pax_global_header00006660000000000000000000000064152621222760014517gustar00rootroot0000000000000052 comment=ecfd55c747ee1501fd1134f2341a95ebe1f33788 NBISweden-MrBayes-ecfd55c/000077500000000000000000000000001526212227600154155ustar00rootroot00000000000000NBISweden-MrBayes-ecfd55c/.github/000077500000000000000000000000001526212227600167555ustar00rootroot00000000000000NBISweden-MrBayes-ecfd55c/.github/ISSUE_TEMPLATE/000077500000000000000000000000001526212227600211405ustar00rootroot00000000000000NBISweden-MrBayes-ecfd55c/.github/ISSUE_TEMPLATE/bug-report.md000066400000000000000000000022271526212227600235530ustar00rootroot00000000000000--- name: Bug report about: Use this to report a suspected bug --- ### What is the current observed behaviour? ### What is the expected/wanted behaviour? ### How may we reproduce this bug? Steps to reproduce the bug: 1. 2. 3. 4. ### Would you be able to compile and run MrBayes to test fixes to this bug? - [ ] Yes, I know how to use `git` and how to compile MrBayes. - [ ] No, sorry. ### What is the environment that you run MrBayes in? - Operating system (including variant and release): - Version of MrBayes: - If possible, include the output of the `Version` command in MrBayes below: ### Other information that may be of use to us in resolving this issue NBISweden-MrBayes-ecfd55c/.github/ISSUE_TEMPLATE/internal-issue.md000066400000000000000000000003261526212227600244250ustar00rootroot00000000000000--- name: Internal issue about: ONLY for use by developers associated with this project --- NBISweden-MrBayes-ecfd55c/.github/workflows/000077500000000000000000000000001526212227600210125ustar00rootroot00000000000000NBISweden-MrBayes-ecfd55c/.github/workflows/code-tests.yml000066400000000000000000000035761526212227600236220ustar00rootroot00000000000000--- name: Run code build tests on: push: paths-ignore: - 'doc/**' jobs: without-mpi: name: Tests without MPI runs-on: ubuntu-latest strategy: matrix: beagle: [--with-beagle, --without-beagle] steps: - name: Checkout uses: actions/checkout@v2 - name: Install Readline run: | sudo apt-get update sudo apt-get install libreadline-dev - name: Install Beagle if needed if: ${{ matrix.beagle == '--with-beagle' }} run: | sudo apt-get update sudo apt-get install libhmsbeagle-dev - name: Configure project run: ./configure --disable-doc --without-mpi ${{ matrix.beagle }} CC=clang - name: Build project run: make -j 2 - name: Run test (not with Beagle for now) if: ${{ matrix.beagle != '--with-beagle' }} run: make check with-mpi: name: Tests with MPI runs-on: ubuntu-latest strategy: matrix: beagle: [--with-beagle, --without-beagle] steps: - name: Checkout uses: actions/checkout@v2 - name: Install Readline run: | sudo apt-get update sudo apt-get install libreadline-dev - name: Install Beagle if needed if: ${{ matrix.beagle == '--with-beagle' }} run: | sudo apt-get update sudo apt-get install libhmsbeagle-dev - name: Install MPI if needed run: | sudo apt-get update sudo apt-get install libopenmpi-dev - name: Configure project run: ./configure --disable-doc --with-mpi ${{ matrix.beagle }} CC=clang CFLAGS="$( mpicc --showme:compile )" LDFLAGS="$( mpicc --showme:link )" - name: Build project run: make -j 2 - name: Run test (not with Beagle for now) if: ${{ matrix.beagle != '--with-beagle' }} run: make check NBISweden-MrBayes-ecfd55c/.github/workflows/doc-tests.yml000066400000000000000000000007261526212227600234470ustar00rootroot00000000000000--- name: Run documentation build tests on: push: paths-ignore: - 'src/**' jobs: build-doc: runs-on: ubuntu-latest steps: - name: Checkout uses: actions/checkout@v2 - name: Install documentation prerequisites run: | sudo apt-get update sudo apt-get install latexmk texlive-formats-extra - name: Configure project run: ./configure - name: Build project run: make pdf NBISweden-MrBayes-ecfd55c/.gitignore000066400000000000000000000010441526212227600174040ustar00rootroot00000000000000# Generated on OS X sometimes (by dsymutil when using valgrind) *.dSYM # Distribution file (generated by "make distcheck" and "make dist") mrbayes-*.tar.gz # GNU autotools generated files Makefile autom4te.cache config.cache config.h config.log config.status # Generated by Unix build *~ src/*.o src/.deps src/mb src/stamp-h1 # hidden files .gitignore # OS X files .DS_Store Thumbs.db # LaTeX temporary files .texpadtmp # XCode project *.xcodeproj # Tags files TAGS tags # cscope output cscope.out # private file local.env build .vscode NBISweden-MrBayes-ecfd55c/COPYING000066400000000000000000001045131526212227600164540ustar00rootroot00000000000000 GNU GENERAL PUBLIC LICENSE Version 3, 29 June 2007 Copyright (C) 2007 Free Software Foundation, Inc. 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NBISweden-MrBayes-ecfd55c/INSTALL000066400000000000000000000247701526212227600164600ustar00rootroot00000000000000MrBayes 3.2.9 This document describes how to install the most recent release of MrBayes using Homebrew on macOS, Linux, or on Window subsystem for Linux (WSL). It also describes how to build the development sources of MrBayes from the GitHub repository sources. ======================================================================== Installing MrBayes on macOS using Homebrew ------------------------------------------------------------------------ MrBayes is available for macOS through the Homebrew package manager. See https://brew.sh/ for general information about Homebrew and for Homebrew installation instructions. On a macOS system with Homebrew, MrBayes may be installed with: brew install mrbayes The Homebrew package is built with MPI support. The executable is called "mb". ======================================================================== Installing MrBayes on Linux or WSL using Homebrew ("Linuxbrew") ------------------------------------------------------------------------ The instructions for installing MrBayes on Linux systems are identical to those for installing MrBayes on macOS. It relies on Homebrew being available on the Linux system where it's being installed. Installing Homebrew on Linux is a matter of following the instructions at https://docs.brew.sh/Homebrew-on-Linux We have found that the pre-compiled binaries may not work on all systems and that you may get an "Illegal instruction (core dumped)" error when you try starting MrBayes, typically when the code has been compiled on a system with a CPU with SSE, AVX, and/or FMA SIMD extensions and later moved to a system without these extensions. In that case, install the MrBayes package from sources instead. This can still be done through Homebrew like this: brew reinstall -s mrbayes ======================================================================== Installing MrBayes from the GitHub sources on Unix ------------------------------------------------------------------------ Make a shallow clone of the MrBayes repository: git clone --revision=v3.2.9 --depth=1 https://github.com/NBISweden/MrBayes.git ... or download the compressed tar archive from https://github.com/NBISweden/MrBayes/releases/download/v3.2.9/mrbayes-3.2.9.tar.gz ... and unpack it. Building MrBayes using the supplied "configure" script: ------------------------------------------------------------------------ To build MrBayes, you will need a C compiler (gcc or clang works well), the "make" utility, and optionally the "pkg-config" utility. MrBayes may also be compiled with support for the Beagle library and for parallelization using MPI. The code in the MrBayes Git repository comes with a standard GNU "configure" script. On any Unix system, these are the steps required to compile and optionally install MrBayes: * Run "configure" in the top-level directory to create "Makefile" (see "./configure --help" for info about enabling and disabling features of MrBayes, or how to install in non-standard locations etc., and also read further down in this document): ./configure * Make (i.e. compile) the project: make Note that if "pdflatex" or "bibtex" could not be found by the "configure" script, the PDF documentation in doc/manual will not be rebuilt, even if the PDF documents are outdated or missing. * Install it on the system (optional). This will install the executable "mb" in /usr/local/bin, the documentation in /usr/local/share/doc/mrbayes, and the example files in /usr/local/share/examples/mrbayes unless the default installation prefix ("/usr/local") is changed with the "--prefix" option when running "configure". make install * We also support configuring and building the project away from the checked out source repository. This avoids littering the source repository checkout with temporary files. This is done through the following steps: 1. Create and enter a build directory somewhere (its name and location is arbitrary) mkdir build cd build 2. Call the "configure" script ../configure 3. Build and optionally install the project make make install Building MrBayes using the GNU autotools (Autoconf and Automake): ------------------------------------------------------------------------ MrBayes uses the GNU autotools, i.e. Automake and Autoconf, for its configuration and build system. If you tinker with "configure.ac" and/or any of the "Makefile.am" files and then want to regenerate the "configure" script and associated files, there are some prerequisites that need to be in place: * Make sure Automake 1.17 (http://www.gnu.org/software/automake/) is installed. * Make sure Autoconf 2.72 (http://www.gnu.org/software/autoconf/) is installed. The "configure.ac" script uses the "pkg-config" tool for detection of external libraries (Beagle) and also uses m4 macros from the autoconf-archive collection for detection of MPI, Readline, and some architecture-dependent compiler flags. Therefore, * Make sure pkg-config (https://www.freedesktop.org/wiki/Software/pkg-config/) is installed. * Make sure autoconf-archive 2016.03.20 (or later) (http://www.gnu.org/software/autoconf-archive/) is installed. * On some systems that uses metaauto for wrapping multiple versions of the GNU autotools (OpenBSD for example): export AUTOCONF_VERSION=2.72 export AUTOMAKE_VERSION=1.17 * On Ubuntu Linux, installing the following packages installs all of the above prerequisites: 1. automake 2. autoconf 3. pkg-config 4. autoconf-archive These may be installed through sudo apt install automake autoconf pkg-config autoconf-archive When all the prerequisites are in place: * Run "autoreconf" to generate the "configure" script: autoreconf -i * Then continue as outlined earlier in this document. Disabling the SSE, AVX and FMA code paths ------------------------------------------------------------------------ MrBayes will use SSE, AVX and FMA instructions to speed up computations on CPUs that support these instruction sets, if the compiler and build architecture supports it. These code paths are however optional, and if you suspect a bug in part of this code or if you want to move the executable to a machine without these SIMD instructions, you may use "--disable-sse", "--disable-avx" and/or "--disable-fma" to disable it when running the "configure" script. Note that disabling SSE will also disable AVX and FMA, and disabling AVX will also disable FMA. Picking up the Beagle library: ------------------------------------------------------------------------ If configured using "--with-beagle", MrBayes uses the Beagle library (https://github.com/beagle-dev/beagle-lib) if it is available. It will make use of release 4.0.1 of Beagle but should also work with release 2.1.3 or later of the library. If Beagle is installed in a non-default location, there are three ways of telling the "configure" script how to find it: * Use the flag "--with-beagle=PATH" where "PATH" is the prefix path where the Beagle headers and libraries may be found. For example "$HOME/local", "/opt" etc. The paths "/usr/local" and "/usr" are tested if no path is specified. * Point the "PKG_CONFIG_PATH" environment variable to the "pkgconfig" directory that Beagle installed its "hmsbeagle-1.pc" file into. This file is installed if Beagle was compiled from sources, and may be found in the installation's "lib" directory. The detection of Beagle will fall back on using "pkg-config" which picks up this file. * As a last resort, it's also possible to set the "BEAGLE_CFLAGS" and "BEAGLE_LIBS" environment variables prior to running "configure" (the example shows how to use these variables when Beagle was installed under the path "$HOME/local"): env BEAGLE_CFLAGS="-I$HOME/local/include/hmsbeagle-1" \ BEAGLE_LIBS="-L$HOME/local/lib -Wl,-rpath=$HOME/local/lib -lhmsbeagle" \ ./configure Building the Beagle library for MrBayes ------------------------------------------------------------------------ Ideally, you would use a pre-built distribution of the Beagle library, as setting up the required prerequisites for things like OpenCL etc. is a bit cumbersome to say the least. If you none the less want to try building and installing it, this is the executive summary for how to do this on a Unix system, not including installation of prerequisites (since these will differ between systems). It's assumed that the Beagle library is being installed in the default location, under "/usr/local". Note that the Beagle Java library is not needed by MrBayes. git clone --branch=v4.0.1 --depth=1 \ 'https://github.com/beagle-dev/beagle-lib.git' cd beagle-lib ./autogen.sh ./configure --without-jdk LDFLAGS=-Wl,-rpath=/usr/local/lib make sudo make install See also the "INSTALL" document supplied with the Beagle library's source distribution. Picking up and using MPI: ------------------------------------------------------------------------ The "configure" script will find any available MPI C compiler or compiler wrapper and use it (for example the one provided by OpenMPI (https://www.open-mpi.org/) or MPICH (https://www.mpich.org/)) if the "--with-mpi" flag is used. If this flag is not used, or if "--without-mpi" is used, MrBayes will be compiled without support for MPI parallelization. You may set the "MPICC" environment variable to the name or path of a specific MPI C compiler, if you have many to choose from: env MPICC="/usr/local/bin/mpicc" ./configure --with-mpi About support for the GNU Readline library: ------------------------------------------------------------------------ MrBayes will be compiled with support for the GNU Readline Library (https://cnswww.cns.cwru.edu/php/chet/readline/rltop.html) (which allows for command line history within an interactive session, and command completion through pressing the "tab" key) if the Readline library and its associated header files are available. However, it seems as if the Readline library doesn't work well together with common MPI implementations, such as OpenMPI (https://www.open-mpi.org/) and MPICH (https://www.mpich.org/), so if MrBayes is configured with support for MPI parallelization, Readline support will be automatically disabled. A possible workaround is to use a "Readline wrapper", such as "rlwrap" (https://github.com/hanslub42/rlwrap), which gives you persistent command line history between sessions, but no command tab-completion. To use rlwrap with an MPI-enabled MrBayes, simply start MrBayes like this: rlwrap mb # vim: filetype=text NBISweden-MrBayes-ecfd55c/INSTALL-Docker-CentOS.md000066400000000000000000000053631526212227600214120ustar00rootroot00000000000000# Run MrBayes+Beagle3 inside a CentOS 8 Docker container - Status (June 2020): - CUDA: N/A - OpenCL: FAIL - OpenMPI: OK - Beagle3: OK - MrBayes+MPI: OK - MrBayes: OK - This file describes a "proof-of-concept" installation of the develop branch of MrBayes with Beagle. The installation is made inside a Docker container, hence, this is *not* a Dockerfile. - We could not get beagle to work from inside MrBayes unless Beagle is configured using `-rpath`, or `LD_LIBRARY_PATH` is set before running MrBayes. - When configuring the beagle library, we expect to see these warning messages (since we asked for not using OpenCL, CUDA or Java): - `WARNING: OpenCL not found or disabled` - `WARNING: NVIDIA CUDA nvcc compiler not found` - `WARNING: JDK installation not found` - We are not building the LaTeX version of the documentation for MrBayes. For building the documentation, we recommend to use the `latexmk` script. Using the commands below will install the script an all necessary TeX-libraries. yum install -y epel-release yum -y update yum -y install texlive latexmk texlive-wrapfig - When running the MPI (parallel) version of MrBayes inside the Docker container, we need to use `--allow-run-as-root` (since we are starting `mpirun` as user `root`). # Install and run $ docker run -it centos:8 /bin/bash LC_ALL=C # Base system yum -y install \ autoconf \ gcc-c++ \ git \ libtool \ make \ readline-devel # OpenCL # Tue 30 Jun 2020: Beagle compiles with OpenCL, but when run in MrBayes, we get # OpenCL error: Unknown error from file , line 115. #yum -y install \ # 'dnf-command(config-manager)' #yum -y config-manager --set-enabled PowerTools #yum -y update #yum -y install \ # ocl-icd \ # ocl-icd-devel #ln -s /usr/lib64/libOpenCL.so.1 /usr/lib/libOpenCL.so # https://unix.stackexchange.com/questions/292630/how-to-install-opencl-in-centos-7-using-yum # OpenMPI yum -y install \ openmpi-devel source /etc/profile module load mpi # Beagle git clone --depth=1 https://github.com/beagle-dev/beagle-lib.git cd beagle-lib ./autogen.sh LDFLAGS=-Wl,-rpath=/usr/local/lib ./configure --without-jdk --without-cuda --without-opencl --disable-doxygen-doc make -j2 make install cd / # MrBayes git clone --depth=1 --branch=develop https://github.com/NBISweden/MrBayes.git cd MrBayes ./configure --with-mpi --enable-doc=no make -j2 mpirun --allow-run-as-root -np 1 src/mb <<< 'version;showb;quit' make clean ./configure --enable-doc=no make -j2 src/mb <<< 'version;showb;quit' NBISweden-MrBayes-ecfd55c/INSTALL-Docker-Ubuntu.md000066400000000000000000000045711526212227600215410ustar00rootroot00000000000000# Install MrBayes+Beagle3 inside an Ubuntu 20.04 Docker container - Status (June 2020): - CUDA: N/A - OpenCL: OK - OpenMPI: OK - Beagle3: OK - MrBayes+MPI: OK - MrBayes: OK - This file describes a "proof-of-concept" installation of the develop branch of MrBayes with Beagle. The installation is made inside a Docker container, hence, this is *not* a Dockerfile. - Instructions for Debian are identical, but you would use a Debian container such as `debian:sid` instead of `ubuntu:20.04`. - We could not get beagle to work from inside MrBayes unless Beagle is configured using `-rpath`, or `LD_LIBRARY_PATH` is set before running MrBayes. - When configuring the beagle library, we expect to see these warning messages (since we asked for not using CUDA or Java): - `WARNING: NVIDIA CUDA nvcc compiler not found` - `WARNING: JDK installation not found` - We are not building the LaTeX version of the documentation for MrBayes. For building the documentation, we recommend to use the `latexmk` script. Using `apt install latexmk texlive-latex-extra` will install the script an all necessary TeX-libraries. - When running the MPI (parallel) version of MrBayes inside the Docker container, we need to use `--allow-run-as-root` (since we are starting `mpirun` as user `root`). ## Install and run $ docker run -it ubuntu:20.04 /bin/bash # Base system apt update -y && apt upgrade -y DEBIAN_FRONTEND=noninteractive apt install -y tzdata apt install -y \ autoconf \ g++ \ git \ libreadline-dev \ libtool \ make # OpenCL apt install -y \ ocl-icd-opencl-dev \ pocl-opencl-icd # OpenMPI apt install -y \ libopenmpi-dev # Beagle git clone --depth=1 https://github.com/beagle-dev/beagle-lib.git cd beagle-lib ./autogen.sh LDFLAGS=-Wl,-rpath=/usr/local/lib ./configure --without-jdk --disable-doxygen-doc make -j2 make install cd / # MrBayes git clone --depth=1 --branch=develop https://github.com/NBISweden/MrBayes.git cd MrBayes ./configure --with-mpi --enable-doc=no make -j2 # Test MPI (parallel) version mpirun --allow-run-as-root -np 1 src/mb <<< 'version;showb;quit' make clean ./configure --enable-doc=no make -j2 # Test serial version src/mb <<< 'version;showb;quit' NBISweden-MrBayes-ecfd55c/Makefile.am000066400000000000000000000001711526212227600174500ustar00rootroot00000000000000dist_doc_DATA= COPYING README NEWS EXTRA_DIST= INSTALL SUBDIRS= src examples testing if BUILD_DOC SUBDIRS+= doc endif NBISweden-MrBayes-ecfd55c/Makefile.in000066400000000000000000000661171526212227600174750ustar00rootroot00000000000000# Makefile.in generated by automake 1.17 from Makefile.am. # @configure_input@ # Copyright (C) 1994-2024 Free Software Foundation, Inc. # This Makefile.in is free software; the Free Software Foundation # gives unlimited permission to copy and/or distribute it, # with or without modifications, as long as this notice is preserved. # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY, to the extent permitted by law; without # even the implied warranty of MERCHANTABILITY or FITNESS FOR A # PARTICULAR PURPOSE. @SET_MAKE@ VPATH = @srcdir@ am__is_gnu_make = { \ if test -z '$(MAKELEVEL)'; then \ false; \ elif test -n '$(MAKE_HOST)'; then \ true; \ elif test -n '$(MAKE_VERSION)' && test -n '$(CURDIR)'; then \ true; \ else \ false; \ fi; \ } am__make_running_with_option = \ case $${target_option-} in \ ?) ;; \ *) echo "am__make_running_with_option: internal error: invalid" \ "target option '$${target_option-}' specified" >&2; \ exit 1;; \ esac; \ has_opt=no; \ sane_makeflags=$$MAKEFLAGS; \ if $(am__is_gnu_make); then \ sane_makeflags=$$MFLAGS; \ else \ case $$MAKEFLAGS in \ *\\[\ \ ]*) \ bs=\\; \ sane_makeflags=`printf '%s\n' "$$MAKEFLAGS" \ | sed "s/$$bs$$bs[$$bs $$bs ]*//g"`;; \ esac; \ fi; \ skip_next=no; \ strip_trailopt () \ { \ flg=`printf '%s\n' "$$flg" | sed "s/$$1.*$$//"`; \ }; \ for flg in $$sane_makeflags; do \ test $$skip_next = yes && { skip_next=no; continue; }; \ case $$flg in \ *=*|--*) continue;; \ -*I) strip_trailopt 'I'; skip_next=yes;; \ -*I?*) strip_trailopt 'I';; \ -*O) strip_trailopt 'O'; skip_next=yes;; \ -*O?*) strip_trailopt 'O';; \ -*l) strip_trailopt 'l'; skip_next=yes;; \ -*l?*) strip_trailopt 'l';; \ -[dEDm]) skip_next=yes;; \ -[JT]) skip_next=yes;; \ esac; \ case $$flg in \ *$$target_option*) has_opt=yes; break;; \ esac; \ done; \ test $$has_opt = yes am__make_dryrun = (target_option=n; $(am__make_running_with_option)) am__make_keepgoing = (target_option=k; $(am__make_running_with_option)) am__rm_f = rm -f $(am__rm_f_notfound) am__rm_rf = rm -rf $(am__rm_f_notfound) pkgdatadir = $(datadir)/@PACKAGE@ pkgincludedir = $(includedir)/@PACKAGE@ pkglibdir = $(libdir)/@PACKAGE@ pkglibexecdir = $(libexecdir)/@PACKAGE@ am__cd = CDPATH="$${ZSH_VERSION+.}$(PATH_SEPARATOR)" && cd install_sh_DATA = $(install_sh) -c -m 644 install_sh_PROGRAM = $(install_sh) -c install_sh_SCRIPT = $(install_sh) -c INSTALL_HEADER = $(INSTALL_DATA) transform = $(program_transform_name) NORMAL_INSTALL = : PRE_INSTALL = : POST_INSTALL = : NORMAL_UNINSTALL = : PRE_UNINSTALL = : POST_UNINSTALL = : build_triplet = @build@ host_triplet = @host@ @BUILD_DOC_TRUE@am__append_1 = doc subdir = . 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(The bug was introduced in v3.2.4 by mistake.) v3.2.8 (Sep 18 2026) * Use latexmk to build the PDF documentation, if avaliable. * Support MCMC chains longer than 2E9 generations. * Support discrete characters up to 24 states (0-9,A-N for both unordered and ordered characters). * Support continuous characters with missing states (Brownian motion model). * Support serially-sampled tips in the coalescent prior. * Support independent lognormal (ILN) and independent gamma (IGR) clocks. * Support model averaging (rjMCMC) between ILN and IGR relaxed clock models. * Renamed the white noise relaxed clock model to WN. * Updated the parameterization in the TK02 model. * Updated help messages for the FBD model and parameters. * Bug fix to the sigma (sd_log) in lnorm rate variation across sites. * Bug fix to multi-partition Beagle code. * Bug fix to directional state frequency models. * Bug fix to erroneous printing of header alpha_symdir. * Bug fix to setting popsizepr and growthpr for protein data. * Bug fix to setting ratepr for single partition. * Better detection of Beagle library in "configure" script. v3.2.7a (Mar 06 2019): hotfix * PDF manual re-generated from already updated LaTeX sources. v3.2.7 (Feb 13 2019) * Added lognormal and flat ordered dirichelet distributions as alternatives to the gamma prior on site variation across sites. * Added new, optional moves for the fossilized birth-death model. * Added new, optional parsimony-guided moves for non-clock trees. * Support for Beagle 3 added. * Added support for FMA and AVX instructions in built-in likelihood calculators for 4by4 nucleotide models. * Added basic command line option support on Unix systems (-h,-i,-v) * Various minor bug fixes. v3.2.6 (Nov 25 2015) * Add invertebrate mt, Echinoderm and Euplotid codes [lset code=invermt]. * Fix a bug of updating branch lengths in tree-stretch move. * Correct the proposal ratio in the add/delete branch move. * Add more coding options for restriction site chars. v3.2.5 (Apr 8 2015) * Fix pdf in the coalescent prior. * Fix a few issues when appending to the previous run. * Improve the efficiency of moving ancestral fossils. * Add lognormal rate variation among sites [lset rate=lnorm]. * Add parsimony-biased SPR and TBR proposals (for developers). * Other minor bug fixes and improvements. v3.2.4 (Jan 27 2015) * Fix reporting ancestral states under GTR+I+G model. * Fix the sliding window in the move of updating omega in codon model. * Fix the likelihood and proposal ratio of NaN in the multi-species coalescent analysis. * Correct screen output messages for a few commands. * Fix the interaction of MrBayes with Beagle for the Windows executable. * Fix prior pdf of relative rates (rate multipliers). v3.2.3 (Oct 22 2014) * Correct the proposal ratio in the extending TBR move for non-clock tree. * Fix numerical error of calculating proposal ratio in parsimony-biased SPR moves. * Set unconstrained:gammadir prior as default to help avoid overestimating branch lengths. * Add clock:fossilization prior for total-evidence dating analysis. * Add LG model to Aamodelpr [prset aamodelpr=fixed(LG)]. * Rearrange the code to be more concise. * Other minor bug fixes and improvements. v3.2.2 (Aug 22 2013) * Disable LIKE_EPSILON warning for standard users. * Fix segmentation fault errors in 64bit version. * Fix some issues when dealing with constraints. * Fix setting aarevmatpr parameters to fixed for GTR. * Other bug fixes and improvements. v3.2.1 (Feb 8 2012) v3.2.0 (Nov 16 2011) Major release. NBISweden-MrBayes-ecfd55c/README000066400000000000000000000033161526212227600163000ustar00rootroot00000000000000Hi, This is the GitHub repository for MrBayes, a program used for Bayesian inference of phylogeny. Build instructions, very very short version (would install MrBayes as "/usr/local/bin/mb" on a Unix system): git clone --depth=1 https://github.com/NBISweden/MrBayes.git cd MrBayes ./configure make && sudo make install Refer to the document called "INSTALL" for further information. Current repository directory layout: . ├── autotools : Files relating to the build system ├── doc │   └── manual : The manual ├── examples : Example input data sets └── src : C source code The MrBayes web site is available at http://mrbayes.net/ Historical releases are tagged with "v3.2.0" (etc.) from release 3.2.0 onwards. The 'master' branch contains releases. The 'develop' branch should hopefully, at all times, contain working development code that at least compiles. Apart from simple fixes, new development happens on feature branches off the 'develop' branch, and are merged back into 'develop' when finished. This more or less follows the "git-flow" branching model. The 'gh-pages' branch contains the web site (only). The 'testing' branch contains testing data files (only). After release 3.2.6 (November 2015), MrBayes entered "maintenance mode", and its care has been passed over to NBIS (National Bioinformatics Infrastructure Sweden, http://nbis.se/). The original development team is still involved with the program, but any future releases will probably be geared more towards bug-fixes rather than addition of new features. With kind regards, Andreas (Kusalananda) Kähäri, and Johan Nylander NBIS NBISweden-MrBayes-ecfd55c/TODO000066400000000000000000000030531526212227600161060ustar00rootroot00000000000000TODO: * Data parallelization MrBayes currently uses MPI to run different chains in parallel on different processors. However, data sets are becoming a lot larger now with respect to number of sites, so it is becoming important also to distribute likelihood computations on data across processors. This would require some programming but it is not a huge challenge. * Native AVX and FMA code I have added native AVX and FMA code for the 4by4 nucleotide models. It would be straightforward to add native AVX and FMA code for the other models. Particularly the AA-model would probably benefit from this code (when not using the beagle library). * Dynamic scaling Scaling of likelihoods take a surprisingly large amount of time when using the standard 4by4 nucleotide models. Maxim added dynamic scaling code for the Beagle implementation but not for the native likelihoood calculators, which are faster. The dynamic scaling code adjusts the rescaling frequency so that you do not rescale likelihoods more often than needed to avoid numerical exceptions and achieve a reasonable level of precision. For typical-size trees and the 4by4 model, no rescaling is often needed. I added the infrastructure needed to support dynamic scaling for both Beagle and native calculators in December 2015. In principle, it should be straightforward now to rewrite Maxim's dynamic scaling code (which is not so pretty C code) so that it works for both Beagle and native likelihood calculators. 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Compilers that dnl are very slow to start (such as Intel) are listed first. vendors=" intel: __ICC,__ECC,__INTEL_COMPILER ibm: __xlc__,__xlC__,__IBMC__,__IBMCPP__,__ibmxl__ pathscale: __PATHCC__,__PATHSCALE__ clang: __clang__ cray: _CRAYC fujitsu: __FUJITSU sdcc: SDCC,__SDCC sx: _SX nvhpc: __NVCOMPILER portland: __PGI gnu: __GNUC__ sun: __SUNPRO_C,__SUNPRO_CC,__SUNPRO_F90,__SUNPRO_F95 hp: __HP_cc,__HP_aCC dec: __DECC,__DECCXX,__DECC_VER,__DECCXX_VER borland: __BORLANDC__,__CODEGEARC__,__TURBOC__ comeau: __COMO__ kai: __KCC lcc: __LCC__ sgi: __sgi,sgi microsoft: _MSC_VER metrowerks: __MWERKS__ watcom: __WATCOMC__ tcc: __TINYC__ unknown: UNKNOWN " for ventest in $vendors; do case $ventest in *:) vendor=$ventest continue ;; *) vencpp="defined("`echo $ventest | sed 's/,/) || defined(/g'`")" ;; esac AC_COMPILE_IFELSE([AC_LANG_PROGRAM([], [[ #if !($vencpp) thisisanerror; #endif ]])], [break]) done ax_cv_[]_AC_LANG_ABBREV[]_compiler_vendor=`echo $vendor | cut -d: -f1` ]) ])dnl # =========================================================================== # https://www.gnu.org/software/autoconf-archive/ax_compiler_version.html # =========================================================================== # # SYNOPSIS # # AX_COMPILER_VERSION # # DESCRIPTION # # This macro retrieves the compiler version and returns it in the cache # variable $ax_cv_c_compiler_version for C and $ax_cv_cxx_compiler_version # for C++. # # Version is returned as epoch:major.minor.patchversion # # Epoch is used in order to have an increasing version number in case of # marketing change. # # Epoch use: * borland compiler use chronologically 0turboc for turboc # era, # # 1borlanc BORLANDC++ before 5, 2cppbuilder for cppbuilder era, # 3borlancpp for return of BORLANDC++ (after version 5.5), # 4cppbuilder for cppbuilder with year version, # and 5xe for XE era. # # An empty string is returned otherwise. # # LICENSE # # Copyright (c) 2014 Bastien ROUCARIES # # Copying and distribution of this file, with or without modification, are # permitted in any medium without royalty provided the copyright notice # and this notice are preserved. This file is offered as-is, without any # warranty. #serial 14 # for intel AC_DEFUN([_AX_COMPILER_VERSION_INTEL], [ dnl AC_COMPUTE_INT(_ax_[]_AC_LANG_ABBREV[]_compiler_version_major, [__INTEL_COMPILER/100],, AC_MSG_FAILURE([[[$0]] unknown intel compiler version])) AC_COMPUTE_INT(_ax_[]_AC_LANG_ABBREV[]_compiler_version_minor, [(__INTEL_COMPILER%100)/10],, AC_MSG_FAILURE([[[$0]] unknown intel compiler version])) AC_COMPUTE_INT(_ax_[]_AC_LANG_ABBREV[]_compiler_version_patch, [(__INTEL_COMPILER%10)],, AC_MSG_FAILURE([[[$0]] unknown intel compiler version])) ax_cv_[]_AC_LANG_ABBREV[]_compiler_version="$_ax_[]_AC_LANG_ABBREV[]_compiler_version_major.$_ax_[]_AC_LANG_ABBREV[]_compiler_version_minor.$_ax_[]_AC_LANG_ABBREV[]_compiler_version_patch" ]) # for IBM AC_DEFUN([_AX_COMPILER_VERSION_IBM], [ dnl dnl check between z/OS C/C++ and XL C/C++ AC_COMPILE_IFELSE([ AC_LANG_PROGRAM([], [ #if defined(__COMPILER_VER__) choke me; #endif ])], [ AC_COMPUTE_INT(_ax_[]_AC_LANG_ABBREV[]_compiler_version_major, [__xlC__/100],, AC_MSG_FAILURE([[[$0]] unknown IBM compiler major version])) AC_COMPUTE_INT(_ax_[]_AC_LANG_ABBREV[]_compiler_version_minor, [__xlC__%100],, AC_MSG_FAILURE([[[$0]] unknown IBM compiler minor version])) AC_COMPUTE_INT(_ax_[]_AC_LANG_ABBREV[]_compiler_version_patch, [__xlC_ver__/0x100],, AC_MSG_FAILURE([[[$0]] unknown IBM compiler patch version])) AC_COMPUTE_INT(_ax_[]_AC_LANG_ABBREV[]_compiler_version_build, [__xlC_ver__%0x100],, AC_MSG_FAILURE([[[$0]] unknown IBM compiler build version])) ax_cv_[]_AC_LANG_ABBREV[]_compiler_version="$_ax_[]_AC_LANG_ABBREV[]_compiler_version_major.$_ax_[]_AC_LANG_ABBREV[]_compiler_version_minor.$_ax_[]_AC_LANG_ABBREV[]_compiler_version_patch.$_ax_[]_AC_LANG_ABBREV[]_compiler_version_build" ], [ AC_COMPUTE_INT(_ax_[]_AC_LANG_ABBREV[]_compiler_version_patch, [__xlC__%1000],, AC_MSG_FAILURE([[[$0]] unknown IBM compiler patch version])) AC_COMPUTE_INT(_ax_[]_AC_LANG_ABBREV[]_compiler_version_minor, [(__xlC__/10000)%10],, AC_MSG_FAILURE([[[$0]] unknown IBM compiler minor version])) AC_COMPUTE_INT(_ax_[]_AC_LANG_ABBREV[]_compiler_version_major, [(__xlC__/100000)%10],, AC_MSG_FAILURE([[[$0]] unknown IBM compiler major version])) ax_cv_[]_AC_LANG_ABBREV[]_compiler_version="$_ax_[]_AC_LANG_ABBREV[]_compiler_version_major.$_ax_[]_AC_LANG_ABBREV[]_compiler_version_minor.$_ax_[]_AC_LANG_ABBREV[]_compiler_version_patch" ]) ]) # for pathscale AC_DEFUN([_AX_COMPILER_VERSION_PATHSCALE],[ AC_COMPUTE_INT(_ax_[]_AC_LANG_ABBREV[]_compiler_version_major, __PATHCC__,, AC_MSG_FAILURE([[[$0]] unknown pathscale major])) AC_COMPUTE_INT(_ax_[]_AC_LANG_ABBREV[]_compiler_version_minor, __PATHCC_MINOR__,, AC_MSG_FAILURE([[[$0]] unknown pathscale minor])) AC_COMPUTE_INT(_ax_[]_AC_LANG_ABBREV[]_compiler_version_patch, [__PATHCC_PATCHLEVEL__],, AC_MSG_FAILURE([[[$0]] unknown pathscale patch level])) ax_cv_[]_AC_LANG_ABBREV[]_compiler_version="$_ax_[]_AC_LANG_ABBREV[]_compiler_version_major.$_ax_[]_AC_LANG_ABBREV[]_compiler_version_minor.$_ax_[]_AC_LANG_ABBREV[]_compiler_version_patch" ]) # for clang AC_DEFUN([_AX_COMPILER_VERSION_CLANG],[ AC_COMPUTE_INT(_ax_[]_AC_LANG_ABBREV[]_compiler_version_major, __clang_major__,, AC_MSG_FAILURE([[[$0]] unknown clang major])) AC_COMPUTE_INT(_ax_[]_AC_LANG_ABBREV[]_compiler_version_minor, __clang_minor__,, AC_MSG_FAILURE([[[$0]] unknown clang minor])) AC_COMPUTE_INT(_ax_[]_AC_LANG_ABBREV[]_compiler_version_patch, [__clang_patchlevel__],,0) ax_cv_[]_AC_LANG_ABBREV[]_compiler_version="$_ax_[]_AC_LANG_ABBREV[]_compiler_version_major.$_ax_[]_AC_LANG_ABBREV[]_compiler_version_minor.$_ax_[]_AC_LANG_ABBREV[]_compiler_version_patch" ]) # for crayc AC_DEFUN([_AX_COMPILER_VERSION_CRAY],[ AC_COMPUTE_INT(_ax_[]_AC_LANG_ABBREV[]_compiler_version_major, _RELEASE,, AC_MSG_FAILURE([[[$0]] unknown crayc release])) AC_COMPUTE_INT(_ax_[]_AC_LANG_ABBREV[]_compiler_version_minor, _RELEASE_MINOR,, AC_MSG_FAILURE([[[$0]] unknown crayc minor])) ax_cv_[]_AC_LANG_ABBREV[]_compiler_version="$_ax_[]_AC_LANG_ABBREV[]_compiler_version_major.$_ax_[]_AC_LANG_ABBREV[]_compiler_version_minor" ]) # for fujitsu AC_DEFUN([_AX_COMPILER_VERSION_FUJITSU],[ AC_COMPUTE_INT(ax_cv_[]_AC_LANG_ABBREV[]_compiler_version, __FCC_VERSION,, AC_MSG_FAILURE([[[$0]]unknown fujitsu release])) ]) # for GNU AC_DEFUN([_AX_COMPILER_VERSION_GNU],[ AC_COMPUTE_INT(_ax_[]_AC_LANG_ABBREV[]_compiler_version_major, __GNUC__,, AC_MSG_FAILURE([[[$0]] unknown gcc major])) AC_COMPUTE_INT(_ax_[]_AC_LANG_ABBREV[]_compiler_version_minor, __GNUC_MINOR__,, AC_MSG_FAILURE([[[$0]] unknown gcc minor])) AC_COMPUTE_INT(_ax_[]_AC_LANG_ABBREV[]_compiler_version_patch, [__GNUC_PATCHLEVEL__],, AC_MSG_FAILURE([[[$0]] unknown gcc patch level])) ax_cv_[]_AC_LANG_ABBREV[]_compiler_version="$_ax_[]_AC_LANG_ABBREV[]_compiler_version_major.$_ax_[]_AC_LANG_ABBREV[]_compiler_version_minor.$_ax_[]_AC_LANG_ABBREV[]_compiler_version_patch" ]) # For sun AC_DEFUN([_AX_COMPILER_VERSION_SUN],[ m4_define([_AX_COMPILER_VERSION_SUN_NUMBER], [ #if defined(__SUNPRO_CC) __SUNPRO_CC #else __SUNPRO_C #endif ]) AC_COMPUTE_INT(_ax_[]_AC_LANG_ABBREV[]_compiler_version_until59, !!(_AX_COMPILER_VERSION_SUN_NUMBER < 0x1000),, AC_MSG_FAILURE([[[$0]] unknown sun release version])) AS_IF([test "X$_ax_[]_AC_LANG_ABBREV[]_compiler_version_until59" = X1], [dnl AC_COMPUTE_INT(_ax_[]_AC_LANG_ABBREV[]_compiler_version_patch, _AX_COMPILER_VERSION_SUN_NUMBER % 0x10,, AC_MSG_FAILURE([[[$0]] unknown sun patch version])) AC_COMPUTE_INT(_ax_[]_AC_LANG_ABBREV[]_compiler_version_minor, (_AX_COMPILER_VERSION_SUN_NUMBER / 0x10) % 0x10,, AC_MSG_FAILURE([[[$0]] unknown sun minor version])) AC_COMPUTE_INT(_ax_[]_AC_LANG_ABBREV[]_compiler_version_major, (_AX_COMPILER_VERSION_SUN_NUMBER / 0x100),, AC_MSG_FAILURE([[[$0]] unknown sun major version])) ], [dnl AC_COMPUTE_INT(_ax_[]_AC_LANG_ABBREV[]_compiler_version_patch, _AX_COMPILER_VERSION_SUN_NUMBER % 0x10,, AC_MSG_FAILURE([[[$0]] unknown sun patch version])) AC_COMPUTE_INT(_ax_[]_AC_LANG_ABBREV[]_compiler_version_minor, (_AX_COMPILER_VERSION_SUN_NUMBER / 0x100) % 0x100,, AC_MSG_FAILURE([[[$0]] unknown sun minor version])) AC_COMPUTE_INT(_ax_[]_AC_LANG_ABBREV[]_compiler_version_major, (_AX_COMPILER_VERSION_SUN_NUMBER / 0x1000),, AC_MSG_FAILURE([[[$0]] unknown sun major version])) ]) ax_cv_[]_AC_LANG_ABBREV[]_compiler_version="$_ax_[]_AC_LANG_ABBREV[]_compiler_version_major.$_ax_[]_AC_LANG_ABBREV[]_compiler_version_minor.$_ax_[]_AC_LANG_ABBREV[]_compiler_version_patch" ]) AC_DEFUN([_AX_COMPILER_VERSION_HP],[ m4_define([_AX_COMPILER_VERSION_HP_NUMBER], [ #if defined(__HP_cc) __HP_cc #else __HP_aCC #endif ]) AC_COMPUTE_INT(_ax_[]_AC_LANG_ABBREV[]_compiler_version_untilA0121, !!(_AX_COMPILER_VERSION_HP_NUMBER <= 1),, AC_MSG_FAILURE([[[$0]] unknown hp release version])) AS_IF([test "X$_ax_[]_AC_LANG_ABBREV[]_compiler_version_untilA0121" = X1], [dnl By default output last version with this behavior. dnl it is so old ax_cv_[]_AC_LANG_ABBREV[]_compiler_version="01.21.00" ], [dnl AC_COMPUTE_INT(_ax_[]_AC_LANG_ABBREV[]_compiler_version_patch, (_AX_COMPILER_VERSION_HP_NUMBER % 100),, AC_MSG_FAILURE([[[$0]] unknown hp release version])) AC_COMPUTE_INT(_ax_[]_AC_LANG_ABBREV[]_compiler_version_minor, ((_AX_COMPILER_VERSION_HP_NUMBER / 100)%100),, AC_MSG_FAILURE([[[$0]] unknown hp minor version])) AC_COMPUTE_INT(_ax_[]_AC_LANG_ABBREV[]_compiler_version_major, ((_AX_COMPILER_VERSION_HP_NUMBER / 10000)%100),, AC_MSG_FAILURE([[[$0]] unknown hp major version])) ax_cv_[]_AC_LANG_ABBREV[]_compiler_version="$_ax_[]_AC_LANG_ABBREV[]_compiler_version_major.$_ax_[]_AC_LANG_ABBREV[]_compiler_version_minor.$_ax_[]_AC_LANG_ABBREV[]_compiler_version_patch" ]) ]) AC_DEFUN([_AX_COMPILER_VERSION_DEC],[dnl m4_define([_AX_COMPILER_VERSION_DEC_NUMBER], [ #if defined(__DECC_VER) __DECC_VER #else __DECCXX_VER #endif ]) AC_COMPUTE_INT(_ax_[]_AC_LANG_ABBREV[]_compiler_version_patch, (_AX_COMPILER_VERSION_DEC_NUMBER % 10000),, AC_MSG_FAILURE([[[$0]] unknown dec release version])) AC_COMPUTE_INT(_ax_[]_AC_LANG_ABBREV[]_compiler_version_minor, ((_AX_COMPILER_VERSION_DEC_NUMBER / 100000UL)%100),, AC_MSG_FAILURE([[[$0]] unknown dec minor version])) AC_COMPUTE_INT(_ax_[]_AC_LANG_ABBREV[]_compiler_version_major, ((_AX_COMPILER_VERSION_DEC_NUMBER / 10000000UL)%100),, AC_MSG_FAILURE([[[$0]] unknown dec major version])) ax_cv_[]_AC_LANG_ABBREV[]_compiler_version="$_ax_[]_AC_LANG_ABBREV[]_compiler_version_major.$_ax_[]_AC_LANG_ABBREV[]_compiler_version_minor.$_ax_[]_AC_LANG_ABBREV[]_compiler_version_patch" ]) # borland AC_DEFUN([_AX_COMPILER_VERSION_BORLAND],[dnl m4_define([_AX_COMPILER_VERSION_TURBOC_NUMBER], [ #if defined(__TURBOC__) __TURBOC__ #else choke me #endif ]) m4_define([_AX_COMPILER_VERSION_BORLANDC_NUMBER], [ #if defined(__BORLANDC__) __BORLANDC__ #else __CODEGEARC__ #endif ]) AC_COMPILE_IFELSE( [AC_LANG_PROGRAM(, _AX_COMPILER_VERSION_TURBOC_NUMBER)], [dnl TURBOC AC_COMPUTE_INT( _ax_[]_AC_LANG_ABBREV[]_compiler_version_turboc_raw, _AX_COMPILER_VERSION_TURBOC_NUMBER,, AC_MSG_FAILURE([[[$0]] unknown turboc version])) AS_IF( [test $_ax_[]_AC_LANG_ABBREV[]_compiler_version_turboc_raw -lt 661 || test $_ax_[]_AC_LANG_ABBREV[]_compiler_version_turboc_raw -gt 1023], [dnl compute normal version AC_COMPUTE_INT( _ax_[]_AC_LANG_ABBREV[]_compiler_version_minor, _AX_COMPILER_VERSION_TURBOC_NUMBER % 0x100,, AC_MSG_FAILURE([[[$0]] unknown turboc minor version])) AC_COMPUTE_INT( _ax_[]_AC_LANG_ABBREV[]_compiler_version_major, (_AX_COMPILER_VERSION_TURBOC_NUMBER/0x100)%0x100,, AC_MSG_FAILURE([[[$0]] unknown turboc major version])) ax_cv_[]_AC_LANG_ABBREV[]_compiler_version="0turboc:$_ax_[]_AC_LANG_ABBREV[]_compiler_version_major.$_ax_[]_AC_LANG_ABBREV[]_compiler_version_minor"], [dnl special version AS_CASE([$_ax_[]_AC_LANG_ABBREV[]_compiler_version_turboc_raw], [661],[ax_cv_[]_AC_LANG_ABBREV[]_compiler_version="0turboc:1.00"], [662],[ax_cv_[]_AC_LANG_ABBREV[]_compiler_version="0turboc:1.01"], [663],[ax_cv_[]_AC_LANG_ABBREV[]_compiler_version="0turboc:2.00"], [ AC_MSG_WARN([[[$0]] unknown turboc version between 0x295 and 0x400 please report bug]) ax_cv_[]_AC_LANG_ABBREV[]_compiler_version="" ]) ]) ], # borlandc [ AC_COMPUTE_INT( _ax_[]_AC_LANG_ABBREV[]_compiler_version_borlandc_raw, _AX_COMPILER_VERSION_BORLANDC_NUMBER,, AC_MSG_FAILURE([[[$0]] unknown borlandc version])) AS_CASE([$_ax_[]_AC_LANG_ABBREV[]_compiler_version_borlandc_raw], dnl BORLANDC++ before 5.5 [512] ,[ax_cv_[]_AC_LANG_ABBREV[]_compiler_version="1borlanc:2.00"], [1024],[ax_cv_[]_AC_LANG_ABBREV[]_compiler_version="1borlanc:3.00"], [1024],[ax_cv_[]_AC_LANG_ABBREV[]_compiler_version="1borlanc:3.00"], [1040],[ax_cv_[]_AC_LANG_ABBREV[]_compiler_version="1borlanc:3.1"], [1106],[ax_cv_[]_AC_LANG_ABBREV[]_compiler_version="1borlanc:4.0"], [1280],[ax_cv_[]_AC_LANG_ABBREV[]_compiler_version="1borlanc:5.0"], [1312],[ax_cv_[]_AC_LANG_ABBREV[]_compiler_version="1borlanc:5.02"], dnl C++ Builder era [1328],[ax_cv_[]_AC_LANG_ABBREV[]_compiler_version="2cppbuilder:3.0"], [1344],[ax_cv_[]_AC_LANG_ABBREV[]_compiler_version="2cppbuilder:4.0"], dnl BORLANDC++ after 5.5 [1360],[ax_cv_[]_AC_LANG_ABBREV[]_compiler_version="3borlancpp:5.5"], [1361],[ax_cv_[]_AC_LANG_ABBREV[]_compiler_version="3borlancpp:5.51"], [1378],[ax_cv_[]_AC_LANG_ABBREV[]_compiler_version="3borlancpp:5.6.4"], dnl C++ Builder with year number [1392],[ax_cv_[]_AC_LANG_ABBREV[]_compiler_version="4cppbuilder:2006"], [1424],[ax_cv_[]_AC_LANG_ABBREV[]_compiler_version="4cppbuilder:2007"], [1555],[ax_cv_[]_AC_LANG_ABBREV[]_compiler_version="4cppbuilder:2009"], [1569],[ax_cv_[]_AC_LANG_ABBREV[]_compiler_version="4cppbuilder:2010"], dnl XE version [1584],[ax_cv_[]_AC_LANG_ABBREV[]_compiler_version="5xe"], [1600],[ax_cv_[]_AC_LANG_ABBREV[]_compiler_version="5xe:2"], [1616],[ax_cv_[]_AC_LANG_ABBREV[]_compiler_version="5xe:3"], [1632],[ax_cv_[]_AC_LANG_ABBREV[]_compiler_version="5xe:4"], [ AC_MSG_WARN([[[$0]] Unknown borlandc compiler version $_ax_[]_AC_LANG_ABBREV[]_compiler_version_borlandc_raw please report bug]) ]) ]) ]) # COMO AC_DEFUN([_AX_COMPILER_VERSION_COMEAU], [ dnl AC_COMPUTE_INT(_ax_[]_AC_LANG_ABBREV[]_compiler_version_minor, [__COMO_VERSION__%100],, AC_MSG_FAILURE([[[$0]] unknown comeau compiler minor version])) AC_COMPUTE_INT(_ax_[]_AC_LANG_ABBREV[]_compiler_version_major, [(__COMO_VERSION__/100)%10],, AC_MSG_FAILURE([[[$0]] unknown comeau compiler major version])) ax_cv_[]_AC_LANG_ABBREV[]_compiler_version="$_ax_[]_AC_LANG_ABBREV[]_compiler_version_major.$_ax_[]_AC_LANG_ABBREV[]_compiler_version_minor" ]) # KAI AC_DEFUN([_AX_COMPILER_VERSION_KAI],[ AC_COMPUTE_INT(_ax_[]_AC_LANG_ABBREV[]_compiler_version_patch, [__KCC_VERSION%100],, AC_MSG_FAILURE([[[$0]] unknown kay compiler patch version])) AC_COMPUTE_INT(_ax_[]_AC_LANG_ABBREV[]_compiler_version_minor, [(__KCC_VERSION/100)%10],, AC_MSG_FAILURE([[[$0]] unknown kay compiler minor version])) AC_COMPUTE_INT(_ax_[]_AC_LANG_ABBREV[]_compiler_version_major, [(__KCC_VERSION/1000)%10],, AC_MSG_FAILURE([[[$0]] unknown kay compiler major version])) ax_cv_[]_AC_LANG_ABBREV[]_compiler_version="$_ax_[]_AC_LANG_ABBREV[]_compiler_version_major.$_ax_[]_AC_LANG_ABBREV[]_compiler_version_minor.$_ax_[]_AC_LANG_ABBREV[]_compiler_version_patch" ]) dnl LCC dnl LCC does not output version... # SGI AC_DEFUN([_AX_COMPILER_VERSION_SGI],[ m4_define([_AX_COMPILER_VERSION_SGI_NUMBER], [ #if defined(_COMPILER_VERSION) _COMPILER_VERSION #else _SGI_COMPILER_VERSION #endif ]) AC_COMPUTE_INT(_ax_[]_AC_LANG_ABBREV[]_compiler_version_patch, [_AX_COMPILER_VERSION_SGI_NUMBER%10],, AC_MSG_FAILURE([[[$0]] unknown SGI compiler patch version])) AC_COMPUTE_INT(_ax_[]_AC_LANG_ABBREV[]_compiler_version_minor, [(_AX_COMPILER_VERSION_SGI_NUMBER/10)%10],, AC_MSG_FAILURE([[[$0]] unknown SGI compiler minor version])) AC_COMPUTE_INT(_ax_[]_AC_LANG_ABBREV[]_compiler_version_major, [(_AX_COMPILER_VERSION_SGI_NUMBER/100)%10],, AC_MSG_FAILURE([[[$0]] unknown SGI compiler major version])) ax_cv_[]_AC_LANG_ABBREV[]_compiler_version="$_ax_[]_AC_LANG_ABBREV[]_compiler_version_major.$_ax_[]_AC_LANG_ABBREV[]_compiler_version_minor.$_ax_[]_AC_LANG_ABBREV[]_compiler_version_patch" ]) # microsoft AC_DEFUN([_AX_COMPILER_VERSION_MICROSOFT],[ AC_COMPUTE_INT(_ax_[]_AC_LANG_ABBREV[]_compiler_version_minor, _MSC_VER%100,, AC_MSG_FAILURE([[[$0]] unknown microsoft compiler minor version])) AC_COMPUTE_INT(_ax_[]_AC_LANG_ABBREV[]_compiler_version_major, (_MSC_VER/100)%100,, AC_MSG_FAILURE([[[$0]] unknown microsoft compiler major version])) dnl could be overridden _ax_[]_AC_LANG_ABBREV[]_compiler_version_patch=0 _ax_[]_AC_LANG_ABBREV[]_compiler_version_build=0 # special case for version 6 AS_IF([test "X$_ax_[]_AC_LANG_ABBREV[]_compiler_version_major" = "X12"], [AC_COMPUTE_INT(_ax_[]_AC_LANG_ABBREV[]_compiler_version_patch, _MSC_FULL_VER%1000,, _ax_[]_AC_LANG_ABBREV[]_compiler_version_patch=0)]) # for version 7 AS_IF([test "X$_ax_[]_AC_LANG_ABBREV[]_compiler_version_major" = "X13"], [AC_COMPUTE_INT(_ax_[]_AC_LANG_ABBREV[]_compiler_version_patch, _MSC_FULL_VER%1000,, AC_MSG_FAILURE([[[$0]] unknown microsoft compiler patch version])) ]) # for version > 8 AS_IF([test $_ax_[]_AC_LANG_ABBREV[]_compiler_version_major -ge 14], [AC_COMPUTE_INT(_ax_[]_AC_LANG_ABBREV[]_compiler_version_patch, _MSC_FULL_VER%10000,, AC_MSG_FAILURE([[[$0]] unknown microsoft compiler patch version])) ]) AS_IF([test $_ax_[]_AC_LANG_ABBREV[]_compiler_version_major -ge 15], [AC_COMPUTE_INT(_ax_[]_AC_LANG_ABBREV[]_compiler_version_build, _MSC_BUILD,, AC_MSG_FAILURE([[[$0]] unknown microsoft compiler build version])) ]) ax_cv_[]_AC_LANG_ABBREV[]_compiler_version="$_ax_[]_AC_LANG_ABBREV[]_compiler_version_major.$_ax_[]_AC_LANG_ABBREV[]_compiler_version_minor.$_ax_[]_AC_LANG_ABBREV[]_compiler_version_patch.$_ax_[]_AC_LANG_ABBREV[]_compiler_version_build" ]) # for metrowerks AC_DEFUN([_AX_COMPILER_VERSION_METROWERKS],[dnl AC_COMPUTE_INT(_ax_[]_AC_LANG_ABBREV[]_compiler_version_patch, __MWERKS__%0x100,, AC_MSG_FAILURE([[[$0]] unknown metrowerks compiler patch version])) AC_COMPUTE_INT(_ax_[]_AC_LANG_ABBREV[]_compiler_version_minor, (__MWERKS__/0x100)%0x10,, AC_MSG_FAILURE([[[$0]] unknown metrowerks compiler minor version])) AC_COMPUTE_INT(_ax_[]_AC_LANG_ABBREV[]_compiler_version_major, (__MWERKS__/0x1000)%0x10,, AC_MSG_FAILURE([[[$0]] unknown metrowerks compiler major version])) ax_cv_[]_AC_LANG_ABBREV[]_compiler_version="$_ax_[]_AC_LANG_ABBREV[]_compiler_version_major.$_ax_[]_AC_LANG_ABBREV[]_compiler_version_minor.$_ax_[]_AC_LANG_ABBREV[]_compiler_version_patch" ]) # for watcom AC_DEFUN([_AX_COMPILER_VERSION_WATCOM],[dnl AC_COMPUTE_INT(_ax_[]_AC_LANG_ABBREV[]_compiler_version_minor, __WATCOMC__%100,, AC_MSG_FAILURE([[[$0]] unknown watcom compiler minor version])) AC_COMPUTE_INT(_ax_[]_AC_LANG_ABBREV[]_compiler_version_major, (__WATCOMC__/100)%100,, AC_MSG_FAILURE([[[$0]] unknown watcom compiler major version])) ax_cv_[]_AC_LANG_ABBREV[]_compiler_version="$_ax_[]_AC_LANG_ABBREV[]_compiler_version_major.$_ax_[]_AC_LANG_ABBREV[]_compiler_version_minor" ]) # for NVHPC AC_DEFUN([_AX_COMPILER_VERSION_NVHPC],[ AC_COMPUTE_INT(_ax_[]_AC_LANG_ABBREV[]_compiler_version_major, __NVCOMPILER_MAJOR__,, AC_MSG_FAILURE([[[$0]] unknown nvhpc major])) AC_COMPUTE_INT(_ax_[]_AC_LANG_ABBREV[]_compiler_version_minor, __NVCOMPILER_MINOR__,, AC_MSG_FAILURE([[[$0]] unknown nvhpc minor])) AC_COMPUTE_INT(_ax_[]_AC_LANG_ABBREV[]_compiler_version_patch, [__NVCOMPILER_PATCHLEVEL__],, AC_MSG_FAILURE([[[$0]] unknown nvhpc patch level])) ax_cv_[]_AC_LANG_ABBREV[]_compiler_version="$_ax_[]_AC_LANG_ABBREV[]_compiler_version_major.$_ax_[]_AC_LANG_ABBREV[]_compiler_version_minor.$_ax_[]_AC_LANG_ABBREV[]_compiler_version_patch" ]) # for PGI AC_DEFUN([_AX_COMPILER_VERSION_PORTLAND],[ AC_COMPUTE_INT(_ax_[]_AC_LANG_ABBREV[]_compiler_version_major, __PGIC__,, AC_MSG_FAILURE([[[$0]] unknown pgi major])) AC_COMPUTE_INT(_ax_[]_AC_LANG_ABBREV[]_compiler_version_minor, __PGIC_MINOR__,, AC_MSG_FAILURE([[[$0]] unknown pgi minor])) AC_COMPUTE_INT(_ax_[]_AC_LANG_ABBREV[]_compiler_version_patch, [__PGIC_PATCHLEVEL__],, AC_MSG_FAILURE([[[$0]] unknown pgi patch level])) ax_cv_[]_AC_LANG_ABBREV[]_compiler_version="$_ax_[]_AC_LANG_ABBREV[]_compiler_version_major.$_ax_[]_AC_LANG_ABBREV[]_compiler_version_minor.$_ax_[]_AC_LANG_ABBREV[]_compiler_version_patch" ]) # tcc AC_DEFUN([_AX_COMPILER_VERSION_TCC],[ ax_cv_[]_AC_LANG_ABBREV[]_compiler_version=[`tcc -v | $SED 's/^[ ]*tcc[ ]\+version[ ]\+\([0-9.]\+\).*/\1/g'`] ]) # for GNU AC_DEFUN([_AX_COMPILER_VERSION_SDCC],[ AC_COMPUTE_INT(_ax_[]_AC_LANG_ABBREV[]_compiler_version_major, /* avoid parse error with comments */ #if(defined(__SDCC_VERSION_MAJOR)) __SDCC_VERSION_MAJOR #else SDCC/100 #endif ,, AC_MSG_FAILURE([[[$0]] unknown sdcc major])) AC_COMPUTE_INT(_ax_[]_AC_LANG_ABBREV[]_compiler_version_minor, /* avoid parse error with comments */ #if(defined(__SDCC_VERSION_MINOR)) __SDCC_VERSION_MINOR #else (SDCC%100)/10 #endif ,, AC_MSG_FAILURE([[[$0]] unknown sdcc minor])) AC_COMPUTE_INT(_ax_[]_AC_LANG_ABBREV[]_compiler_version_patch, [ /* avoid parse error with comments */ #if(defined(__SDCC_VERSION_PATCH)) __SDCC_VERSION_PATCH #elsif(defined(_SDCC_VERSION_PATCHLEVEL)) __SDCC_VERSION_PATCHLEVEL #else SDCC%10 #endif ],, AC_MSG_FAILURE([[[$0]] unknown sdcc patch level])) ax_cv_[]_AC_LANG_ABBREV[]_compiler_version="$_ax_[]_AC_LANG_ABBREV[]_compiler_version_major.$_ax_[]_AC_LANG_ABBREV[]_compiler_version_minor.$_ax_[]_AC_LANG_ABBREV[]_compiler_version_patch" ]) # main entry point AC_DEFUN([AX_COMPILER_VERSION],[dnl AC_REQUIRE([AX_COMPILER_VENDOR]) AC_REQUIRE([AC_PROG_SED]) AC_CACHE_CHECK([for _AC_LANG compiler version], ax_cv_[]_AC_LANG_ABBREV[]_compiler_version, [ dnl AS_CASE([$ax_cv_[]_AC_LANG_ABBREV[]_compiler_vendor], [intel],[_AX_COMPILER_VERSION_INTEL], [ibm],[_AX_COMPILER_VERSION_IBM], [pathscale],[_AX_COMPILER_VERSION_PATHSCALE], [clang],[_AX_COMPILER_VERSION_CLANG], [cray],[_AX_COMPILER_VERSION_CRAY], [fujitsu],[_AX_COMPILER_VERSION_FUJITSU], [gnu],[_AX_COMPILER_VERSION_GNU], [sun],[_AX_COMPILER_VERSION_SUN], [hp],[_AX_COMPILER_VERSION_HP], [dec],[_AX_COMPILER_VERSION_DEC], [borland],[_AX_COMPILER_VERSION_BORLAND], [comeau],[_AX_COMPILER_VERSION_COMEAU], [kai],[_AX_COMPILER_VERSION_KAI], [sgi],[_AX_COMPILER_VERSION_SGI], [microsoft],[_AX_COMPILER_VERSION_MICROSOFT], [metrowerks],[_AX_COMPILER_VERSION_METROWERKS], [watcom],[_AX_COMPILER_VERSION_WATCOM], [nvhpc],[_AX_COMPILER_VERSION_NVHPC], [portland],[_AX_COMPILER_VERSION_PORTLAND], [tcc],[_AX_COMPILER_VERSION_TCC], [sdcc],[_AX_COMPILER_VERSION_SDCC], [ax_cv_[]_AC_LANG_ABBREV[]_compiler_version=""]) ]) ]) # =========================================================================== # https://www.gnu.org/software/autoconf-archive/ax_ext.html # =========================================================================== # # SYNOPSIS # # AX_EXT # # DESCRIPTION # # Find supported SIMD extensions by requesting cpuid. When a SIMD # extension is found, the -m"simdextensionname" is added to SIMD_FLAGS if # compiler supports it. For example, if "sse2" is available then "-msse2" # is added to SIMD_FLAGS. # # Find other supported CPU extensions by requesting cpuid. When a # processor extension is found, the -m"extensionname" is added to # CPUEXT_FLAGS if compiler supports it. For example, if "bmi2" is # available then "-mbmi2" is added to CPUEXT_FLAGS. # # This macro calls: # # AC_SUBST(SIMD_FLAGS) # AC_SUBST(CPUEXT_FLAGS) # # And defines: # # HAVE_RDRND / HAVE_BMI1 / HAVE_BMI2 / HAVE_ADX / HAVE_MPX # HAVE_PREFETCHWT1 / HAVE_ABM / HAVE_MMX / HAVE_SSE / HAVE_SSE2 # HAVE_SSE3 / HAVE_SSSE3 / HAVE_SSE4_1 / HAVE_SSE4_2 / HAVE_SSE4a # HAVE_SHA / HAVE_AES / HAVE_AVX / HAVE_FMA3 / HAVE_FMA4 / HAVE_XOP # HAVE_AVX2 / HAVE_AVX512_F / HAVE_AVX512_CD / HAVE_AVX512_PF # HAVE_AVX512_ER / HAVE_AVX512_VL / HAVE_AVX512_BW / HAVE_AVX512_DQ # HAVE_AVX512_IFMA / HAVE_AVX512_VBMI / HAVE_ALTIVEC / HAVE_VSX # # LICENSE # # Copyright (c) 2007 Christophe Tournayre # Copyright (c) 2013,2015 Michael Petch # Copyright (c) 2017 Rafael de Lucena Valle # # Copying and distribution of this file, with or without modification, are # permitted in any medium without royalty provided the copyright notice # and this notice are preserved. This file is offered as-is, without any # warranty. #serial 19 AC_DEFUN([AX_EXT], [ AC_REQUIRE([AC_CANONICAL_HOST]) AC_REQUIRE([AC_PROG_CC]) CPUEXT_FLAGS="" SIMD_FLAGS="" case $host_cpu in powerpc*) AC_CACHE_CHECK([whether altivec is supported for old distros], [ax_cv_have_altivec_old_ext], [ if test `/usr/sbin/sysctl -a 2>/dev/null| grep -c hw.optional.altivec` != 0; then if test `/usr/sbin/sysctl -n hw.optional.altivec` = 1; then ax_cv_have_altivec_old_ext=yes fi fi ]) if test "$ax_cv_have_altivec_old_ext" = yes; then AC_DEFINE(HAVE_ALTIVEC,,[Support Altivec instructions]) AX_CHECK_COMPILE_FLAG(-faltivec, SIMD_FLAGS="$SIMD_FLAGS -faltivec", []) fi AC_CACHE_CHECK([whether altivec is supported], [ax_cv_have_altivec_ext], [ if test `LD_SHOW_AUXV=1 /bin/true 2>/dev/null|grep -c altivec` != 0; then ax_cv_have_altivec_ext=yes fi ]) if test "$ax_cv_have_altivec_ext" = yes; then AC_DEFINE(HAVE_ALTIVEC,,[Support Altivec instructions]) AX_CHECK_COMPILE_FLAG(-maltivec, SIMD_FLAGS="$SIMD_FLAGS -maltivec", []) fi AC_CACHE_CHECK([whether vsx is supported], [ax_cv_have_vsx_ext], [ if test `LD_SHOW_AUXV=1 /bin/true 2>/dev/null|grep -c vsx` != 0; then ax_cv_have_vsx_ext=yes fi ]) if test "$ax_cv_have_vsx_ext" = yes; then AC_DEFINE(HAVE_VSX,,[Support VSX instructions]) AX_CHECK_COMPILE_FLAG(-mvsx, SIMD_FLAGS="$SIMD_FLAGS -mvsx", []) fi ;; i[[3456]]86*|x86_64*|amd64*) AC_REQUIRE([AX_GCC_X86_CPUID]) AC_REQUIRE([AX_GCC_X86_CPUID_COUNT]) AC_REQUIRE([AX_GCC_X86_AVX_XGETBV]) eax_cpuid0=0 AX_GCC_X86_CPUID(0x00000000) if test "$ax_cv_gcc_x86_cpuid_0x00000000" != "unknown"; then eax_cpuid0=`echo $ax_cv_gcc_x86_cpuid_0x00000000 | cut -d ":" -f 1` fi eax_cpuid80000000=0 AX_GCC_X86_CPUID(0x80000000) if test "$ax_cv_gcc_x86_cpuid_0x80000000" != "unknown"; then eax_cpuid80000000=`echo $ax_cv_gcc_x86_cpuid_0x80000000 | cut -d ":" -f 1` fi ecx_cpuid1=0 edx_cpuid1=0 if test "$((0x$eax_cpuid0))" -ge 1 ; then AX_GCC_X86_CPUID(0x00000001) if test "$ax_cv_gcc_x86_cpuid_0x00000001" != "unknown"; then ecx_cpuid1=`echo $ax_cv_gcc_x86_cpuid_0x00000001 | cut -d ":" -f 3` edx_cpuid1=`echo $ax_cv_gcc_x86_cpuid_0x00000001 | cut -d ":" -f 4` fi fi ebx_cpuid7=0 ecx_cpuid7=0 if test "$((0x$eax_cpuid0))" -ge 7 ; then AX_GCC_X86_CPUID_COUNT(0x00000007, 0x00) if test "$ax_cv_gcc_x86_cpuid_0x00000007" != "unknown"; then ebx_cpuid7=`echo $ax_cv_gcc_x86_cpuid_0x00000007 | cut -d ":" -f 2` ecx_cpuid7=`echo $ax_cv_gcc_x86_cpuid_0x00000007 | cut -d ":" -f 3` fi fi ecx_cpuid80000001=0 edx_cpuid80000001=0 if test "$((0x$eax_cpuid80000000))" -ge "$((0x80000001))" ; then AX_GCC_X86_CPUID(0x80000001) if test "$ax_cv_gcc_x86_cpuid_0x80000001" != "unknown"; then ecx_cpuid80000001=`echo $ax_cv_gcc_x86_cpuid_0x80000001 | cut -d ":" -f 3` edx_cpuid80000001=`echo $ax_cv_gcc_x86_cpuid_0x80000001 | cut -d ":" -f 4` fi fi AC_CACHE_VAL([ax_cv_have_mmx_os_support_ext], [ ax_cv_have_mmx_os_support_ext=yes ]) ax_cv_have_none_os_support_ext=yes AC_CACHE_VAL([ax_cv_have_sse_os_support_ext], [ ax_cv_have_sse_os_support_ext=no, if test "$((0x$edx_cpuid1>>25&0x01))" = 1; then AC_LANG_PUSH([C]) AC_RUN_IFELSE([AC_LANG_SOURCE([[ #include #include /* No way at ring1 to ring3 in protected mode to check the CR0 and CR4 control registers directly. Execute an SSE instruction. If it raises SIGILL then OS doesn't support SSE based instructions */ void sig_handler(int signum){ exit(1); } int main(void){ signal(SIGILL, sig_handler); /* SSE instruction xorps %xmm0,%xmm0 */ __asm__ __volatile__ (".byte 0x0f, 0x57, 0xc0"); return 0; }]])], [ax_cv_have_sse_os_support_ext=yes], [ax_cv_have_sse_os_support_ext=no], [ax_cv_have_sse_os_support_ext=no]) AC_LANG_POP([C]) fi ]) xgetbv_eax=0 if test "$((0x$ecx_cpuid1>>28&0x01))" = 1; then AX_GCC_X86_AVX_XGETBV(0x00000000) if test x"$ax_cv_gcc_x86_avx_xgetbv_0x00000000" != x"unknown"; then xgetbv_eax=`echo $ax_cv_gcc_x86_avx_xgetbv_0x00000000 | cut -d ":" -f 1` fi AC_CACHE_VAL([ax_cv_have_avx_os_support_ext], [ ax_cv_have_avx_os_support_ext=no if test "$((0x$ecx_cpuid1>>27&0x01))" = 1; then if test "$((0x$xgetbv_eax&0x6))" = 6; then ax_cv_have_avx_os_support_ext=yes fi fi ]) fi AC_CACHE_VAL([ax_cv_have_avx512_os_support_ext], [ ax_cv_have_avx512_os_support_ext=no if test "$ax_cv_have_avx_os_support_ext" = yes; then if test "$((0x$xgetbv_eax&0xe6))" = "$((0xe6))"; then ax_cv_have_avx512_os_support_ext=yes fi fi ]) for ac_instr_info dnl in "none;rdrnd;RDRND;ecx_cpuid1,30;-mrdrnd;HAVE_RDRND;CPUEXT_FLAGS" dnl "none;bmi1;BMI1;ebx_cpuid7,3;-mbmi;HAVE_BMI1;CPUEXT_FLAGS" dnl "none;bmi2;BMI2;ebx_cpuid7,8;-mbmi2;HAVE_BMI2;CPUEXT_FLAGS" dnl "none;adx;ADX;ebx_cpuid7,19;-madx;HAVE_ADX;CPUEXT_FLAGS" dnl "none;mpx;MPX;ebx_cpuid7,14;-mmpx;HAVE_MPX;CPUEXT_FLAGS" dnl "none;prefetchwt1;PREFETCHWT1;ecx_cpuid7,0;-mprefetchwt1;HAVE_PREFETCHWT1;CPUEXT_FLAGS" dnl "none;abm;ABM;ecx_cpuid80000001,5;-mabm;HAVE_ABM;CPUEXT_FLAGS" dnl "mmx;mmx;MMX;edx_cpuid1,23;-mmmx;HAVE_MMX;SIMD_FLAGS" dnl "sse;sse;SSE;edx_cpuid1,25;-msse;HAVE_SSE;SIMD_FLAGS" dnl "sse;sse2;SSE2;edx_cpuid1,26;-msse2;HAVE_SSE2;SIMD_FLAGS" dnl "sse;sse3;SSE3;ecx_cpuid1,1;-msse3;HAVE_SSE3;SIMD_FLAGS" dnl "sse;ssse3;SSSE3;ecx_cpuid1,9;-mssse3;HAVE_SSSE3;SIMD_FLAGS" dnl "sse;sse41;SSE4.1;ecx_cpuid1,19;-msse4.1;HAVE_SSE4_1;SIMD_FLAGS" dnl "sse;sse42;SSE4.2;ecx_cpuid1,20;-msse4.2;HAVE_SSE4_2;SIMD_FLAGS" dnl "sse;sse4a;SSE4a;ecx_cpuid80000001,6;-msse4a;HAVE_SSE4a;SIMD_FLAGS" dnl "sse;sha;SHA;ebx_cpuid7,29;-msha;HAVE_SHA;SIMD_FLAGS" dnl "sse;aes;AES;ecx_cpuid1,25;-maes;HAVE_AES;SIMD_FLAGS" dnl "avx;avx;AVX;ecx_cpuid1,28;-mavx;HAVE_AVX;SIMD_FLAGS" dnl "avx;fma3;FMA3;ecx_cpuid1,12;-mfma;HAVE_FMA3;SIMD_FLAGS" dnl "avx;fma4;FMA4;ecx_cpuid80000001,16;-mfma4;HAVE_FMA4;SIMD_FLAGS" dnl "avx;xop;XOP;ecx_cpuid80000001,11;-mxop;HAVE_XOP;SIMD_FLAGS" dnl "avx;avx2;AVX2;ebx_cpuid7,5;-mavx2;HAVE_AVX2;SIMD_FLAGS" dnl "avx512;avx512f;AVX512-F;ebx_cpuid7,16;-mavx512f;HAVE_AVX512_F;SIMD_FLAGS" dnl "avx512;avx512cd;AVX512-CD;ebx_cpuid7,28;-mavx512cd;HAVE_AVX512_CD;SIMD_FLAGS" dnl "avx512;avx512pf;AVX512-PF;ebx_cpuid7,26;-mavx512pf;HAVE_AVX512_PF;SIMD_FLAGS" dnl "avx512;avx512er;AVX512-ER;ebx_cpuid7,27;-mavx512er;HAVE_AVX512_ER;SIMD_FLAGS" dnl "avx512;avx512vl;AVX512-VL;ebx_cpuid7,31;-mavx512vl;HAVE_AVX512_VL;SIMD_FLAGS" dnl "avx512;avx512bw;AVX512-BW;ebx_cpuid7,30;-mavx512bw;HAVE_AVX512_BW;SIMD_FLAGS" dnl "avx512;avx512dq;AVX512-DQ;ebx_cpuid7,17;-mavx512dq;HAVE_AVX512_DQ;SIMD_FLAGS" dnl "avx512;avx512ifma;AVX512-IFMA;ebx_cpuid7,21;-mavx512ifma;HAVE_AVX512_IFMA;SIMD_FLAGS" dnl "avx512;avx512vbmi;AVX512-VBMI;ecx_cpuid7,1;-mavx512vbmi;HAVE_AVX512_VBMI;SIMD_FLAGS" dnl # do ac_instr_os_support=$(eval echo \$ax_cv_have_$(echo $ac_instr_info | cut -d ";" -f 1)_os_support_ext) ac_instr_acvar=$(echo $ac_instr_info | cut -d ";" -f 2) ac_instr_shortname=$(echo $ac_instr_info | cut -d ";" -f 3) ac_instr_chk_loc=$(echo $ac_instr_info | cut -d ";" -f 4) ac_instr_chk_reg=0x$(eval echo \$$(echo $ac_instr_chk_loc | cut -d "," -f 1)) ac_instr_chk_bit=$(echo $ac_instr_chk_loc | cut -d "," -f 2) ac_instr_compiler_flags=$(echo $ac_instr_info | cut -d ";" -f 5) ac_instr_have_define=$(echo $ac_instr_info | cut -d ";" -f 6) ac_instr_flag_type=$(echo $ac_instr_info | cut -d ";" -f 7) AC_CACHE_CHECK([whether ${ac_instr_shortname} is supported by the processor], [ax_cv_have_${ac_instr_acvar}_cpu_ext], [ eval ax_cv_have_${ac_instr_acvar}_cpu_ext=no if test "$((${ac_instr_chk_reg}>>${ac_instr_chk_bit}&0x01))" = 1 ; then eval ax_cv_have_${ac_instr_acvar}_cpu_ext=yes fi ]) if test x"$(eval echo \$ax_cv_have_${ac_instr_acvar}_cpu_ext)" = x"yes"; then AC_CACHE_CHECK([whether ${ac_instr_shortname} is supported by the processor and OS], [ax_cv_have_${ac_instr_acvar}_ext], [ eval ax_cv_have_${ac_instr_acvar}_ext=no if test x"${ac_instr_os_support}" = x"yes"; then eval ax_cv_have_${ac_instr_acvar}_ext=yes fi ]) if test "$(eval echo \$ax_cv_have_${ac_instr_acvar}_ext)" = yes; then AX_CHECK_COMPILE_FLAG(${ac_instr_compiler_flags}, eval ax_cv_support_${ac_instr_acvar}_ext=yes, eval ax_cv_support_${ac_instr_acvar}_ext=no) if test x"$(eval echo \$ax_cv_support_${ac_instr_acvar}_ext)" = x"yes"; then eval ${ac_instr_flag_type}=\"\$${ac_instr_flag_type} ${ac_instr_compiler_flags}\" AC_DEFINE_UNQUOTED([${ac_instr_have_define}]) else AC_MSG_WARN([Your processor and OS supports ${ac_instr_shortname} instructions but not your compiler, can you try another compiler?]) fi else if test x"${ac_instr_os_support}" = x"no"; then AC_CACHE_VAL(ax_cv_support_${ac_instr_acvar}_ext, eval ax_cv_support_${ac_instr_acvar}_ext=no) AC_MSG_WARN([Your processor supports ${ac_instr_shortname}, but your OS doesn't]) fi fi else AC_CACHE_VAL(ax_cv_have_${ac_instr_acvar}_ext, eval ax_cv_have_${ac_instr_acvar}_ext=no) AC_CACHE_VAL(ax_cv_support_${ac_instr_acvar}_ext, eval ax_cv_support_${ac_instr_acvar}_ext=no) fi done ;; esac AH_TEMPLATE([HAVE_RDRND],[Define to 1 to support Digital Random Number Generator]) AH_TEMPLATE([HAVE_BMI1],[Define to 1 to support Bit Manipulation Instruction Set 1]) AH_TEMPLATE([HAVE_BMI2],[Define to 1 to support Bit Manipulation Instruction Set 2]) AH_TEMPLATE([HAVE_ADX],[Define to 1 to support Multi-Precision Add-Carry Instruction Extensions]) AH_TEMPLATE([HAVE_MPX],[Define to 1 to support Memory Protection Extensions]) AH_TEMPLATE([HAVE_PREFETCHWT1],[Define to 1 to support Prefetch Vector Data Into Caches WT1]) AH_TEMPLATE([HAVE_ABM],[Define to 1 to support Advanced Bit Manipulation]) AH_TEMPLATE([HAVE_MMX],[Define to 1 to support Multimedia Extensions]) AH_TEMPLATE([HAVE_SSE],[Define to 1 to support Streaming SIMD Extensions]) AH_TEMPLATE([HAVE_SSE2],[Define to 1 to support Streaming SIMD Extensions]) AH_TEMPLATE([HAVE_SSE3],[Define to 1 to support Streaming SIMD Extensions 3]) AH_TEMPLATE([HAVE_SSSE3],[Define to 1 to support Supplemental Streaming SIMD Extensions 3]) AH_TEMPLATE([HAVE_SSE4_1],[Define to 1 to support Streaming SIMD Extensions 4.1]) AH_TEMPLATE([HAVE_SSE4_2],[Define to 1 to support Streaming SIMD Extensions 4.2]) AH_TEMPLATE([HAVE_SSE4a],[Define to 1 to support AMD Streaming SIMD Extensions 4a]) AH_TEMPLATE([HAVE_SHA],[Define to 1 to support Secure Hash Algorithm Extension]) AH_TEMPLATE([HAVE_AES],[Define to 1 to support Advanced Encryption Standard New Instruction Set (AES-NI)]) AH_TEMPLATE([HAVE_AVX],[Define to 1 to support Advanced Vector Extensions]) AH_TEMPLATE([HAVE_FMA3],[Define to 1 to support Fused Multiply-Add Extensions 3]) AH_TEMPLATE([HAVE_FMA4],[Define to 1 to support Fused Multiply-Add Extensions 4]) AH_TEMPLATE([HAVE_XOP],[Define to 1 to support eXtended Operations Extensions]) AH_TEMPLATE([HAVE_AVX2],[Define to 1 to support Advanced Vector Extensions 2]) AH_TEMPLATE([HAVE_AVX512_F],[Define to 1 to support AVX-512 Foundation Extensions]) AH_TEMPLATE([HAVE_AVX512_CD],[Define to 1 to support AVX-512 Conflict Detection Instructions]) AH_TEMPLATE([HAVE_AVX512_PF],[Define to 1 to support AVX-512 Conflict Prefetch Instructions]) AH_TEMPLATE([HAVE_AVX512_ER],[Define to 1 to support AVX-512 Exponential & Reciprocal Instructions]) AH_TEMPLATE([HAVE_AVX512_VL],[Define to 1 to support AVX-512 Vector Length Extensions]) AH_TEMPLATE([HAVE_AVX512_BW],[Define to 1 to support AVX-512 Byte and Word Instructions]) AH_TEMPLATE([HAVE_AVX512_DQ],[Define to 1 to support AVX-512 Doubleword and Quadword Instructions]) AH_TEMPLATE([HAVE_AVX512_IFMA],[Define to 1 to support AVX-512 Integer Fused Multiply Add Instructions]) AH_TEMPLATE([HAVE_AVX512_VBMI],[Define to 1 to support AVX-512 Vector Byte Manipulation Instructions]) AC_SUBST(SIMD_FLAGS) AC_SUBST(CPUEXT_FLAGS) ]) # =========================================================================== # https://www.gnu.org/software/autoconf-archive/ax_gcc_x86_avx_xgetbv.html # =========================================================================== # # SYNOPSIS # # AX_GCC_X86_AVX_XGETBV # # DESCRIPTION # # On later x86 processors with AVX SIMD support, with gcc or a compiler # that has a compatible syntax for inline assembly instructions, run a # small program that executes the xgetbv instruction with input OP. This # can be used to detect if the OS supports AVX instruction usage. # # On output, the values of the eax and edx registers are stored as # hexadecimal strings as "eax:edx" in the cache variable # ax_cv_gcc_x86_avx_xgetbv. # # If the xgetbv instruction fails (because you are running a # cross-compiler, or because you are not using gcc, or because you are on # a processor that doesn't have this instruction), # ax_cv_gcc_x86_avx_xgetbv_OP is set to the string "unknown". # # This macro mainly exists to be used in AX_EXT. # # LICENSE # # Copyright (c) 2013 Michael Petch # # This program is free software: you can redistribute it and/or modify it # under the terms of the GNU General Public License as published by the # Free Software Foundation, either version 3 of the License, or (at your # option) any later version. # # This program is distributed in the hope that it will be useful, but # WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General # Public License for more details. # # You should have received a copy of the GNU General Public License along # with this program. If not, see . # # As a special exception, the respective Autoconf Macro's copyright owner # gives unlimited permission to copy, distribute and modify the configure # scripts that are the output of Autoconf when processing the Macro. You # need not follow the terms of the GNU General Public License when using # or distributing such scripts, even though portions of the text of the # Macro appear in them. The GNU General Public License (GPL) does govern # all other use of the material that constitutes the Autoconf Macro. # # This special exception to the GPL applies to versions of the Autoconf # Macro released by the Autoconf Archive. When you make and distribute a # modified version of the Autoconf Macro, you may extend this special # exception to the GPL to apply to your modified version as well. #serial 3 AC_DEFUN([AX_GCC_X86_AVX_XGETBV], [AC_REQUIRE([AC_PROG_CC]) AC_LANG_PUSH([C]) AC_CACHE_CHECK(for x86-AVX xgetbv $1 output, ax_cv_gcc_x86_avx_xgetbv_$1, [AC_RUN_IFELSE([AC_LANG_PROGRAM([#include ], [ int op = $1, eax, edx; FILE *f; /* Opcodes for xgetbv */ __asm__ __volatile__ (".byte 0x0f, 0x01, 0xd0" : "=a" (eax), "=d" (edx) : "c" (op)); f = fopen("conftest_xgetbv", "w"); if (!f) return 1; fprintf(f, "%x:%x\n", eax, edx); fclose(f); return 0; ])], [ax_cv_gcc_x86_avx_xgetbv_$1=`cat conftest_xgetbv`; rm -f conftest_xgetbv], [ax_cv_gcc_x86_avx_xgetbv_$1=unknown; rm -f conftest_xgetbv], [ax_cv_gcc_x86_avx_xgetbv_$1=unknown])]) AC_LANG_POP([C]) ]) # =========================================================================== # https://www.gnu.org/software/autoconf-archive/ax_gcc_x86_cpuid.html # =========================================================================== # # SYNOPSIS # # AX_GCC_X86_CPUID(OP) # AX_GCC_X86_CPUID_COUNT(OP, COUNT) # # DESCRIPTION # # On Pentium and later x86 processors, with gcc or a compiler that has a # compatible syntax for inline assembly instructions, run a small program # that executes the cpuid instruction with input OP. This can be used to # detect the CPU type. AX_GCC_X86_CPUID_COUNT takes an additional COUNT # parameter that gets passed into register ECX before calling cpuid. # # On output, the values of the eax, ebx, ecx, and edx registers are stored # as hexadecimal strings as "eax:ebx:ecx:edx" in the cache variable # ax_cv_gcc_x86_cpuid_OP. # # If the cpuid instruction fails (because you are running a # cross-compiler, or because you are not using gcc, or because you are on # a processor that doesn't have this instruction), ax_cv_gcc_x86_cpuid_OP # is set to the string "unknown". # # This macro mainly exists to be used in AX_GCC_ARCHFLAG. # # LICENSE # # Copyright (c) 2008 Steven G. Johnson # Copyright (c) 2008 Matteo Frigo # Copyright (c) 2015 Michael Petch # # This program is free software: you can redistribute it and/or modify it # under the terms of the GNU General Public License as published by the # Free Software Foundation, either version 3 of the License, or (at your # option) any later version. # # This program is distributed in the hope that it will be useful, but # WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General # Public License for more details. # # You should have received a copy of the GNU General Public License along # with this program. If not, see . # # As a special exception, the respective Autoconf Macro's copyright owner # gives unlimited permission to copy, distribute and modify the configure # scripts that are the output of Autoconf when processing the Macro. You # need not follow the terms of the GNU General Public License when using # or distributing such scripts, even though portions of the text of the # Macro appear in them. The GNU General Public License (GPL) does govern # all other use of the material that constitutes the Autoconf Macro. # # This special exception to the GPL applies to versions of the Autoconf # Macro released by the Autoconf Archive. When you make and distribute a # modified version of the Autoconf Macro, you may extend this special # exception to the GPL to apply to your modified version as well. #serial 10 AC_DEFUN([AX_GCC_X86_CPUID], [AX_GCC_X86_CPUID_COUNT($1, 0) ]) AC_DEFUN([AX_GCC_X86_CPUID_COUNT], [AC_REQUIRE([AC_PROG_CC]) AC_LANG_PUSH([C]) AC_CACHE_CHECK(for x86 cpuid $1 output, ax_cv_gcc_x86_cpuid_$1, [AC_RUN_IFELSE([AC_LANG_PROGRAM([#include ], [ int op = $1, level = $2, eax, ebx, ecx, edx; FILE *f; __asm__ __volatile__ ("xchg %%ebx, %1\n" "cpuid\n" "xchg %%ebx, %1\n" : "=a" (eax), "=r" (ebx), "=c" (ecx), "=d" (edx) : "a" (op), "2" (level)); f = fopen("conftest_cpuid", "w"); if (!f) return 1; fprintf(f, "%x:%x:%x:%x\n", eax, ebx, ecx, edx); fclose(f); return 0; ])], [ax_cv_gcc_x86_cpuid_$1=`cat conftest_cpuid`; rm -f conftest_cpuid], [ax_cv_gcc_x86_cpuid_$1=unknown; rm -f conftest_cpuid], [ax_cv_gcc_x86_cpuid_$1=unknown])]) AC_LANG_POP([C]) ]) # =========================================================================== # https://www.gnu.org/software/autoconf-archive/ax_lib_readline.html # =========================================================================== # # SYNOPSIS # # AX_LIB_READLINE # AX_LIB_READLINE([MIN-VERSION]) # # DESCRIPTION # # Searches for a readline compatible library. If found, defines # `HAVE_LIBREADLINE'. If the found library has the `add_history' function, # sets also `HAVE_READLINE_HISTORY'. Also checks for the locations of the # necessary include files and sets `HAVE_READLINE_H' or # `HAVE_READLINE_READLINE_H' and `HAVE_READLINE_HISTORY_H' or # 'HAVE_HISTORY_H' if the corresponding include files exists. # # If given, MIN-VERSION specifies the minimum acceptable version; if that # version is not found, HAVE_LIBREADLINE and HAVE_READLINE_HISTORY will # not be defined. # # The libraries that may be readline compatible are `libedit', # `libeditline' and `libreadline'. Sometimes we need to link a termcap # library for readline to work, this macro tests these cases too by trying # to link with `libtermcap', `libcurses' or `libncurses' before giving up. # # Here is an example of how to use the information provided by this macro # to perform the necessary includes or declarations in a C file: # # #ifdef HAVE_LIBREADLINE # # if defined(HAVE_READLINE_READLINE_H) # # include # # elif defined(HAVE_READLINE_H) # # include # # else /* !defined(HAVE_READLINE_H) */ # extern char *readline (); # # endif /* !defined(HAVE_READLINE_H) */ # char *cmdline = NULL; # #else /* !defined(HAVE_READLINE_READLINE_H) */ # /* no readline */ # #endif /* HAVE_LIBREADLINE */ # # #ifdef HAVE_READLINE_HISTORY # # if defined(HAVE_READLINE_HISTORY_H) # # include # # elif defined(HAVE_HISTORY_H) # # include # # else /* !defined(HAVE_HISTORY_H) */ # extern void add_history (); # extern int write_history (); # extern int read_history (); # # endif /* defined(HAVE_READLINE_HISTORY_H) */ # /* no history */ # #endif /* HAVE_READLINE_HISTORY */ # # LICENSE # # Copyright (c) 2024 Reuben Thomas # Copyright (c) 2008 Ville Laurikari # # Copying and distribution of this file, with or without modification, are # permitted in any medium without royalty provided the copyright notice # and this notice are preserved. This file is offered as-is, without any # warranty. #serial 9 AU_ALIAS([VL_LIB_READLINE], [AX_LIB_READLINE]) AC_DEFUN([AX_LIB_READLINE], [ AC_CACHE_CHECK([for a readline compatible library], ax_cv_lib_readline, [ ORIG_LIBS="$LIBS" for readline_lib in readline edit editline; do for termcap_lib in "" termcap curses ncurses; do if test -z "$termcap_lib"; then TRY_LIB="-l$readline_lib" else TRY_LIB="-l$readline_lib -l$termcap_lib" fi LIBS="$ORIG_LIBS $TRY_LIB" AC_LINK_IFELSE([AC_LANG_CALL([], [readline])], [ax_cv_lib_readline="$TRY_LIB"]) if test -n "$ax_cv_lib_readline"; then break fi done if test -n "$ax_cv_lib_readline"; then break fi done if test -z "$ax_cv_lib_readline"; then ax_cv_lib_readline="no" fi LIBS="$ORIG_LIBS" ]) if test "$ax_cv_lib_readline" != "no"; then LIBS="$LIBS $ax_cv_lib_readline" AC_CHECK_HEADERS(readline.h readline/readline.h) m4_if([$1], [], [ dnl No version given, so assume it is OK ax_cv_lib_readline_version_ok=yes ], [ dnl Check against given version AC_CACHE_CHECK([check readline is at least version $1], ax_cv_lib_readline_version_ok, [ AC_RUN_IFELSE([ AC_LANG_SOURCE([[ #include #if defined(HAVE_READLINE_READLINE_H) # include #elif defined(HAVE_READLINE_H) # include #endif int main(void) { float min_version = $1; int min_version_major = (int)min_version; int min_version_minor = (int)(min_version * 100.0); int min_version_hex = min_version_minor + (min_version_major * 256); return !(RL_READLINE_VERSION >= min_version_hex); } ]]) ], [ax_cv_lib_readline_version_ok=yes], [ax_cv_lib_readline_version_ok=no], [ax_cv_lib_readline_version_ok=no]) ]) ] ) if test "$ax_cv_lib_readline_version_ok" = "yes"; then AC_DEFINE(HAVE_LIBREADLINE, 1, [Define if you have a readline compatible library]) AC_CACHE_CHECK([whether readline supports history], ax_cv_lib_readline_history, [ ax_cv_lib_readline_history="no" AC_LINK_IFELSE([AC_LANG_CALL([], [add_history])], [ax_cv_lib_readline_history="yes"]) ]) if test "$ax_cv_lib_readline_history" = "yes"; then AC_DEFINE(HAVE_READLINE_HISTORY, 1, [Define if your readline library has \`add_history']) AC_CHECK_HEADERS(history.h readline/history.h) fi else LIBS="$ORIG_LIBS" fi fi ])dnl # =========================================================================== # https://www.gnu.org/software/autoconf-archive/ax_prepend_flag.html # =========================================================================== # # SYNOPSIS # # AX_PREPEND_FLAG(FLAG, [FLAGS-VARIABLE]) # # DESCRIPTION # # FLAG is added to the front of the FLAGS-VARIABLE shell variable, with a # space added in between. # # If FLAGS-VARIABLE is not specified, the current language's flags (e.g. # CFLAGS) is used. FLAGS-VARIABLE is not changed if it already contains # FLAG. If FLAGS-VARIABLE is unset in the shell, it is set to exactly # FLAG. # # NOTE: Implementation based on AX_APPEND_FLAG. # # LICENSE # # Copyright (c) 2008 Guido U. Draheim # Copyright (c) 2011 Maarten Bosmans # Copyright (c) 2018 John Zaitseff # # Copying and distribution of this file, with or without modification, are # permitted in any medium without royalty provided the copyright notice # and this notice are preserved. This file is offered as-is, without any # warranty. #serial 2 AC_DEFUN([AX_PREPEND_FLAG], [dnl AC_PREREQ(2.64)dnl for _AC_LANG_PREFIX and AS_VAR_SET_IF AS_VAR_PUSHDEF([FLAGS], [m4_default($2,_AC_LANG_PREFIX[FLAGS])]) AS_VAR_SET_IF(FLAGS,[ AS_CASE([" AS_VAR_GET(FLAGS) "], [*" $1 "*], [AC_RUN_LOG([: FLAGS already contains $1])], [ FLAGS="$1 $FLAGS" AC_RUN_LOG([: FLAGS="$FLAGS"]) ]) ], [ AS_VAR_SET(FLAGS,[$1]) AC_RUN_LOG([: FLAGS="$FLAGS"]) ]) AS_VAR_POPDEF([FLAGS])dnl ])dnl AX_PREPEND_FLAG # =========================================================================== # https://www.gnu.org/software/autoconf-archive/ax_prog_cc_mpi.html # =========================================================================== # # SYNOPSIS # # AX_PROG_CC_MPI([MPI-WANTED-TEST[, ACTION-IF-FOUND[, ACTION-IF-NOT-FOUND]]]) # # DESCRIPTION # # This macro tries to find out how to compile C programs that use MPI # (Message Passing Interface), a standard API for parallel process # communication (see http://www-unix.mcs.anl.gov/mpi/). The macro has to # be used instead of the standard macro AC_PROG_CC and will replace the # standard variable CC with the found compiler. # # MPI-WANTED-TEST is used to test whether MPI is actually wanted by the # user. If MPI-WANTED_TEST is omitted or if it succeeds, the macro will # try to find out how to use MPI, if it fails, the macro will call # AC_PROG_CC to find a standard C compiler instead. # # When MPI is found, ACTION-IF-FOUND will be executed, if MPI is not found # (or MPI-WANTED-TEST fails) ACTION-IF-NOT-FOUND is executed. If # ACTION-IF-FOUND is not set, the macro will define HAVE_MPI. # # The following example demonstrates usage of the macro: # # # If --with-mpi=auto is used, try to find MPI, but use standard C compiler if it is not found. # # If --with-mpi=yes is used, try to find MPI and fail if it isn't found. # # If --with-mpi=no is used, use a standard C compiler instead. # AC_ARG_WITH(mpi, [AS_HELP_STRING([--with-mpi], # [compile with MPI (parallelization) support. If none is found, # MPI is not used. Default: auto]) # ],,[with_mpi=auto]) # # # AX_PROG_CC_MPI([test x"$with_mpi" != xno],[use_mpi=yes],[ # use_mpi=no # if test x"$with_mpi" = xyes; then # AC_MSG_FAILURE([MPI compiler requested, but couldn't use MPI.]) # else # AC_MSG_WARN([No MPI compiler found, won't use MPI.]) # fi # ]) # # LICENSE # # Copyright (c) 2010,2011 Olaf Lenz # # This program is free software: you can redistribute it and/or modify it # under the terms of the GNU General Public License as published by the # Free Software Foundation, either version 3 of the License, or (at your # option) any later version. # # This program is distributed in the hope that it will be useful, but # WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General # Public License for more details. # # You should have received a copy of the GNU General Public License along # with this program. If not, see . # # As a special exception, the respective Autoconf Macro's copyright owner # gives unlimited permission to copy, distribute and modify the configure # scripts that are the output of Autoconf when processing the Macro. You # need not follow the terms of the GNU General Public License when using # or distributing such scripts, even though portions of the text of the # Macro appear in them. The GNU General Public License (GPL) does govern # all other use of the material that constitutes the Autoconf Macro. # # This special exception to the GPL applies to versions of the Autoconf # Macro released by the Autoconf Archive. When you make and distribute a # modified version of the Autoconf Macro, you may extend this special # exception to the GPL to apply to your modified version as well. #serial 2 AC_DEFUN([AX_PROG_CC_MPI], [ AC_PREREQ(2.50) # Check for compiler # Needs to be split off into an extra macro to ensure right expansion # order. AC_REQUIRE([_AX_PROG_CC_MPI],[_AX_PROG_CC_MPI([$1])]) AS_IF([test x"$_ax_prog_cc_mpi_mpi_wanted" = xno], [ _ax_prog_cc_mpi_mpi_found=no ], [ AC_LANG_PUSH([C]) # test whether MPI_Init is available # We do not use AC_SEARCH_LIBS here, as it caches its outcome and # thus disallows corresponding calls in the other AX_PROG_*_MPI # macros. for lib in NONE mpi mpich; do save_LIBS=$LIBS if test x"$lib" = xNONE; then AC_MSG_CHECKING([for function MPI_Init]) else AC_MSG_CHECKING([for function MPI_Init in -l$lib]) LIBS="-l$lib $LIBS" fi AC_LINK_IFELSE([AC_LANG_CALL([],[MPI_Init])], [ _ax_prog_cc_mpi_mpi_found=yes ], [ _ax_prog_cc_mpi_mpi_found=no ]) AC_MSG_RESULT($_ax_prog_cc_mpi_mpi_found) if test "x$_ax_prog_cc_mpi_mpi_found" = "xyes"; then break; fi LIBS=$save_LIBS done # Check for header AS_IF([test x"$_ax_prog_cc_mpi_mpi_found" = xyes], [ AC_MSG_CHECKING([for mpi.h]) AC_COMPILE_IFELSE([AC_LANG_PROGRAM([#include ])], [ AC_MSG_RESULT(yes)], [ AC_MSG_RESULT(no) _ax_prog_cc_mpi_mpi_found=no ]) ]) AC_LANG_POP([C]) ]) # Finally, execute ACTION-IF-FOUND/ACTION-IF-NOT-FOUND: AS_IF([test x"$_ax_prog_cc_mpi_mpi_found" = xyes], [ ifelse([$2],,[AC_DEFINE(HAVE_MPI,1,[Define if you have the MPI library.])],[$2]) : ],[ $3 : ]) ])dnl AX_PROG_CC_MPI dnl _AX_PROG_CC_MPI is an internal macro required by AX_PROG_CC_MPI. dnl To ensure the right expansion order, the main function AX_PROG_CC_MPI dnl has to be split into two parts. dnl dnl Known MPI C compilers: dnl mpicc dnl mpixlc_r dnl mpixlc dnl hcc dnl mpxlc_r dnl mpxlc dnl sxmpicc NEC SX dnl mpifcc Fujitsu dnl mpgcc dnl mpcc dnl cmpicc dnl cc dnl AC_DEFUN([_AX_PROG_CC_MPI], [ AC_ARG_VAR(MPICC,[MPI C compiler command]) ifelse([$1],,[_ax_prog_cc_mpi_mpi_wanted=yes],[ AC_MSG_CHECKING([whether to compile using MPI]) if $1; then _ax_prog_cc_mpi_mpi_wanted=yes else _ax_prog_cc_mpi_mpi_wanted=no fi AC_MSG_RESULT($_ax_prog_cc_mpi_mpi_wanted) ]) if test x"$_ax_prog_cc_mpi_mpi_wanted" = xyes; then if test -z "$CC" && test -n "$MPICC"; then CC="$MPICC" else AC_CHECK_TOOLS([CC], [mpicc mpixlc_r mpixlc hcc mpxlc_r mpxlc sxmpicc mpifcc mpgcc mpcc cmpicc cc gcc]) fi fi AC_PROG_CC ])dnl _AX_PROG_CC_MPI # =========================================================================== # https://www.gnu.org/software/autoconf-archive/ax_require_defined.html # =========================================================================== # # SYNOPSIS # # AX_REQUIRE_DEFINED(MACRO) # # DESCRIPTION # # AX_REQUIRE_DEFINED is a simple helper for making sure other macros have # been defined and thus are available for use. This avoids random issues # where a macro isn't expanded. Instead the configure script emits a # non-fatal: # # ./configure: line 1673: AX_CFLAGS_WARN_ALL: command not found # # It's like AC_REQUIRE except it doesn't expand the required macro. # # Here's an example: # # AX_REQUIRE_DEFINED([AX_CHECK_LINK_FLAG]) # # LICENSE # # Copyright (c) 2014 Mike Frysinger # # Copying and distribution of this file, with or without modification, are # permitted in any medium without royalty provided the copyright notice # and this notice are preserved. This file is offered as-is, without any # warranty. #serial 2 AC_DEFUN([AX_REQUIRE_DEFINED], [dnl m4_ifndef([$1], [m4_fatal([macro ]$1[ is not defined; is a m4 file missing?])]) ])dnl AX_REQUIRE_DEFINED # pkg.m4 - Macros to locate and utilise pkg-config. -*- Autoconf -*- # serial 12 (pkg-config-0.29.2) dnl Copyright © 2004 Scott James Remnant . dnl Copyright © 2012-2015 Dan Nicholson dnl dnl This program is free software; you can redistribute it and/or modify dnl it under the terms of the GNU General Public License as published by dnl the Free Software Foundation; either version 2 of the License, or dnl (at your option) any later version. dnl dnl This program is distributed in the hope that it will be useful, but dnl WITHOUT ANY WARRANTY; without even the implied warranty of dnl MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU dnl General Public License for more details. dnl dnl You should have received a copy of the GNU General Public License dnl along with this program; if not, write to the Free Software dnl Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA dnl 02111-1307, USA. dnl dnl As a special exception to the GNU General Public License, if you dnl distribute this file as part of a program that contains a dnl configuration script generated by Autoconf, you may include it under dnl the same distribution terms that you use for the rest of that dnl program. dnl PKG_PREREQ(MIN-VERSION) dnl ----------------------- dnl Since: 0.29 dnl dnl Verify that the version of the pkg-config macros are at least dnl MIN-VERSION. Unlike PKG_PROG_PKG_CONFIG, which checks the user's dnl installed version of pkg-config, this checks the developer's version dnl of pkg.m4 when generating configure. dnl dnl To ensure that this macro is defined, also add: dnl m4_ifndef([PKG_PREREQ], dnl [m4_fatal([must install pkg-config 0.29 or later before running autoconf/autogen])]) dnl dnl See the "Since" comment for each macro you use to see what version dnl of the macros you require. m4_defun([PKG_PREREQ], [m4_define([PKG_MACROS_VERSION], [0.29.2]) m4_if(m4_version_compare(PKG_MACROS_VERSION, [$1]), -1, [m4_fatal([pkg.m4 version $1 or higher is required but ]PKG_MACROS_VERSION[ found])]) ])dnl PKG_PREREQ dnl PKG_PROG_PKG_CONFIG([MIN-VERSION]) dnl ---------------------------------- dnl Since: 0.16 dnl dnl Search for the pkg-config tool and set the PKG_CONFIG variable to dnl first found in the path. Checks that the version of pkg-config found dnl is at least MIN-VERSION. If MIN-VERSION is not specified, 0.9.0 is dnl used since that's the first version where most current features of dnl pkg-config existed. AC_DEFUN([PKG_PROG_PKG_CONFIG], [m4_pattern_forbid([^_?PKG_[A-Z_]+$]) m4_pattern_allow([^PKG_CONFIG(_(PATH|LIBDIR|SYSROOT_DIR|ALLOW_SYSTEM_(CFLAGS|LIBS)))?$]) m4_pattern_allow([^PKG_CONFIG_(DISABLE_UNINSTALLED|TOP_BUILD_DIR|DEBUG_SPEW)$]) AC_ARG_VAR([PKG_CONFIG], [path to pkg-config utility]) AC_ARG_VAR([PKG_CONFIG_PATH], [directories to add to pkg-config's search path]) AC_ARG_VAR([PKG_CONFIG_LIBDIR], [path overriding pkg-config's built-in search path]) if test "x$ac_cv_env_PKG_CONFIG_set" != "xset"; then AC_PATH_TOOL([PKG_CONFIG], [pkg-config]) fi if test -n "$PKG_CONFIG"; then _pkg_min_version=m4_default([$1], [0.9.0]) AC_MSG_CHECKING([pkg-config is at least version $_pkg_min_version]) if $PKG_CONFIG --atleast-pkgconfig-version $_pkg_min_version; then AC_MSG_RESULT([yes]) else AC_MSG_RESULT([no]) PKG_CONFIG="" fi fi[]dnl ])dnl PKG_PROG_PKG_CONFIG dnl PKG_CHECK_EXISTS(MODULES, [ACTION-IF-FOUND], [ACTION-IF-NOT-FOUND]) dnl ------------------------------------------------------------------- dnl Since: 0.18 dnl dnl Check to see whether a particular set of modules exists. Similar to dnl PKG_CHECK_MODULES(), but does not set variables or print errors. dnl dnl Please remember that m4 expands AC_REQUIRE([PKG_PROG_PKG_CONFIG]) dnl only at the first occurence in configure.ac, so if the first place dnl it's called might be skipped (such as if it is within an "if", you dnl have to call PKG_CHECK_EXISTS manually AC_DEFUN([PKG_CHECK_EXISTS], [AC_REQUIRE([PKG_PROG_PKG_CONFIG])dnl if test -n "$PKG_CONFIG" && \ AC_RUN_LOG([$PKG_CONFIG --exists --print-errors "$1"]); then m4_default([$2], [:]) m4_ifvaln([$3], [else $3])dnl fi]) dnl _PKG_CONFIG([VARIABLE], [COMMAND], [MODULES]) dnl --------------------------------------------- dnl Internal wrapper calling pkg-config via PKG_CONFIG and setting dnl pkg_failed based on the result. m4_define([_PKG_CONFIG], [if test -n "$$1"; then pkg_cv_[]$1="$$1" elif test -n "$PKG_CONFIG"; then PKG_CHECK_EXISTS([$3], [pkg_cv_[]$1=`$PKG_CONFIG --[]$2 "$3" 2>/dev/null` test "x$?" != "x0" && pkg_failed=yes ], [pkg_failed=yes]) else pkg_failed=untried fi[]dnl ])dnl _PKG_CONFIG dnl _PKG_SHORT_ERRORS_SUPPORTED dnl --------------------------- dnl Internal check to see if pkg-config supports short errors. 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ALPHA_CPU_TYPE=`/usr/sbin/psrinfo -v | sed -n -e 's/^ The alpha \(.*\) processor.*$/\1/p' | head -n 1` case $ALPHA_CPU_TYPE in "EV4 (21064)") UNAME_MACHINE=alpha ;; "EV4.5 (21064)") UNAME_MACHINE=alpha ;; "LCA4 (21066/21068)") UNAME_MACHINE=alpha ;; "EV5 (21164)") UNAME_MACHINE=alphaev5 ;; "EV5.6 (21164A)") UNAME_MACHINE=alphaev56 ;; "EV5.6 (21164PC)") UNAME_MACHINE=alphapca56 ;; "EV5.7 (21164PC)") UNAME_MACHINE=alphapca57 ;; "EV6 (21264)") UNAME_MACHINE=alphaev6 ;; "EV6.7 (21264A)") UNAME_MACHINE=alphaev67 ;; "EV6.8CB (21264C)") UNAME_MACHINE=alphaev68 ;; "EV6.8AL (21264B)") UNAME_MACHINE=alphaev68 ;; "EV6.8CX (21264D)") UNAME_MACHINE=alphaev68 ;; "EV6.9A (21264/EV69A)") UNAME_MACHINE=alphaev69 ;; "EV7 (21364)") UNAME_MACHINE=alphaev7 ;; "EV7.9 (21364A)") UNAME_MACHINE=alphaev79 ;; esac # A Pn.n version is a patched version. # A Vn.n version is a released version. # A Tn.n version is a released field test version. # A Xn.n version is an unreleased experimental baselevel. # 1.2 uses "1.2" for uname -r. OSF_REL=`echo "$UNAME_RELEASE" | sed -e 's/^[PVTX]//' | tr ABCDEFGHIJKLMNOPQRSTUVWXYZ abcdefghijklmnopqrstuvwxyz` GUESS=$UNAME_MACHINE-dec-osf$OSF_REL ;; Amiga*:UNIX_System_V:4.0:*) GUESS=m68k-unknown-sysv4 ;; *:[Aa]miga[Oo][Ss]:*:*) GUESS=$UNAME_MACHINE-unknown-amigaos ;; *:[Mm]orph[Oo][Ss]:*:*) GUESS=$UNAME_MACHINE-unknown-morphos ;; *:OS/390:*:*) GUESS=i370-ibm-openedition ;; *:z/VM:*:*) GUESS=s390-ibm-zvmoe ;; *:OS400:*:*) GUESS=powerpc-ibm-os400 ;; arm:RISC*:1.[012]*:*|arm:riscix:1.[012]*:*) GUESS=arm-acorn-riscix$UNAME_RELEASE ;; arm*:riscos:*:*|arm*:RISCOS:*:*) GUESS=arm-unknown-riscos ;; SR2?01:HI-UX/MPP:*:* | SR8000:HI-UX/MPP:*:*) GUESS=hppa1.1-hitachi-hiuxmpp ;; Pyramid*:OSx*:*:* | MIS*:OSx*:*:* | MIS*:SMP_DC-OSx*:*:*) # akee@wpdis03.wpafb.af.mil (Earle F. Ake) contributed MIS and NILE. case `(/bin/universe) 2>/dev/null` in att) GUESS=pyramid-pyramid-sysv3 ;; *) GUESS=pyramid-pyramid-bsd ;; esac ;; NILE*:*:*:dcosx) GUESS=pyramid-pyramid-svr4 ;; DRS?6000:unix:4.0:6*) GUESS=sparc-icl-nx6 ;; DRS?6000:UNIX_SV:4.2*:7* | DRS?6000:isis:4.2*:7*) case `/usr/bin/uname -p` in sparc) GUESS=sparc-icl-nx7 ;; esac ;; s390x:SunOS:*:*) SUN_REL=`echo "$UNAME_RELEASE" | sed -e 's/[^.]*//'` GUESS=$UNAME_MACHINE-ibm-solaris2$SUN_REL ;; sun4H:SunOS:5.*:*) SUN_REL=`echo "$UNAME_RELEASE" | sed -e 's/[^.]*//'` GUESS=sparc-hal-solaris2$SUN_REL ;; sun4*:SunOS:5.*:* | tadpole*:SunOS:5.*:*) SUN_REL=`echo "$UNAME_RELEASE" | sed -e 's/[^.]*//'` GUESS=sparc-sun-solaris2$SUN_REL ;; i86pc:AuroraUX:5.*:* | i86xen:AuroraUX:5.*:*) GUESS=i386-pc-auroraux$UNAME_RELEASE ;; i86pc:SunOS:5.*:* | i86xen:SunOS:5.*:*) set_cc_for_build SUN_ARCH=i386 # If there is a compiler, see if it is configured for 64-bit objects. # Note that the Sun cc does not turn __LP64__ into 1 like gcc does. # This test works for both compilers. if test "$CC_FOR_BUILD" != no_compiler_found; then if (echo '#ifdef __amd64'; echo IS_64BIT_ARCH; echo '#endif') | \ (CCOPTS="" $CC_FOR_BUILD -m64 -E - 2>/dev/null) | \ grep IS_64BIT_ARCH >/dev/null then SUN_ARCH=x86_64 fi fi SUN_REL=`echo "$UNAME_RELEASE" | sed -e 's/[^.]*//'` GUESS=$SUN_ARCH-pc-solaris2$SUN_REL ;; sun4*:SunOS:6*:*) # According to config.sub, this is the proper way to canonicalize # SunOS6. Hard to guess exactly what SunOS6 will be like, but # it's likely to be more like Solaris than SunOS4. SUN_REL=`echo "$UNAME_RELEASE" | sed -e 's/[^.]*//'` GUESS=sparc-sun-solaris3$SUN_REL ;; sun4*:SunOS:*:*) case `/usr/bin/arch -k` in Series*|S4*) UNAME_RELEASE=`uname -v` ;; esac # Japanese Language versions have a version number like '4.1.3-JL'. SUN_REL=`echo "$UNAME_RELEASE" | sed -e 's/-/_/'` GUESS=sparc-sun-sunos$SUN_REL ;; sun3*:SunOS:*:*) GUESS=m68k-sun-sunos$UNAME_RELEASE ;; sun*:*:4.2BSD:*) UNAME_RELEASE=`(sed 1q /etc/motd | awk '{print substr($5,1,3)}') 2>/dev/null` test "x$UNAME_RELEASE" = x && UNAME_RELEASE=3 case `/bin/arch` in sun3) GUESS=m68k-sun-sunos$UNAME_RELEASE ;; sun4) GUESS=sparc-sun-sunos$UNAME_RELEASE ;; esac ;; aushp:SunOS:*:*) GUESS=sparc-auspex-sunos$UNAME_RELEASE ;; # The situation for MiNT is a little confusing. The machine name # can be virtually everything (everything which is not # "atarist" or "atariste" at least should have a processor # > m68000). The system name ranges from "MiNT" over "FreeMiNT" # to the lowercase version "mint" (or "freemint"). Finally # the system name "TOS" denotes a system which is actually not # MiNT. But MiNT is downward compatible to TOS, so this should # be no problem. atarist[e]:*MiNT:*:* | atarist[e]:*mint:*:* | atarist[e]:*TOS:*:*) GUESS=m68k-atari-mint$UNAME_RELEASE ;; atari*:*MiNT:*:* | atari*:*mint:*:* | atarist[e]:*TOS:*:*) GUESS=m68k-atari-mint$UNAME_RELEASE ;; *falcon*:*MiNT:*:* | *falcon*:*mint:*:* | *falcon*:*TOS:*:*) GUESS=m68k-atari-mint$UNAME_RELEASE ;; milan*:*MiNT:*:* | milan*:*mint:*:* | *milan*:*TOS:*:*) GUESS=m68k-milan-mint$UNAME_RELEASE ;; hades*:*MiNT:*:* | hades*:*mint:*:* | *hades*:*TOS:*:*) GUESS=m68k-hades-mint$UNAME_RELEASE ;; *:*MiNT:*:* | *:*mint:*:* | *:*TOS:*:*) GUESS=m68k-unknown-mint$UNAME_RELEASE ;; m68k:machten:*:*) GUESS=m68k-apple-machten$UNAME_RELEASE ;; powerpc:machten:*:*) GUESS=powerpc-apple-machten$UNAME_RELEASE ;; RISC*:Mach:*:*) GUESS=mips-dec-mach_bsd4.3 ;; RISC*:ULTRIX:*:*) GUESS=mips-dec-ultrix$UNAME_RELEASE ;; VAX*:ULTRIX*:*:*) GUESS=vax-dec-ultrix$UNAME_RELEASE ;; 2020:CLIX:*:* | 2430:CLIX:*:*) GUESS=clipper-intergraph-clix$UNAME_RELEASE ;; mips:*:*:UMIPS | mips:*:*:RISCos) set_cc_for_build sed 's/^ //' << EOF > "$dummy.c" #ifdef __cplusplus #include /* for printf() prototype */ int main (int argc, char *argv[]) { #else int main (argc, argv) int argc; char *argv[]; { #endif #if defined (host_mips) && defined (MIPSEB) #if defined (SYSTYPE_SYSV) printf ("mips-mips-riscos%ssysv\\n", argv[1]); exit (0); #endif #if defined (SYSTYPE_SVR4) printf ("mips-mips-riscos%ssvr4\\n", argv[1]); exit (0); #endif #if defined (SYSTYPE_BSD43) || defined(SYSTYPE_BSD) printf ("mips-mips-riscos%sbsd\\n", argv[1]); exit (0); #endif #endif exit (-1); } EOF $CC_FOR_BUILD -o "$dummy" "$dummy.c" && dummyarg=`echo "$UNAME_RELEASE" | sed -n 's/\([0-9]*\).*/\1/p'` && SYSTEM_NAME=`"$dummy" "$dummyarg"` && { echo "$SYSTEM_NAME"; exit; } GUESS=mips-mips-riscos$UNAME_RELEASE ;; Motorola:PowerMAX_OS:*:*) GUESS=powerpc-motorola-powermax ;; Motorola:*:4.3:PL8-*) GUESS=powerpc-harris-powermax ;; Night_Hawk:*:*:PowerMAX_OS | Synergy:PowerMAX_OS:*:*) GUESS=powerpc-harris-powermax ;; Night_Hawk:Power_UNIX:*:*) GUESS=powerpc-harris-powerunix ;; m88k:CX/UX:7*:*) GUESS=m88k-harris-cxux7 ;; m88k:*:4*:R4*) GUESS=m88k-motorola-sysv4 ;; m88k:*:3*:R3*) GUESS=m88k-motorola-sysv3 ;; AViiON:dgux:*:*) # DG/UX returns AViiON for all architectures UNAME_PROCESSOR=`/usr/bin/uname -p` if test "$UNAME_PROCESSOR" = mc88100 || test "$UNAME_PROCESSOR" = mc88110 then if test "$TARGET_BINARY_INTERFACE"x = m88kdguxelfx || \ test "$TARGET_BINARY_INTERFACE"x = x then GUESS=m88k-dg-dgux$UNAME_RELEASE else GUESS=m88k-dg-dguxbcs$UNAME_RELEASE fi else GUESS=i586-dg-dgux$UNAME_RELEASE fi ;; M88*:DolphinOS:*:*) # DolphinOS (SVR3) GUESS=m88k-dolphin-sysv3 ;; M88*:*:R3*:*) # Delta 88k system running SVR3 GUESS=m88k-motorola-sysv3 ;; XD88*:*:*:*) # Tektronix XD88 system running UTekV (SVR3) GUESS=m88k-tektronix-sysv3 ;; Tek43[0-9][0-9]:UTek:*:*) # Tektronix 4300 system running UTek (BSD) GUESS=m68k-tektronix-bsd ;; *:IRIX*:*:*) IRIX_REL=`echo "$UNAME_RELEASE" | sed -e 's/-/_/g'` GUESS=mips-sgi-irix$IRIX_REL ;; ????????:AIX?:[12].1:2) # AIX 2.2.1 or AIX 2.1.1 is RT/PC AIX. GUESS=romp-ibm-aix # uname -m gives an 8 hex-code CPU id ;; # Note that: echo "'`uname -s`'" gives 'AIX ' i*86:AIX:*:*) GUESS=i386-ibm-aix ;; ia64:AIX:*:*) if test -x /usr/bin/oslevel ; then IBM_REV=`/usr/bin/oslevel` else IBM_REV=$UNAME_VERSION.$UNAME_RELEASE fi GUESS=$UNAME_MACHINE-ibm-aix$IBM_REV ;; *:AIX:2:3) if grep bos325 /usr/include/stdio.h >/dev/null 2>&1; then set_cc_for_build sed 's/^ //' << EOF > "$dummy.c" #include main() { if (!__power_pc()) exit(1); puts("powerpc-ibm-aix3.2.5"); exit(0); } EOF if $CC_FOR_BUILD -o "$dummy" "$dummy.c" && SYSTEM_NAME=`"$dummy"` then GUESS=$SYSTEM_NAME else GUESS=rs6000-ibm-aix3.2.5 fi elif grep bos324 /usr/include/stdio.h >/dev/null 2>&1; then GUESS=rs6000-ibm-aix3.2.4 else GUESS=rs6000-ibm-aix3.2 fi ;; *:AIX:*:[4567]) IBM_CPU_ID=`/usr/sbin/lsdev -C -c processor -S available | sed 1q | awk '{ print $1 }'` if /usr/sbin/lsattr -El "$IBM_CPU_ID" | grep ' POWER' >/dev/null 2>&1; then IBM_ARCH=rs6000 else IBM_ARCH=powerpc fi if test -x /usr/bin/lslpp ; then IBM_REV=`/usr/bin/lslpp -Lqc bos.rte.libc | \ awk -F: '{ print $3 }' | sed s/[0-9]*$/0/` else IBM_REV=$UNAME_VERSION.$UNAME_RELEASE fi GUESS=$IBM_ARCH-ibm-aix$IBM_REV ;; *:AIX:*:*) GUESS=rs6000-ibm-aix ;; ibmrt:4.4BSD:*|romp-ibm:4.4BSD:*) GUESS=romp-ibm-bsd4.4 ;; ibmrt:*BSD:*|romp-ibm:BSD:*) # covers RT/PC BSD and GUESS=romp-ibm-bsd$UNAME_RELEASE # 4.3 with uname added to ;; # report: romp-ibm BSD 4.3 *:BOSX:*:*) GUESS=rs6000-bull-bosx ;; DPX/2?00:B.O.S.:*:*) GUESS=m68k-bull-sysv3 ;; 9000/[34]??:4.3bsd:1.*:*) GUESS=m68k-hp-bsd ;; hp300:4.4BSD:*:* | 9000/[34]??:4.3bsd:2.*:*) GUESS=m68k-hp-bsd4.4 ;; 9000/[34678]??:HP-UX:*:*) HPUX_REV=`echo "$UNAME_RELEASE" | sed -e 's/[^.]*.[0B]*//'` case $UNAME_MACHINE in 9000/31?) HP_ARCH=m68000 ;; 9000/[34]??) HP_ARCH=m68k ;; 9000/[678][0-9][0-9]) if test -x /usr/bin/getconf; then sc_cpu_version=`/usr/bin/getconf SC_CPU_VERSION 2>/dev/null` sc_kernel_bits=`/usr/bin/getconf SC_KERNEL_BITS 2>/dev/null` case $sc_cpu_version in 523) HP_ARCH=hppa1.0 ;; # CPU_PA_RISC1_0 528) HP_ARCH=hppa1.1 ;; # CPU_PA_RISC1_1 532) # CPU_PA_RISC2_0 case $sc_kernel_bits in 32) HP_ARCH=hppa2.0n ;; 64) HP_ARCH=hppa2.0w ;; '') HP_ARCH=hppa2.0 ;; # HP-UX 10.20 esac ;; esac fi if test "$HP_ARCH" = ""; then set_cc_for_build sed 's/^ //' << EOF > "$dummy.c" #define _HPUX_SOURCE #include #include int main () { #if defined(_SC_KERNEL_BITS) long bits = sysconf(_SC_KERNEL_BITS); #endif long cpu = sysconf (_SC_CPU_VERSION); switch (cpu) { case CPU_PA_RISC1_0: puts ("hppa1.0"); break; case CPU_PA_RISC1_1: puts ("hppa1.1"); break; case CPU_PA_RISC2_0: #if defined(_SC_KERNEL_BITS) switch (bits) { case 64: puts ("hppa2.0w"); break; case 32: puts ("hppa2.0n"); break; default: puts ("hppa2.0"); break; } break; #else /* !defined(_SC_KERNEL_BITS) */ puts ("hppa2.0"); break; #endif default: puts ("hppa1.0"); break; } exit (0); } EOF (CCOPTS="" $CC_FOR_BUILD -o "$dummy" "$dummy.c" 2>/dev/null) && HP_ARCH=`"$dummy"` test -z "$HP_ARCH" && HP_ARCH=hppa fi ;; esac if test "$HP_ARCH" = hppa2.0w then set_cc_for_build # hppa2.0w-hp-hpux* has a 64-bit kernel and a compiler generating # 32-bit code. hppa64-hp-hpux* has the same kernel and a compiler # generating 64-bit code. GNU and HP use different nomenclature: # # $ CC_FOR_BUILD=cc ./config.guess # => hppa2.0w-hp-hpux11.23 # $ CC_FOR_BUILD="cc +DA2.0w" ./config.guess # => hppa64-hp-hpux11.23 if echo __LP64__ | (CCOPTS="" $CC_FOR_BUILD -E - 2>/dev/null) | grep -q __LP64__ then HP_ARCH=hppa2.0w else HP_ARCH=hppa64 fi fi GUESS=$HP_ARCH-hp-hpux$HPUX_REV ;; ia64:HP-UX:*:*) HPUX_REV=`echo "$UNAME_RELEASE" | sed -e 's/[^.]*.[0B]*//'` GUESS=ia64-hp-hpux$HPUX_REV ;; 3050*:HI-UX:*:*) set_cc_for_build sed 's/^ //' << EOF > "$dummy.c" #include int main () { long cpu = sysconf (_SC_CPU_VERSION); /* The order matters, because CPU_IS_HP_MC68K erroneously returns true for CPU_PA_RISC1_0. CPU_IS_PA_RISC returns correct results, however. */ if (CPU_IS_PA_RISC (cpu)) { switch (cpu) { case CPU_PA_RISC1_0: puts ("hppa1.0-hitachi-hiuxwe2"); break; case CPU_PA_RISC1_1: puts ("hppa1.1-hitachi-hiuxwe2"); break; case CPU_PA_RISC2_0: puts ("hppa2.0-hitachi-hiuxwe2"); break; default: puts ("hppa-hitachi-hiuxwe2"); break; } } else if (CPU_IS_HP_MC68K (cpu)) puts ("m68k-hitachi-hiuxwe2"); else puts ("unknown-hitachi-hiuxwe2"); exit (0); } EOF $CC_FOR_BUILD -o "$dummy" "$dummy.c" && SYSTEM_NAME=`"$dummy"` && { echo "$SYSTEM_NAME"; exit; } GUESS=unknown-hitachi-hiuxwe2 ;; 9000/7??:4.3bsd:*:* | 9000/8?[79]:4.3bsd:*:*) GUESS=hppa1.1-hp-bsd ;; 9000/8??:4.3bsd:*:*) GUESS=hppa1.0-hp-bsd ;; *9??*:MPE/iX:*:* | *3000*:MPE/iX:*:*) GUESS=hppa1.0-hp-mpeix ;; hp7??:OSF1:*:* | hp8?[79]:OSF1:*:*) GUESS=hppa1.1-hp-osf ;; hp8??:OSF1:*:*) GUESS=hppa1.0-hp-osf ;; i*86:OSF1:*:*) if test -x /usr/sbin/sysversion ; then GUESS=$UNAME_MACHINE-unknown-osf1mk else GUESS=$UNAME_MACHINE-unknown-osf1 fi ;; parisc*:Lites*:*:*) GUESS=hppa1.1-hp-lites ;; C1*:ConvexOS:*:* | convex:ConvexOS:C1*:*) GUESS=c1-convex-bsd ;; C2*:ConvexOS:*:* | convex:ConvexOS:C2*:*) if getsysinfo -f scalar_acc then echo c32-convex-bsd else echo c2-convex-bsd fi exit ;; C34*:ConvexOS:*:* | convex:ConvexOS:C34*:*) GUESS=c34-convex-bsd ;; C38*:ConvexOS:*:* | convex:ConvexOS:C38*:*) GUESS=c38-convex-bsd ;; C4*:ConvexOS:*:* | convex:ConvexOS:C4*:*) GUESS=c4-convex-bsd ;; CRAY*Y-MP:*:*:*) CRAY_REL=`echo "$UNAME_RELEASE" | sed -e 's/\.[^.]*$/.X/'` GUESS=ymp-cray-unicos$CRAY_REL ;; CRAY*[A-Z]90:*:*:*) echo "$UNAME_MACHINE"-cray-unicos"$UNAME_RELEASE" \ | sed -e 's/CRAY.*\([A-Z]90\)/\1/' \ -e y/ABCDEFGHIJKLMNOPQRSTUVWXYZ/abcdefghijklmnopqrstuvwxyz/ \ -e 's/\.[^.]*$/.X/' exit ;; CRAY*TS:*:*:*) CRAY_REL=`echo "$UNAME_RELEASE" | sed -e 's/\.[^.]*$/.X/'` GUESS=t90-cray-unicos$CRAY_REL ;; CRAY*T3E:*:*:*) CRAY_REL=`echo "$UNAME_RELEASE" | sed -e 's/\.[^.]*$/.X/'` GUESS=alphaev5-cray-unicosmk$CRAY_REL ;; CRAY*SV1:*:*:*) CRAY_REL=`echo "$UNAME_RELEASE" | sed -e 's/\.[^.]*$/.X/'` GUESS=sv1-cray-unicos$CRAY_REL ;; *:UNICOS/mp:*:*) CRAY_REL=`echo "$UNAME_RELEASE" | sed -e 's/\.[^.]*$/.X/'` GUESS=craynv-cray-unicosmp$CRAY_REL ;; F30[01]:UNIX_System_V:*:* | F700:UNIX_System_V:*:*) FUJITSU_PROC=`uname -m | tr ABCDEFGHIJKLMNOPQRSTUVWXYZ abcdefghijklmnopqrstuvwxyz` FUJITSU_SYS=`uname -p | tr ABCDEFGHIJKLMNOPQRSTUVWXYZ abcdefghijklmnopqrstuvwxyz | sed -e 's/\///'` FUJITSU_REL=`echo "$UNAME_RELEASE" | sed -e 's/ /_/'` GUESS=${FUJITSU_PROC}-fujitsu-${FUJITSU_SYS}${FUJITSU_REL} ;; 5000:UNIX_System_V:4.*:*) FUJITSU_SYS=`uname -p | tr ABCDEFGHIJKLMNOPQRSTUVWXYZ abcdefghijklmnopqrstuvwxyz | sed -e 's/\///'` FUJITSU_REL=`echo "$UNAME_RELEASE" | tr ABCDEFGHIJKLMNOPQRSTUVWXYZ abcdefghijklmnopqrstuvwxyz | sed -e 's/ /_/'` GUESS=sparc-fujitsu-${FUJITSU_SYS}${FUJITSU_REL} ;; i*86:BSD/386:*:* | i*86:BSD/OS:*:* | *:Ascend\ Embedded/OS:*:*) GUESS=$UNAME_MACHINE-pc-bsdi$UNAME_RELEASE ;; sparc*:BSD/OS:*:*) GUESS=sparc-unknown-bsdi$UNAME_RELEASE ;; *:BSD/OS:*:*) GUESS=$UNAME_MACHINE-unknown-bsdi$UNAME_RELEASE ;; arm:FreeBSD:*:*) UNAME_PROCESSOR=`uname -p` set_cc_for_build if echo __ARM_PCS_VFP | $CC_FOR_BUILD -E - 2>/dev/null \ | grep -q __ARM_PCS_VFP then FREEBSD_REL=`echo "$UNAME_RELEASE" | sed -e 's/[-(].*//'` GUESS=$UNAME_PROCESSOR-unknown-freebsd$FREEBSD_REL-gnueabi else FREEBSD_REL=`echo "$UNAME_RELEASE" | sed -e 's/[-(].*//'` GUESS=$UNAME_PROCESSOR-unknown-freebsd$FREEBSD_REL-gnueabihf fi ;; *:FreeBSD:*:*) UNAME_PROCESSOR=`uname -p` case $UNAME_PROCESSOR in amd64) UNAME_PROCESSOR=x86_64 ;; i386) UNAME_PROCESSOR=i586 ;; esac FREEBSD_REL=`echo "$UNAME_RELEASE" | sed -e 's/[-(].*//'` GUESS=$UNAME_PROCESSOR-unknown-freebsd$FREEBSD_REL ;; i*:CYGWIN*:*) GUESS=$UNAME_MACHINE-pc-cygwin ;; *:MINGW64*:*) GUESS=$UNAME_MACHINE-pc-mingw64 ;; *:MINGW*:*) GUESS=$UNAME_MACHINE-pc-mingw32 ;; *:MSYS*:*) GUESS=$UNAME_MACHINE-pc-msys ;; i*:PW*:*) GUESS=$UNAME_MACHINE-pc-pw32 ;; *:SerenityOS:*:*) GUESS=$UNAME_MACHINE-pc-serenity ;; *:Interix*:*) case $UNAME_MACHINE in x86) GUESS=i586-pc-interix$UNAME_RELEASE ;; authenticamd | genuineintel | EM64T) GUESS=x86_64-unknown-interix$UNAME_RELEASE ;; IA64) GUESS=ia64-unknown-interix$UNAME_RELEASE ;; esac ;; i*:UWIN*:*) GUESS=$UNAME_MACHINE-pc-uwin ;; amd64:CYGWIN*:*:* | x86_64:CYGWIN*:*:*) GUESS=x86_64-pc-cygwin ;; prep*:SunOS:5.*:*) SUN_REL=`echo "$UNAME_RELEASE" | sed -e 's/[^.]*//'` GUESS=powerpcle-unknown-solaris2$SUN_REL ;; *:GNU:*:*) # the GNU system GNU_ARCH=`echo "$UNAME_MACHINE" | sed -e 's,[-/].*$,,'` GNU_REL=`echo "$UNAME_RELEASE" | sed -e 's,/.*$,,'` GUESS=$GNU_ARCH-unknown-$LIBC$GNU_REL ;; 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frv:Linux:*:*) GUESS=$UNAME_MACHINE-unknown-linux-$LIBC ;; hexagon:Linux:*:*) GUESS=$UNAME_MACHINE-unknown-linux-$LIBC ;; i*86:Linux:*:*) GUESS=$UNAME_MACHINE-pc-linux-$LIBC ;; ia64:Linux:*:*) GUESS=$UNAME_MACHINE-unknown-linux-$LIBC ;; k1om:Linux:*:*) GUESS=$UNAME_MACHINE-unknown-linux-$LIBC ;; kvx:Linux:*:*) GUESS=$UNAME_MACHINE-unknown-linux-$LIBC ;; kvx:cos:*:*) GUESS=$UNAME_MACHINE-unknown-cos ;; kvx:mbr:*:*) GUESS=$UNAME_MACHINE-unknown-mbr ;; loongarch32:Linux:*:* | loongarch64:Linux:*:*) GUESS=$UNAME_MACHINE-unknown-linux-$LIBC ;; m32r*:Linux:*:*) GUESS=$UNAME_MACHINE-unknown-linux-$LIBC ;; m68*:Linux:*:*) GUESS=$UNAME_MACHINE-unknown-linux-$LIBC ;; mips:Linux:*:* | mips64:Linux:*:*) set_cc_for_build IS_GLIBC=0 test x"${LIBC}" = xgnu && IS_GLIBC=1 sed 's/^ //' << EOF > "$dummy.c" #undef CPU #undef mips #undef mipsel #undef mips64 #undef mips64el #if ${IS_GLIBC} && defined(_ABI64) LIBCABI=gnuabi64 #else #if ${IS_GLIBC} && defined(_ABIN32) LIBCABI=gnuabin32 #else LIBCABI=${LIBC} #endif #endif #if ${IS_GLIBC} && defined(__mips64) && defined(__mips_isa_rev) && __mips_isa_rev>=6 CPU=mipsisa64r6 #else #if ${IS_GLIBC} && !defined(__mips64) && defined(__mips_isa_rev) && __mips_isa_rev>=6 CPU=mipsisa32r6 #else #if defined(__mips64) CPU=mips64 #else CPU=mips #endif #endif #endif #if defined(__MIPSEL__) || defined(__MIPSEL) || defined(_MIPSEL) || defined(MIPSEL) MIPS_ENDIAN=el #else #if defined(__MIPSEB__) || defined(__MIPSEB) || defined(_MIPSEB) || defined(MIPSEB) MIPS_ENDIAN= #else MIPS_ENDIAN= #endif #endif EOF cc_set_vars=`$CC_FOR_BUILD -E "$dummy.c" 2>/dev/null | grep '^CPU\|^MIPS_ENDIAN\|^LIBCABI'` eval "$cc_set_vars" test "x$CPU" != x && { echo "$CPU${MIPS_ENDIAN}-unknown-linux-$LIBCABI"; exit; } ;; mips64el:Linux:*:*) GUESS=$UNAME_MACHINE-unknown-linux-$LIBC ;; openrisc*:Linux:*:*) GUESS=or1k-unknown-linux-$LIBC ;; or32:Linux:*:* | or1k*:Linux:*:*) GUESS=$UNAME_MACHINE-unknown-linux-$LIBC ;; padre:Linux:*:*) GUESS=sparc-unknown-linux-$LIBC ;; parisc64:Linux:*:* | hppa64:Linux:*:*) GUESS=hppa64-unknown-linux-$LIBC ;; parisc:Linux:*:* | hppa:Linux:*:*) # Look for CPU level case `grep '^cpu[^a-z]*:' /proc/cpuinfo 2>/dev/null | cut -d' ' -f2` in PA7*) GUESS=hppa1.1-unknown-linux-$LIBC ;; PA8*) GUESS=hppa2.0-unknown-linux-$LIBC ;; *) GUESS=hppa-unknown-linux-$LIBC ;; esac ;; ppc64:Linux:*:*) GUESS=powerpc64-unknown-linux-$LIBC ;; ppc:Linux:*:*) GUESS=powerpc-unknown-linux-$LIBC ;; ppc64le:Linux:*:*) GUESS=powerpc64le-unknown-linux-$LIBC ;; ppcle:Linux:*:*) GUESS=powerpcle-unknown-linux-$LIBC ;; riscv32:Linux:*:* | riscv32be:Linux:*:* | riscv64:Linux:*:* | riscv64be:Linux:*:*) GUESS=$UNAME_MACHINE-unknown-linux-$LIBC ;; s390:Linux:*:* | s390x:Linux:*:*) GUESS=$UNAME_MACHINE-ibm-linux-$LIBC ;; sh64*:Linux:*:*) GUESS=$UNAME_MACHINE-unknown-linux-$LIBC ;; sh*:Linux:*:*) GUESS=$UNAME_MACHINE-unknown-linux-$LIBC ;; sparc:Linux:*:* | sparc64:Linux:*:*) GUESS=$UNAME_MACHINE-unknown-linux-$LIBC ;; tile*:Linux:*:*) GUESS=$UNAME_MACHINE-unknown-linux-$LIBC ;; vax:Linux:*:*) GUESS=$UNAME_MACHINE-dec-linux-$LIBC ;; x86_64:Linux:*:*) set_cc_for_build CPU=$UNAME_MACHINE LIBCABI=$LIBC if test "$CC_FOR_BUILD" != no_compiler_found; then ABI=64 sed 's/^ //' << EOF > "$dummy.c" #ifdef __i386__ ABI=x86 #else #ifdef __ILP32__ ABI=x32 #endif #endif EOF cc_set_abi=`$CC_FOR_BUILD -E "$dummy.c" 2>/dev/null | grep '^ABI' | sed 's, ,,g'` eval "$cc_set_abi" case $ABI in x86) CPU=i686 ;; x32) LIBCABI=${LIBC}x32 ;; esac fi GUESS=$CPU-pc-linux-$LIBCABI ;; xtensa*:Linux:*:*) GUESS=$UNAME_MACHINE-unknown-linux-$LIBC ;; i*86:DYNIX/ptx:4*:*) # ptx 4.0 does uname -s correctly, with DYNIX/ptx in there. # earlier versions are messed up and put the nodename in both # sysname and nodename. GUESS=i386-sequent-sysv4 ;; i*86:UNIX_SV:4.2MP:2.*) # Unixware is an offshoot of SVR4, but it has its own version # number series starting with 2... # I am not positive that other SVR4 systems won't match this, # I just have to hope. -- rms. # Use sysv4.2uw... so that sysv4* matches it. GUESS=$UNAME_MACHINE-pc-sysv4.2uw$UNAME_VERSION ;; i*86:OS/2:*:*) # If we were able to find 'uname', then EMX Unix compatibility # is probably installed. GUESS=$UNAME_MACHINE-pc-os2-emx ;; i*86:XTS-300:*:STOP) GUESS=$UNAME_MACHINE-unknown-stop ;; i*86:atheos:*:*) GUESS=$UNAME_MACHINE-unknown-atheos ;; i*86:syllable:*:*) GUESS=$UNAME_MACHINE-pc-syllable ;; i*86:LynxOS:2.*:* | i*86:LynxOS:3.[01]*:* | i*86:LynxOS:4.[02]*:*) GUESS=i386-unknown-lynxos$UNAME_RELEASE ;; i*86:*DOS:*:*) GUESS=$UNAME_MACHINE-pc-msdosdjgpp ;; i*86:*:4.*:*) UNAME_REL=`echo "$UNAME_RELEASE" | sed 's/\/MP$//'` if grep Novell /usr/include/link.h >/dev/null 2>/dev/null; then GUESS=$UNAME_MACHINE-univel-sysv$UNAME_REL else GUESS=$UNAME_MACHINE-pc-sysv$UNAME_REL fi ;; i*86:*:5:[678]*) # UnixWare 7.x, OpenUNIX and OpenServer 6. case `/bin/uname -X | grep "^Machine"` in *486*) UNAME_MACHINE=i486 ;; *Pentium) UNAME_MACHINE=i586 ;; *Pent*|*Celeron) UNAME_MACHINE=i686 ;; esac GUESS=$UNAME_MACHINE-unknown-sysv${UNAME_RELEASE}${UNAME_SYSTEM}${UNAME_VERSION} ;; i*86:*:3.2:*) if test -f /usr/options/cb.name; then UNAME_REL=`sed -n 's/.*Version //p' /dev/null >/dev/null ; then UNAME_REL=`(/bin/uname -X|grep Release|sed -e 's/.*= //')` (/bin/uname -X|grep i80486 >/dev/null) && UNAME_MACHINE=i486 (/bin/uname -X|grep '^Machine.*Pentium' >/dev/null) \ && UNAME_MACHINE=i586 (/bin/uname -X|grep '^Machine.*Pent *II' >/dev/null) \ && UNAME_MACHINE=i686 (/bin/uname -X|grep '^Machine.*Pentium Pro' >/dev/null) \ && UNAME_MACHINE=i686 GUESS=$UNAME_MACHINE-pc-sco$UNAME_REL else GUESS=$UNAME_MACHINE-pc-sysv32 fi ;; pc:*:*:*) # Left here for compatibility: # uname -m prints for DJGPP always 'pc', but it prints nothing about # the processor, so we play safe by assuming i586. # Note: whatever this is, it MUST be the same as what config.sub # prints for the "djgpp" host, or else GDB configure will decide that # this is a cross-build. GUESS=i586-pc-msdosdjgpp ;; Intel:Mach:3*:*) GUESS=i386-pc-mach3 ;; paragon:*:*:*) GUESS=i860-intel-osf1 ;; i860:*:4.*:*) # i860-SVR4 if grep Stardent /usr/include/sys/uadmin.h >/dev/null 2>&1 ; then GUESS=i860-stardent-sysv$UNAME_RELEASE # Stardent Vistra i860-SVR4 else # Add other i860-SVR4 vendors below as they are discovered. GUESS=i860-unknown-sysv$UNAME_RELEASE # Unknown i860-SVR4 fi ;; mini*:CTIX:SYS*5:*) # "miniframe" GUESS=m68010-convergent-sysv ;; mc68k:UNIX:SYSTEM5:3.51m) GUESS=m68k-convergent-sysv ;; M680?0:D-NIX:5.3:*) GUESS=m68k-diab-dnix ;; M68*:*:R3V[5678]*:*) test -r /sysV68 && { echo 'm68k-motorola-sysv'; exit; } ;; 3[345]??:*:4.0:3.0 | 3[34]??A:*:4.0:3.0 | 3[34]??,*:*:4.0:3.0 | 3[34]??/*:*:4.0:3.0 | 4400:*:4.0:3.0 | 4850:*:4.0:3.0 | SKA40:*:4.0:3.0 | SDS2:*:4.0:3.0 | SHG2:*:4.0:3.0 | S7501*:*:4.0:3.0) OS_REL='' test -r /etc/.relid \ && OS_REL=.`sed -n 's/[^ ]* [^ ]* \([0-9][0-9]\).*/\1/p' < /etc/.relid` /bin/uname -p 2>/dev/null | grep 86 >/dev/null \ && { echo i486-ncr-sysv4.3"$OS_REL"; exit; } /bin/uname -p 2>/dev/null | /bin/grep entium >/dev/null \ && { echo i586-ncr-sysv4.3"$OS_REL"; exit; } ;; 3[34]??:*:4.0:* | 3[34]??,*:*:4.0:*) /bin/uname -p 2>/dev/null | grep 86 >/dev/null \ && { echo i486-ncr-sysv4; exit; } ;; NCR*:*:4.2:* | MPRAS*:*:4.2:*) OS_REL='.3' test -r /etc/.relid \ && OS_REL=.`sed -n 's/[^ ]* [^ ]* \([0-9][0-9]\).*/\1/p' < /etc/.relid` /bin/uname -p 2>/dev/null | grep 86 >/dev/null \ && { echo i486-ncr-sysv4.3"$OS_REL"; exit; } /bin/uname -p 2>/dev/null | /bin/grep entium >/dev/null \ && { echo i586-ncr-sysv4.3"$OS_REL"; exit; } /bin/uname -p 2>/dev/null | /bin/grep pteron >/dev/null \ && { echo i586-ncr-sysv4.3"$OS_REL"; exit; } ;; m68*:LynxOS:2.*:* | m68*:LynxOS:3.0*:*) GUESS=m68k-unknown-lynxos$UNAME_RELEASE ;; mc68030:UNIX_System_V:4.*:*) GUESS=m68k-atari-sysv4 ;; TSUNAMI:LynxOS:2.*:*) GUESS=sparc-unknown-lynxos$UNAME_RELEASE ;; rs6000:LynxOS:2.*:*) GUESS=rs6000-unknown-lynxos$UNAME_RELEASE ;; PowerPC:LynxOS:2.*:* | PowerPC:LynxOS:3.[01]*:* | PowerPC:LynxOS:4.[02]*:*) GUESS=powerpc-unknown-lynxos$UNAME_RELEASE ;; SM[BE]S:UNIX_SV:*:*) GUESS=mips-dde-sysv$UNAME_RELEASE ;; RM*:ReliantUNIX-*:*:*) GUESS=mips-sni-sysv4 ;; RM*:SINIX-*:*:*) GUESS=mips-sni-sysv4 ;; *:SINIX-*:*:*) if uname -p 2>/dev/null >/dev/null ; then UNAME_MACHINE=`(uname -p) 2>/dev/null` GUESS=$UNAME_MACHINE-sni-sysv4 else GUESS=ns32k-sni-sysv fi ;; PENTIUM:*:4.0*:*) # Unisys 'ClearPath HMP IX 4000' SVR4/MP effort # says GUESS=i586-unisys-sysv4 ;; *:UNIX_System_V:4*:FTX*) # From Gerald Hewes . # How about differentiating between stratus architectures? -djm GUESS=hppa1.1-stratus-sysv4 ;; *:*:*:FTX*) # From seanf@swdc.stratus.com. GUESS=i860-stratus-sysv4 ;; i*86:VOS:*:*) # From Paul.Green@stratus.com. GUESS=$UNAME_MACHINE-stratus-vos ;; *:VOS:*:*) # From Paul.Green@stratus.com. GUESS=hppa1.1-stratus-vos ;; mc68*:A/UX:*:*) GUESS=m68k-apple-aux$UNAME_RELEASE ;; news*:NEWS-OS:6*:*) GUESS=mips-sony-newsos6 ;; R[34]000:*System_V*:*:* | R4000:UNIX_SYSV:*:* | R*000:UNIX_SV:*:*) if test -d /usr/nec; then GUESS=mips-nec-sysv$UNAME_RELEASE else GUESS=mips-unknown-sysv$UNAME_RELEASE fi ;; BeBox:BeOS:*:*) # BeOS running on hardware made by Be, PPC only. GUESS=powerpc-be-beos ;; BeMac:BeOS:*:*) # BeOS running on Mac or Mac clone, PPC only. GUESS=powerpc-apple-beos ;; BePC:BeOS:*:*) # BeOS running on Intel PC compatible. GUESS=i586-pc-beos ;; BePC:Haiku:*:*) # Haiku running on Intel PC compatible. GUESS=i586-pc-haiku ;; ppc:Haiku:*:*) # Haiku running on Apple PowerPC GUESS=powerpc-apple-haiku ;; *:Haiku:*:*) # Haiku modern gcc (not bound by BeOS compat) GUESS=$UNAME_MACHINE-unknown-haiku ;; SX-4:SUPER-UX:*:*) GUESS=sx4-nec-superux$UNAME_RELEASE ;; SX-5:SUPER-UX:*:*) GUESS=sx5-nec-superux$UNAME_RELEASE ;; SX-6:SUPER-UX:*:*) GUESS=sx6-nec-superux$UNAME_RELEASE ;; SX-7:SUPER-UX:*:*) GUESS=sx7-nec-superux$UNAME_RELEASE ;; SX-8:SUPER-UX:*:*) GUESS=sx8-nec-superux$UNAME_RELEASE ;; SX-8R:SUPER-UX:*:*) GUESS=sx8r-nec-superux$UNAME_RELEASE ;; SX-ACE:SUPER-UX:*:*) GUESS=sxace-nec-superux$UNAME_RELEASE ;; Power*:Rhapsody:*:*) GUESS=powerpc-apple-rhapsody$UNAME_RELEASE ;; *:Rhapsody:*:*) GUESS=$UNAME_MACHINE-apple-rhapsody$UNAME_RELEASE ;; arm64:Darwin:*:*) GUESS=aarch64-apple-darwin$UNAME_RELEASE ;; *:Darwin:*:*) UNAME_PROCESSOR=`uname -p` case $UNAME_PROCESSOR in unknown) UNAME_PROCESSOR=powerpc ;; esac if command -v xcode-select > /dev/null 2> /dev/null && \ ! xcode-select --print-path > /dev/null 2> /dev/null ; then # Avoid executing cc if there is no toolchain installed as # cc will be a stub that puts up a graphical alert # prompting the user to install developer tools. CC_FOR_BUILD=no_compiler_found else set_cc_for_build fi if test "$CC_FOR_BUILD" != no_compiler_found; then if (echo '#ifdef __LP64__'; echo IS_64BIT_ARCH; echo '#endif') | \ (CCOPTS="" $CC_FOR_BUILD -E - 2>/dev/null) | \ grep IS_64BIT_ARCH >/dev/null then case $UNAME_PROCESSOR in i386) UNAME_PROCESSOR=x86_64 ;; powerpc) UNAME_PROCESSOR=powerpc64 ;; esac fi # On 10.4-10.6 one might compile for PowerPC via gcc -arch ppc if (echo '#ifdef __POWERPC__'; echo IS_PPC; echo '#endif') | \ (CCOPTS="" $CC_FOR_BUILD -E - 2>/dev/null) | \ grep IS_PPC >/dev/null then UNAME_PROCESSOR=powerpc fi elif test "$UNAME_PROCESSOR" = i386 ; then # uname -m returns i386 or x86_64 UNAME_PROCESSOR=$UNAME_MACHINE fi GUESS=$UNAME_PROCESSOR-apple-darwin$UNAME_RELEASE ;; *:procnto*:*:* | *:QNX:[0123456789]*:*) UNAME_PROCESSOR=`uname -p` if test "$UNAME_PROCESSOR" = x86; then UNAME_PROCESSOR=i386 UNAME_MACHINE=pc fi GUESS=$UNAME_PROCESSOR-$UNAME_MACHINE-nto-qnx$UNAME_RELEASE ;; *:QNX:*:4*) GUESS=i386-pc-qnx ;; NEO-*:NONSTOP_KERNEL:*:*) GUESS=neo-tandem-nsk$UNAME_RELEASE ;; NSE-*:NONSTOP_KERNEL:*:*) GUESS=nse-tandem-nsk$UNAME_RELEASE ;; NSR-*:NONSTOP_KERNEL:*:*) GUESS=nsr-tandem-nsk$UNAME_RELEASE ;; NSV-*:NONSTOP_KERNEL:*:*) GUESS=nsv-tandem-nsk$UNAME_RELEASE ;; NSX-*:NONSTOP_KERNEL:*:*) GUESS=nsx-tandem-nsk$UNAME_RELEASE ;; *:NonStop-UX:*:*) GUESS=mips-compaq-nonstopux ;; BS2000:POSIX*:*:*) GUESS=bs2000-siemens-sysv ;; DS/*:UNIX_System_V:*:*) GUESS=$UNAME_MACHINE-$UNAME_SYSTEM-$UNAME_RELEASE ;; *:Plan9:*:*) # "uname -m" is not consistent, so use $cputype instead. 386 # is converted to i386 for consistency with other x86 # operating systems. if test "${cputype-}" = 386; then UNAME_MACHINE=i386 elif test "x${cputype-}" != x; then UNAME_MACHINE=$cputype fi GUESS=$UNAME_MACHINE-unknown-plan9 ;; *:TOPS-10:*:*) GUESS=pdp10-unknown-tops10 ;; *:TENEX:*:*) GUESS=pdp10-unknown-tenex ;; KS10:TOPS-20:*:* | KL10:TOPS-20:*:* | TYPE4:TOPS-20:*:*) GUESS=pdp10-dec-tops20 ;; XKL-1:TOPS-20:*:* | TYPE5:TOPS-20:*:*) GUESS=pdp10-xkl-tops20 ;; *:TOPS-20:*:*) GUESS=pdp10-unknown-tops20 ;; *:ITS:*:*) GUESS=pdp10-unknown-its ;; SEI:*:*:SEIUX) GUESS=mips-sei-seiux$UNAME_RELEASE ;; *:DragonFly:*:*) DRAGONFLY_REL=`echo "$UNAME_RELEASE" | sed -e 's/[-(].*//'` GUESS=$UNAME_MACHINE-unknown-dragonfly$DRAGONFLY_REL ;; *:*VMS:*:*) UNAME_MACHINE=`(uname -p) 2>/dev/null` case $UNAME_MACHINE in A*) GUESS=alpha-dec-vms ;; I*) GUESS=ia64-dec-vms ;; V*) GUESS=vax-dec-vms ;; esac ;; *:XENIX:*:SysV) GUESS=i386-pc-xenix ;; i*86:skyos:*:*) SKYOS_REL=`echo "$UNAME_RELEASE" | sed -e 's/ .*$//'` GUESS=$UNAME_MACHINE-pc-skyos$SKYOS_REL ;; i*86:rdos:*:*) GUESS=$UNAME_MACHINE-pc-rdos ;; i*86:Fiwix:*:*) GUESS=$UNAME_MACHINE-pc-fiwix ;; *:AROS:*:*) GUESS=$UNAME_MACHINE-unknown-aros ;; x86_64:VMkernel:*:*) GUESS=$UNAME_MACHINE-unknown-esx ;; amd64:Isilon\ OneFS:*:*) GUESS=x86_64-unknown-onefs ;; *:Unleashed:*:*) GUESS=$UNAME_MACHINE-unknown-unleashed$UNAME_RELEASE ;; *:Ironclad:*:*) GUESS=$UNAME_MACHINE-unknown-ironclad ;; esac # Do we have a guess based on uname results? if test "x$GUESS" != x; then echo "$GUESS" exit fi # No uname command or uname output not recognized. set_cc_for_build cat > "$dummy.c" < #include #endif #if defined(ultrix) || defined(_ultrix) || defined(__ultrix) || defined(__ultrix__) #if defined (vax) || defined (__vax) || defined (__vax__) || defined(mips) || defined(__mips) || defined(__mips__) || defined(MIPS) || defined(__MIPS__) #include #if defined(_SIZE_T_) || defined(SIGLOST) #include #endif #endif #endif main () { #if defined (sony) #if defined (MIPSEB) /* BFD wants "bsd" instead of "newsos". Perhaps BFD should be changed, I don't know.... */ printf ("mips-sony-bsd\n"); exit (0); #else #include printf ("m68k-sony-newsos%s\n", #ifdef NEWSOS4 "4" #else "" #endif ); exit (0); #endif #endif #if defined (NeXT) #if !defined (__ARCHITECTURE__) #define __ARCHITECTURE__ "m68k" #endif int version; version=`(hostinfo | sed -n 's/.*NeXT Mach \([0-9]*\).*/\1/p') 2>/dev/null`; if (version < 4) printf ("%s-next-nextstep%d\n", __ARCHITECTURE__, version); else printf ("%s-next-openstep%d\n", __ARCHITECTURE__, version); exit (0); #endif #if defined (MULTIMAX) || defined (n16) #if defined (UMAXV) printf ("ns32k-encore-sysv\n"); exit (0); #else #if defined (CMU) printf ("ns32k-encore-mach\n"); exit (0); #else printf ("ns32k-encore-bsd\n"); exit (0); #endif #endif #endif #if defined (__386BSD__) printf ("i386-pc-bsd\n"); exit (0); #endif #if defined (sequent) #if defined (i386) printf ("i386-sequent-dynix\n"); exit (0); #endif #if defined (ns32000) printf ("ns32k-sequent-dynix\n"); exit (0); #endif #endif #if defined (_SEQUENT_) struct utsname un; uname(&un); if (strncmp(un.version, "V2", 2) == 0) { printf ("i386-sequent-ptx2\n"); exit (0); } if (strncmp(un.version, "V1", 2) == 0) { /* XXX is V1 correct? */ printf ("i386-sequent-ptx1\n"); exit (0); } printf ("i386-sequent-ptx\n"); exit (0); #endif #if defined (vax) #if !defined (ultrix) #include #if defined (BSD) #if BSD == 43 printf ("vax-dec-bsd4.3\n"); exit (0); #else #if BSD == 199006 printf ("vax-dec-bsd4.3reno\n"); exit (0); #else printf ("vax-dec-bsd\n"); exit (0); #endif #endif #else printf ("vax-dec-bsd\n"); exit (0); #endif #else #if defined(_SIZE_T_) || defined(SIGLOST) struct utsname un; uname (&un); printf ("vax-dec-ultrix%s\n", un.release); exit (0); #else printf ("vax-dec-ultrix\n"); exit (0); #endif #endif #endif #if defined(ultrix) || defined(_ultrix) || defined(__ultrix) || defined(__ultrix__) #if defined(mips) || defined(__mips) || defined(__mips__) || defined(MIPS) || defined(__MIPS__) #if defined(_SIZE_T_) || defined(SIGLOST) struct utsname *un; uname (&un); printf ("mips-dec-ultrix%s\n", un.release); exit (0); #else printf ("mips-dec-ultrix\n"); exit (0); #endif #endif #endif #if defined (alliant) && defined (i860) printf ("i860-alliant-bsd\n"); exit (0); #endif exit (1); } EOF $CC_FOR_BUILD -o "$dummy" "$dummy.c" 2>/dev/null && SYSTEM_NAME=`"$dummy"` && { echo "$SYSTEM_NAME"; exit; } # Apollos put the system type in the environment. test -d /usr/apollo && { echo "$ISP-apollo-$SYSTYPE"; exit; } echo "$0: unable to guess system type" >&2 case $UNAME_MACHINE:$UNAME_SYSTEM in mips:Linux | mips64:Linux) # If we got here on MIPS GNU/Linux, output extra information. cat >&2 <&2 <&2 </dev/null || echo unknown` uname -r = `(uname -r) 2>/dev/null || echo unknown` uname -s = `(uname -s) 2>/dev/null || echo unknown` uname -v = `(uname -v) 2>/dev/null || echo unknown` /usr/bin/uname -p = `(/usr/bin/uname -p) 2>/dev/null` /bin/uname -X = `(/bin/uname -X) 2>/dev/null` hostinfo = `(hostinfo) 2>/dev/null` /bin/universe = `(/bin/universe) 2>/dev/null` /usr/bin/arch -k = `(/usr/bin/arch -k) 2>/dev/null` /bin/arch = `(/bin/arch) 2>/dev/null` /usr/bin/oslevel = `(/usr/bin/oslevel) 2>/dev/null` /usr/convex/getsysinfo = `(/usr/convex/getsysinfo) 2>/dev/null` UNAME_MACHINE = "$UNAME_MACHINE" UNAME_RELEASE = "$UNAME_RELEASE" UNAME_SYSTEM = "$UNAME_SYSTEM" UNAME_VERSION = "$UNAME_VERSION" EOF fi exit 1 # Local variables: # eval: (add-hook 'before-save-hook 'time-stamp) # time-stamp-start: "timestamp='" # time-stamp-format: "%:y-%02m-%02d" # time-stamp-end: "'" # End: NBISweden-MrBayes-ecfd55c/autotools/config.sub000077500000000000000000001077561526212227600214510ustar00rootroot00000000000000#! /bin/sh # Configuration validation subroutine script. # Copyright 1992-2024 Free Software Foundation, Inc. # shellcheck disable=SC2006,SC2268 # see below for rationale timestamp='2024-01-01' # This file is free software; you can redistribute it and/or modify it # under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, but # WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU # General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program; if not, see . # # As a special exception to the GNU General Public License, if you # distribute this file as part of a program that contains a # configuration script generated by Autoconf, you may include it under # the same distribution terms that you use for the rest of that # program. This Exception is an additional permission under section 7 # of the GNU General Public License, version 3 ("GPLv3"). # Please send patches to . # # Configuration subroutine to validate and canonicalize a configuration type. # Supply the specified configuration type as an argument. # If it is invalid, we print an error message on stderr and exit with code 1. # Otherwise, we print the canonical config type on stdout and succeed. # You can get the latest version of this script from: # https://git.savannah.gnu.org/cgit/config.git/plain/config.sub # This file is supposed to be the same for all GNU packages # and recognize all the CPU types, system types and aliases # that are meaningful with *any* GNU software. # Each package is responsible for reporting which valid configurations # it does not support. The user should be able to distinguish # a failure to support a valid configuration from a meaningless # configuration. # The goal of this file is to map all the various variations of a given # machine specification into a single specification in the form: # CPU_TYPE-MANUFACTURER-OPERATING_SYSTEM # or in some cases, the newer four-part form: # CPU_TYPE-MANUFACTURER-KERNEL-OPERATING_SYSTEM # It is wrong to echo any other type of specification. # The "shellcheck disable" line above the timestamp inhibits complaints # about features and limitations of the classic Bourne shell that were # superseded or lifted in POSIX. However, this script identifies a wide # variety of pre-POSIX systems that do not have POSIX shells at all, and # even some reasonably current systems (Solaris 10 as case-in-point) still # have a pre-POSIX /bin/sh. me=`echo "$0" | sed -e 's,.*/,,'` usage="\ Usage: $0 [OPTION] CPU-MFR-OPSYS or ALIAS Canonicalize a configuration name. 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*) basic_machine=$field1 basic_os=$field2 ;; esac ;; esac ;; *) # Convert single-component short-hands not valid as part of # multi-component configurations. case $field1 in 386bsd) basic_machine=i386-pc basic_os=bsd ;; a29khif) basic_machine=a29k-amd basic_os=udi ;; adobe68k) basic_machine=m68010-adobe basic_os=scout ;; alliant) basic_machine=fx80-alliant basic_os= ;; altos | altos3068) basic_machine=m68k-altos basic_os= ;; am29k) basic_machine=a29k-none basic_os=bsd ;; amdahl) basic_machine=580-amdahl basic_os=sysv ;; amiga) basic_machine=m68k-unknown basic_os= ;; amigaos | amigados) basic_machine=m68k-unknown basic_os=amigaos ;; amigaunix | amix) basic_machine=m68k-unknown basic_os=sysv4 ;; apollo68) basic_machine=m68k-apollo basic_os=sysv ;; apollo68bsd) basic_machine=m68k-apollo basic_os=bsd ;; aros) basic_machine=i386-pc basic_os=aros ;; aux) basic_machine=m68k-apple basic_os=aux ;; balance) basic_machine=ns32k-sequent basic_os=dynix ;; blackfin) basic_machine=bfin-unknown basic_os=linux ;; cegcc) basic_machine=arm-unknown basic_os=cegcc ;; convex-c1) basic_machine=c1-convex basic_os=bsd ;; convex-c2) basic_machine=c2-convex basic_os=bsd ;; convex-c32) basic_machine=c32-convex basic_os=bsd ;; convex-c34) basic_machine=c34-convex basic_os=bsd ;; convex-c38) basic_machine=c38-convex basic_os=bsd ;; cray) basic_machine=j90-cray basic_os=unicos ;; crds | unos) basic_machine=m68k-crds basic_os= ;; da30) basic_machine=m68k-da30 basic_os= ;; decstation | pmax | pmin | dec3100 | decstatn) basic_machine=mips-dec basic_os= ;; delta88) basic_machine=m88k-motorola basic_os=sysv3 ;; dicos) basic_machine=i686-pc basic_os=dicos ;; djgpp) basic_machine=i586-pc basic_os=msdosdjgpp ;; ebmon29k) basic_machine=a29k-amd basic_os=ebmon ;; es1800 | OSE68k | ose68k | ose | OSE) basic_machine=m68k-ericsson basic_os=ose ;; gmicro) basic_machine=tron-gmicro basic_os=sysv ;; go32) basic_machine=i386-pc basic_os=go32 ;; h8300hms) basic_machine=h8300-hitachi basic_os=hms ;; h8300xray) basic_machine=h8300-hitachi basic_os=xray ;; h8500hms) basic_machine=h8500-hitachi basic_os=hms ;; harris) basic_machine=m88k-harris basic_os=sysv3 ;; hp300 | hp300hpux) basic_machine=m68k-hp basic_os=hpux ;; hp300bsd) basic_machine=m68k-hp basic_os=bsd ;; hppaosf) basic_machine=hppa1.1-hp basic_os=osf ;; hppro) basic_machine=hppa1.1-hp basic_os=proelf ;; i386mach) basic_machine=i386-mach basic_os=mach ;; isi68 | isi) basic_machine=m68k-isi basic_os=sysv ;; m68knommu) basic_machine=m68k-unknown basic_os=linux ;; magnum | m3230) basic_machine=mips-mips basic_os=sysv ;; merlin) basic_machine=ns32k-utek basic_os=sysv ;; mingw64) basic_machine=x86_64-pc basic_os=mingw64 ;; mingw32) basic_machine=i686-pc basic_os=mingw32 ;; mingw32ce) basic_machine=arm-unknown basic_os=mingw32ce ;; monitor) basic_machine=m68k-rom68k basic_os=coff ;; morphos) basic_machine=powerpc-unknown basic_os=morphos ;; moxiebox) basic_machine=moxie-unknown basic_os=moxiebox ;; msdos) basic_machine=i386-pc basic_os=msdos ;; msys) basic_machine=i686-pc basic_os=msys ;; mvs) basic_machine=i370-ibm basic_os=mvs ;; nacl) basic_machine=le32-unknown basic_os=nacl ;; ncr3000) basic_machine=i486-ncr basic_os=sysv4 ;; netbsd386) basic_machine=i386-pc basic_os=netbsd ;; netwinder) basic_machine=armv4l-rebel basic_os=linux ;; news | news700 | news800 | news900) basic_machine=m68k-sony basic_os=newsos ;; news1000) basic_machine=m68030-sony basic_os=newsos ;; necv70) basic_machine=v70-nec basic_os=sysv ;; nh3000) basic_machine=m68k-harris basic_os=cxux ;; nh[45]000) basic_machine=m88k-harris basic_os=cxux ;; nindy960) basic_machine=i960-intel basic_os=nindy ;; mon960) basic_machine=i960-intel basic_os=mon960 ;; nonstopux) basic_machine=mips-compaq basic_os=nonstopux ;; os400) basic_machine=powerpc-ibm basic_os=os400 ;; OSE68000 | ose68000) basic_machine=m68000-ericsson basic_os=ose ;; os68k) basic_machine=m68k-none basic_os=os68k ;; paragon) basic_machine=i860-intel basic_os=osf ;; parisc) basic_machine=hppa-unknown basic_os=linux ;; psp) basic_machine=mipsallegrexel-sony basic_os=psp ;; pw32) basic_machine=i586-unknown basic_os=pw32 ;; rdos | rdos64) basic_machine=x86_64-pc basic_os=rdos ;; rdos32) basic_machine=i386-pc basic_os=rdos ;; rom68k) basic_machine=m68k-rom68k basic_os=coff ;; sa29200) basic_machine=a29k-amd basic_os=udi ;; sei) basic_machine=mips-sei basic_os=seiux ;; sequent) basic_machine=i386-sequent basic_os= ;; sps7) basic_machine=m68k-bull basic_os=sysv2 ;; st2000) basic_machine=m68k-tandem basic_os= ;; stratus) basic_machine=i860-stratus basic_os=sysv4 ;; sun2) basic_machine=m68000-sun basic_os= ;; sun2os3) basic_machine=m68000-sun basic_os=sunos3 ;; sun2os4) basic_machine=m68000-sun basic_os=sunos4 ;; sun3) basic_machine=m68k-sun basic_os= ;; sun3os3) basic_machine=m68k-sun basic_os=sunos3 ;; sun3os4) basic_machine=m68k-sun basic_os=sunos4 ;; sun4) basic_machine=sparc-sun basic_os= ;; sun4os3) basic_machine=sparc-sun basic_os=sunos3 ;; sun4os4) basic_machine=sparc-sun basic_os=sunos4 ;; sun4sol2) basic_machine=sparc-sun basic_os=solaris2 ;; sun386 | sun386i | roadrunner) basic_machine=i386-sun basic_os= ;; sv1) basic_machine=sv1-cray basic_os=unicos ;; symmetry) basic_machine=i386-sequent basic_os=dynix ;; t3e) basic_machine=alphaev5-cray basic_os=unicos ;; t90) basic_machine=t90-cray basic_os=unicos ;; toad1) basic_machine=pdp10-xkl basic_os=tops20 ;; tpf) basic_machine=s390x-ibm basic_os=tpf ;; udi29k) basic_machine=a29k-amd basic_os=udi ;; ultra3) basic_machine=a29k-nyu basic_os=sym1 ;; v810 | necv810) basic_machine=v810-nec basic_os=none ;; vaxv) basic_machine=vax-dec basic_os=sysv ;; vms) basic_machine=vax-dec basic_os=vms ;; vsta) basic_machine=i386-pc basic_os=vsta ;; vxworks960) basic_machine=i960-wrs basic_os=vxworks ;; vxworks68) basic_machine=m68k-wrs basic_os=vxworks ;; vxworks29k) basic_machine=a29k-wrs basic_os=vxworks ;; xbox) basic_machine=i686-pc basic_os=mingw32 ;; ymp) basic_machine=ymp-cray basic_os=unicos ;; *) basic_machine=$1 basic_os= ;; esac ;; esac # Decode 1-component or ad-hoc basic machines case $basic_machine in # Here we handle the default manufacturer of certain CPU types. It is in # some cases the only manufacturer, in others, it is the most popular. w89k) cpu=hppa1.1 vendor=winbond ;; op50n) cpu=hppa1.1 vendor=oki ;; op60c) cpu=hppa1.1 vendor=oki ;; ibm*) cpu=i370 vendor=ibm ;; orion105) cpu=clipper vendor=highlevel ;; mac | mpw | mac-mpw) cpu=m68k vendor=apple ;; pmac | pmac-mpw) cpu=powerpc vendor=apple ;; # Recognize the various machine names and aliases which stand # for a CPU type and a company and sometimes even an OS. 3b1 | 7300 | 7300-att | att-7300 | pc7300 | safari | unixpc) cpu=m68000 vendor=att ;; 3b*) cpu=we32k vendor=att ;; bluegene*) cpu=powerpc vendor=ibm basic_os=cnk ;; decsystem10* | dec10*) cpu=pdp10 vendor=dec basic_os=tops10 ;; decsystem20* | dec20*) cpu=pdp10 vendor=dec basic_os=tops20 ;; delta | 3300 | motorola-3300 | motorola-delta \ | 3300-motorola | delta-motorola) cpu=m68k vendor=motorola ;; dpx2*) cpu=m68k vendor=bull basic_os=sysv3 ;; encore | umax | mmax) cpu=ns32k vendor=encore ;; elxsi) cpu=elxsi vendor=elxsi basic_os=${basic_os:-bsd} ;; fx2800) cpu=i860 vendor=alliant ;; genix) cpu=ns32k vendor=ns ;; h3050r* | hiux*) cpu=hppa1.1 vendor=hitachi basic_os=hiuxwe2 ;; hp3k9[0-9][0-9] | hp9[0-9][0-9]) cpu=hppa1.0 vendor=hp ;; hp9k2[0-9][0-9] | hp9k31[0-9]) cpu=m68000 vendor=hp ;; hp9k3[2-9][0-9]) cpu=m68k vendor=hp ;; hp9k6[0-9][0-9] | hp6[0-9][0-9]) cpu=hppa1.0 vendor=hp ;; hp9k7[0-79][0-9] | hp7[0-79][0-9]) cpu=hppa1.1 vendor=hp ;; hp9k78[0-9] | hp78[0-9]) # FIXME: really hppa2.0-hp cpu=hppa1.1 vendor=hp ;; hp9k8[67]1 | hp8[67]1 | hp9k80[24] | hp80[24] | hp9k8[78]9 | hp8[78]9 | hp9k893 | hp893) # FIXME: really hppa2.0-hp cpu=hppa1.1 vendor=hp ;; hp9k8[0-9][13679] | hp8[0-9][13679]) cpu=hppa1.1 vendor=hp ;; hp9k8[0-9][0-9] | hp8[0-9][0-9]) cpu=hppa1.0 vendor=hp ;; i*86v32) cpu=`echo "$1" | sed -e 's/86.*/86/'` vendor=pc basic_os=sysv32 ;; i*86v4*) cpu=`echo "$1" | sed -e 's/86.*/86/'` vendor=pc basic_os=sysv4 ;; i*86v) cpu=`echo "$1" | sed -e 's/86.*/86/'` vendor=pc basic_os=sysv ;; i*86sol2) cpu=`echo "$1" | sed -e 's/86.*/86/'` vendor=pc basic_os=solaris2 ;; 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See the # GNU General Public License for more details. # You should have received a copy of the GNU General Public License # along with this program. If not, see . # As a special exception to the GNU General Public License, if you # distribute this file as part of a program that contains a # configuration script generated by Autoconf, you may include it under # the same distribution terms that you use for the rest of that program. # Originally written by Alexandre Oliva . case $1 in '') echo "$0: No command. Try '$0 --help' for more information." 1>&2 exit 1; ;; -h | --h*) cat <<\EOF Usage: depcomp [--help] [--version] PROGRAM [ARGS] Run PROGRAMS ARGS to compile a file, generating dependencies as side-effects. Environment variables: depmode Dependency tracking mode. source Source file read by 'PROGRAMS ARGS'. object Object file output by 'PROGRAMS ARGS'. DEPDIR directory where to store dependencies. depfile Dependency file to output. tmpdepfile Temporary file to use when outputting dependencies. libtool Whether libtool is used (yes/no). Report bugs to . GNU Automake home page: . General help using GNU software: . EOF exit $? ;; -v | --v*) echo "depcomp (GNU Automake) $scriptversion" exit $? ;; esac # Get the directory component of the given path, and save it in the # global variables '$dir'. Note that this directory component will # be either empty or ending with a '/' character. This is deliberate. set_dir_from () { case $1 in */*) dir=`echo "$1" | sed -e 's|/[^/]*$|/|'`;; *) dir=;; esac } # Get the suffix-stripped basename of the given path, and save it the # global variable '$base'. set_base_from () { base=`echo "$1" | sed -e 's|^.*/||' -e 's/\.[^.]*$//'` } # If no dependency file was actually created by the compiler invocation, # we still have to create a dummy depfile, to avoid errors with the # Makefile "include basename.Plo" scheme. make_dummy_depfile () { echo "#dummy" > "$depfile" } # Factor out some common post-processing of the generated depfile. # Requires the auxiliary global variable '$tmpdepfile' to be set. aix_post_process_depfile () { # If the compiler actually managed to produce a dependency file, # post-process it. if test -f "$tmpdepfile"; then # Each line is of the form 'foo.o: dependency.h'. # Do two passes, one to just change these to # $object: dependency.h # and one to simply output # dependency.h: # which is needed to avoid the deleted-header problem. { sed -e "s,^.*\.[$lower]*:,$object:," < "$tmpdepfile" sed -e "s,^.*\.[$lower]*:[$tab ]*,," -e 's,$,:,' < "$tmpdepfile" } > "$depfile" rm -f "$tmpdepfile" else make_dummy_depfile fi } # A tabulation character. tab=' ' # A newline character. nl=' ' # Character ranges might be problematic outside the C locale. # These definitions help. upper=ABCDEFGHIJKLMNOPQRSTUVWXYZ lower=abcdefghijklmnopqrstuvwxyz alpha=${upper}${lower} if test -z "$depmode" || test -z "$source" || test -z "$object"; then echo "depcomp: Variables source, object and depmode must be set" 1>&2 exit 1 fi # Dependencies for sub/bar.o or sub/bar.obj go into sub/.deps/bar.Po. depfile=${depfile-`echo "$object" | sed 's|[^\\/]*$|'${DEPDIR-.deps}'/&|;s|\.\([^.]*\)$|.P\1|;s|Pobj$|Po|'`} tmpdepfile=${tmpdepfile-`echo "$depfile" | sed 's/\.\([^.]*\)$/.T\1/'`} rm -f "$tmpdepfile" # Avoid interference from the environment. gccflag= dashmflag= # Some modes work just like other modes, but use different flags. We # parameterize here, but still list the modes in the big case below, # to make depend.m4 easier to write. Note that we *cannot* use a case # here, because this file can only contain one case statement. if test "$depmode" = hp; then # HP compiler uses -M and no extra arg. gccflag=-M depmode=gcc fi if test "$depmode" = dashXmstdout; then # This is just like dashmstdout with a different argument. dashmflag=-xM depmode=dashmstdout fi cygpath_u="cygpath -u -f -" if test "$depmode" = msvcmsys; then # This is just like msvisualcpp but w/o cygpath translation. # Just convert the backslash-escaped backslashes to single forward # slashes to satisfy depend.m4 cygpath_u='sed s,\\\\,/,g' depmode=msvisualcpp fi if test "$depmode" = msvc7msys; then # This is just like msvc7 but w/o cygpath translation. # Just convert the backslash-escaped backslashes to single forward # slashes to satisfy depend.m4 cygpath_u='sed s,\\\\,/,g' depmode=msvc7 fi if test "$depmode" = xlc; then # IBM C/C++ Compilers xlc/xlC can output gcc-like dependency information. gccflag=-qmakedep=gcc,-MF depmode=gcc fi case "$depmode" in gcc3) ## gcc 3 implements dependency tracking that does exactly what ## we want. Yay! Note: for some reason libtool 1.4 doesn't like ## it if -MD -MP comes after the -MF stuff. Hmm. ## Unfortunately, FreeBSD c89 acceptance of flags depends upon ## the command line argument order; so add the flags where they ## appear in depend2.am. Note that the slowdown incurred here ## affects only configure: in makefiles, %FASTDEP% shortcuts this. for arg do case $arg in -c) set fnord "$@" -MT "$object" -MD -MP -MF "$tmpdepfile" "$arg" ;; *) set fnord "$@" "$arg" ;; esac shift # fnord shift # $arg done "$@" stat=$? if test $stat -ne 0; then rm -f "$tmpdepfile" exit $stat fi mv "$tmpdepfile" "$depfile" ;; gcc) ## Note that this doesn't just cater to obsolete pre-3.x GCC compilers. ## but also to in-use compilers like IBM xlc/xlC and the HP C compiler. ## (see the conditional assignment to $gccflag above). ## There are various ways to get dependency output from gcc. Here's ## why we pick this rather obscure method: ## - Don't want to use -MD because we'd like the dependencies to end ## up in a subdir. Having to rename by hand is ugly. ## (We might end up doing this anyway to support other compilers.) ## - The DEPENDENCIES_OUTPUT environment variable makes gcc act like ## -MM, not -M (despite what the docs say). Also, it might not be ## supported by the other compilers which use the 'gcc' depmode. ## - Using -M directly means running the compiler twice (even worse ## than renaming). if test -z "$gccflag"; then gccflag=-MD, fi "$@" -Wp,"$gccflag$tmpdepfile" stat=$? if test $stat -ne 0; then rm -f "$tmpdepfile" exit $stat fi rm -f "$depfile" echo "$object : \\" > "$depfile" # The second -e expression handles DOS-style file names with drive # letters. sed -e 's/^[^:]*: / /' \ -e 's/^['$alpha']:\/[^:]*: / /' < "$tmpdepfile" >> "$depfile" ## This next piece of magic avoids the "deleted header file" problem. ## The problem is that when a header file which appears in a .P file ## is deleted, the dependency causes make to die (because there is ## typically no way to rebuild the header). We avoid this by adding ## dummy dependencies for each header file. Too bad gcc doesn't do ## this for us directly. ## Some versions of gcc put a space before the ':'. On the theory ## that the space means something, we add a space to the output as ## well. hp depmode also adds that space, but also prefixes the VPATH ## to the object. Take care to not repeat it in the output. ## Some versions of the HPUX 10.20 sed can't process this invocation ## correctly. Breaking it into two sed invocations is a workaround. tr ' ' "$nl" < "$tmpdepfile" \ | sed -e 's/^\\$//' -e '/^$/d' -e "s|.*$object$||" -e '/:$/d' \ | sed -e 's/$/ :/' >> "$depfile" rm -f "$tmpdepfile" ;; hp) # This case exists only to let depend.m4 do its work. It works by # looking at the text of this script. This case will never be run, # since it is checked for above. exit 1 ;; sgi) if test "$libtool" = yes; then "$@" "-Wp,-MDupdate,$tmpdepfile" else "$@" -MDupdate "$tmpdepfile" fi stat=$? if test $stat -ne 0; then rm -f "$tmpdepfile" exit $stat fi rm -f "$depfile" if test -f "$tmpdepfile"; then # yes, the sourcefile depend on other files echo "$object : \\" > "$depfile" # Clip off the initial element (the dependent). Don't try to be # clever and replace this with sed code, as IRIX sed won't handle # lines with more than a fixed number of characters (4096 in # IRIX 6.2 sed, 8192 in IRIX 6.5). We also remove comment lines; # the IRIX cc adds comments like '#:fec' to the end of the # dependency line. tr ' ' "$nl" < "$tmpdepfile" \ | sed -e 's/^.*\.o://' -e 's/#.*$//' -e '/^$/ d' \ | tr "$nl" ' ' >> "$depfile" echo >> "$depfile" # The second pass generates a dummy entry for each header file. tr ' ' "$nl" < "$tmpdepfile" \ | sed -e 's/^.*\.o://' -e 's/#.*$//' -e '/^$/ d' -e 's/$/:/' \ >> "$depfile" else make_dummy_depfile fi rm -f "$tmpdepfile" ;; xlc) # This case exists only to let depend.m4 do its work. It works by # looking at the text of this script. This case will never be run, # since it is checked for above. exit 1 ;; aix) # The C for AIX Compiler uses -M and outputs the dependencies # in a .u file. In older versions, this file always lives in the # current directory. Also, the AIX compiler puts '$object:' at the # start of each line; $object doesn't have directory information. # Version 6 uses the directory in both cases. set_dir_from "$object" set_base_from "$object" if test "$libtool" = yes; then tmpdepfile1=$dir$base.u tmpdepfile2=$base.u tmpdepfile3=$dir.libs/$base.u "$@" -Wc,-M else tmpdepfile1=$dir$base.u tmpdepfile2=$dir$base.u tmpdepfile3=$dir$base.u "$@" -M fi stat=$? if test $stat -ne 0; then rm -f "$tmpdepfile1" "$tmpdepfile2" "$tmpdepfile3" exit $stat fi for tmpdepfile in "$tmpdepfile1" "$tmpdepfile2" "$tmpdepfile3" do test -f "$tmpdepfile" && break done aix_post_process_depfile ;; tcc) # tcc (Tiny C Compiler) understand '-MD -MF file' since version 0.9.26 # FIXME: That version still under development at the moment of writing. # Make that this statement remains true also for stable, released # versions. # It will wrap lines (doesn't matter whether long or short) with a # trailing '\', as in: # # foo.o : \ # foo.c \ # foo.h \ # # It will put a trailing '\' even on the last line, and will use leading # spaces rather than leading tabs (at least since its commit 0394caf7 # "Emit spaces for -MD"). "$@" -MD -MF "$tmpdepfile" stat=$? if test $stat -ne 0; then rm -f "$tmpdepfile" exit $stat fi rm -f "$depfile" # Each non-empty line is of the form 'foo.o : \' or ' dep.h \'. # We have to change lines of the first kind to '$object: \'. sed -e "s|.*:|$object :|" < "$tmpdepfile" > "$depfile" # And for each line of the second kind, we have to emit a 'dep.h:' # dummy dependency, to avoid the deleted-header problem. sed -n -e 's|^ *\(.*\) *\\$|\1:|p' < "$tmpdepfile" >> "$depfile" rm -f "$tmpdepfile" ;; ## The order of this option in the case statement is important, since the ## shell code in configure will try each of these formats in the order ## listed in this file. A plain '-MD' option would be understood by many ## compilers, so we must ensure this comes after the gcc and icc options. pgcc) # Portland's C compiler understands '-MD'. # Will always output deps to 'file.d' where file is the root name of the # source file under compilation, even if file resides in a subdirectory. # The object file name does not affect the name of the '.d' file. # pgcc 10.2 will output # foo.o: sub/foo.c sub/foo.h # and will wrap long lines using '\' : # foo.o: sub/foo.c ... \ # sub/foo.h ... \ # ... set_dir_from "$object" # Use the source, not the object, to determine the base name, since # that's sadly what pgcc will do too. set_base_from "$source" tmpdepfile=$base.d # For projects that build the same source file twice into different object # files, the pgcc approach of using the *source* file root name can cause # problems in parallel builds. Use a locking strategy to avoid stomping on # the same $tmpdepfile. lockdir=$base.d-lock trap " echo '$0: caught signal, cleaning up...' >&2 rmdir '$lockdir' exit 1 " 1 2 13 15 numtries=100 i=$numtries while test $i -gt 0; do # mkdir is a portable test-and-set. if mkdir "$lockdir" 2>/dev/null; then # This process acquired the lock. "$@" -MD stat=$? # Release the lock. rmdir "$lockdir" break else # If the lock is being held by a different process, wait # until the winning process is done or we timeout. while test -d "$lockdir" && test $i -gt 0; do sleep 1 i=`expr $i - 1` done fi i=`expr $i - 1` done trap - 1 2 13 15 if test $i -le 0; then echo "$0: failed to acquire lock after $numtries attempts" >&2 echo "$0: check lockdir '$lockdir'" >&2 exit 1 fi if test $stat -ne 0; then rm -f "$tmpdepfile" exit $stat fi rm -f "$depfile" # Each line is of the form `foo.o: dependent.h', # or `foo.o: dep1.h dep2.h \', or ` dep3.h dep4.h \'. # Do two passes, one to just change these to # `$object: dependent.h' and one to simply `dependent.h:'. sed "s,^[^:]*:,$object :," < "$tmpdepfile" > "$depfile" # Some versions of the HPUX 10.20 sed can't process this invocation # correctly. Breaking it into two sed invocations is a workaround. sed 's,^[^:]*: \(.*\)$,\1,;s/^\\$//;/^$/d;/:$/d' < "$tmpdepfile" \ | sed -e 's/$/ :/' >> "$depfile" rm -f "$tmpdepfile" ;; hp2) # The "hp" stanza above does not work with aCC (C++) and HP's ia64 # compilers, which have integrated preprocessors. The correct option # to use with these is +Maked; it writes dependencies to a file named # 'foo.d', which lands next to the object file, wherever that # happens to be. # Much of this is similar to the tru64 case; see comments there. set_dir_from "$object" set_base_from "$object" if test "$libtool" = yes; then tmpdepfile1=$dir$base.d tmpdepfile2=$dir.libs/$base.d "$@" -Wc,+Maked else tmpdepfile1=$dir$base.d tmpdepfile2=$dir$base.d "$@" +Maked fi stat=$? if test $stat -ne 0; then rm -f "$tmpdepfile1" "$tmpdepfile2" exit $stat fi for tmpdepfile in "$tmpdepfile1" "$tmpdepfile2" do test -f "$tmpdepfile" && break done if test -f "$tmpdepfile"; then sed -e "s,^.*\.[$lower]*:,$object:," "$tmpdepfile" > "$depfile" # Add 'dependent.h:' lines. sed -ne '2,${ s/^ *// s/ \\*$// s/$/:/ p }' "$tmpdepfile" >> "$depfile" else make_dummy_depfile fi rm -f "$tmpdepfile" "$tmpdepfile2" ;; tru64) # The Tru64 compiler uses -MD to generate dependencies as a side # effect. 'cc -MD -o foo.o ...' puts the dependencies into 'foo.o.d'. # At least on Alpha/Redhat 6.1, Compaq CCC V6.2-504 seems to put # dependencies in 'foo.d' instead, so we check for that too. # Subdirectories are respected. set_dir_from "$object" set_base_from "$object" if test "$libtool" = yes; then # Libtool generates 2 separate objects for the 2 libraries. These # two compilations output dependencies in $dir.libs/$base.o.d and # in $dir$base.o.d. We have to check for both files, because # one of the two compilations can be disabled. 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END } # TODO: better error handling in option parsing (in particular, ensure # TODO: $log_file, $trs_file and $test_name are defined). test_name= # Used for reporting. log_file= # Where to save the result and output of the test script. trs_file= # Where to save the metadata of the test run. expect_failure=0 color_tests=0 merge=0 ignore_exit=0 comments=0 diag_string='#' while test $# -gt 0; do case $1 in --help) print_usage; exit $?;; --version) echo "$me (GNU Automake) $scriptversion"; exit $?;; --test-name) test_name=$2; shift;; --log-file) log_file=$2; shift;; --trs-file) trs_file=$2; shift;; --color-tests) color_tests=$2; shift;; --expect-failure) expect_failure=$2; shift;; --enable-hard-errors) shift;; # No-op. --merge) merge=1;; --no-merge) merge=0;; --ignore-exit) ignore_exit=1;; --comments) comments=1;; --no-comments) comments=0;; --diagnostic-string) diag_string=$2; shift;; --) shift; break;; -*) usage_error "invalid option: '$1'";; esac shift done test $# -gt 0 || usage_error "missing test command" case $expect_failure in yes) expect_failure=1;; *) expect_failure=0;; esac if test $color_tests = yes; then init_colors=' color_map["red"]="" # Red. color_map["grn"]="" # Green. color_map["lgn"]="" # Light green. color_map["blu"]="" # Blue. color_map["mgn"]="" # Magenta. color_map["std"]="" # No color. color_for_result["ERROR"] = "mgn" color_for_result["PASS"] = "grn" color_for_result["XPASS"] = "red" color_for_result["FAIL"] = "red" color_for_result["XFAIL"] = "lgn" color_for_result["SKIP"] = "blu"' else init_colors='' fi # :; is there to work around a bug in bash 3.2 (and earlier) which # does not always set '$?' properly on redirection failure. # See the Autoconf manual for more details. :;{ ( # Ignore common signals (in this subshell only!), to avoid potential # problems with Korn shells. Some Korn shells are known to propagate # to themselves signals that have killed a child process they were # waiting for; this is done at least for SIGINT (and usually only for # it, in truth). Without the `trap' below, such a behavior could # cause a premature exit in the current subshell, e.g., in case the # test command it runs gets terminated by a SIGINT. Thus, the awk # script we are piping into would never seen the exit status it # expects on its last input line (which is displayed below by the # last `echo $?' statement), and would thus die reporting an internal # error. # For more information, see the Autoconf manual and the threads: # # trap : 1 3 2 13 15 if test $merge -gt 0; then exec 2>&1 else exec 2>&3 fi "$@" echo $? ) | LC_ALL=C ${AM_TAP_AWK-awk} \ -v me="$me" \ -v test_script_name="$test_name" \ -v log_file="$log_file" \ -v trs_file="$trs_file" \ -v expect_failure="$expect_failure" \ -v merge="$merge" \ -v ignore_exit="$ignore_exit" \ -v comments="$comments" \ -v diag_string="$diag_string" \ ' # TODO: the usages of "cat >&3" below could be optimized when using # GNU awk, and/on on systems that supports /dev/fd/. # Implementation note: in what follows, `result_obj` will be an # associative array that (partly) simulates a TAP result object # from the `TAP::Parser` perl module. ## ----------- ## ## FUNCTIONS ## ## ----------- ## function fatal(msg) { print me ": " msg | "cat >&2" exit 1 } function abort(where) { fatal("internal error " where) } # Convert a boolean to a "yes"/"no" string. function yn(bool) { return bool ? "yes" : "no"; } function add_test_result(result) { if (!test_results_index) test_results_index = 0 test_results_list[test_results_index] = result test_results_index += 1 test_results_seen[result] = 1; } # Whether the test script should be re-run by "make recheck". function must_recheck() { for (k in test_results_seen) if (k != "XFAIL" && k != "PASS" && k != "SKIP") return 1 return 0 } # Whether the content of the log file associated to this test should # be copied into the "global" test-suite.log. function copy_in_global_log() { for (k in test_results_seen) if (k != "PASS") return 1 return 0 } function get_global_test_result() { if ("ERROR" in test_results_seen) return "ERROR" if ("FAIL" in test_results_seen || "XPASS" in test_results_seen) return "FAIL" all_skipped = 1 for (k in test_results_seen) if (k != "SKIP") all_skipped = 0 if (all_skipped) return "SKIP" return "PASS"; } function stringify_result_obj(result_obj) { if (result_obj["is_unplanned"] || result_obj["number"] != testno) return "ERROR" if (plan_seen == LATE_PLAN) return "ERROR" if (result_obj["directive"] == "TODO") return result_obj["is_ok"] ? "XPASS" : "XFAIL" if (result_obj["directive"] == "SKIP") return result_obj["is_ok"] ? "SKIP" : COOKED_FAIL; if (length(result_obj["directive"])) abort("in function stringify_result_obj()") return result_obj["is_ok"] ? COOKED_PASS : COOKED_FAIL } function decorate_result(result) { color_name = color_for_result[result] if (color_name) return color_map[color_name] "" result "" color_map["std"] # If we are not using colorized output, or if we do not know how # to colorize the given result, we should return it unchanged. return result } function report(result, details) { if (result ~ /^(X?(PASS|FAIL)|SKIP|ERROR)/) { msg = ": " test_script_name add_test_result(result) } else if (result == "#") { msg = " " test_script_name ":" } else { abort("in function report()") } if (length(details)) msg = msg " " details # Output on console might be colorized. print decorate_result(result) msg # Log the result in the log file too, to help debugging (this is # especially true when said result is a TAP error or "Bail out!"). print result msg | "cat >&3"; } function testsuite_error(error_message) { report("ERROR", "- " error_message) } function handle_tap_result() { details = result_obj["number"]; if (length(result_obj["description"])) details = details " " result_obj["description"] if (plan_seen == LATE_PLAN) { details = details " # AFTER LATE PLAN"; } else if (result_obj["is_unplanned"]) { details = details " # UNPLANNED"; } else if (result_obj["number"] != testno) { details = sprintf("%s # OUT-OF-ORDER (expecting %d)", details, testno); } else if (result_obj["directive"]) { details = details " # " result_obj["directive"]; if (length(result_obj["explanation"])) details = details " " result_obj["explanation"] } report(stringify_result_obj(result_obj), details) } # `skip_reason` should be empty whenever planned > 0. function handle_tap_plan(planned, skip_reason) { planned += 0 # Avoid getting confused if, say, `planned` is "00" if (length(skip_reason) && planned > 0) abort("in function handle_tap_plan()") if (plan_seen) { # Error, only one plan per stream is acceptable. testsuite_error("multiple test plans") return; } planned_tests = planned # The TAP plan can come before or after *all* the TAP results; we speak # respectively of an "early" or a "late" plan. If we see the plan line # after at least one TAP result has been seen, assume we have a late # plan; in this case, any further test result seen after the plan will # be flagged as an error. plan_seen = (testno >= 1 ? LATE_PLAN : EARLY_PLAN) # If testno > 0, we have an error ("too many tests run") that will be # automatically dealt with later, so do not worry about it here. If # $plan_seen is true, we have an error due to a repeated plan, and that # has already been dealt with above. Otherwise, we have a valid "plan # with SKIP" specification, and should report it as a particular kind # of SKIP result. if (planned == 0 && testno == 0) { if (length(skip_reason)) skip_reason = "- " skip_reason; report("SKIP", skip_reason); } } function extract_tap_comment(line) { if (index(line, diag_string) == 1) { # Strip leading `diag_string` from `line`. line = substr(line, length(diag_string) + 1) # And strip any leading and trailing whitespace left. sub("^[ \t]*", "", line) sub("[ \t]*$", "", line) # Return what is left (if any). return line; } return ""; } # When this function is called, we know that line is a TAP result line, # so that it matches the (perl) RE "^(not )?ok\b". function setup_result_obj(line) { # Get the result, and remove it from the line. result_obj["is_ok"] = (substr(line, 1, 2) == "ok" ? 1 : 0) sub("^(not )?ok[ \t]*", "", line) # If the result has an explicit number, get it and strip it; otherwise, # automatically assign the next test number to it. if (line ~ /^[0-9]+$/ || line ~ /^[0-9]+[^a-zA-Z0-9_]/) { match(line, "^[0-9]+") # The final `+ 0` is to normalize numbers with leading zeros. result_obj["number"] = substr(line, 1, RLENGTH) + 0 line = substr(line, RLENGTH + 1) } else { result_obj["number"] = testno } if (plan_seen == LATE_PLAN) # No further test results are acceptable after a "late" TAP plan # has been seen. result_obj["is_unplanned"] = 1 else if (plan_seen && testno > planned_tests) result_obj["is_unplanned"] = 1 else result_obj["is_unplanned"] = 0 # Strip trailing and leading whitespace. sub("^[ \t]*", "", line) sub("[ \t]*$", "", line) # This will have to be corrected if we have a "TODO"/"SKIP" directive. result_obj["description"] = line result_obj["directive"] = "" result_obj["explanation"] = "" if (index(line, "#") == 0) return # No possible directive, nothing more to do. # Directives are case-insensitive. rx = "[ \t]*#[ \t]*([tT][oO][dD][oO]|[sS][kK][iI][pP])[ \t]*" # See whether we have the directive, and if yes, where. pos = match(line, rx "$") if (!pos) pos = match(line, rx "[^a-zA-Z0-9_]") # If there was no TAP directive, we have nothing more to do. if (!pos) return # Let`s now see if the TAP directive has been escaped. For example: # escaped: ok \# SKIP # not escaped: ok \\# SKIP # escaped: ok \\\\\# SKIP # not escaped: ok \ # SKIP if (substr(line, pos, 1) == "#") { bslash_count = 0 for (i = pos; i > 1 && substr(line, i - 1, 1) == "\\"; i--) bslash_count += 1 if (bslash_count % 2) return # Directive was escaped. } # Strip the directive and its explanation (if any) from the test # description. result_obj["description"] = substr(line, 1, pos - 1) # Now remove the test description from the line, that has been dealt # with already. line = substr(line, pos) # Strip the directive, and save its value (normalized to upper case). sub("^[ \t]*#[ \t]*", "", line) result_obj["directive"] = toupper(substr(line, 1, 4)) line = substr(line, 5) # Now get the explanation for the directive (if any), with leading # and trailing whitespace removed. sub("^[ \t]*", "", line) sub("[ \t]*$", "", line) result_obj["explanation"] = line } function get_test_exit_message(status) { if (status == 0) return "" if (status !~ /^[1-9][0-9]*$/) abort("getting exit status") if (status < 127) exit_details = "" else if (status == 127) exit_details = " (command not found?)" else if (status >= 128 && status <= 255) exit_details = sprintf(" (terminated by signal %d?)", status - 128) else if (status > 256 && status <= 384) # We used to report an "abnormal termination" here, but some Korn # shells, when a child process die due to signal number n, can leave # in $? an exit status of 256+n instead of the more standard 128+n. # Apparently, both behaviors are allowed by POSIX (2008), so be # prepared to handle them both. See also Austin Group report ID # 0000051 exit_details = sprintf(" (terminated by signal %d?)", status - 256) else # Never seen in practice. exit_details = " (abnormal termination)" return sprintf("exited with status %d%s", status, exit_details) } function write_test_results() { print ":global-test-result: " get_global_test_result() > trs_file print ":recheck: " yn(must_recheck()) > trs_file print ":copy-in-global-log: " yn(copy_in_global_log()) > trs_file for (i = 0; i < test_results_index; i += 1) print ":test-result: " test_results_list[i] > trs_file close(trs_file); } BEGIN { ## ------- ## ## SETUP ## ## ------- ## '"$init_colors"' # Properly initialized once the TAP plan is seen. planned_tests = 0 COOKED_PASS = expect_failure ? "XPASS": "PASS"; COOKED_FAIL = expect_failure ? "XFAIL": "FAIL"; # Enumeration-like constants to remember which kind of plan (if any) # has been seen. It is important that NO_PLAN evaluates "false" as # a boolean. NO_PLAN = 0 EARLY_PLAN = 1 LATE_PLAN = 2 testno = 0 # Number of test results seen so far. bailed_out = 0 # Whether a "Bail out!" directive has been seen. # Whether the TAP plan has been seen or not, and if yes, which kind # it is ("early" is seen before any test result, "late" otherwise). plan_seen = NO_PLAN ## --------- ## ## PARSING ## ## --------- ## is_first_read = 1 while (1) { # Involutions required so that we are able to read the exit status # from the last input line. st = getline if (st < 0) # I/O error. fatal("I/O error while reading from input stream") else if (st == 0) # End-of-input { if (is_first_read) abort("in input loop: only one input line") break } if (is_first_read) { is_first_read = 0 nextline = $0 continue } else { curline = nextline nextline = $0 $0 = curline } # Copy any input line verbatim into the log file. print | "cat >&3" # Parsing of TAP input should stop after a "Bail out!" directive. if (bailed_out) continue # TAP test result. if ($0 ~ /^(not )?ok$/ || $0 ~ /^(not )?ok[^a-zA-Z0-9_]/) { testno += 1 setup_result_obj($0) handle_tap_result() } # TAP plan (normal or "SKIP" without explanation). else if ($0 ~ /^1\.\.[0-9]+[ \t]*$/) { # The next two lines will put the number of planned tests in $0. sub("^1\\.\\.", "") sub("[^0-9]*$", "") handle_tap_plan($0, "") continue } # TAP "SKIP" plan, with an explanation. else if ($0 ~ /^1\.\.0+[ \t]*#/) { # The next lines will put the skip explanation in $0, stripping # any leading and trailing whitespace. This is a little more # tricky in truth, since we want to also strip a potential leading # "SKIP" string from the message. sub("^[^#]*#[ \t]*(SKIP[: \t][ \t]*)?", "") sub("[ \t]*$", ""); handle_tap_plan(0, $0) } # "Bail out!" magic. # Older versions of prove and TAP::Harness (e.g., 3.17) did not # recognize a "Bail out!" directive when preceded by leading # whitespace, but more modern versions (e.g., 3.23) do. So we # emulate the latter, "more modern" behavior. else if ($0 ~ /^[ \t]*Bail out!/) { bailed_out = 1 # Get the bailout message (if any), with leading and trailing # whitespace stripped. The message remains stored in `$0`. sub("^[ \t]*Bail out![ \t]*", ""); sub("[ \t]*$", ""); # Format the error message for the bailout_message = "Bail out!" if (length($0)) bailout_message = bailout_message " " $0 testsuite_error(bailout_message) } # Maybe we have to look for diagnostic comments too. else if (comments != 0) { comment = extract_tap_comment($0); if (length(comment)) report("#", comment); } } ## -------- ## ## FINISH ## ## -------- ## # A "Bail out!" directive should cause us to ignore any following TAP # error, as well as a non-zero exit status from the TAP producer. if (!bailed_out) { if (!plan_seen) { testsuite_error("missing test plan") } else if (planned_tests != testno) { bad_amount = testno > planned_tests ? "many" : "few" testsuite_error(sprintf("too %s tests run (expected %d, got %d)", bad_amount, planned_tests, testno)) } if (!ignore_exit) { # Fetch exit status from the last line. exit_message = get_test_exit_message(nextline) if (exit_message) testsuite_error(exit_message) } } write_test_results() exit 0 } # End of "BEGIN" block. ' # TODO: document that we consume the file descriptor 3 :-( } 3>"$log_file" test $? -eq 0 || fatal "I/O or internal error" # Local Variables: # mode: shell-script # sh-indentation: 2 # eval: (add-hook 'before-save-hook 'time-stamp) # time-stamp-start: "scriptversion=" # time-stamp-format: "%:y-%02m-%02d.%02H" # time-stamp-time-zone: "UTC0" # time-stamp-end: "; # UTC" # End: NBISweden-MrBayes-ecfd55c/configure000077500000000000000000011234141526212227600173320ustar00rootroot00000000000000#! /bin/sh # Guess values for system-dependent variables and create Makefiles. # Generated by GNU Autoconf 2.72 for MrBayes 3.2.9. # # # Copyright (C) 1992-1996, 1998-2017, 2020-2023 Free Software Foundation, # Inc. # # # This configure script is free software; the Free Software Foundation # gives unlimited permission to copy, distribute and modify it. ## -------------------- ## ## M4sh Initialization. ## ## -------------------- ## # Be more Bourne compatible DUALCASE=1; export DUALCASE # for MKS sh if test ${ZSH_VERSION+y} && (emulate sh) >/dev/null 2>&1 then : emulate sh NULLCMD=: # Pre-4.2 versions of Zsh do word splitting on ${1+"$@"}, which # is contrary to our usage. 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"./$cache_file";; esac fi else { printf "%s\n" "$as_me:${as_lineno-$LINENO}: creating cache $cache_file" >&5 printf "%s\n" "$as_me: creating cache $cache_file" >&6;} >$cache_file fi # Test code for whether the C compiler supports C89 (global declarations) ac_c_conftest_c89_globals=' /* Does the compiler advertise C89 conformance? Do not test the value of __STDC__, because some compilers set it to 0 while being otherwise adequately conformant. */ #if !defined __STDC__ # error "Compiler does not advertise C89 conformance" #endif #include #include struct stat; /* Most of the following tests are stolen from RCS 5.7 src/conf.sh. */ struct buf { int x; }; struct buf * (*rcsopen) (struct buf *, struct stat *, int); static char *e (char **p, int i) { return p[i]; } static char *f (char * (*g) (char **, int), char **p, ...) { char *s; va_list v; va_start (v,p); s = g (p, va_arg (v,int)); va_end (v); return s; } /* C89 style stringification. */ #define noexpand_stringify(a) #a const char *stringified = noexpand_stringify(arbitrary+token=sequence); /* C89 style token pasting. Exercises some of the corner cases that e.g. old MSVC gets wrong, but not very hard. */ #define noexpand_concat(a,b) a##b #define expand_concat(a,b) noexpand_concat(a,b) extern int vA; extern int vbee; #define aye A #define bee B int *pvA = &expand_concat(v,aye); int *pvbee = &noexpand_concat(v,bee); /* OSF 4.0 Compaq cc is some sort of almost-ANSI by default. It has function prototypes and stuff, but not \xHH hex character constants. These do not provoke an error unfortunately, instead are silently treated as an "x". The following induces an error, until -std is added to get proper ANSI mode. Curiously \x00 != x always comes out true, for an array size at least. It is necessary to write \x00 == 0 to get something that is true only with -std. */ int osf4_cc_array ['\''\x00'\'' == 0 ? 1 : -1]; /* IBM C 6 for AIX is almost-ANSI by default, but it replaces macro parameters inside strings and character constants. */ #define FOO(x) '\''x'\'' int xlc6_cc_array[FOO(a) == '\''x'\'' ? 1 : -1]; int test (int i, double x); struct s1 {int (*f) (int a);}; struct s2 {int (*f) (double a);}; int pairnames (int, char **, int *(*)(struct buf *, struct stat *, int), int, int);' # Test code for whether the C compiler supports C89 (body of main). ac_c_conftest_c89_main=' ok |= (argc == 0 || f (e, argv, 0) != argv[0] || f (e, argv, 1) != argv[1]); ' # Test code for whether the C compiler supports C99 (global declarations) ac_c_conftest_c99_globals=' /* Does the compiler advertise C99 conformance? */ #if !defined __STDC_VERSION__ || __STDC_VERSION__ < 199901L # error "Compiler does not advertise C99 conformance" #endif // See if C++-style comments work. #include extern int puts (const char *); extern int printf (const char *, ...); extern int dprintf (int, const char *, ...); extern void *malloc (size_t); extern void free (void *); // Check varargs macros. These examples are taken from C99 6.10.3.5. // dprintf is used instead of fprintf to avoid needing to declare // FILE and stderr. #define debug(...) dprintf (2, __VA_ARGS__) #define showlist(...) puts (#__VA_ARGS__) #define report(test,...) ((test) ? puts (#test) : printf (__VA_ARGS__)) static void test_varargs_macros (void) { int x = 1234; int y = 5678; debug ("Flag"); debug ("X = %d\n", x); showlist (The first, second, and third items.); report (x>y, "x is %d but y is %d", x, y); } // Check long long types. #define BIG64 18446744073709551615ull #define BIG32 4294967295ul #define BIG_OK (BIG64 / BIG32 == 4294967297ull && BIG64 % BIG32 == 0) #if !BIG_OK #error "your preprocessor is broken" #endif #if BIG_OK #else #error "your preprocessor is broken" #endif static long long int bignum = -9223372036854775807LL; static unsigned long long int ubignum = BIG64; struct incomplete_array { int datasize; double data[]; }; struct named_init { int number; const wchar_t *name; double average; }; typedef const char *ccp; static inline int test_restrict (ccp restrict text) { // Iterate through items via the restricted pointer. // Also check for declarations in for loops. for (unsigned int i = 0; *(text+i) != '\''\0'\''; ++i) continue; return 0; } // Check varargs and va_copy. static bool test_varargs (const char *format, ...) { va_list args; va_start (args, format); va_list args_copy; va_copy (args_copy, args); const char *str = ""; int number = 0; float fnumber = 0; while (*format) { switch (*format++) { case '\''s'\'': // string str = va_arg (args_copy, const char *); break; case '\''d'\'': // int number = va_arg (args_copy, int); break; case '\''f'\'': // float fnumber = va_arg (args_copy, double); break; default: break; } } va_end (args_copy); va_end (args); return *str && number && fnumber; } ' # Test code for whether the C compiler supports C99 (body of main). ac_c_conftest_c99_main=' // Check bool. _Bool success = false; success |= (argc != 0); // Check restrict. if (test_restrict ("String literal") == 0) success = true; char *restrict newvar = "Another string"; // Check varargs. success &= test_varargs ("s, d'\'' f .", "string", 65, 34.234); test_varargs_macros (); // Check flexible array members. struct incomplete_array *ia = malloc (sizeof (struct incomplete_array) + (sizeof (double) * 10)); ia->datasize = 10; for (int i = 0; i < ia->datasize; ++i) ia->data[i] = i * 1.234; // Work around memory leak warnings. free (ia); // Check named initializers. struct named_init ni = { .number = 34, .name = L"Test wide string", .average = 543.34343, }; ni.number = 58; int dynamic_array[ni.number]; dynamic_array[0] = argv[0][0]; dynamic_array[ni.number - 1] = 543; // work around unused variable warnings ok |= (!success || bignum == 0LL || ubignum == 0uLL || newvar[0] == '\''x'\'' || dynamic_array[ni.number - 1] != 543); ' # Test code for whether the C compiler supports C11 (global declarations) ac_c_conftest_c11_globals=' /* Does the compiler advertise C11 conformance? */ #if !defined __STDC_VERSION__ || __STDC_VERSION__ < 201112L # error "Compiler does not advertise C11 conformance" #endif // Check _Alignas. char _Alignas (double) aligned_as_double; char _Alignas (0) no_special_alignment; extern char aligned_as_int; char _Alignas (0) _Alignas (int) aligned_as_int; // Check _Alignof. enum { int_alignment = _Alignof (int), int_array_alignment = _Alignof (int[100]), char_alignment = _Alignof (char) }; _Static_assert (0 < -_Alignof (int), "_Alignof is signed"); // Check _Noreturn. int _Noreturn does_not_return (void) { for (;;) continue; } // Check _Static_assert. struct test_static_assert { int x; _Static_assert (sizeof (int) <= sizeof (long int), "_Static_assert does not work in struct"); long int y; }; // Check UTF-8 literals. #define u8 syntax error! char const utf8_literal[] = u8"happens to be ASCII" "another string"; // Check duplicate typedefs. typedef long *long_ptr; typedef long int *long_ptr; typedef long_ptr long_ptr; // Anonymous structures and unions -- taken from C11 6.7.2.1 Example 1. struct anonymous { union { struct { int i; int j; }; struct { int k; long int l; } w; }; int m; } v1; ' # Test code for whether the C compiler supports C11 (body of main). ac_c_conftest_c11_main=' _Static_assert ((offsetof (struct anonymous, i) == offsetof (struct anonymous, w.k)), "Anonymous union alignment botch"); v1.i = 2; v1.w.k = 5; ok |= v1.i != 5; ' # Test code for whether the C compiler supports C11 (complete). ac_c_conftest_c11_program="${ac_c_conftest_c89_globals} ${ac_c_conftest_c99_globals} ${ac_c_conftest_c11_globals} int main (int argc, char **argv) { int ok = 0; ${ac_c_conftest_c89_main} ${ac_c_conftest_c99_main} ${ac_c_conftest_c11_main} return ok; } " # Test code for whether the C compiler supports C99 (complete). ac_c_conftest_c99_program="${ac_c_conftest_c89_globals} ${ac_c_conftest_c99_globals} int main (int argc, char **argv) { int ok = 0; ${ac_c_conftest_c89_main} ${ac_c_conftest_c99_main} return ok; } " # Test code for whether the C compiler supports C89 (complete). ac_c_conftest_c89_program="${ac_c_conftest_c89_globals} int main (int argc, char **argv) { int ok = 0; ${ac_c_conftest_c89_main} return ok; } " as_fn_append ac_header_c_list " stdio.h stdio_h HAVE_STDIO_H" as_fn_append ac_header_c_list " stdlib.h stdlib_h HAVE_STDLIB_H" as_fn_append ac_header_c_list " string.h string_h HAVE_STRING_H" as_fn_append ac_header_c_list " inttypes.h inttypes_h HAVE_INTTYPES_H" as_fn_append ac_header_c_list " stdint.h stdint_h HAVE_STDINT_H" as_fn_append ac_header_c_list " strings.h strings_h HAVE_STRINGS_H" as_fn_append ac_header_c_list " sys/stat.h sys_stat_h HAVE_SYS_STAT_H" as_fn_append ac_header_c_list " sys/types.h sys_types_h HAVE_SYS_TYPES_H" as_fn_append ac_header_c_list " unistd.h unistd_h HAVE_UNISTD_H" # Auxiliary files required by this configure script. ac_aux_files="compile config.guess config.sub missing install-sh tap-driver.sh" # Locations in which to look for auxiliary files. ac_aux_dir_candidates="${srcdir}/autotools" # Search for a directory containing all of the required auxiliary files, # $ac_aux_files, from the $PATH-style list $ac_aux_dir_candidates. # If we don't find one directory that contains all the files we need, # we report the set of missing files from the *first* directory in # $ac_aux_dir_candidates and give up. ac_missing_aux_files="" ac_first_candidate=: printf "%s\n" "$as_me:${as_lineno-$LINENO}: looking for aux files: $ac_aux_files" >&5 as_save_IFS=$IFS; IFS=$PATH_SEPARATOR as_found=false for as_dir in $ac_aux_dir_candidates do IFS=$as_save_IFS case $as_dir in #((( '') as_dir=./ ;; */) ;; *) as_dir=$as_dir/ ;; esac as_found=: printf "%s\n" "$as_me:${as_lineno-$LINENO}: trying $as_dir" >&5 ac_aux_dir_found=yes ac_install_sh= for ac_aux in $ac_aux_files do # As a special case, if "install-sh" is required, that requirement # can be satisfied by any of "install-sh", "install.sh", or "shtool", # and $ac_install_sh is set appropriately for whichever one is found. if test x"$ac_aux" = x"install-sh" then if test -f "${as_dir}install-sh"; then printf "%s\n" "$as_me:${as_lineno-$LINENO}: ${as_dir}install-sh found" >&5 ac_install_sh="${as_dir}install-sh -c" elif test -f "${as_dir}install.sh"; then printf "%s\n" "$as_me:${as_lineno-$LINENO}: ${as_dir}install.sh found" >&5 ac_install_sh="${as_dir}install.sh -c" elif test -f "${as_dir}shtool"; then printf "%s\n" "$as_me:${as_lineno-$LINENO}: ${as_dir}shtool found" >&5 ac_install_sh="${as_dir}shtool install -c" else ac_aux_dir_found=no if $ac_first_candidate; then ac_missing_aux_files="${ac_missing_aux_files} install-sh" else break fi fi else if test -f "${as_dir}${ac_aux}"; then printf "%s\n" "$as_me:${as_lineno-$LINENO}: ${as_dir}${ac_aux} found" >&5 else ac_aux_dir_found=no if $ac_first_candidate; then ac_missing_aux_files="${ac_missing_aux_files} ${ac_aux}" else break fi fi fi done if test "$ac_aux_dir_found" = yes; then ac_aux_dir="$as_dir" break fi ac_first_candidate=false as_found=false done IFS=$as_save_IFS if $as_found then : else case e in #( e) as_fn_error $? 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"invalid value of canonical host" "$LINENO" 5;; esac host=$ac_cv_host ac_save_IFS=$IFS; IFS='-' set x $ac_cv_host shift host_cpu=$1 host_vendor=$2 shift; shift # Remember, the first character of IFS is used to create $*, # except with old shells: host_os=$* IFS=$ac_save_IFS case $host_os in *\ *) host_os=`echo "$host_os" | sed 's/ /-/g'`;; esac printf "%s\n" "#define HOST_TYPE \"$host\"" >>confdefs.h printf "%s\n" "#define HOST_CPU \"$host_cpu\"" >>confdefs.h # NB: DEFAULT CFLAGS: # Using --enable-debug sets CFLAGS to "-g" ($debug_cflags) # Not using --enable-debug sets CFLAGS to "-O3 -DNDEBUG" ($release_cflags) # "-std=c99 -pedantic" will always be added ($default_cflags) # # (warning flags are added by the AX_CFLAGS_WARN_ALL macro further down) debug_cflags="-g" release_cflags="-O3 -DNDEBUG" default_cflags="-std=c99 -pedantic" # Optional feature: debug # Check whether --enable-debug was given. if test ${enable_debug+y} then : enableval=$enable_debug; enable_debug=$enableval else case e in #( e) enable_debug=no ;; esac fi # Handle CFLAGS at once, before AX_PROG_CC_MPI further down. if test "x$enable_debug" = "xyes" then : our_cflags="$debug_cflags" else case e in #( e) our_cflags="$release_cflags" ;; esac fi # Use our_cflags, the default_cflags, # plus any CFLAGS given to us by the user. CFLAGS="$our_cflags $default_cflags $CFLAGS" # Optional features: SSE, AVX, FMA # Check whether --enable-sse was given. if test ${enable_sse+y} then : enableval=$enable_sse; enable_sse=$enableval else case e in #( e) enable_sse=yes ;; esac fi # Check whether --enable-avx was given. if test ${enable_avx+y} then : enableval=$enable_avx; enable_avx=$enableval else case e in #( e) enable_avx=yes ;; esac fi # Check whether --enable-fma was given. if test ${enable_fma+y} then : enableval=$enable_fma; enable_fma=$enableval else case e in #( e) enable_fma=yes ;; esac fi # Optional feature: Enable/disable building of documentation under doc/ # (enabled by default) # Check whether --enable-doc was given. if test ${enable_doc+y} then : enableval=$enable_doc; enable_doc=$enableval else case e in #( e) enable_doc=yes ;; esac fi # Optional external libraries: MPI, Readline, Beagle # Check whether --with-mpi was given. if test ${with_mpi+y} then : withval=$with_mpi; else case e in #( e) with_mpi=no ;; esac fi # Check whether --with-readline was given. if test ${with_readline+y} then : withval=$with_readline; else case e in #( e) with_readline=check ;; esac fi # Check whether --with-beagle was given. if test ${with_beagle+y} then : withval=$with_beagle; else case e in #( e) with_beagle=no ;; esac fi # Checks for programs. if test "x$ac_cv_env_PKG_CONFIG_set" != "xset"; then if test -n "$ac_tool_prefix"; then # Extract the first word of "${ac_tool_prefix}pkg-config", so it can be a program name with args. set dummy ${ac_tool_prefix}pkg-config; ac_word=$2 { printf "%s\n" "$as_me:${as_lineno-$LINENO}: checking for $ac_word" >&5 printf %s "checking for $ac_word... " >&6; } if test ${ac_cv_path_PKG_CONFIG+y} then : printf %s "(cached) " >&6 else case e in #( e) case $PKG_CONFIG in [\\/]* | ?:[\\/]*) ac_cv_path_PKG_CONFIG="$PKG_CONFIG" # Let the user override the test with a path. ;; *) as_save_IFS=$IFS; IFS=$PATH_SEPARATOR for as_dir in $PATH do IFS=$as_save_IFS case $as_dir in #((( '') as_dir=./ ;; */) ;; *) as_dir=$as_dir/ ;; esac for ac_exec_ext in '' $ac_executable_extensions; do if as_fn_executable_p "$as_dir$ac_word$ac_exec_ext"; then ac_cv_path_PKG_CONFIG="$as_dir$ac_word$ac_exec_ext" printf "%s\n" "$as_me:${as_lineno-$LINENO}: found $as_dir$ac_word$ac_exec_ext" >&5 break 2 fi done done IFS=$as_save_IFS ;; esac ;; esac fi PKG_CONFIG=$ac_cv_path_PKG_CONFIG if test -n "$PKG_CONFIG"; then { printf "%s\n" "$as_me:${as_lineno-$LINENO}: result: $PKG_CONFIG" >&5 printf "%s\n" "$PKG_CONFIG" >&6; } else { printf "%s\n" "$as_me:${as_lineno-$LINENO}: result: no" >&5 printf "%s\n" "no" >&6; } fi fi if test -z "$ac_cv_path_PKG_CONFIG"; then ac_pt_PKG_CONFIG=$PKG_CONFIG # Extract the first word of "pkg-config", so it can be a program name with args. set dummy pkg-config; ac_word=$2 { printf "%s\n" "$as_me:${as_lineno-$LINENO}: checking for $ac_word" >&5 printf %s "checking for $ac_word... " >&6; } if test ${ac_cv_path_ac_pt_PKG_CONFIG+y} then : printf %s "(cached) " >&6 else case e in #( e) case $ac_pt_PKG_CONFIG in [\\/]* | ?:[\\/]*) ac_cv_path_ac_pt_PKG_CONFIG="$ac_pt_PKG_CONFIG" # Let the user override the test with a path. ;; *) as_save_IFS=$IFS; IFS=$PATH_SEPARATOR for as_dir in $PATH do IFS=$as_save_IFS case $as_dir in #((( '') as_dir=./ ;; */) ;; *) as_dir=$as_dir/ ;; esac for ac_exec_ext in '' $ac_executable_extensions; do if as_fn_executable_p "$as_dir$ac_word$ac_exec_ext"; then ac_cv_path_ac_pt_PKG_CONFIG="$as_dir$ac_word$ac_exec_ext" printf "%s\n" "$as_me:${as_lineno-$LINENO}: found $as_dir$ac_word$ac_exec_ext" >&5 break 2 fi done done IFS=$as_save_IFS ;; esac ;; esac fi ac_pt_PKG_CONFIG=$ac_cv_path_ac_pt_PKG_CONFIG if test -n "$ac_pt_PKG_CONFIG"; then { printf "%s\n" "$as_me:${as_lineno-$LINENO}: result: $ac_pt_PKG_CONFIG" >&5 printf "%s\n" "$ac_pt_PKG_CONFIG" >&6; } else { printf "%s\n" "$as_me:${as_lineno-$LINENO}: result: no" >&5 printf "%s\n" "no" >&6; } fi if test "x$ac_pt_PKG_CONFIG" = x; then PKG_CONFIG="" else case $cross_compiling:$ac_tool_warned in yes:) { printf "%s\n" "$as_me:${as_lineno-$LINENO}: WARNING: using cross tools not prefixed with host triplet" >&5 printf "%s\n" "$as_me: WARNING: using cross tools not prefixed with host triplet" >&2;} ac_tool_warned=yes ;; esac PKG_CONFIG=$ac_pt_PKG_CONFIG fi else PKG_CONFIG="$ac_cv_path_PKG_CONFIG" fi fi if test -n "$PKG_CONFIG"; then _pkg_min_version=0.9.0 { printf "%s\n" "$as_me:${as_lineno-$LINENO}: checking pkg-config is at least version $_pkg_min_version" >&5 printf %s "checking pkg-config is at least version $_pkg_min_version... " >&6; } if $PKG_CONFIG --atleast-pkgconfig-version $_pkg_min_version; then { printf "%s\n" "$as_me:${as_lineno-$LINENO}: result: yes" >&5 printf "%s\n" "yes" >&6; } else { printf "%s\n" "$as_me:${as_lineno-$LINENO}: result: no" >&5 printf "%s\n" "no" >&6; } PKG_CONFIG="" fi fi if test "$enable_doc" = "yes"; then BUILD_DOC_TRUE= BUILD_DOC_FALSE='#' else BUILD_DOC_TRUE='#' BUILD_DOC_FALSE= fi if test -z "$BUILD_DOC_TRUE"; then : # Test whether we can rebuild the documentation. # This does not affect the pre-built PDF documents unless these are deleted. # Extract the first word of "pdflatex", so it can be a program name with args. set dummy pdflatex; ac_word=$2 { printf "%s\n" "$as_me:${as_lineno-$LINENO}: checking for $ac_word" >&5 printf %s "checking for $ac_word... " >&6; } if test ${ac_cv_path_PDFLATEX+y} then : printf %s "(cached) " >&6 else case e in #( e) case $PDFLATEX in [\\/]* | ?:[\\/]*) ac_cv_path_PDFLATEX="$PDFLATEX" # Let the user override the test with a path. ;; *) as_save_IFS=$IFS; IFS=$PATH_SEPARATOR for as_dir in $PATH do IFS=$as_save_IFS case $as_dir in #((( '') as_dir=./ ;; */) ;; *) as_dir=$as_dir/ ;; esac for ac_exec_ext in '' $ac_executable_extensions; do if as_fn_executable_p "$as_dir$ac_word$ac_exec_ext"; then ac_cv_path_PDFLATEX="$as_dir$ac_word$ac_exec_ext" printf "%s\n" "$as_me:${as_lineno-$LINENO}: found $as_dir$ac_word$ac_exec_ext" >&5 break 2 fi done done IFS=$as_save_IFS ;; esac ;; esac fi PDFLATEX=$ac_cv_path_PDFLATEX if test -n "$PDFLATEX"; then { printf "%s\n" "$as_me:${as_lineno-$LINENO}: result: $PDFLATEX" >&5 printf "%s\n" "$PDFLATEX" >&6; } else { printf "%s\n" "$as_me:${as_lineno-$LINENO}: result: no" >&5 printf "%s\n" "no" >&6; } fi # Extract the first word of "bibtex", so it can be a program name with args. set dummy bibtex; ac_word=$2 { printf "%s\n" "$as_me:${as_lineno-$LINENO}: checking for $ac_word" >&5 printf %s "checking for $ac_word... " >&6; } if test ${ac_cv_path_BIBTEX+y} then : printf %s "(cached) " >&6 else case e in #( e) case $BIBTEX in [\\/]* | ?:[\\/]*) ac_cv_path_BIBTEX="$BIBTEX" # Let the user override the test with a path. ;; *) as_save_IFS=$IFS; IFS=$PATH_SEPARATOR for as_dir in $PATH do IFS=$as_save_IFS case $as_dir in #((( '') as_dir=./ ;; */) ;; *) as_dir=$as_dir/ ;; esac for ac_exec_ext in '' $ac_executable_extensions; do if as_fn_executable_p "$as_dir$ac_word$ac_exec_ext"; then ac_cv_path_BIBTEX="$as_dir$ac_word$ac_exec_ext" printf "%s\n" "$as_me:${as_lineno-$LINENO}: found $as_dir$ac_word$ac_exec_ext" >&5 break 2 fi done done IFS=$as_save_IFS ;; esac ;; esac fi BIBTEX=$ac_cv_path_BIBTEX if test -n "$BIBTEX"; then { printf "%s\n" "$as_me:${as_lineno-$LINENO}: result: $BIBTEX" >&5 printf "%s\n" "$BIBTEX" >&6; } else { printf "%s\n" "$as_me:${as_lineno-$LINENO}: result: no" >&5 printf "%s\n" "no" >&6; } fi # Extract the first word of "latexmk", so it can be a program name with args. set dummy latexmk; ac_word=$2 { printf "%s\n" "$as_me:${as_lineno-$LINENO}: checking for $ac_word" >&5 printf %s "checking for $ac_word... " >&6; } if test ${ac_cv_path_LATEXMK+y} then : printf %s "(cached) " >&6 else case e in #( e) case $LATEXMK in [\\/]* | ?:[\\/]*) ac_cv_path_LATEXMK="$LATEXMK" # Let the user override the test with a path. ;; *) as_save_IFS=$IFS; IFS=$PATH_SEPARATOR for as_dir in $PATH do IFS=$as_save_IFS case $as_dir in #((( '') as_dir=./ ;; */) ;; *) as_dir=$as_dir/ ;; esac for ac_exec_ext in '' $ac_executable_extensions; do if as_fn_executable_p "$as_dir$ac_word$ac_exec_ext"; then ac_cv_path_LATEXMK="$as_dir$ac_word$ac_exec_ext" printf "%s\n" "$as_me:${as_lineno-$LINENO}: found $as_dir$ac_word$ac_exec_ext" >&5 break 2 fi done done IFS=$as_save_IFS ;; esac ;; esac fi LATEXMK=$ac_cv_path_LATEXMK if test -n "$LATEXMK"; then { printf "%s\n" "$as_me:${as_lineno-$LINENO}: result: $LATEXMK" >&5 printf "%s\n" "$LATEXMK" >&6; } else { printf "%s\n" "$as_me:${as_lineno-$LINENO}: result: no" >&5 printf "%s\n" "no" >&6; } fi fi if test -n "$PDFLATEX" && test -n "$BIBTEX"; then HAVE_LATEX_TRUE= HAVE_LATEX_FALSE='#' else HAVE_LATEX_TRUE='#' HAVE_LATEX_FALSE= fi if test -z "$BUILD_DOC_TRUE"; then : if test -z "$HAVE_LATEX_TRUE"; then : else { printf "%s\n" "$as_me:${as_lineno-$LINENO}: WARNING: pdflatex and/or bibtex is missing, documentation will not be rebuilt" >&5 printf "%s\n" "$as_me: WARNING: pdflatex and/or bibtex is missing, documentation will not be rebuilt" >&2;} fi fi if test -n "$LATEXMK"; then HAVE_LATEXMK_TRUE= HAVE_LATEXMK_FALSE='#' else HAVE_LATEXMK_TRUE='#' HAVE_LATEXMK_FALSE= fi # Determine if we can (and want) to use an MPI C compiler. DEPDIR="${am__leading_dot}deps" ac_config_commands="$ac_config_commands depfiles" { printf "%s\n" "$as_me:${as_lineno-$LINENO}: checking whether ${MAKE-make} supports the include directive" >&5 printf %s "checking whether ${MAKE-make} supports the include directive... " >&6; } cat > confinc.mk << 'END' am__doit: @echo this is the am__doit target >confinc.out .PHONY: am__doit END am__include="#" am__quote= # BSD make does it like this. echo '.include "confinc.mk" # ignored' > confmf.BSD # Other make implementations (GNU, Solaris 10, AIX) do it like this. echo 'include confinc.mk # ignored' > confmf.GNU _am_result=no for s in GNU BSD; do { echo "$as_me:$LINENO: ${MAKE-make} -f confmf.$s && cat confinc.out" >&5 (${MAKE-make} -f confmf.$s && cat confinc.out) >&5 2>&5 ac_status=$? echo "$as_me:$LINENO: \$? = $ac_status" >&5 (exit $ac_status); } case $?:`cat confinc.out 2>/dev/null` in #( '0:this is the am__doit target') : case $s in #( BSD) : am__include='.include' am__quote='"' ;; #( *) : am__include='include' am__quote='' ;; esac ;; #( *) : ;; esac if test "$am__include" != "#"; then _am_result="yes ($s style)" break fi done rm -f confinc.* confmf.* { printf "%s\n" "$as_me:${as_lineno-$LINENO}: result: ${_am_result}" >&5 printf "%s\n" "${_am_result}" >&6; } # Check whether --enable-dependency-tracking was given. if test ${enable_dependency_tracking+y} then : enableval=$enable_dependency_tracking; fi if test "x$enable_dependency_tracking" != xno; then am_depcomp="$ac_aux_dir/depcomp" AMDEPBACKSLASH='\' am__nodep='_no' fi if test "x$enable_dependency_tracking" != xno; then AMDEP_TRUE= AMDEP_FALSE='#' else AMDEP_TRUE='#' AMDEP_FALSE= fi { printf "%s\n" "$as_me:${as_lineno-$LINENO}: checking whether to compile using MPI" >&5 printf %s "checking whether to compile using MPI... " >&6; } if test "x$with_mpi" != "xno"; then _ax_prog_cc_mpi_mpi_wanted=yes else _ax_prog_cc_mpi_mpi_wanted=no fi { printf "%s\n" "$as_me:${as_lineno-$LINENO}: result: $_ax_prog_cc_mpi_mpi_wanted" >&5 printf "%s\n" "$_ax_prog_cc_mpi_mpi_wanted" >&6; } if test x"$_ax_prog_cc_mpi_mpi_wanted" = xyes; then if test -z "$CC" && test -n "$MPICC"; then CC="$MPICC" else if test -n "$ac_tool_prefix"; then for ac_prog in mpicc mpixlc_r mpixlc hcc mpxlc_r mpxlc sxmpicc mpifcc mpgcc mpcc cmpicc cc gcc do # Extract the first word of "$ac_tool_prefix$ac_prog", so it can be a program name with args. set dummy $ac_tool_prefix$ac_prog; ac_word=$2 { printf "%s\n" "$as_me:${as_lineno-$LINENO}: checking for $ac_word" >&5 printf %s "checking for $ac_word... " >&6; } if test ${ac_cv_prog_CC+y} then : printf %s "(cached) " >&6 else case e in #( e) if test -n "$CC"; then ac_cv_prog_CC="$CC" # Let the user override the test. else as_save_IFS=$IFS; IFS=$PATH_SEPARATOR for as_dir in $PATH do IFS=$as_save_IFS case $as_dir in #((( '') as_dir=./ ;; */) ;; *) as_dir=$as_dir/ ;; esac for ac_exec_ext in '' $ac_executable_extensions; do if as_fn_executable_p "$as_dir$ac_word$ac_exec_ext"; then ac_cv_prog_CC="$ac_tool_prefix$ac_prog" printf "%s\n" "$as_me:${as_lineno-$LINENO}: found $as_dir$ac_word$ac_exec_ext" >&5 break 2 fi done done IFS=$as_save_IFS fi ;; esac fi CC=$ac_cv_prog_CC if test -n "$CC"; then { printf "%s\n" "$as_me:${as_lineno-$LINENO}: result: $CC" >&5 printf "%s\n" "$CC" >&6; } else { printf "%s\n" "$as_me:${as_lineno-$LINENO}: result: no" >&5 printf "%s\n" "no" >&6; } fi test -n "$CC" && break done fi if test -z "$CC"; then ac_ct_CC=$CC for ac_prog in mpicc mpixlc_r mpixlc hcc mpxlc_r mpxlc sxmpicc mpifcc mpgcc mpcc cmpicc cc gcc do # Extract the first word of "$ac_prog", so it can be a program name with args. set dummy $ac_prog; ac_word=$2 { printf "%s\n" "$as_me:${as_lineno-$LINENO}: checking for $ac_word" >&5 printf %s "checking for $ac_word... " >&6; } if test ${ac_cv_prog_ac_ct_CC+y} then : printf %s "(cached) " >&6 else case e in #( e) if test -n "$ac_ct_CC"; then ac_cv_prog_ac_ct_CC="$ac_ct_CC" # Let the user override the test. else as_save_IFS=$IFS; IFS=$PATH_SEPARATOR for as_dir in $PATH do IFS=$as_save_IFS case $as_dir in #((( '') as_dir=./ ;; */) ;; *) as_dir=$as_dir/ ;; esac for ac_exec_ext in '' $ac_executable_extensions; do if as_fn_executable_p "$as_dir$ac_word$ac_exec_ext"; then ac_cv_prog_ac_ct_CC="$ac_prog" printf "%s\n" "$as_me:${as_lineno-$LINENO}: found $as_dir$ac_word$ac_exec_ext" >&5 break 2 fi done done IFS=$as_save_IFS fi ;; esac fi ac_ct_CC=$ac_cv_prog_ac_ct_CC if test -n "$ac_ct_CC"; then { printf "%s\n" "$as_me:${as_lineno-$LINENO}: result: $ac_ct_CC" >&5 printf "%s\n" "$ac_ct_CC" >&6; } else { printf "%s\n" "$as_me:${as_lineno-$LINENO}: result: no" >&5 printf "%s\n" "no" >&6; } fi test -n "$ac_ct_CC" && break done if test "x$ac_ct_CC" = x; then CC="" else case $cross_compiling:$ac_tool_warned in yes:) { printf "%s\n" "$as_me:${as_lineno-$LINENO}: WARNING: using cross tools not prefixed with host triplet" >&5 printf "%s\n" "$as_me: WARNING: using cross tools not prefixed with host triplet" >&2;} ac_tool_warned=yes ;; esac CC=$ac_ct_CC fi fi fi fi ac_ext=c ac_cpp='$CPP $CPPFLAGS' ac_compile='$CC -c $CFLAGS $CPPFLAGS conftest.$ac_ext >&5' ac_link='$CC -o conftest$ac_exeext $CFLAGS $CPPFLAGS $LDFLAGS conftest.$ac_ext $LIBS >&5' ac_compiler_gnu=$ac_cv_c_compiler_gnu if test -n "$ac_tool_prefix"; then # Extract the first word of "${ac_tool_prefix}gcc", so it can be a program name with args. set dummy ${ac_tool_prefix}gcc; ac_word=$2 { printf "%s\n" "$as_me:${as_lineno-$LINENO}: checking for $ac_word" >&5 printf %s "checking for $ac_word... " >&6; } if test ${ac_cv_prog_CC+y} then : printf %s "(cached) " >&6 else case e in #( e) if test -n "$CC"; then ac_cv_prog_CC="$CC" # Let the user override the test. else as_save_IFS=$IFS; IFS=$PATH_SEPARATOR for as_dir in $PATH do IFS=$as_save_IFS case $as_dir in #((( '') as_dir=./ ;; */) ;; *) as_dir=$as_dir/ ;; esac for ac_exec_ext in '' $ac_executable_extensions; do if as_fn_executable_p "$as_dir$ac_word$ac_exec_ext"; then ac_cv_prog_CC="${ac_tool_prefix}gcc" printf "%s\n" "$as_me:${as_lineno-$LINENO}: found $as_dir$ac_word$ac_exec_ext" >&5 break 2 fi done done IFS=$as_save_IFS fi ;; esac fi CC=$ac_cv_prog_CC if test -n "$CC"; then { printf "%s\n" "$as_me:${as_lineno-$LINENO}: result: $CC" >&5 printf "%s\n" "$CC" >&6; } else { printf "%s\n" "$as_me:${as_lineno-$LINENO}: result: no" >&5 printf "%s\n" "no" >&6; } fi fi if test -z "$ac_cv_prog_CC"; then ac_ct_CC=$CC # Extract the first word of "gcc", so it can be a program name with args. set dummy gcc; ac_word=$2 { printf "%s\n" "$as_me:${as_lineno-$LINENO}: checking for $ac_word" >&5 printf %s "checking for $ac_word... " >&6; } if test ${ac_cv_prog_ac_ct_CC+y} then : printf %s "(cached) " >&6 else case e in #( e) if test -n "$ac_ct_CC"; then ac_cv_prog_ac_ct_CC="$ac_ct_CC" # Let the user override the test. else as_save_IFS=$IFS; IFS=$PATH_SEPARATOR for as_dir in $PATH do IFS=$as_save_IFS case $as_dir in #((( '') as_dir=./ ;; */) ;; *) as_dir=$as_dir/ ;; esac for ac_exec_ext in '' $ac_executable_extensions; do if as_fn_executable_p "$as_dir$ac_word$ac_exec_ext"; then ac_cv_prog_ac_ct_CC="gcc" printf "%s\n" "$as_me:${as_lineno-$LINENO}: found $as_dir$ac_word$ac_exec_ext" >&5 break 2 fi done done IFS=$as_save_IFS fi ;; esac fi ac_ct_CC=$ac_cv_prog_ac_ct_CC if test -n "$ac_ct_CC"; then { printf "%s\n" "$as_me:${as_lineno-$LINENO}: result: $ac_ct_CC" >&5 printf "%s\n" "$ac_ct_CC" >&6; } else { printf "%s\n" "$as_me:${as_lineno-$LINENO}: result: no" >&5 printf "%s\n" "no" >&6; } fi if test "x$ac_ct_CC" = x; then CC="" else case $cross_compiling:$ac_tool_warned in yes:) { printf "%s\n" "$as_me:${as_lineno-$LINENO}: WARNING: using cross tools not prefixed with host triplet" >&5 printf "%s\n" "$as_me: WARNING: using cross tools not prefixed with host triplet" >&2;} ac_tool_warned=yes ;; esac CC=$ac_ct_CC fi else CC="$ac_cv_prog_CC" fi if test -z "$CC"; then if test -n "$ac_tool_prefix"; then # Extract the first word of "${ac_tool_prefix}cc", so it can be a program name with args. set dummy ${ac_tool_prefix}cc; ac_word=$2 { printf "%s\n" "$as_me:${as_lineno-$LINENO}: checking for $ac_word" >&5 printf %s "checking for $ac_word... " >&6; } if test ${ac_cv_prog_CC+y} then : printf %s "(cached) " >&6 else case e in #( e) if test -n "$CC"; then ac_cv_prog_CC="$CC" # Let the user override the test. else as_save_IFS=$IFS; IFS=$PATH_SEPARATOR for as_dir in $PATH do IFS=$as_save_IFS case $as_dir in #((( '') as_dir=./ ;; */) ;; *) as_dir=$as_dir/ ;; esac for ac_exec_ext in '' $ac_executable_extensions; do if as_fn_executable_p "$as_dir$ac_word$ac_exec_ext"; then ac_cv_prog_CC="${ac_tool_prefix}cc" printf "%s\n" "$as_me:${as_lineno-$LINENO}: found $as_dir$ac_word$ac_exec_ext" >&5 break 2 fi done done IFS=$as_save_IFS fi ;; esac fi CC=$ac_cv_prog_CC if test -n "$CC"; then { printf "%s\n" "$as_me:${as_lineno-$LINENO}: result: $CC" >&5 printf "%s\n" "$CC" >&6; } else { printf "%s\n" "$as_me:${as_lineno-$LINENO}: result: no" >&5 printf "%s\n" "no" >&6; } fi fi fi if test -z "$CC"; then # Extract the first word of "cc", so it can be a program name with args. set dummy cc; ac_word=$2 { printf "%s\n" "$as_me:${as_lineno-$LINENO}: checking for $ac_word" >&5 printf %s "checking for $ac_word... " >&6; } if test ${ac_cv_prog_CC+y} then : printf %s "(cached) " >&6 else case e in #( e) if test -n "$CC"; then ac_cv_prog_CC="$CC" # Let the user override the test. else ac_prog_rejected=no as_save_IFS=$IFS; IFS=$PATH_SEPARATOR for as_dir in $PATH do IFS=$as_save_IFS case $as_dir in #((( '') as_dir=./ ;; */) ;; *) as_dir=$as_dir/ ;; esac for ac_exec_ext in '' $ac_executable_extensions; do if as_fn_executable_p "$as_dir$ac_word$ac_exec_ext"; then if test "$as_dir$ac_word$ac_exec_ext" = "/usr/ucb/cc"; then ac_prog_rejected=yes continue fi ac_cv_prog_CC="cc" printf "%s\n" "$as_me:${as_lineno-$LINENO}: found $as_dir$ac_word$ac_exec_ext" >&5 break 2 fi done done IFS=$as_save_IFS if test $ac_prog_rejected = yes; then # We found a bogon in the path, so make sure we never use it. set dummy $ac_cv_prog_CC shift if test $# != 0; then # We chose a different compiler from the bogus one. # However, it has the same basename, so the bogon will be chosen # first if we set CC to just the basename; use the full file name. shift ac_cv_prog_CC="$as_dir$ac_word${1+' '}$@" fi fi fi ;; esac fi CC=$ac_cv_prog_CC if test -n "$CC"; then { printf "%s\n" "$as_me:${as_lineno-$LINENO}: result: $CC" >&5 printf "%s\n" "$CC" >&6; } else { printf "%s\n" "$as_me:${as_lineno-$LINENO}: result: no" >&5 printf "%s\n" "no" >&6; } fi fi if test -z "$CC"; then if test -n "$ac_tool_prefix"; then for ac_prog in cl.exe do # Extract the first word of "$ac_tool_prefix$ac_prog", so it can be a program name with args. set dummy $ac_tool_prefix$ac_prog; ac_word=$2 { printf "%s\n" "$as_me:${as_lineno-$LINENO}: checking for $ac_word" >&5 printf %s "checking for $ac_word... " >&6; } if test ${ac_cv_prog_CC+y} then : printf %s "(cached) " >&6 else case e in #( e) if test -n "$CC"; then ac_cv_prog_CC="$CC" # Let the user override the test. else as_save_IFS=$IFS; IFS=$PATH_SEPARATOR for as_dir in $PATH do IFS=$as_save_IFS case $as_dir in #((( '') as_dir=./ ;; */) ;; *) as_dir=$as_dir/ ;; esac for ac_exec_ext in '' $ac_executable_extensions; do if as_fn_executable_p "$as_dir$ac_word$ac_exec_ext"; then ac_cv_prog_CC="$ac_tool_prefix$ac_prog" printf "%s\n" "$as_me:${as_lineno-$LINENO}: found $as_dir$ac_word$ac_exec_ext" >&5 break 2 fi done done IFS=$as_save_IFS fi ;; esac fi CC=$ac_cv_prog_CC if test -n "$CC"; then { printf "%s\n" "$as_me:${as_lineno-$LINENO}: result: $CC" >&5 printf "%s\n" "$CC" >&6; } else { printf "%s\n" "$as_me:${as_lineno-$LINENO}: result: no" >&5 printf "%s\n" "no" >&6; } fi test -n "$CC" && break done fi if test -z "$CC"; then ac_ct_CC=$CC for ac_prog in cl.exe do # Extract the first word of "$ac_prog", so it can be a program name with args. set dummy $ac_prog; ac_word=$2 { printf "%s\n" "$as_me:${as_lineno-$LINENO}: checking for $ac_word" >&5 printf %s "checking for $ac_word... " >&6; } if test ${ac_cv_prog_ac_ct_CC+y} then : printf %s "(cached) " >&6 else case e in #( e) if test -n "$ac_ct_CC"; then ac_cv_prog_ac_ct_CC="$ac_ct_CC" # Let the user override the test. else as_save_IFS=$IFS; IFS=$PATH_SEPARATOR for as_dir in $PATH do IFS=$as_save_IFS case $as_dir in #((( '') as_dir=./ ;; */) ;; *) as_dir=$as_dir/ ;; esac for ac_exec_ext in '' $ac_executable_extensions; do if as_fn_executable_p "$as_dir$ac_word$ac_exec_ext"; then ac_cv_prog_ac_ct_CC="$ac_prog" printf "%s\n" "$as_me:${as_lineno-$LINENO}: found $as_dir$ac_word$ac_exec_ext" >&5 break 2 fi done done IFS=$as_save_IFS fi ;; esac fi ac_ct_CC=$ac_cv_prog_ac_ct_CC if test -n "$ac_ct_CC"; then { printf "%s\n" "$as_me:${as_lineno-$LINENO}: result: $ac_ct_CC" >&5 printf "%s\n" "$ac_ct_CC" >&6; } else { printf "%s\n" "$as_me:${as_lineno-$LINENO}: result: no" >&5 printf "%s\n" "no" >&6; } fi test -n "$ac_ct_CC" && break done if test "x$ac_ct_CC" = x; then CC="" else case $cross_compiling:$ac_tool_warned in yes:) { printf "%s\n" "$as_me:${as_lineno-$LINENO}: WARNING: using cross tools not prefixed with host triplet" >&5 printf "%s\n" "$as_me: WARNING: using cross tools not prefixed with host triplet" >&2;} ac_tool_warned=yes ;; esac CC=$ac_ct_CC fi fi fi if test -z "$CC"; then if test -n "$ac_tool_prefix"; then # Extract the first word of "${ac_tool_prefix}clang", so it can be a program name with args. set dummy ${ac_tool_prefix}clang; ac_word=$2 { printf "%s\n" "$as_me:${as_lineno-$LINENO}: checking for $ac_word" >&5 printf %s "checking for $ac_word... " >&6; } if test ${ac_cv_prog_CC+y} then : printf %s "(cached) " >&6 else case e in #( e) if test -n "$CC"; then ac_cv_prog_CC="$CC" # Let the user override the test. else as_save_IFS=$IFS; IFS=$PATH_SEPARATOR for as_dir in $PATH do IFS=$as_save_IFS case $as_dir in #((( '') as_dir=./ ;; */) ;; *) as_dir=$as_dir/ ;; esac for ac_exec_ext in '' $ac_executable_extensions; do if as_fn_executable_p "$as_dir$ac_word$ac_exec_ext"; then ac_cv_prog_CC="${ac_tool_prefix}clang" printf "%s\n" "$as_me:${as_lineno-$LINENO}: found $as_dir$ac_word$ac_exec_ext" >&5 break 2 fi done done IFS=$as_save_IFS fi ;; esac fi CC=$ac_cv_prog_CC if test -n "$CC"; then { printf "%s\n" "$as_me:${as_lineno-$LINENO}: result: $CC" >&5 printf "%s\n" "$CC" >&6; } else { printf "%s\n" "$as_me:${as_lineno-$LINENO}: result: no" >&5 printf "%s\n" "no" >&6; } fi fi if test -z "$ac_cv_prog_CC"; then ac_ct_CC=$CC # Extract the first word of "clang", so it can be a program name with args. set dummy clang; ac_word=$2 { printf "%s\n" "$as_me:${as_lineno-$LINENO}: checking for $ac_word" >&5 printf %s "checking for $ac_word... " >&6; } if test ${ac_cv_prog_ac_ct_CC+y} then : printf %s "(cached) " >&6 else case e in #( e) if test -n "$ac_ct_CC"; then ac_cv_prog_ac_ct_CC="$ac_ct_CC" # Let the user override the test. else as_save_IFS=$IFS; IFS=$PATH_SEPARATOR for as_dir in $PATH do IFS=$as_save_IFS case $as_dir in #((( '') as_dir=./ ;; */) ;; *) as_dir=$as_dir/ ;; esac for ac_exec_ext in '' $ac_executable_extensions; do if as_fn_executable_p "$as_dir$ac_word$ac_exec_ext"; then ac_cv_prog_ac_ct_CC="clang" printf "%s\n" "$as_me:${as_lineno-$LINENO}: found $as_dir$ac_word$ac_exec_ext" >&5 break 2 fi done done IFS=$as_save_IFS fi ;; esac fi ac_ct_CC=$ac_cv_prog_ac_ct_CC if test -n "$ac_ct_CC"; then { printf "%s\n" "$as_me:${as_lineno-$LINENO}: result: $ac_ct_CC" >&5 printf "%s\n" "$ac_ct_CC" >&6; } else { printf "%s\n" "$as_me:${as_lineno-$LINENO}: result: no" >&5 printf "%s\n" "no" >&6; } fi if test "x$ac_ct_CC" = x; then CC="" else case $cross_compiling:$ac_tool_warned in yes:) { printf "%s\n" "$as_me:${as_lineno-$LINENO}: WARNING: using cross tools not prefixed with host triplet" >&5 printf "%s\n" "$as_me: WARNING: using cross tools not prefixed with host triplet" >&2;} ac_tool_warned=yes ;; esac CC=$ac_ct_CC fi else CC="$ac_cv_prog_CC" fi fi test -z "$CC" && { { printf "%s\n" "$as_me:${as_lineno-$LINENO}: error: in '$ac_pwd':" >&5 printf "%s\n" "$as_me: error: in '$ac_pwd':" >&2;} as_fn_error $? "no acceptable C compiler found in \$PATH See 'config.log' for more details" "$LINENO" 5; } # Provide some information about the compiler. printf "%s\n" "$as_me:${as_lineno-$LINENO}: checking for C compiler version" >&5 set X $ac_compile ac_compiler=$2 for ac_option in --version -v -V -qversion -version; do { { ac_try="$ac_compiler $ac_option >&5" case "(($ac_try" in *\"* | *\`* | *\\*) ac_try_echo=\$ac_try;; *) ac_try_echo=$ac_try;; esac eval ac_try_echo="\"\$as_me:${as_lineno-$LINENO}: $ac_try_echo\"" printf "%s\n" "$ac_try_echo"; } >&5 (eval "$ac_compiler $ac_option >&5") 2>conftest.err ac_status=$? if test -s conftest.err; then sed '10a\ ... rest of stderr output deleted ... 10q' conftest.err >conftest.er1 cat conftest.er1 >&5 fi rm -f conftest.er1 conftest.err printf "%s\n" "$as_me:${as_lineno-$LINENO}: \$? = $ac_status" >&5 test $ac_status = 0; } done cat confdefs.h - <<_ACEOF >conftest.$ac_ext /* end confdefs.h. */ int main (void) { ; return 0; } _ACEOF ac_clean_files_save=$ac_clean_files ac_clean_files="$ac_clean_files a.out a.out.dSYM a.exe b.out" # Try to create an executable without -o first, disregard a.out. # It will help us diagnose broken compilers, and finding out an intuition # of exeext. { printf "%s\n" "$as_me:${as_lineno-$LINENO}: checking whether the C compiler works" >&5 printf %s "checking whether the C compiler works... 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We should not override ac_cv_exeext if it was cached, # so that the user can short-circuit this test for compilers unknown to # Autoconf. for ac_file in $ac_files '' do test -f "$ac_file" || continue case $ac_file in *.$ac_ext | *.xcoff | *.tds | *.d | *.pdb | *.xSYM | *.bb | *.bbg | *.map | *.inf | *.dSYM | *.o | *.obj ) ;; [ab].out ) # We found the default executable, but exeext='' is most # certainly right. break;; *.* ) if test ${ac_cv_exeext+y} && test "$ac_cv_exeext" != no; then :; else ac_cv_exeext=`expr "$ac_file" : '[^.]*\(\..*\)'` fi # We set ac_cv_exeext here because the later test for it is not # safe: cross compilers may not add the suffix if given an '-o' # argument, so we may need to know it at that point already. # Even if this section looks crufty: it has the advantage of # actually working. break;; * ) break;; esac done test "$ac_cv_exeext" = no && ac_cv_exeext= else case e in #( e) ac_file='' ;; esac fi if test -z "$ac_file" then : { printf "%s\n" "$as_me:${as_lineno-$LINENO}: result: no" >&5 printf "%s\n" "no" >&6; } printf "%s\n" "$as_me: failed program was:" >&5 sed 's/^/| /' conftest.$ac_ext >&5 { { printf "%s\n" "$as_me:${as_lineno-$LINENO}: error: in '$ac_pwd':" >&5 printf "%s\n" "$as_me: error: in '$ac_pwd':" >&2;} as_fn_error 77 "C compiler cannot create executables See 'config.log' for more details" "$LINENO" 5; } else case e in #( e) { printf "%s\n" "$as_me:${as_lineno-$LINENO}: result: yes" >&5 printf "%s\n" "yes" >&6; } ;; esac fi { printf "%s\n" "$as_me:${as_lineno-$LINENO}: checking for C compiler default output file name" >&5 printf %s "checking for C compiler default output file name... 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For instance with Cygwin, 'ls conftest' will # work properly (i.e., refer to 'conftest.exe'), while it won't with # 'rm'. for ac_file in conftest.exe conftest conftest.*; do test -f "$ac_file" || continue case $ac_file in *.$ac_ext | *.xcoff | *.tds | *.d | *.pdb | *.xSYM | *.bb | *.bbg | *.map | *.inf | *.dSYM | *.o | *.obj ) ;; *.* ) ac_cv_exeext=`expr "$ac_file" : '[^.]*\(\..*\)'` break;; * ) break;; esac done else case e in #( e) { { printf "%s\n" "$as_me:${as_lineno-$LINENO}: error: in '$ac_pwd':" >&5 printf "%s\n" "$as_me: error: in '$ac_pwd':" >&2;} as_fn_error $? "cannot compute suffix of executables: cannot compile and link See 'config.log' for more details" "$LINENO" 5; } ;; esac fi rm -f conftest conftest$ac_cv_exeext { printf "%s\n" "$as_me:${as_lineno-$LINENO}: result: $ac_cv_exeext" >&5 printf "%s\n" "$ac_cv_exeext" >&6; } rm -f conftest.$ac_ext EXEEXT=$ac_cv_exeext ac_exeext=$EXEEXT cat confdefs.h - <<_ACEOF >conftest.$ac_ext /* end confdefs.h. */ #include int main (void) { FILE *f = fopen ("conftest.out", "w"); if (!f) return 1; return ferror (f) || fclose (f) != 0; ; return 0; } _ACEOF ac_clean_files="$ac_clean_files conftest.out" # Check that the compiler produces executables we can run. If not, either # the compiler is broken, or we cross compile. { printf "%s\n" "$as_me:${as_lineno-$LINENO}: checking whether we are cross compiling" >&5 printf %s "checking whether we are cross compiling... " >&6; } if test "$cross_compiling" != yes; then { { ac_try="$ac_link" case "(($ac_try" in *\"* | *\`* | *\\*) ac_try_echo=\$ac_try;; *) ac_try_echo=$ac_try;; esac eval ac_try_echo="\"\$as_me:${as_lineno-$LINENO}: $ac_try_echo\"" printf "%s\n" "$ac_try_echo"; } >&5 (eval "$ac_link") 2>&5 ac_status=$? printf "%s\n" "$as_me:${as_lineno-$LINENO}: \$? = $ac_status" >&5 test $ac_status = 0; } if { ac_try='./conftest$ac_cv_exeext' { { case "(($ac_try" in *\"* | *\`* | *\\*) ac_try_echo=\$ac_try;; *) ac_try_echo=$ac_try;; esac eval ac_try_echo="\"\$as_me:${as_lineno-$LINENO}: $ac_try_echo\"" printf "%s\n" "$ac_try_echo"; } >&5 (eval "$ac_try") 2>&5 ac_status=$? printf "%s\n" "$as_me:${as_lineno-$LINENO}: \$? = $ac_status" >&5 test $ac_status = 0; }; }; then cross_compiling=no else if test "$cross_compiling" = maybe; then cross_compiling=yes else { { printf "%s\n" "$as_me:${as_lineno-$LINENO}: error: in '$ac_pwd':" >&5 printf "%s\n" "$as_me: error: in '$ac_pwd':" >&2;} as_fn_error 77 "cannot run C compiled programs. If you meant to cross compile, use '--host'. See 'config.log' for more details" "$LINENO" 5; } fi fi fi { printf "%s\n" "$as_me:${as_lineno-$LINENO}: result: $cross_compiling" >&5 printf "%s\n" "$cross_compiling" >&6; } rm -f conftest.$ac_ext conftest$ac_cv_exeext \ conftest.o conftest.obj conftest.out ac_clean_files=$ac_clean_files_save { printf "%s\n" "$as_me:${as_lineno-$LINENO}: checking for suffix of object files" >&5 printf %s "checking for suffix of object files... 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BEAGLE_CFLAGS= BEAGLE_LIBS= # Try direct probing by prefix. old_CPPFLAGS=$CPPFLAGS old_LDFLAGS=$LDFLAGS old_LIBS=$LIBS case $with_beagle in #( yes|check) : beagle_prefixes="/usr/local /usr" ;; #( *) : beagle_prefixes="$with_beagle" ;; #( *) : ;; esac for beagle_prefix in $beagle_prefixes; do BEAGLE_CFLAGS="-I$beagle_prefix/include/libhmsbeagle-1" BEAGLE_LIBS="-L$beagle_prefix/lib -Wl,-rpath,$beagle_prefix/lib -lhmsbeagle" CPPFLAGS="$old_CPPFLAGS $BEAGLE_CFLAGS" LDFLAGS="$old_LDFLAGS" LIBS="$BEAGLE_LIBS $old_LIBS" { printf "%s\n" "$as_me:${as_lineno-$LINENO}: checking whether Beagle works under $beagle_prefix" >&5 printf "%s\n" "$as_me: checking whether Beagle works under $beagle_prefix" >&6;} cat confdefs.h - <<_ACEOF >conftest.$ac_ext /* end confdefs.h. */ #include int main (void) { (void) beagleCreateInstance; ; return 0; } _ACEOF if ac_fn_c_try_link "$LINENO" then : beagle_found=yes; break else case e in #( e) BEAGLE_CFLAGS=; BEAGLE_LIBS= ;; esac fi rm -f core conftest.err conftest.$ac_objext conftest.beam \ conftest$ac_exeext conftest.$ac_ext done CPPFLAGS=$old_CPPFLAGS LDFLAGS=$old_LDFLAGS LIBS=$old_LIBS # Fallback to pkg-config only for --with-beagle or --with-beagle=check. if test "x$beagle_found" != xyes then : case $with_beagle in #( yes|check) : BEAGLE_CFLAGS= BEAGLE_LIBS= if test "x$ac_cv_env_PKG_CONFIG_set" != "xset"; then if test -n "$ac_tool_prefix"; then # Extract the first word of "${ac_tool_prefix}pkg-config", so it can be a program name with args. set dummy ${ac_tool_prefix}pkg-config; ac_word=$2 { printf "%s\n" "$as_me:${as_lineno-$LINENO}: checking for $ac_word" >&5 printf %s "checking for $ac_word... " >&6; } if test ${ac_cv_path_PKG_CONFIG+y} then : printf %s "(cached) " >&6 else case e in #( e) case $PKG_CONFIG in [\\/]* | ?:[\\/]*) ac_cv_path_PKG_CONFIG="$PKG_CONFIG" # Let the user override the test with a path. ;; *) as_save_IFS=$IFS; IFS=$PATH_SEPARATOR for as_dir in $PATH do IFS=$as_save_IFS case $as_dir in #((( '') as_dir=./ ;; */) ;; *) as_dir=$as_dir/ ;; esac for ac_exec_ext in '' $ac_executable_extensions; do if as_fn_executable_p "$as_dir$ac_word$ac_exec_ext"; then ac_cv_path_PKG_CONFIG="$as_dir$ac_word$ac_exec_ext" printf "%s\n" "$as_me:${as_lineno-$LINENO}: found $as_dir$ac_word$ac_exec_ext" >&5 break 2 fi done done IFS=$as_save_IFS ;; esac ;; esac fi PKG_CONFIG=$ac_cv_path_PKG_CONFIG if test -n "$PKG_CONFIG"; then { printf "%s\n" "$as_me:${as_lineno-$LINENO}: result: $PKG_CONFIG" >&5 printf "%s\n" "$PKG_CONFIG" >&6; } else { printf "%s\n" "$as_me:${as_lineno-$LINENO}: result: no" >&5 printf "%s\n" "no" >&6; } fi fi if test -z "$ac_cv_path_PKG_CONFIG"; then ac_pt_PKG_CONFIG=$PKG_CONFIG # Extract the first word of "pkg-config", so it can be a program name with args. set dummy pkg-config; ac_word=$2 { printf "%s\n" "$as_me:${as_lineno-$LINENO}: checking for $ac_word" >&5 printf %s "checking for $ac_word... " >&6; } if test ${ac_cv_path_ac_pt_PKG_CONFIG+y} then : printf %s "(cached) " >&6 else case e in #( e) case $ac_pt_PKG_CONFIG in [\\/]* | ?:[\\/]*) ac_cv_path_ac_pt_PKG_CONFIG="$ac_pt_PKG_CONFIG" # Let the user override the test with a path. ;; *) as_save_IFS=$IFS; IFS=$PATH_SEPARATOR for as_dir in $PATH do IFS=$as_save_IFS case $as_dir in #((( '') as_dir=./ ;; */) ;; *) as_dir=$as_dir/ ;; esac for ac_exec_ext in '' $ac_executable_extensions; do if as_fn_executable_p "$as_dir$ac_word$ac_exec_ext"; then ac_cv_path_ac_pt_PKG_CONFIG="$as_dir$ac_word$ac_exec_ext" printf "%s\n" "$as_me:${as_lineno-$LINENO}: found $as_dir$ac_word$ac_exec_ext" >&5 break 2 fi done done IFS=$as_save_IFS ;; esac ;; esac fi ac_pt_PKG_CONFIG=$ac_cv_path_ac_pt_PKG_CONFIG if test -n "$ac_pt_PKG_CONFIG"; then { printf "%s\n" "$as_me:${as_lineno-$LINENO}: result: $ac_pt_PKG_CONFIG" >&5 printf "%s\n" "$ac_pt_PKG_CONFIG" >&6; } else { printf "%s\n" "$as_me:${as_lineno-$LINENO}: result: no" >&5 printf "%s\n" "no" >&6; } fi if test "x$ac_pt_PKG_CONFIG" = x; then PKG_CONFIG="" else case $cross_compiling:$ac_tool_warned in yes:) { printf "%s\n" "$as_me:${as_lineno-$LINENO}: WARNING: using cross tools not prefixed with host triplet" >&5 printf "%s\n" "$as_me: WARNING: using cross tools not prefixed with host triplet" >&2;} ac_tool_warned=yes ;; esac PKG_CONFIG=$ac_pt_PKG_CONFIG fi else PKG_CONFIG="$ac_cv_path_PKG_CONFIG" fi fi if test -n "$PKG_CONFIG"; then _pkg_min_version=0.9.0 { printf "%s\n" "$as_me:${as_lineno-$LINENO}: checking pkg-config is at least version $_pkg_min_version" >&5 printf %s "checking pkg-config is at least version $_pkg_min_version... " >&6; } if $PKG_CONFIG --atleast-pkgconfig-version $_pkg_min_version; then { printf "%s\n" "$as_me:${as_lineno-$LINENO}: result: yes" >&5 printf "%s\n" "yes" >&6; } else { printf "%s\n" "$as_me:${as_lineno-$LINENO}: result: no" >&5 printf "%s\n" "no" >&6; } PKG_CONFIG="" fi fi if test -n "$PKG_CONFIG" then : { printf "%s\n" "$as_me:${as_lineno-$LINENO}: trying pkg-config fallback for $beagle_pkg_name" >&5 printf "%s\n" "$as_me: trying pkg-config fallback for $beagle_pkg_name" >&6;} pkg_failed=no { printf "%s\n" "$as_me:${as_lineno-$LINENO}: checking for $beagle_pkg_name" >&5 printf %s "checking for $beagle_pkg_name... " >&6; } if test -n "$BEAGLE_CFLAGS"; then pkg_cv_BEAGLE_CFLAGS="$BEAGLE_CFLAGS" elif test -n "$PKG_CONFIG"; then if test -n "$PKG_CONFIG" && \ { { printf "%s\n" "$as_me:${as_lineno-$LINENO}: \$PKG_CONFIG --exists --print-errors \"\$beagle_pkg_name\""; } >&5 ($PKG_CONFIG --exists --print-errors "$beagle_pkg_name") 2>&5 ac_status=$? printf "%s\n" "$as_me:${as_lineno-$LINENO}: \$? = $ac_status" >&5 test $ac_status = 0; }; then pkg_cv_BEAGLE_CFLAGS=`$PKG_CONFIG --cflags "$beagle_pkg_name" 2>/dev/null` test "x$?" != "x0" && pkg_failed=yes else pkg_failed=yes fi else pkg_failed=untried fi if test -n "$BEAGLE_LIBS"; then pkg_cv_BEAGLE_LIBS="$BEAGLE_LIBS" elif test -n "$PKG_CONFIG"; then if test -n "$PKG_CONFIG" && \ { { printf "%s\n" "$as_me:${as_lineno-$LINENO}: \$PKG_CONFIG --exists --print-errors \"\$beagle_pkg_name\""; } >&5 ($PKG_CONFIG --exists --print-errors "$beagle_pkg_name") 2>&5 ac_status=$? printf "%s\n" "$as_me:${as_lineno-$LINENO}: \$? = $ac_status" >&5 test $ac_status = 0; }; then pkg_cv_BEAGLE_LIBS=`$PKG_CONFIG --libs "$beagle_pkg_name" 2>/dev/null` test "x$?" != "x0" && pkg_failed=yes else pkg_failed=yes fi else pkg_failed=untried fi if test $pkg_failed = yes; then { printf "%s\n" "$as_me:${as_lineno-$LINENO}: result: no" >&5 printf "%s\n" "no" >&6; } if $PKG_CONFIG --atleast-pkgconfig-version 0.20; then _pkg_short_errors_supported=yes else _pkg_short_errors_supported=no fi if test $_pkg_short_errors_supported = yes; then BEAGLE_PKG_ERRORS=`$PKG_CONFIG --short-errors --print-errors --cflags --libs "$beagle_pkg_name" 2>&1` else BEAGLE_PKG_ERRORS=`$PKG_CONFIG --print-errors --cflags --libs "$beagle_pkg_name" 2>&1` fi # Put the nasty error message in config.log where it belongs echo "$BEAGLE_PKG_ERRORS" >&5 { printf "%s\n" "$as_me:${as_lineno-$LINENO}: pkg-config did not find $beagle_pkg_name" >&5 printf "%s\n" "$as_me: pkg-config did not find $beagle_pkg_name" >&6;} elif test $pkg_failed = untried; then { printf "%s\n" "$as_me:${as_lineno-$LINENO}: result: no" >&5 printf "%s\n" "no" >&6; } { printf "%s\n" "$as_me:${as_lineno-$LINENO}: pkg-config did not find $beagle_pkg_name" >&5 printf "%s\n" "$as_me: pkg-config did not find $beagle_pkg_name" >&6;} else BEAGLE_CFLAGS=$pkg_cv_BEAGLE_CFLAGS BEAGLE_LIBS=$pkg_cv_BEAGLE_LIBS { printf "%s\n" "$as_me:${as_lineno-$LINENO}: result: yes" >&5 printf "%s\n" "yes" >&6; } { printf "%s\n" "$as_me:${as_lineno-$LINENO}: pkg-config found $beagle_pkg_name" >&5 printf "%s\n" "$as_me: pkg-config found $beagle_pkg_name" >&6;} beagle_libdir=`$PKG_CONFIG --libs-only-L "$beagle_pkg_name" | sed -n 's/^-L//p'` if test -n "$beagle_libdir" then : BEAGLE_LIBS="-Wl,-rpath,$beagle_libdir $BEAGLE_LIBS" fi beagle_found=yes fi fi ;; #( *) : { printf "%s\n" "$as_me:${as_lineno-$LINENO}: not using pkg-config fallback because a specific Beagle prefix was given" >&5 printf "%s\n" "$as_me: not using pkg-config fallback because a specific Beagle prefix was given" >&6;} ;; #( *) : ;; esac fi ;; esac fi if test "x$beagle_found" = xyes then : printf "%s\n" "#define BEAGLE_ENABLED 1" >>confdefs.h # Feature test for Beagle v3.1+. old_CPPFLAGS=$CPPFLAGS old_LIBS=$LIBS CPPFLAGS="$CPPFLAGS $BEAGLE_CFLAGS" LIBS="$BEAGLE_LIBS $LIBS" cat confdefs.h - <<_ACEOF >conftest.$ac_ext /* end confdefs.h. */ #include int main (void) { (void) beagleSetCPUThreadCount; ; return 0; } _ACEOF if ac_fn_c_try_link "$LINENO" then : printf "%s\n" "#define BEAGLE_V3_ENABLED 1" >>confdefs.h BEAGLE_CFLAGS="$BEAGLE_CFLAGS -pthread" fi rm -f core conftest.err conftest.$ac_objext conftest.beam \ conftest$ac_exeext conftest.$ac_ext CPPFLAGS=$old_CPPFLAGS LIBS=$old_LIBS { printf "%s\n" "$as_me:${as_lineno-$LINENO}: using BEAGLE_CFLAGS=\"$BEAGLE_CFLAGS\"" >&5 printf "%s\n" "$as_me: using BEAGLE_CFLAGS=\"$BEAGLE_CFLAGS\"" >&6;} { printf "%s\n" "$as_me:${as_lineno-$LINENO}: using BEAGLE_LIBS=\"$BEAGLE_LIBS\"" >&5 printf "%s\n" "$as_me: using BEAGLE_LIBS=\"$BEAGLE_LIBS\"" >&6;} else case e in #( e) if test "x$with_beagle" != xcheck then : as_fn_error $? "--with-beagle was given, but Beagle could not be found" "$LINENO" 5 fi ;; esac fi fi # Check for readline, but only if we don't have MPI, because apparently # it won't work. ac_header= ac_cache= for ac_item in $ac_header_c_list do if test $ac_cache; then ac_fn_c_check_header_compile "$LINENO" $ac_header ac_cv_header_$ac_cache "$ac_includes_default" if eval test \"x\$ac_cv_header_$ac_cache\" = xyes; then printf "%s\n" "#define $ac_item 1" >> confdefs.h fi ac_header= ac_cache= elif test $ac_header; then ac_cache=$ac_item else ac_header=$ac_item fi done if test $ac_cv_header_stdlib_h = yes && test $ac_cv_header_string_h = yes then : printf "%s\n" "#define STDC_HEADERS 1" >>confdefs.h fi if test "x$use_mpi" = "xno" then : if test "x$with_readline" != "xno" then : { printf "%s\n" "$as_me:${as_lineno-$LINENO}: checking for a readline compatible library" >&5 printf %s "checking for a readline compatible library... " >&6; } if test ${ax_cv_lib_readline+y} then : printf %s "(cached) " >&6 else case e in #( e) ORIG_LIBS="$LIBS" for readline_lib in readline edit editline; do for termcap_lib in "" termcap curses ncurses; do if test -z "$termcap_lib"; then TRY_LIB="-l$readline_lib" else TRY_LIB="-l$readline_lib -l$termcap_lib" fi LIBS="$ORIG_LIBS $TRY_LIB" cat confdefs.h - <<_ACEOF >conftest.$ac_ext /* end confdefs.h. */ /* Override any GCC internal prototype to avoid an error. Use char because int might match the return type of a GCC builtin and then its argument prototype would still apply. The 'extern "C"' is for builds by C++ compilers; although this is not generally supported in C code supporting it here has little cost and some practical benefit (sr 110532). */ #ifdef __cplusplus extern "C" #endif char readline (void); int main (void) { return readline (); ; return 0; } _ACEOF if ac_fn_c_try_link "$LINENO" then : ax_cv_lib_readline="$TRY_LIB" fi rm -f core conftest.err conftest.$ac_objext conftest.beam \ conftest$ac_exeext conftest.$ac_ext if test -n "$ax_cv_lib_readline"; then break fi done if test -n "$ax_cv_lib_readline"; then break fi done if test -z "$ax_cv_lib_readline"; then ax_cv_lib_readline="no" fi LIBS="$ORIG_LIBS" ;; esac fi { printf "%s\n" "$as_me:${as_lineno-$LINENO}: result: $ax_cv_lib_readline" >&5 printf "%s\n" "$ax_cv_lib_readline" >&6; } if test "$ax_cv_lib_readline" != "no"; then LIBS="$LIBS $ax_cv_lib_readline" ac_fn_c_check_header_compile "$LINENO" "readline.h" "ac_cv_header_readline_h" "$ac_includes_default" if test "x$ac_cv_header_readline_h" = xyes then : printf "%s\n" "#define HAVE_READLINE_H 1" >>confdefs.h fi ac_fn_c_check_header_compile "$LINENO" "readline/readline.h" "ac_cv_header_readline_readline_h" "$ac_includes_default" if test "x$ac_cv_header_readline_readline_h" = xyes then : printf "%s\n" "#define HAVE_READLINE_READLINE_H 1" >>confdefs.h fi ax_cv_lib_readline_version_ok=yes if test "$ax_cv_lib_readline_version_ok" = "yes"; then printf "%s\n" "#define HAVE_LIBREADLINE 1" >>confdefs.h { printf "%s\n" "$as_me:${as_lineno-$LINENO}: checking whether readline supports history" >&5 printf %s "checking whether readline supports history... " >&6; } if test ${ax_cv_lib_readline_history+y} then : printf %s "(cached) " >&6 else case e in #( e) ax_cv_lib_readline_history="no" cat confdefs.h - <<_ACEOF >conftest.$ac_ext /* end confdefs.h. */ /* Override any GCC internal prototype to avoid an error. Use char because int might match the return type of a GCC builtin and then its argument prototype would still apply. The 'extern "C"' is for builds by C++ compilers; although this is not generally supported in C code supporting it here has little cost and some practical benefit (sr 110532). */ #ifdef __cplusplus extern "C" #endif char add_history (void); int main (void) { return add_history (); ; return 0; } _ACEOF if ac_fn_c_try_link "$LINENO" then : ax_cv_lib_readline_history="yes" fi rm -f core conftest.err conftest.$ac_objext conftest.beam \ conftest$ac_exeext conftest.$ac_ext ;; esac fi { printf "%s\n" "$as_me:${as_lineno-$LINENO}: result: $ax_cv_lib_readline_history" >&5 printf "%s\n" "$ax_cv_lib_readline_history" >&6; } if test "$ax_cv_lib_readline_history" = "yes"; then printf "%s\n" "#define HAVE_READLINE_HISTORY 1" >>confdefs.h ac_fn_c_check_header_compile "$LINENO" "history.h" "ac_cv_header_history_h" "$ac_includes_default" if test "x$ac_cv_header_history_h" = xyes then : printf "%s\n" "#define HAVE_HISTORY_H 1" >>confdefs.h fi ac_fn_c_check_header_compile "$LINENO" "readline/history.h" "ac_cv_header_readline_history_h" "$ac_includes_default" if test "x$ac_cv_header_readline_history_h" = xyes then : printf "%s\n" "#define HAVE_READLINE_HISTORY_H 1" >>confdefs.h fi fi else LIBS="$ORIG_LIBS" fi fi fi else case e in #( e) if test "x$with_readline" = "xyes" then : { printf "%s\n" "$as_me:${as_lineno-$LINENO}: WARNING: --with-readline was given, but MPI support requires readline to be disabled. Retry with --without-mpi if you *really* need readline support" >&5 printf "%s\n" "$as_me: WARNING: --with-readline was given, but MPI support requires readline to be disabled. Retry with --without-mpi if you *really* need readline support" >&2;} fi ;; esac fi # Checks for header files. ac_fn_c_check_header_compile "$LINENO" "float.h" "ac_cv_header_float_h" "$ac_includes_default" if test "x$ac_cv_header_float_h" = xyes then : printf "%s\n" "#define HAVE_FLOAT_H 1" >>confdefs.h fi ac_fn_c_check_header_compile "$LINENO" "limits.h" "ac_cv_header_limits_h" "$ac_includes_default" if test "x$ac_cv_header_limits_h" = xyes then : printf "%s\n" "#define HAVE_LIMITS_H 1" >>confdefs.h fi ac_fn_c_check_header_compile "$LINENO" "memory.h" "ac_cv_header_memory_h" "$ac_includes_default" if test "x$ac_cv_header_memory_h" = xyes then : printf "%s\n" "#define HAVE_MEMORY_H 1" >>confdefs.h fi ac_fn_c_check_header_compile "$LINENO" "stdlib.h" "ac_cv_header_stdlib_h" "$ac_includes_default" if test "x$ac_cv_header_stdlib_h" = xyes then : printf "%s\n" "#define HAVE_STDLIB_H 1" >>confdefs.h fi ac_fn_c_check_header_compile "$LINENO" "string.h" "ac_cv_header_string_h" "$ac_includes_default" if test "x$ac_cv_header_string_h" = xyes then : printf "%s\n" "#define HAVE_STRING_H 1" >>confdefs.h fi # Checks for typedefs, structures, and compiler characteristics. ac_fn_c_check_type "$LINENO" "long long" "ac_cv_type_long_long" "$ac_includes_default" if test "x$ac_cv_type_long_long" = xyes then : fi ac_fn_c_check_type "$LINENO" "size_t" "ac_cv_type_size_t" "$ac_includes_default" if test "x$ac_cv_type_size_t" = xyes then : else case e in #( e) printf "%s\n" "#define size_t unsigned int" >>confdefs.h ;; esac fi ac_fn_c_check_type "$LINENO" "_Bool" "ac_cv_type__Bool" "$ac_includes_default" if test "x$ac_cv_type__Bool" = xyes then : printf "%s\n" "#define HAVE__BOOL 1" >>confdefs.h fi { printf "%s\n" "$as_me:${as_lineno-$LINENO}: checking for stdbool.h that conforms to C99 or later" >&5 printf %s "checking for stdbool.h that conforms to C99 or later... " >&6; } if test ${ac_cv_header_stdbool_h+y} then : printf %s "(cached) " >&6 else case e in #( e) cat confdefs.h - <<_ACEOF >conftest.$ac_ext /* end confdefs.h. */ #include /* "true" and "false" should be usable in #if expressions and integer constant expressions, and "bool" should be a valid type name. Although C99 requires bool, true, and false to be macros, C23 and C++11 overrule that, so do not test for that. Although C99 requires __bool_true_false_are_defined and _Bool, C23 says they are obsolescent, so do not require them. */ #if !true #error "'true' is not true" #endif #if true != 1 #error "'true' is not equal to 1" #endif char b[true == 1 ? 1 : -1]; char c[true]; #if false #error "'false' is not false" #endif #if false != 0 #error "'false' is not equal to 0" #endif char d[false == 0 ? 1 : -1]; enum { e = false, f = true, g = false * true, h = true * 256 }; char i[(bool) 0.5 == true ? 1 : -1]; char j[(bool) 0.0 == false ? 1 : -1]; char k[sizeof (bool) > 0 ? 1 : -1]; struct sb { bool s: 1; bool t; } s; char l[sizeof s.t > 0 ? 1 : -1]; /* The following fails for HP aC++/ANSI C B3910B A.05.55 [Dec 04 2003]. */ bool m[h]; char n[sizeof m == h * sizeof m[0] ? 1 : -1]; char o[-1 - (bool) 0 < 0 ? 1 : -1]; /* Catch a bug in an HP-UX C compiler. See https://gcc.gnu.org/ml/gcc-patches/2003-12/msg02303.html https://lists.gnu.org/r/bug-coreutils/2005-11/msg00161.html */ bool p = true; bool *pp = &p; int main (void) { bool ps = &s; *pp |= p; *pp |= ! p; /* Refer to every declared value, so they cannot be discarded as unused. */ return (!b + !c + !d + !e + !f + !g + !h + !i + !j + !k + !l + !m + !n + !o + !p + !pp + !ps); ; return 0; } _ACEOF if ac_fn_c_try_compile "$LINENO" then : ac_cv_header_stdbool_h=yes else case e in #( e) ac_cv_header_stdbool_h=no ;; esac fi rm -f core conftest.err conftest.$ac_objext conftest.beam conftest.$ac_ext ;; esac fi { printf "%s\n" "$as_me:${as_lineno-$LINENO}: result: $ac_cv_header_stdbool_h" >&5 printf "%s\n" "$ac_cv_header_stdbool_h" >&6; } # Get compiler's vendor and version { printf "%s\n" "$as_me:${as_lineno-$LINENO}: checking for C compiler vendor" >&5 printf %s "checking for C compiler vendor... " >&6; } if test ${ax_cv_c_compiler_vendor+y} then : printf %s "(cached) " >&6 else case e in #( e) vendors=" intel: __ICC,__ECC,__INTEL_COMPILER ibm: __xlc__,__xlC__,__IBMC__,__IBMCPP__,__ibmxl__ pathscale: __PATHCC__,__PATHSCALE__ clang: __clang__ cray: _CRAYC fujitsu: __FUJITSU sdcc: SDCC,__SDCC sx: _SX nvhpc: __NVCOMPILER portland: __PGI gnu: __GNUC__ sun: __SUNPRO_C,__SUNPRO_CC,__SUNPRO_F90,__SUNPRO_F95 hp: __HP_cc,__HP_aCC dec: __DECC,__DECCXX,__DECC_VER,__DECCXX_VER borland: __BORLANDC__,__CODEGEARC__,__TURBOC__ comeau: __COMO__ kai: __KCC lcc: __LCC__ sgi: __sgi,sgi microsoft: _MSC_VER metrowerks: __MWERKS__ watcom: __WATCOMC__ tcc: __TINYC__ unknown: UNKNOWN " for ventest in $vendors; do case $ventest in *:) vendor=$ventest continue ;; *) vencpp="defined("`echo $ventest | sed 's/,/) || defined(/g'`")" ;; esac cat confdefs.h - <<_ACEOF >conftest.$ac_ext /* end confdefs.h. */ int main (void) { #if !($vencpp) thisisanerror; #endif ; return 0; } _ACEOF if ac_fn_c_try_compile "$LINENO" then : break fi rm -f core conftest.err conftest.$ac_objext conftest.beam conftest.$ac_ext done ax_cv_c_compiler_vendor=`echo $vendor | cut -d: -f1` ;; esac fi { printf "%s\n" "$as_me:${as_lineno-$LINENO}: result: $ax_cv_c_compiler_vendor" >&5 printf "%s\n" "$ax_cv_c_compiler_vendor" >&6; } { printf "%s\n" "$as_me:${as_lineno-$LINENO}: checking for a sed that does not truncate output" >&5 printf %s "checking for a sed that does not truncate output... " >&6; } if test ${ac_cv_path_SED+y} then : printf %s "(cached) " >&6 else case e in #( e) ac_script=s/aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa/bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb/ for ac_i in 1 2 3 4 5 6 7; do ac_script="$ac_script$as_nl$ac_script" done echo "$ac_script" 2>/dev/null | sed 99q >conftest.sed { ac_script=; unset ac_script;} if test -z "$SED"; then ac_path_SED_found=false # Loop through the user's path and test for each of PROGNAME-LIST as_save_IFS=$IFS; IFS=$PATH_SEPARATOR for as_dir in $PATH do IFS=$as_save_IFS case $as_dir in #((( '') as_dir=./ ;; */) ;; *) as_dir=$as_dir/ ;; esac for ac_prog in sed gsed do for ac_exec_ext in '' $ac_executable_extensions; do ac_path_SED="$as_dir$ac_prog$ac_exec_ext" as_fn_executable_p "$ac_path_SED" || continue # Check for GNU ac_path_SED and select it if it is found. # Check for GNU $ac_path_SED case `"$ac_path_SED" --version 2>&1` in #( *GNU*) ac_cv_path_SED="$ac_path_SED" ac_path_SED_found=:;; #( *) ac_count=0 printf %s 0123456789 >"conftest.in" while : do cat "conftest.in" "conftest.in" >"conftest.tmp" mv "conftest.tmp" "conftest.in" cp "conftest.in" "conftest.nl" printf "%s\n" '' >> "conftest.nl" "$ac_path_SED" -f conftest.sed < "conftest.nl" >"conftest.out" 2>/dev/null || break diff "conftest.out" "conftest.nl" >/dev/null 2>&1 || break as_fn_arith $ac_count + 1 && ac_count=$as_val if test $ac_count -gt ${ac_path_SED_max-0}; then # Best one so far, save it but keep looking for a better one ac_cv_path_SED="$ac_path_SED" ac_path_SED_max=$ac_count fi # 10*(2^10) chars as input seems more than enough test $ac_count -gt 10 && break done rm -f conftest.in conftest.tmp conftest.nl conftest.out;; esac $ac_path_SED_found && break 3 done done done IFS=$as_save_IFS if test -z "$ac_cv_path_SED"; then as_fn_error $? "no acceptable sed could be found in \$PATH" "$LINENO" 5 fi else ac_cv_path_SED=$SED fi ;; esac fi { printf "%s\n" "$as_me:${as_lineno-$LINENO}: result: $ac_cv_path_SED" >&5 printf "%s\n" "$ac_cv_path_SED" >&6; } SED="$ac_cv_path_SED" rm -f conftest.sed { printf "%s\n" "$as_me:${as_lineno-$LINENO}: checking for C compiler version" >&5 printf %s "checking for C compiler version... " >&6; } if test ${ax_cv_c_compiler_version+y} then : printf %s "(cached) " >&6 else case e in #( e) case $ax_cv_c_compiler_vendor in #( intel) : if ac_fn_c_compute_int "$LINENO" "__INTEL_COMPILER/100" "_ax_c_compiler_version_major" "" then : else case e in #( e) { { printf "%s\n" "$as_me:${as_lineno-$LINENO}: error: in '$ac_pwd':" >&5 printf "%s\n" "$as_me: error: in '$ac_pwd':" >&2;} as_fn_error $? "_AX_COMPILER_VERSION_INTEL unknown intel compiler version See 'config.log' for more details" "$LINENO" 5; } ;; esac fi if ac_fn_c_compute_int "$LINENO" "(__INTEL_COMPILER%100)/10" "_ax_c_compiler_version_minor" "" then : else case e in #( e) { { printf "%s\n" "$as_me:${as_lineno-$LINENO}: error: in '$ac_pwd':" >&5 printf "%s\n" "$as_me: error: in '$ac_pwd':" >&2;} as_fn_error $? "_AX_COMPILER_VERSION_INTEL unknown intel compiler version See 'config.log' for more details" "$LINENO" 5; } ;; esac fi if ac_fn_c_compute_int "$LINENO" "(__INTEL_COMPILER%10)" "_ax_c_compiler_version_patch" "" then : else case e in #( e) { { printf "%s\n" "$as_me:${as_lineno-$LINENO}: error: in '$ac_pwd':" >&5 printf "%s\n" "$as_me: error: in '$ac_pwd':" >&2;} as_fn_error $? "_AX_COMPILER_VERSION_INTEL unknown intel compiler version See 'config.log' for more details" "$LINENO" 5; } ;; esac fi ax_cv_c_compiler_version="$_ax_c_compiler_version_major.$_ax_c_compiler_version_minor.$_ax_c_compiler_version_patch" ;; #( ibm) : cat confdefs.h - <<_ACEOF >conftest.$ac_ext /* end confdefs.h. */ int main (void) { #if defined(__COMPILER_VER__) choke me; #endif ; return 0; } _ACEOF if ac_fn_c_try_compile "$LINENO" then : if ac_fn_c_compute_int "$LINENO" "__xlC__/100" "_ax_c_compiler_version_major" "" then : else case e in #( e) { { printf "%s\n" "$as_me:${as_lineno-$LINENO}: error: in '$ac_pwd':" >&5 printf "%s\n" "$as_me: error: in '$ac_pwd':" >&2;} as_fn_error $? "_AX_COMPILER_VERSION_IBM unknown IBM compiler major version See 'config.log' for more details" "$LINENO" 5; } ;; esac fi if ac_fn_c_compute_int "$LINENO" "__xlC__%100" "_ax_c_compiler_version_minor" "" then : else case e in #( e) { { printf "%s\n" "$as_me:${as_lineno-$LINENO}: error: in '$ac_pwd':" >&5 printf "%s\n" "$as_me: error: in '$ac_pwd':" >&2;} as_fn_error $? "_AX_COMPILER_VERSION_IBM unknown IBM compiler minor version See 'config.log' for more details" "$LINENO" 5; } ;; esac fi if ac_fn_c_compute_int "$LINENO" "__xlC_ver__/0x100" "_ax_c_compiler_version_patch" "" then : else case e in #( e) { { printf "%s\n" "$as_me:${as_lineno-$LINENO}: error: in '$ac_pwd':" >&5 printf "%s\n" "$as_me: error: in '$ac_pwd':" >&2;} as_fn_error $? "_AX_COMPILER_VERSION_IBM unknown IBM compiler patch version See 'config.log' for more details" "$LINENO" 5; } ;; esac fi if ac_fn_c_compute_int "$LINENO" "__xlC_ver__%0x100" "_ax_c_compiler_version_build" "" then : else case e in #( e) { { printf "%s\n" "$as_me:${as_lineno-$LINENO}: error: in '$ac_pwd':" >&5 printf "%s\n" "$as_me: error: in '$ac_pwd':" >&2;} as_fn_error $? "_AX_COMPILER_VERSION_IBM unknown IBM compiler build version See 'config.log' for more details" "$LINENO" 5; } ;; esac fi ax_cv_c_compiler_version="$_ax_c_compiler_version_major.$_ax_c_compiler_version_minor.$_ax_c_compiler_version_patch.$_ax_c_compiler_version_build" else case e in #( e) if ac_fn_c_compute_int "$LINENO" "__xlC__%1000" "_ax_c_compiler_version_patch" "" then : else case e in #( e) { { printf "%s\n" "$as_me:${as_lineno-$LINENO}: error: in '$ac_pwd':" >&5 printf "%s\n" "$as_me: error: in '$ac_pwd':" >&2;} as_fn_error $? "_AX_COMPILER_VERSION_IBM unknown IBM compiler patch version See 'config.log' for more details" "$LINENO" 5; } ;; esac fi if ac_fn_c_compute_int "$LINENO" "(__xlC__/10000)%10" "_ax_c_compiler_version_minor" "" then : else case e in #( e) { { printf "%s\n" "$as_me:${as_lineno-$LINENO}: error: in '$ac_pwd':" >&5 printf "%s\n" "$as_me: error: in '$ac_pwd':" >&2;} as_fn_error $? "_AX_COMPILER_VERSION_IBM unknown IBM compiler minor version See 'config.log' for more details" "$LINENO" 5; } ;; esac fi if ac_fn_c_compute_int "$LINENO" "(__xlC__/100000)%10" "_ax_c_compiler_version_major" "" then : else case e in #( e) { { printf "%s\n" "$as_me:${as_lineno-$LINENO}: error: in '$ac_pwd':" >&5 printf "%s\n" "$as_me: error: in '$ac_pwd':" >&2;} as_fn_error $? "_AX_COMPILER_VERSION_IBM unknown IBM compiler major version See 'config.log' for more details" "$LINENO" 5; } ;; esac fi ax_cv_c_compiler_version="$_ax_c_compiler_version_major.$_ax_c_compiler_version_minor.$_ax_c_compiler_version_patch" ;; esac fi rm -f core conftest.err conftest.$ac_objext conftest.beam conftest.$ac_ext ;; #( pathscale) : if ac_fn_c_compute_int "$LINENO" "__PATHCC__" "_ax_c_compiler_version_major" "" then : else case e in #( e) { { printf "%s\n" "$as_me:${as_lineno-$LINENO}: error: in '$ac_pwd':" >&5 printf "%s\n" "$as_me: error: in '$ac_pwd':" >&2;} as_fn_error $? "_AX_COMPILER_VERSION_PATHSCALE unknown pathscale major See 'config.log' for more details" "$LINENO" 5; } ;; esac fi if ac_fn_c_compute_int "$LINENO" "__PATHCC_MINOR__" "_ax_c_compiler_version_minor" "" then : else case e in #( e) { { printf "%s\n" "$as_me:${as_lineno-$LINENO}: error: in '$ac_pwd':" >&5 printf "%s\n" "$as_me: error: in '$ac_pwd':" >&2;} as_fn_error $? "_AX_COMPILER_VERSION_PATHSCALE unknown pathscale minor See 'config.log' for more details" "$LINENO" 5; } ;; esac fi if ac_fn_c_compute_int "$LINENO" "__PATHCC_PATCHLEVEL__" "_ax_c_compiler_version_patch" "" then : else case e in #( e) { { printf "%s\n" "$as_me:${as_lineno-$LINENO}: error: in '$ac_pwd':" >&5 printf "%s\n" "$as_me: error: in '$ac_pwd':" >&2;} as_fn_error $? "_AX_COMPILER_VERSION_PATHSCALE unknown pathscale patch level See 'config.log' for more details" "$LINENO" 5; } ;; esac fi ax_cv_c_compiler_version="$_ax_c_compiler_version_major.$_ax_c_compiler_version_minor.$_ax_c_compiler_version_patch" ;; #( clang) : if ac_fn_c_compute_int "$LINENO" "__clang_major__" "_ax_c_compiler_version_major" "" then : else case e in #( e) { { printf "%s\n" "$as_me:${as_lineno-$LINENO}: error: in '$ac_pwd':" >&5 printf "%s\n" "$as_me: error: in '$ac_pwd':" >&2;} as_fn_error $? "_AX_COMPILER_VERSION_CLANG unknown clang major See 'config.log' for more details" "$LINENO" 5; } ;; esac fi if ac_fn_c_compute_int "$LINENO" "__clang_minor__" "_ax_c_compiler_version_minor" "" then : else case e in #( e) { { printf "%s\n" "$as_me:${as_lineno-$LINENO}: error: in '$ac_pwd':" >&5 printf "%s\n" "$as_me: error: in '$ac_pwd':" >&2;} as_fn_error $? "_AX_COMPILER_VERSION_CLANG unknown clang minor See 'config.log' for more details" "$LINENO" 5; } ;; esac fi if ac_fn_c_compute_int "$LINENO" "__clang_patchlevel__" "_ax_c_compiler_version_patch" "" then : else case e in #( e) 0 ;; esac fi ax_cv_c_compiler_version="$_ax_c_compiler_version_major.$_ax_c_compiler_version_minor.$_ax_c_compiler_version_patch" ;; #( cray) : if ac_fn_c_compute_int "$LINENO" "_RELEASE" "_ax_c_compiler_version_major" "" then : else case e in #( e) { { printf "%s\n" "$as_me:${as_lineno-$LINENO}: error: in '$ac_pwd':" >&5 printf "%s\n" "$as_me: error: in '$ac_pwd':" >&2;} as_fn_error $? "_AX_COMPILER_VERSION_CRAY unknown crayc release See 'config.log' for more details" "$LINENO" 5; } ;; esac fi if ac_fn_c_compute_int "$LINENO" "_RELEASE_MINOR" "_ax_c_compiler_version_minor" "" then : else case e in #( e) { { printf "%s\n" "$as_me:${as_lineno-$LINENO}: error: in '$ac_pwd':" >&5 printf "%s\n" "$as_me: error: in '$ac_pwd':" >&2;} as_fn_error $? "_AX_COMPILER_VERSION_CRAY unknown crayc minor See 'config.log' for more details" "$LINENO" 5; } ;; esac fi ax_cv_c_compiler_version="$_ax_c_compiler_version_major.$_ax_c_compiler_version_minor" ;; #( fujitsu) : if ac_fn_c_compute_int "$LINENO" "__FCC_VERSION" "ax_cv_c_compiler_version" "" then : else case e in #( e) { { printf "%s\n" "$as_me:${as_lineno-$LINENO}: error: in '$ac_pwd':" >&5 printf "%s\n" "$as_me: error: in '$ac_pwd':" >&2;} as_fn_error $? "_AX_COMPILER_VERSION_FUJITSUunknown fujitsu release See 'config.log' for more details" "$LINENO" 5; } ;; esac fi ;; #( gnu) : if ac_fn_c_compute_int "$LINENO" "__GNUC__" "_ax_c_compiler_version_major" "" then : else case e in #( e) { { printf "%s\n" "$as_me:${as_lineno-$LINENO}: error: in '$ac_pwd':" >&5 printf "%s\n" "$as_me: error: in '$ac_pwd':" >&2;} as_fn_error $? "_AX_COMPILER_VERSION_GNU unknown gcc major See 'config.log' for more details" "$LINENO" 5; } ;; esac fi if ac_fn_c_compute_int "$LINENO" "__GNUC_MINOR__" "_ax_c_compiler_version_minor" "" then : else case e in #( e) { { printf "%s\n" "$as_me:${as_lineno-$LINENO}: error: in '$ac_pwd':" >&5 printf "%s\n" "$as_me: error: in '$ac_pwd':" >&2;} as_fn_error $? "_AX_COMPILER_VERSION_GNU unknown gcc minor See 'config.log' for more details" "$LINENO" 5; } ;; esac fi if ac_fn_c_compute_int "$LINENO" "__GNUC_PATCHLEVEL__" "_ax_c_compiler_version_patch" "" then : else case e in #( e) { { printf "%s\n" "$as_me:${as_lineno-$LINENO}: error: in '$ac_pwd':" >&5 printf "%s\n" "$as_me: error: in '$ac_pwd':" >&2;} as_fn_error $? "_AX_COMPILER_VERSION_GNU unknown gcc patch level See 'config.log' for more details" "$LINENO" 5; } ;; esac fi ax_cv_c_compiler_version="$_ax_c_compiler_version_major.$_ax_c_compiler_version_minor.$_ax_c_compiler_version_patch" ;; #( sun) : if ac_fn_c_compute_int "$LINENO" "!!( #if defined(__SUNPRO_CC) __SUNPRO_CC #else __SUNPRO_C #endif < 0x1000)" "_ax_c_compiler_version_until59" "" then : else case e in #( e) { { printf "%s\n" "$as_me:${as_lineno-$LINENO}: error: in '$ac_pwd':" >&5 printf "%s\n" "$as_me: error: in '$ac_pwd':" >&2;} as_fn_error $? "_AX_COMPILER_VERSION_SUN unknown sun release version See 'config.log' for more details" "$LINENO" 5; } ;; esac fi if test "X$_ax_c_compiler_version_until59" = X1 then : if ac_fn_c_compute_int "$LINENO" " #if defined(__SUNPRO_CC) __SUNPRO_CC #else __SUNPRO_C #endif % 0x10" "_ax_c_compiler_version_patch" "" then : else case e in #( e) { { printf "%s\n" "$as_me:${as_lineno-$LINENO}: error: in '$ac_pwd':" >&5 printf "%s\n" "$as_me: error: in '$ac_pwd':" >&2;} as_fn_error $? "_AX_COMPILER_VERSION_SUN unknown sun patch version See 'config.log' for more details" "$LINENO" 5; } ;; esac fi if ac_fn_c_compute_int "$LINENO" "( #if defined(__SUNPRO_CC) __SUNPRO_CC #else __SUNPRO_C #endif / 0x10) % 0x10" "_ax_c_compiler_version_minor" "" then : else case e in #( e) { { printf "%s\n" "$as_me:${as_lineno-$LINENO}: error: in '$ac_pwd':" >&5 printf "%s\n" "$as_me: error: in '$ac_pwd':" >&2;} as_fn_error $? "_AX_COMPILER_VERSION_SUN unknown sun minor version See 'config.log' for more details" "$LINENO" 5; } ;; esac fi if ac_fn_c_compute_int "$LINENO" "( #if defined(__SUNPRO_CC) __SUNPRO_CC #else __SUNPRO_C #endif / 0x100)" "_ax_c_compiler_version_major" "" then : else case e in #( e) { { printf "%s\n" "$as_me:${as_lineno-$LINENO}: error: in '$ac_pwd':" >&5 printf "%s\n" "$as_me: error: in '$ac_pwd':" >&2;} as_fn_error $? "_AX_COMPILER_VERSION_SUN unknown sun major version See 'config.log' for more details" "$LINENO" 5; } ;; esac fi else case e in #( e) if ac_fn_c_compute_int "$LINENO" " #if defined(__SUNPRO_CC) __SUNPRO_CC #else __SUNPRO_C #endif % 0x10" "_ax_c_compiler_version_patch" "" then : else case e in #( e) { { printf "%s\n" "$as_me:${as_lineno-$LINENO}: error: in '$ac_pwd':" >&5 printf "%s\n" "$as_me: error: in '$ac_pwd':" >&2;} as_fn_error $? "_AX_COMPILER_VERSION_SUN unknown sun patch version See 'config.log' for more details" "$LINENO" 5; } ;; esac fi if ac_fn_c_compute_int "$LINENO" "( #if defined(__SUNPRO_CC) __SUNPRO_CC #else __SUNPRO_C #endif / 0x100) % 0x100" "_ax_c_compiler_version_minor" "" then : else case e in #( e) { { printf "%s\n" "$as_me:${as_lineno-$LINENO}: error: in '$ac_pwd':" >&5 printf "%s\n" "$as_me: error: in '$ac_pwd':" >&2;} as_fn_error $? "_AX_COMPILER_VERSION_SUN unknown sun minor version See 'config.log' for more details" "$LINENO" 5; } ;; esac fi if ac_fn_c_compute_int "$LINENO" "( #if defined(__SUNPRO_CC) __SUNPRO_CC #else __SUNPRO_C #endif / 0x1000)" "_ax_c_compiler_version_major" "" then : else case e in #( e) { { printf "%s\n" "$as_me:${as_lineno-$LINENO}: error: in '$ac_pwd':" >&5 printf "%s\n" "$as_me: error: in '$ac_pwd':" >&2;} as_fn_error $? "_AX_COMPILER_VERSION_SUN unknown sun major version See 'config.log' for more details" "$LINENO" 5; } ;; esac fi ;; esac fi ax_cv_c_compiler_version="$_ax_c_compiler_version_major.$_ax_c_compiler_version_minor.$_ax_c_compiler_version_patch" ;; #( hp) : if ac_fn_c_compute_int "$LINENO" "!!( #if defined(__HP_cc) __HP_cc #else __HP_aCC #endif <= 1)" "_ax_c_compiler_version_untilA0121" "" then : else case e in #( e) { { printf "%s\n" "$as_me:${as_lineno-$LINENO}: error: in '$ac_pwd':" >&5 printf "%s\n" "$as_me: error: in '$ac_pwd':" >&2;} as_fn_error $? "_AX_COMPILER_VERSION_HP unknown hp release version See 'config.log' for more details" "$LINENO" 5; } ;; esac fi if test "X$_ax_c_compiler_version_untilA0121" = X1 then : ax_cv_c_compiler_version="01.21.00" else case e in #( e) if ac_fn_c_compute_int "$LINENO" "( #if defined(__HP_cc) __HP_cc #else __HP_aCC #endif % 100)" "_ax_c_compiler_version_patch" "" then : else case e in #( e) { { printf "%s\n" "$as_me:${as_lineno-$LINENO}: error: in '$ac_pwd':" >&5 printf "%s\n" "$as_me: error: in '$ac_pwd':" >&2;} as_fn_error $? "_AX_COMPILER_VERSION_HP unknown hp release version See 'config.log' for more details" "$LINENO" 5; } ;; esac fi if ac_fn_c_compute_int "$LINENO" "(( #if defined(__HP_cc) __HP_cc #else __HP_aCC #endif / 100)%100)" "_ax_c_compiler_version_minor" "" then : else case e in #( e) { { printf "%s\n" "$as_me:${as_lineno-$LINENO}: error: in '$ac_pwd':" >&5 printf "%s\n" "$as_me: error: in '$ac_pwd':" >&2;} as_fn_error $? "_AX_COMPILER_VERSION_HP unknown hp minor version See 'config.log' for more details" "$LINENO" 5; } ;; esac fi if ac_fn_c_compute_int "$LINENO" "(( #if defined(__HP_cc) __HP_cc #else __HP_aCC #endif / 10000)%100)" "_ax_c_compiler_version_major" "" then : else case e in #( e) { { printf "%s\n" "$as_me:${as_lineno-$LINENO}: error: in '$ac_pwd':" >&5 printf "%s\n" "$as_me: error: in '$ac_pwd':" >&2;} as_fn_error $? "_AX_COMPILER_VERSION_HP unknown hp major version See 'config.log' for more details" "$LINENO" 5; } ;; esac fi ax_cv_c_compiler_version="$_ax_c_compiler_version_major.$_ax_c_compiler_version_minor.$_ax_c_compiler_version_patch" ;; esac fi ;; #( dec) : if ac_fn_c_compute_int "$LINENO" "( #if defined(__DECC_VER) __DECC_VER #else __DECCXX_VER #endif % 10000)" "_ax_c_compiler_version_patch" "" then : else case e in #( e) { { printf "%s\n" "$as_me:${as_lineno-$LINENO}: error: in '$ac_pwd':" >&5 printf "%s\n" "$as_me: error: in '$ac_pwd':" >&2;} as_fn_error $? "_AX_COMPILER_VERSION_DEC unknown dec release version See 'config.log' for more details" "$LINENO" 5; } ;; esac fi if ac_fn_c_compute_int "$LINENO" "(( #if defined(__DECC_VER) __DECC_VER #else __DECCXX_VER #endif / 100000UL)%100)" "_ax_c_compiler_version_minor" "" then : else case e in #( e) { { printf "%s\n" "$as_me:${as_lineno-$LINENO}: error: in '$ac_pwd':" >&5 printf "%s\n" "$as_me: error: in '$ac_pwd':" >&2;} as_fn_error $? "_AX_COMPILER_VERSION_DEC unknown dec minor version See 'config.log' for more details" "$LINENO" 5; } ;; esac fi if ac_fn_c_compute_int "$LINENO" "(( #if defined(__DECC_VER) __DECC_VER #else __DECCXX_VER #endif / 10000000UL)%100)" "_ax_c_compiler_version_major" "" then : else case e in #( e) { { printf "%s\n" "$as_me:${as_lineno-$LINENO}: error: in '$ac_pwd':" >&5 printf "%s\n" "$as_me: error: in '$ac_pwd':" >&2;} as_fn_error $? "_AX_COMPILER_VERSION_DEC unknown dec major version See 'config.log' for more details" "$LINENO" 5; } ;; esac fi ax_cv_c_compiler_version="$_ax_c_compiler_version_major.$_ax_c_compiler_version_minor.$_ax_c_compiler_version_patch" ;; #( borland) : cat confdefs.h - <<_ACEOF >conftest.$ac_ext /* end confdefs.h. */ int main (void) { #if defined(__TURBOC__) __TURBOC__ #else choke me #endif ; return 0; } _ACEOF if ac_fn_c_try_compile "$LINENO" then : if ac_fn_c_compute_int "$LINENO" " #if defined(__TURBOC__) __TURBOC__ #else choke me #endif " "_ax_c_compiler_version_turboc_raw" "" then : else case e in #( e) { { printf "%s\n" "$as_me:${as_lineno-$LINENO}: error: in '$ac_pwd':" >&5 printf "%s\n" "$as_me: error: in '$ac_pwd':" >&2;} as_fn_error $? "_AX_COMPILER_VERSION_BORLAND unknown turboc version See 'config.log' for more details" "$LINENO" 5; } ;; esac fi if test $_ax_c_compiler_version_turboc_raw -lt 661 || test $_ax_c_compiler_version_turboc_raw -gt 1023 then : if ac_fn_c_compute_int "$LINENO" " #if defined(__TURBOC__) __TURBOC__ #else choke me #endif % 0x100" "_ax_c_compiler_version_minor" "" then : else case e in #( e) { { printf "%s\n" "$as_me:${as_lineno-$LINENO}: error: in '$ac_pwd':" >&5 printf "%s\n" "$as_me: error: in '$ac_pwd':" >&2;} as_fn_error $? "_AX_COMPILER_VERSION_BORLAND unknown turboc minor version See 'config.log' for more details" "$LINENO" 5; } ;; esac fi if ac_fn_c_compute_int "$LINENO" "( #if defined(__TURBOC__) __TURBOC__ #else choke me #endif /0x100)%0x100" "_ax_c_compiler_version_major" "" then : else case e in #( e) { { printf "%s\n" "$as_me:${as_lineno-$LINENO}: error: in '$ac_pwd':" >&5 printf "%s\n" "$as_me: error: in '$ac_pwd':" >&2;} as_fn_error $? "_AX_COMPILER_VERSION_BORLAND unknown turboc major version See 'config.log' for more details" "$LINENO" 5; } ;; esac fi ax_cv_c_compiler_version="0turboc:$_ax_c_compiler_version_major.$_ax_c_compiler_version_minor" else case e in #( e) case $_ax_c_compiler_version_turboc_raw in #( 661) : ax_cv_c_compiler_version="0turboc:1.00" ;; #( 662) : ax_cv_c_compiler_version="0turboc:1.01" ;; #( 663) : ax_cv_c_compiler_version="0turboc:2.00" ;; #( *) : { printf "%s\n" "$as_me:${as_lineno-$LINENO}: WARNING: [_AX_COMPILER_VERSION_BORLAND] unknown turboc version between 0x295 and 0x400 please report bug" >&5 printf "%s\n" "$as_me: WARNING: [_AX_COMPILER_VERSION_BORLAND] unknown turboc version between 0x295 and 0x400 please report bug" >&2;} ax_cv_c_compiler_version="" ;; esac ;; esac fi else case e in #( e) # borlandc if ac_fn_c_compute_int "$LINENO" " #if defined(__BORLANDC__) __BORLANDC__ #else __CODEGEARC__ #endif " "_ax_c_compiler_version_borlandc_raw" "" then : else case e in #( e) { { printf "%s\n" "$as_me:${as_lineno-$LINENO}: error: in '$ac_pwd':" >&5 printf "%s\n" "$as_me: error: in '$ac_pwd':" >&2;} as_fn_error $? "_AX_COMPILER_VERSION_BORLAND unknown borlandc version See 'config.log' for more details" "$LINENO" 5; } ;; esac fi case $_ax_c_compiler_version_borlandc_raw in #( 512 ) : ax_cv_c_compiler_version="1borlanc:2.00" ;; #( 1024) : ax_cv_c_compiler_version="1borlanc:3.00" ;; #( 1024) : ax_cv_c_compiler_version="1borlanc:3.00" ;; #( 1040) : ax_cv_c_compiler_version="1borlanc:3.1" ;; #( 1106) : ax_cv_c_compiler_version="1borlanc:4.0" ;; #( 1280) : ax_cv_c_compiler_version="1borlanc:5.0" ;; #( 1312) : ax_cv_c_compiler_version="1borlanc:5.02" ;; #( 1328) : ax_cv_c_compiler_version="2cppbuilder:3.0" ;; #( 1344) : ax_cv_c_compiler_version="2cppbuilder:4.0" ;; #( 1360) : ax_cv_c_compiler_version="3borlancpp:5.5" ;; #( 1361) : ax_cv_c_compiler_version="3borlancpp:5.51" ;; #( 1378) : ax_cv_c_compiler_version="3borlancpp:5.6.4" ;; #( 1392) : ax_cv_c_compiler_version="4cppbuilder:2006" ;; #( 1424) : ax_cv_c_compiler_version="4cppbuilder:2007" ;; #( 1555) : ax_cv_c_compiler_version="4cppbuilder:2009" ;; #( 1569) : ax_cv_c_compiler_version="4cppbuilder:2010" ;; #( 1584) : ax_cv_c_compiler_version="5xe" ;; #( 1600) : ax_cv_c_compiler_version="5xe:2" ;; #( 1616) : ax_cv_c_compiler_version="5xe:3" ;; #( 1632) : ax_cv_c_compiler_version="5xe:4" ;; #( *) : { printf "%s\n" "$as_me:${as_lineno-$LINENO}: WARNING: [_AX_COMPILER_VERSION_BORLAND] Unknown borlandc compiler version $_ax_c_compiler_version_borlandc_raw please report bug" >&5 printf "%s\n" "$as_me: WARNING: [_AX_COMPILER_VERSION_BORLAND] Unknown borlandc compiler version $_ax_c_compiler_version_borlandc_raw please report bug" >&2;} ;; esac ;; esac fi rm -f core conftest.err conftest.$ac_objext conftest.beam conftest.$ac_ext ;; #( comeau) : if ac_fn_c_compute_int "$LINENO" "__COMO_VERSION__%100" "_ax_c_compiler_version_minor" "" then : else case e in #( e) { { printf "%s\n" "$as_me:${as_lineno-$LINENO}: error: in '$ac_pwd':" >&5 printf "%s\n" "$as_me: error: in '$ac_pwd':" >&2;} as_fn_error $? "_AX_COMPILER_VERSION_COMEAU unknown comeau compiler minor version See 'config.log' for more details" "$LINENO" 5; } ;; esac fi if ac_fn_c_compute_int "$LINENO" "(__COMO_VERSION__/100)%10" "_ax_c_compiler_version_major" "" then : else case e in #( e) { { printf "%s\n" "$as_me:${as_lineno-$LINENO}: error: in '$ac_pwd':" >&5 printf "%s\n" "$as_me: error: in '$ac_pwd':" >&2;} as_fn_error $? "_AX_COMPILER_VERSION_COMEAU unknown comeau compiler major version See 'config.log' for more details" "$LINENO" 5; } ;; esac fi ax_cv_c_compiler_version="$_ax_c_compiler_version_major.$_ax_c_compiler_version_minor" ;; #( kai) : if ac_fn_c_compute_int "$LINENO" "__KCC_VERSION%100" "_ax_c_compiler_version_patch" "" then : else case e in #( e) { { printf "%s\n" "$as_me:${as_lineno-$LINENO}: error: in '$ac_pwd':" >&5 printf "%s\n" "$as_me: error: in '$ac_pwd':" >&2;} as_fn_error $? "_AX_COMPILER_VERSION_KAI unknown kay compiler patch version See 'config.log' for more details" "$LINENO" 5; } ;; esac fi if ac_fn_c_compute_int "$LINENO" "(__KCC_VERSION/100)%10" "_ax_c_compiler_version_minor" "" then : else case e in #( e) { { printf "%s\n" "$as_me:${as_lineno-$LINENO}: error: in '$ac_pwd':" >&5 printf "%s\n" "$as_me: error: in '$ac_pwd':" >&2;} as_fn_error $? "_AX_COMPILER_VERSION_KAI unknown kay compiler minor version See 'config.log' for more details" "$LINENO" 5; } ;; esac fi if ac_fn_c_compute_int "$LINENO" "(__KCC_VERSION/1000)%10" "_ax_c_compiler_version_major" "" then : else case e in #( e) { { printf "%s\n" "$as_me:${as_lineno-$LINENO}: error: in '$ac_pwd':" >&5 printf "%s\n" "$as_me: error: in '$ac_pwd':" >&2;} as_fn_error $? "_AX_COMPILER_VERSION_KAI unknown kay compiler major version See 'config.log' for more details" "$LINENO" 5; } ;; esac fi ax_cv_c_compiler_version="$_ax_c_compiler_version_major.$_ax_c_compiler_version_minor.$_ax_c_compiler_version_patch" ;; #( sgi) : if ac_fn_c_compute_int "$LINENO" " #if defined(_COMPILER_VERSION) _COMPILER_VERSION #else _SGI_COMPILER_VERSION #endif %10" "_ax_c_compiler_version_patch" "" then : else case e in #( e) { { printf "%s\n" "$as_me:${as_lineno-$LINENO}: error: in '$ac_pwd':" >&5 printf "%s\n" "$as_me: error: in '$ac_pwd':" >&2;} as_fn_error $? "_AX_COMPILER_VERSION_SGI unknown SGI compiler patch version See 'config.log' for more details" "$LINENO" 5; } ;; esac fi if ac_fn_c_compute_int "$LINENO" "( #if defined(_COMPILER_VERSION) _COMPILER_VERSION #else _SGI_COMPILER_VERSION #endif /10)%10" "_ax_c_compiler_version_minor" "" then : else case e in #( e) { { printf "%s\n" "$as_me:${as_lineno-$LINENO}: error: in '$ac_pwd':" >&5 printf "%s\n" "$as_me: error: in '$ac_pwd':" >&2;} as_fn_error $? "_AX_COMPILER_VERSION_SGI unknown SGI compiler minor version See 'config.log' for more details" "$LINENO" 5; } ;; esac fi if ac_fn_c_compute_int "$LINENO" "( #if defined(_COMPILER_VERSION) _COMPILER_VERSION #else _SGI_COMPILER_VERSION #endif /100)%10" "_ax_c_compiler_version_major" "" then : else case e in #( e) { { printf "%s\n" "$as_me:${as_lineno-$LINENO}: error: in '$ac_pwd':" >&5 printf "%s\n" "$as_me: error: in '$ac_pwd':" >&2;} as_fn_error $? "_AX_COMPILER_VERSION_SGI unknown SGI compiler major version See 'config.log' for more details" "$LINENO" 5; } ;; esac fi ax_cv_c_compiler_version="$_ax_c_compiler_version_major.$_ax_c_compiler_version_minor.$_ax_c_compiler_version_patch" ;; #( microsoft) : if ac_fn_c_compute_int "$LINENO" "_MSC_VER%100" "_ax_c_compiler_version_minor" "" then : else case e in #( e) { { printf "%s\n" "$as_me:${as_lineno-$LINENO}: error: in '$ac_pwd':" >&5 printf "%s\n" "$as_me: error: in '$ac_pwd':" >&2;} as_fn_error $? "_AX_COMPILER_VERSION_MICROSOFT unknown microsoft compiler minor version See 'config.log' for more details" "$LINENO" 5; } ;; esac fi if ac_fn_c_compute_int "$LINENO" "(_MSC_VER/100)%100" "_ax_c_compiler_version_major" "" then : else case e in #( e) { { printf "%s\n" "$as_me:${as_lineno-$LINENO}: error: in '$ac_pwd':" >&5 printf "%s\n" "$as_me: error: in '$ac_pwd':" >&2;} as_fn_error $? "_AX_COMPILER_VERSION_MICROSOFT unknown microsoft compiler major version See 'config.log' for more details" "$LINENO" 5; } ;; esac fi _ax_c_compiler_version_patch=0 _ax_c_compiler_version_build=0 # special case for version 6 if test "X$_ax_c_compiler_version_major" = "X12" then : if ac_fn_c_compute_int "$LINENO" "_MSC_FULL_VER%1000" "_ax_c_compiler_version_patch" "" then : else case e in #( e) _ax_c_compiler_version_patch=0 ;; esac fi fi # for version 7 if test "X$_ax_c_compiler_version_major" = "X13" then : if ac_fn_c_compute_int "$LINENO" "_MSC_FULL_VER%1000" "_ax_c_compiler_version_patch" "" then : else case e in #( e) { { printf "%s\n" "$as_me:${as_lineno-$LINENO}: error: in '$ac_pwd':" >&5 printf "%s\n" "$as_me: error: in '$ac_pwd':" >&2;} as_fn_error $? "_AX_COMPILER_VERSION_MICROSOFT unknown microsoft compiler patch version See 'config.log' for more details" "$LINENO" 5; } ;; esac fi fi # for version > 8 if test $_ax_c_compiler_version_major -ge 14 then : if ac_fn_c_compute_int "$LINENO" "_MSC_FULL_VER%10000" "_ax_c_compiler_version_patch" "" then : else case e in #( e) { { printf "%s\n" "$as_me:${as_lineno-$LINENO}: error: in '$ac_pwd':" >&5 printf "%s\n" "$as_me: error: in '$ac_pwd':" >&2;} as_fn_error $? "_AX_COMPILER_VERSION_MICROSOFT unknown microsoft compiler patch version See 'config.log' for more details" "$LINENO" 5; } ;; esac fi fi if test $_ax_c_compiler_version_major -ge 15 then : if ac_fn_c_compute_int "$LINENO" "_MSC_BUILD" "_ax_c_compiler_version_build" "" then : else case e in #( e) { { printf "%s\n" "$as_me:${as_lineno-$LINENO}: error: in '$ac_pwd':" >&5 printf "%s\n" "$as_me: error: in '$ac_pwd':" >&2;} as_fn_error $? "_AX_COMPILER_VERSION_MICROSOFT unknown microsoft compiler build version See 'config.log' for more details" "$LINENO" 5; } ;; esac fi fi ax_cv_c_compiler_version="$_ax_c_compiler_version_major.$_ax_c_compiler_version_minor.$_ax_c_compiler_version_patch.$_ax_c_compiler_version_build" ;; #( metrowerks) : if ac_fn_c_compute_int "$LINENO" "__MWERKS__%0x100" "_ax_c_compiler_version_patch" "" then : else case e in #( e) { { printf "%s\n" "$as_me:${as_lineno-$LINENO}: error: in '$ac_pwd':" >&5 printf "%s\n" "$as_me: error: in '$ac_pwd':" >&2;} as_fn_error $? "_AX_COMPILER_VERSION_METROWERKS unknown metrowerks compiler patch version See 'config.log' for more details" "$LINENO" 5; } ;; esac fi if ac_fn_c_compute_int "$LINENO" "(__MWERKS__/0x100)%0x10" "_ax_c_compiler_version_minor" "" then : else case e in #( e) { { printf "%s\n" "$as_me:${as_lineno-$LINENO}: error: in '$ac_pwd':" >&5 printf "%s\n" "$as_me: error: in '$ac_pwd':" >&2;} as_fn_error $? "_AX_COMPILER_VERSION_METROWERKS unknown metrowerks compiler minor version See 'config.log' for more details" "$LINENO" 5; } ;; esac fi if ac_fn_c_compute_int "$LINENO" "(__MWERKS__/0x1000)%0x10" "_ax_c_compiler_version_major" "" then : else case e in #( e) { { printf "%s\n" "$as_me:${as_lineno-$LINENO}: error: in '$ac_pwd':" >&5 printf "%s\n" "$as_me: error: in '$ac_pwd':" >&2;} as_fn_error $? "_AX_COMPILER_VERSION_METROWERKS unknown metrowerks compiler major version See 'config.log' for more details" "$LINENO" 5; } ;; esac fi ax_cv_c_compiler_version="$_ax_c_compiler_version_major.$_ax_c_compiler_version_minor.$_ax_c_compiler_version_patch" ;; #( watcom) : if ac_fn_c_compute_int "$LINENO" "__WATCOMC__%100" "_ax_c_compiler_version_minor" "" then : else case e in #( e) { { printf "%s\n" "$as_me:${as_lineno-$LINENO}: error: in '$ac_pwd':" >&5 printf "%s\n" "$as_me: error: in '$ac_pwd':" >&2;} as_fn_error $? "_AX_COMPILER_VERSION_WATCOM unknown watcom compiler minor version See 'config.log' for more details" "$LINENO" 5; } ;; esac fi if ac_fn_c_compute_int "$LINENO" "(__WATCOMC__/100)%100" "_ax_c_compiler_version_major" "" then : else case e in #( e) { { printf "%s\n" "$as_me:${as_lineno-$LINENO}: error: in '$ac_pwd':" >&5 printf "%s\n" "$as_me: error: in '$ac_pwd':" >&2;} as_fn_error $? "_AX_COMPILER_VERSION_WATCOM unknown watcom compiler major version See 'config.log' for more details" "$LINENO" 5; } ;; esac fi ax_cv_c_compiler_version="$_ax_c_compiler_version_major.$_ax_c_compiler_version_minor" ;; #( nvhpc) : if ac_fn_c_compute_int "$LINENO" "__NVCOMPILER_MAJOR__" "_ax_c_compiler_version_major" "" then : else case e in #( e) { { printf "%s\n" "$as_me:${as_lineno-$LINENO}: error: in '$ac_pwd':" >&5 printf "%s\n" "$as_me: error: in '$ac_pwd':" >&2;} as_fn_error $? "_AX_COMPILER_VERSION_NVHPC unknown nvhpc major See 'config.log' for more details" "$LINENO" 5; } ;; esac fi if ac_fn_c_compute_int "$LINENO" "__NVCOMPILER_MINOR__" "_ax_c_compiler_version_minor" "" then : else case e in #( e) { { printf "%s\n" "$as_me:${as_lineno-$LINENO}: error: in '$ac_pwd':" >&5 printf "%s\n" "$as_me: error: in '$ac_pwd':" >&2;} as_fn_error $? "_AX_COMPILER_VERSION_NVHPC unknown nvhpc minor See 'config.log' for more details" "$LINENO" 5; } ;; esac fi if ac_fn_c_compute_int "$LINENO" "__NVCOMPILER_PATCHLEVEL__" "_ax_c_compiler_version_patch" "" then : else case e in #( e) { { printf "%s\n" "$as_me:${as_lineno-$LINENO}: error: in '$ac_pwd':" >&5 printf "%s\n" "$as_me: error: in '$ac_pwd':" >&2;} as_fn_error $? "_AX_COMPILER_VERSION_NVHPC unknown nvhpc patch level See 'config.log' for more details" "$LINENO" 5; } ;; esac fi ax_cv_c_compiler_version="$_ax_c_compiler_version_major.$_ax_c_compiler_version_minor.$_ax_c_compiler_version_patch" ;; #( portland) : if ac_fn_c_compute_int "$LINENO" "__PGIC__" "_ax_c_compiler_version_major" "" then : else case e in #( e) { { printf "%s\n" "$as_me:${as_lineno-$LINENO}: error: in '$ac_pwd':" >&5 printf "%s\n" "$as_me: error: in '$ac_pwd':" >&2;} as_fn_error $? "_AX_COMPILER_VERSION_PORTLAND unknown pgi major See 'config.log' for more details" "$LINENO" 5; } ;; esac fi if ac_fn_c_compute_int "$LINENO" "__PGIC_MINOR__" "_ax_c_compiler_version_minor" "" then : else case e in #( e) { { printf "%s\n" "$as_me:${as_lineno-$LINENO}: error: in '$ac_pwd':" >&5 printf "%s\n" "$as_me: error: in '$ac_pwd':" >&2;} as_fn_error $? "_AX_COMPILER_VERSION_PORTLAND unknown pgi minor See 'config.log' for more details" "$LINENO" 5; } ;; esac fi if ac_fn_c_compute_int "$LINENO" "__PGIC_PATCHLEVEL__" "_ax_c_compiler_version_patch" "" then : else case e in #( e) { { printf "%s\n" "$as_me:${as_lineno-$LINENO}: error: in '$ac_pwd':" >&5 printf "%s\n" "$as_me: error: in '$ac_pwd':" >&2;} as_fn_error $? "_AX_COMPILER_VERSION_PORTLAND unknown pgi patch level See 'config.log' for more details" "$LINENO" 5; } ;; esac fi ax_cv_c_compiler_version="$_ax_c_compiler_version_major.$_ax_c_compiler_version_minor.$_ax_c_compiler_version_patch" ;; #( tcc) : ax_cv_c_compiler_version=`tcc -v | $SED 's/^[ ]*tcc[ ]\+version[ ]\+\([0-9.]\+\).*/\1/g'` ;; #( sdcc) : if ac_fn_c_compute_int "$LINENO" "/* avoid parse error with comments */ #if(defined(__SDCC_VERSION_MAJOR)) __SDCC_VERSION_MAJOR #else SDCC/100 #endif " "_ax_c_compiler_version_major" "" then : else case e in #( e) { { printf "%s\n" "$as_me:${as_lineno-$LINENO}: error: in '$ac_pwd':" >&5 printf "%s\n" "$as_me: error: in '$ac_pwd':" >&2;} as_fn_error $? "_AX_COMPILER_VERSION_SDCC unknown sdcc major See 'config.log' for more details" "$LINENO" 5; } ;; esac fi if ac_fn_c_compute_int "$LINENO" "/* avoid parse error with comments */ #if(defined(__SDCC_VERSION_MINOR)) __SDCC_VERSION_MINOR #else (SDCC%100)/10 #endif " "_ax_c_compiler_version_minor" "" then : else case e in #( e) { { printf "%s\n" "$as_me:${as_lineno-$LINENO}: error: in '$ac_pwd':" >&5 printf "%s\n" "$as_me: error: in '$ac_pwd':" >&2;} as_fn_error $? "_AX_COMPILER_VERSION_SDCC unknown sdcc minor See 'config.log' for more details" "$LINENO" 5; } ;; esac fi if ac_fn_c_compute_int "$LINENO" " /* avoid parse error with comments */ #if(defined(__SDCC_VERSION_PATCH)) __SDCC_VERSION_PATCH #elsif(defined(_SDCC_VERSION_PATCHLEVEL)) __SDCC_VERSION_PATCHLEVEL #else SDCC%10 #endif " "_ax_c_compiler_version_patch" "" then : else case e in #( e) { { printf "%s\n" "$as_me:${as_lineno-$LINENO}: error: in '$ac_pwd':" >&5 printf "%s\n" "$as_me: error: in '$ac_pwd':" >&2;} as_fn_error $? "_AX_COMPILER_VERSION_SDCC unknown sdcc patch level See 'config.log' for more details" "$LINENO" 5; } ;; esac fi ax_cv_c_compiler_version="$_ax_c_compiler_version_major.$_ax_c_compiler_version_minor.$_ax_c_compiler_version_patch" ;; #( *) : ax_cv_c_compiler_version="" ;; esac ;; esac fi { printf "%s\n" "$as_me:${as_lineno-$LINENO}: result: $ax_cv_c_compiler_version" >&5 printf "%s\n" "$ax_cv_c_compiler_version" >&6; } printf "%s\n" "#define COMPILER_VENDOR \"$ax_cv_c_compiler_vendor\"" >>confdefs.h printf "%s\n" "#define COMPILER_VERSION \"$ax_cv_c_compiler_version\"" >>confdefs.h # Add warning flags to compile command line. ac_ext=c ac_cpp='$CPP $CPPFLAGS' ac_compile='$CC -c $CFLAGS $CPPFLAGS conftest.$ac_ext >&5' ac_link='$CC -o conftest$ac_exeext $CFLAGS $CPPFLAGS $LDFLAGS conftest.$ac_ext $LIBS >&5' ac_compiler_gnu=$ac_cv_c_compiler_gnu { printf "%s\n" "$as_me:${as_lineno-$LINENO}: checking CFLAGS for most reasonable warnings" >&5 printf %s "checking CFLAGS for most reasonable warnings... " >&6; } if test ${ac_cv_cflags_warn_all+y} then : printf %s "(cached) " >&6 else case e in #( e) ac_cv_cflags_warn_all="" ac_save_cflags_warn_all_found="yes" case "$ax_cv_c_compiler_vendor" in #( intel) : ac_cv_cflags_warn_all="-w2" ;; #( ibm) : ac_cv_cflags_warn_all="-qsrcmsg -qinfo=all:noppt:noppc:noobs:nocnd" ;; #( pathscale) : ;; #( clang) : ac_cv_cflags_warn_all="-Wall" ;; #( cray) : ac_cv_cflags_warn_all="-h msglevel 2" ;; #( fujitsu) : ;; #( sdcc) : ;; #( sx) : ac_cv_cflags_warn_all="-pvctl,fullmsg" ;; #( portland) : ;; #( gnu) : ac_cv_cflags_warn_all="-Wall" ;; #( sun) : ac_cv_cflags_warn_all="-v" ;; #( hp) : ac_cv_cflags_warn_all="+w1" ;; #( dec) : ac_cv_cflags_warn_all="-verbose -w0 -warnprotos" ;; #( borland) : ;; #( comeau) : ;; #( kai) : ;; #( lcc) : ;; #( sgi) : ac_cv_cflags_warn_all="-fullwarn" ;; #( microsoft) : ;; #( metrowerks) : ;; #( watcom) : ;; #( tcc) : ;; #( unknown) : ac_cv_cflags_warn_all="" ac_save_cflags_warn_all_found="no" ;; #( *) : { printf "%s\n" "$as_me:${as_lineno-$LINENO}: WARNING: Unknown compiler vendor returned by AX_COMPILER_VENDOR" >&5 printf "%s\n" "$as_me: WARNING: Unknown compiler vendor returned by AX_COMPILER_VENDOR" >&2;} ac_cv_cflags_warn_all="" ac_save_cflags_warn_all_found="no" ;; esac if test "x$ac_save_cflags_warn_all_found" = "xyes" then : if test "x$ac_cv_cflags_warn_all" != "x" then : if test ${CFLAGS+y} then : case " $CFLAGS " in #( *" $ac_cv_cflags_warn_all "*) : { { printf "%s\n" "$as_me:${as_lineno-$LINENO}: : CFLAGS already contains \$ac_cv_cflags_warn_all"; } >&5 (: CFLAGS already contains $ac_cv_cflags_warn_all) 2>&5 ac_status=$? printf "%s\n" "$as_me:${as_lineno-$LINENO}: \$? = $ac_status" >&5 test $ac_status = 0; } ;; #( *) : CFLAGS="$ac_cv_cflags_warn_all $CFLAGS" { { printf "%s\n" "$as_me:${as_lineno-$LINENO}: : CFLAGS=\"\$CFLAGS\""; } >&5 (: CFLAGS="$CFLAGS") 2>&5 ac_status=$? printf "%s\n" "$as_me:${as_lineno-$LINENO}: \$? = $ac_status" >&5 test $ac_status = 0; } ;; esac else case e in #( e) CFLAGS=$ac_cv_cflags_warn_all { { printf "%s\n" "$as_me:${as_lineno-$LINENO}: : CFLAGS=\"\$CFLAGS\""; } >&5 (: CFLAGS="$CFLAGS") 2>&5 ac_status=$? printf "%s\n" "$as_me:${as_lineno-$LINENO}: \$? = $ac_status" >&5 test $ac_status = 0; } ;; esac fi fi else case e in #( e) true ;; esac fi ;; esac fi { printf "%s\n" "$as_me:${as_lineno-$LINENO}: result: $ac_cv_cflags_warn_all" >&5 printf "%s\n" "$ac_cv_cflags_warn_all" >&6; } ac_ext=c ac_cpp='$CPP $CPPFLAGS' ac_compile='$CC -c $CFLAGS $CPPFLAGS conftest.$ac_ext >&5' ac_link='$CC -o conftest$ac_exeext $CFLAGS $CPPFLAGS $LDFLAGS conftest.$ac_ext $LIBS >&5' ac_compiler_gnu=$ac_cv_c_compiler_gnu # Check for SIMD (etc.) extensions supported by hardware and compiler. ac_ext=c ac_cpp='$CPP $CPPFLAGS' ac_compile='$CC -c $CFLAGS $CPPFLAGS conftest.$ac_ext >&5' ac_link='$CC -o conftest$ac_exeext $CFLAGS $CPPFLAGS $LDFLAGS conftest.$ac_ext $LIBS >&5' ac_compiler_gnu=$ac_cv_c_compiler_gnu { printf "%s\n" "$as_me:${as_lineno-$LINENO}: checking for x86 cpuid output" >&5 printf %s "checking for x86 cpuid output... " >&6; } if test ${ax_cv_gcc_x86_cpuid_+y} then : printf %s "(cached) " >&6 else case e in #( e) if test "$cross_compiling" = yes then : ax_cv_gcc_x86_cpuid_=unknown else case e in #( e) cat confdefs.h - <<_ACEOF >conftest.$ac_ext /* end confdefs.h. */ #include int main (void) { int op = , level = 0, eax, ebx, ecx, edx; FILE *f; __asm__ __volatile__ ("xchg %%ebx, %1\n" "cpuid\n" "xchg %%ebx, %1\n" : "=a" (eax), "=r" (ebx), "=c" (ecx), "=d" (edx) : "a" (op), "2" (level)); f = fopen("conftest_cpuid", "w"); if (!f) return 1; fprintf(f, "%x:%x:%x:%x\n", eax, ebx, ecx, edx); fclose(f); return 0; ; return 0; } _ACEOF if ac_fn_c_try_run "$LINENO" then : ax_cv_gcc_x86_cpuid_=`cat conftest_cpuid`; rm -f conftest_cpuid else case e in #( e) ax_cv_gcc_x86_cpuid_=unknown; rm -f conftest_cpuid ;; esac fi rm -f core *.core core.conftest.* gmon.out bb.out conftest$ac_exeext \ conftest.$ac_objext conftest.beam conftest.$ac_ext ;; esac fi ;; esac fi { printf "%s\n" "$as_me:${as_lineno-$LINENO}: result: $ax_cv_gcc_x86_cpuid_" >&5 printf "%s\n" "$ax_cv_gcc_x86_cpuid_" >&6; } ac_ext=c ac_cpp='$CPP $CPPFLAGS' ac_compile='$CC -c $CFLAGS $CPPFLAGS conftest.$ac_ext >&5' ac_link='$CC -o conftest$ac_exeext $CFLAGS $CPPFLAGS $LDFLAGS conftest.$ac_ext $LIBS >&5' ac_compiler_gnu=$ac_cv_c_compiler_gnu ac_ext=c ac_cpp='$CPP $CPPFLAGS' ac_compile='$CC -c $CFLAGS $CPPFLAGS conftest.$ac_ext >&5' ac_link='$CC -o conftest$ac_exeext $CFLAGS $CPPFLAGS $LDFLAGS conftest.$ac_ext $LIBS >&5' ac_compiler_gnu=$ac_cv_c_compiler_gnu { printf "%s\n" "$as_me:${as_lineno-$LINENO}: checking for x86-AVX xgetbv output" >&5 printf %s "checking for x86-AVX xgetbv output... " >&6; } if test ${ax_cv_gcc_x86_avx_xgetbv_+y} then : printf %s "(cached) " >&6 else case e in #( e) if test "$cross_compiling" = yes then : ax_cv_gcc_x86_avx_xgetbv_=unknown else case e in #( e) cat confdefs.h - <<_ACEOF >conftest.$ac_ext /* end confdefs.h. */ #include int main (void) { int op = , eax, edx; FILE *f; /* Opcodes for xgetbv */ __asm__ __volatile__ (".byte 0x0f, 0x01, 0xd0" : "=a" (eax), "=d" (edx) : "c" (op)); f = fopen("conftest_xgetbv", "w"); if (!f) return 1; fprintf(f, "%x:%x\n", eax, edx); fclose(f); return 0; ; return 0; } _ACEOF if ac_fn_c_try_run "$LINENO" then : ax_cv_gcc_x86_avx_xgetbv_=`cat conftest_xgetbv`; rm -f conftest_xgetbv else case e in #( e) ax_cv_gcc_x86_avx_xgetbv_=unknown; rm -f conftest_xgetbv ;; esac fi rm -f core *.core core.conftest.* gmon.out bb.out conftest$ac_exeext \ conftest.$ac_objext conftest.beam conftest.$ac_ext ;; esac fi ;; esac fi { printf "%s\n" "$as_me:${as_lineno-$LINENO}: result: $ax_cv_gcc_x86_avx_xgetbv_" >&5 printf "%s\n" "$ax_cv_gcc_x86_avx_xgetbv_" >&6; } ac_ext=c ac_cpp='$CPP $CPPFLAGS' ac_compile='$CC -c $CFLAGS $CPPFLAGS conftest.$ac_ext >&5' ac_link='$CC -o conftest$ac_exeext $CFLAGS $CPPFLAGS $LDFLAGS conftest.$ac_ext $LIBS >&5' ac_compiler_gnu=$ac_cv_c_compiler_gnu CPUEXT_FLAGS="" SIMD_FLAGS="" case $host_cpu in powerpc*) { printf "%s\n" "$as_me:${as_lineno-$LINENO}: checking whether altivec is supported for old distros" >&5 printf %s "checking whether altivec is supported for old distros... " >&6; } if test ${ax_cv_have_altivec_old_ext+y} then : printf %s "(cached) " >&6 else case e in #( e) if test `/usr/sbin/sysctl -a 2>/dev/null| grep -c hw.optional.altivec` != 0; then if test `/usr/sbin/sysctl -n hw.optional.altivec` = 1; then ax_cv_have_altivec_old_ext=yes fi fi ;; esac fi { printf "%s\n" "$as_me:${as_lineno-$LINENO}: result: $ax_cv_have_altivec_old_ext" >&5 printf "%s\n" "$ax_cv_have_altivec_old_ext" >&6; } if test "$ax_cv_have_altivec_old_ext" = yes; then printf "%s\n" "#define HAVE_ALTIVEC /**/" >>confdefs.h { printf "%s\n" "$as_me:${as_lineno-$LINENO}: checking whether the C compiler accepts -faltivec" >&5 printf %s "checking whether the C compiler accepts -faltivec... " >&6; } if test ${ax_cv_check_cflags___faltivec+y} then : printf %s "(cached) " >&6 else case e in #( e) ax_check_save_flags=$CFLAGS if test x"$GCC" = xyes ; then add_gnu_werror="-Werror" fi CFLAGS="$CFLAGS -faltivec $add_gnu_werror" cat confdefs.h - <<_ACEOF >conftest.$ac_ext /* end confdefs.h. */ int main (void) { ; return 0; } _ACEOF if ac_fn_c_try_compile "$LINENO" then : ax_cv_check_cflags___faltivec=yes else case e in #( e) ax_cv_check_cflags___faltivec=no ;; esac fi rm -f core conftest.err conftest.$ac_objext conftest.beam conftest.$ac_ext CFLAGS=$ax_check_save_flags ;; esac fi { printf "%s\n" "$as_me:${as_lineno-$LINENO}: result: $ax_cv_check_cflags___faltivec" >&5 printf "%s\n" "$ax_cv_check_cflags___faltivec" >&6; } if test "x$ax_cv_check_cflags___faltivec" = xyes then : SIMD_FLAGS="$SIMD_FLAGS -faltivec" else case e in #( e) : ;; esac fi fi { printf "%s\n" "$as_me:${as_lineno-$LINENO}: checking whether altivec is supported" >&5 printf %s "checking whether altivec is supported... " >&6; } if test ${ax_cv_have_altivec_ext+y} then : printf %s "(cached) " >&6 else case e in #( e) if test `LD_SHOW_AUXV=1 /bin/true 2>/dev/null|grep -c altivec` != 0; then ax_cv_have_altivec_ext=yes fi ;; esac fi { printf "%s\n" "$as_me:${as_lineno-$LINENO}: result: $ax_cv_have_altivec_ext" >&5 printf "%s\n" "$ax_cv_have_altivec_ext" >&6; } if test "$ax_cv_have_altivec_ext" = yes; then printf "%s\n" "#define HAVE_ALTIVEC /**/" >>confdefs.h { printf "%s\n" "$as_me:${as_lineno-$LINENO}: checking whether the C compiler accepts -maltivec" >&5 printf %s "checking whether the C compiler accepts -maltivec... " >&6; } if test ${ax_cv_check_cflags___maltivec+y} then : printf %s "(cached) " >&6 else case e in #( e) ax_check_save_flags=$CFLAGS if test x"$GCC" = xyes ; then add_gnu_werror="-Werror" fi CFLAGS="$CFLAGS -maltivec $add_gnu_werror" cat confdefs.h - <<_ACEOF >conftest.$ac_ext /* end confdefs.h. */ int main (void) { ; return 0; } _ACEOF if ac_fn_c_try_compile "$LINENO" then : ax_cv_check_cflags___maltivec=yes else case e in #( e) ax_cv_check_cflags___maltivec=no ;; esac fi rm -f core conftest.err conftest.$ac_objext conftest.beam conftest.$ac_ext CFLAGS=$ax_check_save_flags ;; esac fi { printf "%s\n" "$as_me:${as_lineno-$LINENO}: result: $ax_cv_check_cflags___maltivec" >&5 printf "%s\n" "$ax_cv_check_cflags___maltivec" >&6; } if test "x$ax_cv_check_cflags___maltivec" = xyes then : SIMD_FLAGS="$SIMD_FLAGS -maltivec" else case e in #( e) : ;; esac fi fi { printf "%s\n" "$as_me:${as_lineno-$LINENO}: checking whether vsx is supported" >&5 printf %s "checking whether vsx is supported... " >&6; } if test ${ax_cv_have_vsx_ext+y} then : printf %s "(cached) " >&6 else case e in #( e) if test `LD_SHOW_AUXV=1 /bin/true 2>/dev/null|grep -c vsx` != 0; then ax_cv_have_vsx_ext=yes fi ;; esac fi { printf "%s\n" "$as_me:${as_lineno-$LINENO}: result: $ax_cv_have_vsx_ext" >&5 printf "%s\n" "$ax_cv_have_vsx_ext" >&6; } if test "$ax_cv_have_vsx_ext" = yes; then printf "%s\n" "#define HAVE_VSX /**/" >>confdefs.h { printf "%s\n" "$as_me:${as_lineno-$LINENO}: checking whether the C compiler accepts -mvsx" >&5 printf %s "checking whether the C compiler accepts -mvsx... " >&6; } if test ${ax_cv_check_cflags___mvsx+y} then : printf %s "(cached) " >&6 else case e in #( e) ax_check_save_flags=$CFLAGS if test x"$GCC" = xyes ; then add_gnu_werror="-Werror" fi CFLAGS="$CFLAGS -mvsx $add_gnu_werror" cat confdefs.h - <<_ACEOF >conftest.$ac_ext /* end confdefs.h. */ int main (void) { ; return 0; } _ACEOF if ac_fn_c_try_compile "$LINENO" then : ax_cv_check_cflags___mvsx=yes else case e in #( e) ax_cv_check_cflags___mvsx=no ;; esac fi rm -f core conftest.err conftest.$ac_objext conftest.beam conftest.$ac_ext CFLAGS=$ax_check_save_flags ;; esac fi { printf "%s\n" "$as_me:${as_lineno-$LINENO}: result: $ax_cv_check_cflags___mvsx" >&5 printf "%s\n" "$ax_cv_check_cflags___mvsx" >&6; } if test "x$ax_cv_check_cflags___mvsx" = xyes then : SIMD_FLAGS="$SIMD_FLAGS -mvsx" else case e in #( e) : ;; esac fi fi ;; i[3456]86*|x86_64*|amd64*) eax_cpuid0=0 ac_ext=c ac_cpp='$CPP $CPPFLAGS' ac_compile='$CC -c $CFLAGS $CPPFLAGS conftest.$ac_ext >&5' ac_link='$CC -o conftest$ac_exeext $CFLAGS $CPPFLAGS $LDFLAGS conftest.$ac_ext $LIBS >&5' ac_compiler_gnu=$ac_cv_c_compiler_gnu { printf "%s\n" "$as_me:${as_lineno-$LINENO}: checking for x86 cpuid 0x00000000 output" >&5 printf %s "checking for x86 cpuid 0x00000000 output... " >&6; } if test ${ax_cv_gcc_x86_cpuid_0x00000000+y} then : printf %s "(cached) " >&6 else case e in #( e) if test "$cross_compiling" = yes then : ax_cv_gcc_x86_cpuid_0x00000000=unknown else case e in #( e) cat confdefs.h - <<_ACEOF >conftest.$ac_ext /* end confdefs.h. */ #include int main (void) { int op = 0x00000000, level = 0, eax, ebx, ecx, edx; FILE *f; __asm__ __volatile__ ("xchg %%ebx, %1\n" "cpuid\n" "xchg %%ebx, %1\n" : "=a" (eax), "=r" (ebx), "=c" (ecx), "=d" (edx) : "a" (op), "2" (level)); f = fopen("conftest_cpuid", "w"); if (!f) return 1; fprintf(f, "%x:%x:%x:%x\n", eax, ebx, ecx, edx); fclose(f); return 0; ; return 0; } _ACEOF if ac_fn_c_try_run "$LINENO" then : ax_cv_gcc_x86_cpuid_0x00000000=`cat conftest_cpuid`; rm -f conftest_cpuid else case e in #( e) ax_cv_gcc_x86_cpuid_0x00000000=unknown; rm -f conftest_cpuid ;; esac fi rm -f core *.core core.conftest.* gmon.out bb.out conftest$ac_exeext \ conftest.$ac_objext conftest.beam conftest.$ac_ext ;; esac fi ;; esac fi { printf "%s\n" "$as_me:${as_lineno-$LINENO}: result: $ax_cv_gcc_x86_cpuid_0x00000000" >&5 printf "%s\n" "$ax_cv_gcc_x86_cpuid_0x00000000" >&6; } ac_ext=c ac_cpp='$CPP $CPPFLAGS' ac_compile='$CC -c $CFLAGS $CPPFLAGS conftest.$ac_ext >&5' ac_link='$CC -o conftest$ac_exeext $CFLAGS $CPPFLAGS $LDFLAGS conftest.$ac_ext $LIBS >&5' ac_compiler_gnu=$ac_cv_c_compiler_gnu if test "$ax_cv_gcc_x86_cpuid_0x00000000" != "unknown"; then eax_cpuid0=`echo $ax_cv_gcc_x86_cpuid_0x00000000 | cut -d ":" -f 1` fi eax_cpuid80000000=0 ac_ext=c ac_cpp='$CPP $CPPFLAGS' ac_compile='$CC -c $CFLAGS $CPPFLAGS conftest.$ac_ext >&5' ac_link='$CC -o conftest$ac_exeext $CFLAGS $CPPFLAGS $LDFLAGS conftest.$ac_ext $LIBS >&5' ac_compiler_gnu=$ac_cv_c_compiler_gnu { printf "%s\n" "$as_me:${as_lineno-$LINENO}: checking for x86 cpuid 0x80000000 output" >&5 printf %s "checking for x86 cpuid 0x80000000 output... " >&6; } if test ${ax_cv_gcc_x86_cpuid_0x80000000+y} then : printf %s "(cached) " >&6 else case e in #( e) if test "$cross_compiling" = yes then : ax_cv_gcc_x86_cpuid_0x80000000=unknown else case e in #( e) cat confdefs.h - <<_ACEOF >conftest.$ac_ext /* end confdefs.h. */ #include int main (void) { int op = 0x80000000, level = 0, eax, ebx, ecx, edx; FILE *f; __asm__ __volatile__ ("xchg %%ebx, %1\n" "cpuid\n" "xchg %%ebx, %1\n" : "=a" (eax), "=r" (ebx), "=c" (ecx), "=d" (edx) : "a" (op), "2" (level)); f = fopen("conftest_cpuid", "w"); if (!f) return 1; fprintf(f, "%x:%x:%x:%x\n", eax, ebx, ecx, edx); fclose(f); return 0; ; return 0; } _ACEOF if ac_fn_c_try_run "$LINENO" then : ax_cv_gcc_x86_cpuid_0x80000000=`cat conftest_cpuid`; rm -f conftest_cpuid else case e in #( e) ax_cv_gcc_x86_cpuid_0x80000000=unknown; rm -f conftest_cpuid ;; esac fi rm -f core *.core core.conftest.* gmon.out bb.out conftest$ac_exeext \ conftest.$ac_objext conftest.beam conftest.$ac_ext ;; esac fi ;; esac fi { printf "%s\n" "$as_me:${as_lineno-$LINENO}: result: $ax_cv_gcc_x86_cpuid_0x80000000" >&5 printf "%s\n" "$ax_cv_gcc_x86_cpuid_0x80000000" >&6; } ac_ext=c ac_cpp='$CPP $CPPFLAGS' ac_compile='$CC -c $CFLAGS $CPPFLAGS conftest.$ac_ext >&5' ac_link='$CC -o conftest$ac_exeext $CFLAGS $CPPFLAGS $LDFLAGS conftest.$ac_ext $LIBS >&5' ac_compiler_gnu=$ac_cv_c_compiler_gnu if test "$ax_cv_gcc_x86_cpuid_0x80000000" != "unknown"; then eax_cpuid80000000=`echo $ax_cv_gcc_x86_cpuid_0x80000000 | cut -d ":" -f 1` fi ecx_cpuid1=0 edx_cpuid1=0 if test "$((0x$eax_cpuid0))" -ge 1 ; then ac_ext=c ac_cpp='$CPP $CPPFLAGS' ac_compile='$CC -c $CFLAGS $CPPFLAGS conftest.$ac_ext >&5' ac_link='$CC -o conftest$ac_exeext $CFLAGS $CPPFLAGS $LDFLAGS conftest.$ac_ext $LIBS >&5' ac_compiler_gnu=$ac_cv_c_compiler_gnu { printf "%s\n" "$as_me:${as_lineno-$LINENO}: checking for x86 cpuid 0x00000001 output" >&5 printf %s "checking for x86 cpuid 0x00000001 output... " >&6; } if test ${ax_cv_gcc_x86_cpuid_0x00000001+y} then : printf %s "(cached) " >&6 else case e in #( e) if test "$cross_compiling" = yes then : ax_cv_gcc_x86_cpuid_0x00000001=unknown else case e in #( e) cat confdefs.h - <<_ACEOF >conftest.$ac_ext /* end confdefs.h. */ #include int main (void) { int op = 0x00000001, level = 0, eax, ebx, ecx, edx; FILE *f; __asm__ __volatile__ ("xchg %%ebx, %1\n" "cpuid\n" "xchg %%ebx, %1\n" : "=a" (eax), "=r" (ebx), "=c" (ecx), "=d" (edx) : "a" (op), "2" (level)); f = fopen("conftest_cpuid", "w"); if (!f) return 1; fprintf(f, "%x:%x:%x:%x\n", eax, ebx, ecx, edx); fclose(f); return 0; ; return 0; } _ACEOF if ac_fn_c_try_run "$LINENO" then : ax_cv_gcc_x86_cpuid_0x00000001=`cat conftest_cpuid`; rm -f conftest_cpuid else case e in #( e) ax_cv_gcc_x86_cpuid_0x00000001=unknown; rm -f conftest_cpuid ;; esac fi rm -f core *.core core.conftest.* gmon.out bb.out conftest$ac_exeext \ conftest.$ac_objext conftest.beam conftest.$ac_ext ;; esac fi ;; esac fi { printf "%s\n" "$as_me:${as_lineno-$LINENO}: result: $ax_cv_gcc_x86_cpuid_0x00000001" >&5 printf "%s\n" "$ax_cv_gcc_x86_cpuid_0x00000001" >&6; } ac_ext=c ac_cpp='$CPP $CPPFLAGS' ac_compile='$CC -c $CFLAGS $CPPFLAGS conftest.$ac_ext >&5' ac_link='$CC -o conftest$ac_exeext $CFLAGS $CPPFLAGS $LDFLAGS conftest.$ac_ext $LIBS >&5' ac_compiler_gnu=$ac_cv_c_compiler_gnu if test "$ax_cv_gcc_x86_cpuid_0x00000001" != "unknown"; then ecx_cpuid1=`echo $ax_cv_gcc_x86_cpuid_0x00000001 | cut -d ":" -f 3` edx_cpuid1=`echo $ax_cv_gcc_x86_cpuid_0x00000001 | cut -d ":" -f 4` fi fi ebx_cpuid7=0 ecx_cpuid7=0 if test "$((0x$eax_cpuid0))" -ge 7 ; then ac_ext=c ac_cpp='$CPP $CPPFLAGS' ac_compile='$CC -c $CFLAGS $CPPFLAGS conftest.$ac_ext >&5' ac_link='$CC -o conftest$ac_exeext $CFLAGS $CPPFLAGS $LDFLAGS conftest.$ac_ext $LIBS >&5' ac_compiler_gnu=$ac_cv_c_compiler_gnu { printf "%s\n" "$as_me:${as_lineno-$LINENO}: checking for x86 cpuid 0x00000007 output" >&5 printf %s "checking for x86 cpuid 0x00000007 output... " >&6; } if test ${ax_cv_gcc_x86_cpuid_0x00000007+y} then : printf %s "(cached) " >&6 else case e in #( e) if test "$cross_compiling" = yes then : ax_cv_gcc_x86_cpuid_0x00000007=unknown else case e in #( e) cat confdefs.h - <<_ACEOF >conftest.$ac_ext /* end confdefs.h. */ #include int main (void) { int op = 0x00000007, level = 0x00, eax, ebx, ecx, edx; FILE *f; __asm__ __volatile__ ("xchg %%ebx, %1\n" "cpuid\n" "xchg %%ebx, %1\n" : "=a" (eax), "=r" (ebx), "=c" (ecx), "=d" (edx) : "a" (op), "2" (level)); f = fopen("conftest_cpuid", "w"); if (!f) return 1; fprintf(f, "%x:%x:%x:%x\n", eax, ebx, ecx, edx); fclose(f); return 0; ; return 0; } _ACEOF if ac_fn_c_try_run "$LINENO" then : ax_cv_gcc_x86_cpuid_0x00000007=`cat conftest_cpuid`; rm -f conftest_cpuid else case e in #( e) ax_cv_gcc_x86_cpuid_0x00000007=unknown; rm -f conftest_cpuid ;; esac fi rm -f core *.core core.conftest.* gmon.out bb.out conftest$ac_exeext \ conftest.$ac_objext conftest.beam conftest.$ac_ext ;; esac fi ;; esac fi { printf "%s\n" "$as_me:${as_lineno-$LINENO}: result: $ax_cv_gcc_x86_cpuid_0x00000007" >&5 printf "%s\n" "$ax_cv_gcc_x86_cpuid_0x00000007" >&6; } ac_ext=c ac_cpp='$CPP $CPPFLAGS' ac_compile='$CC -c $CFLAGS $CPPFLAGS conftest.$ac_ext >&5' ac_link='$CC -o conftest$ac_exeext $CFLAGS $CPPFLAGS $LDFLAGS conftest.$ac_ext $LIBS >&5' ac_compiler_gnu=$ac_cv_c_compiler_gnu if test "$ax_cv_gcc_x86_cpuid_0x00000007" != "unknown"; then ebx_cpuid7=`echo $ax_cv_gcc_x86_cpuid_0x00000007 | cut -d ":" -f 2` ecx_cpuid7=`echo $ax_cv_gcc_x86_cpuid_0x00000007 | cut -d ":" -f 3` fi fi ecx_cpuid80000001=0 edx_cpuid80000001=0 if test "$((0x$eax_cpuid80000000))" -ge "$((0x80000001))" ; then ac_ext=c ac_cpp='$CPP $CPPFLAGS' ac_compile='$CC -c $CFLAGS $CPPFLAGS conftest.$ac_ext >&5' ac_link='$CC -o conftest$ac_exeext $CFLAGS $CPPFLAGS $LDFLAGS conftest.$ac_ext $LIBS >&5' ac_compiler_gnu=$ac_cv_c_compiler_gnu { printf "%s\n" "$as_me:${as_lineno-$LINENO}: checking for x86 cpuid 0x80000001 output" >&5 printf %s "checking for x86 cpuid 0x80000001 output... " >&6; } if test ${ax_cv_gcc_x86_cpuid_0x80000001+y} then : printf %s "(cached) " >&6 else case e in #( e) if test "$cross_compiling" = yes then : ax_cv_gcc_x86_cpuid_0x80000001=unknown else case e in #( e) cat confdefs.h - <<_ACEOF >conftest.$ac_ext /* end confdefs.h. */ #include int main (void) { int op = 0x80000001, level = 0, eax, ebx, ecx, edx; FILE *f; __asm__ __volatile__ ("xchg %%ebx, %1\n" "cpuid\n" "xchg %%ebx, %1\n" : "=a" (eax), "=r" (ebx), "=c" (ecx), "=d" (edx) : "a" (op), "2" (level)); f = fopen("conftest_cpuid", "w"); if (!f) return 1; fprintf(f, "%x:%x:%x:%x\n", eax, ebx, ecx, edx); fclose(f); return 0; ; return 0; } _ACEOF if ac_fn_c_try_run "$LINENO" then : ax_cv_gcc_x86_cpuid_0x80000001=`cat conftest_cpuid`; rm -f conftest_cpuid else case e in #( e) ax_cv_gcc_x86_cpuid_0x80000001=unknown; rm -f conftest_cpuid ;; esac fi rm -f core *.core core.conftest.* gmon.out bb.out conftest$ac_exeext \ conftest.$ac_objext conftest.beam conftest.$ac_ext ;; esac fi ;; esac fi { printf "%s\n" "$as_me:${as_lineno-$LINENO}: result: $ax_cv_gcc_x86_cpuid_0x80000001" >&5 printf "%s\n" "$ax_cv_gcc_x86_cpuid_0x80000001" >&6; } ac_ext=c ac_cpp='$CPP $CPPFLAGS' ac_compile='$CC -c $CFLAGS $CPPFLAGS conftest.$ac_ext >&5' ac_link='$CC -o conftest$ac_exeext $CFLAGS $CPPFLAGS $LDFLAGS conftest.$ac_ext $LIBS >&5' ac_compiler_gnu=$ac_cv_c_compiler_gnu if test "$ax_cv_gcc_x86_cpuid_0x80000001" != "unknown"; then ecx_cpuid80000001=`echo $ax_cv_gcc_x86_cpuid_0x80000001 | cut -d ":" -f 3` edx_cpuid80000001=`echo $ax_cv_gcc_x86_cpuid_0x80000001 | cut -d ":" -f 4` fi fi if test ${ax_cv_have_mmx_os_support_ext+y} then : printf %s "(cached) " >&6 else case e in #( e) ax_cv_have_mmx_os_support_ext=yes ;; esac fi ax_cv_have_none_os_support_ext=yes if test ${ax_cv_have_sse_os_support_ext+y} then : printf %s "(cached) " >&6 else case e in #( e) ax_cv_have_sse_os_support_ext=no, if test "$((0x$edx_cpuid1>>25&0x01))" = 1; then ac_ext=c ac_cpp='$CPP $CPPFLAGS' ac_compile='$CC -c $CFLAGS $CPPFLAGS conftest.$ac_ext >&5' ac_link='$CC -o conftest$ac_exeext $CFLAGS $CPPFLAGS $LDFLAGS conftest.$ac_ext $LIBS >&5' ac_compiler_gnu=$ac_cv_c_compiler_gnu if test "$cross_compiling" = yes then : ax_cv_have_sse_os_support_ext=no else case e in #( e) cat confdefs.h - <<_ACEOF >conftest.$ac_ext /* end confdefs.h. */ #include #include /* No way at ring1 to ring3 in protected mode to check the CR0 and CR4 control registers directly. Execute an SSE instruction. If it raises SIGILL then OS doesn't support SSE based instructions */ void sig_handler(int signum){ exit(1); } int main(void){ signal(SIGILL, sig_handler); /* SSE instruction xorps %xmm0,%xmm0 */ __asm__ __volatile__ (".byte 0x0f, 0x57, 0xc0"); return 0; } _ACEOF if ac_fn_c_try_run "$LINENO" then : ax_cv_have_sse_os_support_ext=yes else case e in #( e) ax_cv_have_sse_os_support_ext=no ;; esac fi rm -f core *.core core.conftest.* gmon.out bb.out conftest$ac_exeext \ conftest.$ac_objext conftest.beam conftest.$ac_ext ;; esac fi ac_ext=c ac_cpp='$CPP $CPPFLAGS' ac_compile='$CC -c $CFLAGS $CPPFLAGS conftest.$ac_ext >&5' ac_link='$CC -o conftest$ac_exeext $CFLAGS $CPPFLAGS $LDFLAGS conftest.$ac_ext $LIBS >&5' ac_compiler_gnu=$ac_cv_c_compiler_gnu fi ;; esac fi xgetbv_eax=0 if test "$((0x$ecx_cpuid1>>28&0x01))" = 1; then ac_ext=c ac_cpp='$CPP $CPPFLAGS' ac_compile='$CC -c $CFLAGS $CPPFLAGS conftest.$ac_ext >&5' ac_link='$CC -o conftest$ac_exeext $CFLAGS $CPPFLAGS $LDFLAGS conftest.$ac_ext $LIBS >&5' ac_compiler_gnu=$ac_cv_c_compiler_gnu { printf "%s\n" "$as_me:${as_lineno-$LINENO}: checking for x86-AVX xgetbv 0x00000000 output" >&5 printf %s "checking for x86-AVX xgetbv 0x00000000 output... " >&6; } if test ${ax_cv_gcc_x86_avx_xgetbv_0x00000000+y} then : printf %s "(cached) " >&6 else case e in #( e) if test "$cross_compiling" = yes then : ax_cv_gcc_x86_avx_xgetbv_0x00000000=unknown else case e in #( e) cat confdefs.h - <<_ACEOF >conftest.$ac_ext /* end confdefs.h. */ #include int main (void) { int op = 0x00000000, eax, edx; FILE *f; /* Opcodes for xgetbv */ __asm__ __volatile__ (".byte 0x0f, 0x01, 0xd0" : "=a" (eax), "=d" (edx) : "c" (op)); f = fopen("conftest_xgetbv", "w"); if (!f) return 1; fprintf(f, "%x:%x\n", eax, edx); fclose(f); return 0; ; return 0; } _ACEOF if ac_fn_c_try_run "$LINENO" then : ax_cv_gcc_x86_avx_xgetbv_0x00000000=`cat conftest_xgetbv`; rm -f conftest_xgetbv else case e in #( e) ax_cv_gcc_x86_avx_xgetbv_0x00000000=unknown; rm -f conftest_xgetbv ;; esac fi rm -f core *.core core.conftest.* gmon.out bb.out conftest$ac_exeext \ conftest.$ac_objext conftest.beam conftest.$ac_ext ;; esac fi ;; esac fi { printf "%s\n" "$as_me:${as_lineno-$LINENO}: result: $ax_cv_gcc_x86_avx_xgetbv_0x00000000" >&5 printf "%s\n" "$ax_cv_gcc_x86_avx_xgetbv_0x00000000" >&6; } ac_ext=c ac_cpp='$CPP $CPPFLAGS' ac_compile='$CC -c $CFLAGS $CPPFLAGS conftest.$ac_ext >&5' ac_link='$CC -o conftest$ac_exeext $CFLAGS $CPPFLAGS $LDFLAGS conftest.$ac_ext $LIBS >&5' ac_compiler_gnu=$ac_cv_c_compiler_gnu if test x"$ax_cv_gcc_x86_avx_xgetbv_0x00000000" != x"unknown"; then xgetbv_eax=`echo $ax_cv_gcc_x86_avx_xgetbv_0x00000000 | cut -d ":" -f 1` fi if test ${ax_cv_have_avx_os_support_ext+y} then : printf %s "(cached) " >&6 else case e in #( e) ax_cv_have_avx_os_support_ext=no if test "$((0x$ecx_cpuid1>>27&0x01))" = 1; then if test "$((0x$xgetbv_eax&0x6))" = 6; then ax_cv_have_avx_os_support_ext=yes fi fi ;; esac fi fi if test ${ax_cv_have_avx512_os_support_ext+y} then : printf %s "(cached) " >&6 else case e in #( e) ax_cv_have_avx512_os_support_ext=no if test "$ax_cv_have_avx_os_support_ext" = yes; then if test "$((0x$xgetbv_eax&0xe6))" = "$((0xe6))"; then ax_cv_have_avx512_os_support_ext=yes fi fi ;; esac fi for ac_instr_info in "none;rdrnd;RDRND;ecx_cpuid1,30;-mrdrnd;HAVE_RDRND;CPUEXT_FLAGS" "none;bmi1;BMI1;ebx_cpuid7,3;-mbmi;HAVE_BMI1;CPUEXT_FLAGS" "none;bmi2;BMI2;ebx_cpuid7,8;-mbmi2;HAVE_BMI2;CPUEXT_FLAGS" "none;adx;ADX;ebx_cpuid7,19;-madx;HAVE_ADX;CPUEXT_FLAGS" "none;mpx;MPX;ebx_cpuid7,14;-mmpx;HAVE_MPX;CPUEXT_FLAGS" "none;prefetchwt1;PREFETCHWT1;ecx_cpuid7,0;-mprefetchwt1;HAVE_PREFETCHWT1;CPUEXT_FLAGS" "none;abm;ABM;ecx_cpuid80000001,5;-mabm;HAVE_ABM;CPUEXT_FLAGS" "mmx;mmx;MMX;edx_cpuid1,23;-mmmx;HAVE_MMX;SIMD_FLAGS" "sse;sse;SSE;edx_cpuid1,25;-msse;HAVE_SSE;SIMD_FLAGS" "sse;sse2;SSE2;edx_cpuid1,26;-msse2;HAVE_SSE2;SIMD_FLAGS" "sse;sse3;SSE3;ecx_cpuid1,1;-msse3;HAVE_SSE3;SIMD_FLAGS" "sse;ssse3;SSSE3;ecx_cpuid1,9;-mssse3;HAVE_SSSE3;SIMD_FLAGS" "sse;sse41;SSE4.1;ecx_cpuid1,19;-msse4.1;HAVE_SSE4_1;SIMD_FLAGS" "sse;sse42;SSE4.2;ecx_cpuid1,20;-msse4.2;HAVE_SSE4_2;SIMD_FLAGS" "sse;sse4a;SSE4a;ecx_cpuid80000001,6;-msse4a;HAVE_SSE4a;SIMD_FLAGS" "sse;sha;SHA;ebx_cpuid7,29;-msha;HAVE_SHA;SIMD_FLAGS" "sse;aes;AES;ecx_cpuid1,25;-maes;HAVE_AES;SIMD_FLAGS" "avx;avx;AVX;ecx_cpuid1,28;-mavx;HAVE_AVX;SIMD_FLAGS" "avx;fma3;FMA3;ecx_cpuid1,12;-mfma;HAVE_FMA3;SIMD_FLAGS" "avx;fma4;FMA4;ecx_cpuid80000001,16;-mfma4;HAVE_FMA4;SIMD_FLAGS" "avx;xop;XOP;ecx_cpuid80000001,11;-mxop;HAVE_XOP;SIMD_FLAGS" "avx;avx2;AVX2;ebx_cpuid7,5;-mavx2;HAVE_AVX2;SIMD_FLAGS" "avx512;avx512f;AVX512-F;ebx_cpuid7,16;-mavx512f;HAVE_AVX512_F;SIMD_FLAGS" "avx512;avx512cd;AVX512-CD;ebx_cpuid7,28;-mavx512cd;HAVE_AVX512_CD;SIMD_FLAGS" "avx512;avx512pf;AVX512-PF;ebx_cpuid7,26;-mavx512pf;HAVE_AVX512_PF;SIMD_FLAGS" "avx512;avx512er;AVX512-ER;ebx_cpuid7,27;-mavx512er;HAVE_AVX512_ER;SIMD_FLAGS" "avx512;avx512vl;AVX512-VL;ebx_cpuid7,31;-mavx512vl;HAVE_AVX512_VL;SIMD_FLAGS" "avx512;avx512bw;AVX512-BW;ebx_cpuid7,30;-mavx512bw;HAVE_AVX512_BW;SIMD_FLAGS" "avx512;avx512dq;AVX512-DQ;ebx_cpuid7,17;-mavx512dq;HAVE_AVX512_DQ;SIMD_FLAGS" "avx512;avx512ifma;AVX512-IFMA;ebx_cpuid7,21;-mavx512ifma;HAVE_AVX512_IFMA;SIMD_FLAGS" "avx512;avx512vbmi;AVX512-VBMI;ecx_cpuid7,1;-mavx512vbmi;HAVE_AVX512_VBMI;SIMD_FLAGS" # do ac_instr_os_support=$(eval echo \$ax_cv_have_$(echo $ac_instr_info | cut -d ";" -f 1)_os_support_ext) ac_instr_acvar=$(echo $ac_instr_info | cut -d ";" -f 2) ac_instr_shortname=$(echo $ac_instr_info | cut -d ";" -f 3) ac_instr_chk_loc=$(echo $ac_instr_info | cut -d ";" -f 4) ac_instr_chk_reg=0x$(eval echo \$$(echo $ac_instr_chk_loc | cut -d "," -f 1)) ac_instr_chk_bit=$(echo $ac_instr_chk_loc | cut -d "," -f 2) ac_instr_compiler_flags=$(echo $ac_instr_info | cut -d ";" -f 5) ac_instr_have_define=$(echo $ac_instr_info | cut -d ";" -f 6) ac_instr_flag_type=$(echo $ac_instr_info | cut -d ";" -f 7) { printf "%s\n" "$as_me:${as_lineno-$LINENO}: checking whether ${ac_instr_shortname} is supported by the processor" >&5 printf %s "checking whether ${ac_instr_shortname} is supported by the processor... " >&6; } if eval test \${ax_cv_have_${ac_instr_acvar}_cpu_ext+y} then : printf %s "(cached) " >&6 else case e in #( e) eval ax_cv_have_${ac_instr_acvar}_cpu_ext=no if test "$((${ac_instr_chk_reg}>>${ac_instr_chk_bit}&0x01))" = 1 ; then eval ax_cv_have_${ac_instr_acvar}_cpu_ext=yes fi ;; esac fi eval ac_res=\$ax_cv_have_${ac_instr_acvar}_cpu_ext { printf "%s\n" "$as_me:${as_lineno-$LINENO}: result: $ac_res" >&5 printf "%s\n" "$ac_res" >&6; } if test x"$(eval echo \$ax_cv_have_${ac_instr_acvar}_cpu_ext)" = x"yes"; then { printf "%s\n" "$as_me:${as_lineno-$LINENO}: checking whether ${ac_instr_shortname} is supported by the processor and OS" >&5 printf %s "checking whether ${ac_instr_shortname} is supported by the processor and OS... " >&6; } if eval test \${ax_cv_have_${ac_instr_acvar}_ext+y} then : printf %s "(cached) " >&6 else case e in #( e) eval ax_cv_have_${ac_instr_acvar}_ext=no if test x"${ac_instr_os_support}" = x"yes"; then eval ax_cv_have_${ac_instr_acvar}_ext=yes fi ;; esac fi eval ac_res=\$ax_cv_have_${ac_instr_acvar}_ext { printf "%s\n" "$as_me:${as_lineno-$LINENO}: result: $ac_res" >&5 printf "%s\n" "$ac_res" >&6; } if test "$(eval echo \$ax_cv_have_${ac_instr_acvar}_ext)" = yes; then as_CACHEVAR=`printf "%s\n" "ax_cv_check_cflags__${ac_instr_compiler_flags}" | sed "$as_sed_sh"` { printf "%s\n" "$as_me:${as_lineno-$LINENO}: checking whether the C compiler accepts ${ac_instr_compiler_flags}" >&5 printf %s "checking whether the C compiler accepts ${ac_instr_compiler_flags}... 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Jukes and Cantor (1969) @InProceedings{jukes69, author = {T. H. Jukes and C. R. Cantor}, editor = {H. N. Munro}, title = {Evolution of protein molecules}, booktitle = {Mammalian {P}rotein {M}etabolism}, publisher = {Academic Press}, year = 1969, pages = {21--123} } % 2. (Kimura, 1980; @Article{kimura80, author = {M. Kimura}, title = {A simple method for estimating evolutionary rates of base substitutions through comparative studies of nucleotide sequences}, journal = {Journal of Molecular Evolution}, year = 1980, volume = 16, pages = {111--120} } % 3. Hasegawa et al., 1985) @Article{hasegawa85, author = {M. Hasegawa and H. Kishino and T. Yano}, title = {Dating the human-ape splitting by a molecular clock of mitochondrial {DNA}}, journal = {Journal of Molecular Evolution}, year = 1985, volume = 22, pages = {160--174} } % 4. (Yang, 1993) @Article{yang93, author = {Z. Yang}, title = {Maximum likelihood estimation of phylogeny from {DNA} sequences when substitution rates differ over sites}, journal = {Molecular Biology and Evolution}, year = 1993, volume = 10, pages = {1396--1401} } % 5. Goldman and Yang, 1994; @Article{goldman94, author = {N. Goldman and Z. Yang}, title = {A codon-based model of nucleotide substitution for protein-coding {DNA} sequences}, journal = {Molecular Biology and Evolution}, year = 1994, volume = 11, pages = {725--736} } % 6. Muse and Gaut, 1994 @Article{muse94, author = {S. V. Muse and B. S. Gaut}, title = {A likelihood approach for comparing synonymous and nonsynonymous nucleotide substitution rates with application to the chloroplast genome}, journal = {Molecular Biology and Evolution}, year = 1994, volume = 11, pages = {715--724} } % 7. Tuffley and Steel, 1997 @Article{tuffley97, author = {C. Tuffley and M. Steel}, title = {Links between maximum likelihood and maximum parsimony under a simple model of site substitution}, journal = {Bulletin of Mathematical Biology}, year = 1997, volume = 59, pages = {581--607} } % 8. Huelsenbeck and Ronquist, [YEAR]; (MrBayes) @Article{huelsenbeck01c, author = {J. P. Huelsenbeck and F. Ronquist}, title = {{MRBAYES}: {B}ayesian inference of phylogenetic trees}, journal = {Bioinformatics}, year = 2001, volume = 17, pages = {754--755} } % 9. Ronquist and Huelsenbeck, [YEAR]) (MrBayes) @Article{ronquist03, author = {F. Ronquist and J. P. Huelsenbeck}, title = {MrBayes 3: {B}ayesian phylogenetic inference under mixed models}, journal = {Bioinformatics}, year = 2003, volume = 19, pages = {1572--1574} } % 10. (Felsenstein, 1981) @Article{felsenstein81, author = {J. Felsenstein}, title = {Evolutionary trees from {DNA} sequences: {A} maximum likelihood approach}, journal = {Journal of Molecular Evolution}, year = 1981, volume = 17, pages = {368--376} } % 11. Nielsen and Yang (1998) @Article{nielsen98, author = {R. Nielsen and Z. Yang}, title = {Likelihood models for detecting positively selected amino acid sites and applications to the {HIV-1} envelope gene}, journal = {Genetics}, year = 1998, volume = 148, pages = {929--93} } % 12. Hasegawa et al. (1984; HKY @Article{hasegawa84, author = {M. Hasegawa and T. Yano and H. Kishino}, title = {A new molecular clock of mitochondrial {DNA} and the evolution of {H}ominoids}, journal = {Proc. Japan Acad. Ser. B}, year = 1984, volume = 60, pages = {95--98} } % 13. Hasegawa et al. (1985; HKY % 14. Felsenstein's (1981) % 15. Swofford, 2002 @Book{swofford02, author = {D. L. Swofford}, title = {{PAUP*}: {P}hylogenetic {A}nalysis {U}sing {P}arsimony and {O}ther {M}ethods}, publisher = {Sinauer Associates, Inc.}, year = 2002, address = {Sunderland, Massachusetts} } % 16. The program MrBayes (Huelsenbeck and Ronquist, [year]; % 17. Ronquist and Huelsenbeck, [year]) % 18. Chapter [NUMBER] (Larget, [YEAR]) %%%%%%% MISSING % 19. Indeed, it has been known for at least a decade now that the interpretation of nonparametric bootstrap values on phylogenetic trees is problematic, for a variety of reasons summarized in [REFS] %%%%%%% MISSING % 20. Huelsenbeck and Rannala (In Press) %%%%%%% MISSING % 21. Suzuki and Nei ([YEAR]) %%%%%%% MISSING % 22. Huelsenbeck and Rannala (In Press) % 23. simulation studies that examine the performance of phylogenetic methods (see [REFS]) %%%%%%% MISSING % 24. Foster ([YEAR]) has relaxed the assumption that nucleotide frequencies are constant over time and %%%%%%% MISSING % 25. [PEOPLE] ([YEAR]) relaxed the assumption that the GC content is a constant over a phylogenetic tree %%%%%%% MISSING % 26. Cox test ([REF]) %%%%%%% MISSING % 27. Jeffreys (1961) % 28. Bayes factors can be used to choose among substitution models (Huelsenbeck et al., In Press) %%%%%%% MISSING % 29. Jukes-Cantor (1969) or % 30. 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(2001) NBISweden-MrBayes-ecfd55c/doc/SMME_paper/src/Huelsenbeck_Ronquist_SMME_2005.tex000066400000000000000000004373221526212227600270610ustar00rootroot00000000000000%%%%%%%%%%%%%%%%%%%%%%%%%% bayes.tex %%%%%%%%%%%%%%%%%%%%%%%%%%% % % Using the Springer-Verlag contributed book template % %%%%%%%%%%%%%%%%%%%%%%%% Springer-Verlag %%%%%%%%%%%%%%%%%%%%%%%%%% \documentclass{svmult} %\usepackage{makeidx} % allows index generation \usepackage{graphicx} % standard LaTeX graphics tool when including figure files \usepackage{multicol} % used for the two-column index \usepackage{lmodern} % Try to account for missing font sizes (JN: 19 jul 2019) \usepackage{url} \usepackage{amssymb} \usepackage{amsmath} \usepackage{natbib} %\usepackage{filecontents} % For adding Bibliography without explicit citations %\makeindex % used for the subject index % % please use the style sprmidx.sty with % % your makeindex program %\usepackage{showidx} \begin{document} \nocite{*} \title*{Bayesian Analysis of Molecular Evolution using {MrBayes}} \titlerunning{Bayesian Analysis of Phylogeny} \author{John P. Huelsenbeck and Fredrik Ronquist} \institute{$^1$Division of Biological Sciences, University of California at San Diego\\La Jolla, CA 92093 \texttt{johnh@biomail.ucsd.edu}\\ $^2$School of Computational Science and Information Technology, Florida State University, Tallahassee, FL 32306-4120 \texttt{ronquist@csit.fsu.edu} } \maketitle \section{Introduction} \label{intro.sec} Stochastic models of evolution play a prominent role in the field of molecular evolution; they are used in applications as far ranging as phylogeny estimation, uncovering the pattern of DNA substitution, identifying amino acids under directional selection, and in inferring the history of a population using models such as the coalescence. The models used in molecular evolution have become quite sophisticated over time. In the late 1960's one of the first stochastic models applied to molecular evolution was introduced by Jukes and Cantor (1969) to describe how substitutions might occur in a DNA sequence. This model was quite simple, really having only one parameter---the amount of change between two sequences---and assumed that all of the different substitution types had an equal probability of occurring. A familiar story, and one of the greatest successes of molecular evolution, has been the gradual improvement of models to describe new observations as they were made. For example, the observation that transitions (substitutions between the nucleotides $A \leftrightarrow G$ and $C \leftrightarrow T$) occur more frequently than transversions (changes between the nucleotides $A \leftrightarrow C$, $A \leftrightarrow T$, $C \leftrightarrow G$, $G \leftrightarrow T$) spurred the development of DNA substitution models that allow the transition rate to differ from the transversion rate (Kimura, 1980; Hasegawa et al., 1985). Similarly, the identification of widespread variation in rates across sites led to the development of models of rate variation (Yang, 1993), and also to more sophisticated models that incorporate constraints on amino acid replacement (Goldman and Yang, 1994; Muse and Gaut, 1994). More recently, rates have been allowed to change on the tree (the covarion-like models of Tuffley and Steel, 1997), and can explain patterns such as many substitutions at a site in one clade and few if any substitutions at the same position in another clade of roughly the same age. The fundamental importance of stochastic models in molecular evolution is this: they contain parameters, and if specific values can be assigned to these parameters based on observations, such as an alignment of DNA sequences, then biologists can learn something about how molecular evolution has occurred. This point is a very basic one, but important. It implies that in addition to careful consideration to the development of models, one needs to be able to efficiently {\it estimate} the parameters of the model. By efficient, we mean the ability to accurately estimate the parameters of an evolutionary model based on as little data as possible. There are only a handful of methods that have been used to estimate parameters of evolutionary models. These include the parsimony, distance, maximum likelihood, and Bayesian methods. In this chapter, we will concentrate on Bayesian estimation of evolutionary parameters. More specifically, we will show how the program MrBayes (Huelsenbeck and Ronquist, [YEAR]; Ronquist and Huelsenbeck, [YEAR]) can be used to investigate several important questions in molecular evolution in a Bayesian framework. \section{Maximum likelihood and Bayesian estimation} \label{sec:2} Unlike the parsimony and distance methods, maximum likelihood and Bayesian inference take full advantage of the information contained in an alignment of DNA sequences when estimating parameters of an evolutionary model. Both maximum likelihood and Bayesian estimation rely on the likelihood function. The likelihood is proportional to the probability of observing the data, conditioned on the parameters of the model: $$ \ell(\mbox{Parameter}) = \mbox{Constant} \times \mbox{Prob}[\mbox{Data} | \mbox{Parameter}] $$ where the constant is arbitrary. The probability of observing the data conditioned on specific parameter values is calculated using stochastic models. Details about how the likelihood can be calculated for an alignment of DNA or protein sequences can be found elsewhere (Felsenstein, 1981). Here, we have written the likelihood function with only one parameter. However, for the models typically used in molecular evolution, there are many parameters. We make the notational change in what follows by denoting parameters with the greek symbol $\theta$ and the data as $\mathbf{X}$ so that the likelihood function for multiple parameter models is $$ \ell(\theta_1, \theta_2, \ldots, \theta_n) = K \times f(\mathbf{X} | \theta_1, \theta_2, \ldots, \theta_n) $$ where $K$ is the constant. In a maximum likelihood analysis, the combination of parameters that maximizes the likelihood function is the best estimate, called the maximum likelihood estimate. In a Bayesian analysis, on the other hand, the object is to calculate the joint posterior probability distribution of the parameters. This is calculated using Bayes's theorem as $$ f(\theta_1, \theta_2, \ldots, \theta_n | \mathbf{X}) = {\ell(\theta_1, \theta_2, \ldots, \theta_n) \times f(\theta_1, \theta_2, \ldots, \theta_n) \over f(\mathbf{X}) } $$ where $f(\theta_1, \theta_2, \ldots, \theta_n | \mathbf{X})$ is the posterior probability distribution, $\ell(\theta_1, \theta_2, \ldots, \theta_n)$ is the likelihood function, and $f(\theta_1, \theta_2, \ldots, \theta_n)$ is the prior probability distribution for the parameters. The posterior probability distribution of parameters can then be used to make inferences. Although both maximum likelihood and Bayesian analysis are based upon the likelihood function, there are fundamental differences about how they treat parameters. Many of the parameters of an evolutionary model are not of direct interest to the biologist. For example, for someone interested in detecting adaptive evolution at the molecular level, the details of the phylogenetic history of the sequences sampled is not of direct interest, whereas other aspect of the model are of interest. The parameters not of direct interest are usually called nuisance parameters. There are a few standard ways to deal with nuisance parameters. One way is to maximize the likelihood with respect to these nuisance parameters. It is understood, then, that inferences about the parameters of interest depend upon the nuisance parameters taking fixed values. This is the approach usually taken in maximum likelihood analyses and also in empirical Bayes analyses. The other approach assigns a prior probability distribution to the nuisance parameters. The maximum likelihood or posterior probabilities are calculated by integrating over all possible values of the nuisance parameters, weighting each by its (prior) probability. This approach is rarely taken in maximum likelihood analyses (where it is called the integrated likelihood approach) and is the standard method for accounting for nuisance parameters in a Bayesian analysis, where all of the parameters of the model are assigned a prior probability distribution. The advantage of marginalization is that inferences about the parameters of interest do not depend upon any particular value for the nuisance parameters. The disadvantage, of course, is that it may be difficult to specify a reasonable prior model for the parameters. Maximum likelihood and Bayesian analyses also differ in how they interpret parameters of the model. Maximum likelihood does not treat the parameters of the model as random variables (variables that can take their value by chance) whereas in a Bayesian analysis, everything---the data and the parameters---are treated as random variables. This is not to say that a Bayesian does not think that there is only one actual value for a parameter (such as a phylogenetic tree) but rather that his or her uncertainty about the parameter is described by the posterior probability distribution. In some ways, the treatment of all of the variables as random quantities simplifies a Bayesian analysis. First, one is always dealing with probability distributions. If one is interested in only the phylogeny of a group of organisms say, then one would base inferences on the marginal posterior probability distribution of phylogeny. The marginal posterior probability of a parameter is calculated by integrating over all possible values of the other parameters, weighting each by its probability. This means that an inference of phylogeny does not critically depend upon another parameter taking a specific value. Another simplification in a Bayesian analysis is that uncertainty in a parameter can be easily described. After all, the probability distribution of the parameter is available, so specifics about the mean, variance, and a range that contains most of the posterior probability for the parameter can be directly calculated from the marginal posterior probability distribution for that parameter. In a maximum likelihood analysis, on the other hand, the parameters of the model are not treated as random variables, so probabilities cannot be directly assigned to the parameters. If one wants to describe the uncertainty in an estimate obtained using maximum likelihood, one has to go through the thought experiment of collecting many data sets of the same size as the original, with parameters set to the maximum likelihood values. One then asks what the range of maximum likelihood estimates would be for the parameter of interest on the imaginary data. In practice, many studies in molecular evolution apply a hybrid approach that combines ideas from maximum likelihood and Bayesian analysis. For example, in what is now a classic study, Nielsen and Yang (1998) identified amino acid positions in a protein coding DNA sequence under the influence of positive selection using Bayesian methods; the posterior probability that each amino acid position is under directional selection was calculated. However, they used maximum likelihood to estimate all of the parameters of the model. This approach can be called an empirical Bayes approach because of its reliance on Bayesian reasoning for the parameter of interest (the probability a site is under positive selection) and maximum likelihood for the nuisance parameters. In the following section, we describe three uses of Bayesian methods in molecular evolution: phylogeny estimation, analysis of complex data, and estimating divergence times. We hope to show the ease with which parameters can be estimated, the uncertainty in the parameters can be described, and uncertainty about important parameters can be incorporated into a study in a Bayesian framework. \section{Applications of Bayesian estimation in molecular evolution} \label{sec:3} \subsection{A brief introduction to models of molecular evolution} Before delving into specific examples of the application of Bayesian inference in molecular evolution, the reader needs some background on the modeling assumptions made in a Bayesian analysis. Many of these assumptions are shared by maximum likelihood and distance-based methods. Typically, the models used in molecular evolution have three components. First, they assume a tree relating the samples. Here, the samples might be DNA sequences collected from different species, or different individuals within a population. In either case, a basic assumption is that the samples are related to one another through an (unknown) tree. This would be a species tree for sequences sampled from different species, or perhaps a coalescence tree for sequences sampled from individuals from within a population. Second, they assume that the branches of the tree have an (unknown) length. Ideally, the length of a branch on a tree is in terms of time. However, in practice it is difficult to determine the duration of a branch on a tree in terms of time. Instead, the lengths of the branches on the tree are in terms of expected change per character. Figure~1 shows some examples of trees with branch lengths. The main points the reader should remember are: (1) Trees can be rooted or unrooted. Rooted trees have a time direction whereas unrooted trees do not. Most methods of phylogenetic inference, including most implementations of maximum likelihood and Bayesian analysis, work on unrooted trees; trees must then be rooted using some other criterion, such as the outgroup criterion. (2) The space of possible trees is huge. The number of possible unrooted trees for $n$ species is $B(n) = {(2n-5)! \over 2^{n-3}(n-3)!}$. This means that for a relatively small problem of only $n=50$ species, there are about $B(50) = 2.838 \times 10^{74}$ possible unrooted trees that can explain the phylogenetic relationships of the species. \begin{figure}[t] \centering \includegraphics[height=3in]{fig1} \caption{\textbf{\textsc{Example of unrooted and rooted trees}}. An unrooted tree of four species (center) with the branch lengths drawn proportional to their length in terms of expected number of substitutions per site. The five trees surrounding the central, unrooted, tree show the five possible rooted trees that result from the unrooted tree. } \label{fig1} \end{figure} The third component of a model of molecular evolution is a process that describes how the characters change on the phylogeny. All model-based methods of phylogenetic inference, including maximum likelihood and Bayesian estimation of phylogeny, currently assume that character change occurs according to a continuous-time Markov chain. At the heart of any continuous-time Markov chain is a matrix of rates, specifying the rate of change from one state to another. For example, the instantaneous rate of change under the model described by Hasegawa et al. (1984, 1985; hereafter called the HKY85 model) is \begin{equation*} {\mathbf Q} = \{q_{ij}\} = \left( \begin{array}{cccc} - & \pi_C & \kappa \pi_G & \pi_T \\ \pi_A & - & \pi_G & \kappa \pi_T \\ \kappa \pi_A & \pi_C & - & \pi_T \\ \pi_A & \kappa \pi_C & \pi_G & - \\ \end{array} \right) \mu \end{equation*} This matrix specifies the rate of change from one nucleotide to another; the rows and columns of the matrix are ordered $A, C, G, T$, so that the rate of change from $C \rightarrow G$ is $q_{CG} = \pi_G$. Similarly, the rates of change between $C \rightarrow T$, $G \rightarrow A$, and $T \rightarrow C$, are $q_{CT} = \kappa \pi_T$, $q_{GA} = \kappa \pi_A$, and $q_{TG} = \pi_G$, respectively. The diagonals of the rate matrix, denoted with the dash, are specified such that each row sums to zero. Finally, the rate matrix is rescaled such that the mean rate of substitution is one. This can be accomplished by setting $\mu = -1 / \sum_{i\in \{A,C,G,T\}} \pi_i q_{ii}$. This rescaling of the rate matrix such that the mean rate is one allows the branch lengths on the phylogenetic tree to be interpreted as expected number of nucleotide substitutions per site. We will make a few important points about the rate matrix. First, the rate matrix may have free parameters. For example, the HKY85 model has the parameters $\kappa$, $\pi_A$, $\pi_C$, $\pi_G$, and $\pi_T$. The parameter $\kappa$ is the transition/transversion rate bias; when $\kappa = 1$ transitions occur at the same rate as transversions. Typically, the transition/transversion rate ratio, estimated using maximum likelihood or Bayesian inference, is estimated to be greater then one; transitions occur at a higher rate than transversions. The other parameters---$\pi_A$, $\pi_C$, $\pi_G$, and $\pi_T$---are the base frequencies, and have a biological interpretation as the frequency of the different nucleotides and are also, incidentally, the stationary probabilities of the process (more on stationary probabilities later). Second, the rate matrix, ${\mathbf Q}$, can be used to calculate the transition probabilities and the stationary distribution of the substitution process. The transition probabilities and stationary distribution play a key role in calculating the likelihood, and we will spend more time here developing an intuitive understanding of these concepts. \subsubsection{Transition probabilities} Let us consider a specific example of a rate matrix, with all of the parameters of the model taking specific values. For example, if we use the HKY85 model and fix the parameters to $\kappa = 5$, $\pi_A = 0.4$, $\pi_C = 0.3$, $\pi_G = 0.2$, and $\pi_T = 0.1$, we get the following matrix of instantaneous rates $$ {\mathbf Q} = \{q_{ij}\} = \left( \begin{array}{rrrr} -0.886 & 0.190 & 0.633 & 0.063 \\ 0.253 & -0.696 & 0.127 & 0.316 \\ 1.266 & 0.190 & -1.519 & 0.063 \\ 0.253 & 0.949 & 0.127 & -1.329 \\ \end{array} \right) $$ Note that these numbers are not special in any particular way. That is to say, they are not based upon any observations from a real data set, but are rather arbitrarily picked to illustrate a point. The point is that one can interpret the rate matrix in the physical sense of specifying how changes occur on a phylogenetic tree. Consider the very simple case of a single branch on a phylogenetic tree. Let's assume that the branch is $v=0.5$ in length and that the ancestor of the branch is the nucleotide $G$. The situation we have is something like that shown in Figure~2A. How can we simulate the evolution of the site starting from the $G$ at the ancestor? The rate matrix tells us how to do this. First of all, because the current state of the process is $G$, the only relevant row of the rate matrix is the third one: $$ {\mathbf Q} = \{q_{ij}\} = \left( \begin{array}{cccc} \cdot & \cdot & \cdot & \cdot \\ \cdot & \cdot & \cdot & \cdot \\ 1.266 & 0.190 & -1.519 & 0.063 \\ \cdot & \cdot & \cdot & \cdot \\ \end{array} \right) $$ The overall rate of change away from nucleotide $G$ is $q_{GA} + q_{GC} + q_{GT} = 1.266 + 0.190 + 0.063 = 1.519$. Equivalently, the rate of change away from nucleotide $G$ is simply $-q_{GG} = 1.519$. We wait an exponentially distributed amount of time with rate 1.519 until the next substitution occurs. One can easily construct exponential random variables from uniform random variables using the equation \begin{figure}[t] \centering \includegraphics[height=1.75in]{fig2} \caption{\textbf{\textsc{Simulation under the HKY85 substitution process}}. A single realization of the substitution process under the HKY85 model when $\kappa = 5$, $\pi_A = 0.4$, $\pi_C = 0.3$, $\pi_G = 0.2$, and $\pi_T = 0.1$. The length of the branch is $v = 0.5$ and the starting nucleotide is $G$ (red). A, The process starts in nucleotide G. B, The first change is 0.152 units up the branch. C, the change is from G to A (blue). The time at which the next change occurs exceeds the total branch length, so the process ends in state C. } \label{fig2} \end{figure} $$ t = -{1 \over \lambda} \log_e(u) $$ where $\lambda$ is the rate and $u$ is a uniform(0,1) random number. For example, our calculator has a uniform(0,1) random number generator. The first number it generated is $u = 0.794$. This means that the next time at which a substitution occurs is 0.152 up from the root of the tree (using $\lambda = 1.519$; Figure~2B). The rate matrix also specifies the probabilities of a change from $G$ to the nucleotides $A$, $C$, and $T$. These probabilities are $$ \begin{array}{ccc} G \rightarrow A: {1.266\over 1.519}=0.833, & G \rightarrow C: {0.190\over 1.519}=0.125, & G \rightarrow T: {0.063 \over 1.519}=0.042 \\ \end{array} $$ To determine what nucleotide the process changes to we would generate another uniform(0,1) random number (again called $u$). If $u$ is between 0 and 0.833, we will say that we had a change from $G$ to $A$. If the random number is between 0.833 and 0.958 we will say that we had a change from $G$ to $C$. Finally, if the random number $u$ is between 0.958 and 1.000, we will say we had a change from $G$ to $T$. The next number generated on our calculator was $u = 0.102$, which means the change was from $G$ to $A$. The process is now in a different state (the nucleotide $A$) and the relevant row of the rate matrix is $$ {\mathbf Q} = \{q_{ij}\} = \left( \begin{array}{cccc} -0.886 & 0.190 & 0.633 & 0.063 \\ \cdot & \cdot & \cdot & \cdot \\ \cdot & \cdot & \cdot & \cdot \\ \cdot & \cdot & \cdot & \cdot \\ \end{array} \right) $$ We wait an exponentially distributed amount of time with parameter $\lambda = 0.886$ until the next substitution occurs. When the substitution occurs, it is to a $C$, $G$, or $T$ with probabilities ${0.190 \over 0.886} = 0.214$, ${0.633 \over 0.886} = 0.714$, and ${0.063 \over 0.886} = 0.072$, respectively. This process of generating random and exponentially distributed times until the next substitution occurs and then determining (randomly) what nucleotide the change is to is repeated until the process exceeds the length of the branch. The state the process is in when it passes the end of the branch is recorded. In the example of Figure~2, the process started in state $G$ and ended in state $A$. (The next uniform random variable generated on our calculator was $u = 0.371$, which means that the next substitution would occur 1.119 units above the substitution from $G \rightarrow A$. The process is in the state $A$ when it passed the end of the branch.) The only non-random part of the entire procedure was the initial decision to start the process in state $G$. All other aspects of the simulation used a uniform random number generator and our knowledge of the rate matrix to simulate a single realization of the HKY85 process of DNA substitution. \begin{figure}[t] \centering \includegraphics[height=2in]{fig3} \caption{\textbf{\textsc{Simulations under the HKY85 substitution process}}. One hundred simulations under the HKY85 model when $\kappa = 5$, $\pi_A = 0.4$, $\pi_C = 0.3$, $\pi_G = 0.2$, and $\pi_T = 0.1$. The length of the branch is $v = 0.5$ and the starting nucleotide is always $G$. Blue, A; green, C; red: G; yellow, T. } \label{fig3} \end{figure} This Monte Carlo procedure for simulating the HKY85 process of DNA substitution can be repeated. Figure~3 shows 100 realizations of the HKY85 substitution process where each simulation started with the nucleotide $G$. The following table summarizes the results of the 100 simulations: \begin{center} \begin{tabular}{ccc} Starting & Ending & Number of \\ Nucleotide & Nucleotide & Replicates \\ \hline G & A & 27 \\ G & C & 10 \\ G & G & 59 \\ G & T & 4 \\ \end{tabular} \end{center} This table can be interpreted as a Monte Carlo approximation of the {\it transition probabilities} from nucleotide $G$ to nucleotide $i \in (A,C,G,T)$. Specifically, the Monte Carlo approximations are $p_{GA}(0.5) \approx 0.27$, $p_{GC}(0.5) \approx 0.10$, $p_{GG}(0.5) \approx 0.59$, and $p_{GT}(0.5) \approx 0.04$. These approximate probabilities are all conditioned on the starting nucleotide being $G$ and the branch length being $v = 0.5$. Figure~4 shows simulations when the starting nucleotide is $A$, $C$, $G$, or $T$ (the branch length remains $v=0.5$). The simulations allow us to fill out the following table with the approximate transition probabilities: \begin{center} \begin{tabular}{crcccc} & & \multicolumn{4}{c}{Ending} \\ & & \multicolumn{4}{c}{Nucleotide} \\ & & A & C & G & T \\ \cline{2-6} & A \vline & 0.67 & 0.13 & 0.20 & 0.00 \\ Starting & C \vline & 0.13 & 0.70 & 0.07 & 0.10 \\ Nucleotide & G \vline & 0.27 & 0.10 & 0.59 & 0.04 \\ & T \vline & 0.12 & 0.30 & 0.08 & 0.50 \\ \cline{2-6} \end{tabular} \end{center} Clearly, these numbers are only crude approximations to the true transition probabilities; after all, each row in the table is based on only 100 Monte Carlo simulations. However, they do illustrate the meaning of the transition probabilities; the transition probability, $p_{ij}(v)$, is the probability that the substitution process ends in nucleotide $j$ conditioned on it starting in nucleotide $i$ after an evolutionary amount of time $v$. The table of approximate transition probabilities, above, can be interpreted as a matrix of probabilities, usually denoted ${\mathbf P}(v)$. Fortunately, we do not need to rely on Monte Carlo simulation to approximate the transition probability matrix. Instead, we can calculate the transition probability matrix exactly using matrix exponentiation: $$ {\mathbf P}(v) = e^{{\mathbf Q} v} $$ For the case we have been simulating, the exact transition probabilities (to four decimal places) are $$ {\mathbf P}(0.5) = \{p_{ij}(0.5)\} = \left( \begin{array}{rrrr} 0.7079 & 0.0813 & 0.1835 & 0.0271 \\ 0.1085 & 0.7377 & 0.0542 & 0.0995 \\ 0.3670 & 0.0813 & 0.5244 & 0.0271 \\ 0.1085 & 0.2985 & 0.0542 & 0.5387 \\ \end{array} \right) $$ The transition probability matrix accounts for all the possible ways the process could end up in nucleotide $j$ after starting in nucleotide $i$. In fact, each of the infinite possibilities is weighted by its probability under the substitution model. \begin{figure}[t] \centering \includegraphics[height=2in]{fig4} \caption{\textbf{\textsc{Simulations under the HKY85 substitution process}}. One hundred simulations under the HKY85 model when $\kappa = 5$, $\pi_A = 0.4$, $\pi_C = 0.3$, $\pi_G = 0.2$, and $\pi_T = 0.1$. The starting nucleotide is A, C, G, or T for the upper left, upper right, lower left and lower right panels, respectively. The length of the branch is $v = 0.5$. Blue, A; green, C; red: G; yellow, T. } \label{fig4} \end{figure} \subsubsection{Stationary distribution} The transition probabilities provide the probability of ending in a particular nucleotide after some specific amount of time (or opportunity for substitution, $v$). These transition probabilities are conditioned on starting in a particular nucleotide. What do the transition probability matrices look like as $v$ increases? The following transition probability matrices show the effect of increasing branch length: $$ \begin{array}{cc} {{\mathbf P}(0.00) = \left( \begin{array}{rrrr} 1.000 & 0.000 & 0.000 & 0.000 \\ 0.000 & 1.000 & 0.000 & 0.000 \\ 0.000 & 0.000 & 1.000 & 0.000 \\ 0.000 & 0.000 & 0.000 & 1.000 \\ \end{array} \right)} & {{\mathbf P}(0.01) = \left( \begin{array}{rrrr} 0.991 & 0.002 & 0.006 & 0.001 \\ 0.003 & 0.993 & 0.001 & 0.003 \\ 0.013 & 0.002 & 0.985 & 0.001 \\ 0.003 & 0.009 & 0.001 & 0.987 \\ \end{array} \right)} \end{array} $$ $$ \begin{array}{cc} {{\mathbf P}(0.10) = \left( \begin{array}{rrrr} 0.919 & 0.018 & 0.056 & 0.006 \\ 0.024 & 0.934 & 0.012 & 0.029 \\ 0.113 & 0.018 & 0.863 & 0.006 \\ 0.025 & 0.086 & 0.012 & 0.877 \\ \end{array} \right)} & {{\mathbf P}(0.50) = \left( \begin{array}{rrrr} 0.708 & 0.081 & 0.184 & 0.027 \\ 0.106 & 0.738 & 0.054 & 0.100 \\ 0.367 & 0.081 & 0.524 & 0.027 \\ 0.109 & 0.299 & 0.054 & 0.539 \\ \end{array} \right)} \end{array} $$ $$ \begin{array}{cc} {{\mathbf P}(1.00) = \left( \begin{array}{rrrr} 0.580 & 0.141 & 0.232 & 0.047 \\ 0.188 & 0.587 & 0.094 & 0.131 \\ 0.464 & 0.141 & 0.348 & 0.047 \\ 0.188 & 0.394 & 0.094 & 0.324 \\ \end{array} \right)} & {{\mathbf P}(5.00) = \left( \begin{array}{rrrr} 0.411 & 0.287 & 0.206 & 0.096 \\ 0.383 & 0.319 & 0.192 & 0.106 \\ 0.411 & 0.287 & 0.206 & 0.096 \\ 0.383 & 0.319 & 0.192 & 0.107 \\ \end{array} \right)} \end{array} $$ $$ \begin{array}{cc} {{\mathbf P}(10.0) = \left( \begin{array}{rrrr} 0.401 & 0.299 & 0.200 & 0.099 \\ 0.399 & 0.301 & 0.199 & 0.100 \\ 0.401 & 0.299 & 0.200 & 0.099 \\ 0.399 & 0.301 & 0.199 & 0.100 \\ \end{array} \right)} & {{\mathbf P}(100) = \left( \begin{array}{rrrr} 0.400 & 0.300 & 0.200 & 0.100 \\ 0.400 & 0.300 & 0.200 & 0.100 \\ 0.400 & 0.300 & 0.200 & 0.100 \\ 0.400 & 0.300 & 0.200 & 0.100 \\ \end{array} \right)} \\ \end{array} $$ (Each matrix was calculated under the HKY85 model with $\kappa = 5$, $\pi_A = 0.4$, $\pi_C = 0.3$, $\pi_G = 0.2$, and $\pi_T = 0.1$.) Note that as the length of a branch, $v$, increases, the probability of ending up in a particular nucleotide converges to a single number, regardless of the starting state. For example, the probability of ending up in $C$ is about 0.300 when the branch length is $v=100$. This is true regardless of whether the process starts in $A$, $C$, $G$, or $T$. The substitution process has in a sense `forgotten' its starting state. The stationary distribution is the probability of observing a particular state when the branch length increases without limit ($v \rightarrow \infty$). The stationary probabilities of the four nucleotides are $\pi_A = 0.4$, $\pi_C = 0.3$, $\pi_G = 0.2$, and $\pi_T = 0.1$ for the example discussed above. The models typically used in phylogenetic analyses have the stationary probabilities built into the rate matrix, ${\mathbf Q}$. You will notice that the rate matrix for the HKY85 model has parameters $\pi_A$, $\pi_C$, $\pi_G$, and $\pi_T$, and that the stationary frequencies of the four nucleotides for our example match the input values for our simulations. Building the stationary frequency of the process into the rate matrix, while somewhat unusual, makes calculating the likelihood function easier. For one, specifying the stationary distribution saves the time of figuring out what the stationary distribution is (which involves solving the equation $\pi {\mathbf Q} = {\mathbf 0}$). For another, it allows one to more easily specify a time reversible substitution model. [A time reversible substitution model has the property that $\pi_i q_{ij} = \pi_j q_{ji}$ for all $i, j \in (A,C,G,T)$, $i \neq j$.] Practically speaking, time reversibility means that we can work with unrooted trees instead of rooted trees. \subsubsection{Calculating the likelihood} The transition probabilities and stationary distribution are used when calculating the likelihood. For example, consider the following alignment of sequences for five species\footnote{This alignment was simulated on the tree of Figure~5 under the HKY85 model of DNA substitution. Parameter values for the simulation can be found in the caption of Table~1.}: \begin{verbatim} Species 1 TAACTGTAAAGGACAACACTAGCAGGCCAGACGCACACGCACAGCGCACC Species 2 TGACTTTAAAGGACGACCCTACCAGGGCGGACACAAACGGACAGCGCAGC Species 3 CAAGTTTAGAAAACGGCACCAACACAACAGACGTATGCAACTGACGCACC Species 4 CGAGTTCAGAAGACGGCACCAACACAGCGGACGTATGCAGACGACGCACC Species 5 TGCCCTTAGGAGGCGGCACTAACACCGCGGACGAGTGCGGACAACGTACC \end{verbatim} This is clearly a rather small alignment of sequences to use for estimating phylogeny, but it will illustrate how likelihoods are calculated. The likelihood is the probability of the alignment of sequences, conditioned on a tree with branch lengths. The basic procedure is to calculate the probability of each site (column) in the matrix. Assuming that the substitutions are independent across sites, the probability of the entire alignment is simply the product of the probabilities of the individual sites. \begin{figure}[t] \centering \includegraphics[height=2in]{fig5} \caption{\textbf{\textsc{A tree with states assigned to the tips}}. One of the possible (rooted) trees describing the evolutionary history of the five species. The states at the first site in the alignment of the text are shown at the tips of the tree. The states at the interior nodes of the tree are also shown, though in reality these states are not observed. The length of the $i$th branch is denoted $v_i$. } \label{fig5} \end{figure} How is the likelihood at a single site calculated? Figure~5 shows the observations at the first site ($T$, $T$, $C$, $C$, and $T$) at the tips of one of the possible phylogenetic trees for five species. The tree in Figure~5 is unusual in that we will assume that the nucleotide states at the interior nodes of the tree are also known. This is clearly a bad assumption, because we cannot directly observe the nucleotides that occurred at any point on the tree in the distant past. For now, however, ignore this fact and bear with us. The probability of observing the configuration of nucleotides at the tips and interior nodes of the tree in Figure~5 is \begin{eqnarray*} \lefteqn{\Pr(TTCCT , ATCG | \tau, {\mathbf v}, \theta ) =} \\ & & \pi_G \, p_{GA}(v_3) \, p_{AT}(v_1) \, p_{AT}(v_2) \, p_{GC}(v_8) \, p_{CT}(v_6) \, p_{CT}(v_7) \, p_{TC}(v_4) \, p_{TC}(v_5) \end{eqnarray*} Here we show the probability of the observations (TTCCT) and the states at the interior nodes of the tree (ATCG) conditioned on the tree ($\tau$), branch lengths (${\mathbf v}$), and other model parameters ($\theta$). Note that to calculate the probability of the states at the tips of the tree, we used the stationary probability of the process ($\pi$) and also the transition probabilities [$p_{ij}(v)$]. The stationary probability of the substitution process was used to calculate the probability of the nucleotide at the root of the tree. In this case, we are assuming that the substitution process has been running a very long time before it reached the root of our five species tree. We then use the transition probabilities to calculate the probabilities of observing the states at each end of the branches. When taking the product of the transition probabilities, we are making the additional assumption that the substitutions on each branch of the tree are independent of one another. This is probably a reasonable assumption for real data sets. The probability of observing the states at the tips of the tree, described above, was conditioned on the interior nodes of the tree taking specific values (in this case $ATCG$). To calculate the unconditional probability of the observed states at the tips of the tree, we sum over all possible combinations of nucleotide states that can be assigned to the interior nodes of the tree $$ \Pr(TTCCT | \tau, {\mathbf v}, \theta ) = \sum_{w} \sum_{x} \sum_{y} \sum_{z} \Pr(TTCCT , w x y z | \tau, {\mathbf v}, \theta ) $$ where $w,x,y,z \in (A,C,G,T)$. Averaging the probabilities over all combinations of states at the interior nodes of the tree accomplishes two things. First, we remove the assumption that the states at the interior nodes take specific values. Second, because the transition probabilities account for all of the possible ways we could have state $i$ at one end of a branch and state $j$ at the other, the probability of the site is also averaged over all possible character histories. Here, we think of a character history as one realization of changes on the tree that is consistent with the observations at the tips of the tree. For example, the parsimony method, besides calculating the minimum number of changes on the tree, also provides a character history; the character history favored by parsimony is the one that minimizes the number of changes required to explain the data. In the case of likelihood-based methods, the likelihood accounts for all possible character histories, with each history weighted by its probability under the substitution model. Nielsen ([ref]) described a method for sampling character histories in proportion to their probability that relies on the interpretation of the rate matrix as specifying waiting times between substitutions. His method provides a means to reconstruct the history of a character that does not inherit the flaws of the parsimony method. Namely, Nielsen's method allows multiple changes on a single branch and also allows for non-parsimonious reconstructions of a character's history. \begin{table}[b] \centering \caption{\textbf{\textsc{Probabilities of individual sites}}. The probabilities of the fifty sites for the example alignment from the text. The likelihoods are calculated assuming the tree of Figure~5 with the branch lengths being $v_1 = 0.1$, $v_2 = 0.1$, $v_3 = 0.2$, $v_4 = 0.1$, $v_5 = 0.1$, $v_6 = 0.1$, $v_7 = 0.2$, and $v_8 = 0.1$. The substitution model parameters were also fixed, with $\kappa = 5$, $\pi_A = 0.4$, $\pi_C = 0.3$, $\pi_G = 0.2$, and $\pi_T = 0.1$.} \begin{tabular}{cccccccccc} Site & Prob. & Site & Prob. & Site & Prob. & Site & Prob. & Site & Prob. \\ \hline 1 & 0.004025 & 11 & 0.029483 & 21 & 0.179392 & 31 & 0.179392 & 41 & 0.003755 \\ 2 & 0.001171 & 12 & 0.006853 & 22 & 0.001003 & 32 & 0.154924 & 42 & 0.005373 \\ 3 & 0.008008 & 13 & 0.024885 & 23 & 0.154924 & 33 & 0.007647 & 43 & 0.016449 \\ 4 & 0.002041 & 14 & 0.154924 & 24 & 0.179392 & 34 & 0.000936 & 44 & 0.029483 \\ 5 & 0.005885 & 15 & 0.007647 & 25 & 0.005719 & 35 & 0.024885 & 45 & 0.154924 \\ 6 & 0.000397 & 16 & 0.024124 & 26 & 0.001676 & 36 & 0.000403 & 46 & 0.047678 \\ 7 & 0.002802 & 17 & 0.154924 & 27 & 0.000161 & 37 & 0.024124 & 47 & 0.010442 \\ 8 & 0.179392 & 18 & 0.004000 & 28 & 0.154924 & 38 & 0.154924 & 48 & 0.179392 \\ 9 & 0.024124 & 19 & 0.154924 & 29 & 0.001171 & 39 & 0.011088 & 49 & 0.002186 \\ 10 & 0.024885 & 20 & 0.004025 & 30 & 0.047678 & 40 & 0.000161 & 50 & 0.154924 \\ \hline \end{tabular} \label{tab1} \end{table} Before moving on to some applications of Bayesian estimation in molecular evolution, we will make two final points. First, in practice no computer program actually evaluates all combinations of nucleotides that can be assigned to the interior nodes of a tree when calculating the probability of observing the data at a site. There are simply too many combinations for trees of even small size. For example, for a tree of 100 species, there are 99 interior nodes and $4.02 \times 10^{59}$ combinations of nucleotides at the ancestral nodes on the tree. Instead, Felsenstein's (1981) pruning algorithm is used to calculate the likelihood at a site. Felsenstein's method is mathematically equivalent to the summation shown above, but can evaluate the likelihood at a site in a fraction of the time it would take to plow through all combinations of ancestral states. Second, the overall likelihood of a character matrix is the product of the site likelihoods. If we assume that the tree of Figure~5 is correct (with all of the parameters taking the values specified in the caption of Table~1), then the probability of observing the data is $$ 0.004025 \times 0.001171 \times 0.008008 \times \ldots \times 0.154924 = 1.2316 \times 10^{-94} $$ where there are fifty factors, each factor representing the probability of an individual site (column) in the alignment. Table~1 shows the probabilities of all fifty sites for the tree of Figure~5. Note that the overall probability of observing the data is a very small number ($\approx 10^{-94}$). This is typical of phylogenetic problems and results from the simple fact that many numbers between 0 and 1 are multiplied together. Computers cannot accurately hold very small numbers in memory. Programmers avoid this problem of computer ``underflow'' by using the log probability of observing the data. The log probability of observing the sample alignment of sequences presented earlier is $\log_e \ell = \log_e(1.2316 \times 10^{-94}) = -216.234734$. The log likelihood can be accurately stored in computer memory. \subsection{Phylogeny estimation} \subsubsection{Frequentist and Bayesian perspectives on phylogeny estimation} The phylogenetic model described in the preceding section has numerous parameters. Minimally, the parameters include the topology of the tree and the lengths of the branches on the tree. In the following, we imagine that every possible tree is labelled: $\tau_1, \tau_2, \ldots, \tau_{B(n)}$. Each tree has its own set of branches, and each branch has a length in terms of expected number of substitutions per site. The lengths of the branches on the $i$th tree are denoted ${\mathbf v}_i = (v_1, v_2, \ldots, v_{2n-3})$. In addition, there may be parameters associated with the substitution model. The parameters of the substitution model will be denoted ${\mathbf \theta}$. For the HKY85 model the parameters are ${\mathbf \theta} = (\kappa, \pi_A, \pi_C, \pi_G, \pi_T)$, but other substitution models may have more or fewer parameters than the HKY85 model. When all of the parameters are specified, one can calculate the likelihood function using the general ideas described in the previous section. The likelihood will be denoted $\ell(\tau_i, {\mathbf v}_i, {\mathbf \theta})$ and is proportional to the probability of observing the data conditioned on the model parameters taking specific values ($\ell(\tau_i, {\mathbf v}_i, {\mathbf \theta}) \propto \Pr[ {\mathbf X} | \tau_i, {\mathbf v}_i, {\mathbf \theta} ] $; the alignment of sequences is ${\mathbf X}$). Which of the possible trees best explains the alignment of DNA sequences? This is among the most basic questions asked in many molecular evolution studies. In a maximum likelihood analysis the answer is straight forward: the best estimate of phylogeny is the tree that maximizes the likelihood. This is equivalent to finding the tree that makes the observations most probable. For the toy alignment of sequences given in the previous section, the likelihood is maximized when the tree of Figure~5 is used. The other 14 possible trees had a lower likelihood. (This is not surprising because the sequences were simulated on the tree of Figure~5.) How was the maximum likelihood tree found? In this case, the program PAUP* (Swofford, 2002) visited each of the 15 possible trees. For each tree, it found the combination of parameters that maximized the likelihood. In this analysis, we assumed the HKY85 model, so the parameters included the transition/transversion rate ratio and the nucleotide frequencies. After maximizing the likelihood for each tree, the program picked that tree with the largest likelihood as the best estimate of phylogeny. The approach was described earlier in this chapter; the nuisance parameters (here all of the parameters except for the topology of the tree) are dealt with by maximizing the likelihood with respect to them. The tree of Figure~5 has a maximum likelihood score of $-211.25187$. The parameter estimates on this tree are: $\hat{v}_1 = 0.182$, $\hat{v}_2 = 0.124$, $\hat{v}_{3 + 8} = 0.226$, $\hat{v}_4 = 0.162$, $\hat{v}_5 = 0.018$, $\hat{v}_6 = 0.159$, $\hat{v}_7 = 0.199$, $\hat{\kappa} = 5.73$, $\hat{\pi}_A = 0.329$, $\hat{\pi}_C = 0.329$, $\hat{\pi}_G = 0.253$, and $\hat{\pi}_T = 0.089$. The method of maximum likelihood and the program PAUP*, often used to find maximum likelihood trees, are described in more detail in Chapter [NUMBER]. Importantly, there are many computational shortcuts that can be taken to speed up calculation of the maximum likelihood tree. In a Bayesian analysis, inferences are based upon the posterior probability distribution of the parameters. The joint posterior probability of all the parameters is calculated using Bayes's theorem as $$ \Pr[\tau_i, {\mathbf v}_i, {\mathbf \theta} | {\mathbf X}] = {\Pr[ {\mathbf X} | \tau_i, {\mathbf v}_i, {\mathbf \theta} ] \times \Pr[\tau_i, {\mathbf v}_i, {\mathbf \theta}] \over \Pr[ {\mathbf X}] } $$ The posterior probability is equal to the likelihood ($\Pr[ {\mathbf X} | \tau_i, {\mathbf v}_i, {\mathbf \theta} ]$) times the prior probability of the parameters ($\Pr[\tau_i, {\mathbf v}_i, {\mathbf \theta}]$) divided by a normalizing constant ($\Pr[ {\mathbf X}]$). The normalizing constant involves a summation over all possible trees and, for each tree, integration over all possible combinations of branch lengths and parameter values. Clearly, the Bayesian method is similar to the method of maximum likelihood; after all, both methods make the same assumptions about the evolutionary process and use the same likelihood function. However, the Bayesian method treats all of the parameters as random variables (note that the posterior probability is the probability of the parameters) and the method also incorporates any prior information the biologist might have about the parameters through the prior probability distribution of the parameters. Unfortunately, one cannot calculate the posterior probability distribution of trees analytically. Instead, one resorts to a heuristic algorithm to approximate posterior probabilities of trees. The program MrBayes (Huelsenbeck and Ronquist, [year]; Ronquist and Huelsenbeck, [year]) uses Markov chain Monte Carlo (MCMC) to approximate posterior probabilities of phylogenetic trees (and the posterior probability density of the model parameters). Briefly, a Markov chain is constructed that has as its state space the parameter values of the model and a stationary distribution that is the posterior probability of the parameters. Samples drawn from this Markov chain while at stationarity are valid, albeit dependent, samples from the posterior probability distribution of the parameters. If one is interested in the posterior probability of a particular phylogenetic tree, one simply notes the fraction of the time the Markov chain visited that tree; the proportion of the time the chain visits the tree is an approximation of that tree's posterior probability. A thorough discussion of MCMC is beyond the scope of this chapter. However, an excellent description of MCMC and its applications in molecular evolution can be found in Chapter [NUMBER] (Larget, [YEAR]). \begin{table}[b] \centering \caption{\textbf{\textsc{Summary statistics for the marginal posterior probability density distributions of the substitution parameters}}. The mean, median, variance, and 95\% credible interval of the marginal posterior probability density distribution of the substitution parameters of the HKY85 model. The parameters are discussed in the text.} \begin{tabular}{cccccc} & & & \multicolumn{2}{c}{95\% Cred. Interval} & \\ \cline{4-5} Parameter & Mean & Variance & Lower & Upper & Median \\ \hline $V$ & 0.990 & 0.025 & 0.711 & 1.333 & 0.980 \\ $\kappa$ & 5.576 & 4.326 & 2.611 & 10.635 & 5.219 \\ $\pi_A$ & 0.323 & 0.002 & 0.235 & 0.418 & 0.323 \\ $\pi_C$ & 0.331 & 0.002 & 0.238 & 0.433 & 0.329 \\ $\pi_G$ & 0.252 & 0.002 & 0.176 & 0.340 & 0.250 \\ $\pi_T$ & 0.092 & 0.001 & 0.047 & 0.152 & 0.090 \\ \hline \end{tabular} \label{tab2} \end{table} We performed a Bayesian analysis on the simulated data set discussed above under the HKY85 model. (We describe how to do the Bayesian analyses performed in this chapter in the second appendix.) This is a rather ideal situation because the example data were simulated on the tree of Figure~5 under the HKY85 model; the model assumed in the Bayesian analysis is not misspecified. We ran a Markov chain for 1,000,000 cycles using the program MrBayes. The Markov chain visited the tree shown in Figure~5 about 99\% of the time; the MCMC approximation of the posterior probability of the tree in Figure~5, then, is about 0.99. This can be considered strong evidence in favor of that tree. The posterior probabilities of phylogenetic trees was calculated by integrating over uncertainty in the other model parameters (such as branch lengths, the transition/tranversion rate ratio, and base frequencies). However, we can turn the study around and ask questions about the parameters of the substitution model. Table~2 shows information on the posterior probability density distribution of the substitution model parameters. The table shows the mean, median, and variance of the marginal posterior probability distribution for the tree length ($V$), transition/transversion rate ratio ($\kappa$), and base frequencies ($\pi_A$, $\pi_C$, $\pi_G$, $\pi_T$). The table also shows the upper and lower limits of an interval that contains 95\% of the posterior probability for each parameter. The table shows, for example, that with probability 0.95 the transition/transversion rate ratio is in the interval (2.611, 10.635). In reality, the transition/transversion rate ratio was in that interval (the data matrix was simulated with $\kappa = 5$). The mean of the posterior probability distribution for $\kappa$ was 5.576 (which is fairly close to the true value). The interval we constructed that contains the true value of the parameter with 0.95 probability is called a 95\% credible interval. One can construct a credible set of trees in a similar manner; simply order the trees from highest to lowest posterior probability, and put the trees into a set (starting from the tree with highest probability) until the cumulative probability of trees in the set is 0.95. One of the great strengths of the Bayesian approach is the ease with which the results of an analysis can be summarized and interpreted. The posterior probability of a tree has a very simple and direct interpretation: the posterior probability of a tree is the probability that the tree is correct, assuming that the substitution model is correct. It is worth considering how uncertainty in parameter estimates is evaluated in a more traditional phylogenetic approach. Because the tree is not considered a random quantity in other types of analyses, such as a maximum likelihood phylogenetic analysis, one cannot directly assign a probability to the tree. Instead one has to resort to a rather complicated thought experiment. The thought experiment goes something like this. Assuming that the phylogenetic model is correct and that the parameter estimates take the maximum likelihood values (or better yet, their true values), what would the parameter estimates look like on simulated data sets of the same size as the original data matrix? The distribution of parameter estimates that would be generated in such a study represent the sampling distribution of the parameter. One could construct an interval from the sampling distribution that contains 95\% of the parameter estimates from the simulated replicates, and this would be called a confidence interval. A 95\% confidence interval is a random interval containing the true value of the parameter with probability 0.95. Very few people have constructed confidence intervals/sets of phylogenetic trees using simulation. The simulation approach we just described is referred to as the parametric bootstrap. A related approach, called the nonparametric bootstrap, generates data matrices of the same size as the original by randomly sampling columns (sites) of the original data matrix with replacement. Each matrix generated using the bootstrap procedure is then analyzed using maximum likelihood under the same model as in the original analysis. The nonparametric bootstrap is widely used in phylogenetic analysis. \subsubsection{Interpreting posterior probabilities on trees} One of the concerns in Bayesian phylogenetic analysis is the interpretation of the posterior probabilities on trees, or the probabilities of individual clades on trees. The posterior probabilities are usually compared to the nonparametric bootstrap proportions, and many have expressed the concern that the posterior probabilities on clades are too high, or that the posterior probabilities do not have an easy interpretation. We find this concern somewhat frustrating, mostly because the implicit assumption is that the nonparametric bootstrap proportions are in some way the correct number that should be assigned to a tree, and that any method that gives a different number is in some way suspect. However, it is not clear that the nonparametric bootstrap values on phylogenetic trees should be the gold standard. Indeed, it has been known for at least a decade now that the interpretation of nonparametric bootstrap values on phylogenetic trees is problematic, for a variety of reasons summarized in [REFS]. \begin{figure}[t] \centering \includegraphics[height=3in]{fig6} \caption{\textbf{\textsc{The meaning of posterior probabilities under the model}}. The relationship between the posterior probability of a phylogenetic tree and the probability that the tree is correct when all of the assumptions of the analysis are satisfied.} \label{fig6} \end{figure} What does the posterior probability of a phylogenetic tree represent? Huelsenbeck and Rannala (In Press) performed a small simulation study that did two things. First, it pointed out that the technique many people used to evaluate the meaning of posterior probabilities was incorrect if the intention was to investigate the best-case scenario for the method (i.e., the situation in which the Bayesian method does not misspecify the model). Second, it pointed out that the common interpretation of the posterior probability of a phylogenetic tree is correct; the posterior probability of a phylogenetic tree is the probability that the tree is correct. The catch is that this is true only when the assumptions of the analysis are correct. Figure~6 summarizes the salient points of the Huelsenbeck and Rannala (In Press) study. The experimental design was as follows. They first randomly sampled a tree, branch lengths, and substitution model parameters from the prior probability distribution of the parameters. (The tree was a small one, with only six species.) This is the main difference between their analysis and all others; they treated the prior model seriously, and generated samples from it instead of considering the parameters of the model fixed when doing the simulations. For each sample from the prior they simulated a data matrix of 100 sites. They then analyzed the simulated data matrix under the correct analysis. Figure~6 summarizes the results of 10,000 such simulations for each panel. They simulated data under a very simple model (the JC69 model in which the base frequencies are all equal and the rates of substitution between states are the same) and a complicated model (the GTR+$\Gamma$ model in which the nucleotide frequencies are free to vary, the rates of substitution between states are allowed to differ, and the rates across sites are gamma distributed). In both cases, the relationship between posterior probabilities and the probability that the tree is correct is linear; the posterior probability of a tree is the probability that the tree is correct, at least when the assumptions of the phylogenetic analysis are satisfied. Importantly, to our knowledge posterior probabilities are the only measure of support that have this simple interpretation. \begin{figure}[t] \centering \includegraphics[height=3in]{fig7} \caption{\textbf{\textsc{The meaning of posterior probabilities when the model is incorrect}}. The relationship between the posterior probability of a phylogenetic tree and the probability that the tree is correct when all of the assumptions of the analysis are not met. } \label{fig7} \end{figure} Of course, to some extent the simulation results shown in Figure~6 are superfluous; the posterior probabilities have always been known to have this interpretation, and the simulations merely confirm the analytical expectation (and incidentally are additional evidence that the program MrBayes is generating valid draws from the posterior probability distribution of trees, at least for simple problems). The more interesting case is when the assumptions of the analysis are incorrect. Suzuki and Nei ([YEAR]) attempted to do such an analysis. Unfortunately, they violated the assumptions of the analysis in a very peculiar way; they simulated data sets when the underlying phylogeny differed from one gene region to another. This scenario is not a universal concern in phylogenetic analysis (though it can be a problem in the analysis of closely related species, in bacterial phylogenetics, or in population studies). The common worry is that the substitution model is incorrect. Huelsenbeck and Rannala (In Press) performed a few simulations when the assumptions of the analysis are incorrect (Figure~7). The top panel in Figure~7 shows the case when the evolutionary model is not incorporating some important parameters (the model is under-specified). In this case the relationship between posterior probabilities and the probability that the tree is correct is not linear. Instead, the method places too much posterior probability on incorrect trees. The situation is not so dire when the evolutionary model has unnecessary parameters (bottom panel in Figure~7). \subsubsection{Bayesian model choice} It appears that Bayesian analysis can be sensitive to model misspecification. It is important to note that the best tree selected under the Bayesian criterion is unlikely to differ significantly from the maximum likelihood tree, mostly because the prior should have a small effect on phylogeny choice when the data set is reasonably large. It is also important to note that it is not really a problem with the Bayesian method, but rather with the models used to analyze the data. In a sense, biologists have a method in hand that, in principle, has some very desirable properties: it is fast, allows analysis of complex models in a timely way, and has a correct and simple interpretation, when the assumptions of the analysis are satisfied. The simulation studies summarized in the previous section, along with scores of simulation studies that examine the performance of phylogenetic methods (see [REFS]), suggest that it is important to analyze sequence data under as realistic a model as possible. Unfortunately, even the most complicated models currently used in phylogenetic analysis are quite simple and fail to capture important evolutionary processes that generated the sequence data. Phylogenetic models need to be improved to capture evolutionary processes most likely to influence phylogeny estimation. It is impossible to know with certainty what advances will be made in improving phylogenetic models, but we can speculate on what the future might hold. For one, it seems important to relax the assumption that the substitution process in homogeneous over the entire phylogenetic history of the organisms under study. This assumption might be relaxed in a number of ways. For example, Foster ([YEAR]) has relaxed the assumption that nucleotide frequencies are constant over time and [PEOPLE] ([YEAR]) relaxed the assumption that the GC content is a constant over a phylogenetic tree. Other such improvements are undoubtedly in store, and Bayesian methods are likely to play an important role in evaluating such models. We can also imagine upper bounds on how many parameters can be added to a phylogenetic model while still maintaining the ability to estimate them from sequence data. It is not clear how close we currently are to that situation. We know that maximum likelihood is consistent for the models typically used in phylogenetic analysis, but we do not know if consistency will be maintained for nonhomogeneous models, or other models that account for other evolutionary processes. We can be certain that analysis of more parameter-rich models will be quite complicated, and may require a different perspective on model choice than the one that is wide spread in phylogenetics today. Currently, selecting the best model for a particular alignment of DNA sequences is a straight-forward affair. For example, the substitution models implemented in the program PAUP* are all a special case of the general time reversible (GTR) model. The GTR model has instantaneous rate matrix \begin{equation*} {\mathbf Q} = \{q_{ij}\} = \left( \begin{array}{cccc} - & r_{AC} \pi_C & r_{AG} \pi_G & r_{AT} \pi_T \\ r_{AC} \pi_A & - & r_{CG} \pi_G & r_{CT} \pi_T \\ r_{AG} \pi_A & r_{CG} \pi_C & - & r_{GT} \pi_T \\ r_{AT} \pi_A & r_{CT} \pi_C & r_{GT} \pi_G & - \\ \end{array} \right) \mu \end{equation*} Other commonly used models of phylogenetic analysis are all special cases of the GTR model, with constraints on the parameters of the GTR model. For example, the HKY85 model constrains the transitions to be one rate ($r_{AG} = r_{CT}$) and the transversions to have another, potentially different, rate ($r_{AC} = r_{AT} = r_{CG} = r_{GT}$). The Felsenstein (F81, 1981) model further constrains the transitions and transversions to have the same rate ($r_{AC} = r_{AG} = r_{AT} = r_{CG} = r_{CT = }r_{GT}$). These models are nested, one within the other. The F81 model is a special case of the HKY85 and the HKY85 model is a special case of the GTR model. In the programs PAUP* and MrBayes, these different models are set using the ``nst'' option: nst can be set to `1', `2', or `6' for the F81, HKY85, or GTR models, respectively. Because the models are nested, one can choose an appropriate model using likelihood ratio tests. The likelihood ratio for a comparison of the F81 and HKY85 models is $$ \Lambda = {\max[\ell(\mbox{F81})] \over \max[\ell(\mbox{HKY85})]} $$ Because the models are nested, $\Lambda \leq 1$ and $-2\log_e \Lambda$ follows a $\chi^2$ distribution with one degree of freedom under the null hypothesis. This type of test can be applied to a number of nested models in order to choose the best of them. This approach is easy to perform by hand using a program like PAUP*, but has also been automated in the program Modeltest. The current machinery for model choice appears to work quite well when the universe of candidate models is limited (as is the current case in phylogenetics). But what happens when we reach that happy situation in which the universe of candidate models (pool of models to choose among) is large and the relationship among the models is not nested? There are a number of alternative ways model choice can be performed in this situation. One could use information criteria, such as the Akaike Information Criterion (AIC) to choose among a pool of candidate models. Or, one could use the Cox test ([REF]) which uses the likelihood ratio as the test statistic, but simulates the null distribution. One might also use Bayes factors to choose among models. Here we will describe how Bayes factors, calculated using MCMC, can be used to choose among a potentially large set of candidate models. The Bayes factor for a comparison of two models, $M_1$ and $M_2$, is $$ BF_{12} = {\Pr[X | M_1] \over \Pr[X | M_2]} $$ A Bayes factor greater than one is support for $M_1$, whereas the opposite is true for Bayes factors less than one. Note that the Bayes factor is simply the ratio of the marginal likelihoods of the two models. The Bayes factor integrates over uncertainty in the parameters. The likelihood ratio, on the other hand, maximizes the likelihood with respect to the parameters of the model. Jeffreys (1961) provided a table for the interpretation of Bayes factors. In general, the Bayes factor describes the degree by which you change your opinion about rival hypotheses after observing data. Here we will describe how Bayes factors can be used to choose among substitution models (Huelsenbeck et al., In Press). First, we will note that the universe of possible time-reversible substitution models is much larger than typically implemented in phylogenetic programs. Appendix~1 shows all of the possible time-reversible substitution models. There are 203 of them, though only a few of them have been named (formally described in a paper). We use a special notation to describe each of these models. We assign index values to each of the six substitution rates, in the order $AC, AG, AT, CG, CT, GT$. If a model has the constraint that $r_i = r_j$, then the index value for those two rates is the same. Moreover, the index number for the first rate is always 1, and indices are labelled sequentially. So, for example, ``111111'' denotes the Jukes-Cantor (1969) or Felsenstein (1981) model and ``121121'' denotes the Kimura (1980), Hasegawa et al.\ (1984, 1985), or Felsenstein (1984) model. The simplest model is ``111111'' and the most complex is the GTR model, ``123456''. The program PAUP* can implement all of these models through a little-used option (the command ``lset nst=6 rmatrix=estimate rclass=(abbcba)'' implements one of the unnamed models, constraining $r_{AC} = r_{GT}$ and $r_{AG} = r_{AT} = r_{CT}$ with $r_{CG}$ having another independent rate]. The interested reader can contact J.P.H. for a file that instructs the program PAUP* to maximize the likelihood for each of the 203 possible substitution models. This would allow one to choose among substitution models using AIC, or related information criteria. \begin{table}[b] \centering \caption{\textbf{\textsc{The best models for 16 data sets using Bayes factors}}. PP, the model with the highest posterior probability, with its corresponding probability; BF, the Bayes factor for the best model.} \begin{tabular}{l c c c} \hline Name & PP & BF & 95\% Credible Set of Models \\ \hline Angiosperms & 189 (0.57) & 265.9 & (189, 125, 193, 203) \\ Archaea & 168 (0.74) & 584.3 & (168, 198, 193) \\ Bats & 112 (0.34) & 103.0 & (112, 50, 162, 125, 147, 152, 90, \\ & & & 122, 183, 157, 15, 189, 191) \\ Butterflies & 136 (0.27) & 74.4 & (136, 162, 112, 90, 201, 191, \\ & & & 183, 168, 40, 125, 198, 152) \\ Crocodiles & 125 (0.35) & 108.6 & (125, 40, 166, 168, 134, 189, 191, 162, 193) \\ Gophers & 40 (0.46) & 174.8 & (40, 112, 162, 15, 50, 125, 189, 147, \\ & & & 95, 138, 90, 136, 183, 201, 168) \\ HIV-1 ({\it env}) & 25 (0.29) & 83.0 & (25, 60, 50, 64, 125, 100, 102, 97, 164, 169, 152,\\ & & & 173, 159, 157, 147, 175, 171, 191, 189, 193, 140, 199)\\ HIV-1 ({\it pol}) & 50 (0.62) & 321.7 & (50, 125, 157, 152, 147, 193) \\ Lice & 15 (0.56) & 255.4 & (15, 40, 90, 117, 50, 122, \\ & & & 95, 136, 166, 125, 112) \\ Lizards & 193 (0.68) & 434.7 & (193, 138, 200, 203, 157) \\ Mammals & 193 (0.64) & 353.1 & (193, 203) \\ Parrotfish & 162 (0.56) & 257.6 & (162, 189, 201) \\ Primates & 15 (0.32) & 91.5 & (15, 40, 112, 162, 95, 138, 90, 136, \\ & & & 50, 125, 166, 122, 168, 117, 85) \\ Vertebrates & 125 (0.20) & 51.6 & (125, 40, 168, 64, 134, 189, 193, 166, 191, \\ & & & 162, 171, 136, 198, 138, 175, 200, 173) \\ Water snakes & 166 (0.54) & 237.6 & (166, 191, 117, 152, 134, 200, 198, 177) \\ Whales & 15 (0.60) & 111.1 & (15, 40, 117, 95, 85, 122, 112, 90, 162, 50, 134, 166) \\ \hline \end{tabular} \label{tab3} \end{table} To calculate the Bayes factors for the different substitution models, we first need to calculate the posterior probability for each of the possible models. We do this using MCMC. Here, the goal is to construct a Markov chain that visits substitution models in proportion to their posterior probability. We could not use the normal theory for constructing a Markov chain for MCMC analysis because the dimensionality of the problem changes from model to model; the 203 models often differ in the number of substitution rates. Instead, we constructed a Markov chain using reversible jump to visit candidate substitution models. Reversible jump MCMC is described in more detail by Larget (Chapter [NUMBER]). The program we wrote uses two proposal mechanisms to move among models. One proposal mechanism takes a group of substitution rates that are constrained to be the same, and splits them into two groups with potentially different rates. The other mechanism takes two groups of substitution rates, each of which has substitutions constrained to be the same, and merges the two groups into one. To start with, let's examine the simple data matrix that we have been using throughout this chapter: the five species matrix of 50 sites simulated under the HKY85 model on the tree of Figure~5. Up to now, we have been performing all of our analyses---maximum likelihood and Bayesian---under the HKY85 model of DNA substitution (the true model) for this alignment. However, which model is selected as best using the Bayesian reversible jump MCMC approach? Is the true model, or at least one similar to the true model, chosen as the best? We ran the reversible jump MCMC program for a total of 10,000,000 cycles on the small simulated data set. The true model ($M_{15}$, 121121) was visited with the highest frequency; this model was visited 14.2\% of the time, which means the posterior probability of this model is about 0.142. What is the Bayes factor for a comparison of $M_{15}$ to all of the other models ($M_{15}^C$)? As described above, the Bayes factor is the ratio of the marginal likelihoods. It also can be calculated, however, as the ratio of the posterior odds to the prior odds of the two hypotheses of interest: $$ BF_{12} = {\Pr[X | M_1] \over \Pr[X | M_2]} = { {\Pr[M_1 | X] \over \Pr[M_2 | X]} \over {\Pr[M_1] \over \Pr[M_2]} } $$ The posterior probability of $M_{15}$ is $\Pr[M_{15} | X] = 0.142$ and the posterior probability of all of the other models against which we are comparing $M_{15}$ is just $\Pr[M_{15}^C | X] = 1 - \Pr[M_{15} | X] = 1 - 0.142 = 0.858$. We also know the prior probabilities of the hypotheses. We assumed a uniform prior on all of the possible models, so the prior probability of any specific model is $1 / 203 = 0.0049$. The Bayes factor for a comparison of $M_{15}$ to the other models is then $$ BF_{12} = { {\Pr[M_{15} | X] \over \Pr[M_{15}^C | X]} \over {\Pr[M_{15}] \over \Pr[M_{15}^C]} } = { {0.142 \over 0.858} \over {1/203 \over 202/203} } = 33.4 $$ This means that we change our mind about the relative tenability of the two hypotheses by a factor of about 33 after observing the small data matrix. A Bayes factor of 33 would be considered strong evidence in favor of the model (Jeffreys, 1961). We can also construct a 95\% credible set of models. This is a set of models that has a cumulative posterior probability of 0.95. The 95\% credible set included 41 models, which in order were 121121, 121131, 123123, 121321, 121341, 123143, 121323, 123321, 121343, 123121, 123341, 121123, 123323, 123141, 121134, 123343, 121331, 121345, 123423, 123421, 123451, 123453, 123145, 121324, 123124, 123324, 123424, 123454, 123345, 123456, 121133, 123441, 121334, 121333, 123443, 123425, 123313, 121111, 123131, 121344, and 123331. Note that the best of these models (the first 16, in fact, which have a cumulative posterior probability of 0.72) do not constrain a transition to have the same rate as a transversion. One can see that the second-best model ($M_{40}$, 121131) has this property. The second best model also happens to be a named one (it is the model described by Tamura and Nei, 1993). The third best model, however, is not a named one. Huelsenbeck et al. (In Press) examined 16 data sets using the approach described here. The details about the data sets can be found in that paper. Table~3 summarizes the results. In most cases, the posterior probability was spread across a hand full of models. The Bayes factors ranged from 51.6 to over 500, suggesting that all of the alignments contained considerable information about which models are preferred. Also, one can see that for 14 of the 16 data matrices, the 95\% credible set contains models that do not constrain transitions to have the same rate as transversions. The best models are usually variants of the model first proposed by Kimura (1980). The exceptions are the HIV-{\it env} and vertebrate $\beta$-globin alignments. The Bayesian approach helped us find these unusual models, that would not usually be considered in a more traditional approach to model choice. Practicing biologists already favor `automated' approaches to choosing among models. The program Modeltest (Posada and Crandall, [YEAR]) is very popular for this reason; even though the universe of models of interest to the biologist (i.e., implemented in a computer program) is of only moderate size, it is convenient to have a program that automatically considers each of these models and returns the best of them. The program Modeltest, for example, typically looks at seven of the 203 possible time-reversible substitution models, considering only nested models that are implemented in most phylogeny packages. One could reasonably argue that the number of models currently implemented is small enough that one could perform model choice by hand, with the corresponding advantage that it promotes a more intimate exploration of the data by the biologist, promotes understanding of the models, and keeps the basic scientific responsibility of choosing which hypotheses to investigate in the biologist's hands. However, as models become more complicated and the number of possible models increases, it becomes more difficult to perform model choice by hand. In such cases, an approach like the one described here might be useful. \subsection{Inferring phylogeny under complex models} Alignments that contain multiple genes, or data of different types, are becoming much more common. It is now relatively easy to sequence multiple genes for any particular phylogenetic analysis, leading to data sets that were uncommon just a few years ago. For example, consider the data set collected by Kim et al. (2003) which is fairly typical of those that are now collected for phylogenetic problems. They looked at sequences from three different genes sampled from 27 leaf beetles: the second variable region (D2) of the nuclear rRNA large subunit (28S), and partial sequences from a nuclear gene (EF-1$\alpha$) and a mitochondrial gene (COI). They also had information from 49 morphological characters. [Although the program MrBayes can analyze morphological data in combination with molecular data, using the approach described by Lewis, (YEAR), we do not examine the morphological characters of the Kim et al. study in this chapter. This is a book on molecular evolution, after all.] The molecular characters of the Kim et al. (2003) study were carefully aligned; the ribosomal sequences were aligned using the secondary structure as a guide and the protein-coding genes were aligned first by the translated amino acid sequence. For illustrative purposes, we are going to consider the amino acid sequences from the COI gene and not the complete DNA sequence. This is probably not the best approach because there is information in the DNA sequence that is being lost when only the amino acid sequence of the gene is considered. However, we want to show how data of different types can be analyzed in MrBayes. The data from the Kim et al. (2003) study that we examine, then, consists of three parts: the nucleotide sequences from the 28S rRNA gene, the nucleotide sequences from the EF-1$\alpha$ gene, and the amino acid sequences from the COI gene. Each of these partitions of the data require careful consideration. To begin with, it is clear that the same sort of continuous-time Markov chain model is not going to be appropriate for each of these gene regions. After all, the nucleotide part of the alignment has only four states whereas the amino acid part of the alignment (the COI gene) has 20 potential states. We could resort to a very simple partitioned analysis, treating all of the nucleotide sequences with one model, and the amino acid sequences with another. However, this approach, too, has problems. Is it really reasonable to treat the protein coding DNA sequences in the same way as the ribosomal sequences? Moreover, in this case we have information on the secondary structure of the ribosomal gene; we know which nucleotides probably form Watson-Crick pairs in the stem regions of the ribosomal gene. It seems sensible that this information should be accommodated in the analysis of the sequences. One of the strengths of likelihood-based approaches in general, and the program MrBayes in particular, is that heterogeneous data of the type collected by Kim et al. (2003) can be included in a single analysis, with the peculiarities of the substitution process in each partition accounted for. Here are the special considerations we think each data partition of the Kim et al. (2003) study raise: \begin{description} \item[ {\bf Stem regions of the 28S rRNA nucleotide sequences.}] Although the assumption of independence across sites (invoked when one multiplies the probabilities of columns in the alignment to get the likelihood) is not necessarily a good one for any data set, it seems especially bad for the stem regions of ribosomal genes. The secondary structure in ribosomal genes plays an important functional role. The functional importance of secondary structure in ribosomal genes causes non-independence of substitutions in sites participating in a Watson-Crick pair: specifically, if a mutation occurs in one member of a base pair in a functionally important stem, natural selection causes the rate of substitution to be higher for compensatory changes. That is, individuals with a mutation that restores the base pairing have a higher fitness than individuals that do not carry the mutation, and the mutation may eventually become fixed in the population. The end result of natural selection acting on maintenance of stems is a signature of covariation between paired nucleotides.\\ Sch\"oniger and von Haeseler (1994) described a model that accounts for the non-independence of substitutions in stem regions of ribosomal genes. They suggest that instead of modeling the substitution process on a site-by-site basis, as was then typical and as described earlier in this chapter, that instead substitutions be modeled on both of the nucleotides participating in the stem pair bond---the doublet. Instead of four states, the doublet model of Sch\"oniger and von Haeseler (1994) has 16 states (all possible doublets: AA, AC, AG, AU, $\ldots$, UA, UC, UG, UU). The instantaneous rate matrix, instead of being $4 \times 4$, is now $16 \times 16$. Each element of the rate matrix, ${\mathbf Q}$ can be specified as follows: $$ q_{ij} = \left\{ \begin{array}{r@{\quad:\quad}l} {\kappa \pi}_j & \mbox{transition} \\ { \pi}_j & \mbox{transversion} \\ 0 & \mbox{{\it i} and {\it j} differ at two positions} \\ \end{array} \right. $$ Note that this model only allows a single substitution in an instant of time; substitutions between doublets like $AA \rightarrow CG$ have an instantaneous rate of zero. This is not to say that transitions between such nucleotides is not allowed, only that a minimum of two substitutions is required. Just as there are different parameterizations of the $4 \times 4$ models, one can have different parameterizations of the doublet model. The one described here allows a transition/transversion rate bias. However, one could construct a doublet model under any of the models shown in Appendix 1. \\ \item[ {\bf Loop regions of the 28S rRNA nucleotide sequences.}] We will use a more traditional $4 \times 4$ model for the loop regions of the ribosomal genes. Nucleotides in the loop regions presumably do not participate in any strong interactions with other sites (at least that we can identify before hand). \\ \item[ {\bf EF-1$\alpha$ nucleotide sequences.}] Special attention should be paid to the choice of model for protein coding genes, where the structure of the codon causes heterogeneity at the different codon positions, along with potential non-independence of substitutions within the codon. The rate of substitution is the most obvious difference at different codon positions. Because of the redundancy of the genetic code, typically second positions are the most conservative and third codon positions are the least conservative. Often people approach this problem of rate variation by grouping the nucleotides at the first, second, and third codon positions into different partitions, and allow the overall rate of substitution to differ at the different positions. Another approach, and the one we take here, is to stretch the model of DNA substitution around the codon (Goldman and Yang, 1994; Muse and Gaut, 1994). We now have 64 possible states (the triplets AAA, AAC, AAG, AAT, ACA, $\ldots$, TTT), and instead of a $4 \times 4$---or even a $16 \times 16$---rate matrix, we have a $64 \times 64$ instantaneous rate matrix describing the continuous-time Markov chain. Usually, the stop codons are excluded from the state space, and the rate matrix, now $61 \times 61$ for the universal code, is $$ q_{ij} = \left\{ \begin{array}{r@{\quad:\quad}l} {\omega \kappa \pi}_j & \mbox{nonsynonymous transition} \\ {\omega \pi}_j & \mbox{nonsynonymous transversion} \\ { \kappa \pi}_j & \mbox{synonymous transition} \\ { \pi}_j & \mbox{synonymous transversion} \\ 0 & \mbox{{\it i} and {\it j} differ at more than one position} \end{array} \right. $$ where $\omega$ is the nonsynonymous/synonymous rate ratio, $\kappa$ is the transition/transversion rate ratio, and ${\pi}_j$ is the stationary frequency of codon $j$ (Goldman and Yang 1994; Muse and Gaut 1994). This matrix specifies the rate of change from codon $i$ to codon $j$. This rate matrix, like the $4 \times 4$ and $16 \times 16$ rate matrices, only allows one substitution at a time.\\ The traditional codon model, described here, does not allow the nonsynonymous/synonymous rate to vary across sites. This assumption has been relaxed. Nielsen and Yang (1998) allowed the $\omega$ at a site to be a random variable. Their method allows $\omega$ to vary across the sequence and also the identification of amino acid positions under directional, or positive, selection. The program PAML (Yang, [YEAR]) implements an empirical Bayes approach to identifying amino acid positions under positive selection. MrBayes uses the same general idea to identify positive selection, but implements a fully Bayesian approach, integrating over uncertainty in model parameters (Huelsenbeck and Dyer, In Press). Here, we will not allow the nonsynonymous/synonymous rate to vary across sites. \\ \item[ {\bf COI amino acid sequences.}] In some ways, modeling the amino acid sequences is more complicated than the nucleotide sequences. Some sort of continuous-time Markov chain with 20 states seems appropriate. The most general time-reversible substitution model for amino acids is \begin{equation*} {\mathbf Q} = \{q_{ij}\} = \left( \begin{array}{ccccccc} - & r_{AR} \pi_R & r_{AN} \pi_N & \hspace{0.1in} \cdots \hspace{0.1in} & r_{AW} \pi_W & r_{AY} \pi_Y & r_{AV} \pi_V \\ r_{AR} \pi_A & - & r_{RN} \pi_N & \cdots & r_{RW} \pi_W & r_{RY} \pi_Y & r_{RV} \pi_V \\ r_{AN} \pi_A & r_{RN} \pi_R & - & \cdots & r_{NW} \pi_W & r_{NY} \pi_Y & r_{NV} \pi_V \\ \vdots & \vdots & \vdots & \ddots & \vdots & \vdots & \vdots \\ r_{AW} \pi_A & r_{RW} \pi_R & r_{NW} \pi_N & \cdots & - & r_{WY} \pi_Y & r_{WV} \pi_V \\ r_{AY} \pi_A & r_{RY} \pi_R & r_{NY} \pi_N & \cdots & r_{YW} \pi_W & - & r_{YV} \pi_V \\ r_{AV} \pi_A & r_{RV} \pi_R & r_{NV} \pi_N & \cdots & r_{WV} \pi_W & r_{YV} \pi_Y & - \\ \end{array} \right) \mu \end{equation*} (The dots represent rows and columns that are not shown. The entire matrix is too large to be printed nicely on the page.) There are a total of 208 free parameters; 19 of these free parameters involve the stationary frequencies of the amino acids. Knowing 19 of the amino acid frequencies allows you to calculate the frequency of the 20th, so there are a total of 19 free parameters. Similarly, there are a total of $20 \times 19 / 2 - 1 = 189$ rate parameters. Contrast this with the codon model. The size of the rate matrix for the codon model is much larger than the size of the amino acid rate matrix ($61 \times 61 = 3721$ versus $20 \times 20 = 400$). However, there are fewer free parameters for even the most general time reversible codon model than there are for the most general time reversible amino acid model (66 and 208 for the codon and amino acid matrix, respectively). Of course, the reason the codon model has so few parameters for its size is that many of the entries in the matrix are zero.\\ Molecular evolutionists have come up with a unique solution to the problem of the large number of potential free parameters in the amino acid matrices. They fix them all to specific values. The parameters are estimated once on large data bases of amino acid sequence alignments. The details of how to do this are beyond the scope of this chapter. But, the end result is that we have a number of amino acid rate matrices, each with no free parameters (nothing to estimate) that are designed for specific types of data. These matrices go by different names: Poisson (Bishop and Friday, 1987), Jones (Jones et al., 1992), Dayhoff (Dayhoff et al., 1978), Mtrev (Adachi and Hasegawa. 1996), Mtmam (Cao et al., 1998), WAG (Whelan and Goldman, 2001), Rtrev (Dimmic et al., 2002), Cprev (Adachi et al., 2000), Blossum (Henikoff and Henikoff, 1992), and Vt (Muller and Vingron. 2000). The amino acid models are designed for use with different types of data. For example, WAG was estimated on nuclear genes, Cprev on chloroplast genes, and Rtrev on viral genes. Which of these models is the appropriate one for the mitochondrial COI gene sequences for leaf beetles? It is not clear which one we should use; nobody has ever designed a mitochondrial amino acid model for insects, much less leaf beetles. It might make sense to use one of the mitochondrial matrices, such as the Mtrev or Mtmam models. However, we can do better than this. Instead of assuming a specific model for the analyses, we can let the amino acid model be a random variable. We will assume that the ten amino acid models listed above all have equal prior probability. We will use MCMC to sum over the uncertainty in the models. This is the same approach described in the previous section, where we used reversible jump MCMC to choose among all possible time-reversible nucleotide substitution models. Fortunately, we do not need to resort to reversible jump MCMC here because all of the parameters of the models are fixed. We do not change dimensions when going from one amino acid model to another. \end{description} There are only a few other caveats to consider before we can actually start our analysis of the leaf beetle data with the complex substitution model. Many of the parameters of the model for the individual partitions are shared across partitions. These parameters include the tree, branch lengths, and the rates of substitution under the GTR model for the nucleotide data. Because we are mostly interested in estimating phylogeny here, we will assume that the same tree underlies each of the partitions. That is, we will not allow one tree for the EF-1$\alpha$ gene and another for the loop regions of the 28S ribosomal gene. This seems like a reasonable choice as we have no {\it a priori} reason to expect the trees for each partition to differ. However, we might expect the rates of substitution to differ systematically across genes (some might be more evolutionarily constrained) and also rates to vary from site to site within a gene. We do the following to account for rate variation across and within partitions. Across partitions, we apply a site specific model by introducing a single parameter for each partition that increases or decreases the rate of substitution for all of the sites within the gene. For example, if the rate multipliers were $m_1 = 0.1$, $m_2 = 1.0$, $m_3 = 2.0$, and $m_4 = 0.9$, then the first and fourth partitions would have, on average, a rate of substitution lower than the mean rate and the third partition would have a rate greater than the mean rate. In this hypothetical example, the second partition has a rate exactly equal to the mean rate of substitution. Site specific models are often denoted in the literature by `SS'; the GTR model with site specific rate variation is denoted `GTR+SS'. The site specific model, although it allows rates to vary systematically from one partition to another, does not account for among site rate variation within a partition. Here we assume that the rate at a site is a random variable drawn from a gamma distribution. This is commonly assumed in the literature, and gamma rate variation models are often denoted with a `$\Gamma$'. We are assuming a mixture of rate variation models, so our models could be denoted something like `GTR+SS+$\Gamma$'. The modeling assumptions we are making can be summarized in a table: \begin{center} \begin{tabular}{lccc} & & Substitution & Rate \\ Partition & \# States & Model & Variation \\ \hline Stem & 16 & GTR & Gamma \\ Loop & 4 & GTR & Gamma \\ EF-1$\alpha$ & 61 & GTR & Equal \\ COI & 20 & Mixture & Gamma \\ \end{tabular} \end{center} We will also allow parameters that could potentially be constrained to be equal across partitions, such as the shape parameters of the gamma rate variation model, to be different. The parameters of the model that need to be estimated, then, include: \begin{center} \begin{tabular}{cl} Parameters & Notes \\ \hline $\tau$ \& ${\mathbf v}$ & Tree and branch lengths, shared across all of the partitions \\ $\pi_{AA} \ldots \pi_{UU}$ & State frequencies for the stem region partition \\ $\pi_{A} \ldots \pi_{T}$ & State frequencies for the loop region partition \\ $\pi_{AAA} \ldots \pi_{TTT}$ & Codon frequencies for the EF-1$\alpha$ gene \\ $\pi_{A} \ldots \pi_{V}$ & Amino acid frequencies for the COI gene \\ $r^{(1)}_{AC} \ldots r^{(1)}_{GT}$ & The GTR rate parameters for the loop region partition\\ $r^{(2)}_{AC} \ldots r^{(2)}_{GT}$ & The GTR rate parameters for the stem region partition\\ $r^{(3)}_{AC} \ldots r^{(3)}_{GT}$ & The GTR rate parameters for the EF-1$\alpha$ gene \\ $\omega$ & The nonsynonymous/synonymous rate ratio for the EF-1$\alpha$ gene\\ $\alpha_1$ & The gamma shape parameter for the loop region partition \\ $\alpha_2$ & The gamma shape parameter for the stem region partition \\ $\alpha_4$ & The gamma shape parameter for the COI amino acid data\\ $m_1$ & The rate multiplier for the loop region partition \\ $m_2$ & The rate multiplier for the stem region partition \\ $m_3$ & The rate multiplier for the EF-1$\alpha$ gene \\ $m_4$ & The rate multiplier for the COI gene \\ $S$ & The amino acid model for the COI gene \\ \end{tabular} \end{center} Note that we are allowing most of the parameters to be estimated independently for each gene partition. It is not clear that this is the best strategy. For example, the data might be consistent with some of the parameters being constrained to be the same across partitions. This would allow us to be more parsimonious with our parameters. However, at this time there is no easy way of deciding which pattern of constraints is the best for partitioned data. We used MrBayes to analyze the data under the complicated substitution model. We ran a MCMC algorithm for 3,000,000 update cycles, sampling the chain every 100th cycle. Figure~8 shows a majority rule consensus tree of the trees that were visited during the course of the MCMC analysis (the tree is based on samples taken during the last two million cycles of the chain). \begin{figure}[t] \centering \includegraphics[height=5in]{fig8} \caption{\textbf{\textsc{Bayesian phylogenetic tree of leaf beetles}}. A majority rule tree of the trees sampled during the course of the MCMC analysis. The numbers at the interior nodes are the marginal posterior probability of the clade being correct.} \label{fig8} \end{figure} The tree has additional information on it. For one, the numbers at the interior nodes represent the posterior probability of that clade being correct (again, assuming the model is correct). For another, the branch lengths on the majority rule tree are proportional to the mean of the posterior probability of the branch length. The Bayesian analysis also provided information on the parameters of the model. Appendix 3 summarizes the marginal posterior probability of each parameter. There are a few points to note here. First, the nonsynonymous/synonymous rate ratio ($\omega$) is estimated to be a very small number. This is consistent with the EF-1$\alpha$ gene being under strong purifying selection. Second, the rate multiplier parameters for the site specific model ($m_1, m_2, m_3, m_4$) indicate that the rate of substitution is different for the gene regions. The stem partition of the ribosomal gene is the most conservative. Third, the doublet stationary frequency parameters ($\pi_{AA} \ldots \pi_{TT}$) are consistent with a pattern of higher rates to Watson-Crick doublets; note that the stationary frequency is highest for the AT, TA, GC, and CG doublets. Finally, in this analysis we allowed the stationary frequencies of the states to be random variables, and integrated over uncertainty in them. All of the state frequency parameters were given a flat Dirichlet prior. Although the base frequencies are commonly estimated via maximum likelihood for simple ($4 \times 4$) models, they are rarely estimated for codon models. Instead, they are usually estimated by using the observed frequencies of the nucleotides at the three codon positions to predict the codon frequencies. In the Bayesian analysis, on the other hand, estimating these parameters is not too onerous. The only parameter not shown in Appendix 3 is the amino acid model, which was treated as unknown in this analysis. The Markov chain proposed moves among 10 different amino acid models (the ones listed earlier). The chain visited the Mtrev model almost all of the time, giving it a posterior probability of 1.0. The results of the Bayesian analysis confirm our guess that the Mtrev should be the most reasonable of the amino acid models, because it was estimated using a data base of mitochondrial sequences. Importantly, we did not need to rely on our guess of what amino acid model to use, and could let the data inform us about the fit of the alternative models. \subsection{Estimating divergence times} The molecular clock hypothesis states that substitutions accumulate at roughly the same rate along different lineages of a phylogenetic tree ([REF]). Besides being among the earliest ideas in molecular evolution, the molecular clock hypothesis is an immensely useful one. If true, it suggests a way to estimate the divergence times of species with poor fossil records. The idea, in its simplest form, is shown in Figure~9. The figure shows a tree of three species. The numbers on the branches are the branch lengths, in terms of expected number of substitutions per site. Note that the branch lengths on the tree satisfy the molecular clock hypothesis; if you sum the lengths of the branches from the root to each of the tips, you get the same number (0.4). One can estimate branch lengths under the molecular clock hypothesis by constraining the branch lengths to have this property. Figure~9 shows the second key assumption that must be made to estimate divergence times. We assume that the divergence of at least one of the clades on the tree is known. In this hypothetical example, we assume that species A and B diverged five million years ago. We have calibrated the molecular clock. The calibration is this: if five million years have elapsed since the common ancestor of A and B, then 0.1 substitutions is equal to five million years. Together, the assumptions of a molecular clock and a calibration allow us to infer that the ancestor of the three species must have diverged 20 million years ago. \begin{figure}[t] \centering \includegraphics[height=2in]{fig9} \caption{\textbf{\textsc{Estimating divergence times using the molecular clock}}. A tree of three species showing how divergence times can be estimated.} \label{fig9} \end{figure} There are numerous potential problems with the simple picture we presented. We will list them here: \begin{itemize} \item Substitutions may not accumulate at the same rate along different lineages. In fact, it is easy to test the molecular clock hypothesis, using for example a likelihood ratio test (Felsentein, 1981). The molecular clock hypothesis is usually rejected for real data sets. \item Even if the molecular clock is true, we do not know the lengths of the branches with certainty. In fact, there are potential errors not only in the branch lengths but also in the tree. \item We do not know the divergence times of any of the species on the tree with absolute certainty. This uncertainty should in some way be accommodated. \end{itemize} The first problem---that substitutions may not accumulate at a constant rate along the phylogenetic tree---has received the most attention from biologists. Many statistical tests have been devised to examine whether rates really are constant over the tree. As already mentioned, applying these tests to real data usually results in the molecular clock being rejected. However, it is still possible that divergence times can be estimated even if the clock is not perfect. Perhaps the tests of the molecular clock are sensitive enough to pick up on small amounts of rate variation, but that the degree of rate variation does not scupper our ability to estimate divergence times. Some biologists have attempted to account for the variation in rates. One approach is to find taxa that are the worst offenders of the clock and either eliminate them, or allow a different rate just for those taxa. Another approach specifies a parametric model describing how substitution rates change on the tree. These relaxed clock models still allow estimation of divergence times, but may correct for limited degrees of rate variation across lineages. To date, two different models have been proposed for allowing rates to vary across the tree ([REFS]), and in both cases, a Bayesian MCMC approach was taken to estimate parameters. In the remainder of this section, we will assume that the molecular clock is true, or at least that if the molecular clock is violated, we can still meaningfully estimate divergence times. The point of this section is not to provide a definitive answer to the divergence time of any particular group, but rather to show how uncertainty in the tree, branch lengths, and calibration times can be accounted for in a Bayesian analysis. We examine two data sets. The first data set included complete mitochondrial protein-coding sequences from 23 mammals ({\sc Arnason} {\it et al}.\ 1997). We excluded the platypus ({\it Ornithorhynchus anatinus}) and the guinea pig ({\it Cavia porcellus}) from our analysis. We analyzed the alignment of mitochondrial sequences under the GTR+SS model of DNA substitution. The data were partitioned by codon position, and the rates for first, second, and third positions estimated. The second data set consists of 104 amino acid sequences sampled from mouse, rate, an artiodactyl, human, and chicken collated by Nei et al. (2001). Nei et al. (2001) were mainly interested in estimating the divergence times of the rodents and the rodent-human split, and pointed out the importance of taking a multi-gene approach to divergence time estimation. We analyze their data using the partitioned approach, described in the previous section. We partition the data by gene, resulting in 104 divisions in the data. We allow rates to vary systematically across genes using the site specific model. We allow rates to vary within genes by treating the rate of substitution at an amino acid position as a gamma-distributed random variable. We allow different gamma shape parameters for each partition. Moreover, we allow a different amino acid model for each partition, with the actual identity of the amino acid model being unknown. For both data sets, we constrained the branch lengths to obey the molecular clock hypothesis. MrBayes was used to approximate the joint posterior probability of all of the parameters of the evolutionary model. For the mammalian mitochondrial alignment, we ran the MCMC algorithm for a total of one million cycles and based inferences on samples taken during the last 900,000 MCMC cycles. For the amino acid alignments, we ran two independent Markov chains, each for a total of three million update cycles. We combined the samples taken after the 500,000th cycle. For the mammalian data set, we had a total of 9,000 trees with branch lengths that were sampled from the posterior probability distribution of trees. Each of the trees obeyed the molecular clock, meaning that the if one were to take a direct path from each tip of the tree to the root, and sum the lengths of the branches on each path, one would obtain the same number. Importantly, the lengths of the branches and the topology of the tree differed from one sample to another. The differences reflect the uncertainty in the data about the tree and branch lengths. The final missing ingredient is a calibration time for some divergence time on the tree. We used the divergence between the cow and the whales as the calibration. Our first analysis of these samples will reflect the typical approach taken when estimating divergence times; we will assume that the divergence between cows and whales was {\it precisely} 56.5 million years ago. This is a reasonable guess at the divergence time of cows and whales. Figure~10 shows the posterior probability distribution of the divergence time at the root of the tree, corresponding to the divergence of marsupial and placental mammals. \begin{figure}[t] \centering \includegraphics[height=2.3in]{fig10} \caption{\textbf{\textsc{The posterior probability density distribution of the divergence time of placental and marsupial mammals}}. The distributions were calculated assuming the divergence time between cows and whales was precisely 56.5 million years [Fixed(56.5)], uniformly distributed between two times (U) or no less than 56.5 million years, with an exponentially declining prior into the past [56.5 + Exp(0.2)]. K, J, and Tr are the Cretaceous, Jurassic, and Triassic time periods, respectively.} \label{fig10} \end{figure} The top-left panel, marked `Fixed(56.5)' shows the posterior probability of the marsupial-placental split when the cow and whales are assumed to diverge precisely 56.5 million years ago. It shows that even when we assume that the molecular clock is true and the calibration time is known without error, that there is considerable uncertainty about the divergence time. The 95\% credible interval for the divergence of marsupials from placentals is (115.6, 145.1), a span of about 30 million years in the early Cretaceous. In fact, it is easy to calculate the probability that the divergence time was in any specific time interval; with (posterior) probabilities 0.0, 0.97, 0.03, and 0.0, the divergence was in the late Cretaceous, early Cretaceous, late Jurassic, and middle Jurassic, respectively. These probabilities account for the uncertainty in the topology of the tree, branch lengths on the tree, and parameters of the substitution model, but do assume that the calibration time was perfectly known. The other three panels in Figure~10 show the posterior probability distribution of the divergence of marsupial and placental mammals when the calibration is not assumed known with certainty. In two of the analyses, we assumed that the cow and whales diverged at some unknown time, constrained to lie in an interval. The probability of the divergence at any time in the interval was uniformly distributed. The last analysis, shown in the lower-right panel of Figure~10, assumed that the divergence of cows and whales occurred no more recently than 56.5 million years, and was exponentially distributed before then (an offset exponential prior). As expected, the effect of introducing uncertainty in the calibration times is reflected in a posterior probability distribution that is more spread out. The additional uncertainty can be neatly summarized by the 95\% credible intervals: \begin{center} \begin{tabular}{lcc} Prior & Credible Interval &Size \\ \hline Fixed(56.5) & (115.6, 145.1) & 29.5\\ U(50, 60) & (107.8, 145.8) & 38.0 \\ U(50, 70) & (110.3, 166.9) & 56.6\\ 56.5 + Exp(0.2) & (119.8, 175.6) & 55.8\\ \end{tabular} \end{center} The column marked `Size' shows the duration of the credible interval in millions of years. Clearly, introducing uncertainty in the calibration time is reflected in the posterior probability distribution; and the credible interval becomes larger as more uncertainty is introduced into the calibration time. \begin{figure}[t] \centering \includegraphics[height=2.3in]{fig11} \caption{\textbf{\textsc{The distribution of best amino acid models for the 104 amino acid alignments}}. The number of alignments for which each amino acid model was best for the Nei et al. (2001) study.} \label{fig11} \end{figure} The results from the analysis of the 104 concatenated amino acid alignments was similar to that of the mammalian mitochondrial data. However, the model for the amino acid data sets was quite complicated. Besides the tree and branch lengths, there were 104 gamma shape parameters, 104 rate multipliers for the site specific model, and 104 unknown amino acid models to estimate. We do not attempt to summarize the information for all of these parameters here. We only show the results for the amino acid models. Figure~11 shows which models were chosen as best for the various amino acid alignments. in 82 cases, the model of Jones et al. (1992) was chosen as best. The Dayhoff and Wag models (Dayhoff et al., 1978; Whelan and Goldman, 2001) were chosen 11 times each. The other seven amino acid models were never chosen as the best one in any of the 104 alignments, though some did receive considerable posterior probability. There was no uncertainty in the topology of the tree chosen using the Bayesian method (Figure~12). \begin{figure}[b] \centering \includegraphics[height=1.5in]{fig12} \caption{\textbf{\textsc{The best tree for the 104 amino acid alignments}}. This tree had a posterior probability approximated to be 1.0 by the MCMC algorithm. The lengths of the branches are the mean of the posterior probability distribution.} \label{fig12} \end{figure} Nei et al. (2001) assumed as the calibration time the divergence of birds and mammals, which they assumed occurred 310 million years ago. Table~4 summarizes the results of the divergence times for three clades on the tree, assuming the calibration time of Nei et al. (2001) as well as three other calibrations which allow for uncertainty in the divergence time of birds and mammals. \begin{table}[t] \centering \caption{\textbf{\textsc{Credible intervals for divergence times of the amino acid data}}. The 95\% credible intervals for the divergence of mouse from rat, human from rodents, and the time at the root of the tree for four different calibrations of the bird-mammal split.} \begin{tabular}{l c c c} \hline Calibration & Mouse-Rat & Human-Rodent & Root \\ \hline 310 & (25.9, 33.4) & (84.5, 97.5) & (448.3, 487.8) \\ U(288, 310) & (25.0, 33.0) & (80.6, 97.5) & (427.7, 491.8) \\ 288 + Exp(0.1) & (24.6, 32.6) & (79.8, 96.6) & (423.3, 495.1) \\ 288 + Exp(0.05) & (24.9, 34.9) & (80.4, 106.5) & (426.4, 551.6) \\ \hline \end{tabular} \label{tab4} \end{table} As might be expected, the uncertainty is greater for the older divergences. Also, having a calibration time that is older than the group of interest makes the posterior probability distribution less vulnerable to errors in the calibration time. The prior models for the uncertainty in the calibration times we used here are largely arbitrary, and chosen mostly to make the point that errors in calibration times can be accounted for in a Bayesian analysis and that these errors can make a difference in the results (at least, these errors can make a difference in how much one believes the results). Experts in the fossils from these groups would place very different priors on the calibration times. For example Philip Gingerich (pers. comm.) would place a much smaller error on the divergence times between cow and whales than we did here; the fossil record for this group is rich, and it is unlikely that cows and whales diverged as early as 100 million years ago (our offset exponential prior places some weight on this hypothesis, along with divergences that are much earlier). Lee (1999) pointed out that the widely used bird-mammal calibration of 310 million years is poorly chosen. The earliest synapsids (fossils on the lineage leading to modern day mammals) is from the upper Pennsylvanian, about 288 million years ago. This is much more recent than the calibration of 310 million years used by some to calibrate the molecular clock. \section{Conclusions} \label{sec:4} In this chapter, we have attempted to demonstrate some of the power and flexibility of the Bayesian approach to the inference of phylogeny and molecular evolution. The most important aspect we want to convey is the efficiency of the Bayesian MCMC methodology in addressing complex models. Current statistical analyses of molecular evolution are based on very simple models, inspired by the apparent simplicity of molecular sequences. But beyond the simple sequences of symbols lies tremendous evolutionary complexity. Approaches that ignore this complexity do not utilize the molecular information efficiently and are prone to produce erroneous inferences. Modelling the complexity of molecular evolution more accurately will be critical to future progress in statistical analysis of molecular evolution. The Bayesian MCMC approach provides promising tools for the analysis of these realistic evolutionary models. \newpage \section*{Appendix 1} {\bf All possible time-reversible models of DNA substitution} \begin{center} \begin{tabular}{cccccc} \hline $M_{1} = 111111$ & $M_{35} = 122322$ & $M_{69} = 121322$ & $M_{103} = 112132$ & $M_{137} = 121314$ & $M_{171} = 112343$ \\ $M_{2} = 122222$ & $M_{36} = 122232$ & $M_{70} = 121232$ & $M_{104} = 112123$ & $M_{138} = 121134$ & $M_{172} = 112334$ \\ $M_{3} = 121111$ & $M_{37} = 122223$ & $M_{71} = 121223$ & $M_{105} = 111233$ & $M_{139} = 112341$ & $M_{173} = 112342$ \\ $M_{4} = 112111$ & $M_{38} = 123111$ & $M_{72} = 122312$ & $M_{106} = 111232$ & $M_{140} = 112314$ & $M_{174} = 112324$ \\ $M_{5} = 111211$ & $M_{39} = 121311$ & $M_{73} = 122321$ & $M_{107} = 111223$ & $M_{141} = 112134$ & $M_{175} = 112234$ \\ $M_{6} = 111121$ & $M_{40} = 121131$ & $M_{74} = 122132$ & $M_{108} = 112233$ & $M_{142} = 111234$ & $M_{176} = 123412$ \\ $M_{7} = 111112$ & $M_{41} = 121113$ & $M_{75} = 122123$ & $M_{109} = 112323$ & $M_{143} = 123344$ & $M_{177} = 123421$ \\ $M_{8} = 112222$ & $M_{42} = 112311$ & $M_{76} = 122231$ & $M_{110} = 112332$ & $M_{144} = 123434$ & $M_{178} = 123142$ \\ $M_{9} = 121222$ & $M_{43} = 112131$ & $M_{77} = 122213$ & $M_{111} = 121233$ & $M_{145} = 123443$ & $M_{179} = 123124$ \\ $M_{10} = 122122$ & $M_{44} = 112113$ & $M_{78} = 123311$ & $M_{112} = 121323$ & $M_{146} = 123244$ & $M_{180} = 123241$ \\ $M_{11} = 122212$ & $M_{45} = 111231$ & $M_{79} = 123131$ & $M_{113} = 121332$ & $M_{147} = 123424$ & $M_{181} = 123214$ \\ $M_{12} = 122221$ & $M_{46} = 111213$ & $M_{80} = 123113$ & $M_{114} = 122133$ & $M_{148} = 123442$ & $M_{182} = 121342$ \\ $M_{13} = 122111$ & $M_{47} = 111123$ & $M_{81} = 121331$ & $M_{115} = 122313$ & $M_{149} = 122344$ & $M_{183} = 121324$ \\ $M_{14} = 121211$ & $M_{48} = 122333$ & $M_{82} = 121313$ & $M_{116} = 122331$ & $M_{150} = 122343$ & $M_{184} = 121234$ \\ $M_{15} = 121121$ & $M_{49} = 123233$ & $M_{83} = 121133$ & $M_{117} = 123123$ & $M_{151} = 122334$ & $M_{185} = 122341$ \\ $M_{16} = 121112$ & $M_{50} = 123323$ & $M_{84} = 123211$ & $M_{118} = 123132$ & $M_{152} = 123423$ & $M_{186} = 122314$ \\ $M_{17} = 112211$ & $M_{51} = 123332$ & $M_{85} = 123121$ & $M_{119} = 123213$ & $M_{153} = 123432$ & $M_{187} = 122134$ \\ $M_{18} = 112121$ & $M_{52} = 123322$ & $M_{86} = 123112$ & $M_{120} = 123231$ & $M_{154} = 123243$ & $M_{188} = 123455$ \\ $M_{19} = 112112$ & $M_{53} = 123232$ & $M_{87} = 122311$ & $M_{121} = 123312$ & $M_{155} = 123234$ & $M_{189} = 123454$ \\ $M_{20} = 111221$ & $M_{54} = 123223$ & $M_{88} = 122131$ & $M_{122} = 123321$ & $M_{156} = 123342$ & $M_{190} = 123445$ \\ $M_{21} = 111212$ & $M_{55} = 122332$ & $M_{89} = 122113$ & $M_{123} = 123444$ & $M_{157} = 123324$ & $M_{191} = 123453$ \\ $M_{22} = 111122$ & $M_{56} = 122323$ & $M_{90} = 121321$ & $M_{124} = 123433$ & $M_{158} = 123144$ & $M_{192} = 123435$ \\ $M_{23} = 111222$ & $M_{57} = 122233$ & $M_{91} = 121312$ & $M_{125} = 123343$ & $M_{159} = 123414$ & $M_{193} = 123345$ \\ $M_{24} = 112122$ & $M_{58} = 121333$ & $M_{92} = 121231$ & $M_{126} = 123334$ & $M_{160} = 123441$ & $M_{194} = 123452$ \\ $M_{25} = 112212$ & $M_{59} = 123133$ & $M_{93} = 121213$ & $M_{127} = 123422$ & $M_{161} = 121344$ & $M_{195} = 123425$ \\ $M_{26} = 112221$ & $M_{60} = 123313$ & $M_{94} = 121132$ & $M_{128} = 123242$ & $M_{162} = 121343$ & $M_{196} = 123245$ \\ $M_{27} = 121122$ & $M_{61} = 123331$ & $M_{95} = 121123$ & $M_{129} = 123224$ & $M_{163} = 121334$ & $M_{197} = 122345$ \\ $M_{28} = 121212$ & $M_{62} = 112333$ & $M_{96} = 112331$ & $M_{130} = 122342$ & $M_{164} = 123413$ & $M_{198} = 123451$ \\ $M_{29} = 121221$ & $M_{63} = 112322$ & $M_{97} = 112313$ & $M_{131} = 122324$ & $M_{165} = 123431$ & $M_{199} = 123415$ \\ $M_{30} = 122112$ & $M_{64} = 112232$ & $M_{98} = 112133$ & $M_{132} = 122234$ & $M_{166} = 123143$ & $M_{200} = 123145$ \\ $M_{31} = 122121$ & $M_{65} = 112223$ & $M_{99} = 112321$ & $M_{133} = 123411$ & $M_{167} = 123134$ & $M_{201} = 121345$ \\ $M_{32} = 122211$ & $M_{66} = 123122$ & $M_{100} = 112312$ & $M_{134} = 123141$ & $M_{168} = 123341$ & $M_{202} = 112345$ \\ $M_{33} = 123333$ & $M_{67} = 123212$ & $M_{101} = 112231$ & $M_{135} = 123114$ & $M_{169} = 123314$ & $M_{203} = 123456$ \\ $M_{34} = 123222$ & $M_{68} = 123221$ & $M_{102} = 112213$ & $M_{136} = 121341$ & $M_{170} = 112344$ & \\ \hline \end{tabular} \end{center} \newpage \section*{Appendix 2} {\bf Using MrBayes 3.0.} MrBayes 3.0 (Huelsenbeck and Ronquist, 2001; Ronquist and Huelsenbeck, 2003) is a program distributed free of charge and can be downloaded from the WWW at {\tt http://www.mrbayes.net}. The program takes as input an alignment of DNA, RNA, amino acid, or restriction site site data (matrices of morphological characters can be input too). The program uses Markov chain Monte Carlo to approximate the joint posterior probability distribution of the phylogenetic tree, branch lengths, and substitution model parameters. The parameter values sampled by the Markov chain are saved to two files; one file contains the trees that were sampled whereas the other file has the parameter values that were sampled. The program also provides some commands for summarizing the results. The basic steps (and commands) that need to be executed to perform a Bayesian analysis of phylogeny using MrBayes include: (1) reading in the data file (`execute [file name]'); (2) setting the model (using the `lset' and `prset' commands); (3) running the Markov chain Monte Carlo algorithm (using the `mcmc' command); and (4) summarizing the results (using the `sumt' and `sump' commands). The program has extensive online help, which can be reached using the `help' command. We urge the user to explore the available commands and the extensive amount we have written about each by exploring the `help' option. \bigskip \noindent {\bf Analyzing the `toy' example of simulated data.} The data matrix analyzed in numerous places in the text was simulated on the tree of Figure~5 under the HKY85 model of DNA substitution. The specific HKY85 parameter values and the branch lengths used for the simulation can be found in the text. The input file contained the alignment of sequences and the commands: \scriptsize \begin{verbatim} begin data; dimensions ntax=5 nchar=50; format datatype=dna; matrix Species_1 TAACTGTAAAGGACAACACTAGCAGGCCAGACGCACACGCACAGCGCACC Species_2 TGACTTTAAAGGACGACCCTACCAGGGCGGACACAAACGGACAGCGCAGC Species_3 CAAGTTTAGAAAACGGCACCAACACAACAGACGTATGCAACTGACGCACC Species_4 CGAGTTCAGAAGACGGCACCAACACAGCGGACGTATGCAGACGACGCACC Species_5 TGCCCTTAGGAGGCGGCACTAACACCGCGGACGAGTGCGGACAACGTACC ; end; begin mrbayes; lset nst=2 rates=equal; mcmc ngen=1000000 nchains=1 samplefreq=100 printfreq=100; sumt burnin=1001; sump burnin=1001; end; \end{verbatim} \normalsize The actual alignment is in a NEXUS file format. More accurately, the input file format is NEXUS(ish), because we do not implement all of the NEXUS standards in the program, and have extended the format in some (unlawful) ways. The data are contained in the `data block' which starts with a `begin data' command and ends with an `end' command. The next block is specific to the program, and is called a `MrBayes' block. Other programs will simply skip this block of commands, just as MrBayes skips over foreign blocks it does not understand. All of the commands that can be issued to the program via the command line can also be embedded directly into the file. This facilitates batch processing of data sets. The first command sets the model to the HKY85 with no rate variation across sites. The second command runs the MCMC algorithm, and the third and fourth commands summarize the results of the MCMC analysis, discarding the first 1001 samples taken by the chain. Inferences, then, are based on the last 9,000 samples taken from the posterior probability distribution. \bigskip \noindent {\bf Analyzing the leaf beetle data under a complicated model.} The following shows the data and MrBayes block used in the analysis of the Kim et al. (2003) alignment of three different genes. We do not show the entire alignment, though we do show the most relevant portions of the data block. Specifically, we show that you need to specify the datatype as mixed when you perform a simultaneous Bayesian analysis on different types of data: \scriptsize \begin{verbatim} begin data; dimensions ntax=27 nchar=1090; format datatype=mixed(rna:1-516,dna:517-936,protein:937-1090) gap=- missing=?; matrix Orsodacne gGGUAAACCUNAGaA [ 1060 other sites ] DPILYQHLFWFFGHP Chrysomela GGGUAAACCUGAGAA [ 1060 other sites ] DPILYQHLFWFFGHP Altica --------------- [ 1060 other sites ] DPILYQHLFWFFGHP Agelastica GGGUAAACCUGAGAA [ 1060 other sites ] DPILYQHLFWFFGHP Monolepta GGGUAAACCUGAGAA [ 1060 other sites ] DPILYQHLFWFFGHP Phyllobrotica ---------UGANAA [ 1060 other sites ] DPILYQHLFWFFGHP Allochroma GGGUAAaCcUGAgAA [ 1060 other sites ] DPILYQHLFWFFGHP Chrysolina GGGUAAACCUGAGAA [ 1060 other sites ] DPILYQHLFWFFGHP Aphthona GGGUAACCCUGAGAA [ 1060 other sites ] ??????????????? Chaetocnema --------------- [ 1060 other sites ] DPILYQHLFWFFGHP Systena ---CCGACCUGAGAA [ 1060 other sites ] DPILYQHLFWFFGHP Monocesta ----------GAGAA [ 1060 other sites ] DPILYQHLFWFFGHP Disonycha -------------AA [ 1060 other sites ] DPILYQHLFWFFGHP Blepharida --------------- [ 1060 other sites ] DPILYQHLFWFFGHP Galeruca GGGUAAACCUGAGAA [ 1060 other sites ] DPILYQHLFWFFGHP Orthaltica GGGUAAACCUGAGAA [ 1060 other sites ] DPILYQHLFWFFGHP Paropsis GGGUAAACCUGAGAA [ 1060 other sites ] DPILYQHLFWFFGHP Timarcha -----AACCUGAGAA [ 1060 other sites ] DPILYQHLFWFFGHP Zygograma GGGUAAACCUGAGAA [ 1060 other sites ] DPILYQHLFWFFGHP Syneta -----GAACUUACAA [ 1060 other sites ] DPILYQHLFWFFGHP Dibolia ggguaaaccugagaa [ 1060 other sites ] DPILYQHLFWFFGHP Sangariola --------------- [ 1060 other sites ] DPILYQHLFWFFGHP Aulacophora -----------AGAA [ 1060 other sites ] DPILYQHLFWFFGHP Diabrotica GGGUAAACcUGAgAA [ 1060 other sites ] DPILYQHLFWFFGHP Diorhabda -----------AGAA [ 1060 other sites ] DPILYQHLFWFFGHP Schematiza -----????UGAGAA [ 1060 other sites ] DPILYQHLFWFFGHP Oides GGGUAACCCUGAGAA [ 1060 other sites ] DPILYQHLFWFFGHP ; end; begin mrbayes; pairs 22:497, 21:498, 20:499, 19:500, 18:501, 17:502, 16:503, 33:172, 34:171, 35:170, 36:169, 37:168, 38:167, 45:160, 46:159, 47:158, 48:157, 49:156, 50:155, 51:154, 53:153, 54:152, 55:151, 59:150, 60:149, 61:148, 62:147, 63:146, 86:126, 87:125, 88:124, 89:123, 187:484, 186:485, 185:486, 184:487, 183:488, 182:489, 191:295, 192:294, 193:293, 194:292, 195:291, 196:290, 197:289, 198:288, 199:287, 200:286, 201:283, 202:282, 203:281, 204:280, 205:279, 206:278, 213:268, 214:267, 215:266, 216:265, 217:264, 226:259, 227:258, 228:257, 229:256, 230:255, 231:254, 232:253, 233:252, 304:477, 305:476, 306:475, 307:474, 308:473, 316:335, 317:334, 318:333, 319:332, 336:440, 337:439, 338:438, 339:437, 340:436, 341:435, 343:422, 344:421, 345:420, 346:419, 347:418, 348:417, 349:416, 351:414, 352:413, 353:412, 354:411, 355:408, 356:407, 357:406, 358:405, 359:404, 360:403, 361:402, 369:400, 370:399, 371:398, 372:397, 373:396, 376:394, 377:393, 379:392, 380:391, 381:390; charset ambiguously_aligned = 92-103 108-122 234-251 320-327 449-468; charset stems = 22 497 21 498 20 499 19 500 18 501 17 502 16 503 33 172 34 171 35 170 36 169 37 168 38 167 45 160 46 159 47 158 48 157 49 156 50 155 51 154 53 153 54 152 55 151 59 150 60 149 61 148 62 147 63 146 86 126 87 125 88 124 89 123 187 484 186 485 185 486 184 487 183 488 182 489 191 295 192 294 193 293 194 292 195 291 196 290 197 289 198 288 199 287 200 286 201 283 202 282 203 281 204 280 205 279 206 278 213 268 214 267 215 266 216 265 217 264 226 259 227 258 228 257 229 256 230 255 231 254 232 253 233 252 304 477 305 476 306 475 307 474 308 473 316 335 317 334 318 333 319 332 336 440 337 439 338 438 339 437 340 436 341 435 343 422 344 421 345 420 346 419 347 418 348 417 349 416 351 414 352 413 353 412 354 411 355 408 356 407 357 406 358 405 359 404 360 403 361 402 369 400 370 399 371 398 372 397 373 396 376 394 377 393 379 392 380 391 381 390; charset loops = 1-15 23-32 39-44 52 56-58 64-85 90-122 127-145 161-166 173-181 188-190 207-212 218-225 234-251 260-263 269-277 284 285 296-303 309-315 320-331 342 350 362-368 374 375 378 382-389 395 401 409 410 415 423-434 441-472 478-483 490-496 504-516; charset rna = 1-516; charset dna = 517-936; charset protein = 937-1090; charset D2 = 1-516; charset EF1a = 517-936; charset EF1a1st = 517-936\3; charset EF1a2nd = 518-936\3; charset EF1a3rd = 519-936\3; charset CO1aa = 937-1090; partition by_gene_and_pos = 5:rna,EF1a1st,EF1a2nd,EF1a3rd,CO1aa; partition by_gene = 3:rna,EF1a,CO1aa; partition by_gene_and_struct = 4:stems,loops,EF1a,CO1aa; exclude ambiguously_aligned; set partition = by_gene_and_struct; lset applyto=(1) nucmodel=doublet; lset applyto=(2) nucmodel=4by4; lset applyto=(3) nucmodel=codon; lset applyto=(1,2,4) rates=gamma; lset nst=6; prset ratepr=variable aamodelpr=mixed; unlink shape=(all) revmat=(all); mcmc ngen=3000000 nchains=1 samplefreq=100 printfreq=100; sumt burnin=10001; sump burni=10001; end; \end{verbatim} \normalsize The commands in the MrBayes block show how to specify a very complicated model. First, we specify which nucleotides pair with one another using the pairs command. We then specify a number of character sets, using the `charset' command. Specifying character sets saves the hassle of having to type in a long list of character number every time you want to refer to some division of the data (such as a gene). We then specify three character partitions. A character partition divides the data into groups of characters. Each character in the matrix must be assigned to one, and only one, group. For example, one of the partitions we define (by\_gene) divides the characters into three groups. When a data file is executed it sets up a default partition of the data, that groups characters by data type. We need to tell the program which of the four partitions to use (where the four partitions are default, by\_gene\_and\_pos, by\_gene, and by\_gene\_and\_struct). We do this using the set command. Finally, we use lset and prset to specify different models to different groups of characters. In fact, with the applyto option in lset and prset and the link and unlink commands, one can specify a very large number of possible models that currently cannot be implemented with any other phylogeny program. The last three commands in run the MCMC algorithm and then summarize the results. \bigskip \noindent {\bf Analyzing the 104 amino acid alignments.} The analysis of the data collated by Nei et al. (2001) was conceptually simple, though laborious, to set up. The data block, as usual, has the alignment, this time in interleaved format. The MrBayes block has 104 character set definitions, specifies a partition, grouping positions by gene, sets the partition, and then sets up a model in which the parameters are estimated independently for each gene and that enforces the molecular clock. \scriptsize \begin{verbatim} begin data; dimensions ntax=5 nchar=48092; format datatype=protein interleave=yes; matrix [The data for the 104 alignments was here. We do not include it here for obvious reasone (see the nchar command, above).] ; end; begin mrbayes; charset M00007 = 1 - 112; charset M00008 = 113 - 218; charset M00037 = 219 - 671; [There were another 98 character set definitions which we have deleted here.] charset N01447 = 45917 - 46694; charset N01456 = 46695 - 47285; charset N01479 = 47286 - 48092; partition by_gene = 104:M00007,M00008,M00037,[98 other partitions],N01447,N01456,N01479; set autoclose=yes nowarn=yes; set partition=by_gene; outgroup xenopus; lset rates=gamma; prset ratepr=variable aamodel=mixed brlenspr=clock:uniform; unlink shape=(all) aamodel=(all); mcmcp ngen=30000000 nchains=1 samplefreq=1000 savebrlens=yes; end; \end{verbatim} \normalsize \newpage \section*{Appendix 3} {\bf Parameter estimates for the leaf beetle data.} The numbers are the mean and 95\% credible interval of the posterior probability density distribution for each parameter. \begin{center} \begin{tabular}{lc lc lc} Param. & Mean (CI) & Param. & Mean (CI) & Param. & Mean (CI) \\ \hline $V$ & 3.495 (3.209, 3.828) & $\pi_{G}$ & 0.222 (0.180, 0.267) & $\pi_{GAC}$ & 0.012 (0.008, 0.016) \\ $r_{CT}^{(1)}$ & 0.428 (0.187, 0.850) & $\pi_{T}$ & 0.285 (0.240, 0.332) & $\pi_{GAG}$ & 0.007 (0.006, 0.009) \\ $r_{CG}^{(1)}$ & 0.616 (0.166, 1.616) & $\pi_{AAA}$ & 0.023 (0.020, 0.024) & $\pi_{GAT}$ & 0.018 (0.016, 0.019) \\ $r_{AT}^{(1)}$ & 2.130 (0.703, 5.436) & $\pi_{AAC}$ & 0.006 (0.006, 0.008) & $\pi_{GCA}$ & 0.014 (0.012, 0.018) \\ $r_{AG}^{(1)}$ & 0.780 (0.340, 1.594) & $\pi_{AAG}$ & 0.019 (0.014, 0.023) & $\pi_{GCC}$ & 0.023 (0.019, 0.027) \\ $r_{AC}^{(1)}$ & 0.828 (0.214, 2.240) & $\pi_{AAT}$ & 0.005 (0.004, 0.006) & $\pi_{GCG}$ & 0.005 (0.005, 0.005) \\ $r_{CT}^{(2)}$ & 3.200 (2.037, 4.915) & $\pi_{ACA}$ & 0.011 (0.007, 0.013) & $\pi_{GCT}$ & 0.036 (0.034, 0.037) \\ $r_{CG}^{(2)}$ & 0.335 (0.116, 0.683) & $\pi_{ACC}$ & 0.021 (0.017, 0.024) & $\pi_{GGA}$ & 0.019 (0.014, 0.022) \\ $r_{AT}^{(2)}$ & 0.994 (0.522, 1.699) & $\pi_{ACG}$ & 0.006 (0.004, 0.009) & $\pi_{GGC}$ & 0.013 (0.006, 0.015) \\ $r_{AG}^{(2)}$ & 2.805 (1.702, 4.447) & $\pi_{ACT}$ & 0.025 (0.019, 0.027) & $\pi_{GGG}$ & 0.004 (0.004, 0.006) \\ $r_{AC}^{(2)}$ & 1.051 (0.541, 1.880) & $\pi_{AGA}$ & 0.020 (0.013, 0.021) & $\pi_{GGT}$ & 0.018 (0.015, 0.019) \\ $r_{CT}^{(3)}$ & 2.292 (1.471, 3.555) & $\pi_{AGC}$ & 0.016 (0.014, 0.019) & $\pi_{GTA}$ & 0.022 (0.017, 0.028) \\ $r_{CG}^{(3)}$ & 1.021 (0.400, 2.127) & $\pi_{AGG}$ & 0.004 (0.001, 0.007) & $\pi_{GTC}$ & 0.014 (0.008, 0.014) \\ $r_{AT}^{(3)}$ & 1.320 (0.766, 2.184) & $\pi_{AGT}$ & 0.001 (0.001, 0.002) & $\pi_{GTG}$ & 0.014 (0.012, 0.016) \\ $r_{AG}^{(3)}$ & 2.276 (1.424, 3.621) & $\pi_{ATA}$ & 0.003 (0.003, 0.004) & $\pi_{GTT}$ & 0.020 (0.016, 0.020) \\ $r_{AC}^{(3)}$ & 1.041 (0.575, 1.756) & $\pi_{ATC}$ & 0.025 (0.024, 0.029) & $\pi_{TAC}$ & 0.033 (0.030, 0.034) \\ $\omega$ & 0.010 (0.010, 0.012) & $\pi_{ATG}$ & 0.014 (0.009, 0.017) & $\pi_{TAT}$ & 0.011 (0.010, 0.016) \\ $\pi_{AA}$ & 0.001 (0.000, 0.004) & $\pi_{ATT}$ & 0.026 (0.016, 0.029) & $\pi_{TCA}$ & 0.020 (0.017, 0.026) \\ $\pi_{AC}$ & 0.004 (0.000, 0.008) & $\pi_{CAA}$ & 0.015 (0.011, 0.019) & $\pi_{TCC}$ & 0.026 (0.023, 0.033) \\ $\pi_{AG}$ & 0.006 (0.003, 0.012) & $\pi_{CAC}$ & 0.010 (0.009, 0.014) & $\pi_{TCG}$ & 0.015 (0.014, 0.016) \\ $\pi_{AT}$ & 0.122 (0.086, 0.170) & $\pi_{CAG}$ & 0.009 (0.006, 0.011) & $\pi_{TCT}$ & 0.025 (0.024, 0.037) \\ $\pi_{CA}$ & 0.003 (0.000, 0.008) & $\pi_{CAT}$ & 0.009 (0.005, 0.010) & $\pi_{TGC}$ & 0.003 (0.003, 0.005) \\ $\pi_{CC}$ & 0.005 (0.001, 0.013) & $\pi_{CCA}$ & 0.022 (0.021, 0.024) & $\pi_{TGG}$ & 0.014 (0.008, 0.016) \\ $\pi_{CG}$ & 0.257 (0.191, 0.319) & $\pi_{CCC}$ & 0.012 (0.011, 0.014) & $\pi_{TGT}$ & 0.001 (0.001, 0.003) \\ $\pi_{CT}$ & 0.002 (0.000, 0.005) & $\pi_{CCG}$ & 0.008 (0.003, 0.010) & $\pi_{TTA}$ & 0.020 (0.013, 0.025) \\ $\pi_{GA}$ & 0.001 (0.000, 0.003) & $\pi_{CCT}$ & 0.008 (0.007, 0.010) & $\pi_{TTC}$ & 0.045 (0.044, 0.049) \\ $\pi_{GC}$ & 0.284 (0.222, 0.353) & $\pi_{CGA}$ & 0.002 (0.001, 0.004) & $\pi_{TTG}$ & 0.025 (0.025, 0.026) \\ $\pi_{GG}$ & 0.003 (0.000, 0.008) & $\pi_{CGC}$ & 0.009 (0.009, 0.009) & $\pi_{TTT}$ & 0.011 (0.010, 0.011) \\ $\pi_{GT}$ & 0.078 (0.057, 0.106) & $\pi_{CGG}$ & 0.001 (0.000, 0.000) & $\alpha_1$ & 0.422 (0.308, 0.570) \\ $\pi_{TA}$ & 0.145 (0.103, 0.190) & $\pi_{CGT}$ & 0.016 (0.014, 0.016) & $\alpha_2$ & 0.381 (0.296, 0.484) \\ $\pi_{TC}$ & 0.004 (0.001, 0.008) & $\pi_{CTA}$ & 0.005 (0.004, 0.010) & $\alpha_4$ & 0.226 (0.175, 0.288) \\ $\pi_{TG}$ & 0.073 (0.056, 0.093) & $\pi_{CTC}$ & 0.016 (0.015, 0.020) & $m_1$ & 0.708 (0.553, 0.894) \\ $\pi_{TT}$ & 0.003 (0.001, 0.008) & $\pi_{CTG}$ & 0.042 (0.036, 0.046) & $m_2$ & 0.870 (0.732, 1.027) \\ $\pi_{A}$ & 0.252 (0.209, 0.301) & $\pi_{CTT}$ & 0.042 (0.034, 0.048) & $m_3$ & 1.274 (1.171, 1.378) \\ $\pi_{C}$ & 0.239 (0.199, 0.284) & $\pi_{GAA}$ & 0.034 (0.031, 0.044) & $m_4$ & 0.856 (0.651, 1.100) \\ \hline \end{tabular} \end{center} \clearpage %% \begin{filecontents}{Huelsenbeck_Ronquist_SMME_2005.bib} %% % # Citations used in `Huelsenbeck_Ronquist_SMME_2005.tex` (in order) %% % 1. Jukes and Cantor (1969) %% @InProceedings{jukes69, %% author = {T. H. Jukes and C. R. Cantor}, %% editor = {H. N. Munro}, %% title = {Evolution of protein molecules}, %% booktitle = {Mammalian {P}rotein {M}etabolism}, %% publisher = {Academic Press}, %% year = 1969, %% pages = {21--123} %% } %% % 2. (Kimura, 1980; %% @Article{kimura80, %% author = {M. Kimura}, %% title = {A simple method for estimating evolutionary rates of base substitutions through comparative studies of nucleotide sequences}, %% journal = {Journal of Molecular Evolution}, %% year = 1980, %% volume = 16, %% pages = {111--120} %% } %% % 3. Hasegawa et al., 1985) %% @Article{hasegawa85, %% author = {M. Hasegawa and H. Kishino and T. Yano}, %% title = {Dating the human-ape splitting by a molecular clock of mitochondrial {DNA}}, %% journal = {Journal of Molecular Evolution}, %% year = 1985, %% volume = 22, %% pages = {160--174} %% } %% % 4. (Yang, 1993) %% @Article{yang93, %% author = {Z. Yang}, %% title = {Maximum likelihood estimation of phylogeny from {DNA} sequences when substitution rates differ over sites}, %% journal = {Molecular Biology and Evolution}, %% year = 1993, %% volume = 10, %% pages = {1396--1401} %% } %% % 5. Goldman and Yang, 1994; %% @Article{goldman94, %% author = {N. Goldman and Z. Yang}, %% title = {A codon-based model of nucleotide substitution for protein-coding {DNA} sequences}, %% journal = {Molecular Biology and Evolution}, %% year = 1994, %% volume = 11, %% pages = {725--736} %% } %% % 6. Muse and Gaut, 1994 %% @Article{muse94, %% author = {S. V. Muse and B. S. Gaut}, %% title = {A likelihood approach for comparing synonymous and nonsynonymous nucleotide substitution rates with application to the chloroplast genome}, %% journal = {Molecular Biology and Evolution}, %% year = 1994, %% volume = 11, %% pages = {715--724} %% } %% % 7. Tuffley and Steel, 1997 %% @Article{tuffley97, %% author = {C. Tuffley and M. Steel}, %% title = {Links between maximum likelihood and maximum parsimony under a simple model of site substitution}, %% journal = {Bulletin of Mathematical Biology}, %% year = 1997, %% volume = 59, %% pages = {581--607} %% } %% % 8. Huelsenbeck and Ronquist, [YEAR]; (MrBayes) %% @Article{huelsenbeck01c, %% author = {J. P. Huelsenbeck and F. Ronquist}, %% title = {{MRBAYES}: {B}ayesian inference of phylogenetic trees}, %% journal = {Bioinformatics}, %% year = 2001, %% volume = 17, %% pages = {754--755} %% } %% % 9. Ronquist and Huelsenbeck, [YEAR]) (MrBayes) %% @Article{ronquist03, %% author = {F. Ronquist and J. P. Huelsenbeck}, %% title = {MrBayes 3: {B}ayesian phylogenetic inference under mixed models}, %% journal = {Bioinformatics}, %% year = 2003, %% volume = 19, %% pages = {1572--1574} %% } %% % 10. (Felsenstein, 1981) %% @Article{felsenstein81, %% author = {J. Felsenstein}, %% title = {Evolutionary trees from {DNA} sequences: {A} maximum likelihood approach}, %% journal = {Journal of Molecular Evolution}, %% year = 1981, %% volume = 17, %% pages = {368--376} %% } %% % 11. Nielsen and Yang (1998) %% @Article{nielsen98, %% author = {R. Nielsen and Z. Yang}, %% title = {Likelihood models for detecting positively selected amino acid sites and applications to the {HIV-1} envelope gene}, %% journal = {Genetics}, %% year = 1998, %% volume = 148, %% pages = {929--93} %% } %% % 12. Hasegawa et al. (1984; HKY %% @Article{hasegawa84, %% author = {M. Hasegawa and T. Yano and H. Kishino}, %% title = {A new molecular clock of mitochondrial {DNA} and the evolution of {H}ominoids}, %% journal = {Proc. Japan Acad. Ser. B}, %% year = 1984, %% volume = 60, %% pages = {95--98} %% } %% % 13. Hasegawa et al. (1985; HKY %% % 14. Felsenstein's (1981) %% % 15. Swofford, 2002 %% @Book{swofford02, %% author = {D. L. Swofford}, %% title = {{PAUP*}: {P}hylogenetic {A}nalysis {U}sing {P}arsimony and {O}ther {M}ethods}, %% publisher = {Sinauer Associates, Inc.}, %% year = 2002, %% address = {Sunderland, Massachusetts} %% } %% % 16. The program MrBayes (Huelsenbeck and Ronquist, [year]; %% % 17. Ronquist and Huelsenbeck, [year]) %% % 18. Chapter [NUMBER] (Larget, [YEAR]) %% %%%%%%% MISSING %% % 19. Indeed, it has been known for at least a decade now that the interpretation of nonparametric bootstrap values on phylogenetic trees is problematic, for a variety of reasons summarized in [REFS] %% %%%%%%% MISSING %% % 20. Huelsenbeck and Rannala (In Press) %% %%%%%%% MISSING %% % 21. Suzuki and Nei ([YEAR]) %% %%%%%%% MISSING %% % 22. Huelsenbeck and Rannala (In Press) %% % 23. simulation studies that examine the performance of phylogenetic methods (see [REFS]) %% %%%%%%% MISSING %% % 24. Foster ([YEAR]) has relaxed the assumption that nucleotide frequencies are constant over time and %% %%%%%%% MISSING %% % 25. [PEOPLE] ([YEAR]) relaxed the assumption that the GC content is a constant over a phylogenetic tree %% %%%%%%% MISSING %% % 26. Cox test ([REF]) %% %%%%%%% MISSING %% % 27. Jeffreys (1961) %% % 28. Bayes factors can be used to choose among substitution models (Huelsenbeck et al., In Press) %% %%%%%%% MISSING %% % 29. Jukes-Cantor (1969) or %% % 30. Felsenstein (1981) model %% % 31. Kimura (1980), %% % 32. Hasegawa et al.\ (1984,), %% % 33. Hasegawa et al.\ 1985), %% % 34. Felsenstein (1984) %% %%%%%%% MISSING %% % 35. Larget (Chapter [NUMBER]) %% %%%%%%% %% % 36. (Jeffreys, 1961) %% @Book{jeffreys61, %% author = {H. Jeffreys}, %% title = {Theory of {P}robability}, %% publisher = {Oxford University Press}, %% year = 1961, %% address = {Oxford} %% } %% % 37. Tamura and Nei, 1993 %% @Article{tamura93, %% author = {K. Tamura and M. Nei}, %% title = {Estimation of the number of nucleotide substitutions in the control region of mitochondrial DNA in humans and chimpanzees}, %% journal = {Molecular Biology and Evolution}, %% year = 1993, %% volume = 10, %% pages = {512--526} %% } %% % 38. Huelsenbeck et al. (In Press) %% %%%%%%% %% % 39. Kimura (1980) %% % 40. Modeltest (Posada and Crandall, [YEAR]) %% @article{posada98, %% author = {David Posada}, %% title = {Model{T}est: testing the model of {DNA} substitution}, %% journal = {Bioinformatics}, %% year = {1998}, %% volume = {14}, %% pages = {817--818} %% } %% % 41. Kim et al. (2003) %% %%%%%%% %% % 42. Lewis, (YEAR) (Mk) %% @Article{lewis01, %% author = {P. O. Lewis}, %% title = {A likelihood approach to estimating phylogeny from discrete morphological character data}, %% journal = {Systematic Biology}, %% year = 2001, %% volume = 50, %% pages = {913--925} %% } %% % 43. Kim et al. (2003) %% @Article{kim03, %% author = {S. Kim and K. M. Kjer and C. N. Duckett}, %% year = {2003}, %% title = {Comparison between molecular and morphological-based phylogenies of galerucine/alticine leaf beetles ({C}oleoptera: {C}hrysomelidae)}, %% journal = {Insect Systematics and Evolution}, %% volume = {34}, %% pages = {53--64}, %% } %% % 44. Kim et al. (2003) %% % 45. Kim et al. (2003) %% % 46. Kim et al. (2003) %% % 47. Sch\"oniger and von Haeseler (1994) %% @Article{schoniger94, %% author = {M. Sch{\"o}niger and A. von Haeseler}, %% title = {A stochastic model and the evolution of autocorrelated DNA sequences}, %% journal = {Molecular Phylogenetics and Evolution}, %% year = 1994, %% volume = 3, %% pages = {240--247} %% } %% % 48. Sch\"oniger and von Haeseler (1994) %% % 49. Goldman and Yang, 1994; %% % 50. Muse and Gaut, 1994 %% % 51. Goldman and Yang 1994; %% % 52. Muse and Gaut 1994 %% % 53. Nielsen and Yang (1998) %% % 54. program PAML (Yang, [YEAR]) %% %%%%%%% %% % 55. Huelsenbeck and Dyer, In Press %% @Article{huelsenbeck04c, %% author = {J. P. Huelsenbeck and K. Dyer}, %% title = {Bayesian estimation of positively selected sites}, %% journal = {Journal of Molecular Evolution}, %% year = 2004, %% volume = 58, %% pages = {661--672} %% } %% % 56. Poisson (Bishop and Friday, 1987), %% @Article{bishop85, %% author = {M. J. Bishop and A. E. Friday}, %% title = {Evolutionary trees form nucleic acid and protein sequences}, %% journal = {Proceedings of the Royal Society of London, B}, %% year = 1985, %% volume = 226, %% pages = {271--302} %% } %% % 57. Jones (Jones et al., 1992), %% @Article{jones92, %% author = {D. T. Jones and W. R. Taylor and J. M. Thornton}, %% title = {The rapid generation of mutation data matrices from protein sequences}, %% journal = {Computer Applications in the Biosciences (CABIOS)}, %% year = 1992, %% volume = 8, %% pages = {275--282} %% } %% % 58. Dayhoff (Dayhoff et al., 1978), %% @InProceedings{dayhoff78, %% author = {M. O. Dayhoff and R. M. Schwartz and B. C. 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Hasegawa}, %% title = {Plastid genome phylogeny and a model of amino acid substitution for proteins encoded by chloroplast {DNA}}, %% journal = {Journal of Molecular Evolution}, %% year = 2000, %% volume = 50, %% pages = {348--358} %% } %% % 64. Blossum (Henikoff and Henikoff, 1992), and %% @Article{henikoff92, %% author = {S. Henikoff and J. G. Henikoff}, %% title = {Amino acid substitution matrices from protein blocks}, %% journal = {Proceedings of the National Academy of Science, U.S.A.}, %% year = 1992, %% volume = 89, %% pages = {10915--10919} %% } %% % 65. Vt (Muller and Vingron. 2000). %% @Article{muller00, %% author = {T. Muller and M. Vingron}, %% title = {Modeling amino acid replacement}, %% journal = {Journal of Computational Biology}, %% year = 2000, %% volume = 7, %% pages = {761--776} %% } %% % 66. The molecular clock hypothesis states that substitutions accumulate at roughly the same rate along different lineages of a phylogenetic tree ([REF]). %% %%%%%%% MISSING %% % 67. To date, two different models have been proposed for allowing rates to vary across the tree ([REFS]), %% %%%%%%% MISSING %% % 68. To date, two different models have been proposed for allowing rates to vary across the tree ([REFS]), %% % 69. Arnason} {\it et al}.\ 1997) %% %%%%%%% MISSING %% % 70. Nei et al. (2001) %% %%%%%%% MISSING %% % 71. Nei et al. (2001) %% % 72. Nei et al. (2001) %% % 73. Jones et al. (1992) %% % 74. Dayhoff et al., 1978; %% % 75. Whelan and Goldman, 2001 %% % 76. Nei et al. (2001) %% % 77. Lee (1999) %% %%%%%%% MISSING %% % 78. Huelsenbeck and Ronquist, 2001; %% % 79. Ronquist and Huelsenbeck, 2003 %% % 80. Kim et al. (2003) %% % 81. Nei et al. (2001) %% %% \end{filecontents} \bibliographystyle{plain} \bibliography{Huelsenbeck_Ronquist_SMME_2005} %\printindex \end{document} NBISweden-MrBayes-ecfd55c/doc/SMME_paper/src/README.md000066400000000000000000000044771526212227600221740ustar00rootroot00000000000000# Files for building "Bayesian Analysis of Molecular Evolution using MrBayes" - Last modified: fre jul 26, 2019 07:04 - Sign: Johan Nylander ## Description * The files in this directory are "reversed engineered" to be able to build the LaTeX file `Huelsenbeck_Ronquist_SMME_2005.tex`. The build process works, but see **Issues** below. * This is a **draft version** to what was published as > Huelsenbeck J.P., Ronquist F. (2005) Bayesian Analysis of Molecular Evolution > Using MrBayes. Pp 183-226 in: Statistical Methods in Molecular Evolution. Statistics > for Biology and Health. Springer, New York, NY. > > DOI: > > URL: * The style class file `svmult.cls` is from Springer, and was downloaded on 19 July 2019 from [https://sv.sharelatex.com/templates/books/springer's-edited-book-svmult](https://sv.sharelatex.com/templates/books/springer's-edited-book-svmult) ## Files included Huelsenbeck_Ronquist_SMME_2005.tex Huelsenbeck_Ronquist_SMME_2005.bib svmult.cls fig1.png fig2.png fig3.png fig4.png fig5.png fig6.png fig7.png fig8.png fig9.png fig10.png fig11.png fig12.png README.md ## Compile pdflatex Huelsenbeck_Ronquist_SMME_2005.tex bibtex Huelsenbeck_Ronquist_SMME_2005 pdflatex Huelsenbeck_Ronquist_SMME_2005 Or, if `latexmk` is available: latexmk -pdf Huelsenbeck_Ronquist_SMME_2005.tex ## Issues - The bibliography list is not yet complete. See the file `Huelsenbeck_Ronquist_SMME_2005.bib`. - Figures are not original versions. I did not have access to the original image material, so all PNG images are screenshots(!) from the original publication. - "`Overfull \hbox`" issues -- several. I changed font size for some of the verbatim listings in the Appendix 2 to accommodate some warnings. - "`Underfull \hbox`" issues -- several. No action taken. - `LaTeX Font Warning: Font shape `OT1/lmr/bx/sc' undefined`. No action taken. - Use of alternative command: ``` Package amsmath Warning: Foreign command \over; (amsmath) \frac or \genfrac should be used instead (amsmath) on input line 108. ``` This can be adressed by using, for example, `\frac{a}{b}` instead of `{a \over b}`. No action taken. 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\DeclareOption{nosechang}{\let\if@sechang\iffalse} % hanging sectioning titles \def\ClassInfoNoLine#1#2{% \ClassInfo{#1}{#2\@gobble}% } % \DeclareOption{graybox}{% \AtEndOfClass{% Loading color package \RequirePackage{color}% % defining values of gray \definecolor{shadecolor}{gray}{.85}% \definecolor{tintedcolor}{gray}{.80}% \RequirePackage{framed}% % \newenvironment{tinted}{% \def\FrameCommand{\colorbox{tintedcolor}}% \MakeFramed {\FrameRestore}}% {\endMakeFramed}% % \renewenvironment{svgraybox}% {\fboxsep=12pt\relax \begin{shaded}% \list{}{\leftmargin=12pt\rightmargin=2\leftmargin\leftmargin=\z@\topsep=\z@\relax}% \expandafter\item\parindent=\svparindent \hskip-\listparindent}% {\endlist\end{shaded}}% % \renewenvironment{svtintedbox}% {\fboxsep=12pt\relax \begin{tinted}% \list{}{\leftmargin=12pt\rightmargin=2\leftmargin\leftmargin=\z@\topsep=\z@\relax}% \expandafter\item\parindent=\svparindent \relax}% {\endlist\end{tinted}}% % }} % \let\SVMultOpt\@empty \DeclareOption*{\InputIfFileExists{sv\CurrentOption.clo}{% \global\let\SVMultOpt\CurrentOption}{% \ClassWarning{Springer-SVMult}{Specified option or subpackage "\CurrentOption" \MessageBreak not found - passing it to article class \MessageBreak -}\PassOptionsToClass{\CurrentOption}{article}% }} \ProcessOptions\relax \ifx\SVMultOpt\@empty\relax \ClassInfoNoLine{Springer-SVMult}{extra/valid Springer sub-package \MessageBreak not found in option list - using "global" style}{} \fi \LoadClass[\@rticle@options]{article} \raggedbottom % various sizes and settings for contributed works \setlength{\textwidth}{117mm} %\setlength{\textheight}{12pt}\multiply\textheight by 45\relax \setlength{\textheight}{191mm} \setlength{\topmargin}{0cm} \setlength\oddsidemargin {63\p@} \setlength\evensidemargin {63\p@} \setlength\marginparwidth{90\p@} \setlength\headsep {12\p@} \newdimen\svparindent \setlength{\svparindent}{12\p@} \parindent\svparindent \newdimen\bibindent \setlength\bibindent{\parindent} \setlength{\parskip}{\z@ \@plus \p@} \setlength{\hfuzz}{2\p@} \setlength{\arraycolsep}{1.5\p@} \frenchspacing \tolerance=500 \predisplaypenalty=0 \clubpenalty=10000 \widowpenalty=10000 \setlength\footnotesep{7.7\p@} \newdimen\betweenumberspace % dimension for space between \betweenumberspace=5\p@ % number and text of titles \newdimen\headlineindent % dimension for space of \headlineindent=2.5cc % number and gap of running heads % fonts, sizes, and the like \renewcommand\normalsize{% \@setfontsize\normalsize\@xpt\@xiipt \abovedisplayskip 10\p@ % \@plus2\p@ \@minus5\p@ \abovedisplayshortskip \z@ % \@plus3\p@ \belowdisplayshortskip 6\p@ %\@plus3\p@ \@minus3\p@ \belowdisplayskip \abovedisplayskip \let\@listi\@listI} \normalsize \renewcommand\small{% \@setfontsize\small{8.5}{10}% \abovedisplayskip 8.5\p@ % \@plus3\p@ \@minus4\p@ \abovedisplayshortskip \z@ %\@plus2\p@ \belowdisplayshortskip 4\p@ %\@plus2\p@ \@minus2\p@ \def\@listi{\leftmargin\leftmargini \parsep \z@ \@plus\p@ \@minus\p@ \topsep 6\p@ \@plus2\p@ \@minus4\p@ \itemsep\z@}% \belowdisplayskip \abovedisplayskip } % \let\footnotesize=\small % \renewcommand\Large{\@setfontsize\large{14}{16}} \newcommand\LArge{\@setfontsize\Large{16}{18}} \renewcommand\LARGE{\@setfontsize\LARGE{18}{20}} % \newenvironment{petit}{\par\addvspace{6\p@}\small}{\par\addvspace{6\p@}} % % modification of automatic positioning of floating objects \setlength\@fptop{\z@ } \setlength\@fpsep{12\p@ } \setlength\@fpbot{\z@ \@plus 1fil } \def\textfraction{.01} \def\floatpagefraction{.8} \setlength{\intextsep}{20\p@ \@plus 2\p@ \@minus 2\p@} \setlength\textfloatsep{24\p@ \@plus 2\p@ \@minus 4\p@} \setcounter{topnumber}{4} \def\topfraction{.9} \setcounter{bottomnumber}{2} \def\bottomfraction{.7} \setcounter{totalnumber}{6} % % size and style of headings \newcommand{\partnumsize}{\LArge} \newcommand{\partnumstyle}{\bfseries\boldmath} \newcommand{\partsize}{\LARGE} \newcommand{\partstyle}{\bfseries\boldmath} \newcommand{\chapnumsize}{\Large} \newcommand{\chapnumstyle}{\bfseries\boldmath} \newcommand{\chapsize}{\LArge} \newcommand{\chapstyle}{\bfseries\boldmath} \newcommand{\chapauthsize}{\normalsize} \newcommand{\chapauthstyle}{\bfseries\boldmath} \newcommand{\mottosize}{\small} \newcommand{\mottostyle}{\itshape\unboldmath\raggedright} \newcommand{\secsize}{\large} \newcommand{\secstyle}{\bfseries\boldmath} \newcommand{\subsecsize}{\large} \newcommand{\subsecstyle}{\bfseries\itshape\boldmath} \newcommand{\subsubsecstyle}{\bfseries\boldmath} % \def\cleardoublepage{\clearpage\if@twoside \ifodd\c@page\else \hbox{}\newpage\if@twocolumn\hbox{}\newpage\fi\fi\fi} \newcommand{\clearemptydoublepage}{% \clearpage{\pagestyle{empty}\cleardoublepage}} \newcommand{\startnewpage}{\if@openright\clearemptydoublepage\else\clearpage\fi} % MiniTOC % one outputstream for all minitocs \newwrite\minitoc \let\MiniTOC=N % switch for MT processing in .aux files \newcounter{minitocdepth} \setcounter{minitocdepth}{0} % stolen from LaTeX.ltx - read miniTOC and redirect output stream \long\def \protected@immwrite#1#2#3{% \begingroup \let\thepage\relax #2% \let\protect\@unexpandable@protect \edef\reserved@a{\immediate\write#1{#3}}% \reserved@a \endgroup \if@nobreak\ifvmode\nobreak\fi\fi} % \newcommand{\@mtstarttoc}[1] {\begingroup \makeatletter \immediate\write\@auxout{\string\immediate\string\closeout\string\minitoc}% \typeout{input jobname.#1}% \small \@input{\jobname.#1}% \protected@immwrite\@auxout {\let\label\@gobble \let\index\@gobble \let\glossary\@gobble}% {\immediate\openout\minitoc \jobname.#1\relax} \global\@nobreakfalse\endgroup} % \newcommand{\@mtstarttocquiet}[1] {\begingroup \makeatletter \protected@write\@auxout {\let\label\@gobble \let\index\@gobble \let\glossary\@gobble}% {\immediate\openout\minitoc \jobname.#1\relax} \global\@nobreakfalse\endgroup} % \newcommand{\mtaddtocont}[1] {\protected@write \@auxout {\let\label\@gobble \let\index\@gobble \let\glossary\@gobble}% {\string\@mtwritefile{#1}}} % \newcommand{\@mtwritefile}[1]{\if Y\MiniTOC \@temptokena{#1} \immediate\write\minitoc{\the\@temptokena}\fi} \AtEndDocument{\immediate\write\@auxout{\string\immediate\string\closeout\string\minitoc}} \newcommand{\dominitoc}{% switch \let\MiniTOC=Y \protected@immwrite\@auxout{}{\let\MiniTOC=Y}% \ifnum \c@minitocdepth<1 \@mtstarttocquiet{t\thecontribution}\relax \else \@mtstarttoc{t\thecontribution}\par\addvspace\bigskipamount \fi} % redefinition of \part \renewcommand\part{\clearemptydoublepage \thispagestyle{empty} \if@twocolumn \onecolumn \@tempswatrue \else \@tempswafalse \fi \@ifundefined{thispagecropped}{}{\thispagecropped} \secdef\@part\@spart} \def\@part[#1]#2{\ifnum \c@secnumdepth >-2\relax \refstepcounter{part} \addcontentsline{toc}{part}{\partname\ \thepart\thechapterend\hspace{\betweenumberspace}% #1}\else \addcontentsline{toc}{part}{#1}\fi \markboth{}{} {\raggedleft \hyphenpenalty \@M \interlinepenalty\@M \ifnum \c@secnumdepth >-2\relax \normalfont\partnumsize\partnumstyle %\vrule height 34pt width 0pt depth 0pt% \partname\ \thepart %\llap{\smash{\lower 5pt\hbox to\textwidth{\hrulefill}}} \par \vskip 2\p@ \fi \partsize\partstyle #2\par}\@endpart} % % \@endpart finishes the part page % \def\@endpart{\vfil\newpage \if@twoside \hbox{} \thispagestyle{empty} \newpage \fi \if@tempswa \twocolumn \fi} % \def\@spart#1{{\raggedleft \normalfont\partsize\partstyle #1\par}\@endpart} % \newenvironment{partbacktext}{\def\@endpart{\vfil\newpage}} {\thispagestyle{empty} \newpage} % % (re)define sectioning \setcounter{secnumdepth}{3} \def\seccounterend{} \def\seccountergap{\hskip\betweenumberspace} \def\@seccntformat#1{\csname the#1\endcsname\seccounterend\seccountergap\ignorespaces} % \let\firstmark=\botmark % \@ifundefined{thechapterend}{\def\thechapterend{}}{} % \if@sechang \def\sec@hangfrom#1{\setbox\@tempboxa\hbox{#1}% \hangindent\wd\@tempboxa\noindent\box\@tempboxa} \else \def\sec@hangfrom#1{\setbox\@tempboxa\hbox{#1}% \hangindent\z@\noindent\box\@tempboxa} \fi \def\chap@hangfrom#1{\if!#1!\else \@chapapp\ #1\vskip2pt\fi} \def\schap@hangfrom{\chap@hangfrom{}} \newcounter{chapter} \newif\if@mainmatter \@mainmattertrue \newcommand\frontmatter{\startnewpage \@mainmatterfalse\pagenumbering{roman} \setcounter{page}{5}} % \newcommand\mainmatter{\clearemptydoublepage \@mainmattertrue \markboth{}{} \pagenumbering{arabic}} % \newcommand\backmatter{% \setcounter{minitocdepth}{0}% \pagestyle{headings}% \clearemptydoublepage %\@mainmatterfalse \let\appendix=\bppendix \def\bibsection{\chapter*{\refname}\@mkboth{\refname}{\refname}% \addcontentsline{toc}{chapter}{\refname}% \csname biblst@rthook\endcsname\par}% } \renewenvironment{titlepage} {% \cleardoublepage \if@twocolumn \@restonecoltrue\onecolumn \else \@restonecolfalse\newpage \fi \thispagestyle{empty}% \addtocounter{page}\m@ne \def\and{\unskip, } \parindent=\z@ \pretolerance=10000 \rightskip=0pt plus 1fil \large % default size for titlepage \vspace*{2em} % Vertical space above title. }{{\LARGE % each author set in \LARGE \lineskip .5em \@author \par}% \vskip 2cm % Vertical space after author. {\Huge\bfseries\@title \par}% Title set in \Huge size and bold face \vskip 1cm % Vertical space after title. \if!\@subtitle!\else {\LARGE\ignorespaces\@subtitle \par} \vskip 1cm % Vertical space after subtitle. \fi \if!\@date!\else \@date \par \vskip 1.5em % Vertical space after date. \fi \vfill {\Large\bfseries Springer\par} %\vskip 3pt %\itshape % Berlin\enspace Heidelberg\enspace New\kern0.1em York\\ % Hong\kern0.2em Kong\enspace London\\ % Milan\enspace Paris\enspace Tokyo\par \if@restonecol\twocolumn \else \newpage \fi \if@twoside\else \setcounter{page}\@ne \fi \clearheadinfo } \def\@chapapp{\chaptername} \newdimen\mottowidth \newcommand\motto[2][77mm]{% \setlength{\mottowidth}{#1}% \gdef\m@ttotext{#2}} % \newcommand{\processmotto}{\@ifundefined{m@ttotext}{}{% \setbox0=\hbox{\vbox{\hyphenpenalty=50 \begin{flushright} \begin{minipage}{\mottowidth} \vrule\@width\z@\@height21\p@\@depth\z@ \normalfont\mottosize\mottostyle\m@ttotext \end{minipage} \end{flushright}}}% \@tempdima=\pagetotal \advance\@tempdima by\ht0 \ifdim\@tempdima<157\p@ \multiply\@tempdima by-1 \advance\@tempdima by157\p@ \vskip\@tempdima \fi \box0\par \global\let\m@ttotext=\undefined}} \newcommand{\chapsubtitle}[1]{% \gdef\ch@psubtitle{#1}} % \newcommand{\processchapsubtit}{\@ifundefined{ch@psubtitle}{}{% {\normalfont\chapnumsize\chapnumstyle \vskip 14\p@ \ch@psubtitle \par} \global\let\ch@psubtitle=\undefined}} \newcommand{\chapauthor}[1]{% \gdef\ch@pauthor{#1}} % \newcommand{\processchapauthor}{\@ifundefined{ch@pauthor}{}{% {\normalfont\chapauthsize\chapauthstyle \vskip 20\p@ \ch@pauthor \par} \global\let\ch@pauthor=\undefined}} \newcommand\chapter{\startnewpage \@ifundefined{thispagecropped}{}{\thispagecropped} \thispagestyle{bchap}% \if@chapnum\else \begingroup \let\@elt\@stpelt \csname cl@chapter\endcsname \endgroup \fi \global\@topnum\z@ \@afterindentfalse \secdef\@chapter\@schapter} \def\@chapter[#1]#2{\if@chapnum % war mal \ifnum \c@secnumdepth >\m@ne \refstepcounter{chapter}% \if@mainmatter \typeout{\@chapapp\space\thechapter.}% \addcontentsline{toc}{chapter}{\protect \numberline{\thechapter\thechapterend}#1}% \else \addcontentsline{toc}{chapter}{#1}% \fi \else \addcontentsline{toc}{chapter}{#1}% \fi \chaptermark{#1}% \addtocontents{lof}{\protect\addvspace{10\p@}}% \addtocontents{lot}{\protect\addvspace{10\p@}}% \if@twocolumn \@topnewpage[\@makechapterhead{#2}]% \else \@makechapterhead{#2}% \@afterheading \fi} \def\@schapter#1{\if@twocolumn \@topnewpage[\@makeschapterhead{#1}]% \else \@makeschapterhead{#1}% \@afterheading \fi} %%changes position and layout of numbered chapter headings \def\@makechapterhead#1{{\parindent\z@\raggedright\normalfont \hyphenpenalty \@M \interlinepenalty\@M \if@chapnum \chapnumsize\chapnumstyle \@chapapp\ \thechapter\thechapterend\par \vskip 2\p@ \fi \chapsize\chapstyle \ignorespaces#1\par\nobreak \processchapsubtit \processchapauthor \processmotto \ifdim\pagetotal>167\p@ \vskip 11\p@ \else \@tempdima=167\p@\advance\@tempdima by-\pagetotal \vskip\@tempdima \fi}} %%changes position and layout of unnumbered chapter headings \def\@makeschapterhead#1{{\parindent \z@ \raggedright\normalfont \hyphenpenalty \@M \interlinepenalty\@M \chapsize\chapstyle \ignorespaces#1\par\nobreak \processmotto \ifdim\pagetotal>167\p@ \vskip 11\p@ \else \@tempdima=168\p@\advance\@tempdima by-\pagetotal \vskip\@tempdima \fi}} % % dedication environment \newenvironment{dedication} {\clearemptydoublepage \thispagestyle{empty} \vspace*{13\baselineskip} \large\itshape \let\\\@centercr\@rightskip\@flushglue \rightskip\@rightskip \leftskip4cm\parindent\z@\relax \everypar{\parindent=\svparindent\let\everypar\empty}}{\clearpage} % % predefined unnumbered headings \newcommand{\preface}[1][\prefacename]{\chapter*{#1}\markboth{#1}{#1}} \newcommand{\foreword}[1][\forewordname]{\chapter*{#1}\markboth{#1}{#1}} \newcommand{\contributors}[1][\contriblistname]{\chapter*{#1}\markboth{#1}{#1}} \newcommand{\extrachap}[1]{\chapter*{#1}\markboth{#1}{#1}} % same with TOC entry \newcommand{\Extrachap}[1]{\chapter*{#1}\markboth{#1}{#1}% \addcontentsline{toc}{chapter}{#1}} % measures and setting of sections \renewcommand\section{\@startsection{section}{1}{\z@}% {-30\p@}% \p@lus -4\p@ \@minus -4\p@}% {16\p@}% \p@lus 4\p@ \@minus 4\p@}% {\normalfont\secsize\secstyle \rightskip=\z@ \@plus 8em\pretolerance=10000 }} \renewcommand\subsection{\@startsection{subsection}{2}{\z@}% {-30\p@}% \p@lus -4\p@ \@minus -4\p@}% {16\p@}% \p@lus 4\p@ \@minus 4\p@}% {\normalfont\subsecsize\subsecstyle \rightskip=\z@ \@plus 8em\pretolerance=10000 }} \renewcommand\subsubsection{\@startsection{subsubsection}{3}{\z@}% {-24\p@}% \p@lus -4\p@ \@minus -4\p@}% {12\p@}% \p@lus 4\p@ \@minus 4\p@}% {\normalfont\normalsize\subsubsecstyle \rightskip=\z@ \@plus 8em\pretolerance=10000 }} \renewcommand\paragraph{\@startsection{paragraph}{4}{\z@}% {-24\p@}% \p@lus -4\p@ \@minus -4\p@}% {12\p@}% \p@lus 4\p@ \@minus 4\p@}% {\normalfont\normalsize\upshape \rightskip=\z@ \@plus 8em\pretolerance=10000 }} \renewcommand\subparagraph{\@startsection{paragraph}{4}{\z@}% {-18\p@}% \p@lus -4\p@ \@minus -4\p@}% {6\p@}% \p@lus 4\p@ \@minus 4\p@}% {\normalfont\normalsize\itshape \rightskip=\z@ \@plus 8em\pretolerance=10000 }} \newcommand\runinhead{\@startsection{paragraph}{4}{\z@}% {-6\p@}% \p@lus -4\p@ \@minus -4\p@}% {-6\p@}% {\normalfont\normalsize\bfseries\boldmath \rightskip=\z@ \@plus 8em\pretolerance=10000 }} \newcommand\subruninhead{\@startsection{paragraph}{4}{\z@}% {-6\p@}% \p@lus -4\p@ \@minus -4\p@}% {-6\p@}% {\normalfont\normalsize\itshape \rightskip=\z@ \@plus 8em\pretolerance=10000 }} % Appendix \renewcommand\appendix{\par} %article appendix \newcommand\bppendix{\startnewpage %book appendix \pagestyle{headings} \stepcounter{chapter} \setcounter{chapter}{0} \stepcounter{section} \setcounter{section}{0} \setcounter{equation}{0} \setcounter{figure}{0} \setcounter{table}{0} \setcounter{footnote}{0} \let\if@chapnum=\iftrue \def\@chapapp{\appendixname} \renewcommand\thechapter{\@Alph\c@chapter} \renewcommand\thesection{\thechapter.\@arabic\c@section} \renewcommand\thesubsection{\thesection.\@arabic\c@subsection} \renewcommand\theequation{\thechapter.\@arabic\c@equation} \renewcommand\thefigure{\thechapter.\@arabic\c@figure} \renewcommand\thetable{\thechapter.\@arabic\c@table}} \def\hyperhrefextend{\ifx\hyper@anchor\@undefined\else {\@currentHref}\fi} \def\runinsep{} \def\aftertext{\unskip\runinsep} % % \def\@ssect#1#2#3#4#5{% \@tempskipa #3\relax \ifdim \@tempskipa>\z@ \begingroup #4{% \@hangfrom{\hskip #1}% \raggedright \hyphenpenalty \@M \interlinepenalty \@M #5\@@par}% \endgroup \else \def\@svsechd{#4{\hskip #1\relax #5}}% \fi \@xsect{#3}} % \def\@sect#1#2#3#4#5#6[#7]#8{% \ifnum #2>\c@secnumdepth \let\@svsec\@empty \else \refstepcounter{#1}% \protected@edef\@svsec{\@seccntformat{#1}\relax}% \fi \@tempskipa #5\relax \ifdim \@tempskipa>\z@ \begingroup #6\relax \sec@hangfrom{\hskip #3\relax\@svsec}% {\raggedright \hyphenpenalty \@M \interlinepenalty \@M #8\@@par}% \endgroup \csname #1mark\endcsname{#7}% \addcontentsline{toc}{#1}{% \ifnum #2>\c@secnumdepth \else \protect\numberline{\csname the#1\endcsname}% \fi #7}% \ifnum #2>\c@minitocdepth \else \mtaddtocont{\protect\contentsline \ifnum #2>\@ne{mtsec}\else{mtchap}\fi \ifnum #2>\c@secnumdepth {#7}% \else {\protect\numberline{\csname the#1\endcsname}#7}% \fi {\thepage}\hyperhrefextend}% \fi \else \def\@svsechd{% #6\hskip #3\relax \@svsec #8\aftertext\ignorespaces \csname #1mark\endcsname{#7}% \addcontentsline{toc}{#1}{% \ifnum #2>\c@secnumdepth \else \protect\numberline{\csname the#1\endcsname}% \fi #7}}% \fi \@xsect{#5}} % figures and tables are processed in small print \def \@floatboxreset {% \reset@font \small \@setnobreak \@setminipage } \def\fps@figure{htbp} \def\fps@table{htbp} % % Frame for paste-in figures or tables \def\mpicplace#1#2{% #1 =width #2 =height \vbox{\hbox to #1{\vrule\@width \fboxrule \@height #2\hfill}}} % \newenvironment{svgraybox}% {\ClassWarning{Springer-SVMono}{Environment "svgraybox" not available,\MessageBreak switching over to "quotation" environment;\MessageBreak specify documentclass option "graybox",\MessageBreak see SVMono documentation -}% \par\addvspace{6pt} \list{}{\listparindent12\p@% \leftmargin=12\p@% \itemindent \listparindent \rightmargin \leftmargin \parsep \z@ \@plus\p@}% \expandafter\item\parindent=\svparindent \relax\hskip-\listparindent}% {\endlist}% % \newenvironment{svtintedbox}% {\ClassWarning{Springer-SVMono}{Environment "svtintedbox" not available,\MessageBreak switching over to "quotation" environment;\MessageBreak specify documentclass option "graybox",\MessageBreak see SVMono documentation -}% \par\addvspace{6pt} \list{}{\listparindent12\p@% \leftmargin=12\p@% \itemindent \listparindent \rightmargin \leftmargin \parsep \z@ \@plus\p@}% \expandafter\item\parindent=\svparindent \relax\hskip-\listparindent}% {\endlist}% % \renewenvironment{quotation} {\par\addvspace{6pt} \list{}{\listparindent12\p@% \leftmargin=12\p@% \itemindent \listparindent \rightmargin \leftmargin \parsep \z@ \@plus\p@% \small}% \item\relax\hskip-\listparindent} {\endlist} % \renewenvironment{quote} {\par\addvspace{6pt} \list{}{\leftmargin=12\p@% \rightmargin\leftmargin \parsep=3\p@ \small}% \item\relax} {\endlist} % labels of enumerate \renewcommand\labelenumii{\theenumii.} \renewcommand\theenumii{\@alph\c@enumii} % labels of itemize \renewcommand\labelitemi{\textbullet} \renewcommand\labelitemii{\textendash} \let\labelitemiii=\labelitemiv % labels of description \renewcommand*\descriptionlabel[1]{\hspace\labelsep #1\hfil} % fixed indentation for standard itemize-environment \newdimen\svitemindent \setlength{\svitemindent}{\parindent} % make indentations changeable \def\setitemindent#1{\settowidth{\labelwidth}{#1}% \let\setit@m=Y% \leftmargini\labelwidth \advance\leftmargini\labelsep \def\@listi{\leftmargin\leftmargini \labelwidth\leftmargini\advance\labelwidth by -\labelsep \parsep=\parskip \topsep=\medskipamount \itemsep=\parskip \advance\itemsep by -\parsep}} \def\setitemitemindent#1{\settowidth{\labelwidth}{#1}% \let\setit@m=Y% \leftmarginii\labelwidth \advance\leftmarginii\labelsep \def\@listii{\leftmargin\leftmarginii \labelwidth\leftmarginii\advance\labelwidth by -\labelsep \parsep=\parskip \topsep=6\p@ \itemsep=\parskip \advance\itemsep by -\parsep}} % % adjusted environment "description" % if an optional parameter (at the first two levels of lists) % is present, its width is considered to be the widest mark % throughout the current list. \def\description{\@ifnextchar[{\@describe}{\list{}{\labelwidth\z@ \labelsep=12pt\relax %!!!!!!!!! \leftmargini=12pt\relax %!!!!!!!!! \leftmargin=12pt\relax %!!!!!!!!! \itemindent-\leftmargin \let\makelabel\descriptionlabel}}} % \def\describelabel#1{#1\hfil} \def\@describe[#1]{\labelsep=12pt\relax \relax\ifnum\@listdepth=0 \setitemindent{#1}\else\ifnum\@listdepth=1 \setitemitemindent{#1}\fi\fi \list{--}{\let\makelabel\describelabel}} % \def\itemize{% \ifnum \@itemdepth >\thr@@\@toodeep\else \advance\@itemdepth\@ne \ifx\setit@m\undefined \ifnum \@itemdepth=1 \leftmargini=\svitemindent \labelwidth\leftmargini\advance\labelwidth-\labelsep \leftmarginii=\leftmargini \leftmarginiii=\leftmargini \fi \fi \edef\@itemitem{labelitem\romannumeral\the\@itemdepth}% \expandafter\list \csname\@itemitem\endcsname {\def\makelabel##1{\rlap{##1}\hss}}% \fi} % \def\enumerate{% \ifnum \@enumdepth >\thr@@\@toodeep\else \advance\@enumdepth\@ne \ifx\setit@m\undefined \ifnum \@enumdepth=1 \leftmargini=\svitemindent \labelwidth\leftmargini\advance\labelwidth-\labelsep \leftmarginii=\leftmargini \leftmarginiii=\leftmargini \fi \fi \edef\@enumctr{enum\romannumeral\the\@enumdepth}% \expandafter \list \csname label\@enumctr\endcsname {\usecounter\@enumctr\def\makelabel##1{\hss\llap{##1}}}% \fi} % \newdimen\verbatimindent \verbatimindent\parindent \def\verbatim{\advance\@totalleftmargin by\verbatimindent \@verbatim \frenchspacing\@vobeyspaces \@xverbatim} % % special signs and characters \newcommand{\D}{\mathrm{d}} \newcommand{\E}{\mathrm{e}} \let\eul=\E \newcommand{\I}{{\rm i}} \let\imag=\I % % the definition of uppercase Greek characters % Springer likes them as italics to depict variables \DeclareMathSymbol{\Gamma}{\mathalpha}{letters}{"00} \DeclareMathSymbol{\Delta}{\mathalpha}{letters}{"01} \DeclareMathSymbol{\Theta}{\mathalpha}{letters}{"02} \DeclareMathSymbol{\Lambda}{\mathalpha}{letters}{"03} \DeclareMathSymbol{\Xi}{\mathalpha}{letters}{"04} \DeclareMathSymbol{\Pi}{\mathalpha}{letters}{"05} \DeclareMathSymbol{\Sigma}{\mathalpha}{letters}{"06} \DeclareMathSymbol{\Upsilon}{\mathalpha}{letters}{"07} \DeclareMathSymbol{\Phi}{\mathalpha}{letters}{"08} \DeclareMathSymbol{\Psi}{\mathalpha}{letters}{"09} \DeclareMathSymbol{\Omega}{\mathalpha}{letters}{"0A} % the upright forms are defined here as \var \DeclareMathSymbol{\varGamma}{\mathalpha}{operators}{"00} \DeclareMathSymbol{\varDelta}{\mathalpha}{operators}{"01} \DeclareMathSymbol{\varTheta}{\mathalpha}{operators}{"02} \DeclareMathSymbol{\varLambda}{\mathalpha}{operators}{"03} \DeclareMathSymbol{\varXi}{\mathalpha}{operators}{"04} \DeclareMathSymbol{\varPi}{\mathalpha}{operators}{"05} \DeclareMathSymbol{\varSigma}{\mathalpha}{operators}{"06} \DeclareMathSymbol{\varUpsilon}{\mathalpha}{operators}{"07} \DeclareMathSymbol{\varPhi}{\mathalpha}{operators}{"08} \DeclareMathSymbol{\varPsi}{\mathalpha}{operators}{"09} \DeclareMathSymbol{\varOmega}{\mathalpha}{operators}{"0A} % Upright Lower Case Greek letters without using a new MathAlphabet \newcommand{\greeksym}[1]{\usefont{U}{psy}{m}{n}#1} \newcommand{\greeksymbold}[1]{{\usefont{U}{psy}{b}{n}#1}} \newcommand{\allmodesymb}[2]{\relax\ifmmode{\mathchoice {\mbox{\fontsize{\tf@size}{\tf@size}#1{#2}}} {\mbox{\fontsize{\tf@size}{\tf@size}#1{#2}}} {\mbox{\fontsize{\sf@size}{\sf@size}#1{#2}}} {\mbox{\fontsize{\ssf@size}{\ssf@size}#1{#2}}}} \else \mbox{#1{#2}}\fi} % Definition of lower case Greek letters \newcommand{\ualpha}{\allmodesymb{\greeksym}{a}} \newcommand{\ubeta}{\allmodesymb{\greeksym}{b}} \newcommand{\uchi}{\allmodesymb{\greeksym}{c}} \newcommand{\udelta}{\allmodesymb{\greeksym}{d}} \newcommand{\ugamma}{\allmodesymb{\greeksym}{g}} \newcommand{\umu}{\allmodesymb{\greeksym}{m}} \newcommand{\unu}{\allmodesymb{\greeksym}{n}} \newcommand{\upi}{\allmodesymb{\greeksym}{p}} \newcommand{\utau}{\allmodesymb{\greeksym}{t}} % redefines the \vec accent to a bold character - if desired \def\fig@type{arrow}% temporarily abused \ifx\vec@style\fig@type\else \@ifundefined{vec@style}{% \def\vec#1{\ensuremath{\mathchoice {\mbox{\boldmath$\displaystyle\mathbf{#1}$}} {\mbox{\boldmath$\textstyle\mathbf{#1}$}} {\mbox{\boldmath$\scriptstyle\mathbf{#1}$}} {\mbox{\boldmath$\scriptscriptstyle\mathbf{#1}$}}}}% } {\def\vec#1{\ensuremath{\mathchoice {\mbox{\boldmath$\displaystyle#1$}} {\mbox{\boldmath$\textstyle#1$}} {\mbox{\boldmath$\scriptstyle#1$}} {\mbox{\boldmath$\scriptscriptstyle#1$}}}}% } \fi % tensor \def\tens#1{\relax\ifmmode\mathsf{#1}\else\textsf{#1}\fi} % end of proof symbol \newcommand\qedsymbol{\hbox{\rlap{$\sqcap$}$\sqcup$}} \newcommand\qed{\relax\ifmmode\else\unskip\quad\fi\qedsymbol} \newcommand\smartqed{\renewcommand\qed{\relax\ifmmode\qedsymbol\else {\unskip\nobreak\hfil\penalty50\hskip1em\null\nobreak\hfil\qedsymbol \parfillskip=\z@\finalhyphendemerits=0\endgraf}\fi}} % \newif\if@numart \@numarttrue \def\ds@numart{\@numarttrue \@takefromreset{figure}{chapter}% \@takefromreset{table}{chapter}% \@takefromreset{equation}{chapter}% \def\thesection{\@arabic\c@section}% \def\thefigure{\@arabic\c@figure}% \def\thetable{\@arabic\c@table}% \def\theequation{\arabic{equation}}% \def\thesubequation{\arabic{equation}\alph{subequation}}} % \def\ds@book{\@numartfalse \def\thesection{\thechapter.\@arabic\c@section}% \def\thefigure{\thechapter.\@arabic\c@figure}% \def\thetable{\thechapter.\@arabic\c@table}% \def\theequation{\thechapter.\arabic{equation}}% \@addtoreset{section}{chapter}% \@addtoreset{figure}{chapter}% \@addtoreset{table}{chapter}% \@addtoreset{equation}{chapter}} % % Ragged bottom for the actual page \def\thisbottomragged{\def\@textbottom{\vskip\z@ \@plus.0001fil \global\let\@textbottom\relax}} % This is texte.tex % it defines various texts and their translations % called up with documentstyle options \def\switcht@albion{% \def\abbrsymbname{List of Abbreviations and Symbols}% \def\abstractname{Abstract}% \def\ackname{Acknowledgements}% \def\andname{and}% \def\bibname{References}% \def\lastandname{, and}% \def\appendixname{Appendix}% \def\chaptername{Chapter}% \def\claimname{Claim}% \def\conjecturename{Conjecture}% \def\contentsname{Contents}% \def\corollaryname{Corollary}% \def\definitionname{Definition}% \def\emailname{e-mail}% \def\examplename{Example}% \def\exercisename{Exercise}% \def\figurename{Fig.}% \def\forewordname{Foreword}% \def\keywordname{{\bf Key words:}}% \def\indexname{Index}% \def\lemmaname{Lemma}% \def\contriblistname{List of Contributors}% \def\listfigurename{List of Figures}% \def\listtablename{List of Tables}% \def\mailname{{\it Correspondence to\/}:}% \def\noteaddname{Note added in proof}% \def\notename{Note}% \def\partname{Part}% \def\prefacename{Preface}% \def\problemname{Problem}% \def\proofname{Proof}% \def\propertyname{Property}% \def\propositionname{Proposition}% \def\questionname{Question}% \def\refname{References}% \def\remarkname{Remark}% \def\seename{see}% \def\solutionname{Solution}% \def\subclassname{{\it Subject Classifications\/}:}% \def\tablename{Table}% \def\theoremname{Theorem}} \switcht@albion % Names of theorem like environments are already defined % but must be translated if another language is chosen % % French section \def\switcht@francais{\svlanginfo \def\abbrsymbname{Liste des abbr\'eviations et symboles}% \def\abstractname{R\'esum\'e.}% \def\ackname{Remerciements.}% \def\andname{et}% \def\lastandname{ et}% \def\appendixname{Appendice}% \def\bibname{Bibliographie}% \def\chaptername{Chapitre}% \def\claimname{Pr\'etention}% \def\conjecturename{Hypoth\`ese}% \def\contentsname{Table des mati\`eres}% \def\corollaryname{Corollaire}% \def\definitionname{D\'efinition}% \def\emailname{e-mail}% \def\examplename{Exemple}% \def\exercisename{Exercice}% \def\figurename{Fig.}% \def\forewordname{Avant-propos}% \def\keywordname{{\bf Mots-cl\'e:}}% \def\indexname{Index}% \def\lemmaname{Lemme}% \def\contriblistname{Liste des contributeurs}% \def\listfigurename{Liste des figures}% \def\listtablename{Liste des tables}% \def\mailname{{\it Correspondence to\/}:}% \def\noteaddname{Note ajout\'ee \`a l'\'epreuve}% \def\notename{Remarque}% \def\partname{Partie}% \def\prefacename{Pr\'eface}% \def\problemname{Probl\`eme}% \def\proofname{Preuve}% \def\propertyname{Caract\'eristique}% %\def\propositionname{Proposition}% \def\questionname{Question}% \def\refname{Litt\'erature}% \def\remarkname{Remarque}% \def\seename{voir}% \def\solutionname{Solution}% \def\subclassname{{\it Subject Classifications\/}:}% \def\tablename{Tableau}% \def\theoremname{Th\'eor\`eme}% } % % German section \def\switcht@deutsch{\svlanginfo \def\abbrsymbname{Abk\"urzungs- und Symbolverzeichnis}% \def\abstractname{Zusammenfassung}% \def\ackname{Danksagung}% \def\andname{und}% \def\lastandname{ und}% \def\appendixname{Anhang}% \def\bibname{Literaturverzeichnis}% \def\chaptername{Kapitel}% \def\claimname{Behauptung}% \def\conjecturename{Hypothese}% \def\contentsname{Inhaltsverzeichnis}% \def\corollaryname{Korollar}% %\def\definitionname{Definition}% \def\emailname{E-mail}% \def\examplename{Beispiel}% \def\exercisename{\"Ubung}% \def\figurename{Abb.}% \def\forewordname{Geleitwort}% \def\keywordname{{\bf Schl\"usselw\"orter:}}% \def\indexname{Sachverzeichnis}% %\def\lemmaname{Lemma}% \def\contriblistname{Mitarbeiter}% \def\listfigurename{Abbildungsverzeichnis}% \def\listtablename{Tabellenverzeichnis}% \def\mailname{{\it Correspondence to\/}:}% \def\noteaddname{Nachtrag}% \def\notename{Anmerkung}% \def\partname{Teil}% \def\prefacename{Vorwort}% %\def\problemname{Problem}% \def\proofname{Beweis}% \def\propertyname{Eigenschaft}% %\def\propositionname{Proposition}% \def\questionname{Frage}% \def\refname{Literaturverzeichnis}% \def\remarkname{Anmerkung}% \def\seename{siehe}% \def\solutionname{L\"osung}% \def\subclassname{{\it Subject Classifications\/}:}% \def\tablename{Tabelle}% %\def\theoremname{Theorem}% } \def\getsto{\mathrel{\mathchoice {\vcenter{\offinterlineskip \halign{\hfil $\displaystyle##$\hfil\cr\gets\cr\to\cr}}} {\vcenter{\offinterlineskip\halign{\hfil$\textstyle##$\hfil\cr\gets \cr\to\cr}}} {\vcenter{\offinterlineskip\halign{\hfil$\scriptstyle##$\hfil\cr\gets \cr\to\cr}}} {\vcenter{\offinterlineskip\halign{\hfil$\scriptscriptstyle##$\hfil\cr \gets\cr\to\cr}}}}} \def\lid{\mathrel{\mathchoice {\vcenter{\offinterlineskip\halign{\hfil $\displaystyle##$\hfil\cr<\cr\noalign{\vskip1.2\p@}=\cr}}} {\vcenter{\offinterlineskip\halign{\hfil$\textstyle##$\hfil\cr<\cr \noalign{\vskip1.2\p@}=\cr}}} {\vcenter{\offinterlineskip\halign{\hfil$\scriptstyle##$\hfil\cr<\cr \noalign{\vskip\p@}=\cr}}} {\vcenter{\offinterlineskip\halign{\hfil$\scriptscriptstyle##$\hfil\cr <\cr \noalign{\vskip0.9\p@}=\cr}}}}} \def\gid{\mathrel{\mathchoice {\vcenter{\offinterlineskip\halign{\hfil $\displaystyle##$\hfil\cr>\cr\noalign{\vskip1.2\p@}=\cr}}} {\vcenter{\offinterlineskip\halign{\hfil$\textstyle##$\hfil\cr>\cr \noalign{\vskip1.2\p@}=\cr}}} {\vcenter{\offinterlineskip\halign{\hfil$\scriptstyle##$\hfil\cr>\cr \noalign{\vskip\p@}=\cr}}} {\vcenter{\offinterlineskip\halign{\hfil$\scriptscriptstyle##$\hfil\cr >\cr \noalign{\vskip0.9\p@}=\cr}}}}} \def\grole{\mathrel{\mathchoice {\vcenter{\offinterlineskip \halign{\hfil $\displaystyle##$\hfil\cr>\cr\noalign{\vskip-\p@}<\cr}}} {\vcenter{\offinterlineskip\halign{\hfil$\textstyle##$\hfil\cr >\cr\noalign{\vskip-\p@}<\cr}}} {\vcenter{\offinterlineskip\halign{\hfil$\scriptstyle##$\hfil\cr >\cr\noalign{\vskip-0.8\p@}<\cr}}} {\vcenter{\offinterlineskip\halign{\hfil$\scriptscriptstyle##$\hfil\cr >\cr\noalign{\vskip-0.3\p@}<\cr}}}}} \def\bbbr{{\rm I\!R}} %reelle Zahlen \def\bbbm{{\rm I\!M}} \def\bbbn{{\rm I\!N}} %natuerliche Zahlen \def\bbbf{{\rm I\!F}} \def\bbbh{{\rm I\!H}} \def\bbbk{{\rm I\!K}} \def\bbbp{{\rm I\!P}} \def\bbbone{{\mathchoice {\rm 1\mskip-4mu l} {\rm 1\mskip-4mu l} {\rm 1\mskip-4.5mu l} {\rm 1\mskip-5mu l}}} \def\bbbc{{\mathchoice {\setbox0=\hbox{$\displaystyle\rm C$}\hbox{\hbox to\z@{\kern0.4\wd0\vrule\@height0.9\ht0\hss}\box0}} {\setbox0=\hbox{$\textstyle\rm C$}\hbox{\hbox to\z@{\kern0.4\wd0\vrule\@height0.9\ht0\hss}\box0}} {\setbox0=\hbox{$\scriptstyle\rm C$}\hbox{\hbox to\z@{\kern0.4\wd0\vrule\@height0.9\ht0\hss}\box0}} {\setbox0=\hbox{$\scriptscriptstyle\rm C$}\hbox{\hbox to\z@{\kern0.4\wd0\vrule\@height0.9\ht0\hss}\box0}}}} \def\bbbq{{\mathchoice {\setbox0=\hbox{$\displaystyle\rm Q$}\hbox{\raise 0.15\ht0\hbox to\z@{\kern0.4\wd0\vrule\@height0.8\ht0\hss}\box0}} {\setbox0=\hbox{$\textstyle\rm Q$}\hbox{\raise 0.15\ht0\hbox to\z@{\kern0.4\wd0\vrule\@height0.8\ht0\hss}\box0}} {\setbox0=\hbox{$\scriptstyle\rm Q$}\hbox{\raise 0.15\ht0\hbox to\z@{\kern0.4\wd0\vrule\@height0.7\ht0\hss}\box0}} {\setbox0=\hbox{$\scriptscriptstyle\rm Q$}\hbox{\raise 0.15\ht0\hbox to\z@{\kern0.4\wd0\vrule\@height0.7\ht0\hss}\box0}}}} \def\bbbt{{\mathchoice {\setbox0=\hbox{$\displaystyle\rm T$}\hbox{\hbox to\z@{\kern0.3\wd0\vrule\@height0.9\ht0\hss}\box0}} {\setbox0=\hbox{$\textstyle\rm T$}\hbox{\hbox to\z@{\kern0.3\wd0\vrule\@height0.9\ht0\hss}\box0}} {\setbox0=\hbox{$\scriptstyle\rm T$}\hbox{\hbox to\z@{\kern0.3\wd0\vrule\@height0.9\ht0\hss}\box0}} {\setbox0=\hbox{$\scriptscriptstyle\rm T$}\hbox{\hbox to\z@{\kern0.3\wd0\vrule\@height0.9\ht0\hss}\box0}}}} \def\bbbs{{\mathchoice {\setbox0=\hbox{$\displaystyle \rm S$}\hbox{\raise0.5\ht0\hbox to\z@{\kern0.35\wd0\vrule\@height0.45\ht0\hss}\hbox to\z@{\kern0.55\wd0\vrule\@height0.5\ht0\hss}\box0}} {\setbox0=\hbox{$\textstyle \rm S$}\hbox{\raise0.5\ht0\hbox to\z@{\kern0.35\wd0\vrule\@height0.45\ht0\hss}\hbox to\z@{\kern0.55\wd0\vrule\@height0.5\ht0\hss}\box0}} {\setbox0=\hbox{$\scriptstyle \rm S$}\hbox{\raise0.5\ht0\hbox to\z@{\kern0.35\wd0\vrule\@height0.45\ht0\hss}\raise0.05\ht0\hbox to\z@{\kern0.5\wd0\vrule\@height0.45\ht0\hss}\box0}} {\setbox0=\hbox{$\scriptscriptstyle\rm S$}\hbox{\raise0.5\ht0\hbox to\z@{\kern0.4\wd0\vrule\@height0.45\ht0\hss}\raise0.05\ht0\hbox to\z@{\kern0.55\wd0\vrule\@height0.45\ht0\hss}\box0}}}} \def\bbbz{{\mathchoice {\hbox{$\textstyle\sf Z\kern-0.4em Z$}} {\hbox{$\textstyle\sf Z\kern-0.4em Z$}} {\hbox{$\scriptstyle\sf Z\kern-0.3em Z$}} {\hbox{$\scriptscriptstyle\sf Z\kern-0.2em Z$}}}} \let\ts\, \setlength\arrayrulewidth{.5\p@} \def\svhline{% \noalign{\ifnum0=`}\fi\hrule \@height2\arrayrulewidth \futurelet \reserved@a\@xhline} \setlength \labelsep {5\p@} \setlength\leftmargini {17\p@} \setlength\leftmargin {\leftmargini} \setlength\leftmarginii {\leftmargini} \setlength\leftmarginiii {\leftmargini} \setlength\leftmarginiv {\leftmargini} \setlength\labelwidth {\leftmargini} \addtolength\labelwidth{-\labelsep} \def\@listI{\leftmargin\leftmargini \parsep=\parskip \topsep=\medskipamount \itemsep=\parskip \advance\itemsep by -\parsep} \let\@listi\@listI \@listi \def\@listii{\leftmargin\leftmarginii \labelwidth\leftmarginii \advance\labelwidth by -\labelsep \parsep=\parskip \topsep=6\p@ \itemsep=\parskip \advance\itemsep by -\parsep} \def\@listiii{\leftmargin\leftmarginiii \labelwidth\leftmarginiii\advance\labelwidth by -\labelsep \parsep=\parskip \topsep=\z@ \itemsep=\parskip \advance\itemsep by -\parsep \partopsep=\topsep} \setlength\arraycolsep{1.5\p@} \setlength\tabcolsep{1.5\p@} \def\tableofcontents{\chapter*{\contentsname\markboth{{\contentsname}}% {{\contentsname}}} \def\authcount##1{\setcounter{auco}{##1}\setcounter{@auth}{1}} \def\lastand{\ifnum\value{auco}=2\relax \unskip{} \andname\ \else \unskip \lastandname\ \fi}% \def\and{\stepcounter{@auth}\relax \ifnum\value{@auth}=\value{auco}% \lastand \else \unskip, \fi}% \@starttoc{toc}\if@restonecol\twocolumn\fi} \setcounter{tocdepth}{2} \def\l@part#1#2{\addpenalty{\@secpenalty}% \addvspace{1em \@plus\p@}% \begingroup \parindent \z@ \rightskip \z@ \@plus 5em % \hrule\vskip5\p@ \bfseries\boldmath \leavevmode #1\par % \vskip5\p@ % \hrule \vskip\p@ \nobreak \addvspace{1em \@plus\p@}% \endgroup} \def\@dotsep{2} \def\addnumcontentsmark#1#2#3{% \addtocontents{#1}{\protect\contentsline{#2}{\protect\numberline {\thechapter}#3}{\thepage}}} \def\addcontentsmark#1#2#3{% \addtocontents{#1}{\protect\contentsline{#2}{#3}{\thepage}}} \def\addcontentsmarkwop#1#2#3{% \addtocontents{#1}{\protect\contentsline{#2}{#3}{0}}} \def\@adcmk[#1]{\ifcase #1 \or \def\@gtempa{\addnumcontentsmark}% \or \def\@gtempa{\addcontentsmark}% \or \def\@gtempa{\addcontentsmarkwop}% \fi\@gtempa{toc}{chapter}} \def\addtocmark{\@ifnextchar[{\@adcmk}{\@adcmk[3]}} \def\l@chapter#1#2{\par\addpenalty{-\@highpenalty} \addvspace{1.0em \@plus \p@} \@tempdima \tocchpnum \begingroup \parindent \z@ \rightskip \@tocrmarg \advance\rightskip by \z@ \@plus 2cm \parfillskip -\rightskip \pretolerance=10000 \leavevmode \advance\leftskip\@tempdima \hskip -\leftskip {\bfseries\boldmath#1}\ifx0#2\hfil\null \else \nobreak \leaders\hbox{$\m@th \mkern \@dotsep mu\hbox{.}\mkern \@dotsep mu$}\hfill \nobreak\hbox to\@pnumwidth{\hfil #2}% \fi\par \penalty\@highpenalty \endgroup} \newcommand{\tocauthorstyle}{\upshape} \newcommand{\toctitlestyle}{\bfseries} \def\l@title#1#2{\addpenalty{-\@highpenalty} \addvspace{8\p@ \@plus \p@} \@tempdima \z@ \begingroup \tocchpnum \z@ \calctocindent \parindent \z@ \rightskip \@tocrmarg \advance\rightskip by \z@ \@plus 2cm \pretolerance=10000 \parfillskip -\@tocrmarg \leavevmode \advance\leftskip\@tempdima \hskip -\leftskip {\toctitlestyle#1}%\nobreak \leaders\hbox{$\m@th \mkern \@dotsep mu.\mkern \@dotsep mu$}\hfill \nobreak\hbox to\@pnumwidth{\hss #2}% \par \penalty\@highpenalty \endgroup} \def\l@titlech#1#2{\addpenalty{-\@highpenalty} \addvspace{8\p@ \@plus \p@} \@tempdima=\tocchpnum \begingroup \parindent \z@ \rightskip \@tocrmarg \advance\rightskip by \z@ \@plus 2cm \pretolerance=10000 \parfillskip -\@tocrmarg \leavevmode \advance\leftskip\@tempdima \hskip -\leftskip {\toctitlestyle#1}%\nobreak \leaders\hbox{$\m@th \mkern \@dotsep mu.\mkern \@dotsep mu$}\hfill \nobreak\hbox to\@pnumwidth{\hss #2}% \par \penalty\@highpenalty \endgroup} \newcommand{\tocaftauthskip}{\z@} \def\l@author#1#2{%\addpenalty{\@highpenalty} \@tempdima \z@ \begingroup \pretolerance=10000 \parindent \z@ \rightskip \@tocrmarg \advance\rightskip by \z@ \@plus 2cm %\parfillskip -\@tocrmarg \leavevmode \advance\leftskip\@tempdima \hskip -\leftskip {\tocauthorstyle#1}\nobreak %\leaders\hbox{$\m@th \mkern \@dotsep mu.\mkern %\@dotsep mu$}\hfill %\nobreak\hbox to\@pnumwidth{\hss #2}% \par \penalty\@highpenalty \addvspace{\tocaftauthskip}\endgroup} \def\l@authorch#1#2{%\addpenalty{\@highpenalty} \@tempdima=\tocchpnum \begingroup \pretolerance=10000 \parindent \z@ \rightskip \@tocrmarg \advance\rightskip by \z@ \@plus 2cm %\parfillskip -\@tocrmarg \leavevmode \advance\leftskip\@tempdima %\hskip -\leftskip {\tocauthorstyle#1}\nobreak %\leaders\hbox{$\m@th \mkern \@dotsep mu.\mkern %\@dotsep mu$}\hfill %\nobreak\hbox to\@pnumwidth{\hss #2}% \par \penalty\@highpenalty \addvspace{\tocaftauthskip}\endgroup} \newdimen\tocchpnum \newdimen\tocsecnum \newdimen\tocsectotal \newdimen\tocsubsecnum \newdimen\tocsubsectotal \newdimen\tocsubsubsecnum \newdimen\tocsubsubsectotal \newdimen\tocparanum \newdimen\tocparatotal \newdimen\tocsubparanum \tocchpnum=20\p@ % chapter {\bf 88.} \@plus 5.3\p@ \tocsecnum=28.5\p@ % section 88.8. plus 4.722\p@ \tocsubsecnum=36.5\p@ % subsection 88.8.8 plus 4.944\p@ \tocsubsubsecnum=43\p@ % subsubsection 88.8.8.8 plus 4.666\p@ \tocparanum=45\p@ % paragraph 88.8.8.8.8 plus 3.888\p@ \tocsubparanum=53\p@ % subparagraph 88.8.8.8.8.8 plus 4.11\p@ \def\calctocindent{% \tocsectotal=\tocchpnum \advance\tocsectotal by\tocsecnum \tocsubsectotal=\tocsectotal \advance\tocsubsectotal by\tocsubsecnum \tocsubsubsectotal=\tocsubsectotal \advance\tocsubsubsectotal by\tocsubsubsecnum \tocparatotal=\tocsubsubsectotal \advance\tocparatotal by\tocparanum} \calctocindent \def\@dottedtocline#1#2#3#4#5{% \ifnum #1>\c@tocdepth \else \vskip \z@ \@plus.2\p@ {\leftskip #2\relax \rightskip \@tocrmarg \advance\rightskip by \z@ \@plus 2cm \parfillskip -\rightskip \pretolerance=10000 \parindent #2\relax\@afterindenttrue \interlinepenalty\@M \leavevmode \@tempdima #3\relax \advance\leftskip \@tempdima \null\nobreak\hskip -\leftskip {#4}\nobreak \leaders\hbox{$\m@th \mkern \@dotsep mu\hbox{.}\mkern \@dotsep mu$}\hfill \nobreak \hb@xt@\@pnumwidth{\hfil\normalfont \normalcolor #5}% \par}% \fi} % \def\l@section{\@dottedtocline{1}{\tocchpnum}{\tocsecnum}} \def\l@subsection{\@dottedtocline{2}{\tocsectotal}{\tocsubsecnum}} \def\l@subsubsection{\@dottedtocline{3}{\tocsubsectotal}{\tocsubsubsecnum}} \def\l@paragraph{\@dottedtocline{4}{\tocsubsubsectotal}{\tocparanum}} \def\l@subparagraph{\@dottedtocline{5}{\tocparatotal}{\tocsubparanum}} \renewcommand\listoffigures{% \chapter*{\listfigurename \markboth{\listfigurename}{\listfigurename}}% \@starttoc{lof}% } \renewcommand\listoftables{% \chapter*{\listtablename \markboth{\listtablename}{\listtablename}}% \@starttoc{lot}% } \newenvironment{thecontriblist} {\par \addvspace{\bigskipamount} \parindent\z@ \rightskip\z@ \@plus 40\p@ \def\iand{\\[\medskipamount]\let\and=\nand}% \def\nand{\ifhmode\unskip\nobreak\fi\ $\cdot$ }% \let\and=\nand \def\at{\\\let\and=\iand}% } {\par \addvspace{\bigskipamount}} \renewcommand\footnoterule{% \kern-3\p@ \hrule\@width 36mm \kern2.6\p@} \newdimen\foot@parindent \foot@parindent 10.83\p@ \footnotesep 9\p@ \AtBeginDocument{% \renewcommand\@makefntext[1]{% \parindent 12\p@ \noindent \mbox{\@makefnmark} #1}} % \if@spthms % Definition of the "\spnewtheorem" command. % % Usage: % % \spnewtheorem{env_nam}{caption}[within]{cap_font}{body_font} % or \spnewtheorem{env_nam}[numbered_like]{caption}{cap_font}{body_font} % or \spnewtheorem*{env_nam}{caption}{cap_font}{body_font} % % New is "cap_font" and "body_font". It stands for % fontdefinition of the caption and the text itself. % % "\spnewtheorem*" gives a theorem without number. % % A defined spnewthoerem environment is used as described % by Lamport. % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \def\@thmcountersep{.} \def\@thmcounterend{.} \newcommand\nocaption{\noexpand\@gobble} \newdimen\spthmsep \spthmsep=3pt \def\spnewtheorem{\@ifstar{\@sthm}{\@Sthm}} % definition of \spnewtheorem with number \def\@spnthm#1#2{% \@ifnextchar[{\@spxnthm{#1}{#2}}{\@spynthm{#1}{#2}}} \def\@Sthm#1{\@ifnextchar[{\@spothm{#1}}{\@spnthm{#1}}} \def\@spxnthm#1#2[#3]#4#5{\expandafter\@ifdefinable\csname #1\endcsname {\@definecounter{#1}\@addtoreset{#1}{#3}% \expandafter\xdef\csname the#1\endcsname{\expandafter\noexpand \csname the#3\endcsname \noexpand\@thmcountersep \@thmcounter{#1}}% \expandafter\xdef\csname #1name\endcsname{#2}% \global\@namedef{#1}{\@spthm{#1}{\csname #1name\endcsname}{#4}{#5}}% \global\@namedef{end#1}{\@endtheorem}}} \def\@spynthm#1#2#3#4{\expandafter\@ifdefinable\csname #1\endcsname {\@definecounter{#1}% \expandafter\xdef\csname the#1\endcsname{\@thmcounter{#1}}% \expandafter\xdef\csname #1name\endcsname{#2}% \global\@namedef{#1}{\@spthm{#1}{\csname #1name\endcsname}{#3}{#4}}% \global\@namedef{end#1}{\@endtheorem}}} \def\@spothm#1[#2]#3#4#5{% \@ifundefined{c@#2}{\@latexerr{No theorem environment `#2' defined}\@eha}% {\expandafter\@ifdefinable\csname #1\endcsname {\global\@namedef{the#1}{\@nameuse{the#2}}% \expandafter\xdef\csname #1name\endcsname{#3}% \global\@namedef{#1}{\@spthm{#2}{\csname #1name\endcsname}{#4}{#5}}% \global\@namedef{end#1}{\@endtheorem}}}} \def\@spthm#1#2#3#4{\topsep 7\p@ \@plus2\p@ \@minus4\p@ \labelsep=\spthmsep\refstepcounter{#1}% \@ifnextchar[{\@spythm{#1}{#2}{#3}{#4}}{\@spxthm{#1}{#2}{#3}{#4}}} \def\@spxthm#1#2#3#4{\@spbegintheorem{#2}{\csname the#1\endcsname}{#3}{#4}% \ignorespaces} \def\@spythm#1#2#3#4[#5]{\@spopargbegintheorem{#2}{\csname the#1\endcsname}{#5}{#3}{#4}\ignorespaces} \def\normalthmheadings{\def\@spbegintheorem##1##2##3##4{\trivlist \item[\hskip\labelsep{##3##1\ ##2\@thmcounterend}]##4} \def\@spopargbegintheorem##1##2##3##4##5{\trivlist \item[\hskip\labelsep{##4##1\ ##2}]{##4(##3)\@thmcounterend\ }##5}} \normalthmheadings \def\reversethmheadings{\def\@spbegintheorem##1##2##3##4{\trivlist \item[\hskip\labelsep{##3##2\ ##1\@thmcounterend}]##4} \def\@spopargbegintheorem##1##2##3##4##5{\trivlist \item[\hskip\labelsep{##4##2\ ##1}]{##4(##3)\@thmcounterend\ }##5}} % definition of \spnewtheorem* without number \def\@sthm#1#2{\@Ynthm{#1}{#2}} \def\@Ynthm#1#2#3#4{\expandafter\@ifdefinable\csname #1\endcsname {\global\@namedef{#1}{\@Thm{\csname #1name\endcsname}{#3}{#4}}% \expandafter\xdef\csname #1name\endcsname{#2}% \global\@namedef{end#1}{\@endtheorem}}} \def\@Thm#1#2#3{\topsep 7\p@ \@plus2\p@ \@minus4\p@ \@ifnextchar[{\@Ythm{#1}{#2}{#3}}{\@Xthm{#1}{#2}{#3}}} \def\@Xthm#1#2#3{\@Begintheorem{#1}{#2}{#3}\ignorespaces} \def\@Ythm#1#2#3[#4]{\@Opargbegintheorem{#1} {#4}{#2}{#3}\ignorespaces} \def\@Begintheorem#1#2#3{#3\trivlist \item[\hskip\labelsep{#2#1\@thmcounterend}]} \def\@Opargbegintheorem#1#2#3#4{#4\trivlist \item[\hskip\labelsep{#3#1}]{#3(#2)\@thmcounterend\ }} % initialize theorem environment \if@envcntshowhiercnt % show hierarchy counter \def\@thmcountersep{.} \spnewtheorem{theorem}{Theorem}[\envankh]{\bfseries}{\itshape} \@addtoreset{theorem}{chapter} \else % theorem counter only \spnewtheorem{theorem}{Theorem}{\bfseries}{\itshape} \if@envcntreset \@addtoreset{theorem}{chapter} \if@envcntresetsect \@addtoreset{theorem}{section} \fi \fi \fi %definition of divers theorem environments \spnewtheorem*{claim}{Claim}{\itshape}{\rmfamily} \spnewtheorem*{proof}{Proof}{\itshape}{\rmfamily} % \if@envcntsame % all environments like "Theorem" - using its counter \def\spn@wtheorem#1#2#3#4{\@spothm{#1}[theorem]{#2}{#3}{#4}} \else % all environments with their own counter \if@envcntshowhiercnt % show hierarchy counter \def\spn@wtheorem#1#2#3#4{\@spxnthm{#1}{#2}[\envankh]{#3}{#4}} \else % environment counter only \if@envcntreset % environment counter is reset each section \if@envcntresetsect \def\spn@wtheorem#1#2#3#4{\@spynthm{#1}{#2}{#3}{#4} \@addtoreset{#1}{chapter}\@addtoreset{#1}{section}} \else \def\spn@wtheorem#1#2#3#4{\@spynthm{#1}{#2}{#3}{#4} \@addtoreset{#1}{chapter}} \fi \else \let\spn@wtheorem=\@spynthm \fi \fi \fi % \let\spdefaulttheorem=\spn@wtheorem % \spn@wtheorem{case}{Case}{\itshape}{\rmfamily} \spn@wtheorem{conjecture}{Conjecture}{\itshape}{\rmfamily} \spn@wtheorem{corollary}{Corollary}{\bfseries}{\itshape} \spn@wtheorem{definition}{Definition}{\bfseries}{\rmfamily} \spn@wtheorem{example}{Example}{\itshape}{\rmfamily} \spn@wtheorem{exercise}{Exercise}{\bfseries}{\rmfamily} \spn@wtheorem{lemma}{Lemma}{\bfseries}{\itshape} \spn@wtheorem{note}{Note}{\itshape}{\rmfamily} \spn@wtheorem{problem}{Problem}{\bfseries}{\rmfamily} \spn@wtheorem{property}{Property}{\itshape}{\rmfamily} \spn@wtheorem{proposition}{Proposition}{\bfseries}{\itshape} \spn@wtheorem{question}{Question}{\itshape}{\rmfamily} \spn@wtheorem{solution}{Solution}{\bfseries}{\rmfamily} \spn@wtheorem{remark}{Remark}{\itshape}{\rmfamily} % \newenvironment{theopargself} {\def\@spopargbegintheorem##1##2##3##4##5{\trivlist \item[\hskip\labelsep{##4##1\ ##2}]{##4##3\@thmcounterend\ }##5} \def\@Opargbegintheorem##1##2##3##4{##4\trivlist \item[\hskip\labelsep{##3##1}]{##3##2\@thmcounterend\ }}}{} \newenvironment{theopargself*} {\def\@spopargbegintheorem##1##2##3##4##5{\trivlist \item[\hskip\labelsep{##4##1\ ##2}]{\hspace*{-\labelsep}##4##3\@thmcounterend}##5} \def\@Opargbegintheorem##1##2##3##4{##4\trivlist \item[\hskip\labelsep{##3##1}]{\hspace*{-\labelsep}##3##2\@thmcounterend}}}{} % \spn@wtheorem{prob}{\nocaption}{\bfseries}{\rmfamily} \newcommand{\probref}[1]{\textbf{\ref{#1}} } \newenvironment{sol}{\par\addvspace{6pt}\noindent\probref}{\par\addvspace{6pt}} % \fi \def\@takefromreset#1#2{% \def\@tempa{#1}% \let\@tempd\@elt \def\@elt##1{% \def\@tempb{##1}% \ifx\@tempa\@tempb\else \@addtoreset{##1}{#2}% \fi}% \expandafter\expandafter\let\expandafter\@tempc\csname cl@#2\endcsname \expandafter\def\csname cl@#2\endcsname{}% \@tempc \let\@elt\@tempd} % redefininition of the captions for "figure" and "table" environments % \@ifundefined{floatlegendstyle}{\def\floatlegendstyle{\bfseries}}{} \def\floatcounterend{\enspace} \def\capstrut{\vrule\@width\z@\@height\topskip} \@ifundefined{captionstyle}{\def\captionstyle{\normalfont\small}}{} \@ifundefined{instindent}{\newdimen\instindent}{} \long\def\@caption#1[#2]#3{\par\addcontentsline{\csname ext@#1\endcsname}{#1}{\protect\numberline{\csname the#1\endcsname}{\ignorespaces #2}}\begingroup \@parboxrestore\if@minipage\@setminipage\fi \@makecaption{\csname fnum@#1\endcsname}{\ignorespaces #3}\par \endgroup} \def\twocaptionwidth#1#2{\def\first@capwidth{#1}\def\second@capwidth{#2}} % Default: .46\textwidth \twocaptionwidth{.46\textwidth}{.46\textwidth} \def\leftcaption{\refstepcounter\@captype\@dblarg% {\@leftcaption\@captype}} \def\rightcaption{\refstepcounter\@captype\@dblarg% {\@rightcaption\@captype}} \long\def\@leftcaption#1[#2]#3{\addcontentsline{\csname ext@#1\endcsname}{#1}{\protect\numberline{\csname the#1\endcsname}{\ignorespaces #2}}\begingroup \@parboxrestore \vskip\figcapgap \@maketwocaptions{\csname fnum@#1\endcsname}{\ignorespaces #3}% {\first@capwidth}\ignorespaces\hspace{.073\textwidth}\hfill% \endgroup} \long\def\@rightcaption#1[#2]#3{\addcontentsline{\csname ext@#1\endcsname}{#1}{\protect\numberline{\csname the#1\endcsname}{\ignorespaces #2}}\begingroup \@parboxrestore \@maketwocaptions{\csname fnum@#1\endcsname}{\ignorespaces #3}% {\second@capwidth}\par \endgroup} \long\def\@maketwocaptions#1#2#3{% \parbox[t]{#3}{{\floatlegendstyle #1\floatcounterend}#2}} \def\fig@pos{l} \newcommand{\leftfigure}[2][\fig@pos]{\makebox[.4635\textwidth][#1]{#2}} \let\rightfigure\leftfigure \newdimen\figgap\figgap=0.5cm % hgap between figure and sidecaption % \long\def\@makesidecaption#1#2{\@tempdimb=3.6cm \setbox0=\vbox{\hsize=\@tempdimb \captionstyle{\floatlegendstyle #1\floatcounterend}#2}% \ifdim\instindent<\z@ \ifdim\ht0>-\instindent \advance\instindent by\ht0 \typeout{^^JClass-Warning: Legend of \string\sidecaption\space for \@captype\space\csname the\@captype\endcsname ^^Jis \the\instindent\space taller than the corresponding float - ^^Jyou'd better switch the environment. }% \instindent\z@ \fi \else \ifdim\ht0<\instindent \advance\instindent by-\ht0 \advance\instindent by-\dp0\relax \advance\instindent by\topskip \advance\instindent by-11\p@ \else \advance\instindent by-\ht0 \instindent=-\instindent \typeout{^^JClass-Warning: Legend of \string\sidecaption\space for \@captype\space\csname the\@captype\endcsname ^^Jis \the\instindent\space taller than the corresponding float - ^^Jyou'd better switch the environment. }% \instindent\z@ \fi \fi \parbox[b]{\@tempdimb}{\captionstyle{\floatlegendstyle #1\floatcounterend}#2% \ifdim\instindent>\z@ \\ \vrule\@width\z@\@height\instindent \@depth\z@ \fi}} \def\sidecaption{\@ifnextchar[\sidec@ption{\sidec@ption[b]}} % \newbox\bildb@x % \def\sidec@ption[#1]#2\caption{% \setbox\bildb@x=\hbox{\ignorespaces#2\unskip}% \if@twocolumn \ifdim\hsize<\textwidth\else \ifdim\wd\bildb@x<\columnwidth \typeout{Double column float fits into single column - ^^Jyou'd better switch the environment. }% \fi \fi \fi \instindent=\ht\bildb@x \advance\instindent by\dp\bildb@x \if t#1 \else \instindent=-\instindent \fi \@tempdimb=\hsize \advance\@tempdimb by-\figgap \advance\@tempdimb by-\wd\bildb@x \ifdim\@tempdimb<3.6cm \ClassWarning{SVMult}{\string\sidecaption: No sufficient room for the legend; ^^Jusing normal \string\caption}% \unhbox\bildb@x \let\@capcommand=\@caption \else % \ifdim\@tempdimb<4.5cm % \ClassWarning{SVMono}{\string\sidecaption: Room for the legend very narrow; % ^^Jusing \string\raggedright}% \toks@\expandafter{\captionstyle\sloppy \rightskip=\z@\@plus6mm\relax}% \def\captionstyle{\the\toks@}% % \fi \let\@capcommand=\@sidecaption % \leavevmode % \unhbox\bildb@x % \hfill \fi \refstepcounter\@captype \@dblarg{\@capcommand\@captype}} \long\def\@sidecaption#1[#2]#3{\addcontentsline{\csname ext@#1\endcsname}{#1}{\protect\numberline{\csname the#1\endcsname}{\ignorespaces #2}}\begingroup \@parboxrestore \@makesidecaption{\csname fnum@#1\endcsname}{\ignorespaces #3}% \hfill \unhbox\bildb@x \par \endgroup} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \def\fig@type{figure} \def\leftlegendglue{\relax} \newdimen\figcapgap\figcapgap=5\p@ % vgap between figure and caption \newdimen\tabcapgap\tabcapgap=3\p@ % vgap between caption and table \long\def\@makecaption#1#2{% \captionstyle \ifx\@captype\fig@type \vskip\figcapgap \fi \setbox\@tempboxa\hbox{{\floatlegendstyle #1\floatcounterend}% \capstrut #2}% \ifdim \wd\@tempboxa >\hsize {\floatlegendstyle #1\floatcounterend}\capstrut #2\par \else \hbox to\hsize{\leftlegendglue\unhbox\@tempboxa\hfil}% \fi \ifx\@captype\fig@type\else \vskip\tabcapgap \fi} \newcounter{merk} \def\endfigure{\resetsubfig\end@float} \@namedef{endfigure*}{\resetsubfig\end@dblfloat} \def\resetsubfig{\global\let\last@subfig=\undefined} \def\r@setsubfig{\xdef\last@subfig{\number\value{figure}}% \setcounter{figure}{\value{merk}}% \setcounter{merk}{0}} \def\subfigures{\refstepcounter{figure}% \@tempcnta=\value{merk}% \setcounter{merk}{\value{figure}}% \setcounter{figure}{\the\@tempcnta}% \def\thefigure{\if@numart\else\thechapter.\fi \@arabic\c@merk\alph{figure}}% \let\resetsubfig=\r@setsubfig} \def\samenumber{\addtocounter{\@captype}{-1}% \@ifundefined{last@subfig}{}{\setcounter{merk}{\last@subfig}}} % redefinition of the "bibliography" environment % \def\biblstarthook#1{\gdef\biblst@rthook{#1}} % \AtBeginDocument{% \ifx\chpbibl\undefined \def\bibsection{\section*{\refname}\ifx\sectionmark\@gobble\else \markright{\refname}\fi \addcontentsline{toc}{section}{\refname}% \mtaddtocont{\protect\contentsline{mtchap}{\refname}{\thepage}\hyperhrefextend}% \csname biblst@rthook\endcsname\par} \else \def\bibsection{\chapter*{\refname}\@mkboth{\refname}{\refname}% \addcontentsline{toc}{chapter}{\refname}% \csname biblst@rthook\endcsname\par} \fi} \ifx\oribibl\undefined % Springer way of life \renewenvironment{thebibliography}[1]{\bibsection \global\let\biblst@rthook=\undefined \def\@biblabel##1{##1.} \small \list{\@biblabel{\@arabic\c@enumiv}}% {\settowidth\labelwidth{\@biblabel{#1}}% \leftmargin\labelwidth \advance\leftmargin\labelsep \if@openbib \advance\leftmargin\bibindent \itemindent -\bibindent \listparindent \itemindent \parsep \z@ \fi \usecounter{enumiv}% \let\p@enumiv\@empty \renewcommand\theenumiv{\@arabic\c@enumiv}}% \if@openbib \renewcommand\newblock{\par}% \else \renewcommand\newblock{\hskip .11em \@plus.33em \@minus.07em}% \fi \sloppy\clubpenalty4000\widowpenalty4000% \sfcode`\.=\@m} {\def\@noitemerr {\@latex@warning{Empty `thebibliography' environment}}% \endlist} \def\@lbibitem[#1]#2{\item[{[#1]}\hfill]\if@filesw {\let\protect\noexpand\immediate \write\@auxout{\string\bibcite{#2}{#1}}}\fi\ignorespaces} \else % original bibliography is required \let\bibname=\refname \renewenvironment{thebibliography}[1] {\chapter*{\bibname \markboth{\bibname}{\bibname}}% \list{\@biblabel{\@arabic\c@enumiv}}% {\settowidth\labelwidth{\@biblabel{#1}}% \leftmargin\labelwidth \advance\leftmargin\labelsep \@openbib@code \usecounter{enumiv}% \let\p@enumiv\@empty \renewcommand\theenumiv{\@arabic\c@enumiv}}% \sloppy \clubpenalty4000 \@clubpenalty \clubpenalty \widowpenalty4000% \sfcode`\.\@m} {\def\@noitemerr {\@latex@warning{Empty `thebibliography' environment}}% \endlist} \fi \let\if@threecolind\iffalse \def\threecolindex{\let\if@threecolind\iftrue} \def\indexstarthook#1{\gdef\indexst@rthook{#1}} \renewenvironment{theindex} {\if@twocolumn \@restonecolfalse \else \@restonecoltrue \fi \columnseprule \z@ \columnsep 1cc \@nobreaktrue \if@threecolind \begin{multicols}{3}[\chapter*{\indexname}% \else \begin{multicols}{2}[\chapter*{\indexname}% \fi {\csname indexst@rthook\endcsname}]% \global\let\indexst@rthook=\undefined \markboth{\indexname}{\indexname}% \addcontentsline{toc}{chapter}{\indexname}% \parindent\z@ \rightskip\z@ \@plus 40\p@ \parskip\z@ \@plus .3\p@\relax \flushbottom \let\item\@idxitem \def\,{\relax\ifmmode\mskip\thinmuskip \else\hskip0.2em\ignorespaces\fi}% \normalfont\small} {\end{multicols} \global\let\if@threecolind\iffalse \if@restonecol\onecolumn\else\clearpage\fi} \def\idxquad{\hskip 10\p@}% space that divides entry from number \def\@idxitem{\par\setbox0=\hbox{--\,--\,--\enspace}% \hangindent\wd0\relax} \def\subitem{\par\noindent\setbox0=\hbox{--\enspace}% second order \kern\wd0\setbox0=\hbox{--\,--\,--\enspace}% \hangindent\wd0\relax}% indexentry \def\subsubitem{\par\noindent\setbox0=\hbox{--\,--\enspace}% third order \kern\wd0\setbox0=\hbox{--\,--\,--\enspace}% \hangindent\wd0\relax}% indexentry \def\indexspace{\par \vskip 10\p@ \@plus5\p@ \@minus3\p@\relax} % LaTeX does not provide a command to enter the authors institute % addresses. The \institute command is defined here. \newcounter{@inst} \newcounter{@auth} \newcounter{auco} \newdimen\instindent \newbox\authrun \newtoks\authorrunning \newtoks\tocauthor \newbox\titrun \newtoks\titlerunning \newtoks\toctitle \def\clearheadinfo{\gdef\@author{No Author Given}% \gdef\@title{No Title Given}% \gdef\@subtitle{}% \gdef\@institute{}% \gdef\@thanks{}% \global\titlerunning={}\global\authorrunning={}% \global\toctitle={}\global\tocauthor={}} \def\institute#1{\gdef\@institute{#1}} \def\title{\@ifstar\s@title\n@title} \def\s@title#1{\gdef\@title{#1}\ds@numart} \def\n@title#1{\gdef\@title{#1}\ds@book} \def\institutename {\begingroup \if!\@institute!\else \def\thanks##1{\unskip{}}% \def\iand{\\[5pt]\let\and=\nand}% \def\nand{\ifhmode\unskip\nobreak\fi\ $\cdot$ }% \let\and=\nand \def\at{\\\let\and=\iand}% \footnotetext[0]{\kern-\bibindent \ignorespaces\@institute}\vspace{5dd}\fi \endgroup }% \def\@fnsymbol#1{\ensuremath{\ifcase#1\or\star\or{\star\star}\or {\star{\star}\star}\or \dagger\or \ddagger\or \mathchar "278\or \mathchar "27B\or \|\or **\or \dagger\dagger \or \ddagger\ddagger \else\@ctrerr\fi}} \def\inst#1{\unskip$^{#1}$} \def\fnmsep{\unskip$^,$} \def\subtitle#1{\gdef\@subtitle{#1}} \clearheadinfo \def\@bfdottedtocline#1#2#3#4#5{% \ifnum #1>\c@minitocdepth \else \par \if@minipage\else\addvspace{5\p@}\fi {\leftskip #2\relax \rightskip \@tocrmarg \advance\rightskip by \z@ \@plus 2cm \parfillskip -\rightskip \pretolerance=10000 \parindent #2\relax\@afterindenttrue \interlinepenalty\@M \leavevmode \@tempdima #3\relax \advance\leftskip \@tempdima \null\nobreak\hskip -\leftskip {\bfseries#4}\nobreak \leaders\hbox{$\m@th \mkern \@dotsep mu\hbox{.}\mkern \@dotsep mu$}\hfill \nobreak \hb@xt@\@pnumwidth{\hfil\normalfont \normalcolor #5}% \par\addvspace{5\p@}}% \fi} \def\@rmdottedtocline#1#2#3#4#5{% \ifnum #1>\c@minitocdepth \else \vskip \z@ \@plus.2\p@ {\leftskip #2\relax \rightskip \@tocrmarg \advance\rightskip by \z@ \@plus 2cm \parfillskip -\rightskip \pretolerance=10000 \parindent #2\relax\@afterindenttrue \interlinepenalty\@M \leavevmode \@tempdima #3\relax \advance\leftskip \@tempdima \null\nobreak\hskip -\leftskip {#4}\nobreak \leaders\hbox{$\m@th \mkern \@dotsep mu\hbox{.}\mkern \@dotsep mu$}\hfill \nobreak \hb@xt@\@pnumwidth{\hfil\normalfont \normalcolor #5}% \par}% \fi} %def\l@mtchap{\@bfdottedtocline{1}{\z@}{\tocsectotal}} \def\l@mtchap{\@rmdottedtocline{1}{\z@}{\tocsecnum}} \def\l@mtsec{\@rmdottedtocline{1}{\tocsecnum}{\tocsubsecnum}} \newcounter{contribution} \renewcommand\maketitle{\par\startnewpage \stepcounter{section}% \setcounter{section}{0}% \setcounter{subsection}{0}% \setcounter{figure}{0} \setcounter{table}{0} \setcounter{equation}{0} \setcounter{footnote}{0}% \if@numart \stepcounter{chapter}% \addtocounter{chapter}{-1}% \else \refstepcounter{chapter}% \fi \stepcounter{contribution}% \immediate\write\@auxout{\string\immediate\string\closeout\string\minitoc}% \immediate\write\@auxout{\let\MiniTOC=N}% % try to be hyperref-compatible \csname phantomsection\endcsname \begingroup \parindent=\z@ %%%%%%%%% \renewcommand\thefootnote{\@fnsymbol\c@footnote}% % \renewcommand\thefootnote{\@fnsymbol\c@footnote}% \def\@makefnmark{$^{\@thefnmark}$}% \renewcommand\@makefntext[1]{% \noindent \hb@xt@\bibindent{\hss\@makefnmark\enspace}##1\vrule height0pt width0pt depth8pt} % \if@twocolumn \ifnum \col@number=\@ne \@maketitle \else \twocolumn[\@maketitle]% \fi \else \newpage \global\@topnum\z@ % Prevents figures from going at top of page. \@maketitle \fi \@ifundefined{thispagecropped}{}{\thispagecropped} \thispagestyle{bchap}\@thanks % \def\\{\unskip\ \ignorespaces}\def\inst##1{\unskip{}}% \def\thanks##1{\unskip{}}\def\fnmsep{\unskip}% \instindent=\hsize \advance\instindent by-\headlineindent \if@numart % keine Nummer \if!\the\toctitle!\addcontentsline{toc}{title}{\@title}\else \addcontentsline{toc}{title}{\the\toctitle}\fi \else \if!\the\toctitle!\addcontentsline{toc}{titlech}{\protect\numberline{\thechapter\thechapterend}\@title}\else \addcontentsline{toc}{titlech}{\protect\numberline{\thechapter\thechapterend}\the\toctitle}\fi \fi \if@runhead \if!\the\titlerunning!\else \edef\@title{\the\titlerunning}% \fi \global\setbox\titrun=\hbox{\small\rm\unboldmath\if@numart\else \@seccntformat{chapter}\fi \ignorespaces\@title}% \ifdim\wd\titrun>\instindent \typeout{Title too long for running head. Please supply}% \typeout{a shorter form with \string\titlerunning\space prior to \string\maketitle}% \global\setbox\titrun=\hbox{\small\rm Title Suppressed Due to Excessive Length}% \fi \xdef\@title{\copy\titrun}% \fi % \if!\the\tocauthor!\relax {\def\and{\noexpand\protect\noexpand\and}% \protected@xdef\toc@uthor{\@author}}% \else \def\\{\noexpand\protect\noexpand\newline}% \protected@xdef\scratch{\the\tocauthor}% \protected@xdef\toc@uthor{\scratch}% \fi \addtocontents{toc}{\noexpand\protect\noexpand\authcount{\the\c@auco}}% \if@numart \addcontentsline{toc}{author}{\toc@uthor}% \else \addcontentsline{toc}{authorch}{\toc@uthor}% \fi \if@runhead \if!\the\authorrunning! \value{@inst}=\value{@auth}% \setcounter{@auth}{1}% \else \edef\@author{\the\authorrunning}% \fi \global\setbox\authrun=\hbox{\small\unboldmath\@author\unskip}% \ifdim\wd\authrun>\instindent \typeout{Names of authors too long for running head. 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In vim: %%%% JN ':set textwidth=99' followed by ':set colorcolumn=+1' %%%% JN: NOTES: %%%% JN: Since we are not yet ready with the text, placement of tables (figures) are %%%% JN: not a priority. This will have to be set at a later stage. \begin{document} \title{MrBayes version 3.2 Manual: \\ Tutorials and Model Summaries} \date{\large Draft version, July 2020} \author{Fredrik Ronquist, John P. Huelsenbeck, \\ Maxim Teslenko, Chi Zhang \\ Johan A. A. Nylander} \maketitle \tableofcontents \newpage \frontmatter \chapter{Preface}\label{preface} When we started working on MrBayes in 1999--2000, we knew that there was a need for a Bayesian MCMC package for phylogenetics, which was more flexible and easier to use than the software available then. Nevertheless, we were astonished by how quickly users took MrBayes to their hearts. The original release paper \citep{huelsenbeck01c}, which appeared in Bioinformatics in 2001, quickly became a fast-track paper in Computer Science, as did the release note for version 3 \citep{ronquist03}, published in Bioinformatics in 2003. To date, these two papers have accumulated more than 20,000 citations. With the release of version 3.2 \citep{ronquist12a}, MrBayes has come of age. Through the years, we have added to, extended, and rewritten the program to cover most of the models used in standard statistical phylogenetic analyses today. We have also implemented a number of techniques to speed up calculations, improve convergence, and facilitate Bayesian model averaging and model choice. Version 3.2 has also undergone considerably more testing than previous versions of MrBayes. This is not to say that MrBayes 3.2 is bug free but it should be considerably more stable than previous versions. As in previous versions, we have done our best to document all of the available models and tools in the online help and in this manual. Version 3.2 of MrBayes also marks the end of the road for us in terms of major development. Extending the original code has become increasingly difficult, and with version 3.2 we are at a point where we feel we need to explore new approaches to Bayesian phylogenetics. Perhaps most importantly, model specifications in MrBayes are strongly limited by the constraints of the Nexus language. In a separate project, RevBayes, we hope to provide a generic computing environment that allows users to build complex phylogenetic models interactively, from small building blocks. However, such flexibility requires more of the user, and it may not be popular with everyone. For standard analyses, MrBayes will remain adequate for years to come and it is our intention to maintain the code base as long as the program remains heavily used and we have the resources to do so. The MrBayes project would not have been possible without the help from many people. First and foremost, we would like to thank Maxim Teslenko, who has played an important role in fixing bugs and adding functionality to MrBayes during the last couple of years. Among other things, Maxim is responsible for a large part of the work involved in supporting BEAGLE, and in implementing the stepping-stone method for estimating marginal likelihoods. Maxim has also contributed several sections to this manual. We are grateful for the financial support of the project provided by the Swedish Research Council, the National Institutes of Health, and the National Science Foundation. We are also deeply indebted to students, colleagues and numerous users of MrBayes, who have helped the project along in important ways. We are not going to try to enumerate them here, as any attempt to do so is likely to result in major omissions. However, we do want to express the overwhelming feeling of gratitude we feel for the generosity with which people have shared ideas, bug fixes and other valuable tips through the years. This feedback alone makes all the hours we have put into developing MrBayes worthwhile. Thank you, all of you! Last but not least, we would like to thank our families for the unwavering support they have provided throughout the project. During intense programming periods, or when we have taught MrBayes workshops around the world, they have had to cope with absent-minded fathers, aloof visitors, and absent husbands. We realize that the childish enthusiasm we have shown when a new model resulted in some incomprehensible numbers scrolling by on the screen has been poor compensation. Thank you so much for all of your support and your sacrifices; we love you! \vspace{1cm} November, 2011\hfill\\ \textit{Fredrik Ronquist}\hfill\\ \textit{John Huelsenbeck}\hfill\\ \mainmatter \chapter{Introduction}\label{intro} MrBayes 3 is a program for Bayesian inference and model choice across a large space of phylogenetic and evolutionary models. The program has a command-line interface and should run on a variety of computer platforms, including large computer clusters and multicore machines. Depending on the settings, MrBayes analyses may demand a lot of your machine, both in terms of memory and processor speed. Many users therefore run more challenging analyses on dedicated computing machines or clusters. Several computing centers around the globe provide web access to such services. This said, many standard analyses run fine on common desktop machines. This manual explains how to use the program. After introducing you to the program in this chapter, we first walk you through a simple analysis (chapter \ref{tutorialSimple}), which will get you started, and a more complex partitioned analysis, which uses more of the program's capabilities (chapter \ref{tutorialPartitioned}). This is followed by a set of shorter tutorials covering a range of common types of analyses (chapter \ref{tutorialAdvanced}). We then cover the capabilities of the program in more detail (chapter \ref{advancedTopics}), followed by some details on the evolutionary models that are implemented (chapter \ref{evolutionaryModels}). The manual ends with a series of diagrams giving a graphical overview of the models and some proposal mechanisms implemented in the program (Appendix \ref{appendixOverview}). For more detailed information about commands and options in MrBayes, see the command reference that can either be downloaded from the program web site or generated from the program itself (see section \ref{gettingHelp} below). All the information in the command reference is also available on-line when using the program. The manual assumes that you are familiar with the basic concepts of Bayesian phylogenetics. If you are new to the subject, we recommend one of several recent reviews \citep{lewis01, holder03, ronquist10}. The early papers introducing Bayesian phylogenetic methods \citep{li96, mau96, rannala96, mau97, larget99, mau99, newton99} are also worthwhile reading. The basic MCMC techniques are described in \citet{metropolis53} and \citet{hastings70}, and the Metropolis-coupled MCMC used by MrBayes was introduced by \citet{geyer91}. Some recent general textbooks on Bayesian inference and MCMC methods include \citet{gilks96a}, \citet{carlin00}, \citet{gelman03}, and \citet{gamerman06}. \section{Conventions Used in this Manual} Throughout the document, we use \texttt{typewriter font} for things you see on screen or in a data file, and \tb{bold typewriter font} for things you should type in. Alternative commands, options, file names, etc are also given in \texttt{typewriter font}. Furthermore, a ``\texttt{MrBayes >}'' will indicate the prompt in MrBayes, while the ``\texttt{\$}'' indicates your computer prompt (inside your shell/Terminal). \section{Acquiring and Installing MrBayes} MrBayes 3 is distributed without charge by download from the MrBayes web site, \url{http://mrbayes.net}, or from the linked code repository on GitHub \url{https://github.com/NBISweden/MrBayes}. If someone has given you a copy of MrBayes 3, we strongly suggest that you download the most recent version from the official MrBayes site. The site also gives detailed information about installation on different platforms (Window, Macintosh, Unix) and describes how you can report bugs or contribute to the project. MrBayes 3 is a plain-vanilla program that uses a command line interface and therefore behaves virtually the same on all platforms --- Macintosh, Windows and Unix. Perhaps the easiest way of installing the program is to compile it from source code in a Unix environment, as described in the \texttt{README} and \texttt{INSTALL} documents in the MrBayes repository. The program may also be available as binaries that run on Windows or Macintosh computers. MrBayes comes with reasonably fast code for computing likelihoods, partly implemented using so-called SIMD (single instruction, multiple data) technologies like SSE, AVX and FMA. However, MrBayes will run faster with the specialized BEAGLE library \citep{ayres12}, which makes even better use of the available computing resources. For instance, BEAGLE allows you to take advantage of the processing power of your graphics card, which the standard MrBayes code does not. Installing the BEAGLE library and linking it to MrBayes can be challenging; please follow the instructions in the \texttt{INSTALL} file. MrBayes is available both in a serial version and in a parallel version that uses MPI (message passing interface) instructions to distribute computations across several processors or processor cores. The serial version does not support multithreading, which means that you will not be able to utilize more than one core on a multi-core machine for a single MrBayes analysis. If you want to utilize all cores, you need to run the MPI version of MrBayes. The MPI version must be compiled from source and will only run under Unix/Linux. Most Windows machines allow you to install a parallel Linux partition, and all Macintosh computers come with a Unix system under the hood, which will support MPI. Refer to the \texttt{README} and \texttt{INSTALL} documents for further instructions on how to compile and run the MPI version. The MrBayes package comes with example data files. These are intended to show various types of analyses you can perform with the program, and you can use them as templates for your own analyses. In the tutorials given in chapters \ref{tutorialSimple} to \ref{tutorialAdvanced}, you can learn more about how to set up various types of analyses based on these example data files. \newpage \section{Getting Started} Start MrBayes by double-clicking the application icon (or typing \tb{./mb} or simply \tb{mb} depending on your system) and you will see the following information: \begin{figure}[H] \centering \begin{BVerbatim} MrBayes v3.2.7 (Bayesian Analysis of Phylogeny) Distributed under the GNU General Public License Type "help" or "help " for information on the commands that are available. Type "about" for authorship and general information about the program. MrBayes > \end{BVerbatim} \end{figure} Note the \texttt{MrBayes >} prompt at the bottom, which tells you that MrBayes is ready for your commands. \subsubsection{Changing the Size of the MrBayes Window} Some MrBayes commands will output a lot of information and write fairly long lines, so you may want to change the size of the MrBayes window to make it easier to read the output. On Macintosh and Unix machines, you should be able to increase the window size simply by dragging the margins. On a Windows machine, you cannot increase the size of the window beyond the preset value by simply dragging the margins but you can change both the size of the screen buffer and the console window by right-clicking on the title bar of the MrBayes window and then selecting ``Properties'' in the menu that appears. Make sure the ``Layout'' tab is selected in the window that appears, and then set the ``Screen Buffer Size'' and ``Window Size'' to the desired values. \section{Getting Help} \label{gettingHelp} At the \texttt{MrBayes >} prompt, type \tb{help} to see a list of the commands available in MrBayes. Most commands allow you to set values (options) for different parameters. If you type \tb{help }, where \texttt{} is any of the listed commands, you will see the help information for that command as well as a description of the available options. For most commands, you will also see a list of the current settings at the end. Try, for instance, \tb{help lset} or \tb{help mcmc}. The \texttt{lset} settings table at the end should look like this: \begin{figure}[h] \centering \begin{BVerbatim} Parameter Options Current Setting ------------------------------------------------------------------ Nucmodel 4by4/Doublet/Codon/Protein 4by4 Nst 1/2/6/Mixed 1 Code Universal/Vertmt/Invermt/Yeast/Mycoplasma/ Ciliate/Echinoderm/Euplotid/Metmt Universal Ploidy Haploid/Diploid/Zlinked Diploid Rates Equal/Gamma/LNorm/Propinv/ Invgamma/Adgamma/Kmixture Equal Ngammacat 4 Nlnormcat 4 Nmixtcat 4 Nbetacat 5 Omegavar Equal/Ny98/M3 Equal Covarion No/Yes No Coding All/Variable/Informative/Nosingletons Noabsencesites/Nopresencesites/ Nosingletonabsence/Nosingletonpresence All Parsmodel No/Yes No ------------------------------------------------------------------ \end{BVerbatim} \end{figure} Note that MrBayes 3 supports abbreviations of commands and options, so in many cases it is sufficient to type the first few letters of a command or option instead of the full name. A complete list of commands and options can be generated as an ASCII text file by giving the command \texttt{manual} to MrBayes. See the output of the command \tb{help manual} for more information. \section{Reporting and Fixing Bugs} If you find a bug in MrBayes, we are grateful if you tell us about it using the bug reporting functions of GitHub, as explained on the MrBayes web site (\url{www.mrbayes.net}). Advanced users may be interested in fixing bugs themselves in the source code. Refer to section \ref{modifyingOrExtendingMrBayes} of this manual for information on how to contribute bug fixes, improved algorithms or expanded functionality to other users of MrBayes. \section{License and Warranty} MrBayes is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3 of the License, or (at your option) any later version. The program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details (\url{http://www.gnu.org/copyleft/gpl.html}). \section{Citing the Program} If you wish to cite the program, you can simply refer to the most recent release note \citep{ronquist12b}. That is, \begin{quote}\footnotesize Ronquist, F., M. Teslenko, P. van der Mark, D. Ayres, A. Darling, S. H\"{o}hna, B. Larget, L. Liu, M. A. Suchard, and J. P. Huelsenbeck. 2012. MrBayes 3.2: efficient Bayesian phylogenetic inference and model choice across a large model space. Systematic Biology 61:539--542. DOI: 10.1093/sysbio/sys029 \end{quote} This manual should be cited as an online publication, for example: \begin{quote}\footnotesize Ronquist, F., J. P. Huelsenbeck, M. Teslenko, C, Zhang, and J. A. A Nylander (2020) MrBayes version 3.2 manual: tutorials and model summaries. Distributed with the software from \url{https://github.com/NBISweden/MrBayes} \end{quote} For more tips on citations, you can run the \texttt{citations} command in the program, which will give you a number of other relevant citations for the program and its models and algorithms. \chapter{Tutorial: A Simple Analysis}\label{tutorialSimple} This section walks you through a simple MrBayes example analysis to get you started. It is based on the \texttt{primates.nex} data file and will guide you through a basic Bayesian MCMC analysis of phylogeny, explaining the most important features of the program. There are two versions of the tutorial. You will first find a Quick-Start version for impatient users who want to get an analysis started immediately. The rest of the section contains a much more detailed description of the same analysis. \section{Quick Start Version} There are four steps to a typical Bayesian phylogenetic analysis using MrBayes: \begin{enumerate} \item Read the Nexus data file \item Set the evolutionary model \item Run the analysis \item Summarize the samples \end{enumerate} In more detail, each of these steps is performed as described in the following paragraphs: \tb{1}. At the \texttt{MrBayes >} prompt, type \tb{execute primates.nex}. This will bring the data into the program. When you only give the data file name (\texttt{primates.nex}), MrBayes assumes that the file is in the current directory. If this is not the case, you have to use the full or relative path to your data file, for example \texttt{execute ../examples/primates.nex}. If you are running your own data file for this tutorial, beware that it may contain some MrBayes commands that can change the behavior of the program; delete those commands or put them in square brackets to follow this tutorial. \tb{2}. At the \texttt{MrBayes >} prompt, type \tb{lset nst=6 rates=invgamma}. This sets the evolutionary model to the GTR substitution model with gamma-distributed rate variation across sites and a proportion of invariable sites (``GTR + I + $\Gamma$''). If your data are not DNA or RNA, if you want to invoke a different model, or if you want to use non-default priors, refer to the rest of this manual and the Appendix for more help. \tb{3.1}. At the \texttt{MrBayes >} prompt, type \tb{mcmc ngen=20000 samplefreq=100}\\ \tb{printfreq=100 diagnfreq=1000}. This will start the Markov chain Monte Carlo simulation. It will also ensure that you get at least 200 samples from the posterior probability distribution, and that diagnostics are calculated every 1,000 generations. For larger data sets you probably want to run the analysis longer and sample less frequently. The default sample and print frequency is 500, the default diagnostic frequency is 5,000, and the default run length is 1,000,000. You can find the predicted remaining time to completion of the analysis in the last column printed to screen. \tb{3.2}. If the standard deviation of split frequencies is below $0.01$ after 20,000 generations, stop the run by answering \tb{no} when the program asks \texttt{Continue the analysis? (yes/no)}. Otherwise, keep adding generations until the value falls below $0.01$. If you are interested mainly in the well-supported parts of the tree, a standard deviation below $0.05$ may be adequate. \tb{4.1}. Type \tb{sump} to summarize the parameter values using the same burn-in as the diagnostics in the \texttt{mcmc} command. The program will output a table with summaries of the samples of the substitution model parameters, including the mean, mode, and 95 \% credibility interval (region of Highest Posterior Density, HPD) of each parameter. Make sure that the potential scale reduction factor (PSRF) is reasonably close to $1.0$ for all parameters; if not, you need to run the analysis longer. The same applies for the effective sample size (ESS). If the ESSs are somewhere below 100, you may want to run longer. \tb{4.2}. Summarize the trees using the same burn-in as the \texttt{mcmc} command by typing \tb{sumt}. The program will output a cladogram with the posterior probabilities for each split and a phylogram with mean branch lengths. Both trees will also be printed to a file that can be read by FigTree \citep{rambaut12} and other tree-drawing programs, such as TreeView \citep{page96} and Mesquite \citep{maddison06}. It does not have to be more complicated than this; however, as you get more proficient you will probably want to know more about what is happening behind the scenes. The rest of this section explains each of the steps in more detail and introduces you to all the implicit assumptions you are making and the machinery that MrBayes uses in order to perform your analysis. \section{Thorough Version} \subsection{Getting Data into MrBayes} To get data into MrBayes, you need a so-called Nexus file that contains aligned nucleotide or amino acid sequences, morphological (``standard'') data, restriction site (binary) data, or any mix of these four data types. The Nexus data file is often generated by another program, such as Mesquite \citep{maddison06}. Note, however, that MrBayes version 3 does not support the full Nexus standard, so you may have to do a little editing of the file for MrBayes to process it properly. In particular, MrBayes uses a fixed set of symbols~\footnote{The supported symbols are \{A, C, G, T, R, Y, M, K, S, W, H, B, V, D, N\} for DNA data, \{A, C, G, U, R, Y, M, K, S, W, H, B, V, D, N\} for RNA data, \{A, R, N, D, C, Q, E, G, H, I, L, K, M, F, P, S, T, W, Y, V, X\} for protein data, \{0, 1\} for restriction (binary) data, and \{0, 1, 2, 3, 4, 5, 6, 5, 7, 8, 9\} for standard (morphology) data.} for each data type and does not support user-defined symbols. In addition to the standard one-letter ambiguity symbols for DNA and RNA listed above, ambiguity can also be expressed using the Nexus parenthesis notation. For instance, if there is uncertainty whether a taxon have state 2 or 3 for a specific character, it can be coded as (23) (or (2,3)). The Nexus format also allows coding taxa as polymorphic --- where a taxon truly have both character states 2 and 3. One would then use braces instead of paranthesis: \{23\}. MrBayes, however, does not make any distinction between polymorphic states or uncertainty in states, but treat them the same way (as uncertainty), so it does not matter whether you use parentheses or braces. If you have other symbols in your matrix than the ones supported by MrBayes, you need to replace them before processing the \texttt{DATA} block in MrBayes. You also need to remove the \texttt{Equate} and \texttt{Symbols} statements in the \texttt{Format} line if they are included. Unlike the Nexus standard, MrBayes supports data blocks that contain mixed data types as described in the tutorial in chapter \ref{tutorialPartitioned}. To put the data into MrBayes type \tb{execute $<$filename$>$} at the \texttt{MrBayes >} prompt, where \tb{$<$filename$>$} is the name of the input file. To process our example file, type \tb{execute primates.nex} or simply \tb{exe primates.nex} to save some typing (MrBayes allows you to use the shortest unambiguous version of a command). Note that the input file must be located in the same folder (directory) where you started the MrBayes application (or else you will have to give the path to the file) and the name of the input file should not have blank spaces, or it will have to be quoted. If everything proceeds normally, MrBayes will acknowledge that it has read the data in the \texttt{DATA} block of the Nexus file by outputting some information about the file read in. \subsection{Specifying a Model} All of the commands are entered at the \texttt{MrBayes >} prompt. At a minimum two commands, \texttt{lset} and \texttt{prset}, are required to specify the evolutionary model that will be used in the analysis. Usually, it is also a good idea to check the model settings prior to the analysis using the \texttt{showmodel} command. In general, \texttt{lset} is used to define the structure of the model and \texttt{prset} is used to define the prior probability distributions on the parameters of the model. In the following, we will specify a GTR + I + $\Gamma$ model (a General Time Reversible model with a proportion of invariable sites and a gamma-shaped distribution of rates across sites) for the evolution of the mitochondrial sequences and we will check all of the relevant priors. We assume that you are familiar with the common stochastic models of molecular evolution. In general, a good start is to type \tb{help lset}. Ignore the help information for now and concentrate on the table at the bottom of the output, which specifies the current settings. It should look like this: \begin{figure}[h] \centering \begin{BVerbatim} Model settings for partition 1: Parameter Options Current Setting ------------------------------------------------------------------ Nucmodel 4by4/Doublet/Codon/Protein 4by4 Nst 1/2/6/Mixed 1 Code Universal/Vertmt/Invermt/Yeast/Mycoplasma/ Ciliate/Echinoderm/Euplotid/Metmt Universal Ploidy Haploid/Diploid/Zlinked Diploid Rates Equal/Gamma/LNorm/Propinv/ Invgamma/Adgamma/Kmixture Equal Ngammacat 4 Nlnormcat 4 Nmixtcat 4 Nbetacat 5 Omegavar Equal/Ny98/M3 Equal Covarion No/Yes No Coding All/Variable/Informative/Nosingletons Noabsencesites/Nopresencesites/ Nosingletonabsence/Nosingletonpresence All Parsmodel No/Yes No ------------------------------------------------------------------ \end{BVerbatim} \end{figure} First, note that the table is headed by \texttt{Model settings for partition 1}. By default, MrBayes divides the data into one partition for each type of data you have in your \texttt{DATA} block. If you have only one type of data, all data will be in a single partition by default. How to change the partitioning of the data will be explained in the tutorial in chapter \ref{tutorialPartitioned}. The \texttt{Nucmodel} setting allows you to specify the general type of DNA model. The \texttt{Doublet} option is for the analysis of paired stem regions of ribosomal DNA and the \texttt{Codon} option is for analyzing the DNA sequence in terms of its codons. We will analyze the data using a standard nucleotide substitution model, in which case the default \texttt{4by4} option is appropriate, so we will leave \texttt{Nucmodel} at its default setting. The general structure of the substitution model is determined by the \texttt{Nst} setting. By default, all substitutions have the same rate (\texttt{Nst=1}), corresponding to the F81 model (or the JC model if the stationary state frequencies are forced to be equal using the \texttt{prset} command, see below). We want the GTR model (\texttt{Nst=6}) instead of the F81 model so we type \tb{lset nst=6}. MrBayes should acknowledge that it has changed the model settings. The \texttt{Code} setting is only relevant if the \texttt{Nucmodel} is set to \texttt{Codon}. The \texttt{Ploidy} setting is also irrelevant for us. However, we need to change the \texttt{Rates} setting from the default \texttt{Equal} (no rate variation across sites) to \texttt{Invgamma} (gamma-shaped rate variation with a proportion of invariable sites). Do this by typing \tb{lset rates=invgamma}. Again, MrBayes will acknowledge that it has changed the settings. We could have changed both \texttt{lset} settings at once if we had typed \tb{lset nst=6 rates=invgamma} in a single line. We will leave the \texttt{Ngammacat} setting (the number of discrete categories used to approximate the gamma distribution) at the default of 4. In most cases, four rate categories are sufficient. It is possible to increase the accuracy of the likelihood calculations by increasing the number of rate categories. However, the time it will take to complete the analysis will increase in direct proportion to the number of rate categories you use, and the effects on the results will be negligible in most cases. Of the remaining settings, it is only \texttt{Covarion} and \texttt{Parsmodel} that are relevant for single nucleotide models. We will use neither the parsimony model nor the covariotide model for our data, so we will leave these settings at their default values. If you type \tb{help lset} now to verify that the model is correctly set, the table should look like this: \begin{figure}[H] \centering \begin{BVerbatim} Model settings for partition 1: Parameter Options Current Setting ------------------------------------------------------------------ Nucmodel 4by4/Doublet/Codon/Protein 4by4 Nst 1/2/6/Mixed 6 Code Universal/Vertmt/Invermt/Yeast/Mycoplasma/ Ciliate/Echinoderm/Euplotid/Metmt Universal Ploidy Haploid/Diploid/Zlinked Diploid Rates Equal/Gamma/LNorm/Propinv/ Invgamma/Adgamma/Kmixture Invgamma Ngammacat 4 Nlnormcat 4 Nmixtcat 4 Nbetacat 5 Omegavar Equal/Ny98/M3 Equal Covarion No/Yes No Coding All/Variable/Informative/Nosingletons Noabsencesites/Nopresencesites/ Nosingletonabsence/Nosingletonpresence All Parsmodel No/Yes No ------------------------------------------------------------------ \end{BVerbatim} \end{figure} \subsection{Setting the Priors} We now need to set the priors for our model. There are six types of parameters in the model: the topology, the branch lengths, the four stationary frequencies of the nucleotides, the six different nucleotide substitution rates, the proportion of invariable sites, and the shape parameter of the gamma distribution of rate variation. The default priors in MrBayes work well for most analyses, and we will not change any of them for now. By typing \tb{help prset} you can obtain a list of the default settings for the parameters in your model. The table at the end of the help information reads: \begin{Verbatim}[fontsize=\footnotesize] Model settings for partition 1: Parameter Options Current Setting ------------------------------------------------------------------ Tratiopr Beta/Fixed Beta(1.0,1.0) Revmatpr Dirichlet/Fixed Dirichlet(1.0,1.0,1.0,1.0,1.0,1.0) Aamodelpr Fixed/Mixed Fixed(Poisson) Aarevmatpr Dirichlet/Fixed Dirichlet(1.0,1.0,...) Omegapr Dirichlet/Fixed Dirichlet(1.0,1.0) Ny98omega1pr Beta/Fixed Beta(1.0,1.0) Ny98omega3pr Uniform/Exponential/Fixed Exponential(1.0) M3omegapr Exponential/Fixed Exponential Codoncatfreqs Dirichlet/Fixed Dirichlet(1.0,1.0,1.0) Statefreqpr Dirichlet/Fixed Dirichlet(1.0,1.0,1.0,1.0) Shapepr Uniform/Exponential/Fixed Exponential(1.0) Ratecorrpr Uniform/Fixed Uniform(-1.0,1.0) Pinvarpr Uniform/Fixed Uniform(0.0,1.0) Covswitchpr Uniform/Exponential/Fixed Uniform(0.0,100.0) Symdirihyperpr Uniform/Exponential/Fixed Fixed(Infinity) Topologypr Uniform/Constraints/Fixed/ Uniform Speciestree Brlenspr Unconstrained/Clock/Fixed Unconstrained:GammaDir(1.0,0.100,1.0,1.0) Treeagepr Gamma/Uniform/Fixed/ Gamma(1.00,1.00) Truncatednormal/Lognormal/ Offsetlognormal/Offsetgamma/ Offsetexponential Speciationpr Uniform/Exponential/Fixed Exponential(10.0) Extinctionpr Beta/Fixed Beta(1.0,1.0) Fossilizationpr Beta/Fixed Beta(1.0,1.0) SampleStrat Random/Diversity/Cluster/ Random FossilTip Sampleprob 1.00000000 Popsizepr Lognormal/Gamma/Uniform/ Gamma(1.0,10.0) Normal/Fixed Popvarpr Equal/Variable Equal Nodeagepr Unconstrained/Calibrated Unconstrained Clockratepr Fixed/Normal/Lognormal/ Fixed(1.00) Exponential/Gamma Clockvarpr Strict/Cpp/TK02/WN/IGR/ILN Strict Cppratepr Fixed/Exponential Exponential(0.10) Cppmultdevpr Fixed Fixed(0.40) TK02varpr Fixed/Exponential/Uniform Exponential(1.00) WNvarpr Fixed/Exponential/Uniform Exponential(10.00) IGRvarpr Fixed/Exponential/Uniform Exponential(1.00) ILNvarpr Fixed/Exponential/Uniform Exponential(1.00) Ratepr Fixed/Variable=Dirichlet Fixed Generatepr Fixed/Variable=Dirichlet Fixed ------------------------------------------------------------------ \end{Verbatim} We need to focus on \texttt{Revmatpr} (for the six substitution rates of the GTR rate matrix), \texttt{Statefreqpr} (for the stationary nucleotide frequencies of the GTR rate matrix), \texttt{Shapepr} (for the shape parameter of the gamma distribution of rate variation), \texttt{Pinvarpr} (for the proportion of invariable sites), \texttt{Topologypr} (for the topology), and \texttt{Brlenspr} (for the branch lengths). The default prior probability density is a flat Dirichlet (all values are $1.0$) for both \texttt{Revmatpr} and \texttt{Statefreqpr}. This is appropriate if we want estimate these parameters from the data assuming no prior knowledge about their values. It is possible to fix the rates and nucleotide frequencies but this is generally not recommended. However, it is occasionally necessary to fix the nucleotide frequencies to be equal, for instance in specifying the JC and SYM models. This would be achieved by using \texttt{prset statefreqpr=fixed(equal)}. If we wanted to specify a prior that put more emphasis on equal nucleotide frequencies than the default flat Dirichlet prior, we could for instance use \texttt{prset statefreqpr=Dirichlet(10,10,10,10)} or, for even more emphasis on equal frequencies, \texttt{prset statefreqpr=Dirichlet(100,100,100,100)}. The sum of the numbers in the Dirichlet distribution determines how focused the distribution is, and the balance between the numbers determines the expected proportion of each nucleotide (in the order A, C, G, and T). Usually, there is a connection between the parameters in the Dirichlet distribution and the observations. For example, you can think of a Dirichlet(150,100,90,140) distribution as one arising from observing (roughly) 150 A's, 100 C's, 90 G's and 140 T's in some set of reference sequences. If the reference sequences are independent but clearly relevant to the analysis of your sequences, it might be reasonable to use those numbers as a prior in your analysis. In our analysis, we will be cautious and leave the prior on state frequencies at its default setting. If you have changed the setting according to the suggestions above, you need to change it back by typing \tb{prset statefreqpr=Dirichlet(1,1,1,1)} or \tb{prs st=Dir(1,1,1,1)} if you want to save some typing. Similarly, we will leave the prior on the substitution rates at the default flat Dirichlet(1,1,1,1) distribution. The \texttt{Shapepr} parameter determines the prior for the $\alpha$ (shape) parameter of the gamma distribution of rate variation. We will leave it at its default setting, an Exponential distribution with mean 1.0. This will put highest probability on low $\alpha$ values (see section \ref{gammaDistributedRateModel}). The prior for the proportion of invariable sites is set with \texttt{Pinvarpr}. The default setting is a uniform distribution between 0 and 1, an appropriate setting if we don't want to assume any prior knowledge about the proportion of invariable sites. For topology, the default \texttt{Uniform} setting for the \texttt{Topologypr} parameter puts equal probability on all distinct, fully resolved topologies. The alternative is to introduce some constraints on the tree topology, but we will not attempt that in this analysis. The \texttt{Brlenspr} parameter can either be set to unconstrained, clock-constrained, or fixed. If \texttt{Fixed} is chosen then branch lengths will not be updated by the MCMC in the program but will be fixed to the current tree (requires a user-provided starting tree). %%%%% JN: Add reference to description of user provided starting tree? For trees without a molecular clock (``unconstrained'') there are currently five different branch length priors that can be applied. The default is a compound Dirichlet prior, which essentially puts a prior on the total length on the tree, and then assigns prior lengths on individual branches by dividing the tree length using a Dirichlet prior. %%%% JN: Add text here More details can be found in build in help documentation (by issuing \tb{help prset}). \subsection{Checking the Model} To check the model before we start the analysis, type \tb{showmodel}. This will give an overview of the model settings. In our case, the output will be as follows: \begin{Verbatim}[fontsize=\footnotesize] Model settings: Data not partitioned -- Datatype = DNA Nucmodel = 4by4 Nst = 6 Substitution rates, expressed as proportions of the rate sum, have a Dirichlet prior (1.00,1.00,1.00,1.00,1.00,1.00) Covarion = No # States = 4 State frequencies have a Dirichlet prior (1.00,1.00,1.00,1.00) Rates = Invgamma The distribution is approximated using 4 categories. Shape parameter is exponentially distributed with parameter (1.00). Proportion of invariable sites is uniformly dist- ributed on the interval (0.00,1.00). Active parameters: Parameters --------------------- Revmat 1 Statefreq 2 Shape 3 Pinvar 4 Ratemultiplier 5 Topology 6 Brlens 7 --------------------- 1 -- Parameter = Revmat Type = Rates of reversible rate matrix Prior = Dirichlet(1.00,1.00,1.00,1.00,1.00,1.00) 2 -- Parameter = Pi Type = Stationary state frequencies Prior = Dirichlet 3 -- Parameter = Alpha Type = Shape of scaled gamma distribution of site rates Prior = Exponential(1.00) 4 -- Parameter = Pinvar Type = Proportion of invariable sites Prior = Uniform(0.00,1.00) 5 -- Parameter = Ratemultiplier Type = Partition-specific rate multiplier Prior = Fixed(1.0) 6 -- Parameter = Tau Type = Topology Prior = All topologies equally probable a priori Subparam. = V 7 -- Parameter = V Type = Branch lengths Prior = Unconstrained:GammaDir(1.0,0.1000,1.0,1.0) \end{Verbatim} Note that we have seven types of parameters in our model. All of these parameters, except the rate multiplier, will be estimated during the analysis (to fix them to some predetermined values, use the \texttt{prset} command and specify a fixed prior). To see more information about each parameter, including its starting value, use the \texttt{showparams} command. The \texttt{startvals} command allows one to set the starting values, separately for each chain if desired. \subsection{Setting up the Analysis} The analysis is started by issuing the \texttt{mcmc} command. However, before doing this, we recommend that you review the run settings by typing \tb{help mcmc}. In our case, we will get the following table at the bottom of the output: \begin{Verbatim}[xleftmargin=15mm,fontsize=\footnotesize] Parameter Options Current Setting -------------------------------------------------------- Ngen 1000000 Nruns 2 Nchains 4 Temp 0.100000 Reweight , 0.00 v 0.00 ^ Swapfreq 1 Nswaps 1 Samplefreq 500 Printfreq 1000 Printall Yes/No Yes Printmax 8 Mcmcdiagn Yes/No Yes Diagnfreq 5000 Diagnstat Avgstddev/Maxstddev Avgstddev Minpartfreq 0.10 Allchains Yes/No No Allcomps Yes/No No Relburnin Yes/No Yes Burnin 0 Burninfrac 0.25 Stoprule Yes/No No Stopval 0.05 Savetrees Yes/No No Checkpoint Yes/No Yes Checkfreq 2000 Filename primates.nex.

Startparams Current/Reset Current Starttree Current/Random/ Current Parsimony Nperts 0 Data Yes/No Yes Ordertaxa Yes/No No Append Yes/No No Autotune Yes/No Yes Tunefreq 100 -------------------------------------------------------- \end{Verbatim} The \texttt{Ngen} setting is the number of generations for which the analysis will be run. It is useful to run a small number of generations first to make sure the analysis is correctly set up and to get an idea of how long it will take to complete a longer analysis. We will start with 20,000 generations but you may want to start with an even smaller number for a larger data set. To change the \texttt{Ngen} setting without starting the analysis we use the \texttt{mcmcp} command, which is equivalent to \texttt{mcmc} except that it does not start the analysis. Type \tb{mcmcp ngen=20000} to set the number of generations to 20,000. You can type \tb{help mcmc} to confirm that the setting was changed appropriately. By default, MrBayes will run two simultaneous, completely independent analyses starting from different random trees (\texttt{Nruns=2}). Running more than one analysis simultaneously allows MrBayes to calculate convergence diagnostics on the fly, which is helpful in determining when you have a good sample from the posterior probability distribution. The idea is to start each run from a different, randomly chosen tree. In the early phases of the run, the two runs will sample very different trees but when they have reached convergence (when they produce a good sample from the posterior probability distribution), the two tree samples should be very similar. To make sure that MrBayes compares tree samples from the different runs, check that \texttt{Mcmcdiagn} is set to yes and that \texttt{Diagnfreq} is set to some reasonable value. The default value of 5,000 is more appropriate for a larger analysis, so change the setting so that we compute diagnostics every 1,000th generation instead by typing \tb{mcmcp diagnfreq=1000}. MrBayes will now calculate various run diagnostics every \texttt{Diagnfreq} generation and print them to a file with the name \texttt{.mcmc}. The most important diagnostic, a measure of the similarity of the tree samples in the different runs, will also be printed to screen every \texttt{Diagnfreq} generation. Every time the diagnostics are calculated, either a fixed number of samples (\texttt{burnin}) or a percentage of samples (\texttt{burninfrac}) from the beginning of the chain is discarded. The \texttt{relburnin} setting determines whether a fixed burnin (\texttt{relburnin=no}) or a burnin percentage (\texttt{relburnin=yes}) is used. By default, MrBayes will discard the first 25 \% samples from the cold chain (\texttt{relburnin=yes} and \texttt{burninfrac=0.25}). By default, MrBayes uses Metropolis coupling to improve the MCMC sampling of the target distribution. The \texttt{Swapfreq}, \texttt{Nswaps}, \texttt{Nchains}, and \texttt{Temp} settings together control the Metropolis coupling behavior. When \texttt{Nchains} is set to 1, no heating is used. When \texttt{Nchains} is set to a value $n$ larger than 1, then $n - 1$ heated chains are used. By default, \texttt{Nchains} is set to 4, meaning that MrBayes will use 3 heated chains and one ``cold'' chain. In our experience, heating is essential for some data sets but it is not needed for others. Adding more than three heated chains may be helpful in analyzing large and difficult data sets. The time complexity of the analysis is directly proportional to the number of chains used (unless MrBayes runs out of physical RAM memory, in which case the analysis will suddenly become much slower), but the cold and heated chains can be distributed among processors in a cluster of computers and among cores in multicore processors using the MPI version of the program, greatly speeding up the calculations. See also section \ref{metropolisCoupling} for more information on Metropolis coupling. The \texttt{Samplefreq} setting determines how often the chain is sampled; the default is every 500 generations. This works well for moderate-sized analyses but our analysis is so small and is likely to converge so rapidly that it makes sense to sample more often. Let us sample the chain every 100th generation instead by typing \tb{mcmcp samplefreq=100}. For really large data sets that take a long time to converge, you may even want to sample less frequently than the default, or you will end up with very large files containing tree and parameter samples. When the chain is sampled, the current values of the model parameters are printed to file. The substitution model parameters are printed to a \texttt{.p} file; a tab delimited text file that can be imported into most statistics and graphing programs~\footnote{In our case, there will be one file for each independent analysis, and they will be called \texttt{primates.nex.run1.p} and \texttt{primates.nex.run2.p}}. The topology and branch lengths are printed to a \texttt{.t} file; a Nexus tree file that can be imported into programs like PAUP* \citep{swofford98}, Mesquite, and FigTree~\footnote{In our case, \texttt{primates.nex.run1.t} and \texttt{primates.nex.run2.t}}. The root of the \texttt{.p} and \texttt{.t} file names can be altered using the \texttt{Filename} setting. The \texttt{Printfreq} parameter controls the frequency with which brief info about the analysis is printed to screen. The default value is 1,000. Let us change it to match the sample frequency by typing \tb{mcmcp printfreq=100}. When you set up your model and analysis (the number of runs and heated chains), MrBayes creates starting values for the model parameters. A different random tree with predefined branch lengths is generated for each chain and most substitution model parameters are set to predefined values. For instance, stationary state frequencies start out being equal and unrooted trees have all branch lengths set to $0.1$. The starting values can be changed by using the \tb{Startparams} command. For instance, user-defined trees can be read into MrBayes by executing a Nexus file with a \texttt{TREES} block. The available user trees can then be assigned to different chains using the \texttt{Startparams} command. After a completed analysis, MrBayes keeps the parameter values of the last generation and will use those as the starting values for the next analysis unless the values are reset using \texttt{mcmc starttree=random startparams=reset}. Since version 3.2, MrBayes prints all parameter values of all chains (cold and heated) to a checkpoint file every \texttt{Checkfreq} generations, by default every 2,000 generations. The checkpoint file has the suffix \texttt{.ckp}. If you run an analysis and it is stopped prematurely, you can restart it from the last checkpoint by using \texttt{mcmc append=yes}. MrBayes will start the new analysis from the checkpoint; it will even read in all the old trees and include them in the convergence diagnostics. At the end of the new run, you will have parameter and tree files that are indistinguishable from those you would have obtained from an uninterrupted analysis. \subsection{Running the Analysis} Finally, we are ready to start the analysis. Type \tb{mcmc}. MrBayes will first print information about the model and then list the proposal mechanisms that will be used in sampling from the posterior distribution. In our case, the proposals are the following: \begin{figure}[h] \centering \begin{BVerbatim} The MCMC sampler will use the following moves: With prob. Chain will use move 0.93 % Dirichlet(Revmat) 0.93 % Slider(Revmat) 0.93 % Dirichlet(Pi) 0.93 % Slider(Pi) 1.85 % Multiplier(Alpha) 1.85 % Slider(Pinvar) 9.26 % ExtSPR(Tau,V) 9.26 % ExtTBR(Tau,V) 9.26 % NNI(Tau,V) 9.26 % ParsSPR(Tau,V) 37.04 % Multiplier(V) 12.96 % Nodeslider(V) 5.56 % TLMultiplier(V) \end{BVerbatim} \end{figure} The exact set of proposals and their relative probabilities may differ depending on the exact version of the program that you are using. Note that MrBayes will spend most of its effort changing the topology (Tau) and branch length (V) parameters. In our experience, topology and branch lengths are the most difficult parameters to integrate over and we therefore let MrBayes spend a large proportion of its time proposing new values for those parameters. The proposal probabilities and tuning parameters can be changed with the \texttt{Propset} command, but be warned that inappropriate changes of these settings may destroy any hopes of achieving convergence. After the initial log likelihoods, MrBayes will print the state of the chains every 100th generation, like this (with a 13 column output. Here we show 11 for readability): \begin{Verbatim}[fontsize=\scriptsize] Chain results (20000 generations requested): 0 -- [-8849.255] (-8762.724) (-8987.103) (-8764.951) * [-8802.317] ... 100 -- (-6964.051) [-6564.318] (-6585.769) (-7091.917) * (-6978.196) ... -- 0:00:00 200 -- (-6331.866) (-6429.968) [-6064.254] (-6553.628) * (-6373.717) ... -- 0:00:00 300 -- (-6244.179) (-6321.599) [-6025.263] (-6348.894) * (-6300.468) ... -- 0:00:00 400 -- (-6206.423) (-6220.442) [-5983.629] (-6294.857) * (-6161.822) ... -- 0:00:00 500 -- (-6186.992) (-6143.353) [-5976.339] (-6255.509) * (-6093.662) ... -- 0:00:00 600 -- (-6176.634) (-6139.131) [-5960.670] (-6214.204) * (-6091.991) ... -- 0:00:00 700 -- (-6112.019) (-6103.034) [-5944.847] (-6145.807) * (-6026.301) ... -- 0:00:27 800 -- (-6082.030) (-6049.371) [-5952.450] (-6076.208) * (-6029.975) ... -- 0:00:24 900 -- (-6071.824) (-6040.252) [-5931.429] (-6040.448) * (-6011.106) ... -- 0:00:21 1000 -- (-6018.607) (-5969.229) [-5921.752] (-6031.270) * (-6006.262) ... -- 0:00:19 Average standard deviation of split frequencies: 0.028570 1100 -- (-6002.039) (-5931.818) [-5903.026] (-6030.727) * (-6012.134) ... -- 0:00:17 ... 19100 -- (-5723.134) (-5728.224) [-5724.674] (-5722.268) * (-5729.337) ... -- 0:00:00 19200 -- (-5724.510) (-5726.167) [-5727.321] (-5726.341) * (-5732.863) ... -- 0:00:00 19300 -- (-5723.553) (-5729.459) [-5725.622] (-5728.011) * (-5726.456) ... -- 0:00:00 19400 -- (-5726.202) (-5729.561) [-5726.133] (-5737.466) * [-5726.154] ... -- 0:00:00 19500 -- [-5720.607] (-5730.406) (-5729.461) (-5725.360) * [-5721.939] ... -- 0:00:00 19600 -- (-5717.783) (-5732.331) (-5725.260) [-5727.780] * [-5727.295] ... -- 0:00:00 19700 -- [-5717.220] (-5726.029) (-5723.541) (-5727.178) * (-5729.795) ... -- 0:00:00 19800 -- [-5723.804] (-5719.361) (-5721.537) (-5731.023) * (-5727.843) ... -- 0:00:00 19900 -- (-5725.641) (-5726.235) [-5725.040] (-5723.821) * (-5728.999) ... -- 0:00:00 20000 -- (-5731.566) (-5723.933) [-5726.992] (-5727.836) * (-5728.071) ... -- 0:00:00 Average standard deviation of split frequencies: 0.000520 Continue with analysis? (yes/no): \end{Verbatim} If you have the terminal window wide enough, each generation of the chain will print on a single line. The first column lists the generation number. The following four columns with negative numbers each correspond to one chain in the first run. Each column represents one physical location in computer memory, and the chains shift positions in the columns as the run proceeds (it is actually only the temperature that is shifted). The numbers are the log likelihood values of the chains. The chain that is currently the cold chain has its value surrounded by square brackets, whereas the heated chains have their values surrounded by parentheses. When two chains successfully change states, they trade column positions (places in computer memory). If the Metropolis coupling works well, the cold chain should move around among the columns; this means that the cold chain successfully swaps states with the heated chains. If the cold chain gets stuck in one of the columns, then the heated chains are not successfully contributing states to the cold chain, and the Metropolis coupling is inefficient. The analysis may then have to be run longer. You can also try to reduce the temperature difference between chains, which may increase the efficiency of the Metropolis coupling. The star column separates the two different runs (with four log-likelihood columns each). The last column gives the time left to completion of the specified number of generations. This analysis approximately takes 1 second per 1,000 generations. Because different moves are used in each generation, the exact time varies somewhat for each set of 100 generations, and the predicted time to completion will be unstable in the beginning of the run. After a while, the predictions will become more accurate and the estimated remaining time will decrease more evenly between generations. \subsection{When to Stop the Analysis} At the end of the run, MrBayes asks whether or not you want to continue with the analysis. Before answering that question, examine the average standard deviation of split frequencies. As the two runs converge onto the stationary distribution, we expect the average standard deviation of split frequencies to approach zero, reflecting the fact that the two tree samples become increasingly similar. In our case, the average standard deviation is down to 0.0 already after 1,000 generations and then stays at very low values throughout the run. Your values can differ slightly because of stochastic effects but should show a similar trend. In larger and more difficult analyses, you will typically see the standard deviation of split frequencies come down much more slowly towards 0.0; the standard deviation can even increase temporarily, especially in the early part of the run. A rough guide is that an average standard deviation below $0.01$ is very good indication of convergence, while values between $0.01$ and $0.05$ may be adequate depending on the purpose of your analysis. The \texttt{sumt} command (see below) allows you to examine the error (standard deviation) associated with each clade in the tree. Typically, most of the error is associated with clades that are not very well supported (posterior probabilities well below $0.95$), and getting accurate estimates of those probabilities may not be an important depending on the purpose of the analysis. Given the extremely low value of the average standard deviation at the end of the run, there appears to be no need to continue the analysis beyond 20,000 generations so when MrBayes asks \texttt{Continue with analysis? (yes/no):} stop the analysis by typing \tb{no}. Although we recommend using a convergence diagnostic, such as the standard deviation of split frequencies, there are also simpler but less powerful methods of determining when to stop the analysis. The simplest technique is to examine the log likelihood values (or, more exactly, the log probability of the data given the parameter values) of the cold chain, that is, the values printed to screen within square brackets. In the beginning of the run, the values typically increase rapidly (the absolute values decrease, since these are negative numbers). In our case, the values increase from below $-8,400$ to around $-5,725$ in the first few thousand generations. This is the ``burn-in'' phase and the corresponding samples are typically discarded. Once the likelihood of the cold chain stops to increase and starts to randomly fluctuate within a more or less stable range, the run may have reached stationarity, that is, it may be producing a good sample from the posterior probability distribution. At stationarity, we also expect different, independent runs to sample similar likelihood values. Trends in likelihood values can be deceiving though; you're more likely to detect problems with convergence by comparing split frequencies than by looking at likelihood trends. When you stop the analysis, MrBayes will print several types of information useful in optimizing the analysis. This is primarily of interest if you have difficulties in obtaining convergence, which is unlikely to happen with this analysis. We give a few tips on how to improve convergence at the end of the following section. \subsection{Summarizing Samples of Model Parameters} During the run, samples of the substitution model parameters have been written to the \texttt{.p} files every \texttt{samplefreq} generation. These files are tab-delimited text files that look something like this: \begin{figure}[h] \centering \begin{BVerbatim}[fontsize=\scriptsize] [ID: 0184438524] Gen LnL LnPr TL r(A<->C) ... pi(T) alpha pinvar 0 -8.849255e+03 4.262056e+01 4.200000e-01 1.666667e-01 ... 2.500000e-01 1.000000e+00 0.000000e+00 100 -6.564318e+03 1.426025e+01 1.722886e+00 1.499856e-01 ... 2.500000e-01 1.000000e+00 8.643869e-02 ... 19900 -5.725040e+03 4.019204e+00 2.936465e+00 4.674104e-02 ... 2.517984e-01 4.555687e-01 8.178856e-02 20000 -5.726992e+03 2.358156e+00 3.184675e+00 4.674104e-02 ... 2.517984e-01 4.689211e-01 4.150474e-02 \end{BVerbatim} \end{figure} The first number, in square brackets, is a randomly generated ID number that lets you identify the analysis from which the samples come. The next line contains the column headers, and is followed by the sampled values. From left to right, the columns contain: (1) the generation number (\texttt{Gen}); (2) the log likelihood of the cold chain (\texttt{LnL}); (3) the log likelihood of the prior (\texttt{LnPr}); %%%% JN: Add correct, short description of LnPr! (4) the total tree length (the sum of all branch lengths, \texttt{TL}); (5) the six GTR rate parameters (\texttt{r(A<->C)}, \texttt{r(A<->G)} etc); (6) the four stationary nucleotide frequencies (\texttt{pi(A)}, \texttt{pi(C)} etc); (7) the shape parameter of the gamma distribution of rate variation (\texttt{alpha}); and (8) the proportion of invariable sites (\texttt{pinvar}). If you use a different model for your data set, the \texttt{.p} files will of course be different. MrBayes provides the \texttt{sump} command to summarize the sampled parameter values. By default, the \texttt{sump} command uses the same burn-in as the convergence diagnostics in the \texttt{mcmc} command. This should be appropriate if we use these diagnostics to determine when we have an appropriate sample from the posterior. Thus, we can summarize the information in the \texttt{.p} file by simply typing \textbf{sump}. By default, \texttt{sump} will summarize the information in the \texttt{.p} file or files generated most recently, but the filename can be changed if necessary. The \texttt{relburnin=yes} option specifies that we want to give the burn-in in terms of a fraction (relative burn-in) rather than as an absolute value. The \texttt{burninfrac} option specifies the desired burn-in fraction. The \texttt{sump} command will first generate a plot of the generation versus the log probability of the data (the log likelihood values). If we are at stationarity, this plot should look like ``white noise'', that is, there should be no tendency of increase or decrease over time. The plot should look something like this: \begin{figure}[h] \centering \begin{BVerbatim}[fontsize=\scriptsize] +------------------------------------------------------------+ -5718.97 | 2 | | 1 21 2 11 2 2 1 2 | | 2 2 1 1 2 1 | | 2 1 2 1 2 1 2 1 1 1 | | 1 2 2 1 2 22 1 22 * 21 2 | | 1 2 2 1 1 22 2111 1 1| | 1 1 2 2 12 2 1 21 121 1 1 *22| | 1 1 21 1 2*2 1 1 121 22 1 1 | | 2 1 1 22 2 | | 21 1 2 1 2 | | 2 1 1 2 2 | | 11 2 2 | | 2 2 1 | |2 2 | |1 1 | +------+-----+-----+-----+-----+-----+-----+-----+-----+-----+ -5734.79 ^ ^ 5000 20000 \end{BVerbatim} \end{figure} If you see an obvious trend in your plot, either increasing or decreasing, you very likely need to run the analysis longer to get an adequate sample from the posterior probability distribution. Furthermore, if you see a clear separation of the two chains (all the 1:s vs. all the 2:s), that is also an indication that the two runs did not converge on the same target distribution, and that you might require longer runs. At the bottom of the \texttt{sump} output, there is a table summarizing the samples of the parameter values: \begin{figure}[h] \centering \begin{BVerbatim}[fontsize=\scriptsize] 95% HPD Interval -------------------- Parameter Mean Variance Lower Upper Median min ESS* avg ESS PSRF+ -------------------------------------------------------------------------------------------------- TL 3.157259 0.110374 2.498745 3.787651 3.133277 14.34 54.22 0.999 r(A<->C) 0.042789 0.000086 0.024422 0.056206 0.042196 16.58 38.21 0.999 r(A<->G) 0.467506 0.002449 0.368281 0.554633 0.469500 11.91 15.60 0.998 r(A<->T) 0.035821 0.000061 0.023179 0.051031 0.036272 15.00 18.17 1.007 r(C<->G) 0.032281 0.000197 0.007671 0.058215 0.029646 19.70 38.75 1.019 r(C<->T) 0.399001 0.001674 0.325954 0.474863 0.399301 17.63 24.72 1.007 r(G<->T) 0.022602 0.000289 0.000512 0.054123 0.017937 8.58 30.61 1.074 pi(A) 0.356156 0.000166 0.331900 0.378428 0.356370 56.29 70.70 1.009 pi(C) 0.321006 0.000156 0.300738 0.344609 0.321326 15.28 48.65 1.006 pi(G) 0.080853 0.000047 0.067370 0.093071 0.080178 19.82 30.34 1.004 pi(T) 0.241985 0.000119 0.223346 0.265659 0.241647 21.20 52.94 1.002 alpha 0.533518 0.024674 0.329657 0.817921 0.488774 61.71 94.24 1.030 pinvar 0.111808 0.005969 0.001041 0.252672 0.103843 71.95 95.00 1.018 -------------------------------------------------------------------------------------------------- \end{BVerbatim} \end{figure} For each parameter, the table lists the mean and variance of the sampled values, the lower and upper boundaries of the 95 \% credibility interval, and the median of the sampled values. The parameters are the same as those listed in the .p files: the total tree length (\texttt{TL}), the six reversible substitution rates (\texttt{r(A<->C)}, \texttt{r(A<->G)}, etc), the four stationary state frequencies (\texttt{pi(A)}, \texttt{pi(C)}, etc), the shape of the gamma distribution of rate variation across sites (\texttt{alpha}), and the proportion of invariable sites (\texttt{pinvar}). Note that the six rate parameters of the GTR model are given as proportions of the rate sum (the Dirichlet parameterization). This parameterization has some advantages in the Bayesian context; in particular, it allows convenient formulation of priors. If you want to scale the rates relative to the G--T rate, just divide all rate proportions by the G--T rate proportion. The last three columns in the table contains information about the mixing and convergence of the MCMC. The ESS (Effective Sample Size; \citet{ripley87}) is the (estimated) effective number of independent draws from the posterior distribution that the MCMC managed to sample. A small ESS ($<100$) would indicate poor mixing behaviour, suggesting that you need to run longer simulations. The Potential Scale Reduction Factor (PSRF; \citet{gelman92}), is based on comparing the estimated between-chain variance with the within-chain variance for a parameter. If we have a good sample from the posterior probability distribution, these values should be close to $1.0$. A reasonable goal might be to aim for values between $1.0$ and $1.2$ but it can be difficult to achieve this for all parameters in the model in larger and more complicated analyses. \subsection{Summarizing Tree Samples} Trees and branch lengths are printed to the \texttt{.t} files. These files are Nexus-formatted tree files with a structure like this: \begin{figure}[H] \centering \begin{BVerbatim} #NEXUS [ID: 0184438524] [Param: tree] begin trees; translate 1 Tarsius_syrichta, 2 Lemur_catta, 3 Homo_sapiens, 4 Pan, 5 Gorilla, 6 Pongo, 7 Hylobates, 8 Macaca_fuscata, 9 M_mulatta, 10 M_fascicularis, 11 M_sylvanus, 12 Saimiri_sciureus; tree gen.0 = [&U] ((10:2.000000e-02,(((((12:2.000000e-02,... ... tree gen.20000 = [&U] ((((7:1.869397e-01,(6:1.712180e-01,... end; \end{BVerbatim} \end{figure} To summarize the tree and branch length information, while using the same burnin as above when summarizing the other model parameters, type \tb{sumt}. The \texttt{sumt} command will output, among other things, summary statistics for the taxon bipartitions, a tree with clade credibility (posterior probability) values, and a phylogram (if branch lengths have been saved). The output first gives a key to each partition in the tree sample using dots for the taxa that are on one side of the partition and stars for the taxa on the other side. For instance, the 14th partition (ID 14) in the output below represents the clade \textit{Homo} (taxon 3) and \textit{Pan} (taxon 4), since there are stars in the third and fourth positions and a dot in all other positions. \begin{Verbatim}[xleftmargin=15mm,fontsize=\footnotesize] List of taxa in bipartitions: 1 -- Tarsius_syrichta 2 -- Lemur_catta 3 -- Homo_sapiens 4 -- Pan 5 -- Gorilla 6 -- Pongo 7 -- Hylobates 8 -- Macaca_fuscata 9 -- M_mulatta 10 -- M_fascicularis 11 -- M_sylvanus 12 -- Saimiri_sciureus Key to taxon bipartitions (saved to file "primates.nex.parts"): ID -- Partition ------------------ 1 -- .*********** 2 -- .*.......... 3 -- ..*......... 4 -- ...*........ 5 -- ....*....... 6 -- .....*...... 7 -- ......*..... 8 -- .......*.... 9 -- ........*... 10 -- .........*.. 11 -- ..........*. 12 -- ...........* 13 -- .......****. 14 -- ..**........ 15 -- ..********** 16 -- .......**... 17 -- ..*********. 18 -- ..*****..... 19 -- ..****...... 20 -- ..***....... 21 -- .......***.. ------------------ \end{Verbatim} Then it gives a table over the informative bipartitions (the ones with more than one taxon included), specifying the number of times the partition was sampled (\texttt{\#obs}), the probability of the partition (\texttt{Probab.}), the standard deviation of the partition frequency (\texttt{Sd(s)}) across runs, the min and max of the standard deviation across runs (\texttt{Min(s)} and \texttt{Max(s)}) and finally the number of runs in which the partition was encountered. In our analysis, there is overwhelming support for a single tree, so all partitions in this tree have a posterior probability of $1.0$ (or very close to $1.0$): %%%% JN: This table needs to be placed accurately \begin{figure}[H] \centering \begin{BVerbatim} Summary statistics for informative taxon bipartitions (saved to file "primates.nex.tstat"): ID #obs Probab. Sd(s)+ Min(s) Max(s) Nruns ---------------------------------------------------------------- 13 302 1.000000 0.000000 1.000000 1.000000 2 14 302 1.000000 0.000000 1.000000 1.000000 2 15 302 1.000000 0.000000 1.000000 1.000000 2 16 302 1.000000 0.000000 1.000000 1.000000 2 17 302 1.000000 0.000000 1.000000 1.000000 2 18 302 1.000000 0.000000 1.000000 1.000000 2 19 302 1.000000 0.000000 1.000000 1.000000 2 20 302 1.000000 0.000000 1.000000 1.000000 2 21 301 0.996689 0.004683 0.993377 1.000000 2 ---------------------------------------------------------------- \end{BVerbatim} \end{figure} We then get a table summarizing branch and node parameters, in our case the branch lengths. The indices in this table refer to the key to partitions. %%%% JN: This table needs to be placed accurately \begin{Verbatim}[fontsize=\footnotesize] Summary statistics for branch and node parameters (saved to file "primates.nex.vstat"): 95% HPD Interval -------------------- Parameter Mean Variance Lower Upper Median PSRF+ Nruns -------------------------------------------------------------------------------------- length[1] 0.531436 0.008411 0.378396 0.744535 0.530547 1.006 2 length[2] 0.372177 0.005349 0.239386 0.522698 0.367659 0.998 2 length[3] 0.051257 0.000138 0.028920 0.073525 0.050418 1.000 2 length[4] 0.064483 0.000190 0.041188 0.094009 0.064052 0.997 2 length[5] 0.060566 0.000223 0.032939 0.087111 0.059372 0.997 2 length[6] 0.151097 0.000855 0.102240 0.215567 0.147996 1.008 2 length[7] 0.181224 0.001226 0.118437 0.244907 0.175819 1.007 2 length[8] 0.019283 0.000038 0.007797 0.031080 0.018823 1.007 2 length[9] 0.022357 0.000045 0.010939 0.034789 0.021591 0.997 2 length[10] 0.057082 0.000131 0.038134 0.079448 0.055902 1.002 2 length[11] 0.076942 0.000490 0.037907 0.122557 0.075693 0.998 2 length[12] 0.465255 0.007233 0.280258 0.608086 0.465252 0.997 2 length[13] 0.297782 0.005323 0.163584 0.431633 0.291874 0.999 2 length[14] 0.031277 0.000159 0.010224 0.054248 0.029928 0.997 2 length[15] 0.132457 0.002687 0.034449 0.226634 0.126070 0.999 2 length[16] 0.134438 0.001637 0.067021 0.221458 0.131206 1.002 2 length[17] 0.275791 0.003801 0.175685 0.405595 0.273442 1.000 2 length[18] 0.060090 0.000501 0.020345 0.106085 0.059751 0.997 2 length[19] 0.036908 0.000128 0.016550 0.057338 0.035712 1.002 2 length[20] 0.088113 0.000594 0.038292 0.129339 0.085182 0.998 2 length[21] 0.047375 0.000386 0.012946 0.087369 0.047096 1.000 2 -------------------------------------------------------------------------------------- \end{Verbatim} For instance, \texttt{length[14]} is the length of the branch corresponding to partition ID 14. As we noted above, this is the branch grouping humans and chimps. The meaning of most of the values in this table is obvious. The last two columns give a convergence diagnostic, the Potential Scale Reduction Factor (PSRF), and the number of runs in which the partition was encountered. The PSRF diagnostic is the same used for the regular parameter samples, and it should approach $1.0$ as runs converge. This table is followed by two trees. The clade credibility tree (upper tree) gives the probability of each partition or clade in the tree: \begin{figure}[H] \centering \begin{BVerbatim}[fontsize=\scriptsize] Clade credibility values: /---------------------------------------------------------- Tarsius_syrichta (1) | |---------------------------------------------------------- Lemur_catta (2) | | /-------- Homo_sapiens (3) | /---100--+ | | \-------- Pan (4) | /--100--+ | | \----------------- Gorilla (5) | /--100--+ + | \------------------------- Pongo (6) | /--100--+ | | \--------------------------------- Hylobates (7) | | | | /-------- Macaca_fuscata (8) | /---100--+ /---100--+ | | | | \-------- M_mulatta (9) | | | /--100--+ | | | | \----------------- M_fascicularis (10) \--100--+ \------100------+ | \------------------------- M_sylvanus (11) | \-------------------------------------------------- Saimiri_sciure~ (12) \end{BVerbatim} \end{figure} The phylogram (lower tree) gives the branch lengths measured in expected substitutions per site: \begin{figure}[H] \centering \begin{BVerbatim}[fontsize=\scriptsize] Phylogram (based on average branch lengths): /--------------------------------------- Tarsius_syrichta (1) | |--------------------------- Lemur_catta (2) | | /---- Homo_sapiens (3) | /-+ | | \----- Pan (4) | /------+ | | \---- Gorilla (5) | /---+ + | \----------- Pongo (6) | /---------+ | | \------------- Hylobates (7) | | | | /- Macaca_fuscata (8) | /--------+ /--+ | | | | \- M_mulatta (9) | | | /---+ | | | | \---- M_fascicularis (10) \---------------------+ \-------------------+ | \------ M_sylvanus (11) | \---------------------------------- Saimiri_sciure~ (12) |-------------| 0.200 expected changes per site \end{BVerbatim} \end{figure} In the background, the \texttt{sumt} command creates five additional files. The first is a \texttt{.parts} file, which contains the key to taxon bipartitions. The second and third are the \texttt{.tstat} and \texttt{.vstat} files, which contain the summaries of partition statistics and branch length statistics, respectively. The next is the \texttt{.con.tre} file, which includes the consensus trees. By default, the consensus tree with all the relevant clade support values and branch length information is printed in a format suitable for FigTree. Once the \texttt{.con.tre} file is opened in FigTree, you can display statistics such as the posterior probability and its associated standard deviation on each clade in the tree. Alternatively, you can specify \texttt{conformat=simple} when doing \texttt{sumt} to get the consensus tree in a simpler format that can be opened in most tree drawing programs. The simple format contains two different trees, one with both branch lengths and support values, and one with only branch lengths. Be sure to select the appropriate one if you wish to display both branch lengths and support values. The last file generated by the \texttt{sumt} command is the \texttt{.trprobs} file, which contains the trees that were found during the MCMC search, sorted by posterior probability. This allows you to examine the trees contained in various credible sets of trees. For instance, the 95~\% credible set contains the most probable trees up to an accumulated posterior probability of 95~\%. In our case, this file will only contain one tree, but data sets with more topological uncertainty can produce long lists of trees in the \texttt{.trprobs} files. \chapter{Tutorial: A Partitioned Analysis}\label{tutorialPartitioned} MrBayes handles a wide variety of data types and models, as well as any mix of these models. In this example we will look at how to set up a simple analysis of a combined data set, consisting of data from four genes and morphology for 30 taxa of gall wasps and outgroups. A similar approach can be used, e.g., to set up a partitioned analysis of molecular data coming from different genes. The data set for this tutorial is found in the file \texttt{cynmix.nex}. \section{Getting Mixed Data into MrBayes} First, open up the Nexus data file in a text editor. The \texttt{DATA} block of the Nexus file should look familiar but there are some differences compared to the \texttt{primates.nex} file in the format statement: \begin{figure}[h] \begin{BVerbatim} Format datatype=mixed(Standard:1-166,DNA:167-3246) interleave=yes gap=- missing=?; \end{BVerbatim} \end{figure} First, the datatype is given as mixed followed by a specification of the content of the matrix: it contains standard (morphology) characters in columns 1--166 and DNA characters in the remaining columns. The mixed datatype is an extension to the Nexus standard, which originated in MrBayes 3. It may not be compatible with other phylogenetics programs. Second, the matrix is specified to be interleaved. It is often convenient to specify mixed data in interleaved format, with each block consisting of a natural subset of the matrix, such as the morphological data or one of the gene regions. \section{Dividing the Data into Partitions} By default, MrBayes partitions the data according to data type. There are only two data types in the matrix, so the default model will include only a morphology (standard) and a DNA partition. To divide the DNA partition into gene regions or other appropriate subsets, it is convenient to first specify character sets. In principle, this can be done from the command line but it is easier to do it in a \texttt{MRBAYES} block in the data file. At the end of the \texttt{cynmix.nex}, there is such a block (starting with \texttt{begin mrbayes;}). Any text between square brackets (\texttt{[]}) are comments and ignored by MrBayes. Note also that each command ends with a semicolon. The relevant lines for setting up partitions starts with the command \texttt{charset}. That command simply associates a name with a set of characters. For instance, the character set \texttt{COI} is defined to include characters 167 to 1244. The next step is to define a partition of the data according to genes and morphology. This is accomplished with the line: \begin{figure}[h] \begin{BVerbatim}[fontsize=\small] partition favored = 5: morphology, COI, EF1a, LWRh, 28S; \end{BVerbatim} \end{figure} The elements of the \texttt{partition} command are: (1) the name of the partitioning scheme (\texttt{favored}); (2) an equal sign ($=$); (3) the number of character divisions in the scheme (\texttt{5}); (4) a colon (:); and (5) a list of the characters in each division, separated by commas. The list of characters can simply be an enumeration of the character numbers (the above line is equivalent to \texttt{partition favored = 5: 1-166, 167-1244, 1245-1611, 1612-2092, 2093-3246;}) but it is often more convenient to use predefined character sets as given in the \texttt{MRBAYES} block. The \texttt{end;} statement closes the \texttt{MRBAYES} block. When we read this block into MrBayes, we will have defined variables that can be used for setting up a partitioned analysis with the first character division being morphology, the second division being the COI gene, etc. Exit your text editor, launch MrBayes and type \tb{execute cynmix.nex} to read in your data and the variables. The final step is to tell MrBayes that we want to work with this partitioning of the data instead of the default partitioning. We do this using \texttt{set}: \begin{figure}[h] \begin{BVerbatim} set partition = favored; \end{BVerbatim} \end{figure} \section{Specifying a Partitioned Model} Before starting to specify the partitioned model, it is useful to examine the default model. Type \tb{showmodel} and you should get this table as part of the output: \begin{figure}[h] \centering \begin{BVerbatim} Active parameters: Partition(s) Parameters 1 2 3 4 5 ------------------------------ Statefreq 1 2 2 2 2 Ratemultiplier 3 3 3 3 3 Topology 4 4 4 4 4 Brlens 5 5 5 5 5 ------------------------------ \end{BVerbatim} \end{figure} There is a lot of other useful information in the output of \texttt{showmodel} but this table is the key to the partitioned model. We can see that there are five partitions in the model and five active (free) parameters. There are two stationary state frequency parameters, one for the morphological data (parameter 1) and one for the DNA data (parameter 2). Then there is also a ratemultiplier (3), a topology parameter (4) and a set of branch length parameters (5). All three are the same for all partitions. Now, assume we want a separate GTR + I + $\Gamma$ model for each gene partition. All the parameters should be estimated separately for the individual genes. Assume further that we want the overall evolutionary rate to be (potentially) different across partitions, and that we want to assume gamma-shaped rate variation for the morphological data. We can obtain this model by using \texttt{lset} and \texttt{prset} with the \texttt{applyto} mechanism, which allows us to apply the settings to specific partitions. For instance, to apply a GTR + I + $\Gamma$ model to the molecular partitions, we type \tb{lset applyto=(2,3,4,5) nst=6 rates=invgamma}. This will produce the following table when \tb{showmodel} is invoked: \begin{figure}[H] \centering \begin{BVerbatim} Active parameters: Partition(s) Parameters 1 2 3 4 5 ----------------------------- Revmat . 1 1 1 1 Statefreq 2 3 3 3 3 Shape . 4 4 4 4 Pinvar . 5 5 5 5 Ratemultiplier 6 6 6 6 6 Topology 7 7 7 7 7 Brlens 8 8 8 8 8 ----------------------------- \end{BVerbatim} \end{figure} As you can see, all molecular partitions now evolve under the correct model but all parameters (\texttt{statefreq}, \texttt{revmat}, \texttt{shape}, \texttt{pinvar}) are shared across partitions. To unlink them such that each partition has its own set of parameters, type: \tb{unlink revmat=(all)} \tb{pinvar=(all)} \tb{shape=(all)} \tb{statefreq=(all)}. Gamma-shaped rate variation for the morphological data is enforced by typing \tb{lset applyto=(1) rates=gamma}. The trickiest part is to allow the overall rate to be different across partitions. This is achieved using the \texttt{ratepr} parameter of the \texttt{prset} command. By default, \texttt{ratepr} is set to \texttt{fixed}, meaning that all partitions have the same overall rate. By changing this to \texttt{variable}, the rates are allowed to vary under a flat Dirichlet prior. To allow all our partitions to evolve under different rates, type \tb{prset applyto=(all) ratepr=variable}. The model is now essentially complete but there is one final thing to consider. Typically morphological data matrices do not include all types of characters. Specifically, morphological data matrices do not usually include any constant (invariable) characters. Sometimes, autapomorphies are not included either, and the matrix is restricted to parsimony-informative characters. For MrBayes to calculate the probability of the data correctly, we need to inform it of this ascertainment (coding) bias. By default, MrBayes assumes that standard data sets include all variable characters but no constant characters. If necessary, one can change this setting using \texttt{lset coding}. We will leave the \texttt{coding} setting at the default, though, which is \texttt{variable} for standard (morphology) data. Now, \tb{showmodel} should produce this table: \begin{figure}[H] \centering \begin{BVerbatim}[fontsize=\small] Active parameters: Partition(s) Parameters 1 2 3 4 5 ------------------------------ Revmat . 1 2 3 4 Statefreq 5 6 7 8 9 Shape 10 11 12 13 14 Pinvar . 15 16 17 18 Ratemultiplier 19 19 19 19 19 Topology 20 20 20 20 20 Brlens 21 21 21 21 21 ------------------------------ \end{BVerbatim} \end{figure} \section{Running the Analysis} When the model has been completely specified, we can proceed with the analysis essentially as described above in the tutorial for the \texttt{primates.nex} data set. However, in the case of the \texttt{cynmix.nex} dataset, the analysis will have to be run longer before it converges. When looking at the parameter samples from a partitioned analysis, it is useful to know that the names of the parameters are followed by the character division (partition) number in curly braces. For instance, \texttt{pi(A)\{3\}} is the stationary frequency of nucleotide A in character division 3, which is the EF1a division in the above analysis. In this section we have used a separate Nexus file for the \texttt{MRBAYES} block. Although one can add this command block to the data file itself, there are several advantages to keeping the commands and the data blocks separate. For example, one can create a set of different analyses with different parameters in separate command files and submit all those files to a job scheduling system on a computer cluster. It is important to remember, though, that MrBayes uses the name of the file containing the character matrix as the default for all output files. Thus, if you run all your analyses in the same directory, results from different analyses will overwrite each other. To change this behavior, include the command \texttt{mcmcp filename=$<$filename$>$;} in each of your run files, just before issuing the \texttt{mcmc} command, using a different file name for each run file. For instance, if you wish to name the output files from one analysis using the root \texttt{analysis1}, you use the line \texttt{mcmcp filename=analysis1;}. The files will then be named \texttt{analysis1.run1.t}, \texttt{analysis1.run2.t}, etc. An alternative approach is to run each analysis in a separate directory, in which case the naming of the output files will not be an issue. \chapter{More Tutorials}\label{tutorialAdvanced} In this chapter, we provide a number of brief tutorials that cover a wide range of common models and analyses in MrBayes. We use a slightly different format for these tutorials. First, we assume that you are familiar with the basic working of the program, so we skip uninteresting details when they should be obvious. Also, in addition to the conventions used previously, we will often simply give the command line, including the \texttt{MrBayes >} prompt. What you need to type in is simply the text after the prompt. You may wish to start each tutorial by switching off warnings and asking MrBayes to quietly finish each analysis after the requested number of generations, without asking you if you want to extend the analysis. You can accomplish this with: \begin{Verbatim} MrBayes > set autoclose=yes nowarn=yes \end{Verbatim} The tutorials are largely independent of each other, so it should be possible to skip directly to a tutorial that is of particular interest. However, some tutorials form a natural sequence of successively more advanced analyses of the same data set, so following all of the tutorials in order should give some added insight. The tutorials by no means cover the entire range of models or analytical options supported by MrBayes. In chapters \ref{advancedTopics} and \ref{evolutionaryModels} we describe all component models and algorithms available, so refer to these for complete coverage. A graphical overview of the models implemented in MrBayes 3.2 is provided in Appendix~\ref{appendixOverview}. \section{An Amino Acid Analysis} In this tutorial, we will explore a couple of different approaches to amino acid analyses. First, we will ask MrBayes to integrate over a predetermined set of fixed rate matrices, and then we will repeat the analysis using a fixed rate matrix. To start the tutorial, first read in the amino acid data: \begin{Verbatim} MrBayes > execute avian_ovomucoids.nex \end{Verbatim} To ask MrBayes to sample across fixed amino acid rate matrices, we use the \texttt{prset} command: \begin{Verbatim} MrBayes > prset aamodelpr=mixed \end{Verbatim} Now we run the analysis as usual. The analysis is much slower than a normal nucleotide analysis, so let us try first and see if we can get convergence without using Metropolis coupling (heated chains). We do this by asking for a single chain per analysis using \texttt{nchains=1} in the \texttt{mcmc} command. Although convergence might be slightly slower if measured in terms of the number of generations, the analysis will run much faster without the overhead of the heated chains. We are still using the default of two parallel runs, so we can monitor topological convergence on the fly. For the dataset in this tutorial, our previous results indicate that about 300,000 generations are sufficient for decent topological convergence. Run 300,000 generations without heating using the command: \begin{Verbatim} MrBayes > mcmc nchains=1 ngen=300000 \end{Verbatim} Now we can summarize the parameter and tree samples as usual using the \texttt{sumt} and \texttt{sump} commands. The output of the \texttt{sump} command will include a table giving the posterior probabilities of the amino acid models: \begin{figure}[h] \centering \begin{BVerbatim} Model probabilities above 0.050 Estimates saved to file "avian_ovomucoids.nex.mstat". Posterior Standard Min. Max. Model Probability Deviation Probability Probability ------------------------------------------------------------------------- aamodel[Jones] 1.000 0.000 1.000 1.000 ------------------------------------------------------------------------- \end{BVerbatim} \end{figure} This particular dataset overwhelmingly supports the Jones model, so this may be the only model sampled after the burn-in phase. This means that the move mechanism trying to change the amino acid model will rarely or ever have its proposals accepted after the burn-in phase. However, the sample from the posterior may nevertheless be accurate. To determine whether we have an appropriate sample from the posterior, we simply compare the model probabilities across independent runs. The range of probabilities, from minimum to maximum, across independent analyses is given in the table. In our case, we have two independent analyses, both of which are putting all of the probability on the Jones model (both minimum and maximum probabilities are $1.000$). This is good evidence that we have an adequate sample from the posterior, since each run started with a randomly selected amino acid rate matrix. If desired, it is possible to run the same data set under another amino acid model, for instance the Dayhoff model, by fixing the rate matrix. Do this by running the analysis with the following commands: \begin{Verbatim} MrBayes > prset aamodelpr=fixed(dayhoff) MrBayes > mcmc nchains=1 ngen=300000 \end{Verbatim} Note that the likelihoods printed to screen during the run are much worse (about 100 log likelihood units lower) than those obtained when we sampled over rate matrices according to their posterior probabilities. As you will discover, the analysis will not be much faster per generation if we fix the rate matrix, which may be somewhat counter-intuitive. The reason is that only a single rate matrix is used in any one generation of the chain, even if we change the rate matrix between generations. The effect of sampling across rate matrices on the speed of convergence is data-dependent. Convergence may be slower or faster when you mix over rate matrices, depending on the effects of the rate matrices on the complexity of the posterior. We end this tutorial by pointing out that protein-coding nucleotide sequences can also be analyzed using amino acid models in MrBayes. MrBayes simply uses the specified genetic code to translate the codons to amino acids, generating relevant ambiguous amino acid state sets when appropriate, before running the analysis. For instance, we would run a mixed amino acid model analysis on the protein-coding viral sequences in the \texttt{replicase.nex} data set using: \begin{Verbatim} MrBayes > execute replicase.nex MrBayes > lset nucmodel=protein MrBayes > prset aamodelpr=mixed MrBayes > mcmc nchains=1 ngen=300000 \end{Verbatim} \section{Identifying Positively Selected Sites} The purpose of this analysis is to identify positively selected sites in a protein-coding viral sequence. We will use the data in the file \texttt{replicase.nex}. First, we read in the data: \begin{Verbatim} MrBayes > execute replicase.nex \end{Verbatim} Then we specify that we want to use a codon model with omega variation across sites, and that we want MrBayes to report positive selection probabilities and site omega values (dN/dS ratios): \begin{Verbatim} MrBayes > lset nucmodel=codon omegavar=ny98 MrBayes > report possel=yes siteomega=yes \end{Verbatim} This is going to be a slow analysis, so we will run relatively short analyses and will therefore reset the sampling frequency and the frequency with which we print to screen to once every 20 generations, so that we can monitor the run adequately and get more samples than we would otherwise. In the same vein, we will increase the frequency with which we calculate convergence diagnostics to once every 500 generations. Even though it is possible to speed up the analysis by running it without Metropolis coupling, our results indicate that the mixing across model parameters benefits considerably from the heated chains in this case, so we will not attempt that. Our results indicate that it is possible to get decent if not perfect convergence within about 10,000 generations, so let us try that: \begin{Verbatim} MrBayes > mcmcp printfreq=20 samplefreq=20 diagnfreq=500 MrBayes > mcmc ngen=10000 \end{Verbatim} Now we examine the parameter samples by using the \tb{sump} command. The output will dominated by a long table summarizing the estimates of the posterior distributions of the model parameters, including the probabilities of sites being in the positively selected class, and the weighted site omega values (dN/dS ratios): \begin{figure}[H] \centering \begin{BVerbatim}[fontsize=\scriptsize] 95% HPD Interval ------------------- Parameter Mean Variance Lower Upper Median min ESS* avg ESS PSRF+ -------------------------------------------------------------------------------------------------- TL 6.063468 0.869372 4.156027 7.813660 6.025831 5.08 12.36 1.014 omega(-) 0.069961 0.000184 0.047403 0.095480 0.068025 6.48 7.61 1.048 omega(N) 1.000000 0.000000 1.000000 1.000000 1.000000 NA NA NA omega(+) 1.657224 1.111911 1.000940 4.105477 1.112150 34.57 84.80 0.999 pi(-) 0.827655 0.001925 0.764926 0.906755 0.828821 10.20 11.70 0.999 pi(N) 0.125519 0.002765 0.009573 0.206275 0.128064 16.95 33.05 1.166 pi(+) 0.046826 0.003448 0.000100 0.172577 0.015650 20.67 43.00 1.119 pi(AAA) 0.014750 0.000007 0.011227 0.019912 0.014817 10.95 12.02 1.010 pi(AAC) 0.014741 0.000009 0.009607 0.019714 0.015200 7.43 37.31 1.488 pi(AAG) 0.021098 0.000010 0.015839 0.026900 0.020483 11.36 17.71 1.089 ... pr+(1,2,3) 0.012812 0.000861 0.000000 0.059747 0.000743 40.76 171.75 1.080 pr+(4,5,6) 0.000332 0.000000 0.000000 0.001532 0.000036 43.44 195.80 1.092 pr+(7,8,9) 0.000068 0.000000 0.000000 0.000316 0.000003 83.01 223.65 1.054 ... omega(1,2,3) 0.103900 0.002723 0.054415 0.176367 0.089053 16.88 20.23 1.014 omega(4,5,6) 0.071107 0.000195 0.048813 0.097142 0.069085 6.47 7.37 1.041 omega(7,8,9) 0.070189 0.000188 0.048528 0.096028 0.068360 6.46 7.48 1.046 ... -------------------------------------------------------------------------------------------------- \end{BVerbatim} \end{figure} First, note that the effective sample size (ESS) is still somewhat low for many parameters. The total ESS across the two runs is the double of the average ESS (avg ESS). The total in this analysis is typically below the recommended minimum of 100 to 200, so we need to run the analysis longer to get a publication-quality sample from the posterior. The potential scale reduction factor (PSRF) also indicates that there is some heterogeneity between the two independent runs. Nevertheless, the analysis is clearly approaching convergence. We should not have to run it much longer to get an accurate sample from the posterior, and we might expect the sample obtained so far to be reasonably close to the correct values. Now focus on the summaries of the parameter samples. From the top, the parameters are total tree length (\texttt{TL}), the omega values (dN/dS ratios) for the negatively selected, neutral, and positively selected sites (\texttt{omega(-)}, \texttt{omega(N)}, and \texttt{omega(+)}), the frequencies of the site categories (\texttt{pi(-)}, \texttt{pi(N)}, and \texttt{pi(+)}), and the stationary state frequencies of the codons (\texttt{pi(AAA)}, \texttt{pi(AAC)}, etc). The table ends with the probabilities of individual sites being in the positively selected class, with \texttt{pr+(1,2,3)} being the probability of the codon corresponding to nucleotide sites 1--3 being positively selected, \texttt{pr+(4,5,6)} being the probability for the codon corresponding to sites 4--6 being positively selected, etc. The positive selection probabilities are followed by the site omega values, which represent a weighted average for each site across all categories of omega values, with \texttt{omega(1,2,3)} being the value for the codon corresponding to nucleotide sites 1--3, \texttt{omega(4,5,6)} the value for the codon corresponding to sites 4--6, etc. Looking at the values, we note that about 85 \% (\texttt{pi(-)}) of the sites are under strong constraining selection (\texttt{omega(-)=0.0699}). Only a small fraction of sites, around 5 \%, are under positive selection (\texttt{pi(+)}). Examination of the positive selection probabilities and codon site omega values reveals which sites are most likely to be experiencing positive selection (a high value for \texttt{pr+(n,n,n)}, and \texttt{omega(n,n,n)} $> 1.0$). % For instance, the evidence is strong that site (292,293,294) is under positive selection % (\texttt{pr+(292,293,294) = 0.998}, \texttt{omega(292,293,294) = 1.21}). %%%% JN: My rerun using 3.2.7 did not find any pr+ value > 0.282567, %%%% JN: and for site (292,293,294), I get pr+(292,293,294) = 0.000015, omega(292,293,294) = 0.070020 \section{Sampling Across the GTR Model Space} \label{sampleGTR} A standard approach to Bayesian phylogenetics is to first select an appropriate substitution model using a model testing approach, such as those implemented in jModelTest \citep{posada08} or MrModeltest \citep{nylander04b}. An alternative, more elegant approach, is to sample across the substitution model space in the Bayesian MCMC analysis itself \citep{huelsenbeck04d}, removing the need for a priori model testing. The purpose of this tutorial is to demonstrate how to set up an analysis that integrates out the uncertainty about the correct substitution model by sampling across the entire general time reversible (GTR) model space. We will use the data in the file \texttt{primates.nex}. First, we read in the data: \begin{Verbatim} MrBayes > execute primates.nex \end{Verbatim} Then we specify that we want to use a gamma model of rate variation across sites, and that we want to sample across the GTR model space: \begin{Verbatim} MrBayes > lset nst=mixed rates=gamma \end{Verbatim} We can now run the analysis as usual. The \tb{sump} command will give relevant summaries of the parameter samples, including the probabilities of the sampled substitution models. We assess convergence by making sure that these probabilities are the same across independent runs. This is a sample table from an analysis using two independent runs on the tutorial data set: \begin{figure}[H] \centering \begin{BVerbatim} Model probabilities above 0.050 Estimates saved to file "primates.nex.mstat". Posterior Standard Min. Max. Model Probability Deviation Probability Probability ----------------------------------------------------------------------------- gtrsubmodel[121123] 0.196 0.015 0.186 0.207 gtrsubmodel[121121] 0.099 0.007 0.094 0.104 gtrsubmodel[121321] 0.066 0.017 0.053 0.078 gtrsubmodel[121324] 0.063 0.013 0.054 0.072 gtrsubmodel[121323] 0.062 0.008 0.057 0.068 gtrsubmodel[123323] 0.055 0.004 0.053 0.058 ----------------------------------------------------------------------------- \end{BVerbatim} \end{figure} The models are labeled using a so-called restricted growth function. The six different substitution rates are given in the order $r_{AC}$, $r_{AG}$, $r_{AT}$, $r_{CG}$, $r_{CT}$, $r_{GT}$. The first rate is labeled ``1''. The next rate is labeled with the next higher, unused integer if it is different, and otherwise with the same integer as the partition it belongs to. For instance, \texttt{gtrsubmodel[111111]} refers to the Jukes--Cantor or F81 model, \texttt{gtrsubmodel[123456]} to the GTR model, and \texttt{gtrsubmodel[121121]} to the HKY model, and so on. For this particular data set, we observe that there is considerable uncertainty concerning the correct substitution model. The model with the highest posterior probability is \texttt{gtrsubmodel[121123]}, which differs from the HKY model only in that the G--T substitution rate is different from the other transversion rates, presumably lower. The HKY model has the second best posterior probability of all the models reaching above the reporting threshold of $0.050$. All other estimates of model parameters derived from this analysis, the topology estimate for instance, will be based on an average across the sampled substitution models, each one weighted according to its posterior probability. To assess convergence when you sample across the GTR substitution model space, focus on the variation across runs in estimated model probabilities. The table gives the standard deviation as well as the range of estimated values across runs. Here, we see some heterogeneity across runs in model probabilities, but most models receive similar probabilities in the two independent runs. \section{Testing a Topological Hypothesis} In this tutorial, we will use Bayes factor comparisons to test a topological hypothesis, namely that humans are more closely related to chimps than to other primates. Specifically, we will contrast the hypothesis that humans and chimps form a monophyletic group ($M_{1}$), with the hypothesis that humans and chimps do not form a monophyletic group ($M_{2}$). In order to do this, we need to compute the ratio of the marginal likelihoods of the two models, $M_{1}$ and $M_{2}$. This ratio is known as the Bayes factor, $B_{12}$, and is a summary of the evidence provided by the data in favor of model 1 over model 2 \citep{kass95} (see also section \ref{bayesianModelChoice}). We will use the data in the file \texttt{primates.nex} for the analysis. First, read in the data and then specify a GTR + I + $\Gamma$ model: %% PV: I need to save some lines here for later \begin{Verbatim} MrBayes > execute primates.nex MrBayes > lset nst=6 rates=invgamma \end{Verbatim} Before we can test the hypothesis, we need to specify a hard constraint and a negative constraint. The hard constraint will allow us to force a partition to always be present in the sampled trees. The negative constraint will allow us to sample across all trees that do not contain the specified partition. Specify the constraints using the following commands: \begin{Verbatim} MrBayes > constraint humanchimp = Homo_sapiens Pan MrBayes > constraint nohumanchimp negative = Homo_sapiens Pan \end{Verbatim} MrBayes provides two methods for estimating marginal model likelihoods. The first is based on the harmonic mean of the likelihood values of the MCMC samples. It is simple to compute but it is a pretty rough estimate of the model likelihood. To obtain a more accurate model likelihood, MrBayes provides the stepping-stone method. First, let us use the harmonic mean estimate of the model likelihoods of the two models we want to compare. First we enforce the positive constraint, run an MCMC analysis with 100,000 generations, and use \texttt{sump} to get the harmonic mean estimate (we also use \texttt{set autoclose=yes nowarnings=yes} for allowing execution without being prompted for answering \texttt{yes/no} between steps): \begin{Verbatim} MrBayes > set autoclose=yes nowarnings=yes MrBayes > prset topologypr=constraints(humanchimp) MrBayes > mcmc ngen=100000 MrBayes > sump \end{Verbatim} In the output from the \texttt{sump} command, focus on the table summarizing the likelihoods of the MCMC samples: \begin{figure}[H] \centering \begin{BVerbatim} Estimated marginal likelihoods for runs sampled in files "primates.nex.run1.p" and "primates.nex.run2.p": (Use the harmonic mean for Bayes factor comparisons of models) (Values are saved to the file primates.nex.lstat) Run Arithmetic mean Harmonic mean -------------------------------------- 1 -5719.86 -5730.72 2 -5720.03 -5730.95 -------------------------------------- TOTAL -5719.94 -5730.84 -------------------------------------- \end{BVerbatim} \end{figure} It is the harmonic mean that we will use as the initial estimate of the model likelihood. The estimate of the logarithm of the model likelihood is $-5730.84$, with a little bit of heterogeneity between the runs. Now we enforce the negative constraint, and repeat the procedure using: \begin{Verbatim} Mrbayes > prset topologypr=constraints(nohumanchimp) Mrbayes > mcmc ngen=100000 Mrbayes > sump \end{Verbatim} Our run produced the following estimates of the log of the model likelihoods: \begin{figure}[h] \centering \begin{BVerbatim} Run Arithmetic mean Harmonic mean -------------------------------------- 1 -5726.95 -5735.94 2 -5727.59 -5738.19 -------------------------------------- TOTAL -5727.22 -5737.59 -------------------------------------- \end{BVerbatim} \end{figure} The harmonic mean estimate for this model is $-5737.59$ in log units, which is about 6 log units worse than the previous model---wich gives us a $2logB_{12} = 13.5$. A value $10$ is typically considered ``very strong'' evidence in favor of the better model (See table \ref{table:bftable} on page~\pageref{table:bftable}). Thus, the harmonic mean estimator indicates that we have very strong evidence in favor of human and chimp being each others closest relatives, with Gorilla and other primates being more distant. It may also be interesting to look at the best estimate of the phylogeny under the assumption that humans and chimps are not each others sister groups. Do this by typing \tb{sumt}. As you will see, this tree groups chimps (\textit{Pan}) and gorillas (\textit{Gorilla}) together, with humans \textit{Homo sapiens} being just outside, as one might have expected. Let us now repeat the comparison using the more accurate stepping-stone sampling approach. Instead of using the \texttt{mcmc} command followed by the \texttt{sump} command, we simply use the \texttt{ss} command instead, which will produce the estimated model likelihood directly. The stepping-stone analysis moves from the posterior to the prior through a number of steps (MCMC analyses) in which the sampled distribution is a mixture of varying proportions of the two. Ideally, one would like to see evidence of convergence among independent runs in each of the steps of the stepping-stone sampling algorithm. As the algorithm moves close to the prior, however, we expect this to be difficult. This will be especially true for the topology parameter, as the number of trees with similar probability will become huge for any reasonably-sized set of taxa. To help you monitor convergence during stepping-stone sampling, MrBayes will print the average standard deviation of split frequencies across runs if you use at least two independent, parallel runs. Each step in the algorithm is treated as an independent MCMC sampling problem, and where most of the parameters in the \texttt{mcmcp} and \texttt{mcmc} commands are applicable to the MCMC analysis in each step. For example, the \texttt{samplefreq} and the \texttt{burnin} parameters, respectively, are used for setting sampling frequency and burnin behaviour within each step. In addition, the \texttt{ss} command also uses its own burnin parameter, \texttt{Burninss}, for specifying the number of samples to be discarded before sampling of the first step starts. The length of the initial burn-in can be specified either in terms of the number of samples to be discarded (positive numbers) or in terms of step lengths (negative numbers). By default, the initial burn-in phase is as long as one of the subsequent steps in the algorithm, that is, \texttt{burninss=-1}. For example, assume that \texttt{Burninss} is set to $-1$, \texttt{Nsteps} to $49$, \texttt{Ngen} to $300000$ and \texttt{samplefreq} to $500$. The algorithm will then run 49 steps, each with 6,000 generations ($Ngen/Nsteps+1$), which is the number of generations per step left---after a burnin of (the length of) one step is removed. To obtain an adequate sample from most of the steps in the algorithm, we will use the settings given above. To monitor convergence, twice during each step, we set the diagnostics frequency to once every 3,000 generations. Stepping-stone analysis under the two models using these settings will be generated by the following commands: \begin{Verbatim} MrBayes > prset topologypr=constraints(humanchimp) MrBayes > ss ngen=300000 diagnfreq=3000 MrBayes > prset topologypr=constraints(nohumanchimp) MrBayes > ss \end{Verbatim} The output from the \texttt{ss} command will include the following table, which is given here for the first model: \begin{figure}[h] \centering \begin{BVerbatim} Marginal likelihood (in natural log units) estimated using stepping-stone sampling based on 49 steps with 6000 generations (12 samples) within each step. Run Marginal likelihood (ln) ----------------------------- 1 -5797.07 2 -5795.82 ----------------------------- Mean: -5796.26 \end{BVerbatim} \end{figure} In our run, the second model produced the following version of the same table: \begin{figure}[H] \centering \begin{BVerbatim} Run Marginal likelihood (ln) ----------------------------- 1 -5805.21 2 -5807.67 ----------------------------- Mean: -5805.82 \end{BVerbatim} \end{figure} The model likelihoods are thus $-5796.26$ for the first model,and $-5805.82$ for the second (in natural log units). We first note that both likelihoods are considerably smaller than the corresponding values based on the harmonic mean estimator. This is an expected effect due to the reliance of the harmonic mean estimator on rare samples of very low likelihood. Short runs are unlikely to include such samples, resulting in the harmonic mean estimator usually being biased upwards in practice. We also note that the more accurate model likelihoods suggest a larger difference between the two models, about 9 log likelihood units (with a $2*logB_{12} = 19,12$). Thus, we conclude that the better model (humans and chimps do form a monophyletic group) is very strongly supported by the Bayes factor. Finally, let us examine one of the convergence diagnostics plots from one of our stepping-stone analyses: \begin{figure}[H] \centering \begin{BVerbatim}[fontsize=\scriptsize] Plot of average standard deviation of split frequencies across steps. Points at -1.0 (y-axis) indicate that there were no splits above minimum frequency for corresponding step. +------------------------------------------------------------+ 0.101 | * * * | | * * * | | * * * ** * *| | ** * * * * | | * * * * * | | *** ** | | * ** * | | | | | | * * * | | * * * | | | | * * | | | |*** * * ** * | +------+-----+-----+-----+-----+-----+-----+-----+-----+-----+ 0.000 ^ ^ 1 49 \end{BVerbatim} \end{figure} Here, we see the expected pattern of low average standard deviation of split frequencies in the early steps of the algorithm (to the left in the diagram), and difficulties of sampling across topology space when we move closer to the prior (to the right in the diagram). By increasing the length of each step, it should be possible to improve convergence also in the steps close to the prior, and therefore increase the precision of the estimated model likelihoods. However, the similarity between the two independent estimates of the model likelihood we obtained in this analysis suggests that the accuracy is already reasonably good. It might be worth repeating that for any difficult MCMC scenario (such as getting an accurate estimate from the stepping-stone algorithm), it is well worth trying to repeat the analyses, perhaps with slightly different settings, to look for robustness. In the case of the \texttt{ss} command, one might also use the parameter \texttt{fromprior=yes}, which starts from the prior and goes towards the posterior (the opposite direction from the default settings). The two approaches should, in theory, give the same estimate (plus/minus the MCMC error). \section{Testing the Strict Molecular Clock} \label{strictClock} In this tutorial, we will use a Bayes factor comparison to test the strict clock model against the non-clock model for an example data set, \texttt{primates.nex}. The data set is unusual in that a standard non-clock analysis suggests that the evolution of these sequences may be fairly well explained by a strict clock. The analysis is quite similar to the previous one, so you might find it useful to go through that tutorial first. The basic idea is that we want to compare the marginal likelihoods of the two models (non-clock and strict clock) against each other. To do that we need to estimate the marginal likelihoods either using the rough harmonic mean method or the more accurate stepping-stone method. We will use both in this tutorial and compare the results. As usual, we first read in the data and then we specify a GTR + I + $\Gamma$ model: \begin{Verbatim} MrBayes > execute primates.nex MrBayes > lset nst=6 rates=invgamma \end{Verbatim} To estimate marginal likelihoods of the two models using harmonic means of the likelihoods of the MCMC samples, we simply run an ordinary MCMC analysis and obtain the appropriate values using the standard \texttt{sump} command. Specifically, we first run the standard non-clock model for 100,000 generations and summarize the obtained samples using \texttt{sump} (we also use \texttt{set autoclose=yes nowarnings=yes} for allowing execution without being prompted for answering \texttt{yes/no} between steps): \begin{Verbatim} MrBayes > set autoclose=yes nowarnings=yes MrBayes > mcmc ngen=100000 MrBayes > sump \end{Verbatim} \begin{figure}[h] \centering \begin{BVerbatim} Run Arithmetic mean Harmonic mean -------------------------------------- 1 -5720.09 -5732.97 2 -5719.24 -5730.36 -------------------------------------- TOTAL -5719.58 -5732.35 -------------------------------------- \end{BVerbatim} \end{figure} In the \texttt{sump} output, we focus on the table summarizing the means of the likelihoods of the MCMC samples obtained after the burn-in. \begin{figure}[h] \centering \begin{BVerbatim} Run Arithmetic mean Harmonic mean -------------------------------------- 1 -5720.09 -5732.97 2 -5719.24 -5730.36 -------------------------------------- TOTAL -5719.58 -5732.35 -------------------------------------- \end{BVerbatim} \end{figure} The harmonic mean suggests that the marginal likelihood of the standard non-clock model is $-5732.35$ log units. Notice, however, some heterogeneity between the runs. Now we repeat the procedure under the strict clock model. To enforce branch lengths to conform to a strict clock, we choose one of the clock priors for the branch lengths. Specifically, we are going to use the uniform prior on clock trees. The other commands are the same as those used previously: \begin{Verbatim} Mrbayes > prset brlenspr=clock:uniform Mrbayes > mcmc ngen=100000 Mrbayes > sump \end{Verbatim} Again, we focus on the table summarizing the means of the likelihoods of the MCMC samples: \begin{figure}[h] \centering \begin{BVerbatim} Run Arithmetic mean Harmonic mean -------------------------------------- 1 -5719.56 -5729.48 2 -5719.12 -5731.20 -------------------------------------- TOTAL -5719.32 -5730.67 -------------------------------------- \end{BVerbatim} \end{figure} The harmonic mean is lower for the strict-clock model than for the non-clock model, the log values being $-5730.67$ and $-5732.35$, respectively. This is as we suspected, but the difference is small and there is overlap between the estimates obtained in individual runs. In this case, we undoubtedly need the increased accuracy of the stepping-stone sampling method. Before moving on to the stepping-stone analysis, first compute a summary of the tree samples using the \texttt{sumt} command: \begin{Verbatim} Mrbayes > prset sumt \end{Verbatim} Now focus on the phylogram, the second tree, in the output from this command. It should look something like this: \begin{figure}[H] \centering \begin{BVerbatim}[fontsize=\scriptsize] Phylogram (based on median node depths): /------------------------------------------- Tarsius_syrichta (1) /--------------+ | \------------------------------------------- Lemur_catta (2) | | /----- Homo_sapiens (3) | /-+ | | \----- Pan (4) | /-------+ | | \------- Gorilla (5) | /---+ + | \--------------- Pongo (6) | /-------------+ | | \------------------- Hylobates (7) | | | | /-- Macaca_fuscata (8) | /-----------+ /--+ | | | | \-- M_mulatta (9) | | | /--+ | | | | \----- M_fascicularis (10) \------------+ \------------------------+ | \-------- M_sylvanus (11) | \--------------------------------------------- Saimiri_sciure~ (12) --|---------|--------|--------|---------|--------|--------| 0.60 0.50 0.40 0.30 0.20 0.10 0.00 [Expected changes per site] \end{BVerbatim} \end{figure} We briefly want to point out two things. First, all tips in the tree have the same distance to the root in the consensus phylogram, the summary tree that is based on branch lengths. This is because the default assumption in a strict-clock model is that the tips are of the same age. This assumption can be altered as shown below in the tutorial on dating. Second, the clock tree is rooted, unlike a tree resulting from a non-clock analysis. An unrooted tree is drawn by convention such that it is rooted on the outgroup taxon. This means that it will have a basal trichotomy, or a basal polytomy of higher degree if outgroup relationships are unresolved. The specified outgroup taxon (by default the first taxon in the matrix) will be the first member lineage of the basal trichotomy. A strict-clock analysis, however, produces a rooted tree, which should have a dichotomy at the root unless there is uncertainty concerning the resolution near the base of the tree. Such a tree does not have to be artificially rooted for drawing purposes using an outgroup. Thus, \emph{the outgroup setting is irrelevant in analyses of clock tree models}. The outgroup setting is only used when drawing unrooted trees. Now let us continue with a more accurate assessment of the marginal model likelihoods using the stepping-stone method. It estimates the model likelihood by sampling a series of distributions that represent different mixtures of the posterior distribution and the prior distribution \citep{xie11}. The stepping-stone algorithm is invoked with the \texttt{ss} command, and it uses the same computational machinery as the \texttt{mcmc} command for each of the steps. Let us run the algorithm for 50 steps (the default) of 5,000 generations each, which makes a total of 250,000 generations. We will adjust the default frequency of calculating convergence diagnostics to once every 2,500 generations to get two reports from each step in the algorithm. By default, the algorithm uses an initial burn-in corresponding to one step, which corresponds to setting the \texttt{burninss} option to $-1$. The total length of the analysis will therefore be 255,000 generations. As before, the run needs to be repeated for each of the two models. The following set of commands will run stepping-stone sampling on the non-clock model. Note that we first reset the prior on branch lengths to the default, an independent exponential prior with the rate parameter $10.0$. An alternative way of resetting the model to the default is to execute the data set again. If you choose that alternative, do not forget to specify the GTR + I + $\Gamma$ model before running the stepping-stone method. \begin{Verbatim} MrBayes > prset brlenspr=unconstrained:exp(10.0) MrBayes > ss ngen=255000 diagnfreq=2500 \end{Verbatim} The estimated marginal likelihoods are found in a table printed by the \texttt{ss} command after the analysis is completed. Here are the estimates from our run: \begin{figure}[h] \centering \begin{BVerbatim} Run Marginal likelihood (ln) ------------------------------ 1 -5798.62 2 -5799.24 ------------------------------ Mean: -5798.88 \end{BVerbatim} \end{figure} We now run the same analysis on the strict clock model: \begin{Verbatim} MrBayes > prset brlenspr=clock:uniform MrBayes > ss ngen=255000 diagnfreq=2500 \end{Verbatim} We got the following estimates of the marginal model likelihoods: \begin{figure}[h] \centering \begin{BVerbatim} Run Marginal likelihood (ln) ------------------------------ 1 -5783.66 2 -5784.14 ------------------------------ Mean: -5783.87 \end{BVerbatim} \end{figure} First we note that there is good agreement between the two independent analyses for both models, indicating that we now have more accurate estimates of the marginal model likelihoods. If we compare these estimates with the harmonic mean estimates, it is striking that they are both much lower. Although the harmonic mean estimator is unbiased if you let the number of samples go to infinity, it relies heavily on rarely sampled states with low likelihood. Short runs tend not to include such samples, so the harmonic mean estimate is often considerably inflated in practice. We also see that the strict-clock model is now almost $15$ log likelihood units better than the non-clock model. A difference exceeding 5 log likelihood units is usually considered very strong evidence in favor of the better model \citep{kass95} (see also Table \ref{table:bftable} on page~\pageref{table:bftable}). If you are familiar with likelihood ratio tests, it may seem counter-intuitive that the strict clock model can be so strongly preferred. After all, the strict clock is just a special case of the non-clock model, so the (maximum) likelihood \emph{must} necessarily be higher, or at least as high, for the latter. How can the strict-clock model still be preferred in the Bayes factor comparison? The answer has to do with the number of parameters. For a data set with $n$ taxa, a non-clock model has roughly $2n$ independent branch length parameters, whereas a strict clock model only has around $n$ independent node times. The other model parameters, the ones associated with the topology and the substitution model, are so few in comparison to the branch lengths or node times that they can be neglected. Thus, a strict clock model is much more parsimonious than a non-clock model in the number of parameters, and in Bayesian model choice, there is a built-in correction for the number of parameters. One way of explaining the correction is by noting that comparing marginal model likelihoods is the same thing as comparing the average likelihood across the joint prior distribution over all model parameters. By including more parameters, we are almost always going to increase the peak likelihoods, but whether the average likelihood increases is a complex function of the distribution of likelihood values with respect to the joint prior distribution. If the simpler model already captures values close to the peak likelihoods of the more complex model, then the latter is likely to have a lower average likelihood when the values are spread out across its larger parameter space. This is what happens here, resulting in a strong preference for the strict clock model. For a brief discussion on convergence diagnostics and the plot printed by the \texttt{ss} command, see the previous tutorial. \section{Using a Relaxed Clock Model}\label{relaxedClock} In this analysis, we will set up a relaxed clock model for the same data set, \texttt{primates.nex}, used in the previous tutorials. A relaxed clock model is intermediate between a strict clock model and a non-clock model. It has one or a few more parameters than a strict clock model, but much fewer than a non-clock model. Unlike a non-clock model, it produces a rooted tree, but the information about the position of the root is not as strong as in a non-clock analysis. Because the information about the position of the root might be weak, it is often beneficial to add a rooting constraint to a relaxed clock analysis. It can help a relaxed clock analysis correctly infer rate variation close to the base of the tree, variation that might otherwise cause rooting artifacts. It can also help speed up convergence, since it may be difficult for an MCMC analysis to mix adequately across multiple alternative root positions in a relaxed clock tree; this is going to be the case for this data set. To speed up convergence, and to show you how to set up a rooting constraint, we will choose the rooting option for this tutorial. Specifically, we will accept the common view that lemurs form the sister group of other primates. As usual, we first read in the data, and then we specify a GTR + I + $\Gamma$ model (we also use \texttt{set autoclose=yes nowarnings=yes} for allowing execution without being prompted for answering \texttt{yes/no} between steps): \begin{Verbatim}[fontsize=\small] MrBayes > set autoclose=yes nowarnings=yes MrBayes > execute primates.nex MrBayes > lset nst=6 rates=invgamma \end{Verbatim} To specify a rooting constraint, we first need to know the order of the taxa in the data matrix, or their names. We can do this by looking into the data file or by printing the matrix using the \tb{showmatrix} command. In either case, we will discover that the only lemur in the matrix, \textit{Lemur catta}, is taxon number 2. To specify a constraint that includes all taxa but the single taxon 2, we need to use the following command: \begin{Verbatim} MrBayes > constraint ingroup partial = 1 3-. : 2 \end{Verbatim} The \texttt{constraint} command first gives the name of the constraint (`ingroup'), then the type of constraint (`partial'). The list of taxa are given after the equal sign using a list of taxa, where the taxa can be specified using either their index in the matrix (1, 2, etc) or their name. A period is synonymous with the last taxon in the matrix, and a range is indicated using a dash. In our case, we constrain taxon 2 (\textit{Lemur catta}) to always be outside taxa number 1, and 3 to 12, in a rooted tree. See section \ref{unconstrainedAndConstrainedTopology} for more information of using constraints. To enforce the constraint, we also need to set the topology prior using: \begin{Verbatim} MrBayes > prset topologypr=constraints(ingroup) \end{Verbatim} Inside the parenthesis, it is possible to give one or more previously defined constraints, separated by a comma. We are now ready to specify the relaxed clock model, which is done in two steps. First, we select an underlying strict clock model. Let us use the simple uniform clock model, which would be specified with the command: \begin{Verbatim} MrBayes > prset brlenspr=clock:uniform \end{Verbatim} Other options include the coalescent and the birth-death models. In the next step, we are going to ``relax'' the strict clock assumption by selecting a model for the variation of the clock rate across lineages. MrBayes offers five relaxed clock models: the Thorne--Kishino 2002 (TK02) \citep{thorne02}, the compound poisson process (CPP) \citep{huelsenbeck00a}, the white noise (WN) \citep{lepage07}, the independent gamma rates (IGR), and independent lognormal (ILN) \citep{drummond06} models. We note here that, since version 3.2.8, the name `WN' is used to refer to `IGR' in old versions, while `IGR' is used to refer to a different model. In the new IGR model, the rates are i.i.d. gamma distributions across branches and the variance does not dependent on branch lengths as in the WN model. The ILN model differs from IGR in that the rate variation follows i.i.d. lognormal distributions instead of gamma distributions. Let us use the WN model, which is a continuous uncorrelated model of rate variation across lineages. We invoke this model using: \begin{Verbatim} MrBayes > prset clockvarpr=wn \end{Verbatim} Each relaxed clock model has one or more additional parameters. In the case of the WN model, it is the rate at which the variance of the effective branch length increases over time, \texttt{WNvarpr}. Because the strict clock model fits this data set so well (see previous tutorial), we do not expect the variance to increase much over time. The default prior for this parameter, an exponential distribution with rate $10.0$, should be more than generous enough for our analysis. To check the model settings, it is wise to first run the \tb{showmodel} command. Once you are convinced that the model settings are OK, run the analysis for 100,000 generation using: \begin{Verbatim} MrBayes > mcmc ngen=100000 \end{Verbatim} We can now verify using the \tb{sumt} command that the tree is rooted correctly and that it looks reasonable. We focus on the phylogram, the second tree to be displayed: \begin{figure}[H] \centering \begin{BVerbatim} Phylogram (based on median node depths): /---------------------------------------------------------- Lemur_catta (2) | |/--------------------------------------------------------- Tarsius_syrichta (1) || || /----- Homo_sapiens (3) || /-+ +| | \----- Pan (4) || /-------+ || | \------- Gorilla (5) || /---+ || | \--------------- Pongo (6) \+ /------------+ | | \------------------- Hylobates (7) | | | | /-- Macaca_fuscata (8) | /----------+ /---+ | | | | \-- M_mulatta (9) | | | /--+ | | | | \------ M_fascicularis (10) \-------------+ \----------------------+ | \--------- M_sylvanus (11) | \------------------------------------------- Saimiri_sciure~ (12) ----------|---------|--------|---------|---------|--------| 0.50 0.40 0.30 0.20 0.10 0.00 [Expected changes per site] \end{BVerbatim} \end{figure} Note that the strict clock tree that we obtained in the previous analysis was rooted differently, with \textit{Lemur} and \textit{Tarsius} together forming the sister group of the remaining primates. The reason that the strict clock analysis prefers to put the root there can be understood by going back to the non-clock tree (see chapter \ref{tutorialSimple}). By placing the root in this position, we minimize the difference between the non-clock and strict clock branch lengths. If our rooting assumption is correct, then there must have been some changes in the evolutionary rate close to the root. Specifically, we should see a deceleration in the \textit{Lemur} and \textit{Tarsius} lineages, or an acceleration in their sister lineages. We can look for this pattern in the summary statistics for the branch and node parameters, output by \texttt{sumt} before the trees. The table is preceded by a key to the taxon bipartitions, which is critical to interpreting where the different parameters belong. The key consists of two parts. First a list of the taxon labels with their corresponding number: \begin{figure}[h] \centering \begin{BVerbatim} List of taxa in bipartitions: 1 -- Tarsius_syrichta 2 -- Lemur_catta 3 -- Homo_sapiens 4 -- Pan 5 -- Gorilla 6 -- Pongo 7 -- Hylobates 8 -- Macaca_fuscata 9 -- M_mulatta 10 -- M_fascicularis 11 -- M_sylvanus 12 -- Saimiri_sciureus \end{BVerbatim} \end{figure} The second part consists of a ``dot-and-star''-table, indicating which taxa that belong to which partition. For example ID 17 describes a partition that divides taxa nr 3 and 4 against the rest. \begin{figure}[H] \centering \begin{BVerbatim} ID -- Partition ------------------ 0 -- ************ 1 -- *........... 2 -- .*.......... 3 -- ..*......... 4 -- ...*........ 5 -- ....*....... 6 -- .....*...... 7 -- ......*..... 8 -- .......*.... 9 -- ........*... 10 -- .........*.. 11 -- ..........*. 12 -- ...........* 13 -- .......****. 14 -- .......***.. 15 -- ..*********. 16 -- ..********** 17 -- ..**........ 18 -- .......**... 19 -- ..***....... 20 -- *.********** 21 -- ..*****..... 22 -- ..****...... ------------------ \end{BVerbatim} \end{figure} We first identify the taxon ID of \textit{Lemur} and find that it is 2. Now we find the partition ID that has a single star in the second position; it is the bipartition that separates \textit{Lemur catta} from all other taxa. Not surprisingly, the bipartition ID is 2. The sister lineage of \textit{L. catta} includes all taxa except itself. To find the corresponding bipartition ID, we need to find the ID that has a partition descriptor with a single dot in the second position; all other positions should be filled with stars. This bipartition has ID 20. Similarly, we find that the bipartition IDs of \textit{Tarsius syrichta} and its sister lineage are 1 and 16, respectively. Now we examine the posterior distribution of WN branch rates for these taxon bipartitions. If the clock is accelerated, the branch rate should be above $1.0$; if the rate is decelerated, the branch rate should be below $1.0$. The relevant rows of the table are: \begin{figure}[H] \centering \begin{BVerbatim} 95% HPD Interval ------------------ Parameter Mean Variance Lower Upper Median PSRF+ Nruns -------------------------------------------------------------------------------- WNBrlens_rate[1] 1.007293 0.003879 0.888841 1.124742 1.009719 1.003 2 WNBrlens_rate[2] 0.929211 0.007155 0.758538 1.053767 0.938230 1.044 2 ... WNBrlens_rate[16] 1.117410 0.065654 0.808765 1.595881 1.064906 1.018 2 ... WNBrlens_rate[20] 0.765294 0.301914 0.020191 1.648757 0.743613 1.000 2 \end{BVerbatim} \end{figure} We see that there is considerable uncertainty about the branch rates but some of our predictions may have some support. The evolutionary rate seems slightly decelerated in the lineage leading to \textit{Lemur catta}, with bipartition ID 2, but no evidence about a decelerated in the \textit{Tarsius syrichta} lineage. On the other hand, there seems to be an acceleration in the sister lineage to \textit{T. syrichta} with bipartition ID 16, which would be in accordance with our predictions. To help you visualize parameters such as branch rates inferred by a relaxed clock model, \texttt{sumt} produces a \texttt{.con.tre} file that contains summaries of all branch and node parameter samples in a format that the program FigTree can display. This allows you, for instance, to color branches according to inferred evolutionary rates. If you performed the Bayes factor comparison between the strict clock and non-clock models for this data set in the previous tutorial, you may wonder how the relaxed clock model stacks up. To make the comparison fair, let us compare the strict clock and relaxed clock models using the same rooting constraint that we enforced above. To get an accurate comparison, we run the stepping-stone sampling method. We refer to the previous two tutorials for details, but briefly give the commands here for the two stepping-stone analyses that are required for this comparison: \begin{Verbatim} MrBayes > ss ngen=500000 diagnfreq=2500 MrBayes > prset clockvarpr=strict MrBayes > ss \end{Verbatim} The results summarized by the \texttt{ss} command are shown here, with the relaxed clock model first (upper table), followed by the strict clock model (lower table). \begin{figure}[h] \centering \begin{BVerbatim} Run Marginal likelihood (ln) ------------------------------ 1 -6357.90 2 -6358.78 ------------------------------ Mean: -6358.25 Run Marginal likelihood (ln) ------------------------------ 1 -6371.97 2 -6372.22 ------------------------------ Mean: -6372.08 \end{BVerbatim} \end{figure} By comparing the model likelihoods we see that the relaxed clock model is a better explanation of the data. The difference is almost 14 log units, which is considered very strong evidence in favor of the better model \citep{kass95}. \section{Node Dating and Total-Evidence Dating} In this tutorial, we will use three different approaches to dating. First, we will show how to calibrate a tree based on information about the clock rate (rate dating), then we will date the tree based on node calibrations (node dating), and finally we will use total-evidence dating (or tip dating), in which the tree is dated based on simultaneous analysis of fossil and recent taxa. Rate dating and node dating will be demonstrated on a small data set, \texttt{primates.nex}, which has been analyzed in several previous tutorials, while a much larger data set, \texttt{hymfossil.nex} will be used to demonstrate total-evidence dating and node dating in a more typical setting. We begin by reading in the smaller data set using the command \texttt{execute} (we also use \texttt{set autoclose=yes nowarnings=yes} for allowing execution without being prompted for answering \texttt{yes/no} between steps): \begin{Verbatim} MrBayes > set autoclose=yes nowarnings=yes MrBayes > execute primates.nex \end{Verbatim} Next, we set the substitution model to GTR + I + $\Gamma$: \begin{Verbatim} MrBayes > lset nst=6 rates=invgamma \end{Verbatim} We know from previous tutorials (see sections \ref{strictClock} and \ref{relaxedClock}) that a strict clock model provides a good fit to the data set. Specifically, we choose a strict clock model with uniform branch lengths using the command: \begin{Verbatim} MrBayes > prset brlenspr=clock:uniform \end{Verbatim} By default, the clock rate is going to be fixed to $1.0$, which means that the age of the nodes in the tree will be measured in terms of the number of expected substitutions per site. If we want a dated tree, it is sufficient to change this assumption. For instance, let us assume that we know that the rate is approximately $0.01 \pm 0.005$ substitutions per site per million years. To use this assumption, we could for instance use a normal distribution as the prior for the clock rate, using $0.01$ as the mean and $0.005$ as the standard deviation. Since we give these values using millions of years as the unit, the resulting tree will be calibrated in millions of years. Setting the prior on the clock rate is thus achieved with: \begin{Verbatim} MrBayes > prset clockratepr=normal(0.01,0.005) \end{Verbatim} There is one more thing to consider before we can run the analysis. By default, the clock rate is fixed to $1.0$. This means that the tree age (or height, if you wish) measured in substitutions per site is the same as tree age measured in time units. As soon as these two measures are decoupled, it is important to consider whether the clock tree prior is defined in terms of substitution units or time units. MrBayes uses clock tree priors that are defined on parameters (speciation rate, extinction rate etc) measured in time rather than substitution units. The uniform clock tree prior has only one prior parameter, tree age. The default prior is an exponential distribution with rate parameter $1.0$, which usually works well as long as the clock rate is fixed to $1.0$. When we change the clock prior, we usually need to reconsider this prior. For instance, we might want to modify the prior so that it has the same effect on branch lengths measured in substitution units. In our case, we want to change the default prior, with expected mean $1.0$, to one with expected mean $1.0 / 0.01 = 100$, that is, the original value divided by the expected clock rate. %%%% JN: Orphan, commented, sentence below. Include or not? %Since the expectation of the exponential distribution is the same as the inverse of the rate % parameter, we thus need to modify the tree age prior to an exponential distribution with the % rate $0.01$. Here we specify a gamma distribution with mean 100 and standard deviation 50. We do this using the following command line. \begin{Verbatim} MrBayes > prset treeagepr=gamma(100,50) \end{Verbatim} Now let us run a short MCMC analysis using 100,000 generations and the default settings. After that we use the \texttt{sumt} command to examine the calibrated tree: \begin{Verbatim} MrBayes > mcmc ngen=100000 MrBayes > sumt \end{Verbatim} The resulting dated phylogram should look something like this: \begin{figure}[H] \centering \begin{BVerbatim}[fontsize=\scriptsize] /-------------------------------------------- Tarsius_syrichta (1) /-------------+ | \-------------------------------------------- Lemur_catta (2) | | /----- Homo_sapiens (3) | /-+ | | \----- Pan (4) | /-------+ | | \------- Gorilla (5) | /----+ + | \--------------- Pongo (6) | /-------------+ | | \-------------------- Hylobates (7) | | | | /-- Macaca_fuscata (8) | /-----------+ /--+ | | | | \-- M_mulatta (9) | | | /--+ | | | | \----- M_fascicularis (10) \-----------+ \-------------------------+ | \-------- M_sylvanus (11) | \---------------------------------------------- Saimiri_sciure~ (12) ------|---------|----------|---------|----------|---------| 50.0 40.0 30.0 20.0 10.0 0.0 \end{BVerbatim} \end{figure} The phylogram gives the median age estimates for all nodes in the tree in user-defined time units, which is million years in our case. For instance, the analysis dates the oldest split in the tree at around 53 Ma (million years ago), and the split between humans (\textit{Homo sapiens}) and chimps (\textit{Pan}) at about 5 Ma. If we are interested in a more detailed summary of the age estimates, we need to go into the parameter summary table printed above the trees. Before doing that, however, we need to identify the bipartition IDs that we are interested in. We do that by referring to the bipartition table: \begin{Verbatim}[xleftmargin=15mm,fontsize=\footnotesize] List of taxa in bipartitions: 1 -- Tarsius_syrichta 2 -- Lemur_catta 3 -- Homo_sapiens 4 -- Pan 5 -- Gorilla 6 -- Pongo 7 -- Hylobates 8 -- Macaca_fuscata 9 -- M_mulatta 10 -- M_fascicularis 11 -- M_sylvanus 12 -- Saimiri_sciureus Key to taxon bipartitions (saved to file "primates.nex.parts"): ID -- Partition ------------------ 0 -- ************ 1 -- *........... 2 -- .*.......... 3 -- ..*......... 4 -- ...*........ 5 -- ....*....... 6 -- .....*...... 7 -- ......*..... 8 -- .......*.... 9 -- ........*... 10 -- .........*.. 11 -- ..........*. 12 -- ...........* 13 -- ..***....... 14 -- ..********** 15 -- ..****...... 16 -- .......**... 17 -- ..*********. 18 -- ..*****..... 19 -- .......****. 20 -- ..**........ 21 -- .......***.. 22 -- **.......... ------------------ \end{Verbatim} For instance, assume that we are interested in the age of the oldest split in the tree, and the split between humans and chimps. The oldest split in the tree corresponds to the partition description with all stars, that is, partition ID 0. Humans have taxon ID 3 and chimps the taxon ID 4. The partition description corresponding to the human--chimp split thus should have stars in positions 3 and 4, and dots in all other positions. This is partition ID 20 (in your analysis, the partition IDs may be different). Now we go into the table of node and branch parameter estimates, and find the corresponding age estimates. Here are the relevant rows in this table in our analysis: \begin{figure}[H] \centering \begin{BVerbatim}[fontsize=\scriptsize] 95% HPD Interval -------------------- Parameter Mean Variance Lower Upper Median PSRF+ Nruns -------------------------------------------------------------------------------------- age[0] 59.590844 588.972831 26.805076 114.390416 52.435516 0.999 2 age[20] 5.361829 4.555993 2.300513 10.178956 4.955494 1.000 2 -------------------------------------------------------------------------------------- \end{BVerbatim} \end{figure} For instance, we see that the posterior distribution of the age of the human--chimp split has a mean of $5.36$ Ma, a median of $4.96$ Ma, and a 95\% HPD ranging from $2.30$ to $10.18$ Ma. By default, the \texttt{sumt} command will print all the summaries of node and branch parameters to the tree in the file with the ending \texttt{.con.tre} in a format that can be understood by the program FigTree. When you open the file in FigTree, you will have access to a range of options for visualizing the parameter summaries of your interest. We now turn our attention to dating of the tree using node calibrations (node dating). Assume, for instance, that we have independent evidence suggesting that the human--chimp split occurred at least $5$ Ma and not before $7$ Ma. We might then choose to date the tree using a uniform prior from $5$ to $7$ Ma on the human--chimp split. Dating the tree using a calibration node does not free us from setting appropriate priors on tree age and clock rate. We would like those priors to be set such that the main dating information comes from the calibrated node. Given how diffuse the divergence time estimates were under the priors chosen previously, it is clear that dating the human--chimp node as suggested will add information to the analysis. Thus, let us stick with the previous priors for tree age and clock rate, and just add the human--chimp calibration. This is done in four steps. First, we define a constraint for the node of interest. Second, we associate the node with a calibration assumption. Third, we enforce the constrained nodes. Fourth, we enforce node ages to obey calibrations. Specifically, the MrBayes commands are: \begin{Verbatim} MrBayes > constraint humanchimp = Homo_sapiens Pan MrBayes > calibrate humanchimp=uniform(5,7) MrBayes > prset topologypr=constraints(humanchimp) MrBayes > prset nodeagepr=calibrated \end{Verbatim} After running the analysis and summarizing the tree samples, we can examine the resulting phylogram. It should look something like this: \begin{figure}[h] \centering \begin{BVerbatim}[fontsize=\scriptsize] /-------------------------------------------- Tarsius_syrichta (1) /-------------+ | \-------------------------------------------- Lemur_catta (2) | | /----- Homo_sapiens (3) | /-+ | | \----- Pan (4) | /-------+ | | \------- Gorilla (5) | /---+ + | \--------------- Pongo (6) | /-------------+ | | \------------------- Hylobates (7) | | | | /-- Macaca_fuscata (8) | /-----------+ /--+ | | | | \-- M_mulatta (9) | | | /---+ | | | | \----- M_fascicularis (10) \------------+ \-----------------------+ | \--------- M_sylvanus (11) | \--------------------------------------------- Saimiri_sciure~ (12) -------|--------|-------|--------|-------|--------|-------| 60.0 50.0 40.0 30.0 20.0 10.0 0.0 \end{BVerbatim} \end{figure} If we look at the detailed summary of the age estimate of the human--chimp split, we now see that the distribution has a mean and a median of $6.0$, and the HPD interval ranges from $5.0$ to $6.9$ (your results may be slightly different). Thus, the posterior is virtually the same as the prior for the calibration node, meaning that the tree age and clock rate priors have little influence on the dating of the tree. Also note that the tree is now estimated to be slightly older than previously, as one might have expected given that the previous median age estimate for the human--chimp split was around $5$ Ma. Regardless of how one approaches dating, it is important to keep in mind that the posterior distribution will always depend to a considerable extent on the prior. Therefore, it is important to consider the priors on the clock tree parameters (such as the tree age for the uniform prior), the node ages and the clock rate carefully. It is good practice to examine the influence of these priors on the dating results. \vspace{1cm} Now we turn our attention to a larger data set, and more complex dating scenarios. The data set, \texttt{hymfossil.nex}, is from a study of the early radiation of the Hymenoptera \citep{ronquist12b}, and includes 68 recent taxa and 45 fossil taxa. The data matrix comprises about 5 kb data from seven molecular markers for recent taxa, and about 350 morphological characters coded for both recent and fossil taxa. At the end of the data file, in the \texttt{MRBAYES} block, you will find two dating analyses specified. The first is based on the standard node dating approach, in which the fossil record is used to derive calibration points that can help date the tree of extant taxa. The second uses total-evidence dating, in which the fossils are analyzed together with the extant taxa. The advantage of the total-evidence approach is that it integrates out the uncertainty in the placement of the fossils in the tree, while simultaneously extracting the dating information they provide. Thus, it avoids the problem of translating the fossil record into node calibrations, and it does not suffer from the possible artifacts resulting from locking the clades corresponding to calibration nodes in the phylogenetic analysis. Specifically, both analyses specified in the \texttt{hymfossil.nex} data file use two standard calibration nodes for the outgroups, one corresponding to the root of the tree and the other to the ancestor of Holometabola. In addition, the node dating analysis relies on seven hymenopteran calibration points derived from the 45 hymenopteran fossils. The fossils themselves, however, are not included in the analysis. In the total-evidence analysis, no node calibrations are used for the Hymenoptera. Instead, dating is based on simultaneous analysis of the fossil and recent taxa, taking the age of the fossils into account. To run any of these two analyses, simply uncomment one of the two analysis blocks by removing the pair of square brackets enclosing it. Then you execute the data file from MrBayes and use the standard \texttt{mcmc} command to run the analysis. We will not comment the results here but will simply walk you through the \texttt{MRBAYES} block in the data file to help explain all the steps needed in setting up the node dating and total-evidence dating analyses. The block starts out by defining a number of character sets. Then there is a set of lines that define the model for the morphological partition. It first defines a set of morphological characters to be ordered (\texttt{morph\_ordered}), and then a set of morphological characters to be excluded (\texttt{morph\_excluded}, \texttt{morph\_constant}). Then these characters are ordered and excluded with the following two lines: \begin{Verbatim} ctype ordered: morph_ordered; exclude morph_excluded morph_constant; \end{Verbatim} The next set of lines define the partitioning of the data: \begin{Verbatim} partition without_CO1_3 = 7: MV MS, 12S 16S, 18S, 28S, CO1_12 CO1_3, Ef1aF1_12 Ef1aF2_12, Ef1aF1_3 Ef1aF2_3; exclude CO1_3; set partition = without_CO1_3; \end{Verbatim} Note that the morphological data (sets \texttt{MV} and \texttt{MS}) are lumped into one partition, as are the 12S and 16S sequences (because these are very short sequences). The protein-coding CO1, Ef1aF1, and Ef1aF2 sequences are each divided into two character sets: one for the first and second codon positions and one for the third codon positions. Then the first and second codon positions of Ef1aF1 and Ef1aF2, both nuclear protein-coding genes, are lumped into one partition, while the third codon positions of these sequences are modeled in a separate partition. Finally, only the the first and second codon positions of CO1 are included in the analysis, in a separate partition; the third codon positions of CO1 are excluded. MrBayes does not accept a partition definition that does not include all characters, so we need to include the third codon position sites of CO1 in one partition, it does not matter which, before excluding them from the analysis. Here, they are included in the same partition as the first and second codon position sites of CO1 before being excluded. Finally, we tell MrBayes that the partition we just defined is the one we want to use with the \texttt{set} command. The next lines define the substitution models for the data partitions based on a priori model testing. You should already be familiar with the structure of these commands; if not, refer to the tutorial in chapter \ref{tutorialPartitioned}. The following block first defines an outgroup, to be used in drawing trees from unrooted non-clock analyses. This line is irrelevant for the dating analyses, since they produce rooted trees. The next line defines a set of taxa including the fossils, called \texttt{fossils}: \begin{Verbatim} taxset fossils= Triassoxyela Asioxyela ... \end{Verbatim} This will be convenient when we want to exclude the fossils from the node dating analysis. Finally, all the constraints corresponding to the calibration nodes are defined: \begin{Verbatim} constraint root = 1-.; constraint Hymenoptera = 10-69; constraint Holometabola = 3-69; constraint Holometabola_withFossils = 3-114; ... \end{Verbatim} Note that the file uses two versions of the holometabola constraint, one with the fossils for the total-evidence analysis and one without for the node dating analysis. Strictly speaking, this is not necessary because MrBayes will remove excluded taxa from the constraint before using it, so the second version of the constraint would have been adequate for both analyses. We now come to the specification of the two dating analyses. The first is the total-evidence analysis, which starts out by specifying the date of all fossil taxa: \begin{Verbatim} calibrate Triassoxyela=Fixed(235) Asioxyela=Fixed(235) ... \end{Verbatim} Here, we assume that the uncertainty concerning the age of each fossil is negligible compared to other sources of uncertainty, so we simply fix the age of each fossil instead of associating it with a prior probability distribution. The next three lines set up an WN relaxed clock model. You should recognize the commands from the tutorial in section \ref{relaxedClock}. \begin{Verbatim} prset brlenspr=clock:uniform; prset clockvarpr=wn; prset wnvarpr=exp(37); \end{Verbatim} An appropriate prior for the \texttt{wnvarpr} parameter, the variance increase parameter of the WN model, was found by simulations. Specifically, strict-clock and non-clock branch lengths were inferred on the same topology, and then simulations under the WN model were used to try to match the variation observed between them \citep{ronquist12b}. Stepping-stone sampling indicated that the WN model matched these data better than the TK02 relaxed clock model provided by MrBayes. The final five lines define a prior on the clock rate and sets the two outgroup calibration points using the same commands discussed previously in this tutorial: \begin{Verbatim} prset clockratepr=lognorm(-7.1,0.5); calibrate root=offsetexp(315,396); calibrate Holometabola_with_fossils=offsetexp(302,396); prset topologypr=constraints(root,Holometabola_with_fossils); prset nodeagepr=calibrated; \end{Verbatim} Note that a tree age prior is not used here; instead, we define a root calibration point. MrBayes will use the root calibration as your tree age prior if you provide one; otherwise, it is the \texttt{treeagepr} setting that will be used to define the uniform prior on clock trees. The specific parameter values used in defining the priors on the calibration points and the clock rate are derived from the fossil record \citep{ronquist12b}. This completes the definition of the total-evidence analysis. Let us now turn our attention to the node-dating analysis. It starts out by deleting the fossils from the analysis, using the previously defined taxon set called \texttt{fossils}: \begin{Verbatim} delete fossils \end{Verbatim} %%%% JN: Note, the following paragraph describes non-existing commands in the hymfossils.nex! %%%% JN: The current hymfossil.nex have the fossilized BD prior instead of "the relaxed clock model %%%% JN: in the same way as the total-evidence analysis"! It then continues by defining the relaxed clock model in the same way as the total-evidence analysis. This is followed by a specification of the clock rate prior and then the specification of the two outgroup and seven ingroup calibration points. The commands are essentially the same used in the total-evidence analysis. Note that all nine constraints have to be enforced when specifying the topology prior: \begin{Verbatim} prset topologypr=constraints(root,Holometabola,Hymenoptera,...,Vespina); \end{Verbatim} This completes the description of the analysis specifications. As you will notice, the file ends by setting up some \texttt{mcmc} parameters using the \texttt{mcmcp} command but it leaves it up to you to actually start the analysis. Another total-evidence dating tutorial using the Hymenoptera data (truncated) is provided by Zhang (\url{https://arxiv.org/abs/1603.05707}). It uses the fossilized birth-death model \citep{Stadler:2010fn,Heath:2014hn,Zhang:2016kf} as a tree prior. %%%% JN: The current hymfossil.nex have the fossilized BD prior instead of "the relaxed clock model %%%% JN: in the same way as the total-evidence analysis"! \section{Inferring Ancestral States} The purpose of the analysis discussed in this tutorial is to infer the ancestral state probabilities for a given node in a phylogeny, while simultaneously accommodating uncertainty in all other model parameters, including the topology of other parts of the tree. We will use the \texttt{cynmix.nex} dataset for the tutorial. First, we read in the data (and use \texttt{set autoclose=yes nowarnings=yes} for allowing execution without being prompted for answering \texttt{yes/no} between steps): \begin{Verbatim} MrBayes > set autoclose=yes nowarnings=yes MrBayes > execute cynmix.nex \end{Verbatim} This is the same data set we used for the partitioned analysis in chapter \ref{tutorialPartitioned}. It is a mixed data set consisting of morphological and molecular data. Let us assume we are interested in inferring the likely ancestral states of one of the morphological characters, say the first one in the matrix, for a particular ancestor in the tree, the ancestor of all gall wasps (family Cynipidae). The family includes taxa 1--29 of the 32 taxa included in the matrix. To make sure we can infer the states of the morphological character of interest without inferring the states of all other morphological characters, we will treat it in a separate data partition. Otherwise, we will use the same partitioned model as in chapter \ref{tutorialPartitioned}. To set up the model, we need to type in the following lines: \begin{Verbatim} Mrbayes > partition ancstates = 6: 1,2-166,COI,EF1a,LWRh,28S Mrbayes > set partition=ancstates Mrbayes > lset applyto=(2) rates=gamma Mrbayes > lset applyto=(3,4,5,6) rates=invgamma nst=6 Mrbayes > unlink statefreq=(all) revmat=(all) pinvar=(all) shape=(all) Mrbayes > prset ratepr=variable \end{Verbatim} Now we need to enforce a topological constraint corresponding to the family Cynipidae, and then ask MrBayes to report ancestral states for that node and the relevant partition. We force the node corresponding to the Cynipidae to always be present in the sampled trees by first defining and then enforcing the constraint: \begin{Verbatim} MrBayes > constraint cynipidae=1-29 MrBayes > prset topologypr=constraints(cynipidae) \end{Verbatim} To ask MrBayes to report ancestral states for the first partition, which contains only the first morphological character, we use: \begin{Verbatim} MrBayes > report applyto=(1) ancstates=yes \end{Verbatim} We now run the analysis, say for 30,000 generations, which is barely enough to get adequate convergence. After the analysis is completed, we summarize the parameter samples, including those of the ancestral state probabilities, using \texttt{sump}: \begin{Verbatim} MrBayes > mcmc ngen=30000 MrBayes > sump \end{Verbatim} The output from \texttt{sump} will include the state probabilities of the first character for the node corresponding to the most recent common ancestor of the Cynipidae. Here are the relevant lines in the parameter table from our analysis (they are found at the bottom of the table), based on a run of 1 M generations: \begin{figure}[h] \centering \begin{BVerbatim} Parameter Mean Variance min ESS* avg ESS PSRF+ -------------------------------------------------------------------- p(0){1@cynipidae} 0.547910 0.019476 ... 36.72 43.47 1.000 p(1){1@cynipidae} 0.452090 0.019476 ... 36.72 43.47 1.000 -------------------------------------------------------------------- \end{BVerbatim} \end{figure} The first line gives the probability of state 0 for character 1 at the constrained node labeled ``cynipidae''. The second line gives the probability of state 1 for the same character and the same node. The results indicate that the probability of the ancestral state being 0 at this node is only slightly higher than the probability of the state being 1. \section{The Multi-Species Coalescent}\label{theMultiSpeciesCoalescent} %%%% JN: tor 2 jul 2020 21:44:19 Need to verify that commands work %%%% JN: Adding `set autoclose=yes nowarnings=yes` to avoid explaining the warning about clock rate %%%% JN: when starting the `mcmc`. See github issue #117. In this tutorial, we will set up an analysis using the multi-species coalescent. Unlike standard models, which are based on sequence concatenation, this model does not assume that gene trees are identical to the species tree. Instead, we model the evolution of individual gene trees within species trees using the coalescent model, extended to multiple species \citep{liu07,edwards07}. The multi-species coalescent will take into account that gene trees and species trees may be different because of lineage sorting (deep coalescence). However, it does not model introgression or hybridization. For this tutorial, we will use the \texttt{finch.nex} data set, which contains data from 30 loci for a set of four individuals belonging to four different species. As usual, we first read in the data (we also use \texttt{set autoclose=yes nowarnings=yes} for allowing execution without being prompted for answering \texttt{yes/no} between steps): \begin{Verbatim} MrBayes > set autoclose=yes nowarnings=yes MrBayes > execute finch.nex \end{Verbatim} The data file contains a \texttt{MRBAYES} block, with commands to define a partition that divides the data into the 30 separate genes. Lets start by making sure this partition is used: \begin{Verbatim} MrBayes > set partition = genes \end{Verbatim} The next step in specifying the multi-species coalescent is to define what species to which the different sequenced individuals belong. This is done using the \texttt{speciespartition} command. By default, it is assumed that each individual belongs to a separate species, which is the case we are dealing with, but let us nevertheless be explicit: \begin{Verbatim} MrBayes > speciespartition species=SpeciesA:1,SpeciesB:2, SpeciesC:3,SpeciesD:4 MrBayes > set speciespartition=species \end{Verbatim} The first line (broken here into two to fit the page, but you need to enter it as a single line in the console window) simply names each of the species and then lists the sequences that belong to that species. The list of sequences is a standard taxon list, as used by the \texttt{taxset} command. This means that you can use either the index or the name of a sequence (``taxon''). Ranges are specified using a dash, and a period can be used as a synonym of the last sequence in the matrix. Here, each species that we name is represented by a single sequence, specified by its row index in the data matrix. We now need to unlink the topology parameter of the 30 gene partitions, which is done using the \texttt{unlink} command: \begin{Verbatim} MrBayes > unlink topology=(all) \end{Verbatim} To set the multi-species coalescent as the prior for each of the gene trees, we need to say both that the branch lengths should come from a coalescent process within a species tree,and that the topology should be constrained by the species tree. This is achieved using the following commands: \begin{Verbatim} MrBayes > prset topologypr=speciestree MrBayes > prset brlenspr=clock:speciestree \end{Verbatim} Note that these settings need to go together to invoke the multi-species coalescent. Once the multi-species coalescent is invoked, the ordinary rate multiplier is split into a within-gene and an among-gene rate multiplier. The within-gene rate multiplier is referred to simply as the rate multiplier, while the among-gene rate multiplier is called the generate multiplier. Examine the model parameters by using the \tb{showmodel} command to verify this. We now turn our attention to the substitution model. Assume that we would like to use an HKY model with site rates modeled using a discrete gamma. This would be accomplished using the line: \begin{Verbatim} MrBayes > lset nst=2 rates=gamma \end{Verbatim} Finally, we need to consider the prior on the population size. By default, it will be assumed that the population size is the same for all lineages in the tree. Let us change that to make the population size variable across lineages. For the prior on the population size, let us use a lognormal distribution with the mean on the log scale being equal to a population size of 100, and the standard deviation on the log scale being equal to 10. Since the natural log of 100 is about $4.6$, and the natural log of 10 is about $2.3$, this would be achieved using the lines: \begin{Verbatim} MrBayes > prset popvarpr=variable MrBayes > prset popsizepr=lognormal(4.6,2.3) \end{Verbatim} We can now sample from the model to estimate the posterior probability distribution. This is a large model with many parameters, so we need to run a fair number of generations to get adequate convergence. To speed up the analysis, let us try to run it without heating (Metropolis coupling). We do that by setting the number of chains to one, \texttt{nchains=1}. Under these settings, our run seemed to produce an adequate sample within 1 M generations. To run an analysis of this length without Metropolis coupling, use: \begin{Verbatim} MrBayes > mcmc ngen=1000000 nchains=1 \end{Verbatim} By default, MrBayes runs two analysis in parallel, and computes convergence diagnostics on all gene trees as well as on the species tree. Ideally, we would like to see topological convergence on all trees. This is a sample output from our analysis: \begin{figure}[h] \centering \begin{BVerbatim} Average standard deviation of split frequencies for gene tree 1: 0.007974 Average standard deviation of split frequencies for gene tree 2: 0.016684 Average standard deviation of split frequencies for gene tree 3: 0.015212 ... Average standard deviation of split frequencies for species tree (tree 31): 0.043427 \end{BVerbatim} \end{figure} In this list, the first 30 topologies correspond to the gene trees, and the last one to the species tree. To summarize the tree samples, simply use the \tb{sumt} command, which will process all of the gene trees and then the species tree. Focus on the last tree, which is the species tree. It should look something like this: \begin{figure}[h] \centering \begin{BVerbatim}[fontsize=\scriptsize] Clade credibility values: /------------------------------------------------------------------ SpeciesD (4) | + /--------------------------------- SpeciesA (1) | | \---------------100--------------+--------------------------------- SpeciesB (2) | \--------------------------------- SpeciesC (3) Phylogram (based on median node depths): /------------------------------------------------------------------ SpeciesD (4) | + /--- SpeciesA (1) | | \--------------------------------------------------------------+--- SpeciesB (2) | \--- SpeciesC (3) --------|-------|--------|-------|-------|--------|-------|-------| 7.00e-036.00e-03 5.00e-034.00e-033.00e-03 2.00e-031.00e-030.00e+00 [Expected changes per site] \end{BVerbatim} \end{figure} These summaries of the tree samples show that the species tree topology is partly unresolved. Note also that the species phylogram is shallow, so the sequences are quite similar to each other. In the partition tables printed above the trees, it is possible to find the posterior probability for each of the individual clades found in the tree samples. \chapter{Working with MrBayes}\label{advancedTopics} In this chapter, we cover the current capabilities of MrBayes, and provide some guidance on how you work most effectively with the program. We also discuss how to compile the program from source, and how to modify and extend it. Finally, we answer some frequently asked questions, and list the major differences between versions 3.1 and 3.2. The chapter ends with some pointers to additional information about the program. The text is intended as a complement to the tutorials and the online information available by using the \texttt{help} command. We strongly recommend that you run through the relevant tutorials before looking for additional information in this chapter. Often, we will simply describe the relevant commands, so that you can obtain the appropriate online help information in the program itself. The online help information is always likely to be the most reliable, detailed, and up-to-date source of information on how to use MrBayes. \section{Reading in Data} The basic mechanism for bringing data into MrBayes is to execute a Nexus data file using the \texttt{execute} command. MrBayes supports much of the Nexus standard \citep{Maddison97} but not all of it. The most frequent problems in reading Nexus data files into MrBayes are caused by the lack of support for the \texttt{equate} macro and the \texttt{symbols} list in the \texttt{format} command. These mechanisms are used to define non-standard symbols for use in a data matrix specification, but MrBayes does not support such non-standard symbols. Instead, MrBayes assumes a fixed set of standard symbols for the supported data types. These standard symbols include widely recognized ambiguity codes. Specifically, the symbols understood by MrBayes are \{A, C, G, T, R, Y, M, K, S, W, H, B, V, D, N\} for DNA data, \{A, C, G, U, R, Y, M, K, S, W, H, B, V, D, N\} for RNA data, \{A, R, N, D, C, Q, E, G, H, I, L, K, M, F, P, S, T, W, Y, V, X\} for protein data, \{0, 1\} for restriction (binary) data, and \{0, 1, 2, 3, 4, 5, 6, 5, 7, 8, 9\} for standard (morphology) data. In addition to these symbols, MrBayes supports the use of separate symbols for gaps, missing characters, and ``match'' characters; the latter correspond to characters that match the state in the first taxon in the matrix. The special symbols are defined using the \texttt{gap}, \texttt{missing}, and \texttt{matchchar} parameters of the \texttt{format} command. For instance, \texttt{missing=?} specifies that a questionmark is used to denote missing characters. Typically, the restriction to standard symbols is not a problem. However, some programs that write data to Nexus files will use \texttt{equate} macros and \texttt{symbols} lists even when they are not needed. If this is the case, you can simply delete the \texttt{equate} and \texttt{symbols} part of your \texttt{format} statement. Otherwise, you will have to do some editing of your data matrix to translate the symbols to the standard symbols used by MrBayes. MrBayes supports all mechanisms defined in the Nexus standard for specifying partial ambiguities or polymorphisms. The states involved in the ambiguity can be specified with or without a separating comma, and they can be bracketed by curly braces or parentheses. For instance, using \texttt{(A,G)} or \texttt{\{AG\}} for a nucleotide character would both signify an ambiguity as to whether the correct state is A or G. This would be equivalent to using the standard ambiguity code \texttt{R}. Like other statistical phylogenetics programs, MrBayes treats partial ambiguity and polymorphism the same. All the character states in the specified set are assigned a conditional likelihood of $1.0$; states that are not in the set have $0.0$ likelihood. Since version 3.2, MrBayes supports Nexus files that divide up the character matrix into a \texttt{TAXA} block and a \texttt{CHARACTERS} block instead of using a single \texttt{DATA} block. If you have formatting errors in your matrix, MrBayes should report the line and position of the error, which should help you find the problem. After you have successfully read a data matrix into MrBayes, you can examine it using the \texttt{showmatrix} command. \section{Reading in Trees} MrBayes uses standard Nexus tree specifications to read in user-defined trees. To read in a user-defined tree, you place the tree(s) inside a Nexus \texttt{TREES} block, either in the same file as your data, or in a separate file. The tree, along with any branch lengths, will be read into memory by the \texttt{execute} command. If you have your tree in a separate file, say \texttt{usertree.tre}, you first need to load your data matrix, and then issue the command \texttt{execute usertree.tre}. It is important that the spelling of taxon labels in the user tree matches those in the data matrix. Here is an example of a simple Nexus file containing a \texttt{TREES} block with a single tree in it: \begin{Verbatim} #NEXUS begin trees; tree mytree = [&R] ((SpeciesA:0.15,SpeciesB:0.20):0.10,SpeciesC:0.25); end; \end{Verbatim} Typically, a Nexus \texttt{TREES} block will start with a \texttt{translate} statement, which allows taxon names to be replaced by indices in the tree specifications. If a \texttt{translate} statement is not present, the taxon names need to be spelled out in each tree statement. Note that the Nexus tree statement includes a name for each tree; in this case, the tree is named \texttt{mytree}. It is this name that is used in MrBayes to refer to the tree and its branch lengths. The command \texttt{showusertrees} will print to screen a simple representation of the user-defined trees that are currently held in memory. If two user-defined trees have the same name, the second tree, when read in, will replace the first. All user-defined trees will be cleared from memory when a new data matrix is read in. The Nexus tree format allows you to specify both trees with branch lengths, and topologies --- trees without branch lengths. The Nexus format also allow for branch labels, such as support values, or bracketed comments inside the tree specifications, but MrBayes can not read that information, and any such information needs to be excluded before attempting to read the \texttt{TREES} block. Both rooted and unrooted trees are handled, and the trees need not be fully resolved. Typically, a special comment is used to explicitly declare whether a tree is rooted or unrooted. The special comments start with a bracket followed immediately by an ampersand. For instance, the special comment \texttt{[\&R]} is used to declare that a tree is rooted. When MrBayes prints trees to file, it uses such special comments to specify branch and node parameters that belong to the tree, for instance, branch rate parameters in a relaxed clock model. If you are interested in manipulating these parameters, we suggest that you set up the model you are interested in and then run a short MCMC analysis to the first checkpoint. The correct specification of all tree parameters can then be found by examining the checkpoint file, which will include a \texttt{TREES} block with all relevant parameter values specified. \section{Dealing with Models} In this section, we discuss how to set up a model in MrBayes, how to choose a model, and how to detect and solve problems associated with model selection. As we will discover, one can either choose a model before the analysis using a priori testing procedures, or use Bayesian model choice criteria to select among models analyzed by MrBayes. Under certain conditions, MrBayes can automatically sample across a large model space. We also discuss how to detect and address problems with the selected models, and share some of our experiences with models for various types of data. \subsection{Setting up a Model} Since version 3.2, MrBayes sets up a default model as soon as you have read in a data matrix. The default model will have one partition for each data type, and will apply a simple substitution model to each of them. You modify the model settings primarily by using the \texttt{lset} and \texttt{prset} commands. Appendix \ref{appendixOverview} contains graphical summaries of the available models for different data types, and the appropriate \texttt{lset} and \texttt{prset} parameters needed to invoke them. Chapter \ref{evolutionaryModels} gives more detail about each model and how you set it up in MrBayes. In principle, the \texttt{lset} command is used to set the structure of the model, while the \texttt{prset} command is used to set the prior parameters. However, the distinction is somewhat arbitrary, so check the options of both commands if you have difficulties finding a model option where you expect it to be. Several auxiliary commands are used to support the specification of models. The \texttt{constraints} command allows you to specify constraints on the topology. Importantly, it allows you to fix and name interior nodes in the tree. The \texttt{calibrate} command is used to associate tips or fixed interior nodes with prior probability distributions on age. The \texttt{pairs} command defines pairs (doublets) for models on RNA stem sequences. To set up a partitioned model, you first define the partition using the \texttt{partition} and \texttt{set} commands, possibly using previously named character sets specified with the \texttt{charset} command. Once that has been accomplished, you use the \texttt{applyto} option of \texttt{lset} and \texttt{prset} to apply model settings to particular parts of the data set. To link or unlink parameters across partitions, you use the \texttt{link} and \texttt{unlink} commands, respectively. For more instructions on how to set up a partitioned model, see the tutorial in chapter \ref{tutorialPartitioned} and the model specification in the \texttt{cynmix.nex} example file. More details are provided in section \ref{partitionedModels}. You often want to specify models using different subsets of the characters or the taxa in a data matrix. The easiest way of doing this is to first specify appropriate character and taxon sets using the \texttt{charset} and \texttt{taxaset} commands. Both commands use a similar syntax: the set is given a name, which is followed by a list of the included characters or taxa. The list can be based on character and taxon indices, or on taxon names in the case of \texttt{taxaset}. A range of taxa or characters is specified by giving the first member, a dash, and then the last member in the range. A dot (period) can be used to represent the last character or taxon. Finally, a backslash can be used to specify a step criterion after a range. For instance, \texttt{1-.\\3} would refer to every third character from character 1 to the end. This mechanism is commonly used to group sites in protein-coding sequences based on their position in the codon. Character and taxon sets are usually best defined in a \texttt{MRBAYES} block in the data file. Here we give some examples of character and taxon sets defined in such a block. In principle, the same commands could be issued from the command line, without the ending semicolon. For these examples, we assume that the gene sequence starts with the first character being a site in the first codon position. Comments are given in square brackets; they are ignored by MrBayes when the data file is processed. \begin{Verbatim} charset COI = 1-756; [Identify a gene using a range] charset COI_2nd = 2-756; \ 3 [Identify second codon position sites] taxaset fossils = 10-30; [Create set with taxon 10 through 30] \end{Verbatim} Once character and taxon sets have been defined, they can be used to identify subsets for inclusion in a particular model. Characters are excluded using the \texttt{exclude} command, and included using the \texttt{include} command. For taxa, the corresponding commands are \texttt{delete} and \texttt{restore}. Note that the \texttt{delete} command does not delete the taxa from memory, it only results in the taxa being excluded temporarily until the next \texttt{restore} command is issued. For a simple example, assume that we have a data set consisting of a mix of fossil and recent taxa, and would like to run an analysis on only recent taxa. We can do this by defining a taxon set called \texttt{fossils} and then exclude it from the analysis by using the following pattern (given here as lines in a data file, with an ending semicolon): \begin{Verbatim} taxaset fossils = 10-30; delete fossils; \end{Verbatim} For a second example, consider a mix of three genes: the mitochondrial gene COI and the nuclear genes Ef1a and CAD. First, let us set up a partition by gene: \begin{Verbatim} charset COI = 1-756; charset Ef1a = 757-1785; charset CAD = 1786-.; partition genes = 3:COI,Ef1a,CAD; set partition=genes; \end{Verbatim} Now assume we want to run an analysis with only the nuclear genes. Intuitively, one might expect that this could be accomplished by simply using \texttt{exclude COI}. However, MrBayes does not allow parts in a data partition to be empty, so this would clash with the partition called \texttt{genes}. We first need to set up an appropriate partition that will not have any empty parts after COI has been excluded. The following pattern is one possibility: \begin{Verbatim} partition nuclear_genes = 2:Ef1a COI,CAD; set partition=nuclear_genes; exclude COI; \end{Verbatim} This will give the appropriate two-gene partition with COI excluded. We end by noting that it is often better to partition sites in protein-coding sequences based on the codon position they occupy rather than on the gene to which they belong. \subsection{Selecting a Model for Analysis} MrBayes implements a vast number of different models. How does one select an appropriate model for analysis? There is a number of sophisticated techniques that can help you with this decision, but to some degree it still relies on your familiarity with the problem and your background knowledge of evolutionary processes. In particular, it is the choice of partitioning scheme and the type of substitution model (nucleotide, doublet, amino acid or codon) that necessarily depend to a large extent on your input. Let us first discuss the selection of substitution model type. For non-coding sequence data, the choice is straightforward, as a standard four-by-four nucleotide model is the only sensible option. Ribosomal sequences are more difficult. If it is possible to reliably separate out the stem regions, it would be advantageous to use a doublet model for those and a standard four-by-four model for the loops. The reason is that the doublet model captures the dependencies between the substitutions at the paired stem sites. If the stem sites are modeled with the standard four-by-four model, the changes in stem sites will be weighted too heavily and this might cause problems if there are homoplasies in these characters. Of course, a doublet model is computationally more complex than a four-by-four model, so if this means you cannot get MCMC convergence in a reasonable amount of time, you may still be better off with the four-by-four model. If it is difficult or impossible to separate out the stem sites, then a standard four-by-four nucleotide model is the only option. Alignment is often a significant issue when analyzing ribosomal sequences. There are Bayesian approaches that accommodate alignment uncertainty, but none of them is implemented currently in MrBayes. For protein-coding sequences, there are numerous options. Is it best to analyze such sequences using a nucleotide, amino acid or codon model? In principle, a codon model could also capture evolutionary processes at both the amino acid and nucleotide levels, making it difficult to beat. The codon models currently implemented in MrBayes should do a decent job at modeling nucleotide substitution processes, but amino acid evolution is only represented by a single rate, omega, the ratio of nonsynonymous to synonymous changes. While the most sophisticated codon models allow omega to vary across sites, different amino acid substitutions always have the same rate. This is unlike the amino acid models, which can potentially model each of the 190 pairs of amino acids as having a separate substitution rate. The amino acid models, however, completely ignore nucleotide substitution processes. None of the models implemented in MrBayes consider the dependency among sites caused by the constraints on the 3D structure of the resulting protein. On top of the model adequacy considerations, there is also computational complexity. Amino acid models are roughly 25 times slower than nucleotide models, and codon models are roughly 10 times slower than amino-acid models. If you are able to use BEAGLE to compute likelihoods on the GPU, you may observe less severe performance penalties, but amino-acid and codon models are still likely to be a lot slower than nucleotide models. This slowdown may compromise your ability to obtain an adequate sample of the posterior probability distribution, offsetting any gains in model adequacy. There is much to suggest that, currently, four-by-four nucleotide models typically give you the best bang for the buck. However, when analyzing more difficult phylogenetic problems, especially problems involving deep divergences, amino acid models may be the best choice. Codon models, as currently implemented in MrBayes, are chiefly recommended for those interested in inferring dN/dS ratios and in finding positively selected sites. However, future studies may well show that, for difficult phylogenetic problems, more complex codon models may offer an interesting alternative to amino acid or nucleotide models. When it comes to partitioning schemes, a standard approach is to group molecular data by gene. However, in most cases, you will be better off partitioning the sites based on other criteria. For nucleotide models, for instance, we often partition by origin (nuclear or mitochondrial) and codon position rather than by gene. A good scheme might be to group first and second codon position sites in one part and third codon position sites in another. One of several reasons for preferring such a scheme is that sites in a protein-coding sequence often show a distinct bimodal pattern of rate variation across sites, with third codon position sites being significantly faster than first and second codon position sites. Such a bimodal distribution can often not be modeled adequately with the gamma or invariable sites plus gamma models of rate variation across sites. In fact, we have seen several instances where this misfit causes long branch attraction phenomena. With amino acid models, it seems plausible that dividing up the sites by the selective environment of the amino acid residue will be better than dividing up the sites simply by gene. For instance, hydrophilic sites in one group and hydrophobic sites in another. Unfortunately, such a partitioning scheme requires detailed knowledge of protein structure or careful analysis of the aligned character matrix. MrBayes currently does not implement models that automatically finds these partitions and models for you. If you have decided on using a four-by-four nucleotide model, then a very popular approach for selecting a specific substitution model is to use so-called model testing, as implemented in jModelTest \citep{posada08} or MrModelTest \citep{nylander04b}. The basic idea is to find a rough estimate of the tree, and then use one of several information criteria to find the best rate matrix and model of rate variation across sites. Often, such a procedure will suggest not only a specific model but also a set of specific parameter estimates. Especially in older maximum likelihood algorithms for phylogenetic inference, there was a big performance penalty for estimating parameter values, so it was often a good idea to initially fix parameters to the rough estimates produced by the model-selection procedure. However, fixing values is against the Bayesian philosophy of integrating out uncertainty where possible. Furthermore, in the Bayesian approach, the computational penalty associated with estimating parameters rather than fixing them prior to analysis is typically insignificant. For these reasons, one would generally advise against fixing parameter values in Bayesian inference. Parameter estimates from a priori model testing also tend to be quite imprecise, so if one insists on fixing parameter estimates, it would probably be better to use more accurate estimates. Another potential problem that may arise is that jModelTest suggests a specific rate matrix that is not implemented in MrBayes. However, the Bayesian approach is robust to slight overparameterization, so we suggest that you simply substitute the preferred model with the next more complex model available in MrBayes. Since version 3.2, MrBayes can automatically sample across the entire general time-reversible (GTR)model space (see section \ref{modelJumping} below), which basically renders a priori model testing superfluous. An additional advantage of this approach is that your results will be based on a weighted average of the best substitution models instead of being based exclusively on a single model. The only potential drawback is that you may experience problems with mixing over the substitution model space. In our limited experience, however, this has not been a problem. If it is, you will detect it easily by examining the \texttt{sump} output from the analysis sampling across the GTR model space, as described in the tutorial in section \ref{sampleGTR}. A similar option of sampling across rate matrices is available for fixed amino-acid rate matrices, as described below. If you are interested in fixing parameters in MrBayes to specific values, you can do that using \texttt{prset =fixed ()}. For instance, if you want to fix the shape parameter of the gamma model to $0.12$, use \texttt{prset shapepr=fixed(0.05)}. The graphical model summaries in Appendix \ref{appendixOverview} give the relevant \texttt{prset} options for particular types of parameters. In general, the default prior distributions used by MrBayes are so-called flat, uninformative, or vague distributions. This is an appropriate choice if you want your posterior distribution to primarily be based on the data at hand rather than on your background knowledge. However, uninformative priors demand more of the data than more informative alternatives. Simply put, you can use a more complex model if you have informative priors than if you are using vague priors. This will be reflected both in the likelihood of obtaining symptoms of over-parameterization (see the following section) and in the Bayesian model choice criteria. A more informative prior will reduce the problems of over-parameterization and will tilt Bayesian model choice in favor of more complex models. If you want to use more informative priors in your analyses, it is easy to change the default ones using the \texttt{prset} command. Remember that the prior distributions are part of a Bayesian model. Thus, if you want to compare the results of MrBayes with those of other Bayesian programs, remember to check that the priors are the same. In many cases, one ends up with a small set of candidate models that cannot be separated meaningfully based on a priori considerations. If so, then it usually makes sense to analyze all models using the full Bayesian MCMC machinery. The stepping-stone algorithm (see below) can then be used to select among them based on Bayes factors and Bayesian model choice criteria. \subsection{Over-parameterization} Even though Bayesian inference is robust to modest over-parameterization, you can definitely run into problems if you introduce parameters willy-nilly in your model. What types of models should be avoided and what are the symptoms of over-parameterization? A common cause of over-parameterization is the thought-less application of too complex rate matrices or models of rate variation across sites to data sets with little variation across the tree. For instance, a group of sites that hardly vary over the tree is unlikely to give much information about the six relative substitution rates of the GTR model. Therefore, the posterior is likely to largely reflect the prior. Similarly, if roughly half of the sites in your sequence are invariant across taxa, then it may be difficult to fit a gamma distribution to the few remaining variable sites. Similarly, if you partition your data sets into many small parts there may be too little information to accurately infer the relative evolutionary rate of each part. A common symptom of over-parameterization is that you obtain posterior distributions that seem extreme in some sense. For instance, trees may have excessively long branches, some substitution rates may be extremely large or small, or partition rate multipliers may be extremely small or large. These phenomena are caused by the vague default priors used in MrBayes. When the posterior is dominated by the vague default priors, it will include values that are typically more extreme than you expect. One way of understanding this is that you really have much more background knowledge than represented by the vague default priors. A phenomenon related to over-parameterization is ``burn out''. The likelihood monitored during the MCMC sampling typically increases in the early phase of a run (burn in) and then stabilizes close to the peak values. However, with over-parameterized models, one sometimes finds that the likelihood first increases but then \emph{decreases} significantly before stabilizing. The cause of this behavior is the default starting values used in MrBayes. They often tend to be closer to the maximum likelihood estimate (MLE) of the parameter than a random sample from the default vague prior. For instance, the starting value of all relative rate parameters is set to $1.0$ by default. In most cases, you expect the bulk of the probability mass in the posterior probability distribution to be in the region close to the MLE. However, if your model is over-parameterized, there may not be enough information to focus the posterior around the MLE. Then it is possible that there is another region in parameter space with only moderate height (lower likelihood values) but considerably larger probability mass than the MLE region. It is like comparing the mass of a tower to the mass of a huge office complex. Even if the tower is considerably higher, its mass is going to be only a fraction of the mass of the office complex. Under these conditions, the likelihood will initially climb towards the peak of the MLE region, since the sampling starts close to that region of parameter space. After a while, it will find the larger probability mass, and the likelihood will decrease. A particularly common situation is that the MLE region, which includes trees with short branch lengths, has considerably lower probability mass than a much larger region of tree space at longer branch lengths. The reason is that a standard phylogenetic model, particularly a non-clock model, is entirely dominated by the many branch length parameters. For instance, a non-clock GTR + I + $\Gamma$ model for 100 taxa has 10 free substitution model parameters but roughly 200 branch length parameters. As a branch becomes longer, its exact value has little influence on likelihood and we can end up with the ``huge office complex'' situation. Regardless of the cause, there are two ways of correcting an over-parameterized model for a given data set: either you decrease the model complexity by reducing the number of model parameters, or you incorporate more background information into your prior distributions. \subsection{Bayesian Model Choice} \label{bayesianModelChoice} Once a small set of models have been chosen for analysis, they can be compared using Bayesian model choice criteria. This is typically done through the calculation of Bayes factors, which can be interpreted as indicators of the strength of the evidence in favor of the best of two models. Bayes factor is given by $$ B_{12} = \frac{P(D|H_{1})}{P(D|H_{2})} $$ where $D$ is the data thought to have been generated by any of the candidate hypotheses $H_{k}$ ($k = 1,2$). The expression $P(D|H_{k})$ is also called the model-, or marginal likelihood. The Bayes factors values are typically interpreted according to recommendations presented by \citet{kass95} (see Table \ref{table:bftable}). \begin{longtable}[h!]{@{}lll@{}} \toprule \(2\log_{e}(B_{12})\) & \(B_{12}\) & Evidence against \(H_{2}\)\tabularnewline \midrule \endhead \(0\) to \(2\) & \(1\) to \(3\) & Not worth more than a bare mention\tabularnewline \(2\) to \(6\) & \(3\) to \(20\) & Positive\tabularnewline \(6\) to \(10\) & \(20\) to \(150\) & Strong\tabularnewline \(>10\) & \(>150\) & Very strong\tabularnewline \bottomrule \caption{Interpretation of the Bayes factor ($B_{12}$). Based on \citep{kass95}.} \label{table:bftable} \end{longtable} Unlike a hierarchical likelihood ratio test, the models compared with Bayes factors need not be hierarchically nested. A Bayes factor is calculated simply as the ratio of the marginal likelihoods of the two models being compared. The logarithm of the Bayes factor is the difference in the logarithms of the marginal model likelihoods. The marginal likelihood of a model is difficult to estimate accurately but a very rough estimate may be obtained from the harmonic mean of the likelihood values of the MCMC samples \citep{newton94}. MrBayes calculates this estimator when you summarize your samples with the command \texttt{sump}. In the output from the \texttt{sump} command, you will find the following table (it might look a little different depending on how many simultaneous runs you have performed; this table is for two runs): \begin{figure}[h] \centering \begin{BVerbatim} Estimated marginal likelihoods for runs sampled in files "replicase.nex.run1.p", "replicase.nex.run2.p", etc: (Use the harmonic mean for Bayes factor comparisons of models) Run Arithmetic mean Harmonic mean -------------------------------------- 1 -5838.81 -5847.20 2 -5838.21 -5849.17 -------------------------------------- TOTAL -5838.47 -5848.61 -------------------------------------- \end{BVerbatim} \end{figure} For instance, assume we want to compare a GTR model with an HKY model. Then simply run two separate analyses, one under each model, and estimate the logarithm of the marginal likelihoods for the two models (using only samples from the stationary phase of the runs). Then simply take the difference between the logarithms of the harmonic means and find the corresponding interpretation in the table of \citet{kass95} (Table \ref{table:bftable}). A worked example might look like this: Assume that we wish to compare two models $M_{1}$ and $M_{2}$ on the same data. We run two separate MCMC simulations, one for each model, and $M_{1}$ gives a marginal likelihood of $-5848.61$, and $M_{2}$ gives a marginal likelihood of $-5857.70$. The Bayes factor is the ratio between the model likelihoods. Since we are approximating the model likelihoods by the harmonic mean on a log scale, we have $$ log (B_{12}) = log \Big(\frac{P(D|M_{1})}{P(D|M_{2})}\Big) = log(P(D|M_{1})) - log(P(D|M_{2})) $$ and by using the first column in Table \ref{table:bftable} for interpretation, we multiply by $2$ to get $2 log(B_{12}) = 2 * (-5848.61 - (-5857.70)) = 18.18$. According to Table \ref{table:bftable}, any value over $10$ for $2 log(B_{12})$ is considered ``Very strong evidence against $M_{2}$'' (compared to $M_{1}$), and this might also be our conclusion in our comparison. The same approach can be used to compare any pair of models you are interested in. For instance, one model might have a group constrained to be monophyletic while the other is unconstrained, or one model can have $\Gamma$-shaped rate variation while the other assumes equal rates across sites. As stated above, models need not be hierarchically nested. An interesting property of the Bayes factor comparisons is that it can favor either the more complex model or the simpler model, so they need not be corrected for the number of parameters in the models being compared. Additional discussion of Bayesian model testing, with several examples, is found in \citet{nylander04}. \subsection{Model Jumping} \label{modelJumping} Bayesian MCMC model jumping provides a convenient alternative to model selection prior to the analysis. In model jumping, the MCMC sampler explores different models and weights the results according to the posterior probability of each model. In MrBayes, the model-jumping approach is available for two sets of models. One is for all models where a four-by-four nucleotide model is a component, i.e., doublet and codon models in addition to the ordinary nucleotide models. Rather than selecting a substitution model before the analysis, the user can now sample across all 203 possible time-reversible rate matrices according to their posterior probability \citep{huelsenbeck04d}. This can be accomplished simply by using the option \texttt{mixed} for the \texttt{Nst} parameter in the \texttt{lset} command. For example: \begin{Verbatim} MrBayes > lset applyto=(all) nst=mixed \end{Verbatim} Currently, the program only applies model-jumping among subclasses in the GTR matrix, so there is not possible to sample across, for example, a GTR model with or without any assumption of rate variation across sites (e.g. GTR vs. GTR + $\Gamma$ vs. GTR + I + $\Gamma$). The second scenario where the program can apply model jumping is is the estimation of fixed-rate amino-acid substitution models. In this situation, the Markov chain will sample over models according to its probability. Currently, ten different rate matrices will be sampled (see section \ref{estimatingTheFixedRateModel}). To achive the integration over rate matrices, one need to use the \texttt{mixed} option for \texttt{Aamodelpr} parameter in the \texttt{prset} command. For example: \begin{Verbatim} MrBayes > prset applyto=(all) aamodelpr=mixed \end{Verbatim} %%%% JN: Section not finished % \section{Starting Values} % \subsection{Default Starting Values} % \subsection{Changing Starting Values} % One can change the default, or current, starting values for parameters in a model using the command % \texttt{startvals}. The basic format is: % % \begin{Verbatim} % startvals =(,,...,) % \end{Verbatim} % % for all substitution model parameters. The format is slightly different for parameters that are % written to a tree file: % % \begin{Verbatim} % startvals = % \end{Verbatim} % % This version of the command will look for a tree with the specified name among the trees read in % previously when parsing a tree block. The information stored in that tree will be used to set the % starting value of the parameter. The parameters that are set using this mechanism include topology % and branch length parameters, as well as relaxed clock branch rates, cpp events and cpp branch rate % multipliers. %%%% JN: Sections planned, but not written % \section{Sampling the Posterior} % \subsection{Convergence and Mixing} % \subsection{Proposal Mechanisms} % \subsubsection{Scalar Moves} % \tb{\it Slider} % \tb{\it Scaler} % \subsubsection{Simplex Moves} % \tb{\it Dirichlet Move} % \tb{\it Pair Slider} % % \subsubsection{Tree Moves} % \tb{\it Node Slider} % \tb{\it LOCAL} % \tb{\it Nearest Neighbor Interchange} % \tb{\it Subtree Pruning and Regrafting} % \tb{\it Subtree Swapping} % \tb{\it Tree Bisection and Reconnection} % \tb{\it Parsimony-Biased SPR} \subsection{Tuning Proposals} \subsubsection{Manual Tuning} If you are using automatic optimization of proposal tuning parameters (auto-tuning), and your runs are reasonably long so that MrBayes has sufficient time to find the best settings, you should not have to adjust proposal tuning parameters manually. Auto-tuning is turned on by default and will take care of this for you. However, auto-tuning does not change the frequencies with which different proposals are tried. By default, MrBayes uses topology proposals quite frequently, while other parameters are changed more rarely. If it is easy to get topological convergence on your data set, you may want to decrease the frequency of topology proposals to get an adequate sample of other model parameters more quickly. There is also a number of proposal mechanisms that are turned off by default, in particular topology proposals. If you have difficulties getting topological convergence, it might sometimes help to select a different mix of topology moves than the one used by default. For instance, the random SPR move tends to do well on some data sets but it is switched off by default because, in general, it is less efficient than the default moves. You can add and remove proposal mechanisms, or change the frequency with which they are used, by adjusting their relative proposal probabilities using the \texttt{propset} command. Use \texttt{showmoves allavailable=yes} first to see a list of all the available moves. In general, however, if your chains are not converging then it may be more productive to add more heated chains, especially if you have access to a large computer cluster, than it is to try to fine-tune proposal tuning parameters or proposal frequencies. %%%% JN: Section planned, but not written % \subsubsection{Adaptive Tuning} \subsection{Monitoring Convergence} \label{monitoringConvergence} One of the most powerful ways of assessing MCMC convergene, is to compare samples obtained from independent MCMC analyses. It is typically the tree samples that are most divergent in phylogenetic analyses, and we therefore introduced the average standard deviation of split frequencies (ASDSF; \citep{lakner08}) in MrBayes to allow quantitative assessment of the similarity among such samples. ASDSF is calculated by comparing split or clade frequencies across multiple independent MCMC runs that ideally should be started from different randomly chosen starting trees \citep{lakner08}. Specifically, let $f_i^j$ be the fraction of the trees from run $i$ containing the split or clade (taxon bipartition) $j$. Then ASDSF is defined as \begin{equation*} \text{ASDSF} = \frac{1}{M} \sum_j \left( \sqrt{ \frac{1}{N-1} \sum_i (f_i^j - \bar{f}^j)^2} \, \right) \end{equation*} where $N$ is the number of runs, $M$ is the total number of splits or clades present in the tree samples being compared, and $\bar{f}^j$ is the mean fraction of trees across all runs containing split or clade $j$. ASDSF should approach $0.0$ as runs converge to the same distribution. The frequencies of rare splits or clades are difficult to estimate accurately and these groupings are usually of marginal interest. Therefore, it may be advantageous to exclude them from the diagnostic. MrBayes allows the user to set a cutoff frequency (using the \texttt{minpartfreq} option for the \texttt{sumt} command); all splits or clades occurring minimally at that frequency (default value $0.10$), in at least one of the runs will be incorporated in the ASDSF. To allow users to monitor MCMC progress, MrBayes can run several analyses in parallel (by ensuring that the \texttt{nruns} parameter for the \texttt{mcmc} command is $>1$) and report the average (ASDSF), or maximum standard deviation of split frequencies at regular intervals. For example (command given on several lines for readability): \begin{Verbatim} MrBayes > mcmcp nruns=2 mcmcdiagn=yes diagnfreq=5000 MrBayes > mcmcp diagnstat=avgstddev minpartfreq=0.10 MrBayes > mcmcp relburnin=yes burninfrac=0.25 savetrees=no \end{Verbatim} All options given for the \texttt{mcmcp} command above are the default settings for MrBayes 3.2 (effectively the same as typing \tb{mcmc}), but we want to highlight the relevant parts. Setting \texttt{nruns=2} will ensure that we can compare independent runs (two in this case), each started from a random starting tree. By using \texttt{mcmcdiagn=yes}, MrBayes will print a MCMC-diagnostics file, ending in \texttt{.mcmc}, with the convergence diagnostics calculated every \texttt{diagnfreq} generation. Here we have chosen the standard deviation of split frequencies to be based on the average (\texttt{diagnstat=avgstddev}), but there is also a choice of using the maximum (\texttt{diagnstat=maxstddev}). The \texttt{minpartfreq=0.10} will discard any partitions with a frequency $<0.10$ from being included in the calculation of the diagnostic statistic. The \texttt{savetrees} option becomes relevant if you are using a relative burnin for run-time convergence diagnostics (as given by \texttt{relburnin=yes burninfrac=0.25}. Tree samples need to be deleted from split frequency counters as the cut-off point for the burnin moves during the run. If \t{savetrees} is set to \texttt{no}, tree samples to be discarded are read back in from file. If \texttt{savetrees} is set to \texttt{yes}, the tree samples to be removed will be stored in the internal memory instead. This can use up a lot of memory in large analyses. If one wishes to rely on the ASDSF as the crucial statistic for deciding when to stop a run, then MrBayes provides a convenient ``automatic stopping rule''. For example (note that other default values are omitted): \begin{Verbatim} MrBayes > mcmc stoprule=yes stopval=0.01 \end{Verbatim} In the example above, we start a MCMC run and allow MrBayes to check the ASDSF (every \texttt{diagnfreq} generation, see above), and if the ASDSF value is below $0.01$, the run will automatically stop. In order for the stopping rule to work, more than one run needs to be started. More detailed diagnostics can be obtained using the \texttt{sump} and \texttt{sumt} commands after a run has completed. They include ASDSF across runs for each of the sampled clades in addition to the potential scale reduction factor (PSRF; \citep{gelman92}) for branch lengths, node times, and substitution model parameters. PSRF compares the variance within and between runs and should approach $1.0$ as runs converge. MrBayes 3.2 also reports the effective sample size (ESS; \citep{ripley87}), the number of effectively independent draws from the posterior distribution, widely used for single-run convergence diagnostics. %%%% JN: mention that one can use sumt, sump, compare tree, plot etc using mrbayes after a run, %%%% JN: or other software potentially both during (gnuplot, R), and after a run (Tracer, AWTY/RWTY). \subsection{Metropolis Coupling} \label{metropolisCoupling} MrBayes uses Metropolis coupling to improve the MCMC sampling of the target distribution. The \texttt{Swapfreq}, \texttt{Nswaps}, \texttt{Nchains}, and \texttt{Temp} settings together control the Metropolis coupling behavior. When \texttt{Nchains} is set to 1, no heating is used. When \texttt{Nchains} is set to a value $n$ larger than 1, then $n - 1$ heated chains are used. By default, \texttt{Nchains} is set to 4, meaning that MrBayes will use 3 heated chains and one ``cold'' chain. In our experience, heating is essential for some data sets but it is not needed for others. Adding more than three heated chains may be helpful in analyzing large and difficult data sets. The time complexity of the analysis is directly proportional to the number of chains used, but the cold and heated chains can be distributed among processors in a cluster of computers and among cores in multicore processors using the MPI version of the program, greatly speeding up the calculations. MrBayes uses an incremental heating scheme, in which chain $i$ is heated by raising its posterior probability to the power $1/ (1 + i\lambda)$, where $\lambda$ is the heating coefficient controlled by the \texttt{Temp} parameter. Every \texttt{Swapfreq} generation, two chains are picked at random and an attempt is made to swap their states. For many analyses, the default settings should work nicely. If you are running many more than three heated chains, however, you may want to increase the number of swaps (\texttt{Nswaps}) that are tried each time the chain stops for swapping. If the frequency of swapping between chains that are adjacent in temperature is low, you may want to decrease the \texttt{Temp} parameter. As illustrated by several of the tutorials above, it is sometimes possible to get a reasonable sample from the posterior without using Metropolis coupling, which will save you a lot of computational effort. The risk is that you will have to start over if you have difficulties getting convergence. Turn off heating by setting the \texttt{mcmc} option \texttt{nchains} to 1 and switch it on by setting \texttt{nchains} to a value larger than $1$. If you do use heated chains, try to make sure that the acceptance rates of swaps between adjacent chains are in the approximate range of 10 to 70 \%. The acceptance rates are printed to the \texttt{.mcmc} file and to screen at the end of the run. The latter output contains a table that looks like this (you find the critical values in a different format in the \texttt{.mcmc} file): \begin{figure}[h] \centering \begin{BVerbatim} 1 2 3 4 -------------------------- 1 | 0.53 0.26 0.12 2 | 3347 0.55 0.31 3 | 3295 3337 0.53 4 | 3332 3396 3293 \end{BVerbatim} \end{figure} It is the values just above the diagonal, $\{0.53, 0.55, 0.53\}$ in this case, that you should focus on. If the acceptance rates are low, say below 10 \%, you can try decreasing the temperature constant (\texttt{mcmc temp=}); if the acceptance rates are high, say above 70 \%, you can try increasing it. Acceptance rates outside the optimal range do not invalidate the results from your analysis, but they do suggest that you could make your analysis more efficient. We often observe considerable improvements in convergence and mixing for difficult problems when you use Metropolis coupling. If your chains are not converging, it may worthwhile to explore the possibility of adding more heated chains, especially if you have access to a cluster running the MPI version of MrBayes. The MPI version scales almost linearly with the number of chains, which means that you can add chains without making the analysis slower, provided that the number of chains per core stays the same. For more details on the MPI version, see the next section and chapter \ref{advancedTopics}. If you decide to experiment with an increased number of chains, you may wish to simultaneously decrease the heating coefficient, so that the temperature difference between the cold chain and the hottest chain stays the same. See chapter \ref{metropolisCoupling} for more details on the heating coefficient. \subsection{Improving Convergence} There are several things you can do to improve the efficiency of your analysis. The simplest is to just increase the length of the run. However, the computational cost of doing so may be prohibitive. A better way is then to try improving the mixing behavior of the chain. First, examine the acceptance rates of the proposal mechanisms used in your analysis (output at the end of the run). The Metropolis proposals used by MrBayes work best when their acceptance rate is neither too low nor too high. A rough guide is to try to get them within the range of 10~\% to 70~\%. Rates outside this range are not necessarily a big problem but they typically mean that the analysis is inefficient. If the rate is too high, you can make the proposal bolder by changing tuning parameters (see Appendix) using the \texttt{props} command. Be warned, however, that changing tuning parameters of proposals and proposal probabilities may destroy any hope of getting convergence. For instance, you need at least one move changing each parameter in your model. The next step is to examine the heating parameters if you are using Metropolis-coupled MCMC. If acceptance rates for the swaps between adjacent chains (the values close to the diagonal in the swap statistics matrix) are low, then it might be a good idea to decrease the temperature to make the cold and heated chains more similar to each other so that they can change states more easily. If acceptance rates for the swaps is high, then you may try to increase the temperature in order to flatten space for heated chain facilitating for them escape from local minima. Finaly you may consider to increase number of parallel chain to improve the efficiency of the Metropolis coupling. A good way of improving convergence is to start the analysis from a good tree instead of starting it from a randomly chosen tree. To do this, you first define a good tree, with or without branch lengths, and put it in the \texttt{TREES} block of a Nexus file. Say you name the tree \texttt{usertree}. Then start the chains from this tree using \texttt{startvals tau=usertree} just before you issue your \texttt{mcmc} command. Note that changing the number of chains or runs after you have set the starting tree will cause the starting trees to be changed. Therefore, set the number of runs and chains with \texttt{mcmcp} before you set the starting tree and run your analysis with the \texttt{mcmc} command. A disadvantage with starting the analysis from a good tree is that it is more difficult to detect problems with convergence using independent runs. A compromise is to start each chain from a slightly perturbed version of a good tree. MrBayes can introduce random perturbations of a starting tree; this is requested using \texttt{mcmcp nperts=}. \subsection{Checkpointing} \label{checkpointing} If you are running a long analysis and want to extend it, or if there is a risk that a long analysis will be inadvertently interrupted by hardware failure or other factors that are out of your control, checkpointing will come handy. The option \texttt{checkpoint} for the \texttt{mcmc} command will create a check-pointing file contaning all the current parameter values of all chains. This is done on a regular interval as set by the \texttt{checkfreq} option (units are MCMC generations). The check-point file is written in the current directory with file suffix \texttt{.ckp}. To start from an existing \texttt{.ckp} file, you need to use the \texttt{append=yes} option when starting a new (continued) MCMC analysis using the \texttt{mcmc} command. Also make sure that the output file names used in the previous run are the same as those in the current run. \section{Stepping-stone Sampling} \subsection{Getting Started using Stepping-stone Sampling} Since version 3.2 MrBayes allows you to use stepping-stone sampling \citep{xie11} to estimating the marginal likelihood of the currently specified model. Stepping-stone sampling (SS) is considerably more accurate than the harmonic mean of the likelihoods from a standard MCMC run on the model (calculated by the \texttt{sump} command) and it was shown to be more efficient than thermodynamic integration \citep{xie11}. It is very easy to use stepping-stone sampling to estimate marginal likelihood. All you should do is to set up your model as usual, but instead of issuing command \texttt{mcmc} you issue command \texttt{ss} with the same arguments. In other words you may literally substitute \texttt{mcmc} with \texttt{ss} in your dataset file or the command sequence fed to MrBayes and that would produce estimate of marginal likelihood of your model. The only thing that you would need to adjust in order to get accurate estimate is to increase number of generations. You would probably need to increase number of generation by a factor of 10 compared with your ordinary \texttt{mcmc}. In most of the cases it does not have to be more complicated than that. For example, if you want to estimate marginal likelihood of a model which is setup in file \texttt{primates.nex} then you may issue the commands: \begin{Verbatim} MrBayes > execute primates.nex MrBayes > ss ngen=1000000 samplefreq=1000 \end{Verbatim} Once completed, a table containing estimate of marginal likelihood for each run is printed along with the mean value: \begin{figure}[h] \centering \begin{BVerbatim} Marginal likelihood (in natural log units) estimated using stepping-stone sampling based on 50 steps with 196000 generations (196 samples) within each step. Run Marginal likelihood (ln) ------------------------------ 1 -6346.22 2 -6348.62 ------------------------------ Mean: -6346.82 \end{BVerbatim} \end{figure} Note that unlike with ordinary MCMC run you can not stop the SS-run and process results for sampled generations or extend your run beyond originally specified length. Thus, it is extremely important to chose right length of the SS-run at the beginning, you will not be able to change it later on. That said, it does not mean that if your SS-run did not finish it is completly wasted. As with any other MCMC run, if you enable checkpointing you can ``resurrect'' you run from the last checkpoint. Now you know how to get the result, but how much we can trust the numbers? As usually you need to have at least two independent runs in order to get a feeling of result quality. The first thing to check would be to look at the difference of estimates among runs. Another thing that you may look at is the graph plotted below the table with marginal likelihood estimates: \begin{figure}[h] \centering \begin{BVerbatim}[fontsize=\scriptsize] Plot of average standard deviation of split frequencies across steps. Points at -1.0 (y-axis) indicate that there were no splits above minimum frequency for corresponding step. +------------------------------------------------------------+ 0.119 | * | | * * | | * | | * * * | | ** * * * ** | | * * ** *****| | * | | * * * | | | | * * | | * * * * | | * ** | | ** * | | * ** | |* * * * * ** * | +------+-----+-----+-----+-----+-----+-----+-----+-----+-----+ 0.016 ^ ^ 1 50 \end{BVerbatim} \end{figure} The dots on this graph represent deviation of split frequencies among different runs as SS-run progresses. Ideally the values of deviation of split frequencies in the beginning of SS-run (x-axis around 1) should be as good as at the end of your ordinary MCMC analyzes on this dataset. It is quite normal that towards right hand side of the graph the values start to increase. If you have datasets with many taxons then towards the end of the run you may even get values indicating that there were no splits above minimum frequency. \subsection{How Stepping-stone Sampling Works} Stepping-stone sampling uses importance sampling to estimate each ratio in a series of discrete steps bridging the posterior and prior distributions. The importance distributions that are used are called power posterior distributions, and are defined as $Prior*Likelihood^{\beta}$. By varying $\beta$ from 1 to 0, we get a series of distributions that connect the posterior ($\beta$ = 1) to the prior ($\beta$ = 0). The power posterior distributions are sampled using MCMC. First, we start a standard MCMC chain on the posterior distribution, and let it run until we have reached the criterion specified by the \texttt{burninss} option. After this, we step through the power posterior distributions until we reach the prior distribution. In each of the \texttt{nsteps} steps, we sample from a new power posterior distribution with a distinct $\beta$ value. The $\beta$ values correspond to \texttt{nsteps} evenly spaced quantiles in a Beta(\texttt{alpha},1.0) distribution. For the first sampling step, the $\beta$ value is equal to the last quantile, i.e., it is close to $1.0$. For each successive step, the $\beta$ value takes on the value of the next quantile, in decreasing order, until it reaches the value of $0.0$. %%%% JN: Check if this applies: "However, the \texttt{mcmc} burnin parameters are ignored." The \texttt{ss} procedure uses the same machinery as the standard \texttt{mcmc} algorithm, and shares most of its parameters with the \texttt{mcmc} and \texttt{mcmcp} commands. All \texttt{mcmc} parameters, except those related to burnin, have the same meaning and usage in the \texttt{ss} command as they have in the \texttt{mcmc} command. However, the \texttt{mcmc} burnin parameters are ignored. Instead, the \texttt{ss} command uses its own burnin parameter, \texttt{burninss}. The \texttt{ss} command also has its own parameters for specifying the number of steps and the shape of the Beta distribution from which the $\beta$ values are computed. Note that the \texttt{ngen} parameter of \texttt{mcmc} is used to set the maximum number of generations processed, including both the burnin and the following steps in the stepping-stone sampling phase. For instance, assume that \texttt{burninss} is set to $-1$, \texttt{Nsteps} to $49$, \texttt{ngen} to $1000000$ and \texttt{Samplefreq} to $1000$. We will then get 1,000 samples in total (1,000,000/1,000). These will fall into 50 bins, one of which represents the burnin and is discarded. Each step in the algorithm will thus be represented by 20 samples. The marginal likelihood estimated by SS is the multiplication of the ratios obtained from each step. Information about value of the ratio estimated at each step and run could be found in a file with \texttt{.ss} extension created during SS analyses. Since each ratio is stored in log units, the marginal likelihood in log units is obtained simply by summing of the log ratios across all steps. Examining the values of ratio estimates allows you to asses there quality and judge convergence of different runs for specific steps. If you observe huge differences among estimates for the same step then the runs did not converge for the step. If you observe poor convergence for later steps then in your next SS analyzes, you may consider reducing \texttt{alpha} to increase skewness of the $\beta$ values. If you have diagnostic turned on, the file contains information about standard deviation of split frequencies for each step. So, instead of looking at the graph with split frequencies plotted at the end of SS analyzes, you may examine the \texttt{.ss} file to analyze precise values. If you have dataset with many taxa ($>20$) you should not worry if standard deviation of split frequencies is poor, or even can not be evaluated for later steps. %%%% JN: Sections planned, but not started: % \section{Analyzing the Posterior Sample} % \subsection{Reporting Parameter Values} % \subsubsection{Parameter Formats} % \subsubsection{dN/dS Ratios} % \subsubsection{Site Rates} % \subsubsection{Tree Parameters} % \subsubsection{Node Ages} %%%% JN: The following section is a duplicate. Move to/replace "4.8 Inferring Ancestral States" % \subsubsection{Ancestral State Reconstruction} % MrBayes allows you to infer ancestral states at ancestral nodes using the full hierarchical % Bayesian approach (integrating out uncertainty concerning topology and other model parameters). The % basic approach is described by \citet{huelsenbeck01a} as well as in a recent review % \citep{ronquist04a}. You first need to constrain the node you want to infer ancestral states for % using a \texttt{constraint} definition and the \texttt{topologypr=constraints(...)} command as described % above for constrained topology models. Then ancestral state reconstruction is requested using % \tb{report ancstates=yes.} % % The probability of each state will be printed to the \texttt{.p} file(s) under the heading % \texttt{p(){character\_number}}. For instance, the probability of an A at site $215$ in a % nucleotide data set would be found under the heading \texttt{p(A)\{215\}}. If you constrain several % nodes in your data set, the node number will be given as well. If you had constrained two nodes, % the probabilities of the above character would be distinguished as \texttt{p(A)\{215@1\}} and % \texttt{p(A)\{215@2\}}. However, if you are interested in inferring ancestral states at two or more % different nodes, we recommend running separate analyses, each constraining a single node. The % reason is that when you focus on one node, you probably want to integrate over uncertainty in the % rest of the tree, including the potential uncertainty concerning the presence of the other node(s). % % Often, there is interest in mapping only one or a few characters onto trees inferred using largely % other types of data. For instance, a behavioral or ecological trait may be mapped onto trees based % on molecular data. To do this type of analysis in MrBayes, you would set up a mixed data set % including both the character to be mapped and the data used to infer the phylogeny, with the % character to be mapped in a separate data partition. How to do this is explained in the tutorial % given in section \ref{tutorialPartitioned} of this manual as well as in the description of % partitioned models above. Typically, you also want to assume that the evolutionary rate for the % mapped character is proportional to that of the other data (rather than identical). This is % achieved by setting up a partitioned rate model using \texttt{prset ratepr=variable}. Then you need to % set up a constraint for the node of interest, as described above. Finally, you request that % ancestral states are inferred for the partition with the mapped character (there is no need to wade % through ancestral state probabilities for the other partition(s)). For instance, if the character % to be mapped is in partition 2, request ancestral state sampling using \texttt{prset applyto=(2) % ancstates=yes.} Now only the ancestral states for the character of interest will be printed to the % \texttt{.p} file(s). The sampled values can be summarized as usual with the \texttt{sump} command. \subsection{Convergence Diagnostics} The convergence diagnostics provided by MrBayes are quite powerful (see section \ref{monitoringConvergence}) but they certainly do not exhaust the possibilities. Several programs will read MrBayes output files and will provide you with a number of additional ways in which you can examine the output from your analysis. Two of the most popular tools are Tracer \citep{rambaut18} for the parameter (\texttt{.p}) files, and AWTY \citep{nylander07} for the tree (\texttt{.t}, \texttt{.con.tre}) files. Among other things, they provide nice graphical representations of the output from MCMC analyses. Another set of tools is provided by the CODA \citep{plummer06} and RWTY \citep{warren17} packages for R \citep{rcoreteam18}. %%%% JN: Planned sections but not started: % \subsection{Plotting} % \subsection{Tree Summaries} \section{Working Effectively With MrBayes} \subsection{Running MrBayes in Batch Mode} When you become more familiar with MrBayes, you will undoubtedly want to run it in batch mode instead of typing all commands at the prompt. This is done by adding a \texttt{MRBAYES} block to a Nexus file, either the same file containing the \texttt{DATA} block or a separate Nexus file. The \texttt{MRBAYES} block simply contains the commands as you would have given them from the command line, with the difference that each command line is ended with a semi-colon. For instance, a \texttt{MRBAYES} block that performs three single-run analyses of the data set in \texttt{primates.nex} under the GTR + $\Gamma$ model and stores each result in a separate file is given below: \begin{figure}[h] \centering \begin{BVerbatim}[fontsize=\small] begin mrbayes; set autoclose=yes nowarnings=yes; execute primates.nex; lset nst=6 rates=gamma; mcmc nruns=1 ngen=10000 samplefreq=10 file=primates.nex1; mcmc file=primates.nex2; mcmc file=primates.nex3; end; \end{BVerbatim} \end{figure} Since this file contains the \texttt{execute} command, it must be in a file separate from the \texttt{primates.nex} file. You start the analysis simply by typing \tb{execute }, where filename is the name of the file containing the \texttt{MRBAYES} block. The \texttt{set} command is needed to change the behavior of MrBayes such that it is appropriate for batch mode. When \texttt{autoclose=yes}, MrBayes will finish the MCMC analysis without asking you whether you want to add more generations. When \texttt{nowarnings=yes}, MrBayes will overwrite existing files without warning you, so make sure that your batch file does not inadvertently cause the deletion of previous result files that should be saved for future reference. The Unix version of MrBayes can execute batch files in the background from the command prompt. Just type \tb{mb > log.txt \&} at the Unix prompt, where \texttt{} is the name of your Nexus batch file, to have MrBayes run in the background, logging its output to the file \texttt{log.txt}. If you want MrBayes to process more than one file, just list the files one after the other with space between them, before the output redirection sign (\texttt{>}). When MrBayes is run in this way, it will quit automatically when it has processed all files; it will also terminate with an error signal if it encounters an error. Alternatively, the Unix version of MrBayes can also be run in batch mode using input redirection. For that you need a text file containing the commands exactly as you would have typed them from the command line. For instance, assume that your data set is in \texttt{primates.nex} and that you want to perform the same analyses specified above. Then type \tb{mb < batch.txt > log.txt \&} (note that both angle brackets are essential and must be included in the command line) with the \texttt{batch.txt} file containing this text: \begin{figure}[h] \centering \begin{BVerbatim}[fontsize=\small] set autoclose=yes nowarnings=yes execute primates.nex lset nst=6 rates=gamma mcmc ngen=10000 savebrlens=yes file=primates.nex1 mcmc file=primates.nex2 mcmc file=primates.nex3 quit \end{BVerbatim} \end{figure} The \texttt{quit} command forces MrBayes to terminate. %%%% JN: Planned section but not started: % \subsection{Running MrBayes on a Cluster} \subsection{Turbo-charging MrBayes with BEAGLE and MPI} To speed up MrBayes analyses, you can try using the BEAGLE library or the MPI version of MrBayes. Let us start by examining the options provided by BEAGLE. This is a library that contains code for efficient likelihood calculations on both CPUs and GPUs. The CPU (central processing unit) is the core component of all computers, while a GPU (graphics processing unit) belongs on the video card that comes with most modern computers. The GPU specializes in performing small tasks on large sets of numbers very rapidly. Its power is typically used in processing video data for screen output, but recently it has become much easier to use GPUs for ordinary calculations. BEAGLE takes advantage of this for likelihood calculations. By default, the Mac and Windows versions of MrBayes will come with the BEAGLE library installed. However, it is turned off, so you have to specifically change the settings to use BEAGLE. Regardless of your machine, you should be able to use BEAGLE on your CPU. However, it is the GPU code that will give you the dramatic speedups, and it currently requires that you have an NVIDIA video card and the appropriate CUDA drivers. By default, MrBayes will use its built-in single-precision SSE likelihood calculators, which run on the CPU. They are quite fast on most machines, and should be similar in performance to the CPU routines in the BEAGLE library. The \texttt{set} command is used to switch to the BEAGLE likelihood calculators, and to set various BEAGLE-related options. Before looking into that, however, it may be a good idea to examine the available BEAGLE CPU and GPU resources. You can find out by using the following command: \begin{Verbatim} MrBayes > showbeagle \end{Verbatim} If BEAGLE finds an available GPU resource, it will be listed here. This is the listing from a machine with an available GPU detected by BEAGLE: \begin{figure}[h] \centering \begin{BVerbatim} Available resources reported by beagle library: Resource 0: Name: CPU Flags: PROCESSOR_CPU PRECISION_DOUBLE PRECISION_SINGLE COMPUTATION_SYNCH EIGEN_REAL EIGEN_COMPLEX SCALING_MANUAL SCALING_AUTO SCALING_ALWAYS SCALING_DYNAMIC SCALERS_RAW SCALERS_LOG VECTOR_NONE VECTOR_SSE THREADING_NONE Resource 1: Name: GeForce 9400M Desc: Global memory (MB): 254 | Clock speed (Ghz): 1.10 | Number of cores: 16 Flags: PROCESSOR_GPU PRECISION_SINGLE COMPUTATION_SYNCH EIGEN_REAL EIGEN_COMPLEX SCALING_MANUAL SCALING_AUTO SCALING_ALWAYS SCALING_DYNAMIC SCALERS_RAW SCALERS_LOG VECTOR_NONE THREADING_NONE \end{BVerbatim} \end{figure} It is the second entry that corresponds to the GPU. If you do not have one or more GPUs listed here, you will have to run the BEAGLE code on the processor. By focusing on the table printed at the end of the output from the \textbf{help set} command, you can quickly find the current BEAGLE settings. Here is the table with only the relevant BEAGLE settings included: \begin{figure}[h] \centering \begin{BVerbatim} Parameter Options Current Setting -------------------------------------------------------- Usebeagle Yes/No No Beagledevice CPU/GPU CPU Beagleprecision Single/Double Double Beaglescaling Always/Dynamic Always Beaglesse Yes/No No Beagleopenmp Yes/No No -------------------------------------------------------- \end{BVerbatim} \end{figure} If you run BEAGLE on the CPU, then your best option (when these lines are written) is likely to be the double-precision SSE code with dynamic scaling. This would be requested with the following lines: \begin{Verbatim} MrBayes > set usebeagle=yes beagledevice=cpu beagleprecision=double MrBayes > set beaglescaling=dynamic beaglesse=yes \end{Verbatim} The default MrBayes likelihood calculator uses single-precision SSE code, which should theoretically be the fastest option on the CPU. Currently, BEAGLE only supports double-precision SSE code, which is slower. However, the code we use to call BEAGLE supports smart dynamic scaling, unlike the code calling the default calculator, which may well compensate for the slowdown caused by the increased precision. Therefore, you need to test both options before knowing which one will be faster on your machine. To use the GPU, you simply switch from CPU to GPU (if you have an available GPU). With the GPU, the fastest option should be the single-precision code with dynamic scaling. The SSE option is not applicable in the GPU case. The fastest GPU option should therefore be: \begin{Verbatim} MrBayes > set usebeagle=yes beagledevice=gpu MrBayes > set beagleprecision=single beaglescaling=dynamic \end{Verbatim} The GPU code can be a lot faster than the CPU code, particularly for amino acid and codon models. However, the length of the sequences also influences the speed-up. In general, the longer the sequences are, the better GPU performance you can expect. If the sequences are short, the overhead involved in shuffling data to and from the GPU may well overshadow any performance gain you get in the computation step. Try the various calculator options out in short runs before you decide on the best option for longer runs. If you run multiple simultaneous runs or use Metropolis coupling, which is standard practice with MrBayes, then you can speed up your analyses considerably by using the MPI version of MrBayes. MPI stands for Message Passing Interface, which is a method for farming out computations to a set of parallel processors. To use MPI successfully, you need to have a machine with several processors or processor cores, and you need to be able to run an MPI implementation, such as Open MPI \url{www.open-mpi.org}. You can install Open MPI on virtually any machine running Unix or Linux, including all modern Macs. If you have a Windows machine, you can convert it to a dual-boot machine that will also run Linux in a separate partition, if you wish to run MPI on it. Computer clusters will almost always support MPI. The MPI version of MrBayes is different from the serial version put on your machine by the standard Mac and Windows installers. Therefore, you need to compile the MPI version of MrBayes from source code. It is not difficult, if you are willing to run a few commands from a console window. Refer to chapter \ref{advancedTopics} for detailed instructions. The MPI version of MrBayes farms out each chain to a different core or processor on your computer or computer cluster. To use MPI efficiently, you should make sure that the number of chains in your analysis is a multiple of the number of cores or processors on your system. For instance, assume you are running four runs with four chains each, giving a total of 16 chains. Compared to the serial version of MrBayes, which uses just a single processor or core for this analysis, the MPI version can run the analysis almost twice as fast if it has access to two cores, almost four times as fast if it has access to four cores, and almost 16 times as fast if it has access to 16 cores. This is the maximum speedup; you will not be able to run the analysis on more cores than there are chains in the analysis. The MPI version of MrBayes can use the BEAGLE library. However, modern computers almost always have more cores than GPUs. This means that BEAGLE may not help much in an MPI setting unless you are running on a specially designed cluster. \section{Working with the Source Code} Compiling MrBayes from source code can be done with several different compilers targeting all the common operating systems: Macintosh, Windows, and Unix. The easiest way to build MrBayes on a Unix-like system is to use the GNU tool-chain. You can also compile MrBayes with Xcode (on a Macintosh) and the Microsoft Visual Studio suite (on a Windows machine). For a more updated description on compiling the current MrBayes code, please refer to the GitHub pages \url{https://github.com/NBISweden/MrBayes}, and especially the files \texttt{INSTALL}, and \texttt{README} that comes with the distribution. \subsection{Compiling and Running the MPI Version of MrBayes} \label{MPIversion} Metropolis coupling or heating is well suited for parallelization. MrBayes 3 takes advantage of this and uses MPI to distribute heated and cold chains among available processors \citep{altekar04}. For instance, the default of two runs with four chains each makes for a total of eight chains, which the MPI version will farm out to different processors. The speedup is almost linear with the number of processors. Assume, for instance, that you want to run an analysis with four runs and four chains for a total of 16 chains. Compared to the serial version, this analysis will run almost twice as fast on two processors, almost four times as fast on four processors, and almost sixteen times as fast on sixteen processors. MrBayes will refuse to use more processors than the total number of chains, as there is no additional speedup beyond this point. The MPI version will run both on clusters and on single machines with multicore CPUs, provided that the system supports MPI. The dominant current implementation of MPI is Open MPI, which runs on most Unix or Unix-like systems, including Macintoshes. For more information on Open MPI, see \url{openmpi.org}. Once you have Open MPI installed, compiling and running the parallel version of MrBayes will essentially be the same as if you were running on a computer cluster, with each core behaving as a separate processor. Before running the MPI version of MrBayes, you will have to compile it on a system with an mpi compiler in addition to \texttt{autoconf} and \texttt{make}. Find out whether your system has an mpi compiler by typing \tb{which mpicc}. If the system responds by printing a path, you have an MPI compiler, otherwise you do not. In addition, your system needs to have the relevant libraries in the correct locations, which is typically the case if you have an MPI compiler installed. Once you have ensured that the system environment supports MPI compilation, you need to run the configure script with the \texttt{--with-mpi=yes} option. Otherwise, the compilation process is quite similar to the one used to produce the serial version (please refer to the \texttt{INSTALL} file that comes with the MrBayes distribution from GitHub for details). A successful compilation will produce an MPI-enabled version of MrBayes called \texttt{mb}. Note that the default name of the executable is the same as for the serial version, \texttt{mb}. This means that the parallel version will overwrite any previously existing serial version kept in the same directory. If you would like to have both the serial and parallel versions installed on the same system, you probably want to name the differently and put both in your path. We often rename the parallel executable \texttt{pmb} to separate it from the serial version. Although the MPI version is a lot faster than the serial version, it has one drawback: it does not support the readline library. This means that you do not have access to your command history, or command completion with the tab key. Therefore, we often prefer to run short experimental analyses and interactive post-hoc analyses using the serial version, even though we typically use the MPI version for most MCMC analyses. How you run the MPI version depends on your system. Some implementations of MPI require you to invoke a daemon or virtual machine to set up the MPI execution environment. Once the MPI environment is ready, a parallel MrBayes job is typically started with a line such as \begin{Verbatim} $ mpirun -np 4 mb batch.nex > log.txt & \end{Verbatim} This line calls the MPI version of MrBayes (just called \texttt{mb} here) and asks it to run the file \texttt{batch.nex} using four processors (\texttt{-np 4}), saving the screen output to the file \texttt{log.txt}. Specifically, the angle bracket is the output redirection flag, and the ampersand at the end ensures that the job is posted quietly so that you get the prompt back immediately. If you keep both a serial and a parallel version of MrBayes on your system, make sure you are using the parallel version with your \texttt{mpirun} command. When executing the command, you should get a process ID number back. It is handy to keep this number for future reference. For instance, you might want to kill your job if you find you made a mistake when starting it. You can do this using \tb{kill -9 }, where \texttt{} is the process ID. To examine how the analysis is doing, you can simply look at the last few lines in the screen dump file. Say that you dump the screen output to a file called \texttt{log.txt}. We can look at the end of that file by using \tb{tail log.txt}. If you would like the output to be dynamically updated, you can use \tb{tail -f log.txt} instead, in which case each new line printed to the file \texttt{log.txt} will be echoed to screen. To stop the display of new lines, press Control-C (this will only stop the echoing of new lines, it will not affect the analysis). Another way of examining how the analysis is doing is to look into the output files from the analysis, such as the \texttt{.mcmc} file. We strongly recommend that you use checkpointing with the MPI version of MrBayes. This allows you to restart the analysis from the last checkpoint, should your analysis crash or be terminated prematurely. By default, MrBayes outputs a checkpoint file every 2,000 generations. See section \ref{checkpointing} for more details on checkpointing and how to restart your analysis from a checkpoint file. \subsection{Compiling the Development Version of MrBayes}\label{compileDevVersion} If you want to use recently added features that have not made it to an official release yet, you may want to compile the most recent development version of MrBayes. First a word of warning, however. The development version may not be as stable as the latest official release, so use it at your own risk. If you decide you want to go ahead, the first step is to download (clone) the code repository of MrBayes from GitHub. On a Unix system with \texttt{git} installed, this is accomplished using the command line: \begin{Verbatim} $ git clone --depth=1 https://github.com/NBISweden/MrBayes.git \end{Verbatim} In the copy of the \texttt{MrBayes} repository you get on the local system, you will find the compile instructions in the file \texttt{INSTALL}. \subsection{Modifying or Extending MrBayes}\label{modifyingOrExtendingMrBayes} If you are interested in investigating or working with the source code, you first need to download the latest development version from the MrBayes repository on GitHub \url{https://github.com/NBISweden/MrBayes}. The download process was briefly described above in section \ref{compileDevVersion}. If you are interested in contributing code with bug fixes, the best way is to create a ``git pull request'' using the functionality on GitHub. See \url{https://help.github.com/articles/about-pull-requests/} for more information on pull requests. \section{Frequently Asked Questions} \label{FAQ} \tb{\it{How are gaps and missing characters treated?}} MrBayes uses the same method as most maximum likelihood programs: it treats gaps and missing characters as missing data. Thus, gaps and missing characters will not contribute any phylogenetic information. There is no way in which you can treat gaps as a fifth state in MrBayes (but see below for information on how you can use gap information in your analysis). \tb{\it{How do I use gap information in my analysis?}} Often, insertion and deletion events contain phylogenetically useful information. Although MrBayes 3 is not able to do statistical multiple sequence alignment, treating the insertion-deletion process under a realistic stochastic model, there is nevertheless a way of using some of the information in the indel events in your MrBayes analysis: Code the indel events as binary characters (presence/absence of the gap) and include them as a separate binary (restriction) data partition in your analysis. See more information on this possibility in the section on the binary model in this manual. \tb{\it{How many data partitions can I have in MrBayes?}} Since version 3.2, MrBayes does not limit the maximum number of partitions. You can use as many partitions as you can fit in the available memory. However, it may be difficult to reach convergence if your model includes an excessive number of parameters. \tb{\it{Does MrBayes run faster on a dual-processor or dual-core machine?}} Yes, if you are using MPI versions of MrBayes or along with BEAGLE library you enable multithreading while compiling MrBayes. If you run long and demanding analyses on a Mac, Unix, or Linux machine, you should consider using the MPI version of MrBayes. For instructions on compiling and running the MPI version, see section \ref{MPIversion}. \tb{\it{How much memory is required?}} You can calculate the amount of memory needed to store the conditional likelihoods for an analysis roughly as 2 $\times$ (\# taxa) $\times$ (\# chains in total + 1) $\times$ (\# states in the Q matrix) $\times$ (\# gamma categories) $\times$ 4 bytes (for the single-precision float version of the code; double the memory requirement for the double-precision code). Here (\# chains in total) is the number of chains allocated for a given MrBayes process. In case of serial version of MrBayes all (\# chains per run) $\times$ (\# runs) chains are allocated to the only MrBayes process. In the case of an MPI run, multiple MrBayes processes are created and each of them is responsible for only a portion of the total number of chains. For example if we have 2 runs with 4 chains in each of the runs and we invoke an MPI run with 4 processes, then each process receives only 2 chains. The program will need slightly more memory for various book-keeping purposes but the bulk of the memory required for an analysis is typically occupied by the conditional likelihoods. \tb{\it{How do I fix the tree during an analysis?}} %%%% JN: What is the limit (number X) for reading trees? Need to specify that below: %%%% JN: "You can read in up to $?$ trees." MrBayes allows you to fix the tree topology using the \texttt{topologypr} parameter of the \texttt{prset} command. Before doing that, you need to read in the topology you want to enforce. This is done by putting the topology in a regular \texttt{TREES} block in a NEXUS file. The tree in the NEXUS file is then read into memory using the \texttt{execute} command of MrBayes. You can read in up to $?$ trees. Each tree is referred to by the name it has been given in the NEXUS file. Assume for instance that you named your tree topology ``mytree''. Then you fix the topology parameter of your phylogenetic model to the topology of that tree with the MrBayes command \texttt{prset topologypr=fixed(mytree)}. Note that the procedure above only fixes the tree topology, not the branch lengths. If you want to fix the branch lengths too, you need to use the \texttt{brlenspr} parameter of the \texttt{prset} command. First you fix the topology as described above, then you fix the branch lengths using \texttt{prset brlenspr=fixed(mytree)}. If it is a clock tree, you should use \texttt{prset brlenspr=clock:fixed(mytree)}. \tb{\it{Why does MrBayes not use my starting values?}} A number of commands or actions will result in MrBayes resetting some or all of the starting values. For instance, starting values will be reset if you change the partitioning scheme, if you change the number of runs or chains, or if you change any model settings. MrBayes will warn you that it resets the starting values in these cases, but it is easy to miss that warning. If you want to set the starting values yourself, make sure the \texttt{startvals} command is the last command issued before you start the analysis. In addition, make sure you do not change the number of runs or chains, or reset the starting values in your \texttt{mcmc} command. If you need to change the number of runs or chains, then do this in a separate \texttt{mcmcp} command before you set the starting values. \tb{\it{I set the seed and swapseed to the same number. Why are the analyses not repeatable?}} Trying to exactly replicate an analysis is not normally something you would want to do, unless you are trying to replicate a bug before submitting a bug report. If you really do want to make sure that you can run analyses that are identical down to the last decimal, you need to be aware that, since version 3.2, MrBayes generates a default model, including starting values, already when it reads in a character matrix. Setting the seed and swapseed will not change those starting values. Thus, unless you make sure you reset all starting values after you have set the seeds, different runs will have different starting values and will produce slightly different results. The easiest way of making sure analyses are repeatable is to set the seed and swapseed before reading in the character matrix. \section{More Information} For more information on how to use MrBayes, turn to the MrBayes web site (\texttt{www.mrbayes.net}). \chapter{Evolutionary Models in MrBayes}\label{evolutionaryModels} MrBayes implements a wide variety of evolutionary models for nucleotide, amino acid, restriction site (binary), and standard discrete data. In addition, there are several different ways of modeling the process generating phylogenetic trees. An overview of all the models is given in diagrammatic form in the Appendix. Here, we provide a brief description of each model with some comments on their implementation in MrBayes and advice on how you can apply them successfully to your data. \section{Nucleotide Models} MrBayes implements a large number of DNA substitution models. These models are of three different types. The ``\texttt{4by4}'' models are the usual simple models of nucleotide evolution. The ``\texttt{Doublet}'' model is intended for stem regions of ribosomal DNA, where nucleotides evolve in pairs. Finally, the ``\texttt{Codon}'' models group the nucleotides in triplets and model evolution based on these. The type of nucleotide model is set in MrBayes with \texttt{lset nucmodel}; for instance, if you want to use the doublet model, the command is \texttt{lset nucmodel=doublet}. The default setting is \texttt{4by4}. In addition to these model types, MrBayes also supports translation of protein-coding nucleotide data to amino acids before analysis, effectively treating the nucleotide data as if it were amino acid data. To request this, use the command \texttt{lset nucmodel=aa}. The translation uses the current setting of the genetic code. The default code is the universal genetic code; several others can be selected using \texttt{lset code}; for instance, if you want to use the metazoan mitochondrial code, specify \texttt{lset code=metmt}. The translation takes ambiguities into account. For instance, assume that the observed codon is \texttt{A?A}, that is, the second codon site is completely ambiguous. Then each of the possible unambiguous codons is translated to an amino acid separately, and all unique amino acids are then included in the final state set. Once the nucleotide sequences have been translated to amino acids, the analysis proceeds as if you had started with amino acid data. See section \ref{amino-acidModels} for details on the available amino acid models. \subsection{Simple Nucleotide Models} There has been more work based on the simple four by four nucleotide models than on any other type of evolutionary model for molecular data. MrBayes 3 implements three main types of models; you select among those by setting the number of substitution types using \texttt{lset nst} to 1, 2, or 6. The widely used General Time Reversible (GTR) model has six substitution types (\texttt{lset nst=6}), one for each pair of nucleotides. The (unscaled) instantaneous rate matrix for the GTR model is (note that we order the rows and columns alphabetically -- A, C, G, T -- unlike some other authors) \[ Q=\begin{bmatrix} & [A] & [C] & [G] & [T]\\ [A]& - & \pi_C r_{AC} & \pi_G r_{AG} & \pi_T r_{AT}\\ [C]& \pi_A r_{AC} & - & \pi_G r_{CG} &\pi_T r_{CT}\\ [G]& \pi_A r_{AG} & \pi_C r_{CG}& - & \pi_T r_{GT}\\ [T]& \pi_A r_{AT} & \pi_C r_{CT}&\pi_G r_{GT} & - \end{bmatrix} \] The GTR model (Tavare, 1986) has four stationary state frequencies ($\pi_A, \pi_C, \pi_G, \pi_T$) and six rate parameters ($r_{AC}, r_{AG}, r_{AT}, r_{CG}, r_{CT}, r_{GT}$). In total, there are eight free parameters, since one of the stationary state frequencies and one of the substitution rates are determined by the others. By default, MrBayes uses a ``flat'' Dirichlet distribution (with all distribution parameters set to $1.0$) as the prior for both the stationary state frequencies and the substitution rates. This is a reasonable uninformative prior that should work well for most analyses. During the analysis, both the stationary state frequencies and substitution rates are estimated. If you want to fix the stationary state frequencies or substitution rates, you can do that by using the \texttt{prset} command. For instance, assume that we want to fix the stationary state frequencies to be equal, converting the GTR model to the so-called SYM model. This is achieved by \texttt{prset statefreqpr = fixed(0.25,0.25,0.25,0.25)} or, more conveniently, \texttt{prset statefreqpr = fixed(equal)}. The substitution rate matrix now becomes \[ Q=\begin{bmatrix} & [A] & [C] & [G] & [T]\\ [A]& - & 0.25 r_{AC} & 0.25 r_{AG} & 0.25 r_{AT}\\ [C]& 0.25 r_{AC} & - & 0.25 r_{CG} &0.25 r_{CT}\\ [G]& 0.25 r_{AG} & 0.25 r_{CG}& - & 0.25 r_{GT}\\ [T]& 0.25 r_{AT} & 0.25 r_{CT}&0.25 r_{GT} & - \end{bmatrix} \] and the stationary state frequencies are no longer estimated during the analysis. Similarly, it is possible to fix the substitution rates of the GTR model using \texttt{prset revmatpr=fixed}. Assume, for instance, that we want to fix the substitution rates to be ($r_{AC}$ = 0.11, $r_{AG}$ = 0.22, $r_{AT}$ = 0.12, $r_{CG}$ = 0.14, $r_{CT}$ = 0.35, $r_{GT}$ = 0.06). Then the correct statement would be \texttt{prset revmatpr = fixed(0.11,0.22,0.12,0.14,0.35,0.06)}. Note the order of the rates. The substitution rates can be given either as percentages of the rate sum, as here, or they can be scaled to the $r_{GT}$ rate. The former representation is the simplex (Dirichlet) parameterization used internally by MrBayes. By default, MrBayes reports substitution rates in the simplex format but you can request conversion of sampled rates to the scaled format using the \texttt{report} command if you prefer this representation. One disadvantage with the scaled format is that the posterior distribution tends to be strongly skewed such that the arithmetic mean of the sampled values is considerably higher than the mode and the median. Therefore, consider using the median instead of the mean when reporting a posterior distribution sampled in the scaled format. Before using the GTR model for some of your data, we recommend that you make sure there are at least a few possible substitutions of each type. For instance, if there is not a single GT substitution in your data, it will be difficult to estimate the GT substitution rate. In such cases, you should consider either the HKY model (\texttt{nst=2}) or the F81 model (\texttt{nst=1}) instead of the GTR model. The HKY model (Hasegawa, Kishino and Yano, 1985) has different rates for transitions ($r_{ti}$) and transversions ($r_{tv}$): \[ Q=\begin{bmatrix} & [A] & [C] & [G] & [T]\\ [A]& - & \pi_C r_{tv} & \pi_G r_{ti} & \pi_T r_{tv}\\ [C]& \pi_A r_{tv} & - & \pi_G r_{tv} & \pi_T r_{ti}\\ [G]& \pi_A r_{ti} & \pi_C r_{tv} & - & \pi_T r_{tv}\\ [T]& \pi_A r_{tv} & \pi_C r_{ti} & \pi_G r_{tv} & - \end{bmatrix} \] The HKY model is often parameterized in terms of the ratio between the transition and transversion rates, $\kappa = r_{ti}/r_{tv}$, and this is the default format used by MrBayes when reporting samples from the posterior distribution. Internally, however, MrBayes uses the simplex (Dirichlet) parameterization in which the transition and transversion rates are expressed as percentages of the (unscaled) rate sum. If you prefer, you can have MrBayes report the sampled values using the Dirichlet format instead of the ratio format by using the command \texttt{report tratio=dirichlet}. The caveats described above for the GT-scaled substitution rates also apply to the transition/transversion rate ratio. In the \texttt{.p} files, the ratio will be referred to as kappa and the transition and transversion rate proportions will be referred to as \texttt{ti} and \texttt{tv}. The setting of the \texttt{report tratio} option will determine whether you will see a single \texttt{kappa} column or the two \texttt{ti} and \texttt{tv} columns. As with the GTR model, you can fix both the stationary state frequencies and the transition / transversion rate ratio of the HKY model. If you fix the stationary state frequencies to be equal, you will get the K2P model \citep{kimura80}. Finally, MrBayes implements the F81 model \citep{felsenstein81}, which assumes that all substitution rates are equal but stationary state frequencies are not, that is \[ Q=\begin{bmatrix} & [A] & [C] & [G] & [T] \\ [A]& - & \pi_C & \pi_G & \pi_T\\ [C]& \pi_A & - & \pi_G &\pi_T \\ [G]& \pi_A & \pi_C& - & \pi_T\\ [T]& \pi_A & \pi_C&\pi_G & - \end{bmatrix} \] If the stationary state frequencies are fixed to be equal using \texttt{prset statefreqpr = fixed(equal)}, you will get the simplest of all nucleotide substitution models, the JC model \citep{jukes69}. A large number of other constrained versions of the GTR model are often used in Maximum Likelihood inference. Why do we not allow more substitution model types in MrBayes? One of the most important advantages of the Bayesian approach is that it allows you to integrate out uncertainty concerning the model parameters you have little information about. Thus, Bayesian inference is relatively robust to slight over-parameterization of your model. In addition, the MCMC sampling procedure is typically efficient in dealing with complex multi-parametric models. For these reasons, it is less important in the Bayesian context to find the simplest possible model that can reasonably represent your data. Therefore, if your intended model is not available in the program, we suggest that you simply select the next more complex model implemented in MrBayes. How do I select the best substitution model for my analysis? A standard approach is to use a model testing procedure, such as those implemented in, e.g., ModelTest \citep{posada98}, jModelTest \citep{posada08}, or MrModelTest \citep{nylander04b}. However, a priori model testing comes with a number of serious drawbacks. It typically restricts the testing to a rough topology estimate, and at the end of the day you have to put all your money down on a single substitution model. An alternative approach, which is more truly Bayesian, is to integrate out the uncertainty concerning the correct structure of the substitution model. This is accomplished by having the MCMC procedure sample across substitution models \citep{huelsenbeck04d}, a method made available in version 3.2 of MrBayes. This will result in all parameter estimates being averaged across substitution models, with each substitution model being weighted according to its posterior probability. For example, to sample across substitution models, simply use the command \texttt{lset nst=mixed} before running the analysis. See sections \ref{modelJumping} and \ref{bayesianModelChoice} for more details. \subsection{The Doublet Model} The doublet model of MrBayes is intended for stem regions of ribosomal sequences, where nucleotides pair with each other to form doublets. The nucleotide pairing results in strong correlation of substitutions across sites; when there is a substitution at one site, it is typically accompanied by a compensatory substitution at the paired site. If the correlation between paired sites is not accounted for, parametric statistical methods will overestimate the confidence we should have in the best tree(s). Incidentally, the same is true for parsimony and the non-parametric bootstrap. There are various ways to model the evolution of nucleotide doublets. One method is to focus on the common doublets, A--T and C--G in particular. MrBayes uses a more complex model, originally formulated by Schoniger and von Haeseler (1994), where all doublets are taken into account. The central idea in this model is that one common doublet is converted into another through a two-step process. In the first step, one of the nucleotides is substituted with another according to a standard four by four model of nucleotide change. In the second step, the matching nucleotide is changed according to the same standard four by four model. Thus, in this model there is no momentary change from one doublet to another doublet; this always occurs through an intermediate, rare doublet. Assume that we are using a GTR model for the single nucleotide substitutions, that $i$ and $j$ are two different doublets, that $d_{ij}$ is the minimum number of nucleotides that must be changed when going between $i$ and $j$, and that $mn$ is the pair of nucleotides that change when going between $i$ and $j$ when $d_{ij}$ = 1. Then the elements $q_{ij}$ of the instantaneous rate matrix $Q$ of the doublet model can be expressed as follows \[ q_{ij} = \left\{ \begin{array}{l l} 0 & \quad \text{if} ~d_{ij}>1 \\ \pi_j r_{min} & \quad \text{if} ~d_{ij}=1\\ \end{array} \right. \] for the case when $i$ differs from $j$; the diagonal elements ($i = j$) are determined, as usual, to balance the rows in the instantaneous rate matrix to sum to zero. This gives the instantaneous rate matrix (only 7 rows and columns out of 16 shown): \footnotesize \[ Q=\begin{bmatrix} & [AA] & [AC] & [AG] & [AT] & [CA] & [CC] & \cdots & [TT]\\ [AA]& - & \pi_{AC} r_{AC} & \pi_{AG} r_{AG} & \pi_{AT} r_{AT}& \pi_{CA} r_{AC} & 0 & \cdots & 0\\ [AC]& \pi_{AA} r_{AC} & - & \pi_{AG} r_{CG} & \pi_{AT} r_{CT}& 0 & \pi_{CC} r_{AC} & \cdots & 0\\ [AG]& \pi_{AA} r_{AG} & \pi_{AC} r_{CG} & - & \pi_{AT} r_{GT}& 0 & 0 & \cdots & 0 \\ [AT]& \pi_{AA} r_{AT} & \pi_{AC} r_{CT} & \pi_{AG} r_{GT}& -& 0 & 0 & \cdots & \pi_{TT} r_{AT}\\ [CA]& \pi_{AA} r_{AC} & 0 & 0 & 0 & - & \pi_{CC} r_{AC} & \cdots & 0\\ [CC]& 0 & \pi_{AC} r_{AC} & 0 & 0 & \pi_{CA} r_{AC} & - & \cdots & 0\\ \cdots& \cdots& \cdots& \cdots& \cdots& \cdots& \cdots& \cdots& \cdots\\ [TT]& 0 & 0 & 0 &\pi_{AT} r_{AT} & 0 & 0 & \cdots & -\\ \end{bmatrix} \] \normalsize Instead of using the GTR model, we could of course have used the HKY or F81 model, resulting in obvious modifications of the $r_{mn}$ values. Use \texttt{lset nst} to change among those options; for instance, use \texttt{lset nst=6} to choose the GTR model. The default model is the F81 model (\texttt{lset nst=1}). Before the doublet model can be used, it is necessary to specify all the nucleotide pairs in the alignment. This is done using the \texttt{pairs} command, most conveniently in a \texttt{MRBAYES} block in a data file. For instance, \texttt{pairs 1:10, 2:9;} would pair nucleotide 1 with 10, and 2 with 9. See the \texttt{kim.nex} data file for an example of an analysis using the doublet model. \subsection{Codon Models} The codon models implemented in MrBayes are based on the first formulations of such models (\citet{goldman94}; \citet{muse94}). The approach is similar to that used in the doublet model. A codon can change to another only through steps of one nucleotide change at a time. These steps are modeled using a standard four by four nucleotide model. There is one additional complication though: some nucleotide changes are synonymous while others lead to changes in amino acids and thus may be subject to selection (a factor modifying the substitution rate). Assume that $i$ and $j$ are different codons, that $a(i)$ is the amino acid coded for by codon $i$, that $d_{ij}$ is the minimum number of nucleotide changes involved in changing between them, and that $\omega$ is the ratio of the non-synonymous to the synonymous substitution rate. The off-diagonal elements of the instantaneous rate matrix can now be defined as \[ q_{ij} = \left\{ \begin{array}{l l} 0 & \quad \text{if $d_{ij}>$1 } \\ \pi_j r_{min} & \quad \text{if $d_{ij}$=1 and $a(i)=a(j)$}\\ \pi_j r_{min} \omega & \quad \text{if $d_{ij}$=1 and $a(i)\neq a(j)$}\\ \end{array} \right. \] with the diagonal elements being defined to balance the rows of the instantaneous rate matrix, as usual. The single-nucleotide substitution rates can be modeled using the GTR, the F81, or the JC model, as before. Use \texttt{lset nst} to change among those options; for instance, use \texttt{lset nst=6} to choose the GTR model. The default model is the F81 model (\texttt{lset nst=1}). Invoking the codon model is easy; just make sure that the aligned DNA or RNA sequences start with a nucleotide in codon position 1 and that they end with a nucleotide in codon position 3. Also, make sure that the sequences do not contain any stop codons. To figure out whether a codon is a stop codon and whether a particular single-nucleotide change involves an amino acid change, MrBayes uses one of several genetic codes. By default, MrBayes uses the universal code but you can select other codes by using the \texttt{lset code} command. Note that the codon models are computationally demanding. Whereas the computations for the simple four by four models need to deal with only 16 $Q$-matrix and transition probability elements ($4*4$), the codon model computations need to process more than 3,600 $Q$-matrix and transition probability elements resulting in these runs being roughly 200 times slower. The codon models also require much more memory than the four by four models, about 16 times as much. The simplest codon model, described above, assumes that all amino acid sites are subject to the same level of selection (the same $\omega$ factor). However, MrBayes also implements models accommodating variation in selection across sites. This allows you to detect positively selected amino-acid sites using a full hierarchical Bayes analysis (that is, an analysis that does not fix tree topology, branch lengths or any substitution model parameters but instead integrates out uncertainty in all these factors). The $\omega$ variation models work much like the model accommodating rate variation across sites according to a gamma distribution. The likelihood of each site is calculated under several different $\omega$ values and then the values are summed to give the site likelihood. A difference is that the stationary frequency of each omega category is estimated, instead of being fixed as in the case of the gamma distribution. There are two variants implemented in MrBayes, and they are invoked using the \texttt{lset omegavar} option. In the Ny98 model \citep{nielsen98}, there are three classes with potentially different $\omega$ values: 0 $< \omega_1 <$ 1, $\omega_2$ = 1, and $\omega_3 >$ 1. The M3 model also has three classes of $\omega$ values but these values are less constrained in that they only have to be ordered $\omega_1 < \omega_2 < \omega_3$. If, for instance, you would like to invoke the M3 model, use the command \texttt{lset omegavar=M3}. When you use a model with variation in selection pressure across sites, you probably want to infer the positively selected sites. If you select \texttt{report possel=yes} before you start your analysis, MrBayes will calculate the probability of each site being in a positively selected omega class. For the M3 model, for instance, the likelihood of the site is calculated under each of the three categories, taking the category frequencies into account, and then the likelihoods are summed to yield the total likelihood of the site. Finally, the proportion of this sum originating from categories that are positively selected (those that have an $\omega$ value larger than 1); this is the posterior probability of the site being positively selected. Once the probabilities of each site being positively selected are printed to file, they can be summarized using the standard \texttt{sump} command. When interpreting the output from the Ny98 model, it is helpful to know that \texttt{pi(-)}, \texttt{pi(N)}, and \texttt{pi(+)} are the frequencies of the negatively selected, neutral and positively selected categories, respectively, and \texttt{omega(-)}, \texttt{omega(N)}, and \texttt{omega(+)} are the corresponding $\omega$ values. The M3 model parameters are instead labeled \texttt{pi(1)}, \texttt{pi(2)}, and \texttt{pi(3)} for the category frequencies and \texttt{omega(1)}, \texttt{omega(2)}, and \texttt{omega(3)} for the $\omega$ values. The probability of a codon being positively selected is labeled by the site numbers in the original alignment. Thus \texttt{pr+(16,17,18)} is the probability of the codon corresponding to the original nucleotide alignment sites 16, 17, and 18 being in a positively selected omega category. \section{Amino-acid Models} \label{amino-acidModels} MrBayes implements a large number of amino-acid models. They fall in two distinct categories: the fixed-rate models and the variable-rate models. The former have both the stationary state frequencies and the substitution rates fixed, whereas one or both of these are estimated in the latter. \subsection{Fixed Rate Models} The Poisson model \citep{bishop87} is the simplest of the fixed rate models. It assumes equal stationary state frequencies and equal substitution rates; thus, it is analogous to the JC model for nucleotide characters. The rest of the fixed-rate models have unequal but fixed stationary state frequencies and substitution rates reflecting estimates of protein evolution based on some large training set of proteins. These models include the Dayhoff model \citep{dayhoff78}, the Mtrev model \citep{adachi96}, the Mtmam model (\citet{cao98}; \citet{yang98}), the WAG model \citep{whelan01},the Rtrev model \citep{dimmic02}, the Cprev model \citep{adachi00}, the Vt model \citep{muller00} and the Blosum62 model \citep{henikoff92}. Each model is appropriate for a particular type of proteins. For instance, if you are analyzing mammal mitochondrial proteins, you might want to use the Mtmam model. Invoke that model by using \texttt{prset aamodelpr=fixed(mtmam)}. \subsection{Estimating the Fixed Rate Model} \label{estimatingTheFixedRateModel} MrBayes allows a convenient way of estimating the fixed-rate model for your amino acid data instead of specifying it prior to the analysis. If you choose this option, MrBayes will allow the MCMC sampler to explore all of the fixed-rate models listed above, including the Poisson model, by regularly proposing new models. When the MCMC procedure has converged, each model will contribute to your results in proportion to its posterior probability. For instance, if you are analyzing mammal mitochondrial proteins, it is likely that the Mtmam model will contribute most to the posterior distribution but it is possible that some other models, for instance the Mtrev model, will contribute significantly too. A nice feature of the MCMC model estimation is that the extra computational cost is negligible. To allow so-called model jumping between fixed-rate amino acid models, simply set the prior for the amino acid model to mixed, \texttt{prset aamodelpr=mixed}, prior to analysis. During the run, MrBayes will print the index of the current model to the \texttt{.p} file(s) in the \texttt{aamodel} column. The indices of the models are as follows: 0 = Poisson, 1 = Jones, 2 = Dayhoff, 3 = Mtrev, 4 = Mtmam, 5 = Wag, 6 = Rtrev, 7 = Cprev, 8 = Vt, 9 = Blosum. When you use the \texttt{sump} command, you will get a table with the names of the amino acid models and their posterior probabilities. \subsection{Variable Rate Models} There are two variable-rate models implemented in MrBayes for amino acid data. The $Equalin$ $model$ is a ``glorified'' F81 model in that it allows the stationary state frequencies of all amino acids to be different but assumes the same substitution rate. Thus, the instantaneous rate matrix for this model is \footnotesize \[ Q=\begin{bmatrix} & [A] & [R] & [N] & [D] & [C] & [Q] & \cdots & [V]\\ [A]& - & \pi_{R} & \pi_{N} & \pi_{D}& \pi_{C}& \pi_{Q} & \cdots & \pi_V\\ [R]& \pi_{A} &- & \pi_{N} & \pi_{D}& \pi_{C}& \pi_{Q} & \cdots & \pi_V\\ [N]& \pi_{A} &\pi_{R} &- & \pi_{D}& \pi_{C}& \pi_{Q} & \cdots & \pi_V\\ [D]& \pi_{A} &\pi_{R} & \pi_{N}&- & \pi_{C}& \pi_{Q} & \cdots & \pi_V\\ [C]& \pi_{A} &\pi_{R} & \pi_{N}& \pi_{D}&- & \pi_{Q} & \cdots & \pi_V\\ [Q]& \pi_{A} &\pi_{R} & \pi_{N}& \pi_{D}& \pi_{C} &- & \cdots & \pi_V\\ \cdots& \cdots& \cdots& \cdots& \cdots& \cdots& \cdots& \cdots& \cdots\\ [V]& \pi_{A} &\pi_{R} & \pi_{N}& \pi_{D}& \pi_{C} &\pi_Q & \cdots &- \\ \end{bmatrix} \] \normalsize and it has 19 free parameters (20 stationary state frequencies minus one because the frequencies sum to 1) that will be estimated from data. The other variable-rate model is the ``glorified'' GTR model, which allows all stationary state frequencies and substitution rates to vary. Thus, the instantaneous rate matrix for this model is \footnotesize \[ Q=\begin{bmatrix} & [A] & [R] & [N] & [D] & [C] & [Q] & \cdots & [V]\\ [A]& - & \pi_{R} r_{AR}& \pi_{N} r_{AN} & \pi_{D} r_{AD}& \pi_{C} r_{AC}& \pi_{Q} r_{AQ} & \cdots & \pi_V r_{AV}\\ [R]& \pi_{A} r_{AR} &- & \pi_{N} r_{RN}& \pi_{D} r_{RD}& \pi_{C} r_{RC}& \pi_{Q} r_{RQ} & \cdots & \pi_V r_{RV}\\ [N]& \pi_{A} r_{AN}&\pi_{R} r_{RN}&- & \pi_{D} r_{ND}& \pi_{C} r_{NC}& \pi_{Q} r_{NQ} & \cdots & \pi_V r_{NV}\\ [D]& \pi_{A} r_{AD} &\pi_{R} r_{RD} & \pi_{N} r_{ND}&- & \pi_{C} r_{DC}& \pi_{Q} r_{DQ} & \cdots & \pi_V r_{DV}\\ [C]& \pi_{A} r_{AC} &\pi_{R} r_{RC} & \pi_{N} r_{NC}& \pi_{D} r_{DC}&- & \pi_{Q} r_{CQ} & \cdots & \pi_V r_{CV}\\ [Q]& \pi_{A} r_{AQ} &\pi_{R} r_{RG} & \pi_{N} r_{NQ}& \pi_{D} r_{DQ}& \pi_{C} r_{CQ} &- & \cdots & \pi_V r_{QV}\\ \cdots& \cdots& \cdots& \cdots& \cdots& \cdots& \cdots& \cdots& \cdots\\ [V]& \pi_{A} r_{AV} &\pi_{R} r_{RV} & \pi_{N} r_{NV}& \pi_{D} r_{DV}& \pi_{C} r_{CV} &\pi_Q r_{QV}& \cdots & - \end{bmatrix} \] \normalsize and the model has 19 free stationary state frequency parameters and 189 free substitution rate parameters. The Bayesian MCMC approach is good at handling uncertainty in multiparameter models, so the GTR model may be used successfully with moderate-size data sets, but the model is so parameter-rich that you need a fairly sizable data set to be able to estimate all parameters with reasonable precision. The $GTR$ $model$ can be used to express a user-derived fixed rate model other than those already implemented in MrBayes. Simply use the \texttt{prset} command to fix the stationary state frequencies and substitution rates of the GTR model to the desired values. You need to set two options, \texttt{prset aarevmatpr=fixed(<190 comma-separated values>)} and \texttt{prset statefreqpr=fixed(<20 comma-separated values>)}. Once the values are fixed prior to analysis, the MCMC procedure will not change them and they will remain the same throughout the analysis. \section{Restriction Site (Binary) Model} MrBayes implements a simple F81-like model for restriction sites and other binary data. The instantaneous rate matrix for this model is simply \[ Q=\begin{bmatrix} & [0] & [1]\\ [0]& - & \pi_{1}\\ [1]& \pi_{0}& - \\ \end{bmatrix} \] Any asymmetry in the rate of 0 to 1 and 1 to 0 transitions is expressed in terms of the stationary state frequencies. Thus, if the stationary frequencies are $\pi_0 = 0.25$ and $\pi_1 = 0.75$, then the rate of 0 to 1 transitions is 3 times as high as the rate of transitions in the other direction ($\pi_1 / \pi_0 = 3$). A problem with some binary data sets, notably restriction sites, is that there is an ascertainment (coding) bias such that certain characters will always be missing from the observed data. It is impossible, for instance, to detect restriction sites that are absent in all of the studied taxa. MrBayes corrects for this bias by calculating likelihoods conditional on the unobservable characters being absent (Felsenstein, 1992). The ascertainment (coding) bias is selected using \texttt{lset coding}. There are five options: (1) there is no bias, all types of characters could, in principle, be observed (\texttt{lset coding=all}); (2) characters that are absent (state 0) in all taxa cannot be observed (\texttt{lset coding=noabsencesites}); (3) characters that are present (state 1) in all taxa cannot be observed (\texttt{lset coding=nopresencesites}); (4) characters that are constant (either state 0 or 1) in all taxa cannot be observed (\texttt{lset coding=variable}); and (5) only characters that are parsimony-informative have been scored (\texttt{lset coding=informative}). For restriction sites it is typically true that all-absence sites cannot be observed, so the correct coding bias option is \texttt{noabsencesites}. The binary model is useful for a number of character types other than restriction sites. For instance, the model can be used for gap characters. The presence and absence of gaps must be coded consistently for all characters; let us assume here that absence of a gap is coded as 0 and presence as 1. Since the detection of gaps is typically contingent on observing some sequence length variation, neither all-absence nor all-presence characters can be observed. Thus, the correct ascertainment bias for gap characters is \texttt{variable}. The parameters $\pi_0$ and $\pi_1$ would represent the rate at which insertions and deletions occur, respectively (assuming that state 0 denotes absence of a gap). The binary model can also be used for ecological, morphological, or other binary characters of arbitrary origin. However, if the binary model is applied to more than one character, then there is an implicit assumption that the state labels are not arbitrary for those characters. That is, the 0 state in one character must somehow be comparable to the 0 state in the others. For instance, 0 could mean absence (or presence) of a particular type of feature, such as a wing vein, a restriction site, or a gap in a DNA sequence. It is not appropriate to apply the default binary model to a set of characters where the state labels are arbitrary, as is true of most morphological characters. Thus, we can possibly estimate the rate of loss versus gain of wing veins over a set of consistently coded wing venation characters, but we cannot compare the rate of loss of antennal articles to the rate at which a yellow patch evolves into a green patch. If state labels are truly arbitrary, then the stationary state frequencies of the binary model must be fixed to be equal, such that the estimation of model parameters becomes independent of the labeling of character states. An alternative is to consider the standard model, which provides more sophisticated ways of dealing with arbitrary state labels. When is the correction for ascertainment bias important? This is strongly dependent on the size of the tree (the sum of the branch lengths on the tree). The larger the tree, the less important the correction for ascertainment bias becomes. In our experience, when there are more than 20--30 taxa, even the most severe bias (only informative characters observed) is associated with an insignificant correction of the likelihood values. \section{Standard Discrete (Morphology) Model} The model used by MrBayes for ``standard'' discrete data is based on the ideas originally presented by \citet{lewis01}. Essentially, the model is analogous to a JC model except that it has a variable number of states, from 2 to 10. For instance, a three-state standard character would be evolving according to the instantaneous rate matrix \[ Q=\begin{bmatrix} & [0] & [1] & [2]\\ [0]& - & 1 & 1\\ [1]& 1& - & 1\\ [2]& 1& 1 & -\\ \end{bmatrix} \] Because all rates are the same, we can maintain the essential property of standard characters, namely that state labels are arbitrary. Thus, the standard model assures that you will get the same results regardless of the way in which you label the states. This applies to the standard, program default, assumption of unordered character states. In ordered characters, on the other hand, evolution between some states is assumed to go through intermediate states. MrBayes implements a stochastic model for such characters. For a three-state character assumed to be ordered (by convention in the sequence $0 \leftrightarrow 1 \leftrightarrow 2$), the instantaneous rate matrix is \[ Q=\begin{bmatrix} & [0] & [1] & [2]\\ [0]& - & 1 & 0\\ [1]& 1& - & 1\\ [2]& 0& 1 & -\\ \end{bmatrix} \] Note that the instantaneous rate of going between the two end states is 0, that is, a transition from 0 to 2, or from 2 to 0, has to go through state 1. By default, MrBayes treats all standard characters as unordered. To change this, use the \texttt{ctype} command. For instance, if you want to treat characters 3 and 4 as ordered you need to include the statement \texttt{ctype ordered: 3 4;} in your \texttt{MRBAYES} block (or enter it using the command line, if you prefer). The number of states of each standard character is determined by MrBayes simply by looking at the state codes in your matrix. Thus, a three-state model will be used for a three-state character and a six-state model for a six-state character. MrBayes does not check that all state codes are used, it simply finds the largest state code in the matrix for each character. Thus, make sure that you use the state codes 0, 1, and 2 for a three-state character and state codes 0, 1, 2, 3, 4, and 5 for a six-state character. Because state labels are arbitrary in the standard model, we cannot estimate unequal stationary state frequencies or substitution rates (recall that the stationary state frequencies are an important factor in determining the latter). However, it is still possible to allow the state frequencies (rates) to vary over sites according to some appropriate distribution. MrBayes uses a symmetric Dirichlet distribution for this purpose. For binary data, the analogy of the Dirichlet distribution is called the beta distribution; MrBayes uses Dirichlet and beta interchangeably for the distribution depending on context. The approach is similar to the one used to allow rate variation across sites according to a gamma distribution: we calculate the likelihood of a site assuming different discrete categories of asymmetry and then we sum the values to obtain the site likelihood. The symmetric Dirichlet distribution has one parameter that determines its shape, just like the alpha parameter determines the shape of the gamma distribution. The larger the parameter of the symmetric Dirichlet, the less transition rate (stationary frequency) asymmetry there is across sites. By default, the parameter is fixed to infinity (\texttt{prset symdirihyperpr=fixed(infinity)}); this corresponds to the standard assumption of no transition rate asymmetry across sites: the rate of going from 0 to 1 is equal to the rate of going from 1 to 0 for all characters. The prior is called a hyperprior because it concerns a distribution that controls the distributions of other model parameters (stationary state frequencies in this case). If you want to allow transition rate (stationary frequency) asymmetry in standard data, then simply select another (hyper)prior. For instance, you can fix the parameter to $1.0$, which would result in a uniform prior on the proportions of the state frequencies. In practice, MrBayes uses a discrete approximation of the Dirichlet distribution for binary characters; five categories are used by default (change this with \texttt{lset nbetacat}). For instance, assume that we fix the hyperprior to $1.0$ and then evaluate the likelihood of one binary character using five discrete beta categories. MrBayes would then calculate the likelihood of the character assuming that the stationary state frequencies of the two states were $0.1:0.9$, $0.3:0.7$, $0.5:0.5$, $0.7:0.3$ and $0.9:0.1$. The five category likelihoods would then be multiplied by $0.20$ (there is a probability of $0.20$ of being in each of the categories) and then summed up to give the total likelihood of the character. For multistate characters, MrBayes does not use the discrete approximation; instead, it uses the MCMC procedure to explore different stationary state frequency proportions. \section{Parsimony Model} MrBayes implements an incredibly parameter-rich model, first described by \citet{tuffley97}. It orders trees in terms of their maximum likelihood in the same way as the parsimony method would order them in terms of their parsimony score; hence we call it the parsimony model. The model is also referred to as the No-Common-Mechanism model because it treats each branch length for each character as a separate, completely independent parameter. In principle, a Bayesian MCMC analysis using the parsimony model should integrate out the branch lengths but MrBayes 3 uses a simpler approach, in which the branch lengths are fixed to their maximum likelihood values (infinity if there is a change on the branch and zero otherwise). This type of approach, where some parameters are fixed prior to the Bayesian analysis according to some non-Bayesian estimate, is typically referred to as an empirical Bayes method. Future versions of MrBayes may implement the true (hierarchical) Bayesian approach to the parsimony model but we expect the results to be very similar under both approaches. The parsimony model is much less parsimonious with respect to parameters than any other model implemented in MrBayes. Consider, for instance, an analysis of 1,000 characters and 100 taxa. The parsimony model would have about 200,000 free parameters ($200$ branches times $1000$ characters). A more typical GTR + I + $\Gamma$ model would have only little more than 200 parameters, about 1,000 times fewer parameters. In this sense, the standard stochastic models are much more parsimonious than the parsimony model. Several problems are associated with the excessive number of parameters. Statistical inconcistency is perhaps the best known of these but, more fundamentally, a model like the parsimony model does not offer much in terms of generalities that can be inferred about the evolutionary process. The Goldman \citep{goldman93} model is another example of a parameter-rich stochastic model that orders trees in the same way as the parsimony method. In this model, the branch lengths are the same but the ancestral states are estimated for all characters and all nodes in the tree. For an analysis of 100 taxa and 1,000 characters, this results in approximately 100,000 free parameters. The Goldman model is actually very similar to the No-Common-Mechanism model; it makes little difference if the character- and treesection-specific parameters are introduced at the nodes or at the branches. The Goldman model is not implemented in MrBayes. We would like to emphasize that we do not recommend the use of the parsimony model. We have included it in MrBayes only to allow users to explore its properties and contrast it with the other models implemented in the program. The parsimony model is not the default model used for standard (morphological) data in MrBayes. The default standard data model is described above and is similar to the models used for nucleotide and amino acid data. By default, MrBayes does not use the parsimony model at all; you have to invoke it using \texttt{lset parsmodel=yes}. \section{Rate Variation Across Sites} By default, MrBayes assumes that all characters evolve at the same rate (measured in expected changes per site over the tree). There are different ways in which you can allow rate variation across sites. The simplest method is to assume that rates vary over sites according to a gamma distribution \citep{yang93}. The gamma model can be combined with spatial autocorrelation between the rates at adjacent sites, the autocorrelated gamma model. A completely different approach to rate variation across sites is to allow a proportion of sites to be invariable. This model can be combined with the gamma model but not with the autocorrelated gamma model. In addition, MrBayes also implements new ways of modelling rate-variation (log normal, and $k$-mixtures, see below). Finally, it is possible to divide characters into groups evolving at different rates, the partitioned or site specific rate model. \subsection{Gamma-distributed Rate Model} \label{gammaDistributedRateModel} The commonly used model of gamma-shaped rate variation across sites is invoked using \texttt{lset rates=gamma}. The shape of the gamma distribution is determined by the so-called $\alpha$ (alpha) parameter. When this parameter is small (below $1$), the distribution takes on an L-shaped form with a few sites evolving rapidly, while most sites are conserved. Conversely, when $\alpha$ is above $1$ the distribution becomes similar to a normal distribution with less and less variation in rates across sites as $\alpha$ becomes larger. %%%% JN: Include the classical Figure? In practice, the gamma distribution is approximated using a small number of discrete rate categories \citep{yang94a}. By default, four rate categories are used; you can change this setting by using \texttt{lset ngammacat}. For instance, if you want to use eight discrete rate categories the appropriate command is \texttt{lset ngammacat=8}. The computational complexity is proportional to the number of categories used. An analysis with four discrete gamma categories is four times as slow as an analysis with no rate variation across sites and twice as fast as one with eight categories. The shape parameter ($\alpha$) of the gamma distribution is similar to the shape parameter of the Dirichlet distribution of stationary state frequencies used for standard data (see above) in that it controls the distribution of another model parameter (the site rates). Therefore, the prior probability distribution used for the shape parameter can be referred to as a hyperprior. The default prior used in MrBayes is an exponential distribution with the rate set to $1.0$ (\texttt{prset shapepr=exponential(1.0)}). Note that he mean of the exponential distribution is the inverse of the rate, so if, for example, changing the rate of the exponential distribution to $10.0$, will move the mean from $1.0$ to $1/10.0 = 0.1$. The sampled values of the shape parameter are found under the column heading \texttt{alpha} in the \texttt{.p} file(s). \subsection{Autocorrelated Gamma Model} In this model, rates vary across sites according to an autocorrelated gamma model where the rate at each site depends to some extent on the rates at adjacent sites \citep{yang95a}. The spatial autocorrelation is measured by the $\rho$ (rho) parameter, which ranges from -1 (negative autocorrelation, that is, adjacent sites tend to have wildly different rates) to 1 (adjacent sites have very similar rates). The default prior probability for $\rho$ is a uniform distribution covering the entire interval $(-1,1)$. In the worst case, a small symmetric tree, the extra computational complexity incurred by invoking the auto correlated gamma model instead of the gamma model is comparable to a doubling of the number of taxa in the analysis. In more typical cases, moderate to large data sets, the additional computational cost is negligible and equivalent to adding a single taxon. As with the gamma model, the autocorrelated gamma distribution is approximated with a number of discrete rate categories determined by \texttt{lset ngammacat}. As described by Yang \citep{yang95a}, protein-coding sequences tend to have a three-position offset in their autocorrelation. That is, the first codon position sites tend to have rates that are correlated with the adjacent first-position sites, second-position site rates are correlated with adjacent second-position rates, etc. You can take this effect into account by partitioning your sites into the three codon positions and then applying a separate autocorrelated gamma model to each of the categories. You might want to invoke an autocorrelated gamma model with the same correlation coefficient for a dataset consisting of several concatenated genes. If so, it is necessary to inform MrBayes about the break points between different genes. Otherwise, the rates at the first site of each gene except the first one will be erroneously compared to the rates at the last site in the preceding gene. The command \texttt{databreaks} is provided for this purpose. For instance, if there are only two genes in your data set, the first with 960 sites, you would specify the break between them with the statement \texttt{databreaks 960}. Note that you specify the break by giving the last sequence site before the break. The \texttt{databreaks} command is only needed when you invoke a single autocorrelated gamma model for a multigene dataset. The \texttt{databreaks} command cannot be used to partition a data set. %%%% JN: This subsection needs to be expanded \subsection{Lognormal-distributed Rates Across Sites} \texttt{LNorm} --- Lognormal-distributed rates across sites. The rate at a site is drawn from a lognormal distribution. the lognormal distribiton has a single parameter, sigma (SD) that describes how much rates vary (mean fixed to $log(1.0) = 0.0$). You invoke this assumption about rate variation acrros sites by the command \texttt{lset rates=lnorm}. The number of discrete categories used for the approximation of the lognormal distribution can be set by \texttt{lset nlnormcat=}. Default value is 4. %%%% JN: This subsection needs to be expanded \subsection{Site Rates from a Mixture with $k$ Categories} \texttt{Kmixture} --- Site rates come from a mixture with $k$ categories. Category rates are drawn from an ordered flat Dirichlet distribution with mean rather than sum equal to $1.0$. You invoke this assumption about rate variation acrros sites by the command \texttt{lset rates=kmixture}. To set the number of components in the $k$-mixture model, you use \texttt{lset nmixtcat=}. Default value is 4. \subsection{Proportion of Invariable Sites} A completely different approach to rate variation is to allow a proportion of sites to be invariable. This model is invoked using \texttt{lset rates=propinv}. The proportion of invariable sites is referred to as \texttt{pinvar;} it can vary from $0$ (no invariable sites) to $1$ (all sites are invariable). The default prior is a uniform distribution on the interval $(0,1)$; change it using \texttt{prset pinvarpr}. The proportion of invariable sites model can be combined with the gamma model using \texttt{lset rates=invgamma}. Although this model is slightly better than the simple gamma model for many data sets, it sometimes results in a bimodal or ridge-like posterior probability distribution. In particular, it is not uncommon to see two peaks in the posterior, one with a low proportion of invariable sites and significant rate variation in the gamma distribution (low alpha value) and the other with a high proportion of invariable sites and moderate amounts of rate variation in the gamma distribution (moderately high alpha value). If you have a posterior of this kind, you should not be surprised if Metropolis-coupling results in rapid (instantaneous) shifts from one mode to the other during the stationary phase of the analysis. The reason for this is that different chains are likely to explore different peaks in the posterior, and successful swapping involving the cold chain is likely to result in mode-jumping. Also, you should consider presenting the entire distribution of the sampled \texttt{alpha} and \texttt{pinvar} parameters since simple point estimates of each parameter would be misleading. \subsection{Partitioned (Site Specific) Rate Model} For protein-coding nucleotide sequences, a site-specific rate model is often used, allowing each codon position (first, second and third codon position sites) to have its own rate. This results in a model with three rates, two of which are free to vary (since the average rate is $1.0$ by definition). More generally, we might have different character divisions (separate genes, morphology, etc) which potentially evolve at very different rates. In MrBayes 3, we provide a general mechanism for setting up these models based on partitioning the data set and then unlinking parameters across the partitions. Assume for instance that we want to set up a site-specific rate model for a data set with one sequence. We first set up the codon site partitioning scheme using the following lines in a MrBayes block: \begin{figure}[h] \centering \begin{BVerbatim}[fontsize=\small] charset pos1 = 1-.\3; charset pos2 = 2-.\3; charset pos3 = 3-.\3; partition by_codon = 3:pos1,pos2,pos3; set partition = by_codon; \end{BVerbatim} \end{figure} The character sets are first defined using the dot sign (.) to mark the last character in the data set and the $\backslash$3 sequence to include every third character in the specified range. Then a partitioning scheme called \texttt{by\_codon} is defined using the previously named character sets. Finally, the partitioning scheme called \texttt{by\_codon} is invoked using the \texttt{set} command. When we process these commands in MrBayes using the \texttt{execute} command, the characters are divided into three sets corresponding to the codon positions. By default, however, all model parameters including the rate will be shared across partitions. To allow the rates to differ across partitions, we need to change the prior for rates using \texttt{prset}. Specifically, \texttt{prset ratepr=variable} invokes partition-specific rates. The partition-specific rate parameter is referred to as \texttt{ratemult}, and the individual rates are labeled \texttt{m\{1\}}, \texttt{m\{2\}}, etc for the rate (multiplier) of character division 1, 2, etc. See below for more information on how to set up partitioned models. \subsection{Inferring Site Rates} When you are allowing rate variation across sites, you may be interested in inferring the rates at each individual site. By default, the site rates are not sampled during a MCMC run. You need to request the sampling of these values using \texttt{report siterates=yes}. The rates will be referred to as \texttt{r()}. For instance, \texttt{r(45)} is the inferred rate at site (character) 45 of your data set. \section{Rate Variation Across the Tree} \subsection{The Covarion Model} For both nucleotide sequence and amino-acid data, MrBayes allows rates to change across the tree under a covarion-like model (\citet{tuffley98}; \citet{huelsenbeck02a}; see also \citet{galtier01}). Specifically, the covarion-like model assumes that a site is either ``on'' or ``off''. When it is on, it evolves under a standard four-by-four nucleotide or 20 by 20 amino acid model but when it is off, it does not change at all. The switching between on and off states is controlled by two rate parameters, $s_{01}$ (from off to on) and $s_{10}$ (from on to off). The instantaneous rate matrix of the nucleotide variant (also referred to as the covariotide model), assuming a GTR model for the on state, is \footnotesize \[ Q=\begin{bmatrix} & [A_{off}] & [C_{off}] & [G_{off}] & [T_{off}] & [A_{on}] & [C_{on}] & [G_{on}] & [T_{on}]\\ [A_{off}]& - & 0 & 0 & 0 & s_{01} & 0 & 0 & 0 \\ [C_{off}]& 0 & - & 0 & 0 & 0 & s_{01} & 0 & 0 \\ [G_{off}]& 0 & 0 & - & 0 & 0 & 0 & s_{01} & 0 \\ [T_{off}]& 0 & 0 & 0 & - & 0 & 0 & 0 & s_{01} \\ [A_{on}]& s_{10} & 0 & 0 & 0 & - & \pi_C r_{AC} k & \pi_G r_{AG} k & \pi_T r_{AT} k \\ [C_{on}]& 0 & s_{10} & 0 & 0 & \pi_A r_{AC} k & - & \pi_G r_{CG} k & \pi_T r_{CT} k \\ [G_{on}]& 0 & 0 & s_{10} & 0 & \pi_A r_{AG} k & \pi_C r_{CG} k & - & \pi_T r_{GT} k \\ [T_{on}]& 0 & 0 & 0 & s_{10} & \pi_A r_{AT} k & \pi_C r_{CT} k & \pi_G r_{GT} k &- \\ \end{bmatrix} \] \normalsize where $k$ is a scaling constant determined by the proportion of time the sites spend in the ``on'' state. The matrix can be simplified into \[ Q=\begin{bmatrix} R_1 & R_2\\ R_3 & k R_4\\ \end{bmatrix} \] where each $R$ element is a four by four matrix: $R_1$ contains the rates in the off state (all rates are 0), $R_2$ and $R_3$ describe the switching process (the diagonal elements are either $s_{01}$ or $s_{10}$), and $R_4$ is the chosen model for the evolution in the ``on'' state. The covarion-like model can be described as a general case of the proportion of invariable sites model \citep{huelsenbeck02a}. As the switching rates go to zero, the proportion of these rates represented by the switch to the off state ($s_{10}$) becomes identical to the proportion of invariable sites. When the switching rates are zero, there is no exchange between the on and off states and the characters in the off state remain off throughout the tree; in other words, they are invariable sites. Note that the covarion-like model implemented in MrBayes differs from the original covarion model in that sites switch completely independently of each other between the on and off states. To invoke the covarion-like model, simply use \texttt{lset covarion=yes} and then choose the desired nucleotide or amino acid model using the other \texttt{lset} and the \texttt{prset} options. The covarion-like model can be combined with the gamma model of rate variation across sites. %%%% JN: Section planned, but not started %\subsection{Relaxed Clock Models} \section{Topology and Branch Length Models} The topology and branch length models in MrBayes are set using the \texttt{prset topologypr} options, which deal with the tree topology prior, and the \texttt{prset brlenspr} options, which deal with the branch lengths. \subsection{Unconstrained and Constrained Topology} \label{unconstrainedAndConstrainedTopology} In MrBayes 3, there are four choices for the prior probability distribution on topology: \texttt{uniform}, \texttt{speciestree}, \texttt{fixed}, or \texttt{constraints}. By default, topologies are not constrained in the prior (\texttt{prset topologypr=uniform}), resulting in equal prior probability being associated with all possible labeled trees (unless a different topology prior is induced by the branch length model, see below). There are some cases where one wish to constrain the topology. For example, if you are interested in branch lengths or other parameters in your model, but where the tree topology is assumed to be true, you may apply a \texttt{fixed} prior probality for the tree. This will keep the topology (the relationships between the clades) fixed, but still integrate over branch lengths in the MCMC (if you also want to fix branch lengths, you do that by using the \texttt{brlenspr} option). Another special case is when you are using the multi-species coalescent to infer species trees from gene trees. Then the topology is constrained to fold inside a species tree together with other (gene) trees. This is accomplished by using the \texttt{speciestree} argument for the \texttt{topologypr} option. See section \ref{theMultiSpeciesCoalescent} for more information on the multi-species coalescent. Other scenarios might be when you want to contrast a hypothesis of monophyly for a group with the more general hypothesis with no topological constraints; or you may want to infer ancestral states for a particular node in the tree. In both those cases, you need to specify the constraint(s) first by listing the taxa that should form a monophyletic group. There are three types of constraints implemented in MrBayes v3.2. The type of the constraint is specified by using one of the three keywords \texttt{hard}, \texttt{negative}, or \texttt{partial} right after the name of the constraint (see example below). If no type is specified, then the constraint is assumed to be hard. In a rooted tree, a \texttt{hard} constraint forces the taxa in the list to form a monophyletic group. In an unrooted tree, the taxon split that separates the taxa in the list from other taxa is forced to be present. The interpretation of this depends on whether the tree is rooted on a taxon outside the list or a taxon in the list. If the outgroup is excluded, the taxa in the list are assumed to form a monophyletic group, but if the outgroup is included, the taxa that are not in the list are forced together. A \texttt{negative} constraint ``bans'' all the trees that have the listed taxa in the same subtree. In other words, it is the opposite of a hard constraint. A \texttt{partial} or ``backbone'' constraint is defined in terms of two sets of taxa separated by a colon character. The constraint forces all taxa in the first list to form a monophyletic group that does not include any taxon in the second list. Taxa that are not included in either list can be placed in any position on the tree, either inside or outside the constrained group. In an unrooted tree, the two taxon lists can be switched with each other with no effect. For a rooted tree, it is the taxa in the first list that have to be monophyletic, that is, these taxa must share a common ancestor not shared with any taxon in the second list. The taxa in the second list may or may not fall in a monophyletic group depending on the rooting of the tree. A list of taxa can be specified using a taxset, taxon names, taxon numbers, or any combination of the above, sepatated by spaces. The constraint is treated as an absolute requirement of trees, that is, trees that are not compatible with the constraint have zero prior (and hence zero posterior) probabilty. If you are interested in inferring ancestral states for a particular node, you need to hard constrain that node first using the \texttt{constraint} command. The same applies if you wish to calibrate an interior node in a dated analysis. It is important to note that simply defining a constraint using this command is not sufficient for the program to actually implement the constraint in an analysis. You must also enforce the constraints using \texttt{prset topologypr = constraints ()}. Examples: \begin{Verbatim} MrBayes > constraint myclade = Homo Pan Gorilla \end{Verbatim} Defines a hard constraint forcing \textit{Homo}, \textit{Pan}, and \textit{Gorilla} to form a monophyletic group or a split that does not include any other taxa. \begin{Verbatim} MrBayes > constraint forbiddenclade negative = Homo Pan Gorilla \end{Verbatim} Defines a negative constraint that associates all trees where \textit{Homo}, \textit{Pan}, and \textit{Gorilla} form a monophyletic group with zero posterior probability. In other words, such trees will not be sampled during MCMC. \begin{Verbatim} MrBayes > constraint backbone partial = Homo Gorilla : Mus \end{Verbatim} Defines a partial constraint that keeps \textit{Mus} outside of the clade defined by the most recent common ancestor of \textit{Homo} and \textit{Gorilla}. Other taxa are allowed to sit anywhere in the tree. Note that this particular constraint is meaningless in unrooted trees. MrBayes does not assume anything about the position of the outgroup unless it is explicitly included in the partial constraint. Therefore a partial constraint must have at least two taxa on each side of the `\texttt{:}' to be useful in analyses of unrooted trees. The case is different for rooted trees, where it is sufficient for a partial constraint to have more than one taxon before the `\texttt{:}', as in the example given above, to constrain tree space. When you define constraints, make sure that you have the outgroup selected correctly. By default, MrBayes uses the first taxon in the data matrix as the outgroup. You can change this by using the \texttt{outgroup} command. For instance, if you want taxon number 7 called \textit{Hylobates} to be the outgroup, either use \texttt{outgroup 7} or \texttt{outgroup Hylobates}. MrBayes 3.2 only allows a single taxon as the outgroup. To define a more complex constraint tree, simply combine constraints into a list when issuing the \texttt{prset topologypr} command. \subsection{Non-clock (Standard) Trees} %%%% JN: Paragraph taken directly from built-in documentation. Might need some softening. If you do not want to enforce a molecular clock, you choose an unconstrained branch length prior. Actually, you do not have to do anything because unconstrained branch lengths are the default. You can associate unconstrained branch lengths with five alternative prior distributions. The first two are the simple uniform and exponential priors. The uniform prior takes two parameters, the lower and upper bound of the uniform distribution, respectively. The exponential prior takes a single parameter, the rate of the exponential distribution. The mean of the exponential distribution is the inverse of the rate. For instance, an \texttt{exp(10)} distribution has an expected mean of $0.1$. MrBayes also offers three more complex prior distributions on unconstrained branch lengths. The two-exponential prior (\citet{yang05}; \citet{yang07}) uses two different exponential distributions, one for internal and one for external branch lengths. The two-exponential prior is invoked using \texttt{prset brlenspr=unconstrained:twoexp(,)}, where \texttt{r\_I} is a number specifying the rate of the exponential distribution on internal branch lengths ($r_{I}$), while \texttt{r\_E} is the rate for external branch lengths ($r_{E}$). The prior mean for internal branch lengths is then $1/r_{I}$, and for external ones is $1/r_{E}$. For instance, to set prior mean of internal branch lengths to $0.01$, and external ones to $0.1$, use \texttt{twoexp(100,10)}. The setting \texttt{twoexp(10,10)} is equivalent to \texttt{exp(10)}. The compound Dirichlet priors \texttt{gammadir(,,,)} and \texttt{invgamdir(,,,)} specify a fairly diffuse prior on tree length $T$, and then partition the tree length into branch lengths according to a Dirichlet distribution \citep{rannala12}. If $T$ is considered drawn from a gamma distribution with parameters $a_{T}$ and $b_{T}$, and with mean $a_{T}/b_{T}$, we recommend setting $a_{T} = 1$; if it is instead considered drawn from an inverse gamma (\texttt{invgamma}) distribution with parameters $a_{T}$ and $b_{T}$, and with mean $b_{T}/(a_{T} - 1)$, then we recommend setting $a_{T} = 3$. In the latter case, $b_{T}$ should be chosen so that the prior mean of $T$ is reasonable for the data. In the former case, setting $b_{T} = 0.1$ (corresponding to a mean tree length of $10$) should be appropriate for a wide range of tree lengths (at least in the interval 1 to 100). The concentration parameter a of the Dirichlet distribution is inversely related to the variance of the branch lengths, while $c$ is the ratio of the prior means for the internal and external branch lengths. The default setting, $a$ = $c$ = $1$, specifies a uniform Dirichlet distribution of branch lengths given the tree length. For instance, \texttt{gammadir(1,0.1,1,1)} specifies a compound Dirichlet prior on branch lengths, where tree length is associated with a gamma distribution with mean $10$, and branch length proportions are associated with a uniform Dirichlet distribution (default). \subsection{Strict Clock Trees} MrBayes implements three strict clock models: the simple (uniform) model, the birth-death model, and the coalescence model. The birth-death model and coalescence models both have additional parameters describing the tree-generating process, whereas the simple model does not. In the $birth$-$death$ $model$ (see \citet{yang97b}, for a Bayesian implementation), trees are generated according to a birth-death model with a speciation and an extinction rate. The model, as implemented in MrBayes, can also be associated with a sampling probability of terminal lineages. The priors for these three parameters are set using the \texttt{speciationpr, extinctionpr,} and \texttt{sampleprob} parameters of the \texttt{prset} command. In the $coalescence$ $model$, the tree generating process is looked at from the opposite perspective, backward in time. Instead of lineages branching, this model sees them as coalescing into fewer and fewer ancestral lineages. This process occurs at a rate determined by the $\theta$ (theta) parameter; it is also affected by the ploidy of the data (haploid or diploid). The prior for the theta parameter is set using \texttt{prset thetapr}. The ploidy level is set using \texttt{lset ploidy}. The $simple$ $clock$ model is invoked by \texttt{prset brlenspr=clock:uniform}. There is only one additional parameter in the simple clock model, namely the total tree height/age. The prior for this parameter is set by \texttt{prset treeagepr}. The default prior is an gamma distribution with mean 1.0 (\texttt{gamma(1.0,1.0)}, i.e. \texttt{exp(1.0)}). \subsection{Relaxed Clock Trees} {\large\color{red} Please note, this section of the manual is not revised for v.3.2.7!} %%%% JN: subsection need to be expanded. I pasted a block of text from another section below MrBayes offers five relaxed clock models: the Thorne--Kishino 2002 (TK02) \citep{thorne02}, the compound poisson process (CPP) \citep{huelsenbeck00a}, the white noise (WN) \citep{lepage07}, the independent gamma rates (IGR), and independent lognormal (ILN) \citep{drummond06} models. We note here that, since version 3.2.8, the name `WN' is used to refer to `IGR' in old versions, while `IGR' is used to refer to a different model. In the new IGR model, the rates are i.i.d. gamma distributions across branches and the variance does not dependent on branch lengths as in the WN model. The ILN model differs from IGR in that the rate variation follows i.i.d. lognormal distributions instead of gamma distributions. \section{Partitioned Models} \label{partitionedModels} MrBayes provides great flexibility in setting up partitioned models. By default, the characters are divided into partitions based on the data type. If there is only one data type in the matrix, then all characters will be in a single partition. The default partitioning scheme is called \texttt{default}. For information on how to set up a file with mixed data types, see the example file \texttt{cynmix.nex} and the tutorial in section \ref{tutorialPartitioned} of this manual. You can easily set up partitioning schemes that divide the characters up further than the default partition does using the \texttt{partition} command. The most convenient way of partitioning data is to define character sets first using the \texttt{charset} command. For instance, assume that you have concatenated nucleotide sequences from three genes in your data set of length 1962, 1050, and 2082 sites, respectively. Then you create character sets for those three genes using \begin{Verbatim} charset gene1 = 1-1962; charset gene2 = 1963-3012; charset gene3 = 3013-.; \end{Verbatim} in a \texttt{MRBAYES} block. Note the use of the dot (\texttt{.}) as a synonym of the last site. You can also use the ``backslash n'' sequence to include every $n$th character in the preceding range of characters (see the description of the site-specific rate model above). Once the character sets are defined, the partitioning scheme based on the genes is defined with the \texttt{partition} command and selected using the \texttt{set} command: \begin{Verbatim} partition by_gene = 3:gene1,gene2,gene3; set partition = by_gene; \end{Verbatim} Here, \texttt{by\_gene} is the name we chose for the partitioning scheme. The name is followed by an equal sign, the number of partitions, and then a comma-separated list of characters to include in each partition. Note that MrBayes requires the partitioning scheme to include all characters. Say, for instance, that you wanted to run an analysis with only gene 1 and gene 2. Then define a two-partition scheme and exclude the characters represented by gene 3: \begin{Verbatim} partition genes_1_2 = 2:gene1,gene2 gene3; exclude gene3; set partition = genes_1_2; \end{Verbatim} If the only purpose of the partition \texttt{genes\_1\_2} is to allow exclusion of gene 3, then gene 3 can of course be included in either of the two partitions before being excluded. Once the partitions have been correctly set up, MrBayes allows you to set models for individual partitions using the \texttt{lset applyto} and \texttt{prset applyto} mechanism. For instance, assume that we have two partitions, a standard data partition (partition 1) and a nucleotide partition (partition 2), and want to apply a GTR model to the nucleotide data, gamma-shaped rate variation to both partitions, and allow the partition rates to be different. Then we would use the commands \begin{Verbatim} lset applyto=(2) nst=6; lset applyto=(all) rates=gamma; prset applyto=(all) ratepr=variable; \end{Verbatim} By default, all model parameters that are identical and have the same prior probability distribution associated with them, are linked across partitions (they are assumed to be one and the same parameter). To unlink parameters, use the \texttt{unlink} command. For instance, assume that we want to unlink the shape parameter across the partitions discussed above (after all, why should the standard data and the molecular data have the same distribution of rates across sites?). This would be achieved using \begin{Verbatim} unlink shape=(all); \end{Verbatim} If you unlink parameters by mistake, they can be linked again using the \texttt{link} command. All of the commands mentioned above and given as they would appear in a \texttt{MRBAYES} block in a Nexus file can of course be entered from the command line as well (without the trailing semicolon). However, it is often more convenient to have them in either your data file or in a separate Nexus file that you process after you have read in your data. MrBayes will keep the data set in memory until you read in a new \texttt{DATA} block, so you can have your different \texttt{MRBAYES} blocks pertaining to the same data file distributed over as many separate Nexus files as you like. We recommend that, before you run your analysis, you check the current model settings using the \texttt{showmodel} command. This command will list all the active parameters and how they are linked across partitions, as well as the priors associated with each parameter. Finally, we want to give you a warning. Even though MrBayes allows you to easily set up extremely complex and parameter-rich models, and the Bayesian MCMC approach is good at handling such models, think carefully about the parameters you introduce in your model. There should be at least some reasonable chance of estimating the parameters based on your data. For instance, a common mistake is to use a separate GTR model for a partition with so few substitutions that there is not a single observation for some rate categories. Making sure there are at least some observations allowing you to estimate each parameter is good practice. Over-parameterized models often result in problems with convergence in addition to the excessive variance seen in the parameter estimates. \appendix \chapter{Overview of Models and Moves}\label{appendixOverview} \addtocontents{toc}{\protect\setcounter{tocdepth}{0}} \hspace{-12mm}\includegraphics[width=0.85\textheight]{Appendix_Fig1.pdf}\newpage \hspace{-8mm}\includegraphics[width=0.85\textheight]{Appendix_Fig2.pdf} \includegraphics[width=0.8\textheight]{Appendix_Fig3.pdf} \addcontentsline{toc}{chapter}{Bibliography} \bibliographystyle{sysbio} \bibliography{bayes} \end{document} NBISweden-MrBayes-ecfd55c/doc/manual/src/bayes.bib000066400000000000000000005437511526212227600220260ustar00rootroot00000000000000@string{AHG = "Annals of Human Genetics"} @string{AJHG = "American Journal of Human Genetics"} @string{ARB = "Annual Review of Biochemistry"} @string{ARCB = "Annual Review of Cell Biology"} @string{BMCBI = "BMC Bioinformatics"} @string{BI = "Bioinformatics"} @string{BIOGEN = "Biochemical Genetics"} @string{BJLS = "Biological Journal of the Linnean Society"} @string{BMB = "Bulletin of Mathematical Biology"} @string{CABIOS = "Computer Applications in the Biosciences"} @string{CELL = "Cell"} @string{CACM = "Communications of the ACM"} @string{COCB = "Current Opinion in Cell Biology"} @string{COGD = "Current Opinion in Genetics and Development"} @string{COSB = "Current Opinion in Structural Biology"} @string{CSHSQB = "Cold Spring Harbor Symposia Quantitative Biology"} @string{ComputChem = "Computers and Chemistry"} @string{EMBO = "EMBO Journal"} @string{EVO = "Evolution"} @string{GEN = "Genetics"} @string{GB = "Genome Biology"} @string{GR = "Genome Research"} @string{JBSD = "Journal of Biomolecular Structure and Dynamics"} @string{JComputBiol = "Journal of Computational Biology"} @string{JCB = JComputBiol} @string{JMB = "Journal of Molecular Biology"} @string{JME = "Journal of Molecular Evolution"} @string{JRSS = "Journal of the Royal Statistical Society, B"} @string{JTB = "Journal of Theoretical Biology"} @string{MBE = "Molecular Biology and Evolution"} @string{MBIO = "Mathematical Biosciences"} @string{MCB = "Molecular Cell Biology"} @string{ME = "Methods in Enzymology"} @string{MPE = "Molecular Phylogenetics and Evolution"} @string{NAR = "Nucleic Acids Research"} @string{NC = "Neural Computation"} @string{NNB = "Nature New Biology"} @string{NG = "Nature Genetics"} @string{NB = "Nature Biotechnology"} @string{Nature = "Nature"} @string{PE = "Protein Engineering"} @string{PLOSCOMPBIO = "PLoS Computational Biology"} @string{PNAS = "Proceedings of the National Academy of Sciences of the USA"} @string{PROT = "Proteins"} @string{PROTSCI = "Protein Science"} @string{SIAM = "SIAM Journal of Applied Mathematics"} @string{SYSB = "Systematic Biology"} @string{SZ = "Systematic Zoology"} @string{Science = "Science"} @string{S = Science} @string{TIBTECH = "Trends in Biotechnology"} @string{TIGS = "Trends in Genetics"} @string{UKSB = "University of Kansas Scientific Bulletin"} %********************************************************************************* % A %********************************************************************************* @Article{adachi92, author = {J. 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As such, it is % non-standard LaTeX, and requires a special package file to function properly. % Such a package is natbib.sty by Patrick W. Daly % The form of the \bibitem entries is % \bibitem[Jones et al.(1990)]{key}... % \bibitem[Jones et al.(1990)Jones, Baker, and Smith]{key}... % The essential feature is that the label (the part in brackets) consists % of the author names, as they should appear in the citation, with the year % in parentheses following. There must be no space before the opening % parenthesis! % With natbib v5.3, a full list of authors may also follow the year. % In natbib.sty, it is possible to define the type of enclosures that is % really wanted (brackets or parentheses), but in either case, there must % be parentheses in the label. % The \cite command functions as follows: % \citet{key} ==>> Jones et al. (1990) % \citet*{key} ==>> Jones, Baker, and Smith (1990) % \citep{key} ==>> (Jones et al., 1990) % \citep*{key} ==>> (Jones, Baker, and Smith, 1990) % \citep[chap. 2]{key} ==>> (Jones et al., 1990, chap. 2) % \citep[e.g.][]{key} ==>> (e.g. Jones et al., 1990) % \citep[e.g.][p. 32]{key} ==>> (e.g. Jones et al., p. 32) % \citeauthor{key} ==>> Jones et al. % \citeauthor*{key} ==>> Jones, Baker, and Smith % \citeyear{key} ==>> 1990 %--------------------------------------------------------------------- ENTRY { address annote author booktitle chapter edition editor eid howpublished institution journal key language month note number organization pages publisher school series title type volume year } {} { label extra.label sort.label short.list } INTEGERS { output.state before.all mid.sentence after.sentence after.block } FUNCTION {init.state.consts} { #0 'before.all := #1 'mid.sentence := #2 'after.sentence := #3 'after.block := } STRINGS { s t} % -- rdmp mid.sentence outputs space, not comma FUNCTION {output.nonnull} { 's := output.state mid.sentence = { " " * write$ } { output.state after.block = { add.period$ write$ newline$ "\newblock " write$ } { output.state before.all = 'write$ { add.period$ " " * write$ } if$ } if$ mid.sentence 'output.state := } if$ s } FUNCTION {output} { duplicate$ empty$ 'pop$ 'output.nonnull if$ } FUNCTION {output.check} { 't := duplicate$ empty$ { pop$ "empty " t * " in " * cite$ * warning$ } 'output.nonnull if$ } FUNCTION {fin.entry} { add.period$ write$ newline$ annote missing$ { "\bibAnnoteFile{" cite$ * "}" * write$ newline$ } { "\bibAnnote{" cite$ * "}{" * annote * "}" * write$ newline$ } if$ } FUNCTION {new.block} { output.state before.all = 'skip$ { after.block 'output.state := } if$ } FUNCTION {new.sentence} { output.state after.block = 'skip$ { output.state before.all = 'skip$ { after.sentence 'output.state := } if$ } if$ } FUNCTION {add.blank} { " " * before.all 'output.state := } FUNCTION {date.block} { new.sentence } FUNCTION {not} { { #0 } { #1 } if$ } FUNCTION {and} { 'skip$ { pop$ #0 } if$ } FUNCTION {or} { { pop$ #1 } 'skip$ if$ } FUNCTION {new.block.checkb} { empty$ swap$ empty$ and 'skip$ 'new.block if$ } FUNCTION {field.or.null} { duplicate$ empty$ { pop$ "" } 'skip$ if$ } FUNCTION {emphasize} { duplicate$ empty$ { pop$ "" } { "\emph{" swap$ * "}" * } if$ } FUNCTION {tie.or.space.prefix} { duplicate$ text.length$ #3 < { "~" } { " " } if$ swap$ } FUNCTION {capitalize} { "u" change.case$ "t" change.case$ } FUNCTION {space.word} { " " swap$ * " " * } % Here are the language-specific definitions for explicit words. % Each function has a name bbl.xxx where xxx is the English word. % The language selected here is ENGLISH FUNCTION {bbl.and} { "and"} FUNCTION {bbl.etal} { "et~al." } FUNCTION {bbl.editors} { "eds." } FUNCTION {bbl.editor} { "ed." } FUNCTION {bbl.edby} { "edited by" } FUNCTION {bbl.edition} { "ed." } FUNCTION {bbl.volume} { "vol." } FUNCTION {bbl.of} { "of" } FUNCTION {bbl.number} { "no." } FUNCTION {bbl.nr} { "no." } FUNCTION {bbl.in} { "in" } FUNCTION {bbl.pages} { "pages" } FUNCTION {bbl.page} { "page" } FUNCTION {bbl.chapter} { "chap." } FUNCTION {bbl.techrep} { "Tech. Rep." } FUNCTION {bbl.mthesis} { "Master's thesis" } FUNCTION {bbl.phdthesis} { "Ph.D. thesis" } MACRO {jan} {"Jan."} MACRO {feb} {"Feb."} MACRO {mar} {"Mar."} MACRO {apr} {"Apr."} MACRO {may} {"May"} MACRO {jun} {"Jun."} MACRO {jul} {"Jul."} MACRO {aug} {"Aug."} MACRO {sep} {"Sep."} MACRO {oct} {"Oct."} MACRO {nov} {"Nov."} MACRO {dec} {"Dec."} MACRO {acmcs} {"ACM Computing Surveys"} MACRO {acta} {"Acta Informatica"} MACRO {cacm} {"Communications of the ACM"} MACRO {ibmjrd} {"IBM Journal of Research and Development"} MACRO {ibmsj} {"IBM Systems Journal"} MACRO {ieeese} {"IEEE Transactions on Software Engineering"} MACRO {ieeetc} {"IEEE Transactions on Computers"} MACRO {ieeetcad} {"IEEE Transactions on Computer-Aided Design of Integrated Circuits"} MACRO {ipl} {"Information Processing Letters"} MACRO {jacm} {"Journal of the ACM"} MACRO {jcss} {"Journal of Computer and System Sciences"} MACRO {scp} {"Science of Computer Programming"} MACRO {sicomp} {"SIAM Journal on Computing"} MACRO {tocs} {"ACM Transactions on Computer Systems"} MACRO {tods} {"ACM Transactions on Database Systems"} MACRO {tog} {"ACM Transactions on Graphics"} MACRO {toms} {"ACM Transactions on Mathematical Software"} MACRO {toois} {"ACM Transactions on Office Information Systems"} MACRO {toplas} {"ACM Transactions on Programming Languages and Systems"} MACRO {tcs} {"Theoretical Computer Science"} FUNCTION {bibinfo.check} { swap$ duplicate$ missing$ { pop$ pop$ "" } { duplicate$ empty$ { swap$ pop$ } { swap$ pop$ } if$ } if$ } FUNCTION {bibinfo.warn} { swap$ duplicate$ missing$ { swap$ "missing " swap$ * " in " * cite$ * warning$ pop$ "" } { duplicate$ empty$ { swap$ "empty " swap$ * " in " * cite$ * warning$ } { swap$ pop$ } if$ } if$ } STRINGS { bibinfo} INTEGERS { nameptr namesleft numnames } FUNCTION {format.names} { 'bibinfo := duplicate$ empty$ 'skip$ { 's := "" 't := #1 'nameptr := s num.names$ 'numnames := numnames 'namesleft := { namesleft #0 > } { s nameptr duplicate$ #1 > { "{f.~}{vv~}{ll}{, jj}" } { "{vv~}{ll}{, f.}{, jj}" } if$ format.name$ bibinfo bibinfo.check 't := nameptr #1 > { namesleft #1 > { ", " * t * } { numnames #2 > { "," * } 'skip$ if$ s nameptr "{ll}" format.name$ duplicate$ "others" = { 't := } { pop$ } if$ t "others" = { " " * bbl.etal * } { bbl.and space.word * t * } if$ } if$ } 't if$ nameptr #1 + 'nameptr := namesleft #1 - 'namesleft := } while$ } if$ } FUNCTION {format.names.ed} { 'bibinfo := duplicate$ empty$ 'skip$ { 's := "" 't := #1 'nameptr := s num.names$ 'numnames := numnames 'namesleft := { namesleft #0 > } { s nameptr "{f.~}{vv~}{ll}{, jj}" format.name$ bibinfo bibinfo.check 't := nameptr #1 > { namesleft #1 > { ", " * t * } { numnames #2 > { "," * } 'skip$ if$ s nameptr "{ll}" format.name$ duplicate$ "others" = { 't := } { pop$ } if$ t "others" = { " " * bbl.etal * } { bbl.and space.word * t * } if$ } if$ } 't if$ nameptr #1 + 'nameptr := namesleft #1 - 'namesleft := } while$ } if$ } FUNCTION {format.key} { empty$ { key field.or.null } { "" } if$ } FUNCTION {format.authors} { author "author" format.names } FUNCTION {get.bbl.editor} { editor num.names$ #1 > 'bbl.editors 'bbl.editor if$ } FUNCTION {format.editors} { editor "editor" format.names duplicate$ empty$ 'skip$ { "," * " " * get.bbl.editor * } if$ } FUNCTION {select.language} { duplicate$ empty$ 'pop$ { language empty$ 'skip$ { "{\selectlanguage{" language * "}" * swap$ * "}" * } if$ } if$ } FUNCTION {format.note} { note empty$ { "" } { note #1 #1 substring$ duplicate$ "{" = 'skip$ { output.state mid.sentence = { "l" } { "u" } if$ change.case$ } if$ note #2 global.max$ substring$ * "note" bibinfo.check } if$ } FUNCTION {format.title} { title duplicate$ empty$ 'skip$ { "t" change.case$ } if$ "title" bibinfo.check duplicate$ empty$ 'skip$ { select.language } if$ } FUNCTION {format.full.names} {'s := "" 't := #1 'nameptr := s num.names$ 'numnames := numnames 'namesleft := { namesleft #0 > } { s nameptr "{vv~}{ll}" format.name$ 't := nameptr #1 > { namesleft #1 > { ", " * t * } { s nameptr "{ll}" format.name$ duplicate$ "others" = { 't := } { pop$ } if$ t "others" = { " " * bbl.etal * } { numnames #2 > { "," * } 'skip$ if$ bbl.and space.word * t * } if$ } if$ } 't if$ nameptr #1 + 'nameptr := namesleft #1 - 'namesleft := } while$ } FUNCTION {author.editor.key.full} { author empty$ { editor empty$ { key empty$ { cite$ #1 #3 substring$ } 'key if$ } { editor format.full.names } if$ } { author format.full.names } if$ } FUNCTION {author.key.full} { author empty$ { key empty$ { cite$ #1 #3 substring$ } 'key if$ } { author format.full.names } if$ } FUNCTION {editor.key.full} { editor empty$ { key empty$ { cite$ #1 #3 substring$ } 'key if$ } { editor format.full.names } if$ } FUNCTION {make.full.names} { type$ "book" = type$ "inbook" = or 'author.editor.key.full { type$ "proceedings" = 'editor.key.full 'author.key.full if$ } if$ } FUNCTION {output.bibitem} { newline$ "\bibitem[{" write$ label write$ ")" make.full.names duplicate$ short.list = { pop$ } { * } if$ "}]{" * write$ cite$ write$ "}" write$ newline$ "" before.all 'output.state := } FUNCTION {n.dashify} { 't := "" { t empty$ not } { t #1 #1 substring$ "-" = { t #1 #2 substring$ "--" = not { "--" * t #2 global.max$ substring$ 't := } { { t #1 #1 substring$ "-" = } { "-" * t #2 global.max$ substring$ 't := } while$ } if$ } { t #1 #1 substring$ * t #2 global.max$ substring$ 't := } if$ } while$ } % -- rdmp FUNCTION {word.in} { bbl.in emphasize " " * } %FUNCTION {word.in} %{ bbl.in capitalize % emphasize % " " * } FUNCTION {format.date} { year "year" bibinfo.check duplicate$ empty$ { "empty year in " cite$ * "; set to ????" * warning$ pop$ "????" } 'skip$ if$ extra.label * before.all 'output.state := after.sentence 'output.state := } FUNCTION {format.btitle} { title "title" bibinfo.check duplicate$ empty$ 'skip$ { select.language } if$ } FUNCTION {either.or.check} { empty$ 'pop$ { "can't use both " swap$ * " fields in " * cite$ * warning$ } if$ } FUNCTION {format.bvolume} { volume empty$ { "" } { bbl.volume volume tie.or.space.prefix "volume" bibinfo.check * * series "series" bibinfo.check duplicate$ empty$ 'pop$ { swap$ bbl.of space.word * swap$ emphasize * } if$ "volume and number" number either.or.check } if$ } FUNCTION {format.number.series} { volume empty$ { number empty$ { series field.or.null } { series empty$ { number "number" bibinfo.check } { output.state mid.sentence = { bbl.number } { bbl.number capitalize } if$ number tie.or.space.prefix "number" bibinfo.check * * bbl.in space.word * series "series" bibinfo.check * } if$ } if$ } { "" } if$ } FUNCTION {format.edition} { edition duplicate$ empty$ 'skip$ { output.state mid.sentence = { "l" } { "t" } if$ change.case$ "edition" bibinfo.check " " * bbl.edition * } if$ } INTEGERS { multiresult } FUNCTION {multi.page.check} { 't := #0 'multiresult := { multiresult not t empty$ not and } { t #1 #1 substring$ duplicate$ "-" = swap$ duplicate$ "," = swap$ "+" = or or { #1 'multiresult := } { t #2 global.max$ substring$ 't := } if$ } while$ multiresult } %-- rdmp spell out Pages FUNCTION {format.pages} { pages empty$ { "" } { pages multi.page.check { "Pages~" pages n.dashify * } { "Page~" pages * } if$ } if$ } %FUNCTION {format.pages} %{ pages duplicate$ empty$ 'skip$ % { duplicate$ multi.page.check % { % bbl.pages swap$ % n.dashify % } % { % bbl.page swap$ % } % if$ % tie.or.space.prefix % "pages" bibinfo.check % * * % } % if$ %} FUNCTION {format.journal.pages} { pages duplicate$ empty$ 'pop$ { swap$ duplicate$ empty$ { pop$ pop$ format.pages } { ":" * swap$ n.dashify "pages" bibinfo.check * } if$ } if$ } FUNCTION {format.journal.eid} { eid "eid" bibinfo.check duplicate$ empty$ 'pop$ { swap$ duplicate$ empty$ 'skip$ { ":" * } if$ swap$ * } if$ } FUNCTION {format.vol.num.pages} { volume field.or.null duplicate$ empty$ 'skip$ { "volume" bibinfo.check } if$ eid empty$ { format.journal.pages } { format.journal.eid } if$ } FUNCTION {format.chapter.pages} { chapter empty$ 'format.pages { type empty$ { bbl.chapter } { type "l" change.case$ "type" bibinfo.check } if$ chapter tie.or.space.prefix "chapter" bibinfo.check * * pages empty$ 'skip$ { ", " * format.pages * } if$ } if$ } FUNCTION {format.booktitle} { booktitle "booktitle" bibinfo.check } FUNCTION {format.in.ed.booktitle} { format.booktitle duplicate$ empty$ 'skip$ { editor "editor" format.names.ed duplicate$ empty$ 'pop$ { get.bbl.editor swap$ "," * " " * swap$ * "(" swap$ * ")" * swap$ " " * swap$ * } if$ word.in swap$ * } if$ } FUNCTION {format.thesis.type} { type duplicate$ empty$ 'pop$ { swap$ pop$ "t" change.case$ "type" bibinfo.check } if$ } FUNCTION {format.tr.number} { number "number" bibinfo.check type duplicate$ empty$ { pop$ bbl.techrep } 'skip$ if$ "type" bibinfo.check swap$ duplicate$ empty$ { pop$ "t" change.case$ } { tie.or.space.prefix * * } if$ } FUNCTION {format.article.crossref} { word.in " \cite{" * crossref * "}" * } FUNCTION {format.book.crossref} { volume duplicate$ empty$ { "empty volume in " cite$ * "'s crossref of " * crossref * warning$ pop$ word.in } { bbl.volume capitalize swap$ tie.or.space.prefix "volume" bibinfo.check * * bbl.of space.word * } if$ " \cite{" * crossref * "}" * } FUNCTION {format.incoll.inproc.crossref} { word.in " \cite{" * crossref * "}" * } FUNCTION {format.org.or.pub} { 't := "" address empty$ t empty$ and 'skip$ { t empty$ { address "address" bibinfo.check * } { t * address empty$ 'skip$ { ", " * address "address" bibinfo.check * } if$ } if$ } if$ } FUNCTION {format.publisher.address} { publisher "publisher" bibinfo.warn format.org.or.pub } FUNCTION {format.organization.address} { organization "organization" bibinfo.check format.org.or.pub } FUNCTION {article} { output.bibitem format.authors "author" output.check author format.key output format.date "year" output.check date.block format.title "title" output.check new.sentence crossref missing$ { journal "journal" bibinfo.check "journal" output.check add.blank format.vol.num.pages output } { format.article.crossref output.nonnull format.pages output } if$ format.note output fin.entry } FUNCTION {book} { output.bibitem author empty$ { format.editors "author and editor" output.check editor format.key output } { format.authors output.nonnull crossref missing$ { "author and editor" editor either.or.check } 'skip$ if$ } if$ format.date "year" output.check date.block format.btitle "title" output.check crossref missing$ { format.bvolume output new.sentence format.number.series output format.edition output format.publisher.address output } { new.sentence format.book.crossref output.nonnull } if$ format.note output fin.entry } FUNCTION {booklet} { output.bibitem format.authors output author format.key output format.date "year" output.check date.block format.title "title" output.check new.sentence howpublished "howpublished" bibinfo.check output address "address" bibinfo.check output format.note output fin.entry } FUNCTION {inbook} { output.bibitem author empty$ { format.editors "author and editor" output.check editor format.key output } { format.authors output.nonnull crossref missing$ { "author and editor" editor either.or.check } 'skip$ if$ } if$ format.date "year" output.check date.block format.btitle "title" output.check crossref missing$ { format.bvolume output format.chapter.pages "chapter and pages" output.check new.sentence format.number.series output format.edition output format.publisher.address output } { format.chapter.pages "chapter and pages" output.check new.sentence format.book.crossref output.nonnull } if$ format.note output fin.entry } % -- rdmp Moved pages FUNCTION {incollection} { output.bibitem format.authors "author" output.check author format.key output format.date "year" output.check date.block format.title "title" output.check new.sentence crossref missing$ { format.chapter.pages output format.in.ed.booktitle "booktitle" output.check format.bvolume output format.number.series output format.edition output new.sentence format.publisher.address output } { format.incoll.inproc.crossref output.nonnull format.chapter.pages output } if$ format.note output fin.entry } % -- rdmp Moved pages FUNCTION {inproceedings} { output.bibitem format.authors "author" output.check author format.key output format.date "year" output.check date.block format.title "title" output.check new.sentence crossref missing$ { format.chapter.pages output format.in.ed.booktitle "booktitle" output.check format.bvolume output format.number.series output publisher empty$ { format.organization.address output } { organization "organization" bibinfo.check output format.publisher.address output } if$ } { format.incoll.inproc.crossref output.nonnull format.pages output } if$ format.note output fin.entry } FUNCTION {conference} { inproceedings } FUNCTION {manual} { output.bibitem format.authors output author format.key output format.date "year" output.check date.block format.btitle "title" output.check new.sentence organization "organization" bibinfo.check output address "address" bibinfo.check output format.edition output format.note output fin.entry } FUNCTION {mastersthesis} { output.bibitem format.authors "author" output.check author format.key output format.date "year" output.check date.block format.btitle "title" output.check new.sentence bbl.mthesis format.thesis.type output.nonnull school "school" bibinfo.warn output address "address" bibinfo.check output format.note output fin.entry } FUNCTION {misc} { output.bibitem format.authors output author format.key output format.date "year" output.check date.block format.title output new.sentence howpublished "howpublished" bibinfo.check output format.note output fin.entry } FUNCTION {phdthesis} { output.bibitem format.authors "author" output.check author format.key output format.date "year" output.check date.block format.btitle "title" output.check new.sentence bbl.phdthesis format.thesis.type output.nonnull school "school" bibinfo.warn output address "address" bibinfo.check output format.note output fin.entry } FUNCTION {proceedings} { output.bibitem format.editors output editor format.key output format.date "year" output.check date.block format.btitle "title" output.check format.bvolume output format.number.series output publisher empty$ { format.organization.address output } { organization "organization" bibinfo.check output format.publisher.address output } if$ format.note output fin.entry } FUNCTION {techreport} { output.bibitem format.authors "author" output.check author format.key output format.date "year" output.check date.block format.title "title" output.check new.sentence format.tr.number output.nonnull institution "institution" bibinfo.warn output address "address" bibinfo.check output format.note output fin.entry } FUNCTION {unpublished} { output.bibitem format.authors "author" output.check author format.key output format.date "year" output.check date.block format.title "title" output.check format.note "note" output.check fin.entry } FUNCTION {default.type} { misc } READ FUNCTION {sortify} { purify$ "l" change.case$ } INTEGERS { len } FUNCTION {chop.word} { 's := 'len := s #1 len substring$ = { s len #1 + global.max$ substring$ } 's if$ } FUNCTION {format.lab.names} { 's := "" 't := s #1 "{vv~}{ll}" format.name$ s num.names$ duplicate$ #2 > { pop$ " " * bbl.etal * } { #2 < 'skip$ { s #2 "{ff }{vv }{ll}{ jj}" format.name$ "others" = { " " * bbl.etal * } { bbl.and space.word * s #2 "{vv~}{ll}" format.name$ * } if$ } if$ } if$ } FUNCTION {author.key.label} { author empty$ { key empty$ { cite$ #1 #3 substring$ } 'key if$ } { author format.lab.names } if$ } FUNCTION {author.editor.key.label} { author empty$ { editor empty$ { key empty$ { cite$ #1 #3 substring$ } 'key if$ } { editor format.lab.names } if$ } { author format.lab.names } if$ } FUNCTION {editor.key.label} { editor empty$ { key empty$ { cite$ #1 #3 substring$ } 'key if$ } { editor format.lab.names } if$ } FUNCTION {calc.short.authors} { type$ "book" = type$ "inbook" = or 'author.editor.key.label { type$ "proceedings" = 'editor.key.label 'author.key.label if$ } if$ 'short.list := } FUNCTION {calc.label} { calc.short.authors short.list "(" * year duplicate$ empty$ short.list key field.or.null = or { pop$ "" } 'skip$ if$ * 'label := } FUNCTION {sort.format.names} { 's := #1 'nameptr := "" s num.names$ 'numnames := numnames 'namesleft := { namesleft #0 > } { s nameptr "{vv{ } }{ll{ }}{ f{ }}{ jj{ }}" format.name$ 't := nameptr #1 > { " " * namesleft #1 = t "others" = and { "zzzzz" * } { t sortify * } if$ } { t sortify * } if$ nameptr #1 + 'nameptr := namesleft #1 - 'namesleft := } while$ } FUNCTION {sort.format.title} { 't := "A " #2 "An " #3 "The " #4 t chop.word chop.word chop.word sortify #1 global.max$ substring$ } FUNCTION {author.sort} { author empty$ { key empty$ { "to sort, need author or key in " cite$ * warning$ "" } { key sortify } if$ } { author sort.format.names } if$ } FUNCTION {author.editor.sort} { author empty$ { editor empty$ { key empty$ { "to sort, need author, editor, or key in " cite$ * warning$ "" } { key sortify } if$ } { editor sort.format.names } if$ } { author sort.format.names } if$ } FUNCTION {editor.sort} { editor empty$ { key empty$ { "to sort, need editor or key in " cite$ * warning$ "" } { key sortify } if$ } { editor sort.format.names } if$ } FUNCTION {presort} { calc.label label sortify " " * type$ "book" = type$ "inbook" = or 'author.editor.sort { type$ "proceedings" = 'editor.sort 'author.sort if$ } if$ #1 entry.max$ substring$ 'sort.label := sort.label * " " * title field.or.null sort.format.title * #1 entry.max$ substring$ 'sort.key$ := } ITERATE {presort} SORT STRINGS { last.label next.extra } INTEGERS { last.extra.num number.label } FUNCTION {initialize.extra.label.stuff} { #0 int.to.chr$ 'last.label := "" 'next.extra := #0 'last.extra.num := #0 'number.label := } FUNCTION {forward.pass} { last.label label = { last.extra.num #1 + 'last.extra.num := last.extra.num int.to.chr$ 'extra.label := } { "a" chr.to.int$ 'last.extra.num := "" 'extra.label := label 'last.label := } if$ number.label #1 + 'number.label := } FUNCTION {reverse.pass} { next.extra "b" = { "a" 'extra.label := } 'skip$ if$ extra.label 'next.extra := extra.label duplicate$ empty$ 'skip$ { "{\natexlab{" swap$ * "}}" * } if$ 'extra.label := label extra.label * 'label := } EXECUTE {initialize.extra.label.stuff} ITERATE {forward.pass} REVERSE {reverse.pass} FUNCTION {bib.sort.order} { sort.label " " * year field.or.null sortify * " " * title field.or.null sort.format.title * #1 entry.max$ substring$ 'sort.key$ := } ITERATE {bib.sort.order} SORT FUNCTION {begin.bib} { preamble$ empty$ 'skip$ { preamble$ write$ newline$ } if$ "\begin{thebibliography}{" number.label int.to.str$ * "}" * write$ newline$ "\providecommand{\natexlab}[1]{#1}" write$ newline$ "\providecommand{\selectlanguage}[1]{\relax}" write$ newline$ "\providecommand{\bibAnnoteFile}[1]{%" write$ newline$ " \IfFileExists{#1}{\begin{quotation}\noindent\textsc{Key:} #1\\" write$ newline$ " \textsc{Annotation:}\ \input{#1}\end{quotation}}{}}" write$ newline$ "\providecommand{\bibAnnote}[2]{%" write$ newline$ " \begin{quotation}\noindent\textsc{Key:} #1\\" write$ newline$ " \textsc{Annotation:}\ #2\end{quotation}}" write$ newline$ } EXECUTE {begin.bib} EXECUTE {init.state.consts} ITERATE {call.type$} FUNCTION {end.bib} { newline$ "\end{thebibliography}" write$ newline$ } EXECUTE {end.bib} %% End of customized bst file %% %% End of file `sysbio.bst'. 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Syrmaticus_reevesii .RF..K.......LVAED.H...T.....S.........I..ERNGEAL.AS-..E...ATME.R..N..G........N...SR..E Chrysolophus_amherstiae .RFL.....S.D-LVAED.H...T...............I..EHDG?AL.AS-..E...ATME.R..N..G........N...N.... Polyplectron_bicalcaratum .RF....K...D.LA.EEVR...T.....D.S..RD...I..EHDR?????S-..E.Q.TTTEHRVNNE.G......K.N..VS.... Argusianus_argus_argus .RF........D.LVSEDRH...T.....H..T......I..EHD?EAL.A--..EH..AT.EDR..N.I...D..L..N...S.... Pavo_cristatus .RF..A.....D.LVSED.H...T.....H.........I..EHDREAL.AS-..E...AT.EHR..N..G........N...S.... Afropavo_congensis .RF........D.SAS.D.R...T.....H.........I..EHDGEAL.AS-..E...ATMEQR..N..G........N...S.... Numida_meleagris .RF..A.....D.LVTED.R...T......D........I.?????EAL.A--..E...ATME.R..N..D........N...S.... Acryllium_vulturinum .RF..A.....D.LVIED.R...T......D........I..EHD???L.A--..E...ATME.R..N..D........N...S.... Meleagris_gallopavo .RF........D.LVTED.R...T...H.-.........I..EHDREAL.AS-..E...AT.E.R..N..G........N...S.... Grus_carunculatus .T...........LVT.......T..................DHDKEAT..--......AT.E..F...........D.N...S.... Anthropoides_virgo .T...........LVT.......T..................DHDKEVT..--......AT.E..F...........D.N...S.... Grus_vipio IT...........LVT.......T..................DHDKEAT..--......AT.E..F...........D.N...S.... Fulica_atra .T...........LVT.....V.TN......S..........DYDKEVT..--..G.Q.AS.E.VF.N.....D..AD.N...S.... Vanellus_spinosus .T...........LVT.......T..........L.......DYDKEVI..--......AS................D.N...S..E. Larus_rudibundus .T...........LAT.A...V....................DYDKEDI..--......AS................D.N...S..E. Turnix_sylvatica .RF........DT.AD.D.P.........-.M.......I..EHD??T???S-..E...GMMERL..N..ND.......N...N...E Gallirallus_australis .T.........V.LVT.NI..V.TN...T..S.I...S....DYD???T..??..G.QSA.Q..VF.N........AD.N...S.... Geococcyx_californianus .A...A......ALVTTARLH..T....G.....L.H..I..DYNKEVI.S--.N.....S.L....N..G.....AD.N...S.... Dacelo_novaeguineae .......D.....LVTE......T.R................EHDKEAI..-Q..EH..AT...RI.......D..MD.N...S.... Carpococcyx_renauldi .R...S......GLATT.R....T....G.....L....I..DYD???I..--.......T.ED...NI.H..Y..AH.N..FS.... Podargus_strigoides .T.......S...LVDEV.....T..........L.-..I..DRDK??I..--....Q..MG...............D.N...N.... ; end; [ begin mrbayes; [The following line will let the mcmc chain integrate over the ten fixed amino-acid rate matrices. You can also fix the rates to those in one of the rate matrices.] prset aamodelpr=mixed; end; ] NBISweden-MrBayes-ecfd55c/examples/codon.nex000066400000000000000000000031521526212227600210520ustar00rootroot00000000000000#NEXUS [ Example command file, which reads in a data file and sets up different codon models. Use one of the blocks to set up the model you are interested in. ] begin mrbayes; [The following line is useful for automatic execution with no warnings issued before files are overwritten and automatic termination of chains after the prespecified number of generations.] set autoclose=yes nowarn=yes; [Read in a data file with protein-coding sequences] execute replicase.nex; [Use ONE of the blocks below depending one what model you like] [One omega value for the gene The estimated single omega value, valid for the entire gene or gene fragment, is printed to the .p file(s)] lset nucmodel=codon; [Omega variation across sites with three classes, omega_1 < 1, omega_2 = 1, omega_3 > 1. The estimated omega_1 and omega_3 are printed to the .p file(s) as omega(-) and omega(+). If you set report possel=yes, you will also get the probability of each site being in the positively selected omega class. If you set report siteomega=yes, you will also get the omega value for each site.] lset nucmodel=codon omegavar=ny98; report possel=yes; report siteomega=yes; [Omega variation across sites with three classes, omega_1 < omega_2 < omega_3. Otherwise very similar to the Nielsen and Yang model.] lset nucmodel=codon omegavar=m3; report possel=yes; [These models are slow, so print to screen more frequently] mcmcp printfreq = 10; [Only the mcmc command is missing now to run the analysis] end; NBISweden-MrBayes-ecfd55c/examples/cynmix.nex000066400000000000000000003424251526212227600212700ustar00rootroot00000000000000#NEXUS [ Data from: Nylander JAA, Ronquist F, Huelsenbeck JP, Nieves-Aldrey JL. 2004. Bayesian phylogenetic analysis of combined data. Systematic Biology 53:47-67. ] Begin data; Dimensions ntax=32 nchar=3246; Format datatype=mixed(Standard:1-166,DNA:167-3246) interleave=yes gap=- missing=?; Matrix Ibalia 000000000000002-000000000000??00000000000000100{01}0100001-00100000-0000000000010000-000000000011010000-0000000000-0---000000000200---01010000100000000-000000??00000000- Synergus 1-1-100000000202110201011010110100000000010121001120101010101000000001100020000000111000102010000211010010001001110110000011100000100111000000011112100001001101100005 Periclistus 1-1-100000000202110201011110110100000000010100101000101010011000000001100021001000111000112010000211010010000001110111000011100000100111010000111112100001001111100002 Ceroptres 1-1-1000100002021002010111101001000000000111000???10101010010000000000100021001000111000100000000200010010101001010111000011100000100111100000101112101001100111????05 Synophromorpha 1-1-00001000021-100101001111110100000000010100101000100010010000000000100001200000111000110000000200010010001001000111000011100001120011110000111112100001001101100002 Xestophanes 1-1-00001000011-10-110001011010100000000010110101000100010010101000000100001201000121000110000000100111010001000000111000011100000110011100000010012100000100120001002 Diastrophus 011-10101000021-10-210001011010100000100000011101000100010012000000000001101201100101000110000000100100000000000000011000011100001120011100100000012000000100120001002 Gonaspis 1-1-10001000011-10-210001011010100000100000021101000100010012000000000000101201000100000110000000200110000000000000111000011100101120011100101000010-00000100120001002 Liposthenes_gle 01000030000101022020000010110111000001001000101100001000100120000000010000112011001010001120100001001110000000000000100000100010011210111-0201100012000000000120000003 Liposthenes_ker 0000102000000101????????????????????????????110???0?10001001000000??00000011101100101000112010000????1000000000??????1?1?0100000011210111-01011000???0?000?001?1????03 Antistrophus 1-010120000?01001010000110110001000001020000101???001011111100210000000002210011101210101110200001001100021000000000110000100000011210011-1201000112000000000120????00 Rhodus 1-01000000000000????????????0??????????0????1010000?10101111000000??00000121001110121010112010000????1000200001??????1?000100010011211011-12010000?200?001?001?0????03 Hedickiana 1-01010000000000?????0??10101??????1???0????0010000110101111000000??00000121001100121010111010000???11000100000??????1?000100000011200111012010001??00?001?00??0????03 Neaylax 1-01010000000000100000011010000100010000000000{01}???0110001111000000000000002100110010201011{12}010000100111001000000000011000010000001120011100101000110-00001000110????03 Isocolus 0100001000000100100100011110000100000000000010110001101010110000000000000020000010002000112010000100010001001000000010000010001001020011011100000111000001001100000100 Aulacidea 00000000000001001002000110100001000000000000101???01101010110000000000000021000000000000112010000100010001001000000011000010000001010011110100100111000001001100????00 Panteliella 0000003000100200??1?000????0??010?00????????200???1?101010111020?0??000001210010101200001{12}2110010???11?01100?????00????0?010?000021210111-020000?1?????000100120????03 Barbotinia 1-010100000?0100002000000011000100010200000020010021101010111001000000000021001000100000111000000100110000101000000011000010000001110111110100001012010000100011001101 Aylax 0100002000100101101000010010100110010200000020010021101010110000000003000021001010100000110010000100110000000000001011010010010001120111110200100010-10000000110001101 Iraella 0100003000100100101100010010100110010200000020{01}???21101010110001100000000021000010110010121020000100111000000000001011000000020001120111110100100010-00000000020????01 Timaspis 0000002000100201001100010010100101000000000021100120102010201001100003000021011000111000121001100000111100000100001011000010010000010101100210000010-01000000120000100 Phanacis_1 00011000001001011011000110101001010000000010201???0110201021000010000300012101100012100012102100000011011000000000101100001001000011011111021000001000001000000200??00 Phanacis_2 00000020001002000002010110101001110000000000111???01102010210001100003000021010000122000121021100000110010000010001011000010010000010111100110000010-00000100020????00 Eschatocerus 000010310-1012002002000100101-110?101-0---20112101311--1112100011011120-0021-1001--321-012102110-00010--0011-1-1001001-0-000010001131--01-1200001-10-010-00?0120010106 Diplolepis 000000300010120220020000001011111100120000202110013011-11021100110111201002101001001200011000100000010010001-110001011001000000111010111100210000010-01000000010????02 Pediaspis 000000201011021-20100000001111111100121000{012}021010111100?101101011110010110011011011001-1110001001100101100000010000011001100010001120011100210000010-01010100020001117 Plagiotrochus 000000000110120120010000001201101100121000102000013011-01021001110100001101120001111100012{01}0010011001011001001110110?1011000010002120011100210000000-010-0110120011015 Andricus 0{01}00002001{01}01202200100001012011{01}1000111000{01}0{12}100003111-1101110101010000110{12}1{01}000011020001100000{01}010010{01}0001010110100110110010100{01}2120111110210{01}01110-01111110010011015 Neuroterus 00001031021112022020001000121110111012121020310???3011-0102110101011010110012100010320001200011000001011001001-1011001111001020002120011000200000000-01110110000????15 Biorhiza 010000310211120220200010001201101110121110202100103111-110111011101101011001200001101000110000010100101100100011010001111001020012120011100210000000-01111100020010015 Parnips 1-00000000000100???????????????1????????????11110001111000101000?-??00000021001000021000112000000?0??00100000000?????0?00010?001000001010002110001???-?00??001?1????01 Paramblynotus 0100000002000000100000000000110100000100000010{01}10000001000-0000000000000000000000--00000000001000200100-1-001000000-010000100000000111100-01000000020000000001000???0- Ibalia GCTGCTATATTTTATTTTTGGGATTTGATCCGGAATAATTGGATCTAGACTAAGAATAATTATTCGAATAGAATTAGGAGCCCCTTCCCAATTAATTGGGAATGATCAAATTTATAATTCTATTGTAACAATTCATGCTTTTATTATAATTTTTTTTATAGTAATA Synergus TATACTATACTTTATATTTGGTATTTGAACTGGAATAATTGGATCAGCATTAAGATTAATTATTCGTATAGAATTAAGATCAACATTACAATTAATTAATAATGATCAAATTTATAATTCAATTGTAACTGCTCATGCATTTATCATAATTTTTTTTATAGTAATA Periclistus ATTAATATATTTTATTTTAGGGATTTGATCAGGTATAATTGGGTCAAGATTAAGAATAATTATTCGATTAGAACTTGGTAATCCTTTACAATTAATTGGAAATGATCAAATTTATAATTCAATTGTAACTGTTCATGCATTTATTATAATTTTTTTTATAGTTATA Ceroptres TATATTATATTTTATATTTGGGGTATATTCTGGAATAATTGGATCATCCTTAAGAATAATTATTCGAATAGAATTAGGAACTCCTACTCAGTTAATTAATAATGACCAAATTTATAATTCAATTGTAACTGCTCATGCTTTTATTATAATTTTTTTTATAGTTATA Synophromorpha AATACTATATTTTATTTTTGGAATTTGATCTGGTATAATTGGATCTAGATTAAGAATAATTATTCGAATAGAACTTGGTAATCCATCTCAATTAATTGGAAATGATCAAATTTATAATTCAATTGTTACAATTCATGCTTTTATTATAATTTTTTTTATAGTTATA Xestophanes AATATTATATTTTATTTTTGGAATTTGATCTGGTATAATTGGATCTAGATTAAGAATAATTATTCGAATAGAACTTGGAATTCCTACACAATTAATTGGTAATGACCAAATTTATAATTCAATTGTAACAATTCATGCATTTATTATAATTTTTTTTATAGTAATA Diastrophus AATATTATATATAGTTTTTGGTATTTGATCTGGAATAATTGGATCAAGATTAAGAATAATTATTCGTACAGAACTTGGAACTCCTACACAATTAATTGGTAATGATCAAATTTATAACTCAATTGTTACAATTCATGCATTTATTATAATTTTTTTTATAGTAATA Gonaspis TATATTATATATAATTTTTGGTGTTTGATCTGGCATAATTGGGTCTAGATTAAGAATAATTATTCGGACAGAACTTGGTACACCATTACAATTAATTGGTAATGATCAAATTTATAATTCAATTGTTACAACTCATGCATTTATTATAATTTTTTTTATAGTTATA Liposthenes_gle AATTTTATATTTTATTTTTGGTATATGATCAGGTATATTAGGTTCAGCTTTAAGAATGATTATTCGTATAGAGTTAGGGACTCCCTCTCAATTAATTGGAAATGATCAAATTTATAATACAATTGTTACAGCTCATGCTTTTGTAATAATTTTTTTTATGGTTATA Liposthenes_ker AATATTATACTTTATATTTGGAATCTGATCTGGAATAATTGGATCAGGATTAAGAATAATTATCCGTATAGAACTAGGATCCCCCGGCCAACTAATTGGAAATGATCAAATCTATAACTCTATCGTTACAGCTCATGCATTTATTATAATTTTTTTTATAGTAATA Antistrophus TATATTATACTTTTTATTTGGAATTTGATCTGGATTAATTGGATCAGCTTTAAGTATACTTATTCGAATAGAACTAGGAACCCCTTCTCAATTAATTGGTAATGATCAAATTTATAATTCAGTAGTTACTTCTCATGCTTTTGTAATAATTTTTTTTATAGTAATA Rhodus ATTACTTTATTTTTTATTCGGTATATGATCAGGAATAGTTGGAGCAAGATTAAGAGTTATTATCCGTATAGAACTAGGAACCCCCTCTCAACTACTTGAAAATGACCAAGTTTATAATTCAATTGTAACTGCTCATGCATTTATCATAATCTCCTTTATAGTCATA Hedickiana TATATTATATTTTTTATTTGGTGCATGATCTGGGACTATTGGATCTGCATTAAGTATACTTATTCGTAGAGAATTAGGGACTCCAAATCAATTTATTGGAAATGATCAAATTTATAATTCAATTGTCACATCTCATGCATTTGTAATAATTTTTTTTATAGTTATA Neaylax TATATTATACTTTTTATTTGGGATTTGGTCAGGAATTATTGGATCTGCATTAAGAATAATTATTCGAATAGAATTAGGGTCACCCTCCCCATTAATTGGTAATGACCAAATTTATAATTCAATTGTTACTGCTCATGCATTTGTAATAATTTTTTTTATAGTCATG Isocolus AATATTATATTTTTTATTTGGTATTTGATCAGGGATAATTGGGTCTGCTTTGAGAGTAATTATTCGAATAGAGTTAGGGACTCCTGGGCAATTAATTGGAAATGATCAAATTTATAATTCAATTGTTACAGCTCATGCTTTTGTAATAATTTTTTTTATAGTAATA Aulacidea AATATTATATTTTTTATTTGGAATTTGATCTGGAATAATTGGATCAGCATTAAGAATAATTATTCGTTTAGAATTAGGGACCTCTGGACAATTAATTGGAGATGATCAAGTTTATAACTCTATTGTTACAGCTCATGCATTCGTAATAATTTTTTTTATAGTTATA Panteliella ATTATTATATTTTATATTTGGTATTTGAGCAGGAATAATTGGGCCAGCTTTAAGAATAATTATTCGTATAGAGTTAGGGTTGCCTTCCCAATTAATTGGAAATGATCAAATTTATAATTCTATTGTTACGGCTCACGCTTTTATTATAATTTTTTTTATAGTTATA Barbotinia AATATTATATTTTATATTTGGAATCTGATCAGGAATAATTGGGTCAGCTTTAAGAATATTAATTCGAATGGAATTAGGTACTCCAGGTCAATTAATTGGTAATGATCAAATTTACAATTCAATTGTTACTATTCATGCATTTATTATAATTTTTTTTATAGTTATA Aylax TATATTATATTTTATATTTGGAATTTGATCTGGAATAATTGGATCTGCTTTAAGTATATTAATTCGAATAGAATTAGGAACACCTAATCAATTAATTGGAAATGATCAAGTTTATAATTCAATTGTTACTACTCATGCATTTATTATAATTTTTTTTATAGTTATA Iraella AATAATATATTTTATTTTTGGTATTTGATCAGGAATAATTGGATCAGCCTTAAGAATATTAATTCGAATAGAATTAGGTACTCCAGGTCAATTAATTGGAAATGATCAAATTTATAATTCAATTGTTACAGCTCATGCTTTTATTATAATTTTTTTTATAGTTATA Timaspis AATATTATATTTTATTTTTGGTATTTGATCTGGAATAATTGGATCAGCTTTAAGAATAATTATTCGTATAGAATTAGGGACTCCTTCACAATTAATTGGTAATGATCAAATTTATAATTCAGTAGTTACAGCTCATGCTTTTATTATAATTTTTTTTATAGTTATA Phanacis_1 TATATTATATTTTGTATTTGGGATTTGATCAGGAATAATTGGGTCTGCTTTAAGTATAATTATTCGGATAGAATTAGGTACACCTTCCCAATTAATTGGGAATGATCAGATTTATAATGCTATTGTTACAGCTCATGCATTTATTATAATTTTTTTTATAGTTATA Phanacis_2 GGTATTATATTTTATTTTTGGGATTTGATCTGGTATAATTGGAACAGGATTAAGATTAATTATTCGTATAGAATTAGGGTCTCCTTCACAATTAATTGGGAATGATCAGATTTATAACTCAGTTGTTACTGCTCATGCATTTATTATAATTTTTTTTATGGTTATA Eschatocerus AATCATATATTTTATTTTAGGAATTTGATCAGGAATTTTAGGGGCATCATTAAGTATACTTATTCGAATAGAATTAGGTACCCCTAATCAATTTATTGGAAATGATCAAATTTATAATTCTATTGTAACAGCTCATGCATTTATCATAATTTTCTTTATAGTTATA Diplolepis TGTATTATATTTTATATTTGGTATTTGGTGTGGGATGGTTGGGGCAGCTTTAAGAATAATTATTCGTATTGAGTTAGGAATAACAGGGCAGTTAATTGGTAATGATCAAATTTATAATTCTATTGTTACTTCTCATGCTTTTATTATAATTTTTTTTATAGTTATG Pediaspis AATATTATATTTTATTTTTGGAACATGATCTGGAATAATAGGAAGTTCATTAAGAATAATTATTCGAATAGAATTAGGAATACCTGGACAATTAATTAGAAATGATCAAATTTATAATATAATTGTAACTTCTCATGCTTTTATTATAATTTTTTTTATAGTAATA Plagiotrochus TATATTATATTTTATATCTGGAATCTGATCAGGATTAATTGGATCAAGATTAAGAATAATTATTCGAATAGAATTAGGAACCCCTTCACAATTAATTGGAAATGATCAACTCTATAACTCAATCGTAACTGCACATGCATTTATTATAATTTTTTTTATAGTTATA Andricus AATATTATATTTTATATTTGGTATTTGATCAGGAATAATTGGATCAGGATTGAGAATAATTATTCGAATAGAGTTAGGGATACCTTTACAATTAATTGGAAATGACCAAATTTATAATTCTATTGTTACTGCTCATGCTTTTGTAATAATTTTTTTTATAGTTATA Neuroterus AATATTATATTTTATATTTGGAATTTGAGCAGGAATAATTGGATCAGGATTAAGAATAATTATTCGAATAGAATTAGGGATACCTTTACAATTAATTGGAAATGATCAAATTTATAACTCTATTGTTACAGCCCATGCTTTTGTAATAATTTTTTTTATAGTAATA Biorhiza TATAATATATTTTATATTTGGAATTTGATCAGGAATAATTGGATCTAGTTTAAGAATAATTATTCGAATAGAATTAGGGACTCCATTACAATTAATTATAAATGATCAAATTTATAATTCAATTGTTACAGCTCATGCTTTTGTAATAATTTTTTTTATAGTTATA Parnips AATATTATATTTTATTTTTGGAATTTGATCAGGAATTATTGGGTCAGCATTAAGAATAATTATTCGAATAGAATTAGGGACTCCTGAACAATTAATTGGGAATGACCAAATTTATAATTCTATTGTGACAGCTCATGCTTTTATTATAATTTTTTTTATAGTTATA Paramblynotus TATCTTATATTTTATTTTTGGAATATGAGCAGGAATAATTGGAGCATCAATAAGAATAATTATTCGTATAGAATTAGGTACACCAAATCAATTAATTAATAATGATCAAATTTATAATTCTATTGTAACTGCTCATGCATTTATTATAATTTTTTTTATAGTTATA Ibalia CCTATTATAGTAGGGGGATTTGGAAATTATTTAATTCCTTTAATATTAGCAATACCAGATATGGCTTTCCCTCGTTTAAATAATATAAGATTTTGGTTATTAATTCCTGCCCTAATATTTTTAATATCTGGAATATTCATTGATCAGGGAGCAGGGACGGGATGAA Synergus CCAATTACAATTGGAGGATTTAGAAATTACCTAATTCCATTAATATTAAGAACTCCTGATATAGCTTTCCCACGACTTAATAATATAAGATTTTGATTATTAATCCCATCTTTAATTTTATTAACATCAAGTATATTTATTGACCAAGGAGCTGGAAGAGGTTGAA Periclistus CCTATTATAGTTGGAGGATTTGGTAATTATTTAATTCCTATTATATTAATTACTCCTGATATGGCTTTTCCTCGATTAAATAATATAAGTTATTGACTTTTAATTCCCTCTTTATTATTAATATTATCTACAATATTTATTGATCAGGGTGCAGGAACAGGATGAA Ceroptres CCTATTATAGTAGGTGGATTTGGAAATTATATGATTCCTTTAATATTAATTTCTCCTGATATAGCATATCCTCGTTTAAATAATATAAGGTTTTGATTATTAATTCCTTCTTTAATTTTATTGATCATGGGAATATTTATTGATCAAGGGGCGGGGACAGGATGAA Synophromorpha CCTATTATAGTTGGTGGATTTGGAAATTATTTAATTCCTTTAATATTATCAGTTCCTGATATAGCTTTTCCACGAATAAATAATATAAGGTATTGACTTTTAATTCCTTCATTATTATTAATAGTATCAAGAATATTTATTGATCAAGGAGCAGGAACAGGATGAA Xestophanes CCTATTATAGTAGGAGGATTTGGAAATTATTTAATTCCTTTAATATTATCTGTACCTGATATAGCTTTTCCTCGAATAAATAATATAAGATATTGACTTTTAGTCCCTTCTTTATTATTAATAATATCTAGAATATTTATTGATCAAGGAGCAGGTACAGGATGAA Diastrophus CCAATTATAGTAGGAGGATTTGGAAATTATTTAATTCCTTTAATATTATCAGTACCTGATATAGCTTTTCCTCGAATAAATAATATAAGATTTTGACTTTTAGTCCCTTCTTTATTATTAATAATTTCTAGTATATGTGTAGATCAAGGGTCTGGTACAGGTTGAA Gonaspis CCTATTATAGTTGGGGGATTTGGAAATTATTTAATTCCTCTTATATTAAGGGTTCCTGATATAGCTTTCCCTCGTATAAATAATATAAGATTTTGGCTTTTAATTCCTTCTTTAATATTAATATTATCTAGAATATATGTTGATCAAGGATCAGGGACAGGATGAA Liposthenes_gle CCAATTATAGTAGGAGGATTTGGAAATTATTTAATTCCATTAATATTATCAGTACCTGATATAGCTTTCCCTCGTTTAAATAATTTAAGATATTGAATATTAGTCCCTGCATTATTATTATTATCATCAAGAATGTTTATTGATCAAGGGGCGGGTACAGGTTGAA Liposthenes_ker CCTATTATAGTTGGAGGTTTTGGAAACTATTTAATTCCATTAATAATAACGGCCCCTGATATAGCTTTCCCTCGATTAAATAATATAAGATACTGAATATTAATCCCAGCCTTATTATTATTAATTTCAAGAATATTTATTGATGAAGGTGCTGGAACTGGATGAA Antistrophus CCAATTATAGTAGGGGGATTTGGTAATTATTTAATTCCTTTAATATTATCAGCTCCTGATATAGCTTTCCCACGTTTAAATAATATAAGATTTTGATTATTAATTCCTTCTATATTATTAATATTATCAAGAATTTTTATTGATAGAGGAGCAGGGACTGGATGAA Rhodus CCAATTATAGTAGGAGGATTTGGTAATTATTTAACTCCTCTAATATTATCCTCACCAGACATAGCTTTCCCTCGATTAAATAATATAAGATTCTGATTATTAATCCCAGCATTATTATTATTATTATCTAGAATACTTATTGATCAAGGGGCTGGAACAGGATGAA Hedickiana CCTATTATAGTTGGTGGATTTGGCAATTATTTAATTCCTTTAATAATTTCAGCTCCTGATATAGCTTTCCCTCGATTAAATAATTTAAGATATTGGTTACTTGCCCCAGCTTTATTATTATTATTATCAAATTTATTTATTGATCAGGGGGCTGGGACTGGATGAA Neaylax CCTATTATAGTAGGGGGGTTTGGAAATTATTTAATTCCTTTAATATTAACAGCCCCAGATATAGCTTTCCCACGATTAAATAATATAAGATATTGATTACTACCCCCAGCATTATTTTTATTACTTTCTAGTATATTTATTGATCAAGGGGCAGGGACAGGATGAA Isocolus CCAATTATAGTTGGAGGATTTGGGAATTATTTAGTTCCTTTAATGTTAACTGCCCCAGATATAGCATTCCCTCGATTAAATAATATAAGATATTGGCTTTTAATCCCTTCTTTATTATTAATAATAACAAGAATATTTATTGACCAAGGAGGGGGTACTGGATGAA Aulacidea CCAATTATAGTAGGTGGATTTGGTAATTATTTAATTCCTTTAATATTGACAGCACCTGATATGGCATTTCCACGATTAAATAATATAAGATACTGATTATTACTTCCAGCTTTATTATTAATAATAACTAGGATATTTATTGATCAAGGGGCCGGTACTGGGTGGA Panteliella CCAATTATAGTTGGTGGGTTTGTAAATTATTTAGTTCCATTAATGTTATCAGCCCCTGATATAGCTTTCCCTCGTTTAAATAACATAAGATATTGATTATTAATCCCTTCATTATTACTATTGTTATCTAGAATTTTTATTGATCAGAGGGCAGGGACGGGGTGAA Barbotinia CCTCTTATATTAGGAGGGTTTGGTAATTATTTAATTCCATTAATATTATCAGCTCCTGATATAGCTTTCCCTCGATTAAATAATATAAGATATTGATTATTAATCCCACCATTATTATTATTATTATCAAGAATATTAGTAGATCAAGGGGCAGGGACAGGATGAA Aylax CCTATTATAGTTAGAGGATTTGGTAATTATTTAGTACCTTTAATATTAACAGCTCCTGATATAGCTTTCCCTCGATTAAATAATATAAGATATTGATTATTAATCCCCTCTATATTATTATTATTATCAAGATTATTTATTGATGAAGGGGCAGGGACAGGATGAA Iraella CCAATTATAGTTGGAGGATTTGGAAATTATTTAATTCCTTTAATACTAACAGCACCTGACATAGCTTTTCCACGATTAAATAATATAAGATATTGATTATTAATTCCTTCTTTATTATTATTATTATTAAGAATATTTGTTGATCAGGGAGCTGGAACAGGTTGAA Timaspis CCAATTATAGTAGGAGGATTTGGTAATTATTTAATTCCTTCAATATTAAGAATTCCCGATATAGCTTTCCCTCGAATAAATAATATAAGTTATTGGTTATTAATCCCTTCTTTATTTTTATTATTATCAGGGATATTTATTGATCAAGGGGCTGGAACAGGATGAA Phanacis_1 CCAATTATAGTTGGAGGATTTGGGAATTATTTAGTCCCTTTAATATTAAGAGTTCCTGATATAGCTTTCCCTCGAATAAATAATATAAGATATTGATTATTAATCCCAGCTTTATTATTATTATTATCTAGAATAGTAGTTGATCAAGGGGCTGGGACAGGATGAA Phanacis_2 CCTATTATAGTAGGGGGGTTTGGGAATTATTTAATTCCATTAATATTAAGAATTCCTGATATAGCATTTCCTCGAATAAATAATATAAGATATTGATTATTAATCCCTTCATTAATGTTATTATTATCTAGGATAATTGTTGATCAAGGGGCTGGGACAGGATGAA Eschatocerus CCTATTATAATTGGAGGATTTGGTAATTATTTAATTCCTTTAATACTTGGGGTTCCAGATATATGCTTTCCACGATTAAATAATTTAAGATTTTGGTTACTTCTACCTTCTTTAGGTTTATTATTAAGAAGAATGCTTTTAGATTCTGGTGCGGGTACAGGATGAA Diplolepis CCTTTAATATTAGGGGGATTTGGTAATTATTTAATTCCATTAATATTATCTTCTCCAGATATATCTTTTCCTCGATTAAATAATATAAGATTTTGATTATTAGTTCCTTCTTTTATTTTGTTAATTATAAGTATATTTATTGATCAAGGAGCAGGTACAGGGTGAA Pediaspis CCTATTATATTAGGAGGATTTGGTAATTATTTAATTCCTTTAATATTAATAAGACCAGATATATCTTTTCCTCGATTAAATAATTTAAGATTTTGATTATTAATTCCTTCTTTAATTTTATTAACATCAAGAATATTTATTGATCAAGGAGCTGGAACTGGTTGAA Plagiotrochus CCAATTATAGTTGGAGGATTTGGTAATTATTTAATTCCTCTAATATTAATTGCTCCGGATATAGCATTCCCTCGTTTAAATAATATAAGATATTGATTATTAACTCCTGCTCTATTACTATTAATATCAAGAATATTTATTGATCAAGGAGCAGGAACAGGATGAA Andricus CCAATTATAGTTGGGGGGTTTGGAAATTATTTAGTTCCATTAATATTAACTGCTCCTGATATAGCTTTTCCTCGATTAAATAATATAAGATATTGACTTTTAATTCCTTCTTTATTTTTATTATTAGCAGGGATATTAGTTGATCAAGGGGCTGGAACAGGATGAA Neuroterus CCTATTATAGTAGGAGGGTTTGGTAATTATTTAGTTCCTTTAATATTAGCTGCTCCAGATATAGCTTTTCCTCGATTAAATAACATAAGATATTGATTATTAATCCCTTCATTATTATTATTATTAGCCGGAATATTAGTGGACCAAGGGGCAGGAACAGGATGAA Biorhiza CCTATTATAGTTGGAGGATTTGGAAACTATTTAGTGCCTTTAATGTTAGTAATTCCTGATATATCTTTCCCTCGATTAAATAATATAAGATATTGACTTTTAATTCCTTCATTATTTTTATTATTATCTGGTATATTAGTTGATCAGGGAGCTGGAACAGGATGAA Parnips CCTATTATAGTAGGAGGGTTTGGAAATTATTTAGTTCCATTAATATTATCTGCCCCTGATATGGCTTTTCCTCGATTAAATAATATAAGATATTGATTATTAATCCCTTCTTTAATTTTATTAATAATAGGTATATTTGTTGATCAAGGAGCTGGGACTGGGTGAA Paramblynotus CCAATTATAGTTGGAGGGTTTGGAAATTATTTAATTCCAATTATATTAATTTTACCTGATATATCATATCCACGTTTAAATAATATAAGATATTGATTATTACCCCCTTCTTTATTTTTATTAATTTCAAGAATAATAATTGATCAAGGAGCAGGAACTGGATGAA Ibalia CAGTTTATCCTCCTTTATCTTCTTATTTAGGCCACCCTAGGATTTCAGTTGATTTAACTATTTTTTCTTTACATTTAAGGGGAGTTTCTTCTATTTTAGGTTCTATTAATTTTATTACAACTATTTTAAATATACGA------ATTATAATAATAGATAAAATTAC Synergus CAGTTTATCCACCTTTATCTTCAAATTTAGGACATCCAGGAATTTCAGTAGATTTAGCTATTTATTCATTACATATATCAGGAATTTCATCAATTTTAGGTTCAATTAATTTTATTACCACAATTTTAAATATACGTCCTAATTTTATAATAATAGATAAAATTTC Periclistus CTGTTTATCCTCCTTTATCCTCAAATTTAGGTCATAATACAATTTGTGTAGATTTAATTATTTTTTCATTACATTTGACTGGTATTTCTTCAATTTTAGGAGGGATTAATTTTATCACTACAATTTTAAATATACGACCAAATTTAATATCTATAGATAAAATTAC Ceroptres CAGTGTATCCCCCTTTATCTTCTAGGTTAGGGCATTCAGGGTTATCTGTAGATTTAACTATTTATTCATTACATTTAAGAGGAATTTCTTCTATTTTAGGATCCATTAATTTTATTTCTTCTATTATAAATATACGCCCTAAATTAATATTAATAGATAAAATTTC Synophromorpha CTGTGTATCCTCCTTTATCTTCAAATTTAGGTCATAATGGAATATCAGTTGATTTAGTAATTTTTTCTTTACATTTAAGAGGTATTTCTTCAATTTTAGGTTCAATTAATATTATTACTACAATTTTAAATATACGACCTTATTTAATATGTATAGATAAAATTAC Xestophanes CTATTTATCCTCCTTTATCTTCAAATTTAGGACATAATGGTATTTCAGTTGATTTAGTAATTTTTTCTTTACATTTAAGAGGAATTTCATCAATTATAGGATCAATTAATATTATTACTACAATTTTAAATATACGACCATATTTAATATATATAGATAAAATTAC Diastrophus CTGTTTATCCCCCTTTATCTTCAACTTTAGGACATAATGGTATTTCAGTAGATTTAGTAATTTATTCATTACATTTAAGAGGCATTTCATCTATTATAGGGTCAATTAATATTATTACAACAATTTTAAATATACGACCATATTTATTATCTATAGATAAAATTAG Gonaspis CGGTTTATCCTCCTTTATCTTCAATTTTGGGTCATAATGGTATTTCTGTTGATTTAGTAATTTATTCTTTACATTTAAGAGGAGTTTCTTCTATTTTAGGATCTATTAATATTATCACAACAATTTTAAATATACGTCCATATTTATTAAGAATAGATAAAATTAG Liposthenes_gle CAGTATATCCCCCATTGTCATCTAATTTAGGCCATGCAGGGATATCAGTAGATTTAACTATTTATTCTTTACATATAAGAGGAATTTCTTCAATTTTAGGGTCAATTAATTTTATTACAACAATTTTAAATTTACGACCAAATATAGTTAGAATAGATAAAATTTC Liposthenes_ker CAGTTTACCCACCTTTATCATCTTTATTAAGACATAGAGGAATCTCCGTAGATTTAACAATTTATTCCCTTCATATAAGAGGAATTTCATCAATTTTAGGATCAATTAATTTTATCTCAACTATCTTAAACATACGACCCAATAATTTGTCAATAGATAAAATTTC Antistrophus CAATTTACCCCCCACTTTCTTCATTAAATGGTCATTCAGGTATTTCTGTAGATTTAACTATTTATTCATTACATATAAGAGGAATTTCATCAATTTTAGGTTCAATTAATTTTATTACAACAATTTTAAATATACGACCTAATTTAATGTCTATAGATAAAATTAC Rhodus CAATTTACCCCCCATTATCTTCAACCATAGGCCATAATAGAATCTCTATAGATTTAGTTATTTACTCTCTTCATATAAGTGGAATTTCCTCTATTTTAAGATCTATCAATTTTATTACAACTATTTTAAATATACGCCCCCCTATAATCAGAATAGATAAATTATC Hedickiana CAGTTTATCCTCCATTATCTTCTTCTATTGGACATGAAGGAATTTCAGTAGATTTAATTATTTATGCATTACATTTAAGAGGGATCTCTTCAATTTTAGGGTCAATTAATTTTATTACCACTATTTTAAATATACGACCTGAAAAAGTTTCTATAGATAAAATTTC Neaylax CAATTTATCCTCCTTTATCTTCAAGATTAGGCCATATAGGGGTTTCTGTTGATTTAGTGATTTACTCTTTACACTTAAGAGGAGTATCTTCAATTTTAGGGTCAATTAATTTCATTACTACAATTTTAAACATACGCCCAAATATAATTACTATAGATAAAATTTC Isocolus CAGTTTACCCCCCTTTATCTTCAAATATTGGGCATTTAGGAGTTTCAGTAGATTTAATTATTTATTCATTACATATAAGAGGGGTTTCTTCAATTTTAGGGTCAATTAATTTTATTACAACTATTTTAAATATGCGACCTAATAATTTATCAATAGATAAAATTTC Aulacidea CAGTTTATCCACCCTTATCTTCAAATATAGGTCATTCTGGAATTTCAGTTGATCTTGTTATTTATTCATTACACATAAGAGGTATTTCTTCAATTTTAGGGTCCATTAATTTTATTACCACTATTTTAAATATACGACCAAATATATTATCTATAGATAAGATTTC Panteliella CAGTCTACCCTCCATTATCATCAAATTTAGGGCATAGAGGAATATCAGTTGACCTAACAATTTATTCATTACATATAAGAGGGGTATCGTCAATTTTACGATCAATTAATTTTATTACTACAATTTTAAATATGCGTCCTTTATACATATCAATAGATAAAATTTC Barbotinia CGGTTTATCCTCCTTTATCTTCAAATATTGGGCATCCAGGAATTTCAGTAGATTTAACAATTTACTCTTTACATTTAACAGGAATTTCATCGATTTTAGGATCAATTAATTTTATTACAACAATTTTAAATATACGACCAGAAATATTAACTATAGATAAGATTTC Aylax CAGTTTATCCTCCTTTATCTACAGAAGTTAGACATTCAGGTATATCTGTTGATTTAATTATTTATTCTTTACATTTAAGAGGAATTTCTTCTATTTTAGGTTCTATTAATTTTATTACAACAATTTTAAATATACGTCCAAATATATTAACAATAGATAAAATTCC Iraella CAGTTTATCCTCCTTTATCTTCAAATATAGGTCATCCAGGGATCTCTGTAGATCTTACAATTTATTCTTTACATTTAAGAGGGAYTTCTTCAATTTTAGGGTCAATTAACTTTATTACAACTATTTTAAATATACGACCTTATATAGTTACTATAGATAAAATTTC Timaspis CTGTATATCCTCCTTTATCTTCAAATATAGGTCATTTAGGTTTATCAGTAGATTTAACTATTTATTCTTTACATATAAGAGGGGTATCTTCAATTTTAGGGTCTATTAATTTTATTACAACAATTTTAAATATACGACCTTTAATAATATCAATAGATAAAATTTC Phanacis_1 CTGTGTATCCACCTTTATCTTCAAATATTGGTCATTTAGGTATTTCAGTAGATTTAACAATTTATTCATTACATATAAGAGGAATTTCATCAATTTTAGGATCAATTAATTTTATTACTACAATTTTAAATATACGACCTTTAAAAATAATAATAGATAAAGTTAC Phanacis_2 CTGTTTATCCTCCTTTATCTTCTAATATAGGGCATTTAGGAATATCAGTTGATTTAACTATTTACTCTTTACATATAAGGGGGGTTTCTTCTATTTTAGGATCAATTAATTTTATTACTACTATTTTAAATATACGACCCATAAAAATAACTATAGATAAAATTGC Eschatocerus CAGTTTATCCTCCTTTATCTTCTCTTATTGGACATCCTGGAGTATCTGTTGATTTTGCAATTTTTTCTTTACATTTAAGGGGAGCATCCTCTATTTTAGGATCTATTAACTTTATTTCCACAATTATTAATATACGGACAATGAAATTTTCAATAGATAAAATTTC Diplolepis CTGTTTATCCTCCATTATCATTAAATATTGGGCATGAAGGGGTTTCTGTTGATTTAGTAATTTTTTCATTACATTTAAGTGGGATTTCATCAATTTTAGGTTCAATTAATTTTATTACTACAATTTTAAATATACGTCCTGTAATAATAAGAATAGAAAAAATTAC Pediaspis CTATTTACCCTCCTTTATCTTCAAATATAGGTCATATAGGAATTTCTATGGATTTAATTATTTTTTCTTTACATATAAGTGGAATATCTTCAATTTTAGGTTCAATTAATTTTATTACTACTATTTTAAATATACGTCCATTAAATTTAACTATAGATAAAATTTC Plagiotrochus CCATTTACCCCCCATTATCATCCAATTTAGGACATTCAGGAATTTCAGTNGATTTGACTATTTATTCTCTTCATATAAGAGGAATTTCTTCAATTTTAGGATCAATTAATTTCATTACAACAATTTTAAATATACGCCCTTATTTAATATCTATAGATAAAATTCC Andricus CTGTTTATCCTCCTTTATCTTCAAATTTAGGACACCCAGGAATTTCTGTTGATTTAACTATTTATTCTTTACATTTAACTGGGATTTCCTCAATTTTAGGTTCAATTAATTTTATTACAACAATTTTAAATATACGACCTAATTTAATAGATATAGATAAAATCCC Neuroterus CTGTTTATCCTCCTTTATCCTCAAATTTAGGGCATCCAGGTATCTCTGTAGACTTAACTATTTATTCTTTACATTTAACAGGAATTTCTTCAATTTTAGGTTCAATTAATTTTATTACTACAATTTTAAATATACGTCCTAATTTAATAGAAATAGATAAAATTCC Biorhiza CTGTTTACCCTCCTTTATCTTCAAATTTAGGACATTCAGGAATTTCTGTTGATTTAACAATTTATTCTTTACATTTAACTGGAATTTCTTCAATTTTAGGATCAATTAATTTTATTACTACAATTTTAAATATACGATCTAATTTAATAACAATAGATAAAATTCC Parnips CTGTTTACCCTCCTTTATCAGCAAATTTAGGTCATCCAGGAGTTTCAGTTGATTTAACTATTTTTTCTTTACATTTAAGTGGAATTTCATCTATTTTAGGATCAATTAATTTTATTTCAACTATTTTAAATATACGACCTAATTATGTATCTATAGATAAAATTTC Paramblynotus CTGTTTATCCTCCTCTTTCATCAAATTTAGGTCATAGAGGAATTTCTGTAGATTTAACAATTTTTTCTTTACATTTAAGTGGTGTATCTTCAATTTTAGGATCAATTAATTTTATTACTACAATTTTAAATATACGATTAAATAATATAAGTATAGATAAAATTAC Ibalia TTTATTTATTTGATCTATTTTATTGACTACAATTTTATTATTATTATCTTTACCTGTATTAGCAGGAGGAATTACAATATTATTGTTTGATCGTAATTTAAATACTTCATTTTTTGACCCCATAGGGGGAGGGGACCCTATTTTATACCAACATTTATTTTGATTT Synergus ACTATTTATTTGATCAATTTTACTAACTACAATCTTATTATTATTATCATTACCTGTTCTAGCTGGAGGAATTACAATATTATTATTCGATCGTAATATAAACACATCATTTTTTGACCCTATAGGAGGAGGAGACCCTATCTTATATCAACACTTATTTTGATTT Periclistus TTTATTTGTTTGATCTATTTTTTTAACAGTAATTTTATTAGTAGTTTCTCTCCCAGTATTAGCTGGTGGTATTACAATATTATTATTTGATCGAAATATAAATACTTCTTTTTTTGATCCATTAGGAGGTGGAGATCCTATTTTATATCAACATTTATTTTGATTT Ceroptres TTTATTTATTTGATCAATTTTATTAACTACTATTTTATTATTATTATCTTTACCTGTTTTAGCTGGAGGAATTACAATATTATTATTTGATCGAAATATAAATACTTCATTTTTTGATCCTATAGGAGGAGGTGACCCAATTTTATATCAACATTTATTTTGATTT Synophromorpha TTTATTTATTTGATCTTTTATTTTAACAACAATTTTATTATTATTATCATTACCTGTATTAGCAGGAGGAATTACAATATTATTATTTGATCGAAATATAAATTCTTCTTTTTTTGATCCATTAGGAGGTGGTGATCCTATTCTTTATCAACATTTATTTTGATTT Xestophanes TTTATTTATTTGATCTTTATTTTTAACTACAATTTTATTATTATTATCATTACCTGTATTAGCTGGGGGAATTACAATATTATTATTTGATCGAAATATAAATTCTTCTTTTTTTGATCCATTAGGAGGAGGAGACCCTATTCTTTATCAACATTTATTTTGATTT Diastrophus TTTATTTACTTGATCTATCTTTTTAACTACTATTTTATTATTATTATCTTTACCAGTGTTAGCCGGAGGAATTACTATATTATTAATAGATCGAAATATAAATTCTTCTTTTTTTGACCCTTTAGGAGGAGGGGATCCTATTCTTTATCAACATTTATTTTGATTT Gonaspis GTTATTTATTTGATCTATTTTATTAACAACTATTTTATTATTATTATCTTTACCAGTTTTAGCAGGAGGAATTACTATATTATTAATAGATCGAAATATAAATTCTTCTTTTTTCGATCCGTTAGGTGGAGGAGATCCTATTCTTTATCAACATTTATTTTGATTT Liposthenes_gle TTTATTTATATGATCAATTTTTTTAACTACTATTTTACTTTTATTATCTTTACCAGTGTTAGCGGGAGGAATTACAATATTATTATTTGACCGAAATTTAAATACTTCGTTTTTTGACCCCTTAGGAGGAGGAGACCCTATTTTATATCAACATTTATTTTGATTT Liposthenes_ker TTTATTTTCCTGATCAATTTTACTAACGACAACTTTATTATTATTATCATTGCCAGTATTAGCAGGAGGAATTACAATACTTCTTTTTGACCGTAATATAAATACTTCTTTTTTTGACCCTATAGGAGGAGGAGACCCAATTTTATATCAACATCTATTTTGATTC Antistrophus ATTATTTTCTTGATCAATTATATTAACAACTATTTTATTATTATTATCTTTACCAGTTTTAGCTGGAGGAATTACAATATTACTCTTTGATCGAAATATAAATACATCTTTTTTTGATCCTTTAGGGGGAGGAGATCCAATTTTATATCAACATTTATTTTGATTT Rhodus ATTATTTTTATGGTCCATTTTATTAACTACTATTTTATTATTACTTTCCTTACCCGTCTTAGCTGGTGGAATTACAATATTACTTTTTGATCGAAATTTTAATACCTCCTTTTTTGACCCTATAGGAGGTGGAGACCCAATCTTATTCCAACATTTATTTTGATTC Hedickiana ATTATTTTCATGATCTATTTTATTAACAACAATTTTACTTTTACTTTCTTTACCTGTATTAGCAGGAGGTATTACAATATTACTTTTTGATCGTAATTTAAATACATCTTTTTTTGACCCAATAGGAGGGGGGGATCCAATTTTATACCAACATTTATTTTGATTT Neaylax TTTATTTCTATGATCAATTTTTTTAACTACAATTTTACTATTACTTTCATTACCTGTATTAGCCGGGGGGATTACAATATTATTATTTGATCGAAACTTAAACACATCTTTCTTTGACCCAATAGGAGGAGGAGACCCTATTTTATTCCAGCATTTATTTTGATTT Isocolus TTTATTTATTTGATCAATTTTATTAACAACAATTTTATTATTATTATCTTTACCTGTGTTAGCTGGAGGAATTACTATATTATTATTTGACCGAAATTTAAATACTTCTTTTTTTGACCCTATAGGAGGAGGAGACCCAATTTTATACCAACATTTATTTTGATTT Aulacidea ATTATTTATATGGTCAATTTCTTTAACAACAATTTTATTATTATTATCATTGCCTGTATTAGCTGGAGGAATTACAATATTATTATTTGATCGAAATATAAATACTTCATTTTTTGACCCTATAGGAGGAGGAGACCCTGTGTTATATCAACATTTATTTTGATTT Panteliella ATTATTCATGTGAACTATTCTATTAACTACTATTTTATTATTATTATCTTTACCTGTTTTAGCTGGAGGAATTACAATATTATTATTTGATCGTAATTTAAATACCTCATTTTTTGATCCTGTAGGCGGGGGAGAGCCAGTATTATACCAACATTTATTTTGATTT Barbotinia TTTATTTATTTGATCAATTTTTTTAACAACTATTTTACTATTACTTTCTTTACCTGTTTTAGCTGGAGGAATTACAATATTATTATTTGATCGTAATATAAATACTTCATTTTTTGATCCAATAGGAGGGGGTGACCCAATTTTATATCAACATTTATTTTGATTT Aylax TTTATTTATTTGATCTATTTTTTGAACAACAATTTTATTATTATTATCTTTACCAGTTTTAGCTGGGGGAATTACAATATTATTATTTGATCGGAATATAAATACATCTTTTTTTGACCCTATAGGGGGAGGAGATCCAATTTTATATCAACATTTATTTTGATTT Iraella TTTATTTATTTGATCAATTTTTTTAACAACAATTTTATTATTATTATCTTTACCAGTATTAGCAGGTGGAATTACAATATTATTATTTGATCGTAATATAAATACTTCATTTTTTGACCCAATAGGAGGAGGAGATCCAGTATTATATCAACATTTATTTTGATTT Timaspis TTTATTTGTATGATTTATTTTTTTAACAACTATTTTATTACTATTATCTTTACCTGTTTTAGCAGGAGGGATTACAATATTATTATTTGATCGAAATTTAAATACTTCTTTTTTTGACCCTATAGGAGGAGGAGATCCTGTATTATATCAACATTTATTTTGATTT Phanacis_1 TTTATTTGTTTGATCAATTTTTTCAACAACTATTTTATTATTATTATCTTTACCAGTTCTTGCTGGAGGTATTACTATATTATTATTTGATCGAAATTTAAATACTTCATTTTTTGACCCTATAGGAGGAGGAGACCCAGTATTATACCAACATTTATTTTGATTT Phanacis_2 TTTATTTGTTTGATCGATTTTTTTAACAACAATTTTACTATTATTATCTTTACCTGTATTAGCGGGAGGAATTACAATATTATTATTTGATCGAAATTTAAATACATCTTTTTTTGACCCTATAGGAGGGGGGGACCCTGTATTATACCAACATTTATTTTGATTT Eschatocerus TTTGTTTGTTTGATCTATTTTATTAACTACTATTTTATTATTATTATCTTTACCTGTATTAGCAGGAGGAATTACAATACTTTTATTTGATCGAAATCTAAATACTTCTTTCTTTGACCCTATTGGAGGTGGGGACCCCATTTTATATCAACATTTATTTTGATTT Diplolepis TTTATTTTCTTGATCAATTTTATTAACTACTATTTTATTATTATTGTCTTTACCTGTTTTAGCTGGGGGTATTACTATATTATTATTTGATCATAATTTAAATACTTCTTTCTTTGATCCTATAGGGGGCGGGGATCCAGTTTTATATCAACATTTATTTTGATTT Pediaspis ATTATTTACATGATCAATTTTATTAACAACAATTTTATTATTATTATCATTACCTGTTTTAGCTGGAGGAATTACAATATTATTATTTGATCGTAATTTAAATACTACTTTTTTTGATCCTATAGGAGGCGGAGATCCAATTTTATTTCAACATTTATTTTGATTT Plagiotrochus TCTTTTTGTATGATCAATTTTTTTAACTACAATTTTATTATTATTATCATTACCAGTATTAGCAGGTGCAATTACTATGTTACTTTTTGATCGAAATATAAATACTTCTTTTTTTGACCCAACTGGAGGAGGAGATCCTATCTTATATCAACATTTATTTTGATTT Andricus TTTATTTGTTTGATCAATTTTTTTAACAACTATTTTATTATTATTATCTTTACCTATTTTAGCTGGTGCAATTACTATATTATTATTTGATCGTAATATAAATACTTCATTTTTTGATCCTATAGGAGGAGGGGATCCAATTTTATACCAACATTTATTTTGATTT Neuroterus TTTATTTGTTTGATCTATTTTTTTAACTACAATTTTATTACTTTTATCTTTACCAATTTTAGCAGGAGCAATTACAATATTATTATTTGATCGAAATATAAATACCTCATTTTTTGATCCTATAGGAGGGGGGGATCCTATTTTATATCAACATTTATTTTGATTT Biorhiza TTTATTTGTATGATCAATTTTTTTAACTACAATTTTATTATTATTATCTTTACCAATTTTAGCCGGAGCAATTACAATATTATTATTTGATCGTAATATAAATACTTCTTTTTTTGACCCTATAGGAGGAGGAGACCCAATTTTATATCAACATTTATTTTGATTT Parnips TTTATTTATTTGATCTATTTTATTAACAACTATTTTACTTTTATTGTCATTACCTGTATTGGCTGGAGGGATTACTATATTATTGTTTGATCGGAATTTAAATACTTCATTTTTTGATCCTTTAGGAGGTGGGGACCCTGTTTTATATCAACATTTATTTTGATTT Paramblynotus ATTATTTATTTGATCTATTTATTTAACAACAATTTTATTATTATTATCTTTACCAGTTTTAGCAGGAGGAATTACAATATTATTATTTGATCGTAATTTAAATACTTCATTTTTTGATCCAATAGGTGGAGGAGATCCAATTCTTTACCAACATTTATTTTGGTTT Ibalia TTTGGTCATCCAGAAGTTTATATTTTAATTTTACCTGGATTTGGAATAATTTCTCATATAATTTATACAGAATGTGGTAAAAAAAGAACTTTTGGGGCATTAAGAATAATATATGCAATAATTTCTATTGGGATATTAGGATTTATCGTATGAGGACATCATATAT Synergus TTTGGTCACCCCGAAGTATATATTTTAATTTTACCAGGTTTTGGAATAATTTCACATATAATTTACATAGAATCAGGAAAAAAAATAACTTTTGGATCTTTAGGAATAATATATGCAATAATTTCAATTGGAATTTTAGGATTTATTGTATGAGGGCATCATATAT Periclistus TTTGGTCATCCTGAAGTTTATATTTTAATTTTACCTGGATTTGGAATAGTATCTCATATAATTTATACTGAGTGTGGTAAAAAAGGTACTTTTGGATCTTTAGGAATAATATATGCAATAATTTCAATTGGATTATTAGGATTTGTTGTTTGAGGGCATCATATAT Ceroptres TTTGGACATCCTGAAGTTTATATTTTAATTTTACCAGGATTTGGAATAATTTCTCATATAATTTATTTAGAAACTGGAAAAAAAATTACTTTTGGATCTTTAGGAATAATTTATGCAATAATTTCTATTGGAATGTTAGGTTTTATTGTATGAGGACATCATATAT Synophromorpha TTTGGTCATCCTGAAGTTTATATTTTAATTTTACCTGGATTTGGAATAATTTCTCATATAATTTGTGTAGAATGTGGAAAAAAAAATACTTTTGGTTCATTAGGAATAATATATGCTATAATTTCTATTGGTATATTAGGATTTATTGTTTGAGGACATCATATAT Xestophanes TTTGGTCATCCTGAAGTTTATATTTTAATTTTACCGGGATTTGGTATAATTTCTCATATAATTTTTATAGAATGTGGAAAAAAAAATACTTTTGGTTCTTTAGGAATAATTTATGCTATAATTTCTATTGGAATATTAGGATTTATTGTTTGAGGGCATCATATAT Diastrophus TTTGGACACCCTGAAGTTTATATTTTAATTTTACCTGGATTTGGAATAATTTCTCATATAATTTATACAGAATGTGGAAAAAAAATTACTTTTGGTTCTTTAGGAATAATATATGCAATAATTTCTATTGGAATATTAGGATTTATTGTATGAGGACATCATATAT Gonaspis TTTGGTCATCCTGAAGTTTATATTTTAATTTTACCAGGATTTGGAATAATTTCTCATATAATTTATACAGAATGTGGAAAAAAAATTACTTTTGGATCTTTAGGTATAATATATGCTATAATTTCTATTGGAATATTAGGATTTATTGTTTGAGGTCATCATATAT Liposthenes_gle TTTGGTCATCCTGAAGTTTATATTTTAATTTTGCCCGGATTTGGAATAATTTCTCATATAATTTATTTAGAATGTGGGAAAAAAATTACATTTGGGTCTTTAGGTATAATATATGCAATAATTTCAATTGGAATATTAGGATTTATTGTATGGGGTCATCATATAT Liposthenes_ker TTTGGTCATCCTGAAGTTTATATTTTAATTCTTCCAGGATTTGGAATAGTCTCTCACTTAATTTATTCTGAATGTGGAAAAAAAAACACTTTTGGATCCTTAGGGATAACATATGCTATAATCTCTATTGGATTATTAGGATTTATTGTCTGAGGACATCATATAT Antistrophus TTTGGTCACCCAGAAGTTTATATTTTAATTTTACCAGGATTTGGAATAATTTCCCATATAATTTATTCAGAATGTGGTAAAAAAACAACATTTGGTGTATTAGGAATAATTTATGCTTTAATTTCAATTGGTATATTAGGATTTATTGTATGAGGACATCATATAT Rhodus TTTGGTCACCCTGAAGTCTATATCTTAATTTTACCTGGATTTGGTATAATCTCTCATATAATTTATATAGAATGTGGAAAACCCACAACCTTTGGTTCATTAGGTATAATATACGCAATAATTTCAATTGGAATATTAGGATTTATCGTATGAGGACATCATATAT Hedickiana TTTGGTCACCCAGAAGTTTATATTTTAATTCTTCCAGGATTTGGAATAATTTCTCATATAATTTATTCAGAATGTGGTAAAAAAACAACATTTGGTTCATTAGGGATATTATATGCAATAATTTCTATTGGAATATTAGGTTTTATTGTTTGAGGCCATCATATAT Neaylax TTTGGTCATCCAGAAGTTTATATTTTAATTTTACCTGGATTTGGAATAATTTCTCATATAATTTTTTTAGAGTGTGGGAAAAAAACAACATTTGGGTCATTAGGAATAATATACGCCATAATTTCAATTGGAATACTAGGATTTATTGTATGAGGCCATCATATAT Isocolus TTTGGTCACCCAGAAGTTTATATTTTAATTTTACCTGGATTTGGAATAATTTCTCATATAATTTATAGTGAGTGTGGGAAAAAAACTACATTTGGATCTTTAGGAATAATATATGCTATAATTTCAATTGGTGTTTTAGGGTTTCTTGTTTGAGGTCATCATATAT Aulacidea TTTGGTCATCCTGAGGTTTATATTTTAATTTTACCAGGATTTGGGATAATTTCACATATAATTTATATAGAATGTGGTAAAAGAATTACTTTTGGAGCTTTAGGGATAATATATGCAATAATTTCCATTGGAATATTAGGATTTATTGTTTGAGGCCATCACATAT Panteliella TTTTGTCACCCTGAAGTTTATATTCCAATTTTAGCTGGATTTGGGATAGTGTCTCATATAATTTATTTAGAATGTGGGGAAAAAATTACATTTGGGTCTTTAGGAATAATATACGCAATAATTTCAATTGGTATATTAGGTTTTATTGTATGAGGACATCATATAT Barbotinia TTTGGTCATCCTGAAGTTTATATTTTAATTTTACCTGGGTTTGGAATAATTTCTCATATAATTTATAGAGAATGTGGAAAAAAAACTACTTTTGGATCATTAGGTATAATATATGCAATAATTTCAATTGGTTTACTAGGATTTATTGTTTGAGGTCATCATATAT Aylax TTTGGTCATCCTGAAGTTTATATTTTAATTTTACCAGGATTTGGTATAATTTCTCATATAATTTATAGTGAAAGAGGAAAAAAAACTACTTTTGGATCTTTAGGAATAATTTATGCTATAATTTCAATTGGATTATTAGGATTTATTGTATGAGGGCATCATATAT Iraella TTTGGTCATCCTGAAGTATATATTTTAATTTTACCAGGATTTGGGATAATTTCTCATATAATTTATAATGAATGTGGGAAAAAAACTACTTTTGGATCTTTAGGAATAATATATGCAATAATTTCTATTGGAATATTAGGATTTATTGTTTGAGGTCATCATATGT Timaspis TTTGGCCATCCAGAAGTTTATATTTTAATTTTACCAGGATTTGGAATAATTTCACATATAATTTACACAGAATGTGGGAAAAAAATTACTTTTGGATCTTTAGGAATAATATATGCAATAATTTCTATTGGAATATTAGGGTTTATTGTATGAGGCCATCATATAT Phanacis_1 TTTGGTCATCCAGAAGTTTATATTTTAATTTTACCTGGATTTGGAATAATCTCTCATATAATTTATAATGAATGTGGTAAAAAAATTACTTTTGGATCATTAGGAATAATATACGCTATAATTTCAATTGGTATATTAGGATTTATTGTATGAGGTCACCATATAT Phanacis_2 TTTGGGCATCCTGAAGTGTATATTTTAATTCTTCCAGGATTTGGGATAGTGTCTCATATAATTTTTACTGAATGTGGACAAAAAACTACTTTTGGATCATTAGGGATAATATATGCTATAATTTCAATTGGAATATTAGGATTTATTGTTTGAGGTCATCATATAT Eschatocerus TTTGGTCATCCTGAAGTTTATATTTTAATTTTACCTGGATTTGGAATAATTTCTCATATAATTTTTACTGAATGTGGTAAAAAATCTACTTTTGGTTCTTTAGGAATATTATATGCTATAATTTCTATTGGAGCTTTAGGATTTATGGTTTGAGGTCATCATATAT Diplolepis TTTGGTCATCCAGAAGTTTATATTTTAATTTTGCCAGGATTTGGTATAATTTCTCATATAATTTATTCTGAATGTGGTAAAAAATTTACTTTTGGTTCATTAGGTATGATATATGCTATAATTTCAATTGGTATATTAGGGTTTATTGTATGAGGACATCATATAT Pediaspis TTTGGACACCCTGAAGTTTATATTTTAATTTTACCTGGATTTGGAATAATTTCTCATATAATTACAAATGAATGTGGAAAAAAAACTACTTTTGGTTCTTTAGGAATAATATATGCTATAATTTCAATTGGTATATTAGGTTTTATTGTATGAGGACATCATATAT Plagiotrochus TTTGGTCATCCTGAAGTTTATATTTTAATTTTACCTGGGTTTGGAATAATTTCTCATATAATTTATTTGGAATGTGGAAAAAAAAATACTTTTGGATCATTAGGAATAATATATGCTATAAATTCAATTGGTATATTAGGATTTATTGTATGAGGTCATCATATAT Andricus TTTGGTCATCCAGAAGTTTATATTTTAATTTTACCTGCTTTTGGTATAATTTCTCATATAATTTATATAGAATGTGGGAAAAAAAATACTTTTGGTTCATTAGGAATAATATATGCTATAATTTCAATTGGTATATTAGGATTTATTGTTTGGGGTCATCATATAT Neuroterus TTTGGGCATCCAGAAGTTTATATTTTAATTTTACCAGCATTTGGTATAATTTCACATATAATTTATATAGAATGTGGAAAAAAAAATACTTTTGGGTCTTTAGGAATAATATATGCAATAATTTCAATTGGTATATTAGGATTTATTGTTTGAGGTCATCATATAT Biorhiza TTTGGGCACCCAGAAGTTTATATTTTAATTTTACCTGCTTTTGGAATAGTTTCTCATATAATTTATATAGAATGTGGAAAAAAAAATACTTTTGGATCTTTGGGAATAATATATGCAATAATTTCAATTGGTATATTAGGATTTATTGTTTGAGGACATCATATAT Parnips TTTGGTCATCCAGAAGTATATATTTTAATTTTACCAGGATTTGGTATAATTTCCCATATAATTTATACAGAATGTGGAAAAAAAACTACATTTGGATCTTTAGGAATAATATATGCTATAATTTCTATTGGAATATTAGGGTTTATTGTTTGAGGCCATCATATAT Paramblynotus TTTGGCCATCCAGAAGTTTACATTTTAATTTTATCAGGATTTGGTATAATTTCTCATATGATTTATACTGAATGTGGGAAAAAAAATACTTTTGGTTCATTAGGAATAATTTATGCAATAATTTCTATTGGTATGTTAGGATTTATTGTTTGAGGTCATCATATAT Ibalia TTACTGTAGGGATAGATGTAGATACTCGAGCTTATTATACTTCTGTAACTATAATTATTGCTATCCCAACAGGAATTAAAATTTTTAGATGATTAGCTAGGATATATGGGTCTAAAATTAAATTTAATTTATCTATTATTTGATCAATTGGATTTATTTTTTTATT Synergus TTACAGTAGGTATAGATGTAGATACTCGAGCTTATTACACATCTGCGACTATAATTATTGCTATCCCAACAGGTATTAAAATTTTTAGTTGATTAGCAACTATATATGGGTCAAAAATTAAATTTAATTTATCTATAATTTGATCTATAGGATTTATTTTTTTATT Periclistus TTACTATTGGTATAGATATTGACACGCGAGCTTATTATACTTCTGTAACAATAGTTATTGCTATTCCTACAGGAATAAAAATTTTTAGATGACTAGCAACAATATATGGTTCAAAAATTAATTGTAGGGTTCCTATATTTTGATCATTAGGTTTTATTTTTTTATT Ceroptres TTACAGTAGGAATAGATGTAGATACTCGAGCTTATTATACTTCAGCTACTATAATTATTGCAATTCCCACAGGAATCAAAATTTTTAGATGATTGGCTACAATATATGGATCAAAATTAGACATTAATATTTCAATAATATGATCTTTAGGATTTATTTTTTTATT Synophromorpha TTACTGTAGGAATAGATATTGATACACGAGCTTATTATACATCTGTAACTATAATTATTGCAATTCCYACGGGAATTAATATTTTTAGATGATTAGCTACAATATATGGATCAAAAATTAATTTTAATTTATCAATTATATGATCATTAGGTTTTATTTTTTTATT Xestophanes TTACTGTAGGAATAGATATTGATACACGAGCTTATTATACTTCTGTAACTATAATTATTGCAATTCCTACTGGAATTAAAATTTTTAGATGATTAGCTACAATATATGGATCAAAAATTAATTTTAATTTATCAATTAAATGATCATTAGGATTTATTTTTTTATT Diastrophus TTACTGTAGGGATAGATATTGATACACGAGCTTATTATACTTCAGTAACTATAATTATTGCTATTCCAACAGGAATTAAAATTTTTAGATGGTTAGCAACAATATATGGTACAAAAATTAATTTTAATCCTTCAATTATTTGATCATTAGGCTTTATTTTTTTATT Gonaspis TTACTGTAGGTATAGATATTGATACTCGAGCTTATTATACGTCAGTAACTATAATTATTGCAATTCCTACAGGGATTAAAATTTTTAGATGATTAGCTACTATATATGGATCAAAAATTAATTTTAATCCCTCAATTATTTGATCATTAGGCTTTATTTTTTTATT Liposthenes_gle TTACTGTAGGAATAGATGTAGATACACGGGCATACTACACTTCAGCTACAATAATTATTGCAGTCCCTACAGGTATTAAAATTTTTAGATGATTGGCAACAATATATGGGTCAAGAATTAATTTAAATTTATCAATTATATGATCTTTAGGGTTTATTTTTTTATT Liposthenes_ker TTACTGTAGGAATGGATGTCGACACCCGAGCTTATTATACTTCTGCAACTATAATTATTGCAATTCCACCAGGAATCAAAATTTTTAGATGATTAGCTACAATATATGGATCTAAAATTTACATAAATTTATCAATTTTATGATCCTTAGGATTTATTTTTCTATT Antistrophus TTACAGTTGGCATAGATGTAGATACACGAGCCTATTACACATCAGCAACTATAATTATTGCTATTCCTACAGGTATTAAAATTTTTAGTTGATTGGCAACAATATATGGAATAAAGATTAATTATAATTTATCAATAATTTGAGCTTTAGGATTTATCTTTTTATT Rhodus TTACTGTAGGAATAGATGTAGACACCCGAGCATATTACACATCCGCTACCATAATTATTGCAATTCCTACTGGAATTAAAATTTTTAGGTGATTAGCTACAATATACGGATCAAAAATTAATTTTAATTTATCCATTATATGATCCTTAGGATTTATTTTTTTATT Hedickiana TTACTGTGGGTATAGATGTAGATACACGAGCTTATTACACATCAGCAACTATAATTATTGCAGTTCCTACTGGTATTAAAGTATTTAGTTGATTGGCATCAATATATGGAGCAAAAGTTAATTTTAATTTATCAATTCTTTGATCATTAGGATTTATTTTTTTATT Neaylax TTACTGTTGGGATAGATGTTGATACACGTGCATATTATACTTCAGCAACAATAATTATTGCAATTCCTACTGGTATTAAAATTTTTAGATGATTAGCAACTATTTATGGAACAAAAATTAATTTTAATCCATCAATTATTTGATCTTTAGGGTTTATTTTTTTATT Isocolus TTACAGTAGGGATAGATGTAGATACACGAGCTTATTATACTTCAGTAACAATAATTATTGCAATCCCTACAAGTATTAAAATTTTTAGATGATTAGCTACTATACATGGTACAAAAATTAATTTTAACCTATCTATTTTATGATCTTTAGGATTTGTATTTTTATT Aulacidea TTACTGTAGGGATAGATGTTGATACACGAGCTTATTATACTTCAGCAACGATAGTAATTGCTATCCCTACAGGAATTAAAATTTTTAGGTGACTTGCAACTATATATGGATCTAAAATTAATTTTAATTTATCAATTATATGATCATTAGGTTTTATTTTTTTATT Panteliella TTACTGTTGGGATAGATGTAGATACACGAGCTTATTACACTTCAGCCACTATAATTATTGCAATTTCTACAGGAATTATAATTTTTAGATGATTAGCTACTATATATGGATCAAAGATTGATTTTAATTTATCTATATTATGATCTTTAGGATTTATTTTTTTATT Barbotinia TTACTGTTGGGATAGATGTAGATACACGAGCTTATTATACATCAGCAACAATGGTAATTGCTATTCCTACAGGAATTAAAATTTTTAGGTGATTAGCAACTATATATGGATCAAAAATTAATTTTAATTTATCAATAATTTGATCATTAGGATTTATTTTTTTATT Aylax TTACTGTTGGTATAGATGTAGATACTCGTGCTTATTATACTTCTGCTACAATAGTAATTGCTATCCCCACAGGAATTAAAATTTTTAGATGACTTGCTACTATATATGGATCAAAAATTGATTTTAATTTATCTATAATTTGATCATTAGGATTTATTTTTTTATT Iraella TTACTGTAGGAATAGATGTTGATACACGAGCTTATTACACTTCAGCAACAATAGTTATTGCAATTCCTACAGGTATTAAAATTTTTAGTTGATTAGCAACTATATATGGATCAAAAATTGATTTAAATTTATCAATAATTTGATCTTTAGGTTTCATTTTTTTATT Timaspis TTACTGTAGGCATAGATGTTGATACTCGAGCTTATTACACTTCTGCAACTATAATTATTGCTATTCCAACAGGTATTAAAATTTTTAGATGATTAGCAACAATATATGGATCAAAATTAAATTTTAGTTTATCTATAATTTGATCTTTAGGTTTTATTTTTTTATT Phanacis_1 TTACTGTAGGGATAGATGTAGACACCCGAGCTTATTACACATCAGCTACAATAATTATTGCTATCCCTACTGGGATTAAAATTTTTAGTTGGTTAGCAACAATATATGGGGCTAAAGTAGATTTTAATTTATCAATAATATGAGCTTTAGGGTTTATTTTTTTATT Phanacis_2 TTACTGTTGGAATAGATGTAGATACACGAGCTTATTACACTTCAGCAACAATAGTAATTGCTATCCCAACTGGTATTAAAATTTTTAGATGGTTAGCTACTATATATGGATCAAAAGTAGAATTTAACCCTTCAATTATATGGGCTTTAGGGTTTATTTTTTTATT Eschatocerus TTACAGTAGGTATAGATGTTGATACTCGAGCTTATTTTACTTCTGTAACTATAATTATTGCTATTCCTACTGGAATTAAAATTTTTAGATGATTAGCTACTATATATGGATCAAAAATAAAATTTAGATTACCTATATTATGATCTTTAGGATTTGTATTTTTATT Diplolepis TTACTGTTGGTATAGATATTGATACACGGGCTTATTATACATCAGTTACTATAATTATTGCTGTTCCTACAGGTATTAAAATTTTTAGGTGATTGGCTACTATATATGGAGCAAAAATTGATTATAATTTGGCTATAATTTGATCATTAGGATTTATTTTTTTATT Pediaspis TTACTGTAGGAATAGACATTGATACACGAGCTTATTATACATCAGCTACTATAATTATTGCAGTTCCAACAGGAATTAAAATTTTTAGATGATTAGCAACAATATATGGATCAAAAATTAAATTAAATATAATTATAATTTGATCTTTAGGTTTTATTTTTTTATT Plagiotrochus TAACTGTAGGCATAGATATTGATACACGAGCTTATTACACATCTGTAACTATAATTATTGCTATTCCTACAGGAATTAAAATTTTTAGATGATTAGCAACAATATATGGAACTAAAATAAATTTTAACTTATCAATTATATGAGCTCTAGGGTTTATCTTTTTATT Andricus TTACTGTAGGTATAGATATTGATACACGAGCTTATTATACATCAGTAACAATAATTATTGCTATTCCTACAGGAATTAAAATTTTTAGGTGGTTAGCAACTATATATGGATCAAAAATTAATTTTAATTTATCAATTATATGATCTTTAGGATTTATTTTTTTATT Neuroterus TTACTGTAGGAATAGATATTGACACACGAGCTTATTATACTTCAGTAACAATAATTATTGCGATTCCTACAGGAATTAAAATTTTTAGATGATTAGCTACAATATATGGATCAAAAATTAATTTTAATTTATCAATTATATGAGCTTTAGGGTTTATTTTTTTATT Biorhiza TTACTGTAGGGATAGATATTGATACACGAGCTTATTATACTTCAGTTACAATAATTATTGCAATTCCTACAGGTATTAAAATTTTTAGATGATTAGCTACTATATATGGGTCAAAAATTAATTTTAATTTATCTACTATATGAGCTTTAGGATTTATTTTTTTATT Parnips TTACGGTAGGTATAGATGTAGATACACGAGCTTATTATACTTCTGCTACTATGATTATTGCAGTTCCTACAGGAATTAAAATTTTTAGATGATTAGCAACAATATATGGATCAAAAATTAAATATAATTTATCAATAATATGAGCTATTGGATTTGTATTTTTATT Paramblynotus TTACTGTTGGAATAGATGATGATACCCGTGCCTATTATACTTCTGTAACTATAATTATTGCAATCCCAACTGGTATTAAAATTTTTAGATGATTAGCAACAATATATGGATCAAAAATTTTAATAAATTTATCAATAATTTGATCATTAGGATTTATTTTTTTATT Ibalia TTCTTTTGGGGGAATAACTGGAGTTACTTTATCAAACTCTTCTATTGACATTATTCTTCATGATACTTATTATGTAGTAGCNCATCGACATTGCTCTCTGGAAATTTGAAACAGCCAAATATTACGTAACCATTATCGATGCACCAGGACATCGTGATTTCATAAA Synergus TTCATTTGGAGGAATAACAGGAATTACTTTAGCAAATTCTTCGATTGATATTATAATACATGATACTTATTATGTAGTAGCCGATCGACATTGCTCTCTGGAAATTCGAAACCGCCAAGTACTACGTAACCATCATTGATGCACCAGGTCATCGTGATTTCATAAA Periclistus TTCATTTGGAGGATTAACAGGAATTACATTATCTAATTCTACTATTGATATTATATTACATGATACTTATTATGTAGTTGCNGATCGACATTGCTCTCTGGAAATTTGAAACTGCCAAGTATTACGTGACCATCATTGATGCACCAGGACATCGTGATTTCATCAA Ceroptres TTCAATAGGAGGAATAACAGGAGTAACTTTATCTAATTCTTCTATTGATATTATTTTGCATGATACTTATTATGTAGTAGCT???????????????????????????????????????????????????????????????????????????????????? Synophromorpha TTCAATTGGAGGAATAACAGGAGTAACTTTACCTAATTCTTCAATTGATATTATTTTACATGATACTTATTATGTAGTGGCTGATCGACATTGCTCTCTGGAAATTTGAAACTGCCAAGTATTACGTGACCATCATTGATGCACCAGGACATCGTGATTTCATCAA Xestophanes TTCAATTGGAGGAATAACAGGAGTAACTTTATCTAATTCTTCTATTGATATTATTTTACATGATACTTATTATGTAGTAGCTGATCGACATTGCTCTCTGGAAATTTGAAACTGCCAAGTATTACGTGACAATCATTGATGCACCAGGACATCGTGATTTCATCAA Diastrophus TTCAATTGGGGGAATAACAGGGGTAACTTTAGCTAATTCATCAATTGATATTATTTTACATGATACTTATTATGTAGTGGCTGATCGACATTGCTCTCTGGAAATTTGAAACTGCCAAGTATTATGTGACTATCATTGATGCACCAGGACATCGTGATTTCATCAA Gonaspis TTCAATTGGGGGAATAACAGGAGTAACTTTATCTAATTCATCAATTGATATTATTTTACATGATACTTATTATGTAGTTGCTGATCGACATTGCTCTCTGGAAATTTGAAACTGCCAAGTATTACGTGACCATCATTGATGCACCAGGACATCGTGATTTCATCAG Liposthenes_gle TTCTATTGGTGGATTAACAGGTGTGACTCTTTCTAATTCATCGATTGATATTATTTTACATGACACTTATTATGTAGTGGCTTATCGACATTGCTCTGTGGAAATTTGAAACAGCCAAGTACTACGTGACCATCATTGATGCACCAGGACACCGTGATTTCATAAA Liposthenes_ker TTCTATTGGAGGAATTACTGGTATTACCTTAGCAAATTCTTCTCTAGACATTATCCTTCATGACACTTACTATGTTGTAGCATATTGACATTGCTCTGTGGAAATTTGAAACTGCCAAGTACTACGTGACCATCATTGATGCACCAGGACATCGTGATTTCATAAA Antistrophus TTCTTTTGGAGGATTAACAGGAATTACTTTATCAAATTCTTCAATTGATATTTCTTTACGTGATACATATTATGTAGTTGCTTATCGACATTGCTCTGTGGAAATTTGAAACTGCCAGGTACTACGTGACCATCATTGATGCTCCAGGACATCGTGATTTCATAAA Rhodus TTCTTTAGGAGGATTAACAGGGGTTACTTTAGCAAATTCTTCAATTGACATTATTTTACATGATACTTACTATGTAGTAGCCTATCGACATTGCTCTGTGGAAGTTTGAAACTGCCAAGTACTACGTGACCATAATTGATGCACCAGGACATCGTGATTTCATAAA Hedickiana TTCACTTGGAGGAGTGACAGGTGTTACTTTATCTAATTCATCAATTGATATTGTTATACATGATACTTATTATGTTGTTGCATATCGACATTGCTCTGTGGAAGTTTGAAACTGCCAAGTACTACGTGACCATAATTGATGCACCAGGACATCGTGATTTTATAAA Neaylax TTCAATTGGGGGGATGACAGGAGTTACACTATCTAATTCTTCAATTGATATTATTATACATGATACTTATTATGTCGTAGCTTATCGACATTGCTCTGTGGAAGTTTGAAACTGCCAAGTACTACGTGACCATAATTGATGCACCAGGACATCGTGATTTCATAAA Isocolus TTCATTAGGGGGGATAACAGGAGTAACATTAGCAAATTCATCTATTGACATTATTTTACATGATACTTATTATGTAGTTGCATATCGACATTGCTCTGTGGAAATTTGAAACTGCCAAGTACTACGTGACCATCATTGATGCACCAGGACATCGTGATTTCATAAA Aulacidea TTCTTTAGGTGGTTTGACAGGAGTCACATTATCAAATTCTTCACTTGATATTATTTTACATGATACTTATTATGTAGTGGCTTATCGACATTGCTCTGTGGAAATTTGAAACTGCCAAGTACTACGTGACCATCATTGATGCACCAGGACATCGTGATTTCATAAA Panteliella TTCAATTGGAGGATTAACTGGAGTAACTTTATCAAATTCATCAATTGATATTGTTATACATGACACTTATTATGTTGTGGCNTATCGACATTGCTCTGTGGAAATTTGAAACTGCCAAGTACTACGTAACCATCATTGATGCACCAGGGCATCGTGATTTCATAAA Barbotinia TTCTCTTGGAGGAATTACAGGAATTACTTTATCTAATTCTTCAATTGATGTTATTTTACATGATACATATTATGTCGTAGCNGATCGACATTGCTCTCTGGAAATTTGAAACTGCCAAGTATTACGTAACCATCATCGATGTACCAGGACATCGTGATTTCATCAA Aylax TTCAATTGGAGGTATTACAGGAGTAACCTTATCAAATTCTTCAATTGATATTGTTTTGCATGATACATATTATGTTGTAGCTGATCGACATTGCTCTCTGGAAATTTGAAACTGCCAAGTATTACGTAACCATCATCGATGCACCAGGACATCGTGATTTCATCAA Iraella TTCAGTTGGGGGAATTACAGGAGTAACTTTATCAAATTCATCTATTGATATTATTTTACATGATACTTATTATGTAGTGGCTGATCGACATTGCTCTCTGGAAATTTGAAACTGCCAAGTATTACGTAACCATCATCGATGCACCAGGACATCGTGATTTCATCAA Timaspis TTCTATAGGAGGATTAACTGGTGTAACATTATCAAATTCATCAATTGATTTAATATTACATGATACTTATTATGTTGTAGCTAATCGACATTGCTCTCTGGAAATTTGAAACTGCCAAGTATTACGTGACAATCATTGATGCACCAGGGCATCGTGATTTTATCAA Phanacis_1 TTCAATAGGGGGATTAACTGGAGTTACTTTATCAAATTCTTCTATTGATTTGATTTTACATGATACTTATTATGTAGTTGCTAATCGACATTGCTCTGTGGAAATTTGAAACTGCCAAGTATTACGTGACAATCATTGATGCACCAGGGCATCGTGATTTTATTAA Phanacis_2 TTCAGTAGGGGGTTTAACAGGAGTAACTTTGGCTAATTCTTCAATTGATTTAATTATACATGATACATATTATGTAGTGGCTAATCGACATTGCTCTCTGGAAATTTGAAACTGCCAAGTATTATGTAACGATCATTGATGCACCAGGCCATCGTGATTTTATTAA Eschatocerus TACTCTTGGTGGTCTAACTGGAGTTACTTTATCAAATTCATCAATTGATATTATCTTACATGATACATATTATGTAGTAGCTAATCGATATTGCTCTCTGGAAATTTGAAACGGCGAAATATTATGTTACCATAATTGATGCACCAGGACATCGTGATTTCATCAA Diplolepis TTCTTTAGGAGGATTAACAGGAGTTTTATTATCTAATTCATCCATTGATATTATTTTACATGATACATATTATGTTGTTGCNCATTGATATCGCTCTTTGGAAATTCGAAACAGCCAAATATTACGTGACCATCATCGATGCACCAGGACATCGTGATTTTATAAA Pediaspis TTCAATTGGAGGAATAACAGGAGTAACTTTATCAAATTCATCAATTGATATTATTTTACATGATACATATTATGTTGTAGCTTATCGACATTGCCCTCTGGAAATTCGAGACAGCCAAGTATTACGTAACCATCATCGATGCACCAGGACATCGTGATTTCATNAA Plagiotrochus TTCAATTGGAGGTATAACAGGAGTAACATTAGCAAATTCTTCTATTGATATTATTATACATGATACATATTATGTTGTAGCNGATCGACATTGCTCTCTGGAAATTTGAAACTGCCAAGTATTACGTCACCATCATTGATGCACCAGGACATCGTGATTTCATCAA Andricus TTCTATTGGAGGAATAACTGGAGTTACATTAGCTAATTCATCTATTGATATTGTTATACATGATACTTATTATGTTGTTGCNAATCGACATTGCTCTCTGGAAATTTGAAACAGCGAAATATTACGTGACCATCATTGATGCACCAGGACATCGTGATTTCATCAA Neuroterus TTCAATTGGAGGAATAACAGGTGTAACATTAGCAAATTCATCAATTGATATTGTTATACATGATACTTATTATGTTGTTGCTAATCGACATCGCTCTCTGGAAATTTGAAACGGCGAAATATTATGTGACCATAATTGATGCACCAGGACATCGTGATTTTATCAA Biorhiza TTCTATTGGAGGAATAACTGGTGTAACATTAGCTAATTCATCAATTGATATTATTATACATGATACTTATTATGTTGTTGCTAATCGATATTGCTCTCTGGAAATTTGAAACGGCGAAATATTATGTTACCATAATTGATGCACCAGGACATCGTGATTTCATCAA Parnips TTCTATTGGAGGTATGACTGGAGTAACATTATCAAATTCTTCAATTGATATTATTTTACATGATACTTATTATGTAGTTGCTCATTGACATTGCTCTTTGGAAATTCGAAACAGCCAAGTATTACGTTACCATCATCGATGCACCAGGTCATCGTGATTTCATAAA Paramblynotus TTCAATAGGTGGAGTTACAGGAATTACTTTATCTAATTCTTCTATTGATGTAATTTTACATGATACTTACTATGTAGTAGCTCATAGACATCGCTCTCTGGAAATTCGAAACAGCCAAGTATTACGTAACCATCATCGATGCACCGGGACATCGTGATTTCATAAA Ibalia AAACATGATCACTGGAACAAGTCAAGCTGATTGTGCAGTTTTGATAGTAGCAGCAGGAATTGGAGAATTCGAAGCAGGAATTTCAAAGAATGGTCAAACTCGTGAACATGCTCTTTTAGCTTTCACCCTGGGAGTGAAACAATTGATTGTTGGAGTCAACAAGATG Synergus AAACATGATTACGGGAACGAGTCAAGCTGATTGTGCAGTGCTGATTGTAGCAGCTGGAATTGGTGAATTCGAAGCTGGAATTTCGAAAAATGGTCAGACTCGTGAACATGCTTTATTGGCTTTCACCCTGGGAGTGAAGCAATTGATTGTGGGAGTCAACAAGATG Periclistus AAACATGATCACTGGAACAAGTCAGGCTGACTGTGCAGTGCTGATCGTAGCTGCTGGAATTGGAGAATTTGAAGCTGGAATCTCGAAAAATGGTCAAACTCGTGAACATGCTTTATTGGCTTTCACCTTGGGAGTGAAGCAATTGATTGTGGGAGTCAATAAGATG Ceroptres ?????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Synophromorpha AAACATGATCACTGGAACAAGTCAGGCTGACTGTGCTGTGCTGATCGTAGCAGCTGGAATTGGAGAATTTGAAGCCGGAATTTCGAAAAATGGTCAAACTCGTGAACATGCATTATTGGCTTTCACCCTGGGAGTGAAGCAATTGATTGTGGGAGTCAATAAGATG Xestophanes AAACATGATCACTGGAACAAGTCAGGCTGACTGTGCTGTGCTGATCGTAGCAGCTGGAATTGGAGAATTTGAAGCTGGAATTTCGAAAAATGGTCAAACTCGTGAACATGCTTTATTGGCTTTCACTCTGGGAGTGAAGCAATTGATTGTGGGAGTCAATAAGATG Diastrophus AAACATGATCACTGGAACAAGTCAGGCTGACTGTGCAGTGCTGATCGTAGCTGCTGGAATTGGAGAATTTGAAGCTGGAATTTCGAAAAATGGTCAAACTCGTGAACATGCTTTATTGGCTTTCACACTGGGAGTGAAGCAATTGATTGTGGGAGTCAATAAGATG Gonaspis AAACATGATCACTGGAACAAGTCAGGCTGACTGTGCAGTGCTGATCGTAGCTGCTGGAATTGGAGAATTTGAAGCTGGAATTTCGAAAAATGGTCAAACTCGTGAACATGCTTTATTGGCTTTCACACTGGGAGTTAAGCAATTGATTGTGGGAGTCAATAAGATG Liposthenes_gle AAACATGATCACTGGTACGAGTCAAGCTGATTGTGCTGTGCTGATTGTAGCAGCTGGAATTGGAGAATTCGAAGCTGGAATTTCCAAAAATGGTCAAACTCGGGAGCATGCTTTATTGGCTTTCACCCTTGGAGTGAAGCAATTGATTGTGGGAGTCAATAAGATG Liposthenes_ker AAACATGATCACTGGTACGAGTCAAGCTGACTGTGCTGTGCTGATTGTAGCAGCTGGAATTGGAGAATTCGAAGCTGGAATATCCAAAAATGGTCAAACTCGAGAACATGCTTTATTGGCTTTCACCCTCGGAGTGAAGCAATTGATTGTGGGAGTCAATAAGATG Antistrophus AAACATGATCACTGGTACGAGTCAAGCTGATTGTGCTGTGCTGATTGTAGCAGCTGGAATTGGAGAATTCGAAGCTGGAATTTCTAAAAATGGTCAAACTCGAGAGCATGCTTTATTGGCTTTCACCCTCGGAGTAAAGCAGTTGATTGTGGGAGTCAATAAGATG Rhodus AAACATGATCACTGGTACGAGTCAAGCTGATTGTGCTGTGTTGATTGTAGCAGCTGGAATTGGAGAATTCGAAGCTGGAATTTCCAAAAATGGTCAAACTCGAGAGCATGCTTTATTGGCTTTCACCCTCGGAGTAAAGCAGTTGATTGTGGGAGTCAATAAGATG Hedickiana AAACATGATCACTGGTACGAGTCAAGCTGATTGTGCTGTGCTGATTGTAGCAGCTGGAATTGGAGAATTCGAAGCTGGAATTTCCAAAAATGGTCAAACTCGAGAGCATGCTTTATTGGCTCTCACCCTTGGAGTAAAGCAGTTGATTGTGGGAGTCAATAAGATG Neaylax AAACATGATCACTGGTACGAGTCAAGCTGATTGTGCTGTACTGATTGTAGCAGCTGGAATTGGAGAATTCGAAGCTGGGATTTCCAAAAATGGTCAAACTCGGGAGCATGCTTTATTAGCTTTCACCCTCGGTGTAAAGCAGTTGATTGTGGGAGTCAATAAGATG Isocolus AAACATGATCACTGGTACTAGTCAAGCTGATTGTGCTGTGCTGATTGTAGCAGCTGGAATTGGGGAATTCGAAGCTGGAATATCCAAAAATGGTCAAACTCGAGAGCATGCTTTATTAGCTTTCACCCTGGGAGTGAAGCAATTGATTGTGGGAGTCAATAAGATG Aulacidea GAACATGATTACTGGTACGAGTCAAGCTGATTGCGCTGTGCTGATTGTAGCAGCTGGAATTGGAGAATTCGAAGCTGGAATTTCAAAAAATGGTCAAACTCGAGAGCATGCTTTATTGGCTTTCACCCTCGGAGTGAAGCAATTGATTGTCGGAGTCAATAAGATG Panteliella AAACATGATCACTGGTACGAGTCAAGCTGATTGTGCTGTGCTGATTGTAGCAGCTGGAATTGGAGAATTCGAAGCTGGAATTTCCAAAAATGGTCAAACTCGAGAACATGCTTTATTGGCCTTCACCCTTGGAGTGAAGCAATTGATTGTGGGAGTTAATAAGATG Barbotinia AAATATGATCACTGGAACGAGTCAAGCTGATTGTGCAGTGCTGATTGTAGCGGCTGGAATTGGAGAATTCGAAGCTGGAATTTCGAAAAATGGTCAAACTCGTGAACATGCTTTATTGGCTTTCACTCTGGGAGTGAAGCAGTTGATTGTAGGGGTCAACAAGATG Aylax AAACATGATCACTGGAACGAGTCAAGCTGATTGTGCAGTACTGATTGTAGCAGCTGGAATTGGAGAATTCGAAGCTGGTATTTCGAAAAATGGTCAAACTCGTGAACATGCTTTATTGGCTTTTACTCTAGGAGTGAAGCAATTGATTGTAGGGGTCAACAAGATG Iraella AAACATGATCACTGGAACGAGTCAAGCTGATTGTGCAGTGCTGATTGTAGCAGCTGGAATTGGAGAATTCGAAGCTGGTATTTCGAAAAATGGTCAAACTCGTGAACATGCTTTATTGGCTTTCACTCTGGGAGTAAAGCAATTGATTGTAGGGGTCAATAAGATG Timaspis AAACATGATCACTGGAACAAGTCAAGCTGACTGTGCAGTGCTAATCGTAGCAGCTGGAATTGGAGAATTTGAAGCTGGAATTTCAAAAAATGGTCAAACTCGTGAACATGCTTTATTGGCTTTCACTCTGGGAGTGAAGCAACTGATTGTGGGTGTCAATAAGATG Phanacis_1 AAACATGATCACTGGAACAAGTCAAGCCGATTGTGCAGTGCTGATCGTAGCAGCTGGAATTGGAGAATTCGAAGCTGGAATTTCAAAAAATGGCCAAACTCGAGAACATGCTTTATTGGCTTTCACTCTGGGAGTGAAGCAACTGATTGTAGGTGTCAATAAGATG Phanacis_2 AAACATGATCACTGGAACAAGTCAAGCTGACTGTGCAGTGCTGATCGTAGCAGCTGGAATTGGAGAATTCGAAGCTGGAATTTCCAAAAATGGTCAAACTCGTGAACATGCTTTATTGGCTTTCACTCTGGGAGTGAAGCAACTGATTGTGGGTGTCAATAAGACG Eschatocerus AAACATGATCACTGGAACGAGTCAAGCTGATTGTGCTGTACTGATAGTAGCAGCTGGAATTGGAGAATTCGAGGCTGGAATTTCGAAAAATGGTCAAACTCGTGAACATGCTTTATTGGCTTTCACTCTGGGAGTGAAGCAATTGATTGTGGGAGTCAATAAGATG Diplolepis AAACATGATTACTGGAACAAGTCAAGCTGATTGTGCAGTGTTAATAGTAGCAGCTGGAATAGGAGAATTCGAAGCTGGAATTTCAAAAAATGGCCAAACTCGTGAACATGCTCTACTTGCTTTTACCTTGGGAGTAAAGCAATTGATTGTCGGAGTCAATAAAATG Pediaspis AAATATGATTACTGGGACGAGTCAAGCTGATTGTGCAGTTCTGATAGTAGCAGCTGGAATAGGAGAATTCGAAGCTGGAATTTCCAAAAATGGTCAAACTCGTGAACATGCTCTACTGGCCTTCACCTTGGGAGTGAAGCAATTGATCGTTGGAGTCAATAAGATG Plagiotrochus AAACATGATCACTGGAACGAGTCAAGCTGATTGTGCAGTGCTGATTGTGGCAGCTGGAATTGGAGAATTCGAAGCTGGAATTTCGAAAAATGGTCAAACTCGTGAACATGCTTTATTGGCTCTCACTCTTGGAGTGAAGCAATTAATTGTAGGAGTTAATAAGATG Andricus AAACATGATCACTGGAACGAGTCAAGCTGATTGTGCTGTGCTGATCGTAGCAGCTGGAATTGGAGAATTCGAGGCTGGAATTTCGAAAAATGGTCAAACTCGTGAACATGCTTTATTGGCTTTCACTCTGGGAGTGAAGCAATTGATTGTGGGAGTCAATAAGATG Neuroterus AAACATGATCACTGGAACGAGTCAAGCTGATTGTGCTGTACTGATCGTAGCAGCTGGAATTGGAGAATTCGAGGCTGGGATTTCGAAAAATGGTCAAACTCGTGAGCATGCTTTATTGGCTTTCACTCTGGGAGTGAAACAATTGATTGTAGGAGTCAATAAGATG Biorhiza AAACATGATCACTGGAACGAGTCAAGCTGATTGTGCTGTACTGATAGTAGCAGCTGGAATTGGAGAATTCGAGGCTGGAATTTCGAAAAATGGTCAAACTCGTGAACATGCTTTATTGGCTTTCACTCTGGGAGTGAAGCAATTGATTGTGGGAGTCAATAAGATG Parnips AAACATGATCACTGGAACGAGTCAAGCTGATTGTGCAGTTTTAATAGTAGCAGCAGGAATTGGAGAATTCGAAGCTGGAATTTCAAAGAATGGTCAAACTCGTGAACATGCTCTTTTGGCTTTCACCCTAGGAGTGAAGCAATTGATCGTTGGAGTCAATAAGATG Paramblynotus GAACATGATTACTGGAACTAGTCAGGCTGACTGTGCAGTTTTGATAGTAGCTGCAGGAATTGGAGAATTCGAGGCTGGAATTTCAAAGAATGGCCAAACTCGTGAACATGCCCTTTTGGCGTTCACCTTAGGAGTGAAGCAATTGATTGTTGGAGTCAACAAGATG Ibalia GATATGACTGATCCACCATATTCTGAGAGTCGATTTGAAGAAATCAAAAAAGAGGTATCTTCATACATCAAGAAAATTGGCTACAATACTGCTTCAGTTGCATTTGTTCCCATTTCT????????????????????????????????????????????????? Synergus GATATGACTGATCCACCATATTCTGAGAGTCGATTCGAAGAAATAAAAAAAGAGGTTTCTTCATACATCAAGAAAATTGGTTACAATACCGCTTCAGTTGCATTTGTTCCAATTTCTTCTAGGTCCATTTATATGTGAGATGTACGGAATGTTTGGCTCCCTATTC Periclistus GATATGACTGATCCGCCATATTCTGAGACTCGATTTGAAGAAATCAAGAAAGAGGTGTCTTCATACATCAAGAAAATTGGTTACAATACTGCTTCGGTAGCATTTGTTCCAATTTCTTCTTGGTCCATTTATGTGTGAGATCTACGCAATGCTTGGCTCCCTATTC Ceroptres ?????????????????????????????????????????????????????????????????????????????????????????????????????????????????????TCTTGGACCATTTATATGCGAGATTTACGCAATGCTTGGCTCCCTATTC Synophromorpha GATATGACTGATCCACCATATTCTGAGAGTCGATTTGAAGAAATCAAGAAAGAGGTGTCTTCATACATCAAGAAAATTGGTTACAATACTGCTTCGGTTGCATTTGTTCCAATTTCT????????????????????????????????????????????????? Xestophanes GATATGACTGATCCACCATATTCTGAGAGTCGATTTGAAGAAATCAAGAAAGAGGTGTCTTCATACATCAAGAAAATTGGTTACAATACTGCTTCGGTTGCATTTGTTCCAATTTCT????????????????????????????????????????????????? Diastrophus GGCATGACTGATCCACCATATTCTGAGAGTCGATTTGAAGAAATCAAGAAAGAGGTGTCTTCATACATCAAGAAAATTGGTTTCAATACTGCTTCGGTTGCATTTGTTCCAATTTCT????????????????????????????????????????????????? Gonaspis GACATGACTGATCCACCATATTCTGAGAGTCGATTTGAAGAAATCAAGAAAGAGGTGTCATCATACATCAAGAAAATTGGTTACAATACTGCTTCGGTTGCATTTGTTCCAATTTCT????????????????????????????????????????????????? Liposthenes_gle GATATGACTGATCCACCATACTCTGAGAGTCGATTCGAAGAAATCAAAAAAGAGGTTTCTTCATACATCAAGAAAATTGGTTATAATACTGCGTCGGTTGCATTTGTTCCAATTTCTTCTTGGTCCATTTTTATGTGAAATATACGCAATGCTTGGCTCCCTATTT Liposthenes_ker GATATGACTGATCCACCATACTCTGAGAGTCGCTTTGAAGAAATCAAAAAAGAGGTTTCTTCATACATCAAGAAAATTGGCTATAATACTGCGTCGGTTGCATTTGTTCCAATTTCTTCTTGGCCCATTTTTATGTGAGATGTACGGAATGTTTGGTTCCCTATTC Antistrophus GATATGACTGATCCACCGTACTCTGAGAGTCGATTTGAAGAAATCAAAAAAGAAGTTTCTTCATACATCAAGAAAATTGGTTATAATACTGCGTCAGTTGCATTTGTTCCAATTTCTTCTTGGTCCATTTTTATGTGAGATGTACGCAATGCTTGGCTCCCTATTC Rhodus GATATGACTGATCCACCATACTCTGAGAGTCGATTTGAAGAAATCAAAAAAGAGGTTTCTTCATACATCAAGAAAATTGGTTATAATACTGCGTCGGTTGCATTTGTTCCAATTTCTTCTTGGTCCATTTTTATGTGAGATGTACGCAATGTTTGGCTCCCTATTT Hedickiana GATATGATTGATCCACCATACTCTGAGAGTCGATTTGAAGAAATCAAAAAAGAGGTTTCTTCATACATCAAGAAAATTGGTTATAATACTGCGTCGGTTGCATTTGTTCCAATTTCTTCTTGGTCCATTTTTATGTGAGATGTACGGAATGTTTGGCTCCCTGTTT Neaylax GATATGACTGATCCACCATACTCTGAGAGTCGATTTGAAGAAATCAAAAAAGAGGTTTCTTCATACATCAAGAAAATTGGTTATAATACTGCGTCGGTTGCATTTGTTCCAATTTCTTCTTGGTCCATTTTTATGTGAGATGTACGGAATGTTTGGCTCCCTATTA Isocolus GATATGACTGATCCACCATACTCCGACAGTCGATTTGAAGAAATCAAAAAAGAGGTTTCTTCATACATCAAGAAAATTGGTTATAATACTGCGTCGGTTGCATTTGTTCCAATTTCTTCTTGGTCCGTTTTTATGTGAGATGTACGCAATGCTTGGCTCCCTATTC Aulacidea GATATGACTGATCCACCATACTCTGAGAGCCGATTTGAAGAAATCAAAAAAGAGGTTTCTTCATACATCAAGAAAATTGGTTATAATACTGCATCGGTTGCATTTGTTCCAATTTCTTCTTGGTCCATTTTTATGTGAGATGTACGGAATGTTYGGCTCCCTATTC Panteliella GATATGACTGATCCACCATACTCTGAGAGTCGATTTGAAGAAATCAAAAAAGAGGTTTCTTCATACATCAAGAAAATTGGTTATAATACTGCGTCGGTTGCATTTGTTCCAATTTCTTCTTGGTCCATTTTTATGTGAGATGTACGGAATGTTTGGCTCCCTTTTC Barbotinia GATATGACTGATCCTCCATATTCTGAAAGTCGATTTGAAGAAATAAAGAAAGAGGTTTCTTCATACATCAAGAAAATTGGTTACAATACTGCTTCGGTTGCATTTGTTCCAATTTCTTCTTGGTCCATTTATATGTGAGATATATGCAATGCTTGGCTCCCTATTT Aylax GATATGACTGATCCTCCATATTCTGAAAGTCGATTTGAAGAAATAAAGAAAGAGGTTTCTTCATACATCAAGAAAATTGGTTACAATACTGCTTCGGTTGCATTTGTTCCAATTTCTTCTTGGTCCATTTATATGTGAGATGTACGGAATGTTTGGCTCTCTATTT Iraella GATATGACTGATCCTCCATATTCTGAAAGTCGATTTGAAGAAATTAAGAAAGAAGTTTCTTCATACATCAAGAAAATTGGTTACAATACTGCTTCGGTCGCATTTGTTCCAATTTCT????????????????????????????????????????????????? Timaspis GACATGGCTGATCCATCATATTCTGAGAGTCGATTTGAAGAAATCAAAAAAGAGGTTTCTTCATACATCAAAAAAATTGGATACAATACTGCTTCGGTTGCATTTGTACCAATTTCTTCTTGGTCCATTTTTATGTGAGATATATGCAATGCTTGGCTCCCTATTC Phanacis_1 GACATGACTGATCCACCATATTCTGAAAGTCGATTTGAAGAAATCAAAAAAGAGGTTTCTTCATACATCAAAAAAATTGGATACAATACTGCTTCGGTTGCATTTGTTCCAATTTCT????????????????????????????????????????????????? Phanacis_2 GACATGACTGACCCACCATATTCTGAGAGTCGATTTGAAGAAATCAAAAAAGAGGTTTCTTCATACATCAAAAAAATTGGATACAATACTGCTTCGGTTGCATTTGTTCCAATTTCT????????????????????????????????????????????????? Eschatocerus GATATGACTGATCCACCATATTCTGAGAGTCGATTTGAAGAAATAAAAAAAGAGGTTTCCTCATACATCAAGAAAATTGGTTACAATACTTCTTCGGTTGCATTTGTTCCAATTTCTATTTGGACCACTCTTCTGTCAAATATACGCATTGCTCGGCTCACTTTTT Diplolepis GATATGACTGACCCACCATATTCAGAGAGCCGATTTGAAGAAATCAAAAAAGAAGTTTCTTCATACATCAAGAAAATTGGTTACAATACAGCTTCAGTTGCATTTGTTCCCATTTCTNNNNGGTCCATTCTTGTGTGAGATATACGCTTTACTTGGTTCTCTATTT Pediaspis GATATGACTGACCCACCATATTCTGAAAGTCGATTTGAAGAAATCAAAAAAGAGGTTTCTTCATACATCAAAAAAATTGGTTACAATACAGCTTCGGTTGCATTTGTTCCCATTTCTGTTTGGGCCATTTATATGTGAGCTGTATGCATTGTTCGGTTCCCTATTC Plagiotrochus GATATGACTGATCCGCCATATTCTGAGAGTCGATTTGAAGAAATCAAGAAAGAGGTGTCTTCATACATAAAGAAAATTGGTTACAATACTGCCTCGGTTGCATTTGTTCCAATTTCAACATGGACCTTTTATGTGCGAGATGTACGCAATGTTTGGCTCCCTATTC Andricus GATATGACCGATCCACCATATTCTGAAAGTCGTTTTGAAGAAATCAAGAAAGAGGTTTCCTCATACATCAAGAAAATTGGTTACAATACTTCTTCGGTTGCATTTGTTCCAATTTCTTCTTGGTCCATTTATGTGTGAGATCTACGCAATGCTTGGCTCCCTATTC Neuroterus GATATGACCGATCCACCATATTCTGAGAGTCGATTTGAAGAAATCAAAAAAGAGGTTTCCTCGTACATCAAGAAGATTGGTTACAATACTTCTTCGGTTGCATTTGTTCCAATTTCT????????????????????????????????????????????????? Biorhiza GATATGACTGATCCACCATATTCTGAGAGTCGATTTGAAGAAATAAAAAAAGAGGTTTCCTCATACATCAAGAAAATTGGTTACAATACTTCTTCGGTTGCATTTGTTCCAATTTCTTCTTGGTCCATTTATATGTGAGATCTACGCAATGCTTGGCTCCTTATTC Parnips GATATGACTGACCCACCATATTCTGAGAGCCGATTTGAGGAAATAAAAAAGGAAGTTTCTTCATACATCAAGAAAATTGGCTACAATACTGTTTCGGTTGCATTTGTTCCCATTTCTATTGGGGCCATTTATGTGTGAAATGTATGGACTAACTGGCTCTCTATTC Paramblynotus GACATGACTGATCCACCTTATTCTGAAAGTCGTTTTGAGGAGATCAAGAAAGAGGTTTCCTCATACATCAAGAAGATTGGCTACAATACAGCATCAGTTGCATTTGTACCAATTTCT????????????????????????????????????????????????? Ibalia ?????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Synergus GGATGTGGCTCTATTTGGACAATGTGTATGATTGCTTTCGACCGATACAACGTTATCGTAAAAGGTTTAGCTGGCAAGCCCTTAACTATCAGTGGTGCAATTCTGCGCATTGCTTTTCTCTGGATCTGGGCAGTAATTTGGACAGTTGCACCAATGATAGGATGGA Periclistus GGATGTGGCTCCATTTGGACAATGTGTATGATTGCTTTCGATCGATACAATGTTATCGTAAAGGGTCTGGCTGGCAAACCGCTATCTATCAGTGGTGCAATTCTGCGCATTGTTGGACTCTGGGTCTGGGCAGTAATTTGGACCATTGCACCAATGATAGGATGGA Ceroptres GGATGTGCCTCCATTTGGACGATGTGTATGATTGCTTTCGACCGATACAATGTCATCGTGAAAGGTTTAGCTGGAAAGCCGCTAACTATCAGTGGTGCAATTCTGCGTATTATTGGTCTCTGGGCCTGGGCAATAGTTTGGACCGTTGCCCCAATGTTAGGATGGA Synophromorpha ?????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Xestophanes ?????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Diastrophus ?????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Gonaspis ?????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Liposthenes_gle GGATGCGGCTCCATTTGGACAATGTGTATGATTGCTTTCGATCGATACAATGTTATCGTAAAGGGCTTAGCAGGCAAGCCCTTAACTATCACTGGTGCAATTCTGCGGATTGTTGGTCTCTGGGTTTGGGCAGTTGTTTGGACCATTGCACCAATGATAGGATGGA Liposthenes_ker GGATGCGGCTCCATTTGGACAATGTGTATGATTGCTTTCGATCGATATAATGTTATCGTAAAAGGCTTAGCAGGCAAGCCCTTAACTATCACTGGTGCAACTCTGCGGATTATTGGTCTCTGGGCTTGGGCAGTCGTTTGGACCATTGCACCAATGATAGGATGGA Antistrophus GGATGCGGCTCCATTTGGACAATGTGTATGATTGCTTTCGATCGATACAATGTTATTGTAAAAGGCTTAGCAGGCAAGCCCTTAACTATCACTGGTGCAATTCTGCGGATTGTTGGTCTCTGGGTTTGGGCAGTCGTTTGGACTATTGCACCAATGATAGGATGGA Rhodus GGATGCGGCTCCATTTGGACAATGTGTATGATTGCTTTCGATCGATACAATGTTATTGTAAAAGGCTTAGCAGGCAAGCCCTTAACTATCACTGGTGCAGTTCTGCGGATTGTTGGTCTCTGGGTTTGGGCAGTCGTTTGGACCATTGCACCAATGATAGGATGGA Hedickiana GGATGCGGCTCCATTTGGACAATGTGTATGATTGCTTTCGATCGATACAATGTTATTGTAAAAGGCTTAGCAGGCAAGCCCTTAACTATCACTGGTGCAATTCTGCGGATTGTTGGTCTATGGGTTTGGGCAGTCGTTTGGACCATTGCACCAATGATAGGATGGA Neaylax GGATGCGGCTCCATCTGGACAATGTGTATGATTGCTTTCGATCGATACAATGTTATTGTAAAAGGCTTAGCAGGCAAGACCTTAACTATCACTGGCGCAATTCTGCGGATTGTTGGTCTCTGGGTTTGGGCAGTCGTTTGGACCATTGCACCAATGATAGGATGGA Isocolus GGATGTGGCTCCATTTGGACAATGTGTATGATTGCTTTTGACCGATATAATGTTATCGTAAAAGGTTTAGCTGGCAAGCCCTTGACTATCACTGGTGCAATTCTGCGGATTGTTGGTCTCTGGATTTGGGCAGTCGTTTGGACCATTGCACCAATGATAGGATGGA Aulacidea GGATGTGGCTCCATTTGGACAATGTGTATGATTGCTTTTGACCGATATAATGTTATCGTAAAAGGCTTAGCTGGCAAGCCCTTAACTATCACTGGTGCAATTCTGCGGATTGTTGGTCTCTGGGTTTGGGCAGTTGTTTGGACCATTGCACCAATGATAGGATGGA Panteliella GGATGTGGCTCCATTTGGACAATGTGTATGATTGCTTTCGACCGATACAATGTTATCGTAAAAGGCTTGGCTGGCAAGCCCTTAACTATCACTGGTGCAATTCTGCGGATTGTTGGTCTCTGGGTTTGGGCAGTCATTTGGACCATTGCCCCAATGATAGGATGGA Barbotinia GGATGTGGCTCCATTTGGACAATGTGTATGATTGCTTTCGACCGATACAATGTTATCGTAAAAGGTTTAGCTGGCAAGCCATTAACTATCAGTGGTGCAATTCTGCGTATTGTTTTTCTCTGGGTCTGGGCGGTAGTTTGGACCATTGCACCAATGATAGGATGGA Aylax GGATGTGGATCCATTTGGACAATGAGTATGATTGCTTTCGACCGATACAATGTTATCGTAAAAGGTTTAGCTGGCAAGCCATTAACTATCAGTGGTGCAATTCTGCGTATTGNTTTCCTCTGGCTCTGGGCGGTAATTNGGACCATTGCACCAATGATAGGATGGA Iraella ?????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Timaspis GGATGTGGCTCCATTTGGACAATGTGTATGATTGCTTTTGATCGATACAATGTTATTGTAAAAGGTTTAGCTGGCAAGCCATTAACTATCAGTGGTGCAATTCTACGCATTGTTGGTCTCTGGGTCTGGGCTGTCATTTGGACCATTGCACCAATGCTAGGATGGA Phanacis_1 ?????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Phanacis_2 ?????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Eschatocerus GGCTGTGCTTCAATCTGGACAATGTGTTTGATTGCCTTCGACAGGTACAATGTCATTGTCAAGGGTCTAGCTGGAAAACCTTTGACCATAACCGGTGCAGTCTTGCGTATATTTGCTCTCTGGATCTGGGCATTGATTTGGACAGTTGCACCAATGTTTGGATGGA Diplolepis GGATGTGGCTCCATTTGGACAATGTGTATGATTGCTTTTGATAGGTACAATGTAATAGTGAAAGGTTTGGCTGGGAAGCCCTTAACAATCACCGGTGCAATTATACGCATAATTGGCCTTTGGGTCTGGGCCATTATTTGGACTATTGCGCCAATGTTTGGATGGA Pediaspis GGATGTGGCTCCATTTGGACAATGTGTATGATTGCTTTCGACAGATACAACGTAATCGTAAAAGGCTTAGCTGGAAAGCCCTTAACCATAAGCGGTGCAATTATTCGTATAATTGGTCTCTGGGTCTGGGCCGTGATTTGGACCGTTGCACCAATATTTGGATGGA Plagiotrochus GGATGCGGCTCCATTTGGACGATGTGTATGATAGCTTTCGACCGATACAATGTTATCGTGAAGGGATTAGCTGGAAAGCCGTTAACTATCAGTGGTGCAATTCTGCGCATTGTTGGACTCTGGGTTTGGGCAGTAATTTGGACAATTGCACCAATGTTAGGATGGA Andricus GGATGTGTCTCCATTTGGACAATGTGTATGATTGCTTTTGATCGATACAATGTCATTGTAAAGGGTTTAGCTGGCAAGCCACTAACTATCAGTGGTGCAATTCTGCGCATTGTTGGTCTCTGGGTCTGGGCAATAATTTGGACCATTGCACCAATGTTGGGCTGGA Neuroterus ?????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Biorhiza GGATGTGCCTCCATTTGGACAATGTGTATGATTGCTTTCGACCGATACAATGTCATTGTAAAGGGTTTAGCTGGAAAGCCACTCACTATCAGTGGTGCAATTCTGCGTATTGCTGGTCTCTGGGTATGGGCAGTAATTTGGACCATTGCACCAATGTTGGGCTGGA Parnips GGATGTGGGTCCATTTGGACAATGTGCATGATTGCGTTCGACAGATACAACGTAATAGTAAAAGGTTTGGCAGGAAAACCTTTAACCATCAGTGGTGCAATTCTTCGCATAGTTGGTCTCTGGGTCTGGGCCGTTATTTGGACCATTGCACCAATGATAGGATGGA Paramblynotus ?????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Ibalia ?????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Synergus ATCGGTACGTTCCGGAGGGTAACATGACAGCTTGCGGAACTGACTACCTGACTAAGGATTGGTTATCGAGGTCTTACATCATTGTATACAGCGTCTTCGTATACTTCATGCCACTTTTCCTCATCATATACAGTTACTATTTCATCATTGCTGCTGTATCTGCCCA Periclistus ATCGGTATGTTCCTGAAGGCAATTTGACAGCTTGCGGAACTGACTATCTGAGTAAGGATTGGTTGTCGAGGTCTTACATTCTTGTATACAGCGTCTTCGTATACTTCATGCCACTTTTCCTCATCATATACAGTTACTATTTCATCATCGCTGCTGTATCTGCTCA Ceroptres ATCGGTACGTTCCCGAAGGCAACATGACAGCTTGTGGAACTGACTATCTGACTAAGGATTGGTTCTCGAGATCTTACATTATTATATATAGCGTCTTCGTATACTTTGCGCCACTTTTCCTCATCATATACAGTTACTATTTCATCATTGCTGCTGTATCTGCTCA Synophromorpha ?????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Xestophanes ?????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Diastrophus ?????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Gonaspis ?????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Liposthenes_gle ATCGGTACGTTCCCGAGGGTAACATGACAGCTTGTGGAACTGATTATCTAACCAAAGATTGGTTCTCAAGGTCTTATATACTTATATACAGTGTCTTTGTATACTTCATGCCACTTTTTCTCATCATATACAGCTACTATTTTATCATTGCTGCTGTATCTGCTCA Liposthenes_ker ATCGGTACGTTCCCGAGGGTAACATGACAGCTTGTGGAACTGACTATCTAACCAAAGATTGGTTCTCGAGGTCTTATATACTTATATACAGYGTCTTTGTATACTTCATGCCACTTTTTCTCATCATATACAGCTACTATTTTATCATTGCTGCTGTATCTGCTCA Antistrophus ATCGGTACGTTCCCGAAGGTAACATGACAGCTTGTGGAACTGACTATCTAACTAAAGATTGGTTCTCGAGGTCTTATATAATTATATACAGTGTCTTCGTATACTTCATGCCACTTTTTCTCATCATATACAGTTACTATTTTATCATTGCTGCTGTATCTGCTCA Rhodus ATCGGTACGTTCCCGAAGGCAATATGACAGCTTGTGGAACTGACTATCTAACTAAAGACTGGTTCTCGAGGTCTTATATACTTATATACAGTGTCTTCGTATACTTCATGCCACTTTTTCTCATTATATACAGCTACTATTTTATCATTGCTGCTGTATCTGCTCA Hedickiana ATCGGTACGTTCCCGAAGGCAACATGACAGCTTGTGGAACTGACTATCTAACTAAAGACTGGTTCTCGAGGTCTTATATACTTATATACAGTGTCTTCGTATACTTTATGCCACTTTTTCTCATCATATACAGCTACTATTTTATCATTGCTGCTGTATCTGCTCA Neaylax ATCGGTACGTTCCCGAAGGCAACATGACAGCTTGTGGAACTGACTATCTAACTAAAGACTGGTTCTCGAGGTCTTATATACTTATATACAGTGTCTTCGTATACTTCATGCCACTTTTTCTCATCATATACAGCTACTATTTTATCATTGCTGCTGTATCTGCTCA Isocolus ATCGGTACGTTCCCGAGGGCAACATGACAGCTTGTGGAACTGACTACTTGACTAAGGATTGGTTCTCGAGGTCCTATATACTTATATACAGTGTCTTCGTATATTTCATGCCACTTTTTCTCATCATATACAGCTACTATTTCATCATTGCTGCTGTATCTGCTCA Aulacidea ATCGGTATGTTCCCGAGGGTAACATGACAGCTTGTGGAACTGACTATCTAACTAAGGATTGGTTCTCCAGGTCCTTCATACTTGTATACAGTATCTTCGTATACTTCATGCCACTTTTTCTCATCATATACAGCTACTATTTCATCATTGCTGCTGTATCTGCTCA Panteliella ATCGGTACGTTCCCGAGGGCAATATGACAGCTTGTGGAACTGACTATCTGAATAAAGATTGGTTCTCGAGGTCTTATATACTTATATACAGTGTCTTTGTATACTTTATGCCACTTTTTCTCATTATATACAGTTACTATTTCATCATTGCTGCTGTATCTGCCCA Barbotinia ATCGGTACGTTCCCGAGGGCAACATGACAGCTTGTGGAACTGACTATCTAACTAAGGATTGGTTCTCGAGGTCTTACATTCTTGTATACAGCGTCTTCGTATACTTCATGCCACTTTTCCTCATCATCTACAGTTACTATTTCATCATTGCTGCTGTATCTGCTCA Aylax ATCGGTACGTNCCCGAGGGTAACATGACAGCTTGTGGAACTGACTATCTANCTAAGGATTGGTTCTCGAGGT---------------------------------------------------------------------------------------------- Iraella ?????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Timaspis ATCGGTATGTTCCTGAAGGTAACATGACAGCTTGTGGGACTGACTATTTGACTAAGAACTGGTTCTCGAGGTCTTACATCCTTATATACAGCATTTTCGTGTATTTCATGCCACTTTTCCTCATAATATACAGTTACTATTTCATCATTGCTGCTGCATCTGCTCA Phanacis_1 ?????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Phanacis_2 ?????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Eschatocerus ATCGGTATGTACCTGAAGGTAATCTGACAGCATGTGGTACTGATTATTTGAGCAAGGACTGGCTCTCAAGATCCTACCTCCTCGTATATGGTTTCTTCGTATACTTCATGCCGCTTTTCCTGATCATCTATAGCTATTATTTTATAATCGCAGCCGTATCTGCCCA Diplolepis ATCGGTATGTACCTGAAGGTAACATGACAGCTTGCGGAACTGATTATTTAAGTAAAGACTGGTTCTCGAGGTCTTACATCCTTGTATACAGTATCTTCGTATACTATATGCCGCTTTTCCTTATCATATACAGTTACTATTTTATCATCTCAGCTGTATCTGCTCA Pediaspis ATCGGTATGTACCCGAGGGTAACATGACAGCTTGCGGAACTGACTATCTCAGTAAGGACTGGTTTTCGAGGTCTTACATCATTGTTTACAGTATCTTCGTGTACTACATGCCACTTTTCCTCATCATATACAGCTACTACTTTATCATCTCAGCTGTATCTGCTCA Plagiotrochus ATCGGTACGTTCCCGAGGGCAACATGACAGCTTGTGGAACTGACTATTTGACTAAGGATTGGTTCTCGAGGTCTTACATACTTGTATATAGCGTCTTTGTATACTTCATGCCACTTTTCCTTATCATATACAGTTACTATTTCATCATTGCTGCTGTAACAGCTCA Andricus ATCGGTATGTTCCCGAGGGCAACATGACAGCATGTGGAACTGACTATCTGTCTAAGGATTGGTTCTCGAGGTCTTACATTATTGTATACAGCATCTTTGTATACTTTATGCCACTTTTCCTCATCATATACAGTTACTATTTTATCATTGCTGCTGTAACTGCTCA Neuroterus ?????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Biorhiza ATCGGTATGTTCCTGAGGGCAACATGACAGCATGTGGAACTGACTATCTGTCTAAGGATTGGTTCTCAAGGTCTTACATTCTTGTATACAGCATCTTTGTATACTTTATGCCACTTTTCCTTATAATATACAGTTACTATTTCATCATTGCTGCTGTAACTGCTCA Parnips ATCGGTACGTACCCGAAGGTAACTTGACAGCTTGTGGAACTGACTATCTGACACAGGATTGGTTCTCCAAGTCTTATATCCTTGTATACAGTGTTTTCGTATATTTCATGCCACTTTTCCTCATCATATACAGCTATTATTTCATCATCGCAGCTGTATCTGCTCA Paramblynotus ?????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Ibalia ????????????????????????????????????????????????????????????????????????????????????????????????????------------------------CTAANGCTAAATAC-AACCACGAGACCGATAGCGAACAAGTA Synergus TGAAAAGGCTATGCGTGAACAGGCCAAAAAGATGAATGTAGCTTCTCTACGATCATCTGACAATCAAAATACGAGTGCTGAACATAAACTTGCCAAGGTACTCTAAGTGGGTGGTAAACTCCATCTAAGGCTAAATAC-ACCCACGAGACCGATAGCGAACAAGTA Periclistus CGAAAAAGCAATGCGTGAACAGGCCAAAAAGATGAATGTAGCTTCTCTACGATCATCCGATAATCAAAATACGAGTGCTGAACATAAACTCGCCAAGGTA------------------------------------------------------------AAGGTA Ceroptres CGAAAAAGCAATGCGTGAACAGGCCAAAAAGATGAATGTAGCATCGCTACGATCATCCGACAATCAAAATACGAGCGCTGAACATAAACTCGCCAAGGTACTCTAAGTGGGTGGTAAACTCCATCTAAGGCTAAATAC-AACCACGAGACCGATAGCGAACAAGTA Synophromorpha ????????????????????????????????????????????????????????????????????????????????????????????????????CTCTAAGTGGGTGGTAAACTCCATCTAAGGCTAAATAC-AACCACGAGACCGATAGCGAACAAGTA Xestophanes ????????????????????????????????????????????????????????????????????????????????????????????????????------------------------------------------------ACCGATAGCGAACAAGTA Diastrophus ????????????????????????????????????????????????????????????????????????????????????????????????????------------------------------------------------ACCGATNGCGAACAAGTA Gonaspis ????????????????????????????????????????????????????????????????????????????????????????????????????CTCTAAGTGGGTGGTAAACTCCATCTAAGGCTAAATAC-GACCACGAGACCGATAGCGAACAAGTA Liposthenes_gle CGAAAAAGCAATGCGTGAACAAGCCAAAAAGATGAATGTAGCTTCTCTCCGATCCTCCGACAACCAAAATACGAGTGCTGAACATAAGCTCGCAAAGGTACTCTAAGTGGGTGGTAAACTCCATCTAAGGCTAAATAC-AACCACGAGACCGATAGCGAACAAGTA Liposthenes_ker CGAAAAAGCAATGCGTGAACAAGCCAAAAAGATGAATGTAGCTTCTCTACGATCCTCCGACAACCAAAATACGAGTGCTGAACATAAGCTCGCAAAGGTACTCTAAGTGGGTGGTAAACTCCATCTAAGGCTAAATAC-AACCACGAGACCGATAGCGAACAAGTA Antistrophus CGAAAAAGCAATGCGTGAACAAGCCAAAAAGATGAATGTAGCTTCTCTACGATCCTCCGATAACCAAAATACGAGTGCTGAACATAAGCTCGCAAAGGTACTCTAAGTGGGTGGTAAACTCCATCTAAGGCTAAATAC-AACCACGAGACCGATAGCGAACAAGTA Rhodus CGAAAAAGCAATGCGTGAACAAGCCAAAAAGATGAATGTAGCTTCTCTACGATCCTCCGACAACCAAAATACGAGTGCTGAACATAAGCTCGCAAAGGTA---------------------------------------------ACCACTAGCGAN---CAAGTA Hedickiana CGAAAAAGCAATGCGTGAACAAGCCAAAAAGATGAATGTAGCTTCTCTACGATCCTCCGATAACCAAAATACG---------------------------CTCTAAGTGGGTGGTAAACTCCATCTAAGGCTAAATAC-AACCACGAGACCGATAGCGAACAAGTA Neaylax CGAAAAAGCAATGCGTGAACAAGCCAAAAAGATGAATGTAGCTTCTCTACGATCCTCCGACAACCAAAATACGAGTGCTGAACATAAGCTCGCAAAGGTACTCTAAGTGGGTGGTAAACTCCATCTAAGGCTAAATAC-AACCACGAGACCGATAGCGAACAAGTA Isocolus CGAAAAAGCAATGCGTGAGCAAGCCAAAAAGATGAATGTAGCTTCTCTACGATCCTCCGACAATCAAAACACGAGTGCTGAACATAAGCTCGCAAAAGTA---------------------------------AATATGACNCACGAGACCGATAGCGAACAAGTA Aulacidea CGAAAAAGCAATGCGTGAGCAAGCCAAAAAGATGAATGTAGCTTCTCTACGATCCTCTGACAACCAAAACACGAGTGCTGAACATAAGCTGGCAAAGGTA---------------------------------------------ACAACTGGCGCTG--AAGNTA Panteliella CGAAAAAGCAATGCGTGAACAAGCCAAAAAGATGAATGTAGCTTCTCTACGATCCTCCGACAACCAAAATACGAGTGCAGAACATAAGCTCGCAAAGGTT-------------------------------GAGATATCAACCACGAGACCGATAGCGAACAAGTA Barbotinia TGAAAAAGCTATGCGTGAACAGGCCAAAAAGAWGAATGTAGCTTCTCTACGATCATCCGACAATCAAAATACGAGTGCTGAACATAAACTCGCCAAGGCA------------------------------------------CACGAGACCGATAGCGAACAAGTA Aylax ----------------------------------------------------------------------------------------------------CTCTAAGTGGGTGGTAAACTCCATCTAAGGCTAAATAT-AACCACGAGACCGATAGCGAACAAGTA Iraella ????????????????????????????????????????????????????????????????????????????????????????????????????------------------------------------------------------------CAAGTA Timaspis CGAAAAAGCAATGCGTGAACAAGCAAAAAAGATGAATGTAGCTTCTCTACGATCGTCCGATAATCAGAATACGAGTGCTGAGCATAAGCTCGCAAAGGTA------------------------------------------------ACCGATAGCGAACAAGTA Phanacis_1 ????????????????????????????????????????????????????????????????????????????????????????????????????CTCTAAGTGGGTGGTAAACTCCATCTAAGGCTAAATAC-AACCACGAGACCGATAGCGAACAAGTA Phanacis_2 ????????????????????????????????????????????????????????????????????????????????????????????????????-------------------------------------------------CCGATAGCGAACAAGTA Eschatocerus TGAGAAAGCCATGCGAGAACAGGCCAAAAAGATGAATGTAGCTTCCCTGAGATCATCAGATAATCAGAATACAAGCACTGAACACAAACTTGCAAAAGTACTCTAAGTGGGTGGTAAACTCCATCTAAGGCTAAATAT-AACCACGAGACCGATAGCGAACAAGTA Diplolepis CGAAAAAGCAATGCGCGAACAGGCCAAAAAGATGAACGTAGCTTCTCTACGTTCATCTGACAATGCAAACACAAGTGCTGAGCATAAACTCGCAAAGGTA----------------------------------ATAT-AACCACGAGACCGATAGCGAACAAGTA Pediaspis CGAAAAAGCAATGCGTGAACAGGCCAAAAAGATGAACGTAGCTTCTCTACGATCATCAGACAATGCAAATACGAGTGCAGAGCATAAACTCGCAAAAGTA----------------------------------ATAT-AACCACGAGACCGATAGCGAACAAGTA Plagiotrochus TGAAAAAGCAATGCGTGAGCAGGCCAAAAAGATGAATGTGGCTTCCTTACGATCATCGGACAATCAAAATACAAGTGCTGAACATAAACTTGCCAAGGTA--------------------------------------GGTAAACCGAACTGGCGCTG--TTGGCG Andricus CGAAAAAGCAATGCGCGAACAGGCCAAAAAGATGAACGTGGCTTCCCTACGATCATCTGACAATCAAAATACGAGTGCCGAACACAAGCTCGCTAAGGTT-----------------------------------ATACAACCACGAGACCGATAGCGAACAAGTA Neuroterus ????????????????????????????????????????????????????????????????????????????????????????????????????------------------------------------------CACGAGACCGATAGCGAACAAGTA Biorhiza CGAAAAAGCAATGCGTGAACAGGCCAAAAAAATGAACGTCGCTTCCCTTCGATCATCCGACAATCAAAATACGAGTGCTGAGCATAAGCTCGCCAAGGTT----------------------------------------------AGACCGATAGCGAACAAGTA Parnips CGAAAAAGCTATGCGTGAACAGGCCAAAAAGATGAACGTAGCTTCTCTACGATCATCCGATAATCAAAATTCAAGTGCTGAACATAAGCTCGCGAAGGTA--------------------------------------------------CNATAGCGAACAAGTA Paramblynotus ????????????????????????????????????????????????????????????????????????????????????????????????????CTCTAAGTGGGTGGTAAACTCCATCTAAGGCTAAATAC-ACCCACGAGACCGATAGCGAACAAGTA Ibalia CCGTGAGGGAAAGTTGAAAAGAACTTTGAAGAGAGAGTTCAAGAGTACGTGAAACCGTTCAGGGGTAAACCTGAGAAACCCAAAAGATCGAATGGGGAGATTCATCGTCAGCGGCTTTGGCTTTCGTGTGGTTTTGTGATGTTT-CGGGATTTC----GGTCTCGT Synergus CCGTGAGGGAAAGTTGAAAAGAACTTTGAAGAGAGAGTTCAAGAGTACGTGAAACCGTTCAGGGGTAAACCTGAGAAACCTAAAAGATCGAATGGGGAGATTCATCGTCAGCGACTTTGGCTTTCGTGTGGTGC-GTGATGCT--CGAGATTTC----GGTCTCGC Periclistus CCGTGAGGGAAAGTTGAAAAGAACTTTGAAGAGAGAGTTCAAGAGTACGTGAAACCGTTCAGGGGTAAACCTGAGAAACCCAAAAGATCGAATGGGGAGATTCATCGTCAGCGACTTTGGCTTTCGTGTGTGTC-GCGATGTT--CGGGGCTTC----GGTCTCGC Ceroptres CCGTGAGGGAAAGTTGAAAAGAACTTTGAAGAGAGAGTTCAAGAGTACGTGAAACCGTTCAGGGGTAAACCTGAGAAACCCAAAAGATCGAATGGGGAGATTCATCGTCAGCGGCCTTGGCTTTCGTGTGGTTT-GCGATGTT--CGGGGCTTC----GGTCTCGC Synophromorpha CCGTGAGGGAAAGTTGAAAAGAACTTTGAAGAGAGAGTTCAAGAGTACGTGAAACCGTTCAGGGGTAAACCTGAGAAACCCAAAAGATCGAATGGGGAGATTCATCGTCAGCGACTCTGGCTTTCGTGTGGTTC-GCGATGTT--CGGGGCTTC----GGTCTCGC Xestophanes CCGTGAGGGAAAGTTGAAAAGAACTTTGAAGAGAGAGTTCAAGAGTACGTGAAACCGTTCAGGGGTAAACCTGAGAAACCCAAAAGATCGAATGGGGAGATTCATCGTCAGCGACTCTGGCTTTCGTGTGGTTC-GCGATGTT--CGGGACTTT----GGTCTTGC Diastrophus CCGTGAGGGAAAGTTGAAAAGAACTTTGAAGAGAGAGTTCAAGAGTACGTGAAACCGTTCAGGGGTAAACCTGAGAAACCCAAAAGATCGAATGGGGAGATTCATCGTCAGCGACTCTGGCTTGCGTGTGGTTC-GCGATGCT--CGGGACTTC----GGTCTCGC Gonaspis CCGTGAGGGAAAGTTGAAAAGAACTTTGAAGAGAGAGTTCAAGAGTACGTGAAACCGTTCAGGGGTAAACCTGAGAAACCCAAAAGATCGAATGGGGAGATTCATCGTCAGCGACTCTGGCTTGCGTGTGGTTT-GCGATGCT--CGGGGCTTC----GGTCTCGT Liposthenes_gle CCGTGAGGGAAAGTTGAAAAGAACTTTGAAGAGAGAGTTCAAGAGTACGTGAAACCGTTCAGGGGTAAACCTGAGAAACCCAAAAGATCGAATGGGGAGATTCATCGTCAGCGGCTTTGGCTTTCGTGTGGTTC-GCGATGTT--CGAGATTCC----GATCTCGT Liposthenes_ker CCGTGAGGGAAAGTTGAAAAGAACTTTGAAGAGAGAGTTCAAGAGTACGTGAAACCGTTCAGGGGTAAACCTGAGAAACCCAAAAGATCGAATGGGGAGATTCACCGTCAGCGGCTTTGGCTTTCGTGTGGTTC-GCGATGTT--CGAGACTTC----GGTCTCGC Antistrophus CCGTGAGGGAAAGTTGAAAAGAACTTTGAAGAGAGAGTTCAAGAGTACGTGAAACCGTTCAGGGGTAAACCTGAGAAACCCAAAAGATCGAATGGGGAGATTCATCGTCAGCGGCTTTGGCTTTCGTGTGGTTC-GTGATGCT--CGAGATTTT----TGTCTCGT Rhodus CCGTGAGGGAAAGTTGAAAAGAACTTTGAAGAGAGAGTTCAAGAGTACGTGAAACCGTTCAGGGGTAAACCTGAGAAACCCAAAAGATCGAATGGGGAGATTCATCGTCAGCGGCTTTGGCTTTCGTGTGATTC-GCGATGCT--TGAGATTTC----GGTCTCGT Hedickiana CCGTGAGGGAAAGTTGAAAAGAACTTTGAAGAGAGAGTTCAAGAGTACGTGAAACCGTTCAGGGGTAAACCTGAGAAACCCAAAAGATCGAATGGGGAGATTCATCGTCAGCGGCTTTGGCTTTCGTGTGGTTC-GCGATGCT--CGGGATTTC----GGTCTCGT Neaylax CCGTGAGGGAAAGTTGAAAAGAACTTTGAAGAGAGAGTTCAAGAGTACGTGAAACCGTTCAGGGGTAAACCTGAGAAACCCAAAAGATCGAATGGGGAGATTCATCGTCAGCGGCTTTGGCTTTCGTGTGGTTC-GCGATGCT--CGGGATTTC----GGTCTCGT Isocolus CCGTGAGGGAAAGTTGAAAAGAACTTTGAAGAGAGAGTTCAAGAGTACGTGAAACCGTTCAGGGGTAAACCTGAGAAACCCAAAAGATCGAATGGGGAGATTCATCGTCAGCGGCTTTGGCTTTCGTGTGGTTC-GCGATGTT--CGGGATTTC----GGTCTCGC Aulacidea CCGTGAGGGAAAGTTGAAAAGAACTTTGAAGAGAGAGTTCAAGAGTACGTGAAACCGTTCAGGGGTAAACCTGAGAAACCCAAAAGATCGAATGGGGAGATTCATCGTCAGCGGCTTTGGCTTTCGTGTGGTAT-GCGATGTT--CGGGATTTC----GGTCTCGT Panteliella CCGTGAGGGAAAGTTGAAAAGAACTTTGAAGAGAGAGTTCAAGAGTACGTGAAACCGTTCAGGGGTAAACCTGAGAAACCCAAAAGATCGAATGGGGAGATTCATCGTCAGCGGCTTTGGCTTTCGTGTGGTTC-GCGATGTT--CGGGATTTC----GATCTCGT Barbotinia CCGTGAGGGAAAGTTGAAAAGAACTTTGAAGAGAGAGTTCAAGAGTACGTGAAACCGTTCAGGGGTAAACCTGAGAAACCCAAAAGATCGAATGGGGAGATTCATCGTCAACGGCTTTGGCTTTCGTGTGGTTC-GCGATGTT--CGGGACTTC----GGTCTCGC Aylax CCGTGAGGGAAAGTTGAAAAGAACTTTGAAGAGAGAGTTCAAGAGTACGTGAAACCGTTCAGGGGTAAACCTGAGAAACCCAAAAGATCGAATGGGGAGATTCATCGTCAACGGCTTTGGCTTTCGTGTGGTTC-GCGATGTT--CGGGTCTTC----GGTCTCGC Iraella CCGTGAGGGAAAGTTGAAAAGAACTTTGAAGAGAGAGTTCAAGAGTACGTGAAACCGTTCAGGGGTAAACCTGAGAAACCCAAAAGATCGAATGGGGAGATTCATCGTCAACGGCTTTGGCTTTCGTGTGGTTC-GCGATGTT--CGGGACTTC----GGTCTCGC Timaspis CCGTGAGGGAAAGTTGAAAAGAACTTTGAAGAGAGAGTTCAAGAGTACGTGAAACCGTTCAGGGGTAAACCTGAGAAACCCAAAAGATCGAATGGAGAGATTCATCGTCAGCGGCTTTGGCTTTCGTGTGGTTC-GTGATGTT--CATGACTTC----GGTCTTGT Phanacis_1 CCGTGAGGGAAAGTTGAAAAGAACTTTGAAGAGAGAGTTCAAGAGTACGTGAAACCGTTCAGGGGTAAACCTGAGAAACCCAAAAGATCGAATGGGGAGATTCATCGTCAGCGGCTTTGGCTTTCGTGTGGTTC-GTGATGTT--CAAGACTTC----GGTTTTGC Phanacis_2 CCGTGAGGGAAAGTTGAAAAGAACTTTGAAGAGAGAGTTCAAGAGTACGTGAAACCGTTCAGGGGTAAACCTGAGAAACCCAAAAGATCGAATGGGGAGATTCATCGTCAGCGGCTTTGGCTTTCGTGTGGTTC-GTGATGTT--CGTGACTTT----GGTCTTGC Eschatocerus CCGTGAGGGAAAGTTGAAAAGAACTTTGAAGAGAGAGTTCAAGAGTACGTGAAACCGTTCAGGGGTAAACCTGAGAAACCCAAAAGATCGAATGAGGAGATTCATCGTCAATGTTTTTGGCTTTCCTGTGATTTAGTGATGTTT-TATAATCTTTTTAGATTATAT Diplolepis CCGTGAGGGAAAGTTGAAAAGAACTTTGAAGAGAGAGTTCAAGAGTACGTGAAACCGTTCAGGGGTAAACCTGAGAAACCCAAAAGGTCGAATGG-GAGACTCATCGTCAATAACTTTGGCTTTCGTGTGATTA-GTGATGTT--CGTAATTTC----GATTACGT Pediaspis CCGTGAGGGAAAGTTGAAAAGAACTTTGAAGAGAGAGTTCAAGAGTACGTGAAACCGTTCAGGGGTAAACCTGAGAAACCCAAAAGATCGAATGGGGAGATTCATCGTCAGCGGCATTGGCTTTCGTGTGGTTC-ACGATGTTTGCAGGACCCTCGCGGGTCTTGT Plagiotrochus C-GTGAGGGAAAGTTGAAAAGAACTTTGAAGAGAGAGTTCAAGAGTACGTGAAACCGTTCAGGGGTAAACCTGAGAAACCCAAAAGATCGAATGGGGAGATTCATCGTCAGCGGCTTTGGCTTTCGTGTGGTAC-GCGATGCGA-TGGGATTTC----GGTCACAT Andricus CCGTGAGGGAAAGTTGAAAAGAACTTTGAAGAGAGAGTTCAAGAGTACGTGAAACCGTTCAGGGGTAAACCTGAGAAACCCAAAAGATCGAATGGGGAGATTCATCGTCAGCGGCTTTGGCTTTCGTGTGGTTC-ACGATGTT--CGAGACTTT----GGTCTTG- Neuroterus CCGTGAGGGAAAGTTGAAAAGAACTTTGAAGAGAGAGTTCAAGAGTACGTGAAACCGTTCAGGGGTAAACCTGAGAAACCCAAAAGATCGAATGGGGAGATTCATCGTCAGCGGCTTTGGCTTTCGTGTGGTTC-ACGATGTT--CGAGACTTT----GGTCTTG- Biorhiza CCGTGAGGGAAAGTTGAAAAGAACTTTGAAGAGAGAGTTCAAGAGTACGTGAAACCGTTCAGGGGTAAACCTGAGAAACCCAAAAGATCGAATGGGGAGATTCATCGTCAGCGGCTCTGGCTTTCGTGTGGTTC-ACGATGTT--TGAGACTTT----GGTCTTA- Parnips CCGTGAGGGAAAGTTGAAAAGAACTTTGAAGAGAGAGTTCAAGAGTACGTGAAACCGTTCAGGGGTAAACCTGAGAAACCCAAAAGATCGAATGGGGAGATTCATCGTCAGCGGCTTTGGCTTTCGTGTGGTTC-GCGATGCT--CGTGACTTCT---GGTCACGT Paramblynotus CCGTGAGGGAAAGTTGAAAAGAACTTTGAAGAGAGAGTTCAAGAGTACGTGAAACCGTTCAGGGGTAAACCTGAGAAATCCAAAAGATCGAATGGGGAGATTCATCGTCAGCGGCTTTGGCTTTCGTGTGGTTTTGTGATGTT--CGAGACCTC----GGTCCTGT Ibalia AG--CACGCGGCCACTGCGGT-ATGTCTGGAGTC-GTCGGCGTGCACTTCTCCTCTAGTAGGACGTCGCGACCCGTTGGG-TGTCGGTTT--ACGGTCTGG-GTGCGTTGCCTGTTCCCTCGCATT-TACGTGCGC-TTGGGACCAGACCCCCGGTACTAACTGAC Synergus GG--TACACGTCCACTGCGTT-ATGTCTAGAGTC-GTCGGCGTGCACTTCTCCCCTAGTAGGACGTCGTGACCCGTTGGG-TGTTGGTTT--ACGGTCTGG-GCG-GTTGCCTGTTCTATTGCAT---TTAT--GTATATGGAGCAGACCCCCGGTT--ACCTGAC Periclistus AG--CACGCGTCCACTGCGGT-ATGTCTAGGGTC-GTCGGCGTGCACTTCTCCCCTAGTAGGACGTTACGACCCGTTAGG-TGTTGGTTT--ACGGTCTGG-GTG-GTTGCCTGTTTCGTTGCAC--TTTGT--GCGTACGGAGCAGACCCCCGGTA--ACCTGAC Ceroptres GA--CACGCGTCCACTGCGGT-ATGTTCTTGGTC-GTCGGCGTGCACTTCTCCCCTAGTAGGACGTCGTGACCCGTTGGG-TGTTGGTTT--ACGGCCTGGTGTG-GTTGCCTGTTTCGTCGCACAATTTATTTGTGCACGGAGCAGACCCCCGGAT--ACCCGAC Synophromorpha AG--CACGCGTCCACTGCGGT-ATGTCTAGGGCC-GTCGGCGTGCACTTCTCCCCTAGTAGGACGTTACGACCCGTTGGG-TGTTGGTTT--ACGGTCTGG-GTG-GTTGACTGTTTCGTCGCAC--TCTGTGCGTGTACGGAGCAGACCCCCGATC--ACCTGAC Xestophanes AG--CACGCGTCCACTGCGGTTATGTCTAGGGAC-GTCGGCGTGCACTTCTCCCCTAGTAGGACGTTACGACCCGTTGGG-TGTTGGTTT--ACGGTCTGG-GTG-GTTGACTGTTTCGTCGCAC--TTTGTGCGTGTACGGAGCAGGCCCCCGGTC--ACCTGAC Diastrophus AG--CACGCGTCCACTGCGGT-ATGTCTAGGGCC-GTCGGCGTGCACTTCTCCTCTAGTAGGACGTTACGACCCGTTGGG-TGTTGGTTT--ACGGTCTGG-GTG-GTTGCCTGCTTTGTCGCAC--TCTGT--GTGTACGGAGTAGACCCCCGGTT--ACCTAAC Gonaspis AG--CACGCGTCCACTGCGGT-ATGTCTAGGGCC-GTCGGCGTGCACTTCTCCTCTAGTAGGACGTTACGACCCGTTGGG-TGTTGGTTT--ACGGTCTGG-GTG-GTTGCCTGTTTCGTCACAC--TCTGT--GTGTACGGAGTAGACCCCCGGTT--ACCTAAC Liposthenes_gle AG--CACACGTCCACTGCGTT-ATGTCCGGAGTC-GTCGGCGTGCACTTCTCCCCTAGTAGGACGTCGTGACCCGTTGGG-TGTTGGTTT--ACGGTCTGG-ATG-GTTGCCTGTTCCGTTGCAT--TT-ATGCGC--ACGGGACAGACCTCCGGTT--ACCTGAC Liposthenes_ker AG--CACGCGTCCACTGCGTT-ATGTCCGGAGTC-GTCGGCGTGCACTTCTCCCCTAGTAGGACGTCGTGACCCGTTGGG-TGTTGGTTT--ACGGTCTGG-GTG-GTTGCCTGTTCCGTCGCAT--TT-ATGCGC--ACGGGGCAGACCCCCGGTT--ACCTGAC Antistrophus AG--CACATGTCCACTGCGTT-ATGTCTGGAGTC-GTCGGTGTGCACTTCTCCCCTAGTAGGACGTCGTGACCCGTTGGG-TGTTGGTTT--ACGGTCTGG-GTG-GTTGCCTGTTTCGTCGCAT--TT-ATGCGT--ACGGTTCAGACCCCCGGGTT-TCCTGAC Rhodus AG--CACACGTCCACTGCGTTTATGTCCGGAGTC-GTCGGCGTGCACTTCTCCCCTAGTAGGACGTCGTGACCCGTTGGG-TGTTGGTTTTTACGGTCTGG-GTGTGTTGCCTGTTCCGTCGTAT--TTTATACGC--ACGGGGCAGACCCCCGGTT--TCCTGAC Hedickiana AG--CACGCGTCCACTGCGTT-ATGTCTGGAGTC-GTCGGCGTGCACTTCTCCCCTAGTAGGACGTCGTGACCCGTTGGG-TGTTGGTTT--ACGGTCTGG-GTG-GTTGCCTGTTCCGTCGTAT--TT-ATGCGT--ACGGAGCAGACCCCCGG-TA-TCCTGAC Neaylax AG--CACGCGTCCACTGCGTT-ATGTCCGGAGTC-GTCGGCGTGCACTTCTCCCCTAGTAGGACGTCGTGACCCGTTGGG-TGTTGGTTT--ACGGTCTGG-GTG-GTTGCCTGTTCCGTCGTAT--TT-ATGCGC--ACGGAGCAGACCCCCGG-TT-TCCTGAC Isocolus AG--CACACGTCCACTGCGTT-ATGTCTGAAGTC-GTCGGCGTGCACTTCTCCCCTAGTAGGACGTCGTGACCCGTTAGG-TGTTGGTCT--ACGGTCTGG-GTG-GTTGCCTGTACCGTCACAT--TT-ATGCGC--ACGGGACAGACCCCCAGATTTACCTGAC Aulacidea AG--CACGCGTCCACTGCGGT-ATGTCCGGAGTC-GTCGGCGTGCACTTCTCCCCTAGTAGGACGTCGTGACCCGTTGGG-TGTTGGTTT--ACGGTCTAG-GTG-GTTGCCTGTTCCGTCACAT--TT-ATGCGT--ACGGTGCAGACCCCTGGCT--ACCTGAC Panteliella AG--CACGCGACCACTGCGTT-ATGTCTGGAGTC-GTCGGCGTGCACTTCTCCTCTAGTAGGACGTCGTGACCCGTTGGG-TGTTGGTCT--ACGGTCTGG-GTG-GTTGCCTGTTCCGTCGCAT--TT-ATGCGC--ACGGGGCAGACCCCTGGGT--ACCTGAC Barbotinia AG--CACGCGTCCACTGCGGT-ATGTCTGGAGTC-GTCGGCGTGCACTTCTCCCCTAGTAGGACGTCGTGACCCGTTGGG-TGTTGGTTT--ACGGTCTGG-GTG-GTTGCCTGTTCCGTCGCAT--TTTATGCGT--ACGGAGCAGACCCCTGGTA--ACCCGAC Aylax AG--CACGCGTCCACTGCGGT-ATGTCTGGAGTC-GTCGGCGTGCACTTCTCCCCTAGTAGGACGTCGTGACCCGTTGGG-TGTTGGTTT--ACGGTCTGG-GTG-GTTGCCTGTTTCGTCGCAT--TTTATGCGT--ACGGAGCAGACCCCTGGTA--ACCCGAC Iraella AG--CACGCGTCCACTGCGGT-ATGTCCGGAGTC-GTCGGCGTGCACTTCTCCCCTAGTAGGACGTCGTGACCCGTTGGG-TGTTGGTTT--ACGGTCTAG-GTG-GTTGCCTGTTCCGTCACAT--TTTATGCGT--ACGGAGCAGACCCCTGGTA--ACCCGAC Timaspis AG--CACACGTCCACTGCGGT-ATGTCCGGAGTC-GTCGGCGTGCACTTCTCCCCTAGTAGAACGTCGTGACCCGTTGGA-TGTTGGTTT--ACGGTC-TGGGTG-GTTGCCTGTTCCATTGCAT---TTATGCGC--ATGGAGCATACCCCTGGTT--ACCTGAC Phanacis_1 AG--CACACGTCCACTGCGGT-ATGTCCAGAGTC-GTCGGCGTGCACTTCTCCCCTAGTAGAACGTCGTGACCCGTTGGG-TGTTGGTTT--ACGGTCCTAGGTC-GTTGTCTGTTTCGTCGCAT---TTATGCGC--ACGAAGCATACCCCAGGTT--ACCTGAC Phanacis_2 AG--CACACGTCCACTGCGGT-ATGTCCGGGGTC-GTCGGCGTGCACTTCTCCCCTAGTAGAACGTCGTGACCCGTTGGG-TGTTGGTTT--ACGTTC-TGGGTG-GTTGCCTGTTCCGTTGTAT---TTATACGT--ACGGAGCATACTCCCGGTT--AACTGAC Eschatocerus GATACACACAATCACTGCGGT-ATGTCTAAATTCTGTCGGCGTGCACTTCTCCTCTAGTAGGACGTCGCGACCCGTTAGAATATTGATTT--ACGGCATAAATTTGATTGTTTGTATTAT-ATATA-TTTATTTATATATAATATATATCATAAATTT-GTNTAAT Diplolepis GG--CACACTGTCACTGCGGT-ATGTCTGAAGTTTGTCGGCGTGCATTTCTCCCTT-GTAGGACGTCGCGACCCGTTGGG-TGTCGGTTT--ACGGTCTAG-GTG-GTTGACTGTTTTGTCAAAA--TTTTTTTGT--ATAAAGCAGACCCCTGGAT--ACCTGAC Pediaspis TT--CACGCGTCCACTGCGTT-ATGTCCGGTGTC-GTCGGCGTGCACTTCTCCTCTAGTAGGACGTCGCGACCCGTTGGG-TGTCGGTTT--ACGGTCCGG-GTG-GTTGCCTGTTCTATCGTAC---TTGTTTACGTATAGAGCAGACCCTCGGTA--ATCTGAC Plagiotrochus AG--CACGCGTCCACTGCGTA-ATGTCCGGAGAC-GTCGGCGTGCACTTCTCCCCTAGTAGGACGTCGTGACCCGTTGGG-TGTTGGTCT--ACGGTCCGG-GTG-GTTGCCTGTTCCGTCGCAT--TTTATGCGTT-GCGGAGCAGACCCCCGGTA--ACCCGAC Andricus CG--CACGCGTCCACTGCGGT-ATGTCCGGAGTC-GTCGGCGTGCACTTCTCCCCTAGTAGGACGTCGTGACCCGTTGGG-TGTTGGTTT--ACGGTCTGG-GTG-GTTGCCTGTTCCGTCGCAT--TTTATGCGC--ACGGAGCAGACCCCCGTGT--ACCCGAC Neuroterus GG--CACGCGTCCACTGCGGT-ATGTCTGGAGTC-GTCGGCGTGCACTTCTCCCCTAGTAGGACGTCGTGACCCGTTGGG-TGTTGGTTT--ACGGTCTGG-GTG-GTTGCCTGTTCCGTCGCAT--TTTATGCGC--ACGGAGCAGACCCCCGTGT--ACCCGAC Biorhiza GG--CACGCGTCCACTGCGGT-ATGTCTGGAGTC-GTCGGCGTGCACTTCTCCCCTAGTAGGACGTCGTGACCCGTTGGG-TGTTGGTTT--ACGGTCTGG-GTG-GTTGCCTGTTACGTCGCAT--TTTATGCGC--ACGGAGCAGACCCCCGTGT--ACCCGAC Parnips GG--CACACGTCCACTGCGGT-ATGCCCGGAGTC-GTCGGCGTGCACTTCTCCCCTAGTAGGACGTCGCGACCCGTTGGG-TGTCGGTTT--ACGAACTGG-GTG-GTTGCCTGTTCCGTCGCATGAATAATGTGT--ATGGTGCAGACCCCTGGAT--ATCTGAC Paramblynotus GA--CACACGATCACTGCGGT-ATGTCTGAAGTC-GTCGGCGTGCACTTCTCCCCTAGTAGGACGTCGCGACCCGTTGGG-TGTCGGTTT--ACGATCTAG-GTG-GTTGCCTGTTCCGTCACACA-TTACTGTGT--ACGGAGTAGACCCCTGGTT--ATCTGAC Ibalia CGACTGCCCGGCGGTACTCGCACGGTA-TCGGGCCGCACTAA---GGTGCGTCGGACCCGCTGCAAGCGAGATCAG-TGAT-ACCCGGAGGTGCGGACCTAGTGCCGTCCCCGGGCCTGGTCAGCTGTTGGTC-GGCGGTGT-TCTCAGACTGGCTCGT--TTGAA Synergus CAACTGCCTGGCGGTACTCGTATGGTA-TCGGGCCGCACTTT----GTGCGTCGGGCCCGTCGCAAGCTAGATCAG-TGTT-TCTCGGAGGTGCGGACCTAGTGCCGTCCCCGGGCCTGGTCAGCTGTTGGTC-GACGGTGT-TCTCGGACAGGCTCAT--TATGA Periclistus CAACTGCCCGACGGTACTCGTACGGTA-TCGGGCCGCACTTT----GTGCGTCGGGCCCGTCGCAAGCTTGAGCAG-TGTTATCCCGGATGTGCGGACCTAGTGCCGTCACCGGGCCTGGTCAGCTGTTGGTC-GACGGTGT-TCTCGGACTGGCTCAA--AACAA Ceroptres CAACTGCCCGGCGGTACTCGCACGGTA-TCGGGCCGCACTTT---TGTGCGTCGGGCCCGTCGCAAGCGAGATCAG-TGTT-ACCCGGATGTGCGGACCTAGTGCCGTCGCCGGGCCTGCTCAGCTGTTGGTC-GGCGGTGT-TCTCGGACTGGCTCAA--AATTA Synophromorpha CAACTGCCCGACGGTACTCGTACGGTA-TCGGGCCGCACTTT----GTGCGTCGGGCTCGTCGCAAGCGCGAGCAGGTGTTTATCCGGATGTACGGACCTAGTGCCGTCACCGGGCCTGATCAGCTGTTGGTC-GACGGTGTACCTCGGACTGGCTCAA--AATAA Xestophanes CAACTGCCCGACGGTACTCGTACGGTA-TCGGGCCGCACTTT----GTGCGTCGGGCCCGTCGCAAGCGCGAGCAGGTGTTTATCCGGATGTACGGACCTAGTGCCGTCACCGGGCCTGATCAGCTGTTGGTC-GACGGTGT-TCTCGGACTGGCTCAA--AATAA Diastrophus CAACTGCCCGGCGGTCCTCGTACGGTA-TCGGGCCGCACTCT----GTGCGTCGGGCCCGTCGCAAGCGCGAGCAG-TGTT-ACCTGGATGTGCGGACCTAGTGCCGTCACCGGGCCTGATCAGCTGTTGGTC-GACGGTGT-TCTCGGACTGGCTCAA------- Gonaspis CAACTGCCCGGCGGTCCTCGTACGGTA-TCGGGCCGCACTTT----GTGCGTCGGGCCCGTCGCAAGCGCGAGCAG-TGTT-ACCCGGATGTGCGGACCTAGTGCCGTCACCGGGCCTGATCAGCTGTTGGTC-GACGGTGT-TCTCGGACTGGCTCAA------- Liposthenes_gle CAACTGCCCGGCGGTACTCGCACGGTA-TCGGGCCGCACTTA---TGTGCGTCGGACTCGCCGCAAGCGAGATCAG-TGTT-ACCCGTAGGTATGGACATAGTGCCGTCCCCGGGCCTGATCAGCTGTTGGTC-GGCGATGT-TCTTAGACAGGCTCA---CCTAA Liposthenes_ker CAACTGCCCGGCGGTACTCGCATGGTA-TCGGGCCGCACT-A---TGTGCGTCGGGCCCGCCGCAAGCGAGATCAG-TGTT-TCCCGGAGGTGCGGACCTAGCGCCGTCCCCGGGCCTGGTCAGCTGTTGGTC-GGCGGTGT-TCTCAGACAGGCTCA---TCTAA Antistrophus CAACTGCCCGGCGGTACTCGCACGGTA-TCGGGCCGCATTA----TGTGCGTCGGTCCCGCCGCAAGCGAGATCAG-TGTT-TCCCGGAGGTGCGGACCTAGAGCCGTCCCCGGGCCTGGTCAGCTGTTGGTT-GGCGGTGT-TCTTAGACAGGCTCA---TA-GA Rhodus CAACTGCCTGACGGTACTCGCACGGTA-TCGGGCCGCACTA----TGTGCGTCGGGCCCGCCGCAAGCGAGATCAG-TGTT-TCCCGGTGGTACGGACCTAGCGCCGTCACCGGGCCTGGTCAGCTGTTGGTC-GGCGGTGT-TCTCAGACAGGCTCA---TACAA Hedickiana CAACTGCCCGGCGGTACTCGCACGGTA-TCGGGCCGCACTA----TGTGCGTCGGGCCCGCCGCAAGCGAGATCAG-TGTT-TCCCGGAGGTACGGACCTAGCGCCGTCCCCGGGCCTGGTCAGCTGTTGGTC-GGCGGTGT-TCTCAGACAGGCTCA---TCCAA Neaylax CAACTGCCCGGCGGTACTCGCACGGTA-TCGGGCCGCACTA----TGTGCGTCGGGCCCGCCGCAAGCGAGATCAG-TGTT-TCCCGGAGGTACGGACCTAGCGCCGTCCCCGGGCCTGGTCAGCTGTTGGTC-GGCGGTGT-TCTCAGACAGGCTCA---TCTAA Isocolus CAACTGCCTGGCGGTACTCGCACGGTA-TCGGGCCGCACTAA---TGTGCGTCGGGCCCACCGCAAGCGAGATCAG-TGCT-TCCCGGAGGTTCGGACTTAGCGCCGTCCCCGGGCCTGGTCAGCTGTTGGTC-GGTGGTGT-TCTCAGACAGGCTCA---TCGAA Aulacidea CAACTGCCCGGCGGTACTCGCACGGTA-TTGGGCCGCACTA----TGTGCGTCGGACCCGCCGCAAGCGAGATCAG-TGTTTTCCCGGAGTTACGGAGCTAGTGCCGTTCCCGGGCCTGGTCAGCTGTTGGTC-GGCGGTGT-TCTCAGACAGGCTCAT--CAAAA Panteliella -AACTGCCCGGCGGTACTCGCACGGTA-TCGGGCCGCACTA----TGTGCGTCGGGCTCGCCACAAGCGAGATCAG-TGTT-TCCCGGAGGTTCGGACCTAGCGCCGTCCCCGGGCCTGGTCAGCTGTTGGTT-GGCGATGT-TCTCAGACAGGCTCA---TCAAA Barbotinia CAACTGCTCGGCGGTACTCGCACGGTA-TCGAGCCGCACTA----TGTGCGTCGGGCCCGTCGCAAGCGAGATCAG-TGAT-ACCCGGATGTGCGGACCTAGTGCCGTCACCGGGCCTGGTCAGCTGTTGGCC-GACGGTGT-TCTCGGACAGGCTCAT--TC--- Aylax CAACTGCTCGGCGGTACTCGCACGGTA-TCGAGCCGCACTA----TGTGCGTCGGGCCCGTCGCAAGCGAGATCAG-TGTT-ACCCGGATGTGCGGACTTAGTGCCGTCACCGGGCCTGGTCAGCTGTTGGCC-GACGGTGT-TCTCGGACTGGCTCAT--TT--- Iraella CAACTGCTCGGCGGTACTCGCACGGTA-TCGAGCCGCACTA----TGTGCGTCGGACCCGTCGCAAGCGAGATCAG-TGTT-ACCCGGAGGTGCGGACCTAGTGCCGTCCCCGGGCCTGGTCAGCTGTTGGCC-GGCGGTGT-TCTCGGACAGGCTCAT--TTTGA Timaspis CAACTGCCCGGCGGTACTCACACGGTA-TCGGGCCGCACTTA---CGTGCGTCAGGCCCGTCGCAAGCGAGATCAG-TGTTT-CCCGGAGGTGCGGACCTAGCGCCGTCCCCGGGCCTGGTCAGCTGTTGGTC-GACGGTGT-TCTCTGACAGGCTCAT--TGAAA Phanacis_1 CAACTGCCCGGCGGTACTCGCACGGTA-TCGAGCCGCA-TTA---TGTGCGTCGGGCCCGTCGCAAGTGAGATCAG-CGTTA-CCCGGAGGTGCGGACCTAGCGCCGTCCCCGGGCCTGATCAGCTGTTGGTC-GGCGGTGT-TCTCTGACAGGCTCAT--C---A Phanacis_2 CAACTGCCCGGCGGTACTCGTATGGTA-TCGAGCCGCACTTA---TGTGCGTCGGGCCCGTCGCAAGCGAGATCAG-TGTTTACCCGGAGGTGCGGACCTAGCGCCGTCCCCGGGCCTGGTCAGCTGTTGGCC-GTCGGTGT-TCTCTGACAGGCTCAT--T---- Eschatocerus CAATTGTCTGACGGTACTAATAAGGGTATTGGGCCGCATTTTTAATATGCGTCTAATCCGTTACAAGCTAGAATTAGTGTT-ACTTAGTTTTACGAACCAAGTTTCGTTTCTGAGCCTAATCAGCTGTTGGTTTAGCGGTTAATCTTAGACTGGCTCAAAACATAC Diplolepis CGACTGCCCGGCGGTATTTGAACGGTAATCGAGCCGCACATA---AGTGCGTCAGGCCCACTGCAAGCGAGATCAG-TGTTAACCTGGAGGTGCGGACCTAGTGCCGTCCCCAGGCCTGGTCAGCTGTTGGTC-GGTGGTGT-TCTCGGACTGGCTCAT--AATTA Pediaspis CGACTGCTCGGCGGTACTCGTAAGGTA-TCGGGCCGCACTTG---TGTGCGTCGGGCCCGCTGCAAGCCGGATCAG-TGTT-ACCCGGAGGTGCGGACCTAGTGCCGTCCCCGGGCCTGATCAGCTGTTGGTC-GGCGGTGT-TCTCGGACTGGCTCAT--CGAAA Plagiotrochus CAACTGCCCGGCGGTACTCGCAAGGTA-TCGGGCCGCACAT----TGTGCGTCGGGCCCGTCGCAAGCGAGATCAG-TGTTATCCTGGAGGTGCGGACCTAGTGCCGTCCCCGGGCCTGGTCAGCTGTTGGTC-GACGGTGT-TCTCGGACTGGCTC----TAAAC Andricus CAACTGCCCGGCGGTACTCGCACGGTA-TCGGGCCGCACTTA---CGTGCGTCGGGCCCGTCGCAAGCGAGATCA-GTGTT-ACCCGGAGGTGCGGACCTAGTGCCGTCCCCGGGCCTGGTCAGCTGTTGGTC-GGCGGTGT-TCTCGGACAGGCTCAT--CCAAA Neuroterus CAACTGCCCGGCGGTACTCGCACGGTA-TCGGGCCGCACTTA---CGTGCGTCGGGCCCGCCGCAAGCGAGATCAAGTGTT-TCCCGGAGGTGCGGACCTAGTGCCGTCCCCGGGCCTGGTCAGCTGTTGGTC-GGCGGTGT-TCTCGGACAGGCTCAT--CCAAA Biorhiza CAACTGCCCGGCGGTACTCGCACGGTA-TCGGGCCGCACTTA---CGTGCGTCGGGCCCGTCGCAAGCGAGATCA-GTGTT-ACCCGGAGGTGCGGACCTAGTGCCGTCCCTGGGCCTGGTCAGCTGTTGGTC-GGCGGTGT-TCTCGGACAGGCTCAT--CCAAA Parnips CGACTGCCTGGCGGTACTCGCACGGTA-TCGAGCCGCACTA----CGTGCGTCGGGCCCGCCGCAAGCGAGATCAG-TGAT-ACCCGGAGGTGCGGACCTAGTGCCGTCCCCGGGCCTGGTCAGCTGTTGGTC-GGCGGTGT-TCTCGGACTGGCTCAT--CTCAA Paramblynotus CGACTGCCCGGCGGTACTCGCACGGTA-TCGAGTCGCACTA----TGTGCGTCCGGCCCGCTGCAAGCGAGATCAG-TGTT-ACCCGGAGGTGCGGACCTGGTGCCGTCCCCGGGCCTGCTCAGCTGTTGGTT-AGCGGTGT-TCTCGGACTGACTCGT--CTCAG Ibalia TGAAT-----CATTACCGGTCGGCGACGCTACTGCTTTGGGTACTTTCAGGACCCGTCTTGAAACACGGACCAAGGAGTCTAACATGTGCGCGAGTCATTGGGATCG--CAAAACCTAAAGGCATAATGAAAGTGAAGGTCGACCTTGCGTCGACTAAGGGAGGAT Synergus -------------TACCGGTCGGCGACGCTACTGCTTTAGGTACTTTCAGGACCCGTCTTGAAACACGGACCAAGGAGTCTAACATGTGCGCGAGTCATTGGGACCG--CAAAACCTAAAGGCGTAATGAAAGTGAAGGTCGATCTTGCGTCGACTAAGGGAGGAT Periclistus TTAATA----TGATACCGGTCAGTGACGCTACTGCTTTGGGTACTTTCAGGACCCGTCTTGAAACACGGACCAAGGAGTCTAACATGTGCGCGAGTCATTGGGACCG--CAAAACCTAAAGGCGTAATGAAAGTGAAGGTCGACCTTGCGTCGACTAAGGGAGGAT Ceroptres T----------GTTACCGGTCGGCGACGCTACTGCTTTGGGTACTTTCAGGACCCGTCTTGAAACACGGACCAAGGAGTCTAACATGTGCGCGAGTCATTGGGACCG--CAAAACCTAAAGGCGTAATGAAAGTGAAGGTCGACCTTGCGTCGACTAAGGGAGGAT Synophromorpha ATGA---------TACCGGTCAGTGACGCTACTGCTTTGGGTACTTTCAGGACCCGTCTTGAAACACGGACCAAGGAGTCTAACATGTGCGCGAGTCATTGGGACCG--CAAA-CCTAAAGGCGTAATGAAAGTGAAGGTCGACCTTGCGTCGACTAAGGGAGGAT Xestophanes ATGAA--------TACCGGTCAGTGACGCTACTGCTTTGGGTACTTTCAGGACCCGTCTTGAAACACGGACCAAGGAGTCTAACATGTGCGCGAGTCATTGGGACCG--CAAAACCTAAAGGCGTAATGAAAGTGAAGGTCGACCTTGCGTCGACTAAGGGAGGAT Diastrophus -------------TACCGGTCAGTGACGCTACTGCTTTGGGTACTTTCAGGACCCGTCTTGAAACACGGACCAAGGAGTCTAACATGTGCGCGAGTCATTGGGACCG--CAAAACCTAAAGGCGTAATGAAAGTGAAGGTCGACCATGCGTCCACTAAGGGAGGAT Gonaspis -------------TACCGGTCAGTGACGCTACTGCTTTGGGTACTTTCAGGACCCGTCTTGAAACACGGACCAAGGAGTCTAACATGTGCGCGAGTCATTGGGACCG--CAAAACCTAAAGGCGTAATGAAAGTAAAGGTCGACCATGCGTCGACTAAGGGAGGAT Liposthenes_gle AAATT-----TGATACCGGTCGGCGACGCTACTGCTTTGGGTACTTTCAGGACCCGTCTTGAAACACGGACCAAGGAGTCTAACATGTGCGCGAGTCATTGGGACCG--CAAAACCTAAAGGCGCAATGAAAGTGAAGGTCGACCTTGCGTCGACTGAGGGAGGAT Liposthenes_ker AA--------TGATACCGGTCGGCGACGCTACTGCTTTGGGTACTTTCAGGACCCGTCTTGAAACACGGACCAAGGAGTCTAACATGTGCGCGAGTCATTGGGACCG--CAAAACCTAAAGGCGTAATGAAAGTGAAGGTCGACCTTGCGTCGACTGAGGGAGGAT Antistrophus AAA-------TGATACCGGTCGACGACGCTACTGCTTTGGGTACTTTCAGGACCCGTCTTGAAACACGGACCAAGGAGTCTAACATGTGCGCGAGTCATTGGGACCG--CAAAACCTAAAGGCGTAATGAAAGTGAAGGTCGACCTTGCGTCGACTGAGGGAGGAT Rhodus AAATG-----ATATACCGGTCGGCGACGCTATTGCTTTGGGTACTTTCAGGACCCGTCTTGAAACACGGACCAAGGAGTCTAACATGTGCGCGAGTCATTGGGACCG--CAAAACCTAAAGGCGTAATGAAAGTGAAGGTCGACCTTGCGTCGACTGAGGGAGGAT Hedickiana AAA-------TGATACCGGTCGACGACGCTACTGCTTTGGGTACTTTCAGGACCCGTCTTGAAACACGGACCAAGGAGTCTAACATGTGCGCGAGTCATTGGGACCG--CAAAACCTAAAGGCGTAATGAAAGTGAAGGTCGACCTTGCGTCGACTGAGGGAGGAT Neaylax AAA-------TGATACCGGTCGGCGACGCTACTGCTTTGGGTACTTTCAGGACCCGTCTTGAAACACGGACCAAGGAGTCTAACATGTGCGCGAGTCATTGGGACCG--CAAAACCTAAAGGCGTAATGAAAGTGAAGGTCGACCTTGCGTCGACTGAGGGAGGAT Isocolus CA--------TGATACCGGTCGGCGACGCTACTGCTTTGGGTACTTTCAGGACCCGTCTTGAAACACGGACCAAGGAGTCTAACATGTGCGCAAGTCATTGGGACTG--AAAAACCTAAAGGCGTAATGAAAGTGAAGGTCGACCTTGTGTCGACTG-GGGAGGAT Aulacidea TATTA-----TGATACCGGTCGGCGACGCTACTGCTTTGGGTACTTTCAGGACCCGTCTTGAAACACGGACCAAGGAGTCTAACATGTGCGCGAGTCATTGGGACAG--CAAAACCTAAAGGCGTAATGAAAGTGAAGGTCGACCTTGCGTCGACTAAGGGAGGAT Panteliella AA--------TGATACCGGTCGACGACGCTACTGCTTTGGGTACTTTCAGGACCCGTCTTGAAACACGGACCAAGGAGTCTAACATGTGCGCGAGTCATTGGGACCG--CAAAACCTAAAGGCGTAATGAAAGTGAAGGTCGACCTTGCGTCGACTGAGGGAGGAT Barbotinia ATA-------TGATACCGGTCGGCGACGCTACTGCTTTGGGTACTTTCAGGACCCGTCTTGAAACACGGACCAAGGAGTCTAACATGTACGCGAGTCATTGGGACCG--CAAAACCTAAAGGCGTAATGAAAGTGAAGGTCGACCTTGCGTCGACTGAGGGAGGAT Aylax ATA-------TGATACCGGTCAGCGACGCTACTGCTTTGGGTACTTTCAGGACCCGTCTTGAAACACGGACCAAGGAGTCTAACATGTGCGCGAGTCATTGGGACCG--CAAAACCTAAAGGCGTAATGAAAGTGAAGGTCGACCTTGCGTCGACTGAGGGAGGAT Iraella ATA-------TGATACCGGTCGGCGACGCTACTGCTTTGGGTACTTTCAGGACCCGTCTTGAAACACGGACCAAGGAGTCTAACATGTGCGCGAGTCATTGGGACTG--CAAAACCTAAAGGCGTAATGAAAGTGAAGGTCGACCTTGCGTCGACTGAGGGAGGAT Timaspis AAAAT------GATACCGGTCAGCGACGCTATTGCTTTGGGTACTTTCAGGACCCGTCTTGAAACACGGACCAAGGAGTCTAACATGTACGCAAGTCATTGGGACTG--CAAAACCTAAAGGCGTAATGAAAGTGAAGGTCGACCTTGCGTCGACTAAGGGAGGAT Phanacis_1 AAAAT------GATACCGGTCGGCGACGCTACTGCTTTGGGTACTTTCAGGACCCGTCTTGAAACACGGACCAAGGAGTCTAACATGTGCGCAAGTCATTGGGATTG--CAAAACCTAAAGGCGTAATGAAAGTGAAGGTCGACCTTGCGTCGACTAAGGGAGGAT Phanacis_2 AAAAG------GATACCGGTCGGCGACGCTACTGCTTTGGGTACTTTCAGGACCCGTCTTGAAACACGGACCAAGGAGTCTAACATGTGCGCAAGTCATTGGGACTGG-TAAAACCTAAAGGCGTAATGAAAGTGAAGGTCGACCTTGCGTCGACTAAGGGAGGAT Eschatocerus AATTTTTGTATGATACCTGTCAGCGACGCTACTGCTTTGGGTACTTTCAGGACCCGTCTTGAAACACGGACCAAGGAGTCTAACATGTGCGCAAGTCATTGGGATTTAATAAAACCTAAAGGCGTAATGAAAGTAAAGGTCGATCTTGTGTCGATTGAGGGAGGAT Diplolepis TA----------ATACCGGTCAGCGACGCTATTGCTTTGGGTACTTTCAGGACCCGTCTTGAAACACGGACCAAGGAGTCTAACATGTGCGCGAGTCATTGGGACTT--GAAAACCTAAAGGCATAATGAAAGTGAAGGTCGGCCTTGCGTCGACTGAGGGAGGAT Pediaspis TGTTGTTATATGATACCGGTCGGCGACGCTACTGCTTTGGGTACTTTCAGGACCCGTCTTGAAACACGGACCAAGGAGTCTAACATGTGCGCGAGTCATTGGGACTG--CAAAACCTAAAGGCGTAATGAAAGTGAAGGTCGATCTTGCGTCGACTGAGGGAGGAT Plagiotrochus AATTG-----AAATACCGGTCGGCGACGCTACTGCTTTGGGTACTTTCAGGACCCGTCTTGAAACACGGACCAAGGAGTCTAACATGTTCGCGAGTCATTGGGACCG--CAAAACCTAAAGGCGAAATGAAAGTGAAGGTCGACCTCGCGTCGACTGAGGGAGGAT Andricus AT---------GATACCGGTCGGCGACGCTACTGCTTTGGGTACTTTCAGGACCCGTCTTGAAACACGGACCAAGGAGTCTAACATGTGCGCGAGTCATTGGGACCG--CAAAACCTAAAGGCGTAATGAAAGTGAAGGTCGACCTTGCGTCGACTAAGGGAGGAT Neuroterus AT---------GATACCGGTCGGCGACGCTACTGCTTTGGGTACTTTCAGGACCCGTCTTGAAACACGGACCAAGGAGTCTAACATGTGCGCGAGTCATTGGGACCG--CAAAACCTAAAGGCGTAATGAAAGTGAAGGTCGACCTTGCGTCGACTAAGGGAGGAT Biorhiza AT---------GATACCGGTCGGCGACGCTACTGCTTTGGGTACTTTCAGGACCCGTCTTGAAACACGGACCAAGGAGTCTAACATGTGCGCGAGTCATTGGGACCG--CAAAACCTAAAGGCGTAATGAAAGTGAAGGTCGACCTTGCGTCGACTAAGGGAGGAT Parnips TTATT-----GAATACCGGTCGGCGACGCTACTGCTTTGGGTACTTTCAGGACCCGTCTTGAAACACGGACCAAGGAGTCTAACATGTGCGCGAGTCATTGGGACTG--CAAAACCTAAAGGCGTAATGAAAGTGAAGGTCGACCTTGCGTCGACTGAGGGAGGAT Paramblynotus TTACT-----GAATACCGGTCGGCGACGCTACTGCTTTGGGTACTTTCAGGACCCGTCTTGAAACACGGACCAAGGAGTCTAACATGTGCGCGAGTCATTGGGACCG--CAAAACCTAAAGGCGTAATGAAAGTGAAGGTCGATTTTGTGTCGACTAAGGGAGGAT Ibalia GGGCT-TCATCACGATGAAGCCCCGCACTCCCGGGGCGTCTCGTACTCAATGCGAGTAGAGGCGCACCCAGAGCGTACACGTTGGGACCCGAAAGATGGTGAACTATGCCTGGTCAGGACGAAGTCAGGGGAAACCCTGATGGAGGTCCGTAGCGATTCTGACGTG Synergus GGGTT-GTGTCACGATGCAGCCCCGCACTCCCGGGGCGTCTCGTACTCATTGCGAGTTGAGGCGCACCCAGAGCGTACACGTTGGGACCCGAAAGATGGTGAACTATGCCTGGTCAGGACGAAGTCAGGGGAAACCCTGATGGAGGTCCGTAGCGATTCTGACGTG Periclistus GGGTT-GCGTCACGATGCAGCCCCGCACTCCCGGGGCGTCTCGTACTCATTGCGAGTAGAGGCGCACCCAGAGCGTACACGTTGGGACCCGAAAGATGGTGAACTATGCCTGGTCAGGACGAAGTCAGGGGAAACCCTGATGGAGGTCCGTAGCGATTCTGACGTG Ceroptres GGGTT-GCGTTACGATGCAGCCCCGCACTCCCGGGGCGTCTCGTACTCATTGCGAGTAGAGGCGCACCTAGAGCGTACACGTTGGGACCCGAAAGATGGTGAACTATGCCTGGTCAGGACGAAGTCAGGGGAAACCCTGATGGAGGTCCGTAGCGATTCTGACGTG Synophromorpha GGGTT-GCGTCACGATGCAGCCCCGCACTCCCGGGGCGTCTCATACTCATTGCGAGTAGAGGCGCACCCAGAGCGTACACGTTGGGACCCGAAAGATGGTGAACTATGCCTGGTCAGGACGAAGTCAGGGGAAACCCTGATGGAGGTCCGTAGCGATTCTGACGTG Xestophanes GGGTTTGCGTCACGATGCAGCCCCGCACTCCCGGGGCGTCTCGTACTCATTAAGAGTAGAGGCGCACCCAGAGCGTACACGTTGGGACCCGAAAGATGGTGAACTATGCCTGGTCAGGACGAAGTCAGGGGAAACCCTGATGGAGGTCCGTAGCGATTCTGACGTG Diastrophus GGGTT-GCGTCACGATGCAGCCCCGCACTCCCGGGGCGTCTCATACTCATTGCGAGTAGAGGCGCACCCAGAGCGTACACGTTGGGACCCGAAAGATGGTGAACTATGCCTGGTCAGGACGAAGTCAGGGGAAACCCTGATGGAGGTCCGTAGCGATTCTGACGTG Gonaspis GGGTT-GCGTCACGATGCAGCCCCGCACTCCCGGGGCGTCTCATACTCATTGCGAGTAGAGGCGCACCCAGAGCGTACACGTTGGGACCCGAAAGATGGTGAACTATGCCTGGTCAGGACGAAGTCAGGGGAAACCCTGATGGAGGTCCGTAGCGATTCTGACGTG Liposthenes_gle GGGTT-GCGTCACGATGCAGCCCCGCACTCCCGGGGCGTCTCATACTCATTGCGAGTAGAGGCGCACCCAGAGCGTACACGTTGGGACCCGAAAGATGGTGAACTATGCCTGGTCAGGACGAAGTCAGGGGAAACCCTGATGGAGGTCCGTAGCGATTCTGACGTG Liposthenes_ker GGGTT-GCGTCACGATGCAGCCCCGCACTCCCGGGGCGTCTCATACTCATTGCGAGTAGAGGCGCACCCAGAGCGTACACGTTGGGACCCGAAAGATGGTGAACTATGCCTGGTCAGGACGAAGTCAGGGGAAACCCTGATGGAGGTCCGTAGCGATTCTGACGTG Antistrophus GGGTT-GCGTTACGATGCAGCCCCGCACTCCCGGGGCGTCTCATACACATTACGAGTAGAGGCGCACCCAGAGCGTACACGTTGGGACCCGAAAGATGGTGAACTATGCCTGGTCAGGACGAAGTCAGGGGAAACCCTGATGGAGGTCCGTAGCGATTCTGACGTG Rhodus GGGTT-GTGTCACGATGCAGCTCCGCACTCCCGGGGCGTCTCATACTCATTGCGAGTAGAGGCGCACCCAGAGCGTACACGTTGGGACCCGAAAGATGGTGAACTATGCCTGGTCAGGACGAAGTCAGGGGAAACCCTGATGGAGGTCCGTAGCGATTCTGACGTG Hedickiana GGGTT-GCATTACGATGCAGCCCCGCACTCCCGGGGCGTCTCATACTCATTGCGAGTAGAGGCGCACCCAGAGCGTACACGTTGGGACCCGAAAGATGGTGAACTATGCCTGGTCAGGACGAAGTCAGGGGAAACCCTGATGGAGGTCCGTAGCGATTCTGACGTG Neaylax GGGTT-GCGTCACGATGCAGCCCCGCACTCCCGGGGCGTCTCATACTCATTGCGAGTAGAGGCGCACCCAGAGCGTACACGTTGGGACCCGAAAGATGGTGAACTATGCCTGGTCAGGACGAAGTCAGGGGAAACCCTGATGGAGGTCCGTAGCGATTCTGACGTG Isocolus GGGCT-GCGTTACGATGCAGCCTCGCACTCCCGGGGCGTCTCGTACTCATTACGAGTAGAGGCGCACCCAGAGCGTACACGTTGGGACCCGAAAGATGGTGAACTATGCCTGGTCAGGACGAAGTCAGGGGAAACCCTGATGGAGGTCCGTAGCGATTCTGACGTG Aulacidea GGGTT-ACGTTACGATGTAGCCCCGCACTCCCGGGGCGTCTCGTACTCATTATGAGTAGAGGCGCACCCAGAGCGTACACGTTGGGACCCGAAAGATGGTGAACTATGCCTGGTCAGGACGAAGTCAGGGGAAACCCTGATGGAGGTCCGTAGCGATTCTGACGTG Panteliella GGGTT-GCATCACGATGCAGCCCCGCACTCCCGGGGCGTCTCATACTCATTGCGAGTAGAGGCGCACCCAGAGCGTACACGTTGGGACCCGAAAGATGGTGAACTATGCCTGGTCAGGACGAAGTCAGGGGAAACCCTGATGGAGGTCCGTAGCGATTCTGACGTG Barbotinia GGGTT-ACGTTACGATGTAACCCCGCACTCCCGGGGCGTCTCGTACTCATTGCGAGTAGAGGCGCACCCAGAGCGTACACGTTGGGACCCGAAAGATGGTGAACTATGCCTGGTCAGGACGAAGTCAGGGGAAACCCTGATGGAGGTCCGTAGCGATTCTGACGTG Aylax GGGTT-GCGTTACGATGTAACCCCGCACTCCCGGGGCGTCTCGTACTCATTGCGAGTAGAGGCGCACCCAGAGCGTACACGTTGGGACCCGAAAGATGGTGAACTATGCCTGGTCAGGACGAAGTCAGGGGAAACCCTGATGGAGGTCCGTAGCGATTCTGACGTG Iraella GGGTT-ACGTTACGATGTAACCCCGCACTCCCGGGGCGTCTCGTACTCATTGCGAGTAGAGGCGCACCCAGAGCGTACACGTTGGGACCCGAAAGATGGTGAACTATGCCTGGTCAGGACGAAGTCAGGGGAAACCCTGATGGAGGTCCGTAGCGATTCTGACGTG Timaspis GAGCT-GCGTCACGATGCAGCTCCGCACTCCCGGGGCGTCTCATACTCATTGCGAGTAGAGGCGCACCCAGAGCGTACACGTTGGGACCCGAAAGATGGTGAACTATGCCTGGTCAGGACGAAGTCAGGGGAAACCCTGGTGGAGGTCCGTAGCGATTCTGACGTG Phanacis_1 GGGTT-GCGTCACGATGCAGCCCCGCACTCCCGGGGCGTCTCATACTCATTGCGAGTAGAGGCGCACCCAGAGCGTACACGTTGGGACCCGAAAGATGGTGAACTATGCCTGGTCAGGACGAAGTCAGGGGAAACCCTGATGGAGGTCCGTAGCGATTCTGACGTG Phanacis_2 GGGTT-ATATCACGATGTAGCCCCGCACTCCCGGGGCGTCTCATACTCATTGCGAGTAGAGGCGCACCCAGAGCGTACACGTTGGGACCCGAAAGATGGTGAACTATGCCTGGTCAGGACGAAGTCAGGGGAAACCCTGATGGAGGTCCGTAGCGATTCTGACGTG Eschatocerus GGGTT-ACATTACGATGTTTCCTCGCACTCCCGGGGCGTTTTATACTCATTGCGAGTAGAGGCGCACCCAGAGCGTACACGTTGGGACCCGAAAGATGGTGAACTATGCCTGGTCAGGACGAAGTCAGGGGAAACCCTGATGGAGGTCCGTAGCGATTCTGACGTG Diplolepis GGACT-GTATTACGATGCAGCCCCGCACTCCCGGGGCGTCTCGTACTCATTACGAGTAGAGGCGCACCCAGAGCGTACACGTTGGGACCCGAAAGATGGTGAACTATGCCTGGTCAGGACGAAGTCAGGGGAAACCCTGATGGAGGTCCGTAGCGATTCTGACGTG Pediaspis GGGTT-GCGTTACGATGCAGCCCCGCACTCCCGGGGCGTCTCGTACTCATTGCGAGTAGAGGCGCACCCAGAGCGTACACGTTGGGACCCGAAAGATGGTGAACTATGCCTGGTCAGGACGAAGTCAGGGGAAACCCTGATGGAGGTCCGTAGCGATTCTGACGTG Plagiotrochus GGGTT-GCGTCACGATGCAGCCCCGCACTCCCGGGGCGTCTCGTACTCATTGCGAGTAGAGGCGCACCCAGAGCGTACACGTTGGGACCCGAAAGATGGTGAACTATGCCTGGCCAGGACGAAGTCAGGGGAAACCCTGATGGAGGTCCGTAGCGATTCTGACGTG Andricus GGGTT-GCGTCACGATGCAGCCCCGCACTCCCGGGGCGTCTCGTACTCATTGCGAGTAGAGGCGCACCCAGAGCGTACACGTTGGGACCCGAAAGATGGTGAACTATGCCTGGTCAGGACGAAGTCAGGGGAAACCCTGATGGAGGTCCGTAGCGATTCTGACGTG Neuroterus GGGTT-GCGTCACGATGCAGCCCCGCACTCCCGGGGCGTCTCGTACTCATTGCGAGTAGAGGCGCACCCAGAGCGTACACGTTGGGACCCGAAAGATGGTGAACTATGCCTGGTCAGGACGAAGTCAGGGGAAACCCTGATGGAGGTCCGTAGCGATTCTGACGTG Biorhiza GGGTT-GCGTCACGATGCAGCCCCGCACTCCCGGGGCGTCTCGTACTCATTGCGAGTAGAGGCGCACCCAGAGCGTACACGTTGGGACCCGAAAGATGGTGAACTATGCCTGGTCAGGACGAAGTCAGGGGAAACCCTGATGGAGGTCCGTAGCGATTCTGACGTG Parnips GGGTT-GCGTTACGATGCAGCCCCGCACTCCCGGGGCGTCTCGTACTCATTGCGAGTAGAGGCGCACCCAGAGCGTACACGTTGGGACCCGAAAGATGGTGAACTATGCCTGGTCAGGACGAAGTCAGGGGAATCCCTGATGGAGGTCCGTAGCGATTCTGACGTG Paramblynotus GGGTT-GTGTTACGATGCAATCCCGCACTCCCGGGGCGTCTCGTACTCATTGCGAGTAGAGGCGCACCCAGAGCGTACACGTTGGGACCCGAAAGATGGTGAACTATGCCTGGTCAGGACGAAGTCAGGGGAAACCCTGATGGAGGTCCGTAGCGATTCTGACGTG Ibalia CAAATCGATCGTCGGAACTGGGTATAGGGGCGAAAGACTAATCGAACCATCTAGTAGCTGGTTCCCTCCGAAGTTTCCCTCAGGATAGCTGGCACTCGCGTGA-GCGAGTCTCATCTGGTAAAGCGAATGATTAGAGGCCTTGGGGCCGAAACGACCTCAACCTAT Synergus CAAATCGATCGTCGGAACTGGGTATAGGGGCGAAAGACTAATCGAACCATCTAGTAGCTGGTTCCCTCCGAAGTTTCCCTCAGGATAGCTGGCACTCGCGTGA-GCGAGTCTCATCTGGTAAAGCGAATGATTAGAGGCCTTGGGGCCGAAACGACCTCAACCTAT Periclistus CAAATCGATCGTCGGAACTGGGTATAGGGGCGAAAGACTAATCGAACCATCTAGTAGCTGGTTCCCTCCGAAGTTTCCCTCAGGATAGCTGGCACTCGCGTGA-GCGAGTCTCATCTGGTAAAGCGAATGATTAGAGGCCTTGGGGCCGAAACGACCTCAACCTAT Ceroptres CAAATCGATCGTCGGAACTGGGTATAGGGGCGAAAGACTAATCGAACCATCTAGTAGCTGGTTCCCTCCGAAGTTTCCCTCAGGATAGCTGGCACTCGCGTGA-GCGAGTCTCATCTGGTAAAGCGAATGATTAGAGGCCTTGGGGCCGAAACGACCTCAACCTAT Synophromorpha CAAATCGATCGTCGGAACTGGGTATAGGGGCGAAAGACTAATCGAACCATCTAGTAGCTGGTTCCCTCCGAAGTTTCCCTCAGGATAGCTGGCACTCGCGTGA-GCGAGTCTCATCTGGTAAAGCGAATGATTAGAGGCCTTGGGGCCGAAACGACCTCAACCTAT Xestophanes CAAATCGATCGTCGGAACTGGGTATAGGGGCGAAAGACTAATCGAACCATCTAGTAGCTGGTTCCCTCCGAAGTTTCCCTCAGGATAGCTGGCACTCGCGTGA-GCGAGTCTCATCTGGTAAAGCGAATGATTAGAGGCCTTGGGGCCGAAACGACCTCAACCTAT Diastrophus CAAATCGATCGTCGGAACTGGGTATAGGGGCGAAAGACTAATCGAACCATCTAGTAGCTGGTTCCCTCCGAAGTTTCCCTCAGGATAGCTGGCACTCGCGTGA-GCGAGTCTCATCTGGTAAAGCGAATGATTAGAGGCCTTGGGGCCGAAACGACCTCAACCTAT Gonaspis CAAATCGATCGTCGGAACTGGGTATAGGGGCGAAAGACTAATCGAACCATCTAGTAGCTGGTTCCCTCCGAAGTTTCCCTCAGGATAGCTGGCACTCGCGTGA-GCGAGTCTCATCTGGTAAAGCGAATGATTAGAGGCCTTGGGGCCGAAACGACCTCAACCTAT Liposthenes_gle CAAATCGATCGTCGGAACTGGGTATAGGGGCGAAAGACTAATCGAACCATCTAGTAGCTGGTTCCCTCCGAAGTTTCCCTCAGGATAGCTGGCACTCGCGTGA-GCGAGTCTCATCTGGTAAAGCGAATGATTAGAGGCCTTGGGGCCGAAACGACCTCAACCTAT Liposthenes_ker CAAATCGATCGTCGGAACTGGGTATAGGGGCGAAAGACTAATCGAACCATCTAGTAGCTGGTTCCCTCCGAAGTTTCCCTCAGGATAGCTGGCACTCGCGTGA-GCGAGTCTCATCTGGTAAAGCGAATGATTAGAGGCCTTGGGGCCGAAACGACCTCAACCTAT Antistrophus CAAATCGATCGTCGGAACTGGGTATAGGGGCGAAAGACTAATCGAACCATCTAGTAGCTGGTTCCCTCCGAAGTTTCCCTCAGGATAGCTGGCACTCGCGTGA-GCGAGTCTCATCTGGTAAAGCGAATGATTAGAGGCCTTGGGGCCGAAACGACCTCAACCTAT Rhodus CAAATCGATCGTCGGAACTGGGTATAGGGGCGAAAGACTAATCGAACCATCTAGTAGCTGGTTCCCTCCGAAGTTTCCCTCAGGATAGCTGGCACTCGCGTGA-GCGAGTCTCATCTGGTAAAGCGAATGATTAGAGGCCTTGGGGCCGAAACGACCTCAACCTAT Hedickiana CAAATCGATCGTCGGAACTGGGTATAGGGGCGAAAGACTAATCGAACCATCTAGTAGCTGGTTCCCTCCGAAGTTTCCCTCAGGATAGCTGGCACTCGCGTGA-GCGAGTCTCATCTGGTAAAGCGAATGATTAGAGGCCTTGGGGCCGAAACGACCTCAACCTAT Neaylax CAAATCGATCGTCGGAACTGGGTATAGGGGCGAAAGACTAATCGAACCATCTAGTAGCTGGTTCCCTCCGAAGTTTCCCTCAGGATAGCTGGCACTCGCGTGA-GCGAGTCTCATCTGGTAAAGCGAATGATTAGAGGCCTTGGGGCCGAAACGACCTCAACCTAT Isocolus CAAATCGATCGTCGGAACTGGGTATAGGGGCGAAAGACTAATCGAACCATCTAGTAGCTGGTTCCCTCCGAAGTTTCCCTCAGGATAGCTGGCACTCGCGTGA-GCGAGTCTCATCTGGTAAAGCGAATGATTAGAGGCCTTGGGGCCGAAACGACCTCAACCTAT Aulacidea CAAATCGATCGTCGGAACTGGGTATAGGGGCGAAAGACTAATCGAACCATCTAGTAGCTGGTTCCCTCCGAAGTTTCCCTCAGGATAGCTGGCACTCGCGTGA-GCGAGTCTCATCTGGTAAAGCGAATGATTAGAGGCCTTGGGGCCGAAACGACCTCAACCTAT Panteliella CAAATCGATCGTCGGAACTGGGTATAGGGGCGAAAGACTAATCGAACCATCTAGTAGCTGGTTCCCTCCGAAGTTTCCCTCAGGATAGCTGGCACTCGCGTGA-GCGAGTCTCATCTGGTAAAGCGAATGATTAGAGGCCTTGGGGCCGAAACGACCTCAACCTAT Barbotinia CAAATCGATCGTCGGAACTGGGTATAGGGGCGAAAGACTAATCGAACCATCTAGTAGCTGGTTCCCTCCGAAGTTTCCCTCAGGATAGCTGGCACTCGCGTGA-GCGAGTCTCATCTGGTAAAGCGAATGATTAGAGGCCTTGGGGCCGAAACGACCTCAACCTAT Aylax CAAATCGATCGTCGGAACTGGGTATAGGGGCGAAAGACTAATCGAACCATCTAGTAGCTGGTTCCCTCCGAAGTTTCCCTCAGGATAGCTGGCACTCGCGTGA-GCGAGTCTCATCTGGTAAAGCGAATGATTAGAGGCCTTGGGGCCGAAACGACCTCAACCTAT Iraella CAAATCGATCGTCGGAACTGGGTATAGGGGCGAAAGACTAATCGAACCATCTAGTAGCTGGTTCCCTCCGAAGTTTCCCTCAGGATAGCTGGCACTCGCGTGA-GCGAGTCTCATCTGGTAAAGCGAATGATTAGAGGCCTTGGGGCCGAAACGACCTCAACCTAT Timaspis CAAATCGATCGTCGGAACTGGGTATAGGGGCGAAAGACTAATCGAACCATCTAGTAGCTGGTTCCCTCCGAAGTTTCCCTCAGGATAGCTGGCACTCGCGTGA-GCGAGTCTCATCTGGTAAAGCGAATGATTAGAGGCCTTGGGGCCGAAACGACCTCAACCTAT Phanacis_1 CAAATCGATCGTCGGAACTGGGTATAGGGGCGAAAGACTAATCGAACCATCTAGTAGCTGGTTCCCTCCGAAGTTTCCCTCAGGATAGCTGGCACTCGCGTGA-GCGAGTCTCATCTGGTAAAGCGAATGATTAGAGGCCTTGGGGCCGAAACGACCTCAACCTAT Phanacis_2 CAAATCGATCGTCGGAACTGGGTATAGGGGCGAAAGACTAATCGAACCATCTAGTAGCTGGTTCCCTCCGAAGTTTCCCTCAGGATAGCTGGCACTCGCGTGA-GCGAGTCTCATCTGGTAAAGCGAATGATTAGAGGCCTTGGGGCCGAAACGACCTCAACCTAT Eschatocerus CAAATCGATCGTCGGAACTGGGTATAGGGGCGAAAGACTAATCGAACCATCTAGTAGCTGGTTCCCTCCGAAGTTTCCCTCAGGATAGCTGGCACTTGCATTAAGCGAGTCTCATCTGGTAAAGCGAATGATTAGAGGCCTTGGGGCCGAAACGACCTCAACCTAT Diplolepis CAAATCGATCGTCGGAACTGGGTATAGGGGCGAAAGACTAATCGAACCATCTAGTAGCTGGTTCCCTCCGAAGTTTCCCTCAGGATAGCTGGCACTCGAGTGA-GCGAGTCTCATCTGGTAAAGCGAATGATTAGAGGCCTTGGGGCCGAAACGACCTCAACCTAT Pediaspis CAAATCGATCGTCGGAACTGGGTATAGGGGCGAAAGACTAATCGAACCATCTAGTAGCTGGTTCCCTCCGAAGTTTCCCTCAGGATAGCTGGCACTCGTGTGA-TTGAGTCTCATCTGGTAAAGCGAATGATTAGAGGCCTTGGGGCCGAAACGACCTCAACCTAT Plagiotrochus CAAATCGATCGTAGGAGCTGGGTATAGGGGCGAAAGACTAATCGAACCATCTAGTAGCTGGTTCCCTCCGAAGTTTCCCTCAGGATAGCTGGCACTCGCGTGAAGCGAGTCTCATCTGGTAAAGCGAATGATTAGAGGCCTTGGGGCCGAAACGACCTCAACCTAT Andricus CAAATCGATCGTCGGAACTGGGTATAGGGGCGAAAGACTAATCGAACCATCTAGTAGCTGGTTCCCTCCGAAGTTTCCCTCAGGATAGCTGGCACTCGCGTGA-GCGAGTCTCATCTGGTAAAGCGAATGATTAGAGGCCTTGGGGCCGAAACGACCTCAACCTAT Neuroterus CAAATCGATCGTCGGAACTGGGTATAGGGGCGAAAGACTAATCGAACCATCTAGTAGCTGGTTCCCTCCGAAGTTTCCCTCAGGATAGCTGGCACTCGCGTGA-GCGAGTCTCATCTGGTAAAGCGAATGATTAGAGGCCTTGGGGCCGAAACGACCTCAACCTAT Biorhiza CAAATCGATCGTCGGAACTGGGTATAGGGGCGAAAGACTAATCGAACCATCTAGTAGCTGGTTCCCTCCGAAGTTTCCCTCAGGATAGCTGGCACTCGCGTGA-GCGAGTCTCATCTGGTAAAGCGAATGATTAGAGGCCTTGGGGCCGAAACGACCTCAACCTAT Parnips CAAATCGATCGTCGGAACTGGGTATAGGGGCGAAAGACTAATCGAACCATCTAGTAGCTGGTTCCCTCCGAAGTTTCCCTCAGGATAGCTGGCACTCGCGTGA-GCGAGTCTCATCTGGTAAAGCGAATGATTAGAGGCCTTGGGGCCGAAACGACCTCAACCTAT Paramblynotus CAAATCGATCGTCGGAACTGGGTATAGGGGCGAAAGACTAATCGAACCATCTAGTAGCTGGTTCCCTCCGAAGTTTCCCTCAGGATAGCTGGCACTCGCGTGA-GCGAGTCTCATCTGGTAAAGCGAATGATTAGAGGCCTTGGGGCCGAAACGACCTCAACCTAT Ibalia TCTCAAACTTTAAATGGGTGAGATCTCTGGCTTGCTTGAACG-ATGAAGCCACGAGATTT---CGGATCAGAGTGCCAAGTGGGCCAATTTT Synergus TCTCAAACTTTAAATGGGTGAGATCTCTGGCTTGCTTGAACA-GTGAAGCCACGAGATTT---CGGATCAGAGTGCCAAGTGGGCCAATTTT Periclistus TCTCAAACTTTAAATGGGTGAGATCTCTGGCTTGCTTGAACA-GTGAAGCCACGAGATTT---CGGATCAGAGTGCCAAGTGGGCCAATTTT Ceroptres TCTCAAACTTTAAATGGGTGAGATCTCTGGCTTGCTTGAACA-GTGAAGCCACGAGATTT---CGGATCAGAGTGCCAAGTGGGCCAATTTT Synophromorpha TCTCAAACTTTAAATGGGTGAGATCTCTGGCTTGCTTGAATA-GTGAAGCCACGAGATTT---CGGATCAGAGTGCCAAGTGGGCCAATTTT Xestophanes TCTCAAACTTTAAATGGGTGAGATCTCTGGCTTGCTTGAATA-------------------------------------------------- Diastrophus TCTCAAACTTTAAATGGGTGAGATCTCTGGCTTGCTTGAATA-GTGAAGCCACGAGATTT---CGGATCAGAGTGCCAAGTGGGCCAATTTT Gonaspis TCTCAAACTTTAAATGGGTGAGATCTCTGGCTTGCTTGAATA-GTGAAGCCACGAGATTT---CGGATCAGAGTGCCAAGTGGGCCAATTTT Liposthenes_gle TCTCAAACTTTAAATGGGTGAGATCTCTGGCTTGCTTGAACA-GTGAAGCCACGAGATTT---CGGATCAGAGTGCCAAGTGGGCCAATTTT Liposthenes_ker TCTCAAACTTTAAATGGGTGAGATCTCTGGCTTGCTTGAACA-GTGAAGCCACGAGATTT---CGGATCAGAGTGCCAAGTGGGCCAATTTT Antistrophus TCTCAAACTTTAAATGGGTGAGATCTCTGGCTTGCTTGAAAA-GTGAAGCCACGAGATTT---CGGATCAGAGTGCCAAGTGGGCCAATTTT Rhodus TCTCAAACTTTAAATGGGTGAGATCTCTGGCTTGCTTGAACA-GTGAAGCCACGAGATTT---CGGATCAGAGTGCCAAGTGGGCCAATTTT Hedickiana TCTCAAACTTTAAATGGGTGAGATCTCTGGCTTGCTTGAACA-GTGAAGCCAAGAGATTT---CGGATCAGAGTGCCAAGTGGGCCAATTTT Neaylax TCTCAAACTTTAAATGGGTGAGATCTCTGGCTTGCTTGAACA-GTGAAGCCACGAGATTT---CGGATCAGAGTGCCAAGTGGGCCAATTTT Isocolus TCTCAAACTTTAAATGGGTGAGATCTCTGGCTTGCTTGAACA-GTGAAGCCACGAGATTT---CGGATCAGAGTGCCAAGTGGGCCAATTTT Aulacidea TCTCAAACTTTAAATGGGTGAGATCTCTGGCTTGCTTGAACA-GTGAAGCCACGAGATTT---CGGATCAGAGTGCCAAGTGGGCCAATTTT Panteliella TCTCAAACTTTAAATGGGTGAGATCTCTGGCTTGCTTGAACA-GTNAAGCCAC--------------------------------------- Barbotinia TCTCAAACTTTAAATGGGTGAGATCTCTGGCTTGCTTGAACA-GTGAAGCCACGAGATTT---CGGATCAGAGTGCCAAGTGGGCCATTTTT Aylax TCTCAAACTTTAAATGGGTGAGATCTCTGGCTTGCTTGAACA-GTGAAGCCACGAGATTT---CGGATCAGAGTGCCAAGTGGGCCATTTTT Iraella TCTCAAACTTTAAATGGGTGAGATCTCTGGCTTGCTTGAACA-GTGAAGCCACGAGATTT---CGGATCAGAGTGCCAAGTGGGCCATTTTT Timaspis TCTCAAACTTTAAATGGGTGAGATCTCTGGCTTGCTTGAATA-GTGAAGCCACGAGATTT---CGGATCAGAGTGCCAAGTGGGCCAATTTT Phanacis_1 TCTCAAACTTTAAATGGGTGAGATCTCTGGCTTGCTTGAACA-GTGAAGCCACGAGATTT---CGGATCAGAGTGCCAAGTGGGCCAATTTT Phanacis_2 TCTCAAACTTTAAATGGGTGAGATCTCTGGCTTGCTTGAACA-GTGAAGCCACGAGATTT---CGGATCAGAGTGCCAAGTGGGCCAATTTT Eschatocerus TCTCAAACTTTAAATGGGTGAGATCTCTGGCTTGCTTGAATTTATGAAGCCATGAGATATAATTGGATCAGAGTGCCAAGTGGGCCAATTTT Diplolepis TCTCAAACTTTAAATGGGTGAGATCTCTGGCTTGCTTGAACA-TTGAAGCCATGAGATTT---CGGATCAGAGTGCCAAGTGGGCCAATTTT Pediaspis TCTCAAACTTTAAATGGGTGAGATCTCTGGCTTGCTTGAACA-GTGAAGCCACGAGATTT---CGGATCAGAGTGCCAAGTGGGCCAATTTT Plagiotrochus TCTCAAACTTTAAATGGGTGAGATCTCTGGCTTGCTTGAACA-GTGAAGCCACGAGATTTT-TCGGATCAGAGTGCCAAGTGGGCCAATTTT Andricus TCTCAAACTTTAAATGGGTGAGATCTCTGGCTTGCTTGAACA-GTGAAGCCACGAGATTT---CGGATCAGAGTGCCAAGTGGGCCAATTTT Neuroterus TCTCAAACTTTAAATGGGTGAGATCTCTGGCTTGCTTGAACA-GTGAAGCCACGAGATTT---CGGATCAGAGTGCCAAGTGGGCCAATTTT Biorhiza TCTCAAACTTTAAATGGGTGAGATCTCTGGCTTGCTTGAACA-GTGAAGCCACGAGATTT---CGGATCAGAGTGCCAAGTGGGCCAATTTT Parnips TCTCAAACTTTAAATGGGTGAGATCTCTGGCTTGCTTGAACG-GTGAAGCCATGAGATTT---CGGATCAGAGTGCCAAGTGGGCCAATTTT Paramblynotus TCTCAAACTTTAAATGGGTGAGATCTCTGGCTTGCTTGAACT-GTGAAGCCATGAGATTT---CGGATCAGAGTGCCAAGTGGGCCAATTTT ; End; begin mrbayes; [This block defines several different character sets that could be used in partitioning these data and then defines and enforces a partition called favored.] charset morphology = 1-166; charset COI = 167-1244; charset EF1a = 1245-1611; charset LWRh = 1612-2092; charset 28S = 2093-3246; partition favored = 5: morphology, COI, EF1a, LWRh, 28S; [The following lines set up a particular model (the one discussed in the MrBayes manual). Uncomment this block if you want to set up the model when executing this file instead of specifying it yourself.] [ set partition=favored; lset app=(1) rates=gamma; lset app=(2,3,4,5) rates=invgamma nst=6; unlink revmat=(all) pinvar=(all) shape=(all) statefreq=(all); prset applyto=(all) ratepr=variable; ] end; NBISweden-MrBayes-ecfd55c/examples/finch.nex000066400000000000000000002056731526212227600210530ustar00rootroot00000000000000#NEXUS [ Data from: Jennings, W.B. and Edwards, S.V. Speciational history of Australian grass finches (Poephila) inferred from 30 gene trees. Evolution 59:2033-2047, 2005. This data set consists of 30 loci with one allele for each of three ingroup species and one outgroup species. ] Begin data; Dimensions ntax=4 nchar=16119; Format datatype=DNA gap=- missing=? matchchar=. interleave; Matrix Q097 TTCTTTTTGTCCTTTCACTAGAAAATCTTGGCTTATCTTCAAATACAAAAAAAAAAAGAAAAATTAACAGCATTTGAGTTTCAATACCGAGTATTTAAATAAATTTCAGTGTCACAAAAATACTGAAAAAATTAAAAAAAATTATATCTCTCAATACTGTGACTGAGGGTGTGACCATAATGAATAAAATTTGAACTTGGTCATAAGAACTAATGACTTTTAAGCTGATTTGCCCTTGAGAAAAAGACTGAAACTTAGGATAGCTTTGCCCAAGTGAAAACTCTAACCACAGAATATGCCAAGTTGGAAGGGGCCCATAAGGATCATCAAGCTTAACGCCTGTCTCCACACAGGACCAAGATTCTTTTCTTCAACTCTGTCAGGCTTGCTGCTGGAACCCTTCCCTGGGGAGCCTGTTCCAGTGCCCAACCACCCTCTAGGGGAAGAAACTTTTTCTTATATCCAACCTAAAC W097 TTCTTTTTGTCCTTTCACTAGAAAATCTTGGCTTATCTTCAAATACAAAAAAAAAAAGAAAAATTAACAGCATTTGAGTTTCAATACCGAGTTTTAAAATAAATTTCAGTGTCACAAAAATACTGAAAAAATTAAAAAAAATTATATCTCTCAATACTGTGACTGAGGGTGTGACCATAATGAATAAAATTTGAACTTGGTCATAAGAACTAATGACTTTTAAGCTGATTTGCCCTTGAGAAAAAGACTGAAACTTAGGATAGCTTTGCCCAAGTGAAAACTCTAACCACAGAATATGCCAAGTTGGAAGGGGCCCATAAGGATCATCAAGCTTAACGCCTGTCTCCACACAGGACCAAGATTCTTTTCTTCAACTCTGTCAGGCTTGCTGCTGGAACCCTTCCCTGGGGAGCCTGTTCCAGTGCCCAACCACCGTCTAGGGGAAGAAACTTTTTCTTATATCCAACCTAAAC B097 TTCTTTTTGTCCTTTCACTAGAAAATCTTGGCTTATCTTCAAATACAAAAAAAAAAAGAAAAATTAACAGCATTTGAGTTTCAATACCGAGTTTTTAAATAAATTTCAGTGTCACAAAAATACTGAAAAAATTAAAAAAAATTATATCTCTCAATACTGTGACTGAGGGTGTGACCATAATGAATAAAATTTGAACTTGGTCATAAGAACTAATGACTTTTAAGCTGATTTGCCCTTGAGAAAAAGACTGAAACTTAGGATAGCTTTGCCCAAGTGAAAACTCTAACCACAGAATATGCCAAGTTGGAAGGGGCCCATAAGGATCATCAGGCTTAACGCCTGTCTCCACACAGGACCAAGATTCTTTTCTTCAACTCTGTCAGGCTTGCTGCTGGAACCCTTCCCTGGGGAGCCTGTTCCAGTGCCCAACCACCCTCTATGGGAAGAAACTTTTTCTTACATCCAACCTAAAC O097 GTCTTTTTGTCCTTTCACTAGAAAATCTTGGCTTATCTTCAAATACAAAAAAAAAAAGAAAAATTAACAGCATTTGAGTTTCAATACCAAGTTTTTAAATAAATTTCAGTATCACAAAAATGCTGAAAAAATTAAAAAAAAATATATCTCTCAATACTGTGACTGAGGGTGTGACCATAATGAATAAAATTTGAACTTGGTCATAAGAACTAATGACTTTTAAGCTGATTTGTCCTTGAGAAAAAGACTAAAACTTAGGATAGCTTTGCCCAAGTGAAAACGCTAACTACAGAATATGCCAAGTTGGAAGGGGCCCATAAGGATCATCAAGCTTAACGCCTGTCTCCACACAGGATCAAGATTCTTTTCTTCAACTCTGTCCGGCTTGCTGCTGGGACCCTTCCCTGGGGAGCCTGTTCCAGTGCCCAACCACCCTCTGGGGTAAGAAACTTTTTCTTATATTCAACCTAAAC Q097 GCCATTTCATCCCAGCACAAACCTGGGAGCTCCAGCTGTGTCTGGAAAGCTTCGATGGGATCCCACCAGTCTGCAAAGCTTTTGTGTCAGGTATAGAGATTGATGCATCAGCCTTGGGCTCTCAGAAATGCACCTGGTTTGTAGTGAATGTGAGATGTGTAACTGCTTAGTCAAGGACAAACCTTTTTCCTATTCTCCTCTCTGTACTGGCTTTTAAAATGTAGTATTTAGTGAAAAAAAACTCTCAATGATGTGTGTGCTACAAACCAGTTGTCCCAGTCAAAGCTATTTCAATAAGCAGGAAATAATTTCTGTTATAACTTCAATTTTCAAGAACAGATTTTCTTGACTTGATTGCTTAAAATTGTTTGGGTTTATGTGGTGTTTTTTACCATTTGCACCAGTTTAATGTGTACAGCATTTTTTTTTTTTCTGGATTCTTTCTAGAAACTTAACCTACAAAGAGGAAAGACTTTTTTATTGTCTTGAGGTGTGTTCAGAAAAAGTAAAATAGATCTTTTGGAAAAAGACCTGTGATCAGATTTTTGCGAAGCAAAA W097 GCCATTTCATCCCAGCACAAACCTGGGAGCTCCAGCTGTGTCTGGAAAGCTTCGATGGGATCCCACCAGTCTGCAAAGCTTTTGTGTCAGGTATAGAGATTGATGCATCAGCCTTGGGCTCTCAGAAATGCACCTGGTTTGTAGTGAATGTGAGATGTGTAACTGCTTAGTCAAGGACAAACCTTTTTCCTATTCTCCTCTCTGTACTGGCTTTTAAAATGTAGTATTTAGTGAAAAAAAAGTCTCAATGATGTGTGTGCTACAAACCAGTTGTCCCAGTCAAAGTTATTTCAATAAGCAGGAAATAATTTCTGTTATAACTTCAGTTTTCAAGAACAGATTTTCTTCACTTGATTGCTTAAAATTGTTTGGGTTTATGTGGTGTTTTTTACCATTTGCACCAGTTTAATGTGTACAGCATTTTTTTTTTTTCTGGATTCTTTCTAGAAACTTAACCTACAAAGAGGAAAGACTTTTTTATTGTCTTGAGGTGTGTTCAGAAAAAGTAAAATAGATCTTTTGGAAAAAGACCTGTGATCAGATTTTTGCGAAGCAAAA B097 GCCATTTCATCCCAGCACAAAGCTGGGAGCTCCAGCTGTGGCTGGAAAGCTTCGATGGGATCCCACCAGTCTGCAAAGCTTTTGTGTCAGGTATAGAGATTGATGCATCAGCCTTGGGCTCTCAGAAATGCACCTGGTTTGTAGTGAATGTGAGATGTGTAACTGCTTAGTCAAGGACAAACCTTTTTCCTATTCTCCTCTCTGTACTGGCTTTTAAAATGTAGTATTTAGTGAAAAAAAACTCTCAATGATGTGTGTGCTACAAACCAGTTGTCCCAGTCAAAGTTATTTCAATAAGCAGGAAATAATTTCTGTTATAACTTCAATTTTCAAGAACAGATTTTCTTGACTTGATTGCTTAAAATTGTTTGGGTTTATGTGGTGTTTTTTACCATTTGCACCAGTTTAATGTGTACAGCATTTTTTTTTTTTCTGGATTCTTTCTAGAAACTTAACCTACAAAGAGGAAAGACTTTTTTATTGTCTTGAGGTGTGTTCAGAAAAAGTAAAATAGATCTTTTGGAAAAAGACCTGTGATCAGATTTTTGCGAAGCAAAA O097 GCCATTTCATCCCAGCACAAAGCTGGGAGCTCCAGCTGTGGCTGAAAAACTTCGATGGGATCCCACCGGTCTGCAAAGCTTTTGTGTCAGGTATAGAGATTGATGCATCAGCCCTGGGCTCTCAGAAATGCACCTGGTTTGTAGTGAATGTGAGATGTGTAACTGCTTAGTCAAGGACAAACCTTTTTCCTATTCTCCTCTCTATATTGGCTTTTAAAATGCAGTATTTAGTGAAAAAAAAGTCTCAATGATTTGTGTGCTACAAACCAGTTGTCCCAGTCAAAGTTATTTCAATAAGCAGGAAATTATTTCTTTTATAACTTCAATTTTCAAGAACAGATTTTCTTGACTTGATTGCTTAAAATTGTTTGGGTTTATGTGGTGTTTTGTACCATTTGCACCAGTTTAATGTGTACAGCATTTTTTTTTTTTCTGGATTCTTTCTAGGAACTTAACCTACAAAGAGGAAAGACTTTTTTATTGTCTTGAGGTGTGTTCAGAAAAAGTAAAATAGATCTTTTGGAAAAAGACCTGTGATCAGATTTTTGCTAAGCAAAA Q097 ACTTCATGCAGGTCTCAGACTGGAATCCCAGATAAAACGGGCACTGCAGGGTAGTTTATTTTGTTCAAATGTAGCGATAGCACAAACCTGGTTATGGGTCACATTCAAGAGGCAGACAGTGAATTAGATGGTGGGGGTTAAAAAATGTTATTGAGGATACTTTTATGAGCGAAAAACCCACTGAACATTACCCAGGGACCTGGGCAGGCCTGTTGGGCGTGTCATGAGTTCCATTCCAAAAGTTTGGCAGAAGAAAACAGGCAATAGGTAGCTTCAGAGAAGCAGCCAGTCATCATTTTCCTCAGGGCATTTGAGCTCCTGGTTCCTCAGGCTGTAGATGAGGGGGTTCAGGGATGGAGGCACCACCGAGTACAGAACTGACACTGCCAGATCTAGGGATGGGGAGGAGAGGGAGGGGGCTTTAGGTAGGCAAAGGCTGCGGTGCAGAGAAACAGGGAGAGCACAGCCAGGTGACGGAGGCAGGTAGAAAAGGCTTTGTGCCGTCTCTGCTCAGAGGGAATCCTCAGCACAGCCCTGAAGATCTCCACATAGGAGAAAACCA W097 ACTTCATGCAGGTCTCAGACTGGAATCCCAGATAAAACAGGCACTGCAGGGTAGTTTATTTTGTTCAAATGTAGCGACAGGACAAACCTGGTTATGGGTCACATTCAAGAGGCAGACAGTGAATTAGATGGTGGGGGTTAAAAAATGTTATTGAGGATACTTTTATGAGCGAAAAACCCACTGAACATTACCCAGGGACCTGGGCAGGCCTGTTGGGCGTGTCATGAGTTCCATTCCAAAAGTTTGGCAGAAGAAAACAGGCAATAGGTAGCTTCAGAGAAGCAGCCAGTCATCATTTTCCTCAGGGCATTTGAGCTCCTGGTTCCTCAGGCTATAGATGAGGGGGTTCAGGGATGGAGGCACCACCGAGTACAGAACTGACACTGCCAGATCTAGGGATGGGGAGGAGAGGGAGGGGGCTTTAGGTAGGCAAAGGCTGCGGTGCAGAGAAACAGGGAGAGCACAGCCAGGTGACGGAGGCAGGTAGAAAAGGCTTTGTGCCGTCTCTGCTCAGAGGGAATCCTCAGCACAGCCCTGAAGATCTCCACATAGGAGAAAACCA B097 ACTTCATGCAGGTCTCAGACTGGAATCCCAGATAAAACAGGCACTGCAGGGTAGTTTATTTTGTTCAAATGTAGCGATAGCACAAACCTGGTTATGGGTCACATTCAAGAGGCAGACAGTGAATTAGATGGTGGGGGTTAAAAACTGTTATTGAGGATACTTTTATGAGCGAAAAACCCACTGAACATTACCCAGGGACCTGGGCAGGCCTGTTGGGCGTGTCATGAGTTCCATTCCAAAAGTTTGGCAGAAGAAAACAGGCAATAGGTAGCTTCAGAGAAGCAGCCAGTCATCATTTTCCTCAGGGCATTTGAGCTCCTGGTTTCTCAGGCTGTAGATGAGGGGGTTCAGGGATGGAGGCACCACCAAGTACAGAACTGACACTGCCAGATCTAGGGATGGGGAGGAGAGGGAGGGGGCTTTAGGTAGGCAAAGGCTGTGGTGCAGAGAAACAAGGAAAGCACAGCCAGGTGACGGAGGCAGGTAGAAAAGGCTTTGTCCCGTCTCTGCTCAGAGGGAATCCTCAGCACAGCCCTGAAGATCTCCACATAGGAGAAAACCA O097 ACTTCATGCAGGCCTCAGACTGGAATCCCAGATAAAACAGGCACTGCAGGGTAGTTTATTTTGTTCAAATGTAGCGATAGTACAAACCTGGTTATGGGTCACATCCAAGAGGCAGACAGTGAATTAGATGGTGGGGGTTAAAAAATATTATTGAGGATACTTTTATGAGCGAAAAACCCACTGAACATTTCCCAGGGACCTGGGCAGGCCTGTTGGGCGTGTCATGAGTTCCATTCCAAAAGTCTGGCAGAAGAAAACAGGAAATAGGTAGCTTCAGAGAAGCAGCCAGTCATCATTTTCCTCAGGGCATTTGAGCTCCTGGTTCCTCAGGCTGTAGATGAGGGGGTTCAGGGCTGGAGGCACCACCGAGTACAGAACTTACACTGCCAGATCCAGGGATGGGAGGAGAGGGAAGGGGGCTTCAGGTAGGCAAAGGCTGCGGTGCAGAGAAACAAGGAGAGCACAGCCAGGTGACGGAGGCAGGTAGAAAAGGCTTTGTGCCATCCCTGCTCAGAGGGAGTCCTCAGCACAGCCCTGAAGATCTCCACATAGGAGAAAACCA Q097 TCTTAAAAAATGAAATATTTTAAAAATATTTAAATCAAATCCTCACATTTTATAAAGAGGTCTGTTAAGAAGCACAGCAGTGTACATCATAAACCAAGCCTTCACTTAAATGGATTTCTAGAAAATATCAAAAAGGACTTAGGACTCTTCAAGGAGAATTTATTTGTTTCATGGATATTTTAGATGGGCAAATCCCATAGCCCTGAATAAAACAAAATTCTTACAATTGAGATTGAGAGGAATAACCAGTGATCAAGACTTCTGGAAGCCCTGAAGATGACTGAGAAAATAACTGTTGCCTCAAGGAGCTCATAGCAAAATACAAGGCACCACAAAACCACTGATATGGGGGAAAAGTAGAAATTTCTATCCTTCCTCCCCTCCATGAGGCAAAAAAGGTATGTTTATCATCCCAGGTATCAATGGAGTTTTGGAGCAAGGGAAGAAATGTAACCAGATTTTTGGATGCCAGTCAGGGCCTTTAAGCTCCTTCTCTGGCCGTTCAGGTCACTTTTACCTATGTGAGAGGGGAATAAAGCTCACAGATGTTTGTTTGGGTCCAGCTGGCTGCAGATATCCTGCTCATAGAGATGCAGAGCAGAAATACTTGT W097 TCTTAAAAAATGAAATATTTAAAAAATATTTCAATCAAATCCTCACATTTTATAAAGAGGTCTGTTAAGAAGCACAGCAGTGTACATCATAAACCAAGCCTTCACTTAAATGGATTTCTAGAAAATATCAAAAAGGACTTAGGACTCTTCAAGGAGAATTTATTTGTTTCATGGATATTTTAGATGGGCAAATCCCATAGCCCTGAATAAAACAAAATTCTTACAATTGAGATTGAGAGGAATAACCAGTGATCAAGACTTCTGGAAGCCCTGAAGATGACTGAGAAAATAACTGTTGCCTCAAGGAGCTCATAGCAAAATACAAGGCACCACAAAACCACTGATATGGGGGAAAAGTAGAAATTTCTATCCTTCCTCCCCTCCATGAGGCAAAAAAGGTATGTTTGTCATCCCAGGTATCAATGGAGTTTTGGAGCAAGGGAAGAAATGTAACCAGATTTTTGGATGCCAGTCAGGGCCTTTAAGCTCCTTCTCTGGCCGTTCAGGTCACTTTTACCTATGTGAGAGGGGAATAAAGCTCACAGATGTTTGTTTGGGTCCAGCTGGCTGCAGATATCCTGCTCATAGAGATGCAGAGCAGAAATACTTGT B097 TCTTAAAAAATGAAATATTTTAAAAATATTTCAATCAAATCCTCACATTTTATAAAGAGGTCTGTTAAGAAGCACAGCAGTGTACATCATAAACCAAGCCTTCACTTAAATGGATTTCTAGAAAATATCAAAAAGGACTTAGGACTCTTCAAGGAGAATTTATTTGTTTCACGGATATTTTAGATGGGCAAATCCCATAGTCCTGAATAAAACAAAATTCTTACAATTGAGATTGAGAGGAATAACCAGTGATCAAGGCTTCTGGAAGCCCTGAAGATGACTGAGAAAATAACTGTTGCTTCAAGGAGTTCATAGCAAAATACAAGGCACCACAAAACCACTGATATGGGGGAAAAATAGAAATTTCTATCCTTCCTCCCCTCCATGAGGCAAAAAAGGTATGTTTATCATCCCAGGTATCAATGGAGTTTTGGAGCAAGGGAAGAAATGTAACCAGATTTTTGGATGCCAGTCAGGGCCTTTAGGCTCCTTCTCTGGCCATTCAGGTCACTTTTACCTATGTGAGAGGGGAATAAAGCTCACAGATGCTTGCTTGGGTCCAGCTGGCTGCAGATATCCTGCTCATAGAGATGCAGAGCAGAAATACTTGT O097 TCTCAAAATATGAAATATTTTAAAAATATTTCAATCAAATCCTCACATTTTATAAAGGGGTCTGTTAAGAAGCACAGCAGTTTACATCATAAACCAAGCCTTCACTTAAATGGATTTCTAGAAAATATCAAAAAGGACTTAGGACTCTTCAAGGAGAACTTATTTATTTCATGGATATTTTAGATGGGCAAATCCCATAGCCCTGAATAAAACAAAATTCTTACAATTGAGATTGAGAGGAATAACCAGTGATCAAGACTTCTGTAAGCCATGAAGATGACTGAGAAAATAACTGTTGCCTCAAGGAGCTCATAGCAAAATACAAGGCACCACAAAACCACTGATATGGGGGAAAAGTAGAAATTTCTATCCTTCCTCCCCTCCATGAGGCAAAAAAGGTATGTTTATCATCCCATGTATCAATGGAGTTTTGGAGCAAGGGAAGAAATGTAACCAGATTTTTGGATGCCAGTCAGGGCCTTTAAGCTCCTCCTCTGGCCGTTCAGGTCACTTTTACCTATGTGAGAGGGGAATAAAGCTCACAGATGTTTGTTTGGGTCCAGCTGGCTGCAGATATCCTGCTCATAGAGATGCAAAGCAGAAACACTTGT Q097 ATATTCATATATCCTTCATGGTTATGCATACATTCTATTTAAAACAAGAAACTCTGTCTGTTATATGCCAACTGTTTCCTTTAATCCCCATGGCAAGGCCTGAAGAAATTAGCTTCTTCTGACAAGAAAGCCATAAATTCCTCTTCTCTGGAAGATTTAGGCGTTCTGTGATGGTTACGTCAAAGCGAGTCTCTCATTCCTTAAAATAAAAAACAAAACACATAAGGAGTTTCTATTTTAACTACTAAAGCTTCATTTTAACTACAAAACTACATTTACCATGCTATTAAGTTGTTAATA W097 ATATTCATATATCCTTCATGGTTATGCATACATTCTATTTAAAACAAGAAACTCTGTCTGTTATATGCCAACTGTTTCCTTTAATCCCCATGGCAAGGCCTGAAGAAATTAGCTTCTTCTGACAAGAAAGCCATAAATTCCTCTTCTCTGGAAGATTTAGGCGTTCTGTGATGGTTACGTCAAAGCGAGTCTCTCATTCCTTAAAATAAAAAACAAAACACATAAGGAGTTTCTATTTTAACTACTAAAGCTTCATTTTAACGACTAAACTACATTTACCATGCTATTAAAATGTTAATA B097 ATATTCATATATCCTTCATGGTTATGCATACATTCTATTTAAAACAAGAAACTCTGTCTGTTATATGCCAACTGTTTCCTTTAATCCCCATGGCAAGGCCCGAAGAAATTAGCTTCTTCTGACAAGAAAACCATAAATTCCTCTTCTCTGGAAGATTTAGGTGTTCTGTGATGGTTACATCAAAGCGAGTCTCTCATTCCTTAAAATAAAAAACAAAACACATATGGAGTTTCTATTTTAACTACTAAAGCTTCATTTTAACTACAAAACTACATTTCCCATGCTATTAAAATGTTAATA O097 ATATTCATATAGCCTTCATGGTTATGCATACATTCTATTTAAAACAAGAAACTCTGTCTGTTATATGCCAACTGTTTCCTTTAATCCCCATGGCAAGGCCTGAAGAAATTAGCTTCTTCTGACAAGAAAGCCATAAATTCCTCTTCTCTGGAAGATTTAGGTGTTCTGTGATGGTTACCTCAAAGCGAGTCTCTCAGTCCTTAAAATAAAAAACAAAAGACATAAGGAGTTTCTATTTTAACTATTAAAGCTTCATTTTAACTACAAAACTACATTTACCATGCTATTAAAATGTTAATA Q097 TGCCAGACCACACACACTCTGGGCTCTGCTGAGTATTTGTGTAAAACTCAATGTATATTCACATCCCTGAAGTCTGGGAGACAACCCAAATTCCTGGGAGAGTGCTACCTTTCCTTCCTGCTGGAAATGGCTGAGAAGTTCCCAGCTTCCCTGGGCTCCCAGCTCCTCCACTCCAGGATATCTGGGTGTGTCCAAAGGGCTGGGATTTTGGGTCTTTTCCTCAGGGAAAGATGCTCCCTCCTCCAAGCAGACTGGGTGTTCTCAGGGAGTATTCCAACGGAGCTGGCAGGGGCAAAGCACAGCTCAGGCAAGAGGAGTTGACTCCCACAGGGTACAGTTCCTCATGGCAGGAGGAGCCATGGTTGGTGAATGTTGAAATGTGTTCTGGGCTCCAGGGAAGCTGAAAATTGTTGGAGAAGCAATTCTTGGGAGATCATAAATCCCAGAATGGTTTGGGTTGGAAGGGACGTGAAAAAACATGTGGTCCCAACCCCCTGCCATGGCAGGGATACCTTCCACTATCCCAGGCTGCTCCAAGCCCTGTCCAACCTGGCCTTGGACACTTCCAGGGATTCAGGGGCAGCCACAGCTTCTCTGGGCACCCTGTGCCAGGGCCTC W097 TGCCAGACCACACACACTCTGGGCTCTGCTGAGTATTTGTGTAAAACTCAATGTGTATTCACATCCCTGAAGTCTGGGAGACAACCCAAATTCCTGGGAGAGTGCTACCTTTCCTTCCTGCTGGAAATGGCTGAGAAGTTCCCAGCTTCCCTGGGCTCCCAGCTCCTCCACTCCAGGATATCTGGGTGTGTCCAAAGGGCTGGGATTTTGGGTCTTTTCCTCAGGGAAAGATGCTCCCTCCTCCAAGCAGACTGGGTGTTCTCAGGGAGTATTCCAACGGAGCTGGCAGGGGCAAAGCACAGCTCAGGCAAGAGGAGTTGACTCCCACAGGGTACAGGTCCTCATGGCAGGAGGAGCCATGGTTGGTGAATGTTGAAATGTGTTCTGGGCTCCAGGGAAGCTGAAAATTGTTGGAGAAGCAATTCTTGGGAGATCATAAATCCCAGAATGGTTTGGGTTGGAAGGGACGTGAAAAAACATGTGGTCCCAAACCCCTGCCATGGCAGGGATACCTTCCACTACCCCAGGCTGCTCCAAGCCCTGTCCAACCTGGCCTTGGACACTTCCAGGGATTCAGGGGCAGCCACAGCTTCTCTGGGCACCCTGTGCCAGGGCCTC B097 TGTCAGACCACACACACTCTGGGCTCTGCTGAGTATTTGTGTAAAACTCAATGTATATTCACATCCCTGAAGTCTGGGAGACAACCCAAATTCCTGGGAGAGTGCTACCTTTCCTTCCTGCTGGAAATGGCTGAGAAGTTCCCAGCTTCCCTGGGCTCCCAGCTCCTCCACTCCAGGATATCTGGGTGTGTCCAAAGGGCTGGGATTTTGGGTCTTTTCCTCAGGGAAAGATGCTCCCTCCTCCAAGCAGACTGGGTGTTCTCAGGGAGTATTCCAACGGAGCTGGCAGGGGCAAAGCACAGCTCAGGCAAGAGGAGTTGACTCCCACAGGGTACAGTTCCTCATGGCAGGAGGAGCCATGGTTGGTGAATGTTGAAATGTGTTCTGGGCTCCAGGGAAGCTGAAAATTGTTGGAGAAGCAATTCTTGGGAGATCATAAATCCCAGAATGGTTTGGGTTGGAAGAGACGTGAAAAAACATGTGGTCCCAAACCCCTGCCATGGCAGGGATACCTTCCACTATCCCAGGCTGCTCCAAGCCCTGTCCAACCTGGCCTTGGACACTTCCAGGGATTCAGGGGCAGCCACAGCTTCTCTGGGCACCCTGTGCCAGGGCCTC O097 TGCCAGACCACACACACTCTGGGCTCTGCTGAGTATTTGTGTAAAACTCAATGTATATTCACATTCCTGAAGTCTGGGAGACAATCCAAATTCCTGGGAGAGTGCTACCTTTCCTTCCTGCTGGAAATGGCTGAGAAGTTCCCAGCTTCCCTGGGCTCCCAGCTCCTCCACTCCAGGATATCTGGGTGTGTCCAAAGGGCTGGGATTTTGGGTCTTTTCCTCAGGGAAAGATACTCCCTCCTCCAAGCAGACTGGGTGTTCTCAGGGAGTATTCCAACGGAGCTGGCAGGGGGAAAGCACAGCTCAGGCAAGAGGAGTTGACTCCCACAGGGTGCAGTTCCTCATGGCAGGAGGAGCCATGATTGGTGAATGTTGAAATGTGTTCTGGGCTCCAGGGAAGCTGAAAATTGTTGGAGAAGCAATTCTTGGGAGATCATAAATCCCAGAATGGTTTGGGTTGGAAGGGACCTGAAAAAACATGTGGTCCCAAACCCCTGCCATGGCAGGGATACCTTCCACTATCTCAGGTGGCTCCAAGCCCTGTCCAGCCTGGCCTTGGACACTTCCAGGGATTCAGGGGCAGCCACAGCTTCTCTGGGCACCCTGTGCCAGGGCCTC Q097 GGTAAAGTTAAAACCAGAAAAAGTATGTATTGATACAAAAAAGAGAGCTCATCTATCATTTCACAGCAAAAGATGCATGTCGTATGAGGAATTTAAAACAATAAGAAAAGCTGTTGGTTTTGCTTCTCCAGACTGGGAGAATCAGTGGAGCAGCAACAGAAAGGGAGTTTGGAAGCTGTGACAGGCAATGAGGCACAGCTCAGGTGGGAGTGGGCCCTGCCAAGGCTCTGGCCAACAGCCTTTATCTCACCAGAAACTGAAGTGTTTTGTAAGAGTTCATAAAATACTTCATTACATTTTTGCAGCAACAGATTAATTAGTCTTTAGTCTGAGTTGCCCTTTCACCTGCTGATTTTTTCTACTATTTCTCTGCTCTCCTAAACAGTCTCTCTTTCAGTAACTCCCTGGAGCACATCTCTGGCACCACGTCCTTCCCCACATCATCCCCCCTTCTGTCATCCCCCTGCACAGCCAGGGCCCTGCGAGCACTCCAAATTCAGAGTAAGGCAGGCCCCTTCCAGGAGGCTGAGAGGGGAAAC W097 GGTAAAGTTAAAACCAGAAAAAGTATGTATTGATACAAAAAAGAGAGCTCATCTATCATTTCACAGCAAAAGATGCATGTCGTATGAGGAATTTAAAACAATAAGAAAAGCTGTTGGTTTTGCTTCTCCAGACTGGGAGAATCAGTGGAGCAGCAACAGAAAGGGAGTTTGGAAGCTGTGACAGGCAATGAGGCACAGCTCAGGTGTGAGTGGGCCCTGCCAAGGCTCTGGCCAACAGCCTTTATCTCACCAGAAACTGAAGTGTTTTGTAAGAGTTCATAAAATACTTCATTACATTTTTGCAGCAACAGATTAATTAGTCTTTAGTCTGAGTTGCCCTTTCACCTGCTGATTTTTTCTACTATTTCTCTGCTCTCCTAAATAGTCTCTCTTTCAGTAACTCCCTGGAGCACATCTCTGGCACCACATCCTTCCCCACATCATCCCCCCTTCTGTCATCCCCCTGCACAGCCAGGGCCCTGCGAGCACTCCAAATTCAGAGTAAGGCAGGCCCCTTCCAGGAGGCTGAGAGGGGAAAC B097 GGTAAAGTTAAAACCAGAAAAAGTATGTATTGATACAAAAAAGAGAGCTCATCTATCATTTCACAGCAAAAGATGCATGTCATATGAGGAATTTAAAACAATAAGAAAAGCTGTTGGTTTTGCTTCTCCAGACTGGGAGAATCAGTGGAGCAGCAACAGAAAGGGAGTTTGGAAGCTGTGACAGGCAATGAGGCACAGCTCAGGTGGGAGTGGGCCCTGCCAAGGCTCTGGCCAACAGCCTTTATCTCACCAGAAACTGAAGTGTTTTGTAAGAGTTCATAAAATACTTCATTACATTTTTGCAGCAACAGATTAATTAGTCTTTAGTCTGAGTTGCCCTTTCACCTGCTGATTTTTTCTACTATTTCTCTGCTCTCCTAAACAGTCTCTCTTTCAGTAACTCCCTGGAGCACATCTCTGGCACCACGTCCTTCCCCACATCATCCCCCCTGCTGTCATCCCCCTGCACAGCCAGGGCCCTGCGAGCACTCCAAATTCAGAGTAAGGCAGGCCCCTTCCAGGAGGCTGAGAGGGGAAAC O097 GGTAAAGTTAAAACCAGAAAAAGTATGTATTGATACAAAAAAGAGAGCTCATCTATCATTTCACAGCAAAAGATGCGTGTCATATGATGAATTTAAAACAATAAGAAAAGCTGTTGGTTTTGCTTCTCCAGACTGGGAGAATCGGTGGAGCAGCAACAGAAAGGGAGTTTGGAAGCTGTGACAGGCAATGAGGCACAGCTCAGGTGTGAGTGGGCCCTGCCAAGGCTCTGGCCAACAGCCTTTATCTGACCAGAAACTGAAGTGTTTTGTAAGAGTTCATAAAATACTTCATTACATTTTTGCAGCAACAGATTAATTAGTCTTTAGTCTGAGTTGCCCTTTCACCTGCTGATTTTCTCTACTATTTCTCTGCTCTCCCAAACAGTCTCTCTTTTAGTAACTCCCTGGAGCACATCACTGGCACCACGTCCTTCCCCACATCATCCCCCCTTCTGTCATCCCCCTGCACAGCCAGGGCCCTGCGAGCACTCCAAATTCAGAGTAAGGCAGGCCCCTTCCAGGAGGCTGAGAGGGGAAAC Q097 TTGAATTTCAAAATAATTTGGTGTTGGGAAGTATACACGAAGGCATCTCTTCTCACACTTGCTCACATTTTCCTGTCTTTCCAATTAGCACCCTCACGTTCACATTGTTTAAAGAAGATTAGCTTGAGTTTTTGTTCTTTAAATTCTGGTATTTGTCAAAGGTCTTTGGAGCTGGACAGCAAAATCATACTATATGCAGGAAAACATGAAAAATCACCACTATGAATTCTGAGAACCAAATCCTTTTATTAAAAAAAAAAGTATTTGGAAGTCTTGTTTTCTGTTTCCCTGCTTCTAGTGGCACCACCTTGTATAATATTTTCCATAAACAGATCAATTTCCATTTTAATAATGGATTATTTTTTATAACCGTTCCCAGAATGGCAGCTGCTGGTACATTGCTCCACGCTCTCGGTGATTCTGAAGTTCAGCAACTTTAGTTAACTTCCATTGTAAAGATATTAATTTGTTCCTAGGCCAGTGTTGTGCTTGGTGTTTGAGTTCTCCTCCTCCATGTACTCTCCTTCACCCTGATGCACATCTTGCTGTGCATCTGCTCATCTTTATCTGCTCTTTCCATCAGACCCGGTGGTTTCTCTTGCAGCACAGAC W097 TTGAATTTCAAAATAATTTGGTGTTGGGAAGTATACACGAGGGCATCTCTTCTCACACTTGCGCACATTTTCCTGTCTTTCCAATTAGCACCCTCACGTTCACATTGTTTAGAGAAGATTAGCTTGAGTTTTTGTTCTTTAAATTCTGGTATTTGTCAAAGGTCTTTGGAGCTGGACAGCAAAATCATTGTATATGCAGGAAAACATGAAAAACCACCACTATGAATTCTGAGAACCAAATCCTTTTATTAAAAAAAAAAGTATTTGGAAGTCTTGTTTTCTGTTTCCCTGCTTCTACTGGCACCACCTTGTATAATATTTTCCATAAACAGATCAATTTCCATTTTAATAATAGATTATTTTTTATAACCGTTCCCAGAATGTCGGCTGCTGGTACGTTGCTCCACACTCTCGGTGATTCTGAAGTTCAGCAACTTTAGTTAACTTCCATTGTAAAGATATTAATTTGTTCCTAGGCCAGTGTTGTGCTTGGTGTTTGAGTTCTCCTCCTCCATGTACTCTCCTTCACCCTGATGCACATCTTGCTGTGCATCTGCTCATCTTTATCTGCTCTTTCCATCAGACCCGGTGGTTTCTCTTGCAGCACAGAC B097 TTGAATTTCAAAATAATTTGGTGTTGGGAAGTATACACGAGGGCATCTCTTCTCATACTTGCGCACATTTTCCTGTCTTTCCAATTAGCACCCTCACGTTCACATTGTTTAAAGAAGATTAGCTTGAGTTTTTGTTCTTTAAATTCTGGTATTTGTCAAAGGTCTTTGGAGCTGGACAGCAAAATCATTGTATATGCAGGAAAACATGAAAAACCACCACTATGAATTCTGAGAACCAAATCCTTTTATTAAAAAAAAAAGTGTTTGGAAGTCTTGTTTTCTGTTTCCCTGCTTCTACTGGCACCACCTTGTATAATATTTTCCATAAACAGATCAATTTCCATTTTAATAATAGATTATTTTTTATAACCGTTCCCAGAATGTCGGCTGCTGGTACGTTGCTCCACACTCTCGGTGATTCTGAAGTTCAGCAACTTTAGTTAACTTCCATTGTAAAGATATTAATTTGTTCCTAGGCCAGTGTTGTGCTTGGTGTTTGAGTTCTCCTCCTCCATGTACCCTCCTTCACCCTGATGCACATCTTGCTGTGCATCTGCTCATCTTTATCTGGTCTTTCCATCAGACCCGGTGGTTTCTCTTGCAGCACAGAC O097 TTGAATTTCAAAATAATTTGGTGTTGGGAAGTATACACGAAGGCATCTCTTCTCACACTTGTGCACATTTTCCTGCCTTTCCAATTAGCACCCTCACGTTCACATTGTTTAAAGAAGATTAGCTTGAGTTTTTGTTCTTTAAATTCTGGTATTTGTCAAAGGTCTTTGGAGCTGGACAGCAAAATCATTGTATATGCAGGAAAACATGAAAAACCACCACTATGAATTCTGAGAACCAAATCCTTTTATTAAAAAAAAAAGTATTTGGAAGTCTTGTTTTCTGTTTCCCTGCTTCTACTGGCACCACCTTGTATAATATTTTCCATAAACAGATCAATTTCCATTTTAATAATAGATTATTTTTTATAACCATTCCCAGAATGGCGGCTGCTGGTACATTGCTCCACGCTCTCGGTGATTCTGAAGTTCAGCAACTTTAGTTAACTTCCATTGTAAAGATATTAATTTGTTCCTAGGCCAGTGTTGTGCTTGGTGTTTGAGTTCTCCTCCTCCATGTACTCTCCTTCACCTTGATGCACATCTTGCTGTGCATCTGCTCATCTTTATCTGCTCTTTCCATCAGACCCGGTGGTTTCTCTTGCAGCACAGAC Q097 CCAGGACTAAATTCTTTATGCTGAGAGGTGACATGCTGTGCAAGTAGTTTTATTGAAATTAATGCAGTTACACCCAGGAAAGAGAGTGCTCAACAGGACTGAGATTGCAGGAATCTGACCAGCAGTGTTTTACACTTAGCTTCCAAATTGCTGTATTGCTGTCACCAGGTTAATCCTCATAACATCTCACTGTGCAAATTTCTAAGGTACTTAAAATACCTTTGTATTAACTGGAAACAGTTTATCAGACACAATTTAGGTCTTTCTTCTGTAATGCATTTTAGAATGAATTTTTAAAACGTGTATTTTTTTGTGCTTGAGTATTCCTGCACACTTCATGCAAATTATTTGCAACTAAAAGGCAGCATGAGATTTATTTTTTTTATGCAATTCATTTTGATTTGTGAGGTGACATGATTTTTTCATCACTAAAACCCATTAAGCAGGGAAATCAATAATATTCTTGGTTCTCGTTCATGTAGGAATTCTTTCCTTATAGATAGCTTTGCTTA W097 CCAGGACTAAATTCTTTATGCTGAGAGGTGACATGCTGTGCAAGTAGTTTTATTGAAATTAATGCAGTTACACCCAGGAAAGAGAGTGCTCAACAGGACTGAGATTGCAGGAATCTGACCAGCAGTGTTTTACACTTAGCTTCCAAATTGCTGTATTGCTGTCACCAGGTTAATCCTCATAACATCTCACTGTGCAAATTTCTAAGGTACTTAAAATACCTTTGTATTAACTGGAAACAGTTTATCAGACACAATTTAGGTCTTTCTTCTGTAATGCATTTTAGAATGAATTTTTAAAACGTGTATTTTTTTGTGCTTGAGTATTCCTGCACACTTCATGCAAATTATTTGCAACTAAAAGGCAGCATGAGATTTATTTTTTTTATGCAATTCATTTTGATTTGTGAGGTGACATGATTTTTTCATCACTAAAACCCATTAAGCATGGAAATCAATAATATTCTTGGTTCTCGTTCATGTAGGAATTCTTTCCTTATAGATAGCTTTGCTTA B097 CCAGGACTAAATTCTTTATGCTGAGAGGTGACATGCTGTGCAAGTAGTTTTATTGAAATTAATGCAGTTACACCCAGGAAAGAGAGTGCTCAACAGGACTGGGATTGCAGGAATGTGACCAGCAGTGTTTTACACTTAGCTTCCAAATTGCTGTATTGCTGTCACCAGGTTAATCCTCATAACATCTCACTGTGCAAATTTCTAAGGTACTTAAAATACCTTTGTATTAACTGGAAACAGTTTATCAGACACAATTTAGGTCTTTCTTCTGTAATGCATTTTAGAATGAATTTTTAAAACGTGTATTTTTTTGTGCTTGAGTATTCCTGCACACTTCATGCAAATTATTTGCAACTAAAAGGCAGCATGAGATTTATTTTTTTTATGCAATTCATTTTGATTTGTGAGGTGACATGATTTTTTCATCACTAAAACCCATTAAGCAGGGAAATCAATAATATTCTTGGTTCTCGTTCATGTAGGAATTCTTTCCTTATAGATAGCTTTGCTTA O097 CCAGGACTAAATTCTTTATGCTGAGAGGTGACATGCTGTGCAAGTAGTTTTATTGAAATTAATGCAGTTACACCCAGGAAAGAGAGTGCTCAACAGGACTGAGATTGCAGGAATCTGACCAGCAGTGTTTTGCACTTAGCTTCCAAATTGCTGTATTGCTGTCACCAGGTTAATCCTCATAACATCTCACTGTGCAAATTTCTAAGGTACTTAAAATACCTTTGTATTAACTGGAAACAGTTTATCAGACACAATTTAGGTCTTTCTTCTGTAATGCATTTTAGAATGAATTTTTAAAATGTGTATTTTTTTGTGCTTGAGTATTCCTGCACACTTCATGCAAATTATTTGCAACTAAAAGGCAGCATGAGATTTATTTTTTTTATGCAATTCATTTTGATTTGTGAGGTGACATGATTTTTTCATCACTAAAACCCATTAAGCAGGGAAATCAATAATATTCTTGGTTCTCGTTCATGCGGGAATTCTTTCCTTATAGATAGTTTTGCTTA Q097 TAGTATAGAGATCAAGCAGCTCAGAGCTGAGAGGAGTTTATTTGCACCACCAAGTGCGATTGTGGCTGAATTCTGACTCCAGATGGGCACAGTTTCAGCCCAATGAAACCTCATGGATGCCAACAATTTCCTGCCAGTATAAAAAGTTCACACTTCCAGCGTTCATTTTAATAAAGGCTTCTGGATATACATATTTGTCTCTGAATTGCTAGAGATCCCCATCTCTGAAGAGAAGACATAAAATAAAAATTTAGTACCTCAGTGCAGGAAATAGAGCTTTGAAAGCATGTCCGAGAAGAGTGTTTGTAGCCCAGATTGTTTTAGTTATACAGGAGGACCAAAACTCAGCTATTTGCACAAATCCAAAGTTTTACAATAGAAATGACTGTGTGTGATCAACTAGGAATAACAGGGCGGAGGAGTATCAGCAAAGGAGTTGAAGTGCTCACTGCCAGCCTTGCTAGTGTGCCATCCTGTGCCAAATGCATAATGAACTTCTGGCAATTGAGTGGCCACTCAGTACCAAGATCACGGGCATCTGAGGTTCCTTATTAAACTGTGAATACTCCATATCTGCTGTTTATTATATG W097 TAGTATAGAGATCAAGCAGCTCAGAGCTGAGAGGAGTTTATTTGCACCACCAAGTGCGATTGTGGCTGAATTCTGACTCCAGATGGGCACAGTTTCAGCCCAATGAAACCTCATGGATGCCAACAATTTCCTGCCAGTATAAAAAGTTCACACTTCCAGCGTTCATTTTAATAAAGGCTTCTGGATATACATATTTGTCTCTGAATTACTAGAGATCCCCATCTCTGAAGAGAAGACATAAAATAAAAATTTAGTACCTCAGTGCAGGAAATAGAGCTTTGAAAGCATGTCCGAGAAGAGTGTTTGTAGCCCAGATTGTTTTAGTTATACAGGAGGACCAAAACTCAGCTATTTGCACAAATCCAAAGTTTTACAATAGAAATGACTGTGTGTGATCAACTAGGAATAACAGGGCGGAGGAGTATCAGCAAAGGAGTTGAAGTGCTCACTGCCAGCCTTGCTAGTGTGCCATCCTGTGCCAAATGCATAATGGACTTCTGGCAATTGAGTGGCCACTCAGTACCAAGATCACGGGCATCTGAGGTTCCTTATTAAACTGTGAATACTCCATATCTGCTGTTTATTATATG B097 TAGTATAGAGATCAAGCAGCTCAGAGCTGAGAGGAGTTTATTTGCACCACCAAGTGCGATTGTGGCTGAATTCTGACTCCAGATGGGCACAGTTTCAGCCCAATGAAACCTCATGGATGCCAACAATTTCCTGCCAGTATAAAAAGTTCACACTTCCAGCGTTCATTTTAATAAAGGCTTCTGGATATACATATTTGTCTCTGAATTACTAGAGATCCCCATCTCTGAAGAGAAGACATAAAATAAAAATTTAGTACCTCAGTGCAGGAAATAGAGCTTTGAAAGCATGTCCGAGAAGAGTGTTTGTAGCCCAGATTGTTTTAGTTATACAGGAGGACCAAAACTCAGCTATTTGCACAAATCCAAAGTTTTACAATAGAAATGACTGTGTGTGATCAACTAGGAATAACAGGGCGGAGGAGTATCAGCAAAGGAGTTGAAGTGCTCACTGCCAGCCTTGCTAGTGTGCCATCCTGTGCCAAATGCATAATGAACTTCTGGCAATTGAGTGGCCATTCAGTACCAAGATCACGGGCATCTGAGGTTCCTTATTAAACTGTAAATACTCCATATCTGCTGTTTATTATATG O097 TAGTATAGAGATCAAGCAGCTCAGAGCTGAGAGGAGTTTATTTGCACCACCAAGTGCGATTGTGGCTGAATTCTGACTCCAGATGGGCACAGTTTCAGCCCAATGAAACCTCATGGATGCCAACAATTTCCTGCCAGTATAAAAAGTTCACACTTCCAGTGTTCATTTTAATAAAGGCTTCTGGATATACAGATTTGTCTCTGAATTACTAGAGATCCCCATCTCTGAAGAGAAAACATAAAATAAAAATTTAGTACCTCAGTGCAGGAAATAGAGCTTTGAAAGCATGTCCGAGAAGAGTGTTTGTAGCCCAGATTGTTTTAGTTATACAGGAGGACCAAAACTCAACTATTTGCACAAATCCAAAGTTTTGCAATAGAAATGACTGTGCGTGGTCAACTAGGAATAACAGTGCTGAGGAGTATCAGCAAAGGAGTTGAAATGCTCACTGCCAGCCTTGCTAGTGTGCCATCCTGTGCCAAATGCATAATGAACTTCTGGCAATTCAGTGGCCACTCAGTACCAAGATCACGGGCGTCTGAGTTTCCTTATAAAACTGTGAATACTCCATATCGGCTGTTTATTATACG Q097 AGAGTTGCACTTTACACTGATGTCATCGACATAGGCAAGCTCTCCACTCTAGGTCTAAATATTAGTAGTTCAAAAACAACTTTAATATCTAGCATTTAATAGCTAAAGGGACTCTACTTTTTTCTTTCATTTTGCTGTTATTTCTACAACTCTTCCCTCTTCATTCTGAATTACAATACAAATGTCTTGTACCATAATTTCTATGTGAAATTCTAGACCAACTGAAATATATAGGACTTGGATTATTAACATCAAAGGTCTAAAATTTATCTCAGTTTCTATACTTCCTGTGCATTAAGGAGACAAATCCTGCCTTTTCATCTCATCTTTATTAAAATAAAAGAATAACAACCAAAGATGGCAGAGAAGCCATCTCAGCATATGAGAGCATGAACTGGACTACTCTGGAGGAAATTACACTATAAATCTTTATCTACCAATTCAAAGTAATTCACCATTTATTAAATTTGATATAAATGTGGAGAAACTTTCTCATTATTGTCCAGTGTGCCACAGCTACACAACTTGGTCCTTGTGGTTATGA W097 AGAGTTGCACTTTACACTGATGTCATCGACATAGGCAAGCTCTCCACTCTAGGTCTAAATATTAGTAGTTCAAAAACAACTTTAATATCTAGCATTTAATAGCTAAAGGGACTCTACTTTTTTCTTTCATTTTGCTGTTATTTCTACAACTCTTCCCTCTTCATTCTGAATTACAATACAAATGTCTTGTACCATAATTTCTATGTGAAATTCTAGACCAACTGAAATATATAGGACTTGGATTATTAACATCAAAGGTCTAAAATTTATCTCAGTTTCTATACTTCCTGTGCATTAAGGAGACAAATCCTGCCTTTTCATCTCATCTTTATTAAAATAAAAGAATAACAACCAAAGATGGCAGAGAAGCCATCTCAGCATATGAGAGCATGAACTGGACTACTCTGGAGGAAATTACACTATAAATCTTTATCTACCAATTCAAAGTAATTCACCATTTATTAAATTTGATATAAATGTGGAGAAACTTTCTCATTATTGTCCAGTGTGCCACAGCTACACAACTTGGTCCTTGTGGTTATGA B097 AGAGTTGCACCTTACACTGATGTCATCGACATAGGCAAGCTCTCCACTCTAGGTCTAAATATTAGTAGTTCAAAAACAACTTTAATATCTAGCATTTAATAGCTAAAGGGACTCTACTTTTTTCTTTCATTTTGCTGTTATTTCTACAACTCTTCCCTCTTCATTCTGAATTACAATACAAATGTCTTGTACTATAATTTCTATGTGAAATTCTAGACCAACTGAAATATATAGGACTTGGATTATTAACATCAAAGGTCTAAAATTTATCTCAGTTTCTATACTTCCTGTGCATTAAGGAGACAAATCCTGCCTTTTCATCTCATCTTTATTAAAATAAAAGAATAACAACCAAAGATGGCAGAGAAGCCATCTCAGCATATGAGAGCATGAACTGGACTACTCTGGAGGAAATTACACTATAAATCTTTATCTACCAATTCAAAGTAATTCACCATTTATTAAATTTGATATAAATGTGGAGAAACTTTCTCATTATTGTCCAGTGTGCCACAGCTACACAACTTGGTCCTTGTGGTTATGA O097 AGAGTTACACCTTACACTGATGTCATCGACATAAGCAAGCTCTCCACTCTAGGTCTAAATATTAGTAGTTCAAAAACAACTTTAATATCTAGAATTTAATAGCTAAAGGGACTCTACTTTTTTCTTTCATTTTGCTGTTATTTCTACAACTCTTCCCTCTTCATTCTGAATTACAATACAAATGTCTTGTACCAGAATTTCTATGTGAAATTCCAGACCAACTGAAATATATAGGACTTGGATTATTAACATCAAAGGTCTAAAATTTATCTCAGTTTCTATACTTCCTGTGCATTAAGGAGACAAATCCTGCCTTTTCATCTCATCTTTATTAAAATAAAAGAATAACAACCAAAGATGGCAGAGAAGCCATCTCAGCATATGAGAACATGAACTGGACTACTCTGGAGGAAATTACACTATAAATCTTTATCTACCAATTCAAAGTAATTCACCATTTATTAAATTTGATATAAATGTGGAGAAACTTTCTCATTATTGTCCAGTGTGACACATCTACACAACTTGGTCCTTGTGGTTATGA Q097 CAAGGTGCTCTCAGGTTGCATCATGCTTCCCACATCAGACTCTTCCCTTGTCCCACAAATCCATAAATAAGTTTTGGACGACAGGGAGAGAAAATCCAATTCCTTGACTGAAATAACAGATTTATCAGATAAAATAAACCAGTACATGAATTCATCCTCAAAGCCCCTTCACCCTCCTCTAACAACATGACAGCAAACTGAGACCAATAGCTCCTCCCCTGATAATCTACAGCAGAGATTGTCCTGGAAAGGAGGACAGGGGTTTTATCAGAGGAAATTAACAGCTCCTGGTCTCTGCTCAGCCCATTAGCATCAGCTGTGCTGTGTTAGTTAGCTGACATGAGTTCATGCCTGGAGTGTTACCTAAAATGTGTATCTATCCGGAGATGAGGTCAAATATATGTAAATATTGGGATGAGTCTCAGCAGAAGAGTGTAAACAAAGTTTTGGCAATTTTTCATACACTCCCCAGGATGAAAACTTCTTTTTTTCACTACTTATATTGTAATTAGGACCAAATATTCAGGGCCAAAATTGCAGCTCACATGGAATGTCATTGTTGCTTTGGTTTAGACCACTTCAAAATAATG W097 CAAGGTGCTCTCAGGTTGCATCATGCTTCCCACATCAGACTCTTCCCTTGTCCCACAAATCCATAAATAAGTTTTGGAGGACAGGGAGAGAAAATCCAATTCCTTGACTTAAATAACAGATTTATCAGATAAAATAAACCAGTACATGAATTCATCCTCAAAGCCCCTTCACCCTCCTCTAACAACAGGACAGCAAACTGAGACCAATAGCTCCTCCCCTGATAATCTACAGCAGAGATTGTCCTGGAAAGGAGGACAGGGGTTTTATCAGAGGAAATTAACAGCTCCTGGTCTCTGCTCAGCCCATTAGCATCAGCTGTGCTGTGTTAGTTAGCTGACATGAGTTCATGCCTGGAGTGTTACCTAAAATGTGTATCTATCTGGAGATGAGGTCAAATATATATAAATATTGGGATGAGTCTCAGCAGAAGAGTGTAAACAAAGTTTTGGCAAATTTTCATACACTCCCCAGGATGAAAACTTCTTTTTTTTACTCCTTATATTGTAATTAGGACCAAATATTCAGGGCCAAAATTGCAGCTCACATGGAATGTCATTGCTAGTTTGGCTTAGACCACCTCAAAATAATG B097 CAAGGTGCTCTCAGGTTGCATCATGCTTCCCACATCAGACTCTTCCCTTGTCCCACAAATCCATAAATAAGTTTTGGAGGACAGGGAGAGAAAATCCAATTCCTTGACTTAAATAACAGATTTATCAGATAAAATAAACCAGTACATGAATTCATCCTCAAAGCCCCTTCACCCTCCTCTAACAACAGGACAGCAAACTGAGACCAATAGCTCCTCCCCTGATAATCTACAGCAGAGATTGTCCTGGAAAGGAGGACAGGGGTTTTATCAGAGGAAATTAACAGCTCCTGGTCTCTGCTCAGCCCATTAGCATCAGCTGTGCTGTGTTAGTTAGCTGACATGAGTTCATGCCTGGAGTGTTACCTAAAATGTGTATCTATCTGGAGATGAGGTCAAATATATATAAATATTGGGATGAGTCTCAGCAGAAGAGTGTAAGCAAAGTTTTGGCAAATTTTCATACACTCCCCAGGATGAAAACTTCTTTTTTTTACTCCTTATATTGTAATTAGGACCAAATATTCAGGGCCAAAATTGCAGCTCACATGGAATGTCATTGTTGGTTTGGTTTAGACCACTTCAAAATAATG O097 CAAGGTGCTCTCAGGTTGCATCATGCTTCCCACATCAGACTCTTCCCTTGTCCCACAAATCCATAAATAAATTTTGGAGGACAGGGAGAGAAAATCCAATTCCTTGATTGCAATAACAGATTTATCAGATAAAATAAACCAGTACATGAATTCATCCTCAAAGCCCCTTCACCCTCCTCTAACAACACGACAGCAAACTGAGACCAATAGCTCCTCCCCTGATAATCTACAGCAGAGATTGTCCTGGAAAGGAGGACAGAGGTTTTATCAGAGGAAATTAACAGCTCCTGGTCTCTGCTCAGCCCATTAGCATCAGCTGTGCTGTGGTAGTTAGCTGACATGAGTTCATGCCTGGAGTGTTACCTAAAATGTGTATATATCCGGAGATTAGGTCAAATATATATAAATATTGGGATGAGTCTCAGCAGAAGAGTGTAAACAAAGTTTTGGCAATTTTTCATACACTCCCCAGGATGAAAACTTCTTTTTTTTACTCCTTATATTGTAATTAGGACCAAATATTCAAGGCCAAAATTGCGGCTCACATGGAATGTCATTGTTGTTTTGGTTTAGACCATTTCAAAATAATG Q097 ATAATGTTTATTCCAGATTGCTTTGTGACTCTGACTTAAGGTCCCTGTCACTCTGCAGCTATGGCTGCCAGGAGAGATGAGCCGTGTTAACTCTGTATGTTCTAAAGCAGCAATCAGGCACATATAAAAACTGTACATAAATATGTCAGTTTTACAAAAGGACATAATAACTAGAAGTGAGGATGGAATGCTCCTGAAAAATCAGTATTGGAAAGGCTGAATAAAGGGTTGGGGATAGAGCAGTAGATCACTACACAAATTAGAGGAACATCGTATTTTTGGATGAATACCCTATAGGCTCTCTGCATAAACAAATGAACAAACAGATCAATAAAGAGCTTTATCAAGAGTATAGTTTATAAGCATGACTAATTAAAACATGGGGAAGTCTACCTCAAAGCTACCTCACTATTCCCCCTGCCTGTGCTTCCTTTGATTTTTCCCAGGAGCTCACTCTAGTAACCT W097 ATAATGTTTATTCCAGATTGCTTTGTGACTCTGACTTAAGGTCCCTGTCACTCTGCAGCTATGGCTGCCAGGAGAGATGAGCCGTGTTAACTCTGTATGTTCTAAAGCAGCAATCAGGCACATATAAAAACTGTACATAAATATGTCAGTTTTACAAAAGGACATAATAACTAGAAGTGAGGATGGAATGCTCCTGAAAAATCAGTATTGGAAAGGCTGAATAAAGGGTTGGGGATAGAGCAGTAGATCACTACACAAATTAGAGGAACATCGTATTTTTGGATGAATACCCTATAGGCTCTCTGCATAAACAAATGAACAAACAGATCAATAAAGAGCTTTATCAAGAGTATAGTTTATAAGCATGACTAATTAAAACATGGGGAAGTCTACCTCAAAGCTACCTCACTATTCCCCCTGCCTGTGCTTCCTTTGATTTTTCCCAGGAGCTCACTCTAGTAACCT B097 ATAATGCTTATTCCAGATTGCTTTGTGACTCTGACTTAAGGTCCCTGTCACTCTGCAGCTATGGCTGCCAGGAGAGATGAGCCGTGTTAACTCTGTATGTTCTAAAGCAGCAATCAGGCACATATAAAAACTGTACATAAATATGTCAGTTTTACAAAAGGACATAATAACTAGAAGTGAGGATGGAATGCTCCTGAAAAATCAGTATTGGAAAGGCTGAATAAAGGGTTGGGGATAGAGCAGTAGATCACTACACAAATTAGAGGAACATCGTATTTTTGGATGAATACCCTATAGGCTCTCTGCATAAACAAATGAACAAACAGATCAATAAAGAGCTTTATCAAGAGTATAGTTTATAAGCATGACTAATTAAAACATGGGGAAGTCTACCTCAAAGCTACCTCACTATTCCCCCTGCCTGTGCTTCCTTTGATTTTTCCCAGGAGCTCACTCTAGTAACCT O097 ATAATGTTTATTCCAGATTGCTTTGTGACTCTGACTTAAGGTCCCTGTCACTCTGCAGCTATGGCTGCCAGGAGAGGTGAGCCGTGTTAACTCTGTATGTTCTAAAGCAGCAATCAGGCACATATAAAAACTGTACATAAATATGTCAGTTATACAAAAGGACATAATAACTAGAAGTGAGGATGGAATGCTCCTGAAAAATCAGTATTGGAAAGGCTGAATAAAGGGTTGGGGATAGAGCAGTAGATCACTACACAAATTAGAGGAACATCGTATTTTTGGATGAATACCCTATAGGCTCTCTGCATAAACAAATGAACCAACAGATCAATAAAGAGCTTTATTAAGAGTATAGTTTATAAGCATGACTAATTAAAACATGGGGAAGTCTACCTCAAAGCTACCTCACTATTCCCCCTGCCTGTGCTTCCTTTGATTTTTCCCAGGAGCTCACTCTAGTAACTT Q097 CGTGAATCCTCTCTGGAAGACCCATTGACAGGAGAGAGCAGTTGCCTCGTAACTTTACCTGAAAGGCATTAAAATGATACAGTATGTAACTTCAGGATGTCCTTGAATAGGTCCTGTTCCCAGAAGATCCTCACAGGAAGCTATGTACTGTGAATGATGCATCTCAAAATAACCACGAGCTCGAGTTTATTCAGGAGCCATTTTGTGTGTGCAGATCCACAGACACACAGGCACACGTCCCTAAGTACCCACGGTGTGCATGTGTTGTGTGTGTTATTTACACTACATGTAAACAGTACCAACACCAATAGCAACTCAAAGACCTTTTCCTCCAAGTGTTCGGGCACAGAACGCTGGCTGGACTCGGCCGGGGTTCCCTGAGTTGTGTGCTTGCAGGATCAGGCCCTGTGCTAGTATTTAAGAAAATACTTTTTTATTAGTACTGGTTAAGGTTTTTCTATTTTTCACTTTTGCCAAAAATGTTTTGCACTTAAAACGTGACGTGTCTCGTAATGGCAGTGGATCTGTCTGAGAGTCAGCGAGTAGCTCACTTTGACATAAGAATGATCC W097 CGTGAATCCTCTCTGGAAGACCCATTGACAGGAGAGAGCAGTTGCCTCGTAACTTTACCTGAAAGGCATTAAAATGATACAGTATGTAACTTCAGGATGTCCTTGAATAGGTCCTGTTCCCAGAAGATCCTCACAGGAAGCTATGTACTGTGAATGATGCATCTCAAAATAACCACGAGCTCGAGTTTATTCAGGAGCCATTTTGTGTGTGCAGATCCACAGACACACAGGCACACGTCCCTAAGTACCCACGGTGTGCATGTGTTGTGTGTGTTATTTACACTACATGTAAACAGTACCAACACCAATAGCAACTCAAAGACCTTTTCCTCCAAGTGTTCGGGCACAGAACGCTGGCTGGACTCGGCCGGGGTTCCCTGAGTTGTGTGCTTGCAGGATCAGGCCCTGTGCTAGTATTTAAGAAAATACTTTTTTATTAGTACTGGTTAAGGTTTTTCTATTTTTCACTTTTGCCAAAAATGTTTTGCACTTAAAACGTGACGTGTCTCGTAATGGCAGTGGATCTGTCTGAGAGTCAGCGAGTAGCTCACTTTGACATAAGAATGATCC B097 CGTGAATCCTCTCTGGAAGACCCATTGACAGGAGAGAGCAGTTGCCTCGTAACTTTACCTGAAAGGCATTAAAATGATACAGTATGTAACTTCAGGATGTCCTTGAATAGGTCCTGTTCCCAGAAGATCCTCACAGGAAGCTATGTACTGTGAATGATGCATCTCAAAATAACCACGAGCTCGAGTTTATTCAGGAGCCATTTTGTGTGTGCAGATCCACAGACACACAGGCACACGTCCCTAAGTACCCACGGTGTGCATGTGTTGTGTGTGTTATTTACACTACATGTAAACAGTACCAACACCAATAGCAACTCAAAGACCTTTTCCTCCAAGTGTTCGGGCACAGAACGCTGGCTGGACTCGGCCGGGGTTCCCTGAGTTGTGTGCTTGCAGGATCAGGCCCTGTGCTAGTATTTAAGAAAATACTTTTTTATTAGTACTGGTTAAGGTTTTTCTATTTTTCACTTTTGCCAAAAATGTTTTGCACTTAAAACGTGACGTGTCTCGTAATGGCAGTGGATCTGTCTGAGAGTCAGCGAGTAGCTCACTTTGACATAAGAATGATCC O097 CGTGAATCCTCTCTGGAAGACCCATTGACAGGAGAGAGCAGTTGCCTCGTAACTTTACCTGAAAGGCATTAAAATGATACAGTATGTAACTTCAGGATGTCCCTGAATAGGTCCTGTTCCCAGAAGATCCTCACAGGAAGCTATGTACTGTGAATGATGCATCTCAAAATAACCACGAGCTCGAGTTTATTCAGGAGCCATTTTGTGTGTGCAGATCCACAGACACACCGGCACACGTCCCTAAGTACCCACGGTGTGCATGTGTTGTGTGTGTTATTTACACTACATGTAAACAGTACCAACACCAATAGCAACTCAAAGACCTTTTCCTCCAAGTGTTCAGGCACAGAACCCTGGCTGGACTCGGCCGGGGTTCCCTGAGTTGTGTGCTTGCAGGATCAGGCCCTGTGCTAGTATTTAAGAAAATACTTTTTTATTAGTACTGGTTAAGGTTTTTCTATTTTTCACTTTTGCCAAAAATGTTTTGCACTTAAAACGTGACGTGTCTCGTAATGGCAGTGGATCTGTCTGAGAGTCAGCGAGTAACTCCCTTTGACGTAAGAATGATCC Q097 TCTTTATCCTTGTTTGCCTTATTTGATGGAAACAATTGCTCTTCTACCTGAAAATATGCAAATTTACAGGATGAATAGGAGGCATTTCATTCCATGCCCAGTTGTTGACAATTCTCAGAAATCACTTCAGAAATTTCAGTATTTTTCTCCTGCCCAGTGTCTGAATAATAGAGGATAAATAAACAGCCAAATGGAGTATATTAAATTTTGACACGTACTGATTAATGTTTCAAGTAGACAAAGAGTTTTGGGGTTTTTTTAAGGCAAAAATTAGTATTTTTCCTCCTGTTGCCTTACTCAGCATAACAATTTCTGGAGGACACTTGTGCAAATGCTCTGTAGTTGGTGCCAATTTCAGACTGCACTGGTGAAGAGTGGGACAAGACAGGAACACCCCTGTGTGTGTGTGTGGAATCATCACTGCTAGCAGCAGTTAGGAAATGGACTGAGCATTTGGTGATGCAGTTTGGCAGGAGTTTTTACAGCAGAAGCAGAATTCTTGCAGTGCTCCTTTGTGCTCTGCCCTCTGCAGTGCTCAGAGCATTGCAGGGCTGAGGGAAGGCACTTCATCCCAGCGTGGAACTGGTGCCTTTTGGGGCTGCCTAAAAACAG W097 TCTTTATCCTTGTTTGCCTTATTTGGTGGAAACAATTGCTCTTCTACCTGAAAATATGCAAATTTACAGGATGAATAGGAGGCATTTCATTCCATGCCCAGTTGTTGACAATTCTCAGAAATCACTTCAGAAATTTCAGTATTTTTCTCCTGCCCAGTGTCTGAATAATAGAGGATAAATAAACAGCCAAATGGAGTATATTAAATTTTGACACGTACTGATTAATGTTTCAAGCAGACAAAGAGTTTTGGGGTTTTTTTAAGGCAAAAATTAGTATTTTTCCTCCTGTTGCCTTACTCAGCATAACAATTTCTGGAGGACACTTGTGCAAGTGCTCTGTAGTTGGTGCCATTTTCAGACTGCACTGGTGAAGAGTGGGACAAGACAGGAACACCCCTGTGCGTGTGTGTGGAATCATCACTGCTAGCAGCAGTTAGGAAATGGACTGAGCCTTTGGTGATGCAGTTTGGCAGGAGTTTTTACAGCAGAAGCAGAATTCTTGCAGTGCTCCTTTGTGCTCTGCCCTCTGCAGTGCTCAGAGCATTGCAGGGCTGAGGGAAGGCACTTCATCCCAGTGTGGAACTGGTGCCTTTTGGGGCTGCCTAAAAACAG B097 TCTTTATCCTTGTTTGCCTTATTTGATGGAAACAATTGCTCTTCTACCTGAAAATATGCAAATTTACAGGATGAATAGGAGGCATTTCATTCCATGCCCAGTTGTTGACAATTCTCAGAAATCACTTCAGAAATTTCAGTATTTTTCTCCTGCCCAGTGTCTGAATAATAGAGGATAAATAAACAGCCAAATGGAGTATATTAAATTTTGACATGTACTGATTAATGTTTCAAGCAGACAAAGAGTTTTGGGGTTTTTTTAAGGCAAAAATTAGTATTTTTCCTCCTGTTGCCTTACTCAGCATAACAATTTCTGGAGGACACTTGTGCAAATGCTCTGTAGTTGGTGCCATTTTCAGACTGCACTGGTGAAGAGTGGGACAAGACAGGAACACCCCTGTGTGTGTGTGTGGAATCATCACTGCTAGCAGCAGTTAGGAAATGGACTGAGCCTTTGGCGATGCAGTTTGGCAGGAGTTTTTACAGCAGAAGCAGAATTCTTGCAGTGCTCCTTTGTGCTCTGCCCTCTGCAGTGCTCAGAGCATTGCAGGGGTGAGGGAAGGCACTTCATCCCAGCGTGGAACTGGTGCCTTTTGGGGCTGCCTAAAAACAG O097 ???????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Q097 CTCCCCAGTCCTGCTGGTGGGGAGTGAGCAAGCAGCTGCGTGGGGCTTGATCCTGGCTATGGCTAAACCATGACAGAGCCACAGACCACATCCTTCCTTTGCCACTGCTGTTTCTTTGTGATAGTCCAATTACCTCTCAGTGTATCCAATATTCCTCAATTTATAATATCAAGAATTTTTCTTATACTCAAAAAGGTAACACTTTTTATTCTTTTATAGGTTGGCTTTAAATGCAGTCATTTTTCACTTGGTAATGTCATGGCAGTGTTCATGCTGCTTTCCTTTCTATTCTTTTTCTCTTCTCCCCCCAGGATGACAAAAACCTTTAACCCACCCTGACTGTGTTGCAATGAGAGTAATTTACTTATTATGGGCTGGTGTTAGGCTTTGTAGCTTGGTGTTGCTTGAGAGGGTGCTCCCAGGGTCTGTGACTCTGAAAGGGCTTCTTTTTGGAGTAAGAGCAATCCTTCATTTTGCAGACTGTCTCAAGGGATGAAAAC W097 CTCCCCAGTCCTGCTGGTGGGGAGTGAGCAAGCAGCTGCGTGGGGCTTGATCCTGGCTACGGCTAAACCATGACAGAGCCACAGACCACATCCTTCCTTTGCCACTGCTGTTTCTTTGTGATAGTCCAATTACCTCTCAGTGTATCCAATATTCCTCAATTTATAATATCAAGAATTTTTCTTATACTCAAAAAGGTAACACTTTTTATTCTTTTATAGGTTGGCTTTAAATGCAGTCATTTTTCACTTGGTAATGTCATGGCAGTGTTCATGCTGCTTTCCTTTCTATTCTTTTTCTCTTCTCCCCCCAGGATGACAAAAACCTTTAACCCACCCTGACTGTGTTGCAATGAGAGTAATTTACTTATTATGGGCTGGTGTTAGGCTTTGTAGCTTGGTGTTGCTTGAGAGGGTGCTCCCAGGGTCTGTGACTCTGAAAGGGCTTCTTTTAGGAGTAAGAGCAATCCTTCATTTTGCAGACTGTCTCAAGGGATGAAAAC B097 CTCCCCAGTCCCGCTGGTGGGGAGTGAGCAAGCAGCTGCGTGGGGCTTGATCCTGGCTACGGCTAAACCATGACAGAGCCACAGACCACATCCTTCCTTTGCCACTGCTGTTTCTTTGTGATAGTCCAATTACCTCTCAGTGTATCCAATAGTCCTCAATTTGTAATCTCAATAATTTTTCTTATACTCAAAAAGGTAACACTTTTTATTCTTTTATAGGTTGGCTTTAAATGCAGTCATTTTTCACTTGGTAATGTCATGGCACTGTTCATGCTGCTTTCCTTTCTATTCTTTCTCTCTTCTCCCCCCAGGATGACAAAAACCTTTAACCCACCCTAACTGTGTTGCAATGAGAGTAATTTACCTATTATGGGCTGGTGTTTGTCTTTGTAGCTTGGTGTTGCTTGAGAGGGTACTCCCAGGGCTTGTGACTCTGAAAGGGCTTCTTTTAGGAGTAAGAGCAATCCTTCATTTTGCAGACTGTCTGAAGGGACGAAAAC O097 CTCCCCAGTCCTGCTGGTGGGGAGTGAGCAGGCAGCTGCGTGGGGCTTGATCCTGGCTACGGCTAAACCATGACAGAGCCACAGACCACATCCTTCCTTTGCCACTGCTGTTTCTTTGTGATAGTCCAATTACCTCTCAGTGTATCCAATAGTCCTCAATTTATAGTCTCAATAATTTTTCTTATACTCAAAAAGGTAACACTTTTTATTCTTTTATAGGTTGGCTTTAAATGCAGTCATTTTTCACTTGGTAATGTCATGGCAGTATTCATGCTGCTTTCCTTTCTATTCTTTCTCTCTTCTCCCCCCAGGATGACAAAAACCTTTAACCCACCCTAACTGTGTTGCAATGAGAGTAATTTACTTACTATGGGCTGGTGTTAGGCTTTGTAGCTTGGTGTTGCTTGAGGGGGTACTCCCAGGGCTTGTGACTCTGAAAGGGCTTCTTTTAGGAGTAAGAGCAATCCTTCATTTTGCAGACTGTCTGAAGGGACGAAAAC Q097 AAAGCATAGAGCTTTACAGAAAGGCTGGCAAATATCTCAATTTTTCAGTAGGAGGGTACCTTTTCCAGTAAATAACATGTTCACCTTAAAAATACCTCTACAGAGTTTTTAGTTGAAAAGCTGTGATTCAGAATTAGAAAAGTTGTAGCTGATAATTGGAACAGTTTGTTTTATAAGTTCTGCCCACTGTCAGTTGCCCAGATCGTCTATGTGTAGGACAGCTCCTAGATCGTCCACAGATAAGCCCATGAAAGAGAAGCAGAACACAAGCACCTAAACTGCAAATTCCCACATTAAGGTCTCCAGATAAAACAATGCCAGATTTCAAATTTATGTCTGGCAGTTGGTTTGTTTTTCTCAAAAGTATTAATTTGCATCAGGTGAAGGTAAAAACAGGGACCTGAATCATCTTCTAGCCAGACCAATTAATTATCAATAAAAATGCTGTATGTATTGCATAAGAATTTCATTGATGAAATGTGCAGTGGAGTAAAATCCACTTTCTTTTCTGTAGCGCTGGGGGCTGTTGCCTTTTTACGCAGTAAAATGAGGCTTGGGAAGAGAAAGTGACT W097 AAAGCATAGAGCTTTACAGAAAGGCTGGCAAATATCTCAATTTTTCAGTAGGAGGGTACCTTTTCCAGTAAATAACATGTTCACCTTAAAAATACCTCTACAGAGTTTTTAGTTGAAAAGCTGTGATTCAGAATTAGAAAAGTTGTAGCTGATAATTGGAACAGTTTGTTTTATAAGTTCTGCCCACTGTCAGTTGCCCAGATCATCTATGTGTAGGACAGCTCCTAGATCGTCCACAGATAAGCCCATGAAAGAGAAGCAGAACACAAGCACCTAAACTGCAAATTCCCACATTAAGGTCTCCAGATAAAACAATGCCAGATTTCAAATTTATGTCTGGCAGTTGGTTTGTTTTTCTCAAAAGTATTAATTTGCATCAGGTGAAGGTAAAAACAGGGACCTGAATCATCTTCTAGCCAGACCAATTAATTATCAATAAAAATGCTGTATGTATTGCATAAGAATTTCATTGATGAGATGTGCAGTGGAGTAAAATCCACTTTCTTTTCTGTAGCGCTGGGGGCTGTTGCCTTTTTACGCAGTAAAATGAGGCTTGGGAAGAGAAAGTGACT B097 AAAGCATAGAGCTTTACAGAAAGGTTGGCAAATATCTCAATTTTTCAGTAGGAGGGTACCTTTTCCAGTAAATAACATGTTCACCTTAAAAATGCCTCTACAGAGTTTTTAGTTGAAAAGCTGTGATTCAGAATTAGAAAAGTTGTAGCTGATAATTGGAACAGTTTGTTTTAGAAGTTCTGCCCACTGTCAGTTGCCCAGATCGTCTCTGTGTAGGACAGCTCCTAGATCGTCCACAGATAAGCCCATGAAAGAGAAGCAGAACACAAGCACCTAAACTGCAAATTCCCACATTAAGGTTGCCAGATAAAACAATGCCAGATTTCAAATTTATGTCTGGCAGTTGGTTTGTTTTTCTCAAAAGTATTAATTTGCATCAGGTGAAGGTAAAAACAGGGACCTGAATCATCTTCTAGCCAGACCAATTAATTATCAATAAAAATGCCGTATGTATTGCATAAGAATTTCATTGATGAAATGTGCAGTGGAGTAAAATCCACTTTCTTTTCTGTAGCGCTGGGGGCTGTTGCCTTTTTACGCAGTAAAATGAGGCTTGGGAAGAGAAAGTGACT O097 AAAGCATAGAGCTTTACAGAAAGACTGGCAAAATTCTCAATTTTTCAGTAGGAGGGTACCTTTTCCAGTGAATAACATGTTCACTTTATAAATGCCTCTACAGAGTTTTTAGTTGAAAAGCTGTGATTCAGAATTAGAAAAGTTGTAGCTGATAATTGGAACAGTTTGTTTTAGAAGTTCTGCCCACTGTCAGTTGCCCAGATTGTCTCTGTGTAGGACAGCTCCTAGATGGTCCACAGATAAGCCCATGAAAGAGAAGCAGAACACAAGCACCTAAACTGCAAATTCCCACATTAAGGTCTCCAGATAAAACAATGCCAGATTTCAAATTTATGTCTGGCAGTTGGTTTGTTTTTCTCAAAAGTATTAATTTGCATCAGGTGAAGGTAAAAACAGGGACCTGAATCACCTTCTAGCCAGACCAATTAATTATCAATAAAAATGCAGTATGTATTGCATAAGAATTTAATTGATGAAATGTGCAGTGGAATAAAATCCACTTTCTTTTCTGTAGCGCTGGGGGCTGTTGCCTTTTTACGCAGTAAAATGAGGCTTGGGAAGAGAAAGTGGCT Q097 TAGTGATACTAAACAGACTATGTATGTTTTAAAACAAATGTAACTATGTTCCGGTGGATTCCCTGTATAATACTTCCCAGATGTTGGTGGTATAGTTCAAAATCTGGACCTGTGCAGCTGTAGTTACTCTTCTTGACAGTCAGAGTTCCAGTTCTGTATTACTGGCTTTTATAGCAACTGGAGAGAGCCAAGTGGCAGAGCAGTTAACTGTGGATGGATACTGAAAATGTCTGAGAATAGATTAAAACTATCTCCTTTGAAAATGCAGCTGGATGAGGGGAGTAGCTCCAGAGTGAGCTGGCCTGGACTTTGTCTGTGGGTCACTGCTTATACCTGCTCAGAGCACAGAGGATAATTTAGGGTTCAGGCTGGCAGCACGTAGGTTTTGCAGGGAGGGCTGGAGTAAGATTTCTGGTATTTTCTGATCTTCTGTCTGATAGCTGGTGTGTTGAGTTTTTATGCCTCTCCTGCTTGGAAGATGAACTTGGAGTGTAAAAAACTGTTAAGTATAGGGCACTCCACACCACAGCCATCACACTTGTCTGCTTCTCTGCTTTATTGCAGTGAGCAAGTACATGTGTTATTTACAGATA W097 AAGTGATACTAAACAGACTATGTATGTTTTAAAACAAATGTAACTGTGTTCTGGTGGATTCCCTGTATAATACTTCCCAGATGTTGGTGGTATAGTTCAAAATCTGGACCTGTGCAGCTGTAGTTACTCTTCTTGACAGTCAGAGTTCCAGTTCTGTATTACTGGCTTTTATAGCAACTGGAGAGAGCCAAGTGGCAGAGCAGTTAACTGTGGATGGATACTGAAAATGTCTGAGAATAGATTAAAACTATCTCCTTTGAAAATGCAGCTGGATGAGGGGAGTAGCTCCAGAGTGATCTGGCCTGGACTTTGTCTGTGGGTCACTGCTTATACCTGCTCAGAGCACAGAGGATAATTTAGGGTTCAGGCTGGCAGCATGTAGGTTTTGCAGGGAGGGCTGGAGTAAGATTTCTGGTATTTTCTGATCTTCTGTCTGACAGCTGGTGTGTTGAGTTTTTAAGCCTCTCCTGCTTGGAAGATGAACTTGGAGTGTAAAAAACTGTTAAGTATAGGGCACTCCACACCACAGCCATCACACTTGTCTGCTTCTCTGCTTTATTGCAGTGAGCAAGGACATGTGTTATTTACAGATA B097 TAGTGATATTAAACAGACTATGTATGTTTTAAAACAAATGTAACTGTGTTCCGGTGGATTCCCTGTATAATACTTCCCAGATGTTGGTGGTATAGTTCAAAATCTGGACCTGTGCAGCTGTAGTTACTCTTCTTGACAGTCAGAGTTCCAGTTCTGTATTACTGGCTTTTATAGCAACTGGAGAGAGCCAAGTGGCAGAGCAGTTAACTGTGGATGGATACTGAAAATGTCTGAGAATAGATTAAAACTATCTCCTTTGAAAATGCAGCTGGATGAGGGGAGTAGCTCCAGAGTGAGCTGGCCTGGACTTTGTCTGTGGGCCACTGCTTATACCTGCTCAGAGCACAGAGGATAATTTAGGGTTCAGGGTGGCAGCACCTAGGTTTTGCAGGGAGGGCTGGAGTAAAATTTCTGGTATTTTCTGATCTCCTGTCTGATAGCTGGTGTGTTGAGTTTTTAAGCCTCTCCTGCTTGGAAGATGAACTTGGAGTGTAAAAAACTGTTAAGTATAGGGCACTTCACACCACAGCCATCACACTTGTCTGCTTCTCTGCTTTATTGCAGTGAGCAAGGACATGTGTTATTTACAGATA O097 TAGTGATACTAAACAGACTATGTATGTTTTAAAACAAATGTAACTGTGTTCCGGTGGATTCCCTGTATAATACTTCCCAGATGTTGGTGGTATAGTTCAAAATCTGGACCTGTGCAGCTATAGTTACTCTTCTTGACAGTCAGAGTTCCAGTTCTGTATTACTGGCTTTTATAGCAACTGGAGAGAGCCAAGTGGCAGAGCAGTTAACTGTGGATGGATACTGAAAATGTCTGAGAATAGATTAAAACTATCTCCTTTGAAAATGCAGCTGGATGAGGGGAGTAGCTCCAGAGTGAGCTGGCCTGGACTTTGTCTGTGGGTCACTGCTTATACCTGCTCAGAGCACAGAGGATAATTTAGGGTTCAGGCTGGCAGCACCTAGGTTTTGCAGGGAGGGCTGGAGTAAGATTTCTGGTATTTTCTGATCTTCTGTCTGATAGCTGGTGTGTTGAGTTTTTAAGCCTCTCCTGCTTGGAAGATGAACTTGGAGTGTAAAAAACTGTTAAGTATAGGGCACTCCACACCACAGCCATCACACTTGTCTGCTTCTCTGCTTTATTGCAGTGAGCAAGTACATGTGTTATTTACAGATA Q097 ATTTCTGATGTTGTTACAGGTTTTACCATGATGGCAAACTCCCTCCAGCATCAGCATCAGAATGTCTTGTACCGTCTACTCTTAATGAAGCACTGCCCACCCAAAACAGGTGAAAGTTTTCCATCCCAGGACAAGGATGGCATCCTGTGACTCAACATCCTTGTACGCTGTAAGTGGTCCAAGTGTGGCTTTCCATGTGAGAGCTGCTGGGTAATCCAAACTCCTAGCTGTGGTAGGAAATGAGCCAGCAGGGATAGCTGAGGCTGAAAAAGATGGGCTGCTTCAAGACACTTCCCAGTGGGACTCTGAAGGGAGCCACTGTCTCTTGCTGAGAAGCAATCCTGTTTCATGAGAGTTGTATTTTGAGCCTTGTGGTTCTTGGCCACATATGAAAATTTCTATATGAAAATTTTGCTGTGAAGCAGAGAGGGTTGGAAAAGTAGGCCCCTCCCTTGTCTTTTGCTAGTCTAGAAATATCATTAGATGTGAAATCACATTCTTAATTTATGCTGGCAGGTGATTAGGGCAGACCCAGCATGAAGTTGCCAATTGCAATATGTGAAAGTTCCTACTAATAATTTGGGTAGGTTTCATTTCTTTAACAACGCTTTGCAACCCATGGGTTATGTGTCTGCCAGACTCCAGCACCT W097 ATTTCTGATGTTGTTACAGGCTTTACCATGATGGCAAACTCCCTCCAGCATCAGCATCAGAATGTCTTGAACCGTCTACTCTTAATGAAGCACTGCCCACCCAAAACAGGTGAAAGTTTTCCATCCCAGGACAAGGATGGCATCCTGTGACTCAACATCCTTGTATGCTGTAAGTGGTCCAAGTGTGGCTTTCCATGTGAGAGCTGCTGGGTAATCCAAACTCCTAGCTGTGGTAGGAAATGAGCCAGCAGGGACAGCTGAGGCTGAAAAAGATGGGCTGCTTCAAGACACTTCCCAGTGGGACTCTGAAGGGAGCCACTGTCTCTTGCTGAGAAGCAATCCTGTTTCATGAGAGTTGTATTTTGAGCCTTGTGGTTCTTGGCCACATATGAAAATTTCTATATGAAAATTTTGCTGTGAAGCAGAGAGGGTTGGAAAAGTAGGCCCCTCCCTTGTCTTTTGCTAGTCTAGAAATATCATTAGATGTGAAATCACATTCTTAATTTATGCTGGCAGGTGATTAGGGCAGACCCAGCATGAGGCTGCCAATTGCAATATGTGAAAGTTCCTACTAATAATTTGGGTAGGTTTCATTTCTTTAACAACGCATTGCAACCCATGGGTTACGTGTCTGCCAGACTCCAGCACCT B097 ATTTCTGATGTTGTTACAGGTTTTACCATGATGAAAAACTCCCTCCAGCATCAGCATCAGAATGTCTTGTACCGTCTGCTCTTAATGAAGCACTGCCCACCCAGAACAGGTGAAAGTTTTCCATCCCAGGACAAGGATGGCATCCTGTGACTCAACATCCTTGTATGCTGTAAGTGGTCCAAGTGTGGCTTTCCATGTGAGAGCTGCTGGGTAATCCAGACTCCTAGCTGTGGTAGGAAATGAGCCAGCAGGGATAGCTGAGGCTGAAAAAGATGGGCTGCTTCAAGACACTTCCCAGTGGGACTCTGAAGGGAGCCACTGTCTCTTGCTGAGAAGCAATCCTGTTTCATGAGAGTTGTATTTTGAGCCTTGTGGTTCTTGGCCACATATGAAAATTTCTATATGAAAATTTTGCTGTGAAGCAGAGAGGGTTGGAAAAGTAGGCCCCCCCCTTGTCTTTTGCTAGTCTAGAAATATCATTAGATGTGAAATCACATTCTTAATTTATGCTGGCAGGTGATTAGGGCAGACCCAGCATGAGGCTGCCAATTGCAATATGTGAAAGTTCCTACTAATAATTTGGGTAGGTTTCATTTCTTTAACAACGCATTGCAACCCATGGGTTACGTGTCTGCCAGACTCCAGCACCT O097 ATTTCTGATGTTGTTACCGGTTTTACCATGATGGCAAATTCCCTCCAGCATCAGCATCAGAATGTCTTGTACCGTCTGCTCTTAATGAAGCACTGCCCACCCAAAACAGGTGAAAGTTTTCCATCCCAGGACAAGGATGACATCCTGTGACTCAACATCCTTGTATGCTGTAAGTGGTCCAAGTGTGGCTTTCCATGTGAGAGCTGCTGGGTAATCCAAACTCCTTGCTGTGGTAGGAAATGAGCCAGCAGGGATAGCTGAGGCTGAAAAAGATGGGCTGCTTCAAGACACTTCCCAGTGGGACTCTGAAGGGAGCCACTGTCTCTTGCTGAGAAGCAATCCTGTTTCATGAGAGTTGTATTTTGAGTCTTGTGGTTCTTGGCCACATATGAAAATTTCTATATGAAAATTTTGCTGTGAAGCAGAGAGGGTTGGAAAAGTAGGCCCCTCCCTTGTCTTTTGCTAGTCTAGAAATATCATTAGATGTGAAATCACATTCTTAATTTATGCTGGCAGGTGATTAGGGCAGACCCAGCATGAGGCTGCCAATTGCAATGTGTGAGAGTTCCTACTAATAACTTGGGTAGGTTTCATTTCTTTAACAACGCATTGCAACCCATGGGTTACGTGTCTGCCAGACTCCAGCACCT Q097 CTGTGTATAGTCCTAAAGTGATAAAAATTACATATGACTAGGACGTCTTTCACCTGCCTCAAAAAGAGCAAAAGGTCAAATTTGATTCTAGATGGTCACATAGGCTTTCAGCCTTGAACATGATGTTTTTCTTAAATGTCAGTGATGACATGAAAGGACAGATACCTAAGTAGCCACTTATTCTTAATTCTACCAGAGGGACAAACAGGATATATTTAATTTAATTGACTGATATTCTCACTTTCAGAAAGTGGAACCAGTTATAGGTTATCCAATTTTCTCTTTTGGACTTGTATCTGACATAATTGTGATAGTGTAACAGAACGAAGTGGCAAAATCCACCCCTTCCATGAAGAAATCTAGAGAAAAAAACCTGGTTAGGGCCTTCTCATTCTCTACAATTACCTGAAAGGAGATTGTGATGAGGTAAATGTTTGTCTCTTCTCCCTAGTTACAAGAGAAGAAATGGCTTCGAGCTGTGCCAGGGGGAATTTAGACTGAATATTAGGAAATCTTTTTTCATGAGAAGGGCTGTCAAGCATTGGTACAGGCTGCCCAGATAATCAATTTATTCACCATCCCTGGATGTATTTCAAATGTGTAGATATGGCACTTAGGGATATGGTCTAGTGATAGACT W097 CTGTGTATAGTCCTAAAGTGATAAAAATTACATATGACTAGGATGTCTTTCACCTGCCTCAAAAAGAGCAAAAGGTCAAATTTGATTCTAGATGGTCACATAGGCTTTCAGCCTTGAACATGATGTTTTTCTTAAGTGCCAGTGATGACATGAAAGGACAGATACCTAAGTAGCCACTTATTCTTAATTCTACCAGAGGGACAAACAGGATATATTTAATTTAATTGACTGATATTCTCACTTTCAGAAAGTGGAACCAGTTATAGGTTATCCAATTTTCTCTTTTGGACTTGTATCTGACATAATTGTGATAGTGTAACAGAACGAAGTGGCAAAATCTACCCCTTCCATGAAGAAATCTAGAGAAAAAAACCTGGTTAGGGCCTTCTCATTCTCTACAATTACCTGAAAGGAGATTGTGATGAGGTGAATGTTTGTCTCTTCTCCCTAGTTACAAGAGAAGAAATGGCTTCGAGCTGTGCCAGGGGGAATTTAGACTGAATATTAGGAAATTTTTTTTCATGAGAAGGGCTGTCAAGCATTGGTACAGGCTGCCCAGATAATCAATTTATTCACCATCTCTGGATGTATTTCAAATGTGTAGATATGGCGCTTAGGGATATGGTCTAGTGATAGACT B097 CTGTGTATAGTCCTAAAGTGATAAAAATTACATATGACTAGGATGTCTTTCACCTGCCTCAAAAAGAGCAAAAGGTCAAATTTGATTCTAGATGGTCACATAGGCTTTCAACCTTGAACATGATGTTTTTCTTAAATGTCAGTGATGACATGAAAGGACAGATACCTAAGTAGCCACTTATTCTTAATTCTACCAGAGGGACAAACAGGATATATTTAATTTAATTGACTGATATTCTCACTTTCAGAAAGTGGAACCAGTTATAGGTTATCCAATTTTCTCTTTTGGACTTGTATCTGACATAATTGTGATAGTGCAACAGAACGAAGTGGCAAAATCCACCTCTTCCATGAAGAAATCTAGAGAAAAAAACCTGGTTAGGGCCTTCTCATTCTCTACAATTACCTGAAAGGAGATTGTGATGAGGTGAATGTTTGTCTCTTCTCCCTAGTTACAAGAGAAGAAATGGCTTCGAGCTGTGCCAGGGGGAATTTAGACTGAATATTAGGAAATTTTTTTTCATGAGAAGGGCTGTCAAGCATTGGTACAGGCTGCCCAGATAATCAATTTATTCACCATCCCTGGATGTATTTCAAATGTGTAGATATGGCGCTTAGGGATATGGTCTAGTGATAGACT O097 CTGTGTATAGTCCTAAAGTGATAAAAATTACATATGACTAGGACGTCTTTCACCTACCTCAAAAAGAGCAAAAGGTCAAATTTGATTCTAGATGGTCACATAGGCTTTCAGCCTTGAACATGATGTTTTTCTTAAATGTCAGTGATGACATGAAAGGACAGATACCTAAGTAGCCACTTATTCTTAATTCTACCAGAGGGACAAACAGGATATATTTAATTTAATTGACTGATATTCTCACTTTCAGAAAGTGGAACCAGTTATAGGTTATCCAATTTTCTCTTTTGGACTTGTATCTGACATAATTGTGATAGTATAACAGAACCAAGTGGCAAAATCCACCCCTTCCATGAAGAAATCTAGAGAAAAAAACCTGATTAGGGCCTTCTCATTCTCTACAATTACCTGAAAGGAGATTGTGATGAGGTGGATGTTTGTCTCTTCTCCCTAGTTACAAGAGAAGAAATGACTTCAGGCTGTGCCAGGGGGAATTTAGACTGAATATTAGGAATTTTTTTTTCATGAGAAGGGCTGTCAAGCATTGGTACAGGCTGCCCAGATAATCAATTTATTCACCATCCCTGGATGTATTTCAAATGTGTAGATGTGGTGCTTAGGGATATGGTCTAGTGATAGACT Q097 TTCCTGACCATTCAGTAAGCAAGTCATCTCTGCGCAGGAACCCACACAGTTTAAAAGCAGCATAATTCCCATTTATTCCTTCAAACAATTATTTTAAATTCCTCTTGCAGTGAGAGAACAGCCTTAAAATAGGCTCCATACTTGAACATTAGTTCCAGCAAAGCAGATTCCAGTGAATTGTGAGGCATTTCTGTCACCTGGTGCCAAACCCAGCCACTCTGATGTTAAGATAAAAACATCCTGAATCTGCCCAGCAGGATAAGAAAAACATATTTTGTGTTGCTTACACTCTCACTGGTTCCTTGCCAAAAAGACAGTGTTTTTAAAACTTGTAAACTATTCACTTTTCTAACTTAAGCATTGCAATGTTTTTAACCTTTCCATCGAGTTTCTGGGGATGCTCTGGTGGTCCATGGCAGAGTGTGGACCCTGGCAGTCACCTCTTATTTAAATTATGGGAGAATCCAAAGAAAAGAGATGTTAATGAGGAGGGAACAATCTCTCCAGACACATTGTTCATAGCACATATTGATTTCTAGCATGTCATGGCGAGAATTCATTTATGCTTTTTACATTTTTAACAACCTGGCTATTTTTACTCTCCTCATCCTCTCAA W097 TTCCTGACCATTCAATGAGCAAGTCATCTCTGTGCAGGAACCCACACAGTTTAAAAGCAGCATAATTCCCATTTATTCCTTCAAACAATTATTTTAAATTCCTCTTGCAGTGAGAGAACAGCCTTAAAATAGGCTCCATACTTGAACATTAGTTCCAGCAAAGCAGATTCCAGTGAATTGTGAGGCATTTCTGTCACCTGGTGCCAAACCCAGCCACTCTGATGTTAAGATAAAAACATCCTGAATCTGCCCAGCAGGATAAGAAAAACATATTTTGTGTTGCTTACACTCTCACTGGTTCCTTGCCAAAAAGACAGTGTTTTTAAAACTTTTAAACTATTCACTTTTCTAACTTAAGCATTGCAATGTTTGTAACATTTCCATCCAGTTTCTGGGGATGCTCTGGTGGTCCATGGCAGAGTGTGAACCCTGGCAGTCACCTCTTATTTAAATTATGGGAGAATCCAAAGAAAAGAGATGTTAATGAGGAGGGAACAATCTCTCCAGACACATTGTTCATAGCACATATTGATTTCTAGCATGTCACGGCGAGAATTCATTTATGCTTTTTACATTTTTAACAACCTGGCTATTTTTACTCTCCTCATCCTCTCAA B097 TTCCTGACCATTCAGTGAGCAAGTCATCTCTGTGCAGGAACCCACACAGTTTAAAAGCAGCATAATTCCCATTTATTCCTTCAAACAATTATTTTAAATTCCTCTTGCAGTGAGAGAACAGCCTTAAAATAGGCTCCATACTTGAACATTAGTTCCAGCAAAGTAGATTCCAGTGAATTGTGAGGCATTTCTGTCACCTGGTGCCAAACCCAGCCACTCTGATGTTAAGATAAAAACATCCTGAATCTGCCCAGCAGGATAAGGAAAACATATTTTGTGTTGCTTACACTCTCACTGGTTCCTTGCCAAAAAGACAGTGTTTTTAAAACTTGTAAACTATTCACTTTTCTAACTTAAGCATTGCAATGTTTGTAACCTTTCCATCCAGTTTCTGGGGATGCTCTGGTGGTCCATGGCAGAGGCTGGACCCTGGTAGTCACTTCTTATTTAAATTATGGGAGAATCCAAAGAAAAGAGATGTTAATGAGGAGGGAACAATCTCTCCAGACACATTATTCATAGCACATATTGATTTCTAGCATGTCATGGCGAGAATTCATTTATGCTTTTTACATTTTTAACAACCTGGCTATTTTTACTCTCCTCATCCTCTCAA O097 TTCCTGACCATTCAGTGAGCAAGTAATTCTTGTGCAGGAACCCACACAGTTTAAAAGCAGCATAATTCCCATTTATTCCTTCAAACAATTATTTTAAATTCCTCTTGCAGTGAGAGAACAGCCTTAAAATAGGCTCCATACTTGAACATTAGTTCCAGCAAAGCAGATTCCAGTGAATTGTGAGGCATTTCTGTCACCTGGTGCCAAACCCAGCCACTCTGATGTTAAGATAAAAACATCCTGATTCTGCCCAGCAGGATAAGGAAAACATATTTTGTGTTGTTTACACTCTCATTGGTTCCTTGCCAAAAAGACAGTGTTTTTAAAACTTTTAAACTATTCCCTTTTCTAACTTAAGCATTGCAATGTTTGCAACCTTTCCATCCAGTTTCTGGGGATGCTCTGGTGGTCCATGGCAGAGGCTGGACCCTGGCAGTCACCTCATATTTAAATTATGGGAGAATCCAAAGAAAAGAGATGTTAATGAGGAGGGAACAATCTCTCCAGACACATTGTTCATAGCACATATTGATTTCTAGCATGTCATGGCGAGAATTCATTTATGCTTTTTACATTTTTAACAACCTGGCTATTTTTACTCTCCTCATCCTCTCAA Q097 ATATTTACAGAGCTACAGCAGTGATTAATAACAGGAGTTACTTACCAGCTCCCCCCACCCTGAATTTAAAAGTCCATACTTACCAGAAGACATGGCTACAAGCTGTTCACCTTTTAGCTGTGTTTCTGCAGACTCCAGTGCTGCTTCCAGCCTGCTCACTAACATTTGCAGTCACTTTAGACATCCTGTGCTGGGATAGATGCTGCCTGATGTGAGCTTGATCTCACAAAATCACTTAAAAAGAAAAGCCTGGTGAGTACTGCCTTACGCCCAGACCTTTCTGCCTTTGCTCCAGTTTGCCACAATTGCTGTGCTAAAGCGATTCCTCAGGCTGGGGCAGTACTCTAAGAGCAGATTAATTTGTCACTGACTGCAAAGGGAGGCAAGGGGAGAGGAGGAAGAGTTCTCTCCCTGCTCTCAAATCCCCTCTGAGGTCCCCAAACTGCGGCGCTGACACCGGCATGGGGGCACAGCTTCCTCCAGTCAGGCCTACTGTAGTGCCGGCTGCTCCCGGCATGGCAGCTCCCAACTTATCCCTCAAACACAAAAGCATCTGTTCCTCCAAAAGTTTC W097 ATATTTACAGAGCTACAGCAGTGATTAATAACAGGAGTTACTTACCAGCTCCCCCCACCCTGAATTTAAAAGTCCATACTTACCAGAAGACATGGCTACAAGCTGTTCACCTTTTAGCTGTGTTTCTGCAGACTCCGGTGCTGCTTCCAGCCTGCTCACTAACATTTGCAGTCACTTTAGACATCCTGTGCTGGGATAGATGCTGCCTGATGTGAGCTTGATCTCACAAAATCACTTAAAAACAAAAGCCTGGTGAGTACTGCCTTACGCCCAGACCTTTCTGCCTTTGCTCCAGTTTGCCACAATTGCTGTGCTAAAGCGATTCCTCAGGCTGGGGCAGTACTCTAAGAGCAGATTAATTTGTCACTGACTGCAAAGGGAGGCAAGGGGAGAGGAGGAAGAGTTCTCTCCCTGCTCTCAAATCCCCTCTGAGGTCCCCAAACTGCGGCGCTGACACCGGCATGGGGGCACAGCTTCCTCCAGTCAGGCCTACTGTAGTGCCGGCTGCTCCCGGCATGGCAGCTCCCAACTTATCCCTCAAACACAAAAGCATCTGTTCCTCCAAAAGTTTC B097 ATATTTACAGAGCTACAGCAGTGATTAATAACAGGAGTTACTTACCAGCTCCCCCCACCCTGAATTTAAAAGTCCATACTTACCAGAAGACATGTCTACAAGCTGTTCACCTTTTAGCTGTGTTTCTGCAGACTCCAGTGCTGCTTCCAGCCTGCTCACTAACATTTGCAGTCACTTTAGACATCCTGTGCTGGGATAGATGCTGCCTGATGTGAGCTTGATCTCACAAAATCACTTAAAAAGAAAAGCCTGGTGAGTACTGCCTTATGCCCAGACCTTTCTGCCTTTGCTCCAGTTTGCCACAATTGCTGTGCTAAAGCGATTCCTCAGGCTGGGGCAGTACTCTAAGAGCAGATTAATTTGTCACTGACTGCAAAGGGAGGCAAGGGGAGAGGAGGAAGAGTTCTCTTCCTGCTCTCAAATCCCCTCTGAGGTCCCCAAACTGCGGCACTGACACCGGCATGGGGGCACAGCTTCCTCCAGTCAGGCCTACTGTAGTGCCGGCTGCTCCCGGCATGGCAGCTCCCAACTTATCCCTCAAACACAAAAGCATCTGTTCCTCCAAAAGTTTC O097 ATATTTACAGAGCTATAGCAGTGATTAATAACAGGAGTTACTTACCAGCTCCCCCCACCCTGAATTTAAAAGTCCATACTTACCAGAAGACATGGCTACAAGCTGTTCACCTTTTAGCTGTGTTTCTGCAGACTCCAGTGCTGTTTCCAGCCTGCTCACTAACATTTGCAGTCACTTTAGACATCCTGTGCTGGGATAGATGCTGCCTGATGTGAGCTTGATCTCACAAAATCACTTAAAAAGAAAAGCCTGGTGAGTACTGCGTTACGCCCAGACCTTTCTGCCTTTGCTCCAGTTTGCCACAATTGCTGTGCTAAAGCGATTCCTCAGGCTGGGGCAGTACTCTAAGAGCAGATTAATTTGTCACTGACTGCAAAGGGAGGCAAGTGGAGAGGAGGAAGAGTTCTCTCCCTGCTCTCAAATCCCCTCTGAGGTCCCCAGACTGCGGCGCTGACACCGGCATGGGGGCACAGCTTCCTCCAGTCAGGCCTACTGTAGTGCCGGCTGCTCCCGGCATGGCAGCTCCCAACTTATCCCTCAAACACAAAAGCATCTGTTCCTCCAAAAGTTTC Q097 CATAGATATATTTGTGTAATTATAGGGTGAGTGATTAATGATGAGGCAACATTAGGAACAGCCCAAATCTTTGTTCCTGTCAGCATATCAGAAAGCTAATACAGAAACTTGTGCAGAAGGACATCAGCAAAGTGAATGAAATACAAAAAAACAGAAGCAGTTTAAAGATATGGCTAAGGAAAAAGAGTAAGACAATAATGAAGAGATTAGAGTACAAATAATCCCAATTGTCATGCAAAGAGGTTGTATCTTTCTTCCATTTAAGTAGAGTAACAAGTTAATTATAAAAATTCAGCTAAATGTCAAGAGGTCACCATGAATAGGAAGACAAAAGCATGAAATCCAATTTAAACCAAATTTTCTTATTGCTAAAAACTGACCCAAAAAAAGGTAACCCTCCCAAAA W097 CATAGTTATATTTGTGTAATTGTAGGGTGAGTGATTAATGATGAGGCAACATTAGGAACAGCCCAAATCTTTGTTCCTGTCAGCATTTCAGAAAGCTAATACAGAAACTTGTGCAGAAGAACATCAGCAAAGTGAATGAAACACAAAAAAACAGGAGCAGTTTAAAGATATGGCTAAGGAAAAAGAGTAAGACAATAATGAAGAGATTAGAGTACAAATAATCCCAATTGTCATGCAAAGAGGTTGTATCTTTCTTCCATTTAAGGAGAGTAACAAGTTAATTATAAAAATTCAGCTAAATGTCAAGAGGTCACCATGAATAGGAAGACAAAAGCATGAAATCCAATTTAAACCAATTTTTCTTATTGCTAAAAACTGACCCAAAAAAAGGTAACCCTCCCAAAA B097 CATAGATATATTTGTGTAATTATAGGGTGAGTGATTAATGATGAGGCAACATTAGGAACAGCCCAAATCTTTGTTCCTGTCAGCATATCAGAAAGCTAATACAGAAACTTGTGCAGAAGGACATCAGCAAAGTGAATGAAATACAAAAAAACAGAAGCAGTTTAAAGATATGGCTAAGGAAAAAGAGTAAGACAATAATGAAGAGATTAGAGTACAAATAATCCCAATTGTCATGCAAAGAGGTTGTATCTTTCTTCCATTTAAGTAGAGTAACAAGTTAATTATAAAAATTCAGCTAAATGTCAAGAGGTCACCATGAATAGGAAGACAAAAGCATGAAATCCAATTTAAACCAAATTTTCTTATTGCTAAAAACTGACCCAAAAAAAGGTAACCCTCCCAAAA O097 CAGACTTATATTTGTGTAATTATAGGGTGAGTGATTAATGATGAGGCAACATTAGGAACAGCCCAAATCTTTGTTCCTGTCAGCATTTCAGAAAGCTAATACAGAAACTTGTGCAGAAGAACATCAGCAAAGTGAATAAAACACAAAAAACCAGGAGCAGTTTAAAGATACGGCTAAGGAAAAAGAGTAAGACAATAATGAAGAGATTAGAGTACAAATAATCCCAATTGTCATGCAAAGAGGTTGTATCTTTCTTCCATTTAAGGAGAGTAACAAGTTAATTATAAAAATTCAGCTAAATGTCAAGAGGTCACCATGAATAGGAAGACAAAAGCATGAAATCCAATTTAAACCAATTTTTCTTATTGCTAAAAACTGACCCAAAAAAAGGTAACCCTCCCAAAA Q097 ATCCCATAAAAAACTTCAGCAGCACTCAGTCCAAACCCACTATTTAGGTTTCTTCTTGGGAAAAAAAAGGCAGCAAATTGTGATGATTAATGTGGAAGTCCCAGGGAAAGCAATGGGTATAAAGGGCTCTTCATTTTTGACAGGTGCATTGTGCACTCTGGGCTCCCAGCCAAGACACATTGTCATCAAATGTTAATCAAATCTGCAAATAATATACAAAGTATTAAATTGTGCTTATTCTCAGAGTAATTTGAGACATAAATTTATCAAGCTGTGCTATATGTTAAGGTCCCTTAAAATGAAGAAAATCAGGACTATGAGGCAGATAAAGAGATTGGGACAACAAAGGGCTTTCTGTGAGCCCTGGTTCAGGAGAACTCCTCTCTGTATTTTCCTTCAGAAAAGGTTCTGATGTAACCAAGTAAATCCACAAGTAAAGAGAAGCAGGCTGGGATGAGAAAACATGTGAAACCTCTCTGCAGCTGCACACTTAAAATAGTAATTTCAGTTTTCTGCATGTGGCTGTGCCCTTCCTCACCAGGACTGTGGCTGCAGGTCCCACCCCCCAGCAGTTATTCGCACCTGAGAAAAACATCTGTATTAGCATAAGAAACTCAGTTTTCAGGCAGGAGGACTTTTTGCA W097 ATCCCATAAAAAACTTCAGCAGCACTCAGTCCAAACCCACTATTTAAGTTTCTTTTTGGGAAAAAAAAGGCAGCAAATTGTGATGATTAATGTGGAAGTCCCAGGGAAAGCAATGGGTATAAAGAGCTCTTCATTTTTGACAGGTGCATTGTGCACTCTGGGCTCCCAGCCAAGACACATTGTCATCAAATGTTAATCAAATCTGCAAATAATATACAAAGTATTAAATTGTGCTTATTCTCAGAGTAATTTGAGACATAAATTTATCAAGCTGTGCTATATGTTAAGGTCCCTTAAAATGAAGAAAATCAGGACTATGAGGCAGATAAAGAGATTGGGACAACAAAGGGCTTTCTGTGAGCCCTGGTTCAGGAGAACTCCTCTCCGTATTTTCCTTCAGAAAAGGTTCTGATGTAAACAAGTAAATCCACAAGTAAAGAGAAGCAGGCTGGGATGAGAAAACATGTGAAACCTCTCTGCAGCTGCACACTCAAAATAGTAATTTCAGTTTTCTGCATGTGGCTGTGCCCTTCCTCACCAGAACTGTGGCTGCAGGTCCCACCCCCCAGCAGTTATTCGCACCTGAGAAAAACATCTGTATTAGCATAAGAAACTCAGTTTTCAGGCAGGAGGACTTTTTGCA B097 ATCCCATAAAAAACTTCAGCAGCACTCAGTCCAAACCCACTATTTAGGTTTCTTTTTGGGAAAAAAAAGGCAGCAAATTGTGATGATTAATGTGGAAGTCCCAGGGAAAGCAATGGGTATAAAGGGCTCTTCATTTTTGACAGGTGCATTGTGCACTCTGGGCTCCCAGCCAAGACACATTGTCATCAAATGTTAATCAAATCTGCAAATAATATACAAAGTATTAAATTGTGCTTATTCTCAGAGTAATTTGAGACATAAATTTATCAAGCTGTGCTATATGTTAAGGTCCCTTAAAATGAAGAAAATCAGGACTATGAGGCAGATAAAGAGATTGGGACAACAAAGGGCTTTCTGTGAGCCCTGGTTCAGGAGAACTCCTCTCAGTATTTTCCTTCAGAAAAGGTTCTGATGTAAACAAGTAAATCCAAAAGTAAAGAGAAGCAGGCTGGGATGAGAAAACATGTGAAACCTCTCTGCAGCTGCACACTCAAAATAGTAATTTCAGTTTTCTGCATGTGGCTGTGCCCTTCCTCACCAGGACTGTGGCTGCAGGTCCCACCCCCAAGCAGTTATTCGCACCTGAGAAAAACATCTGTATTAGCATAAGAAACTCAGTTTTCAGGCAGGAGGACTTTTTGCA O097 ATCCCATAAAAAACTTCAGCAGCACTCAGTCCAAACCCACTATTTAGGTTTCTTTTTGGGAAAAAAAAGGCAGCAAATTGTGATGATTAATGTGGAAGTCCCAGGGAAAGCAATGGGTATAAAGGGCTCTTCATTTTTGACAGGTGCATTGTGCACTCTGGGCTCCCAGCCAAGACACATTGTCATCAAATGTTAATCAAATCTGCAAATAATATACAAAGTATTAAATTGTGCTTATTCTCAGAGTAATTTGAGACATAAATTTATCAAGCTGTGCTATATGTTAAGGTCCCTAAAAATGAAGAAAATCAGGACTATGAGGCAGATAAAGAGATTGGGACAACAAAGGGCTTTCTGTGAGCCCTGGTTCAGGAGAACTCCTCTCAGTATTTTCCTTCAGAAAAGGTTCTGATGTAAACAAGTAAATCCACAAGTAAAGAGAAGCAGGCTGGGATGAGAAAACATGTGAAACCTCTCTGCAGCTGCACACTTAAAATAGTAATTTCAGTTTTCTGCATGTGGCTGTGCCCTTCCTCACCAGGACTGTGGCTGCAGGTCCCACCCCCCAGCAGTTATTTGCACCTGAGAAAAACATCTGTATTAGCATAAGAAACTCAGTTTTCAGGCAGGAGGACTTTTTGCA Q097 GGATTTCATAGACTCTACTCAGTAGAATACTCTAACATTGGGAATTAACTGGAGTGCTTTTTCTTTTGACCAAGCATATCAAAGTCTGGTTTCAATGTGTAATAAAACCAGAATGATACCTATGGGACAAAGGGCAAGACACATCTTGAACTGGCATGGTGAAATTATCTGTAAGCATGTAGATTGAAAGGAAATAGCTCATACACATCCAATGGGAGAAAAATAATTACGGTGTTCTAGTGTTGGGTTATGAAAACAGAGGCAGGAGAAGCCCTTAGCTGGGCAGGTTGTCAAACCAAAGAAAAAAAAATATCCATTACTTCCAAAATAAAGCTCCTCAGATTATTCTGCTGGTTGCACACAGTGATTATCTCCTGCCTTTGAAATGAGATCTGATTTTGCTCTGGGGTCTGGTGAGTCCAACTCACCTTTAAAGGTCCAACCCCCTCGCA W097 GGATTTCATAGACTCTACTCAGTAGAATATTCTAACATTGGGAATTAACTGGAGTGCTTTTTCTTTTGACCAAGCAAGTCAAAGTCTGGTTTCAATGTGTAATAAAACCAGAATGATACCTATGGGACAAAGGGCAAGACACATCTTGAACTGGCATGGTGAAATTATCTGTAAGCATGTAGATTGAAAGGAAATAGCTCATACACATCCAATGGGAGAAAAATAATTACAGTGTTCTAGTGTTGGGTTATGAAAACAGAGGCAGGAGAAGCCCTTAGCTGGGCAGGTTGTCAAACCAAAGGAAAAAAAATATCCATTACTTCCAAAATAAAGCTCCTCAGATTATTCTGCTGGTTGCACACAGTGATTATCTCCTGCCTTTGAAATGAGATCTGATTTTGCTCTGGGGTTTGGTGAGTCCAACTCACCTTTAAAGGTCCAACCCCCTCGCA B097 GGATTTCATAGACTCTACTCAGTAGAATATTCTAACATTGGGAATTAACTGGAGTGCTTTTTCTTTTGACCAAGCATGTCAAAGTCTGATTTCAATGTGTAATAAAACCAGAATGATCCCTATGGGACAAAGGGCAAGACACATCTTGAACTGGCATGGTGAAATTACCTGTAAGCATGTAGATTGAAAGGAAATAGCTCATACACATCCAATGGGAGAAAAATAATTATGGTGTTCTAGTGTTGGGTTATGAAAACAGAGGCAGGTGAAGCCCTTAGCTGGGCAGGCTGTCAAACCAAAGAAAACAAAATATCCATTACTTCCAAAATAAAGCTTCTCAGATTATTCTGCTGGTTGCACACAGTGATTATCTCCTGCCTTTGAAATGAGATCTGATTTTGCTCTGGGGTTTGGTGAGTCCAACTCACCTTTAAAGGTCCAACCCCCTCGCA O097 ???????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Q097 CATTTCTGCATCTTCTGCTTCTTGCTGAGTTGAGTTATAAAATATTAGAAAAGCCATATTGCACAGCTTTTAGCCACTAATATATATGTGCAATCTTAAATATCAACAGCTTCAGCCATTTTTTGTTAATGTTAGGTCTGGATGTGATTTCTTAACTAAAGACAGCTTCCTTAGCTTACCTCATATCATGGCATCTGGTATCTCTAATTGGAATCATTTTCATAATAACCATGAGCAATATACATTACCATCTAAATATGAAGGCTGACTGCAATACAGACTTGTCTACAGCTTTTTAAGTATCCAGGATCCTGTCCAAAGCCCCTTAGACTCGAGGGAAAGACTCCTGCTGATGTATCTTATTTAAATTAGGTGCAATGTACTAAAAAACTCTCCTAGATTCCATATAGGAAACCAGAAATCTATTCCATAATAGTAATAACAATAATCAGTGTAACTCTGTCAAACCCCTCCAAAGGTGGTGTCCCATTGTGTCACTAAATCAGGCCTAATTTAGTCA W097 CATTTCTGCATCTTCTGCTTCTTGCTGAGTTGAGTTATAAAATATTAGAAAAGCCATATTGCACAGCTTTTAGCCACTAATATATATGTGCAATCTTAAATATCAACAGCTTCAGCCATTTTTTGTTAATGTTAGGTCTGGATGTGATTTCTTAACTAAAGACAGCTTCCTTAGCTTACCTCATATCATGGCATCTGGTATCTCTAATTGGAATCATTTTCATAATAACCATGAGCAATATACATTACCATCTAAATATGAAGGCTGACTGCAATACAGACTTGTCTACAGCTTTTTAAGTATCCAGGATCCTGTCCAAAGCCCCTTAGACTCGAGGGAAAGACTCCTGCTGATATATCTTATTTAAATTAGGTGCAATGTATTAAAAAACTCTCCTAGATTCCATATAGGAAACCAGAAATCTATTCAATAATAGTAATAACAATAATCAGTGTAACTCTGTCAAACCCCTCCAAAGGTGGTGTCCCATTGTGTCACTAAATCAGGCCTAATTTAGTCA B097 CATTTCTGCATCTTCTGCTTCTTGCTGAGTTGAGTTATAAAATATTAGAAAAGCCATATTGCACAGCTTTTAGCCACTAATATATATGTGCAATCTTAAATATCAACAGCTTCAGCCATTTTTTGTTAATGTTAGGTCTGGATGTGATTTCTTAACTAAAGACAGCTTCCTTAGCTTACCTCATATCATGGCATCTGGTATCTCTAATTGGAATCATTTTCATAATAACCATGAGCAATATACATTACCATCTAAATATGAAGGCTGACTGCAATACAGACTTGTCTACAGCTTTTTAAGTATCCAGGATCCTGTCCAAAGCCCCTTAGACTCGAGGGAAAGACTCCTGCTGATATATCTTATTTAAATTAGGTGCAACGTACTAAAAAACTCTCCTAGATTCCATATAGGAAACCAGAAATCTATTCAATAATAGTAATAACAATAATCAGTGTAACTCTGTCAAACCCCTCCAAAGGTGGTGTCCCATTGTGTCACTAAATCAGGCCTAATTTAGTCA O097 CATTTCTGCATCTTCTGCTTCTTGCTGAGTTGAGTTATAAAATATTAGAAAAGCCATATTGCACAGCTTTTAGCCACTAATATATATGCGCAATCTTAAATATCAACAGCTTCAGCCATTTTTTGTTAATGTTAGGTCTGGATGTGATTTCTTAACTAAAGACAGCTTCCTTAGCTTACCTCATATCATGGCATCTGGTATCTCTAATTGGAATCATTTTCATAATAACCATGAGCAATATACATTACCATCTAAATATGAAGGCTGACTGCAATACAGACTTGTCTACAGCTTTTTAAGTATCCAGGATCCTGTCCAAAGCCCCTTAGACTCGAGGGAAAGACTCTTGCTGATATATCTTATTTAAATTAGGTGCAACGTATCAAAAAACTCTCCTAGATTCCATATAGGAAACCAGAAATCTATTCAATAATAGTAATAACAATAATCAGTGTAACTCTGTCTAACCCCTCCAAAGGTGGTGTCCCATTGTGTCACTAAATCAGGTCTAATTTAGTCA Q097 TGAATTCTTCTCAGTCTCCATTCGTTTGATAGCTTCCAAAATAAGGGTGGTATATGCCTTAGGTACTATATTTACCACAAGAAGGTCATTCCACAAAGCTGCTGGATCTCTCCATTGATCCAACTCTCGCCATTTGATACTCCTCCTGTTGTCTGTGAGACCAAAGAAACCACTAACATGAACTGGAAGTCCAGTTTTGCTTTCTTCACCTGGAGGCAAAGGCAGAAAACAGAAAGCTCTTCCTGAAAAGTCTGCTACAGCTCCATTTTCCTCCTCGTTAGCTGCTAAAGACATTGCTATTCCAATAGTAGGAACAAATTTCAAGTCATCAGCTAAACAATCCAGTTCTGTGCACATTCCTCGACCACCCACACAATTACACACTAACCAAGATGTTTTCTGTGCGTCCTTAACACTCTCATCTTCTACAACTATATTTACATGGTATGTCACACATGTTATGCTATTGCTTGGAACTCTCTTACAGTACTGATTTATTGCAGTTCCCAGGATTTTGATAGAATTGGGTCTTTCGTGTTTCAAGGCCTTGTTCTCGCTGGCAGTTACTCTAAACACCAGCCTCTCTGTTCCATCAGCTTCCCGAACGTGCAATGAAACATCCTGAACACTCTTCAGGAAAAGCAATAC W097 TGAATTCTTCTCAGTCTCCATTCGTTTGATAGCTTCCAAAATAAGGGTGGTATATGCCTTAGGTACTATATTTACCACAAGAAGGTCATTCCACAAAGCTGCTGGATCTCTCCATTGATCCAACTCTCGCCATTTGATACTCCTCCTGTTGTCTGTGAGACCAAAGAAACCACTAACATGAACTGGAAGTCCAGTTTTGCTTTCTTCACCTGGAGGCAAAGGCAGAAAACAGAAAGCTCTTCCTGAAAAGTCTGCTACAGCTCCATTTTCCTCCTCGTTAGCTGCTAAAGACATTGCTATTCCAATAGTAGGAACAAATTTCAAGTCATCAGCTAAACAATCCAGTTCTGTGCACATTCCTCGACCACCCACACAATTACACACTAACCAAGATGTTTTCTGTGCGTCCTTAACACTCTCATCTTCTACAACTATATTTACATGGTATGTCACACATGTTATGCTATTGCTTGGAACTCTCTTACAGTACTGATTTATTGCAGTTCCCAGGATTTTGATAGAATTGGGTCTTTCGTGTTTCAAGGCCTTGTTCTCGCTGGCAGTTACTCTAAACACCAGCCTCTCTGTTCCATCAGCTTCCCGAACGTGCAATGAAACATCCTGAACACTCTTCAGGAAAAGCAATAC B097 TGAATTCTTCTCAGTCTCCATTCGTTTGATAGCTTCCAAAATAAGGGTGGTATATGCCTTAGGTACTATATTTACCACAAGAAGGTCATTCCACAAAGCTGCTGGATCTCTCCATTGATCCAACTCTCGCCATTTGATACTCCTCCTGTTGTCTGTGAGACCAAAGAAACCACTAACATGAACTGGAAGTCCAGTTTTGCTTTCTTCACCTGGAGGCAAAGGCAGAAAACAGAAAGCTCTTCCTGAAAAGTCTGCTACAGCTCCATTTTCCTCCTCGTTAGCTGCTAAAGACATTGCTATTCCAATAGTAGGAACAAATTTCAAGTCATCAGCTAAACAATCCAGTTCTGTGCACATTCCTCGACCACCCACACAATTACACACTAACCAAGATGTTTTCTGTGCATCCTTAACACTCTCATCTTCTACAACTATATTTACATGGTATGTCACACATGTTATGCTATTGCTTGGAACTCTCTTACAGTACTGATTTATTGCAGTTCCCAGGATTTTGATAGAATTGGGTCTTTCGTGTTTCAAGGCCTTGTTCTCGCTGGCAGTTACTCTAAACACCAGCCTCTCTGTTCCATCAGCTTCCCGAACGTGCAATGAAACATCCTGAACACTCTTCAGGAAAAGCAATAC O097 ???????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Q097 GGTGGCAAGAAAATCTACAAAGCTGTTATTTATTTATTTGTTTGTTTGTTTCCAGCCACCATCCTTGTAATTTTTCCTGTGCCTTAAGAAAAAAATCAGTATTCTTGCTCTGATCTTCTGAGAAGGTTTTCCTCCATTGTTTGATACTCGGTTTTGATTTTGCTCCAGTGACTTCTTGCTTTTGTTTAAAGTTCCAAGTTCTTAAAATCCAGAAAATCGCTTCTTGTTTTCTTCCAGATGTTGATCCAATAGTCAGGTATCTGCAGCTTCTCTTGACCCATCAGGTTGTTATTCTGTGACCCAAATCCCCAAGTCCCTCCCACTCACTGATGGTTTCCAAAATTTTCTGTAACGCTTCTTAAACATTTAAGAAATTATTTATGGAAATATGTTGCAAAAACTTAGTGTGTCTTCATGGTTTTAATGACGAACTCTGCAAATACAGCTTTGCTATGTGAAAAGGCTACTGCTTACAACAACATAGTCCTTGGGTCAGTAATTTCCAATCCCTGAGCAGGTTCGGTGATGAGAAATGCATAAATGTGGGTTTCTGATCCTGTGAGAAGCTGAGTGTTTTGATTCAGCCCATTCTCAAGCTGA W097 GGTGGCAAGAAAATCTACAAAGCTGTTATTTATTTATTTGTTTGTTTGTTTCTAGCCACCATCCTTGTAATTTTTCCTGTGCCTTAAGAAAAAAATCAGTATTCTTGCTCTGATCTTCTGAGAAGGTTTTCCTCCATTGCTTGATACTCGGTTTTGATTTTGCTCCAGTGACTTCTTGCTTTTGCTTAAAGTTCCAAGTTCTTAAAATCCAGAAAATCGCTTCTTGTTTTCTTCCAGATGTTGATCCAATAGTCAGGTATCTGCAGCTTCTCTTGACCCATCAGGTTGTTATTCTGTGACCCAAATCCCCAAGTCCCTCCCACTCACTGATGGTTTCCAAAATTTTCTGTAATGCTTCTTAAACATTTAAGAAATTATTTATGGAAATATGTTGCAAAAACTTAGTGTGTCTTCATGGTTTTAATGACGAACTCTGCAAATACAGCTTTGCTATGTGAAAAGGCTATTGCTTACAACAACATAGTCCTTGGGTCAGTAATTTCCAATCCCTGAGCAGGTTCGGTGATGAGAAATGCATAAATGTGGGTTTCTGATCCTGTGAGAAGCTGAGTGTTTTGATTCAGCCCATTCTCAAGCTGA B097 GGTGGCAAGAAAATCTACAAAGCTGTTATTTATTTGTTTGTTTGTTTGTTTCTAGCCACCATCCTTGTAATTTTTCCTGTGCCTTAAGAAAAAAATCAGTATTCTTGCTCTGATCTTCTGAGAAGGTTTTCCTCCATTGCTTGATACTCGGTTTTGATTTTGCTCCAGTGACTTCTTGCTTTTGCTTAAAGTTCCAAGTTCTTAAAATCCAGAAAATCGCTTCTTGTTTTCTTCCAGATGTTGATCCAATAGTCAGGTATCTGCAGCTTCTCTTGACCCATCAGGTTGTTATTCTGTGACCCAAATCCCCAAGTCCCTCCCACTCACTGATGGTTTCCAAAATTTTCTGTAATGCTTCTTAAACATTTAAGAAATTATTTATGGAAATATGTTGCAAAAACTTAGTGTGTCTTCATGGTTTTAATGACGAACTCTGCAAATACAGCTTTGCTATGTGAAAAGGCTATTGCTTACAACAACATAGTCCTTGGGTCAGTAATTTCCAATCCCTGAGCAGGTTCGGTGATGAGAAATGCATAAATGTGGGTTTCTGATCCTGTGAGAAGCTGAGTGTTTTGATTCAGCCCATTCTCAAGCTGA O097 GGTGGCAAGAAAATCTACAAAGCTGTTATTTATTTATTTGTTTGTTTGTTTCCAGCCACCATCCTTGTAATTTTTCCTGTGCCTTAAGAAAAAAATCAGTATTCTTGCTCTGATCTTCTGAGAAGATTTTCTTCCATTGCTTGATACTCGCTTTTGATTTTGCTCCAGTGACTTCTTGCTTTTGTTTAAAGTTCCAAGTTCTTAAAATCCAGAAAATCTCTTCTTGTTTTCTTCCAGGTGTTGATCCAATAGTCAGGTATCTGCAGCTTCTCTTGACCCATCAGATTGTTATTCTGTGACCCAAATCCCCAAGTCCCTCCCACTCACTGATGGTTTCCAAAATTTTCTGTAACGCTTCTTAAACATTTAAGAAATTATTTATGGAAATATATTGCAAAAACTTAATGTGTCTTCATGGTTTTAATGAGGAACTCTGCAAATACAGCTTTGCTGTGTGAAAAGGTTATTGCTTCCAACAACATAGTCCTTGGGTCAGTAATTTCCAATCCCTGAGCAGGTTCGGTGATGAGAAATGCATAAATGTGGGTTCCTGATCCTGTGAGAAGCTGAGTGTTTTGATTCAGCCCATTCTCAAGCTGA Q097 GCTGTAGAGTGCACAGCTCACCCTGGCATTCAGTGCATAGTGAAGGCAATTGCAAAAAACCAGCAGCTGCTCACTCCCAAACACAAGGCGCTGTCTATGTCCTTGCTTCCCGAGCAACTCAACGCAGCCCTTCTTTTCTGCAGCTGCCATCGAGTTCTCTGCATCTCGCAAGCTCAGGGCACGAACAAGAATTCTCCAGGACGTCTGCATTAGGACCAAGTCAAAACCCAGGAAAGTCGTCCTGTAACACCTGCCAGAAGCATGCCAGTGGTGAATTGAAAGGGCAGGCAGCTGCAGCCACCGTCGTCTAGCAGTGCCAAGGCTGCGCACACAAGCCG W097 GCTGTAGAGTGCACAGCTCATCCTGGCATTCAGTGCACAGTGAAGGCAATTGCAAAAAACCAGCAGCTGCTCACTCCCAAACACAAGGCGCTGTCTATGTCCTTGCTTCCCGAGCAACTCAGCGCAGCCCTTCATTTCTGCAGCTGCCATCGAGTTCTCTGCATCTCAGAAGCTCAGGGCACGAACAAGAATTCTCCAGGACATCTGCATTAGGACCAAGTCAAAACCCAGGAAAGTCGTCCTGGAACACCTGCCAGAAGAATGCCAGTAGTGAATTGAAAGGGCAGGCAGTTGCAGCCACCGTCCTCTAGCAGTGCCAAGGCATCGCACACAAGCCG B097 GCTGTAGAGTGCACAGCTCATCCTGGCATTCAGTGCACAGTGAAGACAATTGCAAAAAACCAGCAGCTGCTCACTCCCAAACACAAGGCGCTGTCTATGTCCTTGCTTCCCGAGCAACTCAGCGCAGCCCTTCATTTCTGCAGCTGCCATCGAGTTCTCTGCATCTCAGAAGCTCAGGGCACGAACAAGAATTCTCCAGGACGTCTGCATCAGGACCAAGTCAAAACCCAGGAAAGTCGTCCTGGAACACCCGCCAGAAGCATGCCAGTGGTGAATTGAAAGAGCAGGCAGCTGCAGCCACCCTCCTCTAGCAGTGCCAAGGCATCGCACACAAGCCG O097 GTAGAGGAGTGCACAGCTCCACCTGACATTCAGTGCACAGCGAAGTCAACTGCAAAAAACCAGCAGCTGCTCACTCCCAAACACTAAGCGCTGTTAATGTCCTTGCTTCCTGAGCGACTCAGCACAGCCCTTCTTTTCTGTAGCTGCCATCGATATCTCTGCAACTCGTAAACTCAGGGTACGAACAAGAATTCTCCAGATCGTCTGCATCAGGACCAAGTCGAAACCCAGGAAAGTCGTCCTGGAGCACCTGCCAGAAGCATGCCAGAGGTGAACTGAAAGGGCTGGCAGCTGCAGCCACCGTCCTCTAACAGTACCGAGGCAGCGCACACAAGCCG Q097 ATCAAGTGTTCTGACTTTTTGAGATATTGAGCAAGATAAATTAAAAGCAAGGTGATTATGCAAAATATTGCTAGTATTTCCTCTAGTAAGAGTAATGCTTCTTGCAGCAAATGAGGGAATCTGTGAATGTGTCTTGGGATTTTCAAATACCAATTCTAAAGAATGGAGTCTAACTGAAAGGATTTCTCCTGCTTCCTTGTTTTGGCTATTGCTTGA W097 ATCAAGTGTTCTGACTTTTTGAGATATTGAGCAAGATAAATTAAAAGCAAGGTGATTATGCAAAATATTGCTAGTATTTCCACTAGTAAGAGTAATGCTTCTTGCAGCAAATGAGGGAATCTGTGAATGCGTCTTGGGATTTTCAAATACCAATTCTAAAGAATGGAGTCTAACTGAAAGGATTTCTCCTGCTTCCTTGTTTTGGCTATTGCTTGA B097 ATCAAGTGTTCTGACTTTTTGAGATATTGAGCAAGATAAATTAAAAGCAAGGTGATTATGCAAAATATTGCTAGTATTTCCACTAGTAAGAGTAATGCTTCTTGCAGCAAATGAGGGAATCTGTGAATGCGTCTTGGGATTTTCAAATACCAATTCTAAAGAATGGAGTCTAACTGAAAGGATTTCTCCTGCTTCCTTGTTTTGGCTATTGCTTGA O097 ATCAAGTGTTCTGACTTTTTGAGATATTGAGCAAGATAAATTAAAAGCAAGGTGATTATGCAAAATATTGCTAGTATTTCCACTAGTAAGAGTAATGCTTCTTGCAGCAAATGAGGGAATCTGTGAATGTGTCTTGGGATTTTCAAATACCAATTCTAAAGAATGGAGTCTAACTGAAAGGATTTCTCCTGCTTCCTTGTTTTGATTATTGCTTGA ; End; begin mrbayes; set autoclose=yes nowarn=yes; outgroup 4; CHARSET locus097 = 1-473; CHARSET locus098 = 474-1031; CHARSET locus118 = 1032-1593; CHARSET locus119 = 1594-2204; CHARSET locus120 = 2205-2504; CHARSET locus122 = 2505-3122; CHARSET locus130 = 3123-3661; CHARSET locus104 = 3662-4272; CHARSET locus129 = 4273-4784; CHARSET locus143 = 4785-5374; CHARSET locus146 = 5375-5918; CHARSET locus111 = 5919-6508; CHARSET locus135 = 6509-6973; CHARSET locus148 = 6974-7543; CHARSET locus182 = 7544-8155; CHARSET locus200 = 8156-8655; CHARSET locus209 = 8656-9227; CHARSET locusB098 = 9228-9820; CHARSET locus184 = 9821-10470; CHARSET locus185 = 10471-11109; CHARSET locus186 = 11110-11725; CHARSET locus187 = 11726-12297; CHARSET locus188 = 12298-12702; CHARSET locus192 = 12703-13345; CHARSET locus193 = 13346-13797; CHARSET locus195 = 13798-14317; CHARSET locus198 = 14318-14965; CHARSET locus199 = 14966-15565; CHARSET locusB200 = 15566-15903; CHARSET locus103 = 15904-16119; partition genes = 30: locus097,locus098,locus118,locus119,locus120,locus122,locus130,locus104, locus129,locus143,locus146,locus111,locus135,locus148,locus182,locus200, locus209,locusB098,locus184,locus185,locus186,locus187,locus188,locus192, locus193,locus195,locus198,locus199,locusB200,locus103; set partition = genes; speciespartition test = SpQ: 1, SpW: 2, SpB: 3, SpO: 4; set speciespartition = test; unlink topology = (all); prset brlenspr = clock:speciestree; prset topologypr = speciestree; prset popvarpr = variable; prset popsizepr = gamma(1,100); [mcmc ngen = 10000000; sump; sumt;] end; NBISweden-MrBayes-ecfd55c/examples/hym.nex000066400000000000000000000372771526212227600205640ustar00rootroot00000000000000#NEXUS [ Truncated data from: Ronquist F, Klopfstein S, Vilhelmsen L, Schulmeister S, Murray DL, Rasnitsyn AP. 2012. A total-evidence approach to dating with fossils, applied to the early radiation of the Hymenoptera. Systematic Biology 61:973-999. ] [ Used in Zhang, C. 2017. Molecular Clock Dating using MrBayes. arXiv:1603.05707] [ Version: fre 3 jul 2020 14:59:27 Sign: JN] Begin data; Dimensions ntax=20 nchar=510; Format datatype=mixed(standard:1-200,DNA:201-510) interleave=yes gap=- missing=?; Matrix [Morphology, Vilhelmsen, 200 chars] Raphidioptera 0000000000 000011?001 ?000?00000 01-210?100 000000-000 10000-0??1 011000???1 -2-00?100? 00--000000 -0-0?1-000 ?2?0000??? 0-00?0--00 001?010100 0000100031 01001010?0 ???0011100 000000???? 011--100?? ??01000000 010-02000- Xyela 0010000100 0010000-00 0010111010 0001010000 0000010110 0001100010 0200000000 0100010000 0100011000 0000000100 0002100000 1000010000 00?0010000 0100100000 0000000100 0011100010 0000000000 1000000000 0000010200 001010000- Runaria 0010000010 0100000-00 002-001000 00010?0010 1110020110 1100100020 0210100100 110001000? 0???0??0?3 2102110??? 000?1??110 101?11?010 0000000000 1100000021 110?1??210 100110?120 1-11001000 1100000100 0000010210 00????0010 Diprion 00100???0? ?010000-?0 ?100001??? ?00?00?010 110002?2?? ?100100030 0?102001?? 001000000? 010001?003 ?101?1100? ?0??1????? 10111121?? ?00?000000 ??00?0?000 ?1001012?? ?????11121 0-1101-000 1100000100 00000101?0 ?010?0100- Phylacteophaga 0010000000 0000000-00 0000001000 000110?010 1200021211 10001111-0 0210200111 100000000? 00--1?0003 2102110001 221210011? 1211112111 00?1000010 120010103- 1110101210 0110111021 0-0101-000 4101010100 10000101?0 0010101010 Onycholyda 00110???0? ??110100?2 0000?22??? ?00101?100 010002?2?? ??01111011 021000?0?? 0100010000 0???0????3 ?100?10100 ?00?1????? 1?0??10000 ??0?01?000 ??00100000 ???0010??? ????100000 0001000000 1001000000 0000011200 0010111?1? Cephus 0011010020 0110011000 1000?01000 0001020000 0000120210 1001111010 0210021100 1101110000 0100010113 1100000110 1001100000 101100--00 0001110100 0200100011 1100120100 1011100021 0001100000 1101000100 00001101?0 0010100010 Sirex 0111110020 0110010100 0000?01000 0003120111 00000202?1 1101110020 0200021100 110001101? 0???0??110 ?10001?120 0102100??? 1000010020 0001000100 0100110000 1000102100 0111100101 0101000000 1100010100 00000101?0 0021100011 Orussus 1111110001 1110110112 1000101000 0012101110 2100120210 10111101-0 0201021100 1101221220 1---1?2022 021201-121 1002110111 1000010001 0011010010 0201100101 1111101210 0111101121 0-11200011 3101101111 10000101?1 202110000- Vespidae 00111100?1 011011?112 1000101102 ?011001110 2102121211 1011111011 0210221110 1101211221 0101132123 220011?121 2212110111 100000--01 0011011-10 120211013- 1111101211 1110112121 0101200000 4101010110 10012101?0 000-000011 Asioxyela ?????????? ?????????? ?????????? ?????????? ?????????? ?????????? ?????????? ?????????? ?????????? ?????????? ?????????? ?????10??? ?????????? ?????????? ?????????? ????????00 0000000000 1????????? ?????????? ?????????? Nigrimonticola 0????????? ????0????? ??10?1???? ????????00 ?????????? ?????????? ?????????? ??????000? ????????0? ?????00??? ????10???? 1????????? ?????????0 ?????????? ?????????? ????????0? 00?0?00000 ?????????0 ????01?2?? ?010???00? Xyelotoma 0????????? ????0????? ??10?0???? ????????10 ?????????? ?????????? ????2????? ??????000? ?????????? ?????01??? ?????????? ?2???1???? ?????????? ?????????? ?????????? ???????100 0-0?00???? 1?????0100 ??0?0????? ?????????? Undatoma ?????????? ?????????? ?????????? ????????10 ??100??2?? ?00?11001? ?????????? 01000?000? ??????0?0? ?????1-??? ?????????? 1????11??? ????????00 ?????????? ?????????? ????????21 1001001000 1?????01?0 0???0?0200 ?0???0???? Dahuratoma 0?1??????? ????0????? ??10?????? ????????00 ?????????? ?????????? ?????????? ??????000? ??????0??? ?????????? ?????????? ?0???11??? ?????????? ?????????? ?????????? ????????00 1-01001000 1?010?0100 000?0??100 ?0???????? Cleistogaster 0????????? ????0????? ??000????? ????????10 ?????????? ?????1???? ?????????? 1101??022? ??????2??? ?????01??? ?????????? 1????0--?? ???????011 ???2????3? ?????????? ????????21 0101200000 3?????1101 100?2?02-1 ?02?10?0?? Ghilarella 0????????? ?????????? ?????0???? ????????00 ?????????? ?????????? ?????????? ??????100? ?????????? ?????01??? ?????????? ?????1???? ????????00 ?????????? ?????????? ????????21 000100-100 1????????? 000?(0,1)?0??? ?0???????? Mesorussus 11???????? ?????1???? ??020(0,1)???? ?????????? 21???????? ?????1???? ?????????? ?????????? ?????????? ?????????? ?????????? 1????????? ?????????? ?????????? ?????????? ?????????? ?????????? ?????????? ?????????? ?????????? Prosyntexis 00???????? ?????????? ??00?????? ????????00 ???00????? ?????????? ?????????? ?????????? ?????????? ?????????? ?????????? 1????????? ????????00 ?????????? ?????????? ????????21 000110-100 1?????11?? ?00?1??2-0 ?0??1????? Pseudoxyelocerus ?????????? ?????????? ?????????? ?????????? ?????????? ?????????? ?????????? ?????????? ?????????? ?????????? ?????????? ?????????? ?????????? ?????????? ?????????? ????????10 1001001000 1????????? ?????????? ?????????? [16S, 100 sites] Raphidioptera ATAATCATTTGTTTCTTAATTAGGGACTAGTATGAAGGATTGGACGAGATATTTTCTGTCTCAAATTTTATTTTTTAGTAAAAAAGCT-AAAATTTATTT Xyela ATAATCATTAGTCTTTTAATTGAGGGCTGGAATGAATGATTGAACGAAGTATTAACTGTCTCGAATTTAATTTTTGAGTTAAAAAGCT-TAAATTTTATT Runaria ATAATCATTAGTCTTTTAATTGAAGACTTGTATGAAGGATTGAATGAAATATATACTGTCTCGAATTTAATTTTTGAGTTAAAAAGCT-TAAATATTATT Diprion ATAATAATTTGTCCTCTAATTAAGGACTAGAATGAATGATTTGACGAAGTTAAAACTTTCTCGAAATTAATTTTTAAGTTAAAAAGCT-TAAATAATATT Phylacteophaga ATAATAATTAGTCTATTAATTGTAGGCTGGTATGAAAGATTGAACGAGGTAAATACTGTCTCGAATTTAATTTTTGAGTTAAAAAGCT-TAAATTTTATT Onycholyda ATAATCATTAGTCTTTTAATTAAGGTCTAGAATGAAGGATTGAACGAAGTAAAATCTGTCTCAAATTTAATATTTAAGTCAAAAAGCT-TAAATAAATTT Cephus ATAATCATTAGTTTTTTAATTGAAAGCTTGTATGAATGATTGAATAAAATATTTATTGTCTCAAATTTAATTTTTAAATTAAAAAGTT-TAAATTTTATT Sirex ATAATCAATTGTTTTTTAATTTAAATCTAGAATGAATGGTTTAACGAAATATTAACTGTCTCGAATTTAAATTTTTAGTAAAAATGCTGAAAATATATTT Orussus ATAATCAATAGTTCTTTAATTGGGAACTTGTATGAAGGATTGAACGAGATAGTAACTTTCTAGAATTTAATTTTTAGATTAAAAAGTC-TAAATTTAATT Vespidae ATAATCAATAGTTTTTTAATTGGAAACTGGAATGAAGGAGTGGATGAAATATAGATTGTCTCGAATTTAAATTAAAAGTAAAAATGCT-TTTATATAGTT Asioxyela ???????????????????????????????????????????????????????????????????????????????????????????????????? Nigrimonticola ???????????????????????????????????????????????????????????????????????????????????????????????????? Xyelotoma ???????????????????????????????????????????????????????????????????????????????????????????????????? Undatoma ???????????????????????????????????????????????????????????????????????????????????????????????????? Dahuratoma ???????????????????????????????????????????????????????????????????????????????????????????????????? Cleistogaster ???????????????????????????????????????????????????????????????????????????????????????????????????? Ghilarella ???????????????????????????????????????????????????????????????????????????????????????????????????? Mesorussus ???????????????????????????????????????????????????????????????????????????????????????????????????? Prosyntexis ???????????????????????????????????????????????????????????????????????????????????????????????????? Pseudoxyelocerus ???????????????????????????????????????????????????????????????????????????????????????????????????? [EF1aF2, 210 sites] Raphidioptera GCTGAGCGTGAGCGTGGTATCACTATAGATATCGCACTGTGGAAATTTGAAACTGCAAAATACTATGTCACTATAATTGATGCTCCGGGCCATCGTGATTTCATTAAGAACATGATTACGGGTACGTCCCAGGCCGATTRTGCTGTCTTGATAGTTGCGGCCGGCACAGGAGAATTCGAAGCTGGTATTTCAAAGAACGGACAAACTCGT Xyela GCTGAGCGYGARCGTGGTATCACCATCGACATTGCTCTGTGGAAGTTCGAGACCGCCAGGTACTACGTCACCATCATCGACGCCCCTGGCCACAGGGATTTCATCAAGAACATGATCACTGGTACCTCTCAGGCCGATTGCGCCGTACTTATCGTCGCTGCCGGTACTGGAGAATTTGAAGCCGGTATCTCCAAGAACGGACAGACCCGC Runaria GCCGAGCGTGAGCGTGGTATCACCATCGATATCGCCCTGTGGAAATTCGAGACGGCCAAATACTACGTGACGATCATCGACGCCCCGGGGCACAGAGATTTCATCAAGAACATGATCACCGGAACTTCGCAGGCCGATTGCGCCGTACTGATCGTCGCCGCAGGTACCGGAGAGTTCGAGGCCGGTATCTCCAAGAACGGCCAGACCCGC Diprion GCTGAGCGTGAACGTGGTATCACAATTGATATCGCACTTTGGAAATTTGAAACTGCCAAGTACTATGTGACTATCATCGATGCGCCGGGACACAGAGATTTCATTAAGAACATGATCACTGGTACGTCTCAGGCCGACTGTGCAGTGTTGATTGTGGCTGCTGGTACGGGAGAATTCGAAGCTGGTATTTCCAAGAACGGTCAGACCCGT Phylacteophaga GCTGAGCGTGAACGTGGTATCACCATTGACATTGCTCTGTGGAAGTTTGAGACTGCCAAGTACTACGTTACCATCATTGACGCCCCTGGACACAGAGATTTCATTAAGAACATGATCACTGGAACGTCGCAAGCTGATTGTGCTGTGTTGATCGTCGCTGCTGGTACGGGAGAATTCGAGGCTGGTATCTCGAAAAATGGGCAGACTCGT Onycholyda GCTGAGCGTGANCGTGGTATCACCATCGACATTGCCCTGTGGAAGTTCGAGACCGCCAAGTACTATGTCACCATCATCGACGCCCCCGGTCACAGGGATTTCATCAAGAACATGATCACTGGTACTTCGCAGGCTGATTGTGCTGTGCTGATCGTCGCTGCCGGTACGGGAGAGTTCGAGGCTGGTATCTCGAAGAACGGACAGACCCGC Cephus GCCGAGCGTGAACGTGGTATCACCATTGACATTGCTCTCTGGAAATTCGAGACGGCCAAATATTACGTGACCATCATCGATGCTCCCGGACACAGAGATTTCATCAAGAACATGATCACGGGAACGAGTCAGGCCGATTGTGCTGTGCTAATTGTGGCAGCTGGTACTGGAGAGTTCGAGGCGGGTATCTCGAAGAACGGACAGACCCGT Sirex GCTGAGCGCGAACGTGGTATCACCATTGACATTGCCCTGTGGAAATTCGAAACGGCAAAATATTACGTTACTATTATTGATGCTCCCGGACACAGAGATTTCATCAAGAACATGATCACTGGAACTTCGCAAGCCGATTGTGCAGTTCTGATTGTCGCGGCTGGTACCGGAGAATTTGAGGCCGGTATTTCGAAAAATGGACAGACTCGT Orussus GCTGAGCGYGARCGTGGTATCACTATTGACATTGCCTTGTGGAAGTTTGAAACGGCTAAATACTATGTCACTATTATCGATGCGCCTGGACACAGAGACTTTATTAAAAACATGATTACTGGAACCTCGCAAGCTGACTGCGCTGTACTCATTGTCGCCGCAGGTACCGGCGAGTTTGAAGCCGGTATCTCCAAGAATGGACAGACTCGT Vespidae GCTGAGCGCGAGCGTGGTATCACCATTGACATTGCTCTTTGGAAATTCGAAACTTCTAAATACTACGTCACAATTATTGACGCACCTGGACACAGAGATTTCATCAAGAACATGATCACTGGAACCTCGCAAGCAGATTGCGCTGTGCTGATCGTAGCTGCTGGTACAGGAGAATTTGAGGCTGGTATTTCTAAGAACGGACAAACGCGT Asioxyela ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Nigrimonticola ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Xyelotoma ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Undatoma ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Dahuratoma ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Cleistogaster ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Ghilarella ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Mesorussus ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Prosyntexis ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Pseudoxyelocerus ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ ; End; [ Begin mrbayes; set autoclose=yes nowarnings=yes; [3.2 Data partitions] charset MV = 1-200; charset 16S = 201-300; charset Ef1a = 301-510; charset Ef1a12 = 301-510\3 302-510\3; charset Ef1a3 = 303-510\3; partition four = 4: MV, 16S, Ef1a12, Ef1a3; set partition = four; [3.3 Substitution models] exclude 7 31 61 83 107 121 122 133 182 183 198; lset applyto = (1) coding = variable rates = gamma; ctype ordered: 20 23 27 30 36 41 42 44 46 48 59 65 75 78 79 89 99 112 117 134 146 157 159 171 185 191 193 196; lset applyto = (2,3,4) nst = 6 rates = gamma; unlink statefreq = (all) revmat = (all) shape = (all); prset applyto = (all) ratepr = variable; [3.4 Relaxed clock models] prset clockratepr = lognorm(-7,0.6); [prset clockvarpr = tk02;] [prset tk02varpr = exp(1);] prset clockvarpr = igr; prset igrvarpr = exp(10); outgroup Raphidioptera; taxset fossils = Asioxyela Nigrimonticola Xyelotoma Undatoma Dahuratoma Cleistogaster Ghilarella Mesorussus Prosyntexis Pseudoxyelocerus; constraint root = 1-.; constraint HymenFossil = 2-.; constraint Hymenoptera = 2-10; constraint Holometabola = 1-10; constraint Tenthredinidae = 3-5; constraint CepSirOruApo = 7-10; [ [3.5 Total-evidence dating] calibrate Asioxyela = unif(228,242) Nigrimonticola = unif(152,163) Xyelotoma = unif(152,163) Undatoma = unif(145,152) Dahuratoma = fixed(134) Cleistogaster = unif(168,191) Ghilarella = unif(113,125) Mesorussus = unif(94,100) Prosyntexis = unif(80,86) Pseudoxyelocerus = fixed(182); prset nodeagepr = calibrated; prset brlenspr = clock:fossilization; prset samplestrat = diversity; prset sampleprob = 0.0001; prset speciationpr = exp(10); prset extinctionpr = beta(1,1); prset fossilizationpr = beta(1,1); prset treeagepr = offsetexp(300,390); prset topologypr = constraint(HymenFossil); mcmcp nrun = 2 nchain = 4 ngen = 500000 samplefr = 100; mcmcp filename = hym.te printfr = 1000 diagnfr = 5000; mcmc; sump; sumt; delete fossils; sumt output = hym.rf; ] [ [3.6 Node dating] delete fossils; exclude 24 130 168; [calibrate root = offsetexp(300,390) Tenthredinidae = offsetgamma(100,150,25) CepSirOruApo = truncatednormal(140,175,25);] calibrate Tenthredinidae = offsetgamma(100,150,25) CepSirOruApo = truncatednormal(140,175,25); prset nodeagepr = calibrated; [prset brlenspr = clock:fossilization;] [prset fossilizationpr = fixed(0);] [prset sampleprob = 0.0001;] [prset samplestrat = diversity;] prset brlenspr = clock:birthdeath; prset samplestrat = diversity; prset sampleprob = 0.0001; prset speciationpr = exp(10); prset extinctionpr = beta(1,1); prset treeagepr = offsetexp(300,390); prset topologypr = constraint(Hymenoptera,Tenthredinidae,CepSirOruApo); mcmcp nrun = 2 nchain = 4 ngen = 500000 samplefr = 100; mcmcp filename = hym.nd printfr = 1000 diagnfr = 5000; mcmcp startp = reset startt = random; mcmc; sump; sumt; ] [ [3.7 Non-clock analysis] delete fossils; prset brlenspr = uncons:gammadir(1,1,1,1); prset topologypr = uniform; mcmcp nrun = 2 nchain = 4 ngen = 500000 samplefr = 100; mcmcp filename = hym.un printfr = 1000 diagnfr = 5000; mcmc; sump; sumt; ] End; ] NBISweden-MrBayes-ecfd55c/examples/hymfossil.nex000066400000000000000000024235661526212227600220060ustar00rootroot00000000000000#NEXUS [ Data from: Ronquist F, Klopfstein S, Vilhelmsen L, Schulmeister S, Murray DL, Rasnitsyn AP. 2012. A total-evidence approach to dating with fossils, applied to the early radiation of the Hymenoptera. Systematic Biology 61:973-999. ] [ Version: fre 3 jul 2020 17:03:07 Sign: JN ] Begin data; Dimensions ntax=114 nchar=5449; Format datatype=mixed(standard:1-353,DNA:354-5449) interleave=yes gap=- missing=?; Matrix [Morphology, Vilhelmsen] Orthoptera ?????????? ?????????? ?????????? ?????????? ?????????? ?????????? ?????????? ?????????? ?????????? ?????????? ?????????? ?????????? ?????????? ?????????? ?????????? ?????????? ?????????? ?????????? ?????????? ?????????? ?????????? ?????????? ?????????? ?????? Paraneoptera 0000001000 0000100-10 0000?110?0 ?1-230??00 00?--0-010 1100000??1 00000?0000 000000000? 1---0?0003 ?000?1-000 ?20010000? 0?00?0--00 ?0??010010 ?000100101 ?0101--0?0 ?0??0001?1 ?010-1-111 011--10101 111100-000 000-020110 000---1?-0 ?00??00100 ---------- ------ Chrysopidae 0000000000 0?00010010 0000?00000 01-110?100 00?010-000 ?000000??1 011000?0?? -2-00?000? 011000000? ?0-0?1-000 121000000? 0-00?0--00 0010000100 ?000?00000 ?100102000 00??010100 00?0?????? 011--100?? ???1000000 010--?---- ---1--0?0- -----????? 0014010002 -0001- Raphidioptera 0000000000 000011?001 ?000?00000 01-210?100 000000-000 10000-0??1 011000???1 -2-00?100? 00--000000 -0-0?1-000 ?2?0000??? 0-00?0--00 001?010100 0000100031 01001010?0 ???0011100 000000???? 011--100?? ??01000000 010-02000- --01--101- ---??00000 0004001002 -0001- Coleo_Polyphaga 0010011000 0??011001? ?000?00010 ?1-11???00 00?-----00 111100-001 0000021001 00000?0000 1---1000-3 ?0?0?1-000 0200100000 0?0000--00 0010000010 0100100001 ?001102000 00000001-- ?--------- -11--110?? 000000000? 0????????? ?????????? ??????010? ?????????? ?????? Coleo_Adephaga ?????????? ?????????? ?????????? ?????????? ?????????? ?????????? ?????????? ?????????? ?????????? ?????????? ?????????? ?????????? ?????????? ?????????? ?????????? ?????????? ?????????? ?????????? ?????????? ?????????? ?????????? ?????????? ?????????? ?????? Lepid_Micropterigidae 0000001100 0010000-10 0000?11010 1000220000 00?000-00? 0000000001 01?001???? -00000000? 0?????000? ?0???1-001 ?2?0000??? 0?01?0--00 10??111100 1000?00101 00?01--001 0??0010-00 0??0?????? ?11--111?? ???1000000 02-------- ---------- -----00100 001401?010 0?001- Lepid_Papilionidae ?????????? ?????????? ?????????? ?????????? ?????????? ?????????? ?????????? ?????????? ?????????? ?????????? ?????????? ?????????? ?????????? ?????????? ?????????? ?????????? ?????????? ?????????? ?????????? ?????????? ?????????? ?????????? ?????????? ?????? Mecoptera 0000-00000 0010010011 0000?01000 11-3200?00 011000-0?0 0000101??1 0100--1101 00000?1000 00--000003 ?0?0?1-000 ?21000010? 0-00?0--00 10000110?0 ?000100001 0100102001 000?010100 0??0?????? 011--100?? ??01010000 02-------- ---------- -----00000 0004000010 00001- Xyela 0010000100 0010000-00 0010111010 0001010000 0000010110 0001100010 0200000000 0100010000 0100011000 0000000100 0002100000 1000010000 00?0010000 0100100000 0000000100 0011100010 0000000000 1000000000 0000010200 001010000- -000000010 0000011011 0111000011 00001- Macroxyela 0010000100 0010000-00 0010011010 01-0010000 00000101?? ?101101010 0200000000 010001000? 0100010000 ?000?00100 0002100??? 1000010000 ?00?010000 ?100000000 00000001?? ????100000 0000000000 1000000000 00000101?0 001010000- --00000000 0000101011 0101000011 000000 Runaria 0010000010 0100000-00 002-001000 00010?0010 1110020110 1100100020 0210100100 110001000? 0???0??0?3 2102110??? 000?1??110 101?11?010 0000000000 1100000021 110?1??210 100110?120 1-11001000 1100000100 0000010210 00????0010 0000????0? ?00??01001 ?????????? ?????? Paremphytus 0010000010 0??0000-00 002-?0100? ?0010??010 11?002?1?? ?100100020 02101001?0 01000?000? 0100?10003 ?102?1?101 ?002100??? 101011?01? ?000000000 ?100000021 ?1001122?? ????101120 1-11001000 1100000100 0000010210 00????0010 000?????0? ??0??01001 ?????????? ?????? Blasticotoma 0010000010 0?00000-?? ?010?01000 ?00101?010 111002?1?? ?100100020 021010???? 010001000? 0100010003 ?102?1?001 0002100??? 101??1001? ?00?000000 ?10000?021 ?1?01122?? ????101120 1?11001000 1100000100 0000010210 0010100010 0000000?00 0100001001 0101000012 -00001 Tenthredo 0010000000 0000000-00 0000001000 ?001000010 11100202?? ?100111031 02102001?1 0100000001 0100010001 ?102111001 0002100110 1011?10010 ?00?000000 ?100011001 ?1001012?? ????111121 0-01001000 11000?0100 00000101?0 0010101010 1000110?00 ?10??11001 0102000010 10001- Aglaostigma 001000?0?0 0000000-00 ?00000100? ?001000010 12?001?2?? ??001001-1 0???2????? 010000000? ?????????1 2102111??? ????1????? 1????100?? ???????000 ??00011021 ???0101??? ???????120 0-010?1000 1100000100 00000101?0 0010?01010 1010110100 ?10??11001 0?02000?10 10???? Dolerus 0010000000 0?00000-00 0000001000 ?001000010 11100202?? ?100111031 02102001?0 0100000001 0100010001 ?102?11001 0002100110 1011110011 ?000000000 ?100000001 ?1?01012?? ????111120 0-0100-100 1100000100 00000101?0 0010101010 1100110?00 ?10??11001 0102000010 10001- Selandria 001000?0?0 0000000-00 ?00000100? ?001000010 120012?2?? ??00100031 021020?1?0 0100000001 0???0????3 ?102?11000 ?01?1??1?? 1?1??11011 ????10?000 ??00011001 ???0101??? ????111120 0-01001000 1100000100 00000101?? 00???????? ?????????? ?????11001 ???2????10 ?????? Strongylogaster 00100????? ?000000-00 0000001??? ?00?00?010 110002?2?? ??0010?031 0???2??1?? 0100000001 010001?003 ?102?1?00? ????1????? 1????100?? ??0????000 ??00?1?001 ???0101??? ???????120 0-01001000 1100000100 00000101?0 0010?01?10 ????????00 ?????11001 0?02000?10 10???? Monophadnoides 00100???0? ?000000-?0 ?000001??? ?00?00?010 111002?2?? ??00100031 0???2????? 010000000? ?????????3 ?121?1???? ????1????? 1????100?? ??0????000 ??0001?001 ???0101??? ???????120 0-01001000 310?010101 10000101?? 00???????? ?????????? ?????11001 ???2??0?10 10???? Metallus 00100???0? ?000000-?0 0000001??? ?00?0??010 110012?2?? ??00100031 0???2????? 000000000? ?????????3 ?102?11??? ????1????? 1????100?? ???????000 ??00010001 ???0100??? ???????121 0-11001000 310??10101 10000101?? 00???????? ?????????? ?????11001 ???2??0?10 00???? Athalia 0010000000 0010000-01 0000?01000 0001000010 1100020211 1000100031 0210210110 00000?000? 00--0?0001 0102111001 0212100??? 1011110011 0000000000 0100100001 1100101201 0011111121 0-01001000 1100000100 00000101?0 0010101010 1000110100 ?10??11001 0102000010 10001- Taxonus 00100???0? ?000000-00 ?000001??? ?00100?010 111002?2?? ??00111031 02?02001?? 0100000001 0???0????1 ?102?1100? ?00?1????? 1?1??1001? ??0?00?000 ??000??001 ???0101??? ????111120 0-01001000 110?010100 00000101?? 00???????? ?????????? ?????11001 0?02000010 10???? Hoplocampa 00100???0? ?000000-?1 ?000001??? ?00?0??010 110002?2?? ??0010?031 0???2????? 0?0???0001 ?????????3 ?101?1???? ????1????? 1????110?? ??0????000 ??0000104- ???0101??? ???????120 0-11001000 2100000100 00000101?0 00???????? ?????????0 ?????11001 0?03000010 1????? Nematinus 00100???0? ?000000-?1 ?000001??? ?00100?010 110002?2?? ??00100031 0???2????? 0?0000000? ?????????1 ?101?1???? ????1????? 1????110?? ??0????000 ??000?0001 ???0101??? ???????120 0-1101-000 3100000100 00000101?0 00???????? ?????????? ?????11001 0??3000?10 1????? Nematus 0010000000 0??0000-01 0000001000 ?00100?010 11100202?? ?000101031 02102001?0 010000000? 00--0?0001 0101?11001 0002100??? 1011111011 ?00?000000 ?200001001 ?1001012?? ????111120 0-1101-{0,1}00 3100000100 00000101?0 0010101010 1100110?01 ?00??11001 01031?0010 10001- Cladius 00100???0? ?010000-?1 ?000001??? ?00100?010 110012?2?? ??00101031 0???2????? 000000000? ?????????3 ?101?11??? ????1????? 1????110?? ??0????000 ??000?104- ???0101??? ???????120 0-1101-000 2100010100 00000101?0 00????1?10 ?????????0 ?????11001 ???3????10 1????? Monoctenus 0010000000 0??0000-00 010000100? ?00100?010 110002?2?? ?100100031 02102001?0 001000000? 0100010001 0101?11001 0002100??0 1011112111 ?00?000000 0100101000 ?1?01012?? ????111120 0-1101-000 21000?0100 00000101?0 001010100- -010010?00 ?01??11001 0104100010 1?001- Gilpinia 0010000000 0110000-00 0100001000 0001000010 1100020211 1100100031 0210200110 00100?0001 0100010002 0101111001 0002100110 1011112111 0000000000 0100110000 1100101211 1011111120 0-1101-000 11000?0100 00000101?0 001010100- -010010100 ?01??11001 0104100010 10001- Diprion 00100???0? ?010000-?0 ?100001??? ?00?00?010 110002?2?? ?100100030 0?102001?? 001000000? 010001?003 ?101?1100? ?0??1????? 10111121?? ?00?000000 ??00?0?000 ?1001012?? ?????11121 0-1101-000 1100000100 00000101?0 ?010?0100- -010010?00 ?01??11001 ???41?0?10 1????? Cimbicinae 0010100000 0??0001-00 0001001000 ?00100?010 13000202?? ?1011001-1 1210210110 001000000? 0101030003 ?10111?001 0012100??0 1210112101 0101011010 1100101-3- 1110101211 111?111121 0-11001000 1100010100 0000010200 0010101010 0100110100 ?01??11001 0105000010 11001- Abia 0010000000 0010100-01 0001?01000 0001000010 1300020211 11011001-1 1210210110 00100?000? 0101030003 210111?001 2012100??? 1110012101 ?101011010 1100101-3- 1110101211 1111111121 0-11001000 2100010100 0000010200 0010101010 1100110100 ?01??11001 0105000010 11001- Corynis 00100000?0 0??0010002 0001?01000 ?0010??010 11000102?? ?100100021 0210210??? 00000?100? 1---0?0002 0102?11001 ?212100??? 1010111100 ?00?011010 ?200111-3- ?1101012?? ????111121 0-11001000 21000?0100 0000010200 0010101010 1100110?00 ?00??11001 ???5??0??0 ?1???? Arge 0010000000 0000000-00 002-001000 0001000010 1200021211 1100110031 0210210111 0000000001 0100010003 2101101001 0212100110 1211112111 0001000000 111010103- 1110101211 1101111020 0-0101-000 2100010100 00000101?0 0010101010 11010-0001 0000011001 0106000010 00001- Sterictiphora 0010000000 0??0000-00 002-?01000 ?00100?010 12000212?? ?100110031 0210200111 000000000? 0100010003 ?101?01001 ?212100??? 1211?1211? ?00?000010 ??10?0103- ?1101012?? ????111121 0-1101-110 21000?0100 00000101?0 0010101010 11010-0?01 ?00??11001 0106?00010 0??00? 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Brachysyntexis 00???????? ?????????? ?????????? ????????00 ?????????? ?????????? ?????????? ??????000? ?????????? ?????????? ?????????? ?????10??? ?????????0 ?????????? ?????????? ????????20 000100-100 1????????0 000?0????? ?0???????? ?????????? ?????????? ?????????? ?????? Symphytopterus 00??0????? ????1????? ?0000(0,1)???? ????????00 ?????????? ?????????? ?????????? ???????20? ?????????? ?????????? ?????????? 1????0--?? ????????10 ???2????3? ?????????? ???????121 0101200000 2?????01(0,1)(0,1) 10010?0??? ?0??1????? ???0?????? ?????????? ?????????? ?????? Eoxyela 0????????? ????0????? ?-??????????????????????????????????????????????????????000????????????????00????????0?????????11???????????00????????????????????????????0000000000001?????0???00000?01???0?????0?????0????0??????????????????????????? Liadoxyela 0????????? ?????????? ?010???????????????????????????????0????????????????????000????????????????00????????????????????????????????0????????????????????????????00000010000?1?????????????0?01???????????????????????????0?(0,1)?????????????????? Abrotoxyela 0????????? ????0????? ?010?(0,1)????????????0?????????????????????????????????????000??????????????????????????0???????????????????????0????????????????????????????0000000000001?????0?00000?0??1???0?????????????????????????(0,1)?1???????????????? Pseudoxyelocerus ?????????? ?????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????1010010010001????????????????????????????????????????????????????????????????? Palaeathalia 0????????? ?????????? ??00?????????????????????????????????????????????????????????????????????????????????????????????????????????0????????????????????????????201-1?001?????????0100?00?0??????????????????????????????????????????????????? Ferganolyda ?????????? ?????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????0000010000001????????????????????????????????????????????????????????????????? PamphiliidaeUndesc ?????????? ?????1?0?? ?????2?????????????????????????????????????????????????????????????????????00????????0???????????????????????0????????????????????????????000001000???1?????????????0??(1,2)???0???????????????????????????????????????????? Rudisiricius 00???????? ????0????? ?0?0?2????????????0?????????????????????????????????????0??????????????????01????????0???????????????????????0????????????????????????????210?010000001?????0100000?0??(1,2)?????????????????????????????(0,1)?????????????????? Sogutia ?????????? ?????????? ????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????0001000???1?????????????0??????0???????????????????????????????????????????? Xyelula 00???????? ?????????? ??10?(0,1)?????????????????????????????1????????????????????000????????????????????????????????????10????????????0????????????????????????????2100000000001?????????????0??2???0?????0?????0???????????????????????????????? Brigittepterus ?????????? ?????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????2100012000001?????????????0??????0???????????????????????????????????????????? Grimmaratavites ?????????? ?????????? ??????????????????0?????????????????????????????????????0?2???????(0,1)????????????????????????????11????????????0????????0???????????????????2100010000001?????????????(1,2)?02???0???0?0?????0????0??????????????????????????? [Morphology, new characters, Schulmeister] Orthoptera ???? ?????????? ??????? ? ?? ?????? Paraneoptera ???? ?????????? ??????? ? ?? ?????? Chrysopidae ?000 1????00??? ?00??0- - -? ??00?? Raphidioptera 0000 1????00??? 1??000- - -0 -0???? Coleo_Polyphaga 2000 0???????0? ???000- - -? ?0???? Coleo_Adephaga ???? ?????????? ??????? ? ?? ?????? Lepid_Micropterigidae 0010 1????00??? ?00??0- - -? ?00??? Lepid_Papilionidae ???? ?????????? ??????? ? ?? ?????? Mecoptera 0000 1????0000? 100000- - -0 0000?? Xyela 2010 2000000100 0000010 0 00 000000 Macroxyela 2000 2000000000 0000010 0 00 000000 Runaria 2001 2001001101 0??0020 0 0? 0000?? Paremphytus 200? ?001001101 ??????? ? ?? ??00?? Blasticotoma 2001 2001001101 0??002? ? ?? ??000- Tenthredo 2001 200010??00 1000021 1 00 000001 Aglaostigma 2001 2000101101 1000021 0 00 000001 Dolerus 2001 200000??00 1000021 0 00 000011 Selandria 2001 200020??01 1??0021 1 00 000001 Strongylogaster 2001 200020??00 1000021 1 00 000001 Monophadnoides 2001 2000-01101 1000021 1 00 0000?1 Metallus 2001 2000-01101 100002? ? ?0 010001 Athalia 2001 2000001101 1000020 0 00 0000?1 Taxonus 2001 2000001100 1000021 0 00 000001 Hoplocampa 2001 2000-01101 1000021 1 00 000002 Nematinus 2001 2000-01101 000002? ? ?0 000002 Nematus 2001 2000-01101 0000021 1 00 0000?2 Cladius 2001 2000-01101 0000021 1 00 000002 Monoctenus 0001 2000-01101 1000021 0 00 000001 Gilpinia 0001 2000101101 1000021 0 00 0000?1 Diprion 0001 2000101101 1000021 0 00 0000?1 Cimbicinae 2001 2001101101 1000021 0 0? 0000?1 Abia 2001 2001-01101 1000021{0,1}00 000001 Corynis 2001 2001-01101 1000021 0 00 0000?1 Arge 2001 2010-01101 0000020 0 00 000003 Sterictiphora 2001 2100-01101 0000020 0 00 000002 Perga 200- 20?1-???1- 0??0020 0 0? 0?00?? Phylacteophaga 200- 2000-0111- 1??0020 0 00 0000?? Lophyrotoma ?001 2011-0111- 0??0020 0 00 0000?? Acordulecera 2001 2000-0111- 100002? ? ?0 0000?2 Decameria ?00- 2010-0111- 0??0020 0 00 0000?? Neurotoma 0001 2001001101 100001? ? ?? 0?000- Onycholyda 0001 2001001101 1000010 0 00 0000?- Pamphilius 0001 2001001101 0000010 0 0? 0?000- Cephalcia 0001 2000001101 0000010 0 00 00000- Acantholyda 0001 2000001101 0??0010 0 00 00000- Megalodontesc 0001 2001001101 0000010 0 00 00000- Megalodontessk 0001 2001001101 0??0010 0 00 ?0000- Cephus 0001 2000111101 0000021 0 00 01000- Calameuta 0001 2000111100 0000021 0 00 0100?- Hartigia 0001 2000111100 0000020 0 00 01000- Syntexis 0001 2100001101 000000- - -? 01101- Sirex 0000 1111001101 ?001010 0 1? 00201- Xeris 0000 111100111- 1??110- - -0 00201- Urocerus 0000 1111001111 ?001010 0 10 0020?- Tremex 1000 111100111- 1001110 0 1? 00201- Xiphydria 0000 2001001101 1000020 0 00 00000- Orussus 2100 21-0-0111- 111000- - -0 00010- StephanidaeA 0001 2100-0111- 10?200- - -? 1000?- StephanidaeB ???? ?????????? ??????? ? ?? ?????? Megalyra 1000 2?00-2111- 1??100- - -? 0????- Trigonalidae 0001 2000-2111- ???002? ? ?? 0????- Chalcidoidea ???? ?????????? ??????? ? ?? ?????? Evanioidea 1001 2000-0111- 1??000- - -? 0????- Ichneumonidae 0000 2000-0111- 100000- - -0 1000?- Cynipoidea 1000 2001-0111- ???000- - -0 0?00?- ApoideaA 1001 2000-0111- 1000020 0 01 000?0- ApoideaB ???? ?????????? ??????? ? ?? ?????? ApoideaC ???? ?????????? ??????? ? ?? ?????? Vespidae 1001 2000-0111- 100000- - -1 100?0- Triassoxyela ???? ?0000????? ??????? ? ?? ?????? Asioxyela ???? ?0000??1?? ??????? ? ?? ?????? Nigrimonticola 000? ?00??????? ??????? ? ?? ?00??? Gigantoxyelinae 100? ?000000000 ??????? ? ?? ?00??? Spathoxyela 100? ?000000100 ??????? ? ?? ?00??? Xyela_mesozoica 200? ?0000??1?? ??????? ? ?? ?00??? Angaridyela 000? ?0000001?? ??????? ? ?? ?00??? Xyelotoma 20?? ????00?1?? ??????? ? ?? ?00??? Undatoma 200? ?0010011?? ??????? ? ?? ?00??? Dahuratoma 20?? ?000001101 ??????? ? ?? ?00??? Mesolyda 00?? ?000001101 ??????? ? ?? ?00??? Turgidontes (0,1)??? ?00?0????? ??????? ? ?? ?0???? Aulidontes ???? ?00?0?1101 ??????? ? ?? ?????? Protosirex 00?? ?001001001 ??????? ? ?? ?????? Aulisca 00?? ?0000????? ????0?? ? ?? ?00??? Anaxyela 0??? ?0000?1101 ??????? ? ?? ?????? Syntexyela 00?? ?000001101 ??????? ? ?? ?0???? Karatavites 00?? ?000001101 ??????? ? ?? ?????? Stephanogaster 00?? ?000-0111- ??????? ? ?? ?00??? Leptephialtites 00?? ?000-?111- ??????? ? ?? ?00??? Cleistogaster 00?? ?000-0111- ??????? ? ?? ?00??? Sepulca ???? ?00?001100 ??????? ? ?? ?0???? Onochoius 20?? ?00?0?1??? ??????? ? ?? ?00??? Ghilarella ???? ?000011100 ??????? ? ?? ?00??? Paroryssus ???? ?000-011?? ??????? ? ?? ?????? Praeoryssus 0??? ?00?-??1?? ??????? ? ?? ??0??? Mesorussus 200? ?????????? ??????? ? ?? ?????? Trematothorax 000? ?000001101 ???00?? ? ?? ?0???? Thoracotrema ??0? ?0000?1101 ??????? ? ?? ?0???? Prosyntexis 0?0? ?000101101 ??????? ? ?? ?????? Kulbastavia ???? ?000001101 ??????? ? ?? ?00??? Brachysyntexis 00?? ?000001101 ??????? ? ?? ?00??? Symphytopterus 00?? ?000-0111- ????0?? ? ?? ?00??? Eoxyela ???? ?000000?00 0?????? ? ?? ?000?? Liadoxyela 0??? ?0000????? ??????? ? ?? ?????? Abrotoxyela ?0?? ?00000??00 ??0?0?? ? ?? ?000?? Pseudoxyelocerus ???? ?0000????? ??????? ? ?? ?????? Palaeathalia 20?? ?????0??01 ??????? ? ?? ??0??? Ferganolyda ???? ?0000????? ??????? ? ?? ?????? PamphiliidaeUndesc ???? ????0????? 0?????? ? ?? ?000?? Rudisiricius 0??? 2000001101 0?????? ? ?? ?0???? Sogutia ???? ?00?0????? ??????? ? ?? ?????? Xyelula 20?? ?0000????? ??????? ? ?? ?000?? Brigittepterus ???? ?0000????? ??????? ? ?? ?????? Grimmaratavites ???? 00000????? ??????? ? ?? ??00?? [Morphology, male terminalia, Schulmeister] Orthoptera ???? ?????????? ?????????? ?????????? ?????? ? ??? ?????????? ? ? ???????? ?????????? ?? ? ??????? ??? Paraneoptera ???? ----?????? ?????????1 ???0????2? ?????? ? ??? ?????????? ? ? ??0????? ???????000 ?? ? ??????? ??? Chrysopidae ???? ?????????? ?????????? ?????????? ?????? ? ??? ?????????? ? ? ???????? ?????????? ?? ? ??????? ??? Raphidioptera ???? ----?????? ?????????1 ???0????2? ?????? ? ??? ???????0?? ? ? ??0????? ?????????? ?? ? ??????? ??? Coleo_Polyphaga ???? ?????????? ?????????? ???00????? ?????? ? ??? ???????0?0 ? ? ??0????? ?????????? ?? 0 0?0???? 0?? Coleo_Adephaga ???? ?????????? ?????????? ?????????? ?????? ? ??? ?????????? ? ? ???????? ?????????? ?? ? ??????? ??? Lepid_Micropterigidae ???? ----?????? ?????????1 ???0????2? ?????? ? ??? ?????????? ? ? ??0????? ?????????? ?? ? ??????? ??? Lepid_Papilionidae ???? ?????????? ?????????? ?????????? ?????? ? ??? ?????????? ? ? ???????? ?????????? ?? ? ??????? ??? Mecoptera ???? ----?????? ?????????1 ???0????2? ?????? ? ??? ???????0?0 ? ? ??0????? ???????0?? ?? 0 0?0???0 010 Xyela 0-00 ?000000000 2000000100 0001010000 000100 0 101 0000000100 0 1 00100000 0000000000 00 2 0000010 000 Macroxyela 0-00 0000000000 2000000100 0000010000 000100 0 101 0000000100 0 1 00100000 0000000000 00 1 0000010 000 Runaria 0-00 0000000201 4010020211 0000010000 000101 0 00? 0-?0001010 0 1 00100010 0000000000 00 0 0020010 000 Paremphytus 0-00 0000000211 1010000211 0000000000 000101 0 000 0-10001010 0 1 10100010 00010000?0 0? 0 0020010 000 Blasticotoma ???? ?????????? ?????????1 ???0000000 000100 ? 000 0-?0001010 0 ? ??1?001? ?000000000 00 0 0?0???0 000 Tenthredo 2000 2001000101 3000000211 0001000000 000300 0 101 0-?1000000 0 0 00100010 0000000020 00 2 0001020 100 Aglaostigma 2000 2001000001 3010000211 0001000000 000300 0 211 0-11000000 0 2 ?0100010 0000000010 10 1 0001010 000 Dolerus 2000 --11100101 3010000--1 0001000002 000310 0 101 0-?1000000 0 0 10100010 0000000000 00 2 0001020 000 Selandria 2000 2001200101 1010000211 0001000000 000300 0 100 0-11000000 0 2 00100010 0000000040 00 1 0001020 000 Strongylogaster 2000 2000000101 1010000211 0001000000 000300 0 110 0-11000000 1 2 00100010 0000000030 00 1 0001020 000 Monophadnoides 2000 --01100001 3000000211 0001000001 000300 0 111 0-11000000 0 1 00100010 0000000000 2- 2 0001010 000 Metallus 2000 --11200101 1010000--1 0001000011 000300 0 111 0-110000?0 0 1 10100010 0000000000 2- 1 0001013 000 Athalia 2000 2000000101 1000000--1 0001000000 000200 0 201 0-11000000 0 0 00100010 0000000000 00 3 0101010 000 Taxonus 2000 2001000101 2000000211 0001000000 000300 1 101 0-11000000 0 2 00100010 0000000000 00 2 0001020 000 Hoplocampa 2000 2100000101 1010000211 0001020000 000300 0 111 0-11000010 0 1 10100010 0000000000 00 2 0001020 000 Nematinus 2000 2000000101 1010000211 0001000000 000300 0 110 0-11000011 0 2 00100010 0000000000 00 1 0001021 000 Nematus 2000 2000000101 0010000211 0001000000 000300{0,1}110 0-11000001 0{1,2}10100010 0000000000 00{1,2}0001024 000 Cladius 2000 2000200201 1000000--1 0001000000 000200 1 101 0-?1000010 0 1 10100010 0000000030 00 1 0001011 000 Monoctenus 2010 2011000101 1010000--1 0001000000 000300 0 11? 0-11000000 1 2 00110010 0000000000 00 3 0001020 000 Gilpinia 2010 2001000001 1010032--1 0001000100 000300 1 111 0-11000000 1 3 10110010 0000000020 00 3 0200010 000 Diprion 2110 2001000101 101003?--1 0001000100 000300 0 11? 0-11000000 1 0 10110010 0000000020 00 3 0100010 ??0 Cimbicinae 2110 2001100101 3000002201 0001000000 000200 0 101 0-10000000 0 2 00101010 0000000030 00 2 0000030 000 Abia 2110 2001000101 3000000--1 0001000000 000200 0 101 0-10000000 0 2 10111010 0000000000 00{1,3}0000030 000 Corynis 2000 2001200101 3000020201 0001000000 000100 0 101 0-?0000100 0 2 00111010 ?000000000 00 3 0001032 ??0 Arge 0-00 --11000001 3110010201 0001000011 100100 0 001 0-10100000 0 2 11100010 0000100130 ?? 0 0301010 000 Sterictiphora 0-00 --01000011 3000010201 0001010001 101100 0 001 0-10100000 0 0 01100010 0000100130 01 0 0001020 000 Perga ??00 ----00000? ?210??0??1 00010-002- --0100 0 101 0-?0000000 0 ? ?110001? ?000100??? ?? ? ?3??01? ??? Phylacteophaga ??00 ----0000?1 1010010--1 00010-002- --0110 0 ?0? 0-10000000 ? 2 0110001? ?000000000 2- 0 0331010 001 Lophyrotoma 0-00 ----200001 1200010201 00010-002- --0100 0 101 0-10000000 0 3 01101010 0000100000 01 0 0301010 100 Acordulecera 1-00 ----000001 3200010201 00010-002- --0100 0 100 0-10000000 0 2 01100010 0000100000 ?? 0 000?010 021 Decameria 1-00 ----000001 3200010201 00010-002- --0100 0 001 0-10000000 0 2 11100010 0000100000 01 0 0301010 001 Neurotoma 1-00 ?10?000?00 3020??0100 0000000000 000000 ? ?00 01?0011000 0 ? ?010001? ?000000000 00 0 0200010 000 Onycholyda 1-00 0100000000 3000000100 0000010000 000000 0 101 0110011000 0 3 00100010 0000000000 00 0 0200010 000 Pamphilius 1-00 ?100000?00 302000?1?0 0000010000 000000 ? ?00 01?0011000 0 ? ?010011? ?00000000? 0? 0 0200010 000 Cephalcia 2-00 0100000100 3020020100 0000010000 000000 0 101 0110011000 0 3 00100110 0000000000 00 0 0200010 000 Acantholyda ??0? ??0??00?00 30?00?01?0 0000010000 000000 ? ?01 01?00?1000 0 ? ?0100110 0000000000 ?0 0 020?01? 000 Megalodontesc 1-00 0100000011 3021100100 0000000010 000000 1 101 0110010000 0 4 10110010 0000000040 00 1 0201021 000 Megalodontessk ??00 01??000211 3021100??0 0000000010 000000 1 101 01?0010000 0 4 10110010 ?000000??? ?? 1 0201021 ??0 Cephus 1-00 2000000201 1020111--- 0010000000 000100 0 000 0-----1000 0 2 0-100110 0000010000 2- 0 0300010 000 Calameuta 1-00 2001000211 1020111--- 0010000000 000100 0 000 0-----1000 0 2 0-100110 0000010000 2- 0 0300000 000 Hartigia 1-00 1000100201 1020111--- 0010000000 000100 0 000 0-----0000 0 2 0-100110 0000010000 2- 0 030000? 000 Syntexis ???? 001100000? 3000?20--1 0000010000 000100 1 001 0-10001000 0 3 0010001? ?000?00??? ?? ? 0?0???? ??? Sirex 1-0? 00?0100000 30200?0000 00?0010000 001100 0 ?0? 01?000?000 0 ? ?-100?1? ?000?10?0? ?? ? ?3110?? 000 Xeris 1-00 0000100000 3020000000 0010010000 001100 0 10? 0110001000 0 1 0-100111 ?000010000 00 1 0311011 000 Urocerus 1-00 0000110000 3020020000 0010010000 001100 0 10? 01?0001000 0 1 0-100011 0000010000 00 1 0311010 000 Tremex 1-00 0?001000?0 3020000010 0000000000 001100 0 100 01?0001000 0 ? 1-120011 ?000?10??? ?? 1 0310011 001 Xiphydria 1-00 1000000001 1000010000 0000000000 010100 1 101 0110001000 0 2 00100010 0000000000 00 0 0020010 000 Orussus 1-01 100010000? ?000031--- 0000100000 000100 0 101 0-----1000 0 3 00100000 0011000030 ?0 0 0001112 200 StephanidaeA 1-20 1001100201 1000011--- 0000100000 010000 0 301 0-----1000 0 2 10101001 0001001001 ?? 1 0102000 020 StephanidaeB ???? ?????????? ?????????? ?????????? ?????? ? ??? ?????????? ? ? ???????? ?????????? ?? ? ??????? ??? Megalyra ???? ?????????? ??????1--- ?????00?0? 011100 ? ?0? 0-----?000 0 ? ??11001? ??00?00??? ?? 0 0102002 020 Trigonalidae 1-20 200000000? 3000??2--1 ?000020000 010100 1 ?00 0-1-000000 0 ? 1011001? ?00000000? ?? 0 0102010 210 Chalcidoidea ???? ?????????? ?????????? ?????????? ?????? ? ??? ?????????? ? ? ???????? ?????????? ?? ? ??????? ??? Evanioidea ???? ???0?????? ?0????1--- ??0?010000 010100 1 ?00 0-----?000 0 ? ?01?000? ?00000??0? ?? 0 0100010 200 Ichneumonidae 0-00 1000100211 3000001--- 0100100010 110100 0 000 0-----1000 0 2 00100010 0000000?00 2- 0 0031000 200 Cynipoidea ???? ?????????? ??????1--- ?????10000 000100 ? ?0? 0-----?000 0 ? 0?10011? ?000?00??? ?? 0 ?--1--2 001 ApoideaA 1-00 2000000201 1000131--- 0000021000 010000 0 300 0-----0000 1 ? 01100010 0100001001 20 0 0201011 211 ApoideaB ???? ?????????? ?????????? ?????????? ?????? ? ??? ?????????? ? ? ???????? ?????????? ?? ? ??????? ??? ApoideaC ???? ?????????? ?????????? ?????????? ?????? ? ??? ?????????? ? ? ???????? ?????????? ?? ? ??????? ??? Vespidae 1-00 1010000101 1000031--- 1000011010 010000 1 201 1-----1000 0 0 0?110001 0000001050 20 1 1102011 211 Triassoxyela ???? ?????????? ?????????? ?????????? ?????? ? ??? ?????????? ? ? ???????? ?????????? ?? ? ??????? ??? Asioxyela ???? ?????????? ?????????? ?????????? ?????? ? ??? ?????????? ? ? ???????? ?????????? ?? ? ??????? ??? Nigrimonticola ???? ?????????? ?????????? ?????????? ?????? ? ??? ?????????? ? ? ???????? ?????????? ?? ? ??????? ??? Gigantoxyelinae ???? ?????????? ?????????? ?????????? ?????? ? ??? ?????????? ? ? ???????? ?????????? ?? ? ??????? ??? Spathoxyela ???? ?????????? ?????????? ?????????? ?????? ? ??? ?????????? ? ? ???????? ?????????? ?? ? ??????? ??? Xyela_mesozoica ???? ?????????? ?????????? ?????????? ?????? ? ??? ?????????? ? ? ???????? ?????????? ?? ? ??????? ??? Angaridyela ???? ?????????? ?????????? ?????????? ?????? ? ??? ?????????? ? ? ???????? ?????????? ?? ? ??????? ??? Xyelotoma ???? ?????????? ?????????? ?????????? ?????? ? ??? ?????????? ? ? ???????? ?????????? ?? ? ??????? ??? Undatoma ???? ?????????? ?????????? ?????????? ?????? ? ??? ?????????? ? ? ???????? ?????????? ?? ? ??????? ??? Dahuratoma ???? ?????????? ?????????? ?????????? ?????? ? ??? ?????????? ? ? ???????? ?????????? ?? ? ??????? ??? Mesolyda ???? ?????????? ?????????? ?????????? ?????? ? ??? ?????????? ? ? ???????? ?????????? ?? ? ??????? ??? Turgidontes ???? ?????????? ?????????? ?????????? ?????? ? ??? ?????????? ? ? ???????? ?????????? ?? ? ??????? ??? Aulidontes ???? ?????????? ?????????? ?????????? ?????? ? ??? ?????????? ? ? ???????? ?????????? ?? ? ??????? ??? Protosirex ???? ?????????? ?????????? ?????????? ?????? ? ??? ?????????? ? ? ???????? ?????????? ?? ? ??????? ??? Aulisca ???? ?????????? ?????????? ?????????? ?????? ? ??? ?????????? ? ? ???????? ?????????? ?? ? ??????? ??? Anaxyela ???? ?????????? ?????????? ?????????? ?????? ? ??? ?????????? ? ? ???????? ?????????? ?? ? ??????? ??? Syntexyela ???? ?????????? ?????????? ?????????? ?????? ? ??? ?????????? ? ? ???????? ?????????? ?? ? ??????? ??? Karatavites ???? ?????????? ?????????? ?????????? ?????? ? ??? ?????????? ? ? ???????? ?????????? ?? ? ??????? ??? Stephanogaster ???? ?????????? ?????????? ?????????? ?????? ? ??? ?????????? ? ? ???????? ?????????? ?? ? ??????? ??? Leptephialtites ???? ?????????? ?????????? ?????????? ?????? ? ??? ?????????? ? ? ???????? ?????????? ?? ? ??????? ??? Cleistogaster ???? ?????????? ?????????? ?????????? ?????? ? ??? ?????????? ? ? ???????? ?????????? ?? ? ??????? ??? Sepulca ???? ?????????? ?????????? ?????????? ?????? ? ??? ?????????? ? ? ???????? ?????????? ?? ? ??????? ??? Onochoius ???? ?????????? ?????????? ?????????? ?????? ? ??? ?????????? ? ? ???????? ?????????? ?? ? ??????? ??? Ghilarella ???? ?????????? ?????????? ?????????? ?????? ? ??? ?????????? ? ? ???????? ?????????? ?? ? ??????? ??? Paroryssus ???? ?????????? ?????????? ?????????? ?????? ? ??? ?????????? ? ? ???????? ?????????? ?? ? ??????? ??? Praeoryssus ???? ?????????? ?????????? ?????????? ?????? ? ??? ?????????? ? ? ???????? ?????????? ?? ? ??????? ??? Mesorussus ???? ?????????? ?????????? ?????????? ?????? ? ??? ?????????? ? ? ???????? ?????????? ?? ? ??????? ??? Trematothorax ???? ?????????? ?????????? ?????????? ?????? ? ??? ?????????? ? ? ???????? ?????????? ?? ? ??????? ??? Thoracotrema ???? ?????????? ?????????? ?????????? ?????? ? ??? ?????????? ? ? ???????? ?????????? ?? ? ??????? ??? Prosyntexis ???? ?????????? ?????????? ?????????? ?????? ? ??? ?????????? ? ? ???????? ?????????? ?? ? ??????? ??? Kulbastavia ???? ?????????? ?????????? ?????????? ?????? ? ??? ?????????? ? ? ???????? ?????????? ?? ? ??????? ??? Brachysyntexis ???? ?????????? ?????????? ?????????? ?????? ? ??? ?????????? ? ? ???????? ?????????? ?? ? ??????? ??? Symphytopterus ???? ?????????? ?????????? ?????????? ?????? ? ??? ?????????? ? ? ???????? ?????????? ?? ? ??????? ??? Eoxyela ???? ?????????? ?????????? ?????????? ?????? ? ??? ?????????? ? ? ???????? ?????????? ?? ? ??????? ??? Liadoxyela ???? ?????????? ?????????? ?????????? ?????? ? ??? ?????????? ? ? ???????? ?????????? ?? ? ??????? ??? Abrotoxyela ???? ?????????? ?????????? ?????????? ?????? ? ??? ?????????? ? ? ???????? ?????????? ?? ? ??????? ??? Pseudoxyelocerus ???? ?????????? ?????????? ?????????? ?????? ? ??? ?????????? ? ? ???????? ?????????? ?? ? ??????? ??? Palaeathalia ???? ?????????? ?????????? ?????????? ?????? ? ??? ?????????? ? ? ???????? ?????????? ?? ? ??????? ??? Ferganolyda ???? ?????????? ?????????? ?????????? ?????? ? ??? ?????????? ? ? ???????? ?????????? ?? ? ??????? ??? PamphiliidaeUndesc ???? ?????????? ?????????? ?????????? ?????? ? ??? ?????????? ? ? ???????? ?????????? ?? ? ??????? ??? Rudisiricius ???? ?????????? ?????????? ?????????? ?????? ? ??? ?????????? ? ? ???????? ?????????? ?? ? ??????? ??? Sogutia ???? ?????????? ?????????? ?????????? ?????? ? ??? ?????????? ? ? ???????? ?????????? ?? ? ??????? ??? Xyelula ???? ?????????? ?????????? ?????????? ?????? ? ??? ?????????? ? ? ???????? ?????????? ?? ? ??????? ??? Brigittepterus ???? ?????????? ?????????? ?????????? ?????? ? ??? ?????????? ? ? ???????? ?????????? ?? ? ??????? ??? Grimmaratavites ???? ?????????? ?????????? ?????????? ?????? ? ??? ?????????? ? ? ???????? ?????????? ?? ? ??????? ??? [12S 354-559, 203 chars] Orthoptera ?????????????????????????????????????????????????????????????????????????????????????? ?????????????????? ?????????????????????????????????????????????? ?????????????????????????????? ??????????????????????? Paraneoptera ?????????????????????????????????????????????????????????????????????????????????????? ?????????????????? ?????????????????????????????????????????????? ?????????????????????????????? ??????????????????????? Chrysopidae ?????????????????????????????????????????????????????????????????????????????????????? ?????????????????? ?????????????????????????????????????????????? ?????????????????????????????? ??????????????????????? Raphidioptera ?????????????????????????????????????????????????????????????????????????????????????? ?????????????????? ?????????????????????????????????????????????? ?????????????????????????????? ??????????????????????? Coleo_Polyphaga ?????????????????????????????????????????????????????????????????????????????????????? ?????????????????? ?????????????????????????????????????????????? ?????????????????????????????? ??????????????????????? Coleo_Adephaga ?????????????????????????????????????????????????????????????????????????????????????? ?????????????????? ?????????????????????????????????????????????? ?????????????????????????????? ??????????????????????? Lepid_Micropterigidae ?????????????????????????????????????????????????????????????????????????????????????? ?????????????????? ?????????????????????????????????????????????? ?????????????????????????????? ??????????????????????? Lepid_Papilionidae ?????????????????????????????????????????????????????????????????????????????????????? ?????????????????? ?????????????????????????????????????????????? ?????????????????????????????? ??????????????????????? Mecoptera ?????????????????????????????????????????????????????????????????????????????????????? ?????????????????? ?????????????????????????????????????????????? ?????????????????????????????? ??????????????????????? Xyela TCTTTAAACTTAAAGAATTTGGCGGTATTTTAGTCTTTTCAGAGGAACTTGTCCCGTAATC-GATAATCCTCGATA-ATCTTACTT AATTTGTATACCGCCGTT TTCAGGTCAAGGTGCAGTT-TATAATTAAG--TGGAGATGAGTTAC ATAT--TAAAATGAAGGTGGATTTGAAGGT TTGATTATAGCTCTAAAATATGT Macroxyela TCTTGAAACTTAAAGAATTTGGCGGTGTTTTAGTCTTTTCAGAGGAACCTGTCCTGTAATT-GATATTCCACAATA-ATCTTACTT AATTTGTATACCGCCGTT TTCAGGTCAAGGTGCAGTT-AATAATTAAG--AAGAGATGGGTTAC AAAA--TAAAATGAAGGTGGATTTGAAAGT TTGATTATAGCTCTAGAACATGT Runaria TCTTAAAATTCAAGAGATTTGGCGGTATTC-TATCAAATTAGGGGAATTTGCTTATGAATT-GATAATCCGCAAGAAATCTTACTT AATTTATATGTCGCCGTT TTCAGGT-AAGATGTAGTT-TATAATAAAG--GTTAAGTGAATTAC ATAT--ATAATTGAAGGTGGACTTAAAAGT TTGAATTTAGATCTAGAATATGT Paremphytus ?????????????????????????????????????????????????????????????????????????????????????? ?????????????????? ?????????????????????????????????????????????? ?????????????????????????????? ??????????????????????? Blasticotoma TCTTGAAACTCAAGAGATTTGGCGGTACTTTAATCTAATTAGGGGAATTTGTTTATTAAAT-GATGATCCACAAGGCATCTTACTT AATTTATATGTCGCCGTT TTCAGGT-AAGATGTAGTT-TATAGTAGAG--TTTAAATGAGTTAC ATAT--TTAGTTGAAGGTGGACTTAAAAGT TTGAATATAGTGCTAAAGTATGT Tenthredo TCTTTAAACTTAAAAAAATTGGCGGTATTTAAATCTAATTAGGGGAATTTGTCTTTTAAAT-GATAATCCTCAAGATACCTTACTT AATTTATTTATTGCCGTT TTCAGGTCTTTATGTAGTT-TATAATAAAG--TTAAGATGAATTAC ATTT--ATAATTGAAAGTGGACTTAATAGT TTGAATTAAGAATTAAA-TATGT Aglaostigma TCTTTAAACTTAAAAAAATTGGCGGTGTTTAAGTCTGATTAGGGGAATTTGTCTTTTAAAT-GATATTCCGCAAGATACCTTACTT AATTTATTTATTGCCGTT TTCAGGTCTTTATGTAGTT-TATAATAAAG--TTTAGATGAATTAC AATT--TTTATTGAAAGTGGACTTAGTAGT TTGAATTTAGAATTAAA-CATGT Dolerus TCTTTAAACTTAAAAAAATTGGCGGTATTTAAATCTAATTAGGGGAATTTGTTTATTAAAT-GATAATCCGCAAGATACCTTACTT AATTTATTTATTGTCGTT TTCAGATCATTATGTAGTT-TATATTAAAG--TTTAAATGAATTAC ATAT--ATAATTTAAAGTGGACTTAATAGT TTGAATTAAGAATTAAA-TATGT Selandria TCTTTAAACTTAAAAAAATTGGCGGTATTTAAATCTAATTAGGGGAATTTGTTTATTAAAT-GATAATCCTCAAGAAACCTTACTT AATTTATTTATTGCCGTT TTCAGGTCATTATGTAGTT-AATATTAAAG--TTAAAATGAATTAC ATAT--TTAATTAAAATTGGACTTAATAGT TTGAAATTAGAATTAAA-TATG- Strongylogaster TCTTTAAACTTAAAAAAATTGGCGGTATTTAAATCTGATTAGGGGAATTTGTTTATTAAAT-GATAATCCTCAAGAAACCTTACTT AACTTATTTATTGCCGTT TTCAGGTCATTATGTAGTT-TATATTAAAG--TTTAGATGAATTAC ATAT--TTTATTGAAAGTGGACTTAATAGT TTGAAATAAGAA-TTAAATATGT Monophadnoides TCTTTAAACTTAAAAAAATTGGCGGTATTTAAATCTAATTAGGGGAACTTGTCTTTAAAAT-GATAGTCCGCAAGAAACCTTACTT AATTTATTTATTGCCGTT TTCAGGTCTTTATGTAGTT-TATATTAAAG--TTAAGATGAGTTAC ATTT--TTAATTGAAAGTGGACTTAATAGT TTGAATTAAGAA-TTAAATATGT Metallus TCTTTAAATTTAAAAAAATTGGCGGTATTTAAATCTAATCAGGGGAACTTGTTTTTTAATT-GATAATCCTCAAAGTACCTTACTT AATTTATTTATTGCCGTT TTCAGGTCATTATGTAGTT-TATATTAAAG--TTAAAATGAGTTAC ATAT--ATAATTAAAATTGGACTTGATAGT TTGAATTAAGAA-TTAAATATGT Athalia TCTTTAAATTTAAAAAAATTGGCGGTATTTAAAACTAATTAGGGGAACTTGTTTTTTAAAT-GATAATCCGCATGATACCTTACTT AATTTATTTACTGCCGTT TTCAGGTCATGGTGTAGTT-TATATTAAAG--TTAAAATGAGTTAC ATAT--TAAATTAAAGGTGGACTTAATAGT TTGAATTAAGAATTAAA-TGTGT Taxonus TCTTTAAATTTAAAAAAATTGGCGGTATTTAAATCTAATTAGGGGAATTTGTTTATTAAAT-GATAATCCGCAAGAGACCTTACTT AATTTATTTATTGCCGTT TTCAGGTCATTATGTAGTT-TATATTAAAG--TTTAAATGAATTAC ATAT--ATTTTTGAAGGTGGACTTAATAGT TTGAATTAAGAA-TTAAATATGT Hoplocampa TCTTTAAACTTAAAAAAATTGGCGGTATTTAAAACTAATTAGGGGAACTTGTCTTTTAAAT-GATAATCCGCAAGAAACCTTACTT AATTTGTTTATTGCCGTT TTCAGGTCATTATGTAGTT-TATATTAAAG--TTAAGATGAGTTAC ATAT--AAAATTAAAAGTGGACTTAATAGT TTGAATTTAGAA-TTAAATATGT Nematinus TCTTTAAACTTAAAAAAATTGGCGGTATTTAACTCTAATTAGGGGAATTTGTCTTTAAAAT-GATAATCCTCAAGAAACCTTACTT AATTTATTTATTTCCGTT TTCAGGTCATTATGTAGTT-TATATTAAAG--TTAAGATGAATTAC ATAT--AAAATTAAAAGTGGACTTAATAGT TTGAATTTAGAA-TTAAATATGT Nematus TCTTTAAACTTAAAAAAATTGGCGGTATTTAAATCTAATTAGGGGAATTTGTCTTTTAAAT-GATAATCCGCAAGAAACCTTACTT AATTTGTTTATTGCCGTT TTCAGGTCATTATGTAGTT-TATATTAAAG--TTAAGATGAATTAC AAAT--TAAATTGAAAATGGACTTAATAGT TTGAATTTAGAATTAAA-TATGT Cladius TCTTTAAACTTAAAAAAATTGGCGGTATTTAAGTCTAATTAGGGGAATTTGTTTTTTAAAT-GATAATCCGCAAGATACCTTACTT AATTTGTTTATTTCCGTT TTCAGGTCTATATGCAGTT-TATATTAAAG--TTAAAATGAATTAC ATAT--TAATTTGAAGGTGGACTTATAAGT TTGAATAAAGAATTAAA-TATGT Monoctenus ACTTTAAATTTAAAAAAATTGGCGGTATTTAAAACTAATTAGGGGGGTTTGTTTAATAAAC-GATGATCCGCGTGAAATCTTACTT AATTTATTTATTTTCGTT TTCAGATCAAGATGTAGTT-TATATTAAAG--TTTAAATGAATTAC ATAT--TTAATTGAAATTGGACTTAATAGT TTGAATTTAGAATTAAA-TATGT Gilpinia TCTTTAAAATTAAAAAAATTGGCTGTATTTAATACTAATTAGGGGAATTTGTTTATTAAAC-GATGGTCCGCGATATATCTTACTT AATTTATTTATTTTCGTT TTCAGATCAAGATGTAGTT-TATATTAAAG--ATTAAATGAATTAC AAAT--TTAATTGAAATTGGACTTAATAGT TTGAATTTAGAA-TTAAATAAGT Diprion TCTTGAAAATTAAAAAAATTGGCGGTATTTAAAACTAATTAGGGGAATTTGTTTATAAAAC-GATGATCCACGAGGAATCTTACTT AGCTTATTTATTTTCGTT TTCAGATCAAGATGTAGCT-TATATTAAAG--TTTAAATGAATTAC ATAT--TTATTTGAAATTGGACTTAATAGT TTGAATTTAGAATTAAA-TGTGT Cimbicinae TCTTTAAACTTAAAAAAATTGGCGGTATTTAAATCTAATTAGGGGAATTTGTTTTTTAATT-GTTAATACTCAAAAAACCTTACTT AATTTGTTTATTGTCGTT TTCAGATCATGATGCAGTT-TATATTAAAG--TTAAAATTAATTAC AAAT--GTTTTAGAAGGTGGACTTAATAGT TTGAATTTAGAATTAAA-TATGT Abia TCTTTAAACTTAAAAAAATTGGAGGTATTTAAATCTAATTAGGGGAATTTGTTTTTTAAAT-GATAATCCACAAGATACCTTACTT AATTTGTTTATTGTCGTT TTCAGATCATAGTGCAG-T-AATAATAAAG--TTAAAATGAGTTAC ATAT--TTTATTAAAAGTGGACTTAATAGT TTGAATTTAGAATTAAA-TATGT Corynis TCTTTAAACTTAAAAAAATTGGCGGTATTTAAATCTAATTAGGGGAATTTGTTTTTTAATC-GATGATCCGCGTGATACCTTACTT AATTAGTTTATTTCCGTT TTCAGGTCAAGATGCTGTT-TATATTAAAG--TTAAAATGAATTAC ATAT--TTAATTGAAAGTGGACTTAATAGT TTGAATTTAGACTTAAA-TATGT Arge TCTTTAAATTCAAAAAATTTGGCGGTATTT-TATCTAATTAGGGGAACTTGTTTAATGAAT-GATATTCCTCGAGTTACCTTACTT AATTTATGTATTTCCGTT TTCAGGTCAAAGTGTAGTT-TATATAAAAG--TTAAAATGAGTTAC ATTTAATTAATTGAAGGTGGACTTAATAGT TTGAATAAAGATCTAAAATATGT Sterictiphora TCTTTAAATTCAAAAAAATTGGCGGTATTT-TATCTAATTAGGGGAATTTGTTTTATAAAT-GATAATCCACGAGGAACCTTACTT AATTTGTGTATTTCCGTT TTCAGGTCAAAGTGCAGTT-TATGAGTGAG--AAAAAATGAGTTAC ATAA--ATTAAATAAGGAGGACTTAATAGT TTGAATTAAGATCTAGAATGTGT Perga TCTTTAAAATCAAAAAAATTGGCGGTATTT-TATCTAATTAGGGGAATTTGTTTATTAAAT-GATAATCCGCGAGAAATCTTACTT AATATATGTACTTCCGTT TTCAGGTCAAAGTGTAGTT-TATAAAAAAG--TTTAAATGAATTAC ATAT--TTAAATGAAGGTGGACTTAATAGT TTGAAAAAAGATCTAAAATATGT Phylacteophaga TCTTTAAATTTAAAAAAGTTGGCGGTATTT-TATCTAATTAGGGGAATTTGTTTTTTAAAT-GATGATCCGCGAGAAATCTTACTT AGTTTATGTATTTCCGTT TTCAGGTCAAAGTGTAGCT-TATAAAAAAG--TTAAAATGAATTAC AAAT--TTAAATGAAGGTGGACTTAATAGT TTGAATATAGGTCTAAAATATGT Lophyrotoma ATTTTAAAATTAAAGAATTTGGCGGTATTT-TTTCTAATTAGGGGAATTTGTCTTTTAAAT-GATAATCCACGAGGAACCTTACTT AATTTATGTATTTCCGTT TTCAGGTCAAAGTATAGTT-TATAAAATAG--TAAAGATGAATTAC ATTT--ATTTTTGAAGGTGGACTTAATAGT TTGAAAAAGAAT-TAAATTATGT Acordulecera TCTTTAAATCTAAAAAAATTGGCGGTATTT-TATCTAATTAGGGGAATTTGTTTATTAAAT-GATAATCCGCGAGATACCTTACTT AATTTGTATACTTCCGTT TTCAGGTCAAAGTGCAGTT-TATTAAAAAG--TATAAATGAATTAC ATAT--TTAAATGAAGGTGGACTTAATAGT TTGAAAATAGATCTAAAATATGT Decameria TCTTTAAATTTAAAAAAATTGGCAGTATTT-TATCTTATTAGGGGAATTTGTTTAATAAAT-GATAATCCACGAGAAACCTTACTT AATTTATGTATTTCCGTT TTCAGGTCACAATGTAGTT-TATAATAAAG--TTAAAATGAATTAC ATAT--TTAATTGAAGGTGGACTTAATAGT TTGAAAGAAGATCTAAAATATGT Neurotoma ?????????????????????????????????????????????????????????????????????????????????????? ?????????????????? ?????????????????????????????????????????????? ?????????????????????????????? ??????????????????????? Onycholyda TCTTTAAATTTAATGAATTTGACGGTATTATAATCTATTTAGAGGAACCTGTATTTTAATT-GATAATCCACGATAAATTTTACTT AGTTTGTATACCGCCGTT TTCAGGTCAAGGTGCAGCT-AATAATTAAG--TTAATATGGGTTAC ATAA--AATTATTAAAGTGGATTTAAAAGT TTGAATTTAGCTCTATAATATGT Pamphilius TCTTTAAATTTAATGAATTTGGCGGTATTATAATCTAATTAGAGGAACCTGTATTTTAATT-GATAATCCACGATAAATTTTACTT AGTTTGTATACCGCCGTT TTCAGGTCAAGGTGCAACT-AATAATTAAG--TTAATATGGGTTAC ATAA--AATTATTAAAGTGGATTTAAAAGT TTGAATATAGCTCTATAATATGT Cephalcia TCTTTAAAATTAATAAATTTGGCGGTATTATAATCTAATTAGAGGAACCTGTATTTTAATC-GATAATCCACGATAAATTTTACTT AATATGTATACCGCCGTT TTCAGGTCAAGGTGCAGTT-TATAATTAAG--TATATATGGGTTAC ATTT--AATTATTAAAGTGGATTTAAAAGT TTGATTATAGCTCTATAATATGT Acantholyda ?????????????????????????????????????????????????????????????????????????????????????? ?????????????????? ?????????????????????????????????????????????? ?????????????????????????????? ??????????????????????? Megalodontesc TCTTTAAAGTTAATAATTTTGGCGGTATTATAGTCATTTTAGAGGAACCTGTGTTATAATT-GATGGTCCACAATTTATTCTACTT AGTTTATATACCGCCGTT TTCAGGTCAAGGTGTAGTT-TATAGTTAAG--TAAATATGGGTTAC A-AT--TATTATGAAAATGGATTTAAAAGT TTGATTTATGCTCTATAATATG- Megalodontessk ?????????????????????????????????????????????????????????????????????????????????????? ?????????????????? ?????????????????????????????????????????????? ?????????????????????????????? ??????????????????????? Cephus TCTTGAAACTTTAAGAATTTGACGGTAATTCATTCAATTTAGAGGAACTTGTTTTTTAATT-GATAATCCACGATATAAATTACCT TGTTTGTATACCGTCGTT TTCAGATCAAGGTGCAGCTATTTATTTAGG--TTAAAATGAGTTAC ATAT--TTAAATTAAGGTGGATTTAAAAGT TTGAGATTTGTTTTGAATTATGT Calameuta TCTTGAAACTTTAAGAATTTGACGGTAATTCATTCATTTTAGAGGAACTTGTTTTTTAATT-GATATTCCACGATAAAATTTACCT TGTTTGTATATCGTCGTT TTCAGATCAAGGTGCAGCTATTTATTTAGG--TTAAAATGAGTTAC ATTT--TTAAATTAAGGTGGATTTAAAAGT ----------------------- Hartigia TCATGAAACTTAAAGAATTTGACGGTAATTCATTCATATTAGAGGAACTTGTCTTTTAATT-GATAATCCACGATAAAATTTACCT TATTTATATACCGTCGTT TTCAGATCAAGGTGTAGTA-TATATTTAGG--TTAAGATGAGTTAC ATAT--TTTATTGAAAGTGGATTTAAAAGT TTGATAAA--------------- Syntexis TCTCGAAATTTAAAAAATTTGACGGTATTTTAAT-TAATCAGGGGAACTTGTTCATTAAATCGATAGTCCACGATAGAAACTACTT AGCTTGTATACCGTCGTT TTCAGATCAAGGTGCAGTT-TATAGTTAAG--ATAGAATGAGTTAC TTTT--ATTAATGAAGGTGGATTTGAAAGT TTGATTAATACAATAAAATATGT Sirex ?????????????????????????????????????????????????????????????????????????????????????? ?????????????????? ?????????????????????????????????????????????? ?????????????????????????????? ??????????????????????? Xeris ?????????????????????????????????????????????????????????????????????????????????????? ?????????????????? ?????????????????????????????????????????????? ?????????????????????????????? ??????????????????????? Urocerus ?????????????????????????????????????????????????????????????????????????????????????? ?????????????????? ?????????????????????????????????????????????? ?????????????????????????????? ??????????????????????? Tremex ?????????????????????????????????????????????????????????????????????????????????????? ?????????????????? ?????????????????????????????????????????????? ?????????????????????????????? ??????????????????????? Xiphydria ?????????????????????????????????????????????????????????????????????????????????????? ?????????????????? ?????????????????????????????????????????????? ?????????????????????????????? ??????????????????????? Orussus ACTTAAAATCTAAAGAATTTGACGGTATTTTAATAAAATCAGAGGAACTTGTCATTTAATTCGATAATCCACGATAGAATATACCT AACTTGTATACCTCCGTT TTCAGGTCAAGGTGTAGTG-AATAGCAGGGGAGAAAGATGAGTTAC TTTA--ATTAATGAAAATGGATTTGAAGGT CTGATTAAAATACTAAAATATGT StephanidaeA TCAATAAATTTAATAGATTTGGCGGTATTTTAAA-TTTTTAGGGGAACTTGTATTATAATT-GATATTCCACGATTGGATTTACTT AATTTATGTATTGTCGTT TTCAGATCAAAATGTAGTT-TATATTAAAG-AAAATTATGAGTTAC ATTT--ATTTTTGAAATTGGATTTAAAAGT TTGAATAA-TTATTGAAATATGT StephanidaeB ?????????????????????????????????????????????????????????????????????????????????????? ?????????????????? ?????????????????????????????????????????????? ?????????????????????????????? ??????????????????????? Megalyra ?????????????????????????????????????????????????????????????????????????????????????? ?????????????????? ?????????????????????????????????????????????? ?????????????????????????????? ??????????????????????? Trigonalidae ?????????????????????????????????????????????????????????????????????????????????????? ?????????????????? ?????????????????????????????????????????????? ?????????????????????????????? ??????????????????????? Chalcidoidea ?????????????????????????????????????????????????????????????????????????????????????? ?????????????????? ?????????????????????????????????????????????? ?????????????????????????????? ??????????????????????? Evanioidea ?????????????????????????????????????????????????????????????????????????????????????? ?????????????????? ?????????????????????????????????????????????? ?????????????????????????????? ??????????????????????? Ichneumonidae TCATTAAACTTAAAAAAAATGGCGGTTTTTTATTCTTATTAGAGGAATTTGTTTATTAATTTGATAATCCACGATAGGATGTACTT AGTTTATATATTGTCGTT TTCAGATCAAAATGTAGCT--TTTTTAAAG--ATAAAATGAATTAC ATTT------ATGAAGGTGGATTTAAAAGT TTGAATAA-GTTTTAAAAAATGT Cynipoidea ?????????????????????????????????????????????????????????????????????????????????????? ?????????????????? ?????????????????????????????????????????????? ?????????????????????????????? ??????????????????????? ApoideaA ?????????????????????????????????????????????????????????????????????????????????????? ?????????????????? ?????????????????????????????????????????????? ?????????????????????????????? ??????????????????????? ApoideaB ?????????????????????????????????????????????????????????????????????????????????????? ?????????????????? ?????????????????????????????????????????????? ?????????????????????????????? ??????????????????????? ApoideaC ?????????????????????????????????????????????????????????????????????????????????????? ?????????????????? ?????????????????????????????????????????????? ?????????????????????????????? ??????????????????????? Vespidae ?????????????????????????????????????????????????????????????????????????????????????? ?????????????????? ?????????????????????????????????????????????? ?????????????????????????????? ??????????????????????? Triassoxyela ?????????????????????????????????????????????????????????????????????????????????????? ?????????????????? ?????????????????????????????????????????????? ?????????????????????????????? ??????????????????????? Asioxyela ?????????????????????????????????????????????????????????????????????????????????????? ?????????????????? ?????????????????????????????????????????????? ?????????????????????????????? ??????????????????????? Nigrimonticola ?????????????????????????????????????????????????????????????????????????????????????? ?????????????????? ?????????????????????????????????????????????? ?????????????????????????????? ??????????????????????? Gigantoxyelinae ?????????????????????????????????????????????????????????????????????????????????????? ?????????????????? ?????????????????????????????????????????????? ?????????????????????????????? ??????????????????????? Spathoxyela ?????????????????????????????????????????????????????????????????????????????????????? ?????????????????? ?????????????????????????????????????????????? ?????????????????????????????? ??????????????????????? Xyela_mesozoica ?????????????????????????????????????????????????????????????????????????????????????? ?????????????????? ?????????????????????????????????????????????? ?????????????????????????????? ??????????????????????? Angaridyela ?????????????????????????????????????????????????????????????????????????????????????? ?????????????????? ?????????????????????????????????????????????? ?????????????????????????????? ??????????????????????? Xyelotoma ?????????????????????????????????????????????????????????????????????????????????????? ?????????????????? ?????????????????????????????????????????????? ?????????????????????????????? ??????????????????????? Undatoma ?????????????????????????????????????????????????????????????????????????????????????? ?????????????????? ?????????????????????????????????????????????? ?????????????????????????????? ??????????????????????? Dahuratoma ?????????????????????????????????????????????????????????????????????????????????????? ?????????????????? ?????????????????????????????????????????????? ?????????????????????????????? ??????????????????????? Mesolyda ?????????????????????????????????????????????????????????????????????????????????????? ?????????????????? ?????????????????????????????????????????????? ?????????????????????????????? ??????????????????????? Turgidontes ?????????????????????????????????????????????????????????????????????????????????????? ?????????????????? ?????????????????????????????????????????????? ?????????????????????????????? ??????????????????????? Aulidontes ?????????????????????????????????????????????????????????????????????????????????????? ?????????????????? ?????????????????????????????????????????????? ?????????????????????????????? ??????????????????????? Protosirex ?????????????????????????????????????????????????????????????????????????????????????? ?????????????????? ?????????????????????????????????????????????? ?????????????????????????????? ??????????????????????? Aulisca ?????????????????????????????????????????????????????????????????????????????????????? ?????????????????? ?????????????????????????????????????????????? ?????????????????????????????? ??????????????????????? Anaxyela ?????????????????????????????????????????????????????????????????????????????????????? ?????????????????? ?????????????????????????????????????????????? ?????????????????????????????? ??????????????????????? Syntexyela ?????????????????????????????????????????????????????????????????????????????????????? ?????????????????? ?????????????????????????????????????????????? ?????????????????????????????? ??????????????????????? Karatavites ?????????????????????????????????????????????????????????????????????????????????????? ?????????????????? ?????????????????????????????????????????????? ?????????????????????????????? ??????????????????????? Stephanogaster ?????????????????????????????????????????????????????????????????????????????????????? ?????????????????? ?????????????????????????????????????????????? ?????????????????????????????? ??????????????????????? Leptephialtites ?????????????????????????????????????????????????????????????????????????????????????? ?????????????????? ?????????????????????????????????????????????? ?????????????????????????????? ??????????????????????? Cleistogaster ?????????????????????????????????????????????????????????????????????????????????????? ?????????????????? ?????????????????????????????????????????????? ?????????????????????????????? ??????????????????????? Sepulca ?????????????????????????????????????????????????????????????????????????????????????? ?????????????????? ?????????????????????????????????????????????? ?????????????????????????????? ??????????????????????? Onochoius ?????????????????????????????????????????????????????????????????????????????????????? ?????????????????? ?????????????????????????????????????????????? ?????????????????????????????? ??????????????????????? Ghilarella ?????????????????????????????????????????????????????????????????????????????????????? ?????????????????? ?????????????????????????????????????????????? ?????????????????????????????? ??????????????????????? Paroryssus ?????????????????????????????????????????????????????????????????????????????????????? ?????????????????? ?????????????????????????????????????????????? ?????????????????????????????? ??????????????????????? Praeoryssus ?????????????????????????????????????????????????????????????????????????????????????? ?????????????????? ?????????????????????????????????????????????? ?????????????????????????????? ??????????????????????? Mesorussus ?????????????????????????????????????????????????????????????????????????????????????? ?????????????????? ?????????????????????????????????????????????? ?????????????????????????????? ??????????????????????? Trematothorax ?????????????????????????????????????????????????????????????????????????????????????? ?????????????????? ?????????????????????????????????????????????? ?????????????????????????????? ??????????????????????? Thoracotrema ?????????????????????????????????????????????????????????????????????????????????????? ?????????????????? ?????????????????????????????????????????????? ?????????????????????????????? ??????????????????????? Prosyntexis ?????????????????????????????????????????????????????????????????????????????????????? ?????????????????? ?????????????????????????????????????????????? ?????????????????????????????? ??????????????????????? Kulbastavia ?????????????????????????????????????????????????????????????????????????????????????? ?????????????????? ?????????????????????????????????????????????? ?????????????????????????????? ??????????????????????? Brachysyntexis ?????????????????????????????????????????????????????????????????????????????????????? ?????????????????? ?????????????????????????????????????????????? ?????????????????????????????? ??????????????????????? Symphytopterus ?????????????????????????????????????????????????????????????????????????????????????? ?????????????????? ?????????????????????????????????????????????? ?????????????????????????????? ??????????????????????? Eoxyela ?????????????????????????????????????????????????????????????????????????????????????? ?????????????????? ?????????????????????????????????????????????? ?????????????????????????????? ??????????????????????? Liadoxyela ?????????????????????????????????????????????????????????????????????????????????????? ?????????????????? ?????????????????????????????????????????????? ?????????????????????????????? ??????????????????????? Abrotoxyela ?????????????????????????????????????????????????????????????????????????????????????? ?????????????????? ?????????????????????????????????????????????? ?????????????????????????????? ??????????????????????? Pseudoxyelocerus ?????????????????????????????????????????????????????????????????????????????????????? ?????????????????? ?????????????????????????????????????????????? ?????????????????????????????? ??????????????????????? Palaeathalia ?????????????????????????????????????????????????????????????????????????????????????? ?????????????????? ?????????????????????????????????????????????? ?????????????????????????????? ??????????????????????? Ferganolyda ?????????????????????????????????????????????????????????????????????????????????????? ?????????????????? ?????????????????????????????????????????????? ?????????????????????????????? ??????????????????????? PamphiliidaeUndesc ?????????????????????????????????????????????????????????????????????????????????????? ?????????????????? ?????????????????????????????????????????????? ?????????????????????????????? ??????????????????????? Rudisiricius ?????????????????????????????????????????????????????????????????????????????????????? ?????????????????? ?????????????????????????????????????????????? ?????????????????????????????? ??????????????????????? Sogutia ?????????????????????????????????????????????????????????????????????????????????????? ?????????????????? ?????????????????????????????????????????????? ?????????????????????????????? ??????????????????????? Xyelula ?????????????????????????????????????????????????????????????????????????????????????? ?????????????????? ?????????????????????????????????????????????? ?????????????????????????????? ??????????????????????? Brigittepterus ?????????????????????????????????????????????????????????????????????????????????????? ?????????????????? ?????????????????????????????????????????????? ?????????????????????????????? ??????????????????????? Grimmaratavites ?????????????????????????????????????????????????????????????????????????????????????? ?????????????????? ?????????????????????????????????????????????? ?????????????????????????????? ??????????????????????? [16S 560-781, 222 chars] Orthoptera ATAATCATTAGTCTCTTAATTAGGGGCTGGAATGAATGGCTTGACGAGAAATTAACTGTCTC GAATTTAACTTTTGAGTTAAAAGGCT-TAAATTTTTCTTAAAGACGAGAAGACCCTATAGAGCTT TTTTGTTGGGGT-GAC-ATGAAGAATAAATAAACTCT TACCTTAGGGATAACAGCGTAATTGTTTTTGAGAGCTCATATTGACAAAGCAGATTGC Paraneoptera ATAATCATTTGTCTTTTAATTGAGGGCTTGTATGAAAGGTTGGACAAGATATTAACTTTCTT GAACTTAATTTTTAAGTTAAAAAGCT-TAAATTTTTTTGTTGGACGAGAAGACCCTATAGATCTT TTTTGTTGGGGT-GAC-TGTAGAATTTTATGAACTTC TACCTTAGGGATAACAGCGTAATTTCTTTGGAGAGTTCATATTGATAAAGGAGTTTGC Chrysopidae ATAATCATTTGTCTTTTAATTGAAGGCTGGAATGAATGGTTGAATGAAGTATTAACTGTCTC GAATTTAGCTTTTTAATCAAAAGGTT-TAAATGAAATTAAAAGACGAGAAGACCCTATAGATTTT TTTGATTGGGGT-GAT-TAAAAAATTTAATAAACTTT TACCTTAGGGATAACAGCGTAATTTTTTTAGAGAGTTCTTATCGATAAAAAAGATTGC Raphidioptera ATAATCATTTGTTTCTTAATTAGGGACTAGTATGAAGGATTGGACGAGATATTTTCTGTCTC AAATTTTATTTTTTAGTAAAAAAGCT-AAAATTTATTTAAAAGACGAGAAGACCCTATAGAGTTT TTTTATTGGGGT-GAT-AGAAAAATTTATAAAACTTT TACCTTAGGGATAACAGCGTTATTATTTTGGAGAGTTCTAATCAATAAAATAGTTTGC Coleo_Polyphaga ATAATAAATAGTTTTTTGATTGAAAGCTGGAATGAAGGATTGGACGAGAAAATTACTGTCTC GAATTTAACTTTTTAGTTAAAAGGCT-AAAATTTTTTTAAAAGACGAGAAGACCCTATAGAGTTT TTTGGTTGGGGT-GAT-TAAAAAATTTGTTAAACTTT TACCTTAGGGATAACAGCGTAATATATTTTTGAAGTTCTTATTTAAAAATTTGTTTGC Coleo_Adephaga ATAATCATTAGTTTTTTAATTAAAAGCTTGTATGAAAGGCTGGACGAGGTAAAATCTGTCTC GAATTTAACTTTTAAGTTAAAAGGCT-TAAATAATATTAAAAGACGAGAAGACCCTATAGATCTT TTTTATTGGGGT-AAT-AGAAAGATTAAATTAATTCT TACCTTAGGGATAACAGCGTAATTTTTTTAGAGAGTTCATATCGATAAAAAAGTTTGC Lepid_Micropterigidae ?????????????????????????????????????????????????????????????? ????????????????????????????????????????????????????????????????? ????????????????????????????????????? ?????????????????????????????????????????????????????????? Lepid_Papilionidae ATAATCATTAGTCTTTTAATTGATGACTGGTATGAAAGATTGGACGAAATATATTCTGTCTC AAATTTAATTTTTTAGTAAAAAAGCT-AAAATGATTTTAAAAGACGAGAAGACCCTATAGAGTTT TTTTGTTGGGGT-GAT-AGAAAAATTAAATTAACTTT TACCTTAGGGATAACAGCGTAATTTTTCTTTTTAGTTCAAATAGGAAGAAAAGTTTGC Mecoptera ATAATAATTAGTCTTTTAATTGAGGGCTAGAATGAATGATTGAACGAGGTGTTAACTGTCTC TAATTTAACTTTTGAGTTAAAAGGCT-TAAATATAATTAAAGGACGAGAAGACCCTATAGAACTT TTTTGTTGGGGT-GAC-AGGAAGATTTATAAAACTCT TACCTTAGGGATAACAGCGTAATTTTTTTTGAGAGTTCTTATCGACAAAAAAGATTGC Xyela ATAATCATTAGTCTTTTAATTGAGGGCTGGAATGAATGATTGAACGAAGTATTAACTGTCTC GAATTTAATTTTTGAGTTAAAAAGCT-TAAATTTTATTAAAGGACGAGAAGACCCTATAGAGCTT TTTTATTGGGGT-GAT-AGGAAGATTAAATTAACTCT TACCTTAGGGATAACAGCGTAATTTTTTTTGAGAGTTCATATCGACAAAAAGGATTGC Macroxyela ATAATCATTAGTCTTTTAATTAAGGGCTAGAATGAACGATTGGACGAAGTATTAACTGTCTC GAATTTAATTTTTGAGTTAAAAAGCT-TAAATTTAATTAAAGGACGAGAAGACCCTATAGAGCTT TTTTATTGGGGT-GAT-AAGAAGATTAAAATAACTCT TACCTTAGGGATAACAGCGTAATTTTTTTTGAGAGTTCTTATCGACAAAATAGATTGC Runaria ATAATCATTAGTCTTTTAATTGAAGACTTGTATGAAGGATTGAATGAAATATATACTGTCTC GAATTTAATTTTTGAGTTAAAAAGCT-TAAATATTATTAGAGGACGAGAAGACCCTATAGAGTTT TTTTGTTGGGGT-GAT-GGGAAAATTTGTTAAACTTT TACCTTAGGGATAACAGCGTAATTTTTTTTTAAAGTTCATATTTGAAGAAAAGATTGC Paremphytus ?????????????????????????????????????????????????????????????? ????????????????????????????????????????????????????????????????? ????????????????????????????????????? ?????????????????????????????????????????????????????????? Blasticotoma ATAATAATTAGTCTCTTAATTAGAGACTGGAATGAATGATTGAATGTAATATATACTGTCTC GAATTTAATTTTTGAGTGAAAAAGCT-TAAATTTAATTAAGGGACGAGAAGACCCTATAGAGTTT TTTGGTTGGGGT-GAT-TGGAAAATTTATTGAACTTT TACCTTAGGGATAACAGCGTAATTTTTTTTGAAAGTTCTTATTGATAAAAGAGATTGC Tenthredo ATAATAATTAGTCTTTTAATTGGGGGCTTGTATGAATGATTGGATGAGATATAAACTGTCTT TAATTTAATTTTTAAGTTAAAAAGCT-TAAATATTTTTAAAGGACGAGAAGACCCTATAGAGTTT TTTTGTTGGGGC-GAT-GAAAAAATTTAATAAACTTT TACCTTAGGGATAACAGCGTTATTTTTTTT-AGAGTTCTTATCAATAAAAGAGATTGC Aglaostigma ATAATAATTAGTCTTTTAATTGGAGGCTGGAATGAATGATTGGATGAGATATAGTCTGTCTT TAATTTAATTTTTAAGTCAAAAAGCT-TAAATAATTTTAAAGGACGAGAAGACCCTATAGAGTTT TTTTATTGGGGC-GAT-AGAAAAATTAATTAAACTTT TACCTTAGGGATAACAGCGTAATTTTTTTT-AGAGTTCTTATCGATAATAAAGATTGC Dolerus ATAATAATTTGTCTTTTAATTAAAGGCTGGAATGAAAGATTGAACGAGATATAAACTGTCTT TAATTTAATATTTAAGTCAAAAAGCT-TAAATTTTTTTAAAGGACGAGAAGACCCTATAGAGTTT TTTTATTGGGGT-GAT-AAAGAAATTTAATAAACTTT TACCTTAGGGATAACAGCGTTATTTTTTTT-AGAGTACTTATCGATATTAAAGATTGC Selandria ATAATAATTTGTCTTTTAATTAAAGACTAGAATGAAAGATTTAACGAAGTAAAAACTGTATC AAATTTAATTTTTAAGTTAAAAAGCT-TAAATATTTTTAAAGGACGAGAAGACCCTATAGAGTTT TTTTATTGGGGT-GAT-AAAAAAATTAAATAAACTTT TACCTTAGGGATAACAGCGTTATTTTTTTT-AGAGTATTTATCAATAAGAAAGATTGC Strongylogaster ATAATAATTTGTCTTTTAATTAAAGGCTGGTATGAAAGATTTGACGAGATAAATTCTGTCTC TAATTTAATTTTTAAGTTAAAAAGCT-TAAATTTTTTTAAAGGACGAGAAGACCCTATAGAGTTT TTTTATTGGGGC-GAT-AATAAAATTTAATTAACTTT TACCTTAGGGATAACAGCGTTATTTTTTTT-AGAGTACTTATCGATAAAAAAGATTGC Monophadnoides ATAATTAGTCTTGTAATTTGAGGCTGGAATGAACGATTGAATGAGGTAAATACTGTCTTTAT TAATTTAATTTTTGAGTTAAAAAGCT-CAAATTTTTTTATAGGACGAGAAGACCCTATAGAGTTT TTTTATTGGGGC-GAT-AGAAAAATTTAATAAACTTT TACCTTAGGGATAACAGCGTTATTTTTTTT-AGAGTTCTTATCGATATAAAAGATTGC Metallus ATAATAATTAGTCTTTTAATTAGAGGCTGGAATGAATGATTTAATGAAATATAATCTGTTTC AAATTTAATTTTTAAGTTAAAAAGCT-TAAATTTTTTTAAAGGACGAGAAGACCCTATAGAGTTT TTTTGTTGGGGC-GAT-GGGAAAATTTAATTAACTTT TACCTTAGGGATAACAGCGTTATTTTTTTT-AAAGTTCATATTTATAAAAGAGATTGC Athalia ATAATAATTTGTCTTTTAATTGAAGGCTGGTATGAATGATTTGATGAAGTAAAAACTGTCTC TAATTTTATTTTTTAGTTAAAAAGCT-AAAATATTATTAAAGGACGAGAAGACCCTATAGAGTTT TTTTGTTGGGGT-GAT-AGGAAAATTAATTAAACTTT TACCTTAGGGATAACAGCGTTATTTTTTTT-AAAGTTCTTATTTATAAAAAAGATTGC Taxonus ATAATAATTAGCCTTTTAATTTGAGGCTGGTATGAATGATTTGATGAAGTATAAACTGTCTC TAATTTAATTTTTAAGTTAAAAAGCT-TAAATTTATTTAAAGGACGAGAAGACCCTATAGAGTTT TTTTATTGGGGC-GAT-AGAAAAATTTAATAAACTTT TACCTTAGGGATAACAGCGTTATTTTTTTT-AAAGTTCTTATTTATAAGAAAGATTGC Hoplocampa ATAATAATTTGTCTTATAATTTAAGGCTGGAATGAAGGATTGGACGAGGTATAAACTGTCTC GAATTTAATTTTTAAGTTAAAAAGCT-TAAATTTATTTAAAGGACGAGAAGACCCTATAGAGTTT TTTTATTGGGGC-GAT-AGAAAAATTTAATTAACTTT TACCTTAGGGATAACAGCGTTATTTTTTTT-AGAGTTCTTATCGATAAAAAAGATTGC Nematinus ATAATAATTTGTCTTTTAATTAAAGGCTGGTATGAAGGATTGAACGAAGTATAAACTGTCTC GAATTTAATTTTTAAGTTAAAAAGCT-TAAATTTATTTAAAGGACGAGAAGACCCTATAGAGTTT TTTTATTGGGGC-GAT-TGAAAAATTTAATAAACTTT TACCTTAGGGATAACAGCGTTATTTTTTTT-AGAGTTCTTATCGATAATAAAGATTGC Nematus ATAATAATTTGTCTTTTAATTAAAGGCTGGAATGAATGATTGGACGAGGTATAATCTGTCTC GAATTTAATTTTTAAGTTAAAAAGCT-TAAATAAATTTAAAGGACGAGAAGACCCTATAGAGTTT TTTTATTGGGGC-GAT-AGAAAAATTTATTAAACTTT TACCTTAGGGATAACAGCGTTATTTTTTTT-AGAGTTCTTATTGATAAAAAAGATTGC Cladius ATAATAATTTGTCTTTTAATTTAAGGCTGGAATGAATGATTGGTCAAGGTATAAACTGTCTT GAATTTAATTTTTAAGTCAAAAAGCT-TAAATAAATTTAAAGGACGAGAAGACCCTATAGAGTTT TTTTGTTGGGGC-GAT-AGAAAAATTTAATAAACTTT TACCTTAGGGATAACAGCGTTATTTTTTTT-AGAGTTCTTATCAATAAAGAAGATTGC Monoctenus ATAATAATTTGTCTTTTAATTTAGGACTAGAATGAATGATTTAACGAAGTTTAAGCTGTCTT GAATTTAATTTTTAAGTTAAAAAGCT-TAAATTTTTTTATAGGACGAGAAGACCCTATAGAGTTT TTTTATTGGGGT-GAT-AATAAAATTTTAATAACTTT TACCTTAGGGATAACAGCGTAATTTTTTTT-AAAGTTCTTATTAACATTAAAGATTGC Gilpinia ATAATAATTTGTCCTTTAATTTAGGACTAGAATGAATGATTTGACGAAGTTAAAGCTGTCTT GAATTTAATTTTTAAGTTAAAAAGCT-TAAATTTTATTAAAGGACGAGAAGACCCTATAGAGTTT TTTTGTTGGGGT-GAT-AATTAAATTTAATTAACTTT TACCTTAGGGATAACAGCGTTATTTTTTTT-AGAGTTCTTATTAACATTAAAAATTGC Diprion ATAATAATTTGTCCTCTAATTAAGGACTAGAATGAATGATTTGACGAAGTTAAAACTTTCTC GAAATTAATTTTTAAGTTAAAAAGCT-TAAATAATATTAAAGGACGAGAAGACCCTATAGAGTTT TTTTATTGGGGA-GAT-AATTAAATTTAAAAAACTTT TACCTTAGGGATAACAGCGTTATTTTATTA-AAAGTTCTTATTAACAATAAAAATTGC Cimbicinae ATAATAATTAGTCTTTTAATTGAAGGCTGGCATGAATGATTGGACAAAATATAAACTGTCTT GAATTTAATTTTTAAGTTAAAAAGCT-TAAATTTTTTTAAAGGACGAGAAGACCCTATAGAGTTT TTTTATTGGGGT-GAT-AGGAAAATTTAATAAACTTT TACCTTAGGGATAACAGCGTAATTTTTTTT-AGAGTTCTTATCGATTTAAAAGATTGC Abia ATAATAATTAGTTTTTTAATTGAAGGCTGGAATGAAAGATTGGACAAAATAAAAACTGTCTT GAAATTAATTTTTAAGTTAAAAAGCT-TAAATTATATTAAAGGACGAGAAGACCCTATAGAGTTT TTTTATTGGGGT-GAT-AAATAAATTTATTTAACTTT TACCTTAGGGATAACAGCATAATTTTTTTT-AAAGTTCTTATTGGTAAAAAAGATTGT Corynis ATAATAATTTGTCTTTTAATTAAAGGCTAGAATGAATGATTGGACGAGATAAAATCTGTCTC GAATTTAATTTTTAAGTTAAAAAGCT-TAAATTTTTTTAAAGGACGAGAAGACCCTATAGAGTTT TTTTATTGGGGT-GAT-AGGAATATTAATAGAACTTT TACCTTAGGGATAACAGCGTAATTTTTTTT-AGAGTTCTTATCGAAAAAGAAGATTGC Arge ATAATAATTAGTCTTTTAAATGAAGGCTGGAATGAATGATTGGACGAGGTAGATTCTGTCTC AAATTTAATTTTTAAGTTAAAAAGCT-TAAATATTATTAAAGGACGAGAAGACCCTATAGAGCTT TTTTATTGGGGC-GAT-AGGAAAATTTATTTAACTTT TACCTTAGGGATAACAGCGTTATTTTTTTTTAGAGTTCATATTAATAAAAAAGATTGC Sterictiphora ATAATAATTAGTCTTTTAATTGAAGGCTAGTATGAAGGATTGAACGAGATATATACTGTCTC TAATTTAATTTTTAAGTTAAAAAGCT-TAAATTTTATTAAAGGACGAGAAGACCCTATAGAGCTT TTTTGTTGGGGC-GAC-AGGAAAATTTAATAAACTTT TACCTTAGGGATAACAGCGTTATTTTTTTTTAGAGTTCATATTGATAAAAAAGATTGC Perga ATAATAATTAGTCTTTTAATTGGAGGCTGGTATGAAGGATTGGACGAGATAAATACTGTCTC TAATTTAATTTTTAAGTAAAAAAGCT-TAAATTTAATTATAGGACGAGAAGACCCCATAGAGTTT TTTTATTGGGGC-GAT-AGAAAAATTTAATAAACTTT TACCTTGGGGATAACAGCGTTATTTTTTTTAGAAGTTCATATTAATAGAAAAGATTGC Phylacteophaga ATAATAATTAGTCTATTAATTGTAGGCTGGTATGAAAGATTGAACGAGGTAAATACTGTCTC GAATTTAATTTTTGAGTTAAAAAGCT-TAAATTTTATTAGAGGACGAGAAGACCCTATAGAACTT TTTTATTGGGGC-GAT-AGGAAAATTTAATAAACTTT TACCTTAGGGATAACAGCGTTATTTTTTTTAGAAGTTCATATTAATAAAAAAGATTGC Lophyrotoma ATAATAAATAGTCTTTTAATTGAAGGCTGGTATGAAAGATTGGACGAGATAATTACTGTCTC GAATTTTATTTTTAAGTTAAAAAGCT-TAAATTTAATTAAAGGACGAGAAGACCCTATAGAGCTT TTTTATTGGGGC-GAT-AATAAAATTTATTAAACTTT TACCTTAGGGATAACAGCGTTATTTTTTTATTAAGTTCTTATTAATGAAAGAGTTTGC Acordulecera ATAGTAATTAGTCTTTTAATTAAAGGCTGGAATGAAAGATTGAACGAGATAAATTCTGTCTC AAATTTAATTTTTAAGTTAAAAAGCT-TAAATTTTATTAAAGGACGAGAAGACCCTATAGAACTT TTTTATTGGGGC-GAT-AAGAAAATTTAAATAACTTT TACCTTAGGGATAACAGCGTTATTTTTTTT-AAAGTTCATATTAATAAAAAAGATTGC Decameria ATAATAATTTGTTTTTTAAATGAAAACTGGAATGAATGATTGGACGAGAAATTAACTGTCTC GAATTTAATCTTTAAGTAAAAAAGCT-TAAATTTTATTAAAGGACGAGAAGACCCTATAGAATTT TTATATTGGGGT-GAT-AGAAAAATTTAAATAATTTT TTCATTATGAATACAAAGATTATTGATTTT-AAAATTTAGATTAATAGAATAAATTAC Neurotoma ATAATCATTAGTCTTTTAATTAAGGTCTAGTATGAATGATTGAACGAAGTATTAACTGTCTC AAATTTAATTTTTAAGTTAAAAAGCT-TAAATAATTTTAAAGGACGAGAAGACCCTATAGAACTT TTTTATTGGGGT-GAT-AAAAAGATTAATTTAACTCT TACCTTAGGGATAACAGCGTAATTTTTTTTTAGAGTTCATATTAACAAAATAGATTAC Onycholyda ATAATCATTAGTCTTTTAATTAAGGTCTAGAATGAAGGATTGAACGAAGTAAAATCTGTCTC AAATTTAATATTTAAGTCAAAAAGCT-TAAATAAATTTAAAAGACGAGAAGACCCTATAGAATTT TTTTATTGGGGT-GAT-AAAAAGATAAAAGTAACTCT TACCTTAGGGATAACAGCGTAATTTTTTTTTAAAGTTCATATTAATAAATTAGATTAC Pamphilius ATAATCATTAGTCTTTTAATTAAGGTCTAGAATGAAGGATTGAACGAAATAAAATCTGTCTC AAATTTAATATTTAAGTTAAAAAACT-TAAATAAAATTAAAAGACGAGAAGACCCTATAGAATTT TTTTATTGGGGT-GAT-AAAAAGATAAAAATAACTCT TACCTTAGGGATAACAGCGTAATTTTTTTTAAAAGTTCATATTAATAAATTAGATTAC Cephalcia ATAATCATTAGTCTTTTAATTAAAGTCTTGAATGAAGGATTGAACGAAGTATAATCTGTCTC AAAATTAATTTTTAAGTTAAAAAGCT-TAAATTATTTTAATAGACGAGAAGACCCTATAGAACTT TTTTGTTGGGGT-GAC-AGGAAAATTAAATAAACTTT TACCTTAGGGATAACAGCGTAATTATTTTTTAAAGTTCATATTAACAAAATAGATTAC Acantholyda ATAATCACTAGTCTTTTAATTGAAGTCTTGTATGAAAGATTGCACGAAATATAATCTGTCTC GAAATTAATTTTTAAGTTAAAAAGCT-TAAATTAATTTAAAAGACGAGAAGACCCTATAGAACTT TTTTGTTGGGGT-GAT-AGGAAAATTTAATAAACTTT TACCTTAGGGATAACAGCGTAATTATTTTTTAGAGTTCATATTGACAAAATAGATTAC Megalodontesc ATAATCATTAGTCTCTTAATTAGAGTCTTGTATGAAAGATTTGATGAAATATTAACTGTCTC AAATTTAATTTTTAAGTCAAAAAGCT-TAAATAAATTTAGAGGACGAGAAGACCCTATAGAACTT TTTCATTGGGAT-GAT-GAATAGATTAATTTAACTCT TACCTTAGGGATAACAGCGTAATTTTTTTTTAAAGTTCATATTAACAAAAAAGATTGC Megalodontessk ?????????????????????????????????????????????????????????????? ????????????????????????????????????????????????????????????????? ????????????????????????????????????? ?????????????????????????????????????????????????????????? Cephus ATAATCATTAGTTTTTTAATTGAAAGCTTGTATGAATGATTGAATAAAATATTTATTGTCTC AAATTTAATTTTTAAATTAAAAAGTT-TAAATTTTATTATAGGACGAGAAGACCCTATAGATTTT TTTTGTTGGGGT-GAC-ATTAAAATTAAATTAACTTT TACCTTAGGGATAACAGCGTAATTTATTTGGAGAGATCATATTAATAAATAAGTTTGC Calameuta ATAATCATTAGTTTTTTAATTGAAAGCTTGTATGAATGATTGGATAAAATATTAATTGTCTC AAATTTAATTTTTAAATTAAAAAGTT-TAAATTTAATTAGAGGACGAGAAGACCCTGTAGATTTT TTTTGTTGGGGT-GAC-ATTAAAATTAAATTAACTTT TACCTTAGGGATAACAGCGTAATTTATTTGGAGAGATCATATTAATAAATAAGTTTGC Hartigia ATAATCATTAGTTTTTTAATTGAAAGCTTGTATGAACGATTGGATAAGATATTAATTGTCTC AAATTTAATTTTTAAGTTAAAAAGCT-TAAATATTATTAAAGGACGAGAAGACCCTATAGATTTT TTTTGTTGGGGT-GAC-ATTAAAATTTAATTAACTTT TACCTTAGGGATAACAGCGTAATTTATTTGGAGAGATCATATTAATAAATAAGTTTGC Syntexis ATAATCATTAGTTTTTTAATTGAAGGCTGGAATGAATGATTGGACGAGATATTTACTGTCTC AAATTTATAATTTAATTTAAAATAGT-TAAATGAAATTAAAAGACGAGAAGACCCTATAGA---- -----TTGGGGT-GAT-TTTAGAATTTAAAAAATTTC TACCTTAGGGATAACAGCGTAATTTTTTTGGAAAGATCATATTTATAAAAAAGTTTGC Sirex ATAATCAATTGTTTTTTAATTTAAATCTAGAATGAATGGTTTAACGAAATATTAACTGTCTC GAATTTAAATTTTTAGTAAAAATGCTGAAAATATATTTAAAAGACGAGAAGACCCTATAGAATTT TTTAATTGGGGA-AAT-TTTAAAA---AAAAATTTTT TACCTTAGGGATAACAGCATAATTTTTTTTGAAAGTTCAAATTGATAAAAAAGATTAT Xeris ATAATCAATTGTTTTTTAATTTAAAGCTGGAATGAAAGGTTTAACGAGATATTTACTGTTTC GAATTTAAATTTTTAGTAAAAATGCTGAAAATATGATTAAAAGACGAGAAGACCCTATAGAATTT TTTAGTTGGGGA-AAT-TTTAAAAAAAAAAGAAATTT TACCTTAGGGATAACAGCATAATTTTTTTTGAAAGTTCTCATAGATAAAAAAGATTGT Urocerus ATAATCAATTGTTTTTTAATTAAAAGCTTGAATGAATGGTTGAACGAGATATAAACTGTTTC GAATTTAAATTTTTAGTAAAAATGCT-AAAATATGATTTAAAGACGAGAAGACCCTATAGAATTT TTTAATTGGGGC-AAT-TTTAGAA---AAAAAAATTT TACCTTAGGGATAACAGCATAATTTTTTTTGAAAGTTCAAATTAATAAAAGAGATTGT Tremex ATAATCAATTGTTTTTTAAATAAAAACTTGAATGAATGGTTTTACGAAATAAATACTGTCTC GAATTTAAAATTTTTGTAAAAATACA-AAAATTTAAATTATAGACGAGAAGACCCTA-AGAATTT TTTTATTGGGGA-AATATTTAAAA--ATAAAAAATTT TACCTTAGGGATAACAGCATAATTTTTTTTGAAAGTTCTTATTAATAAAAAAGATTGT Xiphydria ATAATCATTAGTTTTTTAATTAAAATCTAGAATGAATGATTGAATGAAAAATTATCTGTCTC AAAATTAATTTTTAAGTAAAAAAGCT-TAAATTTTTTTAAAGGACGAGAAGACCCTATAGAGTTT TTTTATTGGGGT-GAT-AATAAAATTAAATTAACTTT TACCTTAGGGATAACAGCATAATAATTTTGGAGAGATCAAATCAATAAAATTTTTTAT Orussus ATAATCAATAGTTCTTTAATTGGGAACTTGTATGAAGGATTGAACGAGATAGTAACTTTCTA GAATTTAATTTTTAGATTAAAAAGTC-TAAATTTAATTAAAAGACGATAAGACCCTATAGAATTT TTTTATTGGGGA-GAT-ATTAAGATTAAAATAACTCT TACCTTAGGGATAACAGCGTAATTCTTTTTGAAAGTTCATATTAATAAAAGAGTTTGC StephanidaeA ATAATAAATAGTTTATTAAAAGTAAACTTGAATGAAGGATTGAATGAGATATAAACTTTATA GAATTTATTTATTAATTAAAAAAAAT-TAATTGATTTTAAAAGACAATAAGACCCTTTAGAATTT TTATGATGGGGC-ATT-AACAAAATTTAATAAAATTT TACCTAAGGGATAACAGCGTAATTATTTTGGATAGTTCATATTGATAAAATAGTTTGC StephanidaeB ATAATAAATAGTTTATTAAAAATAGACTAGTATGAAAGATTAAATGAAATATTAACTTTATA GAATTTTTTAATTAATTAAAAAAAAT-TAATTATTTTTAGGGGACGATAAGACCCTTTAAAATTT TTTTAATGGGGC-ATT-ATTAAAATTTAAATAAATTT TACTTAAGGGATAACAGCGTAATAAATTTAGATAGATCATATTGATAAATTTGATTGC Megalyra ATAATAAATAGTTTTTTTATTGAAAGCTTGAATGAATGGTTTAATGAAAAAATAACTGTCTA AAATTTTATATTTAAGTTAAAAAGCT-TAATTTAATTTAAGTGACGAGAAGACCCTATAGAGTTT TTTGATTGGGGG-GAT-TAATAAATAAATTTAACTTT TACCTTAGGGATAACAGCATAATTTTTTTAGAGAGATCACATTAATAAAAAAGATTAT Trigonalidae ATAATAATTTGTCCTATAATTAAGGAATTGAATGAATGATTTAATGAAAAAATTATTGTCTC AAATTTATTTTTTAAGTTAAAAAACT-TAAATAGAATTAAAAGACGAGAAGACCCTATAGAACTT TTTAATTGGGAG-GAT-TATTAAATTTAATTAACTTT TACCTTAGGGATAACAGCATAATTTTTTTAAATAGTTCATATTGATAAAAAAGTTTAT Chalcidoidea ATAATAATTTGGCTTTTTATTGAAGTCTGGAATGAATGATTTGATGAAGTAATAACTGTCTC GAATTTAATT-TTAAGTAAAAATTCT-TAAATTTAATTAAAAGACGAGAAGACCCTATAGAATTT TTTTTTTGGGGT-AGA-AATAAAATTTAAAGAATTTT TACCTTAGGGATAACAGCGTAATTTTTTTGGAGAGATCATATTGATAAAGAAAATTGC Evanioidea ATAATAAATAGTTTATTAATTGTGAACTGGAATGAATGATTTAATAAGATATAAACTGTATC AATTTAAATTTTAAAGTAAAAAAGCT-TAAATGAATTTGGGGGACGAGAAGACCCTATAGATTTT TTTGGATGGGGA-GAT--------------------- TACCTTAGGGATAACAGCATAATATAATTATTTAGATCTTATAAATAATAGTGATTGT Ichneumonidae AAAATCAATAGTTTTATAATTGAAAGCTGGAATGAAGGATTGAATGAAATATAGACTGTCTC TTAATTTTCTTTTTAGTTAAAAAGCT-AAAATTTTATTAAAAGACGAGAAGACCCTATAGATTTT TTTGGTTGGGGC-AAT-TATTAAATTAATTAAATTTT TACCTTAGGGATAACAGCGTAATTTTTTTGGAGAGATCTTATTAATAAAAAAGTTTGC Cynipoidea ATAATCATTTGTCTTTTAATTAAAGACTAGAATGAAAGATTAAATAAAATATTAACTATATT AAAATTTATTTTTAAGTAAAAAATCT-TAAATATTATTATTAGACGATAAGACCCTATAGAACTT TTATATTGGGGG-GAT-AAAAAAATTAAATAAATTTT TACCTTAGGGATAACAGCATAATTTTTTTAAATAGTTCTTATTGATAAAAAAGATTAT ApoideaA ATAATAAATTGTCTTTTAATTGAAGGCTTGGATGAAAGAATTGACGAAATATAATCTGTCTA TAATTTAAAATTTTAGTTAAAATACT-AAAATTATTTTAAAAGACGAGAAGACCCTATAGAATTT TTTAATTGGGAG-GAT-TTTTATATTTAATTAACTTT TACCTTAGGGATAACAGCGTAATATTTTTGGAGAGATCATATTAAAAAAAATGTTTGC ApoideaB NNNNNNNNNNGTCTTTTAATTGAAGGCTGGGATGAAAGAATTAACGAGATAAAGACTGTCTC TAAATTAAAATTTTAGTTAAAATGCT-AAAATAAATAAATTAGACGAGAAGACCCTATAGAATTT TTTTATTGGGAG-GAT-ATTTATAATTAATTATCTAT TACCTTAGGGATAACAGCGTAATATTTTTTTAAAGATCATATTG?????????????? ApoideaC ATAATAAATTGTCTTTTAATTAAAGGCTGGAATGAAAGAATTAATGAAATATATACTGTCTT GAAATTAAATTTTAAGTAAAAATTCT-TAAATAAATTTATGGGACGATAAGACCCTATAAAATTT TTT-ATTGGGAG-GAT-AATTAAATTTAATAAACTTT TACTTTAGGGATAACAGCGTAATATCTTTTTATAGATCATATTAAAAAAGATGATTGC Vespidae ATAATCAATAGTTTTTTAATTGGAAACTGGAATGAAGGAGTGGATGAAATATAGATTGTCTC GAATTTAAATTAAAAGTAAAAATGCT-TTTATATAGTTATTAGACGAGAAGACCCTATAGAATTT TTTTATTGGGAG-GAT-ATTTAAATTTGAGTAACTTT TACCTTAGGGATAACAGCGTAATAGTTTNGGAGAGTTCATATCGATAAAATTGTTAGC Triassoxyela ?????????????????????????????????????????????????????????????? ????????????????????????????????????????????????????????????????? ????????????????????????????????????? ?????????????????????????????????????????????????????????? Asioxyela ?????????????????????????????????????????????????????????????? ????????????????????????????????????????????????????????????????? ????????????????????????????????????? ?????????????????????????????????????????????????????????? Nigrimonticola ?????????????????????????????????????????????????????????????? ????????????????????????????????????????????????????????????????? ????????????????????????????????????? ?????????????????????????????????????????????????????????? Gigantoxyelinae ?????????????????????????????????????????????????????????????? ????????????????????????????????????????????????????????????????? ????????????????????????????????????? ?????????????????????????????????????????????????????????? Spathoxyela ?????????????????????????????????????????????????????????????? ????????????????????????????????????????????????????????????????? ????????????????????????????????????? ?????????????????????????????????????????????????????????? Xyela_mesozoica ?????????????????????????????????????????????????????????????? ????????????????????????????????????????????????????????????????? ????????????????????????????????????? ?????????????????????????????????????????????????????????? Angaridyela ?????????????????????????????????????????????????????????????? ????????????????????????????????????????????????????????????????? ????????????????????????????????????? ?????????????????????????????????????????????????????????? Xyelotoma ?????????????????????????????????????????????????????????????? ????????????????????????????????????????????????????????????????? ????????????????????????????????????? ?????????????????????????????????????????????????????????? Undatoma ?????????????????????????????????????????????????????????????? ????????????????????????????????????????????????????????????????? ????????????????????????????????????? ?????????????????????????????????????????????????????????? Dahuratoma ?????????????????????????????????????????????????????????????? ????????????????????????????????????????????????????????????????? ????????????????????????????????????? ?????????????????????????????????????????????????????????? Mesolyda ?????????????????????????????????????????????????????????????? ????????????????????????????????????????????????????????????????? ????????????????????????????????????? ?????????????????????????????????????????????????????????? Turgidontes ?????????????????????????????????????????????????????????????? ????????????????????????????????????????????????????????????????? ????????????????????????????????????? ?????????????????????????????????????????????????????????? Aulidontes ?????????????????????????????????????????????????????????????? ????????????????????????????????????????????????????????????????? ????????????????????????????????????? ?????????????????????????????????????????????????????????? Protosirex ?????????????????????????????????????????????????????????????? ????????????????????????????????????????????????????????????????? ????????????????????????????????????? ?????????????????????????????????????????????????????????? Aulisca ?????????????????????????????????????????????????????????????? ????????????????????????????????????????????????????????????????? ????????????????????????????????????? ?????????????????????????????????????????????????????????? Anaxyela ?????????????????????????????????????????????????????????????? ????????????????????????????????????????????????????????????????? ????????????????????????????????????? ?????????????????????????????????????????????????????????? Syntexyela ?????????????????????????????????????????????????????????????? ????????????????????????????????????????????????????????????????? ????????????????????????????????????? ?????????????????????????????????????????????????????????? Karatavites ?????????????????????????????????????????????????????????????? ????????????????????????????????????????????????????????????????? ????????????????????????????????????? ?????????????????????????????????????????????????????????? Stephanogaster ?????????????????????????????????????????????????????????????? ????????????????????????????????????????????????????????????????? ????????????????????????????????????? ?????????????????????????????????????????????????????????? Leptephialtites ?????????????????????????????????????????????????????????????? ????????????????????????????????????????????????????????????????? ????????????????????????????????????? ?????????????????????????????????????????????????????????? Cleistogaster ?????????????????????????????????????????????????????????????? ????????????????????????????????????????????????????????????????? ????????????????????????????????????? ?????????????????????????????????????????????????????????? Sepulca ?????????????????????????????????????????????????????????????? ????????????????????????????????????????????????????????????????? ????????????????????????????????????? ?????????????????????????????????????????????????????????? Onochoius ?????????????????????????????????????????????????????????????? ????????????????????????????????????????????????????????????????? ????????????????????????????????????? ?????????????????????????????????????????????????????????? Ghilarella ?????????????????????????????????????????????????????????????? ????????????????????????????????????????????????????????????????? ????????????????????????????????????? ?????????????????????????????????????????????????????????? Paroryssus ?????????????????????????????????????????????????????????????? ????????????????????????????????????????????????????????????????? ????????????????????????????????????? ?????????????????????????????????????????????????????????? Praeoryssus ?????????????????????????????????????????????????????????????? ????????????????????????????????????????????????????????????????? ????????????????????????????????????? ?????????????????????????????????????????????????????????? Mesorussus ?????????????????????????????????????????????????????????????? ????????????????????????????????????????????????????????????????? ????????????????????????????????????? ?????????????????????????????????????????????????????????? Trematothorax ?????????????????????????????????????????????????????????????? ????????????????????????????????????????????????????????????????? ????????????????????????????????????? ?????????????????????????????????????????????????????????? Thoracotrema ?????????????????????????????????????????????????????????????? ????????????????????????????????????????????????????????????????? ????????????????????????????????????? ?????????????????????????????????????????????????????????? Prosyntexis ?????????????????????????????????????????????????????????????? ????????????????????????????????????????????????????????????????? ????????????????????????????????????? ?????????????????????????????????????????????????????????? Kulbastavia ?????????????????????????????????????????????????????????????? ????????????????????????????????????????????????????????????????? ????????????????????????????????????? ?????????????????????????????????????????????????????????? Brachysyntexis ?????????????????????????????????????????????????????????????? ????????????????????????????????????????????????????????????????? ????????????????????????????????????? ?????????????????????????????????????????????????????????? Symphytopterus ?????????????????????????????????????????????????????????????? ????????????????????????????????????????????????????????????????? ????????????????????????????????????? ?????????????????????????????????????????????????????????? Eoxyela ?????????????????????????????????????????????????????????????? ????????????????????????????????????????????????????????????????? ????????????????????????????????????? ?????????????????????????????????????????????????????????? Liadoxyela ?????????????????????????????????????????????????????????????? ????????????????????????????????????????????????????????????????? ????????????????????????????????????? ?????????????????????????????????????????????????????????? Abrotoxyela ?????????????????????????????????????????????????????????????? ????????????????????????????????????????????????????????????????? ????????????????????????????????????? ?????????????????????????????????????????????????????????? Pseudoxyelocerus ?????????????????????????????????????????????????????????????? ????????????????????????????????????????????????????????????????? ????????????????????????????????????? ?????????????????????????????????????????????????????????? Palaeathalia ?????????????????????????????????????????????????????????????? ????????????????????????????????????????????????????????????????? ????????????????????????????????????? ?????????????????????????????????????????????????????????? Ferganolyda ?????????????????????????????????????????????????????????????? ????????????????????????????????????????????????????????????????? ????????????????????????????????????? ?????????????????????????????????????????????????????????? PamphiliidaeUndesc ?????????????????????????????????????????????????????????????? ????????????????????????????????????????????????????????????????? ????????????????????????????????????? ?????????????????????????????????????????????????????????? Rudisiricius ?????????????????????????????????????????????????????????????? ????????????????????????????????????????????????????????????????? ????????????????????????????????????? ?????????????????????????????????????????????????????????? Sogutia ?????????????????????????????????????????????????????????????? ????????????????????????????????????????????????????????????????? ????????????????????????????????????? ?????????????????????????????????????????????????????????? Xyelula ?????????????????????????????????????????????????????????????? ????????????????????????????????????????????????????????????????? ????????????????????????????????????? ?????????????????????????????????????????????????????????? Brigittepterus ?????????????????????????????????????????????????????????????? ????????????????????????????????????????????????????????????????? ????????????????????????????????????? ?????????????????????????????????????????????????????????? Grimmaratavites ?????????????????????????????????????????????????????????????? ????????????????????????????????????????????????????????????????? ????????????????????????????????????? ?????????????????????????????????????????????????????????? [18S 782-1672, 891 char] Orthoptera ATAGCGTATATTAAAGTTGTTGCGGTTAAAAAGCTCGTAGTTGGATTTGTGTCCCACGCTGTTGGTTCACCGCC-CGTCG--GTGTTT-AACTGGCATGTATCGTGGGACGTCCTGCCGGTGGGG ----------------------CCTACCGCGGTGCTCTTTACCGAGTGTCGAGGTGGGCCGGCACGTTTACTTTGAACAAATTAGAGTGCTTAAAGCAGGC--------CCTGAATACTGTGTGCATGGAATAATGGAATAGGACCTCGGTTCTATTTTGTTGGTTTTCGGAA-CCCGAGGTAATGATTAATAGGGACAGGCGGGGGCATTCGTATTGCGACGTTAGAGGTGAAATTCTTGGACCGTCGCAAGACGAAGCGAAGCGAAAGCATTTGCCAAGTATGTTTTCATTAATCAAGAACGAAAGTTAGAGGTTCGAAGGCGATCAGATACCGCCCTAGTTCTAACCATAATCGATGCCAGCCAGCGATCCGCCGCAGTTCCTCCGATGACTCGGCGGGCAGCCTCCGGGAAACCAAAGCTTTTGGGTTCCGGGGGAAGTATGGTTGCAAAGCTGAAACTTAAAGGAATTGACGGAAGGGCACCACCAGGAGTGGAGCCTGCGGCTTAATTTGACTCAACACGGGAAACCTCACCAGGCCCGGACACCGGAAGGATTGACAGATTGATAGCTCTTTCTTGATTCGGTGGGTGGTGGTGCATGGCCGTTCTTAGTTGGTGGAGCGATTTGTCTGGTTAATTCCGATAACGAACGAGACTC???????????????????????????????????????????????????????????????????????????????????????????????????????? Paraneoptera ATAGCGTATATTAAAGTTGTTGCGGTTAAAAAGCTCGTAGTTGGATCTGTGTTTCACGCTGACGGTTCACCGTTTTGTCG--GTGTTT-AACTGTCATGT--CGTGGTACGTCCTGCCGGT---- ----------------------CCTGTCGCGGTGCTCTTTACCGAGTGTCGAGGCGGGCCGGCACGTTTACCTTGAACAAATTTGAGTGCTTAAAGCAGGCTAT----GCCGGAAAACTGTGTGCATGGAATAATATAATAGGACCGCGGTTCTATTTTGTTGGTTTTCAGAG-TCTGAGGTAATGATTAATAGGGCCCGGCGGGGGCATTAGTATTGCGACGTTAGAGGTGAAATTCTTGGATCGTCGCAAGACTGACCTAAGCGAAAGCATTTGCCAAGGATGCTTCCATTAATCAAGAACGAAAGTTAGAGGTTCGAAGGCGATCAGATACCGCCCTAGTTCTAACCATAAACGATGCCAGCCAACGATCCGTCGCAGTTCCTCCGATGACTCGACGGGCAGCTTCCGGGAAACTAAAGCATTTGGGTTCCGGGGGAAGTATGGTTGCAAAGCTGAAACTTAAAGGAATTGACGGAAAGGCACCACCAGGAGTGGAGCCTGCGGCTTAATTTGACTCAACACGGGAAATCTCACCAGGCCAGAACACTAGATGGATTGACAGATTGAGAGCTCTTTCTTGATTTAGTGGGTGGTGGTGCATGGCCGTTCTTAGTTGGTGGAGCGATTTGTCTGGTTAATTCCGATAACGAACGAGACTCCAGCCTACTAAATAGACGG-------------------------------------------------------------------------CTTCTTAGAGGG Chrysopidae ATAGCGTATATTAAAGTTGTTGCGGTTAAAAAGCTCGTAGTCGAATCTGTGTCCTACACTGTTGGTTCAATGCT-CG-CA--TTGTTT-AACTAGCATGTTATGTGGGACGTCCTACCGGTGGGT ----------------------CCCGTCGCGGTGCTCTTAATTGAGTGTCGAGGTGGGCCGGTACGTTTACTTTGAACAAATTAGAGTGCTTAAGGCAGGCTCA----GCCTGAATATTGTGTGCATGGAATAATGGAATAGGACCTCGGTTCTATTTTGTTGGTTTTCGGAACTCCGAGGTAATGATTAATACGGACAGATGGGGGCATTCGTATTGCGACGTTAGAGGTGAAATTCTTGGATCGTCGCAAGACGGACAGAAGCGAAAGCATTTGCCAAAAATGTTTTGATTGATCAAGAACGAAAGTTAGAGGTTCGAAGGCGATCAGATACCGCCCTAGTTCTAACCATAAACGATGCCAGCTAGCGATCCGCCGAAGTTCCTCCGATGACTCGGCGGGCAGCTTCCGGGAAACCAAAGCTTTTGGGTTCCGGGGGAAGTATGGTTGCAAAGCTGAAACTTAAAGGAATTGACGGAAGGGCACCACCAGGAGTGGAGCCTGCGGCTTAATTTGACTCAACACGGGAAACCTCACCAGGCCCGGACACCGGAAGGATTGACAGATTGAGAGCTCTTTCTTGATTCGGTGGGTGGTGGTGCATGGCCGTTCTTAGTTGGTGGAGCGATTTGTCTGGTTAATTCCGATAACGAACGAGACTCTAGCCTGCTAAATAGACGT---------------------------------------------GGTTTTTACTGCCGGCGTACAAATAATTCTTCTTAGAGGG Raphidioptera ??????????????????????????????????????????????????GTCCCGCGCTGTCGGGTNCACCGC-TC-GC-GGTGTTC-AACTGACATGTTTTGCGGGACGTCCTACCGGTGGGC ----------------------CCTGTCGCGGTGCTCTTCATTGAGTGTCGAGGTGGGCCGGTACATTNACTTTGAACAAATTAGAGTGCTCAAGGCAAGCTAA----GCCTGAATATTGTGTGCATGGAATAATGGAATAGGACCTCGGTTCTATTTTGTTGGTTTTCGGAACTCCGAGGTAATGATTAATACGGACAGATGGGGGCATTCGTATTGCGGCGTTAGAGGTGAAATTCTTGGATCGTCGCAAGACGGACAGAAGCGAAAGCATTTGCCAAAAATGTTTTGATTGATCAAGAACGAAAGTTAGAGGTTCGAAGGCGATCAGATACCGCCCTAGTTCTAACCATAAACGATGCCAGCCAGTGATGCGTCGAAGTTCCTCCGATGACTCGACGTGCAACTTTCGGGAAACCAAAGCTTTTGGGTTCCGGGGGAAGTATGGTTGCAAAGCTGAAACTTAAAGGAATTGACGGAAGGGCACCACCAGGAGTGGAGCCTGCGGCTTAATTTGACTCAACACGGGAAACCTCACCAGGCCCGGACACCGGAAGGATTGACAGATTGAGAGCTCTTTCTTGATTCGGTGGGTGGTGGTGCATGGCCGTTCTTAGTTGGTGGAGCGATTTGTCTGGTTAATTCCGATAACGAACGAGACTCTAGCCTGCTAAATAGACGT---------------------------------------------GGTTTTTACTACCGGC???????????????????????? Coleo_Polyphaga ????CGTATATTAAAGTTGTTGCGGTTAAAAAGCTCGTAGTCGAATTTGTGTCCCGCGCCGTCGGTTCGCCGCT-CG-CG--GCGTTT-AATTGGCGTG--TTGCGGGACGTCATGACGGTGGGC ----------------------CCCGTCGCGGTGCTCTTCGTTGAGTGTCGAGGCGGGCCGTCACGTTTACTTTGAACAAATTAGAGTGCTTAAAGCAGGCTAA----GCCTGGATATTGTGTGCATGGAATAATAAAATAGGATCTCGGTTCTATTTTGTTGGTTTTCGGAACTCCGAGGTAATGATTAATAGGAACGGATGGGGGCATTCGTATTGCGACGTTAGAGGTGAAATTCTTGGATCGTCGCAAGACGGACTAAAGCGAAAGCATTTGCCAAAAACGCTTTCATTGATCAAGAACGAAAGTTAGAGGTTCGAAGGCGATCAGATACCGCCCTAGTTCTAACCATAAACGATGCCAGCCAGCGATCCGCCGAAGTTCCTCCGATGACTCGGCGGGCAGCTTCCGGGAAACCAAAGCTTTTGGGTTCCGGGGGAAGTATGGTTGCAAAGCTGAAACTTAAAGGAATTGACGGAAGGGCACCACCAGGAGTGGAGCCTGCGGCTTAATTTGACTCAACACGGGAAACCTCACCAGGTCCGGACACCGGAAGGATTGACAGATTGAGAGCTCTTTCTTGATTCGGTGGGTGGTGGTGCATGGCCGTTCTTAGTTGGTGGAGCGATTTGTCTGGTTAATTCCGATAACGAACGAGACTCTGGCCTACTAAATAGGCGA---------------------------------------------GGTTTTTACTGTCGACGC-TATATAAATCTTCTTAGAGGG Coleo_Adephaga ATAGCGTATATTAAAGTTGTTGCGGTTAAAAAGCTCGTAGTTGAATCTGTGTCCCGCGCTGTCGGTTCACCGCC-CG-CG--GTGTTT-AACTGGCACGA-TCGCGGGACGTCCTGCCGGTGGAT ----------------------CCTGTCGCGGTGCTCTTAATTGAGTGTCGAGGCGGGCCGGCACGTTTACTTTGAACAAATTAGAGTGCTCAAAGCAGGCTAA----GCCTGAATACTGTGTGCATGGAATAATGGAACAGGACCTCGGTTCTATTTTGTTGGTTTTCGGAACCCCGATGTAATGATTAATAGGGACGGATGGGGGCATTCGTATTGCGACGTTAGAGGTGAAATTCTTGGATCGTCGCAAGACGGACAGAAGCGAAAGCATTTGCCAAAAATGTTTTCATTAATCAAGAACGAAAGTCAGAGGTTCGAAGGCGATCAGATACCGCCCTAGTTCTGACCATAAACGATGCCAGCTAGCGATCCGCCGACGTTCCTCCGATGACTCGGCGGGCAGCTTCCGGGAAACCAAAGCTTTTGGGTTCCGGGGGAAGTATGGTTGCAAAGCTGAAACTTAAAGGAATTGACGGAAGGGCACCACCAGGAGTGGAGCCTGCGGCTTAATTTGACTCAACACGGGAAACCTCACCAGGCCCGGACACCGGAAGGATTGACAGATTGAGAGCTCTTTCTTGATTCGGTGGGTGGTGGTGCATGGCCGTTCTTAGTTGGTGGAGCGATTTGTCTGGTTAATTCCGATAACGAACGAGACTCTAGCCTGCTAACTAGGCGT---------------------------------------------GGTTTTTACTACCGGCGTACAATTGAATCTTCTTAGAGGG Lepid_Micropterigidae ATAGCATATTCTAGAATTGTTGCGGTTAAAAAGCTCGTAGTTGCATTTGTGTGCCGCACTGCCGGTCCATCGCT-CA-CG--CGGTGC-GACTGGCAGGC-TTGCGGGGCATATCGCCGGTGAGC ----------------------CCTGTCGCGGTGCTCTTCGCTGAGTGTCGAGATGGGCCGGCAATTTTACTTTGAACGAATTAGAGTGCTCAAAGCGGGCTAT----GTTTGGATAAAG-GTGCATGGAATAATGGAATATGATCATGGTTTTATTTTGTTGGTTTTAGAGA--CCAAGATAATGATTAATGGAAGCAACTGGGGGCATTCGTATTGCGACGTTAGAGGTGAAATTCTTGGATCGTCGCAAGACGGACCACTGCGAAAGCATTTGCCAAAGGTGTTTTCATCAATCAAGAACGAAAGTTAGAGGTTCGAAGGCGATTAGATACCGCCCTAGTTCTAACCATAAATGTTGCTGTCTAACGATCCGCCGACGTTACTCTAATGGCTCGGCGGGCAGCTTACGGGAAACCAAAGATTTTGAGCTCCGGGGGGAGTATGGTTGCAAAACTGAAACTTAAAGGAATTGACGGAAGGGCACCACCAGGAGTGGAGCCTGCGGCTTAATTTGACTCAACACGGGAAAACTCACCAGGCCCGGACACCGGAAGGATTGACAGATTGATAGCTCTTTCATGATTCGGTGGGTGGTGGTGCATGGCCGTTCTTAGTTGGTGGAGTGATTTGTCAGGTTAATTCCGGTAACGAACGAGATTCTAGCCTGCTAAATAGGCGT-------------------------------------------------------------------------CTTCTTAGAGGG Lepid_Papilionidae ATAGCGTATACTAAAATTGTTGCGGTTAAAAAGCTCGTAGTTGCATTTGTGCGCCGCGCTGTCGGTGCACCGCT-TC-CG--CGGTGA-TACTGACACGT-CTGCGGAGCATATCGTCGGTGAGC ----------------------CCTATCGCGGTGCTCTTCGGTGAGTGTCGAGATGGGCCGACAATTTTACTTTGAACAAATTAGAGTGCTCAAAGCGGGCTCA----GCTTGAATATTTCGTGCATGGAATAATAGAATATGATCTCGGTTCTATTTTGTTGGTTTTCAGAACTCCGAGGTAATGATTAATAGGGATAACTGGGGGCATTCGTATTGCGACGTTAGAGGTGAAATTCTTGGATCGTCGCAAGACGAACATCAGCGAAAGCATTTGCCAAAGGTGTTTTCATCAATCAAGAACGAAAGTTAGAGGTTCGAAGGCGATTAGATACCGCCCTAGTTCTAACCGTAAAT-ATGTCATCTAGCGATCCGCCGACGTTACTACTATGGCTCGGCGGGCAGCTTCCGGGAAACCAAAGATTTTGGACTCCGGGGGGAGTATGGTTGCAAAGCTGAAACTTAAAGGAATTGACGGAAGGGCACCACCAGGAGTGGAGCCTGCGGCTTAATTTGACTCAACACGGGAAATCTCACCAGGCCCGGACACCGGAAGGATTGACAGATTAACAGCTCTTTCTTGATTCGGTGGGTGGTGGTGCATGGCCGTTCTTAGTTGGTGGAGCGATTTGTCTGGTTAATTCCGGTAACGAACGAGACTCTAGCCTGCTAAATAGGCGT-------------------------------------------------------------------------CTTCTTAGAGGG Mecoptera ATAGCGTATATTAAAGTTGTTGCGGTTAAAAAGCTCGTAGTTGAATTTGTGTCCCATACTGTCGGTTCACCGCT-CG-CG--GTGTTT-AACTGGCATGA--TATGGGACGTCCTATCGGTGGGT ----------------------CTCATCACGGTGCTCTTAAACGAGTGTCGAGGTGGGCCGATACGTTTACTTTGAACAAATTAGAGTGCTTAAAGCAGGCTAA----GCCTAAATATTGTGTGCATGGAATAATAGAATAGGACCTCGTTTCTATTTTGTTGGTTTTCGGAATTCCGAGGTAATGATTAATAGGGACAACTGGGGGCATTCGTATTGCGACGTTAGAGGTGAAATTCTTGGATCGTCGCAAGACGGACAGAAGCGAAAGCATTTGCCAAATGTGTTTTCATCAATCAAGAACGAAAGTTAGAGGTTCGAAGGCGATCAGATACCGCCCTAGTTCTAACCATAAACGATGCCAGCTAGCGATCCGCCAAAGTTCCTCCGATGACTTGGCGGGCAGCTTCCGGGAAACCAAAGCTTTTGGGTTCCGGGGGAAGTATGGTTGCAAAGCTGAAACTTAAAGGAATTGACGGAAGGGCACCACCAGGAGTGGAGCCTGCGGCTTAATTTGACTCAACACGGGAAACCTCACCAGGCCCGGACACTGGAAGGATTGACAGATTGAGAGCTCTTTCTTGATTCAGTGGGTGGTGGTGCATGGCCGTTCTTAGTTGGT-GAGCGATTTGTCTGGTTAATTCCGATAACGAACGAGACTCTGGCCTGCTAAATAGGCGT---------------------------------------------AGTTTTTACTACTGGCGTACAAACAATTCTTCTTAGAGGG Xyela ATAGCGTATATTAAAGTTGTTGCGGTTAAAAAGCTCGTAGTTGAATCTGTGTCCCGCGCTGTCGGTTCACCGCT-CG-CG--GTGTTT-AACTGGCATGA-TTGCGGGACGTCCTACCGGTGGGC GGGCG--GCCCAACT-AATA-TCCCATCGCGGTGCTCTTTACTGAGTGTCGAGGTGGGCCGGTACGTTTACTTTGAACAAATTAGAGTGCTTAAAGCAGGCTATCTTCGCCTGAATACTGTGTGCATGGAATAATGGAATAGGACCTCGGTTCTATTTTGTTGGTTTTCGGAACCCCGAGGTAATGATTAATAGGGACAGATGGGGGCATTCGTATTGCGACGTTAGAGGTGAAATTCTTGGATCGTCGCAAGACGGACAGAAGCGAAAGCATTTGCCAAAAATGTTTTCTTTAATCAAGAACGAAAGTTAGAGGTTCGAAGGCGATCAGATACCGCCCTAGTTCTAACCATAAACGATGCCAGCTAGCGATCCGCCGAAGTTCCTCCGATGACTCGGCGGGCAGCTTCCGGGAAACCAAAGCTTTTGGGTTCCGGGGGAAGTATGGTTGCAAAGCTGAAACTTAAAGGAATTGACGGAAGGGCACCACCAGGAGTGGAGCCTGCGGCTTAATTTGACTCAACACGGGAAACCTCACCAGGCCCGGACACCGGAAGGATTGACAGATTGATAGCTCTTTCTTGATTCGGTGGGTGGTGGTGCATGGCCGTTCTTAGTTGGTGGAGCGATTTGTCTGGTTAATTCCGATAACGAACGAGACTCTAGCCTGCTAAATAGGCGTACTTTCCGGTATCTCGAAGGCCCCCGGCTCTTCACGGGGTCGGGCGGTTTTTACTACCGGCGTACAAATAAATCTTCTTAGAGGG Macroxyela ATAGCGTATATTAAAGTTGTTGCGGTTAAAAAGCTCGTAGTTGAATCTGTGTCCCACGCTGTCGGTTCACCGCT-CG-CG--GTGTTT-AACTGGCATGA-TTGTGGGACGTCCTACCGGTGGGC GGGCG--GCCCAACT-AATA-TCCCATCGCGGTGCTCTTTACTGAGTGTCGAGGTGGGCCGGTACGTTTACTTTGAACAAATTAGAGTGCTTAAAGCAGGCTATCTTCGCCTGAATACTGTGTGCATGGAATAATGGAATAGGACCTCGGTTCTATTTTGTTGGTTTTCGGAACCCCGAGGTAATGATTAATAGGGACAGATGGGGGCATTCGTATTGCGACGTTAGAGGTGAAATTCTTGGATCGTCGCAAGACGGACAGAAGCGAAAGCATTTGCCAAAAATGTTTTCTTTAATCAAGAACGAAAGTTAGAGGTTCGAAGGCGATCAGATACCGCCCTAGTTCTAACCATAAACGATGCCAGCTAGCGATCCGCCGAAGTTCCTCCGATGACTCGGCGGGCAGCTTCCGGGAAACCAAAGCTTTTGGGTTCCGGGGGAAGTATGGTTGCAAAGCTGAAACTTAAAGGAATTGACGGAAGGGCACCACCAGGAGTGGAGCCTGCGGCTTAATTTGACTCAACACGGGAAACCTCACCAGGCCCGGACACCGGAAGGATTGACAGATTGATAGCTCTTTCTTGATTCGGTGGGTGGTGGTGCATGGCCGTTCTTAGTTGGTGGAGCGATTTGTCTGGTTAATTCCGATAACGAACGAGACTCTAGCCTGCTAAATAGGCGTACTTTCCGGTATCTCGAAGGCCCCCGGC--TTC----GGTCGGGTGGTTTTTACTACCGGCGTACAAATAAATCTTCTTAGAGGG Runaria ATAGCGTATATTAAAGTTGTTGCGGTTAAAAAGCTCGTAGTTGAATCTGTGTCCCGCGCTGTCGGTTCACCGCT-CG-CG--GTGTTT-AACTGGCATGA-TTGCGGGACGTCCTACCGGTGGGC GGGCG--GCCCAACT-AATA-TCCCATCGCGGTGCTCTTTACTGAGTGTCGAGGTGGGCCGGTACGTTTACTTTGAACAAATTAGAGTGCTTAAAGCAGGCTATCCTCGCCTGAATACTGTGTGCATGGAATAATGGAATAGGACCTCGGTTCTATTTTGTTGGTTTTCGGAACCCCGAGGTAATGATTAATAGGGACAGATGGGGGCATTCGTATTGCGACGTTAGAGGTGAAATTCTTGGATCGTCGCAAGACGGACAGAAGCGAAAGCATTTGCCAAAAATGTTTTCTTTAATCAAGAACGAAAGTTAGAGGTTCGAAGGCGATCAGATACCGCCCTAGTTCTAACCATAAACGATGCCAGCTAGCGATCCGCCGAAGTTCCTCCGATGACTCGGCGGGCAGCTTCCGGGAAACCAAAGCTTTTGGGTTCCGGGGGAAGTATGGTTGCAAAGCTGAAACTTAAAGGAATTGACGGAAGGGCACCACCAGGAGTGGAGCCTGCGGCTTAATTTGACTCAACACGGGAAACCTCACCAGGCCCGGACACCGGAAGGATTGACAGATTGATAGCTCTTTCTTGATTCGGTGGGTGGTGGTGCATGGCCGTTCTTAGTTGGTGGAGCGATTTGTCTGGTTAATTCCGATAACGAACGAGATTCTAGCCTGCTAAATAGGCGTACTTTCCGGTATCTCGAAGGCCCCCGGC--CTC----GGTCGGGTGGTTTTTACTACCGGCGTACAAATAAATCTTCTTAGAGGG Paremphytus ????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ?????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Blasticotoma ATAGCGTATATTAAAGTTGTTGCGGTTAAAAAGCTCGTAGTTGAATCTGTGTCCCGCGCTGTCGGTTCACCGCT-CG-CG--GTGTTT-AACTGGCATGA-TTGCGGGACGTCCTACCGGTGGGC GGGCG--GCCCAACT-AATA-TCCCATCGCGGTGCTCTTTACTGAGTGTCGAGGTGGGCCGGTACGTTTACTTTGAACAAATTAGAGTGCTTAAAGCAGGCTATCCTCGCCTGAATACTGTGTGCATGGAATAATGGAATAGGACCTCGGTTCTATTTTGTTGGTTTTCGGAACCCCGAGGTAATGATTAATAGGGACAGATGGGGGCATTCGTATTGCGACGTTAGAGGTGAAATTCTTGGATCGTCGCAAGACGGACAGAAGCGAAAGCATTTGCCAAAAATGTTTTCTTTAATCAAGAACGAAAGTTAGAGGTTCGAAGGCGATCAGATACCGCCCTAGTTCTAACCATAAACGATGCCAGCTAGCGATCCGCCGAAGTTCCTCCGATGACTCGGCGGGCAGCTTCCGGGAAACCAAAGCTTTTGGGTTCCGGGGGAAGTATGGTTGCAAAGCTGAAACTTAAAGGAATTGACGGAAGGGCACCACCAGGAGTGGAGCCTGCGGCTTAATTTGACTCAACACGGGAAACCTCACCAGGCCCGGACACCGGAAGGATTGACAGATTGATAGCTCTTTCTTGATTCGGTGGGTGGTGGTGCATGGCCGTTCTTAGTTGGTGGAGCGATTTGTCTGGTTAATTCCGATAACGAACGAGATTCTAGCCTGCTAAATAGGCGTACTTTCCGGTATCTCGAAGGCCCCCGGC--TTC----GGTCGGGTGGTTTTTACTACCGGCGTACAAATAAATCTTCTTAGAGGG Tenthredo ATAGCGTATATTAAAGTTGTTGCGGTTAAAAAGCTCGTAGTTGAATCTGTGTCCCACGCTGCCGGTTCGCCGCT-CG-CG--GTGTTT-AACTGGCATGA-TTGTGGGACGTCCTACCGGTGGGC GGGCG--GCCCAACT-AATA-TCCCATCGCGGTGCTCTTCACTGAGTGTCGAGGTGGGCCGGTACGTTTACTTTGAACAAATTAGAGTGCTTAAAGCAGGCTATTTTCGCTTGAATACTGTGTGCATGGAATAATGGAATAGGACCTCGGTTCTATTTTGTTGGTTTTCGGAACCCCGAGGTAATGATTAATAGGGACAGATGGGGGCATTCGTATTGCGACGTTAGAGGTGAAATTCTTGGATCGTCGCAAGACGGACAGAAGCGAAAGCATTTGCCAAAAATGTTTTCTTTAATCAAGAACGAAAGTTAGAGGTTCGAAGGCGATCAGATACCGCCCTAGTTCTAACCATAAACGATGCCAGCTAGCGATCCGCCGAAGTTCCTCCGATGACTCGGCGGGCAGCTTCCGGGAAACCAAAGCTTTTGGGTTCCGGGGGAAGTATGGTTGCAAAGCTGAAACTTAAAGGAATTGACGGAAGGGCACCACCAGGAGTGGAGCCTGCGGCTTAATTTGACTCAACACGGGAAACCTCACCAGGCCCGGACACCGGAAGGATTGACAGATTGAGAGCTCTTTCTTGATTCGGTGGGTGGTGGTGCATGGCCGTTCTTAGTTGGTGGAGCGATTTGTCTGGTTAATTCCGTTAACGAACGAGACTCTAGCCTGCTAAATAGGCGTACTTTCCGGTATCTCGAAGGCCCCCGGC--TTC----GGTCGGGCGGTTTTTACTACCGGCGTACAAATAAATCTTCTTAGAGGG Aglaostigma ATAGCGTATATTAAAGTTGTTGCGGTTAAAAAGCTCGTAGTTGAATCTGTGTCCCACGCTGCCGGTTCGCCGCT-CG-CG--GTGTTT-AACTGGCATGA-TTGTGGGACGTCCTACCGGTGGGC GGGCG--GCCCAACT-AATA-TCCCATCGCGGTGCTCTTCACTGAGTGTCGAGGTGGGCCGGTACGTTTACTTTGAACAAATTAGAGTGCTTAAAGCAGGCTATTTTCGCTTGAATACTGTGTGCATGGAATAATGGAATAGGACCTCGGTTCTATTTTGTTGGTTTTCGGAACCCCGAGGTAATGATTAATAGGGACAGATGGGGGCATTCGTATTGCGACGTTAGAGGTGAAATTCTTGGATCGTCGCAAGACGGACAGAAGCGAAAGCATTTGCCAAAAATGTTTTCTTTAATCAAGAACGAAAGTTAGAGGTTCGAAGGCGATCAGATACCGCCCTAGTTCTAACCATAAACGATGCCAGCTAGCGATCCGCCGAAGTTCCTCCGATGACTCGGCGGGCAGCTTCCGGGAAACCAAAGCTTTTGGGTTCCGGGGGAAGTATGGTTGCAAAGCTGAAACTTAAAGGAATTGACGGAAGGGCACCACCAGGAGTGGAGCCTGCGGCTTAATTTGACTCAACACGGGAAACCTCACCAGGCCCGGACACCGGAAGGATTGACAGATTGAGAGCTCTTTCTTGATTCGGTGGGTGGTGGTGCATGGCCGTTCTTAGTTGGTGGAGCGATTTGTCTGGTTAATTCCGATAACGAACGAGACTCTAGCCTGCTAAATAGGCGTACTTTCCGGTATCTCGAAGGCCCCCGGC--TTC----GGTTGGGCGGTTTTTACTACCGGCGTACAAATAAATCTTCTTAGAGGG Dolerus ATAGCGTATATTAAAGTTGTTGCGGTTAAAAAGCTCGTAGTTGAATCTGTGTCCCACGCTGCCGGTTCGCCGCT-CG-CG--GTGTTT-AACTGGCATGA-TTGTGGGACGTCCTACCGGTGGGC GGGCG--GCCCAACT-AATA-TCCCATCGCGGTGCTCTTCACTGAGTGTCGAGGTGGGCCGGTACGTTTACTTTGAACAAATTAGAGTGCTTAAAGCAGGCTATTTTCGCCTGAATACTGTGTGCATGGAATAATGGAATAGGACCTCGGTTCTATTTTGTTGGTTTTCGGAACCCCGAGGTAATGATTAATAGGGACAGATGGGGGCATTCGTATTGCGACGTTAGAGGTGAAATTCTTGGATCGTCGCAAGACGGACAGAAGCGAAAGCATTTGCCAAAAATGTTTTCTTTAATCAAGAACGAAAGTTAGAGGTTCGAAGGCGATCAGATACCGCCCTAGTTCTAACCATAAACGATGCCAGCTAGCGATCCGCCGAAGTTCCTCCGATGACTCGGCGGGCAGCTTCCGGGAAACCAAAGCTTTTGGGTTCCGGGGGAAGTATGGTTGCAAAGCTGAAACTTAAAGGAATTGACGGAAGGGCACCACCAGGAGTGGAGCCTGCGGCTTAATTTGACTCAACACGGGAAACCTCACCAGGCCCGGACACCGGAAGGATTGACAGATTGAGAGCTCTTTCTTGATTCGGTGGGTGGTGGTGCATGGCCGTTCTTAGTTGGTGGAGCGATTTGTCTGGTTAATTCCGATAACGAACGAGACTCTAGCCTGCTAAATAGGCGTACTTTCCGGTATCTCGAAGGCCCCCGGC--TTC----GGTCGGGTGGTTTTTACTACCGGCGTACAAATAAATCTTCTTAGAGGG Selandria ATAGCGTATATTAAAGTTGTTGCGGTTAAAAAGCTCGTAGTTGAATCTGTGTCCCACGCTGCCGGTTCGCCGCT-CG-CG--GTGTTT-AACTGGCATGA-TTGTGGGACGTCCTGCCGGTGGGC GGGCG--GCCCAACT-AATAATCCCGTCGCGGTGCTCTTCACTGAGTGTCGAGGTGGGCCGGTACGTTTACTTTGAACAAATTAGAGTGCTTAAAGCAGGCTATTTTCGCCTGAATACTGTGTGCATGGAATAATGGAATAGGACCTCGGTTCTATTTTGTTGGTTTTCGGAACCCCGAGGTAATGATTAATAGGGACAGATGGGGGCATTCGTATTGCGACGTTAGAGGTGAAATTCTTGGATCGTCGCAAGACGGACAGAAGCGAAAGCATTTGCCAAAAATGTTTTCTTTAATCAAGAACGAAAGTTAGAGGTTCGAAGGCGATCAGATACCGCCCTAGTTCTAACCATAAACGATGCCAGCTAGCGATCCGCCGAAGTTCCTCCGATGACTCGGCGGGCAGCTTCCGGGAAACCAAAGCTTTTGGGTTCCGGGGGAAGTATGGTTGCAAAGCTGAAACTTAAAGGAATTGACGGAAGGGCACCACCAGGAGTGGAGCCTGCGGCTTAATTTGACTCAACACGGGAAACCTCACCAGGCCCGGACACCGGAAGGATTGACAGATTGAGAGCTCTTTCTTGATTCGGTGGGTGGTGGTGCATGGCCGTTCTTAGTTGGTGGAGCGATTTGTCTGGTTAATTCCGATAACGAACGAGACTCTAGCCTGCTAAATAGGCGTACTTTCCGGTATCTCGAAGGCCCCCGGC--TTC----GGTCGGGCGGTTTTTACTACCGGCGTACAAATAAATCTTCTTAGAGGG Strongylogaster ATAGCGTATATTAAAGTTGTTGCGGTTAAAAAGCTCGTAGTTGAATCTGTGTCCCACGCTGCCGGTTCGCCGCT-CG-CG--GTGTTT-AACTGGCATGA-TTGTGGGACGTCCTACCGGTGGGC GGGCG--GCCCAACT-AATA-TCCCATCGCGGTGCTCTTCACTGAGTGTCGAGGTGGGCCGGTACGTTTACTTTGAACAAATTAGAGTGCTTAAAGCAGGCTATTTTCGCCTGAATACTGTGTGCATGGAATAATGGAATAGGACCTCGGTTCTATTTTGTTGGTTTTCGGAACCCCGAGGTAATGATTAATAGGGACAGATGGGGGCATTCGTATTGCGACGTTAGAGGTGAAATTCTTGGATCGTCGCAAGACGGACAGAAGCGAAAGCATTTGCCAAAAATGTTTTCTTTAATCAAGAACGAAAGTTAGAGGTTCGAAGGCGATCAGATACCGCCCTAGTTCTAACCATAAACGATGCCAGCTAGCGATCCGCCGAAGTTCCTCCGATGACTCGGCGGGCAGCTTCCGGGAAACCAAAGCTTTTGGGTTCCGGGGGAAGTATGGTTGCAAAGCTGAAACTTAAAGGAATTGACGGAAGGGCACCACCAGGAGTGGAGCCTGCGGCTTAATTTGACTCAACACGGGAAACCTCACCAGGCCCGGACACCGGAAGGATTGACAGATTGAGAGCTCTTTCTTGATTCGGTGGGTGGTGGTGCATGGCCGTTCTTAGTTGGTGGAGCGATTTGTCTGGTTAATTCCGATAACGAACGAGACTCTAGCCTGCTAAATAGGCGTACTTTCCGGTATCTCGAAGGCCCCCGGC--TTC----GGTCGGGCGGTTTTTACTACCGGCGTACAAATAAATCTTCTTAGAGGG Monophadnoides ATAGCGTATATTAAAGTTGTTGCGGTTAAAAAGCTCGTAGTTGAATCTGTGTCCCACGCTGCCGGTTCGCCGCT-CG-CG--GTGTTT-AACTGGCATGA-TTGTGGGACGTCCTACCGGTGGGC GGGCG--GCCCAACT-AATA-TCCCATCGCGGTGCTCTTCACTGAGTGTCGAGGTGGGCCGGTACGTTTACTTTGAACAAATTAGAGTGCTTAAAGCAGGCTATTTTCGCTTGAATACTGTGTGCATGGAATAATGGAATAGGACCTCGGTTCTATTTTGTTGGTTTTCGGAACCCCGAGGTAATGATTAATAGGGACAGATGGGGGCATTCGTATTGCGACGTTAGAGGTGAAATTCTTGGATCGTCGCAAGACGGACAGAAGCGAAAGCATTTGCCAAAAATGTTTTCTTTAATCAAGAACGAAAGTTAGAGGTTCGAAGGCGATCAGATACCGCCCTAGTTCTAACCATAAACGATGCCAGCTAGCGATCCGCCGAAGTTCCTCCGATGACTCGGCGGGCAGCTTCCGGGAAACCAAAGCTTTTGGGTTCCGGGGGAAGTATGGTTGCAAAGCTGAAACTTAAAGGAATTGACGGAAGGGCACCACCAGGAGTGGAGCCTGCGGCTTAATTTGACTCAACACGGGAAACCTCACCAGGCCCGGACACCGGAAGGATTGACAGATTGAGAGCTCTTTCTTGATTCGGTGGGTGGTGGTGCATGGCCGTTCTTAGTTGGTGGAGCGATTTGTCTGGTTAATTCCGATAACGAACGAGACTCTAGCCTGCTAAATAGGCGTACTTCCCGGTATCTCGAAGGCCTCCGGC--TTC----GGTCGGGCGGTTTTTACTACCGGCGTACAAATAAATCTTCTTAGAGGG Metallus ATAGCGTATATTAAAGTTGTTGCGGTTAAAAAGCTCGTAGTTGAATCTGTGTCCCACGCTGCCGGTTCGCCGCT-CG-CG--GTGTTC-AACTGGCATGA-TTGTGGGACGTCCTACCGGTGGGC GGGCG--GCCCAACT-AATA-TCCCATCGCGGTGCTCTTCACTGAGTGTCGAGGTGGGCCGGTACGTTTACTTTGAACAAATTAGAGTGCTTAAAGCAGGCTATTTTCGCTTGAATACTGTGTGCATGGAATAATGGAATAGGACCTCGGTTCTATTTTGTTGGTTTTAGGAACCCCGAGGTAATGATTAATAGGGACAGATGGGGGCATTCGTATTGCGACGTTAGAGGTGAAATTCTTGGATCGTCGCAAGACGGACAGAAGCGAAAGCATTTGCCAAAAATGTTTTCTTTAATCAAGAACGAAAGTTAGAGGTTCGAAGGCGATCAGATACCGCCCTAGTTCTAACCATAAACGATGCCAGCTAGCGATCCGCCGAAGTTCCTCCGATGACTCGGCGGGCAGCTTCCGGGAAACCAAAGCTTTTGGGTTCCGGGGGAAGTATGGTTGCAAAGCTGAAACTTAAAGGAATTGACGGAAGGGCACCACCAGGAGTGGAGCCTGCGGCTTAATTTGACTCAACACGGGAAACCTCACCAGGCCCGGACACCGGAAGGATTGACAGATTGAGAGCTCTTTCTTGATTCGGTGGGTGGTGGTGCATGGCCGTTCTTAGTTGGTGGAGCGATTTGTCAGGTTAATTCCGATAACGAACGAGACTCTAGCCTGCTAAATAGGCGTACTTTCCGGTATCTCGAAGGCCCCCGGC--TTC----GGTCGGGTGGTTTTTACTACCGGCGTACAAATAAATCTTCTTAGAGGG Athalia ATAGCGTATATTAAAGTTGTTGCGGTTAAAAAGCTCGTAGTTGAATCTGTGTCCCACGCTGTCGGTTCACCGCT-CG-CG--GTGTTT-AACTGGCATGA-TTGTGGGACGTCCTACCGGTGGGC GGGCG--GCCCAACT-AATA-TCCCATCGCGGTGCTCTTCATTGAGTGTCGAGGTGGGCCGGTACGTTTACTTTGAACAAATTAGAGTGCTTAAAGCAGGCTATTTTCGCCTGAATACTGTGTGCATGGAATAATGGAATAGGACCTCGGTTCTATTTTGTTGGTTTTCGGAACCCCGAGGTAATGATTAATAGGAACAGATGGGGGCATTCGTATTGCGACGTTAGAGGTGAAATTCTTGGATCGTCGCAAGACGGACAGAAGCGAAAGCATTTGCCAAAAATGTTTTCTTTAATCAAGAACGAAAGTTAGAGGTTCGAAGGCGATCAGATACCGCCCTAGTTCTAACCATAAACGATGCCAGCTAGCGATCCGCCGAAGTTCCTCCGATGACTCGGCGGGCAGCTTCCGGGAAACCAAAGCTTTTGGGTTCCGGGGGAAGTATGGTTGCAAAGCTGAAACTTAAAGGAATTGACGGAAGGGCACCACCAGGAGTGGAGCCTGCGGCTTAATTTGACTCAACACGGGAAACCTCACCAGGCCCGGACACCGGAAGGATTGACAGATTGAGAGCTCTTTCTTGATTCGGTGGGTGGTGGTGCATGGCCGTTCTTAGTTGGTGGAGCGATTTGTCTGGTTAATTCCGATAACGAACGAGACTCTAGCCTGCTAAATAGGCGTACTTTCCGGTATCTCGAAGGCCCCCGGC--TTC----GGTCGGGAGGTTTTTACTACCGGCGTACAAATAAATCTTCTTAGAGGG Taxonus ATAGCGTATATTAAAGTTGTTGCGGTTAAAAAGCTCGTAGTTGAATCTGTGTCCCACGCTGTCGGTTCGCCGCT-CG-CG--GTGTTT-AACTGGCATGA-TTGTGGGACGTCCTACCGGTGGGC GGGCG--GCCCAACT-AATA-TCCCATCGCGGTGCTCTTCACTGAGTGTCGAGGTGGGCCGGTACGTTTACTTTGAACAAATTAGAGTGCTTAAAGCAGGCTATTTTCGCCTGAATACTGTGTGCATGGAATAATGGAATAGGACCTCGGTTCTATTTTGTTGGTTTTCGGAACCCCGAGGTAATGATTAATAGGGACAGATGGGGGCATTCGTATTGCGACGTTAGAGGTGAAATTCTTGGATCGTCGCAAGACTGACAGAAGCGAAAGCATTTGCCAAAAATGTTTTCTTTAATCAAGAACGAAAGTTAGAGGTTCGAAGGCGATCAGATACCGCCCTAGTTCTAACCATAAACGATGCCAGCTAGCGATCCGCCGAAGTTCCTCCGATGACTCGGCGGGCAGCTTCCGGGAAACCAAAGCTTTTGGGTTCCGGGGGAAGTATGGTTGCAAAGCTGAAACTTAAAGGAATTGACGGAAGGGCACCACCAGGAGTGGAGCCTGCGGCTTAATTTGACTCAACACGGGAAACCTCACCAGGCCCGGACACCGGAAGGATTGACAGATTGAGAGCTCTTTCTTGATTCGGTGGGTGGTGGTGCATGGCCGTTCTTAGTTGGTGGAGCGATTTGTCTGGTTAATTCCGATAACGAACGAGACTCTAGCCTGCTAAATAGGCGTACTTTCCGGTATCTCGAAGGCCCCCGGC--TTC----GGTCGGGTGGTTTTTACTACCGGCGTACAAATAAATCTTCTTAGAGGG Hoplocampa ATAGCGTATATTAAAGTTGTTGCGGTTAAAAAGCTCGTAGTTGAATCTGTGTCCCACGCTGTCGGTTCGCCGCT-CG-CG--GTGTTC-AACTGGCATGA-TTGTGGGACGTCCTACCGGTGGGC GGGCG--GCCCAACT-AATA-TCCCATCGCGGTGCTCTTCACTGAGTGTCGAGGTGGGCCGGTACGTTTACTTTGAACAAATTAGAGTGCTTAAAGCAGGCTATTTTCGCCTGAATACTGTGTGCATGGAATAATGGAATAGGACCTCGGTTCTATTTTGTTGGTTTTCGGAACCCCGAGGTAATGATTAATAGGGACAGATGGGGGCATTCGTATTGCGACGTTAGAGGTGAAATTCTTGGATCGTCGCAAGACGGACAGAAGCGAAAGCATTTGCCAAAAATGTTTTCTTTAATCAAGAACGAAAGTTAGAGGTTCGAAGGCGATCAGATACCGCCCTAGTTCTAACCATAAACGATGCCAGCTAGCGATCCGCCGAAGTTCCTCCGATGACTCGGCGGGCAGCTTCCGGGAAACCAAAGCTTTTGGGTTCCGGGGGAAGTATGGTTGCAAAGCTGAAACTTAAAGGAATTGACGGAAGGGCACCACCAGGAGTGGAGCCTGCGGCTTAATTTGACTCAACACGGGAAACCTCACCAGGCCCGGACACCGGAAGGATTGACAGATTGAGAGCTCTTTCTTGATTCGGTGGGTGGTGGTGCATGGCCGTTCTTAGTTGGTGGAGCGATTTGTCTGGTTAATTCCGATAACGAACGAGACTCTAGCCTGCTAAATAGGCGTACTTTCCGGTATCTCGAAGGCCCCCGGC--TTC----GGTCGGGCGGTTTTTACTACCGGCGTACAAATAAATCTTCTTAGAGGG Nematinus ATAGCGTATATTAAAGTTGTTGCGGTTAAAAAGCTCGTAGTTGAATCTGTGTCCCACGCTGTCGGTTCGCCGCT-CG-CG--GTGTTT-AACTGGCATGA-TTGTGGGACGTCCTACCGGTGGGC GGGCG--GCCCAACT-AATA-TCCCATCGCGGTGCTCTTCACTGAGTGTCGAGGTGGGCCGGTACGTTTACTTTGAACAAATTAGAGTGCTTAAAGCAGGCTACTTTCGCCTGAATACTGTGTGCATGGAATAATGGAATAGGACCTCGGTTCTATTTTGTTGGTTTTCGGAACCCCGAGGTAATGATTAATAGGGACAGATGGGGGCATTCGTATTGCGACGTTAGAGGTGAAATTCTTGGATCGTCGCAAGACGGACAGAAGCGAAAGCATTTGCCAAAAATGTTTTCTTTAATCAAGAACGAAAGTTAGAGGTTCGAAGGCGATCAGATACCGCCCTAGTTCTAACCATAAACGATGCCAGCTAGCGATCCGCCGAAGTTCCTCCGATGACTCGGCGGGCAGCTTCCGGGAAACCAAAGCTTTTGGGTTCCGGGGGAAGTATGGTTGCAAAGCTGAAACTTAAAGGAATTGACGGAAGGGCACCACCAGGAGTGGAGCCTGCGGCTTAATTTGACTCAACACGGGAAACCTCACCAGGCCCGGACACCGGAAGGATTGACAGATTGAGAGCTCTTTCTTGATTCGGTGGGTGGTGGTGCATGGCCGTTCTTAGTTGGTGGAGCGATTTGTCTGGTTAATTCCGATAACGAACGAGACTCTAGCCTGCTAAATAGGCGTACTTTCCGGTATCTCGAAGGCCCCCGGC--TTC----GGTCGGGCGGTTTTTACTACCGGCGTACAAATAAATCTTCTTAGAGGG Nematus ATAGCGTATATTAAAGTTGTTGCGGTTAAAAAGCTCGTAGTTGAATCTGTGTCCCACGCTGTCGGTTCGCCGCT-CG-CG--GTGTTT-AACTGGCATGA-TTGTGGGACGTCCTACCGGTGGGC GGGCG--GCCCAACT-AATA-TCCCATCGCGGTGCTCTTCACTGAGTGTCGAGGTGGGCCGGTACGTTTACTTTGAACAAATTAGAGTGCTTAAAGCAGGCTACTTTCGCCTGAATACTGTGTGCATGGAATAATGGAATAGGACCTCGGTTCTATTTTGTTGGTTTTCGGAACCCCGAGGTAATGATTAATAGGGACAGATGGGGGCATTCGTATTGCGACGTTAGAGGTGAAATTCTTGGATCGTCGCAAGACGGACAGAAGCGAAAGCATTTGCCAAAAATGTTTTCTTTAATCAAGAACGAAAGTTAGAGGTTCGAAGGCGATCAGATACCGCCCTAGTTCTAACCATAAACGATGCCAGCTAGCGATCCGCCGAAGTTCCTCCGATGACTCGGCGGGCAGCTTCCGGGAAACCAAAGCTTTTGGGTTCCGGGGGAAGTATGGTTGCAAAGCTGAAACTTAAAGGAATTGACGGAAGGGCACCACCAGGAGTGGAGCCTGCGGCTTAATTTGACTCAACACGGGAAACCTCACCAGGCCCGGACACCGGAAGGATTGACAGATTGAGAGCTCTTTCTTGATTCGGTGGGTGGTGGTGCATGGCCGTTCTTAGTTGGTGGAGCGATTTGTCTGGTTAATTCCGATAACGAACGAGACTCTAGCCTGCTAAATAGGCGTACTTTCCGGTATCTCGAAGGCCCCCGGC--TTC----GGTCGGGCGGTTTTTACTACCGGCGTACAAATAAATCTTCTTAGAGGG Cladius ATAGCGTATATTAAAGTTGTTGCGGTTAAAAAGCTCGTAGTTGAATCTGTGTCCCACGCTGTCGGTTCGTCGCT-CG-CG--GCGTTT-AACTGGCATGA-TTGTGGGACGTCCTACCGGTGGGC GGGCG--GCCCAACT-AATA-TCCCATCGCGGTGCTCTTCACTGAGTGTCGAGGTGGGCCGGTACGTTTACTTTGAACAAATTAGAGTGCTTAAAGCAGGCTATCTTCGCCTGAATACTGTGTGCATGGAATAATGGAATAGGACCTCGGTTCTATTTTGTTGGTTTTAGGAACCCCGAGGTAATGATTAATAGGAACAGATGGGGGCATTCGTATTGCGACGTTAGAGGTGAAATTCTTGGATCGTCGCAAGACGGACAGAAGCGAAAGCATTTGCCAAAAATGTTTTCTTTAATCAAGAACGAAAGTTAGAGGTTCGAAGGCGATCAGATACCGCCCTAGTTCTAACCATAAACGATGCCAGCTAGCGATCCGCCGAAGTTCCTCCGATGACTCGGCGGGCAGCTTCCGGGAAACCAAAGCTTTTGGGTTCCGGGGGAAGTATGGTTGCAAAGCTGAAACTTAAAGGAATTGACGGAAGGGCACCACCAGGAGTGGAGCCTGCGGCTTAATTTGACTCAACACGGGAAACCTCACCAGGCCCGGACACCGGAAGGATTGACAGATTGATAGCTCTTTCTTGATTCGGTGGGTGGTGGTGCATGGCCGTTCTTAGTTGGTGGAGCGATTTGTCTGGTTAATTCCGATAACGAACGAGACTCTAGCCTGCTAAATAGGCGTACTTTCCGGTATCTCGAAGGCCCCCGGC--CTC----GGTCGGGCGGTTTTTACTACCGGCGTACAAATAAATCTTCTTAGAGGG Monoctenus ATAGCGTATATTAAAGTTGTTGCGGTTAAAAAGCTCGTAGTTGAATCTGTGTCCCACGCTGTCGGTTCACCGCT-CG-CG--GTGTTT-AACTGGCATGA-TTGTGGGACGTCCTACCGGTGGGC GGGCG--GCCCAACT-AATA-TCCCATCGCGGTGCTCTTCGCTGAGTGTCGAGGTGGGCCGGTACGTTTACTTTGAACAAATTAGAGTGCTTAAAGCAGGCTATTTTCGCCTGAATACTGTGTGCATGGAATAATGGAATAGGACCTCGGTTCTATTTTGTTGGTTTTCGGAACCCCGAGGTAATGATTAATAGGGACAGATGGGGGCATTCGTATTGCGACGTTAGAGGTGAAATTCTTGGATCGTCGCAAGACGGACAGAAGCGAAAGCATTTGCCAAAAATGTTTTCTTTAATCAAGAACGAAAGTTAGAGGTTCGAAGGCGATCAGATACCGCCCTAGTTCTAACCATAAACGATGCCAGCTAGCGATCCGCCGAAGTTCCTCCGATGACTCGGCGGGCAGCTTCCGGGAAACCAAAGCTTTTGGGTTCCGGGGGAAGTATGGTTGCAAAGCTGAAACTTAAAGGAATTGACGGAAGGGCACCACCAGGAGTGGAGCCTGCGGCTTAATTTGACTCAACACGGGAAACCTCACCAGGCCCGGACACCGGAAGGATTGACAGATTGAGAGCTCTTTCTTGATTCGGTGGGTGGTGGTGCATGGCCGTTCTTAGTTGGTGGAGCGATTTGTCTGGTTAATTCCGATAACGAACGAGACTCTAGCCTGCTAAATAGGCGTACTTTCCGGTATCTCGAAGGCCCCCGGC--TTC----GGTCGGGCGGTTTTTACTACCGGCGTACAAATAAATCTTCTTAGAGGG Gilpinia ATAGCGTATATTAAAGTTGTTGCGGTTAAAAAGCTCGTAGTTGAATCTGTGTCCCACGCTGTCGGTTCACCGCT-CG-CG--GTGTCT-AACTGGCATGA-TTGTGGGACGTCCTACCGGTGGGC GGGCG--GCCCAACT-AATA-TCCCATCGCGGTGCTCTTCACTGAGTGTCGAGGTGGGCCGGTACGTTTACTTTGAACAAATTAGAGTGCTTAAAGCAGGCTATTTTCGCCTGAATACTGTGTGCATGGAATAATGGAATAGGACCTCGGTTCTATTTTGTTGGTTTTCGGAACCCCGAGGTAATGATTAATAGGAACAGATGGGGGCATTCGTATTGCGACGTTAGAGGTGAAATTCTTGGATCGTCGCAAGACGGACAGAAGCGAAAGCATTTGCCAAAAATGTTTTCTTTAATCAAGAACGAAAGTTAGAGGTTCGAAGGCGATCAGATACCGCCCTAGTTCTAACCATAAACGATGCCAGCTAGCGATCCGCCGAAGTTCCTCCGATGACTCGGCGGGCAGCTTCCGGGAAACCAAAGCTTTTGGGTTCCGGGGGAAGTATGGTTGCAAAGCTGAAACTTAAAGGAATTGACGGAAGGGCACCACCAGGAGTGGAGCCTGCGGCTTAATTTGACTCAACACGGGAAACCTCACCAGGCCCGGACACCGGAAGGATTGACAGATTGAGAGCTCTTTCTTGATTCGGTGGGTGGTGGTGCATGGCCGTTCTTAGTTGGTGGAGCGATTTGTCTGGTTAATTCCGATAACGAACGAGACTCTAGCCTGCTAAATAGGCGTACTTCCCGGTATCTCGAAGGCCCCCGGC--TTC----GGTCGGGCGGTTTTTACTACCGGCGTACAAATAAATCTTCTTAGAGGG Diprion ATAGCGTATATTAAAGTTGTTGCGGTTAAAAAGCTCGTAGTTGAATCTGTGTCCCACGCTGTCGGTTCACCGCT-CG-CG--GTGTCT-AACTGGCATGA-TTGTGGGACGTCCTACCGGTGGGC GGGCG--GCCCAACT-AATA-TCCCATCGCGGTGCTCTTCACTGAGTGTCGAGGTGGGCCGGTACGTTTACTTTGAACAAATTAGAGTGCTTAAAGCAGGCTATTTTCGCCTGAATACTGTGTGCATGGAATAATGGAATAGGACCTCGGTTCTATTTTGTTGGTTTTCGGAACCCCGAGGTAATGATTAATAGGGACAGATGGGGGCATTCGTATTGCGACGTTAGAGGTGAAATTCTTGGATCGTCGCAAGACGGACAGAAGCGAAAGCATTTGCCAAAAATGTTTTCTTTAATCAAGAACGAAAGTTAGAGGTTCGAAGGCGATCAGATACCGCCCTAGTTCTAACCATAAACGATGCCAGCTAGCGATCCGCCGAAGTTCCTCCGATGACTCGGCGGGCAGCTTCCGGGAAACCAAAGCTTTTGGGTTCCGGGGGAAGTATGGTTGCAAAGCTGAAACTTAAAGGAATTGACGGAAGGGCACCACCAGGAGTGGAGCCTGCGGCTTAATTTGACTCAACACGGGAAACCTCACCAGGCCCGGACACCGGAAGGATTGACAGATTGAGAGCTCTTTCTTGATTCGGTGGGTGGTGGTGCATGGCCGTTCTTAGTTGGTGGAGCGATTTGTCTGGTTAATTCCGATAACGAACGAGACTCTAGCCTGCTAAATAGGCGTACTTTCCGGTATCTCGAAGGCCCCCGGC--TTC----GGTCGGGCGGTTTTTACTACCGGCGTACAAATAAATCTTCTTAGAGGG Cimbicinae ATAGCGTATATTAAAGTTGTTGCGGTTAAAAAGCTCGTAGTTGAATCTGTGTGCCACGCTGTCGGTTCACCGCT-CG-CG--GTGTTT-AACTGGCATGA-TTGTGGGACGTCCTACCGGTGGGC GGGCG--GCCCAACT-AATA-TCCCATCGCGGTGCTCTTCGTTGAGTGTCGAGGTGGGCCGGTACGTTTACTTTGAACAAATTAGAGTGCTTAAAGCAGGCTAGTTTCGCCTGAATACTGTGTGCATGGAATAATGGAATAGGACCTCGGTTCTATTTTGTTGGTTTTCGGAACCCCGAGGTAATGATTAACAGGGACAGATGGGGGCATTCGTATTGCGACGTTAGAGGTGAAATTCTTGGATCGTCGCAAGACGGACAGAAGCGAAAGCATTTGCCAAAAATGTTTTCTTTAATCAAGAACGAAAGTTAGAGGTTCGAAGGCGATCAGATACCGCCCTAGTTCTAACCATAAACGATGCCAGCTAGCGATCCGCCGAAGTTCCTCCGATGACTCGGCGGGCAGCTTCCGGGAAACCAAAGCTTTTGGGTTCCGGGGGAAGTATGGTTGCAAAGCTGAAACTTAAAGGAATTGACGGAAGGGCACCACCAGGAGTGGAGCCTGCGGCTTAATTTGACTCAACACGGGAAACCTCACCAGGCCCGGACACCGGAAGGATTGACAGATTGAGAGCTCTTTCTTGATTCGGTGGGTGGTGGTGCATGGCCGTTCTTAGTTGGTGGAGCGATTTGTCTGGTTAATTCCGATAACGAACGAGACTCTAGCCTGCTAAATAGGCGTACTTTCCGGTATCTCGAAGGCCCCCGGC--TTC----GGTCGGGCGGTTTTTACTACCGGCGTACAAATAAATCTTCTTAGAGGG Abia ATAGCGTATATTAAAGTTGTTGCGGTTAAAAAGCTCGTAGTTGAATCTGTGTGCCACGCTGTCGGTTCACCGCT-CG-CG--GTGTTT-AACTGGCATGA-CTGTGGGACGTCCTACCGGTGGGC GGGCG--GCCCAACT-AATA-TCCCATCGCGGTGCTCTTCGCTGAGTGTCGAGGTGGGCCGGTACGTTTACTTTGAACAAATTAGAGTGCTTAAAGCAGGCTATTTTCGCCTGAATACTGTGTGCATGGAATAATGGAATAGGACCTCGGTTCTATTTTGTTGGTTTTCGGAACCCCGAGGTAATGATTAACAGGAACAGATGGGGGCATTCGTATTGCGACGTTAGAGGTGAAATTCTTGGATCGTCGCAAGACGGACAGAAGCGAAAGCATTTGCCAAAAATGTTTTCCTTAATCAAGAACGAAAGTTAGAGGTTCGAAGGCGATCAGATACCGCCCTAGTTCTAACCATAAACGATGCCAGCTAGCGATCCGCCGAAGTTCCTCCGATGACTCGGCGGGCAGCTTCCGGGAAACCAAAGCTTTTGGGTTCCGGGGGAAGTATGGTTGCAAAGCTGAAACTTAAAGGAATTGACGGAAGGGCACCACCAGGAGTGGAGCCTGCGGCTTAATTTGACTCAACACGGGAAACCTCACCAGGCCCGGACACCGGAAGGATTGACAGATTGAGAGCTCTTTCTTGATTCGGTGGGTGGTGGTGCATGGCCGTTCTTAGTTGGTGGAGCGATTTGTCTGGTTAATTCCGATAACGAACGAGACTCTAGCCTGCTAAATAGGCGTACTTTCCGGTATCTCGAAGGCCCCCGGC--TTC----GGTCGGGCGGTTTTTACTACCGGCGTACAAATTAATCTTCTTAGAGGG Corynis ATAGCGTATATTAAAGTTGTTGCGGTTAAAAAGCTCGTAGTTGAATCTGTGTCCCACGCTGTCGGTTCACCGCT-CG-CG--GTGTTT-AACTGGCATGA-TTGTGGGACGTCCTACCGGTGGGC GGGCG--GCCCAACT-AATA-TCCCATCGCGGTGCTCTTCGCTGAGTGTCGAGGTGGGCCGGTACGTTTACTTTGAACAAATTAGAGTGCTTAAAGCAGGCTAGTTTCGCCTGAATACTGTGTGCATGGAATAATGGAATAGGACCTCGGTTCTATTTTGTTGGTTTTCGGAACCCCGAGGTAATGATTAATAGGGACAGATGGGGGCATTCGTATTGCGACGTTAGAGGTGAAATTCTTGGATCGTCGCAAGACGGACAGAAGCGAAAGCATTTGCCAAAAATGTTTTCTTTAATCAAGAACGAAAGTTAGAGGTTCGAAGGCGATCAGATACCGCCCTAGTTCTAACCATAAACGATGCCAGCTAGCGATCCGCCGAAGTTCCTCCGATGACTCGGCGGGCAGCTTCCGGGAAACCAAAGCTTTTGGGTTCCGGGGGAAGTATGGTTGCAAAGCTGAAACTTAAAGGAATTGACGGAAGGGCACCACCAGGAGTGGAGCCTGCGGCTTAATTTGACTCAACACGGGAAACCTCACCAGGCCCGGACACCGGAAGGATTGACAGATTGAGAGCTCTTTCTTGATTCGGTGGGTGGTGGTGCATGGCCGTTCTTAGTTGGTGGAGCGATTTGTCTGGTTAATTCCGATAACGAACGAGACTCTAGCCTGCTAAATAGGCGTACTTTCCGGTATCTCGAAGGCCCCCGGC--TTC----GGTCGGGTGGTTTTTACTACCGGCGTACAAATAAATCTTCTTAGAGGG Arge ATAGCGTATATTAAAGTTGTTGCGGTTAAAAAGCTCGTAGTTGAATCTGTGTCCCGCGCTGTCGGTTCACCGCT-CG-CG--GTGTTT-AACTGGCATGA-TTGCGGGACGTCCTACCGGTGGGC GGGCG--GCCCAACT-AATA-TCCCATCGCGGTGCTCTTTATTGAGTGTCGAGGTGGGCCGGTACGTTTACTTTGAACAAATTAGAGTGCTTAAAGCAGGCTATTTTCGCCTGAATACTGTGTGCATGGAATAATGGAATAGGACCTCGGTTCTATTTTGTTGGTTTTCGGAACCCCGAGGTAATGATTAATAGGAACAGATGGGGGCATTCGTATTGCGACGTTAGAGGTGAAATTCTTGGATCGTCGCAAGACGGACAGAAGCGAAAGCATTTGCCAAAAATGTTTTCATTAATCAAGAACGAAAGTTAGAGGTTCGAAGGCGATCAGATACCGCCCTAGTTCTAACCATAAACGATGCCAGCTAGCGATCCGCCGAAGTTCCTCCGATGACTCGGCGGGCAGCTTCCGGGAAACCAAAGCTTTTGGGTTCCGGGGGAAGTATGGTTGCAAAGCTGAAACTTAAAGGAATTGACGGAAGGGCACCACCAGGAGTGGAGCCTGCGGCTTAATTTGACTCAACACGGGAAACCTCACCAGGCCCGGACACCGGAAGGATTGACAGATTGAGAGCTCTTTCTTGATTCGGTGGGTGGTGGTGCATGGCCGTTCTTAGTTGGTGGAGCGATTTGTCTGGTTAATTCCGATAACGAACG?????????????????????????????????????????????????????????????????????????????????????????????????????????????? Sterictiphora ATAGCGTATATTAAAGTTGTTGCGGTTAAAAAGCTCGTAGTTGAATCTGTGTCCCACGCTGTCGGTTCACCGCT-CG-CG--GTGTTT-AACTGGCATGA-TTGTGGGACGTCCTACCGGTGGGC GGGCG--GCCCAACT-AATA-TCCCATCGCGGTGCTCTTTACTGAGTGTCGAGGTGGGCCGGTACGTTTACTTTGAACAAATTAGAGTGCTTAAAGCAGGCTATTTTCGCCTGAATACTGTGTGCATGGAATAATGGAATAGGACCTCGGTTCTATTTTGTTGGTTTTCGGAACCCCGAGGTAATGATTAATAGGAACAGATGGGGGCATTCGTATTGCGACGTTAGAGGTGAAATTCTTGGATCGTCGCAAGACGGACAGAAGCGAAAGCATTTGCCAAAAATGTTTTCTTTAATCAAGAACGAAAGTTAGAGGTTCGAAGGCGATCAGATACCGCCCTAGTTCTAACCATAAACGATGCCAGCTAGCGATCCGCCGAAGTTCCTCCGATGACTCGGCGGGCAGCTTCCGGGAAACCAAAGCTTTTGGGTTCCGGGGGAAGTATGGTTGCAAAGCTGAAACTTAAAGGAATTGACGGAAGGGCACCACCAGGAGTGGAGCCTGCGGCTTAATTTGACTCAACACGGGAAACCTCACCAGGCCCGGACACCGGAAGGATTGACAGATTGAGAGCTCTTTCTTGATTCGGTGGGTGGTGGTGCATGGCCGTTCTTAGTTGGTGGAGCGATTTGTCTGGTTAATTCCGATAACGAACGAGACTCTAGCCTGCTAAATAGGCGTACTTTCCGGTATCTCGAAGGCCCCCGGC--TTC----GGTCGTGGGGTTTTTACTACCGGCGTACAAATAAATCTTCTTAGAGGG Perga ATAGCGTATATTAAAGTTGTTGCGGTTAAAAAGCTCGTAGTTGAATCTGTGTCCCACGCTGCCGGTTCACCGCT-CG-CG--GTGTTC-AACTGGCATGA-TTGTGGTACGTCCTACCGGTGGGC GGGCG--GCCCAACT-TGTA-TCCCATCGCGGTGCTCTTCACCGAGTGTCGAGGTGGGCCGGTACGTTTACTTTGAACAAATCAGAGTGCTTAAAGCAGGCCGTTCTCGCCTGAATACTGTGTGCATGGAATAATGGAATAGGACCTCGGTTCTATTTTGTTGGCTTTCGGAACCCCGAGGTAATGATTAATAGGAACAGATGGGGGCATTCGTATTGCGACGTTAGAGGTGAAATTCTTGGATCGTCGCAAGACGGACAGAAGCGAACGCATTTGCCAAAAATGTTTTCCTTAATCAAGAACGAAAGTTAGAGGTTCGAAGGCGATCAGATACCGCCCTAGTTCTAACCATAAACGATGCCAGCTAGCGATCCGCCGAAGTTCCTCCGATGACTCGGCGGGCAGCTTCCGGGAAACCAAAGCTTTTGGGTTCCGGGGGAAGTATGGTTGCAAAGCTGAAACTTAAAGGAATTGACGGAAGGGCACCACCAGGAGTGGAGCCTGCGGCTTAATTTGACTCAACACGGGAAACCTCACCAGGCCCGGACACCGGAAGGATTGACAGATTGAGAGCTCTTTCTTGATTCGGTGGGTGGTGGTGCATGGCCGTTCTTAGTTGGTGGAGCGATTTGTCTGGTTAATTCCGATAACGAACGAGACTCTAGCCTGCTAAATAGGCGTACTTACCGGTATCTCGAAGGCCCCCGGC--CTC----GGTCGCGGGGTTTTTACTACCGGCGTACAAATAAATCTTCTTAGAGGG Phylacteophaga ATAGCGTATATTAAAGTTGTTGCGGTTAAAAAGCTCGTAGTTGAATCTGTGTCCCACGCTGTCGGTTCACCGCT-CG-CG--GTGTTC-AACTGGCATGA-TTGTGGTACGTCCTACCGGTGGGC GGGCG--GCCCAACT-TATA-TCCCATCGCGGTGCTCTTCGCCGAGTGTCGAGGTGGGCCGGTACGTTTACTTTGAACAAATTAGAGTGCTTAAAGCAGGCCGTTTTCGCCTGAATACTGTGTGCATGGAATAATGGAATAGGACCTCGGTTCTATTTTGTTGGCTTTCGGAACCCCGAGGTAATGATTAATAGGAACAGATGGGGGCATTCGTATTGCGACGTTAGAGGTGAAATTCTTGGATCGTCGCAAGACGGACAGAAGCGAACGCATTTGCCAAAAATGTTTTCATTAATCAAGAACGAAAGTTAGAGGTTCGAAGGCGATCAGATACCGCCCTAGTTCTAACCATAAACGATGCCAGCTAGCGATCCGCCGAAGTTCCTCCGATGACTCGGCGGGCAGCTTCCGGGAAACCAAAGCTTTTGGGTTCCGGGGGAAGTATGGTTGCAAAGCTGAAACTTAAAGGAATTGACGGAAGGGCACCACCAGGAGTGGAGCCTGCGGCTTAATTTGACTCAACACGGGAAACCTCACCAGGCCCGGACACCGGAAGGATTGACAGATTGAGAGCTCTTTCTTGATTCGGTGGGTGGTGGTGCATGGCCGTTCTTAGTTGGTGGAGCGATTTGTCTGGTTAATTCCGATAACGAACGAGACTCTAGCCTGCTAAATAGGCGTACTTACCGGTATCTCGAAGGCCCCTGGC-CTTCC---GGCCGCGGGGTTTTTACTACCGGCGTACAAATAAATCTTCTTAGAGGG Lophyrotoma ATAGCGTATATTAAAGTTGTTGCGGTTAAAAAGCTCGTAGTTGAATCTGTGTCCCACGCTGTCGGTTCACCGCT-CG-CG--GTGTTC-AACTGGCATGA-TTGTGGTACGTCCTACCGGTGGGC GGGCG--GCCCAACT-TATA-TCCCATCGCGGTGCTCTTTACCGAGTGTCGAGGTGGGCCGGTACGTTTACTTTGAACAAATTAGAGTGCTTAAAGCAGGCCATTTTCGCCTGAATACTGTGTGCATGGAATAATGGAATAGGACCTCGGTTCTATTTTGTTGGCTTTCGGAACCCCGAGGTAATGATTAATAGGAACAGATGGGGGCATTCGTATTGCGACGTTAGAGGTGAAATTCTTGGATCGTCGCAAGACGGACAGAAGCGAACGCATTTGCCAAAAATGTTTTCCTTAATCAAGAACGAAAGTTAGAGGTTCGAAGGCGATCAGATACCGCCCTAGTTCTAACCATAAACGATGCCAGCTAGCGATCCGCCGAAGTTCCTCCGATGACTCGGCGGGCAGCTTCCGGGAAACCAAAGCTTTTGGGTTCCGGGGGAAGTATGGTTGCAAAGCTGAAACTTAAAGGAATTGACGGAAGGGCACCACCAGGAGTGGAGCCTGCGGCTTAATTTGACTCAACACGGGAAACCTCACCAGGCCCGGACACCGGAAGGATTGACAGATTGAGAGCTCTTTCTTGATTCGGTGGGTGGTGGTGCATGGCCGTTCTTAGTTGGTGGAGCGATTTGTCTGGTTAATTCCGATAACGAACGAGACTCTAGCCTGCTAAATAGGCGTACTTTCCGGTATCTCGAAGGCCCCCGGC--CTC----GGTCGTGGGGTTTTTACTACCGGCGTACAAACAAATCTTCTTAGAGGG Acordulecera ATAGCGTATATTAAAGTTGTTGCGGTTAAAAAGCTCGTAGTTGAATCTGTGTCCCACGCTGTCGGTTCACCGCT-CG-CG--GTGTTC-AACTGGCATGA-TTGTGGTACGTCCTACCGGTGGGC GGGCG--GCCCAACT-TATA-TCCCATCGCGGTGCTCTTTACCGAGTGTCGAGGTGGGCCGGTACGTTTACTTTGAACAAATTAGAGTGCTTAAAGCAGGCCGTTTTCGCCTGAATACTGTGTGCATGGAATAATGGAATAGGACCTCGGTTCTATTTTGTTGGCTTTCGGAACCCCGAGGTAATGATTAATAGGAACAGATGGGGGCATTCGTATTGCGACGTTAGAGGTGAAATTCTTGGATCGTCGCAAGACGGACAGAAGCGAAAGCATTTGCCAAAAATGTTTTCCTTAATCAAGAACGAAAGTTAGAGGTTCGAAGGCGATCAGATACCGCCCTAGTTCTAACCATAAACGATGCCAGCTAGCGATCCGCCGAAGTTCCTCCGATGACTCGGCGGGCAGCTTCCGGGAAACCAAAGCTTTTGGGTTCCGGGGGAAGTATGGTTGCAAAGCTGAAACTTAAAGGAATTGACGGAAGGGCACCACCAGGAGTGGAGCCTGCGGCTTAATTTGACTCAACACGGGAAACCTCACCAGGCCCGGACACCGGAAGGATTGACAGATTGAGAGCTCTTTCTTGATTCGGTGGGTGGTGGTGCATGGCCGTTCTTAGTTGGTGGAGCGATTTGTCTGGTTAATTCCGATAACGAACGAGACTCTAGCCTGCTAAATAGGCGTACTTACCGGTATCTCGAAGGCCCCCGGC--CTC----GGTCGCGGGGTTTTTACTACCGGCGTACAAATAAATCTTCTTAGAGGG Decameria ATAGCGTATATTAAAGTTGTTGCGGTTAAAAAGCTCGTAGTTGAATCTGTGTCCCACGCTGTCGGTTCACCGCT-CG-CG--GTGTTC-AACTGGCATGA-TTGTGGTACGTCCTACCGGTGGGC GGGCG--GCCCAGCT-TATA-TCCCATCGCGGTGCTCTTTACCGAGTGTCGAGGTGGGCCGGTACGTTTACTTTGAACAAATTAGAGTGCTTAAAGCAGGCCAATTTCGCCTGAATACTGTGTGCATGGAATAATGGAATAGGACCTCGGTTCTATTTTGTTGGCTTTCGGAACCCCGAGGTAATGATTAATAGGAACAGATGGGGGCATTCGTATTGCGACGTTAGAGGTGAAATTCTTGGATCGTCGCAAGACGGACAGAAGCGAAAGCATTTGCCAAAAATGTTTTCTTTAATCAAGAACGAAAGTTAGAGGTTCGAAGGCGATCAGATACCGCCCTAGTTCTAACCATAAACGATGCCAGCTAGCGATCCGCCGAAGTTCCTCCGATAACTCGGCGGGCAGCTTCCGGGAAACCAAAGCTTTTGGGTTCCGGGGGAAGTATGGTTGCAAAGCTGAAACTTAAAGGAATTGACGGAAGGGCACCACCAGGAGTGGAGCCTGCGGCTTAATTTGACTCAACACGGGAAACCTCACCAGGCCCGGACACCGGAAGGATTGACAGATTGAGAGCTCTTTCTTGATTCGGTGGGTGGTGGTGCATGGCCGTTCTTAGTTGGTGGAGCGATTTGTCTGGTTAATTCCGATAACGAACGAGACTCTAGCCTGCTAAATAGGCGTACTTACCGGTATCTCGAAGGCCCCCGGC--CTC----GGTCGCGGGGTTTTTACTACCGGCGTACAAATAAATCTTCTTAGAGGG Neurotoma ATAGCGTATATTAAAGTTGTTGCGGTTAAAAAGCTCGTAGTTGAATCTGTGTCCCACGCTGTCGGTTCACCGCT-CG-CG--GTGTTT-AACTGGCATGA-CTGTGGGACGTCCTACCGGTGGGC GGGCG--GCCCAACT-AATA-TCCCATCGCGGTGCTCTTCACTGAGTGTCGAGGTGGGCCGGTACGTTTACTTTGAACAAATTAGAGTGCTTAAAGCAGGCTATCTTCGCCTGAATACTGTGTGCATGGAATAATGGAATAGGACCTCGGTTCTATTTTGTTGGTTTTCGGAACCCCGAGGTAATGATTAATAGGGACAGATGGGGGCATTCGTATTGCGACGTTAGAGGTGAAATTCTTGGATCGTCGCAAGACGGACAGAAGCGAAAGCATTTGCCAAAAATGTTTTCTTTAATCAAGAACGAAAGTTAGAGGTTCGAAGGCGATCAGATACCGCCCTAGTTCTAACCATAAACGATGCCAGCTAGCGATCCGCCGAAGTTCCTCCGATGACTCGGCGGGCAGCTTCCGGGAAACCAAAGCTTTTGGGTTCCGGGGGAAGTATGGTTGCAAAGCTGAAACTTAAAGGAATTGACGGAAGGGCACCACCAGGAGTGGAGCCTGCGGCTTAATTTGACTCAACACGGGAAACCTCACCAGGCCCGGACACCGGAAGGATTGACAGATTGATAGCTCTTTCTTGATTCGGTGGGTGGTGGTGCATGGCCGTTCTTAGTTGGTGGAGCGATTTGTCTGGTTAATTCCGATAACGAACGAGACTCTAGCCTGCTAAATAGGCGTACTTTCCGGTATCTCGAAGGCCCCCGGC--TTC----GGTCGGGTGGTTTTTACTACCGGCGTACAAATAAATCTTCTTAGAGGG Onycholyda ATAGCGTATATTAAAGTTGTTGCGGTTAAAAAGCTCGTAGTTGAATCTGTGTCCCACGCTGTCGGTTCACCGCT-CG-CG--GTGTTT-AACTGGCATGA-CTGTGGGACGTCCTACCGGTGGGC GGGCG--GCCCAACT-AATA-TCCCATCGCGGTGCTCTTTACTGAGTGTCGAGGTGGGCCGGTACGTTTACTTTGAACAAATTAGAGTGCTTAAAGCAGGCTATTTTCGCCTGAATACTGTGTGCATGGAATAATGGAATAGGACCTCGGTTCTATTTTGTTGGTTTTCGGAACCCCGAGGTAATGATTAATAGGGACAGATGGGGGCATTCGTATTGCGACGTTAGAGGTGAAATTCTTGGATCGTCGCAAGACGGACAGAAGCGAAAGCATTTGCCAAAAATGTTTTCTTTAATCAAGAACGAAAGTTAGAGGTTCGAAGGCGATCAGATACCGCCCTAGTTCTAACCATAAACGATGCCAGCTAGCGATCCGCCGACGTTCCTCCGATGACTCGGCGGGCAGCTTCCGGGAAACCAAAGCTTTTGGGTTCCGGGGGAAGTATGGTTGCAAAGCTGAAACTTAAAGGAATTGACGGAAGGGCACCACCAGGAGTGGAGCCTGCGGCTTAATTTGACTCAACACGGGAAACCTCACCAGGCCCGGACACCGGAAGGATTGACAGATTGATAGCTCTTTCTTGATTCGGTGGGTGGTGGTGCATGGCCGTTCTTAGTTGGTGGAGCGATTTGTCTGGTTAATTCCGATAACGAACGAGACTCTAGCCTGCTAAATAGGCGTACTTTCCGGTATCTCGAAGGCCCCCGGC--TTC----GGTCGGGTGGTTTTTACTACCGGCGTACAAATAAATCTTCTTAGAGGG Pamphilius ATAGCGTATATTAAAGTTGTTGCGGTTAAAAAGCTCGTAGTTGAATCTGTGTCCCACGCTGTCGGTTCACCGCT-CG-CG--GTGTTT-AACTGGCATGA-CTGTGGGACGTCCTACCGGTGGGC GGGCG--GCCCAACT-AATA-TCCCATCGCGGTGCTCTTTACTGAGTGTCGAGGTGGGCCGGTACGTTTACTTTGAACAAATTAGAGTGCTTAAAGCAGGCTATCTTCGCCTGAATACTGTGTGCATGGAATAATGGAATAGGACCTCGGTTCTATTTTGTTGGTTTTCGGAACCCCGAGGTAATGATTAATAGGGACAGATGGGGGCATTCGTATTGCGACGTTAGAGGTGAAATTCTTGGATCGTCGCAAGACGGACAGAAGCGAAAGCATTTGCCAAAAATGTTTTCTTTAATCAAGAACGAAAGTTAGAGGTTCGAAGGCGATCAGATACCGCCCTAGTTCTAACCATAAACGATGCCAGCTAGCGATCCGCCGACGTTCCTCCGATGACTCGGCGGGCAGCTTCCGGGAAACCAAAGCTTTTGGGTTCCGGGGGAAGTATGGTTGCAAAGCTGAAACTTAAAGGAATTGACGGAAGGGCACCACCAGGAGTGGAGCCTGCGGCTTAATTTGACTCAACACGGGAAACCTCACCAGGCCCGGACACCGGAAGGATTGACAGATTGATAGCTCTTTCTTGATTCGGTGGGTGGTGGTGCATGGCCGTTCTTAGTTGGTGGAGCGATTTGTCTGGTTAATTCCGATAACGAACGAGACTCTAGCCTGCTAAATAGGCGTACTTTCCGGTATCTCGAAGGCCCCCGGC--TTC----GGTCGGGTGGTTTTTACTACCGGCGTACAAATAAATCTTCTTAGAGGG Cephalcia ATAGCGTATATTAAAGTTGTTGCGGTTAAAAAGCTCGTAGTTGAATCTGTGTCCCACGCTGTCGGTTCACCGCT-CG-CG--GTGTTT-AACTGGCATGA-CTGTGGGACGTCCTACCGGTGGGC GGGCG--GCCCAACT-AATA-TCCCATCGCGGTGCTCTTCACTGAGTGTCGAGGTGGGCCGGTACGTTTACTTTGAACAAATTAGAGTGCTTAAAGCAGGCTATCTTCGCCTGAATACTGTGTGCATGGAATAATGGAATAGGACCTCGGTTCTATTTTGTTGGTTTTCGGAACCCCGAGGTAATGATTAATAGGAACAGATGGGGGCATTCGTATTGCGACGTTAGAGGTGAAATTCTTGGATCGTCGCAAGACGGACAGAAGCGAAAGCATTTGCCAAAAATGTTTTCATTAATCAAGAACGAAAGTTAGAGGTTCGAAGGCGATCAGATACCGCCCTAGTTCTAACCATAAACGATGCCAGCTAGCGATCCGCCGAAGTTCCTCCGATGACTCGGCGGGCAGCTTCCGGGAAACCAAAGCTTTTGGGTTCCGGGGGAAGTATGGTTGCAAAGCTGAAACTTAAAGGAATTGACGGAAGGGCACCACCAGGAGTGGAGCCTGCGGCTTAATTTGACTCAACACGGGAAACCTCACCAGGCCCGGACACCGGAAGGATTGACAGATTGATAGCTCTTTCTTGATTCGGTGGGTGGTGGTGCATGGCCGTTCTTAGTTGGTGGAGCGATTTGTCTGGTTAATTCCGATAACGAACGAGACTCTAGCCTGCTAAATAGGCGTACTTTCCGGTATCTCGAAGGCCCCCGGC--TTC----GGTCGGGTGGTTTTTACTACCGGCGTACAAATAAATCTTCTTAGAGGG Acantholyda ATAGCGTATATTAAAGTTGTTGCGGTTAAAAAGCTCGTAGTTGAATCTGTGTCCCACGCTGTCGGTTCACCGCT-CG-CG--GTGTTT-AACTGGCATGA-CTGTGGGACGTCCTACCGGTGGGC GGGCG--GCCCAACT-AATA-TCCCATCGCGGTGCTCTTGATTGAGTGTCGAGGTGGGCCGGTACGTTTACTTTGAACAAATTAGAGTGCTTAAAGCAGGCTATCTTCGCCTGAATACTGTGTGCATGGAATAATGGAATAGGACCTCGGTTCTATTTTGTTGGTTTTCGGAACCCCGAGGTAATGATTAATAGGAACAGATGGGGGCATTCGTATTGCGACGTTAGAGGTGAAATTCTTGGATCGTCGCAAGACGGACAGAAGCGAAAGCATTTGCCAAAAATGTTTTCATTAATCAAGAACGAAAGTTAGAGGTTCGAAGGCGATCAGATACCGCCCTAGTTCTAACCATAAACGATGCCAGCTAGCGATCCGCCGAAGTTCCTCCGATGACTCGGCGGGCAGCTTCCGGGAAACCAAAGCTTTTGGGTTCCGGGGGAAGTATGGTTGCAAAGCTGAAACTTAAAGGAATTGACGGAAGGGCACCACCAGGAGTGGAGCCTGCGGCTTAATTTGACTCAACACGGGAAACCTCACCAGGCCCGGACACCGGAAGGATTGACAGATTGATAGCTCTTTCTTGATTCGGTGGGTGGTGGTGCATGGCCGTTCTTAGTTGGTGGAGCGATTTGTCTGGTTAATTCCGATAACGAACGAGACTCTAGCCTGCTAAATAGGCGTACTTTCCGGTATCTCGAAGGCCCCCGGC--CTC----GGTCGGGTGGTTTTTACTACCGGCGTACAAATAAATCTTCTTAGAGGG Megalodontesc ATAGCGTATATTAAAGTTGTTGCGGTTAAAAAGCTCGTAGTTGAATTTGTGTCCCACGCTGTCGGTTCACCGCT-CG-CG--GTGTTT-AACTGACATGA-TTGTGGGACGTCCTACCGGTGGGC GGGCG--GCCCAACT-AATA-TCCCATCGCGGTGCTCTTTATTGAGTGTCGAGGTGGGCCGGTACGTTTACTTTGAACAAATTAGAGTGCTTAAAGCAGGCTATTCTCGCCTGAATACTGTGTGCATGGAATAATGGAATAGGACCTCGGTTCTATTTTGTTGGTTTTCGGAACCCCGAGGTAATGATTAATAGGGACAGATGGGGGCATTCGTATTGCGACGTTAGAGGTGAAATTCTTGGATCGTCGCAAGACGGACAGAAGCGAAAGCATTTGCCAAAAATGTTTTCTTTAATCAAGAACGAAAGTTAGAGGTTCGAAGGCGATCAGATACCGCCCTAGTTCTAACCATAAACGATGCCAGCTAGCGATCCGCCGAAGTTCCTCCGATGACTCGGCGGGCAGCTTCCGGGAAACCAAAGCTTTTGGGTTCCGGGGGAAGTATGGTTGCAAAGCTGAAACTTAAAGGAATTGACGGAAGGGCACCACCAGGAGTGGAGCCTGCGGCTTAATTTGACTCAACACGGGAAACCTCACCAGGCCCGGACACCGGAAGGATTGACAGATTGATAGCTCTTTCTTGATTCGGTGGGTGGTGGTGCATGGCCGTTCTTAGTTGGTGGAGCGATTTGTCTGGTTAATTCCGATAACGAACGAGACTCTAGCCTGCTAACTAGGCGTACTTTCCGGTATCTTGAAGGTCCTCGGC--CTAC--GGGTCGGGCGGTCCTTACTACCGGCGTACAAATAATTCTTCTTAGAGGG Megalodontessk ????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ?????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Cephus ATAGCGTATATTAAAGTTGTTGCGGTTAAAAAGCTCGTAGTTGAATCTGTGTCCCACGCTGTCGGTTCATCGCT-CG-CG--GTGTTT-AACTGGCATGA-CTGTGGGACGTCCTACCGGTGGGC GGGCG--GCCCAACT-AATA-TCCCATCGCGGTGCTCTTTACCGAGTGTCGAGGTGGGCCGGTACGTTTACTTTGAACAAATTAGAGTGCTTAAAGCAGGCTACCTTCGCCTGAATACTGCGTGCATGGAATAATGGAATAGGACCTCGGTTCTATTTTGTTGGTTTTCGGAACCCCGAGGTAATGATTAATAGGGACAGATGGGGGCATTCGTATTGCGACGTTAGAGGTGAAATTCTTGGATCGTCGCAAGACGGACAGAAGCGAAAGCATTTGCCAAAAATGTTTTCATTAATCAAGAACGAAAGTTAGAGGTTCGAAGGCGATCAGATACCGCCCTAGTTCTAACCATAAACGATGCCAGCTAGCGATCCGCCGAAGTTCCTCCGATGACTCGGCGGGCAGCTTCCGGGAAACCAAAGCTTTTGGGTTCCGGGGGAAGTATGGTTGCAAAGCTGAAACTTAAAGGAATTGACGGAAGGGCACCACCAGGAGTGGAGCCTGCGGCTTAATTTGACTCAACACGGGAAACCTCACCAGGCCCGGACACCGGAAGGATTGACAGATTGATAGCTCTTTCTTGATTCGGTGGGTGGTGGTGCATGGCCGTTCTTAGTTGGTGGAGCGATTTGTCTGGTTAATTCCGATAACGAACGAGACTCTAGCCTGCTAAATAGGCGTAATTCATGGTATCTCGAAGGCCCTCGAC--TTC----GGTCGGGCGGTTTTTACTACCGACGTACAAACAAATCTTCTTAGAGGG Calameuta ATAGCGTATATTAAAGTTGTTGCGGTTAAAAAGCTCGTAGTTGAATCTGTGTCCCACGCTGTCGGTTCATCGCT-CG-CG--GTGTTT-AACTGGCATGA-CTGTGGGACGTCCTACCGGTGGGC GGGCG--GCCCAACT-AATA-TCCCATCGCGGTGCTCTTTACCGAGTGTCGAGGTGGGCCGGTACGTTTACTTTGAACAAATTAGAGTGCTTAAAGCAGGCTACCTTCGCCTGAATACTGCGTGCATGGAATAATGGAATAGGACCTCGGTTCTATTTTGTTGGTTTTCGGAACCCCGAGGTAATGATTAATAGGGACAGATGGGGGCATTCGTATTGCGACGTTAGAGGTGAAATTCTTGGATCGTCGCAAGACGGACAGAAGCGAAAGCATTTGCCAAAAATGTTTTCATTAATCAAGAACGAAAGTTAGAGGTTCGAAGGCGATCAGATACCGCCCTAGTTCTAACCATAAACGATGCCAGCTAGCGATCCGCCGAAGTTCCTCCGATGACTCGGCGGGCAGCTTCCGGGAAACCAAAGCTTTTGGGTTCCGGGGGAAGTATGGTTGCAAAGCTGAAACTTAAAGGAATTGACGGAAGGGCACCACCAGGAGTGGAGCCTGCGGCTTAATTTGACTCAACACGGGAAACCTCACCAGGCCCGGACACCGGAAGGATTGACAGATTGATAGCTCTTTCTTGATTCGGTGGGTGGTGGTGCATGGCCGTTCTTAGTTGGTGGAGCGATTTGTCTGGTTAATTCCGATAACGAACGAGACTCTAGCCTGCTAAATAGGCGTAATTCATGGTATCTCGAAGGCCCTCGAC--TTC----GGTCGGGCGGTTTTTACTACCGACGTACAAACAAATCTTCTTAGAGGG Hartigia ATAGCGTATATTAAAGTTGTTGCGGTTAAAAAGCTCGTAGTTGAATCTGTGTCCCACGCTGTCGGTTCATCGCT-CG-CG--GTGTTT-AACTGGCATGA-CTGTGGGACGTCCTACCGGTGGGC GGGCG--GCCCAACT-AATA-TCCCATCGCGGTGCTCTTTACCGAGTGTCGAGGTGGGCCGGTACGTTTACTTTGAACAAATTAGAGTGCTTAAAGCAGGCTACCTTCGCCTGAATACTGCGTGCATGGAATAATGGAATAGGACCTCGGTTCTATTTTGTTGGTTTTCGGAACCCCGAGGTAATGATTAATAGGGACAGATGGGGGCATTCGTATTGCGACGTTAGAGGTGAAATTCTTGGATCGTCGCAAGACGGACAGAAGCGAAAGCATTTGCCAAAAATGTTTTCATTAATCAAGAACGAAAGTTAGAGGTTCGAAGGCGATCAGATACCGCCCTAGTTCTAACCATAAACGATGCCAGCTAGCGATCCGCCGAAGTTCCTCCGATGACTCGGCGGGCAGCTTCCGGGAAACCAAAGCTTTTGGGTTCCGGGGGAAGTATGGTTGCAAAGCTGAAACTTAAAGGAATTGACGGAAGGGCACCACCAGGAGTGGAGCCTGCGGCTTAATTTGACTCAACACGGGAAACCTCACCAGGCCCGGACACCGGAAGGATTGACAGATTGATAGCTCTTTCTTGATTCGGTGGGTGGTGGTGCATGGCCGTTCTTAGTTGGTGGAGCGATTTGTCTGGTTAATTCCGATAACGAACGAGACTCTAGCCTGCTAAATAGGCGTACTTTATGGTATCTCGAAGGCCCTCGGC--TTC----GGTCGGGCGGTTTTTACTACCGACGTACAAACAAATCTTCTTAGAGGG Syntexis ATAGCGTATATTAAAGTTGTTGCGGTTAAAAAGCTCGTAGTTGAATCTGTGTGCCACGCTGCCGGTTCACCGCT-CG-CG--GTGTTT-AACTGGCATGA-TTGTGGTACGTCCTACCGGTGGGC GGGCG--GCCCAACT-AATA-TCCCATCGCGGTGCTCTTCATTGAGTGTCGAGGTGGGCCGGTACGTTTACTTTGAACAAATTAGAGTGCTCAAAGCAGGCTAACTTCGCCTGAATACTGCGTGCATGGAATAATGGAATAGGACCTCGGTTCTATTTTGTTGGTTTTCGGAACCCCGAGGTAATGATTAATAGGGACAGATGGGGGCATTCGTATTGCGACGTTAGAGGTGAAATTCTTGGATCGTCGCAAGACGGACAGAAGCGAAAGCATTTGCCAAAAATGTTTTCATTAATCAAGAACGAAAGTTAGAGGTTCGAAGGCGATCAGATACCGCCCTAGTTCTAACCATAAACGATGCCAGCTAGCGATCCGCCGAAGTTCCTCCGATGACTCGGCGGGCAGCTTCCGGGAAACCAAAGCTTTTGGGTTCCGGGGGAAGTATGGTTGCAAAGCTGAAACTTAAAGGAATTGACGGAAGGGCACCACCAGGAGTGGAGCCTGCGGCTTAATTTGACTCAACACGGGAAACCTCACCAGGCCCGGACACCGGAAGGATTGACAGATTGATAGCTCTTTCTTGATTCGGTGGGTGGTGGTGCATGGCCGTTCTTAGTTGGTGGAGCGATTTGTCTGGTTAATTCCGATAACGAACGAGACTCTAGCCTGCTAAATAGGCGTAATTTATGGTATCTCGAAGGCCCCCGGC--TTC----GGTCGGGTGGTTTTTACTACCGACGTAAAAACAAATCTTCTTAGAGGG Sirex ATAGCGTATATTAAAGTTGTTGCGGTTAAAAAGCTCGTAGTTGAATCTGTGTGTCACGCTGTCGGTTCACCGCT-CG-CG--GTGTTTTAACTGGCATGA-C-GTGGTACGTCCTACCGGTGGGC GGGCG--GCCCGACT-AATA-TCCCATCGCGGTGCTCTTCACCGGGTGTCGAGGTGGGCCGGTACGTTTACTTTGAACAAATTAGAGTGCTCAAAGCAGGCTATCCTCGCCTGAATACTGCGTGCATGGAATAATGGAATAGGACCTCGGTTCTATTTTGTTGGTTTTCGGAACCCCGAGGTAATGATTAATAGGGACAGATGGGGGCATTCGTATTGCGACGTTAGAGGTGAAATTCTTGGATCGTCGCAAGACGGACAGAAGCGAAAGCATTTGCCAAAAATGTTTTCATTAATCAAGAACGAAAGTTAGAGGTTCGAAGGCGATCAGATACCGCCCTAGTTCTAACCATAAACGATGCCAGCTAGCGATCCGCCGAAGTTCCTCCGATGACTCGGCGGGCAGCTTCCGGGAAACCAAAGCTTTTGGGTTCCGGGGGAAGTATGGTTGCAAAGCTGAAACTTAAAGGAATTGACGGAAGGGCACCACCAGGAGTGGAGCCTGCGGCTTAATTTGACTCAACACGGGAAACCTCACCAGGCCCGGACACCGGAAGGATTGACAGATTGATAGCTCTTTCTTGATTCGGTGGGTGGTGGTGCATGGCCGTTCTTAGTTGGTGGAGCGATTTGTCTGGTTAATTCCGATAACGAACGAGACTCTAGCCTGCTAAATAGGCGTACCTTCTGGTATCTCGAAGGCCCCCGGC-CCTCC---GGCCGGTGGGTTTTTACTACCGACGTACAAACAAATCTTCTTAGAGGG Xeris ATAGCGTATATTAAAGTTGTTGCGGTTAAAAAGCTCGTAGTTGAATCTGTGTGTCACGCTGTCGGTTCACCGCT-CG-CG--GTGTCT-AACTGGCATGA-C-GTGGTACGTCCTACCGGTGGGC GGGCG--GCCCGACT-AATA-TCCCATCGCGGTGCTCTTCACCGGGTGTCGAGGTGGGCCGGTACGTTTACTTTGAACAAATTAGAGTGCTCAAAGCAGGCTATCCTCGCCTGAATACTGCGTGCATGGAATAATGGAATAGGACCTCGGTTCTATTTTGTTGGTTTTCGGAACCCCGAGGTAATGATTAATAGGGACAGATGGGGGCATTCGTATTGCGACGTTAGAGGTGAAATTCTTGGATCGTCGCAAGACGGACAGAAGCGAAAGCATTTGCCAAAAATGTTTTCATTAATCAAGAACGAAAGTTAGAGGTTCGAAGGCGATCAGATACCGCCCTAGTTCTAACCATAAACGATGCCAGCTAGCGATCCGCCGAAGTTCCTCCGATGACTCGGCGGGCAGCTTCCGGGAAACCAAAGCTTTTGGGTTCCGGGGGAAGTATGGTTGCAAAGCTGAAACTTAAAGGAATTGACGGAAGGGCACCACCAGGAGTGGAGCCTGCGGCTTAATTTGACTCAACACGGGAAACCTCACCAGGCCCGGACACCGGAAGGATTGACAGATTGATAGCTCTTTCTTGATTCGGTGGGTGGTGGTGCATGGCCGTTCTTAGTTGGTGGAGCGATTTGTCTGGTTAATTCCGATAACGAACGAGACTCTAGCCTGCTAAATAGGCGTACCTTCTGGTATCTCGAAGGCCCCCGGC-CTTCC---GGCCGGTGGGTTTTTACTACCGACGTACAAACAAATCTTCTTAGAGGG Urocerus ATAGCGTATATTAAAGTTGTTGCGGTTAAAAAGCTCGTAGTTGAATCTGTGTGTCACGCTGTCGGTTCACCGCT-CG-CG--GTGTTT-AACTGGCATGA-C-GTGGTACGTCCTACCGGTGGGC GGGCG--GCCCGACT-AATA-TCCCATCGCGGTGCTCTTCACCGGGTGTCGAGGTGGGCCGGTACGTTTACTTTGAACAAATTAGAGTGCTCAAAGCAGGCTATCCTCGCCTGAATACTGCGTGCATGGAATAATGGAATAGGACCTCGGTTCTATTTTGTTGGTTTTCGGAACCCCGAGGTAATGATTAATAGGGACAGATGGGGGCATTCGTATTGCGACGTTAGAGGTGAAATTCTTGGATCGTCGCAAGACGGACAGAAGCGAAAGCATTTGCCAAAAATGTTTTCATTAATCAAGAACGAAAGTTAGAGGTTCGAAGGCGATCAGATACCGCCCTAGTTCTAACCATAAACGATGCCAGCTAGCGATCCGCCGAAGTTCCTCCGATGACTCGGCGGGCAGCTTCCGGGAAACCAAAGCTTTTGGGTTCCGGGGGAAGTATGGTTGCAAAGCTGAAACTTAAAGGAATTGACGGAAGGGCACCACCAGGAGTGGAGCCTGCGGCTTAATTTGACTCAACACGGGAAACCTCACCAGGCCCGGACACCGGAAGGATTGACAGATTGATAGCTCTTTCTTGATTCGGTGGGTGGTGGTGCATGGCCGTTCTTAGTTGGTGGAGCGATTTGTCTGGTTAATTCCGATAACGAACGAGACTCTAGCCTGCTAAATAGGCGTACCTTCTGGTATCTCGAAGGCCCCCGGC-CTTCC---GGCCGGCGGGTTTTTACTACCGACGTACAAACAAATCTTCTTAGAGGG Tremex ATAGCGTATATTAAAGTTGTTGCGGTTAAAAAGCTCGTAGTTGAATTTGTGTGTCACGCTGTCGGTTCACCGCT-CG-CG--GTGTTT-AACTGGCATGA-C-GTGGTACGTCCTACCGGTGGGC GGGCG--GCCCGACT-AATA-TCCCATCGCGGTGCTCTTCACCGAGTGTCGAGGTGGGCCGGTACGTTTACTTTGAACAAATTAGAGTGCTCAAAGCAGGCTATCCTCGCCTGAATACTGCGTGCATGGAATAATGGAATAGGACCTCGGTTCTATTTTGTTGGTTTTCGGAACCCCGAGGTAATGATTAATAGGGACAGATGGGGGCATTCGTATTGCGACGTTAGAGGTGAAATTCTTGGATCGTCGCAAGACGGACAGAAGCGAAAGCATTTGCCAAAAATGTTTTCATTAATCAAGAACGAAAGTTAGAGGTTCGAAGGCGATCAGATACCGCCCTAGTTCTAACCATAAACGATGCCAGCTAGCGATCCGCCGAAGTTCCTCCGATGACTCGGCGGGCAGCTTCCGGGAAACCAAAGCTTTTGGGTTCCGGGGGAAGTATGGTTGCAAAGCTGAAACTTAAAGGAATTGACGGAAGGGCACCACCAGGAGTGGAGCCTGCGGCTTAATTTGACTCAACACGGGAAACCTCACCAGGCCCGGACACCGGAAGGATTGACAGATTGATAGCTCTTTCTTGATTCGGTGGGTGGTGGTGCATGGCCGTTCTTAGTTGGTGGAGCGATTTGTCTGGTTAATTCCGATAACGAACGAGACTCTAGCCTGCTAAATAGGCGTACCTTCTGGTATCTCGAAGGCCCCCGGC-CTTCT---GGTCGGTGGGTTTTTACTACCGACGTACAAACAAATCTTCTTAGAGGG Xiphydria ATAGCGTATATTAAAGTTGTTGCGGTTAAAAAGCTCGTAGTTGAATCTGTGTCCCACGCTGTCGGTTCACCGCT-CG-CG--GTGTTT-AACTGGCATGA-TTGTGGGACGTCCTACCGGTGGGC GGGCG--GCCCAACT-AATA-TCCCATCGCGGTGCTCTTCATTGAGTGTCGAGGTGGGCCGGTACGTTTACTTTGAACAAATTAGAGTGCTTAAAGCAGGCTACCTTCGCCTGAATACTGCGTGCATGGAATAATGGAATAGGACCTCGGTTCTATTTTGTTGGTTTTCGGAACCCCGAGGTAATGATTAATAGGGACAGATGGGGGCATTCGTATTGCGACGTTAGAGGTGAAATTCTTGGATCGTCGCAAGACGGACAGAAGCGAAAGCATTTGCCAAAAATGTTTTCATTAATCAAGAACGAAAGTTAGAGGTTCGAAGGCGATCAGATACCGCCCTAGTTCTAACCATAAACGATGCCAGCTAGCGATCCGCCGAAGTTCCTCCGATGACTCGGCGGGCAGCTTCCGGGAAACCAAAGCTTTTGGGTTCCGGGGGAAGTATGGTTGCAAAGCTGAAACTTAAAGGAATTGACGGAAGGGCACCACCAGGAGTGGAGCCTGCGGCTTAATTTGACTCAACACGGGAAACCTCACCAGGCCCGGACACCGGAAGGATTGACAGATTGATAGCTCTTTCTTGATTCGGTGGGTGGTGGTGCATGGCCGTTCTTAGTTGGTGGAGCGATTTGTCTGGTTAATTCCGATAACGAACGAGACTCTAGCCTGCTAAATAGGCGTACTTTCTGGTATCTCGAAGGCCCCCGGC--CTC----GGTCGGGTGGTTTTTACTACCGACGTACAAACAAATCTTCTTAGAGGG Orussus ATAGCGTATATTAAAGTTGTTGCGGTTAAAAAGCTCGTAGTTGAATCTGTGTGTCACGCTGTCGGTTCATCGCT-CG-CG--GTGTTT-A-CTGACATGA-C-GTGGGACGTCCTACCGGTGGGC AGGCG--GTCCAACT-AATA-TCCCATCGCGGTGCTCTTCACTGAGTGCCGAGGTGGGCCGGTACGTTTACTTTGAACAAATTAGAGTGCTTAAAGCAGGCTGTCTTCGCCTGAATACTGTGTGCATGGAATAATGGAATAGGACCTCGGTTCTATTTTGTTGGTTTTCGGAACCCCGAGGTAATGATTAATAGGGACAGATGGGGGCATTCGTATTGCGACGTTAGAGGTGAAATTCTTGGATCGTCGCAAGACGGACAGAAGCGAAAGCATTTGCCAAAAATGTTTTCATTAATCAAGAACGAAAGTTAGAGGTTCGAAGGCGATCAGATACCGCCCTAGTTCTAACCATAAACGATGCCAGCTAGCGATCCGCCGAAGTTCCTCCGATGACTCGGCGGGCAGCTTCCGGGAAACCAAAGCTTTTGGGTTCCGGGGGAAGTATGGTTGCAAAGCTGAAACTTAAAGGAATTGACGGAAGGGCACCACCAGGAGTGGAGCCTGCGGCTTAATTTGACTCAACACGGGAAACCTCACCAGGCCCGGACACCGGAAGGATTGACAGATTGATAGCTCTTTCTTGATTCGGTGGGTGGTGGTGCATGGCCGTTCTTAGTTGGTGGAGCGATTTGTCTGGTTAATTCCGATAACGAACGAGACTCTAGCCTGCTAAATAGGCGTAACTTATGGTATCTCTAAGGCCCCCGTC-GTTC----TGTCGGGCGGTTCTTACTACCGACGTACAAACAAATCTTCTTAGAGGG StephanidaeA ????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ?????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? StephanidaeB ATAGCGTATATTAAAGTTGTTGCGGTTAAAAAGCTCGTAGTTGAATCTGTGTGCCACGCTGTCGGTTCGTCGCT-CG-CG--TCGTTT-AACTTGCAGTT-T-GTGGGACGTCCTACCGGTGGAC GAGCG--GTCCAAAT-AATA-TCCCATCGCGGTGCTCTTAACTGAGTGTCGAGGTGGGCCGGTACGTTTACTTTGAACAAATTAGAGTGCTCAAAGCAGGCTACCTTCGCCTGAATACTGTGTGCATGGAATAATGGAATAGGACCTCGGTTCTATTTTGTTGGTTTTCGGAACCCCGAGGTAATGATTAATAGGGACAGATGGGGGCATTCGTATTGCGACGTTAGAGGTGAAATTCTTGGATCGTCGCAAGACGGACAGAAGCGAAAGCATTTGCCAAAAATGTTTTCATTAATCAAGAACGAAAGTTAGAGGTTCGAAGGCGATCAGATACCGCCCTAGTTCTAACCATAAACGATGCCAGCTAGCGATCCGCCGAAGTTCCTCCGATGACTCGGCGGGCAGCTTCCGGGAAACCAAAGCTTTTGGGTTCCGGGGGAAGTATGGTTGCAAAGCTGAAACTTAAAGGAATTGACGGAAGGGCACCACCAGGAGTGGAGCCTGCGGCTTAATTTGACTCAACACGGGAAACCTCACCAGGCCCGGACACCGGAAGGATTGACAGATTGACAGCTCTTTCTTGATTCGGTGGGTGGTGGTGCATGGCCGTTCTTAGTTGGTGGAGCGATTTGTCTGGTTAATTCCGATAACGAACGAGACTCTAGCCTGCTAAATAGGCGTACTTTCTGGTATCTCGAAGGCCCCCGGC--CTC-----GCCGGGTGGTTTTTACTACCAACGTACAAACCAATCTTCTTAGAGGG Megalyra ATAGCGTATATTAAAGTTGTTGCGGTTAAAAAGCTCGTAGTTGAATCTGTGTGCCACGGTGTCGGTTCACTGCT-CG-CGCGGTGTTT-A-CTTGGCACT-C-GTGGTACGTCCTACCGGTGGGC GGGCG--GCCCAGCT-AATA-TCCCATCGCGGTGCTCTTTATCGAGTGTCGAGGTGGGCCGGTACGTTTACTTTGAACAAATTAGAGTGCTTAAAGCAGGCTATTTTCGCCTGAATACTGTGTGCATGGAATAATGGAATAGGACCTCGGTTCTATTTTGTTGGTTTTCGGAACCCCGAGGTAATGATTAATAGGGACAGATGGGGGCATTCGTATTGCGACGTTAGAGGTGAAATTCTTGGATCGTCGCAAGACGGACAGAAGCGAAAGCATTTGCCAAAAATGTTTTCATTAATCAAGAACGAAAGTTAGAGGTTCGAAGGCGATCAGATACCGCCCTAGTTCTAACCATAAACGATGCCAGCTAGCGATCCGCCGAAGTTCCTCCGATGACTCGGCGGGCAGCTTCCGGGAAACCAAAGCTTTTGGGTTCCGGGGGAAGTATGGTTGCAAAGCTGAAACTTAAAGGAATTGACGGAAGGGCACCACCAGGAGTGGAGCCTGCGGCTTAATTTGACTCAACACGGGAAACCTCACCAGGCCCGGACACCGGAAGGATTGACAGATTGACAGCTCTTTCTTGATTCGGTGGGTGGTGGTGCATGGCCGTTCTTAGTTGGTGGAGCGATTTGTCTGGTTAATTCCGATAACGAACGAGACTCTAGCCTGCTAAATAGGCGTACTTTCTGGTATCTCGAAGGCCCTCGGC-TTTT----AGTCGGGTGGTTTTTACTACCAACGTACACACAAATCTTCTTAGAGGG Trigonalidae ATAGCGTATATTAAAGTTGTTGCGGTTAAAAAGCTCGTAGTTGAATCTGTGTGCCACACTGTCGGTTCATCGCT-CG-CG--GTGTTT-AACTGGCATGA-TTGGGGGACGTCCTACCGGTGGGY GGGCG--GTCCAACT-AATA-TCCCATCGCGGTGCTCTTAAYYGAGTGTCGAGGTGGGCCGGTACGTTTACTTTGAACAAATTAGAGTGCTTAAAGCAGGCTATTTTCGCCTGAATACTGTGTGCATGGAATAATGGAATAGGACCTCGGTTCTATTTTGTTGGTTTTCGGAACCCCGAGGTAATGATTAATAGGGACAGATGGGGGCATTCGTATTGCGACGTTAGAGGTGAAATTCTTGGATCGTCGCAAGACGRACAGAAGCGAAAGCATTTGCCAAAAATGTTTTCATTAATCAAGAACGAAAGTTAGAGGTTCGAAGGCGATCAGATACCGCCCTAGTTCTAACCATAAACGATGCCAGCTAGCGATCCGCCGAAGTTCCTCCGATGACTCGGCGGGCAGCTTCCGGGAAACCAAAGCTTTTGGGTTCCGGGGGAAGTATGGTTGCAAAGCTGAAACTTAAAGGAATTGACGGAAGGGCACCACCAGGAGTGGAGCCTGCGGCTTAATTTGACTCAACACGGGAAACCTCACCAGGCCCGGACACCGGAAGGATTGACAGATTGAYAGCTCTTTCTTGATTCGGTGGGTGGTGGTGCATGGCCGTT??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Chalcidoidea ATAGCGTATATTAAAGTTGTTGCGGTTAAAAAGCTCGTAGTTGAATCTGTGTGCCACGCTGTCGGTTCATCGCT-CG-CG--ATGTTT-AACTGACATGA-TTGTGGGACGTCCTACCGGTGGGT GGGCA--GCCCAGCT-AATA-TCCCATCGCGGTGCTCTTTACCGAGTGTCGAGGTGGGCCGGTACGTTTACTTTGAACAAATTAGAGTGCTCAAAGCAGGCCATGTTCGCCTGAATACTGTGTGCATGGAATAATGGAATAGGACCTCGGTTCTATTTTGTTGGTTTTCGGAACCCCGAGGTAATGATTAATAGGGACAGATGGGGGCATTCGTATTGCGACGTTAGAGGTGAAATTCTTGGATCGTAGCAAGACGGACAGAAGCGAAAGCATTTGCCAAAAATGTTTTCATTAATCAAGAACGAAAGTTAGAGGTTCGAAGGCGATCAGATACCGCCCTAGTTCTAACCATAAACGATGCCAGCTAGCGATCCGCCGAAGTTCCTCCGATGACTCGGCGGGCAGCTTCCGGGAAACCAAAGCTTTTGGGTTCCGGGGGAAGTATGGTTGCAAAGCTGAAACTTAAAGGAATTGACGGAAGGGCACCACCAGGAGTGGAGCCTGCGGCTTAATTTGACTCAACACGGGAAACCTCACCAGGCCCGGACACCGGAAGGATTGACAGATTGATAGCTCTTTCTTGATTCGGTGGGTGGTGGTGCATGGCCGTTCTTAGTTGGTGGAGCGATTTGTCTGGTTAATTCCGATAACGAAC??????????????????????????????????????????????????????????????????????????????????????????????????????????????? Evanioidea ATAGCGTATATTAAAGTTGTTGCGGTTAAAAAGCTCGTAGTTGAATCTGTGTCTCACGCTGTCGGTTCGCCGCT-CG-CG--GTGCTC-A-CTGACATGA-C-GTGGTACGTCCTACCGGTGGAC GGGCG--GTCCAGTT-GCAA-TCCTGTCGCGGTGCTCTTTACTGAGTGTCGAGGCGGGCCGGTACGTTTACTTTGAACAAATTAGAGTGCTTAAAGCAGGCTAACTTCGCCTGAATACTGTGTGCATGGAATAATGGAATAGGACCTCGGTTCTATTTTGTTGGTTTTCGGAACCCCGAGGTAATGATTAATAGGGACAGATGGGGGCATTCGTATTGCGACGTTAGAGGTGAAATTCTTGGATCGTCGCAAGACGGACAGAAGCGAAAGCATTTGCCAAAAATGTTTTCATTAATCAAGAACGAAAGTTAGAGGTTCGAAGGCGATCAGATACCGCCCTAGTTCTAACCATAAACGATGCCAGCTAGCGATCCGCCGAAGTTCCTCCGATGACTCGGCGGGCAGCTTCCGGGAAACCAAAGCTTTTGGGTTCCGGGGGAAGTATGGTTGCAAAGCTGAAACTTAAAGGAATTGACGGAAGGGCACCACCAGGAGTGGAGCCTGCGGCTTAATTTGACTCAACACGGGAAACCTCACCAGGCCCGGACACCGGAAGGATTGACAGATTGATAGCTCTTTCTTGATTCGGTGGGTGGTGGTGCATGGCCGTTCTTAGTTGGTGGAGCGATTTGTCTGGTTAATTCCGATAACGAACGAGACTCTAGCCTGCTAAATAGGCGTACC-CATGGTATCTCGAAGGCCCGCGGC--TTC----GGTCGCGTGGTTTTTACTACCGGCGTACAAACAAATCTTCTTAGAGGG Ichneumonidae ATAGCGTATATTAAAGTTGTTGCGGTTAAAAAGCTCGTAGTTGAATCTGTGTGTCACGATGTTGGTTCACCGCT-CG-CG--GTGTTT-AACTAGCATGC-C-GTGAGACGTCCTACCGGTGGGC GGGCG--GTCCAACT-AATA-TCCCATCGCGGTGCTCTTCACTGAGTGTCGAGGTGGGCCGGTACGTTTACTTTGAACAAATTAGAGTGCTTAAAGCAGGCTATCTTCGCCTGAATACTGTGTGCATGGAATAATGGAATAGGACCTCGGTTCTATTTTGTTGGTTTTCGGAACCCCGAGGTAATGATTAATAGGGACAGATGGGGGCATTCGTATTGCGACGTTAGAGGTGAAATTCTTGGATCGTCGCAAGACGGACAGAAGCGAAAGCATTTGCCAAAAATGTTTTCATTAATCAAGAACGAAAGTTAGAGGTTCGAAGGCGATCAGATACCGCCCTAGTTCTAACCATAAACGATGCCAGCTAGCGATCCGCCGAAGTTCCTCCGATGACTCGGCGGGCAGCTTCCGGGAAACCAAAGCTTTTGGGTTCCGGGGGAAGTATGGTTGCAAAGCTGAAACTTAAAGGAATTGACGGAAGGGCACCACCAGGAGTGGAGCCTGCGGCTTAATTTGACTCAACACGGGAAACCTCACCAGGCCCGGACACCGGAAGGATTGACAGATTGATAGCTCTTTCTTGATTCGGTGGGTGGTGGTGCATGGCCGTTCTTAGTTGGTGGAGCGATTTGTCTGGTTAATTCCGATAACGAACGAGACTCTAGCCTGCTAAATAGGCGTACTTTATGGTATCTCGAAGGCCCCCGGC--CTA----GGTCGGGCGGTTTTTACTACCGACGTACAAACAAATCTTCTTAGAGGG Cynipoidea ATAGCGTATATTAAAGTTGTTGCGGTTAAAAAGCTCGTAGTTGAATCTGTGTCGCACGCTGTCGGTTCGCCGCT-CG-CG--GTGTTT-AACTGACATGC-TCGTGAGACGTCCTACCGGTGGGC GAGCG--GTCCAACT-AATA-TCCCATCGCGGTGCTCTTAATTGAGTGTCGAGGTGGGCCGGTACGTTTACTTTGAACAAATTAGAGTGCTTAAAGCAGGCTACCTTTGCCTGAATACTGTGTGCATGGAATAATGGAATAGGACCTCGGTTCTATTTTGTTGGTTTTCGGAAATCCGAGGTAATGATTAATAGGGACAGATGGGGGCATTCGTATTGCGACGTTAGAGGTGAAATTCTTGGATCGTCGCAAGACGGACAGAAGCGAAAGCATTTGCCAAAAATGTTTTCATTAATCAAGAACGAAAGTTAGAGGTTCGAAGGCGATCAGATACCGCCCTAGTTCTAACCATAAACGATGCCAGCTAGCGATCCGCCGAAGTTCCTCCGATGACTCGGCGGGCAGCTTCCGGGAAACCAAAGCTTTTGGGTTCCGGGGGAAGTATGGTTGCAAAGCTGAAACTTAAAGGAATTGACGGAAGGGCACCACCAGGAGTGGAGCCTGCGGCTTAATTTGACTCAACACGGGAAACCTCACCAGGCCCGGACACCGGAAGGATTGACAGATTGAGAGCTCTTTCTTGATTCGGTGGGTGGTGGTGCATGGCCGTTCTTAGTTGGTGGAGCGATTTGTCTGGTTAATTCCGATAACGAACGAGACTCTAGCCTGCTAAATAGGCGTACATTCTGGTATCTTGAAGGCTCCCGGC--TTC----GGCCGGTGGGTTTTTACTACCAACGTACAAATAAATCTTCTTAGAGGG ApoideaA ATAGCGTATATTAAAGTTGTTGCGGTTAAAAAGCTCGTAGTTGAATCTGTGTAACACAGTGTCGGTTCACCGCT-CG-CG--GTGTTC-AACTGGCATTA-C-GTGTTGCGTCCTACCGGTGGAC GGGCG--GTCCGACC-AATA-TCCTAGCCCGGTGCTCTTCATTGAGTGTCGAGTTGGGCCGGTACGTTTACTTTGAACAAATTAGAGTGCTCAAAGCAGGCTATCCTCGCCTGAATACTGTGTGCATGGAATAATGGAATAGGACCTCGGTTCTATTTTGTTGGTTTTCGGAACCCCGAGGTAATGATTAATAGGGACAGATGGGGGCATTCGTATTGCGACGTTAGAGGTGAAATTCTTGGATCGTCGCAAGACGGACAGAAGCGAAAGCATTTGCCAAAAATGTTTTCATTAATCAAGAACGAAAGTTAGAGGTTCGAAGGCGATCAGATACCGCCCTAGTTCTAACCATAAACGATGCCAGCTAGCGATCCGCCGTAGTTCCTCCGATGACTCGGCGGGCAGCTTCCGGGAAACCAAAGCTTTTGGGTTCCGGGGGAAGTATGGTTGCAAAGCTGAAACTTAAAGGAATTGACGGAAGGGCACCACCAGGAGTGGAGCCTGCGGCTTAATTTGACTCAACACGGGAAACCTCACCAGGCCCAGACACCGGAAGGATTGACAGATTGATAGCTCTTTCTTGATTCGGTGGGTGGTGGTGCATGGCCGTTCTTAGTTGGTGGAGCGATTTGTCTGGTTAATTCCGATAACGAACGAGACTCTAGCCTGCTAAATAGGCGTACTTTATGGTATCTCGAAGGCC-CCGGC--TTCA---CGGTCGGTGGTTTTTACTACCAACGTACTTACAAATCTTCTTAGAGGG ApoideaB ATAGCGTATATTAAAGTTGTTGCGGTTAAAAAGCTCGTAGTTGAATCTGTGTAACACAGTGTCGGTTCACCGCT-CG-CG--GTGTTT-AACTGGCATTA-C-GTGTTGCGTCCTACCGGTGGGC GGGCG--GTCCAACC-AATA-TCCTAACCCGGTGCTCTTGACTGAGTGTCGAGTTGGGCCGGTACGTTTACTTTGAACAAATTAGAGTGCTCAAAGCAGGCTACTTTCGCCTGAATACTGTGTGCATGGAATAATGGAATAGGACCTCGGTTCTATTTTGTTGGTTTTCGGAACCCCGAGGTAATGATTAATAGGGACAGATGGGGGCATTCGTATTGCGACGTTAGAGGTGAAATTCTTGGATCGTCGCAAGACGGACAGAAGCGAAAGCATTTGCCAAAAATGTTTTCATTAATCAAGAACGAAAGTTAGAGGTTCGAAGGCGATCAGATACCGCCCTAGTTCTAACCATAAACGATGCCAGCTAGCGATCCGCCGAAGTTCCTCCGATGACTCGGCGGGCAGCTTCCGGGAAACCAAAGCTTTTGGGTTCCGGGGGAAGTNNNNNNNNNNNNNNNNNNNTTAAAGGAATTGACGGAAGGGCACCACCAGGAGTGGAGCCTGCGGCTTAATTTGACTCAACACGGGAAACCTCACCAGGCCCGGACACCGGAAGGATTGACAGATTGATAGCTCTTTCTTGATTCGGTGGGTGGTGGTGCATGGCCGTTCTTAGTTGGTGGAGCGATTTGTCTGGTTAATTCCGATAACGAACGAGACTCTAGCCTGCTAAATAGGCGTAATTCATGGTATCTCGAAGGCCCTCGGC--TAC----GGTCGGGCGGTTTTTACTACCAACGTACCAACAAATCTTCTTAGAGGG ApoideaC ATAGCGTATATTAAAGTTGTTGCGGTTAAAAAGCTCGTAGTTGAATCTGTGTGTCACAGTGTCGGTTCACCGCT-CG-CG--GTGTTT-AACTGGCATTA-T-GTGGTACGTCCTACCGGTGGGC GGGCG--GTCCAACT-AATA-TCCCATCGCGGTGCTCTTTACTGAGTGTCGAGGTGGGCCGGTACGTTTACTTTGAACAAATTAGAGTGCTCAAAGCAGGCTACCTTCGCCTGAATACTCTGTGCATGGAATAATGGAATAGGACCTCGGTTCTATTTTGTTGGTTTTCGGAACCCCGAGGTAATGATTAATAGGGACAGATGGGGGCATTCGTATTGCGACGTTAGAGGTGAAATTCTTGGATCGTCGCAAGACGGACAGAAGCGAAAGCATTTGCCAAAAATGTTTTCATTAATCAAGAACGAAAGTTAGAGGTTCGAAGGCGATCAGATACCGCCCTAGTTCTAACCATAAACGATGCCAGCTAGCGATCCGCCGAAGTTCCTCCGATGACTCGGCGGGCAGCTTCCGGGAAACCAAAGCTTTTGGGTTCCGGGGGAAGTATGGTTGCAAAGCTGAAACTTAAAGGAATTGACGGAAGGGCACCACCAGGAGTGGAGCCTGCGGCTTAATTTGACTCAACACGGGAAACCTCACCAGGCCCGGACACCGGAAGGATTGACAGATTGATAGCTCTTTCTTGATTCGGTGGGTGGTGGTGCATGGCCGTTCTTAGTTGGTGGAGCGATTTGTCTGGTTAATTCCGATAACGAACGAGACTCTAGCCTGCTAAATAGACGTATCTTATGGTATCTCGAAGGCCCCCGGC--TTC----GGTCGGAGGGTTTTTACTACCAACGTACAAACAAATCTTCTTAGAGGG Vespidae ATAGCGTATATTAAAGTTGTTGCGGTTAAAAAGCTCGTAGTTGAATCTGT-TTTCACACTGTCGGTTCACCGCT-CG-CG--GTGTTT-A-CTGGCATGA-T-GTGGGACGTCCTACCGGTGGAC GGGCG--GTCCAAAT-AATA-TCCCATCGC-GTGCTCTTCATTGAGTGTCGAGATGGGCCGGTACGTTTACTTTGAACAAATTAGAGTGCTTAAAGCAGGCTAGCCTCGCCTGAATACTGTGTGCATGGAATAATGGAATAGGACCTCGGTTCTATTTTGTTGGTTTTCGGAACACCGAGGTAATGATTAATAGGGACAGATGGGGGCATTCGTATTGCGACGTTAGAGGTGAAATTCTTGGACCGTCGCAAGACGAACGGAAGCGAAAGCATTTGCCAAAAATGTTTTCATTAATCAAGAACGAAAGTTAGAGGTTCGAAGGCGATCAGATACCGCCCTAGTTCTAACCATAAACGATGCCAGCTAGCGATCCGCCGAAGTTCCTCCTATGACTCGGCGGGCAGCTTCCGGGAAACCAAAGCTTTTGGGTTCCGGGGGAAGTATGGTTGCAAAGCTGAAACTTAAAGGAATTGACGGAAGGGCACCACCAGGAGTGGAGCCTGCGGCTTAATTTGACTCAACACGGGAAACCTCACCAGGCCCGGACACCGGAAGGATTGACAGATTGATAGCTCTTTCTTGATTCGGTGGGTGGTGGTGCATGGCCGTTCTTAGTTGGTGGAGCGATTTGTCTGGTTAATTCCGATAACGAACGAGACTC???????????????????????????????????????????????????????????????????????????????????????????????????????? Triassoxyela ????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ?????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Asioxyela ????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ?????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Nigrimonticola ????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ?????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Gigantoxyelinae ????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ?????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Spathoxyela ????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ?????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Xyela_mesozoica ????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ?????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Angaridyela ????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ?????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Xyelotoma ????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ?????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Undatoma ????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ?????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Dahuratoma ????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ?????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Mesolyda ????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ?????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Turgidontes ????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ?????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? 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Brigittepterus ????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ?????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Grimmaratavites ????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ?????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? [28S 1673-2224, 552 char] Orthoptera CCAAGGAGTCTAACATGTGCGCGAGTCATTGGG AAACCTAAAGGCGTAATGAAAGTGAAGGT-------------------CCGAGGGAGGATGGGC-------------------CCGCCTTCACGGGGCGGCGGCCTCCG-CACTCCGGGGTGAGGCGCACCTAGAGCGTACACGTTGGGACCCGAAAGATGGTGAACTATGCCTGGCCAGGACGAAGTCAGGGGAAACCCTGATGGAGGTCCGTAGCGATTCTGACGTGCAAATCGATCGTCGGAGCTGGGTATAGGGGCGAAAGACTAATCGAACCATCTAGTAGCTGGTTCCCTCCGAAGTTTCCCTCAGGATAGCTGGTGCTC- CGAGTCTCATCCGGTAAAGCGAATGATTAGAGGCCTTGGGGCCGAAACGACCTCAACCTATTCTCAAACTTTAAATGGGTGAGATCTCCGGCTTGCTTGATAT------------GCTGAAGCCGCGAG--CGAA---------CGAC-------------TCGGATCGGAGT-GCCAAGTGGGCCACTTTT Paraneoptera CCAAGGAGTTTAGCGTGTGCGCGAGTCATTGAG AAACTCAAAGGCAAAATGAAAGTGAAGGA-------------------CTAAGGGAAGATGGAT-----------------TTCCGCACTCCCGGGGCGTCTCT-ATCT-CATTGCGAGAGGAGGCGCACCTAGAGCGTACACGCTGATACCCGAAAGATGGTGAACTATGCCTGGCCAGGACGAAGCCAGGGGAAACCCTGGTGGAGGTCCGCAGCGATTCTGACGTGCAAATCGATCGTCGGAGCTGGGTTTAGGGGCGAAAGACCAATCGAACCATCTAGTAGCTGGTTCCCTCTGAAGTTTCCCTCAGGATAGCTTGCGCTC- CGAGTCTCATCCGGTAGAGCGAATGATTAGAGGCCTTGGGGTCGAAACGACCTCAACCTATTCTCAAACTCCAAATAGATGAGAACCCTGGCTTTCTTGAACT-------------ATGAAGCTAGGGG--TT-----------------------------CGGATCAGAGT-GCCAAGTGGGCCAATTTT Chrysopidae CCAAGGAGTCTAGCATGTACGCAAGTCATTGGG AAACCCAAAGGCGTAATGAAAGTAAAGGT-------------------CCTAGGGAGGATATAT-----------------TGTCGCACTCCCGGGGCGTCTTA-TTCTTCAATAGAATAAGAGTCGCACCTAGAGCGTACACGCTGGGACCCGAAAGATGGTGAACTATGCCTGGTCAGGACGAAGTCAGGGGAAACCCTGATGGAGGTCCGTAGCGATTCTGACGTGCAAATCGATCGTCGGAACTGGGTATAGGGGCGAAAGACTAATCGAACCATCTAGTAGCTGGTTCCCTCCGAAGTTTCCCTCAGGATAGCTGGCGCTC- TGAGTCTCATCCGGTAAAGCGAATGATTAGAGGCCTTGGGGCCGAAACGACCTCAACCTATTCTCAAACTTTAAATGGGTGAGATCTCTAGCTTGCTTAAAATTACATATTTATGTATGAAGCTACGAG--ATT-----------AAAAAGTTATTTTTAATTGGATCAGAGT-GCCAAGTGGGCCACTTTT Raphidioptera CCAAGGAGTCTAGCATGTGCGCGAGTCACTGAG AAACTCAAAGGCGAAATGAAAGTGAAAGC-------------------CCGAGGGAAGATCGGT-----------------CCGCGCATTCCCGGGGCGTCTCG-TTCT-CATTGCGAGATGAGGCGCACCTAGAGCGTACACGCTGGGACCCGAAAGATGGTGAACTATGCCTGGTCAGGACGAAGTCAGGGGAAACCCTGATGGAGGTCCGTAGCGATTCTGACGTGCAAATCGATCGTCGGAACTGGGTATAGGGGCGAAAGACTAATCGAACCATCTAGTAGCTGGTTCCCTCCGAAGTTTCCCTCAGGATAGCTGGCGCTC- CGAGTCTCATCTGGTAAAGCGAATGATTAGAGGCCTTGGGGCCGAAACGACCTCAACCTATTCTCAAACTTTAAATGGGTGAGATCTCCGGCTTGCTTGAACT------------AGTGAAGCCGCGAG--ACAAA--------TTA---------------TGGATCAGAGT-GCCAAGTGGGCCACTTTT Coleo_Polyphaga CCAAGGAGTCTAGCATGTGCGCGAGTCATTGGG AAACCTAAAGGCGCAATGAAAGTGAAGG-----------------------AGGATGGGCGGGC-----------------CCTCGCACTCCCGGGGCGTCTCG-CACT-CATCGCGAGGCGAGGCGCACCACGAGCGTACACGCTGGGACCCGAAAGATGGTGAACTATGCCTGGTCAGGACGAAGTCAGGGGAAACCCTGATGGAGGTCCGTAGCGATTCTGACGTGCAAATCGATCGTCGGAACTGGGTATAGGGGCGAAAGACTAATCGA-CCATCTAGTAGCTGGTTCCTTCCG---------------------------- ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Coleo_Adephaga ???????GTCTAGCATGTGCGCAAGTCATTGGG AAACCTAAAGGCGTAATGAAAGTGAA-------------------------AGGGAAGAT-------------------------GCATTCCCGGGGCGTCGCG-TACT-TATTGCAAGTTGCGGCGCACCAAGAGCGTACACGCTGGGACCCGAAAGATGGTGAACTATGCCTGGTCAGGACGAAGTCAGGGGAAACCCTGATGGAGGTCCGCAGCGATTCTGACGTGCAAATCGATCGCATGAACTGGGTATAGGGGCGAAAGACTAATCGAACCATC??????????????????????????????????????????????? ???????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Lepid_Micropterigidae ???????GTCTAGCATGTGCGCGAGCCATTGGG AAACCCACAGGCGCAATGAAAGTGAA-------------------------GACGATGGT-------------------------GCACTCCCGGGGCGTCTTGTCCGCAATCTGTCGGACTGGGCGCACCGATAGCGTAAATGCTGGTACCCGAAAGATGGTGAACTATGCCTGGTCAGGTTGAAGTCAGGGGAAACTCTGATGGAGGACCGTAGCGATTCTGACGTGCAAATCGATCGTCTGAACTGGGTATAGGGGCGAAAGACTAATCGAACCATCTAGTAGCTGGTTCCATCCGAAGTTTCCCTCAGGATAGCTGGCGCTA- GCAGTCTCATACGGTAAAGCGAATGATTAGAGGCCTTGGGGCCGAAACGGCCTCAACCTATTCTCAAACTTTAAATGGGTGAGAACACCGGCTTGCTCGAATT-------------GTGAAGCCG-GTG---------------------------------CAGATGATAGT-GCCAAGTGGGCCAATTTT Lepid_Papilionidae ???????GTCTAGCATGTGTGCGAGTCATTGAG AAACTGAAAGGCGTAACGAAAGTGAA-------------------------AGGGAGGAT-------------------------GCACTCCCGAGGCGTCTCGTTTCCAATCTGTGAATGTAGGCGCGCTCTGAGCAGAGATGCTGGGACCCGAAAGATGGTGAACTATGCCTGGTCAGGTCGAAGTCAGGGGAAACCCTGATGGAGGACCGTAGCGATTCTGACGTGCAAATCGATCGTCGGAACTGGGTATAGGGGCGAAAGACTAATCGAACCATC??????????????????????????????????????????????? ???????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Mecoptera CCAAGGAGTCTAGCATGTGCGCAAGTCATTGGG AAACCTAAAGGCGTAATGAAAGTAAA-------------------------AGGGAAGAT-------------------------GCATTCCCTGGACGTCTCA-TATT-CATTGCGAATAGAGGCGTACCAAGAGCGTACACGCTGGGACCCGAAAGATGGTGAACTATGCCTGGTCAGGTCGAAGTCAGGGGAAACCCTGATGGAGGACCGTAGCGATTCTGACGTGCAAATCGATCGTCGGAACTGGGTATAGGGGCGAAAGACTAATCGAACCATCTAGTAGCTGGTTCCCTCCGAAGTTTCCCTCAGGATAGCTGGCA---- CGAGTCTCATCCGGTAAAGCGAATGATTAGAGGCCTTGGGGCCGAAACGACCTCAACCTATTCTCAAACTTTAAATGGGTGAGATCTCTGGCTTGCTTAAATA-------------GTGAAGCCA-GAG--AT-----------TT-----------------GGATCAGAGT-GCCAAGTGGGCCACTTTT Xyela CCAAGGAGTCTAACATGTGCGCGAGTCATTGGG AAACCTAAAGGCGCAATGAAAGTGAAGGT-CAGGCC-T-C-GCGCCGACCGAGGGAGGATGGGCCGCGTC--ACGATGCGGCCCCGCACTCCCGGGGCGTCTCG-TTCT-CATCGCGAGATGAGGCGCACCCAGAGCGTACACGTTGGGACCCGAAAGATGGTGAACTATGCCTGGTCAGGACGAAGTCAGGGGAAACCCTGATGGAGGTCCGTAGCGATTCTGACGTGCAAATCGATCGTCGGAACTGGGTATAGGGGCGAAAGACTAATCGAACCATCTAGTAGCTGGTTCCCTCCGAAGTTTCCCTCAGGATAGCTGGCACTCG CGAGTCTCATCCGGTAAAGCGAATGATTAGAGGCCTTGGGGCCGAAACGACCTCAACCTATTCTCAAACTTTAAATGGGTGAGATCTCTGGCTTGCTTGAACG--TTTAATT----TTGAAGCCACGAG--AT-----------TT----------------CGGATCAGAGT-GCCAAGTGGGCCACTTTT Macroxyela CCAAGGAGTCTAACATGTGCGCGAGTCATTGGG AAACCTAAAGGCGCAATGAAAGTGAAGGT-CA-GCC-T-C-GCGCTGACCGAGGGAGGATGGGCCGCGTC--ACGATGCGGCCCCGCACTCCCGGGGCGTCTCG-TTCT-CATCGCGAGAAGAGGCGCACCCAGAGCGTACACGTTGGGACCCGAAAGATGGTGAACTATGCCTGGTCAGGACGAAGTCAGGGGAAACCCTGATGGAGGTCCGTAGCGATTCTGACGTGCAAATCGATCGTCGGAACTGGGTATAGGGGCGAAAGACTAATCGAACCATCTAGTAGCTGGTTCCCTCCGAAGTTTCCCTCAGGATAGCTGGCACTCG CGAGTCTCATCCGGTAAAGCGAATGATTAGAGGCCTTGGGGCCGAAACGACCTCAACCTATTCTCAAACTTTAAATGGGTGAGATCTCTGGCTTGCTTGAACG-------------TTGAAGCCACGAG--AT-----------TT----------------CGGATCAGAGT-GCCAAGTGGGCCACTTTT Runaria CCAAGGAGTCTAACATGTGCGCGAGTCATTGGG AAACCTAAAGGCGCAATGAAAGTGAAGGT-CG-GCC-T-T-GCGCCGACCGAGGGAGGATGGGCCGCGTC--ACGATGCGGCCCCGCACTCCCGGGGCGTCTCG-TTCT-CATCGCGAGAGGAGGCGCACCCAGAGCGTACACGTTGGGACCCGAAAGATGGTGAACTATGCCTGGTCAGGACGAAGTCAGGGGAAACCCTGATGGAGGTCCGTAGCGATTCTGACGTGCAAATCGATCGTCGGAACTGGGTATAGGGGCGAAAGACTAATCGAACCATCTAGTAGCTGGTTCCCTCCGAAGTTTCCCTCAGGATAGCTGGCACTCG CGAGTCTCATCCGGTAAAGCGAATGATTAGAGGCCTTGGGGCCGAAACGACCTCAACCTATTCTCAAACTTTAAATGGGTGAGATCTCTGGCTTGCTTGAACT-------------ATGAAGCCACGAG--AT-----------TT----------------CGGATCAGAGT-GCCAAGTGGGCCACTTTT Paremphytus ????????????????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ???????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Blasticotoma CCAAGGAGTCTAACATGTGCGCGAGTCATTGGG AAACCTAAAGGCGCAATGAAAGTGAAGGT-CA-GCC-T-T-GCGCTGACCGAGGGAGGATGGGCCGCGTC--ACGATGCGGCCCCGCACTCCCGGGGCGTCTCG-TTCT-CATTGCGAGAGGAGGCGCACCCAGAGCGTACACGTTGGGACCCGAAAGATGGTGAACTATGCCTGGTCAGGACGAAGTCAGGGGAAACCCTGATGGAGGTCCGTAGCGATTCTGACGTGCAAATCGATCGTCGGAACTGGGTATAGGGGCGAAAGACTAATCGAACCATCTAGTAGCTGGTTCCCTCCGAAGTTTCCCTCAGGATAGCTGGCACTCG CGAGTCTCATCCGGTAAAGCGAATGATTAGAGGCCTTGGGGCCGAAACGACCTCAACCTATTCTCAAACTTTAAATGGGTGAGATCTCTGGCTTGCTTGAACT-------------ATGAAGCCACGAG--AT-----------TT----------------CGGATCAGAGT-GCCAAGTGGGCCACTTTT Tenthredo CCAAGGAGTCTAACATGTGCGCGAGTCATTGGG AAACCTAAAGGCGAAATGAAAGTGAAGGT-CA-GCC-T-A-GCGCTGACCGAGGGAGGATGGGCCGCGTC--ACGATGCGGCCCCGCACTCCCGGGGCGTCTCG-TTCT-CATTGCGAGGAGAGGCGCACCCAGAGCGTACACGTTGGGACCCGAAAGATGGTGAACTATGCCTGGTCAGGACGAAGTCAGGGGAAACCCTGATGGAGGTCCGTAGCGATTCTGACGTGCAAATCGATCGTCGGAACTGGGTATAGGGGCGAAAGACTAATCGAACCATCTAGTAGCTGGTTCCCTCCGAAGTTTCCCTCAGGATAGCTGGCACTCG CGAGTCTCATCCGGTAAAGCGAATGATTAGAGGCCTTGGGGCCGAAACGACCTCAACCTATTCTCAAACTTTAAATGGGTGAGATCTCTGGCTTGCTTGAACT-------------ATGAAGCCACGAG--AT-----------TT----------------CGGATCAGAGT-GCCAAGTGGGCCACTTTT Aglaostigma CCAAGGAGTCTAACATGTGCGCGAGTCATTGGG AAACCTAAAGGCGAAATGAAAGTGAAGGT-CA-GCC-T-A-GCGCTGACCGAGGGAGGATGGGCCGCGTC--ACGATGCGGCCCCGCACTCCCGGGGCGTCTCG-TTCT-CATTGCGAGGAGAGGCGCACCCAGAGCGTACACGTTGGGACCCGAAAGATGGTGAACTATGCCTGGTCAGGACGAAGTCAGGGGAAACCCTGATGGAGGTCCGTAGCGATTCTGACGTGCAAATCGATCGTCGGAACTGGGTATAGGGGCGAAAGACTAATCGAACCATCTAGTAGCTGGTTCCCTCCGAAGTTTCCCTCAGGATAGCTGGCACTCG CGAGTCTCATCCGGTAAAGCGAATGATTAGAGGCCTTGGGGCCGAAACGACCTCAACCTATTCTCAAACTTTAAATGGGTGAGATCTCTGGCTTGCTTGAACT-------------ATGAAGCCACGAG--AT-----------TT----------------CGGATCAGAGT-GCCAAGTGGGCCACTTTT Dolerus CCAAGGAGTCTAACATGTGCGCGAGTCATTGGG AAACCTAAAGGCGAAATGAAAGTAAAGGT-CA-GCC-T-T-GCGCTGACCGAGGGAGGATGGGCCGCGTC--ACGATGCGGCCCCGCACTCCCGGGGCGTCTCG-TTCT-CATTGCGAGAAGAGGCGCACCCAGAGCGTACACGTTGGGACCCGAAAGATGGTGAACTATGCCTGGTCAGGACGAAGTCAGGGGAAACCCTGATGGAGGTCCGTAGCGATTCTGACGTGCAAATCGATCGTCGGAACTGGGTATAGGGGCGAAAGACTAATCGAACCATCTAGTAGCTGGTTCCCTCCGAAGTTTCCCTCAGGATAGCTGGCACTCG CGAGTCTCATCCGGTAAAGCGAATGATTAGAGGCCTTGGGGCCGAAACGACCTCAACCTATTCTCAAACTTTAAATGGGTGAGATCTCTGGCTTGCTTGAACT-------------ATGAAGCCACGAG--AT-----------TT----------------CGGATCAGAGT-GCCAAGTGGGCCACTTTT Selandria CCAAGGAGTCTAACATGTGCGCGAGTCATTGGG AAACCTAAAGGCGAAATGAAAGTGAAGGT-CT-GCC-T-A-GCGCTGACCGAGGGAGGATGGGCCGCGTC--ACGATGCGGTCCCGCACTCCCGGGGCGTCTCG-TTCT-CATTACGAGAAGAGGCGCACCCAGAGCGTACACGTTGGGACCCGAAAGATGGTGAACTATGCCTGGTCAGGACGAAGTCAGGGGAAACCCTGATGGAGGTCCGTAGCGATTCTGACGTGCAAATCGATCGTCGGAACTGGGTATAGGGGCGAAAGACTAATCGAACCATCTAGTAGCTGGTTCCCTCCGAAGTTTCCCTCAGGATAGCTGGCACTCG CGAGTCTCATCCGGTAAAGCGAATGATTAGAGGCCTTGGGGCCGAAACGACCTCAACCTATTCTCAAACTTTAAATGGGTGAGATCTCTGGCTTGCTTGAACT-------------ATGAAGCCACGAG--AT-----------TT----------------CGGATCAGAGT-GCCAAGTGGGCCACTTTT Strongylogaster CCAAGGAGTCTAACATGTGCGCGAGTCATTGGG AAACCTAAAGGCGAAATGAAAGTGAAGGT-CA-GCC-T-A-GCGCTGGCCGAGGGAGGATGGGCCGCGTC--ACGATGCGGTCCCGCACTCCCGGGGCGTCTCG-TTCT-CATTACGAGAAGAGGCGCACCCAGAGCGTACACGTTGGGACCCGAAAGATGGTGAACTATGCCTGGTCAGGACGAAGTCAGGGGAAACCCTGATGGAGGTCCGTAGCGATTCTGACGTGCAAATCGATCGTCGGAACTGGGTATAGGGGCGAAAGACTAATCGAACCATCTAGTAGCTGGTTCCCTCCGAAGTTTCCCTCAGGATAGCTGGCACTCG CGAGTCTCATCCGGTAAAGCGAATGATTAGAGGCCTTGGGGCCGAAACGACCTCAACCTATTCTCAAACTTTAAATGGGTGAGATCTCTGGCTTGCTTGAACT-------------ATGAAGCCACGAG--AT-----------TT----------------CGGATCAGAGT-GCCAAGTGGGCCACTTTT Monophadnoides CCAAGGAGTCTAACATGTGCGCGAGTCATTGGG AAGCCTAAAGGCGAAATGAAAGTAAAGGT-CA-GCC-T-A-GCGCTGACCGAGGGAGGATGGGCCGCGTC--ACGATGCGGCCCCGCACTCCCGGGGCGTCTCG-TTCT-CATTGCGAGAAGAGGCGCACCCAGAGCGTACACGTTGGGACCCGAAAGATGGTGAACTATGCCTGGTCAGGACGAAGTCAGGGGAAACCCTGATGGAGGTCCGTAGCGATTCTGACGTGCAAATCGATCGTCGGAACTGGGTATAGGGGCGAAAGACTAATCGAACCATCTAGTAGCTGGTTCCCTCCGAAGTTTCCCTCAGGATAGCTGGCACTCG CGAGTCTCATCCGGTAAAGCGAATGATTAGAGGCCTTGGGGCCGAAACGACCTCAACCTATTCTCAAACTTTAAATGGGTGAGATCTCTGGCTTGCTTGAACT-------------ATGAAGCCACGAG--AT-----------TT----------------CGGATCAGAGT-GCCAAGTGGGCCACTTTT Metallus CCAAGGAGTCTAACATGTGCGCGAGTCATTGGG AAGCCTAAAGGCGAAATGAAAGTGAAGGT-CA-GCC-T-C-GCGCTGACCGAGGGAGGATGGGCCGCGTC--ACGATGCGGCCCCGCACTCCCGGGGCGTCTCG-TTCT-CATTGCGAGAAGAGGCGCACCCAGAGCGTACACGTTGGGACCCGAAAGATGGTGAACTATGCCTGGTCAGGACGAAGTCAGGGGAAACCCTGATGGAGGTCCGTAGCGATTCTGACGTGCAAATCGATCGTCGGAACTGGGTATAGGGGCGAAAGACTAATCGAACTATCTAGTAGCTGGTTCCCTCCGAAGTTTCCCTCAGGATAGCTGGCACTCG CGAGTCTCATCCGGTAAAGCGAATGATTAGAGGCCTTGGGGCCGAAACGACCTCAACCTATTCTCAAACTTTAAATGGGTGAGATCTCTGGCTTGCTTGAACT-------------ATGAAGCCACGAG--AT-----------TT----------------CGGATCAGAGT-GCCAAGTGGGCCACTTTT Athalia CCAAGGAGTCTAACATGTGCGCGAGTCATTGGG AAACCTAAAGGCGCAATGAAAGTAAAGGT-CA-GCC-T-A-GCGCTGACCGAGGGAGGATGGGTCGCGTC--ACGATGCGGCCCCGCACTCCCGGGGCGTCTCG-TTCT-CATTGCGAGAAGAGGCGCACCCAGAGCGTACACGTTGGGACCCGAAAGATGGTGAACTATGCCTGGTCAGGACGAAGTCAGGGGAAACCCTGATGGAGGTCCGTAGCGATTCTGACGTGCAAATCGATCGTCGGAACTGGGTATAGGGGCGAAAGACTAATCGAACCATCTAGTAGCTGGTTCCCTCCGAAGTTTCCCTCAGGATAGCTGGCACTCG CGAGTCTCATCCGGTAAAGCGAATGATTAGAGGCCTTGGGGCCGAAACGACCTCAACCTATTCTCAAACTTTAAATGGGTGAGATCTCTGGCTTGCTTGAACT-------------ATGAAGCCACGAG--AT-----------CT----------------CGGATCAGAGT-GCCAAGTGGGCCACTTTT Taxonus CCAAGGAGTCTAACATGTGCGCGAGTCATTGGG AAGCCTAAAGGCGAAATGAAAGTGAAGGT-CA-GCC-T-A-GCGCTGACCGAGGGAGGATGGGCCGCGTC--ACGATGCGGCCCCGCACTCCCGGGGCGTCTCG-TTCT-CATTGCGAGAAGAGGCGCACCCAGAGCGTACACGTTGGGACCCGAAAGATGGTGAACTATGCCTGGTCAGGACGAAGTCAGGGGAAACCCTGATGGAGGTCCGTAGCGATTCTGACGTGCAAATCGATCGTCGGAACTGGGTATAGGGGCGAAAGACTAATCGAACTATCTAGTAGCTGGTTCCCTCCGAAGTTTCCCTCAGGATAGCTGGCACTCG CGAGTCTCATCCGGTAAAGCGAATGATTAGAGGCCTTGGGGCCGAAACGACCTCAACCTATTCTCAAACTTTAAATGGGTGAGATCTCTGGCTTGCTTGAACT-------------ATGAAGCCACGAG--AT-----------TT----------------CGGATCAGAGT-GCCAAGTGGGCCACTTTT Hoplocampa CCAAGGAGTCTAACATGTGCGCGAGTCATTGGG AAACCTAAAGGCGAAATGAAAGTGAAGGT-CA-GCC-T-C-GCGCTGACCGAGGGAGGATGGGCCGCGTC--ACGATGCGGCCCCGCACTCCCGGGGCGTCTCG-TTCT-CATTGCGAGAAGAGGCGCACCCAGAGCGTACACGTTGGGACCCGAAAGATGGTGAACTATGCCTGGTCAGGACGAAGTCAGGGGAAACCCTGATGGAGGTCCGTAGCGATTCTGACGTGCAAATCGATCGTCGGAACTGGGTATAGGGGCGAAAGACTAATCGAACCATCTAGTAGCTGGTTCCCTCCGAAGTTTCCCTCAGGATAGCTGGCACTCG CGAGTCTCATCCGGTAAAGCGAATGATTAGAGGCCTTGGGGCCGAAACGACCTCAACCTATTCTCAAACTTTAAATGGGTGAGATCTCTGGCTTGCTTGAACT-------------ATGAAGCCACGAG--AT-----------TT----------------CGGATCAGAGT-GCCTAGTGGGCCACTTTT Nematinus CCAAGGAGTCTAACATGTGCGCGAGTCATTGGG AAACCTAAAGGCGAAATGAAAGTGAAGGT-CA-GCC-T-C-GCGCTGACCGAGGGAGGATGGGCCGCGTC--ACGATGCGGTCCCGCACTCCCGGGGCGTCTCG-TTCT-CATTGCGAGAAGAGGCGCACCCAGAGCGTACACGTTGGGACCCGAAAGATGGTGAACTATGCCTGGTCAGGACGAAGTCAGGGGAAACCCTGATGGAGGTCCGTAGCGATTCTGACGTGCAAATCGATCGTCGGAACTGGGTATAGGGGCGAAAGACTAATCGAACCATCTAGTAGCTGGTTCCCTCCGAAGTTTCCCTCAGGATAGCTGGCACTCG CGAGTCTCATCCGGTAAAGCGAATGATTAGAGGCCTTGGGGCCGAAACGACCTCAACCTATTCTCAAACTTTAAATGGGTGAGATCTCTGGCTTGCTTGAACT-------------ATGAAGCCACGAG--AT-----------TT----------------CGGATCAGAGT-GCCAAGTGGGCCACTTTT Nematus CCAAGGAGTCTAACATGTGCGCGAGTCATTGGG AAACCTAAAGGCGAAATGAAAGTGAAGGT-CA-GCC-T-C-GCGCTGACCGAGGGAGGATGGGCCGCGTC--ACGATGCGGCCCCGCACTCCCGGGGCGTCTCG-TTCT-CATTGCGAGAAGAGGCGCACCCAGAGCGTACACGTTGGGACCCGAAAGATGGTGAACTATGCCTGGTCAGGACGAAGTCAGGGGAAACCCTGATGGAGGTCCGTAGCGATTCTGACGTGCAAATCGATCGTCGGAACTGGGTATAGGGGCGAAAGACTAATCGAACCATCTAGTAGCTGGTTCCCTCCGAAGTTTCCCTCAGGATAGCTGGCACTCG CGAGTCTCATCCGGTAAAGCGAATGATTAGAGGCCTTGGGGCCGAAACGACCTCAACCTATTCTCAAACTTTAAATGGGTGAGATCTCTGGCTTGCTTGAACT-------------ATGAAGCCACGAG--AT-----------TT----------------CGGATCAGAGT-GCCAAGTGGGCCACTTTT Cladius CCAAGGAGTCTAACATGTGCGCGAGTCATTGGG AAACCTAAAGGCGAAATGAAAGTAAAGGT-CA-GCC-T-C-GCGCTGACCGAGGGAGGATGGGCCGCGTC--ACGATGCGGCCCCGCACTCCCGGGGCGTCTCG-TTCT-CATTGCGAGAAGAGGCGCACCCAGAGCGTACACGTTGGGACCCGAAAGATGGTGAACTATGCCTGGTCAGGACGAAGTCAGGGGAAACCCTGATGGAGGTCCGTAGCGATTCTGACGTGCAAATCGATCGTCGGAACTGGGTATAGGGGCGAAAGACTAATCGAACCATCTAGTAGCTGGTTCCCTCCGAAGTTTCCCTCAGGATAGCTGGCACTCG CGAGTCTCATCCGGTAAAGCGAATGATTAGAGGCCTTGGGGCCGAAACGACCTCAACCTATTCTCAAACTTTAAATGGGTGAGATCTCTGGCTTGCTTGAACT-------------ATGAAGCCACGAG--AT-----------TT----------------CGGATCAGAGT-GCCAAGTGGGCCACTTTT Monoctenus CCAAGGAGTCTAACATGTGCGCGAGTCATTGGG AAACCTAAAGGCGAAATGAAAGTGAAGGT-CA-GCC-T-A-GCGCTGACCGAGGGAGGATGGGTCGCGTC--ACGATGCGGCCCCGCACTCCCGGGGCGTCTCG-TTCT-CATTGCGAGAAGAGGCGCACCCAGAGCGTACACGTTGGGACCCGAAAGATGGTGAACTATGCCTGGTCAGGACGAAGTCAGGGGAAACCCTGATGGAGGTCCGTAGCGATTCTGACGTGCAAATCGATCGTCGGAACTGGGTATAGGGGCGAAAGACTAATCGAACCATCTAGTAGCTGGTTCCCTCCGAAGTTTCCCTCAGGATAGCTGGCACTCG CGAGTCTCATCCGGTAAAGCGAATGATTAGAGGCCTTGGGGCCGAAACGACCTCAACCTATTCTCAAACTTTAAATGGGTGAGATCTCTGGCTTGCTTGAACT-------------ATGAAGCCACGAG--AT-----------TT----------------CGGATCAGAGT-GCCAAGTGGGCCACTTTT Gilpinia CCAAGGAGTCTAACATGTGCGCGAGTCATTGGG AAACCTAAAGGCGAAATGAAAGTAAAGGT-CG-GCC-C-A-GCGCCGACCGAGGGAGGATGGGTCGCGTC--ACGATGCGACCCCGCACTCCCGGGGCGTCTCG-TTCT-CATTGCGAGAAGAGGCGCACCCAGAGCGTACACGTTGGGACCCGAAAGATGGTGAACTATGCCTGGTCAGGACGAAGTCAGGGGAAACCCTGATGGAGGTCCGTAGCGATTCTGACGTGCAAATCGATCGTCGGAACTGGGTATAGGGGCGAAAGACTAATCGAACCATCTAGTAGCTGGTTCCCTCCGAAGTTTCCCTCAGGATAGCTGGCACTCG CGAGTCTCATCCGGTAAAGCGAATGATTAGAGGCCTTGGGGCCGAAACGACCTCAACCTATTCTCAAACTTTAAATGGGTGAGATCTCTGGCTTGCTTGAACT-------------ATGAAGCCACGAG--AT-----------CT----------------CGGATCAGAGT-GCCAAGTGGGCCACTTTT Diprion CCAAGGAGTCTAACATGTGCGCGAGTCATTGGG AAACCTAAAGGCGAAATGAAAGTAAAGGT-CA-GCC-C-A-GCGCTGACCGAGGGAGGATGGGCCGCGTC--ACGATGCGGCCCCGCACTCCCGGGGCGTCTCG-TTCT-CATCGCGAGAAGAGGCGCACCCAGAGCGTACACGTTGGGACCCGAAAGATGGTGAACTATGCCTGGTCAGGACGAAGTCAGGGGAAACCCTGATGGAGGTCCGTAGCGATTCTGACGTGCAAATCGATCGTCGGAACTGGGTATAGGGGCGAAAGACTAATCGAACCATCTAGTAGCTGGTTCCCTCCGAAGTTTCCCTCAGGATAGCTGGCACTCG CGAGTCTCATCCGGTAAAGCGAATGATTAGAGGCCTTGGGGCCGAAACGACCTCAACCTATTCTCAAACTTTAAATGGGTGAGATCTCTGGCTTGCTTGAACT-------------ATGAAGCCACGAG--AT-----------CT----------------CGGATCAGAGT-GCCAAGTGGGCCACTTTT Cimbicinae CCAAGGAGTCTAACATGTGCGCGAGTCATTGGG AAACCTAAAGGCGCAATGAAAGTAAAGGT-CA-GCC-T-A-GCGCTGACCGAGGGAGGATGGGCCGCGTC--ACGATGCGGCCCCGCACTCCCGGGGCGTCTCG-TTCT-CATTGCGAGAAGAGGCGCACCCAGAGCGTACACGTTGGGACCCGAAAGATGGTGAACTATGCCTGGTCAGGACGAAGTCAGGGGAAACCCTGATGGAGGTCCGTAGCGATTCTGACGTGCAAATCGATCGTCTGAACTTGGTATAGCGGCGAAAGACTAATCGAACTATCTAGTAGCTGGTTCCCTCCGAAGTTTCCCTCAGGATAGCTGGCACTCG CGAGTCTCATCCGGTAAAGCGAATGATTAGAGGCCTTGGGGCCGAAACGACCTCAACCTATTCTCAAACTTTAAATGGGTGAGATCTCTGGCTTGCTTGAACT-------------ATGAAGCCACGAG--AT-----------TT----------------CGGATCAGAGT-GCCTAGTGGGCCACTTTT Abia CCAAGGAGTCTAACATGCGCGCGAGTCATTGGG AAGCCTAAAGGCGCAATGAAAGTGAAGGTACG-GCC-A-A-GCGCCTGCCGAGGGAGGATGGGCCGCGTC--ACGATGCGGCCCCGCACTCCCGGGGCGTCTCG-TTCT-CATTGCGAGAAGAGGCGCACCCAGAGCGTACACGTTGGGACCCGAAAGATGGTGAACTATGCCTGGTCAGGACGAAGTCAGGGGAAACCCTGATGGAGGTCCGTAGCGATTCTGACGTGCAAATCGATCGTCGGAACTGGGTATAGGGGCGAAAGACTAATCGAACCATCTAGTAGCTGGTTCCCTCCGAAGTTTCCCTCAGGATAGCTGGCACTCG AGAGTCTCATCCGGTAAAGCGAATGATTAGAGGCCTTGGGGCCGAAACGACCTCAACCTATTCTCAAACTTTAAATGGGTGAGATCTCTGGCTTGCTTGAACT-------------ATGAAGCCACGAG--AT-----------CT----------------CGGATCAGAGT-GCCCAGTGGGCCACTTTT Corynis CCAAGGAGTCTAACATGTGCGCGAGTCATTGGG AAACCTAAAGGCGAAATGAAAGTAAAGGT-CA-GCC-T-A-GCGCTGACCGAGGGAGGATGGGCCGCGTC--ACGATGCGGCCCCGCACTCCCGGGGCGTCTCG-TTCT-CATTGCGAGAAGAGGCGCACCCAGAGCGTACACGTTGGGACCCGAAAGATGGTGAACTATGCCTGGTCAGGACGAAGTCAGGGGAAACCCTGATGGAGGTCCGTAGCGATTCTGACGTGCAAATCGATCGTCGGAACTGGGTATAGGGGCGAAAGACTAATCGAACCATCTAGTAGCTGGTTCCCTCCGAAGTTTCCCTCAGGATAGCTGGCACTCG CGAGTCTCATCCGGTAAAGCGAATGATTAGAGGCCTTGGGGCCGAAACGACCTCAACCTATTCTCAAACTTTAAATGGGTGAGATCTCTGGCTTGCTTGAACT-------------ATGAAGCCACGAG--AT-----------CT----------------CGGATCAGAGT-GCCAAGTGGGCCACTTTT Arge NNNNNGAGTCTAGCATGTGCGCGAGTCATTGGG AAACCTAAAGGCGCAATGAAAGTGAAGGT-CA-ACC-A-C-GCGTTGACCGAGGGAGGATGGGTCGCGTC--GCGGCGCGGTCCCGCACTCCCGGGGCGTCTCG-TTCT-CATCGCGAGAAGAGGCGCACCCAGAGCGTACACGTTGGGACCCGAAAGATGGTGAACTATGCCTGGTCAGGACGAAGTCAGGGGAAACCCTGATGGAGGTCCGTAGCGATTCTGACGTGCAAATCGATCGTCGGAACTGGGTATAGGGGCGAAAGACTAATCGAACCATCTAGTAGCTGGTTCCCTCCGAAGTTTCCCTCAGGATAGCTGGCACTCG CGAGTCTCATCCGGTAAAGCGAATGATTAGAGGCCTTGGGGCCGAAACGACCTCAACCTATTCTCAAACTTTAAATGGGTGAGATCTCTGGCTTGCTTGAACC-------------ATGAAGCCACGAG--AT-----------TT----------------CGGATCAGAGT-GCCAAGTGGGCCACTTTT Sterictiphora CCAAGGAGTCTAACATGTGCGCGAGTCATTGGG ATACCTAAAGGCGCAATGAAAGTGAAGGT-CA-ACCCT-C-GCGTTGACCGAGGGAGGATGGGCCGCGTC--ACGATGCGGCCCCGCACTCCCGGGGCGTCTCG-TTCT-CATCGCGAGAAGAGGCGCACCCAGAGCGTACACGTTGGGACCCGAAAGATGGTGAACTATGCCTGGTCAGGACGAAGTCAGGGGAAACCCTGATGGAGGTCCGTAGCGATTCTGACGTGCAAATCGATCGTCGGAACTGGGTATAGGGGCGAAAGACTAATCGAACCATCTAGTAGCTGGTTCCCTCCGAAGTTTCCCTCAGGATAGCTGGCACTCG CGAGTCTCATCCGGTAAAGCGAATGATTAGAGGCCTTGGGGCCGAAACGACCTCAACCTATTCTCAAACTTTAAATGGGTGAGATCTCTGGCTTGCTTGAACT-------------ATGAAGCCACGAG--AT-----------TT----------------CGGATCAGAGT-GCCAAGTGGGCCACTTTT Perga CCAAGGAGTCTAACATGTGCGCGAGTCATTGGG AAACCTAAAGGCGCAATGAAAGTAAATGT-TC-GCCCT-C-GCGCGGACAGAGGGAGGATGGATCGCGTC--ACGATGCGGTCTCGCACTCCCGGGGCGTCTCG-TTCT-CATTGCGAGAAGAGGCGCACCCAGAGCGTACACGTTGGGACCCGAAAGATGGTGAACTATGCCTGGTCAGGACGAAGTCAGGGGAAACCCTGATGGAGGTCCGTAGCGATTCTGACGTGCAAATCGATCGTCGGAACTGGGTATAGGGGCGAAAGACTAATCGAACCATCTAGTAGCTGGTTCCCTCCGAAGTTTCCCTCAGGATAGCTGGCACTCG CGAGTCTCATTCGGTAAAGCGAATGATTAGAGGCCTTGGGGCCGAAACGACCTCAACCTATTCTCAAACTTTAAATGGGTGAGATCTCTGGCTTGCTTGAACC-------------ATGAAGCCACGAG--AT-----------TT----------------CGGATCAGAGT-GCCAAGTGGGCCACTTTT Phylacteophaga CCAAGGAGTCTAACATGTGCGCGAGTCATTGGG AAACCTAAAGGCGCAATGAAAGTGAATGT-CA-GCC-T-T-GCGCTGACAGAGGGAGGATGGGTCGCGTC--ACGATGCGGCCCCGCACTCCCGGGGCGTCTCG-TTCT-CATTGCGAGGAGAGGCGCACCCAGAGCGTACACGTTGGGACCCGAAAGATGGTGAACTATGCCTGGTCAGGACGAAGTCAGGGGAAACCCTGATGGAGGTCCGTAGCGATTCTGACGTGCAAATCGATCGTCGGAACTGGGTATAGGGGCGAAAGACTAATCGAACCATCTAGTAGCTGGTTCCCTCCGAAGTTTCCCTCAGGATAGCTGGCACTCG CGAGTCTCATTCGGTAAAGCGAATGATTAGAGGCCTTGGGGCCGAAACGACCTCAACCTATTCTCAAACTTTAAATGGGTGAGATCTCTGGCTTGCTTGAACC-------------ATGAAGCCACGAG--AT-----------TT----------------CGGATCAGAGT-GCCAAGTGGGCCACTTTT Lophyrotoma CCAAGGAGTCTAACATGTGCGCGAGTCATTGGG AAACCTAAAGGCGCAATGAAAGTAAATGT-CG-GTC-A-C-GCGCCGACAGAGGGAGGATGGGCCGCGTC--ACGATACGGCCCCGCACTCCCGGGGCGTCTCG-TTCT-CATTGCGAGAAGAGGCGCACCCAGAGCGTACACGTTGGGACCCGAAAGATGGTGAACTATGCCTGGTCAGGACGAAGTCAGGGGAAACCCTGATGGAGGTCCGTAGCGATTCTGACGTGCAAATCGATCGTCGGAACTGGGTATAGGGGCGAAAGACTAATCGAACCATCTAGTAGCTGGTTCCCTCCGAAGTTTCCCTCAGGATAGCTGGCACTCG CGAGTCTCATTCGGTAAAGCGAATGATTAGAGGCCTTGGGGCCGAAACGACCTCAACCTATTCTCAAACTTTAAATGGGTGAGATCTCTGGCTTGCTTGAATC-------------GTGAAGCCACGAG--AT-----------TT----------------CGGATCAGAGT-GCCAAGTGGGCCACTTTT Acordulecera CCAAGGAGTCTAACATGTGCGCGAGTCATTGGG AAACCTAAAGGCGCAATGAAAGTGAATGT-CG-GCC-T-T-GAGTCGAGAGAGGGAGGATGAGCCGCGTC--ACGATGCGGCTCCGCACTCCCGGGGCGTCTCG-TTCT-CATTGCGAGAAGAGGCGCACCCAGAGCGTACACGTTGGGACCCGAAAGATGGTGAACTATGCCTGGTCAGGACGAAGTCAGGGGAAACCCTGATGGAGGTCCGTAGCGATTCTGACGTGCAAATCGATCGTCGGAACTGGGTATAGGGGCGAAAGACTAATCGAACCATCTAGTAGCTGGTTCCCTCCGAAGTTTCCCTCAGGATAGCTGGCACTCG AGAGTCTCATTCGGTAAAGCGAATGATTAGAGGCCTTGGGGCCGAAACGACCTCAACCTATTCTCAAACTTTAAATGGGTGAGATCTCTGGCTTGCTTGAACT-------------ATGAAGCCACGAG--AT-----------TT----------------CGGATCAGAGT-GCCAAGTGGGCCATTTTT Decameria CCAAGGAGTCTAACATGTGCGCGAGTCATTGGG ACACCTAAAGGCGCAATGAAAGTAAATGT-CG-TCCATTCGACGTCGACAGAGGGAGGATGGGCCGCGTC--TCGATGCGGCCCCGCACTCCCGGGGCGTCTCG-TTCT-CATTGCGAGAAGAGGCGCACCCAGAGCGTACACGTTGGGACCCGAAAGATGGTGAACTATGCCTGGTCAGGACGAAGTCAGGGGAAACCCTGATGGAGGTCCGTAGCGATTCTGACGTGCAAATCGATCGTCGGAACTGGGTATAGGGGCGAAAGACTAATCGAACCATCTAGTAGCTGGTTCCCTCCGAAGTTTCCCTCAGGATAGCTGGCGCTCG CGAGTCTCATTCGGTAAAGCGAATGATTAGAGGCCTTGGGGCCGAAACGACCTCAACCTATTCTCAAACTTTAAATGGGTGAGATCTCTGGCTTGCTTGAACT-------------ATGAAGCCACGAG--AT-----------TT----------------CGGATCAGAGT-GCCAAGTGGGCCACTTTT Neurotoma CCAAGGAGTCTAACATGTGCGCGAGTCATTGGG AAACCTAAAGGCGCAATGAAAGTGAAGGTCCA-GCC-T-C-GCGCTGACCGAGGGAGGATGGGCCGCGTC--ACGATGCGGCCCCGCACTCCCGGGGCGTCTCG-TTCT-CATTGCGAGATGAGGCGCACCCAGAGCGTACACGTTGGGACCCGAAAGATGGTGAACTATGCCTGGTCAGGACGAAGTCAGGGGAAACCCTGATGGAGGTCCGTAGCGATTCTGACGTGCAAATCGATCGTCGGAACTGGGTATAGGGGCGAAAGACTAATCGAACCATCTAGTAGCTGGTTCCCTCCGAAGTTTCCCTCAGGATAGCTGGCACTCG CGAGTCTCATCCGGTAAAGCGAATGATTAGAGGCCTTGGGGCCGAAACGACCTCAACCTATTCTCAAACTTTAAATGGGTGAGATCTCTGGCTTGCTCGAACG-------------TTGAAGCCACGAG--AT-----------TT----------------CGGATCAGAGT-GCCAAGTGGGCCACTTTT Onycholyda CCAAGGAGTCTAACATGTGCGCGAGTCATTGGG AAACCTAAAGGCGCAATGAAAGTGAAGGTCCA-GCC-T-C-GCGCTGACCGAGGGAGGATGGGCCGCGTC--ACGATGCGGCCCCGCACTCCCGGGGCGTCTCG-TTCT-CATTGCGAGATGAGGCGCACCCAGAGCGTACACGTTGGGACCCGAAAGATGGTGAACTATGCCTGGTCAGGACGAAGTCAGGGGAAACCCTGATGGAGGTCCGTAGCGATTCTGACGTGCAAATCGATCGTCGGAACTGGGTATAGGGGCGAAAGACTAATCGAACCATCTAGTAGCTGGTTCCCTCCGAAGTTTCCCTCAGGATAGCTGGCACTCG CGAGTCTCATCCGGTAAAGCGAATGATTAGAGGCCTTGGGGCCGAAACGACCTCAACCTATTCTCAAACTTTAAATGGGTGAGATCTCTGGCTTGCTCGAACG-------------TTGAAGCCACGAG--AT-----------TT----------------CGGATCAGAGT-GCCAAGTGGGCCACTTTT Pamphilius CCAAGGAGTCTAACATGTGCGCGAGTCATTGGG AAACCTAAAGGCGCAATGAAAGTGAAGGTCCA-GCC-T-C-GCGCTGACCGAGGGAGGATGGGCCGCGTC--ACGATGCGGCCCCGCACTCCCGGGGCGTCTCG-TTCT-CATTGCGAGATGAGGCGCACCCAGAGCGTACACGTTGGGACCCGAAAGATGGTGAACTATGCCTGGTCAGGACGAAGTCAGGGGAAACCCTGATGGAGGTCCGTAGCGATTCTGACGTGCAAATCGATCGTCGGAACTGGGTATAGGGGCGAAAGACTAATCGAACCATCTAGTAGCTGGTTCCCTCCGAAGTTTCCCTCAGGATAGCTGGCACTCG CGAGTCTCATCCGGTAAAGCGAATGATTAGAGGCCTTGGGGCCGAAACGACCTCAACCTATTCTCAAACTTTAAATGGGTGAGATCTCTGGCTTGCTCGAACG-------------TTGAAGCCACGAG--AT-----------TT----------------CGGATCAGAGT-GCCAAGTGGGCCACTTTT Cephalcia CCAAGGAGTCTAACATGTGCGCGAGTCATTGGG AAACCTAAAGGCGCAATGAAAGTGAAGGTACA-GCC-T-C-GCGCTTACCGAGGGAGGATGGGCCGCGTC--ACGATGCGGCCCCGCACTCCCGGGGCGTCTCG-TTCT-CATTGCGAGATGAGGCGCACCCAGAGCGTACACGTTGGGACCCGAAAGATGGTGAACTATGCCTGGTCAGGACGAAGTCAGGGGAAACCCTGATGGAGGTCCGTAGCGATTCTGACGTGCAAATCGATCGTCGGAACTGGGTATAGGGGCGAAAGACTAATCGAACCATCTAGTAGCTGGTTCCCTCCGAAGTTTCCCTCAGGATAGCTGGCACTCG CGAGTCTCATCCGGTAAAGCGAATGATTAGAGGCCTTGGGGCCGAAACGACCTCAACCTATTCTCAAACTTTAAATGGGTGAGATCTCTGGCTTGCTCGAACG-------------TTGAAGCCACGAG--AT-----------TT----------------CGGATCAGAGT-GCCAAGTGGGCCACTTTT Acantholyda CCAAGGAGTCTAACATGTGCGCGAGTCATTGGG AAACCTAAAGGCGCAATGAAAGTGAAGGTCCA-GCC-T-C-GCGCTGACCGAGGGAGGATGGGCCGCGTC--ACGATGCGGCCCCGCACTCCCGGGGCGTCTCG-TTCT-CATTGCGAGATGAGGCGCACCCAGAGCGTACACGTTGGGACCCGAAAGATGGTGAACTATGCCTGGTCAGGACGAAGTCAGGGGAAACCCTGATGGAGGTCCGTAGCGATTCTGACGTGCAAATCGATCGTCGGAACTGGGTATAGGGGCGAAAGACTAATCGAACCATCTAGTAGCTGGTTCCCTCCGAAGTTTCCCTCAGGATAGCTGGCACTCG CGAGTCTCATCCGGTAAAGCGAATGATTAGAGGCCTTGGGGCCGAAACGACCTCAACCTATTCTCAAACTTTAAATGGGTGAGATCTCTGGCTTGCTTGAACT-------------ATGAAGCCACGAG--AT-----------TT----------------CGGATCAGAGT-GCCAAGTGGGCCACTTTT Megalodontesc CCAAGGAGTCTAACATGTGCGCGAGTCATTGGG AAACCTAAAGGCGCAATGAAAGTGAAGGT-CG-GCC-TTT-GTGCCGACCGAGGGAGGATGGGTCGCGTC--AAGATGCGGCCCCGCACTCCCGGGGCGTCTCG-TTCT-CATTGCGAGAAGAGGCGCACCCAGAGCGTACACGTTGGGACCCGAAAGATGGTGAACTATGCCTGGTCAGGACGAAGTCAGGGGAAACCCTGATGGAGGTCCGTAGCGATTCTGACGTGCAAATCGATCGTCGGAACTGGGTATAGGGGCGAAAGACTAATCGAACCATCTAGTAGCTGGTTCCCTCCGAAGTTTCCCTCAGGATAGCTGGCACTCG CGAGTCTCATCCGGTAAAGCGAATGATTAGAGGCCTTGGGGCCGAAACGACCTCAACCTATTCTCAAACTTTAAATGGGTGAGATCTCTGGCTTGCTTGAACG-------------TTGAAGCCATGAG--AT-----------TT----------------CGGATCAGAGT-GCCAAGTGGGCCACTTTT Megalodontessk ????????????????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ???????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Cephus CCAAGGAGTCTAACATGTGCGCGAGTCATTGGG AAGCCTAAAGGCGCAATGAAAGTGAAGGT-CG-GCC-T-C-GCGCCGACCGAGGGAGGATGGGCCGCGTC--ACGATGCGGCTCCGCACTCCCGGGGCGTCTCG-TTCT-CATTGCGAGAGGAGGCGCACCCAGAGCGTACACGTTGGGACCCGAAAGATGGTGAACTATGCCTGGTCAGGACGAAGTCAGGGGAAACCCTGATGGAGGTCCGTAGCGATTCTGACGTGCAAATCGATCGTCGGAACTGGGTATAGGGGCGAAAGACTAATCGAACCATCTAGTAGCTGGTTCCCTCCGAAGTTTCCCTCAGGATAGCTGGCACTCG CGAGTCTCATCTGGTAAAGCGAATGATTAGAGGCCTTGGGGCCGAAACGACCTCAACCTATTCTCAAACTTTAAATGGGTGAGATCTCTGGCTTGCTTGAACGAAAAATTTAAAAATTGAAGCCACGAG--ATA----------TTAT------TGAAA-TACGGATCAGAGT-GCCAAGTGGGCCACTTTT Calameuta CCAAGGAGTCTAACATGTGCGCGAGTCATTGGG AAGCCCAAAGGCGCAATGAAAGTGAAGGT-CG-GCC-T-C-GCGCCGACCGAGGGAGGATGGGCCGCGTC--ACGATGCGGCTCCGCACTCCCGGGGCGTCTCG-TTCT-CATTGCGAGAGGAGGCGCACCCAGAGCGTACACGTTGGGACCCGAAAGATGGTGAACTATGCCTGGTCAGGACGAAGTCAGGGGAAACCCTGATGGAGGTCCGTAGCGATTCTGACGTGCAAATCGATCGTCGGAACTGGGTATAGGGGCGAAAGACTAATCGAACCATCTAGTAGCTGGTTCCCTCCGAAGTTTCCCTCAGGATAGCTGGCACTCG CGAGTCTCATCTGGTAAAGCGAATGATTAGAGGCCTTGGGGCCGAAACGACCTCAACCTATTCTCAAACTTTAAATGGGTGAGATCTCTGGCTTGCTTGAACGAAAAATTTAAAAATTGAAGCCACGAG--ATA----------TTAT------TGAAA-TACGGATCAGAGT-GCCAAGTGGGCCACTTTT Hartigia CCAAGGAGTCTAACATGTGCGCGAGTCATTGGG AAGCCTAAAGGCGCAATGAAAGTGAAGGT-CG-GCC-T-C-GCGCCGACCGAGGGAGGATGGGTCGCGTC--ACGATGCGGCCCCGCACTCCCGGGGCGTCTCG-TTCT-CATTGCGAGAGGAGGCGCACCCAGAGCGTACACGTTGGGACCCGAAAGATGGTGAACTATGCCTGGTCAGGACGAAGTCAGGGGAAACCCTGATGGAGGTCCGTAGCGATTCTGACGTGCAAATCGATCGTCGGAACTGGGTATAGGGGCGAAAGACTAATCGAACCATCTAGTAGCTGGTTCCCTCCGAAGTTTCCCTCAGGATAGCTGGCACTCG CGAGTCTCATCTGGTAAAGCGAATGATTAGAGGCCTTGGGGCCGAAACGACCTCAACCTATTCTCAAACTTTAAATGGGTGAGATCTCTGGCTTGCTCGAACGAAAAATTTAAAAATTGAAGCCACGAG--ATACATATATCATTTATGATACATGAAAATACGGATCAGAGT-GCCAAGTGGGCCACTTTT Syntexis CCAAGGAGTCTAACATGTGCGCGAGTCATTGGG AAGCCTAAAGGCGCAATGAAAGTGAAGGT-CG--TC-T-T---GACGACCGAGGGAGGATGGGTCGCGA---A--A-GCGGCCCCGCACTCCCGGGGCGTCTCG-TTCT-CATTGCGAGAAGAGGCGCACCCAGAGCGTACACGTTGGGACCCGAAAGATGGTGAACTATGCCTGGTCAGGACGAAGTCAGGGGAAACCCTGATGGAGGTCCGTAGCGATTCTGACGTGCAAATCGATCGTCGGAACTGGGTATAGGGGCGAAAGACTAATCGAACCATCTAGTAGCTGGTTCCCTCCGAAGTTTCCCTCAGGATAGCTGGCACTCG CGAGTCTCATCCGGTAAAGCGAATGATTAGAGGCCTTGGGGCCGAAACGACCTCAACCTATTCTCAAACTTTAAATGGGTGAGATCTCTGGCTTGCTTGAACG--------------TGAAGCCACGAG--AT-----------TT----------------CGGATCAGAGT-GCCAAGTGGGCCACTTTT Sirex CCAAGGAGTCTAACATGTGCGCGAGTCATTGGG AAGCCCAGAGGCGCAATGAAAGTGAAGGC-CGCGTC-T-CGGTCGCGGCCGAGGGAGGATGGG----------------------GCACTCCCGGGGCGTCTCG-GCCT-CATCGCGAGGCGAGGCGCACCCAGAGCGTACACGTTGGGACCCGAAAGATGGTGAACTATGCCTGGTCAGGACGAAGTCAGGGGAAACCCTGATGGAGGTCCGTAGCGATTCTGACGTGCAAATCGATCGTCGGAACTGGGTATAGGGGCGAAAGACTAATCGAACCATCTAGTAGCTGGTTCCCTCCGAAGTTTCCCTCAGGATAGCTGGCGCTCG CGAGTCTCATCCGGTAAAGCGAATGATTAGAGGCCTTGGGGCCGAAACGACCTCAACCTATTCTCAAACTTTAAATGGGTGAGATCTCTGGCTTGCTCGAACG-------------TTGAAGCCACGAG--AT-----------------------------CGGATCAGAGT-GCCAAGTGGGCCACTTTT Xeris CCAAGGAGTCTAACATGTGCGCGAGTCATTGGG AAGCCTAAAGGCGCAATGAAAGTGAAGGC-CGTGTC-T-CGGTCACGGCCGAGGGAGGATGGG----------------------GCACTCCCGGGGCGTCTCG-GTCT-CATCGCGAGGCGAGGCGCACCCAGAGCGTACACGTTGGGACCCGAAAGATGGTGAACTATGCCTGGTCAGGACGAAGTCAGGGGAAACCCTGATGGAGGTCCGTAGCGATTCTGACGTGCAAATCGATCGTCGGAACTGGGTATAGGGGCGAAAGACTAATCGAACCATCTAGTAGCTGGTTCCCTCCGAAGTTTCCCTCAGGATAGCTGGCGCTCG CGAGTCTCATCCGGTAAAGCGAATGATTAGAGGCCTTGGGGCCGAAACGACCTCAACCTATTCTCAAACTTTAAATGGGTGAGATCTCTGGCTTGCTCGAACG-------------TTGAAGCCACGAG--AT-----------------------------CGGATCAGAGT-GCCAAGTGGGCCACTTTT Urocerus CCAAGGAGTCTAACATGTGCGCGAGTCATTGGG AAGCCTAAAGGCGCAATGAAAGTGAAGGC-CGTGTC-T-CGGTCACGGCCGAGGGAGGATGGG----------------------GCACTCCCGGGGCGTCTCG-GCCT-CATTGCGAGGCGAGGCGCACCCAGAGCGTACACGTTGGGACCCGAAAGATGGTGAACTATGCCTGGTCAGGACGAAGTCAGGGGAAACCCTGATGGAGGTCCGTAGCGATTCTGACGTGCAAATCGATCGTCGGAACTGGGTATAGGGGCGAAAGACTAATCGAACCATCTAGTAGCTGGTTCCCTCCGAAGTTTCCCTCAGGATAGCTGGCGCTCG CGAGTCTCATCCGGTAAAGCGAATGATTAGAGGCCTTGGGGCCGAAACGACCTCAACCTATTCTCAAACTTTAAATGGGTGAGATCTCTGGCTTGCTCGAACG-------------TTGAAGCCACGAG--AT-----------------------------CGGATCAGAGT-GCCAAGTGGGCCACTTTT Tremex CCAAGGAGTCTAACATGTGCGCAAGTCATTGGG AAGCCCAAAGGCGCAATGAAAGTGAAGGC-CGTGCC-T-CGATCACGGCCGAGGGAGGATGGG----------------------GCACTCCCGGGGCGTCTCG-GCCT-CATTGCGAGGCGAGGCGCACCCAGAGCGTACACGTTGGGACCCGAAAGATGGTGAACTATGCCTGGTCAGGACGAAGTCAGGGGAAACCCTGATGGAGGTCCGTAGCGATTCTGACGTGCAAATCGATCGTCGGAACTGGGTATAGGGGCGAAAGACTAATCGAACCATCTAGTAGCTGGTTCCCTCCGAAGTTTCCCTCAGGATAGCTGGCGCTCG CGAGTCTCATCCGGTAAAGCGAATGATTAGAGGCCTTGGGGCCGAAACGACCTCAACCTATTCTCAAACTTTAAATGGGTGAGATCTCTGGCTTGCTCGAACG-------------TTGAAGCCACGAG--AT-----------------------------CGGATCAGAGT-GCCAAGTGGGCCACTTTT Xiphydria CCAAGGAGTCTAACATGTGCGCGAGTCATTGGG AAACCTAAAGGCGCAATGAAAGTGAAAGT-CG-GCC-T-C-GCGCCGACCGAGGGAGGATGGGTCGCGTC--ACGATGCGGCCCCGCACTCCCGGGGCGTCTCG-TTCT-CATTGCGAGAGGAGGCGCACCCAGAGCGTACACGTTGGGACCCGAAAGATGGTGAACTATGCCTGGTCAGGACGAAGTCAGGGGAAACCCTGATGGAGGTCCGTAGCGATTCTGACGTGCAAATCGATCGTCGGAACTGGGTATAGGGGCGAAAGACTAATCGAACCATCTAGTAGCTGGTTCCCTCCGAAGTTTCCCTCAGGATAGCTGGCACTCG CGAGTCTCATCCGGTAAAGCGAATGATTAGAGGCCTTGGGGCCGAAACGACCTCAACCTATTCTCAAACTTTAAATGGGTGAGATCTCTGGCTTGCTTGAACG--------------TGAAGCCACGAG--AT-----------TT----------------CGGATCAGAGT-GCCAAGTGGGCCACTTTT Orussus CCAAGGAGTCTAACATGTGCGCGAGTCATTGGG AAGCCTAAAGGCGAAATGAAAGTGAAGGT-CG-GCC-C-A-GCGCCGACCGAGGGAGGATGGACGGCGTC--ACGATGCCGTCCCGCACTCCCGGGGCGTCTCG-TTCT-CATTGCGAGAAGAGGCGCACCCAGAGCGTACACGTTGGGACCCGAAAGATGGTGAACTATGCCTGGTCAGGACGAAGTCAGGGGAAACCCTGATGGAGGTCCGTAGCGATTCTGACGTGCAAATCGATCGTCGGAACTGGGTATAGGGGCGAAAGACTAATCGAACCATCTAGTAGCTGGTTCCCTCCGAAGTTTCCCTCAGGATAGCTGGCACTCG CGAGTCTCATCTGGTAAAGCGAATGATTAGAGGCCTTGGGGCCGAAACGACCTCAACCTATTCTCAAACTTTAAATGGGTGAGATCTCTGGCTTGCTTGAACT-------------ATGAAGCCACGAG--AT-----------CA----------------TGGATCAGAGT-GCCAAGTGGGCCAATTTT StephanidaeA CCAAGGAGTCTAACATGTGCGCGAGTCATTGGG AACCCTAAAGGCGCAATGAAAGTGAAGGT-CG-TCC-T-C-GCGACGACCGAGGGAGGATGGGCCGCGTC--ACGATGCGGCCCCGCACTCCCGGGGCGTCTCG-TTCT-CATTGCGAGATGAGGCGCACCCAGAGCGTACACGTTGGGACCCGAAAGATGGTGAACTATGCCTGGTCAGGACGAAGTCAGGGGAAACCCTGATGGAGGTCCGTAGCGATTCTGACGTGCAAATCGATCGTCGGAACTGGGTATAGGGGCGAAAGACTAATCGAACCATCTAGTAGCTGGTTCCCTCCGAAGTTTCCCTCAGGATAGCTGGCACTCG CGAGTCTCATCTGGTAAAGCGAATGATTAGAGGCCTTGGGGCCGAAACGACCTCAACCTATTCTCAAACTTTAAATGGGTGAGATCTCTGGCTTGCTCGAACG-------------TTGAAGCCACGAG--AT----------TCA----------------CGGATCAGAGT-GCCAAGTGGGCCAATTTT StephanidaeB CCAAGGAGTCTAACATGTGCGCGAGTCATTGGG AAACCTAAAGGCGCAATGAAAGTGAAGGT-CG-TCC-A-C-GCGACGACCGAGGGAGGATGGGCCGCG-C--ATGCTGCGGCCCCGCACTCCCGGGGCGTCTCG-TTCT-CATCGCGAGAGGAGGCGCACCCAGAGCGTACACGTTGGGACCCGAAAGATGGTGAACTATGCCTGGTCAGGACGAAGTCAGGGGAAACCCTGATGGAGGTCCGTAGCGATTCTGACGTGCAAATCGATCGTCGGAACTGGGTATAGGGGCGAAAGACTAATCGAACCATCTAGTAGCTGGTTCCCTCCGAAGTTTCCCTCAGGATAGCTGGCACTCG CGAGTCTCATCTGGTAAAGCGAATGATTAGAGGCCTTGGGGCCGAAACGACCTCAACCTATTCTCAAACTTTAAATGGGTGAGATCTCTGGCTTGCTTGAACG-------------TTGAAGCCACGAG--AT-----------TT----------------CGGATCAGAGT-GCCAAGTGGGCCAATTTT Megalyra CCAAGGAGTCTAACATGTGCGCGAGTCATTGGG AAGCCTAAAGGCGCAATGAAAGTGAAGCT-CG-ACC-T-C-GCGTCGATCGAGGGAGGATGGACCGCGTCTAACGACGC-GTCCCGCACTCCCGGGGCGTCTCG-TTCT-CATCACGAGGAGAGGCGCACCCAGAGCGTACACGTTGGGACCCGAAAGATGGTGAACTATGCCTGGTCAGGACGAAGTCAGGGGAAACCCTGATGGAGGTCCGTAGCGATTCTGACGTGCAAATCGATCGTCGGAACTGGGTATAGGGGCGAAAGACTAATCGAACCATCTAGTAGCTGGTTCCCTCCGAAGTTTCCCTCAGGATAGCTGGCACTCG CGAGTCTCATCTGGTAAAGCGAATGATTAGAGGCCTTGGGGCCGAAACGACCTCAACCTATTCTCAAACTTTAAATGGGTGAGATCTCTGGCTTGCTTGAACGTT-----------TTGAAGCCACGAG--AT-----------TT----------------CGGATCAGAGT-GCCAAGTGGGCCAATTTT Trigonalidae CCAAGGAGTCTAACATGTGCGCGAGTCATTGGG AAACCTAAAGGCGCAATGAAAGTGAAGGT-CG-GCC-T-C-GCGCCGACCGAGGGAGGATGGGCCGCGTT--TCGATGCGGCCCCGCACTCCCGGGGCGTCTCG-TTCT-CATCGCGAGAAGAGGCGCACCCAGAGCGTACACGTTGGGACCCGAAAGATGGTGAACTATGCCTGGTCAGGACGAAGTCAGGGGAAACCCTGATGGAGGTCCGTAGCGATTCTGACGTGCAAATCGATCGTCGGAACTGGGTATAGGGGCGAAAGACTAATCGAACCATCTAGTAGCTGGTTCCCTCCGAAGTTTCCCTCAGGATAGCTGGCACTCG CGAGTCTCATCTGGTAAAGCGAATGATTAGAGGCCTTGGGGCCGAAACGACCTCAACCTATTCTCAAACTTTAAATGGGTGAGATCTCTGGCTTGCTTGAACG-------------TTGAAGCCACGAG--AT-----------TT----------------CGGATCAGAGT-GCCAAGTGGGCCAATTTT Chalcidoidea CCAAGGAGTCTAACATGTACGCGAGTCATTGGG AAGCCTAAAGGCGCAATGAAAGTGAAGGT-CG-ACCCT-A-GCGTCGACCGAGGGAGGATGGCTCGCGTC--ACGATGCGAGTCCGCACTCCCGGGGCGTCTCG-TGCT-CATTGCGAGCAGAGGCGCACCTAGAGCGTACACGTTGGGACCCGAAAGATGGTGAACTATGCCTGGTCAGGACGAAGTCAGGGGAAACCCTGATGGAGGTCCGTAGCGATTCTGACGTGCAAATCGATCGTCGGAACTGGGTATAGGGGCGAAAGACTAATCGAACCATCTAGTAGCTGGTTCCCTCCGAAGTTTCCCTCAGGATAGCTGGCACTCG CGAGTCTCATCTGGTAAAGCGAATGATTAGAGGCCTTGGGGCCGAAACGACCTCAACCTATTCTCAAACTTTAAATGGGTGAGATGACTGGCTTGCTTGAACG-------------TTGAAGCCATGTCAGACA----------TTAAAGCGA---------AGGATCAGAGT-GCCAAGTGGGCCAATTTT Evanioidea CCAAGGAGTCTAACATGTGCGCGAGTCATTGGG AAACCCAAAGGCGCAATGAAAGTGAAGAA-CG-GCC-T-C-GCGCCGTTCGAGGGAGGATGGGTCGCCTGC-ACG--GCGACCCCGCACTCCCGGGGCGTCTCT-ATCT-CATTGCGAGGGGAGGCGCACCCAGAGCGTACACGTTGGGACCCGAAAGATGGTGAACTATGCCTGGTCAGGACGAAGTCAGGGGAAACCCTGATGGAGGTCCGTAGCGATTCTGACGTGCAAATCGATCGTCGGAACTGGGTATAGGGGCGAAAGACTAATCGAACCATCTAGTAGCTGGTTCCCTCCGAAGTTTCCCTCAGGATAGCTGGCACTCG CGAGTCTCATCTGGTAAAGCGAATGATTAGAGGCCTTGGGGCCGAAACGACCTCAACCTATTCTCAAACTTTAAATGGGTGAGATCTCTGGCTTGCTCGAACG-------------ATGAAGCCAAGAGAGAG---------CGTT----------------TGGATCAGAGT-GCCAAGTGGGCCAATTTT Ichneumonidae CCAAGGAGTCTAACATGTGCGCGAGTCATTGGG AAGCCCAAAGGCGCAATGAAAGTGAAGGT-CG-GCC-T-C-GCGCCGACCGAGGGAGGATGGGCCGCGTC--ACGATGCGGTCCCGCACTCCCGGGGCGTCTCG-TTCT-CATTGCGAGAAGAGGCGCACCCAGAGCGTACACGTTGGGACCCGAAAGATGGTGAACTATGCCTGGTCAGGACGAAGTCAGGGGAAACCCTGATGGAGGTCCGTAGCGATTCTGACGTGCAAATCGATCGTCGGAACTGGGTATAGGGGCGAAAGACTAATCGAACCATCTAGTAGCTGGTTCCCTCCGAAGTTTCCCTCAGGATAGCTGGCACTCG CGAGTCTCATCTGGTAAAGCGAATGATTAGAGGCCTTGGGGCCGAAACGACCTCAACCTATTCTCAAACTTTAAATGGGTGAGATCTCTGGCTTGCTTGAACG-------------TTGAAGCCACGAG--AT-----------TT----------------CGGATCAGAGT-GCCAAGTGGGCCAATTTT Cynipoidea CCAAGGAGTCTAACATGTGCGCGAGTCATTGGG AAACCTAAAGGCATAATGAAAGTGAAGGT-CG-ACC-T-T-GCGTCGACTAAGGGAGGATGGGCTTCATC--ACGATGAAGCCTCGCACTCCCGGGGCGTCTCG-TACT-CATTGCGAGTAGAGGCGCACCCAGAGCGTACACGTTGGGACCCGAAAGATGGTGAACTATGCCTGGTCAGGACGAAGTCAGGGGAAACCCTGATGGAGGTCCGTAGCGATTCTGACGTGCAAATCGATCGTCGGAACTGGGTATAGGGGCGAAAGACTAATCGAACCATCTAGTAGCTGGTTCCCTCCGAAGTTTCCCTCAGGATAGCTGGCACTCG CGAGTCTCATCTGGTAAAGCGAATGATTAGAGGCCTTGGGGCCGAAACGACCTCAACCTATTCTCAAACTTTAAATGGGTGAGATCTCTGGCTTGCTTGAACG-------------ATGAAGCCACGAG--AT-----------TT----------------CGGATCAGAGT-GCCAAGTGGGCCAATTTT ApoideaA CCAAGGAGTCTAACATGTACGCGAGTCATTGGG AAACCTAAAGGCGTAATGAAAGTGAAAGT-CG-TCC-A-TAGCGTCGACCGAGGGAGGATGGGCCGCGTT--ACGATTCGGCCTCGCACTCCCGGGGCGTCTCG-TTCT-CATTGCGAGAAGAGGCGCACCTAGAGCGTACACGTTGGGACCCGAAAGATGGTGAACTATGCCTGGTCAGGACGAAGTCAGGGGAAATCCTGATGGAGGTCCGTAGCGATTCTGACGTGCAAATCGATCGTCGGAACTGGGTATAGGGGCGAAAGACTAATCGAACCATCTAGTAGCTGGTTCCCTCCGAAGTTTCCCTCAGGATAGCTGGCACTCG TGAGTCTCATCTGGTAAAGCGAATGATTAGAGGCCTTGGGGCCGAAACGACCTCAACCTATTCTCAAACTTTAAATGGGTGAGATCTCTGGCTTGCTTGAACT-------------ATGAAGCCACGAG--AT-----------TT----------------CGGATCAGAGTTGCCAAGTGGGCCAATTTT ApoideaB CCAAGGAGTCTAACATGTACGCGAGTCATTGGG AAACCTAAAGGCGTAATGAAAGTGAAGGT-CG-GCC-T-TGGTGTCGACCGAGGGAGGATGGGCCGCGTT--ACGATTCGGCCTCGCACTCCCGGGGCGTCTCG-TTCT-CATTGCGAGAAGAGGCGCACCTAGAGCGTACACGTTGGGACCCGAAAGATGGTGAACTATGCCTGGTCAGGACGAAGTCAGGGGAAACCCTGATGGAGGTCCGTAGCGATTCTGACGTGCAAATCGATCGTCGGAACTGGGTATAGGGGCGAAAGACTAATCGAACCATCTAGTAGCTGGTTCCCTCCGAAGTTTCCCTCAGGATAGCTGGCACTCG TGAGTCTCATCTGGTAAAGCGAATGATTAGAGGCCTTGGGGCCGAAACGACCTCAACCTATTCTCAAACTTTAAATGGGTGAGATCTCTGGCTTGCTTGAACT-------------ATGAAGCCACGAG--AT-----------TT----------------CGGATCAGAGT-GCCAAGTGGGCCAATTTT ApoideaC CCAAGGAGTCTAACATGTGCGCGAGTCATTGGG AAACCTAAAGGCGAAATGAAAGTGAAGGT-CG-GCC-T-TAGCGTCGACCTAGGGAGGATGGGCCGCGTT--ATGATGCGGCCTCGCACTCCCGGGGCGTTTCG-TTCT-CATTGCGAGAAGAGGCGCACCTAGAGCGTACACGTTGGGACCCGAAAGATGGTGAACTATGCCTGGTCAGGACGAAGTCAGGGGAAACCCTGATGGAGGTCCGTAGCGATTCTGACGTGCAAATCGATCGTCGGAACTGGGTATAGGGGCGAAAGACTAATCGAACCATCTAGTAGCTGGTTCCCTCCGAAGTTTCCCTCAGGATAGCTGGCACTCG CGAGTCTCATCTGGTAAAGCGAATGATTAGAGGCCTTGGGGCCGAAACGACCTCAACCTATTCTCAAACTTTAAATGGGTGAGATCTCTGGCTTGCTTGAAAC-------------GTGAAGCCACGAG--AT-----------TT----------------TGGATCAGAGT-GCCAAGTGGGCCAATTTT Vespidae CCAAGGAGTCTAACATGTGCGCGAGTCATTGGG AAGCCTAAAGGCGTAATGAAAGTGAAGGT-CG-ACC-T-C-GCGTCGACCGAGGGAGGATGGGCCGCGTT--AAGACGCGGCTTCGCACTCCCGGGGCGTCTCG-TTCT-CATTGCGAGAAGAGGCGCACCCAGAGCGTACACGTTGGGACCCGAAAGATGGTGAACTATGCCTGGTCAGGACGAAGTCAGGGGAAACCCTGATGGAGGTCCGTAGCGATTCTGACGTGCAAATCGATCGTCGGAACTGGGTATAGGGGCGAAAGACTAATCGAACCATCTAGTAGCTGGTTCCCTCCGAAGTTTCCCTCAGGATAGCTGGCACTCG CGAGTCTCATCTGGTAAAGCGAATGATTAGAGGCCTTGGGGCCGAAACGACCTCAACCTATTCTCAAACTTTAAATGGGTGAGATCTCTGGCTTGCTTGAACC-------------ATGAAGCCATGAGAGAT-----------TT----------------CGGATCAGAGT-GCCAAGTGGGCCAATTTT Triassoxyela ????????????????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ???????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Asioxyela ????????????????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ???????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Nigrimonticola ????????????????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ???????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Gigantoxyelinae ????????????????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ???????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Spathoxyela ????????????????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ???????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Xyela_mesozoica ????????????????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ???????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Angaridyela ????????????????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ???????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Xyelotoma ????????????????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ???????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Undatoma ????????????????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ???????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Dahuratoma ????????????????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ???????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Mesolyda ????????????????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ???????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Turgidontes ????????????????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ???????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Aulidontes ????????????????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ???????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Protosirex ????????????????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ???????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Aulisca ????????????????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ???????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Anaxyela ????????????????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ???????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Syntexyela ????????????????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ???????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Karatavites ????????????????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ???????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Stephanogaster ????????????????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ???????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Leptephialtites ????????????????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ???????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Cleistogaster ????????????????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ???????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Sepulca ????????????????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ???????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Onochoius ????????????????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ???????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Ghilarella ????????????????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ???????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Paroryssus ????????????????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ???????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Praeoryssus ????????????????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ???????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Mesorussus ????????????????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ???????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Trematothorax ????????????????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ???????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Thoracotrema ????????????????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ???????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Prosyntexis ????????????????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ???????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Kulbastavia ????????????????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ???????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Brachysyntexis ????????????????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ???????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Symphytopterus ????????????????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ???????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Eoxyela ????????????????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ???????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Liadoxyela ????????????????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ???????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Abrotoxyela ????????????????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ???????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Pseudoxyelocerus ????????????????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ???????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Palaeathalia ????????????????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ???????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Ferganolyda ????????????????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ???????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? PamphiliidaeUndesc ????????????????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ???????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Rudisiricius ????????????????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ???????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Sogutia ????????????????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ???????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Xyelula ????????????????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ???????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Brigittepterus ????????????????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ???????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Grimmaratavites ????????????????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ???????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? [COI 2225-3268, 1044 chars] Orthoptera GCTGGAATAGTAGGAACATCAATAAGAATAATTATTCGTGCAGAACTTGGACAACCAGGATCTATAATTGGAGATGATCAAATTTATAATGTTGTTATTACAGCACATGCATTTGTAATAATCTTCTTTATGGTAATACCTATTATAATTGGAGGATTT GGTAATTGATTAGTTCCACTAATAATTGGTGCACCAGATATAGCCTTTCCACGAATAAATAATATAAGTTTTTGACTTCTACCACCATCATTAACTCTTTTGATTACATCATCTATAGTTGATAGAGGTGCAGGAACAGGATGAACAGTTTACCCACCTCTTGCAAGAGCAATTGCTCATAGAGGAAGATCAGTAGACCTGGCTATCTTTTCATTACATTTAGCTGGTATTTCATCAATTCTAGGAGCAGTTAATTTCATTACAACCACAATCAATATGCGATCGGAAAGAATAACACTGGATCAAACACCACTATTTGTCTGATCAGTAGCAATTACAGCACTTTTATTACTTCTGTCATTACCAGTACTAGCAGGAGCAATTACTATATTATTAACTGATCGAAATTTAAATACATCATTCTTTGATCCAGCTGGAGGTGGAGACCCAATTCTTTATCAACATTTATTTTGATTCTTTGGACATCCTGAAGTT TACATTATAATTTTACCAGGATTTGGTATTATTTCACACATTGTTTGTCAAGAAAGCGGAAAAATTGAATCATTTGGAACTCTAGGAATAATTTATGCAATATTATCAATT GGATTAATAGGTTTTATTGTATGAGCACATCATATATTTACAGTAGGTATAGATGTTGATACACGAGCTTACTTTACATCAGCAACAATAATTATTGCAGTACCAACAGGAATTAAAGTATTTAGATGGTTAGCAACTCTTTATGGAACTAAATTTAATTTTAACCCACCATTATTA---TGAGCTCTTGGATTTATTTTCCTATTTACAATTGGTGGATTAACAGGATTAGTTTTAGCAAATTCATCTCTTGATATTGTTCTACACGATACTTATTAT Paraneoptera GCTGGAATAATAGGAACATCCCTTAGATGAATTATTCGAATCGAGTTAGGACAACCTGGATCATTTATTGGAGACGACCAAATTTATAATGTAGTTGTAACAGCCCATGCCTTCGTCATAATTTTCTTTATAGTTATACCCATTATAATTGGAGGCTTT GGAAACTGACTTGTACCCTTAATAATTGGTGCCCCAGACATAGCATTCCCTCGAATAAATAATATAAGATTTTGACTCCTACCCCCAGCATTAACCCTCCTTCTAGTAAGAAGATTAGTAGAAAGAGGAGCTGGAACAGGATGAACAGTTTACCCTCCCCTATCAAGAAATATCGCACATAGAGGAGCATCAGTAGATATAGCAATTTTCTCATTGCACTTAGCCGGAGTCTCATCAATTCTAGGAGCAGTAAATTTTATCTCTACCATTATTAATATACGCCCCGCAGGTATACGACCTGATCGAATTCCATTATTTGTCTGATCAGTAGGTATTACTGCCCTCTTATTACTTCTAAGACTCCCTGTCCTTGCAGGAGCTATTACAATACTTTTAACAGACCGAAACTTTAACACTTCATTCTTTGATCCAGCTGGAGGAGGAGACCCGATTCTTTATCAACATTTATTTTGATTCTTTGGACACCCTGAAGTA TACATTCTTATTTTACCAGGATTCGGCTTAATCTCACATATTATCGCAATAGAGACCGGAAAAAATGAAGCTTTTGGGGCACTCGGAATAATTTATGCCATATTAGCCATT GGCTTATTAGGGTTCATTGTATGGGCACACCACATATTTACTGTAGGTATAGACGTAGACACACGTGCATACTTTACCTCAGCTACTATAATCATTGCTGTACCCACAGGTATTAAAATCTTTAGATGATTAGCCACCCTTCATGGAAGAATAATATTATTCTCACCAAGAATTCTA---TGAGCTCTTGGGTTTGTTTTTCTATTCACTATTGGAGGACTTACTGGAGTCATTCTAGCAAACTCGAGAATTGATATTGTACTTCACGATACATATTAC Chrysopidae --------------------------------------------------------------------------------------------------------------------------------------------------------------- ---------------------------------------------------------------------------ATATTACCTCCCTCATTAACTTTATTACTTGCTTCTTCTATAGTAGAAAGAGGAGCTGGAACTGGTTGAACAGTTTACCCTCCTTTATCATCAAGAATTGCACATGCTGGAGCTTCTGTTGATTTAGCTATTTTTAGTTTACATCTTGCCGGTATTTCATCAATTTTAGGGGCAGTAAATTTCATTACAACAGTAATTAATATACGATTAAGTTATATAACATTAGATCGTATACCATTATTTGTATGATCAGTAGTAATTACAGCTTTACTATTATTACTTTCATTGCCTGTATTAGCTGGTGCTATTACTATATTATTAACTGATCGTAATTTAAATACTTCATTTTTTGACCCTGCGGGAGGAGGAGACCCAATTTTATATCAACATTTATTTTGATTCTTTGGACATCCTGAAGTA TATATTTTAATTTTACCAGGATTTGGAATAATTAGTCATATTATTGCTCAAGAAAGTGGAAAAAAGGAAACCTTTGGATCTTTAGGAATAATTTATGCTATATTAGCTATT GGATTATTAGGATTTATTGTTTGAGCTCATCATATATTTACTGTTGGAATAGATGTTGATACTCGAGCTTACTTTACATCAGCTACAATAATTATTGCAGTTCCTACTGGTATTAAGGTATTTAGTTGATTAGCTACTTTACATGGAACTCAATTTACATATAGTCCAGCTTTATTA---TGAAGTTTAGGATTTGTATTTTTATTTACTGTTGGAGGATTAACTGGAGTTGTATTAGCTAATTCATCAATTGATATTATTCTTCATGATACTTATTAT Raphidioptera TCTGGAATAATTGGAACCTCTTTAAGCTTATTAATTCGATCAGAACTAGGAATACCAGGATCTCTTATTGGAGATGATCAAATCTATAATGTTATTGTTACAGCTCATGCTTTTATTATAATTTTTTTTATAGTTATACCTATTATAATTGGAGGATTT GGAAATTGATTAGTCCCTTTAATACTAGGAGCTCCAGACATAGCTTTTCCTCGTTTAAATAATATAAGATTTTGACTACTTCCTCCCTCTATTACTCTCCTCTTAAGAAGAAGAATAAGAGAAAGAGGAGCTGGTACAGGATGAACTGTTTACCCTCCTTTATCAAGAAACATTGCTCACGCTGGAGCATCCGTAGATTTAACTATTTTTAGACTTCACTTAGCAGGTGTATCTTCTATTCTAGGAGCAATTAATTTTATTACTACTACTTTAAACATACGGTCAAAAATATTATCATTAGAACGAATACCTTTATTTGTTTGATCTGTTGCAATCACAGCATTATTATTACTATTATCACTGCCAGTACTAGCTGGTGCTATTACTATACTTTTAACTGACCGAAATTTAAATACTTCTTTTTTTGATCCAGCTGGAGGAGGAGACCCAATTCTATACCAACACCTTTTCTGATTTTTTGGACACCCAGAAGTT TACATTTTAATTTTACCTGGGTTTGGAATAATTTCTCATATTATTAGACAAGAAAGAGGAAAAAAAGAAACATTTGGATCTTTAGGAATAATTTATGCCATACTATCAATT GGATTATTAGGATTCGTTGTTTGAGCACACCATATATTCACTATTGGAATAGATGTTGATACTCGAGCCTATTTCACTTCTGCTACAATAATTATCGCCGTTCCCACTGGAATTAAAATTTTTAGTTGATTAGCCACCCTTCATGGATCTCAATTAACTTTTTCACCATCCCTATTA---TGAGCTTTAGGATTTGTATTTTTATTTACCGTAGGAGGTTTAACTGGAGTTATTTTAGCAAATTCTTCTATTGATATCATTCTTCATGACACTTATTAT Coleo_Polyphaga TCGGGATCTTTAGGATTAGCCTTAAGATTATTAATTCGAGCTGAATTAGGAACTCCAGGAACTTTAATTGGTAATGATCAAATTTATAATGTTATTGTTACTGCTCATGCTTTTATCATAATTTTCTTCATAGTTATACCTATTATAATTGGTGGATTT GGGAATTGATTAGTACCTTTAATACTAGGGGCTCCAGATATAGCATTCCCTCGAATAAATAATATAAGATTTTGATTTCTCCCACCCTCTTTAATATTTTTATTAATAAGAAGAATAGTAGAAAGAGGGGCAGGAACAGGGTGAACTGTTTATCCTCCCCTTTCAGCTAATATTGCTCATAGAGGCCCTTCAGTTGATTTAGCTATTTTTAGACTTCATATAGCAGGGATCTCTTCAATTTTAGGAGCAGTAAACTTTATTTCAACTATTCTCAATATAAAGCCTCCCAGAATAAAGTTTGACCAAATACCTTTATTTGTTTGATCTGTAGGAATTACTGCATTACTATTACTTTTATCTTTACCTGTATTAGCAGGAGCTATTACAATACTTTTATCAGATCGTAATTTAAATACATCATTTTTTGATCCAATAGGGGGTGGGGACCCAATTCTTTATCAACATTTATTCTGATTCTTTGGTCATCCTGAAGTT TATATTTTAATTCTCCCAGGATTTGGACTTATTTCTCATATTGTTTCTCAAGAAAGAGGAAAAAAGGAAACATTTGGTTGTATTGGAATAATTTATGCTATATTAGCTATT GGTTTATTAGGATTTGTTGTTTGAGCACATCATATGTTCACAGTTGGAATAGACGTTGATACTCGAGCTTACTTCACATCAGCTACAATAATTATTGCTGTTCCAACAGGAATTAAAATTTTTAGTTGACTAGCAACTCTGCATGGTTCTAAAATTAGATGAACTCCTCAAATACTT---TGAGCAACAGGATTTATTTTTCTATTTACAGTTGGAGGATTAACAGGAGTAATTTTAGCCAAT-CTTCAATTGATATTATTTTACATGAACCTAATTA? Coleo_Adephaga TCGGGGATAGTGGGAACTTCTTTAAGAATACTAATTCGAGCAGAATTAGGAAACCCAGGATCTTTAATTGGAGACGATCAAATTTATAATGTTATTGTAACAGCTCATGCTTTTGTAATAATTTTTTTCATAGTTATACCTATTATAATTGGGGGATTT GGAAATTGATTAGTGCCACTAATATTAGGGGCCCCAGATATGGCATTCCCTCGAATAAATAATATAAGATTCTGGCTTCTCCCTCCTTCTTTGACTTTACTTCTTATAAGAAGAGTGGTAGAAAAAGGTGCAGGGACAGGATGAACGGTTTACCCTCCCCTATCATCTGGAATTGCCCATAGAGGTGCTTCGGTAGACTTAGCTATTTTTAGATTACATTTAGCTGGTATTTCATCAATTTTAGGAGCAGTAAATTTTATTACAACAATTATTAATATACGATCAATCGGAATAACATTTGACCGAATACCTTTATTTGTATGATCGGTGGGTATCACAGCATTATTACTTTTACTTTCTCTCCCAGTATTAGCTGGAGCTATTACTATACTTTTAACAGACCGAAACTTAAACACATCATTTTTTGACCCAGCTGGAGGAGGTGATCCTATTTTATATCAACATTTATTTTGATTTTTTGGACATCCTGAAGTA TATATTTTAATTTTACCTGGATTTGGTATAATTTCACATATCATCAGACAAGAAAGAGGAAAAAAAGAAACCTTTGGATCTTTAGGAATAATTTACGCTATATTAGCTATT GGATTATTAGGATTTGTTGTATGAGCACATCATATATTTACTGTAGGAATAGATGTTGATACTCGAGCTTATTTTACCTCTGCAACAATAATCATTGCAGTACCTACAGGAATTAAAATTTTTTCTTGATTAGCAACTCTTCACGGAACTCAGATTTCTTACAGCCCCTCATTACTT---TGAGCTCTAGGATTTGTGTTTTTATTTACTGTTGGAGGACTAACTGGAGTAGT?????????????????????????????????????????????? Lepid_Micropterigidae ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Lepid_Papilionidae GCAAGAATATTAGGAACCTCTTTAAGTTTATTAATTCGAACTGAATTGGGAACTCCAGGTTCTTTAATCGGAGATGATCAAATTTATAATACTATTGTTACAGCTCACGCTTTCATTATAATTTTTTTTATAGTTATACCAATTATAATTGGAGGATTT GGAAATTGACTAGTACCATTAATATTAGGGGCCCCTGATATAGCTTTCCCCCGAATAAATAATATAAGATTTTGACTTTTACCCCCTTCCTTAACTCTTTTAATTTCAAGAATAATTGTTGAAAATGGAGCTGGAACTGGATGAACTGTTTATCCCCCCCTTTCCTCAAATATTGCCCATGGAAGAAGATCAGTAGATTTAGTTATTTTCTCGCTTCACTTAGCGGGAATTTCCTCAATTTTAGGAGCAATTAATTTTATTACCACAATTATTAATATACGAATTAATAATATATCTTTTGATCAAATACCTTTATTTGTATGAGCTGTAGGAATTACAGCCTTATTACTACTTCTTTCTCTACCTGTTTTAGCCGGAGCTATTACTATATTATTAACAGATCGAAATTTAAATACATCATTTTTTGACCCTGCGGGAGGAGGAGATCCTATTTTATACCAACATTTATTTTGATTTTTTGGCCATCCAGAAGTT TATATTTTAATTTTACCAGGATTTGGTATAATTTCACATATTATTTCTCAAGAAAGAGGAAAAAAAGAAACATTCGGATGTTTAGGTATAATTTATGCTATGATAGCAATT GGATTATTAGGATTTATTGTATGAGCACATCACATATTTACAGTAGGAATAGATACAGATACTCGAGCCTATTTTACTTCAGCAACAATAATTATTGCAGTTCCTACAGGAATTAAAATTTTTAGTTGATTAGCCACTCTTCATGGAACTCAAATTAACTATAGACCTTCAATTTTA---TGAAGTTTAGGATTTGTATTTCTCTTTACAGTTGGGGGATTAACAGGAGTAATTTTAGCTAATTCTTCTATTGATGTCACTTTACATGATACTTATTAT Mecoptera TCTGGTATAGTCGGAACTTCTCTAAGATTACTTATTCGAGCAGAATTAGGTCAACCAGGAGCCTTAATTGGAGACGATCAAATTTTTAATGTAATTGTCACTGCTCATGCTTTCGTAATAATTTTTTTTATAGTTATGCCAATTTTAATTGGAGGATTT GGAAATTGATTAGTTCCATTAATATTAGGAGCTCCCGATATAGCATTCCCTCGAATAAATAATATAAGATTTTGGTTATTACCTCCTTCTTTAACTTTACTACTCACTAGCTCATTAGTCGAAAATGGAGCTGGGACTGGTTGAACAGTGTACCCTCCGTTATCGTCTACCATTGCTCATGCCGGAGCTTCAGTTGATTTAGCAATTTTTTCATTACATTTAGCTGGAATTTCTTCTATTTTAGGGGCAGTAAATTTTATTACAACAGTAATTAATATACGATCAACAGGTATAACTCTTGATCGAATACCATTATTTGTTTGATCTGTAGCTATTACTGCTTTATTATTATTGTTATCTTTACCAGTTTTAGCTGGGGCTATTACAATACTTCTTACGGATCGAAATTTAAATACATCATTTTTTGATCCTGCGGGAGGAGGAGATCCTATTTTATATCAACATTTATTTTGATTTTTTGGTCATCCTGAAGTA TATATTTTAATTCTACCAGGATTCGGAATAATTTCTCATATTATTTCACAAGAATCAGGTAAAAAGGAAACATTTGGAACTCTTGGAATAATTTATGCTATATTAGCAATT GGACTTTTAGGATTTGTTGTTTGAGCTCATCATATATTTACAGTTGGGATAGATGTTGATACTCGAGCTTATTTTACTTCAGCTACAATAATTATTGCTGTTCCAACAGGAATTAAAATTTTTAGTTGATTAGCTACCTTACACGGATCCCAATTTACTTATAGCCCATCATTACTT---TGAGCTCTTGGATTTGTATTTTTATTCACAGTTGGAGGTTTAACAGGTGTTGTATTAGCAAATTCTTCTTTAGATATTATTTTACATGATACTTATTAT Xyela TCAGGAATAGTAGGAACATCATTAAGAATACTAATCCGAGCTGAATTAGGAACTCCTGGAACATTAATTGGAAATGATCAAATTTATAATGTTATTGTTACAGCTCATGCATTTGTTATAATTTTTTTTATAGTAATACCAATTATAATCGGGGGATTT GGAAACTGACTAGTTCCATTAATATTAGGGGCTCCAGATATAGCATTTCCACGATTAAATAATATAAGTTTTTGATTATTACCTCCTTCATTAACTCTATTATTAGCAAGAAGAATTGTTGATACAGGAGCAGGTACTGGATGAACTGTATATCCACCTCTATCTAGAGGTATAGCTCATGCAGGAGCCTCTGTTGATTTAGCTATCTTTTCCTTACATTTAGCTGGAATTTCTTCTATTCTAGGAGCTATTAATTTTATTACAACAATTATTAATATACGAACAACGGGTATAAACTTTGATCGTATCCCATTATTTGTATGAGCTGTTGGAATTACAGCTTTATTATTACTATTATCACTTCCCGTATTAGCTGGGGCTATCACAATACTACTAACAGATCGTAATTTAAATACATCTTTCTTTGACCCAGCTGGAGGAGGAGACCCAATTTTATATCAACATTTATTTTGATTTTTTGGACACCCTGAAGTT TATATTTTAATTTTACCGGCCTTTGGAATAATTTCTCATATTGTTTCTCAAGAAAGAGGTAAAAAGGAAGCATTTGGAACTCTAGGTATAATTTATGCTATATTATCAATT ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Macroxyela TCAGGAATAGTTGGAACTTCATTAAGTATATTAATTCGTGCAGAACTAGGAACCCCAGGGTCTCTAATTGGAAATGATCAAATTTACAATGTAATTGTAACTGCTCATGCATTTATTATAATTTTTTTCATAGTTATACCTATTATAATCGGAGGATTT GGAAACTGATTAGTTCCTCTAATATTAGGAGCCCCTGATATAGCATTTCCCCGAATAAATAATATAAGATTTTGACTATTACCCCCAGCTCTAACTTTATTACTAGCCAGAAGAATTGTAGGGTCAGGAGCTGGTACAGGATGAACAGTTTATCCACCACTTTCAAGTAATACAGGACATGCAGGAGCTTCCGTTGATCTAGCAATTTTTTCTTTGCACTTAGCTGGAATTTCCTCAATTTTAGGAGCAGTAAACTTTATTACTACTATTATCAATATACGATCAACAGGTATTAAATTTGATCGAATACCCTTATTTGTATGATCTGTAGGAATTACAGCTTTATTATTATTATTATCACTACCTGTATTAGCTGGAGCTATTACTATATTATTAACTGATCGTAATTTAAATACATCATTTTTTGATCCTGCAGGAGGAGGAGACCCTATTTTATATCAACATTTATTTTGATTTTTTGGACACCCAGAAGTT TATATTTTAATTTTGCCAGGATTCGGTATAATTTCACATATTATTTCTGAAGAAAGAGGAAAAAAAGAAACATTTGGATCTTTAGGTATAATTTACGCTATACTAACAATT GGATTATTAGGATTTGTTGTTTGAGCTCACCACATATTTACTGTTGGAATAGATGTTGATACACGTGCATATTTTACCTCTGCTACCATAATTATTGCTGTACCTACAGGAATTAAAATCTTCAGATGATTAGCCACATTACATGGAACAAAATTATCTTACAGACCTTCATTATTG---TGAGCACTAGGATTTGTATTTTTATTTACTATAGGAGGACTAACTGGAGTTGTTCTAGCTAACTCATCTATTGATATTGTTCTTCATGACACATACTAC Runaria TCTGGCATAATTGGTACTTCCTTTAGTGTATTAATCCGATTAGAACTAAGAACACCTACTAGATTTTTAGGAAGAGATCAAACTTATAATACTTTAGTAACCTCTCATGCATTCTTAATGATTTTTTTTATAGTTATACCTATTATAATTGGAGGATTC GGTAATTGATTAGTACCCTTAATAATCTCTGCCCCTGACATAGCCTTCCCTCGAATAAATAATTCAAGATTTTGATTATTGCCCCCCTCTCTTATTTTATTAACTTCAAGAAGATTTATTGATCTAGGATCTGGTACAGGATGAACAATTTACCCCCCTCTATCAAGAAATTTAGGACATGCTAGATCATCTATAGACTTAACAATTTTTTCCTTACATCTAGCTGGAATTTCATCAATTATAGGTGCTATTAACTTTATTTCAACAACTATTAATATACGAAATAAAGGAATAACATCTGAACGACTTACATTATTCACATGATCTGTAGCAATTACTGCATTATTGTTAATACTATCATTACCTGTATTAGCAGGAGCCATCACAATATTACTGACAGACCGAAATTTAAATACATCATTTTTCGACCCTTCTGGGGGTGGAGACCCGATTCTATTCCAACACTTATTTTGATTCTTTGGACACCCTGAAGTA TACATTTTAATTTTACCTGGATTTGGAATTATTTCCCATATTATTTATCAAGAAGCAGGAAAAAAAGAAACATTTGGAACTTTAGGAATAATCTATGCTATATTAACAATT GGCATTCTAGGATTTGTTGTTTGAGCTCATCATATATTTACAGTAGGAATAGATGTTGATACTCGAGCATACTTTACAGCCGCTACAATAATTATTGCCGTTCCCACTGGAATTAAAATTTTTAGATGATTAGCAACAATCCACGGAACCCAACTAATTTATACCCCTTCAATTTTA---TGAACACTAGGATTTATTTTTCTATTTACTTTAGGAGGACTAACAGGTATTATATTATCTAACTCATCATTAGATATTATTTTACATGATACATATTAT Paremphytus ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Blasticotoma TCAGGAATAATTGGGACCTCATTCAGAATCCTTATTCGATTAGAATTAAGAACTCCTAACAGTCTTCTAAGTAATGACCAAACATACAACACTCTAGTAACCTCTCATGCCTTCTTAATAATTTTCTTCATAGTTATACCAATCATAATTGGAGGGTTC GGAAATTGACTAATCCCCCTCATAATTTCAGCCCCTGATATAGCTTTTCCTCGTATAAACAACTCTAGATTCTGACTTCTACCCCCTTCTCTAATTCTACTAACTTCAAGAAACTTTATTGACTTAGGAACAGGAACAGGATGAACTATCTACCCTCCTCTATCAAGAAACTTAGGTCATGCAAGATCTTCAATAGACCTAACTATTTATTCCCTACATATAGCAGGTATTTCCTCAATTATAGGAGCTATTAATTTTATCTCTACAACAATTAACATACGAAATAAAGGAATATCCTCTGAACGACTAACCTTATTCACTTGATCAGTATCAATCACAGCACTACTTTTAATATTATCATTACCCGTTCTAGCAGGGGCTATCACAATATTATTAACAGACCGAAACTTAAACACATCATTTTTTGACCCAGCTGGAGGAGGAGACCCCATCCTATTCCAACACCTATTTTGATTTTTTGGGCACCCAGAAGTT TATATTTTAATCTTACCAGGATTCGGAATTATTTCACATATCATTTATCAAGAAGCAGGGAAAAAAGAAACATTTGGAACTTTAGGAATAATCTATGCTATAATAACCATC GGAATCCTAGGATTTATTGTCTGAGCCCACCATATATTTACAGTAGGAATAGATGTAGATACCCGAGCATACTTCACAGCAGCCACTATGATTATCGCTATCCCTACAGGAATCAAAATTTTTAGATGATTAGCTACTCTTCATGGAACTCAACTAATATACTCCCCCCCAATCCTA---TGAACTTTAGGATTTATCTTCTTATTCACCTTAGGAGGATTGACAGGAATTATATTATCCAATTCATCTCTAGACATTATTCTACACGATACTTACTAT Tenthredo TCAGGTATATTAGGATTATCTTTCAGAATATTAATTCGAACAGAATTAGGAATACCAGGATCAATAATTGGTGATGATCAAATTTATAATGTTATTGTAACATCACATGCTTTTCTTATAATTTTTTTTATAGTTATACCAATCATAATTGGAGGATTT GGAAACTGATTAATTCCTTTAATATTAGGAGCTCCTGATATAGCATTTCCTCGATTAAATAATATAAGATTTTGATTTTTACCACCATCAATCACTCTTCTTCTATCAAGAAGAATAGTAAATTCAGGTTCAGGAACAGGATGAACAGTATACCCACCCCTATCTAATAACATATCACATGCAGGAGCATCTGTAGATTTAACAATTTTTTCTTTACACTTAGCAGGAATTTCATCAATTTTAGGAGCAATTAACTTTATTTCTACTATAATTAATATACGAGTAAAAGGAATAAAATTTGAACGAATACCTTTATTTATTTGAGCAGTATCATTAACAGCATTATTATTATTATTATCATTACCAGTATTAGCTGGTGCAATTACTATACTATTAACTGATCGAAACTTAAATACTTCATTCTTTGACCCTTCAGGAGGAGGAGATCCAATTTTATACCAACATCTATTTTGATTCTTTGGACATCCTGAAGTA TATATTTTAATTATCCCAGCATTTGGAATAATTTCTCATATTATTTCTCAAGAATCAGGAAAAAAAGAAACATTTGGAACATTAGGAATAATTTATGCAATAATAACTATT ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Aglaostigma TCAGGAATACTAGGTTTGTCTTTTAGAATACTTATTCGAATAGAACTAGGAATACCAGGTTCAATAATTGGAGATGATCAAATTTATAATGTAATTGTAACATCACATGCATTTTTAATAATTTTTTTCATAGTTATACCAATTATAATCGGAGGATTT GGAAATTGACTAATCCCTTTAATATTAGGGGCCCCTGATATAGCATTTCCACGATTAAATAATATAAGATTTTGATTTTTACCCCCATCAATCACTTTACTTTTATCAAGAAGAATAGTTAATTCAGGATCAGGAACTGGATGAACAGTATACCCTCCCTTATCTAATAGAATCTCACATGCAGGAGCTTCTGTAGATATAACTATTTTTTCTCTTCATTTAGCTGGAATTTCCTCAATTCTAGGAGCCATTAATTTTATCTCAACAATAATTAATATACAAGTAAAAGGAATAAAATTTGAACGTATACCTTTATTCATTTGAGCTGTATCATTAACAGCTTTATTACTACTTTTATCACTTCCTGTATTAGCAGGAGCTATCACTATATTATTAACAGATCGAAATCTTAATACATCATTTTTTGACCCTTCGGGGGGAGGTGACCCCATCCTTTACCAACATTTATTTTGATTCTTTGGACACCCAGAAGTT TATATTTTAATTATTCCCGCTTTTGGAATAATTTCACACATTATTTCTCAAGAATCAGGGAAAAAAGAAACTTTTGGGACAGTAGGAAT?????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Dolerus TCTGGTATATTAGGATTATCATTTAGAATATTAATTCGAACTGAATTAAGAATACCAGGTTCTATTATTAATGATAATCAATTATATAATGTTATTGTAACTTCACATGCATTTTTAATAATTTTTTTTATAGTTATACCTATTATAATTGGAGGATTT GGAAATTGATTAGTTCCTATAATATTAGGAGCCCCTGATATAGCATTTCCTCGATTAAATAATATAAGATTTTGATTTTTACCTCCATCTATTATATTATTACTATCCAGAAGATTAGTAAATTCAGGTTCAGGAACCGGATGAACAATTTATCCTCCATTATCTAGAAGAATTTTTCATGCTGGAGCATCAGTAGATATAACAATTTTTTCTCTTCATTTAGCAGGAATTTCTTCAATTTTAGGAGCTATTAATTTTATTTCAACAATAATTAATATAAAAATTAAAGGAATAAATTTTGAACGTATACCTTTATTTGTATGAGCAGTATTATTAACTACTTTACTTTTATTATTATCATTACCTGTTTTAGCAGGTGCAATTACTATATTATTAACTGATCGAAATTTAAATACATCATTTTTTGATCCTTCAGGAGGAGGAGATCCAGTTTTATATCAACATTTATTTTGATTTTTTGGTCATCCAGAAGTA TATATTTTAATTATTCCTGCATTTGGAATAATTTCTCATATTATTTCACAAGATTCAGGAAAAAAAGAAACATTTGGAACTTTAGGAATAATTTATGCAATAATAACAATT ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Selandria TCAGGAATATTAGGAATATCTTTTAGAATATTAATTCGAATAGAATTAGGAATACCAGGATCTTTAATTGGTAATGATCAAATTTATAATGTTATTGTTACTTCACATGCTTTTTTAATAATTTTTTTTATAGTTATACCTATTATAATTGGAGGATTT GGAAATTGACTAATCCCTCTAATATTAGGAGCACCAGACATAGCTTTTCCTCGATTAAATAATATAAGATTTTGATTTTTACCACCTTCATTAATCTTATTATTAACTAGAAGGTTAGTAAATTCAGGATCAGGTACAGGATGAACAGTATATCCACCTTTATCAAGAACAATTTCACATGCAGGATCTTCAGTTGATATAACAATTTTTTCTTTACATTTAGCTGGAATTTCATCAATTCTTGGGGCAATTAATTTTATTTCAACAATAATTAATATACGAATTATAAGAATAAAAATTGAACAAATACCTTTATTTATTTGAGCAGTTTCTATTACAGCCCTATTACTTTTATTATCACTACCAGTTTTAGCAGGAGCTATTACAATACTATTAACAGATCGAAATTTAAATACATCATTTTTTGATCCATCTGGAGGAGGAGATCCAATTTTATATCAACATTTATTTTGATTTTTTGGACATCCAGAAGTT TATATTCTTATTATTCCCGCATTTGGAATAATTTCTCATATTATTTCTCAAGAATCAGGTAAAAAAGAAACATTTGGAACATTAGGAATAATTTATGCTATAATAGCAATT ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Strongylogaster TCAGGTATATTAGGTTTATCATTTAGAATACTAATTCGTACAGAATTAAGAATACCTGGATCAATAATTGGAAATGATCAAATTTATAATGTAATTGTTACATCTCACGCATTCTTAATAATTTTTTTTATAGTTATACCAATTATAATTGGAGGTTTT GGAAATTGATTAATTCCTTTAATATTAGGAGCCCCAGATATAGCATTTCCCCGACTCAATAATATAAGATTTTGATTTTTACCCCCTTCATTAATTTTAATTCTTTTTAGAAGTATAATTAATTCAGGTTCAGGAACAGGATGAACAGTATACCCACCTTTATCAAGAAGAATTTCACATGCTGGAGCATCAGTAGATATAACTATTTTTTCATTACATCTTGCTGGAATTTCTTCAATTTTAGGAGCAATTAATTTTATTTCTACTATAATTAATATACGAATTAAAGGAATAAAGTTTGAACGAATACCTCTCTTTATTTGAGCAGTTTCACTTACAGCTCTATTACTTTTATTATCTTTACCAGTATTAGCAGGTGCAATTACTATATTACTTACAGACCGAAATTTAAATACTTCATTTTTCGATCCTTCAGGAGGAGGAGATCCAATCTTATATCAACATTTATTTTGATTTTTTGGACATCCTGAAGTA TATATTTTAATTATCCCAGCATTTGGAATAATTTCTCATATTATTTCTCAAGAATCTGGAAAAAAAGAAACATTTGGAACTTTAGGAATAATTTATGCTATATTAACTATT GGTTTATTAGGATTCGTAGTATGAGCTCATCATATATTTACTATTGGAATAGATGTTGATACACGAGCATATTTTACTGCAGCTACTATAATTATTGCTATTCCCACAGGAATTAAAATTTTTAGATGATTAGCTACAATTAATGGATCAAAATTAATTTTTAATCCATCAATACTT---TGAACTCTAGGTTTCGTATTTTTATTTACAATAGGAGGTTTAACAGGTATTATATTAGCTAATTCATCAATTGAT------------------------ Monophadnoides ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Metallus TCAGGAATATTAGGTTTATCTTTTAGAATATTAATCCGAACAGAATTAAGCATACCTGGCTCATTAATTGGTGATGATCAAATTTATAATATAATTGTTACATCTCATGCATTTTTAATAATTTTTTTTATAGTTATACCTATTATAATTGGAGGATTC GGAAACTGATTAATTCCACTAATGTTAGGAGCCCCTGATATAGCATTTCCACGATTAAACAACATAAGATTTTGATTCTTACCCCCTTCAATCTTATTTTTATTATCAAGAAGAATGGTAAATTCAGGAACAGGAACAGGTTGAACTGTTTATCCCCCTTTATCAAATAGAATATCTCATGCTGGAGCATCTGTTGATTTAACAATTTTCTCTTTACATTTAGCAGGAATTTCATCAATTTTAGGAGCTATTAATTTTATTTCAACCGTAATCAATATACGAATTATCGGAATAAATTTAGAACGAATACCTTTATTTATTTGAGCAGTAGTACTAACAGCATTATTATTATTATTATCTTTACCAGTACTAGCTGGAGCTATTACTATATTATTAACAGATCGAAATTTAAATACATCATTCTTTGACCCTTCAGGTGGAGGAGATCCAATTCTTTACCAACATTTATTCTGATTTTTTGGCCACCCAGAAGTT TACATTTTAATTATTCCAGCTTTCGGAATAATTTCACATATTATTTCTCAAGAATCAGGAAAAAAAGAAACATTTGGAACAATTGGAATAATTTATGCAATATTAACAATT ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Athalia TCAGGAATAATTGGATTATCAATAAGAATAATCATTCGAAGAGAATTAAGTTCAAGAAATTCTTTTATTAAAAATGATCAAATTTATAATACTATTGTTACATCACATGCTTTTCTAATAATTTTTTTTATAGTAATACCCATTATAATTGGTGGATTT GGTAATTGATTAATTCCATTAATATTAGGAGCTCCTGATATAGCCTTCCCTCGAATAAATAATATAAGATTTTGATTCTTACCACCATCATTAATTTTATTATTATCTAGAAGATTAATTAATACTGGTTCAGGAACAGGATGAACAGTTTACCCACCACTTTCTAGAAATGCCTCTCATGCAGGAGCATCCGTTGATTTAACAATTTTCTCTTTACATTTAGCAGGAATTTCATCTATTCTAGGAGCAATTAATTTTATTTCAACTATTTTTAATATACGAATAAATGGAATAACAAACGAACGAATAAGATTATTTTCATGATCAGTATTATTAACAGCTATTTTATTACTTTTATCATTACCTGTTTTAGCAGGAGCAATTACAATATTATTAACTGACCGAAACTTAAATACATCATTTTTTGATCCTTCTGGAGGAGGTGATCCTATTTTATACCAACATTTATTTTGATTTTTTGGACATCCTGAAGTT TACATTTTAATTTTACCAGCATTTGGAATAATTTCTCATATTATTTCWCAAGAATCAGGAAAAAAAGAAACTTTTGGAACACTAGGAATAATTTATGCAATATCAAGAATT GGATTATTAGGTTTTATTGTTTGAGCTCATCATATATTTACAGTTGGAATAGATGTAGATACTCGAGCCTACTTTACAGCAGCAACAATAATTATTGCAATTCCAACAGGAATTAAAATTTTTAGATGAATAGCAACTATATATGGTAATAAAATATTTTTTACACCATCTATATTA---TGAACATTAGGATTTATTTTTCTCTTTACTTTAGGAGGATTGACAGGAATTATTTTATCAAATTCATCAATTGATATTATTTTACATGACACATATTAT Taxonus TCAGGAATAATTGGAATATCATTTAGAATATTAATTCGAATAGAACTAGGAAGTCCTGGAGCAGTAATTGGAAATGATCAAATTTACAATGTAATTGTTACTTCTCATGCTTTTTTAATAATTTTTTTTATAGTAATACCAGTAATAATTGGTGGATTT GGAAATTGATTAATTCCTTTAATATTAGGAGCCCCTGATATAGCATTTCCACGATTAAATAATATAAGATTTTGATTTTTACCCCCATCAATTATTTTATTATTATCAAGAAGAATGGTAAATTCAGGTTCAGGGACTGGTTGAACTGTTTATCCACCTCTCTCAAGAAGAATTTCACACGCTGGACCATCTGTAGATTTAACTATTTTTTCCCTTCATTTAGCAGGAGTTTCATCAATTTTAGGTGCAATAAACTTTATTTCAACAGTAATTAATATAAGAGTTAAAGGAATAGATTCAGAACGAATACCTTTATTTATTTGAGCTGTTTCTTTAACTGCTTTATTATTATTATTATCTTTACCTGTATTAGCTGGAGCTATTACTATATTATTAACAGATCGAAATTTAAATACAACTTTTTTTGATCCTTCAGGAGGAGGAGATCCAATTTTATATCAACATTTATTTTGATTTTTTGGACACCCTGAAGTT TATATTTTAATTATTCCAGCATTCGGAGTAATCTCTCATATTATATCTCAAGAATCAGGAAAAAAAGAAACATTTGGAACACTTGGAATAATTTATGCTATATTAACTATT ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Hoplocampa TCTGGAATACTAGGGTTATCATTTAGAATACTAATTCGAACTGAATTAGGAATACCAGGATCAATAATTGGTAATGATCAAATCTATAATGTAATTGTAACATCACATGCTTTTTTAATAATTTTTTTTATAGTTATACCTATTATAATCGGAGGGTTT GGAAATTGATTAGTACCTTTAATACTAGGAGCCCCAGATATAGCTTTCCCCCGATTAAATAACATAAGATTTTGATTTTTACCTCCTTCATTAATTTTACTTTTATCAAGAAGAATAGTAAATTCTGGGTCAGGAACAGGATGAACAGTATACCCTCCCTTATCCAGAAGAATTTCACATGCAGGTGCATCAGTTGATTTAACTATTTTCTCTTTACATTTAGCAGGAATTTCATCAATTCTAGGGGCAATTAATTTTATTTCTACAATAATTAATATACGAACTAAAGGAATAAAATTTGAACGTATACCTTTATTTATTTGAGCAGTTTCCCTAACTGCTTTATTATTATTATTATCATTACCAGTATTAGCAGGAGCTATTACTATATTATTAACTGACCGTAACTTAAATACATCATTTTTTGACCCTGCAGGAGGAGGTGACCCTATTTTATACCAACACTTATTTTGATTTTTTGGACACCCTGAAGTC TATATTTTAATTATTCCTGCCTTTGGAATAATCTCTCATATTATTTCACAAGAAGCAGGGAAAAAAGAAACATTCGGAACTCTAGGTATAATTTATGCTATAATAACTATT GGACTCTTAGGTTTTGTAGTTTGAGCTCACCATATATTTACTGTAGGTATAGATGTTGATACACGAGCTTATTTTACTGCAGCAACAATAATTATTGCAATTCCAACAGGAATTAAAATTTTTAGATGATTAGCAACTCTTTATGGATCAAAAATTATTTTTAATCCATCAATACTA---TGAACACTAGGATTTGTATTTTTATTTACCTCAGGGGGATTAACTGGAATTATTTTATCAAACTCATCTGTTGAC------------------------ Nematinus --------------------------------------------------------------------------------------------------------------------------------------------------------------- ------------------------------------------------CCACGTCTTAATAATATAAGATTTTGATTTTTACCTCCTTCATTAATTTTATTACTTTCAAGAAGAATAGTAAATTCAGGTTCAGGAACAGGATGAACTGTATATCCACCATTATCTAGGAGAATATCTCACGCAGGTGCATCAGTTGATATAACAATTTTTTCTTTACATCTTGCAGGAATCTCATCAATTTTAGGAGCTATCAATTTTATTTCTACTATAATTAATATACGAATTAAAGGAATAAGATTTGAACAAATACCTTTATTTATTTGAGCTGTAACATTAACTGCTTTATTATTACTTTTATCTTTACCAGTATTAGCAGGAGCTATTACAATATTATTAACTGATCGTAATTTAAATACATCATTTTTTGATCCTTCAGGAGGAGGAGATCCAATCCTTTATCAACATCTATTTTGATTTTTTGGTCATCCAGAAGTT TATATTCTAATTATTCCCGCATTTGGAATAATTTCTCATATTATTTCTCAAGAATCAGGTAAAAAAGAAACTTTTGGAACACTAGGAATAATTTATGCTATAATAACTATT GGATTATTAGGATTTGTAGTATGAGCTCATCATATATTTACTATTGGAATAGATGTTGATACACGAGCTTATTTCACAGCAGCCACAATAATTATTGCCATCCCAACAGGAATCAAAATTTTTAGTTGATTAGCTACAATTTATGGATCTCAAGTTATTTTCAATCCTTCAATAATA---TGAACTTTAGGATTTGTATTTTTATTTACTTCAGGAGGACTAACAGGAATTATTTTATCAAATTCATCAATTGAT------------------------ Nematus TCTGGGATATTAGGATTATCATTTAGAATACTAATTCGAACAGAATTAGGAATACCAGGATCAATAATTGGAGATGATCAAATTTACAATGTAATTGTAACATCTCATGCATTTTTAATAATTTTTTTTATAGTTATACCAATTATAATTGGAGGATTT GGAAACTGATTAGTTCCATTAATATTAGGAGCTCCTGATATAGCATTTCCACGATTGAATAATATAAGATTTTGATTTTTACCACCTTCACTAATCCTACTCCTTTCAAGAAGAATAGTAAATTCAGGATCAGGAACAGGATGAACTGTTTATCCACCCTTATCAAGAAGAATTTCTCATTCAGGTGCATCAGTTGATATAACTATTTTTTCATTACATTTAGCAGGAATTTCATCTATTTTAGGAGCAATTAATTTCATTTCTACAGTAATTAATATAAAACTAAAAAGAATAAGATTTGAACAAATACCTCTATTTATTTGAGCAGTAACTTTAACTGCACTTCTACTTCTTTTATCTCTTCCAGTATTAGCAGGAGCTATTACAATACTTCTTACAGATCGAAACTTAAATACATCATTTTTTGATCCTTCAGGAGGAGGAGATCCAATTTTATACCAACATTTATTTTGATTTTTTGGACATCCAGAAGTT TATATTTTAATTATTCCAGCATTTGGAATAATTTCTCATATTATTTCACAAGATTCAGGAAAGAAAGAAACGTTTGGTACNNNAGGNNNNNTTTATGCTATAATNNCAATT ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Cladius TCAGGAATATTAGGACTCTCATTTAGAATACTTATTCGAACAGAACTAGGAATACCAGGATCCTTAATTGGAGATGATCAAATTTATAATGTAATTGTAACTTCACACGCATTTTTAATAATTTTCTTTATAGTAATACCTATTATAATTGGAGGATTT GGAAACTGACTAGTACCTTTAATATTAGGAGCCCCCGATATAGCATTTCCTCGATTAAATAATATAAGATTTTGATTTTTACCACCATCACTAATTCTATTACTTTCAAGAAGAATAGTAAATTCAGGTTCTGGAACAGGTTGAACAGTTTATCCTCCTTTATCAAGAAGAATTTCACATGCAGGGGCATCAGTAGATCTTACCATTTTCTCTCTTCATTTAGCAGGAATTTCATCAATTCTTGGAGCAATTAATTTCATTTCAACAATAATTAATATAAAAGTAAAAGGAATAAGATTTGAACGAATACCTCTATTTGTTTGAGCAGTATCATTAACAGCTTTATTACTTCTTCTTTCACTACCTGTACTAGCCGGAGCAATTACTATACTTCTCACAGATCGAAACTTAAATACATCATTTTTTGACCCATCAGGAGGAGGAGACCCAATTTTATACCAACACTT???????????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Monoctenus TCAGGGATATTAGGATTATCATCAAGAATAATTATTCGGATAGAATTAGGAATACCAGGTTCATTAATTGGAGATGATCAAATTTATAATGTAATTGTAACTTCACATGCATTTTTAATAATTTTTTTTTTAGTTATACCTATTATAATTGGGGGGTTT GGTAATTGGTTAGTTCCTCTTATATTAGGAGCTCCAGATATAGCTTTCCCACGGATAAATAATATAAGATTTTGATTTTTACCTCCATCTTTAATTTTAATAATTACAAGAATAATAGTAGAATCAGGATCAGGAACGGGGTGAACAGTTTATCCCCCGTTATCTAGAAGATTGGGACATTCTGGTAGATCTGTAGATTTAACTATTTTTTCACTTCATTTAGCAGGAATTTCTTCTATTTTAGGGGCTATTAATTTTATTTCAACAATAATTAATATACGAATAAATGGTATAAATTTTGATCAACTTTCTCTTTTTGTATGATCAGTTAAAATTACAGCAATTTTATTATTATTATCTTTACCTGTTTTAGCTGGCGCTATTACTATATTACTTACAGATCGAAATTTAAATACAACATTTTTTGATCCTGCTGGAGGAGGGGACCCAATTTTATATCAACATTTATTTTGATTTTTTGGGCATCCGGAGGTT TATATTTTAATTTTACCTGCTTTTGGTATAATTTCTCATATTATTTCTCAAGAATCAGGTAAAAAGGAAACATTTGGAACATTGGGGATAATTTATGCAATAATAACTATT GGATTATTAGGATTTATTGTATGGGCACATCATATATTTACTGTAGGAATAGATGTTGATACTCGAGCATATTTTACAGCGGCAACTATAATTATTGCAATTCCTACGGGGATTAAAATTTTTAGATGATTAGCTACATTATATGGATCAAATATAAAATTTAATTCTGTTACATTA---TGAGCATTAGGTTTTATTTTTTTATTTACAATAGGGGGATTAACTGGTATTATATTATCAAATTCTTCTATTGATATTATTTTACATGATACATATTAT Gilpinia TCAGGAATATTAGGATTATCATCAAGAATAATTATTCGAACAGAATTAGGAATACCTGGATCATTTATTGGTGATGATCAAATTTATAATATAATTGTTACATCTCATGCATTTTTAATAATTTTTTTTATAGTAATACCTATTATAATTGGAGGATTT GGAAATTGACTAGTACCTTTAATATTAGGAGCCCCAGACATAGCTTTTCCTCGAATAAATAACATAAGATTTTGACTTCTACCACCTTCATTAATTTTATTAATATCAAGAAGATTAGTTGATTCAGGAACTGGAACAGGATGAACTGTATACCCACCTCTATCAAGAAATTTAGGTCATTCAGGACCATCAGTAGATTATACAATTTTTTCACTTCATATAGCAGGAATTTCATCTATTTTAGGAGCAATTAATTTTATTTCTACAATAATTAATATACGACCAAAAAGAATAAATTTTGAACAAATACCATTATTTGTGTGATCAGTTAAATTAACTGCTATTCTTTTATTATTATCACTACCAGTATTAGCTGGGGCTATTACTATATTATTAACAGATCGAAATATAAATACAAGATTTTTTGATCCATCAGGTGGGGGTGATCCAATTCTTTATCAACATCTATTTTGATTTTTTGGTCATCCAGAAGTA TATATTTTAATTCTTCCAGCATTTGGAATAATTTCACATATCATTTCACAAAAATCAGGAAAAAAAGAAACCTTTGGAACATTAGGAATAATTTATGCTATAATAACAATT ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Diprion TCTGGAATACTAGGATTATCATCGAGAATGATCATTCGGATAGAGTTAGGTACCCCAGGATCATTTATTGGTGACGATCAAATTTATAATATAATTGTTACATCTCACGCATTTTTAATAATTTTTTTTATAGTAATACCTATTATAATTGGAGGATTT GGAAACTGATTAGTACCCTTAATATTAGGAGCACCAGATATAGCATTTCCTCGAATAAATAATATAAGATTTTGACTTTTACCTCCTTCATTAATTATACTTTTATCAAGAAGAATTGTTGATTCAGGAACAGGAACAGGATGAACAATCTATCCTCCTTTATCAAGAAATTTAGGTCATTCAGGACCATCAACTGATTATACAATTTTTTCTTTACATATAGCAGGGATTTCATCTATTATAGGAGCAATTAATTTTATTTCTACTATATTAAATATACATTTAAAAGGAATAAAATTAGACCAATTAACTCTTTTTATTTGAGCAGTTAAATTAACAGCTATTCTATTATTACTTTCTTTACCAGTTTTAGCAGGAGCTATTACAATACTTTTAAGAGATCGAAATTTAAATACAACATTTTTTGATCCTTCAGGTGGTGGTGATCCAATTTTATATCAACATTTATTTTGATTCTTTGGACATCCAGAAGTT TATATTTTAATTTTACCAGCATTTGGAATAATTTCACATATTATTTCTCAAAGATCAGGTAAAAAAGAAACTTTCGGAACTTTAGGAATAATTTATGCAATAATAACAATT GGAGTACTAGGATTTGTAGTATGAGCACATCATATATTTACTGTAGGAATAGATGTTGACACACGAGCATATTTTACAGCTGCAACTATAGTAATTGCGATTCCAACAGGAATTAAAATTTTTAGATGAATAGCAACTCTTTATGGATCAAGAATTAATTTTAAACCTTCACTAATA---TGAACTTTAGGATTCATTTTTCTATTTACTATAGGAGGATTAACTGGAATTATACTATCTAATTCATCAATTGATATTATTCTTCATGATACATATTAT Cimbicinae TCAGGAATATTAGGTTTATCATTTAGAATAATTATTCGAACTGAATTAGGTACACCAGGATCTTTAATTGGAAATGATCAAATTTATAATGTTGTAGTTACTTCTCATGCATTTTTAATAATTTTCTTTATAGTAATACCTATTATAATTGGAGGATTT GGAAATTGATTAATTCCTCTAATATTAGGGGCTCCTGACATAGCATTCCCACGTTTAAATAACATAAGATTTTGATTACTTCCCCCATCAATCATTTTTCTACTTTCTAGAAGAATTGTAAATTCAGGAAGAGGAACAGGATGAACAGTTTATCCTCCTCTTTCAAGAAATTTAGGACATGCAGGTGCTTCAGTTGATTTAACTATTTTCTCACTTCATTTAGCAGGAATCTCTTCAATTCTTGGAGCAATTAATTTTATTTCAACAATAATTAATATAAAAATCAAAACTATAAATTATGATCAACTACCCCTTTTTGTTTGAGCAACTTCATTAACTACATTATTACTTTTATTATCATTACCAGTATTAGCTGGAGCGATCACTATACTATTAACAGATCGAAATTTAAATACATCATTTTTTGATCCTGCAGGAGGAGGAGATCCAATTTTATATCAACATTTATTTTGATTTTTTGGACATCCAGAAGTT TATATTTTAATTTTACCAGGATTTGGGGTAATTTCCCATATTATCTCTCAAGAATCAGGAAAAAAAGAAACATTTGGAACTCTAGGTATAATTTATGCTATAATAGCAATT GGATTATTAGGATTTGTAGTTTGAGCCCATCATATATTTACTATTGGAATAGACGTTGATACACGAGCATACTTTACAGCAGTAACAATAATTATTGCTATTCCTACTGGAATTAAAATTTTTAGTTGACTAGCAACTATATATGGATCTAAAATTAATTCTAATCCATCTACATTA---TGAACTTTAGGATTTGTATTCTTATTTACATTAGGAGGACTTACAGGAATCATATTATCAAATTCATCTGTAGATATTATTCTTCATGATACATATTAT Abia TCAGGAATAATTGGAATATCTTTTAGTATAATTATTCGAACAGAATTAGGAACAGCAGGATCATTAATTGGAGATGACCAAATTTATAATGTTGTAGTAACATCTCATGCATTTTTAATAATCTTCTTTATAGTAATACCTATTATAATTGGAGGATTC GGAAATTGACTTGTACCACTAATATTAGGAGCTCCAGATATAGCTTTTCCACGATTAAATAATATAAGATTTTGACTATTACCACCATCAATTATTTTACTATTATCAAGAAGAATTGTAAATTCAGGTAGAGGAACAGGATGAACAGTTTATCCTCCTTTATCAAGAAGATTAGGACATGCAGGACCATCTGTTGATTTAACTATTTTTTCATTACATTTAGCAGGTATTTCTTCTATTTTAGGGGCTGTAAATTTTATTTCAACTACAATTAATATAAAAATTAACTCAATAAAATATGATCAATTATCATTATTTATTTGAGCAACAGTTTTAACTACTATTTTACTATTATTATCTTTACCAGTATTAGCAGGAGCAATTACTATATTATTAACTGATCGTAATTTAAATACTTCATTTTTTGATCCTGCAGGAGGAGGAGATCCTATTTTATACCAACACTTATTTTGATTTTTTGGTCATCCAGAAGTT TATATTCTAATTTTACCTGGATTTGGTGTAATTTCTCATATTATTGCTCAAGAATCAGGTAAAAAAGAATCATTCGGAGCTCTTAGAATAATTTATGCTATATCAGCTATT GGAATTTTAGGATTTGTAGTATGAGCCCATCATATATTTACTATTGGTATAGATATTGATACACGAGCTTATTTTACAGCAGTAACCATAATTATTGCTATTCCTACAGGAATTAAAATTTTTAGATGAGTAGCAACTTTACATGGATCTAAAATAAAATTTACTCCATCAATACTA---TGAACATTAGGATTTGTATTTTTATTTACCAGAGGAGGATTAACAGGAATTATACTAGCAAATTCATCAATTGATATTATTTTACATGATACTTATTAT Corynis TCAGGTATATTAGGTTTATCATTTAGTATATTAATTCGTAGAGAATTAGGTACACCAAATTCTTTAATTGGAGATGATCAAATCTATAATACAGTAGTAACTTCTCATGCATTTTTAATAATTTTTTTTATAGTAATACCAGTAATAATTGGAGGATTT GGAAACTGATTAATTCCTTTAATACTAGGAGCACCAGATATAGCATTTCCTCGTTTAAATAATATAAGATTTTGATTATTACCTCCATCATTATTAATATTATTAATAAGAAGAATTATTGATTCAGGAGCAGGAACTGGATGAACAATTTATCCACCATTATCAAGAAATTTAGCTCATGCAGGAGCTTCTGTAGATATAACAATTTTCTCGCTACATATAGCAGGAATCTCTTCAATTTTAGGAGCTATTAATTTTATTTCTACTATTATAAATATAAAAATTAATGGAATAAAATTTGATCAATTACCTTTATTTGTTTGAGCCGTAATTTTAACTACTATTTTATTATTATTATCATTACCTGTTTTAGCAGGAGCTATTACAATATTATTAACAGATCGAAATTTAAATACATCATTTTTTGATCCATCAGGAGGAGGAGATCCAATCTTATATCAACATTTATTCTGATTTTTTGGCCACCCAGAAGTG TATATTTTAATTTTACCGGCATTTGGTGTTATTTCTCATATTATTTCTAACGAATCAGGAAAAAAAGAAACATTTGGAACATTAGGTATAATTTATGCTATATTATCAATT GGTTTATTAGGTTTTGTAGTTTGAGCTCATCATATATTTACTGTTGGTATAGATGTTGATACACGAGCATATTTTACTGCAGTAACAATAATAATTGCAATTCCTACAGGAATTAAAATTTTTAGATGATTAGCAACTCTATTCGGAACAATTATAAAATCAATTCCTTCAATATTA---TGAACAATAGGATTTGTATTCTTATTTACAATAGGAGGATTAACAGGTATCATTTTATCTAATTCATCAATTGATGTAATTTTACATGATACATATTAT Arge GCAGGAATAATAGGACTTTCATTTAGAATAATTATTCGTACAGAATTAAGTTCAGTAGGGTCATTAATTAAAGATGATCAAATTTATAATACAATTGTCACATCACATGCATTTTTAATAATTTTTTTTATAGTTATACCTATTATAATTGGAGGATTT GGAAATTGATTAATTCCATTAATATTAGGGGCTCCTGATATAGCATTTCCACGATTAAATAATATAAGATTTTGATTTTTACCTCCTTCATTAATTCTTTTAACAATTAGAAGATTTGTTGATTCAGGAACTGGTACAGGTTGAACAGTTTATCCCCCCTTATCAAGTAATATTTCTCATTCAGGTTCATCTGTAGATTTAACTATTTTCTCCTTACATTTAGCAGGAATTTCATCAATTTTAGGAGCTATTAATTTTATTTCTACAATTATTAATATAAAAATTTTAGGAATATCTTTTGATAAAATACCATTATTTATTTGATCAGTTAAAATTACAGCAATTTTATTACTTCTTTCATTACCTGTATTAGCAGGAGCTATTACAATATTATTAACAGATCGTAATTTAAATACTTCATTTTTTGACCCTTCAGGTGGAGGAGACCCTATTTTATACCAACATTTATTTTGATTTTTTGGACACCCTGAAGTT TATATTTTAATTCTCCCCGCATTTGGAATTATTTCACATATTATTTCACAAGAATCTGGTAAAAAAGAAACATTTGGAGTTTTAGGTATAATTTATGCTATATTAACAATT GGAATTTTAGGATTTATTGTTTGAGCTCATCATATATTTACTGTAGGTATAGATGTTGATACACGAGCTTATTTTACTGCTGCTACTATAATTATTGCAGTTCCTACAGGTATTAAAATTTTTAGTTGAATAGCTACAATTCATGGATCAAAAATTATTTATTCTCCAACTATATTA---TGAACTTTAGGATTYATTYTCTTATTTACATTAGKAGGATTAACAGGKATTATTTTATCWMATTCATCAATTGATATTATTTTACATGATACATA?TAT Sterictiphora TCAGGAATAATAGGACTATCATTCAGAATAATTATCCGGACAGAATTAAGTTCAGTAGGATCACTTATTGGAGATGACCAAATCTACAATGTATTAGTAACATCTCATGCATTTTTAATAATTTTTTTTATAGTAATACCAATTATAATTGGAGGATTT GGAAACTGATTAATTCCTTTAATATTAGGGGCACCTGACATAGCTTTCCCCCGACTTAATAATATAAGATTTTGACTACTCCCCCCTTCATTAATATTATTAACAATTAGTAGATTTACTGATTCAGGAGCAGGTACAGGATGAACAGTATACCCTCCTTTATCAAGTAATATTGCTCACGCAGGAAGATCAGTAGACTTAACAATTTTTTCTTTACATTTAGCAGGAATCTCATCAATTCTAGGGGCAGTAAATTTTATTTCAACAGTAATTAATATACGATCATTAGGAATATCATTTGATAAAATACCTTTATTTATTTGAGCAGTAGCAATCACAGCTTTATTATTATTAATTTCCTTACCTGTATTAGCAGGAGCAATTACAATACTATTAACAGACCGAAACCTAAACACTTCTTTCTTTGACCCTTCAGGAGGAGGGGACCCTGTACTTTACCAACACTTATTCTGATTTTTTGGCCACCCAGAAGTT TATATTCTTATTTTACCAGCCTTTGGTATTATTTCTCATATTATTTCACAAGAATCAGGAAAAAAAGAAACATTCGGAACTTTAGGTATAATTTATGCAATAACAGCTATT ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Perga TCAGGAATAATTGGATTATCATTCAGAATAATTATTCGAACAGAAATAATAACAACAGGATCTTTTATTGGAGATGATCAAATCTATAATGTAATTGTCACATCTCATGCATTTTTAATAATTTTTTTTATAGTTATACCTATTATAATAGGAGGATTT GGAAATTGATTATTACCTTTAATATTAGGAGCTCCTGATATGGCATTTCCTCGACTTAATAATTTAAGATTTTGACTACTACCCCCCTCATTAATCCTACTAACATTTAGTAGATTTATTAACTCAGGATCTGGAACAGGATGAACTGTCTATCCTCCCCTTTCAAGTAATATTGCCCATGCTGGAACTTCTGTTGATATAACTATTTTTTCATTACATATAGCAGGAATTTCATCAATTCTAGGAGCTATTAATTTTGTATCAACTATAATTAATATACGAGCATCAGAAATAAGACTAGATAAAATACCTTTATTTATTTGAGCTATTACTATTACCGCTATTTTATTAATTATCTCTTTACCAGTTTTAGCAGGAGCTATCACTATATTACTTACAGATCGAAACTTAAATACATCATTTTTTGACCCATCAGGAGGAGGAGATCCAATTTTATACCAACACTTATTTTGATTCTTTGGACATCCAGAAGTA TATATTTTAATTTTACCTGCTTTTGGTATTATCTCTCATACAATCTCTCAAGAATCTGGTAAAAAAGAAACTTTCGGAACTTTAGGAATAATTTATGCAATATCAGCCATT GGCCTACTTGGATTTATTGTATGAGCTCATCATATATTTACTGTAGGAATAGATGTAGATACTCGAGCTTATTTTACTGCTGCAACAATAATTATTGCTGTACCCACAGGAATTAAAATTTTTAGATGACTTGCAACAATTCACGGATCAAAAATCTCCTACACTCCTAATATAATA---TGAACCCTAGGATTTGTTTTTTTATTCACTATAGGAGGATTAACTGGAATTATACTCTCTAACTCATCAATTGATATCATTATACACGATACATATTAT Phylacteophaga TCAGGAATAATTGGATTATCTTTCAGAATAATTATTCGAACTGAACTTAATACATCAGGATCATTTATTGGAGATGATCAAACATATAATGTTATAGTGACATCTCATGCATTTTTAATAATTTTCTTTATAGTAATACCAGTAATAATAGGAGGATTT GGAAATTGATTAGTACCCTTAATATTAGGAGCACCAGATATAGCTTTTCCACGATTAAATAACCTTAGATTTTGATTACTACCCCCATCCCTAATCTTACTAACAATAAGAAATTTAATTGATTCAGGATCAGGGACAGGATGAACAGTTTATCCACCTCTTTCAAGTAATATCGCTCATGCAGGATGTTCTGTAGACATAACAATTTTTTCTCTTCATATAGCAGGTATTTCTTCCATTCTTGGAGCAATTAACTTTATCTCAACTATAATTAATATACGAGCTTCTAATATAAGACTAGATAAATTATCATTATTAATTTGAGCAATTACAATTACAGCAATTTTACTAGTTATTTCTCTTCCAGTACTAGCTGGTGCAATTACTATACTTTTAACAGATCGAAACCTAAATACATCATTCTTCGACCCATCAGGAGGAGGAGATCCTATTTTATATCAACATTTATTTTGATTCTTTGGACATCCCGAAGTA TATATTCTTATTCTACCAGCATTTGGAATTATTTCACATATTATTTCTCAAGAATCTGGAAAAAAAGAAACTTTTGGAACATTAGGAATAATTTATGCAATATCAGCTATC GGATTGCTAGGATTTATTGTATGAGCCCATCACATATTTACTATTGGAATAGATGTTGATACTCGAGCATATTTTACTGCAGCAACAATAATTATTGCTGTTCCTACAGGAATCAAAATTTTTAGATGATTAGCAACTATTCACGGATCAAAAATAATTTATACACCTAATATACTT---TGAACATTGGGATTTGTATTTCTTTTCACTATAGGAGGACTAACAGGAATTATTTTATCTAACTCTTCTATTGATATTATTATACATGACACATACTAC Lophyrotoma TCAGGAATATTAGGGTTATCTTTTAGAATAATTATTCGAACAGAATTAATAACACTAGGAACATTTATTGGTAATGATCAAATTTATAATGTTCTTGTAACATCTCATGCATTTTTAATAATTTTTTTTATAGTTATACCAATTATAATAGGAGGGTTT GGAAATTGATTACTACCATTAATACTTGGTGCACCAGACATAGCATTTCCTCGTTTAAATAATTTAAGATTTTGACTTTTACCCCCTTCATTAATTTTACTGTTAATAAGAAGATTAATTGARTCAGGAAGAGGGACAGGATGAACTGTTTACCCACCATTATCAAGAAATTTATCTCATTCAGGGTCATCTGTAGATTTAACAATTTTTTCATTACATATGGCAGGAGTATCATCAATTTTAGGAGCAATCAATTTTGTTTCTACAGTAATTAATATACGAACTAAAGGAATACAATTAAGTATAATATCATTAATAATTTGAGCTATTACAATTACAGCTATTTTATTAATTATTTCATTACCAGTTTTAGCAGGAGCTATTACTATACTTTTAACTGACCGAAATTTAAATACATCATTTTTTGATCCTTCAGGAGGAGGAGATCCTATTCTTTATCAACATTTATTTTGATTTTTTGGTCACCCTGAAGTT TATATTTTAATTTTACCAGCATTTGGTATTATTTCCCATATTATTTCTCAAGARTCAGGTAAAAAAGAAACTTTTGGTACTTTAGGAATAATTTATGCAATATCTTCTATT GGTTTATTAGGATTTATTGTTTGAGCTCATCATATATTTACTGTAGGTATAGATGTTGATACACGAGCTTATTTTACAGCAGCCACTATAATTATTGCTATTCCTACAGGGATTAAAATTTTTAGATGACTAGCAACAATTCATGGAACAAAATTAATTTATTCACCAAATATATTA---TGAACTCTAGGATTTGTTTTTTTATTTACTATAGGAGGACTTACTGGAATTATTTTATCAAATTCATCAATTGATATTATTTTACATGATACTTATTAT Acordulecera TCGGGAATAATTGGACTATCATTTAGTATAATTATCCGAACAGAGCTTAGAACTACAGGATCATTTATCGGTAATGATCAAATTTATAATGTAATTGTAACATCTCATGCATTTTTAATAATTTTTTTTATAGTTATACCAGTTATAATAGGAGGATTT GGAAACTGATTAATTCCACTAATATTAGGAGCTCCTGATATAGCTTTCCCACGAATAAATAATTTAAGATTTTGACTTCTTCCTCCCTCACTAATTTTATTAACATTTAGAAGTTTTATTGATTCAGGATCAGGAACAGGATGAACAGTTTACCCTCCTTTATCAAGAAATATTGCTCATGCAGGAACATCCGTAGATATAACTATTTTTTCCCTTCATATAGCAGGAATTTCTTCAATTTTAGGGGCAATTAATTTTATTTCAACCATAATTAATATACGAACACCCGGAATAAGATTCGATAAAATACCATTATTAATTTGAGCAGTTATAATTACAGCTATCCTTCTAGTTATCTCTTTACCCGTTCTAGCTGGAGCTATTACTATATTATTAACAGATCGAAATCTAAATACCTCATTTTTTGATCCTTCAGGGGGGGGGGACCCTATTCTATACCAACACTTATTCTGATTTTTTGGACACCCAGAAGTT TATATTTTAATTCTCCCCGCTTTTGGTATTATTTCTCATATTATTTCTCAAGAATCAGGTAAAAAGGAAACTTTTGGGACATTAGGAATAATTTATGCTATATCAGCTATT ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Decameria TCAGGTATACTTGGATTATCATTTAGAATAATTATTCGTTCTCAATTAAGAACTTCTGGCACACTAATTTCAAATGACCAAATTTATAATGTTATTGTAACATCACATGCATTTTTAATAATTTTTTTTATAGTAATACCAATCATAATAGGAGGATTT GGAAATTGATTAATTCCTTTAATAATTGGGGCCCCTGATATAGCTTTTCCTCGAATAAATAATTTAAGATTTTGGTTTTTACCTCCTTCTCTAATCCTTTTATTGATAAGTAGAATAACAGATTCTGGATCTGGAACAGGTTGAACTGTGTATCCACCATTATCCAACAATATTGCCCATGCAGGAGCATCTGTTGACATAACTATCTTCTCTTTGCATATAGCAGGTATTTCATCAATTCTTGGAGCTATTAATTTTGTATCAACTATAATTAATATACGAACTTCAGAAATAAATATAGATAAGCTTCCTCTTTTTATTTGATCAATTACAGTAACTGCCATTTTATTAATTATTTCTTTACCTGTATTAGCCGGGGCAATCACTATACTACTTACTGATCGTAATTTAAATACATCATTTTTTGATCCTTCAGGAGGAGGGGACCCTATTTTATACCAACATTTATTCTGATTTTTTGGCCACCCAGAAGTT TACATTTTAATCTTACCAGCATTTGGAATTATTTCTCATGTAATTTCTCAAGAATCTGGAAAAAAAGAAACATTTGGATCTCTAGGAATAATTTATGCTATAATAGCTATT ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Neurotoma -------------------------------------------------------------------------ATGATCAAATTTATAATGTAATTGTTACTGCCCATGCATTTTTAATAATTTTCTTTATAGTTATACCTATAATAATTGGGGGATTC GGAAACTGATTAGTTCCTTTAATATTAGGAGCCCCTGATATAGCATTCCCACGAATAAATAATATAAGATTTTGACTCCTACCTCCTTCCTTAGTTTTATTACTAACTAGAAGAATTATAAATAAAGGAACGGGAACTGGTTGAACAGTTTACCCCCCTTTATCATCAATCTTATCTCATAGAGGTTCATCTGTAGATTTCGCAATTTTTTCATTACATATAGCTGGAATCTCTTCTATTATAGGAGCTATTAATTTTATTTCAACAATTATAAACATAAAAATTCCAGGATTAAGTCTTGATCAATTATCATTATTTACTTGATCAGTTCTAAATACTGCAATCTTACTATTACTATCATTACCAGTATTAGCAGGAGCCATTACTATATTATTAACAGATCGTAATTTAAATACTTCATTTTTTGACCCTGCAGKAGGAGGAGAYCCTGTTCTTTATCANCATTTATTTTGATTTTTTGGACACCCAGAAGTA TATATTCNNATCNTANNNNNNTTNNGAATAATTTCTCATATAATTTTCCATGAATCAGGAAAAAAAGAAACTTTCGGAACATTAGGAATAATCTACGCAATAATTTCTATT GGAATTCTTGGATTTGTTGTTT----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- Onycholyda TCAGGAATATTAGGAACTTCAATAAGAATATTAATTCGAATAGAATTAGGRACTCCAGGATCTTTAATTGGRGATGATCAAATTTATAATGTTATTGTCACAGCTCATGCATTTTTAATAATTTTTTTTATAGTTATACCAATAATAATTGGAGGATTT GGAAATTGACTTGTTCCATTAATATTAGGAGCTCCTGATATAGCATTTCCTCGAATAAAYAATATAAGATTTTGATTATTACCACCTTCATTAATTTTTTTAATTTCTAGAAGAATTATAAATAAAGGAGCAGGTACAGGATGAACAGTTTATCCTCCTTTATCTTCAAATTTAACTCATACTGGTTCATCAGTAGATTTTGCAATTTTTTCCTTACATATRGCTGGWATTTCTTCAATTATAGGAGCAATTAATTTTATTTCAACAATTTTAAATATACGAATTTCAGGATTATTTATAGATCAAATATCATTATTTATTTGATCAGTATTTATTACRGCTTTTTTATTATTATTATCATTACCTGTATTAGCAGGTGCAATTACAATATTATTAACAGACCGTAATTTAAATACTTCATTTTTTGATCCAGCAGGAGGGGGTGATCCAGTTTTATAYCAACATTTATTTTGRTTTTTTGGTCACCCTGAAGTT TATATTTTAATTTTACCTGGATTTGGAATAATTTCTCATATAATTTTTCATGAATCTGGTAAAAAGGAAACATTTGGTACATTAGGAATAATTTATGCAATAATTTCTATT GGAATTTTAGGATTTATTGTATGAGCTCATCATATATTTACAGTTGGAATAGATGTTGATACTCGAGCTTATTTTACATCTGCTACTTTAATTATTGCTGTTCCTACAGGAATTAAAGTTTTTAGATGAATAGCAACTATACATGGAATAAAATTTAATTTAAATCCTTCTATATTA---TGAGCTTCAGGTTTTGTATTTTTATTTACAGTTGGGGGATTAACTGGTGTAGTATTAGCAAATTCATCTATTGATATTATTTTACATGATACATATTAT Pamphilius TCAGGGATATTAGGTACTTCAATAAGAATATTAATTCGAATAGAATTAGGAACCCCAGGATCTTTAATTGGAGATGATCAAATTTATAATGTTATTGTTACTGCTCATGCATTTTTAATGATTTTTTTTATAGTTATACCAATAATAATTGGAGGATTT GGAAATTGATTAGTTCCATTAATATTAGGAGCTCCAGATATAGCTTTTCCTCGAATAAATAATATAAGATTTTGATTACTTCCTCCTTCATTAATTTTTTTAATTTCTAGAAGAATTATAAATAAGGGTGCAGGTACAGGTTGAACAGTTTATCCACCTTTATCTTCAAATTTATCTCATACTGGATCATCAGTTGATTTTGCAATTTTTTCTTTACATATAGCAGGAATTTCTTCAATTATAGGAGCAATTAATTTTATTTCAACAATTTTAAATATGCGAATTTCAGGTTTAATAATAGATCAAATATCATTATTTATTTGATCGGTATTTATTACAGCTTTTTTATTATTATTATCATTACCAGTTTTAGCCGGGGCAATTACTATACTATTAACAGATCGAAATTTAAATACTTCATTTTTTGATCCAGCTGGAGGAGGAGATCCTGTTTTATACCAACATTTATTTTGATTTTTTGGTCACCCAGAAGTT TATATTTTAATTTTACCTGGATTTGGGATAATTTCACATATAATTTTTAATGAATCGGGAAAAAAGGAAACATTTGGAACATTAGGAATAATTTATGCTATAATTTCTATT GGAATTTTAGGTTTTATTGTATGAGCTCATCATATATTTACTATTGGAATAGATGTTGATACTCGAGCATATTTTACTTCTGCTACATTAATTATTGCTGTTCCTACAGGAATTAAAGTATTTAGATGAATAGCTACTATGCACGGAATAAAATTTAATTTAAATCCTTCTATATTA---TGAGCTTCAGGTTTTGTTTTTTTATTTACTGTTGGTGGTTTAACTGGAGTAGTATTAGCTAATTCTTCAATTGATATTATTCTTCATGATACATATTAN Cephalcia TCAGGGATATTAGGGCTTTCAATAAGAATATTAATTCGATTAGAATTAGGGACTCCTGGTTCATTAATTGGTGATGATCAGATTTATAATGTTATTGTAACAGCACATGCATTTTTAATAATTTTTTTTATAGTTATACCAATAATAATTGGAGGATTT GGAAATTGATTAGTCCCTTTAATATTGGGAGCCCCTGATATAGCATTTCCTCGGATAAATAATATAAGATTTTGATTATTACCCCCTTCTTTAATTATATTATTAATTAGAAGAATTATAAATAAAGGAGCAGGAACTGGATGAACAGTTTATCCACCTCTTTCTTCTAATTTAACTCATACTGGATCATCAGTAGATTTTGCTATTTTTTCTTTACATATAGCTGGGATTTCATCAATTATAGGGGCYATTAATTTTATTTCTACAGTAATTAATATACGTACATCTGGTATAYTATTTGATCAAATATCTTTATTTATTTGATCAGTAGTAATTACTGCTTTATTATTACTTTTATCATTACCAGTYTTAGCAGGAGCTATTACTATATTATTAACAGACCGAAACTTAAATACATCTTTTTTTGATCCAGCAGGYGGGGGTGATCCTGTTTTATATCAACATTTATTTTGATTTTTTGGTCATCCTGAAGTT TATATTTTAATTTTACCTGGTTTTGGATTAATTTCCCATATTATTTTTCATGAATCAAAAAAAAAAGAAACTTTTGGAACTTTAGGTATAATTTATGCAATAATTTCTATT ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Acantholyda TCAGGAATATTAGGATTATCAATAAGAATATTAATTCGACTAGAATTAGGAACCCCAGGTTCATTAATTGGTAATGATCAAATTTATAATACAATTGTTACAGCACATGCATTCTTAATAATTTTCTTTATAGTAATACCAATAATGATTGGAGGTTTT GGAAATTGATTAGTTCCACTAATATTAGGAGCCCCAGATATAGCTTTTCCTCGAATAAATAATATAAGATTTTGATTATTACCCCCATCATTAATTATATTATTAACCAGAAGAATTATAAATAAAGGGGCAGGAACAGGATGGACAGTTTATCCCCCACTTTCTTCTAATCTAACTCATATAGGATCATCAGTAGATTTTGCTATTTTCTCCTTACATATAGCAGGAATTTCATCAATTATAGGAGCTATTAATTTCATTTCTACAGTAATTAATATACGAATTTATGGAATATCATTTGATCAAATATCTTTATTTATTTGATCAGTAGTTATTACTGCTTTATTACTATTATTATCATTACCAGTTTTAGCTGGAGCTATTACTATATTATTAACTGATCGAAATTTAAATACTTCATTTTTTGATCCGGCTGGAGGAGGGGATCCTGTATTATATCAACATCTATTTTGATTTTTTGGGCATCCAGAAGTA TATATTTTAATTTTACCAGGATTTGGTTTAATTTCACATATAATTTTCCATGAATCAAAAAAAAAAGAAACATTTGGAACATTAGGTATAATTTATGCCATAATTTCTATT GGTATTCTAGGATTTGTAGTTTGAGCTCATCATATATTTACTATTGGAATAGACGTAGATACTCGAGCATATTTTACATCTGCTACTATAATTATTGCAGTACCTACTGGAATTAAAGTATTTAGATGATTAGCTACTATACATGGAATAAAATTTTCTTCTACCCCTTCATTTCTA---TGATCATTAGGATTTGTATTCCTTTTTACTGTGGGAGGATTAACAGGAGTAATTTTAGCTAATTCATCAATTGATATTATAATACATGATACATATTAT Megalodontesc TCAGGACTTATTGGAACATCTATAAGAATATTAATTCGAATAGAACTTAGAGCCCCAGGATCATTAATTGGAAATGACCAAATCTATAATGTTATTGTTACAGCCCATGCTTTTGTAATAATTTTTTTTATAGTTATACCTTTAATAATTGGAGGATTT GGAAATTGATTAATTCCTCTAATACTAGGAGCCCCAGATATAGCTTTTCCCCGATTAAATAATTTAAGATTTTGATTACTTCCTCCTTCTTTAATATTTCTAATTTCTAGAAGAATAATAAATAAAGGAGTAGGAACAGGATGAACTGTATATCCCCCATTATCTTCTAATATTTCTCATTCTGGACCTGCTATTGATTTTGCAATTTTTTCTTTACATATTGCAGGAATCTCTTCAATCTTAGGGGCTATAAATTTCATTACTACTATTATTAATATACGTAGATGAGGATTTAAATTAGATAAAATTTCTTTATTTATCTGATCAGTAGGTCTAACCGCCCTTCTCCTGCTCTTATCATTACCTGTATTAGCAGGAGCTATTACTATACTTCTTACTGATCGAAATTTAAATACTTCATTTTTTGACCCAGCAGGGGGTGGTGACCCAATTCTTTATCAACACTTATTTTGATTTTTTGGTCATCCTGAAGTT TATATTTTAATTCTCCCAGGATTTGGTATAATTTCTCATATCATTTATCAAGAAAGATTTAAAAAAGAATCATTTGGTTCTTTAGGAATAATTTTTGCCATAATTTCTATT GGATTATTAGGATTTATTGTATGAGCTCATCATATATTTACTGTAGGTATAGATATTGATACACGAGCTTACTTTACATCAGCAACAATAATTATTGCTATTCCTACAGGAATTAAAGTATTTAGATGAATTGCCACATTCCACGGAAACAAATTTTATTTTTCTCCAACTATCCTA---TGAAGATTAGGATTTATTTTTTTATTTACTATTGGTGGATTAACTGGAATTATTTTATCTAATTCTTCTATAGATATTATCTTACATGATTCATATTAT Megalodontessk ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Cephus GCAGGAATACTAGGAACATCCCTAAGATTAATTATCCGAGCTGAATTAGGTACTCCAGGCCCTTTAATCAATAATGACCAAATTTATAATTCAATTGTAACTGCTCATGCTTTTATTATAATTTTTTTTATAATTATACCAGTTATAATTGGTGGGTTT GGAAACTGATTAATTCCTCTAATATTAGGAGCTCCTGATATAGCATTCCCCCGAATAAATAATATAAGATTTTGATTACTACCACCCTCATTAACATTATTATTAATTAGAAGATTAACTAATCAAGGAGTAGGAACAGGATGAACAGTATACCCCCCCTTATCAAACAATCTTTACCATAGAGGGCCAGCAGTTGACCTAGCTATTTTTTCCTTACATTTAGCAGGAATATCATCAATCATAGGTGCTATTAACTTCATTTCTACAATTATTAATATGCGACCGGTTGGATTAAAATTAGACCAAATATCTTTATTTAGATGAGCTGTAGGAATTACAGCAATCTTACTACTATTATCACTACCTGTATTAGCTGGTGCAATTACTATACTATTAACAGATCGAAACTTAAACACAACATTCTTTGACCCTGCTGGTGGAGGAGATCCTATTTTATATCAACATTTATTTTGATTTTTTGGGCACCCAGAAGTT TATATTTTAATTTTACCTGGATTTGGTATAATTTCTCATATAATTTTTTCAGAAACTGGTAAAAAAGAAACATTTGGAATATTAGGAATAATTTATGCAATAACAGCAATT GGGATTTTAGGTTTTGTTGTATGAGCTCACCATATATTCACAGTTGGAATAGACGTAGATACACGAGCTTACTTTACATCTGCAACATTAATTATTGCAGTACCTACTGGAATTAAAGTATTTAGATGATTAGCAACTTTACATGGATTTAAATTTAATAACTCTCCAACAATATTA---TGATCAATTGGATTTATTTTCTTATTTACCCTTGGTGGATTAACAGGAATTACTTTAGCCAATTCATCTATTGATATTATCTTACATGACACATATTAT Calameuta GCAGGAATACTTGGAACTTCCTTAAGATTAATTATTCGAGCAGAATTGGGAACACCAGGACCATTAATTAATAACGATCAAATTTATAATTCAATTGTAACAGCTCATGCATTTATTATAATTTTCTTCATAATTATGCCTATTATAATTGGAGGATTT GGAAATTGACTAATTCCATTAATATTAGGAGCCCCAGATATAGCTTTTCCCCGAATAAATAATATAAGATTTTGATTATTACCCCCCTCACTTACTCTCCTTTTAATCAGAAGATTAATTAATCAAGGTGCTGGAACAGGGTGAACTGTATACCCCCCCTTATCTAATAATCTATACCATAGAGGACCTTCAGTAGATTTAGCTATTTTTTCTCTTCATTTAGCTGGAATATCTTCAATTATAGGAGCTATTAATTTTATTTCAACAATTATTAATATACGACCTATAGGCCTATCAATTGACCAAATATCTTTATTTACATGAGCAGTTGGAATTACAGCAATTCTCCTACTTTTATCTCTTCCTGTTTTAGCAGGAGCTATCACCATACTTCTAACAGATCGAAATTTAAATACTACATTTTTTGATCCTGCAGGAGGGGGAGACCCTATTTTATATCAACACTTATTTTGATTCTTTGGCCATCCCGAAGTA TATATTCTAATTTTACCAGGATTTGGAATAATTTCTCATATAATTTTTTCTGAAACAGGAAAAAAAGAAACATTTGGAATATTAGGTATAATTTATGCAATATCTGCTATT GGAATTTTAGGATTTGTAGTATGAGCCCATCATATATTTACTGTAGGAATAGATGTAGATACCCGGGCATATTTCACATCCGCAACATTAATTATTGCTGTACCTACGGGAATTAAAGTATTTAGATGATTAGCAACATTACATGGATTTAAATTCAATAACTCTCCAACAATATGA---TGATCTATTGGATTTATTTTTTTATTTACATTAGGTGGATTAACAGGAATCACACTAGCCAATTCATCAATTGATATTATTCTTCACGATACATACTAT Hartigia GCAGGAATACTAGGAACTTCTCTTAGTATAATTATTCGAACAGAACTAGGAAACCCTGGACCACTTATTAATAATGATCAAATTTATAATTCTATCGTAACTGCTCACGCTTTTATTATAATTTTTTTTATAATTATACCTATTATAATTGGGGGGTTT GGAAATTGATTAGTGCCTTTAATACTAGGAGCTCCTGATATAGCATTCCCTCGATTAAATAATATAAGATTTTGACTCCTTCCCCCATCATTAACTTTATTATTATCAAGTAATTTAATTAATAATGGAGTTGGAACAGGATGAACTGTTTACCCTCCTTTATCAAGAAATTTATATCATTCAGGCCCTTCTGTTGATTTAGCTATTTTTGCACTTCACTTAGCAGGAATATCTTCGATTCTTGGTGCTATTAATTTTATTTCAACTATTATTAATATGCGCCCTGTAGGTTTATACCTAGATCAAATATCATTATTTACATGAGCTGTAGGAATTACAGCAATTTTACTCTTATTATCACTACCTGTTTTAGCCGGAGCCATTACTATACTTTTAACAGATCGAAACTTAAATACATCCTTTTTTGACCCTAGAGGGGGAGGAGATCCTATTTTATTCCAACATTTATTCTGATTTTTTGGTCACCCTGAAGTA TACATTTTAATTTTACCAGGATTCGGAATAATCTCTCATATAATTTTTTCAGAAACAGGAAAAAAAGAGACTTTTGGAATACTTGGTATAATTTATGCAATATCTGCTATT GGTGTCCTAGGTTTTGTTGTATGGGCTCACCATATATTTACAGTAGGAATAGACGTAGATACACGAGCTTATTTTACATCAGCAACCTTAATTATTGCTGTTCCAACGGGTATTAAAGTATTCAGATGATTAGCCACTCTCCATGGATTTAAATTTAATAATTCCCCTACAATACATTGATCAATTGGATTTATTTTTCTATTTACATTAGGAGGTCTAACAGGAGTAACCTTAGCAAATTCATCAATTGATATTATTCTTCATGATACATATTTAGTA Syntexis TGTGGATTTATTGGATCATCACTAAGAATTCTAATTCGAACAGAACTAGGGACCCCAAAAAGACTCTTTATAAATGATCAGCTTTATAACTCTTTTGTTACAGCTCATGCTTTTATCATAATCTTCTTTATAGTAATACCTATTATAATTGGTGGTTTT GGAAATTGATTAATTCCCTTAATGATTGGAGCCCCTGATATAGCATTTCCCCGAATAAATAATTTAAGATTCTGACTCCTTATTCCCTCAATCTCACTCTTATCATTCAGTCTTCTAGTAGGGACAGGAACAGGGACAGGATGAACAGTCTACCCCCCTCTATCCAGAAATTTATTTCACTCTTCTATATCAGTAGATATATCAATTTTCTCTCTTCATTTAGCAGGAATATCCTCAATTATAGGGGCAATTAATTTCATCTCAACTATCTACAATATACGACCCTTTTCTCTTCAATTTGATCAAATTCCTTTATTTTCATGAGCTGTTTTACTTACTGCTATTCTTCTTCTTCTCTCTCTCCCCGTTCTTGCTGGGGCAATTACCATACTACTAACTGATCGAAATTTAAATACTTCATTTTTTGACCCATCAGGAGGAGGGGATCCAATCCTTTATCAACATCTTTTTTGATTCTTTGGGCACCCTGAAGTC TATATTCTTATTCTACCTGGATTTGGAATAATCTCTCATATAATTTCCCAAGAAAGAGGAAAAAAAGAAACATTTGGTTCAATGGGAATAATTTACGCAATAATTTCAATT GGTTTCCTAGGTTTTATTGTTTGAGCACACCACATATACACTATTGGAATAGATGTCGATACTCGAGCTTATTTCACTTCTGCAACTATAATTATTGCAATTCCTACAGGAATTAAAATTTTTAGATGACTTGCAACATTCCACGGATCTAAATTCTATTTTTCTCCTAATCTAATT---TGAATTTTAGGATTTATTTTTCTTTTTACTTTAGGAGGATTAACTGGCGTAATTCTAGCTAATTCATCAATTGATATAATTTTACATGATTCATATTAT Sirex GCAGGAATWATTGGATCCTCTATAAGAATTATTATTCGAACAGAATTAATATCTTCTAATCCTTTTATTACTAATGATCATTTATTTAATTCAATTATCACAAGTCATGGATTAATTATAATTTTCTTCATAATTATACCAATTATAATAGGAGGATTT GGAAATTGATTACTCCCTTTAATTTTAAATTCACCTGATATAGCTTTTCCTCGAATAAATAATTTTAGATTCTGACTCCTTCCTCCTGCTTTATTACTTTTATTATTAAGAATACTAACTAGTTTAGGTCCAGGAACTGGTTGAACTCTTTATCCTCCTCTTTCTTCTTTTCCTTCTCACTCAAATATATCTGTTGATTTAAGAATTTTATCTATACATTTAGCAGGAATTTCATCAATTTTAGGATCAATTAATTTTATTTCAACTTTTATTAATATACATTGCTCTTCATTAAATATAGAACATTTACCTTTATTTACATGATCTATTAATATTACCGCAATCTTACTAGTAATTTCATTACCTGTTCTTGCTGGAGCAATCACTATACTTTTAACAGATCGAAATTTTAATACAACTTTTTTTGATCCTGCTGGAGGAGGAGATCCTATTTTATTTCAACATTTATTTTGATTTTTTGGTCANNNNNNNNNN NNNNNNNNNNNNNNNCCAGGATTTGGAATAATTTCTCATTTAATTATTAATGAAAGAAGAAAAAAAGAAACTTTTGGTACATTAAGTATAATTTACGCAATTATTTCAATT GGTCTACTAGGATTTATTGTATGAGCTCATCATATATTTACTGTTGGTTTAGATATTGATACTCGTGCTTACTTTACATCAGCTACAATAATTATTGCTATTCCAACAGGAATTAAAATTTTTAGATGAATTGCAACTCTTTATGGTTCCCCCCTAAAAATTACTCCAATTTCTTTA---TGAGTAATAGGATTCATTTTCCTTTTTACCATTGGAGGACTCTCAGGAATTATACTTTCAAACTCCTCTCTTGATGTAATACTTCATGATTCATACTAT Xeris GCCGGAATTATTGGAGCTTCAATAAGAATAATTATTCGAATAGAATTAATATCCCCCCACCCATTTATTTTTAATGATCATCTATTTAATTCAGTCATTACCTCTCATGGACTTTTAATAATTTTCTTTATAATCATACCTATCATAATAGGTGGATTT GGAAACTGACTATTTCCAATAATACTAAATTCACCTGACATAGCTTTTCCACGAATAAATAATTTTAGTTTTTGACTCCTCCCCCCTTCACTCCTTTTCCTTCTACTTAGAATAACTTCAGGAATAGGACCTGGTACAGGATGAACTTTATACCCCCCCCTATCTTCTTTTCCATCCCACTCTAATCTTTCAGTAGACTTCACTATTCTTTCTATACACCTTGCAGGTATCTCCTCAATTCTTGGATCAATTAATTTCATCTCAACTTTTATTAATATACATCCCCATTCATTAAATTCTGAACACATACCTCTCTTACCATGATCAATTAATGTCACAGCCATCCTACTTATTATTTCTCTACCTGTTCTTGCAGGTGCTATTACCATATTATTAACTGATCGAAATTTTAATACTACATTCTTTGACCCAGCTGGAGGAGGAGATCCAATTTTATTTCAACATTTATTTTGATTTTTCGGACACCCTGAAGTT TATATTTTAATTCTACCAGGATTTGGAATAATCTCCCATTTAATTATCAATGAAAGAAGAAAAAAAGAAACATTTGGTGCATTAAGAATAATTTATGCAATCTTATCTATT GGTCTTTTAGGATTTATTGTATGAGCCCATCACATATTTACTGTAGGTTTAGATGTAGATACTCGTGCATACTTTACTTCAGCAACAATAATTATTGCTATCCCCACAGGAATTAAAATCTTTAGATGAATTGCAACTATTTATTGTTCCCCCACAAAAATAACCCCAATTATCCTA---TGAACTTTAGGTTTTATTTTCCTATTTACTATAGGAGGTCTTTCAGGAATTATATTATCAAATTCATCTTTAGATGTAATTCTTCATGACTCCTATTAT Urocerus GCAGGAATTATTGGTTCATCAATAAGAATAATTATTCGTATTGAACTAATATCTCCTCAACCCTTTTTTATCAATGATCACCTTTTTAATTCAATTATTACTGCTCATGGACTATTAATAATTTTTTTCATAATTATACCTATTATAATAGGAGGATTT GGAAATTGATTAATTCCCCTATCTATAAATTCTCCTGATATAGCATTTCCTCGAATAAATAATTTAAGATTTTGACTCCTTCCACCTGCTTTATTTATTCTATTACTTAGAATACTTACAGGATTTGGACCAGGGACAGGATGAACTTTTTATCCCCCTCTATCCTCCTTTCCCTCTCATTCCAGTCTTGCAATTGATTTAAGTATTCTTTCTATACACTTAGCTGGACTATCTTCAATTATAGGATCAATTAATTTTATTACAACTTTTATTAATATACATTGTTCTTCATTAAACTTTGAACATCTATCTTTATTTTCATGATCAATTAATATTACTGCAATTCTTTTAATTATCTCCCTTCCTGTTCTAGCAGGAACAATCACTATACTTTTTACTGATCGAAATTTTAATTCAACATTTTTTGATCCTTCTGGAGGGGGAGACCCTATTTTATTTCAACATTTATTTTGATTCTTTGGTCATCCTGAAGTT TATATTTTAATTTTACCCGGATTTGGAATAATCTCTCATTTAATTATTAATGAAAGAGGAAAAAAAGAAACTTTTGGTGCTTTAAGAATAATTTATGCTATTATTTCAATT GGTCTCCTTGGATTTATTGTCTGAGCCCATCATATATTTACTGTAGGATTAGATATTGATACACGAGCCTACTTTACAGCTGCGACAATAATTATTGCAATTCCTACAGGAATTAAAATTTTTAGATGAATTGCAACTTTATACTCTTCACCATTAAAAATTTCCCCTATTTCTTTA---TGATCTGTTGGATTTATTTTTCTCTTTACAATTGGTGGTCTCTCAGGAATTATACTTTCTAACTCATCTCTAGATATTATTCTACATGATTCTTATTAT Tremex GCAGGAATTATTGGATCTTCCTTAAGTATAATTATTCGATTAGAATTAATAAATTCAATACCTTTTCTTTATAATGAACAATTATTTAACTCTATCATTACTGCTCATGGGATTTTAATAATTTTTTTTATAATTATACCCATAATAATAGGAGGATTT GGAAATTGACTTTTACCTATGTTCTTAAATTGTCCTGATATAGCTTTTCCTCGAATAAATAATTTTAGATTCTGACTACTCCCTCCTTCTATTTCTCTTTTACTCTTAGCTATATTATCTAGGTCAGGTCCAGGTACTGGATGAACTCTTTACCCTCCTTTGTCTTCAATTCCAGCACATAGAAATTTTTGTGTAGATTTAAGAATTTTATCTATACATTTAGCAGGAATTTCCTCTATTTTAAGATCAATTAATTTTATTACAACATTCACAAATATATATATTTACTCATTAAATATAGAAAATATTCCCTTGTTTTCTTGATCGGTAATAATTACTGCAATCCTTCTAATTATCTCTTTACCAGTTCTTGCTGGGGCCATTACAATACTTTTAACAGATCGAAACTTTAATACTTCATTTTTTGACCCTGCTGGAGGAGGAGATCCAATCCTCTTTCAACATTTATTTTGATTTTTCGGACATCCTGAAGTA TATATTTTAATTCTCCCAGGATTTGGAATAATTTCTCATTTAATTATCCAAGAAAGAGGAAAAAAAGAAACTTTTGGTACTTTAAGAATAATTTATGCTATTACCACAATT GGTTTACTTGGTTTTATTGTATGAGCTCACCATATATTTACAGTAGGATTAGATATTGATACTCGAGCTTATTTTACTTCAGCAACAATAATTATTGCCATTCCTACTGGAATTAAAATTTTTAGATGACTTGCTACATTATATAGGGTCCCTTTAAAAATTACTCCGATTTCCTTA---TGAATTATTGGTTTTATTTTCTTGTTTACTATTGGAGGACTTTCTGGAATCATACTTTCAAATTCAGCTCTTGATACAATACTTCATGATTCCTATTAT Xiphydria TCAGGAATAATAGGAACATCCTTAAGAATTTTAATTCGAAATGAATTAGGATCTCCAAGATCTATAATTAATAATGATCAAATTTATAACTCAATTGTTACTGCACATGCATTTATTATAATTTTTTTTATAGCTATACCTATTATAATAGGAGGATTT GGAAACTGATTAGTTCCACTAATATTAGGAGCTCCTGATATAGCTTTTCCACGAATAAATAATATAAGATTCTGATTATTACCACCTTCTTTAATTTTATTAATTTCAAGAAATTTATCAAATTCAGGTGTGGGGTGTGGTTGAACAATGTATCCTCCTTTATCTTCAAATTTAAGACATCTAGGACCAGCAATAGATTTAACAATTTTTTCACTTCATATTGCAGGAATTTCTTCAATTATAGGAGCTATTAATTTTATCTCTACTATTATTAATATACGACCCATAAAAATTTCATTTGATCGTATTCCTTTATTTTGTTGATCAATTATAATTACTGCAATTCTTTTACTTCTTTCTCTTCCTGTACTAGCAGGATCAATTACTATATTATTAACTGATCGAAATTTAAATACATCATTTTTTGATCCTGCAGGAGGAGGGGATCCAATTCTTTTTCAACATTTATTTTGATTTTTTGGTCATCCAGAAGTT TATATTTTAATTTTACCCGGATTTGGTATAATTTCTCATATAATTTCTCAAGAAAGAGGTAAAAAAGAAACTTTTAGATCTTTAGGAATAATTTATGCTATATTAACTATT GGATTTTTAGGATTTATTGTTTGAGCTCATCATATATTTACTGTAGGAATAGATGTTGATACCCGAGCTTATTTTACTTCAGCAACATTAATTATTGCTATTCCCACTGGGATCAAAGTTTTTAGATGATTAGCTACATTAAATGGAACAAAAATTAAATTTTCTCCATTAATAATG---TGAAGCTTAGGTTTTATTTTTCTTTTTACTATAGGTGGTTTAACAGGTATTATTCTATCTAATTCTTCAATTGATATTATCTTACATGATACATATTAT Orussus GCTGGTATAGTTGGATCTTCTTTAAGTATAATTATTCGTATAGAATTAGGTACCCCTGGTCCTCTTATTGGTAATGATCAAATTTATAATTCTATTGTAACCTCTCATGCATTTGTTATAATTTTTTTTATAGTTATACCAATTCTTATTGGAGGATTT GGTAATTGATTAATTCCTCTTATACTAGGAGCTCCAGATATAGCATTTCCCCGAATAAATAATATAAGTTTCTGACTTCTTCCTCCTGCTCTTATTATCTTATCATCAAGTATATTCACTAGTAAAGGTGTTGGAACAGGATGAACTGTTTACCCTCCACTTTCTTCA---TCAGGCCATGTAGGTCTAGCAGTTGATTTATCTATTTTTTCCCTCCACATTGCTGGTATATCTTCTATTATAGGAGCCATTAACTTTATTTCTACAATTATTAATATACGTAATAAAAGTCTTACTATAGATAAAATTTCTCTATTATCGTGATCTACTAACATTACTGCTATTCTTCTACTTTTATCTCTTCCAGTGCTAGCAGGAGCTATCACTATACTTTTAACTGACCGTAATTTTAATACATCTTTCTTTGATCCTGCTGGTGGAGGAGACCCAATTTTATACCAACATCTATTTTGATTTTTTGGTCATCCAGAAGTT TATATTCTTATTCTACCTGGATTTGGAATAATCTCTCATATAATCACTAAAGAAGGTGGTAAAAAAGAAACCTTTGGAACTTTAGGAATAATTTATGCTATACTAGCAATT GGATTCTTAGGGTTTGTAGTATGGGCTCATCATATATTTACTGTAGGAATAGATGTAGATACTCGAGCTTACTTCACTTCAGCTACTATAATTATTGCTGTACCAACAGGAATTAAAGTATTTAGCTGACTGGCTACTATAAATGGTATAAAATTAGAATTAAATCTTAATAATATA---TGATCCTTAGGATTTGTATTTCTTTTTACTGTTGGAGGTCTTACAGGTGTTATATTATCAAATTCATCTATTGATATTATTTTACATGACACTTATTAT StephanidaeA TCAGGTATCCTTGGATCATCAATAAGTATAATTATTCGTATAGAACTCAGTATACCAGGAATTATAATTGGAAATGATCAAATTTATAATTCAATTGTAACAGCTCATGCTTTTATTATAATTTTTTTTATAATTATACCAATTATAATTGGAGGATTT GGAAATTGACTAATTCCTATAATAATTGGAGTTCCTGATATAGCATTTCCTCGAATAAATAATATAAGATTCTGATTACTTCCTCCTTCATTAATTTTATTAATTTCAAGAATAATTACAGAAATTGGAATTGGAACAGGATGAACTATTTATCCCCCATTATCATCTAATTTAGGTCACAATAGTAAATCAGTAGATATAACTATTTTTTCTCTTCACATTGCAGGAATTTCATCAATTATAGGAGCAATTAACTTTATTTCAACAATTTTAAATATAATCCCAAATGGAATAATAAAAAAAAATATTCCACTAATACCCTGAGCCACAATAATTACAGCATTTCTTCTTCTTCTATCTTTACCAGTTCTTGCTGGAGCAATTACTATACTTTTAACAGATCGAAATATTAATACATCATTTTTTGATCCAGCAGGAGGTGGAGACCCAATCTTATTTCAACATTTATTTTGATTTTTTGGACATCCTGAAGTT TATATTTTAATTCTTCCAGGATTTGGAATAATCTCCCATATTATTACCAATGAAAGAGGAAAAAAAGAAACATTTGGAAGAACTGGAATAATTTATGCTATAATAACCATT GGAATACTAGGATTTATTGTTTGAGCTCATCATATATTTACTGTTGGTCTTGATGTAGATACACGAGCCTATTTTACTTCAGCAACTATAATTATTGCAATTCCAACAGGAATTAAAATCTTTAGATGACTAGCTTCTTTTTATGGAAGAAAAATATATTTATCACCAACAATTTTA---TGATCAATAGGATTTATTTTTCTATTTACCATTGGTGGATTAACTGGAATTATATTATCAAATTCATCCATTGATCTAATTATACATGATACATATTAT StephanidaeB TCAGGACTATTTGGATCTTCAATAAGATTAATTATTCGAATAGAATTAAGTATACCAGGACAAATAATTGGTAATGACCAAATTTATAATTCAATTGTTACAGCCCATGCATTTGTAATAATTTTTTTTATAATTATACCATTAATAATTGGGGGATTT GGTAATTGATTAATTCCATTAATATTAGGGGCCCCAGATATAGCTTTCCCTCGAATAAATAATATAAGATTTTGATTACTACCCCCATCAATTATACTATTAATTTCAAGAATAATAACTGATTCAGGTGCTGGAACAGGATGAACTATTTACCCCCCCCTATCCTCTAATATAGGACACAATAGAAAAACAATTGACTTAACAATTTTTTCATTACACCTTGCTGGAATCTCTTCAATCATAGGAGCAATTAATTTTATTTCCACAATTTTAAATATAACCCCCATAGGAATAAAATTAAAAAATATAACATTATTTACATGAGCAACCTTTATTACTGCTATTTTACTACTATTATCACTACCAGTTTTAGCTGGGGCTATTACTATACTATTAACCGATCGAAATATCAATACATCCTTTTTTGACCCCTCAGGAGGAGGAGACCCAATTTTATATCAACATTTATTTTGATTTTTTGGACATCCAGAAGTA TATATCTTAATTCTTCCTGGGTTTGGAATAATTTCACATATTATTACTAATGAAAGAGGAAAAAAAGMAACATTTGGARCTATTGGAATAATTTATGCAATAACTGCTATT GGAATTTTAGGATTTATTGTTTGAGCTCATCATATGTTTACAGTTGGTTTAGATGTTGATACACGAGCATATTTTACTTCAGCTACAATAATTATTGCTATTCCTACTGGAATTAAAATTTTTAGTTGACTAGCTTCATTTTATGGTAGAAAATTAAATTTTTCCCCACCTATTTTA---TGATCAATAGGATTTATTTTTCTATTTACATTAGGAGGTTTAACAGGAATTATATTGTCTAATTCATCTATTGATCTTGTTTTACATGACACATATTAC Megalyra TCTGGGCTAGTTGGGTCTTCAATAAGTATATTAATTCGAATTGAATTAATGACACCTGGATCTTTTATAGGGAATGATCAGTTATATAATTCTTTTGTTACTGGTCATGCATTTATTATAATTTTTTTTATAGTAATACCTTTTATAATAGGGGGGTTT GGAAATTTTTTAGTTCCTTTAATATTAGGTACTCCAGATATATCTTACCCTCGAATAAATAATATAAGATTTTGATTATTAATTCCTTCAATAGTATTTTTAATTATAAGAATATTTATTGGAGAGGGGGTAGGGACAGGTTGAACGGTTTATCCCCCTTTATCATCAATTAGTTATCATTCAAGTTTAAGAGTTGATTTAGCAATTTTTTCATTACATATTGCAGGGATATCTTCTATTTTAGGAGCTATTAATTTTATAAGAACAATTATTAATATAAA------AAGATTTAAGTTTAGTATATTATCTTTATTTGTATGATCAATTTTTATTACTGCTATTTTATTATTATTGTCATTACCTGTCTTAGCTGGGGCAATTACAATATTATTAACTGATCGAAATTTAAATTCTACATTTTTTGACCCTTCAGGGGGAGGGGATCCAATTTTATATCAGCATTTATTTTGATTTTTTGGTCATCCAGAAGTT TATATTTTAATTTTACCTGGATTTGGAATTTTTTCTTATATTATTAGGTCAGAAAGAGGGAAAAAGGAATCATTTGGAACTTTGGGTATAATTTATGCAATATGTTCAATT GGGTTATTAGGATTTTTAGTATGAGCTCATCATATATTTACTGTTGGGTTAGATGTTGATACACGGGCGTATTTTACTGCAGCAACGATATTAATTGCAATTCCTACAGGGATTAAAGTTTTTACTTGATTAATAACTTTAATAGGGTATCGAGAATGTAATTATTCTCCTTCATTATATTGAGCTTTAGGATTTATTTTTTTATTTACATTAGGAGGGTTAACTGGGGTTTTATTGTCTAATTCTTCAATTGATTTGGTTCTTCATGATACATAT??? Trigonalidae TCTGGAATAATTGGAGTTGGATTAAGATTAATTATTCGGTTAGAATTAGGAATTGTTGGTAAGTTATTAGGGAATGATCAAATTTATAATAGGGTTGTTACTGCTCATGCTTTTATTATAATTTTTTTTATAGTTATACCTTTTATAATTGGAGGATTT GGTAATTGATTAATTCCTTTAATATTAGGAGCTCCTGACATATCTTTTCCTCGAATGAATAATATGAGATTTTGATTATTACCTCCATCAATTTTTTTACTTTTAATTAGAGGGTTTATTAATATAGGTGTAGGAACAGGATGAACTGTTTATCCACCTTTATCTAATATATTATATCATAGGGGGGTATCAGTTGATTTATGTATTTTTTCTTTACATATAGCTGGAATTTCTTCAATTATAGGAGCTATTAATTTTATTACAACAATTTTAAGAATAAAAAATATTAAAATAAATTTTTCACAAATTAACTTATTATCATGATCTGTTTTAATTACAGCTGTTTTATTATTGTTATCATTACCGGTTTTAGCAGGAGCTATCACTATATTATTATTTGATCGTAATTTTAATACAACATTTTTTGATCCTTCTGGAGGGGGAGATCCAATTTTATATCAACATTTATTT------------------------ -------------------------------TTTCACATATAATTATTAATGAKAGAGGAAAAAGAGAAGTATTTGGTACATTAGGAATAATTTATGCTATAATTTCTATT GGAATTTTAGGATTTATTGTTTGAGCTCATCATATATTTACTGTTGGATTAGATGTTGATACACGGGCATATTTTACTTCTGCAACAATAATTATTGCTATTCCTACAGGTGTAAAAGTTTTTAGTTGACTTTCAACATTATATGGAGTAAAATTACGATATTCAATTGTTATATTATGATCAATTGGATTTATTTTTTTATTTACATTGGGAGGATTAACAGGAATTTTACTTTCAAATTCATCTATTGATTTAATTATACATGATACATATTA???? Chalcidoidea --------------------------------------------------------------------------------------------------------------------------------------------------------------- -------------------------------------------------------------------GATTTTGATTATTACCTCCTAGATTATTATTGTTATTATTAAGAATATTTTTAGGAGCAGGAACTGGAACAGGATGAACAGTTTATCCTCCTTTATCTACA---GACGGTCATTCAGGTCCATCTGTAGATTTATCAATTTTTTCTTTGCATATAGCTGGAATAAGATCTATTATAGGATCAATTAATTTTATTTCTACAATTTGTAATATAAA------GATTTATAAAATAGAAATAATTTCTTTATTTTCTTGATCAATATTATTAACAGCAATTTTATTATTATTATCTTTACCAGTATTAGCAGGAGCCATTACTATATTATTATTTGATCGAAATTTAAATACATCTTTTTTTGATCCAATAGGAGGGGGTGATCCAATTTTGTATCAACATTTATTTTGATTTTTTGGTCACCCAGAAGTT TATATTTTAATTTTACCAGGATTTGGTTTAATTTCTCATATAATTTGTATAAAAAATTTTAAAAAAGAAACTTTTGGAATTATAGGTATAATTTATGCAATAATTTCAATT GGAATTTTAGGATTTATTGTTTGAGCTCATCATATATTTACTGTCGGAATAGATGTAGATACTCGGGCATATTTCACTTCTGCTACTATAATTATTGCTATCCCTACTGGAATTAAAGTTTTTAGATGATTAGCAACAATATTAGGAAATAAAATAGACTTATCTATTACTAATATA---TGATTATTAGGATTTATTTTTTTATTTACAATTGGAGGTTTAACTGGAATTGTTTTATCAAATTCATCAATTGATATTGTTTTACATGATACTTATTAT Evanioidea -CAGGAACCTTAGGCTCCTCTCTTAGTATAATTATTCGCCTTGAACTTGCAACCCCCCATCGATTTATCGGAAACGAACAAGTCTATAATTCTATTGTTACTATACATGCCTTTGTTATAATTTTTTTTATAGTTATACCCTTAATAATTGGAGGATTC GGAAACTGAATAATTCCACTTATAATTGGAGCCCCTGATCTCGCTTTCCCCCGACTAAATAATATAAGTTTCTGACTCCTACCTCCCTCACTTCTTCTTCTAGTAGGAGCAGGATACCTTAACTCAGGAAGAGGAACTGGATGAACTATCTATCCACCCCTTTCTGCTGCTTCATTCCACAGAGGTTATTCTGTTGATCTCTCTCTTTTTTCACTACATTTGTCTGGAATAAGATCAATTTTAGGTTCTATTAATTTTCTTGTAAGTATCTTTAACATAAAACTTAAACCTCTTAAATGAAACCATCTTACCCTTTTCACTTGATCCATTATTTTAACAACAATTCTTCTTCTTCTTTCTATCCCCGTTCTAGCAGGAGCTATTACAATACTTATTATAGACCGAAACACAAATACTTCATTCTTCGATCCATTCGGGGGTGGAGATCCTGTTCTATTCCAACACTTATTCTGATTTTTTGGTCACCC------- --------------------------------------------------------------------------------------------------------------- --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- Ichneumonidae TCAGGAATAATCGGAACTTCATTAAGATTAATTATTCGTTTAGAACTTGGAAATCCAGGGTTTTTAATTAATAATGATCAAATTTACAATACAATAGTTACAGCACACGCTTTTATTATAATTTTTTTTTTTGTAATACCTATTATAATTGGAGGATTT GGAAACTGACTCATTCCTTTAATATTAGGAAGTCCAGATATAGCCTTTCCACGTATAAATAATTTAAGATTTTGACTTTTACCACCATCACTTATATTATTAATTGCAAGTAGAATTACTAATCAAGGAACAGGAACAGGATGAACGGTTTATCCTCCATTATCATTAAATATAAATCATAATGGATCATCAGTTGATTTATCAATTTTTTCTCTTCACATAGCTGGGATATCCTCAATTTTAGGAGCAATTAATTTTATTTCAACAATTCTATTTATAATCCCTTTAAAAATAAAACTAGAACAAATACCTTTATTCTCATGATCAATTTTAATTACAACAATTTTATTACTTTTAGCAGTTCCAGTATTAGCTGGAGCAATCACTATACTTCTATCTGACCGTAATTTAAATACATCATTCTTTGATCCATCCGGCGGAGGTGACCCTATTCTTTATCAACATTTATTCTGATTTTTTGGCCACCCTGAAGTT TATATTCTCATTTTACCAGGATTTGGAATAATTTCACATATAATCTTTAATAAAAGAAAAAAAAAAGAAACATTTGGATGTCTAAGAATAATTTATGCCATATTAACCATT GGATTTTTAGGATTTATTGTATGAGCTCATCATATATTTACTATTGGTATAGATGTTGATACACGAGCTTACTTTACATCAGCTACCATAATCATTGCAATTCCCACTGGAATTAAAGTATTTAGTTGATTAGCAACTATTCATGGAACAAAAATAAATTCTAACCCTATTAATTTA---TGATCAATTGGATTTATTTTCTTATTTACAATTGGAGGATTAACTGGTGTTATTCTTGCAAATTCATCAATTGACATTGTTCTACATGATACATATTAT Cynipoidea TCAGGATTAATTGGAGCTAGTATAAGATTAATTATCCGWATAGAATTAGGGATACCTGGGCAATTAATTAATAATGATCAAATTTATAATTCTATTGTAACTATTCATGCTTTTATTATAATTTTTTTTATAGTKATACCTATTATAGTAGGAGGATTT GGRAATTATTTAATTCCTTTAATATTAGTTGTTCCAGATATGGCTTTCCCTCGATTAAATAATATAAGATTTTGATTATTARTCCCTGCTCAGGTATTTTTAATMGCTGGGTTAWTAATTGATCAAGGAGCMGGTACTGGATGAACTGTTTATCCTCCTTTATCTTCWTTATTRGGACATTCTAGAATTTCAGTTGATTTAACTATTTTTTCTTTACATTTAAGAGGAATCTCTTCAATTTTAGGATCTATTAATTTTATTAGAACAMTTTATAAKATACG---TYW---AATAAGAATAGATMAAATYACTCTATWTGATCGATCAATGTTATWAACARCTRTWCTTYTATTATTATMTTTACCTGTATTAGCAGGAGGAATCACTATRTTAYTATWKGAKSGRRAKTTAAATACTTCTTTTTTTGATCCAATAGGAGGAGGGGATCCAATCTTATACCAACATTTATTTTGATTTTTTGGACATCCTGAAGTT TATATTTTAATTTTACCTGGATTTGGAATAATTTCACATATTATTTACACTGAATGTGGAAAAAAAAGAACTTTTGGAGCTCTAGGAATAATATATGCTATAATTTCAATT GGAATATTAGGATTTATTGTATGAGGCCATCATATATTTACAGTAGGAATAGATGTAGATACTCGAGCTTATTATACTTCAGTAACTATAGTTATTGCAATTCCTACAGGAATCAAAATTTTTAGTTGATTAGCTAGAATATATGGATCTAAAATTAATTACACTCCTTCAATAATT---TGATCTATTGGATTTATTTTTTTATTTTCTATTGGAGGAATAACAGGAGTAACTTTATCTAATTCTTCAATT??????????????????????????? ApoideaA AGAGGAATAATTGGATCAGCATTAAGAATATTAATTCGTATAGAATTAGGAACACCAAGAAAATTAATTGGAAACGATCAAATTTATAATAGAATTGTTACAGCTCACGCTTTTGTAATAATTTTTTTTATAGTAATACCATTTATAATCGGAGGATTT GGAAATTGATTAGTACCTATGATATTAGGAGCCCCAGATATAGCCTATCCACGAATAAATAATATAAGATTTTGATTATTACCACCATCATTATTTTTATTAATTACAAGAAATACAATTAACTCAGGAACAGGAACAGGATGAACAGTCTATCCACCTCTATCATCAAATATATTTCATAGAGGAGCATGTGTAGATTATAGAATTTTTTCATTACATATAGCAGGGATATCTTCAATTATAGGAGCTATTAATTTCATTACAACAATTATTAATATAAAAAATATATATATACCTATAAACTTAATTAATCTTTTTAGATGATCTATTCTCATTACAGCATTTTTATTAGTTATTTCTCTACCAGTTCTAGCAGGAGCCATCACTATATTATTAACAGATCGTAATTTAAATACTTCATTTTTCGACCCAGCTGGAGGAGGTGATCCAATCTTATATCAACACTTATTTTGATTTTTTGGTCATCCAGAAGTT TACATTCTAATTTTACCAGGATTTGGATTAATTTCTCAAATTATTATTAACGAAAGAGGAAAAAAAGAATCATTTGGAACTATTGGTATAATTTATGCTATAATAACTATT GGACTATTAGGATTTATTGTTTGAGCCCATCATATATTTACTGTAGGATTAGATGTAGATACTCGAGCTTATTTCACTTCAGCTACTATAATTATTGCAATTCCAACAGGTATTAAAGTTTTCAGATGAATAGCAACTTTTTCAGGTTCTAAAATCAAAATAAATCCATCATCTCTT---TGATCAATAGGATTTATTATACTTTTTACTATTGGAGGTCTAACAGGAATTATTTTATCAAATTCATCAATCGATATTGTTCTTCATGATACTTATTAT ApoideaB GCAGGTATATATGGAGCTTCATTAAGAATAATTATTCGAATAGAACTAGGTACACCTGGAAGATTAATTGGAAATGATCAAATTTACAATAGAATTGTTACAGCCCACGCATTTATTATAATTTTTTTTATAATTATACCATTTATAATTGGAGGTTTC GGAAATTGATTGATTCCATTAATACTAGGAGCACCAGACATAGCTTATCCTCGAATAAATAATATAAGATTTTGATTATTACCCCCATCCTTATTCTTATTAATCTCTAGAAGAATTGTGGATTCAGGAACAGGGACAGGATGGACAGTCTATCCTCCTTTATCTTCAAATTTATTCCATAATGGAATATCTGTAGACCTAAGAATCTTATCCCTTCATTTAGCTGGAGCTTCATCTTTAATAGGTACAATTAATTTTATTGCAACAATTATAAATATAAAAAATTCTAACTTATCAATAGAAAAAATTCCTTTATTTTCTTGATCAATTTTAATCACTACATTTTTATTACTTTTATCATTACCAGTACTAGCCGGAGCAATTACTATACTCTTAACTGATCGAAATTTAAATACATCATTTTTTGACCCTACGGGAGGAGGAGATCCAATCTTATATCAACATTTATTCTGATTTTTTGGACATCCAGAAGTT TATATTTTAATTTTACCTGGATTTGGATTAATCTCCCAAATAATTACTAATGAAAGAGGAAAAAAAGAATCATTTGGAACTATGGGAATAATTTATGCCATACTAACTATT GGAATTTTAGGTTTTATTGTATGGGCCCATCATATATTTACTGTAGGATTAGATGTAGATACTCGAGCCTATTTTACCTCAGCTACTATAATTATTGCAGTACCTACTGGAATTAAAGTATTTAGATGATTAGCCTCATTAAGAGGAATAGTCTTTAATCCATCCCCATCTATTCTA---TGATCCATAGGATTCATTTTACTCTTTACAATTGGTGGATTAACAGGAATTATTTTATCAAACTCTTCAATTGATATTATCTTACACGACACTTATTAT ApoideaC TCAGGAATAATTGGAACTTCTATGAGATTTATTATTCGTATAGAATTAAGTAATCCTGGTGAATGAATTATAAATGATCAAATTTATAATTCNTTAGTNACNGCTCATGCTTTTTTAATAATTTTTTTTATAGTAATACCTTTTATAATTGGGGGATTT GGTAATTGATTANTTCCTTTAATATTAGGATGTCCTGATATAGCTTTCCCTCGAATAAACAATATTAGATTTTGATTATTACCCCCATCTTTAATTTTATTATTATTAAGAAATTTATTTAATATTACTCCNGGAACAGGATGAACAGTTTATCCTCCATTATCTTCATCAATATTTCATAGATCTCCTTCAGTTGATTTAGCAATTTTTTCNCTTCATATATCTGGTATTTCTTCTATTATAGGAGCTATAAATTTTATAGTTACAATTATATTAATAAAAAATATTTCAATAAATTATGATCAAATTAATTTATTTTCATGATCAATTTTCATTACAGCAATTTTGTTATTATTATCATTACCTGTTTTAGCTGGTGCTATTACTATATTATTATTTGATCGAAATTTTAATACTTCATTTTTTGATCCTATAGGAGGAGGAGATCCAATTTTATATCAACATTTATTTTGATTTTTTGGTCATCCTGAAGTT TATATTTTAATTTTACCNGGNTTTGGGTTAATTTCACAAATTATTATAAATGAAAGAGGGAAAAAAGAGATTTTTGGTAATATTGGAATAATTTATGCAATAATAGGAATT GGATTATTAGGATTTATTGTATGAGCTCATCATATATTTACTGTGGGGTTAGATGTTGATACTCGAGCTTATTTTACTTCAGCAACTATAATTATTGCAGTTCCAACAGGAATTAAAGTATTTAGATGATTAACTACTTATCATGGAGTTAAATTAGAATTTTTTAATAATTCAATTATTTGAAGATTTGGTTTTATTTTTTTATTTACTTTAGGGGGTTTAACTGGAATTATATTATCAAATTCTTCAATTGATATTATTATACATGATACTTATTAT Vespidae ???????TAATTGGAAGATCATTAAGATTAATTATTCGTTTAGAATTAGGTACACCTTTATCAATTATTAATAATGATCAAATCTATAATTCAATTATTACAGCTCACGCATTAATTATAATTTTTTTCATAGTTATACCATTTATAATTGGAGGATTT GGAAATTGATTAATTCCCTTAATATTAGGTGCCCCAGATATAGCGTTCCCACGAATAAATAATATAAGATTTTGATTATTACCACCATCATTATTATTATTAATTATTAGAAATATTAATGATATAGGAGTAGGAACTGGATGAACTTTATATCCACCTTTATCATCTAATATAGGACACAATTCATCATCAGTTGATTTTGCTATTTTCTCTCTTCACATCGCAGGAATTTCATCAATTATAGGAGCTATTAATTTCATCGTAACAATCTTAAATATACATATTAAAACTCATTCATTAAATTTTTTACCTATATTTACATGATCAGTTTTAATTACTGCAATTTTACTTTTATTATCTTTACCTGTCCTAGCCGGAGCAATTACCATACTATTAACTGATCGGAATATTAATACATCTTTTTTTGATCCATTAGGAGGTGGAGATCCAATTTTATTCCAACATTTATTTTGATTTTTTGGACATCCAGAAGTA TATATTTTAATTTTACCAGGATTTGGAATTATTTCACAAATTATTACCAATGAAACAGGAAAAAAAGAAATTTTTGGATCTTTAGGAATGATTTACGCTATAGTATCAATT GGAATTTTAGGATTTATTGTATGAGCTCATCATATATTTACAGTTGGATTAGATATTGATACACGTGCATATTTTACCTCAGCAACAATAATTATTGCTATTCCAACAGGAATTAAAGTTTTTAGTTGAATAGCTACAATTTATGGATCAAAATTAAATTTTTCACCAGCAATAATT---TGATCTATAGGATTTATTTTCCTCTTCACAATAGGAGGTCTTACAGGAATTATTTTATCAAATTCATCTCTTGATATTATTCTTCATGATACATATTAT Triassoxyela ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Asioxyela ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Nigrimonticola ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Gigantoxyelinae ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Spathoxyela ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Xyela_mesozoica ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Angaridyela ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Xyelotoma ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Undatoma ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Dahuratoma ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Mesolyda ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Turgidontes ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Aulidontes ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? 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Sogutia ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Xyelula ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Brigittepterus ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Grimmaratavites ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????? ??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? [EF1a F2 3269-4360, 1092 chars] Orthoptera GCCGAACGTGAACGTGGTATTACTATTGATATTGCTCTGTGGAAATTCGAAACTAGCAAATACTATGTTACTATCATTGATGCCCCTGGTCACAGAGATTTCATTAAGAACATGATCACTGGCACTTCCCAGGCTGACTGTGCGGTGTTGATTGTTGCTGCGGGTACTGGTGAGTTTGAAGCCGGTATTTCCAAGAACGGACAGACCCGTGAACACGCTTTGCTGGCTTTCACACTTGGTGTGAAGCAGTTGATTGTGGGTGTTAATAAGATGGATTCTACAGAGCCTCCCTACAGCGAGGCTCGTTTTGAGGAAATCAAGAAGGAAGTTAGCAATTATATTAAGAAGATTGGTTACAACCCTGCTGCTGTTGCTTTTGTGCCCATTTCTGGGTGGCATGGTGACAACATGTTGGAGCACTCCGACAAAATGGGATGGTTTAAGGGTTGGAATATTGAACGCAAGGAAGGAAAGGCTGAAGGAAAGACTCTTATTGAAGCTTTGGATGCCATCCTTCCTCCTTCTAGGCCAACTGAGAAGCCCCTTAGGCTTCCTCTTCAGGATGTATACAAAATTGGTGGTATTGGAACAGTGCCCGTGGGCCGTGTTGAAACTGGTGTTCTGAAGCCAGGTATGGTGGTGACTTTTGCCCCAGCCAATCTAACCACTGAAGTGAAGTCTGTGGAGATGCACCATGAAGCTCTCCAAGAAGCTGTTCCTGGAGACAACGTTGGTTTCAACGTCAAGAACGTCTCTGTTAAGGAATTGCGTCGTGGTTATGTTGCTGGAGATTCTAAAAACAATCCTCCTAAGAGTGCTGCTGATTTCCTTGCTCAGGTCATTGTTCTTAACCATCCTGGTCAAATTTCCAATGGTTATACTCCAGTTTTGGATTGCCACACTGCTCATATTGCCTGCAAATTTGCAGAAATTAAGGAAAAGTGCGACCGTCGTACTGGCAAGACTACTGAAGATAATCCAAAGTCTATCAAATCTGGTGATGCTGCCATTGTCAACTTGGTTCCCACAAAACCCATGTGTGTGGAATCCTTCCAAGAATTTCCTCCTCTGGGACGGTTTGCTGTGCGTGAT Paraneoptera GCTGAACGTGAGCGTGGTATTACTATCGATATTGCCCTTTGGAAATTTGAAACTGCCAAGTACTATGTTACCATCATTGATGCTCCCGGACACAGAGATTTCATCAAGAACATGATCACAGGAACTTCTCAGGCTGATTGTGCTGTACTGATTGTGGCTGCTGGTACCGGTGAGTTTGAAGCTGGCATTTCCAAGAACGGTCAGACTCGTGAACATGCTCTCCTTGCTTTTACTTTGGGTGTGAAACAACTGATTGTTGGAGTTAACAAGATGGATTCTACTGAACCCGCTTACAGTGAAAGCCGATTTGAGGAAATCAAGAAAGAAGTCAGTAACTACATCAAGAAAATCGGTTACAACCCAGCAGCTGTTGCTTTCGTGCCCATTTCTGGATGGCACGGAGATAATATGCTGGAGCCCTCTGACAAGATGCCATGGTTCAAGGGGTGGGCCATCGAGCGTAAGGAAGGCAAGGCTGAGGGCAAGTGCTTGATTGAAGCTTTAGATGCCATTCTTCCTCCATCAAGACCTACTGAGAAGCCCCTCAGGCTGCCTCTTCAGGATGTATACAAGATTGGTGGTATTGGAACAGTGCCTGTAGGCCGAGTAGAGACTGGAGTGCTTAAACCTGGTATGGTGGTCACTTTCGCCCCTGCTAATCTGACTACTGAAGTCAAGTCTGTTGAAATGCACCACGAAGCTCTCCAGGAGGCCGTCCCTGGAGACAATGTGGGCTTCAACGTTAAGAACGTTTCTGTCAAAGAATTGAGGCGAGGTTTTGTTGCTGGGGACTCCAAATCTAACCCACCCAAGGCTGCCGCTGACTTCACTGCACAGGTTATTGTTCTGAACCATCCTGGTCAAATCTCCAACGGATATACTCCTGTCCTCGACTGTCACACTGCTCACATTGCGTGCAAATTCGCAGAGATCAAAGAAAAGTGCGACCGTCGTACTGGCAAGACTACTGAAGATAATCCAAAATCCATAAAGTCTGGTGATGCGGCTATCATCACTTTGGTTCCTTCCAAACCCATGTGTGTCGAGAGTTTCCAGGAGTTCCCACCTCTGGGACGTTTCGCTGTGCGTGAC Chrysopidae GCTGAGCGTGARCGTGGTATCACGATTGAWATTGCGTTATGGAAATTCGAAACTGCTAAATATTACGTGACTATCATCGATGCCCCAGGACATCGTGATTTTATTAAAAATATGATTACGGGTACATCACAAGCCGATTGTGCTGTTCTAATTGTTGCTGCCGGTACAGGAGAATTTGAGGCTGGTATTTCAAAAAATGGYCAAACTCGTGAACATGCCCTACTTGCGTTTACACTTGGTGTTAAACAATTGATTGTTGGAGTTAATAAAATGGATTCGACAGAGCCTNCATATAGYGAGGCACGCTATGAAGAAATCAAGAAAGAAGTATCTTCGTATATTAAAAAAATTGGATATAATCCAGCTGCTGTTGCTTTTGTGCCAATTTCGGGTTGGCATGGTGACAATATGCTTGAACCATCTGAAAAGATGCCATGGTTTAAAGGATGGACAATTGATCGAAAAGAAGGTAAAGCGGAAGGGAAATGTCTTATCGAAGCTTTAGATGCAATCATGCCACCGAGTCGTCCTACTGAGAAACCATTACGACTNCCATTACAGGATGTATACAAGATTGGTGGTATAGGAACAGTTCCTGTTGGTCGTGTGGAAACAGGAATTTTAAAACCGGGTTGTATTGTTGTATTCGCTCCAGCAAATTTAACAACTGAAGTCAAATCAGTCGAAATGCATCATGAAGCTTTACAAGAAGCCTTACCAGGTGATAATGTTGGATTTAACGTGAAGAACGTTTCTGTTAAAGAGCTACGACGTGGATATGTAGCTGGTGATTCGAAAAATAATCCTCCACGTGGTGCAGCTGATTTTACTGCCCAAGTAATTGTATTAAACCATCCCGGACAAATTAGTAATGGTTATACACCAGTTTTAGATTGCCACACAGCTCATATTGCTTGTAAATTTTCTGAAATTAAAGAAAAAGTAGATCGTCGTACTGGAAAAACAACCGAAGAACAACCAAAATCGATTAAATCAGGTGACGCAGCTATTATAACATTGGTGCCAACAAAACCAATGTGCGTTGAATCATTTCAAGAGTTTCCACCATTAGGTCGTTTTGCAGTACGTGAC Raphidioptera GCTGAGCGTGAGCGTGGTATCACTATAGATATCGCACTGTGGAAATTTGAAACTGCAAAATACTATGTCACTATAATTGATGCTCCGGGCCATCGTGATTTCATTAAGAACATGATTACGGGTACGTCCCAGGCCGATTRTGCTGTCTTGATAGTTGCGGCCGGCACAGGAGAATTCGAAGCTGGTATTTCAAAGAACGGACAAACTCGTGAACATGCTTTGCTCGCTTTCACGCTCGGCGTAAAACAATTGATCGTCGGCGTCAATAAAATGGACTCGACCGATCCACCCTACAGTGAAACGCGATATGAGGAAATTAAAAAAGAAGTCTCCTCGTACATTAAAAAGATTGGATATAATYCGGCAGCGGTGGCTTTTGTTCCTATTTCTGGGTGGCACGGAGACAACATGCTGGAACCGTCTGAAAAAATGCCGTGGTTCAAGGGATGGAACGTCGATCGTAAAGAGGGAAAAGCTGAAGGCAAAACTCTCATTGAAGCTCTGGACGCAATTCTGCCCCCTTCGCGACCGACAGATAAACCATTGCGCCTACCGTTACAGGATGTATACAAGATCGGAGGCATCGGTACAGTTCCAGTAGGTCGTGTCGAAACTGGAGTTCTTAAACCGGGCGCGGTCGTTGTGTTTGCGCCTGCCAACATTACCACTGAAGTTAAATCCGTAGAAATGCATCATGAAGCGCTTCAANAAGCNCTGCCAGGNGACAATGTTGGTTTCACCGTTAAGAATGTATCGGTAAAAGAATTGCGTCGTGGATATGTAGCAGGAGATTCCAAAAACAACCCGCCGAAAGGCGCANCCGATTTCACATCCCAAGTTATTGTCCTGAATCATCCGGGTCAAATCAGTAATGGATACACGCCCGTGCTCGACTGTCATACGGCTCACATTGCGTGCAAATTTTCCGAAATCAAAGANAAGGTTGACCGTCGTACAGGCAAAACAACGGAAGAGAACCCAAAATCNATTAAATCTGGTGNCGCTGCAATTATTACTCTTGTCCCAACCAARCCTATGTGTGTTGAATCGTTCCAGGAGTTCCCTCCGTTGGGTCGTTTTGCTGTTCGTGAC Coleo_Polyphaga GCCGAGCGTGAACGTGGTATCACCATCGATATCGCGCTGTGGAAATTCGAAACTTCCAAATACTACGTTACTATCATTGACGCTCCCGGCCACAGAGATTTCATCAAGAACATGATCACGGGGACGTCTCAGGCCGATTGCGCCGTTTTGATCGTGGCCGCAGGTACCGGTGAGTTCGAAGCCGGCATCTCCAAGAACGGACAGACCCGCGAACACGCCCTTCTGGCCTTCACTCTGGGAGTCAAACAACTGATCGTTGGTGTGAACAAGATGGACTCCACTGAACCACCATACAGCGAAGCTAGATACGAAGAAATCAAAAAGGAAGTGTCTTCCTACATCAAGAAGATCGGCTACAACCCAGCCGCTGTTGCCTTCGTACCCATCTCCGGGTGGCACGGGGACAACATGTTGGAACCCTCCACCAAAATGCCCTGGTTCAAGGGGTGGAACATCGAACGCAAGGAGGGCAAAGCTGAGGGCAAATGCTTGATCGAGGCCCTTGATGCCATCCTCCCGCCCACGCGCCCCACCGACAAGCCCCTCCGTCTTCCGCTCCAGGACGTGTACAAGATCGGCGGTATTGGAACAGTACCAGTCGGTCGTGTCGAAACCGGAGTCTTGAAACCCGGAATGGTGGTCGTGTTCGCGCCCGCTAACATCACGACTGAAGTCAAGTCCGTCGAAATGCACCACGAGGCGCTCCCGGAGGCCGTGCCCGGCGACAACGTCGGCTTCAACGTGAAGAACGTGTCCGTGAAGGAGTTGCGTCGTGGGTACGTGGCCGGCGACTCGAAGGCCAGCCCCCCGAAAGGCGCGGCTGACTTCTTGGCGCAAGTTATCGTGTTGAACCACCCTGGACAAATTTCGAACGGTTACACCCCCGTTCTTGACTGTCACACCGCTCACATCGCGTGCAAGTTCGCCGAAATCAAAGAAAAG------------------------------------------------------------------------------------------------------------------------------------------------------ Coleo_Adephaga GCAGAACGCGAGCGTGGCATCACCATTGATATTGCATTGTGGAAGTTCGAAACCGCAAAATATTATGTCACCATCATTGATGCCCCTGGTCACAGAGATTTCATTAAGAACATGATCACTGGTACATCCCAAGCTGATTGTGCCGTACTCATTGTCGCTGCCGGTACTGGTGAATTTGAAGCAGGTATCTCCAAAAATGGTCAAACCCGTGAACACGCTCTTCTTGCCTTCACCCTTGGAGTCAAACAACTAATTGTTGGCGTCAACAAGATGGACTCTACCGAACCAGCTTACAGCGAACCCCGTTTCGAAGAAATCAAAAAAGAAGTCTCTTCCTACATCAAGAAGATCGGTTACAACCCAGCTGCTGTAGCCTTCGTACCAATCTCTGGCTGGCACGGAGACAACATGCTTGAACCATCTACCAAGATGCCATGGTTCAAGGGATGGGCTATTGAACGCAAAGAAGGCAAAGCTGACGGTAAATGTCTCATTGAAGCACTCGATGCCATCCTTCCACCAAGCAGACCCACAGAAAAGCCTCTACGTCTTCCACTCCAAGATGTCTACAAAATTGGCGGTATCGGAACAGTACCAGTTGGTCGTGTTGAAACTGGTGTTCTTAAACCAGGCATGGTTGTCGTCTTCGCCCCAGCCAATATCACCACAGAAGTAAAGTCCGTTGAAATGCACCACGAAGCACTCCAAGAAGCCGTTCCCGGAGATAACGTTGGTTTCAACGTTAAGAACGTCTCCGTCAAAGAATTGCGTCGTGGTTACGTCGCTGGTGATTCCAAAGCCAGTCCACCCAGAGGAGCCGCTGACTTTACCGCTCAGGTTATTGTATTGAACCATCCAGGCCAAATCAGCAACGGATACACACCTGTGCTCGATTGCCACACTGCACATATTGCATGCAAATTCGCCGAAATCAAAGAGAAGGTCGACAGACGTACTGGCAAGACCACCGAAGAGAACCCCAAGGCTATCAAGTCCGGTGATGCCGCCATCGTTATCTTGGTACCAAGCAAACCCATGTGCGTTGAATCCTTCCAGGAATTCCCACCTCTAGGACGTTTCGCCGTCCGTGAC Lepid_Micropterigidae -------------GTGGTATCACCATCGACATCGCCCTGTGGAAGTTTGAAACCGCCAAGNACTACGTCACCATCATCGACGCCCCCGGACACAGGGATTTCATCAAGAACATGATCACGGGAACGTCCCAGGCCGACTGCGCTGTACTCNTCGTNGCCGCCGGTACNGGTGAATTCGARGCGGGCATCTCGAAGAACGGCCAGACCCGCGAACACGCTCTCCTTGCCTTCACGCTCGGAGTGAAACAACTCATCGTCGGCGTCAACAAGATGGACTCCACCGAGCCGCCTTACAGCGAGCCCCGCTTCGAAGAAATCAAAAAAGAAGTGTCTTCTTACATCAAGAAGATCGGTTATAACCCAGCCGCCGTCGCTTTCGTGCCCATCTCC?????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????TCGGAACGGTGCCCGTCGGCCGAGTGGAGACCGGAATCCTCAAGCCCGGCACCGTGGTCGTTTTCGCCCCGGCCAACATCACCACCGAAGTCAAGTCCGTTGAGATGCACCACGAAGCTCTCCAGGAGGCCGTTCCCGGAGACAACGTGGGTTTCAACGTAAAGAACGTTGCCGTGAAGGATCTTCGCCGTGGTTACGTCGCCGGAGATACTAAGAATAACCCCCCGAAGGGAGCTGGTGACTTCACCGCGCAGGTCATTGTTCTCAACCACCCCGGACAGATCTCAAACGGTTACACGCCTGTCCTCGATTGCCACACCGCTCACATTGCCTGCAAATTCGCAGAGATCAAAGAGAAAGTCGACCGTCGTACTGGCAAATCCACCGAAGACAACCCGAAAGCCATTAAATCGGGAGACGCCGCCATCGTTATCCTGGTCCCGTCCAAGCCGATGTGCGTCGAGTCCTTCCAGGAGTTCCCCCCTCTCGGCAGATTCGCCGTGCGC--- Lepid_Papilionidae GCTGAGCGTGAGCGTGGTATCACCATCGATATCGCCCTGTGGAAGTTCGAGACCAGCAAATACTATGTCACCATCATTGACGCTCCCGGACACAGAGATTTCATCAAAAACATGATCACAGGAACATCGCAGGCTGACTGCGCCGTGCTCATCGTCGCTGCCGGTACCGGTGAGTTTGAAGCGGGTATCTCCAAGAACGGACAGACCCGTGAACACGCGCTCCTCGCGTTCACACTCGGTGTCAAGCAACTGATTGTTGGTGTCAACAAAATGGACTCCACTGAGCCGCCATACAGCGAGTCTCGCTTCGAGGAAATCAAGAAGGAAGTATCTTCATACATCAAGAAGATCGGTTACAACCCAGCTGCTGTCGCTTTCGTACCCATTTCTGGCTGGCACGGAGATAACATGCTGGAGCCATCCACCAAAATGCCCTGGTTCAAGGGGTGGAATGTTGAGCGTAAGGAGGGCAAGGCTGAGGGTAAATGCCTGATTGAAGCTCTGGATGCCATCCTGCCCCCAGCTCGTCCCACCGACAAGGCTCTCCGTCTTCCCCTGCAAGACGTTTACAAAATCGGCGGTATTGGTACAGTGCCCGTAGGCAGAGTTGAAACCGGTGTCCTCAAGCCAGGTACCATCGTCGTGTTTGCGCCTGCTAACATCACCACTGAAGTRAAATCTGTTGAAATGCACCACGAAGCTCTCCAGGAGGCTGTACCTGGTGACAATGTTGGTTTCAACGTAAAGAACGTTTCCGTCAAGGAGTTGCGTCGTGGTTATGTCGCTGGCGACTCCAAAAACAACCCACCCAGGGGAGCTGCAGACTTCACTGCACAGGTCATCGTTCTCAACCACCCTGGTCAAATCTCCAATGGTTATACACCAGTGTTGGATTGCCACACAGCTCACATTGCCTGCAAATTCGCCGAAATTAAAGAGAAGGTTGACCGTCGTACTGGTAAATCCACTGAAGACAACCCTAAGTCCATCAAATCTGGTGATGCCGCCATTGTCAACCTCGTGCCCTCCAAGCCCYTGTGTGTGGAATCCTTCCAGGAATTCCCACCTCTTGGTCGTTTCGCCGTGCGT--- Mecoptera ---GGGCGTGAACGTGGTATCACCATTGATATTGCTTTGTGGAAATTCGAAACCGCCAAATATTATGTTACAATCATCGATGCTCCTGGACACAGAGATTTCATTAAGAACATGATTACTGGAACATCTCAGGCTGATTGTGCTGTGCTCATTGTTGCTGCTGGTACTGGTGAATTCGAAGCTGGTATCTCTAAAAATGGACAGACCCGTGAACATGCCCTCCTTGCCTTCACTCTTGGTGTCAAACAATTGATTGTCGGTGTCAACAAGATGGACTCCACTGAACCTCCATACAGCGAATCTCGTTTCGAGGAAATCAAAAAGGAAGTATCTTCATACATTAAGAAGATCGGTTACAACCCTGCTGCTGTTGCTTTCGTACCAATCTCTGGATGGCACGGAGATAACATGTTAGAAGTTTCCACGAAGATGAGTTGGTTCAAGGGATGGAACGTTGAACGCAAAGAAGGCAAGGCTGATGGTAAATGTCTCATTGAAGCTTTGGACGCAATCCTTCCACCTGCTCGTCCCACAGACAAACCCCTCCGTCTTCCACTCCAGGATGTCTACAAAATCGGTGGAATCGGAACAGTACCAGTTGGACGTGTTGAAACTGGTATATTAAAACCCGGTACCGTTGTTGTATTCGCACCAGCTAACCTTACTACTGAAGTCAAGTCCGTCGAAATGCACCACGAAGCCCTCCAAGAAGCTGTTCCTGGAGACAATGTTGGTTTCAACGTAAAGAACGTATCTGTCAAGGAATTACGTCGTGGTTACGTTGCTGGAGATTCCAAGGCCAGTCCACCCAAGGGAGCCGCTGATTTCACTGCTCAGGTCATCGTCCTCAATCACCCCGGACAAATTAGCAACGGATACACACCTGTACTCGATTGCCACACCGCCCATATTGCCTGCAAATTCGCCGAAATCAAAGAAAAGGTCGATCGTCGTACTGGAAAATCAACTGAAGATAACCCAAAAGCCATTAAATCTGGTGATGCCGCTATTGTCAACTTAGTACCCTCTAAACCCATGTGCGTTGAATCTTTCCAAGAATTCCCTCCACTTGGACGTTTTGCTGTTCGTGAC Xyela GCTGAGCGYGARCGTGGTATCACCATCGACATTGCTCTGTGGAAGTTCGAGACCGCCAGGTACTACGTCACCATCATCGACGCCCCTGGCCACAGGGATTTCATCAAGAACATGATCACTGGTACCTCTCAGGCCGATTGCGCCGTACTTATCGTCGCTGCCGGTACTGGAGAATTTGAAGCCGGTATCTCCAAGAACGGACAGACCCGCGAGCACGCCCTTCTCGCTTTCACCCTTGGAGTGAAGCAGCTGATCGTTGGAGTCAACAAGATGGACTCCACCGAGCCCCCGTACTCCGAGGCCCGTTTCGAGGAAATCAAGAAGGAAGTTTCCTCATACATCAAGAAGATCGGTTACAACCCCTCCAACGTTCCTTTCGTGCCCATCTCCGGATGGCATGGAGACAACATGTTGGAGGCTTCCTCCAAGATGCCATGGTTCAAGGGATGGGCTGTGGAGCGCAAGGAAGGAAAGGCTGACGGCAAGTGCCTGATTGAGGCCCTTGACGCTATCCTCCCCCCCAGCAGGCCCACCGACAAGGCCCTCCGTCTTCCCCTTCAGGACGTGTACAAGATTGGTGGTATCGGAACGGTACCCGTTGGCCGTGTCGAGACTGGTCTCCTGAAGCCCGGTATGCTCGTGACTTTCGCCCCCGCTGGTCTGACCACTGAGGTTAAGTCCGTCGAGATGCATCACGAAGCCCTCGTTGAAGCCGTTCCCGGTGACAACGTTGGTTTCAACGTCAAGAACGTGTCCGTCAAGGAATTGCGTCGTGGATACGTCGCTGGTGACTCCAAGAGCAACCCCCCTAAGGGCGCTGCTGACTTCACCGCTCAGGTTATCGTGCTGAACCACCCCGGTCAGATCAGCAACGGATACACTCCCGTGCTCGATTGCCACACAGCTCACATTGCCTGCAAGTTCGCCGAGATCAAGGAGAAGTGCGACCGTCGTACCGGCAAGACCACCGAGGAGAACCCCAAGGCCATCAAATCTGGTGACGCTGCCATTGTCAACCTAGTCCCCAGCAAGCCCATGTGCGTCGAGGCCTTCCAGGAGTTCCCNCCCTTGGGACGTTTCGCTGTACGTGAC Macroxyela GCTGAGCGTGARCGTGGTATCACCATCGATATTGCTCTGTGGAAGTTCGAGACTGCCAAGTACTATGTCACCATCATTGACGCCCCTGGCCACAGGGATTTCATCAAGAACATGATCACTGGTACATCCCAGGCCGATTGCGCCGTGTTGATCGTCGCTGCTGGTACTGGAGAATTTGAGGCTGGTATCTCCAAGAACGGACAGACCCGTGAGCACGCCCTTCTCGCCTTCACCCTTGGAGTGAAGCAGCTGATTGTTGGTGTTAACAAGATGGACTCCACCGAGCCCCCGTACTCTGAGGCCCGTTTCGAAGAAATCAAGAAGGAAGTCTCTTCGTACATCAAGAAGATCGGTTACAACCCCGCCGCTGTCGCTTTCGTGCCCATCTCTGGATGGCACGGAGACAACATGTTGGAGTCTTCCAGCAAGATGCCATGGTTCAAGGGATGGGCTGTGGAGCGCAAGGAAGGCAAGGCTGACGGCAAGTGCCTCATTGAGGCCCTTGACGCCATCCTGCAACCTAGCAGACCCACTGACAAGCCCCTTCGTCTTCCCCTTCAGGACGTCTACAAGATTGGTGGTATTGGAACGGTACCCGTCGGCCGTGTGGAGACTGGTCTCTTGAAACCCGGTATGGTTGTGACCTTCGCGCCCGCTGGTTTGACCACTGAAGTCAAGTCCGTCGAGATGCACCACGAAGCCCTCACTGAAGCCGTTCCCGGTGACAATGTCGGTTTCAACGTAAAGAACGTTTCCGTCAAGGAATTGCGTCGTGGATACGTCGCTGGTGACTCCAAGAGCAACCCCCCTAAGGGCGCTGCTGACTTCACCGCTCAGGTTATTGTGCTGAACCATCCTGGTCAGATCAGCAACGGATACACACCCGTGCTCGATTGCCACACAGCTCACATTGCCTGCAAGTTCGCTGAGATCAARGAAARGTGCGACCGTCGTACTGGCAAAAYCACTGAGGAGAACCCCAAGGCCATCAAGTCTGGTGACGCCGCCATCGTCAACCTGGTCCCCAGCAAGCCCATGTGCGTTGAGGCCTTCCAAGAGTTCCCTCCCCTGGGACGTTTCGCTGTACGTGAC Runaria GCCGAGCGTGAGCGTGGTATCACCATCGATATCGCCCTGTGGAAATTCGAGACGGCCAAATACTACGTGACGATCATCGACGCCCCGGGGCACAGAGATTTCATCAAGAACATGATCACCGGAACTTCGCAGGCCGATTGCGCCGTACTGATCGTCGCCGCAGGTACCGGAGAGTTCGAGGCCGGTATCTCCAAGAACGGCCAGACCCGCGAGCACGCGCTGCTCGCCTTCACTCTGGGCGTCAAGCAGCTCATCGTCGGCGTCAACAAAATGGACAACACCGAACCCCCGTACTCGGAGTCTCGCTTCGAGGAAATCAAGAAGGAAGTATCTTCCTACATCAAGAAGATCGGCTACAATCCCGCCGCCGTCGCTTTCGTTCCCATCTCCGGATGGCACGGAGACAACATGCTGGAACCGTCCACCAAGATGCCGTGGTTCAAGGGATGGGCCGTCGAACGTAAGGAGGGCAAAGCCGACGGCAAGTGCCTCATCGAAGCCCTGGACGCTATCCTCCCCCCCAGCAGACCTACCGACAAGGCTCTTCGCCTTCCCCTTCAGGATGTGTACAAGATTGGCGGTATTGGAACGGTACCCGTCGGTCGTGTCGAGACTGGCGTGCTGAAGCCCGGCATGGTCGTGACTTTCGCGCCGGCCGGTCTGACCACCGAAGTAAAGTCCGTGGAGATGCACCACGAGGCTCTCGTCGAGGCCGTGCCCGGCGACAACGTTGGTTTTAACGTCAAGAACGTCTCCGTCAAGGAGCTGCGACGTGGATTCGTCGCCGGCGACTCCAAGAACAACCCGCCCAAGGGCGCCGCCGACTTTACCGCTCAGGTCATCGTGTTGAATCACCCCGGTCAAATCTGCAACGGATACACTCCCGTGCTCGATTGTCACACGGCCCATATCGCCTGCAAGTTCGCGGAAATCAAGGAGAAGTGCGACCGTCGTACCGGCAAGACCACCGAGGAGAATCCCAAGGCCATCAAGTCCGGGGACGCGGCTATCGTCAATCTGGTACCCAGCAAGCCTATGTGCGTCGAGGCTTTCCAGGAGTTCCCTCCTCTCGGACGCTTCGCCGTGCGTGAC Paremphytus ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Blasticotoma ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Tenthredo ---------------------------------------------------------------------------------------------------------------ATGATCACTGGTACTTCGCAGGCTGATTGCGCTGTATTGATAGTAGCAGCTGGTACGGGTGAATTCGAAGCTGGTATTTCAAAGAATGGTCAAACCCGAGAGCACGCACTTCTTGCTTTTACTCTTGGAGTTAAACAGCTGATTGTTGGTGTTAACAAAATGGATTCAACCGACCCACCGTACTCAGAGGCTCGTTATGAGGAAATTAAGAAGGAAGTTTCTTCTTACATTAAGAAAATTGGATACAATCCACTCGCCGTCGCTTTCGTACCTATCTCAGGATGGCACGGAGACAACATGCTCGAACCATCTAGCAAAATGCCATGGTTTAAGGGATGGGCTGTTGAACGTAAGGAAGGAAAGGCTGACGGCAAGTGCCTCATTGAAGCTCTTGACGCTATCCTACCGCCAAGCAGACCCACCGACAAGGCTCTCCGTCTTCCCCTTCAGGACGTCTACAAGATTGGAGGTATTGGAACGGTCCCAGTCGGTCGTGTAGAAACTGGAGTACTGAAGCCTGGTACCGTTGTGACTTTCGCACCTGCCGGACTAACGACTGAAGTTAAATCCGTCGAGATGCATCACGAAGCTCTCGTCGAAGCTGTACCCGGCGACAACGTTGGTTTCAACGTAAAGAACATCTCCGTCAAGGATCTTCGTCGTGGATACGTTGCCGGTGACTCAAAGAACAACCCGCCTAAAGGCGCTGCCGACTTCACCGCTCAGGTAATCGTGTTGAACCATCCGGGTCAAATCAGCAACGGTTACACGCCTGTGTTGGATTGTCACACAGCTCATATAGCGTGTAAATTCGCTGAGATAAAGGAGAAGTGTGACCGACGTACGGGTAAAACAACTGAAGAAAATCCAAAGGCTATAAAATCCGGAGACGCTGCGATCGTGAACCTTGTACCAAGCAAGCCAATGTGCGTTGAAGCTTTCCAGGAGTTTCCACCATTGGGACGTTTCGCTGTACGTGAC Aglaostigma ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Dolerus ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------GTCAAGGAACTTCGTCGTGGATACGTCGCTGGCGACTCCAAGAACAACCCGCCCAAGGGGGCCGCCGATTTCACCGCTCAGGTAATCGTGTTGAACCACCCTGGTCAAATCAGCAACGGTTACACGCCAGTGTTGGATTGCCACACAGCCCATATAGCGTGTAAATTCGCAGAGATAAAAGAGAAGTGTGACAGACGTACGGGAAAAACAACCGAAGAAAATCCAAAAGCAATAAAATCCGGAGACGCGGCCATCGTAAACTTGGTACCCAGCAAGCCCATGTGCGTCGAGGCTTCCCAGGAGTTCCC---------------------------- Selandria ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Strongylogaster ------------------------------------------------------------TACTACGTAACAATCATCGACGCTCCCGGTCACAGAGATTTCATCAAGAACATGATCACCGGTACGTCGCAGGCTGATTGCGCCGTACTCATCGTAGCTGCTGGTACGGGAGAATTTGAAGCTGGTATTTCAAAGAACGGACAAACCCGTGAGCACGCTCTTCTCGCTTTCACTCTTGGAGTAAAGCAGCTGATCGTTGGAGTTAACAAAATGGATTCCACCGAACCCCCGTACTCCGAAGCTCGTTTCGAGGAAATCAAGAAGGAAGTATCTTCCTACATTAAGAAGATCGGTTACAATCCTGCCGCTGTTGCTTTCGTACCAATCTCCGGTTGGCACGGAGACAACATGCTCGAACCATCCTCAAAGATGCCATGGTTCAAGGGATGGGCTGTCGAACGTAAGGAAGGCAAGGCTGACGGCAAGTGCCTCATCGAAGCTCTCGACGCTATCCTTCAACCCAGCCGTCCAACCGACAAGGCTCTCCGTCTTCCCCTTCAGGACGTCTACAAGATTGGTGGTATCGGAACGGTCCCAGTCGGTCGTGTAGAAACTGGAGTCTTGAAACCAGGTACCGTTGTTACATTCGCTCCTGCTGGACTCACCACTGAAGTTAAATCCGTTGAAATGCACCACGAAGCTCTGGCAGAAGCTGTTCCCGGTGACAACGTTGGTTTCAACGTTAAGAACGTTTCCGTTAAGGAACTTAGACGTGGATACGTAGCTGGTGACTCAAAGAACAACCCACCAAAGGGTGCCGCCGACTTCACCGCTCAG--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- Monophadnoides ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Metallus ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Athalia GCAGAGCGTGAACGTGGTATCACGATCGATATTGCTCTGTGGAAGTTTGAGACTTCTAGATACTACGTAACAATCATCGACGCGCCCGGACACAGAGATTTTATCAAGAACATGATCACTGGTACGTCGCAGGCCGATTGTGCGGTGCTGATCGTTGCTGCTGGTACTGGAGAATTCGAGGCTGGTATTTCCAAGAGCGGACAAACCCGTGAGCATGCACTCCTTGCCTTCACTCTTGGTGTCAAGCAGCTCATCGTAGGAGTCAACAAAATGGACTCGACCGAACCACCATTCTCTGAATCACGTTTCGAAGAAATCAAGAAGGAAGTTTCTTCCTACATCAAAAAGATCGGTTACAACCCAGCTGCTGTAGCATTCGTGCCAATCTCTGGATGGCATGGAGACAACATGCTTGAGCCATCCAGCAAAATGCCGTGGTTCAAGGGATGGGCTGTTGAGCGTAAGGAAGGCAAGGCTGATGGCAAGTGCCTCATCGAAGCTCTTGACGCCATCCTGCCACCCAGCAGACCCACTGACAAGGCTCTCCGTCTTCCTCTTCAGGATGTTTACAAGATTGGTGGTATTGGAACGGTACCAGTCGGTCGTGTTGAAACTGGTGTCCTGAAGCCAGGTACTGTCGTCACCTTTGCCCCAGCTGGACTTACTACTGAAGTCAAGTCCGTCGAAATGCACCACGAAGCTCTCCTTGAAGCCGTTCCCGGTGACAATGTTGGTTTCAATGTGAAGAACGTCTCCGTCAAGGAACTCCGACGTGGATACGTTGCTGGTGATACGAAGAACAACCCACCAAAGGGAGCCGCTGACTTCACTGCTCAGGTNATCGTGCTGAACCACCCCGGTCAAATTAGCAAGGGATACACGCCTGTGTTGGATTGCCACACGGCTCATATCGCTTCAAAGTTTGCACAGATTAAGGAGAAGTGCGACCGCCGTACGGGTAAAACAACTGAAGAGAATCCAAGGGCGATCAAATCTGGAGATGCTGCAATNGTAACCCTGGTGCCAAGCAAGCCAATGTGCGTTGAGGCTTTCCAGGAATTCCCACCTTTGGGACG---------------- Taxonus ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Hoplocampa ------------------------------------------------------------TACTATGTCACGATCATCGATGCCCCAGGACACAGAGATTTCATCAAGAACATGATCACGGGAACGTCGCAGGCCGATTGCGCTGTGTTGATTGTAGCCGCTGGTACGGGAGAGTTCGAGGCTGGTATTTCCAAGAACGGTCAGACCCGCGAGCATGCGCTCCTCGCCTTCACCCTTGGAGTAAAGCAGCTGATCGTCGGAGTCAACAAAATGGACTCCACCGAGCCACCCTACTCCGAGTCTCGTTTCGAGGAAATCAAGAAGGAAGTGTCCTCTTACATCAAAAAGATCGGTTACAATCCCGCCGCTGTTGCTTTCGTTCCCATCTCTGGATGGCACGGAGATAACATGCTTGAGCCATCCACCAAAATGGGGTGGTTCAAGGGATGGGCTGTTGAGCGCAAGGAAGGAAAGGCTGACGGCAAGTGCCTCATCGAAGCTCTCGACGCTATCCTGCCACCCAGCAGACCCACCGACAAGGCTCTCCGTCTTCCCCTTCAGGACGTCTACAAGATTGGTGGTATTGGAACGGTACCAGTTGGTCGTGTAGAAACCGGAGTCTTGAAGCCAGGTACTGTGGTAACATTCGCTCCTGCTGGACTCACCACTGAAGTAAAGTCCGTCGAGATGCACCACGAAGCTCTCGTCGAAGCCGTTCCCGGCGACAACGTTGGTTTCAACGTAAAGAACGTTTCCGTCAAAGAACTCCGTCGTGGATACGTTGCCGGTGACTCGAAGAACAACCCGCCCAAGGGCGCCGCCGACTTCACCGCTCAG--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- Nematinus ------------------------------------------------------------TACTACGTCACAATCATCGACGCCCCTGGACACAGAGATTTCATCAAGAACATGATCACTGGTACGTCGCAGGCCGATTGTGCAGTGTTGATCGTAGCCGCTGGTACCGGAGAGTTTGAAGCTGGTATTTCCAAGAACGGTCAGACCCGTGAGCACGCTCTCCTTGCCTTCACTCTTGGAGTCAAGCAGCTCATCGTCGGAGTCAACAAAATGGACTCCACCGAGCCACCCTACTCCGAATCCCGTTTCGAGGAAATCAAGAAGGAAGTCTCATCCTACATCAAGAAGATCGGTTACAACCCAGCTGCCGTTCCCTTCGTTCCCATCTCCGGATGGCACGGAGACAACATGCTCGAGCCATCCAGCAAAATGGGATGGTTCAAGGGATGGGCTGTTGAGCGCAAGGAAGGCAAGGCTGACGGCAAGTGCCTCATCGAAGCTCTCGACGCTATCCTGCCACCCAGCAGACCCACCGACAAGGCTCTTCGTCTTCCCCTTCAGGACGTCTACAAGATTGGTGGTATCGGAACGGTACCAGTTGGTCGTGTGGAAACTGGAGTCCTGAAGCCAGGTACTGTTGTAACATTCGCTCCTGCTGGACTCACCACTGAAGTCAAGTCCGTTGAGATGCACCACGAAGCTCTCGTCGAAGCCGTTCCCGGTGACAACGTCGGTTTCAACGTAAAGAACGTCTCCGTCAAGGAACTCCGTCGTGGATACGTTGCCGGTGACTCAAAGAACAACCCGCCCAAGGGAGCTGCCGACTTCAACGCTCAG--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- Nematus GCTGAGCGBGARCGTGGTATCACCATCGACATTGCCCTRTGGAAGTTCGAGACTGGCAAATACTACGTGACGATCATCGACGCCCCGGGACACAGAGATTTCAWCAAGAACATGATCACTGGTACGTCGCAGGCCGATTGTGCGGTGYTGATCGTAGCCGCCGGTACCGGAGAATTCGAGGCTGGTATCTCCAAGAACGGACAGACCCGCGAGCACGCGCTTCTCGCCTTCACTCTTGGAGTCAAGCAGCTGATCGTAGGAGTCAACAAAATGGACTCCACCGAGCCACCCTACTCAGAGGCCCGTTTCGAGGAAATCAAGAAGGAAGTCTCCTCCTACATCAAGAAGATCGGTTACAACCCAGCCGCCGTTGCCTTCGTTCCCATCTCAGGATGGCACGGAGACAACATGCTCGAGCCATCCACCAAAATGGGATGGTTCAAGGGATGGGCTGYCGAGCGCAAGGAAGGAAAGGCCGACGGCAAGTGCCTCATCGAAGCTCTTGACGCTATCCTCCAACCCAGCAGACCCACCGACAAGGCTCTCCGTCTTCCCCTTCAGGATGTCTACAAGATTGGTGGTATCGGAACGGTACCAGYTGGTCGTGTAGAAACTGGAGTCCTGAAGCCAGGTACTGTTGTAACATTCGCTCCTGCTGGACTCACCACCGAAGTCAAGTCAGTTGAGATGCACCACGAAGCTCTCGTCGAAGCTGTTCCCGGTGACAACGTTGGTTTCAACGTAAAGAACGTCTCCGTCAAGGAACTCCGTCGTGGATACGTCGCTGGTGACTCAAAGAACAACCCACCCAAGGGAGCCGCCGACTTCAACGCTCAGGTCATCGTTCTGAACCACCCTGGTCAGATCAGCAACGGTTACACTCCAGTGTTGGATTGCCACACCGCTCACATAGCGTGCAAGTTCGCAGAGATCAAGGAGAAGTGTGACAGACGTACGGGAAAAACTACCGAGGAAAATCCTAAGGCTATCAAATCCGGAGACGCGGCGATAGTCACGCTGATACCCAGCAAGCCGATGTGCGTCGAAGCTTTCCAGGAGTTCCCACCTCTGGGACGTTTCGCCGTGCGTGAC Cladius ------------------------------------------------------------TACTATGTCACGATCATCGATGCCCCTGGACACAGAGATTTCATTAAGAACATGATCACTGGAACGTCGCAGGCGGATTGTGCTGTGTTGATTGTAGCTGCAGGTACCGGAGAGTTCGAGGCTGGTATTTCCAAGAACGGACAAACCCGTGAGCACGCGCTTCTTGCCTTCACTCTTGGAGTCAAGCAGCTGATTGTTGGAGTCAACAAAATGGACTCTACCGAGCCACCCTACTCTGAGGCTCGTTTCGAGGAAATCAAGAAGGAAGTCTCATCTTACATCAAGAAGATCGGTTACAACCCAGCGGCTGTTGCCTTTGTTCCCATCTCTGGATGGCACGGAGACAACATGCTTGAGCCTTCCACCAAAATGGGATGGTTCAAGGGATGGGCTGTCGAACGCAAGGAAGGAAAGGCTGACGGCAAGTGCCTCATCGAAGCTCTTGATGCTATCCTGCCGCCCAGCAGACCCACCGACAAGGCTCTCCGTCTTCCTCTCCAGGACGTCTACAAGATTGGTGGTATCGGAACAGTACCAGTCGGTCGTGTAGAAACTGGAGTCCTGAAGCCAGGTACTGTGGTAACATTCGCTCCTGCTGGTCTCACCACTGAAGTCAAGTCAGTCGAGATGCACCACGAAGCCCTTGTTGAAGCCGTTCCCGGTGACAATGTTGGTTTCAACGTTAAGAACGTCTCTGTCAAGGAACTCCGTCGTGGATACGTTGCCGGTGACTCTAAGAACAACCCACCCAAGGGTGCCGCTGACTTCAACGCTCAG--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- Monoctenus GCTGAGCGTGAACGTGGTATCACRATCGACATCGCTCTGTGGAAGTTTGAGACTGCCAGGTACTATGTGACTATCATCGATGCACCTGGACACAGGGATTTCATCAAGAACATGATCACCGGTACGTCGCAGGCCGACTGCGYGGTGTTGATCGTTGCTGCGGGAACGGGAGAATTCGAGGCTGGTATTTCTAAGAACGGTCAGACCCGCGAGCACGCCCTGCTCGCCTTCACTCTTGGAGTCAAGCAGCTCATCGTCGGAGTCAACAAAATGGACTCTACTGAGCCGCCGTACTCCGAGGCTCGTTTTGAGGAAATCAAGAAGGAAGTCTCGTCATACATCAAGAAAATCGGTTACAATCCTGCTGCCGTAGCCTTCGTCCCAATTTCCGGATGGCACGGAGACAACATGCTCGAACCCTCGAGCAAAATGCCATGGTTCAAGGGGTGGGCCGTTGAACGCAAAGAAGGAAAGGCTGACGGCAAGTGCCTCATTGAAGCTCTTGACGCCATCCTACCACCCAGCAGACCCACCGACAAAGCTCTGCGTCTTCCCCTGCAGGACGTATACAAGATTGGAGGTATCGGAACAGTACCCGTAGGCCGTGTCGAGACCGGAGTCCTGAAACCAGGTACCGTCGTTACCTTCGCCCCTGCCGGACTTACGACTGAAGTAAAATCCGTCGAAATGCATCACGAAGCT---------------------------------------------------------------------------------------------------------------------------------------GTAATCGTATTGAACCACCCCGGTCAGATAAGCAACGGTTACACGCCTGTACTGGATTGTCACACAGCTCACATAGCGTGCAAGTTCGCTGAGATCAAGGAGAAATGTGACCGACGTACCGGTAAAACGACGGAAGAAAATCCAAAGGCGATAAAATCAGGAGACGCAGCGATAGTGAACCTGGTACCCAGCAAGCCAATGTGCGTCGAGGCCTTCCAAGAATTCCCATCACTTGGACGTTTCGCTGCACGTGAT Gilpinia ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Diprion GCTGAGCGTGAACGTGGTATCACAATTGATATCGCACTTTGGAAATTTGAAACTGCCAAGTACTATGTGACTATCATCGATGCGCCGGGACACAGAGATTTCATTAAGAACATGATCACTGGTACGTCTCAGGCCGACTGTGCAGTGTTGATTGTGGCTGCTGGTACGGGAGAATTCGAAGCTGGTATTTCCAAGAACGGTCAGACCCGTGAGCACGCACTACTTGCCTTCACCCTCGGCGTCAAGCAGCTCATCGTTGGTGTCAATAAAATGGACTCCACTGAGCCGCCATACTCGGAGGCTCGTTTCGAGGAAATTAAGAAGGAAGTTTCCTCTTATATCAAAAAGATTGGTTACAACCCAGCCGCTGTCGCTTTCGTTCCCATCTCAGGGTGGCACGGAGACAACATGCTCGAGCCCTCGAGCAAGATGCCATGGTTCAAGGGATGGGCCGTCGAACGCAAGGAAGGCAAAGCTGATGGAAAGTGTCTCATTGAGGCTCTCGATGCCATCCTTCCACCCAGCAGACCTACCGATAAGGCTCTGCGTCTTCCCCTGCAGGACGTTTACAAGATTGGAGGTATTGGAACGGTGCCCGTAGGCCGTGTAGAAACTGGAGTTCTGAAGCCAGGTACCGTCGTAACCTTTGCTCCAGCAGGTCTCACGACTGAAGTAAAATCCGTCGAGATGCACCACGAGGCTTTGACTGAAGCCGTGCCCGGCGACAACGTTGGTTTCAACGTAAAGAACGTNTCCGTCAAGGAACTTCGACGTGGATACGTTGCTGGTGACTCTAAGAACAATCCGCCCAAGGGCGCCGCCGATTTCACCGCTCAGGTAATCGTATTGAACCACCCCGGTCAGATCAGCAACGGTTATACGCCAGTGTTGGACTGTCACACGGCACATATAGCGTGCAAGTTCGCCGAGATTAAGGAGAAATGCGACCGACGTACGGGTAAAACGACTGAGGAGAATCCAAAGGCGATAAAGTCGGGAGACGCGGCCATCGTGACTCTGGTGCCGAGCAAGCCGATGTGCGTCGAAGCCTTCCAAGAATTCCCGCCATTGGGTCGCTTCGCTGCACGTGAT Cimbicinae GCTGAGCGYGARCGTGGTATCACCATTGACATTGCCCTGTGGAAGTTCGAGACGAGCAGGTACTACGTGACGATCATCGACGCCCCTGGTCACAGAGATTTCATCAAGAACATGATCACTGGTACCTCACAGGCTGATTGCGCTGTGTTGATTGTCGCCGCTGGTACGGGAGAGTTCGAGGCTGGTATCTCAAAGAACGGACAAACCCGAGAGCACGCGCTTCTCGCCTTCACTCTTGGAGTCAAGCAGCTGATTGTTGGAGTTAACAAAATGGACTCCACCGAGCCCCCGTACTCCGAGTCTCGTTTCGAAGAAATCAAGAAGGAAGTTTCATCCTACATCAAGAAGATCGGTTACAACCCTGCCGCCGTCGCTTTCGTTCCCATCTCTGGATGGCACGGAGACAACATGCTTGAGCYATCGAGCAAGATGCCGTGGTTCAAGGGGTGGGCTGTTGRACGCAAGGAAGGCAAGGCTGATGGCAAATGCCTCATCGAAGCCCTCGATGCTATCCTCCCACCCAGCAGACCTACCGACAAGGCTCTGCGTCTCCCCCTCCAGGACGTCTACAAGATCGGTGGTATTGGAACGGTACCCGTTGGTCGAGTTGAAACTGGTGTGTTGAAGCCTGGTACCGTCGTTACTTTCGCTCCCGCTGGACTCACTACTGAAGTAAAGTCTGTTGAAATGCACCACGAAGCTCTCGTTGAAGCCGTTCCCGGTGACAACGTTGGTTTCAACGTTAAGAACGTCTCCGTCAAGGAACTTCGTCGTGGATACGTTGCTGGCGACTCTAAGAMCAACCCACCCAAGGGAGCTGCCGATTTCACTGCTCAGGTCATCGTCCTGAACCACCCTGGCCAGATCAGCAACGGTTATACACCGGTGTTGGATTGTCACACCGCACATATAGCGTGCAAATTCGCTGAAATTAAAGAGAAGTGCGACCGACGTACGGGAARAACAACAGAAGAGAATCCTAARGCGATAAAATCCGGAGACGCTGCGATAGTAAATCTGGTACCTAGCAAGCCAATGTGCGTTGAAGCTTTCCAAGAATTCCCACCGCTGGGACGTTTCGCCGTACGTGAC Abia GCTGAGCGTGAACGTGGTATTACCATCGACATTGCCCTGTGGAAGTTTGAGACTGGCAGATACTACGTGACAATCATCGACGCCCCTGGACACAGAGATTTCATTAAGAACATGATCACTGGTACTTCCCAGGCTGATTGTGCAGTGTTGATAGTCGCCGCTGGTACGGGAGAGTTCGAGGCTGGTATCTCAAAGAACGGACAAACCCGAGAGCACGCTCTTCTTGCTTTCACTCTTGGCGTAAAGCAGCTCATCGTCGGAGTTAACAAAATGGACTCCACCGAGCCTCCCTACTCCGAATCTCGATTCGAGGAAATCAAGAAGGAAGTATCATCCTACATCAAGAAGATCGGTTACAACCCTGCCGCCGTCGCTTTCGTTCCCATCTCTGGATGGCACGGAGACAACATGCTTGAGCCCTCTAGCAAGATGCCGTGGTTCAAGGGATGGGCTGTTGAGCGTAAGGAAGGCAAAGCTGACGGCAAGTGCCTGATCGAAGCTCTCGATGCTATCCTCCCACCCAGCAGACCAACCGATAAGGCTCTGCGTCTTCCCCTCCAGGACGTCTACAAAATTGGTGGTATTGGAACGGTACCCGTTGGTCGTGTCGAAACCGGTGTGCTGAANCCCGGTACCGTCGTAACTTTTGCTCCCGCTGGACTCACCACTGAAGTAAAGTCCGTTGAGATGCACCACGAAGCTCTCGTTGAAGCCGTTCCCGGTGACAACGTTGGTTTCAACGTAAAGAACGTCTCCGTTAAGGAACTTCGTCGTGGATATGTCGCTGGCGACTCTAAGAACAACCCACCCAAGGGAGCTGCNGANTTTACNGCTCAGGTTATCGTTCTGAACCATCCTGGTCAGATTAGCAACGGTTACACACCGGTTTTGGATTGCCACACCGCACATATAGCGTGCAAATTCGCTGAAATCAAAGAGAAGTGCGACCGACGTACGGGAAAAACAACAGAAGAGAATCCTAAGGCGATAAAATCTGGAGATGCTGCGATAGTTAATCTTGTACCTAGCAAGCCTATGTGCGTCGAGGCTTCCCAGGAATTCCCACCTTTGGGACGTTTC------------ Corynis GCTGAACGTGAACGTGGTATCACCATTGATATTGCTCTGTGGAAGTTCGAGACTAGCAAGTACTATGTAACCATCATTGATGCTCCTGGTCACAGAGATTTCATCAAGAACATGATCACTGGTACCTCTCAGGCCGATTGTGCGGTGTTGATTGTTGCCGCTGGTACAGGAGAGTTTGAAGCTGGTATTTCGAAGAATGGTCAGACCCGTGAGCACGCGCTCCTTGCTTTCACTCTTGGTGTAAAGCAGCTGATTGTTGGAGTGAACAAGATGGATTCCACTGAGCCTCCTTACTCTGAAGCTCGTTTTGAGGAAATTAAGAAGGAAGTATCGTCGTACATCAAGAAAATTGGTTACAACCCTGCAGCTGTTGCTTTCGTTCCCATTTCTGGATGGCACGGAGACAACATGCTCGAACAATCCTCCAAAATGCCATGGTTCAAGGGATGGAGCGTTGAGCGCAAAGAAGGCAAGGCTGATGGCAAGTGCTTGATTGAAGCTCTCGATGCTATCCTCCCACCCAGCAGACCTACCGACAAGGCTCTCCGACTTCCCCTTCAGGACGTCTACAAGATTGGTGGTATTGGAACTGTACCTGTCGGTCGTGTTGAAACCGGTGTCTTGAAGCCTGGTACCGTTGTTACATTCGCACCTGCAGGACTTACCACTGAAGTAAAATCCGTTGAAATGCATCACGAAGCTCTCGTCGAGGCTGTGCCTGGTGACAACGTTGGATTCAACGTAAAGAACGTCTCCGTAAAGGAACTTCGTCGTGGATACGTCGCTGGCGACTCAAAGAACAACCCGCCTAGGGGAGCAGCTGACTTCACCGCTCAGGTCATCGTTCTGAACCATCCAGGACAGATTAGCAACGGTTATACTCCAGTTTTGGATTGCCACACCGCACACATAGCGTGCAAGTTTGCCGAAATCAAGGAAAAGTGTGACCGACGTACGGGTAAAACAACTGAGGAAAATCCAAAGGCGATAAAGTCAGGAGATGCTGCGATAGTGAATCTGGTACCCAGCAAACCAATGTGCGTTGAGGCTTTCCAAGAATTTCCACCTTTGGGACGTTTTGCTGTACGTGAC Arge GCTGAACGTGAACGTGGTATCACCATCGATATCGCTCTGTGGAAGTTCGAGACTGCCAAGTACTACGTAACGATCATTGACGCTCCTGGACACAGGGATTTCATCAAGAACATGATCACTGGTACCTCCCAGGCTGATTGCGCCGTGCTCATTGTCGCCGCTGGTACGGGAGAGTTCGAGGCTGGTATCTCCAAGAATGGACAGACTCGCGAGCACGCTCTTCTCGCTTTCACTTTGGGAGTGAAACAGCTGATTGTCGGAGTCAACAAAATGGACTCTACCGAACCCCCTTACTCCGAGTCTCGTTTCGAGGAAATCAAGAAAGAAGTATCGTCGTACATCAAGAAGATCGGTTACAACCCTGCTGCCGTTGCCTTCGTACCCATTTCCGGATGGCACGGAGACAACATGTTGGAACCATCCACTAAGATGCCGTGGTTCAAGGGATGGGCTGTTGAACGTAAGGAAGGTAAAGCGGACGGCAAGTGCCTGATCGAAGCCCTCGACGCTATCCTTCCACCCAGCAGACCCACCGACAAGGCTCTTCGTCTGCCCCTGCAGGACGTCTACAAGATTGGTGGTATCGGAACGGTACCCGTCGGTCGTGTCGAAACTGGTGTCCTGAAACCAGGTACCGTCGTCACTTTCGCACCAGCTGGCCTCACGACTGAAGTAAAGTCCGTGGAAATGCACCACGAAGCCCTCGTCGAAGCTGTTCCCGGAGACAACGTTGGTTTCAACGTTAAAAACGTCTCCGTTAAGGAATTGCGTCGTGGTTACGTCGCCGGTGACTCCAAGAACAACCCGCCCAGAGGAGCTGCTGATTTCACGGCTCAGGTTATCGTGTTGAACCATCCTGGTCAGATCAGCAACGGTTATACTCCAGTTTTGGATTGCCACACGGCCCACATCGCGTGCAAGTTCGCGGAGATCAAGGAGAAGTGTGACCGACGTACCGGTAAAACGACGGAAGAGAACCCAAAGGCGATCAAATCTGGTGACGCCGCGATCGTGAACCTGGTCCCGAGCAAGCCAATGTGCGTCGAATCTTTCCAGGAGTTCCCACCGTTGGG------------------- Sterictiphora ---------------------------------------------------------------------------------------------------------------ATGATCACTGGTACCTCTCAGGCCGATTGCGCTGTGCTGATTGTCGCTGCCGGTACGGGAGAATTCGAGGCTGGTATCTCGAAGAATGGCCAGACCCGTGAGCACGCTCTGCTTGCTTTCACCTTGGGAGTCAAGCAGCTGATCGTTGGAGTCAACAAAATGGACTCCACCGAGCCCCCCTTCTCTGAGTCCCGTTTCGAGGAAATCAAGAAAGAAGTCTCCTCCTACATCAAGAAGATCGGTTACAACCCAGCTGCCGTTGCCTTCGTTCCCATCTCCGGATGGCACGGTGACAACATGTTGGAGGTCTCCACCAAGATGCCATGGTTCAAGGGATGGGCTGTTGAGCGCAAGGAAGGCAAGGCCGACGGCAAGTGCCTGATCGAAGCTCTCGACGCTATCCTGCCCCCTAGCAGACCCACCGACAAGGCTCTGCGTCTGCCCCTGCAGGACGTTTACAAGATTGGTGGTATTGGAACGGTACCCGTCGGTCGTGTCGAGACTGGTGTGCTGAAGCCCGGTACCGTCGTCACTTTCGCGCCTGCTGGTCTGACCACTGAAGTCAAATCCGTGGAAATGCACCACGAAGCCCTCACTGAGGCCGTTCCCGGAGACAACGTTGGTTTCAACGTTAAGAACGTCTCCGTCAAGGAATTGCGTCGTGGATACGT---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- Perga ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Phylacteophaga GCTGAGCGTGAACGTGGTATCACCATTGACATTGCTCTGTGGAAGTTTGAGACTGCCAAGTACTACGTTACCATCATTGACGCCCCTGGACACAGAGATTTCATTAAGAACATGATCACTGGAACGTCGCAAGCTGATTGTGCTGTGTTGATCGTCGCTGCTGGTACGGGAGAATTCGAGGCTGGTATCTCGAAAAATGGGCAGACTCGTGAGCATGCCCTCCTAGCTTTCACTCTGGGAGTGAAGCAACTCATTGTTGGAGTGAACAAGATGGATTCCACTGAGCCGCCATACTCTGAGGCTCGATTTGAGGAAATTAAGAAGGAAGTTTCCTCATACATCAAGAAAATTGGGTATAACCCAGCTGCCGTTGCCTTCGTCCCCATTTCTGGATGGCACGGAGACAACATGTTGGAACCTTCCACCAAAATGTCCTGGTTCAAGGGATGGGCTGTGGAACGTAAGGAAGGCAAGGCTGACGGCAAGTGCCTCATTGAAGCTCTAGACGCCATCCTTCCCCCCAGCAGACCCACCGACAAGGCTCTGCGTCTCCCTCTGCAGGACGTGTACAAAATCGGCGGTATTGGAACGGTACCCGTCGGTCGTGTTGAGACTGGAGTGTTGAAACCAGGTATGGTTGTGACCTTCGCTCCAGCTGGTCTCACCACTGAAGTCAAGTCTGTCGAGATGCACCACGAAGCCCTCACGGAGGCTGTTCCCGGAGACAACGTTGGTTTCAACGTCAAGAACGTCTCCGTCAAGGAATTGCGTCGTGGATACGTGGCTGGCGACTCAAAAGCCAACCCACCGCGAGGAGCAGCTGACTTCACTGCTCAGGTCATCGTGTTGAACCATCCCGGACAAATTAGCAACGGATACACGCCAGTGCTGGATTGCCACACCGCTCACATCGCCTGCAAGTTCGCTGAAATCAAAGAGAAGTGTGACCGACGTACCGGTAAAACGACGGAAGAGAACCCGAAGGCTATCAAATCTGGGGACGCTGCGATTGTGAATCTCGTACCCAGCAAGCCTATGTGCGTGGAAGCCTTCCAGGAGTTCCCGCCTT----------------------- Lophyrotoma ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Acordulecera GCAGAGCGTGAACGTGGTATCACCATCGACATTGCCCTGTGGAAGTTCGAGACTGCCAAATACTACGTAACGATCATCGACGCTCCAGGACACAGAGATTTCATCAAGAACATGATCACTGGTACGTCCCAGGCTGACTGCGCCGTCTTGATCGTTGCTGCCGGTACCGGAGAATTTGAGGCTGGTATCTCGAAGAACGGACAAACTCGCGAGCACGCTCTCCTTGCCTTCACTTTGGGAGTGAAGCAGCTGATCGTAGGAGTTAACAAGATGGACTCCACCGAGCCACCATACTCCGAAGCTCGTTTCGAGGAAATCAAGAAGGAAGTGTCTTCTTACATCAAGAAAATTGGTTACAATCCAGTTGCTGTACCATTCGTTCCCATCTCTGGATGGCACGGAGACAACATGTTGGAAGTTTCCAGCAAAATGAACTGGTTCAAGGGATGGTCTGTCGAGCGCAAGGAAGGCAAGGCTGACGGCAAGTGCTTGATTGAAGCTCTTGACGCTATCCTGCCACCCAGCCGACCCACCGACAAGGCACTCCGTCTTCCCCTTCAGGATGTCTACAAAATTGGTGGTATTGGAACAGTACCCGTCGGTCGTGTCGAGACTGGTGTCATCAAGCCCGGTATGGTCGTCGTTTTCGCTCCAGCTGGTCTGACGACTGAAGTCAAGTCAGTCGAGATGCACCACGAAGCCCTCGTCGAAGCCGTTCCCGGCGACAACGTTGGTTTCAACGTCAAGAACGTGTCCGTCAAGGAATTGCGTCGTGGATACGTCGCCGGTGACACGAAGTCCAACCCACCCAAGGGAGCTGCCGACTTCACGGCTCAGGTCATCGTCCTGAACCACCCTGGACAAATCAGCAACGGATACACTCCGGTTTTGGATTGCCACACCGCTCACATCGCGTGCAAGTTCGCCGAGATCAAGGAGAAGTGCGACCGACGTACCGGCAAAACCACCGAAGAAAATCCCAAGGCAATCAAGTCCGGAGACGCAGCTATCGTCAACCTAGTGCCCAGCAAACCAATGTGCGTCGAAGCTTTCCAGGAGTTCCCACCTCTGGGTCGTTTCGCTGTCCNTGAC Decameria ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Neurotoma ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Onycholyda GCTGAGCGTGANCGTGGTATCACCATCGACATTGCCCTGTGGAAGTTCGAGACCGCCAAGTACTATGTCACCATCATCGACGCCCCCGGTCACAGGGATTTCATCAAGAACATGATCACTGGTACTTCGCAGGCTGATTGTGCTGTGCTGATCGTCGCTGCCGGTACGGGAGAGTTCGAGGCTGGTATCTCGAAGAACGGACAGACCCGCGAGCACGCCCTGCTCGCCTTCACCCTTGGAGTGAAGCAGCTGATCGTTGGTGTCAACAAGATGGACTCCTCCGAACCCCCGTACTCTGAGACCCGTTTCGAGGAAACCAAGAAGGAAGTTTCTTCTTTCCTTAAGAAAATCGGTTACAATCCAGCTGCCGTCGCTTTCGTGCCCATTTCCGGATGGAACGGAGACAACATGTTGGAGGCCTCCGCCAAAATGCCATGGTTCAAGGGATGGGCTGTT---------------------------------------------------------------------------------------------------------GACGTTTACAAAATTGGTGGTATTGGAACGGTACCCGTTGGCCGTGTGGAGACTGGTCTGCTTAAGCCTGGTATGGTCGTAACTTTCGCCCCTGCTGGTCTGACGACTGAAGTTAAGTCCGTTGAGATGCACCACGAAGCCCTCGTCGAGGCTGTGCCCGGAGACAACGTCGGTTTCAACGTCAAGAACGTTTCCGTCAAGGAATTGCGTCGTGGATACGTCGCTGGTGAC------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Pamphilius GCTGAGCGTGAACGTGGTATCACCATCGACATTGCCCTGTGGAAGTTCGAGACCGCCAAGTACTATGTCACCATCATCGACGCCCCCGGTCACAGGGATTTCATCAAGAACATGATCACTGGTACTTCGCAGGCTGATTGTGCTGTGCTGATCGTCGCTGCCGGTACGGGAGAGTTCGAGGCTGGTATCTCGAAGAACGGACAGACCCGTGAGCACGCCCTGCTCGCCTTCACCCTTGGAGTGAAGCAGCTGATCGTTGGTGTCAACAAGATGGACTCCTCCGAACCCCCGTACTCTGAGACCCGTTTCGAGGAAATCAAGAAGGAAGTTTCTTCTTACATTAAGAAAATCGGTTACAACCCAGCTGCCGTCGCTTTCGTGCCCATTTCCGGATGGAACGGAGACAACATGTTGGAGCCCTCCGCAAACATGCCATGGTTCAAGGGATGGGCTGTGGACCGCAAGGAAGGCAAGGCTGAAGGCAAATGCCTGATTGAGGCCCTCGACGCTATCCTGCCACCTTCCAGGCCAACCAACAAGCCCCTTCGTCTTCCCCTCCAGGACGTTTACAAAATTGGTGGTATTGGAACGGTACCCGTTGGCCGTGTGGAGACTGGTCTGCTCAAGCCTGGTATGGTCGTTACTTTCGCCCCTGCTGGTCTGACGACTGAAGTTAAGTCCGTTGAGATGCACCACGAAGCCCTCGTCGAGGCTGTGCCCGGAGACAACGTCGGTTTCAACGTCAAGAACGTTTCCGTCAAGGAATTGCGTCGTGGATACGTCGCTGGTGACTCAAAGAGCAACCCACCCAGGGGTGCTGCTGACTTCAACGCTCAGGTCATTGTACTGAACCACCCTGGTCAGATCAGCAACGGCTACACTCCAGTGCTTGATTGCCACACGGCTCACATTGCCTGCAAATTCGCTGAGATCAAAGAGAAGTGTGACCGTCGTACCGGCAAAACCACCGAAGAAAACCCCAAGGCCATCAAGTCTGGAGACGCCGCCATCGTCACCCTAGTGCCCAGCAAGCCCATGTGCGTCGAGCCTTCCCAGGAGTTCCCTCCTTTGGGACGTTTCGCTGCACGTGAT Cephalcia ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------TCCGTCAAGGAATTGCGTCGTGGATACGTCGCTGGTGACTCAAAGAGCAACCCACCCAGGGGTGCTGCTGACTTCAACGCTCAGGTCATNGTACTGAACCACCCTGGTCAGATCAGCAACGGCTACACTCCAGTGCTCGATTGCCACACGGCTCACATGGCCTGCAAATTCGCTGAGATCAAAGAGAAGTGTGACCGTCGTACCGGCAAAACCACCGAAGAAAACCCCAAGGCCATCAAGTCTGGAGACGCCGCCATCGTCACCCTAGTGCCCAGCAAGCCCATGTGCGTCGAGGCTTCCCAGGAGTTCCCTCCTTTAGGACGTTTCGCNGTNCGTGAC Acantholyda ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Megalodontesc GCTGAGCGCGAACGTGGTATCACCATTGATATTGCCCTCTGGAAGTTTGAAACCTCCAAGTACTACGTCACCATCATTGATGCTCCAGGACACAGGGATTTCATTAAGAACATGATTACTGGAACTTCTCAGGCTGATTGTGCCGTATTGATCGTTGCTGCTGGTACTGGTGAATTCGAAGCCGGTATCTCAAAGAACGGTCAGACTCGTGAGCACGCCCTGTTGGCTTTCACTCTTGGTGTCAAACAATTGATTGTTGGTGTCAACAAGATGGATTCAACCGAACCTCCCTACTCCGAATCCCGTTTCGAGGAAATCAAGAAAGAAGTTTCTTCGTACATCAAGAAAATTGGTTACAATCCTGACGCCGTTGCTTTCGTACCTATCTCTGGATGGCACGGAGATAACATGTTGGAGCCATCTTCCAAAATGCCATGGTTCAAGGGATGGGCTGTTGAACGTAAAGATGGAAACGCTAAGGGTAATTGCCTCATCGAAGCTCTCGACGCCATCCTCCCTCCCAGCAGACCCACTGACAAGCCCCTCCGTCTTCCATTGCAGGACGTTTACAAAATTGGTGGTATCGGAACGGTACCCGTTGGTCGTGTAGAAACTGGTATGCTGAAACCAGGTATGGTCGTTACATTCGCCCCCGCTGGTCTGACCACTGAAGTTAAATCCGTGGAAATGCACCACGAAGCCTTTACGGAGGCTGTGCCCGGTGACAACGTTGGTTTCAACGTCAAGAACGTTTCTGTTAAGGAATTGCGTCGTGGATACGTAGCTGGTGACTCTAAGAACAATCCACCAAGGGGTGCTGCTGACTTCACAGCACAGGTCATCGTGTTGAATCACCCTGGTCAAATCAGTAACGGATACACGCCTGTGTTGGATTGCCACACGGCTCACATCGCTTGCAAGTTCGCTGAAATCAAAGAGAAGTGCGACCGTCGTACTGGTAAAACCACTGAAGAAAACCCTAAGGCTATTAAATCCGGAGACGCTGCGATTGTTAACCTCGTCCCCAGCAAGCCCATGTGTGTAGAGGCCTTCCAAGAGTTCCCGCCTTTAGGACG---------------- Megalodontessk ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Cephus GCCGAGCGTGAACGTGGTATCACCATTGACATTGCTCTCTGGAAATTCGAGACGGCCAAATATTACGTGACCATCATCGATGCTCCCGGACACAGAGATTTCATCAAGAACATGATCACGGGAACGAGTCAGGCCGATTGTGCTGTGCTAATTGTGGCAGCTGGTACTGGAGAGTTCGAGGCGGGTATCTCGAAGAACGGACAGACCCGTGAGCACGCACTTCTTGCTTTCACCCTGGGTGTCAAGCAACTCATCGTCGGTGTCAACAAGATGGACTTCACCACACCTCCATACTCTGAGGCTCGTTACGAGGAAATCAAGAAGGAAGTATCGTCATACATTAAGAAGATTGGTTACAACCCATCTGCGGTTGCCTTTGTTCCCATTTCCGGATGGAACGGCGACAACATGTTGGAGCCCTCCGCCAAAATGCCATGGTTCAAGGGATGGACTGTGGAACGTAAGGAAGGCAAGGTTGAAGGAAAGACCCTCATTGAGGCCCTAGACGCTATCCTTCCACCTTCCAGGCCCACCGACAAGCCCCTGCGTCTTCCTCTCCAGGACGTATACAAGATTGGTGGTATTGGAACAGTACCCGTTGGTCGTGTTGAGACTGGTGTCTTGAAACCCTCTATGGTGGTGACTTTCGCTCCCGCTGGAATTACCACTGAAGTCAAGTCCGTTGAAATGCATCACGAGGCCCTGACGGAAGCTGTTCCCGGAGACAATGTTGGTTTCAACGTCAAGAGCGTCTCCGTGAAGGAATTGCGTCGTGGATACGTCGCTGGAGACTCCAAAAACAATCCACCCAAGGGTGCTGCTGACTTCACTGCACAGGTCATCGTGCTGAACCACCCTGGTCAGATTAGCAACGGATACACGCCCGTGTTGGATTGCCACACGGCACACATTGCGTGCAAGTTCGCAGAGATCAAGGAGAAGTGCGACCGTCGTACCGGCAAAACCACCGAGGAGAACCCGAAGGCAATCAAGTCCGGTGACGCCGCCATCGTCAATCTGGTGCCCAGCAAGCCCATGTGCGTCGAGGCTTTCCAGGAGTTCCCTCCC------------------------ Calameuta GCTGAGCGYGARCGTGGTATCACCATTGACATTGCTCTATGGARATTCGAGACGGCCAAGTATTACGTGACCATCATCGATGCTCCTGGACACAGAGATTTCATTAAGAACATGATCACGGGAACGAGTCAGGCTGATTGTGCTGTGCTGATCGTGGCAGCTGGTACCGGAGAGTTCGAAGCGGGTATCTCCAAGAACGGACAGACCCGYGAGCACGCACTTCTTGCTTTCACCCTGGGTGTCAAGCAGCTCATCGTCGGTGTCAACAAGATGGACTTCACCACACCCCCATACTCTGAGGCTCGTTACGAAGAAATCAAGAAGGAAGTATCGTCATACATCAAGAAGATTGGTTACAACCCAGCTGCTGTTGCCTTCGTTCCCATTTCCGGRTGGAACGGCGACAACATGTTGGAGCCCTCCGCCAAAATGCCATGGTTCAAGGGATGGACTGTGGAACGTAAGGAAGGCAAGGTTGAAGGAAAGTGCCTCATTGAGGCCCTAGACGCTATCCTCCCACCTTYCAGGCCCACCGACAAGCCCCTGCGTCTTCCTCTCCAGGACGTGTACAAGATTGGTGGTATTGGAACAGTACCCGTTGGTCGTGTGAAGACTGGTGTCTTGAAACCCTCTATGTTGGTGACTTTCGCTCCCGCTGGAATCACCACTGAAGTCAAGTCCGTYGAAATGCATCACGAGGCTCTAACGGAAGCTGTTCCCGGGGACAACGTTGGTTTCAACGTCAAGAGCGTCTCCGTCAAGGAATTGCGTCGTGGATACGTCGCTGGAGACTCCAAAAATAATCCACCCAAGGGTGCTGCTGACTTCACTGCACAGGTCATCGTGCTGAACCACCCTGGTCAGATTAGCAACGGATACACGCCCGTGCTGGATTGCCACACGGCACACAYTGCGTGCAAGTTCGCAGAGATCAAGGAGAAGTGCGACCGTCGTACCGGCAAAACTACCGAGGAAAACCCGAAGGCAATCAAGTCCGGTGACGCCGCCATCGTCAATCTGGTGCCCAGCAAGCCCATGTGCGTCGAGGCTTTCCAGGAGTTCCCTCCCCTGGGTCGTTTCGCCGTTCGTGAC Hartigia GCTGANCGCGAACGTGGTATCACCATTGACATTGCTCTCTGGAAATTCGAGACGGCCAAGTATTACGTGACCATCATCGATGCTCCTGGACACAGAGATTTCATCAAGAACATGATCACGGGAACAAGTCAGGCTGATTGTGCTGTGTTAATCGTGGCAGCTGGTACTGGAGAGTTCGAAGCGGGTATCTCCAAGAACGGACAGACCCGTGAACACGCACTTCTTGCTTTCACCTTGGGTGTCAAGCAGCTCATCGTCGGTGTCAACAAGATGGACTCCACTGCACCACCATACTCTGAGGCCCGTTTCGAGGAAATCAAGAAAGAAGTATCGTCATACATCAAGAAGATCGGTTATAACCCAGCTGCTGTTGCCTTCGTTCCCATTTCCGGATGGAATGGCGACAACATGCTGGAGGCCTCCGCCAAAATGCCATGGTTCAAGGGATGGCTTGTGGAACGTAAGGAAGGCAAGGTTGAAGGAAAGTGCCTCATTGAGGCCCTAGATGCTATCCTCCCACCTTCCAGGCCCACCGACAAGCCTCTGCGTCTTCCTCTCCAGGACGTCTACAAGATTGGTGGTATCGGAACAGTACCCGTTGGTCGTGTGGAGACTGGTGTCTTGAAACCCTCTATGGTGGTGACTTTCGCTCCTGCTGGAATCACCACTGAAGTCAAGTCCGTTGAAATGCATCACGAAGCCCTGACGGAGGCTGTTCCCGGAGACAATGTTGGTTTCAACGTCAAGAGCGTCTCCGTCAAGGAATTGCGTCGTGGATACGTCGCTGGAGACTCCAAAAACAATCCACCTAAGGGTGCTGCTGACTTCACTGCACAGGTCATCGTGCTGAACCACCCTGGTCAGATTAGCAATGGATACACGCCCGTGCTGGATTGCCACACGGCTCACATCGCGTGCAAGTTCGCAGAGATCAAGGAGAAGTGCGACCGTCGTACCGGCAAAACCACTGAGGAGAACCCGAAGGCAATCAAATCCGGTGACGCTGCCATCGTCAACCTGGTGCCCAGCAAGCCCATGTGCGTCGAGGCTTTCCAGGAGTTCCCTCCCTTGGGTCGTTTCGCCGTTCGTGAC Syntexis GCTGANCGTGAGCGTGGTATCACCATCGACGTTGCCCTGTGGAAGTTCGAAACGGGCAAGTACTATGTCACTATTATCGACGCCCCTGGACACAGAGATTTCATCAAGAACATGATTACCGGAACCTCGCAGGCTGACTGCGCAGTCTTGATCGTCGCGGCTGGTACTGGTGAATTTGAGGCTGGTATCTCGAAGAACGGACAGACCCGTGAGCACGCTCTGCTTGCCTTCACTCTTGGTGTGAAGCAGCTGATCGTCGGTGTTAACAAGATGGACTCCACCGAGCCCCCGTATTCGGAAGCTCGTTTCGAGGAAATCAAGAAGGAAGTGTCTTCTTACATCAAGAAGATCGGTTACAATCCCGCTGCCGTCGCTTTTGTACCTATTTCTGGCTGGCACGGTGATAACATGTTGGAGCCGTCCTCCAAAATGCCATGGTTCAAGGGATGGAACGTCGACCGCAAGGAAGGCAAGGGTGAAGGCAAGTGCCTCATCGAAGCTCTCGATGCCATCCTGCCACCCAGTAGGCCAACCGACAAGCCGCTCCGTCTTCCCCTCCAGGACGTGTACAAGATTGGTGGTATCGGAACGGTACCAGTCGGTCGTGTTGAGACCGGTGTCCTGAAGCCCGGTATGGTGGTTACGTTTGCCCCTGCTGGACTGACGACTGAAGTCAAATCCGTTGAAATGCATCACGAGGCTCTGACCGAAGCTGTACCCGGTGACAACGTCGGTTTCAACGTCAAGAACGTATCTGTCAAGGAGTTGCGTCGTGGATACGTCGCTGGTGACTCCAAGAATAATCCCCCCAAAGGAGCTGCTGACTTCACTGCACAGGTAATTGTGCTCAACCACCCTGGTCAGATCAGCAATGGATACACGCCCGTGTTGGATTGCCACACGGCCCACATTGCTTGCAAGTTTGCCGAGATCAAGGAGAAGTGCGACCGTCGTACGGGAAAGACCACAGAGGAAAATCCTAAGGCCATCAAGTCTGGTGATGCCGCCATCGTGAACCTCGTGCCGAGCAAACCCATGTGCGTCGAGGCTTTCCAGGAATTCCCTCCTCTAGGACGTTTC------------ Sirex GCTGAGCGCGAACGTGGTATCACCATTGACATTGCCCTGTGGAAATTCGAAACGGCAAAATATTACGTTACTATTATTGATGCTCCCGGACACAGAGATTTCATCAAGAACATGATCACTGGAACTTCGCAAGCCGATTGTGCAGTTCTGATTGTCGCGGCTGGTACCGGAGAATTTGAGGCCGGTATTTCGAAAAATGGACAGACTCGTGAGCATGCTTTGCTCGCTTTCACTCTCGGCGTCAAGCAATTGATCGTTGGTGTGAACAAGATGGACTCTACCGAACCTCCTTACTCCGAAGCCCGTTTCGAGGAAATTAAGAAAGAAGTCTCCTCGTACATTAAAAAGATTGGTTACAATCCTAGTGCTGTTGCCTTTGTACCAATTTCTGGCTGGCACGGAGACAACATGTTAGAAAGTTCTTCTAAAATGCCCTGGTTCAAGGGATGGAACGTGGAGCGCAAGGAGGGCAAGGCTGAAGGCAAGTGCCTCATCGAAGCACTCGATGCCATTTTGCCACCTTCCAGGCCAACTGACAAGCCTCTGCGTCTTCCCCTTCAG------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------GTTATTGTGCTGAACCATCCTGGTCAGATTAGCAATGGATACACGCCTGTGTTAGATTGTCACACGGCTCACATTGCATGCAAATTCGCCGAGATCAAGGAGAAGTGCGATCGTCGTACGGGAAAAACCACGGAAGAAAATCCTAAGGCCATCAAATCTGGCGATGCTGCAATTGTGAACCTCGTGCCGAGCAAACCCATGTGCGTCGAATCTTTCCAAGAATTCCCACCGTTGGCACGTTTTGCGGTGCGTGAC Xeris ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Urocerus ------------------------------ATTGCCCTCTGGAAATTTGAAACGGCAAAGTACTACGTTACCATCATCGATGCTGGCGGACACAGAGATTTCATCAAGAACATGATCACCGGAACTTCGCAAGCCGATTGTGCAGTTCTAATTGTCGCGGGTGGCACCGGAGAGTTTGAGGCGGGTATTTCAAAAAATGGACAGACTCGCGAGCATGCTTTACTCGCGTTCACTCTCGGTGTCAAGCAATTGATCGTTGGTGTGAACAAGATGGACTCTACCGAACCTCCTTATTCCGAGTCCCGTTTCGAGGAAATCAAGAAGGAAGTGTCCTCGTACATCAAAAAGATTGGTTACAATCCTAGTGCCGTTGCCTTCGTACCAATTTCTGGCTGGCACGGAAACAATATGTTGGAAAGTTCTTCTAAAATGCCCTGGTTCAAGGGATGGAACGTAGAGCGCAAGGAAGGCAAGGCCAAAGGCAAGTGCCTCATCGAAGCACTCAATGCCATCCTGCCACCTTCCAGGCCAACTGACAAGCCTCTCCGTCTTCCCCTTCAG------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------TCTGTCAAGGAACTGCGTCGTGGATACGTCGCTGGCGACTCGAAGAACAATCCTCCCAAAGGCGCCGCTGATTTCACTGCACAGGTTATCGTACTGAACCATCCTGGTCAGATTAGCAACGGGTACACACCTGTGCTGGATTGTCACACGGCTCACATTGCGTGCAAATTCGCCGAGATTAAGGAGAAGTGCGATCGTCGTACGGGAAAGACCACCGAAGAAAATCCCAAGGCCATCAAATCTGGCGATGCCGCCATTGTGAACCTCGTGCCGAGCAAACCCATGTGCGTCGAATCTTTCCAAGAATTTCCCCCGTTGGGACGTTTCGCCGTTCGTGAC Tremex -----------------------------CATTGCTCTCTGGAAATTTGAAACAGCCAAATACTATGTTACTATTATTGATGCTCCCGGACACAGAGATTTTATTAAAAACATGATTACTGGAACTTCACAAGCCGATTGTGCAGTTTTAATCGTCGCAGCTGGTACCGGAGAGTTCGAAGCGGGTATTTCAAAAAACGGACAGACACGTGAGCATGCTTTGCTCGCTTTCACTCTTGGTGTCAAGCAACTGATTGTTGGTGTTAATAAGATGGACTCTACCGAACCTCCTTATTCCGAGTCTCGTTTCGAGGAAATCAAGAAGGAAGTGTCCTCGTACATTAAGAAGATTGGTTACAATCCTAGTGCTGTTGCTTTCGTACCTATTTCTGGCTGGCACGGAGACAATATGTTAGAAAGTTCTGCTAAAATGCCCTGGTTCAAGGGATGGAACGTGGAGCGCAAGGAAGGAAAGGCCAATGGNAACACCCTCATCGAGGCACTCGATGCTATCTTGCCGCCTTCCAGGCCAACTGACAAGCCTCTACGTCTTCCCCTCCAGGATGTGTACAAGATTGGTGGTATTGAAACAGTACCCGTTGGTCGTGTCGAGACTGGTGTACTGAAACCCGGTATGGTGGTTACCTTTGCTCCTGCTGGAATCACCACTGAAGTTAAATCCGTTGAAATGCATCACGAGGCCTTGACCGAGGCTGTACCGGGTGACAATGTTGGTTTCAACGTAAAGAACGTTTCCGTCAAGGAATTACGTCGTGGATACGTTACTGGTGACTCGAAGAACAATCCTCCCAGAGGTGCCGCTGATTTCACTGCACAGGTCATTGTATTGAATCACCCTGGTCAGATAAGCAATGGATACACGCCCGTGCTGGATTGTCACACGGCTCACATTGCCTGCAAATTCGCTGAGATCAAGGAGAAGTGCGACCGTCGTACGGGAAAGACCACAGAGGAAAATCCCAAGGCGATAAAATCTGGCGACGCTGCCATTGTAAACCTCGTCCCGAGCAAACCCATGTGCGTCGGAGCTTCCCAGGAGTTTCCCCCGTTGGGACGTTTCGCCGTTCGTGAC Xiphydria GCTGAGCGCGAGCGTGGTATCACCATCGACATCGCTCTCTGGAAGTTTGAGACGAACAAGTACTATGTCACGATCATCGACGCCCCGGGACATAGAGATTTCATCAAAAACATGATCACGGGAACCTCGCAAGCTGACTGCGCCGTTTTGATTGTCGCTGCCGGTACCGGCGAATTCGAGGCCGGTATCTCGAAAAATGGACAAACCCGCGAACACGCTCTGCTCGCCTTCACCCTCGGCGTGAAGCAGCTGATCGTCGGCGTGAACAAGATGGACTCCACAGAGCCTCCCTACTCGGAGGCCCGTTTCGAAGAAATTAAGAAAGAAGTTTCCTCNTACATCAAGAAGATCGGTTACAATCCCGCCGCTGTGGCTTTCGTCCCCATTTCCGGCTGGCACGGAGACAACATGCTCGAGCCCTCCTCCAAAATGCCNTGGATCAAGGGATGGGCCGTGGAGCGCAAAAAAGGCAAGGCCGATGGCAAATGCCTGATCGAAGCTCTCAATGCCATCCTGCCACCTTCCAGGCCAACTGACAAGCCTCTCCGTCTTCCCTTTCAGGATGTATACAAAATTGGTGGAATCGGAACGGTACCAGTCGGTCGTGTGGAGACTGGTGTCCTCAAGCCAGGCATGGTTGTTACATTCGCACCCGCGGGCCTGACGACTGAAGTTAAGTCCGTAGAGATGCACCACGAAGCCCTGACCGAGGCCGTCCCGGGTGACAACGTTGGTTTCAACGTTAAGAACGTCTCAGTCAAGGAATTGCGTCGTGGTTACGTCGCTGGTGACTCGAAAAATAACCCGCCCAGAGGAGCAGCTGACTTCACTGCACAGGTTATCGTGCTGAATCACCCTGGTCAAATCAGCAACGGATATACGCCAGTGTTGGACTGTCACACGGCCCACATTGCGTGCAAGTTCGCTGAGATCAAGGAGAAGTGCGACCGTCGTACGGGAAAGACCACCGAAGAGAATCCCAAGTCCATCAAGTCTGGCGATGCCGCTATTGTGACTCTTGTGCCCAGCAAACCCATGTGCGTCGAGGCCTTCCAAGAGTTCCCTCCGTTAGGACGGTTCGCAGTGCGTGAC Orussus GCTGAGCGYGARCGTGGTATCACTATTGACATTGCCTTGTGGAAGTTTGAAACGGCTAAATACTATGTCACTATTATCGATGCGCCTGGACACAGAGACTTTATTAAAAACATGATTACTGGAACCTCGCAAGCTGACTGCGCTGTACTCATTGTCGCCGCAGGTACCGGCGAGTTTGAAGCCGGTATCTCCAAGAATGGACAGACTCGTGAGCACGCTCTGCTCGCGTTTACCCTTGGCGTCAAGCAGCTGATCGTTGGTGTCAATAAAATGGACTCCACCGAACCCCCATACTCGGAAACTCGATTCGAGGAAATTAAGAAGGAAGTGTCGTCGTACATTAAGAAAATCGGTTACAACCCTGCTGCTGTAGCGTTTGTACCGATCTCAGGATGGCACGGAGATAACATGTTGGAATCCTCTTCCAAGATGCCTTGGTTCAAGGGATGGACCGTGGAACGAAAGGAAGGCAAGACGGAGGGCAAGTGCCTCATCGAAGCCCTCGATGCTATCTTGCCCCCTACCAGGCCTACCGACARGCCTCTGCGTCTTCCYCTCCAGGACGTGTACAAGATCGGCGGTATCGGAACGGTACCGGTAGGTCGTGTGGAGACTGGCGTTCTGAAACCCGGTATGGTGGTTACGTTTGCTCCTGCTGCCCTGACCACCGAAGTGAAGTCGGTCGAGATGCATCACGAGGCCTTGGTGGAGGCCGTTCCCGGTGACAACGTAGGTTTCAACGTGAAAAACGTGCCTGTTAAGGATCTGCGTCGAGGATACGTAGCTGGTGATTCGAAAAATAATCCCCCAAGGGGAGCCGCTGACTTCACTGCACAGGTAATCGTATTAAACCACCCTGGCCAAATTCAGGACGGGTACACGCCCGTGTTGGATTGTCACACGGCCCACATTGCTTGCAAGTTCGCCGAGATCAAGGAAAAGTGCGACCGTCGTACGGGAAAGAGTACCGAAGTCAACCCGAAGTCCATCAAGACCGGCGACGCCGCCATCGTGACCCTTGTGCCCACCAAGCCCATGTGCGTCGGAGCCTTCCAGGAGTTCCCACCCCTGGGACGATTCGCGGTGCGTGAC StephanidaeA ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ StephanidaeB ---------------------ACCATCGACATTGCTCTGTGGAAGTTTGAAACTGCCAAATACTATGTCACGATTATTGATGCTCCAGGACATAGAGATTTTATCAAGAATATGATTACTGGAACATCACAAGCTGATTGTGCAGTATTAATTGTGGCAGCTGGCACTGGAGAATTTGAAGCTGGAATTTCGAAAAATGGACAAACTCGTGAACATGCATTACTTGCTTTCACTTTGGGTGTCAAACAGCTAATTGTGGGTGTTAATAAGATGGACTCAACTGATCCTCCATATTCTGAGGCTCGTTTTGAGGAAATAAAGAAAGAAGTTTCTTCGTATATTAAAAAAATTGGTTATAATCCTGCTGGAGTTGCATTTGTGCCAATTTCTGGTTGGCATGGTGATAATATGTTGGAGGTTTCTCCTAAAATGCCTTGGTTTAAGGGATGGAATGTTGAACGTAAGGAAGGTAAAGGTGAAGGAAAATGCCTTATTGAAGCTCTTGATGCTATTTTACCTCCTACAAGACCAACAGATAAGCCTCTCCGTCTTCCTCTCCAGGACGTGTACAAAATTGGTGGTATTGGAACAGTACCAGTTGGTCGTGTGGAGACTGGTGTGCTAAAACCTGGTATGGTAGTCACATTTGCACCTGCTGGTCTAACAACTGAAGTAAAATCTGTTGAAATGCATCATGAAGCATTGCAAGAAGCAGTCCCCGGAGACAATGTTGGTTTCAATGTTAAAAACGTATCTGTAAAAGAATTGCGT--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- Megalyra GCTGAGCGTGAGCGTGGTATCACGATCGATGTCGCTCTGTGGAAGTTTGAGACATCCAAGTACTATGTTACCATCATTGACGCTCCTGGGCACAGAGATTTCATCAAGAACATGATCACTGGAACGTCGCAGGCCGACTGCGCGGTACTGATCGTTGCTGCTGGTACTGGAGAGTTCGAAGCTGGTATTTCCAAGAATGGACAAACCCGTGAGCATGCTCTGCTCGCCTTCACCTTGGGTGTCAAGCAACTGATCGTTGGTGTGAACAAAATGGACTCCACCGAGCCTCCGTACTCGGAGTCGCGTTTTGAGGAAATCAAGAAGGAAGTTTCCTCCTACATCAAGAAGATCGGTTACAATCCGGCTGCCGTCGCCTTTGTCCCGATCTCTGGATGGCACGGAGACAACATGTTGGAGGTCTCCTCCAAAATGCCCTGGTTCAAGGGATGGGTTGTTGAGCGCAAGGAAGGAAAGGCAGAAGGAAAGTGCCTCATTGAGGCTCTTGACGCCATCCTCCCGCCTTCCAGGCCTACCGACAAGGCTCTCCGTCTTCCTCTGCAGGACGTGTACAAAATCGGCGGTATCGGAACGGTACCCGTCGGTCGTGTCGAGACTGGTGTCCTGAAGGCCGGTATGGTCGTGACGTTCGCCCCCGCTGGGCTGACTACTGAAGTTAAGTCCGTGGAAATGCACCACGAAGCACTCACAGAAGCTGTCCCCGGAGACAACGTTGGCGTCAACGTCAAGAACGTGCCCGTTAA-GAGCTGCGTCGTGGTTACGTAGCTGGAGACTCTAAGAACCAACCTCCCAAGGGAGCTTCTGACTTCACCGCACAGGTGATTGTGCTGAACCATCCTGGTCAAATCAGCAACGGTTACACGCCCGTGCTGGATTGCCACACGGCTCACATTGCCTGCAAGTTCGCCGAGATCAAAGAGAAGTGCGACCGTCGTACCGGCAAGACCACCGAAGAGAACCCTAAGGCCATCAAGTCCGGCGACGCGGCCATCGTGAACCTCATACCGACCAAGCCCATGTGCGTCGAGGCCTTCCAAGAGTTCC----------------------------- Trigonalidae GCTGAGCGTGAACGTGGTATTACCATTGACATTGCCCTCTGGAAATTCGAGACTGCCAAGTACTACGTCACCATCATCGACGCCCCAGGCCACAGAGATTTCATCAAGAACATGATCACTGGTACATCGCAGGCTGATTGCGCCGTGTTGATTGTCGCTGCTGGTACTGGAGAGTTCGAAGCTGGTATCTCGAAGAACGGACAAACCCGTGAGCACGCTCTGCTTGCTTTCACCCTCGGTGTCAAGCAGCTCATCGTCGGAGTCAACAAGATGGATTCCACCGAGCCCCCGTACTCGGAAGCCCGTTTCGAAGAAATTAAGAAAGAAGTATCGTCGTACATCAAGAAAATCGGTTACAATCCAGCGGCGGTTGCTTTCGTGCCCATCTCTGGTTGGCACGGAGACAACATGTTGGAAACTTCTTCCAAGATGCCATGGTTCAAGGGATGGGCCGTAGACCGTAAAGAAGGAAAGGCTGACGGTAAATGCCTTATCGAGGCCCTCGACGCTATCCTGCCACCTTCCAGACCTACCGACAAGGCTCTCCGTCTTCCGCTTCAGGACGTGTACAAGATTGGTGGTATCGGAACTGTACCGGTCGGCCGTGTGGAGACTGGTCTACTGAAACCCGGTATGCTCGTCACCTTCGCACCTGCCGGTCTGACCACCGAAGTTAAGTCCGTCGAGATGCACCACGAAGCTCTCGTCGAGGCTGTCCCCGGTGACAATGTCGGTTTCAACGTCAAGAACGTACCTGTCAAGGAATTGCGTCGTGGATACGTCGCTGGTGATTCCAAAAACAACCCACCCAAGGGCGCTGCCGACTTCACCGCGCAGGTCATTGTGCTGAACCACCCCGGTCAGATCAGCAACGGNTACACGCCCGTGCTCGATTGTCACACGGCTCACATCGCCTGCAAGTTCGCCGAGATCAAGGAGAAGTGCGACCGTCGTACCGGCAAGACCACCGAAGAGAACCCGAAGGCCATCAAGTCCGGCGACGCAGCTATCGTGAACCTCGTGCCGAGCAAGCCCATGTGCGTCGAGGCTTTCCAAGAGTTCCCGCCATTGGGACGTTTCGCGGTGCGTGAC Chalcidoidea ---------------------------------------TGGAAATTCGAGACTTCCAAGTACTACGTCACCATCATCGACGCTCCCGGACACAGAGATTTCATCAAGAACATGATCACTGGTACGTCGCAGGCCGATTGCGCCGTGTTGATCGTCGCTGCCGGTACTGGTGAATTCGAAGCCGGTATCTCCAAGAACGGTCAGACCCGTGAGCACGCTCTGCTTGCCTTCACTCTTGGTGTCAAGCAGTTGATCGTTGGTGTCAACAAGATGGACTCCACCGAGCCACCCTACTCGGAGGCTCGTTTCGAGGAAATCAAGAAGGAAGTATCTTCCTACATCAAGAAGATCGGTTACAACCCAGCCGCTGTTGCCTTCGTACCCATCTCTGGATGGCACGGAGACAACATGTTGGAGCCATCCACCAAAATGCCCTGGTTCAAGGGATGGGCTGTTGAGCGCAAGGAAGGCAAGGCCGACGGCAAGTGCCTCATCGAAGCTCTTGACGCCATCCTGCCACCCAGCAGGCCCACCGACAAAGCCCTTCGTCTTCCCCTCCAGGACGTCTACAAGATTGGTGGTATTGGAACGGTACCCGTCGGTCGTGTCGAGACTGGTGTGCTGAAGCCCGGTATGGTCGTCACCTTCGCCCCAGCTGGACTCACCACTGAGGTTAAGTCCGTCGAAATGCACCACGAAGCTCTGACTGAGGCCGTCCCCGGAGACAACGTTGGTTTCAACGTTAAGAACGTTTCCGTCAAGGACTTGCGTCGTGGATACGTCGCTGGTGACTCCAAGAACAACCCACCCAAGGGAGCCGCTGACTTCACCGCTCAGGTCATCGTCCTGAACCACCCTGGTCAGATCAGCAACGGATACACGCCCGTGCTCGATTGCCACACGGCCCACATTGCTTGCAAGTTCGCCGAAATCAAGGAGAAGTGCGACCGTCGTACCGGTAAAACCACCGAAGAGAACCCCAAGGCCATCAAGTCTGGTGATGCCGCCATCGTCAACCTTGTGCCCAGCAAGCCCATGTGCGTAGAGGCCTTCCAGGATTTCCCACCCCTGGGACGTTTCGCTGTACGTGAC Evanioidea ------CGTGAACGTGGTATCACCATTGATATCGCCTTGTGGAAGTTCGAGACGGCAAAGTACTACGTGACATTCATCGACGCCCCGGGACACAGGTTCTTCATAAAGAACATGATCACCGGTACATCGCAGGCTGACTGCGCGGTATTGATCGTCGCCGCCGGTACCGGCGAGTTCGAGGCGGGCATCTCGAAGAACGGGCAGACGCGCGAGCACGCCCTCCTCGCTTTCACCCTGGGAGTCAAGCATCTGATCGTCGGGGTCAACAAGATGGACTCCACCGAGCCGCCCTACTCCGAGGCTCGCTTCGAGGAGATCAAGAAGGAGGTCTCCTCGTACATCAAGAAGATCGGCTACAACCCGGCGGCTGTTGCCTTCGTGCCTATCTCCGGGTGGCACGGCGACAACATGCTGGAGAGCTCCTCCAAGATGCCCTGGTTCAAGGGATGGATGGTCGAGCGTAAGGAGGGCAAGGCCGAGGGCAAATGCCTCATCGAAGCTCTCGATGCCATCCTTCCACCCAGCCGGCCTACCGACAAGGCCCTCCGTCTTCCTCTCCAGGACGTGTACAAAATCGGTGGAATTGGAACGGTGCCAGTCGGTCGTGTGGAAACCGGCATCCTGAAGCCCGGAATGGTCGTGACCTTCGCGCCCGCCGGTCTCACCACTGAAGTCAAGTCCGTAGAAATGCACCACGAGGCCCTGACAGAAGCCGTRCCGGGTGACAACGTTGGCTTCAACGTTAAGAACGTCTCCGTCAAAGAACTGCGTCGCGGATATGTTGCCGGTGAC------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Ichneumonidae GCTGAGCGTGAGCGTGGTATCACCATCGATATCGCTCTATGGAAGTTCGAGACTGCTAAATATTACGTGACGATCATCGATGCTCCCGGACACAGAGATTTCATCAAAAACATGATCACCGGCACGTCGCAAGCCGATTGCGCTGTTCTCATCGTAGCTGCTGGTACTGGTGAATTTGAGGCCGGTATCTCGAAGAACGGACAGACCCGAGAGCACGCCCTCCTCGCCTTCACCCTTGGCGTCAAGCAGCTCATCGTCGGCGTCAACAAGATGGACTCGACCGAACCTCCCTATTCCGAGTCTCGTTTCGAGGAAATTAAGAAGGAAGTCTCGTCTTACATCAAGAAAATTGGTTATAATCCGGCAGCAGTCGCTTTCGTACCAATTTCCGGATGGCACGGTGATAACATGTTGGAGCCATCCTCCAAAATGCCATGGTTCANGGGATGGGCTGTTGAGCGCAAGGAAGGCAAAGCCGATGGCAAGTGCCTCATCGAAGCACTCGATGCTATTTTGCCACCCTCCAGGCCAACTGACAAGGCCCTTCGTCTTCCCCTCCAGGACGTTTACAAGATTGGCGGTATTGGAACAGTGCCCGTCGGTCGTGTTGAGACCGGTGTCCTTAAACCCGGTATGGTCGTGACCTTTGCACCAGCTGGTCTCACGACTGAAGTTAAGTCCGTCGAGATGCACCACGAGGCCCTCGTCGAGGCTGTTCCCGGTGACAATGTCGGTTTCAACGTCAAGAACGTTTCCGTCAAGGAATTACGTCGTGGCTACGTTGCTGGTGACTCCAAGAACAATCCACCCAGAGGTGCTGCCGATTTCAATGCTCAGGTCATCGTCCTCAACCATCCTGGTCAGATCAGCAATGGATACACACCCGTGTTGGATTGCCACACGGCTCACATCGCTTGCAAATTCGCCGAGATCAAAGAAAAGTGCGACCGTCGTACTGGCAAAACGACCGAGGAAAATCCAAAGGCCATTAAATCCGGCGATGCCGCTATTGTAAATCTCGTACCGAGCAAACCCATGTGCGTTGAGGCTTTCCAAGAATTCCCACCTCTTGGACGTTTTGCAGTTCGTGAC Cynipoidea GCTGAGCGTGAACGTGGTATCACCATCGATATCGCTCTATGGAAGTTTGAGACCGCGAAGTATTACGTCACCATCATTGACGCCCCCGGACACAGAGATTTCATTAAGAACATGATCACTGGAACTTCGCAAGCTGACTGCGCCGTGTTGATCGTCGCTGCCGGTACCGGAGAATTCGAAGCTGGTATTTCGAAGAATGGACAAACCCGTGAACATGCTCTGCTCGCTTTTACCCTGGGAGTCAAGCAGCTTATCGTCGGAGTCAACAAGATGGACTCCACCGAGCCTCCATACTCTGAGTCTCGTTTCGAGGAAATTAAGAAGGAAGTCTCTTCTTATATCAAAAAGATCGGTTACAATCCTGCCTCTGTTGCTTTCGTTCCCATCTCAGGATGGCACGGAGACAACATGTTGGAGCCTTCCTCTAAAATGCCTTGGTTCAAGGGATGGGCGGTCGAACGTAAGGAGGGAAAAGCTGAAGGCAAGTGCCTCATTGAAGCCCTTGATGCCATCTTACCTCCCAGCAGACCTACCGACAAGGCGCTGCGTCTTCCCCTCCAGGACGTCTACAAGATCGGTGGTATCGGAACGGTGCCAGTCGGTCGAGTGGAGACTGGTGTTTTGAAGCCCGGTATGGTAGTTACTTTCGCCCCTGCTGGATTGACCACTGAAGTTAAGTCTGTCGAGATGCACCACGAAGCACTGACAGAGGCTGTTCCTGGTGACAACGTTGGTTTCAACGTTAAAAACGTCTCCGTTAAGGATCTGCGTCGTGGTTATGTCGCCGGAGATTCCAAAAACAACCCACCCAAGGGTGCTGCTGACTTCACCGCACAAGTTATCGTCTTGAACCATCCCGGACAAATTAGCAACGGCTACACGCCAGTGTTGGATTGCCACACGGCCCACATTGCCTGTAAATTCGCCGAGATTAAGGAAAAGTGTGACAGACGTACCGGAAAAACTACTGAGGAGAACCCTAAGGCCATCAAGTCTGGAGACGCAGCTATTGTAAACCTCGTTCCAAGCAAACCAATGTGTGTCGAGGCTTTCCAGGAGTTCCCTCCATTGGGACGTTTCGCCGTTCGTGAC ApoideaA -----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------TGGTGACAATGTTGGTTTCAACGTTAAGAACGTCTCCGTTAAGGAATTGCGTCGTGGTTACGTAGCTGGTGACTCCAAGAACAACCCACCAAGGGGTGCTGCTGACTTTACCGCACAAGTCATCGTGCTGAACCACCCTGGCCAGATCAGCAGCGGATACACCCCAGTGTTGGATTGTCACACTGCTCACATTGCGTGTAAGTTCGCCGAGATGAAGGAGAAGTGTGACCGTCGTACCGGTAAGACCACCGAGGAGAATCCAAAGTCCATCAAATCTGGCGACGCCGCCATCGTCATCCTCGTGCCGAGCAAGCCCATGTGCGTGGAAGCTTTCCAAGAGTTCCCGCCACTGGGACGCTTCGCTGTCCGTGAC ApoideaB ------------------ATTATCATTGATATTGCCCTGGGGAAGTTCGAAACCGCCAAATACTATGTCACAATTATTGACGCTCCTGGACACAAAGATTTCATCAAGAACATGATTCATGGTACCTCGCAGGCCGACTGTGCAGTACTGATTGTTGCCGCCGGAACTGGAGAGTTCGAAGCCGGTATTTCGAAAAATGGACAAACTCGTGAGCATGCTCTGCTCGCTTTTACTCTTGGTGTAAAGCAGTTGATTGTTGGTGTTAACAAGATGGACTCCACTGAGCCACCATACTCCGAAGCCAGATTCGAGGAAATCAAGAAGGAAGTATCTTCCTACATCAGGAAAATCGGTTATAATCCAGCTGCTGTTGCATTCGTGCCCATTTCTGGTTGGCATGGAGACAACATGTTGGAGGCTTCTTCCAAAATGGTCTGGTTCAAAGGATGGGCTGTTGAGCGCAAAGAGGGCAAGGCTGATGGCAAGACCCTCATCGAAGCTCTCGATGCCATCCTCCCACCCACTAGGCCCACAGACAAGGCTCTCCGTCTTCCCCTCCAGGACGTGTACAAAATCGGTGGTATCGGAACAGTACCCGTTGGTCGTGTGAAAACTGGTGTGTTGAAGCCGGGTATGGTTGTCACATTCGTTCCAGTTGGACTCACCACTGAAGTTAAGTCCGTTGAAATGCACCACGAAGCTCTGCAAGAAGCTGTTCCTGGTGACAACGTTGGTTTCAACGTTAAGAACGTATCTGTTAAGGAATTACGTCGTGGCTACGTAGCCGGTGACTCCAAAAACAGCCCACCACGCGGTGCCGCTGACTTCACTGCACAGGTTATCGTGCTGAACCACCCGGGCCAAATCACTAACGGATACACGCCAGTGTTGGATTGTCACACTGCCCACATCGCGTGTAAGTTCGCCGAAATCAAGGAGAAGTGCGACCGTCGTACCGGAAAGACCACTGAGGAGAATCCAAAATTCATAAAATCCGGAGATGCGGCCATTGTTACGCTCGTGCCGAGCAAGCCAATGTGCGTGGAGGCTTTCCAAGAATTCCCTCCTCTGGGACGTTTCGCTGTCCGTGAC ApoideaC GCTGAACGTGAACGTGGTATTACCATTGACATTGCTTTATGGAAATTTGAAACTTCCAAGTACTATGTTACTATTATCGATGCTCCGGGGCATAGAGATTTCATCAAAAACATGATTACCGGTACCTCACAAGCTGACTGTGCAGTATTAATTGTTGCTGCTGGTACTGGCGAGTTTGAAGCTGGTATTTCAAAGAATGGACAAACCCGTGAGCATGCTTTGCTTGCTTTTACTCTCGGTGTAAAACAGTTGATCGTTGGTGTTAACAAGATGGACTCCACTGAGCCACCATATTCTGAAGTCCGATTTGAAGAAATTAAGAAGGAAGTGTCATCTTACATCAAGAAGATCGGTTACAATCCTGCTGGAGTAGCATTTGTGCCAATTTCTGGTTGGCACGGAGATAATATGTTGGAGATTTCTTCAAAAATGCCTTGGTTCAAGGGATGGACCGTGGAACGTAAAGAAGGTAAAGTTGAGGGAAAATGCCTTATTGAAGCGCTTGACGCCATTCTTCCACCATCTAGACCTACGGACAAGGCTCTCCGTCTTCCTCTTCAAGATGTATACAAAATTGGTGGTATTGGAACTGTACCTGTTGGTCGTGTTGAAACTGGTGTGTTGAAGCCAGGTATGGTTGTTACATTTGCTCCAGCTGGTTTGACCACTGAAGTTAAATCCGTCGAAATGCATCACGAAGCTTTGCCAGAAGCTGTTCCTGGTGATAACGTTGGTTTCAACGTTAAGAACGTGGCAGTAAAAGAATTGCGTCGTGGTTATGTAACTGGTGACTCCAAGAATAA------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- Vespidae GCTGAGCGCGAGCGTGGTATCACCATTGACATTGCTCTTTGGAAATTCGAAACTTCTAAATACTACGTCACAATTATTGACGCACCTGGACACAGAGATTTCATCAAGAACATGATCACTGGAACCTCGCAAGCAGATTGCGCTGTGCTGATCGTAGCTGCTGGTACAGGAGAATTTGAGGCTGGTATTTCTAAGAACGGACAAACGCGTGAGCATGCTTTGCTCGCTTTCACTCTTGGTGTGAAGCAACTCATCGTTGGTGTTAACAAAATGGACTCTACCGAGCCACCCTACTCCGAGACTCGATTCGAAGAAATTAAGAAGGAGGTCTCTTCGTACATTAAAAAAATTGGTTACAATCCTGTAACCGTCGCATTTGTACCAATCTCTGGCTGGCACGGAGACAACATGTTGGAAGTGTCTAGCAAGATGCCATGGTTCAAGGGATGGGTAGTAGAACGTAAAGAAGGCAAGGCAGAAGGAAAATGTCTTATCGAAGCTCTTGACGCCATCCTTCCACCTACCAGACCTACAGACAAGGCACTCCGTCTTCCACTTCAGGATGTGNACAAGATCGGTGGTATCGGAACGGTACCGGTCGGTCGTGTTGAAACCGGTGTATTGAAGCCGGGTATGGTTGTTACATTTGCTCCGGCGGGTCTTACCNCCGAAGTTAAATCCGTTGAAATGCATCACGAAGC------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- Triassoxyela ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Asioxyela ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Nigrimonticola ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Gigantoxyelinae ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Spathoxyela ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Xyela_mesozoica ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Angaridyela ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Xyelotoma ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Undatoma ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Dahuratoma ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Mesolyda ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Turgidontes ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Aulidontes ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Protosirex ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Aulisca ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Anaxyela ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Syntexyela ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Karatavites ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Stephanogaster ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Leptephialtites ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Cleistogaster ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Sepulca ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Onochoius ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Ghilarella ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Paroryssus ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Praeoryssus ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Mesorussus ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Trematothorax ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Thoracotrema ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Prosyntexis ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Kulbastavia ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Brachysyntexis ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Symphytopterus ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Eoxyela ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Liadoxyela ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Abrotoxyela ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Pseudoxyelocerus ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Palaeathalia ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Ferganolyda ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ PamphiliidaeUndesc ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Rudisiricius ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Sogutia ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Xyelula ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Brigittepterus ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Grimmaratavites ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ [EF1a F1 4361-5452, 1092 chars] Orthoptera ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Paraneoptera ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Chrysopidae ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Raphidioptera ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Coleo_Polyphaga ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Coleo_Adephaga ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Lepid_Micropterigidae ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Lepid_Papilionidae ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Mecoptera ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Xyela GCCGAGCGCGAGCGTGGTATCACGATCGATATCGCCCTCTGGAAATTTGAGACAGCCAAATACTATGTAACCATCATCGATGCTCCGGGACACAGGGATTTCATCAAGAACATGATCACGGGAACGAGTCAAGCAGATTGCGCCGTACTCATAGTAGCCGCGGGTACCGGAGAATTTGAAGCTGGTATTTCCAAGAACGGGCAGACTCGCGAACACGCTCTGCTCGCGTTCACTCTGGGAGTCAAACAGCTGATCGTAGGGGTCAATAAGATGGACTCCACCGAACCTCCTTATTCTGAGAGCCGATTCGAAGAGATCAAGAAGGAAGTCTCCTCCTACATTAAGAAAATCGGATACAACCCTGCTTCTGTCGCTTTCGTCCCAATCTCAGGCTGGCACGGAGACAACATGCTGGAACCGTCTCCTAAGATGGCATGGTACAAGGGCTGGAAAGTTGAGCGCAAGGATGGTAACGCTGACGGCAAGTGTCTGATAGAAGCTTTGGATGCAATTTTGCCTCCATCCCGCCCCACTGATAAGGCTCTGCGTCTTCCCCTGCAGGATGTCTACAAAATCGGTGGTATCGGGACCGTACCGGTCGGTCGTGTCGAAACTGGTATCCTGAAACCAGGAATGCTCGTAACTTTCGCTCCAGCGGCTTTGACCACTGAAGTAAAGTCTGTTGAAATGCACCATGAAGCGTTGACCGAGGCTCTACCCGGCGACAACGTCGGGTTTAACGTGAAGAACATTTCGGTCAAAGAATTGCGTCGTGGTTACGTTGCTGGTGATTCTAAGAATTCGCCACCAAGAGGCGCGGCTGATTTTACCGCTCAGGTTATCGTTCTGAACCATCCTGGCCAAATCAGCAACGGTTATACTCCTGTGCTCGACTGCCACACGGCTCATATTGCTTGCAAATTCGCTGAGATCAAGGAGAAGTGCGATCGTCGTACGGGAAAGACAACGGAGGAAAATCCAAAAAGTATCAAGAGCGGGGACGCTGCGTTAGTCATGCTACAGCCCAGCAAGCCGATGTGCGTGGAGGCGTTCCAGGAGTTTCCGCCGTTGGGACG---------------- Macroxyela GCTGAGCGCGAGCGTGGTATCACGATCGATATCGCCCTTTGGAAATTCGAAACAGCCAAGTACTATGTAACCATCATCGACGCTCCGGGACACAGAGATTTCATCAAGAACATGATAACGGGAACGAGTCAAGCGGATTGCGCTGTACTCATAGTAGCAGCAGGTACCGGAGAGTTTGAAGCGGGCATTTCTAAAAACGGCCAAACTCGCGAACACGCTCTTCTCGCGTTCACTCTGGGTGTGAAACAGCTGATCGTAGGAGTCAATAAGATGGACTCCACCGAGCCTCCTTATTCCGAGAGTCGGTTCGAAGAGATCAAGAAAGAGGTATCGTCCTACATCAAGAAGATCGGGTATAATCCAGCTTCGGTTGCATTCGTGCCGATCTCAGGCTGGCACGGTGATAACATGCTTGAACCGTCTCCCAAGATGGCCTGGTACAAAGGCTGGAAAGTCGAACGCAAGGATGGTAACGCTGATGGTAAATGTCTGATAGAAGCTTTGGACGCTATTTTACCTCCGTCACGCCCGACTGACAAGGCTCTGCGTCTTCCGCTGCAGGATGTCTACAAAATCGGTGGTATTGGAACTGTACCCGTTGGTCGTGTCGAAACCGGTATCCTGAAACCTGGAATGCTCGTGACTTTCGCTCCTGCAGCACTGACCACTGAGGTGAAATCCGTTGAAATGCATCACGAGGCGCTGACCGAGGCTTTACCCGGTGACAACGTCGGGTTCAACGTGAAGAACATTTCGGTTAAAGAATTGCGTCGTGGTTACGTTGCCGGCGATTCCAAGAACTCGCCTCCTAGGGGTGCTGCTGATTTTACCGCTCAGGTCATTGTCCTGAACCATCCCGGGCAAATCAGCAACGGCTACACTCCGGTGCTTGATTGCCACACGGCCCATATTGCGTGCAAATTTGCTGAAATAAAAGAGAAATGCGACCGTCGTACGGGAAAAACAACAGAAGAAAATCCAAAGAGTATCAAGAGCGGGGACGCTGCGATAGTCATGCTGCAGCCCAGCAAGCCCATGTGCGCGGAGGCGTTCCAGGAATTTCCACCATTGGGACGCTTTGCTGTTCGTGAC Runaria --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------TGTGGGTTTCAACGTGAAGAACATCTCCGTCAAGGAACTGCGTCGCGGCTACGTCGCCGGCGATTCCAAAAATTCGCCGCCGAGAGGCGCCGCCGACTTCACGGCCCAGGTCATCGTGCTGAATCATCCCGGGCAAATTAACAACGGCTATACGCCGGTGCTGGATTGCCACACGGCGCATATCGCCTGCAAGTTCGCCGAGATCAAGGAGAAATGCGATCGTCGTACCGGAAAAACTACGGAGGAGAATCCAAAGAGTATTAAGAGCGGAGACGCCGCGATAGTGATGCTGCAGCCGAGCAAGCCCATGTGCGTCGAAGCTTTCCAAGAATTTCCCCCTCTGGGTCGCTTCGCCGTACGCGAC Paremphytus ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Blasticotoma ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Tenthredo ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Aglaostigma ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Dolerus GCTGAGCGTGAACGTGGTATCACAATCGACATCGCTCTATGGAAATTCGAAACAGCTAAATACTACGTAACCATAATCGACGCACCGGGCCACAGAGACTTCATCAATAACATGATCACCGGAACAAGTCAAGCCGATTGCGCGGTGCTGATAGTGGCAGCTGGAACCGGTGAATTCGAAGCCGGAATATCGAAGAATGGACAAACCCGAGAGCACGCTCTTCTCGCCTTCACTTTGGGAGTTAAACAACTAATCGTCGGCGTGAACAAGATGGATTACACCGAGCCACCGTACTCGGAATGCCGGTTCGAGGAGATAAAGAAAGAAGTATCCTCCTACATAAAGAAGATCGGCTACTATCCAGCGTCAGTAGCTTTCGTTCCGATCTCCGGATGGCACGGAGACAACATGCTAGAACCCTCTCCGAAGATGCCGTGGTACAAGGGGTGGAAAGTTGAGAGAAAAGACGGTAACGCTGATGGGAAGTGTCTGATCGAAGCGTTGGATGCCATCCTACCACCGTCTCGTCCCACTGATAAAGCTCTGCGTCTGCCTCTTCAGGATGTCTATAAAATTGGTGGAATTGGAACCGTCCCCGTTGGCCGGGTAGAAACTGGCATCTTGAAGCCCGGTATGCTCGTCACCTTTGCTCCTGCCGCTTTAACAACCGAAGTTAAATCCGTCGAAATGCATCACGAGGC------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- Selandria ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Strongylogaster ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Monophadnoides ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Metallus ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Athalia GCTGAGCGYGAACGTGGTATCACTATCGATATCGCTTTGTGGAAGTTTGAAACGGCCAAATATTACGTGACAATTATCGACGCTCCGGGTCACAGGGATTTTATAAAGAACATGATCACCGGCACGAGCCAAGCCGACTGCGCGGTGCTGATAGTAGCTGCGGGTACCGGAGAATTCGAGGCCGGAATATCGAAGAATGGTCAAACTCGGGAGCACGCACTCCTGGCATTTACTTTGGGTGTTAAGCAGCTGATAGTTGGTGTTAACAAAATGGACTCGACCGAACCACCGTACTCGGAAGTTCGTTTCGAAGAGATAAAGAAGGAAGTGTCATCGTACATAAAGAAAATTGGCTACTACCCAACTTCCGTAGCATTTGTTCCAATATCCGGATGGAACGGGGACAACATGCTCGAACCGTCTCCAAAAATGCCGTGGTACAAGGGCTGGAAAGTTGAACGTAAAGACGGAAACGCCGACGGCAAATGTTTGATCGAAGCTCTGGACGCCATTTTACCCCCGTCTCGGCCAACCGACAAAGCTCTTCGTCTYCCGCTTCAAGACGTCTACAAAATCGGGGGCATCGGAACGGTCCCGGTYGGGAGAGTCGAGACGGGAATTTTGAAGCCTGGAATGGTGGTTACTTTCGCCCCAGCAGCACTTACTACCGAAGTAAAATCGGTCGAGATGCACCACGAGGCTCTGACGGAAGCTCTTCCCGGCGACAACGTCGGTTTCAACGTGAAGAACATCTCGGTCAAAGAATTACGTCGCGGTTACGTGGCCGGTGATTCGAAAAATTCGCCGCCGAGAGGAGCATCCGATTTCACGGCGCAAGTAATCGTCCTTAATCATCCGGGACAAATAAGTAACGGATACACGCCCGTCCTCGATTGCCACACGGCTCACATCGCTTGCAAATTTGCTGAGATTAAGGAAAAGTGCGATCGTCGTTCAGGAAAAACAACCGAAGAAAATCCTAAGAGCATCAAAAGCGGGGACGCTGCTATCGTGATGTTACAGCCGAGTAAGCCGATGTGCGTCGAAGCTTTTCAAGAATTTCCACCACTCGGACGTTTCGCTGTGCGCGAC Taxonus ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Hoplocampa ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Nematinus ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Nematus GCTGAGCGYGARCGTGGTATCACGATCGACATCGCTCTGTGGAAGTTCGAAACGGCCCGGTACTACGTTACCATAATCGACGCCCCGGGTCACAGAGACTTCATAAAGAACATGATAACCGGGACCAGCCAAGCCGACTGCGCGGTCCTCATAGTCGCCGCTGGAACCGGCGAATTCGAAGCAGGGATATCGAAGAACGGCCAGACCCGCGAGCACGCCCTCCTCGCCTTCACTCTAGGAGTGAAGCAGCTGATCGTCGGCGTGAACAAGATGGACTCGACCGAGCCACCCTACTCCGAATGCCGGTTCGAGGAGATCAAGAAGGAGGTGTCCTCCTACATCAAGAAGATCGGCTACCACCCGGCCTCCGTCGCCTTCGTGCCGATCTCCGGCTGGCATGGAGACAACATGCTCGAACCATCCCCGAAGATGCCTTGGTACAAGGGGTGGAAGGTGGAGCGCAAGGAGGGCAACGGTGATGGCAAGTGCCTCATCGAAGCCCTGGACGCGATCCTGCCGCCTTCGCGACCGACGGACAAGGCTCTGCGGCTCCCGCTCCAGGACGTATACAAAATCGGCGGTATCGGAACCGTCCCGGTTGGCCGGGTCGAGACCGGGATCCTGAAGCCGGGGATGCTGGTGACCTTCGCGCCAGCTGCGCTTACGACCGAGGTCAAGTCTGTYGARATGCATCACGARGCNCTGCCTGAAGCTCTGCCTGGGGACAACGTTGGTTTCAACGTGAAGAACATATCCGTGAAGGAACTCCGCCGCGGATACGTTGCCGGGGACTCGAAGAACTCGCCGCCCAGGGGGGCGGCCGACTTCACGGCCCAGGTGATCGTCCTGAATCATCCGGGACAGATCAGCAACGGGTACACTCCGGTGCTCGACTGTCACACCGCTCATATAGCTTGCAAGTTCGCTGAGATTAAGGAGAAGTGTGATCGCCGTACGGGAAAAACTACCGAGGAGAATCCTAAGAGCATCAAGAGCGGAGATGCGGCCATCGTGATGCTCCAGCCGAGCAAGCCGATGTGCGTCGAAGCTTTCCAGGAATTTCCGCCACTGGGACGATTCGCTGTACGCGAC Cladius ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Monoctenus GCTGAGCGTGAACGTGGTATCACGATCGACATAGCCCTGTGGAAATTCGAGACGGCCAAGTACTACGTGACCATAATTGACGCGCCGGGTCACCGGGACTTCATAAAGAACATGATAACCGGCACGAGCCAGGCCGACTGCGTGGTTCTGATAGTGGCCGCAGGAACCGGCGAATTCGAGGCCGGGATATCGAAGAACGGCCAGACCCGTGAACACGCCCTGCTGGCGTTCACGCTCGGGGTGAAGCAGCTGATCGTCGGCGTGAACAANATGGACTCGACCGAGCCGCCGTACTCCGAGTGCCGGTTCGAGGAGATCAAGAAGGAGGTATCGTCCTACATAAAGAAGATCGGCTACTACCCGGCCTCGGTCGCCTTCGTGCCGATCTCGGGATGGCACGGCGACAACATGCTCGAGCCCTCGCCCAAGATGCCGTGGTACAAGGGGTGGCGGGTCGAACGCAAGGACGGCAACGCCGACGGCAAGTGCCTGATCGAGGCCCTGGACGCCATCCTGCCGCCGTCCCGGCCGACGGACAAGGCGCTGCGCCTGCCGCTGCAGGACGTCTACAAGATCGGCGGAATCGGGACCGTGCCGGTGGGCCGCGTCGAGACCGGGATCCTGAAGCCCGGGATGCTCGTGACCTTCGCACCCGCCGCACTCACCACCGAGGTAAAGTCCGTTGAAATGCATCACGAGGC------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- Gilpinia ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Diprion GCTGAGCGTGAGCGGGGAATAACGATCGACATAGCTCTCTGGAAGTTCGAGACGGCCAAGTACTACGTGACGATAATAGACGCGCCCGGTCACCGTGACTTCATAAAGAACATGATAACCGGGACCAGTCAGGCGGACTGCGCTGTGCTGATAGTGGCGGCAGGAACAGGGGAATTCGAGGCCGGGATATCGAAGAACGGACAGACGCGGGAACACGCCCTCCTGGCCTTCACCCTGGGGGTGAAACAGCTGATCGTGGGTGTGAACAAGATGGACTCGACGGAGCCGCCGTACTCGGAGAGCCGGTTCGAGGAGATCAAGAAGGAGGTCTCCTCCTACATAAAGAAGATCGGCTACTACCCGGCCTCCGTTGCCTTTGTCCCGATCTCAGGATGGCACGGGGACAACATGCTCGAACCCTCGCCGAAGATGCCCTGGTACAAGGGGTGGAAGGTTGAGAGGAAGGACGGCAACGCCGACGGCAAGTGTCTGATCGAAGCTCTCGACGCCATCCTGCCGCCGTCGAGACCGACGGACAAGGCGCTCCGACTTCCGCTCCAGGACGTCTACAAGATCGGCGGCATCGGAACCGTCCCCGTCGGCAGGGTAGAGACTGGCATCCTGAAGCCGGGGATGCTGGTCACCTTTGCCCCCGCGGCCCTTACGACGGAGGTAAAATCCGTCGAGATGCACCACGAGGCTC--------------------------------------------------------------CTGCGTCGGGGTTACGTGGCTGGGGATTCAAAGAACTCGCCGCCGCGCGGCGCGGCTGATTTTACCGCTCAAGTAATCGTCCTCAATCATCCGGGTCAGATCAGCAACGGTTACACCCCCGTTCTCGACTGCCACACCGCTCATATCGCTTGCAAGTTCGCCGAGATCAAGGAGAAGTGCGACCGCAGGACCGGCAAAACTACCGAGGAAAATCCTAAGAGCATCAAAAGCGGCGACGCCGCCATCGTCATGCTGCAACCCAGCAAGCCGATGTGCGTCGAAGCTTTTCAGGAGTTTCCACCCCTCGGACGTTTCGCT--------- Cimbicinae ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Abia ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Corynis ---GAGCGCGAGCGTGGCATCACAATCGACATAGCCCTGTGGAAATTCGAAACAGCCAAGTACTACGTGACCATAATCGACGCCCCGGGACACCGGGACTTCATCAAGAACATGATCACCGGAACGAGTCAGGCCGATTGTGCCGTGTTAATAGTAGCAGCTGGAACAGGAGAATTCGAGGCTGGAATCTCAAAAAACGGTCAGACCCGCGAACACGCGCTCCTGGCCTTTACCCTGGGTGTCAAGCAATTGATCGTAGGAGTCAACAAGATGGACTCGACCGAACCACCCTATTCCGAATGCCGGTTCGAGGAGATCAAAAAAGAGGTGTCGTCCTACATCAAGAAGATCGGCTACAACCCGGTCTCCGTCGCCTTCGTACCCATCTCTGGCTGGCATGGCGACAATATGTTGGAACCATCCCCGAAAATGGCTTGGTACAAGGGTTGGAAGGTCGAGCGCAAGGATGGAAACGCCGATGGCAAATGTCTGATCGAGGCCCTGGATGCCATTCTTCCACCATCTCGACCCACCGACAAGGCTCTGCGTTTTCCGCTGCAGGACGTCTACAAAATCGGCGGAATCGGGACCGTNCCNGTTGGCCGCGTNGAGACNGGCATCCTGAAGCCNGGGATGCTCGTNACCTTTGCTCCCGCCGCCCTNACGACCGAGGTAAANTCCGTTGAGATGCATCACGANGCNCTGACCGAGGCTCTNCCCGGTGACAACGTTGGTTTCAACGTGAAGAACATCTTNGTCAAGGAACTGCGTCGCGGCTACGTTGCTGGAGATTCGAAAAATTCACCGCCTANAGGTGCCGCCGATTTTACCGCTCAAGTAATTGTGTTGAATCATCCTGGACAAATTAGCAATGGGTATACCCCGGTACTTGATTGTCATACGGCTCATATTGCTTGCAAATTTGCTGAAATTAAGGAGAAATGCGATCGTCGTACCGGTAAAACAACTGAGGAAAATCCTAAGAGTATTAAAAGCGGTGATGCTGCCATTGTTATGTTGCANCCAAGCAAGCCGATGTGCGTTGAAGCTTTTCAAGAATTTCCACCATCTGGACGTTTCGCTGTGCGCGAC Arge ---------------------------------------TGGAAATTTGAGACAGCTAAGTATTACGTGACTATAATCGACGCTCCGGGTCACAGAGATTTCATAAAAAACATGATTACCGGGACGAGTCAGGCGGAGTGCGCGGTGTTGATCGTGGCTGCCGGTACCGGTGAATTCGAGGCTGGTATCTCGAAGAATGGGCAGACGCGCGAGCATGCTCTGCTCGCTTTTACGCTGGGAGTGAAACAGCTGATAGTTGGTGTGAACAAGATGGATTCCACGGAGCCACCCTATTCGGAATGTCGTTTCGAGGAGATTAAGAGAGAAGTGTCTTCTTACATCAAAAAGATCGGCTACTATCCCGGAGCCGTAGCTTTCGTTCCCATCTCCGGATGGCACGGTGATAACATGCTGGAACCATCGCCCAAGATGCCTTGGTACAAGGGATGGAAGGTCGAGCGTAAGGAGGGATCCGCGGATGGAAAATGTTTGATCGAAGCTTTGGATGCTATTTTGCCACCGTCTCGACCTACCGACAAGGCTCTGCGGCTACCATTGCAGGACGTCTACAAAATCGGTGGAATCGGCACAGTTCCGGTTGGTCGGGTCGAGACCGGTATCCTGAAACCCGGGATGCTGGTTACCTTCGCTCCTGCCGCCCTGACGACGGAAGTTAAATCTGTGGAGATGCATCACGAAGCGTTGACCGAAGCTCTGCCAGGTGACAACGTTGGCTTCAACGTGAAGAATATCTCCGTGAAGGAATTGCGTCGCGGTTACGTAGCTGGAGACTCGAAGAACTCGCCTCCGAGGGGTGCCTCGGATTTCACGGCTCAGGTTATCGTGCTCAATCATCCTGGGCAAATCAGTAACGGCTACACGCCGGTACTCGATTGTCATACGGCTCACATCGCTTGCAAATTTGCCGAGATAAAGGAGAAGTGCGATCGTCGTACGGGGCGAACAACGGAAGAAAATCCGAAGAGCATCAAGACAGGCGACGCCGCTATAGTCATGCTTCAGCCAAGCAAGCCGATGTGCGTACGAGCGTTTCAGGAGTTCCCTCTGCTGGGACGATTCGCTGTACGGGAC Sterictiphora GCCGAGCGCGAGCGCGGCATCACGATCGACATCGCGCTGTGGAAGTTCGAGACCGCCAAGTATTACGTGACCATAATCGACGCGCCGGGGCACCGGGACTTCATCAAGAACATGATAACCGGGACGAGCCAGGCGGACTGTGCCGTGCTGATCGTGGCCGCTGGTACCGGCGAATTTGAGGCGGGCATCTCGAAGAACGGGCAGACCCGCGAGCACGCTCTCCTCGCGTTCACCCTGGGCGTGAAGCAGCTGATTGTCGGCGTCAACAAGATGGACTCGACCGAGCCACCCTACTCCGAGTGTCGGTTCGAGGAGATCAAGAAAGAGGTGTCCTCGTACATCAAGAAGATCGGCTACTATCCGGGATCCGTGGCCTTCGTTCCCATCTCCGGGTGGCACGGCGATAACATGCTGGAACCGTCCCCGAAGATGGCCTGGTACAAGGGGTGGAAGGTCGACCGCAAAGAGGGCTCCGCCGACGGGAAGTGCCTCATCGAGGCGCTGGACGCGATCTTGCCACCGTCGCGTCCCACCGACAAGGCTTTGCGACTGCCACTTCAGGACGTCTACAAGATCGGTGGTATCGGCACGGTGCCGGTGGGTCGCGTCGAGACCGGCATCCTGAAGCCGGGAATGCTGGTGACCTTCGCGCCGGCAGCGCTGACGACCGAGGTCAAGTCCGTGGAGATGCACCACGAAGCGCTGACCGAGGCCCTACCGGGCGACAACGTTGGCTTCAACGTGAAGAACATCTCGGTCAAGGAGCTGCGTCGCGGCTACGTGGCCGGGGACTCGAAGAATTCCCCACCTCGGGGCGCCTCCGACTTCACGGCCCAAGTCATCGTGCTGAATCATCCGGGCCAGATCAGCAACGGGTACACGCCGGTCCTCGACTGTCACACTGCCCATATCGCGTGCAAGTTCGCCGAGATCAAGGAGAAGTGCGACCGTCGTACCGGAAAAACTACGGAGGAGAACCCGAAGAGCATCAAGAGCGGCGACGCCGCCATAGTCACGCTGCAGCCCAGCAAGCCGATGTGCGTCGAAGCCTTTCAGGAGTTTCCACCGCTGGGACGATTTGCTGTACGGGAC Perga ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Phylacteophaga ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Lophyrotoma ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Acordulecera ------CGTGAGCGCGGCATAACCATCGACATAGCTCTGTGGAAGTTCGAGACAGCCAAGTACTACGTAACCATAATCGACGCCCCTGGTCACCGAGACTTCATCAAGAACATGATCACGGGAACGAGTCAGGCTGATTGCGCCGTGCTGATAGTAGCTGCAGGAACCGGCGAGTTTGAGGCGGGAATTTCGAAGAACGGGCAGACTCGAGAGCACGCATTGCTGGCTTTCACTTTGGGAGTGAAACAGCTAATCGTCGGAGTGAACAAGATGGATTCAACCGAACCACCCTATTCGGAAACCCGTTTCGAGGAGATCAAGAAGGAGGTGTCNTCCTACATCAAGAAGATCGGCTACTATCCTGGAGCTGTGGCATTCGTTCCCATCTCTGGATGGCACGGAGACAACATGCTGGAACCGTCTCCGAAAATGGCTTGGTACAAGGGATGGAAGGTCGAGCGAAAGGAGGGCAACGCTGATGGNAAATGTNTGATCGAGGCCTTGGACGCCATTCTACCATCGTCGCGACCTACTGACAAGGCTCTGCGGCTACCATTGCAGGACGTTTACAAGATCGGAGGCATTGGNACAGTACCGGTGGGNCGAGTCGAGACTGGCATTTTAAAACCTGGAATGGTTGTGACCTTTGCACCCGCTGCGCTAACTACAGAAGTCAAATCTGTCGAGATGCATCNCGAGGCTTTGGTCGAAGCTCTCCCCGGGGATAACGTGGGATTCAACGTGAAGAACATCTCGGTCAAGGAACTTCGCCGCGGTTACGTTGCCGGNGACTCGAAGAATTCTCCGCCACGTGGCGCTGCTGACTTCACTGCTCAAGTCATCGTCCTCAACCATCCGGGACAAATCAGCAACGGATACCCTCCGGTGCTTGACTGCCACACTGCGCACATTGCCTGCAAGTTCGCCGAGATCAAAGAGAAGTGCGACAGACGCACTGGCAAGACTACCGAGGAAAATCCCAAGAGCATCAAGAGTGGCGATGCAGCCATAGTGGTCCTGCAACCCAGCAAACCCATGTGCGTCGAAGCTTTCCAGGA---------------------------------- Decameria ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Neurotoma ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Onycholyda GCTGAGCGCGAACGTGGTATCACCATCGACATCGCCCTCTGGAAGTTCGAAACCGCCAAGTACTACGTGACCATCATCGACGCGCCGGGTCACCGTGACTTCATCAAGAACATGATAACCGGAACGAGCCAGGCGGACTGCGCCGTCCTGATCGTCGCCGCCGGAACCGGCGAGTTCGAGGCCGGCATTTCCAAAAACGGCCAAACCCGCGAGCACGCGCTTCTAGCCTTCACCCTGGGGGTCAAGCAGCTCATCGTCGGCGTCAACAAGATGGACTCAACGGAACCGCCCTACTCCGAGAGCCGCTTCGAGGAAATCAAGAAGGAGGTCTCCTCGTACATCAAGAAGATCGGCTACAACCCCCCCTCCGTGGCCTTCGTGCCCATATCCGGATGGCACGGGGACAACATGCTGGAACCATCGCCGAAGATGGCCTGGTACAAGGGCTGGAAGGTGGAGCGCAAGGACGGCAACGCCGACGGCAAATGTCTGATCGAGGCGCTCGACGCGATCCTTCCGCCATCAAGGCCCACCGATAAGGCCCTCCGTCTGCCGCTGCAGGACGTCTACAAGATCGGCGGGATTGGGACCGTTCCAGTGGGACGCGTGGAGACGGGCATCCTGAAGCCGGGCATGCTGGTGACCTTCGCCCCGGCCGCCTTGACGACTGAAGTCAAGAGCGTGGAGATGCACCATGAGGCCCTGACCGAGGCCCTACCCGGGGACAACGTGGGCTTCAACGTTAAGAACATCTCCGTGAAGGAGCTGCGTCGCGGCTACGTTGCCGGGGACTCGAAGAACCAGCCGCCCAAGGGCGCTGCCGATTTCACGGCCCAGGTCATCGTCCTGAACCACCCGGGGCAGATCTGCAACGGCTATACGCCGGTCTTGGACTGCCACACGGCGCACATCGCGTGCAAGTTTGCGGAGATAAAGGAGAAGTGCGATCGTCGAACTGGGAAGACCACGGAGGAGAACCCGAAGAGCATCAAGAGCGGTGATGCTGCGATCGTGATGCTGCAGCCGAGCAAGCCGATGTGCGTCGAGGCGTTCCAGGAGTTTCCGCCTCTTGGACGCTTTGCTGTTCGCGAC Pamphilius ------------CGCGGTATANCCATCGACATCGCCCTCTGGAAGTTCGAAACCGCCAAGTACTACGTGACCATCATCGACGCCCCGGGTCACCGTGACTTCATCAAGAACATGATAACCGGGACGAGCCAGGCGGACTGCGCCGTCCTGATCGTCGCCGCCGGAACCGGCGAGTTCGAGGCCGGCATTTCCAAAAACGGNCAGACCCGCGAGCACGCGCTNCTAGCCTTCACCCTGGGGGTCAAGCAGCTCATCGTCGGCGTCAACAAGATGGACTCAACGGAGCCGCCCTACTCCGAGAGCCGCTTCGAGGAAATCAAGAAGGAGGTCTCCTCGTACATCAAGAAGATCGGCTACAACCCCCCCTCCGTGGCCTTCGTGCCCATATCCGGATGGCACGGGGACAACATGCTGGAACCATCGCCGAAGATGGCCTGGTACAAGGGCTGGAAGGTGGAGCGCAAGGACGGCAATGCCGACGGCAAATGCCTGATCGAGGCGCTCGACGCGATCCTTCCGCCGTCAAGGCCCACCGATAAGGCCCTCCGTCTGCCGCTGCAGGACGTCTACAAGATCGGCGGGATTGGGACCGTTCCAGTGGGACGCGTGGAGACGGGCATCCTGAAGCCGGGCATGCTGGTCACCTTCGCCCCGGCCGCCTTGACGACCGAAGTCAAGAGCGTGGAGATGCACCATGAGGCCCTGACCGAGGTCCTTCCCGGGGATAACGTGGGCTTCAACGTTAAGAACATCTCCGTGAAGGAACTGCGTCGNGGCTACGTTGCCGGGGACTCGAAGAACCAGCCGCCCAAGGGCGCTGCCGATTTCACGGCCCAGGTCATCGTCCTGAACCATCCGGGGCAGATNTGCAACGGCTATACGCCGGTCTTGGACTGTCACACGGCGCACATCGCGTGCAAGTTTGCGGAGATAAAGGAGAAGTGCGATCGTCGAACTGGGAAGACCACGGAGGAGAACCCGAAGAGCATAAAAAGCGGTGATGCTGCGATCGTGATGCTGCGGCCGAGCAAGCCGATGTGCGTTGAGGCTTTCCAGGAGTTTCCGC-------------------------- Cephalcia GCTGAGCGCGAACGTGGTATCACCATCGACATCGCCCTCTGGAAGTTCGAAACCGCCAAGTACTACGTGACCATCATCGACGCCCCGGGTCACCGTGACTTCATCAAGAACATGATAACCGGCACGAGCCAGGCGGACTGCGCCGTTCTCATCGTCGCCGCCGGAACCGGCGAGTTCGAGGCCGGTATTTCCAAAAACGGACAGACCCGCGAGCACGCTCTCCTCGCCTTCACCCTGGGCGTGAAGCAGCTCATAGTCGGCGTCAACAAGATGGATTCCACCGAGCCACCCTACTCCGAAAGCCGCTTCGAGGAGATCAAGAAGGAGGTCTCGTCGTACATCAAGAAGATCGGATACAACCCTCCCTCCGTCGCCTTCGTGCCCATCTCCGGGTGGCACGGGGACAACATGCTGGAACCCTCTCCGAAGATGGCTTGGTACAAGGGCTGGAAGGTGGAGCGCAAGGACGGCAACGCCGACGGCAAGTGCCTGATCGAGGCGCTCGACGCGATCCTGCCGCCGTCAAGACCCACCGATAAGGCCCTCCGTTTGCCGCTGCAGGACGTCTACAAGATCGGCGGAATTGGKACCGTTCCAGTGGGACGCGTGGAGACGGGCATCCTGAAGCCGGGCATGCTGGTGACATTCGCGCCGGCTGCCCTGACGACTGAGGTCAAGTCAGTCGARATGCATCACGARGC------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- Acantholyda GCTGAGCGCGAACGCGGCATCACCATCGACATCGCTCTCTGGAAGTTCGAAACCGCCAAGTACTACGTGACCATCATCGACGCCCCGGGTCACCGTGACTTCATCAAGAACATGATAACCGGGACGAGCCAGGCGGACTGCGCCGTCCTGATCGTCGCCGCCGGAACCGGCGAGTTCGAGGCCGGCATTTCCAAAAACGGNCAGACCCGCGAGCACGCGCTNCTAGCCTTCACCCTGGGGGTCAAGCAGCTCATCGTCGGCGTCAACAAGATGGACTCAACGGAGCCGCCCTACTCCGAGAGCCGCTTCGAGGAAATCAAGAAGGAGGTCTCCTCGTACATCAAGAAGATCGGCTACAACCCCCCCTCCGTGGCCTTCGTGCCCATATCCGGATGGCACGGGGACAACATGCTGGAACCATCGCCGAAGATGGCCTGGTACAAGGGCTGGAAGGTGGAGCGCAAGGACGGCAATGCCGACGGCAAATGCCTGATCGAGGCGCTCGACGCGATCCTTCCGCCGTCAAGGCCCACCGATAAGGCCCTCCGTCTGCCGCTGCAGGACGTCTACAAGATCGGCGGGATTGGGACCGTTCCAGTGGGACGCGTGGAGACGGGCATCCTGAAGCCGGGCATGCTGGTCACCTTCGCCCCGGCCGCCTTGACGACCGAAGTCAAGAGCGTGGAGATGCACCATGAGGCCCTGACCGAGGTCCTTCCCGGGGATAACGTGGGCTTCAACGTTAAGAACATCTCCGTGAAGGAACTGCGTCGNGGCTACGTTGCCGGGGACTCGAAGAACCAGCCGCCCAAGGGCGCTGCCGATTTCACGGCCCAGGTCATCGTCCTGAACCATCCGGGGCAGATCTGCAACGGCTATACGCCGGTCTTGGACTGTCACACGGCGCACATCGCGTGCAAGTTTGCGGAGATAAAGGAGAAGTGCGATCGTCGAACTGGGAAGACCACGGAGGAGAACCCGAAGAGCATAAAAAGCGGTGATGCTGCGATCGTGATGCTGCGGCCGAGCAAGCCGATGTGCGTTGAGGCTTTCCAGGAGTTTCCGCC------------------------- Megalodontesc GCTGAACGAGAACGTGGTATCACCATTGACATCGCCCTGTGGAAGTTCGAAACTGCCAAGTACTACGTGACCATCATCGACGCCCCGGGTCACAGGGACTTCATAAAGAACATGATAACCGGAACTAGCCAAGCCGATTGCGCTGTGTTGATAGTTGCAGCCGGAACCGGTGAATTCGAGGCTGGGATCTCGAAGAACGGGCAAACCCGAGAGCACGCNTTGCTGGCGTTTACCCTAGGGGTAAAACAATTGATCGTCGGGGTGAACAAGATGGACTCAACCGAGCCACCTTACTCCGAGAGTCGCTTCGAAGAGATAAAGAAAGAAGTGTCCTCTTACATCAAGAAGATCGGCTACAACCCCCCTTCCGTAGCCTTCGTCCCAATTTCCGGGTGGCACGGCGACAACATGTTGGAACCCTCGCCGAAGATGCCATGGTACAAGGGGTGGAAGGTCGAACGAAAGGACGGAAACGCCGACGGGAAGTGTCTGATCGAAGCCCTGGACGCAATTCTTCCTCCTTCGAGACCTACGGACAAGGCATTGCGTCTGCCTCTGCAGGACGTCTACAAGATCGGGGGCATTGGAACGGTCCCGGTCGGTCGTGTGGAGACCGGGATCCTAAAACCCGGGATGCTCGTGACCTTCGCTCCGGCAGCATTGACTACGGAGGTCAAGTCCGTCGAGATGCATCACGAGGCTCTAACCGAGGCTTTGCCAGGTGACAACGTAGGCTTCAACGTGAAGAACATCTCCGTGAAGGAGTTGCGTCGCGGGTACGTGGCTGGAGATTCGAAGAACCAGCCGCCGAAAGGAGCTTCTGACTTCACCGCTCAGGTCATCGTCCTGAACCACCCGGGACAAATCAGCAACGGGTATACCCCGGTGTTGGATTGTCACACGGCTCACATCGCGTGCAAGTTTGCTGAGATAAAGGAGAAGTGCGATCGTCGTACGGGAAAAACTACGGAGGAGAACCCTAAGAGCATCAAGAGCGGAGATGCGGCTATTGTGATGCTGCAACCCAGCAAGCCGTTGTGCGTCGAGGCTTTCCAGGAGTTTCCGCC------------------------- Megalodontessk ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Cephus GCTGAGCGTGAACGTGGCATCACTATTGATATTGCTCTTTGGAAGTTTGAAACAGCTAAGTACTATGTAACCATCATCGATGCCCCTGGACATAGAGATTTCATAAAGAACATGATCACTGGCACAAGTCAAGCTGACTGCGCTGTGCTCATAGTGGCAGCTGGTATAGGAGAGTTCGAGGCAGGCATTTCTAAAAATGGACAAACCCGTGAACATGCATTACTTGCATTCACTTTGGGTGTTAAGCAGCTGATTGTTGGAATCAATAAAATGGACATGACCGAACCACCATATTCTGAAAGTCGCTTTGAGGAAATCAAAAAGGAAGTCTCCTCATACATCAAGAAGATTGGGTATAACACTGCTTCTGTAGCTTTTGTACCCATCTCAGGTTGGCATGGTGATAATATGCTTGAGCCGTCTCCAAAAACATCATGGTACAAGGGCTGGAAAGTAGAGCGTAAAGATGGCAATGCTGATGGAAAAACTTTGATCGAAGCATTGGATGCTATTCTTCCACCTTCTCGTCCTACAGATAAAGCATTGCGGCTTCCACTACAAGATGTTTACAAGATTGGTGGTATTGGTACAGTGCCTGTGGGTCGCGTTGAAACTGGTATTTTGAAGCCAGGCATGGTGGTAACTTTTGCTCCTGCAGCTCTTACTACTGAAGTAAAATCTGTGGAGATGCATCACGAAGCCCTGCCTGAGGCTGTGCCTGGTGACAATGTAGGCTTCAACGTGAAAAACATCTCAGTGAAGGAATTGCGTCGTGGCTACGTAGCTGGGGATTCAAAAAATCAGCCACCCCGTGGAGCCGCTGATTTCACTGCTCAGGTTATCGTACTCAATCATCCCGGTCAAATTAGCAATGGTTACACTCCTGTTCTTGATTGTCATACTGCTCACATCGCATGCAAATTCGCTGAGATAAAAGAAAAATGTGACCGTCGTACCGGAAAAACAACTGAGGAAAATCCGAAAAGCATTAAGAGCGGCGACGCTGCCATAGTAATGCTGCAACCGAGTAAGCCGATGTGCGTTGAAGCTTTTCAGGAATTCCCGCCTATGGGACGCTTCGCAGTTCG---- Calameuta GCTGAGCGYGARCGTGGTATCACTATTGACATTGCTCTGTGGAAGTTTGAAACAGCCAAGTACTACGTAACCATCATCGATGCACCTGGACATAGAGATTTCATAAAGAACATGATCACTGGCACAAGTCAGRCTGACTGCGCTGTGCTCATAGTGGCAGCTGGTGTAGGAGAGTTTGAGGCAGGCATTTCGAAAAACGGACAGACCCGTGAACATGCATTGCTTGCATTCACTYTGGGTGTTAAACAGCTGATTGTTGGAATCAATAAAATGGACACGACCGAACCACCATATTCCGAAAGTCGCTTTGAGGAAATCAAAAAGGAAGTCTCCTCGTACATCAAGAAGATTGGGTATAACACTGCTTCCGTAGCCTTTGTACCCATCTCAGGTTGGCATGGTGATAATATGCTTGAGCCATCTCCAAAAACATCATGGTACAAGGGCTGGAAAGTAGAGCGAAAGGATGGCAATGCTGATGGAAAAACTTTGATCGAAGCACTGGATGCTATTCTTCCACCTTCTCGTCCTACGGACAAAGCATTGCGGCTTCCACTGCAAGATGTTTACAAGATTGGCGGGATTGGTACAGTGCCTGTGGGTCGCGTTGAAACTGGTATTTTGAAGCCAGGCATGGTGGTAACTTTTGCTCCTGCAGCCCTTACCACCGAAGTCAAATCTGTCGAGATGCATCACGAAGCACTGCCTGAAGCTGTGCCCGGTGACAACGTGGGCTTTAACGTGAAGAACATCTCAGTGAAGGAATTGCGTCGTGGCTACGTAGCTGGAGATTCGAAAAATCAGCCACCCCGTGGAGCCGCTGATTTCACTGCTCAGGTTATCGTACTCAATCATCCCGGTCAGATTAGCAATGGTTACACTCCTGTTCTCGATTGTCACACTGCCCACATCGCGTGCAAATTCGCTGAGATAAAAGAAAAATGTGACCGTCGTACCGGAAAAACAACAGAGGAAAATCCGAAAAGCATTAAGAGCGGCGACGCTGCCATAGTAATGCTGCAACCGAGTAAGCCGATGTGCGTTGAAGCTTTTCAGGAATTCCCGCCTCTGGGGCGCTTCGCAGTACGCGAC Hartigia GCTGAGCGCGAACGCGGTATCACAATCGACATAGCCCTGTGGAAATTCGAAACAGCCAAGTACTACGTGACGATAATCGACGCCCCGGGACACCGGGACTTCATCAAGAACATGATCACCGGAACGAGTCAGGCCGATTGTGCCGTGTTAATAGTAGCAGCTGGAACAGGAGAATTCGAGGCTGGAATCTCAAAAAACGGTCAGACCCGCGAACACGCGCTCCTGGCATTTACCCTGGGTGTCAAGCAATTGATCGTAGGAGTCAACAAGATGGACTCGACCGAACCACCCTATTCCGAGAGTAGATTCGAGGAGATCAAAAAAGAAGTGTCGTCCTACATCAAGAAGATCGGCTACAACACGTCCTCGGTGGCATTCGTTCCGATCTCAGGCTGGCACGGGGACAATATGTTGGAACCCTCCCCCAAAATGGCTTGGTACAAGGGTTGGAAAGTGGAACGCAAGGATGGCAATGCCGATGGCAAAACACTGATCGAGGCATTGGACGCAATTTTACCACCGTCGCGTCCAACCGACAAGGCACTTCGTCTTCCTCTTCAGGATGTCTACAAAATCGGCGGAATCGGAACAGTGCCAGTTGGTCGTGTAGAAACTGGCATCTTGAAGCCGGGAATGCTCGTGACTTTTGCCCCGGCAGCTCTTACCACTGAAGTTAAATCCGTCGAGATGCACCACGAGGCACTGACCGAAGCTCTACCCGGCGACAACGTTGGCTTCAACGTGAAGAACATCTCGGTKAAGGAATTACGTCGTGGCTATGTAGCAGGGGATTCAAAAAATCAACCACCTCGTGGAGCCGCCGATTTCACTGCTCAGGTTATCGTACTGAATCATCCCGGTCAGATCAGCAATGGTTATACACCGGTTCTTGATTGTCATACTGCTCACATCGCGTGCAAATTCGCCGAGATAAAAGAAAAATGTGACCGTCGTACCGGAAAAACAACTGAGGAAAACCCGAAAAGTATNAAAAGTGGCGACGCTGCCATAGTAATGCTGCAACCGAGTAAGCCAATGTGCGTTGAAGCTTTTCAGGAATTCCCGCCTTTGGGGCGCTTTGCAGTGCGCGAC Syntexis ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Sirex GCCGAACGCGAACGTGGTATCACGATCGATATAGCCCTGTGGAAATTCGAAACGGCCAAGTACTACGTNACGATAATCGACGCCCCGGGTCACCGAGACTTCATCAAAAACATGATCACAGGAACGAGTCAAGCCGATTGTGCAGTGCTCATTGTAGCAGCTGGAACTGGTGAATTTGAGGCTGGAATCTCNAAAAATGGTCAAACCCGTGAACACGCTCTCCTGGCATTTACCTTGGGAGTGAAACAATTGATCGTCGGAGTCAACAAGATGGACTCGACCGAGCCACCCTATTCTGAGAGCCGATTCGAAGAGATCAAGAAAGAAGTGTCATCCTACATCAAGAAGATCGGATACAACACGGCCTCAGTGGCATTCGTTCCAATTTCAGGATGGCATGGGGACAATATGTTGGAACCATCTCCCAAAATGGCTTGGTACAAAGGTTGGAAAGTGGAACGTAAGGACGGCAATGCCGATGGCAAGACTCTGATCGAAGCATTAGACGCAATTTTACCACCATCCCGTCCAACCGACAAGGCTCTTCGTCTTCCACTTCAAGATGTCTACAAGATCGGTGGAATCGGAACAGTTCCAGTTGGTAGAGTCGAAACTGGCATTTTAAAACCAGGAATGCTCGTAACTTTTGCCCCAGCAGCTCTTACCACCGAAGTCAAGTCCGTCGAGATGCACCATGAGGCTCTGACCGAGGCTTTGCCTGGTGACAACGTGGGCTTCAACGTAAAAAACATCTCGGTTAAGGAATTACGTCGTGGTTACGTGGCTGGAGACTCGAAAAATCAACCCCCTAAAGGTGCTGCTGACTTTACCGCTCAGGTAATCGTACTGAATCATCCTGGGCAAATCAGCAATGGCTACACGCCTGTTCTTGACTGCCATACCGCTCATATTGCCTGCAAATTTGCCGAGATCAAGGAAAAGTGCGATCGTCGAACTGGCAAGACTACCGAGGAGAATCCAAAGAGCATCAAAAGCGGCGACGCGGCAATAGTCATACTGCAGCCAAGTAAGCCGATGTGCGTTGAGGCTTTTCAAG----------------------------------- Xeris ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Urocerus ---------------------------------------TGGAAATTCGAAACAGCCAAGTACTACGTGACGATAATCGACGCCCCGGGACACCGGGACTTCATCAAGAACATGATCACCGGAACGAGTCAGGCCGATTGTGCCGTGTTAATAGTAGCAGCTGGAACANGAGAATTCGAGGCTGGAATCTCAAAAAACGGTCAGACCCGCGAACACGCGCTCCTGGTATTTACCCTGGGTGTCAAGCAATTGATCGTAGGAGTCAACAAGATGGACTCGACCGAGCCACCCTATTCCGAGAGTAGATTCGAGGAGATCAAAAAAGAAGTGTCGTCCTACATCAAGAAGATCGGCTACAACACGTCCTCGGTGGCATTCGTTCCGATCTCAGGCTGGCACGGGGACAATATGTTGGAACCCTCCCCCAAAATGGCTTGGTACAAGGGTTGGAAAGTGGAACGCAAGGATGGCAATGCCGATGGCAAAACACTGATCGAGGCATTGGACGCAATTTTACCACCGTCGCGTCCAACCGACAAGGCACTTCGTCTTCCTCTTCAGGATGTCTACAAAATCGGCGGAATCGGAACAGTGCCGGTTGGTCGTGTAGAAACTGGCATCTTGAAGCCGGGAATGCTCGTGACTTTTGCCCCGGCAGCTCTTACCACTGAAGTTAAATCCGTCGAGATGCACCACGAGGCACTGACCGAAGCTCTACCCGGCGACAACGTTGGCTTCAACGTGAAGAACATCTCGGTTAAGGAATTACGTCGTGGTTACGTGGCTGGAGACTCGAAGAATCAGCCGCCCAAAGGTGCTGCCGACTTTACTGCCCAGGTGATCGTGCTGAATCATCCTGGACAGATCAGCAATGGTTACACGCCGGTTCTCGACTGTCACACCGCCCATATTGCCTGCAAGTTTGCCGAGATCAAGGAAAAGTGCGATCGTCGTACCGGCAAGACGACCGAGGAGAATCCTAAGAGCATTAAGAGCGGCGATGCTGCAATAGTCATGCTACAGCCGAGCAAACCGATGTGCGTTGAGGCTTTCCAAGAGTTTCCGCCGTTGGGACGCTTTGCTGTTCGCAAC Tremex ---------------------------------------TGGAAATTCGAAACTGCCAAGTATTACGCTACGATCATCGACGCGCCGGGTCATCGGGACTTCATCAAGAACATGATAACTGGAACGAGTCAGGCCGATTGCGCGGTGCTGATAGTGGCCGCTGGAACCGGAGAATTCGAGGCTGGAATTTCAAAAAATGGTCAAACGCGAGAACACCCTCTCCTGGTATTCACCCTTGGGGTCAAGCAATTGATCGTCGGGGTCAATAAAATGGACTCGACCGAGCCGGCCTATTCCGAGAGCCGATTCGAGGAGATCAAGAAGGAGGTGTCGTCGTACATCAAGAAGATCGGCTACAACCCGGCCTCGGTGGCGTTCGTCCCGATTTCAGGTTGGCACGGGGACAACATGCTGGAGCCATCCCCGAAAATGGCCTGGTACAAGGGTTGGAAGGTCGAACGTAAGGACGGTAATGCCGACGGTAAGACCCTGATCGAGGCCCTGGACGCGATCCTGCCGCCATCGCGTCCGACCGACAAGGCCCTTCGTCTTCCTCTTCAGGACGTTTACAAGATCGGGGGAATCGAAACGGTCCCGGTCGGTCGGGTCGAAACCGGGATCCTAAAGCCAGGAATGCTCGTAACCTTTGCCCCGGCCGCCCTTACGACCGAGGTCAAGTCCGTCGAGATGCACCACGAGGCCCTGACCGAGGCCCTACCCGGCGACAACGTGGGCTTCAACGTGAAGAACATCTCGGTCAAGGAGCTGCGCCGCGGCTACGTGGCCGGGGACTCGAAGAATCAGCCACCGAAAGGCGCGGCCGACTTCACCGCCCAGGTCATCGTCCTGAATCATCCTGGACAGATCAGCAATGGCTACACTCCCGTTCTCGATTGCCACACCGCTCATATAGCCTGCAAATTCGCCGAGATCAAGGAGAAGTGCGATCGTCGAACCGGAAAGACCACGGAAGAGAATCCGAAGAGCATCAAGAGCGGCGACGCGGCGATAGTTATGCTGCAACCCAGCAAGCCGATGTGCGTGGAAGCCTTCCAGGAGTTCCCGCCGCTGGGACGCTTTGCTGTTCGCGAC Xiphydria GCTGAGCGCGAGCGTGGTATCACCATCGACATCGCGCTCTGGAAATTTGAGACGGCCAAGTACTACGTGACGATAATCGACGCGCCCGGTCATCGGGATTTTATCAAGAACATGATCACCGGTACTAGTCAGGCCGATTGCGCGGTACTGATCGTCGCCGCCGGTACCGGGGAATTCGAAGCGGGCATCTCGAAGAACGGCCAGACTCGCGAACACGCCCTACTCGCCTTCACTCTCGGCGTGAAGCAGCTGATCGTCGGCGTCAATAAGATGGACTCGACCGAGCCGCCTTATTCGGAGAGTCGCTTCGAGGAGATAAAAAAAGAGGTGTCCTCCTACATCAAGAAAATCGGCTACAACACAGCGTCGGTCGCCTTCGTGCCGATTTCCGGCTGGCACGGCGACAATATGCTCGAGCCCTCCCCGAAGATGCCTTGGTACAAGGGTTGGAAGGTGGAGCGCAAGGACAACACCGCCGACGGAAGGACGCTCATCGAGGCGCTCGACGCCATCCTGCCGCCTTCGCGTCCCACCGACAAGGCTCTCCGTCTGCCGCTGCAGGATGTCTACAAGATCGGTGGCATCGGTACCGTACCGGTGGGTCGCGTCGAAACCGGTATCTTGAAACCGGGCATGCTCGTCACCTTCGCACCGGCCGCTCTCACCACCGAAGTCAAGAGCGTCGAGATGCACCACGAGGCCCTCACCGAAGCCGTGCCCGGTGACAACGTCGGCTTCAACGTCAAGAACATTTCCGTGAAGGAGTTGCGTCGCGGATATGTGGCTGGCGACTCCAAGAACCAGCCGCCGAGGGGAGCCGCCGATTTTACTGCTCAAGTGATCGTTCTGAATCACCCCGGTCAGATCAGCAACGGCTACACGCCCGTGCTCGATTGTCACACTGCTCACATCGCCTGTAAGTTCGCCGAGATCAAGGAGAAGTGCGACCGTCGTACCGGAAAGATGACCGAGGAGAACCCGAGGTGCATCAAGAGCGGTGACGCAGCGATAGTGATGCTCCAGCCGAGCAAGCCCATGTGCGTCGAGGCTTTTCAGGAGTTTCCGCCGCTGGGACGCTTCGCCGTGCGCAAC Orussus GCTGAGCGTGAACGTGGTATCACGATCGACATAGCGCTCTGGAAGTTCGAGACGGCAAAGTACTACGTGACCATAATCGACGCACCGGGTCACCGAGACTTCATCAAGAACATGATAACGGGAACAAGCCAGGCGGACTGCGCGGTGCTGATAGTGGCAGCAGGAATCGGTGAATTCGAGGCAGGGATCTCGAAAAACGGGCAGACCAGAGAGCACGCCCTGCTGGCGTTCACCCTGGGAGTGAAACAGCTGATCGTCGGGGTGAACAAAATGGACATGACCGAGCCCCCGTACTCGGAGAGCCGCTTCGAGGAGATCAAGAAGGAGGTCTCCTCCTACATCAAGAAGATCGGCTACAACACCGGGTCCGTGGCCTTCGTGCCCATCTCCGGCTGGCACGGCGACAACATGTTGGAGCCGTCCCCGAAGACGCCCTGGTACAAGGGGTGGAAAGTCGAGCGTAAAGACGGCAACGCCGACGGGAAGACCCTGATCGAGGCTCTGGACGCCATCTTGCCACCCTCTCGACCCACCGACAAGGCTCTGCGACTTCCTCTTCAGGACGTCTACAAGATAGGCGGCATCGGAACCGTCCCGGTCGGCAGAGTGGAGACCGGTATCCTGAAGCCGGGGATGTTGGTCACCTTCGCCCCTGCTGCCCTGACCACCGAGGTCAAGTCCGTTGAAATGCATCACGAAGC------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- StephanidaeA GCCGAACGTGAACGCGGTATTACCATCGATATTGCTTTGTGGAAATTCGAGACAGCCAAGTACTATGTGACTATTATCGATGCTCCTGGTCATCGTGATTTTATCAAGAACATGATAACCGGTACCAGTCAAGCCGATTGCGCGGTATTGATAGTAGCAGCTGGAATAGGTGAATTCGAGGCTGGAATCTCAAAAAGCGGTCAAACCCGCGAACATGCTCTATTGGCATTCACGTTGGGTGTCAAACAACTGATCGTTGGAGTGAACAAGATGGATATGACCAAACCACCATATTCGGAAAATCGATTTGAAGAGATCAAGAAGGAAGTATCTTCCTACATCAAGAAGATCGGTTATAATACCGCTTCGGTGGCGTTTGTACCGATTTCCGGTTGGTACGGGGATAATATGTTGGAACCGACACCCAAAACGTCGTGGTACAAAGGATGGAAGGTGGAAAGAAAAGATGGCAATGCAGATGGCAAAACTTTGATCGAAGCTCTTGATGCAATTTTACCACCTTCCAGACCCACCGACAAGGCATTGCGTTTACCACTTCAAGACGTTTATAAGATCGGTGGTATCGGTACAGTACCTGTCGGTCGTGTGGAGACTGGTATTTTGAAACCAGGTATGCTGGTTACTTTTGCCCCAGCGGCATTGACCACTGAAGTTAAATCGGTTGAAATGCACCACGAAGCTTTGCCGGAAGCTTTACCCGGCGACAACGTTGGCTTCAACGTTAAGAATATNTCCGTTAAGGAATTGAGGCGTGGCTATGTAGCTGGCGATTCGAAAAATCAACCTCCGAAAGGAGCATCCGATTTTACCGCTCAAGTGATCGTTCTTAATCATCCTGGTCAAATCAGCAACGGTTATACGCCAGTTCTTGATTGTCACACGGCTCATATTGCTTGCAAATTTGCAGAAATTAAAGAAAAGTGTGACCGTCGTACTGGAAAAACTACCGAAGAAAATCCCAAAAGCATCAAAAGCGGTGATGCGGCGATAGTGATGCTTCAACCGACCAAACCGATGTGTGTTGAAGCTTTTCAAGAGTTTCCACCCTTAGGACGCTTTGCCGTACGCGAC StephanidaeB ---------------------ACCATCGATATTGCTTTGTGGAAATTCGAGACAGCTAAATATTATGTGACCATCATCGATGCTCCTGGTCATCGTGATTTTATCAAGAATATGATAACGGGTACCAGTCAAGCCGATTGCGCCGTATTGATAGTAGCGGCTGGAATAGGTGAATTCGAGGCTGGAATTTCGAAAAGTGGACAAACTCGCGAACATGCTCTTTTGGCGTTCACTTTGGGCGTTAAACAATTGATCGTTGGAGTAAATAAGATGGATATGACCAAACCACCATATTCGGAGAGTAGATTCGAAGAGATCAAGAAAGAAGTATCTTCCTACATTAAGAAGATCGGTTATAATACTGCTTCGGTGGCATTTGTACCGATTTCCGGTTGGTACGGGGATAATATGTTGGAATCGTCACCCAAAACATCTTGGTACAAAGGATGGAAAGTGGAAAGAAAGGATGGCAATGCCGATGGGAAAACTTTGATCGAAGCTCTCGATGCGATTTTACCACCTTCCAGACCGACCGATAAGGCACTGCGTTTGCCACTTCAAGATGTTTATAAGATAGGTGGTATTGGTACCGTACCTGTTGGTCGTGTGGAGACCGGTATTTTGAAACCGGGTATGCTCGTAACTTTTGCTCCAGCAGCGTTGACCACTGAAGTTAAATCGGTTGAAATGCATCATGAAGCTCTGCCGGAAGCTTTACCCGGTGATAACGTTGGTTTCAACGTTAAAAATATCTCCGTGAAGGAATTGAGACGCGGCTATGT---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- Megalyra ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Trigonalidae ---------------GGTATCACTATCGANATCGCTCTATGGAAATTCGAGACGGCCAAGTATTACGTGACCATCATCGACGCGCCGGGTCATCGCGACTTCATCAAGAACATGATAACCGGAACTAGCCAAGCGGATTGCGCCGTCNTGGTCGTGGCTGCGGGTATCGGNGAGTTCGAGGCNGGGATATCGAAGAACGGCCAGACCCGCGAACACGCCCTGCTGGCGTTCACCCTGGGCGTCAAGCAGCTCATCGTCGGCGTGAATAAGATGGACATGACCGAGCCACCTTACTCGGAGCCTCGCTTCGAGGAGATCAAGAAAGAAGTTTCCTCGTACATCAAGAAGATCGGCTACAACACCGCGTCCGTGGCCTTCGTGCCGATATCCGGTTGGCACGGCGACAACATGCTGGAACCCTCGCCGAAGACTTCCTGGTACAAGGGTTGGAAGGTCGAGCGCAAGGACGGCAACGCCGACGGCAAGACGCTCATCGAAGCTCTCGACGCCATTCTCCCACCGTCCAGACCTACGGACAAGGCTTTGCGTCTACCTCTGCAGGATGTTTACAAGATCGGTGGCATCGGNACNGAACCNGTTGGTCGAGTGGAGACAGGTATCCTCAAACCAGGT---------------------------------------------------------------------------------------------------------------------------GTGAAGGAACTTCGACGCGGTTACGTGGCCGGCGACTCCAAGAATCAACCACCGAGAGGCGCAGCTGATTTTACCGCTCAGGTTATCGTGCTCAATCATCCTGGACAGATCAGCAACGGCTACACGCCGGTTCTCGACTGCCACACTGCTCACATCGCTTGCAAGTTCGCCGAGATCAAGGAAAAGTGCGATCGTCGTACGGGCAAGACCACCGAGGAAAACCCAAAGAGTATCAAGAGCGGCGACGCGGCCATAGTTACCCTACAACCTACGAAACCCATGTGCGTGGAAGCTTTCCCGGAATTCCCACCACTCGGTCGCTTCGCTGTACGTGAC Chalcidoidea ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Evanioidea GCTGAGCGTGAACGTGGTATCACGATCGACATCGCCCTGTGGAAGTTCGAGACTGCCAAGTACTACGTGACGATAATCGACGCGCCTGGACACCGGGACTTCATAAAGAACATGATAACCGGGACGAGTCAGGCGGATTGCGCGGTCCTGATCGTCGCAGCCGGGTTAGGCGAGTTCGAGACCGGCATCTCGAAGAACGGGCAGACGCGCGAGCACGCCCTGCTCGCGTTCACCCTGGGCGTCAAGCAGCTCATCGTCGGGGTCAATAAGATGGACCAGACCCAGCCGCCATACTCGGAGGACCGCTTCGAGGAGATCAAGAAGGAGGTCTCGTCCTACATCAAGAAGATCGGCTACAACCCCGCCTCCGTCGCCTTCGTGCCCATCTCCGGGTGGCACGGGGACAACATGCTCGAGTCCTCCCCGAAGACGTCCTGGTACAAGGGGTGGAAGGTCGAGCGCAAGGACGGCAACGCCGACGGGAAGACCCTCATCGAAGCCCTCGACGCCATCCTCCCGCCGTCCAGACCCACCGACAAGGCTCTTCGACTGCCGCTCCAGGACGTCTACAAGATCGGCGGCATCGGCACCGTTCCCGTCGGCCGCGTCGAGACGGGCATCCTGAAACCCGGTATGCTGGTGACCTTCGCACCGGCGGCTCTGACGACGGAGGTAAAGTCCGTGGAGATGCATCACGAGGCCCTGACCGAGGCCCTGCCCGGGGACAACGTCGGGTTCAACGTGAAGAACATCTCCGTGAAGGAGCTGCGCCGCGGTTACGTCGCCGGGGACTCAAAGAACCAGCCTCCGAGAGGCGCGGCCGATTTCACGGCCCAAGTAATCGTCCTTAATCATCCGGGTCAGATAAGCAACGGGTACACGCCCGTCCTTGACTGCCACACGGCTCACATCGCGTGCAAATTCGCCGAGATCAAGGAGAAGTGCGACCGTCGTACGGGCAAGACGACGGAGGAGAACCCGAAGAGCATCAAGAGCGGGGACGCGGCTATCGTCATGCTCCAGCCGACGAAGCCGATGTGCGTCGAGGCTTTCCAGGAGTTCCCGCCGTTGGGACGTTTCGCCGTCCGGGAC Ichneumonidae GCCGAAAGAGAGCGTGGAATAACAATCGATATTGCAATGTGGAAATTTGAAACAGCCAAATATTACATAACCATAATCGATGCACCGGGACATCGCGACTTTATAAAAAATATGATAACCGGAACAAGCCAAGCAGATTGCGCTGTCCTCATAGTCGCTGCCGGAATCGGTGAATTTGAGGCTGGAATATCGAAAAACGGACAAACACGCGAGCATGCTCTGCTCGCATTCACTCTTGGTGTCAAACAGCTCATAGTCGGTGTCAATAAAATGGACATGACCGAGCCACCATACTCCGAGACACGTTTCGAAGAAATAAAGAAGGAAGTTTCGTCCTACATAAAGAAAATCGGTTACAACACGTCATCGGTCGCTTTTGTGCCGATTTCTGGTTGGCACGGTGACAATATGCTCGAATCGTCACCCAAAACAGCTTGGTACAAGGGCTGGAAAGTTGAACGCAAAGATGGTAACGGCGAGGGTAAAACTTTGATCGAGGCACTTGATGCTATTTTACCGCCGTCACGTCCAACCGATAAGGCACTTCGTTTACCTCTTCAAGATGTGTACAAAATTGGTGGTATCGGCACTGTGCCAGTTGGTCGAGTCGAGACCGGGATACTCAAACCAGGTATGTTGGTGACATTTGCCCCAGCCGCTTTGACAACGGAAGTTAAATCCGTGGAAATGCATCATGAGGCATTGACAGTCGCCATGCCCGGTGACAATGTTGGATTTAATGTCAAAAACATATCAGTCAAGGAATTACGTCGGGGCTATGTTGCCGGAGATTCAAAAAATCAGCCACCTCGTGGTGCATCGGATTTCACCGCACAGGTTATCGTGCTCAATCATCCGGGACGGATAAGCAACGGTTACACGCCCGTTCTCGATTGTCATACGGCTCACATAGCTTGCAAATTTGCCGAGATAAAGGAAAAATGTGATCGTCGTACTGGCAAAACAACTGAGGAAAATCCAAAGAGCATAAAAAGCGGTGATGCTGCTATGGTCGTTCTCCAACCCACCAAGCCAATGTGTGTCGAGGCTTCCAAGGAATTCCCGCCCTTAGGACGTTTC------------ Cynipoidea GCCGAACGTGAACGTGGTATCACCATTGACATTGCTCTTTGGAAATTCGAAACAGCGAAATATTATGTAACCATCATTGATGCACCGGGACATCGTGATTTCATAAAAAACATGATCACTGGAACAAGTCAAGCTGATTGTGCAGTTTTGATAGTGGCAGCGGGAATTGGAGAATTCGAAGCTGGAATTTCAAAGAATGGTCAAACTCGTGAACATGCTCTTTTGGCTTTCACTCTGGGAGTCAAACAATTGATTGTTGGTGTCAACAAAATGGATATGACCGATCCACCATATTCCGAAAGTCGATTTGAAGAAATTAAGAAAGAGGTTTCATCATACATCAAGAAAATTGGCTACAATACAGCTTCGGTTGCTTTTGTTCCTATTTCTGGATGGCATGGAGATAACATGTTGGAACCTTCTCCAAAAACTCCTTGGTACAAGGGATGGAAGGTCGAACGTAAAGATGGAAATGCNGATGGAAAAACATTNATTGAGTCTCTGGATGCTATTCTGCCTCCATCTCGACCAACTGATAAGGCCCTGANACTTCCTCTTCAAGATGTCTACAANATTGGAGGAATTGGAACTGTCCCAGTTGGACGAGTTGAGACTGGAATTTTGAAACCAGGTAT-------------------------------------------------------------------------------------GGGGACAACGTTGGTTTCAACGTGAAAAATATTTCAGTAAAAGAACTACGACGTGGATACGTTGCTGGAGATTCAAAGAACCAGCCTCCCAGAGGAGCAGCTGATTTTACTGCTCAGGTGATTGTTCTCAATCATCCTGGTCAAATCAGCAATGGATATACCCCAGTGTTGGATTGTCACACGGCTCATATTGCCTGCAAATTTGCTGAAATCAAGGAAAAATGCGACCGTCGAACTGGAAAAACTACAGAGGAAAATCCAAAAAGTATCAAAAGTGGAGATGCTGCTATAGTGATGCTGCAACCGACCAAGCCGATGTGTGTGGAAGCATTCCAGGAATTTCCACCTCTGGGAAGA--------------- ApoideaA GCTGAACGCGAGCGCGGCATCACCATCGACATCGCTTTATGGAAATTCGAAACTGCCAAGTACTACGTGACCATCATCGACGCCCCGGGTCATCGAGATTTCATCAAAAACATGATCACCGGCACGAGTCAGGCCGATTGCGCGGTATTGATCGTCGCGGCCGGCATCGGCGAGTTCGAGGCCGGGATCTCGAAGAACGGACAAACTCGGGAGCACGCGTTGCTCGCGTTCACGCTCGGCGTTAAACAGCTTATCGTCGGCGTGAACAAGATGGACATGACCGACCCGCCGTACTCGGAAAGCCGTTTCGAGGAGATCAAGAAGGAGGTGTCCTCCTACATCAAGAAGATCGGGTACAACACCGCGTCCGTTGCGTTCGTCCCGATCTCCGGCTGGCACGGGGACAACATGCT-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------TTGGGGACAACGTTGGTTTCAACGTGAAGAACATCTCCGTGAAGGAGCTGAGGCGCGGCTACGTGGCCGGCGACTCCAAGAACCAGCCGCCACGGGGGGCGGCCGACTTCACCGCCCAGGTGATCGTCCTGAATCACCCGGGCCAGATCAGCAGCGGCTACACGCCGGTCCTCGACTGCCACACCGCCCATATCGCGTGTAAGTTCGCCGAGATGAAAGAGAAGTGCGACCGTCGTACCGGCAAGACCACCGAGGAGAATCCGAACAGCATCAAGAGCGGCGATGCTGCGATCGTGTTGCTGCAGCCGACCAAGCCGATGTGCGTCGAGTCGTTCCAGGAGTTCCCGCCGCTCGGTCGCTTCGCCGTCCGCAAC ApoideaB -----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------TGGGGACAATGTTGGTTTCAACGTGAAGAACATCTCCGTGAAGGAGCTGAGACGCGGCTACGTGGCCGGTGATTCGAAGAATCAGCCTCCGCGAGGGGCTGCCGACTTCACGGCCCAAGTGATCGTGCTGAACCATCCGGGGCAGATCAACAACGGGTACACGCCTGTGCTTGACTGCCACACCGCTCATATCGCGTGCAAATTTGCCGAGATAAAGGAGAAGTGTGACCGTCGTACCGGTAAAACGACCGAAGAGAATCCGAAGAGCATCAAGAGCGGAGACGCGGCCATCGTGCTGCTACAGCCGATGAAGCCGATGTGCGTCGAGGCGTTCCAGGAATTCCCGCCTCTGGGTCGCTTCGCTGTGCGCGAC ApoideaC ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Vespidae GCTGAGCGYGARCGTGGTATCACSATAGACATCGCCCTTTGGAAATTCGAGACCGCCAAGTACTACGTGACGATCATCGATGCGCCTGGTCACCGTGATTTCATCAAGAATATGRTAACCGGTACGAGCCAGGCAGATTGCGCGGTCCTTATCGTGGCCGCCGGTATCGGCGAGTTCGAGGCTGGAATTTCGAAGAACGGTCAGACGCGCGAACACGCYTTGCTCGCGTTCACCCTCGGCGTCAAGCAGCTCATCGTCGGCGTCAACAAGATGGACATGACCGATCCGCCGTACTCGGAGAGTCGCTTCGAAGAAATCAAAAAGGAGGTGTCGTCTTACATCAAGAAGATCGGATACAACACGGCATCGGTCGCGTTCGTCCCGATTTCTGGCTGGCACGGTGACAACATGCTCGAGCCATCACCGAAAACTATCTGGTACAAGGGTTGGAAGGTCGAACGTAAGGACGGCAACGCCGACGGGAARACTCTGATCGAAGCACTCGACGCCATACTACCGCCATCCARACCAACCGACAAGGCACTTCGTCTGCCTCTTCAGGACGTCTACAAGATAGGTGGTATCGGCACCGTGCCCGTTGGTCGCGTCGAAACCGGCATCCTTAAACCAGGTATGCTGGTGACCTTCGCTCCGGCCGCGCTAACCACCGAAGTGAAATCCGTCGARATGCATCACGAGGCCTTGACGGAGGCCCTACCAGGCGACAACGTTGGCTTCAACGTTAAGAACATCTCGGTGAAGGAGTTGAGGCGCGGCTACGTCGCCGGCGATTCGAAGAATCAGCCGCCTCGCGGCGCGGCCGACTTCACCGCTCAGGTAATCGTACTGAATCATCCAGGACAGATCAGCAACGGATACACGCCCGTCCTCGACTGTCACACCGCTCACATCGCCTGCAAGTTCGCCGAGATAAAGGAGAAATGCGACCGTCGTACCGGTAAAACCACCGARGAGAATCCAAGAAGTATAAAGAGCGGGGACGCGGCCATCGTGATGTTGCAGCCGWCCAAGCCGATGTGCGTCGAGGCTTTCCAAGAGTTCCCGCCTCTTGGTCGTTTCGCCGTCCGCGAC Triassoxyela ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Asioxyela ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Nigrimonticola ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Gigantoxyelinae ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Spathoxyela ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Xyela_mesozoica ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Angaridyela ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Xyelotoma ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Undatoma ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Dahuratoma ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Mesolyda ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Turgidontes ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Aulidontes ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Protosirex ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Aulisca ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Anaxyela ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Syntexyela ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Karatavites ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Stephanogaster ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Leptephialtites ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Cleistogaster ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Sepulca ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Onochoius ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Ghilarella ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Paroryssus ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Praeoryssus ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Mesorussus ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Trematothorax ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Thoracotrema ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Prosyntexis ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Kulbastavia ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Brachysyntexis ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Symphytopterus ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Eoxyela ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Liadoxyela ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Abrotoxyela ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Pseudoxyelocerus ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Palaeathalia ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Ferganolyda ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ PamphiliidaeUndesc ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Rudisiricius ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Sogutia ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Xyelula ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Brigittepterus ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Grimmaratavites ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ ; end; [ begin mrbayes; [5444 chars, 345 morphology with 8 deleted 5099 basepairs 2184 Ef1a and 2915 from Susanne] charset MV = 1-236; charset MS = 237-353; charset 12S = 354-556; charset 16S = 557-778; charset 18S = 779-1669; charset 28S = 1670-2221; charset CO1 = 2222-3265; charset CO1_12 = 2222-3265\3 2223-3265\3; charset CO1_3 = 2224-3265\3; charset Ef1aF2 = 3266-4357; charset Ef1aF2_12 = 3266-4357\3 3267-4357\3; charset Ef1aF2_3 = 3268-4357\3; charset Ef1aF1 = 4358-5449; charset Ef1aF1_12 = 4358-5449\3 4359-5449\3; charset Ef1aF1_3 = 4360-5449\3; charset Ef1a = 3266-5449; charset morph_ordered = 20 23 27 30 35 36 41 42 44 46 48 59 65 75 78 79 89 99 112 117 134 146 157 159 171 185 191 192 193 196 218 228 229 230 237 263 266 288 296 299 304 343 347 349; charset morph_excluded = 96 136 212 216 217 218 219 220; charset morph_constant = 277 331; ctype ordered: morph_ordered; exclude morph_excluded morph_constant; partition without_CO1_3 = 7: MV MS, 12S 16S, 18S, 28S, CO1_12 CO1_3, Ef1aF1_12 Ef1aF2_12, Ef1aF1_3 Ef1aF2_3; exclude CO1_3; set partition = without_CO1_3; lset applyto=(1) coding=variable rates=gamma; lset applyto=(2,3,5,6,7) nucmodel=4by4 nst=6 rates=gamma covarion=no; lset applyto=(4) nucmodel=4by4 nst=6 rates=gamma covarion=no; prset applyto=(4) statefreqpr=fixed(equal); unlink statefreq=(all) revmat=(all) shape=(all); prset applyto=(all) ratepr=variable; outgroup Orthoptera; taxset fossils = Triassoxyela Asioxyela Nigrimonticola Gigantoxyelinae Spathoxyela Xyela_mesozoica Angaridyela Xyelotoma Undatoma Dahuratoma Mesolyda Turgidontes Aulidontes Protosirex Aulisca Anaxyela Syntexyela Karatavites Stephanogaster Leptephialtites Cleistogaster Sepulca Onochoius Ghilarella Paroryssus Praeoryssus Mesorussus Trematothorax Thoracotrema Prosyntexis Kulbastavia Brachysyntexis Symphytopterus Eoxyela Liadoxyela Abrotoxyela Pseudoxyelocerus Palaeathalia Ferganolyda PamphiliidaeUndesc Rudisiricius Sogutia Xyelula Brigittepterus Grimmaratavites; constraint root = 1-.; constraint Hymenoptera = 10-69; constraint Holometabola = 3-69; constraint Holometabola_with_fossils = 3-114; constraint Pamphilioidea = 41-47; constraint Siricoidea = 51-55; constraint Apocrita = 58-69; constraint Xyelidae = 10-11; constraint Tenthredinidae = 15-40; constraint Vespina = 57-69; [ ======================================== ] [ First dating ("Total-evidence") analysis ] [ ======================================== ] [ calibrate Triassoxyela=fixed(235) Asioxyela=fixed(235) Nigrimonticola=fixed(157) Gigantoxyelinae=fixed(135) Spathoxyela=fixed(135) Xyela_mesozoica=fixed(135) Angaridyela=fixed(135) Xyelotoma=fixed(157) Undatoma=fixed(148) Dahuratoma=fixed(134) Mesolyda=fixed(157) Turgidontes=fixed(134) Aulidontes=fixed(157) Protosirex=fixed(157) Aulisca=fixed(157) Anaxyela=fixed(157) Syntexyela=fixed(157) Karatavites=fixed(157) Stephanogaster=fixed(157) Leptephialtites=fixed(157) Cleistogaster=fixed(179) Sepulca=fixed(157) Onochoius=fixed(135) Ghilarella=fixed(119) Paroryssus=fixed(157) Praeoryssus=fixed(157) Mesorussus=fixed(97) Trematothorax=fixed(135) Thoracotrema=fixed(119) Prosyntexis=fixed(83) Kulbastavia=fixed(157) Brachysyntexis=fixed(157) Symphytopterus=fixed(157) Eoxyela=fixed(179) Liadoxyela=fixed(179) Abrotoxyela=fixed(164) Pseudoxyelocerus=fixed(182) Palaeathalia=fixed(135) Ferganolyda=fixed(179) PamphiliidaeUndesc=fixed(164) Rudisiricius=fixed(164) Sogutia=fixed(187) Xyelula=fixed(182) Brigittepterus=fixed(182) Grimmaratavites=fixed(182); prset brlenspr=clock:uniform; prset clockvarpr=wn; prset wnvarpr=exp(37); prset clockratepr=lognorm(-7.1,0.5); calibrate root=offsetexp(315,396); calibrate Holometabola_with_fossils=offsetexp(302,396); prset topologypr=constraints(root,Holometabola_with_fossils); prset nodeagepr=calibrated; ] [ ====================================== ] [ Second dating ("node-dating") analysis ] [ ====================================== ] [ Commands missing/incomplete/not checked in this block! ] [ delete fossils; prset brlenspr=clock:fossilization; prset speciationpr=exp(20); prset extinctionpr=beta(1,1); prset fossilizationpr=beta(1,1); prset sampleprob=0.0005; prset samplestrat=diversity; prset topologypr=constraints(root,Hymenoptera,Holometabola,Pamphilioidea,Siricoidea,Apocrita,Xyelidae,Tenthredinidae,Vespina); prset nodeagepr=calibrated; ] mcmcp nruns=4 temp=0.07 ngen=100000000 samplefr=1000 printfr=10000 diagnfr=50000; mcmc; sump burninfrac=0.4; sumt burninfrac=0.4; [ ============================================================ ] [ Use these two lines for completing the total-evidence dating ] [ ============================================================ ] [ delete fossils; sumt burninfrac=0.4 output=totev; ] end; ] NBISweden-MrBayes-ecfd55c/examples/kim.nex000066400000000000000000002043451526212227600205370ustar00rootroot00000000000000#NEXUS [Kim, S., K. M. Kjer, and C. N. Duckett. 2003. Comparison between molecular and morphological-based phylogenies of galerucine/alticine leaf beetles (Coleoptera: Chrysomelidae). Insect Syst. Evol. 34:53-64.] begin data; dimensions ntax=27 nchar=1742; format datatype=mixed(rna:1-516,dna:517-1398,protein:1399-1692,standard:1693-1742) gap=- missing=? interleave=yes; matrix [ 1 ] [ 11 1111 1111222 2222222333] [ 12345678901 2345 6789012 3456789012] [ --------------------------------------] Orsodacne [---------------kaCGu-CAG] gGGUAAACCUN AGaA AcccnaA AGGUCGAAUg Chrysomela [-----------------CGUUCAG] GGGUAAACCUG AGAA ACCCGAA AGGUCGAAAG Altica [------------------------] ----------- ---- -CCCGAA AGGUCGAAAG Agelastica [----------------UUAUUCAG] GGGUAAACCUG AGAA ACCCGAA AGGUCGAAAG Monolepta [---------CGUGAA-CCGUUCAG] GGGUAAACCUG AGAA ACCCGAA AGGUCGAAAG Phyllobrotica [------------------------] ---------UG ANAA ACCCGAA AGGUCGAAAG Allochroma [------------nUgaCCGUUCAg] GGGUAAaCcUG AgAA aCCcGAA AGGUCgAAAG Chrysolina [-------------------UUCAG] GGGUAAACCUG AGAA ACCCGAA AGGUCGAAAG Aphthona [AGUCAAGAGUACGUGACCGUUCAG] GGGUAACCCUG AGAA ACCCGAA AGGUCGAAAG Chaetocnema [------------------------] ----------- ---- -CCCGAA AGGUCGAAAG Systena [------------------------] ---CCGACCUG AGAA ACCCGAA AGGUCGAAAG Monocesta [------------------------] ----------G AGAA ACCCGAA AGGUCGAAAG Disonycha [------------------------] ----------- --AA ACCCGAA AGGUCGAAAG Blepharida [------------------------] ----------- ---- -CCCGAA AGGUCGAAAG Galeruca [--------------CAUCGUUCAG] GGGUAAACCUG AGAA ACCCGAA AGGUCGAAAG Orthaltica [---------------ACCGUUCAG] GGGUAAACCUG AGAA ACCCGAA AGGUCGAAAG Paropsis [-------------------UUCAG] GGGUAAACCUG AGAA ACCCGAA AGGUCGAAAG Timarcha [------------------------] -----AACCUG AGAA ACCCGAA AGGUCGAAAG Zygograma [------------UGAACCGUUCAG] GGGUAAACCUG AGAA ACCCGAA AGGUCGAAAG Syneta [------------------------] -----GAACUU ACAA ACCCGAA AGGUCGAAAG Dibolia [------aguacguaaaccguucag] ggguaaaccug agaa acccgaa aggucgaaag Sangariola [------------------------] ----------- ---- -CCCGAA AGGUCGAAAG Aulacophora [------------------------] ----------- AGAA ACCCGAA AGGUCGAAAG Diabrotica [---------------ACcGuUCA-] GGGUAAACcUG AgAA ACCcGAA AG-UCGAAAG Diorhabda [------------------------] ----------- AGAA ACCCGAA AGGUCGAAAG Schematiza [------------------------] -----????UG AGAA ACCCGAA AGGUCGAAAG Oides [--UCAAGAGUACGUGACCGUU-AG] GGGUAACCCUG AGAA ACCCGAA AGGUCGAAAG [ *2 2a 2b 2c 2e I1 I2 2ei 2ci 2bi 2ai 2i* ] [ 1111 1111 111111111111111 1111 111111111111 1111111 11111 111 1111111 111111 111111] [ 333333 344444 4444455 5 555 555 56666 66666677 77777 777888888 8888 99 999999990000 0000 001111111111222 2222 222333333333 3444444 44445 555 5555556 666666 666777] [ 345678 901234 5678901 2 345 678 90123 45678901 23456 789012345 6789 01 234567890123 4567 890123456789012 3456 789012345678 9012345 67890 123 4567890 123456 789012] [ ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------] Orsodacne gGGAAA UUCAuU crCGUUU C NAA --C rUsgg s-nc---- -CG-- ggUyraCgN CGAG AC GGGCN-CGCcg- UUcg Cr-CCAC-------G CUcg CUCCGUCGUCC- GGCGCGC CCGGC GUC GAACGCG UGCACU UUuCcC Chrysomela GGGAAA UUCAUU CGCGUUU C GUC --U GCCGG C-GGUGAG -CG-- GGUGCGUGA CGAA CG GUG--------- UUCG ---CGCC-------G UUCG CGCUGCUCGUUU UCUGGCA CCGGU GAC GAACGCG UGCACU UUUCCC Altica AGGAAA UUCAUU CGCGUCU C GAC --U GUCGG A-GUUGAA -AG-- UUUGUGUGA CGGA UU ACG--------- UUUA ---CGUC-------G UCCG CGCCUCUCGUU- CUUCGAA CCGAU GUG GAACGCG UGCACU UUUCCU Agelastica AGGAAA UUCAUU CGCGUUU C GAU --C GUCGA A-GUCGAA -CG-- UUCGUAUGA CGAA CG ACGU-------- UUCG --GCGUC-------G UUUG CUUCCUACGUU- UUUCUAA UCGAU GUC GAACGCG UGCACU UUUCCU Monolepta AGGAAA UUCAUU CGCGUUU C GGC --- GUCGA A-GUUAAG -CG-- UUUGUAUGA CGAA AG AUGU-------- UUCG --GCGUU-------U UUUG CUUCUUACGUU- UUUCGAG UCGAU GUC GAACGCG UGCACU UUUCCU Phyllobrotica GGGAAA UUCAUU CGCGUUU C GGC --U GUCGA A-GUUGAA -UG-- UAUGUAUGA CGGA UG GCGU-------- UUCG --ACGUC-------G UCUG CAUCUUGCGUU- CUUCAAG UCGAU GUC GAACGCG UGCACU UUUCCU Allochroma GGGAAA UUCaUU CgCGUUU C GgC --U GUCGg A-GUUGgA -UG-- UUCGUGUGA CGgA Ug ACG--------- UUCA ---CGUC-------G UUUG CaCCUUACGUC- UUUCGAA UCGAC GUC GAACGCG UGCaCU UUUCCU Chrysolina GGGAAA UUCAUU CGCGUUU C GAU --C GUUGG C-GAUUGA -CG-- GACUAGUGA CGGA UG GCG--------- UUCG ---CGUC-------G UCUG CGCAGUCUGUU- UAUCGAA CUGAC GUC GAACGCG UGCACU UUUCCC Aphthona GGGAAA UUCAUU CGCGUUU C GGC --C GUCAG A-GGU-GA -UG-- UUGU-GUGA CGGA UG GCG--------- UUCG ---CGUC-------G UCUG CGCCUUGCGUC- CAUCUAA CUGGC GUC GAACGCG UGCACU UUUCCC Chaetocnema GGGAAA UUCAUU CGCGUUU C GAC --C GUCGG A-GUUGGA -CG-- UUCGAGUGA CGGA CG ACG--------- UUCG -CGCGUC-------G UCUG CGCCGUUCGUU- CAUCGAA CUGAC GUC GAACGCG UGCACU UUUCCC Systena AGGAAA UUCAUU CGCGUUU C GAU UUU GUCGG A-GUUGAG -CG-- UUUCGGCGA CGAA UG ACG--------- UUUU AUAUGUC-------G UUUG AGUCAUUCGUU- CUUCGAA CCGAU AUC GAACGCG UGCACU UUUCCU Monocesta AGGAAA UUCAUU CGCGUUU C GAU --C GUCGA AAGUUGAA -UGUA UUUGUAUGA CGGA UG ACGUGUGUGCGC UUCG GCUCUCUCAUGUUUA UCCG CAUAUUAUAUU- CUUCGAA UCGAU AUC GAACGCG UGCACU UUUCCU Disonycha GGGAAA UUCAUU CGCGUUU C GAU --G AUCGA A-GGCUGG -CG-- UGUUUAUGA CGGA UC GUAUA------- ???? U---UUUUAUAUU-G UUCG CAUAUCACGUU- CGUUUAA UCGAU UUC GAACGCG UGCACU UUUCCU Blepharida GGGAAA UUCAUU CGCGUUU C AGC --C GUUGG A-GUUGAA -CG-- UUCGCGUGA CGGA CG ACG--------- UUCG ---CGUC-------G UUUG CGCCUUUCGUU- CUACUAA CCGAC GUU GAACGCG UGCACU UUUCCU Galeruca AGGAAA UUCAUU CGCGUUU C GAU --C GUUGA A-GUUGAA -UG-- UUUGUAUGA CGGA UG AUGU------GC UUCG --GCGC--AUGU--G UUUG CAUCUUAUGUU- CUUCGAA UCGAU GUC GAACGCG UGCACU UUUCCU Orthaltica GGGAAA UUCAUU CGCGUUU C GGC --C GUCGG A-GUUGUG -CG-- UUCGUGUGA CGGA UG GCG--------- CUU- --GCGUC-------G UCCG CGCCUUACGUG- CUUCGAA CCGGC GUC GAACGCG UGCACU UUUCCU Paropsis GGGAAA UUCAUU CGCGUUU C GAC --U GUCGG C-GAUAAG -UG-- AUCUAGUGA CGGA UG GCG--------- UUCG -CGCGUC-------G UCUG CGCAGUUUAUU- UAUCGAA CCGGC GUC GAACGCG UGCACU UUUCCU Timarcha GGGAAA UUCAUU CGCGUUU C GAU --U GUCGG C-GAUAAA -CG-- AGCGAGUGA CGGA CG ACGU----ACG- UUCG -CGCGUAC--GUU-U UCUG CUUCUUUCGUU- UAGGUAA -CGAC GCC GAACGCC UGCACU UUUCCC Zygograma GGGAAA UUCAUU CGCGUUU C GGU --C GUCGG C-UAUCGA -CG-- GGCUAGUGA CGG- UC GGCG-------- UUCG ---CGUC-------G UCUG CGCAGUCUGUC- GAUUGAA CCGAU AUC GAACGCG UGCACU UUUCCC Syneta GGGAAA UUCAUU CGCGUUU C GAC --C GUCGA U-GACGAG -CG-- GUCGAGUGA CGGA UG GCG--------- UUCG ---CGUC-------G UCCG CGCCUUCUGUU- CGUAGAA UCGGC GUC GAACGCG UGCACU UUUCCC Dibolia gggaaa uucauu cgcguuu c ggc -uc gucgg a-ggagaa -CG-- ucggcguga cgga ug acg--------- uucg -cgcguc-------g uucg cacccuucguu- cuuuuua ccggc guc gaacgug ugcacu uuuccu Sangariola GGGAAA UUCAUU CGCGUUU C GAC --U GUCGG A-GUUGGA -CG-- UUCGAGUGA CGGA CG ACG--------- UUUG ---CGCG-------G UUUG CGCCUUACGUU- UAUCGAA CCGAC GUC GAACGCG UGCACU UUUCCU Aulacophora GGGAAA UUCAUU CCCUUUU C GGC --U GUCGA C-GUUAAA -CG-- UUUAUACGA CGGA UG GCGU-------- UUCG --GCGUC-------G UCUG CGUUUUAUGUU- UUUCGAA UCGAU GUC GAACGCG UGCACU UUUCCU Diabrotica AGgAAA UUCAUU CGCGUUU C GAC --U GUCGA A-GUUGGA -UG-- UUUGUAUGA CGGA UA GCGU-------- UUCG --GCGUC-------G UCCG CUUCUUAUAUU- CUUCGUG UCGAU GUC GAACGCG UGCACU UUUCCU Diorhabda AGGAAA UUCAUU CGCGUUU C GAU --A AUUGA A-GUUGAA CCG-- UUUGUAUGA CGGA UA UUGU--UUGUGU UUCG GCAUAUUAC-AAUAA AUCG CGUUUUACGUU- UUUCUAA UCGAU AUC GAACGCG UGCACU UUUCCU Schematiza GGGAAA UUCAUU CGCGUUU C GAu --C GUCUA C-GUUAAA -UG-- UUUGUAUGA CGGA UG AGAUA-CGUCG- UUCG CGGCGCCU----GAG UCCG CAUCUUAUGUU- UAUCUAA UAGAU GUC GAACGCG UGCACU UUUCCU Oides GGGAAA UUCAUU CGCGUUU C GAU --U GUUGA U-AUCGAG -CA-- UUAGUAUGA CGGU AA aCGu-------- UUCG AUGu---------UU AUUG CAUCUUUuGGU- CUUUuAA UCGAU AUC GAACGCG UGCACU UUUCCU [ 3 x xa y z za I3 zai zi yi xai xi ] [ 111111111 111111 111 111111 1112 222222 222222 22222 22222222 22222222 222222222222222222 22222222 2222 22222 222222222 222222 22 2222 222222] [ 777777788 888888 889 999999 9990 000000 000111 11111 11222222 22223333 333333444444444455 55555555 6666 66666 677777777 778888 88 8888 999999] [ 345678901 234567 890 123456 7890 123456 789012 34567 89012345 67890123 456789012345678901 23456789 0123 45678 901234567 890123 45 6789 012345] [ ----------------------------------------------------------------------------------------------------------------------------------------------------------------] Orsodacne CUAGUAGGA CGUCGU GAU CCGUUG GACG UCGGUC UACGGC CCGCC GGUUAGCC CGUCCGGG GUAAACGCUUCGCGGCGU CCCGGGCG GACC GGCGG UGU---CCC GGCCGG CU CGUC CGACGG Chrysomela CUAGUAGGA CGUCGC GAU CCGUUG GGUG UCGGUC UAAGGC UCGAG GUGGAGCC CGCGUGGU CGCUUCGGCGU------- GUCGCGCG GACC CUCGA AAU---CCU GGCCGA CU UGCU CGACGG Altica CUAGUAGGA CGUCGC GAU CCGUUG GGUG UCUGUC UAAGAC UUGGG GUGGAGCC CACAUGGC UUUU-------------- GUCGCGUG GACC CUCGA UGU---UCC GACAGA CU CGCU CGACGG Agelastica CUAGUAGGA CGUCGC GAU CCGUUG GGUG UCUGUC UAAGAC UCGGG GCGGAGCC CGUGCGGU UAUUAUUAUUUUA----- AUCGUGCG GACC CUCGA UGU---UCC GACAGA CU CGCU CGACGG Monolepta CUAGUAGGA CGUCGC GAU CCGUUG GGUG UCUGUC UAGGGU UCGAG GUGGAGCC CACGUAAU UUC--------------G AUUGCGUG GACC CUUGA UGU---CCC GACAGA CU CGCU CGACGG Phyllobrotica CUAGUAGAA CGUCGC GAU CCGUUG GGUG UCUGUC UAAGGU GUGAG GUGUAGCC CACGCGAU UUUU-------------G AUCGUGUG GACC CUCGA UAU---CCC GACAGA CU CGCU CGACGG Allochroma CUAGUAGGa CGUCgC GAU CCGUUG GGUG UCUGUC uAAgAC UCGGG GUGgAGUC CACGUGGC UUUU-------------G GUCGCGUG AaCC CUCGA UGU---GCC GaCAGA CU CGCU CGACGG Chrysolina CUAGUAGGA CGUUGC GAU CCGUUG GGUG UCGGUC UAAGGC CCGAG GUGGAGCC CACGUGAA CGUUUCGGCG-------- UUUGCGUG GACC CUCGG UGU---CCC GGUCGA CU CGCU CGACGG Aphthona CUAGUAGGA CGUCGC GAU CCGUUG GGUG UCUGUC UAAGAC UCGGG GUGGAGCC CAGAUGAC CACG-------------- GUCGUUUG GACC CUCGA UGU---UCC GACAGA CU CGCU CGACGG Chaetocnema CUAGUAGGA CGUCGC GAU CCGUUG GGUG UUUGUC UAAGAC UUGAG GUGGAGCC CGCGAGAC UUA--------------G GUCGCGCG GACC CUUGA UGU---UCC GACCAA CC CGCU CGACGG Systena CUAGUAGGA CGUCGC GAU CCGUUG GGUG UCUGUC UAAGAC CCGAG GUGGAGUC CACGAAGC GUUUUUC----------- GCUuCGUG AACC CUUGG UGUAUAUCC GACAGA CU CGCU CGACGG Monocesta CUAGUAGAA CGUCGU GAU CCGUUG GGUG UUUGUC UAAGAC UCGAG GUGGAGCC CACAUGAU UUUUUGUC---------G AUUAUGUG GACC CUCUA UGUUCUUCC GACAGA CU CGCU CGACGG Disonycha CCAGUAGGA CGUCGC GAU CCGUUG GGUG UCUGUC UAAGAA UUGAG GUGGAGAC CAUGUGGC UUUUU------------A GUUGCUUG GACC CUUGA AUU---UCC GACAGA CU CGCU CGACGG Blepharida CUAGUAGGA CGUCGC GAU CCGUUG GGUG UCUGUC UAAGAC UCGGG GUGGAGCC CGCGUGGC AUUUU------------- GUCGCGCG GACC CUCGA AGU---UCC GACAGA CU CGCU CGACGG Galeruca CUAGUAGGA CGUCGU GAU CCGUUG GGUG UCUGUC UAAGAC UCGGG GUGGAGAC CACGUGAU UUUUC------------G AUUAUGUG GACC CUCGA UGU---UCC GACAGA CU CGCU CGACGG Orthaltica CUAGUAGGA CGUCGC GAU CCGUUG GGUG UCUGUC UAAGGC UUGGG GUGGAGCC CACGUGGA CGUUUCACGACGUA---- UCCGCGUG GACC CUCGA CGU---CCC GGCAGA CU CGCU CGACGG Paropsis CUAGUAGGA CGUUGC GAU CCGUUG GGUG UCGGUC UAAGGC UCGAG GUGGAGCC CACGUGGA CGUUUCGGCG-------- UUCGCGUG GACc CUCGG UGU---CCC GGCCGA CU CGCU CGACGG Timarcha CUAGUAGGA CGUCGC GAU CCGUUG GGUG UCGGUC UAAGGC UCGCG GUGGAGCC CGCGCGGU UUAUA------------- GCcGCGCG GACC CGCGA UAU---CCC GACCGA CC UGCU CGACGG Zygograma CUAGUAGGA CGUUGC GAU CCGUUG GGUG UCGGUC UAAGGC UCGAG GUGGAGCC CACGCGAA CGUUUCGGCGUC------ UUCGCGUG GACC CUCGA UGU---CCC GGCCGA CU CGCU CGACGG Syneta CUAGUAGGA CGUCGC GAU CCGUUG GGUG UCGGUC UAAGGC CCUCG GUGGAGCC CAGUAGAC GUUUCGAC---------- GUUGCGUG GACC CGAGG CGU---CCC GGCCGG CC CGCU CGACGG Dibolia cuaguacca cgucgc gau ucguug ggug ucuguc uaagac ucgag guggagcc cgcgcgau uu------------aaac gucgc-cg gacc cucga ugc---ucc gacaga cu cgcu cgacgg Sangariola CUAGUAGGA CGUCGC GAU CCGUUG GGUG UUUGUC UAAGAC UCGAG GUGGAGUC CACGUGGC UUUU-------------G GUCGCGUG AACC CUUGA UGU---GCC GACAGA CU CGCU CGACGG Aulacophora CUAGUAGGA CGUCGC GAU CCGUUG GGUG UCUGUC UAAGGU UCGAA GUGGAGAC CACGCGAU UUUUUU-----------G AUCGUAUG GACC UUCGU UAU---CCC GACAGA CU CGCU CGACGG Diabrotica CUAGUAGGA CGUCGC GAU CCGUUG GGUG UCUGUC UAAGAC UCGAG GUGGAGCC CGCGUAAU UUUU-------------A AUUAUGCG GACC CUUGG UGU---UCC GACAGA CU CACU CGACGG Diorhabda CUAGUAGGA CGUCGU GAU CCGUUG GGUG UCUGUC UAAGAC UCGAG GUGGAGAC CACUUGAU UUUAUUC---------GA AUUAggUg GACC CUCGA UGU---UCC GACAGA CC CGCU CGACGG Schematiza CUAGUAGGA CGUCGU GAU CCGUUG GGUG UCUGUC UAAGGC UCGGG GUGGAGCC CACGUGAU UUUUU------------A AUUAUGUG GACC CUCGA UGU---UCC GACAGA CU CGCU CGACGG Oides CUAGUAGGA CGUCGC GAU CCGUUG GGUG UUUGUC UAAGGU UUGAG GUGGAGCC CGCGUGAU UUCUU------------A AUUGCGCG GACC CUCAU UAU---CCC GACAGA CU CGCU CGACGG [ 3a 3b I4 3bi ] [ 22223333 33333 3333333 3333 33333333 3333 3333] [ 99990000 00000 0111111 1111 22222222 2233 3333] [ 67890123 45678 9012345 6789 01234567 8901 2345] [ -----------------------------------------------] Orsodacne UAAGAAUU AGGCG CGGGGCC GCUA --CCACU- CGGU UAGC Chrysomela UAUAAGGA AGGCG CGAGGCC GCUA -----CU- CAGU UAGC Altica UAUGCUAA AGGCG CGGGGCC GCUA -----CU- CAGU UAGC Agelastica UACGCUAA UGGCG UGGGGCC GCUA ------UC UUUU UAGC Monolepta UAUGCUAA UGGCG UAAGGCC GCUA -----CUU AAGU UAGC Phyllobrotica UAUACUAA UGGCG CGAGGCC GCUA -----CUU AAGU UAGC Allochroma UAUGCUAA UGGCG CGGGgCC GCUA -----Cu- CAGu UAGC Chrysolina UAUAAUGA UGGCG CGGGGCC GCUA -----CU- UAGU UAGC Aphthona UAUGCUAA AGGCG CGGGGCC GCUA ------UU CAGU UAGC Chaetocnema UAUACGAA UGGCG CCGGGCC GCUA C---ACU- UAGU UAGC Systena UAUGCAAA UGGCG CGAGGCC GCUA -A--A--- CAAU UAGC Monocesta UAUAAAUA UGGCG CGGGGCC GCUA CACAUUA- CAGU UAGC Disonycha UAUAACGA UGGCG CAAGGCC GCUA C--AUU-- CAGU UAGC Blepharida UAUGCUAA UGGCG CGGGGCC GCUA ------UU CAGU UAGC Galeruca UAUAAAUA UGGCG CGGGGCC GCUA -----CAU UAGU UAGC Orthaltica UACAUAGA UGGCG UGGGGCC GCUA -----CU- CAGU UAGC Paropsis UAUAAUGA UGGCG CGGGGCC GCUA -----CU- CAGU UAGC Timarcha UAUAAAGA UGGCG CGAGGCC GCUA CUUCGUA- GAAU UAGC Zygograma UAUAAUGA UGGCG CGGGGCC GCUA -----CA- UAGU UAGC Syneta UAUCAUUA AGGCG CGAGGCC GCUA -----CUA CAGU UAGC Dibolia uaugcuaa uggcg cgaggcc gcua -------u caau uagc Sangariola UAUGCUAA UGGCG CGGGGCC GCUA -----CUC UAGU UAGC Aulacophora UAUACUAA UGGCG CGAGGCC GCUA -----UUU UAAU UAGC Diabrotica UAUACUAA UGGCG CGGGgCC GCUA -----CUU UAGU UAGC Diorhabda UAGAAAUA UGGCG CGGGGCC GCUA -----GAU UAAU UAGC Schematiza UAUACAUA UGGCG CGGGGCC GCUA -----CA- CAGU UAGC Oides UAUAUUGA UGGCG CGGGGCC GCUA -----CUU UAGU UAGC [ 3b2 3c 3d 3e 3f 3g 3gi 3fi 3ei 3di 3ci 3b2i ] [ 333333 3 3333333 3 3333 3333333 33333 33 33333 33 333333 33333333 33333 3 33334 4 4444444 44 4444 4 4444444 44444444444 4 444444] [ 333344 4 4444444 5 5555 5555566 66666 66 67777 77 777788 88888888 99999 9 99990 0 0000000 01 1111 1 1111222 22222223333 3 333334] [ 678901 2 3456789 0 1234 5678901 23456 78 90123 45 678901 23456789 01234 5 67890 1 2345678 90 1234 5 6789012 34567890123 4 567890] [ ----------------------------------------------------------------------------------------------------------------------------------------------------] Orsodacne GUCCGG C CCGCCGC A AGCA CGUCCGG CGUUC GG CACGG CG AU-CGr ACCCGGUG CCGGU C CCGUC C CCCGGCG AC UGUU G GCGGCGG AGCU------- C UCGGAC Chrysomela GUUCGG C UCGCGGC A AGCG CGUUCGG CGU-- UU ACGGC GA -U-CGG ACCUGGUG CCGAU U CUGUC C CCGGACG AC UGUU G GCCGCGA UGCU------- C UCGAAC Altica GUUCGG C CCGUAGC A AGCA CGUUCUG UGU-- UU GACGG UG AU-CGG ACCUGGUG CCGAU U CUGUC C CAGAACG AC UGUU G GUUGCGG UGUU------- C UCGAAC Agelastica GUUCGG C CCGUAGC A AGCG CGUUUUG UGU-- UU gACGG UC AU-CGG AUCUAACG CCGAU C UCGUC C CGAAACG AC UGUU G GUUACGG CGUU------- C UCGAAC Monolepta GUUCGG C CCGUAGC A AGCG CGUUCUG UG--- UU GACGG CC AU-CGG ACCUGGUG CCGAU U CUGUC C CGGAACG AC UGUU G GUUGCGG UGUU------- C UCGAAC Phyllobrotica GUUCGG C CCGUAGC A AGCA CGUUCUG CGU-- Uw GAUGG WC AU-CGG ACCUGGUG CCGAU C CUGUC C CGGAACG AC UGUU G GUUACGG NGUU------- C UCGAAC Allochroma GUUCGG C CCGUGGC A AGCa CGUUcUG UGU-- UU GACGg CG AU-CGG ACCUGGUG CCGAU U CUGUC C CGGAaCG AC UGUu G GcUaCgG UGuU------- C UCGAAC Chrysolina GUUCGG C UCGUGGC A AGCG CGUUCGG UGU-- UU GACGG CG AU-CGG ACCUAGUG CCGAU C CUGUC C CCGGGCG AC UGUU G GCUGCGA UGUC------- C UCGAAC Aphthona GUUCGG C CCGUAUC A AGCA CGUUCUG CGU-- UU GAUGG CG AU-CGG AUCUGAUG CCGAU U CUGUC C CGGAACG AC UGUU G GGUGCGG UGUU------- C UCGAAC Chaetocnema GUUCGG G CCGUAGC A AGCA CGUUCCG CGU-- UU GACGG CG AU-CGG ACCUGGUG CCGAU U CUGUC C CGGAACG AC UGUU G GUUACGG UGUU------- C UCGAAC Systena GUUCGG C UCGUAGC A AGCA CGUUCCG UGU-- UU GAUGG UG AU-CGG ACCUAGUG CCGAU U CUGUC C CGGAAUG AC UGUU G GUUGCGA UGUU------- C UCGAAC Monocesta GUUCGG C CCGUAGC A AGCA CGUUUCG UGU-- UU GACGG CG AU-CGG ACCUAGUG CCGAU U CUGUC C CGGAACG AC UGUU G GUUACGG UGUUUUUUUAU C UCGAAC Disonycha GUUCGG C CCGUAGC A AGCG CGUUCUG UGU-- UU GACGG CA AUUCGG ACCUAGCG CCGAU U CCGUU C CAGAACG AC UGUU G GUUGCGG UGCU------- C UCGAAC Blepharida GUUCGG C CCGUAAC A AGCA CGUUCUG CGU-- UU GACGG CG AU-CGG ACCUGGUG CCGAU U CUGUC C CGGAACG AC UGUU G GUUGCGG UGUU------- C UCGAAC Galeruca GUUCGG C CCGUAGC A AGCA CGUUUCG CGU-- UU GACGG CG AU-CGG ACCUAGUG CCGAU U CUGUC C CGGAACG AC UGUU G GUUACGG UGUU------- C UCGAAC Orthaltica GUUCGG C CCGUAGC A AGCG CGUUCUG UGU-- UU GAUGG CG AU-CGG ACCUGGUG CCGAU U CUGUC C CAGAACG AC UGUU G GCUGCGG UGUU------- C UCGAAC Paropsis GUUCGG C UCGUGGC A AGCA CGUUCGG UGU-- UU GACGG CG AU-CGG ACCUAGUG CCGAU C CUGUC C CCGUACG AC UGUU G GCUGCGA UGUU------- C UCGAAC Timarcha GUUCGG C CCGUAGC A AGCG CGUUCGU CGU-- UA GACGG CG AU-CGG ACCUAGCG CCGGU C CUGUC C GCGAACG AC UGUU G GCUGCGG UGAU------- C UCGAAC Zygograma GUUCGG C UCGUGGC A AGCG CGAACGG CGU-- UU GACGG CG AU-CGG ACCUAGCG CCGAU C CUGUC C CCGUUCG AC UGUU G GCUGCGA UGUU------- C UCGAAC Syneta GUUCGG C CCGCGGC A AGCA CGUUCGG CGU-- UU GACGG AG AU-CGG ACCUAGUG CCGGU G CCGUC C CCGAGCG AC UGUU G GCCGUGG UGUU------- C UCGAAC Dibolia guucga c ccguagc a agcg cguuccg CGU-- uu gacgg cg au-cgg aucugaug ccgau u cuguc c cggaacg ac uguu g guuacgg uguuuu---au c UCGAAC Sangariola GUUCGG C CCGUAGC A AGCA CGUUCUG UGU-- UU GACGG CG AU-CGG ACCUGGUG CCGAU U CUGUC C CGGAACG AC UGUU G GCUGCGG UGUU------- C UCGAAC Aulacophora GUUCGG C CCGUAGC A AGCA CGUUCUG CGU-- UU GACGG CC AU-CGG ACCUGGUG CCGAU U CUGUC C CGGAACG AC UGUU G GUUGCGG UGUU------- C UCGAAC Diabrotica GUUCGG C CCGUAGC A AGCA CGUUCUG UGU-- UU GACGG CG AU-CGG ACCUGGUG CCGAU U CUGUC C CAGAACG AC UGUU G GUUACGG UGUU------- C UCGAAC Diorhabda GUUCGG C CCGUAGC A AGAG CGUUUCG UGU-- UU GACGG UA AU-CGG ACAUAAUG CCGAU U CUGUC C CGGAACG AC UGUU G GUUACGG UGUU------- C UCGAAC Schematiza GUUCGG C CCGUAGC A AGCA CGUUUCG CGU-- UU GACGG CG AU-CGG ACCUAGUG CCGAU U CUGUC C CGGAACG AC UGUU G GUUACGG UGUU------- C UCGAAC Oides GUUCGG C CCGUAGC A AGCA CGUUCUG UGU-- UU GACAG UC AU-CGG ACCUGGUG CCGAU U CUGUC C CGGAACG AC UGUU G GUUACGG UGUU------- C UCGAAC [ I5 3ai 3i 1i ] [ 44444444 44444444444444444444 4444 44444 44444 4444444 4444444 4445555 5555555555555] [ 44444444 45555555555666666666 6777 77777 77888 8888888 9999999 9990000 0000001111111] [ 12345678 90123456789012345678 9012 34567 89012 3456789 0123456 7890123 4567890123456] [ ------------------------------------------------------------------------------------------] Orsodacne AGACCUCC GUU----------------- -AAA CGCCG -AUCG -ACGACg CUAUAGC u?????? ?????????????[??????????-------------] Chrysomela AGACCUCG AAUCUC-------------- GAAA CGCCG -AUCU -GCGACG CUAUAGC UUUGGGU ACUUUCAGGACCC[-UC---AA---------------] Altica AGACCUCG UU------------------ GAAA CGCCG -AUCA -GCGACG CUAUAGC UUUGGGU ACUUUCAGGACCC[-U---------------------] Agelastica AGACCUCA UCGUUCGAUUUUUCGAAAUC GAAA CGCCG -AUCA -GCGACG CUAUAGA UUUGGGU ACUUUCAGGACCC[-U---------------------] Monolepta AGACCUUA UAUUU--------------- GAAA CGCCG -AUCA -GCGACK CUAUAGA UUUGGGU ACUUUCAGGACCC[GUCUUGAA-C-------------] Phyllobrotica AGACCUCN AUUAU--------------- GAAA CGCCG -AUCA -SCGACG CNAUAGA UUUGGGN ACUUUCAGGACCC[GUYUUGAAAC-------------] Allochroma AgaCCUCG uu------------------ GAAA cgccg -auca -?????? ??????? ??????? ????????????-[-----------------------] Chrysolina AGACCUCG UU------------------ GAAA CGCCG -AUCU -GCAACG CUAUAGC UUUGGGU ACUUUCAGGACCC[-UC---AA---------------] Aphthona AGACCUCG UU------------------ GAAA CGCCG -AUCA AGCGACG CUAUAGC UUUGGGU ACUUUCAGGACCC[-UC---AA---------------] Chaetocnema AGACC-CG UU------------------ GAAA CGCCG -AUCA -GCGACG CAAUAGC UUUGGGU ACUUUCAGGACCC[-UC---AA---------------] Systena AGACCUCG UU------------------ GAAA CGCCG -AUCU -GCGACG CUAUAGU UUUGGGU AAUUUCAGGACCC[------A----------------] Monocesta AGACCUCG UUU----------------- GAAA CGCCG -AUCA -ACGACG CUAUAGA UUUGGGU ACUUUCAGGACCC[-----------------------] Disonycha AGACCUUG UU------------------ GAAA CGCCG -AUCA -GCGACG AUUUAGC UUUGGGU ACUUUCAGGACCC[-U---------------------] Blepharida AGACCUCG UU------------------ GAAA CGCCG -AUCA -GCGACG CUAUAGC UUUGGGU ACUUUCAGGACCC[-UC---AA---------------] Galeruca AGACCUCG UUUU---------------- GAAA CGCCG -AUCA -ACGACG CUAUAGA UUUGGGU ACUUUCAGGACCC[-UC---A----------------] Orthaltica AGACCUCU UU------------------ GAAA CGCCG -AUCA -GCGACG CUAUAGC UUUGGGU ACUUUCAGGACCC[-UC---AA---------------] Paropsis AGACCUCG UU------------------ GAAA CGCCG -AUCU -GCAACG CUAUAGC UUUGGGU ACUUUCAGGACCC[-UC---AA---------------] Timarcha AGACCUCG UC------------------ GAAA CGCCG -AUCU -GCGACG CUAUAGC UUUGGGU ACUUUCAGGACCC[-UU---AA---------------] Zygograma AGACCUCG UC------------------ GAAA CGCCG -AUCU -GCAACG CUAUAGC UUUGGGU ACUUUCAGGACCC[-UC---AA---------------] Syneta AGACCUCG UCG----------------- AAAA CGCCG AAUCU -GCGACG CUAUAGC UUUGGGU ACUUUCAGGACCC[-----------------------] Dibolia agaCCUCG uc------------------ GAAA cgccg -aucg -gcgacg cuauagc uuugggu acuuucaggaccc[gucuugaaaccgg----------] Sangariola AGACCUCG UU------------------ GAAA CGCCG -AUCA -GCGACG CUAUAGC UUUGGGU ACUUUCAGGACCC[-----------------------] Aulacophora AGACCUCG UUU----------------- GAAA CGCCG -AUAA -GCGACG CUAAAGA UUUGGGU ACUUUCAGGACCC[-----------------------] Diabrotica AGACCUCG UAU----------------- GAAA CGCCG -AUCA -GCGACG CUCUAGA UuuGGGu ACAucgAGGACCC[-----------------------] Diorhabda AGACCUCG UUUU---------------- GAAA CGCCG -AUCA -ACGACG CUAUCGA UUUGGGU AAUUUCAGGACCC[-UC---A----------------] Schematiza AGACCUCG ACUU---------------- GAAA CGCCG -AUCA -ACGACG CUAUAGA UUUGGGU ACUUUCAGGACCC[-----------------------] Oides AGACCUCG UUUU---------------- GAAA CGCCG -AUCA -GCGACG CUAUAGA UUUGGGU ACUUUCAGGACCc[GUCUUGAAaCACGGACCAAGG--] [EF1a] Orsodacne ???????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Chrysomela UCCUUCAAGUAUGCGUGGGUACUUGACAAACUGAAAGCCGAACGUGAACGUGGUAUCACCAUCGAUAUCGCUUUGUGGAAGUUCGAAACUGCCAAGUAUUAUGUUACCAUCAUUGAUGCUCCUGGACACAGAGAUUUCAUCAAGAACAUGAUCACUGGUACAUCGCAGGCCGAUUGUGCCGUACUCAUUGUUGCUGCUGGUACUGGUGAAUUUGAAGCCGGUAUCUCGAAAAACGGACAGACCCGUGAACACGCACUGCUCGCAUUCACCCUUGGAGUAAAACAACUUAUUGUUGGUGUCAACAAAAUGGAUUCGACUGAACCACCGUACAGUGAGUCUCGUUUCGAAGAAAUCAAAAAGGAGGUAUCCUCCUACAUCAAGAAGAUUGGUUACAACCCGGCAGCUGUCGCUUUCGUACCA Altica UCYUUCAAAUAUGCAUGGGUACUUGACAAACUUAAGGCUGAACGUGAACGUGGUAUUACCAUUGAUAUUGCUUUAUGGAAAUUCGAAACAGCUAAAUACUAUGUUACCAUUAUUGAUGCCCCCGGACACAGAGAUUUCAUCAAGAACAUGAUUACUGGUACAUCACAAGCUGAUUGUGCUGUACUUAUUGUAGCCGCUGGUACCGGUGAAUUCGAAGCUGGUAUUUCAAAGAAUGGACAAACACGUGAACACGCUCUUCUUGCUUUUACUCUUGGAGUAAAACAACUUAUUGUUGGUGUUAACAAAAUGGACUCAACUGAACCACCAUACAGUGAAGCUCGUUUCGAUGAAAUUAAGAAAGAAGUAUCAUCUUACAUCAAGAAAAUCGGUUACAAUCCCGCUGCCGUUGCUUUCGUGCCA Agelastica UCCUUUAAAUACGCAUGGGUGCUGGAUAAACUUAAGGCAGAACGUGAACGCGGCAUcACUAUCGACAUCGCUCUAUGGAAAUUCGAAACUGCCAAAUAUUACGUGACCAUCAUUGAUGCUCCAGGACACAGAGAUUUCAUUAAGAACAUGAUCACUGGCACUUCUCAAGCUGAUUGUGCCGUACUCAUCGUAGCUGCCGGUACCGGAGAAUUCGAGGCCGGUAUCUCCAAGAACGGCCAGACGCGCGAACACGCUCUGCUGGCUUUCACCCUCGGAGUCAAGCAGCUGAUAGUCGGAGUCAACAAGAUGGACUCCACAGAACCGCCAUACUCCGAGgCGCGUUUUGAGGAAAUCAAGAAGGAAGUAUCGUCGUACAUCAAGAAGAUCGGCUACAAUCCGGCCGCCGUCGCCUUUGUACCG Monolepta UCYUUYAAAUAUGCAUGGGUACUUGACAAACUUAAGGCUGAACGUGAACGUGGUAUUACAAUUGAUAUUGCCUUGUGGAAAUUCGAAACAGCUAAAUACUAUGUAACCAUCAUUGAUGCCCCUGGACACAGAGAUUUCAUCAAAAACAUGAUUACUGGUACUUCACAGGCUGAUUGUGCUGUGCUUAUUGUGGCUGCUGGUACUGGAGAAUUCGAAGCUGGUAUUUCAAAGAACGGACAAACACGUGAACACGCUCUCCUCGCCUUCACCCUUGGAGUAAAACAACUUAUCGUUGGUGUCAACAAAAUGGACUCUACCGAACCCCCAUACAGUGAAGCACGUUUCGAGGAAAUUAAGAAAGAAGUAUCCUCUUACAUUAAGAAGAUUGGUUACAACCCUGCUGCUGUUGCCUUUGUACCA Phyllobrotica ???????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Allochroma UCCUUCAAAUAUGCUUGGGUACUUGACAAACUUAAGGCCGAACGUGAACGUGGUAUCACCAUUGAUAUUGCUUUGUGGAAAUUCGAGACUGCUAAAUAUUAUGUAACCAUCAUUGAUGCUCCCGGACACAGAGAUUUCAUCAAAAACAUGAUUACUGGCACUUCCCAAGCUGACUGUGCCGUAUUGAUUGUUGCUGCUGGUACAGGUGAAUUUGAAGCUGGAAUAUCAAAGAACGGACAAACACGUGAACAUGCCUUGUUAGCCUUUACUCUUGGAGUAAAACAACUUAUUGUUGGAGUUAACAAAAUGGAUUCCACUGAACCCCCAUACAGCGAAGCUCGUUUUGAAGAAAUCAAGAAGGAAGUGUCCUCAUACAUUAAAAAAAUUGGUUACAACCCUGCUGCUGUAGCUUUUGUGCCA Chrysolina UCCUUCAAGUACGCGUGGGUACUUGACAAACUUAAGGCUGAACGUGAACGUGGUAUCACUAUCGAUAUUGCAUUGUGGAAGUUCGAAACCGCGAAAUACUAUGUAACCAUUAUUGAUGCACCUGGGCACAGAGAUUUCAUCAAGAACAUGAUCACUGGUACAUCUCAGGCUGAUUGCGCUGUACUCAUUGUGGCUGCUGGUACUGGUGAGUUUGAAGCUGGUAUCUCGAAGAAUGGACAAACUCGUGAACAUGCUCUACUUGCCUUUACUCUUGGAGUGAAACAAAUUAUCAUAGGAGUCAACAAAAUGGAUUCAACUGAACCACCCUACAGUGAAUCUCGUUUUGAGGAAAUCAAGAAAGAAGUGUCUUCAUACAUCAAGAAAAUUGGUUACAACCCAGCUGCAGUCGCCUUUGUGCCA Aphthona UCCUUCAAAUAUGCCUGGGUACUGGACAAACUGAAGGCCGAACGCGAACGCGGUAUCACCAUUGAUAUUGCUUUGUGGAAAUUCGAGACUGCCAAAUACUAUGUAACCAUCAUUGAUGCCCCUGGACACAGAGAUUUCAUCAAGAACAUGAUUACUGGAACUUCCCAAGCUGAUUGUGCCGUGCUCAUCGUUGCUGCCGGUACUGGUGAAUUCGAAGCCGGAAUUUCAAAGAACGGACAAACCCGUGAACACGCUCUUCUUGCUUUCACUCUUGGAGUAAAACAACUUAUCGUUGGAGUCAACAAAAUGGAUUCCACUGAACCYCCAUACAGYGAAGCUCGUUUUGAAGAAAUCAAGAAGGAAGUGUCCUCUUACAUCAAGAAGAUCGGUUACAACCCUGCUGCUGUUGCUUUUGUACCA Chaetocnema UCCUUCAAGUACGCCUGGGUACUGGACAAACUUAAGGCUGAACGUGAACGUGGUAUCACCAUCGAUAUUGCUUUGUGGAAAUUCGAAACUGCCAAAUACUACGUAACCAUCAUUGAUGCCCCUGGACACAGAGAUUUCAUCAAGAACAUGAUUACUGGAACAUCCCAAGCUGAUUGUGCCGUGCUUAUUGUAGCUGCCGGUACUGGUGAAUUCGAAGCUGGUAUUUCCAAGAACGGACAAACUCGUGAACACGCUCUGCUUGCUUUCACUCUUGGAGUAAAACAACUUAUCGUUGGUGUUAACAAGAUGGAUUCCACUGAACCCCCAUACAGUGAAGCCCGUUUUGAAGAAAUCAAGAAGGAAGUGUCUUCGUACAUCAAGAAGAUCGGUUACAACCCUGCUGCUGUUGCUUUCGUACCA Systena UCUUUCAAAUAUGCCUGGGUACUCGACAAACUGAAGGCUGAACGUGAACGUGGUAUCACCAUUGAUAUUGCAUUGUGGAAAUUCGAAACAGCUAAAUACUAUGUAACUAUUAUUGAUGACCCUGGACAUAGAGAUUUCAUCAAAAACAUGAUUACCGGUACAUCUCAAGCUGAUUGUGCCGUAUUGAUUGUAGCUGCAGGUACCGGUGAAUUCGAAGCUGGUAUUUCAAAGAACGGACAAACACGUGAACACGCUUUACUCGCCUUCACUCUAGGAGUAAAACAACUUAUUGUUGGAGUUAACAAAAUGGAUUCAACUGAACCACCAUACAGUGAAGCCCGUUUAGAAGAAAUCAAGAAAGAAGUAUCUUCAUACAUCAAGAAGAUUGGUUACAAUCCUGCAGCUGUAGCCUUCGUUCCA Monocesta UCCUUUAAAUAUGCAUGGGUGCUCGACAAACUUAAAGCUGAACGCGAGCGUGGUAUCACCAUCGAUAUUGCUUUAUGGAAAUUCGAAACAGCUAAAUACUAYGUAACCAUUAUUGAUGCCCCUGGGCACAGAGAUUUCAUUAAGAACAUGAUUACUGGAACAUCUCAGGCUGAUUGUGCCGUGCUCAUUGUAGCUGCUGGUACUGGAGAAUUUGAAGCUGGUAUUUCCAAGAACGGACAAACCCGUGAACAYGCUCUCCUUGCUUUCACUCUUGGAGUAAAACAACUUAUUGUUGGAGUCAACAAAAUGGACUCUACUGAACCACCAUACAGUGAAUCACGUUUUGAAGAAAUCAAGAAAGAAGUUUCCUCAUACAUUAAAAAGAUUGGAUACAAUCCUGCUGCUGUUGCCUUUGUUCCC Disonycha UCUUUCAAAUACGCNUGGGUACUCGACAAAUUGAAGGCUGAACGUGAACGUGGUAUCACCAUYGAUAUYGCYUUGUGGAAAUUCGAAACAUCYAAAUAYUAYGUAACCAUCAUUGAUGCUCCCGGGCACAGRGAUUUCAUCAAGAACAUGAUCACCGGUACAUCCCAAGCUGAUUGUGCYGUACUCAUYGUAGCUGCUGGUACUGGUGAAUUCGAAGCUGGUAUCUCAAAGAACGGRCAAACCCGUGAACAYGCCCUUCUGGCUUUCACUCUUGGAGUAAAACAACUCaUUGUUGGUGUYAACAARAUGGACUCCACCGAACCACCAUACAGUGAAgCCCGUUUCGAAGAAAUCAAGAAGGAAGUAUCUUCUUACAUCAAGAAGAUUGGYUACAAYCCAGCAGCUGUUGCCUUUGUCCCA Blepharida UCCUUCAAAUACGCCUGGGUACUCGACAAACUUAAGGCUGAACGUGAACGUGGUAUCACCAUUGAUAUUGCUUUGUGGAAAUUCGAAACCGCUAAAUACUAUGUAACUAUCAUUGAUGCACCUGGACACAGAGAUUUCAUCAAGAACAUGAUUACUGGUACAUCCCAAGCUGAUUGUGCCGUACUUAUCGUCGCUGCUGGUACUGGUGAAUUUGAAGCUGGUAUUUCAAAGAACGGCCAAACACGUGAACACGCUUUACUUGCCUUCACCCUUGGAGUAAAACAACUUAUUGUUGGUGUCAACAAAAUGGACUCGACUGAACCCCCAUACAGCGAAgCCCGUUUCGAGGAAAUCAAGAAGGAAGUAUCUUCUUACAUUAAGAAAAUUGGUUACAAUCCUGCCGCUGUAGCCUUUGUACCA Galeruca UCUUUUAAAUAUGCCUGGGUAUUGGAUAAGCUUAAAGCAGAACGCGAACGUGGUAUcACCAUCGACAUAGCUUUAUGGAAAUUUGAAACCGCUAAGUAUUAUGUCACUAUUAUUGACGCUCCAGGACAUAGAGAUUUUAUUAAGAACAUGAUUACUGGAACAUCCCAAGCUGAUUGUGCCGUGCUUAUUGUAGCAGCUGGUACGGGAGAAUUUGAAGCCGGUAUUUCGAAAAAUGGACAAACUCGUGAACACGCUCUGCUUGCAUUUACUCUUGGCGUCAAACAACUUAUUGUAGGAGUUAAUAAGAUGGAUUCAACGGAACCGCCAUAUUCCGAAgCGCGUUAUGAAGAAAUCAAGAAAGAAGUAUCUUCGUACAUUAAGAAAAUUGGAUACAAUCCGGCAGCUGUAGCAUUUGUUCCU Orthaltica UCCUUCAAAUAUGCUUGGGUACUUGACAAACUCAAGGCCGAACGCGAACGCGGUAUCACCAUUGACAUUGCUUUGUGGAAAUUCGAAACUGCUAAAUACUACGUAACCAUCAUUGAUGCCCCUGGACACAGAGAUUUCAUCAAGAACAUGAUUACUGGAACUUCCCAAGCUGAUUGUGCUGUGCUCAUCGUCGCUGCCGGUACUGGUGAAUUUGAAGCCGGAAUUUCAAAGAACGGACAAACUCGUGAACACGCUCUUCUUGCUUUCACUCUUGGAGUAAAACAACUUAUCGUUGGAGUUAACAAAAUGGACUCCACUGAACCCCCAUACAGCGAAGCUCGUUUUGAAGAAAUCAAGAAGGAAGUGUCCUCGUACAUCAAGAAGAUCGGUUACAACCCUGCUGCUGUAGCCUUUGUACCA Paropsis UCCUUCAAGUACGCUUGGGUUCUGGACAAACUGAAAGCUGAACGUGAACGAGGUAUCACCAUCGACAUUGCUCUAUGGAAAUUUGAAACCGCCAAGUAUUACGUGACCAUUAUUGAUGCUCCUGGCCAUAGAGAUUUCAUUAAGAACAUGAUCACUGGUACUUCUCAGGCUGAUUGUGCUGUACUUAUUGUAGCUGCAGGAACAGGAGAAUUUGAGGCUGGUAUUUCUAAAAAUGGACAAACUCGUGAACACGCUUUGCUGGCUUUCACUCUGGGAGUGAAGCAACUUAUCGUAGGAGUCAACAAAAUGGACUCCACUGAACCACCGUACAGCGACUCUCGUUUUGAGGAAAUCAAGAAGGAAGUUUCGUCUUACAUCAAGAAGAUUGGCUACAACCCAGCCGCUGUGGCUUUCGUACCc Timarcha UCCUUCAAGUAUGCCUGGGUCCUGGACAAAUUGAAAGCAGAACGUGAACGCGGUAUUACCAUCGAUAUUGCGCUGUGGAAAUUCGAAACGGCCAAGUAUUACGUGACUAUUAUUGAUGCGCCUGGUCACAGGGAUUUCAUAAAGAACAUGAUCACCGGUACUUCCCAGGCCGAUUGCGCUGUUCUGAUCGUAGCCGCUGGUACAGGCGAAUUUGAAGCGGGCAUUUCGAAAAACGGCCAGACUCGCGAACACGCUCUGUUAGCUUUCACUCUGGGUGUUAAACAACUAAUCGUAGGCGUCAACAAAAUGGAUUCCACCGAACCUCCCUAUUCUGAGACCCGUUUCGAGGAGAUUAAGAAAGAAGUAUCAUCCUACAUUAAAAAAAUUGGGUAUAAUCCAGCAGGCGUUCCUUUCGUACCA Zygograma UCCUUCAAGUACGCGUGGGUGCUGGACAAGCUCAAAGCUGAACGCGAACGAGGCAUCACCAUCGACAUCGCUCUGUGGAAGUUCGAGACGGCCAAGUACUACGUGACCAUCAUCGACGCUCCCGGACACAGAGACUUCAUCAAGAACAUGAUCACUGGCACUUCUCAGGCGGACUGCGCCGUGCUGAUCGUAGCUGCAGGCACCGGCGAGUUCGAGGCCGGCAUCUCCAAGAAUGGGCAGACUCGCGAGCACGCUCUGCUGGCCUUCACUCUGGGCGUGAAGCAGCUCAUCGUCGGAGUCAACAAGAUGGACUCCACUGAGCCUCCGUACUCUGAGACGCGUUUCGAGGAGAUCAAGAAGGAGGUGUCUUCGUACAUCAAGAAGAUCGGCUACAAUCCGGCAGCCGUCGCUUUCGUUCCG Syneta UCCUUCAAAUAUGCUUGGGUACUUGACAAGCUCAAAGCUGAACGUGAACGUGGUAUUACCAUAGAUAUUGCAUUGUGGAAAUUCGAAACUGCCAAAUACUAUGUGACAAUCAUUGAUGCCCCUGGGCACAGAGAUUUCAUCAAGAACAUGAUCACUGGUACGUCGCAGGCCGAUUGUGCCGUACUCAUUGUAGCUGCUGGUACUGGUGAAUUCGAAGCUGGUAUUUCAAAGAAUGGACAGACUCGUGAACACGCGUUGCUAGCGUUUACUCUUGGAGUGAAACAACUUGUCGUAGGCGUUAACAAAAUGGACUCGACUGAACCACCUUACAGCGAACCCCGUUUCGAAGAAAUUAAAAAGGAAGUAUCAUCAUACAUCAAAAAGAUUGGUUACAACCCAGCAGCAGUUGCGUUCGUACCA Dibolia ???????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Sangariola UCCUUCAAGUACGCCUGGGUACUGGACAAACUUAAGGCUGAACGCGAACGCGGUAUCACCAUYGAUAUUGCUUUGUGGAAAUUCGAAACNGCCAAAUACUACGUAACCAUCAUUGAUGCCCCCGGACACAGAGAUUUCAUCAAGAACAUGAUUACUGGAACAUCCCAAGCUGAUUGUGCCGUACUUAUUGUAGCUGCUGGUACUGGAGAAUUCGAAGCUGGUAUUUCCAAGAACGGACAAACCCGUGAACACGCUUUGCUUGCUUUCACCCUUGGAGUAAAACAACUUAUUGUGGGUGUAAACAAAAUGGACUCCACUGAACCCCCAUACAGUGAggCNCGUUUYGAAGAAAUCAAGAAGGAAGUUUCCUCGUACAUUAAGAAGAUUGGCUACAACCCUGCUGCUGUUGCUUUCGUUCCA Aulacophora UCCUUCAAAUAUGCCUGGGUACUCGACAAACUGAAGGCUGAACGUGAACGCGGUAUCACCAUCGACAUUGCUUUGUGGAAAUUUGAAACAUCUAAAUACUAUGUAACAAUUAUAGACGCCCCUGGACACAGAGAUUUCAUCAAAAACAUGAUUACUGGUACUUCCCAAGCUGAUUGUGCCGUACUCAUAGUAGCUGCCGGUACUGGUGAAUUUGAAGCUGGUAUUUCUAAAAACGGACAAACUCGUGAACAYGCCCUUCUGGCCUUUACUUUGGGAGUCAAACAACUCAUCGUUGGUGUUAACAAGAUGGAUUCUACUGAACCCCCAUACAGAGAAGCACGUUUUGAAGAAAUUAAAAAAGAAGUUUCCUCGUACAUCAAGAAGAUUGGAUACAAUCCUGCUGCUGUAGCGUUCGUACCA Diabrotica ???????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Diorhabda UCCUUCAAGUAUGCCUGGGUACUGGACAAACUUAAAGCCGAGCGUGAACGUGGUAUCACUAUUGACAUAGCUUUGUGGAAAUUUGAAACCUCUAAGUACUACGUAACCAUCAUUGACGCUCCAGGGCACAGAGAUUUCAUCAAGAACAUGAUUACUGGAACAUCCCAAGCUGAUUGUGCAGUGCUUAUUGUAGCUGCCGGUACUGGAGAAUUCGAGGCUGGUAUUUCCAAGAAUGGACAAACACGUGAACACGCUCUUCUUGCAUUCACUCUUGGAGUAAAACAGCUCAUUGUAGGAGUCAACAAAAUGGACUCUACUGAACCACCAUACAGUGAAGCGCGUUUUGAAGAAAUCAAGAAAGAAGUAUCCUCGUACAUUAAGAAGAUUGGAUACAACCCUGCUGCCGUUGCUUUCGUACCA Schematiza UCUUUCAAAUAUGCCUGGGUACUUGACAAACUUAAGGCUGAACGUGAACGUGGUAUUACCAUUGACAUCGCAUUAUGGAAAUUCGAAACAGCCAAGUACUAUGUAACCAUUAUCGAUGCCCCUGGACACAGAGAUUUCAUUAAAAACAUGAUUACUGGAACUUCACAAGCUGAUUGCGCAGUACUUAUUGUAGCUGCUGGUACUGGAGAAUUUGAAGCUGGUAUCUCUAAGAACGGACAAACACGUGAACAUGCUCUUCUUGCAUUCACUCUUGGAGUAAAACAACUUAUUGUUGGUGUUAACAAAAUGGACUCUACUGAACCACCAUACAGUGAAUCACGUUUUGAAGAAAUUAAGAAAGAAGUAUCCUCAUACAUAAAGAAAAUUGGUUACAAUCCAGCUGCAGUAGCCUUCGUUCCA Oides UCCUUCAAGUACGCCUGGGUACUGGACAAGCUCAAAGCUGAACGCGAGCGCGGUAUCACCAUCGAUAUCGCUUUGUGGAAAUUCGAAACGGCCAAGUACUACGUAACCAUCAUUGAUGCCCCCGGACACAGGGAUUUCAUCAAGAACAUGAUCACCGGAACGUCCCAAGCCGAUUGCGCCGUGCUCAUCGUCGCCGCCGGUACUGGCGAGUUCGAAGCUGGUAUAUCCAAGAACGGACAGACCCGCGAGCACGCUCUGCUGGCUUUCACUUUGGGAGUCAAGCAACUCAUCGUCGGUGUCAACAAGAUGGAUUCCACUGAGCCUCCGUACAGUGAAGCCCGUUUCGAAGAAAUCAAGAAGGAAGUUUCGUCGUACAUCAAGAAGAUCGGUUACAAUCCGGCUGCUGUUGCUUUCGUUCCA [CO1] Orsodacne ggNgcHccHgaYaUAGCUUUUCCUCGAAUAAAUAAUAUAAGAUUUUGACUUUUGCCCCCAAGUCUAACUUUAUUAUUAAUAAGAAGAAUUGUAGAAAGAGGGGCUGGUACAGGUUGAACAGUUUACCCUCCUCUUUCCUCAAACAUUGCCCAUAGGGGUUCCUCUGUUGAUUUAGCAAUUUUUAGACUUCAUUUAGCAGGAAUUUCAUCUAUUCUAGGAGCGGUAAAUUUUAUUACAACAGUGAUUAACAUACGACCAGUUGGGAUAUCUUUAGAUAAAAUACCUUUAUUUGUAUGAGCAGUAAUUAUUACUGCUGUUUUAUUAUUACUUUCUUUACCCGUUUUAGCAGGUGCAAUUACUAUACUUCUAACAGAUCGUAAUAUUAAUACAUCUUUUUUUGACCCUUCUGGAGGGGGGGACCCAAUUCUUUACCAACAUUUAUUUUGAUUUUUUGGUCAUCCU Chrysomela GGUGCCCCAGAUAUAGCUUUCCCUCGAUUAAAUAACAUAAGAUUCUGACUUCUCCCCCCUUCUCUAUUUCUUCUUUUAAUAAGAAGAAUUGUAGAAAAUGGGGUAGGUACUGGUUGGACAGUCUACCCCCCUUUAUCAACCAAUAUCGCGCAUAGAGGAUCUUCAGUUGAUCUCGCUAUUUUUAGACUUCAUCUUGCCGGAAUCUCAUCAAUUUUAGGGGCAAUUAAUUUUAUCACUACAGUAAUUAAUAUACGGCCUGAAGGGAUAAAUUUUGAACAAACUCCUCUUUUUGUAUGGGCUGUUUUAAUCACAGCUAUCCUUUUACUUUUAUCCUUACCAGUUCUGGCUGGUGCAAUCACAAUACUUUUAACUGAUCGAAAUCUUAAUACUUCUUUUUUUGACCCAGCAGGUGGGGGAGACCCAAUUUUAUACCAACACUUAUUUUGAUUUUUUGGCCACCCU Altica ggDgcYccHgaUaURgcHUUYccNcgDaURaaYaaUAUAagAUUCUGGCUACUCCCACCAUCAAUUUUUUUAcUUUUAAUAAGAAGAUUAGUGGAGAGaGGDGCDGGAACUGGAUGAACAGUUUAUCCHCCUCUAUCUUCAAAUCUUGCACAUARWGGACCAUCUGUUGAUUUAGCYAUCUUCAGUCUUCAUUUAGCAGGAAUUUCAUCAAUUUUAGGUGCUAUUAAUUUUAUUACAACAAUAAUUAAUAUGCGACCACAAGGAAUGUCAAUAGACCAAAUACCUUUAUUUGUAUGAGCAGUAUUUAUUACGGCCAUCCUUUUACUUUUAUCUUUACCAGUCCUAGCCGGUGCAAUUACAAUAUUACUUACAGAUCGUAAUUUAAAUACAUCUUUUUUUGAACCAGCHGGVggVGGDGAYCCWAUUUUaUAYCAACAYUUAUUYUGRUUYUUYGGNCAYCCY Agelastica GGUGCUCCAGAUAUGGCAUUUCCACGAAUAAAUAAUAUAAGAUUUUGAYUAUUACCCCCAUCAAUUUUUUUAUUAAUUAUAAGUAGAAUUGUAGAAAGAGGAGCAGGAACUGGAUGAACAGUAUACCCUCCUUUAUCCUCUAAUAUUGCCCAUAAUGGAUCAUCCGUAGAUCUGGCUAUUUUUAGUCUUCAUUUAGCUGGAAUCUCAUCAAUUUUAGGUGCUAUUAAUUUUAUUACAACAGUUAUUAAUAUGCGACCAAAUGGUAUAAGAUUUGACCGCAUACCUUUAUUUGUUUGAGCAGUAGUAAUUACUGCAGUUUUACUAUUGUUAUCUUUACCUGUUUUAGCUGGGGCUAUUACAAUACUAUUAACUGACCGAAAUUUAAAUACAUCAUUUUUUGACCCAACCGGUGGAGGUGACCCAAUUCUAUAUCAACACUUAUUUUGAUUUUUUGGUCAUCCU Monolepta ggDgcYccHgaYaURgcHUUYccUcgaaUaaaYaaYaURagDUUYUGAUUacUUCCCCCAUCUUUAUUUUUACUAAUUAUAAGAAGAAUUGUUGAAAGAGGGGCAGGAACAGGAUGAACUGUUUACCCACCUUUAUCAGCCAAUAUUGCCCAUGGAGGAGCUUCAGUAGAUUUAGCAAUUUUUAGACUUCAUUUAGCUGGAAUUUCAUCCAUUUUAGGGGCAAUUAAUUUUAUUACAACAGUAAUUAAUAUACGUCCAGCAGGUAUAACUUUAGAUCGAAUACCAUUAUUUGUAUGAGCUGUAGUUAUUACAGCAAUUUUAUUAUUACUAUCUUUACCUGUAUUAGCWGGAGCAAUUACAAUAUUAUUAACUGAUCGAAAUUUAAACACAUCUUUUUUUGACCCUGCAgGAGGGGGDGAUCCAAUUCUUUACCAACAUUUAUUUUGAUUUUUUGGACAUCCU Phyllobrotica ggDgcYccHgaYaURgcHUUYccUcGAAUAAAUAAUAUAAGGUUUUGACUCUUGCCCCCCUCUCUAUUCUUUUUAAUUAUAAGAAGAAUCGUAGAGAGAGGAGUUGGAACCGGAUGAACAGUUUAUCCCCCCCUUUCAUCAAAUAUUGCUCAUGGUGGGUCUUCAGUUGAUUUAGCUAUCUUUAGAUUACAUCUAGCCGGGAUUUCCUCAAUUUUGGGUGCCAUUAAUUUUAUUACUACCAUUAUUAAUAUACGACCUCAAGGAAUAACUUUAGAUCGAAUACCUCUAUUUGUUUGGGCAGUCUUAAUUACUGCAAUUUUAUUAUUACUUUCAUUACCAGUUUUAGCUGGUGCUAUUACCAUACUUUUAACUGAUCGAAACUUAAAUACAUCAUUUUUCGAUCCUGCCGGGGGAGGAGACCCUAUUUUAUACCAACACUUAUUUUGAUUCUUUGGACAUCCU Allochroma GGAGCUCCAGAUAUAGCUUUUCCCCGAAUAAAUAAUAUAAGAUUUUGGUUAUUACCCCCAUCAUUAUUUUUUUUAUUAUUAAGGAGAUUAGCUGAAAGAGGUGCUGGAACUGGAUGAACUGUUUACCCCCCUUUAUCUGCAAAUAUUGCCCAUAGAGGAUCUUCUGUUGAUUUAGCAAUUUUUAGUCUACAUUUAGCAGGAAUUUCCUCUAUUUUAGGAGCUAUUAAUUUUAUUUCUACAGUAAUUAAUAUACGACCUAAAGGAAUAACUUUAGAUCGAAUACCUCUGUUUGUUUGAGCUGUUACAAUUACAGCAAUUUUACUUUUACUCUCAUUACCAGUUCUAGCAGGAGCUAUUACAAUACUUUUAACUGAUCGAAAUUUAAAUACUUCGUUCUUUGACCCUGCAGGAGGAGGAGAUCCUAUUUUAUACCAACACUUAUUUUGAUUUUUUGGACAUCCU Chrysolina GGAGCUCCAGAUAUAGCAUUCCCUCGAAUAAACAAUAUAAGAUUUUGAUUAUUACCCCCCUCAUUAAUUUUCUUACUAAUAAGAAGAAUCGUAGAAAGAGGAGUGGGAACAGGAUGAACAGUUUAUCCUCCUUUAUCUGCUAAUGUUGCCCAUAGAGGUCCUUCUGUUGAUCUUGCAAUUUUUAGACUUCAUUUAGCUGGAAUUUCCUCUAUUUUAGGGGCAAUCAAUUUUAUUACUACAAUUAUUAAUAUACACCCAACAGGAAUAAAAUUAGAACAAAUACCCCUUUUUUCAUGGGCAGUCUUAAUUACUGCAAUUCUUUUACUUUUAUCCUUACCAGUUUUAGCUGGUGCAAUUACAAUACUAUUAACUGAUCGAAACUUAAAUACCUCUUUUUUUGAUCCUACUAGUGGAGGUGACCCUAUUCUUUACCAGCAUCUAUUCUGAUUUUUUGGGCACCCU Aphthona ?????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Chaetocnema GGAGCUCCUGAUAUAGCAUUUCCACGAAUAAAUAAUAUAAGAUUUUGAUUAUUACCUCCUUCGUUAAUAUUUUUAAAUUUUAGAAGAUUGCUCAAUAAUGGAGUGGGAACAGGUUGAACUGUAUACCCACCUUUAUCAGCAAAUUUAUUUCAUAUAGGAAAUGCUGUUGAUCUAgCUAUUUUUUCUCUUCAUAUAGCAGGUAUUUCUUCUAUUUUAGGGGCUAUUAAUUUCAUCACUACAAUUAUAAAUAUAAAAAUUUUAAACCUUAAAAUAGAUAAAAUUCCACUCUUUGUCUGAUCAAUUUUUAUUACUACAAUUCUUUUACUUUUGUCUCUUCCUGUUUUAGCAGGAGCUAUUACAAUAUUAUUAACUGAUCGAAAUCUCAAUACUUCAUUCUUUGAUCCAGCUGGAGGGGGAGAUCCAAUUUUAUAUCAACACUUAUUCUGAUUUUUCGGACAUCCU Systena GGAGCCCCUGAUAUAGCAUUCCCUCGAAUGAACAAUAUGAGAUUUUGACUUUUACCCCCAUCUUUAUUUUUACUUUUAAUAAGAAGGGUAGUAGAAAGAGGAGCAGGAACAGGAUGAACCGUUUAUCCCCCUCUAGCAUCCAAUAUCGCCCAUGGAGGAUCAUCAGUAGAUUUAGCCAUUUUUAGUUUACACUUAGCAGGAAUCUCUUCAAUUUUAGGAGCUAUUAAUUUUAUUACCACAGUUAUUAAUAUACGACCAAGAGGAAUACUCUUAGACCGUAUGCCAUUAUUUGUAUGAGCAGUAGUAAUUACAGCAAUUUUAUUACUUUUAUCAUUACCUGUUCUUGCAGGUGCUAUUACCAUAUUAUUAACCGACCGAAAUUUAAAUACUUCAUUUUUUGACCCGGCAGGCGGGGGAGACCCAAUUUUAUACCAACAUUUAUUUUGAUUUUUUGGCCAUCCA Monocesta GGUGCUCCAGAUAUAGCAUUCCCUCGAAUAAAUAAUAUAAGAUUUUGAUUAUUACCCCCAUCAUUAUUUUUGUUAAUUAUGAGAAGAAUUGUAGAAAGAGGGGCAGGAACUGGAUGAACAGUUUAUCCCCCACUUUCUUCAAAUAUUGCUCAUGGAGGAUCUUCUGUAGAUUUAGCUAUCUUUAGACUUCAUUUAGCUGGAAUUUCCUCAAUUCUUGGCGCAAUUAAUUUUAUUACUACAACCAUUAAUAUACGACCAAAAGGAAUAUCUUUAGAUCGUAUACCUCUUUUUGUUUGGGCUGUAACUAUUACUGCUAUUCUUUUACUUUUAUCACUUCCUGUUCUUGCUGGUGCAAUUACUAUACUUUUAACUGAUCGAAACUUAAACACCUCUUUUUUUGACCCCGCUGGAGGAGGUGAUCCAAUUCUGUAUCAACAUUUAUUCUGAUUUUUUGGUCAUCCU Disonycha ggDgcYccHgaUaURgcHUUYccNcgDAUAaAUAAUAUAAGAUUUUGACUGUUgcCCccCUCUUUAUUUUUACUUCUUAUAAGAAGAGUUGUUGAAAGAGGGGCAGGAACUGGGUGAACUGUAUACCCUCCCCUUUCCUCAAAUAUCGCUCAUGGAGGCUCUUCUGUAGACUUAGCAAUUUUUAGAUUACAUUUAGCUGGAAUUUCCUCAAUUUUAGGUGCAAUUAAUUUUAUCACCACAAUUUUAAAUAUACGACCUAAUGGAAUAACAUUAGACCGAAUACCCUUAUUUGUUUGAUCGGUAAUCAUUACAGCUAUCCUUUUACUUUUAUCUUUACCUGUUUUAGCAGGAGCAAUUACUAUAUUAUUAACUGAUCGUAAUUUAAAUACUUCUUUCUUUGACCCUGUUGGAGGCGGUGACCCUAUUUUAUACCAACACUUAUUUUGAUUUUUUGGACAYCCY Blepharida ggDgcYccUgaUaURgcAUUCCCCcGGAAUAAUAAUAUAAGAUUCUGACUUCUUCCUCCUUCAUUAUUUUUACUUUUAAUAAGAAGAAUAGUAGAAAGAGGUGCAGGAACUGGUUGAACAGUUUACCCUCCUUUAUCCUCCAAUAUUGCCCAUAAUGGAGCUUCUGUAGAUUUAGCUAUUUUUAGUUUACACCUUGCUGGAAUUUCCUCGAUUUUAGGUGCAAUUAAUUUUAUUACAACAAUCAUUAACAUACGGCCUGCUGGAAUAACCUUAGAUCGAAUACCCCUAUUUGUUUGAGCAGUAGUCAUUACAGCAAUCUUAUUACUUUUAUCUUUACCUGUUCUAGCAGGAGCCAUCACUAUAUUACUAACUGAUCGAAAUUUAAAUACAUCUUUUUUUGAUCCUGCUGGAGGVGGAGAUCCAAUUYUAUACCAACAUUUAUUYUGAUUUUUUGGacacCCC Galeruca GGUGCCCCAGACAUAGCAUUUCCCCGAAUAAAUAACAUGAGAUUUUGAUUAUUACCCCCAUCUUUAUUUCUAUUAAUUAUAAGAAGAAUUGUAGAAAGAGGAGCAGGAACAGGAUGAACAGUUUACCCCCCACUUUCAUCUAAUAUUGCUCACGGAGGUUCUUCAGUAGAUUUAGCUAUUUUUAGGCUACAUUUAGCUGGAAUUUCAUCCAUUUUAGGAGCUAUUAAUUUUAUUACUACAGUAAUUAAUAUACGACCUAAAGGAAUAACUUUAGAUCGAAUACCUCUGUUUGUUUGAGCUGUUACAAUUACAGCAAUUUUACUUUUACUCUCAUUACCAGUUCUAGCAGGAGCAAUCACUAUAUUAUUAACAGAUCGAAAUUUAAAUACAUCAUUUUUUGACCCGGCCGGAGGAGGAGAUCCAAUUUUAUACCAACAUUUAUUUUGAUUCUUUGGACAUCCU Orthaltica ggDgcYccUGAUAUAGCAUUCCCACGUAUAAAUAAUAUAAGAUUUUGAUUAUUACCUCCAUCUUUAUUUUUAUUAAUUAUAAGAAGAAUAGUAGAAAGAGGGGCUGGAACUGGGUGAACUGUGUAUCCUCCUUUAUCUUCUAAUAUUGCUCAUGGAGGCUCUUCAGUUGAUUUAGCUAUUUUUAGACUUCAUUUAGCAGGAAUUUCAUCAAUUCUUGGAGCUAUUAAUUUUAUUACAACUAUUAUUAACAUACGUCCUAAAGGCAUACAACUAGAUCAAAUACCCUUAUUUGUAUGAGCAGUAAUUAUUACAGCAAUUUUACUGCUUUUAUCUCUACCUGUUUUAGCAGGUGCUAUUACUAUAUUAUUAACUGAUCGAAAUUUAAAUACUUCUUUUUUUGAUCCCGCCGGAGGAGGGGACCCAAUUCUCUAUCAACACUUAUUUUGAUUUUUUGGUCACCCA Paropsis GGGGCACCUGAUAUAGCAUUCCCCCGAAUGAACAAUAUGAGAUUUUGACUUCUUCCCCCAUCAUUAUUUUUCUUAUUAAUAAGAAGAAUUGUAGAAAGAGGUGCAGGUACUGGCUGAACAGUUUAUCCCCCACUAUCAGCCAAUGUAGCCCAUAGAGGUUCUUCUGUAGAUCUGGCUAUUUUUAGACUAUAUAUAGCUGGAAUUUCUUCUAUUUUAGGUGCCAUUAAUUUUAUCACAACAAUUAUUAAUAUACGACCAACAGGCAUAUCUAUUGACCGAAUACCGUUAUUUGUCUGAGCUGUAAUAAUUACAGCAAUUUUAUUGUUACUUUCUCUCCCCGUUUUAGCCGGUGCCAUUACUAUGCUAUUAACAGACCGAAAUUUAAAUACUUCAUUUUUUGAUCCAGCAGAUGGGGGAGACCCAAUUUUAUAUCAACAUUUAUUUUGAUUUUUUGGGCAYCCY Timarcha GGAGCACCUGAUAUAGCUUUUCCUCGAAUAAAUAAUAUAAGAUUUUGGCUUUUACCACCAUCCCUAACUUUAUUAUUAUUAAGAAGAAUCACUAAUAAUGGAGCAGGAACUGGUUGAACUGUUUAUCCCCCAUUAUCUGCAAAUCUUGCCCAUAAUGGAUCUUCAGUAGAUUUAGCUAUUUUUAGAUUACAUUUAGCAGGUAUUUCUUCAAUUUUGGGAGCUGCUAAUUUCAUUUCUACAAUUAUAAAUAUACAAAUUUCUGGAAUAAAAUUAGAUCAAAUCUCUUUAUUUCCAUGAUCAGUAAUAAUUACAGCUAUUUUAUUGCUUUUAUCCUUACCUGUAUUAGCAGGAGCAAUUACAAUACUUUUAACUGAUCGAAAUUUAAAUACCUCUUUCUUUGAUCCCUCAGGAGGUGGAGACCCUAUUCUUUAUCAACAUUUAUUUUGAUUUUUUGGUCAUCCY Zygograma GGUGCACCUGAUAUAGCUUUCCCUCGAAUAAAUAAUAUAAGAUUUUGGUUACUUCCUCCAUCAUUAUUAUUUUUAGUAAUGAGAAGAAUCGUUGAAAAUGGUGUAGGGACUGGGUGAACAGUAUAUCCCCCCUUAUCAGCUAAUACUGCCCAUAGAGGAUCAUCUGUCGAUUUAGCAAUUUUUAGGCUUCAUUUAGCAGGAAUUUCCUCUAUUUUAGGAGCAAUUAAUUUUAUUACAACUGUUGCAAAUAUACAUCCAAUAGGAAUAAAAUUAGACCAAAUACCUCUCUUUUCUUGAGCUGUUUUAAUUACAGCUAUUUUAUUAUUAAUUUCACUUCCUGUUUUAGCAGGGGCAAUUACAAUAUUAUUAACAGAUCGAAAUUUAAAUACUUCAUUUUUUGAUCCUGUUAGAGGAGGUGACCCAAUUCUUUACCAACACUUAUUUUGAUUUUUUGGUCACCCU Syneta GGGGCCCCUGAUAUAGCUUUUCCACGAAUAAACAAUAUAAGAUUUUGGCUUUUGCCACCAUCCCUAUCUUUAUUGUUAAUAAGAAGAAUUGUUGAAAAUGGGGCCGGAACCGGCUGAACAGUAUAUCCACCUCUUUCAGCUAAUAUUGCUCAUAGAGGUUCUUCAGUAGACUUAGCAAUCUUUAGGUUACACUUAGCUGGAAUUUCAUCUAUCCUUGGGGCAGUUAAUUUUAUUACUACAGUGAUUAACAUACGUCCUGAAAAAAUAACCUUAGAUCGAAUAUCAUUAUUUAUCUGAGCUGUAACAAUUACAGCAAUUUUAUUAUUAUUAUCUUUACCUGUAUUAGCAGGGGCCAUUACUAUAUUAUUAACAGAUCGAAAUUUAAAUACAUCAUUUUUUGAUCCAGCAGGAGGAGGUGACCCAAUCUUAUAUCAACAUUUAUUCUGAUUUUUUGGACAUCCU Dibolia ????????????AUAGCAUUCCcCCGAAUAaACAAUAUAAGAUUUUGACUUCUGCCCCCCUCAUUAUUCCUUCUUUUAAUAAGAAGAAUAGUAGAAAGAGGAGUCGGAACAGGAUGAACUGUGUACCCCCCACUUUCAUCUAAUAUUGCUCACGGAGGAAGAUCCGUUGAUCUUGCAAUUUUUAGGUUACAUUUAGCAGGAAUCUCUUCUAUUUUAGGUGCAAUUAAUUUUAUUACUACAAUUAUUAAUAUACGACCCAAAGGAAUAAGAUUUGAUCGAAUACCUUUAUUUGUUUGAUCAGUUUUAAUUACUGCAAUUUUAUUACUCUUAUCACUACCUGUACUUGCAGGAGCAAUCACAAUAUUAUUAACAGAUCGAAACUUAAAUACAUCUUUUUUUGACCCCUCAGGAGGUGGAGAUCCAAUUCUUUAUcaacaUUUaUUUUgaUUUUUUggacaUcca Sangariola GGAGCCCCUGAUAUAGCUUUCCCUCGAAUAAAUAAUAUAAGAUUUUGACUUCUACCCCCUUCCUUAUUUCUAUUACUAAUAAGAAGAAUAGUAGAAAGAGGAGCAGGAACAGGAUGAACUGUUUACCCCCCUCUAUCAGCUAAUAUUGCUCAUGGAGGAUCUUCAGUAGAUUUAGCUAUUUUUAGACUACAUCUAGCAGGAAUCUCUUCAAUUUUAGGAGCUAUUAAUUUUAUUACAACUGUUAUUAAUAUACGACCUAUAGGAAUAACUUUAGAUCGAAUACCCCUAUUUGUUUGAGCUGUAAUAAUUACAGCAAUUCUAUUACUUUUAUCCUUGCCAGUAUUAGCUGGAGCAAUUACUAUACUAUUAACUGACCGAAAUUUAAAUACUUCAUUUUUUGAUCCGGCAGGAGGAGGAGACCCUAUUUUAUACCAACACUUAUUUUGAUUUUUUGGACAUCCA Aulacophora GGGGCUCCUGAUAUAGCUUUCCCUCGUAUAAAUAAUAUAAGAUUUUGAUUACUUCCUCCUUCUCUAUUUUUAUUAAUUAUAAGUAGAGUUGUUGAAAGAGGAGCUGGAACUGGCUGAACUGUGUACCCUCCUCUUUCUUCGAAUAUUGCCCAUGGAGGUUCUUCUGUUGAUUUAGCAAUUUUCAGUUUACAUUUAGCCGGAAUUUCUUCAAUUUUAGGAGCAAUUAAUUUUAUCACAACCGUAAUUAAUAUACGUCCUAAAGGAAUAACCCUAGACCGAAUACCACUAUUUGUAUGAGCUGUUGUUAUUACAGCUGUAUUAUUAUUAUUAUCUCUACCAGUUUUAGCUGGAGCUAUUACAAUAUUAUUAACAGAUCGAAAUCUAAAUACUUCCUUUUUUGAUCCUGCUGGUGGAGGAGAUCCUAUUUUAUAUCAACAUUUAUUUUGAUUUUUUGGGCAUCCA Diabrotica ggDgcYccHgaYaUAGCAUUCCCUCGAAUAAAUAAUAUAAGAUUUUGACUUUUACCACCCUCUUUAUUAUUAUUAUUAUUAAGAAGAAUAGUAGAAAAUGGAGUUGGUACCGGAUGAACAGUUUAUCCCCCUUUAUCAUCCAAUAUUGCUCACGGAGGUUCAUCAGUUGAUUUAGCUAUUUUUAGUUUACACUUGGCUGGAAUUUCUUCAAUUUUAGGAGCAAUUAAUUUUAUUACUACAGUAAUUAAUAUACGUCCAAUAGGAAUAACUUUUGACCGAAUACCAUUAUUUGUAUGAGCAGUUGUUAUUACAGCAGUUUUACUACUAUUAUCAUUACCUGUUCUUGCCGGGGCAAUUACAAUAUUAUUAACAGAUCGUAAUCUAAAUACCUCUUUUUUUGAUCCUGCUGGAGGUGGUGAUCCUAUUUUAUAUCAACAUUUAUUUUGAUUUUUCGGACAUCCU Diorhabda GGUGCUCCAGAUAUAGCAUUUCCUCGAAUAAAUAAUAUAAGUUUCUGAUUACUACCUCCUUCUUUAUUUCUUCUAAUUAUAAGAAGAAUCGUAGAAAGAGGUGCAGGAACUGGAUGAACAGUUUAUCCCCCUCUUUCAUCUAAUAUUGCACAUGGAGGAUCUUCAGUAGAUUUAGCUAUUUUUAGUUUACAUUUAGCUGGAAUCUCCUCAAUUUUAGGAGCUAUUAAUUUUAUUACUACUGUAAUUAAUAUACGACCUAAAGGUAUAACUUUAGAUCGAAUGCCUUUAUUUGUAUGAGCCGUUGUAAUUACAGCAAUCCUUUUACUAUUAUCUUUACCAGUUUUAGCCGGAGCUAUUACUAUACUAUUAACUGAUCGAAAUUUAAAUACCUCUUUUUUUGAUCCUGCUGGGGGAGGAGAUCCUAUUUUAUACCAACAUUUAUUUUGAUUUUUUGGACAUCCU Schematiza GGAGCCCCUGACAUAGCCUUUCCUCGAAUAAAUAACUUAAGAUUUUGACUUUUGCCUCCUUCAUUAUUCCUCUUAAUUAUAAGAAGAAUUGUAGAAAGUGGAGCUGGUACUGGUUGAACUGUUUAUCCACCUUUAUCCUCUAAUAUUGCCCAUGGAGGAUCUUCAGUAGAUUUAGCAAUUUUCAGUCUACAUCUUGCCGGAAUUUCAUCAAUUCUAGGAGCAAUUAAUUUUAUUACGACAGUGAUUAAUAUACGACCUAGAGGGAUAAAAUUAGACCGAUUACCUUUAUUUGUUUGAGCUGUAAUAAUUACAGCUAUUUUAUUAUUGUUAUCUCUACCUGUUUUAGCAGGUGCAAUUACUAUGCUUUUAACUGACCGAAAUUUAAAUACUUCAUUUUUUGAUCCUGCUGGAGGAGGAGAUCCUAUUCUUUAUCAACAUUUAUUUUGAUUUUUUGGACACCCU Oides GGUGCCCCCGAUAUAGCAUUUCCACGAAUAAAUAAUAUAAGAUUUUGACUAUUACCCCCUUCUCUUUUACUGUUAAUUAUAAGAAGAAUGGUGGAAAGUGGGGCGGGCACAGGAUGAACAGUUUAUCCCCCUCUUUCAUCUAAUAUUGCUCACAGAGGCUCUAGAGUUGACUUGGCUAUUUUUAGCCUACAUUUAGCCGGAAUCUCAUCUAUUUUAGGAGCAAUUAAUUUUAUUACUACAAUUAUCAAUAUACGACCUUCAAGAAUAUUUAUAGACCAAAUCCCAUUAUUUGUAUGAUCAGUCUUAAUUACAGCUAUUCUUUUACUUCUGUCUCUCCCUGUAUUAGCAGGGGCAAUCACUAUACUUUUAACUGAUCGAAAUUUAAAUACAUCAUUUUUUGACCCUGCAGGAGGUGGUGAUCCAAUCUUAUACCAACAUUUAUUUUGAUUUUUUGGACAUCCU [CO1 protein] Orsodacne GAPDMAFPRMNNMSFWLLPPSLTLLLMSSIVESGAGTGWTVYPPLSSNIAHSGSSVDLAIFSLHLAGISSILGAVNFITTVINMRPVGMSLDKMPLFVWAVIITAVLLLLSLPVLAGAITMLLTDRNINTSFFDPSGGGDPILYQHLFWFFGHP Chrysomela GAPDMAFPRLNNMSFWLLPPSLFLLLMSSIVENGVGTGWTVYPPLSTNIAHSGSSVDLAIFSLHLAGISSILGAINFITTVINMRPEGMNFEQTPLFVWAVLITAILLLLSLPVLAGAITMLLTDRNLNTSFFDPAGGGDPILYQHLFWFFGHP Altica GAPDMAFPRMNNMSFWLLPPSIFLLLMSSLVESGAGTGWTVYPPLSSNLAH?GPSVDLAIFSLHLAGISSILGAINFITTMINMRPQGMSMDQMPLFVWAVFITAILLLLSLPVLAGAITMLLTDRNLNTSFFEPAGGGDPILYQHLFWFFGHP Agelastica GAPDMAFPRMNNMSFWLLPPSIFLLIMSSIVESGAGTGWTVYPPLSSNIAHNGSSVDLAIFSLHLAGISSILGAINFITTVINMRPNGMSFDRMPLFVWAVVITAVLLLLSLPVLAGAITMLLTDRNLNTSFFDPTGGGDPILYQHLFWFFGHP Monolepta GAPDMAFPRMNNMSFWLLPPSLFLLIMSSIVESGAGTGWTVYPPLSANIAHGGASVDLAIFSLHLAGISSILGAINFITTVINMRPAGMTLDRMPLFVWAVVITAILLLLSLPVLAGAITMLLTDRNLNTSFFDPAGGGDPILYQHLFWFFGHP Phyllobrotica GAPDMAFPRMNNMSFWLLPPSLFFLIMSSIVESGVGTGWTVYPPLSSNIAHGGSSVDLAIFSLHLAGISSILGAINFITTIINMRPQGMTLDRMPLFVWAVLITAILLLLSLPVLAGAITMLLTDRNLNTSFFDPAGGGDPILYQHLFWFFGHP Allochroma GAPDMAFPRMNNMSFWLLPPSLFFLLLSSLAESGAGTGWTVYPPLSANIAHSGSSVDLAIFSLHLAGISSILGAINFISTVINMRPKGMTLDRMPLFVWAVTITAILLLLSLPVLAGAITMLLTDRNLNTSFFDPAGGGDPILYQHLFWFFGHP Chrysolina GAPDMAFPRMNNMSFWLLPPSLIFLLMSSIVESGVGTGWTVYPPLSANVAHSGPSVDLAIFSLHLAGISSILGAINFITTIINMHPTGMKLEQMPLFSWAVLITAILLLLSLPVLAGAITMLLTDRNLNTSFFDPTSGGDPILYQHLFWFFGHP Aphthona ?????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Chaetocnema GAPDMAFPRMNNMSFWLLPPSLMFLNFSSLLNNGVGTGWTVYPPLSANLFHMGNAVDLAIFSLHMAGISSILGAINFITTIMNMKILNLKMDKIPLFVWSIFITTILLLLSLPVLAGAITMLLTDRNLNTSFFDPAGGGDPILYQHLFWFFGHP Systena GAPDMAFPRMNNMSFWLLPPSLFLLLMSSVVESGAGTGWTVYPPLASNIAHGGSSVDLAIFSLHLAGISSILGAINFITTVINMRPSGMLLDRMPLFVWAVVITAILLLLSLPVLAGAITMLLTDRNLNTSFFDPAGGGDPILYQHLFWFFGHP Monocesta GAPDMAFPRMNNMSFWLLPPSLFLLIMSSIVESGAGTGWTVYPPLSSNIAHGGSSVDLAIFSLHLAGISSILGAINFITTTINMRPKGMSLDRMPLFVWAVTITAILLLLSLPVLAGAITMLLTDRNLNTSFFDPAGGGDPILYQHLFWFFGHP Disonycha GAPDMAFPRMNNMSFWLLPPSLFLLLMSSVVESGAGTGWTVYPPLSSNIAHGGSSVDLAIFSLHLAGISSILGAINFITTILNMRPNGMTLDRMPLFVWSVIITAILLLLSLPVLAGAITMLLTDRNLNTSFFDPVGGGDPILYQHLFWFFGHP Blepharida GAPDMAFPRNNNMSFWLLPPSLFLLLMSSMVESGAGTGWTVYPPLSSNIAHNGASVDLAIFSLHLAGISSILGAINFITTIINMRPAGMTLDRMPLFVWAVVITAILLLLSLPVLAGAITMLLTDRNLNTSFFDPAGGGDPILYQHLFWFFGHP Galeruca GAPDMAFPRMNNMSFWLLPPSLFLLIMSSIVESGAGTGWTVYPPLSSNIAHGGSSVDLAIFSLHLAGISSILGAINFITTVINMRPKGMTLDRMPLFVWAVTITAILLLLSLPVLAGAITMLLTDRNLNTSFFDPAGGGDPILYQHLFWFFGHP Orthaltica GAPDMAFPRMNNMSFWLLPPSLFLLIMSSMVESGAGTGWTVYPPLSSNIAHGGSSVDLAIFSLHLAGISSILGAINFITTIINMRPKGMQLDQMPLFVWAVIITAILLLLSLPVLAGAITMLLTDRNLNTSFFDPAGGGDPILYQHLFWFFGHP Paropsis GAPDMAFPRMNNMSFWLLPPSLFFLLMSSIVESGAGTGWTVYPPLSANVAHSGSSVDLAIFSLYMAGISSILGAINFITTIINMRPTGMSIDRMPLFVWAVMITAILLLLSLPVLAGAITMLLTDRNLNTSFFDPADGGDPILYQHLFWFFGHP Timarcha GAPDMAFPRMNNMSFWLLPPSLTLLLLSSITNNGAGTGWTVYPPLSANLAHNGSSVDLAIFSLHLAGISSILGAANFISTIMNMQISGMKLDQISLFPWSVMITAILLLLSLPVLAGAITMLLTDRNLNTSFFDPSGGGDPILYQHLFWFFGHP Zygograma GAPDMAFPRMNNMSFWLLPPSLLFLVMSSIVENGVGTGWTVYPPLSANTAHSGSSVDLAIFSLHLAGISSILGAINFITTVANMHPMGMKLDQMPLFSWAVLITAILLLISLPVLAGAITMLLTDRNLNTSFFDPVSGGDPILYQHLFWFFGHP Syneta GAPDMAFPRMNNMSFWLLPPSLSLLLMSSIVENGAGTGWTVYPPLSANIAHSGSSVDLAIFSLHLAGISSILGAVNFITTVINMRPEKMTLDRMSLFIWAVTITAILLLLSLPVLAGAITMLLTDRNLNTSFFDPAGGGDPILYQHLFWFFGHP Dibolia ????MAFPRMNNMSFWLLPPSLFLLLMSSMVESGVGTGWTVYPPLSSNIAHGGSSVDLAIFSLHLAGISSILGAINFITTIINMRPKGMSFDRMPLFVWSVLITAILLLLSLPVLAGAITMLLTDRNLNTSFFDPSGGGDPILYQHLFWFFGHP Sangariola GAPDMAFPRMNNMSFWLLPPSLFLLLMSSMVESGAGTGWTVYPPLSANIAHGGSSVDLAIFSLHLAGISSILGAINFITTVINMRPMGMTLDRMPLFVWAVMITAILLLLSLPVLAGAITMLLTDRNLNTSFFDPAGGGDPILYQHLFWFFGHP Aulacophora GAPDMAFPRMNNMSFWLLPPSLFLLIMSSVVESGAGTGWTVYPPLSSNIAHGGSSVDLAIFSLHLAGISSILGAINFITTVINMRPKGMTLDRMPLFVWAVVITAVLLLLSLPVLAGAITMLLTDRNLNTSFFDPAGGGDPILYQHLFWFFGHP Diabrotica GAPDMAFPRMNNMSFWLLPPSLLLLLLSSMVENGVGTGWTVYPPLSSNIAHGGSSVDLAIFSLHLAGISSILGAINFITTVINMRPMGMTFDRMPLFVWAVVITAVLLLLSLPVLAGAITMLLTDRNLNTSFFDPAGGGDPILYQHLFWFFGHP Diorhabda GAPDMAFPRMNNMSFWLLPPSLFLLIMSSIVESGAGTGWTVYPPLSSNIAHGGSSVDLAIFSLHLAGISSILGAINFITTVINMRPKGMTLDRMPLFVWAVVITAILLLLSLPVLAGAITMLLTDRNLNTSFFDPAGGGDPILYQHLFWFFGHP Schematiza GAPDMAFPRMNNLSFWLLPPSLFLLIMSSIVESGAGTGWTVYPPLSSNIAHGGSSVDLAIFSLHLAGISSILGAINFITTVINMRPSGMKLDRLPLFVWAVMITAILLLLSLPVLAGAITMLLTDRNLNTSFFDPAGGGDPILYQHLFWFFGHP Oides GAPDMAFPRMNNMSFWLLPPSLLLLIMSSMVESGAGTGWTVYPPLSSNIAHSGSSVDLAIFSLHLAGISSILGAINFITTIINMRPSSMFMDQIPLFVWSVLITAILLLLSLPVLAGAITMLLTDRNLNTSFFDPAGGGDPILYQHLFWFFGHP [EF1a amino acids] Orsodacne ???????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Chrysomela SFKYAWVLDKLKAERERGITIDIALWKFETAKYYVTIIDAPGHRDFIKNMITGTSQADCAVLIVAAGTGEFEAGISKNGQTREHALLAFTLGVKQLIVGVNKMDSTEPPYSESRFEEIKKEVSSYIKKIGYNPAAVAFVP Altica SFKYAWVLDKLKAERERGITIDIALWKFETAKYYVTIIDAPGHRDFIKNMITGTSQADCAVLIVAAGTGEFEAGISKNGQTREHALLAFTLGVKQLIVGVNKMDSTEPPYSEARFDEIKKEVSSYIKKIGYNPAAVAFVP Agelastica SFKYAWVLDKLKAERERGITIDIALWKFETAKYYVTIIDAPGHRDFIKNMITGTSQADCAVLIVAAGTGEFEAGISKNGQTREHALLAFTLGVKQLIVGVNKMDSTEPPYSEARFEEIKKEVSSYIKKIGYNPAAVAFVP Monolepta SFKYAWVLDKLKAERERGITIDIALWKFETAKYYVTIIDAPGHRDFIKNMITGTSQADCAVLIVAAGTGEFEAGISKNGQTREHALLAFTLGVKQLIVGVNKMDSTEPPYSEARFEEIKKEVSSYIKKIGYNPAAVAFVP Phyllobrotica ???????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Allochroma SFKYAWVLDKLKAERERGITIDIALWKFETAKYYVTIIDAPGHRDFIKNMITGTSQADCAVLIVAAGTGEFEAGISKNGQTREHALLAFTLGVKQLIVGVNKMDSTEPPYSEARFEEIKKEVSSYIKKIGYNPAAVAFVP Chrysolina SFKYAWVLDKLKAERERGITIDIALWKFETAKYYVTIIDAPGHRDFIKNMITGTSQADCAVLIVAAGTGEFEAGISKNGQTREHALLAFTLGVKQIIIGVNKMDSTEPPYSESRFEEIKKEVSSYIKKIGYNPAAVAFVP Aphthona SFKYAWVLDKLKAERERGITIDIALWKFETAKYYVTIIDAPGHRDFIKNMITGTSQADCAVLIVAAGTGEFEAGISKNGQTREHALLAFTLGVKQLIVGVNKMDSTEPPYSEARFEEIKKEVSSYIKKIGYNPAAVAFVP Chaetocnema SFKYAWVLDKLKAERERGITIDIALWKFETAKYYVTIIDAPGHRDFIKNMITGTSQADCAVLIVAAGTGEFEAGISKNGQTREHALLAFTLGVKQLIVGVNKMDSTEPPYSEARFEEIKKEVSSYIKKIGYNPAAVAFVP Systena SFKYAWVLDKLKAERERGITIDIALWKFETAKYYVTIIDDPGHRDFIKNMITGTSQADCAVLIVAAGTGEFEAGISKNGQTREHALLAFTLGVKQLIVGVNKMDSTEPPYSEARLEEIKKEVSSYIKKIGYNPAAVAFVP Monocesta SFKYAWVLDKLKAERERGITIDIALWKFETAKYYVTIIDAPGHRDFIKNMITGTSQADCAVLIVAAGTGEFEAGISKNGQTREHALLAFTLGVKQLIVGVNKMDSTEPPYSESRFEEIKKEVSSYIKKIGYNPAAVAFVP Disonycha SFKYAWVLDKLKAERERGITIDIALWKFETSKYYVTIIDAPGHRDFIKNMITGTSQADCAVLIVAAGTGEFEAGISKNGQTREHALLAFTLGVKQLIVGVNKMDSTEPPYSEARFEEIKKEVSSYIKKIGYNPAAVAFVP Blepharida SFKYAWVLDKLKAERERGITIDIALWKFETAKYYVTIIDAPGHRDFIKNMITGTSQADCAVLIVAAGTGEFEAGISKNGQTREHALLAFTLGVKQLIVGVNKMDSTEPPYSEARFEEIKKEVSSYIKKIGYNPAAVAFVP Galeruca SFKYAWVLDKLKAERERGITIDIALWKFETAKYYVTIIDAPGHRDFIKNMITGTSQADCAVLIVAAGTGEFEAGISKNGQTREHALLAFTLGVKQLIVGVNKMDSTEPPYSEARYEEIKKEVSSYIKKIGYNPAAVAFVP Orthaltica SFKYAWVLDKLKAERERGITIDIALWKFETAKYYVTIIDAPGHRDFIKNMITGTSQADCAVLIVAAGTGEFEAGISKNGQTREHALLAFTLGVKQLIVGVNKMDSTEPPYSEARFEEIKKEVSSYIKKIGYNPAAVAFVP Paropsis SFKYAWVLDKLKAERERGITIDIALWKFETAKYYVTIIDAPGHRDFIKNMITGTSQADCAVLIVAAGTGEFEAGISKNGQTREHALLAFTLGVKQLIVGVNKMDSTEPPYSDSRFEEIKKEVSSYIKKIGYNPAAVAFVP Timarcha SFKYAWVLDKLKAERERGITIDIALWKFETAKYYVTIIDAPGHRDFIKNMITGTSQADCAVLIVAAGTGEFEAGISKNGQTREHALLAFTLGVKQLIVGVNKMDSTEPPYSETRFEEIKKEVSSYIKKIGYNPAGVPFVP Zygograma SFKYAWVLDKLKAERERGITIDIALWKFETAKYYVTIIDAPGHRDFIKNMITGTSQADCAVLIVAAGTGEFEAGISKNGQTREHALLAFTLGVKQLIVGVNKMDSTEPPYSETRFEEIKKEVSSYIKKIGYNPAAVAFVP Syneta SFKYAWVLDKLKAERERGITIDIALWKFETAKYYVTIIDAPGHRDFIKNMITGTSQADCAVLIVAAGTGEFEAGISKNGQTREHALLAFTLGVKQLVVGVNKMDSTEPPYSEPRFEEIKKEVSSYIKKIGYNPAAVAFVP Dibolia ???????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Sangariola SFKYAWVLDKLKAERERGITIDIALWKFETAKYYVTIIDAPGHRDFIKNMITGTSQADCAVLIVAAGTGEFEAGISKNGQTREHALLAFTLGVKQLIVGVNKMDSTEPPYSEARFEEIKKEVSSYIKKIGYNPAAVAFVP Aulacophora SFKYAWVLDKLKAERERGITIDIALWKFETSKYYVTIIDAPGHRDFIKNMITGTSQADCAVLIVAAGTGEFEAGISKNGQTREHALLAFTLGVKQLIVGVNKMDSTEPPYREARFEEIKKEVSSYIKKIGYNPAAVAFVP Diabrotica ???????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? Diorhabda SFKYAWVLDKLKAERERGITIDIALWKFETSKYYVTIIDAPGHRDFIKNMITGTSQADCAVLIVAAGTGEFEAGISKNGQTREHALLAFTLGVKQLIVGVNKMDSTEPPYSEARFEEIKKEVSSYIKKIGYNPAAVAFVP Schematiza SFKYAWVLDKLKAERERGITIDIALWKFETAKYYVTIIDAPGHRDFIKNMITGTSQADCAVLIVAAGTGEFEAGISKNGQTREHALLAFTLGVKQLIVGVNKMDSTEPPYSESRFEEIKKEVSSYIKKIGYNPAAVAFVP Oides SFKYAWVLDKLKAERERGITIDIALWKFETAKYYVTIIDAPGHRDFIKNMITGTSQADCAVLIVAAGTGEFEAGISKNGQTREHALLAFTLGVKQLIVGVNKMDSTEPPYSEARFEEIKKEVSSYIKKIGYNPAAVAFVP [Morphology] Orsodacne 20002 00000 01000 00100 10010 10110 11011 02100 00110 00001 Chrysomela 00102 10000 00011 10110 12011 10001 01010 10110 00101 01011 Altica 21112 22201 12121 11021 02110 00012 10011 11110 01101 00101 Agelastica 01110 11101 01021 11010 12100 11110 11011 10{01}10 00110 00000 Monolepta 11110 20001 01021 11021 02110 01010 11011 11010 00100 00000 Phyllobrotica ????? ????? ????? ????? ????? ????? ????? ????? ????? ????? Allochroma 11111 20111 12111 01021 02110 00010 10011 11112 01101 00111 Chrysolina 00102 10100 00001 10110 12011 10001 01010 10010 01101 01011 Aphthona 21112 22201 12121 11021 02110 00012 10011 11112 11101 00101 Chaetocnema 21112 22201 12121 11021 02110 00012 10011 11112 11101 00101 Systena 21112 220?1 12121 11021 02110 00112 10011 11111 01101 00101 Monocesta 01110 21101 00121 11110 12200 ?1010 11011 11010 00100 00010 Disonycha 21112 21101 11121 11021 02110 00012 10111 10112 00101 00101 Blepharida 11102 10101 02121 11110 02110 00112 10011 10010 01101 01111 Galeruca 01110 12211 00021 11010 12000 1??10 11010 11010 00100 00000 Orthaltica 01112 20011 12121 11031 02110 ?0001 10001 02112 11101 00000 Paropsis ????? ????? ????? ????? ????? ????? ????? ????? ????? ????? Timarcha 00102 10000 00001 10110 12011 0??01 01011 12??? ?1101 01001 Zygograma ????? ????? ????? ????? ????? ????? ????? ????? ????? ????? Syneta 01012 00010 00111 10?00 11000 01110 01100 10112 00010 10001 Dibolia 21112 22001 12121 11021 02110 00012 10011 11112 01101 00111 Sangariola 21112 22201 12121 11021 02110 00112 10011 11112 00101 00101 Aulacophora 11100 10101 00021 01010 12300 01010 11011 01010 00110 00000 Diabrotica ????? ????? ????? ????? ????? ????? ????? ????? ????? ????? Diorhabda 01110 11001 01021 11011 12000 10010 11010 11010 00100 00000 Schematiza ????? ????? ????? ????? ????? ????? ????? ????? ????? ????? Oides 01110 10001 00021 11120 12001 01010 11110 11110 00110 00001 ; end; begin mrbayes; pairs 22:497, 21:498, 20:499, 19:500, 18:501, 17:502, 16:503, 33:172, 34:171, 35:170, 36:169, 37:168, 38:167, 45:160, 46:159, 47:158, 48:157, 49:156, 50:155, 51:154, 53:153, 54:152, 55:151, 59:150, 60:149, 61:148, 62:147, 63:146, 86:126, 87:125, 88:124, 89:123, 187:484, 186:485, 185:486, 184:487, 183:488, 182:489, 191:295, 192:294, 193:293, 194:292, 195:291, 196:290, 197:289, 198:288, 199:287, 200:286, 201:283, 202:282, 203:281, 204:280, 205:279, 206:278, 213:268, 214:267, 215:266, 216:265, 217:264, 226:259, 227:258, 228:257, 229:256, 230:255, 231:254, 232:253, 233:252, 304:477, 305:476, 306:475, 307:474, 308:473, 316:335, 317:334, 318:333, 319:332, 336:440, 337:439, 338:438, 339:437, 340:436, 341:435, 343:422, 344:421, 345:420, 346:419, 347:418, 348:417, 349:416, 351:414, 352:413, 353:412, 354:411, 355:408, 356:407, 357:406, 358:405, 359:404, 360:403, 361:402, 369:400, 370:399, 371:398, 372:397, 373:396, 376:394, 377:393, 379:392, 380:391, 381:390; charset ambiguously_aligned = 92-103 108-122 234-251 320-327 449-468; charset stems = 22 497 21 498 20 499 19 500 18 501 17 502 16 503 33 172 34 171 35 170 36 169 37 168 38 167 45 160 46 159 47 158 48 157 49 156 50 155 51 154 53 153 54 152 55 151 59 150 60 149 61 148 62 147 63 146 86 126 87 125 88 124 89 123 187 484 186 485 185 486 184 487 183 488 182 489 191 295 192 294 193 293 194 292 195 291 196 290 197 289 198 288 199 287 200 286 201 283 202 282 203 281 204 280 205 279 206 278 213 268 214 267 215 266 216 265 217 264 226 259 227 258 228 257 229 256 230 255 231 254 232 253 233 252 304 477 305 476 306 475 307 474 308 473 316 335 317 334 318 333 319 332 336 440 337 439 338 438 339 437 340 436 341 435 343 422 344 421 345 420 346 419 347 418 348 417 349 416 351 414 352 413 353 412 354 411 355 408 356 407 357 406 358 405 359 404 360 403 361 402 369 400 370 399 371 398 372 397 373 396 376 394 377 393 379 392 380 391 381 390; charset loops = 1-15 23-32 39-44 52 56-58 64-85 90-122 127-145 161-166 173-181 188-190 207-212 218-225 234-251 260-263 269-277 284 285 296-303 309-315 320-331 342 350 362-368 374 375 378 382-389 395 401 409 410 415 423-434 441-472 478-483 490-496 504-516; charset rna = 1 - 516 ; charset dna = 517 - 1398 ; charset protein = 1399 - 1692 ; charset morphology = 1693 - 1742 ; charset D2 = 1 - 516 ; charset EF1a = 517 - 936 ; charset EF1a1st = 517 - 936 \ 3; charset EF1a2nd = 518 - 936 \ 3; charset EF1a3rd = 519 - 936 \ 3; charset CO1 = 937 - 1398 ; charset CO11st = 937 - 1398 \ 3; charset CO12nd = 938 - 1398 \ 3; charset CO13rd = 939 - 1398 \ 3; charset CO1aa = 1399 - 1552 ; charset EF1aa = 1553 - 1692 ; charset Morpho = 1693 - 1742 ; partition by_gene_and_pos = 10:rna,EF1a1st,EF1a2nd,EF1a3rd,CO11st,CO12nd,CO13rd,CO1aa,EF1aa,Morpho; partition by_gene = 6:rna,EF1a,CO1,CO1aa,EF1aa,Morpho; partition by_gene_and_struct = 7:stems,loops,EF1a,CO1,CO1aa,EF1aa,Morpho; [ exclude ambiguously_aligned; set partition = by_gene_and_struct; lset applyto=(4) code=metmt; lset applyto=(1) nucmodel=doublet; lset applyto=(2) nucmodel=4by4; lset applyto=(3,4) nucmodel=codon; lset nst=6; prset ratepr=variable aamodelpr=mixed; ] end; NBISweden-MrBayes-ecfd55c/examples/primates.nex000066400000000000000000000260511526212227600215770ustar00rootroot00000000000000#NEXUS [ Data from: Hayasaka, K., T. Gojobori, and S. Horai. 1988. Molecular phylogeny and evolution of primate mitochondrial DNA. Mol. Biol. Evol. 5:626-644. ] begin data; dimensions ntax=12 nchar=898; format datatype=dna interleave=no gap=-; matrix Tarsius_syrichta AAGTTTCATTGGAGCCACCACTCTTATAATTGCCCATGGCCTCACCTCCTCCCTATTATTTTGCCTAGCAAATACAAACTACGAACGAGTCCACAGTCGAACAATAGCACTAGCCCGTGGCCTTCAAACCCTATTACCTCTTGCAGCAACATGATGACTCCTCGCCAGCTTAACCAACCTGGCCCTTCCCCCAACAATTAATTTAATCGGTGAACTGTCCGTAATAATAGCAGCATTTTCATGGTCACACCTAACTATTATCTTAGTAGGCCTTAACACCCTTATCACCGCCCTATATTCCCTATATATACTAATCATAACTCAACGAGGAAAATACACATATCATATCAACAATATCATGCCCCCTTTCACCCGAGAAAATACATTAATAATCATACACCTATTTCCCTTAATCCTACTATCTACCAACCCCAAAGTAATTATAGGAACCATGTACTGTAAATATAGTTTAAACAAAACATTAGATTGTGAGTCTAATAATAGAAGCCCAAAGATTTCTTATTTACCAAGAAAGTA-TGCAAGAACTGCTAACTCATGCCTCCATATATAACAATGTGGCTTTCTT-ACTTTTAAAGGATAGAAGTAATCCATCGGTCTTAGGAACCGAAAA-ATTGGTGCAACTCCAAATAAAAGTAATAAATTTATTTTCATCCTCCATTTTACTATCACTTACACTCTTAATTACCCCATTTATTATTACAACAACTAAAAAATATGAAACACATGCATACCCTTACTACGTAAAAAACTCTATCGCCTGCGCATTTATAACAAGCCTAGTCCCAATGCTCATATTTCTATACACAAATCAAGAAATAATCATTTCCAACTGACATTGAATAACGATTCATACTATCAAATTATGCCTAAGCTT Lemur_catta AAGCTTCATAGGAGCAACCATTCTAATAATCGCACATGGCCTTACATCATCCATATTATTCTGTCTAGCCAACTCTAACTACGAACGAATCCATAGCCGTACAATACTACTAGCACGAGGGATCCAAACCATTCTCCCTCTTATAGCCACCTGATGACTACTCGCCAGCCTAACTAACCTAGCCCTACCCACCTCTATCAATTTAATTGGCGAACTATTCGTCACTATAGCATCCTTCTCATGATCAAACATTACAATTATCTTAATAGGCTTAAATATGCTCATCACCGCTCTCTATTCCCTCTATATATTAACTACTACACAACGAGGAAAACTCACATATCATTCGCACAACCTAAACCCATCCTTTACACGAGAAAACACCCTTATATCCATACACATACTCCCCCTTCTCCTATTTACCTTAAACCCCAAAATTATTCTAGGACCCACGTACTGTAAATATAGTTTAAA-AAAACACTAGATTGTGAATCCAGAAATAGAAGCTCAAAC-CTTCTTATTTACCGAGAAAGTAATGTATGAACTGCTAACTCTGCACTCCGTATATAAAAATACGGCTATCTCAACTTTTAAAGGATAGAAGTAATCCATTGGCCTTAGGAGCCAAAAA-ATTGGTGCAACTCCAAATAAAAGTAATAAATCTATTATCCTCTTTCACCCTTGTCACACTGATTATCCTAACTTTACCTATCATTATAAACGTTACAAACATATACAAAAACTACCCCTATGCACCATACGTAAAATCTTCTATTGCATGTGCCTTCATCACTAGCCTCATCCCAACTATATTATTTATCTCCTCAGGACAAGAAACAATCATTTCCAACTGACATTGAATAACAATCCAAACCCTAAAACTATCTATTAGCTT Homo_sapiens AAGCTTCACCGGCGCAGTCATTCTCATAATCGCCCACGGGCTTACATCCTCATTACTATTCTGCCTAGCAAACTCAAACTACGAACGCACTCACAGTCGCATCATAATCCTCTCTCAAGGACTTCAAACTCTACTCCCACTAATAGCTTTTTGATGACTTCTAGCAAGCCTCGCTAACCTCGCCTTACCCCCCACTATTAACCTACTGGGAGAACTCTCTGTGCTAGTAACCACGTTCTCCTGATCAAATATCACTCTCCTACTTACAGGACTCAACATACTAGTCACAGCCCTATACTCCCTCTACATATTTACCACAACACAATGGGGCTCACTCACCCACCACATTAACAACATAAAACCCTCATTCACACGAGAAAACACCCTCATGTTCATACACCTATCCCCCATTCTCCTCCTATCCCTCAACCCCGACATCATTACCGGGTTTTCCTCTTGTAAATATAGTTTAACCAAAACATCAGATTGTGAATCTGACAACAGAGGCTTA-CGACCCCTTATTTACCGAGAAAGCT-CACAAGAACTGCTAACTCATGCCCCCATGTCTAACAACATGGCTTTCTCAACTTTTAAAGGATAACAGCTATCCATTGGTCTTAGGCCCCAAAAATTTTGGTGCAACTCCAAATAAAAGTAATAACCATGCACACTACTATAACCACCCTAACCCTGACTTCCCTAATTCCCCCCATCCTTACCACCCTCGTTAACCCTAACAAAAAAAACTCATACCCCCATTATGTAAAATCCATTGTCGCATCCACCTTTATTATCAGTCTCTTCCCCACAACAATATTCATGTGCCTAGACCAAGAAGTTATTATCTCGAACTGACACTGAGCCACAACCCAAACAACCCAGCTCTCCCTAAGCTT Pan AAGCTTCACCGGCGCAATTATCCTCATAATCGCCCACGGACTTACATCCTCATTATTATTCTGCCTAGCAAACTCAAATTATGAACGCACCCACAGTCGCATCATAATTCTCTCCCAAGGACTTCAAACTCTACTCCCACTAATAGCCTTTTGATGACTCCTAGCAAGCCTCGCTAACCTCGCCCTACCCCCTACCATTAATCTCCTAGGGGAACTCTCCGTGCTAGTAACCTCATTCTCCTGATCAAATACCACTCTCCTACTCACAGGATTCAACATACTAATCACAGCCCTGTACTCCCTCTACATGTTTACCACAACACAATGAGGCTCACTCACCCACCACATTAATAACATAAAGCCCTCATTCACACGAGAAAATACTCTCATATTTTTACACCTATCCCCCATCCTCCTTCTATCCCTCAATCCTGATATCATCACTGGATTCACCTCCTGTAAATATAGTTTAACCAAAACATCAGATTGTGAATCTGACAACAGAGGCTCA-CGACCCCTTATTTACCGAGAAAGCT-TATAAGAACTGCTAATTCATATCCCCATGCCTGACAACATGGCTTTCTCAACTTTTAAAGGATAACAGCCATCCGTTGGTCTTAGGCCCCAAAAATTTTGGTGCAACTCCAAATAAAAGTAATAACCATGTATACTACCATAACCACCTTAACCCTAACTCCCTTAATTCTCCCCATCCTCACCACCCTCATTAACCCTAACAAAAAAAACTCATATCCCCATTATGTGAAATCCATTATCGCGTCCACCTTTATCATTAGCCTTTTCCCCACAACAATATTCATATGCCTAGACCAAGAAGCTATTATCTCAAACTGGCACTGAGCAACAACCCAAACAACCCAGCTCTCCCTAAGCTT Gorilla AAGCTTCACCGGCGCAGTTGTTCTTATAATTGCCCACGGACTTACATCATCATTATTATTCTGCCTAGCAAACTCAAACTACGAACGAACCCACAGCCGCATCATAATTCTCTCTCAAGGACTCCAAACCCTACTCCCACTAATAGCCCTTTGATGACTTCTGGCAAGCCTCGCCAACCTCGCCTTACCCCCCACCATTAACCTACTAGGAGAGCTCTCCGTACTAGTAACCACATTCTCCTGATCAAACACCACCCTTTTACTTACAGGATCTAACATACTAATTACAGCCCTGTACTCCCTTTATATATTTACCACAACACAATGAGGCCCACTCACACACCACATCACCAACATAAAACCCTCATTTACACGAGAAAACATCCTCATATTCATGCACCTATCCCCCATCCTCCTCCTATCCCTCAACCCCGATATTATCACCGGGTTCACCTCCTGTAAATATAGTTTAACCAAAACATCAGATTGTGAATCTGATAACAGAGGCTCA-CAACCCCTTATTTACCGAGAAAGCT-CGTAAGAGCTGCTAACTCATACCCCCGTGCTTGACAACATGGCTTTCTCAACTTTTAAAGGATAACAGCTATCCATTGGTCTTAGGACCCAAAAATTTTGGTGCAACTCCAAATAAAAGTAATAACTATGTACGCTACCATAACCACCTTAGCCCTAACTTCCTTAATTCCCCCTATCCTTACCACCTTCATCAATCCTAACAAAAAAAGCTCATACCCCCATTACGTAAAATCTATCGTCGCATCCACCTTTATCATCAGCCTCTTCCCCACAACAATATTTCTATGCCTAGACCAAGAAGCTATTATCTCAAGCTGACACTGAGCAACAACCCAAACAATTCAACTCTCCCTAAGCTT Pongo AAGCTTCACCGGCGCAACCACCCTCATGATTGCCCATGGACTCACATCCTCCCTACTGTTCTGCCTAGCAAACTCAAACTACGAACGAACCCACAGCCGCATCATAATCCTCTCTCAAGGCCTTCAAACTCTACTCCCCCTAATAGCCCTCTGATGACTTCTAGCAAGCCTCACTAACCTTGCCCTACCACCCACCATCAACCTTCTAGGAGAACTCTCCGTACTAATAGCCATATTCTCTTGATCTAACATCACCATCCTACTAACAGGACTCAACATACTAATCACAACCCTATACTCTCTCTATATATTCACCACAACACAACGAGGTACACCCACACACCACATCAACAACATAAAACCTTCTTTCACACGCGAAAATACCCTCATGCTCATACACCTATCCCCCATCCTCCTCTTATCCCTCAACCCCAGCATCATCGCTGGGTTCGCCTACTGTAAATATAGTTTAACCAAAACATTAGATTGTGAATCTAATAATAGGGCCCCA-CAACCCCTTATTTACCGAGAAAGCT-CACAAGAACTGCTAACTCTCACT-CCATGTGTGACAACATGGCTTTCTCAGCTTTTAAAGGATAACAGCTATCCCTTGGTCTTAGGATCCAAAAATTTTGGTGCAACTCCAAATAAAAGTAACAGCCATGTTTACCACCATAACTGCCCTCACCTTAACTTCCCTAATCCCCCCCATTACCGCTACCCTCATTAACCCCAACAAAAAAAACCCATACCCCCACTATGTAAAAACGGCCATCGCATCCGCCTTTACTATCAGCCTTATCCCAACAACAATATTTATCTGCCTAGGACAAGAAACCATCGTCACAAACTGATGCTGAACAACCACCCAGACACTACAACTCTCACTAAGCTT Hylobates AAGCTTTACAGGTGCAACCGTCCTCATAATCGCCCACGGACTAACCTCTTCCCTGCTATTCTGCCTTGCAAACTCAAACTACGAACGAACTCACAGCCGCATCATAATCCTATCTCGAGGGCTCCAAGCCTTACTCCCACTGATAGCCTTCTGATGACTCGCAGCAAGCCTCGCTAACCTCGCCCTACCCCCCACTATTAACCTCCTAGGTGAACTCTTCGTACTAATGGCCTCCTTCTCCTGGGCAAACACTACTATTACACTCACCGGGCTCAACGTACTAATCACGGCCCTATACTCCCTTTACATATTTATCATAACACAACGAGGCACACTTACACACCACATTAAAAACATAAAACCCTCACTCACACGAGAAAACATATTAATACTTATGCACCTCTTCCCCCTCCTCCTCCTAACCCTCAACCCTAACATCATTACTGGCTTTACTCCCTGTAAACATAGTTTAATCAAAACATTAGATTGTGAATCTAACAATAGAGGCTCG-AAACCTCTTGCTTACCGAGAAAGCC-CACAAGAACTGCTAACTCACTATCCCATGTATGACAACATGGCTTTCTCAACTTTTAAAGGATAACAGCTATCCATTGGTCTTAGGACCCAAAAATTTTGGTGCAACTCCAAATAAAAGTAATAGCAATGTACACCACCATAGCCATTCTAACGCTAACCTCCCTAATTCCCCCCATTACAGCCACCCTTATTAACCCCAATAAAAAGAACTTATACCCGCACTACGTAAAAATGACCATTGCCTCTACCTTTATAATCAGCCTATTTCCCACAATAATATTCATGTGCACAGACCAAGAAACCATTATTTCAAACTGACACTGAACTGCAACCCAAACGCTAGAACTCTCCCTAAGCTT Macaca_fuscata AAGCTTTTCCGGCGCAACCATCCTTATGATCGCTCACGGACTCACCTCTTCCATATATTTCTGCCTAGCCAATTCAAACTATGAACGCACTCACAACCGTACCATACTACTGTCCCGAGGACTTCAAATCCTACTTCCACTAACAGCCTTTTGATGATTAACAGCAAGCCTTACTAACCTTGCCCTACCCCCCACTATCAATCTACTAGGTGAACTCTTTGTAATCGCAACCTCATTCTCCTGATCCCATATCACCATTATGCTAACAGGACTTAACATATTAATTACGGCCCTCTACTCTCTCCACATATTCACTACAACACAACGAGGAACACTCACACATCACATAATCAACATAAAGCCCCCCTTCACACGAGAAAACACATTAATATTCATACACCTCGCTCCAATTATCCTTCTATCCCTCAACCCCAACATCATCCTGGGGTTTACCTCCTGTAGATATAGTTTAACTAAAACACTAGATTGTGAATCTAACCATAGAGACTCA-CCACCTCTTATTTACCGAGAAAACT-CGCAAGGACTGCTAACCCATGTACCCGTACCTAAAATTACGGTTTTCTCAACTTTTAAAGGATAACAGCTATCCATTGACCTTAGGAGTCAAAAACATTGGTGCAACTCCAAATAAAAGTAATAATCATGCACACCCCCATCATTATAACAACCCTTATCTCCCTAACTCTCCCAATTTTTGCCACCCTCATCAACCCTTACAAAAAACGTCCATACCCAGATTACGTAAAAACAACCGTAATATATGCTTTCATCATCAGCCTCCCCTCAACAACTTTATTCATCTTCTCAAACCAAGAAACAACCATTTGGAGCTGACATTGAATAATGACCCAAACACTAGACCTAACGCTAAGCTT M_mulatta AAGCTTTTCTGGCGCAACCATCCTCATGATTGCTCACGGACTCACCTCTTCCATATATTTCTGCCTAGCCAATTCAAACTATGAACGCACTCACAACCGTACCATACTACTGTCCCGGGGACTTCAAATCCTACTTCCACTAACAGCTTTCTGATGATTAACAGCAAGCCTTACTAACCTTGCCCTACCCCCCACTATCAACCTACTAGGTGAACTCTTTGTAATCGCGACCTCATTCTCCTGGTCCCATATCACCATTATATTAACAGGATTTAACATACTAATTACGGCCCTCTACTCCCTCCACATATTCACCACAACACAACGAGGAGCACTCACACATCACATAATCAACATAAAACCCCCCTTCACACGAGAAAACATATTAATATTCATACACCTCGCTCCAATCATCCTCCTATCTCTCAACCCCAACATCATCCTGGGGTTTACTTCCTGTAGATATAGTTTAACTAAAACATTAGATTGTGAATCTAACCATAGAGACTTA-CCACCTCTTATTTACCGAGAAAACT-CGCGAGGACTGCTAACCCATGTATCCGTACCTAAAATTACGGTTTTCTCAACTTTTAAAGGATAACAGCTATCCATTGACCTTAGGAGTCAAAAATATTGGTGCAACTCCAAATAAAAGTAATAATCATGCACACCCCTATCATAATAACAACCCTTATCTCCCTAACTCTCCCAATTTTTGCCACCCTCATCAACCCTTACAAAAAACGTCCATACCCAGATTACGTAAAAACAACCGTAATATATGCTTTCATCATCAGCCTCCCCTCAACAACTTTATTCATCTTCTCAAACCAAGAAACAACCATTTGAAGCTGACATTGAATAATAACCCAAACACTAGACCTAACACTAAGCTT M_fascicularis AAGCTTCTCCGGCGCAACCACCCTTATAATCGCCCACGGGCTCACCTCTTCCATGTATTTCTGCTTGGCCAATTCAAACTATGAGCGCACTCATAACCGTACCATACTACTATCCCGAGGACTTCAAATTCTACTTCCATTGACAGCCTTCTGATGACTCACAGCAAGCCTTACTAACCTTGCCCTACCCCCCACTATTAATCTACTAGGCGAACTCTTTGTAATCACAACTTCATTTTCCTGATCCCATATCACCATTGTGTTAACGGGCCTTAATATACTAATCACAGCCCTCTACTCTCTCCACATGTTCATTACAGTACAACGAGGAACACTCACACACCACATAATCAATATAAAACCCCCCTTCACACGAGAAAACATATTAATATTCATACACCTCGCTCCAATTATCCTTCTATCTCTCAACCCCAACATCATCCTGGGGTTTACCTCCTGTAAATATAGTTTAACTAAAACATTAGATTGTGAATCTAACTATAGAGGCCTA-CCACTTCTTATTTACCGAGAAAACT-CGCAAGGACTGCTAATCCATGCCTCCGTACTTAAAACTACGGTTTCCTCAACTTTTAAAGGATAACAGCTATCCATTGACCTTAGGAGTCAAAAACATTGGTGCAACTCCAAATAAAAGTAATAATCATGCACACCCCCATCATAATAACAACCCTCATCTCCCTGACCCTTCCAATTTTTGCCACCCTCACCAACCCCTATAAAAAACGTTCATACCCAGACTACGTAAAAACAACCGTAATATATGCTTTTATTACCAGTCTCCCCTCAACAACCCTATTCATCCTCTCAAACCAAGAAACAACCATTTGGAGTTGACATTGAATAACAACCCAAACATTAGACCTAACACTAAGCTT M_sylvanus AAGCTTCTCCGGTGCAACTATCCTTATAGTTGCCCATGGACTCACCTCTTCCATATACTTCTGCTTGGCCAACTCAAACTACGAACGCACCCACAGCCGCATCATACTACTATCCCGAGGACTCCAAATCCTACTCCCACTAACAGCCTTCTGATGATTCACAGCAAGCCTTACTAATCTTGCTCTACCCTCCACTATTAATCTACTGGGCGAACTCTTCGTAATCGCAACCTCATTTTCCTGATCCCACATCACCATCATACTAACAGGACTGAACATACTAATTACAGCCCTCTACTCTCTTCACATATTCACCACAACACAACGAGGAGCGCTCACACACCACATAATTAACATAAAACCACCTTTCACACGAGAAAACATATTAATACTCATACACCTCGCTCCAATTATTCTTCTATCTCTTAACCCCAACATCATTCTAGGATTTACTTCCTGTAAATATAGTTTAATTAAAACATTAGACTGTGAATCTAACTATAGAAGCTTA-CCACTTCTTATTTACCGAGAAAACT-TGCAAGGACCGCTAATCCACACCTCCGTACTTAAAACTACGGTTTTCTCAACTTTTAAAGGATAACAGCTATCCATTGGCCTTAGGAGTCAAAAATATTGGTGCAACTCCAAATAAAAGTAATAATCATGTATACCCCCATCATAATAACAACTCTCATCTCCCTAACTCTTCCAATTTTCGCTACCCTTATCAACCCCAACAAAAAACACCTATATCCAAACTACGTAAAAACAGCCGTAATATATGCTTTCATTACCAGCCTCTCTTCAACAACTTTATATATATTCTTAAACCAAGAAACAATCATCTGAAGCTGGCACTGAATAATAACCCAAACACTAAGCCTAACATTAAGCTT Saimiri_sciureus AAGCTTCACCGGCGCAATGATCCTAATAATCGCTCACGGGTTTACTTCGTCTATGCTATTCTGCCTAGCAAACTCAAATTACGAACGAATTCACAGCCGAACAATAACATTTACTCGAGGGCTCCAAACACTATTCCCGCTTATAGGCCTCTGATGACTCCTAGCAAATCTCGCTAACCTCGCCCTACCCACAGCTATTAATCTAGTAGGAGAATTACTCACAATCGTATCTTCCTTCTCTTGATCCAACTTTACTATTATATTCACAGGACTTAATATACTAATTACAGCACTCTACTCACTTCATATGTATGCCTCTACACAGCGAGGTCCACTTACATACAGCACCAGCAATATAAAACCAATATTTACACGAGAAAATACGCTAATATTTATACATATAACACCAATCCTCCTCCTTACCTTGAGCCCCAAGGTAATTATAGGACCCTCACCTTGTAATTATAGTTTAGCTAAAACATTAGATTGTGAATCTAATAATAGAAGAATA-TAACTTCTTAATTACCGAGAAAGTG-CGCAAGAACTGCTAATTCATGCTCCCAAGACTAACAACTTGGCTTCCTCAACTTTTAAAGGATAGTAGTTATCCATTGGTCTTAGGAGCCAAAAACATTGGTGCAACTCCAAATAAAAGTAATA---ATACACTTCTCCATCACTCTAATAACACTAATTAGCCTACTAGCGCCAATCCTAGCTACCCTCATTAACCCTAACAAAAGCACACTATACCCGTACTACGTAAAACTAGCCATCATCTACGCCCTCATTACCAGTACCTTATCTATAATATTCTTTATCCTTACAGGCCAAGAATCAATAATTTCAAACTGACACTGAATAACTATCCAAACCATCAAACTATCCCTAAGCTT ; end; NBISweden-MrBayes-ecfd55c/examples/replicase.nex000066400000000000000000000170421526212227600217220ustar00rootroot00000000000000#NEXUS [Bacteriphage RNA sequences from the Huelsenbeck lab] begin data; dimensions ntax=9 nchar=720; format datatype=rna interleave=no gap=- missing=?; matrix [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50] FR GGC AAC GGU --- GUG UUU ACU GUU CCG AAG AAC AAU AAA AUA GAU CGG GCU GCU UGC AAA GAG CCU GAU AUG AAU AUG UAC UUA CAG AAA GGG GUC GGC GGU UUC AUC CGU CGC CGC CUU AAG ACU GUG GGU AUA GAC UUG AAC GAU CAA ACG AUC AAU CAA CGC CUG GCU CAA CAA GGU AGC CGU GAU GGG UCU UUG GCG ACG AUA GAC UUA UCG UCU GCU UCU GAC UCC AUC AGC GAC CGC CUA GUG UGG AGU UUU CUC CCA CCU GAG CUA UAU UCA UAU CUC GAC AUG AUU CGA AGC CAC UAC GGU UAC GUA --- AAU GGC AAG AUG AUU CGU UGG GAA CUA UUU UCG ACG AUG GGU AAU GGG UUC ACC UUU GAA CUA GAG UCC AUG AUU UUC UGG GCU AUA GUC AGG GCU ACU CAG AUC CAU UUU CGU --- AAC ACC GGA ACC AUU GGC AUC UAU GGG GAC GAU AUU AUA UGC CCC ACA GAG AUU GCA CCU CGC GUG CUG GAA GCA CUG AGC UUC UAC GGU UUC AAA CCG AAU CUA CGA AAG ACG --- UUC ACG UCC GGC UCU UUU CGC GAG AGC UGC GGC GCG CAC UAU UUC CGU GGU GUC GAU GUU AAA CCA UUU UAU AUC AAG AAA CCA AUC ACU GAC CUA UUC UCC CUA AUG CUU AUA CUU AAC CGU AUA CGC GGA UGG GGG GUA GUC AAC GGA AUA GCA GAC CCA CGC CUC MS2 GGG AAC GGA --- GUG UUU ACA GUU CCG AAG AAU AAU AAA AUA GAU CGG GCU GCC UGU AAG GAG CCU GAU AUG AAU AUG UAC CUC CAG AAA GGG GUC GGU GCU UUC AUC AGA CGC CGG CUC AAA UCC GUU GGU AUA GAC CUG AAU GAU CAA UCG AUC AAC CAG CGU CUG GCU CAG CAG GGC AGCGUAGAUGGUUCGCUUGCGACGAUAGACUUAUCGUCUGCAUCCGAUUCCAUCUCCGAUCGCCUGGUGUGGAGUUUUCUCCCACCUGAGCUAUAUUCAUAUCUCGAUCGUAUCCGCUCACACUACGGA---GUA---GAUGGCGAGACGAUACGAUGGGAACUAUUUUCCACAAUGGGAAAUGGGUUCACAUUUGAGCUAGAGUCCAUGAUAUUCUGGGCAAUAGUCAAAGCGACCCAAAUCCAUUUUGGU---AACGCCGGAACCAUAGGCAUCUACGGGGACGAUAUUAUAUGUCCCAGUGAGAUUGCACCCCGUGUGCUAGAGGCACUUGCCUACUACGGUUUUAAACCGAAUCUUCGUAAAACG---UUCGUGUCCGGGCUCUUUCGCGAGAGCUGCGGCGCGCACUUUUACCGUGGUGUCGAUGUCAAACCGUUUUACAUCAAGAAACCUGUUGACAAUCUCUUCGCCCUGAUGCUGAUAUUAAAUCGGCUACGGGGUUGGGGAGUUGUCGGAGGUAUGUCAGAUCCACGCCUC GA GGC AAC GGU --- UUG UUU UCU GUU CCG AAG AAC AAU AAA AUA GAU CGG GCU GCC UGU AAG GAG CCU GAU AUG AAU AUG UAC CUU CAG AAG GGG GCG GGA UCU UUU AUA AGA AAA CGC CUU CGC UCC GUC GGU AUA GAU CUU AAC GAU CAG ACG CGC AAU CAG GAA UUA GCC CGA CUU GGC AGCAUUGAUGGUUCGCUCGCUACUAUUGAUCUUAGUAGCGCUAGCGAUUCCAUCUCUGACCGUCUUGUCUGGGAUCUACUUCCGCCGCACGUUUAUUCAUACCUCGCUCGUAUCCGAACAUCGUUCACUAUGAUC---GAUGGGCGUUUACAUAAGUGGGGUCUAUUUUCUACCAUGGGUAAUGGCUUCACGUUCGAACUCGAGUCCAUGAUCUUUUGGGCUUUAAGCAAGAGCAUUAUGCUGUCCAUGGGUGUU---ACUGGCUCAUUAGGCAUCUACGGUGAUGAUAUAAUCGUCCCCGUUGAGUGUCGUCCAACUCUCCUUAAGGUACUAUCCGCUGUAAACUUUCUUCCUAAUGAGGAGAAAACA---UUUACAACGGGUUACUUUCGUGAAAGUUGUGGUGCCCACUUCUUCAAAGAUGCCGACAUGAAACCUUUUUACUGCAAGCGGCCAAUGGAAACCCUUCCCGAUGUCAUGUUGCUAUGCAACAGGAUAAGAGGUUGGCAGACCGUUGGUGGAAUGUCAGAUCCGCGACUC SP UCA --- AAU AAA GCA GUC ACU GUU CCA AAG AAC AGU AAA ACU GAU CGC UGU AUU GCU AUC GAG CCC GGC UGG AAU AUG UUU UUC CAG UUA GGC GUC GGU GCA GUG CUA CGC GAU AGG UUG CGU UUA UGG AAG AUU GAU CUU AAU GAC CAA UCG ACC AAU CAA CGC CUC GCG CGU GAU GGG UCUCUGCUAAAUCAUUUAGCUACCAUAGACUUAUCUGCAGCCAGCGAUUCAAUCAGCCUUAAGCUUGUUGAGUUGCUCAUGCCCCCUGAAUGGUAUGACCUUCUAACGGAUCUCCGAUCCGAUGAAGGAAUACUGCCUGACGGGCGAGUUGUGACCUAUGAGAAAAUAUCCUCCAUGGGUAAUGGCUACACUUUCGAACUCGAGUCGCUUAUUUUUGCGGCUAUCGCUCGAAGUGUGUGCGAGUUACUGGAAAUUGACCAAUCUACUGUUAGCGUGUACGGGGAUGAUAUAAUCAUCGAUACCCGUGCCGCAGCUCCAUUAAUGGAUGUCUUUGAGUACGUCGGGUUCACUCCUAACAGAAAGAAAACG---UUCUGCGAUGGACCCUUCCGCGAAUCGUGCGGUAAGCACUGGUUCCAAGGGGUAGAUGUAACGCCCUUUUACAUACGACGACCAAUACGUUGCCUAGCCGAUAUGAUACUUGUAUUAAAUAGUAUCUAUAGGUGGGGCACUGUUGAUGGCAUAUGGGAUCCUAGAGCA NL95 UCG --- AAU AAA GCA GUC ACU GUU CCA AAG AAC AGU AAA ACU GAU CGC UGC AUU GCU AUC GAG CCC GGC UGG AAU AUG UUU UUC CAG UUA GGC GUC GGU GCU GUG CUC CGU GAU CGG UUG CGC CUU UGG CAU AUU GAU CUC AAU GAU CAA UCU GUU AAU CAG CGC CUC GCA CGU GAU GCA UCGCAGUUGGACCAUUUGGCCACUGUCGAUUUAUCAGCAGCAAGCGAUUCGAUAAGCUUACGGCUUGUUGAACUGCUAAUGCCGCCUGCUUGGUUUGAUCUCCUGACCGAUCUCCGAUCGGACCAGGGAAUCCUGCCUGACGGGCGUGUCGUUACUUACGAGAAAAUAUCCUCCAUGGGUAAUGGCUACACUUUUGAGCUAGAGUCGUUAAUUUUCGCGGCUCUCGCCAGAAGUGUGUGCGAGUUAUUGGACCUUGACCAGUCAACUGUCAGCGUGUACGGUGAUGAUAUAAUCAUCGAUUCACGUGCCGCUGAUGUCCUUAUGGCGGUUUUCGAGUAUGUUGGGUUUACGCCUAAUCGAAAGAAAACU---UUCAUUAAGGGCCCCUUUAGAGAGUCGUGCGGAAAGCACUGGCACUCCGGGGUUGACGUAACGCCCUUUUACAUACGCCGCCCAAUCCGCUGCCUAGCCGACAUGAUACUUGUAUUGAACAGUAUCUACCGGUGGGGUACGAUUGACGGUGUGUGGGAUCCUAGGGUA M11 CCU UUC AAU AAA GCA GUU ACU GUA CCA AAG AAC AGU AAA ACU GAU CGC UGU AUA GCC AUC GAA CCU GGC UGG AAU AUG UUU UUC CAG CUA GGU AUC GGU GGU GUU AUA CGC GAA AAG UUG CGU UUG UGG GGC AUC GAU CUG AAU GAU CAG ACG AUU AAC CAA ACG CGC GCA UAU UUA GGC AGCCGUGAUGAUAAUCUCGCCACGGUGGAUCUCUCAAGAGCUAGCGAUACUAUUUCGCUUGCCCUUGUUGAGCUCCUUAUGCCUCCUGAGUGGUUUAAGGUCCUGUUGGCCUUAAGAUCACCCAAGGGCAUCUUGCCAGAUGGUACCGUCAUUACUUAUGAGAAAAUAUCCUCAAUGGGUAAUGGCUAUACCUUCGAGCUUGAGUCGCUUAUAUUUGCGGCUCUUGCUCGGUCUUUAUGCGAAUUACUGGGCUUACGACCGUCAGAUGUUACGGUCUAUGGCGAUGACAUAAUAUUGCCAUCAGACGCGUGCAGUCCUCUAGUUGAAGUUUUCUCCUAUGUUGGUUUUCGUACCAACAAGAAGAAAACG---UUUUCUAGUGGACCGUUCCGAGAGUCGUGCGGAAAGCACUACUUUUUGGGCGUUGACGUCACACCUUUCUACAUACGUCGCCGUAUAGUGAGUCCCUCCGAUCUCAUACUGGUUUUGAACCAGAUGUAUCGUUGGGCCACAAUUGACGGCGUAUGGGAUCCUAGGGUA MX1 CCU UUC AAU AAA GCA GUU ACU GUA CCA AAG AAC AGU AAA ACU GAU CGC UGC AUC GCU AUC GAG CCA GGC UGG AAU AUG UUU UUC CAG UUG GGC AUU GGU GGC GUA AUU CGC GAA AAG UUG CAC UUG UGG AAU AUC GAC CUG AAU GAU CAG ACG AUU AAC CAG GUG CGC GCA UAU UCA GGC AGCUGUAGCAAUGAACUUGCUACAGUGGAUCUCUCGAGCGCGAGUGAUACUAUUUCGCUUGCGCUCGUUGAGCUCCUGCUACCCCCUGCGUGGUUUAAAGUCCUUACGGACCUUAGGUCACGAAGGGGUAUGUUGCCAGACGGUAGAAUCAUUACCUAUGAGAAAAUUUCCUCAAUGGGUAACGGUUUCACCUUCGAGCUCGAGUCGCUUAUAUUUGCAGCUCUUGCUCGGUCUUUAUGCGAGUUACUGAACUUACAACCGUCGAGUGUCACGGUCUAUGGCGAUGAUAUUAUAUUGCCAUCAGACGCGUGCAGCUCGUUGAUUGAAGUUUUCUCCUACGUAGGUUUUAGAACCAACGAGAAGAAGACC---UUUUUCGACGGGCCGUUCCGAGAGUCGUGCGGAAAGCACUACUUUAUGGGCGUUGACGUCACACCUUUCUACAUACGCCACCGUAUAGUGAGUCCCUCUGAUCUCAUACUGGUUUUGAACCAGAUGUAUCGUUGGGCCACGAUUGAUGGCGUAUGGGAUCCUAGGGUA QB CCU UUU AAU AAA GCA GUU ACU GUA CCU AAG AAC AGU AAG ACA GAU CGU UGU AUU GCU AUC GAA CCU GGU UGG AAU AUG UUU UUC CAA CUG GGU AUC GGU GGC AUU CUA CGC GAU CGG UUG CGU UGC UGG GGU AUC GAU CUG AAU GAU CAG ACG AUA AAU CAG CGC CGC GCU CAC GAA GGC UCCGUUACUAAUAACUUAGCAACGGUUGAUCUCUCAGCGGCAAGCGAUUCUAUAUCUCUUGCCCUCUGUGAGCUCUUAUUGCCCCUAGGCUGGUUUGAGGUUCUUAUGGACCUCAGAUCACCUAAGGGGCGAUUGCCUGACGGUAGUGUUGUUACCUACGAGAAGAUUUCUUCUAUGGGUAACGGUUACACAUUCGAGCUCGAGUCGCUUAUUUUUGCUUCUCUCGCUCGUUCCGUUUGUGAGAUACUGGACUUAGACUCGUCUGAGGUCACUGUUUACGGAGACGAUAUUAUUUUACCGUCCUGUGCAGUCCCUGCCCUCCGGGAAGUUUUUAAGUAUGUUGGUUUUACGACCAAUACUAAAAAGACU---UUUUCCGAGGGGCCGUUCAGAGAGUCGUGCGGCAAGCACUACUAUUCUGGCGUAGAUGUUACUCCCUUUUACAUACGUCACCGUAUAGUGAGUCCUGCCGAUUUAAUACUGGUUUUGAAUAACCUAUAUCGGUGGGCCACAAUUGACGGCGUAUGGGAUCCUAGGGCC PP7 GAC AGC --- CGG UUC GAU UUU GUC GCU AAG ACC GCG AAG GCG GUU CGC UUC AUC GCU AUG GAG CCA GAA CUU AAC AUG CUG CUG CAG AAA UCU GUA GGA GAC ACG AUA AGG GCU GCU CUG CGG AAA GCG GGU AUC GAU CUC AAU ACC CAG CGA CUA AAU CAA GAU CUU GCG UAC CAC GGA UCCGUUUUUCGGAAUCUCGGUACGAUAGAUCUGUCUAGCGCUUCCGAUACGUUAAGCAUUGAACUCGUGCGGCAGUACCUGCCGAAGCGGUUUCUCCGCUAUGUAUUGGAUCUCCGAACCCCCUACACGAGUGUA---GGUGGUAAGAAGCACAGGCUCGAGAAGGUCGCUUCGAUGGGCAACGGGUUCAUUUUUGAACUCCAGAGCCUCAUCUACGCAGCCUUCGCGCAUGCCAUGACGCUAGUAGUAGGAGGAAGAGAAUGCGACAUAGCCAUUUACGGCGAUGAUAUCAUCGUCAGUGAAUGCGUAGUAGAGCCUCUGAUGCAGUUCCUCGAAUGGCAUGGGUUCUGCCCCAAUCUCGAUAAGAGUUAUUGGGGAGGGGAUCCAUUCCGCGAGUCCUGCGGGAAGCACUACUUCGCUGGUCGCGACGUUACCCCUGUCUACGUGAAGGGGGCCCUGGAUAACCUACCUGCCCUUUUCCGUCUCUUCAACUCGUUGAAGCGAUGGGAGGAGCAAACAGGUAUCCGGAUCCCUGACACG ; end; NBISweden-MrBayes-ecfd55c/src/000077500000000000000000000000001526212227600162045ustar00rootroot00000000000000NBISweden-MrBayes-ecfd55c/src/Makefile.am000066400000000000000000000005351526212227600202430ustar00rootroot00000000000000bin_PROGRAMS= mb mb_SOURCES= \ bayes.c \ bayes.h \ best.c \ best.h \ command.c \ command.h \ likelihood.c \ likelihood.h \ mbbeagle.c \ mbbeagle.h \ mcmc.c \ mcmc.h \ model.c \ model.h \ proposal.c \ proposal.h \ sumpt.c 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Huelsenbeck * Dept. Integrative Biology * University of California, Berkeley * Berkeley, CA 94720-3140 * johnh@berkeley.edu * * Fredrik Ronquist * Swedish Museum of Natural History * Box 50007 * SE-10405 Stockholm, SWEDEN * fredrik.ronquist@nrm.se * * With important contributions by * * Paul van der Mark (paulvdm@sc.fsu.edu) * Maxim Teslenko (maxkth@gmail.com) * Chi Zhang (zhangchicool@gmail.com) * * and by many users (run 'acknowledgments' to see more info) * * This program is free software; you can redistribute it and/or * modify it under the terms of the GNU General Public License * as published by the Free Software Foundation; either version 2 * of the License, or (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details (www.gnu.org). * */ #include "bayes.h" #include "command.h" #include "model.h" #include "sumpt.h" #include "utils.h" /* We only do proper command line parsing if we're on a system where the unistd.h header is available */ #ifdef HAVE_UNISTD_H #define UNIX_COMMAND_LINE_PARSING 1 #include /* getopt() */ #endif #ifdef HAVE_LIBREADLINE # if defined(HAVE_READLINE_READLINE_H) # include # elif defined(HAVE_READLINE_H) # include # else # /* Library available, but header files are not. */ # /* This typically happens on Linux where libraries may be split # into runtime and development packages. See Github Issue #182. */ # undef HAVE_LIBREADLINE # undef HAVE_READLINE_HISTORY # endif # endif /* HAVE_LIBREADLINE */ #ifdef HAVE_READLINE_HISTORY # if defined(HAVE_READLINE_HISTORY_H) # include # elif defined(HAVE_HISTORY_H) # include # else # undef HAVE_READLINE_HISTORY # endif #endif /* HAVE_READLINE_HISTORY */ #ifdef HAVE_LIBREADLINE static char **readline_completion(const char *, int, int); #endif /* local prototypes */ int CommandLine (int argc, char **argv); void GetTimeSeed (void); int InitializeMrBayes (void); void PrintHeader (void); int ReinitializeMrBayes (void); /* global variables, declared in this file */ BitsLong bitsLongWithAllBitsSet; /* BitsLong with all bits set, for bit ops */ ModelParams defaultModel; /* Default model; vals set in InitializeMrBayes */ int defTaxa; /* flag for whether number of taxa is known */ int defChars; /* flag for whether number of chars is known */ int defMatrix; /* flag for whether matrix is successful read */ int defPartition; /* flag for whether character partition is read */ int defPairs; /* flag for whether pairs are read */ Doublet doublet[16]; /* holds information on states for doublets */ int fileNameChanged; /* has file name been changed ? */ RandLong globalSeed; /* seed that is initialized at start up */ char **modelIndicatorParams; /* model indicator params */ char ***modelElementNames; /* names for component models */ int nBitsInALong; /* number of bits in a BitsLong */ int nPThreads; /* number of pthreads to use */ int numUserTrees; /* number of defined user trees */ int readComment; /* should we read comment (looking for &) ? */ int readWord; /* should we read word next ? */ RandLong runIDSeed; /* seed used only for determining run ID [stamp] */ RandLong swapSeed; /* seed used only for determining which to swap */ int userLevel; /* user level */ PolyTree *userTree[MAX_NUM_USERTREES];/* array of user trees */ char workingDir[100]; /* working directory */ #if defined (MPI_ENABLED) int proc_id; /* process ID (0, 1, ..., num_procs-1) */ int num_procs; /* number of active processors */ MrBFlt myStateInfo[7]; /* likelihood/prior/heat/ran/moveInfo vals of me */ MrBFlt partnerStateInfo[7]; /* likelihood/prior/heat/ran/moveInfo vals of partner */ #endif int main (int argc, char *argv[]) { int i; # if defined (MPI_ENABLED) int ierror; # endif # if defined (WIN_VERSION) HANDLE scbh; BOOL ok; DWORD lastError; COORD largestWindow; CONSOLE_SCREEN_BUFFER_INFO csbi; int currBottom; char poltmp[256]; scbh = GetStdHandle(STD_OUTPUT_HANDLE); GetConsoleScreenBufferInfo(scbh, &csbi); currBottom = csbi.srWindow.Bottom; largestWindow = GetLargestConsoleWindowSize(scbh); /* Allow for screen buffer 3000 lines long and 140 characters wide */ csbi.dwSize.Y = 3000; csbi.dwSize.X = 140; SetConsoleScreenBufferSize(scbh, csbi.dwSize); /* Allow for maximum possible screen height */ csbi.srWindow.Left = 0; /* no change relative to current value */ csbi.srWindow.Top = 0; /* no change relative to current value */ csbi.srWindow.Right = 0; /* no change relative to current value */ csbi.srWindow.Bottom = largestWindow.Y - currBottom -10; /**/ ok = SetConsoleWindowInfo(scbh, FALSE, &csbi.srWindow); if (ok == FALSE) { lastError = GetLastError(); GetConsoleScreenBufferInfo(scbh, &csbi); sprintf(poltmp, "\nlastError = %ld", lastError); // printf (poltmp); } # endif /* calculate the size of a long - used by bit manipulation functions */ nBitsInALong = sizeof(BitsLong) * 8; for (i=0; i' (i is for interactive)\n", spacer); MrBayesPrint ("%s or use 'set mode=interactive'\n\n", spacer); return (NO_ERROR); } /* normally, we simply wait at the prompt for a user action */ # if defined (MPI_ENABLED) if (proc_id == 0) { /* do not use readline because OpenMPI does not handle it */ MrBayesPrint ("MrBayes > "); fflush (stdin); if (fgets (cmdStr, CMD_STRING_LENGTH - 2, stdin) == NULL) { if (feof(stdin)) MrBayesPrint ("%s End of File encountered on stdin; quitting\n", spacer); else MrBayesPrint ("%s Could not read command from stdin; quitting\n", spacer); strcpy (cmdStr,"quit;\n"); } } ierror = MPI_Bcast (&cmdStr, CMD_STRING_LENGTH, MPI_CHAR, 0, MPI_COMM_WORLD); if (ierror != MPI_SUCCESS) { MrBayesPrint ("%s Problem broadcasting command string\n", spacer); } # else # ifdef HAVE_LIBREADLINE cmdStrP = readline("MrBayes > "); if (cmdStrP!=NULL) { strncpy (cmdStr, cmdStrP, CMD_STRING_LENGTH - 2); # ifdef HAVE_READLINE_HISTORY if (*cmdStrP) add_history (cmdStrP); # endif /* HAVE_READLINE_HISTORY */ free (cmdStrP); } else /* fall through to if (feof(stdin))..*/ # else MrBayesPrint ("MrBayes > "); fflush (stdin); if (fgets (cmdStr, CMD_STRING_LENGTH - 2, stdin) == NULL) # endif { if (feof(stdin)) MrBayesPrint ("%s End of File encountered on stdin; quitting\n", spacer); else MrBayesPrint ("%s Could not read command from stdin; quitting\n", spacer); strcpy (cmdStr,"quit;\n"); } # endif } i = 0; while (cmdStr[i] != '\0' && cmdStr[i] != '\n') i++; cmdStr[i++] = ';'; cmdStr[i] = '\0'; MrBayesPrint ("\n"); if (cmdStr[0] != ';') { /* check that all characters in the string are valid */ if (CheckStringValidity (cmdStr) == ERROR) { MrBayesPrint (" Unknown character in command string\n\n"); } else { expecting = Expecting(COMMAND); message = ParseCommand (cmdStr); if (message == NO_ERROR_QUIT) return (NO_ERROR); if (message == ERROR && quitOnError == YES) { MrBayesPrint ("%s Will exit with signal 1 (error) because quitonerror is set to yes\n", spacer); MrBayesPrint ("%s If you want control to be returned to the command line on error,\n", spacer); MrBayesPrint ("%s use 'mb -i ' (i is for interactive) or use 'set quitonerror=no'\n\n", spacer); return (ERROR); } # if defined (MPI_ENABLED) ierror = MPI_Barrier (MPI_COMM_WORLD); if (ierror != MPI_SUCCESS) { MrBayesPrint ("%s Problem at command barrier\n", spacer); } # endif MrBayesPrint ("\n"); } } } } #ifdef HAVE_LIBREADLINE extern char *command_generator(const char *text, int state); char **readline_completion (const char *text, int start, int stop) { char **matches = (char **) NULL; # ifdef COMPLETIONMATCHES if (start == 0) matches = rl_completion_matches (text, command_generator); # endif return (matches); } #endif void GetTimeSeed (void) { time_t curTime; # if defined (MPI_ENABLED) int ierror; if (proc_id == 0) { curTime = time(NULL); globalSeed = (RandLong)curTime; if (globalSeed < 0) globalSeed = -globalSeed; } ierror = MPI_Bcast(&globalSeed, 1, MPI_LONG, 0, MPI_COMM_WORLD); if (ierror != MPI_SUCCESS) { MrBayesPrint ("%s Problem broadcasting seed\n", spacer); } if (proc_id == 0) { /* Note: swapSeed will often be same as globalSeed */ curTime = time(NULL); swapSeed = (RandLong)curTime; if (swapSeed < 0) swapSeed = -swapSeed; } ierror = MPI_Bcast(&swapSeed, 1, MPI_LONG, 0, MPI_COMM_WORLD); if (ierror != MPI_SUCCESS) { MrBayesPrint ("%s Problem broadcasting swap seed\n", spacer); } if (proc_id == 0) { /* Note: runIDSeed will often be same as globalSeed */ curTime = time(NULL); runIDSeed = (RandLong)curTime; if (runIDSeed < 0) runIDSeed = -runIDSeed; } ierror = MPI_Bcast(&runIDSeed, 1, MPI_LONG, 0, MPI_COMM_WORLD); if (ierror != MPI_SUCCESS) { MrBayesPrint ("%s Problem broadcasting run ID seed\n", spacer); } # else curTime = time(NULL); globalSeed = (RandLong)curTime; if (globalSeed < 0) globalSeed = -globalSeed; /* Note: swapSeed will often be the same as globalSeed */ curTime = time(NULL); swapSeed = (RandLong)curTime; if (swapSeed < 0) swapSeed = -swapSeed; /* Note: runIDSeed will often be the same as globalSeed */ curTime = time(NULL); runIDSeed = (RandLong)curTime; if (runIDSeed < 0) runIDSeed = -runIDSeed; # endif } int InitializeMrBayes (void) { /* this function initializes the program; only call it at the start of execution */ int i, j, growthFxn[6]; nBitsInALong = sizeof(BitsLong) * 8; /* global variable: number of bits in a BitsLong */ userLevel = STANDARD_USER; /* default user level */ readWord = NO; /* should we read a word next ? */ readComment = NO; /* should we read comments? (used by tree cmd) */ fileNameChanged = NO; /* file name changed ? (used by a few commands) */ echoMB = YES; /* flag used by Manual to control printing */ # if defined (MPI_ENABLED) sprintf (manFileName, "commref_mb%sp.txt", VERSION_NUMBER); /* name of command reference file */ # else sprintf (manFileName, "commref_mb%s.txt", VERSION_NUMBER); /* name of command reference file */ # endif for (i=0; i\" for information\n"); MrBayesPrint (" on the commands that are available.\n\n"); MrBayesPrint (" Type \"about\" for authorship and general\n"); MrBayesPrint (" information about the program.\n\n\n"); } int ReinitializeMrBayes (void) { /* this function resets everything dependent on a matrix */ int i; /* reinitialize indentation */ strcpy (spacer, ""); /* holds blanks for indentation */ /* reset all taxa flags */ ResetTaxaFlags(); /* reset all characters flags */ ResetCharacterFlags(); /* chain parameters */ chainParams.numGen = 1000000; /* number of MCMC cycles */ chainParams.sampleFreq = 500; /* frequency to sample chain */ chainParams.printFreq = 1000; /* frequency to print chain */ chainParams.swapFreq = 1; /* frequency of attempting swap of states */ chainParams.numSwaps = 1; /* number of swaps to try each time */ chainParams.isSS = NO; chainParams.startFromPriorSS = NO; chainParams.numStepsSS = 50; chainParams.burninSS = -1; chainParams.alphaSS = 0.4; chainParams.backupCheckSS = 0; chainParams.mcmcDiagn = YES; /* write MCMC diagnostics to file ? */ chainParams.diagnFreq = 5000; /* diagnostics frequency */ chainParams.minPartFreq = 0.10; /* min partition frequency for diagnostics */ chainParams.allChains = NO; /* calculate diagnostics for all chains ? */ chainParams.allComps = NO; /* do not calc diagn for all run comparisons */ chainParams.relativeBurnin = YES; /* use relative burnin? */ chainParams.burninFraction = 0.25; /* default burnin fraction */ chainParams.stopRule = NO; /* should stopping rule be used? */ chainParams.stopVal = 0.05; /* convergence diagnostic value to reach */ chainParams.numRuns = 2; /* number of runs */ chainParams.numChains = 4; /* number of chains */ chainParams.chainTemp = 0.1; /* chain temperature */ chainParams.redirect = NO; /* should printf be to stdout */ strcpy(chainParams.chainFileName, "temp"); /* chain file name for output */ chainParams.chainBurnIn = 0; /* chain burn in length */ chainParams.numStartPerts = 0; /* number of perturbations to starting tree */ strcpy(chainParams.startTree, "Current"); /* starting tree for chain (random/current) */ strcpy(chainParams.startParams, "Current"); /* starting params for chain (reset/current) */ chainParams.saveBrlens = YES; /* should branch lengths be saved */ chainParams.weightScheme[0] = 0.0; /* percent chars to decrease in weight */ chainParams.weightScheme[1] = 0.0; /* percent chars to increase in weight */ chainParams.weightScheme[2] = 1.0; /* weight increment */ chainParams.calcPbf = NO; /* should we calculate the pseudo-BF? */ chainParams.pbfInitBurnin = 100000; /* initial burnin for pseudo BF */ chainParams.pbfSampleFreq = 10; /* sample frequency for pseudo BF */ chainParams.pbfSampleTime = 2000; /* how many cycles to calculate site prob. */ chainParams.pbfSampleBurnin = 2000; /* burnin period for each site for pseudo BF */ chainParams.userDefinedTemps = NO; /* should we use the users temperatures? */ for (i=0; i #endif #include #include #include #include #include #include #include #include #include #include #include /* Set SSE_ENABLED if SSE SIMD extensions available. */ #if defined(HAVE_SSE) && !defined(DISABLE_SSE) #define SSE_ENABLED #endif /* Set AVX_ENABLED if AVX SIMD extensions available. */ #if defined(HAVE_AVX) && !defined(DISABLE_AVX) #define AVX_ENABLED #endif /* Set FMA_ENABLED if FMA SIMD extensions available. */ #if defined(HAVE_FMA3) && !defined(DISABLE_FMA) #define FMA_ENABLED #endif /* Set COMPLETIONMATCHES if we have the readline library */ #ifdef HAVE_LIBREADLINE #define COMPLETIONMATCHES #endif #if defined (MPI_ENABLED) #include "mpi.h" #endif #if defined (BEAGLE_ENABLED) #include "libhmsbeagle/beagle.h" #endif #if !defined (UNIX_VERSION) && !defined (WIN_VERSION) && !defined (MAC_VERSION) # ifdef __MWERKS__ # define MAC_VERSION # elif defined __APPLE__ # define MAC_VERSION # elif defined __unix__ # define UNIX_VERISON # else # define WIN_VERSION # endif #endif #if defined (WIN_VERSION) # include # include #endif /* Previous problems with bitfield operations may have been caused by several things. One potential problem has been that MrBayes has used signed ints or signed longs for the bit operations, and not all compilers perform bitfield operations on signed ints and longs in the expected manner for unsigned ints and longs. Actually, the ANSI standard does not specify how to implement bit operations on signed operands. Another problem is that some bitshift operations in MrBayes have been performed on an integer constant "1". This is probably interpreted by most compilers as a signed int and the result of the bitshift operation is therefore also a signed int. When a signed int has had a different number of bits than the type specified as SafeLong, this may also have resulted in errors on some systems. Both of these problems are fixed now by using unsigned longs for the bitfield operations and by setting the "1" value needed by some bitfield functions using a variable defined as the same type as the bitfield containers themselves. Furthermore, I have separated out the cases where a signed long is required or is more appropriate than the container type of bitfields. FR 2013-07-06 */ typedef unsigned long BitsLong; typedef long RandLong; #define MRBFLT_MAX DBL_MAX /* maximum possible value that can be stored in MrBFlt */ #define MRBFLT_MIN DBL_MIN /* minimum possible value that can be stored in MrBFlt */ #define MRBFLT_NEG_MAX (-DBL_MAX) /* maximum possible negative value that can be stored in MrBFlt */ typedef double MrBFlt; /* double used for parameter values and generally for floating point values, if set to float MPI would not work becouse of use MPI_DOUBLE */ typedef float CLFlt; /* single-precision float used for cond likes (CLFlt) to increase speed and reduce memory requirement, set CLFlt to double if you want increased precision NOTE: CLFlt = double not compatible with SSE_ENABLED, AVX_ENABLED or FMA_ENABLED */ /* * Make sure we define SIMD instruction flags in a stepwise manner. That is, if we have FMA, make sure we have AVX; * if we have AVX, make sure we have SSE. */ #if defined (FMA_ENABLED) # if !defined (AVX_ENABLED) # define AVX_ENABLED # endif #endif #if defined (AVX_ENABLED) # if !defined (SSE_ENABLED) # define SSE_ENABLED # endif #endif /* Likewise, if the user has asked for SIMD instructions to be disabled, do this * too in a stepwise manner */ #ifdef DISABLE_SSE #undef SSE_ENABLED #undef AVX_ENABLED #undef FMA_ENABLED #endif #ifdef DISABLE_AVX #undef AVX_ENABLED #undef FMA_ENABLED #endif #ifdef DISABLE_FMA #undef FMA_ENABLED #endif /* Define compiler for appropriate SIMD vector data alignment and free operations */ #if defined (SSE_ENABLED) # if defined (WIN_VERSION) # define MS_VCPP_SIMD # else # define GCC_SIMD # undef ICC_SIMD # endif #endif /* Include header for SSE code */ #if defined (SSE_ENABLED) # include #endif /* Include header for AVX code */ #if defined (AVX_ENABLED) # include #endif #if defined (FMA_ENABLED) # include #endif /* Define vector code constants */ #define VEC_NONE 0 #define VEC_SSE 1 #define VEC_AVX 2 #define VEC_FMA 3 /* For comparing floating points: two values are the same if the absolute difference is less then this value. */ #ifndef ETA #define ETA (1E-30) #endif /* TEMPSTRSIZE determines size of temporary sprintf buffer (for SafeSprintf) */ /* A patch was sent in by Allen Smith for SafeSprintf, but I could not get it compiled on SGI IRIX 6.5 (too old?) with _xpg5_vsnprintf undefined. The code below is a hack so SafeSprintf never has to reallocate memory. */ #ifdef __sgi #define TEMPSTRSIZE 1000 #else #define TEMPSTRSIZE 200 #endif /* NO_ERROR is defined in bayes.h (as 0) and also in WinError.h (as 0L) ERROR is defined in bayes.h (as 1) and also in WinGDI.h (as 0). we use the bayes.h value */ #undef NO_ERROR #undef ERROR #define NO_ERROR 0 #define ERROR 1 #define NO_ERROR_QUIT 2 #define ABORT 3 #define SKIP_COMMAND 4 #undef FALSE #undef TRUE #define FALSE 0 #define TRUE 1 #define NO 0 #define YES 1 #define UP 0 #define DOWN 1 #define UPPER 0 #define MIDDLE 1 #define LOWER 2 #define NONINTERACTIVE 0 #define INTERACTIVE 1 #define STANDARD_USER 1 #define DEVELOPER 3 #define DIFFERENT 0 #define SAME 1 #define CONSISTENT_WITH 2 #define LINETERM_UNIX 0 #define LINETERM_MAC 1 #define LINETERM_DOS 2 #define SCREENWIDTH 60 #define SCREENWIDTH2 61 #define AVGSTDDEV 0 #define MAXSTDDEV 1 #define NONE 0 #define DNA 1 #define RNA 2 #define PROTEIN 3 #define RESTRICTION 4 #define STANDARD 5 #define MIXED 6 #define CONTINUOUS 7 #define AAMODEL_POISSON 0 #define AAMODEL_JONES 1 #define AAMODEL_DAY 2 #define AAMODEL_MTREV 3 #define AAMODEL_MTMAM 4 #define AAMODEL_WAG 5 #define AAMODEL_RTREV 6 #define AAMODEL_CPREV 7 #define AAMODEL_VT 8 #define AAMODEL_BLOSUM 9 #define AAMODEL_LG 10 #define AAMODEL_EQ 11 #define AAMODEL_GTR 12 /* aa models with free parameters must be listed last */ #define NUCMODEL_4BY4 0 #define NUCMODEL_DOUBLET 1 #define NUCMODEL_CODON 2 #define NUCMODEL_AA 3 #define NST_MIXED -1 /* anything other than 1, 2, or 6 */ #define MISSING 1073741823 // 111111111111111111111111111111, 30 bits #define GAP 1073741824 // 1000000000000000000000000000000 #define UNORD 0 #define ORD 1 #define DOLLO 2 #define IRREV 3 #define IN_CMD 0 #define IN_FILE 1 #define NOTHING 0 #define COMMAND 1 #define PARAMETER 2 #define EQUALSIGN 3 #define COLON 4 #define SEMICOLON 5 #define COMMA 6 #define POUNDSIGN 7 #define QUESTIONMARK 8 #define DASH 9 #define LEFTPAR 10 #define RIGHTPAR 11 #define LEFTCOMMENT 12 #define RIGHTCOMMENT 13 #define ALPHA 14 #define NUMBER 15 #define RETURNSYMBOL 16 #define ASTERISK 17 #define BACKSLASH 18 #define FORWARDSLASH 19 #define EXCLAMATIONMARK 20 #define PERCENT 21 #define QUOTATIONMARK 22 #define WEIRD 23 #define UNKNOWN_TOKEN_TYPE 24 #define LEFTCURL 25 #define RIGHTCURL 26 #define DOLLAR 27 #define AMPERSAND 28 #define VERTICALBAR 29 #define MAX_Q_RATE 100.0f #define MIN_SHAPE_PARAM 0.00001f #define MAX_SHAPE_PARAM 100.0f #define MAX_SITE_RATE 10.0f #define MAX_RATE_CATS 20 #define MAX_RATE_CATS_SQUARED 400 #define BRLENS_MIN 0.00000001f // 1E-8f #define BRLENS_MAX 100.0f /* BRLENS_MIN must be bigger than TIME_MIN */ #define TIME_MIN 1.0E-11f #define TIME_MAX 100.0f #define RELBRLENS_MIN 0.00000001f // 1E-8f #define RELBRLENS_MAX 100.0f #define KAPPA_MIN 0.001f #define KAPPA_MAX 1000.0f #define RATE_MIN 0.000001f #define RATE_MAX 1000.0f #define AA_RATE_MIN 1.0E-15f #define AA_RATE_MAX 1.0E5f #define CPPRATEMULTIPLIER_MIN 0.001f #define CPPRATEMULTIPLIER_MAX 1000.0f #define SYMPI_MIN 0.000001f #define SYMPI_MAX 100.0f #define ALPHA_MIN 0.0001f #define ALPHA_MAX 10000.0f #define DIR_MIN 0.000001f #define PI_MIN 0.000001f #define OFFSETEXPLAMBDA_MIN 0.000001f #define OFFSETEXPLAMBDA_MAX 100000.0f #define TREEHEIGHT_MIN 0.00000001f #define TREEHEIGHT_MAX 1000.0f #define TREEAGE_MIN 0.00000001f #define TREEAGE_MAX 1000000.0f #define CPPLAMBDA_MIN 0.00001f #define CPPLAMBDA_MAX 100.0f #define TK02VAR_MIN 0.000001f #define TK02VAR_MAX 10000.0f #define WNVAR_MIN 0.000001f #define WNVAR_MAX 10000.0f #define INDRVAR_MIN 0.000001f #define INDRVAR_MAX 10000.0f #define OMEGA_MIN 0.001f #define OMEGA_MAX 1000.0f #define POS_MIN 1E-25f #define POS_INFINITY 1E25f #define NEG_INFINITY -1000000.0f /* SK: * Used when doing reversible jump over stationary and directional * model: root frequencies are not used when in stationary model. */ #define NOT_APPLICABLE -9999.0 #define CMD_STRING_LENGTH 100000 #define pos(i,j,n) ((i)*(n)+(j)) #define NUM_ALLOCS 100 #define ALLOC_MATRIX 0 #define ALLOC_CHARINFO 2 #define ALLOC_CHARSETS 3 #define ALLOC_TAXA 4 #define ALLOC_TMPSET 5 #define ALLOC_PARTITIONS 6 #define ALLOC_PARTITIONVARS 7 #define ALLOC_TAXASETS 8 #define ALLOC_CONSTRAINTS 9 #define ALLOC_USERTREE 10 #define ALLOC_SUMTPARAMS 11 #define ALLOC_TERMSTATE 12 #define ALLOC_ISPARTAMBIG 13 #define ALLOC_AVAILNODES 25 #define ALLOC_AVAILINDICES 26 #define ALLOC_CURLNL 28 #define ALLOC_CURLNPR 29 #define ALLOC_CHAINID 30 #define ALLOC_PARAMS 31 #define ALLOC_TREE 32 #define ALLOC_NODES 33 #define ALLOC_LOCTAXANAMES 34 #define ALLOC_COMPMATRIX 39 #define ALLOC_NUMSITESOFPAT 40 #define ALLOC_COMPCOLPOS 41 #define ALLOC_COMPCHARPOS 42 #define ALLOC_ORIGCHAR 43 #define ALLOC_PARAMVALUES 46 #define ALLOC_MCMCTREES 47 #define ALLOC_MOVES 48 #define ALLOC_PRELIKES 52 #define ALLOC_SITEJUMP 54 #define ALLOC_MARKOVTIS 55 #define ALLOC_RATEPROBS 56 #define ALLOC_STDTYPE 57 #define ALLOC_PACKEDTREES 58 #define ALLOC_SUMPSTRING 62 #define ALLOC_SUMPINFO 63 #define ALLOC_SWAPINFO 64 #define ALLOC_SYMPIINDEX 65 #define ALLOC_POSSELPROBS 66 #define ALLOC_PBF 68 #define ALLOC_LOCALTAXONCALIBRATION 69 #define ALLOC_SPR_PARSSETS 72 #define ALLOC_PFCOUNTERS 74 #define ALLOC_FILEPOINTERS 75 #define ALLOC_STATS 76 #define ALLOC_DIAGNTREE 77 #define ALLOC_USEDMOVES 82 #define ALLOC_MODEL 83 #define ALLOC_STDSTATEFREQS 84 #define ALLOC_PRINTPARAM 85 #define ALLOC_TREELIST 86 #define ALLOC_TFILEPOS 87 #define ALLOC_BEST 88 #define ALLOC_SPECIESPARTITIONS 89 #define ALLOC_SS 90 #define LINKED 0 #define UNLINKED 1 /*paramType*/ #define NUM_LINKED 36 #define P_TRATIO 0 #define P_REVMAT 1 #define P_OMEGA 2 #define P_PI 3 #define P_SHAPE 4 #define P_PINVAR 5 #define P_CORREL 6 #define P_SWITCH 7 #define P_RATEMULT 8 #define P_TOPOLOGY 9 #define P_BRLENS 10 #define P_SPECRATE 11 #define P_EXTRATE 12 #define P_FOSLRATE 13 #define P_POPSIZE 14 #define P_AAMODEL 15 #define P_BMCORR 16 #define P_BMSIGMA 17 #define P_GROWTH 18 #define P_CPPMULTDEV 19 #define P_CPPRATE 20 #define P_CPPEVENTS 21 #define P_TK02VAR 22 #define P_TK02BRANCHRATES 23 #define P_IGRVAR 24 #define P_IGRBRANCHRATES 25 #define P_ILNVAR 26 #define P_ILNBRANCHRATES 27 #define P_CLOCKRATE 28 #define P_SPECIESTREE 29 #define P_GENETREERATE 30 #define P_MIXTURE_RATES 31 #define P_MIXEDVAR 32 #define P_MIXEDBRCHRATES 33 #define P_WNVAR 34 #define P_WNBRANCHRATES 35 /* NOTE: If you add another parameter, change NUM_LINKED */ // #define CPPm 0 /* CPP rate multipliers */ // #define CPPi 1 /* CPP independent rates */ #define RCL_IGR 0 #define RCL_ILN 1 /* type of mixed relaxed clock model */ #define MAX_NUM_USERTREES 200 /* maximum number of user trees MrBayes will read */ #define MAX_CHAINS 256 /* maximum number of chains you can run actually only half of it because of m->lnLike[MAX_CHAINS] */ #define MAX_STD_STATES 24 // 0-9 A-N // #define PARAM_NAME_SIZE 400 typedef void * VoidPtr; typedef int (*CmdFxn)(void); typedef int (*ParmFxn)(char *, char *); /* typedef for a ln prior prob fxn */ typedef MrBFlt (*LnPriorProbFxn)(MrBFlt val, MrBFlt *priorParams); /* typedef for a ln prior prob ratio fxn */ typedef MrBFlt (*LnPriorRatioFxn)(MrBFlt newVal, MrBFlt oldVal, MrBFlt *priorParams); typedef struct { MrBFlt sum; /* sum of standard deviations */ MrBFlt max; /* maximum standard deviation */ MrBFlt numPartitions; MrBFlt numSamples; MrBFlt avgStdDev; MrBFlt **pair; } STATS; /* enumeration for calibration prior type */ enum CALPRIOR { unconstrained, fixed, uniform, offsetExponential, truncatedNormal, logNormal, offsetLogNormal, standardGamma, offsetGamma }; enum ConstraintType { PARTIAL, NEGATIVE, HARD }; enum CodingType { ALL = 0, NOABSENCESITES = 1, NOPRESENCESITES = 2, VARIABLE = 3, NOSINGLETONPRESENCE = 4, NOSINGLETONABSENCE = 8, NOSINGLETONS = 12, INFORMATIVE = 15 }; /* typedef for calibration */ typedef struct calibration { char name[100]; enum CALPRIOR prior; MrBFlt priorParams[3]; LnPriorProbFxn LnPriorProb; LnPriorRatioFxn LnPriorRatio; MrBFlt min; MrBFlt max; } Calibration; /* typedef for tree (topology) list element */ typedef struct element { struct element *next; int *order; } TreeListElement; /* typedef for list of trees (topologies) */ typedef struct { TreeListElement *first; TreeListElement *last; } TreeList; /* typedef for packed tree */ typedef struct { int *order; MrBFlt *brlens; } PackedTree; /* typedef for binary tree node */ /* NOTE: Any variable added here must also be copied in CopyTrees */ typedef struct node { char *label; /*!< name of node if tip */ struct node *left, *right, *anc; /*!< pointers to adjacent nodes */ int memoryIndex; /*!< memory index (do not change) */ int index; /*!< index to node (0 to numLocalTaxa for tips) */ int upDateCl; /*!< cond likes need update? */ int upDateTi; /*!< transition probs need update? */ int isLocked; /*!< is node locked? */ int lockID; /*!< id of lock */ int isDated; /*!< is node dated (calibrated)? */ int marked, x, y; /*!< scratch variables */ MrBFlt d; /*!< scratch variable */ BitsLong *partition; /*!< pointer to bitfield describing splits */ MrBFlt length; /*!< length of pending branch */ MrBFlt nodeDepth; /*!< node depth (height) */ MrBFlt age; /*!< age of node */ Calibration *calibration; /*!< pointer to calibration data */ } TreeNode; /* typedef for binary tree */ typedef struct { char name[100]; /*!< name of tree */ int memNodes; /*!< number of allocated nodes (do not exceed!) */ int nNodes; /*!< number of nodes in tree (including lower root in rooted trees) */ int nIntNodes; /*!< number of interior nodes in tree (excluding lower root in rooted trees) */ int isRooted; /*!< is tree rooted? */ int isClock; /*!< is tree clock? */ int isCalibrated; /*!< is tree calibrated? */ int nRelParts; /*!< number of relevant partitions */ int *relParts; /*!< pointer to relevant partitions */ int checkConstraints; /*!< does tree have constraints? */ int nConstraints; /*!< number of constraints */ int *constraints; /*!< pointer to constraints */ int nLocks; /*!< number of constrained (locked) nodes */ TreeNode **allDownPass; /*!< downpass array of all nodes */ TreeNode **intDownPass; /*!< downpass array of interior nodes (including upper but excluding lower root in rooted trees) */ #if defined (BEAGLE_V3_ENABLED) int levelPassEnabled; /*!< are we also doing a level-order traversal? */ TreeNode **intDownPassLevel; /*!< level order downpass array of interior nodes (including upper but excluding lower root in rooted trees) */ #endif TreeNode *root; /*!< pointer to root (lower root in rooted trees) */ TreeNode *nodes; /*!< array containing the nodes */ BitsLong *bitsets; /*!< pointer to bitsets describing splits */ BitsLong *flags; /*!< pointer to cond like flags */ int fromUserTree; /*!< YES is set for the trees whose branch lengths are set from user tree(as start tree or fix branch length prior), NO otherwise */ } Tree; /* typedef for node in polytomous tree */ typedef struct pNode { char label[100]; /*!< name of node if terminal */ struct pNode *left, *sib, *anc; /*!< pointers to adjacent nodes */ int x, y, mark; /*!< scratch variables */ int partitionIndex; /*!< partition index in sumt (scratch) */ int index; /*!< index of node (if < numLocalTaxa = local taxon index) */ int memoryIndex; /*!< immutable index of memory position */ int isLocked; /*!< is the node locked? */ int lockID; /*!< id of lock */ int isDated; /*!< is node dated? */ MrBFlt length; /*!< age of node */ MrBFlt depth; /*!< depth (height) of node */ MrBFlt age; /*!< age of node */ MrBFlt support, f; /*!< scratch variables */ BitsLong *partition; /*!< pointer to partition (split) bitset */ Calibration *calibration; /*!< pointer to dating of node */ } PolyNode; /* typedef for polytomous tree */ typedef struct { char name[100]; /*!< name of tree */ int memNodes; /*!< number of allocated nodes; do not exceed! */ int nNodes; /*!< number of nodes in tree */ int nIntNodes; /*!< number of interior nodes in tree */ PolyNode **allDownPass; /*!< downpass array over all nodes */ PolyNode **intDownPass; /*!< downpass array over interior nodes */ PolyNode *root; /*!< pointer to root (lower for rooted trees */ PolyNode *nodes; /*!< array holding the tree nodes */ BitsLong *bitsets; /*!< bits describing partitions (splits) */ int nBSets; /*!< number of effective branch length sets */ int nESets; /*!< number of breakpoint rate sets */ char **bSetName; /*!< names of effective branch length sets */ char **eSetName; /*!< names of breakpoint rate sets */ int **nEvents; /*!< number of branch events of bp rate set */ MrBFlt ***position; /*!< position of branch events */ MrBFlt ***rateMult; /*!< parameter of branch events */ MrBFlt **effectiveBrLen; /*!< effective branch lengths of ebl set */ int brlensDef; /*!< are brlens defined ? */ int isRooted; /*!< is tree rooted? */ int isClock; /*!< is tree clock? */ int isCalibrated; /*!< is tree calibrated? */ int isRelaxed; /*!< is tree relaxed? */ MrBFlt clockRate; /*!< clock rate */ int popSizeSet; /*!< does tree have a population size set? */ MrBFlt *popSize; /*!< the population size */ char *popSizeSetName; /*!< name of the population size set */ } PolyTree; /* struct for holding model parameter info for the mcmc run */ typedef struct param { int index; /* index to the parameter (0, 1, 2, ...) */ int paramType; /* the type of the parameter */ int paramId; /* unique ID for parameter x prior combination */ MrBFlt *values; /* main values of parameter */ MrBFlt *subValues; /* subvalues of parameter */ int *intValues; /* integer values (model index/growth fxn) */ int nValues; /* number of values */ int nSubValues; /* number of subvalues */ int nIntValues; /* number of intvalues */ MrBFlt min; /* minimum value of parameter */ MrBFlt max; /* maximum value of parameter */ int *relParts; /* pointer to relevant divisions */ int nRelParts; /* number of relevant divisions */ int upDate; /* update flag (for copying) */ struct param **subParams; /* pointers to subparams (for topology) */ int nSubParams; /* number of subparams */ Tree **tree; /* pointer to tree ptrs (for brlens & topology) */ int treeIndex; /* index to first tree in mcmcTree */ int hasBinaryStd; /* has binary standard chars */ int *sympiBsIndex; /* pointer to sympi bsIndex (std chars) */ int *sympinStates; /* pointer to sympi nStates (std chars) */ int *sympiCType; /* pointer to sympi cType (std chars) */ int nSympi; /* number of sympis */ int printParam; /* whether parameter should be printed */ int nPrintSubParams; /* number of subparams that should be printed */ char *paramHeader; /* a string holding header for param values */ char *name; /* string holding name of parameter */ char *paramTypeName; /* pointer to description of parameter type */ int checkConstraints; /* is tree parameter constrained? */ int fill; /* flags whether the parameter should be filled */ int nStdStateFreqs; /* number of std state frequencies */ MrBFlt *stdStateFreqs; /* pointer to std state frequencies */ int **nEvents; /* number of branch events for Cpp model */ /* nEvents[0..2*numCains][0..numNodes=2*numTaxa] */ MrBFlt ***position; /* event positions for Cpp relaxed clock model */ MrBFlt ***rateMult; /* rate multipliers for Cpp relaxed clock model */ int affectsLikelihood; /* does parameter directly influence likelihood? */ MrBFlt* priorParams; /* pointer to the prior parameters */ LnPriorProbFxn LnPriorProb; /* ln prior prob function */ LnPriorRatioFxn LnPriorRatio; /* ln prior prob ratio function */ } Param; /* parameter ID values */ /* identifies unique model parameter x prior combinations */ #define TRATIO_DIR 1 #define TRATIO_FIX 2 #define REVMAT_DIR 3 #define REVMAT_FIX 4 #define OMEGA_DIR 5 #define OMEGA_FIX 6 #define SYMPI_UNI 7 #define SYMPI_UNI_MS 8 #define SYMPI_EXP 9 #define SYMPI_EXP_MS 10 #define SYMPI_FIX 11 #define SYMPI_FIX_MS 12 #define SYMPI_EQUAL 13 #define PI_DIR 14 #define PI_USER 15 #define PI_EMPIRICAL 16 #define PI_EQUAL 17 #define PI_FIXED 18 #define SHAPE_UNI 19 #define SHAPE_EXP 20 #define SHAPE_FIX 21 #define PINVAR_UNI 22 #define PINVAR_FIX 23 #define CORREL_UNI 24 #define CORREL_FIX 25 #define SWITCH_UNI 26 #define SWITCH_EXP 27 #define SWITCH_FIX 28 #define RATEMULT_DIR 29 #define RATEMULT_FIX 30 #define TOPOLOGY_NCL_UNIFORM 31 #define TOPOLOGY_NCL_CONSTRAINED 32 #define TOPOLOGY_NCL_FIXED 33 #define TOPOLOGY_NCL_UNIFORM_HOMO 34 #define TOPOLOGY_NCL_UNIFORM_HETERO 35 #define TOPOLOGY_NCL_CONSTRAINED_HOMO 36 #define TOPOLOGY_NCL_CONSTRAINED_HETERO 37 #define TOPOLOGY_NCL_FIXED_HOMO 38 #define TOPOLOGY_NCL_FIXED_HETERO 39 #define TOPOLOGY_CL_UNIFORM 40 #define TOPOLOGY_CL_CONSTRAINED 41 #define TOPOLOGY_CL_FIXED 42 #define TOPOLOGY_CCL_UNIFORM 43 #define TOPOLOGY_CCL_CONSTRAINED 44 #define TOPOLOGY_CCL_FIXED 45 #define TOPOLOGY_PARSIMONY_UNIFORM 46 #define TOPOLOGY_PARSIMONY_CONSTRAINED 47 #define TOPOLOGY_PARSIMONY_FIXED 48 #define BRLENS_UNI 49 #define BRLENS_EXP 50 #define BRLENS_GamDir 51 #define BRLENS_iGmDir 52 #define BRLENS_twoExp 53 #define BRLENS_FIXED 54 #define BRLENS_CLOCK_UNI 55 #define BRLENS_CLOCK_COAL 56 #define BRLENS_CLOCK_BD 57 #define BRLENS_CLOCK_FIXED 58 #define BRLENS_CLOCK_SPCOAL 59 #define BRLENS_CLOCK_FOSSIL 60 #define BRLENS_PARSIMONY 61 #define SPECRATE_UNI 62 #define SPECRATE_EXP 63 #define SPECRATE_FIX 64 #define EXTRATE_BETA 65 #define EXTRATE_FIX 66 #define FOSLRATE_BETA 67 #define FOSLRATE_FIX 68 #define POPSIZE_UNI 69 #define POPSIZE_GAMMA 70 #define POPSIZE_FIX 71 #define POPSIZE_NORMAL 72 #define POPSIZE_LOGNORMAL 73 #define AAMODEL_FIX 74 #define AAMODEL_MIX 75 #define GROWTH_UNI 76 #define GROWTH_EXP 77 #define GROWTH_FIX 78 #define GROWTH_NORMAL 79 #define OMEGA_BUD 80 #define OMEGA_BUF 81 #define OMEGA_BED 82 #define OMEGA_BEF 83 #define OMEGA_BFD 84 #define OMEGA_BFF 85 #define OMEGA_FUD 86 #define OMEGA_FUF 87 #define OMEGA_FED 88 #define OMEGA_FEF 89 #define OMEGA_FFD 90 #define OMEGA_FFF 91 #define OMEGA_ED 92 #define OMEGA_EF 93 #define OMEGA_FD 94 #define OMEGA_FF 95 #define OMEGA_10UUB 96 #define OMEGA_10UUF 97 #define OMEGA_10UEB 98 #define OMEGA_10UEF 99 #define OMEGA_10UFB 100 #define OMEGA_10UFF 101 #define OMEGA_10EUB 102 #define OMEGA_10EUF 103 #define OMEGA_10EEB 104 #define OMEGA_10EEF 105 #define OMEGA_10EFB 106 #define OMEGA_10EFF 107 #define OMEGA_10FUB 108 #define OMEGA_10FUF 109 #define OMEGA_10FEB 110 #define OMEGA_10FEF 111 #define OMEGA_10FFB 112 #define OMEGA_10FFF 113 #define TOPOLOGY_RCL_UNIFORM 114 #define TOPOLOGY_RCL_CONSTRAINED 115 #define TOPOLOGY_RCL_FIXED 116 #define TOPOLOGY_RCCL_UNIFORM 117 #define TOPOLOGY_RCCL_CONSTRAINED 118 #define TOPOLOGY_RCCL_FIXED 119 #define TOPOLOGY_SPECIESTREE 120 #define TOPOLOGY_FIXED 121 #define CPPRATE_FIX 122 #define CPPRATE_EXP 123 #define CPPMULTDEV_FIX 124 #define CPPEVENTS 125 #define TK02VAR_FIX 126 #define TK02VAR_EXP 127 #define TK02VAR_UNI 128 #define TK02BRANCHRATES 129 #define WNVAR_FIX 130 #define WNVAR_EXP 131 #define WNVAR_UNI 132 #define WNBRANCHRATES 133 #define IGRVAR_FIX 134 #define IGRVAR_EXP 135 #define IGRVAR_UNI 136 #define IGRBRANCHRATES 137 #define ILNVAR_FIX 138 #define ILNVAR_EXP 139 #define ILNVAR_UNI 140 #define ILNBRANCHRATES 141 #define MIXEDVAR_FIX 142 #define MIXEDVAR_EXP 143 #define MIXEDVAR_UNI 144 #define MIXEDBRCHRATES 145 #define CLOCKRATE_FIX 146 #define CLOCKRATE_NORMAL 147 #define CLOCKRATE_LOGNORMAL 148 #define CLOCKRATE_GAMMA 149 #define CLOCKRATE_EXP 150 #define SPECIESTREE_UNIFORM 151 #define GENETREERATEMULT_DIR 152 #define GENETREERATEMULT_FIX 153 #define REVMAT_MIX 154 #define MIXTURE_RATES 155 #define DIRPI_DIRxDIR 156 #define DIRPI_DIRxFIXED 157 #define DIRPI_FIXEDxDIR 158 #define DIRPI_FIXEDxFIXED 159 #define DIRPI_MIX 160 #define TOPOLOGY_RNCL_UNIFORM 161 #define TOPOLOGY_RNCL_CONSTRAINED 162 #define TOPOLOGY_RNCL_FIXED 163 #define BMCORR_UNI 164 #define BMCORR_FIX 165 #define BMSIGMA_UNI 166 #define BMSIGMA_GAMMA 167 #define BMSIGMA_FIX 168 #define EXTRATE_EXP 169 #define FOSLRATE_EXP 170 #if defined (BEAGLE_ENABLED) #define MB_BEAGLE_SCALE_ALWAYS 0 #define MB_BEAGLE_SCALE_DYNAMIC 1 #if defined (_DEBUG) #define MB_PRINT_DYNAMIC_RESCALE_FAIL_STAT #endif #endif /* typedef for a MoveFxn */ typedef int (MoveFxn)(Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp); /* typedef for an ApplicFxn */ typedef int (ApplicFxn)(Param *param); /* typedef for an AutotuneFxn */ typedef void (AutotuneFxn)(MrBFlt acceptanceRate, MrBFlt targetRate, int batch, MrBFlt *tuningParameter, MrBFlt minTuning, MrBFlt maxTuning); /* struct holding info on each move type that the program handles */ typedef struct { MoveFxn *moveFxn; /* pointer to the move function */ ApplicFxn *isApplicable; /* pointer to function determining whether move is applicable to a parameter */ int nApplicable; /* number of relevant params */ int applicableTo[40]; /* pointer to ID of relevant params */ char *name; /* name of the move type */ char *shortName; /* abbreviated name of the move type */ char *paramName; /* name of subparameter if complex parameter */ int subParams; /* are we changing subparams (brlens of topol.) */ char *tuningName[5]; /* name of tuning params */ char *shortTuningName[5];/* short name of tuning params */ MrBFlt relProposalProb; /* default relative proposal probability */ int numTuningParams; /* number of tuning parameters */ MrBFlt tuningParam[5]; /* default tuning parameters for the proposal */ MrBFlt minimum[5]; /* minimum values for tuning params */ MrBFlt maximum[5]; /* maximum values for tuning params */ int parsimonyBased; /* this move is based on parsimony (YES/NO) */ int level; /* user level of this move */ AutotuneFxn *Autotune; /* pointer to the autotuning function */ MrBFlt targetRate; /* default target acceptance rate for autotuning */ } MoveType; /* max number of move types */ #define NUM_MOVE_TYPES 101 /* struct holding info on each move */ /* Note: This allows several different moves to affect the same parameter */ /* It also allows the same move to affect different parameters as before */ /* This is also a good place to keep the proposal probs */ typedef struct { char *name; /* name of the move */ MoveType *moveType; /* pointer to the move type */ MoveFxn *moveFxn; /* pointer to the move function */ Param *parm; /* ptr to parameter the move applies to */ MrBFlt *relProposalProb; /* the relative proposal probability */ MrBFlt *cumProposalProb; /* the cumulative proposal probability */ int *nAccepted; /* number of accepted moves */ int *nTried; /* number of tried moves */ int *nBatches; /* counter for autotuning rounds */ int *nTotAccepted; /* total number of accepted moves */ int *nTotTried; /* total number of tried moves */ MrBFlt *targetRate; /* target acceptance rate for autotuning */ MrBFlt *lastAcceptanceRate;/* acceptance rate in last complete batch */ MrBFlt **tuningParam; /* tuning parameters for the move */ } MCMCMove; typedef int (*LikeDownFxn)(TreeNode *, int, int); typedef int (*LikeRootFxn)(TreeNode *, int, int); typedef int (*LikeScalerFxn)(TreeNode *, int, int); typedef int (*LikeFxn)(TreeNode *, int, int, MrBFlt *, int); typedef int (*TiProbFxn)(TreeNode *, int, int); typedef int (*LikeUpFxn)(TreeNode *, int, int); typedef int (*PrintAncStFxn)(TreeNode *, int, int); typedef int (*StateCodeFxn) (int); typedef int (*PrintSiteRateFxn) (TreeNode *, int, int); typedef int (*PosSelProbsFxn) (TreeNode *, int, int); typedef int (*SiteOmegasFxn) (TreeNode *, int, int); typedef struct cmdtyp { int cmdNumber; char *string; int specialCmd; CmdFxn cmdFxnPtr; short numParms; short parmList[50]; int expect; char *cmdDescription; int cmdUse; int hiding; } CmdType; typedef struct parm { char *string; /* parameter name */ char *valueList; /* list of values that could be input */ ParmFxn fp; /* function pointer */ } ParmInfo, *ParmInfoPtr; typedef struct model { int dataType; /* data type for partition */ int nStates; /* number of states for this type of data */ int codon[64]; /* gives protein ID for each codon */ int codonNucs[64][3]; /* gives triplet for each codon */ int codonAAs[64]; /* gives protein ID for implemented code */ char nucModel[100]; /* nucleotide model used */ char nst[100]; /* number of substitution types */ char statefreqModel[100];/* stationary or directional Markov model */ //SK char parsModel[100]; /* use the (so-called) parsimony model */ char geneticCode[100]; /* genetic code used */ int coding; /* type of patterns encoded */ char codingString[100]; /* string describing type of patterns encoded */ char ploidy[100]; /* ploidy level */ char omegaVar[100]; /* type of omega variation model */ char ratesModel[100]; /* rates across sites model */ int numGammaCats; /* number of categories for gamma approximation */ int numLnormCats; /* number of categories for lnorm approximation */ int numMixtCats; /* number of components of rate mixture */ char useGibbs[100]; /* flags whether Gibbs sampling of discrete gamma is used */ int gibbsFreq; /* frequency of Gibbs resampling of discrete gamma */ int numBetaCats; /* number of categories for beta approximation */ int numM10GammaCats; /* number of cats for gamma approx (M10 model) */ int numM10BetaCats; /* number of cats for beta approx (M10 model) */ char covarionModel[100];/* use covarion model? (yes/no) */ char augmentData[100]; /* should data be augmented */ char tRatioPr[100]; /* prior for ti/tv rate ratio */ MrBFlt tRatioFix; MrBFlt tRatioDir[2]; char revMatPr[100]; /* prior for GTR model */ MrBFlt revMatFix[6]; MrBFlt revMatDir[6]; MrBFlt revMatSymDir; /* prior for mixed GTR subspace model */ char aaModelPr[100]; /* prior for amino acid model */ char aaModel[100]; MrBFlt aaModelPrProbs[10]; char aaRevMatPr[100]; /* prior for aa GTR model */ MrBFlt aaRevMatFix[190]; MrBFlt aaRevMatDir[190]; char omegaPr[100]; /* prior for omega */ MrBFlt omegaFix; MrBFlt omegaDir[2]; char ny98omega1pr[100]; /* prior for class 1 omega (Ny98 model) */ MrBFlt ny98omega1Fixed; MrBFlt ny98omega1Beta[2]; char ny98omega3pr[100]; /* prior for class 3 omega (Ny98 model) */ MrBFlt ny98omega3Fixed; MrBFlt ny98omega3Uni[2]; MrBFlt ny98omega3Exp; char m3omegapr[100]; /* prior for all three omegas (M3 model) */ MrBFlt m3omegaFixed[3]; char m10betapr[100]; /* prior for omega variation (M10 model) */ char m10gammapr[100]; MrBFlt m10betaExp; MrBFlt m10betaUni[2]; MrBFlt m10betaFix[2]; MrBFlt m10gammaExp; MrBFlt m10gammaUni[2]; MrBFlt m10gammaFix[2]; char codonCatFreqPr[100]; /* prior for selection cat frequencies */ MrBFlt codonCatFreqFix[3]; MrBFlt codonCatDir[3]; char stateFreqPr[100]; /* prior for character state frequencies */ MrBFlt stateFreqsFix[200]; MrBFlt stateFreqsDir[200]; char stateFreqsFixType[100]; int numDirParams; char rootFreqPr[100]; /* prior for root state frequencies (dir model)*/ //SK MrBFlt rootFreqsFix[200]; MrBFlt rootFreqsDir[200]; int numDirParamsRoot; char shapePr[100]; /* prior for gamma/lnorm shape parameter */ MrBFlt shapeFix; MrBFlt shapeUni[2]; MrBFlt shapeExp; char pInvarPr[100]; /* prior for proportion of invariable sites */ MrBFlt pInvarFix; MrBFlt pInvarUni[2]; char adGammaCorPr[100]; /* prior for correlation param of adGamma model */ MrBFlt adgCorrFix; MrBFlt adgCorrUni[2]; char covSwitchPr[100]; /* prior for switching rates of covarion model */ MrBFlt covswitchFix[2]; MrBFlt covswitchUni[2]; MrBFlt covswitchExp; char symPiPr[100]; /* prior for pi when unidentifiable states used */ MrBFlt symBetaFix; MrBFlt symBetaUni[2]; MrBFlt symBetaExp; char ratePr[100]; /* prior on rate for a partition */ MrBFlt ratePrDir; char generatePr[100]; /* prior on rate for a gene (one or more partitions) */ MrBFlt generatePrDir; char brownCorrPr[100]; /* prior for correlation of Brownian model */ MrBFlt brownCorrFix; MrBFlt brownCorrUni[2]; char brownScalePr[100]; /* prior for scale (sigma) of Brownian model */ MrBFlt brownScaleFix; MrBFlt brownScaleUni[2]; MrBFlt brownScaleGamma[2]; char topologyPr[100]; /* prior for tree topology */ int topologyFix; /* user tree index for fixed topology */ int *activeConstraints; /* which constraints are active? */ int numActiveConstraints; int numActiveLocks; char brlensPr[100]; /* prior on branch lengths */ int brlensFix; /* user tree index for fixed brlens */ MrBFlt brlensUni[2]; MrBFlt brlensExp; MrBFlt brlens2Exp[2]; MrBFlt brlensDir[4]; MrBFlt brlensGamma[2]; char speciesTreeBrlensPr[100]; /* prior on branch lengths of species tree */ char unconstrainedPr[100]; /* prior on branch lengths if unconstrained */ char clockPr[100]; /* prior on branch if clock enforced */ char clockVarPr[100]; /* prior on clock rate variation (strict, cpp, tk02, wn, igr, iln, ...) */ char nodeAgePr[100]; /* prior on node depths (unconstrained, constraints) */ char speciationPr[100]; /* prior on speciation rate (net diversification) */ MrBFlt speciationFix; MrBFlt speciationUni[2]; MrBFlt speciationExp; char extinctionPr[100]; /* prior on relative extinction rate (turnover) */ MrBFlt extinctionFix; MrBFlt extinctionBeta[2]; MrBFlt extinctionExp; char fossilizationPr[100]; /* prior on fossilization rate (sampling proportion) */ MrBFlt fossilizationFix; MrBFlt fossilizationBeta[2]; MrBFlt fossilizationExp; char sampleStrat[100]; /* taxon sampling strategy (for b-d process) */ int birthRateShiftNum; /* number of birth rate shifts */ MrBFlt birthRateShiftTime[100]; /* birth rate shifting times */ int deathRateShiftNum; /* number of death rate shifts */ MrBFlt deathRateShiftTime[100]; /* death rate shifting times */ int fossilSamplingNum; /* number of fossil sampling rate shiftings / slice sampling events */ MrBFlt fossilSamplingTime[100]; /* fossil sampling rate shifting times */ // MrBFlt *fossilSamplingProb; /* fossil slice sampling probs (rho_i) */ MrBFlt sampleProb; /* extant taxon sampling fraction (rho) */ Calibration treeAgePr; /* prior on tree age for uniform clock trees */ char clockRatePr[100]; /* prior on base substitution rate of tree for clock trees */ MrBFlt clockRateNormal[2]; MrBFlt clockRateLognormal[2]; MrBFlt clockRateGamma[2]; MrBFlt clockRateExp; MrBFlt clockRateFix; char popSizePr[100]; /* prior on population size */ MrBFlt popSizeFix; MrBFlt popSizeUni[2]; MrBFlt popSizeLognormal[2]; MrBFlt popSizeGamma[2]; MrBFlt popSizeNormal[2]; char popVarPr[100]; /* prior on pop. size variation across tree */ char growthPr[100]; /* prior on coalescence growth rate */ MrBFlt growthFix; MrBFlt growthUni[2]; MrBFlt growthExp; MrBFlt growthNorm[2]; char cppRatePr[100]; /* prior on CPP rate */ MrBFlt cppRateFix; MrBFlt cppRateExp; char cppMultDevPr[100]; /* prior on CPP rate multiplier lognormal variance */ MrBFlt cppMultDevFix; char tk02varPr[100]; /* prior on TK02 lognormal rate variance */ MrBFlt tk02varFix; MrBFlt tk02varUni[2]; MrBFlt tk02varExp; char wnvarPr[100]; /* prior on WN gamma distribution variance */ MrBFlt wnvarFix; MrBFlt wnvarUni[2]; MrBFlt wnvarExp; char igrvarPr[100]; /* prior on IGR gamma distribution variance */ MrBFlt igrvarFix; MrBFlt igrvarUni[2]; MrBFlt igrvarExp; char ilnvarPr[100]; /* prior on independent lognormal rate variance */ MrBFlt ilnvarFix; MrBFlt ilnvarUni[2]; MrBFlt ilnvarExp; char mixedvarPr[100]; /* prior on mixed relaxed clock rate variance */ MrBFlt mixedvarFix; MrBFlt mixedvarUni[2]; MrBFlt mixedvarExp; char tratioFormat[30]; /* format used to report tratio */ char revmatFormat[30]; /* format used to report revmat */ char ratemultFormat[30]; /* format used to report ratemult */ char treeFormat[30]; /* format used to report trees/topologies */ char inferAncStates[5]; /* should ancestral states be inferred (Yes/No)? */ char inferSiteOmegas[5]; /* should site omega vals be inferred (Yes/No)? */ char inferSiteRates[5]; /* should site rates be inferred (Yes/No)? */ char inferPosSel[5]; /* should site selection be inferred (Yes/No)? */ } Model, ModelParams; typedef struct chain { long long numGen; /* number of MCMC cycles */ int sampleFreq; /* frequency to sample chain */ int printFreq; /* frequency to print chain */ int swapFreq; /* frequency to attempt swap of states */ int numRuns; /* number of runs */ int numChains; /* number of chains */ int isSS; /* do we do Steppingstone Sampling */ int startFromPriorSS; /* If Yes SS is moving from Prior to Posterior */ int numStepsSS; /* Number of steps in SS */ int burninSS; /* Fixed burnin for SS */ MrBFlt alphaSS; /* Beta values are distributed according to quantiles of Beta(alphaSS,1.0) distribution */ int backupCheckSS; /* Frequency of checkpoints backup */ MrBFlt chainTemp; /* chain temperature */ int userDefinedTemps; /* should we use the users temperatures? */ MrBFlt userTemps[MAX_CHAINS]; /* user-defined chain temperatures */ char chainFileName[100]; /* chain file name for output */ int chainBurnIn; /* chain burn in length */ int numStartPerts; /* number of perturbations to starting tree */ char startTree[100]; /* starting tree for chain (current/random) */ char startParams[100]; /* starting values for chain (current/reset) */ int saveBrlens; /* should branch lengths be saved */ MrBFlt weightScheme[3]; /* percent chars to increase/decrease in weight */ int calcPbf; /* should we calculate the pseudo Bayes factor */ int pbfInitBurnin; /* initial burnin when calculating pseudo BF */ int pbfSampleFreq; /* sample frequency for pseudo BF */ int pbfSampleTime; /* how many cycles to calculate site prob. */ int pbfSampleBurnin; /* burnin period for each site for pseudo BF */ int swapAdjacentOnly; /* whether we only swap adjacent temperatures */ int redirect; /* should output go to stdout */ int allChains; /* should stats be output for all chains? */ int numSwaps; /* number of swaps to try each time */ int mcmcDiagn; /* should mcmc diagnostics be output? */ int diagnFreq; /* mcmc diagnostics frequency */ int diagnStat; /* statistic to use for mcmc diagnostics */ int relativeBurnin; /* should a relative burnin be used ? */ MrBFlt burninFraction; /* the sample fraction to discard as burnin */ int allComps; /* top conv diagnosis for all pairs? */ MrBFlt minPartFreq; /* minimum partition frequency for conv diagn */ MrBFlt stopVal; /* top conv diagn value to reach before stopping */ int stopRule; /* use stop rule? */ STATS *stat; /* ptr to structs with mcmc diagnostics info */ Tree *dtree; /* pointing to tree used for conv diagnostics */ TreeList *treeList; /* vector of tree lists for saving trees */ int saveTrees; /* save tree samples for later removal? */ long long stopTreeGen; /* generation after which no trees need be saved */ fpos_t *tFilePos; /* position for reading trees for removal */ int printMax; /* maximum number of chains to print */ int printAll; /* whether to print all or only cold chains */ int checkPoint; /* should we use check-pointing? */ int checkFreq; /* check-pointing frequency */ int runWithData; /* should we run with or without data? */ int orderTaxa; /* order taxa before printing tree to file? */ int append; /* order taxa before printing tree to file? */ int autotune; /* autotune tuning parameters of proposals ? */ int tuneFreq; /* autotuning frequency */ } Chain; typedef struct modelinfo { /* General model information */ int dataType; /* data type for partition */ int nucModelId; /* structure of nucleotide model */ int nst; /* # substitution types */ int aaModelId; /* amino acid model type */ int parsModelId; /* is parsimony model used YES/NO */ /* Specific model information */ int numRateCats; /* number of rate cats (1 if inapplic.) */ int numBetaCats; /* number of beta cats (1 if inapplic.) */ int numOmegaCats; /* number of omega cats (1 if inapplic.) */ int numTiCats; /* number of cats needing different tis */ int numModelStates; /* number of states including hidden ones */ int numStates; /* number of observable discrete states */ /* Model parameter pointers */ Param *tRatio; /* ptr to tRatio used in model */ Param *revMat; /* ptr to revMat used in model */ Param *omega; /* ptr to omega used in model */ Param *stateFreq; /* ptr to statFreq used in model */ /* SK: same pointer (stateFreq) also * used for the stationary-root freq vector */ Param *mixtureRates; /* ptr to site rate mixture used in model */ Param *shape; /* ptr to shape used in model */ Param *pInvar; /* ptr to pInvar used in model */ Param *correlation; /* ptr to correlation used in model */ Param *switchRates; /* ptr to switchRates (off<->on) */ Param *rateMult; /* ptr to parition rateMult used in model */ Param *geneTreeRateMult; /* ptr to gene tree rateMult used in model */ Param *speciationRates; /* ptr to speciationRates used in model */ Param *extinctionRates; /* ptr to extinctionRates used in model */ Param *fossilizationRates; /* ptr to fossilizationRates */ Param *popSize; /* ptr to population size used in model */ Param *growthRate; /* ptr to growth rate used in model */ Param *topology; /* ptr to topology used in model */ Param *brlens; /* ptr to brlens (and tree) used in model */ Param *speciesTree; /* ptr to species tree used in model */ Param *aaModel; /* ptr to amino acid matrix used */ Param *cppMultDev; /* ptr to cpp ratemult lognormal variance */ Param *cppRate; /* ptr to CPP rate used in model */ Param *cppEvents; /* ptr to CPP events */ Param *tk02var; /* ptr to variance for TK02 relaxed clock */ Param *tk02BranchRates; /* ptr to branch rates for TK02 relaxed clock */ Param *wnvar; /* ptr to gamma var for WN relaxed clock */ Param *wnBranchRates; /* ptr to branch rates for WN relaxed clock */ Param *igrvar; /* ptr to gamma var for IGR relaxed clock */ Param *igrBranchRates; /* ptr to branch rates for IGR relaxed clock */ Param *ilnvar; /* ptr to variance for ILN relaxed clock */ Param *ilnBranchRates; /* ptr to branch rates for ILN relaxed clock */ Param *mixedvar; /* ptr to var for mixed relaxed clock */ Param *mixedBrchRates; /* ptr to branch rates for mixed relaxed clock */ Param *clockRate; /* ptr to clock rate parameter */ Param *brownCorr; /* ptr to Brownian motion correlation parameter */ Param *brownSigma; /* ptr to Brownian motion scale parameter */ /* Information about characters and transformations */ int numChars; /* number of compressed characters */ int numUncompressedChars; /* number of uncompressed characters */ int numDummyChars; /* number of dummy characters */ int compMatrixStart; /* start column in compressed matrix */ int compMatrixStop; /* stop column in compressed matrix */ int compCharStart; /* start char among compressed chars */ int compCharStop; /* stop char among compressed chars */ int parsMatrixStart; /* start column in parsimony matrix */ int parsMatrixStop; /* stop column in parsimony matrix */ int nParsIntsPerSite; /* # parsimony ints per character */ int nCharsPerSite; /* number chars per site (eg 3 for codon) */ int rateProbStart; /* start of rate probs (for adgamma) */ /* Variables for eigen decompositions */ int cijkLength; /* stores length of cijk vector */ int nCijkParts; /* stores number of cijk partitions (1 except for omega/covarion models) */ int upDateCijk; /* whether cijk vector needs to be updated */ /* Variables for standard model */ int *tiIndex; /* index to trans probs for each compressed char*/ int *bsIndex; /* index to stat freqs for each compressed char */ int *nStates; /* # states of each compressed char */ int *cType; /* whether char is ord, unord or irrev */ int *weight; /* prior weight of each compressed char */ int isTiNeeded[MAX_STD_STATES*3]; /* marks whether a trans prob matrix is needed */ /* Gibbs sampling of gamma site rate parameters */ CLFlt ***catLike; /* likelihood for Gibbs sampling of gamma */ CLFlt ***catLnScaler; /* scaler for Gibbs sampling of gamma */ int gibbsGamma; /* flags whether Gibbs sampling of discrete gamma is used */ int gibbsFreq; /* frequency of Gibbs resampling of discrete gamma */ /* Variables for parsimony sets and parsimony calculations */ MrBFlt parsTreeLength[MAX_CHAINS*2]; /* parsimony tree lengths for chains */ BitsLong **parsSets; /* parsimony sets */ int numParsSets; /* number of parsimony sets */ CLFlt *parsNodeLens; /* parsimony node lengths */ int numParsNodeLens; /* number of parsimony node lengths */ /* Miscellaneous parameters */ int mark; /* scratch parameter */ int parsimonyBasedMove; /* is parsimony-based move used (YES/NO) */ /* Variables for conditional likelihoods */ int upDateCl; /* flags whether update of cond likes needed */ int upDateAll; /* flags whether update of entire tree is needed*/ int *isPartAmbig; /* is tip partially ambiguous? */ int **termState; /* index arrays for terminal branch shortcuts */ CLFlt *invCondLikes; /* space for the invariable cond likes */ CLFlt **condLikes; /* space for the cond likes */ CLFlt **tiProbs; /* space for the ti probs */ CLFlt **scalers; /* space for the node and site scalers */ CLFlt **clP; /* handy pointers to cond likes for ti cats */ #if defined (SSE_ENABLED) __m128 **clP_SSE; /* handy pointers to cond likes, SSE version */ int numVecChars; /* number of compact SIMD vectors */ int numFloatsPerVec; /* number of floats per SIMD vector */ CLFlt *lnL_Vec; /* temp storage for log site likes */ CLFlt *lnLI_Vec; /* temp storage for log site invariable likes */ #if defined (AVX_ENABLED) __m256 **clP_AVX; /* handy pointers to cond likes, AVX version */ #endif #endif MrBFlt **cijks; /* space for cijks */ int **condLikeIndex; /* index to cond like space for nodes & chains */ int *condLikeScratchIndex; /* index to scratch space for node cond likes */ int **unscaledNodes; /* # unscaled nodes at a node */ int *unscaledNodesScratch; /* scratch # unscaled nodes at a node */ int **nodeScalerIndex; /* index to scaler space for nodes & chains */ int *nodeScalerScratchIndex; /* index to scratch space for node scalers */ int *siteScalerIndex; /* index to site scaler space for chains */ int siteScalerScratchIndex; /* index to scratch space for site scalers */ int **tiProbsIndex; /* index to ti probs for branches & chains */ int *tiProbsScratchIndex; /* index to scratch space for branch ti probs */ int *cijkIndex; /* index to cijks for chains */ int cijkScratchIndex; /* index to scratch space for cijks */ int numCondLikes; /* number of cond like arrays */ int numScalers; /* number of scaler arrays */ int numTiProbs; /* number of ti prob arrays */ int condLikeLength; /* length of cond like array (incl. ti cats) */ int tiProbLength; /* length of ti prob array */ MrBFlt lnLike[MAX_CHAINS]; /* log like for chain */ CLFlt *ancStateCondLikes; /* ancestral state cond like array */ /* Variables for continuous traits */ CLFlt **ancStates; /* space for the (ancestral) trait states */ CLFlt **bmVars; /* space for the (transformed) branch lengths */ /* Likelihood function pointers */ LikeDownFxn CondLikeDown; /* function for calculating partials */ LikeRootFxn CondLikeRoot; /* function for calculating partials at root */ LikeScalerFxn CondLikeScaler; /* function for scaling partials */ LikeFxn Likelihood; /* function for getting cond likes for tree */ TiProbFxn TiProbs; /* function for calculating transition probs */ LikeUpFxn CondLikeUp; /* final-pass calculation of cond likes */ PrintAncStFxn PrintAncStates; /* function for sampling ancestral states */ StateCodeFxn StateCode; /* function for getting states from codes */ PrintSiteRateFxn PrintSiteRates; /* function for sampling site rates */ PosSelProbsFxn PosSelProbs; /* function for sampling pos. selection probs */ SiteOmegasFxn SiteOmegas; /* function for sampling site omega values */ /* Report variables */ int printAncStates; /* should ancestral states be printed (YES/NO) */ int printSiteRates; /* should site rates be printed (YES/NO) */ int printPosSel; /* should selection be printed (YES/NO) */ int printSiteOmegas; /* should site omegas be printed (YES/NO) */ /* likelihood calculator flags and variables */ int useBeagle; /* use Beagle for this partition? */ int useBeagleMultiPartitions; /* use one Beagle instance for all partitions? */ int useVec; /* use SSE for this partition? */ int* rescaleFreq; /* rescale frequency for each chain */ #if defined (BEAGLE_ENABLED) /* Beagle variables */ int useBeagleResource; /* try to use this BEAGLE resource number */ MrBFlt* branchLengths; /* array of branch lengths for Beagle */ MrBFlt* inRates; /* array of category rates for Beagle */ int* tiProbIndices; /* array of trans prob indices for Beagle */ MrBFlt* logLikelihoods; /* array of log likelihoods from Beagle */ int beagleInstance; /* beagle instance for division */ MrBFlt* inWeights; /* array of weights for Beagle root likelihood */ int* bufferIndices; /* array of partial indices for root likelihood */ int* eigenIndices; /* array of eigen indices for root likelihood */ int* childBufferIndices; /* array of child partial indices (unrooted) */ int* childTiProbIndices; /* array of child ti prob indices (unrooted) */ int* cumulativeScaleIndices; /* array of cumulative scale indices */ int rescaleBeagleAll; /* set to rescale all nodes */ int rescaleFreqOld; /* holds rescale frequency of current state */ int rescaleFreqNew; /* holds temporary new rescale frequency */ int recalculateScalers; /* should we recalculate scalers for current state YES/NO */ int* successCount; /* count of successful computations since last reset of scalers */ int** isScalerNode; /* for each node and chain set to YES if scaled node */ int* isScalerNodeScratch; /* scratch space to hold isScalerNode of proposed state*/ long* beagleComputeCount; /* count of number of calls to likelihood */ int divisionIndex; /* division index number */ BeagleOperation* operations; /* array of operations to be sent to Beagle */ int opCount; /* partial likelihood operations count */ int* scaleFactorsOps; /* array of scaler indices for Beagle operations*/ #if defined (BEAGLE_V3_ENABLED) int numCharsAll; /* number of compressed chars for all divisions */ MrBFlt* logLikelihoodsAll; /* array of log likelihoods for all divisions */ int* cijkIndicesAll; /* cijk array for all divisions */ int* categoryRateIndicesAll; /* category rate array for all divisions */ BeagleOperationByPartition* operationsAll; /* array of all operations across divisions */ BeagleOperationByPartition* operationsByPartition; /* array of division operations to be sent to Beagle */ #endif /* BEAGLE_V3_ENABLED */ #endif /* BEAGLE_ENABLED */ } ModelInfo; typedef struct sumt { int *absentTaxa; /* information on absent taxa */ int brlensDef; /* branch lengths defined? */ char sumtFileName[100]; /* name of input file */ char sumtOutfile[120]; /* name of output file */ char curFileName[120]; /* name of file being processed */ int burnin; /* actual burnin when parsing tree files */ char sumtConType[100]; /* consensus tree type */ int calcTreeprobs; /* should the individual tree probs be calculated*/ int showSumtTrees; /* should the individual tree probs be shown */ int numRuns; /* number of independent analyses to summarize */ int numTrees; /* number of tree params to summarize */ int treeNo; /* current tree being processed */ int orderTaxa; /* order taxa in trees? */ MrBFlt minPartFreq; /* minimum part. freq. for overall diagnostics */ int table; /* show table of partition frequencies? */ int summary; /* show summary diagnostics ? */ int showConsensus; /* show consensus trees ? */ int consensusFormat; /* format of consensus tree */ PolyTree *tree; /* for storing tree read from file */ int *order; /* for storing topology read from file */ int orderLen; /* length of order array */ int numTreesInLastBlock; /* number of trees in last block */ int numTreesEncountered; /* number of trees encountered in total */ int numTreesSampled; /* number of sampled trees in total */ int isRooted; /* is sumt tree rooted ? */ int isRelaxed; /* is sumt tree a relaxed clock tree ? */ int isClock; /* is sumt tree a clock tree ? */ int isCalibrated; /* is sumt tree calibrated ? */ int nESets; /* number of event sets */ int nBSets; /* number of branch rate sets */ char **bSetName; /* name of effective branch length sets */ char **eSetName; /* name of event sets */ int popSizeSet; /* do sumt trees have population size set? */ char *popSizeSetName; /* name of population size set */ int BitsLongsNeeded; /* number of safe longs needed for taxon bits */ int runId; /* id of run being processed */ int numTaxa; /* number of sumt taxa */ char **taxaNames; /* names of sumt taxa */ int *numFileTrees; /* number of trees per file */ int *numFileTreesSampled; /* number of trees sampled per file */ int HPD; /* use highest posterior density? */ int saveBrParams; /* save node/branch parameters? */ MrBFlt minBrParamFreq; /* threshold for printing branch params to file */ } Sumt; /* formats for consensus trees */ #define SIMPLE 0 #define FIGTREE 1 typedef struct comptree { char comptFileName1[120]; /* name of first input file */ char comptFileName2[120]; /* name of second input file */ char comptOutfile[120]; /* name of output file */ int burnin; /* actual burnin used when parsing tree files */ MrBFlt minPartFreq; /* use partitions with frequency >= minPartFreq */ } Comptree; typedef struct sump { char sumpFileName[100]; /* name of input file */ char sumpOutfile[120]; /* name of output file */ //int plot; /* output plot (y/n)? */ int table; /* output table (y/n)? */ int margLike; /* output marginal likelihood (y/n)? */ int numRuns; /* number of independent analyses to summarize */ int allRuns; /* should data for all runs be printed (yes/no)? */ int HPD; /* use highest posterior density? */ MrBFlt minProb; /* cut-off for model probabilities to show */ } Sump; typedef struct sumss { //int plot; /* output plot (y/n)? */ int numRuns; /* number of independent analyses to summarize */ int allRuns; /* should data for all runs be printed (yes/no)? */ int stepToPlot; /* Which step to plot in the step plot */ int askForMorePlots; /* Should user be asked to plot for different discardfraction (y/n)? */ int smoothing; /* An integer indicating number of neighbors to average over when doing smoothing of curves on plots */ MrBFlt discardFraction; /* Proportion of samples discarded when plotting step plot.*/ } Sumss; typedef struct plot { char plotFileName[120]; /* name of input file */ char parameter[100]; /* parameter(s) to be plotted */ char match[100]; /* whether the match needs to be perfect */ } Plot; typedef struct { int numTrees; /* number of trees to reassemble */ int numRuns; /* number of runs to reassemble */ } ReassembleInfo; typedef struct doublet { BitsLong first, second; } Doublet; typedef struct matrix { BitsLong *origin; int rowSize; int nRows; int column; int row; } Matrix; typedef struct charinfo { int dType; int cType; int nStates; int constant[MAX_STD_STATES]; int singleton[MAX_STD_STATES]; int variable; int informative; } CharInfo; typedef struct { int isExcluded; /* is the character excluded */ int numStates; /* number of observed states for the character */ int charType; /* type of character */ int isMissAmbig; /* is the character missing or ambiguous */ int ctype; /* ordering of character */ int charId; /* char ID index for doublet and codon models */ int pairsId; /* char ID for doublets */ int bigBreakAfter; /* is there a large break after this character */ } CharInformation; typedef struct { int isDeleted; /* is the taxon deleted */ int charCount; /* count holder */ } TaxaInformation; typedef struct { MrBFlt curScore; MrBFlt minScore; MrBFlt totalScore; MrBFlt stopScore; MrBFlt warp; TreeNode **leaf; TreeNode **vertex; } TreeInfo; typedef struct { int allavailable; } ShowmovesParams; typedef struct { int numNames; char **names; } NameSet; /* global variables */ extern int abortMove; /* flag determining whether to abort move */ extern int *activeParams[NUM_LINKED]; /* a table holding the parameter status */ extern int *activeParts; /* partitions changes should apply to */ extern int autoClose; /* autoclose */ extern int autoOverwrite; /* Overwrite or append outputfiles when nowarnings=yes */ extern int chainHasAdgamma; /* indicates if chain has adgamma HMMs */ extern Chain chainParams; /* holds parameters for Markov chain */ extern CharInformation *charInfo; /* holds critical information about characters */ extern char **charSetNames; /* holds names of character sets */ extern int *compCharPos; /* char position in compressed matrix */ extern int *compColPos; /* column position in compressed matrix */ extern BitsLong *compMatrix; /* compressed character matrix */ extern int compMatrixRowSize; /* row size of compressed matrix */ extern Comptree comptreeParams; /* holds parameters for comparetree command */ extern char **constraintNames; /* holds names of constraints */ extern BitsLong **definedConstraint; /* holds information about defined constraints */ extern BitsLong **definedConstraintTwo; /* bitfields representing second taxa sets of defined constraints (for PARTIAL constraints) */ extern BitsLong **definedConstraintPruned; /* bitfields representing taxa sets of defined constraints after deleted taxa are removed */ extern BitsLong **definedConstraintTwoPruned; /* bitfields representing second taxa sets of defined constraints after deleted taxa are removed (for PARTIAL constraints) */ extern int dataType; /* type of data */ extern Calibration defaultCalibration; /* default model settings */ extern ModelParams defaultModel; /* default model settings */ extern int defChars; /* flag for whether number of characters is known*/ extern int defMatrix; /* flag for whether matrix is successful read */ extern int defPairs; /* flag for whether constraints on tree are read */ extern int defPartition; /* flag for whether character partition is read */ extern int defTaxa; /* are taxon labels defined ? */ extern Doublet doublet[16]; /* holds information on states for doublets */ extern int echoMB; /* flag used by Manual to prevent echoing */ extern BitsLong expecting; /* variable denoting expected token type */ extern int fileNameChanged; /* has file name been changed? */ extern int foundNewLine; /* whether a new line has been found */ extern char gapId; /* gap character Id */ extern RandLong globalSeed; /* seed that is initialized at start up */ extern char **headerNames; /* string to hold headers in sump and plot */ extern int inComment; /* flag for whether input stream is commented */ extern int inferAncStates; /* should ancestral states be inferred (y/n) */ extern int inferSiteOmegas; /* should site omega values be inferred (y/n) */ extern int inferSiteRates; /* should site rates be inferred (y/n) */ extern int inferPosSel; /* should positive selection be inferred (y/n) */ extern char inputFileName[100]; /* input (NEXUS) file name */ extern int inTreesBlock; /* are we in the sumt block */ extern int inValidCommand; /* a useful flag set whenever you enter a cmd */ extern int isInAmbig, isInPoly; /* flags whether we are within () or {} */ extern int isMixed; /* flags whether dataset is mixed */ extern int inMrbayesBlock; /* flag for whether we are in a mrbayes block */ extern int *intValues; /* integer values of parameters */ extern int isTaxsetDef; /* is a taxon set defined */ extern int isTranslateDef; /* is a translation block defined */ extern int isTranslateDiff; /* is translate different from current taxaset? */ extern int *linkTable[NUM_LINKED]; /* how parameters are linked across parts */ extern int localOutGroup; /* outgroup for non-excluded taxa */ extern char **localTaxonNames; /* points to names of non-excluded taxa */ extern FILE *logFileFp; /* file pointer to log file */ extern char logFileName[100]; /* name of the log file */ extern int logToFile; /* should screen output be logged to a file */ extern char manFileName[100]; /* name of man file */ extern char matchId; /* mach character Id */ extern int *matrix; /* matrix containing original data */ extern int matrixHasPoly; /* flag for whether matrix has polymorphisms */ extern int memAllocs[NUM_ALLOCS]; /* allocated memory flags */ extern int mode; /* mode of program (interactive/noninteractive) */ extern char **modelIndicatorParams; /* model indicator params */ extern char ***modelElementNames; /* names for component models */ extern MCMCMove **moves; /* vector of applicable moves */ extern MoveType moveTypes[NUM_MOVE_TYPES]; /* holds information on the move types */ extern char missingId; /* missing character Id */ extern Tree **mcmcTree; /* pointers to mcmc trees */ extern Model *modelParams; /* holds model params for partitions */ extern ModelInfo *modelSettings; /* stores important info on model params */ extern int nBitsInALong; /* number of bits in a BitsLong */ extern Calibration *nodeCalibration; /* holds information about node calibrations */ extern int noWarn; /* no warnings on overwriting files */ extern int nPThreads; /* number of pthreads to use */ extern int numActiveLocks; /* number of active, locked nodes */ extern int numApplicableMoves; /* number of moves applicable to parameters */ extern int numChar; /* number of characters in character matrix */ extern int numCharSets; /* holds number of character sets */ extern int numComments; /* number of nested comments */ extern int numCompressedChars; /* number of compressed characters */ extern int numCurrentDivisions; /* number of partitions of data */ extern int numDefinedConstraints; /* number of constraints defined */ extern enum ConstraintType *definedConstraintsType; /* Store type of constraint */ extern int numDefinedPartitions; /* number of partitions defined */ extern int numDefinedSpeciespartitions; /* number of species partitions defined */ extern int numGlobalChains; /* number of global chains */ extern int numLocalTaxa; /* number of non-excluded taxa */ extern int numLocalChar; /* number of non-excluded characters */ extern int numMoveTypes; /* the number of move types */ extern int numOpenExeFiles; /* number of execute files open */ extern int numParams; /* number of parameters in model */ extern int numDivisions; /* number of current divisions */ extern int numPrintParams; /* number of substitution model parameters to print */ extern int numPrintTreeParams; /* number of tree model parameters to print */ extern CLFlt *numSitesOfPat; /* no. sites of each pattern */ extern int numSpecies; /* number of species in current speciespartition */ extern int numTaxa; /* number of taxa in character matrix */ extern int numTaxaSets; /* holds number of taxa sets */ extern int numTopologies; /* number of topologies for one chain and state */ extern int numTranslates; /* number of taxa in active translate block */ extern int numTrees; /* number of trees for one chain and state */ extern int numUserTrees; /* number of defined user trees */ extern int *numVars; /* number of variables in setting arrays */ extern int *origChar; /* index from compressed char to original char */ extern int outGroupNum; /* number of outgroup taxon */ extern ParmInfo paramTable[]; /* information on parameters */ extern MrBFlt *paramValues; /* values of parameters */ extern int **partitionId; /* holds information about defined partitions */ extern char **partitionNames; /* hold names of partitions (first is "default") */ extern MrBFlt *parameterValues; /* vector holding sump or plot parameters */ extern Param *params; /* vector of parameters in model */ extern int partitionNum; /* index of current partition */ extern Plot plotParams; /* holds parameters for plot command */ extern int precision; /* precision of samples and summary stats */ extern int *printAncStates; /* divisions to print anc states for */ extern int quitOnError; /* quit on error? */ extern int readComment; /* should we read comment (looking for &)? */ extern int readWord; /* should we read a word next? */ extern ReassembleInfo reassembleParams; /* holds parameters for reassemble command */ extern int replaceLogFile; /* should logfile be replace/appended to */ extern RandLong runIDSeed; /* seed used only for generating run ID [stamp] */ extern BitsLong bitsLongWithAllBitsSet; /* a BitsLong with all bits set, for bit ops */ extern int setUpAnalysisSuccess; /* Set to YES if analysis is set without error */ extern int scientific; /* use scientific format for samples ? */ extern ShowmovesParams showmovesParams; /* holds parameters for Showmoves command */ extern char spacer[10]; /* holds blanks for printing indentations */ extern NameSet *speciesNameSets; /* hold species name sets, one for each speciespartition */ extern int **speciespartitionId; /* holds info about defined speciespartitions */ extern char **speciespartitionNames; /* hold names of speciespartitions (first is "default") */ extern int speciespartitionNum; /* index of current species partition */ extern char stamp[11]; /* holds a unique identifier for each analysis */ extern RandLong swapSeed; /* seed used only for determining which to swap */ extern int state[MAX_CHAINS]; /* state of chain */ extern MrBFlt *stdStateFreqs; /* std char state frequencies */ extern int *stdType; /* compressed std char type: ord, unord, irrev */ extern Sump sumpParams; /* holds parameters for sump command */ extern char sumpToken[]; /* string holding a .p file token */ extern char *sumpTokenP; /* pointer to a .p file token */ extern Sumt sumtParams; /* holds parameters for sumt command */ extern Sumss sumssParams; /* holds parameters for sumss command */ extern int stdStateFreqsRowSize; /* row size for stdStateFreqs */ extern int *sympiIndex; /* sympi state freq index for multistate chars */ extern TaxaInformation *taxaInfo; /* holds critical information about taxa */ extern char **taxaNames; /* holds name of taxa */ extern char **taxaSetNames; /* holds names of taxa sets */ extern BitsLong **taxaSet; /* holds information about defined taxasets */ extern int *tempActiveConstraints; /* info on the active constraints in prset */ extern int *tempLinkUnlink[NUM_LINKED]; /* for changing parameter linkage */ extern int *tempLinkUnlinkVec; /* for changing parameter linkage */ extern MrBFlt *tempNum; /* vector of numbers used for setting arrays */ extern int *tempSet; /* temporarily holds defined character set */ extern int theAmbigChar; /* int containing ambiguous character */ extern int *tiIndex; /* compressed std char ti index */ extern Calibration *tipCalibration; /* holds tip calibrations */ extern char **transFrom; /* translation block information */ extern char **transTo; /* translation block information */ extern int userBrlensDef; /* are the branch lengths on user tree defined */ extern int userLevel; /* the level of the user */ extern PolyTree *userTree[]; /* array of user trees */ extern char workingDir[100]; /* working directory */ #if defined (BEAGLE_ENABLED) extern int tryToUseBEAGLE; /* try to use the BEAGLE library */ extern long beagleFlags; /* BEAGLE requirement flags */ extern int beagleResourceNumber; /* BEAGLE resource number */ extern int* beagleResource; /* BEAGLE resource list */ extern int beagleResourceCount; /* BEAGLE resource list length */ extern int beagleInstanceCount; /* total number of BEAGLE instances */ extern int beagleScalingScheme; /* BEAGLE dynamic scaling */ extern int beagleScalingFrequency; /* BEAGLE rescaling frequency */ extern int recalcScalers; /* should we recalculate scalers for one of divisions for current state YES/NO */ #if defined (BEAGLE_V3_ENABLED) extern int beagleThreadCount; /* max number of BEAGLE CPU threads */ extern int beagleAllFloatTips; /* use floating-point representation for all tips */ #endif #endif /* Aamodel parameters */ extern MrBFlt aaJones[20][20]; /* rates for Jones model */ extern MrBFlt aaDayhoff[20][20]; /* rates for Dayhoff model */ extern MrBFlt aaMtrev24[20][20]; /* rates for mtrev24 model */ extern MrBFlt aaMtmam[20][20]; /* rates for mtmam model */ extern MrBFlt aartREV[20][20]; /* rates for rtREV model */ extern MrBFlt aaWAG[20][20]; /* rates for WAG model */ extern MrBFlt aacpREV[20][20]; /* rates for aacpREV model */ extern MrBFlt aaVt[20][20]; /* rates for VT model */ extern MrBFlt aaBlosum[20][20]; /* rates for Blosum62 model */ extern MrBFlt aaLG[20][20]; /* rates for LG model */ extern MrBFlt jonesPi[20]; /* stationary frequencies for Jones model */ extern MrBFlt dayhoffPi[20]; /* stationary frequencies for Dayhoff model */ extern MrBFlt mtrev24Pi[20]; /* stationary frequencies for mtrev24 model */ extern MrBFlt mtmamPi[20]; /* stationary frequencies for mtmam model */ extern MrBFlt rtrevPi[20]; /* stationary frequencies for rtREV model */ extern MrBFlt wagPi[20]; /* stationary frequencies for WAG model */ extern MrBFlt cprevPi[20]; /* stationary frequencies for aacpREV model */ extern MrBFlt vtPi[20]; /* stationary frequencies for VT model */ extern MrBFlt blosPi[20]; /* stationary frequencies for Blosum62 model */ extern MrBFlt lgPi[20]; /* stationary frequencies for LG model */ #if defined (PRINT_DUMP) FILE *dumpFile; /* for debugging logs */ #endif #if defined (MPI_ENABLED) extern int proc_id; /* process ID (0, 1, ..., num_procs-1) */ extern int num_procs; /* number of active processors */ extern MrBFlt myStateInfo[7]; /* likelihood/prior/heat/ran/moveInfo vals of me */ extern MrBFlt partnerStateInfo[7]; /* likelihood/prior/heat/ran/moveInfo vals of partner */ #endif #endif /* BAYES_H_ */ NBISweden-MrBayes-ecfd55c/src/best.c000066400000000000000000002040241526212227600173070ustar00rootroot00000000000000/* * Best 2.2 * * This file contains the functions * for calculating the probability of * gene trees given the species tree * and the prior probability of the * species tree * * Liang Liu * Department of Statistics * The Ohio State University * Columbus, Ohio * * liuliang@stat.ohio-state.edu */ #include "bayes.h" #include "best.h" #include "command.h" #include "mcmc.h" #include "model.h" #include "proposal.h" #include "utils.h" /****************************** Local functions converted by Fredrik from BEST code *****************************/ int CompareDepths (const void *x, const void *y); int CompareDoubles (const void *x, const void *y); int CompareNodes (const void *x, const void *y); int CompareNodesByX (const void *x, const void *y); int GetSpeciesTreeFromMinDepths (Tree* speciesTree, double *depthMatrix); int GetDepthMatrix(Tree * speciesTree, double *depthMatrix); int GetMeanDist (Tree *speciesTree, double *depthMatrix, double *mean); int GetMinDepthMatrix (Tree **geneTrees, int numGeneTrees, double *depthMatrix); void LineagesIn (TreeNode* geneTreeNode, TreeNode* speciesTreeNode); double LnPriorProbGeneTree (Tree *geneTree, double mu, Tree *speciesTree, double *popSizePtr); double LnProposalProbSpeciesTree (Tree *speciesTree, double *depthMatrix, double expRate); void MapGeneTreeToSpeciesTree (Tree *geneTree, Tree *speciesTree); int ModifyDepthMatrix (double expRate, double *depthMatrix, RandLong *seed); /* Global BEST variables */ BitsLong **speciesPairSets; double *depthMatrix; /* Allocate variables used by best code during mcmc */ void AllocateBestChainVariables (void) { int i, j, index, numUpperTriang, nLongsNeeded; // Free if by mistake variables are already allocated if (memAllocs[ALLOC_BEST] == YES) FreeBestChainVariables (); // Allocate space for upper triangular pair sets numUpperTriang = (numSpecies * (numSpecies-1)) / 2; nLongsNeeded = ((numSpecies - 1) / nBitsInALong) + 1; speciesPairSets = (BitsLong **) SafeCalloc (numUpperTriang, sizeof(BitsLong *)); speciesPairSets[0] = (BitsLong *) SafeCalloc (numUpperTriang*nLongsNeeded, sizeof(BitsLong)); for (i=1; i (*((Depth *)(y))).depth) return 1; else return 0; } /** Compare function (doubles) for qsort */ int CompareDoubles (const void *x, const void *y) { if (*((double *)(x)) < *((double *)(y))) return -1; else if (*((double *)(x)) > *((double *)(y))) return 1; else return 0; } /** Compare function (TreeNode struct) for qsort */ int CompareNodes (const void *x, const void *y) { if ((*((TreeNode **)(x)))->nodeDepth < (*((TreeNode**)(y)))->nodeDepth) return -1; else if ((*((TreeNode **)(x)))->nodeDepth > (*((TreeNode**)(y)))->nodeDepth) return 1; else return 0; } /** Compare function (TreeNode struct; sort by x, then by nodeDepth) for qsort */ int CompareNodesByX (const void *x, const void *y) { if ((*((TreeNode **)(x)))->x < (*((TreeNode**)(y)))->x) return -1; else if ((*((TreeNode **)(x)))->x > (*((TreeNode**)(y)))->x) return 1; else { if ((*((TreeNode **)(x)))->nodeDepth < (*((TreeNode**)(y)))->nodeDepth) return -1; else if ((*((TreeNode **)(x)))->nodeDepth > (*((TreeNode**)(y)))->nodeDepth) return 1; else return 0; } } /**----------------------------------------------------------------- | | FillSpeciesTreeParams: Fill in species trees (start value) | ------------------------------------------------------------------*/ int FillSpeciesTreeParams (RandLong *seed, int fromChain, int toChain) { int i, k, chn, numGeneTrees, freeBestChainVars; Param *p; Tree *speciesTree, **geneTrees; // Allocate space for global best model variables used in this function, in case they are not allocated if (memAllocs[ALLOC_BEST] == NO) { freeBestChainVars = YES; AllocateBestChainVariables(); } else freeBestChainVars = NO; // Use global variable numTopologies to calculate number of gene trees // There is one topology for the species tree, the other ones are gene trees // The number of current divisions is not safe because one gene tree can have // several partitions, for instance if we assign the different genes on the // mitochondrion different substitution models, or if we assign different rates // to the codon site positions in a sequence numGeneTrees = numTopologies - 1; geneTrees = (Tree **) SafeCalloc (numGeneTrees, sizeof(Tree*)); // Build species trees for state 0 for (chn=fromChain; chnparamType == P_SPECIESTREE) { // Find species tree and gene trees speciesTree = GetTree(p, chn, 0); for (i=0; inSubParams; i++) geneTrees[i] = GetTree(p->subParams[i], chn, 0); // Get minimum depth matrix for species tree GetMinDepthMatrix (geneTrees, numGeneTrees, depthMatrix); // Get a species tree from min depth matrix GetSpeciesTreeFromMinDepths(speciesTree, depthMatrix); assert (IsSpeciesTreeConsistent(speciesTree, chn) == YES); // Label the tips if (LabelTree (speciesTree, speciesNameSets[speciespartitionNum].names) == ERROR) { FreeBestChainVariables(); return (ERROR); } } } } // Free gene trees free (geneTrees); // Free best model variables if appropriate if (freeBestChainVars == YES) FreeBestChainVariables(); return (NO_ERROR); } /**----------------------------------------------------------------- | | FreeBestChainVariables: Free best variables used during an mcmc | run. | ------------------------------------------------------------------*/ void FreeBestChainVariables(void) { if (memAllocs[ALLOC_BEST] == YES) { free (speciesPairSets[0]); free (speciesPairSets); speciesPairSets = NULL; } free (depthMatrix); depthMatrix = NULL; memAllocs[ALLOC_BEST] = NO; } /**--------------------------------------------------------------------- | | GetDepthMatrix: | | This algorithm calculates the upper triangular depth matrix for the | species tree. Time complexity O(n^2). | | @param speciesTree The species tree (in) | @param depthMatrix The minimum depth matrix, upper triangular array (out) | @returns Returns ERROR or NO_ERROR ----------------------------------------------------------------------*/ int GetDepthMatrix (Tree *speciesTree, double *depthMatrix) { int i, left, right, numUpperTriang, index, nLongsNeeded, freeBitsets; double maxDepth; TreeNode *p; // Make sure we have bitfields allocated and set if (speciesTree->bitsets == NULL) { AllocateTreePartitions(speciesTree); freeBitsets = YES; } else { ResetTreePartitions(speciesTree); // just in case freeBitsets = NO; } // Calculate number of values in the upper triangular matrix numUpperTriang = numSpecies * (numSpecies - 1) / 2; // Number of longs needed in a bitfield representing a species set nLongsNeeded = ((numSpecies -1) / nBitsInALong) + 1; // Set all cells to max maxDepth = speciesTree->root->left->nodeDepth; // root depth for (i=0; inIntNodes; i++) { p = speciesTree->intDownPass[i]; for (left = FirstTaxonInPartition(p->left->partition, nLongsNeeded); left < numSpecies; left = NextTaxonInPartition(left, p->left->partition, nLongsNeeded)) { for (right = FirstTaxonInPartition(p->right->partition, nLongsNeeded); right < numSpecies; right = NextTaxonInPartition(right, p->right->partition, nLongsNeeded)) { index = UpperTriangIndex(left, right, numSpecies); depthMatrix[index] = p->nodeDepth; } } } // Free partitions if appropriate if (freeBitsets == YES) FreeTreePartitions(speciesTree); return (NO_ERROR); } /**--------------------------------------------------------------------- | | GetMeanDist | | This algorithm calculates the mean distance between a distance matrix | and the minimum depths that define a species tree. | | @param speciesTree The species tree (in) | @param minDepthMatrix The minimum depth matrix, upper triangular array (in) | @param mean The mean distance (out) | @returns Returns ERROR or NO_ERROR ----------------------------------------------------------------------*/ int GetMeanDist (Tree *speciesTree, double *minDepthMatrix, double *mean) { int i, left, right, index, nLongsNeeded, freeBitsets; double dist, minDist=0.0, distSum; TreeNode *p; // Make sure we have bitfields allocated and set if (speciesTree->bitsets == NULL) { AllocateTreePartitions(speciesTree); freeBitsets = YES; } else { ResetTreePartitions(speciesTree); // just in case freeBitsets = NO; } // Number of longs needed in a bitfield representing a species set nLongsNeeded = ((numSpecies -1) / nBitsInALong) + 1; // Loop over interior nodes distSum = 0.0; for (i=0; inIntNodes; i++) { p = speciesTree->intDownPass[i]; p->x = 0; while ((left=FirstTaxonInPartition(p->left->partition, nLongsNeeded)) < numSpecies) { while ((right=FirstTaxonInPartition(p->right->partition, nLongsNeeded)) < numSpecies) { p->x++; index = UpperTriangIndex(left, right, numSpecies); dist = depthMatrix[index] - p->nodeDepth; if (p->x == 1) minDist = dist; else if (dist < minDist) minDist = dist; ClearBit(right, p->right->partition); } ClearBit(left, p->left->partition); } distSum += minDist; } (*mean) = distSum / speciesTree->nIntNodes; // Reset partitions that were destroyed above or free partitions, as appropriate if (freeBitsets == YES) FreeTreePartitions(speciesTree); else ResetTreePartitions(speciesTree); return (NO_ERROR); } /**--------------------------------------------------------------------- | | GetMinDepthMatrix: converted from GetMinDists. | | This algorithm scans the gene trees and calculates the minimum depth | (height) separating species across gene trees. The complexity of the | original algorithm was O(mn^3), where m is the number of gene trees and | n is the number of taxa in each gene tree. I think this algorithm has | complexity that is better on average, but the difference is small. | | I have rewritten the algorithm also to show alternative techniques that | could be used in this and other BEST algorithms. | | @param geneTrees The gene trees (in) | @param depthMatrix The minimum depth matrix, upper triangular array (out) | @returns Returns ERROR or NO_ERROR ----------------------------------------------------------------------*/ int GetMinDepthMatrix (Tree **geneTrees, int numGeneTrees, double *depthMatrix) { int i, j, w, nLongsNeeded, numUpperTriang, index, trace=0; double maxDepth; TreeNode *p; BitsLong **speciesSets; // Allocate space for species partitions nLongsNeeded = ((numSpecies -1) / nBitsInALong) + 1; // number of longs needed in a bitfield representing a species set speciesSets = (BitsLong **) SafeCalloc ((2*(size_t)numLocalTaxa-1), sizeof(BitsLong *)); speciesSets[0] = (BitsLong *) SafeCalloc ((2*(size_t)numLocalTaxa-1)*nLongsNeeded, sizeof(BitsLong)); for (i=1; i<2*numLocalTaxa-1; i++) speciesSets[i] = speciesSets[0] + i*nLongsNeeded; // Set tip species partitions once and for all for (i=0; iroot->left->nodeDepth; for (i=0; inIntNodes; i++) { p = geneTrees[w]->intDownPass[i]; for (j=0; jindex][j] = speciesSets[p->left->index][j] | speciesSets[p->right->index][j]; } // Now order the interior nodes in terms of node depth. We rely on the fact that the // ordered sequence is a valid downpass sequence. O(log n). qsort((void *)(geneTrees[w]->intDownPass), (size_t)(geneTrees[w]->nIntNodes), sizeof(TreeNode *), CompareNodes); // Finally find the minimum for each cell in the upper triangular matrix // This is the time critical step with complexity O(n^3) in the simplest // algorithm version. This algorithm should do a little better in most cases. for (i=0; inIntNodes; j++) { p = geneTrees[w]->intDownPass[j]; // Because nodes are ordered in time, if this test is true then we cannot beat the minimum if (p->nodeDepth > depthMatrix[i]) break; // Check whether the node is a candidate minimum for the species pair // If the test is true, we know from the test above that p->nodeDepth is // either a tie or the new minimum if (IsPartNested(speciesPairSets[i], speciesSets[p->index], nLongsNeeded) == YES) { depthMatrix[i] = p->nodeDepth; break; } } } } // Next gene tree if (trace) { index = 0; printf ("Mindepth matrix\n"); for (i=0;iisRooted = YES; polyTree->isClock = YES; polyTree->root = &polyTree->nodes[2*numSpecies-2]; for (i=0; inodes[i]; p->index = i; p->depth = 0.0; p->left = NULL; if (isib = &polyTree->nodes[i+1]; else p->sib = NULL; p->anc = polyTree->root; } p = polyTree->root; p->index = 2*numSpecies - 2; p->left = &polyTree->nodes[0]; p->sib = NULL; p->anc = NULL; p->depth = -1.0; polyTree->nNodes = numSpecies + 1; polyTree->nIntNodes = 1; GetPolyDownPass(polyTree); ResetPolyTreePartitions(polyTree); /* set bitsets (partitions) for initial tree */ // Resolve bush using sorted depth structs nextNodeIndex = numSpecies; for (i=0; inodes[FirstTaxonInPartition(minDepth[i].pairSet, nLongsNeeded)]; // Descend tree until we find a node within which the pair set is nested do { p = p->anc; } while (!IsPartNested(minDepth[i].pairSet, p->partition, nLongsNeeded)); if (p->left->sib->sib != NULL) { // This node is still a polytomy // Find left and right descendants of new node qPrev = NULL; for (q=p->left; IsSectionEmpty(q->partition, minDepth[i].pairSet, nLongsNeeded); q=q->sib) qPrev = q; rPrev = q; for (r=q->sib; IsSectionEmpty(r->partition, minDepth[i].pairSet, nLongsNeeded); r=r->sib) rPrev = r; // Introduce the new node u = &polyTree->nodes[nextNodeIndex]; u->index = nextNodeIndex; nextNodeIndex++; polyTree->nIntNodes++; polyTree->nNodes++; u->left = q; u->anc = p; if (p->left == q) p->left = u; else qPrev->sib = u; // former upstream sibling to r should point to r->sib if (rPrev == q) u->sib = r->sib; else rPrev->sib = r->sib; if (q->sib == r) u->sib = r->sib; else u->sib = q->sib; u->depth = minDepth[i].depth; // because minDepth structs are sorted, we know this is the min depth assert (u->depth >= 0.0); // Create new taxon set with bitfield operations for (j=0; jpartition[j] = q->partition[j] | r->partition[j]; // Patch the tree together with the new node added q->sib = r; r->sib = NULL; q->anc = u; r->anc = u; } else if (p == polyTree->root && p->depth < 0.0) { // This is the first time we hit the root of the tree && it is resolved p->depth = minDepth[i].depth; assert (p->depth >= 0.0); } // other cases should not be added to tree } // Make sure we have a complete species tree assert (polyTree->nIntNodes == numSpecies - 1); // Set traversal sequences GetPolyDownPass(polyTree); // Set branch lengths from node depths (not done automatically for us) // Make sure all branch lengths are nonnegative (we can have 0.0 brlens, they // should not be problematic in a species tree; they occur when there are // ties in the min depth matrix that have not been modified by the move) for (i=0; inNodes; i++) { p = polyTree->allDownPass[i]; if (p->anc == NULL) p->length = 0.0; else p->length = p->anc->depth - p->depth; if (p->length < 0.0) { FreePolyTree(polyTree); free (minDepth); return (ERROR); } } // Copy to species tree from polytomous tree CopyToSpeciesTreeFromPolyTree (speciesTree, polyTree); // Free locally allocated variables FreePolyTree(polyTree); free (minDepth); return(NO_ERROR); } /**--------------------------------------------------------------------------------------- | | IsSpeciesTreeConsistent: Called when user tries to set a species tree or when | attempting to use a species tree from a check point as starting value. | -----------------------------------------------------------------------------------------*/ int IsSpeciesTreeConsistent (Tree *speciesTree, int chain) { int i, answer, numGeneTrees, numUpperTriang, freeBestVars; double *speciesTreeDepthMatrix; Tree **geneTrees; freeBestVars = NO; if (memAllocs[ALLOC_BEST] == NO) { AllocateBestChainVariables(); freeBestVars = YES; } numGeneTrees = numTrees - 1; geneTrees = (Tree **) SafeCalloc (numGeneTrees, sizeof(Tree *)); for (i=0; iroot, 0, YES); if (freeBestVars == YES) FreeBestChainVariables(); free (speciesTreeDepthMatrix); free (geneTrees); return answer; } /**--------------------------------------------------------------------------------------- | | LineagesIn: Recursive function to get number of gene tree lineages coming into each | branch of the species tree (in ->x of speciestree nodes). We also assign each gene | tree lineage to the corresponding species tree lineage (in ->x of genetree nodes). | Finally, number of coalescent events is recorded (in ->y of speciestree nodes). | Time complexity is O(n). | -----------------------------------------------------------------------------------------*/ void LineagesIn (TreeNode *geneTreeNode, TreeNode *speciesTreeNode) { int nLongsNeeded; if (geneTreeNode->nodeDepth < speciesTreeNode->nodeDepth) { // climb up species tree if (speciesTreeNode->left == NULL) { assert (geneTreeNode->left == NULL); speciesTreeNode->x++; } else { nLongsNeeded = (numSpecies - 1) / nBitsInALong + 1; speciesTreeNode->x++; if (IsPartNested(geneTreeNode->partition, speciesTreeNode->left->partition, nLongsNeeded) == YES) LineagesIn (geneTreeNode, speciesTreeNode->left); else if (IsPartNested(geneTreeNode->partition, speciesTreeNode->right->partition, nLongsNeeded) == YES) LineagesIn (geneTreeNode, speciesTreeNode->right); } } else { // climb up gene tree if (geneTreeNode->left != NULL) LineagesIn(geneTreeNode->left, speciesTreeNode); if (geneTreeNode->right != NULL) LineagesIn(geneTreeNode->right, speciesTreeNode); if (geneTreeNode->left == NULL) { speciesTreeNode->x++; assert (speciesTreeNode->left == NULL); } else { speciesTreeNode->y++; } geneTreeNode->x = speciesTreeNode->index; } } /**----------------------------------------------------------------- | | LnSpeciesTreeProb: Wrapper for LnJointGeneTreeSpeciesTreePr to | free calling functions from retrieving gene and species trees. | ------------------------------------------------------------------*/ double LnSpeciesTreeProb(int chain) { int i, numGeneTrees; double lnProb; Tree **geneTrees, *speciesTree; ModelInfo *m; m = &modelSettings[0]; speciesTree = GetTree(m->speciesTree, chain, state[chain]); numGeneTrees = m->speciesTree->nSubParams; geneTrees = (Tree **) SafeCalloc (numGeneTrees, sizeof(Tree *)); for (i=0; ispeciesTree->nSubParams; i++) geneTrees[i] = GetTree(m->speciesTree->subParams[i], chain, state[chain]); lnProb = LnJointGeneTreeSpeciesTreePr(geneTrees, numGeneTrees, speciesTree, chain); free (geneTrees); return lnProb; } /**----------------------------------------------------------------- | | LnJointGeneTreeSpeciesTreePr: Converted from LnJointGenetreePr, | SPLogLike, SPLogPrior. | | In this function we calculate the entire probability of the species | tree, including its probability given its priors, and the probability | of the gene trees given the species tree. | ------------------------------------------------------------------*/ double LnJointGeneTreeSpeciesTreePr(Tree **geneTrees, int numGeneTrees, Tree *speciesTree, int chain) { double lnPrior, lnLike, clockRate, *popSizePtr, sR, eR; int i; ModelInfo *m; ModelParams *mp; // Get model info for species tree m = &modelSettings[speciesTree->relParts[0]]; // Get model params for species tree mp = &modelParams[speciesTree->relParts[0]]; // Get popSize ptr popSizePtr = GetParamVals(m->popSize, chain, state[chain]); // Get clock rate if (speciesTree->isCalibrated == YES) clockRate = *GetParamVals(m->clockRate, chain, state[chain]); else clockRate = 1.0; // Calculate probability of gene trees given species tree // ShowNodes(speciesTree->root, 0, YES); lnLike = 0.0; for (i=0; ispeciesTreeBrlensPr, "Birthdeath") == 0) { sR = *GetParamVals(m->speciationRates, chain, state[chain]); eR = *GetParamVals(m->extinctionRates, chain, state[chain]); LnBirthDeathPriorPr(speciesTree, clockRate, &lnPrior, sR, eR, mp->sampleStrat, mp->sampleProb); } // The population size is taken care of elsewhere return lnLike + lnPrior; } /**----------------------------------------------------------------- | | LnPriorProbGeneTree: Calculate the prior probability of a gene | tree. | ------------------------------------------------------------------*/ double LnPriorProbGeneTree (Tree *geneTree, double mu, Tree *speciesTree, double *popSizePtr) { int i, k, index, nEvents, trace=0; double N, lnProb, ploidyFactor, theta, timeInterval; TreeNode *p, *q=NULL, *r; ModelParams *mp; // Get model params mp = &modelParams[speciesTree->relParts[0]]; // Find ploidy setting if (strcmp(mp->ploidy, "Diploid") == 0) ploidyFactor = 4.0; else if (strcmp(mp->ploidy, "Haploid") == 0) ploidyFactor = 2.0; else /* if (strcmp(mp->ploidy, "Zlinked") == 0) */ ploidyFactor = 3.0; // Initialize species tree with theta in d for (i=0; inNodes-1; i++) { p = speciesTree->allDownPass[i]; if (strcmp(mp->popVarPr, "Equal") != 0) N = popSizePtr[p->index]; else N = popSizePtr[0]; p->d = ploidyFactor * N * mu; } // Map gene tree to species tree MapGeneTreeToSpeciesTree(geneTree, speciesTree); // Sort gene tree interior nodes first by speciestree branch on which they coalesce, then in time order qsort((void *)(geneTree->intDownPass), (size_t)(geneTree->nIntNodes), sizeof(TreeNode *), CompareNodesByX); // Debug output of qsort result if (trace) { printf ("index -- x -- nodeDepth for gene tree\n"); for (i=0; inIntNodes; i++) printf ("%d -- %d -- %e\n", geneTree->intDownPass[i]->index, geneTree->intDownPass[i]->x, geneTree->intDownPass[i]->nodeDepth); } // Now calculate probability after making sure species tree nodes appear in index order // (the order does not have to be a correct downpass sequence) for (i=0; imemNodes; i++) { p = &(speciesTree->nodes[i]); speciesTree->allDownPass[p->index] = p; } index = 0; lnProb = 0.0; for (i=0; inNodes-1; i++) { p = speciesTree->allDownPass[i]; // Get theta theta = p->d; // Get number of events nEvents = p->y; // Calculate probability lnProb += nEvents * log (2.0 / theta); for (k=p->x; k > p->x - p->y; k--) { q = geneTree->intDownPass[index]; assert (q->x == p->index); if (k == p->x) timeInterval = q->nodeDepth - p->nodeDepth; else { r = geneTree->intDownPass[index-1]; timeInterval = q->nodeDepth - r->nodeDepth; } lnProb -= (k * (k - 1) * timeInterval) / theta; index++; } if (p->x - p->y > 1) { if (nEvents == 0) timeInterval = p->anc->nodeDepth - p->nodeDepth; else timeInterval = p->anc->nodeDepth - q->nodeDepth; assert (p->anc->anc != NULL); assert (timeInterval >= 0.0); k = p->x - p->y; lnProb -= (k * (k - 1) * timeInterval) / theta; } } // Restore downpass sequences (probably not necessary for gene tree, but may be if some // code relies on intDownPass and allDownPass to be in same order) GetDownPass(speciesTree); GetDownPass(geneTree); // Free space FreeTreePartitions(speciesTree); FreeTreePartitions(geneTree); return lnProb; } /**--------------------------------------------------------------------- | | LnProposalProbSpeciesTree: | | This algorithm calculates the probability of proposing a particular | species tree given a distance matrix modified using a scheme based on | truncated exponential distributions with rate expRate. | | @param speciesTree The species tree (in) | @param depthMatrix The minimum depth matrix, upper triangular array (in) | @param expRate Rate of truncated exponential distribution | @returns Returns probability of proposing the species tree ----------------------------------------------------------------------*/ double LnProposalProbSpeciesTree (Tree *speciesTree, double *depthMatrix, double expRate) { int i, left, right, index, nLongsNeeded, freeBitsets; double dist, normConst=1.0, negLambdaX=0.0, eNegLambdaX, density, prob, sumDensRatio, prodProb, lnProb; TreeNode *p; // Make sure we have bitfields allocated and set if (speciesTree->bitsets == NULL) freeBitsets = YES; else freeBitsets = NO; AllocateTreePartitions(speciesTree); // Number of longs needed in a bitfield representing a species set nLongsNeeded = ((numSpecies -1) / nBitsInALong) + 1; // Loop over interior nodes lnProb = 0.0; for (i=0; inIntNodes; i++) { p = speciesTree->intDownPass[i]; p->x = 0; sumDensRatio = 0.0; prodProb = 1.0; for (left = FirstTaxonInPartition(p->left->partition, nLongsNeeded); left < numSpecies; left = NextTaxonInPartition(left, p->left->partition, nLongsNeeded)) { for (right = FirstTaxonInPartition(p->right->partition, nLongsNeeded); right < numSpecies; right = NextTaxonInPartition(right, p->right->partition, nLongsNeeded)) { p->x++; index = UpperTriangIndex(left, right, numSpecies); assert (index < numSpecies*(numSpecies - 1) / 2); dist = depthMatrix[index] - p->nodeDepth; // distance between depth matrix entry and actual species-tree node normConst = 1.0 - exp(-expRate * depthMatrix[index]); // normalization constant because of truncation of exp distribution negLambdaX = - expRate * dist; eNegLambdaX = exp(negLambdaX); density = expRate * eNegLambdaX / normConst; // density for x == dist, f(dist) prob = (1.0 - eNegLambdaX) / normConst; // cumulative prob for x <= dist, F(dist) sumDensRatio += density / prob; // warning: if dist==0, prob is ZERO! prodProb *= prob; } } if (p->x == 1) lnProb += log(expRate) + negLambdaX - log(normConst); else lnProb += log(sumDensRatio * prodProb); } // to avoid lnProposalProb is NaN at initial steps if (lnProb != lnProb) lnProb = 0.0; // Free partitions if appropriate if (freeBitsets == YES) FreeTreePartitions(speciesTree); return (lnProb); } /**----------------------------------------------------------------- | | MapGeneTreeToSpeciesTree: Fold gene tree into species tree. We | are going to use ->x of gene tree to give index of the | corresponding node in the species tree. ->x in the species | tree will give the number of lineages into the corresponding | branch, and ->y will give the number of coalescent events on | that branch. | ------------------------------------------------------------------*/ void MapGeneTreeToSpeciesTree (Tree *geneTree, Tree *speciesTree) { int i, j, nLongsNeeded, trace=0; TreeNode *p; // Initialize species partitions for both gene tree and species tree // This will set the partitions to reflect the partitions in the tree itself, // which is OK for the species tree, but we want the gene tree partitions to // reflect the species partitions and not the gene partitions, so we need to // set them here AllocateTreePartitions(geneTree); AllocateTreePartitions(speciesTree); nLongsNeeded = (numSpecies - 1) / nBitsInALong + 1; for (i=0; inNodes-1; i++) { p = geneTree->allDownPass[i]; ClearBits(p->partition, nLongsNeeded); if (p->left == NULL) SetBit(speciespartitionId[p->index][speciespartitionNum]-1, p->partition); else { for (j=0; jpartition[j] = p->left->partition[j] | p->right->partition[j]; } } // Species tree partitions already set by call to AllocateTreePartitions // Reset ->x and ->y of species tree (->x of gene tree does not need to be initialized) for (i=0; inNodes; i++) { p = speciesTree->allDownPass[i]; p->x = 0; p->y = 0; } // Call recursive function to match gene tree and species tree LineagesIn(geneTree->root->left, speciesTree->root->left); if (trace) { printf ("index -- x -- y for species tree\n"); for (i=0; inNodes-1; i++) printf ("%-2d -- %d -- %d\n", speciesTree->allDownPass[i]->index, speciesTree->allDownPass[i]->x, speciesTree->allDownPass[i]->y); } if (trace) { printf ("index -- x -- nodeDepth for gene tree\n"); for (i=0; inIntNodes; i++) printf ("%-2d -- %d -- %e\n", geneTree->intDownPass[i]->index, geneTree->intDownPass[i]->x, geneTree->intDownPass[i]->nodeDepth); } // Free space FreeTreePartitions(speciesTree); FreeTreePartitions(geneTree); } /**--------------------------------------------------------------------- | | ModifyDepthMatrix: | | This algorithm uses a truncated exponential distribution to modify | a depth matrix. | | @param expRate The rate of the exponential distribution (in) | @param depthMatrix The minimum depth matrix to be modified, upper triangular array (in/out) | @param seed Pointer to seed for random number generator (in/ut) | @returns Returns ERROR or NO_ERROR ----------------------------------------------------------------------*/ int ModifyDepthMatrix (double expRate, double *depthMatrix, RandLong *seed) { int i, numUpperTriang; double u, interval, delta; numUpperTriang = numSpecies * (numSpecies - 1) / 2; for (i=0; irelParts[0]]; // Get species tree (this trick is possible because we always copy tree params) newSpeciesTree = GetTree (m->speciesTree, chain, state[chain]); oldSpeciesTree = GetTree (m->speciesTree, chain, state[chain] ^ 1); // Get gene trees geneTrees = (Tree **) SafeCalloc (2*numGeneTrees, sizeof(Tree *)); for (i=0; ispeciesTree->nSubParams; i++) { geneTrees[i] = GetTree(m->speciesTree->subParams[i], chain, state[chain]); } // Allocate space for depth matrix copy numUpperTriang = numSpecies * (numSpecies - 1) / 2; oldMinDepths = (double *) SafeCalloc (2*numUpperTriang, sizeof(double)); modMinDepths = oldMinDepths + numUpperTriang; // Get min depth matrix for old gene trees GetMinDepthMatrix(geneTrees, numTopologies-1, depthMatrix); // Save a copy for (i=0; irelParts[0]]; // Get species tree (this trick is possible because we always copy tree params) newSpeciesTree = GetTree (m->speciesTree, chain, state[chain]); oldSpeciesTree = GetTree (m->speciesTree, chain, state[chain] ^ 1); // Get gene trees geneTrees = (Tree **) SafeCalloc (2*numGeneTrees, sizeof(Tree *)); for (i=0; ispeciesTree->nSubParams; i++) { geneTrees[i] = GetTree(m->speciesTree->subParams[i], chain, state[chain]); } // Allocate space for depth matrix copy numUpperTriang = numSpecies * (numSpecies - 1) / 2; oldMinDepths = (double *) SafeCalloc (2*numUpperTriang, sizeof(double)); modMinDepths = oldMinDepths + numUpperTriang; // Get min depth matrix for old gene trees GetMinDepthMatrix(geneTrees, numTopologies-1, depthMatrix); // Save a copy for (i=0; irelParts[0]]; // Get species tree (this trick is possible because we always copy tree params) newSpeciesTree = GetTree (m->speciesTree, chain, state[chain]); oldSpeciesTree = GetTree (m->speciesTree, chain, state[chain] ^ 1); // Get gene trees geneTrees = (Tree **) SafeCalloc (2*numGeneTrees, sizeof(Tree *)); for (i=0; ispeciesTree->nSubParams; i++) { geneTrees[i] = GetTree(m->speciesTree->subParams[i], chain, state[chain]); } // Allocate space for depth matrix copy numUpperTriang = numSpecies * (numSpecies - 1) / 2; oldMinDepths = (double *) SafeCalloc (2*numUpperTriang, sizeof(double)); modMinDepths = oldMinDepths + numUpperTriang; // Get min depth matrix for old gene trees GetMinDepthMatrix(geneTrees, numTopologies-1, depthMatrix); // Save a copy for (i=0; irelParts[0]]; /* get gene tree and species tree */ geneTree = GetTree (param, chain, state[chain]); speciesTree = GetTree (m->speciesTree, chain, state[chain]); /* get population size(s) */ popSizePtr = GetParamVals(m->popSize, chain, state[chain]); /* get clock rate */ if (m->clockRate == NULL) clockRate = 1.0; else clockRate = *GetParamVals(m->clockRate, chain, state[chain]); /* pick a node to be changed */ p = geneTree->intDownPass[(int)(RandomNumber(seed)*geneTree->nIntNodes)]; # if defined (DEBUG_CSLIDER) printf ("Before node slider (gene tree):\n"); printf ("Picked branch with index %d and depth %f\n", p->index, p->nodeDepth); if (p->anc->anc == NULL) printf ("Old clock rate: %f\n", clockRate); ShowNodes (t->root, 0, t->isRooted); getchar(); # endif /* get gene tree prior prob before move */ (*lnPriorRatio) -= LnPriorProbGeneTree(geneTree, clockRate, speciesTree, popSizePtr); /* store values needed later for prior calculation (relaxed clocks) */ oldPLength = p->length; if (p->left != NULL) { oldLeftLength = p->left->length; oldRightLength = p->right->length; } else oldLeftLength = oldRightLength = 0.0; /* find species tree branch to which the gene tree node belongs */ MapGeneTreeToSpeciesTree(geneTree, speciesTree); q = NULL; for (i=0; inNodes-1; i++) { q = speciesTree->allDownPass[i]; if (p->x == q->index) break; } assert (q != NULL && p->x == q->index); /* determine lower and upper bound */ /* p is interior node, so p->left is non-NULL */ minDepth = p->left->nodeDepth + POS_MIN; if (p->right->nodeDepth + POS_MIN > minDepth) minDepth = p->right->nodeDepth + POS_MIN; if (q->nodeDepth + POS_MIN > minDepth) minDepth = q->nodeDepth + POS_MIN; if (p->anc->anc == NULL) maxDepth = TREEHEIGHT_MAX; else maxDepth = p->anc->nodeDepth - POS_MIN; /* abort if impossible */ if (minDepth >= maxDepth) { abortMove = YES; return (NO_ERROR); } if (maxDepth-minDepth < window) { window = maxDepth-minDepth; } /* pick the new node depth */ oldDepth = p->nodeDepth; newDepth = oldDepth + (RandomNumber(seed) - 0.5) * window; /* reflect the new node depth */ while (newDepth < minDepth || newDepth > maxDepth) { if (newDepth < minDepth) newDepth = 2.0 * minDepth - newDepth; if (newDepth > maxDepth) newDepth = 2.0 * maxDepth - newDepth; } p->nodeDepth = newDepth; /* determine new branch lengths around p and set update of transition probabilities */ if (p->left != NULL) { p->left->length = p->nodeDepth - p->left->nodeDepth; assert (p->left->length >= POS_MIN); p->left->upDateTi = YES; p->right->length = p->nodeDepth - p->right->nodeDepth; assert (p->right->length >= POS_MIN); p->right->upDateTi = YES; } if (p->anc->anc != NULL) { p->length = p->anc->nodeDepth - p->nodeDepth; assert (p->length >= POS_MIN); p->upDateTi = YES; } /* set flags for update of cond likes from p and down to root */ q = p; while (q->anc != NULL) { q->upDateCl = YES; q = q->anc; } /* calculate proposal ratio */ (*lnProposalRatio) = 0.0; /* calculate prior ratio */ (*lnPriorRatio) += LnPriorProbGeneTree (geneTree, clockRate, speciesTree, popSizePtr); /* adjust proposal and prior ratio for relaxed clock models */ for (i=0; inSubParams; i++) { subParm = param->subParams[i]; if (subParm->paramType == P_CPPEVENTS) { nEvents = subParm->nEvents[2*chain+state[chain]]; lambda = *GetParamVals (modelSettings[subParm->relParts[0]].cppRate, chain, state[chain]); /* proposal ratio */ if (p->left != NULL) { (*lnProposalRatio) += nEvents[p->left->index ] * log (p->left->length / oldLeftLength); (*lnProposalRatio) += nEvents[p->right->index] * log (p->right->length / oldRightLength); } if (p->anc->anc != NULL) (*lnProposalRatio) += nEvents[p->index] * log (p->length / oldPLength); /* prior ratio */ if (p->anc->anc == NULL) // two branches changed in same direction (*lnPriorRatio) += lambda * (2.0 * (oldDepth - newDepth)); else if (p->left != NULL) // two branches changed in one direction, one branch in the other direction (*lnPriorRatio) += lambda * (oldDepth - newDepth); else /* if (p->left == NULL) */ // one branch changed (*lnPriorRatio) += lambda * (newDepth - oldDepth); /* update effective evolutionary lengths */ if (UpdateCppEvolLengths (subParm, p, chain) == ERROR) { abortMove = YES; return (NO_ERROR); } } else if (subParm->paramType == P_TK02BRANCHRATES) { if (subParm->paramType == P_TK02BRANCHRATES) nu = *GetParamVals (modelSettings[subParm->relParts[0]].tk02var, chain, state[chain]); else nu = *GetParamVals (modelSettings[subParm->relParts[0]].mixedvar, chain, state[chain]); rate = GetParamVals (subParm, chain, state[chain]); brlens = GetParamSubVals (subParm, chain, state[chain]); /* no proposal ratio effect */ /* prior ratio */ if (p->left != NULL) { (*lnPriorRatio) -= LnProbLogNormal_Mean_LogVar (rate[p->index], nu*oldLeftLength, rate[p->left->index]); (*lnPriorRatio) -= LnProbLogNormal_Mean_LogVar (rate[p->index], nu*oldRightLength, rate[p->right->index]); (*lnPriorRatio) += LnProbLogNormal_Mean_LogVar (rate[p->index], nu*p->left->length, rate[p->left->index]); (*lnPriorRatio) += LnProbLogNormal_Mean_LogVar (rate[p->index], nu*p->right->length, rate[p->right->index]); } if (p->anc->anc != NULL) { (*lnPriorRatio) -= LnProbLogNormal_Mean_LogVar (rate[p->anc->index], nu*oldPLength, rate[p->index]); (*lnPriorRatio) += LnProbLogNormal_Mean_LogVar (rate[p->anc->index], nu*p->length, rate[p->index]); } /* update effective evolutionary lengths */ if (p->left != NULL) { brlens[p->left->index] = p->left->length * (rate[p->left->index]+rate[p->index])/2.0; brlens[p->right->index] = p->right->length * (rate[p->right->index]+rate[p->index])/2.0; } if (p->anc->anc != NULL) { brlens[p->index] = p->length * (rate[p->index]+rate[p->anc->index])/2.0; } } else if (subParm->paramType == P_WNBRANCHRATES) { if (subParm->paramType == P_WNBRANCHRATES) var = *GetParamVals (modelSettings[subParm->relParts[0]].wnvar, chain, state[chain]); else var = *GetParamVals (modelSettings[subParm->relParts[0]].mixedvar, chain, state[chain]); rate = GetParamVals (subParm, chain, state[chain]); brlens = GetParamSubVals (subParm, chain, state[chain]); if (p->left != NULL) { (*lnPriorRatio) -= LnProbGamma (oldLeftLength/var, oldLeftLength/var, rate[p->left->index ]); (*lnPriorRatio) -= LnProbGamma (oldRightLength/var, oldRightLength/var, rate[p->right->index]); (*lnPriorRatio) += LnProbGamma (p->left->length/var, p->left->length/var, rate[p->left->index ]); (*lnPriorRatio) += LnProbGamma (p->right->length/var, p->right->length/var, rate[p->right->index]); } if (p->anc->anc != NULL) { (*lnPriorRatio) -= LnProbGamma (oldPLength/var, oldPLength/var, rate[p->index]); (*lnPriorRatio) += LnProbGamma (p->length /var, p->length /var, rate[p->index]); } if (p->left != NULL) { brlens[p->left->index ] = rate[p->left->index ] * p->left->length; brlens[p->right->index] = rate[p->right->index] * p->right->length; } if (p->anc->anc != NULL) { brlens[p->index] = rate[p->index] * p->length; } } else if ( subParm->paramType == P_ILNBRANCHRATES || (subParm->paramType == P_MIXEDBRCHRATES && *GetParamIntVals(subParm, chain, state[chain]) == RCL_ILN)) { if (subParm->paramType == P_ILNBRANCHRATES) nu = *GetParamVals (modelSettings[subParm->relParts[0]].ilnvar, chain, state[chain]); else nu = *GetParamVals (modelSettings[subParm->relParts[0]].mixedvar, chain, state[chain]); rate = GetParamVals (subParm, chain, state[chain]); brlens = GetParamSubVals (subParm, chain, state[chain]); if (p->left != NULL) { brlens[p->left->index ] = rate[p->left->index ] * p->left->length; brlens[p->right->index] = rate[p->right->index] * p->right->length; } if (p->anc->anc != NULL) { brlens[p->index] = rate[p->index] * p->length; } } else if ( subParm->paramType == P_IGRBRANCHRATES || (subParm->paramType == P_MIXEDBRCHRATES && *GetParamIntVals(subParm, chain, state[chain]) == RCL_IGR)) { if (subParm->paramType == P_IGRBRANCHRATES) var = *GetParamVals (modelSettings[subParm->relParts[0]].igrvar, chain, state[chain]); else var = *GetParamVals (modelSettings[subParm->relParts[0]].mixedvar, chain, state[chain]); rate = GetParamVals (subParm, chain, state[chain]); brlens = GetParamSubVals (subParm, chain, state[chain]); if (p->left != NULL) { brlens[p->left->index ] = rate[p->left->index ] * p->left->length; brlens[p->right->index] = rate[p->right->index] * p->right->length; } if (p->anc->anc != NULL) { brlens[p->index] = rate[p->index] * p->length; } } } # if defined (DEBUG_CSLIDER) printf ("After node slider (gene tree):\n"); printf ("Old depth: %f -- New depth: %f -- LnPriorRatio %f -- LnProposalRatio %f\n", oldDepth, newDepth, (*lnPriorRatio), (*lnProposalRatio)); ShowNodes (t->root, 0, t->isRooted); getchar(); # endif return (NO_ERROR); } /*------------------------------------------------------------------ | | Move_SpeciesTree: Propose a new species tree | ------------------------------------------------------------------*/ int Move_SpeciesTree (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp) { int i, numGeneTrees, numUpperTriang; double newLnProb, oldLnProb, backwardLnProposalProb, forwardLnProposalProb, *modMinDepths, forwardLambda, backwardLambda, lambdaDiv, mean; Tree *newSpeciesTree, *oldSpeciesTree, **geneTrees; ModelInfo *m; /* get tuning parameter (lambda divider) */ lambdaDiv = mvp[0]; /* calculate number of gene trees */ numGeneTrees = param->nSubParams; /* get model settings */ m = &modelSettings[param->relParts[0]]; /* get new and old species trees */ newSpeciesTree = GetTree (m->speciesTree, chain, state[chain]); oldSpeciesTree = GetTree (m->speciesTree, chain, state[chain] ^ 1); /* get gene trees */ geneTrees = (Tree **) SafeCalloc (numGeneTrees, sizeof(Tree*)); for (i=0; inSubParams; i++) geneTrees[i] = GetTree(param->subParams[i], chain, state[chain]); /* get minimum depth matrix */ GetMinDepthMatrix(geneTrees, numGeneTrees, depthMatrix); /* get forward lambda */ GetMeanDist(oldSpeciesTree, depthMatrix, &mean); forwardLambda = 1.0 / (mean * lambdaDiv); /* make a copy for modification */ numUpperTriang = numSpecies * (numSpecies - 1) / 2; modMinDepths = (double *) SafeCalloc (numUpperTriang, sizeof(double)); for (i=0; istring = s; \ p->fp = f; \ p->valueList = l; \ p++; #define HIDE 0 #define SHOW 1 /* Debugging options */ #undef SHOW_TOKENS #undef ECHO_PROCESSED_COMMANDS /* Local function prototypes */ int AddNameSet(NameSet **nameSetList, int numNameSets, char **nameSet, int numNames); int AddToSet (int i, int j, int k, int id); int AllocCharacters (void); int AllocMatrix (void); int AllocTaxa (void); char ChangeCase (char c); int CharacterCode (char ch, int *charCode, int chType); int CharacterNumber (int charCode, int chType); int CheckInitialPartitions (void); int Dex (TreeNode *p); int DoAbout (void); int DoAcknowledgments (void); int DoBeginParm (char *parmName, char *tkn); int DoBreaks (void); int DoBreaksParm (char *parmName, char *tkn); int DoCalibrate (void); int DoCalibrateParm (char *parmName, char *tkn); int DoCharset (void); int DoCharsetParm (char *parmName, char *tkn); int DoCharStat (void); int DoCitations (void); int DoConstraint (void); int DoConstraintParm (char *parmName, char *tkn); int DoCtype (void); int DoCtypeParm (char *parmName, char *tkn); int DoDelete (void); int DoDeleteParm (char *parmName, char *tkn); int DoDimensions (void); int DoDimensionsParm (char *parmName, char *tkn); int DoDisclaimer (void); int DoEndBlock (void); int DoExecuteParm (char *parmName, char *tkn); int DoExclude (void); int DoExcludeParm (char *parmName, char *tkn); int DoFormat (void); int DoFormatParm (char *parmName, char *tkn); int DoHelp (void); int DoHelpParm (char *parmName, char *tkn); int DoInclude (void); int DoIncludeParm (char *parmName, char *tkn); int DoLog (void); int DoLogParm (char *parmName, char *tkn); int DoManual (void); int DoManualParm (char *parmName, char *tkn); int DoMatrix (void); int DoMatrixParm (char *parmName, char *tkn); int DoNexusParm (char *parmName, char *tkn); int DoOutgroup (void); int DoOutgroupParm (char *parmName, char *tkn); int DoPairs (void); int DoPairsParm (char *parmName, char *tkn); int DoPartition (void); int DoPartitionParm (char *parmName, char *tkn); int DoRestore (void); int DoRestoreParm (char *parmName, char *tkn); int DoSet (void); int DoSetParm (char *parmName, char *tkn); int DoShowBeagle (void); int DoShowMatrix (void); int DoShowUserTrees (void); int DoSpeciespartition (void); int DoSpeciespartitionParm (char *parmName, char *tkn); int DoTaxaset (void); int DoTaxasetParm (char *parmName, char *tkn); int DoTaxaStat (void); int DoTaxlabels (void); int DoTaxlabelsParm (char *parmName, char *tkn); int DoTranslate (void); int DoTranslateParm (char *parmName, char *tkn); int DoTree (void); int DoTreeParm (char *parmName, char *tkn); int DoUserTree (void); int DoUserTreeParm (char *parmName, char *tkn); int DoVersion (void); int FindValidParam (char *tk, int *numMatches); int FreeCharacters (void); int FreeMatrix (void); int FreeTaxa (void); int GetNumPartDivisions (int n); int GetUserHelp (char *helpTkn); int IsAmbig (int charCode, int dType); int IsMissing (int charCode, int dType); int MBResID (char nuc); int NucID (char nuc); void PrintSettings (char *command); void PrintYesNo (int yn, char s[4]); int ProtID (char aa); int SetPartition (int part); int SetSpeciespartition (int part); int SetTaxaFromTranslateTable (void); int StandID (char nuc); void WhatVariableExp (BitsLong exp, char *st); /* globals */ int autoClose; /* autoclose */ int autoOverwrite; /* Overwrite or append outputfiles when nowarnings=yes */ Calibration *calibrationPtr; /* ptr to calibration being set */ CharInformation *charInfo; /* holds critical information about characters */ BitsLong **charSet; /* holds information about defined charsets */ char **charSetNames; /* holds names of character sets */ Comptree comptreeParams; /* holds parameters for comparetree command */ char **constraintNames; /* holds names of constraints */ int dataType; /* type of data */ Calibration defaultCalibration; /* default calibration */ BitsLong **definedConstraint; /* bitfields representing taxa sets of defined constraints */ BitsLong **definedConstraintTwo; /* bitfields representing second taxa sets of defined constraints (used for PARTIAL constraints) */ BitsLong **definedConstraintPruned; /* bitfields representing taxa sets of defined constraints after deleted taxa are removed */ BitsLong **definedConstraintTwoPruned; /* bitfields representing second taxa sets of defined constraints for PARTIAL constraints after deleted*/ /* taxa are removed and for NEGATIVE constraint it contains complements of definedConstraintPruned */ int echoMB; /* flag used by Manual to prevent echoing */ BitsLong expecting; /* variable denoting expected token type */ int foundNewLine; /* whether a new line has been found */ int inComment; /* flag for whether input stream is commented */ int inComparetreeCommand; /* flag set whenever you enter comparetree cmd */ int inferAncStates; /* should ancestral states be inferred (y/n) */ int inferSiteOmegas; /* should site omegas be inferred (y/n) */ int inferSiteRates; /* should site rates be inferred (y/n) */ int inMrbayesBlock; /* flag for whether we are in a mrbayes block */ int inSumtCommand; /* flag set whenever you enter sumt cmd */ int inTreesBlock; /* flag for whether we are in a trees block */ int inValidCommand; /* a useful flag set whenever you enter a cmd */ int isInAmbig, isInPoly; /* flags whether we are within () or {} */ int isTaxsetDef; /* is a taxon set defined */ int isTranslateDef; /* is a translation block defined */ int isTranslateDiff; /* is translate different from current taxaset? */ char logFileName[100]; /* name of the log file */ int logToFile; /* should screen output be logged to a file */ FILE *logFileFp; /* file pointer to log file */ int longIntegerSize; /* size of an unsigned integer */ char manFileName[100]; /* name of the file for the command help info */ int *matrix; /* matrix containing original data */ int matrixHasPoly; /* flag for whether matrix has polymorphisms */ int memAllocs[NUM_ALLOCS]; /* allocated memory flags */ int mode; /* mode of program (interactive/noninteractive) */ Calibration *nodeCalibration; /* holds information about node calibrations */ int noWarn; /* no warnings on overwriting files */ int numChar; /* number of characters in character matrix */ int numCharSets; /* number of character sets */ int numComments; /* counts how deeply nested a comment is */ int numDefinedConstraints; /* number of constraints defined */ int numDefinedPartitions; /* number of partitions defined */ int numDefinedSpeciespartitions; /* number of speciespartitions defined */ int numNamedTaxa; /* number of named taxa during parsing of cmd */ int numOpenExeFiles; /* number of execute files open */ int numSpecies; /* number of species in current speciespartition */ int numTaxa; /* number of taxa in character matrix */ int numTaxaSets; /* number of taxa sets */ int numTranslates; /* number of taxa in active translate block */ int outGroupNum; /* number of outgroup taxon */ ParmInfo paramTable[NUMPARAMS]; /* information on parameters */ char **partitionNames; /* hold names of partitions (first is "default") */ int **partitionId; /* holds information about defined partitions */ int partitionNum; /* index of current partition */ Plot plotParams; /* holds parameters for plot command */ int precision; /* precision of samples and summary stats */ int quitOnError; /* quit on error? */ int replaceLogFile; /* should logfile be replace/appended to */ int scientific; /* use scientific format for samples ? */ char spacer[10]; /* holds blanks for printing indentations */ NameSet *speciesNameSets; /* hold species name sets, one for each speciespartition */ int **speciespartitionId; /* holds info about defined speciespartitions */ char **speciespartitionNames; /* hold names of speciespartitions (first is "default") */ int speciespartitionNum; /* index of current speciespartition */ Sump sumpParams; /* holds parameters for sump command */ Sumt sumtParams; /* holds parameters for sumt command */ Sumss sumssParams; /* holds parameters for sumss command */ TaxaInformation *taxaInfo; /* holds critical information about taxa */ char **taxaNames; /* holds name of taxa */ BitsLong **taxaSet; /* holds information about defined taxasets */ char **taxaSetNames; /* holds names of taxa sets */ int *tempActiveConstraints;/* temporarily holds active constraints size allocated */ enum ConstraintType *definedConstraintsType; /* Store type of constraint */ int *tempSet; /* temporarily holds defined set */ int *tempSetNeg; /* holds bitset of negative set of taxa for partial constraint*/ int theAmbigChar; /* int containing ambiguous character */ Calibration *tipCalibration; /* holds information about node calibrations */ char **transFrom; /* translation block information */ char **transTo; /* translation block information */ int userBrlensDef; /* are the branch lengths on user tree defined */ #if defined (BEAGLE_ENABLED) int tryToUseBEAGLE; /* try to use the BEAGLE library */ int beagleScalingScheme; /* BEAGLE dynamic scaling */ int beagleScalingFrequency;/* BEAGLE dynamic scaling frequency */ long beagleFlags; /* BEAGLE required resource flags */ int beagleResourceNumber; /* BEAGLE resource number */ int *beagleResource; /* BEAGLE resource choice list */ int beagleResourceCount; /* BEAGLE resource choice list length */ int beagleInstanceCount; /* total number of BEAGLE instances */ #if defined (BEAGLE_V3_ENABLED) int beagleThreadCount; /* max number of BEAGLE CPU threads */ int beagleAllFloatTips; /* use floating-point representation for all tips */ #endif #endif /* local (to this file) */ char *tokenP, token[CMD_STRING_LENGTH], *cmdStr=NULL; Calibration defaultCalibration = { "Unconstrained", /* name */ unconstrained, /* prior */ { -1.0, -1.0, -1.0 }, /* priorParams */ NULL, /* LnPriorProb */ NULL, /* LnPriorRatio */ -1.0, /* min */ -1.0 /* max */ }; CmdType commands[] = { /* Information on commands initialization: 1 = Command number (cmdNumber) 2 = Command name (string) 3 = Special command (YES/NO) (specialCmd) 4 = Pointer to finishing function (fp) 5 = Number of valid parameters (numParms) 6 = List of valid parameters (parmList) 7 = Expecting (2^TokenType) (expect) (PARAMETER = 4; SEMICOLON = 32; ALPHA = 16384; ALPHA | QUESTIONMARK | DASH | NUMBER | ASTERISK | EXCLAMATIONMARK | PERCENT | WEIRD | SEMICOLON = 11715360; ALPHA | QUESTIONMARK | DASH | NUMBER | ASTERISK | EXCLAMATIONMARK | PERCENT | WEIRD | VERTICALBAR | SEMICOLON | LEFTPAR | RIGHTPAR | LEFTCURL | RIGHTCURL = 649252640; PARAMETER | SEMICOLON = 36; NUMBER | ALPHA = 49152; ALPHA | SEMICOLON = 16416; EQUALSIGN = 8; NUMBER = 32768) 8 = Description of the command (cmdDescription) 9 = Where should the command be used (cmdUse) (IN_CMD = used from command line or mrbayes block; IN_FILE = used in data block or in tree block) 10 = Should the command be shown when "help" is typed (hiding). #1 #2 #3 #4 #5 #6 #7 #8 #9 #10 -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- */ { 0, "#", NO, NULL, 1, {0}, 4, "", IN_FILE, HIDE }, { 1, "About", NO, DoAbout, 0, {-1}, 32, "Describes the program", IN_CMD, SHOW }, { 2, "Acknowledgments", NO, DoAcknowledgments, 0, {-1}, 32, "Shows program acknowledgments", IN_CMD, SHOW }, { 3, "Begin", NO, NULL, 6, {1,2,3,201,226,227}, 4, "Denotes beginning of block in file", IN_FILE, SHOW }, { 4, "Calibrate", NO, DoCalibrate, 1, {119}, 4, "Assigns dates to terminals or interior nodes", IN_CMD, SHOW }, { 5, "Charset", NO, DoCharset, 1, {15}, 4, "Assigns a group of sites to a set", IN_CMD, SHOW }, { 6, "Charstat", NO, DoCharStat, 0, {-1}, 32, "Shows status of characters", IN_CMD, SHOW }, { 7, "Citations", NO, DoCitations, 0, {-1}, 32, "Citation of program, models, and methods", IN_CMD, SHOW }, { 8, "Comparetree", NO, DoCompareTree, 7, {127,128,129,130,221,222,223}, 36, "Compares the trees from two tree files", IN_CMD, SHOW }, { 9, "Constraint", NO, DoConstraint, 1, {66}, 4, "Defines a constraint on tree topology", IN_CMD, SHOW }, { 10, "Ctype", NO, DoCtype, 1, {65}, 4, "Assigns ordering for the characters", IN_CMD, SHOW }, { 11, "Databreaks", YES, DoBreaks, 1, {93}, 32768, "Defines data breaks for autodiscrete gamma model", IN_CMD, SHOW }, { 12, "Delete", YES, DoDelete, 1, {47}, 49152, "Deletes taxa from the analysis", IN_CMD, SHOW }, { 13, "Dimensions", NO, DoDimensions, 2, {4,5}, 4, "Defines size of character matrix", IN_FILE, SHOW }, { 14, "Disclaimer", NO, DoDisclaimer, 0, {-1}, 32, "Describes program disclaimer", IN_CMD, SHOW }, { 15, "End", NO, DoEndBlock, 0, {-1}, 32, "Denotes end of a block in file", IN_FILE, SHOW }, { 16, "Endblock", NO, DoEndBlock, 0, {-1}, 32, "Alternative way of denoting end of a block", IN_FILE, SHOW }, { 17, "Exclude", YES, DoExclude, 1, {45}, 49152, "Excludes sites from the analysis", IN_CMD, SHOW }, { 18, "Execute", YES, DoExecute, 1, {12}, 16384, "Executes a file", IN_CMD, SHOW }, { 19, "Format", NO, DoFormat, 7, {6,7,8,9,10,219,220}, 4, "Defines character format in data block", IN_FILE, SHOW }, { 20, "Help", YES, DoHelp, 1, {50}, 16416, "Provides detailed description of commands", IN_CMD, SHOW }, { 21, "Include", YES, DoInclude, 1, {46}, 49152, "Includes sites", IN_CMD, SHOW }, { 22, "Link", NO, DoLink, 30, {55,56,57,58,59,60,61,62,63,72,73,74,75,76,105,118,193,194,195,196,197,242,243,252,253,255,256, 270,273,274}, 4, "Links parameters across character partitions", IN_CMD, SHOW }, { 23, "Log", NO, DoLog, 5, {85,86,87,88,89}, 4, "Logs screen output to a file", IN_CMD, SHOW }, { 24, "Lset", NO, DoLset, 20, {28,29,30,31,32,33,34,40,51,52,53,90,91,131,188,189,276,277,280,282},4, "Sets the parameters of the likelihood model", IN_CMD, SHOW }, { 25, "Manual", NO, DoManual, 1, {126}, 36, "Prints a command reference to a text file", IN_CMD, SHOW }, { 26, "Matrix", YES, DoMatrix, 1, {11},649252640, "Defines matrix of characters in data block", IN_FILE, SHOW }, { 27, "Mcmc", NO, DoMcmc, 46, {17,18,19,20,21,22,23,24,25,26,27,84,98,112,113,114,115,116,132,142,143,144,148,149,150,151,152, 153,154,155,156,157,158,159,160,166,169,190,191,198,199,200,202,213,214,215}, 36, "Starts Markov chain Monte Carlo analysis", IN_CMD, SHOW }, { 28, "Mcmcp", NO, DoMcmcp, 46, {17,18,19,20,21,22,23,24,25,26,27,84,98,112,113,114,115,116,132,142,143,144,148,149,150,151,152, 153,154,155,156,157,158,159,160,166,169,190,191,198,199,200,202,213,214,215}, 4, "Sets parameters of a chain (without starting analysis)", IN_CMD, SHOW }, { 29, "Outgroup", YES, DoOutgroup, 1, {78}, 49152, "Changes outgroup taxon", IN_CMD, SHOW }, { 30, "Pairs", YES, DoPairs, 1, {92}, 32768, "Defines nucleotide pairs (doublets) for stem models", IN_CMD, SHOW }, { 31, "Partition", NO, DoPartition, 1, {16}, 4, "Assigns a character partition", IN_CMD, SHOW }, { 32, "Plot", NO, DoPlot, 6, {106,107,108,109,224,225}, 36, "Plots parameters from MCMC analysis", IN_CMD, SHOW }, { 33, "Prset", NO, DoPrset, 44, {35,36,37,38,39,41,42,43,44,54,64,67,68,69,70,71,77,100,101,102,103,104,110,111,117,120,121,133, 168,172,173,174,183,184,185,218,241,246,247,251,254,269,271,272}, 4, "Sets the priors for the parameters", IN_CMD, SHOW }, { 34, "Propset", NO, DoPropset, 1, {186}, 4, "Sets proposal probabilities and tuning parameters", IN_CMD, SHOW }, { 35, "Quit", NO, DoQuit, 0, {-1}, 32, "Quits the program", IN_CMD, SHOW }, { 36, "Report", NO, DoReport, 9, {122,123,124,125,134,135,136,192,217}, 4, "Controls how model parameters are reported", IN_CMD, SHOW }, { 37, "Restore", YES, DoRestore, 1, {48}, 49152, "Restores taxa", IN_CMD, SHOW }, { 38, "Set", NO, DoSet, 23, {13,14,94,145,170,171,179,181,182,216,229,233,234,235,236,238,239,240,245,268,275,278,279}, 4, "Sets run conditions and defines active data partition", IN_CMD, SHOW }, { 39, "Showbeagle", NO, DoShowBeagle, 0, {-1}, 32, "Show available BEAGLE resources", IN_CMD, SHOW }, { 40, "Showmatrix", NO, DoShowMatrix, 0, {-1}, 32, "Shows current character matrix", IN_CMD, SHOW }, { 41, "Showmcmctrees", NO, DoShowMcmcTrees, 0, {-1}, 32, "Shows trees used in MCMC analysis", IN_CMD, SHOW }, { 42, "Showmodel", NO, DoShowModel, 0, {-1}, 32, "Shows model settings", IN_CMD, SHOW }, { 43, "Showmoves", NO, DoShowMoves, 1, {180}, 36, "Shows moves for current model", IN_CMD, SHOW }, { 44, "Showparams", NO, DoShowParams, 0, {-1}, 32, "Shows parameters in current model", IN_CMD, SHOW }, { 45, "Showusertrees", NO, DoShowUserTrees, 0, {-1}, 32, "Shows user-defined trees", IN_CMD, SHOW }, { 46,"Speciespartition", NO,DoSpeciespartition, 1, {244}, 4, "Defines a partition of tips into species", IN_CMD, SHOW }, { 47, "Ss", NO, DoSs, 50, {17,18,19,20,21,22,23,24,25,26,27,84,98,112,113,114,115,116,132,142,143,144,148,149,150,151,152, 153,154,155,156,157,158,159,160,166,169,190,191,198,199,200,202,213,214,215,248,249,250,257}, 36, "Starts stepping-stone sampling", IN_CMD, SHOW }, { 48, "Ssp", NO, DoSsp, 50, {17,18,19,20,21,22,23,24,25,26,27,84,98,112,113,114,115,116,132,142,143,144,148,149,150,151,152, 153,154,155,156,157,158,159,160,166,169,190,191,198,199,200,202,213,214,215,248,249,250,257}, 36,"Sets parameters of stepping-stone analysis (without starting)",IN_CMD, SHOW }, { 49, "Startvals", NO, DoStartvals, 1, {187}, 4, "Sets starting values of parameters", IN_CMD, SHOW }, { 50, "Sump", NO, DoSump, 14, {96,97,137,138,139,140,141,161,162,176,178,211,212,231}, 36, "Summarizes parameters from MCMC analysis", IN_CMD, SHOW }, { 51, "Sumss", NO, DoSumSs, 10, {258,259,260,261,262,263,264,265,266,267}, 36, "Summarizes parameters from stepping-stone analysis", IN_CMD, SHOW }, { 52, "Sumt", NO, DoSumt, 21, {80,81,82,95,146,147,163,164,165,167,175,177,204,205,206,207,208,209,210,230,232}, 36, "Summarizes trees from MCMC analysis", IN_CMD, SHOW }, { 53, "Taxastat", NO, DoTaxaStat, 0, {-1}, 32, "Shows status of taxa", IN_CMD, SHOW }, { 54, "Taxset", NO, DoTaxaset, 1, {49}, 4, "Assigns a group of taxa to a set", IN_CMD, SHOW }, { 55, "Taxlabels", YES, DoTaxlabels, 1, {228}, 49152, "Defines taxon labels", IN_FILE, SHOW }, { 56, "Translate", YES, DoTranslate, 1, {83}, 49152, "Defines alternative names for taxa", IN_FILE, SHOW }, { 57, "Tree", NO, DoTree, 1, {79}, 4, "Defines a tree", IN_FILE, SHOW }, { 58, "Unlink", NO, DoUnlink, 30, {55,56,57,58,59,60,61,62,63,72,73,74,75,76,105,118,193,194,195,196,197,242,243,252,253,255,256, 270,273,274}, 4, "Unlinks parameters across character partitions", IN_CMD, SHOW }, { 59, "Usertree", YES, DoUserTree, 1, {203}, 8, "Defines a single user tree", IN_CMD, HIDE }, { 60, "Version", NO, DoVersion, 0, {-1}, 32, "Shows program version", IN_CMD, SHOW }, { 61, "Compareref", NO, DoCompRefTree, 7, {127,128,129,130,221,222,223}, 36, "Compares the tree to the reference trees", IN_CMD, HIDE }, /* NOTE: If you add a command here, make certain to change NUMCOMMANDS (above, in this file) appropriately! */ { 999, NULL, NO, NULL, 0, {-1}, 32, "", IN_CMD, HIDE } }; int inDataBlock, inForeignBlock, isInterleaved, isFirstMatrixRead, isFirstInterleavedBlock, taxonCount, fromI, toJ, everyK, foundDash, foundSlash, foundFirst, isMixed, whichPartition, isNegative, numDivisions, charOrdering, foundExp, foundColon, isFirstNode, nextAvailableNode, pairId, firstPair, inTaxaBlock, inCharactersBlock, foundEqual; char gapId, missingId, matchId, tempSetName[100], **tempNames; CmdType *commandPtr; /* Points to the commands array entry which corresponds to currently processed command */ ParmInfoPtr paramPtr; /* Points to paramTable table array entry which corresponds to currently processed parameter of current command */ TreeNode *pPtr, *qPtr; enum ConstraintType constraintType; /* Used only in processing of constraint command to indicate the type of constraint */ int AddToGivenSet (int i, int j, int k, int id, int *Set) { int m, n; if (id <= 0) { MrBayesPrint ("%s The id for a temporary set should be greater than 0\n", spacer); return (ERROR); } if (i < 0 && j < 0) return (ERROR); else if (i < 0 && j >= 0) return (ERROR); else if (i >= 0 && j < 0) { if (k >= 0) return (ERROR); else { if (Set[i] != 0) { MrBayesPrint ("%s Character %d defined more than once\n", spacer, i+1); return (ERROR); } Set[i] = id; } } else if (i >= 0 && j >= 0) { if (k < 0) { for (m=i; m<=j; m++) { if (Set[m] != 0) { MrBayesPrint ("%s Character %d defined more than once\n", spacer, m+1); return (ERROR); } Set[m] = id; } } else { n = k; for (m=i; m<=j; m++) { if (n % k == 0) { if (Set[m] != 0) { MrBayesPrint ("%s Character %d defined more than once\n", spacer, m+1); return (ERROR); } Set[m] = id; } n++; } } } return (NO_ERROR); } int AddToSet (int i, int j, int k, int id) { return AddToGivenSet (i, j, k,id, tempSet); } /* AddNameSet: Push a name set onto the end of a list of name sets, with reallocation of list to hold the extra element. The calling function needs to keep track of the counter holding the length of the list. */ int AddNameSet (NameSet **nameSetList, int numNameSets, char **nameSet, int numNames) { int i; (*nameSetList) = (NameSet*) SafeRealloc ((void*)(*nameSetList), ((size_t)numNameSets+1)*sizeof(NameSet)); (*nameSetList)[numNameSets].names = NULL; (*nameSetList)[numNameSets].numNames = numNames; for (i=0; i numTaxa) tempSetSize = numChar; else tempSetSize = numTaxa; tempSet = (int *) SafeRealloc ((void *)tempSet, (size_t)tempSetSize * sizeof(int)); tempSetNeg = (int *) SafeRealloc ((void *)tempSetNeg, (size_t)tempSetSize * sizeof(int)); if (!tempSet || !tempSetNeg) { MrBayesPrint ("%s Problem reallocating tempSet (%d)\n", spacer, tempSetSize * sizeof(int)); goto errorExit; } MrBayesPrint ("%s Allocated matrix\n", spacer); return (NO_ERROR); errorExit: MrBayesPrint ("%s Problem allocating matrix\n", spacer); FreeMatrix(); return (ERROR); } int AllocMatrix (void) { if (memAllocs[ALLOC_TAXA] == NO && AllocTaxa() == ERROR) return ERROR; else return (AllocCharacters()); } int AllocTaxa (void) { int i; if (defTaxa == NO) { MrBayesPrint ("%s Number of taxa not defined\n", spacer); return (ERROR); } if (numTaxa == 0) { MrBayesPrint ("%s Number of taxa is 0\n", spacer); return (ERROR); } /* allocate space for taxa */ if (memAllocs[ALLOC_TAXA] == YES) goto errorExit; taxaNames = NULL; /* This variable is allocated in AddString */ taxaInfo = (TaxaInformation *) SafeMalloc ((size_t) numTaxa * sizeof (TaxaInformation)); if (!taxaInfo) goto errorExit; tipCalibration = (Calibration *) SafeMalloc ((size_t) numTaxa * sizeof (Calibration)); if (!tipCalibration) { free (taxaInfo); taxaInfo = NULL; goto errorExit; } for (i = 0; i < numTaxa; i++) { taxaInfo[i].isDeleted = NO; taxaInfo[i].charCount = 0; } memAllocs[ALLOC_TAXA] = YES; /* taxa sets */ if (memAllocs[ALLOC_TAXASETS] == YES) goto errorExit; taxaSetNames = NULL; taxaSet = NULL; numTaxaSets = 0; memAllocs[ALLOC_TAXASETS] = YES; /* safe to free */ /* species partitions; allocate space and set default species partition */ if (memAllocs[ALLOC_SPECIESPARTITIONS] == YES) goto errorExit; speciespartitionNames = NULL; speciesNameSets = NULL; speciespartitionId = (int **) SafeMalloc ((size_t) numTaxa * sizeof (int *)); for (i = 0; i < numTaxa; i++) { speciespartitionId[i] = (int *) SafeMalloc (sizeof (int)); speciespartitionId[i][0] = i + 1; /* 1-based taxon index, do not ask me why */ } numDefinedSpeciespartitions = 0; /* number of defined species partitions */ memAllocs[ALLOC_SPECIESPARTITIONS] = YES; /* safe to do free */ /* constraints */ if (memAllocs[ALLOC_CONSTRAINTS] == YES) goto errorExit; constraintNames = NULL; definedConstraintsType = NULL; definedConstraint = NULL; definedConstraintTwo = NULL; definedConstraintPruned = NULL; definedConstraintTwoPruned = NULL; numDefinedConstraints = 0; tempActiveConstraints = NULL; memAllocs[ALLOC_CONSTRAINTS] = YES; /* safe to free */ /* translate table */ transFrom = NULL; transTo = NULL; numTranslates = 0; /* tempSet */ if (memAllocs[ALLOC_TMPSET] == YES) goto errorExit; tempSet = (int *) SafeMalloc ((size_t) numTaxa * sizeof (int)); tempSetNeg = (int *) SafeMalloc ((size_t) numTaxa * sizeof (int)); if (!tempSet || !tempSetNeg) goto errorExit; memAllocs[ALLOC_TMPSET] = YES; /* make sure previous user trees are freed */ if (numUserTrees > 0) { MrBayesPrint ("%s Previous user trees not freed\n", spacer); goto errorExit; } MrBayesPrint ("%s Allocated taxon set\n", spacer); return NO_ERROR; errorExit: MrBayesPrint ("%s Problem allocating taxon set\n", spacer); FreeTaxa(); return ERROR; } char ChangeCase (char c) { int x; x = tolower(c); return (x); } int CharacterCode (char ch, int *charCode, int chType) { if (chType == DNA || chType == RNA) { if ((*charCode = NucID (ch)) == -1) { MrBayesPrint ("%s Unrecognized DNA/RNA character '%c'\n", spacer, ch); return (ERROR); } } else if (chType == PROTEIN) { if ((*charCode = ProtID (ch)) == -1) { MrBayesPrint ("%s Unrecognized Protein character '%c'\n", spacer, ch); return (ERROR); } } else if (chType == RESTRICTION) { if ((*charCode = MBResID (ch)) == -1) { MrBayesPrint ("%s Unrecognized Restriction character '%c'\n", spacer, ch); return (ERROR); } } else if (chType == STANDARD) { if ((*charCode = StandID (ch)) == -1) { MrBayesPrint ("%s Unrecognized Standard character '%c'\n", spacer, ch); return (ERROR); } } else if (chType == CONTINUOUS) { MrBayesPrint ("%s CharacterCode function cannot check continuous characters\n", spacer); } else { MrBayesPrint ("%s Unrecognized character type (%d)\n", spacer, chType); return (ERROR); } return (NO_ERROR); } int CharacterNumber (int charCode, int chType) { int i, x = charCode; if (chType == CONTINUOUS) return 0; for (i=0; x!=0; i++) x >>= 1; return (i); } int CheckInitialPartitions (void) { int i; for (i=0; i numDivisions) { MrBayesPrint ("%s The partition for site %d is incorrect\n", spacer, i+1); return (ERROR); } } return (NO_ERROR); } int CheckStringValidity (char *s) { int i, numUnknownChars, tempNumComments, tempInComment; char temp[100]; i = 0; numUnknownChars = 0; tempNumComments = numComments; tempInComment = inComment; while (s[i] != '\0') { if (tempInComment == NO) { if (!IsIn(s[i],"=abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ_0123456789.;:,#()[]?-*/'\\'!%\"&~+^$@|{}`>< ")) { if (IsWhite(s[i]) == 1 || IsWhite(s[i]) == 2) { } else { if (commandPtr == NULL) return (ERROR); MrBayesPrint ("%s Unknown character \"%c\" (ASCII code %d)\n", spacer, s[i], s[i]); if (!strcmp(commandPtr->string,"Matrix")) { if (foundNewLine == NO) { MrBayesPrint ("%s The error is in character %d for taxon %s\n", spacer, taxaInfo[taxonCount-1].charCount+i+1, "???"); /* bug? */ } else { if (taxonCount == 0) MrBayesPrint ("%s The error is in the first taxon name\n", spacer); else { strcpy(temp, taxaNames[taxonCount]); if (isInterleaved == NO) MrBayesPrint ("%s The error is in the name of the taxon following taxon %s\n", spacer, temp); else { MrBayesPrint ("%s The error is in the name of the taxon following taxon %s\n", spacer, temp); MrBayesPrint ("%s in one of the interleaved data blocks\n", spacer); } } } } else if (!strcmp(commandPtr->string,"Execute")) { MrBayesPrint ("%s Assuming irrelevant characters at beginning of file; processing continues\n", spacer); return (NO_ERROR); } return (ERROR); } } if (s[i]=='[') { tempInComment = YES; tempNumComments++; } } else if (tempInComment == YES) { if (s[i]==']') { tempNumComments--; if (tempNumComments == 0) tempInComment = NO; } } i++; } if (numUnknownChars > 0) return (ERROR); else return (NO_ERROR); } /* CheckString: This function simply checks a vector of strings for a match against token. Upon return, matchIndex contains the index of the matched string. An ERROR is returned if there are no matches. */ int CheckString (char **list, int len, char *token, int *matchIndex) { int i; *matchIndex = -1; for (i=0; iindex; } int DoAbout (void) { MrBayesPrint (" --------------------------------------------------------------------------- \n"); MrBayesPrint (" About the program \n"); MrBayesPrint (" \n"); MrBayesPrint (" MrBayes is a program for the Bayesian estimation of phylogeny. Bayesian \n"); MrBayesPrint (" inference of phylogeny is based upon the posterior probability distribution \n"); MrBayesPrint (" of trees. Trees are labelled T1, T2, ..., Tn, where n is the number of \n"); MrBayesPrint (" possible trees. The posterior probability of the i-th tree is calculated \n"); MrBayesPrint (" using Bayes\'s formula as \n"); MrBayesPrint (" \n"); MrBayesPrint (" Pr[Ti | X] = Pr[X | Ti] X Pr[Ti] / Pr[X] \n"); MrBayesPrint (" \n"); MrBayesPrint (" where X is a character matrix. Here, \"Pr[Ti | X]\" is the posterior \n"); MrBayesPrint (" probability of the i-th tree, \"Pr[X | Ti]\" is the likelihood of the \n"); MrBayesPrint (" i-th tree, and \"Pr[Ti]\" is the prior probability of the i-th tree. The \n"); MrBayesPrint (" denominator of Bayes\'s formula (\"Pr[X]\") is a normalizing constant that \n"); MrBayesPrint (" involves a summation over all possible trees. The likelihood, as described \n"); MrBayesPrint (" above, cannot be calculated with knowledge of only the tree\'s topology. You \n"); MrBayesPrint (" also need to have information on the lengths of the branches and on the \n"); MrBayesPrint (" mechanism of character change. Hence, the likelihood (\"Pr[X | Ti]\") \n"); MrBayesPrint (" involves a multidimensional integral over all possible combinations of \n"); MrBayesPrint (" branch lengths and substitution model parameters. \n"); MrBayesPrint (" \n"); MrBayesPrint (" In practice, it is impossible to calculate the posterior probability dist- \n"); MrBayesPrint (" ribution of trees analytically. Instead, the posterior probability \n"); MrBayesPrint (" of trees must be approximated. MrBayes uses a method called Markov chain \n"); MrBayesPrint (" Monte Carlo (MCMC) to approximate the posterior probability of trees. \n"); MrBayesPrint (" The object of MCMC is to construct a Markov chain that has as its state \n"); MrBayesPrint (" space the parameters of the phylogenetic model and a stationary distribution \n"); MrBayesPrint (" that is the posterior probability distribution of trees. MCMC takes valid, \n"); MrBayesPrint (" albeit dependent, samples from the posterior probability distribution of \n"); MrBayesPrint (" trees. The fraction of the time any tree appears in this sample is a \n"); MrBayesPrint (" valid approximation of the posterior probability of the tree. MrBayes keeps \n"); MrBayesPrint (" track of all the parameters of the phylogenetic model. The trees (with branch \n"); MrBayesPrint (" lengths) that were sampled by the MCMC procedure are saved in one file \n"); MrBayesPrint (" (a file with a \".t\" extension) whereas the parameters of the model of \n"); MrBayesPrint (" character change are saved in another file (a file with a \".p\" ext- \n"); MrBayesPrint (" ension). You can summarize the results in the \".t\" and \".p\" files \n"); MrBayesPrint (" using the \"sumt\" and \"sump\" commands, respectively. \n"); MrBayesPrint (" \n"); MrBayesPrint (" MrBayes was originally written by John Huelsenbeck in August of 2000 and was \n"); MrBayesPrint (" intended to be distributed to a small number of people. In March of 2001, \n"); MrBayesPrint (" Fredrik Ronquist started making contributions to the program. The contribu- \n"); MrBayesPrint (" tions were of such a significant nature that he was made a coauthor of the \n"); MrBayesPrint (" program. Version 3 of MrBayes was a fully joint effort, started in the summer \n"); MrBayesPrint (" of 2002 when JPH visited Sweden on a grant from the Wenner-Gren Foundations. \n"); MrBayesPrint (" Several others have contributed to the MrBayes code since then, most notably \n"); MrBayesPrint (" Paul van der Mark, Maxim Teslenko and Chi Zhang, all postdocs/programmers in \n"); MrBayesPrint (" Fredrik's lab. A large number of users and students, too many to list here, \n"); MrBayesPrint (" have also contributed importantly to the project (type 'Acknowledgments' for \n"); MrBayesPrint (" a list of some of them). \n"); MrBayesPrint (" \n"); MrBayesPrint (" From 2003, MrBayes was distributed from SourceForge; the repository was moved \n"); MrBayesPrint (" to GitHub in 2018, when the project entered maintenance phase and the res- \n"); MrBayesPrint (" ponsibility was transferred to National Bioinformatics Infrastructure Sweden \n"); MrBayesPrint (" (https://nbis.se), where Andreas Kahari and Johan Nylander jointly take care \n"); MrBayesPrint (" of bug fixes, documentation updates, and other maintenance tasks. \n"); MrBayesPrint (" --------------------------------------------------------------------------- \n"); return (NO_ERROR); } int DoAcknowledgments (void) { MrBayesPrint (" --------------------------------------------------------------------------- \n"); MrBayesPrint (" Acknowledgments \n"); MrBayesPrint (" \n"); MrBayesPrint (" JPH and FR would like to thank Gautam Altekar, Andrea Betancourt, Jon \n"); MrBayesPrint (" Bollback, Barry Hall, Jimmy McGuire, Rasmus Nielsen, David Swofford, \n"); MrBayesPrint (" Johan Nylander, Mikael Thollesson, and Derrick Zwickl for help during the \n"); MrBayesPrint (" initial development of this program. Gautam Altekar, especially, was instru- \n"); MrBayesPrint (" mental in getting the parallel version of the program working. Important bug- \n"); MrBayesPrint (" fixes and additional functionality was contributed by Clemens Lakner, Sebas- \n"); MrBayesPrint (" tian Hoehna, Paul Lewis, Mark Holder, Julian Catchen and Bret Larget. Marc \n"); MrBayesPrint (" Suchard, Daniel Ayres and Aaron Darling got MrBayes working with beagle and \n"); MrBayesPrint (" contributed a lot of related functionality and bug fixes. Aaron Darling was \n"); MrBayesPrint (" instrumental in getting the Windows installer set up. Liu Liang and Dennis \n"); MrBayesPrint (" Pearl helped integrate MrBayes with BEST. \n"); MrBayesPrint (" \n"); MrBayesPrint (" Bug fixes and user support was provided by Paul van der Mark (2005-2007), \n"); MrBayesPrint (" Maxim Teslenko (2010-2012) and Chi Zhang (2012-2015). From 2015, Andreas \n"); MrBayesPrint (" Kahari and Johan Nylander at the National Bioinformatics Infrastructure \n"); MrBayesPrint (" Sweden (NBIS; https://nbis.se) have been responsible for bug fixes and \n"); MrBayesPrint (" maintenance of the code base. \n"); MrBayesPrint (" \n"); MrBayesPrint (" Our wives -- Edna Huelsenbeck and Eva Ronquist -- showed extraordinary \n"); MrBayesPrint (" patience with us while we spent many late nights programming. \n"); MrBayesPrint (" \n"); MrBayesPrint (" JPH was supported by NSF grants DEB-007540 and MCB-0075404 and a Wenner- \n"); MrBayesPrint (" Gren scholarship while writing this program. FR was supported by grants \n"); MrBayesPrint (" from the Swedish Natural Science Research Council and the Swedish Research \n"); MrBayesPrint (" Council, which also supports NBIS work on MrBayes. \n"); MrBayesPrint (" --------------------------------------------------------------------------- \n"); return (NO_ERROR); } int DoBeginParm (char *parmName, char *tkn) { if (expecting == Expecting(PARAMETER)) { /* set Data (inDataBlock) *************************************************************/ if (!strcmp(parmName, "Data")) { if (FreeModel () == ERROR) return (ERROR); if (FreeMatrix () == ERROR) return (ERROR); MrBayesPrint (" Reading data block\n"); inDataBlock = YES; expecting = Expecting(SEMICOLON); strcpy (spacer, " "); } /* set Characters (inCharactersBlock) *************************************************************/ else if (!strcmp(parmName, "Characters")) { if (FreeModel () == ERROR) return (ERROR); if (FreeCharacters () == ERROR) return (ERROR); MrBayesPrint (" Reading characters block\n"); inCharactersBlock = YES; expecting = Expecting(SEMICOLON); strcpy (spacer, " "); } /* set Taxa (inTaxaBlock) *************************************************************/ else if (!strcmp(parmName, "Taxa")) { if (FreeModel () == ERROR) return (ERROR); if (FreeMatrix () == ERROR) return (ERROR); MrBayesPrint (" Reading taxa block\n"); inTaxaBlock = YES; expecting = Expecting(SEMICOLON); strcpy (spacer, " "); } /* set Mrbayes (inMrbayesBlock) *******************************************************/ else if (!strcmp(parmName, "Mrbayes")) { MrBayesPrint (" Reading mrbayes block\n"); inMrbayesBlock = YES; expecting = Expecting(SEMICOLON); strcpy (spacer, " "); } /* set Trees (inTreesBlock) *******************************************************/ else if (!strcmp(parmName, "Trees")) { MrBayesPrint (" Reading trees block\n"); inTreesBlock = YES; expecting = Expecting(SEMICOLON); strcpy (spacer, " "); } /* set Foreign (inForeignBlock) *******************************************************/ else { MrBayesPrint (" Skipping \"%s\" block\n", tkn); inForeignBlock = YES; expecting = Expecting(SEMICOLON); strcpy (spacer, ""); } } else return (ERROR); return (NO_ERROR); } int DoBreaks (void) { int i, numBreaks; numBreaks = 0; for (i=0; i 0) { if (numBreaks == 1) MrBayesPrint ("%s One data break found after character ", spacer, numBreaks); else MrBayesPrint ("%s %d data breaks found after characters: ", spacer, numBreaks); for (i=0; i numChar) { MrBayesPrint ("%s Character number %d is out of range (should be between %d and %d)\n", spacer, tempInt, 1, numChar); for (i=0; i 1) { MrBayesPrint ("%s Both a taxon and a constraint named ""%s"" encountered -- please rename one\n", spacer, tkn); return (ERROR); } /* get ready to find the equal sign */ expecting = Expecting(EQUALSIGN); } else if (expecting == Expecting(EQUALSIGN)) { /* get ready to find the calibration prior */ expecting = Expecting(ALPHA); } else if (expecting == Expecting(ALPHA)) { /* set the calibration prior type */ if (IsArgValid(tkn,tempStr) == NO_ERROR) { if (!strcmp (tempStr, "Unconstrained")) calPrior = unconstrained; else if (!strcmp (tempStr, "Fixed")) calPrior = fixed; else if (!strcmp (tempStr, "Uniform")) calPrior = uniform; else if (!strcmp (tempStr, "Offsetexponential")) calPrior = offsetExponential; else if (!strcmp (tempStr, "Truncatednormal")) calPrior = truncatedNormal; else if (!strcmp (tempStr, "Lognormal")) calPrior = logNormal; else if (!strcmp (tempStr, "Offsetlognormal")) calPrior = offsetLogNormal; else if (!strcmp (tempStr, "Gamma")) calPrior = standardGamma; else if (!strcmp (tempStr, "Offsetgamma")) calPrior = offsetGamma; if (calPrior == unconstrained) { /* reset the values of the calibration */ MrBayesPrint ("%s Resetting previous calibration for ""%s""\n", spacer, nodeName); calibrationPtr->prior = defaultCalibration.prior; calibrationPtr->priorParams[0] = defaultCalibration.priorParams[0]; calibrationPtr->priorParams[1] = defaultCalibration.priorParams[1]; calibrationPtr->priorParams[2] = defaultCalibration.priorParams[2]; calibrationPtr->LnPriorProb = defaultCalibration.LnPriorProb; calibrationPtr->LnPriorRatio = defaultCalibration.LnPriorRatio; calibrationPtr->min = defaultCalibration.min; calibrationPtr->max = defaultCalibration.max; strcpy(calibrationPtr->name, defaultCalibration.name); expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else { strcpy (calName, tempStr); paramIndex = 0; priorParams[0] = priorParams[1] = priorParams[2] = -1.0; expecting = Expecting(LEFTPAR); } } else { MrBayesPrint ("%s Invalid calibration prior argument \n", spacer); return (ERROR); } } else if (expecting == Expecting(LEFTPAR)) { strcat (calName, "("); expecting = Expecting(NUMBER); } else if (expecting == Expecting(NUMBER)) { sscanf (tkn, "%lf", &tempD); if (paramIndex == 0) { if (calPrior == logNormal) { if (tempD < 0.0) { MrBayesPrint ("%s Mean age must be nonnegative\n", spacer); MrBayesPrint ("%s Parameters of the lognormal distribution used for dating are mean age\n", spacer); MrBayesPrint ("%s and standard deviation, both specified on the linear scale, not as log values.\n", spacer); return (ERROR); } } else if (calPrior == standardGamma) { if (tempD <= 0.0) { MrBayesPrint ("%s Mean parameter must be positive\n", spacer); MrBayesPrint ("%s Parameters of the gamma distribution used for dating are mean age and\n", spacer); MrBayesPrint ("%s standard deviation. In terms of the common shape (alpha) and rate (beta)\n", spacer); MrBayesPrint ("%s parameterization, the expected mean is alpha/beta and the standard\n", spacer); MrBayesPrint ("%s deviation is the square root of (alpha / beta^2).\n", spacer); return (ERROR); } } else if (tempD < 0.0) { if (calPrior == fixed) MrBayesPrint ("%s Fixed age must be nonnegative\n", spacer); else if (calPrior == uniform) { MrBayesPrint ("%s Minimum age must be nonnegative\n", spacer); MrBayesPrint ("%s Parameters of the uniform are minimum age and maximum age.\n", spacer); } else if (calPrior == truncatedNormal) { MrBayesPrint ("%s Offset (minimum or truncation) age must be nonnegative.\n", spacer); MrBayesPrint ("%s Parameters of the truncated normal distribution are offset (minimum\n", spacer); MrBayesPrint ("%s or truncation) age, mean age and standard deviation.\n", spacer); } else if (calPrior == offsetGamma) { MrBayesPrint ("%s Offset age must be nonnegative\n", spacer); MrBayesPrint ("%s Parameters of the offset gamma distribution used for dating are offset age,\n", spacer); MrBayesPrint ("%s mean age, and standard deviation. In terms of the common shape (alpha) and\n", spacer); MrBayesPrint ("%s rate (beta) parameterization, the expected mean is alpha/beta and the standard\n", spacer); MrBayesPrint ("%s deviation is the square root of (alpha / beta^2).\n", spacer); } else if (calPrior == offsetExponential) { MrBayesPrint ("%s Offset age must be nonnegative\n", spacer); MrBayesPrint ("%s Parameters of the offset exponential are offset age and mean age.\n", spacer); } else if (calPrior == offsetLogNormal) { MrBayesPrint ("%s Offset age must be nonnegative\n", spacer); MrBayesPrint ("%s Parameters of the offset lognormal distribution are offset age, mean age,\n", spacer); MrBayesPrint ("%s and standard deviation. All values are specified on the linear scale, not\n", spacer); MrBayesPrint ("%s as log values.\n", spacer); } return (ERROR); } priorParams[0] = tempD; if (calPrior == fixed) expecting = Expecting(RIGHTPAR); else expecting = Expecting(COMMA); } else if (paramIndex == 1) { if (calPrior == uniform) { if (tempD <= priorParams[0]) { MrBayesPrint ("%s Maximum age of uniform distribution must be larger than minimum age\n", spacer); return (ERROR); } } else if (calPrior == offsetExponential) { if (tempD <= priorParams[0]) { MrBayesPrint ("%s Mean age must be larger than offset age.\n", spacer); MrBayesPrint ("%s MrBayes now uses offset and mean rather than offset and rate\n", spacer); MrBayesPrint ("%s as the parameters for the offset exponential distribution.\n", spacer); return (ERROR); } } else if (calPrior == truncatedNormal) { if (tempD <= priorParams[0]) { MrBayesPrint ("%s Mean age must be larger than offset (truncation) age.\n", spacer); MrBayesPrint ("%s Parameters of the truncated normal distribution are offset (minimum\n", spacer); MrBayesPrint ("%s or truncation) age, mean age and standard deviation\n", spacer); return (ERROR); } } else if (calPrior == logNormal) { if (tempD <= 0.0) { MrBayesPrint ("%s Standard deviation must be positive.\n", spacer); MrBayesPrint ("%s Parameters of the lognormal distribution used for dating are mean age\n", spacer); MrBayesPrint ("%s and standard deviation, both specified on the linear scale, not as log values.\n", spacer); return (ERROR); } } else if (calPrior == offsetLogNormal) { if (tempD <= priorParams[0]) { MrBayesPrint ("%s Mean age must be larger than offset age.\n", spacer); MrBayesPrint ("%s Parameters of the offset lognormal distribution are offset age, mean age,\n", spacer); MrBayesPrint ("%s and standard deviation. All values are specified on the linear scale, not\n", spacer); MrBayesPrint ("%s as log values.\n", spacer); return (ERROR); } } else if (calPrior == standardGamma) { if (tempD <= 0.0) { MrBayesPrint ("%s Standard deviation must be positive.\n", spacer); MrBayesPrint ("%s Parameters of the gamma distribution used for dating are mean age and\n", spacer); MrBayesPrint ("%s standard deviation. In terms of the common shape (alpha) and rate (beta)\n", spacer); MrBayesPrint ("%s parameterization, the expected mean is alpha/beta and the standard\n", spacer); MrBayesPrint ("%s deviation is the square root of (alpha / beta^2).\n", spacer); return (ERROR); } } else if (calPrior == offsetGamma) { if (tempD <= 0.0) { MrBayesPrint ("%s Mean age must be positive.\n", spacer); MrBayesPrint ("%s Parameters of the offset gamma distribution used for dating are offset age,\n", spacer); MrBayesPrint ("%s mean age, and standard deviation. In terms of the common shape (alpha) and\n", spacer); MrBayesPrint ("%s rate (beta) parameterization, the expected mean is alpha/beta and the standard\n", spacer); MrBayesPrint ("%s deviation is the square root of (alpha / beta^2).\n", spacer); return (ERROR); } } priorParams[1] = tempD; if (calPrior == uniform || calPrior == standardGamma || calPrior == logNormal || calPrior == offsetExponential) expecting = Expecting(RIGHTPAR); else expecting = Expecting(COMMA); } else /* if (paramIndex == 2) */ { if (calPrior == offsetGamma) { if (tempD <= 0.0) { MrBayesPrint ("%s Standard deviation must be positive.\n", spacer); MrBayesPrint ("%s Parameters of the offset gamma distribution used for dating are offset age,\n", spacer); MrBayesPrint ("%s mean age, and standard deviation. In terms of the common shape (alpha) and\n", spacer); MrBayesPrint ("%s rate (beta) parameterization, the expected mean is alpha/beta and the standard\n", spacer); MrBayesPrint ("%s deviation is the square root of (alpha / beta^2).\n", spacer); return (ERROR); } } else if (calPrior == offsetLogNormal) { if (tempD <= 0.0) { MrBayesPrint ("%s Standard deviation must be positive.\n", spacer); MrBayesPrint ("%s Parameters of the offset lognormal distribution are offset age, mean age,\n", spacer); MrBayesPrint ("%s and standard deviation. All values are specified on the linear scale, not\n", spacer); MrBayesPrint ("%s as log values.\n", spacer); return (ERROR); } } priorParams[2] = tempD; expecting = Expecting(RIGHTPAR); } sprintf (s, "%1.2lf", tempD); strcat (calName, s); } else if (expecting == Expecting(COMMA)) { strcat (calName, ","); paramIndex++; expecting = Expecting(NUMBER); } else if (expecting == Expecting(RIGHTPAR)) { strcat (calName, ")"); if (isTaxon == YES) MrBayesPrint ("%s Setting age of taxon '%s' to %s\n", spacer, nodeName, calName); else MrBayesPrint ("%s Setting age of constraint node '%s' to %s\n", spacer, nodeName, calName); /* set calibration based on collected values and settings */ strcpy(calibrationPtr->name, calName); calibrationPtr->priorParams[0] = priorParams[0]; calibrationPtr->priorParams[1] = priorParams[1]; calibrationPtr->priorParams[2] = priorParams[2]; calibrationPtr->prior = calPrior; if (calPrior == fixed) { calibrationPtr->LnPriorProb = &LnPriorProbFix; calibrationPtr->LnPriorRatio = &LnProbRatioFix; calibrationPtr->min = priorParams[0]; calibrationPtr->max = priorParams[0]; } else if (calPrior == uniform) { calibrationPtr->LnPriorProb = &LnPriorProbUniform; calibrationPtr->LnPriorRatio = &LnProbRatioUniform; calibrationPtr->min = priorParams[0]; calibrationPtr->max = priorParams[1]; } else if (calPrior == offsetExponential) { calibrationPtr->LnPriorProb = &LnPriorProbOffsetExponential_Param_Offset_Mean; calibrationPtr->LnPriorRatio = &LnProbRatioOffsetExponential_Param_Offset_Mean; calibrationPtr->min = priorParams[0]; calibrationPtr->max = POS_INFINITY; } else if (calPrior == truncatedNormal) { calibrationPtr->LnPriorProb = &LnPriorProbTruncatedNormal_Param_Trunc_Mean_Sd; calibrationPtr->LnPriorRatio = &LnProbRatioTruncatedNormal_Param_Trunc_Mean_Sd; calibrationPtr->min = priorParams[0]; calibrationPtr->max = POS_INFINITY; } else if (calPrior == logNormal) { calibrationPtr->LnPriorProb = &LnPriorProbLognormal_Param_Mean_Sd; calibrationPtr->LnPriorRatio = &LnProbRatioLognormal_Param_Mean_Sd; calibrationPtr->min = BRLENS_MIN; calibrationPtr->max = POS_INFINITY; } else if (calPrior == offsetLogNormal) { calibrationPtr->LnPriorProb = &LnPriorProbOffsetLognormal_Param_Offset_Mean_Sd; calibrationPtr->LnPriorRatio = &LnProbRatioOffsetLognormal_Param_Offset_Mean_Sd; calibrationPtr->min = BRLENS_MIN + priorParams[0]; calibrationPtr->max = POS_INFINITY; } else if (calPrior == standardGamma) { calibrationPtr->LnPriorProb = &LnPriorProbGamma_Param_Mean_Sd; calibrationPtr->LnPriorRatio = &LnProbRatioGamma_Param_Mean_Sd; calibrationPtr->min = BRLENS_MIN; calibrationPtr->max = POS_INFINITY; } else if (calPrior == offsetGamma) { calibrationPtr->LnPriorProb = &LnPriorProbOffsetGamma_Param_Offset_Mean_Sd; calibrationPtr->LnPriorRatio = &LnProbRatioOffsetGamma_Param_Offset_Mean_Sd; calibrationPtr->min = BRLENS_MIN + priorParams[0]; calibrationPtr->max = POS_INFINITY; } /* get ready to find more calibrated nodes or taxa, if present */ expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); return (NO_ERROR); } int DoCharset (void) { /* first add set to tempSet */ if (fromI >= 0 && toJ < 0) { if (AddToSet (fromI, toJ, everyK, 1) == ERROR) return (ERROR); } else if (fromI >= 0 && toJ >= 0 && everyK < 0) { if (AddToSet (fromI, toJ, everyK, 1) == ERROR) return (ERROR); } else if (fromI >= 0 && toJ >= 0 && everyK >= 0) { if (AddToSet (fromI, toJ, everyK, 1) == ERROR) return (ERROR); } /* add name to charSetNames */ if (AddString (&charSetNames, numCharSets, tempSetName) == ERROR) { MrBayesPrint ("%s Problem adding charset %s to list\n", spacer, tempSetName); return (ERROR); } /* store charset */ AddBitfield (&charSet, numCharSets, tempSet, numChar); /* increment number of char sets */ numCharSets++; return (NO_ERROR); } int DoCharsetParm (char *parmName, char *tkn) { int i, index, tempInt, allDigit; if (defMatrix == NO) { MrBayesPrint ("%s A matrix must be specified before charsets can be defined\n", spacer); return (ERROR); } if (expecting == Expecting(PARAMETER)) { if (!strcmp(parmName, "Xxxxxxxxxx")) { /* check that the name of the charset is not a number */ allDigit = YES; for (i=0; i<(int)strlen(tkn); i++) { if (tkn[i] == '0' || tkn[i] == '1' || tkn[i] == '2' || tkn[i] == '3' || tkn[i] == '4' || tkn[i] == '5' || tkn[i] == '6' || tkn[i] == '7' || tkn[i] == '8' || tkn[i] == '9' || tkn[i] == '.') {} else allDigit = NO; } if (allDigit == YES) { MrBayesPrint ("%s Charset name may not be a number\n", spacer); return (ERROR); } /* check size of charset name */ if (strlen(tkn) > 99) { MrBayesPrint ("%s Charset name is too long\n", spacer); return (ERROR); } /* check to see if the name has already been used as a charset */ if (numCharSets > 1) { if (CheckString (charSetNames, numCharSets, tkn, &index) == ERROR) { /* if the charset name has not been used, then we should have an ERROR returned */ /* we _want_ to be here */ } else { MrBayesPrint ("%s Charset name has been used previously\n", spacer); return (ERROR); } } /* add the name to the character set */ strcpy (tempSetName, tkn); /* clear tempSet */ for (i=0; i numChar) { MrBayesPrint ("%s Character number %d is out of range (should be between %d and %d)\n", spacer, tempInt, 1, numChar); return (ERROR); } tempInt--; if (foundDash == YES) { if (fromI >= 0) toJ = tempInt; else { MrBayesPrint ("%s Improperly formatted charset\n", spacer); return (ERROR); } foundDash = NO; } else if (foundSlash == YES) { tempInt++; if (tempInt <= 1) { MrBayesPrint ("%s Improperly formatted charset\n", spacer); return (ERROR); } if (fromI >= 0 && toJ >= 0 && fromI < toJ) everyK = tempInt; else { MrBayesPrint ("%s Improperly formatted charset\n", spacer); return (ERROR); } foundSlash = NO; } else { if (fromI >= 0 && toJ < 0) { if (AddToSet (fromI, toJ, everyK, 1) == ERROR) return (ERROR); fromI = tempInt; } else if (fromI < 0 && toJ < 0) { fromI = tempInt; } else if (fromI >= 0 && toJ >= 0 && everyK < 0) { if (AddToSet (fromI, toJ, everyK, 1) == ERROR) return (ERROR); fromI = tempInt; toJ = everyK = -1; } else if (fromI >= 0 && toJ >= 0 && everyK >= 0) { if (AddToSet (fromI, toJ, everyK, 1) == ERROR) return (ERROR); fromI = tempInt; toJ = everyK = -1; } else { MrBayesPrint ("%s Improperly formatted charset\n", spacer); { return (ERROR); } } } expecting = Expecting(ALPHA); expecting |= Expecting(NUMBER); expecting |= Expecting(SEMICOLON); expecting |= Expecting(DASH); expecting |= Expecting(BACKSLASH); } else if (expecting == Expecting(DASH)) { foundDash = YES; expecting = Expecting(NUMBER); } else if (expecting == Expecting(BACKSLASH)) { foundSlash = YES; expecting = Expecting(NUMBER); } else return (ERROR); return (NO_ERROR); } int DoCharStat (void) { int i, j, numDivs; char tempName[100]; if (defMatrix == NO) { MrBayesPrint ("%s A character matrix must be defined first\n", spacer); return (ERROR); } if (numDefinedPartitions == 1) MrBayesPrint ("%s 1 character partition defined:\n", spacer, numDefinedPartitions); else MrBayesPrint ("%s %d character partitions defined:\n", spacer, numDefinedPartitions); for (i=0; i 0) { /* find paired character */ for (j=0; j= 0 && toJ < 0) { if (AddToGivenSet (fromI, toJ, everyK, 1, tset) == ERROR) return (ERROR); } else if (fromI >= 0 && toJ >= 0 && everyK < 0) { if (AddToGivenSet (fromI, toJ, everyK, 1, tset) == ERROR) return (ERROR); } else if (fromI >= 0 && toJ >= 0 && everyK >= 0) { if (AddToGivenSet (fromI, toJ, everyK, 1, tset) == ERROR) return (ERROR); } /* check that this is not a stupid constraint */ howMany = 0; for (i=0; i 99) { MrBayesPrint ("%s Constraint name is too long\n", spacer); return (ERROR); } /* check to see if the name has already been used as a constraint */ if (numDefinedConstraints > 0) { if (CheckString (constraintNames, numDefinedConstraints, tkn, &index) == ERROR) { /* an ERROR returned if the constraint name has not been used. we _want_ to be here */ } else { MrBayesPrint ("%s Constraint name '%s' has been used previously\n", spacer, tkn); return (ERROR); } } /* copy the name to the temporary constraint names string */ strcpy (tempSetName, tkn); /* clear tempSet */ for (i=0; i numTaxa) { MrBayesPrint ("%s Taxon number %d is out of range (should be between %d and %d)\n", spacer, tempInt, 1, numTaxa); return (ERROR); } } tempInt--; if (foundDash == YES) { if (fromI >= 0) toJ = tempInt; else { MrBayesPrint ("%s Improperly formatted constraint\n", spacer); return (ERROR); } foundDash = NO; } else if (foundSlash == YES) { tempInt++; if (tempInt <= 1) { MrBayesPrint ("%s Improperly formatted constraint\n", spacer); return (ERROR); } if (fromI >= 0 && toJ >= 0 && fromI < toJ) everyK = tempInt; else { MrBayesPrint ("%s Improperly formatted constraint\n", spacer); return (ERROR); } foundSlash = NO; } else { if (fromI >= 0 && toJ < 0) { if (AddToGivenSet (fromI, toJ, everyK, 1, tempSetCurrent) == ERROR) return (ERROR); fromI = tempInt; } else if (fromI < 0 && toJ < 0) { fromI = tempInt; } else if (fromI >= 0 && toJ >= 0 && everyK < 0) { if (AddToGivenSet (fromI, toJ, everyK, 1, tempSetCurrent) == ERROR) return (ERROR); fromI = tempInt; toJ = everyK = -1; } else if (fromI >= 0 && toJ >= 0 && everyK >= 0) { if (AddToGivenSet (fromI, toJ, everyK, 1, tempSetCurrent) == ERROR) return (ERROR); fromI = tempInt; toJ = everyK = -1; } else { MrBayesPrint ("%s Improperly formatted constraint\n", spacer); { return (ERROR); } } } expecting = Expecting(ALPHA); expecting |= Expecting(NUMBER); expecting |= Expecting(DASH); expecting |= Expecting(BACKSLASH); if (constraintType != PARTIAL || foundColon == YES) expecting |= Expecting(SEMICOLON); else expecting |= Expecting(COLON); } } else if (expecting == Expecting(BACKSLASH)) { foundSlash = YES; expecting = Expecting(NUMBER); } else if (expecting == Expecting(COLON)) { if (foundColon == YES) { MrBayesPrint ("%s Improperly formatted constraint: two colon characters in constraint command.\n", spacer); return (ERROR); } /* add set to tempSet */ if (fromI >= 0 && toJ < 0) { if (AddToSet (fromI, toJ, everyK, 1) == ERROR) return (ERROR); } else if (fromI >= 0 && toJ >= 0 && everyK < 0) { if (AddToSet (fromI, toJ, everyK, 1) == ERROR) return (ERROR); } else if (fromI >= 0 && toJ >= 0 && everyK >= 0) { if (AddToSet (fromI, toJ, everyK, 1) == ERROR) return (ERROR); } fromI = toJ = everyK = -1; foundDash = foundSlash = NO; foundColon = YES; tempSetCurrent = tempSetNeg; expecting = Expecting(ALPHA); expecting |= Expecting(NUMBER); } else return (ERROR); return (NO_ERROR); } int DoCtype (void) { int i, foundIllegal, marks[5], numAppliedTo; /* add set to tempSet */ if (fromI >= 0 && toJ < 0) { if (AddToSet (fromI, toJ, everyK, 1) == ERROR) return (ERROR); } else if (fromI >= 0 && toJ >= 0 && everyK < 0) { if (AddToSet (fromI, toJ, everyK, 1) == ERROR) return (ERROR); } else if (fromI >= 0 && toJ >= 0 && everyK >= 0) { if (AddToSet (fromI, toJ, everyK, 1) == ERROR) return (ERROR); } /* merge tempSet with ctype */ numAppliedTo = 0; for (i=0; i<5; i++) marks[i] = NO; for (i=0; i 0) { MrBayesPrint ("%s Ctype was applied to %d standard characters\n", spacer, numAppliedTo); } else { MrBayesPrint ("%s No standard characters found to apply ctype to\n", spacer); } # if 0 for (i=0; i numChar) { MrBayesPrint ("%s Character number %d is out of range (should be between %d and %d)\n", spacer, tempInt, 1, numChar); return (ERROR); } tempInt--; if (foundDash == YES) { if (fromI >= 0) toJ = tempInt; else { MrBayesPrint ("%s Improperly formatted ctype\n", spacer); return (ERROR); } foundDash = NO; } else if (foundSlash == YES) { tempInt++; if (tempInt <= 1) { MrBayesPrint ("%s Improperly formatted ctype\n", spacer); return (ERROR); } if (fromI >= 0 && toJ >= 0 && fromI < toJ) everyK = tempInt; else { MrBayesPrint ("%s Improperly formatted ctype\n", spacer); return (ERROR); } foundSlash = NO; } else { if (fromI >= 0 && toJ < 0) { if (AddToSet (fromI, toJ, everyK, 1) == ERROR) return (ERROR); fromI = tempInt; } else if (fromI < 0 && toJ < 0) { fromI = tempInt; } else if (fromI >= 0 && toJ >= 0 && everyK < 0) { if (AddToSet (fromI, toJ, everyK, 1) == ERROR) return (ERROR); fromI = tempInt; toJ = everyK = -1; } else if (fromI >= 0 && toJ >= 0 && everyK >= 0) { if (AddToSet (fromI, toJ, everyK, 1) == ERROR) return (ERROR); fromI = tempInt; toJ = everyK = -1; } else { MrBayesPrint ("%s Improperly formatted ctype\n", spacer); { return (ERROR); } } } expecting = Expecting(ALPHA); expecting |= Expecting(NUMBER); expecting |= Expecting(SEMICOLON); expecting |= Expecting(DASH); expecting |= Expecting(BACKSLASH); } else if (expecting == Expecting(DASH)) { foundDash = YES; expecting = Expecting(NUMBER); } else if (expecting == Expecting(BACKSLASH)) { foundSlash = YES; expecting = Expecting(NUMBER); } else return (ERROR); return (NO_ERROR); } int DoDelete (void) { int i, alreadyDone; MrBayesPrint ("%s Excluding taxa\n", spacer); /* add set to tempSet */ if (fromI >= 0 && toJ < 0) { if (AddToSet (fromI, toJ, everyK, 1) == ERROR) return (ERROR); } else if (fromI >= 0 && toJ >= 0 && everyK < 0) { if (AddToSet (fromI, toJ, everyK, 1) == ERROR) return (ERROR); } else if (fromI >= 0 && toJ >= 0 && everyK >= 0) { if (AddToSet (fromI, toJ, everyK, 1) == ERROR) return (ERROR); } /* merge tempSet with taxaset */ alreadyDone = NO; for (i=0; i= 0 && toJ < 0) { if (AddToSet (fromI, toJ, everyK, 1) == ERROR) return (ERROR); } else if (fromI >= 0 && toJ >= 0) { if (AddToSet (fromI, toJ, everyK, 1) == ERROR) return (ERROR); } tempSet[index] = 1; } } foundDash = NO; fromI = toJ = everyK = -1; expecting = Expecting(ALPHA); expecting |= Expecting(NUMBER); expecting |= Expecting(SEMICOLON); } else if (expecting == Expecting(NUMBER)) { if (strlen(tkn) == 1 && !strcmp(tkn, ".")) tempInt = numTaxa; else { sscanf (tkn, "%d", &tempInt); if (tempInt <= 0 || tempInt > numTaxa) { MrBayesPrint ("%s Taxon number %d is out of range (should be between %d and %d)\n", spacer, tempInt, 1, numTaxa); return (ERROR); } } tempInt--; if (foundDash == YES) { if (fromI >= 0) toJ = tempInt; else { MrBayesPrint ("%s Improperly formatted delete set\n", spacer); return (ERROR); } foundDash = NO; } else { if (fromI >= 0 && toJ < 0) { if (AddToSet (fromI, toJ, everyK, 1) == ERROR) return (ERROR); fromI = tempInt; } else if (fromI < 0 && toJ < 0) { fromI = tempInt; } else if (fromI >= 0 && toJ >= 0 && everyK < 0) { if (AddToSet (fromI, toJ, everyK, 1) == ERROR) return (ERROR); fromI = tempInt; toJ = everyK = -1; } else if (fromI >= 0 && toJ >= 0 && everyK >= 0) { if (AddToSet (fromI, toJ, everyK, 1) == ERROR) return (ERROR); fromI = tempInt; toJ = everyK = -1; } else { MrBayesPrint ("%s Improperly formatted delete set\n", spacer); { return (ERROR); } } } expecting = Expecting(ALPHA); expecting |= Expecting(NUMBER); expecting |= Expecting(SEMICOLON); expecting |= Expecting(DASH); } else if (expecting == Expecting(DASH)) { foundDash = YES; expecting = Expecting(NUMBER); } else return (ERROR); return (NO_ERROR); } int DoDimensions (void) { if (inDataBlock == NO && inTaxaBlock == NO && inCharactersBlock == NO) { MrBayesPrint ("%s Dimensions can only be defined in a data, characters or taxa block\n", spacer); return (ERROR); } /* other problems are detected already when reading in DoDimensionsParm */ if (inDataBlock == YES && (defTaxa == NO || defChars == NO)) { MrBayesPrint ("%s Expecting both Ntax and Nchar to be defined in a data block\n", spacer); return (ERROR); } /* allocate matrix */ if (inTaxaBlock == YES) { if (AllocTaxa () == ERROR) return ERROR; MrBayesPrint ("%s Defining new set of %d taxa\n", spacer, numTaxa); } if (inCharactersBlock == YES) { if (AllocMatrix() == ERROR) return (ERROR); MrBayesPrint ("%s Defining new character matrix with %d characters\n", spacer, numChar); } if (inDataBlock == YES) { if (AllocMatrix() == ERROR) return (ERROR); MrBayesPrint ("%s Defining new matrix with %d taxa and %d characters\n", spacer, numTaxa, numChar); } return (NO_ERROR); } int DoDimensionsParm (char *parmName, char *tkn) { if (expecting == Expecting(PARAMETER)) { expecting = Expecting(EQUALSIGN); } else { /* set Ntax (numTaxa) *****************************************************************/ if (!strcmp(parmName, "Ntax")) { if (inCharactersBlock == YES) { MrBayesPrint ("%s You cannot define ntax in a characters block\n"); return (ERROR); } if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(NUMBER); else if (expecting == Expecting(NUMBER)) { sscanf (tkn, "%d", &numTaxa); defTaxa = YES; expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); } /* set Nchar (numChar) ****************************************************************/ else if (!strcmp(parmName, "Nchar")) { if (inTaxaBlock == YES) { MrBayesPrint ("%s You cannot define nchar in a taxa block\n"); return (ERROR); } if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(NUMBER); else if (expecting == Expecting(NUMBER)) { sscanf (tkn, "%d", &numChar); defChars = YES; expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); } else return (ERROR); } return (NO_ERROR); } int DoDisclaimer (void) { MrBayesPrint (" --------------------------------------------------------------------------- \n"); MrBayesPrint (" Disclaimer \n"); MrBayesPrint (" \n"); MrBayesPrint (" Copyright 2003 by John P. Huelsenbeck and Fredrik Ronquist \n"); MrBayesPrint (" \n"); MrBayesPrint (" This software package is provided \"as is\" and without a warranty of any \n"); MrBayesPrint (" kind. In no event shall the authors be held responsible for any damage \n"); MrBayesPrint (" resulting from the use of this software. The program--including source code, \n"); MrBayesPrint (" example data sets, and executables--is distributed free of charge for \n"); MrBayesPrint (" academic use only. \n"); MrBayesPrint (" --------------------------------------------------------------------------- \n"); return (NO_ERROR); } int DoEndBlock (void) { if (inMrbayesBlock == YES) { MrBayesPrint (" Exiting mrbayes block\n"); inMrbayesBlock = NO; } else if (inDataBlock == YES) { MrBayesPrint (" Exiting data block\n"); inDataBlock = NO; } else if (inCharactersBlock == YES) { MrBayesPrint (" Exiting characters block\n"); inCharactersBlock = NO; } else if (inTaxaBlock == YES) { MrBayesPrint (" Exiting taxa block\n"); if (numNamedTaxa < numTaxa) { MrBayesPrint ("%s Leaving taxa block without taxon labels being defined\n", spacer); FreeTaxa(); } inTaxaBlock = NO; } else if (inTreesBlock == YES) { MrBayesPrint (" Exiting trees block\n"); inTreesBlock = NO; ResetTranslateTable(); } else if (inForeignBlock == YES) { MrBayesPrint (" Exiting foreign block\n"); inForeignBlock = NO; } else { MrBayesPrint (" Unknown \"end\" statement\n"); return (ERROR); } strcpy(spacer,""); /* reset indentation */ return (NO_ERROR); } int DoExecute (void) { int rc, cmdLine, lineTerm, longestLineLength, nErrors; char *s; FILE *fp; CmdType *oldCommandPtr; char *oldTokenP, oldToken[CMD_STRING_LENGTH]; # if defined (MPI_ENABLED) int sumErrors; # endif nErrors = 0; cmdLine = 0; numOpenExeFiles++; s = NULL; if (numOpenExeFiles > 1) MrBayesPrint ("\n%s Executing file \"%s\"...\n\n", spacer, inputFileName); else MrBayesPrint ("%s Executing file \"%s\"\n", spacer, inputFileName); /* Save old command ptr, token pointer and token */ oldCommandPtr = commandPtr; oldTokenP = tokenP; strcpy(oldToken, token); /* open binary file */ if ((fp = OpenBinaryFileR(inputFileName)) == NULL) nErrors++; /* set indentation to 0 */ strcpy (spacer, ""); # if defined (MPI_ENABLED) MPI_Allreduce (&nErrors, &sumErrors, 1, MPI_INT, MPI_SUM, MPI_COMM_WORLD); if (sumErrors > 0) { MrBayesPrint ("%s There was an error on at least one processor\n", spacer); goto errorExit; } # else if (nErrors > 0) goto errorExit; # endif /* find out what type of line termination is used */ lineTerm = LineTermType (fp); if (lineTerm == LINETERM_MAC) MrBayesPrint ("%s Macintosh line termination\n", spacer); else if (lineTerm == LINETERM_DOS) MrBayesPrint ("%s DOS line termination\n", spacer); else if (lineTerm == LINETERM_UNIX) MrBayesPrint ("%s UNIX line termination\n", spacer); else { MrBayesPrint ("%s Unknown line termination\n", spacer); nErrors++; } # if defined (MPI_ENABLED) MPI_Allreduce (&nErrors, &sumErrors, 1, MPI_INT, MPI_SUM, MPI_COMM_WORLD); if (sumErrors > 0) { MrBayesPrint ("%s There was an error on at least one processor\n", spacer); goto errorExit; } # else if (nErrors > 0) goto errorExit; # endif /* find length of longest line */ longestLineLength = LongestLine (fp); MrBayesPrint ("%s Longest line length = %d\n", spacer, longestLineLength); longestLineLength += 10; /* allocate a string long enough to hold a line */ s = (char *)SafeMalloc((size_t)longestLineLength * sizeof(char)); if (!s) { MrBayesPrint ("%s Problem allocating string for reading file\n", spacer); nErrors++; } # if defined (MPI_ENABLED) MPI_Allreduce (&nErrors, &sumErrors, 1, MPI_INT, MPI_SUM, MPI_COMM_WORLD); if (sumErrors > 0) { MrBayesPrint ("%s There was an error on at least one processor\n", spacer); goto errorExit; } # else if (nErrors > 0) goto errorExit; # endif /* close binary file */ SafeFclose (&fp); /* open text file */ if ((fp = OpenTextFileR(inputFileName)) == NULL) { nErrors++; } # if defined (MPI_ENABLED) MPI_Allreduce (&nErrors, &sumErrors, 1, MPI_INT, MPI_SUM, MPI_COMM_WORLD); if (sumErrors > 0) { MrBayesPrint ("%s There was an error on at least one processor\n", spacer); goto errorExit; } # else if (nErrors > 0) goto errorExit; # endif /* parse file, reading each line in turn */ MrBayesPrint ("%s Parsing file\n", spacer); inMrbayesBlock = inDataBlock = inForeignBlock = inTreesBlock = NO; foundNewLine = NO; expecting = Expecting(COMMAND); cmdLine = 0; /* read lines into s until end of file */ while ( fgets(s, longestLineLength, fp) != NULL) { foundNewLine = YES; cmdLine++; /* process string if not empty */ if (strlen(s) > 1) { /* check that all characters in the string are valid */ if (CheckStringValidity (s) == ERROR) { nErrors++; } # if defined (MPI_ENABLED) MPI_Allreduce (&nErrors, &sumErrors, 1, MPI_INT, MPI_SUM, MPI_COMM_WORLD); if (sumErrors > 0) { MrBayesPrint ("%s There was an error on at least one processor\n", spacer); goto errorExit; } # else if (nErrors > 0) goto errorExit; # endif /* interpret commands on line */ rc = ParseCommand (s); if (rc == ERROR) nErrors++; # if defined (MPI_ENABLED) MPI_Allreduce (&nErrors, &sumErrors, 1, MPI_INT, MPI_SUM, MPI_COMM_WORLD); if (sumErrors > 0) { MrBayesPrint ("%s There was an error on at least one processor\n", spacer); goto errorExit; } # else if (nErrors > 0) goto errorExit; # endif if (rc == NO_ERROR_QUIT) nErrors++; # if defined (MPI_ENABLED) MPI_Allreduce (&nErrors, &sumErrors, 1, MPI_INT, MPI_SUM, MPI_COMM_WORLD); if (sumErrors > 0) goto quitExit; # else if (nErrors > 0) goto quitExit; # endif } } MrBayesPrint ("%s Reached end of file\n", spacer); if (inComment == YES) nErrors++; # if defined (MPI_ENABLED) MPI_Allreduce (&nErrors, &sumErrors, 1, MPI_INT, MPI_SUM, MPI_COMM_WORLD); if (sumErrors > 0) { MrBayesPrint ("%s There was an error on at least one processor\n", spacer); goto errorExit; } # else if (nErrors > 0) goto errorExit; # endif if (s) free (s); SafeFclose (&fp); numOpenExeFiles--; if (numOpenExeFiles > 0) { inMrbayesBlock = YES; MrBayesPrint ("\n Returning execution to calling file ...\n\n"); strcpy (spacer, " "); } else strcpy (spacer, ""); commandPtr = oldCommandPtr; return (NO_ERROR); quitExit: if (s) free (s); SafeFclose (&fp); numOpenExeFiles--; if (numOpenExeFiles > 0) { inMrbayesBlock = YES; strcpy (spacer, " "); } else strcpy (spacer, ""); commandPtr = oldCommandPtr; tokenP = oldTokenP; strcpy(token, oldToken); return (NO_ERROR_QUIT); errorExit: if (inComment == YES) { MrBayesPrint ("%s ERROR: Reached end of file while in comment.\n", spacer); inComment = NO; numComments = 0; } if (fp) { MrBayesPrint ("%s The error occurred when reading char. %d-%d on line %d\n", spacer, (size_t)(tokenP-s)-strlen(token)+1, (size_t)(tokenP-s), cmdLine); MrBayesPrint ("%s in the file '%s'\n", spacer, inputFileName); } if (s) free (s); SafeFclose (&fp); /* make sure we exit the block we were reading from correctly */ if (inMrbayesBlock == YES) inMrbayesBlock = NO; else if (inDataBlock == YES) inDataBlock = NO; else if (inTreesBlock == YES) { inTreesBlock = NO; ResetTranslateTable(); } else if (inForeignBlock == YES) inForeignBlock = NO; /* make sure correct return if we came from mrbayes block in another execute file */ if (numOpenExeFiles > 1) { inMrbayesBlock = YES; MrBayesPrint ("\n Returning execution to calling file ...\n\n"); strcpy (spacer, " "); } else { strcpy (spacer, ""); MrBayesPrint ("\n Returning execution to command line ...\n\n"); } numOpenExeFiles--; /* we increase the value above even if no file is successfully opened */ /* restore state of globals */ commandPtr = oldCommandPtr; tokenP = oldTokenP; strcpy(token, oldToken); return (ERROR); } int DoExecuteParm (char *parmName, char *tkn) { if (strlen(tkn)>99) { MrBayesPrint ("%s Maximum allowed length of file name is 99 characters. The given name:\n", spacer); MrBayesPrint ("%s '%s'\n", spacer,tkn); MrBayesPrint ("%s has %d characters.\n", spacer, strlen(tkn)); return (ERROR); } strcpy (inputFileName, tkn); expecting = Expecting (SEMICOLON); return (NO_ERROR); } int DoExclude (void) { int i, alreadyDone; MrBayesPrint ("%s Excluding character(s)\n", spacer); /* add set to tempSet */ if (fromI >= 0 && toJ < 0) { if (AddToSet (fromI, toJ, everyK, 1) == ERROR) return (ERROR); } else if (fromI >= 0 && toJ >= 0 && everyK < 0) { if (AddToSet (fromI, toJ, everyK, 1) == ERROR) return (ERROR); } else if (fromI >= 0 && toJ >= 0 && everyK >= 0) { if (AddToSet (fromI, toJ, everyK, 1) == ERROR) return (ERROR); } /* merge tempSet with charset */ alreadyDone = NO; for (i=0; i numChar) { MrBayesPrint ("%s Character number %d is out of range (should be between %d and %d)\n", spacer, tempInt, 1, numChar); return (ERROR); } tempInt--; if (foundDash == YES) { if (fromI >= 0) toJ = tempInt; else { MrBayesPrint ("%s Improperly formatted exclude set\n", spacer); return (ERROR); } foundDash = NO; } else if (foundSlash == YES) { tempInt++; if (tempInt <= 1) { MrBayesPrint ("%s Improperly formatted exclude set\n", spacer); return (ERROR); } if (fromI >= 0 && toJ >= 0 && fromI < toJ) everyK = tempInt; else { MrBayesPrint ("%s Improperly formatted exclude set\n", spacer); return (ERROR); } foundSlash = NO; } else { if (fromI >= 0 && toJ < 0) { if (AddToSet (fromI, toJ, everyK, 1) == ERROR) return (ERROR); fromI = tempInt; } else if (fromI < 0 && toJ < 0) { fromI = tempInt; } else if (fromI >= 0 && toJ >= 0 && everyK < 0) { if (AddToSet (fromI, toJ, everyK, 1) == ERROR) return (ERROR); fromI = tempInt; toJ = everyK = -1; } else if (fromI >= 0 && toJ >= 0 && everyK >= 0) { if (AddToSet (fromI, toJ, everyK, 1) == ERROR) return (ERROR); fromI = tempInt; toJ = everyK = -1; } else { MrBayesPrint ("%s Improperly formatted exclude set\n", spacer); { return (ERROR); } } } expecting = Expecting(ALPHA); expecting |= Expecting(NUMBER); expecting |= Expecting(SEMICOLON); expecting |= Expecting(DASH); expecting |= Expecting(BACKSLASH); } else if (expecting == Expecting(DASH)) { foundDash = YES; expecting = Expecting(NUMBER); } else if (expecting == Expecting(BACKSLASH)) { foundSlash = YES; expecting = Expecting(NUMBER); } else return (ERROR); return (NO_ERROR); } int DoFormat (void) { if (inDataBlock == NO && inCharactersBlock == NO) { MrBayesPrint ("%s Formats can only be defined in a data or characters block\n", spacer); return (ERROR); } return CheckInitialPartitions(); } int DoFormatParm (char *parmName, char *tkn) { int i, tempInt; char tempStr[100]; if (inDataBlock == NO && inCharactersBlock == NO) { MrBayesPrint ("%s Formats can only be defined in a data or characters block\n", spacer); return (ERROR); } if (defTaxa == NO || defChars == NO) { MrBayesPrint ("%s The dimensions of the matrix must be defined before the format\n", spacer); return (ERROR); } if (expecting == Expecting(PARAMETER)) { expecting = Expecting(EQUALSIGN); if (!strcmp(parmName, "Interleave")) { expecting = Expecting(EQUALSIGN) | Expecting(PARAMETER) | Expecting(SEMICOLON); isInterleaved = YES; } } else { /* set Datatype (dataType) ************************************************************/ if (!strcmp(parmName, "Datatype")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(ALPHA); else if (expecting == Expecting(ALPHA)) { if (IsArgValid(tkn, tempStr) == NO_ERROR) { if (isMixed == NO) { if (!strcmp(tempStr, "Dna")) dataType = DNA; else if (!strcmp(tempStr, "Rna")) dataType = RNA; else if (!strcmp(tempStr, "Protein")) dataType = PROTEIN; else if (!strcmp(tempStr, "Restriction")) dataType = RESTRICTION; else if (!strcmp(tempStr, "Standard")) dataType = STANDARD; else if (!strcmp(tempStr, "Continuous")) dataType = CONTINUOUS; else if (!strcmp(tempStr, "Mixed")) { dataType = MIXED; isMixed = YES; for (i=0; i numChar) { MrBayesPrint ("%s Character number %d is out of range (should be between %d and %d)\n", spacer, tempInt, 1, numChar); return (ERROR); } tempInt--; if (foundDash == YES) { if (fromI >= 0) toJ = tempInt; else { MrBayesPrint ("%s Improperly formatted partition\n", spacer); return (ERROR); } foundDash = NO; } else if (foundSlash == YES) { tempInt++; if (tempInt <= 1) { MrBayesPrint ("%s Improperly formatted partition\n", spacer); return (ERROR); } if (fromI >= 0 && toJ >= 0 && fromI < toJ) everyK = tempInt; else { MrBayesPrint ("%s Improperly formatted partition\n", spacer); return (ERROR); } foundSlash = NO; } else { if (fromI >= 0 && toJ < 0) { if (AddToSet (fromI, toJ, everyK, numDivisions+1) == ERROR) return (ERROR); fromI = tempInt; } else if (fromI < 0 && toJ < 0) { fromI = tempInt; } else if (fromI >= 0 && toJ >= 0 && everyK < 0) { if (AddToSet (fromI, toJ, everyK, numDivisions+1) == ERROR) return (ERROR); fromI = tempInt; toJ = everyK = -1; } else if (fromI >= 0 && toJ >= 0 && everyK >= 0) { if (AddToSet (fromI, toJ, everyK, numDivisions+1) == ERROR) return (ERROR); fromI = tempInt; toJ = everyK = -1; } else { MrBayesPrint ("%s Improperly formatted partition\n", spacer); { return (ERROR); } } } expecting = Expecting(NUMBER); expecting |= Expecting(DASH); expecting |= Expecting(BACKSLASH); expecting |= Expecting(COMMA); expecting |= Expecting(RIGHTPAR); } else if (expecting == Expecting(DASH)) { foundDash = YES; expecting = Expecting(NUMBER); } else if (expecting == Expecting(BACKSLASH)) { foundSlash = YES; expecting = Expecting(NUMBER); } else if (expecting == Expecting(COMMA)) { /* add set to tempSet */ if (fromI >= 0 && toJ < 0) { if (AddToSet (fromI, toJ, everyK, numDivisions+1) == ERROR) return (ERROR); } else if (fromI >= 0 && toJ >= 0 && everyK < 0) { if (AddToSet (fromI, toJ, everyK, numDivisions+1) == ERROR) return (ERROR); } else if (fromI >= 0 && toJ >= 0 && everyK >= 0) { if (AddToSet (fromI, toJ, everyK, numDivisions+1) == ERROR) return (ERROR); } for (i=0; i= 0 && toJ < 0) { if (AddToSet (fromI, toJ, everyK, numDivisions+1) == ERROR) return (ERROR); } else if (fromI >= 0 && toJ >= 0 && everyK < 0) { if (AddToSet (fromI, toJ, everyK, numDivisions+1) == ERROR) return (ERROR); } else if (fromI >= 0 && toJ >= 0 && everyK >= 0) { if (AddToSet (fromI, toJ, everyK, numDivisions+1) == ERROR) return (ERROR); } /* merge tempSet */ for (i=0; i 1) MrBayesPrint ("%s There are a total of %d default data divisions\n", spacer, numDivisions); expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); } /* set Interleave (isInterleaved) *****************************************************/ else if (!strcmp(parmName, "Interleave")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(ALPHA); else if (expecting == Expecting(ALPHA)) { if (IsArgValid(tkn, tempStr) == NO_ERROR) { if (!strcmp(tempStr, "Yes")) isInterleaved = YES; else isInterleaved = NO; } else { MrBayesPrint ("%s Invalid argument for interleaved data\n", spacer); return (ERROR); } if (isInterleaved == YES) MrBayesPrint ("%s Data matrix is interleaved\n", spacer); else MrBayesPrint ("%s Data matrix is not interleaved\n", spacer); expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); } /* set Gap (gapId) ********************************************************************/ else if (!strcmp(parmName, "Gap")) { if (expecting == Expecting(EQUALSIGN)) { expecting = Expecting(ALPHA); expecting |= Expecting(QUESTIONMARK); expecting |= Expecting(DASH); expecting |= Expecting(NUMBER); expecting |= Expecting(ASTERISK); expecting |= Expecting(EXCLAMATIONMARK); expecting |= Expecting(PERCENT); expecting |= Expecting(WEIRD); expecting |= Expecting(VERTICALBAR); } else if (((expecting & Expecting(ALPHA)) == Expecting(ALPHA)) || ((expecting & Expecting(QUESTIONMARK)) == Expecting(QUESTIONMARK)) || ((expecting & Expecting(DASH)) == Expecting(DASH)) || ((expecting & Expecting(NUMBER)) == Expecting(NUMBER)) || ((expecting & Expecting(ASTERISK)) == Expecting(ASTERISK)) || ((expecting & Expecting(EXCLAMATIONMARK)) == Expecting(EXCLAMATIONMARK)) || ((expecting & Expecting(PERCENT)) == Expecting(PERCENT)) || ((expecting & Expecting(WEIRD)) == Expecting(WEIRD)) || ((expecting & Expecting(VERTICALBAR)) == Expecting(VERTICALBAR))) { if (strlen(tkn) == 1) { if (tkn[0] == matchId || tkn[0] == missingId) { MrBayesPrint ("%s Gap character matches matching or missing characters\n", spacer); return (ERROR); } gapId = tkn[0]; } else { MrBayesPrint ("%s Invalid gap argument %s\n", spacer, tkn); return (ERROR); } MrBayesPrint ("%s Gaps coded as %s\n", spacer, tkn); expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); } /* set Missing (missingId) ************************************************************/ else if (!strcmp(parmName, "Missing")) { if (expecting == Expecting(EQUALSIGN)) { expecting = Expecting(ALPHA); expecting |= Expecting(QUESTIONMARK); expecting |= Expecting(DASH); expecting |= Expecting(NUMBER); expecting |= Expecting(ASTERISK); expecting |= Expecting(EXCLAMATIONMARK); expecting |= Expecting(PERCENT); expecting |= Expecting(WEIRD); expecting |= Expecting(VERTICALBAR); } else if (((expecting & Expecting(ALPHA)) == Expecting(ALPHA)) || ((expecting & Expecting(QUESTIONMARK)) == Expecting(QUESTIONMARK)) || ((expecting & Expecting(DASH)) == Expecting(DASH)) || ((expecting & Expecting(NUMBER)) == Expecting(NUMBER)) || ((expecting & Expecting(ASTERISK)) == Expecting(ASTERISK)) || ((expecting & Expecting(EXCLAMATIONMARK)) == Expecting(EXCLAMATIONMARK)) || ((expecting & Expecting(PERCENT)) == Expecting(PERCENT)) || ((expecting & Expecting(WEIRD)) == Expecting(WEIRD)) || ((expecting & Expecting(VERTICALBAR)) == Expecting(VERTICALBAR))) { if (strlen(tkn) == 1) { if (tkn[0] == gapId || tkn[0] == matchId) { MrBayesPrint ("%s Missing character matches matching or gap characters\n", spacer); return (ERROR); } missingId = tkn[0]; } else { MrBayesPrint ("%s Invalid missing argument %s\n", spacer, tkn); return (ERROR); } MrBayesPrint ("%s Missing data coded as %s\n", spacer, tkn); expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); } /* set Matchchar (matchId) ************************************************************/ else if (!strcmp(parmName, "Matchchar")) { if (expecting == Expecting(EQUALSIGN)) { expecting = Expecting(ALPHA); expecting |= Expecting(QUESTIONMARK); expecting |= Expecting(DASH); expecting |= Expecting(NUMBER); expecting |= Expecting(ASTERISK); expecting |= Expecting(EXCLAMATIONMARK); expecting |= Expecting(PERCENT); expecting |= Expecting(WEIRD); expecting |= Expecting(VERTICALBAR); } else if (((expecting & Expecting(ALPHA)) == Expecting(ALPHA)) || ((expecting & Expecting(QUESTIONMARK)) == Expecting(QUESTIONMARK)) || ((expecting & Expecting(DASH)) == Expecting(DASH)) || ((expecting & Expecting(NUMBER)) == Expecting(NUMBER)) || ((expecting & Expecting(ASTERISK)) == Expecting(ASTERISK)) || ((expecting & Expecting(EXCLAMATIONMARK)) == Expecting(EXCLAMATIONMARK)) || ((expecting & Expecting(PERCENT)) == Expecting(PERCENT)) || ((expecting & Expecting(WEIRD)) == Expecting(WEIRD)) || ((expecting & Expecting(VERTICALBAR)) == Expecting(VERTICALBAR))) { if (strlen(tkn) == 1) { if (tkn[0] == gapId || tkn[0] == missingId) { MrBayesPrint ("%s Matching character matches gap or missing characters\n", spacer); return (ERROR); } matchId = tkn[0]; } else { MrBayesPrint ("%s Invalid matchchar argument %s\n", spacer, tkn); return (ERROR); } MrBayesPrint ("%s Matching characters coded as %s\n", spacer, tkn); expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); } /* skip Symbols ***************************************************************/ else if (!strcmp(parmName, "Symbols")) { if (expecting == Expecting(EQUALSIGN)) { MrBayesPrint ("%s WARNING: MrBayes does not support 'symbols' specification; default symbols assumed\n", spacer); readWord=YES; expecting = Expecting(ALPHA); } else if (expecting == Expecting(ALPHA)) { expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); } /* on Equate return ERROR ***************************************************************/ else if (!strcmp(parmName, "Equate")) { MrBayesPrint ("%s ERROR: MrBayes does not support 'Equate' macros; please remove or comment out\n", spacer); return (ERROR); } else return (ERROR); } return (NO_ERROR); } int DoHelp (void) { int i, j, longestDescription; CmdType *p; if (foundFirst == NO) { longestDescription = 0; for (i=1; istring) > longestDescription) longestDescription = (int) strlen(p->string); } MrBayesPrint (" --------------------------------------------------------------------------- \n"); MrBayesPrint (" Commands that are available from the command \n"); MrBayesPrint (" line or from a MrBayes block include: \n"); MrBayesPrint (" \n"); for (i=1; icmdUse == IN_CMD && p->hiding == SHOW) { MrBayesPrint (" %s", p->string); for (j=0; jstring); j++) MrBayesPrint (" "); MrBayesPrint (" -- %s\n", p->cmdDescription); } } MrBayesPrint (" \n"); MrBayesPrint (" Commands that should be in a NEXUS file (data \n"); MrBayesPrint (" block, trees block or taxa block) include: \n"); MrBayesPrint (" \n"); for (i=1; icmdUse == IN_FILE && p->hiding == SHOW) { MrBayesPrint (" %s", p->string); for (j=0; jstring); j++) MrBayesPrint (" "); MrBayesPrint (" -- %s\n", p->cmdDescription); } } MrBayesPrint (" \n"); MrBayesPrint (" Note that this program supports the use of the shortest unambiguous \n"); MrBayesPrint (" spelling of the above commands (e.g., \"exe\" instead of \"execute\"). \n"); MrBayesPrint (" --------------------------------------------------------------------------- \n"); } foundFirst = NO; return (NO_ERROR); } int DoHelpParm (char *parmName, char *tkn) { int i, j, tkLen, targetLen, numDiff, numMatches; CmdType *p, *q=NULL; if (expecting == Expecting(ALPHA)) { p = commands + 0; tkLen = (int) strlen(tkn); numMatches = 0; for (i=0; istring); if (tkLen <= targetLen) { for (j=0, numDiff=0; jstring[j])) numDiff++; } if (numDiff == 0) { numMatches++; q = p; if (tkLen == targetLen) break; } } p++; } if (numMatches == 0) { MrBayesPrint ("%s Could not find command \"%s\"\n", spacer, tkn); return (ERROR); } else if (numMatches == 1) { if (GetUserHelp (q->string) == ERROR) { MrBayesPrint ("%s Problem getting help for command \"%s\"\n", spacer, q->string); } } else { MrBayesPrint ("%s Ambiguous command \"%s\"\n", spacer, tkn); return (ERROR); } expecting = Expecting(SEMICOLON); foundFirst = YES; } else return (ERROR); return (NO_ERROR); } int DoInclude (void) { int i, alreadyDone; MrBayesPrint ("%s Including character(s)\n", spacer); /* add set to tempSet */ if (fromI >= 0 && toJ < 0) { if (AddToSet (fromI, toJ, everyK, 1) == ERROR) return (ERROR); } else if (fromI >= 0 && toJ >= 0 && everyK < 0) { if (AddToSet (fromI, toJ, everyK, 1) == ERROR) return (ERROR); } else if (fromI >= 0 && toJ >= 0 && everyK >= 0) { if (AddToSet (fromI, toJ, everyK, 1) == ERROR) return (ERROR); } /* merge tempSet with excludedChars */ alreadyDone = NO; for (i=0; i numChar) { MrBayesPrint ("%s Character number %d is out of range (should be between %d and %d)\n", spacer, tempInt, 1, numChar); return (ERROR); } tempInt--; if (foundDash == YES) { if (fromI >= 0) toJ = tempInt; else { MrBayesPrint ("%s Improperly formatted include set\n", spacer); return (ERROR); } foundDash = NO; } else if (foundSlash == YES) { tempInt++; if (tempInt <= 1) { MrBayesPrint ("%s Improperly formatted include set\n", spacer); return (ERROR); } if (fromI >= 0 && toJ >= 0 && fromI < toJ) everyK = tempInt; else { MrBayesPrint ("%s Improperly formatted include set\n", spacer); return (ERROR); } foundSlash = NO; } else { if (fromI >= 0 && toJ < 0) { if (AddToSet (fromI, toJ, everyK, 1) == ERROR) return (ERROR); fromI = tempInt; } else if (fromI < 0 && toJ < 0) { fromI = tempInt; } else if (fromI >= 0 && toJ >= 0 && everyK < 0) { if (AddToSet (fromI, toJ, everyK, 1) == ERROR) return (ERROR); fromI = tempInt; toJ = everyK = -1; } else if (fromI >= 0 && toJ >= 0 && everyK >= 0) { if (AddToSet (fromI, toJ, everyK, 1) == ERROR) return (ERROR); fromI = tempInt; toJ = everyK = -1; } else { MrBayesPrint ("%s Improperly formatted include set\n", spacer); { return (ERROR); } } } expecting = Expecting(ALPHA); expecting |= Expecting(NUMBER); expecting |= Expecting(SEMICOLON); expecting |= Expecting(DASH); expecting |= Expecting(BACKSLASH); } else if (expecting == Expecting(DASH)) { foundDash = YES; expecting = Expecting(NUMBER); } else if (expecting == Expecting(BACKSLASH)) { foundSlash = YES; expecting = Expecting(NUMBER); } else return (ERROR); return (NO_ERROR); } int DoLog (void) { if (logToFile == YES) { SafeFclose (&logFileFp); if (replaceLogFile == YES) { if ((logFileFp = OpenTextFileW (logFileName)) == NULL) { logToFile = NO; return (ERROR); } } else { if ((logFileFp = OpenTextFileA (logFileName)) == NULL) { logToFile = NO; return (ERROR); } } MrBayesPrint ("%s Logging screen output to file \"%s\"\n", spacer, logFileName); } else { SafeFclose (&logFileFp); MrBayesPrint ("%s Terminating log output\n", spacer); } return (NO_ERROR); } int DoLogParm (char *parmName, char *tkn) { if (expecting == Expecting(PARAMETER)) { if (!strcmp(parmName, "Start")) { if (logToFile == YES) MrBayesPrint ("%s Logging to file is already on\n", spacer, logFileName); else logToFile = YES; expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else if (!strcmp(parmName, "Stop")) { if (logToFile == NO) MrBayesPrint ("%s Logging to file is already off\n", spacer, logFileName); else logToFile = NO; expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else if (!strcmp(parmName, "Replace")) { replaceLogFile = YES; expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else if (!strcmp(parmName, "Append")) { replaceLogFile = NO; expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else expecting = Expecting(EQUALSIGN); } else { if (!strcmp(parmName, "Filename")) { if (expecting == Expecting(EQUALSIGN)) { expecting = Expecting(ALPHA); readWord = YES; } else if (expecting == Expecting(ALPHA)) { strcpy (logFileName, tkn); expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); } else { MrBayesPrint ("%s Unknown parameter in Log\n", spacer); return (ERROR); } } return (NO_ERROR); } int DoManual (void) { int i, j, logSetting; char title[100]; FILE *fp, *logfp; CmdType *p; /* try to open file, return error if present */ if ((fp = OpenTextFileRQuait(manFileName)) != NULL) { MrBayesPrint ("%s File \"%s\" already exists \n", spacer, manFileName); SafeFclose(&fp); return (ERROR); } /* try to open file for writing, return error if not possible */ if ((fp = OpenTextFileW(manFileName)) == NULL) return (ERROR); /* print message */ MrBayesPrint ("%s Producing command reference file \"%s\"\n", spacer, manFileName); /* temporarily disable normal logging and switch echoing off */ logSetting = logToFile; logfp = logFileFp; echoMB = NO; logToFile = YES; logFileFp = fp; /* produce command reference file */ /* header */ strcpy (title, "Command Reference for MrBayes ver. "); strcat (title, VERSION_NUMBER); i = (70 - (int) strlen (title)) / 2; j = 70 - i - (int) strlen(title); MrBayesPrint (" \n"); MrBayesPrint (" \n"); MrBayesPrint (" \n"); MrBayesPrint (" \n"); MrBayesPrint (" %*c%s%*c \n", i, ' ', title, j, ' '); MrBayesPrint (" \n"); MrBayesPrint (" (c) John P. Huelsenbeck, Fredrik Ronquist \n"); MrBayesPrint (" and Maxim Teslenko \n"); MrBayesPrint (" \n"); /* summary */ MrBayesPrint (" \n"); MrBayesPrint (" *************************************************************************** \n"); MrBayesPrint (" * * \n"); MrBayesPrint (" * 1. Command summary * \n"); MrBayesPrint (" * * \n"); MrBayesPrint (" *************************************************************************** \n"); MrBayesPrint (" \n"); foundFirst = NO; if (DoHelp() == ERROR) { MrBayesPrint ("%s Could not produce command reference summary\n", spacer); goto errorExit; } /* list of MrBayes commands */ MrBayesPrint (" \n"); MrBayesPrint (" *************************************************************************** \n"); MrBayesPrint (" * * \n"); MrBayesPrint (" * 2. MrBayes commands * \n"); MrBayesPrint (" * * \n"); MrBayesPrint (" *************************************************************************** \n"); MrBayesPrint (" \n"); for (i=1; icmdUse == IN_CMD && p->hiding == SHOW) { if (GetUserHelp(p->string)==ERROR) goto errorExit; } } /* list of data or tree block commands */ MrBayesPrint (" \n"); MrBayesPrint (" *************************************************************************** \n"); MrBayesPrint (" * * \n"); MrBayesPrint (" * 3. 'Data' or 'tree' block commands (in #NEXUS file) * \n"); MrBayesPrint (" * * \n"); MrBayesPrint (" *************************************************************************** \n"); MrBayesPrint (" \n"); for (i=1; icmdUse == IN_FILE && p->hiding == SHOW) { if (GetUserHelp(p->string) == ERROR) goto errorExit; } } /* return logging to previous settings and switch echoing on */ SafeFclose (&fp); logToFile = logSetting; logFileFp = logfp; echoMB = YES; MrBayesPrint ("%s Successfully produced command reference file \"%s\"\n", spacer, manFileName); return (NO_ERROR); errorExit: SafeFclose (&fp); logToFile = logSetting; logFileFp = logfp; echoMB = YES; return (ERROR); } int DoManualParm (char *parmName, char *tkn) { if (expecting == Expecting(PARAMETER)) { expecting = Expecting(EQUALSIGN); } else { if (!strcmp(parmName, "Filename")) { if (expecting == Expecting(EQUALSIGN)) { expecting = Expecting(ALPHA); readWord = YES; } else if (expecting == Expecting(ALPHA)) { strcpy (manFileName, tkn); expecting = Expecting(SEMICOLON); } else return (ERROR); } else { MrBayesPrint ("%s Unknown parameter in Manual\n", spacer); return (ERROR); } } return (NO_ERROR); } int DoMatrix (void) { int i, j, hasMissingAmbig; if (taxonCount != numTaxa) { MrBayesPrint ("%s Problem with number of taxa read in (%d taxa read in, while expecting %d)\n", spacer, taxonCount, numTaxa); FreeMatrix(); return (ERROR); } for (i=0; i 1) */ { sprintf (comptreeParams.comptFileName1, "%s.run1.t", inputFileName); sprintf (comptreeParams.comptFileName2, "%s.run2.t", inputFileName); } if (chainParams.numRuns == 1) sprintf (plotParams.plotFileName, "%s.p", inputFileName); else /* if (chainParams.numRuns > 1) */ sprintf (plotParams.plotFileName, "%s.run1.p", inputFileName); strcpy (chainParams.chainFileName, inputFileName); if (chainParams.numRuns > 1) MrBayesPrint ("%s Setting output file names to \"%s.run.\"\n", spacer, chainParams.chainFileName); else MrBayesPrint ("%s Setting output file names to \"%s.\"\n", spacer, chainParams.chainFileName); # if 0 for (i=0; i 0) { if (taxaInfo[taxonCount-1].charCount < numChar) foundNewLine = NO; } } if (taxonCount >= numTaxa && foundNewLine == YES) { if (isInterleaved == YES) { taxonCount = 0; isFirstInterleavedBlock = NO; } else { MrBayesPrint ("%s Too many taxa in matrix\n", spacer); goto errorExit; } } // if (taxaInfo[0].charCount > 4010) i = 1; // why is this here? if (foundNewLine == YES) { /* Should be a taxon. */ if (isFirstInterleavedBlock == YES) { /* If this is the first interleaved block, then we need to add the taxon to the set of taxon names unless there is already a defined taxon set. */ if (strlen(tkn)>99) { MrBayesPrint ("%s Taxon name %s is too long. Maximum 99 characters is allowed.\n", spacer, tkn); goto errorExit; } if (isTaxsetDef == NO && AddString (&taxaNames, taxonCount, tkn) == ERROR) { MrBayesPrint ("%s Problem adding taxon %s to taxon set\n", spacer, tkn); goto errorExit; } if (numTaxa < 10) MrBayesPrint ("%s Taxon %d -> %s\n", spacer, taxonCount+1, tkn); else if (numTaxa < 100 && numTaxa >= 10) MrBayesPrint ("%s Taxon %2d -> %s\n", spacer, taxonCount+1, tkn); else if (numTaxa < 1000 && numTaxa >= 100) MrBayesPrint ("%s Taxon %3d -> %s\n", spacer, taxonCount+1, tkn); else MrBayesPrint ("%s Taxon %4d -> %s\n", spacer, taxonCount+1, tkn); } else { /* If this is not the first interleaved block, then we need to check to see if taxon name is present and in correct place. */ if (CheckString (taxaNames, numTaxa, tkn, &index) == ERROR) { MrBayesPrint ("%s Could not find taxon %s in list of taxa\n", spacer, tkn); goto errorExit; } if (index != taxonCount) { MrBayesPrint ("%s Could not find taxon %s in correct position in list of taxa\n", spacer, tkn); goto errorExit; } } foundNewLine = NO; isNegative = NO; taxonCount++; } else { /* Should be a character (either continuous or otherwise). */ if (charInfo[taxaInfo[taxonCount-1].charCount].charType == CONTINUOUS) { /* If we have a continuous character, then the entire token should either be a question mark, a number or a dash (for a negative sign). */ if (!strcmp(tkn, "?")) { /* store a very large number to represent missing state for now should be fine when data are normalized (between 0 and 1) or standardized */ matrix[pos(taxonCount-1,taxaInfo[taxonCount-1].charCount++,numChar)] = INT_MAX; } else if (!strcmp(tkn, "-")) { /* Dealing with a negative number. We will multiply the next tkn, which had better be a number, by -1. */ isNegative = YES; } else { /* We have a number, we hope. */ if (tkn[0] == matchId) { /* If the token is a matchchar, then things are simple. */ if (taxonCount == 1) { MrBayesPrint ("%s Matching characters cannot be in first taxon\n", spacer); goto errorExit; } charCode = matrix[pos(0,taxaInfo[taxonCount-1].charCount,numChar)]; matrix[pos(taxonCount-1,taxaInfo[taxonCount-1].charCount++,numChar)] = charCode; } else { /* Otherwise, we have a number. Check that it is a valid number first... */ if (!IsIn(tkn[0],"0123456789.")) { MrBayesPrint ("%s Expecting a number for the continuous character\n", spacer); goto errorExit; } /* ... and then put the character into the matrix. Note that matrix is defined as an integer, but we have floating precision continuous characters. To get around this, we multiply the value of the character by 10000 before putting it into the matrix. We will divide by 10000 later on when/if we use the characters. */ sscanf (tkn, "%lf", &charValue); charValue = charValue * 10000.0 + 0.5; if (isNegative == YES) { charValue *= -1.0; isNegative = NO; } matrix[pos(taxonCount-1,taxaInfo[taxonCount-1].charCount++,numChar)] = (int)charValue; } } } else { /* Otherwise, we are dealing with a run-of-the-mill character, and we cannot expect the entire token to contain only a single character. We must, therefore, go through the token character-by-character. */ i = 0; while (tkn[i] != '\0') { /*MrBayesPrint ("%c", tkn[i]);*/ if (tkn[i] == matchId) { if (taxonCount == 1) { MrBayesPrint ("%s Matching characters cannot be in first taxon\n", spacer); goto errorExit; } charCode = matrix[pos(0,taxaInfo[taxonCount-1].charCount,numChar)]; matrix[pos(taxonCount-1,taxaInfo[taxonCount-1].charCount++,numChar)] = charCode; } else { if ((tkn[i] == ')' && isInAmbig == YES) || (tkn[i] == '}' && isInPoly == YES)) { isInAmbig = isInPoly = NO; charCode = theAmbigChar; j = CharacterNumber (charCode, charInfo[taxaInfo[taxonCount-1].charCount].charType); if (j > charInfo[taxaInfo[taxonCount-1].charCount].numStates) charInfo[taxaInfo[taxonCount-1].charCount].numStates = j; matrix[pos(taxonCount-1,taxaInfo[taxonCount-1].charCount++,numChar)] = charCode; theAmbigChar = 0; } else if ((tkn[i] == '(' && isInAmbig == YES) || (tkn[i] == '{' && isInPoly == YES)) { if (isInAmbig == YES) MrBayesPrint ("%s Found an inappropriate \"(\"\n", spacer); else MrBayesPrint ("%s Found an inappropriate \"{\"\n", spacer); goto errorExit; } else if (isInAmbig == YES || isInPoly == YES) { if (tkn[i] == ',') expecting |= Expecting (COMMA); else { if (CharacterCode(tkn[i], &charCode, charInfo[taxaInfo[taxonCount-1].charCount].charType) == ERROR) goto errorExit; if (charCode == MISSING || charCode == GAP) goto errorExit; theAmbigChar |= charCode; expecting ^= Expecting (COMMA); } } else if (tkn[i] == '{' && isInPoly == NO && isInAmbig == NO) { isInPoly = YES; matrixHasPoly = YES; theAmbigChar = 0; } else if (tkn[i] == '(' && isInPoly == NO && isInAmbig == NO) { isInAmbig = YES; theAmbigChar = 0; } else if (tkn[i] == '(' && isInPoly == NO && isInAmbig == NO) { isInAmbig = YES; theAmbigChar = 0; } else { if (CharacterCode(tkn[i], &charCode, charInfo[taxaInfo[taxonCount-1].charCount].charType) == ERROR) { MrBayesPrint ("%s Error while reading character position %d (charCode %d)\n", spacer, taxaInfo[taxonCount-1].charCount+1, charCode); goto errorExit; } if (charCode != MISSING && charCode != GAP) { j = CharacterNumber (charCode, charInfo[taxaInfo[taxonCount-1].charCount].charType); if (j > charInfo[taxaInfo[taxonCount-1].charCount].numStates) charInfo[taxaInfo[taxonCount-1].charCount].numStates = j; } matrix[pos(taxonCount-1,taxaInfo[taxonCount-1].charCount++,numChar)] = charCode; } } i++; } } } return (NO_ERROR); errorExit: numTaxa=taxonCount; FreeMatrix(); return (ERROR); } int DoNexusParm (char *parmName, char *tkn) { if (!strcmp(parmName, "NEXUS")) { MrBayesPrint ("%s Expecting NEXUS formatted file\n", spacer); expecting = Expecting(COMMAND); } else { MrBayesPrint ("%s Found %s\n", spacer, tkn); return (ERROR); } return (NO_ERROR); } int DoOutgroup (void) { MrBayesPrint ("%s Setting outgroup to taxon \"%s\"\n", spacer, taxaNames[outGroupNum]); return (NO_ERROR); } int DoOutgroupParm (char *parmName, char *tkn) { int index, tempInt; if (expecting == Expecting(ALPHA)) { if (CheckString (taxaNames, numTaxa, tkn, &index) == ERROR) { MrBayesPrint ("%s Could not find taxon %s in list of taxa\n", spacer, tkn); return (ERROR); } outGroupNum = index; expecting = Expecting(SEMICOLON); } else if (expecting == Expecting(NUMBER)) { if (CheckString (taxaNames, numTaxa, tkn, &index) == ERROR) { /* OK, as we expect, the taxon is not a digit. So, now we assume that the user is assigning the outgroup by its number */ sscanf (tkn, "%d", &tempInt); if (tempInt < 1 || tempInt > numTaxa) { MrBayesPrint ("%s Taxon number %d is out of range\n", spacer, tempInt); return (ERROR); } outGroupNum = tempInt - 1; } else { outGroupNum = index; } expecting = Expecting(SEMICOLON); } else return (ERROR); return (NO_ERROR); } int DoPairs (void) { MrBayesPrint ("\n"); MrBayesPrint ("%s Successfully defined character pairings\n", spacer); defPairs = YES; foundFirst = NO; return (NO_ERROR); } int DoPairsParm (char *parmName, char *tkn) { int i, tempInt; if (defMatrix == NO) { MrBayesPrint ("%s A matrix must be specified before you can define pairs of characters\n", spacer); return (ERROR); } if (defPairs == YES) { MrBayesPrint ("%s Character pairs have been previously defined \n", spacer); MrBayesPrint ("%s Now overwriting old pairings\n", spacer); for (i=0; i numChar) { MrBayesPrint ("\n"); MrBayesPrint ("%s Character number %d is out of range (should be between %d and %d)\n", spacer, tempInt, 1, numChar); for (i=0; i= 0) if (AddToSet (fromI, toJ, everyK, whichPartition+1) == ERROR) return (ERROR); /* check that all characters are included */ for (i=0; i 99) { MrBayesPrint ("%s Partition name is too long. Max 100 characters\n", spacer); return (ERROR); } /* check to see if the name has already been used as a partition */ if (numDefinedPartitions > 1) { if (CheckString (partitionNames, numDefinedPartitions, tkn, &index) == ERROR) { /* if the partition name has not been used, then we should have an ERROR returned */ /* we _want_ to be here */ } else { MrBayesPrint ("%s Partition name '%s' has been used previously\n", spacer, tkn); return (ERROR); } } /* add the name temporarily to tempSetName */ strcpy (tempSetName, tkn); /* clear tempSet */ for (i=0; i numChar) { MrBayesPrint ("%s Character number %d is out of range (should be between %d and %d)\n", spacer, tempInt, 1, numChar); return (ERROR); } tempInt--; if (foundDash == YES) { if (fromI >= 0) toJ = tempInt; else { MrBayesPrint ("%s Improperly formatted partition\n", spacer); return (ERROR); } foundDash = NO; } else if (foundSlash == YES) { tempInt++; if (tempInt <= 1) { MrBayesPrint ("%s Improperly formatted charset\n", spacer); return (ERROR); } if (fromI >= 0 && toJ >= 0 && fromI < toJ) everyK = tempInt; else { MrBayesPrint ("%s Improperly formatted charset\n", spacer); return (ERROR); } foundSlash = NO; } else { if (fromI >= 0 && toJ < 0) { if (AddToSet (fromI, toJ, everyK, whichPartition+1) == ERROR) return (ERROR); fromI = tempInt; } else if (fromI < 0 && toJ < 0) { fromI = tempInt; } else if (fromI >= 0 && toJ >= 0 && everyK < 0) { if (AddToSet (fromI, toJ, everyK, whichPartition+1) == ERROR) return (ERROR); fromI = tempInt; toJ = everyK = -1; } else if (fromI >= 0 && toJ >= 0 && everyK >= 0) { if (AddToSet (fromI, toJ, everyK, whichPartition+1) == ERROR) return (ERROR); fromI = tempInt; toJ = everyK = -1; } else { MrBayesPrint ("%s Improperly formatted charset\n", spacer); { return (ERROR); } } } expecting = Expecting(ALPHA); expecting |= Expecting(NUMBER); expecting |= Expecting(SEMICOLON); expecting |= Expecting(DASH); expecting |= Expecting(BACKSLASH); expecting |= Expecting(COMMA); } } else if (expecting == Expecting(COMMA)) { /* add set to tempSet */ if (fromI >= 0) if (AddToSet (fromI, toJ, everyK, whichPartition+1) == ERROR) return (ERROR); fromI = toJ = everyK = -1; foundDash = foundSlash = NO; whichPartition++; if (whichPartition > numDivisions) { MrBayesPrint ("%s Too many partitions of the data (expecting %d)\n", spacer, numDivisions); return (ERROR); } expecting = Expecting(NUMBER); expecting |= Expecting(ALPHA); } else if (expecting == Expecting(COLON)) { expecting = Expecting(NUMBER); expecting |= Expecting(ALPHA); } else if (expecting == Expecting(DASH)) { foundDash = YES; expecting = Expecting(NUMBER); } else if (expecting == Expecting(BACKSLASH)) { foundSlash = YES; expecting = Expecting(NUMBER); } else return (ERROR); return (NO_ERROR); } int DoRestore (void) { int i, alreadyDone; MrBayesPrint ("%s Restore taxa\n", spacer); /* add set to tempSet */ if (fromI >= 0 && toJ < 0) { if (AddToSet (fromI, toJ, everyK, 1) == ERROR) return (ERROR); } else if (fromI >= 0 && toJ >= 0 && everyK < 0) { if (AddToSet (fromI, toJ, everyK, 1) == ERROR) return (ERROR); } else if (fromI >= 0 && toJ >= 0 && everyK >= 0) { if (AddToSet (fromI, toJ, everyK, 1) == ERROR) return (ERROR); } /* merge tempSet with excludedTaxa */ alreadyDone = NO; for (i=0; i= 0 && toJ < 0) { if (AddToSet (fromI, toJ, everyK, 1) == ERROR) return (ERROR); } else if (fromI >= 0 && toJ >= 0) { if (AddToSet (fromI, toJ, everyK, 1) == ERROR) return (ERROR); } tempSet[index] = 1; } fromI = toJ = everyK = -1; } expecting = Expecting(ALPHA); expecting |= Expecting(NUMBER); expecting |= Expecting(SEMICOLON); } else if (expecting == Expecting(NUMBER)) { if (strlen(tkn) == 1 && !strcmp(tkn, ".")) { tempInt = numTaxa; } else { sscanf (tkn, "%d", &tempInt); if (tempInt <= 0 || tempInt > numTaxa) { MrBayesPrint ("%s Taxon number %d is out of range (should be between %d and %d)\n", spacer, tempInt, 1, numTaxa); return (ERROR); } } tempInt--; if (foundDash == YES) { if (fromI >= 0) toJ = tempInt; else { MrBayesPrint ("%s Improperly formatted restore set\n", spacer); return (ERROR); } foundDash = NO; } else { if (fromI >= 0 && toJ < 0) { if (AddToSet (fromI, toJ, everyK, 1) == ERROR) return (ERROR); fromI = tempInt; } else if (fromI < 0 && toJ < 0) { fromI = tempInt; } else if (fromI >= 0 && toJ >= 0 && everyK < 0) { if (AddToSet (fromI, toJ, everyK, 1) == ERROR) return (ERROR); fromI = tempInt; toJ = everyK = -1; } else if (fromI >= 0 && toJ >= 0 && everyK >= 0) { if (AddToSet (fromI, toJ, everyK, 1) == ERROR) return (ERROR); fromI = tempInt; toJ = everyK = -1; } else { MrBayesPrint ("%s Improperly formatted restore set\n", spacer); { return (ERROR); } } } expecting = Expecting(ALPHA); expecting |= Expecting(NUMBER); expecting |= Expecting(SEMICOLON); expecting |= Expecting(DASH); } else if (expecting == Expecting(DASH)) { foundDash = YES; expecting = Expecting(NUMBER); } else return (ERROR); return (NO_ERROR); } int DoSet (void) { return (NO_ERROR); } int DoSetParm (char *parmName, char *tkn) { int index; char tempStr[100]; int tempI; if (expecting == Expecting(PARAMETER)) { expecting = Expecting(EQUALSIGN); } else { /* set Autoclose (autoClose) **********************************************************/ if (!strcmp(parmName, "Autoclose")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(ALPHA); else if (expecting == Expecting(ALPHA)) { if (IsArgValid(tkn, tempStr) == NO_ERROR) { if (!strcmp(tempStr, "Yes")) autoClose = YES; else autoClose = NO; } else { MrBayesPrint ("%s Invalid argument for autoclose\n", spacer); return (ERROR); } if (autoClose == YES) MrBayesPrint ("%s Setting autoclose to yes\n", spacer); else MrBayesPrint ("%s Setting autoclose to no\n", spacer); expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); } /* set Nowarnings (noWarn) **********************************************************/ else if (!strcmp(parmName, "Nowarnings")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(ALPHA); else if (expecting == Expecting(ALPHA)) { if (IsArgValid(tkn, tempStr) == NO_ERROR) { if (!strcmp(tempStr, "Yes")) noWarn = YES; else noWarn = NO; } else { MrBayesPrint ("%s Invalid argument for nowarnings\n", spacer); return (ERROR); } if (noWarn == YES) MrBayesPrint ("%s Setting nowarnings to yes\n", spacer); else MrBayesPrint ("%s Setting nowarnings to no\n", spacer); expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); } /* set Quitonerror (quitOnError) **************************************************/ else if (!strcmp(parmName, "Quitonerror")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(ALPHA); else if (expecting == Expecting(ALPHA)) { if (IsArgValid(tkn, tempStr) == NO_ERROR) { if (!strcmp(tempStr, "Yes")) quitOnError = YES; else quitOnError = NO; } else { MrBayesPrint ("%s Invalid argument for quitonerror\n", spacer); return (ERROR); } if (quitOnError == YES) MrBayesPrint ("%s Setting quitonerror to yes\n", spacer); else MrBayesPrint ("%s Setting quitonerror to no\n", spacer); expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); } /* set Autoreplace (autoOverwrite) **************************************************/ else if (!strcmp(parmName, "Autoreplace")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(ALPHA); else if (expecting == Expecting(ALPHA)) { if (IsArgValid(tkn, tempStr) == NO_ERROR) { if (!strcmp(tempStr, "Yes")) { autoOverwrite = YES; MrBayesPrint ("%s Setting autoreplace to yes\n", spacer); } else { autoOverwrite = NO; MrBayesPrint ("%s Setting autoreplace to no\n", spacer); } } else { MrBayesPrint ("%s Invalid argument for autoreplace\n", spacer); return (ERROR); } expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); } /* set Scientific (scientific) *********************************************/ else if (!strcmp(parmName, "Scientific")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(ALPHA); else if (expecting == Expecting(ALPHA)) { if (IsArgValid(tkn, tempStr) == NO_ERROR) { if (!strcmp(tempStr, "Yes")) scientific = YES; else scientific = NO; } else { MrBayesPrint ("%s Invalid argument for Scientific\n", spacer); return (ERROR); } if (scientific == YES) MrBayesPrint ("%s Setting Scientific to Yes\n", spacer); else MrBayesPrint ("%s Setting Scientific to No\n", spacer); expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); } /* set Userlevel (userLevel) **********************************************************/ else if (!strcmp(parmName, "Userlevel")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(ALPHA); else if (expecting == Expecting(ALPHA)) { if (IsArgValid(tkn, tempStr) == NO_ERROR) { if (!strcmp(tempStr, "Standard")) userLevel = STANDARD_USER; else if (!strcmp (tempStr,"Developer")) userLevel = DEVELOPER; } else { MrBayesPrint ("%s Invalid argument for userlevel\n", spacer); return (ERROR); } MrBayesPrint ("%s Setting userlevel to %s\n", spacer, tempStr); if (defMatrix == YES && SetUpAnalysis(&globalSeed) == ERROR) return ERROR; expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); } /* set Npthreads (number of pthreads) ****************************************************/ else if (!strcmp(parmName, "Npthreads")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(NUMBER); else if (expecting == Expecting(NUMBER)) { sscanf (tkn, "%d", &tempI); nPThreads = tempI; MrBayesPrint ("%s Setting Npthreads to %d\n", spacer, nPThreads); expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); } /* set Precision (number of decimals) ****************************************************/ else if (!strcmp(parmName, "Precision")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(NUMBER); else if (expecting == Expecting(NUMBER)) { sscanf (tkn, "%d", &tempI); if (tempI < 3 || tempI > 15) { MrBayesPrint ("%s Precision must be in the range 3 to 15\n", spacer); return ERROR; } precision = tempI; MrBayesPrint ("%s Setting Precision to %d\n", spacer, precision); expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); } /* set Partition (partitionNum) *******************************************************/ else if (!strcmp(parmName, "Partition")) { if (defMatrix == NO) { MrBayesPrint ("%s A character matrix must be defined first\n", spacer); return (ERROR); } if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(ALPHA) | Expecting(NUMBER); else if (expecting == Expecting(ALPHA)) { /* first check to see if name is there */ if (CheckString (partitionNames, numDefinedPartitions, tkn, &index) == ERROR) { MrBayesPrint ("%s Could not find \"%s\" as a defined partition\n", spacer, tkn); return (ERROR); } if (SetPartition (index) == ERROR) return ERROR; if (numCurrentDivisions == 1) MrBayesPrint ("%s Setting %s as the partition (does not divide up characters).\n", spacer, tkn); else MrBayesPrint ("%s Setting %s as the partition, dividing characters into %d parts.\n", spacer, tkn, numCurrentDivisions); if (SetModelDefaults () == ERROR) return (ERROR); if (SetUpAnalysis (&globalSeed) == ERROR) return (ERROR); expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else if (expecting == Expecting(NUMBER)) { sscanf (tkn, "%d", &index); if (index > numDefinedPartitions) { MrBayesPrint ("%s Partition number %d is not a valid partition. Only %d partitions\n", spacer, index, numDefinedPartitions); MrBayesPrint ("%s have been defined.\n", spacer); return (ERROR); } if (index < 1) { MrBayesPrint ("%s Partition number %d is not a valid partition. Must be between 1 and %d.\n", spacer, index+1, numDefinedPartitions); return (ERROR); } if (SetPartition (index) == ERROR) return ERROR; if (numCurrentDivisions == 1) MrBayesPrint ("%s Setting %s as the partition (does not divide up characters).\n", spacer, partitionNames[index]); else MrBayesPrint ("%s Setting %s as the partition, dividing characters into %d parts.\n", spacer, partitionNames[index], numCurrentDivisions); if (SetModelDefaults () == ERROR) return (ERROR); if (SetUpAnalysis (&globalSeed) == ERROR) return (ERROR); expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); } /* set Speciespartition (speciespartitionNum) *******************************************************/ else if (!strcmp(parmName, "Speciespartition")) { if (defTaxa == NO) { MrBayesPrint ("%s A taxaset must be defined first\n", spacer); return (ERROR); } if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(ALPHA) | Expecting(NUMBER); else if (expecting == Expecting(ALPHA)) { /* first check to see if name is there */ if (CheckString (speciespartitionNames, numDefinedSpeciespartitions, tkn, &index) == ERROR) { MrBayesPrint ("%s Could not find \"%s\" as a defined speciespartition\n", spacer, tkn); return (ERROR); } if (SetSpeciespartition (index) == ERROR) return ERROR; MrBayesPrint ("%s Setting %s as the speciespartition, dividing taxa into %d species.\n", spacer, tkn, numSpecies); if (SetModelDefaults () == ERROR) return (ERROR); if (SetUpAnalysis (&globalSeed) == ERROR) return (ERROR); expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else if (expecting == Expecting(NUMBER)) { sscanf (tkn, "%d", &index); if (index > numDefinedSpeciespartitions) { MrBayesPrint ("%s Speciespartition number %d is not valid. Only %d speciespartitions\n", spacer, index, numDefinedSpeciespartitions); MrBayesPrint ("%s have been defined.\n", spacer); return (ERROR); } if (index < 1) { MrBayesPrint ("%s Speciespartition number %d is not valid. Must be between 1 and %d.\n", spacer, index, numDefinedSpeciespartitions); return (ERROR); } if (SetSpeciespartition (index-1) == ERROR) return ERROR; MrBayesPrint ("%s Setting %s as the speciespartition, dividing taxa into %d species.\n", spacer, speciespartitionNames[index-1], numSpecies); if (SetModelDefaults () == ERROR) return (ERROR); if (SetUpAnalysis (&globalSeed) == ERROR) return (ERROR); expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); } /* set Seed (global variable globalSeed) ****************************************************/ else if (!strcmp(parmName, "Seed")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(NUMBER); else if (expecting == Expecting(NUMBER)) { sscanf (tkn, "%d", &tempI); if (tempI == 0 || tempI == 2147483647) { MrBayesPrint ("%s Error: Seed can be any natural number except 0 and 2147483647\n", spacer); return (ERROR); } globalSeed = tempI; MrBayesPrint ("%s Setting seed to %ld\n", spacer, globalSeed); expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); } /* set Swapseed (global variable swapSeed) ***************************************************************/ else if (!strcmp(parmName, "Swapseed")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(NUMBER); else if (expecting == Expecting(NUMBER)) { sscanf (tkn, "%d", &tempI); if (tempI == 0 || tempI == 2147483647) { MrBayesPrint ("%s Error: Swapseed can be any natural number except 0 and 2147483647\n", spacer); return (ERROR); } swapSeed = tempI; MrBayesPrint ("%s Setting swapseed to %ld\n", spacer, swapSeed); expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); } /* set Dir (global variable workingDir) ***************************************************************/ else if (!strcmp(parmName, "Dir")) { if (expecting == Expecting(EQUALSIGN)) { expecting = Expecting(ALPHA); readWord = YES; } else if (expecting == Expecting(ALPHA)) { if (strlen(tkn)>99) { MrBayesPrint ("%s Maximum allowed length of working directory name is 99 characters. The given name:\n", spacer); MrBayesPrint ("%s '%s'\n", spacer,tkn); MrBayesPrint ("%s has %d characters.\n", spacer,strlen(tkn)); return (ERROR); } strcpy (workingDir, tkn); # if defined (WIN_VERSION) /* Reformat to Windows with trailing '\' */ for (index=0; index<(int)strlen(workingDir); index++) { if (workingDir[index] == '/') workingDir[index] = '\\'; } if (strlen(workingDir) > 0 && workingDir[strlen(workingDir)-1] != '\\') strcat(workingDir,"\\"); # else /* Reformat to Unix with trailing '/' */ for (index=0; index<(int)strlen(workingDir); index++) { if (workingDir[index] == '\\') workingDir[index] = '/'; } if (strlen(workingDir) > 0 && workingDir[strlen(workingDir)-1] != '/') strcat(workingDir,"/"); # endif MrBayesPrint ("%s Setting working directory to \"%s\"\n", spacer, workingDir); expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); } /* set Usebeagle (global variable BEAGLE usage) ***************************************************************/ else if (!strcmp(parmName, "Usebeagle")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(ALPHA); else if (expecting == Expecting(ALPHA)) { # if defined (BEAGLE_ENABLED) if (IsArgValid(tkn, tempStr) == NO_ERROR) { if (!strcmp(tempStr, "Yes")) tryToUseBEAGLE = YES; else tryToUseBEAGLE = NO; } else { MrBayesPrint ("%s Invalid argument for usebeagle\n", spacer); return (ERROR); } if (tryToUseBEAGLE == YES) MrBayesPrint ("%s Setting usebeagle to yes\n", spacer); else MrBayesPrint ("%s Setting usebeagle to no\n", spacer); # else BeagleNotLinked(); # endif if (defMatrix == YES && SetUpAnalysis(&globalSeed) == ERROR) return ERROR; expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); } /* set Beagle resource number (global variable BEAGLE flag) ****************************************/ else if (!strcmp(parmName, "Beagleresource")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(NUMBER); else if (expecting == Expecting(NUMBER)) { # if defined (BEAGLE_ENABLED) sscanf (tkn, "%d", &tempI); if (tempI < 0) { MrBayesPrint ("%s Beagleresource must be a valid resource number or 99 to disable resource selection\n", spacer); return ERROR; } beagleResourceNumber = tempI; if (beagleResourceNumber == 99) MrBayesPrint ("%s Setting Beagleresource to %d (auto)\n", spacer, beagleResourceNumber); else MrBayesPrint ("%s Setting Beagleresource to %d\n", spacer, beagleResourceNumber); # else BeagleNotLinked(); # endif if (defMatrix == YES && SetUpAnalysis(&globalSeed) == ERROR) return ERROR; expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); } /* set Beagle resources requirements (global variable BEAGLE flag) ****************************************/ else if (!strcmp(parmName, "Beagledevice")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(ALPHA); else if (expecting == Expecting(ALPHA)) { # if defined (BEAGLE_ENABLED) if (IsArgValid(tkn, tempStr) == NO_ERROR) { long oldFlags = beagleFlags; if (!strcmp(tempStr, "Gpu")) { beagleFlags &= ~BEAGLE_FLAG_PROCESSOR_CPU; beagleFlags |= BEAGLE_FLAG_PROCESSOR_GPU; BeagleAddGPUDevicesToList(&beagleResource, &beagleResourceCount); } else { beagleFlags &= ~BEAGLE_FLAG_PROCESSOR_GPU; beagleFlags |= BEAGLE_FLAG_PROCESSOR_CPU; BeagleRemoveGPUDevicesFromList(&beagleResource, &beagleResourceCount); } if (BeagleCheckFlagCompatability(beagleFlags) == NO) { beagleFlags = oldFlags; } else { if (beagleFlags & BEAGLE_FLAG_PROCESSOR_GPU) MrBayesPrint ("%s Setting beagledevice to GPU\n", spacer); else MrBayesPrint ("%s Setting beagledevice to CPU\n", spacer); } } else { MrBayesPrint ("%s Invalid argument for beagledevice\n", spacer); return (ERROR); } # else BeagleNotLinked(); # endif if (defMatrix == YES && SetUpAnalysis(&globalSeed) == ERROR) return ERROR; expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); } else if (!strcmp(parmName, "Beagleprecision")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(ALPHA); else if (expecting == Expecting(ALPHA)) { # if defined (BEAGLE_ENABLED) if (IsArgValid(tkn, tempStr) == NO_ERROR) { long oldFlags = beagleFlags; if (!strcmp(tempStr, "Single")) { beagleFlags &= ~BEAGLE_FLAG_PRECISION_DOUBLE; beagleFlags |= BEAGLE_FLAG_PRECISION_SINGLE; } else { beagleFlags &= ~BEAGLE_FLAG_PRECISION_SINGLE; beagleFlags |= BEAGLE_FLAG_PRECISION_DOUBLE; } if (BeagleCheckFlagCompatability(beagleFlags) == NO) { beagleFlags = oldFlags; } else { if (beagleFlags & BEAGLE_FLAG_PRECISION_DOUBLE) MrBayesPrint ("%s Setting beagleprecision to double\n", spacer); else MrBayesPrint ("%s Setting beagleprecision to single\n", spacer); } } else { MrBayesPrint ("%s Invalid argument for beagleprecision\n", spacer); return (ERROR); } # else BeagleNotLinked(); # endif if (defMatrix == YES && SetUpAnalysis(&globalSeed) == ERROR) return ERROR; expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); } else if (!strcmp(parmName, "Beagleopenmp")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(ALPHA); else if (expecting == Expecting(ALPHA)) { # if defined (BEAGLE_ENABLED) MrBayesPrint ("%s Setting beagleopenmp no longer supported\n", spacer); return (ERROR); # else BeagleNotLinked(); # endif if (defMatrix == YES && SetUpAnalysis(&globalSeed) == ERROR) return ERROR; expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); } else if (!strcmp(parmName, "Beaglefreq")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(NUMBER); else if (expecting == Expecting(NUMBER)) { # if defined (BEAGLE_ENABLED) sscanf (tkn, "%d", &tempI); if (tempI < 0) { MrBayesPrint ("%s Beaglefreq must be greater than 0\n", spacer); return ERROR; } beagleScalingFrequency= tempI; MrBayesPrint ("%s Setting Beaglefreq to %d\n", spacer, beagleScalingFrequency); # else BeagleNotLinked(); # endif if (defMatrix == YES && SetUpAnalysis(&globalSeed) == ERROR) return ERROR; expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); } else if (!strcmp(parmName, "Beaglesse")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(ALPHA); else if (expecting == Expecting(ALPHA)) { # if defined (BEAGLE_ENABLED) if (IsArgValid(tkn, tempStr) == NO_ERROR) { long oldFlags = beagleFlags; if (!strcmp(tempStr, "Yes")) { beagleFlags |= BEAGLE_FLAG_VECTOR_SSE; } else { beagleFlags &= ~BEAGLE_FLAG_VECTOR_SSE; beagleFlags |= BEAGLE_FLAG_VECTOR_NONE; } if (BeagleCheckFlagCompatability(beagleFlags) == NO) { beagleFlags = oldFlags; } else { if (beagleFlags & BEAGLE_FLAG_VECTOR_SSE) MrBayesPrint ("%s Setting beaglesse to Yes\n", spacer); else MrBayesPrint ("%s Setting beaglesse to No\n", spacer); } } else { MrBayesPrint ("%s Invalid argument for beaglesse\n", spacer); return (ERROR); } # else BeagleNotLinked(); # endif if (defMatrix == YES && SetUpAnalysis(&globalSeed) == ERROR) return ERROR; expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); } else if (!strcmp(parmName, "Beaglethreads")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(ALPHA); else if (expecting == Expecting(ALPHA)) { # if defined (BEAGLE_V3_ENABLED) if (IsArgValid(tkn, tempStr) == NO_ERROR) { if (!strcmp(tempStr, "Yes")) { beagleFlags &= ~BEAGLE_FLAG_THREADING_NONE; beagleFlags |= BEAGLE_FLAG_THREADING_CPP; } else { beagleFlags &= ~BEAGLE_FLAG_THREADING_CPP; beagleFlags |= BEAGLE_FLAG_THREADING_NONE; } if (beagleFlags & BEAGLE_FLAG_THREADING_CPP) MrBayesPrint ("%s Setting beaglethreads to Yes\n", spacer); else MrBayesPrint ("%s Setting beaglethreads to No\n", spacer); } else { MrBayesPrint ("%s Invalid argument for beaglethreads\n", spacer); return (ERROR); } # else BeagleThreadsNotAvailable(); # endif if (defMatrix == YES && SetUpAnalysis(&globalSeed) == ERROR) return ERROR; expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); } else if (!strcmp(parmName, "Beaglethreadcount")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(NUMBER); else if (expecting == Expecting(NUMBER)) { # if defined (BEAGLE_V3_ENABLED) sscanf (tkn, "%d", &tempI); if (tempI < 1) { MrBayesPrint ("%s Beaglethreadcount must be a valid number of threads\n", spacer); return ERROR; } beagleThreadCount = tempI; if (beagleThreadCount == 1) { MrBayesPrint ("%s Setting beaglethreadcount to %d (single-threaded)\n", spacer, beagleThreadCount); beagleFlags &= ~BEAGLE_FLAG_THREADING_CPP; beagleFlags |= BEAGLE_FLAG_THREADING_NONE; } else if (beagleThreadCount == 99) { MrBayesPrint ("%s Setting beaglethreadcount to %d (auto-threading)\n", spacer, beagleThreadCount); beagleFlags &= ~BEAGLE_FLAG_THREADING_NONE; beagleFlags |= BEAGLE_FLAG_THREADING_CPP; } else { MrBayesPrint ("%s Setting beaglethreadcount to %d\n", spacer, beagleThreadCount); beagleFlags &= ~BEAGLE_FLAG_THREADING_NONE; beagleFlags |= BEAGLE_FLAG_THREADING_CPP; } # else BeagleThreadsNotAvailable(); # endif if (defMatrix == YES && SetUpAnalysis(&globalSeed) == ERROR) return ERROR; expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); } else if (!strcmp(parmName, "Beaglefloattips")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(ALPHA); else if (expecting == Expecting(ALPHA)) { # if defined (BEAGLE_V3_ENABLED) if (IsArgValid(tkn, tempStr) == NO_ERROR) { if (!strcmp(tempStr, "Yes")) { beagleAllFloatTips = YES; } else { beagleAllFloatTips = NO; } if (beagleAllFloatTips) MrBayesPrint ("%s Setting beaglefloattips to Yes\n", spacer); else MrBayesPrint ("%s Setting beaglefloattips to No\n", spacer); } else { MrBayesPrint ("%s Invalid argument for beaglefloattips\n", spacer); return (ERROR); } # else MrBayesPrint ("%s Beaglefloattips setting requires BEAGLE v3.1 and higher.\n", spacer); # endif if (defMatrix == YES && SetUpAnalysis(&globalSeed) == ERROR) return ERROR; expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); } #if 0 else if (!strcmp(parmName, "Beaglevec")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(ALPHA); else if (expecting == Expecting(ALPHA)) { # if defined (BEAGLE_ENABLED) if (IsArgValid(tkn, tempStr) == NO_ERROR) { long oldFlags = beagleFlags; if (!strcmp(tempStr, "Sse")) { beagleFlags |= BEAGLE_FLAG_VECTOR_SSE; beagleFlags &= ~BEAGLE_FLAG_VECTOR_AVX; } else if (!strcmp(tempStr, "Avx")) { beagleFlags |= ~BEAGLE_FLAG_VECTOR_AVX; beagleFlags &= ~BEAGLE_FLAG_VECTOR_SSE; } else if (!strcmp(tempStr, "None")) { beagleFlags &= ~BEAGLE_FLAG_VECTOR_SSE; beagleFlags &= ~BEAGLE_FLAG_VECTOR_AVX; } else { MrBayesPrint ("%s Unrecognized argument for beaglevec\n", spacer); } MrBayesPrint ("%s Setting beaglevec to %s\n", spacer, tempStr); } else { MrBayesPrint ("%s Invalid argument for beagleopenmp\n", spacer); return (ERROR); } # else BeagleNotLinked(); # endif if (defMatrix == YES && SetUpAnalysis(&globalSeed) == ERROR) return ERROR; expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); } #endif else if (!strcmp(parmName, "Beaglescaling")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(ALPHA); else if (expecting == Expecting(ALPHA)) { # if defined (BEAGLE_ENABLED) if (IsArgValid(tkn, tempStr) == NO_ERROR) { if (!strcmp(tempStr, "Always")) { beagleScalingScheme = MB_BEAGLE_SCALE_ALWAYS; } else { beagleScalingScheme = MB_BEAGLE_SCALE_DYNAMIC; } if (beagleScalingScheme == MB_BEAGLE_SCALE_ALWAYS) MrBayesPrint ("%s Setting beaglescaling to Always\n", spacer); else MrBayesPrint ("%s Setting beaglescaling to Dynamic\n", spacer); } else { MrBayesPrint ("%s Invalid argument for beaglescaling\n", spacer); return (ERROR); } # else BeagleNotLinked(); # endif if (defMatrix == YES && SetUpAnalysis(&globalSeed) == ERROR) return ERROR; expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); } else return (ERROR); } return (NO_ERROR); } int DoShowMatrix (void) { int i, j, nameLen, start, finish, ct, longestName; char tempStr[100], stride; if (defMatrix == NO) { MrBayesPrint ("%s A character matrix must be defined first\n", spacer); return (ERROR); } longestName = 0; for (i=0; i longestName) longestName = nameLen; } stride = 50; start = finish = 0; do { finish += stride; if (finish > numChar) finish = numChar; MrBayesPrint ("%s ", spacer); for (j=0; j= longestName) { for (j=0; jname); ShowConTree (stdout, userTree[i], 70, NO); MrBayesPrint ("\n"); } } return (NO_ERROR); } int DoShowBeagle (void) { # if defined (BEAGLE_ENABLED) BeaglePrintResources(); # else BeagleNotLinked(); # endif return (NO_ERROR); } int DoTaxlabels (void) { isTaxsetDef = YES; /* add default speciespartition name to list of valid speciespartitions */ if (AddString (&speciespartitionNames, 0, "Default") == ERROR) { MrBayesPrint ("%s Problem adding Default speciespartition to list\n", spacer); return (ERROR); } /* add default species name set */ AddNameSet(&speciesNameSets, 0, taxaNames, numTaxa); /* set number of defined speciespartitions to 1 */ numDefinedSpeciespartitions = 1; return (NO_ERROR); } int DoTaxlabelsParm (char *parmName, char *tkn) { int index; if (inTaxaBlock == NO) { MrBayesPrint ("%s You must be in a taxa block to read a taxlabels command\n", spacer); return (ERROR); } if (defTaxa == NO) { MrBayesPrint ("%s The number of taxa must be given before a set of taxon labels can be read\n", spacer); return ERROR; } if (isTaxsetDef == YES) { MrBayesPrint ("%s A set of taxon labels has already been defined\n", spacer); if (defMatrix == NO) if (WantTo ("Do you want to delete the current set of taxon labels") == NO) return (SKIP_COMMAND); else FreeTaxa(); else if (WantTo ("Do you want to delete the current character matrix") == NO) return (SKIP_COMMAND); else FreeMatrix(); } if (expecting == Expecting(ALPHA) || expecting == Expecting(NUMBER)) { if (CheckString (taxaNames, numNamedTaxa, tkn, &index) == ERROR) { if (strlen(tkn)>99) { MrBayesPrint ("%s Taxon name %s is too long. Maximum 99 characters is allowed.\n", spacer, tkn); return (ERROR); } if (AddString (&taxaNames, numNamedTaxa, tkn) == ERROR) { MrBayesPrint ("%s Problem adding label %s to list of taxon labels\n", spacer, tkn); return (ERROR); } numNamedTaxa++; } else { MrBayesPrint ("%s Taxon label '%s' is included twice in list of taxon labels\n", spacer, tkn); return (ERROR); } if (numNamedTaxa < numTaxa) { expecting = Expecting(ALPHA); expecting |= Expecting(NUMBER); } else expecting |= Expecting(SEMICOLON); } return (NO_ERROR); } int DoSpeciespartition (void) { int i, *partCount; /* add set to tempSet */ if (fromI >= 0) if (AddToSet (fromI, toJ, everyK, whichPartition+1) == ERROR) { for (i=0; i numTaxa) { MrBayesPrint ("%s Speciespartition index for tip %d out of bound (%d)\n", spacer, i+1, tempSet[i]); free (partCount); for (i=0; i 99) { MrBayesPrint ("%s Partition name is too long. Max 100 characters\n", spacer); return (ERROR); } /* check to see if the name has already been used as a partition */ if (numDefinedSpeciespartitions > 0) { if (CheckString (speciespartitionNames, numDefinedSpeciespartitions, tkn, &index) == ERROR) { /* if the partition name has not been used, then we should have an ERROR returned */ /* we _want_ to be here */ } else { MrBayesPrint ("%s Speciespartition name '%s' has been used previously\n", spacer, tkn); return (ERROR); } } /* add the name temporarily to tempSetName */ strcpy (tempSetName, tkn); /* clear tempSet */ for (i=0; i numTaxa) { MrBayesPrint ("%s Tip number %d is out of range (should be between %d and %d)\n", spacer, tempInt, 1, numTaxa); for (i=0; i= 0) toJ = tempInt; else { MrBayesPrint ("%s Improperly formatted speciespartition\n", spacer); for (i=0; i= 0 && toJ >= 0 && fromI < toJ) everyK = tempInt; else { MrBayesPrint ("%s Improperly formatted speciespartition\n", spacer); for (i=0; i= 0 && toJ < 0) { if (AddToSet (fromI, toJ, everyK, whichPartition+1) == ERROR) { for (i=0; i= 0 && toJ >= 0 && everyK < 0) { if (AddToSet (fromI, toJ, everyK, whichPartition+1) == ERROR) return (ERROR); fromI = tempInt; toJ = everyK = -1; } else if (fromI >= 0 && toJ >= 0 && everyK >= 0) { if (AddToSet (fromI, toJ, everyK, whichPartition+1) == ERROR) return (ERROR); fromI = tempInt; toJ = everyK = -1; } else { MrBayesPrint ("%s Improperly formatted speciespartition\n", spacer); { for (i=0; i= 0) if (AddToSet (fromI, toJ, everyK, whichPartition+1) == ERROR) { for (i=0; i numTaxa) { MrBayesPrint ("%s Too many speciespartitions (expecting maximum %d speciespartitions)\n", spacer, numTaxa); for (i=0; i= 0 && toJ < 0) { if (AddToSet (fromI, toJ, everyK, 1) == ERROR) return (ERROR); } else if (fromI >= 0 && toJ >= 0 && everyK < 0) { if (AddToSet (fromI, toJ, everyK, 1) == ERROR) return (ERROR); } else if (fromI >= 0 && toJ >= 0 && everyK >= 0) { if (AddToSet (fromI, toJ, everyK, 1) == ERROR) return (ERROR); } /* add name to taxaSetNames */ if (AddString (&taxaSetNames, numTaxaSets, tempSetName) == ERROR) { MrBayesPrint ("%s Problem adding taxset %s to list\n", spacer, tempSetName); return (ERROR); } /* merge tempSet with taxaSet */ AddBitfield (&taxaSet, numTaxaSets, tempSet, numTaxa); /* increment number of char sets */ numTaxaSets++; /* show taxset (for debugging) */ # if 0 for (i=0; i 99) { MrBayesPrint ("%s Taxset name is too long\n", spacer); return (ERROR); } /* check to see if the name has already been used as a taxset */ if (numTaxaSets > 0) { if (CheckString (taxaSetNames, numTaxaSets, tkn, &index) == ERROR) { /* if the taxset name has not been used, then we should have an ERROR returned */ /* we _want_ to be here */ } else { MrBayesPrint ("%s Taxset name has been used previously\n", spacer); return (ERROR); } } else if (numTaxaSets > 30) { MrBayesPrint ("%s You cannot define more than 30 taxsets\n", spacer); return (ERROR); } /* add the name to the taxa set */ strcpy (tempSetName, tkn); /* clear tempSet */ for (i=0; i numTaxa) { MrBayesPrint ("%s Taxon number %d is out of range (should be between %d and %d)\n", spacer, tempInt, 1, numTaxa); return (ERROR); } } tempInt--; if (foundDash == YES) { if (fromI >= 0) toJ = tempInt; else { MrBayesPrint ("%s Improperly formatted taxset\n", spacer); return (ERROR); } foundDash = NO; } else if (foundSlash == YES) { tempInt++; if (tempInt <= 1) { MrBayesPrint ("%s Improperly formatted taxset\n", spacer); return (ERROR); } if (fromI >= 0 && toJ >= 0 && fromI < toJ) everyK = tempInt; else { MrBayesPrint ("%s Improperly formatted taxset\n", spacer); return (ERROR); } foundSlash = NO; } else { if (fromI >= 0 && toJ < 0) { if (AddToSet (fromI, toJ, everyK, 1) == ERROR) return (ERROR); fromI = tempInt; } else if (fromI < 0 && toJ < 0) { fromI = tempInt; } else if (fromI >= 0 && toJ >= 0 && everyK < 0) { if (AddToSet (fromI, toJ, everyK, 1) == ERROR) return (ERROR); fromI = tempInt; toJ = everyK = -1; } else if (fromI >= 0 && toJ >= 0 && everyK >= 0) { if (AddToSet (fromI, toJ, everyK, 1) == ERROR) return (ERROR); fromI = tempInt; toJ = everyK = -1; } else { MrBayesPrint ("%s Improperly formatted taxset\n", spacer); { return (ERROR); } } } expecting = Expecting(ALPHA); expecting |= Expecting(NUMBER); expecting |= Expecting(SEMICOLON); expecting |= Expecting(DASH); expecting |= Expecting(BACKSLASH); } else if (expecting == Expecting(DASH)) { foundDash = YES; expecting = Expecting(NUMBER); } else if (expecting == Expecting(BACKSLASH)) { foundSlash = YES; expecting = Expecting(NUMBER); } else return (ERROR); return (NO_ERROR); } int DoTaxaStat (void) { int i, j, maxLen, nameLen, nIncludedTaxa; char tempName[100]; if (defMatrix == NO) { MrBayesPrint ("%s A character matrix must be defined first\n", spacer); return (ERROR); } /* find maximum length of taxon name */ maxLen = nIncludedTaxa = 0; for (i=0; i maxLen) maxLen = (int) strlen(tempName); if (taxaInfo[i].isDeleted == NO) nIncludedTaxa++; } MrBayesPrint ("%s Showing taxon status:\n\n", spacer); if (nIncludedTaxa == numTaxa) MrBayesPrint ("%s Number of taxa = %d (all of which are included)\n", spacer, numTaxa); else MrBayesPrint ("%s Number of taxa = %d (of which %d are included)\n", spacer, numTaxa, nIncludedTaxa); MrBayesPrint ("%s Number of constraints = %d\n\n", spacer, numDefinedConstraints); if (numDefinedConstraints > 0) { for (j=0; j 0) { MrBayesPrint ("----"); for (j=0; j%4d (%s) ", spacer, i+1, tempName); else MrBayesPrint ("%s %4d (%s) ", spacer, i+1, tempName); for (j=0; j<(maxLen-nameLen); j++) MrBayesPrint (" "); MrBayesPrint (" -- "); if (taxaInfo[i].isDeleted == YES) MrBayesPrint ("Deleted "); else MrBayesPrint ("Included"); MrBayesPrint (" "); for (j=0; j = [&R] ; tree = [&U] ; tree [&E CppEvents] = [&R] [&clockrate = 1.23] ((1:0.021[&E CppEvents 2: (0.10 1.11,0.83 3.17)],... tree [&B TK02Brlens] = [&R] [&clockrate = 1.23] ((1:0.021[&B TK02Brlens 0.019],... tree [&B IgrBrlens] = [&R] [&clockrate = 1.23] ((1:0.021[&B IgrBrlens 0.019],... Values will be stored in event sets that go with the tree and that are used to initialize the relaxed clock parameters before a run is started. Note that several sets of events can be stored with each tree. */ if (isTaxsetDef == NO) { MrBayesPrint ("%s Taxon labels must be specified before a tree could be red in\n", spacer); return (ERROR); } if (inTreesBlock == NO) { MrBayesPrint ("%s You must be in a trees block to read a tree\n", spacer); return (ERROR); } /* All static parser-state variables (t, pp, qq, nextAvailableNode, etc.) are initialised only when the PARAMETER (tree-name) token is processed. Every other branch dereferences t and/or pp and therefore requires that initialisation to have happened first. If it has not, the call sequence is wrong and we must not proceed. */ if (expecting != Expecting(PARAMETER) && t == NULL) { MrBayesPrint ("%s Unexpected tree token received before tree name was read\n", spacer); return (ERROR); } if (expecting == Expecting(PARAMETER)) { /* this is the name of the tree */ if (inSumtCommand==YES || inComparetreeCommand == YES) { /* we are reading in a tree to sumt or comparetree counters */ t = sumtParams.tree; ResetPolyTree (t); } else { /* we are reading in a user tree */ /* check if the tree exists */ treeIndex = 0; for (i=0; iname) == 0) break; treeIndex = i; if (treeIndex < numUserTrees) { MrBayesPrint ("%s Overwriting tree '%s'.\n", spacer, userTree[treeIndex]); FreePolyTree (userTree[treeIndex]); } if (treeIndex > MAX_NUM_USERTREES) { MrBayesPrint ("%s MrBayes can only store %d user trees.\n", spacer, MAX_NUM_USERTREES); return (ERROR); } if ((userTree[treeIndex] = AllocatePolyTree (numTaxa)) == NULL) return (ERROR); t = userTree[treeIndex]; } strncpy (t->name, tkn, 99); foundColon = foundAmpersand = foundEqual = foundComment = NO; foundE = foundB = foundN = foundFirst = foundClockrate = foundName = NO; eSetIndex = bSetIndex = eventIndex = 0; nextAvailableNode = 0; if (isTranslateDef == YES && isTranslateDiff == YES) nextIntNodeIndex = numTranslates; else nextIntNodeIndex = numTaxa; pp = &t->nodes[nextAvailableNode++]; t->root = pp; t->isRooted = NO; /* expect unrooted tree */ t->isClock = NO; /* expect nonclock tree */ t->isCalibrated = NO; /* expect uncalibrated tree */ t->isRelaxed = NO; /* expect strict clock if clock tree */ t->clockRate = 0.0; /* expect no clock rate */ t->popSizeSet = NO; readComment = YES; expecting = Expecting(EQUALSIGN) | Expecting(LEFTCOMMENT); lastExpecting = expecting; } else if (expecting == Expecting(EQUALSIGN)) { if (foundClockrate == YES) expecting = Expecting(NUMBER); else { for (i=0; i= 2*numTaxa) { MrBayesPrint ("%s Too many nodes on tree '%s'\n", spacer, t->name); if (inSumtCommand == NO && inComparetreeCommand == NO) FreePolyTree (userTree[treeIndex]); return (ERROR); } qq = &t->nodes[nextAvailableNode++]; qq->anc = pp; pp->left = qq; pp->index = nextIntNodeIndex++; pp = qq; expecting = Expecting(LEFTPAR); expecting |= Expecting(ALPHA); expecting |= Expecting(NUMBER); expecting |= Expecting(LEFTCOMMENT); lastExpecting = expecting; } } else if (expecting == Expecting(ALPHA)) { if (foundAmpersand == YES) { if (strcmp(tkn,"E") == 0) { foundE = YES; expecting = Expecting(ALPHA); } else if (strcmp(tkn,"B") == 0) { foundB = YES; expecting = Expecting(ALPHA); } else if (strcmp(tkn,"N") == 0) { foundN = YES; expecting = Expecting(ALPHA); } else if (strcmp(tkn, "R") == 0) { t->isRooted = YES; t->isClock = YES; /* assume clock if rooted */ expecting = Expecting(RIGHTCOMMENT); } else if (strcmp(tkn, "D") == 0) { t->isRooted = YES; /* we use 'D' for 'directed' to indicate a rooted non-clock tree */ expecting = Expecting(RIGHTCOMMENT); } else if (strcmp(tkn, "U") == 0) { t->isRooted = NO; expecting = Expecting(RIGHTCOMMENT); } else if (strcmp(tkn, "clockrate") == 0) { t->isCalibrated = YES; foundClockrate = YES; expecting = Expecting(EQUALSIGN); } else { inComment = YES; numComments++; expecting = lastExpecting; } foundAmpersand = NO; } else if (foundName == YES && foundCurly == YES) { if (strcmp("all",tkn) == 0) { SafeStrcat (&tempNameString,tkn); expecting = Expecting(RIGHTCURL); } else { MrBayesPrint ("%s Unrecognized argument '%s'\n", spacer, tkn); return (ERROR); } } else if (foundE == YES) /* We have seen &E */ { if (foundEqual == NO) /* We have not seen name before and we are in header */ { t->nESets++; t->isRelaxed = YES; t->nEvents = (int **) SafeRealloc ((void *)t->nEvents, t->nESets*sizeof(int *)); t->position = (MrBFlt ***) SafeRealloc ((void *)t->position, t->nESets*sizeof(MrBFlt **)); t->rateMult = (MrBFlt ***) SafeRealloc ((void *)t->rateMult, t->nESets*sizeof(MrBFlt **)); t->nEvents[t->nESets-1] = (int *) SafeCalloc (2*(size_t)numTaxa, sizeof(int)); t->position[t->nESets-1] = (MrBFlt **) SafeCalloc (2*(size_t)numTaxa, sizeof(MrBFlt *)); t->rateMult[t->nESets-1] = (MrBFlt **) SafeCalloc (2*(size_t)numTaxa, sizeof(MrBFlt *)); t->eSetName = (char **) SafeRealloc ((void *)t->eSetName, t->nESets*sizeof(char **)); } SafeStrcpy (&tempNameString,tkn); foundName = YES; expecting = Expecting(LEFTCURL); if (foundEqual == YES) expecting |= Expecting(NUMBER); else expecting |= Expecting(RIGHTCOMMENT); } else if (foundB == YES) { if (foundEqual == NO) { t->nBSets++; t->isRelaxed = YES; t->effectiveBrLen = (MrBFlt **) SafeRealloc ((void *)t->effectiveBrLen, (size_t)(t->nBSets)*sizeof(MrBFlt *)); t->effectiveBrLen[t->nBSets-1] = (MrBFlt *) SafeCalloc (2*(size_t)numTaxa, sizeof(MrBFlt)); for (i=0; i<2*numTaxa; i++) t->effectiveBrLen[t->nBSets-1][i] = 1.0; t->bSetName = (char **) SafeRealloc ((void *)t->bSetName, (size_t)(t->nBSets)*sizeof(char *)); t->bSetName[t->nBSets-1] = (char *) SafeCalloc (strlen(tkn)+1, sizeof(char)); } SafeStrcpy (&tempNameString,tkn); foundName = YES; expecting = Expecting(LEFTCURL); if (foundEqual == YES) expecting |= Expecting(NUMBER); else expecting |= Expecting(RIGHTCOMMENT); } else if (foundN == YES) { if (foundEqual == NO) { if (t->popSizeSet == YES) { MrBayesPrint ("%s Cannot hold more than one population size set\n", spacer); if (inSumtCommand == NO && inComparetreeCommand == NO) FreePolyTree (userTree[treeIndex]); return (ERROR); } t->popSizeSet = YES; if (isTranslateDef == YES && isTranslateDiff == YES) t->popSize = (MrBFlt *) SafeCalloc (2*numTranslates, sizeof(MrBFlt)); else t->popSize = (MrBFlt *) SafeCalloc (2*numLocalTaxa, sizeof(MrBFlt)); } SafeStrcpy (&tempNameString,tkn); foundName = YES; expecting = Expecting(LEFTCURL); if (foundEqual == YES) expecting |= Expecting(NUMBER); else expecting |= Expecting(RIGHTCOMMENT); } else /* taxon name */ { if (isTranslateDef == YES) { /* we are using the translation table */ if (CheckString (transTo, numTranslates, tkn, &index) == ERROR) { MrBayesPrint ("%s Could not find token '%s' in taxon translation table\n", spacer, tkn); if (inSumtCommand == NO && inComparetreeCommand == NO) FreePolyTree (userTree[treeIndex]); return (ERROR); } strcpy (tempName, transFrom[index]); if (isTranslateDiff == NO && CheckString (taxaNames, numTaxa, tempName, &index) == ERROR) { MrBayesPrint ("%s Could not find taxon '%s' in list of taxa\n", spacer, tkn); if (inSumtCommand == NO && inComparetreeCommand == NO) FreePolyTree (userTree[treeIndex]); return (ERROR); } if (tempSet[index] == YES) { MrBayesPrint ("%s Taxon name '%s' already used in tree\n", spacer, tkn); if (inSumtCommand == NO && inComparetreeCommand == NO) FreePolyTree (userTree[treeIndex]); return (ERROR); } tempSet[index] = YES; strcpy (pp->label, tempName); pp->index = index; } else { /* Check to see if the name is in the list of taxon names. */ if (CheckString (taxaNames, numTaxa, tkn, &index) == ERROR) { MrBayesPrint ("%s Could not find taxon '%s' in list of taxa\n", spacer, tkn); if (inSumtCommand == NO && inComparetreeCommand == NO) FreePolyTree (userTree[treeIndex]); return (ERROR); } if (tempSet[index] == YES) { MrBayesPrint ("%s Taxon name '%s' already used in tree\n", spacer, tkn); if (inSumtCommand == NO && inComparetreeCommand == NO) FreePolyTree (userTree[treeIndex]); return (ERROR); } tempSet[index] = YES; strcpy (pp->label, tkn); pp->index = index; } expecting = Expecting(COMMA); expecting |= Expecting(COLON); expecting |= Expecting(RIGHTPAR); } } else if (expecting == Expecting(RIGHTPAR)) { if (foundE == YES) expecting = Expecting(RIGHTCOMMENT); else { if (pp->anc == NULL) { MrBayesPrint ("%s Incorrect tree format: cannot go down\n", spacer);//, tkn if (inSumtCommand == NO && inComparetreeCommand == NO) FreePolyTree (userTree[treeIndex]); return (ERROR); } if (pp->anc->left == pp) { MrBayesPrint ("%s Incorrect tree format: all nodes except tips should have more then one child. Either a single\n", spacer); MrBayesPrint ("%s taxon is surrounded with brackets or there is a clade surrounded by double brackets.\n", spacer); if (inSumtCommand == NO && inComparetreeCommand == NO) FreePolyTree (userTree[treeIndex]); return (ERROR); } pp = pp->anc; if (pp->anc == NULL) { /* finish up tree */ t->nNodes = nextAvailableNode; t->nIntNodes = t->nNodes; for (i=0; inNodes; i++) { if (t->nodes[i].left == NULL) t->nIntNodes--; } GetPolyDownPass(t); /* check that number of taxa is correct */ if (t->isRooted == NO && t->nNodes-t->nIntNodes == t->nIntNodes + 1) t->isRooted = YES; if ((t->isRooted == YES && t->nNodes-t->nIntNodes != t->nIntNodes + 1) || (t->isRooted == NO && t->nNodes-t->nIntNodes != t->nIntNodes + 2)) { /* we are protected from adding too many taxa by taxon-matching code above */ if (t->isRooted == YES && t->nNodes-t->nIntNodes == t->nIntNodes + 2) { MrBayesPrint ("%s The tree is declared as rooted (by comment [&R]) but\n", spacer); MrBayesPrint ("%s the given tree has unrooted structure.\n", spacer); } else MrBayesPrint ("%s Taxa missing in tree, or NOT a binary tree\n", spacer); return (ERROR); } /* check other properties */ if (t->isClock == YES && t->isRooted == NO) { MrBayesPrint ("%s Tree has clock rate but is not rooted\n", spacer); return (ERROR); /* Note: any deviation from an ultrametric tree must be assumed to be due to dated tips at this point */ } if (t->isRelaxed == YES && t->isClock == NO) { MrBayesPrint ("%s Tree has relaxed clock rates but is not a clock tree\n", spacer); return (ERROR); } if (inSumtCommand == NO && inComparetreeCommand == NO) { if (treeIndex == numUserTrees) numUserTrees++; MrBayesPrint ("%s Successfully read tree '%s'\n", spacer, userTree[treeIndex]->name); } if (t->popSize == NULL) { readComment = NO; expecting = Expecting(SEMICOLON); } else { readComment = YES; expecting = Expecting(LEFTCOMMENT); lastExpecting = expecting; } } else { expecting = Expecting(COMMA); expecting |= Expecting(COLON); expecting |= Expecting(RIGHTPAR); } } } else if (expecting == Expecting(COLON)) { foundColon = YES; if (foundE == YES) expecting = Expecting(LEFTPAR); else expecting = Expecting(NUMBER); expecting |= Expecting(LEFTCOMMENT); lastExpecting = expecting; } else if (expecting == Expecting(COMMA)) { if (foundName == YES) { SafeStrcat (&tempNameString,","); expecting = Expecting(NUMBER); } else if (foundE == YES) { expecting = Expecting(NUMBER); } else { if (nextAvailableNode >= 2*numTaxa) { MrBayesPrint ("%s Too many nodes on tree '%s'\n", spacer, t->name); if (inSumtCommand == NO && inComparetreeCommand == NO) FreePolyTree (userTree[treeIndex]); return (ERROR); } qq = &t->nodes[nextAvailableNode++]; pp->sib = qq; qq->anc = pp->anc; pp = qq; expecting = Expecting(LEFTPAR); expecting |= Expecting(ALPHA); expecting |= Expecting(NUMBER); expecting |= Expecting(LEFTCOMMENT); lastExpecting = expecting; } } else if (expecting == Expecting(NUMBER)) { if (foundClockrate == YES) { sscanf (tkn, "%lf", &tempD); t->clockRate = tempD; foundClockrate = NO; expecting = Expecting(RIGHTCOMMENT); } else if (foundName == YES && foundCurly == YES) { /* still assembling name of a param set */ SafeStrcat (&tempNameString,tkn); expecting = Expecting(RIGHTCURL) | Expecting(COMMA); } else if (foundN == YES) { /* we only know now that name is complete if it does not have curlies in it */ foundName = NO; if (strcmp(tempNameString,t->popSizeSetName) != 0) { MrBayesPrint ("%s Could not find population size set '%s'\n", spacer, tempNameString); if (inSumtCommand == NO && inComparetreeCommand == NO) FreePolyTree (userTree[treeIndex]); return (ERROR); } sscanf (tkn, "%lf", &tempD); t->popSize[pp->index] = tempD; foundN = NO; expecting = Expecting(RIGHTCOMMENT); } else if (foundB == YES) { /* we only know now that name is complete if it does not have curlies in it */ foundName = NO; /* find the right effective branch length set */ for (i=0; inBSets; i++) if (strcmp(t->bSetName[i],tempNameString) == 0) break; if (i == t->nBSets) { MrBayesPrint ("%s Could not find effective branch length set '%s'\n", spacer, tempNameString); if (inSumtCommand == NO && inComparetreeCommand == NO) FreePolyTree (userTree[treeIndex]); return (ERROR); } bSetIndex = i; sscanf (tkn, "%lf", &tempD); t->effectiveBrLen[bSetIndex][pp->index] = tempD; foundB = NO; expecting = Expecting(RIGHTCOMMENT); } else if (foundE == YES) { if (foundColon == NO) { /* we only know now that name is complete if it does not have curlies in it */ foundName = NO; /* find the right event set */ for (i=0; inESets; i++) if (strcmp(t->eSetName[i],tempNameString) == 0) break; if (i == t->nESets) { MrBayesPrint ("%s Could not find event set '%s'\n", spacer, tempNameString); if (inSumtCommand == NO && inComparetreeCommand == NO) FreePolyTree (userTree[treeIndex]); return (ERROR); } eSetIndex = i; sscanf (tkn, "%d", &tempInt); if (tempInt < 0) { MrBayesPrint ("%s Wrong number of events (%d) for event set '%s'\n", spacer, tempInt, t->eSetName[eSetIndex]); if (inSumtCommand == NO && inComparetreeCommand == NO) FreePolyTree (userTree[treeIndex]); return (ERROR); } t->nEvents[eSetIndex][pp->index] = tempInt; if (tempInt > 0) { t->position[eSetIndex][pp->index] = (MrBFlt *) SafeCalloc (tempInt, sizeof(MrBFlt)); t->rateMult[eSetIndex][pp->index] = (MrBFlt *) SafeCalloc (tempInt, sizeof(MrBFlt)); expecting = Expecting (COLON); if (inSumtCommand == YES || inComparetreeCommand == YES) expecting |= Expecting (RIGHTCOMMENT); /* we allow empty event specifications in sumt and comparetree */ } else expecting = Expecting (RIGHTCOMMENT); eventIndex = 0; } else if (foundFirst == NO) { /* processing the first number in the cpp event pair */ sscanf (tkn, "%lf", &tempD); t->position[eSetIndex][pp->index][eventIndex] = tempD; expecting = Expecting(NUMBER); foundFirst = YES; } else { /* processing the second number in the cpp event pair */ foundFirst = NO; sscanf (tkn, "%lf", &tempD); t->rateMult[eSetIndex][pp->index][eventIndex] = tempD; eventIndex++; if (eventIndex == t->nEvents[eSetIndex][pp->index]) { expecting = Expecting(RIGHTPAR); foundColon = NO; } else expecting = Expecting(COMMA); } } else if (foundColon == YES) { /* branch length */ sscanf (tkn, "%lf", &tempD); pp->length = tempD; foundColon = NO; t->brlensDef = YES; expecting = Expecting(COMMA); expecting |= Expecting(RIGHTPAR); expecting |= Expecting(LEFTCOMMENT); lastExpecting = expecting; } else /* taxon identifier */ { if (isTranslateDef == YES) { /* we are using the translation table */ if (CheckString (transTo, numTranslates, tkn, &index) == ERROR) { MrBayesPrint ("%s Could not find token '%s' in taxon translation table\n", spacer, tkn); if (inSumtCommand == NO && inComparetreeCommand == NO) FreePolyTree (userTree[treeIndex]); return (ERROR); } strcpy (tempName, transFrom[index]); if (isTranslateDiff == NO && CheckString (taxaNames, numTaxa, tempName, &index) == ERROR) { MrBayesPrint ("%s Could not find taxon '%s' in list of taxa\n", spacer, tkn); if (inSumtCommand == NO && inComparetreeCommand == NO) FreePolyTree (userTree[treeIndex]); return (ERROR); } if (tempSet[index] == YES) { MrBayesPrint ("%s Taxon name '%s' already used in tree\n", spacer, tkn); if (inSumtCommand == NO && inComparetreeCommand == NO) FreePolyTree (userTree[treeIndex]); return (ERROR); } tempSet[index] = YES; strcpy (pp->label, tempName); pp->index = index; } else { /* Simply use taxon number; first check to see if the name is in the list of taxon names. */ if (CheckString (taxaNames, numTaxa, tkn, &index) == ERROR) { /* The number could not be found as a taxon name in the list of taxon names. We will assume that the user has then input taxa as numbers and not the names. */ sscanf (tkn, "%d", &index); if (index < 1 || index > numTaxa) { MrBayesPrint ("%s Taxon number %d is out of range\n", spacer, index); if (inSumtCommand == NO && inComparetreeCommand == NO) FreePolyTree (userTree[treeIndex]); return (ERROR); } index--; if (tempSet[index] == YES) { MrBayesPrint ("%s Taxon name %d has already been used in tree '%s'\n", spacer, index+1, t->name); if (inSumtCommand == NO && inComparetreeCommand == NO) FreePolyTree (userTree[treeIndex]); return (ERROR); } } else { /* The number is in the list of taxon names */ if (index < 0 || index >= numTaxa) { MrBayesPrint ("%s Taxon name %s could not be found\n", spacer, tkn); if (inSumtCommand == NO && inComparetreeCommand == NO) FreePolyTree (userTree[treeIndex]); return (ERROR); } if (tempSet[index] == YES) { MrBayesPrint ("%s Taxon %d has already been used in tree '%s'\n", spacer, index+1, t->name); if (inSumtCommand == NO && inComparetreeCommand == NO) FreePolyTree (userTree[treeIndex]); return (ERROR); } } tempSet[index] = YES; strcpy (pp->label, taxaNames[index]); pp->index = index; } expecting = Expecting(COMMA); expecting |= Expecting(COLON); expecting |= Expecting(RIGHTPAR); expecting |= Expecting(LEFTCOMMENT); lastExpecting = expecting; } } else if (expecting == Expecting(LEFTCOMMENT)) { expecting = Expecting(AMPERSAND); foundComment = YES; } else if (expecting == Expecting(RIGHTCOMMENT)) { if (foundEqual == NO) { /* We may have a complete name of a set of branch parameters, which needs to be recorded */ if (foundName == YES) { if (foundE == YES) { t->eSetName[t->nESets-1] = (char *) SafeCalloc (strlen(tempNameString)+1,sizeof(char)); strcat(t->eSetName[t->nESets-1],tempNameString); } else if (foundB == YES) { t->bSetName[t->nBSets-1] = (char *) SafeCalloc (strlen(tempNameString)+1,sizeof(char)); strcat(t->bSetName[t->nBSets-1],tempNameString); } else if (foundN == YES) { t->popSizeSetName = (char *) SafeCalloc (strlen(tempNameString)+1,sizeof(char)); strcpy(t->popSizeSetName,tempNameString); } foundName = NO; } expecting = Expecting(EQUALSIGN); } else { if (pp->anc == NULL) { if (pp->left == NULL) expecting = Expecting(LEFTPAR); else expecting = Expecting(SEMICOLON); } else if (pp == pp->anc->left) expecting = Expecting(COMMA); else expecting = Expecting(RIGHTPAR); } foundE = foundB = foundN = NO; expecting |= Expecting(LEFTCOMMENT); } else if (expecting == Expecting(AMPERSAND)) { foundAmpersand = YES; foundComment = NO; expecting = Expecting (ALPHA); } else if (foundComment == YES) { numComments++; foundComment = NO; } else if (expecting == Expecting(LEFTCURL)) { if (foundName == YES) { foundCurly=YES; SafeStrcat (&tempNameString,"{"); expecting = Expecting(NUMBER) | Expecting(ALPHA); } else return(ERROR); } else if (expecting == Expecting(RIGHTCURL)) { if (foundName == YES) { SafeStrcat (&tempNameString,"}"); foundCurly=NO; if (foundEqual == NO) { /* We are processing a name of a set of branch params in the header of a tree. */ expecting = Expecting(RIGHTCOMMENT); } else { /* We are processing a param value of a branch param set */ expecting = Expecting(NUMBER); } } else return(ERROR); } return (NO_ERROR); } int DoUserTree (void) { MrBayesPrint ("%s Usertree command deprecated. Define the tree in a treeblock and use 'Startvals' instead.\n", spacer); return (ERROR); } int DoUserTreeParm (char *parmName, char *tkn) { if (expecting == Expecting(EQUALSIGN)) { expecting = Expecting(LEFTPAR); expecting |= Expecting(RIGHTPAR); expecting |= Expecting(COLON); expecting |= Expecting(NUMBER); expecting |= Expecting(ALPHA); expecting |= Expecting(SEMICOLON); } else if (expecting == Expecting(LEFTPAR)) { expecting = Expecting(LEFTPAR); expecting |= Expecting(ALPHA); expecting |= Expecting(NUMBER); } else if (expecting == Expecting(ALPHA)) { expecting = Expecting(COLON); expecting |= Expecting(COMMA); expecting |= Expecting(RIGHTPAR); } else if (expecting == Expecting(NUMBER)) { expecting = Expecting(COLON); expecting |= Expecting(COMMA); expecting |= Expecting(RIGHTPAR); } else if (expecting == Expecting(COLON)) { expecting = Expecting(NUMBER); } else if (expecting == Expecting(COMMA)) { expecting = Expecting(LEFTPAR); expecting |= Expecting(ALPHA); expecting |= Expecting(NUMBER); } else if (expecting == Expecting(RIGHTPAR)) { expecting = Expecting(RIGHTPAR); expecting |= Expecting(COMMA); expecting |= Expecting(COLON); expecting |= Expecting(SEMICOLON); } else return (ERROR); return (NO_ERROR); } int DoVersion (void) { MrBayesPrint (" ---------------------------------------------------------------------------\n"); MrBayesPrint (" Version\n"); MrBayesPrint ("\n"); MrBayesPrint (" MrBayes %s\n", VERSION_NUMBER); MrBayesPrint ("\n"); MrBayesPrint (" Features: "); #ifdef SSE_ENABLED MrBayesPrint (" SSE"); #endif #ifdef AVX_ENABLED MrBayesPrint (" AVX"); #endif #ifdef FMA_ENABLED MrBayesPrint (" FMA"); #endif #ifdef BEAGLE_ENABLED MrBayesPrint (" Beagle"); #endif #ifdef MPI_ENABLED MrBayesPrint (" MPI"); #endif #ifdef HAVE_LIBREADLINE MrBayesPrint (" readline"); #endif MrBayesPrint ("\n"); #if defined(HOST_TYPE) && defined(HOST_CPU) MrBayesPrint (" Host type: %s (CPU: %s)\n", HOST_TYPE, HOST_CPU); #endif #if defined(COMPILER_VENDOR) && defined(COMPILER_VERSION) MrBayesPrint (" Compiler: %s %s\n", COMPILER_VENDOR, COMPILER_VERSION); #endif MrBayesPrint (" ---------------------------------------------------------------------------\n"); return (NO_ERROR); } BitsLong Expecting (int y) { BitsLong x; x = (BitsLong) pow (2.0, (MrBFlt)y); return (x); } #ifdef HAVE_LIBREADLINE /* This function is for commandline substitution: first word is always a command */ char *command_generator (const char *text, int state) { static int list_index, len; char *command; char *dupstring; if (state == 0) { list_index = 0; len = (int) strlen (text); } while ((command = commands[list_index].string) != NULL) { list_index++; if (StrCmpCaseInsensitiveLen (command, text, len) == 0) { /* memory is freed by the readline library so we need a strdup here */ dupstring = SafeMalloc (strlen (command) + 1); strcpy (dupstring, command); return dupstring; } } return NULL; } #endif int FindValidCommand (char *tk, int *numMatches) { int i, j, tkLen, targetLen, numDiff; CmdType *p; p = commands + 0; tkLen = (int) strlen(tk); (*numMatches) = 0; for (i=0; istring); if (tkLen <= targetLen) { for (j=0, numDiff=0; jstring[j])) numDiff++; } if (numDiff == 0) { (*numMatches)++; commandPtr = p; if (tkLen == targetLen) break; } } p++; } inValidCommand = NO; if (*numMatches == 1) { inValidCommand = YES; return (NO_ERROR); } else return (ERROR); } int FindValidParam (char *tk, int *numMatches) { int i, j, tkLen, targetLen, numDiff; CmdType *p; ParmInfoPtr q; if (commandPtr) p = commandPtr; else { MrBayesPrint ("%s Command pointer is NULL\n", spacer); return (ERROR); } tkLen = (int) strlen(tk); *numMatches = 0; for (i=0; inumParms; i++) { q = paramTable + (p->parmList[i]); targetLen = (int) strlen(q->string); /* printf ("%s %d (%s %d)\n", q->string, targetLen, tk, p->numParms); */ if (!strcmp(q->string, "Xxxxxxxxxx")) { (*numMatches)++; paramPtr = q; } else if (tkLen <= targetLen) { for (j=0, numDiff=0; jstring[j])) numDiff++; } if (numDiff == 0) { (*numMatches)++; paramPtr = q; if (tkLen == targetLen) break; } } } if (*numMatches == 1) return (NO_ERROR); else return (ERROR); } int FreeCharacters (void) { int i, memoryLetFree; memoryLetFree = NO; if (memAllocs[ALLOC_TMPSET] == YES) { if (numChar > numTaxa) { tempSet = (int *) SafeRealloc ((void *)tempSet, (size_t)numTaxa*sizeof(int)); tempSetNeg = (int *) SafeRealloc ((void *)tempSetNeg, (size_t)numTaxa*sizeof(int)); } } if (memAllocs[ALLOC_MATRIX] == YES) { free (matrix); matrix = NULL; defMatrix = NO; memAllocs[ALLOC_MATRIX] = NO; memoryLetFree = YES; } if (memAllocs[ALLOC_CHARINFO] == YES) { free (charInfo); charInfo = NULL; memAllocs[ALLOC_CHARINFO] = NO; memoryLetFree = YES; } if (memAllocs[ALLOC_CHARSETS] == YES) { for (i=0; i 0) { MrBayesPrint ("%s Deleting user trees\n", spacer); for (i = 0; i < numUserTrees; i++) { FreePolyTree (userTree[i]); userTree[i] = NULL; } numUserTrees = 0; } FreeCharacters(); if (memoryLetFree == YES) MrBayesPrint ("%s Deleting previously defined taxa\n", spacer); /* reinitialize taxa variables */ ResetTaxaFlags(); return NO_ERROR; } int GetNumPartDivisions (int n) { int i, maxDiv, numDivs, *divFound; maxDiv = 0; for (i=0; i maxDiv) maxDiv = partitionId[i][n]; divFound = (int *) SafeCalloc (maxDiv, sizeof(int)); for (i=0; i tempMax) { *tokenType = NOTHING; token[20]='\0'; MrBayesPrint ("%s Error while parsing a string. Token \"%s...[followed by at least %d more characters]\" is too long.\n", spacer,token,tempMax-token-20); MrBayesPrint ("%s Maximum allowed length of a token is %d\n", spacer,tempMax-token); return (ERROR); } *temp++ = *(*sourceH)++; } *tokenType = ALPHA; } *temp = '\0'; readWord = NO; return (NO_ERROR);; } *tokenType = UNKNOWN_TOKEN_TYPE; if (IsIn(**sourceH,"=")) { *temp++ = *(*sourceH)++; *tokenType = EQUALSIGN; } else if (IsIn(**sourceH,";")) { *temp++ = *(*sourceH)++; *tokenType = SEMICOLON; } else if (IsIn(**sourceH,":")) { *temp++ = *(*sourceH)++; *tokenType = COLON; } else if (IsIn(**sourceH,",")) { *temp++ = *(*sourceH)++; *tokenType = COMMA; } else if (IsIn(**sourceH,"#")) { *temp++ = *(*sourceH)++; *tokenType = POUNDSIGN; } else if (IsIn(**sourceH,"(")) { *temp++ = *(*sourceH)++; *tokenType = LEFTPAR; } else if (IsIn(**sourceH,")")) { *temp++ = *(*sourceH)++; *tokenType = RIGHTPAR; } else if (IsIn(**sourceH,"{")) { *temp++ = *(*sourceH)++; *tokenType = LEFTCURL; } else if (IsIn(**sourceH,"}")) { *temp++ = *(*sourceH)++; *tokenType = RIGHTCURL; } else if (IsIn(**sourceH,"[")) { *temp++ = *(*sourceH)++; *tokenType = LEFTCOMMENT; } else if (IsIn(**sourceH,"]")) { *temp++ = *(*sourceH)++; *tokenType = RIGHTCOMMENT; } else if (IsIn(**sourceH,"?")) { *temp++ = *(*sourceH)++; *tokenType = QUESTIONMARK; } else if (IsIn(**sourceH,"-")) { *temp++ = *(*sourceH)++; *tokenType = DASH; } else if (IsIn(**sourceH,"$")) { *temp++ = *(*sourceH)++; *tokenType = DOLLAR; } else if (IsIn(**sourceH,"\"") && readWord == YES) { (*sourceH)++; while (**sourceH != '"' && **sourceH != '\0') { if (temp > tempMax) { *tokenType = NOTHING; token[20]='\0'; MrBayesPrint ("%s Error while parsing a string. Token \"%s...[followed by at least %d more characters]\" is too long.\n", spacer,token,tempMax-token-20); MrBayesPrint ("%s Maximum allowed length of a token is %d\n", spacer,tempMax-token); return (ERROR); } *temp++ = *((*sourceH)++); } *temp='\0'; *tokenType = ALPHA; (*sourceH)++; readWord = NO; } else if (IsIn(**sourceH,"abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ_0123456789.")) { if (IsIn(**sourceH,"0123456789.")) allNumbers = TRUE; else allNumbers = FALSE; foundExp = foundExpSign = FALSE; *temp++ = *(*sourceH)++; while (IsIn(**sourceH,"abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ_0123456789.-+")) { if (temp > tempMax) { *tokenType = NOTHING; token[20]='\0'; MrBayesPrint ("%s Error while parsing a string. Token \"%s...[followed by at least %d more characters]\" is too long.\n", spacer,token,tempMax-token-20); MrBayesPrint ("%s Maximum allowed length of a token is %d\n", spacer,tempMax-token); return (ERROR); } if (allNumbers == TRUE && !IsIn((*sourceH)[-1],"Ee") && **sourceH=='-') break; else if (allNumbers == TRUE && IsIn(**sourceH,"Ee") && foundExp == NO) foundExp = TRUE; else if (allNumbers == TRUE && IsIn(**sourceH,"+-") && IsIn((*sourceH)[-1],"Ee")) foundExpSign = TRUE; else if (!IsIn(**sourceH,"0123456789.")) allNumbers = FALSE; *temp++ = *(*sourceH)++; } if (allNumbers == TRUE) *tokenType = NUMBER; else *tokenType = ALPHA; } else if (IsIn(**sourceH,"*")) { *temp++ = *(*sourceH)++; *tokenType = ASTERISK; } else if (IsIn(**sourceH,"/")) { *temp++ = *(*sourceH)++; *tokenType = FORWARDSLASH; } else if (IsIn(**sourceH,"'\\'")) { *temp++ = *(*sourceH)++; *tokenType = BACKSLASH; } else if (IsIn(**sourceH,"!")) { *temp++ = *(*sourceH)++; *tokenType = EXCLAMATIONMARK; } else if (IsIn(**sourceH,"%")) { *temp++ = *(*sourceH)++; *tokenType = PERCENT; } else if (IsIn(**sourceH,"\"")) { *temp++ = *(*sourceH)++; *tokenType = QUOTATIONMARK; } else if (IsIn(**sourceH,"&")) { *temp++ = *(*sourceH)++; *tokenType = AMPERSAND; } else if (IsIn(**sourceH,"~+^@{}`><")) { *temp++ = *(*sourceH)++; *tokenType = WEIRD; } else if (IsIn(**sourceH,"|")) { *temp++ = *(*sourceH)++; *tokenType = VERTICALBAR; } *temp = '\0'; return (NO_ERROR); } int GetUserHelp (char *helpTkn) { int i, j, k, tempInt; char tempString[100]; Model *mp; if (!strcmp(helpTkn, "Begin")) { MrBayesPrint (" --------------------------------------------------------------------------- \n"); MrBayesPrint (" Begin \n"); MrBayesPrint (" \n"); MrBayesPrint (" This command is used to format data or commands in the program. The correct \n"); MrBayesPrint (" usage is \n"); MrBayesPrint (" \n"); MrBayesPrint (" begin ; \n"); MrBayesPrint (" \n"); MrBayesPrint (" The two valid uses of the \"begin\" command, then, are \n"); MrBayesPrint (" \n"); MrBayesPrint (" begin data; \n"); MrBayesPrint (" begin mrbayes; \n"); MrBayesPrint (" \n"); MrBayesPrint (" The \"data\" specifier is used to specify the beginning of a data block; your \n"); MrBayesPrint (" character data should follow. For example, the following is an example of \n"); MrBayesPrint (" a data block for four taxa and ten DNA sites: \n"); MrBayesPrint (" \n"); MrBayesPrint (" begin data; \n"); MrBayesPrint (" dimensions ntax=4 nchar=10; \n"); MrBayesPrint (" format datatype=dna; \n"); MrBayesPrint (" matrix \n"); MrBayesPrint (" taxon_1 AACGATTCGT \n"); MrBayesPrint (" taxon_2 AAGGATTCCA \n"); MrBayesPrint (" taxon_3 AACGACTCCT \n"); MrBayesPrint (" taxon_4 AAGGATTCCT \n"); MrBayesPrint (" ; \n"); MrBayesPrint (" end; \n"); MrBayesPrint (" \n"); MrBayesPrint (" The other commands -- dimensions, format, and matrix -- are discussed \n"); MrBayesPrint (" in the appropriate help menu. The only thing to note here is that the \n"); MrBayesPrint (" block begins with a \"begin data\" command. The \"mrbayes\" command is \n"); MrBayesPrint (" used to enter commands specific to the MrBayes program into the file. \n"); MrBayesPrint (" This allows you to automatically process commands on execution of the \n"); MrBayesPrint (" program. The following is a simple mrbayes block: \n"); MrBayesPrint (" \n"); MrBayesPrint (" begin mrbayes; \n"); MrBayesPrint (" charset first = 1-10\\3; \n"); MrBayesPrint (" charset second = 2-10\\3; \n"); MrBayesPrint (" charset third = 3-10\\3; \n"); MrBayesPrint (" end; \n"); MrBayesPrint (" \n"); MrBayesPrint (" This mrbayes block sets off the three \"charset\" commands, used to \n"); MrBayesPrint (" predefine some blocks of characters. The mrbayes block can be very useful. \n"); MrBayesPrint (" For example, in this case, it would save you the time of typing the char- \n"); MrBayesPrint (" acter sets each time you executed the file. Also, note that every \n"); MrBayesPrint (" \"begin \" command ends with an \"end\". Finally, you can \n"); MrBayesPrint (" have so-called foreign blocks in the file. An example of a foreign block \n"); MrBayesPrint (" would be \"begin paup\". The program will simply skip this block. This is \n"); MrBayesPrint (" useful because it means that you can use the same file for MrBayes, PAUP* \n"); MrBayesPrint (" or MacClade (although it isn't clear why you would want to use those other \n"); MrBayesPrint (" programs). \n"); MrBayesPrint (" --------------------------------------------------------------------------- \n"); } else if (!strcmp(helpTkn, "End")) { MrBayesPrint (" --------------------------------------------------------------------------- \n"); MrBayesPrint (" End \n"); MrBayesPrint (" \n"); MrBayesPrint (" This command is used to terminate a data or mrbayes block. The correct \n"); MrBayesPrint (" usage is \n"); MrBayesPrint (" \n"); MrBayesPrint (" end; \n"); MrBayesPrint (" \n"); MrBayesPrint (" For more information on this, check the help for the \"begin\" command. \n"); MrBayesPrint (" --------------------------------------------------------------------------- \n"); } else if (!strcmp(helpTkn, "Endblock")) { MrBayesPrint (" --------------------------------------------------------------------------- \n"); MrBayesPrint (" Endblock \n"); MrBayesPrint (" \n"); MrBayesPrint (" This is an older, deprecated version of \"End\", see that command. \n"); MrBayesPrint (" --------------------------------------------------------------------------- \n"); } else if (!strcmp(helpTkn, "Plot")) { MrBayesPrint (" --------------------------------------------------------------------------- \n"); MrBayesPrint (" Plot \n"); MrBayesPrint (" \n"); MrBayesPrint (" This command plots specified parameters in the .p file or one of the .p files \n"); MrBayesPrint (" created during an MCMC analysis. An x-y graph of the parameter over the course\n"); MrBayesPrint (" of the chain is created. The command can be useful for visually diagnosing \n"); MrBayesPrint (" convergence for many of the parameters of the phylogenetic model. The para- \n"); MrBayesPrint (" meter to be plotted is specified by the \"parameter\" option. Several para- \n"); MrBayesPrint (" meters can be plotted at once by using the \"match\" option, which has a \n"); MrBayesPrint (" default value of \"perfect\". For example, if you were to set \"parameter = pi\"\n"); MrBayesPrint (" and \"match = consistentwith\", then all of the state frequency parameters \n"); MrBayesPrint (" would be plotted. You can also set \"match=all\", in which case all of the \n"); MrBayesPrint (" parameters are plotted. \n"); MrBayesPrint (" \n"); MrBayesPrint (" Note that the \"Sump\" command provides a different set of convergence diag- \n"); MrBayesPrint (" nostics tools that you may also want to explore. Unlike \"Plot\", \"Sump\" can\n"); MrBayesPrint (" compare two or more parameter samples and will calculate convergence diagnos- \n"); MrBayesPrint (" tics as well as parameter summaries for the pooled sample. \n"); MrBayesPrint (" \n"); MrBayesPrint (" Options: \n"); MrBayesPrint (" \n"); MrBayesPrint (" Relburnin -- If this option is set to 'Yes', then a proportion of the \n"); MrBayesPrint (" samples will be discarded as burnin when creating the plot. \n"); MrBayesPrint (" The proportion to be discarded is set with Burninfrac (see \n"); MrBayesPrint (" Burninfrac below). When the Relburnin option is set to 'No', \n"); MrBayesPrint (" then a specific number of samples is discarded instead. This \n"); MrBayesPrint (" number is set by Burnin (see below). Note that the burnin \n"); MrBayesPrint (" setting is shared across the 'comparetree', 'sump' and 'sumt'\n"); MrBayesPrint (" commands. \n"); MrBayesPrint (" Burnin -- Determines the number of samples (not generations) that will \n"); MrBayesPrint (" be discarded when summary statistics are calculated. The \n"); MrBayesPrint (" value of this option is only relevant when Relburnin is set \n"); MrBayesPrint (" to 'No'. \n"); MrBayesPrint (" Burninfrac -- Determines the fraction of samples that will be discarded \n"); MrBayesPrint (" when creating a plot. The value of this parameter is only \n"); MrBayesPrint (" relevant when Relburnin is set to 'Yes'. Example: A value of \n"); MrBayesPrint (" this option of 0.25 means that 25%% of the samples will be \n"); MrBayesPrint (" discarded. \n"); MrBayesPrint (" Filename -- The name of the file to plot. \n"); MrBayesPrint (" Parameter -- Specification of parameters to be plotted. See above for \n"); MrBayesPrint (" details. \n"); MrBayesPrint (" Match -- Specifies how to match parameter names to the Parameter \n"); MrBayesPrint (" specification. See above for details. \n"); MrBayesPrint (" \n"); MrBayesPrint (" Current settings: \n"); MrBayesPrint (" \n"); MrBayesPrint (" Parameter Options Current Setting \n"); MrBayesPrint (" ------------------------------------------------------------ \n"); MrBayesPrint (" Relburnin Yes/No %s \n", chainParams.relativeBurnin == YES ? "Yes" : "No"); MrBayesPrint (" Burnin %d \n", chainParams.chainBurnIn); MrBayesPrint (" Burninfrac %1.2lf \n", chainParams.burninFraction); MrBayesPrint (" Filename %s \n", plotParams.plotFileName); MrBayesPrint (" Parameter %s \n", plotParams.parameter); MrBayesPrint (" Match Perfect/Consistentwith/All %s \n", plotParams.match); MrBayesPrint (" \n"); MrBayesPrint (" --------------------------------------------------------------------------- \n"); } else if (!strcmp(helpTkn, "Dimensions")) { MrBayesPrint (" --------------------------------------------------------------------------- \n"); MrBayesPrint (" Dimensions \n"); MrBayesPrint (" \n"); MrBayesPrint (" This command is used in a data block to define the number of taxa and \n"); MrBayesPrint (" characters. The correct usage is \n"); MrBayesPrint (" \n"); MrBayesPrint (" dimensions ntax= nchar= \n"); MrBayesPrint (" \n"); MrBayesPrint (" The dimensions must be the first command in a data block. The following \n"); MrBayesPrint (" provides an example of the proper use of this command: \n"); MrBayesPrint (" \n"); MrBayesPrint (" begin data; \n"); MrBayesPrint (" dimensions ntax=4 nchar=10; \n"); MrBayesPrint (" format datatype=dna; \n"); MrBayesPrint (" matrix \n"); MrBayesPrint (" taxon_1 AACGATTCGT \n"); MrBayesPrint (" taxon_2 AAGGATTCCA \n"); MrBayesPrint (" taxon_3 AACGACTCCT \n"); MrBayesPrint (" taxon_4 AAGGATTCCT \n"); MrBayesPrint (" ; \n"); MrBayesPrint (" end; \n"); MrBayesPrint (" \n"); MrBayesPrint (" Here, the dimensions command tells MrBayes to expect a matrix with four \n"); MrBayesPrint (" taxa and 10 characters. \n"); MrBayesPrint (" --------------------------------------------------------------------------- \n"); } else if (!strcmp(helpTkn, "Format")) { MrBayesPrint (" --------------------------------------------------------------------------- \n"); MrBayesPrint (" Format \n"); MrBayesPrint (" \n"); MrBayesPrint (" This command is used in a data block to define the format of the char- \n"); MrBayesPrint (" acter matrix. The correct usage is \n"); MrBayesPrint (" \n"); MrBayesPrint (" format datatype= ... =,)'\n"); MrBayesPrint (" and 'invgamdir(,,,)' specify a fairly diffuse\n"); MrBayesPrint (" prior on tree length 'T', and then partition the tree length \n"); MrBayesPrint (" into branch lengths according to a Dirichlet distribution \n"); MrBayesPrint (" (Rannala et al. 2012). If 'T' is considered drawn from a \n"); MrBayesPrint (" gamma distribution with parameters a_T and b_T, and with mean\n"); MrBayesPrint (" a_T/b_T, we recommend setting a_T = 1; if it is instead con- \n"); MrBayesPrint (" sidered drawn from an inverse gamma (invgamma) distribution \n"); MrBayesPrint (" with parameters a_T and b_T, and with mean b_T/(a_T -1), then\n"); MrBayesPrint (" we recommend setting a_T = 3. In the latter case, b_T should \n"); MrBayesPrint (" be chosen so that the prior mean of T is reasonable for the \n"); MrBayesPrint (" data. In the former case, setting b_T = 0.1 (corresponding to\n"); MrBayesPrint (" a mean tree length of 10) should be appropriate for a wide \n"); MrBayesPrint (" range of tree lengths (at least in the interval 1 to 100). \n"); MrBayesPrint (" The concentration parameter a of the Dirichlet distribution \n"); MrBayesPrint (" is inversely related to the variance of the branch lengths, \n"); MrBayesPrint (" while c is the ratio of the prior means for the internal and \n"); MrBayesPrint (" external branch lengths. The default setting, a = c = 1, \n"); MrBayesPrint (" specifies a uniform Dirichlet distribution of branch lengths \n"); MrBayesPrint (" given the tree length. For instance, 'gammadir(1,0.1,1,1)' \n"); MrBayesPrint (" specifies a compound Dirichlet prior on branch lengths, where\n"); MrBayesPrint (" tree length is associated with a gamma distribution with mean\n"); MrBayesPrint (" 10, and branch length proportions are associated with a uni- \n"); MrBayesPrint (" form Dirichlet distribution (default). \n"); MrBayesPrint (" \n"); MrBayesPrint (" For clock trees with calibrated external nodes (fossils), \n"); MrBayesPrint (" MrBayes also offers the fossilized birth-death prior: \n"); MrBayesPrint (" 'clock:fossilization'. \n"); MrBayesPrint (" If 'SampleStrat' is set to 'fossiltip', it assumes that upon \n"); MrBayesPrint (" sampling the lineage is dead and won't produce descendants, \n"); MrBayesPrint (" meaning each fossil sample is a tip. If 'SampleStrat' is set \n"); MrBayesPrint (" to 'random' (default), fossils are sampled serially along the\n"); MrBayesPrint (" birth-death tree (Stadler 2010), so they can be tips or an- \n"); MrBayesPrint (" cestors. See 'Speciationpr', 'Extinctionpr', 'SampleStrat', \n"); MrBayesPrint (" 'Fossilizationpr' for more information. \n"); MrBayesPrint (" \n"); MrBayesPrint (" Treeagepr -- This parameter specifies the prior probability distribution \n"); MrBayesPrint (" on the tree age when a uniform or fossilization prior is used\n"); MrBayesPrint (" on the branch lengths of a clock tree. \n"); MrBayesPrint (" \n"); MrBayesPrint (" The options are: \n"); MrBayesPrint (" \n"); MrBayesPrint (" prset treeagepr = \n"); MrBayesPrint (" \n"); MrBayesPrint (" where is one of \n"); MrBayesPrint (" \n"); MrBayesPrint (" fixed() \n"); MrBayesPrint (" uniform(,) \n"); MrBayesPrint (" offsetexponential(,) \n"); MrBayesPrint (" truncatednormal(,,) \n"); MrBayesPrint (" lognormal(,) \n"); MrBayesPrint (" offsetlognormal(,,) \n"); MrBayesPrint (" gamma(,) \n"); MrBayesPrint (" offsetgamma(,,) \n"); MrBayesPrint (" \n"); MrBayesPrint (" These are the same options used for the 'Calibrate' command. \n"); MrBayesPrint (" Note that, unlike elsewhere in MrMayes, we always use the \n"); MrBayesPrint (" mean and standard deviation of the resulting age distribution\n"); MrBayesPrint (" rather than the standard parameterization, if different. This\n"); MrBayesPrint (" is to facilitate for the users who want to focus on the in- \n"); MrBayesPrint (" formation conveyed about the age. For those who wish to use \n"); MrBayesPrint (" the standard parameterization, there are simple conversions \n"); MrBayesPrint (" between the two. See the 'Calibrate' command for more infor- \n"); MrBayesPrint (" mation. \n"); MrBayesPrint (" \n"); MrBayesPrint (" The tree age is simply the age of the most recent common \n"); MrBayesPrint (" ancestor of the tree. If the clock rate is fixed to 1.0, \n"); MrBayesPrint (" which is the default, the tree age is equivalent to the \n"); MrBayesPrint (" expected number of substitutions from the root to the tip of \n"); MrBayesPrint (" the tree, that is, tree height. The tree age prior ensures \n"); MrBayesPrint (" that the joint probability for the uniform prior (or fossil- \n"); MrBayesPrint (" ization prior) model of branch lengths on a clock tree is \n"); MrBayesPrint (" proper. The default setting is 'gamma(1,1)'. If the root node\n"); MrBayesPrint (" in the tree is calibrated, the root calibration replaces the \n"); MrBayesPrint (" tree age prior. \n"); MrBayesPrint (" Speciationpr -- This parameter sets the prior on the net diversification rate\n"); MrBayesPrint (" i.e., (lambda - mu) in the birth-death model and the general \n"); MrBayesPrint (" case of fossilized birth-death (FBD) model; or (lambda - mu -\n"); MrBayesPrint (" psi) in the special case of the FBD model (fossiltip). \n"); MrBayesPrint (" Values of this parameter are > 0. Prior options: \n"); MrBayesPrint (" \n"); MrBayesPrint (" prset speciationpr = uniform(,) \n"); MrBayesPrint (" prset speciationpr = exponential() \n"); MrBayesPrint (" prset speciationpr = fixed() \n"); MrBayesPrint (" \n"); MrBayesPrint (" This parameter is only relevant if the (fossil) birth-death \n"); MrBayesPrint (" process is selected as the prior on branch lengths. \n"); MrBayesPrint (" Extinctionpr -- This parameter sets the prior on the relative extinction rate\n"); MrBayesPrint (" (turnover), i.e., (mu / lambda) in the birth-death model and \n"); MrBayesPrint (" the general case of the FBD model; or (mu + psi) / lambda in \n"); MrBayesPrint (" the special case of the FBD model (fossiltip). \n"); MrBayesPrint (" Values of this parameter are in range (0,1). Prior options: \n"); MrBayesPrint (" \n"); MrBayesPrint (" prset extinctionpr = beta(,) \n"); MrBayesPrint (" prset extinctionpr = fixed() \n"); MrBayesPrint (" \n"); MrBayesPrint (" This parameter is only relevant if the (fossil) birth-death \n"); MrBayesPrint (" process is selected as the prior on branch lengths. \n"); MrBayesPrint (" Fossilizationpr -- This parameter sets the prior on the relative fossilization \n"); MrBayesPrint (" rate, psi/(mu+psi), in the fossilized birth-death model. \n"); MrBayesPrint (" Values of this parameter are in range (0,1). Prior options: \n"); MrBayesPrint (" \n"); MrBayesPrint (" prset fossilizationpr = beta(,) \n"); MrBayesPrint (" prset fossilizationpr = fixed() \n"); MrBayesPrint (" \n"); MrBayesPrint (" This parameter is only relevant if the fossilized birth-death\n"); MrBayesPrint (" process is selected as the prior on branch lengths. \n"); MrBayesPrint (" If SampleStrat is used to divide up time intervals, it sets \n"); MrBayesPrint (" the i.i.d. prior for the parameter in each interval. \n"); MrBayesPrint (" Sampleprob -- This parameter sets the fraction of extant taxa that are \n"); MrBayesPrint (" sampled in the analysis. This is used with the birth-death \n"); MrBayesPrint (" prior (Yang & Rannala 1997; Stadler 2009; Hohna et al. 2011),\n"); MrBayesPrint (" or the fossilized birth-death prior (Stadler 2010; Heath et \n"); MrBayesPrint (" al. 2014; Zhang et al. 2016) on rooted timetrees. \n"); MrBayesPrint (" Values are in range (0,1], with 1.0 for complete sampling. \n"); MrBayesPrint (" \n"); MrBayesPrint (" prset sampleprob = \n"); MrBayesPrint (" \n"); MrBayesPrint (" SampleStrat -- This parameter sets the strategy under which species were \n"); MrBayesPrint (" sampled in the analysis. For the birth-death prior, 'birth- \n"); MrBayesPrint (" death' (Hohna et al. 2011), three strategies: 'random', \n"); MrBayesPrint (" 'diversity' and 'cluster' sampling can be used for extant \n"); MrBayesPrint (" taxa. No extinct sample (fossil) is allowed in this prior. \n"); MrBayesPrint (" \n"); MrBayesPrint (" For data with extant and extinct samples, use 'prset brlenspr\n"); MrBayesPrint (" =clock:fossilization'. (Stadler 2010; Zhang et al. 2016) \n"); MrBayesPrint (" For the fossilized birth-death prior, 'fossiltip' assumes \n"); MrBayesPrint (" extant taxa are sampled randomly, extinct taxa (fossils) are \n"); MrBayesPrint (" sampled with constant rate, and upon sampling the lineage is \n"); MrBayesPrint (" dead and won't produce any descendant, so fossils are all at \n"); MrBayesPrint (" the tips. Except 'fossiltip', the following strategies allow \n"); MrBayesPrint (" fossils also being ancestors of other samples: 'random' (de- \n"); MrBayesPrint (" fault) assumes extant taxa are sampled uniformly at random \n"); MrBayesPrint (" with probability rho; 'diversity' assumes extant taxa are \n"); MrBayesPrint (" sampled with proportion rho to maximize diversity (Zhang et \n"); MrBayesPrint (" al. 2016). Value of rho is set in Sampleprob (see above). \n"); MrBayesPrint (" \n"); MrBayesPrint (" By default, the rates of speciation, extinction, and fossil \n"); MrBayesPrint (" sampling are constant in the FBD model (Stadler 2010). To \n"); MrBayesPrint (" allow rate variation through time (in a piecewise-constant \n"); MrBayesPrint (" manner), append the number : of shifts and corresponding \n"); MrBayesPrint (" times (in descending order) to the sampling strategy ( \n"); MrBayesPrint (" 'random' or 'diversity'), in the order for relative fossil- \n"); MrBayesPrint (" sampling, net diversification and turnover rates. For example\n"); MrBayesPrint (" '4: 200 150 100 50, 4: 200 150 100 50, 4: 200 150 100 50' \n"); MrBayesPrint (" specifies four shifts (5 epochs) for each rate, all shifting \n"); MrBayesPrint (" at the same time (note that the number and times can also be \n"); MrBayesPrint (" different for each rate, although not necessary). \n"); MrBayesPrint (" Alternatively, simply providing only one series, e.g., \n"); MrBayesPrint (" 'prset samplestrat=random 4: 200 150 100 50' \n"); MrBayesPrint (" allows fossil-sampling rate to shift while keeping the \n"); MrBayesPrint (" speciation and extinction rates constant. \n"); MrBayesPrint (" An implementation note for diversified sampling in the FBD \n"); MrBayesPrint (" model: there is no fossil and no rate shift allowed between \n"); MrBayesPrint (" x_cut (the cut-off time for sampling extant taxa) and the \n"); MrBayesPrint (" present (Zhang et al. 2016). \n"); MrBayesPrint (" \n"); MrBayesPrint (" prset samplestrat = random \n"); MrBayesPrint (" prset samplestrat = diversity \n"); MrBayesPrint (" prset samplestrat = cluster \n"); MrBayesPrint (" prset samplestrat = fossiltip \n"); MrBayesPrint (" prset samplestrat = random : ... ... \n"); MrBayesPrint (" prset samplestrat = diversity : ... ... \n"); MrBayesPrint (" \n"); MrBayesPrint (" Popsizepr -- This parameter sets the prior on the population size compo- \n"); MrBayesPrint (" nent of the coalescent parameter. The options are: \n"); MrBayesPrint (" \n"); MrBayesPrint (" prset popsizepr = uniform(,) \n"); MrBayesPrint (" prset popsizepr = lognormal(,) \n"); MrBayesPrint (" prset popsizepr = normal(,) \n"); MrBayesPrint (" prset popsizepr = gamma(,) \n"); MrBayesPrint (" prset popsizepr = fixed() \n"); MrBayesPrint (" \n"); MrBayesPrint (" This parameter is only relevant if the coalescence process is\n"); MrBayesPrint (" selected as the prior on branch lengths. Note that the set- \n"); MrBayesPrint (" ting of 'ploidy' in 'lset' is important for how this para- \n"); MrBayesPrint (" meter is interpreted. \n"); MrBayesPrint (" Popvarpr -- In a gene tree - species tree model, this parameter deter- \n"); MrBayesPrint (" mines whether the population size is the same for the entire \n"); MrBayesPrint (" species tree ('popvarpr = equal', the default), or varies \n"); MrBayesPrint (" across branches of the species tree ('popvarpr=variable'). \n"); /* MrBayesPrint (" Growthpr -- This parameter sets the prior on the exponential growth \n"); MrBayesPrint (" parameter of the coalescence process. The options are: \n"); MrBayesPrint (" \n"); MrBayesPrint (" prset growthpr = uniform(,) \n"); MrBayesPrint (" prset growthpr = exponential() \n"); MrBayesPrint (" prset growthpr = fixed() \n"); MrBayesPrint (" \n"); MrBayesPrint (" This parameter is only relevant if the coalescence \n"); MrBayesPrint (" process is selected as the prior on branch lengths. \n"); */ MrBayesPrint (" Nodeagepr -- This parameter specifies the assumptions concerning the age \n"); MrBayesPrint (" of the terminal and interior nodes in the tree. The default \n"); MrBayesPrint (" model ('nodeagepr = unconstrained') assumes that all terminal\n"); MrBayesPrint (" nodes are of the same age while the age of interior nodes is \n"); MrBayesPrint (" unconstrained. The alternative ('nodeagepr = calibrated') \n"); MrBayesPrint (" option derives a prior probability distribution on terminal \n"); MrBayesPrint (" and interior node ages from the calibration settings (see \n"); MrBayesPrint (" the 'calibrate' command). The 'nodeagepr' parameter is only \n"); MrBayesPrint (" relevant for clock trees. \n"); MrBayesPrint (" Clockratepr -- This parameter specifies the prior assumptions concerning the\n"); MrBayesPrint (" base substitution rate of the tree, measured in expected num-\n"); MrBayesPrint (" ber of substitutions per site per time unit. The default set-\n"); MrBayesPrint (" ting is 'Fixed(1.0)', which effectively means that the time \n"); MrBayesPrint (" unit is the number of expected substitutions per site. \n"); /* MrBayesPrint (" If you apply age constraints to the tree, the default setting\n"); MrBayesPrint (" changes automatically to 'Exponential()', where '' (the\n"); MrBayesPrint (" rate of exponential) is ten times the age of the maximum age \n"); MrBayesPrint (" constraint. This will give you a very vague prior, which may \n"); MrBayesPrint (" or may not be adequate for your particular problem. \n"); */ MrBayesPrint (" If you do not have any age calibrations in the tree, you can \n"); MrBayesPrint (" still calibrate the tree using 'Clockratepr'. For instance, \n"); MrBayesPrint (" if you know that your sequence data evolve at a rate of 0.20 \n"); MrBayesPrint (" substitutions per million years, you might calibrate the tree\n"); MrBayesPrint (" by fixing the substitution rate to 0.20 using \n"); MrBayesPrint (" \n"); MrBayesPrint (" prset clockratepr = fixed(0.20) \n"); MrBayesPrint (" \n"); MrBayesPrint (" after which the tree will be calibrated using millions of \n"); MrBayesPrint (" years as the unit. \n"); MrBayesPrint (" \n"); MrBayesPrint (" You can also assign a prior probability distribution to the \n"); MrBayesPrint (" substitution rate, accommodating the uncertainty of it. \n"); MrBayesPrint (" When you calibrate the nodes, you should properly set this \n"); MrBayesPrint (" prior to match the time unit of the calibrations. \n"); MrBayesPrint (" You can choose among normal, lognormal, exponential and gamma\n"); MrBayesPrint (" distributions for this purpose. For instance, to assign a \n"); MrBayesPrint (" normal distribution truncated at 0, so that only positive \n"); MrBayesPrint (" values are allowed, and with mean 0.20 and standard deviation\n"); MrBayesPrint (" of 0.02, you would use \n"); MrBayesPrint (" \n"); MrBayesPrint (" prset clockratepr = normal(0.20,0.02) \n"); MrBayesPrint (" \n"); MrBayesPrint (" The lognormal distribution is parameterized in terms of the \n"); MrBayesPrint (" mean and standard deviation on the log scale (natural logs). \n"); MrBayesPrint (" For instance, \n"); MrBayesPrint (" \n"); MrBayesPrint (" prset clockratepr = lognormal(-1.61,0.10) \n"); MrBayesPrint (" \n"); MrBayesPrint (" specifies a lognormal distribution with a mean of log values \n"); MrBayesPrint (" of -1.61 and a standard deviation of log values of 0.10. In \n"); MrBayesPrint (" such a case, the mean value of the lognormal distribution is \n"); MrBayesPrint (" equal to e^(-1.61 + 0.10^2/2) = 0.20. \n"); MrBayesPrint (" \n"); MrBayesPrint (" Note that the 'Clockratepr' parameter has no effect on non- \n"); MrBayesPrint (" clock trees. \n"); MrBayesPrint (" Clockvarpr -- This parameter allows you to specify the type of clock you \n"); MrBayesPrint (" are assuming. The default is 'strict', which corresponds to \n"); MrBayesPrint (" the standard clock model where the evolutionary rate is \n"); MrBayesPrint (" constant throughout the tree. For relaxed clock models, you \n"); MrBayesPrint (" can use 'cpp', 'tk02', 'wn', 'igr', or 'iln'. \n"); MrBayesPrint (" 'cpp' invokes a relaxed clock model where the rate evolves \n"); MrBayesPrint (" according to a Compound Poisson Process (CPP) (Huelsenbeck \n"); MrBayesPrint (" et al., 2000). \n"); MrBayesPrint (" 'tk02' invokes the geometric Brownian Motion model described \n"); MrBayesPrint (" by Thorne and Kishino (2002). [autocorrelated lognormal] \n"); MrBayesPrint (" 'wn' invokes the white noise model (LePage et al., 2007) \n"); MrBayesPrint (" where each branch has an independent rate drawn from a gamma \n"); MrBayesPrint (" distribution with variance proportional to the branch length.\n"); MrBayesPrint (" 'igr' invokes the Independent Gamma Rate model. The differ- \n"); MrBayesPrint (" ence from 'wn' is that these gamma distributions are i.i.d. \n"); MrBayesPrint (" Thus, the variances do not depend on the branch lengths. \n"); MrBayesPrint (" 'iln' invokes the Independent Lognormal model. It is similar \n"); MrBayesPrint (" to 'igr' but the rates are i.i.d. lognormal distributions. \n"); MrBayesPrint (" Each of the relaxed clock models has additional parameters \n"); MrBayesPrint (" with priors. For the CPP model, it is 'cppratepr' and \n"); MrBayesPrint (" 'cppmultdevpr'; for the TK02 model, it is 'tk02varpr'; for \n"); MrBayesPrint (" the WN model, it is 'wnvarpr'; for the IGR model, it is \n"); MrBayesPrint (" 'igrvarpr'; for the ILN model, it is 'ilnvarpr'. \n"); MrBayesPrint (" The 'clockvarpr' parameter is only relevant for clock trees. \n"); MrBayesPrint (" Cppratepr -- This parameter allows you to specify a prior probability \n"); MrBayesPrint (" distribution on the rate of the Poisson process generating \n"); MrBayesPrint (" changes in the evolutionary rate in the CPP relaxed clock \n"); MrBayesPrint (" model. You can either fix the rate or associate it with an \n"); MrBayesPrint (" exponential prior using \n"); MrBayesPrint (" \n"); MrBayesPrint (" prset cppratepr = fixed() \n"); MrBayesPrint (" prset cppratepr = exponential() \n"); MrBayesPrint (" \n"); MrBayesPrint (" For instance, if you fix the rate to 2, then on a branch \n"); MrBayesPrint (" with the length equal to one expressed in terms of average \n"); MrBayesPrint (" expected number of substitution per site, you expect to see, \n"); MrBayesPrint (" on average, two rate-modifying events. \n"); MrBayesPrint (" If you put an exponential(0.1) on the rate, you will be \n"); MrBayesPrint (" estimating the rate against a prior probability distribution \n"); MrBayesPrint (" where the expected rate is 10 (= 1/0.1). \n"); MrBayesPrint (" Cppmultdevpr -- This parameter allows you to specify the standard deviation \n"); MrBayesPrint (" of the log-normal distribution from which the rate multi- \n"); MrBayesPrint (" pliers of the CPP relaxed clock model are drawn. The standard\n"); MrBayesPrint (" deviation is given on the log scale. The default value of 1.0\n"); MrBayesPrint (" thus corresponds to rate multipliers varying from 0.37 (1/e) \n"); MrBayesPrint (" to 2.7 (e) when they are +/- one standard deviation from the \n"); MrBayesPrint (" expected mean. The expected mean of the logarithm of the mul-\n"); MrBayesPrint (" pliers is fixed to 0, ensuring that the expected mean rate is\n"); MrBayesPrint (" 1.0. You can change the default value by using \n"); MrBayesPrint (" \n"); MrBayesPrint (" prset cppmultdevpr = fixed() \n"); MrBayesPrint (" \n"); MrBayesPrint (" where is the standard deviation on the log scale. \n"); MrBayesPrint (" TK02varpr -- This parameter allows you to specify the prior probability \n"); MrBayesPrint (" distribution for the variance of the rate multiplier in the \n"); MrBayesPrint (" Thorne-Kishino ('Brownian motion') relaxed clock model. \n"); MrBayesPrint (" Specifically, the parameter specifies the rate at which the \n"); MrBayesPrint (" variance increases with respect to the base rate of the \n"); MrBayesPrint (" clock. If you have a branch of a length corresponding to 0.4 \n"); MrBayesPrint (" expected changes per site according to the base rate of the \n"); MrBayesPrint (" clock, and the tk02var parameter has a value of 2.0, then the\n"); MrBayesPrint (" rate multiplier at the end of the branch will be drawn from a\n"); MrBayesPrint (" lognormal distribution with a variance of 0.4*2.0 (on the \n"); MrBayesPrint (" linear, not the logarithm scale). The mean is the same as the\n"); MrBayesPrint (" rate multiplier at the start of the branch (again on the \n"); MrBayesPrint (" linear scale). \n"); MrBayesPrint (" You can set the parameter to a fixed value, or specify that \n"); MrBayesPrint (" it is drawn from an exponential or uniform distribution: \n"); MrBayesPrint (" \n"); MrBayesPrint (" prset tk02varpr = fixed() \n"); MrBayesPrint (" prset tk02varpr = exponential() \n"); MrBayesPrint (" prset tk02varpr = uniform(,) \n"); MrBayesPrint (" \n"); MrBayesPrint (" WNvarpr -- This parameter allows you to specify the prior distribution \n"); MrBayesPrint (" for the variance of the independent branch rate (white noise)\n"); MrBayesPrint (" relaxed clock model. Specifically, the parameter specifies \n"); MrBayesPrint (" the rate at which the variance increases with respect to the \n"); MrBayesPrint (" base rate of the clock. If you have a branch of a length \n"); MrBayesPrint (" corresponding to 0.4 expected changes per site according to \n"); MrBayesPrint (" the base rate of the clock, and the wnvar parameter has a \n"); MrBayesPrint (" value of 2.0 , then the effective branch length will be drawn\n"); MrBayesPrint (" from a gamma distribution with a variance of 0.4*2.0. \n"); MrBayesPrint (" You can set the parameter to a fixed value, or specify that \n"); MrBayesPrint (" it is drawn from an exponential or uniform distribution: \n"); MrBayesPrint (" \n"); MrBayesPrint (" prset wnvarpr = fixed() \n"); MrBayesPrint (" prset wnvarpr = exponential() \n"); MrBayesPrint (" prset wnvarpr = uniform(,) \n"); MrBayesPrint (" \n"); MrBayesPrint (" IGRvarpr -- This parameter allows you to specify the prior distribution \n"); MrBayesPrint (" for the variance of the independent gamma rate (IGR) relaxed \n"); MrBayesPrint (" clock model. Specifically, the parameter specifies the rate \n"); MrBayesPrint (" at which the variance increases with respect to the base rate\n"); MrBayesPrint (" of the clock. The difference from the white noise (WN) model \n"); MrBayesPrint (" is that the variance is the same for all branch rates and \n"); MrBayesPrint (" does not depend on the branch length. If igrvar parameter \n"); MrBayesPrint (" has a value of 0.8 , then the rate multiplier will be drawn \n"); MrBayesPrint (" from a gamma distribution with mean 1.0 and variance 0.8. \n"); MrBayesPrint (" You can set the parameter to a fixed value, or specify that \n"); MrBayesPrint (" it is drawn from an exponential or uniform distribution: \n"); MrBayesPrint (" \n"); MrBayesPrint (" prset igrvarpr = fixed() \n"); MrBayesPrint (" prset igrvarpr = exponential() \n"); MrBayesPrint (" prset igrvarpr = uniform(,) \n"); MrBayesPrint (" \n"); MrBayesPrint (" ILNvarpr -- This parameter allows you to specify the prior distribution \n"); MrBayesPrint (" for the variance of the independent lognormal (ILN) relaxed \n"); MrBayesPrint (" clock model. It is similar with IGR but each rate multiplier \n"); MrBayesPrint (" has a lognormal instead of a gamma distribution. If ilnvar \n"); MrBayesPrint (" has a value of 0.8 , then the rate multiplier will be drawn \n"); MrBayesPrint (" from a lognormal distribution with mean 1.0 and variance 0.8.\n"); MrBayesPrint (" You can set the parameter to a fixed value, or specify that \n"); MrBayesPrint (" it is drawn from an exponential or uniform distribution: \n"); MrBayesPrint (" \n"); MrBayesPrint (" prset ilnvarpr = fixed() \n"); MrBayesPrint (" prset ilnvarpr = exponential() \n"); MrBayesPrint (" prset ilnvarpr = uniform(,) \n"); MrBayesPrint (" \n"); MrBayesPrint (" Ratepr -- This parameter allows you to specify the site specific rates \n"); MrBayesPrint (" model or any other model that allows different partitions to \n"); MrBayesPrint (" evolve at different rates. First, you must have defined a \n"); MrBayesPrint (" partition of the characters. For example, you may define a \n"); MrBayesPrint (" partition that divides the characters by codon position, if \n"); MrBayesPrint (" you have DNA data. You can also divide your data using a \n"); MrBayesPrint (" partition that separates different genes from each other. \n"); MrBayesPrint (" The next step is to make the desired partition the active one\n"); MrBayesPrint (" using the set command. For example, if your partition is \n"); MrBayesPrint (" called \"by_codon\", then you make that the active partition \n"); MrBayesPrint (" using \"set partition=by_codon\". Now that you have defined \n"); MrBayesPrint (" and activated a partition, you can specify the rate multi- \n"); MrBayesPrint (" pliers for the various partitions. The options are: \n"); MrBayesPrint (" \n"); MrBayesPrint (" prset ratepr = fixed \n"); MrBayesPrint (" prset ratepr = variable \n"); MrBayesPrint (" prset ratepr = dirichlet(,,...,) \n"); MrBayesPrint (" \n"); MrBayesPrint (" If you specify \"fixed\", then the rate multiplier for \n"); MrBayesPrint (" that partition is set to 1 (i.e., the rate is fixed to \n"); MrBayesPrint (" the average rate across partitions). On the other hand, \n"); MrBayesPrint (" if you specify \"variable\", then the rate is allowed to \n"); MrBayesPrint (" vary across partitions subject to the constraint that the \n"); MrBayesPrint (" average rate of substitution across the partitions is 1. \n"); MrBayesPrint (" You must specify a variable rate prior for at least two \n"); MrBayesPrint (" partitions, otherwise the option is not activated when \n"); MrBayesPrint (" calculating likelihoods. The variable option automatically \n"); MrBayesPrint (" associates the partition rates with a dirichlet(1,...,1) \n"); MrBayesPrint (" prior. The dirichlet option is an alternative way of setting \n"); MrBayesPrint (" a partition rate to be variable, and also gives accurate \n"); MrBayesPrint (" control of the shape of the prior. The parameters of the \n"); MrBayesPrint (" Dirichlet are listed in the order of the partitions that the \n"); MrBayesPrint (" ratepr is applied to. For instance, \"prset applyto=(1,3,4) \n"); MrBayesPrint (" ratepr = dirichlet(10,40,15)\" would set the Dirichlet para- \n"); MrBayesPrint (" meter 10 to partition 1, 40 to partition 3, and 15 to parti- \n"); MrBayesPrint (" tion 4. The Dirichlet distribution is applied to the weighted\n"); MrBayesPrint (" rates; that is, it weights the partition rates according to \n"); MrBayesPrint (" the number of included characters in each partition. \n"); MrBayesPrint (" Generatepr -- This parameter is similar to 'Ratepr' but applies to gene \n"); MrBayesPrint (" trees in the multispecies coalescent, whereas 'Ratepr' app- \n"); MrBayesPrint (" lies to partitions within genes. \n"); MrBayesPrint (" \n"); if (numCurrentDivisions == 0) tempInt = 1; else tempInt = numCurrentDivisions; for (i=0; itRatioPr); if (!strcmp(mp->tRatioPr, "Beta")) MrBayesPrint ("(%1.1lf,%1.1lf)\n", mp->tRatioDir[0], mp->tRatioDir[1]); else MrBayesPrint ("(%1.1lf)\n", mp->tRatioFix); MrBayesPrint (" Revmatpr Dirichlet/Fixed %s", mp->revMatPr); if (!strcmp(mp->revMatPr, "Dirichlet")) MrBayesPrint ("(%1.1lf,%1.1lf,%1.1lf,%1.1lf,%1.1lf,%1.1lf)\n", mp->revMatDir[0], mp->revMatDir[1], mp->revMatDir[2], mp->revMatDir[3], mp->revMatDir[4], mp->revMatDir[5]); else MrBayesPrint ("(%1.1lf,%1.1lf,%1.1lf,%1.1lf,%1.1lf,%1.1lf)\n", mp->revMatFix[0], mp->revMatFix[1], mp->revMatFix[2], mp->revMatFix[3], mp->revMatFix[4], mp->revMatFix[5]); MrBayesPrint (" Aamodelpr Fixed/Mixed %s", mp->aaModelPr); if (!strcmp(mp->aaModelPr, "Fixed")) MrBayesPrint ("(%s)\n", mp->aaModel); else MrBayesPrint ("\n"); MrBayesPrint (" Aarevmatpr Dirichlet/Fixed %s", mp->aaRevMatPr); if (!strcmp(mp->aaRevMatPr, "Dirichlet")) { for (j=1; j<190; j++) if (AreDoublesEqual (mp->aaRevMatDir[0], mp->aaRevMatDir[j], 0.00001) == NO) break; if (j==190) MrBayesPrint ("(%1.1lf,%1.1lf,...)\n", mp->aaRevMatDir[0], mp->aaRevMatDir[0]); else MrBayesPrint (" (use 'Showmodel' to see values set by user)\n"); } else { for (j=1; j<190; j++) if (AreDoublesEqual (mp->aaRevMatFix[0], mp->aaRevMatFix[j], 0.00001) == NO) break; if (j==190) MrBayesPrint ("(%1.1lf,%1.1lf,...)\n", mp->aaRevMatFix[0], mp->aaRevMatFix[0]); else MrBayesPrint (" (use 'Showmodel' to see values set by user)\n"); } MrBayesPrint (" Omegapr Dirichlet/Fixed %s", mp->omegaPr); if (!strcmp(mp->omegaPr, "Dirichlet")) MrBayesPrint ("(%1.1lf,%1.1lf)\n", mp->omegaDir[0], mp->omegaDir[1]); else MrBayesPrint ("(%1.1lf)\n", mp->omegaFix); MrBayesPrint (" Ny98omega1pr Beta/Fixed %s", mp->ny98omega1pr); if (!strcmp(mp->ny98omega1pr, "Beta")) MrBayesPrint ("(%1.1lf,%1.1lf)\n", mp->ny98omega1Beta[0], mp->ny98omega1Beta[1]); else if (!strcmp(mp->ny98omega1pr, "Fixed")) MrBayesPrint ("(%1.1lf)\n", mp->ny98omega1Fixed); MrBayesPrint (" Ny98omega3pr Uniform/Exponential/Fixed %s", mp->ny98omega3pr); if (!strcmp(mp->ny98omega3pr, "Uniform")) MrBayesPrint ("(%1.1lf,%1.1lf)\n", mp->ny98omega3Uni[0], mp->ny98omega3Uni[1]); else if (!strcmp(mp->ny98omega3pr, "Exponential")) MrBayesPrint ("(%1.1lf)\n", mp->ny98omega3Exp); else MrBayesPrint ("(%1.1lf)\n", mp->ny98omega3Fixed); MrBayesPrint (" M3omegapr Exponential/Fixed %s", mp->m3omegapr); if (!strcmp(mp->m3omegapr, "Exponential")) MrBayesPrint ("\n"); else if (!strcmp(mp->m3omegapr, "Fixed")) MrBayesPrint ("(%1.1lf,%1.1lf,%1.1lf)\n", mp->m3omegaFixed[0], mp->m3omegaFixed[1], mp->m3omegaFixed[2]); MrBayesPrint (" Codoncatfreqs Dirichlet/Fixed %s", mp->codonCatFreqPr); if (!strcmp(mp->codonCatFreqPr, "Dirichlet")) MrBayesPrint ("(%1.1lf,%1.1lf,%1.1lf)\n", mp->codonCatDir[0], mp->codonCatDir[1], mp->codonCatDir[2]); else MrBayesPrint ("(%1.1lf,%1.1lf,%1.1lf)\n", mp->codonCatFreqFix[0], mp->codonCatFreqFix[1], mp->codonCatFreqFix[2]); MrBayesPrint (" Statefreqpr Dirichlet/Fixed %s", mp->stateFreqPr); if (!strcmp(mp->stateFreqPr, "Dirichlet")) { if (mp->dataType == DNA || mp->dataType == RNA) { if (!strcmp(mp->nucModel, "4by4")) MrBayesPrint ("(%1.1lf,%1.1lf,%1.1lf,%1.1lf)\n", mp->stateFreqsDir[0], mp->stateFreqsDir[1], mp->stateFreqsDir[2], mp->stateFreqsDir[3]); else MrBayesPrint ("\n"); } else if (mp->dataType == RESTRICTION) { MrBayesPrint ("(%1.1lf,%1.1lf)\n", mp->stateFreqsDir[0], mp->stateFreqsDir[1]); } else MrBayesPrint ("\n"); } else if (!strcmp(mp->stateFreqPr, "Fixed")) { if (mp->dataType == DNA || mp->dataType == RNA) { if (!strcmp(mp->nucModel, "4by4")) MrBayesPrint ("(%1.1lf,%1.1lf,%1.1lf,%1.1lf)\n", mp->stateFreqsFix[0], mp->stateFreqsFix[1], mp->stateFreqsFix[2], mp->stateFreqsFix[3]); else MrBayesPrint ("\n"); } else if (mp->dataType == RESTRICTION) { MrBayesPrint ("(%1.1lf,%1.1lf)\n", mp->stateFreqsFix[0], mp->stateFreqsFix[1]); } else MrBayesPrint ("\n"); } MrBayesPrint (" Shapepr Uniform/Exponential/Fixed %s", mp->shapePr); if (!strcmp(mp->shapePr, "Uniform")) MrBayesPrint ("(%1.1lf,%1.1lf)\n", mp->shapeUni[0], mp->shapeUni[1]); else if (!strcmp(mp->shapePr, "Exponential")) MrBayesPrint ("(%1.1lf)\n", mp->shapeExp); else MrBayesPrint ("(%1.1lf)\n", mp->shapeFix); MrBayesPrint (" Ratecorrpr Uniform/Fixed %s", mp->adGammaCorPr); if (!strcmp(mp->adGammaCorPr, "Uniform")) MrBayesPrint ("(%1.1lf,%1.1lf)\n", mp->adgCorrUni[0], mp->adgCorrUni[1]); else MrBayesPrint ("(%1.1lf)\n", mp->adgCorrFix); MrBayesPrint (" Pinvarpr Uniform/Fixed %s", mp->pInvarPr); if (!strcmp(mp->pInvarPr, "Uniform")) MrBayesPrint ("(%1.1lf,%1.1lf)\n", mp->pInvarUni[0], mp->pInvarUni[1]); else MrBayesPrint ("(%1.1lf)\n", mp->pInvarFix); MrBayesPrint (" Covswitchpr Uniform/Exponential/Fixed %s", mp->covSwitchPr); if (!strcmp(mp->covSwitchPr, "Uniform")) MrBayesPrint ("(%1.1lf,%1.1lf)\n", mp->covswitchUni[0], mp->covswitchUni[1]); else if (!strcmp(mp->covSwitchPr, "Exponential")) MrBayesPrint ("(%1.1lf)\n", mp->covswitchExp); else MrBayesPrint ("(%1.1lf,%1.1lf)\n", mp->covswitchFix[0], mp->covswitchFix[1]); MrBayesPrint (" Symdirihyperpr Uniform/Exponential/Fixed %s", mp->symPiPr); if (!strcmp(mp->symPiPr, "Uniform")) MrBayesPrint ("(%1.1lf,%1.1lf)\n", mp->symBetaUni[0], mp->symBetaUni[1]); else if (!strcmp(mp->covSwitchPr, "Exponential")) MrBayesPrint ("(%1.1lf)\n", mp->symBetaExp); else { if (mp->symBetaFix < 0) MrBayesPrint ("(Infinity)\n"); else MrBayesPrint ("(%1.1lf)\n", mp->symBetaFix); } MrBayesPrint (" Topologypr Uniform/Constraints/Fixed/ %s", mp->topologyPr); if (!strcmp(mp->topologyPr, "Constraints")) { MrBayesPrint ("("); for (j=0; jactiveConstraints[j] == YES) { MrBayesPrint ("%d", j+1); break; } } for (j++; jactiveConstraints[j] == YES) { MrBayesPrint (",%d", j+1); } } MrBayesPrint (")\n"); } else if (!strcmp(mp->topologyPr, "Fixed")) MrBayesPrint ("(%s)\n", userTree[mp->topologyFix]->name); else MrBayesPrint ("\n"); MrBayesPrint (" Speciestree \n"); MrBayesPrint (" Brlenspr Unconstrained/Clock/Fixed %s", mp->brlensPr); if (!strcmp(mp->brlensPr, "Unconstrained")) { if (!strcmp(mp->unconstrainedPr, "Uniform")) MrBayesPrint (":Uni(%1.1lf,%1.1lf)\n", mp->brlensUni[0], mp->brlensUni[1]); else if (!strcmp(mp->unconstrainedPr, "GammaDir")) MrBayesPrint (":GammaDir(%1.1lf,%1.3lf,%1.1lf,%1.1lf)\n", mp->brlensDir[0], mp->brlensDir[1], mp->brlensDir[2], mp->brlensDir[3]); else if (!strcmp(mp->unconstrainedPr, "invGamDir")) MrBayesPrint (":invGamDir(%1.1lf,%1.3lf,%1.1lf,%1.1lf)\n", mp->brlensDir[0], mp->brlensDir[1], mp->brlensDir[2], mp->brlensDir[3]); else if (!strcmp(mp->unconstrainedPr, "twoExp")) MrBayesPrint (":twoExp(%1.1lf,%1.1lf)\n", mp->brlens2Exp[0], mp->brlens2Exp[1]); else MrBayesPrint (":Exp(%1.1lf)\n", mp->brlensExp); } else if (!strcmp(mp->brlensPr, "Clock")) { if (!strcmp(mp->clockPr,"Fixed")) MrBayesPrint (":%s(%s)\n", mp->clockPr, userTree[mp->brlensFix]->name); else MrBayesPrint (":%s\n", mp->clockPr); } else if (!strcmp(mp->brlensPr, "Fixed")) MrBayesPrint ("(%s)\n", userTree[mp->brlensFix]->name); MrBayesPrint (" Treeagepr Gamma/Uniform/Fixed/ %s\n", mp->treeAgePr.name); MrBayesPrint (" Truncatednormal/Lognormal/ \n"); MrBayesPrint (" Offsetlognormal/Offsetgamma/ \n"); MrBayesPrint (" Offsetexponential \n"); MrBayesPrint (" Speciationpr Uniform/Exponential/Fixed %s", mp->speciationPr); if (!strcmp(mp->speciationPr, "Uniform")) MrBayesPrint ("(%1.1lf,%1.1lf)\n", mp->speciationUni[0], mp->speciationUni[1]); else if (!strcmp(mp->speciationPr, "Exponential")) MrBayesPrint ("(%1.1lf)\n", mp->speciationExp); else MrBayesPrint ("(%1.1lf)\n", mp->speciationFix); MrBayesPrint (" Extinctionpr Beta/Fixed %s", mp->extinctionPr); if (!strcmp(mp->extinctionPr, "Beta")) MrBayesPrint ("(%1.1lf,%1.1lf)\n", mp->extinctionBeta[0], mp->extinctionBeta[1]); else if (!strcmp(mp->extinctionPr, "Exponential")) MrBayesPrint ("(%1.1lf)\n", mp->extinctionExp); else MrBayesPrint ("(%1.1lf)\n", mp->extinctionFix); MrBayesPrint (" Fossilizationpr Beta/Fixed %s", mp->fossilizationPr); if (!strcmp(mp->fossilizationPr, "Beta")) MrBayesPrint ("(%1.1lf,%1.1lf)\n", mp->fossilizationBeta[0], mp->fossilizationBeta[1]); else if (!strcmp(mp->fossilizationPr, "Exponential")) MrBayesPrint ("(%1.1lf)\n", mp->fossilizationExp); else MrBayesPrint ("(%1.2lf)\n", mp->fossilizationFix); MrBayesPrint (" SampleStrat Random/Diversity/Cluster/ %s\n", mp->sampleStrat); MrBayesPrint (" FossilTip \n"); MrBayesPrint (" Sampleprob %1.8lf\n", mp->sampleProb); MrBayesPrint (" Popsizepr Lognormal/Gamma/Uniform/ %s", mp->popSizePr); if (!strcmp(mp->popSizePr, "Uniform")) MrBayesPrint ("(%1.1lf,%1.1lf)\n", mp->popSizeUni[0], mp->popSizeUni[1]); else if (!strcmp(mp->popSizePr, "Lognormal")) MrBayesPrint ("(%1.1lf,%1.1lf)\n", mp->popSizeLognormal[0], mp->popSizeLognormal[1]); else if (!strcmp(mp->popSizePr, "Normal")) MrBayesPrint ("(%1.1lf,%1.1lf)\n", mp->popSizeNormal[0], mp->popSizeNormal[1]); else if (!strcmp(mp->popSizePr, "Gamma")) MrBayesPrint ("(%1.1lf,%1.1lf)\n", mp->popSizeGamma[0], mp->popSizeGamma[1]); else MrBayesPrint ("(%1.1lf)\n", mp->popSizeFix); MrBayesPrint (" Normal/Fixed \n"); MrBayesPrint (" Popvarpr Equal/Variable %s\n", mp->popVarPr); /* MrBayesPrint (" Growthpr Uniform/Exponential/ \n"); MrBayesPrint (" Fixed/Normal %s", mp->growthPr); if (!strcmp(mp->growthPr, "Uniform")) MrBayesPrint ("(%1.1lf,%1.1lf)\n", mp->growthUni[0], mp->growthUni[1]); else if (!strcmp(mp->growthPr, "Exponential")) MrBayesPrint ("(%1.1lf)\n", mp->growthExp); else if (!strcmp(mp->growthPr, "Normal")) MrBayesPrint ("(%1.1lf,%1.1lf)\n", mp->growthNorm[0], mp->growthNorm[1]); else MrBayesPrint ("(%1.1lf)\n", mp->growthFix); */ MrBayesPrint (" Nodeagepr Unconstrained/Calibrated %s\n", mp->nodeAgePr); MrBayesPrint (" Clockratepr Fixed/Normal/Lognormal/ %s", mp->clockRatePr); if (!strcmp(mp->clockRatePr, "Fixed")) MrBayesPrint ("(%1.2lf)\n", mp->clockRateFix); else if (!strcmp(mp->clockRatePr,"Exponential")) MrBayesPrint ("(%1.2lf)\n", mp->clockRateExp); else if (!strcmp(mp->clockRatePr,"Normal")) MrBayesPrint ("(%1.2lf,%1.2lf)\n", mp->clockRateNormal[0], mp->clockRateNormal[1]); else if (!strcmp(mp->clockRatePr,"Lognormal")) MrBayesPrint ("(%1.2lf,%1.2lf)\n", mp->clockRateLognormal[0], mp->clockRateLognormal[1]); else { assert (!strcmp(mp->clockRatePr,"Gamma")); MrBayesPrint ("(%1.2lf,%1.2lf)\n", mp->clockRateGamma[0], mp->clockRateGamma[1]); } MrBayesPrint (" Exponential/Gamma \n"); MrBayesPrint (" Clockvarpr Strict/Cpp/TK02/WN/IGR/ILN %s\n", mp->clockVarPr); MrBayesPrint (" Cppratepr Fixed/Exponential %s", mp->cppRatePr); if (!strcmp(mp->cppRatePr, "Fixed")) MrBayesPrint ("(%1.2lf)\n", mp->cppRateFix); else /* if (!strcmp(mp->cppRatePr,"Exponential")) */ MrBayesPrint ("(%1.2lf)\n", mp->cppRateExp); MrBayesPrint (" Cppmultdevpr Fixed %s", mp->cppMultDevPr); MrBayesPrint ("(%1.2lf)\n", mp->cppMultDevFix); MrBayesPrint (" TK02varpr Fixed/Exponential/Uniform %s", mp->tk02varPr); if (!strcmp(mp->tk02varPr, "Fixed")) MrBayesPrint ("(%1.2lf)\n", mp->tk02varFix); else if (!strcmp(mp->tk02varPr,"Exponential")) MrBayesPrint ("(%1.2lf)\n", mp->tk02varExp); else { assert (!strcmp(mp->tk02varPr,"Uniform")); MrBayesPrint ("(%1.2lf,%1.2lf)\n", mp->tk02varUni[0], mp->tk02varUni[1]); } MrBayesPrint (" WNvarpr Fixed/Exponential/Uniform %s", mp->wnvarPr); if (!strcmp(mp->wnvarPr, "Fixed")) MrBayesPrint ("(%1.2lf)\n", mp->wnvarFix); else if (!strcmp(mp->wnvarPr,"Exponential")) MrBayesPrint ("(%1.2lf)\n", mp->wnvarExp); else { assert (!strcmp(mp->wnvarPr,"Uniform")); MrBayesPrint ("(%1.2lf,%1.2lf)\n", mp->wnvarUni[0], mp->wnvarUni[1]); } MrBayesPrint (" IGRvarpr Fixed/Exponential/Uniform %s", mp->igrvarPr); if (!strcmp(mp->igrvarPr, "Fixed")) MrBayesPrint ("(%1.2lf)\n", mp->igrvarFix); else if (!strcmp(mp->igrvarPr,"Exponential")) MrBayesPrint ("(%1.2lf)\n", mp->igrvarExp); else { assert (!strcmp(mp->igrvarPr,"Uniform")); MrBayesPrint ("(%1.2lf,%1.2lf)\n", mp->igrvarUni[0], mp->igrvarUni[1]); } MrBayesPrint (" ILNvarpr Fixed/Exponential/Uniform %s", mp->ilnvarPr); if (!strcmp(mp->ilnvarPr, "Fixed")) MrBayesPrint ("(%1.2lf)\n", mp->ilnvarFix); else if (!strcmp(mp->ilnvarPr,"Exponential")) MrBayesPrint ("(%1.2lf)\n", mp->ilnvarExp); else { assert (!strcmp(mp->ilnvarPr,"Uniform")); MrBayesPrint ("(%1.2lf,%1.2lf)\n", mp->ilnvarUni[0], mp->ilnvarUni[1]); } MrBayesPrint (" Mixedvarpr Fixed/Exponential/Uniform %s", mp->mixedvarPr); if (!strcmp(mp->mixedvarPr, "Fixed")) MrBayesPrint ("(%1.2lf)\n", mp->mixedvarFix); else if (!strcmp(mp->mixedvarPr,"Exponential")) MrBayesPrint ("(%1.2lf)\n", mp->mixedvarExp); else { assert (!strcmp(mp->mixedvarPr,"Uniform")); MrBayesPrint ("(%1.2lf,%1.2lf)\n", mp->mixedvarUni[0], mp->mixedvarUni[1]); } MrBayesPrint (" Ratepr Fixed/Variable=Dirichlet %s", mp->ratePr); if (!strcmp(mp->ratePr, "Dirichlet")) MrBayesPrint ("(...,%1.1lf,...)\n", mp->ratePrDir); else MrBayesPrint ("\n"); MrBayesPrint (" Generatepr Fixed/Variable=Dirichlet %s", mp->generatePr); if (!strcmp(mp->generatePr, "Dirichlet")) MrBayesPrint ("(...,%1.1lf,...)\n", mp->generatePrDir); else MrBayesPrint ("\n"); MrBayesPrint (" \n"); MrBayesPrint (" ------------------------------------------------------------------ \n"); } } else if (!strcmp(helpTkn, "Ctype")) { MrBayesPrint (" --------------------------------------------------------------------------- \n"); MrBayesPrint (" Ctype \n"); MrBayesPrint (" \n"); MrBayesPrint (" This command sets the character ordering for standard-type data. The \n"); MrBayesPrint (" correct usage is: \n"); MrBayesPrint (" \n"); MrBayesPrint (" ctype : \n"); MrBayesPrint (" \n"); MrBayesPrint (" The available options for the specifier are: \n"); MrBayesPrint (" \n"); MrBayesPrint (" unordered -- Movement directly from one state to another is \n"); MrBayesPrint (" allowed in an instant of time. \n"); MrBayesPrint (" ordered -- Movement is only allowed between adjacent characters. \n"); MrBayesPrint (" For example, perhaps only between 0 <-> 1 and 1 <-> 2 \n"); MrBayesPrint (" for a three state character ordered as 0 - 1 - 2. \n"); MrBayesPrint (" irreversible -- Rates of change for losses are 0. \n"); MrBayesPrint (" \n"); MrBayesPrint (" The characters to which the ordering is applied is specified in manner \n"); MrBayesPrint (" that is identical to commands such as \"include\" or \"exclude\". For \n"); MrBayesPrint (" example, \n"); MrBayesPrint (" \n"); MrBayesPrint (" ctype ordered: 10 23 45 \n"); MrBayesPrint (" \n"); MrBayesPrint (" defines characters 10, 23, and 45 to be of type ordered. Similarly, \n"); MrBayesPrint (" \n"); MrBayesPrint (" ctype irreversible: 54-67 71-92 \n"); MrBayesPrint (" \n"); MrBayesPrint (" defines characters 54 to 67 and characters 71 to 92 to be of type \n"); MrBayesPrint (" irreversible. You can use the \".\" to denote the last character, and \n"); MrBayesPrint (" \"all\" to denote all of the characters. Finally, you can use the \n"); MrBayesPrint (" specifier \"\\\" to apply the ordering to every n-th character or \n"); MrBayesPrint (" you can use predefined charsets to specify the character. \n"); MrBayesPrint (" \n"); MrBayesPrint (" Only one ordering can be used on any specific application of ctype. \n"); MrBayesPrint (" If you want to apply different orderings to different characters, then \n"); MrBayesPrint (" you need to use ctype multiple times. For example, \n"); MrBayesPrint (" \n"); MrBayesPrint (" ctype ordered: 1-50 \n"); MrBayesPrint (" ctype irreversible: 51-100 \n"); MrBayesPrint (" \n"); MrBayesPrint (" sets characters 1 to 50 to be ordered and characters 51 to 100 to be \n"); MrBayesPrint (" irreversible. \n"); MrBayesPrint (" \n"); MrBayesPrint (" The ctype command is only sensible with morphological (here called \n"); MrBayesPrint (" \"standard\") characters. The program ignores attempts to apply char- \n"); MrBayesPrint (" acter orderings to other types of characters, such as DNA characters. \n"); MrBayesPrint (" --------------------------------------------------------------------------- \n"); } else if (!strcmp(helpTkn, "Propset")) { MrBayesPrint (" --------------------------------------------------------------------------- \n"); MrBayesPrint (" Propset \n"); MrBayesPrint (" \n"); MrBayesPrint (" This command allows the user to change the details of the MCMC samplers \n"); MrBayesPrint (" (moves) that update the state of the chain. The usage is: \n"); MrBayesPrint (" \n"); MrBayesPrint (" propset $= \n"); MrBayesPrint (" \n"); MrBayesPrint (" Assume we have a branch-length parameter called 'V{all}', which is sampled by \n"); MrBayesPrint (" the move 'Nodeslider(V{all})' (note that the parameter name is included in \n"); MrBayesPrint (" the move name). This move has two tuning parameters: (1) 'prob', the relative \n"); MrBayesPrint (" proposal probability (a weight defining its probability relative to other \n"); MrBayesPrint (" moves); and (2) 'lambda', the tuning parameter of the branch length \n"); MrBayesPrint (" multiplier. A list of the tuning parameters is available by using 'Showmoves' \n"); MrBayesPrint (" (see below). To change the relative proposal probability to 20 and the tuning \n"); MrBayesPrint (" parameter to 0.7, use: \n"); MrBayesPrint (" \n"); MrBayesPrint (" propset Nodeslider(V{all})$prob=20 Nodeslider(V{all})$lambda=0.7 \n"); MrBayesPrint (" \n"); MrBayesPrint (" This change would apply to all chains in all runs. It is also possible to set \n"); MrBayesPrint (" the tuning parameters of individual runs and chains using the format: \n"); MrBayesPrint (" \n"); MrBayesPrint (" propset $(,)= \n"); MrBayesPrint (" \n"); MrBayesPrint (" where and are the index numbers of the run and chain for which \n"); MrBayesPrint (" you want to change the value. If you leave out the index of the run, the \n"); MrBayesPrint (" change will apply to all runs; if you leave out the index of the chain, the \n"); MrBayesPrint (" change will similarly apply to all chains. To switch off the \n"); MrBayesPrint (" Nodeslider(V{all}) move in chain 2 of all runs, use: \n"); MrBayesPrint (" \n"); MrBayesPrint (" propset Nodeslider(V{all})$prob(,2)=0 \n"); MrBayesPrint (" \n"); MrBayesPrint (" It is important to note that all moves are not available until the model has \n"); MrBayesPrint (" been completely defined. Any change to the model will cause all proposal \n"); MrBayesPrint (" tuning parameters to return to their default values. To see a list of all the \n"); MrBayesPrint (" moves that are currently switched on for the model, use 'showmoves'. You can \n"); MrBayesPrint (" also see other available moves by using 'showmoves allavailable=yes'. A list \n"); MrBayesPrint (" of the moves for each parameter in the model is available by using the command\n"); MrBayesPrint (" 'Showparams'. If you change proposal probabilities, make sure that all \n"); MrBayesPrint (" parameters that are not fixed in your model have at least one move switched \n"); MrBayesPrint (" on. \n"); MrBayesPrint (" \n"); MrBayesPrint (" One word of warning: You should be extremely careful when modifying any \n"); MrBayesPrint (" of the chain parameters using 'propset'. It is quite possible to completely \n"); MrBayesPrint (" wreck any hope of achieving convergence by inappropriately setting the \n"); MrBayesPrint (" tuning parameters. In general, you want to set move tuning parameters such \n"); MrBayesPrint (" that the acceptance rate of the move is intermediate (we suggest targeting \n"); MrBayesPrint (" the range 10%% to 70%% acceptance, if possible). If the acceptance rate is \n"); MrBayesPrint (" outside of this range, the MCMC chain will probably not sample that parameter \n"); MrBayesPrint (" very efficiently. The acceptance rates for all moves in the cold chain(s) are \n"); MrBayesPrint (" summarized at the end of each run in the screen output. The acceptance rates \n"); MrBayesPrint (" (potentially for all chains, cold and heated) are also printed to the .mcmc \n"); MrBayesPrint (" file if MCMC convergence diagnostics are turned on (using 'Mcmc' or 'Mcmcp'). \n"); MrBayesPrint (" --------------------------------------------------------------------------- \n"); } else if (!strcmp(helpTkn, "Log")) { MrBayesPrint (" --------------------------------------------------------------------------- \n"); MrBayesPrint (" Log \n"); MrBayesPrint (" \n"); MrBayesPrint (" This command allows output to the screen to also be output to a file. \n"); MrBayesPrint (" The usage is: \n"); MrBayesPrint (" \n"); MrBayesPrint (" log start/stop filename= append/replace \n"); MrBayesPrint (" \n"); MrBayesPrint (" The options are: \n"); MrBayesPrint (" \n"); MrBayesPrint (" Start/Stop -- Starts or stops logging of output to file. \n"); MrBayesPrint (" Append/Replace -- Either append to or replace existing file. \n"); MrBayesPrint (" Filename -- Name of log file (currently, the name of the log \n"); MrBayesPrint (" file is \"%s\").\n", logFileName); MrBayesPrint (" --------------------------------------------------------------------------- \n"); } else if (!strcmp(helpTkn, "Translate")) { MrBayesPrint (" --------------------------------------------------------------------------- \n"); MrBayesPrint (" Translate \n"); MrBayesPrint (" \n"); MrBayesPrint (" This command is used by MrBayes to specify the mapping between taxon names \n"); MrBayesPrint (" and taxon numbers in a Nexus tree file. For instance, \n"); MrBayesPrint (" \n"); MrBayesPrint (" translate \n"); MrBayesPrint (" 1 Homo, \n"); MrBayesPrint (" 2 Pan, \n"); MrBayesPrint (" 3 Gorilla, \n"); MrBayesPrint (" 4 Hylobates; \n"); MrBayesPrint (" \n"); MrBayesPrint (" establishes that the taxon labeled 1 in the trees that follow is Homo, the \n"); MrBayesPrint (" taxon labeled 2 is Pan, etc. \n"); MrBayesPrint (" --------------------------------------------------------------------------- \n"); } else if (!strcmp(helpTkn, "Usertree")) { MrBayesPrint (" --------------------------------------------------------------------------- \n"); MrBayesPrint (" Usertree \n"); MrBayesPrint (" \n"); MrBayesPrint (" This command allows you to specify a user tree. The user tree can then be \n"); MrBayesPrint (" used as a starting tree for a MCMC analysis. The format for the command is \n"); MrBayesPrint (" \n"); MrBayesPrint (" usertree = \n"); MrBayesPrint (" \n"); MrBayesPrint (" For example, \n"); MrBayesPrint (" \n"); MrBayesPrint (" usertree = (A,B,(C,D)) \n"); MrBayesPrint (" \n"); MrBayesPrint (" specifies an unrooted tree of four species. Note that the program re- \n"); MrBayesPrint (" quires that trees are binary (i.e., strictly bifurcating). Hence, there \n"); MrBayesPrint (" can be only one three-way split, as shown in the example. If the tree \n"); MrBayesPrint (" is not binary, the program will return an error. \n"); MrBayesPrint (" --------------------------------------------------------------------------- \n"); } else if (!strcmp(helpTkn, "Mcmc")) { MrBayesPrint (" --------------------------------------------------------------------------- \n"); MrBayesPrint (" Mcmc \n"); MrBayesPrint (" \n"); MrBayesPrint (" This command starts the Markov chain Monte Carlo (MCMC) analysis. The \n"); MrBayesPrint (" posterior probability of phylogenetic trees (and other parameters of the \n"); MrBayesPrint (" substitution model) cannot be determined analytically. Instead, MCMC is \n"); MrBayesPrint (" used to approximate the posterior probabilities of trees by drawing \n"); MrBayesPrint (" (dependent) samples from the posterior distribution. This program can \n"); MrBayesPrint (" implement a variant of MCMC called \"Metropolis-coupled Markov chain Monte \n"); MrBayesPrint (" Carlo\", or MCMCMC for short. Basically, \"Nchains\" are run, with \n"); MrBayesPrint (" Nchains - 1 of them heated. The chains are labelled 1, 2, ..., Nchains. \n"); MrBayesPrint (" The heat that is applied to the i-th chain is B = 1 / (1 + temp X i). B \n"); MrBayesPrint (" is the power to which the posterior probability is raised. When B = 0, all \n"); MrBayesPrint (" trees have equal probability and the chain freely visits trees. B = 1 is \n"); MrBayesPrint (" the \"cold\" chain (or the distribution of interest). MCMCMC can mix \n"); MrBayesPrint (" better than ordinary MCMC; after all of the chains have gone through \n"); MrBayesPrint (" one cycle, two chains are chosen at random and an attempt is made to \n"); MrBayesPrint (" swap the states (with the probability of a swap being determined by the \n"); MrBayesPrint (" Metropolis et al. equation). This allows the chain to potentially jump \n"); MrBayesPrint (" a valley in a single bound. The correct usage is \n"); MrBayesPrint (" \n"); MrBayesPrint (" mcmc = ... = \n"); MrBayesPrint (" \n"); MrBayesPrint (" For example, \n"); MrBayesPrint (" \n"); MrBayesPrint (" mcmc ngen=100000 nchains=4 temp=0.5 \n"); MrBayesPrint (" \n"); MrBayesPrint (" performs a MCMCMC analysis with four chains with the temperature set to \n"); MrBayesPrint (" 0.5. The chains would be run for 100,000 cycles. \n"); MrBayesPrint (" \n"); MrBayesPrint (" Options: \n"); MrBayesPrint (" \n"); MrBayesPrint (" Ngen -- This option sets the number of cycles for the MCMC alg- \n"); MrBayesPrint (" orithm. This should be a big number as you want the chain \n"); MrBayesPrint (" to first reach stationarity, and then remain there for \n"); MrBayesPrint (" enough time to take lots of samples. \n"); MrBayesPrint (" Nruns -- How many independent analyses are started simultaneously. \n"); MrBayesPrint (" Nchains -- How many chains are run for each analysis for the MCMCMC \n"); MrBayesPrint (" variant. The default is 4: 1 cold chain and 3 heated chains. \n"); MrBayesPrint (" If Nchains is set to 1, MrBayes will use regular MCMC sam- \n"); MrBayesPrint (" pling, without heating. \n"); MrBayesPrint (" Temp -- The temperature parameter for heating the chains. The higher \n"); MrBayesPrint (" the temperature, the more likely the heated chains are to \n"); MrBayesPrint (" move between isolated peaks in the posterior distribution. \n"); MrBayesPrint (" However, excessive heating may lead to very low acceptance \n"); MrBayesPrint (" rates for swaps between different chains. Before changing the \n"); MrBayesPrint (" default setting, however, note that the acceptance rates of \n"); MrBayesPrint (" swaps tend to fluctuate during the burn-in phase of the run. \n"); MrBayesPrint (" Reweight -- Here, you specify three numbers, that respectively represent \n"); MrBayesPrint (" the percentage of characters to decrease in weight, the \n"); MrBayesPrint (" percentage of characters to increase in weight, and the \n"); MrBayesPrint (" increment. An increase/decrease in weight is achieved by \n"); MrBayesPrint (" replicating/removing a character in the matrix. This is \n"); MrBayesPrint (" only done to non-cold chains. The format for this parameter \n"); MrBayesPrint (" is \"reweight=(,)\" or \"reweight=(, \n"); MrBayesPrint (" ,)\". \n"); MrBayesPrint (" Swapfreq -- This specifies how often swaps of states between chains are \n"); MrBayesPrint (" attempted. You must be running at least two chains for this \n"); MrBayesPrint (" option to be relevant. The default is Swapfreq=1, resulting \n"); MrBayesPrint (" in Nswaps (see below) swaps being tried each generation of \n"); MrBayesPrint (" the run. If Swapfreq is set to 10, then Nswaps swaps will be \n"); MrBayesPrint (" tried every tenth generation of the run. \n"); MrBayesPrint (" Nswaps -- The number of swaps tried for each swapping generation of the \n"); MrBayesPrint (" chain (see also Swapfreq). \n"); MrBayesPrint (" Samplefreq -- This specifies how often the Markov chain is sampled. You \n"); MrBayesPrint (" can sample the chain every cycle, but this results in very \n"); MrBayesPrint (" large output files. Thinning the chain is a way of making \n"); MrBayesPrint (" these files smaller and making the samples more independent. \n"); MrBayesPrint (" Printfreq -- This specifies how often information about the chain is \n"); MrBayesPrint (" printed to the screen. \n"); MrBayesPrint (" Printall -- If set to NO, only cold chains in a MCMC analysis are printed \n"); MrBayesPrint (" to screen. If set to YES, both cold and heated chains will be \n"); MrBayesPrint (" output. This setting only affects the printing to screen, it \n"); MrBayesPrint (" does not change the way values are written to file. \n"); MrBayesPrint (" Printmax -- The maximum number of chains to print to screen. \n"); MrBayesPrint (" Mcmcdiagn -- Determines whether acceptance ratios of moves and swaps will \n"); MrBayesPrint (" be printed to file. The file will be named similarly to the \n"); MrBayesPrint (" '.p' and '.t' files, but will have the ending '.mcmc'. If \n"); MrBayesPrint (" more than one independent analysis is run simultaneously (see \n"); MrBayesPrint (" Nruns below), convergence diagnostics for tree topology will \n"); MrBayesPrint (" also be printed to this file. The convergence diagnostic used \n"); MrBayesPrint (" is the average standard deviation in partition frequency \n"); MrBayesPrint (" values across independent analyses. The Burnin setting (see \n"); MrBayesPrint (" below) determines how many samples will be discarded as burnin\n"); MrBayesPrint (" before calculating the partition frequencies. The Minpartfreq \n"); MrBayesPrint (" setting (see below) determines the minimum partition frequency\n"); MrBayesPrint (" required for a partition to be included in the calculation. As\n"); MrBayesPrint (" the independent analyses approach stationarity (converge), the\n"); MrBayesPrint (" value of the diagnostic is expected to approach zero. \n"); MrBayesPrint (" Diagnfreq -- The number of generations between the calculation of MCMC \n"); MrBayesPrint (" diagnostics (see Mcmcdiagn above). \n"); MrBayesPrint (" Diagnstat -- The statistic to use for run-time convergence diagnostics. \n"); MrBayesPrint (" Choices are 'Avgstddev' for average standard deviation of \n"); MrBayesPrint (" split frequencies and 'Maxstddev' for maximum standard devia- \n"); MrBayesPrint (" tion of split frequencies. \n"); MrBayesPrint (" Savetrees -- If you are using a relative burnin for run-time convergence \n"); MrBayesPrint (" diagnostics, tree samples need to be deleted from split \n"); MrBayesPrint (" frequency counters as the cut-off point for the burnin moves \n"); MrBayesPrint (" during the run. If 'Savetrees' is set to 'No', tree samples \n"); MrBayesPrint (" to be discarded are read back in from file. If 'Savetrees' is \n"); MrBayesPrint (" set to 'Yes', the tree samples to be removed will be stored \n"); MrBayesPrint (" in the internal memory instead. This can use up a lot of \n"); MrBayesPrint (" memory in large analyses. \n"); MrBayesPrint (" Minpartfreq -- The minimum frequency required for a partition to be included \n"); MrBayesPrint (" in the calculation of the topology convergence diagnostic. The\n"); MrBayesPrint (" partition is included if the minimum frequency is reached in \n"); MrBayesPrint (" at least one of the independent tree samples that are com- \n"); MrBayesPrint (" pared. \n"); MrBayesPrint (" Allchains -- If this option is set to YES, acceptance ratios for moves are \n"); MrBayesPrint (" recorded for all chains, cold or heated. By default, only the \n"); MrBayesPrint (" acceptance ratios for the cold chain are recorded. \n"); MrBayesPrint (" Allcomps -- If this option is set to YES, topological convergence diag- \n"); MrBayesPrint (" nostics are calculated over all pairwise comparisons of runs. \n"); MrBayesPrint (" If it is set to NO, only the overall value is reported. \n"); MrBayesPrint (" Relburnin -- If this option is set to YES, then a proportion of the sampled\n"); MrBayesPrint (" values will be discarded as burnin when calculating the con- \n"); MrBayesPrint (" vergence diagnostic. The proportion to be discarded is set \n"); MrBayesPrint (" with Burninfrac (see below). When the Relburnin option is set \n"); MrBayesPrint (" to NO, then a specific number of samples will be discarded \n"); MrBayesPrint (" instead. This number is set by Burnin (see below). \n"); MrBayesPrint (" Burnin -- Determines the number of samples (not generations) that will \n"); MrBayesPrint (" be discarded when convergence diagnostics are calculated. \n"); MrBayesPrint (" The value of this option is only relevant when Relburnin is \n"); MrBayesPrint (" set to NO. \n"); MrBayesPrint (" BurninFrac -- Determines the fraction of samples that will be discarded \n"); MrBayesPrint (" when convergence diagnostics are calculated. The value of \n"); MrBayesPrint (" this option is only relevant when Relburnin is set to YES. \n"); MrBayesPrint (" Example: A value for this option of 0.25 means that 25%% of \n"); MrBayesPrint (" the samples will be discarded. \n"); MrBayesPrint (" Stoprule -- If this option is set to NO, then the chain is run the number \n"); MrBayesPrint (" of generations determined by Ngen. If it is set to YES, and \n"); MrBayesPrint (" topological convergence diagnostics are calculated (Mcmcdiagn \n"); MrBayesPrint (" is set to YES), then the chain will be stopped before the pre-\n"); MrBayesPrint (" determined number of generations if the convergence diagnostic\n"); MrBayesPrint (" falls below the stop value. \n"); MrBayesPrint (" Stopval -- The critical value for the topological convergence diagnostic.\n"); MrBayesPrint (" Only used when Stoprule and Mcmcdiagn are set to yes, and \n"); MrBayesPrint (" more than one analysis is run simultaneously (Nruns > 1). \n"); MrBayesPrint (" Checkpoint -- If this parameter is set to 'Yes', all the current parameter \n"); MrBayesPrint (" values of all chains will be printed to a check-pointing file \n"); MrBayesPrint (" every 'Checkfreq' generation of the analysis. The file will be\n"); MrBayesPrint (" named .ckp and allows you to restart the analysis \n"); MrBayesPrint (" from the last check point. This can be handy if you are \n"); MrBayesPrint (" running a long analysis and want to extend it, or if there is \n"); MrBayesPrint (" a risk that a long analysis will be inadvertently interrupted \n"); MrBayesPrint (" by hardware failure or other factors that are out of your \n"); MrBayesPrint (" control. \n"); MrBayesPrint (" Checkfreq -- The number of generations between check-pointing. See the \n"); MrBayesPrint (" 'Checkpoint' parameter above for more information. \n"); MrBayesPrint (" Filename -- The name of the files that will be generated. Two files \n"); MrBayesPrint (" are generated: \".t\" and \".p\". \n"); MrBayesPrint (" The .t file contains the trees whereas the .p file con- \n"); MrBayesPrint (" tains the sampled values of the parameters. \n"); MrBayesPrint (" Startparams -- The starting values for the model parameters are set to \n"); MrBayesPrint (" arbitrary or random values when the parameters are created. \n"); MrBayesPrint (" These starting values can be altered using the 'Startvals' \n"); MrBayesPrint (" command. The 'Startparams=reset' option allows you to reset \n"); MrBayesPrint (" the starting values to the default at the start of the ana- \n"); MrBayesPrint (" lysis, overriding any previous user-defined starting values. \n"); MrBayesPrint (" Under the default option, 'current', the chains will use the \n"); MrBayesPrint (" current starting values. \n"); MrBayesPrint (" Starttree -- The starting tree(s) for the chain can either be randomly \n"); MrBayesPrint (" selected or user-defined. It might be a good idea to \n"); MrBayesPrint (" start from randomly chosen trees; convergence seems \n"); MrBayesPrint (" likely if independently run chains, each of which \n"); MrBayesPrint (" started from different random trees, converge to the same \n"); MrBayesPrint (" answer. If you want the chain to start from user-defined \n"); MrBayesPrint (" trees instead, you first need to read in your tree(s) from a \n"); MrBayesPrint (" Nexus file with a 'trees' block, and then you need to set the \n"); MrBayesPrint (" starting tree(s) using the 'Startvals' command. Finally, you \n"); MrBayesPrint (" need to make sure that 'Starttree' is set to 'current'. If \n"); MrBayesPrint (" you do not set the starting tree(s), the chains will start \n"); MrBayesPrint (" with random trees. Setting 'Starttree' to 'random' causes \n"); MrBayesPrint (" new starting trees to be drawn randomly at the start of the \n"); MrBayesPrint (" run, overwriting any previous user-defined starting trees. \n"); MrBayesPrint (" Nperts -- This is the number of random perturbations to apply to the \n"); MrBayesPrint (" user starting tree. This allows you to have something \n"); MrBayesPrint (" between completely random and user-defined trees start \n"); MrBayesPrint (" the chain. \n"); MrBayesPrint (" Data -- When Data is set to NO, the chain is run without data. This \n"); MrBayesPrint (" should be used only for examining induced priors. DO NOT SET \n"); MrBayesPrint (" 'DATA' TO 'NO' UNLESS YOU KNOW WHAT YOU ARE DOING! \n"); MrBayesPrint (" Ordertaxa -- Determines whether taxa should be ordered before trees are \n"); MrBayesPrint (" printed to file. If set to 'Yes', terminals in the sampled \n"); MrBayesPrint (" trees will be reordered to match the order of the taxa in the \n"); MrBayesPrint (" data matrix as closely as possible. By default, trees will be \n"); MrBayesPrint (" printed without reordering of taxa. \n"); MrBayesPrint (" Append -- Set this to 'Yes' to append the results of the current run to \n"); MrBayesPrint (" a previous run. MrBayes will first read in the results of the \n"); MrBayesPrint (" previous run (number of generations and sampled splits) and \n"); MrBayesPrint (" will then continue that run where you left it off. Make sure \n"); MrBayesPrint (" that the output file names used in the previous run are the \n"); MrBayesPrint (" same as those in the current run. \n"); MrBayesPrint (" Autotune -- Set this to 'Yes' to autotune the proposals that change \n"); MrBayesPrint (" substitution model parameters. When set to 'No', the tuning \n"); MrBayesPrint (" parameters are fixed to their starting values. Note that the \n"); MrBayesPrint (" autotuning occurs independently for each chain. The target \n"); MrBayesPrint (" acceptance rate for each move can be changed using the \n"); MrBayesPrint (" 'Propset' command. \n"); MrBayesPrint (" Tunefreq -- When a proposal has been tried 'Tunefreq' times, its tuning \n"); MrBayesPrint (" parameter is adjusted to reach the target acceptance rate \n"); MrBayesPrint (" if 'Autotune' is set to 'Yes'. \n"); MrBayesPrint (" \n"); PrintSettings ("Mcmc"); MrBayesPrint (" --------------------------------------------------------------------------- \n"); } else if (!strcmp(helpTkn, "Mcmcp")) { // PrintYesNo (chainParams.saveBrlens, yesNoStr); MrBayesPrint (" --------------------------------------------------------------------------- \n"); MrBayesPrint (" Mcmcp \n"); MrBayesPrint (" \n"); MrBayesPrint (" This command sets the parameters of the Markov chain Monte Carlo (MCMC) \n"); MrBayesPrint (" analysis without actually starting the chain. This command is identical \n"); MrBayesPrint (" in all respects to Mcmc, except that the analysis will not start after \n"); MrBayesPrint (" this command is issued. For more details on the options, check the help \n"); MrBayesPrint (" menu for Mcmc.\n"); MrBayesPrint (" \n"); PrintSettings ("Mcmc"); MrBayesPrint (" --------------------------------------------------------------------------- \n"); } else if (!strcmp(helpTkn, "Ss")) { MrBayesPrint (" --------------------------------------------------------------------------- \n"); MrBayesPrint (" Ss \n"); MrBayesPrint (" \n"); MrBayesPrint (" This command is used to start stepping-stone sampling, which is an efficient \n"); MrBayesPrint (" and accurate method for estimating the marginal likelihood of the currently \n"); MrBayesPrint (" specified model. It is considerably more accurate than the harmonic mean of \n"); MrBayesPrint (" the likelihoods from a standard MCMC run on the model (calculated by the \n"); MrBayesPrint (" 'Sump' command) but it requires a separate MCMC-like run. To be more specific,\n"); MrBayesPrint (" stepping-stone sampling uses importance sampling to estimate each ratio in a \n"); MrBayesPrint (" series of discrete steps bridging the posterior and prior distributions. \n"); MrBayesPrint (" The importance distributions that are used are called power posterior distri- \n"); MrBayesPrint (" butions, and are defined as prior*(likelihood^beta). By varying beta from 1 to\n"); MrBayesPrint (" 0, we get a series of distributions that connect the posterior (beta = 1) to \n"); MrBayesPrint (" the prior (beta = 0). \n"); MrBayesPrint (" \n"); MrBayesPrint (" The power posterior distributions are sampled using MCMC. First, we start a \n"); MrBayesPrint (" standard MCMC chain on the posterior distribution, and let it run until we \n"); MrBayesPrint (" have reached the criterion specified by the 'Burninss' option. After this, we \n"); MrBayesPrint (" step through the power posterior distributions until we reach the prior dis- \n"); MrBayesPrint (" tribution. In each of the 'Nsteps' steps, we sample from a new power poster- \n"); MrBayesPrint (" ior distribution with a distinct beta value. The beta values correspond to \n"); MrBayesPrint (" 'Nsteps' evenly spaced quantiles in a Beta distribution with the parameters \n"); MrBayesPrint (" 'Alpha' and 1.0. For the first sampling step, the beta value is equal to the \n"); MrBayesPrint (" last quantile, i.e., it is close to 1.0. For each successive step, the beta \n"); MrBayesPrint (" value takes on the value of the next quantile, in decreasing order, until it \n"); MrBayesPrint (" reaches the value of 0.0. If you change value of 'FromPrior' from default 'No'\n"); MrBayesPrint (" to 'Yes' then the direction of power posterior change during SS analysis is \n"); MrBayesPrint (" opposite to the one described above, i.e. we start from sampling prior and \n"); MrBayesPrint (" finish close to posterior. \n"); MrBayesPrint (" \n"); MrBayesPrint (" The 'Ss' procedure uses the same machinery as the standard 'Mcmc' algorithm, \n"); MrBayesPrint (" and shares most of its parameters with the 'Mcmc' and 'Mcmcp' commands. All \n"); MrBayesPrint (" 'Mcmc' parameters, except those related to burnin, have the same meaning and \n"); MrBayesPrint (" usage in the 'Ss' command as they have in the 'Mcmc' command. The 'Mcmc' \n"); MrBayesPrint (" burnin parameters are used to set up burnin within each step. The 'Ss' command\n"); MrBayesPrint (" also uses its own burnin parameter, 'Burninss' (see below for details). The \n"); MrBayesPrint (" 'Ss' command also has its own parameters for specifying the number of steps \n"); MrBayesPrint (" and the shape of the Beta distribution from which the beta values are computed\n"); MrBayesPrint (" (see below). \n"); MrBayesPrint (" \n"); MrBayesPrint (" Note that the 'Ngen' parameter of 'Mcmc' is used to set the maximum number of \n"); MrBayesPrint (" generations processed, including both the burnin and the following steps in \n"); MrBayesPrint (" the stepping-stone sampling phase. For instance, assume that 'Burninss' is set\n"); MrBayesPrint (" to '-1', 'Nsteps' to '49', 'Ngen' to '1000000' and 'Samplefreq' to '1000'. \n"); MrBayesPrint (" We will then get 1,000 samples in total (1,000,000 / 1,000). These will fall \n"); MrBayesPrint (" into 50 bins, one of which represents the burnin and is discarded. Each step \n"); MrBayesPrint (" in the algorithm will thus be represented by 20 samples. \n"); MrBayesPrint (" \n"); MrBayesPrint (" More information on 'Mcmc' parameters is available in the help for the 'Mcmc' \n"); MrBayesPrint (" and 'Mcmcp' commands. Only the exclusive 'Ss' parameters are listed below. \n"); MrBayesPrint (" These can only be set up using the 'Ss' command, while the parameters shared \n"); MrBayesPrint (" with 'Mcmc' and 'Mcmcp' can also be set up using those commands. \n"); MrBayesPrint (" \n"); MrBayesPrint (" The correct usage is \n"); MrBayesPrint (" \n"); MrBayesPrint (" ss = ... = \n"); MrBayesPrint (" \n"); MrBayesPrint (" Note that a command: \n"); MrBayesPrint (" \n"); MrBayesPrint (" ss \n"); MrBayesPrint (" \n"); MrBayesPrint (" would be equivalent to executing two commands: \n"); MrBayesPrint (" \n"); MrBayesPrint (" mcmcp ; \n"); MrBayesPrint (" ss ; \n"); MrBayesPrint (" \n"); MrBayesPrint (" For more information on the stepping-stone algorithm, see: \n"); MrBayesPrint (" \n"); MrBayesPrint (" Xie, W., P. O. Lewis, Y. Fan, L. Kuo, and M.-H. Chen. 2011. Improving marginal\n"); MrBayesPrint (" likelihood estimation for Bayesian phylogenetic model selection. Systematic\n"); MrBayesPrint (" Biology 60:150-160. \n"); MrBayesPrint (" \n"); MrBayesPrint (" Available options: \n"); MrBayesPrint (" (NB: Only exclusive ss parameters listed here. For additional parameters, see \n"); MrBayesPrint (" help on 'Mcmc' or 'Mcmcp'. \n"); MrBayesPrint (" \n"); MrBayesPrint (" Alpha -- The beta values used in the stepping-stone sampling procedure \n"); MrBayesPrint (" correspond to evenly spaced quantiles from a Beta('Alpha',1.0)\n"); MrBayesPrint (" distribution. The parameter 'Alpha' determines the skewness of\n"); MrBayesPrint (" the beta values. If 'Alpha' is set to '1.0', the beta values \n"); MrBayesPrint (" would be spaced uniformly on the interval (0.0,1.0). However, \n"); MrBayesPrint (" better results are obtained if the beta values are skewed. \n"); MrBayesPrint (" Empirically, it was observed that 'Alpha' values in the range \n"); MrBayesPrint (" of 0.3 to 0.5 produce the most accurate results. \n"); MrBayesPrint (" Burninss -- Fixed number of samples discarded before sampling of the first\n"); MrBayesPrint (" step starts. 'Burninss' can be specified using either a pos- \n"); MrBayesPrint (" itive or a negative number. If the number is positive, it is \n"); MrBayesPrint (" interpreted as the number of samples to discard as burnin. If \n"); MrBayesPrint (" the number is negative, its absolute value is interpreted as \n"); MrBayesPrint (" the length of the burnin in terms of the length of each of the\n"); MrBayesPrint (" following steps in the stepping-stone algorithm. For instance,\n"); MrBayesPrint (" a value of '-1' means that the length of the burnin is the \n"); MrBayesPrint (" same as the length of each of the subsequent steps. \n"); MrBayesPrint (" Nsteps -- Number of steps in the stepping-stone algorithm. Typically, a \n"); MrBayesPrint (" number above 30 is sufficient for accurate results. \n"); MrBayesPrint (" FromPrior -- If it is set to 'Yes', it indicates that in the first step we \n"); MrBayesPrint (" sample from the prior, with each consecutive step we sample \n"); MrBayesPrint (" closer to the posterior. 'No' indicates the opposite direction\n"); MrBayesPrint (" of power posterior change, i.e. in the first step we sample \n"); MrBayesPrint (" close to the posterior, and with each consecutive step we \n"); MrBayesPrint (" sample closer to the prior. \n"); MrBayesPrint (" \n"); MrBayesPrint (" Current settings: \n"); MrBayesPrint (" \n"); MrBayesPrint (" Parameter Options Current Setting \n"); MrBayesPrint (" -------------------------------------------------------- \n"); MrBayesPrint (" Alpha %1.2lf\n", chainParams.alphaSS); MrBayesPrint (" BurninSS %d\n", chainParams.burninSS); MrBayesPrint (" Nsteps %d\n", chainParams.numStepsSS); MrBayesPrint (" FromPrior Yes/No %s \n", chainParams.startFromPriorSS == YES ? "Yes" : "No"); MrBayesPrint (" \n"); MrBayesPrint (" --------------------------------------------------------------------------- \n"); } else if (!strcmp(helpTkn, "Ssp")) { // PrintYesNo (chainParams.saveBrlens, yesNoStr); MrBayesPrint (" --------------------------------------------------------------------------- \n"); MrBayesPrint (" Ssp \n"); MrBayesPrint (" \n"); MrBayesPrint (" This command sets the parameters of the stepping-stone sampling \n"); MrBayesPrint (" analysis without actually starting the chain. This command is identical \n"); MrBayesPrint (" in all respects to Ss, except that the analysis will not start after \n"); MrBayesPrint (" this command is issued. For more details on the options, check the help \n"); MrBayesPrint (" menu for Ss.\n"); MrBayesPrint (" \n"); MrBayesPrint (" Current settings: \n"); MrBayesPrint (" \n"); MrBayesPrint (" Parameter Options Current Setting \n"); MrBayesPrint (" -------------------------------------------------------- \n"); MrBayesPrint (" Alpha %1.2lf\n", chainParams.alphaSS); MrBayesPrint (" BurninSS %d\n", chainParams.burninSS); MrBayesPrint (" Nsteps %d\n", chainParams.numStepsSS); MrBayesPrint (" FromPrior Yes/No %s \n", chainParams.startFromPriorSS == YES ? "Yes" : "No"); MrBayesPrint (" \n"); MrBayesPrint (" --------------------------------------------------------------------------- \n"); } else if (!strcmp(helpTkn, "Set")) { MrBayesPrint (" --------------------------------------------------------------------------- \n"); MrBayesPrint (" Set \n"); MrBayesPrint (" \n"); MrBayesPrint (" This command is used to set some general features of the model or program \n"); MrBayesPrint (" behavior. The correct usage is \n"); MrBayesPrint (" \n"); MrBayesPrint (" set = ... = \n"); MrBayesPrint (" \n"); MrBayesPrint (" Available options: \n"); MrBayesPrint (" \n"); MrBayesPrint (" Seed -- Sets the seed number for the random number generator. The \n"); MrBayesPrint (" random number seed is initialized haphazardly at the beg- \n"); MrBayesPrint (" inning of each MrBayes session. This option allows you to \n"); MrBayesPrint (" set the seed to some specific value, thereby allowing you \n"); MrBayesPrint (" to exactly repeat an analysis. If the analysis uses swapping \n"); MrBayesPrint (" between cold and heated chains, you must also set the swap \n"); MrBayesPrint (" seed (see below) to exactly repeat the analysis. \n"); MrBayesPrint (" Swapseed -- Sets the seed used for generating the swapping sequence \n"); MrBayesPrint (" when Metropolis-coupled heated chains are used. This seed \n"); MrBayesPrint (" is initialized haphazardly at the beginning of each MrBayes \n"); MrBayesPrint (" session. This option allows you to set the seed to some \n"); MrBayesPrint (" specific value, thereby allowing you to exactly repeat a \n"); MrBayesPrint (" swap sequence. See also the 'Seed' option. \n"); MrBayesPrint (" Dir -- The working directory. Specifies the absolute or relative path\n"); MrBayesPrint (" to the working directory. If left empty, the working directory\n"); MrBayesPrint (" is the current directory. \n"); MrBayesPrint (" Partition -- Set this option to a valid partition id, either the number or \n"); MrBayesPrint (" name of a defined partition, to enforce a specific partition- \n"); MrBayesPrint (" ing of the data. When a data matrix is read in, a partition \n"); MrBayesPrint (" called \"Default\" is automatically created. It divides the \n"); MrBayesPrint (" data into one part for each data type. If you only have one \n"); MrBayesPrint (" data type, DNA for instance, the default partition will not \n"); MrBayesPrint (" divide up the data at all. The default partition is always \n"); MrBayesPrint (" the first partition, so 'set partition=1' is the same as \n"); MrBayesPrint (" 'set partition=default'. \n"); MrBayesPrint (" Speciespartition -- Set this option to a valid speciespartition id, either the\n"); MrBayesPrint (" number or name of a defined speciespartition, to enforce a \n"); MrBayesPrint (" specific partitioning of taxa to species. When a data matrix \n"); MrBayesPrint (" is read in, a speciespartition called \"Default\" is auto- \n"); MrBayesPrint (" matically created. It assigns one taxon for each species. The \n"); MrBayesPrint (" default speciespartition is always the first speciespartition,\n"); MrBayesPrint (" so 'set speciespartition=1' is the same as \n"); MrBayesPrint (" 'set speciespartition=default'. \n"); MrBayesPrint (" Autoclose -- If autoclose is set to 'yes', then the program will not prompt\n"); MrBayesPrint (" you during the course of executing a file. This is particular-\n"); MrBayesPrint (" ly useful when you run MrBayes in batch mode. \n"); MrBayesPrint (" Nowarnings -- If nowarnings is set to yes, then the program will not prompt \n"); MrBayesPrint (" you when overwriting or appending an output file that is al- \n"); MrBayesPrint (" ready present. If 'nowarnings=no' (the default setting), then \n"); MrBayesPrint (" the program prompts the user before overwriting output files. \n"); MrBayesPrint (" Autoreplace -- When nowarnings is set to yes, then MrBayes will by default \n"); MrBayesPrint (" overwrite output files that already exists. This may cause \n"); MrBayesPrint (" irrecoverable loss of previous results if you have not removed\n"); MrBayesPrint (" or renamed the files from previous runs. To override this be- \n"); MrBayesPrint (" havior, set autoreplace to no, in which case new output will \n"); MrBayesPrint (" be appended to existing files instead. \n"); MrBayesPrint (" Quitonerror -- If quitonerror is set to yes, then the program will quit when \n"); MrBayesPrint (" an error is encountered, after printing an error message. If \n"); MrBayesPrint (" quitonerror is set to no (the default setting), then the \n"); MrBayesPrint (" program will wait for additional commands from the command \n"); MrBayesPrint (" line after the error message is printed. \n"); MrBayesPrint (" Scientific -- Set this option to 'Yes' to write sampled values to file in \n"); MrBayesPrint (" scientific format and to 'No' to write them in fixed format. \n"); MrBayesPrint (" Fixed format is easier for humans to read but you risk losing \n"); MrBayesPrint (" precision for small numbers. For instance, sampled values that\n"); MrBayesPrint (" are less than 1E-6 will print to file as '0.000000' if fixed \n"); MrBayesPrint (" format is used and 'precision' is set to 6. \n"); MrBayesPrint (" Precision -- Precision allows you to set the number of decimals to be prin-\n"); MrBayesPrint (" ted when sampled values are written to file. Precision must be\n"); MrBayesPrint (" in the range 3 to 15. \n"); # if defined (BEAGLE_ENABLED) MrBayesPrint (" Usebeagle -- Set this option to 'Yes' to attempt to use the BEAGLE library \n"); MrBayesPrint (" to compute the phylogenetic likelihood on a variety of high- \n"); MrBayesPrint (" performance hardware including multicore CPUs and GPUs. Some \n"); MrBayesPrint (" models in MrBayes are not yet supported by BEAGLE. \n"); MrBayesPrint (" Beagleresource -- Set this option to the number of a specific resource you \n"); MrBayesPrint (" wish to use with BEAGLE (use 'Showbeagle' to see the list of \n"); MrBayesPrint (" available resources). Set to '99' for auto-resource selection.\n"); MrBayesPrint (" Beagledevice -- Set this option to 'GPU' or 'CPU' to select processor. \n"); MrBayesPrint (" Beagleprecision -- Selection 'Single' or 'Double' precision computation. \n"); MrBayesPrint (" Beaglescaling -- 'Always' rescales partial likelihoods at each evaluation. \n"); MrBayesPrint (" 'Dynamic' rescales less frequently and should run faster. \n"); MrBayesPrint (" Beaglesse -- Use SSE instructions on Intel CPU processors. \n"); # if defined (BEAGLE_V3_ENABLED) MrBayesPrint (" Beaglethreads -- Use threading for parallelism on multi-core CPU processors. \n"); MrBayesPrint (" Beaglethreadcount -- Set maximum number of CPU threads to be used by BEAGLE. \n"); MrBayesPrint (" Set to '99' for auto-threading. \n"); MrBayesPrint (" Beaglefloattips -- Use floating-point representation for tip sequence data. \n"); MrBayesPrint (" Can result in improved performance on GPU devices at the cost \n"); MrBayesPrint (" of additional memory usage. \n"); # endif # endif MrBayesPrint (" \n"); MrBayesPrint (" Current settings: \n"); MrBayesPrint (" \n"); MrBayesPrint (" Parameter Options Current Setting \n"); MrBayesPrint (" -------------------------------------------------------- \n"); MrBayesPrint (" Seed %ld \n", globalSeed); MrBayesPrint (" Swapseed %ld \n", swapSeed); MrBayesPrint (" Dir \"%s\"\n", workingDir); if (defMatrix == YES) MrBayesPrint (" Partition %s\n", partitionNames[partitionNum]); else MrBayesPrint (" Partition \"\"\n"); if (defTaxa == YES) MrBayesPrint (" Speciespartition %s\n", speciespartitionNames[speciespartitionNum]); else MrBayesPrint (" Speciespartition \"\"\n"); MrBayesPrint (" Autoclose Yes/No %s \n", autoClose == YES ? "Yes" : "No"); MrBayesPrint (" Nowarnings Yes/No %s \n", noWarn == YES ? "Yes" : "No"); MrBayesPrint (" Autoreplace Yes/No %s \n", autoOverwrite == YES ? "Yes" : "No"); MrBayesPrint (" Quitonerror Yes/No %s \n", quitOnError == YES ? "Yes" : "No"); MrBayesPrint (" Scientific Yes/No %s \n", scientific == YES ? "Yes" : "No"); MrBayesPrint (" Precision %d \n", precision); # if defined (BEAGLE_ENABLED) MrBayesPrint (" Usebeagle Yes/No %s \n", tryToUseBEAGLE == YES ? "Yes" : "No"); MrBayesPrint (" Beagleresource %d \n", beagleResourceNumber); MrBayesPrint (" Beagledevice CPU/GPU %s \n", beagleFlags & BEAGLE_FLAG_PROCESSOR_GPU ? "GPU" : "CPU"); MrBayesPrint (" Beagleprecision Single/Double %s \n", beagleFlags & BEAGLE_FLAG_PRECISION_SINGLE ? "Single" : "Double"); MrBayesPrint (" Beaglescaling Always/Dynamic %s \n", beagleScalingScheme == MB_BEAGLE_SCALE_ALWAYS ? "Always" : "Dynamic"); MrBayesPrint (" Beaglesse Yes/No %s \n", beagleFlags & BEAGLE_FLAG_VECTOR_SSE ? "Yes" : "No"); # if defined (BEAGLE_V3_ENABLED) MrBayesPrint (" Beaglethreads Yes/No %s \n", beagleFlags & BEAGLE_FLAG_THREADING_CPP ? "Yes" : "No"); MrBayesPrint (" Beaglethreadcount %d \n", beagleThreadCount); MrBayesPrint (" Beaglefloattips Yes/No %s \n", beagleAllFloatTips ? "Yes" : "No"); # endif # endif MrBayesPrint (" \n"); MrBayesPrint (" --------------------------------------------------------------------------- \n"); } else if (!strcmp(helpTkn, "Charset")) { MrBayesPrint (" --------------------------------------------------------------------------- \n"); MrBayesPrint (" Charset \n"); MrBayesPrint (" \n"); MrBayesPrint (" This command defines a character set. The format for the charset command \n"); MrBayesPrint (" is \n"); MrBayesPrint (" \n"); MrBayesPrint (" charset = \n"); MrBayesPrint (" \n"); MrBayesPrint (" For example, \"charset first_pos = 1-720\\3\" defines a character set \n"); MrBayesPrint (" called \"first_pos\" that includes every third site from 1 to 720. \n"); MrBayesPrint (" The character set name cannot have any spaces in it. The slash (\\) \n"); MrBayesPrint (" is a nifty way of telling the program to assign every third (or \n"); MrBayesPrint (" second, or fifth, or whatever) character to the character set. \n"); MrBayesPrint (" This option is best used not from the command line, but rather as a \n"); MrBayesPrint (" line in the mrbayes block of a file. Note that you can use \".\" to \n"); MrBayesPrint (" stand in for the last character (e.g., charset 1-.\\3). \n"); MrBayesPrint (" --------------------------------------------------------------------------- \n"); } else if (!strcmp(helpTkn, "Outgroup")) { MrBayesPrint (" --------------------------------------------------------------------------- \n"); MrBayesPrint (" Outgroup \n"); MrBayesPrint (" \n"); MrBayesPrint (" This command assigns a taxon to the outgroup. The correct usage is: \n"); MrBayesPrint (" \n"); MrBayesPrint (" outgroup / \n"); MrBayesPrint (" \n"); MrBayesPrint (" For example, \"outgroup 3\" assigns the third taxon in the matrix to be \n"); MrBayesPrint (" the outgroup. Similarly, \"outgroup Homo_sapiens\" assigns the taxon \n"); MrBayesPrint (" \"Homo_sapiens\" to be the outgroup (assuming that there is a taxon named \n"); MrBayesPrint (" \"Homo_sapiens\" in the matrix). Only a single taxon can be assigned to \n"); MrBayesPrint (" be the outgroup. \n"); MrBayesPrint (" \n"); if (defTaxa == YES) MrBayesPrint (" Current outgroup: %s (taxon no. %d)\n", taxaNames[outGroupNum], outGroupNum+1); MrBayesPrint (" --------------------------------------------------------------------------- \n"); } else if (!strcmp(helpTkn, "Showusertrees")) { MrBayesPrint (" --------------------------------------------------------------------------- \n"); MrBayesPrint (" Showusertrees \n"); MrBayesPrint (" \n"); MrBayesPrint (" This command shows the currently defined user trees. The correct usage \n"); MrBayesPrint (" is \"showusertrees\". \n"); MrBayesPrint (" --------------------------------------------------------------------------- \n"); } else if (!strcmp(helpTkn, "Showmcmctrees")) { MrBayesPrint (" --------------------------------------------------------------------------- \n"); MrBayesPrint (" Showmcmctrees \n"); MrBayesPrint (" \n"); MrBayesPrint (" This command shows the current trees used by the Markov chains. \n"); MrBayesPrint (" is \"showmcmctrees\". \n"); MrBayesPrint (" --------------------------------------------------------------------------- \n"); } else if (!strcmp(helpTkn, "Deroot")) { MrBayesPrint (" --------------------------------------------------------------------------- \n"); MrBayesPrint (" Deroot \n"); MrBayesPrint (" \n"); MrBayesPrint (" This command deroots the user tree. If the tree is already unrooted, a \n"); MrBayesPrint (" warning is issued. The correct usage is \"deroot\". \n"); MrBayesPrint (" --------------------------------------------------------------------------- \n"); } else if (!strcmp(helpTkn, "Root")) { MrBayesPrint (" --------------------------------------------------------------------------- \n"); MrBayesPrint (" Root \n"); MrBayesPrint (" \n"); MrBayesPrint (" This command roots the tree. If the tree is already rooted, a warning \n"); MrBayesPrint (" is issued. The tree is rooted at the midpoint between the outgroup species \n"); MrBayesPrint (" and the ingroup species. The correct usage is \"root\". \n"); MrBayesPrint (" --------------------------------------------------------------------------- \n"); } else if (!strcmp(helpTkn, "Taxset")) { MrBayesPrint (" --------------------------------------------------------------------------- \n"); MrBayesPrint (" Taxset \n"); MrBayesPrint (" \n"); MrBayesPrint (" This command defines a taxon set. The format for the taxset command \n"); MrBayesPrint (" is \n"); MrBayesPrint (" \n"); MrBayesPrint (" taxset = \n"); MrBayesPrint (" \n"); MrBayesPrint (" For example, \"taxset apes = Homo Pan Gorilla Orang gibbon\" defines a \n"); MrBayesPrint (" taxon set called \"apes\" that includes five taxa (namely, apes). \n"); MrBayesPrint (" You can assign up to 30 taxon sets. This option is best used \n"); MrBayesPrint (" not from the command line but rather as a line in the mrbayes block \n"); MrBayesPrint (" of a file. \n"); MrBayesPrint (" --------------------------------------------------------------------------- \n"); } else if (!strcmp(helpTkn, "Taxlabels")) { MrBayesPrint (" --------------------------------------------------------------------------- \n"); MrBayesPrint (" Taxlabels \n"); MrBayesPrint (" \n"); MrBayesPrint (" This command defines taxon labels. It could be used within taxa block. \n"); MrBayesPrint (" --------------------------------------------------------------------------- \n"); } else if (!strcmp(helpTkn, "Charstat")) { MrBayesPrint (" --------------------------------------------------------------------------- \n"); MrBayesPrint (" Charstat \n"); MrBayesPrint (" \n"); MrBayesPrint (" This command shows the status of all the characters. The correct usage \n"); MrBayesPrint (" is \n"); MrBayesPrint (" \n"); MrBayesPrint (" charstat \n"); MrBayesPrint (" \n"); MrBayesPrint (" After typing \"charstat\", the character number, whether it is excluded \n"); MrBayesPrint (" or included, and the partition identity are shown. The output is paused \n"); MrBayesPrint (" every 100 characters. This pause can be turned off by setting autoclose \n"); MrBayesPrint (" to \"yes\" (set autoclose=yes). \n"); MrBayesPrint (" --------------------------------------------------------------------------- \n"); } else if (!strcmp(helpTkn, "Taxastat")) { MrBayesPrint (" --------------------------------------------------------------------------- \n"); MrBayesPrint (" Taxastat \n"); MrBayesPrint (" \n"); MrBayesPrint (" This command shows the status of all the taxa. The correct usage is \n"); MrBayesPrint (" \n"); MrBayesPrint (" taxastat \n"); MrBayesPrint (" \n"); MrBayesPrint (" After typing \"taxastat\", the taxon number, name, and whether it is \n"); MrBayesPrint (" excluded or included are shown. \n"); MrBayesPrint (" --------------------------------------------------------------------------- \n"); } else if (!strcmp(helpTkn, "Partition")) { MrBayesPrint (" --------------------------------------------------------------------------- \n"); MrBayesPrint (" Partition \n"); MrBayesPrint (" \n"); MrBayesPrint (" This command allows you to specify a character partition. The format for \n"); MrBayesPrint (" this command is \n"); MrBayesPrint (" \n"); MrBayesPrint (" partition = :, ..., \n"); MrBayesPrint (" \n"); MrBayesPrint (" For example, \"partition by_codon = 3:1st_pos,2nd_pos,3rd_pos\" specifies \n"); MrBayesPrint (" a partition called \"by_codon\" which consists of three parts (first, \n"); MrBayesPrint (" second, and third codon positions). Here, we are assuming that the sites \n"); MrBayesPrint (" in each partition were defined using the charset command. You can specify \n"); MrBayesPrint (" a partition without using charset as follows: \n"); MrBayesPrint (" \n"); MrBayesPrint (" partition by_codon = 3:1 4 6 9 12,2 5 7 10 13,3 6 8 11 14 \n"); MrBayesPrint (" \n"); MrBayesPrint (" However, we recommend that you use the charsets to define a set of char- \n"); MrBayesPrint (" acters and then use these predefined sets when defining the partition. \n"); MrBayesPrint (" Also, it makes more sense to define a partition as a line in the mrbayes \n"); MrBayesPrint (" block than to issue the command from the command line (then again, you \n"); MrBayesPrint (" may be a masochist, and want to do extra work). \n"); MrBayesPrint (" --------------------------------------------------------------------------- \n"); } else if (!strcmp(helpTkn, "Exclude")) { MrBayesPrint (" --------------------------------------------------------------------------- \n"); MrBayesPrint (" Exclude \n"); MrBayesPrint (" \n"); MrBayesPrint (" This command excludes characters from the analysis. The correct usage is \n"); MrBayesPrint (" \n"); MrBayesPrint (" exclude \n"); MrBayesPrint (" \n"); MrBayesPrint (" or \n"); MrBayesPrint (" \n"); MrBayesPrint (" exclude - \n"); MrBayesPrint (" \n"); MrBayesPrint (" or \n"); MrBayesPrint (" \n"); MrBayesPrint (" exclude \n"); MrBayesPrint (" \n"); MrBayesPrint (" or some combination thereof. Moreover, you can use the specifier \"\\\" to \n"); MrBayesPrint (" exclude every nth character. For example, the following \n"); MrBayesPrint (" \n"); MrBayesPrint (" exclude 1-100\\3 \n"); MrBayesPrint (" \n"); MrBayesPrint (" would exclude every third character. As a specific example, \n"); MrBayesPrint (" \n"); MrBayesPrint (" exclude 2 3 10-14 22 \n"); MrBayesPrint (" \n"); MrBayesPrint (" excludes sites 2, 3, 10, 11, 12, 13, 14, and 22 from the analysis. Also, \n"); MrBayesPrint (" \n"); MrBayesPrint (" exclude all \n"); MrBayesPrint (" \n"); MrBayesPrint (" excludes all of the characters from the analysis. Excluding all characters \n"); MrBayesPrint (" does not leave you much information for inferring phylogeny. \n"); MrBayesPrint (" --------------------------------------------------------------------------- \n"); } else if (!strcmp(helpTkn, "Include")) { MrBayesPrint (" --------------------------------------------------------------------------- \n"); MrBayesPrint (" Include \n"); MrBayesPrint (" \n"); MrBayesPrint (" This command includes characters that were previously excluded from the \n"); MrBayesPrint (" analysis. The correct usage is \n"); MrBayesPrint (" \n"); MrBayesPrint (" include \n"); MrBayesPrint (" \n"); MrBayesPrint (" or \n"); MrBayesPrint (" \n"); MrBayesPrint (" include - \n"); MrBayesPrint (" \n"); MrBayesPrint (" or \n"); MrBayesPrint (" \n"); MrBayesPrint (" include \n"); MrBayesPrint (" \n"); MrBayesPrint (" or some combination thereof. Moreover, you can use the specifier \"\\\" to \n"); MrBayesPrint (" include every nth character. For example, the following \n"); MrBayesPrint (" \n"); MrBayesPrint (" include 1-100\\3 \n"); MrBayesPrint (" \n"); MrBayesPrint (" would include every third character. As a specific example, \n"); MrBayesPrint (" \n"); MrBayesPrint (" include 2 3 10-14 22 \n"); MrBayesPrint (" \n"); MrBayesPrint (" includes sites 2, 3, 10, 11, 12, 13, 14, and 22 from the analysis. Also, \n"); MrBayesPrint (" \n"); MrBayesPrint (" include all \n"); MrBayesPrint (" \n"); MrBayesPrint (" includes all of the characters in the analysis. Including all of the \n"); MrBayesPrint (" characters (even if many of them are bad) is a very total-evidence-like \n"); MrBayesPrint (" thing to do. Doing this will make a certain group of people very happy. \n"); MrBayesPrint (" On the other hand, simply using this program would make those same people \n"); MrBayesPrint (" unhappy. \n"); MrBayesPrint (" --------------------------------------------------------------------------- \n"); } else if (!strcmp(helpTkn, "Delete")) { MrBayesPrint (" --------------------------------------------------------------------------- \n"); MrBayesPrint (" Delete \n"); MrBayesPrint (" \n"); MrBayesPrint (" This command deletes taxa from the analysis. The correct usage is: \n"); MrBayesPrint (" \n"); MrBayesPrint (" delete ... \n"); MrBayesPrint (" \n"); MrBayesPrint (" A list of the taxon names or taxon numbers (labelled 1 to Ntax in the order \n"); MrBayesPrint (" in the matrix) or taxset(s) can be used. For example, the following: \n"); MrBayesPrint (" \n"); MrBayesPrint (" delete 1 2 Homo_sapiens \n"); MrBayesPrint (" \n"); MrBayesPrint (" deletes taxa 1, 2, and the taxon labelled Homo_sapiens from the analysis. \n"); MrBayesPrint (" You can also use \"all\" to delete all of the taxa. For example, \n"); MrBayesPrint (" \n"); MrBayesPrint (" delete all \n"); MrBayesPrint (" \n"); MrBayesPrint (" deletes all of the taxa from the analysis. Of course, a phylogenetic anal- \n"); MrBayesPrint (" ysis that does not include any taxa is fairly uninteresting. \n"); MrBayesPrint (" --------------------------------------------------------------------------- \n"); } else if (!strcmp(helpTkn, "Restore")) { MrBayesPrint (" --------------------------------------------------------------------------- \n"); MrBayesPrint (" Restore \n"); MrBayesPrint (" \n"); MrBayesPrint (" This command restores taxa to the analysis. The correct usage is: \n"); MrBayesPrint (" \n"); MrBayesPrint (" restore ... \n"); MrBayesPrint (" \n"); MrBayesPrint (" A list of the taxon names or taxon numbers (labelled 1 to Ntax in the order \n"); MrBayesPrint (" in the matrix) or taxset(s) can be used. For example, the following: \n"); MrBayesPrint (" \n"); MrBayesPrint (" restore 1 2 Homo_sapiens \n"); MrBayesPrint (" \n"); MrBayesPrint (" restores taxa 1, 2, and the taxon labelled Homo_sapiens to the analysis. \n"); MrBayesPrint (" You can also use \"all\" to restore all of the taxa. For example, \n"); MrBayesPrint (" \n"); MrBayesPrint (" restore all \n"); MrBayesPrint (" \n"); MrBayesPrint (" restores all of the taxa to the analysis. \n"); MrBayesPrint (" --------------------------------------------------------------------------- \n"); } else if (!strcmp(helpTkn, "Quit")) { MrBayesPrint (" --------------------------------------------------------------------------- \n"); MrBayesPrint (" Quit \n"); MrBayesPrint (" \n"); MrBayesPrint (" This command quits the program. The correct usage is: \n"); MrBayesPrint (" \n"); MrBayesPrint (" quit \n"); MrBayesPrint (" \n"); MrBayesPrint (" It is a very easy command to use properly. \n"); MrBayesPrint (" --------------------------------------------------------------------------- \n"); } else if (!strcmp(helpTkn, "Disclaimer")) { MrBayesPrint (" --------------------------------------------------------------------------- \n"); MrBayesPrint (" Disclaimer \n"); MrBayesPrint (" \n"); MrBayesPrint (" This command shows the disclaimer for the program. In short, the disclaimer \n"); MrBayesPrint (" states that the authors are not responsible for any silly things you may do \n"); MrBayesPrint (" to your computer or any unforeseen but possibly nasty things the computer \n"); MrBayesPrint (" program may inadvertently do to you. \n"); MrBayesPrint (" --------------------------------------------------------------------------- \n"); } else if (!strcmp(helpTkn, "Unlink")) { MrBayesPrint (" --------------------------------------------------------------------------- \n"); MrBayesPrint (" Unlink \n"); MrBayesPrint (" \n"); MrBayesPrint (" This command unlinks model parameters across partitions of the data. The \n"); MrBayesPrint (" correct usage is: \n"); MrBayesPrint (" \n"); MrBayesPrint (" unlink = ( or ) \n"); MrBayesPrint (" \n"); MrBayesPrint (" A little background is necessary to understand this command. Upon exe- \n"); MrBayesPrint (" cution of a file, a default partition is set up. This partition refer- \n"); MrBayesPrint (" enced either by its name (\"default\") or number (0). If your data are \n"); MrBayesPrint (" all of one type, then this default partition does not actually divide up \n"); MrBayesPrint (" your characters. However, if your datatype is mixed, then the default \n"); MrBayesPrint (" partition contains as many divisions as there are datatypes in your \n"); MrBayesPrint (" character matrix. Of course, you can also define other partitions, and \n"); MrBayesPrint (" switch among them using the set command (\"set partition=\"). \n"); MrBayesPrint (" Importantly, you can also assign model parameters to individual part- \n"); MrBayesPrint (" itions or to groups of them using the \"applyto\" option in lset and \n"); MrBayesPrint (" prset. When the program attempts to perform an analysis, the model is \n"); MrBayesPrint (" set for individual partitions. If the same parameter applies to differ- \n"); MrBayesPrint (" partitions and if that parameter has the same prior, then the program \n"); MrBayesPrint (" will link the parameters: that is, it will use a single value for the \n"); MrBayesPrint (" parameter. The program's default, then, is to strive for parsimony. \n"); MrBayesPrint (" However, there are lots of cases where you may want unlink a parameter \n"); MrBayesPrint (" across partitions. For example, you may want a different transition/ \n"); MrBayesPrint (" transversion rate ratio to apply to different partitions. This command \n"); MrBayesPrint (" allows you to unlink the parameters, or to make them different across \n"); MrBayesPrint (" partitions. The converse of this command is \"link\", which links to- \n"); MrBayesPrint (" gether parameters that were previously told to be different. The list \n"); MrBayesPrint (" of parameters that can be unlinked includes: \n"); MrBayesPrint (" \n"); MrBayesPrint (" Tratio -- Transition/transversion rate ratio \n"); MrBayesPrint (" Revmat -- Substitution rates of GTR model \n"); MrBayesPrint (" Omega -- Nonsynonymous/synonymous rate ratio \n"); MrBayesPrint (" Statefreq -- Character state frequencies \n"); MrBayesPrint (" Shape -- Gamma/LNorm shape parameter \n"); MrBayesPrint (" Pinvar -- Proportion of invariable sites \n"); MrBayesPrint (" Correlation -- Correlation parameter of autodiscrete gamma \n"); MrBayesPrint (" Ratemultiplier -- Rate multiplier for partitions \n"); MrBayesPrint (" Switchrates -- Switching rates for covarion model \n"); MrBayesPrint (" Topology -- Topology of tree \n"); MrBayesPrint (" Brlens -- Branch lengths of tree \n"); MrBayesPrint (" Popsize -- Population size for coalescence process \n"); MrBayesPrint (" Growthrate -- Growth rate of coalescence process \n"); MrBayesPrint (" Aamodel -- Aminoacid rate matrix \n"); MrBayesPrint (" Cpprate -- Rate of Compound Poisson Process (CPP) \n"); MrBayesPrint (" Cppmultdev -- Standard dev. of CPP rate multipliers (log scale) \n"); MrBayesPrint (" Cppevents -- CPP events \n"); MrBayesPrint (" TK02var -- Variance increase in TK02 relaxed clock model \n"); MrBayesPrint (" WNvar -- Variance increase in WN relaxed clock model \n"); MrBayesPrint (" IGRvar -- Variance in IGR relaxed clock model \n"); MrBayesPrint (" ILNvar -- Variance in ILN relaxed clock model \n"); // MrBayesPrint (" TK02branchrates -- Branch rates of TK02 relaxed clock model \n"); // MrBayesPrint (" WNbranchrates -- Branch rates of WN relaxed clock model \n"); // MrBayesPrint (" IGRbranchrates -- Branch rates of IGR relaxed clock model \n"); // MrBayesPrint (" ILNbranchrates -- Branch rates of ILN relaxed clock model \n"); MrBayesPrint (" \n"); MrBayesPrint (" For example, \n"); MrBayesPrint (" \n"); MrBayesPrint (" unlink shape=(all) \n"); MrBayesPrint (" \n"); MrBayesPrint (" unlinks the gamma/lnorm shape parameter across all partitions of the data. \n"); MrBayesPrint (" You can use \"showmodel\" to see the current linking status of the \n"); MrBayesPrint (" characters. \n"); MrBayesPrint (" --------------------------------------------------------------------------- \n"); } else if (!strcmp(helpTkn, "Link")) { MrBayesPrint (" --------------------------------------------------------------------------- \n"); MrBayesPrint (" Link \n"); MrBayesPrint (" \n"); MrBayesPrint (" This command links model parameters across partitions of the data. The \n"); MrBayesPrint (" correct usage is: \n"); MrBayesPrint (" \n"); MrBayesPrint (" link = ( or ) \n"); MrBayesPrint (" \n"); MrBayesPrint (" The list of parameters that can be linked includes: \n"); MrBayesPrint (" \n"); MrBayesPrint (" Tratio -- Transition/transversion rate ratio \n"); MrBayesPrint (" Revmat -- Substitution rates of GTR model \n"); MrBayesPrint (" Omega -- Nonsynonymous/synonymous rate ratio \n"); MrBayesPrint (" Statefreq -- Character state frequencies \n"); MrBayesPrint (" Shape -- Gamma/LNorm shape parameter \n"); MrBayesPrint (" Pinvar -- Proportion of invariable sites \n"); MrBayesPrint (" Correlation -- Correlation parameter of autodiscrete gamma \n"); MrBayesPrint (" Ratemultiplier -- Rate multiplier for partitions \n"); MrBayesPrint (" Switchrates -- Switching rates for covarion model \n"); MrBayesPrint (" Topology -- Topology of tree \n"); MrBayesPrint (" Brlens -- Branch lengths of tree \n"); MrBayesPrint (" Popsize -- Population size for coalescence process \n"); MrBayesPrint (" Growthrate -- Growth rate of coalescence process \n"); MrBayesPrint (" Aamodel -- Aminoacid rate matrix \n"); MrBayesPrint (" Cpprate -- Rate of Compound Poisson Process (CPP) \n"); MrBayesPrint (" Cppmultdev -- Standard dev. of CPP rate multipliers (log scale) \n"); MrBayesPrint (" Cppevents -- CPP events \n"); MrBayesPrint (" TK02var -- Variance increase in TK02 relaxed clock model \n"); MrBayesPrint (" WNvar -- Variance increase in WN relaxed clock model \n"); MrBayesPrint (" IGRvar -- Variance in IGR relaxed clock model \n"); MrBayesPrint (" ILNvar -- Variance in ILN relaxed clock model \n"); // MrBayesPrint (" TK02branchrates -- Branch rates of TK02 relaxed clock model \n"); // MrBayesPrint (" WNbranchrates -- Branch rates of WN relaxed clock model \n"); // MrBayesPrint (" IGRbranchrates -- Branch rates of IGR relaxed clock model \n"); // MrBayesPrint (" ILNbranchrates -- Branch rates of ILN relaxed clock model \n"); MrBayesPrint (" \n"); MrBayesPrint (" For example, \n"); MrBayesPrint (" \n"); MrBayesPrint (" link shape=(all) \n"); MrBayesPrint (" \n"); MrBayesPrint (" links the gamma/lnorm shape parameter across all partitions of the data. \n"); MrBayesPrint (" You can use \"showmodel\" to see the current linking status of the \n"); MrBayesPrint (" characters. For more information on this command, see the help menu \n"); MrBayesPrint (" for link's converse, unlink (\"help unlink\"); \n"); MrBayesPrint (" --------------------------------------------------------------------------- \n"); } else if (!strcmp(helpTkn, "Help")) { MrBayesPrint (" --------------------------------------------------------------------------- \n"); MrBayesPrint (" Help \n"); MrBayesPrint (" \n"); MrBayesPrint (" This command provides useful information on the use of this program. The \n"); MrBayesPrint (" correct usage is \n"); MrBayesPrint (" \n"); MrBayesPrint (" help \n"); MrBayesPrint (" \n"); MrBayesPrint (" which gives a list of all available commands with a brief description of \n"); MrBayesPrint (" each or \n"); MrBayesPrint (" \n"); MrBayesPrint (" help \n"); MrBayesPrint (" \n"); MrBayesPrint (" which gives detailed information on the use of . \n"); MrBayesPrint (" --------------------------------------------------------------------------- \n"); } else if (!strcmp(helpTkn, "Sump")) { MrBayesPrint (" --------------------------------------------------------------------------- \n"); MrBayesPrint (" Sump \n"); MrBayesPrint (" \n"); MrBayesPrint (" During an MCMC analysis, MrBayes prints the sampled parameter values to one or\n"); MrBayesPrint (" more tab-delimited text files, one for each independent run in your analysis. \n"); MrBayesPrint (" The command 'Sump' summarizes the information in this parameter file or these \n"); MrBayesPrint (" parameter files. By default, the root of the parameter file name(s) is assumed\n"); MrBayesPrint (" to be the name of the last matrix-containing nexus file. MrBayes also remem- \n"); MrBayesPrint (" bers the number of independent runs in the last analysis that you set up, re- \n"); MrBayesPrint (" gardless of whether you actually ran it. For instance, if there were two in- \n"); MrBayesPrint (" dependent runs, which is the initial setting when you read in a new matrix, \n"); MrBayesPrint (" MrBayes will assume that there are two parameter files with the endings \n"); MrBayesPrint (" '.run1.p' and '.run2.p'. You can change the root of the file names and the \n"); MrBayesPrint (" number of runs using the 'Filename' and 'Nruns' settings. \n"); MrBayesPrint (" \n"); MrBayesPrint (" When you invoke the 'Sump' command, three items are output: (1) a generation \n"); MrBayesPrint (" plot of the likelihood values; (2) estimates of the marginal likelihood of \n"); MrBayesPrint (" the model; and (3) a table with the mean, variance, and 95 percent credible \n"); MrBayesPrint (" interval for the sampled parameters. All three items are output to screen. \n"); MrBayesPrint (" The table of marginal likelihoods is also printed to a file with the ending \n"); MrBayesPrint (" '.lstat' and the parameter table to a file with the ending '.pstat'. For some \n"); MrBayesPrint (" model parameters, there may also be a '.mstat' file. \n"); MrBayesPrint (" \n"); MrBayesPrint (" When running 'Sump' you typically want to discard a specified number or \n"); MrBayesPrint (" fraction of samples from the beginning of the chain as the burn in. This is \n"); MrBayesPrint (" done using the same mechanism used by the 'Mcmc' command. That is, if you \n"); MrBayesPrint (" run an MCMC analysis with a relative burn in of 25 %% of samples for con- \n"); MrBayesPrint (" vergence diagnostics, then the same burn in will be used for a subsequent \n"); MrBayesPrint (" sump command, unless a different burn in is specified. That is, issuing \n"); MrBayesPrint (" \n"); MrBayesPrint (" sump \n"); MrBayesPrint (" \n"); MrBayesPrint (" immediately after 'Mcmc', will result in using the same burn in settings as \n"); MrBayesPrint (" for the 'Mcmc' command. All burnin settings are reset to default values every \n"); MrBayesPrint (" time a new matrix is read in, namely relative burnin ('relburnin=yes') with \n"); MrBayesPrint (" 25 %% of samples discarded ('burninfrac = 0.25'). \n"); MrBayesPrint (" \n"); MrBayesPrint (" Options: \n"); MrBayesPrint (" \n"); MrBayesPrint (" Relburnin -- If this option is set to 'Yes', then a proportion of the \n"); MrBayesPrint (" samples will be discarded as burnin when calculating summary \n"); MrBayesPrint (" statistics. The proportion to be discarded is set with \n"); MrBayesPrint (" 'Burninfrac' (see below). When the 'Relburnin' option is set \n"); MrBayesPrint (" to 'No', then a specific number of samples is discarded \n"); MrBayesPrint (" instead. This number is set by 'Burnin' (see below). Note that\n"); MrBayesPrint (" the burnin setting is shared across the 'Sumt', 'Sump', and \n"); MrBayesPrint (" 'Mcmc' commands. \n"); MrBayesPrint (" Burnin -- Determines the number of samples (not generations) that will \n"); MrBayesPrint (" be discarded when summary statistics are calculated. The \n"); MrBayesPrint (" value of this option is only applicable when 'Relburnin' is \n"); MrBayesPrint (" set to 'No'. \n"); MrBayesPrint (" Burninfrac -- Determines the fraction of samples that will be discarded when\n"); MrBayesPrint (" summary statistics are calculated. The setting only takes \n"); MrBayesPrint (" effect if 'Relburnin' is set to 'Yes'. \n"); MrBayesPrint (" Nruns -- Determines how many '.p' files from independent analyses that \n"); MrBayesPrint (" will be summarized. If Nruns > 1 then the names of the files \n"); MrBayesPrint (" are derived from 'Filename' by adding '.run1.p', '.run2.p', \n"); MrBayesPrint (" etc. If Nruns=1, then the single file name is obtained by \n"); MrBayesPrint (" adding '.p' to 'Filename'. \n"); MrBayesPrint (" Filename -- The name of the file to be summarized. This is the base of the\n"); MrBayesPrint (" file name to which endings are added according to the current \n"); MrBayesPrint (" setting of the 'Nruns' parameter. If 'Nruns' is 1, then only \n"); MrBayesPrint (" '.p' is added to the file name. Otherwise, the endings will \n"); MrBayesPrint (" be '.run1.p', '.run2.p', etc. \n"); MrBayesPrint (" Outputname -- Base name of the file(s) to which 'Sump' results will be \n"); MrBayesPrint (" printed. \n"); MrBayesPrint (" Hpd -- Determines whether credibility intervals will be given as the \n"); MrBayesPrint (" region of Highest Posterior Density ('Yes') or as the interval\n"); MrBayesPrint (" containing the median 95 %% of sampled values ('No'). \n"); MrBayesPrint (" Minprob -- Determines the minimum probability of submodels to be included\n"); MrBayesPrint (" in summary statistics. Only applicable to models that explore \n"); MrBayesPrint (" submodel spaces, like 'nst=mixed' and 'aamodelpr=mixed'. \n"); MrBayesPrint (" \n"); MrBayesPrint (" Current settings: \n"); MrBayesPrint (" \n"); MrBayesPrint (" Parameter Options Current Setting \n"); MrBayesPrint (" -------------------------------------------------------- \n"); MrBayesPrint (" Relburnin Yes/No %s \n", chainParams.relativeBurnin == YES ? "Yes" : "No"); MrBayesPrint (" Burnin %d \n", chainParams.chainBurnIn); MrBayesPrint (" Burninfrac %1.2lf \n", chainParams.burninFraction); MrBayesPrint (" Nruns %d \n", sumpParams.numRuns); if (sumpParams.numRuns == 1) MrBayesPrint (" Filename %s<.p>\n", sumpParams.sumpFileName); else MrBayesPrint (" Filename %s<.run.p>\n", sumpParams.sumpFileName); MrBayesPrint (" Outputname %s<.pstat etc>\n", sumpParams.sumpOutfile); MrBayesPrint (" Hpd Yes/No %s \n", sumpParams.HPD == YES ? "Yes" : "No"); MrBayesPrint (" Minprob %1.3lf \n", sumpParams.minProb); MrBayesPrint (" \n"); MrBayesPrint (" --------------------------------------------------------------------------- \n"); } else if (!strcmp(helpTkn, "Sumss")) { MrBayesPrint (" --------------------------------------------------------------------------- \n"); MrBayesPrint (" Sumss \n"); MrBayesPrint (" \n"); MrBayesPrint (" This command summarizes results of stepping stone analyses. It is a tool to \n"); MrBayesPrint (" investigate the obtained results, and to help find the proper step burn-in. \n"); MrBayesPrint (" To get more help information on stepping-stone analyses, use 'help ss'. \n"); MrBayesPrint (" \n"); MrBayesPrint (" During stepping-stone analysis, MrBayes collects the sampled likelihoods in \n"); MrBayesPrint (" order to estimate the marginal likelihood at the end. It also prints the sam- \n"); MrBayesPrint (" pled parameter values to one or more tab-delimited text files, one for each \n"); MrBayesPrint (" independent run in your analysis. The command 'Sumss' summarizes likelihood \n"); MrBayesPrint (" values stored in these parameter files and calculates marginal likelihood es- \n"); MrBayesPrint (" timates. The names of the files that are summarized are exactly the same as \n"); MrBayesPrint (" the names of the files used for the 'sump' command. In fact, the 'filename' \n"); MrBayesPrint (" setting is a shared setting for the 'sump' and 'sumss' commands. That is, if \n"); MrBayesPrint (" you change the setting in one of the commands, it would change the setting in \n"); MrBayesPrint (" the other command as well. \n"); MrBayesPrint (" \n"); MrBayesPrint (" When you invoke the 'Sumss' command, three items are output: (1) 'Step contri-\n"); MrBayesPrint (" bution table' - summarizes the contribution of each step to the overall esti- \n"); MrBayesPrint (" mate; (2) 'Step plot' - plot of the likelihood values for the initial burn-in \n"); MrBayesPrint (" phase or a chosen step in the stepping-stone algorithm; (3) 'Joined plot' - \n"); MrBayesPrint (" summarizes sampling across all steps in the algorithm. \n"); MrBayesPrint (" \n"); MrBayesPrint (" Step contribution table \n"); MrBayesPrint (" The printed table is similar to the one output to the .ss file. The main pur- \n"); MrBayesPrint (" pose of the table is to summarize marginal likelihood for different values of \n"); MrBayesPrint (" the step burn-in after the stepping stone analysis has finished. The burn-in \n"); MrBayesPrint (" is controlled by the 'Relburnin', 'Burnin' and 'Burninfrac' settings. \n"); MrBayesPrint (" Note that during stepping-stone analyses, step contributions to marginal \n"); MrBayesPrint (" likelihood are calculated based on all generations excluding burn-in. 'Sumss' \n"); MrBayesPrint (" on the other hand makes estimates based only on the sampled generations. This \n"); MrBayesPrint (" may lead to slight difference in results compared to the one printed to the \n"); MrBayesPrint (" .ss file. \n"); MrBayesPrint (" \n"); MrBayesPrint (" Step plot \n"); MrBayesPrint (" The main objective of the plot is to provide a close look at a given step \n"); MrBayesPrint (" in the analysis. Which step is printed here is defined by the 'Steptoplot' \n"); MrBayesPrint (" setting. The plot could be used to inspect if the chosen step burn-in is \n"); MrBayesPrint (" appropriate for the given step. It could also be used to check if the initial \n"); MrBayesPrint (" burn-in phase has converged. Note that the amount of discarded samples is \n"); MrBayesPrint (" controlled by the 'Discardfrac' setting, and not by the ordinary burn-in \n"); MrBayesPrint (" settings. \n"); MrBayesPrint (" \n"); MrBayesPrint (" Joined plot \n"); MrBayesPrint (" Different steps sample from different power posterior distributions. When we \n"); MrBayesPrint (" switch from one distribution to another, it takes some number of generations \n"); MrBayesPrint (" before the chain settles at the correct stationary distribution. This lag is \n"); MrBayesPrint (" called a 'temperature lag' and if the corresponding samples are not removed, \n"); MrBayesPrint (" it will result in a biased estimate. It is difficult to determine the lag be- \n"); MrBayesPrint (" forehand, but MrBayes allows you to explore different step burn-in settings \n"); MrBayesPrint (" after you have finished the stepping-stone algorithm, without having to rerun \n"); MrBayesPrint (" the whole analysis. The 'Joined plot' helps to facilitate the choice of the \n"); MrBayesPrint (" right step burn-in. The plot summarizes samples across all steps and gives you\n"); MrBayesPrint (" a quick overview of the whole analysis. \n"); MrBayesPrint (" \n"); MrBayesPrint (" Specifically, the following procedure is used to obtain the joined plot. Each \n"); MrBayesPrint (" step has the same number N of samples taken. We number each sample 1 to N \n"); MrBayesPrint (" within steps according to the order in which the samples are taken. The first \n"); MrBayesPrint (" sample in each step is numbered 1, and the last sample is N. For each number i\n"); MrBayesPrint (" in [1,..., N], we sum up log likelihoods for all samples numbered i across all\n"); MrBayesPrint (" steps. The joined plot is a graph of the step number versus the normalized \n"); MrBayesPrint (" sums we get in the procedure describe above. This directly visualizes the tem-\n"); MrBayesPrint (" perature lag and allows you to select the appropriate step burn-in. \n"); MrBayesPrint (" \n"); MrBayesPrint (" Ideally, after you discard the appropriate step burn-in, the graph should \n"); MrBayesPrint (" appear as white noise around the estimated value. If you see an increasing or \n"); MrBayesPrint (" decreasing tendency in the beginning of the graph, you should increase the \n"); MrBayesPrint (" step burn-in. If you see an increasing or decreasing tendency across the whole\n"); MrBayesPrint (" graph, then the initial burn-in phase was not long enough. In this case, you \n"); MrBayesPrint (" need to rerun the analysis with a longer initial burn-in. \n"); MrBayesPrint (" \n"); MrBayesPrint (" To make it easier to observe tendencies in the plotted graph you can choose \n"); MrBayesPrint (" different levels of curve smoothing. If 'Smoothing' is set to k, it means that\n"); MrBayesPrint (" for each step i we take an average over step i and k neighboring samples in \n"); MrBayesPrint (" both directions, i.e., the k-smoothed estimate for step i is an average over \n"); MrBayesPrint (" values for steps [i-k,...,i+k]. \n"); MrBayesPrint (" \n"); MrBayesPrint (" \n"); MrBayesPrint (" Options: \n"); MrBayesPrint (" \n"); MrBayesPrint (" Allruns -- If set to 'Yes', it forces all runs to be printed on the same \n"); MrBayesPrint (" graph when drawing joined and step plots. If set to 'No', each\n"); MrBayesPrint (" run is printed on a separate plot. \n"); MrBayesPrint (" Askmore -- Long analyses may produce huge .p files. Reading in them may \n"); MrBayesPrint (" take several minutes. If you want to investigate different \n"); MrBayesPrint (" aspects of your analyses, it could be very inconvenient to \n"); MrBayesPrint (" wait for several minutes each time you want to get a new sum- \n"); MrBayesPrint (" mary for different settings. If you set 'Askmore' to 'YES', \n"); MrBayesPrint (" sumss will read .p files only once. After responding to the \n"); MrBayesPrint (" original query, it will interactively ask you if you wish to \n"); MrBayesPrint (" produce more tables and plots for different settings of \n"); MrBayesPrint (" 'Burnin' or 'Smoothing' (see below). \n"); MrBayesPrint (" Relburnin -- If this option is set to 'Yes', then a proportion of the \n"); MrBayesPrint (" samples from each step will be discarded as burnin when calcu-\n"); MrBayesPrint (" lsting summary statistics. The proportion to be discarded is \n"); MrBayesPrint (" set with 'Burninfrac' (see below). When the 'Relburnin' option\n"); MrBayesPrint (" is set to 'No', then a specific number of samples is discarded\n"); MrBayesPrint (" instead. This number is set by 'Burnin'. Note that the burnin \n"); MrBayesPrint (" settings --- 'Relburnin', 'Burnin', and 'Burninfrac' --- are \n"); MrBayesPrint (" shared across the 'Sumt', 'Sump', 'Sumss' and 'Mcmc' commands.\n"); MrBayesPrint (" Burnin -- Determines the number of samples (not generations) that will \n"); MrBayesPrint (" be discarded from each step when summary statistics are calcu-\n"); MrBayesPrint (" lated. The value of this option is only applicable when \n"); MrBayesPrint (" 'Relburnin' is set to 'No'. \n"); MrBayesPrint (" Burninfrac -- Determines the fraction of samples that will be discarded from\n"); MrBayesPrint (" each step when summary statistics are calculated. The setting \n"); MrBayesPrint (" only takes effect if 'Relburnin' is set to 'Yes'. \n"); MrBayesPrint (" Discardfrac -- Determines the fraction of samples that will be discarded when\n"); MrBayesPrint (" a step plot is printed. It is similar to the 'Burninfrac' set-\n"); MrBayesPrint (" ting, but unlike 'Burninfrac' it is used only for better vis- \n"); MrBayesPrint (" ualization of the step plot. It has no effect on the number of\n"); MrBayesPrint (" samples discarded during marginal likelihood computation. \n"); MrBayesPrint (" Filename -- The name of the file to be summarized. This is the base of the\n"); MrBayesPrint (" file name to which endings are added according to the current \n"); MrBayesPrint (" setting of the 'Nruns' parameter. If 'Nruns' is 1, then only \n"); MrBayesPrint (" '.p' is added to the file name. Otherwise, the endings will \n"); MrBayesPrint (" be '.run1.p', '.run2.p', etc. Note that the 'Filename' setting\n"); MrBayesPrint (" is shared with 'sump' command. \n"); MrBayesPrint (" Nruns -- Determines how many '.p' files from independent analyses that \n"); MrBayesPrint (" will be summarized. If Nruns > 1 then the names of the files \n"); MrBayesPrint (" are derived from 'Filename' by adding '.run1.p', '.run2.p', \n"); MrBayesPrint (" etc. If Nruns=1, then the single file name is obtained by \n"); MrBayesPrint (" adding '.p' to 'Filename'. \n"); MrBayesPrint (" Steptoplot -- Defines which step will be printed in the step plot.If the \n"); MrBayesPrint (" value is set to 0, then the initial sample from the posterior \n"); MrBayesPrint (" will be used. \n"); MrBayesPrint (" Smoothing -- Determines smoothing of the joined plot (see above). A value \n"); MrBayesPrint (" equal to 0 results in no smoothing. \n"); MrBayesPrint (" \n"); MrBayesPrint (" Current settings: \n"); MrBayesPrint (" \n"); MrBayesPrint (" Parameter Options Current Setting \n"); MrBayesPrint (" -------------------------------------------------------- \n"); MrBayesPrint (" Allruns Yes/No %s \n", sumssParams.allRuns == YES ? "Yes" : "No"); MrBayesPrint (" Askmore Yes/No %s \n", sumssParams.askForMorePlots == YES ? "Yes" : "No"); MrBayesPrint (" Relburnin Yes/No %s \n", chainParams.relativeBurnin == YES ? "Yes" : "No"); MrBayesPrint (" Burnin %d \n", chainParams.chainBurnIn); MrBayesPrint (" Burninfrac %1.2lf \n", chainParams.burninFraction); MrBayesPrint (" Discardfrac %1.2lf \n", sumssParams.discardFraction); if (sumpParams.numRuns == 1) MrBayesPrint (" Filename %s<.p>\n", sumpParams.sumpFileName); else MrBayesPrint (" Filename %s<.run.p>\n", sumpParams.sumpFileName); MrBayesPrint (" Nruns %d \n", sumpParams.numRuns); MrBayesPrint (" Steptoplot %d \n", sumssParams.stepToPlot); MrBayesPrint (" Smoothing %d \n", sumssParams.smoothing); MrBayesPrint (" --------------------------------------------------------------------------- \n"); } else if (!strcmp(helpTkn, "Comparetree")) { MrBayesPrint (" --------------------------------------------------------------------------- \n"); MrBayesPrint (" Comparetree \n"); MrBayesPrint (" \n"); MrBayesPrint (" This command compares the trees in two files, called \"filename1\" and \n"); MrBayesPrint (" \"filename2\". It will output a bivariate plot of the split frequencies \n"); MrBayesPrint (" as well as plots of the tree distance as a function of the generation. The \n"); MrBayesPrint (" plots can be used to get a quick indication of whether two runs have con- \n"); MrBayesPrint (" verged onto the same set of trees. The \"Comparetree\" command will also \n"); MrBayesPrint (" produce a \".pairs\" file and a \".dists\" file (these file endings are added \n"); MrBayesPrint (" to the end of the \"Outputname\"). The \".pairs\" file contains the paired \n"); MrBayesPrint (" split frequencies from the two tree samples; the \".dists\" file contains the \n"); MrBayesPrint (" tree distance values. \n"); MrBayesPrint (" \n"); MrBayesPrint (" Note that the \"Sumt\" command provides a different set of convergence diag- \n"); MrBayesPrint (" nostics tools that you may also want to explore. Unlike \"Comparetree\", \n"); MrBayesPrint (" \"Sumt\" can compare more than two tree samples and will calculate consensus \n"); MrBayesPrint (" trees and split frequencies from the pooled samples. \n"); MrBayesPrint (" \n"); MrBayesPrint (" Options: \n"); MrBayesPrint (" \n"); MrBayesPrint (" Relburnin -- If this option is set to 'Yes', then a proportion of the \n"); MrBayesPrint (" samples will be discarded as burnin when calculating summary \n"); MrBayesPrint (" statistics. The proportion to be discarded is set with \n"); MrBayesPrint (" Burninfrac (see below). When the Relburnin option is set to \n"); MrBayesPrint (" 'No', then a specific number of samples is discarded instead.\n"); MrBayesPrint (" This number is set by Burnin (see below). Note that the \n"); MrBayesPrint (" burnin setting is shared with the 'Mcmc', 'Sumt', 'Sump' and \n"); MrBayesPrint (" 'Plot' commands. \n"); MrBayesPrint (" Burnin -- Determines the number of samples (not generations) that will \n"); MrBayesPrint (" be discarded when summary statistics are calculated. The \n"); MrBayesPrint (" value of this option is only relevant when Relburnin is set \n"); MrBayesPrint (" to 'No'. \n"); MrBayesPrint (" BurninFrac -- Determines the fraction of samples that will be discarded \n"); MrBayesPrint (" when summary statistics are calculated. The value of this \n"); MrBayesPrint (" option is only relevant when Relburnin is set to 'Yes'. \n"); MrBayesPrint (" Example: A value for this option of 0.25 means that 25%% of \n"); MrBayesPrint (" the samples will be discarded. \n"); MrBayesPrint (" Minpartfreq -- The minimum probability of partitions to include in summary \n"); MrBayesPrint (" statistics. \n"); MrBayesPrint (" Filename1 -- The name of the first tree file to compare. \n"); MrBayesPrint (" Filename2 -- The name of the second tree file to compare. \n"); MrBayesPrint (" Outputname -- Name of the file to which 'comparetree' results will be \n"); MrBayesPrint (" printed. \n"); MrBayesPrint (" \n"); MrBayesPrint (" Current settings: \n"); MrBayesPrint (" \n"); MrBayesPrint (" Parameter Options Current Setting \n"); MrBayesPrint (" -------------------------------------------------------- \n"); MrBayesPrint (" Relburnin Yes/No %s \n", chainParams.relativeBurnin == YES ? "Yes" : "No"); MrBayesPrint (" Burnin %d \n", chainParams.chainBurnIn); MrBayesPrint (" Burninfrac %1.2lf \n", chainParams.burninFraction); MrBayesPrint (" Minpartfreq %1.2lf \n", comptreeParams.minPartFreq); MrBayesPrint (" Filename1 %s \n", comptreeParams.comptFileName1); MrBayesPrint (" Filename2 %s \n", comptreeParams.comptFileName2); MrBayesPrint (" Outputname %s \n", comptreeParams.comptOutfile); MrBayesPrint (" \n"); MrBayesPrint (" --------------------------------------------------------------------------- \n"); } else if (!strcmp(helpTkn, "Sumt")) { MrBayesPrint (" --------------------------------------------------------------------------- \n"); MrBayesPrint (" Sumt \n"); MrBayesPrint (" \n"); MrBayesPrint (" This command is used to produce summary statistics for trees sampled during \n"); MrBayesPrint (" a Bayesian MCMC analysis. You can either summarize trees from one individual \n"); MrBayesPrint (" analysis, or trees coming from several independent analyses. In either case, \n"); MrBayesPrint (" all the sampled trees are read in and the proportion of the time any single \n"); MrBayesPrint (" taxon bipartition (split) is found is counted. The proportion of the time that\n"); MrBayesPrint (" the bipartition is found is an approximation of the posterior probability of \n"); MrBayesPrint (" the bipartition. (Remember that a taxon bipartition is defined by removing a \n"); MrBayesPrint (" branch on the tree, dividing the tree into those taxa to the left and right \n"); MrBayesPrint (" of the removed branch. This set is called a taxon bipartition.) The branch \n"); MrBayesPrint (" length of the bipartition is also recorded, if branch lengths have been saved \n"); MrBayesPrint (" to file. The result is a list of the taxon bipartitions found, the frequency \n"); MrBayesPrint (" with which they were found, the posterior probability of the bipartition \n"); MrBayesPrint (" and, the mean and variance of the branch lengths or node depths, and various \n"); MrBayesPrint (" other statistics. \n"); MrBayesPrint (" \n"); MrBayesPrint (" The key to the partitions is output to a file with the suffix '.parts'. The \n"); MrBayesPrint (" summary statistics pertaining to bipartition probabilities are output to a \n"); MrBayesPrint (" file with the suffix '.tstat', and the statistics pertaining to branch or node\n"); MrBayesPrint (" parameters are output to a file with the suffix '.vstat'. \n"); MrBayesPrint (" \n"); MrBayesPrint (" A consensus tree is also printed to a file with the suffix '.con.tre' and \n"); MrBayesPrint (" printed to the screen as a cladogram, and as a phylogram if branch lengths \n"); MrBayesPrint (" have been saved. The consensus tree is either a 50 percent majority rule tree \n"); MrBayesPrint (" or a majority rule tree showing all compatible partitions. If branch lengths \n"); MrBayesPrint (" have been recorded during the run, the '.con.tre' file will contain a consen- \n"); MrBayesPrint (" sus tree with branch lengths and interior nodes labelled with support values. \n"); MrBayesPrint (" By default, the consensus tree will also contain other summary information in \n"); MrBayesPrint (" a format understood by the program 'FigTree'. To use a simpler format under- \n"); MrBayesPrint (" stood by other tree-drawing programs, such as 'TreeView', set 'Conformat' to \n"); MrBayesPrint (" 'Simple'. \n"); MrBayesPrint (" \n"); MrBayesPrint (" MrBayes also produces a file with the ending \".trprobs\" that contains a list\n"); MrBayesPrint (" of all the trees that were found during the MCMC analysis, sorted by their \n"); MrBayesPrint (" probabilities. This list of trees can be used to construct a credible set of \n"); MrBayesPrint (" trees. For example, if you want to construct a 95 percent credible set of \n"); MrBayesPrint (" trees, you include all of those trees whose cumulative probability is less \n"); MrBayesPrint (" than or equal to 0.95. You have the option of displaying the trees to the \n"); MrBayesPrint (" screen using the \"Showtreeprobs\" option. The default is to not display the \n"); MrBayesPrint (" trees to the screen; the number of different trees sampled by the chain can \n"); MrBayesPrint (" be quite large. If you are analyzing a large set of taxa, you may actually \n"); MrBayesPrint (" want to skip the calculation of tree probabilities entirely by setting \n"); MrBayesPrint (" 'Calctreeprobs' to 'No'. \n"); MrBayesPrint (" \n"); MrBayesPrint (" When calculating summary statistics you probably want to skip those trees that\n"); MrBayesPrint (" were sampled in the initial part of the run, the so-called burn-in period. The\n"); MrBayesPrint (" number of skipped samples is controlled by the 'Relburnin', 'Burnin', and \n"); MrBayesPrint (" 'Burninfrac' settings, just as for the 'Mcmc' command. Since version 3.2.0, \n"); MrBayesPrint (" the burn-in settings are shared across the 'Sumt', 'Sump' and 'Mcmc' commands.\n"); MrBayesPrint (" That is, changing the burn-in setting for one command will change the settings\n"); MrBayesPrint (" for subsequent calls to any of the other commands. \n"); MrBayesPrint (" \n"); MrBayesPrint (" If you are summarizing the trees sampled in several independent analyses, \n"); MrBayesPrint (" such as those resulting from setting the 'Nruns' option of the 'Mcmc' command \n"); MrBayesPrint (" to a value larger than 1, MrBayes will also calculate convergence diagnostics \n"); MrBayesPrint (" for the sampled topologies and branch lengths. These values can help you \n"); MrBayesPrint (" determine whether it is likely that your chains have converged. \n"); MrBayesPrint (" \n"); MrBayesPrint (" The 'Sumt' command expands the 'Filename' according to the current values of \n"); MrBayesPrint (" the 'Nruns' and 'Ntrees' options. For instance, if both 'Nruns' and 'Ntrees' \n"); MrBayesPrint (" are set to 1, 'Sumt' will try to open a file named '.t'. If 'Nruns' \n"); MrBayesPrint (" is set to 2 and 'Ntrees' to 1, then 'Sumt' will open two files, the first \n"); MrBayesPrint (" named '.run1.t' and the second '.run2.t', etc. By default,\n"); MrBayesPrint (" the 'Filename' option is set such that 'Sumt' automatically summarizes all the\n"); MrBayesPrint (" results from your immediately preceding 'Mcmc' command. You can also use the \n"); MrBayesPrint (" 'Sumt' command to summarize tree samples in older analyses. If you want to do \n"); MrBayesPrint (" that, remember to first read in a matrix so that MrBayes knows what taxon \n"); MrBayesPrint (" names to expect in the trees. Then set the 'Nruns', 'Ntrees' and 'Filename' \n"); MrBayesPrint (" options appropriately if they differ from the MrBayes defaults. \n"); MrBayesPrint (" \n"); MrBayesPrint (" Options: \n"); MrBayesPrint (" \n"); MrBayesPrint (" Relburnin -- If this option is set to YES, then a proportion of the \n"); MrBayesPrint (" samples will be discarded as burnin when calculating summary \n"); MrBayesPrint (" statistics. The proportion to be discarded is set with \n"); MrBayesPrint (" Burninfrac (see below). When the Relburnin option is set to \n"); MrBayesPrint (" NO, then a specific number of samples is discarded instead. \n"); MrBayesPrint (" This number is set by Burnin (see below). Note that the \n"); MrBayesPrint (" burnin setting is shared across the 'Sumt', 'Sump', and \n"); MrBayesPrint (" 'Mcmc' commands. \n"); MrBayesPrint (" Burnin -- Determines the number of samples (not generations) that will \n"); MrBayesPrint (" be discarded when summary statistics are calculated. The \n"); MrBayesPrint (" value of this option is only relevant when Relburnin is set \n"); MrBayesPrint (" to NO. \n"); MrBayesPrint (" BurninFrac -- Determines the fraction of samples that will be discarded \n"); MrBayesPrint (" when summary statistics are calculated. The value of this \n"); MrBayesPrint (" option is only relevant when Relburnin is set to YES. \n"); MrBayesPrint (" Example: A value for this option of 0.25 means that 25%% of \n"); MrBayesPrint (" the samples will be discarded. \n"); MrBayesPrint (" Nruns -- Determines how many '.t' files from independent analyses that\n"); MrBayesPrint (" will be summarized. If Nruns > 1 then the names of the files \n"); MrBayesPrint (" are derived from 'Filename' by adding '.run1.t', '.run2.t', \n"); MrBayesPrint (" etc. If Nruns=1 and Ntrees=1 (see below), then only '.t' is \n"); MrBayesPrint (" added to 'Filename'. \n"); MrBayesPrint (" Ntrees -- Determines how many trees there are in the sampled model. If \n"); MrBayesPrint (" 'Ntrees' > 1 then the names of the files are derived from \n"); MrBayesPrint (" 'Filename' by adding '.tree1.t', '.tree2.t', etc. If there \n"); MrBayesPrint (" are both multiple trees and multiple runs, the filenames will\n"); MrBayesPrint (" be '.tree1.run1.t', '.tree1.run2.t', etc.\n"); MrBayesPrint (" Filename -- The name of the file(s) to be summarized. This is the base of\n"); MrBayesPrint (" the file name, to which endings are added according to the \n"); MrBayesPrint (" current settings of the 'Nruns' and 'Ntrees' options. \n"); MrBayesPrint (" Minpartfreq -- The minimum probability of partitions to include in summary \n"); MrBayesPrint (" statistics. \n"); MrBayesPrint (" Contype -- Type of consensus tree. 'Halfcompat' results in a 50%% major-\n"); MrBayesPrint (" ity rule tree, 'Allcompat' adds all compatible groups to such\n"); MrBayesPrint (" a tree. \n"); MrBayesPrint (" Conformat -- Format of consensus tree. The 'Figtree' setting results in a \n"); MrBayesPrint (" consensus tree formatted for the program FigTree, with rich \n"); MrBayesPrint (" summary statistics. The 'Simple' setting results in a simple \n"); MrBayesPrint (" consensus tree written in a format read by a variety of pro- \n"); MrBayesPrint (" grams. \n"); MrBayesPrint (" Outputname -- Base name of the file(s) to which 'sumt' results will be \n"); MrBayesPrint (" printed. The default is the same as 'Filename'. \n"); MrBayesPrint (" Calctreeprobs -- Determines whether tree probabilities should be calculated. \n"); MrBayesPrint (" Showtreeprobs -- Determines whether tree probabilities should be displayed on \n"); MrBayesPrint (" screen. \n"); MrBayesPrint (" Hpd -- Determines whether credibility intervals will be given as the\n"); MrBayesPrint (" region of Highest Posterior Density ('Yes') or as the inter- \n"); MrBayesPrint (" val containing the median 95 %% of sampled values ('No'). \n"); MrBayesPrint (" Savebrparams -- Set this option to 'yes' to save all sampled branch and node \n"); MrBayesPrint (" parameter values to a separate file with the filename ending \n"); MrBayesPrint (" in '.brparams'. All partitions with a posterior probability \n"); MrBayesPrint (" larger than Minbrparamfreq will be included. \n"); MrBayesPrint (" Minbrparamfreq -- The minimum probability of partitions for which to save \n"); MrBayesPrint (" parameter values to file if 'Savebrparams' is set to 'yes'. \n"); MrBayesPrint (" \n"); MrBayesPrint (" Current settings: \n"); MrBayesPrint (" \n"); MrBayesPrint (" Parameter Options Current Setting \n"); MrBayesPrint (" -------------------------------------------------------- \n"); MrBayesPrint (" Relburnin Yes/No %s \n", chainParams.relativeBurnin == YES ? "Yes" : "No"); MrBayesPrint (" Burnin %d \n", chainParams.chainBurnIn); MrBayesPrint (" Burninfrac %1.2lf \n", chainParams.burninFraction); MrBayesPrint (" Nruns %d \n", sumtParams.numRuns); MrBayesPrint (" Ntrees %d \n", sumtParams.numTrees); if (sumtParams.numRuns == 1 && sumtParams.numTrees == 1) MrBayesPrint (" Filename %s<.t>\n", sumtParams.sumtFileName); else if (sumtParams.numRuns == 1 && sumtParams.numTrees > 1) MrBayesPrint (" Filename %s<.tree.t>\n", sumtParams.sumtFileName); else if (sumtParams.numRuns > 1 && sumtParams.numTrees == 1) MrBayesPrint (" Filename %s<.run.t>\n", sumtParams.sumtFileName); else if (sumtParams.numRuns > 1 && sumtParams.numTrees > 1) MrBayesPrint (" Filename %s<.tree.run.t>\n", sumtParams.sumtFileName); MrBayesPrint (" Minpartfreq %1.2lf \n", sumtParams.minPartFreq); MrBayesPrint (" Contype Halfcompat/Allcompat %s\n", sumtParams.sumtConType); MrBayesPrint (" Conformat Figtree/Simple %s \n", sumtParams.consensusFormat == SIMPLE ? "Simple" : "Figtree"); MrBayesPrint (" Outputname %s<.parts etc>\n", sumtParams.sumtOutfile); MrBayesPrint (" Calctreeprobs Yes/No %s \n", sumtParams.calcTreeprobs == YES ? "Yes" : "No"); MrBayesPrint (" Showtreeprobs Yes/No %s \n", sumtParams.showSumtTrees == YES ? "Yes" : "No"); MrBayesPrint (" Hpd Yes/No %s \n", sumtParams.HPD == YES ? "Yes" : "No"); MrBayesPrint (" Savebrparams Yes/No %s \n", sumtParams.saveBrParams == YES ? "Yes" : "No"); MrBayesPrint (" Minbrparamfreq %1.2lf \n", sumtParams.minBrParamFreq); MrBayesPrint (" \n"); MrBayesPrint (" --------------------------------------------------------------------------- \n"); } else if (!strcmp(helpTkn, "Tree")) { MrBayesPrint (" --------------------------------------------------------------------------- \n"); MrBayesPrint (" Tree \n"); MrBayesPrint (" \n"); MrBayesPrint (" This command is used by MrBayes to write trees to a nexus tree file. Trees \n"); MrBayesPrint (" are written in the Newick format. For instance, \n"); MrBayesPrint (" \n"); MrBayesPrint (" tree ((1,2),3,4); \n"); MrBayesPrint (" \n"); MrBayesPrint (" describes an unrooted tree with taxa 1 and 2 being more closely related to \n"); MrBayesPrint (" each other than to taxa 3 and 4. If branch lengths are saved to file, they \n"); MrBayesPrint (" are given after a colon sign immediately following the terminal taxon or the \n"); MrBayesPrint (" interior node they refer to. An example of an unrooted tree with branch \n"); MrBayesPrint (" lengths is: \n"); MrBayesPrint (" \n"); MrBayesPrint (" tree ((1:0.064573,2:0.029042):0.041239,3:0.203988,4:0.187654); \n"); MrBayesPrint (" \n"); MrBayesPrint (" Trees that are rooted (clock trees) are written with a basal dichotomy \n"); MrBayesPrint (" instead of a basal trichotomy. If the tree described above had been rooted \n"); MrBayesPrint (" on the branch leading to taxon 4, it would have been represented as: \n"); MrBayesPrint (" \n"); MrBayesPrint (" tree (((1,2),3),4); \n"); MrBayesPrint (" \n"); MrBayesPrint (" --------------------------------------------------------------------------- \n"); } else if (!strcmp(helpTkn, "Report")) { MrBayesPrint (" --------------------------------------------------------------------------- \n"); MrBayesPrint (" Report \n"); MrBayesPrint (" \n"); MrBayesPrint (" This command allows you to control how the posterior distribution is \n"); MrBayesPrint (" reported. For rate parameters, it allows you to choose among several popular \n"); MrBayesPrint (" parameterizations. The report command also allows you to request printing of \n"); MrBayesPrint (" some model aspects that are usually not reported. For instance, if a node is \n"); MrBayesPrint (" constrained in the analysis, MrBayes can print the probabilities of the \n"); MrBayesPrint (" ancestral states at that node. Similarly, if there is rate variation in the \n"); MrBayesPrint (" model, MrBayes can print the inferred site rates, and if there is omega varia-\n"); MrBayesPrint (" tion, MrBayes can print the inferred omega (positive selection) values for \n"); MrBayesPrint (" each codon. In a complex model with several partitions, each partition is \n"); MrBayesPrint (" controlled separately using the same 'Applyto' mechanism as in the 'Lset' and \n"); MrBayesPrint (" 'Prset' commands. \n"); MrBayesPrint (" \n"); MrBayesPrint (" Options: \n"); MrBayesPrint (" \n"); MrBayesPrint (" Applyto -- This option allows you to apply the report commands to specific \n"); MrBayesPrint (" partitions. This command should be the first in the list of \n"); MrBayesPrint (" commands specified in 'report'. \n"); MrBayesPrint (" For example, \n"); MrBayesPrint (" \n"); MrBayesPrint (" report applyto=(1,2) tratio=ratio \n"); MrBayesPrint (" \n"); MrBayesPrint (" report applyto=(3) tratio=dirichlet \n"); MrBayesPrint (" \n"); MrBayesPrint (" would result in the transition and transversion rates of the \n"); MrBayesPrint (" first and second partitions in the model being reported as a \n"); MrBayesPrint (" ratio and the transition and transversion rates of the third \n"); MrBayesPrint (" partition being reported as proportions of the rate sum (the \n"); MrBayesPrint (" Dirichlet parameterization). \n"); MrBayesPrint (" Tratio -- This specifies the report format for the transition and trans- \n"); MrBayesPrint (" version rates of a nucleotide substitution model with nst=2. \n"); MrBayesPrint (" If 'ratio' is selected, the rates will be reported as a ratio \n"); MrBayesPrint (" (transition rate/transversion rate). If 'dirichlet' is selected, \n"); MrBayesPrint (" the transition and transversion rates will instead be reported \n"); MrBayesPrint (" as proportions of the rate sum. For example, if the transition \n"); MrBayesPrint (" rate is three times the transversion rate and 'ratio' is selec- \n"); MrBayesPrint (" ted, this will reported as a single value, '3.0'. If 'dirichlet' \n"); MrBayesPrint (" is selected instead, the same rates will be reported using two \n"); MrBayesPrint (" values, '0.75 0.25'. The sum of the Dirichlet values is always 1.\n"); MrBayesPrint (" Although the Dirichlet format may be unfamiliar to some users, \n"); MrBayesPrint (" it is more convenient for specifying priors than the ratio \n"); MrBayesPrint (" format. \n"); MrBayesPrint (" Revmat -- This specifies the report format for the substitution rates of \n"); MrBayesPrint (" a GTR substitution model for nucleotide or amino acid data. If \n"); MrBayesPrint (" 'ratio' is selected, the rates will be reported scaled to the \n"); MrBayesPrint (" G-T rate (for nucleotides) or the Y-V rate (for amino acids). If \n"); MrBayesPrint (" 'dirichlet' is specified instead, the rates are reported as pro- \n"); MrBayesPrint (" portions of the rate sum. For instance, assume that the C-T rate \n"); MrBayesPrint (" is twice the A-G rate and four times the transversion rates, \n"); MrBayesPrint (" which are equal. If the report format is set to 'ratio', this \n"); MrBayesPrint (" would be reported as '1.0 2.0 1.0 1.0 4.0 1.0' since the rates \n"); MrBayesPrint (" are reported in the order rAC, rAG, rAT, rCG, rCT, rGT and scaled\n"); MrBayesPrint (" relative to the last rate, the G-T rate. If 'dirichlet' is selec-\n"); MrBayesPrint (" ted instead, the same rates would have been reported as '0.1 0.2 \n"); MrBayesPrint (" 0.1 0.1 0.4 0.1' since the rates are now scaled so that they sum \n"); MrBayesPrint (" to 1.0. The Dirichlet format is the parameterization used for \n"); MrBayesPrint (" formulating priors on the rates. \n"); MrBayesPrint (" Ratemult -- This specifies the report format used for the rate multiplier of \n"); MrBayesPrint (" different model partitions. Three formats are available. If \n"); MrBayesPrint (" 'scaled' is selected, then rates are scaled such that the mean \n"); MrBayesPrint (" rate per site across partitions is 1.0. If 'ratio' is chosen, \n"); MrBayesPrint (" the rates are scaled relative to the rate of the first parti- \n"); MrBayesPrint (" tion. Finally, if 'dirichlet' is chosen, the rates are given as \n"); MrBayesPrint (" proportions of the rate sum. The latter is the format used \n"); MrBayesPrint (" when formulating priors on the rate multiplier. \n"); MrBayesPrint (" Tree -- This specifies the report format used for the tree(s). Two op- \n"); MrBayesPrint (" tions are available. 'Topology' results in only the topology \n"); MrBayesPrint (" being printed to file, whereas 'brlens' causes branch lengths to \n"); MrBayesPrint (" to be printed as well. \n"); MrBayesPrint (" Ancstates -- If this option is set to 'yes', MrBayes will print the pro- \n"); MrBayesPrint (" bability of the ancestral states at all constrained nodes. Typ- \n"); MrBayesPrint (" ically, you are interested in the ancestral states of only a few \n"); MrBayesPrint (" characters and only at one node in the tree. To perform such \n"); MrBayesPrint (" an analysis, first define and enforce a topology constraint \n"); MrBayesPrint (" using 'constraint' and 'prset topologypr = constraints (...)'. \n"); MrBayesPrint (" Then put the character(s) of interest in a separate partition and\n"); MrBayesPrint (" set MrBayes to report the ancestral states for that partition. \n"); MrBayesPrint (" For instance, if the characters of interest are in partition 2, \n"); MrBayesPrint (" use 'report applyto=(2) ancstates=yes' to force MrBayes to print \n"); MrBayesPrint (" the probability of the ancestral states of those characters at \n"); MrBayesPrint (" the constrained node to the '.p' file. \n"); MrBayesPrint (" Siterates -- If this option is set to 'yes' and the relevant model has rate \n"); MrBayesPrint (" variation across sites, then the site rates, weighted over rate \n"); MrBayesPrint (" categories, will be reported to the '.p' file. \n"); MrBayesPrint (" Possel -- If this option is set to 'yes' and the relevant model has omega \n"); MrBayesPrint (" variation across sites, the probability that each model site \n"); MrBayesPrint (" (codon in this case) is positively selected will be written to \n"); MrBayesPrint (" file. \n"); MrBayesPrint (" Siteomega -- If this option is set to 'yes' and the relevant model has omega \n"); MrBayesPrint (" variation across sites, the weighted omega value (over omega \n"); MrBayesPrint (" categories) for each model site will be reported to file. \n"); MrBayesPrint (" \n"); if (numCurrentDivisions == 0) tempInt = 1; else tempInt = numCurrentDivisions; for (i=0; itratioFormat); MrBayesPrint (" Revmat Ratio/Dirichlet %s \n", mp->revmatFormat); MrBayesPrint (" Ratemult Scaled/Ratio/Dirichlet %s \n", mp->ratemultFormat); MrBayesPrint (" Tree Brlens/Topology %s \n", mp->treeFormat); MrBayesPrint (" Ancstates Yes/No %s \n", mp->inferAncStates); MrBayesPrint (" Siterates Yes/No %s \n", mp->inferSiteRates); MrBayesPrint (" Possel Yes/No %s \n", mp->inferPosSel); MrBayesPrint (" Siteomega Yes/No %s \n", mp->inferSiteOmegas); MrBayesPrint (" \n"); MrBayesPrint (" ------------------------------------------------------------------ \n"); } } else if (!strcmp(helpTkn, "Manual")) { MrBayesPrint (" --------------------------------------------------------------------------- \n"); MrBayesPrint (" Manual \n"); MrBayesPrint (" \n"); MrBayesPrint (" This command allows you to generate a text file containing help information \n"); MrBayesPrint (" on all the available commands. This text file can be used as an up-to-date \n"); MrBayesPrint (" command reference. You can set the name of the text file using the \n"); MrBayesPrint (" \"filename\" option; the default is \"commref_mb.txt\". \n"); MrBayesPrint (" \n"); MrBayesPrint (" Parameter Options Current Setting \n"); MrBayesPrint (" -------------------------------------------------------- \n"); MrBayesPrint (" Filename %s \n", manFileName); MrBayesPrint (" \n"); MrBayesPrint (" --------------------------------------------------------------------------- \n"); } else if (!strcmp(helpTkn, "Showmoves")) { MrBayesPrint (" --------------------------------------------------------------------------- \n"); MrBayesPrint (" Showmoves \n"); MrBayesPrint (" \n"); MrBayesPrint (" This command shows the MCMC samplers (moves) that are switched on for the \n"); MrBayesPrint (" parameters in the current model. The basic usage is \n"); MrBayesPrint (" \n"); MrBayesPrint (" showmoves \n"); MrBayesPrint (" \n"); MrBayesPrint (" If you want to see all available moves, use \n"); MrBayesPrint (" \n"); MrBayesPrint (" showmoves allavailable=yes \n"); MrBayesPrint (" \n"); MrBayesPrint (" If you want to change any of the tuning parameters for the moves, use the \n"); MrBayesPrint (" 'propset' command. \n"); MrBayesPrint (" --------------------------------------------------------------------------- \n"); } else if (!strcmp(helpTkn, "Showparams")) { MrBayesPrint (" --------------------------------------------------------------------------- \n"); MrBayesPrint (" Showparams \n"); MrBayesPrint (" \n"); MrBayesPrint (" This command shows all of the parameters in the current model. The basic \n"); MrBayesPrint (" usage is \n"); MrBayesPrint (" \n"); MrBayesPrint (" showparams \n"); MrBayesPrint (" \n"); MrBayesPrint (" The parameters are listed together with their priors, the available moves, \n"); MrBayesPrint (" and the current value(s), which will be used as the starting values in the \n"); MrBayesPrint (" next MCMC analysis. \n"); MrBayesPrint (" --------------------------------------------------------------------------- \n"); } else if (!strcmp(helpTkn, "Startvals")) { MrBayesPrint (" --------------------------------------------------------------------------- \n"); MrBayesPrint (" Startvals \n"); MrBayesPrint (" \n"); MrBayesPrint (" Use this command to change the current values for parameters in your model. \n"); MrBayesPrint (" These values will be used as the starting values in the next MCMC analysis. \n"); MrBayesPrint (" The basic format is: \n"); MrBayesPrint (" \n"); MrBayesPrint (" startvals =(,,...,) \n"); MrBayesPrint (" \n"); MrBayesPrint (" for all substitution model parameters. The format is slightly different for \n"); MrBayesPrint (" parameters that are written to a tree file: \n"); MrBayesPrint (" \n"); MrBayesPrint (" startvals = \n"); MrBayesPrint (" \n"); MrBayesPrint (" This version of the command will look for a tree with the specified name \n"); MrBayesPrint (" among the trees read in previously when parsing a tree block. The information \n"); MrBayesPrint (" stored in that tree will be used to set the starting value of the parameter. \n"); MrBayesPrint (" The parameters that are set using this mechanism include topology and branch \n"); MrBayesPrint (" length parameters, as well as relaxed clock branch rates, CPP events and \n"); MrBayesPrint (" CPP branch rate multipliers. \n"); MrBayesPrint (" \n"); MrBayesPrint (" The above versions of the command will set the value for all runs and chains. \n"); MrBayesPrint (" You can also set the value for an individual run and chain by using the format\n"); MrBayesPrint (" \n"); MrBayesPrint (" startvals (,)=(,...) \n"); MrBayesPrint (" \n"); MrBayesPrint (" where is the index of the run and the index of the chain. If \n"); MrBayesPrint (" the run index is omitted, the values will be changed for all runs. Similarly, \n"); MrBayesPrint (" if the chain index is omitted, all chains will be set to the specified value. \n"); MrBayesPrint (" For example, if we wanted to set the values of the stationary frequency \n"); MrBayesPrint (" parameter pi{1} to (0.1,0.1,0.4,0.4) for all chains in run 1, and to \n"); MrBayesPrint (" (0.3,0.3,0.2,0.2) for chain 3 of run 2, we would use \n"); MrBayesPrint (" \n"); MrBayesPrint (" startvals pi{1}(1,)=(0.1,0.1,0.4,0.4) pi{1}(2,3)=(0.3,0.3,0.2,0.2) \n"); MrBayesPrint (" \n"); MrBayesPrint (" --------------------------------------------------------------------------- \n"); } else { return (ERROR); } return (NO_ERROR); } /* IsAmbig: This function returns YES if character is set as ambiguous either by using parenthetic notation or by ambiguity codes. It returns NO if character is unambiguous, missing or gapped */ int IsAmbig (int charCode, int dType) { if (dType == DNA || dType == RNA || dType == STANDARD || dType == RESTRICTION || dType == PROTEIN) { if (charCode != MISSING && charCode != GAP) if (NBits(charCode) > 1) return (YES); } else if (dType == CONTINUOUS) { /* do nothing, these cannot be partly ambiguous */ } else { MrBayesPrint ("Unknown datatype in \"IsAmbig\"\n", spacer); } return (NO); } int IsArgValid (char *tk, char *validArg) { int i, j, k, tkLen, targetLen, numDiff, numStrMatches; char tempStr[100]; ParmInfoPtr p; p = paramPtr; tkLen = (int) strlen(tk); numStrMatches = i = j = 0; do { if (p->valueList[i] == '|' || p->valueList[i] == '\0') { tempStr[j++] = '\0'; targetLen = (int) strlen(tempStr); if (tkLen <= targetLen) { numDiff = 0; for (k=0; kvalueList[i]; i++; } while (p->valueList[i] != '\0'); if (numStrMatches == 0) { MrBayesPrint ("%s No valid match for argument \"%s\"\n", spacer, tk); return (ERROR); } else if (numStrMatches == 1) { return (NO_ERROR); } else { MrBayesPrint ("%s Argument \"%s\" is ambiguous\n", spacer, tk); return (ERROR); } } int IsIn (char ch, char *s) { while (*s) { if (*s++ == ch) return 1; } return 0; } int IsMissing (int charCode, int dType) { if (dType == DNA || dType == RNA || dType == PROTEIN || dType == RESTRICTION || dType == STANDARD) { if (charCode == MISSING || charCode == GAP) return (YES); } else if (dType == CONTINUOUS) { if (charCode == INT_MAX) return (YES); } else { MrBayesPrint ("Unknown datatype in \"IsMissing\"\n", spacer); } return (NO); } int IsSame (char *s1, char *s2) { int i, nDiff, isIdentical, len; isIdentical = YES; if (strlen(s1) != strlen(s2)) isIdentical = NO; /* strings cannot be identical because they are different lengths */ /* now, we go through both strings, one character at a time, to see if any are different */ if (strlen(s1) > strlen(s2)) len = (int) strlen(s2); else len = (int) strlen(s1); i = nDiff = 0; while (i < len) { if (tolower(s1[i]) != tolower(s2[i])) nDiff++; i++; } if (nDiff == 0 && isIdentical == YES) return (SAME); else if (nDiff == 0 && isIdentical == NO) return (CONSISTENT_WITH); else return (DIFFERENT); } int IsWhite (char c) { if (c == ' ' || c == '\t' || c == '\n' || c == '\r') { if (c == '\n' || c == '\r') return 2; return 1; } return 0; } int NucID (char nuc) { char n; if (nuc == 'U' || nuc == 'u') n = 'T'; else n = nuc; if (n == 'A' || n == 'a') { return 1; } else if (n == 'C' || n == 'c') { return 2; } else if (n == 'G' || n == 'g') { return 4; } else if (n == 'T' || n == 't') { return 8; } else if (n == 'R' || n == 'r') { return 5; } else if (n == 'Y' || n == 'y') { return 10; } else if (n == 'M' || n == 'm') { return 3; } else if (n == 'K' || n == 'k') { return 12; } else if (n == 'S' || n == 's') { return 6; } else if (n == 'W' || n == 'w') { return 9; } else if (n == 'H' || n == 'h') { return 11; } else if (n == 'B' || n == 'b') { return 14; } else if (n == 'V' || n == 'v') { return 7; } else if (n == 'D' || n == 'd') { return 13; } else if (n == 'N' || n == 'n') { return 15; } else if (n == gapId) { return GAP; } else if (n == missingId) { return MISSING; } else return -1; } /*-------| ParseCommand |------------------------------------------------ | | This function is used to parse a file. The expected format is: | | command parameter=value parameter=value ... ; | | For example, the following is a valid line for this parser: | | lset nst=2; | | In some cases, however, the format is: | | command stuff more stuff ... ; | | For example, when reading a data file, the matrix command might be: | | matrix | taxon_1 data | taxon_2 data | taxon_3 data | ; | | Like before, the command and all of the stuff for that command are | terminated by a semicolon. | */ int ParseCommand (char *s) { int rc, tokenType, inError, numMatches, skipCmd; char errStr[100]; numMatches = 0; /* Avoid gcc warnings (actually set in call to FindValidCommand) */ cmdStr = s; tokenP = &s[0]; # if defined (ECHO_PROCESSED_COMMANDS) MrBayesPrint ("Currently processing command: %s\n", s); # endif inError = skipCmd = NO; do { /* Get the next token. A token is a valid word in a line. Token type is defined in "bayes.h". */ if (GetToken (token, &tokenType, &tokenP)) { inError = YES; break; } if (strlen(token) > 0 || tokenType == ALPHA) { # if defined (SHOW_TOKENS) MrBayesPrint ("%s\n", token); # endif if (tokenType == LEFTCOMMENT) { /* If the token is a left comment "[", then we don't want to actually process commands until we find a right comment. */ /* The exception is if readComment is set to YES, in which case we will leave it to the parser functions to decide on whether they want to read the comment or not */ if (readComment == NO || inComment == YES) { inComment = YES; numComments++; } } if (inComment == NO && inForeignBlock == NO) { if (tokenType != SEMICOLON) { /* If the token is not a semicolon, then we will be processing either a command or a parameter. */ if (expecting == Expecting(COMMAND)) { /* We are expecting to find a command (defined above in "commands[]"). Find the correct command and set a pointer to that command. */ commandPtr = NULL; if (FindValidCommand (token, &numMatches) == ERROR) { /* We couldn't find the command or the user did not specify enough letters to unambiguously determine the command. The command pointer (commandPtr) is NULL. */ if (numMatches == 0) MrBayesPrint ("%s Could not find command \"%s\"\n", spacer, token); else MrBayesPrint ("%s Ambiguous command \"%s\"\n", spacer, token); inError = YES; } else { /* We did find a valid command. Set what we are expecting to see next. */ expecting = commandPtr->expect; /* Check to see if we have one of the so-called special cases in which a command is not necessarily followed by a parameter (e.g., matrix). If we do have a special case, then we want to set the parameter pointer (paramPtr) appropriately. In this case, simply go to the first parameter in the parmList. */ if (commandPtr->specialCmd == YES) { isFirstMatrixRead = YES; foundFirst = NO; paramPtr = paramTable + commandPtr->parmList[0]; } if (strcmp(commandPtr->string, "Execute") == 0) { /* set the tokenizer to recognize quoted strings */ readWord = YES; } } } else { /* We are expecting to find a parameter or a value for the parameter, not a command. */ if ((expecting & Expecting(PARAMETER)) == Expecting(PARAMETER) && (expecting & Expecting(tokenType)) != Expecting(tokenType)) { /* Specifically, if we are here, we need to go through the parameter list, checking to see if the token is a valid parameter. */ expecting = (expecting & Expecting(PARAMETER)); if (FindValidParam (token, &numMatches) == ERROR) { /* The token is not a valid parameter. */ if (numMatches == 0) MrBayesPrint ("%s Could not find parameter \"%s\"\n", spacer, token); else MrBayesPrint ("%s Ambiguous parameter \"%s\"\n", spacer, token); inError = YES; } else { /* The token is a valid parameter. Call the appropriate function ("DoXxxxParm"). */ if ((paramPtr->fp)(paramPtr->string, token) == ERROR) { if (strcmp("Xxxxxxxxxx", paramPtr->string)) MrBayesPrint ("%s Error when setting parameter \"%s\" (1)\n", spacer, paramPtr->string); inError = YES; } } } else { /* Otherwise, we are expecting a value for the parameter. Call the appropriate function ("DoXxxxParm"). */ if ((expecting & Expecting(tokenType)) != 0) expecting = (expecting & Expecting(tokenType)); if ((Expecting(tokenType) & expecting) == Expecting(tokenType)) { if ((paramPtr->fp)(paramPtr->string, token) == ERROR) { if (strcmp("Xxxxxxxxxx", paramPtr->string)) MrBayesPrint ("%s Error when setting parameter \"%s\" (2)\n", spacer, paramPtr->string); inError = YES; } } else { inError = YES; WhatVariableExp (expecting, errStr); MrBayesPrint ("%s Expecting '%s'\n", spacer, errStr+1); /* there will be an initial space in errStr so print from pos 1 */ if (numOpenExeFiles > 0) MrBayesPrint ("%s Instead found '%s' in command '%s'\n", spacer, token, commandPtr->string); else MrBayesPrint ("%s Instead found '%s' in command '%s' at position %d\n", spacer, token, commandPtr->string, tokenP - cmdStr - strlen(token)+1); } } } } else { /* The token is a semicolon. This means that we are at the end of processing one command. We need to clean things up. We do this by calling the finishing function ("DoXxxx"). */ if ((Expecting(SEMICOLON) & expecting) == Expecting(SEMICOLON)) { if (commandPtr->cmdFxnPtr != NULL) { /* Finish up the command here. */ rc = (commandPtr->cmdFxnPtr) (); if (rc == ERROR || rc == ABORT) { if (rc == ABORT) { MrBayesPrint (" Mcmc run aborted\n"); } else if (rc == SKIP_COMMAND) { MrBayesPrint (" Cancelled execution of command\n"); skipCmd = YES; } else { MrBayesPrint ("%s Error in command \"%s\"\n", spacer, commandPtr->string); inError = YES; } } } /* if the user typed "quit", then we want to bail out of this loop, with a NO_ERROR_QUIT */ if (!strcmp(commandPtr->string, "Quit")) return (NO_ERROR_QUIT); expecting = Expecting(COMMAND); } else { inError = YES; WhatVariableExp (expecting, errStr); MrBayesPrint ("%s Expecting %s\n", spacer, errStr); } } } /* Check to see if a comment is terminated. A comment can either be a right comment "]" or, if we were in a foreign nexus block (e.g., a "paup" block) the terminating comment will be "end". */ if (tokenType == RIGHTCOMMENT) { if (inComment == NO && readComment == NO) { MrBayesPrint ("%s Found \"]\", without having previously found \"[\"\n", spacer); inError = YES; } else if (inComment == NO && readComment == YES) { /* This is OK, we just pass through and rely on the command to handle the RIGHTCOMMENT */ } else { numComments--; if (numComments == 0) inComment = NO; } } if ((IsSame(token, "end") == SAME || IsSame(token, "endblock") == SAME) && inForeignBlock == YES) { strcpy (spacer, ""); inForeignBlock = NO; } } } while ((*token || tokenType == ALPHA) && inError == NO && skipCmd == NO); if (inError == YES) { readComment = NO; /* reset this in case it is set to YES in command and we get an error exit */ return (ERROR); } else return (NO_ERROR); } void PrintSettings (char *command) { char yesNoStr[20]; if (!strcmp(command,"Mcmc")) { MrBayesPrint (" Parameter Options Current Setting \n"); MrBayesPrint (" ----------------------------------------------------- \n"); MrBayesPrint (" Ngen %d \n", chainParams.numGen); MrBayesPrint (" Nruns %d \n", chainParams.numRuns); MrBayesPrint (" Nchains %d \n", chainParams.numChains); MrBayesPrint (" Temp %lf \n", chainParams.chainTemp); MrBayesPrint (" Reweight , %1.2lf,%1.2lf \n", chainParams.weightScheme[0], chainParams.weightScheme[1]); MrBayesPrint (" Swapfreq %d \n", chainParams.swapFreq); MrBayesPrint (" Nswaps %d \n", chainParams.numSwaps); MrBayesPrint (" Samplefreq %d \n", chainParams.sampleFreq); MrBayesPrint (" Printfreq %d \n", chainParams.printFreq); PrintYesNo (chainParams.printAll, yesNoStr); MrBayesPrint (" Printall Yes/No %s \n", yesNoStr); MrBayesPrint (" Printmax %d \n", chainParams.printMax); PrintYesNo (chainParams.mcmcDiagn, yesNoStr); MrBayesPrint (" Mcmcdiagn Yes/No %s \n", yesNoStr); MrBayesPrint (" Diagnfreq %d \n", chainParams.diagnFreq); if (chainParams.diagnStat == AVGSTDDEV) strcpy (yesNoStr, "Avgstddev"); else strcpy (yesNoStr, "Maxstddev"); MrBayesPrint (" Diagnstat Avgstddev/Maxstddev %s \n", yesNoStr); MrBayesPrint (" Minpartfreq %1.2lf \n", chainParams.minPartFreq); PrintYesNo (chainParams.allChains, yesNoStr); MrBayesPrint (" Allchains Yes/No %s \n", yesNoStr); PrintYesNo (chainParams.allComps, yesNoStr); MrBayesPrint (" Allcomps Yes/No %s \n", yesNoStr); PrintYesNo (chainParams.relativeBurnin, yesNoStr); MrBayesPrint (" Relburnin Yes/No %s \n", yesNoStr); MrBayesPrint (" Burnin %d \n", chainParams.chainBurnIn); MrBayesPrint (" Burninfrac %1.2lf \n", chainParams.burninFraction); PrintYesNo (chainParams.stopRule, yesNoStr); MrBayesPrint (" Stoprule Yes/No %s \n", yesNoStr); MrBayesPrint (" Stopval %1.2lf \n", chainParams.stopVal); PrintYesNo (chainParams.saveTrees, yesNoStr); MrBayesPrint (" Savetrees Yes/No %s \n", yesNoStr); PrintYesNo (chainParams.checkPoint, yesNoStr); MrBayesPrint (" Checkpoint Yes/No %s \n", yesNoStr); MrBayesPrint (" Checkfreq %d \n", chainParams.checkFreq); MrBayesPrint (" Filename %s.

\n", chainParams.chainFileName); MrBayesPrint (" Startparams Current/Reset %s \n", chainParams.startParams); MrBayesPrint (" Starttree Current/Random/ %s \n", chainParams.startTree); MrBayesPrint (" Parsimony \n"); MrBayesPrint (" Nperts %d \n", chainParams.numStartPerts); PrintYesNo (chainParams.runWithData, yesNoStr); MrBayesPrint (" Data Yes/No %s \n", yesNoStr); MrBayesPrint (" Ordertaxa Yes/No %s \n", chainParams.orderTaxa == YES? "Yes" : "No"); MrBayesPrint (" Append Yes/No %s \n", chainParams.append == YES? "Yes" : "No"); MrBayesPrint (" Autotune Yes/No %s \n", chainParams.autotune == YES? "Yes" : "No"); MrBayesPrint (" Tunefreq %d \n", chainParams.tuneFreq); MrBayesPrint (" \n"); } } void PrintYesNo (int yn, char s[4]) { if (yn == YES) strcpy (s, "Yes"); else strcpy (s, "No"); } int ProtID (char aa) { if (aa == 'A' || aa == 'a') /* Ala */ { return 1; } else if (aa == 'R' || aa == 'r') /* Arg */ { return 2; } else if (aa == 'N' || aa == 'n') /* Asn */ { return 4; } else if (aa == 'D' || aa == 'd') /* Asp */ { return 8; } else if (aa == 'C' || aa == 'c') /* Cys */ { return 16; } else if (aa == 'Q' || aa == 'q') /* Gln */ { return 32; } else if (aa == 'E' || aa == 'e') /* Glu */ { return 64; } else if (aa == 'G' || aa == 'g') /* Gly */ { return 128; } else if (aa == 'H' || aa == 'h') /* His */ { return 256; } else if (aa == 'I' || aa == 'i') /* Ile */ { return 512; } else if (aa == 'L' || aa == 'l') /* Leu */ { return 1024; } else if (aa == 'K' || aa == 'k') /* Lys */ { return 2048; } else if (aa == 'M' || aa == 'm') /* Met */ { return 4096; } else if (aa == 'F' || aa == 'f') /* Phe */ { return 8192; } else if (aa == 'P' || aa == 'p') /* Pro */ { return 16384; } else if (aa == 'S' || aa == 's') /* Ser */ { return 32768; } else if (aa == 'T' || aa == 't') /* Thr */ { return 65536; } else if (aa == 'W' || aa == 'w') /* Trp */ { return 131072; } else if (aa == 'Y' || aa == 'y') /* Tyr */ { return 262144; } else if (aa == 'V' || aa == 'v') /* Val */ { return 524288; } else if (aa == 'X' || aa == 'x') /* Nonidentified */ { return MISSING; } else if (aa == gapId) { return GAP; } else if (aa == missingId) { return MISSING; } else return -1; } int RemoveLastFromString (char *s1) { int i, j, numPrev, numRemoved; /* We remove the last name from the string simply by deleting the last "|". */ i = numPrev = 0; while (s1[i] != '\0') { if (s1[i] == '|') numPrev++; i++; } i = j = numRemoved = 0; while (s1[i] != '\0') { if (s1[i] == '|') j++; if (numPrev == j) { s1[i] = ' '; numRemoved++; break; } i++; } if (numRemoved != 1) { MrBayesPrint ("%s Could not find name to remove\n", spacer); return (ERROR); } return (NO_ERROR); } int MBResID (char nuc) { char n; n = nuc; if (n == '0' || n == 'a' || n == 'A') { return 1; } else if (n == '1' || n == 'b' || n == 'B') { return 2; } else if (n == gapId) { return GAP; } else if (n == missingId) { return MISSING; } else return -1; } /* Reset character flags */ void ResetCharacterFlags (void) { /* reset all characters flags */ numChar = 0; /* number of defined characters */ defChars = NO; /* flag for whether number of characters is known*/ defMatrix = NO; /* flag for whether matrix is successful read */ matrixHasPoly = NO; /* flag for whether matrix has polymorphisms */ isInAmbig = NO; /* flag for whether the parser is within () */ isInPoly = NO; /* flag for whether the parser is within {} */ defPartition = NO; /* flag for whether character partition is read */ defPairs = NO; /* flag indicating whether pairs have been defined */ numDefinedPartitions = 0; /* number of defined partitions */ partitionNum = 0; /* partition number currently enforced */ numCurrentDivisions = 0; /* number of partitions of data */ numCharSets = 0; /* holds number of character sets */ numDivisions = 1; /* holds number of partitions */ isMixed = NO; /* are data mixed ? */ dataType = NONE; /* holds datatype */ matchId = '\0'; /* no default for match character */ gapId = '\0'; /* no default for gap character */ missingId = '\0'; /* no default for missing characters */ } /* Reset taxa flags */ void ResetTaxaFlags (void) { numTaxa = 0; /* number of taxa */ numNamedTaxa = 0; /* number of named taxa */ defTaxa = NO; /* flag for whether number of taxa is known */ isTaxsetDef = NO; /* is a taxlabels set defined */ numDefinedConstraints = 0; /* holds number of defined constraints */ definedConstraint = NULL; definedConstraintTwo = NULL; definedConstraintPruned = NULL; definedConstraintTwoPruned = NULL; constraintNames = NULL; nodeCalibration = NULL; tempActiveConstraints = NULL; /* holds temp info on active constraints */ outGroupNum = 0; /* default outgroup */ numTaxaSets = 0; /* holds number of taxa sets */ } /* SetPartition: Set model partition */ int SetPartition (int part) { int i, j; /* Free space for modelParams and modelSettings */ if (memAllocs[ALLOC_MODEL] == YES) { for (i=0; i numCurrentDivisions) numCurrentDivisions = j; } /* Allocate space for partition models */ modelParams = (Model *) SafeCalloc (numCurrentDivisions, sizeof (Model)); modelSettings = (ModelInfo *) SafeCalloc (numCurrentDivisions, sizeof (ModelInfo)); if (!modelParams || !modelSettings) { MrBayesPrint ("%s Could not allocate modelParams or modelSettings\n", spacer); if (modelParams) free (modelParams); if (modelSettings) free (modelSettings); return (ERROR); } memAllocs[ALLOC_MODEL] = YES; numVars = (int *) SafeRealloc ((void *) numVars, 3 * (size_t)numCurrentDivisions * sizeof(int)); tempLinkUnlinkVec = numVars + numCurrentDivisions; activeParts = numVars + 2*numCurrentDivisions; tempNum = (MrBFlt *) SafeRealloc ((void *) tempNum, 6 * sizeof(MrBFlt)); activeParams[0] = (int *) SafeRealloc ((void *) (activeParams[0]), (size_t)NUM_LINKED * (size_t)numCurrentDivisions * sizeof(int)); for (i=1; i numSpecies) numSpecies = j; } return (NO_ERROR); } int SetTaxaFromTranslateTable (void) { int i; if (numTaxa != 0) return ERROR; for (i=0; i99) { MrBayesPrint ("%s Taxon name %s is too long. Maximum 99 characters is allowed.\n", spacer, transFrom[i]); return (ERROR); } AddString(&taxaNames, numTaxa, transFrom[i]); numTaxa++; } return NO_ERROR; } void SetUpParms (void) { ParmInfoPtr p = paramTable; PARAM (0, "NEXUS", DoNexusParm, "NEXUS|\0"); PARAM (1, "Data", DoBeginParm, "\0"); PARAM (2, "Mrbayes", DoBeginParm, "\0"); PARAM (3, "Trees", DoBeginParm, "\0"); PARAM (4, "Ntax", DoDimensionsParm, "\0"); PARAM (5, "Nchar", DoDimensionsParm, "\0"); PARAM (6, "Interleave", DoFormatParm, "Yes|No|\0"); PARAM (7, "Datatype", DoFormatParm, "Dna|Rna|Protein|Restriction|Standard|Continuous|Mixed|\0"); PARAM (8, "Gap", DoFormatParm, "\0"); PARAM (9, "Missing", DoFormatParm, "\0"); PARAM (10, "Matchchar", DoFormatParm, "\0"); PARAM (11, "MatrixInfo", DoMatrixParm, "\0"); PARAM (12, "Filename", DoExecuteParm, "\0"); PARAM (13, "Autoclose", DoSetParm, "Yes|No|\0"); PARAM (14, "Partition", DoSetParm, "\0"); PARAM (15, "Xxxxxxxxxx", DoCharsetParm, "\0"); PARAM (16, "Xxxxxxxxxx", DoPartitionParm, "\0"); PARAM (17, "Seed", DoMcmcParm, "\0"); PARAM (18, "Ngen", DoMcmcParm, "\0"); PARAM (19, "Samplefreq", DoMcmcParm, "\0"); PARAM (20, "Printfreq", DoMcmcParm, "\0"); PARAM (21, "Nchains", DoMcmcParm, "\0"); PARAM (22, "Temp", DoMcmcParm, "\0"); PARAM (23, "Filename", DoMcmcParm, "\0"); PARAM (24, "Burnin", DoMcmcParm, "\0"); PARAM (25, "Starttree", DoMcmcParm, "Random|Current|User|Parsimony|NJ|\0"); PARAM (26, "Nperts", DoMcmcParm, "\0"); PARAM (27, "Savebrlens", DoMcmcParm, "Yes|No|\0"); PARAM (28, "Nucmodel", DoLsetParm, "4by4|Doublet|Codon|Protein|\0"); PARAM (29, "Nst", DoLsetParm, "1|2|6|Mixed|\0"); PARAM (30, "Aamodel", DoLsetParm, "Poisson|Equalin|Jones|Dayhoff|Mtrev|Mtmam|Wag|Rtrev|Cprev|Vt|Blosum|Blossum|LG|\0"); PARAM (31, "Parsmodel", DoLsetParm, "Yes|No|\0"); PARAM (32, "Omegavar", DoLsetParm, "Equal|Ny98|M3|M10|\0"); PARAM (33, "Code", DoLsetParm, "Universal|Vertmt|Invermt|Mycoplasma|Yeast|Ciliate|Echinoderm|Euplotid|Metmt|\0"); PARAM (34, "Coding", DoLsetParm, "All|Variable|Informative|Nosingletons|Noabsencesites|Nopresencesites|Nosingletonpresence|Nosingletonabsence|\0"); PARAM (35, "Seqerror", DoPrsetParm, "\0"); PARAM (36, "Tratiopr", DoPrsetParm, "Beta|Fixed|\0"); PARAM (37, "Revmatpr", DoPrsetParm, "Dirichlet|Fixed|\0"); PARAM (38, "Omegapr", DoPrsetParm, "Dirichlet|Fixed|\0"); PARAM (39, "Statefreqpr", DoPrsetParm, "Dirichlet|Fixed|\0"); PARAM (40, "Ngammacat", DoLsetParm, "\0"); PARAM (41, "Shapepr", DoPrsetParm, "Uniform|Exponential|Fixed|\0"); PARAM (42, "Ratecorrpr", DoPrsetParm, "Uniform|Fixed|\0"); PARAM (43, "Pinvarpr", DoPrsetParm, "Uniform|Fixed|\0"); PARAM (44, "Covswitchpr", DoPrsetParm, "Uniform|Exponential|Fixed|\0"); PARAM (45, "Xxxxxxxxxx", DoExcludeParm, "\0"); PARAM (46, "Xxxxxxxxxx", DoIncludeParm, "\0"); PARAM (47, "Xxxxxxxxxx", DoDeleteParm, "\0"); PARAM (48, "Xxxxxxxxxx", DoRestoreParm, "\0"); PARAM (49, "Xxxxxxxxxx", DoTaxasetParm, "\0"); PARAM (50, "Xxxxxxxxxx", DoHelpParm, "\0"); PARAM (51, "Applyto", DoLsetParm, "\0"); PARAM (52, "Rates", DoLsetParm, "Equal|Gamma|LNorm|Propinv|Invgamma|Adgamma|Kmixture|\0"); PARAM (53, "Covarion", DoLsetParm, "Yes|No|\0"); PARAM (54, "Applyto", DoPrsetParm, "\0"); PARAM (55, "Tratio", DoLinkParm, "\0"); PARAM (56, "Revmat", DoLinkParm, "\0"); PARAM (57, "Omega", DoLinkParm, "\0"); PARAM (58, "Statefreq", DoLinkParm, "\0"); PARAM (59, "Shape", DoLinkParm, "\0"); PARAM (60, "Pinvar", DoLinkParm, "\0"); PARAM (61, "Correlation", DoLinkParm, "\0"); PARAM (62, "Ratemultiplier", DoLinkParm, "\0"); PARAM (63, "Switchrates", DoLinkParm, "\0"); PARAM (64, "Symdirihyperpr", DoPrsetParm, "Uniform|Exponential|Fixed|\0"); PARAM (65, "Xxxxxxxxxx", DoCtypeParm, "\0"); PARAM (66, "Xxxxxxxxxx", DoConstraintParm, "\0"); PARAM (67, "Topologypr", DoPrsetParm, "Uniform|Constraints|Fixed|Speciestree|\0"); PARAM (68, "Brlenspr", DoPrsetParm, "Unconstrained|Clock|Relaxedclock|Fixed|\0"); PARAM (69, "Speciationpr", DoPrsetParm, "Uniform|Exponential|Fixed|\0"); PARAM (70, "Extinctionpr", DoPrsetParm, "Beta|Exponential|Fixed|\0"); PARAM (71, "Popsizepr", DoPrsetParm, "Lognormal|Uniform|Gamma|Normal|Fixed|\0"); PARAM (72, "Topology", DoLinkParm, "\0"); PARAM (73, "Brlens", DoLinkParm, "\0"); PARAM (74, "Speciationrate", DoLinkParm, "\0"); PARAM (75, "Extinctionrate", DoLinkParm, "\0"); PARAM (76, "Popsize", DoLinkParm, "\0"); PARAM (77, "Ratepr", DoPrsetParm, "Variable|Dirichlet|Fixed|\0"); PARAM (78, "Xxxxxxxxxx", DoOutgroupParm, "\0"); PARAM (79, "Xxxxxxxxxx", DoTreeParm, "\0"); PARAM (80, "Filename", DoSumtParm, "\0"); PARAM (81, "Burnin", DoSumtParm, "\0"); PARAM (82, "Contype", DoSumtParm, "Halfcompat|Allcompat|\0"); PARAM (83, "Xxxxxxxxxx", DoTranslateParm, "\0"); PARAM (84, "Swapfreq", DoMcmcParm, "\0"); PARAM (85, "Start", DoLogParm, "\0"); PARAM (86, "Stop", DoLogParm, "\0"); PARAM (87, "Filename", DoLogParm, "\0"); PARAM (88, "Append", DoLogParm, "\0"); PARAM (89, "Replace", DoLogParm, "\0"); PARAM (90, "Nbetacat", DoLsetParm, "\0"); PARAM (91, "Augment", DoLsetParm, "Yes|No|\0"); PARAM (92, "Xxxxxxxxxx", DoPairsParm, "\0"); PARAM (93, "Xxxxxxxxxx", DoBreaksParm, "\0"); PARAM (94, "Nowarnings", DoSetParm, "Yes|No|\0"); PARAM (95, "Showtreeprobs", DoSumtParm, "Yes|No|\0"); PARAM (96, "Filename", DoSumpParm, "\0"); PARAM (97, "Burnin", DoSumpParm, "\0"); PARAM (98, "Reweight", DoMcmcParm, "\0"); PARAM (99, "Noop", DoMcmcParm, "\0"); PARAM (100, "Ny98omega1pr", DoPrsetParm, "Beta|Fixed|\0"); PARAM (101, "Ny98omega3pr", DoPrsetParm, "Uniform|Exponential|Fixed|\0"); PARAM (102, "Codoncatfreqs", DoPrsetParm, "Dirichlet|Fixed|\0"); PARAM (103, "Sampleprob", DoPrsetParm, "\0"); PARAM (104, "Aamodelpr", DoPrsetParm, "Fixed|Mixed|\0"); PARAM (105, "Aamodel", DoLinkParm, "\0"); PARAM (106, "Filename", DoPlotParm, "\0"); PARAM (107, "Parameter", DoPlotParm, "\0"); PARAM (108, "Match", DoPlotParm, "Perfect|Consistentwith|All|\0"); PARAM (109, "Burnin", DoPlotParm, "\0"); PARAM (110, "Brownscalepr", DoPrsetParm, "Uniform|Gamma|Fixed|\0"); PARAM (111, "Browncorrpr", DoPrsetParm, "Uniform|Fixed|\0"); PARAM (112, "Pbf", DoMcmcParm, "Yes|No|\0"); PARAM (113, "Pbfinitburnin", DoMcmcParm, "\0"); PARAM (114, "Pbfsamplefreq", DoMcmcParm, "\0"); PARAM (115, "Pbfsampletime", DoMcmcParm, "\0"); PARAM (116, "Pbfsampleburnin",DoMcmcParm, "\0"); PARAM (117, "Growthpr", DoPrsetParm, "Uniform|Exponential|Fixed|Normal|\0"); PARAM (118, "Growthrate", DoLinkParm, "\0"); PARAM (119, "Xxxxxxxxxx", DoCalibrateParm, "Unconstrained|Fixed|Uniform|Offsetexponential|Truncatednormal|Lognormal|Offsetlognormal|Gamma|Offsetgamma|\0"); PARAM (120, "Calwaitpr", DoPrsetParm, "Exponential|Fixed|\0"); /* not used but leave it in to not destroy mapping to commands */ PARAM (121, "M3omegapr", DoPrsetParm, "Exponential|Fixed|\0"); PARAM (122, "Applyto", DoReportParm, "\0"); PARAM (123, "Tratio", DoReportParm, "Dirichlet|Ratio|\0"); PARAM (124, "Revmat", DoReportParm, "Dirichlet|Ratio|\0"); PARAM (125, "Ratemult", DoReportParm, "Dirichlet|Scaled|Ratio|\0"); PARAM (126, "Filename", DoManualParm, "\0"); PARAM (127, "Filename1", DoCompareTreeParm, "\0"); PARAM (128, "Filename2", DoCompareTreeParm, "\0"); PARAM (129, "Outputname", DoCompareTreeParm, "\0"); PARAM (130, "Burnin", DoCompareTreeParm, "\0"); PARAM (131, "Ploidy", DoLsetParm, "Haploid|Diploid|Zlinked|\0"); PARAM (132, "Swapadjacent", DoMcmcParm, "Yes|No|\0"); PARAM (133, "Treeagepr", DoPrsetParm, "Fixed|Uniform|Offsetexponential|Truncatednormal|Lognormal|Offsetlognormal|Gamma|Offsetgamma|\0"); PARAM (134, "Ancstates", DoReportParm, "Yes|No|\0"); PARAM (135, "Siterates", DoReportParm, "Yes|No|\0"); PARAM (136, "Possel", DoReportParm, "Yes|No|\0"); PARAM (137, "Plot", DoSumpParm, "Yes|No|\0"); PARAM (138, "Table", DoSumpParm, "Yes|No|\0"); PARAM (139, "Minprob", DoSumpParm, "\0"); PARAM (140, "Printtofile", DoSumpParm, "Yes|No|\0"); PARAM (141, "Outputname", DoSumpParm, "\0"); PARAM (142, "Redirect", DoMcmcParm, "Yes|No|\0"); PARAM (143, "Swapseed", DoMcmcParm, "\0"); PARAM (144, "Runidseed", DoMcmcParm, "\0"); PARAM (145, "Quitonerror", DoSetParm, "Yes|No|\0"); PARAM (146, "Savebrparams", DoSumtParm, "Yes|No|\0"); PARAM (147, "Minbrparamfreq", DoSumtParm, "\0"); PARAM (148, "Minpartfreq", DoMcmcParm, "\0"); PARAM (149, "Allchains", DoMcmcParm, "Yes|No|\0"); PARAM (150, "Mcmcdiagn", DoMcmcParm, "Yes|No|\0"); PARAM (151, "Diagnfreq", DoMcmcParm, "\0"); PARAM (152, "Nruns", DoMcmcParm, "\0"); PARAM (153, "Stoprule", DoMcmcParm, "Yes|No|\0"); PARAM (154, "Stopval", DoMcmcParm, "\0"); PARAM (155, "Relburnin", DoMcmcParm, "Yes|No|\0"); PARAM (156, "Burninfrac", DoMcmcParm, "\0"); PARAM (157, "Allcomps", DoMcmcParm, "Yes|No|\0"); PARAM (158, "Printall", DoMcmcParm, "Yes|No|\0"); PARAM (159, "Printmax", DoMcmcParm, "\0"); PARAM (160, "Data", DoMcmcParm, "Yes|No|\0"); PARAM (161, "Nruns", DoSumpParm, "\0"); PARAM (162, "Allruns", DoSumpParm, "Yes|No|\0"); PARAM (163, "Nruns", DoSumtParm, "\0"); PARAM (164, "Ntrees", DoSumtParm, "\0"); PARAM (165, "Calctreeprobs", DoSumtParm, "Yes|No|\0"); PARAM (166, "Ordertaxa", DoMcmcParm, "Yes|No|\0"); PARAM (167, "Ordertaxa", DoSumtParm, "Yes|No|\0"); PARAM (168, "Aarevmatpr", DoPrsetParm, "Dirichlet|Fixed|\0"); PARAM (169, "Nswaps", DoMcmcParm, "\0"); PARAM (170, "Autoreplace", DoSetParm, "Yes|No|\0"); PARAM (171, "Npthreads", DoSetParm, "\0"); PARAM (172, "Cppratepr", DoPrsetParm, "Fixed|Exponential|\0"); PARAM (173, "Cppmultdevpr", DoPrsetParm, "Fixed|\0"); PARAM (174, "TK02varpr", DoPrsetParm, "Fixed|Exponential|Uniform|\0"); PARAM (175, "Tfile", DoSumtParm, "\0"); PARAM (176, "Pfile", DoSumpParm, "\0"); PARAM (177, "Autocomplete", DoSumtParm, "Yes|No|\0"); PARAM (178, "Autocomplete", DoSumpParm, "Yes|No|\0"); PARAM (179, "Userlevel", DoSetParm, "Standard|Developer|\0"); PARAM (180, "Allavailable", DoShowmovesParm, "Yes|No|\0"); PARAM (181, "Seed", DoSetParm, "\0"); PARAM (182, "Swapseed", DoSetParm, "\0"); PARAM (183, "Clockratepr", DoPrsetParm, "Fixed|Normal|Lognormal|Exponential|Gamma|\0"); PARAM (184, "Nodeagepr", DoPrsetParm, "Unconstrained|Calibrated|\0"); PARAM (185, "Clockvarpr", DoPrsetParm, "Strict|Cpp|TK02|WN|IGR|ILN|Mixed|\0"); PARAM (186, "Xxxxxxxxxx", DoPropsetParm, "\0"); PARAM (187, "Xxxxxxxxxx", DoStartvalsParm, "\0"); PARAM (188, "Usegibbs", DoLsetParm, "Yes|No|\0"); PARAM (189, "Gibbsfreq", DoLsetParm, "\0"); PARAM (190, "Checkpoint", DoMcmcParm, "Yes|No|\0"); PARAM (191, "Checkfreq", DoMcmcParm, "\0"); PARAM (192, "Tree", DoReportParm, "Topology|Brlens|\0"); PARAM (193, "Cpprate", DoLinkParm, "\0"); PARAM (194, "Cppmultdev", DoLinkParm, "\0"); PARAM (195, "Cppevents", DoLinkParm, "\0"); PARAM (196, "TK02var", DoLinkParm, "\0"); PARAM (197, "TK02branchrates",DoLinkParm, "\0"); PARAM (198, "Savetrees", DoMcmcParm, "Yes|No|\0"); PARAM (199, "Diagnstat", DoMcmcParm, "Avgstddev|Maxstddev|\0"); PARAM (200, "Startparams", DoMcmcParm, "Reset|Current|\0"); PARAM (201, "Characters", DoBeginParm, "\0"); PARAM (202, "Startingtrees", DoMcmcParm, "\0"); PARAM (203, "Xxxxxxxxxx", DoUserTreeParm, "\0"); PARAM (204, "Outputname", DoSumtParm, "\0"); PARAM (205, "Table", DoSumtParm, "Yes|No|\0"); PARAM (206, "Summary", DoSumtParm, "Yes|No|\0"); PARAM (207, "Consensus", DoSumtParm, "Yes|No|\0"); PARAM (208, "Minpartfreq", DoSumtParm, "\0"); PARAM (209, "Relburnin", DoSumtParm, "Yes|No|\0"); PARAM (210, "Burninfrac", DoSumtParm, "\0"); PARAM (211, "Relburnin", DoSumpParm, "Yes|No|\0"); PARAM (212, "Burninfrac", DoSumpParm, "\0"); PARAM (213, "Append", DoMcmcParm, "Yes|No|\0"); PARAM (214, "Autotune", DoMcmcParm, "Yes|No|\0"); PARAM (215, "Tunefreq", DoMcmcParm, "\0"); PARAM (216, "Scientific", DoSetParm, "Yes|No|\0"); PARAM (217, "Siteomega", DoReportParm, "Yes|No|\0"); PARAM (218, "IGRvarpr", DoPrsetParm, "Fixed|Exponential|Uniform|\0"); PARAM (219, "Symbols", DoFormatParm, "\0"); PARAM (220, "Equate", DoFormatParm, "\0"); PARAM (221, "Relburnin", DoCompareTreeParm, "Yes|No|\0"); PARAM (222, "Burninfrac", DoCompareTreeParm, "\0"); PARAM (223, "Minpartfreq", DoCompareTreeParm, "\0"); PARAM (224, "Relburnin", DoPlotParm, "Yes|No|\0"); PARAM (225, "Burninfrac", DoPlotParm, "\0"); PARAM (226, "Taxa", DoBeginParm, "\0"); PARAM (227, "Xxxxxxxxxx", DoBeginParm, "\0"); PARAM (228, "Xxxxxxxxxx", DoTaxlabelsParm, "\0"); PARAM (229, "Dir", DoSetParm, "\0"); PARAM (230, "Conformat", DoSumtParm, "Figtree|Simple|\0"); PARAM (231, "Hpd", DoSumpParm, "Yes|No|\0"); PARAM (232, "Hpd", DoSumtParm, "Yes|No|\0"); PARAM (233, "Usebeagle", DoSetParm, "Yes|No|\0"); PARAM (234, "Beagledevice", DoSetParm, "Cpu|Gpu|\0"); PARAM (235, "Beagleprecision",DoSetParm, "Single|Double|\0"); PARAM (236, "Beaglesse", DoSetParm, "Yes|No|\0"); PARAM (237, "Beagleopenmp", DoSetParm, "Yes|No|\0"); /* not in use */ PARAM (238, "Beaglethreads", DoSetParm, "Yes|No|\0"); PARAM (239, "Beaglescaling", DoSetParm, "Always|Dynamic|\0"); PARAM (240, "Beaglefreq", DoSetParm, "\0"); PARAM (241, "Popvarpr", DoPrsetParm, "Equal|Variable|\0"); PARAM (242, "IGRvar", DoLinkParm, "\0"); PARAM (243, "IGRbranchrates", DoLinkParm, "\0"); PARAM (244, "Xxxxxxxxxx", DoSpeciespartitionParm, "\0"); PARAM (245, "Speciespartition", DoSetParm, "\0"); PARAM (246, "Revratepr", DoPrsetParm, "Symdir|\0"); PARAM (247, "Samplestrat", DoPrsetParm, "Random|Diversity|Cluster|FossilTip|\0"); PARAM (248, "Burninss", DoSsParm, "\0"); PARAM (249, "Nsteps", DoSsParm, "\0"); PARAM (250, "Alpha", DoSsParm, "\0"); PARAM (251, "WNvarpr", DoPrsetParm, "Fixed|Exponential|Uniform|\0"); PARAM (252, "WNvar", DoLinkParm, "\0"); PARAM (253, "WNbranchrates", DoLinkParm, "\0"); PARAM (254, "ILNvarpr", DoPrsetParm, "Fixed|Exponential|Uniform|\0"); PARAM (255, "ILNvar", DoLinkParm, "\0"); PARAM (256, "ILNbranchlens", DoLinkParm, "\0"); PARAM (257, "FromPrior", DoSsParm, "Yes|No|\0"); PARAM (258, "Filename", DoSumSsParm, "\0"); PARAM (259, "Burnin", DoSumSsParm, "\0"); PARAM (260, "Nruns", DoSumSsParm, "\0"); PARAM (261, "Allruns", DoSumSsParm, "Yes|No|\0"); PARAM (262, "Askmore", DoSumSsParm, "Yes|No|\0"); PARAM (263, "Relburnin", DoSumSsParm, "Yes|No|\0"); PARAM (264, "Burninfrac", DoSumSsParm, "\0"); PARAM (265, "Discardfrac", DoSumSsParm, "\0"); PARAM (266, "Smoothing", DoSumSsParm, "\0"); PARAM (267, "Steptoplot", DoSumSsParm, "\0"); PARAM (268, "Precision", DoSetParm, "\0"); PARAM (269, "Fossilizationpr", DoPrsetParm, "Beta|Exponential|Fixed|\0"); PARAM (270, "Browncorr", DoLinkParm, "\0"); PARAM (271, "Generatepr", DoPrsetParm, "Variable|Fixed|\0"); PARAM (272, "Mixedvarpr", DoPrsetParm, "Fixed|Exponential|Uniform|\0"); PARAM (273, "Mixedvar", DoLinkParm, "\0"); PARAM (274, "Mixedbrchrates", DoLinkParm, "\0"); PARAM (275, "Beagleresource", DoSetParm, "\0"); PARAM (276, "Nlnormcat", DoLsetParm, "\0"); PARAM (277, "Nmixtcat", DoLsetParm, "\0"); PARAM (278, "Beaglethreadcount", DoSetParm, "\0"); PARAM (279, "Beaglefloattips",DoSetParm, "Yes|No|\0"); PARAM (280, "Statefreqmodel", DoLsetParm, "Stationary|Directional|Mixed|\0"); //SK PARAM (281, "Rootfreqpr", DoPrsetParm, "Dirichlet|Fixed|\0"); //SK PARAM (282, "Statefrmod", DoLsetParm, "Stationary|Directional|Mixed|\0"); //SK /* NOTE: If a change is made to the parameter table, make certain you change NUMPARAMS (now 283; one more than last index) at the top of this file. */ /* CmdType commands[] */ } void ShowNodes (TreeNode *p, int indent, int isThisTreeRooted) { if (p != NULL) { printf (" "); if (p->left == NULL && p->right == NULL && p->anc != NULL) { printf ("%*cN %d (l=%d r=%d a=%d) %1.15lf (%s) isDated=%d ", indent, ' ', Dex(p), Dex(p->left), Dex(p->right), Dex(p->anc), p->length, p->label, p->isDated); } else if (p->left != NULL && p->right == NULL && p->anc == NULL) { if (isThisTreeRooted == NO) { if (p->label[0] == '\0' || p->label[0] == '\n' || p->label[0] == ' ') printf ("%*cN %d (l=%d r=%d a=%d) (---) ", indent, ' ', Dex(p), Dex(p->left), Dex(p->right), Dex(p->anc)); else printf ("%*cN %d (l=%d r=%d a=%d) (%s) ", indent, ' ', Dex(p), Dex(p->left), Dex(p->right), Dex(p->anc), p->label); } else { printf ("%*cN %d (l=%d r=%d a=%d) X.XXXXXX ", indent, ' ', Dex(p), Dex(p->left), Dex(p->right), Dex(p->anc)); } } else { if (p->anc != NULL) { if (p->anc->anc == NULL && isThisTreeRooted == YES) printf ("%*cN %d (l=%d r=%d a=%d) X.XXXXXX ", indent, ' ', Dex(p), Dex(p->left), Dex(p->right), Dex(p->anc)); else printf ("%*cN %d (l=%d r=%d a=%d) %1.15lf ", indent, ' ', Dex(p), Dex(p->left), Dex(p->right), Dex(p->anc), p->length); } } if (isThisTreeRooted == YES) printf ("depth=%1.15lf\n", p->nodeDepth); else printf ("\n"); ShowNodes (p->left, indent + 2, isThisTreeRooted); ShowNodes (p->right, indent + 2, isThisTreeRooted); } } int StandID (char nuc) { /* Note that if you change how many states are recognized, you need to look at IsMissing */ char n = nuc; if (n == '0') { return 1; } else if (n == '1') { return 2; } else if (n == '2') { return 4; } else if (n == '3') { return 8; } else if (n == '4') { return 16; } else if (n == '5') { return 32; } else if (n == '6') { return 64; } else if (n == '7') { return 128; } else if (n == '8') { return 256; } else if (n == '9') { return 512; } else if (n == 'A' || n == 'a') { return 1024; } else if (n == 'B' || n == 'b') { return 2048; } else if (n == 'C' || n == 'c') { return 4096; } else if (n == 'D' || n == 'd') { return 8192; } else if (n == 'E' || n == 'e') { return 16384; } else if (n == 'F' || n == 'f') { return 32768; } else if (n == 'G' || n == 'g') { return 65536; } else if (n == 'H' || n == 'h') { return 131072; } else if (n == 'I' || n == 'i') { return 262144; } else if (n == 'J' || n == 'j') { return 524288; } else if (n == 'K' || n == 'k') { return 1048576; } else if (n == 'L' || n == 'l') { return 2097152; } else if (n == 'M' || n == 'm') { return 4194304; } else if (n == 'N' || n == 'n') { return 8388608; } else if (n == missingId) { return MISSING; } else if (n == gapId) { return GAP; } else return -1; } void State_CODON (char *state, int code, int division) { state[0] = StateCode_NUC4(modelParams[division].codonNucs[code][0]); state[1] = StateCode_NUC4(modelParams[division].codonNucs[code][1]); state[2] = StateCode_NUC4(modelParams[division].codonNucs[code][2]); state[3] = '\0'; } void State_DOUBLET (char *state, int code) { state[0] = code/4 + 'A'; state[1] = code%4 + 'A'; state[2] = '\0'; } int StateCode_AA (int n) { if (n == 0) return 'A'; /* Ala */ else if (n == 1) return 'R'; /* Arg */ else if (n == 2) return 'N'; /* Asn */ else if (n == 3) return 'D'; /* Asp */ else if (n == 4) return 'C'; /* Cys */ else if (n == 5) return 'Q'; /* Gln */ else if (n == 6) return 'E'; /* Glu */ else if (n == 7) return 'G'; /* Gly */ else if (n == 8) return 'H'; /* His */ else if (n == 9) return 'I'; /* Ile */ else if (n == 10) return 'L'; /* Leu */ else if (n == 11) return 'K'; /* Lys */ else if (n == 12) return 'M'; /* Met */ else if (n == 13) return 'F'; /* Phe */ else if (n == 14) return 'P'; /* Pro */ else if (n == 15) return 'S'; /* Ser */ else if (n == 16) return 'T'; /* Thr */ else if (n == 17) return 'W'; /* Trp */ else if (n == 18) return 'Y'; /* Tyr */ else if (n == 19) return 'V'; /* Val */ else return '?'; } int StateCode_NUC4 (int n) { if (n == 0) return 'A'; else if (n == 1) return 'C'; else if (n == 2) return 'G'; else if (n == 3) return 'T'; else return '?'; } int StateCode_Std (int n) { /* max 24 states: 0-9 A-N */ if (n <= 9 && n >= 0) return '0' + n; else if (n == 10) return 'A'; else if (n == 11) return 'B'; else if (n == 12) return 'C'; else if (n == 13) return 'D'; else if (n == 14) return 'E'; else if (n == 15) return 'F'; else if (n == 16) return 'G'; else if (n == 17) return 'H'; else if (n == 18) return 'I'; else if (n == 19) return 'J'; else if (n == 20) return 'K'; else if (n == 21) return 'L'; else if (n == 22) return 'M'; else if (n == 23) return 'N'; else return '?'; } void WhatVariableExp (BitsLong exp, char *st) { int n; strcpy (st, ""); n = 0; if (exp == 0) strcat(st, " nothing"); else { if ((exp & Expecting(COMMAND)) == Expecting(COMMAND)) { strcat(st, " command"); n++; } if ((exp & Expecting(PARAMETER)) == Expecting(PARAMETER)) { if (n > 0) strcat(st, " or"); strcat(st, " parameter"); n++; } if ((exp & Expecting(EQUALSIGN)) == Expecting(EQUALSIGN)) { if (n > 0) strcat(st, " or"); strcat(st, " ="); n++; } if ((exp & Expecting(COLON)) == Expecting(COLON)) { if (n > 0) strcat(st, " or"); strcat(st, " :"); n++; } if ((exp & Expecting(SEMICOLON)) == Expecting(SEMICOLON)) { if (n > 0) strcat(st, " or"); strcat(st, " ;"); n++; } if ((exp & Expecting(COMMA)) == Expecting(COMMA)) { if (n > 0) strcat(st, " or"); strcat(st, " ,"); n++; } if ((exp & Expecting(POUNDSIGN)) == Expecting(POUNDSIGN)) { if (n > 0) strcat(st, " or"); strcat(st, " #"); n++; } if ((exp & Expecting(QUESTIONMARK)) == Expecting(QUESTIONMARK)) { if (n > 0) strcat(st, " or"); strcat(st, " ?"); n++; } if ((exp & Expecting(DASH)) == Expecting(DASH)) { if (n > 0) strcat(st, " or"); strcat(st, " -"); n++; } if ((exp & Expecting(LEFTPAR)) == Expecting(LEFTPAR)) { if (n > 0) strcat(st, " or"); strcat(st, " ("); n++; } if ((exp & Expecting(RIGHTPAR)) == Expecting(RIGHTPAR)) { if (n > 0) strcat(st, " or"); strcat(st, " )"); n++; } if ((exp & Expecting(LEFTCOMMENT)) == Expecting(LEFTCOMMENT)) { if (n > 0) strcat(st, " or"); strcat(st, " ["); n++; } if ((exp & Expecting(RIGHTCOMMENT)) == Expecting(RIGHTCOMMENT)) { if (n > 0) strcat(st, " or"); strcat(st, " ]"); n++; } if ((exp & Expecting(ALPHA)) == Expecting(ALPHA)) { if (n > 0) strcat(st, " or"); strcat(st, " "); n++; } if ((exp & Expecting(NUMBER)) == Expecting(NUMBER)) { if (n > 0) strcat(st, " or"); strcat(st, " "); n++; } if ((exp & Expecting(RETURNSYMBOL)) == Expecting(RETURNSYMBOL)) { if (n > 0) strcat(st, " or"); strcat(st, " return"); n++; } if ((exp & Expecting(ASTERISK)) == Expecting(ASTERISK)) { if (n > 0) strcat(st, " or"); strcat(st, " *"); n++; } if ((exp & Expecting(BACKSLASH)) == Expecting(BACKSLASH)) { if (n > 0) strcat(st, " or"); strcat(st, " /"); n++; } if ((exp & Expecting(BACKSLASH)) == Expecting(BACKSLASH)) { if (n > 0) strcat(st, " or"); strcat(st, " \\"); n++; } if ((exp & Expecting(EXCLAMATIONMARK)) == Expecting(EXCLAMATIONMARK)) { if (n > 0) strcat(st, " or"); strcat(st, " !"); n++; } if ((exp & Expecting(PERCENT)) == Expecting(PERCENT)) { if (n > 0) strcat(st, " or"); strcat(st, " %"); n++; } if ((exp & Expecting(LEFTCURL)) == Expecting(LEFTCURL)) { if (n > 0) strcat(st, " or"); strcat(st, " {"); n++; } if ((exp & Expecting(RIGHTCURL)) == Expecting(RIGHTCURL)) { if (n > 0) strcat(st, " or"); strcat(st, " }"); n++; } if ((exp & Expecting(WEIRD)) == Expecting(WEIRD)) { if (n > 0) strcat(st, " or"); strcat(st, " "); n++; } if ((exp & Expecting(VERTICALBAR)) == Expecting(VERTICALBAR)) { if (n > 0) strcat(st, " or"); strcat(st, " |"); n++; } if ((exp & Expecting(UNKNOWN_TOKEN_TYPE)) == Expecting(UNKNOWN_TOKEN_TYPE)) { if (n > 0) strcat(st, " or"); strcat(st, " no clue"); n++; } } } char WhichAA (int x) { if (x == 1) return ('A'); else if (x == 2) return ('R'); else if (x == 4) return ('N'); else if (x == 8) return ('D'); else if (x == 16) return ('C'); else if (x == 32) return ('Q'); else if (x == 64) return ('E'); else if (x == 128) return ('G'); else if (x == 256) return ('H'); else if (x == 512) return ('I'); else if (x == 1024) return ('L'); else if (x == 2048) return ('K'); else if (x == 4096) return ('M'); else if (x == 8192) return ('F'); else if (x == 16384) return ('P'); else if (x == 32768) return ('S'); else if (x == 65536) return ('T'); else if (x == 131072) return ('W'); else if (x == 262144) return ('Y'); else if (x == 524288) return ('V'); else if (x > 0 && x < 524288) return ('*'); else if (x == MISSING) return ('?'); else if (x == GAP) return ('-'); else return (' '); } MrBFlt WhichCont (int x) { return ((MrBFlt)x / 10000.0); } char WhichNuc (int x) { if (x == 1) return ('A'); else if (x == 2) return ('C'); else if (x == 3) return ('M'); else if (x == 4) return ('G'); else if (x == 5) return ('R'); else if (x == 6) return ('S'); else if (x == 7) return ('V'); else if (x == 8) return ('T'); else if (x == 9) return ('W'); else if (x == 10) return ('Y'); else if (x == 11) return ('H'); else if (x == 12) return ('K'); else if (x == 13) return ('D'); else if (x == 14) return ('B'); else if (x == 15) return ('N'); else if (x == MISSING) return ('?'); else if (x == GAP) return ('-'); else return (' '); } char WhichRes (int x) { if (x == 1) return ('0'); else if (x == 2) return ('1'); else if (x == 3) return ('*'); else if (x == MISSING) return ('N'); else if (x == GAP) return ('-'); else return (' '); } char WhichStand (int x) { if (x == 1) return ('0'); else if (x == 2) return ('1'); else if (x == 4) return ('2'); else if (x == 8) return ('3'); else if (x == 16) return ('4'); else if (x == 32) return ('5'); else if (x == 64) return ('6'); else if (x == 128) return ('7'); else if (x == 256) return ('8'); else if (x == 512) return ('9'); else if (x == 1024) return ('a'); else if (x == 2048) return ('b'); else if (x == 4096) return ('c'); else if (x == 8192) return ('d'); else if (x == 16384) return ('e'); else if (x == 32768) return ('f'); else if (x == 65536) return ('g'); else if (x == 131072) return ('h'); else if (x == 262144) return ('i'); else if (x == 524288) return ('j'); else if (x == 1048576) return ('k'); else if (x == 2097152) return ('l'); else if (x == 4194304) return ('m'); else if (x == 8388608) return ('n'); else if (x > 0 && x < 8388608) return ('*'); else if (x == MISSING) return ('?'); else if (x == GAP) return ('-'); else return (' '); } NBISweden-MrBayes-ecfd55c/src/command.h000066400000000000000000000023761526212227600200030ustar00rootroot00000000000000#ifndef COMMAND_H_ #define COMMAND_H_ int AddString (char ***list, int len, char *token); BitsLong Expecting (int y); int CheckString (char **list, int len, char *token, int *matchIndex); int CheckStringValidity (char *s); int DoExecute (void); int FreeMatrix (void); int GetToken (char *token, int *tokenType, char **sourceH); int FindValidCommand (char *tk, int *numMatches); int IsArgValid (char *s, char *validArg); int IsIn (char ch, char *s); int IsSame (char *s1, char *s2); int IsWhite (char c); int ParseCommand (char *s); void ResetCharacterFlags (void); void ResetTaxaFlags (void); int RootUserTree (TreeNode *p); void SetUpParms (void); void ShowNodes (TreeNode *p, int indent, int isThisTreeRooted); int ShowTree (Tree *t); void State_CODON (char *state, int code, int division); void State_DOUBLET (char *state, int code); int StateCode_AA (int n); int StateCode_NUC4 (int n); int StateCode_Std (int n); char WhichAA (int x); char WhichNuc (int x); char WhichRes (int x); char WhichStand (int x); MrBFlt WhichCont (int x); #endif /* COMMAND_H_ */ NBISweden-MrBayes-ecfd55c/src/config.h.in000066400000000000000000000140531526212227600202320ustar00rootroot00000000000000/* src/config.h.in. Generated from configure.ac by autoheader. */ /* Define if Beagle support is enabled */ #undef BEAGLE_ENABLED /* Define to enable Beagle v3.1 extensions */ #undef BEAGLE_V3_ENABLED /* Compiler vendor */ #undef COMPILER_VENDOR /* Compiler version */ #undef COMPILER_VERSION /* AVX code path should not be compiled. */ #undef DISABLE_AVX /* FMA code path should not be compiled. */ #undef DISABLE_FMA /* SSE code path should not be compiled. */ #undef DISABLE_SSE /* Define to 1 to support Advanced Bit Manipulation */ #undef HAVE_ABM /* Define to 1 to support Multi-Precision Add-Carry Instruction Extensions */ #undef HAVE_ADX /* Define to 1 to support Advanced Encryption Standard New Instruction Set (AES-NI) */ #undef HAVE_AES /* Support Altivec instructions */ #undef HAVE_ALTIVEC /* Define to 1 to support Advanced Vector Extensions */ #undef HAVE_AVX /* Define to 1 to support Advanced Vector Extensions 2 */ #undef HAVE_AVX2 /* Define to 1 to support AVX-512 Byte and Word Instructions */ #undef HAVE_AVX512_BW /* Define to 1 to support AVX-512 Conflict Detection Instructions */ #undef HAVE_AVX512_CD /* Define to 1 to support AVX-512 Doubleword and Quadword Instructions */ #undef HAVE_AVX512_DQ /* Define to 1 to support AVX-512 Exponential & Reciprocal Instructions */ #undef HAVE_AVX512_ER /* Define to 1 to support AVX-512 Foundation Extensions */ #undef HAVE_AVX512_F /* Define to 1 to support AVX-512 Integer Fused Multiply Add Instructions */ #undef HAVE_AVX512_IFMA /* Define to 1 to support AVX-512 Conflict Prefetch Instructions */ #undef HAVE_AVX512_PF /* Define to 1 to support AVX-512 Vector Byte Manipulation Instructions */ #undef HAVE_AVX512_VBMI /* Define to 1 to support AVX-512 Vector Length Extensions */ #undef HAVE_AVX512_VL /* Define to 1 to support Bit Manipulation Instruction Set 1 */ #undef HAVE_BMI1 /* Define to 1 to support Bit Manipulation Instruction Set 2 */ #undef HAVE_BMI2 /* Define to 1 if you have the header file. */ #undef HAVE_FLOAT_H /* Define to 1 if you have the 'floor' function. */ #undef HAVE_FLOOR /* Define to 1 to support Fused Multiply-Add Extensions 3 */ #undef HAVE_FMA3 /* Define to 1 to support Fused Multiply-Add Extensions 4 */ #undef HAVE_FMA4 /* Define to 1 if you have the header file. */ #undef HAVE_HISTORY_H /* Define to 1 if you have the header file. */ #undef HAVE_INTTYPES_H /* Define to 1 if you have the 'm' library (-lm). */ #undef HAVE_LIBM /* Define if you have a readline compatible library */ #undef HAVE_LIBREADLINE /* Define to 1 if you have the header file. */ #undef HAVE_LIMITS_H /* Define to 1 if you have the header file. */ #undef HAVE_MEMORY_H /* Define to 1 if you have the 'memset' function. */ #undef HAVE_MEMSET /* Define to 1 to support Multimedia Extensions */ #undef HAVE_MMX /* Define to 1 to support Memory Protection Extensions */ #undef HAVE_MPX /* Define to 1 if you have the 'pow' function. */ #undef HAVE_POW /* Define to 1 to support Prefetch Vector Data Into Caches WT1 */ #undef HAVE_PREFETCHWT1 /* Define to 1 to support Digital Random Number Generator */ #undef HAVE_RDRND /* Define to 1 if you have the header file. */ #undef HAVE_READLINE_H /* Define if your readline library has \`add_history' */ #undef HAVE_READLINE_HISTORY /* Define to 1 if you have the header file. */ #undef HAVE_READLINE_HISTORY_H /* Define to 1 if you have the header file. */ #undef HAVE_READLINE_READLINE_H /* Define to 1 to support Secure Hash Algorithm Extension */ #undef HAVE_SHA /* Define to 1 if you have the 'sqrt' function. */ #undef HAVE_SQRT /* Define to 1 to support Streaming SIMD Extensions */ #undef HAVE_SSE /* Define to 1 to support Streaming SIMD Extensions */ #undef HAVE_SSE2 /* Define to 1 to support Streaming SIMD Extensions 3 */ #undef HAVE_SSE3 /* Define to 1 to support Streaming SIMD Extensions 4.1 */ #undef HAVE_SSE4_1 /* Define to 1 to support Streaming SIMD Extensions 4.2 */ #undef HAVE_SSE4_2 /* Define to 1 to support AMD Streaming SIMD Extensions 4a */ #undef HAVE_SSE4a /* Define to 1 to support Supplemental Streaming SIMD Extensions 3 */ #undef HAVE_SSSE3 /* Define to 1 if you have the header file. */ #undef HAVE_STDINT_H /* Define to 1 if you have the header file. */ #undef HAVE_STDIO_H /* Define to 1 if you have the header file. */ #undef HAVE_STDLIB_H /* Define to 1 if you have the 'strchr' function. */ #undef HAVE_STRCHR /* Define to 1 if you have the header file. */ #undef HAVE_STRINGS_H /* Define to 1 if you have the header file. */ #undef HAVE_STRING_H /* Define to 1 if you have the header file. */ #undef HAVE_SYS_STAT_H /* Define to 1 if you have the header file. */ #undef HAVE_SYS_TYPES_H /* Define to 1 if you have the header file. */ #undef HAVE_UNISTD_H /* Support VSX instructions */ #undef HAVE_VSX /* Define to 1 to support eXtended Operations Extensions */ #undef HAVE_XOP /* Define to 1 if the system has the type '_Bool'. */ #undef HAVE__BOOL /* Host CPU type */ #undef HOST_CPU /* Host triplet */ #undef HOST_TYPE /* Define if you want to enable MPI */ #undef MPI_ENABLED /* Name of package */ #undef PACKAGE /* Define to the address where bug reports for this package should be sent. */ #undef PACKAGE_BUGREPORT /* Define to the full name of this package. */ #undef PACKAGE_NAME /* Define to the full name and version of this package. */ #undef PACKAGE_STRING /* Define to the one symbol short name of this package. */ #undef PACKAGE_TARNAME /* Define to the home page for this package. */ #undef PACKAGE_URL /* Define to the version of this package. */ #undef PACKAGE_VERSION /* Define to 1 if all of the C89 standard headers exist (not just the ones required in a freestanding environment). This macro is provided for backward compatibility; new code need not use it. */ #undef STDC_HEADERS /* Version number of package */ #undef VERSION /* Define as 'unsigned int' if doesn't define. */ #undef size_t NBISweden-MrBayes-ecfd55c/src/likelihood.c000066400000000000000000014010611526212227600204760ustar00rootroot00000000000000/* * MrBayes 3 * * (c) 2002-2023 * * John P. Huelsenbeck * Dept. Integrative Biology * University of California, Berkeley * Berkeley, CA 94720-3140 * johnh@berkeley.edu * * Fredrik Ronquist * Swedish Museum of Natural History * Box 50007 * SE-10405 Stockholm, SWEDEN * fredrik.ronquist@nrm.se * * With important contributions by * * Paul van der Mark (paulvdm@sc.fsu.edu) * Maxim Teslenko (maxkth@gmail.com) * Chi Zhang (zhangchicool@gmail.com) * * and by many users (run 'acknowledgments' to see more info) * * This program is free software; you can redistribute it and/or * modify it under the terms of the GNU General Public License * as published by the Free Software Foundation; either version 2 * of the License, or (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details (www.gnu.org). * */ #include "bayes.h" #include "likelihood.h" #include "mbbeagle.h" #include "model.h" #include "utils.h" #define LIKE_EPSILON 1.0e-300 /* global variables declared here */ CLFlt *preLikeL; /* precalculated cond likes for left descendant */ CLFlt *preLikeR; /* precalculated cond likes for right descendant*/ CLFlt *preLikeA; /* precalculated cond likes for ancestor */ /* global variables used here but declared elsewhere */ extern int *chainId; extern int numLocalChains; extern int rateProbRowSize; /* size of rate probs for one chain one state */ extern MrBFlt **rateProbs; /* pointers to rate probs used by adgamma model */ /* local prototypes */ MrBFlt GetRate (int division, int chain); int RemoveNodeScalers(TreeNode *p, int division, int chain); #if defined (SSE_ENABLED) int RemoveNodeScalers_SSE(TreeNode *p, int division, int chain); #endif #if defined (AVX_ENABLED) int RemoveNodeScalers_AVX(TreeNode *p, int division, int chain); #endif int SetBinaryQMatrix (MrBFlt **a, int whichChain, int division); int SetNucQMatrix (MrBFlt **a, int n, int whichChain, int division, MrBFlt rateMult, MrBFlt *rA, MrBFlt *rS); int SetStdQMatrix (MrBFlt **a, int nStates, MrBFlt *bs, int cType); int SetProteinQMatrix (MrBFlt **a, int n, int whichChain, int division, MrBFlt rateMult); int UpDateCijk (int whichPart, int whichChain); int IsMissingC (CLFlt value) { if (value > (CLFlt)INT_MAX - 1.0) return YES; else return NO; } int BMVar_Cont (TreeNode *p, int division, int chain) { int i, k, c; MrBFlt baseRate, *catRate, theRate, length; CLFlt *vP, *vL, *vR, *mP, *mL, *mR, m_l, m_r; ModelInfo *m; m = &modelSettings[division]; /* Flip BM var space */ FlipTiProbsSpace (m, chain, p->index); /* get base rate */ baseRate = GetRate(division, chain); /* get category rates */ theRate = 1.0; if (m->shape != NULL) catRate = GetParamSubVals(m->shape, chain, state[chain]); else catRate = &theRate; /* find length which is sigma^2 * t */ if (m->cppEvents != NULL) length = GetParamSubVals(m->cppEvents, chain, state[chain])[p->index]; else if (m->tk02BranchRates != NULL) length = GetParamSubVals(m->tk02BranchRates, chain, state[chain])[p->index]; else if (m->wnBranchRates != NULL) length = GetParamSubVals(m->wnBranchRates, chain, state[chain])[p->index]; else if (m->ilnBranchRates != NULL) length = GetParamSubVals(m->ilnBranchRates, chain, state[chain])[p->index]; else if (m->igrBranchRates != NULL) length = GetParamSubVals(m->igrBranchRates, chain, state[chain])[p->index]; else if (m->mixedBrchRates != NULL) length = GetParamSubVals(m->mixedBrchRates, chain, state[chain])[p->index]; else length = p->length; /* find BM variance pointer */ vP = m->bmVars[m->tiProbsIndex[chain][p->index]]; mP = m->ancStates[m->condLikeIndex[chain][p->index]]; /* fill in values */ if (p->left != NULL && p->right != NULL) // internal node { vL = m->bmVars[m->tiProbsIndex[chain][p->left->index]]; vR = m->bmVars[m->tiProbsIndex[chain][p->right->index]]; mL = m->ancStates[m->condLikeIndex[chain][p->left->index]]; mR = m->ancStates[m->condLikeIndex[chain][p->right->index]]; for (k=0; knumRateCats; k++) for (c=0; cnumChars; c++) { i = k * (m->numChars) + c; /* find descendant states */ if (p->left->left == NULL) m_l = mL[c]; else m_l = mL[i]; if (p->right->left == NULL) m_r = mR[c]; else m_r = mR[i]; /* update BM variances accordingly */ if (IsMissingC(m_l) && IsMissingC(m_r)) vP[i] = 0.0; else if (IsMissingC(m_l)) vP[i] = length * baseRate * catRate[k] + vR[i]; else if (IsMissingC(m_r)) vP[i] = length * baseRate * catRate[k] + vL[i]; else vP[i] = length * baseRate * catRate[k] + (vL[i] * vR[i])/(vL[i] + vR[i]); } } else // tip { for (k=0; knumRateCats; k++) for (c=0; cnumChars; c++) { i = k * (m->numChars) + c; if (IsMissingC(mP[c])) vP[i] = 0.0; else vP[i] = length * baseRate * catRate[k]; } } return NO_ERROR; } int CondLikeDown_Cont (TreeNode *p, int division, int chain) { int i, k, c; CLFlt *lnL, *vL, *vR, *mP, *mL, *mR, m_l, m_r; ModelInfo *m; m = &modelSettings[division]; /* Flip log likelihood space */ FlipCondLikeSpace (m, chain, p->index); /* find log likelihood pointers */ lnL = m->condLikes[m->condLikeIndex[chain][p->index]]; /* find descendant variances */ vL = m->bmVars[m->tiProbsIndex[chain][p->left->index]]; vR = m->bmVars[m->tiProbsIndex[chain][p->right->index]]; /* find character states */ mL = m->ancStates[m->condLikeIndex[chain][p->left->index]]; mR = m->ancStates[m->condLikeIndex[chain][p->right->index]]; mP = m->ancStates[m->condLikeIndex[chain][p->index]]; /* likelihood space is arranged in numRateCats blocks, each block consists of numChars characters */ for (k=0; knumRateCats; k++) { for (c=0; cnumChars; c++) { i = k * (m->numChars) + c; /* find descendant states */ if (p->left->left == NULL) m_l = mL[c]; else m_l = mL[i]; if (p->right->left == NULL) m_r = mR[c]; else m_r = mR[i]; lnL[i] = 0.0; /* save present state and update lnL accordingly */ if (IsMissingC(m_l) && IsMissingC(m_r)) mP[i] = (CLFlt)INT_MAX; // missing else if (IsMissingC(m_l)) mP[i] = m_r; else if (IsMissingC(m_r)) mP[i] = m_l; else { mP[i] = (vR[i]*m_l + vL[i]*m_r) / (vL[i] + vR[i]); lnL[i] += log(2.0 * M_PI) + log(vL[i] + vR[i]); lnL[i] += powf(m_l - m_r, 2) / (vL[i] + vR[i]); lnL[i] *= -0.5; } } } return NO_ERROR; } int CondLikeRoot_Cont (TreeNode *p, int division, int chain) { int i, k, c; CLFlt *lnL, *vL, *vR, *vP, *mP, *mL, *mR, *mA, m_l, m_r, m_a; ModelInfo *m; m = &modelSettings[division]; /* Flip log likelihood space */ FlipCondLikeSpace (m, chain, p->index); /* find log likelihood pointers */ lnL = m->condLikes[m->condLikeIndex[chain][p->index]]; /* find descendant and present variances */ vL = m->bmVars[m->tiProbsIndex[chain][p->left->index]]; vR = m->bmVars[m->tiProbsIndex[chain][p->right->index]]; vP = m->bmVars[m->tiProbsIndex[chain][p->index]]; // vP has been transformed /* find character states */ mL = m->ancStates[m->condLikeIndex[chain][p->left->index]]; mR = m->ancStates[m->condLikeIndex[chain][p->right->index]]; mP = m->ancStates[m->condLikeIndex[chain][p->index]]; mA = m->ancStates[m->condLikeIndex[chain][p->anc->index]]; /* likelihood space is arranged in numRateCats blocks, each block consists of numChars characters */ for (k=0; knumRateCats; k++) { for (c=0; cnumChars; c++) { i = k * (m->numChars) + c; /* find descendant states */ m_a = mA[c]; if (p->left->left == NULL) m_l = mL[c]; else m_l = mL[i]; if (p->right->left == NULL) m_r = mR[c]; else m_r = mR[i]; lnL[i] = 0.0; /* save present state and update lnL accordingly */ if ((IsMissingC(m_l) && IsMissingC(m_r)) || (IsMissingC(m_l) && IsMissingC(m_a)) || (IsMissingC(m_r) && IsMissingC(m_a))) { mP[i] = (CLFlt)INT_MAX; // missing } else if (IsMissingC(m_a)) { mP[i] = (vR[i]*m_l + vL[i]*m_r) / (vL[i] + vR[i]); lnL[i] += log(2.0 * M_PI) + log(vL[i] + vR[i]); lnL[i] += powf(m_l - m_r, 2) / (vL[i] + vR[i]); lnL[i] *= -0.5; } else if (IsMissingC(m_r)) { mP[i] = m_l; lnL[i] += log(2.0 * M_PI) + log(vP[i]); lnL[i] += powf(m_a - mP[i], 2) / vP[i]; lnL[i] *= -0.5; } else if (IsMissingC(m_l)) { mP[i] = m_r; lnL[i] += log(2.0 * M_PI) + log(vP[i]); lnL[i] += powf(m_a - mP[i], 2) / vP[i]; lnL[i] *= -0.5; } else // calculate two normal densities { mP[i] = (vR[i]*m_l + vL[i]*m_r) / (vL[i] + vR[i]); lnL[i] += 2.0 * log(2.0 * M_PI) + log(vL[i] + vR[i]) + log(vP[i]); lnL[i] += powf(m_l - m_r, 2) / (vL[i] + vR[i]) + powf(m_a - mP[i], 2) / vP[i]; lnL[i] *= -0.5; } } } return NO_ERROR; } int CondLikeUp_Cont (TreeNode *p, int division, int chain) { /* This function computes, for every node, the "up message": a Gaussian belief about that node's own value based EXCLUSIVELY on information from outside its own subtree (i.e. excluding its own descendants). Combined with the node's own down-pass estimate (from CondLikeDown_Cont/CondLikeRoot_Cont, which only look at descendants), this gives the final (marginal posterior) ancestral state estimate -- see PrintAncStates_Cont, which performs that last combination at print time. */ int i, k, c; MrBFlt baseRate, *catRate, theRate, length, m1, v1, mA, vA, mS, vS; CLFlt *mUp, *vUp, *mSib, *vSib, *mUpA, *vUpA, *mAnchor; TreeNode *sib; ModelInfo *m; Tree *tree; m = &modelSettings[division]; tree = GetTree (m->brlens, chain, state[chain]); /* get base rate */ baseRate = GetRate(division, chain); /* get category rates */ theRate = 1.0; if (m->shape != NULL) catRate = GetParamSubVals(m->shape, chain, state[chain]); else catRate = &theRate; /* find length which is sigma^2 * t */ if (m->cppEvents != NULL) length = GetParamSubVals(m->cppEvents, chain, state[chain])[p->index]; else if (m->tk02BranchRates != NULL) length = GetParamSubVals(m->tk02BranchRates, chain, state[chain])[p->index]; else if (m->wnBranchRates != NULL) length = GetParamSubVals(m->wnBranchRates, chain, state[chain])[p->index]; else if (m->ilnBranchRates != NULL) length = GetParamSubVals(m->ilnBranchRates, chain, state[chain])[p->index]; else if (m->igrBranchRates != NULL) length = GetParamSubVals(m->igrBranchRates, chain, state[chain])[p->index]; else if (m->mixedBrchRates != NULL) length = GetParamSubVals(m->mixedBrchRates, chain, state[chain])[p->index]; else length = p->length; /* find where to store this node's up message */ mUp = m->ancStates[m->condLikeScratchIndex[p->index]]; vUp = m->bmVars[m->tiProbsScratchIndex[p->index]]; if (p->anc->anc == NULL) { /* the root of a rooted tree carries no observation and has no cond like array */ mAnchor = (tree->isRooted == YES) ? NULL : m->ancStates[m->condLikeIndex[chain][p->anc->index]]; for (k=0; knumRateCats; k++) { for (c=0; cnumChars; c++) { i = k * (m->numChars) + c; if (mAnchor == NULL || IsMissingC(mAnchor[c])) { mUp[i] = (CLFlt) INT_MAX; vUp[i] = 0.0; } else { mUp[i] = mAnchor[c]; vUp[i] = (CLFlt) (length * baseRate * catRate[k]); } } } } else // p is not at the root, combine ancestor's up message with sibling's down message { if (p->anc->left == p) sib = p->anc->right; else sib = p->anc->left; /* find the already-computed up message of the ancestor */ mUpA = m->ancStates[m->condLikeScratchIndex[p->anc->index]]; vUpA = m->bmVars[m->tiProbsScratchIndex[p->anc->index]]; mSib = m->ancStates[m->condLikeIndex[chain][sib->index]]; vSib = m->bmVars[m->tiProbsIndex[chain][sib->index]]; for (k=0; knumRateCats; k++) { for (c=0; cnumChars; c++) { i = k * (m->numChars) + c; mA = mUpA[i]; vA = vUpA[i]; mS = (sib->left == NULL) ? mSib[c] : mSib[i]; vS = vSib[i]; if (IsMissingC(mUpA[i]) && IsMissingC((CLFlt)mS)) { m1 = (CLFlt) INT_MAX; v1 = 0.0; } else if (IsMissingC(mUpA[i])) { m1 = mS; v1 = vS; } else if (IsMissingC((CLFlt)mS)) { m1 = mA; v1 = vA; } else if (vS < TIME_MIN && vA < TIME_MIN) { /* both sources are exact observations (e.g. both fossil ancestors) this should not happen at all! just in case, average them */ m1 = 0.5 * (mA + mS); v1 = 0.0; } else if (vS < TIME_MIN) { /* sibling is an exact observation */ m1 = mS; v1 = 0.0; } else if (vA < TIME_MIN) { /* ancestor's up message is an exact observation */ m1 = mA; v1 = 0.0; } else { /* additive precision-weighted combination */ v1 = vA * vS / (vA + vS); m1 = v1 * (mA/vA + mS/vS); } /* propagate down across p's own branch */ if (IsMissingC((CLFlt) m1)) { mUp[i] = (CLFlt) INT_MAX; vUp[i] = 0.0; } else { mUp[i] = (CLFlt) m1; vUp[i] = (CLFlt) (v1 + length * baseRate * catRate[k]); } } } } return NO_ERROR; } /*---------------------------------------------------------------- | | CondLikeDown_Bin: binary model with or without rate variation | -----------------------------------------------------------------*/ int CondLikeDown_Bin (TreeNode *p, int division, int chain) { int c, k; CLFlt *clL, *clR, *clP, *pL, *pR, *tiPL, *tiPR; ModelInfo *m; /* find model settings for this division */ m = &modelSettings[division]; /* Flip conditional likelihood space */ FlipCondLikeSpace (m, chain, p->index); /* find conditional likelihood pointers */ clL = m->condLikes[m->condLikeIndex[chain][p->left->index ]]; clR = m->condLikes[m->condLikeIndex[chain][p->right->index]]; clP = m->condLikes[m->condLikeIndex[chain][p->index ]]; /* find transition probabilities */ pL = m->tiProbs[m->tiProbsIndex[chain][p->left->index ]]; pR = m->tiProbs[m->tiProbsIndex[chain][p->right->index]]; tiPL = pL; tiPR = pR; for (k=0; knumRateCats; k++) { for (c=0; cnumChars; c++) { *(clP++) = (tiPL[0]*clL[0] + tiPL[1]*clL[1]) *(tiPR[0]*clR[0] + tiPR[1]*clR[1]); *(clP++) = (tiPL[2]*clL[0] + tiPL[3]*clL[1]) *(tiPR[2]*clR[0] + tiPR[3]*clR[1]); clL += 2; clR += 2; } tiPL += 4; tiPR += 4; } return NO_ERROR; } #if defined (SSE_ENABLED) /*---------------------------------------------------------------- | | CondLikeDown_Bin_SSE: binary model with or without rate | variation | -----------------------------------------------------------------*/ int CondLikeDown_Bin_SSE (TreeNode *p, int division, int chain) { int c, k; CLFlt *pL, *pR, *tiPL, *tiPR; __m128 *clL, *clR, *clP; __m128 m1, m2, m3, m4, m5, m6; ModelInfo *m; m = &modelSettings[division]; /* flip state of node so that we are not overwriting old cond likes */ FlipCondLikeSpace (m, chain, p->index); /* find conditional likelihood pointers */ clL = (__m128 *) m->condLikes[m->condLikeIndex[chain][p->left->index ]]; clR = (__m128 *) m->condLikes[m->condLikeIndex[chain][p->right->index]]; clP = (__m128 *) m->condLikes[m->condLikeIndex[chain][p->index ]]; /* find transition probabilities */ pL = m->tiProbs[m->tiProbsIndex[chain][p->left->index ]]; pR = m->tiProbs[m->tiProbsIndex[chain][p->right->index]]; tiPL = pL; tiPR = pR; for (k=0; knumRateCats; k++) { for (c=0; cnumVecChars; c++) { m1 = _mm_load1_ps (&tiPL[0]); m2 = _mm_load1_ps (&tiPR[0]); m5 = _mm_mul_ps (m1, clL[0]); m6 = _mm_mul_ps (m2, clR[0]); m1 = _mm_load1_ps (&tiPL[1]); m2 = _mm_load1_ps (&tiPR[1]); m3 = _mm_mul_ps (m1, clL[1]); m4 = _mm_mul_ps (m2, clR[1]); m5 = _mm_add_ps (m3, m5); m6 = _mm_add_ps (m4, m6); *clP++ = _mm_mul_ps (m5, m6); m1 = _mm_load1_ps (&tiPL[2]); m2 = _mm_load1_ps (&tiPR[2]); m5 = _mm_mul_ps (m1, clL[0]); m6 = _mm_mul_ps (m2, clR[0]); m1 = _mm_load1_ps (&tiPL[3]); m2 = _mm_load1_ps (&tiPR[3]); m3 = _mm_mul_ps (m1, clL[1]); m4 = _mm_mul_ps (m2, clR[1]); m5 = _mm_add_ps (m3, m5); m6 = _mm_add_ps (m4, m6); *clP++ = _mm_mul_ps (m5, m6); clL += 2; clR += 2; } tiPL += 4; tiPR += 4; } return NO_ERROR; } #endif /*---------------------------------------------------------------- | | CondLikeDown_Gen: general n-state model with or without rate | variation | -----------------------------------------------------------------*/ int CondLikeDown_Gen (TreeNode *p, int division, int chain) { int a, b, c, h, i, k, j, shortCut, *lState=NULL, *rState=NULL, nObsStates, nStates, nStatesSquared, preLikeJump; CLFlt likeL, likeR, *pL, *pR, *tiPL, *tiPR, *clL, *clR, *clP; ModelInfo *m; # if !defined (DEBUG_NOSHORTCUTS) int catStart; # endif /* find model settings for this division and nStates, nStatesSquared */ m = &modelSettings[division]; nObsStates = m->numStates; nStates = m->numModelStates; nStatesSquared = nStates * nStates; preLikeJump = nObsStates * nStates; /* flip conditional likelihood space */ FlipCondLikeSpace (m, chain, p->index); /* find conditional likelihood pointers */ clL = m->condLikes[m->condLikeIndex[chain][p->left->index ]]; clR = m->condLikes[m->condLikeIndex[chain][p->right->index]]; clP = m->condLikes[m->condLikeIndex[chain][p->index ]]; /* find transition probabilities */ pL = m->tiProbs[m->tiProbsIndex[chain][p->left->index ]]; pR = m->tiProbs[m->tiProbsIndex[chain][p->right->index]]; /* find likelihoods of site patterns for left branch if terminal */ shortCut = 0; # if !defined (DEBUG_NOSHORTCUTS) if (p->left->left == NULL && m->isPartAmbig[p->left->index] == NO) { shortCut |= 1; lState = m->termState[p->left->index]; tiPL = pL; for (k=a=0; knumRateCats; k++) { catStart = a; for (i=0; iright->left == NULL && m->isPartAmbig[p->right->index] == NO) { shortCut |= 2; rState = m->termState[p->right->index]; tiPR = pR; for (k=a=0; knumRateCats; k++) { catStart = a; for (i=0; inumRateCats; k++) { for (c=0; cnumChars; c++) { for (i=h=0; inumRateCats; k++) { for (c=0; cnumChars; c++) { a = lState[c] + k*(preLikeJump+nStates); for (i=h=0; inumRateCats; k++) { for (c=0; cnumChars; c++) { a = rState[c] + k*(preLikeJump+nStates); for (i=h=0; inumRateCats; k++) { for (c=0; cnumChars; c++) { a = rState[c] + k*(preLikeJump+nStates); b = lState[c] + k*(preLikeJump+nStates); for (i=0; inumStates; nStates = m->numModelStates; nStatesSquared = nStates * nStates; preLikeJump = nObsStates * nStates; /* Flip conditional likelihood space */ FlipCondLikeSpace (m, chain, p->index); /* find conditional likelihood pointers */ clL = (__m128 *)m->condLikes[m->condLikeIndex[chain][p->left->index ]]; clR = (__m128 *)m->condLikes[m->condLikeIndex[chain][p->right->index]]; clP = (__m128 *)m->condLikes[m->condLikeIndex[chain][p->index ]]; /* find transition probabilities */ pL = m->tiProbs[m->tiProbsIndex[chain][p->left->index ]]; pR = m->tiProbs[m->tiProbsIndex[chain][p->right->index]]; /* find likelihoods of site patterns for left branch if terminal */ shortCut = 0; # if !defined (DEBUG_NOSHORTCUTS) if (p->left->left == NULL && m->isPartAmbig[p->left->index] == NO) { shortCut |= 1; lState = m->termState[p->left->index]; tiPL = pL; for (k=a=0; knumRateCats; k++) { catStart = a; for (i=0; iright->left == NULL && m->isPartAmbig[p->right->index] == NO) { shortCut |= 2; rState = m->termState[p->right->index]; tiPR = pR; for (k=a=0; knumRateCats; k++) { catStart = a; for (i=0; inumRateCats; k++) { for (c=0; cnumVecChars; c++) { for (i=h=0; inumRateCats; k++) { for (c=t=0; cnumVecChars; c++) { for (c1=0; c1numFloatsPerVec; c1++,t++) { preLikeLV[c1] = &preLikeL[lState[t] + k*(preLikeJump+nStates)]; } for (i=h=0; inumRateCats; k++) { for (c=t=0; cnumVecChars; c++) { for (c1=0; c1numFloatsPerVec; c1++,t++) { preLikeRV[c1] = &preLikeR[rState[t] + k*(preLikeJump+nStates)]; } for (i=h=0; inumRateCats; k++) { for (c=t=0; cnumVecChars; c++) { for (c1=0; c1numFloatsPerVec; c1++,t++) { preLikeRV[c1] = &preLikeR[rState[t] + k*(preLikeJump+nStates)]; preLikeLV[c1] = &preLikeL[lState[t] + k*(preLikeJump+nStates)]; } for (i=0; inumFloatsPerVec == 4); /* In the following 2 statments we assume that SSE register can hold exactly 4 ClFlts. */ mL = _mm_set_ps (*(preLikeLV[3]++), *(preLikeLV[2]++), *(preLikeLV[1]++), *(preLikeLV[0]++)); mR = _mm_set_ps (*(preLikeRV[3]++), *(preLikeRV[2]++), *(preLikeRV[1]++), *(preLikeRV[0]++)); *(clP++) = _mm_mul_ps (mL,mR); } } } break; } return NO_ERROR; } #endif /*---------------------------------------------------------------- | | CondLikeDown_Gen_GibbsGamma: general n-state model with rate | variation modeled using discrete gamma with Gibbs resampling | -----------------------------------------------------------------*/ int CondLikeDown_Gen_GibbsGamma (TreeNode *p, int division, int chain) { int a, b, c, i, j, r, *rateCat, shortCut, *lState=NULL, *rState=NULL, nObsStates, nStates, nStatesSquared, nGammaCats; CLFlt likeL, likeR, *pL, *pR, *tiPL, *tiPR, *clL, *clR, *clP; ModelInfo *m; # if !defined (DEBUG_NOSHORTCUTS) int k, catStart; # endif /* find model settings for this division and nStates, nStatesSquared */ m = &modelSettings[division]; nObsStates = m->numStates; nStates = m->numModelStates; nStatesSquared = nStates * nStates; /* flip conditional likelihood space */ FlipCondLikeSpace (m, chain, p->index); /* find conditional likelihood pointers */ clL = m->condLikes[m->condLikeIndex[chain][p->left->index ]]; clR = m->condLikes[m->condLikeIndex[chain][p->right->index]]; clP = m->condLikes[m->condLikeIndex[chain][p->index ]]; /* find transition probabilities */ pL = m->tiProbs[m->tiProbsIndex[chain][p->left->index ]]; pR = m->tiProbs[m->tiProbsIndex[chain][p->right->index]]; /* find rate category index and number of gamma categories */ rateCat = m->tiIndex + chain * m->numChars; nGammaCats = m->numRateCats; /* find likelihoods of site patterns for left branch if terminal */ shortCut = 0; # if !defined (DEBUG_NOSHORTCUTS) if (p->left->left == NULL && m->isPartAmbig[p->left->index] == NO) { shortCut |= 1; lState = m->termState[p->left->index]; tiPL = pL; for (k=a=0; kright->left == NULL && m->isPartAmbig[p->right->index] == NO) { shortCut |= 2; rState = m->termState[p->right->index]; tiPR = pR; for (k=a=0; knumChars; c++) { r = (*rateCat++); if (r < nGammaCats) { tiPL = pL + r*nStatesSquared; tiPR = pR + r*nStatesSquared; for (i=0; inumChars; c++) { r = (*rateCat++); if (r < nGammaCats) { tiPR = pR + r*nStatesSquared; a = lState[c] + r*(nStatesSquared+nStates); for (i=0; inumChars; c++) { r = (*rateCat++); if (r < nGammaCats) { tiPL = pL + r*nStatesSquared; a = rState[c] + r*(nStatesSquared+nStates); for (i=0; inumChars; c++) { r = (*rateCat++); if (r < nGammaCats) { a = lState[c] + r*(nStatesSquared+nStates); b = rState[c] + r*(nStatesSquared+nStates); for (i=0; iindex); /* find conditional likelihood pointers */ clL = m->condLikes[m->condLikeIndex[chain][p->left->index ]]; clR = m->condLikes[m->condLikeIndex[chain][p->right->index]]; clP = m->condLikes[m->condLikeIndex[chain][p->index ]]; /* find transition probabilities */ pL = m->tiProbs[m->tiProbsIndex[chain][p->left->index ]]; pR = m->tiProbs[m->tiProbsIndex[chain][p->right->index]]; /* find likelihoods of site patterns for left branch if terminal */ shortCut = 0; # if !defined (DEBUG_NOSHORTCUTS) if (p->left->left == NULL && m->isPartAmbig[p->left->index] == NO) { shortCut |= 1; lState = m->termState[p->left->index]; tiPL = pL; for (k=j=0; knumRateCats; k++) { for (i=0; i<4; i++) { preLikeL[j++] = tiPL[0]; preLikeL[j++] = tiPL[4]; preLikeL[j++] = tiPL[8]; preLikeL[j++] = tiPL[12]; tiPL++; } /* for ambiguous */ for (i=0; i<4; i++) preLikeL[j++] = 1.0; tiPL += 12; } } /* find likelihoods of site patterns for right branch if terminal */ if (p->right->left == NULL && m->isPartAmbig[p->right->index] == NO) { shortCut |= 2; rState = m->termState[p->right->index]; tiPR = pR; for (k=j=0; knumRateCats; k++) { for (i=0; i<4; i++) { preLikeR[j++] = tiPR[0]; preLikeR[j++] = tiPR[4]; preLikeR[j++] = tiPR[8]; preLikeR[j++] = tiPR[12]; tiPR++; } /* for ambiguous */ for (i=0; i<4; i++) preLikeR[j++] = 1.0; tiPR += 12; } } # endif switch (shortCut) { case 0: tiPL = pL; tiPR = pR; for (k=h=0; knumRateCats; k++) { for (c=0; cnumChars; c++) { clP[h++] = (tiPL[AA]*clL[A] + tiPL[AC]*clL[C] + tiPL[AG]*clL[G] + tiPL[AT]*clL[T]) *(tiPR[AA]*clR[A] + tiPR[AC]*clR[C] + tiPR[AG]*clR[G] + tiPR[AT]*clR[T]); clP[h++] = (tiPL[CA]*clL[A] + tiPL[CC]*clL[C] + tiPL[CG]*clL[G] + tiPL[CT]*clL[T]) *(tiPR[CA]*clR[A] + tiPR[CC]*clR[C] + tiPR[CG]*clR[G] + tiPR[CT]*clR[T]); clP[h++] = (tiPL[GA]*clL[A] + tiPL[GC]*clL[C] + tiPL[GG]*clL[G] + tiPL[GT]*clL[T]) *(tiPR[GA]*clR[A] + tiPR[GC]*clR[C] + tiPR[GG]*clR[G] + tiPR[GT]*clR[T]); clP[h++] = (tiPL[TA]*clL[A] + tiPL[TC]*clL[C] + tiPL[TG]*clL[G] + tiPL[TT]*clL[T]) *(tiPR[TA]*clR[A] + tiPR[TC]*clR[C] + tiPR[TG]*clR[G] + tiPR[TT]*clR[T]); clL += 4; clR += 4; } tiPL += 16; tiPR += 16; } break; case 1: tiPR = pR; for (k=h=0; knumRateCats; k++) { for (c=0; cnumChars; c++) { i = lState[c] + k*20; clP[h++] = preLikeL[i++] *(tiPR[AA]*clR[A] + tiPR[AC]*clR[C] + tiPR[AG]*clR[G] + tiPR[AT]*clR[T]); clP[h++] = preLikeL[i++] *(tiPR[CA]*clR[A] + tiPR[CC]*clR[C] + tiPR[CG]*clR[G] + tiPR[CT]*clR[T]); clP[h++] = preLikeL[i++] *(tiPR[GA]*clR[A] + tiPR[GC]*clR[C] + tiPR[GG]*clR[G] + tiPR[GT]*clR[T]); clP[h++] = preLikeL[i++] *(tiPR[TA]*clR[A] + tiPR[TC]*clR[C] + tiPR[TG]*clR[G] + tiPR[TT]*clR[T]); clR += 4; } tiPR += 16; } break; case 2: tiPL = pL; for (k=h=0; knumRateCats; k++) { for (c=0; cnumChars; c++) { i = rState[c] + k*20; clP[h++] = (tiPL[AA]*clL[A] + tiPL[AC]*clL[C] + tiPL[AG]*clL[G] + tiPL[AT]*clL[T]) *preLikeR[i++]; clP[h++] = (tiPL[CA]*clL[A] + tiPL[CC]*clL[C] + tiPL[CG]*clL[G] + tiPL[CT]*clL[T]) *preLikeR[i++]; clP[h++] = (tiPL[GA]*clL[A] + tiPL[GC]*clL[C] + tiPL[GG]*clL[G] + tiPL[GT]*clL[T]) *preLikeR[i++]; clP[h++] = (tiPL[TA]*clL[A] + tiPL[TC]*clL[C] + tiPL[TG]*clL[G] + tiPL[TT]*clL[T]) *preLikeR[i++]; clL += 4; } tiPL += 16; } break; case 3: for (k=h=0; knumRateCats; k++) { for (c=0; cnumChars; c++) { i = j = k*20; i += lState[c]; j += rState[c]; clP[h++] = preLikeL[i++]*preLikeR[j++]; clP[h++] = preLikeL[i++]*preLikeR[j++]; clP[h++] = preLikeL[i++]*preLikeR[j++]; clP[h++] = preLikeL[i++]*preLikeR[j++]; } } } return NO_ERROR; } /*---------------------------------------------------------------- | | CondLikeDown_NUC4_GibbsGamma: 4by4 nucleotide model with rate | variation approximated using Gibbs sampling of gamma | -----------------------------------------------------------------*/ int CondLikeDown_NUC4_GibbsGamma (TreeNode *p, int division, int chain) { int c, h, i, j, r, *rateCat, shortCut, *lState=NULL, *rState=NULL, nGammaCats; CLFlt *clL, *clR, *clP, *pL, *pR, *tiPL, *tiPR; ModelInfo *m; # if !defined (DEBUG_NOSHORTCUTS) int k; # endif m = &modelSettings[division]; /* flip conditional likelihood space */ FlipCondLikeSpace (m, chain, p->index); /* find conditional likelihood pointers */ clL = m->condLikes[m->condLikeIndex[chain][p->left->index ]]; clR = m->condLikes[m->condLikeIndex[chain][p->right->index]]; clP = m->condLikes[m->condLikeIndex[chain][p->index ]]; /* find transition probabilities */ pL = m->tiProbs[m->tiProbsIndex[chain][p->left->index ]]; pR = m->tiProbs[m->tiProbsIndex[chain][p->right->index]]; /* find rate category index and number of gamma categories */ rateCat = m->tiIndex + chain * m->numChars; nGammaCats = m->numRateCats; /* find likelihoods of site patterns for left branch if terminal */ shortCut = 0; # if !defined (DEBUG_NOSHORTCUTS) if (p->left->left == NULL && m->isPartAmbig[p->left->index] == NO) { shortCut |= 1; lState = m->termState[p->left->index]; tiPL = pL; for (k=j=0; kright->left == NULL && m->isPartAmbig[p->right->index] == NO) { shortCut |= 2; rState = m->termState[p->right->index]; tiPR = pR; for (k=j=0; knumChars; c++) { r = rateCat[c]; if (r < nGammaCats) { tiPL = pL + r * 16; tiPR = pR + r * 16; clP[h++] = (tiPL[AA]*clL[A] + tiPL[AC]*clL[C] + tiPL[AG]*clL[G] + tiPL[AT]*clL[T]) *(tiPR[AA]*clR[A] + tiPR[AC]*clR[C] + tiPR[AG]*clR[G] + tiPR[AT]*clR[T]); clP[h++] = (tiPL[CA]*clL[A] + tiPL[CC]*clL[C] + tiPL[CG]*clL[G] + tiPL[CT]*clL[T]) *(tiPR[CA]*clR[A] + tiPR[CC]*clR[C] + tiPR[CG]*clR[G] + tiPR[CT]*clR[T]); clP[h++] = (tiPL[GA]*clL[A] + tiPL[GC]*clL[C] + tiPL[GG]*clL[G] + tiPL[GT]*clL[T]) *(tiPR[GA]*clR[A] + tiPR[GC]*clR[C] + tiPR[GG]*clR[G] + tiPR[GT]*clR[T]); clP[h++] = (tiPL[TA]*clL[A] + tiPL[TC]*clL[C] + tiPL[TG]*clL[G] + tiPL[TT]*clL[T]) *(tiPR[TA]*clR[A] + tiPR[TC]*clR[C] + tiPR[TG]*clR[G] + tiPR[TT]*clR[T]); } else h += 4; clL += 4; clR += 4; } break; case 1: for (c=h=0; cnumChars; c++) { r = rateCat[c]; if (r < nGammaCats) { tiPR = pR + r * 16; i = lState[c] + r * 20; clP[h++] = preLikeL[i++] *(tiPR[AA]*clR[A] + tiPR[AC]*clR[C] + tiPR[AG]*clR[G] + tiPR[AT]*clR[T]); clP[h++] = preLikeL[i++] *(tiPR[CA]*clR[A] + tiPR[CC]*clR[C] + tiPR[CG]*clR[G] + tiPR[CT]*clR[T]); clP[h++] = preLikeL[i++] *(tiPR[GA]*clR[A] + tiPR[GC]*clR[C] + tiPR[GG]*clR[G] + tiPR[GT]*clR[T]); clP[h++] = preLikeL[i++] *(tiPR[TA]*clR[A] + tiPR[TC]*clR[C] + tiPR[TG]*clR[G] + tiPR[TT]*clR[T]); } else h += 4; clR += 4; } break; case 2: for (c=h=0; cnumChars; c++) { r = rateCat[c]; if (r < nGammaCats) { tiPL = pL + r * 16; i = rState[c] + r * 20; clP[h++] = (tiPL[AA]*clL[A] + tiPL[AC]*clL[C] + tiPL[AG]*clL[G] + tiPL[AT]*clL[T]) *preLikeR[i++]; clP[h++] = (tiPL[CA]*clL[A] + tiPL[CC]*clL[C] + tiPL[CG]*clL[G] + tiPL[CT]*clL[T]) *preLikeR[i++]; clP[h++] = (tiPL[GA]*clL[A] + tiPL[GC]*clL[C] + tiPL[GG]*clL[G] + tiPL[GT]*clL[T]) *preLikeR[i++]; clP[h++] = (tiPL[TA]*clL[A] + tiPL[TC]*clL[C] + tiPL[TG]*clL[G] + tiPL[TT]*clL[T]) *preLikeR[i++]; } else h += 4; clL += 4; } break; case 3: for (c=h=0; cnumChars; c++) { r = rateCat[c]; if (r < nGammaCats) { i = lState[c] + r * 20; j = rState[c] + r * 20; clP[h++] = preLikeL[i++]*preLikeR[j++]; clP[h++] = preLikeL[i++]*preLikeR[j++]; clP[h++] = preLikeL[i++]*preLikeR[j++]; clP[h++] = preLikeL[i++]*preLikeR[j++]; } else h += 4; } break; } return NO_ERROR; } #if defined (FMA_ENABLED) /*---------------------------------------------------------------- | | CondLikeDown_NUC4_FMA: 4by4 nucleotide model with or without rate | variation, using AVX + FMA instructions | -----------------------------------------------------------------*/ int CondLikeDown_NUC4_FMA (TreeNode *p, int division, int chain) { int c, k; CLFlt *pL, *pR, *tiPL, *tiPR; __m256 *clL, *clR, *clP; __m256 m1, m2, m3, m4; ModelInfo *m; m = &modelSettings[division]; /* flip state of node so that we are not overwriting old cond likes */ FlipCondLikeSpace (m, chain, p->index); /* find conditional likelihood pointers */ clL = (__m256 *) m->condLikes[m->condLikeIndex[chain][p->left->index ]]; clR = (__m256 *) m->condLikes[m->condLikeIndex[chain][p->right->index]]; clP = (__m256 *) m->condLikes[m->condLikeIndex[chain][p->index ]]; /* find transition probabilities */ pL = m->tiProbs[m->tiProbsIndex[chain][p->left->index ]]; pR = m->tiProbs[m->tiProbsIndex[chain][p->right->index]]; tiPL = pL; tiPR = pR; for (k=0; knumRateCats; k++) { for (c=0; cnumVecChars; c++) { m1 = _mm256_broadcast_ss (&tiPL[AA]); m2 = _mm256_broadcast_ss (&tiPR[AA]); m3 = _mm256_mul_ps (m1, clL[A]); m4 = _mm256_mul_ps (m2, clR[A]); m1 = _mm256_broadcast_ss (&tiPL[AC]); m2 = _mm256_broadcast_ss (&tiPR[AC]); m3 = _mm256_fmadd_ps (m1, clL[C], m3); m4 = _mm256_fmadd_ps (m2, clR[C], m4); m1 = _mm256_broadcast_ss (&tiPL[AG]); m2 = _mm256_broadcast_ss (&tiPR[AG]); m3 = _mm256_fmadd_ps (m1, clL[G], m3); m4 = _mm256_fmadd_ps (m2, clR[G], m4); m1 = _mm256_broadcast_ss (&tiPL[AT]); m2 = _mm256_broadcast_ss (&tiPR[AT]); m3 = _mm256_fmadd_ps (m1, clL[T], m3); m4 = _mm256_fmadd_ps (m2, clR[T], m4); *clP++ = _mm256_mul_ps (m3, m4); m1 = _mm256_broadcast_ss (&tiPL[CA]); m2 = _mm256_broadcast_ss (&tiPR[CA]); m3 = _mm256_mul_ps (m1, clL[A]); m4 = _mm256_mul_ps (m2, clR[A]); m1 = _mm256_broadcast_ss (&tiPL[CC]); m2 = _mm256_broadcast_ss (&tiPR[CC]); m3 = _mm256_fmadd_ps (m1, clL[C], m3); m4 = _mm256_fmadd_ps (m2, clR[C], m4); m1 = _mm256_broadcast_ss (&tiPL[CG]); m2 = _mm256_broadcast_ss (&tiPR[CG]); m3 = _mm256_fmadd_ps (m1, clL[G], m3); m4 = _mm256_fmadd_ps (m2, clR[G], m4); m1 = _mm256_broadcast_ss (&tiPL[CT]); m2 = _mm256_broadcast_ss (&tiPR[CT]); m3 = _mm256_fmadd_ps (m1, clL[T], m3); m4 = _mm256_fmadd_ps (m2, clR[T], m4); *clP++ = _mm256_mul_ps (m3, m4); m1 = _mm256_broadcast_ss (&tiPL[GA]); m2 = _mm256_broadcast_ss (&tiPR[GA]); m3 = _mm256_mul_ps (m1, clL[A]); m4 = _mm256_mul_ps (m2, clR[A]); m1 = _mm256_broadcast_ss (&tiPL[GC]); m2 = _mm256_broadcast_ss (&tiPR[GC]); m3 = _mm256_fmadd_ps (m1, clL[C], m3); m4 = _mm256_fmadd_ps (m2, clR[C], m4); m1 = _mm256_broadcast_ss (&tiPL[GG]); m2 = _mm256_broadcast_ss (&tiPR[GG]); m3 = _mm256_fmadd_ps (m1, clL[G], m3); m4 = _mm256_fmadd_ps (m2, clR[G], m4); m1 = _mm256_broadcast_ss (&tiPL[GT]); m2 = _mm256_broadcast_ss (&tiPR[GT]); m3 = _mm256_fmadd_ps (m1, clL[T], m3); m4 = _mm256_fmadd_ps (m2, clR[T], m4); *clP++ = _mm256_mul_ps (m3, m4); m1 = _mm256_broadcast_ss (&tiPL[TA]); m2 = _mm256_broadcast_ss (&tiPR[TA]); m3 = _mm256_mul_ps (m1, clL[A]); m4 = _mm256_mul_ps (m2, clR[A]); m1 = _mm256_broadcast_ss (&tiPL[TC]); m2 = _mm256_broadcast_ss (&tiPR[TC]); m3 = _mm256_fmadd_ps (m1, clL[C], m3); m4 = _mm256_fmadd_ps (m2, clR[C], m4); m1 = _mm256_broadcast_ss (&tiPL[TG]); m2 = _mm256_broadcast_ss (&tiPR[TG]); m3 = _mm256_fmadd_ps (m1, clL[G], m3); m4 = _mm256_fmadd_ps (m2, clR[G], m4); m1 = _mm256_broadcast_ss (&tiPL[TT]); m2 = _mm256_broadcast_ss (&tiPR[TT]); m3 = _mm256_fmadd_ps (m1, clL[T], m3); m4 = _mm256_fmadd_ps (m2, clR[T], m4); *clP++ = _mm256_mul_ps (m3, m4); clL += 4; clR += 4; } tiPL += 16; tiPR += 16; } return NO_ERROR; } #endif #if defined (AVX_ENABLED) /*---------------------------------------------------------------- | | CondLikeDown_NUC4_AVX: 4by4 nucleotide model with or without rate | variation, using AVX instructions | -----------------------------------------------------------------*/ int CondLikeDown_NUC4_AVX (TreeNode *p, int division, int chain) { int c, k; CLFlt *pL, *pR, *tiPL, *tiPR; __m256 *clL, *clR, *clP; __m256 m1, m2, m3, m4, m5, m6; ModelInfo *m; m = &modelSettings[division]; /* flip state of node so that we are not overwriting old cond likes */ FlipCondLikeSpace (m, chain, p->index); /* find conditional likelihood pointers */ clL = (__m256 *) m->condLikes[m->condLikeIndex[chain][p->left->index ]]; clR = (__m256 *) m->condLikes[m->condLikeIndex[chain][p->right->index]]; clP = (__m256 *) m->condLikes[m->condLikeIndex[chain][p->index ]]; /* find transition probabilities */ pL = m->tiProbs[m->tiProbsIndex[chain][p->left->index ]]; pR = m->tiProbs[m->tiProbsIndex[chain][p->right->index]]; tiPL = pL; tiPR = pR; for (k=0; knumRateCats; k++) { for (c=0; cnumVecChars; c++) { m1 = _mm256_broadcast_ss (&tiPL[AA]); m2 = _mm256_broadcast_ss (&tiPR[AA]); m5 = _mm256_mul_ps (m1, clL[A]); m6 = _mm256_mul_ps (m2, clR[A]); m1 = _mm256_broadcast_ss (&tiPL[AC]); m2 = _mm256_broadcast_ss (&tiPR[AC]); m3 = _mm256_mul_ps (m1, clL[C]); m4 = _mm256_mul_ps (m2, clR[C]); m5 = _mm256_add_ps (m3, m5); m6 = _mm256_add_ps (m4, m6); m1 = _mm256_broadcast_ss (&tiPL[AG]); m2 = _mm256_broadcast_ss (&tiPR[AG]); m3 = _mm256_mul_ps (m1, clL[G]); m4 = _mm256_mul_ps (m2, clR[G]); m5 = _mm256_add_ps (m3, m5); m6 = _mm256_add_ps (m4, m6); m1 = _mm256_broadcast_ss (&tiPL[AT]); m2 = _mm256_broadcast_ss (&tiPR[AT]); m3 = _mm256_mul_ps (m1, clL[T]); m4 = _mm256_mul_ps (m2, clR[T]); m5 = _mm256_add_ps (m3, m5); m6 = _mm256_add_ps (m4, m6); *clP++ = _mm256_mul_ps (m5, m6); m1 = _mm256_broadcast_ss (&tiPL[CA]); m2 = _mm256_broadcast_ss (&tiPR[CA]); m5 = _mm256_mul_ps (m1, clL[A]); m6 = _mm256_mul_ps (m2, clR[A]); m1 = _mm256_broadcast_ss (&tiPL[CC]); m2 = _mm256_broadcast_ss (&tiPR[CC]); m3 = _mm256_mul_ps (m1, clL[C]); m4 = _mm256_mul_ps (m2, clR[C]); m5 = _mm256_add_ps (m3, m5); m6 = _mm256_add_ps (m4, m6); m1 = _mm256_broadcast_ss (&tiPL[CG]); m2 = _mm256_broadcast_ss (&tiPR[CG]); m3 = _mm256_mul_ps (m1, clL[G]); m4 = _mm256_mul_ps (m2, clR[G]); m5 = _mm256_add_ps (m3, m5); m6 = _mm256_add_ps (m4, m6); m1 = _mm256_broadcast_ss (&tiPL[CT]); m2 = _mm256_broadcast_ss (&tiPR[CT]); m3 = _mm256_mul_ps (m1, clL[T]); m4 = _mm256_mul_ps (m2, clR[T]); m5 = _mm256_add_ps (m3, m5); m6 = _mm256_add_ps (m4, m6); *clP++ = _mm256_mul_ps (m5, m6); m1 = _mm256_broadcast_ss (&tiPL[GA]); m2 = _mm256_broadcast_ss (&tiPR[GA]); m5 = _mm256_mul_ps (m1, clL[A]); m6 = _mm256_mul_ps (m2, clR[A]); m1 = _mm256_broadcast_ss (&tiPL[GC]); m2 = _mm256_broadcast_ss (&tiPR[GC]); m3 = _mm256_mul_ps (m1, clL[C]); m4 = _mm256_mul_ps (m2, clR[C]); m5 = _mm256_add_ps (m3, m5); m6 = _mm256_add_ps (m4, m6); m1 = _mm256_broadcast_ss (&tiPL[GG]); m2 = _mm256_broadcast_ss (&tiPR[GG]); m3 = _mm256_mul_ps (m1, clL[G]); m4 = _mm256_mul_ps (m2, clR[G]); m5 = _mm256_add_ps (m3, m5); m6 = _mm256_add_ps (m4, m6); m1 = _mm256_broadcast_ss (&tiPL[GT]); m2 = _mm256_broadcast_ss (&tiPR[GT]); m3 = _mm256_mul_ps (m1, clL[T]); m4 = _mm256_mul_ps (m2, clR[T]); m5 = _mm256_add_ps (m3, m5); m6 = _mm256_add_ps (m4, m6); *clP++ = _mm256_mul_ps (m5, m6); m1 = _mm256_broadcast_ss (&tiPL[TA]); m2 = _mm256_broadcast_ss (&tiPR[TA]); m5 = _mm256_mul_ps (m1, clL[A]); m6 = _mm256_mul_ps (m2, clR[A]); m1 = _mm256_broadcast_ss (&tiPL[TC]); m2 = _mm256_broadcast_ss (&tiPR[TC]); m3 = _mm256_mul_ps (m1, clL[C]); m4 = _mm256_mul_ps (m2, clR[C]); m5 = _mm256_add_ps (m3, m5); m6 = _mm256_add_ps (m4, m6); m1 = _mm256_broadcast_ss (&tiPL[TG]); m2 = _mm256_broadcast_ss (&tiPR[TG]); m3 = _mm256_mul_ps (m1, clL[G]); m4 = _mm256_mul_ps (m2, clR[G]); m5 = _mm256_add_ps (m3, m5); m6 = _mm256_add_ps (m4, m6); m1 = _mm256_broadcast_ss (&tiPL[TT]); m2 = _mm256_broadcast_ss (&tiPR[TT]); m3 = _mm256_mul_ps (m1, clL[T]); m4 = _mm256_mul_ps (m2, clR[T]); m5 = _mm256_add_ps (m3, m5); m6 = _mm256_add_ps (m4, m6); *clP++ = _mm256_mul_ps (m5, m6); clL += 4; clR += 4; } tiPL += 16; tiPR += 16; } return NO_ERROR; } #endif #if defined (SSE_ENABLED) /*---------------------------------------------------------------- | | CondLikeDown_NUC4_SSE: 4by4 nucleotide model with or without rate | variation, using SSE instructions | -----------------------------------------------------------------*/ int CondLikeDown_NUC4_SSE (TreeNode *p, int division, int chain) { int c, k; CLFlt *pL, *pR, *tiPL, *tiPR; __m128 *clL, *clR, *clP; __m128 m1, m2, m3, m4, m5, m6; ModelInfo *m; m = &modelSettings[division]; /* flip state of node so that we are not overwriting old cond likes */ FlipCondLikeSpace (m, chain, p->index); /* find conditional likelihood pointers */ clL = (__m128 *) m->condLikes[m->condLikeIndex[chain][p->left->index ]]; clR = (__m128 *) m->condLikes[m->condLikeIndex[chain][p->right->index]]; clP = (__m128 *) m->condLikes[m->condLikeIndex[chain][p->index ]]; /* find transition probabilities */ pL = m->tiProbs[m->tiProbsIndex[chain][p->left->index ]]; pR = m->tiProbs[m->tiProbsIndex[chain][p->right->index]]; tiPL = pL; tiPR = pR; for (k=0; knumRateCats; k++) { for (c=0; cnumVecChars; c++) { m1 = _mm_load1_ps (&tiPL[AA]); m2 = _mm_load1_ps (&tiPR[AA]); m5 = _mm_mul_ps (m1, clL[A]); m6 = _mm_mul_ps (m2, clR[A]); m1 = _mm_load1_ps (&tiPL[AC]); m2 = _mm_load1_ps (&tiPR[AC]); m3 = _mm_mul_ps (m1, clL[C]); m4 = _mm_mul_ps (m2, clR[C]); m5 = _mm_add_ps (m3, m5); m6 = _mm_add_ps (m4, m6); m1 = _mm_load1_ps (&tiPL[AG]); m2 = _mm_load1_ps (&tiPR[AG]); m3 = _mm_mul_ps (m1, clL[G]); m4 = _mm_mul_ps (m2, clR[G]); m5 = _mm_add_ps (m3, m5); m6 = _mm_add_ps (m4, m6); m1 = _mm_load1_ps (&tiPL[AT]); m2 = _mm_load1_ps (&tiPR[AT]); m3 = _mm_mul_ps (m1, clL[T]); m4 = _mm_mul_ps (m2, clR[T]); m5 = _mm_add_ps (m3, m5); m6 = _mm_add_ps (m4, m6); *clP++ = _mm_mul_ps (m5, m6); m1 = _mm_load1_ps (&tiPL[CA]); m2 = _mm_load1_ps (&tiPR[CA]); m5 = _mm_mul_ps (m1, clL[A]); m6 = _mm_mul_ps (m2, clR[A]); m1 = _mm_load1_ps (&tiPL[CC]); m2 = _mm_load1_ps (&tiPR[CC]); m3 = _mm_mul_ps (m1, clL[C]); m4 = _mm_mul_ps (m2, clR[C]); m5 = _mm_add_ps (m3, m5); m6 = _mm_add_ps (m4, m6); m1 = _mm_load1_ps (&tiPL[CG]); m2 = _mm_load1_ps (&tiPR[CG]); m3 = _mm_mul_ps (m1, clL[G]); m4 = _mm_mul_ps (m2, clR[G]); m5 = _mm_add_ps (m3, m5); m6 = _mm_add_ps (m4, m6); m1 = _mm_load1_ps (&tiPL[CT]); m2 = _mm_load1_ps (&tiPR[CT]); m3 = _mm_mul_ps (m1, clL[T]); m4 = _mm_mul_ps (m2, clR[T]); m5 = _mm_add_ps (m3, m5); m6 = _mm_add_ps (m4, m6); *clP++ = _mm_mul_ps (m5, m6); m1 = _mm_load1_ps (&tiPL[GA]); m2 = _mm_load1_ps (&tiPR[GA]); m5 = _mm_mul_ps (m1, clL[A]); m6 = _mm_mul_ps (m2, clR[A]); m1 = _mm_load1_ps (&tiPL[GC]); m2 = _mm_load1_ps (&tiPR[GC]); m3 = _mm_mul_ps (m1, clL[C]); m4 = _mm_mul_ps (m2, clR[C]); m5 = _mm_add_ps (m3, m5); m6 = _mm_add_ps (m4, m6); m1 = _mm_load1_ps (&tiPL[GG]); m2 = _mm_load1_ps (&tiPR[GG]); m3 = _mm_mul_ps (m1, clL[G]); m4 = _mm_mul_ps (m2, clR[G]); m5 = _mm_add_ps (m3, m5); m6 = _mm_add_ps (m4, m6); m1 = _mm_load1_ps (&tiPL[GT]); m2 = _mm_load1_ps (&tiPR[GT]); m3 = _mm_mul_ps (m1, clL[T]); m4 = _mm_mul_ps (m2, clR[T]); m5 = _mm_add_ps (m3, m5); m6 = _mm_add_ps (m4, m6); *clP++ = _mm_mul_ps (m5, m6); m1 = _mm_load1_ps (&tiPL[TA]); m2 = _mm_load1_ps (&tiPR[TA]); m5 = _mm_mul_ps (m1, clL[A]); m6 = _mm_mul_ps (m2, clR[A]); m1 = _mm_load1_ps (&tiPL[TC]); m2 = _mm_load1_ps (&tiPR[TC]); m3 = _mm_mul_ps (m1, clL[C]); m4 = _mm_mul_ps (m2, clR[C]); m5 = _mm_add_ps (m3, m5); m6 = _mm_add_ps (m4, m6); m1 = _mm_load1_ps (&tiPL[TG]); m2 = _mm_load1_ps (&tiPR[TG]); m3 = _mm_mul_ps (m1, clL[G]); m4 = _mm_mul_ps (m2, clR[G]); m5 = _mm_add_ps (m3, m5); m6 = _mm_add_ps (m4, m6); m1 = _mm_load1_ps (&tiPL[TT]); m2 = _mm_load1_ps (&tiPR[TT]); m3 = _mm_mul_ps (m1, clL[T]); m4 = _mm_mul_ps (m2, clR[T]); m5 = _mm_add_ps (m3, m5); m6 = _mm_add_ps (m4, m6); *clP++ = _mm_mul_ps (m5, m6); clL += 4; clR += 4; } tiPL += 16; tiPR += 16; } return NO_ERROR; } #endif /*---------------------------------------------------------------- | | CondLikeDown_NY98: codon model with omega variation | -----------------------------------------------------------------*/ int CondLikeDown_NY98 (TreeNode *p, int division, int chain) { int a, b, c, h, i, j, k, shortCut, *lState=NULL, *rState=NULL, nStates, nStatesSquared; CLFlt likeL, likeR, *pL, *pR, *tiPL, *tiPR, *clL, *clR, *clP; ModelInfo *m; /* find model settings for this division and nStates, nStatesSquared */ m = &modelSettings[division]; nStates = m->numModelStates; nStatesSquared = nStates * nStates; /* Flip conditional likelihood space */ FlipCondLikeSpace (m, chain, p->index); /* find conditional likelihood pointers */ clL = m->condLikes[m->condLikeIndex[chain][p->left->index ]]; clR = m->condLikes[m->condLikeIndex[chain][p->right->index]]; clP = m->condLikes[m->condLikeIndex[chain][p->index ]]; /* find transition probabilities */ pL = m->tiProbs[m->tiProbsIndex[chain][p->left->index ]]; pR = m->tiProbs[m->tiProbsIndex[chain][p->right->index]]; /* find likelihoods of site patterns for left branch if terminal */ shortCut = 0; # if !defined (DEBUG_NOSHORTCUTS) if (p->left->left == NULL && m->isPartAmbig[p->left->index] == NO) { shortCut |= 1; lState = m->termState[p->left->index]; tiPL = pL; for (k=a=0; knumOmegaCats; k++) { for (i=0; iright->left == NULL && m->isPartAmbig[p->right->index] == NO) { shortCut |= 2; rState = m->termState[p->right->index]; tiPR = pR; for (k=a=0; knumOmegaCats; k++) { for (i=0; inumOmegaCats; k++) { for (c=0; cnumChars; c++) { for (i=h=0; inumOmegaCats; k++) { for (c=0; cnumChars; c++) { a = lState[c] + k*(nStatesSquared+nStates); for (i=h=0; inumOmegaCats; k++) { for (c=0; cnumChars; c++) { a = rState[c] + k*(nStatesSquared+nStates); for (i=h=0; inumOmegaCats; k++) { for (c=0; cnumChars; c++) { a = rState[c] + k*(nStatesSquared+nStates); b = lState[c] + k*(nStatesSquared+nStates); for (i=0; inumModelStates; nStatesSquared = nStates * nStates; /* Flip conditional likelihood space */ FlipCondLikeSpace (m, chain, p->index); /* find conditional likelihood pointers */ clL = (__m128 *)m->condLikes[m->condLikeIndex[chain][p->left->index ]]; clR = (__m128 *)m->condLikes[m->condLikeIndex[chain][p->right->index]]; clP = (__m128 *)m->condLikes[m->condLikeIndex[chain][p->index ]]; /* find transition probabilities */ pL = m->tiProbs[m->tiProbsIndex[chain][p->left->index ]]; pR = m->tiProbs[m->tiProbsIndex[chain][p->right->index]]; /* find likelihoods of site patterns for left branch if terminal */ shortCut = 0; # if !defined (DEBUG_NOSHORTCUTS) if (p->left->left == NULL && m->isPartAmbig[p->left->index] == NO) { shortCut |= 1; lState = m->termState[p->left->index]; tiPL = pL; for (k=a=0; knumOmegaCats; k++) { for (i=0; iright->left == NULL && m->isPartAmbig[p->right->index] == NO) { shortCut |= 2; rState = m->termState[p->right->index]; tiPR = pR; for (k=a=0; knumOmegaCats; k++) { for (i=0; inumOmegaCats; k++) { for (c=0; cnumVecChars; c++) { for (i=h=0; inumOmegaCats; k++) { for (c=t=0; cnumVecChars; c++) { for (c1=0; c1numFloatsPerVec; c1++,t++) { preLikeLV[c1] = &preLikeL[lState[t] + k*(nStatesSquared+nStates)]; } for (i=h=0; inumFloatsPerVec == 4); /* In the following statement we assume that SSE register can hold exactly 4 ClFlts. */ mAcumL = _mm_set_ps (*(preLikeLV[3]++), *(preLikeLV[2]++), *(preLikeLV[1]++), *(preLikeLV[0]++)); mAcumR = _mm_setzero_ps(); for (j=0; jnumOmegaCats; k++) { for (c=t=0; cnumVecChars; c++) { for (c1=0; c1numFloatsPerVec; c1++,t++) { preLikeRV[c1] = &preLikeR[rState[t] + k*(nStatesSquared+nStates)]; } for (i=h=0; inumFloatsPerVec == 4); /* In the following statement we assume that SSE register can hold exactly 4 ClFlts. */ mAcumR = _mm_set_ps (*(preLikeRV[3]++), *(preLikeRV[2]++), *(preLikeRV[1]++), *(preLikeRV[0]++)); mAcumL = _mm_setzero_ps(); for (j=0; jnumOmegaCats; k++) { for (c=t=0; cnumVecChars; c++) { for (c1=0; c1numFloatsPerVec; c1++,t++) { preLikeRV[c1] = &preLikeR[rState[t] + k*(nStatesSquared+nStates)]; preLikeLV[c1] = &preLikeL[lState[t] + k*(nStatesSquared+nStates)]; } for (i=0; inumFloatsPerVec == 4); /* In the following 2 statements we assume that SSE register can hold exactly 4 ClFlts. */ mL = _mm_set_ps (*(preLikeLV[3]++), *(preLikeLV[2]++), *(preLikeLV[1]++), *(preLikeLV[0]++)); mR = _mm_set_ps (*(preLikeRV[3]++), *(preLikeRV[2]++), *(preLikeRV[1]++), *(preLikeRV[0]++)); *(clP++) = _mm_mul_ps (mL,mR); } } } break; } return NO_ERROR; } #endif /*---------------------------------------------------------------- | | CondLikeDown_Std: variable number of states model | with or without rate variation | -----------------------------------------------------------------*/ int CondLikeDown_Std (TreeNode *p, int division, int chain) { int a, c, h, i, j, k, nStates, nCats, tmp; CLFlt *clL, *clR, *clP, *pL, *pR, *tiPL, *tiPR, likeL, likeR; ModelInfo *m; m = &modelSettings[division]; /* Flip conditional likelihood space */ FlipCondLikeSpace (m, chain, p->index); /* find conditional likelihood pointers */ clL = m->condLikes[m->condLikeIndex[chain][p->left->index ]]; clR = m->condLikes[m->condLikeIndex[chain][p->right->index]]; clP = m->condLikes[m->condLikeIndex[chain][p->index ]]; /* find transition probabilities */ pL = m->tiProbs[m->tiProbsIndex[chain][p->left->index ]]; pR = m->tiProbs[m->tiProbsIndex[chain][p->right->index]]; /* Conditional likelihood space is assumed to be arranged in numGammaCats blocks of data. Each block contains all data for one gamma category. Each gamma cat block consist of numChars sequences of data, each of this sequences corresponds to a character of data matrix. A sequence consists of nStates for all non-binary data, otherwise length of sequence is nStates*numBetaCats (i.e. 2*numBetaCats) */ /* calculate ancestral probabilities */ for (k=h=0; knumRateCats; k++) { /* calculate ancestral probabilities */ for (c=0; cnumChars; c++) { nStates = m->nStates[c]; /* the following lines ensure that nCats is 1 unless */ /* the character is binary and beta categories are used */ if (nStates == 2) nCats = m->numBetaCats; else nCats = 1; tmp = k*nStates*nStates; /* tmp contains offset to skip rate cats that already processed*/ tiPL = pL + m->tiIndex[c] + tmp; tiPR = pR + m->tiIndex[c] + tmp; tmp = (m->numRateCats-1)*2*2; /* tmp contains size of block of tpi matrices across all rate cats (minus one) for single beta category. Further used only if character is binary to jump to next beta category */ for (j=0; jindex); /* find conditional likelihood pointers */ clL = m->condLikes[m->condLikeIndex[chain][p->left->index ]]; clR = m->condLikes[m->condLikeIndex[chain][p->right->index]]; clP = m->condLikes[m->condLikeIndex[chain][p->index ]]; clA = m->condLikes[m->condLikeIndex[chain][p->anc->index ]]; /* find transition probabilities (or calculate instead) */ pL = m->tiProbs[m->tiProbsIndex[chain][p->left->index ]]; pR = m->tiProbs[m->tiProbsIndex[chain][p->right->index]]; pA = m->tiProbs[m->tiProbsIndex[chain][p->index ]]; tiPL = pL; tiPR = pR; tiPA = pA; for (k=0; knumRateCats; k++) { for (c=0; cnumChars; c++) { *(clP++) = (tiPL[0]*clL[0] + tiPL[1]*clL[1]) *(tiPR[0]*clR[0] + tiPR[1]*clR[1]) *(tiPA[0]*clA[0] + tiPA[1]*clA[1]); *(clP++) = (tiPL[2]*clL[0] + tiPL[3]*clL[1]) *(tiPR[2]*clR[0] + tiPR[3]*clR[1]) *(tiPA[2]*clA[0] + tiPA[3]*clA[1]); clA += 2; clL += 2; clR += 2; } tiPA += 4; tiPL += 4; tiPR += 4; } return NO_ERROR; } #if defined (SSE_ENABLED) /*---------------------------------------------------------------- | | CondLikeRoot_Bin_SSE:binary model with or without rate | variation | -----------------------------------------------------------------*/ int CondLikeRoot_Bin_SSE (TreeNode *p, int division, int chain) { int c, k; CLFlt *pL, *pR, *pA, *tiPL, *tiPR, *tiPA; __m128 *clL, *clR, *clP, *clA; __m128 m1, m2, m3, m4, m5, m6, m7; ModelInfo *m; m = &modelSettings[division]; /* flip state of node so that we are not overwriting old cond likes */ FlipCondLikeSpace (m, chain, p->index); /* find conditional likelihood pointers */ clL = (__m128 *) m->condLikes[m->condLikeIndex[chain][p->left->index ]]; clR = (__m128 *) m->condLikes[m->condLikeIndex[chain][p->right->index]]; clP = (__m128 *) m->condLikes[m->condLikeIndex[chain][p->index ]]; clA = (__m128 *) m->condLikes[m->condLikeIndex[chain][p->anc->index ]]; /* find transition probabilities */ pL = m->tiProbs[m->tiProbsIndex[chain][p->left->index ]]; pR = m->tiProbs[m->tiProbsIndex[chain][p->right->index]]; pA = m->tiProbs[m->tiProbsIndex[chain][p->index ]]; tiPL = pL; tiPR = pR; tiPA = pA; for (k=0; knumRateCats; k++) { for (c=0; cnumVecChars; c++) { m1 = _mm_load1_ps (&tiPL[0]); m5 = *clL++; m2 = _mm_mul_ps (m1, m5); m1 = _mm_load1_ps (&tiPL[2]); m6 = _mm_mul_ps (m1, m5); m1 = _mm_load1_ps (&tiPL[1]); m5 = *clL++; m3 = _mm_mul_ps (m1, m5); m1 = _mm_load1_ps (&tiPL[3]); m5 = _mm_mul_ps (m1, m5); m4 = _mm_add_ps (m2, m3); /* in m4 we get (tiPL[0]*clL[0] + tiPL[1]*clL[1]) */ m6 = _mm_add_ps (m5, m6); /* in m6 we get (tiPL[2]*clL[0] + tiPL[3]*clL[1]) */ m1 = _mm_load1_ps (&tiPR[0]); m5 = *clR++; m2 = _mm_mul_ps (m1, m5); m1 = _mm_load1_ps (&tiPR[2]); m7 = _mm_mul_ps (m1, m5); m1 = _mm_load1_ps (&tiPR[1]); m5 = *clR++; m3 = _mm_mul_ps (m1, m5); m1 = _mm_load1_ps (&tiPR[3]); m5 = _mm_mul_ps (m1, m5); m1 = _mm_add_ps (m2, m3); /* in m1 we get (tiPR[0]*clR[0] + tiPR[1]*clR[1]) */ m7 = _mm_add_ps (m5, m7); /* in m7 we get (tiPR[2]*clR[0] + tiPR[3]*clR[1]) */ m4 = _mm_mul_ps (m1, m4); /* in m4 we get (tiPL[0]*clL[0] + tiPL[1]*clL[1])*(tiPR[0]*clR[0] + tiPR[1]*clR[1]) */ m7 = _mm_mul_ps (m6, m7); /* in m7 we get (tiPL[2]*clL[0] + tiPL[3]*clL[1])*(tiPR[2]*clR[0] + tiPR[3]*clR[1]) */ m1 = _mm_load1_ps (&tiPA[0]); m5 = *clA++; m2 = _mm_mul_ps (m1, m5); m1 = _mm_load1_ps (&tiPA[2]); m6 = _mm_mul_ps (m1, m5); m1 = _mm_load1_ps (&tiPA[1]); m5 = *clA++; m3 = _mm_mul_ps (m1, m5); m1 = _mm_load1_ps (&tiPA[3]); m1 = _mm_mul_ps (m1, m5); m2 = _mm_add_ps (m2, m3); /* in m1 we get (tiPA[0]*clA[0] + tiPA[1]*clA[1]) */ m1 = _mm_add_ps (m1, m6); /* in m1 we get (tiPA[2]*clA[0] + tiPA[3]*clA[1]) */ *clP++ = _mm_mul_ps (m2, m4); *clP++ = _mm_mul_ps (m1, m7); } tiPL += 4; tiPR += 4; tiPA += 4; } return NO_ERROR; } #endif /*---------------------------------------------------------------- | | CondLikeRoot_Gen: general n-state model with or without rate | variation | -----------------------------------------------------------------*/ int CondLikeRoot_Gen (TreeNode *p, int division, int chain) { int a, b, c, d, h, i, j, k, shortCut, *lState=NULL, *rState=NULL, *aState=NULL, nObsStates, nStates, nStatesSquared, preLikeJump; CLFlt likeL, likeR, likeA, *clL, *clR, *clP, *clA, *pL, *pR, *pA, *tiPL, *tiPR, *tiPA; ModelInfo *m; # if !defined (DEBUG_NOSHORTCUTS) int catStart; # endif /* find model settings for this division and nStates, nStatesSquared */ m = &modelSettings[division]; nObsStates = m->numStates; nStates = m->numModelStates; nStatesSquared = nStates * nStates; preLikeJump = nObsStates * nStates; /* flip state of node so that we are not overwriting old cond likes */ FlipCondLikeSpace (m, chain, p->index); /* find conditional likelihood pointers */ clL = m->condLikes[m->condLikeIndex[chain][p->left->index ]]; clR = m->condLikes[m->condLikeIndex[chain][p->right->index]]; clP = m->condLikes[m->condLikeIndex[chain][p->index ]]; clA = m->condLikes[m->condLikeIndex[chain][p->anc->index ]]; /* find transition probabilities (or calculate instead) */ pL = m->tiProbs[m->tiProbsIndex[chain][p->left->index ]]; pR = m->tiProbs[m->tiProbsIndex[chain][p->right->index]]; pA = m->tiProbs[m->tiProbsIndex[chain][p->index ]]; /* find likelihoods of site patterns for left branch if terminal */ shortCut = 0; # if !defined (DEBUG_NOSHORTCUTS) if (p->left->left == NULL && m->isPartAmbig[p->left->index] == NO) { shortCut |= 1; lState = m->termState[p->left->index]; tiPL = pL; for (k=a=0; knumRateCats; k++) { catStart = a; for (i=0; iright->left == NULL && m->isPartAmbig[p->right->index] == NO) { shortCut |= 2; rState = m->termState[p->right->index]; tiPR = pR; for (k=a=0; knumRateCats; k++) { catStart = a; for (i=0; iisPartAmbig[p->anc->index] == YES) { shortCut = 4; } else { aState = m->termState[p->anc->index]; tiPA = pA; for (k=a=0; knumRateCats; k++) { catStart = a; for (i=0; inumRateCats; k++) { for (c=0; cnumChars; c++) { for (i=h=0; inumRateCats; k++) { for (c=0; cnumChars; c++) { a = aState[c] + k*(preLikeJump+nStates); for (i=h=0; inumRateCats; k++) { for (c=0; cnumChars; c++) { a = lState[c] + k*(preLikeJump+nStates); b = aState[c] + k*(preLikeJump+nStates); for (i=h=0; inumRateCats; k++) { for (c=0; cnumChars; c++) { a = rState[c] + k*(preLikeJump+nStates); b = aState[c] + k*(preLikeJump+nStates); for (i=h=0; inumRateCats; k++) { for (c=0; cnumChars; c++) { a = rState[c] + k*(preLikeJump+nStates); b = lState[c] + k*(preLikeJump+nStates); d = aState[c] + k*(preLikeJump+nStates); for (i=0; inumStates; nStates = m->numModelStates; nStatesSquared = nStates * nStates; preLikeJump = nObsStates * nStates; /* flip state of node so that we are not overwriting old cond likes */ FlipCondLikeSpace (m, chain, p->index); /* find conditional likelihood pointers */ clL = (__m128 *)m->condLikes[m->condLikeIndex[chain][p->left->index ]]; clR = (__m128 *)m->condLikes[m->condLikeIndex[chain][p->right->index]]; clP = (__m128 *)m->condLikes[m->condLikeIndex[chain][p->index ]]; clA = (__m128 *)m->condLikes[m->condLikeIndex[chain][p->anc->index ]]; /* find transition probabilities (or calculate instead) */ pL = m->tiProbs[m->tiProbsIndex[chain][p->left->index ]]; pR = m->tiProbs[m->tiProbsIndex[chain][p->right->index]]; pA = m->tiProbs[m->tiProbsIndex[chain][p->index ]]; /* find likelihoods of site patterns for left branch if terminal */ shortCut = 0; # if !defined (DEBUG_NOSHORTCUTS) if (p->left->left == NULL && m->isPartAmbig[p->left->index] == NO) { shortCut |= 1; lState = m->termState[p->left->index]; tiPL = pL; for (k=a=0; knumRateCats; k++) { catStart = a; for (i=0; iright->left == NULL && m->isPartAmbig[p->right->index] == NO) { shortCut |= 2; rState = m->termState[p->right->index]; tiPR = pR; for (k=a=0; knumRateCats; k++) { catStart = a; for (i=0; iisPartAmbig[p->anc->index] == YES) { shortCut = 4; } else { aState = m->termState[p->anc->index]; tiPA = pA; for (k=a=0; knumRateCats; k++) { catStart = a; for (i=0; inumRateCats; k++) { for (c=0; cnumVecChars; c++) { for (i=h=0; inumRateCats; k++) { for (c=t=0; cnumVecChars; c++) { for (c1=0; c1numFloatsPerVec; c1++,t++) { preLikeAV[c1] = &preLikeA[aState[t] + k*(preLikeJump+nStates)]; } for (i=h=0; inumFloatsPerVec == 4); /* In the following statement we assume that SSE register can hold exactly 4 ClFlts. */ mAcumA = _mm_set_ps (*(preLikeAV[3]++), *(preLikeAV[2]++), *(preLikeAV[1]++), *(preLikeAV[0]++)); mAcumL = _mm_setzero_ps(); mAcumR = _mm_setzero_ps(); for (j=0; jnumRateCats; k++) { for (c=t=0; cnumVecChars; c++) { for (c1=0; c1numFloatsPerVec; c1++,t++) { preLikeLV[c1] = &preLikeL[lState[t] + k*(preLikeJump+nStates)]; preLikeAV[c1] = &preLikeA[aState[t] + k*(preLikeJump+nStates)]; } for (i=h=0; inumFloatsPerVec == 4); /* In the following statement we assume that SSE register can hold exactly 4 ClFlts. */ mAcumL = _mm_set_ps (*(preLikeLV[3]++), *(preLikeLV[2]++), *(preLikeLV[1]++), *(preLikeLV[0]++)); mAcumA = _mm_set_ps (*(preLikeAV[3]++), *(preLikeAV[2]++), *(preLikeAV[1]++), *(preLikeAV[0]++)); mAcumR = _mm_setzero_ps(); for (j=0; jnumRateCats; k++) { for (c=t=0; cnumVecChars; c++) { for (c1=0; c1numFloatsPerVec; c1++,t++) { preLikeRV[c1] = &preLikeR[rState[t] + k*(preLikeJump+nStates)]; preLikeAV[c1] = &preLikeA[aState[t] + k*(preLikeJump+nStates)]; } for (i=h=0; inumFloatsPerVec == 4); /* In the following statement we assume that SSE register can hold exactly 4 ClFlts. */ mAcumR = _mm_set_ps (*(preLikeRV[3]++), *(preLikeRV[2]++), *(preLikeRV[1]++), *(preLikeRV[0]++)); mAcumA = _mm_set_ps (*(preLikeAV[3]++), *(preLikeAV[2]++), *(preLikeAV[1]++), *(preLikeAV[0]++)); mAcumL = _mm_setzero_ps(); for (j=0; jnumRateCats; k++) { for (c=t=0; cnumVecChars; c++) { for (c1=0; c1numFloatsPerVec; c1++,t++) { preLikeRV[c1] = &preLikeR[rState[t] + k*(preLikeJump+nStates)]; preLikeLV[c1] = &preLikeL[lState[t] + k*(preLikeJump+nStates)]; preLikeAV[c1] = &preLikeA[aState[t] + k*(preLikeJump+nStates)]; } for (i=0; inumFloatsPerVec == 4); /* In the following 2 statements we assume that SSE register can hold exactly 4 ClFlts. */ mL = _mm_set_ps (*(preLikeLV[3]++), *(preLikeLV[2]++), *(preLikeLV[1]++), *(preLikeLV[0]++)); mR = _mm_set_ps (*(preLikeRV[3]++), *(preLikeRV[2]++), *(preLikeRV[1]++), *(preLikeRV[0]++)); mA = _mm_set_ps (*(preLikeAV[3]++), *(preLikeAV[2]++), *(preLikeAV[1]++), *(preLikeAV[0]++)); mL = _mm_mul_ps (mL,mR); *(clP++) = _mm_mul_ps (mL,mA); } } } break; } return NO_ERROR; } #endif /*---------------------------------------------------------------- | | CondLikeRoot_Gen_GibbsGamma: general n-state model with rate | variation modeled using a discrete gamma distribution with | Gibbs resampling of rate categories | -----------------------------------------------------------------*/ int CondLikeRoot_Gen_GibbsGamma (TreeNode *p, int division, int chain) { int a, b, c, i, j, r, *rateCat, shortCut, *lState=NULL, *rState=NULL, *aState=NULL, nObsStates, nStates, nStatesSquared, nRateCats; CLFlt likeL, likeR, likeA, *clL, *clR, *clP, *clA, *pL, *pR, *pA, *tiPL, *tiPR, *tiPA; ModelInfo *m; # if !defined (DEBUG_NOSHORTCUTS) int k, catStart; #endif /* find model settings for this division and nStates, nStatesSquared */ m = &modelSettings[division]; nObsStates = m->numStates; nStates = m->numModelStates; nStatesSquared = nStates * nStates; /* flip conditional likelihood space */ FlipCondLikeSpace (m, chain, p->index); /* find conditional likelihood pointers */ clL = m->condLikes[m->condLikeIndex[chain][p->left->index ]]; clR = m->condLikes[m->condLikeIndex[chain][p->right->index]]; clP = m->condLikes[m->condLikeIndex[chain][p->index ]]; clA = m->condLikes[m->condLikeIndex[chain][p->anc->index ]]; /* find transition probabilities (or calculate instead) */ pL = m->tiProbs[m->tiProbsIndex[chain][p->left->index ]]; pR = m->tiProbs[m->tiProbsIndex[chain][p->right->index]]; pA = m->tiProbs[m->tiProbsIndex[chain][p->index ]]; /* find rate category index and number of rate categories */ rateCat = m->tiIndex + chain * m->numChars; nRateCats = m->numRateCats; /* find likelihoods of site patterns for left branch if terminal */ shortCut = 0; # if !defined (DEBUG_NOSHORTCUTS) if (p->left->left == NULL && m->isPartAmbig[p->left->index] == NO) { shortCut |= 1; lState = m->termState[p->left->index]; tiPL = pL; for (k=a=0; kright->left == NULL && m->isPartAmbig[p->right->index] == NO) { shortCut |= 2; rState = m->termState[p->right->index]; tiPR = pR; for (k=a=0; kisPartAmbig[p->anc->index] == YES) { shortCut = 4; } else { aState = m->termState[p->anc->index]; tiPA = pA; for (k=a=0; knumChars; c++) { r = (*rateCat++); if (r < nRateCats) { tiPL = pL + r*nStatesSquared; tiPR = pR + r*nStatesSquared; tiPA = pA + r*nStatesSquared; for (i=0; inumChars; c++) { r = (*rateCat++); if (r < nRateCats) { tiPL = pL + r*nStatesSquared; tiPR = pR + r*nStatesSquared; a = aState[c] + r*(nStatesSquared+nStates); for (i=0; inumChars; c++) { r = (*rateCat++); if (r < nRateCats) { tiPR = pR + r*nStatesSquared; a = lState[c] + r*(nStatesSquared+nStates); b = aState[c] + r*(nStatesSquared+nStates); for (i=0; inumChars; c++) { r = (*rateCat++); if (r < nRateCats) { tiPL = pL + r*nStatesSquared; a = rState[c] + r*(nStatesSquared+nStates); b = aState[c] + r*(nStatesSquared+nStates); for (i=0; iindex); /* find conditional likelihood pointers */ clL = m->condLikes[m->condLikeIndex[chain][p->left->index ]]; clR = m->condLikes[m->condLikeIndex[chain][p->right->index]]; clP = m->condLikes[m->condLikeIndex[chain][p->index ]]; clA = m->condLikes[m->condLikeIndex[chain][p->anc->index ]]; /* find transition probabilities (or calculate instead) */ pL = m->tiProbs[m->tiProbsIndex[chain][p->left->index ]]; pR = m->tiProbs[m->tiProbsIndex[chain][p->right->index]]; pA = m->tiProbs[m->tiProbsIndex[chain][p->index ]]; /* find likelihoods of site patterns for left branch if terminal */ shortCut = 0; # if !defined (DEBUG_NOSHORTCUTS) if (p->left->left == NULL && m->isPartAmbig[p->left->index] == NO) { shortCut |= 1; lState = m->termState[p->left->index]; tiPL = pL; for (k=j=0; knumRateCats; k++) { for (i=0; i<4; i++) { preLikeL[j++] = tiPL[0]; preLikeL[j++] = tiPL[4]; preLikeL[j++] = tiPL[8]; preLikeL[j++] = tiPL[12]; tiPL++; } /* for ambiguous */ for (i=0; i<4; i++) preLikeL[j++] = 1.0; tiPL += 12; } } /* find likelihoods of site patterns for right branch if terminal */ if (p->right->left == NULL && m->isPartAmbig[p->right->index] == NO) { shortCut |= 2; rState = m->termState[p->right->index]; tiPR = pR; for (k=j=0; knumRateCats; k++) { for (i=0; i<4; i++) { preLikeR[j++] = tiPR[0]; preLikeR[j++] = tiPR[4]; preLikeR[j++] = tiPR[8]; preLikeR[j++] = tiPR[12]; tiPR++; } /* for ambiguous */ for (i=0; i<4; i++) preLikeR[j++] = 1.0; tiPR += 12; } } /* find likelihoods of site patterns for anc branch, always terminal */ if (m->isPartAmbig[p->anc->index] == YES) { shortCut = 4; } else { aState = m->termState[p->anc->index]; tiPA = pA; for (k=j=0; knumRateCats; k++) { for (i=0; i<4; i++) { preLikeA[j++] = tiPA[0]; preLikeA[j++] = tiPA[4]; preLikeA[j++] = tiPA[8]; preLikeA[j++] = tiPA[12]; tiPA++; } /* for ambiguous */ for (i=0; i<4; i++) preLikeA[j++] = 1.0; tiPA += 12; } } # else shortCut = 4; # endif switch (shortCut) { case 4: tiPL = pL; tiPR = pR; tiPA = pA; for (k=h=0; knumRateCats; k++) { for (c=0; cnumChars; c++) { clP[h++] = (tiPL[AA]*clL[A] + tiPL[AC]*clL[C] + tiPL[AG]*clL[G] + tiPL[AT]*clL[T]) *(tiPR[AA]*clR[A] + tiPR[AC]*clR[C] + tiPR[AG]*clR[G] + tiPR[AT]*clR[T]) *(tiPA[AA]*clA[A] + tiPA[AC]*clA[C] + tiPA[AG]*clA[G] + tiPA[AT]*clA[T]); clP[h++] = (tiPL[CA]*clL[A] + tiPL[CC]*clL[C] + tiPL[CG]*clL[G] + tiPL[CT]*clL[T]) *(tiPR[CA]*clR[A] + tiPR[CC]*clR[C] + tiPR[CG]*clR[G] + tiPR[CT]*clR[T]) *(tiPA[CA]*clA[A] + tiPA[CC]*clA[C] + tiPA[CG]*clA[G] + tiPA[CT]*clA[T]); clP[h++] = (tiPL[GA]*clL[A] + tiPL[GC]*clL[C] + tiPL[GG]*clL[G] + tiPL[GT]*clL[T]) *(tiPR[GA]*clR[A] + tiPR[GC]*clR[C] + tiPR[GG]*clR[G] + tiPR[GT]*clR[T]) *(tiPA[GA]*clA[A] + tiPA[GC]*clA[C] + tiPA[GG]*clA[G] + tiPA[GT]*clA[T]); clP[h++] = (tiPL[TA]*clL[A] + tiPL[TC]*clL[C] + tiPL[TG]*clL[G] + tiPL[TT]*clL[T]) *(tiPR[TA]*clR[A] + tiPR[TC]*clR[C] + tiPR[TG]*clR[G] + tiPR[TT]*clR[T]) *(tiPA[TA]*clA[A] + tiPA[TC]*clA[C] + tiPA[TG]*clA[G] + tiPA[TT]*clA[T]); clL += 4; clR += 4; clA += 4; } tiPL += 16; tiPR += 16; tiPA += 16; } break; case 0: tiPL = pL; tiPR = pR; for (k=h=0; knumRateCats; k++) { for (c=0; cnumChars; c++) { i = aState[c] + k*20; clP[h++] = (tiPL[AA]*clL[A] + tiPL[AC]*clL[C] + tiPL[AG]*clL[G] + tiPL[AT]*clL[T]) *(tiPR[AA]*clR[A] + tiPR[AC]*clR[C] + tiPR[AG]*clR[G] + tiPR[AT]*clR[T]) *preLikeA[i++]; clP[h++] = (tiPL[CA]*clL[A] + tiPL[CC]*clL[C] + tiPL[CG]*clL[G] + tiPL[CT]*clL[T]) *(tiPR[CA]*clR[A] + tiPR[CC]*clR[C] + tiPR[CG]*clR[G] + tiPR[CT]*clR[T]) *preLikeA[i++]; clP[h++] = (tiPL[GA]*clL[A] + tiPL[GC]*clL[C] + tiPL[GG]*clL[G] + tiPL[GT]*clL[T]) *(tiPR[GA]*clR[A] + tiPR[GC]*clR[C] + tiPR[GG]*clR[G] + tiPR[GT]*clR[T]) *preLikeA[i++]; clP[h++] = (tiPL[TA]*clL[A] + tiPL[TC]*clL[C] + tiPL[TG]*clL[G] + tiPL[TT]*clL[T]) *(tiPR[TA]*clR[A] + tiPR[TC]*clR[C] + tiPR[TG]*clR[G] + tiPR[TT]*clR[T]) *preLikeA[i++]; clL += 4; clR += 4; } tiPL += 16; tiPR += 16; } break; case 1: tiPR = pR; for (k=h=0; knumRateCats; k++) { for (c=0; cnumChars; c++) { i = lState[c] + k*20; j = aState[c] + k*20; clP[h++] = (tiPR[AA]*clR[A] + tiPR[AC]*clR[C] + tiPR[AG]*clR[G] + tiPR[AT]*clR[T]) *preLikeL[i++]*preLikeA[j++]; clP[h++] = (tiPR[CA]*clR[A] + tiPR[CC]*clR[C] + tiPR[CG]*clR[G] + tiPR[CT]*clR[T]) *preLikeL[i++]*preLikeA[j++]; clP[h++] = (tiPR[GA]*clR[A] + tiPR[GC]*clR[C] + tiPR[GG]*clR[G] + tiPR[GT]*clR[T]) *preLikeL[i++]*preLikeA[j++]; clP[h++] = (tiPR[TA]*clR[A] + tiPR[TC]*clR[C] + tiPR[TG]*clR[G] + tiPR[TT]*clR[T]) *preLikeL[i++]*preLikeA[j++]; clR += 4; } tiPR += 16; } break; case 2: tiPL = pL; for (k=h=0; knumRateCats; k++) { for (c=0; cnumChars; c++) { i = rState[c] + k*20; j = aState[c] + k*20; clP[h++] = (tiPL[AA]*clL[A] + tiPL[AC]*clL[C] + tiPL[AG]*clL[G] + tiPL[AT]*clL[T]) *preLikeR[i++]*preLikeA[j++]; clP[h++] = (tiPL[CA]*clL[A] + tiPL[CC]*clL[C] + tiPL[CG]*clL[G] + tiPL[CT]*clL[T]) *preLikeR[i++]*preLikeA[j++]; clP[h++] = (tiPL[GA]*clL[A] + tiPL[GC]*clL[C] + tiPL[GG]*clL[G] + tiPL[GT]*clL[T]) *preLikeR[i++]*preLikeA[j++]; clP[h++] = (tiPL[TA]*clL[A] + tiPL[TC]*clL[C] + tiPL[TG]*clL[G] + tiPL[TT]*clL[T]) *preLikeR[i++]*preLikeA[j++]; clL += 4; } tiPL += 16; } break; case 3: for (k=h=0; knumRateCats; k++) { for (c=0; cnumChars; c++) { a = lState[c] + k*20; i = rState[c] + k*20; j = aState[c] + k*20; clP[h++] = preLikeL[a++]*preLikeR[i++]*preLikeA[j++]; clP[h++] = preLikeL[a++]*preLikeR[i++]*preLikeA[j++]; clP[h++] = preLikeL[a++]*preLikeR[i++]*preLikeA[j++]; clP[h++] = preLikeL[a++]*preLikeR[i++]*preLikeA[j++]; } } break; } return NO_ERROR; } /*---------------------------------------------------------------- | | CondLikeRoot_NUC4_GibbsGamma: 4by4 nucleotide model with rate | variation approximated by Gibbs sampling from gamma | -----------------------------------------------------------------*/ int CondLikeRoot_NUC4_GibbsGamma (TreeNode *p, int division, int chain) { int c, h, i, j, r, *rateCat, shortCut, *lState=NULL, *rState=NULL, *aState=NULL, nRateCats; CLFlt *clL, *clR, *clP, *clA, *pL, *pR, *pA, *tiPL, *tiPR, *tiPA; ModelInfo *m; # if !defined (DEBUG_NOSHORTCUTS) int k; # endif m = &modelSettings[division]; /* flip conditional likelihood space */ FlipCondLikeSpace (m, chain, p->index); /* find conditional likelihood pointers */ clL = m->condLikes[m->condLikeIndex[chain][p->left->index ]]; clR = m->condLikes[m->condLikeIndex[chain][p->right->index]]; clP = m->condLikes[m->condLikeIndex[chain][p->index ]]; clA = m->condLikes[m->condLikeIndex[chain][p->anc->index ]]; /* find transition probabilities (or calculate instead) */ pL = m->tiProbs[m->tiProbsIndex[chain][p->left->index ]]; pR = m->tiProbs[m->tiProbsIndex[chain][p->right->index]]; pA = m->tiProbs[m->tiProbsIndex[chain][p->index ]]; /* find rate category index and number of gamma categories */ rateCat = m->tiIndex + chain * m->numChars; nRateCats = m->numRateCats; /* find likelihoods of site patterns for left branch if terminal */ shortCut = 0; # if !defined (DEBUG_NOSHORTCUTS) if (p->left->left == NULL && m->isPartAmbig[p->left->index] == NO) { shortCut |= 1; lState = m->termState[p->left->index]; tiPL = pL; for (k=j=0; kright->left == NULL && m->isPartAmbig[p->right->index] == NO) { shortCut |= 2; rState = m->termState[p->right->index]; tiPR = pR; for (k=j=0; kisPartAmbig[p->anc->index] == YES) { shortCut = 4; } else { aState = m->termState[p->anc->index]; tiPA = pA; for (k=j=0; knumChars; c++) { r = rateCat[c]; if (r < nRateCats) { tiPL = pL + r * 16; tiPR = pR + r * 16; tiPA = pA + r * 16; clP[h++] = (tiPL[AA]*clL[A] + tiPL[AC]*clL[C] + tiPL[AG]*clL[G] + tiPL[AT]*clL[T]) *(tiPR[AA]*clR[A] + tiPR[AC]*clR[C] + tiPR[AG]*clR[G] + tiPR[AT]*clR[T]) *(tiPA[AA]*clA[A] + tiPA[AC]*clA[C] + tiPA[AG]*clA[G] + tiPA[AT]*clA[T]); clP[h++] = (tiPL[CA]*clL[A] + tiPL[CC]*clL[C] + tiPL[CG]*clL[G] + tiPL[CT]*clL[T]) *(tiPR[CA]*clR[A] + tiPR[CC]*clR[C] + tiPR[CG]*clR[G] + tiPR[CT]*clR[T]) *(tiPA[CA]*clA[A] + tiPA[CC]*clA[C] + tiPA[CG]*clA[G] + tiPA[CT]*clA[T]); clP[h++] = (tiPL[GA]*clL[A] + tiPL[GC]*clL[C] + tiPL[GG]*clL[G] + tiPL[GT]*clL[T]) *(tiPR[GA]*clR[A] + tiPR[GC]*clR[C] + tiPR[GG]*clR[G] + tiPR[GT]*clR[T]) *(tiPA[GA]*clA[A] + tiPA[GC]*clA[C] + tiPA[GG]*clA[G] + tiPA[GT]*clA[T]); clP[h++] = (tiPL[TA]*clL[A] + tiPL[TC]*clL[C] + tiPL[TG]*clL[G] + tiPL[TT]*clL[T]) *(tiPR[TA]*clR[A] + tiPR[TC]*clR[C] + tiPR[TG]*clR[G] + tiPR[TT]*clR[T]) *(tiPA[TA]*clA[A] + tiPA[TC]*clA[C] + tiPA[TG]*clA[G] + tiPA[TT]*clA[T]); } else h += 4; clL += 4; clR += 4; clA += 4; } break; case 0: case 3: for (c=h=0; cnumChars; c++) { r = rateCat[c]; if (r < nRateCats) { tiPL = pL + r * 16; tiPR = pR + r * 16; i = aState[c] + r * 20; clP[h++] = (tiPL[AA]*clL[A] + tiPL[AC]*clL[C] + tiPL[AG]*clL[G] + tiPL[AT]*clL[T]) *(tiPR[AA]*clR[A] + tiPR[AC]*clR[C] + tiPR[AG]*clR[G] + tiPR[AT]*clR[T]) *preLikeA[i++]; clP[h++] = (tiPL[CA]*clL[A] + tiPL[CC]*clL[C] + tiPL[CG]*clL[G] + tiPL[CT]*clL[T]) *(tiPR[CA]*clR[A] + tiPR[CC]*clR[C] + tiPR[CG]*clR[G] + tiPR[CT]*clR[T]) *preLikeA[i++]; clP[h++] = (tiPL[GA]*clL[A] + tiPL[GC]*clL[C] + tiPL[GG]*clL[G] + tiPL[GT]*clL[T]) *(tiPR[GA]*clR[A] + tiPR[GC]*clR[C] + tiPR[GG]*clR[G] + tiPR[GT]*clR[T]) *preLikeA[i++]; clP[h++] = (tiPL[TA]*clL[A] + tiPL[TC]*clL[C] + tiPL[TG]*clL[G] + tiPL[TT]*clL[T]) *(tiPR[TA]*clR[A] + tiPR[TC]*clR[C] + tiPR[TG]*clR[G] + tiPR[TT]*clR[T]) *preLikeA[i++]; } else h += 4; clL += 4; clR += 4; } break; case 1: for (c=h=0; cnumChars; c++) { r = rateCat[c]; if (r < nRateCats) { tiPR = pR + r * 16; i = lState[c] + r * 20; j = aState[c] + r * 20; clP[h++] = (tiPR[AA]*clR[A] + tiPR[AC]*clR[C] + tiPR[AG]*clR[G] + tiPR[AT]*clR[T]) *preLikeL[i++]*preLikeA[j++]; clP[h++] = (tiPR[CA]*clR[A] + tiPR[CC]*clR[C] + tiPR[CG]*clR[G] + tiPR[CT]*clR[T]) *preLikeL[i++]*preLikeA[j++]; clP[h++] = (tiPR[GA]*clR[A] + tiPR[GC]*clR[C] + tiPR[GG]*clR[G] + tiPR[GT]*clR[T]) *preLikeL[i++]*preLikeA[j++]; clP[h++] = (tiPR[TA]*clR[A] + tiPR[TC]*clR[C] + tiPR[TG]*clR[G] + tiPR[TT]*clR[T]) *preLikeL[i++]*preLikeA[j++]; } else h += 4; clR += 4; } break; case 2: for (c=h=0; cnumChars; c++) { r = rateCat[c]; if (r < nRateCats) { tiPL = pL + r * 16; i = rState[c] + r * 20; j = aState[c] + r * 20; clP[h++] = (tiPL[AA]*clL[A] + tiPL[AC]*clL[C] + tiPL[AG]*clL[G] + tiPL[AT]*clL[T]) *preLikeR[i++]*preLikeA[j++]; clP[h++] = (tiPL[CA]*clL[A] + tiPL[CC]*clL[C] + tiPL[CG]*clL[G] + tiPL[CT]*clL[T]) *preLikeR[i++]*preLikeA[j++]; clP[h++] = (tiPL[GA]*clL[A] + tiPL[GC]*clL[C] + tiPL[GG]*clL[G] + tiPL[GT]*clL[T]) *preLikeR[i++]*preLikeA[j++]; clP[h++] = (tiPL[TA]*clL[A] + tiPL[TC]*clL[C] + tiPL[TG]*clL[G] + tiPL[TT]*clL[T]) *preLikeR[i++]*preLikeA[j++]; } else h += 4; clL += 4; } break; } return NO_ERROR; } #if defined (FMA_ENABLED) /*---------------------------------------------------------------- | | CondLikeRoot_NUC4_FMA: 4by4 nucleotide model with or without rate | variation using AVX + FMA instructions | -----------------------------------------------------------------*/ int CondLikeRoot_NUC4_FMA (TreeNode *p, int division, int chain) { int c, k; CLFlt *pL, *pR, *pA, *tiPL, *tiPR, *tiPA; __m256 *clL, *clR, *clP, *clA; __m256 m1, m2, m3, m4, m5, m6; ModelInfo *m; m = &modelSettings[division]; /* flip state of node so that we are not overwriting old cond likes */ FlipCondLikeSpace (m, chain, p->index); /* find conditional likelihood pointers */ clL = (__m256 *) m->condLikes[m->condLikeIndex[chain][p->left->index ]]; clR = (__m256 *) m->condLikes[m->condLikeIndex[chain][p->right->index]]; clP = (__m256 *) m->condLikes[m->condLikeIndex[chain][p->index ]]; clA = (__m256 *) m->condLikes[m->condLikeIndex[chain][p->anc->index ]]; /* find transition probabilities */ pL = m->tiProbs[m->tiProbsIndex[chain][p->left->index ]]; pR = m->tiProbs[m->tiProbsIndex[chain][p->right->index]]; pA = m->tiProbs[m->tiProbsIndex[chain][p->index ]]; tiPL = pL; tiPR = pR; tiPA = pA; for (k=0; knumRateCats; k++) { for (c=0; cnumVecChars; c++) { m1 = _mm256_broadcast_ss (&tiPL[AA]); m2 = _mm256_broadcast_ss (&tiPR[AA]); m3 = _mm256_broadcast_ss (&tiPA[AA]); m4 = _mm256_mul_ps (m1, clL[A]); m5 = _mm256_mul_ps (m2, clR[A]); m6 = _mm256_mul_ps (m3, clA[A]); m1 = _mm256_broadcast_ss (&tiPL[AC]); m2 = _mm256_broadcast_ss (&tiPR[AC]); m3 = _mm256_broadcast_ss (&tiPA[AC]); m4 = _mm256_fmadd_ps (m1, clL[C], m4); m5 = _mm256_fmadd_ps (m2, clR[C], m5); m6 = _mm256_fmadd_ps (m3, clA[C], m6); m1 = _mm256_broadcast_ss (&tiPL[AG]); m2 = _mm256_broadcast_ss (&tiPR[AG]); m3 = _mm256_broadcast_ss (&tiPA[AG]); m4 = _mm256_fmadd_ps (m1, clL[G], m4); m5 = _mm256_fmadd_ps (m2, clR[G], m5); m6 = _mm256_fmadd_ps (m3, clA[G], m6); m1 = _mm256_broadcast_ss (&tiPL[AT]); m2 = _mm256_broadcast_ss (&tiPR[AT]); m3 = _mm256_broadcast_ss (&tiPA[AT]); m4 = _mm256_fmadd_ps (m1, clL[T], m4); m5 = _mm256_fmadd_ps (m2, clR[T], m5); m6 = _mm256_fmadd_ps (m3, clA[T], m6); m4 = _mm256_mul_ps (m4, m5); *clP++ = _mm256_mul_ps (m4, m6); m1 = _mm256_broadcast_ss (&tiPL[CA]); m2 = _mm256_broadcast_ss (&tiPR[CA]); m3 = _mm256_broadcast_ss (&tiPA[CA]); m4 = _mm256_mul_ps (m1, clL[A]); m5 = _mm256_mul_ps (m2, clR[A]); m6 = _mm256_mul_ps (m3, clA[A]); m1 = _mm256_broadcast_ss (&tiPL[CC]); m2 = _mm256_broadcast_ss (&tiPR[CC]); m3 = _mm256_broadcast_ss (&tiPA[CC]); m4 = _mm256_fmadd_ps (m1, clL[C], m4); m5 = _mm256_fmadd_ps (m2, clR[C], m5); m6 = _mm256_fmadd_ps (m3, clA[C], m6); m1 = _mm256_broadcast_ss (&tiPL[CG]); m2 = _mm256_broadcast_ss (&tiPR[CG]); m3 = _mm256_broadcast_ss (&tiPA[CG]); m4 = _mm256_fmadd_ps (m1, clL[G], m4); m5 = _mm256_fmadd_ps (m2, clR[G], m5); m6 = _mm256_fmadd_ps (m3, clA[G], m6); m1 = _mm256_broadcast_ss (&tiPL[CT]); m2 = _mm256_broadcast_ss (&tiPR[CT]); m3 = _mm256_broadcast_ss (&tiPA[CT]); m4 = _mm256_fmadd_ps (m1, clL[T], m4); m5 = _mm256_fmadd_ps (m2, clR[T], m5); m6 = _mm256_fmadd_ps (m3, clA[T], m6); m4 = _mm256_mul_ps (m4, m5); *clP++ = _mm256_mul_ps (m4, m6); m1 = _mm256_broadcast_ss (&tiPL[GA]); m2 = _mm256_broadcast_ss (&tiPR[GA]); m3 = _mm256_broadcast_ss (&tiPA[GA]); m4 = _mm256_mul_ps (m1, clL[A]); m5 = _mm256_mul_ps (m2, clR[A]); m6 = _mm256_mul_ps (m3, clA[A]); m1 = _mm256_broadcast_ss (&tiPL[GC]); m2 = _mm256_broadcast_ss (&tiPR[GC]); m3 = _mm256_broadcast_ss (&tiPA[GC]); m4 = _mm256_fmadd_ps (m1, clL[C], m4); m5 = _mm256_fmadd_ps (m2, clR[C], m5); m6 = _mm256_fmadd_ps (m3, clA[C], m6); m1 = _mm256_broadcast_ss (&tiPL[GG]); m2 = _mm256_broadcast_ss (&tiPR[GG]); m3 = _mm256_broadcast_ss (&tiPA[GG]); m4 = _mm256_fmadd_ps (m1, clL[G], m4); m5 = _mm256_fmadd_ps (m2, clR[G], m5); m6 = _mm256_fmadd_ps (m3, clA[G], m6); m1 = _mm256_broadcast_ss (&tiPL[GT]); m2 = _mm256_broadcast_ss (&tiPR[GT]); m3 = _mm256_broadcast_ss (&tiPA[GT]); m4 = _mm256_fmadd_ps (m1, clL[T], m4); m5 = _mm256_fmadd_ps (m2, clR[T], m5); m6 = _mm256_fmadd_ps (m3, clA[T], m6); m4 = _mm256_mul_ps (m4, m5); *clP++ = _mm256_mul_ps (m4, m6); m1 = _mm256_broadcast_ss (&tiPL[TA]); m2 = _mm256_broadcast_ss (&tiPR[TA]); m3 = _mm256_broadcast_ss (&tiPA[TA]); m4 = _mm256_mul_ps (m1, clL[A]); m5 = _mm256_mul_ps (m2, clR[A]); m6 = _mm256_mul_ps (m3, clA[A]); m1 = _mm256_broadcast_ss (&tiPL[TC]); m2 = _mm256_broadcast_ss (&tiPR[TC]); m3 = _mm256_broadcast_ss (&tiPA[TC]); m4 = _mm256_fmadd_ps (m1, clL[C], m4); m5 = _mm256_fmadd_ps (m2, clR[C], m5); m6 = _mm256_fmadd_ps (m3, clA[C], m6); m1 = _mm256_broadcast_ss (&tiPL[TG]); m2 = _mm256_broadcast_ss (&tiPR[TG]); m3 = _mm256_broadcast_ss (&tiPA[TG]); m4 = _mm256_fmadd_ps (m1, clL[G], m4); m5 = _mm256_fmadd_ps (m2, clR[G], m5); m6 = _mm256_fmadd_ps (m3, clA[G], m6); m1 = _mm256_broadcast_ss (&tiPL[TT]); m2 = _mm256_broadcast_ss (&tiPR[TT]); m3 = _mm256_broadcast_ss (&tiPA[TT]); m4 = _mm256_fmadd_ps (m1, clL[T], m4); m5 = _mm256_fmadd_ps (m2, clR[T], m5); m6 = _mm256_fmadd_ps (m3, clA[T], m6); m4 = _mm256_mul_ps (m4, m5); *clP++ = _mm256_mul_ps (m4, m6); clL += 4; clR += 4; clA += 4; } tiPL += 16; tiPR += 16; tiPA += 16; } return NO_ERROR; } #endif #if defined (AVX_ENABLED) /*---------------------------------------------------------------- | | CondLikeRoot_NUC4_AVX: 4by4 nucleotide model with or without rate | variation using AVX instructions | -----------------------------------------------------------------*/ int CondLikeRoot_NUC4_AVX (TreeNode *p, int division, int chain) { int c, k; CLFlt *pL, *pR, *pA, *tiPL, *tiPR, *tiPA; __m256 *clL, *clR, *clP, *clA; __m256 m1, m2, m3, m4, m5, m6, m7, m8, m9; ModelInfo *m; m = &modelSettings[division]; /* flip state of node so that we are not overwriting old cond likes */ FlipCondLikeSpace (m, chain, p->index); /* find conditional likelihood pointers */ clL = (__m256 *) m->condLikes[m->condLikeIndex[chain][p->left->index ]]; clR = (__m256 *) m->condLikes[m->condLikeIndex[chain][p->right->index]]; clP = (__m256 *) m->condLikes[m->condLikeIndex[chain][p->index ]]; clA = (__m256 *) m->condLikes[m->condLikeIndex[chain][p->anc->index ]]; /* find transition probabilities */ pL = m->tiProbs[m->tiProbsIndex[chain][p->left->index ]]; pR = m->tiProbs[m->tiProbsIndex[chain][p->right->index]]; pA = m->tiProbs[m->tiProbsIndex[chain][p->index ]]; tiPL = pL; tiPR = pR; tiPA = pA; for (k=0; knumRateCats; k++) { for (c=0; cnumVecChars; c++) { m1 = _mm256_broadcast_ss (&tiPL[AA]); m2 = _mm256_broadcast_ss (&tiPR[AA]); m3 = _mm256_broadcast_ss (&tiPA[AA]); m7 = _mm256_mul_ps (m1, clL[A]); m8 = _mm256_mul_ps (m2, clR[A]); m9 = _mm256_mul_ps (m3, clA[A]); m1 = _mm256_broadcast_ss (&tiPL[AC]); m2 = _mm256_broadcast_ss (&tiPR[AC]); m3 = _mm256_broadcast_ss (&tiPA[AC]); m4 = _mm256_mul_ps (m1, clL[C]); m5 = _mm256_mul_ps (m2, clR[C]); m6 = _mm256_mul_ps (m3, clA[C]); m7 = _mm256_add_ps (m4, m7); m8 = _mm256_add_ps (m5, m8); m9 = _mm256_add_ps (m6, m9); m1 = _mm256_broadcast_ss (&tiPL[AG]); m2 = _mm256_broadcast_ss (&tiPR[AG]); m3 = _mm256_broadcast_ss (&tiPA[AG]); m4 = _mm256_mul_ps (m1, clL[G]); m5 = _mm256_mul_ps (m2, clR[G]); m6 = _mm256_mul_ps (m3, clA[G]); m7 = _mm256_add_ps (m4, m7); m8 = _mm256_add_ps (m5, m8); m9 = _mm256_add_ps (m6, m9); m1 = _mm256_broadcast_ss (&tiPL[AT]); m2 = _mm256_broadcast_ss (&tiPR[AT]); m3 = _mm256_broadcast_ss (&tiPA[AT]); m4 = _mm256_mul_ps (m1, clL[T]); m5 = _mm256_mul_ps (m2, clR[T]); m6 = _mm256_mul_ps (m3, clA[T]); m7 = _mm256_add_ps (m4, m7); m8 = _mm256_add_ps (m5, m8); m9 = _mm256_add_ps (m6, m9); m7 = _mm256_mul_ps (m7, m8); *clP++ = _mm256_mul_ps (m7, m9); m1 = _mm256_broadcast_ss (&tiPL[CA]); m2 = _mm256_broadcast_ss (&tiPR[CA]); m3 = _mm256_broadcast_ss (&tiPA[CA]); m7 = _mm256_mul_ps (m1, clL[A]); m8 = _mm256_mul_ps (m2, clR[A]); m9 = _mm256_mul_ps (m3, clA[A]); m1 = _mm256_broadcast_ss (&tiPL[CC]); m2 = _mm256_broadcast_ss (&tiPR[CC]); m3 = _mm256_broadcast_ss (&tiPA[CC]); m4 = _mm256_mul_ps (m1, clL[C]); m5 = _mm256_mul_ps (m2, clR[C]); m6 = _mm256_mul_ps (m3, clA[C]); m7 = _mm256_add_ps (m4, m7); m8 = _mm256_add_ps (m5, m8); m9 = _mm256_add_ps (m6, m9); m1 = _mm256_broadcast_ss (&tiPL[CG]); m2 = _mm256_broadcast_ss (&tiPR[CG]); m3 = _mm256_broadcast_ss (&tiPA[CG]); m4 = _mm256_mul_ps (m1, clL[G]); m5 = _mm256_mul_ps (m2, clR[G]); m6 = _mm256_mul_ps (m3, clA[G]); m7 = _mm256_add_ps (m4, m7); m8 = _mm256_add_ps (m5, m8); m9 = _mm256_add_ps (m6, m9); m1 = _mm256_broadcast_ss (&tiPL[CT]); m2 = _mm256_broadcast_ss (&tiPR[CT]); m3 = _mm256_broadcast_ss (&tiPA[CT]); m4 = _mm256_mul_ps (m1, clL[T]); m5 = _mm256_mul_ps (m2, clR[T]); m6 = _mm256_mul_ps (m3, clA[T]); m7 = _mm256_add_ps (m4, m7); m8 = _mm256_add_ps (m5, m8); m9 = _mm256_add_ps (m6, m9); m7 = _mm256_mul_ps (m7, m8); *clP++ = _mm256_mul_ps (m7, m9); m1 = _mm256_broadcast_ss (&tiPL[GA]); m2 = _mm256_broadcast_ss (&tiPR[GA]); m3 = _mm256_broadcast_ss (&tiPA[GA]); m7 = _mm256_mul_ps (m1, clL[A]); m8 = _mm256_mul_ps (m2, clR[A]); m9 = _mm256_mul_ps (m3, clA[A]); m1 = _mm256_broadcast_ss (&tiPL[GC]); m2 = _mm256_broadcast_ss (&tiPR[GC]); m3 = _mm256_broadcast_ss (&tiPA[GC]); m4 = _mm256_mul_ps (m1, clL[C]); m5 = _mm256_mul_ps (m2, clR[C]); m6 = _mm256_mul_ps (m3, clA[C]); m7 = _mm256_add_ps (m4, m7); m8 = _mm256_add_ps (m5, m8); m9 = _mm256_add_ps (m6, m9); m1 = _mm256_broadcast_ss (&tiPL[GG]); m2 = _mm256_broadcast_ss (&tiPR[GG]); m3 = _mm256_broadcast_ss (&tiPA[GG]); m4 = _mm256_mul_ps (m1, clL[G]); m5 = _mm256_mul_ps (m2, clR[G]); m6 = _mm256_mul_ps (m3, clA[G]); m7 = _mm256_add_ps (m4, m7); m8 = _mm256_add_ps (m5, m8); m9 = _mm256_add_ps (m6, m9); m1 = _mm256_broadcast_ss (&tiPL[GT]); m2 = _mm256_broadcast_ss (&tiPR[GT]); m3 = _mm256_broadcast_ss (&tiPA[GT]); m4 = _mm256_mul_ps (m1, clL[T]); m5 = _mm256_mul_ps (m2, clR[T]); m6 = _mm256_mul_ps (m3, clA[T]); m7 = _mm256_add_ps (m4, m7); m8 = _mm256_add_ps (m5, m8); m9 = _mm256_add_ps (m6, m9); m7 = _mm256_mul_ps (m7, m8); *clP++ = _mm256_mul_ps (m7, m9); m1 = _mm256_broadcast_ss (&tiPL[TA]); m2 = _mm256_broadcast_ss (&tiPR[TA]); m3 = _mm256_broadcast_ss (&tiPA[TA]); m7 = _mm256_mul_ps (m1, clL[A]); m8 = _mm256_mul_ps (m2, clR[A]); m9 = _mm256_mul_ps (m3, clA[A]); m1 = _mm256_broadcast_ss (&tiPL[TC]); m2 = _mm256_broadcast_ss (&tiPR[TC]); m3 = _mm256_broadcast_ss (&tiPA[TC]); m4 = _mm256_mul_ps (m1, clL[C]); m5 = _mm256_mul_ps (m2, clR[C]); m6 = _mm256_mul_ps (m3, clA[C]); m7 = _mm256_add_ps (m4, m7); m8 = _mm256_add_ps (m5, m8); m9 = _mm256_add_ps (m6, m9); m1 = _mm256_broadcast_ss (&tiPL[TG]); m2 = _mm256_broadcast_ss (&tiPR[TG]); m3 = _mm256_broadcast_ss (&tiPA[TG]); m4 = _mm256_mul_ps (m1, clL[G]); m5 = _mm256_mul_ps (m2, clR[G]); m6 = _mm256_mul_ps (m3, clA[G]); m7 = _mm256_add_ps (m4, m7); m8 = _mm256_add_ps (m5, m8); m9 = _mm256_add_ps (m6, m9); m1 = _mm256_broadcast_ss (&tiPL[TT]); m2 = _mm256_broadcast_ss (&tiPR[TT]); m3 = _mm256_broadcast_ss (&tiPA[TT]); m4 = _mm256_mul_ps (m1, clL[T]); m5 = _mm256_mul_ps (m2, clR[T]); m6 = _mm256_mul_ps (m3, clA[T]); m7 = _mm256_add_ps (m4, m7); m8 = _mm256_add_ps (m5, m8); m9 = _mm256_add_ps (m6, m9); m7 = _mm256_mul_ps (m7, m8); *clP++ = _mm256_mul_ps (m7, m9); clL += 4; clR += 4; clA += 4; } tiPL += 16; tiPR += 16; tiPA += 16; } return NO_ERROR; } #endif #if defined (SSE_ENABLED) /*---------------------------------------------------------------- | | CondLikeRoot_NUC4_SSE: 4by4 nucleotide model with or without rate | variation using SSE instructions | -----------------------------------------------------------------*/ int CondLikeRoot_NUC4_SSE (TreeNode *p, int division, int chain) { int c, k; CLFlt *pL, *pR, *pA, *tiPL, *tiPR, *tiPA; __m128 *clL, *clR, *clP, *clA; __m128 m1, m2, m3, m4, m5, m6, m7, m8, m9; ModelInfo *m; m = &modelSettings[division]; /* flip state of node so that we are not overwriting old cond likes */ FlipCondLikeSpace (m, chain, p->index); /* find conditional likelihood pointers */ clL = (__m128 *) m->condLikes[m->condLikeIndex[chain][p->left->index ]]; clR = (__m128 *) m->condLikes[m->condLikeIndex[chain][p->right->index]]; clP = (__m128 *) m->condLikes[m->condLikeIndex[chain][p->index ]]; clA = (__m128 *) m->condLikes[m->condLikeIndex[chain][p->anc->index ]]; /* find transition probabilities */ pL = m->tiProbs[m->tiProbsIndex[chain][p->left->index ]]; pR = m->tiProbs[m->tiProbsIndex[chain][p->right->index]]; pA = m->tiProbs[m->tiProbsIndex[chain][p->index ]]; tiPL = pL; tiPR = pR; tiPA = pA; for (k=0; knumRateCats; k++) { for (c=0; cnumVecChars; c++) { m1 = _mm_load1_ps (&tiPL[AA]); m2 = _mm_load1_ps (&tiPR[AA]); m3 = _mm_load1_ps (&tiPA[AA]); m7 = _mm_mul_ps (m1, clL[A]); m8 = _mm_mul_ps (m2, clR[A]); m9 = _mm_mul_ps (m3, clA[A]); m1 = _mm_load1_ps (&tiPL[AC]); m2 = _mm_load1_ps (&tiPR[AC]); m3 = _mm_load1_ps (&tiPA[AC]); m4 = _mm_mul_ps (m1, clL[C]); m5 = _mm_mul_ps (m2, clR[C]); m6 = _mm_mul_ps (m3, clA[C]); m7 = _mm_add_ps (m4, m7); m8 = _mm_add_ps (m5, m8); m9 = _mm_add_ps (m6, m9); m1 = _mm_load1_ps (&tiPL[AG]); m2 = _mm_load1_ps (&tiPR[AG]); m3 = _mm_load1_ps (&tiPA[AG]); m4 = _mm_mul_ps (m1, clL[G]); m5 = _mm_mul_ps (m2, clR[G]); m6 = _mm_mul_ps (m3, clA[G]); m7 = _mm_add_ps (m4, m7); m8 = _mm_add_ps (m5, m8); m9 = _mm_add_ps (m6, m9); m1 = _mm_load1_ps (&tiPL[AT]); m2 = _mm_load1_ps (&tiPR[AT]); m3 = _mm_load1_ps (&tiPA[AT]); m4 = _mm_mul_ps (m1, clL[T]); m5 = _mm_mul_ps (m2, clR[T]); m6 = _mm_mul_ps (m3, clA[T]); m7 = _mm_add_ps (m4, m7); m8 = _mm_add_ps (m5, m8); m9 = _mm_add_ps (m6, m9); m7 = _mm_mul_ps (m7, m8); *clP++ = _mm_mul_ps (m7, m9); m1 = _mm_load1_ps (&tiPL[CA]); m2 = _mm_load1_ps (&tiPR[CA]); m3 = _mm_load1_ps (&tiPA[CA]); m7 = _mm_mul_ps (m1, clL[A]); m8 = _mm_mul_ps (m2, clR[A]); m9 = _mm_mul_ps (m3, clA[A]); m1 = _mm_load1_ps (&tiPL[CC]); m2 = _mm_load1_ps (&tiPR[CC]); m3 = _mm_load1_ps (&tiPA[CC]); m4 = _mm_mul_ps (m1, clL[C]); m5 = _mm_mul_ps (m2, clR[C]); m6 = _mm_mul_ps (m3, clA[C]); m7 = _mm_add_ps (m4, m7); m8 = _mm_add_ps (m5, m8); m9 = _mm_add_ps (m6, m9); m1 = _mm_load1_ps (&tiPL[CG]); m2 = _mm_load1_ps (&tiPR[CG]); m3 = _mm_load1_ps (&tiPA[CG]); m4 = _mm_mul_ps (m1, clL[G]); m5 = _mm_mul_ps (m2, clR[G]); m6 = _mm_mul_ps (m3, clA[G]); m7 = _mm_add_ps (m4, m7); m8 = _mm_add_ps (m5, m8); m9 = _mm_add_ps (m6, m9); m1 = _mm_load1_ps (&tiPL[CT]); m2 = _mm_load1_ps (&tiPR[CT]); m3 = _mm_load1_ps (&tiPA[CT]); m4 = _mm_mul_ps (m1, clL[T]); m5 = _mm_mul_ps (m2, clR[T]); m6 = _mm_mul_ps (m3, clA[T]); m7 = _mm_add_ps (m4, m7); m8 = _mm_add_ps (m5, m8); m9 = _mm_add_ps (m6, m9); m7 = _mm_mul_ps (m7, m8); *clP++ = _mm_mul_ps (m7, m9); m1 = _mm_load1_ps (&tiPL[GA]); m2 = _mm_load1_ps (&tiPR[GA]); m3 = _mm_load1_ps (&tiPA[GA]); m7 = _mm_mul_ps (m1, clL[A]); m8 = _mm_mul_ps (m2, clR[A]); m9 = _mm_mul_ps (m3, clA[A]); m1 = _mm_load1_ps (&tiPL[GC]); m2 = _mm_load1_ps (&tiPR[GC]); m3 = _mm_load1_ps (&tiPA[GC]); m4 = _mm_mul_ps (m1, clL[C]); m5 = _mm_mul_ps (m2, clR[C]); m6 = _mm_mul_ps (m3, clA[C]); m7 = _mm_add_ps (m4, m7); m8 = _mm_add_ps (m5, m8); m9 = _mm_add_ps (m6, m9); m1 = _mm_load1_ps (&tiPL[GG]); m2 = _mm_load1_ps (&tiPR[GG]); m3 = _mm_load1_ps (&tiPA[GG]); m4 = _mm_mul_ps (m1, clL[G]); m5 = _mm_mul_ps (m2, clR[G]); m6 = _mm_mul_ps (m3, clA[G]); m7 = _mm_add_ps (m4, m7); m8 = _mm_add_ps (m5, m8); m9 = _mm_add_ps (m6, m9); m1 = _mm_load1_ps (&tiPL[GT]); m2 = _mm_load1_ps (&tiPR[GT]); m3 = _mm_load1_ps (&tiPA[GT]); m4 = _mm_mul_ps (m1, clL[T]); m5 = _mm_mul_ps (m2, clR[T]); m6 = _mm_mul_ps (m3, clA[T]); m7 = _mm_add_ps (m4, m7); m8 = _mm_add_ps (m5, m8); m9 = _mm_add_ps (m6, m9); m7 = _mm_mul_ps (m7, m8); *clP++ = _mm_mul_ps (m7, m9); m1 = _mm_load1_ps (&tiPL[TA]); m2 = _mm_load1_ps (&tiPR[TA]); m3 = _mm_load1_ps (&tiPA[TA]); m7 = _mm_mul_ps (m1, clL[A]); m8 = _mm_mul_ps (m2, clR[A]); m9 = _mm_mul_ps (m3, clA[A]); m1 = _mm_load1_ps (&tiPL[TC]); m2 = _mm_load1_ps (&tiPR[TC]); m3 = _mm_load1_ps (&tiPA[TC]); m4 = _mm_mul_ps (m1, clL[C]); m5 = _mm_mul_ps (m2, clR[C]); m6 = _mm_mul_ps (m3, clA[C]); m7 = _mm_add_ps (m4, m7); m8 = _mm_add_ps (m5, m8); m9 = _mm_add_ps (m6, m9); m1 = _mm_load1_ps (&tiPL[TG]); m2 = _mm_load1_ps (&tiPR[TG]); m3 = _mm_load1_ps (&tiPA[TG]); m4 = _mm_mul_ps (m1, clL[G]); m5 = _mm_mul_ps (m2, clR[G]); m6 = _mm_mul_ps (m3, clA[G]); m7 = _mm_add_ps (m4, m7); m8 = _mm_add_ps (m5, m8); m9 = _mm_add_ps (m6, m9); m1 = _mm_load1_ps (&tiPL[TT]); m2 = _mm_load1_ps (&tiPR[TT]); m3 = _mm_load1_ps (&tiPA[TT]); m4 = _mm_mul_ps (m1, clL[T]); m5 = _mm_mul_ps (m2, clR[T]); m6 = _mm_mul_ps (m3, clA[T]); m7 = _mm_add_ps (m4, m7); m8 = _mm_add_ps (m5, m8); m9 = _mm_add_ps (m6, m9); m7 = _mm_mul_ps (m7, m8); *clP++ = _mm_mul_ps (m7, m9); clL += 4; clR += 4; clA += 4; } tiPL += 16; tiPR += 16; tiPA += 16; } return NO_ERROR; } #endif /*---------------------------------------------------------------- | | CondLikeRoot_NY98: codon model with omega variation | -----------------------------------------------------------------*/ int CondLikeRoot_NY98 (TreeNode *p, int division, int chain) { int a, b, c, d, h, i, j, k, shortCut, *lState=NULL, *rState=NULL, *aState=NULL, nStates, nStatesSquared; CLFlt likeL, likeR, likeA, *clL, *clR, *clP, *clA, *pL, *pR, *pA, *tiPL, *tiPR, *tiPA; ModelInfo *m; /* find model settings for this division and nStates, nStatesSquared */ m = &modelSettings[division]; nStates = m->numModelStates; nStatesSquared = nStates * nStates; /* flip state of node so that we are not overwriting old cond likes */ FlipCondLikeSpace (m, chain, p->index); /* find conditional likelihood pointers */ clL = m->condLikes[m->condLikeIndex[chain][p->left->index ]]; clR = m->condLikes[m->condLikeIndex[chain][p->right->index]]; clP = m->condLikes[m->condLikeIndex[chain][p->index ]]; clA = m->condLikes[m->condLikeIndex[chain][p->anc->index ]]; /* find transition probabilities (or calculate instead) */ pL = m->tiProbs[m->tiProbsIndex[chain][p->left->index ]]; pR = m->tiProbs[m->tiProbsIndex[chain][p->right->index]]; pA = m->tiProbs[m->tiProbsIndex[chain][p->index ]]; /* find likelihoods of site patterns for left branch if terminal */ shortCut = 0; # if !defined (DEBUG_NOSHORTCUTS) if (p->left->left == NULL && m->isPartAmbig[p->left->index] == NO) { shortCut |= 1; lState = m->termState[p->left->index]; tiPL = pL; for (k=a=0; knumOmegaCats; k++) { for (i=0; iright->left == NULL && m->isPartAmbig[p->right->index] == NO) { shortCut |= 2; rState = m->termState[p->right->index]; tiPR = pR; for (k=a=0; knumOmegaCats; k++) { for (i=0; iisPartAmbig[p->anc->index] == YES) { shortCut = 4; } else { aState = m->termState[p->anc->index]; tiPA = pA; for (k=a=0; knumOmegaCats; k++) { for (i=0; inumOmegaCats; k++) { for (c=0; cnumChars; c++) { for (i=h=0; inumOmegaCats; k++) { for (c=0; cnumChars; c++) { b = aState[c] + k*(nStatesSquared+nStates); for (i=h=0; inumOmegaCats; k++) { for (c=0; cnumChars; c++) { a = lState[c] + k*(nStatesSquared+nStates); b = aState[c] + k*(nStatesSquared+nStates); for (i=h=0; inumOmegaCats; k++) { for (c=0; cnumChars; c++) { a = rState[c] + k*(nStatesSquared+nStates); b = aState[c] + k*(nStatesSquared+nStates); for (i=h=0; inumOmegaCats; k++) { for (c=0; cnumChars; c++) { a = rState[c] + k*(nStatesSquared+nStates); b = lState[c] + k*(nStatesSquared+nStates); d = aState[c] + k*(nStatesSquared+nStates); for (i=0; inumModelStates; nStatesSquared = nStates * nStates; /* flip state of node so that we are not overwriting old cond likes */ FlipCondLikeSpace (m, chain, p->index); /* find conditional likelihood pointers */ clL = (__m128 *)m->condLikes[m->condLikeIndex[chain][p->left->index ]]; clR = (__m128 *)m->condLikes[m->condLikeIndex[chain][p->right->index]]; clP = (__m128 *)m->condLikes[m->condLikeIndex[chain][p->index ]]; clA = (__m128 *)m->condLikes[m->condLikeIndex[chain][p->anc->index ]]; /* find transition probabilities (or calculate instead) */ pL = m->tiProbs[m->tiProbsIndex[chain][p->left->index ]]; pR = m->tiProbs[m->tiProbsIndex[chain][p->right->index]]; pA = m->tiProbs[m->tiProbsIndex[chain][p->index ]]; /* find likelihoods of site patterns for left branch if terminal */ shortCut = 0; # if !defined (DEBUG_NOSHORTCUTS) if (p->left->left == NULL && m->isPartAmbig[p->left->index] == NO) { shortCut |= 1; lState = m->termState[p->left->index]; tiPL = pL; for (k=a=0; knumOmegaCats; k++) { for (i=0; iright->left == NULL && m->isPartAmbig[p->right->index] == NO) { shortCut |= 2; rState = m->termState[p->right->index]; tiPR = pR; for (k=a=0; knumOmegaCats; k++) { for (i=0; iisPartAmbig[p->anc->index] == YES) { shortCut = 4; } else { aState = m->termState[p->anc->index]; tiPA = pA; for (k=a=0; knumOmegaCats; k++) { for (i=0; inumOmegaCats; k++) { for (c=0; cnumVecChars; c++) { for (i=h=0; inumOmegaCats; k++) { for (c=t=0; cnumVecChars; c++) { for (c1=0; c1numFloatsPerVec; c1++,t++) { preLikeAV[c1] = &preLikeA[aState[t] + k*(nStatesSquared+nStates)]; } for (i=h=0; inumFloatsPerVec == 4); /* In the following statement we assume that SSE register can hold exactly 4 ClFlts. */ mAcumA = _mm_set_ps (*(preLikeAV[3]++), *(preLikeAV[2]++), *(preLikeAV[1]++), *(preLikeAV[0]++)); mAcumL = _mm_setzero_ps(); mAcumR = _mm_setzero_ps(); for (j=0; jnumOmegaCats; k++) { for (c=t=0; cnumVecChars; c++) { for (c1=0; c1numFloatsPerVec; c1++,t++) { preLikeLV[c1] = &preLikeL[lState[t] + k*(nStatesSquared+nStates)]; preLikeAV[c1] = &preLikeA[aState[t] + k*(nStatesSquared+nStates)]; } for (i=h=0; inumFloatsPerVec == 4); /* In the following statement we assume that SSE register can hold exactly 4 ClFlts. */ mAcumL = _mm_set_ps (*(preLikeLV[3]++), *(preLikeLV[2]++), *(preLikeLV[1]++), *(preLikeLV[0]++)); mAcumA = _mm_set_ps (*(preLikeAV[3]++), *(preLikeAV[2]++), *(preLikeAV[1]++), *(preLikeAV[0]++)); mAcumR = _mm_setzero_ps(); for (j=0; jnumOmegaCats; k++) { for (c=t=0; cnumVecChars; c++) { for (c1=0; c1numFloatsPerVec; c1++,t++) { preLikeRV[c1] = &preLikeR[rState[t] + k*(nStatesSquared+nStates)]; preLikeAV[c1] = &preLikeA[aState[t] + k*(nStatesSquared+nStates)]; } for (i=h=0; inumFloatsPerVec == 4); /* In the following statement we assume that SSE register can hold exactly 4 ClFlts. */ mAcumR = _mm_set_ps (*(preLikeRV[3]++), *(preLikeRV[2]++), *(preLikeRV[1]++), *(preLikeRV[0]++)); mAcumA = _mm_set_ps (*(preLikeAV[3]++), *(preLikeAV[2]++), *(preLikeAV[1]++), *(preLikeAV[0]++)); mAcumL = _mm_setzero_ps(); for (j=0; jnumOmegaCats; k++) { for (c=t=0; cnumVecChars; c++) { for (c1=0; c1numFloatsPerVec; c1++,t++) { preLikeRV[c1] = &preLikeR[rState[t] + k*(nStatesSquared+nStates)]; preLikeLV[c1] = &preLikeL[lState[t] + k*(nStatesSquared+nStates)]; preLikeAV[c1] = &preLikeA[aState[t] + k*(nStatesSquared+nStates)]; } for (i=0; inumFloatsPerVec == 4); /* In the following 2 statements we assume that SSE register can hold exactly 4 ClFlts. */ mL = _mm_set_ps (*(preLikeLV[3]++), *(preLikeLV[2]++), *(preLikeLV[1]++), *(preLikeLV[0]++)); mR = _mm_set_ps (*(preLikeRV[3]++), *(preLikeRV[2]++), *(preLikeRV[1]++), *(preLikeRV[0]++)); mA = _mm_set_ps (*(preLikeAV[3]++), *(preLikeAV[2]++), *(preLikeAV[1]++), *(preLikeAV[0]++)); mL = _mm_mul_ps (mL,mR); *(clP++) = _mm_mul_ps (mL,mA); } } } break; } return NO_ERROR; } #endif /*---------------------------------------------------------------- | | CondLikeRoot_Std: variable number of states model | with or without rate variation | -----------------------------------------------------------------*/ int CondLikeRoot_Std (TreeNode *p, int division, int chain) { int a, c, h, i, j, k, nStates=0, nCats=0, tmp; CLFlt *clL, *clR, *clP, *clA, *pL, *pR, *pA, *tiPL, *tiPR, *tiPA, likeL, likeR, likeA; ModelInfo *m; m = &modelSettings[division]; /* flip state of node so that we are not overwriting old cond likes */ FlipCondLikeSpace (m, chain, p->index); /* find conditional likelihood pointers */ clL = m->condLikes[m->condLikeIndex[chain][p->left->index ]]; clR = m->condLikes[m->condLikeIndex[chain][p->right->index]]; clP = m->condLikes[m->condLikeIndex[chain][p->index ]]; clA = m->condLikes[m->condLikeIndex[chain][p->anc->index ]]; /* find transition probabilities (or calculate instead) */ pL = m->tiProbs[m->tiProbsIndex[chain][p->left->index ]]; pR = m->tiProbs[m->tiProbsIndex[chain][p->right->index]]; pA = m->tiProbs[m->tiProbsIndex[chain][p->index ]]; /* calculate ancestral probabilities */ for (k=h=0; knumRateCats; k++) { /* calculate ancestral probabilities */ for (c=0; cnumChars; c++) { nStates = m->nStates[c]; /* the following lines ensure that nCats is 1 unless */ /* the character is binary and beta categories are used */ if (nStates == 2) nCats = m->numBetaCats; else nCats = 1; tmp = k*nStates*nStates; /* tmp contains offset to skip gamma cats that already processed*/ tiPL = pL + m->tiIndex[c] + tmp; tiPR = pR + m->tiIndex[c] + tmp; tiPA = pA + m->tiIndex[c] + tmp; tmp = (m->numRateCats-1)*2*2; /* tmp contains size of block of tpi matrices across all rate cats (minus one) for single beta category. Further used only if character is binary to jump to next beta category */ for (j=0; janc->anc == NULL) { /* this is the root node */ /* find conditional likelihood pointers = down cond likes */ /* use conditional likelihood scratch space for final cond likes */ clDP = m->condLikes[m->condLikeIndex[chain][p->index]]; clFP = m->condLikes[m->condLikeScratchIndex[p->index]]; for (k=0; knumRateCats; k++) { for (c=0; cnumChars; c++) { *(clFP++) = *(clDP++); *(clFP++) = *(clDP++); } } } else { /* find conditional likelihood pointers */ /* use conditional likelihood scratch space for final cond likes */ clFA = m->condLikes[m->condLikeScratchIndex[p->anc->index]]; clFP = m->condLikes[m->condLikeScratchIndex[p->index ]]; clDP = m->condLikes[m->condLikeIndex[chain][p->index ]]; /* find transition probabilities */ tiP = m->tiProbs[m->tiProbsIndex[chain][p->index]]; for (k=0; knumRateCats; k++) { for (c=0; cnumChars; c++) { condLikeUp[0] = condLikeUp[1] = 0.0; sum[0] = tiP[0]*clDP[0] + tiP[1]*clDP[1]; sum[1] = tiP[2]*clDP[0] + tiP[3]*clDP[1]; if (sum[0] != 0.0) condLikeUp[0] = clFA[0] / sum[0]; if (sum[1] != 0.0) condLikeUp[1] = clFA[1] / sum[1]; *(clFP++) = (condLikeUp[0]*tiP[0] + condLikeUp[1]*tiP[1])*clDP[0]; *(clFP++) = (condLikeUp[0]*tiP[2] + condLikeUp[1]*tiP[3])*clDP[1]; clFA += 2; clDP += 2; } tiP += 4; } } return NO_ERROR; } /*---------------------------------------------------------------- | | CondLikeUp_Gen: pull likelihoods up and calculate scaled | finals for an interior node | -----------------------------------------------------------------*/ int CondLikeUp_Gen (TreeNode *p, int division, int chain) { int a, c, i, j, k, nStates, nStatesSquared, nRateCats; CLFlt *clFA, *clFP, *clDP, *tiP, *condLikeUp, sum; ModelInfo *m; /* find model settings for this division */ m = &modelSettings[division]; /* find number of states in the model */ nStates = m->numModelStates; nStatesSquared = nStates * nStates; /* find number of gamma cats */ nRateCats = m->numRateCats; if (m->gibbsGamma == YES) nRateCats = 1; /* use preallocated scratch space */ condLikeUp = m->ancStateCondLikes; /* calculate final states */ if (p->anc->anc == NULL) { /* this is the root node */ /* find conditional likelihood pointers = down cond likes */ /* use conditional likelihood scratch space for final cond likes */ clDP = m->condLikes[m->condLikeIndex[chain][p->index]]; clFP = m->condLikes[m->condLikeScratchIndex[p->index]]; /* final cond likes = downpass cond likes */ for (k=0; knumChars*nStates; c++) *(clFP++) = *(clDP++); } } else { /* find conditional likelihood pointers */ /* use conditional likelihood scratch space for final cond likes */ clFA = m->condLikes[m->condLikeScratchIndex[p->anc->index]]; clFP = m->condLikes[m->condLikeScratchIndex[p->index ]]; clDP = m->condLikes[m->condLikeIndex[chain][p->index ]]; /* find transition probabilities */ tiP = m->tiProbs[m->tiProbsIndex[chain][p->index]]; for (k=0; knumChars; c++) { for (a=j=0; anumRateCats; if (m->gibbsGamma == YES) nRateCats = 1; /* calculate final states */ if (p->anc->anc == NULL) { /* this is the root node */ /* find conditional likelihood pointers = down cond likes */ /* use conditional likelihood scratch space for final cond likes */ clDP = m->condLikes[m->condLikeIndex[chain][p->index]]; clFP = m->condLikes[m->condLikeScratchIndex[p->index]]; /* final cond likes = downpass cond likes */ for (k=0; knumChars; c++) { *(clFP++) = *(clDP++); *(clFP++) = *(clDP++); *(clFP++) = *(clDP++); *(clFP++) = *(clDP++); } } } else { /* find conditional likelihood pointers */ /* use conditional likelihood scratch space for final cond likes */ clFA = m->condLikes[m->condLikeScratchIndex[p->anc->index]]; clFP = m->condLikes[m->condLikeScratchIndex[p->index ]]; clDP = m->condLikes[m->condLikeIndex[chain][p->index ]]; /* find transition probabilities */ tiP = m->tiProbs[m->tiProbsIndex[chain][p->index]]; for (k=0; knumChars; c++) { condLikeUp[A] = condLikeUp[C] = condLikeUp[G] = condLikeUp[T] = 0.0; sum[A] = (tiP[AA]*clDP[A] + tiP[AC]*clDP[C] + tiP[AG]*clDP[G] + tiP[AT]*clDP[T]); sum[C] = (tiP[CA]*clDP[A] + tiP[CC]*clDP[C] + tiP[CG]*clDP[G] + tiP[CT]*clDP[T]); sum[G] = (tiP[GA]*clDP[A] + tiP[GC]*clDP[C] + tiP[GG]*clDP[G] + tiP[GT]*clDP[T]); sum[T] = (tiP[TA]*clDP[A] + tiP[TC]*clDP[C] + tiP[TG]*clDP[G] + tiP[TT]*clDP[T]); if (sum[A] != 0.0) condLikeUp[A] = clFA[A] / sum[A]; if (sum[C] != 0.0) condLikeUp[C] = clFA[C] / sum[C]; if (sum[G] != 0.0) condLikeUp[G] = clFA[G] / sum[G]; if (sum[T] != 0.0) condLikeUp[T] = clFA[T] / sum[T]; /* clFP[A] = (condLikeUp[A]*tiP[AA] + condLikeUp[C]*tiP[CA] + condLikeUp[G]*tiP[GA] + condLikeUp[T]*tiP[TA])*clDP[A]; clFP[C] = (condLikeUp[A]*tiP[AC] + condLikeUp[C]*tiP[CC] + condLikeUp[G]*tiP[GC] + condLikeUp[T]*tiP[TC])*clDP[C]; clFP[G] = (condLikeUp[A]*tiP[AG] + condLikeUp[C]*tiP[CG] + condLikeUp[G]*tiP[GG] + condLikeUp[T]*tiP[TG])*clDP[G]; clFP[T] = (condLikeUp[A]*tiP[AT] + condLikeUp[C]*tiP[CT] + condLikeUp[G]*tiP[GT] + condLikeUp[T]*tiP[TT])*clDP[T]; */ clFP[A] = (condLikeUp[A]*tiP[AA] + condLikeUp[C]*tiP[AC] + condLikeUp[G]*tiP[AG] + condLikeUp[T]*tiP[AT])*clDP[A]; clFP[C] = (condLikeUp[A]*tiP[CA] + condLikeUp[C]*tiP[CC] + condLikeUp[G]*tiP[CG] + condLikeUp[T]*tiP[CT])*clDP[C]; clFP[G] = (condLikeUp[A]*tiP[GA] + condLikeUp[C]*tiP[GC] + condLikeUp[G]*tiP[GG] + condLikeUp[T]*tiP[GT])*clDP[G]; clFP[T] = (condLikeUp[A]*tiP[TA] + condLikeUp[C]*tiP[TC] + condLikeUp[G]*tiP[TG] + condLikeUp[T]*tiP[TT])*clDP[T]; clFA += 4; clFP += 4; clDP += 4; } tiP += 16; } } return NO_ERROR; } /*---------------------------------------------------------------- | | CondLikeUp_Std: pull likelihoods up and calculate scaled | finals for an interior node | -----------------------------------------------------------------*/ int CondLikeUp_Std (TreeNode *p, int division, int chain) { int a, c, i, j, k, t, nStates, nCats, coppySize,tmp; CLFlt *clFA, *clFP, *clDP, *pA, *tiP, condLikeUp[MAX_STD_STATES], sum; ModelInfo *m; /* find model settings for this division */ m = &modelSettings[division]; /* calculate final states */ if (p->anc->anc == NULL) { /* this is the root node */ /* find conditional likelihood pointers = down cond likes */ /* use conditional likelihood scratch space for final cond likes */ clDP = m->condLikes[m->condLikeIndex[chain][p->index]]; clFP = m->condLikes[m->condLikeScratchIndex[p->index]]; coppySize=0; /* final cond likes = downpass cond likes */ for (c=0; cnumChars; c++) { /* calculate nStates and nCats */ nStates = m->nStates[c]; /* the following lines ensure that nCats is 1 unless */ /* the character is binary and beta categories are used */ if (nStates == 2) nCats = m->numBetaCats; else nCats = 1; coppySize+=nCats*nStates; } /* finally multiply with the rate cats */ coppySize *= m->numRateCats; /* copy cond likes */ for (k=0; kcondLikes[m->condLikeScratchIndex[p->anc->index]]; clFP = m->condLikes[m->condLikeScratchIndex[p->index ]]; clDP = m->condLikes[m->condLikeIndex[chain][p->index ]]; /* find transition probabilities */ pA = m->tiProbs[m->tiProbsIndex[chain][p->index]]; for (k=0; knumRateCats; k++) { for (c=0; cnumChars; c++) { /* calculate nStates and nCats */ nStates = m->nStates[c]; /* the following lines ensure that nCats is 1 unless */ /* the character is binary and beta categories are used */ if (nStates == 2) nCats = m->numBetaCats; else nCats = 1; tmp = k*nStates*nStates; /* tmp contains offset to skip rate cats that already processed*/ tiP = pA + m->tiIndex[c] + tmp; tmp = (m->numRateCats-1)*2*2; /* tmp contains size of block of tpi matrices across all rate cats (minus one) for single beta category. Further used only if character is binary to jump to next beta category */ /* now calculate the final cond likes */ for (t=0; tnumModelStates; /* find conditional likelihood pointers */ clPtr = m->condLikes[m->condLikeIndex[chain][p->index]]; clP = m->clP; for (k=0; knumRateCats; k++) { clP[k] = clPtr; clPtr += m->numChars * m->numModelStates; } /* find node scalers */ scP = m->scalers[m->nodeScalerIndex[chain][p->index]]; /* find site scalers */ lnScaler = m->scalers[m->siteScalerIndex[chain]]; /* rescale */ for (c=0; cnumChars; c++) { scaler = 0.0; for (k=0; knumRateCats; k++) { for (n=0; n scaler) scaler = clP[k][n]; } } for (k=0; knumRateCats; k++) { for (n=0; nunscaledNodes[chain][p->index] = 0; return (NO_ERROR); } #if defined (SSE_ENABLED) /*---------------------------------------------------------------- | | CondLikeScaler_Gen_SSE: general n-state model with or without rate | variation | -----------------------------------------------------------------*/ int CondLikeScaler_Gen_SSE (TreeNode *p, int division, int chain) { int c, k, n, nStates; CLFlt *scP, *lnScaler; __m128 *clPtr, **clP, m1; ModelInfo *m; m = &modelSettings[division]; nStates = m->numModelStates; /* find conditional likelihood pointers */ clPtr = (__m128 *) m->condLikes[m->condLikeIndex[chain][p->index]]; clP = m->clP_SSE; for (k=0; knumRateCats; k++) { clP[k] = clPtr; clPtr += m->numVecChars * m->numModelStates; } /* find node scalers */ scP = m->scalers[m->nodeScalerIndex[chain][p->index]]; //scP_SSE = (__m128 *) scP; /* find site scalers */ lnScaler = m->scalers[m->siteScalerIndex[chain]]; /* rescale */ for (c=0; cnumVecChars; c++) { //scaler = 0.0; m1 = _mm_setzero_ps (); for (k=0; knumRateCats; k++) { for (n=0; nnumFloatsPerVec; for (k=0; knumRateCats; k++) { for (n=0; nscalers[m->nodeScalerIndex[chain][p->index]]; for (c=0; cnumChars; c++) { scP[c] = (CLFlt) log (scP[c]); /* store node scaler */ lnScaler[c] += scP[c]; /* add into tree scaler */ } m->unscaledNodes[chain][p->index] = 0; return (NO_ERROR); } #endif /*---------------------------------------------------------------- | | CondLikeScaler_Gen_GibbsGamma: general n-state model with Gibbs | sampling of rate categories in discrete gamma | -----------------------------------------------------------------*/ int CondLikeScaler_Gen_GibbsGamma (TreeNode *p, int division, int chain) { int c, i, j, n, nStates, *rateCat, nRateCats; CLFlt scaler, *clP, *scP, *lnScaler; ModelInfo *m; m = &modelSettings[division]; nStates = m->numModelStates; /* find conditional likelihood pointer */ clP = m->condLikes[m->condLikeIndex[chain][p->index]]; /* flip node scalers */ scP = m->scalers[m->nodeScalerIndex[chain][p->index]]; /* find site scalers */ lnScaler = m->scalers[m->siteScalerIndex[chain]]; /* find rate category index and number of rate categories */ rateCat = m->tiIndex + chain * m->numChars; nRateCats = m->numRateCats; /* scale */ i = j = 0; for (c=0; cnumChars; c++) { if (rateCat[c] < nRateCats) { scaler = 0.0; for (n=0; n scaler) scaler = clP[i]; i++; } for (n=0; nunscaledNodes[chain][p->index] = 0; return (NO_ERROR); } /*---------------------------------------------------------------- | | CondLikeScaler_NUC4: 4by4 nucleotide model with or without rate | variation | -----------------------------------------------------------------*/ int CondLikeScaler_NUC4 (TreeNode *p, int division, int chain) { int c, k; CLFlt scaler, *scP, *lnScaler, *clPtr, **clP; ModelInfo *m; m = &modelSettings[division]; /* find conditional likelihood pointers */ clPtr = m->condLikes[m->condLikeIndex[chain][p->index]]; clP = m->clP; for (k=0; knumRateCats; k++) { clP[k] = clPtr; clPtr += m->numChars * m->numModelStates; } /* find node scalers */ scP = m->scalers[m->nodeScalerIndex[chain][p->index]]; /* find site scalers */ lnScaler = m->scalers[m->siteScalerIndex[chain]]; /* rescale values */ for (c=0; cnumChars; c++) { scaler = 0.0; for (k=0; knumRateCats; k++) { if (clP[k][A] > scaler) scaler = clP[k][A]; if (clP[k][C] > scaler) scaler = clP[k][C]; if (clP[k][G] > scaler) scaler = clP[k][G]; if (clP[k][T] > scaler) scaler = clP[k][T]; } for (k=0; knumRateCats; k++) { clP[k][A] /= scaler; clP[k][C] /= scaler; clP[k][G] /= scaler; clP[k][T] /= scaler; clP[k] += 4; } scP[c] = (CLFlt) log(scaler); /* store node scaler */ lnScaler[c] += scP[c]; /* add into tree scaler */ } m->unscaledNodes[chain][p->index] = 0; /* set unscaled nodes to 0 */ return NO_ERROR; } #if defined (AVX_ENABLED) /*---------------------------------------------------------------- | | CondLikeScaler_NUC4_AVX: 4by4 nucleotide model with or without rate | variation using AVX (or AVX + FMA) code | -----------------------------------------------------------------*/ int CondLikeScaler_NUC4_AVX (TreeNode *p, int division, int chain) { int c, k; CLFlt *scP, *lnScaler; __m256 *clPtr, **clP, *scP_AVX, m1; ModelInfo *m; m = &modelSettings[division]; /* find conditional likelihood pointers */ clPtr = (__m256 *) m->condLikes[m->condLikeIndex[chain][p->index]]; clP = m->clP_AVX; for (k=0; knumRateCats; k++) { clP[k] = clPtr; clPtr += m->numVecChars * m->numModelStates; } /* find node scalers */ scP = m->scalers[m->nodeScalerIndex[chain][p->index]]; scP_AVX = (__m256 *) scP; /* find site scalers */ lnScaler = m->scalers[m->siteScalerIndex[chain]]; /* rescale */ for (c=0; cnumVecChars; c++) { m1 = _mm256_setzero_ps (); for (k=0; knumRateCats; k++) { m1 = _mm256_max_ps (m1, clP[k][A]); m1 = _mm256_max_ps (m1, clP[k][C]); m1 = _mm256_max_ps (m1, clP[k][G]); m1 = _mm256_max_ps (m1, clP[k][T]); } for (k=0; knumRateCats; k++) { *clP[k] = _mm256_div_ps (*clP[k], m1); clP[k]++; *clP[k] = _mm256_div_ps (*clP[k], m1); clP[k]++; *clP[k] = _mm256_div_ps (*clP[k], m1); clP[k]++; *clP[k] = _mm256_div_ps (*clP[k], m1); clP[k]++; } (*scP_AVX++) = m1; } /* update site scalers */ for (c=0; cnumChars; c++) lnScaler[c] += (scP[c] = logf (scP[c])); /* add log of new scaler into tree scaler */ m->unscaledNodes[chain][p->index] = 0; /* set unscaled nodes to 0 */ return NO_ERROR; } #endif #if defined (SSE_ENABLED) /*---------------------------------------------------------------- | | CondLikeScaler_NUC4_SSE: 4by4 nucleotide model with or without rate | variation using SSE code | -----------------------------------------------------------------*/ int CondLikeScaler_NUC4_SSE (TreeNode *p, int division, int chain) { int c, k; CLFlt *scP, *lnScaler; __m128 *clPtr, **clP, *scP_SSE, m1; ModelInfo *m; m = &modelSettings[division]; /* find conditional likelihood pointers */ clPtr = (__m128 *) m->condLikes[m->condLikeIndex[chain][p->index]]; clP = m->clP_SSE; for (k=0; knumRateCats; k++) { clP[k] = clPtr; clPtr += m->numVecChars * m->numModelStates; } /* find node scalers */ scP = m->scalers[m->nodeScalerIndex[chain][p->index]]; scP_SSE = (__m128 *) scP; /* find site scalers */ lnScaler = m->scalers[m->siteScalerIndex[chain]]; /* rescale */ for (c=0; cnumVecChars; c++) { m1 = _mm_setzero_ps (); for (k=0; knumRateCats; k++) { m1 = _mm_max_ps (m1, clP[k][A]); m1 = _mm_max_ps (m1, clP[k][C]); m1 = _mm_max_ps (m1, clP[k][G]); m1 = _mm_max_ps (m1, clP[k][T]); } for (k=0; knumRateCats; k++) { *clP[k] = _mm_div_ps (*clP[k], m1); clP[k]++; *clP[k] = _mm_div_ps (*clP[k], m1); clP[k]++; *clP[k] = _mm_div_ps (*clP[k], m1); clP[k]++; *clP[k] = _mm_div_ps (*clP[k], m1); clP[k]++; } (*scP_SSE++) = m1; } /* update site scalers */ for (c=0; cnumChars; c++) lnScaler[c] += (scP[c] = (CLFlt)(log (scP[c]))); /* add log of new scaler into tree scaler */ m->unscaledNodes[chain][p->index] = 0; /* number of unscaled nodes is 0 */ return NO_ERROR; } #endif /*---------------------------------------------------------------- | | CondLikeScaler_NUC4_GibbsGamma: 4by4 nucleotide model with rate | variation approximated by Gibbs sampling from gamma | -----------------------------------------------------------------*/ int CondLikeScaler_NUC4_GibbsGamma (TreeNode *p, int division, int chain) { int c, i, j, nRateCats, *rateCat; CLFlt scaler, *clP, *scP, *lnScaler; ModelInfo *m; m = &modelSettings[division]; /* find conditional likelihood pointer */ clP = m->condLikes[m->condLikeIndex[chain][p->index]]; /* find node scalers */ scP = m->scalers[m->nodeScalerIndex[chain][p->index]]; /* find site scalers */ lnScaler = m->scalers[m->siteScalerIndex[chain]]; /* find rate category index and number of gamma categories */ rateCat = m->tiIndex + chain * m->numChars; nRateCats = m->numRateCats; /* scale */ i = j = 0; for (c=0; cnumChars; c++) { if (rateCat[c] < nRateCats) { scaler = 0.0; if (clP[i] > scaler) scaler = clP[i]; i++; if (clP[i] > scaler) scaler = clP[i]; i++; if (clP[i] > scaler) scaler = clP[i]; i++; if (clP[i] > scaler) scaler = clP[i]; i++; clP[j++] /= scaler; clP[j++] /= scaler; clP[j++] /= scaler; clP[j++] /= scaler; scP[c] = (CLFlt) log (scaler); /* store node scaler */ lnScaler[c] += scP[c]; /* add into tree scaler */ } else { scP[c] = 0.0; /* store node scaler */ /* no need to add it to the lnScaler */ i += 4; j += 4; } } m->unscaledNodes[chain][p->index] = 0; return NO_ERROR; } /*---------------------------------------------------------------- | | CondLikeScaler_NY98: codon model with omega variation | -----------------------------------------------------------------*/ int CondLikeScaler_NY98 (TreeNode *p, int division, int chain) { int c, k, n, nStates; CLFlt scaler, **clP, *clPtr, *scP, *lnScaler; ModelInfo *m; m = &modelSettings[division]; nStates = m->numModelStates; /* find conditional likelihood pointers */ clPtr = m->condLikes[m->condLikeIndex[chain][p->index]]; clP = m->clP; for (k=0; knumOmegaCats; k++) { clP[k] = clPtr; clPtr += m->numChars * m->numModelStates; } /* find node scalers */ scP = m->scalers[m->nodeScalerIndex[chain][p->index]]; /* find site scalers */ lnScaler = m->scalers[m->siteScalerIndex[chain]]; /* rescale */ for (c=0; cnumChars; c++) { scaler = 0.0; for (k=0; knumOmegaCats; k++) { for (n=0; n scaler) scaler = clP[k][n]; } } for (k=0; knumOmegaCats; k++) { for (n=0; nunscaledNodes[chain][p->index] = 0; return (NO_ERROR); } #if defined (SSE_ENABLED) /*---------------------------------------------------------------- | | CondLikeScaler_NY98_SSE: codon model with omega variation | -----------------------------------------------------------------*/ int CondLikeScaler_NY98_SSE (TreeNode *p, int division, int chain) { int c, k, n, nStates; CLFlt *scP, *lnScaler; __m128 *clPtr, **clP, m1; ModelInfo *m; m = &modelSettings[division]; nStates = m->numModelStates; /* find conditional likelihood pointers */ clPtr = (__m128 *) m->condLikes[m->condLikeIndex[chain][p->index]]; clP = m->clP_SSE; for (k=0; knumOmegaCats; k++) { clP[k] = clPtr; clPtr += m->numVecChars * m->numModelStates; } /* find node scalers */ scP = m->scalers[m->nodeScalerIndex[chain][p->index]]; //scP_SSE = (__m128 *) scP; /* find site scalers */ lnScaler = m->scalers[m->siteScalerIndex[chain]]; /* rescale */ for (c=0; cnumVecChars; c++) { //scaler = 0.0; m1 = _mm_setzero_ps (); for (k=0; knumOmegaCats; k++) { for (n=0; nnumFloatsPerVec; for (k=0; knumOmegaCats; k++) { for (n=0; nscalers[m->nodeScalerIndex[chain][p->index]]; for (c=0; cnumChars; c++) { scP[c] = (CLFlt) log (scP[c]); /* store node scaler */ lnScaler[c] += scP[c]; /* add into tree scaler */ } m->unscaledNodes[chain][p->index] = 0; return (NO_ERROR); } #endif /*---------------------------------------------------------------- | | CondLikeScaler_Std: variable states model with or without | rate variation | -----------------------------------------------------------------*/ int CondLikeScaler_Std (TreeNode *p, int division, int chain) { int c, n, k, nStates, numReps; CLFlt scaler, *clPtr, **clP, *scP, *lnScaler; ModelInfo *m; m = &modelSettings[division]; numReps=0; for (c=0; cnumChars; c++) { if (m->nStates[c] == 2) numReps += m->numBetaCats * 2; else numReps += m->nStates[c]; } /* find conditional likelihood pointers */ clPtr = m->condLikes[m->condLikeIndex[chain][p->index]]; clP = m->clP; for (k=0; knumRateCats; k++) { clP[k] = clPtr; clPtr += numReps; } /* find node scalers */ scP = m->scalers[m->nodeScalerIndex[chain][p->index]]; /* find site scalers */ lnScaler = m->scalers[m->siteScalerIndex[chain]]; /* rescale */ for (c=0; cnumChars; c++) { scaler = 0.0; nStates = m->nStates[c]; if (nStates == 2) nStates = m->numBetaCats * 2; for (k=0; knumRateCats; k++) { for (n=0; n scaler) scaler = clP[k][n]; } } for (k=0; knumRateCats; k++) { for (n=0; nunscaledNodes[chain][p->index] = 0; return NO_ERROR; } /* FlipCijkSpace: Flip space for cijks with scratch area */ void FlipCijkSpace (ModelInfo* m, int chain) { int temp; temp = m->cijkIndex[chain]; m->cijkIndex[chain] = m->cijkScratchIndex; m->cijkScratchIndex = temp; } /* FlipCondLikeSpace: Flip space for conditional likelihoods with scratch area */ void FlipCondLikeSpace (ModelInfo* m, int chain, int nodeIndex) { int temp; temp = m->condLikeIndex[chain][nodeIndex]; m->condLikeIndex[chain][nodeIndex] = m->condLikeScratchIndex[nodeIndex]; m->condLikeScratchIndex[nodeIndex] = temp; } /* FlipNodeScalerSpace: Flip space for node scalers and scaler flag with scratch area */ void FlipNodeScalerSpace (ModelInfo* m, int chain, int nodeIndex) { int temp; temp = m->nodeScalerIndex[chain][nodeIndex]; m->nodeScalerIndex[chain][nodeIndex] = m->nodeScalerScratchIndex[nodeIndex]; m->nodeScalerScratchIndex[nodeIndex] = temp; temp = m->unscaledNodes[chain][nodeIndex]; m->unscaledNodes[chain][nodeIndex] = m->unscaledNodesScratch[nodeIndex]; m->unscaledNodesScratch[nodeIndex] = temp; } /* FlipSiteScalerSpace: Flip space for ln site scalers */ void FlipSiteScalerSpace (ModelInfo *m, int chain) { int temp; # if defined (BEAGLE_ENABLED) int *tempp; # endif temp = m->siteScalerIndex[chain]; m->siteScalerIndex[chain] = m->siteScalerScratchIndex; m->siteScalerScratchIndex = temp; # if defined (BEAGLE_ENABLED) if (m->useBeagle == YES) { tempp = m->isScalerNode[chain]; m->isScalerNode[chain] = m->isScalerNodeScratch ; m->isScalerNodeScratch = tempp; } # endif } /* FlipTiProbsSpace: Flip space for ti probs with scratch area */ void FlipTiProbsSpace (ModelInfo* m, int chain, int nodeIndex) { int temp; temp = m->tiProbsIndex[chain][nodeIndex]; m->tiProbsIndex[chain][nodeIndex] = m->tiProbsScratchIndex[nodeIndex]; m->tiProbsScratchIndex[nodeIndex] = temp; } /*------------------------------------------------------------------ | | Likelihood_Adgamma: all n-state models with autocorrelated | discrete gamma rate variation, NOT morph, restriction, | codon or doublet models; just fill in rateProbs | -------------------------------------------------------------------*/ int Likelihood_Adgamma (TreeNode *p, int division, int chain, MrBFlt *lnL, int whichSitePats) { int c, j, k, i, nStates, nStatesDiv2; MrBFlt *bs, *swr, s01, s10, probOn, probOff, covBF[40]; MrBFlt like, *rP; CLFlt *clP; ModelInfo *m; /* NOTE: whichSitePats offsets numSitesOfPat by whichSitePats X numCompressedChars. This is done so we can use the character reweighting scheme for "heating" chains. This was easy to accomplish for all of the models except this one, which doesn't use numSitesOfPat when calculating likelihoods. Either we disallow autocorrelated rates when using MCMC with character reweighting, or we properly calculate likelihoods when some site patterns have increased or decreased weight. For now, we do not allow MCMCMC with character reweighting with this HMM; we bail out in the function FillNumSitesOfPat if we have Adgamma rate variation and reweighting. */ /* find model settings */ m = &modelSettings[division]; /* get the number of states */ nStates = m->numModelStates; nStatesDiv2 = nStates / 2; /* find base frequencies */ bs = GetParamSubVals (m->stateFreq, chain, state[chain]); /* find conditional likelihood pointer */ clP = m->condLikes[m->condLikeIndex[chain][p->index]]; /* find pointer to rate probabilities */ rP = rateProbs[chain] + state[chain] * rateProbRowSize + m->rateProbStart; /* loop over characters and calculate rate probs */ if (m->switchRates != NULL) { swr = GetParamVals (m->switchRates, chain, state[chain]); s01 = swr[0]; s10 = swr[1]; probOn = s01 / (s01 + s10); probOff = 1.0 - probOn; for (j=0; jnumChars; c++) { for (k=0; knumRateCats; k++) { like = 0.0; for (j=0; jnumModelStates; if (m->pInvar == NULL) { hasPInvar = NO; } else { hasPInvar = YES; pInvar = *(GetParamVals (m->pInvar, chain, state[chain])); clInvar = m->invCondLikes; } /* find conditional likelihood pointers */ clPtr = m->condLikes[m->condLikeIndex[chain][p->index]]; clP = m->clP; for (k=0; knumRateCats; k++) { clP[k] = clPtr; clPtr += m->numChars * m->numModelStates; } /* find base frequencies */ bs = GetParamSubVals (m->stateFreq, chain, state[chain]); /* if covarion model, adjust base frequencies */ if (m->switchRates != NULL) { /* find the stationary frequencies */ swr = GetParamVals(m->switchRates, chain, state[chain]); s01 = swr[0]; s10 = swr[1]; probOn = s01 / (s01 + s10); probOff = 1.0 - probOn; /* now adjust the base frequencies; on-state stored first in cond likes */ for (j=0; jnumRateCats; else freq = (1.0 - pInvar) / m->numRateCats; /* find site scaler */ lnScaler = m->scalers[m->siteScalerIndex[chain]]; /* find nSitesOfPat */ nSitesOfPat = numSitesOfPat + (whichSitePats*numCompressedChars) + m->compCharStart; /* reset lnL */ *lnL = 0.0; /* loop over characters */ if (hasPInvar == NO) { for (c=0; cnumChars; c++) { like = 0.0; for (k=0; knumRateCats; k++) for (j=0; jnumChars; c++) { likeI = like = 0.0; for (k=0; knumRateCats; k++) for (j=0; j 1E-70) { /* forget about like; it is going to be insignificant compared to likeI */ lnLike = log(likeI); } else { /* treat likeI as if 0.0, that is, ignore it completely */ lnLike = log(like) + lnScaler[c]; } } else lnLike = log (like + (likeI / exp (lnScaler[c]))) + lnScaler[c]; /* check against LIKE_EPSILON (values close to zero are problematic) */ if (like < LIKE_EPSILON) { # ifdef DEBUG_LIKELIHOOD MrBayesPrint ("%s WARNING: In LIKE_EPSILON - for division %d char %d has like = %1.30le\n", spacer, division+1, c+1, like); # endif (*lnL) = MRBFLT_NEG_MAX; abortMove = YES; return ERROR; } else { (*lnL) += lnLike * nSitesOfPat[c]; } } } return NO_ERROR; } #if defined (SSE_ENABLED) /*------------------------------------------------------------------ | | Likelihood_Gen_SSE: general n-state model with or without rate | variation | -------------------------------------------------------------------*/ int Likelihood_Gen_SSE (TreeNode *p, int division, int chain, MrBFlt *lnL, int whichSitePats) { int c, j, k, nStates, hasPInvar; MrBFlt like, *bs; MrBFlt s01, s10, probOn, probOff, *swr, covBF[40], freq, likeI, pInvar=0.0, lnLike; CLFlt *lnScaler, *nSitesOfPat, *lnL_SSE, *lnLI_SSE; __m128 *clPtr, **clP, *clInvar=NULL; __m128 m1, mCatLike, mLike, mFreq; ModelInfo *m; /* find model settings and nStates, pInvar, invar cond likes */ m = &modelSettings[division]; nStates = m->numModelStates; if (m->pInvar == NULL) { hasPInvar = NO; } else { hasPInvar = YES; pInvar = *(GetParamVals (m->pInvar, chain, state[chain])); clInvar = (__m128 *) (m->invCondLikes); } /* find conditional likelihood pointers */ clPtr = (__m128 *) (m->condLikes[m->condLikeIndex[chain][p->index]]); clP = m->clP_SSE; for (k=0; knumRateCats; k++) { clP[k] = clPtr; clPtr += m->numVecChars * m->numModelStates; } lnL_SSE = m->lnL_Vec; lnLI_SSE = m->lnLI_Vec; /* find base frequencies */ bs = GetParamSubVals (m->stateFreq, chain, state[chain]); /* if covarion model, adjust base frequencies */ if (m->switchRates != NULL) { /* find the stationary frequencies */ swr = GetParamVals(m->switchRates, chain, state[chain]); s01 = swr[0]; s10 = swr[1]; probOn = s01 / (s01 + s10); probOff = 1.0 - probOn; /* now adjust the base frequencies; on-state stored first in cond likes */ for (j=0; jnumRateCats; else freq = (1.0 - pInvar) / m->numRateCats; mFreq = _mm_set1_ps ((CLFlt)(freq)); /* find site scaler */ lnScaler = m->scalers[m->siteScalerIndex[chain]]; /* find nSitesOfPat */ nSitesOfPat = numSitesOfPat + (whichSitePats*numCompressedChars) + m->compCharStart; /* reset lnL */ *lnL = 0.0; for (c=0; cnumVecChars; c++) { mLike = _mm_setzero_ps (); for (k=0; knumRateCats; k++) { mCatLike = _mm_setzero_ps (); for (j=0; jnumFloatsPerVec; } /* loop over characters */ if (hasPInvar == NO) { for (c=0; cnumChars; c++) { like = m->lnL_Vec[c]; /* check against LIKE_EPSILON (values close to zero are problematic) */ if (like < LIKE_EPSILON) { # ifdef DEBUG_LIKELIHOOD MrBayesPrint ("%s WARNING: In LIKE_EPSILON - for division %d char %d has like = %1.30le\n", spacer, division+1, c+1, like); # endif (*lnL) = MRBFLT_NEG_MAX; abortMove = YES; return ERROR; } else { (*lnL) += (lnScaler[c] + log(like)) * nSitesOfPat[c]; } } } else { /* has invariable category */ for (c=0; cnumVecChars; c++) { mCatLike = _mm_setzero_ps (); for (j=0; jnumFloatsPerVec; } for (c=0; cnumChars; c++) { like = m->lnL_Vec[c]; likeI = m->lnLI_Vec[c]; if (lnScaler[c] < -200.0) { /* we are not going to be able to exponentiate the scaling factor */ if (likeI > 1E-70) { /* forget about like; it is going to be insignificant compared to likeI */ lnLike = log(likeI); } else { /* treat likeI as if 0.0, that is, ignore it completely */ lnLike = log(like) + lnScaler[c]; } } else lnLike = log (like + (likeI / exp (lnScaler[c]))) + lnScaler[c]; /* check against LIKE_EPSILON (values close to zero are problematic) */ if (like < LIKE_EPSILON) { # ifdef DEBUG_LIKELIHOOD MrBayesPrint ("%s WARNING: In LIKE_EPSILON - for division %d char %d has like = %1.30le\n", spacer, division+1, c+1, like); # endif (*lnL) = MRBFLT_NEG_MAX; abortMove = YES; return ERROR; } else { (*lnL) += lnLike * nSitesOfPat[c]; } } } return NO_ERROR; } #endif /*------------------------------------------------------------------ | | Likelihood_Gen_GibbsGamma: general n-state models using | Gibbs resampling of discrete gamma rate categories | -------------------------------------------------------------------*/ int Likelihood_Gen_GibbsGamma (TreeNode *p, int division, int chain, MrBFlt *lnL, int whichSitePats) { int c, j, nStates, nGammaCats, *rateCat; MrBFlt s01, s10, probOn, probOff, *swr; MrBFlt covBF[40], *bs, like; CLFlt *clP, *lnScaler, *nSitesOfPat, *clInvar=NULL; ModelInfo *m; /* find model settings, nStates and invar cond likes */ m = &modelSettings[division]; nStates = m->numModelStates; clInvar = m->invCondLikes; /* find conditional likelihood pointer */ clP = m->condLikes[m->condLikeIndex[chain][p->index]]; /* find base frequencies */ bs = GetParamSubVals (m->stateFreq, chain, state[chain]); /* if covarion model, adjust base frequencies */ if (m->switchRates != NULL) { /* find the stationary frequencies */ swr = GetParamVals(m->switchRates, chain, state[chain]); s01 = swr[0]; s10 = swr[1]; probOn = s01 / (s01 + s10); probOff = 1.0 - probOn; /* now adjust the base frequencies; on-state stored first in cond likes */ for (j=0; jscalers[m->siteScalerIndex[chain]]; /* find nSitesOfPat */ nSitesOfPat = numSitesOfPat + (whichSitePats*numCompressedChars) + m->compCharStart; /* find rate category index and number of gamma categories */ rateCat = m->tiIndex + chain * m->numChars; nGammaCats = m->numRateCats; /* reset lnL */ *lnL = 0.0; /* loop over characters */ if (m->pInvar == NULL) { for (c=0; cnumChars; c++) { like = 0.0; for (j=0; jnumChars; c++) { like = 0.0; if (rateCat[c] < nGammaCats) { for (j=0; jpInvar == NULL) { hasPInvar = NO; } else { hasPInvar = YES; pInvar = *(GetParamVals (m->pInvar, chain, state[chain])); clInvar = m->invCondLikes; } /* find conditional likelihood pointers */ clPtr = m->condLikes[m->condLikeIndex[chain][p->index]]; clP = m->clP; for (k=0; knumRateCats; k++) { clP[k] = clPtr; clPtr += m->numChars * m->numModelStates; } /* find base frequencies */ bs = GetParamSubVals (m->stateFreq, chain, state[chain]); /* find category frequencies */ if (hasPInvar == NO) freq = 1.0 / m->numRateCats; else freq = (1.0 - pInvar) / m->numRateCats; /* find tree scaler */ lnScaler = m->scalers[m->siteScalerIndex[chain]]; /* find nSitesOfPat */ nSitesOfPat = numSitesOfPat + (whichSitePats*numCompressedChars) + m->compCharStart; /* reset lnL */ *lnL = 0.0; /* loop over characters */ if (hasPInvar == NO) { for (c=0; cnumChars; c++) { like = 0.0; for (k=0; knumRateCats; k++) { like += (clP[k][A] * bs[A] + clP[k][C] * bs[C] + clP[k][G] * bs[G] + clP[k][T] * bs[T]); clP[k] += 4; } like *= freq; /* check against LIKE_EPSILON (values close to zero are problematic) */ if (like < LIKE_EPSILON) { # ifdef DEBUG_LIKELIHOOD MrBayesPrint ("%s WARNING: In LIKE_EPSILON - for division %d char %d has like = %1.30le\n", spacer, division+1, c+1, like); # endif (*lnL) = MRBFLT_NEG_MAX; abortMove = YES; return ERROR; } else { (*lnL) += (lnScaler[c] + log(like)) * nSitesOfPat[c]; } } } else { /* has invariable category */ for (c=0; cnumChars; c++) { like = 0.0; for (k=0; knumRateCats; k++) { like += (clP[k][A] * bs[A] + clP[k][C] * bs[C] + clP[k][G] * bs[G] + clP[k][T] * bs[T]); clP[k] += 4; } like *= freq; likeI = (clInvar[A] * bs[A] + clInvar[C] * bs[C] + clInvar[G] * bs[G] + clInvar[T] * bs[T]) * pInvar; if (lnScaler[c] < -200) { /* we are not going to be able to exponentiate the scaling factor */ if (likeI > 1E-70) { /* forget about like; it is going to be insignificant compared to likeI */ like = likeI; } else { /* treat likeI as if 0.0, that is, ignore it completely */ } } else like = like + (likeI / exp (lnScaler[c])); clInvar += 4; /* check against LIKE_EPSILON (values close to zero are problematic) */ if (like < LIKE_EPSILON) { # ifdef DEBUG_LIKELIHOOD MrBayesPrint ("%s WARNING: In LIKE_EPSILON - for division %d char %d has like = %1.30le\n", spacer, division+1, c+1, like); # endif (*lnL) = MRBFLT_NEG_MAX; abortMove = YES; return ERROR; } else { (*lnL) += (lnScaler[c] + log(like)) * nSitesOfPat[c]; } } } return NO_ERROR; } /*------------------------------------------------------------------ | | Likelihood_NUC4_GibbsGamma: 4by4 nucleotide models with rate | variation using Gibbs sampling from gamma rate categories | -------------------------------------------------------------------*/ int Likelihood_NUC4_GibbsGamma (TreeNode *p, int division, int chain, MrBFlt *lnL, int whichSitePats) { int c, i, r, nGammaCats, *rateCat; MrBFlt *bs, like; CLFlt *clP, *lnScaler, *nSitesOfPat, *clInvar; ModelInfo *m; /* find model settings and invar cond likes */ m = &modelSettings[division]; clInvar = m->invCondLikes; /* find conditional likelihood pointer */ clP = m->condLikes[m->condLikeIndex[chain][p->index]]; /* find base frequencies */ bs = GetParamSubVals (m->stateFreq, chain, state[chain]); /* find tree scaler */ lnScaler = m->scalers[m->siteScalerIndex[chain]]; /* find nSitesOfPat */ nSitesOfPat = numSitesOfPat + (whichSitePats*numCompressedChars) + m->compCharStart; /* find rate category index and number of gamma categories */ rateCat = m->tiIndex + chain * m->numChars; nGammaCats = m->numRateCats; /* reset lnL */ *lnL = 0.0; /* loop over characters */ if (m->pInvar == NULL) { for (c=i=0; cnumChars; c++) { like = (clP[A] * bs[A] + clP[C] * bs[C] + clP[G] * bs[G] + clP[T] * bs[T]); clP += 4; /* check against LIKE_EPSILON (values close to zero are problematic) */ if (like < LIKE_EPSILON) { # ifdef DEBUG_LIKELIHOOD MrBayesPrint ("%s WARNING: In LIKE_EPSILON - for division %d char %d has like = %1.30le\n", spacer, division+1, c+1, like); # endif (*lnL) = MRBFLT_NEG_MAX; abortMove = YES; return ERROR; } else { (*lnL) += (lnScaler[c] + log(like)) * nSitesOfPat[c]; } } } else { /* has invariable category */ for (c=i=0; cnumChars; c++) { r = rateCat[c]; if (r < nGammaCats) like = (clP[A] * bs[A] + clP[C] * bs[C] + clP[G] * bs[G] + clP[T] * bs[T]); else like = (clInvar[A] * bs[A] + clInvar[C] * bs[C] + clInvar[G] * bs[G] + clInvar[T] * bs[T]); clInvar += 4; clP += 4; /* check against LIKE_EPSILON (values close to zero are problematic) */ if (like < LIKE_EPSILON) { # ifdef DEBUG_LIKELIHOOD MrBayesPrint ("%s WARNING: In LIKE_EPSILON - for division %d char %d has like = %1.30le\n", spacer, division+1, c+1, like); # endif (*lnL) = MRBFLT_NEG_MAX; abortMove = YES; return ERROR; } else { (*lnL) += (log (like) + lnScaler[c]) * nSitesOfPat[c]; } } } return NO_ERROR; } #if defined (FMA_ENABLED) /*------------------------------------------------------------------ | | Likelihood_NUC4_FMA: 4by4 nucleotide models with or without rate | variation using AVX + FMA code | -------------------------------------------------------------------*/ int Likelihood_NUC4_FMA (TreeNode *p, int division, int chain, MrBFlt *lnL, int whichSitePats) { int c, k, hasPInvar; MrBFlt freq, *bs, pInvar=0.0, like, likeI; CLFlt *lnScaler, *nSitesOfPat, *lnL_Vec, *lnLI_Vec; __m256 *clPtr, **clP, *clInvar=NULL; __m256 mA, mC, mG, mT, mFreq, mPInvar=_mm256_set1_ps(0.0f), mLike; ModelInfo *m; /* find model settings and pInvar, invar cond likes */ m = &modelSettings[division]; if (m->pInvar == NULL) { hasPInvar = NO; } else { hasPInvar = YES; pInvar = *(GetParamVals (m->pInvar, chain, state[chain])); mPInvar = _mm256_set1_ps ((CLFlt)(pInvar)); clInvar = (__m256 *) (m->invCondLikes); } /* find conditional likelihood pointers */ clPtr = (__m256 *) (m->condLikes[m->condLikeIndex[chain][p->index]]); clP = m->clP_AVX; for (k=0; knumRateCats; k++) { clP[k] = clPtr; clPtr += m->numVecChars * m->numModelStates; } lnL_Vec = m->lnL_Vec; lnLI_Vec = m->lnLI_Vec; /* find base frequencies */ bs = GetParamSubVals (m->stateFreq, chain, state[chain]); mA = _mm256_set1_ps ((CLFlt)(bs[A])); mC = _mm256_set1_ps ((CLFlt)(bs[C])); mG = _mm256_set1_ps ((CLFlt)(bs[G])); mT = _mm256_set1_ps ((CLFlt)(bs[T])); /* find category frequencies */ if (hasPInvar == NO) freq = 1.0 / m->numRateCats; else freq = (1.0 - pInvar) / m->numRateCats; mFreq = _mm256_set1_ps ((CLFlt)(freq)); /* find tree scaler */ lnScaler = m->scalers[m->siteScalerIndex[chain]]; /* find nSitesOfPat */ nSitesOfPat = numSitesOfPat + (whichSitePats*numCompressedChars) + m->compCharStart; /* reset lnL */ *lnL = 0.0; /* calculate variable likelihood */ for (c=0; cnumVecChars; c++) { mLike = _mm256_setzero_ps (); for (k=0; knumRateCats; k++) { mLike = _mm256_fmadd_ps (clP[k][A], mA, mLike); mLike = _mm256_fmadd_ps (clP[k][C], mC, mLike); mLike = _mm256_fmadd_ps (clP[k][G], mG, mLike); mLike = _mm256_fmadd_ps (clP[k][T], mT, mLike); clP[k] += 4; } mLike = _mm256_mul_ps (mLike, mFreq); _mm256_store_ps (lnL_Vec, mLike); lnL_Vec += m->numFloatsPerVec; } /* calculate invariable likelihood */ if (hasPInvar == YES) { for (c=0; cnumVecChars; c++) { mLike = _mm256_mul_ps (clInvar[A], mA); mLike = _mm256_fmadd_ps (clInvar[C], mC, mLike); mLike = _mm256_fmadd_ps (clInvar[G], mG, mLike); mLike = _mm256_fmadd_ps (clInvar[T], mT, mLike); mLike = _mm256_mul_ps (mLike, mPInvar); _mm256_store_ps (lnLI_Vec, mLike); clInvar += 4; lnLI_Vec += m->numFloatsPerVec; } } /* accumulate results */ if (hasPInvar == NO) { for (c=0; cnumChars; c++) { like = m->lnL_Vec[c]; /* check against LIKE_EPSILON (values close to zero are problematic) */ if (like < LIKE_EPSILON) { # ifdef DEBUG_LIKELIHOOD MrBayesPrint ("%s WARNING: In LIKE_EPSILON - for division %d char %d has like = %1.30le\n", spacer, division+1, c+1, like); # endif (*lnL) = MRBFLT_NEG_MAX; abortMove = YES; return ERROR; } else { (*lnL) += (lnScaler[c] + log(like)) * nSitesOfPat[c]; } } } else { /* has invariable category */ for (c=0; cnumChars; c++) { like = m->lnL_Vec[c]; likeI = m->lnLI_Vec[c]; if (lnScaler[c] < -200) { /* we are not going to be able to exponentiate the scaling factor */ if (likeI > 1E-70) { /* forget about like; it is going to be insignificant compared to likeI */ like = likeI; } else { /* treat likeI as if 0.0, that is, ignore it completely */ } } else like = like + (likeI / exp (lnScaler[c])); /* check against LIKE_EPSILON (values close to zero are problematic) */ if (like < LIKE_EPSILON) { # ifdef DEBUG_LIKELIHOOD MrBayesPrint ("%s WARNING: In LIKE_EPSILON - for division %d char %d has like = %1.30le\n", spacer, division+1, c+1, like); # endif (*lnL) = MRBFLT_NEG_MAX; abortMove = YES; return ERROR; } else { (*lnL) += (lnScaler[c] + log(like)) * nSitesOfPat[c]; } } } return NO_ERROR; } #endif #if defined (AVX_ENABLED) /*------------------------------------------------------------------ | | Likelihood_NUC4_AVX: 4by4 nucleotide models with or without rate | variation using AVX code | -------------------------------------------------------------------*/ int Likelihood_NUC4_AVX (TreeNode *p, int division, int chain, MrBFlt *lnL, int whichSitePats) { int c, k, hasPInvar; MrBFlt freq, *bs, pInvar=0.0, like, likeI; CLFlt *lnScaler, *nSitesOfPat, *lnL_Vec, *lnLI_Vec; __m256 *clPtr, **clP, *clInvar=NULL; __m256 m1, mA, mC, mG, mT, mFreq, mPInvar=_mm256_set1_ps(0.0f), mLike; ModelInfo *m; /* find model settings and pInvar, invar cond likes */ m = &modelSettings[division]; if (m->pInvar == NULL) { hasPInvar = NO; } else { hasPInvar = YES; pInvar = *(GetParamVals (m->pInvar, chain, state[chain])); mPInvar = _mm256_set1_ps ((CLFlt)(pInvar)); clInvar = (__m256 *) (m->invCondLikes); } /* find conditional likelihood pointers */ clPtr = (__m256 *) (m->condLikes[m->condLikeIndex[chain][p->index]]); clP = m->clP_AVX; for (k=0; knumRateCats; k++) { clP[k] = clPtr; clPtr += m->numVecChars * m->numModelStates; } lnL_Vec = m->lnL_Vec; lnLI_Vec = m->lnLI_Vec; /* find base frequencies */ bs = GetParamSubVals (m->stateFreq, chain, state[chain]); mA = _mm256_set1_ps ((CLFlt)(bs[A])); mC = _mm256_set1_ps ((CLFlt)(bs[C])); mG = _mm256_set1_ps ((CLFlt)(bs[G])); mT = _mm256_set1_ps ((CLFlt)(bs[T])); /* find category frequencies */ if (hasPInvar == NO) freq = 1.0 / m->numRateCats; else freq = (1.0 - pInvar) / m->numRateCats; mFreq = _mm256_set1_ps ((CLFlt)(freq)); /* find tree scaler */ lnScaler = m->scalers[m->siteScalerIndex[chain]]; /* find nSitesOfPat */ nSitesOfPat = numSitesOfPat + (whichSitePats*numCompressedChars) + m->compCharStart; /* reset lnL */ *lnL = 0.0; /* calculate variable likelihood */ for (c=0; cnumVecChars; c++) { mLike = _mm256_setzero_ps (); for (k=0; knumRateCats; k++) { m1 = _mm256_mul_ps (clP[k][A], mA); mLike = _mm256_add_ps (mLike, m1); m1 = _mm256_mul_ps (clP[k][C], mC); mLike = _mm256_add_ps (mLike, m1); m1 = _mm256_mul_ps (clP[k][G], mG); mLike = _mm256_add_ps (mLike, m1); m1 = _mm256_mul_ps (clP[k][T], mT); mLike = _mm256_add_ps (mLike, m1); clP[k] += 4; } mLike = _mm256_mul_ps (mLike, mFreq); _mm256_store_ps (lnL_Vec, mLike); lnL_Vec += m->numFloatsPerVec; } /* calculate invariable likelihood */ if (hasPInvar == YES) { for (c=0; cnumVecChars; c++) { mLike = _mm256_mul_ps (clInvar[A], mA); m1 = _mm256_mul_ps (clInvar[C], mC); mLike = _mm256_add_ps (mLike, m1); m1 = _mm256_mul_ps (clInvar[G], mG); mLike = _mm256_add_ps (mLike, m1); m1 = _mm256_mul_ps (clInvar[T], mT); mLike = _mm256_add_ps (mLike, m1); mLike = _mm256_mul_ps (mLike, mPInvar); _mm256_store_ps (lnLI_Vec, mLike); clInvar += 4; lnLI_Vec += m->numFloatsPerVec; } } /* accumulate results */ if (hasPInvar == NO) { for (c=0; cnumChars; c++) { like = m->lnL_Vec[c]; /* check against LIKE_EPSILON (values close to zero are problematic) */ if (like < LIKE_EPSILON) { # ifdef DEBUG_LIKELIHOOD MrBayesPrint ("%s WARNING: In LIKE_EPSILON - for division %d char %d has like = %1.30le\n", spacer, division+1, c+1, like); # endif (*lnL) = MRBFLT_NEG_MAX; abortMove = YES; return ERROR; } else { (*lnL) += (lnScaler[c] + log(like)) * nSitesOfPat[c]; } } } else { /* has invariable category */ for (c=0; cnumChars; c++) { like = m->lnL_Vec[c]; likeI = m->lnLI_Vec[c]; if (lnScaler[c] < -200) { /* we are not going to be able to exponentiate the scaling factor */ if (likeI > 1E-70) { /* forget about like; it is going to be insignificant compared to likeI */ like = likeI; } else { /* treat likeI as if 0.0, that is, ignore it completely */ } } else like = like + (likeI / exp (lnScaler[c])); /* check against LIKE_EPSILON (values close to zero are problematic) */ if (like < LIKE_EPSILON) { # ifdef DEBUG_LIKELIHOOD MrBayesPrint ("%s WARNING: In LIKE_EPSILON - for division %d char %d has like = %1.30le\n", spacer, division+1, c+1, like); # endif (*lnL) = MRBFLT_NEG_MAX; abortMove = YES; return ERROR; } else { (*lnL) += (lnScaler[c] + log(like)) * nSitesOfPat[c]; } } } return NO_ERROR; } #endif #if defined (SSE_ENABLED) /*------------------------------------------------------------------ | | Likelihood_NUC4_SSE: 4by4 nucleotide models with or without rate | variation | -------------------------------------------------------------------*/ int Likelihood_NUC4_SSE (TreeNode *p, int division, int chain, MrBFlt *lnL, int whichSitePats) { int c, k, hasPInvar; MrBFlt freq, *bs, pInvar=0.0, like, likeI; CLFlt *lnScaler, *nSitesOfPat, *lnL_Vec, *lnLI_Vec; __m128 *clPtr, **clP, *clInvar=NULL; __m128 m1, mA, mC, mG, mT, mFreq, mPInvar=_mm_set1_ps(0.0f), mLike; ModelInfo *m; /* find model settings and pInvar, invar cond likes */ m = &modelSettings[division]; if (m->pInvar == NULL) { hasPInvar = NO; } else { hasPInvar = YES; pInvar = *(GetParamVals (m->pInvar, chain, state[chain])); mPInvar = _mm_set1_ps ((CLFlt)(pInvar)); clInvar = (__m128 *) (m->invCondLikes); } /* find conditional likelihood pointers */ clPtr = (__m128 *) (m->condLikes[m->condLikeIndex[chain][p->index]]); clP = m->clP_SSE; for (k=0; knumRateCats; k++) { clP[k] = clPtr; clPtr += m->numVecChars * m->numModelStates; } lnL_Vec = m->lnL_Vec; lnLI_Vec = m->lnLI_Vec; /* find base frequencies */ bs = GetParamSubVals (m->stateFreq, chain, state[chain]); mA = _mm_set1_ps ((CLFlt)(bs[A])); mC = _mm_set1_ps ((CLFlt)(bs[C])); mG = _mm_set1_ps ((CLFlt)(bs[G])); mT = _mm_set1_ps ((CLFlt)(bs[T])); /* find category frequencies */ if (hasPInvar == NO) freq = 1.0 / m->numRateCats; else freq = (1.0 - pInvar) / m->numRateCats; mFreq = _mm_set1_ps ((CLFlt)(freq)); /* find tree scaler */ lnScaler = m->scalers[m->siteScalerIndex[chain]]; /* find nSitesOfPat */ nSitesOfPat = numSitesOfPat + (whichSitePats*numCompressedChars) + m->compCharStart; /* reset lnL */ *lnL = 0.0; /* calculate variable likelihood */ for (c=0; cnumVecChars; c++) { mLike = _mm_setzero_ps (); for (k=0; knumRateCats; k++) { m1 = _mm_mul_ps (clP[k][A], mA); mLike = _mm_add_ps (mLike, m1); m1 = _mm_mul_ps (clP[k][C], mC); mLike = _mm_add_ps (mLike, m1); m1 = _mm_mul_ps (clP[k][G], mG); mLike = _mm_add_ps (mLike, m1); m1 = _mm_mul_ps (clP[k][T], mT); mLike = _mm_add_ps (mLike, m1); clP[k] += 4; } mLike = _mm_mul_ps (mLike, mFreq); _mm_store_ps (lnL_Vec, mLike); lnL_Vec += m->numFloatsPerVec; } /* calculate invariable likelihood */ if (hasPInvar == YES) { for (c=0; cnumVecChars; c++) { mLike = _mm_mul_ps (clInvar[A], mA); m1 = _mm_mul_ps (clInvar[C], mC); mLike = _mm_add_ps (mLike, m1); m1 = _mm_mul_ps (clInvar[G], mG); mLike = _mm_add_ps (mLike, m1); m1 = _mm_mul_ps (clInvar[T], mT); mLike = _mm_add_ps (mLike, m1); mLike = _mm_mul_ps (mLike, mPInvar); _mm_store_ps (lnLI_Vec, mLike); clInvar += 4; lnLI_Vec += m->numFloatsPerVec; } } /* accumulate results */ if (hasPInvar == NO) { for (c=0; cnumChars; c++) { like = m->lnL_Vec[c]; /* check against LIKE_EPSILON (values close to zero are problematic) */ if (like < LIKE_EPSILON) { # ifdef DEBUG_LIKELIHOOD MrBayesPrint ("%s WARNING: In LIKE_EPSILON - for division %d char %d has like = %1.30le\n", spacer, division+1, c+1, like); # endif (*lnL) = MRBFLT_NEG_MAX; abortMove = YES; return ERROR; } else { (*lnL) += (lnScaler[c] + log(like)) * nSitesOfPat[c]; } } } else { /* has invariable category */ for (c=0; cnumChars; c++) { like = m->lnL_Vec[c]; likeI = m->lnLI_Vec[c]; if (lnScaler[c] < -200) { /* we are not going to be able to exponentiate the scaling factor */ if (likeI > 1E-70) { /* forget about like; it is going to be insignificant compared to likeI */ like = likeI; } else { /* treat likeI as if 0.0, that is, ignore it completely */ } } else like = like + (likeI / exp (lnScaler[c])); /* check against LIKE_EPSILON (values close to zero are problematic) */ if (like < LIKE_EPSILON) { # ifdef DEBUG_LIKELIHOOD MrBayesPrint ("%s WARNING: In LIKE_EPSILON - for division %d char %d has like = %1.30le\n", spacer, division+1, c+1, like); # endif (*lnL) = MRBFLT_NEG_MAX; abortMove = YES; return ERROR; } else { (*lnL) += (lnScaler[c] + log(like)) * nSitesOfPat[c]; } } } return NO_ERROR; } #endif /*------------------------------------------------------------------ | | Likelihood_NY98: Codon model with three selection categories, | after Nielsen and Yang (1998). | -------------------------------------------------------------------*/ int Likelihood_NY98 (TreeNode *p, int division, int chain, MrBFlt *lnL, int whichSitePats) { int c, j, k, nStates; MrBFlt catLike, like, *bs, *omegaCatFreq; CLFlt **clP,*clPtr, *lnScaler, *nSitesOfPat; ModelInfo *m; m = &modelSettings[division]; /* number of states */ nStates = m->numModelStates; /* find conditional likelihood pointers */ clPtr = m->condLikes[m->condLikeIndex[chain][p->index]]; clP = m->clP; for (k=0; knumOmegaCats; k++) { clP[k] = clPtr; clPtr += m->numChars * m->numModelStates; } /* find codon frequencies */ bs = GetParamSubVals (m->stateFreq, chain, state[chain]); /* find category frequencies */ omegaCatFreq = GetParamSubVals (m->omega, chain, state[chain]); /* find site scaler */ lnScaler = m->scalers[m->siteScalerIndex[chain]]; /* find nSitesOfPat */ nSitesOfPat = numSitesOfPat + (whichSitePats*numCompressedChars) + m->compCharStart; *lnL = 0.0; /* reset lnL */ for (c=m->numDummyChars; cnumChars; c++) { like = 0.0; for (k=0; knumOmegaCats; k++) { catLike = 0.0; for (j=0; jnumModelStates; /* find conditional likelihood pointers */ clPtr = (__m128 *) m->condLikes[m->condLikeIndex[chain][p->index]]; clP = m->clP_SSE; for (k=0; knumOmegaCats; k++) { clP[k] = clPtr; clPtr += m->numVecChars * nStates; } /* find codon frequencies */ bs = GetParamSubVals (m->stateFreq, chain, state[chain]); /* find category frequencies */ omegaCatFreq = GetParamSubVals (m->omega, chain, state[chain]); /* find site scaler */ lnScaler = m->scalers[m->siteScalerIndex[chain]]; /* find nSitesOfPat */ nSitesOfPat = numSitesOfPat + (whichSitePats*numCompressedChars) + m->compCharStart; *lnL = 0.0; /* reset lnL */ lnL_Vec = m->lnL_Vec; for (c=0; cnumVecChars; c++) { mLike = _mm_setzero_ps (); for (k=0; knumOmegaCats; k++) { mCatLike = _mm_setzero_ps (); for (j=0; jnumFloatsPerVec; } for (c=m->numDummyChars; cnumChars; c++) { like = m->lnL_Vec[c]; /* check against LIKE_EPSILON (values close to zero are problematic) */ if (like < LIKE_EPSILON) { # ifdef DEBUG_LIKELIHOOD MrBayesPrint ("%s WARNING: In LIKE_EPSILON - for division %d char %d has like = %1.30le\n", spacer, division+1, c+1, like); # endif (*lnL) = MRBFLT_NEG_MAX; abortMove = YES; return ERROR; } else { (*lnL) += (lnScaler[c] + log(like)) * nSitesOfPat[c]; } } return NO_ERROR; } #endif /*------------------------------------------------------------------ | | Likelihood_Res: restriction site model with or without rate | variation | -------------------------------------------------------------------*/ int Likelihood_Res (TreeNode *p, int division, int chain, MrBFlt *lnL, int whichSitePats) { int c, k; MrBFlt *bs, freq, like, pUnobserved, pObserved; CLFlt *clPtr, **clP, *lnScaler, *nSitesOfPat; ModelParams *mp; ModelInfo *m; m = &modelSettings[division]; mp = &modelParams[division]; /* find conditional likelihood pointer */ clPtr = m->condLikes[m->condLikeIndex[chain][p->index]]; clP = m->clP; for (k=0; knumRateCats; k++) { clP[k] = clPtr; clPtr += m->numChars * m->numModelStates; } /* find base frequencies */ /* modified to deal with directional model by SK and FR */ bs = GetParamSubVals (m->stateFreq, chain, state[chain]); if (!strcmp(mp->statefreqModel,"Directional")) { bs += m->numModelStates; } else if (!strcmp(mp->statefreqModel,"Mixed")) { if (bs[m->numModelStates] != NOT_APPLICABLE) bs += m->numModelStates; } /* find category frequencies */ freq = 1.0 / m->numRateCats; /* find site scaler */ lnScaler = m->scalers[m->siteScalerIndex[chain]]; /* find nSitesOfPat */ nSitesOfPat = numSitesOfPat + (whichSitePats*numCompressedChars) + m->compCharStart; *lnL = 0.0; /* reset lnL */ pUnobserved = 0.0; for (c=0; cnumDummyChars; c++) { like = 0.0; for (k=0; knumRateCats; k++) { like += (clP[k][0]*bs[0] + clP[k][1]*bs[1]) * freq; clP[k] += 2; } pUnobserved += like * exp(lnScaler[c]); } pObserved = 1.0 - pUnobserved; if (pObserved < LIKE_EPSILON) { # ifdef DEBUG_LIKELIHOOD MrBayesPrint ("%s WARNING: p(Observed) < LIKE_EPSILON - for division %d p(Observed) = %1.30le\n", spacer, division+1, pObserved); # endif (*lnL) = MRBFLT_NEG_MAX; abortMove = YES; return ERROR; } for (c=m->numDummyChars; cnumChars; c++) { like = 0.0; for (k=0; knumRateCats; k++) { like += (clP[k][0]*bs[0] + clP[k][1]*bs[1]) * freq; clP[k] += 2; } /* check against LIKE_EPSILON (values close to zero are problematic) */ if (like < LIKE_EPSILON) { # ifdef DEBUG_LIKELIHOOD MrBayesPrint ("%s WARNING: In LIKE_EPSILON - for division %d char %d has like = %1.30le\n", spacer, division+1, c+1, like); # endif (*lnL) = MRBFLT_NEG_MAX; abortMove = YES; return ERROR; } else { (*lnL) += (lnScaler[c] + log(like)) * nSitesOfPat[c]; } } /* correct for absent characters */ (*lnL) -= log(pObserved) * (m->numUncompressedChars); return NO_ERROR; } #if defined (SSE_ENABLED) /*------------------------------------------------------------------ | | Likelihood_Res_SSE: restriction site model with or without rate | variation | -------------------------------------------------------------------*/ int Likelihood_Res_SSE (TreeNode *p, int division, int chain, MrBFlt *lnL, int whichSitePats) { int c, k; MrBFlt freq, *bs, like, pUnobserved, pObserved; CLFlt *lnScaler, *nSitesOfPat, *lnL_Vec; __m128 *clPtr, **clP; __m128 m1, mA, mB, mFreq, mLike; ModelInfo *m; ModelParams *mp; /* find model settings and pInvar, invar cond likes */ m = &modelSettings[division]; mp = &modelParams[division]; /* find conditional likelihood pointers */ clPtr = (__m128 *) (m->condLikes[m->condLikeIndex[chain][p->index]]); clP = m->clP_SSE; for (k=0; knumRateCats; k++) { clP[k] = clPtr; clPtr += m->numVecChars * m->numModelStates; } lnL_Vec = m->lnL_Vec; /* find base frequencies */ /* modified to deal with directonal models by SK and FR */ bs = GetParamSubVals (m->stateFreq, chain, state[chain]); if (!strcmp(mp->statefreqModel,"Directional")) { bs += m->numModelStates; } else if (!strcmp(mp->statefreqModel,"Mixed")) { if (bs[m->numModelStates] != NOT_APPLICABLE) bs += m->numModelStates; } mA = _mm_set1_ps ((CLFlt)(bs[0])); mB = _mm_set1_ps ((CLFlt)(bs[1])); freq = 1.0 / m->numRateCats; mFreq = _mm_set1_ps ((CLFlt)(freq)); /* find tree scaler */ lnScaler = m->scalers[m->siteScalerIndex[chain]]; /* find nSitesOfPat */ nSitesOfPat = numSitesOfPat + (whichSitePats*numCompressedChars) + m->compCharStart; /* reset lnL */ *lnL = 0.0; /* calculate variable likelihood */ for (c=0; cnumVecChars; c++) { mLike = _mm_setzero_ps (); for (k=0; knumRateCats; k++) { m1 = _mm_mul_ps (clP[k][0], mA); mLike = _mm_add_ps (mLike, m1); m1 = _mm_mul_ps (clP[k][1], mB); mLike = _mm_add_ps (mLike, m1); clP[k] += 2; } mLike = _mm_mul_ps (mLike, mFreq); _mm_store_ps (lnL_Vec, mLike); lnL_Vec += m->numFloatsPerVec; } pUnobserved = 0.0; for (c=0; cnumDummyChars; c++) { like = m->lnL_Vec[c]; pUnobserved += like * exp(lnScaler[c]); } pObserved = 1.0 - pUnobserved; if (pObserved < LIKE_EPSILON) { # ifdef DEBUG_LIKELIHOOD MrBayesPrint ("%s WARNING: p(Observed) < LIKE_EPSILON - for division %d p(Observed) = %1.30le\n", spacer, division+1, pObserved); # endif (*lnL) = MRBFLT_NEG_MAX; abortMove = YES; return ERROR; } for (c=m->numDummyChars; cnumChars; c++) { like = m->lnL_Vec[c]; /* check against LIKE_EPSILON (values close to zero are problematic) */ if (like < LIKE_EPSILON) { # ifdef DEBUG_LIKELIHOOD MrBayesPrint ("%s WARNING: In LIKE_EPSILON - for division %d char %d has like = %1.30le\n", spacer, division+1, c+1, like); # endif (*lnL) = MRBFLT_NEG_MAX; abortMove = YES; return ERROR; } else { (*lnL) += (lnScaler[c] + log(like)) * nSitesOfPat[c]; } } /* correct for absent characters */ (*lnL) -= log(pObserved) * (m->numUncompressedChars); return NO_ERROR; } #endif /*------------------------------------------------------------------ | | Likelihood_Std: variable states model with or without rate | variation | -------------------------------------------------------------------*/ int Likelihood_Std (TreeNode *p, int division, int chain, MrBFlt *lnL, int whichSitePats) { int b, c, j, k, nBetaCats, nRateCats, nStates, numReps; MrBFlt catLike, catFreq, rateFreq, like, *bs, *bsBase, pUnobserved, pObserved; CLFlt *clPtr, **clP, *lnScaler, *nSitesOfPat; ModelInfo *m; m = &modelSettings[division]; numReps=0; for (c=0; cnumChars; c++) { if (m->nStates[c] == 2) numReps += m->numBetaCats * 2; else numReps += m->nStates[c]; } /* find conditional likelihood pointers */ clPtr = m->condLikes[m->condLikeIndex[chain][p->index]]; clP = m->clP; for (k=0; knumRateCats; k++) { clP[k] = clPtr; clPtr += numReps; } /* find base frequencies */ bsBase = GetParamStdStateFreqs (m->stateFreq, chain, state[chain]); /* find rate category number and frequencies */ nRateCats = m->numRateCats; rateFreq = 1.0 / nRateCats; /* find site scaler */ lnScaler = m->scalers[m->siteScalerIndex[chain]]; /* find nSitesOfPat */ nSitesOfPat = numSitesOfPat + (whichSitePats*numCompressedChars) + m->compCharStart; *lnL = 0.0; /* reset lnL */ if (m->numBetaCats == 1) { pUnobserved = 0.0; catFreq = rateFreq; for (c=j=0; cnumDummyChars; c++) { like = 0.0; nStates = m->nStates[c]; bs = bsBase + m->bsIndex[c]; for (k=0; knumDummyChars; cnumChars; c++) { like = 0.0; nStates = m->nStates[c]; bs = bsBase + m->bsIndex[c]; for (k=0; knumDummyChars; c++) { like = 0.0; nStates = m->nStates[c]; bs = bsBase + m->bsIndex[c]; if (nStates == 2) { nBetaCats = m->numBetaCats; catFreq = rateFreq / nBetaCats; } else { nBetaCats = 1; catFreq = rateFreq; } for (b=0; bnumDummyChars; cnumChars; c++) { like = 0.0; nStates = m->nStates[c]; bs = bsBase + m->bsIndex[c]; if (nStates == 2) { nBetaCats = m->numBetaCats; catFreq = rateFreq / nBetaCats; } else { nBetaCats = 1; catFreq = rateFreq; } for (b=0; bnumUncompressedChars); return NO_ERROR; } int Likelihood_Cont_Debug (TreeNode *p, int division, int chain, MrBFlt *lnL, int whichSitePats) { /* Debug function for continuous-trait likelihood. This works only for a single partition without among-character rate variation. Programmed by Ziye Wang */ int i, c, tipIndex, memIndex; MrBFlt vL, vR, vA, ml, mr, ma; MrBFlt tmplnL; MrBFlt *vP, *mP, *mtmp; Tree *tree; TreeNode *tp, *pL, *pR, *pA, *root; ModelInfo *m; tmplnL = 0.0; m = &modelSettings[division]; tree = GetTree(m->brlens,chain,state[chain]); /* allocate memory for BM variance */ vP = (MrBFlt*) SafeCalloc(tree->nNodes, sizeof(MrBFlt)); /* allocate memory for continuous characters */ mP = (MrBFlt*) SafeCalloc((tree->nNodes)*m->numChars, sizeof(MrBFlt)); /* allocate memory for a temporary array to store original data */ mtmp = (MrBFlt*) SafeCalloc(m->numChars, sizeof(MrBFlt)); /* read in continuous characters */ for (i=0; inNodes; i++) { p = tree->allDownPass[i]; if (p->right == NULL) { tipIndex = p->index; memIndex = p->memoryIndex; for (c=0; cnumChars; c++) { mtmp[c] = matrix[pos(tipIndex,c,numChar)] / 10000.0; // get original data for tip node } memcpy(&mP[memIndex*m->numChars], &mtmp[0], m->numChars*sizeof(MrBFlt)); } } /* calculate BM variance */ for (i=0; inIntNodes; i++) { p = tree->intDownPass[i]; /* find BM variance for left node */ tp = p->left; if (tp->left != NULL && tp->right != NULL) // internal node { vL = vP[tp->left->memoryIndex]; vR = vP[tp->right->memoryIndex]; vP[tp->memoryIndex] = tp->length + (vL * vR)/(vL + vR); } else // tip node { vP[tp->memoryIndex] = tp->length; } /* find BM variance for right node */ tp = p->right; if (tp->left != NULL && tp->right != NULL) // internal node { vL = vP[tp->left->memoryIndex]; vR = vP[tp->right->memoryIndex]; vP[tp->memoryIndex] = tp->length + (vL * vR)/(vL + vR); } else // tip node { vP[tp->memoryIndex] = tp->length; } /* calculate likelihood */ if (p->anc->anc == NULL) // we reach the trichotomy { pA = p->anc; root = p; pL = root->left; pR = root->right; for (c=0; cnumChars; c++) { ml = mP[pL->memoryIndex*m->numChars+c]; // c-th character of left node mr = mP[pR->memoryIndex*m->numChars+c]; // c-th character of right node ma = mP[pA->memoryIndex*m->numChars+c]; // c-th character of ancestral node vL = vP[pL->memoryIndex]; vR = vP[pR->memoryIndex]; vA = root->length; tmplnL += 2.0 * log(2.0 * M_PI); tmplnL += log(vL*vR + vR*vA + vA*vL); tmplnL += (vL*powf(mr-ma, 2) + vR*powf(ml-ma, 2) + vA*powf(mr-ml, 2))/(vL*vR + vR*vA + vA*vL); } } else { vL = vP[p->left->memoryIndex]; vR = vP[p->right->memoryIndex]; for (c=0; cnumChars; c++) { ml = mP[p->left->memoryIndex*m->numChars+c]; // c-th character of left node mr = mP[p->right->memoryIndex*m->numChars+c]; // c-th character of right node /* update likelihood */ tmplnL += log(2.0 * M_PI) + log(vL + vR); tmplnL += powf(ml - mr, 2) / (vL + vR); /* update character of parent node */ mP[p->memoryIndex*m->numChars+c] = (vR*ml + vL*mr) / (vL + vR); } } } *lnL = -0.5 * tmplnL; free(vP); free(mP); free(mtmp); return NO_ERROR; } /*------------------------------------------------------------------ | | CatLnLike_Cont: sum, for each rate category, the contrast log | likelihoods of character c stored in the internal nodes; | returns the largest of them (for numerical stability) | -------------------------------------------------------------------*/ MrBFlt CatLnLike_Cont (int division, int chain, int c, MrBFlt *lnCatL) { int k, n; MrBFlt lmax; CLFlt *lnL; ModelInfo *m; Tree *t; TreeNode *q; m = &modelSettings[division]; t = GetTree(m->brlens, chain, state[chain]); for (k=0; knumRateCats; k++) lnCatL[k] = 0.0; for (n=0; nnIntNodes; n++) { q = t->intDownPass[n]; lnL = m->condLikes[m->condLikeIndex[chain][q->index]]; for (k=0; knumRateCats; k++) lnCatL[k] += lnL[k * (m->numChars) + c]; } lmax = lnCatL[0]; for (k=1; knumRateCats; k++) if (lmax < lnCatL[k]) lmax = lnCatL[k]; return lmax; } /*------------------------------------------------------------------ | | Likelihood_Cont: likelihood for continuous traits //chi | | This function calculates the restricted maximum likelihood (REML) | using phylogenetic independent contrasts (PICs) (Felsenstein 1973, 1981, 1985). | These contrasts, under a BM model, are independent and normally distributed. | The “restricted†part of REML refers to the fact that it calculates likelihood based on a transformed | set of data where the effect of nuisance parameters (the root state in this case) has been removed. | -------------------------------------------------------------------*/ int Likelihood_Cont (TreeNode *p, int division, int chain, MrBFlt *lnL, int whichSitePats) { int k, c; MrBFlt lmax, siteLike, lnCatL[MAX_RATE_CATS]; ModelInfo *m; m = &modelSettings[division]; *lnL = 0.0; /* independent log likelihood values have been calculated and stored in the internal nodes, we just need to sum them up */ for (c=0; cnumChars; c++) { lmax = CatLnLike_Cont (division, chain, c, lnCatL); if (m->numRateCats == 1) *lnL += lnCatL[0]; else // average over the rate categories { siteLike = 0.0; for (k=0; knumRateCats; k++) siteLike += exp(lnCatL[k] - lmax); *lnL += log(siteLike) + lmax - log(m->numRateCats); } } #if defined (DEBUG_LIKELIHOOD_CONT) /* use the straightforward way of calculation for debugging */ MrBFlt tmpLnL = 0.0; Likelihood_Cont_Debug (p, division, chain, &tmpLnL, whichSitePats); printf("-> lnL(dbug): %lf", tmpLnL); printf(" lnL(cont): %lf", *lnL); if (fabs(*lnL - tmpLnL) > 0.01) printf("\tdiff!\n"); else printf("\n"); #endif return NO_ERROR; } /*------------------------------------------------------------------ | | Likelihood_Pars: likelihood under the Tuffley and Steel (1997) | model for characters with constant number of states. The idea | is described in: | | Tuffley, C., and M. Steel. 1997. Links between maximum likelihood | and maximum parsimony under a simple model of site substitution. | Bull. Math. Bio. 59:581-607. | | The likelihood under the Tuffley and Steel (1997) model is: | | L = k^[-(T + n)] | | where L is the likelihood | k is the number of character states | T is the parsimony tree length | n is the number of characters | | The parsimony calculator does not use character packing; this is | to enable reweighting of characters | | Note that this is an empirical Bayes approach in that it uses the | maximum likelihood branch length. | -------------------------------------------------------------------*/ int Likelihood_Pars (TreeNode *p, int division, int chain, MrBFlt *lnL, int whichSitePats) { int c, i, nStates; BitsLong *pL, *pR, *pP, *pA, x; CLFlt nParsChars, treeLength; CLFlt length, *nSitesOfPat, *newNodeLengthPtr, oldNodeLength; Tree *t; ModelInfo *m; /* Find model settings */ m = &modelSettings[division]; /* Get tree */ t = GetTree(m->brlens,chain,state[chain]); /* Get parsimony tree length */ treeLength = (CLFlt) m->parsTreeLength[2 * chain + state[chain]]; /* Get number of states */ nStates = m->numStates; /* Get number of sites of pat */ nSitesOfPat = numSitesOfPat + (whichSitePats*numCompressedChars) + m->compCharStart; /* Now make downpass node by node */ for (i=0; inIntNodes; i++) { p = t->intDownPass[i]; /* continue if no work needs to be done */ if (p->upDateCl == NO) continue; /* flip space */ FlipCondLikeSpace(m, chain, p->index); /* find parsimony sets for the node and its environment */ pL = m->parsSets[m->condLikeIndex[chain][p->left->index ]]; pR = m->parsSets[m->condLikeIndex[chain][p->right->index]]; pP = m->parsSets[m->condLikeIndex[chain][p->index ]]; // oldpP = m->parsSets[m->condLikeScratchIndex[p->index ]]; /* find old and new node lengths */ oldNodeLength = m->parsNodeLens[m->condLikeScratchIndex[p->index]]; newNodeLengthPtr = &m->parsNodeLens[m->condLikeIndex[chain][p->index]]; if (t->isRooted == NO && p->anc->anc == NULL) { pA = m->parsSets[m->condLikeIndex[chain][p->anc->index]]; length = 0.0; for (c=0; cnumChars; c++) { x = pL[c] & pR[c]; if (x == 0) { x = pL[c] | pR[c]; length += nSitesOfPat[c]; } if ((x & pA[c]) == 0) length += nSitesOfPat[c]; pP[c] = x; } treeLength += (length - oldNodeLength); *newNodeLengthPtr = length; } else { length = 0.0; // done = 0; for (c=0; cnumChars; c++) { x = pL[c] & pR[c]; if (x == 0) { x = pL[c] | pR[c]; length += nSitesOfPat[c]; } pP[c] = x; } treeLength += (length - oldNodeLength); *newNodeLengthPtr = length; } } /* Count number of characters in the partition. It is calculated on the fly because this number is going to differ for different chains if character reweighting is used. */ nSitesOfPat = numSitesOfPat + (whichSitePats*numCompressedChars) + m->compCharStart; nParsChars = 0.0; for (c=0; cnumChars; c++) nParsChars += nSitesOfPat[c]; /* Calculate likelihood from parsimony tree length */ *lnL = - ((treeLength + nParsChars) * log (nStates)); /* Store current parsimony tree length */ m->parsTreeLength[2 * chain + state[chain]] = treeLength; return (NO_ERROR); } /*------------------------------------------------------------------ | | Likelihood_Pars: likelihood under the Tuffley and Steel (1997) | model for characters with constant number of states. | | This variant of Likelihood_Pars assumes that the number of states | is variable. It does not take state order into account. | -------------------------------------------------------------------*/ int Likelihood_ParsStd (TreeNode *p, int division, int chain, MrBFlt *lnL, int whichSitePats) { int c, i, *nStates; BitsLong *pL, *pR, *pP, *pA, x; CLFlt *treeLength; CLFlt *nSitesOfPat; Tree *t; ModelInfo *m; /* Find model settings */ m = &modelSettings[division]; /* Get tree */ t = GetTree(m->brlens,chain,state[chain]); /* Allocate space for parsimony tree length */ treeLength = (CLFlt *) SafeCalloc (m->numChars, sizeof (CLFlt)); /* Get number of states */ nStates = m->nStates; /* Get number of sites of pat */ nSitesOfPat = numSitesOfPat + (whichSitePats*numCompressedChars) + m->compCharStart; /* Make downpass node by node; do not skip any nodes */ for (i=0; inIntNodes; i++) { p = t->intDownPass[i]; /* flip space */ FlipCondLikeSpace(m, chain, p->index); /* find parsimony sets for the node and its environment */ pL = m->parsSets[m->condLikeIndex[chain][p->left->index ]]; pR = m->parsSets[m->condLikeIndex[chain][p->right->index]]; pP = m->parsSets[m->condLikeIndex[chain][p->index ]]; if (t->isRooted == NO && p->anc->anc == NULL) { pA = m->parsSets[m->condLikeIndex[chain][p->anc->index]]; for (c=0; cnumChars; c++) { x = pL[c] & pR[c]; if (x == 0) { x = pL[c] | pR[c]; treeLength[c] += nSitesOfPat[c]; } if ((x & pA[c]) == 0) treeLength[c] += nSitesOfPat[c]; pP[c] = x; } } else { for (c=0; cnumChars; c++) { x = pL[c] & pR[c]; if (x == 0) { x = pL[c] | pR[c]; treeLength[c] += nSitesOfPat[c]; } pP[c] = x; } } } /* Calculate the likelihood one character at a time */ *lnL = 0.0; for (c=0; cnumChars; c++) { *lnL -= ((treeLength[c] + nSitesOfPat[c]) * log (nStates[c])); } /* Free space for parsimony character states */ free (treeLength); return (NO_ERROR); } #if defined(BEAGLE_V3_ENABLED) /*----------------------------------------------------------------- | | LaunchLogLikeForBeagleMultiPartition: calculate the log likelihood of the | new state of the chain for all divisions with Beagle | -----------------------------------------------------------------*/ void LaunchLogLikeForBeagleMultiPartition(int chain, MrBFlt* lnL) { int d, divisionCount; int *divisions; ModelInfo *m; divisions = (int *) SafeCalloc (numCurrentDivisions, sizeof(int)); divisionCount = 0; /* Cycle through divisions and recalculate tis and cond likes as necessary. */ /* Code below does not try to avoid recalculating ti probs for divisions */ /* that could share ti probs with other divisions. */ for (d=0; dupDateCl == YES) { if (m->upDateCijk == YES) { if (UpDateCijk(d, chain) == ERROR) { (*lnL) = MRBFLT_NEG_MAX; /* effectively abort the move */ continue; } m->upDateAll = YES; } divisions[divisionCount++] = d; #if defined (DEBUG_MB_BEAGLE_MULTIPART) printf("divisions[%d] = %d\n", divisionCount-1, d); #endif } } LaunchBEAGLELogLikeMultiPartition(divisions, divisionCount, chain, lnL); if (divisionCount != numCurrentDivisions) { for (d=0; dupDateCl == NO) { /* add log likelihood of divisions that were not updated */ (*lnL) += m->lnLike[2*chain + state[chain]]; } } } free(divisions); return; } #endif /* BEAGLE_MULTI_PART_ENABLED */ /*----------------------------------------------------------------- | | LaunchLogLikeForDivision: calculate the log likelihood of the | new state of the chain for a single division | -----------------------------------------------------------------*/ void LaunchLogLikeForDivision(int chain, int d, MrBFlt* lnL) { int i; TreeNode *p; ModelInfo *m; Tree *tree; # if defined (TIMING_ANALIZ) clock_t CPUTimeStart; # endif m = &modelSettings[d]; tree = GetTree(m->brlens, chain, state[chain]); if (m->upDateCijk == YES) { if (UpDateCijk(d, chain)== ERROR) { (*lnL) = MRBFLT_NEG_MAX; /* effectively abort the move */ return; } m->upDateAll = YES; } # if defined (BEAGLE_ENABLED) if (m->useBeagle == YES) { LaunchBEAGLELogLikeForDivision(chain, d, m, tree, lnL); return; } # endif if (m->dataType == CONTINUOUS) { /* pass over tree */ for (i=0; inNodes; i++) { p = tree->allDownPass[i]; /* update the variances */ if (p->anc != NULL && (p->upDateTi == YES || p->upDateCl == YES)) { BMVar_Cont (p, d, chain); } if (p->right != NULL && p->upDateCl == YES) // internal node { if (tree->isRooted == NO && p->anc->anc == NULL) { TIME(m->CondLikeRoot (p, d, chain), CPUCondLikeRoot); } else { TIME(m->CondLikeDown (p, d, chain), CPUCondLikeDown); } } } } else if (m->parsModelId == NO) { /* get site scalers ready */ FlipSiteScalerSpace(m, chain); if (m->upDateAll == YES) ResetSiteScalers(m, chain); else CopySiteScalers(m, chain); /* pass over tree */ for (i=0; inIntNodes; i++) { p = tree->intDownPass[i]; if (p->left->upDateTi == YES) { /* shift state of ti probs for node */ FlipTiProbsSpace (m, chain, p->left->index); m->TiProbs (p->left, d, chain); } if (p->right->upDateTi == YES) { /* shift state of ti probs for node */ FlipTiProbsSpace (m, chain, p->right->index); m->TiProbs (p->right, d, chain); } if (tree->isRooted == NO && p->anc->anc == NULL /* && p->upDateTi == YES */) { /* shift state of ti probs for node (why always upDateTi?) */ FlipTiProbsSpace (m, chain, p->index); m->TiProbs (p, d, chain); } if (p->upDateCl == YES) { if (tree->isRooted == NO) { if (p->anc->anc == NULL) { TIME(m->CondLikeRoot (p, d, chain),CPUCondLikeRoot); } else { TIME(m->CondLikeDown (p, d, chain),CPUCondLikeDown); } } else { TIME(m->CondLikeDown (p, d, chain),CPUCondLikeDown); } if (m->unscaledNodes[chain][p->index] == 0 && m->upDateAll == NO) { #if defined (SSE_ENABLED) if (m->useVec == VEC_SSE) { TIME(RemoveNodeScalers_SSE (p, d, chain),CPUScalersRemove); } #if defined (AVX_ENABLED) else if (m->useVec == VEC_AVX) { TIME(RemoveNodeScalers_AVX (p, d, chain),CPUScalersRemove); } #endif else { TIME(RemoveNodeScalers (p, d, chain),CPUScalersRemove); } # else TIME(RemoveNodeScalers (p, d, chain),CPUScalersRemove); # endif } FlipNodeScalerSpace (m, chain, p->index); m->unscaledNodes[chain][p->index] = 1 + m->unscaledNodes[chain][p->left->index] + m->unscaledNodes[chain][p->right->index]; if (m->unscaledNodes[chain][p->index] >= m->rescaleFreq[chain] && p->anc->anc != NULL) { TIME(m->CondLikeScaler (p, d, chain),CPUScalers); } } } } /* call likelihood function to summarize result */ TIME(m->Likelihood (tree->root->left, d, chain, lnL, (chainId[chain] % chainParams.numChains)),CPULilklihood); return; } /*---------------------------------------------------------------- | | RemoveNodeScalers: Remove node scalers | -----------------------------------------------------------------*/ int RemoveNodeScalers (TreeNode *p, int division, int chain) { int c; CLFlt *scP, *lnScaler; ModelInfo *m; m = &modelSettings[division]; assert (m->unscaledNodes[chain][p->index] == 0); /* find scalers */ scP = m->scalers[m->nodeScalerIndex[chain][p->index]]; /* find site scalers */ lnScaler = m->scalers[m->siteScalerIndex[chain]]; /* remove scalers */ for (c=0; cnumChars; c++) lnScaler[c] -= scP[c]; return NO_ERROR; } #if defined (AVX_ENABLED) /*---------------------------------------------------------------- | | RemoveNodeScalers_AVX: Remove node scalers, AVX code | -----------------------------------------------------------------*/ int RemoveNodeScalers_AVX (TreeNode *p, int division, int chain) { int c; __m256 *scP_AVX, *lnScaler_AVX; ModelInfo *m; m = &modelSettings[division]; assert (m->unscaledNodes[chain][p->index] == 0); /* find scalers */ scP_AVX = (__m256*)(m->scalers[m->nodeScalerIndex[chain][p->index]]); /* find site scalers */ lnScaler_AVX = (__m256*)(m->scalers[m->siteScalerIndex[chain]]); /* remove scalers */ for (c=0; cnumVecChars; c++) { lnScaler_AVX[c] = _mm256_sub_ps(lnScaler_AVX[c], scP_AVX[c]); } return NO_ERROR; } #endif #if defined (SSE_ENABLED) /*---------------------------------------------------------------- | | RemoveNodeScalers_SSE: Remove node scalers, SSE code | -----------------------------------------------------------------*/ int RemoveNodeScalers_SSE (TreeNode *p, int division, int chain) { int c; __m128 *scP_SSE, *lnScaler_SSE; ModelInfo *m; m = &modelSettings[division]; assert (m->unscaledNodes[chain][p->index] == 0); /* find scalers */ scP_SSE = (__m128*)(m->scalers[m->nodeScalerIndex[chain][p->index]]); /* find site scalers */ lnScaler_SSE = (__m128*)(m->scalers[m->siteScalerIndex[chain]]); /* remove scalers */ for (c=0; cnumVecChars; c++) { lnScaler_SSE[c] = _mm_sub_ps(lnScaler_SSE[c], scP_SSE[c]); } return NO_ERROR; } #endif /* CopySiteScalers: Copy site scalers from scratch space into current space */ void CopySiteScalers (ModelInfo *m, int chain) { CLFlt *from, *to; # if defined (BEAGLE_ENABLED) int i, j; # endif # if defined (BEAGLE_ENABLED) if (m->useBeagle == YES) { j = m->siteScalerScratchIndex; for (i=0; inCijkParts; i++) { if (m->useBeagleMultiPartitions == NO) { beagleResetScaleFactors (m->beagleInstance, m->siteScalerIndex[chain] + i); beagleAccumulateScaleFactors (m->beagleInstance, &j, 1, m->siteScalerIndex[chain] + i); } else # if defined (BEAGLE_V3_ENABLED) { beagleResetScaleFactorsByPartition (m->beagleInstance, m->siteScalerIndex[chain] + i, m->divisionIndex); beagleAccumulateScaleFactorsByPartition (m->beagleInstance, &j, 1, m->siteScalerIndex[chain] + i, m->divisionIndex); } # endif /* BEAGLE_V3_ENABLED */ j++; } return; } # endif from = m->scalers[m->siteScalerScratchIndex]; to = m->scalers[m->siteScalerIndex[chain]]; memcpy ((void*) to, (void*) from, (size_t)(m->numChars) * sizeof(CLFlt)); } /*---------------------------------------------------------------------- | | ResetSiteScalers: Set log site scalers to 0.0. | ------------------------------------------------------------------------*/ void ResetSiteScalers (ModelInfo *m, int chain) { int c; CLFlt *lnScaler; #if defined (BEAGLE_ENABLED) if (m->useBeagle == YES) { if (m->useBeagleMultiPartitions == NO) beagleResetScaleFactors(m->beagleInstance, m->siteScalerIndex[chain]); else # if defined (BEAGLE_V3_ENABLED) beagleResetScaleFactorsByPartition(m->beagleInstance, m->siteScalerIndex[chain], m->divisionIndex); # endif /* BEAGLE_V3_ENABLED */ /* TODO: check if nCijkParts scale factors should also be reset here */ return; } #endif lnScaler = m->scalers[m->siteScalerIndex[chain]]; for (c=0; cnumChars; c++) lnScaler[c] = 0.0; } int SetBinaryQMatrix (MrBFlt **a, int whichChain, int division) { MrBFlt scaler, *bs; ModelInfo *m; /* set up pointers to the appropriate model information */ m = &modelSettings[division]; assert (m->numModelStates == 2); bs = GetParamSubVals (m->stateFreq, whichChain, state[whichChain]); scaler = 1.0 / (2*bs[0]*bs[1]); a[0][0]= -bs[1]*scaler; a[0][1]= bs[1]*scaler; a[1][0]= bs[0]*scaler; a[1][1]= -bs[0]*scaler; return (NO_ERROR); } int SetNucQMatrix (MrBFlt **a, int n, int whichChain, int division, MrBFlt rateMult, MrBFlt *rA, MrBFlt *rS) { register int i, j, k; int isTransition=0, nDiff, rtNum=0; MrBFlt scaler, mult=0.0, probOn, sum, *swr, s01, s10, s[4][4], nonsyn, *rateValues=NULL, *bs, dN, dS; ModelInfo *m; ModelParams *mp; # if defined BEAGLE_ENABLED MrBFlt trans; # endif /* set up pointers to the appropriate model information */ mp = &modelParams[division]; m = &modelSettings[division]; assert (m->numModelStates == n); /* All of the models that are set up in this function require the frequencies of the nucleotides (or doublets or codons). They will also require either a transition/transversion rate ratio or the GTR rate parameters. The "rateValues" will either be rateValues[0] = transition/transversion rate (kappa) for nst=2 models or rateValues[0] = A <-> C rate rateValues[1] = A <-> G rate rateValues[2] = A <-> T rate rateValues[3] = C <-> G rate rateValues[4] = C <-> T rate rateValues[5] = G <-> T rate for nst=6 models. */ bs = GetParamSubVals (m->stateFreq, whichChain, state[whichChain]); if (m->nst == 2) { rateValues = GetParamVals(m->tRatio, whichChain, state[whichChain]); # if defined (BEAGLE_ENABLED) /* transversions assumed to have rate 1.0; */ trans = rateValues[0]; if (m->numModelStates == 4) /* code to satisfy Beagle */ { rateValues = (MrBFlt *) SafeCalloc (6, sizeof(MrBFlt)); rateValues[0] = rateValues[2] = rateValues[3] = rateValues[5] =1.0; /* Setting transversions */ rateValues[1] = rateValues[4] = trans; /* Setting transitions */ } # endif } else if (m->nst == 6 || m->nst == NST_MIXED) rateValues = GetParamVals(m->revMat, whichChain, state[whichChain]); # if defined (BEAGLE_ENABLED) else if (m->nst == 1 && m->numModelStates == 4) /* code to satisfy Beagle */ { rateValues = (MrBFlt *) SafeCalloc (6, sizeof(MrBFlt)); for (i=0; i<6; i++) rateValues[i] = 1.0; } # endif if (n == 4) { /* 4 X 4 model: Here, we set the rate matrix for the GTR model (Tavare, 1986). We need not only the 6 rates for this model (rateValues), but also the base frequencies (bs). */ /* set diagonal of Q matrix to 0 */ for (i=0; i<4; i++) a[i][i] = 0.0; /* initialize Q matrix */ scaler = 0.0; for (i=0; i<4; i++) { for (j=i+1; j<4; j++) { if (i == 0 && j == 1) mult = rateValues[0]; else if (i == 0 && j == 2) mult = rateValues[1]; else if (i == 0 && j == 3) mult = rateValues[2]; else if (i == 1 && j == 2) mult = rateValues[3]; else if (i == 1 && j == 3) mult = rateValues[4]; else if (i == 2 && j == 3) mult = rateValues[5]; a[i][i] -= (a[i][j] = bs[j] * mult); a[j][j] -= (a[j][i] = bs[i] * mult); scaler += bs[i] * a[i][j]; scaler += bs[j] * a[j][i]; } } /* rescale Q matrix */ scaler = 1.0 / scaler; for (i=0; i<4; i++) for (j=0; j<4; j++) a[i][j] *= scaler; } else if (n == 8) /* we have a 4 X 4 covarion model */ { /* 8 X 8 covarion model: Here, we set the rate matrix for the covarion model (Tuffley and Steel, 1997). We need the rate parameters of the model (contained in rateValues), the frequencies of the four nucleotides, and the switching rates to completely specify the rate matrix. We first set up the 4 X 4 submatrix that represents changes (the upper left portion of the 8 X 8 matrix). Note that if we have rate variation across sites, that we need to deal with the multiplication in the rate matrix (i.e., we cannot simply deal with rate variation by multiplying the branch length by a rate multiplier as we can with other models). Instead, we multiply the scaled rate matrix by the rate multiplier. */ /* Get the switching rates. The rate of off->on is s01 and the rate of on->off is s10. The stationary probability of the switch process is prob1 = s01/(s01+s10) and prob0 = s10/(s01+s10). */ swr = GetParamVals (m->switchRates, whichChain, state[whichChain]); s01 = swr[0]; s10 = swr[1]; probOn = s01 / (s01 + s10); /* set matrix a to 0 */ for (i=0; i<8; i++) for (j=0; j<8; j++) a[i][j] = 0.0; /* set up the 4 X 4 matrix representing substitutions (s[][]; upper left) */ if (m->nst == 1) { scaler = 0.0; for (i=0; i<4; i++) { for (j=i+1; j<4; j++) { s[i][j] = bs[j]; s[j][i] = bs[i]; scaler += bs[i] * s[i][j] * probOn; scaler += bs[j] * s[j][i] * probOn; } } } else if (m->nst == 2) { scaler = 0.0; for (i=0; i<4; i++) { for (j=i+1; j<4; j++) { if ((i == 0 && j == 2) || (i == 2 && j == 0) || (i == 1 && j == 3) || (i == 3 && j == 1)) mult = rateValues[0]; else mult = 1.0; s[i][j] = bs[j] * mult; s[j][i] = bs[i] * mult; scaler += bs[i] * s[i][j] * probOn; scaler += bs[j] * s[j][i] * probOn; } } } else { scaler = 0.0; for (i=0; i<4; i++) { for (j=i+1; j<4; j++) { if (i == 0 && j == 1) mult = rateValues[0]; else if (i == 0 && j == 2) mult = rateValues[1]; else if (i == 0 && j == 3) mult = rateValues[2]; else if (i == 1 && j == 2) mult = rateValues[3]; else if (i == 1 && j == 3) mult = rateValues[4]; else if (i == 2 && j == 3) mult = rateValues[5]; s[i][j] = bs[j] * mult; s[j][i] = bs[i] * mult; scaler += bs[i] * s[i][j] * probOn; scaler += bs[j] * s[j][i] * probOn; } } } /* rescale off diagonal elements of s[][] matrix */ scaler = 1.0 / scaler; for (i=0; i<4; i++) { for (j=0; j<4; j++) { if (i != j) s[i][j] *= scaler; } } /* now, scale s[][] by rate factor */ for (i=0; i<4; i++) { for (j=0; j<4; j++) { if (i != j) s[i][j] *= rateMult; } } /* put in diagonal elements of s[][] */ for (i=0; i<4; i++) { sum = 0.0; for (j=0; j<4; j++) { if (i != j) sum += s[i][j]; } s[i][i] = -(sum + s10); } /* Now, put s[][] into top left portion of a matrix and fill in the other parts of the matrix with the appropriate switching rates. */ for (i=0; i<4; i++) for (j=0; j<4; j++) a[i][j] = s[i][j]; for (i=4; i<8; i++) a[i][i] = -s01; a[0][4] = s10; a[1][5] = s10; a[2][6] = s10; a[3][7] = s10; a[4][0] = s01; a[5][1] = s01; a[6][2] = s01; a[7][3] = s01; # if 0 for (i=0; i<8; i++) { for (j=0; j<8; j++) printf ("%1.10lf ", a[i][j]); printf ("\n"); } for (i=0; i<4; i++) printf ("%lf ", bs[i]); printf ("\n"); printf ("s01 = %lf s10 = %lf pi1 = %lf pi0 = %lf\n", s01, s10, probOn, 1-probOn); # endif } else if (n == 16) { /* 16 X 16 doublet model: We have a doublet model. The states are in the order AA, AC, AG, AT, CA, CC CG, CT, GA, GC, GG, GT, TA, TC, TG, TT. The rate matrix is straight-forward to set up. We simply multiply the rate parameter (e.g., the ti/tv rate ratio) by the doublet frequencies. */ /* set diagonal of Q matrix to 0 */ for (i=0; i<16; i++) a[i][i] = 0.0; if (m->nst == 1) /* F81-like doublet model */ { scaler = 0.0; for (i=0; i<16; i++) { for (j=i+1; j<16; j++) { if (((doublet[i].first & doublet[j].first) == 0) && ((doublet[i].second & doublet[j].second) == 0)) mult = 0.0; else mult = 1.0; a[i][i] -= (a[i][j] = bs[j] * mult); a[j][j] -= (a[j][i] = bs[i] * mult); scaler += bs[i] * a[i][j]; scaler += bs[j] * a[j][i]; } } } else if (m->nst == 2) /* HKY-like doublet model */ { scaler = 0.0; for (i=0; i<16; i++) { for (j=i+1; j<16; j++) { if (((doublet[i].first & doublet[j].first) == 0) && ((doublet[i].second & doublet[j].second) == 0)) mult = 0.0; else { if ((doublet[i].first & doublet[j].first) == 0) { if ((doublet[i].first + doublet[j].first) == 5 || (doublet[i].first + doublet[j].first) == 10) mult = rateValues[0]; else mult = 1.0; } else { if ((doublet[i].second + doublet[j].second) == 5 || (doublet[i].second + doublet[j].second) == 10) mult = rateValues[0]; else mult = 1.0; } } a[i][i] -= (a[i][j] = bs[j] * mult); a[j][j] -= (a[j][i] = bs[i] * mult); scaler += bs[i] * a[i][j]; scaler += bs[j] * a[j][i]; } } } else /* GTR-like doublet model */ { scaler = 0.0; for (i=0; i<16; i++) { for (j=i+1; j<16; j++) { if (((doublet[i].first & doublet[j].first) == 0) && ((doublet[i].second & doublet[j].second) == 0)) mult = 0.0; else { if ((doublet[i].first & doublet[j].first) == 0) { if ((doublet[i].first + doublet[j].first) == 3) mult = rateValues[0]; else if ((doublet[i].first + doublet[j].first) == 5) mult = rateValues[1]; else if ((doublet[i].first + doublet[j].first) == 9) mult = rateValues[2]; else if ((doublet[i].first + doublet[j].first) == 6) mult = rateValues[3]; else if ((doublet[i].first + doublet[j].first) == 10) mult = rateValues[4]; else mult = rateValues[5]; } else { if ((doublet[i].second + doublet[j].second) == 3) mult = rateValues[0]; else if ((doublet[i].second + doublet[j].second) == 5) mult = rateValues[1]; else if ((doublet[i].second + doublet[j].second) == 9) mult = rateValues[2]; else if ((doublet[i].second + doublet[j].second) == 6) mult = rateValues[3]; else if ((doublet[i].second + doublet[j].second) == 10) mult = rateValues[4]; else mult = rateValues[5]; } } a[i][i] -= (a[i][j] = bs[j] * mult); a[j][j] -= (a[j][i] = bs[i] * mult); scaler += bs[i] * a[i][j]; scaler += bs[j] * a[j][i]; } } } /* rescale Q matrix */ scaler = 1.0 / scaler; for (i=0; i<16; i++) for (j=0; j<16; j++) a[i][j] *= scaler; } else { /* 64(ish) X 64(ish) codon model: Here, we set the rate matrix for the codon model (see Goldman and Yang, 1994). Note that we can specify any general type of codon model, with these constraints: a[i][j] = 0 -> if i and j differ at 2 or 3 nucleotides a[i][j] = rateValues[0] * bs[j] -> if synonymous A <-> C change a[i][j] = rateValues[1] * bs[j] -> if synonymous A <-> G change a[i][j] = rateValues[2] * bs[j] -> if synonymous A <-> T change a[i][j] = rateValues[3] * bs[j] -> if synonymous C <-> G change a[i][j] = rateValues[4] * bs[j] -> if synonymous C <-> T change a[i][j] = rateValues[5] * bs[j] -> if synonymous G <-> T change a[i][j] = rateValues[0] * nonsyn * bs[j] -> if nonsynonymous A <-> C change a[i][j] = rateValues[1] * nonsyn * bs[j] -> if nonsynonymous A <-> G change a[i][j] = rateValues[2] * nonsyn * bs[j] -> if nonsynonymous A <-> T change a[i][j] = rateValues[3] * nonsyn * bs[j] -> if nonsynonymous C <-> G change a[i][j] = rateValues[4] * nonsyn * bs[j] -> if nonsynonymous C <-> T change a[i][j] = rateValues[5] * nonsyn * bs[j] -> if nonsynonymous G <-> T change Other models, such as the one used by Nielsen & Yang (1998) can be obtained from this model by restricting transitions and transversions to have the same rate. nonsyn is the nonsynonymous/synonymous rate ratio (often called the dN/dS ratio). If we are in this part of the function, then we rely on it being called with the "rateMult" parameter specifying the dN/dS ratio. Note that the size of the matrix will never be 64 X 64 as we only consider changes among coding triplets (i.e., we exclude the stop codons). */ /* get the nonsynonymous/synonymous rate ratio */ nonsyn = rateMult; /* set diagonal of Q matrix to 0 */ for (i=0; inst == 1) /* F81-like codon model */ { scaler = 0.0; for (i=0; icodonNucs[i][k] != mp->codonNucs[j][k]) nDiff++; } if (nDiff > 1) { mult = 0.0; } else { if (mp->codonAAs[i] == mp->codonAAs[j]) mult = 1.0; else mult = nonsyn; } a[i][i] -= (a[i][j] = bs[j] * mult); a[j][j] -= (a[j][i] = bs[i] * mult); if (mp->codonAAs[i] == mp->codonAAs[j]) dS += (bs[i] * a[i][j] + bs[j] * a[j][i]); else dN += (bs[i] * a[i][j] + bs[j] * a[j][i]); scaler += bs[i] * a[i][j]; scaler += bs[j] * a[j][i]; } } } else if (m->nst == 2) /* HKY-like codon model */ { scaler = 0.0; for (i=0; icodonNucs[i][k] != mp->codonNucs[j][k]) { nDiff++; if ((mp->codonNucs[i][k] == 0 && mp->codonNucs[j][k] == 2) || (mp->codonNucs[i][k] == 2 && mp->codonNucs[j][k] == 0) || (mp->codonNucs[i][k] == 1 && mp->codonNucs[j][k] == 3) || (mp->codonNucs[i][k] == 3 && mp->codonNucs[j][k] == 1)) isTransition = YES; else isTransition = NO; } } if (nDiff > 1) { mult = 0.0; } else { if (mp->codonAAs[i] == mp->codonAAs[j]) mult = 1.0; else mult = nonsyn; if (isTransition == YES) mult *= rateValues[0]; } a[i][i] -= (a[i][j] = bs[j] * mult); a[j][j] -= (a[j][i] = bs[i] * mult); if (mp->codonAAs[i] == mp->codonAAs[j]) dS += (bs[i] * a[i][j] + bs[j] * a[j][i]); else dN += (bs[i] * a[i][j] + bs[j] * a[j][i]); scaler += bs[i] * a[i][j]; scaler += bs[j] * a[j][i]; } } } else /* GTR-like codon model */ { scaler = 0.0; for (i=0; icodonNucs[i][k] != mp->codonNucs[j][k]) { nDiff++; if ((mp->codonNucs[i][k] == 0 && mp->codonNucs[j][k] == 1) || (mp->codonNucs[i][k] == 1 && mp->codonNucs[j][k] == 0)) rtNum = 0; else if ((mp->codonNucs[i][k] == 0 && mp->codonNucs[j][k] == 2) || (mp->codonNucs[i][k] == 2 && mp->codonNucs[j][k] == 0)) rtNum = 1; else if ((mp->codonNucs[i][k] == 0 && mp->codonNucs[j][k] == 3) || (mp->codonNucs[i][k] == 3 && mp->codonNucs[j][k] == 0)) rtNum = 2; else if ((mp->codonNucs[i][k] == 1 && mp->codonNucs[j][k] == 2) || (mp->codonNucs[i][k] == 2 && mp->codonNucs[j][k] == 1)) rtNum = 3; else if ((mp->codonNucs[i][k] == 1 && mp->codonNucs[j][k] == 3) || (mp->codonNucs[i][k] == 3 && mp->codonNucs[j][k] == 1)) rtNum = 4; else rtNum = 5; } } if (nDiff > 1) { mult = 0.0; } else { if (mp->codonAAs[i] == mp->codonAAs[j]) mult = 1.0; else mult = nonsyn; if (rtNum == 0) mult *= rateValues[0]; else if (rtNum == 1) mult *= rateValues[1]; else if (rtNum == 2) mult *= rateValues[2]; else if (rtNum == 3) mult *= rateValues[3]; else if (rtNum == 4) mult *= rateValues[4]; else mult *= rateValues[5]; } a[i][i] -= (a[i][j] = bs[j] * mult); a[j][j] -= (a[j][i] = bs[i] * mult); if (mp->codonAAs[i] == mp->codonAAs[j]) dS += (bs[i] * a[i][j] + bs[j] * a[j][i]); else dN += (bs[i] * a[i][j] + bs[j] * a[j][i]); scaler += bs[i] * a[i][j]; scaler += bs[j] * a[j][i]; } } } /* rescale Q matrix */ if (m->nucModelId == NUCMODEL_CODON && m->numOmegaCats > 1) { /* If we have a positive selection model with multiple categories, then we do not rescale the rate matrix until we have finished generating all of the necessary rate matrices. The rescaling occurrs in UpDateCijk. */ (*rA) = dN; (*rS) = dS; } else { scaler = 1.0 / scaler; for (i=0; inst == 1 || m->nst == 2) && m->numModelStates == 4) free (rateValues); # endif return (NO_ERROR); } int SetProteinQMatrix (MrBFlt **a, int n, int whichChain, int division, MrBFlt rateMult) { register int i, j, k; int aaModelID; MrBFlt scaler, probOn, sum, *swr, s01, s10, *bs, *rt; ModelInfo *m; /* set up pointers to the appropriate model information */ m = &modelSettings[division]; /* get amino acid model ID AAMODEL_POISSON 0 AAMODEL_JONES 1 AAMODEL_DAY 2 AAMODEL_MTREV 3 AAMODEL_MTMAM 4 AAMODEL_WAG 5 AAMODEL_RTREV 6 AAMODEL_CPREV 7 AAMODEL_VT 8 AAMODEL_BLOSUM 9 AAMODEL_LG 10 AAMODEL_EQ 11 AAMODEL_GTR 12 */ if (m->aaModelId >= 0) aaModelID = m->aaModelId; else aaModelID = (int)*GetParamVals(m->aaModel, whichChain, state[whichChain]); /* Make certain that we have either 20 or 40 states. Anything else means we have a real problem. */ if (n != 20 && n != 40) { MrBayesPrint ("%s ERROR: There should be 20 or 40 states for the aa model\n"); return (ERROR); } if (n == 20) { /* We have a run-of-the-mill amino acid model (i.e., 20 X 20). */ if (aaModelID == AAMODEL_POISSON) { scaler = 1.0 / 19.0; for (i=0; i<20; i++) { for (j=i+1; j<20; j++) { a[i][j] = scaler; a[j][i] = scaler; } } for (i=0; i<20; i++) a[i][i] = -1.0; } else if (aaModelID == AAMODEL_EQ) { bs = GetParamSubVals (m->stateFreq, whichChain, state[whichChain]); for (i=0; i<20; i++) for (j=0; j<20; j++) a[i][j] = 0.0; scaler = 0.0; for (i=0; i<20; i++) { for (j=i+1; j<20; j++) { a[i][i] -= (a[i][j] = bs[j]); a[j][j] -= (a[j][i] = bs[i]); scaler += bs[i] * a[i][j]; scaler += bs[j] * a[j][i]; } } scaler = 1.0 / scaler; for (i=0; i<20; i++) for (j=0; j<20; j++) a[i][j] *= scaler; } else if (aaModelID == AAMODEL_GTR) { bs = GetParamSubVals (m->stateFreq, whichChain, state[whichChain]); rt = GetParamVals (m->revMat, whichChain, state[whichChain]); for (i=0; i<20; i++) for (j=0; j<20; j++) a[i][j] = 0.0; scaler = 0.0; for (i=k=0; i<20; i++) { for (j=i+1; j<20; j++) { a[i][i] -= (a[i][j] = bs[j] * rt[k]); a[j][j] -= (a[j][i] = bs[i] * rt[k]); k++; } } for (i=0; i<20; i++) scaler += -(bs[i] * a[i][i]); for (i=0; i<20; i++) for (j=0; j<20; j++) a[i][j] /= scaler; } else if (aaModelID == AAMODEL_JONES) { for (i=0; i<20; i++) for (j=0; j<20; j++) a[i][j] = aaJones[i][j]; } else if (aaModelID == AAMODEL_DAY) { for (i=0; i<20; i++) for (j=0; j<20; j++) a[i][j] = aaDayhoff[i][j]; } else if (aaModelID == AAMODEL_MTREV) { for (i=0; i<20; i++) for (j=0; j<20; j++) a[i][j] = aaMtrev24[i][j]; } else if (aaModelID == AAMODEL_MTMAM) { for (i=0; i<20; i++) for (j=0; j<20; j++) a[i][j] = aaMtmam[i][j]; } else if (aaModelID == AAMODEL_RTREV) { for (i=0; i<20; i++) for (j=0; j<20; j++) a[i][j] = aartREV[i][j]; } else if (aaModelID == AAMODEL_WAG) { for (i=0; i<20; i++) for (j=0; j<20; j++) a[i][j] = aaWAG[i][j]; } else if (aaModelID == AAMODEL_CPREV) { for (i=0; i<20; i++) for (j=0; j<20; j++) a[i][j] = aacpREV[i][j]; } else if (aaModelID == AAMODEL_VT) { for (i=0; i<20; i++) for (j=0; j<20; j++) a[i][j] = aaVt[i][j]; } else if (aaModelID == AAMODEL_BLOSUM) { for (i=0; i<20; i++) for (j=0; j<20; j++) a[i][j] = aaBlosum[i][j]; } else if (aaModelID == AAMODEL_LG) { for (i=0; i<20; i++) for (j=0; j<20; j++) a[i][j] = aaLG[i][j]; } else { MrBayesPrint ("%s ERROR: Don't understand which amino acid model is needed\n"); return (ERROR); } # if 0 for (i=0; i<20; i++) { for (j=0; j<20; j++) printf ("%1.3lf ", a[i][j]); printf ("\n"); } # endif } else { /* 40 X 40 covarion model: We have a covarion model, and must set up the other quadrants. Note that if we are at this point in the code, that we have already set up the upper left portion of the 40 X 40 rate matrix. Note that if we have rate variation across sites, that we need to deal with the multiplication in the rate matrix (i.e., we cannot simply deal with rate variation by multiplying the branch length by a rate multiplier as we can with other models). Instead, we multiply the scaled rate matrix by the rate multiplier. */ /* Get the switching rates. The rate of off->on is s01 and the rate of on->off is s10. The stationary probability of the switch process is prob1 = s01/(s01+s10) and prob0 = s10/(s01+s10). */ swr = GetParamVals (m->switchRates, whichChain, state[whichChain]); s01 = swr[0]; s10 = swr[1]; probOn = s01 / (s01 + s10); /* set matrix a[][] to 0 */ for (i=0; i<40; i++) for (j=0; j<40; j++) a[i][j] = 0.0; /* fill in upper-left sub matrix (where substitutions occur */ if (aaModelID == AAMODEL_POISSON) { scaler = 0.0; for (i=0; i<20; i++) { for (j=i+1; j<20; j++) { a[i][j] = 0.05; a[j][i] = 0.05; scaler += 0.05 * a[i][j] * probOn; scaler += 0.05 * a[j][i] * probOn; } } } else if (aaModelID == AAMODEL_EQ) { bs = GetParamSubVals (m->stateFreq, whichChain, state[whichChain]); scaler = 0.0; for (i=0; i<20; i++) { for (j=i+1; j<20; j++) { a[i][j] = bs[j]; a[j][i] = bs[i]; scaler += bs[i] * a[i][j] * probOn; scaler += bs[j] * a[j][i] * probOn; } } } else if (aaModelID == AAMODEL_GTR) { bs = GetParamSubVals (m->stateFreq, whichChain, state[whichChain]); rt = GetParamVals (m->revMat, whichChain, state[whichChain]); for (i=0; i<20; i++) for (j=0; j<20; j++) a[i][j] = 0.0; scaler = 0.0; for (i=k=0; i<20; i++) { for (j=i+1; j<20; j++) { a[i][i] -= (a[i][j] = bs[j] * rt[k]); a[j][j] -= (a[j][i] = bs[i] * rt[k]); k++; } } for (i=0; i<20; i++) scaler += -(bs[i] * a[i][i]); for (i=0; i<20; i++) for (j=0; j<20; j++) a[i][j] /= scaler; for (i=0; i<20; i++) { for (j=i+1; j<20; j++) { a[i][j] = bs[j]; a[j][i] = bs[i]; scaler += bs[i] * a[i][j] * probOn; scaler += bs[j] * a[j][i] * probOn; } } } else if (aaModelID == AAMODEL_JONES) { scaler = 0.0; for (i=0; i<20; i++) { for (j=i+1; j<20; j++) { a[i][j] = aaJones[i][j]; a[j][i] = aaJones[j][i]; scaler += jonesPi[i] * a[i][j] * probOn; scaler += jonesPi[j] * a[j][i] * probOn; } } } else if (aaModelID == AAMODEL_DAY) { scaler = 0.0; for (i=0; i<20; i++) { for (j=i+1; j<20; j++) { a[i][j] = aaDayhoff[i][j]; a[j][i] = aaDayhoff[j][i]; scaler += dayhoffPi[i] * a[i][j] * probOn; scaler += dayhoffPi[j] * a[j][i] * probOn; } } } else if (aaModelID == AAMODEL_MTREV) { scaler = 0.0; for (i=0; i<20; i++) { for (j=i+1; j<20; j++) { a[i][j] = aaMtrev24[i][j]; a[j][i] = aaMtrev24[j][i]; scaler += mtrev24Pi[i] * a[i][j] * probOn; scaler += mtrev24Pi[j] * a[j][i] * probOn; } } } else if (aaModelID == AAMODEL_MTMAM) { scaler = 0.0; for (i=0; i<20; i++) { for (j=i+1; j<20; j++) { a[i][j] = aaMtmam[i][j]; a[j][i] = aaMtmam[j][i]; scaler += mtmamPi[i] * a[i][j] * probOn; scaler += mtmamPi[j] * a[j][i] * probOn; } } } else if (aaModelID == AAMODEL_RTREV) { scaler = 0.0; for (i=0; i<20; i++) { for (j=i+1; j<20; j++) { a[i][j] = aartREV[i][j]; a[j][i] = aartREV[j][i]; scaler += rtrevPi[i] * a[i][j] * probOn; scaler += rtrevPi[j] * a[j][i] * probOn; } } } else if (aaModelID == AAMODEL_WAG) { scaler = 0.0; for (i=0; i<20; i++) { for (j=i+1; j<20; j++) { a[i][j] = aaWAG[i][j]; a[j][i] = aaWAG[j][i]; scaler += wagPi[i] * a[i][j] * probOn; scaler += wagPi[j] * a[j][i] * probOn; } } } else if (aaModelID == AAMODEL_CPREV) { scaler = 0.0; for (i=0; i<20; i++) { for (j=i+1; j<20; j++) { a[i][j] = aacpREV[i][j]; a[j][i] = aacpREV[j][i]; scaler += cprevPi[i] * a[i][j] * probOn; scaler += cprevPi[j] * a[j][i] * probOn; } } } else if (aaModelID == AAMODEL_VT) { scaler = 0.0; for (i=0; i<20; i++) { for (j=i+1; j<20; j++) { a[i][j] = aaVt[i][j]; a[j][i] = aaVt[j][i]; scaler += vtPi[i] * a[i][j] * probOn; scaler += vtPi[j] * a[j][i] * probOn; } } } else if (aaModelID == AAMODEL_BLOSUM) { scaler = 0.0; for (i=0; i<20; i++) { for (j=i+1; j<20; j++) { a[i][j] = aaBlosum[i][j]; a[j][i] = aaBlosum[j][i]; scaler += blosPi[i] * a[i][j] * probOn; scaler += blosPi[j] * a[j][i] * probOn; } } } else if (aaModelID == AAMODEL_LG) { scaler = 0.0; for (i=0; i<20; i++) { for (j=i+1; j<20; j++) { a[i][j] = aaLG[i][j]; a[j][i] = aaLG[j][i]; scaler += lgPi[i] * a[i][j] * probOn; scaler += lgPi[j] * a[j][i] * probOn; } } } else { MrBayesPrint ("%s ERROR: Don't understand which amino acid model is needed\n"); return (ERROR); } /* rescale off diagonal elements of Q matrix */ scaler = 1.0 / scaler; for (i=0; i<20; i++) { for (j=0; j<20; j++) { if (i != j) a[i][j] *= scaler; } } /* now, scale by rate factor */ for (i=0; i<20; i++) { for (j=0; j<20; j++) { if (i != j) a[i][j] *= rateMult; } } /* put in diagonal elements */ for (i=0; i<20; i++) { sum = 0.0; for (j=0; j<20; j++) { if (i != j) sum += a[i][j]; a[i][i] = -(sum + s10); } } /* fill in the other three submatrices */ for (i=20; i<40; i++) a[i][i] = -s01; for (i=0; i<20; i++) { a[i][20+i] = s10; a[20+i][i] = s01; } } return (NO_ERROR); } int SetStdQMatrix (MrBFlt **a, int nStates, MrBFlt *bs, int cType) { register int i, j; MrBFlt scaler; /* This function sets up ordered or unordered models for standard characters with unequal stationary state frequencies. It requires the stationary frequencies of the states (passed when calling the function). It also needs to know the number of states and the type (ordered or unordered) of the character. */ /* set Q matrix to 0 */ for (i=0; itiProbs[m->tiProbsIndex[chain][p->index]]; /* get base frequencies */ pis = GetParamSubVals (m->stateFreq, chain, state[chain]); /* get base rate */ baseRate = GetRate (division, chain); /* compensate for invariable sites if appropriate */ if (m->pInvar != NULL) baseRate /= (1.0 - (*GetParamVals(m->pInvar, chain, state[chain]))); /* get category rates */ theRate = 1.0; if (m->shape != NULL) catRate = GetParamSubVals (m->shape, chain, state[chain]); else if (m->mixtureRates != NULL) catRate = GetParamSubVals (m->mixtureRates, chain, state[chain]); else catRate = &theRate; /* rescale beta */ beta = (0.5 / ((pis[0] + pis[2])*(pis[1] + pis[3]) + ((pis[0]*pis[2]) + (pis[1]*pis[3])))); bigPi_j[0] = (pis[0] + pis[2]); bigPi_j[1] = (pis[1] + pis[3]); bigPi_j[2] = (pis[0] + pis[2]); bigPi_j[3] = (pis[1] + pis[3]); /* find length */ if (m->cppEvents != NULL) { length = GetParamSubVals (m->cppEvents, chain, state[chain])[p->index]; } else if (m->tk02BranchRates != NULL) { length = GetParamSubVals (m->tk02BranchRates, chain, state[chain])[p->index]; } else if (m->wnBranchRates != NULL) { length = GetParamSubVals (m->wnBranchRates, chain, state[chain])[p->index]; } else if (m->ilnBranchRates != NULL) { length = GetParamSubVals (m->ilnBranchRates, chain, state[chain])[p->index]; } else if (m->igrBranchRates != NULL) { length = GetParamSubVals (m->igrBranchRates, chain, state[chain])[p->index]; } else if (m->mixedBrchRates != NULL) { length = GetParamSubVals (m->mixedBrchRates, chain, state[chain])[p->index]; } else length = p->length; /* numerical errors will ensue if we allow very large or very small branch lengths, which might occur in relaxed clock models */ /* fill in values */ for (k=index=0; knumRateCats; k++) { t = length * baseRate * catRate[k]; if (t < TIME_MIN) { /* Fill in identity matrix */ for (i=0; i<4; i++) { for (j=0; j<4; j++) { if (i == j) tiP[index++] = 1.0; else tiP[index++] = 0.0; } } } else if (t > TIME_MAX) { /* Fill in stationary matrix */ for (i=0; i<4; i++) for (j=0; j<4; j++) tiP[index++] = (CLFlt) pis[j]; } else { /* calculate probabilities */ for (i=0; i<4; i++) { for (j=0; j<4; j++) { bigPij = bigPi_j[j]; pij = pis[j]; u = 1.0/bigPij - 1.0; x = exp(-beta * t); z = (bigPij - pij) / bigPij; if (i == j) tiP[index++] = (CLFlt) (pij + pij * u * x + z * x); else tiP[index++] = (CLFlt) (pij + pij * u * x - (pij/bigPij) * x); } } } } return NO_ERROR; } /*---------------------------------------------------------------- | | TiProbs_Gen: Calculates transition probabilities for general | models with or without rate variation. This function does | not work with: | | 1. codon models with omega variation or | 2. covarion models with rate variation | | In either of these cases, TiProbs_GenCov is used | -----------------------------------------------------------------*/ int TiProbs_Gen (TreeNode *p, int division, int chain) { register int i, j, k, n, s, index; MrBFlt t, *catRate, baseRate, *eigenValues, *cijk, *bs, EigValexp[64], sum, *ptr, theRate, correctionFactor, length; CLFlt *tiP; ModelInfo *m; m = &modelSettings[division]; n = m->numModelStates; /* find the correction factor to make branch lengths in terms of expected number of substitutions per character */ correctionFactor = 1.0; if (m->dataType == DNA || m->dataType == RNA) { if (m->nucModelId == NUCMODEL_DOUBLET) correctionFactor = 2.0; else if (m->nucModelId == NUCMODEL_CODON) correctionFactor = 3.0; } /* find transition probabilities */ tiP = m->tiProbs[m->tiProbsIndex[chain][p->index]]; /* get base rate */ baseRate = GetRate (division, chain); /* compensate for invariable sites if appropriate */ if (m->pInvar != NULL) baseRate /= (1.0 - (*GetParamVals(m->pInvar, chain, state[chain]))); /* get category rates */ theRate = 1.0; if (m->shape != NULL) catRate = GetParamSubVals (m->shape, chain, state[chain]); else if (m->mixtureRates != NULL) catRate = GetParamSubVals (m->mixtureRates, chain, state[chain]); else catRate = &theRate; /* get eigenvalues and cijk pointers */ eigenValues = m->cijks[m->cijkIndex[chain]]; cijk = eigenValues + (2 * n); /* find length */ if (m->cppEvents != NULL) { length = GetParamSubVals (m->cppEvents, chain, state[chain])[p->index]; } else if (m->tk02BranchRates != NULL) { length = GetParamSubVals (m->tk02BranchRates, chain, state[chain])[p->index]; } else if (m->wnBranchRates != NULL) { length = GetParamSubVals (m->wnBranchRates, chain, state[chain])[p->index]; } else if (m->ilnBranchRates != NULL) { length = GetParamSubVals (m->ilnBranchRates, chain, state[chain])[p->index]; } else if (m->igrBranchRates != NULL) { length = GetParamSubVals (m->igrBranchRates, chain, state[chain])[p->index]; } else if (m->mixedBrchRates != NULL) { length = GetParamSubVals (m->mixedBrchRates, chain, state[chain])[p->index]; } else length = p->length; /* fill in values */ for (k=index=0; knumRateCats; k++) { t = length * baseRate * catRate[k] * correctionFactor; if (t < TIME_MIN) { /* Fill in identity matrix */ for (i=0; i TIME_MAX) { /* Get base freq */ bs = GetParamSubVals(m->stateFreq, chain, state[chain]); /* Fill in stationary matrix */ for (i=0; iindex); for (i=index=0; inumModelStates; /* find the correction factor to make branch lengths in terms of expected number of substitutions per character */ correctionFactor = 1.0; if (m->dataType == DNA || m->dataType == RNA) { if (m->nucModelId == NUCMODEL_DOUBLET) correctionFactor = 2.0; else if (m->nucModelId == NUCMODEL_CODON) correctionFactor = 3.0; } /* find transition probabilities */ tiP = m->tiProbs[m->tiProbsIndex[chain][p->index]]; /* get eigenvalues and cijk pointers */ eigenValues = m->cijks[m->cijkIndex[chain]]; cijk = eigenValues + (2 * n); /* get offset size (we need to move the pointers to the appropriate cijk information for these models) */ sizeOfSingleCijk = m->cijkLength / m->nCijkParts; /* find length */ if (m->cppEvents != NULL) { length = GetParamSubVals (m->cppEvents, chain, state[chain])[p->index]; } else if (m->tk02BranchRates != NULL) { length = GetParamSubVals (m->tk02BranchRates, chain, state[chain])[p->index]; } else if (m->wnBranchRates != NULL) { length = GetParamSubVals (m->wnBranchRates, chain, state[chain])[p->index]; } else if (m->ilnBranchRates != NULL) { length = GetParamSubVals (m->ilnBranchRates, chain, state[chain])[p->index]; } else if (m->igrBranchRates != NULL) { length = GetParamSubVals (m->igrBranchRates, chain, state[chain])[p->index]; } else if (m->mixedBrchRates != NULL) { length = GetParamSubVals (m->mixedBrchRates, chain, state[chain])[p->index]; } else length = p->length; /* numerical errors will ensue if we allow very large or very small branch lengths, which might occur in relaxed clock models */ /* fill in values */ for (k=index=0; knCijkParts; k++) { t = length * correctionFactor; if (t < TIME_MIN) { /* Fill in identity matrix */ for (i=0; i TIME_MAX) { /* Get base freq */ bs = GetParamSubVals(m->stateFreq, chain, state[chain]); /* Fill in stationary matrix */ for (i=0; icijkLength */ if (k+1 < m->nCijkParts) { /* shift pointers */ eigenValues += sizeOfSingleCijk; cijk += sizeOfSingleCijk; } } } # if 0 for (i=index=0; itiProbs[m->tiProbsIndex[chain][p->index]]; /* get kappa */ kap = *GetParamVals (m->tRatio, chain, state[chain]); /* get base frequencies */ pis = GetParamSubVals (m->stateFreq, chain, state[chain]); /* get base rate */ baseRate = GetRate (division, chain); /* compensate for invariable sites if appropriate */ if (m->pInvar != NULL) baseRate /= (1.0 - (*GetParamVals(m->pInvar, chain, state[chain]))); /* get category rates */ theRate = 1.0; if (m->shape != NULL) catRate = GetParamSubVals (m->shape, chain, state[chain]); else if (m->mixtureRates != NULL) catRate = GetParamSubVals (m->mixtureRates, chain, state[chain]); else catRate = &theRate; /* rescale beta */ beta = 0.5 / ((pis[0] + pis[2])*(pis[1] + pis[3]) + kap*((pis[0]*pis[2]) + (pis[1]*pis[3]))); bigPi_j[0] = pis[0] + pis[2]; bigPi_j[1] = pis[1] + pis[3]; bigPi_j[2] = pis[0] + pis[2]; bigPi_j[3] = pis[1] + pis[3]; /* find length */ if (m->cppEvents != NULL) { length = GetParamSubVals (m->cppEvents, chain, state[chain])[p->index]; } else if (m->tk02BranchRates != NULL) { length = GetParamSubVals (m->tk02BranchRates, chain, state[chain])[p->index]; } else if (m->wnBranchRates != NULL) { length = GetParamSubVals (m->wnBranchRates, chain, state[chain])[p->index]; } else if (m->ilnBranchRates != NULL) { length = GetParamSubVals (m->ilnBranchRates, chain, state[chain])[p->index]; } else if (m->igrBranchRates != NULL) { length = GetParamSubVals (m->igrBranchRates, chain, state[chain])[p->index]; } else if (m->mixedBrchRates != NULL) { length = GetParamSubVals (m->mixedBrchRates, chain, state[chain])[p->index]; } else length = p->length; /* numerical errors will ensue if we allow very large or very small branch lengths, which might occur in relaxed clock models */ /* fill in values */ for (k=index=0; knumRateCats; k++) { t = length * baseRate * catRate[k]; if (t < TIME_MIN) { /* Fill in identity matrix */ for (i=0; i<4; i++) { for (j=0; j<4; j++) { if (i == j) tiP[index++] = 1.0; else tiP[index++] = 0.0; } } } else if (t > TIME_MAX) { /* Fill in stationary matrix */ for (i=0; i<4; i++) for (j=0; j<4; j++) tiP[index++] = (CLFlt) pis[j]; } else { /* calculate probabilities */ for (i=0; i<4; i++) { for (j=0; j<4; j++) { bigPij = bigPi_j[j]; pij = pis[j]; u = 1.0/bigPij - 1.0; w = -beta * (1.0 + bigPij * (kap - 1.0)); x = exp(-beta * t); y = exp(w * t); z = (bigPij - pij) / bigPij; if (i == j) tiP[index++] = (CLFlt) (pij + pij * u * x + z * y); else if ((i == 0 && j == 2) || (i == 2 && j == 0) || (i == 1 && j == 3) || (i == 3 && j == 1)) tiP[index++] = (CLFlt) (pij + pij * u * x - (pij/bigPij) * y); else tiP[index++] = (CLFlt) (pij * (1.0 - x)); } } } } return NO_ERROR; } /*----------------------------------------------------------------- | | TiProbs_JukesCantor: update transition probabilities for 4by4 | nucleotide model with nst == 1 (Jukes-Cantor) | with or without rate variation | ------------------------------------------------------------------*/ int TiProbs_JukesCantor (TreeNode *p, int division, int chain) { /* calculate Jukes Cantor transition probabilities */ int i, j, k, index; MrBFlt t, *catRate, baseRate, theRate, length; CLFlt pNoChange, pChange, *tiP; ModelInfo *m; m = &modelSettings[division]; /* find transition probabilities */ tiP = m->tiProbs[m->tiProbsIndex[chain][p->index]]; /* get base rate */ baseRate = GetRate (division, chain); /* compensate for invariable sites if appropriate */ if (m->pInvar != NULL) baseRate /= (1.0 - (*GetParamVals(m->pInvar, chain, state[chain]))); /* get category rates */ theRate = 1.0; if (m->shape != NULL) catRate = GetParamSubVals (m->shape, chain, state[chain]); else if (m->mixtureRates != NULL) catRate = GetParamSubVals (m->mixtureRates, chain, state[chain]); else catRate = &theRate; /* find length */ if (m->cppEvents != NULL) { length = GetParamSubVals (m->cppEvents, chain, state[chain])[p->index]; } else if (m->tk02BranchRates != NULL) { length = GetParamSubVals (m->tk02BranchRates, chain, state[chain])[p->index]; } else if (m->wnBranchRates != NULL) { length = GetParamSubVals (m->wnBranchRates, chain, state[chain])[p->index]; } else if (m->ilnBranchRates != NULL) { length = GetParamSubVals (m->ilnBranchRates, chain, state[chain])[p->index]; } else if (m->igrBranchRates != NULL) { length = GetParamSubVals (m->igrBranchRates, chain, state[chain])[p->index]; } else if (m->mixedBrchRates != NULL) { length = GetParamSubVals (m->mixedBrchRates, chain, state[chain])[p->index]; } else length = p->length; /* numerical errors will ensue if we allow very large or very small branch lengths, which might occur in relaxed clock models */ /* fill in values */ for (k=index=0; knumRateCats; k++) { t = length * baseRate * catRate[k]; if (t < TIME_MIN) { /* Fill in identity matrix */ for (i=0; i<4; i++) { for (j=0; j<4; j++) { if (i == j) tiP[index++] = 1.0; else tiP[index++] = 0.0; } } } else if (t > TIME_MAX) { /* Fill in stationary matrix */ for (i=0; i<4; i++) for (j=0; j<4; j++) tiP[index++] = 0.25; } else { /* calculate probabilities */ pChange = (CLFlt) (0.25 - 0.25 * exp(-(4.0/3.0)*t)); pNoChange = (CLFlt) (0.25 + 0.75 * exp(-(4.0/3.0)*t)); for (i=0; i<4; i++) { for (j=0; j<4; j++) { if (i == j) tiP[index++] = pNoChange; else tiP[index++] = pChange; } } } } return NO_ERROR; } /*----------------------------------------------------------------- | | TiProbs_Res: update transition probabilities for binary | restriction site model with or without rate variation | ------------------------------------------------------------------*/ int TiProbs_Res (TreeNode *p, int division, int chain) { int k, index; MrBFlt baseRate, eV, mu, theRate, v, *bs, *catRate, length; CLFlt *tiP; ModelInfo *m; /* find model settings for the division */ m = &modelSettings[division]; /* find transition probabilities */ tiP = m->tiProbs[m->tiProbsIndex[chain][p->index]]; /* get base rate */ baseRate = GetRate (division, chain); /* get category rates */ theRate = 1.0; if (m->shape != NULL) catRate = GetParamSubVals (m->shape, chain, state[chain]); else if (m->mixtureRates != NULL) catRate = GetParamSubVals (m->mixtureRates, chain, state[chain]); else catRate = &theRate; /* find base frequencies */ bs = GetParamSubVals(m->stateFreq, chain, state[chain]); /* calculate scaling factor */ mu = 1.0 / (2.0 * bs[0] * bs[1]); /* find length */ if (m->cppEvents != NULL) { length = GetParamSubVals (m->cppEvents, chain, state[chain])[p->index]; } else if (m->tk02BranchRates != NULL) { length = GetParamSubVals (m->tk02BranchRates, chain, state[chain])[p->index]; } else if (m->wnBranchRates != NULL) { length = GetParamSubVals (m->wnBranchRates, chain, state[chain])[p->index]; } else if (m->ilnBranchRates != NULL) { length = GetParamSubVals (m->ilnBranchRates, chain, state[chain])[p->index]; } else if (m->igrBranchRates != NULL) { length = GetParamSubVals (m->igrBranchRates, chain, state[chain])[p->index]; } else if (m->mixedBrchRates != NULL) { length = GetParamSubVals (m->mixedBrchRates, chain, state[chain])[p->index]; } else length = p->length; /* numerical errors will ensue if we allow very large or very small branch lengths, which might occur in relaxed clock models */ /* fill in values */ for (k=index=0; knumRateCats; k++) { v = length * baseRate * catRate[k]; if (v < TIME_MIN) { /* Fill in identity matrix */ tiP[index++] = (CLFlt) (bs[0] + bs[1]); tiP[index++] = (CLFlt) (bs[1] - bs[1]); tiP[index++] = (CLFlt) (bs[0] - bs[0]); tiP[index++] = (CLFlt) (bs[1] + bs[0]); } else if (v > TIME_MAX) { /* Fill in stationary matrix */ tiP[index++] = (CLFlt) bs[0]; tiP[index++] = (CLFlt) bs[1]; tiP[index++] = (CLFlt) bs[0]; tiP[index++] = (CLFlt) bs[1]; } else { /* calculate probabilities */ eV = exp(-mu * v); tiP[index++] = (CLFlt) (bs[0] + bs[1] * eV); tiP[index++] = (CLFlt) (bs[1] - bs[1] * eV); tiP[index++] = (CLFlt) (bs[0] - bs[0] * eV); tiP[index++] = (CLFlt) (bs[1] + bs[0] * eV); } } return NO_ERROR; } /*----------------------------------------------------------------- | | TiProbs_Std: update transition probabilities for | variable states model with or without rate variation | ------------------------------------------------------------------*/ int TiProbs_Std (TreeNode *p, int division, int chain) { int b, c, i, j, k, n, s, nStates, index=0; MrBFlt v, eV1, *catRate, baseRate, theRate, *eigenValues, *cijk, sum, *bs, mu, length, stdEv[MAX_STD_STATES][MAX_STD_STATES], expEig[MAX_STD_STATES]; CLFlt pNoChange, pChange, *tiP; ModelInfo *m; # if defined (DEBUG_TIPROBS_STD) int index3; # endif m = &modelSettings[division]; /* find transition probabilities */ tiP = m->tiProbs[m->tiProbsIndex[chain][p->index]]; /* get base rate */ baseRate = GetRate (division, chain); /* get category rates */ theRate = 1.0; if (m->shape != NULL) catRate = GetParamSubVals (m->shape, chain, state[chain]); else if (m->mixtureRates != NULL) catRate = GetParamSubVals (m->mixtureRates, chain, state[chain]); else catRate = &theRate; /* find length */ if (m->cppEvents != NULL) { length = GetParamSubVals (m->cppEvents, chain, state[chain])[p->index]; } else if (m->tk02BranchRates != NULL) { length = GetParamSubVals (m->tk02BranchRates, chain, state[chain])[p->index]; } else if (m->wnBranchRates != NULL) { length = GetParamSubVals (m->wnBranchRates, chain, state[chain])[p->index]; } else if (m->ilnBranchRates != NULL) { length = GetParamSubVals (m->ilnBranchRates, chain, state[chain])[p->index]; } else if (m->igrBranchRates != NULL) { length = GetParamSubVals (m->igrBranchRates, chain, state[chain])[p->index]; } else if (m->mixedBrchRates != NULL) { length = GetParamSubVals (m->mixedBrchRates, chain, state[chain])[p->index]; } else length = p->length; /* numerical errors will ensue if we allow very large or very small branch lengths, which might occur in relaxed clock models; an elegant solution would be to substitute the stationary probs and initial probs but for now we truncate lengths at small or large values TODO */ if (length > BRLENS_MAX) length = BRLENS_MAX; else if (length < BRLENS_MIN) length = BRLENS_MIN; /* find base frequencies */ bs = GetParamStdStateFreqs (m->stateFreq, chain, state[chain]); /* fill in values; this has to be done differently if state freqs are not equal */ if (m->stateFreq->paramId == SYMPI_EQUAL) { /* equal state frequencies */ /* fill in values for unordered characters */ index = 0; # if defined (DEBUG_TIPROBS_STD) index3 = 0; # endif for (nStates=2; nStates<=MAX_STD_STATES; nStates++) { if (m->isTiNeeded[nStates-2] == NO) continue; for (k=0; knumRateCats; k++) { /* calculate probabilities */ v = length * baseRate * catRate[k]; eV1 = exp(-(nStates / (nStates - 1.0)) * v); pChange = (CLFlt) ((1.0 / nStates) - ((1.0 / nStates) * eV1)); pNoChange = (CLFlt) ((1.0 / nStates) + ((nStates - 1.0) / nStates) * eV1); if (pChange<0.0) pChange = (CLFlt) 0.0; for (i=0; iisTiNeeded[nStates+MAX_STD_STATES-4] == NO) continue; /* compute orthonormal eigenvectors (independent of branch length) */ for (i=0; inumRateCats; k++) { /* calculate probabilities */ v = length * catRate[k] * baseRate; /* precompute exp(-mu_s * v) for each eigenvalue (mu_0 = 0) */ expEig[0] = 1.0; for (s=1; sisTiNeeded[0] == YES) { /* cycle through beta and gamma cats */ for (b=0; bnumBetaCats; b++) { mu = 1.0 / (2.0 * bs[0] * bs[1]); for (k=0; knumRateCats; k++) { /* calculate probabilities */ v = length * baseRate * catRate[k]; eV1 = exp(- mu * v); tiP[index++] = (CLFlt) (bs[0] + (bs[1] * eV1)); tiP[index++] = (CLFlt) (bs[1] - (bs[1] * eV1)); tiP[index++] = (CLFlt) (bs[0] - (bs[0] * eV1)); tiP[index++] = (CLFlt) (bs[1] + (bs[0] * eV1)); } /* update stationary state frequency pointer */ bs += 2; } } /* now use general algorithm for the other cases */ if (m->cijkLength > 0) { /* first update cijk if necessary */ if (m->cijkLength > 0 && m->upDateCijk == YES) { if (UpDateCijk (division, chain) == ERROR) return (ERROR); } /* then get first set of eigenvalues */ eigenValues = m->cijks[m->cijkIndex[chain]]; /* and cycle through the relevant characters */ for (c=0; cstateFreq->nSympi; c++) { n = m->stateFreq->sympinStates[c]; /* fill in values */ for (k=0; knumRateCats; k++) { v = length * baseRate * catRate[k]; cijk = eigenValues + (2 * n); for (i=0; iupDateCijk == YES); /* we should only go through here if we have cijk information available for the partition */ if (m->cijkLength > 0) { /* flip cijk space */ FlipCijkSpace(m, whichChain); /* figure out information on either omega values or rate values, if necessary */ if (m->dataType == DNA || m->dataType == RNA) { if (m->nucModelId == NUCMODEL_CODON) /* we have a NY98 model */ { rateOmegaValues = GetParamVals(m->omega, whichChain, state[whichChain]); if (m->numOmegaCats > 1) omegaCatFreq = GetParamSubVals (m->omega, whichChain, state[whichChain]); } else if (m->nCijkParts > 1 && m->nucModelId == NUCMODEL_4BY4 && m->numModelStates == 8) { /* we have a covarion (covariotide) model with rate variation */ if (m->shape != NULL) rateOmegaValues = GetParamSubVals (m->shape, whichChain, state[whichChain]); else if (m->mixtureRates != NULL) rateOmegaValues = GetParamSubVals (m->mixtureRates, whichChain, state[whichChain]); } } else if (m->dataType == PROTEIN) { if (m->nCijkParts > 1) { /* we have a covarion model with rate variation */ if (m->shape != NULL) rateOmegaValues = GetParamSubVals (m->shape, whichChain, state[whichChain]); else if (m->mixtureRates != NULL) rateOmegaValues = GetParamSubVals (m->mixtureRates, whichChain, state[whichChain]); } } else if (m->dataType != STANDARD && m->dataType != RESTRICTION) { MrBayesPrint ("%s ERROR: Should not be updating cijks!\n", spacer); return (ERROR); } if (m->dataType == STANDARD) { /* set pointers and other stuff needed */ numQAllocated = 1; p = m->stateFreq; eigenValues = m->cijks[m->cijkIndex[whichChain]]; q[0] = AllocateSquareDoubleMatrix (MAX_STD_STATES); eigvecs = AllocateSquareDoubleMatrix (MAX_STD_STATES); inverseEigvecs = AllocateSquareDoubleMatrix (MAX_STD_STATES); Ceigvecs = AllocateSquareComplexMatrix (MAX_STD_STATES); CinverseEigvecs = AllocateSquareComplexMatrix (MAX_STD_STATES); bsBase = GetParamStdStateFreqs (m->stateFreq, whichChain, state[whichChain]); /* cycle over characters needing cijks */ for (c=0; cnSympi; c++) { n = p->sympinStates[c]; bs = bsBase + p->sympiBsIndex[c]; cType = p->sympiCType[c]; n3 = n * n * n; eigvalsImag = eigenValues + n; cijk = eigenValues + (2 * n); if (SetStdQMatrix (q[0], n, bs, cType) == ERROR) return (ERROR); isComplex = GetEigens (n, q[0], eigenValues, eigvalsImag, eigvecs, inverseEigvecs, Ceigvecs, CinverseEigvecs); if (isComplex == NO) { CalcCijk (n, cijk, eigvecs, inverseEigvecs); } else { if (isComplex == YES) MrBayesPrint ("%s ERROR: Complex eigenvalues found!\n", spacer); else MrBayesPrint ("%s ERROR: Computing eigenvalues problem!\n", spacer); goto errorExit; } eigenValues += (n3 + (2 * n)); } } else { /* all other data types */ numQAllocated = m->nCijkParts; sizeOfSingleCijk = m->cijkLength / m->nCijkParts; n = m->numModelStates; # if defined (BEAGLE_ENABLED) if (m->useBeagle == YES) eigenValues = m->cijks[m->cijkIndex[whichChain]/m->nCijkParts]; else eigenValues = m->cijks[m->cijkIndex[whichChain]]; # else eigenValues = m->cijks[m->cijkIndex[whichChain]]; # endif eigvalsImag = eigenValues + n; cijk = eigenValues + (2 * n); for (k=0; knCijkParts == 1) { if (m->dataType == DNA || m->dataType == RNA) { if (m->nucModelId == NUCMODEL_CODON) { if (SetNucQMatrix (q[0], n, whichChain, whichPart, rateOmegaValues[0], &rA, &rS) == ERROR) goto errorExit; } else { if (SetNucQMatrix (q[0], n, whichChain, whichPart, 1.0, &rA, &rS) == ERROR) goto errorExit; } } # if defined (BEAGLE_ENABLED) else if (m->dataType == RESTRICTION) { SetBinaryQMatrix (q[0], whichChain, whichPart); } # endif else { if (SetProteinQMatrix (q[0], n, whichChain, whichPart, 1.0) == ERROR) goto errorExit; } isComplex = GetEigens (n, q[0], eigenValues, eigvalsImag, eigvecs, inverseEigvecs, Ceigvecs, CinverseEigvecs); # if defined (BEAGLE_ENABLED) if (isComplex != NO) { if (isComplex == YES) MrBayesPrint ("%s ERROR: Complex eigenvalues found!\n", spacer); else MrBayesPrint ("%s ERROR: Computing eigenvalues problem!\n", spacer); goto errorExit; } if (m->useBeagle == YES) { /* TODO: only allocate this space once at initialization */ beagleEigvecs = (double*) SafeCalloc (2*n*n, sizeof(double)); beagleInverseEigvecs = beagleEigvecs + n*n; for (i=k=0; iuseBeagleMultiPartitions == YES) divisionOffset = (numLocalChains + 1) * m->nCijkParts * m->divisionIndex; beagleSetEigenDecomposition(m->beagleInstance, m->cijkIndex[whichChain] + divisionOffset, beagleEigvecs, beagleInverseEigvecs, eigenValues); free(beagleEigvecs); } else { CalcCijk (n, cijk, eigvecs, inverseEigvecs); } # else if (isComplex == NO) { CalcCijk (n, cijk, eigvecs, inverseEigvecs); } else { if (isComplex == YES) MrBayesPrint ("%s ERROR: Complex eigenvalues found!\n", spacer); else MrBayesPrint ("%s ERROR: Computing eigenvalues problem!\n", spacer); goto errorExit; } # endif } else { /* Here, we calculate the rate matrices (Q) for various nucleotide and amino acid data models. Usually, when the rate matrix is set in SetNucQMatrix, it is scaled such that the average substitution rate is one. However, there is a complication for positive selection models using codon rate matrices. First, we have more than one matrix; in fact, we have as many rate matrices as there are omega values. Second, the mean substitution rate still has to be one. And third, we want the synonymous rate to be the same across the rate matrices. For positive selection models, the Q matrix comes out of SetNucQMatrix unscaled. Once we have all m->nCijkParts rate matrices, we then scale again, this time to ensure that the mean substitution rate is one. */ /* First, calculate rate matrices for each category: */ posScaler = 0.0; for (k=0; knCijkParts; k++) { if (m->dataType == DNA || m->dataType == RNA) { if (SetNucQMatrix (q[k], n, whichChain, whichPart, rateOmegaValues[k], &rA, &rS) == ERROR) goto errorExit; } else { if (SetProteinQMatrix (q[k], n, whichChain, whichPart, rateOmegaValues[k]) == ERROR) goto errorExit; } if (m->nucModelId == NUCMODEL_CODON && m->numOmegaCats > 1) posScaler += omegaCatFreq[k] * (rS + rA); } /* Then rescale the rate matrices, if this is a positive selection model: */ if (m->nucModelId == NUCMODEL_CODON && m->numOmegaCats > 1) { posScaler = 1.0 / posScaler; for (k=0; knCijkParts; k++) { for (i=0; iuseBeagle == YES) { /* TODO: only allocate this space once at initialization */ beagleEigvecs = (double*) SafeCalloc (2*n*n, sizeof(double)); beagleInverseEigvecs = beagleEigvecs + n*n; } # endif for (k=0; knCijkParts; k++) { isComplex = GetEigens (n, q[k], eigenValues, eigvalsImag, eigvecs, inverseEigvecs, Ceigvecs, CinverseEigvecs); # if defined (BEAGLE_ENABLED) if (isComplex != NO) { if (isComplex == YES) MrBayesPrint ("%s ERROR: Complex eigenvalues found!\n", spacer); else MrBayesPrint ("%s ERROR: Computing eigenvalues problem!\n", spacer); goto errorExit; } if (m->useBeagle == YES) { for (i=u=0; iuseBeagleMultiPartitions == YES) divisionOffset = (numLocalChains + 1) * m->nCijkParts * m->divisionIndex; beagleSetEigenDecomposition(m->beagleInstance, m->cijkIndex[whichChain] + k + divisionOffset, beagleEigvecs, beagleInverseEigvecs, eigenValues); } else { CalcCijk (n, cijk, eigvecs, inverseEigvecs); } # else if (isComplex == NO) { CalcCijk (n, cijk, eigvecs, inverseEigvecs); } else { if (isComplex == YES) MrBayesPrint ("%s ERROR: Complex eigenvalues found!\n", spacer); else MrBayesPrint ("%s ERROR: Computing eigenvalues problem!\n", spacer); goto errorExit; } # endif /* shift pointers */ eigenValues += sizeOfSingleCijk; eigvalsImag += sizeOfSingleCijk; cijk += sizeOfSingleCijk; } # if defined (BEAGLE_ENABLED) free(beagleEigvecs); # endif } } for (k=0; kbeagleInstance == -99) { MrBayesPrint ("\n%s Error: Consecutive MCMC runs not currently supported with BEAGLE. Please restart MrBayes.\n\n", spacer); exit(1); } if (m->useBeagle == NO) return ERROR; /* at least one eigen buffer needed */ if (m->nCijkParts == 0) m->nCijkParts = 1; /* allocate memory used by beagle */ m->logLikelihoods = (MrBFlt *) SafeCalloc ((numLocalChains)*m->numChars, sizeof(MrBFlt)); m->inRates = (MrBFlt *) SafeCalloc (m->numRateCats, sizeof(MrBFlt)); m->branchLengths = (MrBFlt *) SafeCalloc (2*numLocalTaxa, sizeof(MrBFlt)); m->tiProbIndices = (int *) SafeCalloc (2*numLocalTaxa, sizeof(int)); m->inWeights = (MrBFlt *) SafeCalloc (m->numRateCats*m->nCijkParts, sizeof(MrBFlt)); m->bufferIndices = (int *) SafeCalloc (m->nCijkParts, sizeof(int)); m->eigenIndices = (int *) SafeCalloc (m->nCijkParts, sizeof(int)); m->childBufferIndices = (int *) SafeCalloc (m->nCijkParts, sizeof(int)); m->childTiProbIndices = (int *) SafeCalloc (m->nCijkParts, sizeof(int)); m->cumulativeScaleIndices = (int *) SafeCalloc (m->nCijkParts, sizeof(int)); m->operations = (BeagleOperation *) SafeCalloc(m->numCondLikes*m->nCijkParts, sizeof(BeagleOperation)); m->scaleFactorsOps = (int *) SafeCalloc (2*numLocalTaxa*m->nCijkParts, sizeof(int)); numPartAmbigTips = 0; if (m->numStates != m->numModelStates) numPartAmbigTips = numLocalTaxa; else { for (i=0; iisPartAmbig[i] == YES || beagleAllFloatTips) #else if (m->isPartAmbig[i] == YES) #endif { numPartAmbigTips++; } } } m->beagleInstance = createBeagleInstance(m, m->nCijkParts, m->numRateCats, m->numModelStates, m->numCondLikes, m->numScalers, m->numChars, m->numTiProbs, numPartAmbigTips, division); if (m->beagleInstance < 0) return ERROR; /* initialize tip data */ inStates = (int *) SafeMalloc (m->numChars * sizeof(int)); if (!inStates) return ERROR; inPartials = (double *) SafeMalloc (m->numChars * m->numModelStates * sizeof(double)); if (!inPartials) return ERROR; for (i=0; iisPartAmbig[i] == NO && !(beagleAllFloatTips)) #else if (m->isPartAmbig[i] == NO) #endif { charBits = m->parsSets[i]; for (c=0; cnumChars; c++) { for (s=j=0; snumModelStates; s++) { if (IsBitSet(s, charBits)) { inStates[c] = s; j++; } } if (j == m->numModelStates) inStates[c] = j; else assert (j==1); charBits += m->nParsIntsPerSite; } beagleSetTipStates(m->beagleInstance, i, inStates); } else /* if (m->isPartAmbig == YES) */ { k = 0; charBits = m->parsSets[i]; for (c=0; cnumChars; c++) { for (s=0; snumModelStates; s++) { if (IsBitSet(s%m->numStates, charBits)) inPartials[k++] = 1.0; else inPartials[k++] = 0.0; } charBits += m->nParsIntsPerSite; } beagleSetTipPartials(m->beagleInstance, i, inPartials); } } free (inStates); free (inPartials); return NO_ERROR; } int createBeagleInstance(ModelInfo *m, int nCijkParts, int numRateCats, int numModelStates, int numCondLikes, int numScalers, int numChars, int numTiProbs, int numPartAmbigTips, int division) { int resource, resourceCount, beagleInstance; BeagleInstanceDetails details; long preferredFlags, requiredFlags; #if defined (BEAGLE_V3_ENABLED) int i; Tree* t; #endif resourceCount = beagleResourceCount; preferredFlags = beagleFlags; requiredFlags = 0L; if (beagleScalingScheme == MB_BEAGLE_SCALE_ALWAYS) { requiredFlags |= BEAGLE_FLAG_SCALERS_LOG; } if (beagleResourceNumber >= 0 && beagleResourceNumber != 99) { resource = beagleResourceNumber; resourceCount = 1; } else if (beagleResourceCount != 0) { # if defined (MPI_ENABLED) resource = beagleResource[(beagleInstanceCount + proc_id) % beagleResourceCount]; # else resource = beagleResource[beagleInstanceCount % beagleResourceCount]; # endif } #if defined (BEAGLE_V3_ENABLED) else if (beagleResourceNumber == 99) { int numInstancePartitions = 1; if (division == -1) { numInstancePartitions = numCurrentDivisions; } MrBayesPrint ("\n%s Running benchmarks to automatically select fastest BEAGLE resource... ", spacer); long benchmarkFlags = BEAGLE_BENCHFLAG_SCALING_NONE; if (beagleScalingScheme == MB_BEAGLE_SCALE_ALWAYS) { benchmarkFlags = BEAGLE_BENCHFLAG_SCALING_ALWAYS; } else if (beagleScalingScheme == MB_BEAGLE_SCALE_DYNAMIC) { benchmarkFlags = BEAGLE_BENCHFLAG_SCALING_DYNAMIC; } // select fastest resource BeagleBenchmarkedResourceList* rBList; rBList = beagleGetBenchmarkedResourceList( numLocalTaxa, numLocalTaxa - numPartAmbigTips, numModelStates, numChars, numRateCats, NULL, 0, preferredFlags, requiredFlags, nCijkParts, numInstancePartitions, 0, benchmarkFlags); # if defined (DEBUG_MB_BEAGLE_FLOW) fprintf(stdout, "Resource benchmarks:\n"); fprintf(stdout, "\ttipCount : %d\n", numLocalTaxa); fprintf(stdout, "\tcompactBufferCount : %d\n", numLocalTaxa - numPartAmbigTips); fprintf(stdout, "\tstateCount : %d\n", numModelStates); fprintf(stdout, "\tpatternCount : %d\n", numChars); fprintf(stdout, "\tcategoryCount : %d\n", numRateCats); fprintf(stdout, "\teigenModelCount : %d\n", nCijkParts); fprintf(stdout, "\tpartitionCount : %d\n", numInstancePartitions); fprintf(stdout, "\tPreferred Flags:"); BeaglePrintFlags(preferredFlags); fprintf(stdout, "\n"); fprintf(stdout, "\tRequired Flags:"); BeaglePrintFlags(requiredFlags); fprintf(stdout, "\n"); fprintf(stdout, "\tBenchmark Flags: "); (benchmarkFlags & BEAGLE_BENCHFLAG_SCALING_ALWAYS ? fprintf(stdout, "BEAGLE_BENCHFLAG_SCALING_ALWAYS") : fprintf(stdout, "BEAGLE_BENCHFLAG_SCALING_DYNAMIC")); fprintf(stdout, "\n"); fprintf(stdout, "\n"); if (rBList != NULL) { for (int i = 0; i < rBList->length; i++) { fprintf(stdout, "\tResource %i:\n\t\tName : %s\n", i, rBList->list[i].name); fprintf(stdout, "\t\tDesc : %s\n", rBList->list[i].description); fprintf(stdout, "\t\tSupport Flags:"); BeaglePrintFlags(rBList->list[i].supportFlags); fprintf(stdout, "\n"); fprintf(stdout, "\t\tRequired Flags:"); BeaglePrintFlags(rBList->list[i].requiredFlags); fprintf(stdout, "\n"); fprintf(stdout, "\t\tBenchmark Results:\n"); fprintf(stdout, "\t\t\tNmbr : %d\n", rBList->list[i].number); fprintf(stdout, "\t\t\tImpl : %s\n", rBList->list[i].implName); fprintf(stdout, "\t\t\tFlags:"); BeaglePrintFlags(rBList->list[i].benchedFlags); fprintf(stdout, "\n"); fprintf(stdout, "\t\t\tPerf : %.4f ms (%.2fx CPU)\n", rBList->list[i].benchmarkResult, rBList->list[i].performanceRatio); } } fprintf(stdout, "\n"); # endif if (rBList != NULL && rBList->length > 0) { double fastestTime = rBList->list[0].benchmarkResult; resource = rBList->list[0].number; resourceCount = 1; for (int i = 1; i < rBList->length; i++) { if (rBList->list[i].benchmarkResult < fastestTime) { fastestTime = rBList->list[i].benchmarkResult; resource = rBList->list[i].number; } } } } #endif /* BEAGLE_V3_ENABLED */ /* TODO: allocate fewer buffers when nCijkParts > 1 */ /* create beagle instance */ beagleInstance = beagleCreateInstance(numLocalTaxa, numCondLikes * nCijkParts, numLocalTaxa - numPartAmbigTips, numModelStates, numChars, ((numLocalChains + 1) * nCijkParts) * numCurrentDivisions, numTiProbs*nCijkParts, numRateCats, numScalers * nCijkParts, (resourceCount == 0 ? NULL : &resource), (resourceCount == 0 ? 0 : 1), preferredFlags, requiredFlags, &details); if (beagleInstance < 0) { MrBayesPrint ("%s Failed to start BEAGLE instance\n", spacer); } else { if (division < 0) { /* do not use multipartition mode (division < 0) for CPU resource */ if (details.flags & BEAGLE_FLAG_FRAMEWORK_CPU) { MrBayesPrint ("\n%s Selected resource is the CPU, changing to multi-instance BEAGLE mode\n", spacer); beagleFinalizeInstance(beagleInstance); return -1; } else { MrBayesPrint ("\n%s Using BEAGLE v%s resource %i for %d division%s:\n", spacer, beagleGetVersion(), details.resourceNumber, numCurrentDivisions, (numCurrentDivisions > 1 ? "s" : "")); } } else { MrBayesPrint ("\n%s Using BEAGLE v%s resource %i for division %d:\n", spacer, beagleGetVersion(), details.resourceNumber, division+1); } MrBayesPrint ("%s Rsrc Name : %s\n", spacer, details.resourceName); MrBayesPrint ("%s Impl Name : %s\n", spacer, details.implName); MrBayesPrint ("%s Flags:", spacer); BeaglePrintFlags(details.flags); MrBayesPrint ("%s MODEL STATES: %d", spacer, numModelStates); MrBayesPrint ("\n"); beagleInstanceCount++; } #if defined (BEAGLE_V3_ENABLED) /* use level-order traversal with CUDA implementation or OpenCL with multi-partition */ if (((details.flags & BEAGLE_FLAG_FRAMEWORK_CUDA) && division < 1 ) || ((details.flags & BEAGLE_FLAG_FRAMEWORK_OPENCL) && division < 0)) { for (i=0; i<(numTrees * 2 * numGlobalChains); i++) { t = mcmcTree[i]; if (t != NULL) { if ((t->intDownPassLevel = (TreeNode **) SafeCalloc (numTaxa, sizeof (TreeNode *))) == NULL) { free (t->nodes); free (t->allDownPass); free (t); return -1; } t->levelPassEnabled = 1; GetDownPass(t); } } } /* set max number of CPU threads to be used by BEAGLE with CPU implementation */ if ((details.flags & BEAGLE_FLAG_FRAMEWORK_CPU) && beagleThreadCount > 1 && beagleThreadCount != 99) { beagleSetCPUThreadCount(beagleInstance, beagleThreadCount); } #endif /* BEAGLE_V3_ENABLED */ return beagleInstance; } /*----------------------------------------------------------------- | | LaunchBEAGLELogLikeForDivision: calculate the log likelihood | of the new state of the chain for a single division | -----------------------------------------------------------------*/ void LaunchBEAGLELogLikeForDivision(int chain, int d, ModelInfo* m, Tree* tree, MrBFlt* lnL) { int i, rescaleFreqNew; int *isScalerNode; TreeNode *p; if (beagleScalingScheme == MB_BEAGLE_SCALE_ALWAYS) { # if defined (DEBUG_MB_BEAGLE_FLOW) MrBayesPrint ("ALWAYS RESCALING\n"); # endif #if defined (DEBUG_MB_BEAGLE_MULTIPART) printf("m->upDateAll %d\n", m->upDateAll); #endif /* Flip and copy or reset site scalers */ FlipSiteScalerSpace(m, chain); if (m->upDateAll == YES) { for (i=0; inCijkParts; i++) { beagleResetScaleFactors(m->beagleInstance, m->siteScalerIndex[chain] + i); } } else CopySiteScalers(m, chain); TreeTiProbs_Beagle(tree, d, chain); TreeCondLikes_Beagle_Always_Rescale(tree, d, chain); TreeLikelihood_Beagle(tree, d, chain, lnL, (chainId[chain] % chainParams.numChains)); } else { /* MB_BEAGLE_SCALE_DYNAMIC */ /* This flag is only valid within this block */ m->rescaleBeagleAll = NO; TreeTiProbs_Beagle(tree, d, chain); if (m->successCount[chain] > 1000) { m->successCount[chain] = 10; m->rescaleFreq[chain]++; /* increase rescaleFreq independent of whether we accept or reject new state */ m->rescaleFreqOld = rescaleFreqNew = m->rescaleFreq[chain]; for (i=0; inIntNodes; i++) { p = tree->intDownPass[i]; if (p->upDateCl == YES) { /* flip to the new workspace since TreeCondLikes_Beagle_Rescale_All() does not do it for (p->upDateCl == YES) since it assumes that TreeCondLikes_Beagle_No_Rescale() did it */ FlipCondLikeSpace (m, chain, p->index); } } goto rescale_all; } if (beagleScalingFrequency != 0 && m->beagleComputeCount[chain] % (beagleScalingFrequency) == 0) { m->rescaleFreqOld = rescaleFreqNew = m->rescaleFreq[chain]; for (i=0; inIntNodes; i++) { p = tree->intDownPass[i]; if (p->upDateCl == YES) { /* flip to the new workspace since TreeCondLikes_Beagle_Rescale_All() does not do it for (p->upDateCl == YES) since it assumes that TreeCondLikes_Beagle_No_Rescale() did it */ FlipCondLikeSpace (m, chain, p->index); } } goto rescale_all; } TreeCondLikes_Beagle_No_Rescale(tree, d, chain); /* Check if likelihood is valid */ if (TreeLikelihood_Beagle(tree, d, chain, lnL, (chainId[chain] % chainParams.numChains)) == BEAGLE_ERROR_FLOATING_POINT) { m->rescaleFreqOld = rescaleFreqNew = m->rescaleFreq[chain]; if (rescaleFreqNew > 1 && m->successCount[chain] < 40) { if (m->successCount[chain] < 10) { if (m->successCount[chain] < 4) { rescaleFreqNew -= rescaleFreqNew >> 3; /* <== we cut up to 12,5% of rescaleFreq */ if (m->successCount[chain] < 2) { rescaleFreqNew -= rescaleFreqNew >> 3; /* to avoid situation when we may stack at high rescaleFreq when new states do not get accepted because of low likelihood but there proposed frequency is high we reduce rescaleFreq even if we reject the last move*/ /* basically the higher probability of proposing of low likelihood state which needs smaller rescaleFreq would lead to higher probability of hitting this code which should reduce rescaleFreqOld thus reduce further probability of hitting this code */ /* at some point this negative feedback mechanism should get in balance with the mechanism of periodically increasing rescaleFreq when long sequence of successes is achieved*/ m->rescaleFreqOld -= m->rescaleFreqOld >> 3; } m->rescaleFreqOld -= m->rescaleFreqOld >> 3; m->rescaleFreqOld--; m->rescaleFreqOld = (m->rescaleFreqOld ? m->rescaleFreqOld:1); m->recalculateScalers = YES; recalcScalers = YES; } } rescaleFreqNew--; rescaleFreqNew = (rescaleFreqNew ? rescaleFreqNew : 1); } m->successCount[chain] = 0; rescale_all: # if defined (DEBUG_MB_BEAGLE_FLOW) MrBayesPrint ("NUMERICAL RESCALING\n"); # endif m->rescaleBeagleAll = YES; FlipSiteScalerSpace(m, chain); isScalerNode = m->isScalerNode[chain]; while_loop: ResetScalersPartition (isScalerNode, tree, rescaleFreqNew); for (i=0; inCijkParts; i++) { beagleResetScaleFactors(m->beagleInstance, m->siteScalerIndex[chain] + i); } TreeCondLikes_Beagle_Rescale_All (tree, d, chain); if (TreeLikelihood_Beagle(tree, d, chain, lnL, (chainId[chain] % chainParams.numChains)) == BEAGLE_ERROR_FLOATING_POINT) { if (rescaleFreqNew > 1) { /* Swap back scalers which were swapped in TreeCondLikes_Beagle_Rescale_All() */ for (i=0; inIntNodes; i++) { p = tree->intDownPass[i]; if (isScalerNode[p->index] == YES) FlipNodeScalerSpace (m, chain, p->index); } rescaleFreqNew -= rescaleFreqNew >> 3; /* <== we cut up to 12,5% of rescaleFreq */ rescaleFreqNew--; /* we cut extra 1 of rescaleFreq */ goto while_loop; } } m->rescaleFreq[chain] = rescaleFreqNew; } } /* Count number of evaluations */ m->beagleComputeCount[chain]++; } void recalculateScalers (int chain) { int i, d, rescaleFreqNew; int *isScalerNode; ModelInfo* m; Tree *tree; #if defined (BEAGLE_V3_ENABLED) int divisionCount; int *divisions; #endif if (modelSettings[0].useBeagleMultiPartitions == NO) { for (d=0; drecalculateScalers == YES) { m->recalculateScalers = NO; tree = GetTree(m->brlens, chain, state[chain]); rescaleFreqNew = m->rescaleFreq[chain]; isScalerNode = m->isScalerNode[chain]; ResetScalersPartition (isScalerNode, tree, rescaleFreqNew); for (i=0; inCijkParts; i++) { beagleResetScaleFactors(m->beagleInstance, m->siteScalerIndex[chain] + i); } /* here it does not matter if we flip CL space or not */ TreeCondLikes_Beagle_Rescale_All (tree, d, chain); } } } #if defined (BEAGLE_V3_ENABLED) else { divisions = (int *) SafeCalloc (numCurrentDivisions, sizeof(int)); divisionCount = 0; for (d=0; drecalculateScalers == YES) { m->recalculateScalers = NO; tree = GetTree(m->brlens, chain, state[chain]); rescaleFreqNew = m->rescaleFreq[chain]; isScalerNode = m->isScalerNode[chain]; ResetScalersPartition (isScalerNode, tree, rescaleFreqNew); for (i=0; inCijkParts; i++) { beagleResetScaleFactorsByPartition(m->beagleInstance, m->siteScalerIndex[chain] + i, m->divisionIndex); } divisions[divisionCount++] = d; } } /* here it does not matter if we flip CL space or not */ TreeCondLikes_BeagleMultiPartition_Rescale_All(divisions, divisionCount, chain); free(divisions); } #endif } void BeagleAddGPUDevicesToList (int **newResourceList, int *beagleResourceCount) { BeagleResourceList* beagleResources; int i, gpuCount; beagleResources = beagleGetResourceList(); if (*newResourceList == NULL) { *newResourceList = (int*) SafeCalloc(sizeof(int), beagleResources->length); } gpuCount = 0; for (i = 0; i < beagleResources->length; i++) { if (beagleResources->list[i].supportFlags & BEAGLE_FLAG_PROCESSOR_GPU) { (*newResourceList)[gpuCount] = i; gpuCount++; } } *beagleResourceCount = gpuCount; } void BeagleRemoveGPUDevicesFromList (int **beagleResource, int *beagleResourceCount) { *beagleResourceCount = 0; } /*----- | | BeaglePrintResources: outputs the available BEAGLE resources | ----------*/ void BeaglePrintResources (void) { int i; BeagleResourceList* beagleResources; beagleResources = beagleGetResourceList(); MrBayesPrint ("%s Available resources reported by beagle library:\n", spacer); for (i=0; ilength; i++) { MrBayesPrint ("\tResource %i:\n", i); MrBayesPrint ("\tName: %s\n", beagleResources->list[i].name); if (i > 0) { MrBayesPrint ("\tDesc: %s\n", beagleResources->list[i].description); } MrBayesPrint ("\tFlags:"); BeaglePrintFlags(beagleResources->list[i].supportFlags); MrBayesPrint ("\n\n"); } MrBayesPrint ("%s BEAGLE version: %s\n", spacer, beagleGetVersion()); } int BeagleCheckFlagCompatability (long inFlags) { if (inFlags & BEAGLE_FLAG_PROCESSOR_GPU) { if (inFlags & BEAGLE_FLAG_VECTOR_SSE) { MrBayesPrint ("%s Simultaneous use of GPU and SSE not available.\n", spacer); return NO; } if (inFlags & BEAGLE_FLAG_THREADING_OPENMP) { MrBayesPrint ("%s Simultaneous use of GPU and OpenMP not available.\n", spacer); return NO; } } return YES; } /*------------------- | | BeaglePrintFlags: outputs beagle instance details | ______________________*/ void BeaglePrintFlags (long inFlags) { int i, k; char *names[] = { "PROCESSOR_CPU", "PROCESSOR_GPU", "PROCESSOR_FPGA", "PROCESSOR_CELL", "PRECISION_DOUBLE", "PRECISION_SINGLE", "COMPUTATION_ASYNCH", "COMPUTATION_SYNCH", "EIGEN_REAL", "EIGEN_COMPLEX", "SCALING_MANUAL", "SCALING_AUTO", "SCALING_ALWAYS", "SCALING_DYNAMIC", "SCALERS_RAW", "SCALERS_LOG", "VECTOR_NONE", "VECTOR_SSE", "THREADING_NONE", #if defined (BEAGLE_V3_ENABLED) "THREADING_CPP", #endif "THREADING_OPENMP" }; long flags[] = { BEAGLE_FLAG_PROCESSOR_CPU, BEAGLE_FLAG_PROCESSOR_GPU, BEAGLE_FLAG_PROCESSOR_FPGA, BEAGLE_FLAG_PROCESSOR_CELL, BEAGLE_FLAG_PRECISION_DOUBLE, BEAGLE_FLAG_PRECISION_SINGLE, BEAGLE_FLAG_COMPUTATION_ASYNCH, BEAGLE_FLAG_COMPUTATION_SYNCH, BEAGLE_FLAG_EIGEN_REAL, BEAGLE_FLAG_EIGEN_COMPLEX, BEAGLE_FLAG_SCALING_MANUAL, BEAGLE_FLAG_SCALING_AUTO, BEAGLE_FLAG_SCALING_ALWAYS, BEAGLE_FLAG_SCALING_DYNAMIC, BEAGLE_FLAG_SCALERS_RAW, BEAGLE_FLAG_SCALERS_LOG, BEAGLE_FLAG_VECTOR_NONE, BEAGLE_FLAG_VECTOR_SSE, BEAGLE_FLAG_THREADING_NONE, #if defined (BEAGLE_V3_ENABLED) BEAGLE_FLAG_THREADING_CPP, #endif BEAGLE_FLAG_THREADING_OPENMP }; int flagCount = 20; #if defined (BEAGLE_V3_ENABLED) flagCount = 21; #endif for (i=k=0; i 0) MrBayesPrint ("\n%s ", spacer); MrBayesPrint (" %s", names[i]); k++; } } } int ScheduleLogLikeForAllDivisions (void) { int d; int divisionsToLaunch = 0; ModelInfo *m; if (numCurrentDivisions < 2) { return 0; } for (d=0; dupDateCl == YES) { divisionsToLaunch++; } } return (divisionsToLaunch > 1); } /*---------------------------------------------------------------- | | TreeCondLikes_Beagle: This routine updates all conditional | (partial) likelihoods of a beagle instance while doing no rescaling. | That potentially can make final likelihood bad then calculation with rescaling needs to be done. | -----------------------------------------------------------------*/ int TreeCondLikes_Beagle_No_Rescale (Tree *t, int division, int chain) { int i, j, cumulativeScaleIndex, op; TreeNode *p; ModelInfo *m; unsigned chil1Step, chil2Step; int *isScalerNode; m = &modelSettings[division]; isScalerNode = m->isScalerNode[chain]; op = 0; for (i=0; inIntNodes; i++) { #if defined (BEAGLE_V3_ENABLED) if (t->levelPassEnabled) { p = t->intDownPassLevel[i]; } else #endif { p = t->intDownPass[i]; } /* check if conditional likelihoods need updating */ if (p->upDateCl == YES) { /* flip to the new workspace */ FlipCondLikeSpace (m, chain, p->index); /* update conditional likelihoods */ m->operations[op].destinationPartials = m->condLikeIndex[chain][p->index ]; m->operations[op].child1Partials = m->condLikeIndex[chain][p->left->index ]; m->operations[op].child1TransitionMatrix = m->tiProbsIndex [chain][p->left->index ]; m->operations[op].child2Partials = m->condLikeIndex[chain][p->right->index]; m->operations[op].child2TransitionMatrix = m->tiProbsIndex [chain][p->right->index]; /* All partials for tips are the same across omega categories, thus we are doing the following two if statements.*/ if (p->left->left== NULL) chil1Step=0; else chil1Step=1; if (p->right->left== NULL) chil2Step=0; else chil2Step=1; m->operations[op].destinationScaleWrite = BEAGLE_OP_NONE; cumulativeScaleIndex = BEAGLE_OP_NONE; if (isScalerNode[p->index] == YES) { m->operations[op].destinationScaleRead = m->nodeScalerIndex[chain][p->index]; } else { m->operations[op].destinationScaleRead = BEAGLE_OP_NONE; } for (j=1; jnCijkParts; j++) { m->operations[op+1].destinationPartials = m->operations[op].destinationPartials + 1; m->operations[op+1].child1Partials = m->operations[op].child1Partials + chil1Step; m->operations[op+1].child1TransitionMatrix = m->operations[op].child1TransitionMatrix + 1; m->operations[op+1].child2Partials = m->operations[op].child2Partials + chil2Step; m->operations[op+1].child2TransitionMatrix = m->operations[op].child2TransitionMatrix + 1; m->operations[op+1].destinationScaleWrite = BEAGLE_OP_NONE; if ( isScalerNode[p->index] == YES ) { m->operations[op+1].destinationScaleRead = m->operations[op].destinationScaleRead + 1; } else { m->operations[op+1].destinationScaleRead = BEAGLE_OP_NONE; } op++; } op++; } } beagleUpdatePartials(m->beagleInstance, m->operations, op, cumulativeScaleIndex); return NO_ERROR; } /*---------------------------------------------------------------- | | TreeCondLikes_Beagle: This routine updates all conditional | (partial) likelihoods of a beagle instance while rescaling at every node. | Note: all nodes get recalculated. | -----------------------------------------------------------------*/ int TreeCondLikes_Beagle_Rescale_All (Tree *t, int division, int chain) { int i, j, cumulativeScaleIndex, op, opJ, opPrev; TreeNode *p; ModelInfo *m; unsigned chil1Step, chil2Step; int *isScalerNode; m = &modelSettings[division]; isScalerNode = m->isScalerNode[chain]; op = 0; for (i=0; inIntNodes; i++) { #if defined (BEAGLE_V3_ENABLED) if (t->levelPassEnabled) { p = t->intDownPassLevel[i]; } else #endif { p = t->intDownPass[i]; } if (p->upDateCl == NO) { //p->upDateCl = YES; /* flip to the new workspace */ FlipCondLikeSpace (m, chain, p->index); } /* update conditional likelihoods */ m->operations[op].destinationPartials = m->condLikeIndex[chain][p->index ]; m->operations[op].child1Partials = m->condLikeIndex[chain][p->left->index ]; m->operations[op].child1TransitionMatrix = m->tiProbsIndex [chain][p->left->index ]; m->operations[op].child2Partials = m->condLikeIndex[chain][p->right->index]; m->operations[op].child2TransitionMatrix = m->tiProbsIndex [chain][p->right->index]; /* All partials for tips are the same across omega categories, thus we are doing the following two if statements.*/ if (p->left->left== NULL) chil1Step=0; else chil1Step=1; if (p->right->left== NULL) chil2Step=0; else chil2Step=1; m->operations[op].destinationScaleRead = BEAGLE_OP_NONE; if (isScalerNode[p->index] == YES) { FlipNodeScalerSpace (m, chain, p->index); m->operations[op].destinationScaleWrite = m->nodeScalerIndex[chain][p->index]; cumulativeScaleIndex = m->siteScalerIndex[chain]; } else { m->operations[op].destinationScaleWrite = BEAGLE_OP_NONE; cumulativeScaleIndex = BEAGLE_OP_NONE; } for (j=1; jnCijkParts; j++) { opJ = op + j * t->nIntNodes; opPrev = op + (j-1) * t->nIntNodes; m->operations[opJ].destinationPartials = m->operations[opPrev].destinationPartials + 1; m->operations[opJ].child1Partials = m->operations[opPrev].child1Partials + chil1Step; m->operations[opJ].child1TransitionMatrix = m->operations[opPrev].child1TransitionMatrix + 1; m->operations[opJ].child2Partials = m->operations[opPrev].child2Partials + chil2Step; m->operations[opJ].child2TransitionMatrix = m->operations[opPrev].child2TransitionMatrix + 1; m->operations[opJ].destinationScaleRead = BEAGLE_OP_NONE; if ( isScalerNode[p->index] == YES ) { m->operations[opJ].destinationScaleWrite = m->operations[opPrev].destinationScaleWrite + 1; } else { m->operations[opJ].destinationScaleWrite = BEAGLE_OP_NONE; } } op++; } cumulativeScaleIndex = m->siteScalerIndex[chain]; for (j=0; jnCijkParts; j++) { beagleUpdatePartials(m->beagleInstance, &m->operations[j*t->nIntNodes], t->nIntNodes, cumulativeScaleIndex); cumulativeScaleIndex++; } return NO_ERROR; } /*---------------------------------------------------------------- | | TreeCondLikes_Beagle: This routine updates and rescales | all conditional (partial) likelihoods of a beagle instance. | -----------------------------------------------------------------*/ int TreeCondLikes_Beagle_Always_Rescale (Tree *t, int division, int chain) { int i, j, op, opJ, opPrev, scaleOp; int destinationScaleRead, cumulativeScaleIndex; TreeNode *p; ModelInfo *m; unsigned chil1Step, chil2Step; m = &modelSettings[division]; op = 0; scaleOp = 0; for (i=0; inIntNodes; i++) { #if defined (BEAGLE_V3_ENABLED) if (t->levelPassEnabled) { p = t->intDownPassLevel[i]; } else #endif { p = t->intDownPass[i]; } /* check if conditional likelihoods need updating */ if (p->upDateCl == YES) { /* remove old scalers */ if (m->upDateAll == NO) { destinationScaleRead = m->nodeScalerIndex[chain][p->index]; for (j=0; jnCijkParts; j++) { m->scaleFactorsOps[scaleOp+j*t->nIntNodes] = destinationScaleRead; destinationScaleRead++; } scaleOp++; } /* flip to the new workspace */ FlipCondLikeSpace (m, chain, p->index); FlipNodeScalerSpace (m, chain, p->index); /* update conditional likelihoods */ m->operations[op].destinationPartials = m->condLikeIndex[chain][p->index ]; m->operations[op].child1Partials = m->condLikeIndex[chain][p->left->index ]; m->operations[op].child1TransitionMatrix = m->tiProbsIndex [chain][p->left->index ]; m->operations[op].child2Partials = m->condLikeIndex[chain][p->right->index]; m->operations[op].child2TransitionMatrix = m->tiProbsIndex [chain][p->right->index]; /* All partials for tips are the same across omega categories, thus we are doing the following two if statements.*/ if (p->left->left== NULL && p->left->right== NULL) chil1Step=0; else chil1Step=1; if (p->right->left== NULL && p->right->right== NULL) chil2Step=0; else chil2Step=1; m->operations[op].destinationScaleWrite = m->nodeScalerIndex[chain][p->index]; m->operations[op].destinationScaleRead = BEAGLE_OP_NONE; for (j=1; jnCijkParts; j++) { opJ = op + j * t->nIntNodes; opPrev = op + (j-1) * t->nIntNodes; m->operations[opJ].destinationPartials = m->operations[opPrev].destinationPartials + 1; m->operations[opJ].child1Partials = m->operations[opPrev].child1Partials + chil1Step; m->operations[opJ].child1TransitionMatrix = m->operations[opPrev].child1TransitionMatrix + 1; m->operations[opJ].child2Partials = m->operations[opPrev].child2Partials + chil2Step; m->operations[opJ].child2TransitionMatrix = m->operations[opPrev].child2TransitionMatrix + 1; m->operations[opJ].destinationScaleWrite = m->operations[opPrev].destinationScaleWrite + 1; m->operations[opJ].destinationScaleRead = BEAGLE_OP_NONE; } #if defined (DEBUG_MB_BEAGLE_MULTIPART) printf("%d %d ", op, m->operations[op].destinationPartials); printf("%d ", m->operations[op].destinationScaleWrite); printf("%d ", m->operations[op].destinationScaleRead); printf("%d ", m->operations[op].child1Partials); printf("%d ", m->operations[op].child1TransitionMatrix); printf("%d ", m->operations[op].child2Partials); printf("%d\n", m->operations[op].child2TransitionMatrix); #endif op++; } } /* end of for */ cumulativeScaleIndex = m->siteScalerIndex[chain]; for (j=0; jnCijkParts; j++) { if (m->upDateAll == NO) { beagleRemoveScaleFactors(m->beagleInstance, &m->scaleFactorsOps[j*t->nIntNodes], scaleOp, cumulativeScaleIndex); } beagleUpdatePartials(m->beagleInstance, &m->operations[j*t->nIntNodes], op, cumulativeScaleIndex); cumulativeScaleIndex++; } return NO_ERROR; } /*--------------------------------------------------------------------------- | | TreeLikelihood_Beagle: Accumulate the log likelihoods calculated by Beagle | at the root. | -----------------------------------------------------------------------------*/ int TreeLikelihood_Beagle (Tree *t, int division, int chain, MrBFlt *lnL, int whichSitePats) { int i, j, c = 0, nStates, hasPInvar, beagleReturn; MrBFlt *swr, s01, s10, probOn, probOff, covBF[40], pInvar=0.0, *bs, freq, likeI, lnLikeI, diff, *omegaCatFreq; CLFlt *clInvar=NULL, *nSitesOfPat; double *nSitesOfPat_Beagle; TreeNode *p; ModelInfo *m; double pUnobserved; # if defined (MB_PRINT_DYNAMIC_RESCALE_FAIL_STAT) static unsigned countBeagleDynamicFail=0; static unsigned countALL=0; # endif /* find root node */ p = t->root->left; /* find model settings and nStates, pInvar, invar cond likes */ m = &modelSettings[division]; nStates = m->numModelStates; if (m->pInvar == NULL) { hasPInvar = NO; } else { hasPInvar = YES; pInvar = *(GetParamVals (m->pInvar, chain, state[chain])); clInvar = m->invCondLikes; } /* find base frequencies */ bs = GetParamSubVals (m->stateFreq, chain, state[chain]); /* if covarion model, adjust base frequencies */ if (m->switchRates != NULL) { /* find the stationary frequencies */ swr = GetParamVals(m->switchRates, chain, state[chain]); s01 = swr[0]; s10 = swr[1]; probOn = s01 / (s01 + s10); probOff = 1.0 - probOn; /* now adjust the base frequencies; on-state stored first in cond likes */ for (j=0; jupDateCijk == YES) { /* set base frequencies in BEAGLE instance */ for (i=0; inCijkParts; i++) beagleSetStateFrequencies(m->beagleInstance, m->cijkIndex[chain] + i, bs); } /* find category frequencies */ if (hasPInvar == NO) freq = 1.0 / m->numRateCats; else freq = (1.0 - pInvar) / m->numRateCats; #if defined (DEBUG_MB_BEAGLE_MULTIPART) printf("freq = %f\n", freq); #endif /* TODO: cat weights only need to be set when they change */ /* set category frequencies in beagle instance */ if (m->numOmegaCats > 1) { omegaCatFreq = GetParamSubVals(m->omega, chain, state[chain]); for (i=0; inCijkParts; i++) { for (j=0; jnumRateCats; j++) m->inWeights[j] = freq * omegaCatFreq[i]; beagleSetCategoryWeights(m->beagleInstance, m->cijkIndex[chain] + i, m->inWeights); } } else if (hasPInvar == YES) { for (i=0; inumRateCats; i++) m->inWeights[i] = freq; beagleSetCategoryWeights(m->beagleInstance, m->cijkIndex[chain], m->inWeights); } /* find nSitesOfPat */ nSitesOfPat = numSitesOfPat + (whichSitePats*numCompressedChars) + m->compCharStart; /* TODO: pattern weights only need to be set when they change */ /* set pattern weights in beagle instance if using dynamic reweighting */ if (chainParams.weightScheme[0] + chainParams.weightScheme[1] > ETA) { nSitesOfPat_Beagle = (double *) SafeMalloc (m->numChars * sizeof(double)); for (c=0; cnumChars; c++) nSitesOfPat_Beagle[c] = numSitesOfPat[m->compCharStart + c]; beagleSetPatternWeights(m->beagleInstance, nSitesOfPat_Beagle); SAFEFREE (nSitesOfPat_Beagle); } /* find root log likelihoods and scalers */ for (i=0; inCijkParts; i++) { m->bufferIndices[i] = m->condLikeIndex[chain][p->index] + i; m->eigenIndices[i] = m->cijkIndex[chain] + i; m->cumulativeScaleIndices[i] = m->siteScalerIndex[chain] + i; if (t->isRooted == NO) { m->childBufferIndices[i] = m->condLikeIndex [chain][p->anc->index]; m->childTiProbIndices[i] = m->tiProbsIndex [chain][p->index] + i; } } #if defined (DEBUG_MB_BEAGLE_MULTIPART) printf("bufferIndices = %d, eigenIndices = %d, cumulativeScaleIndices = %d\n", m->bufferIndices[0], m->eigenIndices[0], m->cumulativeScaleIndices[0]); #endif /* reset lnL */ *lnL = 0.0; /* get root log likelihoods */ if (t->isRooted == YES) { beagleReturn = beagleCalculateRootLogLikelihoods(m->beagleInstance, m->bufferIndices, m->eigenIndices, m->eigenIndices, m->cumulativeScaleIndices, m->nCijkParts, lnL); } else { beagleReturn = beagleCalculateEdgeLogLikelihoods(m->beagleInstance, m->bufferIndices, m->childBufferIndices, m->childTiProbIndices, NULL, NULL, m->eigenIndices, m->eigenIndices, m->cumulativeScaleIndices, m->nCijkParts, lnL, NULL, NULL); } # if defined (MB_PRINT_DYNAMIC_RESCALE_FAIL_STAT) countALL++; # endif if (*lnL > DBL_MAX || *lnL < -DBL_MAX || *lnL != *lnL) { beagleReturn = BEAGLE_ERROR_FLOATING_POINT; } if (beagleReturn == BEAGLE_ERROR_FLOATING_POINT) { # if defined (MB_PRINT_DYNAMIC_RESCALE_FAIL_STAT) countBeagleDynamicFail++; MrBayesPrint ("DEBUG INFO (not an error) countBeagleDynamicFail:%d countALL:%d\n", countBeagleDynamicFail, countALL); # endif return beagleReturn; } assert (beagleReturn == BEAGLE_SUCCESS); m->successCount[chain]++; #if defined (DEBUG_MB_BEAGLE_MULTIPART_SITELNL) beagleGetSiteLogLikelihoods(m->beagleInstance, m->logLikelihoods); printf("lnL = "); for (c=0; cnumChars; c++) { printf("[%d] %f ", c, m->logLikelihoods[c]); } printf("\n"); #endif /* accumulate logs across sites */ if (hasPInvar == NO) { if (m->dataType == RESTRICTION) { beagleGetSiteLogLikelihoods(m->beagleInstance, m->logLikelihoods); (*lnL) = 0.0; pUnobserved = 0.0; for (c=0; cnumDummyChars; c++) { pUnobserved += exp((double)m->logLikelihoods[c]); } /* correct for absent characters */ (*lnL) -= log (1-pUnobserved) * (m->numUncompressedChars); for (; cnumChars; c++) { (*lnL) += m->logLikelihoods[c] * nSitesOfPat[c]; } } /* already done, just check for numerical errors */ assert ((*lnL) == (*lnL)); } else { /* has invariable category */ beagleGetSiteLogLikelihoods(m->beagleInstance, m->logLikelihoods); (*lnL) = 0.0; for (c=0; cnumChars; c++) { likeI = 0.0; for (j=0; jlogLikelihoods[c]; } else diff = -1000.0; if (diff < -200.0) (*lnL) += m->logLikelihoods[c] * nSitesOfPat[c]; else if (diff > 200.0) (*lnL) += lnLikeI * nSitesOfPat[c]; else { (*lnL) += (m->logLikelihoods[c] + log(1.0 + exp(diff))) * nSitesOfPat[c]; } /* check for numerical errors */ assert ((*lnL) == (*lnL)); } } return (NO_ERROR); } /*---------------------------------------------------------------- | | TreeTiProbs_Beagle: This routine updates all transition | probability matrices of a beagle instance. | -----------------------------------------------------------------*/ int TreeTiProbs_Beagle (Tree *t, int division, int chain) { int i, j, k, count; MrBFlt correctionFactor, theRate, baseRate, *catRate, length; TreeNode *p; ModelInfo *m; /* get model settings */ m = &modelSettings[division]; /* find the correction factor to make branch lengths in terms of expected number of substitutions per character */ correctionFactor = 1.0; if (m->dataType == DNA || m->dataType == RNA) { if (m->nucModelId == NUCMODEL_DOUBLET) correctionFactor = 2.0; else if (m->nucModelId == NUCMODEL_CODON) correctionFactor = 3.0; } /* get rate multipliers (for gamma & partition specific rates) */ theRate = 1.0; baseRate = GetRate (division, chain); /* compensate for invariable sites if appropriate */ if (m->pInvar != NULL) baseRate /= (1.0 - (*GetParamVals(m->pInvar, chain, state[chain]))); /* get category rates for gamma */ if (m->shape == NULL) catRate = &theRate; else catRate = GetParamSubVals (m->shape, chain, state[chain]); /* get effective category rates */ for (k=0; knumRateCats; k++) m->inRates[k] = baseRate * catRate[k] * correctionFactor; /* TODO: only need to set category rates when they change */ /* set category rates */ beagleSetCategoryRates(m->beagleInstance, m->inRates); /* get ti prob indices and branch lengths to update */ for (i=count=0; inNodes; i++) { p = t->allDownPass[i]; /* check if transition probs need updating */ if (p->upDateTi == YES) { /* flip transition probability */ FlipTiProbsSpace (m, chain, p->index); /* find length */ if (m->cppEvents != NULL) { length = GetParamSubVals (m->cppEvents, chain, state[chain])[p->index]; } else if (m->tk02BranchRates != NULL) { length = GetParamSubVals (m->tk02BranchRates, chain, state[chain])[p->index]; } else if (m->wnBranchRates != NULL) { length = GetParamSubVals (m->wnBranchRates, chain, state[chain])[p->index]; } else if (m->ilnBranchRates != NULL) { length = GetParamSubVals (m->ilnBranchRates, chain, state[chain])[p->index]; } else if (m->igrBranchRates != NULL) { length = GetParamSubVals (m->igrBranchRates, chain, state[chain])[p->index]; } else if (m->mixedBrchRates != NULL) { length = GetParamSubVals (m->mixedBrchRates, chain, state[chain])[p->index]; } else length = p->length; /* numerical errors might ensue if we allow very large or very small branch lengths, which might occur in relaxed clock models; an elegant solution would be to substitute the stationary probs and initial probs but for now we truncate lengths at small or large values */ if (length > BRLENS_MAX) length = BRLENS_MAX; else if (length < BRLENS_MIN) length = BRLENS_MIN; m->branchLengths[count] = length; /* find index */ m->tiProbIndices[count] = m->tiProbsIndex[chain][p->index]; #if defined (DEBUG_MB_BEAGLE_MULTIPART) printf("%d %f ", count, m->branchLengths[count]); printf(" %d", m->tiProbIndices[count]); printf(" %d", 0); printf(" %d\n", m->cijkIndex[chain]); #endif count++; } } /* TODO: only need to update branches that have changed */ /* calculate transition probabilities */ if (count > 0) { for (i=0; inCijkParts; i++) { beagleUpdateTransitionMatrices(m->beagleInstance, m->cijkIndex[chain] + i, m->tiProbIndices, NULL, NULL, m->branchLengths, count); for (j=0; jtiProbIndices[j]++; } } /* return success */ return NO_ERROR; } #if defined (BEAGLE_V3_ENABLED) /*------------------------------------------------------------------------ | | InitBeagleMultiPartitionInstance: create and initialize a beagle instance for multiple partitions | -------------------------------------------------------------------------*/ int InitBeagleMultiPartitionInstance (void) { int i, j, k, c, s, d, *inStates, numPartAmbigTips, sizePD; int nCijkParts, numRateCats, numModelStates, numCondLikes, numScalers; int numChars, numTiProbs; int *anyDivPartAmbigTip; double *inPartials; ModelInfo *m, *mPrev; BitsLong *charBits; int beagleInstance; m = &modelSettings[0]; if (m->beagleInstance == -99) { MrBayesPrint ("\n%s Error: Consecutive MCMC runs not currently supported with BEAGLE. Please restart MrBayes.\n\n", spacer); exit(1); } if (m->useBeagle == NO) return ERROR; /* check if divisions can be merged into single instance */ for (d=1; duseBeagle == NO || m->nCijkParts != mPrev->nCijkParts || m->numRateCats != mPrev->numRateCats || m->numModelStates != mPrev->numModelStates || m->numCondLikes != mPrev->numCondLikes || m->numScalers != mPrev->numScalers || m->numTiProbs != mPrev->numTiProbs) { #ifdef DEBUG_MB_BEAGLE_MULTIPART printf("m->useBeagle = %d\n m->nCijkParts = %d | mPrev->nCijkParts = %d\n m->numRateCats = %d | mPrev->numRateCats = %d\n m->numModelStates = %d | mPrev->numModelStates = %d\n m->numCondLikes = %d | mPrev->numCondLikes = %d\n m->numScalers = %d | mPrev->numScalers = %d\n m->numTiProbs = %d | mPrev->numTiProbs = %d\n", m->useBeagle, m->nCijkParts , mPrev->nCijkParts , m->numRateCats , mPrev->numRateCats , m->numModelStates, mPrev->numModelStates, m->numCondLikes , mPrev->numCondLikes , m->numScalers , mPrev->numScalers , m->numTiProbs , mPrev->numTiProbs); #endif return ERROR; } } anyDivPartAmbigTip = (int *) SafeCalloc (numLocalTaxa * numCurrentDivisions, sizeof(int)); m = &modelSettings[0]; nCijkParts = (m->nCijkParts == 0 ? 1 : m->nCijkParts); numRateCats = m->numRateCats; numModelStates = m->numModelStates; numCondLikes = m->numCondLikes; numScalers = m->numScalers; numChars = 0; numTiProbs = 0; sizePD = nCijkParts*numCurrentDivisions; m->cijkIndicesAll = (int *) SafeCalloc (sizePD*2*numLocalTaxa, sizeof(int)); m->categoryRateIndicesAll = (int *) SafeCalloc (sizePD*2*numLocalTaxa, sizeof(int)); m->operationsAll = (BeagleOperationByPartition *) SafeCalloc(sizePD*m->numCondLikes, sizeof(BeagleOperationByPartition)); m->tiProbIndices = (int *) SafeCalloc (sizePD*2*numLocalTaxa, sizeof(int)); m->branchLengths = (MrBFlt *) SafeCalloc (sizePD*2*numLocalTaxa, sizeof(MrBFlt)); m->bufferIndices = (int *) SafeCalloc (sizePD, sizeof(int)); m->childBufferIndices = (int *) SafeCalloc (sizePD, sizeof(int)); m->childTiProbIndices = (int *) SafeCalloc (sizePD, sizeof(int)); m->eigenIndices = (int *) SafeCalloc (sizePD, sizeof(int)); m->cumulativeScaleIndices = (int *) SafeCalloc (sizePD, sizeof(int)); for (d=0; dnCijkParts == 0) m->nCijkParts = 1; /* allocate memory used by beagle */ m->inRates = (MrBFlt *) SafeCalloc (m->numRateCats, sizeof(MrBFlt)); m->inWeights = (MrBFlt *) SafeCalloc (m->numRateCats*m->nCijkParts, sizeof(MrBFlt)); m->operationsByPartition = (BeagleOperationByPartition *) SafeCalloc(m->numCondLikes*m->nCijkParts, sizeof(BeagleOperationByPartition)); m->scaleFactorsOps = (int *) SafeCalloc (2*numLocalTaxa*m->nCijkParts, sizeof(int)); numChars += m->numChars; numTiProbs += m->numTiProbs; } m = &modelSettings[0]; m->numCharsAll = numChars; m->logLikelihoodsAll = (MrBFlt *) SafeCalloc ((numLocalChains)*m->numCharsAll, sizeof(MrBFlt)); numPartAmbigTips = 0; if (m->numStates != m->numModelStates) numPartAmbigTips = numLocalTaxa; else { for (i=0; iisPartAmbig[i] == YES) { anyDivPartAmbigTip[i] = YES; numPartAmbigTips++; break; } } } else { anyDivPartAmbigTip[i] = YES; numPartAmbigTips++; } } } beagleInstance = createBeagleInstance(m, nCijkParts, numRateCats, numModelStates, numCondLikes, numScalers, numChars, numTiProbs, numPartAmbigTips, -1); if (beagleInstance < 0) return ERROR; /* allocate space for tip data */ inStates = (int *) SafeMalloc (numChars * sizeof(int)); if (!inStates) return ERROR; inPartials = (double *) SafeMalloc (numChars * numModelStates * sizeof(double)); if (!inPartials) return ERROR; for (d=0; dbeagleInstance = beagleInstance; m->useBeagleMultiPartitions = YES; } /* initialize tip data */ for (i=0; iparsSets[i]; for (c=0; cnumChars; c++) { for (s=j=0; snumModelStates; s++) { if (IsBitSet(s, charBits)) { inStates[k] = s; j++; } } if (j == m->numModelStates) inStates[k] = j; else assert (j==1); charBits += m->nParsIntsPerSite; k++; } } beagleSetTipStates(beagleInstance, i, inStates); } else /* if (m->isPartAmbig == YES) */ { k = 0; for (d=0; dparsSets[i]; for (c=0; cnumChars; c++) { for (s=0; snumModelStates; s++) { if (IsBitSet(s%m->numStates, charBits)) inPartials[k++] = 1.0; else inPartials[k++] = 0.0; } charBits += m->nParsIntsPerSite; } } beagleSetTipPartials(beagleInstance, i, inPartials); } } free (anyDivPartAmbigTip); free (inStates); free (inPartials); return NO_ERROR; } void LaunchBEAGLELogLikeMultiPartition(int* divisions, int divisionCount, int chain, MrBFlt* lnL) { int i, d, dIndex, rescaleDivisionCount, norescaleDivisionCount, beagleReturn; int *isScalerNode, *rescaleDivisions, *norescaleDivisions; MrBFlt rescaleLnL; Tree *tree; TreeNode *p; ModelInfo *m; if (beagleScalingScheme == MB_BEAGLE_SCALE_ALWAYS) { # if defined (DEBUG_MB_BEAGLE_FLOW) MrBayesPrint ("ALWAYS RESCALING\n"); # endif for (d=0; dupDateAll == YES) { for (i=0; inCijkParts; i++) { beagleResetScaleFactorsByPartition(m->beagleInstance, m->siteScalerIndex[chain] + i, m->divisionIndex); } } else { CopySiteScalers(m, chain); } } TreeTiProbs_BeagleMultiPartition(divisions, divisionCount, chain); TreeCondLikes_BeagleMultiPartition_Always_Rescale(divisions, divisionCount, chain); /* TODO integrate only divisions being updated */ TreeLikelihood_BeagleMultiPartition(divisions, divisionCount, chain, lnL, (chainId[chain] % chainParams.numChains)); } else { /* MB_BEAGLE_SCALE_DYNAMIC */ (*lnL) = 0.0; rescaleLnL = 0.0; rescaleDivisions = (int *) SafeCalloc (numCurrentDivisions, sizeof(int)); norescaleDivisions = (int *) SafeCalloc (numCurrentDivisions, sizeof(int)); rescaleDivisionCount = 0; norescaleDivisionCount = 0; TreeTiProbs_BeagleMultiPartition(divisions, divisionCount, chain); for (d=0; dbrlens, chain, state[chain]); /* This flag is only valid within this block */ m->rescaleBeagleAll = NO; if (m->successCount[chain] > 1000) { m->successCount[chain] = 10; m->rescaleFreq[chain]++; /* increase rescaleFreq independent of whether we accept or reject new state */ m->rescaleFreqOld = m->rescaleFreqNew = m->rescaleFreq[chain]; for (i=0; inIntNodes; i++) { p = tree->intDownPass[i]; if (p->upDateCl == YES) { /* flip to the new workspace since TreeCondLikes_Beagle_Rescale_All() does not do it for (p->upDateCl == YES) since it assumes that TreeCondLikes_Beagle_No_Rescale() did it */ FlipCondLikeSpace (m, chain, p->index); } } rescaleDivisions[rescaleDivisionCount++] = dIndex; #if defined (DEBUG_MB_BEAGLE_MULTIPART) printf ("NUMERICAL RESCALING FOR DIVISION %d due to successes\n", dIndex); #endif norescaleDivisions[d] = -1; } else if (beagleScalingFrequency != 0 && m->beagleComputeCount[chain] % (beagleScalingFrequency) == 0) { m->rescaleFreqOld = m->rescaleFreqNew = m->rescaleFreq[chain]; for (i=0; inIntNodes; i++) { p = tree->intDownPass[i]; if (p->upDateCl == YES) { /* flip to the new workspace since TreeCondLikes_Beagle_Rescale_All() does not do it for (p->upDateCl == YES) since it assumes that TreeCondLikes_Beagle_No_Rescale() did it */ FlipCondLikeSpace (m, chain, p->index); } } rescaleDivisions[rescaleDivisionCount++] = dIndex; #if defined (DEBUG_MB_BEAGLE_MULTIPART) printf ("NUMERICAL RESCALING FOR DIVISION %d due to beagleScalingFrequency\n", dIndex); #endif norescaleDivisions[d] = -1; } } if (rescaleDivisionCount < divisionCount) { i = 0; for (d=0; dlnLike[2*chain + state[chain]] > DBL_MAX || m->lnLike[2*chain + state[chain]] < -DBL_MAX || m->lnLike[2*chain + state[chain]] != m->lnLike[2*chain + state[chain]]) { m->rescaleFreqOld = m->rescaleFreqNew = m->rescaleFreq[chain]; if (m->rescaleFreqNew > 1 && m->successCount[chain] < 40) { if (m->successCount[chain] < 10) { if (m->successCount[chain] < 4) { m->rescaleFreqNew -= m->rescaleFreqNew >> 3; /* <== we cut up to 12,5% of rescaleFreq */ if (m->successCount[chain] < 2) { m->rescaleFreqNew -= m->rescaleFreqNew >> 3; /* to avoid situation when we may stack at high rescaleFreq when new states do not get accepted because of low likelihood but there proposed frequency is high we reduce rescaleFreq even if we reject the last move*/ /* basically the higher probability of proposing of low likelihood state which needs smaller rescaleFreq would lead to higher probability of hitting this code which should reduce rescaleFreqOld thus reduce further probability of hitting this code */ /* at some point this negative feedback mechanism should get in balance with the mechanism of periodically increasing rescaleFreq when long sequence of successes is achieved*/ m->rescaleFreqOld -= m->rescaleFreqOld >> 3; } m->rescaleFreqOld -= m->rescaleFreqOld >> 3; m->rescaleFreqOld--; m->rescaleFreqOld = (m->rescaleFreqOld ? m->rescaleFreqOld:1); m->recalculateScalers = YES; recalcScalers = YES; } } m->rescaleFreqNew--; m->rescaleFreqNew = (m->rescaleFreqNew ? m->rescaleFreqNew : 1); } m->successCount[chain] = 0; rescaleDivisions[rescaleDivisionCount++] = dIndex; norescaleDivisions[d] = -1; } else { (*lnL)+= m->lnLike[2*chain + state[chain]]; } } // /* rescaling all divisions */ // for (d=0; d 0) { for (d=0; drescaleBeagleAll = YES; FlipSiteScalerSpace(m, chain); } while_loop: for (d=0; dbrlens, chain, state[chain]); isScalerNode = m->isScalerNode[chain]; ResetScalersPartition (isScalerNode, tree, m->rescaleFreqNew); for (i=0; inCijkParts; i++) { beagleResetScaleFactorsByPartition(m->beagleInstance, m->siteScalerIndex[chain] + i, m->divisionIndex); } } TreeCondLikes_BeagleMultiPartition_Rescale_All (rescaleDivisions, rescaleDivisionCount, chain); if (TreeLikelihood_BeagleMultiPartition(rescaleDivisions, rescaleDivisionCount, chain, &rescaleLnL, (chainId[chain] % chainParams.numChains)) == BEAGLE_ERROR_FLOATING_POINT) { for (d=0; dbrlens, chain, state[chain]); isScalerNode = m->isScalerNode[chain]; if (m->rescaleFreqNew > 1) { /* Swap back scalers which were swapped in TreeCondLikes_Beagle_Rescale_All() */ for (i=0; inIntNodes; i++) { p = tree->intDownPass[i]; if (isScalerNode[p->index] == YES) FlipNodeScalerSpace (m, chain, p->index); } m->rescaleFreqNew -= m->rescaleFreqNew >> 3; /* <== we cut up to 12,5% of rescaleFreq */ m->rescaleFreqNew--; /* we cut extra 1 of rescaleFreq */ } else { m->rescaleFreq[chain] = m->rescaleFreqNew; rescaleDivisions[d] = -1; } } i = 0; for (d=0; d 0) { goto while_loop; } } for (d=0; drescaleFreq[chain] = m->rescaleFreqNew; } (*lnL) += rescaleLnL; } for (d=0; dbeagleComputeCount[chain]++; } free(rescaleDivisions); free(norescaleDivisions); } return; } /*---------------------------------------------------------------- | | TreeTiProbs_BeagleMultiPartition: This routine updates all transition | probability matrices of a beagle instance across all divisions. | -----------------------------------------------------------------*/ int TreeTiProbs_BeagleMultiPartition (int* divisions, int divisionCount, int chain) { int i, j, k, d, dIndex, count, divisionOffset, divisionCijkIndex; MrBFlt correctionFactor, theRate, baseRate, *catRate, length, *branchLengthsAll; TreeNode *p; Tree *t; ModelInfo *m; int *cijkIndicesAll, *tiProbIndicesAll, *categoryRateIndicesAll; cijkIndicesAll = modelSettings[0].cijkIndicesAll; categoryRateIndicesAll = modelSettings[0].categoryRateIndicesAll; tiProbIndicesAll = modelSettings[0].tiProbIndices; branchLengthsAll = modelSettings[0].branchLengths; count = 0; divisionOffset = 0; for (d=0; dbrlens, chain, state[chain]); divisionOffset = m->numTiProbs * m->nCijkParts * m->divisionIndex; #if defined (DEBUG_MB_BEAGLE_MULTIPART) printf("m->upDateAll %d\n", m->upDateAll); #endif /* find the correction factor to make branch lengths in terms of expected number of substitutions per character */ correctionFactor = 1.0; if (m->dataType == DNA || m->dataType == RNA) { if (m->nucModelId == NUCMODEL_DOUBLET) correctionFactor = 2.0; else if (m->nucModelId == NUCMODEL_CODON) correctionFactor = 3.0; } /* get rate multipliers (for gamma & partition specific rates) */ theRate = 1.0; baseRate = GetRate (dIndex, chain); /* compensate for invariable sites if appropriate */ if (m->pInvar != NULL) baseRate /= (1.0 - (*GetParamVals(m->pInvar, chain, state[chain]))); /* get category rates for gamma */ if (m->shape == NULL) catRate = &theRate; else catRate = GetParamSubVals (m->shape, chain, state[chain]); /* get effective category rates */ for (k=0; knumRateCats; k++) m->inRates[k] = baseRate * catRate[k] * correctionFactor; /* TODO: only need to set category rates when they change */ /* set category rates */ beagleSetCategoryRatesWithIndex(m->beagleInstance, m->divisionIndex, m->inRates); divisionCijkIndex = m->cijkIndex[chain] + (numLocalChains + 1) * m->nCijkParts * m->divisionIndex; /* get ti prob indices and branch lengths to update */ for (i=0; inNodes; i++) { p = t->allDownPass[i]; /* check if transition probs need updating */ if (p->upDateTi == YES) { /* flip transition probability */ FlipTiProbsSpace (m, chain, p->index); /* find length */ if (m->cppEvents != NULL) { length = GetParamSubVals (m->cppEvents, chain, state[chain])[p->index]; } else if (m->tk02BranchRates != NULL) { length = GetParamSubVals (m->tk02BranchRates, chain, state[chain])[p->index]; } else if (m->wnBranchRates != NULL) { length = GetParamSubVals (m->wnBranchRates, chain, state[chain])[p->index]; } else if (m->ilnBranchRates != NULL) { length = GetParamSubVals (m->ilnBranchRates, chain, state[chain])[p->index]; } else if (m->igrBranchRates != NULL) { length = GetParamSubVals (m->igrBranchRates, chain, state[chain])[p->index]; } else if (m->mixedBrchRates != NULL) { length = GetParamSubVals (m->mixedBrchRates, chain, state[chain])[p->index]; } else length = p->length; /* numerical errors might ensue if we allow very large or very small branch lengths, which might occur in relaxed clock models; an elegant solution would be to substitute the stationary probs and initial probs but for now we truncate lengths at small or large values */ if (length > BRLENS_MAX) length = BRLENS_MAX; else if (length < BRLENS_MIN) length = BRLENS_MIN; branchLengthsAll[count] = length; tiProbIndicesAll[count] = m->tiProbsIndex[chain][p->index] + divisionOffset; categoryRateIndicesAll[count] = m->divisionIndex; cijkIndicesAll[count] = divisionCijkIndex; #if defined (DEBUG_MB_BEAGLE_MULTIPART) { printf("%2d %f ", count, branchLengthsAll[count]); printf(" %3d", tiProbIndicesAll[count]); printf(" %d", categoryRateIndicesAll[count]); printf(" %f %f %f %f", m->inRates[0], m->inRates[1], m->inRates[2], m->inRates[3]); printf(" %d\n", cijkIndicesAll[count]); } #endif count++; } } } /* TODO: only need to update branches that have changed */ /* calculate transition probabilities */ if (count > 0) { for (i=1; inCijkParts; i++) { for (j=0; jbeagleInstance, cijkIndicesAll, categoryRateIndicesAll, tiProbIndicesAll, NULL, NULL, branchLengthsAll, count * m->nCijkParts); } /* return success */ return NO_ERROR; } /*---------------------------------------------------------------- | | TreeCondLikes_Beagle: This routine updates all conditional | (partial) likelihoods of a beagle instance across all divisions while doing no rescaling. | That potentially can make final likelihood bad then calculation with rescaling needs to be done. | -----------------------------------------------------------------*/ int TreeCondLikes_BeagleMultiPartition_No_Rescale (int* divisions, int divisionCount, int chain) { int i, j, d, dIndex, divisionOffset; int opJ, opPrev, opCountMax, opCountTotal; Tree *t; TreeNode *p; ModelInfo *m; unsigned chil1Step, chil2Step; int *isScalerNode; BeagleOperationByPartition *operationsAll; m = &modelSettings[0]; operationsAll = m->operationsAll; opCountMax = 0; for (d=0; dbrlens, chain, state[chain]); divisionOffset = m->numTiProbs * m->nCijkParts * m->divisionIndex; isScalerNode = m->isScalerNode[chain]; m->opCount = 0; for (i=0; inIntNodes; i++) { if (t->levelPassEnabled) { p = t->intDownPassLevel[i]; } else { p = t->intDownPass[i]; } /* check if conditional likelihoods need updating */ if (p->upDateCl == YES) { /* flip to the new workspace */ FlipCondLikeSpace (m, chain, p->index); /* update conditional likelihoods */ m->operationsByPartition[m->opCount].destinationPartials = m->condLikeIndex[chain][p->index ]; m->operationsByPartition[m->opCount].child1Partials = m->condLikeIndex[chain][p->left->index ]; m->operationsByPartition[m->opCount].child1TransitionMatrix = m->tiProbsIndex [chain][p->left->index ] + divisionOffset; m->operationsByPartition[m->opCount].child2Partials = m->condLikeIndex[chain][p->right->index]; m->operationsByPartition[m->opCount].child2TransitionMatrix = m->tiProbsIndex [chain][p->right->index] + divisionOffset; /* All partials for tips are the same across omega categories, thus we are doing the following two if statements.*/ if (p->left->left== NULL) chil1Step=0; else chil1Step=1; if (p->right->left== NULL) chil2Step=0; else chil2Step=1; m->operationsByPartition[m->opCount].destinationScaleWrite = BEAGLE_OP_NONE; m->operationsByPartition[m->opCount].cumulativeScaleIndex = BEAGLE_OP_NONE; if (isScalerNode[p->index] == YES) { m->operationsByPartition[m->opCount].destinationScaleRead = m->nodeScalerIndex[chain][p->index]; } else { m->operationsByPartition[m->opCount].destinationScaleRead = BEAGLE_OP_NONE; } for (j=1; jnCijkParts; j++) { opJ = m->opCount + j * t->nIntNodes; opPrev = m->opCount + (j-1) * t->nIntNodes; m->operationsByPartition[opJ].destinationPartials = m->operationsByPartition[opPrev].destinationPartials + 1; m->operationsByPartition[opJ].child1Partials = m->operationsByPartition[opPrev].child1Partials + chil1Step; m->operationsByPartition[opJ].child1TransitionMatrix = m->operationsByPartition[opPrev].child1TransitionMatrix + 1; m->operationsByPartition[opJ].child2Partials = m->operationsByPartition[opPrev].child2Partials + chil2Step; m->operationsByPartition[opJ].child2TransitionMatrix = m->operationsByPartition[opPrev].child2TransitionMatrix + 1; m->operationsByPartition[opJ].destinationScaleWrite = BEAGLE_OP_NONE; m->operationsByPartition[opJ].cumulativeScaleIndex = BEAGLE_OP_NONE; if ( isScalerNode[p->index] == YES ) { m->operationsByPartition[opJ].destinationScaleRead = m->operationsByPartition[opPrev].destinationScaleRead + 1; } else { m->operationsByPartition[opJ].destinationScaleRead = BEAGLE_OP_NONE; } } m->opCount++; } } if (m->opCount > opCountMax) opCountMax = m->opCount; } opCountTotal = 0; for (i=0; inCijkParts; j++) { opJ = i + j*t->nIntNodes; for (d=0; dopCount) { operationsAll[opCountTotal].destinationPartials = m->operationsByPartition[opJ].destinationPartials; operationsAll[opCountTotal].destinationScaleWrite = m->operationsByPartition[opJ].destinationScaleWrite; operationsAll[opCountTotal].destinationScaleRead = m->operationsByPartition[opJ].destinationScaleRead; operationsAll[opCountTotal].child1Partials = m->operationsByPartition[opJ].child1Partials; operationsAll[opCountTotal].child1TransitionMatrix = m->operationsByPartition[opJ].child1TransitionMatrix; operationsAll[opCountTotal].child2Partials = m->operationsByPartition[opJ].child2Partials; operationsAll[opCountTotal].child2TransitionMatrix = m->operationsByPartition[opJ].child2TransitionMatrix; operationsAll[opCountTotal].partition = m->divisionIndex; operationsAll[opCountTotal].cumulativeScaleIndex = m->operationsByPartition[opJ].cumulativeScaleIndex; opCountTotal++; } } } } beagleUpdatePartialsByPartition(m->beagleInstance, operationsAll, opCountTotal); return NO_ERROR; } /*---------------------------------------------------------------- | | TreeCondLikes_Beagle: This routine updates all conditional | (partial) likelihoods of a beagle instance across all divisions while rescaling at every node. | Note: all nodes get recalculated, not only touched by move. | -----------------------------------------------------------------*/ int TreeCondLikes_BeagleMultiPartition_Rescale_All (int* divisions, int divisionCount, int chain) { int i, j, d, dIndex, divisionOffset; int opJ, opPrev, opCountMax, opCountTotal; Tree *t; TreeNode *p; ModelInfo *m; unsigned chil1Step, chil2Step; int *isScalerNode; BeagleOperationByPartition *operationsAll; m = &modelSettings[0]; operationsAll = m->operationsAll; opCountMax = 0; t = GetTree(m->brlens, chain, state[chain]); for (d=0; dbrlens, chain, state[chain]); divisionOffset = m->numTiProbs * m->nCijkParts * m->divisionIndex; isScalerNode = m->isScalerNode[chain]; m->opCount = 0; for (i=0; inIntNodes; i++) { if (t->levelPassEnabled) { p = t->intDownPassLevel[i]; } else { p = t->intDownPass[i]; } if (p->upDateCl == NO) { //p->upDateCl = YES; /* flip to the new workspace */ FlipCondLikeSpace (m, chain, p->index); } /* update conditional likelihoods */ m->operationsByPartition[m->opCount].destinationPartials = m->condLikeIndex[chain][p->index ]; m->operationsByPartition[m->opCount].child1Partials = m->condLikeIndex[chain][p->left->index ]; m->operationsByPartition[m->opCount].child1TransitionMatrix = m->tiProbsIndex [chain][p->left->index ] + divisionOffset; m->operationsByPartition[m->opCount].child2Partials = m->condLikeIndex[chain][p->right->index]; m->operationsByPartition[m->opCount].child2TransitionMatrix = m->tiProbsIndex [chain][p->right->index] + divisionOffset; /* All partials for tips are the same across omega categories, thus we are doing the following two if statements.*/ if (p->left->left== NULL) chil1Step=0; else chil1Step=1; if (p->right->left== NULL) chil2Step=0; else chil2Step=1; m->operationsByPartition[m->opCount].destinationScaleRead = BEAGLE_OP_NONE; if (isScalerNode[p->index] == YES) { FlipNodeScalerSpace (m, chain, p->index); m->operationsByPartition[m->opCount].destinationScaleWrite = m->nodeScalerIndex[chain][p->index]; m->operationsByPartition[m->opCount].cumulativeScaleIndex = m->siteScalerIndex[chain]; } else { m->operationsByPartition[m->opCount].destinationScaleWrite = BEAGLE_OP_NONE; m->operationsByPartition[m->opCount].cumulativeScaleIndex = BEAGLE_OP_NONE; } for (j=1; jnCijkParts; j++) { opJ = m->opCount + j * t->nIntNodes; opPrev = m->opCount + (j-1) * t->nIntNodes; m->operationsByPartition[opJ].destinationPartials = m->operationsByPartition[opPrev].destinationPartials + 1; m->operationsByPartition[opJ].child1Partials = m->operationsByPartition[opPrev].child1Partials + chil1Step; m->operationsByPartition[opJ].child1TransitionMatrix = m->operationsByPartition[opPrev].child1TransitionMatrix + 1; m->operationsByPartition[opJ].child2Partials = m->operationsByPartition[opPrev].child2Partials + chil2Step; m->operationsByPartition[opJ].child2TransitionMatrix = m->operationsByPartition[opPrev].child2TransitionMatrix + 1; m->operationsByPartition[opJ].destinationScaleRead = BEAGLE_OP_NONE; if ( isScalerNode[p->index] == YES ) { m->operationsByPartition[opJ].destinationScaleWrite = m->operationsByPartition[opPrev].destinationScaleWrite + 1; m->operationsByPartition[opJ].cumulativeScaleIndex = m->operationsByPartition[opPrev].cumulativeScaleIndex + 1; } else { m->operationsByPartition[opJ].destinationScaleWrite = BEAGLE_OP_NONE; m->operationsByPartition[opJ].cumulativeScaleIndex = BEAGLE_OP_NONE; } } m->opCount++; } if (m->opCount > opCountMax) opCountMax = m->opCount; } for (j=0; jnCijkParts; j++) { opCountTotal = 0; for (i=0; inIntNodes; for (d=0; dopCount) { operationsAll[opCountTotal].destinationPartials = m->operationsByPartition[opJ].destinationPartials; operationsAll[opCountTotal].destinationScaleWrite = m->operationsByPartition[opJ].destinationScaleWrite; operationsAll[opCountTotal].destinationScaleRead = m->operationsByPartition[opJ].destinationScaleRead; operationsAll[opCountTotal].child1Partials = m->operationsByPartition[opJ].child1Partials; operationsAll[opCountTotal].child1TransitionMatrix = m->operationsByPartition[opJ].child1TransitionMatrix; operationsAll[opCountTotal].child2Partials = m->operationsByPartition[opJ].child2Partials; operationsAll[opCountTotal].child2TransitionMatrix = m->operationsByPartition[opJ].child2TransitionMatrix; operationsAll[opCountTotal].partition = m->divisionIndex; operationsAll[opCountTotal].cumulativeScaleIndex = m->operationsByPartition[opJ].cumulativeScaleIndex; opCountTotal++; } } } beagleUpdatePartialsByPartition(m->beagleInstance, operationsAll, opCountTotal); } return NO_ERROR; } /*---------------------------------------------------------------- | | TreeCondLikes_BeagleMultiPartition: This routine updates and rescales all conditional | (partial) likelihoods of a beagle instance across all divisions. | -----------------------------------------------------------------*/ int TreeCondLikes_BeagleMultiPartition_Always_Rescale (int* divisions, int divisionCount, int chain) { int i, j, d, dIndex, destinationScaleRead, cumulativeScaleIndex; int opJ, opPrev, opCountMax, opCountTotal, divisionOffset, scaleOp; Tree *t; TreeNode *p; ModelInfo *m; unsigned chil1Step, chil2Step; BeagleOperationByPartition *operationsAll; m = &modelSettings[0]; operationsAll = m->operationsAll; opCountMax = 0; for (d=0; dbrlens, chain, state[chain]); divisionOffset = m->numTiProbs * m->nCijkParts * m->divisionIndex; m->opCount = 0; scaleOp = 0; for (i=0; inIntNodes; i++) { if (t->levelPassEnabled) { p = t->intDownPassLevel[i]; } else { p = t->intDownPass[i]; } /* check if conditional likelihoods need updating */ if (p->upDateCl == YES) { /* remove old scalers */ if (m->upDateAll == NO) { destinationScaleRead = m->nodeScalerIndex[chain][p->index]; for (j=0; jnCijkParts; j++) { m->scaleFactorsOps[scaleOp+j*t->nIntNodes] = destinationScaleRead; destinationScaleRead++; } scaleOp++; } /* flip to the new workspace */ FlipCondLikeSpace (m, chain, p->index); FlipNodeScalerSpace (m, chain, p->index); /* update conditional likelihoods */ m->operationsByPartition[m->opCount].destinationPartials = m->condLikeIndex[chain][p->index ]; m->operationsByPartition[m->opCount].child1Partials = m->condLikeIndex[chain][p->left->index ]; m->operationsByPartition[m->opCount].child1TransitionMatrix = m->tiProbsIndex [chain][p->left->index ] + divisionOffset; m->operationsByPartition[m->opCount].child2Partials = m->condLikeIndex[chain][p->right->index]; m->operationsByPartition[m->opCount].child2TransitionMatrix = m->tiProbsIndex [chain][p->right->index] + divisionOffset; /* All partials for tips are the same across omega categories, thus we are doing the following two if statements.*/ if (p->left->left== NULL && p->left->right== NULL) chil1Step=0; else chil1Step=1; if (p->right->left== NULL && p->right->right== NULL) chil2Step=0; else chil2Step=1; m->operationsByPartition[m->opCount].destinationScaleWrite = m->nodeScalerIndex[chain][p->index]; m->operationsByPartition[m->opCount].cumulativeScaleIndex = m->siteScalerIndex[chain]; m->operationsByPartition[m->opCount].destinationScaleRead = BEAGLE_OP_NONE; for (j=1; jnCijkParts; j++) { opJ = m->opCount + j * t->nIntNodes; opPrev = m->opCount + (j-1) * t->nIntNodes; m->operationsByPartition[opJ].destinationPartials = m->operationsByPartition[opPrev].destinationPartials + 1; m->operationsByPartition[opJ].child1Partials = m->operationsByPartition[opPrev].child1Partials + chil1Step; m->operationsByPartition[opJ].child1TransitionMatrix = m->operationsByPartition[opPrev].child1TransitionMatrix + 1; m->operationsByPartition[opJ].child2Partials = m->operationsByPartition[opPrev].child2Partials + chil2Step; m->operationsByPartition[opJ].child2TransitionMatrix = m->operationsByPartition[opPrev].child2TransitionMatrix + 1; m->operationsByPartition[opJ].destinationScaleWrite = m->operationsByPartition[opPrev].destinationScaleWrite + 1; m->operationsByPartition[opJ].cumulativeScaleIndex = m->operationsByPartition[opPrev].cumulativeScaleIndex + 1; m->operationsByPartition[opJ].destinationScaleRead = BEAGLE_OP_NONE; } m->opCount++; } } /* end of nIntNodes for */ if (m->opCount > opCountMax) { opCountMax = m->opCount; } if (m->upDateAll == NO) { cumulativeScaleIndex = m->siteScalerIndex[chain]; for (j=0; jnCijkParts; j++) { beagleRemoveScaleFactorsByPartition(m->beagleInstance, &m->scaleFactorsOps[j*t->nIntNodes], scaleOp, cumulativeScaleIndex, m->divisionIndex); cumulativeScaleIndex++; } } } for (j=0; jnCijkParts; j++) { opCountTotal = 0; for (i=0; inIntNodes; for (d=0; dopCount) { operationsAll[opCountTotal].destinationPartials = m->operationsByPartition[opJ].destinationPartials; operationsAll[opCountTotal].destinationScaleWrite = m->operationsByPartition[opJ].destinationScaleWrite; operationsAll[opCountTotal].destinationScaleRead = m->operationsByPartition[opJ].destinationScaleRead; operationsAll[opCountTotal].child1Partials = m->operationsByPartition[opJ].child1Partials; operationsAll[opCountTotal].child1TransitionMatrix = m->operationsByPartition[opJ].child1TransitionMatrix; operationsAll[opCountTotal].child2Partials = m->operationsByPartition[opJ].child2Partials; operationsAll[opCountTotal].child2TransitionMatrix = m->operationsByPartition[opJ].child2TransitionMatrix; operationsAll[opCountTotal].partition = m->divisionIndex; operationsAll[opCountTotal].cumulativeScaleIndex = m->operationsByPartition[opJ].cumulativeScaleIndex; #if defined (DEBUG_MB_BEAGLE_MULTIPART) { printf("%2d %3d ", opCountTotal, operationsAll[opCountTotal].destinationPartials); printf("%3d ", operationsAll[opCountTotal].destinationScaleWrite); printf("%3d ", operationsAll[opCountTotal].destinationScaleRead); printf("%3d ", operationsAll[opCountTotal].child1Partials); printf("%3d ", operationsAll[opCountTotal].child1TransitionMatrix); printf("%3d ", operationsAll[opCountTotal].child2Partials); printf("%3d ", operationsAll[opCountTotal].child2TransitionMatrix); printf("%d ", operationsAll[opCountTotal].partition); printf("%3d\n", operationsAll[opCountTotal].cumulativeScaleIndex); } #endif opCountTotal++; } } } beagleUpdatePartialsByPartition(m->beagleInstance, operationsAll, opCountTotal); } return NO_ERROR; } /*--------------------------------------------------------------------------- | | TreeLikelihood_BeagleMultiPartition: Accumulate the log likelihoods calculated by Beagle | at the root across all divisions. | -----------------------------------------------------------------------------*/ int TreeLikelihood_BeagleMultiPartition (int* divisions, int divisionCount, int chain, MrBFlt *lnL, int whichSitePats) { int i, j, d, c = 0, nStates, beagleReturn, site, dIndex, divisionOffset; int hasPInvar, hasAnyPInvar, hasAnyDataRestriction; MrBFlt *swr, s01, s10, probOn, probOff, covBF[40], pInvar=0.0, *bs, freq, likeI, lnLikeI, diff, *omegaCatFreq, *lnLDiv; CLFlt *clInvar=NULL, *nSitesOfPat; double *nSitesOfPat_Beagle; TreeNode *p; Tree *t, *tZero; ModelInfo *m; double pUnobserved; # if defined (MB_PRINT_DYNAMIC_RESCALE_FAIL_STAT) static unsigned countBeagleDynamicFail=0; static unsigned countALL=0; # endif hasAnyPInvar = NO; hasAnyDataRestriction = NO; m = &modelSettings[0]; nStates = m->numModelStates; for (d=0; dnCijkParts * m->divisionIndex; t = GetTree(m->brlens, chain, state[chain]); /* find root node */ p = t->root->left; if (m->pInvar == NULL) { hasPInvar = NO; } else { hasPInvar = YES; hasAnyPInvar = YES; pInvar = *(GetParamVals (m->pInvar, chain, state[chain])); #if defined (DEBUG_MB_BEAGLE_MULTIPART) printf("pInvar[%d] = %f\n", dIndex, *(GetParamVals (m->pInvar, chain, state[chain]))); #endif } if (m->dataType == RESTRICTION) hasAnyDataRestriction = YES; /* find base frequencies */ bs = GetParamSubVals (m->stateFreq, chain, state[chain]); /* if covarion model, adjust base frequencies */ if (m->switchRates != NULL) { /* find the stationary frequencies */ swr = GetParamVals(m->switchRates, chain, state[chain]); s01 = swr[0]; s10 = swr[1]; probOn = s01 / (s01 + s10); probOff = 1.0 - probOn; /* now adjust the base frequencies; on-state stored first in cond likes */ for (j=0; jupDateCijk == YES) { /* set base frequencies in BEAGLE instance */ for (i=0; inCijkParts; i++) beagleSetStateFrequencies(m->beagleInstance, m->cijkIndex[chain] + i + divisionOffset, bs); } /* find category frequencies */ if (hasPInvar == NO) freq = 1.0 / m->numRateCats; else freq = (1.0 - pInvar) / m->numRateCats; #if defined (DEBUG_MB_BEAGLE_MULTIPART) printf("freq = %f\n", freq); #endif /* TODO: cat weights only need to be set when they change */ /* set category frequencies in beagle instance */ if (m->numOmegaCats > 1) { omegaCatFreq = GetParamSubVals(m->omega, chain, state[chain]); for (i=0; inCijkParts; i++) { for (j=0; jnumRateCats; j++) m->inWeights[j] = freq * omegaCatFreq[i]; beagleSetCategoryWeights(m->beagleInstance, m->cijkIndex[chain] + i + divisionOffset, m->inWeights); } } else if (hasPInvar == YES) { for (i=0; inumRateCats; i++) m->inWeights[i] = freq; beagleSetCategoryWeights(m->beagleInstance, m->cijkIndex[chain] + divisionOffset, m->inWeights); } } /* TODO: pattern weights only need to be set when they change */ /* set pattern weights in beagle instance if using dynamic reweighting */ if (chainParams.weightScheme[0] + chainParams.weightScheme[1] > ETA) { nSitesOfPat_Beagle = (double *) SafeMalloc (m->numCharsAll * sizeof(double)); site = 0; for (d=0; dbeagleInstance, nSitesOfPat_Beagle); SAFEFREE (nSitesOfPat_Beagle); } tZero = GetTree(modelSettings[0].brlens, chain, state[chain]); /* find root log likelihoods and scalers */ j=0; for (i=0; inCijkParts; i++) { for (d=0; dbrlens, chain, state[chain]); p = t->root->left; divisionOffset = (numLocalChains + 1) * m->nCijkParts * m->divisionIndex; modelSettings[0].bufferIndices[j] = m->condLikeIndex[chain][p->index] + i; modelSettings[0].eigenIndices[j] = m->cijkIndex[chain] + i + divisionOffset; modelSettings[0].cumulativeScaleIndices[j] = m->siteScalerIndex[chain] + i; #if defined (DEBUG_MB_BEAGLE_MULTIPART) printf("dIndex = %d, bufferIndices = %d, eigenIndices = %d, cumulativeScaleIndices = %d\n", dIndex, modelSettings[0].bufferIndices[j], modelSettings[0].eigenIndices[j], modelSettings[0].cumulativeScaleIndices[j]); #endif if (tZero->isRooted == NO) { divisionOffset = m->numTiProbs * m->nCijkParts * m->divisionIndex; modelSettings[0].childBufferIndices[j] = m->condLikeIndex[chain][p->anc->index]; modelSettings[0].childTiProbIndices[j] = m->tiProbsIndex [chain][p->index] + i + divisionOffset; #if defined (DEBUG_MB_BEAGLE_MULTIPART) printf("childBufferIndices = %d, childTiProbIndices = %d\n", modelSettings[0].childBufferIndices[j], modelSettings[0].childTiProbIndices[j]); #endif } j++; } } /* reset lnL */ *lnL = 0.0; /* get root log likelihoods */ if (tZero->isRooted == YES) { beagleReturn = beagleCalculateRootLogLikelihoodsByPartition(modelSettings[0].beagleInstance, modelSettings[0].bufferIndices, modelSettings[0].eigenIndices, modelSettings[0].eigenIndices, modelSettings[0].cumulativeScaleIndices, divisions, divisionCount, modelSettings[0].nCijkParts, modelSettings[0].logLikelihoodsAll, /* assuming numChars >= numDivisions */ lnL); } else { beagleReturn = beagleCalculateEdgeLogLikelihoodsByPartition( modelSettings[0].beagleInstance, modelSettings[0].bufferIndices, modelSettings[0].childBufferIndices, modelSettings[0].childTiProbIndices, NULL, NULL, modelSettings[0].eigenIndices, modelSettings[0].eigenIndices, modelSettings[0].cumulativeScaleIndices, divisions, divisionCount, modelSettings[0].nCijkParts, modelSettings[0].logLikelihoodsAll, lnL, NULL, NULL, NULL, NULL); } # if defined (MB_PRINT_DYNAMIC_RESCALE_FAIL_STAT) countALL++; # endif if (*lnL > DBL_MAX || *lnL < -DBL_MAX || *lnL != *lnL) { beagleReturn = BEAGLE_ERROR_FLOATING_POINT; } for (d=0; dlnLike[2*chain + state[chain]] = modelSettings[0].logLikelihoodsAll[d]; if (m->lnLike[2*chain + state[chain]] > DBL_MAX || m->lnLike[2*chain + state[chain]] < -DBL_MAX || m->lnLike[2*chain + state[chain]] != m->lnLike[2*chain + state[chain]]) { beagleReturn = BEAGLE_ERROR_FLOATING_POINT; } else { m->successCount[chain]++; } } if (beagleReturn == BEAGLE_ERROR_FLOATING_POINT) { # if defined (MB_PRINT_DYNAMIC_RESCALE_FAIL_STAT) countBeagleDynamicFail++; MrBayesPrint ("DEBUG INFO (not an error) countBeagleDynamicFail:%d countALL:%d\n", countBeagleDynamicFail, countALL); # endif } #if defined (DEBUG_MB_BEAGLE_MULTIPART_SITELNL) beagleGetSiteLogLikelihoods(modelSettings[0].beagleInstance, modelSettings[0].logLikelihoodsAll); printf("lnL = "); site = 0; for (d=0; dlnLike[2*chain + state[chain]]; if (!(*lnLDiv > DBL_MAX || *lnLDiv < -DBL_MAX || *lnLDiv != *lnLDiv)) { /* find nSitesOfPat */ nSitesOfPat = numSitesOfPat + (whichSitePats*numCompressedChars) + m->compCharStart; site = 0; for (i=0; ipInvar == NULL) { if (m->dataType == RESTRICTION) { (*lnLDiv) = 0.0; pUnobserved = 0.0; for (c=0; cnumDummyChars; c++) { pUnobserved += exp((double)modelSettings[0].logLikelihoodsAll[site]); site++; } /* correct for absent characters */ (*lnLDiv) -= log (1-pUnobserved) * (m->numUncompressedChars); for (; cnumChars; c++) { (*lnLDiv) += modelSettings[0].logLikelihoodsAll[site] * nSitesOfPat[c]; site++; } } } else { (*lnLDiv) = 0.0; /* has invariable category */ pInvar = *(GetParamVals (m->pInvar, chain, state[chain])); clInvar = m->invCondLikes; /* find base frequencies */ bs = GetParamSubVals (m->stateFreq, chain, state[chain]); /* if covarion model, adjust base frequencies */ if (m->switchRates != NULL) { /* find the stationary frequencies */ swr = GetParamVals(m->switchRates, chain, state[chain]); s01 = swr[0]; s10 = swr[1]; probOn = s01 / (s01 + s10); probOff = 1.0 - probOn; /* now adjust the base frequencies; on-state stored first in cond likes */ for (j=0; jnumChars; c++) { likeI = 0.0; for (j=0; j 200.0) (*lnLDiv) += lnLikeI * nSitesOfPat[c]; else { (*lnLDiv) += (modelSettings[0].logLikelihoodsAll[site] + log(1.0 + exp(diff))) * nSitesOfPat[c]; } site++; } } (*lnL) += m->lnLike[2*chain + state[chain]]; } } /* check for numerical errors */ assert ((*lnL) == (*lnL)); } return beagleReturn; } #endif /* BEAGLE_V3_ENABLED */ #endif /* BEAGLE_ENABLED */ void BeagleNotLinked (void) { MrBayesPrint ("%s BEAGLE library is not linked to this executable.\n", spacer); } void BeagleThreadsNotAvailable (void) { MrBayesPrint ("%s BEAGLE CPU threading requires v3.1 and higher of the library.\n", spacer); } NBISweden-MrBayes-ecfd55c/src/mbbeagle.h000066400000000000000000000040561526212227600201200ustar00rootroot00000000000000#ifndef MBBEAGLE_H_ #define MBBEAGLE_H_ void BeaglePrintResources (void); void BeaglePrintFlags (long inFlags); void BeagleNotLinked (void); void BeagleThreadsNotAvailable (void); int BeagleCheckFlagCompatability (long inFlags); void BeagleAddGPUDevicesToList (int **beagleResource, int *beagleResourceCount); void BeagleRemoveGPUDevicesFromList (int **beagleResource, int *beagleResourceCount); int ScheduleLogLikeForAllDivisions (void); void LaunchBEAGLELogLikeForDivision (int chain, int d, ModelInfo* m, Tree* tree, MrBFlt* lnL); void *LaunchThreadLogLikeForDivision (void *arguments); MrBFlt LaunchLogLikeForAllDivisionsInParallel (int chain); void recalculateScalers (int chain); int InitBeagleInstance (ModelInfo *m, int division); int createBeagleInstance(ModelInfo *m, int nCijkParts, int numGammaCats, int numModelStates, int numCondLikes, int numScalers, int numChars, int numTiProbs, int numPartAmbigTips, int division); int TreeCondLikes_Beagle_Always_Rescale (Tree *t, int division, int chain); int TreeLikelihood_Beagle (Tree *t, int division, int chain, MrBFlt *lnL, int whichSitePats); int TreeTiProbs_Beagle (Tree *t, int division, int chain); int TreeCondLikes_Beagle_No_Rescale (Tree *t, int division, int chain); int TreeCondLikes_Beagle_Rescale_All (Tree *t, int division, int chain); int InitBeagleMultiPartitionInstance (void); void LaunchBEAGLELogLikeMultiPartition(int* divisions, int divisionCount, int chain, MrBFlt* lnL); int TreeTiProbs_BeagleMultiPartition (int* divisions, int divisionCount, int chain); int TreeCondLikes_BeagleMultiPartition_No_Rescale (int* divisions, int divisionCount, int chain); int TreeCondLikes_BeagleMultiPartition_Rescale_All (int* divisions, int divisionCount, int chain); int TreeCondLikes_BeagleMultiPartition_Always_Rescale (int* divisions, int divisionCount, int chain); int TreeLikelihood_BeagleMultiPartition (int* divisions, int divisionCount, int chain, MrBFlt *lnL, int whichSitePats); //extern char *beagleGetVersion (void); #endif /* MBBEAGLE_H_ */ NBISweden-MrBayes-ecfd55c/src/mcmc.c000066400000000000000000026357261526212227600173140ustar00rootroot00000000000000/* * MrBayes 3 * * (c) 2002-2023 * * John P. Huelsenbeck * Dept. Integrative Biology * University of California, Berkeley * Berkeley, CA 94720-3140 * johnh@berkeley.edu * * Fredrik Ronquist * Swedish Museum of Natural History * Box 50007 * SE-10405 Stockholm, SWEDEN * fredrik.ronquist@nrm.se * * With important contributions by * * Paul van der Mark (paulvdm@sc.fsu.edu) * Maxim Teslenko (maxkth@gmail.com) * Chi Zhang (zhangchicool@gmail.com) * * and by many users (run 'acknowledgments' to see more info) * * This program is free software; you can redistribute it and/or * modify it under the terms of the GNU General Public License * as published by the Free Software Foundation; either version 2 * of the License, or (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details (www.gnu.org). * */ #undef DEBUG_TIME #if defined (DEBUG_TIME) #define _POSIX_C_SOURCE 199309L #include #endif #include "bayes.h" #include "best.h" #include "command.h" #include "likelihood.h" #include "mbbeagle.h" #include "mcmc.h" #include "model.h" #include "proposal.h" #include "sumpt.h" #include "utils.h" #if defined(__MWERKS__) #include "SIOUX.h" #endif #include #include #if defined (WIN_VERSION) && !defined (__GNUC__) #define VISUAL #else typedef void (*sighandler_t) (int); #endif #define GIBBS_SAMPLE_FREQ 100 /* generations between gibbs sampling of gamma cats */ #define MAX_SMALL_JUMP 10 /* threshold for precalculating trans probs of adgamma model */ #define BIG_JUMP 100 /* threshold for using stationary approximation */ #define MAX_RUNS 120 /* maximum number of independent runs */ #define PFILE 0 #define TFILE 1 #define CALFILE 2 #define MCMCFILE 3 #define MAXTUNINGPARAM 10000 /* limit to ensure convergence for autotuning */ #define SAMPLE_ALL_SS /* if defined makes ss sample every generation instead of every sample frequency */ #define BEAGLE_RESCALE_FREQ 160 #define BEAGLE_RESCALE_FREQ_DOUBLE 10 /* The factor by which BEAGLE_RESCALE_FREQ get multiplied if double precision is used */ #define TARGETLENDELTA 100 /* debugging compiler statements */ #undef DEBUG_SETUPTERMSTATE #undef DEBUG_RUNCHAIN #undef DEBUG_NOSHORTCUTS #undef DEBUG_NOSCALING #undef DEBUG_TIPROBS_STD #undef DEBUG_RUN_WITHOUT_DATA #undef DEBUG_CONSTRAINTS #undef DEBUG_LNLIKELIHOOD /* slow if defined!! */ #undef DEBUG_LIKELIHOOD #undef DEBUG_FBDPR // #undef FBDPR_CondOnN #undef SHOW_MOVE #if defined (MPI_ENABLED) #define ERROR_TEST2(failString,X1,X2) \ MPI_Allreduce (&nErrors, &sumErrors, 1, MPI_INT, MPI_SUM, MPI_COMM_WORLD);\ if (sumErrors > 0)\ {\ MrBayesPrint ("%s "failString"\n", spacer);\ X1;X2;\ } #else #define ERROR_TEST2(failString,X1,X2) \ if (nErrors > 0)\ {\ MrBayesPrint ("%s "failString"\n", spacer);\ X1;X2;\ } #endif /* local (to this file) data types */ typedef struct pfnode { struct pfnode *left; struct pfnode *right; int *count; BitsLong *partition; } PFNODE; /* local prototypes */ int AddTreeSamples (int from, int to, int saveToList); PFNODE *AddPartition (PFNODE *r, BitsLong *p, int runId); int AddTreeToPartitionCounters (Tree *tree, int treeId, int runId); int AttemptSwap (int swapA, int swapB, RandLong *seed); void BuildExhaustiveSearchTree (Tree *t, int chain, int nTaxInTree, TreeInfo *tInfo); int BuildStepwiseTree (Tree *t, int chain, RandLong *seed); int CalcLikeAdgamma (int d, Param *param, int chain, MrBFlt *lnL); void CalcPartFreqStats (PFNODE *p, STATS *stat); void CalcTopoConvDiagn (int numSamples); #ifdef VISUAL BOOL WINAPI CatchInterrupt (DWORD signum); #else void CatchInterrupt (int signum); #endif int CheckTemperature (void); void CloseMBPrintFiles (void); PFNODE *CompactTree (PFNODE *p); int ConfirmAbortRun(void); void CopyParams (int chain); void CopyPFNodeDown (PFNODE *p); void CopyTrees (int chain); int ExtendChainQuery (void); int FillNumSitesOfPat (void); TreeNode *FindBestNode (Tree *t, TreeNode *p, TreeNode *addNode, CLFlt *minLength, int chain); void FreeChainMemory (void); MrBFlt GetFitchPartials (ModelInfo *m, int chain, int source1, int source2, int destination); void GetStamp (void); void GetSwappers (int *swapA, int *swapB, int run); void GetTempDownPassSeq (TreeNode *p, int *i, TreeNode **dp); int GetTotalRateShifts (Model *mp, MrBFlt *shiftTimes); MrBFlt GibbsSampleGamma (int chain, int division, RandLong *seed); int InitAdGamma(void); int InitChainCondLikes (void); int InitContStates (void); int InitEigenSystemInfo (ModelInfo *m); int InitInvCondLikes (void); int InitParsSets (void); int InitPrintParams (void); int IsPFNodeEmpty (PFNODE *p); PFNODE *LargestNonemptyPFNode (PFNODE *p, int *i, int j); MrBFlt LogLike (int chain); MrBFlt LogPrior (int chain); int LnBirthDeathPriorPrRandom (Tree *t, MrBFlt clockRate, MrBFlt *prob, MrBFlt sR, MrBFlt eR, MrBFlt sF); int LnBirthDeathPriorPrDiversity (Tree *t, MrBFlt clockRate, MrBFlt *prob, MrBFlt sR, MrBFlt eR, MrBFlt sF); int LnBirthDeathPriorPrCluster (Tree *t, MrBFlt clockRate, MrBFlt *prob, MrBFlt sR, MrBFlt eR, MrBFlt sF); int LnFossilizedBDPriorFossilTip (Tree *t, MrBFlt clockRate, MrBFlt *prob, MrBFlt *sR, MrBFlt *eR, MrBFlt *fR, MrBFlt sF); int LnFossilizedBDPriorRandom (Tree *t, MrBFlt clockRate, MrBFlt *prob, MrBFlt *lambda, MrBFlt *mu, MrBFlt *psi, MrBFlt *rho, MrBFlt *t_sl, int n_sl); int LnFossilizedBDPriorDiversity (Tree *t, MrBFlt clockRate, MrBFlt *prob, MrBFlt *lambda, MrBFlt *mu, MrBFlt *psi, MrBFlt *rho, MrBFlt *t_sl, int n_sl); MrBFlt LnP0 (MrBFlt t, MrBFlt l, MrBFlt m); MrBFlt LnP0Subsample (MrBFlt t, MrBFlt l, MrBFlt m, MrBFlt f); MrBFlt LnP1 (MrBFlt t, MrBFlt l, MrBFlt m); MrBFlt LnP1Subsample (MrBFlt t, MrBFlt l, MrBFlt m, MrBFlt f); MrBFlt LnP0_fossil (MrBFlt t, MrBFlt lambda, MrBFlt mu, MrBFlt psi, MrBFlt c1, MrBFlt c2); MrBFlt LnP1_fossil (MrBFlt t, MrBFlt rho, MrBFlt c1, MrBFlt c2); MrBFlt LnQi_fossil (MrBFlt t, MrBFlt *t_f, int sl, MrBFlt *c1, MrBFlt *c2); MrBFlt LnPi_fossil (MrBFlt t, MrBFlt *t_f, int sl, MrBFlt *c1, MrBFlt *c2, MrBFlt *lambda, MrBFlt *mu, MrBFlt *psi); int NewtonRaphsonBrlen (Tree *t, TreeNode *p, int chain); void NodeToNodeDistances (Tree *t, TreeNode *fromNode); int PickProposal (RandLong *seed, int chainIndex); int NumCppEvents (Param *p, int chain); int PosSelProbs (TreeNode *p, int division, int chain); #if defined (SSE_ENABLED) int PosSelProbs_SSE (TreeNode *p, int division, int chain); #endif int PreparePrintFiles (void); int PrintAncStates_Bin (TreeNode *p, int division, int chain); int PrintAncStates_Gen (TreeNode *p, int division, int chain); int PrintAncStates_NUC4 (TreeNode *p, int division, int chain); int PrintAncStates_Std (TreeNode *p, int division, int chain); int PrintAncStates_Cont (TreeNode *p, int division, int chain); int PrintCheckPoint (long long gen); int PrintMCMCDiagnosticsToFile (long long curGen); #if defined (MPI_ENABLED) int PrintMPISlaves (FILE *fp); #endif void PrintParamValues (Param *p, int chain, char *s); int PrintParsMatrix (void); int PrintSiteRates_Gen (TreeNode *p, int division, int chain); int PrintSiteRates_Std (TreeNode *p, int division, int chain); int PrintSiteRates_Cont (TreeNode *p, int division, int chain); int PrintStates (long long curGen, int coldId); int PrintStatesToFiles (long long curGen); int PrintSwapInfo (void); int PrintTermState (void); void PrintTiProbs (CLFlt *tP, MrBFlt *bs, int nStates); int PrintTopConvInfo (void); void PrintToScreen (long long curGen, long long startGen, time_t endingT, time_t startingT); int PrintTree (long long curGen, Param *treeParam, int chain, int showBrlens, MrBFlt clockRate); MrBFlt PropAncFossil (Param *param, int chain); #if defined (MPI_ENABLED) int ReassembleMoveInfo (void); int ReassembleParamVals (int *curId); int ReassembleSwapInfo (void); int ReassembleTuningParams (void); void RedistributeMoveInfo (void); int RedistributeParamVals (void); int RedistributeTuningParams (void); #endif int RemovePartition (PFNODE *r, BitsLong *p, int runId); int RemoveTreeFromPartitionCounters (Tree *tree, int treeId, int runId); int RemoveTreeSamples (int from, int to); int ReopenMBPrintFiles (void); void ResetChainIds (void); void ResetFlips(int chain); int ReusePreviousResults(int *numSamples, int); int RunChain (RandLong *seed); int SafeSprintf (char **target, int *targetLen, char *fmt, ...); void SetChainIds (void); void SetFileNames (void); int SetLikeFunctions (void); int SetLocalChainsAndDataSplits (void); int SetModelInfo (void); int SetMoves (void); int SetBinaryQMatrix (MrBFlt **a, int whichChain, int division); int SetNucQMatrix (MrBFlt **a, int n, int whichChain, int division, MrBFlt rateMult, MrBFlt *rA, MrBFlt *rS); int SetProteinQMatrix (MrBFlt **a, int n, int whichChain, int division, MrBFlt rateMult); int SetStdQMatrix (MrBFlt **a, int nStates, MrBFlt *bs, int cType); int SetUpPartitionCounters (void); int SetUpTermState (void); int SetUsedMoves (void); int ShowMoveSummary (void); void ShowValuesForChain (int chn); int SiteOmegas (TreeNode *p, int division, int chain); #if defined (SSE_ENABLED) int SiteOmegas_SSE (TreeNode *p, int division, int chain); #endif PFNODE *SmallestNonemptyPFNode (PFNODE *p, int *i, int j); PFNODE *Talloc (void); void Tfree (PFNODE *r); MrBFlt Temperature (int x); void TouchAllCijks (int chain); void TouchAllPartitions (void); void TouchAllTrees (int chain); void TouchEverything (int chain); /* globals declared here and used elsewhere */ int *bsIndex; /* compressed std stat freq index */ Chain chainParams; /* parameters of Markov chain */ int *compCharPos; /* char position in compressed matrix */ int *compColPos; /* column position in compressed matrix */ BitsLong *compMatrix; /* compressed character matrix */ int compMatrixRowSize; /* row size of compressed matrix */ char inputFileName[100]; /* input (NEXUS) file name */ MoveType moveTypes[NUM_MOVE_TYPES]; /* holds information on the move types */ int numCompressedChars; /* number of compressed characters */ int numMoveTypes; /* the number of move types */ CLFlt *numSitesOfPat; /* no. sites of each pattern */ int *origChar; /* index from compressed char to original char */ char stamp[11]; /* holds a unique identifier for each analysis */ MrBFlt *stdStateFreqs; /* std char state frequencies */ int *stdType; /* compressed std char type: ord, unord, irrev */ int *tiIndex; /* compressed std char ti index */ #if defined (BEAGLE_ENABLED) int recalcScalers; /* should we recalculate scalers for current state YES/NO */ #endif /* globals used here but declared elsewhere (in likelihood.c) */ extern CLFlt *preLikeL; /* precalculated cond likes for left descendant */ extern CLFlt *preLikeR; /* precalculated cond likes for right descendant*/ extern CLFlt *preLikeA; /* precalculated cond likes for ancestor */ extern int IsMissingC (CLFlt value); /* local (to this file) variables */ int numLocalChains; /* number of Markov chains */ int *chainId = NULL; /* information on the id (0 ...) of the chain */ MrBFlt *curLnL = NULL; /* stores log likelihood */ MrBFlt *curLnPr = NULL; /* stores log prior probability */ int stepRelativeBurninSS; /* Should we use relative burn in within each step or not */ MrBFlt powerSS; /* power (beta) in power posterior distribution used in SS */ MrBFlt *marginalLnLSS = NULL; /* marginal likelihood obtained using steppingstone sampling */ MrBFlt *stepAcumulatorSS = NULL; /* accumulates likelihoods for current step in SS */ MrBFlt *stepScalerSS = NULL; /* scaler of stepAcumulatorSS in log scale in SS */ MrBFlt *splitfreqSS = NULL; /* array holding split frequencies for each step in SS */ int *sympiIndex; /* sympi state freq index for multistate chars */ int stdStateFreqsRowSize; /* row size for std state frequencies */ int *weight; /* weight of each compressed char */ int *chainTempId; /* info ton temp, change to float holding temp? */ int state[MAX_CHAINS]; /* state of chain */ int augmentData; /* are data being augmented for any division? */ int *nAccepted; /* counter of accepted moves */ int *termState = NULL; /* stores character states of tips */ int *isPartAmbig = NULL; /* records whether tips are partially ambiguous */ BitsLong **parsPtrSpace = NULL; /* space holding pointers to parsimony sets */ BitsLong ***parsPtr = NULL; /* pointers to pars state sets for chain & node */ CLFlt *parsNodeLengthSpace = NULL; /* space for parsimony node lengths */ CLFlt **parsNodeLen = NULL; /* pointers to pars node lengths for chains */ char *printString; /* string for printing to a file */ size_t printStringSize; /* length of printString */ MCMCMove **usedMoves; /* vector of pointers to used moves */ int numUsedMoves; /* the number of moves used by chain */ Param **printParam; /* vector of pointers to normal params to print */ int numPrintParams; /* the number of normal params to print */ Param **printTreeParam; /* vector of pointers to tree params to print */ Param **topologyParam; /* vector of pointers to topology params */ int numPrintTreeParams; /* the number of tree params to print */ int codon[6][64]; /* holds info on amino acids coded in code */ int chainHasAdgamma; /* indicates if chain has adgamma HMMs */ int inferPosSel; /* indicates if positive selection is inferred */ MrBFlt *posSelProbs; /* probs. for positive selection */ int hasMarkovTi[MAX_SMALL_JUMP]; /* vector marking size of observed HMM jumps */ int *siteJump; /* vector of sitejumps for adgamma model */ MrBFlt **rateProbs; /* pointers to rate probs used by adgamma model */ MrBFlt *rateProbSpace; /* space for rate probs used by adgamma model */ int rateProbRowSize; /* size of rate probs for one chain one state */ MrBFlt **markovTi[MAX_SMALL_JUMP]; /* trans prob matrices used in calc of adgamma */ MrBFlt **markovTiN; /* trans prob matrices used in calc of adgamma */ int whichReweightNum; /* used for setting reweighting of char pats */ int ***swapInfo; /* keeps track of attempts & successes of swaps */ int tempIndex; /* keeps track of which user temp is specified */ int abortMove; /* flag determining whether to abort move */ PFNODE **partFreqTreeRoot; /* root of tree(s) holding partition freqs */ int nLongsNeeded; /* number of longs needed for partitions */ BitsLong **partition; /* matrix holding partitions */ MrBFlt *maxLnL0 = NULL; /* maximum likelihood */ FILE *fpMcmc = NULL; /* pointer to .mcmc file */ FILE **fpParm = NULL; /* pointer to .p file(s) */ FILE ***fpTree = NULL; /* pointer to .t file(s) */ FILE *fpSS = NULL; /* pointer to .ss file */ static int requestAbortRun; /* flag for aborting mcmc analysis */ int *topologyPrintIndex; /* print file index of each topology */ int *printTreeTopologyIndex; /* topology index of each tree print file */ long long numPreviousGen; /* number of generations in run to append to */ #if defined (MPI_ENABLED) int lowestLocalRunId; /* lowest local run Id */ int highestLocalRunId; /* highest local run Id */ #endif #if defined (PRINT_DUMP) FILE **fpDump = NULL; /* pointer to .dump file(s) */ #endif /* AddPartition: Add a partition to the tree keeping track of partition frequencies */ PFNODE *AddPartition (PFNODE *r, BitsLong *p, int runId) { int i, comp; if (r == NULL) { /* new partition */ r = Talloc (); /* create a new node */ if (r == NULL) return NULL; for (i=0; ipartition[i] = p[i]; for (i=0; icount[i] = 0; r->count[runId] = 1; r->left = r->right = NULL; } else { for (i=0; ipartition[i] != p[i]) break; } if (i == nLongsNeeded) comp = 0; else if (r->partition[i] < p[i]) comp = -1; else comp = 1; if (comp == 0) /* repeated partition */ r->count[runId]++; else if (comp < 0) /* greater than -> into left subtree */ { if ((r->left = AddPartition (r->left, p, runId)) == NULL) { Tfree (r); return NULL; } } else { /* smaller than -> into right subtree */ if ((r->right = AddPartition (r->right, p, runId)) == NULL) { Tfree (r); return NULL; } } } return r; } int AddToPrintString (char *tempStr) { size_t len1, len2; len1 = strlen(printString); len2 = strlen(tempStr); if (len1 + len2 + 5 > printStringSize) { printStringSize += len1 + len2 - printStringSize + 200; printString = (char*)SafeRealloc((void*)printString, printStringSize * sizeof(char)); if (!printString) { MrBayesPrint ("%s Problem reallocating printString (%d)\n", spacer, printStringSize * sizeof(char)); goto errorExit; } } strcat(printString, tempStr); // printf ("printString(%d) -> \"%s\"\n", printStringSize, printString); return (NO_ERROR); errorExit: return (ERROR); } /* AddTreeSamples: Add tree samples from .t files to partition counters. if saveToList == YES then also save trees in tree list */ int AddTreeSamples (int from, int to, int saveToList) { int i, j, k, longestLine; BitsLong lastBlock; char *word, *s, *lineBuf; FILE *fp; Tree *t; char *tempStr; int tempStrSize = TEMPSTRSIZE; if (from > to) return (NO_ERROR); # if defined (MPI_ENABLED) if (proc_id != 0) return (NO_ERROR); # endif tempStr = (char *) SafeMalloc((size_t)tempStrSize * sizeof(char)); if (!tempStr) { MrBayesPrint ("%s Problem allocating tempString (%d)\n", spacer, tempStrSize * sizeof(char)); return (ERROR); } for (i=0; i=from) { s = strtok (NULL, ";"); while (*s != '(') s++; StripComments(s); if (ResetTopology (t, s) == ERROR) { SafeFclose (&fp); free (lineBuf); free (tempStr); return ERROR; } if (AddTreeToPartitionCounters (t, i, j) == ERROR) { SafeFclose (&fp); free (lineBuf); free (tempStr); return ERROR; } if (saveToList == YES) if (AddToTreeList(&chainParams.treeList[numTopologies*j+i],t) == ERROR) return (ERROR); } } SafeFclose (&fp); free (lineBuf); } /* next run */ } /* next tree */ free (tempStr); return (NO_ERROR); } /* AddTreeToPartitionCounters: Break a tree into partitions and add those to counters */ int AddTreeToPartitionCounters (Tree *tree, int treeId, int runId) { int i, j, nTaxa; TreeNode *p; if (tree->isRooted == YES) nTaxa = tree->nNodes - tree->nIntNodes - 1; else nTaxa = tree->nNodes - tree->nIntNodes; for (i=0; inIntNodes-1; i++) { p = tree->intDownPass[i]; assert (p->index >= tree->nNodes - tree->nIntNodes - (tree->isRooted == YES ? 1 : 0)); for (j=0; jindex][j] = partition[p->left->index][j] | partition[p->right->index][j]; } if ((partFreqTreeRoot[treeId] = AddPartition (partFreqTreeRoot[treeId], partition[p->index], runId)) == NULL) { MrBayesPrint ("%s Could not allocate space for new partition in AddTreeToPartitionCounters\n", spacer); return ERROR; } } return NO_ERROR; } int AttemptSwap (int swapA, int swapB, RandLong *seed) { int d, tempX, reweightingChars, isSwapSuccessful, chI, chJ, runId; MrBFlt tempA, tempB, lnLikeA, lnLikeB, lnPriorA, lnPriorB, lnR, r, lnLikeStateAonDataB=0.0, lnLikeStateBonDataA=0.0, lnL; ModelInfo *m; Tree *tree; # if defined (MPI_ENABLED) int numChainsForProc, tempIdA=0, tempIdB=0, proc, procIdForA=0, procIdForB=0, tempIdMy=0, procIdPartner=0, whichElementA=0, whichElementB=0, lower, upper, areWeA, doISwap, ierror, myId, partnerId, i, run; MrBFlt swapRan; MPI_Status status[2]; MPI_Request request[2]; # endif # if defined (MPI_ENABLED) /* get the number of chains handled by this proc */ /* the number will be corrected further down for unbalanced scenarios */ numChainsForProc = (int) (chainParams.numChains * chainParams.numRuns / num_procs); # endif /* are we using character reweighting? */ reweightingChars = NO; if ((chainParams.weightScheme[0] + chainParams.weightScheme[1]) > 0.00001) reweightingChars = YES; # if defined (MPI_ENABLED) /* figure out processors involved in swap */ lower = upper = 0; for (proc=0; proc= lower && swapA < upper) { procIdForA = proc; whichElementA = swapA - lower; } if (swapB >= lower && swapB < upper) { procIdForB = proc; whichElementB = swapB - lower; } lower = upper; } /* NOTE: at this point, procIdForA and procIdForB * * store the proc id's of swapping procs. Also, * * whichElementA and whichElementB store the * * chainId[] index of swapping procs */ /* figure out if I am involved in the swap */ doISwap = areWeA = NO; if (proc_id == procIdForA) { doISwap = YES; areWeA = YES; } else if (proc_id == procIdForB) { doISwap = YES; } /* chain's that do not swap, continue to the next iteration */ if (doISwap == YES) { /* no need to communicate across processors if swapping chains are in the same proc */ if (procIdForA == procIdForB) { if (reweightingChars == YES) { /* use character reweighting */ lnLikeStateAonDataB = 0.0; for (d=0; dbrlens, whichElementA, state[whichElementA]); lnL = 0.0; m->Likelihood (tree->root->left, d, whichElementA, &lnL, chainId[whichElementB] % chainParams.numChains); lnLikeStateAonDataB += lnL; } lnLikeStateBonDataA = 0.0; for (d=0; dbrlens, whichElementB, state[whichElementB]); lnL = 0.0; m->Likelihood (tree->root->left, d, whichElementB, &lnL, chainId[whichElementA] % chainParams.numChains); lnLikeStateBonDataA += lnL; } } /*curLnPr[whichElementA] = LogPrior(whichElementA); curLnPr[whichElementB] = LogPrior(whichElementB);*/ /* then do the serial thing - simply swap chain id's */ tempA = Temperature (chainId[whichElementA]); tempB = Temperature (chainId[whichElementB]); lnLikeA = curLnL[whichElementA]; lnLikeB = curLnL[whichElementB]; if (chainParams.isSS == YES) { lnLikeA *= powerSS; lnLikeB *= powerSS; } lnPriorA = curLnPr[whichElementA]; lnPriorB = curLnPr[whichElementB]; if (reweightingChars == YES) { if (chainParams.isSS == YES) lnR = (tempB * (lnLikeStateAonDataB*powerSS + lnPriorA) + tempA * (lnLikeStateBonDataA*powerSS + lnPriorB)) - (tempA * (lnLikeA + lnPriorA) + tempB * (lnLikeB + lnPriorB)); else lnR = (tempB * (lnLikeStateAonDataB + lnPriorA) + tempA * (lnLikeStateBonDataA + lnPriorB)) - (tempA * (lnLikeA + lnPriorA) + tempB * (lnLikeB + lnPriorB)); } else lnR = (tempB * (lnLikeA + lnPriorA) + tempA * (lnLikeB + lnPriorB)) - (tempA * (lnLikeA + lnPriorA) + tempB * (lnLikeB + lnPriorB)); if (lnR < -100.0) r = 0.0; else if (lnR > 0.0) r = 1.0; else r = exp(lnR); isSwapSuccessful = NO; if (RandomNumber(seed) < r) { /* swap chain id's (heats) */ tempX = chainId[whichElementA]; chainId[whichElementA] = chainId[whichElementB]; chainId[whichElementB] = tempX; if (reweightingChars == YES) { curLnL[whichElementA] = lnLikeStateAonDataB; curLnL[whichElementB] = lnLikeStateBonDataA; } isSwapSuccessful = YES; } chI = chainId[whichElementA]; chJ = chainId[whichElementB]; if (chainId[whichElementB] < chainId[whichElementA]) { chI = chainId[whichElementB]; chJ = chainId[whichElementA]; } runId = chI / chainParams.numChains; chI = chI % chainParams.numChains; chJ = chJ % chainParams.numChains; swapInfo[runId][chJ][chI]++; if (isSwapSuccessful == YES) swapInfo[runId][chI][chJ]++; } /* we need to communicate across processors */ else { if (reweightingChars == YES) { /* If we are reweighting characters, then we need to do an additional communication to figure out the chainId's of the partner. We need to have this information so we can properly calculate likelihoods with switched observations. */ if (areWeA == YES) { lnLikeStateAonDataB = 0.0; myId = chainId[whichElementA]; ierror = MPI_Isend (&myId, 1, MPI_INT, procIdForB, 0, MPI_COMM_WORLD, &request[0]); if (ierror != MPI_SUCCESS) { return (ERROR); } ierror = MPI_Irecv (&partnerId, 1, MPI_INT, procIdForB, 0, MPI_COMM_WORLD, &request[1]); if (ierror != MPI_SUCCESS) { return (ERROR); } ierror = MPI_Waitall (2, request, status); if (ierror != MPI_SUCCESS) { return (ERROR); } for (d=0; dbrlens, whichElementA, state[whichElementA]); lnL = 0.0; m->Likelihood (tree->root->left, d, whichElementA, &lnL, partnerId); lnLikeStateAonDataB = lnL; } } else { lnLikeStateBonDataA = 0.0; myId = chainId[whichElementB]; ierror = MPI_Isend (&myId, 1, MPI_INT, procIdForA, 0, MPI_COMM_WORLD, &request[0]); if (ierror != MPI_SUCCESS) { return (ERROR); } ierror = MPI_Irecv (&partnerId, 1, MPI_INT, procIdForA, 0, MPI_COMM_WORLD, &request[1]); if (ierror != MPI_SUCCESS) { return (ERROR); } ierror = MPI_Waitall (2, request, status); if (ierror != MPI_SUCCESS) { return (ERROR); } for (d=0; dbrlens, whichElementB, state[whichElementB]); lnL = 0.0; m->Likelihood (tree->root->left, d, whichElementB, &lnL, partnerId); lnLikeStateBonDataA = lnL; } } } if (areWeA == YES) { /*curLnPr[whichElementA] = LogPrior(whichElementA);*/ /* we are processor A */ tempIdA = chainId[whichElementA]; lnLikeA = curLnL[whichElementA]; lnPriorA = curLnPr[whichElementA]; swapRan = RandomNumber(seed); myStateInfo[0] = lnLikeA; myStateInfo[1] = lnPriorA; myStateInfo[2] = tempIdA; myStateInfo[3] = swapRan; myStateInfo[4] = 0.0; if (reweightingChars == YES) { myStateInfo[2] = lnLikeStateAonDataB; tempIdB = partnerId; } ierror = MPI_Isend (&myStateInfo, 5, MPI_DOUBLE, procIdForB, 0, MPI_COMM_WORLD, &request[0]); if (ierror != MPI_SUCCESS) { return (ERROR); } ierror = MPI_Irecv (&partnerStateInfo, 5, MPI_DOUBLE, procIdForB, 0, MPI_COMM_WORLD, &request[1]); if (ierror != MPI_SUCCESS) { return (ERROR); } ierror = MPI_Waitall (2, request, status); if (ierror != MPI_SUCCESS) { return (ERROR); } lnLikeA = curLnL[whichElementA]; lnLikeB = partnerStateInfo[0]; if (chainParams.isSS == YES) { lnLikeA *= powerSS; lnLikeB *= powerSS; } lnPriorA = curLnPr[whichElementA]; lnPriorB = partnerStateInfo[1]; if (reweightingChars == YES) lnLikeStateBonDataA = partnerStateInfo[2]; else tempIdB = partnerStateInfo[2]; tempA = Temperature (tempIdA); tempB = Temperature (tempIdB); if (reweightingChars == YES) { if (chainParams.isSS == YES) lnR = (tempB * (lnLikeStateAonDataB*powerSS + lnPriorA) + tempA * (lnLikeStateBonDataA*powerSS + lnPriorB)) - (tempA * (lnLikeA + lnPriorA) + tempB * (lnLikeB + lnPriorB)); else lnR = (tempB * (lnLikeStateAonDataB + lnPriorA) + tempA * (lnLikeStateBonDataA + lnPriorB)) - (tempA * (lnLikeA + lnPriorA) + tempB * (lnLikeB + lnPriorB)); } else lnR = (tempB * (lnLikeA + lnPriorA) + tempA * (lnLikeB + lnPriorB)) - (tempA * (lnLikeA + lnPriorA) + tempB * (lnLikeB + lnPriorB)); if (lnR < -100.0) r = 0.0; else if (lnR > 0.0) r = 1.0; else r = exp(lnR); /* process A's random number is used to make the swap decision */ isSwapSuccessful = NO; if (swapRan < r) { /* swap chain id's (heats) */ isSwapSuccessful = YES; tempIdMy = chainId[whichElementA]; procIdPartner = procIdForB; if (reweightingChars == YES) chainId[whichElementA] = tempIdB; else chainId[whichElementA] = (int)(partnerStateInfo[2]); if (reweightingChars == YES) { curLnL[whichElementA] = lnLikeStateAonDataB; } } /* only processor A keeps track of the swap success/failure */ chI = tempIdA; chJ = tempIdB; if (tempIdB < tempIdA) { chI = tempIdB; chJ = tempIdA; } runId = chI / chainParams.numChains; chI = chI % chainParams.numChains; chJ = chJ % chainParams.numChains; swapInfo[runId][chJ][chI]++; if (isSwapSuccessful == YES) { swapInfo[runId][chI][chJ]++; /* exchange the move info */ for (i=0; inAccepted[tempIdA]; myStateInfo[1] = usedMoves[i]->nTried[tempIdA]; myStateInfo[2] = usedMoves[i]->nBatches[tempIdA]; myStateInfo[3] = usedMoves[i]->nTotAccepted[tempIdA]; myStateInfo[4] = usedMoves[i]->nTotTried[tempIdA]; myStateInfo[5] = usedMoves[i]->lastAcceptanceRate[tempIdA]; if (usedMoves[i]->moveType->numTuningParams > 0) myStateInfo[6] = usedMoves[i]->tuningParam[tempIdA][0]; else myStateInfo[6] = 0.0; ierror = MPI_Isend (&myStateInfo, 7, MPI_DOUBLE, procIdForB, 0, MPI_COMM_WORLD, &request[0]); if (ierror != MPI_SUCCESS) { return (ERROR); } ierror = MPI_Irecv (&partnerStateInfo, 7, MPI_DOUBLE, procIdForB, 0, MPI_COMM_WORLD, &request[1]); if (ierror != MPI_SUCCESS) { return (ERROR); } ierror = MPI_Waitall (2, request, status); if (ierror != MPI_SUCCESS) { return (ERROR); } usedMoves[i]->nAccepted[tempIdB] = (int)partnerStateInfo[0]; usedMoves[i]->nTried[tempIdB] = (int)partnerStateInfo[1]; usedMoves[i]->nBatches[tempIdB] = (int)partnerStateInfo[2]; usedMoves[i]->nTotAccepted[tempIdB] = (int)partnerStateInfo[3]; usedMoves[i]->nTotTried[tempIdB] = (int)partnerStateInfo[4]; usedMoves[i]->lastAcceptanceRate[tempIdB] = partnerStateInfo[5]; if (usedMoves[i]->moveType->numTuningParams > 0) usedMoves[i]->tuningParam[tempIdB][0] = partnerStateInfo[6]; usedMoves[i]->nAccepted[tempIdA] = 0; usedMoves[i]->nTried[tempIdA] = 0; usedMoves[i]->nBatches[tempIdA] = 0; usedMoves[i]->lastAcceptanceRate[tempIdA] = 0.0; usedMoves[i]->nTotAccepted[tempIdA] = 0; usedMoves[i]->nTotTried[tempIdA] = 0; if (usedMoves[i]->moveType->numTuningParams > 0) usedMoves[i]->tuningParam[tempIdA][0] = 0.0; } } } else { /*curLnPr[whichElementB] = LogPrior(whichElementB);*/ /* we are processor B */ tempIdB = chainId[whichElementB]; lnLikeB = curLnL[whichElementB]; lnPriorB = curLnPr[whichElementB]; swapRan = -1.0; myStateInfo[0] = lnLikeB; myStateInfo[1] = lnPriorB; myStateInfo[2] = tempIdB; myStateInfo[3] = swapRan; myStateInfo[4] = 0.0; if (reweightingChars == YES) { myStateInfo[2] = lnLikeStateBonDataA; tempIdA = partnerId; } ierror = MPI_Isend (&myStateInfo, 5, MPI_DOUBLE, procIdForA, 0, MPI_COMM_WORLD, &request[0]); if (ierror != MPI_SUCCESS) { return (ERROR); } ierror = MPI_Irecv (&partnerStateInfo, 5, MPI_DOUBLE, procIdForA, 0, MPI_COMM_WORLD, &request[1]); if (ierror != MPI_SUCCESS) { return (ERROR); } ierror = MPI_Waitall (2, request, status); if (ierror != MPI_SUCCESS) { return (ERROR); } lnLikeB = curLnL[whichElementB]; lnLikeA = partnerStateInfo[0]; lnPriorB = curLnPr[whichElementB]; lnPriorA = partnerStateInfo[1]; if (reweightingChars == YES) lnLikeStateAonDataB = partnerStateInfo[2]; else tempIdA = partnerStateInfo[2]; tempB = Temperature (tempIdB); tempA = Temperature (tempIdA); if (chainParams.isSS == YES) { lnLikeA *= powerSS; lnLikeB *= powerSS; } if (reweightingChars == YES) { if (chainParams.isSS == YES) lnR = (tempB * (lnLikeStateAonDataB*powerSS + lnPriorA) + tempA * (lnLikeStateBonDataA*powerSS + lnPriorB)) - (tempA * (lnLikeA + lnPriorA) + tempB * (lnLikeB + lnPriorB)); else lnR = (tempB * (lnLikeStateAonDataB + lnPriorA) + tempA * (lnLikeStateBonDataA + lnPriorB)) - (tempA * (lnLikeA + lnPriorA) + tempB * (lnLikeB + lnPriorB)); } else lnR = (tempB * (lnLikeA + lnPriorA) + tempA * (lnLikeB + lnPriorB)) - (tempA * (lnLikeA + lnPriorA) + tempB * (lnLikeB + lnPriorB)); if (lnR < -100.0) r = 0.0; else if (lnR > 0.0) r = 1.0; else r = exp(lnR); /* we use process A's random number to make the swap decision */ isSwapSuccessful = NO; if (partnerStateInfo[3] < r) { isSwapSuccessful = YES; tempIdMy = chainId[whichElementB]; procIdPartner = procIdForA; if (reweightingChars == YES) chainId[whichElementB] = tempIdA; else chainId[whichElementB] = (int)(partnerStateInfo[2]); if (reweightingChars == YES) { curLnL[whichElementB] = lnLikeStateBonDataA; } /* swap the move info */ for (i=0; inAccepted[tempIdB]; myStateInfo[1] = usedMoves[i]->nTried[tempIdB]; myStateInfo[2] = usedMoves[i]->nBatches[tempIdB]; myStateInfo[3] = usedMoves[i]->nTotAccepted[tempIdB]; myStateInfo[4] = usedMoves[i]->nTotTried[tempIdB]; myStateInfo[5] = usedMoves[i]->lastAcceptanceRate[tempIdB]; if (usedMoves[i]->moveType->numTuningParams > 0) myStateInfo[6] = usedMoves[i]->tuningParam[tempIdB][0]; else myStateInfo[6] = 0.0; ierror = MPI_Isend (&myStateInfo, 7, MPI_DOUBLE, procIdForA, 0, MPI_COMM_WORLD, &request[0]); if (ierror != MPI_SUCCESS) { return (ERROR); } ierror = MPI_Irecv (&partnerStateInfo, 7, MPI_DOUBLE, procIdForA, 0, MPI_COMM_WORLD, &request[1]); if (ierror != MPI_SUCCESS) { return (ERROR); } ierror = MPI_Waitall (2, request, status); if (ierror != MPI_SUCCESS) { return (ERROR); } usedMoves[i]->nAccepted[tempIdA] = (int)partnerStateInfo[0]; usedMoves[i]->nTried[tempIdA] = (int)partnerStateInfo[1]; usedMoves[i]->nBatches[tempIdA] = (int)partnerStateInfo[2]; usedMoves[i]->nTotAccepted[tempIdA] = (int)partnerStateInfo[3]; usedMoves[i]->nTotTried[tempIdA] = (int)partnerStateInfo[4]; usedMoves[i]->lastAcceptanceRate[tempIdA] = partnerStateInfo[5]; if (usedMoves[i]->moveType->numTuningParams > 0) usedMoves[i]->tuningParam[tempIdA][0] = partnerStateInfo[6]; usedMoves[i]->nAccepted[tempIdB] = 0; usedMoves[i]->nTried[tempIdB] = 0; usedMoves[i]->nBatches[tempIdB] = 0; usedMoves[i]->nTotAccepted[tempIdB] = 0; usedMoves[i]->nTotTried[tempIdB] = 0; usedMoves[i]->lastAcceptanceRate[tempIdB] = 0.0; if (usedMoves[i]->moveType->numTuningParams > 0) usedMoves[i]->tuningParam[tempIdB][0] = 0.0; } } } /*We exchange only if swap successful and (my id is cold or partner id is cold)*/ if (chainParams.isSS == YES && isSwapSuccessful == YES && (tempIdMy % chainParams.numChains == 0 || (areWeA == YES && chainId[whichElementA] % chainParams.numChains == 0) || (areWeA == NO && chainId[whichElementB] % chainParams.numChains == 0))) { run = tempIdMy/chainParams.numChains; myStateInfo[0] = tempIdMy; myStateInfo[1] = marginalLnLSS [ run ]; myStateInfo[2] = stepAcumulatorSS[ run ]; myStateInfo[3] = stepScalerSS [ run ]; ierror = MPI_Isend (&myStateInfo, 4, MPI_DOUBLE, procIdPartner, 0, MPI_COMM_WORLD, &request[0]); if (ierror != MPI_SUCCESS) { return (ERROR); } ierror = MPI_Irecv (&partnerStateInfo, 4, MPI_DOUBLE, procIdPartner, 0, MPI_COMM_WORLD, &request[1]); if (ierror != MPI_SUCCESS) { return (ERROR); } ierror = MPI_Waitall (2, request, status); if (ierror != MPI_SUCCESS) { return (ERROR); } /*we swap chains from the same run*/ assert (run == (int)partnerStateInfo[0]/chainParams.numChains); /*If my chain is the cold chain then send current SS values of corresponding run*/ if (tempIdMy % chainParams.numChains == 0) { assert ((int)partnerStateInfo[0] % chainParams.numChains != 0); assert (partnerStateInfo[1] == 0.0); marginalLnLSS [ run ] = (MrBFlt) 0.0; stepAcumulatorSS[ run ] = (MrBFlt) 0.0; stepScalerSS [ run ] = (MrBFlt) 0.0; } else if ((int)partnerStateInfo[0] % chainParams.numChains == 0) { marginalLnLSS [ run ] = (MrBFlt) partnerStateInfo[1]; stepAcumulatorSS[ run ] = (MrBFlt) partnerStateInfo[2]; stepScalerSS [ run ] = (MrBFlt) partnerStateInfo[3]; } } } } # else if (reweightingChars == YES) { /* use character reweighting */ lnLikeStateAonDataB = 0.0; for (d=0; dbrlens, swapA, state[swapA]); lnL = 0.0; m->Likelihood (tree->root->left, d, swapA, &lnL, chainId[swapB] % chainParams.numChains); lnLikeStateAonDataB += lnL; } lnLikeStateBonDataA = 0.0; for (d=0; dbrlens, swapB, state[swapB]); lnL = 0.0; m->Likelihood (tree->root->left, d, swapB, &lnL, chainId[swapA] % chainParams.numChains); lnLikeStateBonDataA += lnL; } } assert (fabs((curLnPr[swapA]-LogPrior(swapA))/curLnPr[swapA]) < 0.0001); assert (fabs((curLnPr[swapB]-LogPrior(swapB))/curLnPr[swapB]) < 0.0001); tempA = Temperature (chainId[swapA]); tempB = Temperature (chainId[swapB]); lnLikeA = curLnL[swapA]; lnLikeB = curLnL[swapB]; lnPriorA = curLnPr[swapA]; lnPriorB = curLnPr[swapB]; if (chainParams.isSS == YES) { lnLikeA *= powerSS; lnLikeB *= powerSS; } if (reweightingChars == YES) { if (chainParams.isSS == YES) lnR = (tempB * (lnLikeStateAonDataB*powerSS + lnPriorA) + tempA * (lnLikeStateBonDataA*powerSS + lnPriorB)) - (tempA * (lnLikeA + lnPriorA) + tempB * (lnLikeB + lnPriorB)); else lnR = (tempB * (lnLikeStateAonDataB + lnPriorA) + tempA * (lnLikeStateBonDataA + lnPriorB)) - (tempA * (lnLikeA + lnPriorA) + tempB * (lnLikeB + lnPriorB)); } else lnR = (tempB * (lnLikeA + lnPriorA) + tempA * (lnLikeB + lnPriorB)) - (tempA * (lnLikeA + lnPriorA) + tempB * (lnLikeB + lnPriorB)); if (lnR < -100.0) r = 0.0; else if (lnR > 0.0) r = 1.0; else r = exp (lnR); isSwapSuccessful = NO; if (RandomNumber(seed) < r) { tempX = chainId[swapA]; chainId[swapA] = chainId[swapB]; chainId[swapB] = tempX; if (reweightingChars == YES) { curLnL[swapA] = lnLikeStateAonDataB; curLnL[swapB] = lnLikeStateBonDataA; } isSwapSuccessful = YES; } chI = chainId[swapA]; chJ = chainId[swapB]; if (chainId[swapB] < chainId[swapA]) { chI = chainId[swapB]; chJ = chainId[swapA]; } runId = chI / chainParams.numChains; chI = chI % chainParams.numChains; chJ = chJ % chainParams.numChains; swapInfo[runId][chJ][chI]++; if (isSwapSuccessful == YES) swapInfo[runId][chI][chJ]++; # endif return (NO_ERROR); } /* Autotune Dirichlet move */ void AutotuneDirichlet (MrBFlt acceptanceRate, MrBFlt targetRate, int batch, MrBFlt *alphaPi, MrBFlt minTuning, MrBFlt maxTuning) { MrBFlt delta, logTuning, newTuning; delta = 1.0 / sqrt(batch); delta = 0.01 < delta ? 0.01 : delta; logTuning = log(*alphaPi); if (acceptanceRate > targetRate) logTuning -= delta; else logTuning += delta; newTuning = exp(logTuning); if (newTuning > minTuning && newTuning < maxTuning) *alphaPi = newTuning; } /* Autotune multiplier move */ void AutotuneMultiplier (MrBFlt acceptanceRate, MrBFlt targetRate, int batch, MrBFlt *lambda, MrBFlt minTuning, MrBFlt maxTuning) { MrBFlt delta, logTuning, newTuning; delta = 1.0 / sqrt(batch); delta = 0.01 < delta ? 0.01 : delta; logTuning = log(*lambda); if (acceptanceRate > targetRate) logTuning += delta; else logTuning -= delta; newTuning = exp(logTuning); if (newTuning > minTuning && newTuning < maxTuning) *lambda = newTuning; } /* Autotune sliding window move */ void AutotuneSlider (MrBFlt acceptanceRate, MrBFlt targetRate, int batch, MrBFlt *width, MrBFlt minTuning, MrBFlt maxTuning) { MrBFlt delta, logTuning, newTuning; delta = 1.0 / sqrt(batch); delta = 0.01 < delta ? 0.01 : delta; logTuning = log(*width); if (acceptanceRate > targetRate) logTuning += delta; else logTuning -= delta; newTuning = exp(logTuning); if (newTuning > minTuning && newTuning < maxTuning) *width = newTuning; } /* used in rjMCMC to achieve higher acceptance rate of the move */ MrBFlt initial_w = 2.0, previousRate = 0.0; int previousDirection = 1; void AutotuneRJClocks (MrBFlt acceptanceRate, MrBFlt targetRate, int batch, MrBFlt *w, MrBFlt minTuning, MrBFlt maxTuning) { MrBFlt delta, newTuning; // a hack to disable autotune: propset rj_clocks$targetrate = 0.5 if (targetRate < 0.99) return; delta = 1.0 / sqrt(batch); delta = 0.1 < delta ? 0.1 : delta; if (batch == 1) initial_w = *w; // record the initial value if (previousRate == 0.0 && acceptanceRate == 0.0) { newTuning = initial_w; // fall back } else if (acceptanceRate > previousRate && previousDirection > 0) { newTuning = *w + delta; // move on } else if (acceptanceRate > previousRate && previousDirection < 0) { newTuning = *w - delta; // move on } else if (acceptanceRate < previousRate && previousDirection > 0) { newTuning = *w - delta; previousDirection = -1; // turn around } else // (acceptanceRate < previousRate && previousDirection < 0) { newTuning = *w + delta; previousDirection = 1; // turn around } previousRate = acceptanceRate; if (newTuning > minTuning && newTuning < maxTuning) *w = newTuning; } void BuildExhaustiveSearchTree (Tree *t, int chain, int nTaxInTree, TreeInfo *tInfo) { int i; TreeNode *p, *q, *r; if (nTaxInTree == t->nIntNodes + 1) { /* Get downpass */ GetDownPass (t); /* Calculate cost of this tree and add to counter */ tInfo->curScore = GetParsimonyLength (t, chain); if (tInfo->curScore < tInfo->minScore) { tInfo->totalScore *= pow ((tInfo->warp/3.0) / (1.0 - tInfo->warp), tInfo->minScore - tInfo->curScore); tInfo->totalScore += 1.0; tInfo->minScore = tInfo->curScore; } else tInfo->totalScore += pow (tInfo->warp/3.0, tInfo->curScore - tInfo->minScore) * pow (1.0-tInfo->warp, tInfo->minScore - tInfo->curScore); } else { /* find node to connect */ q=tInfo->leaf[nTaxInTree]; /* add using this ancestral node */ p=tInfo->vertex[nTaxInTree-1]; q->anc=p; p->right=q; for (i=0;i<2*nTaxInTree-1;i++) { /* find node to connect to */ if (i>=nTaxInTree) r=tInfo->vertex[i-nTaxInTree]; else r=tInfo->leaf[i]; /* add to this node */ p->left=r; if (r->anc==NULL) p->anc=NULL; else { p->anc=r->anc; if (r->anc->left==r) r->anc->left=p; else r->anc->right=p; } r->anc=p; /* next level */ BuildExhaustiveSearchTree (t, chain, nTaxInTree+1, tInfo); if (tInfo->stopScore > 0.0 && tInfo->totalScore >= tInfo->stopScore) return; /* restore tree before trying next possibility */ r->anc=p->anc; if (r->anc!=NULL) { if (r->anc->left==p) r->anc->left=r; else r->anc->right=r; } } } } /*------------------------------------------------------------------ | | BuildParsTrees: Fill in trees using random add seq with parsimony | ------------------------------------------------------------------*/ int BuildParsTrees (RandLong *seed, int fromChain, int toChain) { int k, chn; Param *p, *q; Tree *tree; /* Build starting trees for state 0 */ for (chn=fromChain; chnparamType == P_TOPOLOGY) { assert (p->nSubParams == 1); q = p->subParams[0]; tree = GetTree (q, chn, 0); /* fixed topology */ if (p->paramId == TOPOLOGY_RCL_FIXED || p->paramId == TOPOLOGY_RCCL_FIXED || p->paramId == TOPOLOGY_CL_FIXED || p->paramId == TOPOLOGY_CCL_FIXED || p->paramId == TOPOLOGY_NCL_FIXED || p->paramId == TOPOLOGY_PARSIMONY_FIXED) { MrBayesPrint ("%s Tree %s is fixed so a parsimony-based starting tree is not built\n", spacer, p->name); return (NO_ERROR); } /* constrained topology */ else if (tree->nConstraints > 0) { MrBayesPrint ("%s Tree %s is constrained and parsimony-based starting trees are not implemented for constrained trees (yet)\n", spacer, p->name); return (NO_ERROR); } /* random topology */ else { if (BuildStepwiseTree (tree, chn, &globalSeed) == ERROR) return (ERROR); } if (InitializeTreeCalibrations (tree) == ERROR) return (ERROR); FillTopologySubParams(p, chn, 0, seed); } } } return (NO_ERROR); } /* build (starting) topology stepwise */ int BuildStepwiseTree (Tree *t, int chain, RandLong *seed) { int i, j, nTips; TreeNode *p, *q, *r; CLFlt length; // Allocate parsimony matrix if not done already /* get the tips */ for (i=j=0; inNodes; i++) { p = t->allDownPass[i]; if (p->left == NULL && p->right == NULL) t->allDownPass[j++] = p; else if (p->right == NULL && p->anc == NULL && t->isRooted == NO) t->allDownPass[j++] = p; } nTips = j; /* order the tips randomly, use last as root */ for (i=0; iallDownPass[i]; t->allDownPass[i] = t->allDownPass[j]; t->allDownPass[j] = p; } /* build first tree */ j = 0; q = t->allDownPass[0]; r = t->allDownPass[1]; p = t->intDownPass[j++]; q->anc = p; r->anc = p; p->left = q; p->right = r; q = t->allDownPass[nTips-1]; q->anc = NULL; q->right = NULL; q->left = p; p->anc = q; t->root = q; /* add nodes one at a time */ for (i=2; iallDownPass[i]; p = t->intDownPass[j++]; GetParsDP(t, t->root->left, chain); GetParsFP(t, t->root->left, chain); q = FindBestNode(t, t->root->left, r, &length, chain); p->left = q; p->right = r; p->anc = q->anc; if (q->anc->left == q) q->anc->left = p; else q->anc->right = p; q->anc = p; r->anc = p; } /* take care of the root */ if (t->isRooted == YES) { r = t->root; q = t->allDownPass[t->nNodes-1]; p = t->intDownPass[j]; q->anc = q->right = NULL; q->left = p; p->anc = q; p->left = r->left; p->right = r; p->left->anc = p; r->left = r->right = NULL; r->anc = p; t->root = q; } GetDownPass (t); return (NO_ERROR); } /*------------------------------------------------------------------ | | CalcLikeAdgamma: calc likelihood for one adgamma correlation HMM | -------------------------------------------------------------------*/ int CalcLikeAdgamma (int d, Param *param, int chain, MrBFlt *lnL) { int c, i, j, nRates, posit, lastCharId; MrBFlt logScaler, max, prob, *F, *oldF, *tempF, fSpace[2][MAX_RATE_CATS]; MrBFlt *rP; CLFlt freq, *lnScaler; ModelInfo *m; ModelParams *mp; BitsLong *inHMM; /* find nRates for first division in HMM */ m = &modelSettings[d]; mp = &modelParams[d]; nRates = m->numRateCats; /* calculate rate category frequencies */ freq = (CLFlt) ((CLFlt) 1.0 / nRates); /* find Markov trans probs */ F = GetParamSubVals (param,chain, state[chain]); for (i=posit=0; iscalers[m->siteScalerIndex[chain]]; /* find rate probs for this chain and state */ rP = rateProbs[chain] + state[chain] * rateProbRowSize; /* set bit vector indicating divisions in this HMM */ inHMM = (BitsLong *) SafeCalloc (((param->nRelParts/nBitsInALong) + 1), sizeof(BitsLong)); for (i=0; inRelParts; i++) { if (modelSettings[param->relParts[i]].shape == modelSettings[d].shape) { SetBit(param->relParts[i], inHMM); } } /* Perform the so-called forward algorithm of HMMs */ /* set up the space for f(c,i) */ F = fSpace[0]; oldF = fSpace[1]; for (c=0; crateProbStart + (compCharPos[c] - m->compCharStart) * m->numRateCats; for (i=0; i max) max = F[i]; } for (i=0; idataType == DNA || mp->dataType == RNA) && m->dataType == PROTEIN && charInfo[c].charId == lastCharId) continue; else lastCharId = charInfo[c].charId; /* skip if not in HMM */ if (IsBitSet(partitionId[c][partitionNum] - 1, inHMM) == NO) continue; /* switch F and oldF, since the previous F is now old */ tempF = F; F = oldF; oldF = tempF; /* find the position of the rate probs */ m = &modelSettings[partitionId[c][partitionNum] - 1]; posit = m->rateProbStart + (compCharPos[c] - m->compCharStart) * m->numRateCats; /* calculate the HMM forward probs fi(x) at site x in HMM */ if (siteJump[c] <= MAX_SMALL_JUMP) { max = 0.0; for (i=0; i max) max = F[i]; } } else if (siteJump[c] < BIG_JUMP) /* intermediate jump, calculate trans probs */ { MultiplyMatrixNTimes(nRates, markovTi[0], siteJump[c], markovTiN); max = 0.0; for (i=0; i max) max = F[i]; } } else /* big jump, use stationary freqs */ { max = 0.0; for (i=0; i max) max = F[i]; } } /* rescale and adjust total scaler with HMM scaler and site scaler */ for (i=0; inumSamples); if ((MrBFlt)min != chainParams.minPartFreq * stat->numSamples) min++; /* recursively compute partition frequencies for all subpartitions */ if (p->left != NULL) CalcPartFreqStats (p->left, stat); if (p->right != NULL) CalcPartFreqStats (p->right, stat); for (i=0; icount[i] >= min) break; } if (i == n) return; sum = 0.0; sumsq = 0.0; for (i=0; icount[i]) / (MrBFlt) (stat->numSamples); sum += f; sumsq += f * f; } f = (sumsq - sum * sum / n) / (n - 1); if (f < 0.0) stdev = 0.0; else stdev = sqrt (f); stat->sum += stdev; if (stdev > stat->max) stat->max = stdev; stat->numPartitions++; if (chainParams.allComps == YES) { for (i=0; icount[i] < min && p->count[j] < min) continue; sum = 0.0; sumsq = 0.0; f = (MrBFlt) (p->count[i]) / (MrBFlt) (stat->numSamples); sum += f; sumsq += f * f; f = (MrBFlt) (p->count[j]) / (MrBFlt) (stat->numSamples); sum += f; sumsq += f * f; f = (sumsq - sum * sum / 2.0); if (f < 0.0) stdev = 0.0; else stdev = sqrt (f); if (chainParams.diagnStat == AVGSTDDEV) stat->pair[i][j] += stdev; else if (stdev > stat->pair[i][j]) stat->pair[i][j] = stdev; stat->pair[j][i]++; } } } } /*---------------------------------------------------------------- | | CalcTopoConvDiagn: Calculate average and max standard deviation | in clade credibility (partition frequency) values | ----------------------------------------------------------------*/ void CalcTopoConvDiagn (int numSamples) { int i, j, n; STATS *stat; for (n=0; nnumSamples = numSamples; stat->numPartitions = 0.0; stat->sum = 0.0; stat->max = 0.0; if (chainParams.allComps == YES) { for (i=0; ipair[i][j] = 0.0; } CalcPartFreqStats (partFreqTreeRoot[n], stat); stat->avgStdDev = stat->sum / stat->numPartitions; } } /* used in DoCompRefTree */ void PartFreq (PFNODE *p, STATS *stat, int *ntrees) { int i, n = chainParams.numRuns; MrBFlt f, sum, sumsq, stdev; /* recursively compute partition frequencies for all subpartitions */ if (p->left != NULL) PartFreq (p->left, stat, ntrees); if (p->right != NULL) PartFreq (p->right, stat, ntrees); sum = sumsq = 0.0; for (i=0; icount[i]) / (MrBFlt)ntrees[i]; sum += f; sumsq += f * f; } f = (sumsq - sum * sum / n) / (n - 1); if (f < 0.0) stdev = 0.0; else stdev = sqrt (f); stat->sum += stdev; if (stat->max < stdev) stat->max = stdev; stat->numPartitions++; } void CalcTopoConvDiagn2 (int *nTrees) { int n; STATS *stat; for (n=0; nnumPartitions = 0.0; stat->sum = stat->max = 0.0; PartFreq (partFreqTreeRoot[n], stat, nTrees); stat->avgStdDev = stat->sum / stat->numPartitions; } } int CheckTemperature (void) { if (chainParams.userDefinedTemps == YES) { if (AreDoublesEqual(chainParams.userTemps[0], 1.0, ETA)==NO) { MrBayesPrint ("%s The first user-defined temperature must be 1.0.\n", spacer); return (ERROR); } } return (NO_ERROR); } void CloseMBPrintFiles (void) { int i, n; # if defined (MPI_ENABLED) if (proc_id != 0) return; # endif for (n=0; nleft, &i, 0); r = LargestNonemptyPFNode (p->right, &j, 0); if (q != NULL || r != NULL) { if (i < j) q = r; for (i=0; icount[i] = q->count[i]; q->count[i] = 0; } for (i=0; ipartition[i] = q->partition[i]; } } p->left = CompactTree (p->left); p->right = CompactTree (p->right); /* delete on the way down if empty */ if (IsPFNodeEmpty(p) == YES) { Tfree (p); return NULL; } else return p; } /*----------------------------------------------------------------- | | CopyParams: copy parameters of touched divisions | -----------------------------------------------------------------*/ void CopyParams (int chain) { int i, j, k, fromState, toState, *fromInt, *toInt; MrBFlt *from, *to; ModelInfo *m; Param *p; /* copy all params */ /* now done for all vars, can also be done for only touched vars */ /* but then m->upDateCl must be kept separate for each chain! */ for (i=0; inValues; j++) to[j] = from[j]; from = GetParamSubVals (p, chain, state[chain]); to = GetParamSubVals (p, chain, (state[chain] ^ 1)); for (j=0; jnSubValues; j++) to[j] = from[j]; fromInt = GetParamIntVals (p, chain, state[chain]); toInt = GetParamIntVals (p, chain, (state[chain] ^ 1)); for (j=0; jnIntValues; j++) toInt[j] = fromInt[j]; if (p->nStdStateFreqs > 0) { from = GetParamStdStateFreqs (p, chain, state[chain]); to = GetParamStdStateFreqs (p, chain, state[chain] ^ 1); for (j=0; jnStdStateFreqs; j++) to[j] = from[j]; } if (p->paramType == P_CPPEVENTS) { fromState = 2*chain + state[chain]; toState = 2*chain + (state[chain] ^ 1); for (j=0; j<2*numLocalTaxa-2; j++) { if (p->nEvents[toState][j] != p->nEvents[fromState][j]) { if (p->nEvents[fromState][j] == 0) { free (p->position[toState][j]); p->position[toState][j] = NULL; free (p->rateMult[toState][j]); p->rateMult[toState][j] = NULL; } else if (p->nEvents[toState][j] == 0) { p->position[toState][j] = (MrBFlt *) SafeCalloc (p->nEvents[fromState][j], sizeof (MrBFlt)); p->rateMult[toState][j] = (MrBFlt *) SafeCalloc (p->nEvents[fromState][j], sizeof (MrBFlt)); } else { p->position[toState][j] = (MrBFlt *) SafeRealloc ((void *)p->position[toState][j], p->nEvents[fromState][j] * sizeof (MrBFlt)); p->rateMult[toState][j] = (MrBFlt *) SafeRealloc ((void *)p->rateMult[toState][j], p->nEvents[fromState][j] * sizeof (MrBFlt)); } p->nEvents[toState][j] = p->nEvents[fromState][j]; } if (p->nEvents[fromState][j] > 0) { for (k=0; knEvents[fromState][j]; k++) { p->position[toState][j][k] = p->position[fromState][j][k]; p->rateMult[toState][j][k] = p->rateMult[fromState][j][k]; } } } } } /* copy division params (model settings) for chain */ /* reset division update flags */ fromState = 2 * chain + state[chain]; toState = 2 * chain + (state[chain] ^ 1); for (i=0; ilnLike[toState] = m->lnLike[fromState]; if (m->parsModelId == YES) m->parsTreeLength[toState] = m->parsTreeLength[fromState]; m->upDateCl = NO; m->upDateCijk = NO; m->upDateAll = NO; } return; } /*----------------------------------------------------------------- | | CopyTrees: copies touched trees for chain | resets division and node update flags in the process | Note: partition information of nodes are not copied if | either source or destination tree does not have bitsets allocated | -----------------------------------------------------------------*/ void CopyTrees (int chain) { int i, j, n, nTaxa, nLongsNeeded; TreeNode *p, *q; Tree *from, *to; /* reset division update flags */ for (i=0; ibitsets != NULL && to->bitsets != NULL) { if (from->isRooted == NO) nTaxa = from->nNodes - from->nIntNodes; else nTaxa = from->nNodes - from->nIntNodes - 1; nLongsNeeded = (int)((nTaxa - 1) / nBitsInALong) + 1; } else nLongsNeeded = 0; /* copy nodes */ for (j=0; jnNodes; j++) { /* copy pointers */ p = from->nodes + j; q = to->nodes + j; if (p->anc != NULL) q->anc = to->nodes + p->anc->memoryIndex; else q->anc = NULL; if (p->left != NULL) q->left = to->nodes + p->left->memoryIndex; else q->left = NULL; if (p->right != NULL) q->right = to->nodes + p->right->memoryIndex; else q->right = NULL; CopyTreeNodes (q, p, nLongsNeeded); q->upDateCl = q->upDateTi = NO; /* reset update flags */ } for (i=0; inIntNodes; i++) { to->intDownPass[i] = to->nodes + from->intDownPass[i]->memoryIndex; #if defined (BEAGLE_V3_ENABLED) if (from->levelPassEnabled) { to->intDownPassLevel[i] = to->nodes + from->intDownPassLevel[i]->memoryIndex; } #endif } for (i=0; inNodes; i++) { to->allDownPass[i] = to->nodes + from->allDownPass[i]->memoryIndex; } to->root = to->nodes + from->root->memoryIndex; /* rest of tree info is constant and need not be copied */ } return; } #ifdef VISUAL BOOL WINAPI CatchInterrupt (DWORD signum) { /* set up signal handler to do the same */ MrBayesPrint ("\n Ctrl-C detected\n"); requestAbortRun = YES; return TRUE; } #else void CatchInterrupt (int signum) { /* set up signal handler to do the same */ signal (signum, CatchInterrupt); requestAbortRun = YES; MrBayesPrint ("\n Ctrl-C detected\n"); } #endif /*---------------------------------------------------------------- | | DebugNodeScalers: Calculate node scalers sum | -----------------------------------------------------------------*/ CLFlt DebugNodeScalers (TreeNode *p, int division, int chain) { int c; CLFlt *scP; CLFlt sum=0.0; ModelInfo *m; m = &modelSettings[division]; /* find scalers */ scP = m->scalers[m->nodeScalerIndex[chain][p->index]]; /* remove scalers */ for (c=0; cnumChars; c++) sum += scP[c]; return sum; } /*---------------------------------------------------------------- | | DebugTreeScalers: Calculate DebugNodeScalers for each node and printit | -----------------------------------------------------------------*/ void DebugTreeScalers(int chain, int d) { int i; TreeNode *p; ModelInfo *m; Tree *tree; m = &modelSettings[d]; tree = GetTree(m->brlens, chain, state[chain]); if (m->parsModelId == NO) { for (i=0; inIntNodes; i++) { p = tree->intDownPass[i]; if (m->unscaledNodes[chain][p->index] == 0) { printf ("Node:%d Sum scalers:%f\n",p->index,DebugNodeScalers(p, d, chain)); } } } } int DoMcmc (void) { RandLong seed; int rc, i, j, run; char c; FILE *tempFile; char temp[20]; char *strBuf,*tmpcp; double tmp; # if defined (BEST_MPI_ENABLED) Tree *tree; # endif # if !defined (VISUAL) && !defined (MPI_ENABLED) sighandler_t sigint_oldhandler, sigterm_oldhandler; # endif numPreviousGen = 0; /* Make sure this is reset */ /* Check to see that we have a data matrix. Otherwise, the MCMC is rather pointless. */ if (defMatrix == NO) { MrBayesPrint ("%s A character matrix must be defined first\n", spacer); goto errorExit; } if (setUpAnalysisSuccess == NO) { MrBayesPrint ("%s The analysis could not be started because there was an error during its setup.\n", spacer); MrBayesPrint ("%s Refer to error messages printed during model set up to address the problem.\n", spacer); goto errorExit; } /* set file names */ sumtParams.numRuns = chainParams.numRuns; sumpParams.numRuns = chainParams.numRuns; sumssParams.numRuns = chainParams.numRuns; if (fileNameChanged == YES) { SetFileNames(); fileNameChanged = NO; } MrBayesPrint ("%s Running Markov chain\n", spacer); /* Check the chain temperature parameters */ if (CheckTemperature () == ERROR) goto errorExit; /* Set the chain random number seeds here. We have two seeds. One (called swapSeed) is only used to determine which two chains will swap states in the next trial. The other (called seed) is the seed for our work-horse pseudorandom number generator. Note that if we are doing MPI, we want the swap seed to be the same for every processor. This is taken care of when we initialize things in the program. If we are doing MPI, we also want to make certain that seed is different for every processor. */ # if defined (MPI_ENABLED) seed = globalSeed + (proc_id + 1); if (seed < 0) seed = -seed; # else seed = globalSeed; # endif /* Get a unique identifier (stamp) for this run. This is used as an identifier for each mcmc analysis. It uses runIDSeed to initialize the stamp. All of the processors should have the same seed, so this should be safe. */ GetStamp (); MrBayesPrint ("%s Seed = %d\n", spacer, seed); MrBayesPrint ("%s Swapseed = %d\n", spacer, swapSeed); /* Show the model to make sure the user sees it before running the analysis */ if (ShowModel() == ERROR) goto errorExit; MrBayesPrint ("\n"); /* Warn the user or stop analysis in case the model is strange */ if (CheckModel() == ERROR) goto errorExit; /* Determine the number of local chains and data splits */ if (SetLocalChainsAndDataSplits() == ERROR) goto errorExit; /* Set up the moves to be used */ if (SetUsedMoves () == ERROR) goto errorExit; /* Check to see that we have at least one move. Otherwise, the MCMC is rather pointless. */ if (numUsedMoves == 0) { MrBayesPrint ("%s No move is currently switched on.\n", spacer); MrBayesPrint ("%s There must be at least one move to run an MCMC analysis.\n", spacer); MrBayesPrint ("%s Switch on moves using the 'propset' command.\n", spacer); goto errorExit; } /* Show summary table of moves that will be used */ if (ShowMoveSummary () == ERROR) goto errorExit; /* Set the likelihood function pointers. */ if (SetLikeFunctions () == ERROR) goto errorExit; /* Set up number of characters of each character pattern. */ if (FillNumSitesOfPat () == ERROR) goto errorExit; /* Initialize parsimony sets. */ if (InitParsSets() == ERROR) goto errorExit; /* Set up a terminal state index matrix for local compression. */ if (SetUpTermState() == ERROR) goto errorExit; /* Initialize conditional likelihoods and transition probabilities for chain (the working space). */ if (InitChainCondLikes () == ERROR) goto errorExit; /* Initialize adgamma conditional likelihoods */ if (InitAdGamma () == ERROR) goto errorExit; /* Initialize invariable conditional likelihoods. */ if (InitInvCondLikes() == ERROR) goto errorExit; /* Initialize node states for continuous traits. */ if (InitContStates() == ERROR) goto errorExit; /* Allocate BEST chain variables */ if (numTopologies > 1 && !strcmp(modelParams[0].topologyPr,"Speciestree")) AllocateBestChainVariables(); /* allocate SS memory for the chains if needed */ if (chainParams.isSS == YES) { if (memAllocs[ALLOC_SS] == YES) { MrBayesPrint ("%s SS is already allocated\n", spacer); goto errorExit; } else if ((marginalLnLSS = (MrBFlt *) SafeCalloc (chainParams.numRuns, sizeof(MrBFlt))) == NULL) { MrBayesPrint ("%s Problem allocating marginalLnLSS\n", spacer); goto errorExit; } else if ((stepScalerSS = (MrBFlt *) SafeCalloc (chainParams.numRuns, sizeof(MrBFlt))) == NULL) { MrBayesPrint ("%s Problem allocating stepScalerSS\n", spacer); free (marginalLnLSS); goto errorExit; } else if ((stepAcumulatorSS = (MrBFlt *) SafeCalloc (chainParams.numRuns, sizeof(MrBFlt))) == NULL) { MrBayesPrint ("%s Problem allocating stepAcumulatorSS\n", spacer); free (stepScalerSS); free (marginalLnLSS); goto errorExit; } else if ((splitfreqSS = (MrBFlt *) SafeCalloc (chainParams.numStepsSS*numTopologies, sizeof(MrBFlt))) == NULL) { MrBayesPrint ("%s Problem allocating splitfreqSS\n", spacer); free (stepScalerSS); free (marginalLnLSS); free (stepAcumulatorSS); goto errorExit; } else memAllocs[ALLOC_SS] = YES; } /* Either append to previous run or deal with starting values */ if (chainParams.append == YES) { /* Continue old run */ /* Get starting values from checkpoint file */ MrBayesPrint ("%s Getting values from previous run\n", spacer); strcpy(inputFileName, chainParams.chainFileName); strcat(inputFileName, ".ckp"); if (OpenTextFileR(inputFileName) == NULL) { MrBayesPrint ("%s Could not find the checkpoint file '%s'.\n", spacer, inputFileName); MrBayesPrint ("%s Make sure it is in the working directory.\n", spacer); goto errorExit; } if (DoExecute () == ERROR) goto errorExit; /* Get number of generations to start from and SS information if needed */ temp[0] = '\0'; numPreviousGen = 0; # if defined (MPI_ENABLED) if (proc_id == 0) { # endif tempFile = OpenBinaryFileR (inputFileName); do { c = fgetc(tempFile); } while (c!=':' && c!=EOF); if (c!=EOF) { do { c = fgetc(tempFile); } while (c!=':' && c!=EOF); } if (c!=EOF) { do { c = fgetc(tempFile); } while (!isdigit(c) && c!=EOF); } if (c!=EOF) { i=0; while (c >= '0' && c <= '9' && i < 18) { temp[i++] = c; c = fgetc(tempFile); } temp[i] = '\0'; sscanf(temp, "%lli", &numPreviousGen); } if (chainParams.isSS==YES && c!=EOF) { do { c = fgetc(tempFile); } while (c!=':' && c!=EOF); strBuf = (char *) SafeCalloc (chainParams.numRuns*20,sizeof(char)); if (fgets(strBuf,chainParams.numRuns*20,tempFile)==NULL) { MrBayesPrint ("%s Error: Reading SsAcumulators from .ckp file fails.\n", spacer); free(strBuf); goto errorExit; } tmpcp=strtok(strBuf," "); for (run=0; run= chainParams.numGen) { MrBayesPrint ("%s The specified number of generations (%lli) was already finished in\n", spacer, chainParams.numGen); MrBayesPrint ("%s the previous run you are trying to append to.\n", spacer); goto errorExit; } else MrBayesPrint ("%s Using samples up to generation %lli from previous analysis.\n", spacer, numPreviousGen); } else { /* New run */ /* deal with starting param values */ if (!strcmp(chainParams.startParams,"Reset")) { MrBayesPrint ("%s Resetting starting values for substitution model parameters\n", spacer); FillNormalParams (&seed, 0, numLocalChains); } /* deal with starting treeparam values */ if (!strcmp(chainParams.startTree,"Random")) { MrBayesPrint ("%s Resetting starting trees and tree parameters\n", spacer); FillTreeParams (&seed, 0, numLocalChains); } else if (!strcmp(chainParams.startTree,"Parsimony")) { MrBayesPrint ("%s Rebuilding starting trees using random addition sequences and parsimony\n", spacer); BuildParsTrees (&seed, 0, numLocalChains); } /* Perturb start trees if requested */ if (chainParams.numStartPerts > 0) { MrBayesPrint ("%s Randomly perturbing starting trees\n", spacer); for (i=0; i INT_MAX ) { MrBayesPrint ("%s Maximum %d samples allowed. Decrease 'ngen' or increase 'samplefreq'.\n", spacer, INT_MAX); return (ERROR); } chainParams.numGen = tempL; MrBayesPrint ("%s Setting number of generations to %lli\n", spacer, chainParams.numGen); expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else { free (tempStr); return (ERROR); } } /* set Samplefreq (sampleFreq) ********************************************************/ else if (!strcmp(parmName, "Samplefreq")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(NUMBER); else if (expecting == Expecting(NUMBER)) { sscanf (tkn, "%d", &tempI); if (tempI < 1) { MrBayesPrint ("%s Sampling chain too infrequently\n", spacer); free (tempStr); return (ERROR); } if ( chainParams.numGen / tempI > INT_MAX ) { MrBayesPrint ("%s Maximum %d samples allowed. Decrease 'ngen' or increase 'samplefreq'.\n", spacer, INT_MAX); return (ERROR); } chainParams.sampleFreq = tempI; MrBayesPrint ("%s Setting sample frequency to %d\n", spacer, chainParams.sampleFreq); expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else { free (tempStr); return (ERROR); } } /* set Printfreq (printFreq) **********************************************************/ else if (!strcmp(parmName, "Printfreq")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(NUMBER); else if (expecting == Expecting(NUMBER)) { sscanf (tkn, "%d", &tempI); if (tempI < 1) { MrBayesPrint ("%s Printing to screen too infrequently\n", spacer); free(tempStr); return (ERROR); } chainParams.printFreq = tempI; MrBayesPrint ("%s Setting print frequency to %d\n", spacer, chainParams.printFreq); expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else { free(tempStr); return (ERROR); } } /* set Printmax (printMax) **********************************************************/ else if (!strcmp(parmName, "Printmax")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(NUMBER); else if (expecting == Expecting(NUMBER)) { sscanf (tkn, "%d", &tempI); if (tempI < 1) { MrBayesPrint ("%s You need to print at least one chain\n", spacer); return (ERROR); } chainParams.printMax = tempI; MrBayesPrint ("%s Setting maximum number of chains to print to screen to %d\n", spacer, chainParams.printMax); expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else { free(tempStr); return (ERROR); } } /* set Printall (printAll) ********************************************************/ else if (!strcmp(parmName, "Printall")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(ALPHA); else if (expecting == Expecting(ALPHA)) { if (IsArgValid(tkn, tempStr) == NO_ERROR) { if (!strcmp(tempStr, "Yes")) chainParams.printAll = YES; else chainParams.printAll = NO; } else { MrBayesPrint ("%s Invalid argument for Printall\n", spacer); free (tempStr); return (ERROR); } if (chainParams.allChains == YES) MrBayesPrint ("%s Printing all chains to screen\n", spacer); else MrBayesPrint ("%s Printing only cold chains to screen\n", spacer); expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else { free(tempStr); return (ERROR); } } /* set Swapfreq (swapFreq) ************************************************************/ else if (!strcmp(parmName, "Swapfreq")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(NUMBER); else if (expecting == Expecting(NUMBER)) { sscanf (tkn, "%d", &tempI); if (tempI < 1) { MrBayesPrint ("%s Swapping states too infrequently\n", spacer); free(tempStr); return (ERROR); } chainParams.swapFreq = tempI; MrBayesPrint ("%s Setting swap frequency to %d\n", spacer, chainParams.swapFreq); expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else { free (tempStr); return (ERROR); } } /* set Nswaps (numSwaps) ************************************************************/ else if (!strcmp(parmName, "Nswaps")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(NUMBER); else if (expecting == Expecting(NUMBER)) { sscanf (tkn, "%d", &tempI); if (tempI < 1) { MrBayesPrint ("%s There must be at least one swap per swapping cycle\n", spacer); free (tempStr); return (ERROR); } chainParams.numSwaps = tempI; MrBayesPrint ("%s Setting number of swaps per swapping cycle to %d\n", spacer, chainParams.numSwaps); expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else { free(tempStr); return (ERROR); } } /* set Allchains (allChains) ********************************************************/ else if (!strcmp(parmName, "Allchains")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(ALPHA); else if (expecting == Expecting(ALPHA)) { if (IsArgValid(tkn, tempStr) == NO_ERROR) { if (!strcmp(tempStr, "Yes")) chainParams.allChains = YES; else chainParams.allChains = NO; } else { MrBayesPrint ("%s Invalid argument for Allchains\n", spacer); free (tempStr); return (ERROR); } if (chainParams.allChains == YES) MrBayesPrint ("%s Calculating MCMC diagnostics for all chains\n", spacer); else MrBayesPrint ("%s Calculating MCMC diagnostics only for cold chain(s)\n", spacer); expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else { free (tempStr); return (ERROR); } } /* set Allcomps (allComps) ************************************************************/ else if (!strcmp(parmName, "Allcomps")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(ALPHA); else if (expecting == Expecting(ALPHA)) { if (IsArgValid(tkn, tempStr) == NO_ERROR) { if (!strcmp(tempStr, "Yes")) chainParams.allComps = YES; else chainParams.allComps = NO; } else { MrBayesPrint ("%s Invalid argument for Allcomps\n", spacer); free (tempStr); return (ERROR); } if (chainParams.allComps == YES) MrBayesPrint ("%s Calculating MCMC diagnostics for all pairwise run comparisons\n", spacer); else MrBayesPrint ("%s Only calculating overall MCMC diagnostics\n", spacer); expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else { free(tempStr); return (ERROR); } } /* set Mcmcdiagn (mcmcDiagn) ********************************************************/ else if (!strcmp(parmName, "Mcmcdiagn")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(ALPHA); else if (expecting == Expecting(ALPHA)) { if (IsArgValid(tkn, tempStr) == NO_ERROR) { if (!strcmp(tempStr, "Yes")) chainParams.mcmcDiagn = YES; else chainParams.mcmcDiagn = NO; } else { MrBayesPrint ("%s Invalid argument for MCMC diagnostics\n", spacer); free(tempStr); return (ERROR); } if (chainParams.mcmcDiagn == YES) MrBayesPrint ("%s Setting calculation of MCMC diagnostics ('Mcmcdiagn') to yes\n", spacer); else MrBayesPrint ("%s Setting calculation of MCMC diagnostics ('Mcmcdiagn') to no\n", spacer); expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else { free(tempStr); return (ERROR); } } /* set Diagnfreq (diagnFreq) ************************************************************/ else if (!strcmp(parmName, "Diagnfreq")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(NUMBER); else if (expecting == Expecting(NUMBER)) { sscanf (tkn, "%d", &tempI); if (tempI < 1) { MrBayesPrint ("%s Diagnosing MCMC behavior too infrequently\n", spacer); free(tempStr); return (ERROR); } chainParams.diagnFreq = tempI; MrBayesPrint ("%s Setting diagnosing frequency to %d\n", spacer, chainParams.diagnFreq); expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else { free(tempStr); return (ERROR); } } /* set Savetrees (saveTrees) ********************************************************/ else if (!strcmp(parmName, "Savetrees")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(ALPHA); else if (expecting == Expecting(ALPHA)) { if (IsArgValid(tkn, tempStr) == NO_ERROR) { if (!strcmp(tempStr, "Yes")) chainParams.saveTrees = YES; else chainParams.saveTrees = NO; } else { MrBayesPrint ("%s Invalid argument for Savetrees\n", spacer); free(tempStr); return (ERROR); } if (chainParams.saveTrees == YES) MrBayesPrint ("%s Saving trees for MCMC diagnostics in memory (if needed)\n", spacer); else MrBayesPrint ("%s Not saving trees for MCMC diagnostics in memory\n", spacer); expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else { free(tempStr); return (ERROR); } } /* set Diagnstat (diagnStat) ********************************************************/ else if (!strcmp(parmName, "Diagnstat")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(ALPHA); else if (expecting == Expecting(ALPHA)) { if (IsArgValid(tkn, tempStr) == NO_ERROR) { if (!strcmp(tempStr, "Avgstddev")) chainParams.diagnStat = AVGSTDDEV; else /* if (!strcmp(tempStr, "Maxstddev")) */ chainParams.diagnStat = MAXSTDDEV; } else { MrBayesPrint ("%s Invalid argument for Savetrees\n", spacer); free(tempStr); return (ERROR); } if (chainParams.diagnStat == AVGSTDDEV) MrBayesPrint ("%s Setting diagnostics statistic to Avgstddev\n", spacer); else /* if (chainParams.diagnStat == MAXSTDDEV) */ MrBayesPrint ("%s Setting diagnostics statistic to Maxstddev\n", spacer); expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else { free(tempStr); return (ERROR); } } /* set Checkpoint (checkPoint) ********************************************************/ else if (!strcmp(parmName, "Checkpoint")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(ALPHA); else if (expecting == Expecting(ALPHA)) { if (IsArgValid(tkn, tempStr) == NO_ERROR) { if (!strcmp(tempStr, "Yes")) chainParams.checkPoint = YES; else chainParams.checkPoint = NO; } else { MrBayesPrint ("%s Invalid argument for 'Checkpoint' (check-pointing)\n", spacer); free(tempStr); return (ERROR); } if (chainParams.checkPoint == YES) MrBayesPrint ("%s Setting check-pointing ('Checkpoint') to yes\n", spacer); else MrBayesPrint ("%s Setting check-pointing ('Checkpoint') to no\n", spacer); expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else { free(tempStr); return (ERROR); } } /* set Checkfreq (checkFreq) ************************************************************/ else if (!strcmp(parmName, "Checkfreq")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(NUMBER); else if (expecting == Expecting(NUMBER)) { sscanf (tkn, "%d", &tempI); if (tempI < 100) { MrBayesPrint ("%s Check-pointing frequency must be at least 100\n", spacer); free(tempStr); return (ERROR); } chainParams.checkFreq = tempI; MrBayesPrint ("%s Setting check-pointing frequency to %d\n", spacer, chainParams.checkFreq); expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else { free(tempStr); return (ERROR); } } /* set Minpartfreq (minPartFreq) ************************************************************/ else if (!strcmp(parmName, "Minpartfreq")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(NUMBER); else if (expecting == Expecting(NUMBER)) { sscanf (tkn, "%lf", &tempD); if (tempD < 0.01) { MrBayesPrint ("%s Minimum partition frequency too low (< 0.01)\n", spacer); free (tempStr); return (ERROR); } if (tempD > 0.8) { MrBayesPrint ("%s Minimum partition frequency too high (> 0.8)\n", spacer); free (tempStr); return (ERROR); } chainParams.minPartFreq = tempD; MrBayesPrint ("%s Setting minimum partition frequency to %.2f\n", spacer, chainParams.minPartFreq); expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else { free(tempStr); return (ERROR); } } /* set Nruns (numRuns) ****************************************************************/ else if (!strcmp(parmName, "Nruns")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(NUMBER); else if (expecting == Expecting(NUMBER)) { sscanf (tkn, "%d", &tempI); if (tempI < 1) { MrBayesPrint ("%s Too few runs (minimum of 1 run)\n", spacer); free(tempStr); return (ERROR); } if (tempI > MAX_RUNS) { MrBayesPrint ("%s Too many runs (maximum of %d runs)\n", spacer, MAX_RUNS); free(tempStr); return (ERROR); } if (ChangeNumRuns (chainParams.numRuns, tempI) == ERROR) return (ERROR); chainParams.numRuns = tempI; fileNameChanged = YES; MrBayesPrint ("%s Setting number of runs to %d\n", spacer, chainParams.numRuns); expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else { free(tempStr); return (ERROR); } } /* set Nchains (numChains) ************************************************************/ else if (!strcmp(parmName, "Nchains")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(NUMBER); else if (expecting == Expecting(NUMBER)) { sscanf (tkn, "%d", &tempI); if (tempI < 1) { MrBayesPrint ("%s Too few chains (minimum of 1 chain)\n", spacer); free(tempStr); return (ERROR); } if (tempI > MAX_CHAINS) { MrBayesPrint ("%s Too many chains (maximum of %d chains)\n", spacer, MAX_CHAINS); free(tempStr); return (ERROR); } if (ChangeNumChains (chainParams.numChains, tempI) == ERROR) return (ERROR); chainParams.numChains = tempI; MrBayesPrint ("%s Setting number of chains to %d\n", spacer, chainParams.numChains); expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else { free(tempStr); return (ERROR); } } /* set Temp (chainTemp) ***************************************************************/ else if (!strcmp(parmName, "Temp")) { if (expecting == Expecting(EQUALSIGN)) { tempIndex = 0; expecting = Expecting(NUMBER) | Expecting(LEFTPAR); } else if (expecting == Expecting(LEFTPAR)) { chainParams.userDefinedTemps = YES; expecting = Expecting(NUMBER); } else if (expecting == Expecting(RIGHTPAR)) { MrBayesPrint ("%s Setting user-defined temperatures\n", spacer); expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else if (expecting == Expecting(COMMA)) { expecting = Expecting(NUMBER); } else if (expecting == Expecting(NUMBER)) { if (chainParams.userDefinedTemps == NO) { sscanf (tkn, "%lf", &tempD); chainParams.chainTemp = tempD; MrBayesPrint ("%s Setting heating parameter to %lf\n", spacer, chainParams.chainTemp); expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else { if (tempIndex >= MAX_CHAINS) { MrBayesPrint ("%s Too many user-defined temperatures (%d maximum)\n", spacer, MAX_CHAINS); free(tempStr); return (ERROR); } sscanf (tkn, "%lf", &tempD); chainParams.userTemps[tempIndex++] = tempD; expecting = Expecting(COMMA) | Expecting(RIGHTPAR); } } else { free(tempStr); return (ERROR); } } /* set Reweight (weightScheme) ********************************************************/ else if (!strcmp(parmName, "Reweight")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(LEFTPAR); else if (expecting == Expecting(LEFTPAR)) { expecting = Expecting(NUMBER); whichReweightNum = 0; } else if (expecting == Expecting(NUMBER)) { if (whichReweightNum < 0 || whichReweightNum > 2) { free(tempStr); return (ERROR); } sscanf (tkn, "%lf", &tempD); chainParams.weightScheme[whichReweightNum] = tempD; if (whichReweightNum < 2) { if (tempD < 0.0 || tempD > 100.0) { MrBayesPrint ("%s The reweighting parameter must be between 0 and 100\n", spacer); chainParams.weightScheme[0] = chainParams.weightScheme[1] = 0.0; chainParams.weightScheme[2] = 1.0; free(tempStr); return (ERROR); } } else { if (tempD <= 0.0 || tempD > 1.0) { MrBayesPrint ("%s The reweighting increment must be between 0 and 1\n", spacer); chainParams.weightScheme[0] = chainParams.weightScheme[1] = 0.0; chainParams.weightScheme[2] = 1.0; free(tempStr); return (ERROR); } } if (whichReweightNum == 0) { expecting = Expecting(COMMA); } else if (whichReweightNum == 1) { if (chainParams.weightScheme[0] + chainParams.weightScheme[1] > 100.0) { MrBayesPrint ("%s The sum of the reweighting parameters cannot exceed 100 %%\n", spacer); chainParams.weightScheme[0] = chainParams.weightScheme[1] = 0.0; chainParams.weightScheme[2] = 1.0; free(tempStr); return (ERROR); } expecting = Expecting(COMMA) | Expecting(RIGHTPAR); } else { expecting = Expecting(RIGHTPAR); } whichReweightNum++; } else if ((expecting & Expecting(COMMA)) == Expecting(COMMA)) expecting = Expecting(NUMBER); else if ((expecting & Expecting(RIGHTPAR)) == Expecting(RIGHTPAR)) { if (chainParams.weightScheme[0] >= 100.0) { MrBayesPrint ("%s Cannot decrease weight of all characters\n", spacer); chainParams.weightScheme[0] = chainParams.weightScheme[1] = 0.0; chainParams.weightScheme[2] = 1.0; free(tempStr); return (ERROR); } MrBayesPrint ("%s Setting reweighting parameter to (%1.2lf v, %1.2lf ^) increment = %1.2lf\n", spacer, chainParams.weightScheme[0], chainParams.weightScheme[1], chainParams.weightScheme[2]); expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else { free(tempStr); return (ERROR); } } /* set Filename (chainFileName) *******************************************************/ else if (!strcmp(parmName, "Filename")) { if (expecting == Expecting(EQUALSIGN)) { expecting = Expecting(ALPHA); readWord = YES; } else if (expecting == Expecting(ALPHA)) { sscanf (tkn, "%s", tempStr); if (strlen(tempStr)>99) { MrBayesPrint ("%s Maximum allowed length of chain file name is 99 characters. The given name:\n", spacer); MrBayesPrint ("%s '%s'\n", spacer,tempStr); MrBayesPrint ("%s has %d characters.\n", spacer,strlen(tempStr)); return (ERROR); } strcpy (chainParams.chainFileName, tempStr); fileNameChanged = YES; expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else { free(tempStr); return (ERROR); } } /* set Relburnin (relativeBurnin) ********************************************************/ else if (!strcmp(parmName, "Relburnin")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(ALPHA); else if (expecting == Expecting(ALPHA)) { if (IsArgValid(tkn, tempStr) == NO_ERROR) { if (!strcmp(tempStr, "Yes")) chainParams.relativeBurnin = YES; else chainParams.relativeBurnin = NO; } else { MrBayesPrint ("%s Invalid argument for Relburnin\n", spacer); free(tempStr); return (ERROR); } if (chainParams.relativeBurnin == YES) MrBayesPrint ("%s Using relative burnin (a fraction of samples discarded).\n", spacer); else MrBayesPrint ("%s Using absolute burnin (a fixed number of samples discarded).\n", spacer); expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else { free (tempStr); return (ERROR); } } /* set Burnin (chainBurnIn) ***********************************************************/ else if (!strcmp(parmName, "Burnin")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(NUMBER); else if (expecting == Expecting(NUMBER)) { sscanf (tkn, "%d", &tempI); chainParams.chainBurnIn = tempI; MrBayesPrint ("%s Setting chain burn-in to %d\n", spacer, chainParams.chainBurnIn); expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else { free(tempStr); return (ERROR); } } /* set Burninfrac (burninFraction) ************************************************************/ else if (!strcmp(parmName, "Burninfrac")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(NUMBER); else if (expecting == Expecting(NUMBER)) { sscanf (tkn, "%lf", &tempD); if (tempD < 0.01) { MrBayesPrint ("%s Burnin fraction too low (< 0.01)\n", spacer); free(tempStr); return (ERROR); } if (tempD > 0.50) { MrBayesPrint ("%s Burnin fraction too high (> 0.50)\n", spacer); free(tempStr); return (ERROR); } chainParams.burninFraction = tempD; MrBayesPrint ("%s Setting burnin fraction to %.2f\n", spacer, chainParams.burninFraction); expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else { free(tempStr); return (ERROR); } } /* set Stoprule (stopRule) ********************************************************/ else if (!strcmp(parmName, "Stoprule")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(ALPHA); else if (expecting == Expecting(ALPHA)) { if (IsArgValid(tkn, tempStr) == NO_ERROR) { if (!strcmp(tempStr, "Yes")) chainParams.stopRule = YES; else chainParams.stopRule = NO; } else { MrBayesPrint ("%s Invalid argument for Stoprule\n", spacer); free(tempStr); return (ERROR); } if (chainParams.stopRule == YES) MrBayesPrint ("%s Using stopping rule.\n", spacer); else MrBayesPrint ("%s Not using stopping rule.\n", spacer); expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else { free(tempStr); return (ERROR); } } /* set Stopval (stopVal) ************************************************************/ else if (!strcmp(parmName, "Stopval")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(NUMBER); else if (expecting == Expecting(NUMBER)) { sscanf (tkn, "%lf", &tempD); if (tempD < 0.000001) { MrBayesPrint ("%s Stop value too low (< 0.000001)\n", spacer); free(tempStr); return (ERROR); } if (tempD > 0.20) { MrBayesPrint ("%s Stop value too high (> 0.20)\n", spacer); free(tempStr); return (ERROR); } chainParams.stopVal = tempD; MrBayesPrint ("%s Setting burnin fraction to %.2f\n", spacer, chainParams.burninFraction); expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else { free(tempStr); return (ERROR); } } /* set Starttree (startTree) **************************************************/ else if (!strcmp(parmName, "Starttree")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(ALPHA); else if (expecting == Expecting(ALPHA)) { if (IsArgValid(tkn, tempStr) == NO_ERROR) { if (!strcmp(tempStr,"User")) { MrBayesPrint ("%s The 'user' setting of 'Starttree' is deprecated. Set starting trees using 'Startvals' instead.\n", spacer); free (tempStr); return (ERROR); } else strcpy(chainParams.startTree, tempStr); } else { MrBayesPrint ("%s Invalid 'Starttree' argument '%s'. This is a bug -- please report.\n", spacer, tempStr); free(tempStr); return (ERROR); } MrBayesPrint ("%s Setting 'Starttree' to \"%s\"\n", spacer, chainParams.startTree); expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else { free(tempStr); return (ERROR); } } /* set Startingtrees (deprecated) **************************************************/ else if (!strcmp(parmName, "Startingtrees")) { free (tempStr); MrBayesPrint ("%s Parameter 'Startingtrees' is deprecated. Use the 'Starttree' parameter or the 'Startvals' command instead.\n", spacer); return (ERROR); } /* set Nperts (numStartPerts) *********************************************************/ else if (!strcmp(parmName, "Nperts")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(NUMBER); else if (expecting == Expecting(NUMBER)) { sscanf (tkn, "%d", &tempI); chainParams.numStartPerts = tempI; MrBayesPrint ("%s Setting number of perturbations to start tree to %d\n", spacer, chainParams.numStartPerts); expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else { free(tempStr); return (ERROR); } } /* set Startparams (startParams) **************************************************/ else if (!strcmp(parmName, "Startparams")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(ALPHA); else if (expecting == Expecting(ALPHA)) { if (IsArgValid(tkn, tempStr) == NO_ERROR) strcpy(chainParams.startParams, tempStr); else { MrBayesPrint ("%s Invalid 'Startparams' argument\n", spacer); free(tempStr); return (ERROR); } MrBayesPrint ("%s Setting 'Startparams' to \"%s\"\n", spacer, chainParams.startParams); expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else { free(tempStr); return (ERROR); } } /* set Savebrlens (saveBrlens) ********************************************************/ else if (!strcmp(parmName, "Savebrlens")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(ALPHA); else if (expecting == Expecting(ALPHA)) { if (IsArgValid(tkn, tempStr) == NO_ERROR) { if (!strcmp(tempStr, "Yes")) ; /* This is the only option in version 3.2 */ else MrBayesPrint ("%s WARNING: Ignoring savebrlens setting; since version 3.2, branch lengths are always saved\n", spacer); } else { MrBayesPrint ("%s Invalid argument for savebrlens\n", spacer); free(tempStr); return (ERROR); } expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } } /* set Redirect (redirect) ********************************************************/ else if (!strcmp(parmName, "Redirect")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(ALPHA); else if (expecting == Expecting(ALPHA)) { if (IsArgValid(tkn, tempStr) == NO_ERROR) { if (!strcmp(tempStr, "Yes")) chainParams.redirect = YES; else chainParams.redirect = NO; } else { MrBayesPrint ("%s Invalid argument for redirecting output\n", spacer); free(tempStr); return (ERROR); } if (chainParams.redirect == YES) MrBayesPrint ("%s Setting program to redirect output\n", spacer); else MrBayesPrint ("%s Setting program not to redirect output\n", spacer); expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else { free(tempStr); return (ERROR); } } /* set Data (runWithData) ************************************************************/ else if (!strcmp(parmName, "Data")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(ALPHA); else if (expecting == Expecting(ALPHA)) { if (IsArgValid(tkn, tempStr) == NO_ERROR) { if (!strcmp(tempStr, "Yes")) chainParams.runWithData = YES; else chainParams.runWithData = NO; } else { MrBayesPrint ("%s Invalid argument for Data\n", spacer); free(tempStr); return (ERROR); } if (chainParams.runWithData == NO) MrBayesPrint ("%s Running without data (WARNING: use this only for checking priors!)\n", spacer); else MrBayesPrint ("%s Running with data (standard analysis)\n", spacer); expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else { free(tempStr); return (ERROR); } } /* set Ordertaxa (chainParams.orderTaxa) *********************************************/ else if (!strcmp(parmName, "Ordertaxa")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(ALPHA); else if (expecting == Expecting(ALPHA)) { if (IsArgValid(tkn, tempStr) == NO_ERROR) { if (!strcmp(tempStr, "Yes")) chainParams.orderTaxa = YES; else chainParams.orderTaxa = NO; } else { MrBayesPrint ("%s Invalid argument for ordertaxa\n", spacer); free(tempStr); return (ERROR); } if (chainParams.orderTaxa == YES) MrBayesPrint ("%s Setting ordertaxa to yes\n", spacer); else MrBayesPrint ("%s Setting ordertaxa to no\n", spacer); expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else { free(tempStr); return (ERROR); } } /* set Append (chainParams.append) *********************************************/ else if (!strcmp(parmName, "Append")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(ALPHA); else if (expecting == Expecting(ALPHA)) { if (IsArgValid(tkn, tempStr) == NO_ERROR) { if (!strcmp(tempStr, "Yes")) chainParams.append = YES; else chainParams.append = NO; } else { MrBayesPrint ("%s Invalid argument for append\n", spacer); free(tempStr); return (ERROR); } if (chainParams.append == YES) MrBayesPrint ("%s Setting append to yes\n", spacer); else MrBayesPrint ("%s Setting append to no\n", spacer); expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else { free(tempStr); return (ERROR); } } /* set Autotune (chainParams.autotune) *********************************************/ else if (!strcmp(parmName, "Autotune")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(ALPHA); else if (expecting == Expecting(ALPHA)) { if (IsArgValid(tkn, tempStr) == NO_ERROR) { if (!strcmp(tempStr, "Yes")) chainParams.autotune = YES; else chainParams.autotune = NO; } else { MrBayesPrint ("%s Invalid argument for Autotune\n", spacer); free(tempStr); return (ERROR); } if (chainParams.autotune == YES) MrBayesPrint ("%s Setting Autotune to yes\n", spacer); else MrBayesPrint ("%s Setting Autotune to no\n", spacer); expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else { free(tempStr); return (ERROR); } } /* set Tunefreq (tuneFreq) ************************************************************/ else if (!strcmp(parmName, "Tunefreq")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(NUMBER); else if (expecting == Expecting(NUMBER)) { sscanf (tkn, "%d", &tempI); if (tempI < 10) { MrBayesPrint ("%s Autotuning frequency must be at least 10\n", spacer); free(tempStr); return (ERROR); } chainParams.tuneFreq = tempI; MrBayesPrint ("%s Setting autotuning frequency to %d\n", spacer, chainParams.tuneFreq); expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else { free(tempStr); return (ERROR); } } /* set Swapadjacent (swapAdjacentOnly) **************************************************/ else if (!strcmp(parmName, "Swapadjacent")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(ALPHA); else if (expecting == Expecting(ALPHA)) { if (IsArgValid(tkn, tempStr) == NO_ERROR) { if (!strcmp(tempStr, "Yes")) chainParams.swapAdjacentOnly = YES; else chainParams.swapAdjacentOnly = NO; } else { MrBayesPrint ("%s Invalid argument for Swapadjacent\n", spacer); free(tempStr); return (ERROR); } if (chainParams.swapAdjacentOnly == YES) MrBayesPrint ("%s Setting program to attempt swaps only between chains of adjacent temperatures\n", spacer); else MrBayesPrint ("%s Setting program to attempt all possible swaps between chains\n", spacer); expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else { free(tempStr); return (ERROR); } } else { free(tempStr); return (ERROR); } } free(tempStr); return (NO_ERROR); } int DoSs (void) { int ret, oldBurnin; if (chainParams.numGen/chainParams.sampleFreq <= chainParams.burninSS) {/*Do not change print out to generations vs samples because of danger of overflow*/ MrBayesPrint ("%s ERROR: Burnin %d samples is too large compared with requested total %d samples (%lli generations).\n", spacer ,chainParams.burninSS, chainParams.numGen/chainParams.sampleFreq, chainParams.numGen); return ERROR; } oldBurnin = chainParams.burninSS;; stepRelativeBurninSS = chainParams.relativeBurnin; chainParams.relativeBurnin = YES; if (chainParams.burninSS < 0) chainParams.burninSS = (int)(chainParams.numGen / ((chainParams.numStepsSS-chainParams.burninSS)*(long long)(chainParams.sampleFreq))); chainParams.isSS = YES; ret=DoMcmc(); chainParams.isSS = NO; chainParams.burninSS = oldBurnin; chainParams.relativeBurnin = stepRelativeBurninSS; return ret; } int DoSsp (void) { return NO_ERROR; } int ExhaustiveParsimonySearch (Tree *t, int chain, TreeInfo *tInfo) { int i, j, k; TreeNode *p; for (i=j=k=0; inNodes; i++) { p = t->allDownPass[i]; if (p->left == NULL || p->right == NULL) tInfo->leaf[j++] = p; else tInfo->vertex[k++] = p; } tInfo->leaf[0]->anc = tInfo->leaf[1]->anc = tInfo->vertex[0]; tInfo->vertex[0]->left = tInfo->leaf[0]; tInfo->vertex[0]->right = tInfo->leaf[1]; tInfo->leaf[t->nIntNodes+1]->left = tInfo->vertex[0]; tInfo->vertex[0]->anc = tInfo->leaf[t->nIntNodes+1]; BuildExhaustiveSearchTree (t, chain, 2, tInfo); return (NO_ERROR); } int ExtendChainQuery (void) { int extendChain, additionalCycles; char s[100]; # if defined (MPI_ENABLED) if (proc_id == 0) { MrBayesPrint ("\n"); extendChain = WantTo ("Continue with analysis"); } MPI_Bcast (&extendChain, 1, MPI_INT, 0, MPI_COMM_WORLD); if (extendChain == YES) { if (proc_id == 0) { additionalCycles = 0; do { if (additionalCycles < 0) MrBayesPrint ("%s Number must be greater than or equal to 0: ", spacer); else MrBayesPrint ("%s Additional number of generations: ", spacer); if (fgets (s, 100, stdin) == NULL) { printf ("Error in function: %s at line: %d in file: %s", __func__, __LINE__, __FILE__); } sscanf (s, "%d", &additionalCycles); } while (additionalCycles < 0); MrBayesPrint ("\n"); } MPI_Bcast (&additionalCycles, 1, MPI_INT, 0, MPI_COMM_WORLD); return (additionalCycles); } else return (0); # else MrBayesPrint ("\n"); extendChain = WantTo ("Continue with analysis"); if (extendChain == YES) { additionalCycles = 0; do { if (additionalCycles < 0) MrBayesPrint ("%s Number must be greater than or equal to 0: ", spacer); else MrBayesPrint ("%s Additional number of generations: ", spacer); if (fgets (s, 20, stdin) == NULL) { printf ("Error in function: %s at line: %d in file: %s", __func__, __LINE__, __FILE__); } sscanf (s, "%d", &additionalCycles); } while (additionalCycles < 0); MrBayesPrint ("\n"); return (additionalCycles); } else return (0); # endif } int FillNumSitesOfPat (void) { int i, j, n, *increased, *decreased, nToDecrease, nToIncrease, whichToChange; MrBFlt ran, sum; CLFlt wtIncrement; wtIncrement = (CLFlt) chainParams.weightScheme[2]; increased = decreased = NULL; /* reallocate numSitesOfPat */ if (memAllocs[ALLOC_NUMSITESOFPAT] == NO) { MrBayesPrint ("%s numSitesOfPat is not allocated\n", spacer); goto errorExit; } memAllocs[ALLOC_NUMSITESOFPAT] = NO; numSitesOfPat = (CLFlt *) SafeRealloc((void *) numSitesOfPat, numCompressedChars * chainParams.numChains * sizeof(MrBFlt)); if (!numSitesOfPat) { MrBayesPrint ("%s Problem reallocating numSitesOfPat (%d)\n", spacer, numCompressedChars * chainParams.numChains * sizeof(MrBFlt)); goto errorExit; } memAllocs[ALLOC_NUMSITESOFPAT] = YES; /* copy first numCompressedChars into the remaining bits */ if (chainParams.numChains > 1) { for (i=0; i 1) { if (chainParams.weightScheme[0] + chainParams.weightScheme[1] > 0.0001) MrBayesPrint ("%s Reweighting of characters for chains 1 to %d\n", spacer, chainParams.numChains); /* check that we don't have an HMM */ if (chainHasAdgamma == YES && chainParams.weightScheme[0] + chainParams.weightScheme[1] > 0.0001) { MrBayesPrint ("%s Reweighting of characters is not allowed with an autocorrelated gamma model\n", spacer); goto errorExit; } /* how many characters */ n = 0; for (i=0; i 0.0001) { MrBayesPrint ("%s Decreasing weight of %d characters\n", spacer, nToDecrease); MrBayesPrint ("%s Increasing weight of %d characters\n", spacer, nToIncrease); } /* allocate memory */ increased = (int *)SafeMalloc(2 * (size_t)numCompressedChars * sizeof(int)); if (!increased) { MrBayesPrint ("%s Problem reallocating increased (%d)\n", spacer, numCompressedChars * chainParams.numChains * sizeof(int)); goto errorExit; } decreased = increased + numCompressedChars; /* reweight characters for each chain */ for (j=1; j= numCompressedChars) continue; } while (decreased[whichToChange] >= numSitesOfPat[0 * numCompressedChars + whichToChange]); decreased[whichToChange]++; numSitesOfPat[j * numCompressedChars + whichToChange] -= wtIncrement; if (numSitesOfPat[j * numCompressedChars + whichToChange] < 0) { MrBayesPrint ("%s Problem reweighting characters\n", spacer); goto errorExit; } } /* increase weight of characters */ for (i=0; i= numCompressedChars) continue; } while ((increased[whichToChange] + decreased[whichToChange]) >= numSitesOfPat[0 * numCompressedChars + whichToChange]); increased[whichToChange]++; numSitesOfPat[j * numCompressedChars + whichToChange] += wtIncrement; if (numSitesOfPat[j * numCompressedChars + whichToChange] < 0) { MrBayesPrint ("%s Problem reweighting characters\n", spacer); goto errorExit; } } } /* free allocated memory */ free (increased); } # if 0 /* print site patterns for each chain */ for (i=0; inRelParts; n++) { division = t->relParts[n]; /* Find model settings */ m = &modelSettings[division]; /* Find number of site patterns */ nSitesOfPat = numSitesOfPat + ((1 % chainParams.numChains) * numCompressedChars) + m->compCharStart; /* Find final-pass parsimony sets for the node and its ancestor */ pP = m->parsSets[p->index ]; pA = m->parsSets[p->anc->index ]; pX = m->parsSets[addNode->index]; for (c=0; cnumChars; c++) { if (((pP[c] | pA[c])&pX[c]) == 0) fpLength += nSitesOfPat[c]; } } /* If tip, this is the best node and its length is the min length */ if (p->left == NULL) { *minLength = fpLength; return p; } /* Find best node and min length above this node */ if (p->left != NULL) { q = FindBestNode(t, p->left, addNode, minLength, chain); r = FindBestNode(t, p->right, addNode, &length, chain); if (length < *minLength) { *minLength = length; q = r; } } /* Return best node and min length */ if (*minLength < fpLength) return q; else /* This node is the best */ { *minLength = fpLength; return p; } } void FreeChainMemory (void) { int i, j, k, nRates; ModelInfo *m; /* free model variables for Gibbs gamma */ for (i=0; iparsSets) { for (j=0; jnumParsSets; j++) free (m->parsSets[j]); free (m->parsSets); m->parsSets = NULL; } if (m->parsNodeLens) { free(m->parsNodeLens); m->parsNodeLens = NULL; } } /* free model variables for conditional likelihoods */ for (i=0; icondLikes) { for (j=0; jnumCondLikes; j++) { # if defined (SSE_ENABLED) if (m->useVec != NO) ALIGNEDSAFEFREE (m->condLikes[j]); else free (m->condLikes[j]); # else free (m->condLikes[j]); # endif } free (m->condLikes); m->condLikes = NULL; } if (m->ancStates) { for (j=0; jnumCondLikes; j++) free (m->ancStates[j]); free (m->ancStates); m->ancStates = NULL; } if (m->bmVars) { for (j=0; jnumTiProbs; j++) free (m->bmVars[j]); free (m->bmVars); m->bmVars = NULL; } if (m->scalers) { for (j=0; jnumScalers; j++) # if defined (SSE_ENABLED) if (m->useVec != VEC_NONE) ALIGNEDSAFEFREE (m->scalers[j]); else free (m->scalers[j]); # else free (m->scalers[j]); # endif free (m->scalers); m->scalers = NULL; } if (m->clP) { free (m->clP); m->clP = NULL; } # if defined (SSE_ENABLED) if (m->useVec != VEC_NONE) { if (m->clP_SSE) { free (m->clP_SSE); m->clP_SSE = NULL; } #if defined (AVX_ENABLED) if (m->clP_AVX) { free (m->clP_AVX); m->clP_AVX = NULL; } #endif if (m->lnL_Vec) ALIGNEDSAFEFREE (m->lnL_Vec); if (m->lnLI_Vec) ALIGNEDSAFEFREE (m->lnLI_Vec); } # endif if (m->tiProbs) { for (j=0; jnumTiProbs; j++) free (m->tiProbs[j]); free (m->tiProbs); m->tiProbs = NULL; } if (m->cijks) { for (j=0; jcijks[j]); free (m->cijks); m->cijks = NULL; } if (m->condLikeIndex) { for (j=0; jcondLikeIndex[j]); free (m->condLikeIndex); m->condLikeIndex = NULL; } if (m->condLikeScratchIndex) { free (m->condLikeScratchIndex); m->condLikeScratchIndex=NULL; } if (m->tiProbsIndex) { for (j=0; jtiProbsIndex[j]); free (m->tiProbsIndex); m->tiProbsIndex = NULL; } if (m->tiProbsScratchIndex) { free (m->tiProbsScratchIndex); m->tiProbsScratchIndex = NULL; } if (m->nodeScalerIndex) { for (j=0; jnodeScalerIndex[j]); free (m->nodeScalerIndex); m->nodeScalerIndex = NULL; } if (m->nodeScalerScratchIndex) { free (m->nodeScalerScratchIndex); m->nodeScalerScratchIndex = NULL; } if (m->unscaledNodes) { for (j=0; junscaledNodes[j]); free (m->unscaledNodes); m->unscaledNodes = NULL; } if (m->unscaledNodesScratch) { free (m->unscaledNodesScratch); m->unscaledNodesScratch = NULL; } if (m->siteScalerIndex) { free (m->siteScalerIndex); m->siteScalerIndex = NULL; } if (m->cijkIndex) { free (m->cijkIndex); m->cijkIndex = NULL; } if (m->ancStateCondLikes) { free (m->ancStateCondLikes); m->ancStateCondLikes = NULL; } # if defined (BEAGLE_ENABLED) if (m->useBeagle == NO) continue; if (!(m->useBeagleMultiPartitions == YES && m->divisionIndex > 0)) { beagleFinalizeInstance(m->beagleInstance); m->beagleInstance = -99; // TODO: allow consecutive 'mcmc' runs using BEAGLE } SAFEFREE(m->logLikelihoods); SAFEFREE(m->inRates); SAFEFREE(m->branchLengths); SAFEFREE(m->tiProbIndices); SAFEFREE(m->inWeights); SAFEFREE(m->bufferIndices); SAFEFREE(m->eigenIndices); SAFEFREE(m->childBufferIndices); SAFEFREE(m->childTiProbIndices); SAFEFREE(m->cumulativeScaleIndices); m->isScalerNodeScratch += numLocalTaxa; SAFEFREE(m->isScalerNodeScratch); for (j=0; jisScalerNode[j] += numLocalTaxa; SAFEFREE(m->isScalerNode[j]); } SAFEFREE(m->isScalerNode); SAFEFREE(m->beagleComputeCount); SAFEFREE(m->successCount); SAFEFREE(m->rescaleFreq); SAFEFREE(m->operations); SAFEFREE(m->scaleFactorsOps); # if defined (BEAGLE_V3_ENABLED) if (m->useBeagleMultiPartitions == YES) { if (m->divisionIndex == 0) { SAFEFREE(m->logLikelihoodsAll); SAFEFREE(m->cijkIndicesAll); SAFEFREE(m->categoryRateIndicesAll); SAFEFREE(m->operationsAll); } SAFEFREE(m->operationsByPartition); } # endif /* BEAGLE_V3_ENABLED */ # endif /* BEAGLE_ENABLED */ } if (memAllocs[ALLOC_CURLNL] == YES) /*alloc in RunChain()*/ { free (maxLnL0); free (curLnL); memAllocs[ALLOC_CURLNL] = NO; } if (memAllocs[ALLOC_SS] == YES) /*alloc in mcmc()*/ { free (marginalLnLSS); free (stepScalerSS); free (stepAcumulatorSS); free (splitfreqSS); memAllocs[ALLOC_SS] = NO; } if (memAllocs[ALLOC_CURLNPR] == YES) /*alloc in RunChain()*/ { free (curLnPr); memAllocs[ALLOC_CURLNPR] = NO; } if (memAllocs[ALLOC_CHAINID] == YES) /*alloc in RunChain()*/ { free (chainId); memAllocs[ALLOC_CHAINID] = NO; } if (memAllocs[ALLOC_USEDMOVES] == YES) /*alloc in setUsedMoves()*/ { free (usedMoves); memAllocs[ALLOC_USEDMOVES] = NO; } if (memAllocs[ALLOC_TERMSTATE] == YES) /*alloc in SetUpTermState()*/ { free (termState); termState = NULL; memAllocs[ALLOC_TERMSTATE] = NO; } if (memAllocs[ALLOC_ISPARTAMBIG] == YES) /*alloc in SetUpTermState()*/ { free (isPartAmbig); isPartAmbig = NULL; memAllocs[ALLOC_ISPARTAMBIG] = NO; } if (memAllocs[ALLOC_PRELIKES] == YES) /*alloc in InitCondLike()*/ { free (preLikeL); preLikeL = NULL; memAllocs[ALLOC_PRELIKES] = NO; } if (memAllocs[ALLOC_RATEPROBS] == YES) /*alloc in InitAdGamma() */ { free (rateProbSpace); free (rateProbs); rateProbs = NULL; memAllocs[ALLOC_RATEPROBS] = NO; } if (memAllocs[ALLOC_SITEJUMP] == YES) /*alloc in InitAdGamma() */ { free (siteJump); siteJump = NULL; memAllocs[ALLOC_SITEJUMP] = NO; } if (memAllocs[ALLOC_MARKOVTIS] == YES) /*alloc in InitAdGamma() */ { for (i=0; inParsIntsPerSite <= 2 && m->nParsIntsPerSite > 0); assert (source1 >= 0 && source1 < m->numParsSets); assert (source2 >= 0 && source2 < m->numParsSets); assert (destination >= 0 && destination < m->numParsSets); /* find parsimony sets for the nodes */ pS1 = m->parsSets[source1 ]; pS2 = m->parsSets[source2 ]; pD = m->parsSets[destination]; /* Find number of site patterns */ nSitesOfPat = numSitesOfPat + ((1 % chainParams.numChains) * numCompressedChars) + m->compCharStart;//chainId[chain] if (m->nParsIntsPerSite == 1) { for (c=0; cnumChars; c++) { x[0] = pS1[c] & pS2[c]; if (x[0] == 0) { length += nSitesOfPat[c]; x[0] = pS1[c] | pS2[c]; } pD[c] = x[0]; } } else /* if (m->nParsIntsPerSite == 2) */ { assert (m->nParsIntsPerSite == 2); for (c=i=0; cnumChars; c++) { x[0] = pS1[i] & pS2[i]; x[1] = pS1[i+1] & pS2[i+1]; if ((x[0] | x[1]) == 0) { length += nSitesOfPat[c]; x[0] = pS1[i] | pS2[i]; x[1] = pS1[i+1] | pS2[i+1]; } pD[i++] = x[0]; pD[i++] = x[1]; } } return length; } MrBFlt GetParsDP (Tree *t, TreeNode *p, int chain) { int n, division; MrBFlt length; ModelInfo *m; length = 0.0; if (p->left != NULL) { length += GetParsDP (t, p->left, chain); length += GetParsDP (t, p->right, chain); for (n=0; nnRelParts; n++) { division = t->relParts[n]; /* Find model settings */ m = &modelSettings[division]; /* Get Fitch partials and length */ length += GetFitchPartials(m, chain, p->left->index, p->right->index, p->index); } } return length; } void GetParsFP (Tree *t, TreeNode *p, int chain) { int i, c, n, division; BitsLong *pL, *pR, *pP, *pA, x[2]; ModelInfo *m; if (p->left != NULL) { for (n=0; nnRelParts; n++) { division = t->relParts[n]; /* Find model settings */ m = &modelSettings[division]; assert (m->nParsIntsPerSite == 1 || m->nParsIntsPerSite == 2); /* find parsimony sets for the node and its environment */ pL = m->parsSets[p->left->index ]; pR = m->parsSets[p->right->index]; pP = m->parsSets[p->index ]; pA = m->parsSets[p->anc->index ]; if (m->nParsIntsPerSite == 1) { for (c=0; cnumChars; c++) { x[0] = pP[c] & pA[c]; if (x[0] != pA[c]) { /* means that we allow change of state from p to a */ if ((pL[c] & pR[c]) != 0) x[0] = ((pL[c] | pR[c]) & pA[c]) | pP[c] ; /* We still allow only one change from both children of p through p to a. So state from a that belong to one of the children of p can be added to pP[c], if p assume the state then the only change would be on the other child. */ else x[0] = pP[c] | pA[c]; /* Here we allow two change from both children of p through p to a. Adding pA[c] to pP[c] means that if p assume state exclusive in a then even if both children will be in different state from p we still get optimal parsimony */ } pP[c] = x[0]; } } else /* if (m->nParsIntsPerSite == 2) */ { for (c=i=0; cnumChars; c++) { x[0] = pP[i] & pA[i]; x[1] = pP[i+1] & pA[i+1]; if (x[0] != pA[i] || x[1] != pA[i+1]) { x[0] = pL[i] & pR[i]; x[1] = pL[i+1] & pR[i+1]; if ((x[0] | x[1]) != 0) { x[0] = ((pL[i] | pR[i]) & pA[i]) | pP[i] ; x[1] = ((pL[i+1] | pR[i+1]) & pA[i+1]) | pP[i+1] ; } else { x[0] = pP[i] | pA[i]; x[1] = pP[i+1] | pA[i+1]; } } pP[i++] = x[0]; pP[i++] = x[1]; } } } GetParsFP (t, p->left, chain); GetParsFP (t, p->right, chain); } } int GetParsimonyBrlens (Tree *t, int chain, MrBFlt *brlens) { int c, i, j, n, division; BitsLong *pP, *pA; CLFlt *nSitesOfPat; TreeNode *p; ModelInfo *m; /* Reset all brlens */ for (i=0; inNodes-1; i++) brlens[i] = 0.0; /* Get final parsimony state sets */ GetParsDP(t, t->root->left, chain); GetParsFP(t, t->root, chain); /* Get all branch lengths, looping over divisions */ for (n=0; nnRelParts; n++) { division = t->relParts[n]; /* Find model settings */ m = &modelSettings[division]; /* Find number of site patterns */ nSitesOfPat = numSitesOfPat + ((chainId[chain] % chainParams.numChains) * numCompressedChars) + m->compCharStart; /* Record branch lengths in downpass */ for (i=0; inNodes-1; i++) { p = t->allDownPass[i]; /* Find final-pass parsimony sets for the node and its ancestor */ pP = m->parsSets[p->index ]; pA = m->parsSets[p->anc->index]; if (m->nParsIntsPerSite == 1) { for (c=0; cnumChars; c++) { if ((pP[c] & pA[c]) == 0) brlens[i] += nSitesOfPat[c]; } } else { for (c=j=0; cnumChars; c++, j+=2) { if ((pP[j] & pA[j]) == 0 && (pP[j+1] & pA[j+1]) == 0) brlens[i] += nSitesOfPat[c]; } } } } return (NO_ERROR); } MrBFlt GetParsimonyLength (Tree *t, int chain) { int c, i, n, division; BitsLong *pP, *pA; CLFlt *nSitesOfPat; MrBFlt length; TreeNode *p; ModelInfo *m; /* Get length down to internal root */ length = GetParsDP(t, t->root->left, chain); /* return if rooted */ if (t->isRooted == NO) return length; /* add in the last terminal if not rooted */ for (n=0; nnRelParts; n++) { division = t->relParts[n]; /* Find model settings */ m = &modelSettings[division]; /* Find number of site patterns */ nSitesOfPat = numSitesOfPat + ((chainId[chain] % chainParams.numChains) * numCompressedChars) + m->compCharStart; /* Deal with last branch */ p = t->intDownPass[t->nIntNodes-1]; /* find downpass parsimony sets for the node and its environment */ pP = m->parsSets[p->index ]; pA = m->parsSets[p->anc->index]; if (m->nParsIntsPerSite == 1) { for (c=0; cnumChars; c++) { if ((pP[c] & pA[c]) == 0) { length += nSitesOfPat[c]; } } } else /* if (m->nParsIntsPerSite == 2) */ { for (c=i=0; cnumChars; c++, i+=2) { if ((pP[i] & pA[i]) == 0 && (pP[i+1] & pA[i+1]) == 0) { length += nSitesOfPat[c]; } } } } return length; } void GetParsimonySubtreeRootstate (Tree *t, TreeNode *root, int chain) { int c, i, n, division; BitsLong *pD, *pP, *pA, x[2]; TreeNode *p; ModelInfo *m; /* Loop over divisions */ for (n=0; nnRelParts; n++) { division = t->relParts[n]; /* Find model settings */ m = &modelSettings[division]; assert (m->nParsIntsPerSite == 1 || m->nParsIntsPerSite == 2); for (i=0; inNodes; i++) { p = t->allDownPass[i]; p->marked = NO; } p = root; while (p->anc != NULL) { p->marked = YES; p = p->anc; } /* Make uppass node by node */ for (i=t->nIntNodes-1; i>=0; i--) { p = t->intDownPass[i]; /* continue if no work needs to be done */ if (p->marked == NO) continue; /* find downpass and uppass parsimony sets for the node and its environment */ pP = m->parsSets[p->index ]; if (p->left->marked == YES) pD = m->parsSets[p->right->index]; else pD = m->parsSets[p->left->index ]; pA = m->parsSets[p->anc->index ]; if (m->nParsIntsPerSite == 1) { for (c=0; cnumChars; c++) { x[0] = pD[c] & pA[c]; if (x[0] == 0) { x[0] = (pD[c] | pA[c]); } pP[c] = x[0]; } } else if (m->nParsIntsPerSite == 2) { for (c=i=0; cnumChars; c++, i+=2) { x[0] = pD[i ] & pA[i ]; x[1] = pD[i+1] & pA[i+1]; if (x[0] + x[1] == 0) { x[0] = (pD[i ] | pA[i ]); x[1] = (pD[i+1] | pA[i+1]); } pP[i ] = x[0]; pP[i+1] = x[1]; } } if (p == root) break; } } return; } /* GetRate: retrieve the base rate for the division and chain in current state */ MrBFlt GetRate (int division, int chain) { Param *p; MrBFlt *values, rate; int i; rate = 0.0; p = modelSettings[division].rateMult; values = GetParamVals (p, chain, state[chain]); if (p->nValues == 1) rate = values[0]; else { for (i=0; inRelParts; i++) { if (p->relParts[i] == division) { rate = values[i]; break; } } } p = modelSettings[division].geneTreeRateMult; if (p != NULL) { values = GetParamVals (p, chain, state[chain]); for (i=0; inRelParts; i++) { if (p->relParts[i] == division) { rate *= values[i]; break; } } } return rate; } void GetStamp (void) { int i; for (i=0; i<10; i++) stamp[i] = '0' + (int)(RandomNumber(&runIDSeed) * 10); stamp[10] = '\0'; MrBayesPrint ("%s MCMC stamp = %s\n", spacer, stamp); } void GetSwappers (int *swapA, int *swapB, int run) { int i; /* this works for both the serial and parallel versions because the swapSeed is identical for all processors, ensuring they all get the same sequence of chainIds to swap */ # if defined (MPI_ENABLED) /* For now, we only allow random swaps in the MPI version. Other schemes require tagging of messages, or a dedicated server node doing message processing. */ (*swapA) = (int) (RandomNumber(&swapSeed) * chainParams.numChains); (*swapB) = (int) (RandomNumber(&swapSeed) * (chainParams.numChains - 1)); if ((*swapB) == (*swapA)) (*swapB) = chainParams.numChains - 1; # else if (chainParams.swapAdjacentOnly == NO) { (*swapA) = (int) (RandomNumber(&swapSeed) * chainParams.numChains); (*swapB) = (int) (RandomNumber(&swapSeed) * (chainParams.numChains - 1)); if ((*swapB) == (*swapA)) (*swapB) = chainParams.numChains - 1; } else { (*swapA) = (int) (RandomNumber(&swapSeed) * (chainParams.numChains - 1)); (*swapB) = (*swapA) + 1; } # endif i = run * chainParams.numChains; (*swapA) += i; (*swapB) += i; return; } void GetTempDownPassSeq (TreeNode *p, int *i, TreeNode **dp) { if (p != NULL) { GetTempDownPassSeq (p->left, i, dp); GetTempDownPassSeq (p->right, i, dp); dp[(*i)++] = p; } } MrBFlt GibbsSampleGamma (int chain, int division, RandLong *seed) { int c, i, k, *rateCat, nStates, nRateCats, nPosRateCats, id; CLFlt **catLike, **catLnScaler, *lnScaler, maxLnScaler, *clRoot, f, bs[64], *clInvar, pInvar, freq; MrBFlt ran, lnL, *bsVals, deltaLnL, temp; ModelInfo *m; Tree *t; TreeNode *p; m = &modelSettings[division]; /* find base frequencies */ bsVals = GetParamSubVals (m->stateFreq, chain, state[chain]); nStates = m->numModelStates; for (i=0; iscalers[m->siteScalerIndex[chain]]; /* find category like array and associated scaler */ catLike = m->catLike[chain]; catLnScaler = m->catLnScaler[chain]; /* find rate category index */ rateCat = m->tiIndex + chain*m->numChars; /* find number of rate cats and gamma cats */ nRateCats = nPosRateCats = m->numRateCats; if (m->pInvar != NULL) nRateCats++; /* find tree */ t = GetTree (m->brlens, chain, state[chain]); /* find invar cond likes (if we have invariable rate category) */ clInvar = m->invCondLikes; /* get pInvar */ if (m->pInvar == NULL) pInvar = 0.0; else pInvar = (CLFlt) *GetParamVals (m->pInvar, chain, state[chain]); freq = ((CLFlt)1.0 - pInvar) / nPosRateCats; /* get chain temperature */ temp = Temperature (chainId[chain]); id = chainId[chain] % chainParams.numChains; /* calculate rate probs */ for (k=0; knumChars; c++) rateCat[c] = k; for (i=0; inIntNodes; i++) { p = t->intDownPass[i]; if (t->isRooted == NO && p->anc->anc == NULL) m->CondLikeRoot (p, division, chain); else m->CondLikeDown (p, division, chain); if (m->unscaledNodes[chain][p->index] == 0) m->CondLikeScaler (p, division, chain); } /* find root conditional likes */ p = t->root->left; clRoot = m->condLikes[m->condLikeIndex[chain][p->index]]; for (c=0; cnumChars; c++) { catLike[k][c] = 0.0; for (i=0; ipInvar != NULL) { k = nPosRateCats; for (c=0; cnumChars; c++) { catLike[k][c] = 0.0; for (i=0; inumChars; c++) { /* find max scaler */ maxLnScaler = catLnScaler[0][c]; for (k=1; k maxLnScaler && catLike[k][c] > 0.0) maxLnScaler = catLnScaler[k][c]; } /* scale values */ for (k=0; knIntNodes; i++) { p = t->intDownPass[i]; if (t->isRooted == NO && p->anc->anc == NULL) m->CondLikeRoot (p, division, chain); else m->CondLikeDown (p, division, chain); if (m->unscaledNodes[chain][p->index] == 0) m->CondLikeScaler (p, division, chain); } lnL = 0.0; m->Likelihood (t->root->left, division, chain, &lnL, (chainId[chain] % chainParams.numChains)); deltaLnL = lnL - m->lnLike[2*chain + state[chain]]; m->lnLike[2*chain + state[chain]] = lnL; return (deltaLnL); } /*------------------------------------------------------------------------ | | InitAdGamma: initialize variables for adgamma model. | -------------------------------------------------------------------------*/ int InitAdGamma (void) { int d, c, i, j, k, maxRates, *corrModel; ModelInfo *m; /* take care of adgamma model */ if (chainHasAdgamma == NO) return (NO_ERROR); MrBayesPrint ("%s Initializing autocorrelated discrete gamma model\n", spacer); /* allocate corr space */ corrModel = (int *) SafeCalloc (numCurrentDivisions, sizeof(int)); if (!corrModel) return ERROR; /* allocate siteJump */ if (memAllocs[ALLOC_SITEJUMP] == YES) { MrBayesPrint ("%s siteJump not free in InitAdGamma\n", spacer); free (corrModel); return ERROR; } siteJump = (int *) SafeCalloc (numChar, sizeof(int)); if (siteJump) memAllocs[ALLOC_SITEJUMP] = YES; else { MrBayesPrint ("%s Problem allocating siteJump in InitAdGamma (%d ints)\n", spacer, numChar); free (corrModel); return ERROR; } /* reset vector indicating the matrices needed */ for (i=0; icorrelation == NULL || m->mark == YES) continue; m->mark = YES; for (i=0; icorrelation->nRelParts; i++) { if (modelSettings[m->correlation->relParts[i]].shape == modelSettings[d].shape) { modelSettings[m->correlation->relParts[i]].mark = YES; corrModel[m->correlation->relParts[i]] = k; } } k++; if (m->numRateCats > maxRates) maxRates = m->numRateCats; } for (c=0; c=0; j--) { if (corrModel[partitionId[j][partitionNum] - 1] == k) break; } if (j<0) siteJump[c] = 0; else if (charInfo[j].bigBreakAfter == YES) siteJump[c] = BIG_JUMP; else { siteJump[c] = c - j; hasMarkovTi[c-j-1] = YES; } } /* check if any HMM is empty */ k=0; for (i=0; i k) k = corrModel[i]; } for (i=1; i<=k; i++) { for (c=j=0; c= MAX_SMALL_JUMP && markovTiN) memAllocs[ALLOC_MARKOVTIS] = YES; else { MrBayesPrint ("%s Problem allocating MarkovTis in InitAdGamma (%d MrBFlt)\n", spacer, 2 * MAX_RATE_CATS * MAX_RATE_CATS); for (i=0; icorrelation != NULL) { m->rateProbStart = i; i += m->numRateCats * m->numChars; } } rateProbRowSize = i; /* allocate space */ if (memAllocs[ALLOC_RATEPROBS] == YES) { MrBayesPrint ("%s Space for rate probs not free in InitAdGamma\n", spacer); free (corrModel); return ERROR; } rateProbSpace = (MrBFlt *) SafeMalloc (2 * numLocalChains * rateProbRowSize * sizeof(MrBFlt)); rateProbs = (MrBFlt **) SafeMalloc (numLocalChains * sizeof(MrBFlt *)); if (!rateProbSpace || !rateProbs) { MrBayesPrint ("%s Problem allocating rate probs\n", spacer); if (rateProbSpace) free (rateProbSpace); if (rateProbs) free (rateProbs); free (corrModel); return ERROR; } else memAllocs[ALLOC_RATEPROBS] = YES; /* set chain rateProbs pointers */ for (i=j=0; igibbsGamma == NO) continue; m->tiIndex = (int *) SafeMalloc (numLocalChains * m->numChars * sizeof (int)); if (!m->tiIndex) return ERROR; m->catLike = (CLFlt ***) SafeMalloc (numLocalChains * sizeof (CLFlt **)); if (!m->catLike) return ERROR; m->catLnScaler = (CLFlt ***) SafeMalloc (numLocalChains * sizeof (CLFlt **)); if (!m->catLnScaler) return ERROR; if (m->pInvar == NULL) nRateCats = m->numRateCats; else nRateCats = m->numRateCats + 1; for (i=0; icatLike[i] = (CLFlt **) SafeCalloc (nRateCats, sizeof (CLFlt *)); if (!m->catLike[i]) return ERROR; m->catLnScaler[i] = (CLFlt **) SafeCalloc (nRateCats, sizeof (CLFlt *)); if (!m->catLnScaler[i]) return ERROR; for (j=0; jcatLike[i][j] = (CLFlt *) SafeCalloc (m->numChars, sizeof (CLFlt)); if (!m->catLike[i][j]) return ERROR; m->catLnScaler[i][j] = (CLFlt *) SafeCalloc (m->numChars, sizeof (CLFlt)); if (!m->catLnScaler[i][j]) return ERROR; } } } return NO_ERROR; } /*------------------------------------------------------------------------ | | InitChainCondLikes: (1) calculate size of cond like arrays, tiprob arrays | and scaler arrays; (2) allocate space for cond like, tiprob and | scaler arrays; (3) allocate and set node indices pointing to | cond like and scaler arrays; (4) initialize tip cond likes; | (5) allocate space for precalculated cond likes; (6) allocate | space for adgamma probs, if used. | -------------------------------------------------------------------------*/ int InitChainCondLikes (void) { int c, d, i, j, k, s, t, numReps, condLikesUsed, nIntNodes, nNodes, clIndex, tiIndex, scalerIndex, indexStep; BitsLong *charBits; CLFlt *cL; ModelInfo *m; # if defined (SSE_ENABLED) int j1; # endif # if defined (BEAGLE_ENABLED) int divisionOffset; double *nSitesOfPat; MrBFlt freq; # endif # if defined (BEAGLE_V3_ENABLED) int *nPartsOfPat; # endif /* Figure out how large cond like array is needed, and how many cond like, scaler and tiprob arrays are needed. Also check for possible use of Beagle */ condLikesUsed = NO; for (d=0; dnumChars); /* initialize model settings for chain cond likes */ m->condLikeLength = 0; m->numCondLikes = 0; if (m->parsModelId == YES || m->dataType == CONTINUOUS) continue; condLikesUsed = YES; /* figure out length of cond like array */ if (m->dataType == STANDARD) { for (c=0; cnumChars; c++) { numReps = m->numRateCats; if (m->nStates[c] == 2) numReps *= m->numBetaCats; m->condLikeLength += m->nStates[c] * numReps; } } else { if (m->gibbsGamma == YES) m->condLikeLength = m->numChars * m->numModelStates; else m->condLikeLength = m->numChars * m->numRateCats * m->numOmegaCats * m->numModelStates; # if defined (BEAGLE_ENABLED) /* tentatively decide on whether to use Beagle */ if (tryToUseBEAGLE == YES) { if (m->printAncStates == YES || m->printSiteRates == YES || m->printPosSel ==YES || m->printSiteOmegas==YES) { MrBayesPrint ("%s Non-beagle version of conditional likelihood calculator will be used for division %d due to\n", spacer, d+1); MrBayesPrint ("%s request of reporting 'ancestral states', 'site rates', 'pos selection' or 'site omegas'.\n", spacer); } else if (m->switchRates != NULL) { MrBayesPrint ("%s Non-beagle version of conditional likelihood calculator will be used for division %d due to\n", spacer, d+1); MrBayesPrint ("%s the covarion model not being supported in the current beagle implementation in MrBayes.\n", spacer); } else if (m->gibbsGamma == NO) m->useBeagle = YES; } # endif } /* find size of tree */ nIntNodes = GetTree(m->brlens, 0, 0)->nIntNodes; nNodes = GetTree(m->brlens, 0, 0)->nNodes; /* figure out number of cond like arrays */ m->numCondLikes = (numLocalChains + 1) * (nIntNodes); m->numCondLikes += numLocalTaxa; /* # if !defined (DEBUG_NOSHORTCUTS) for (i=0; iisPartAmbig[i] == NO && m->dataType != STANDARD) m->numCondLikes--; } # endif */ /* figure out number of node and site scalers */ m->numScalers = (numLocalChains + 1) * (nIntNodes + 1); /* add 1 for site scalers */ /* figure out length of ti prob array and number of ti prob arrays */ m->tiProbLength = 0; if (m->dataType == STANDARD) { m->numTiCats = 0; /* We do not have repeated similar transition probability matrices */ if (m->stateFreq->paramId == SYMPI_EQUAL) { for (k = 0; k < MAX_STD_STATES-1; k++) /* UNORD */ { if (m->isTiNeeded[k] == YES) m->tiProbLength += (k + 2) * (k + 2) * m->numRateCats; } for (k = MAX_STD_STATES-1; k < 2*MAX_STD_STATES-3; k++) /* ORD */ { if (m->isTiNeeded[k] == YES) m->tiProbLength += (k-MAX_STD_STATES+4) * (k-MAX_STD_STATES+4) * m->numRateCats; } } else { /* deal with unequal state frequencies */ if (m->isTiNeeded[0] == YES) m->tiProbLength += 4 * m->numRateCats * m->numBetaCats; for (c=0; cnumChars; c++) { if (m->nStates[c] > 2 && (m->cType[c] == UNORD || m->cType[c] == ORD)) { m->tiProbLength += (m->nStates[c] * m->nStates[c]) * m->numRateCats; } } } } else { m->numTiCats = m->numRateCats * m->numBetaCats * m->numOmegaCats; /* A single partition has either gamma, beta or omega categories */ m->tiProbLength = m->numModelStates * m->numModelStates * m->numTiCats; } m->numTiProbs = (numLocalChains + 1) * nNodes; /* set info about eigen systems */ if (InitEigenSystemInfo (m) == ERROR) return (ERROR); } /* check if conditional likelihoods are needed */ /* set up use of beagle, if applicable */ if (condLikesUsed == YES) { MrBayesPrint ("%s Initializing conditional likelihoods\n", spacer); # if defined (BEAGLE_V3_ENABLED) /* Try to use a multipartition instance of beagle. * The call to InitBeagleMultiPartitionsInstance will detect if a division has useBeagle == NO, and * will return an error if so. The function will also set the useBeagleMultiPartitions flag for all * partitions if it is possible to use a multipartition instance of Beagle. We can safely ignore * the ERROR returned by InitBeagleMultiPartitionsInstance as it leaves everything in the correct * state if it fails to implement a multipartition instance of beagle. */ if (beagleResourceNumber != 0 && numCurrentDivisions > 1) InitBeagleMultiPartitionInstance(); # endif # if defined (BEAGLE_ENABLED) /* Try to use single-partition instances of beagle */ if (modelSettings[0].useBeagleMultiPartitions == NO) { for (d=0; duseBeagle == YES && InitBeagleInstance(m, d) == ERROR) m->useBeagle = NO; } } # endif } /* allocate space and fill in info for tips */ for (d=0; ddataType == CONTINUOUS) continue; # if defined (BEAGLE_ENABLED) /* if using beagle, adjust SIMD settings */ if (m->useBeagle == YES) { m->useVec = VEC_NONE; # if defined (SSE_ENABLED) m->numFloatsPerVec = 0; # endif } # endif /* get size of tree */ nIntNodes = GetTree(m->brlens,0,0)->nIntNodes; nNodes = GetTree(m->brlens,0,0)->nNodes; /* allocate and set indices from tree nodes to cond like arrays */ /* we set them up first because they are needed also for parsimony partitions */ m->condLikeIndex = (int **) SafeMalloc (numLocalChains * sizeof(int *)); if (!m->condLikeIndex) return (ERROR); for (j=0; jcondLikeIndex[j] = (int *) SafeMalloc (nNodes * sizeof(int)); if (!m->condLikeIndex[j]) return (ERROR); for (i=0; icondLikeIndex[j][i] = -1; } /* set up indices for terminal nodes */ clIndex = 0; if (m->useBeagle == YES) indexStep = m->nCijkParts; else indexStep = 1; for (i=0; iuseBeagle == NO && m->useVec == VEC_NONE && m->isPartAmbig[i] == NO && m->dataType != STANDARD) continue; */ # endif for (j=0; jcondLikeIndex[j][i] = clIndex; clIndex += 1; /* even for multiple omega cat we need only one set of conditional likelihoods for terminals for all chains.*/ } /* reserve private space for parsimony-based moves if parsimony model is used */ if (m->parsModelId == YES && m->parsimonyBasedMove == YES) clIndex += nIntNodes; /* set up indices for internal nodes */ for (j=0; jcondLikeIndex[j][i+numLocalTaxa] = clIndex; clIndex += indexStep; } } /* allocate and set up scratch cond like indices */ m->condLikeScratchIndex = (int *) SafeMalloc (nNodes * sizeof(int)); if (!m->condLikeScratchIndex) return (ERROR); for (i=0; icondLikeScratchIndex[i] = -1; for (i=0; icondLikeScratchIndex[i+numLocalTaxa] = clIndex; clIndex += indexStep; } /* parsimony models need nothing of the below */ if (m->parsModelId == YES) continue; /* allocate space for conditional likelihoods */ if (m->useBeagle == NO && m->useVec == VEC_NONE) MrBayesPrint ("%s Using standard non-SSE likelihood calculator for division %d (%s-precision)\n", spacer, d+1, (sizeof(CLFlt) == 4 ? "single" : "double")); else if (m->useBeagle == NO && m->useVec == VEC_SSE) MrBayesPrint ("%s Using standard SSE likelihood calculator for division %d (single-precision)\n", spacer, d+1); else if (m->useBeagle == NO && m->useVec == VEC_AVX) MrBayesPrint ("%s Using standard AVX likelihood calculator for division %d (single-precision)\n", spacer, d+1); else if (m->useBeagle == NO && m->useVec == VEC_FMA) MrBayesPrint ("%s Using standard FMA likelihood calculator for division %d (single-precision)\n", spacer, d+1); else if (m->useBeagle == NO) { MrBayesPrint ("%s WARNING! Using unknown SIMD likelihood calculator for division %d (single-precision)\n", spacer, d+1); return (ERROR); } if (m->useBeagle == NO) { /* allocate cond like space */ m->condLikes = (CLFlt**) SafeMalloc(m->numCondLikes * sizeof(CLFlt*)); if (!m->condLikes) return (ERROR); for (i=0; inumCondLikes; i++) { # if defined (SSE_ENABLED) if (m->useVec != VEC_NONE) { /* calculate number SSE chars */ m->numVecChars = ((m->numChars - 1) / m->numFloatsPerVec) + 1; /* allocate space with padding (m->condLikeLength is without padding) */ if (m->gibbsGamma == YES) numReps = 1; else numReps = m->numRateCats * m->numOmegaCats; k = m->numVecChars * m->numFloatsPerVec * m->numModelStates * numReps; if (m->useVec == VEC_AVX || m->useVec == VEC_FMA) m->condLikes[i] = (CLFlt*) AlignedMalloc (k * sizeof(CLFlt), 32); else m->condLikes[i] = (CLFlt*) AlignedMalloc (k * sizeof(CLFlt), 16); if (!m->condLikes[i]) return (ERROR); /* start by filling all with 0.0f; pad when filling in tips */ for (j=0; jcondLikes[i][j] = 0.0f; } else { m->condLikes[i] = (CLFlt*) SafeMalloc(m->condLikeLength * sizeof(CLFlt)); if (!m->condLikes[i]) return (ERROR); } # else m->condLikes[i] = (CLFlt*) SafeMalloc(m->condLikeLength * sizeof(CLFlt)); if (!m->condLikes[i]) return (ERROR); # endif } /* allocate scaler space and pointers for scaling */ m->scalers = (CLFlt**) SafeMalloc(m->numScalers * sizeof(CLFlt*)); if (!m->scalers) return (ERROR); for (i=0; inumScalers; i++) { # if defined (SSE_ENABLED) if (m->useVec != VEC_NONE) { /* allocate space with padding */ if (m->useVec == VEC_SSE) m->scalers[i] = (CLFlt*) AlignedMalloc (m->numVecChars * m->numFloatsPerVec * sizeof(CLFlt), 16); else if (m->useVec == VEC_AVX || m->useVec == VEC_FMA) m->scalers[i] = (CLFlt*) AlignedMalloc (m->numVecChars * m->numFloatsPerVec * sizeof(CLFlt), 32); if (!m->scalers[i]) return (ERROR); for (j=0; jnumVecChars*m->numFloatsPerVec; j++) m->scalers[i][j] = 0.0f; } else { m->scalers[i] = (CLFlt*) SafeMalloc (m->numChars * sizeof(CLFlt)); if (!m->scalers[i]) return (ERROR); } # else m->scalers[i] = (CLFlt*) SafeMalloc (m->numChars * sizeof(CLFlt)); if (!m->scalers[i]) return (ERROR); # endif } /* allocate stuff for facilitating scaling and accumulation of cond likes */ if (m->dataType == STANDARD) { m->clP = (CLFlt **) SafeMalloc(m->numRateCats * sizeof(CLFlt *)); if (!m->clP) return (ERROR); } else { m->clP = (CLFlt **) SafeMalloc(m->numTiCats * sizeof(CLFlt *)); if (!m->clP) return (ERROR); # if defined (SSE_ENABLED) if (m->useVec == VEC_SSE) { m->clP_SSE = (__m128 **) SafeMalloc(m->numTiCats * sizeof(__m128 *)); if (!m->clP_SSE) return (ERROR); m->lnL_Vec = AlignedMalloc (m->numVecChars * m->numFloatsPerVec * sizeof(CLFlt*), 16); m->lnLI_Vec = AlignedMalloc (m->numVecChars * m->numFloatsPerVec * sizeof(CLFlt*), 16); if (!m->lnL_Vec || !m->lnLI_Vec) return (ERROR); } #if defined (AVX_ENABLED) else if (m->useVec == VEC_AVX) { m->clP_AVX = (__m256 **) SafeMalloc(m->numTiCats * sizeof(__m256 *)); if (!m->clP_AVX) return (ERROR); m->lnL_Vec = AlignedMalloc (m->numVecChars * m->numFloatsPerVec * sizeof(CLFlt*), 32); m->lnLI_Vec = AlignedMalloc (m->numVecChars * m->numFloatsPerVec * sizeof(CLFlt*), 32); if (!m->lnL_Vec || !m->lnLI_Vec) return (ERROR); } #endif #if defined (FMA_ENABLED) else if (m->useVec == VEC_FMA) { m->clP_AVX = (__m256 **) SafeMalloc(m->numTiCats * sizeof(__m256 *)); if (!m->clP_AVX) return (ERROR); m->lnL_Vec = AlignedMalloc (m->numVecChars * m->numFloatsPerVec * sizeof(CLFlt*), 32); m->lnLI_Vec = AlignedMalloc (m->numVecChars * m->numFloatsPerVec * sizeof(CLFlt*), 32); if (!m->lnL_Vec || !m->lnLI_Vec) return (ERROR); } #endif # endif } /* allocate tiprob space */ m->tiProbs = (CLFlt**) SafeMalloc(m->numTiProbs * sizeof(CLFlt*)); if (!m->tiProbs) return (ERROR); for (i=0; inumTiProbs; i++) { m->tiProbs[i] = (CLFlt*) SafeMalloc(m->tiProbLength * sizeof(CLFlt)); if (!m->tiProbs[i]) return (ERROR); } } /* allocate eigen system space (needed also for Beagle version */ if (m->nCijkParts > 0) { m->cijks = (MrBFlt**) SafeMalloc((numLocalChains + 1) * sizeof(MrBFlt*)); if (!m->cijks) return ERROR; for (i=0; icijks[i] = (MrBFlt*) SafeMalloc(m->cijkLength * sizeof(MrBFlt)); if (!m->cijks[i]) return (ERROR); } } /* allocate and set indices from tree edges to ti prob arrays */ m->tiProbsIndex = (int **) SafeMalloc (numLocalChains * sizeof(int *)); if (!m->tiProbsIndex) return (ERROR); for (i=0; itiProbsIndex[i] = (int *) SafeMalloc (nNodes * sizeof(int)); if (!m->tiProbsIndex[i]) return (ERROR); } /* set up indices for nodes */ tiIndex = 0; for (i=0; itiProbsIndex[i][j] = tiIndex; tiIndex += indexStep; } } /* allocate and set up scratch transition prob indices */ m->tiProbsScratchIndex = (int *) SafeMalloc (nNodes * sizeof(int)); if (!m->tiProbsScratchIndex) return (ERROR); for (i=0; itiProbsScratchIndex[i] = tiIndex; tiIndex += indexStep; } /* allocate and set up rescale frequency */ m->rescaleFreq = (int*) SafeMalloc((numLocalChains) * sizeof(int)); for (i=0; inumModelStates == 4) m->rescaleFreq[i] = 1; else m->rescaleFreq[i] = 1; } /* allocate and set up number of unscaled nodes + scratch space */ m->unscaledNodes = (int **) SafeCalloc (numLocalChains, sizeof(int *)); if (!m->unscaledNodes) return (ERROR); for (i=0; iunscaledNodes[i] = (int *) SafeCalloc (nNodes, sizeof(int)); if (!m->unscaledNodes[i]) return (ERROR); } m->unscaledNodesScratch = (int *) SafeCalloc (nNodes, sizeof (int)); if (!m->unscaledNodesScratch) return (ERROR); /* allocate and set up node scaler indices */ scalerIndex = 0; m->nodeScalerIndex = (int **) SafeMalloc (numLocalChains * sizeof(int *)); if (!m->nodeScalerIndex) return (ERROR); for (i=0; inodeScalerIndex[i] = (int *) SafeMalloc (nNodes * sizeof(int)); if (!m->nodeScalerIndex[i]) return (ERROR); for (j=0; jnodeScalerIndex[i][j] = -1; for (j=0; jnodeScalerIndex[i][j+numLocalTaxa] = scalerIndex; scalerIndex += indexStep; } } m->nodeScalerScratchIndex = (int *) SafeMalloc (nNodes * sizeof (int)); if (!m->nodeScalerScratchIndex) return (ERROR); for (i=0; inodeScalerScratchIndex[i] = -1; for (i=0; inodeScalerScratchIndex[i+numLocalTaxa] = scalerIndex; scalerIndex += indexStep; } /* allocate and set up site scaler indices */ m->siteScalerIndex = (int *) SafeMalloc (numLocalChains * sizeof(int)); if (!m->siteScalerIndex) return (ERROR); for (i=0; isiteScalerIndex[i] = scalerIndex; scalerIndex += indexStep; } m->siteScalerScratchIndex = scalerIndex; # if defined (BEAGLE_ENABLED) /* used only with Beagle advanced dynamic rescaling where we set scaler nodes for each partition */ if (m->useBeagle == YES) { m->successCount = (int*) SafeMalloc((numLocalChains) * sizeof(int)); m->beagleComputeCount = (long *) SafeMalloc(sizeof(long) * numLocalChains); t=BEAGLE_RESCALE_FREQ/m->numModelStates; if (beagleFlags & BEAGLE_FLAG_PRECISION_DOUBLE) /*if double presition is used*/ t*=BEAGLE_RESCALE_FREQ_DOUBLE; for (i=0; irescaleFreq[i] = t; } m->isScalerNode = (int**) SafeMalloc((numLocalChains) * sizeof(int*)); /* we will use m->isScalerNode[chain][node->index] to determine whether the node is scaled or not. We do it only for internal nodes whose indexes start from numLocalTaxa thus we skew the pointer */ m->isScalerNodeScratch = (int*) SafeMalloc(nIntNodes * sizeof(int)) - numLocalTaxa; assert (NO == 0); /* SafeMalloc set the allocated memory to 0 while we need to set it to NO */ for (i=0; iisScalerNode[i] = (int*) SafeMalloc(nIntNodes * sizeof(int)) - numLocalTaxa; } } # endif /* allocate and set up cijk indices */ if (m->nCijkParts > 0) { m->cijkIndex = (int *) SafeMalloc (numLocalChains * sizeof(int)); if (!m->cijkIndex) return (ERROR); for (i=0; icijkIndex[i] = i*indexStep; m->cijkScratchIndex = numLocalChains*indexStep; } # if defined (BEAGLE_ENABLED) /* Set up nSitesOfPat for Beagle */ if (m->useBeagle == YES) { if (m->useBeagleMultiPartitions == NO) { nSitesOfPat = (double *) SafeMalloc (m->numChars * sizeof(double)); for (c=0; cnumChars; c++) nSitesOfPat[c] = numSitesOfPat[m->compCharStart + c]; beagleSetPatternWeights(m->beagleInstance, nSitesOfPat); free (nSitesOfPat); nSitesOfPat = NULL; /* Set up scalers for Beagle */ for (i=0; inumScalers*m->nCijkParts; i++) beagleResetScaleFactors(m->beagleInstance, i); } /* find category frequencies */ if (m->pInvar == NO) { freq = 1.0 / m->numRateCats; /* set category frequencies in beagle instance */ if (m->numOmegaCats <= 1) { divisionOffset = 0; if (m->useBeagleMultiPartitions == YES) divisionOffset = (numLocalChains + 1) * m->nCijkParts * m->divisionIndex; for (i=0; inumRateCats; i++) m->inWeights[i] = freq; for (i=0; i< (numLocalChains); i++) { beagleSetCategoryWeights(m->beagleInstance, m->cijkIndex[i] + divisionOffset, m->inWeights); } beagleSetCategoryWeights(m->beagleInstance, m->cijkScratchIndex + divisionOffset, m->inWeights); } } } # endif /* fill in tip conditional likelihoods */ if (m->dataType == STANDARD) { clIndex = 0; for (i=0; icondLikes[clIndex++]; for (t=0; tnumRateCats;t++) { charBits = m->parsSets[i]; for (c=0; cnumChars; c++) { if (m->nStates[c] == 2) numReps = m->numBetaCats; else numReps = 1; for (k=0; knStates[c]; s++) { if (IsBitSet(s, charBits)) (*cL) = 1.0; cL++; } } charBits += m->nParsIntsPerSite; } } } } else if (m->useBeagle == NO) { if (m->gibbsGamma == YES) numReps = m->numTiCats / m->numRateCats; else numReps = m->numTiCats; clIndex = 0; for (i=0; iisPartAmbig[i] == NO && m->dataType != RESTRICTION) continue; */ # endif cL = m->condLikes[clIndex++]; # if defined (SSE_ENABLED) /* deal with setup for SIMD code */ if (m->useVec != VEC_NONE) { for (k=0; kparsSets[i]; for (c=0; cnumChars/m->numFloatsPerVec; c++) { for (j=0; jnumModelStates/m->numStates; j++) { for (s=0; snumStates; s++) { for (j1=0; j1numFloatsPerVec; j1++) { if (IsBitSet(s, charBits + j1*m->nParsIntsPerSite)) (*cL) = 1.0; cL++; } } } charBits += m->numFloatsPerVec * m->nParsIntsPerSite; } if (m->numChars % m->numFloatsPerVec > 0) { /* add last characters and pad */ for (j=0; jnumModelStates/m->numStates; j++) { for (s=0; snumStates; s++) { for (j1=0; j1numChars%m->numFloatsPerVec; j1++) { if (IsBitSet(s, charBits + j1*m->nParsIntsPerSite)) (*cL) = 1.0; cL++; } for (; j1numFloatsPerVec; j1++) { (*cL) = 1.0; cL++; } } } } } } else { for (k=0; kparsSets[i]; for (c=0; cnumChars; c++) { for (j=0; jnumModelStates/m->numStates; j++) { for (s=0; snumStates; s++) { if (IsBitSet(s, charBits)) (*cL) = 1.0; cL++; } } charBits += m->nParsIntsPerSite; } } } # else for (k=0; kparsSets[i]; for (c=0; cnumChars; c++) { for (j=0; jnumModelStates/m->numStates; j++) { for (s=0; snumStates; s++) { if (IsBitSet(s, charBits)) (*cL) = 1.0; cL++; } } charBits += m->nParsIntsPerSite; } } # endif } } if (m->printAncStates == YES) { m->ancStateCondLikes = (CLFlt *) SafeMalloc (m->condLikeLength * sizeof(CLFlt)); if (!m->ancStateCondLikes) return (ERROR); } } #if defined (BEAGLE_V3_ENABLED) if (modelSettings[0].useBeagleMultiPartitions == YES) { nSitesOfPat = (double *) SafeMalloc (modelSettings[0].numCharsAll * sizeof(double)); nPartsOfPat = (int *) SafeMalloc (modelSettings[0].numCharsAll * sizeof(int )); j = 0; for (d=0; dnumChars; c++) { nSitesOfPat[j] = numSitesOfPat[m->compCharStart + c]; nPartsOfPat[j] = m->divisionIndex; j++; } } beagleSetPatternWeights(modelSettings[0].beagleInstance, nSitesOfPat); beagleSetPatternPartitions(modelSettings[0].beagleInstance, numCurrentDivisions, nPartsOfPat); free (nSitesOfPat); free (nPartsOfPat); nSitesOfPat = NULL; nPartsOfPat = NULL; /* Set up scalers for Beagle */ for (i=0; idataType == STANDARD || m->dataType == CONTINUOUS || m->parsModelId == YES) continue; i = (m->numModelStates + 1) * m->numModelStates * m->numTiCats; if (i > j) j = i; } if (j > 0) /* don't bother allocating precalculated likelihoods if we only have parsimony model or morphological characters */ { if (memAllocs[ALLOC_PRELIKES] == YES) { MrBayesPrint ("%s Space for preLikes not free in InitChainCondLikes\n", spacer); return ERROR; } preLikeL = (CLFlt *) SafeMalloc (3 * j * sizeof(CLFlt)); if (!preLikeL) { MrBayesPrint ("%s Problem allocating preLikes\n", spacer); return ERROR; } memAllocs[ALLOC_PRELIKES] = YES; preLikeR = preLikeL + j; preLikeA = preLikeR + j; } return NO_ERROR; } /*------------------------------------------------------------------------ | | Allocate space for log likelihoods, BM variances, | and ancestral and tip states for continuous traits | -------------------------------------------------------------------------*/ int InitContStates (void) { int c, d, i, j, nIntNodes, nNodes, clIndex, tiIndex; long state; ModelInfo *m; Tree *t; for (d=0; ddataType != CONTINUOUS) continue; /* we have no SIMD code for this partition */ MrBayesPrint ("%s Using standard non-SSE likelihood calculator for division %d (%s-precision)\n", spacer, d+1, (sizeof(CLFlt) == 4 ? "single" : "double")); /* find size of tree */ t = GetTree(m->brlens, 0, 0); nIntNodes = t->nIntNodes; nNodes = t->nNodes; /* figure out length of likelihood array */ m->condLikeLength = m->numChars * m->numRateCats; /* allocate space for the log likelihoods and node states */ m->numCondLikes = (numLocalChains + 1) * nIntNodes + numLocalTaxa; m->condLikes = (CLFlt**) SafeMalloc(m->numCondLikes * sizeof(CLFlt*)); m->ancStates = (CLFlt**) SafeMalloc(m->numCondLikes * sizeof(CLFlt*)); if (!m->condLikes || !m->ancStates) return (ERROR); for (i=0; inumCondLikes; i++) { m->condLikes[i] = (CLFlt*) SafeCalloc(m->condLikeLength, sizeof(CLFlt)); m->ancStates[i] = (CLFlt*) SafeCalloc(m->condLikeLength, sizeof(CLFlt)); if (!m->condLikes[i] || !m->ancStates[i]) return (ERROR); } /* allocate space for the variances (transformed branch lengths) */ m->numTiProbs = (numLocalChains + 1) * nNodes; // temporarily use numTiProbs m->bmVars = (CLFlt**) SafeMalloc(m->numTiProbs * sizeof(CLFlt*)); if (!m->bmVars) return (ERROR); for (i=0; inumTiProbs; i++) { m->bmVars[i] = (CLFlt*) SafeCalloc(m->condLikeLength, sizeof(CLFlt)); if (!m->bmVars[i]) return (ERROR); } /* allocate and set indices from tree nodes to likelihood arrays */ m->condLikeIndex = (int **) SafeMalloc(numLocalChains * sizeof(int *)); if (!m->condLikeIndex) return (ERROR); for (j=0; jcondLikeIndex[j] = (int *) SafeMalloc(nNodes * sizeof(int)); if (!m->condLikeIndex[j]) return (ERROR); for (i=0; icondLikeIndex[j][i] = -1; } m->condLikeScratchIndex = (int *) SafeMalloc(nNodes * sizeof(int)); if (!m->condLikeScratchIndex) return (ERROR); for (i=0; icondLikeScratchIndex[i] = -1; /* set up indices for tip and internal nodes */ clIndex = 0; for (i=0; icondLikeIndex[j][i] = clIndex; clIndex += 1; } for (i=0; icondLikeIndex[j][i+numLocalTaxa] = clIndex; clIndex += 1; } } for (i=0; icondLikeScratchIndex[i+numLocalTaxa] = clIndex; clIndex += 1; } /* allocate and set indices from tree edges to bm var arrays (temporarily use tiProbsIndex) */ m->tiProbsIndex = (int **) SafeMalloc(numLocalChains * sizeof(int *)); if (!m->tiProbsIndex) return (ERROR); for (j=0; jtiProbsIndex[j] = (int *) SafeMalloc(nNodes * sizeof(int)); if (!m->tiProbsIndex[j]) return (ERROR); } m->tiProbsScratchIndex = (int *) SafeMalloc(nNodes * sizeof(int)); if (!m->tiProbsScratchIndex) return (ERROR); /* set up indices for nodes */ tiIndex = 0; for (j=0; jtiProbsIndex[j][i] = tiIndex; tiIndex += 1; } } for (i=0; itiProbsScratchIndex[i] = tiIndex; tiIndex += 1; } /* fill in tip states */ /* we do this for one chain as all chains point to the same space */ for (i=0; icondLikeIndex[0][i]; for (c=0, j=m->compMatrixStart; jcompMatrixStop; j++, c++) { /* matrix[pos(i,origChar[c],numChar)] holds the int value, but we will have trouble if some taxa or/and characters are deleted. compMatrix has the actual data used in inference, but returns unsigned long, need to make sure we get the correct value (with the sign) back here */ state = (long)compMatrix[pos(i,j,compMatrixRowSize)]; if (state == INT_MAX) m->ancStates[clIndex][c] = (CLFlt)INT_MAX; else m->ancStates[clIndex][c] = (CLFlt)state / 10000.0; } } /* check values if continuous characters are normalized (between 0 and 1) */ if (!strcmp(modelParams[d].nst, "1")) { for (i=0; icondLikeIndex[0][i]; for (c=0; cnumChars; c++) { if (!IsMissingC(m->ancStates[clIndex][c]) && (m->ancStates[clIndex][c] < 0.0 || m->ancStates[clIndex][c] > 1.0)) { MrBayesPrint ("%s Continuous characters are assumed normalized (between 0 and 1), but found\n", spacer); MrBayesPrint ("%s %.3f in taxon %d char %d ...\n", spacer, m->ancStates[clIndex][c], i+1, c+1); MrBayesPrint ("%s Please set 'nst=2' in 'lset' if the characters are standardized (or unscaled).\n", spacer); return ERROR; } } } } /* for (i=0; icondLikeIndex[0][i]; for (c=0; cnumChars; c++) printf("%.3f ", m->ancStates[clIndex][c]); printf("\n"); } */ } return (NO_ERROR); } /*------------------------------------------------------------------------ | | InitEigenSystemInfo: set info about eigen decompositions | -------------------------------------------------------------------------*/ int InitEigenSystemInfo (ModelInfo *m) { int ts; if (m->dataType == STANDARD) { /* dealt with in ProcessStdChars */ return (NO_ERROR); } m->cijkLength = 0; m->nCijkParts = 0; if (m->dataType == PROTEIN) { ts = m->numModelStates; m->cijkLength = (ts * ts * ts) + (2 * ts); m->nCijkParts = 1; if (m->switchRates != NULL) /* covarion model */ { m->cijkLength *= m->numRateCats; m->nCijkParts = m->numRateCats; } } else if (m->dataType == DNA || m->dataType == RNA) { if (m->nucModelId == NUCMODEL_4BY4) { if (m->switchRates==NULL && m->nst != 6 && m->nst != NST_MIXED) { m->cijkLength = 0; m->nCijkParts = 0; # if defined (BEAGLE_ENABLED) ts = m->numModelStates; m->cijkLength = (ts * ts * ts) + (2 * ts); m->nCijkParts = 1; # endif } else { ts = m->numModelStates; m->cijkLength = (ts * ts * ts) + (2 * ts); m->nCijkParts = 1; } if (m->switchRates != NULL) { m->cijkLength *= m->numRateCats; m->nCijkParts = m->numRateCats; } } else if (m->nucModelId == NUCMODEL_DOUBLET) { ts = m->numModelStates; m->cijkLength = (ts * ts * ts) + (2 * ts); m->nCijkParts = 1; } else if (m->nucModelId == NUCMODEL_CODON) { ts = m->numModelStates; m->cijkLength = (ts * ts * ts) + (2 * ts); m->cijkLength *= m->numOmegaCats; m->nCijkParts = m->numOmegaCats; } else { MrBayesPrint ("%s ERROR: Something is wrong if you are here.\n", spacer); return ERROR; } } # if defined (BEAGLE_ENABLED) else if (m->dataType == RESTRICTION) { assert (m->numModelStates == 2); ts = 2; m->cijkLength = (ts * ts * ts) + (2 * ts); m->nCijkParts = 1; } # endif return (NO_ERROR); } /*------------------------------------------------------------------------ | | InitFinalStateCondLikes: allocate space for final conditional | likelihoods if needed | -------------------------------------------------------------------------*/ int InitFinalStateCondLikes (void) { int d; ModelInfo *m; for (d=0; dprintAncStates == YES) { m->ancStateCondLikes = (CLFlt *) SafeMalloc (m->condLikeLength * sizeof(CLFlt)); if (!m->ancStateCondLikes) return ERROR; } } return (NO_ERROR); } /*------------------------------------------------------------------------ | | InitInvCondLikes: allocate and initialize invariable conditional | likelihoods if needed | | NB! Fills in invariable cond likes for all hidden states; this | is convenient although some space is wasted | -------------------------------------------------------------------------*/ int InitInvCondLikes (void) { int c, d, i, s, isConstant, usingInvCondLikes; BitsLong *charBits; CLFlt *cI; ModelInfo *m; # if defined (SSE_ENABLED) int c1; # endif /* allocate space for invariable cond likes */ usingInvCondLikes = NO; for (d=0; dpInvar == NULL) continue; usingInvCondLikes = YES; # if defined (SSE_ENABLED) if ( m->useVec != VEC_NONE ) { c1 = m->numVecChars * m->numFloatsPerVec * m->numModelStates; if (m->useVec == VEC_AVX || m->useVec == VEC_FMA) m->invCondLikes = (CLFlt *) AlignedMalloc (c1 * sizeof(CLFlt), 32); else m->invCondLikes = (CLFlt *) AlignedMalloc (c1 * sizeof(CLFlt), 16); for (i=0; iinvCondLikes[i] = 0.0f; } else # endif { m->invCondLikes = (CLFlt *) SafeMalloc (m->numChars * m->numModelStates * sizeof(CLFlt)); } if (!m->invCondLikes) return ERROR; } if (usingInvCondLikes == NO) return NO_ERROR; MrBayesPrint ("%s Initializing invariable-site conditional likelihoods\n", spacer); /* fill in invariable-site conditional likelihoods */ for (d=0; dpInvar == NULL) continue; cI = m->invCondLikes; if (m->dataType == STANDARD) { for (c=0; cnumChars; c++) { for (s=0; snStates[c]; s++) { isConstant = YES; for (i=0; iparsSets[i][c*m->nParsIntsPerSite]; if (IsBitSet(s, charBits) == NO) { isConstant = NO; break; } } if (isConstant == YES) *cI = 1.0; else *cI = 0.0; cI++; } } } else /* all other models for which pInvar is applicable */ { assert (m->numModelStates == m->numStates); # if defined (SSE_ENABLED) if ( m->useVec != VEC_NONE ) { for (c=0; cnumChars/m->numFloatsPerVec; c++) { for (s=0; snumModelStates; s++) { for (c1=0; c1numFloatsPerVec; c1++) { isConstant = YES; //charBits = parsMatrix + m->parsMatrixStart + ((c * m->numFloatsPerVec) + c1) * m->nParsIntsPerSite; for (i=0; iparsSets[i][((c * m->numFloatsPerVec) + c1) *m->nParsIntsPerSite]; if (IsBitSet(s, charBits) == NO) { isConstant = NO; break; } //charBits += parsMatrixRowSize; } if (isConstant == YES) *cI = 1.0; cI++; } } } if (m->numChars % m->numFloatsPerVec != 0) { for (s=0; snumModelStates; s++) { for (c1=0; c1numChars%m->numFloatsPerVec; c1++) { isConstant = YES; //charBits = parsMatrix + m->parsMatrixStart + (((m->numChars / m->numFloatsPerVec) * m->numFloatsPerVec) + c1) * m->nParsIntsPerSite; for (i=0; iparsSets[i][(((m->numChars / m->numFloatsPerVec) * m->numFloatsPerVec) + c1) *m->nParsIntsPerSite]; if (IsBitSet(s, charBits) == NO) { isConstant = NO; break; } //charBits += parsMatrixRowSize; } if (isConstant == YES) *cI = 1.0; cI++; } for (; c1numFloatsPerVec; c1++) { *cI = 1.0; cI++; } } } } else # endif { for (c=0; cnumChars; c++) { for (s=0; snumModelStates; s++) { isConstant = YES; for (i=0; iparsSets[i][c*m->nParsIntsPerSite]; if (IsBitSet(s, charBits) == NO) { isConstant = NO; break; } } if (isConstant == YES) *cI = 1.0; cI++; } } } } } /* next division */ # if 0 invCondLikeSize = 0; for (d=0; dpInvar == NULL) continue; cI = m->invCondLikes; if (m->dataType == STANDARD) { } else { for (c=0; cnumChars; c++) { printf ("%4d -- ", c); for (s=0; snumModelStates; s++) { printf ("%1.0lf", *cI); cI++; } printf ("\n"); } } } # endif return NO_ERROR; } /*------------------------------------------------------------------------ | | InitParsSets: allocate space for and set parsimony state sets | -------------------------------------------------------------------------*/ int InitParsSets (void) { int c, i, j, d, nIntNodes, nNodes, nuc1, nuc2, nuc3, codingNucCode, allNucCode; BitsLong allAmbig, x, x1, x2, x3, *longPtr, bitsLongOne=1; long state; ModelInfo *m; ModelParams *mp; /* find number and size of parsimony sets and node lengths */ for (d=0; ddataType == CONTINUOUS) m->nParsIntsPerSite = 1; else m->nParsIntsPerSite = 1 + m->numStates / nBitsInALong; /* Calculate number of nodes and number of internal nodes */ nIntNodes = GetTree(m->brlens,0,0)->nIntNodes; nNodes = GetTree(m->brlens,0,0)->nNodes; /* Calculate number of parsimony sets */ m->numParsSets = numLocalTaxa; if (m->parsimonyBasedMove == YES || !strcmp(chainParams.startTree, "Parsimony")) m->numParsSets += nIntNodes; if (m->parsModelId == YES) m->numParsSets += (numLocalChains + 1) * nIntNodes; if (m->parsModelId == YES) m->numParsNodeLens = (numLocalChains + 1) * nNodes; else m->numParsNodeLens = 0; } /* then allocate space for the sets and node lengths */ for (d=0; dparsSets = (BitsLong **) SafeCalloc (m->numParsSets, sizeof(BitsLong*)); if (!m->parsSets) return (ERROR); for (i=0; inumParsSets; i++) { m->parsSets[i] = (BitsLong *) SafeCalloc (m->numChars*m->nParsIntsPerSite, sizeof(BitsLong)); if (!m->parsSets[i]) return (ERROR); } if (m->numParsNodeLens > 0) { m->parsNodeLens = (CLFlt *) SafeCalloc (m->numParsNodeLens, sizeof(CLFlt)); if (!m->parsNodeLens) return (ERROR); } } /* finally fill in tip parsimony sets */ for (d=0; ddataType == CONTINUOUS) { for (i=0; icompMatrixStart; jcompMatrixStop; j++, c++) { state = (long)compMatrix[pos(i,j,compMatrixRowSize)]; if (!strcmp(mp->nst, "1")) // normalized (between 0 and 1) { if (state == INT_MAX) // missing m->parsSets[i][c] = 15; else if ((CLFlt)state > 7500.0) m->parsSets[i][c] = 8; else if ((CLFlt)state > 5000.0) m->parsSets[i][c] = 4; else if ((CLFlt)state > 2500.0) m->parsSets[i][c] = 2; else m->parsSets[i][c] = 1; } else // standardized { if (state == INT_MAX) // missing m->parsSets[i][c] = 3; else if ((CLFlt)state > 0.0) m->parsSets[i][c] = 2; else m->parsSets[i][c] = 1; } } } } else if (m->nCharsPerSite == 1 && m->nParsIntsPerSite == 1) { allAmbig = (bitsLongOne<nStates) - 1UL; for (i=0; icompMatrixStart; jcompMatrixStop; j++, c++) { x = compMatrix[pos(i,j,compMatrixRowSize)]; if (x == MISSING || x == GAP) m->parsSets[i][c] = allAmbig; else m->parsSets[i][c] = x; } } } else if (!strcmp(mp->nucModel, "Doublet") && (mp->dataType == DNA || mp->dataType == RNA)) { allAmbig = 15; for (i=0; icompMatrixStart; jcompMatrixStop; j+=m->nCharsPerSite, c++) { /* fetch the original values x1 and x2 */ x1 = compMatrix[pos(i,j,compMatrixRowSize)]; if (x1 == MISSING || x1 == GAP) x1 = allAmbig; x2 = compMatrix[pos(i,j+1,compMatrixRowSize)]; if (x2 == MISSING || x2 == GAP) x2 = allAmbig; /* squeeze them together in the new value x */ x = 0; for (nuc1=0; nuc1<4; nuc1++) { for (nuc2=0; nuc2<4; nuc2++) { if (IsBitSet(nuc1,&x1) == YES && IsBitSet(nuc2, &x2) == YES) x |= (bitsLongOne<<(nuc1*4 + nuc2)); } } m->parsSets[i][c] = x; } } } else if (!strcmp(mp->nucModel, "Codon") && (mp->dataType == DNA || mp->dataType == RNA)) { allAmbig = 15; for (i=0; icompMatrixStart; jcompMatrixStop; j+=m->nCharsPerSite, c++) { /* fetch the original values x1, x2, and x3*/ x1 = compMatrix[pos(i,j,compMatrixRowSize)]; if (x1 == MISSING || x1 == GAP) x1 = allAmbig; x2 = compMatrix[pos(i,j+1,compMatrixRowSize)]; if (x2 == MISSING || x2 == GAP) x2 = allAmbig; x3 = compMatrix[pos(i,j+2,compMatrixRowSize)]; if (x3 == MISSING || x3 == GAP) x3 = allAmbig; /* squeeze them together in the new long string pointed to by longPtr */ longPtr = &m->parsSets[i][c*m->nParsIntsPerSite]; allNucCode = codingNucCode = 0; for (nuc1=0; nuc1<4; nuc1++) for (nuc2=0; nuc2<4; nuc2++) for (nuc3=0; nuc3<4; nuc3++) { if (mp->codon[allNucCode] != 21) { if (IsBitSet(nuc1, &x1) == YES && IsBitSet(nuc2, &x2) == YES && IsBitSet(nuc3, &x3) == YES) SetBit(codingNucCode, longPtr); codingNucCode++; } allNucCode++; } } } } else if (!strcmp(mp->nucModel, "Protein") && (mp->dataType == DNA || mp->dataType == RNA)) { allAmbig = 15; for (i=0; icompMatrixStart; jcompMatrixStop; j+=m->nCharsPerSite, c++) { /* fetch the original values x1, x2, and x3*/ x1 = compMatrix[pos(i,j,compMatrixRowSize)]; if (x1 == MISSING || x1 == GAP) x1 = allAmbig; x2 = compMatrix[pos(i,j+1,compMatrixRowSize)]; if (x2 == MISSING || x2 == GAP) x2 = allAmbig; x3 = compMatrix[pos(i,j+2,compMatrixRowSize)]; if (x3 == MISSING || x3 == GAP) x3 = allAmbig; /* squeeze them together in the new long string pointed to by longPtr */ longPtr = &m->parsSets[i][c*m->nParsIntsPerSite]; /* m->nParsIntsPerSite should be 1 */ allNucCode = 0; for (nuc1=0; nuc1<4; nuc1++) for (nuc2=0; nuc2<4; nuc2++) for (nuc3=0; nuc3<4; nuc3++) { if (mp->codon[allNucCode] != 21) { if (IsBitSet(nuc1, &x1) == YES && IsBitSet(nuc2, &x2) == YES && IsBitSet(nuc3, &x3) == YES) SetBit(mp->codon[allNucCode]-1, longPtr); } allNucCode++; } } } } else { MrBayesPrint ("%s Unrecognized data format during bitset compression\n", spacer); return ERROR; } } return (NO_ERROR); } /*------------------------------------------------ | | InitPrintParams: Set up arrays of print para- | meters and print tree parameters | ------------------------------------------------*/ int InitPrintParams (void) { int i, j, k, k1=0; Param *p; /* count number of model params to print */ numPrintParams = 0; for (i=0; iprintParam == YES && p->paramType != P_TOPOLOGY && p->paramType != P_BRLENS && p->paramType != P_SPECIESTREE && p->paramType != P_CPPEVENTS && p->paramType != P_TK02BRANCHRATES && p->paramType != P_WNBRANCHRATES && p->paramType != P_IGRBRANCHRATES && p->paramType != P_ILNBRANCHRATES && p->paramType != P_MIXEDBRCHRATES) numPrintParams++; } /* count number of tree params to print */ numPrintTreeParams = 0; for (i=0; iparamType == P_TOPOLOGY) { /* always print parsimony topology (printParam == YES), otherwise */ /* print topology only if brlens never requested (nPrintSubParams == 0)*/ if (p->printParam == YES || p->nPrintSubParams == 0) numPrintTreeParams++; } else if (p->paramType == P_BRLENS) { /* print only if brlens (or events) requested for at least one partition */ if (p->printParam == YES || p->nPrintSubParams > 0) numPrintTreeParams++; } else if (p->paramType == P_SPECIESTREE) { /* always print if printParam set to YES */ if (p->printParam == YES) numPrintTreeParams++; } } /* allocate space */ printParam = (Param **) SafeCalloc (numPrintParams + numPrintTreeParams + numTopologies, sizeof(Param *)); topologyPrintIndex = (int *) SafeCalloc (numTopologies + numPrintTreeParams, sizeof(int)); if (!printParam || !topologyPrintIndex) { free (printParam); free (topologyPrintIndex); MrBayesPrint ("%s Could not allocate printParam vector in InitPrintParams\n", spacer); return (ERROR); } printTreeParam = printParam + numPrintParams; topologyParam = printTreeParam + numPrintTreeParams; printTreeTopologyIndex = topologyPrintIndex + numTopologies; memAllocs[ALLOC_PRINTPARAM] = YES; /* assign normal print params */ for (i=j=0; iprintParam == YES && p->paramType != P_TOPOLOGY && p->paramType != P_BRLENS && p->paramType != P_SPECIESTREE && p->paramType != P_CPPEVENTS && p->paramType != P_TK02BRANCHRATES && p->paramType != P_WNBRANCHRATES && p->paramType != P_IGRBRANCHRATES && p->paramType != P_ILNBRANCHRATES && p->paramType != P_MIXEDBRCHRATES) printParam[j++] = p; } /* assign tree print params */ for (i=j=k=0; iparamType == P_TOPOLOGY) { /* always print parsimony topology (printParam == YES), otherwise */ /* print topology only if brlens never requested (nPrintSubParams == 0)*/ if (p->printParam == YES || p->nPrintSubParams == 0) printTreeParam[k++] = p; } else if (p->paramType == P_BRLENS) { /* print only if brlens (or events) requested for at least one partition */ if (p->printParam == YES || p->nPrintSubParams > 0) printTreeParam[k++] = p; } else if (p->paramType == P_SPECIESTREE) { if (p->printParam == YES) printTreeParam[k++] = p; } } /* find topologies, topology file index, and printtree topology index */ for (i=0; iparamType == P_SPECIESTREE) { topologyParam[j] = p; for (k=0; kparamType == P_TOPOLOGY) { topologyParam[j] = p; for (k=0; knSubParams; k++) { for (k1=0; k1subParams[k]) break; if (k1 < numPrintTreeParams) break; } topologyPrintIndex[j] = k1; printTreeTopologyIndex[k1] = j; } j++; } } return (NO_ERROR); } int IsPFNodeEmpty (PFNODE *p) { int i; for (i=0; icount[i] > 0) break; } if (i == chainParams.numRuns) return YES; else return NO; } /* LargestNonemptyPFNode: recursive function to largest nonempty node in a subtree */ PFNODE *LargestNonemptyPFNode (PFNODE *p, int *i, int j) { PFNODE *q; ++j; if (p == NULL) return NULL; q = LargestNonemptyPFNode (p->left, i, j); if (q != NULL) { return q; } else if (IsPFNodeEmpty (p) == NO) { *i = j; return p; } else { return LargestNonemptyPFNode (p->right, i, j); } } /* ln prior ratio for clock trees */ int LogClockTreePriorRatio (Param *param, int chain, MrBFlt *lnPriorRatio) { MrBFlt oldLnPrior, newLnPrior, theta, N, growth, clockRate, sF, *sR, *eR, *fR; char *sS; Model *mp; ModelInfo *m; Tree *newTree, *oldTree; TreeNode *p, *q=NULL; int i, j; (*lnPriorRatio) = 0.0; mp = &modelParams[param->relParts[0]]; m = &modelSettings[param->relParts[0]]; newTree = GetTree (m->brlens, chain, state[chain]); oldTree = GetTree (m->brlens, chain, state[chain] ^ 1); if (m->clockRate != NULL) clockRate = *GetParamVals(m->clockRate, chain, state[chain]); else clockRate = 1.0; /* calculate prior ratio on brlens of clock tree */ if (!strcmp(mp->clockPr,"Coalescence")) { /* coalescence prior */ /* first calculate theta as 4*N*mu, 3*N*mu or 2*N*mu */ N = *(GetParamVals (m->popSize, chain, state[chain])); if (!strcmp(mp->ploidy, "Diploid")) theta = 4 * N * clockRate; else if (!strcmp(mp->ploidy, "Zlinked")) theta = 3 * N * clockRate; else theta = 2 * N * clockRate; /* deal with growth */ if (!strcmp(mp->growthPr, "Fixed")) growth = mp->growthFix; else growth = *(GetParamVals (m->growthRate, chain, state[chain])); if (LnCoalescencePriorPr (oldTree, &oldLnPrior, theta, growth) == ERROR) { MrBayesPrint ("%s Problem calculating prior for coalescence process\n", spacer); return (ERROR); } if (LnCoalescencePriorPr (newTree, &newLnPrior, theta, growth) == ERROR) { MrBayesPrint ("%s Problem calculating prior for coalescence process\n", spacer); return (ERROR); } (*lnPriorRatio) = (newLnPrior - oldLnPrior); } else if (!strcmp(mp->clockPr,"Birthdeath")) { /* birth-death prior */ sR = GetParamVals (m->speciationRates, chain, state[chain]); eR = GetParamVals (m->extinctionRates, chain, state[chain]); sS = mp->sampleStrat; sF = mp->sampleProb; if (LnBirthDeathPriorPr (oldTree, clockRate, &oldLnPrior, *sR, *eR, sS, sF) == ERROR) { MrBayesPrint ("%s Problem calculating prior for birth-death process\n", spacer); return (ERROR); } if (LnBirthDeathPriorPr (newTree, clockRate, &newLnPrior, *sR, *eR, sS, sF) == ERROR) { MrBayesPrint ("%s Problem calculating prior for birth-death process\n", spacer); return (ERROR); } (*lnPriorRatio) = (newLnPrior - oldLnPrior); } else if (!strcmp(mp->clockPr,"Fossilization")) { /* fossilized birth-death prior */ sR = GetParamVals (m->speciationRates, chain, state[chain]); eR = GetParamVals (m->extinctionRates, chain, state[chain]); fR = GetParamVals (m->fossilizationRates, chain, state[chain]); sF = mp->sampleProb; sS = mp->sampleStrat; if (LnFossilizationPriorPr (oldTree, clockRate, &oldLnPrior, sR, eR, fR, sF, sS) == ERROR) { MrBayesPrint ("%s Problem calculating prior for fossilized birth-death process\n", spacer); return (ERROR); } if (LnFossilizationPriorPr (newTree, clockRate, &newLnPrior, sR, eR, fR, sF, sS) == ERROR) { MrBayesPrint ("%s Problem calculating prior for fossilized birth-death process\n", spacer); return (ERROR); } (*lnPriorRatio) = (newLnPrior - oldLnPrior); } else if (!strcmp(mp->clockPr,"Uniform")) { oldLnPrior = LnUniformPriorPr(oldTree, clockRate); newLnPrior = LnUniformPriorPr(newTree, clockRate); (*lnPriorRatio) = (newLnPrior - oldLnPrior); } else if (!strcmp(mp->clockPr,"Speciestreecoalescence")) { // Defer this calculation to the BEST code } assert (*lnPriorRatio > NEG_INFINITY); /* take care of calibrations */ if (newTree->isCalibrated == YES) { for (i=0; inNodes-1; i++) { p = newTree->allDownPass[i]; if (p->isDated == NO) continue; for (j=0; jnNodes-1; j++) { q = oldTree->allDownPass[j]; if (p->lockID == q->lockID) break; } assert (j != oldTree->nNodes-1); if (p->isDated == YES && p->calibration->prior != fixed) { (*lnPriorRatio) += p->calibration->LnPriorRatio(p->age, q->age, p->calibration->priorParams); } } } return (NO_ERROR); } /*----------------------------------------------------------------- | | LogLike: calculate the log likelihood of the new state of the chain | -----------------------------------------------------------------*/ MrBFlt LogLike (int chain) { int i, d; ModelInfo *m; MrBFlt chainLnLike, lnL; /* initialize chain cond like */ chainLnLike = 0.0; if (chainParams.runWithData == NO) return (chainLnLike); # if defined (DEBUG_RUN_WITHOUT_DATA) return (chainLnLike); # endif # if defined (BEAGLE_V3_ENABLED) if (modelSettings[0].useBeagleMultiPartitions == YES) { /* Launch all divisions that require updating in one Beagle instance, concurrently */ LaunchLogLikeForBeagleMultiPartition(chain, &chainLnLike); } else { # endif /* Cycle through divisions and recalculate tis and cond likes as necessary. */ /* Code below does not try to avoid recalculating ti probs for divisions */ /* that could share ti probs with other divisions. */ for (d=0; dupDateCl == YES) { /* Work has been delegated to a separate function so we can wrap */ /* a thread around it */ LaunchLogLikeForDivision(chain, d, &(m->lnLike[2 * chain + state[chain]])); } if (abortMove == YES) return MRBFLT_NEG_MAX; chainLnLike += m->lnLike[2*chain + state[chain]]; } # if defined (BEAGLE_V3_ENABLED) } # endif #if defined (DEBUG_MB_BEAGLE_MULTIPART) printf("chainLnLike = %f\n", chainLnLike); #endif /* unmark all divisions */ if (chainHasAdgamma == YES) { for (d=0; dmark = NO; } /* update HMM likelihoods if appropriate */ for (d=0; dupDateCl == YES && m->correlation != NULL && m->mark != YES) { lnL = 0.0; CalcLikeAdgamma(d, m->correlation, chain, &lnL); /* store the value for the cases where the HMM is not touched */ m->lnLike[2*chain + state[chain]] = lnL; /* add it to chainLnLike - it was not added above since the division */ /* lnL was set to zero after the update call to Likelihood_Adgamma */ chainLnLike += lnL; /* set mark for other divisions in the HMM (i.e., those with the same correlation parameter AND the same shape parameter) */ for (i=0; icorrelation->nRelParts; i++) { if (modelSettings[m->correlation->relParts[i]].shape == modelSettings[d].shape) { modelSettings[m->correlation->relParts[i]].mark = YES; } } } } } return (chainLnLike); } MrBFlt LogOmegaPrior (MrBFlt w1, MrBFlt w2, MrBFlt w3) { /* This function returns the log prior probability of the ratio on three omegas. Here, we have three nonsynonymous/synonymous rate ratios, denoted w1, w2, and w3. They have the property that w1 < w2 < w3. Remember that w1 = dN1/dS, w2 = dN2/dS, and w3 = dN3/dS. We assume that dN1, dN2, dN3, and dS are all independent draws from the same exponential distribution, and that dN1, dN2, and dN3 are the order statistics. The w1, w2, and w3, then, are all scaled to the same dS r.v. */ MrBFlt lnProb; lnProb = log(36.0) - 4.0 * log(1.0 + w1 + w2 + w3); return (lnProb); } /* ------------------------------------------------------------------------------------------------------------- */ /* Joint distribution of branch lengths t under gamma-Dirichlet prior: */ /* (Zhang et al. 2012, Eq. 4; Rannala et al. 2012, Eq. 36): */ /* ln[f(t|aT,bT,a,c)] = (aT - a*s - a*c*(s-3)) * ln(T) - bT * T + (a-1) * sum[ln(t_j)] + (a*c-1) * sum[ln(t_k)] */ /* + aT * ln(bT) - lnG(aT) - lnB(a,c) */ /* */ /* Joint distribution of branch lengths t under invgamma-Dirichlet prior: */ /* (Zhang et al. 2012, Eq. 6; Rannala et al. 2012, Eq. 39): */ /* ln[f(t|aT,bT,a,c)] = (-aT - a*s - a*c*(s-3)) * ln(T) - bT / T + (a-1) * sum[ln(t_j)] + (a*c-1) * sum[ln(t_k)] */ /* + aT * ln(bT) - lnG(aT) - lnB(a,c) */ /* also see DoCitations() */ /* ------------------------------------------------------------------------------------------------------------- */ /* external (tip): 1, internal: 0 */ #define IsTip(Node) (Node->index < numTaxa || (Node->anc)->index < numTaxa) MrBFlt LogDirPrior (Tree *t, ModelParams *mp, int PV) { /* ln prior prob. under Dirichlet priors and twoExp prior //chi */ int i, numBranches, nb[2] = {0,0}; MrBFlt lnprior = 0.0, tb[2] = {0,0}, treeL = 0.0; MrBFlt aT, bT, a, c; TreeNode *p; /* Make sure this works for both rooted and non-rooted non-clock trees */ /* For rooted trees, the last two nodes do not have branches under them; for unrooted * trees, it is only the last node that does not have a branch. */ numBranches = (t->isRooted == YES? t->nNodes - 2 : t->nNodes - 1); /* Not safe, should define Macros. YES or NO should never be defined to 2 or 3 or 4! */ /* PV is 2 or 3: Dirichlet priors */ if (PV == 2 || PV == 3) { /* partially for calculating lnPriorRatio, full part is in LogPrior() */ aT = mp->brlensDir[0]; bT = mp->brlensDir[1]; a = mp->brlensDir[2]; c = mp->brlensDir[3]; for (i = 0; i < numBranches; i++) { p = t->allDownPass[i]; treeL += p->length; nb[IsTip(p)]++; tb[IsTip(p)] += log(p->length); } lnprior += (a-1)*tb[1] + (a*c -1)*tb[0]; if (PV == 2) lnprior += (aT - a*nb[1] - a*c*nb[0]) * log(treeL) - bT*treeL; else lnprior += (-aT - a*nb[1] - a*c*nb[0]) * log(treeL) - bT/treeL; } /* or 4: twoExp prior */ else if (PV == 4) { for (i = 0; i < numBranches; i++) { p = t->allDownPass[i]; nb[IsTip(p)]++; tb[IsTip(p)] += p->length; } for (i = 0; i < 2; i++) lnprior += nb[i] * log(mp->brlens2Exp[i]) - tb[i] * (mp->brlens2Exp[i]); } return lnprior; } MrBFlt LogPrior (int chain) { int i, j, c, n, nStates, *nEvents, sumEvents, *ist, nRates, nParts[6], numBranches; const int *rateCat; MrBFlt *st, *sst, lnPrior, sum, x=0.0, clockRate, theta, popSize, growth, *alphaDir, newProp[190], sF, *sR, *eR, *fR, freq, pInvar, lambda, sigma, nu, var, **rateMultiplier; char *sS; CLFlt *nSitesOfPat; Param *p; ModelParams *mp; ModelInfo *m; Tree *t; TreeNode *branch, *q; /* In the stepping-stone method, constants that appear in prior probability density functions must be fully specified. It is not necessary for Bayesian MCMC analyses because such constants cancel out. */ lnPrior = 0.0; for (n=0; nrelParts[0]] == NO) continue; # endif st = GetParamVals (p, chain, state[chain]); sst = GetParamSubVals (p, chain, state[chain]); mp = &modelParams[p->relParts[0]]; m = &modelSettings[p->relParts[0]]; if (p->paramType == P_TRATIO) { /* tratio parameter */ if (p->paramId == TRATIO_DIR) { alphaDir = mp->tRatioDir; /* we convert here from the ratio parameterization used in the parameter struct to the simplex parameterization used for the prior */ newProp[0] = st[0] / (st[0] + 1.0); newProp[1] = (1.0 - newProp[0]); x = LnGamma(alphaDir[0]+alphaDir[1]) - LnGamma(alphaDir[0]) - LnGamma(alphaDir[1]); for (i=0; i<2; i++) x += (alphaDir[i]-1.0)*log(newProp[i]); lnPrior += x; } } else if (p->paramType == P_REVMAT) { /* revmat parameter */ if (p->paramId == REVMAT_DIR) { if (p->nValues == 6) alphaDir = mp->revMatDir; else /* if (p->nValues == 190) */ alphaDir = mp->aaRevMatDir; sum = 0.0; for (i=0; inValues; i++) sum += alphaDir[i]; x = LnGamma(sum); for (i=0; inValues; i++) x -= LnGamma(alphaDir[i]); for (i=0; inValues; i++) x += (alphaDir[i] - 1.0) * log(st[i]); lnPrior += x; } else if (p->paramId == REVMAT_MIX) { assert (p->nValues == 6); alphaDir = &mp->revMatSymDir; ist = GetParamIntVals(p, chain, state[chain]); /* the growth fxn */ nRates = GetKFromGrowthFxn(ist); /* get the actual rate proportions of the current groups */ for (i=0; iparamType == P_OMEGA) { /* account for prior on omega proportion if 1 omega category */ if (p->paramId == OMEGA_DIR) { alphaDir = mp->omegaDir; /* convert from ratio parameterization to simplex representation */ newProp[0] = st[0] / (st[0] + 1.0); newProp[1] = 1.0 - newProp[0]; x = LnGamma(alphaDir[0]+alphaDir[1]) - LnGamma(alphaDir[0]) - LnGamma(alphaDir[1]); for (i=0; i<2; i++) x += (alphaDir[i]-1.0)*log(newProp[i]); lnPrior += x; } /* account for stationary state frequencies of M3 and ny98 omega categories */ if (p->paramId == OMEGA_BUD || p->paramId == OMEGA_BED || p->paramId == OMEGA_BFD || p->paramId == OMEGA_FUD || p->paramId == OMEGA_FED || p->paramId == OMEGA_FFD || p->paramId == OMEGA_ED || p->paramId == OMEGA_FD) { alphaDir = mp->codonCatDir; x = 0.0; for (i=0; i<3; i++) x += (alphaDir[i]-1.0)*log(sst[i]); lnPrior += x; } /* account for beta prior on omeganeg in NY98 */ if (p->paramId == OMEGA_BUD || p->paramId == OMEGA_BUF || p->paramId == OMEGA_BED || p->paramId == OMEGA_BEF || p->paramId == OMEGA_BFD || p->paramId == OMEGA_BFF) { alphaDir = mp->ny98omega1Beta; newProp[0] = st[0] / (st[0] + 1.0); newProp[1] = 1.0 - newProp[0]; x = 0.0; for (i=0; i<2; i++) x += (alphaDir[i]-1.0)*log(newProp[i]); lnPrior += x; } /* account for omegapos in NY98 with uniform prior prob */ if (p->paramId == OMEGA_BUD || p->paramId == OMEGA_BUF || p->paramId == OMEGA_FUD || p->paramId == OMEGA_FUF) { lnPrior += log(1.0) - log(mp->ny98omega3Uni[1] - mp->ny98omega3Uni[0]); } /* account for omegapos in NY98 with exponential prior prob */ if (p->paramId == OMEGA_BED || p->paramId == OMEGA_BEF || p->paramId == OMEGA_FED || p->paramId == OMEGA_FEF) { lnPrior += (log(mp->ny98omega3Exp) - mp->ny98omega3Exp * st[2]); } /* account for omegas in M3, which can only be exponential; if fixed, ln prior prob is 0 */ if (p->paramId == OMEGA_EF || p->paramId == OMEGA_ED) { lnPrior += LogOmegaPrior (st[0], st[1], st[2]); } } else if (p->paramType == P_PI) { /* state frequencies parameter */ if (p->paramId == PI_DIR) { nStates = p->nSubValues; lnPrior += LnDirichlet(st, sst, nStates); } else if (strcmp(mp->statefreqModel,"Stationary") != 0) { /* now for directional evolution: first process states */ nStates = m->numModelStates; if (p->paramId == DIRPI_DIRxDIR || p->paramId == DIRPI_DIRxFIXED || p->paramId == DIRPI_MIX) { lnPrior += LnDirichlet(st, sst, nStates); } /* now same for the respective values of root frequencies */ if (p->paramId == DIRPI_DIRxDIR || p->paramId == DIRPI_FIXEDxDIR || (p->paramId == DIRPI_MIX && sst[nStates] != NOT_APPLICABLE)) //SK todo: check if this works! { lnPrior += LnDirichlet(&st[nStates], &sst[nStates], nStates); } } else if (p->paramId == SYMPI_EXP || p->paramId == SYMPI_EXP_MS) { lnPrior += - mp->symBetaExp * st[0] + log(mp->symBetaExp); } else if (p->paramId == SYMPI_UNI || p->paramId == SYMPI_UNI_MS) { lnPrior += log(1.0) - log(mp->symBetaUni[1] - mp->symBetaUni[0]); } if (p->paramId == SYMPI_EXP_MS || p->paramId == SYMPI_UNI_MS || p->paramId == SYMPI_FIX_MS) { sst = GetParamStdStateFreqs(p, chain, state[chain]); sst += 2*m->numBetaCats; for (i=0; inSympi; i++) { nStates = p->sympinStates[i]; x = LnGamma(nStates*st[0]) - nStates*LnGamma(st[0]); for (j=0; jparamType == P_MIXTURE_RATES) { /* site rate mixture; first deal with dirichlet prior */ nStates = p->nSubValues; sum = 0.0; for (i=0; iparamType == P_SHAPE) { /* gamma/lnorm shape parameter */ if (p->paramId == SHAPE_UNI) { lnPrior += log(1.0) - log(mp->shapeUni[1] - mp->shapeUni[0]); } else if (p->paramId == SHAPE_EXP) { lnPrior += log(mp->shapeExp) - mp->shapeExp * st[0]; } for (i=0; inRelParts; i++) { m = &modelSettings[p->relParts[i]]; if (m->gibbsGamma == YES) { if (m->pInvar == NULL) lnPrior += log(1.0/m->numRateCats) * m->numUncompressedChars; else { rateCat = m->tiIndex + chain * m->numChars; pInvar = *GetParamVals (m->pInvar, chain, state[chain]); nSitesOfPat = numSitesOfPat + m->compCharStart; freq = (1.0 - pInvar)/m->numRateCats; for (c=0; cnumChars; c++) { if (rateCat[c] < m->numRateCats) lnPrior += log(freq) * nSitesOfPat[c]; else lnPrior += log(pInvar) * nSitesOfPat[c]; } } } } } else if (p->paramType == P_PINVAR) { /* proportion of invariable sites parameter */ lnPrior += log(1.0) - log(mp->pInvarUni[1] - mp->pInvarUni[0]); } else if (p->paramType == P_CORREL) { /* adGamma model parameter */ lnPrior += log(1.0) - log(mp->adgCorrUni[1] - mp->adgCorrUni[0]); } else if (p->paramType == P_SWITCH) { /* switching rate parameter of covarion model */ if (p->paramId == SWITCH_UNI) { lnPrior += log(1.0) - log(mp->covswitchUni[1] - mp->covswitchUni[0]); } else if (p->paramId == SWITCH_EXP) { lnPrior += log(mp->covswitchExp) - mp->covswitchExp * st[0]; } } else if (p->paramType == P_RATEMULT && p->nValues > 1) { nStates = p->nValues; sum = 0.0; for (i=0; iparamType == P_GENETREERATE && p->nValues > 1) { nStates = p->nValues; sum = 0.0; for (i=0; iparamType == P_TOPOLOGY) { // Note that a topology can have several unlinked branch length subparameters but only // one set of clock branch lengths. To find all the branch length subparameters of a // topology, cycle through the p->subParams, which will contain at least one branch length // parameter. t = GetTree (p, chain, state[chain]); if (t->isClock == YES) continue; /* prior probability taken care of in the brlens parameter */ if (t->nLocks > 0) { for (i=0; inNodes-1; i++) { branch = t->allDownPass[i]; if (branch->left == NULL) branch->x = 1; else branch->x = branch->left->x + branch->right->x; if (branch->isLocked == YES || branch->anc->anc == NULL) { for (j = 2*branch->x - 3; j>=1; j-=2) { lnPrior -= log ((MrBFlt)j); } branch->x = 1; } } } else { for (j = 2*(t->nNodes-t->nIntNodes)-5; j>=1; j-=2) { lnPrior -= log ((MrBFlt)j); } } } else if (p->paramType == P_BRLENS) { /* branch lengths */ t = GetTree (p, chain, state[chain]); if (t->isClock == YES) { if (p->paramId == BRLENS_CLOCK_UNI) { /* uniformly distributed branch lengths */ clockRate = *(GetParamVals (m->clockRate, chain, state[chain])); lnPrior += LnUniformPriorPr(t, clockRate); } else if (p->paramId == BRLENS_CLOCK_COAL) { /* coalescence prior */ popSize = *(GetParamVals (m->popSize, chain, state[chain])); clockRate = *(GetParamVals (m->clockRate, chain, state[chain])); if (strcmp(mp->ploidy, "Diploid") == 0) theta = 4.0 * popSize * clockRate; else if (strcmp(mp->ploidy, "Zlinked") == 0) theta = 3.0 * popSize * clockRate; else theta = 2.0 * popSize * clockRate; if (!strcmp(mp->growthPr, "Fixed")) growth = mp->growthFix; else growth = *(GetParamVals (m->growthRate, chain, state[chain])); if (LnCoalescencePriorPr (t, &x, theta, growth) == ERROR) { MrBayesPrint ("%s Problem calculating prior for coalescence process\n", spacer); } lnPrior += x; /* Note that x is assigned an appropriate value in the call to LnCoalescencePriorPr above */ } else if (p->paramId == BRLENS_CLOCK_BD) { /* birth-death prior */ sR = GetParamVals (m->speciationRates, chain, state[chain]); eR = GetParamVals (m->extinctionRates, chain, state[chain]); sS = mp->sampleStrat; sF = mp->sampleProb; if (m->clockRate != NULL) clockRate = *(GetParamVals (m->clockRate, chain, state[chain])); else clockRate = 1.0; if (LnBirthDeathPriorPr (t, clockRate, &x, *sR, *eR, sS, sF) == ERROR) { MrBayesPrint ("%s Problem calculating prior for birth-death process\n", spacer); } lnPrior += x; /* Note that x is assigned an appropriate value in the call to LnBirthDeathPriorPr above */ } else if (p->paramId == BRLENS_CLOCK_FOSSIL) { /* fossilized birth-death prior */ sR = GetParamVals (m->speciationRates, chain, state[chain]); eR = GetParamVals (m->extinctionRates, chain, state[chain]); fR = GetParamVals (m->fossilizationRates, chain, state[chain]); sF = mp->sampleProb; sS = mp->sampleStrat; if (m->clockRate != NULL) clockRate = *(GetParamVals (m->clockRate, chain, state[chain])); else clockRate = 1.0; if (LnFossilizationPriorPr (t, clockRate, &x, sR, eR, fR, sF, sS) == ERROR) { MrBayesPrint ("%s Problem calculating prior for fossilized birth-death process\n", spacer); } lnPrior += x; /* Note that x is assigned an appropriate value in the call to LnFossilizationPriorPr above */ } else if (p->paramId == BRLENS_CLOCK_SPCOAL) { /* delegate this calculation to the P_SPECIESTREE parameter */ } if (t->isCalibrated == YES) { /* take care of calibrations */ for (i=0; inNodes-1; i++) { q = t->allDownPass[i]; if (q->isDated == YES && q->calibration->prior != fixed) { lnPrior += q->calibration->LnPriorProb(q->age, q->calibration->priorParams); } } } } else { /* compute the number of branches depending on whether the tree is rooted */ if (t->isRooted == YES) numBranches = t->nNodes - 2; else numBranches = t->nNodes - 1; if (p->paramId == BRLENS_UNI) { for (i=0; iallDownPass[i]; lnPrior += log(1.0) - log(mp->brlensUni[1] - BRLENS_MIN); } } else if (p->paramId == BRLENS_EXP) { for (i=0; iallDownPass[i]; lnPrior += log(mp->brlensExp) - mp->brlensExp * branch->length; } } /* Dirichlet priors */ else if (p->paramId == BRLENS_GamDir) { lnPrior += LogDirPrior(t, mp, 2); lnPrior += (mp->brlensDir[0]) * log(mp->brlensDir[1]) - LnGamma(mp->brlensDir[0]) + LnGamma (mp->brlensDir[2] * numTaxa + mp->brlensDir[2] * mp->brlensDir[3] * (numBranches-numTaxa)) - numTaxa * LnGamma(mp->brlensDir[2]) - (numBranches-numTaxa) * LnGamma(mp->brlensDir[2] * mp->brlensDir[3]); } else if (p->paramId == BRLENS_iGmDir) { lnPrior += LogDirPrior(t, mp, 3); lnPrior += (mp->brlensDir[0]) * log(mp->brlensDir[1]) - LnGamma(mp->brlensDir[0]) + LnGamma (mp->brlensDir[2] * numTaxa + mp->brlensDir[2] * mp->brlensDir[3] * (numBranches-numTaxa)) - numTaxa * LnGamma(mp->brlensDir[2]) - (numBranches-numTaxa) * LnGamma(mp->brlensDir[2] * mp->brlensDir[3]); } /* twoExp prior */ else if (p->paramId == BRLENS_twoExp) lnPrior += LogDirPrior(t, mp, 4); } } else if (p->paramType == P_SPECRATE) { /* speciation rate parameter */ if (p->paramId == SPECRATE_UNI) { for (i=0; inValues; i++) lnPrior += log(1.0) - log(mp->speciationUni[1] - mp->speciationUni[0]); } else if (p->paramId == SPECRATE_EXP) { for (i=0; inValues; i++) lnPrior += log(mp->speciationExp) - mp->speciationExp * st[i]; } } else if (p->paramType == P_EXTRATE) { /* extinction rate parameter */ if (p->paramId == EXTRATE_BETA) { for (i=0; inValues; i++) { alphaDir = mp->extinctionBeta; newProp[0] = st[i]; newProp[1] = (1.0 - newProp[0]); lnPrior += LnGamma(alphaDir[0]+alphaDir[1]) - LnGamma(alphaDir[0]) - LnGamma(alphaDir[1]); lnPrior += (alphaDir[0]-1.0)*log(newProp[0]) + (alphaDir[1]-1.0)*log(newProp[1]); } } else if (p->paramId == EXTRATE_EXP) { for (i=0; inValues; i++) lnPrior += log(mp->extinctionExp) - mp->extinctionExp * st[i]; } } else if (p->paramType == P_FOSLRATE) { /* fossilization rate parameter */ if (p->paramId == FOSLRATE_BETA) { for (i=0; inValues; i++) { alphaDir = mp->fossilizationBeta; newProp[0] = st[i]; newProp[1] = (1.0 - newProp[0]); lnPrior += LnGamma(alphaDir[0]+alphaDir[1]) - LnGamma(alphaDir[0]) - LnGamma(alphaDir[1]); lnPrior += (alphaDir[0]-1.0)*log(newProp[0]) + (alphaDir[1]-1.0)*log(newProp[1]); } } else if (p->paramId == FOSLRATE_EXP) { for (i=0; inValues; i++) lnPrior += log(mp->fossilizationExp) - mp->fossilizationExp * st[i]; } } else if (p->paramType == P_POPSIZE) { /* neutral coalescence population size parameter; one value, or one value per branch of species tree */ for (i=0; inValues; i++) { lnPrior += p->LnPriorProb(st[i], p->priorParams); } } else if (p->paramType == P_GROWTH) { /* population growth parameter */ if (p->paramId == GROWTH_UNI) { lnPrior += log(1.0) - log(mp->growthUni[1] - mp->growthUni[0]); } else { lnPrior += p->LnPriorProb(st[0], p->priorParams); } } else if (p->paramType == P_AAMODEL) { lnPrior += sst[(int)st[0]]; } else if (p->paramType == P_BMCORR) { if (p->paramId == BMCORR_UNI) { lnPrior += log(1.0) - log(mp->brownCorrUni[1] - mp->brownCorrUni[0]); } } else if (p->paramType == P_BMSIGMA) { if (p->paramId == BMSIGMA_UNI) { lnPrior += log(1.0) - log(mp->brownScaleUni[1] - mp->brownScaleUni[0]); } else if (p->paramId == BMSIGMA_GAMMA) { lnPrior += LnProbGamma(mp->brownScaleGamma[0], mp->brownScaleGamma[1], st[0]); } } else if (p->paramType == P_CPPRATE) { /* rate (lambda) of comp poisson process of relaxed clock */ if (p->paramId == CPPRATE_EXP) { lnPrior += log (mp->cppRateExp) - mp->cppRateExp * st[0]; } } else if (p->paramType == P_CPPMULTDEV) { /* standard deviation (log) of lognormal distribution of rate multipliers for cpp relaxed clock */ /* only fixed value allowed currently */ } else if (p->paramType == P_CPPEVENTS) { /* events of CPP relaxed clock process */ lambda = *GetParamVals (m->cppRate, chain, state[chain]); sigma = *GetParamVals (m->cppMultDev, chain, state[chain]); nEvents = p->nEvents[2*chain+state[chain]]; rateMultiplier = p->rateMult[2*chain+state[chain]]; /* cpp events */ sumEvents = 0; for (i=0; i<2*numLocalTaxa-2; i++) sumEvents += nEvents[i]; t = GetTree (p, chain, state[chain]); lnPrior += - lambda * TreeLength (p, chain) + (sumEvents * log (lambda)); /* rate multipliers */ for (i=0; i<2*numLocalTaxa-2; i++) { for (j=0; jnNodes-2; i++) { branch = t->allDownPass[i]; assert (fabs(branch->length - (branch->anc->nodeDepth - branch->nodeDepth)) < 0.000001); } } else if (p->paramType == P_TK02VAR) { /* variance of rates (nu) in Thorne-Kishino model */ if (p->paramId == TK02VAR_EXP) { lnPrior += log (mp->tk02varExp) - mp->tk02varExp * st[0]; } else if (p->paramId == TK02VAR_UNI) { lnPrior += log(1.0) - log (mp->tk02varUni[1] - mp->tk02varUni[0]); } } else if (p->paramType == P_TK02BRANCHRATES) { /* branch rates of Thorne-Kishino model */ t = GetTree (p, chain, state[chain]); if (p->paramType == P_TK02BRANCHRATES) nu = *GetParamVals (m->tk02var, chain, state[chain]); else nu = *GetParamVals (m->mixedvar, chain, state[chain]); for (i=0; inNodes-2; i++) { branch = t->allDownPass[i]; if (branch->length > 0.0) // not ancestral fossil lnPrior += LnProbLogNormal_Mean_LogVar (st[branch->anc->index], nu*branch->length, st[branch->index]); } // mean is on natural scale and variance is on log scale } else if (p->paramType == P_WNVAR) { /* variance of rates in white noise model */ if (p->paramId == WNVAR_EXP) { lnPrior += log (mp->wnvarExp) - mp->wnvarExp * st[0]; } else if (p->paramId == WNVAR_UNI) { lnPrior += log(1.0) - log (mp->wnvarUni[1] - mp->wnvarUni[0]); } } else if (p->paramType == P_WNBRANCHRATES) { /* branch rates of white noise model */ t = GetTree (p, chain, state[chain]); if (p->paramType == P_WNBRANCHRATES) var = *GetParamVals (m->wnvar, chain, state[chain]); else var = *GetParamVals (m->mixedvar, chain, state[chain]); for (i=0; inNodes-2; i++) { branch = t->allDownPass[i]; if (branch->length > 0.0) // not ancestral fossil lnPrior += LnProbGamma (branch->length/var, branch->length/var, st[branch->index]); } } else if (p->paramType == P_ILNVAR) { /* variance of rates in independent lognormal rates model */ if (p->paramId == ILNVAR_EXP) { lnPrior += log (mp->ilnvarExp) - mp->ilnvarExp * st[0]; } else if (p->paramId == ILNVAR_UNI) { lnPrior += log(1.0) - log (mp->ilnvarUni[1] - mp->ilnvarUni[0]); } } else if (p->paramType == P_ILNBRANCHRATES || (p->paramType == P_MIXEDBRCHRATES && *GetParamIntVals(p, chain, state[chain]) == RCL_ILN)) { /* branch rates of independent branch rate model */ t = GetTree (p, chain, state[chain]); if (p->paramType == P_ILNBRANCHRATES) nu = *GetParamVals (m->ilnvar, chain, state[chain]); else nu = *GetParamVals (m->mixedvar, chain, state[chain]); for (i=0; inNodes-2; i++) { branch = t->allDownPass[i]; if (branch->length > 0.0) // not ancestral fossil lnPrior += LnProbLogNormal_Mean_Var (1.0, nu, st[branch->index]); } // both mean and variance are on the natural scale } else if (p->paramType == P_IGRVAR) { /* variance of rates in independent gamma rates model */ if (p->paramId == IGRVAR_EXP) { lnPrior += log (mp->igrvarExp) - mp->igrvarExp * st[0]; } else if (p->paramId == IGRVAR_UNI) { lnPrior += log(1.0) - log (mp->igrvarUni[1] - mp->igrvarUni[0]); } } else if (p->paramType == P_IGRBRANCHRATES || (p->paramType == P_MIXEDBRCHRATES && *GetParamIntVals(p, chain, state[chain]) == RCL_IGR)) { /* branch rates of independent branch rate model */ t = GetTree (p, chain, state[chain]); if (p->paramType == P_IGRBRANCHRATES) var = *GetParamVals (m->igrvar, chain, state[chain]); else var = *GetParamVals (m->mixedvar, chain, state[chain]); for (i=0; inNodes-2; i++) { branch = t->allDownPass[i]; if (branch->length > 0.0) // not ancestral fossil lnPrior += LnProbGamma (1.0/var, 1.0/var, st[branch->index]); } } else if (p->paramType == P_MIXEDVAR) { /* hyper prior of rates in mixed rel clock model */ if (p->paramId == MIXEDVAR_EXP) { lnPrior += log (mp->mixedvarExp) - mp->mixedvarExp * st[0]; } else if (p->paramId == MIXEDVAR_UNI) { lnPrior += log(1.0) - log (mp->mixedvarUni[1] - mp->mixedvarUni[0]); } } else if (p->paramType == P_CLOCKRATE) { /* base rate of molecular clock */ lnPrior += p->LnPriorProb(st[0], p->priorParams); } else if (p->paramType == P_SPECIESTREE) { /* calculate prior */ lnPrior += LnSpeciesTreeProb(chain); } } assert (lnPrior == lnPrior); # if defined (BEST_MPI_ENABLED) /* Assemble prior probabilities across processors */ myLnPrior = lnPrior; MPI_AllReduce (&myLnPrior, &lnPrior, MPI_DOUBLE, MPI_SUM, MPI_COMM_WORLD); # endif return (lnPrior); } int LnBirthDeathPriorPr (Tree *t, MrBFlt clockRate, MrBFlt *prob, MrBFlt sR, MrBFlt eR, char *sS, MrBFlt sF) { if (!strcmp(sS, "Random")) { return LnBirthDeathPriorPrRandom (t, clockRate, prob, sR, eR, sF); } else if (!strcmp(sS, "Diversity")) { return LnBirthDeathPriorPrDiversity (t, clockRate, prob, sR, eR, sF); } else if (!strcmp(sS, "Cluster")) { return LnBirthDeathPriorPrCluster (t, clockRate, prob, sR, eR, sF); } else { MrBayesPrint ("%s ERROR: Sampling strategy for birth-death process not implemented\n", spacer); return (ERROR); } } /*--------------------------------------------------------------------------------- | | LnBirthDeathPriorPrRandom | | t_4 (age of tips) | \ \ \ / | \ \ \ / | \ \ \ / | \ \ \ / | \ \ \/ t_3 | \ \ / | \ \ / | \ \ / | \ \ / | \ \/ t_2 | \ / | \ / | \ / | \ / | \/ t_1 (age of most recent common ancestor) | | | This function calculates the probability of a rooted tree under the neutral | birth death prior with constant birth and death rates, conditioned on t_1, | the most recent common ancestor. We assume rho-sampling, that is, a constant | sampling probability rho on extant taxa, which is known. | | T: the unlabeled oriented tree, which is equivalent to a set of unordered | speciation times from a point process | tau: the labeled unoriented tree | b: birth (speciation) rate | d: death (extinction) rate | f: sampling fraction | n: number of (extant) taxa in the sampled tree | | Eq.3 * Eq.A0 in Hohna et al. 2011 MBE | ---------------------------------------------------------------------------------*/ int LnBirthDeathPriorPrRandom (Tree *t, MrBFlt clockRate, MrBFlt *prob, MrBFlt sR, MrBFlt eR, MrBFlt sF) { int i, n, m; MrBFlt *nt, lambda, mu, tmrca; TreeNode *p; Model *mp; /* transform to standard variables */ lambda = sR / (1.0 - eR); mu = eR * lambda; n = t->nIntNodes+1; m = (int)floor(n/sF+0.5); /* equal to round(n/sF) plus it is compatible with MS Visual Studio */ if (AreDoublesEqual(lambda, mu, ETA) == YES) { MrBayesPrint ("\n ERROR: Critical branching process for random sampling not implemented\n"); return (ERROR); } /* allocate space for the speciation times */ nt = (MrBFlt *)SafeMalloc((size_t)(t->nIntNodes) * sizeof(MrBFlt)); if (!nt) { MrBayesPrint ("\n ERROR: Problem allocating nt\n"); return (ERROR); } /* get the node times and put them into a vector */ for (i=0; inIntNodes; i++) { p = t->intDownPass[i]; nt[i] = p->nodeDepth / clockRate; } /* time of most recent common ancestor */ tmrca = nt[t->nIntNodes-1]; // t->root->left->nodeDepth / clockRate /* calculate probability of tree using standard variables */ (*prob) = log(m-1) - log(n-1); // constant (*prob) += (m-2.0) * (LnP0(tmrca, lambda, mu) + log(lambda) - log(mu)); (*prob) += 2.0 * (LnP1(tmrca, lambda, mu) - log(1.0 - exp(LnP0(tmrca, lambda, mu)))); (*prob) += (n-2.0) * (log(lambda*sF + (lambda-lambda*sF-mu)*exp((mu-lambda)*tmrca)) - log(sF*(1-exp((mu-lambda)*tmrca)))); for (i=0; inIntNodes-1; i++) (*prob) += LnP1Subsample(nt[i], lambda, mu, sF); /* conversion to labeled tree from oriented tree */ // (*prob) += (n-1.0)*log(2.0) - LnFactorial(n); // constant /* condition on tmrca, calibrations are dealt with separately */ mp = &modelParams[t->relParts[0]]; if (t->root->left->isDated == NO) (*prob) += mp->treeAgePr.LnPriorProb(tmrca, mp->treeAgePr.priorParams); /* free memory */ free (nt); return (NO_ERROR); } /*--------------------------------------------------------------------------------- | | LnBirthDeathPriorPrDiversity | | Eq.A1 in Hohna et al. 2011 MBE | ---------------------------------------------------------------------------------*/ int LnBirthDeathPriorPrDiversity (Tree *t, MrBFlt clockRate, MrBFlt *prob, MrBFlt sR, MrBFlt eR, MrBFlt sF) { int i, n, m; MrBFlt *nt, lambda, mu, nt_min, tmrca, ln_p0_t1; TreeNode *p; Model *mp; /* transform to standard variables */ lambda = sR / (1.0 - eR); mu = eR * lambda; n = t->nIntNodes+1; m = (int)floor(n/sF+0.5); /* equal to round(n/sF) plus it is compatible with MS Visual Studio */ if (AreDoublesEqual(lambda, mu, ETA) == YES) { MrBayesPrint ("\n ERROR: Critical branching process for diversity sampling not implemented\n"); return (ERROR); } /* allocate space for the speciation times */ nt = (MrBFlt *)SafeMalloc((size_t)(t->nIntNodes) * sizeof(MrBFlt)); if (!nt) { MrBayesPrint ("\n ERROR: Problem allocating nt\n"); return (ERROR); } /* get the node times and put them into a vector */ for (i=0; inIntNodes; i++) { p = t->intDownPass[i]; nt[i] = p->nodeDepth / clockRate; } /* time of most recent common ancestor */ tmrca = nt[t->nIntNodes-1]; // t->root->left->nodeDepth / clockRate /* find the youngest interal node */ nt_min = nt[0]; for (i=0; inIntNodes-1; i++) { if (nt_min > nt[i]) nt_min = nt[i]; } ln_p0_t1 = LnP0(tmrca, lambda, mu); (*prob) = 0; // LnFactorial(m-1) - LnFactorial(n-1) - LnFactorial(m-n); // constant (*prob) += (m-2.0) * (ln_p0_t1 + log(lambda)) + (n-m)*log(mu); (*prob) += 2.0 * (LnP1(tmrca, lambda, mu) - log(1.0 - exp(ln_p0_t1))); (*prob) += (m-n) * (LnP0(nt_min, lambda, mu) - ln_p0_t1); for (i=0; inIntNodes-1; i++) (*prob) += LnP1(nt[i], lambda, mu) - ln_p0_t1; /* conversion to labeled tree from oriented tree */ // (*prob) += (n-1.0)*log(2.0) - LnFactorial(n); // constant /* condition on tmrca, calibrations are dealt with separately */ mp = &modelParams[t->relParts[0]]; if (t->root->left->isDated == NO) (*prob) += mp->treeAgePr.LnPriorProb(tmrca, mp->treeAgePr.priorParams); /* free memory */ free (nt); return (NO_ERROR); } /*--------------------------------------------------------------------------------- | | LnBirthDeathPriorPrCluster | | Eq.A2 in Hohna et al. 2011 MBE | ---------------------------------------------------------------------------------*/ int LnBirthDeathPriorPrCluster (Tree *t, MrBFlt clockRate, MrBFlt *prob, MrBFlt sR, MrBFlt eR, MrBFlt sF) { int i, n, m; MrBFlt *nt, lambda, mu, nt_2, tmrca, ln_p0_t1; TreeNode *p; Model *mp; /* transform to standard variables */ lambda = sR / (1.0 - eR); mu = eR * lambda; n = t->nIntNodes+1; m = (int)floor(n/sF+0.5); /* equal to round(n/sF) plus it is compatible with MS Visual Studio */ if (AreDoublesEqual(lambda, mu, ETA) == YES) { MrBayesPrint ("\n ERROR: Critical branching process for cluster sampling not implemented\n"); return (ERROR); } /* allocate space for the speciation times */ nt = (MrBFlt *)SafeMalloc((size_t)(t->nIntNodes) * sizeof(MrBFlt)); if (!nt) { MrBayesPrint ("\n ERROR: Problem allocating nt\n"); return (ERROR); } /* get the node times and put them into a vector */ for (i=0; inIntNodes; i++) { p = t->intDownPass[i]; nt[i] = p->nodeDepth / clockRate; } /* time of most recent common ancestor */ tmrca = nt[t->nIntNodes-1]; // t->root->left->nodeDepth / clockRate /* find the second oldest interal node */ nt_2 = nt[0]; for (i=0; inIntNodes-1; i++) { if (nt_2 < nt[i]) nt_2 = nt[i]; } ln_p0_t1 = LnP0(tmrca, lambda, mu); (*prob) = 0; // LnFactorial(m-1) - LnFactorial(n-1) - LnFactorial(m-n); // constant (*prob) += (m-2.0) * (ln_p0_t1 + log(lambda)) + (n-m)*log(mu); (*prob) += 2.0 * (LnP1(tmrca, lambda, mu) - log(1.0 - exp(ln_p0_t1))); (*prob) += (m-n) * log(1.0 - exp(LnP0(nt_2, lambda, mu))/exp(ln_p0_t1)); for (i=0; inIntNodes-1; i++) (*prob) += LnP1(nt[i], lambda, mu) - ln_p0_t1; /* conversion to labeled tree from oriented tree */ // (*prob) += (n-1.0)*log(2.0) - LnFactorial(n); // constant /* condition on tmrca, calibrations are dealt with separately */ mp = &modelParams[t->relParts[0]]; if (t->root->left->isDated == NO) (*prob) += mp->treeAgePr.LnPriorProb(tmrca, mp->treeAgePr.priorParams); /* free memory */ free (nt); return (NO_ERROR); } /* | | The probability of having zero lineages remaining after time t in the | birth-death process. | | param: t - speciation time | param: b - birth rate | param: d - death rate | return: log probability of zero remaining lineages | */ MrBFlt LnP0 (MrBFlt t, MrBFlt b, MrBFlt d) { MrBFlt p0t; p0t = d*(1.0-exp((d-b)*t)) / (b -d*exp((d-b)*t)); return (log(p0t)); } /* The probability that a lineage at time t has 0 sampled descendants at time 0 */ MrBFlt LnP0Subsample (MrBFlt t, MrBFlt b, MrBFlt d, MrBFlt f) { MrBFlt p0t; p0t = (f*d + (b*(1.0-f) -d)*exp((d-b)*t)) / (f*b + (b*(1.0-f) -d)*exp((d-b)*t)); return (log(p0t)); } /* | | The probability of having one lineage remaining after time t | in the birth-death process. | | param: t - speciation time | param: b - birth rate | param: d - death rate | return: log probability of one remaining lineage | */ MrBFlt LnP1 (MrBFlt t, MrBFlt b, MrBFlt d) { MrBFlt p1t; p1t = 2.0*log(b-d) - (b-d)*t; p1t -= 2.0*log(b -d*exp((d-b)*t)); return p1t; } /* The probability that a lineage at time t has 1 sampled descendants at time 0 */ MrBFlt LnP1Subsample (MrBFlt t, MrBFlt b, MrBFlt d, MrBFlt f) { MrBFlt p1t; p1t = (b-d) / (f*b + (b*(1.0-f)-d)*exp((d-b)*t)); return (2.0*log(p1t) + log(f) - (b-d)*t); } /* probability that an individual alive at time t before today has no sampled extinct or extant descendants */ MrBFlt LnP0_fossil (MrBFlt t, MrBFlt lambda, MrBFlt mu, MrBFlt psi, MrBFlt c1, MrBFlt c2) { MrBFlt other; // c1 = sqrt(pow(lambda-mu-psi, 2) + 4*lambda*psi); // c2 = (-lambda + mu + 2*lambda*rho + psi) / c1; other = (exp(-c1 *t) * (1 - c2) - (1 + c2)) / (exp(-c1 *t) * (1 - c2) + (1 + c2)); return log(lambda + mu + psi + c1 * other) - log(2.0*lambda); } /* probability that an individual alive at time t before today has precisely one sampled extant descendant and no sampled extinct descendant */ MrBFlt LnP1_fossil (MrBFlt t, MrBFlt rho, MrBFlt c1, MrBFlt c2) { MrBFlt other; // c1 = sqrt(pow(lambda-mu-psi, 2) + 4*lambda*psi); // c2 = (-lambda + mu + 2*lambda*rho + psi) / c1; other = 2.0 * (1- c2*c2) * exp(-c1 *t) + pow(1-c2, 2) * exp(-2 *c1 *t) + pow(1+c2, 2); return log(4.0) + log(rho) - c1 *t - log(other); } /* return which time interval t is in */ int Slice_i (MrBFlt t, MrBFlt *t_sl, int n_sl) { int i = 0; assert (t > 0.0 && n_sl >= 0); /* we need some tolerance here, for t[i] < t <= t[i-1] to return i */ while (t < t_sl[i] + BRLENS_MIN/5) { i++; if (i > n_sl) return n_sl; } return i; } /* probability density of an individual at time t giving rise to an edge between time t and t_i with q_i(t_i) = 1 */ MrBFlt LnQi_fossil (MrBFlt t, MrBFlt *t_sl, int n_sl, MrBFlt *c1, MrBFlt *c2) { MrBFlt lnq; int i = Slice_i (t, t_sl, n_sl); lnq = log(4.0) +c1[i] *(t_sl[i] -t); lnq -= 2.0 * log(1 +c2[i] +(1 -c2[i]) *exp(c1[i] *(t_sl[i] -t))); return lnq; } /* an individual at time t has no sampled descendants when the process is stopped (i.e., at time t_s), with t_i < t <= t_{i-1} (i = 1,..,s) */ MrBFlt LnPi_fossil (MrBFlt t, MrBFlt *t_sl, int n_sl, MrBFlt *c1, MrBFlt *c2, MrBFlt *lambda, MrBFlt *mu, MrBFlt *psi) { MrBFlt other; int i = Slice_i (t, t_sl, n_sl); other = lambda[i] +mu[i] +psi[i] -c1[i] * (1 +c2[i] -(1 -c2[i]) *exp(c1[i] *(t_sl[i] -t))) / (1 +c2[i] +(1 -c2[i]) *exp(c1[i] *(t_sl[i] -t))); return log(other) - log(2 *lambda[i]); } int LnFossilizationPriorPr (Tree *t, MrBFlt clockRate, MrBFlt *prob, MrBFlt *sR, MrBFlt *eR, MrBFlt *fR, MrBFlt sF, char *sS) { /* fossilization priors //chi */ int n_sl, i, i1,i2,i3, max_intv, rtn_val; MrBFlt *t_sl, *lambda, *mu, *psi, *rho; Model *mp; mp = &modelParams[t->relParts[0]]; if (!strcmp(sS, "FossilTip")) { assert (mp->fossilSamplingNum + mp->birthRateShiftNum + mp->deathRateShiftNum == 0); return LnFossilizedBDPriorFossilTip (t, clockRate, prob, sR, eR, fR, sF); } else if (strcmp(sS, "Random") && strcmp(sS, "Diversity")) { MrBayesPrint ("%s Sampling strategy %s for fossilized birth-death process not implemented\n", spacer, sS); return (ERROR); } /* the following models allow rate shifts */ /* alloc memory for time of each shift */ max_intv = mp->fossilSamplingNum + mp->birthRateShiftNum + mp->deathRateShiftNum +2; t_sl = (MrBFlt *)SafeMalloc(max_intv * sizeof(MrBFlt)); if (!t_sl) { MrBayesPrint ("%s ERROR: Problem allocating t_sl in FossilizedBirthDeathPrior\n", spacer); return (ERROR); } n_sl = GetTotalRateShifts(mp, t_sl); // number of total slices /* alloc memory for the parameters */ lambda = (MrBFlt *)SafeMalloc((size_t)(n_sl +2) * sizeof(MrBFlt)); mu = (MrBFlt *)SafeMalloc((size_t)(n_sl +2) * sizeof(MrBFlt)); psi = (MrBFlt *)SafeMalloc((size_t)(n_sl +2) * sizeof(MrBFlt)); rho = (MrBFlt *)SafeMalloc((size_t)(n_sl +2) * sizeof(MrBFlt)); if (!lambda || !mu || !psi || !rho) { MrBayesPrint ("%s ERROR: Problem allocating memory in FossilizedBirthDeathPrior\n", spacer); return (ERROR); } /* initialization */ for (i1 = i2 = i3 = 0, i = 0; i <= n_sl; i++) { if (mp->birthRateShiftNum > 0 && mp->birthRateShiftTime[i1] > t_sl[i] +BRLENS_MIN/2) i1++; if (mp->deathRateShiftNum > 0 && mp->deathRateShiftTime[i2] > t_sl[i] +BRLENS_MIN/2) i2++; if (mp->fossilSamplingNum > 0 && mp->fossilSamplingTime[i3] > t_sl[i] +BRLENS_MIN/2) i3++; if (!strcmp(mp->extinctionPr, "Exponential") || !strcmp(mp->fossilizationPr, "Exponential")) { // direct lambda,mu,psi parameterization lambda[i] = sR[i1]; mu[i] = eR[i2]; psi[i] = fR[i3]; } else // d=lambda-mu, r=mu/lambda, s=psi/(mu+psi) { lambda[i] = sR[i1] / (1.0 - eR[i2]); mu[i] = lambda[i] * eR[i2]; psi[i] = mu[i] * fR[i3] / (1.0 - fR[i3]); } rho[i] = 1E-20; // to avoid prob being -inf # ifdef DEBUG_FBDPR printf ("%d: sR=%lf eR=%lf fR=%lf\n", i+1, sR[i1], eR[i2], fR[i3]); # endif } t_sl[n_sl] = 0.0; rho[n_sl] = sF; // 0 < sF <= 1 if (!strcmp(sS, "Random")) rtn_val = LnFossilizedBDPriorRandom (t, clockRate, prob, lambda, mu, psi, rho, t_sl, n_sl); else rtn_val = LnFossilizedBDPriorDiversity(t, clockRate, prob, lambda, mu, psi, rho, t_sl, n_sl); /* free memory */ free(t_sl); free(lambda); free(mu); free(psi); free(rho); return rtn_val; } /*--------------------------------------------------------------------------------- | | time ___ 0 | \ / | | \ / |___ y2 | \ \ / | | \ \ / | | \ \/ x3 ___| | \ / |___ y1 | \ \ / | | \ \ / | | \ \ / | | \ \/ x2 ___| | \ / | | \ / | | \ / | | \ / | | \/ x1 ___| | | | T: oriented tree | b: birth (speciation) rate | d: death (extinction) rate | p: extant sampling prob | q: fossil sampling rate | n: number of extant taxa | m: number of fossil tips | | | [p1(x1)]^2 n+m-1 m q | f(T|tmrca) = ---------------- * prod b*p1(xi) * prod ----- . | [1 - p0(x1)]^2 i=2 i=1 p1(yi) | | f(tmrca) ~ uniform, gamma, etc (see treeAge). | ---------------------------------------------------------------------------------*/ int LnFossilizedBDPriorFossilTip (Tree *t, MrBFlt clockRate, MrBFlt *prob, MrBFlt *sR, MrBFlt *eR, MrBFlt *fR, MrBFlt sF) { /* special case: upon sampling the lineage is dead and won't produce descendants. Each extinct sample is a tip */ int i; MrBFlt x, lambda, mu, rho, psi, tmrca, c1, c2; TreeNode *p; Model *mp; /* sR = lambda-mu-psi, eR = (mu+psi)/lambda, fR = psi/(mu+psi) */ lambda = sR[0] / (1.0 - eR[0]); mu = lambda * eR[0] * (1.0 - fR[0]); psi = lambda * eR[0] * fR[0]; rho = sF; tmrca = t->root->left->nodeDepth / clockRate; c1 = sqrt(sR[0]*sR[0] + 4 *lambda *psi); c2 = (2 *lambda *rho - sR[0]) / c1; /* calculate prior prob of the fbd tree */ (*prob) = 0.0; for (i = 0; i < t->nNodes -1; i++) { p = t->allDownPass[i]; x = p->nodeDepth / clockRate; if (p->left != NULL && p->right != NULL) // internal { if (p != t->root->left) (*prob) += log(lambda) + LnP1_fossil(x, rho, c1, c2); } else if (p->left == NULL && p->right == NULL) // tip { if (p->nodeDepth > 0.0) { (*prob) += log(psi) - LnP1_fossil(x, rho, c1, c2); // m++; } // else n++; } } (*prob) += 2.0 * LnP1_fossil(tmrca, rho, c1, c2); /* condition on sampling at least one individual in both subtrees leading to the root */ (*prob) -= 2.0 * log(1 - exp(LnP0_fossil(tmrca, lambda, mu, psi, c1, c2))); /* condition on tmrca, calibrations are dealt with separately */ mp = &modelParams[t->relParts[0]]; if (t->root->left->isDated == NO) (*prob) += mp->treeAgePr.LnPriorProb(tmrca, mp->treeAgePr.priorParams); /* conversion to labeled tree from oriented tree, constant */ // (*prob) += (n + m - 1) * log(2.0) - LnFactorial(n) - LnFactorial(m); return (NO_ERROR); } int GetTotalRateShifts (Model *mp, MrBFlt *shiftTimes) { int i, j, sLen, sTotal; /* get the total number of rate shifts */ sTotal = mp->fossilSamplingNum; for (i = 0; i < mp->fossilSamplingNum; i++) shiftTimes[i] = mp->fossilSamplingTime[i]; sLen = sTotal; for (i = 0; i < mp->birthRateShiftNum; i++) { for (j = 0; j < sLen; j++) if (fabs(mp->birthRateShiftTime[i] - shiftTimes[j]) <= BRLENS_MIN/2) break; if (fabs(mp->birthRateShiftTime[i] - shiftTimes[j]) > BRLENS_MIN/2) shiftTimes[sTotal++] = mp->birthRateShiftTime[i]; } sLen = sTotal; for (i = 0; i < mp->deathRateShiftNum; i++) { for (j = 0; j < sLen; j++) if (fabs(mp->deathRateShiftTime[i] - shiftTimes[j]) <= BRLENS_MIN/2) break; if (fabs(mp->deathRateShiftTime[i] - shiftTimes[j]) > BRLENS_MIN/2) shiftTimes[sTotal++] = mp->deathRateShiftTime[i]; } /* sort shiftTimes[] in descending order */ SortMrBFlt_Des (shiftTimes, 0, sTotal-1); return sTotal; } /*--------------------------------------------------------------------------------- | | LnFossilizedBDPriorRandom | | Gavryushkina, A., D. Welch, T. Stadler, and A. Drummond. 2014. | Bayesian inference of sampled ancestor trees for epidemiology and fossil calibration. PLoS Comp. Biol. | Zhang C., T. Stadler, S. Klopfstein, T. A. Heath, and F. Ronquist. 2016. | Total-Evidence Dating under the Fossilized Birth-Death Process. Syst. Biol. | | | 0 _____________ t3, rho3 | \ / | | \ / |___ y2 | \ \ / _________|_________ t2, rho2 | \ \ / | | \ \/ x3 ___| | \ / |___ y1 | \ \ / | | _\ \ / _________|_________ t1, rho1 | \ \ / | | \ \/ x2 ___| | \ / | | \ / | | \ / | | \ / | | \/ x1 ___|_________ t_mrca | | | sl = 2, t1 > t2 > t3 = 0 | E = 2, K = 1, M = 2 | ---------------------------------------------------------------------------------*/ int LnFossilizedBDPriorRandom (Tree *t, MrBFlt clockRate, MrBFlt *prob, MrBFlt *lambda, MrBFlt *mu, MrBFlt *psi, MrBFlt *rho, MrBFlt *t_sl, int n_sl) { /* Fossils are sampled with piecewise constant rates in the past. Extant taxa are sampled uniformly at random at present. */ int i, j, M, E; MrBFlt x, tmrca, *c1, *c2, *p_t; TreeNode *p; Model *mp; tmrca = t->root->left->nodeDepth / clockRate; assert (t_sl[0] < tmrca); c1 = (MrBFlt *)SafeMalloc((size_t)(n_sl +1) * sizeof(MrBFlt)); c2 = (MrBFlt *)SafeMalloc((size_t)(n_sl +1) * sizeof(MrBFlt)); p_t = (MrBFlt *)SafeMalloc((size_t)(n_sl +1) * sizeof(MrBFlt)); if (!c1 || !c2 || !p_t) { MrBayesPrint ("%s ERROR: Problem allocating memory in FossilizedBirthDeathPrior\n", spacer); return (ERROR); } for (i = n_sl; i >= 0; i--) { c1[i] = sqrt(pow(lambda[i]-mu[i]-psi[i], 2) + 4*lambda[i]*psi[i]); if (i == n_sl) c2[i] = ((1 - 2* (1-rho[i])) *lambda[i] +mu[i] +psi[i]) /c1[i]; else c2[i] = ((1 - 2* (1-rho[i]) *p_t[i+1]) *lambda[i] +mu[i] +psi[i]) /c1[i]; if (i > 0) p_t[i] = (lambda[i] +mu[i] +psi[i] -c1[i] * (1 +c2[i] -(1 -c2[i]) *exp(c1[i] *(t_sl[i] -t_sl[i-1]))) / (1 +c2[i] +(1 -c2[i]) *exp(c1[i] *(t_sl[i] -t_sl[i-1])))) *0.5/lambda[i]; else p_t[i] = (lambda[i] +mu[i] +psi[i] -c1[i] * (1 +c2[i] -(1 -c2[i]) *exp(c1[i] *(t_sl[i] -tmrca))) / (1 +c2[i] +(1 -c2[i]) *exp(c1[i] *(t_sl[i] -tmrca)))) *0.5/lambda[i]; # ifdef DEBUG_FBDPR printf ("%d: lambda=%lf mu=%lf psi=%lf rho=%lf t=%lf\n", i+1, lambda[i], mu[i], psi[i], rho[i], t_sl[i]); printf (" A=%lf B=%lf p%d(t%d)=%lf\n", c1[i], c2[i], i+1, i, p_t[i]); if (i == 0) printf ("0: tmrca=%lf\n", tmrca); # endif } /* calculate prior prob of the fbd tree */ (*prob) = 0.0; for (M = E = 0, i = 0; i < t->nNodes -1; i++) { p = t->allDownPass[i]; x = p->nodeDepth / clockRate; if (p->left != NULL && p->right != NULL) // internal { if (p->left->length > 0.0 && p->right->length > 0.0) { if (p != t->root->left) (*prob) += log(lambda[Slice_i(x, t_sl, n_sl)]) + LnQi_fossil(x, t_sl, n_sl, c1,c2); } else { for (j = 0; j < n_sl; j++) if (AreDoublesEqual(p->nodeDepth, t_sl[j]*clockRate, BRLENS_MIN/5) == YES) break; if (j == n_sl) /* fossil ancestor between t[j-1] and t[j] */ { (*prob) += log(psi[Slice_i(x, t_sl, n_sl)]); } else /* fossil ancestor at slice time t[j] */ { (*prob) += log(rho[j]) - log(1 - rho[j]); } // K++; /* number of fossil ancestors */ } } else if (p->left == NULL && p->length > 0.0) // tip { if (p->nodeDepth > 0.0) { for (j = 0; j < n_sl; j++) if (AreDoublesEqual(p->nodeDepth, t_sl[j]*clockRate, BRLENS_MIN/5) == YES) break; if (j == n_sl) /* fossil tip between t[j-1] and t[j] */ { (*prob) += LnPi_fossil(x, t_sl, n_sl, c1,c2, lambda,mu,psi) - LnQi_fossil(x, t_sl, n_sl, c1,c2); (*prob) += log(psi[Slice_i(x, t_sl, n_sl)]); } else /* fossil tip at slice time t[j] */ { (*prob) += log(p_t[j+1]) + log(rho[j]); } M++; /* number of fossil tips */ } else { (*prob) += log(rho[n_sl]); // 0 < rho[sl] <= 1 E++; /* number of extant taxa */ } } for (j = 0; j < n_sl; j++) /* degree-two vertices at slice time t_j */ { if (p->length > 0.0 && (p->nodeDepth +BRLENS_MIN/10 < t_sl[j]*clockRate) && (t_sl[j]*clockRate < p->anc->nodeDepth +BRLENS_MIN/10)) { (*prob) += LnQi_fossil(t_sl[j], t_sl, n_sl, c1,c2) + log(1 - rho[j]); } } } /* condition on sampling a fossil OR a extant taxa */ (*prob) += 2.0 * (LnQi_fossil(tmrca, t_sl, n_sl, c1,c2) - log(1- p_t[0])); /* conversion to labeled tree from oriented tree */ (*prob) += (M + E - 1) * log(2.0); // - LnFactorial(E + M + K); // # permutation is constant /* condition on tmrca, calibrations are dealt with separately */ mp = &modelParams[t->relParts[0]]; if (t->root->left->isDated == NO) (*prob) += mp->treeAgePr.LnPriorProb(tmrca, mp->treeAgePr.priorParams); # ifdef DEBUG_FBDPR printf ("M=%d E=%d\n", M, E); printf ("prob=%lf\n", *prob); # endif /* free memory */ free(c1); free(c2); free(p_t); return (NO_ERROR); } /*--------------------------------------------------------------------------------- | | LnFossilizedBDPriorDiversity | | Zhang C., T. Stadler, S. Klopfstein, T. A. Heath, and F. Ronquist. 2016. | Total-Evidence Dating under the Fossilized Birth-Death Process. Syst. Biol. | ---------------------------------------------------------------------------------*/ int LnFossilizedBDPriorDiversity (Tree *t, MrBFlt clockRate, MrBFlt *prob, MrBFlt *lambda, MrBFlt *mu, MrBFlt *psi, MrBFlt *rho, MrBFlt *t_sl, int n_sl) { /* Fossils are sampled with piecewise constant rates in the past. Extant taxa are sampled with prop sF to maximize diversity. */ int i, E; MrBFlt tmrca, x_cut, sF, M_x; TreeNode *p; /* record extant sampling fraction */ sF = rho[n_sl]; /* get the cutoff time */ x_cut = tmrca = t->root->left->nodeDepth / clockRate; for (E = 0, i = 0; i < t->nNodes -1; i++) { p = t->allDownPass[i]; if (p->left == NULL && p->nodeDepth == 0.0) // extant tip E++; if (p->left == NULL && p->nodeDepth > 0.0) // fossil if (x_cut > p->nodeDepth / clockRate) x_cut = p->nodeDepth / clockRate; if (p->left != NULL && p->right != NULL && p->left->length > 0.0 && p->right->length > 0.0) // internal node if (x_cut > p->nodeDepth / clockRate) x_cut = p->nodeDepth / clockRate; } /* lower the cutoff time if not compatible */ if (n_sl > 0 && x_cut > t_sl[n_sl -1]) x_cut = t_sl[n_sl -1]; /* add one slice to shift psi to 0 */ t_sl[n_sl] = x_cut * 0.95; rho[n_sl] = 0.0; t_sl[n_sl+1] = 0.0; rho[n_sl +1] = 1.0; // assuming complete sampling lambda[n_sl+1] = lambda[n_sl]; mu[n_sl+1] = mu[n_sl]; psi[n_sl+1] = 0.0; /* first calculate the prob density of the tree assuming complete sampling */ if (LnFossilizedBDPriorRandom (t, clockRate, prob, lambda, mu, psi, rho, t_sl, n_sl+1) == ERROR) return (ERROR); /* number of extant taxa not sampled */ M_x = (int)floor(E/sF + 0.5) - E; /* equal to round(E/sF) plus it is compatible with MS Visual Studio */ /* then correct the prob density of the tree for diversified sampling of extant */ if ((lambda[n_sl]-mu[n_sl])*t_sl[n_sl] > 1E-6) (*prob) += M_x * (log(lambda[n_sl] * (1.0 - exp((mu[n_sl]-lambda[n_sl])*t_sl[n_sl]))) - log(lambda[n_sl] - mu[n_sl] * exp((mu[n_sl]-lambda[n_sl])*t_sl[n_sl]))); else if ((mu[n_sl]-lambda[n_sl])*t_sl[n_sl] > 1E-6) (*prob) += M_x * (log(lambda[n_sl] * (1.0 - exp((lambda[n_sl]-mu[n_sl])*t_sl[n_sl]))) - log(mu[n_sl] - lambda[n_sl] * exp((lambda[n_sl]-mu[n_sl])*t_sl[n_sl]))); else // for numerical stability (*prob) += M_x * log(lambda[n_sl] / (mu[n_sl] + 1.0/t_sl[n_sl])); # ifdef DEBUG_FBDPR printf ("prob=%lf\n", *prob); # endif return (NO_ERROR); } /*--------------------------------------------------------------------------------- | | LnCoalescencePriorPr | | This function calculates the probability of a tree under the neutral | coalescence prior with a (potentially) exponentially growing population. | We assume a rooted tree that satisfies the molecular clock constraint. The | Tree is labelled as follows: | | t_4 ___ | \ \ \ / \ | \ \ \ / | | I_4 \ \ \ / | g_4 | \ \ \ / | | \ \ \/ t_3 ___ / | \ \ / \ | \ \ / | | I_3 \ \ / | g_3 | \ \ / | | \ \/ t_2 ___ / | \ / \ | \ / | | I_2 \ / | g_2 | \ / | | \/ t_1 ___ / | | Each interval on the tree is specified by successive coalescence events. | These intervals are denoted I_2, I_3, I_4, ..., with the subscript denoting | how many lineages exist in that interval. The time of each coalescence event | is designated t_1, t_2, t_3, ..., where the subscript denotes the number | of lineages that exist after the coalescence (t_3, for instance, would be | the time of the coalescence that went from four lineages to three lineages). | The duration of the i-th interval is designated g_i. | | The probability of the coalescence tree is: | | prob = (k_C_2 / (N(g_k + t_k))) * exp(-integral_(from x=t_k, to g_k + t_k) (k_C_2 / N(x)) dx) | | where N(x) = N(0) * exp(-r*x). For the constant population size case, | N(0) = N_e. r is the population growth parameter for the exponentially | growing population. Here, theta = N(0) * mu when organisms are haploid and | theta = 2 * N(0) * mu when the organism is diploid. | | Below, ct holds the n - 1 coalescence times (t_i, above) sorted from the | smallest to the largest. Remember that t_4 < t_3 < t_2, etc. | | 2010-03-23 | The original function (described above) was incorrect in that it used theta = 2 * N * mu | in one part of the equation and theta = 4 * N * mu in another. It is now corrected to | consistently use theta = 4 * N * mu, which is the standard for diploid populations | and theta = 2 * N * mu for haploid populations. The calculations are the same for the | diploid and haploid cases, only the interpretation is different. See, e.g., Felsenstein | (2004; Inferring Phylogenies). -- Fredrik. | ---------------------------------------------------------------------------------*/ int LnCoalescencePriorPr_Contemp (Tree *t, MrBFlt *prob, MrBFlt theta, MrBFlt growth) { int i, j, k, nNodes; MrBFlt *ct, tempD, lastCoalescenceTime, coalescenceTime, intervalLength; TreeNode *p; /* allocate space for the coalescence times */ ct = (MrBFlt *)SafeMalloc((size_t)(t->nIntNodes) * sizeof(MrBFlt)); if (!ct) { MrBayesPrint ("\n ERROR: Problem allocating ct\n"); return (ERROR); } /* get the coalescence times and put them into a vector */ for (i=j=0; inIntNodes; i++) { p = t->intDownPass[i]; if (p->anc != NULL) ct[j++] = p->nodeDepth; // Don't divide clockRate here, as mu is already in theta } nNodes = j; /* sort the coalescence times */ SortMrBFlt_Asc (ct, 0, nNodes-1); /*for (i=0, k=numLocalTaxa; inNodes -1; // number of coalescent and tip times nCoals = t->nIntNodes; // number of coalescent events /* allocate space for the coalescence and tip times */ ct = (MrBFlt *)SafeMalloc((size_t)nTimes * sizeof(MrBFlt)); /* allocate space for the number of lineages */ k = (int *)SafeMalloc((size_t)nTimes * sizeof(int)); if (!ct || !k) { MrBayesPrint ("%s ERROR: Problem allocating coalescenct times\n", spacer); return (ERROR); } /* get the coalescence and tip times and put them into a vector */ for (i = 0; i < nTimes; i++) { p = t->allDownPass[i]; ct[i] = p->nodeDepth; // do not divide clockRate here, as mu is already in theta } /* sort the times array in increasing order */ SortMrBFlt_Asc (ct, 0, nTimes -1); /* get the number of lineages in each time interval */ for (i = 0; i < nTimes -1; i++) { mid = (ct[i] + ct[i+1]) / 2.0; k[i] = 0; for (j = 0; j < nTimes -1; j++) { // loop over lineages p = t->allDownPass[j]; if (p->nodeDepth <= mid && p->anc->nodeDepth > mid) k[i]++; } } k[nTimes -1] = 1; /* for (i = 0; i < nTimes; i++) printf ("%3d %lf %d\n", i, ct[i], k[i]); */ /* calculate probability of the tree */ if (AreDoublesEqual (growth, 0.0, 0.000001) == YES) { /* use this if there is no population growth */ tempD = 0.0; for (i = 0; i < nTimes -1; i++) { duration = ct[i+1] - ct[i]; tempD += - k[i] * (k[i] -1) * duration / theta; } (*prob) = nCoals * log(2.0 / theta) + tempD; } else { /* use this if the population is growing exponentially */ tempD = 0.0; for (i = 0; i < nTimes -1; i++) { tempD += - k[i] * (k[i] -1) * (exp(growth * ct[i+1]) - exp(growth * ct[i])) / (theta * growth); if (k[i+1] < k[i]) // for a coalescent event tempD += growth * ct[i+1]; } (*prob) = nCoals * log(2.0 / theta) + tempD; } /* free memory */ free (ct); free (k); return (NO_ERROR); } /*--------------------------------------------------------------------------------- | | LnUniformPriorPr | | This function calculates the probability of a calibrated clock tree under the | uniform prior probability distribution on node depths. The tree is labeled as | follows: | interval between tip dates | t_0 t_1 time ____ 0 (interval between t_0 and t_1, duration 0) | \ / | | \ t_2 / |___ 1 (interval between t_1 and t_2) | \ \ / | | \ \ / | | \ \/ 0.33 ___| 2 (interval between t_2 and t_3) | \ t_3 / |___ | \ \ / | | \ \ / | | \ \ / | | \ t_4 \/ 0.67 ___| | \ \ / | 3 (interval between t_3 and root) | \ \/ | | \ / | Note that t_4 is irrelevant for intervals | \ / | because we need not have a single coalescent | \/ 1.00 ___|___ event beneath t_4 except for the root. | | The probability depends on the number of nodes falling in each interval between | tip dates, the time interval on which each node depth can vary (if it is interior | node number i, it can vary on the interval (t_{i+1},1)). Finally, the probability | is multiplied by the number of coalescent histories compatible with the node order. | ---------------------------------------------------------------------------------*/ MrBFlt LnUniformPriorPr (Tree *t, MrBFlt clockRate) { int i, j, k, *nLineages=NULL, nDatedTips, nLineagesIn, nLineagesOut, nTips; MrBFlt lnProb, treeAge, *nodeDepths=NULL; TreeNode *p, *root; Model *mp; lnProb = 0.0; mp = &modelParams[t->relParts[0]]; treeAge = t->root->left->nodeDepth / clockRate; assert (t->root->left->isDated == NO || AreDoublesEqual(treeAge, t->root->left->age, 0.000001) == YES); /* Calculate number of tips for convenience */ nTips = t->nNodes - t->nIntNodes - 1; /* First take tree age into account f(t_0) */ if (t->root->left->isDated == YES) { lnProb += 0.0; /* calibrations are dealt with separately in calling function */ } else { lnProb += mp->treeAgePr.LnPriorProb(treeAge, mp->treeAgePr.priorParams); } /* If tree is not calibrated or only root is calibrated, it is easy */ for (i=j=0; inNodes-2; i++) { if (t->allDownPass[i]->isDated == YES) j++; } if (j == 0) { /* Calculate simple probability f(tau|t_0) */ lnProb += (nTips - 1.0)*log(2.0) - LnFactorial(nTips) - log(nTips-1.0) - (nTips - 2.0)*log(treeAge); assert (lnProb > NEG_INFINITY); return lnProb; } /* We have a tree with interior or tip calibrations */ /* Color subtrees by assigning an index 1,...,k to the x variable of each node, where k is the total number of separate subtrees. A subtree is characterized by having all interior nodes being unconstrained and all tips being either terminals or constrained interior nodes. The root of a subtree may be the root of the tree, or an interior node that is constrained and dated, or an interior node that is constrained but not dated. */ i = 0; ColorClusters (t->root->left, &i); /* Get the probability for each subtree */ for (i=0; inIntNodes; i++) { p = t->intDownPass[i]; /* Skip unless this is the root of a subtree */ if (p->anc->anc != NULL && p->isDated != YES) continue; /* Save the root of the subtree */ root = p; /* Create an array containing the sorted times */ /* Allocate space for node depths */ nDatedTips = NumDatedTips (root); nodeDepths = (MrBFlt *) SafeRealloc ((void *)nodeDepths, (nDatedTips+1)*sizeof(MrBFlt)); /* Get the dated node depths and sort them. The call to GetDatedNodeDepths also returns the root node depth into nodeDepths, which is convenient. For now, this only works for dated tips, not for constrained but undated interior nodes. */ GetDatedNodeDepths (root, nodeDepths); SortMrBFlt_Asc (nodeDepths, 0, nDatedTips); /* use index of left and right in call */ /* Get probability due to the uniform node depths; we do not use first and last tip depth for obvious reasons (see figure above) */ for (j=1; jnodeDepth - nodeDepths[j]) / clockRate); /* Get probability due to sorting of interior node depths */ /* First get the potential number of lineages leaving each interval j at time nodeDepths[j+1] */ nLineages = (int *) SafeRealloc ((void *)nLineages, nDatedTips*sizeof(int)); for (j=0; jnIntNodes; j++) { p = t->intDownPass[j]; if (p->x != root->x || p == root || p->isDated == YES) continue; for (k=0; knodeDepth < nodeDepths[k+1]) nLineages[k]--; } /* Now get the density effect of the sorting constraints */ for (j=1; j 1 && nLineagesIn - nLineagesOut >= 1) { lnProb += LnFactorial (nLineagesIn-1) - LnFactorial(nLineagesOut-1); } } /* Finally get the effect of the number of possible coalescent histories */ for (j=1; jx = root->anc->x; } free (nodeDepths); free (nLineages); assert (lnProb > NEG_INFINITY); return lnProb; } #if 0 /*------------------------------------------------------------------------ | | NewtonRaphsonBrlen: Find one maximum likelihood branch length using | the Newton-Raphson method. This function assumes that the tree is | a non-clock tree. For clock trees, you have to optimize the node | depths instead. | ------------------------------------------------------------------------*/ int NewtonRaphsonBrlen (Tree *t, TreeNode *p, int chain) { int c, i, j, s, k, n, d, division, nIterations, maxNumIterations, index, *rateCat, r; MrBFlt vOld, *pi, *cijk, *eigenValues, *catRate, baseRate, length, theRate, tolerance, expLambdaV[64], pInvar=0.0, likeI=0.0, sum=0.0, sum1, sum2, beta, w, v, x, y, expBetaV, expWV, kappa, bigPi, *ptr; CLFlt *nSitesOfPat, *clP, *clA, *tiP, *tiP1, *tiP2, like, like1, like2, CLsum, CLsum1, CLsum2, freq, sumLike1, sumLike2, *lnScaler=NULL, *clInvar=NULL; ModelInfo *m; /* TODO: Standard model (also check RES for ascertainment bias) */ tolerance = 0.001; maxNumIterations = 5; nIterations = 0; do { /* reset f'(v) and f''(v) sums */ sumLike1 = sumLike2 = 0.0; /* cycle over character partitions */ for (d=0; dnRelParts; d++) { division = t->relParts[d]; /* get pointer to model */ m = &modelSettings[division]; /* get number of model states */ n = m->numModelStates; /* find conditional likelihoods */ clP = m->condLikes[m->condLikeIndex[chain][p->index ]]; clA = m->condLikes[m->condLikeIndex[chain][p->anc->index]]; /* get state frequencies */ pi = GetParamSubVals (m->stateFreq, chain, state[chain]); /* get base rate */ theRate = 1.0; baseRate = GetRate (division, chain); /* get category rates */ if (m->shape == NULL) catRate = &theRate; else catRate = GetParamSubVals (m->shape, chain, state[chain]); /* find category frequencies and some additional stuff for the invar model */ if (m->pInvar == NULL) freq = (CLFlt) (1.0 / m->numRateCats); else { /* invariable sites model */ pInvar = * GetParamVals(m->pInvar, chain, state[chain]); freq = (CLFlt) ((1.0 - pInvar) / m->numRateCats); baseRate /= (1.0 - pInvar); clInvar = m->invCondLikes; lnScaler = m->scalers[m->siteScalerIndex[chain]]; } /* find the branch lengths times any correction factor to make them in terms of expected number of substitutions per character */ length = p->length; if (m->dataType == DNA || m->dataType == RNA) { if (m->nucModelId == NUCMODEL_DOUBLET) length *= 2.0; else if (m->nucModelId == NUCMODEL_CODON) length *= 3.0; } /* find nSitesOfPat */ nSitesOfPat = numSitesOfPat + ((chainId[chain] % chainParams.numChains)*numCompressedChars) + m->compCharStart; /* get tiProbs for current length; use the scratch location */ FlipTiProbsSpace(m, chain, p->index); m->TiProbs (p, division, chain); tiP = m->tiProbs[m->tiProbsIndex[chain][p->index]]; FlipTiProbsSpace(m, chain, p->index); /* allocate space for first and second derivatives */ tiP1 = (CLFlt *) SafeCalloc (2*m->tiProbLength, sizeof (CLFlt)); if (!tiP1) return (ERROR); tiP2 = tiP1 + m->tiProbLength; /* calculate first and second derivatives of P(v): P'(v) and P''(v) */ index = 0; if (m->TiProbs == &TiProbs_Fels || m->TiProbs == &TiProbs_Res) { /* calculate beta */ sum = 0.0; for (i=0; inumModelStates; i++) for (j=i+1; jnumModelStates; j++) sum += pi[i]*pi[j]; beta = 0.5 / sum; /* calculate derivatives */ for (k=0; knumRateCats; k++) { v = length * catRate[k] * baseRate; expBetaV = exp (- beta * v); for (i=0; inumModelStates; i++) { for (j=0; jnumModelStates; j++) { if (i == j) { x = - beta * (1.0 - pi[j]) * expBetaV; tiP1[index] = (CLFlt) x; tiP2[index] = (CLFlt) (- beta * x); } else { x = beta * pi[j] * expBetaV; tiP1[index] = (CLFlt) x; tiP2[index] = (CLFlt) (- beta * x); } index++; } } } } else if (m->TiProbs == &TiProbs_Hky) { /* get kappa */ kappa = *GetParamVals (m->tRatio, chain, state[chain]); /* calculate beta */ sum = 0.0; for (i=0; inumModelStates; i++) { for (j=i+1; jnumModelStates; j++) { if (j - i == 2) sum += kappa * pi[i] * pi[j]; else sum += pi[i] * pi[j]; } } beta = 0.5 / sum; /* calculate derivatives */ for (k=0; knumRateCats; k++) { v = length * catRate[k] * baseRate; expBetaV = exp (- beta * v); for (i=0; inumModelStates; i++) { for (j=0; jnumModelStates; j++) { s = (j + 2) % 4; bigPi = pi[j] + pi[s]; w = (1.0 + bigPi * (kappa - 1.0)) * beta; expWV = exp (- w * v); if (i == j) { x = - beta * ((pi[j]/bigPi) - pi[j]) * expBetaV; y = - w * (pi[s]/bigPi) * expWV; tiP1[index] = (CLFlt) (x + y); tiP2[index] = (CLFlt) (- beta * x - w * y); } else if (abs(i-j) == 2) { x = - beta * pi[j] * ((pi[j]/bigPi) - pi[j]) * expBetaV; y = w * (pi[j]/bigPi) * expWV; tiP1[index] = (CLFlt) (x + y); tiP2[index] = (CLFlt) (- beta * x - w * y); } else { x = beta * pi[j] * expBetaV; tiP1[index] = (CLFlt) x; tiP2[index] = (CLFlt) (- beta * x); } index++; } } } } else if (m->TiProbs == &TiProbs_Gen || m->TiProbs == &TiProbs_GenCov) { /* get eigenvalues and cijk pointers */ eigenValues = m->cijks[m->cijkIndex[chain]]; cijk = eigenValues + (2 * n); /* calculate P'(v) and P''(v) */ for (k=0; knumRateCats; k++) { v = length * catRate[k]; for (s=0; s 0.000001) { x = eigenValues[s] * (*ptr++) * expLambdaV[s]; sum1 += x; sum2 += eigenValues[s] * x; } else ptr += n; } tiP1[index] = (CLFlt) ((sum1 < 0.0) ? 0.0 : sum); tiP2[index] = (CLFlt) ((sum2 < 0.0) ? 0.0 : sum); index++; } } } } else if (m->TiProbs == &TiProbs_GenCov) { /* get eigenvalues and cijk pointers */ eigenValues = m->cijks[m->cijkIndex[chain]]; cijk = eigenValues + (2 * n); /* calculate P'(v) and P''(v) */ for (k=0; knumTiCats; k++) { if (m->numRateCats > 1) v = length * catRate[k]; else v = length; for (s=0; s 0.000001) { x = eigenValues[s] * (*cijk++) * expLambdaV[s]; sum1 += x; sum2 += eigenValues[s] * x; } } tiP1[index] = (CLFlt) ((sum1 < 0.0) ? 0.0 : sum); tiP2[index] = (CLFlt) ((sum2 < 0.0) ? 0.0 : sum); index++; } } /* get new eigenvalues and cijks */ eigenValues += m->cijkLength / m->nCijkParts; cijk = eigenValues + 2 * n; } } /* calculate f(v), f'(v) and f''(v) for this partition */ /* note that the scalers are irrelevant because they disappear when we take the derivative of the log likelihood */ if (m->gibbsGamma == YES) { /* find rate category index */ rateCat = m->tiIndex + chain*m->numChars; for (c=0; cnumChars; c++) { like = like1 = like2 = 0.0; r = rateCat[c]; if (r < m->numRateCats) { index = r*m->numModelStates*m->numModelStates; for (j=0; jnumChars; c++) { like = like1 = like2 = 0.0; index = 0; for (k=0; knumTiCats; k++) { for (j=0; jpInvar != NULL) { /* get right like; like1 and like2 not affected */; for (i=0; i 1E-70) { /* forget about like; it is going to be insignificant compared to likeI */ like = (CLFlt) likeI; } else { /* treat likeI as if 0.0, that is, ignore it completely */ like = like + (CLFlt)(0.0); } } else /* take both likeI and like into account */ like = like + (CLFlt) (likeI * exp (-lnScaler[c])); } sumLike1 += nSitesOfPat[c] * (like1 / like); sumLike2 += nSitesOfPat[c] * (like2 * like - like1 * like1) / (like * like); } } free (tiP1); } vOld = p->length; p->length -= sumLike2 / sumLike1; nIterations++; } while (fabs(p->length - vOld) > tolerance && nIterations < maxNumIterations); return (NO_ERROR); } #endif void NodeToNodeDistances (Tree *t, TreeNode *fromNode) { int i; TreeNode *p; /* set all distances to 0.0 and also set marks on all nodes to NO */ for (i=0; inNodes; i++) { p = t->allDownPass[i]; p->x = NO; p->d = 0.0; } /* find distances, and mark path, below "fromNode" */ for (i=0; inNodes; i++) { p = t->allDownPass[i]; if (p == fromNode) { p->x = YES; } if (p->left != NULL && p->right != NULL && p->anc != NULL) { if (p->left->x == YES) { p->x = YES; p->d = p->left->d + p->left->length; } else if (p->right->x == YES) { p->x = YES; p->d = p->right->d + p->right->length; } } } /* find all other distances */ for (i=t->nNodes-1; i>=0; i--) { p = t->allDownPass[i]; if (p->anc == NULL) { if (p == fromNode) p->d = 0.0; else p->d = p->left->d + p->left->length; } else { if (p->x == NO) { p->d = p->anc->d + p->length; } } } } int NumCppEvents (Param *p, int chain) { int i, *nEvents, sumEvents; nEvents = p->nEvents[2*chain+state[chain]]; sumEvents = 0; for (i=0; i<2*numLocalTaxa-2; i++) sumEvents += nEvents[i]; return (sumEvents); } /*---------------------------------------------------------------------- | | OpenNewMBPrintFile: Open a file the first time for printing | ------------------------------------------------------------------------*/ FILE *OpenNewMBPrintFile (char *fileName) { int overWrite; FILE *fp; /* Open file, use noWarn to determine if the user should be prompted to have the file over-written or appended. */ if (noWarn == YES) { /* overwrite or append file, if already present */ if ((fp = TestOpenTextFileR(fileName)) != NULL) { SafeFclose (&fp); if (autoOverwrite == NO) { MrBayesPrint ("%s Appending to file \"%s\"\n", spacer, fileName); return (OpenTextFileA(fileName)); } else MrBayesPrint ("%s Overwriting file \"%s\"\n", spacer, fileName); } return (OpenTextFileW(fileName)); } else { /* prompt user if file is already present */ if ((fp = TestOpenTextFileR(fileName)) != NULL) { SafeFclose (&fp); MrBayesPrint ("\n"); MrBayesPrint ("%s File \"%s\" already exists\n", spacer, fileName); overWrite = WantTo ("Overwrite information in this file"); if (overWrite == YES) { MrBayesPrint ("%s Overwriting file \"%s\"\n", spacer, fileName); return (OpenTextFileW(fileName)); } else { MrBayesPrint ("%s Appending to file \"%s\"\n", spacer, fileName); return (OpenTextFileA(fileName)); } } else { /* file is not present */ return (OpenTextFileW(fileName)); } } } int PickProposal (RandLong *seed, int chainIndex) { MrBFlt ran; int i; ran = RandomNumber(seed); for (i=0; usedMoves[i]->cumProposalProb[chainIndex] <= ran; i++); return i; } /* Calculate positive selection probabilities */ int PosSelProbs (TreeNode *p, int division, int chain) { int c, j, k, nStates; MrBFlt catLike, *like, *bs, *omegaCatFreq, *omega, posProb, *ps, sum; CLFlt **clP; ModelInfo *m; /* find model partition */ m = &modelSettings[division]; /* allocate space for conditional likelihood pointer array and site likelihood array */ clP = (CLFlt **) calloc (m->numOmegaCats, sizeof(CLFlt *)); like = (MrBFlt *) calloc (m->numOmegaCats, sizeof(MrBFlt)); if (!clP || !like) { MrBayesPrint ("%s ERROR: Out of memory in PosSelProbs\n", spacer); free (clP); free (like); return (ERROR); } /* number of states */ nStates = m->numModelStates; /* find conditional likelihoods */ clP[0] = m->condLikes[m->condLikeIndex[chain][p->index]]; for (k=1; knumOmegaCats; k++) clP[k] = clP[0] + k*m->numModelStates*m->numChars; /* find base frequencies */ bs = GetParamSubVals (m->stateFreq, chain, state[chain]); /* find category frequencies */ omegaCatFreq = GetParamSubVals (m->omega, chain, state[chain]); /* get category omegas */ omega = GetParamVals (m->omega, chain, state[chain]); /* find posSelProbs */ ps = posSelProbs + m->compCharStart; for (c=0; cnumChars; c++) { sum = 0.0; for (k=0; knumOmegaCats; k++) { like[k] = 0.0; catLike = 0.0; for (j=0; jnumOmegaCats; k++) { if (omega[k] > 1.0) posProb += like[k] / sum; } ps[c] = posProb; } free (clP); free (like); return NO_ERROR; } #if defined (SSE_ENABLED) /* Calculate positive selection probabilities (SSE version) */ int PosSelProbs_SSE (TreeNode *p, int division, int chain) { int i, c1, c2, j, k, nStates; CLFlt **catLike, *siteLike; MrBFlt *bs, *omegaCatFreq, *omega, posProb, *ps; __m128 m1, m2, *clPtr, **clP, mSiteLike, *mCatLike; ModelInfo *m; /* find model partition */ m = &modelSettings[division]; /* number of states */ nStates = m->numModelStates; /* find base frequencies */ bs = GetParamSubVals (m->stateFreq, chain, state[chain]); /* find category frequencies */ omegaCatFreq = GetParamSubVals (m->omega, chain, state[chain]); /* get category omegas */ omega = GetParamVals (m->omega, chain, state[chain]); /* allocate space for category likelihood arrays */ catLike = (CLFlt **) calloc (m->numOmegaCats, sizeof(CLFlt *)); mCatLike = (__m128 *) calloc (m->numOmegaCats, sizeof(__m128)); if (!catLike || !mCatLike) { MrBayesPrint ("%s ERROR: Out of memory in PosSelProbs_SSE\n", spacer); free (catLike); free (mCatLike); return (ERROR); } /* find conditional likelihood pointers */ clPtr = (__m128 *) m->condLikes[m->condLikeIndex[chain][p->index]]; clP = m->clP_SSE; for (k=0; knumOmegaCats; k++) { clP[k] = clPtr; clPtr += m->numVecChars * nStates; catLike[k] = (CLFlt *) (&(mCatLike[k])); } siteLike = (CLFlt *) (&mSiteLike); /* find posSelProbs */ ps = posSelProbs + m->compCharStart; for (c1=c2=0; c1numVecChars; c1++) { mSiteLike = _mm_setzero_ps (); for (k=0; knumOmegaCats; k++) { mCatLike[k] = _mm_setzero_ps(); m1 = _mm_setzero_ps (); for (j=0; jnumFloatsPerVec && c2 < m->numChars; ++i, ++c2) { posProb = 0.0; for (k=0; knumOmegaCats; k++) { if (omega[k] > 1.0) { posProb += catLike[k][i] / siteLike[i]; } } ps[c2] = posProb; } } free (catLike); free (mCatLike); return NO_ERROR; } #endif /* Calculate omega values for each site */ int SiteOmegas (TreeNode *p, int division, int chain) { int c, j, k, nStates; MrBFlt catLike, *like, *bs, *omegaCatFreq, *omega, siteOmega, *ps, sum; CLFlt **clP; ModelInfo *m; /* find model partition */ m = &modelSettings[division]; /* allocate space for conditional likelihood pointer array and site likelihood array */ clP = (CLFlt **) calloc (m->numOmegaCats, sizeof(CLFlt *)); like = (MrBFlt *) calloc (m->numOmegaCats, sizeof(MrBFlt)); if (!clP || !like) { MrBayesPrint ("%s ERROR: Out of memory in SiteOmegas\n", spacer); free (clP); free (like); return (ERROR); } /* number of states */ nStates = m->numModelStates; /* find conditional likelihoods */ clP[0] = m->condLikes[m->condLikeIndex[chain][p->index]]; for (k=1; knumOmegaCats; k++) clP[k] = clP[0] + k*m->numModelStates*m->numChars; /* find base frequencies */ bs = GetParamSubVals (m->stateFreq, chain, state[chain]); /* find category frequencies */ omegaCatFreq = GetParamSubVals (m->omega, chain, state[chain]); /* get category omegas */ omega = GetParamVals (m->omega, chain, state[chain]); /* find site omegas (using posSelProbs space) */ ps = posSelProbs + m->compCharStart; for (c=0; cnumChars; c++) { sum = 0.0; for (k=0; knumOmegaCats; k++) { like[k] = 0.0; catLike = 0.0; for (j=0; jnumOmegaCats; k++) { siteOmega += (like[k]/sum) * omega[k]; } ps[c] = siteOmega; } free (clP); free (like); return NO_ERROR; } #if defined (SSE_ENABLED) /* Calculate omega values for each site (SSE version) */ int SiteOmegas_SSE (TreeNode *p, int division, int chain) { int i, c1, c2, j, k, nStates; CLFlt **catLike, *siteLike; MrBFlt *bs, *omegaCatFreq, *omega, siteOmega, *ps; __m128 m1, m2, *clPtr, **clP, mSiteLike, *mCatLike; ModelInfo *m; /* find model partition */ m = &modelSettings[division]; /* number of states */ nStates = m->numModelStates; /* find base frequencies */ bs = GetParamSubVals (m->stateFreq, chain, state[chain]); /* find category frequencies */ omegaCatFreq = GetParamSubVals (m->omega, chain, state[chain]); /* get category omegas */ omega = GetParamVals (m->omega, chain, state[chain]); /* allocate space for category likelihood arrays */ catLike = (CLFlt **) calloc (m->numOmegaCats, sizeof(CLFlt *)); mCatLike = (__m128 *) calloc (m->numOmegaCats, sizeof(__m128)); if (!catLike || !mCatLike) { MrBayesPrint ("%s ERROR: Out of memory in SiteOmegas_SSE\n", spacer); free (catLike); free (mCatLike); return (ERROR); } /* find conditional likelihood pointers */ clPtr = (__m128 *) m->condLikes[m->condLikeIndex[chain][p->index]]; clP = m->clP_SSE; for (k=0; knumOmegaCats; k++) { clP[k] = clPtr; clPtr += m->numVecChars * nStates; catLike[k] = (CLFlt *) (&(mCatLike[k])); } siteLike = (CLFlt *) (&mSiteLike); /* find site omegas (using posSelProbs space) */ ps = posSelProbs + m->compCharStart; for (c1=c2=0; c1numVecChars; c1++) { mSiteLike = _mm_setzero_ps (); for (k=0; knumOmegaCats; k++) { mCatLike[k] = _mm_setzero_ps(); m1 = _mm_setzero_ps (); for (j=0; jnumFloatsPerVec && c2 < m->numChars; ++i, ++c2) { siteOmega = 0.0; for (k=0; knumOmegaCats; k++) { siteOmega += (catLike[k][i] / siteLike[i]) * omega[k]; } ps[c2] = siteOmega; } } free (catLike); free (mCatLike); return NO_ERROR; } #endif /*---------------------------------------------------------------------- | | PreparePrintFiles: Prepare .t, .p, and .mcmc files for printing | ------------------------------------------------------------------------*/ int PreparePrintFiles (void) { int i, n, previousResults, oldAutoOverwrite, oldNoWarn; char localFileName[100], fileName[220], bkupName[220]; FILE *tempFile; #if defined (MPI_ENABLED) if (proc_id != 0) return (NO_ERROR); #endif oldNoWarn = noWarn; oldAutoOverwrite = autoOverwrite; /* Allocate space for file pointers */ if (memAllocs[ALLOC_FILEPOINTERS] == YES) { MrBayesPrint ("%s File pointers already allocated in PreparePrintFiles\n", spacer); return ERROR; } fpMcmc = NULL; fpSS = NULL; fpParm = NULL; fpTree = NULL; fpParm = (FILE **) SafeCalloc (chainParams.numRuns, sizeof (FILE *)); if (fpParm == NULL) { MrBayesPrint ("%s Could not allocate fpParm in PreparePrintFiles\n", spacer); return ERROR; } memAllocs[ALLOC_FILEPOINTERS] = YES; fpTree = (FILE ***) SafeCalloc (chainParams.numRuns, sizeof (FILE **)); if (fpTree == NULL) { MrBayesPrint ("%s Could not allocate fpTree in PreparePrintFiles\n", spacer); return ERROR; } fpTree[0] = (FILE **) SafeCalloc (numTrees*chainParams.numRuns, sizeof (FILE *)); if (fpTree[0] == NULL) { MrBayesPrint ("%s Could not allocate fpTree[0] in PreparePrintFiles\n", spacer); return ERROR; } for (i=1; i 1 && chainParams.numRuns == 1) sprintf (fileName, "%s.tree%d.t", localFileName, i+1); else if (numTrees == 1 && chainParams.numRuns > 1) sprintf (fileName, "%s.run%d.t", localFileName, n+1); else sprintf (fileName, "%s.tree%d.run%d.t", localFileName, i+1, n+1); if ((tempFile = TestOpenTextFileR(fileName)) != NULL) { fclose(tempFile); previousResults = YES; } } } if (previousResults == YES) { MrBayesPrint("\n"); MrBayesPrint("%s There are results from a previous run saved using the same filename(s).\n", spacer); if (WantTo("Do you want to overwrite these results") == YES) { MrBayesPrint("\n"); noWarn = YES; autoOverwrite = YES; } else { MrBayesPrint("\n"); MrBayesPrint("%s Please specify a different file name before running the MCMC analysis.\n", spacer); MrBayesPrint("%s You can do that using 'Mcmc filename='. You can also move or\n", spacer); MrBayesPrint("%s rename the old result files.\n", spacer); return ERROR; } } } /* Prepare the .mcmc file */ if (chainParams.mcmcDiagn == YES) { sprintf (fileName, "%s.mcmc", chainParams.chainFileName); if ((fpMcmc = OpenNewMBPrintFile (fileName)) == NULL) { noWarn = oldNoWarn; autoOverwrite = oldAutoOverwrite; return (ERROR); } } /* Prepare the .p and .t files */ for (n=0; n 1 && chainParams.numRuns == 1) sprintf (fileName, "%s.tree%d.t", localFileName, i+1); else if (numTrees == 1 && chainParams.numRuns > 1) sprintf (fileName, "%s.run%d.t", localFileName, n+1); else sprintf (fileName, "%s.tree%d.run%d.t", localFileName, i+1, n+1); if ((fpTree[n][i] = OpenNewMBPrintFile (fileName)) == NULL) { noWarn = oldNoWarn; autoOverwrite = oldAutoOverwrite; return (ERROR); } } } /* Prepare the .ss file */ if (chainParams.isSS == YES) { sprintf (fileName, "%s.ss", chainParams.chainFileName); if ((fpSS = OpenNewMBPrintFile (fileName)) == NULL) { noWarn = oldNoWarn; autoOverwrite = oldAutoOverwrite; return (ERROR); } } /* Remove previous chekpoint file if present */ sprintf (fileName, "%s%s.ckp", workingDir, chainParams.chainFileName); strcpy (bkupName, fileName); strcat (bkupName, "~"); remove (bkupName); rename (fileName, bkupName); # if defined (PRINT_DUMP) fpDump = (FILE **) SafeCalloc (chainParams.numRuns, sizeof (FILE *)); for (n=0; nstateFreq, chain, state[chain]); /* find frequencies of rate categories */ freq = 1.0 / m->numRateCats; /* find the conditional likelihoods from the final pass */ clFP = m->condLikes[m->condLikeScratchIndex[p->index]]; clP = m->clP; for (k=0; knumRateCats; k++) { clP[k] = clFP; clFP += m->numChars * m->numModelStates; } /* find the preallocated working space */ cL = m->ancStateCondLikes; /* cycle over the compressed characters */ for (c=0; cnumChars; c++) { cL[0] = cL[1] = 0.0; for (k=0; knumRateCats; k++) { cL[0] += clP[k][0]; cL[1] += clP[k][1]; clP[k]+=2; } cL[0] *= (CLFlt) (bs[0] * freq); cL[1] *= (CLFlt) (bs[1] * freq); sum = cL[0] + cL[1]; assert (cL[0]==cL[0]); assert (cL[1]==cL[1]); assert (sum<9999999999999999999999999999999.0); cL[0] /= sum; cL[1] /= sum; assert (cL[0]==cL[0]); assert (cL[1]==cL[1]); cL += 2; } /* print the resulting conditional likelihoods cycling over uncompressed chars */ for (c=0; ccompCharStart; cL = m->ancStateCondLikes + (i*2); SafeSprintf (&tempStr, &tempStrSize, "\t%s", MbPrintNum(cL[0])); if (AddToPrintString (tempStr) == ERROR) return (ERROR); SafeSprintf (&tempStr, &tempStrSize, "\t%s", MbPrintNum(cL[1])); if (AddToPrintString (tempStr) == ERROR) return (ERROR); } free (tempStr); return NO_ERROR; } /*---------------------------------------------------------------- | | PrintAncStates_Gen: print ancestral states after final pass | General model with or without rate variation | -----------------------------------------------------------------*/ int PrintAncStates_Gen (TreeNode *p, int division, int chain) { int c, i, k, nStates, hasPInvar, nGammaCats; const int *rateCat; MrBFlt *bsVals; CLFlt *cL, sum, pInvar=0.0, freq, f, bs[64]; const CLFlt *clFP, *clInvar=NULL, *lnScaler, **clP; char *tempStr, *printedChar; int tempStrSize = TEMPSTRSIZE; ModelInfo *m; tempStr = (char *) SafeMalloc((size_t)tempStrSize * sizeof(char)); if (!tempStr) { MrBayesPrint ("%s Problem allocating tempString (%d)\n", spacer, tempStrSize * sizeof(char)); return (ERROR); } if (!strcmp(modelParams[division].nucModel,"Codon") || !strcmp(modelParams[division].nucModel,"Protein") || !strcmp(modelParams[division].nucModel,"Doublet")) { assert (modelParams[division].dataType == DNA || modelParams[division].dataType == RNA); /* Note that we can have matrix with Protein datatype which is not and should not be covered here */ printedChar = (char *) SafeMalloc (numChar*sizeof(char)); } else { printedChar = NULL; } /* find model settings for this division */ m = &modelSettings[division]; /* find number of states */ nStates = m->numModelStates; /* find state frequencies */ bsVals = GetParamSubVals (m->stateFreq, chain, state[chain]); for (i=0; ipInvar == NULL) hasPInvar = NO; else { hasPInvar = YES; clInvar = m->invCondLikes; pInvar = (CLFlt) *GetParamVals (m->pInvar, chain, state[chain]); } /* find number of rate categories */ nGammaCats = m->numRateCats; /* find frequencies of rate categories (only relevant if gibbsGamma == NO) */ freq = ((CLFlt)1.0 - pInvar) / nGammaCats; /* find site scaler */ lnScaler = m->scalers[m->siteScalerIndex[chain]]; /* find rate category index */ rateCat = m->tiIndex + chain*m->numChars; /* find the conditional likelihoods from the final pass */ clFP = m->condLikes[m->condLikeScratchIndex[p->index]]; /* find the preallocated working space */ cL = m->ancStateCondLikes; /* cycle over the compressed characters */ if (m->gibbsGamma == YES) { for (c=0; cnumChars; c++) { sum = 0.0; if (rateCat[c] < nGammaCats) { for (i=0; iclP; for (k=0; knumRateCats; k++) { clP[k] = clFP; clFP += m->numChars * m->numModelStates; } for (c=0; cnumChars; c++) { for (i=0; i 0) { sum = 0.0; for (i=0; i 0.0) { if (lnScaler[c] < -100.0) { /* only invar cond likes are relevant */ for (i=0; i 0.0); for (i=0; icompCharStart; cL = m->ancStateCondLikes + (i*nStates); if (printedChar) { for (i=c+1; istateFreq, chain, state[chain]); bs[A] = (CLFlt) bsVals[A]; bs[C] = (CLFlt) bsVals[C]; bs[G] = (CLFlt) bsVals[G]; bs[T] = (CLFlt) bsVals[T]; /* find invar cond likes */ if (m->pInvar == NULL) hasPInvar = NO; else { hasPInvar = YES; clInvar = m->invCondLikes; pInvar = (CLFlt) *GetParamVals (m->pInvar, chain, state[chain]); } /* find number of rate categories */ nGammaCats = m->numRateCats; /* find frequencies of rate categories (only relevant if gibbsGamma == NO) */ if (hasPInvar == NO) freq = (CLFlt) 1.0 / nGammaCats; else freq = ((CLFlt)1.0 - pInvar) / nGammaCats; /* find site scaler */ lnScaler = m->scalers[m->siteScalerIndex[chain]]; /* find rate category index */ rateCat = m->tiIndex + chain*m->numChars; /* find the conditional likelihoods from the final pass */ clFP = m->condLikes[m->condLikeScratchIndex[p->index]]; /* find the preallocated working space */ cL = m->ancStateCondLikes; /* cycle over the compressed characters */ if (m->gibbsGamma == YES) { for (c=0; cnumChars; c++) { if (rateCat[c] < nGammaCats) { cL[A] = clFP[A] * (CLFlt) bs[A]; cL[C] = clFP[C] * (CLFlt) bs[C]; cL[G] = clFP[G] * (CLFlt) bs[G]; cL[T] = clFP[T] * (CLFlt) bs[T]; } else { cL[A] = clInvar[A] * (CLFlt) bs[A]; cL[C] = clInvar[C] * (CLFlt) bs[C]; cL[G] = clInvar[G] * (CLFlt) bs[G]; cL[T] = clInvar[T] * (CLFlt) bs[T]; } sum = cL[A] + cL[C] + cL[G] + cL[T]; cL[A] /= sum; cL[C] /= sum; cL[G] /= sum; cL[T] /= sum; clInvar += 4; clFP += 4; cL += 4; } } else { /* find conditional likelihood pointers */ clP = (const CLFlt**)m->clP; for (k=0; knumRateCats; k++) { clP[k] = clFP; clFP += m->numChars * m->numModelStates; } for (c=0; cnumChars; c++) { cL[A] = cL[C] = cL[G] = cL[T] = 0.0; for (k=0; k 0.0) { if (lnScaler[c] < -100.0) { /* only invar cond likes are relevant */ cL[A] = clInvar[A] * bs[A] * pInvar; cL[C] = clInvar[C] * bs[C] * pInvar; cL[G] = clInvar[G] * bs[G] * pInvar; cL[T] = clInvar[T] * bs[T] * pInvar; } else { f = (CLFlt)exp(lnScaler[c]) * freq; cL[A] = clInvar[A] * bs[A] * pInvar + cL[A] * f; cL[C] = clInvar[C] * bs[C] * pInvar + cL[C] * f; cL[G] = clInvar[G] * bs[G] * pInvar + cL[G] * f; cL[T] = clInvar[T] * bs[T] * pInvar + cL[T] * f; } } clInvar += 4; } sum = cL[A] + cL[C] + cL[G] + cL[T]; cL[A] /= sum; cL[C] /= sum; cL[G] /= sum; cL[T] /= sum; cL += 4; } } /* print the resulting conditional likelihoods cycling over uncompressed chars */ for (c=0; ccompCharStart; cL = m->ancStateCondLikes + (i*4); SafeSprintf (&tempStr, &tempStrSize, "\t%s", MbPrintNum(cL[A])); if (AddToPrintString (tempStr) == ERROR) return ERROR; SafeSprintf (&tempStr, &tempStrSize, "\t%s", MbPrintNum(cL[C])); if (AddToPrintString (tempStr) == ERROR) return ERROR; SafeSprintf (&tempStr, &tempStrSize, "\t%s", MbPrintNum(cL[G])); if (AddToPrintString (tempStr) == ERROR) return ERROR; SafeSprintf (&tempStr, &tempStrSize, "\t%s", MbPrintNum(cL[T])); if (AddToPrintString (tempStr) == ERROR) return ERROR; } free (tempStr); return NO_ERROR; } /*---------------------------------------------------------------- | | PrintAncStates_Std: print ancestral states after final pass | Standard model with or without rate variation | -----------------------------------------------------------------*/ int PrintAncStates_Std (TreeNode *p, int division, int chain) { int c, i, j, k, s, nStates, numReps; MrBFlt *bsBase, *bs, freq; CLFlt *clFP, *cL, sum, **clP; char *tempStr; int tempStrSize = TEMPSTRSIZE; ModelInfo *m; tempStr = (char *) SafeMalloc((size_t)tempStrSize * sizeof(char)); if (!tempStr) { MrBayesPrint ("%s Problem allocating tempString (%d)\n", spacer, tempStrSize * sizeof(char)); return (ERROR); } /* find model settings for this division */ m = &modelSettings[division]; /* find state frequencies, base index */ bsBase = GetParamStdStateFreqs (m->stateFreq, chain, state[chain]); /* find the conditional likelihoods from the final pass */ clFP = m->condLikes[m->condLikeScratchIndex[p->index]]; numReps=0; for (c=0; cnumChars; c++) { if (m->nStates[c] == 2) numReps += m->numBetaCats * 2; else numReps += m->nStates[c]; } /* find conditional likelihood pointers */ clP = m->clP; for (k=0; knumRateCats; k++) { clP[k] = clFP; clFP += numReps; } /* find the preallocated working space */ cL = m->ancStateCondLikes; /* cycle over the compressed characters */ for (c=0; cnumChars; c++) { nStates = m->nStates[c]; bs = bsBase + m->bsIndex[c]; for (s=0; snumBetaCats * m->numRateCats); for (i=0; inumBetaCats; i++) { for (k=0; knumRateCats; k++) { for (s=0; snumRateCats); for (k=0; knumRateCats; k++) { for (s=0; scompCharStart; for (i=j=0; inStates[i]; cL = m->ancStateCondLikes + j; for (i=0; inStates[k]; i++) { SafeSprintf (&tempStr, &tempStrSize, "\t%s", MbPrintNum(cL[i])); if (AddToPrintString (tempStr) == ERROR) return (ERROR); } } free (tempStr); return NO_ERROR; } /*---------------------------------------------------------------- | | PrintAncStates_Cont: print ancestral states of continuous | characters with or without rate variation | -----------------------------------------------------------------*/ int PrintAncStates_Cont (TreeNode *p, int division, int chain) { int c, k, i; CLFlt *mDP, *vL, *vR, *mL, *mR, *mUp, *vUp, m_l, m_r; MrBFlt m_d, v_d, m_u, v_u, m_f, v_f, avg, lmax, catLike, siteLike, mFVals[MAX_RATE_CATS], lnCatL[MAX_RATE_CATS]; char *tempStr; int tempStrSize = TEMPSTRSIZE; ModelInfo *m; tempStr = (char *) SafeMalloc((size_t)tempStrSize * sizeof(char)); if (!tempStr) { MrBayesPrint ("%s Problem allocating tempString (%d)\n", spacer, tempStrSize * sizeof(char)); return (ERROR); } /* find model settings for this division */ m = &modelSettings[division]; /* this node's own down-pass estimate (based on its descendants only) */ mDP = m->ancStates[m->condLikeIndex[chain][p->index]]; /* the stored variance also covers this node's own branch, so recompute the descendant-only part (cf. BMVar_Cont) */ vL = m->bmVars[m->tiProbsIndex[chain][p->left->index]]; vR = m->bmVars[m->tiProbsIndex[chain][p->right->index]]; mL = m->ancStates[m->condLikeIndex[chain][p->left->index]]; mR = m->ancStates[m->condLikeIndex[chain][p->right->index]]; /* the "up message" computed by CondLikeUp_Cont: information from everywhere else in the tree, excluding this node's own subtree */ mUp = m->ancStates[m->condLikeScratchIndex[p->index]]; vUp = m->bmVars[m->tiProbsScratchIndex[p->index]]; /* combine the down-pass estimate with the up message (additive, precision-weighted, to avoid the numerical instability of "dividing out" a contribution from an already-combined estimate) to get the final marginal estimate for each rate category, then average the estimates weighted by the posterior probability */ for (c=0; cnumChars; c++) { for (k=0; knumRateCats; k++) { i = k * (m->numChars) + c; m_l = (p->left->left == NULL) ? mL[c] : mL[i]; m_r = (p->right->left == NULL) ? mR[c] : mR[i]; if (IsMissingC(m_l) && IsMissingC(m_r)) v_d = 0.0; else if (IsMissingC(m_l)) v_d = vR[i]; else if (IsMissingC(m_r)) v_d = vL[i]; else v_d = (vL[i] * vR[i]) / (vL[i] + vR[i]); m_d = mDP[i]; m_u = mUp[i]; v_u = vUp[i]; if (IsMissingC((CLFlt)m_d) && IsMissingC((CLFlt)m_u)) m_f = (CLFlt) INT_MAX; else if (IsMissingC((CLFlt)m_d)) m_f = m_u; else if (IsMissingC((CLFlt)m_u)) m_f = m_d; else if (v_d < TIME_MIN && v_u < TIME_MIN) m_f = 0.5 * (m_d + m_u); else if (v_d < TIME_MIN) m_f = m_d; else if (v_u < TIME_MIN) m_f = m_u; else { v_f = v_d * v_u / (v_d + v_u); m_f = v_f * (m_d/v_d + m_u/v_u); } mFVals[k] = m_f; } if (m->numRateCats == 1) { avg = mFVals[0]; } else { lmax = CatLnLike_Cont (division, chain, c, lnCatL); avg = siteLike = 0.0; for (k=0; knumRateCats; k++) { catLike = exp(lnCatL[k] - lmax); siteLike += catLike; avg += catLike * mFVals[k]; } avg /= siteLike; } SafeSprintf (&tempStr, &tempStrSize, "\t%s", MbPrintNum(avg)); if (AddToPrintString (tempStr) == ERROR) return (ERROR); } free (tempStr); return NO_ERROR; } /*----------------------------------------------------------------------- | | PrintCheckPoint: Print checkpoint to file | ------------------------------------------------------------------------*/ int PrintCheckPoint (long long gen) { int i, j, k, k1, nErrors=0, run, chn, nValues, tempStrSize = TEMPSTRSIZE, hasEvents, *intValue, oldPrecision; char bkupFileName[220], oldBkupFileName[220], ckpFileName[220], *tempString=NULL; MrBFlt *value, clockRate; Param *p = NULL, *subParm = NULL; Tree *t; FILE *fp = NULL; MCMCMove *mv; /*ModelInfo *m = NULL;*/ #if defined (MPI_ENABLED) int sumErrors=0,ierror; MrBFlt r, sum; #endif /* use high precision for checkpointing */ oldPrecision = precision; precision = 15; /* allocate tempString */ if ((tempString = (char *) SafeCalloc (tempStrSize, sizeof(char))) == NULL) nErrors++; #if defined (MPI_ENABLED) if (proc_id == 0) { #endif if (nErrors == 0) { /* figure out check-point file names */ sprintf(ckpFileName, "%s.ckp", chainParams.chainFileName); strcpy (bkupFileName, ckpFileName); strcat (bkupFileName, "~"); strcpy (oldBkupFileName, bkupFileName); strcat (oldBkupFileName, "~"); /* shift check-point files */ remove (oldBkupFileName); rename (bkupFileName, oldBkupFileName); rename (ckpFileName, bkupFileName); /* create new ckp file */ if ((fp = OpenTextFileW (ckpFileName)) == NULL) { MrBayesPrint ("%s Problem opening checkpoint file\n", spacer); nErrors++; } } #if defined (MPI_ENABLED) } /* end of if (proc_id == 0)*/ #endif ERROR_TEST2("",free(tempString),return(ERROR)); /* write file header */ MrBayesPrintf (fp, "#NEXUS\n[ID: %s]\n[generation: %lli]\n", stamp, gen); MrBayesPrintf (fp, "[seed: %lli, swapseed: %lli]\n", globalSeed, swapSeed); if (chainParams.isSS == YES) { /* dump to .ckp file current step contribution */ MrBayesPrintf (fp, "[SsAcumulators:"); # if defined (MPI_ENABLED) for (j=0; j 0) { MrBayesPrint ("%s Memory allocation error on at least one processor\n", spacer); free (tempString); return ERROR; } # else if (nErrors > 0) { free (tempString); return ERROR; } # endif */ /* print trees (but not species trees) */ for (i=0; iparamType != P_BRLENS && p->paramType != P_TOPOLOGY) continue; if (p->paramType == P_TOPOLOGY && p->subParams[0] != p) continue; hasEvents = NO; for (j=0; jname) == ERROR) nErrors++; if (nErrors == 0 && AddToPrintString (tempString) == ERROR) nErrors++; if (p->paramType == P_BRLENS && p->nSubParams > 0) { for (k=0; knSubParams; k++) { subParm = p->subParams[k]; if (subParm->paramType == P_CPPEVENTS) { hasEvents = YES; if (SafeSprintf (&tempString, &tempStrSize, " [&E %s]", subParm->name) == ERROR) nErrors++; if (nErrors == 0 && AddToPrintString (tempString) == ERROR) nErrors++; } if (nErrors == 0 && (subParm->paramType == P_CPPEVENTS || subParm->paramType == P_TK02BRANCHRATES || subParm->paramType == P_IGRBRANCHRATES || subParm->paramType == P_ILNBRANCHRATES || subParm->paramType == P_MIXEDBRCHRATES || subParm->paramType == P_WNBRANCHRATES)) { if (SafeSprintf (&tempString, &tempStrSize, " [&B %s]", subParm->name) == ERROR) nErrors++; if (nErrors == 0 && AddToPrintString (tempString) == ERROR) nErrors++; } } } if (t->isRooted == YES && t->isClock == NO) SafeSprintf (&tempString, &tempStrSize, " = [&D] "); else if (t->isRooted == YES && t->isClock == YES) { clockRate = *GetParamVals(modelSettings[p->relParts[0]].clockRate, j, state[j]); SafeSprintf (&tempString, &tempStrSize, " = [&R] [&clockrate = %s] ", MbPrintNum(clockRate)); } else /* if (t->isRooted == NO) */ SafeSprintf (&tempString, &tempStrSize, " = "); if (nErrors == 0 && AddToPrintString (tempString) == ERROR) nErrors++; /* write the tree in (extended) Newick format */ if (nErrors == 0) { if (p->paramType == P_TOPOLOGY) WriteNoEvtTreeToPrintString (t->root->left, j, p, NO, t->isRooted); else if (hasEvents == NO) WriteNoEvtTreeToPrintString (t->root->left, j, p, YES, t->isRooted); else WriteEventTreeToPrintString (t->root->left, j, p, YES); if (AddToPrintString (";\n") == ERROR) nErrors++; } } MrBayesPrintf (fp, "%s", printString); #if defined (MPI_ENABLED) MPI_Allreduce (&nErrors, &sumErrors, 1, MPI_INT, MPI_SUM, MPI_COMM_WORLD); if (sumErrors > 0) { MrBayesPrint ("%s Print error on at least one processor\n", spacer); goto errorExit; } if (PrintMPISlaves(fp) == ERROR) goto errorExit; #else if (nErrors > 0) goto errorExit; #endif strcpy (printString, ""); strcpy (tempString, ""); } MrBayesPrintf (fp, "end;\n\n"); /* print species trees */ if (strcmp(modelParams[0].topologyPr,"Speciestree") == 0) { /* get the first species tree */ for (i=0; iparamType == P_SPECIESTREE) break; } t = GetTree(p, 0, state[0]); #if defined (MPI_ENABLED) if (proc_id == 0) { #endif /* write the block header and translate block */ MrBayesPrintf (fp, "\nbegin trees;\n"); PrintTranslateBlock (fp, t); #if defined (MPI_ENABLED) } #endif for (j=0; jname) == ERROR) nErrors++; if (nErrors == 0 && AddToPrintString (tempString) == ERROR) nErrors++; subParm = modelSettings[p->relParts[0]].popSize; if (subParm->nValues > 1) { if (SafeSprintf (&tempString, &tempStrSize, " [&N %s]", subParm->name) == ERROR) nErrors++; if (nErrors == 0 && AddToPrintString (tempString) == ERROR) nErrors++; } clockRate = *GetParamVals(modelSettings[p->relParts[0]].clockRate, j, state[j]); SafeSprintf (&tempString, &tempStrSize, " = [&R] [&clockrate = %s] ", MbPrintNum(clockRate)); if (nErrors == 0 && AddToPrintString (tempString) == ERROR) nErrors++; /* write the tree in (extended) Newick format */ if (nErrors == 0) { WriteNoEvtTreeToPrintString (t->root->left, j, p, YES, t->isRooted); if (AddToPrintString (";\n") == ERROR) nErrors++; } } MrBayesPrintf (fp, "%s", printString); #if defined (MPI_ENABLED) MPI_Allreduce (&nErrors, &sumErrors, 1, MPI_INT, MPI_SUM, MPI_COMM_WORLD); if (sumErrors > 0) { MrBayesPrint ("%s Print error on at least one processor\n", spacer); goto errorExit; } if (PrintMPISlaves(fp) == ERROR) goto errorExit; #else if (nErrors > 0) goto errorExit; #endif strcpy (printString, ""); strcpy (tempString, ""); MrBayesPrintf (fp, "end;\n\n"); } /* start startvals block */ MrBayesPrintf (fp,"begin mrbayes;\n"); MrBayesPrintf (fp, "\tstartvals\n"); /* first print topology values */ for (i=0; iparamType == P_TOPOLOGY) { for (j=0; jname, run, chn, t->name) == ERROR) nErrors++; if (nErrors == 0 && AddToPrintString (tempString) == ERROR) nErrors++; } MrBayesPrintf (fp, "%s", printString); # if defined (MPI_ENABLED) MPI_Allreduce (&nErrors, &sumErrors, 1, MPI_INT, MPI_SUM, MPI_COMM_WORLD); if (sumErrors > 0) { MrBayesPrint ("%s Print error on at least one processor\n", spacer); goto errorExit; } if (PrintMPISlaves(fp) == ERROR) goto errorExit; # else if (nErrors > 0) goto errorExit; # endif strcpy (printString, ""); strcpy (tempString, ""); } } /* now print branch lengths and relaxed clock parameters */ for (i=0; iparamType == P_BRLENS) { for (j=0; jname, run, chn, t->name) == ERROR) nErrors++; if (nErrors == 0 && AddToPrintString (tempString) == ERROR) nErrors++; for (k=0; knSubParams; k++) { if (nErrors == 0 && SafeSprintf (&tempString, &tempStrSize, "\t\t%s(%d,%d)=%s\n", p->subParams[k]->name, run, chn, t->name) == ERROR) nErrors++; if (nErrors == 0 && AddToPrintString (tempString) == ERROR) nErrors++; } } MrBayesPrintf (fp, "%s", printString); # if defined (MPI_ENABLED) MPI_Allreduce (&nErrors, &sumErrors, 1, MPI_INT, MPI_SUM, MPI_COMM_WORLD); if (sumErrors > 0) { MrBayesPrint ("%s Print error on at least one processor\n", spacer); goto errorExit; } if (PrintMPISlaves(fp) == ERROR) goto errorExit; # else if (nErrors > 0) goto errorExit; # endif strcpy (printString, ""); strcpy (tempString, ""); } } /* now print species tree and population size parameters */ for (i=0; iparamType == P_SPECIESTREE) { for (j=0; jname, run, chn, t->name) == ERROR) nErrors++; if (nErrors == 0 && AddToPrintString (tempString) == ERROR) nErrors++; if (modelSettings[p->relParts[0]].popSize->nValues > 1) if (nErrors == 0 && SafeSprintf (&tempString, &tempStrSize, "\t\t%s(%d,%d)=%s\n", modelSettings[p->relParts[0]].popSize->name, run, chn, t->name) == ERROR) nErrors++; if (nErrors == 0 && AddToPrintString (tempString) == ERROR) nErrors++; } MrBayesPrintf (fp, "%s", printString); # if defined (MPI_ENABLED) MPI_Allreduce (&nErrors, &sumErrors, 1, MPI_INT, MPI_SUM, MPI_COMM_WORLD); if (sumErrors > 0) { MrBayesPrint ("%s Print error on at least one processor\n", spacer); goto errorExit; } if (PrintMPISlaves(fp) == ERROR) goto errorExit; # else if (nErrors > 0) goto errorExit; # endif strcpy (printString, ""); strcpy (tempString, ""); } } /* now print param values */ for (i=0; iparamType == P_PI && modelSettings[p->relParts[0]].dataType != STANDARD) { value = GetParamSubVals (p, j, state[j]); nValues = p->nSubValues; } else { value = GetParamVals (p, j, state[j]); nValues = p->nValues; } if (nErrors == 0 && SafeSprintf (&tempString, &tempStrSize, "\t\t%s(%d,%d)=(%.15le", p->name, run, chn, value[0]) == ERROR) nErrors++; if (nErrors == 0 && AddToPrintString (tempString) == ERROR) nErrors++; for (k=1; knIntValues > 0) { intValue = GetParamIntVals (p, j, state[j]); nValues = p->nIntValues; for (k=0; knSympi > 0) { value = GetParamStdStateFreqs (p, j, state[j]); if (p->hasBinaryStd == YES) value += 2 * modelSettings[p->relParts[0]].numBetaCats; for (k=0; knSympi; k++) { for (k1=0; k1sympinStates[k]; k1++) { if (nErrors==0 && SafeSprintf (&tempString, &tempStrSize, ",%.15le", *value++) == ERROR) nErrors++; if (nErrors == 0 && AddToPrintString (tempString) == ERROR) nErrors++; } } } /* print extra params for omega */ if (p->paramType == P_OMEGA) { value = GetParamSubVals (p, j, state[j]); nValues = p->nSubValues/2; for (k=0; k 0) { MrBayesPrint ("%s Print error on at least one processor\n", spacer); goto errorExit; } if (PrintMPISlaves(fp) == ERROR) goto errorExit; #else if (nErrors > 0) goto errorExit; #endif strcpy (printString, ""); strcpy (tempString, ""); } /* end startvals statement */ MrBayesPrintf (fp, "\t;\n"); /* print tuning parameters of moves */ MrBayesPrintf (fp, "\tpropset\n"); for (i=0; imoveType->Autotune == NULL) continue; /* tuning parameter(s) do not change */ for (j=0; j$(,)= */ if (nErrors == 0 && SafeSprintf (&tempString, &tempStrSize, "\t\t%s$%s(%d,%d)=%.15le\n", mv->name, mv->moveType->shortTuningName[0], run, chn, mv->tuningParam[chainId[j]][0]) == ERROR) nErrors++; if (nErrors == 0 && AddToPrintString (tempString) == ERROR) nErrors++; } MrBayesPrintf (fp, "%s", printString); #if defined (MPI_ENABLED) MPI_Allreduce (&nErrors, &sumErrors, 1, MPI_INT, MPI_SUM, MPI_COMM_WORLD); if (sumErrors > 0) { MrBayesPrint ("%s Print error on at least one processor\n", spacer); goto errorExit; } if (PrintMPISlaves(fp) == ERROR) goto errorExit; #else if (nErrors > 0) goto errorExit; #endif strcpy (printString, ""); strcpy (tempString, ""); } /* end propset statement */ MrBayesPrintf (fp, "\t;\n"); /* end mrbayes block */ MrBayesPrintf (fp, "end;\n\n"); /* change precision back */ precision = oldPrecision; SafeFclose (&fp); free (tempString); free (printString); printStringSize = 0; return (NO_ERROR); errorExit: precision = oldPrecision; free (tempString); free (printString); printString = NULL; printStringSize = 0; SafeFclose (&fp); return (ERROR); } /*---------------------------------------------------------------------- | | PrintMCMCDiagnosticsToFile: Print acceptance ratios, swapping | frequencies, and convergence diagnostics to file. | ------------------------------------------------------------------------*/ int PrintMCMCDiagnosticsToFile (long long curGen) { int i, j, n; MCMCMove *theMove; char *diagnstat; /* Simply print header if curGen == 0 */ if (curGen == 0) { // MrBayesPrintf (fpMcmc, "[LEGEND:]\n"); MrBayesPrintf (fpMcmc, "[ID: %s]\n", stamp); MrBayesPrintf (fpMcmc, "[ Gen -- Generation]\n"); if (chainParams.allChains == YES) MrBayesPrintf (fpMcmc, "[ ()$acc_run2_chn3 -- Acceptance rate of move changing parameter in run 2, chain 3]\n"); else /* if (chainParams.allChains == NO) */ MrBayesPrintf (fpMcmc, "[ ()$acc_run2 -- Acceptance rate of move changing parameter 1 in run 2]\n"); if (chainParams.numChains > 1 && chainParams.numRuns > 1) MrBayesPrintf (fpMcmc, "[ Swap(1<>2)$acc_run3 -- Acceptance rate of swaps between chains 1 and 2 in run 3]\n"); else if (chainParams.numChains > 1 && chainParams.numRuns == 1) MrBayesPrintf (fpMcmc, "[ Swap(1<>2)$acc -- Acceptance rate of swaps between chains 1 and 2]\n"); if (chainParams.diagnStat == AVGSTDDEV) diagnstat = "Average"; else diagnstat = "Maximum"; if (chainParams.numRuns > 1 && numTopologies == 1 && chainParams.allComps == NO) MrBayesPrintf (fpMcmc, "[ StdDev(s) -- %s standard deviation of split frequencies]\n", diagnstat); else if (chainParams.numRuns > 1 && numTopologies == 1 && chainParams.allComps == YES) MrBayesPrintf (fpMcmc, "[ StdDev(s)(run1-run2) -- %s standard deviation of split frequencies between runs 1 and 2]\n", diagnstat); else if (chainParams.numRuns > 1 && numTopologies > 1 && chainParams.allComps == NO) MrBayesPrintf (fpMcmc, "[ StdDev(s.tree1) -- %s standard deviation of split frequencies for topology 1]\n", diagnstat); else if (chainParams.numRuns > 1 && numTopologies > 1 && chainParams.allComps == YES) MrBayesPrintf (fpMcmc, "[ StdDev(s.tree1)(run1-run2) -- %s standard deviation of split frequencies between runs 1 and 2 for topology 1]\n", diagnstat); MrBayesPrintf (fpMcmc, "Gen"); for (n=0; nmoveType->shortName, theMove->parm->name, n+1, i+1); if (theMove->moveType->Autotune != NULL && chainParams.autotune == YES) MrBayesPrintf (fpMcmc, "\t%s(%s)$%s_run%d_chn%d", theMove->moveType->shortName, theMove->parm->name, theMove->moveType->shortTuningName[0], n+1, i+1); } } } else { for (i=0; imoveType->shortName, theMove->parm->name); if (theMove->moveType->Autotune != NULL && chainParams.autotune == YES) MrBayesPrintf (fpMcmc, "\t%s(%s)$%s", theMove->moveType->shortName, theMove->parm->name, theMove->moveType->shortTuningName[0]); } else { MrBayesPrintf (fpMcmc, "\t%s(%s)$acc_run%d", theMove->moveType->shortName, theMove->parm->name, n+1); if (theMove->moveType->Autotune != NULL && chainParams.autotune == YES) MrBayesPrintf (fpMcmc, "\t%s(%s)$%s_run%d", theMove->moveType->shortName, theMove->parm->name, theMove->moveType->shortTuningName[0], n+1); } } } if (chainParams.numChains > 1) { for (i=0; i%d)$acc", i+1, j+1); else MrBayesPrintf (fpMcmc, "\tSwap(%d<>%d)$acc(%d)", i+1, j+1, n+1); } } } } if (chainParams.numRuns > 1) { for (n=0; n 1 && ReassembleSwapInfo() == ERROR) return (ERROR); if (proc_id != 0) return (NO_ERROR); #endif MrBayesPrintf (fpMcmc, "%lli", curGen); for (n=0; nnBatches[j] < 1) MrBayesPrintf (fpMcmc, "\tNA"); else MrBayesPrintf (fpMcmc, "\t%.6f", theMove->lastAcceptanceRate[j]); if (theMove->moveType->Autotune != NULL && chainParams.autotune == YES) MrBayesPrintf (fpMcmc, "\t%.6e", theMove->tuningParam[j][0]); } } } else { j = n*chainParams.numChains; for (i=0; inBatches[j] < 1) MrBayesPrintf (fpMcmc, "\tNA"); else MrBayesPrintf (fpMcmc, "\t%.6f", theMove->lastAcceptanceRate[j]); if (theMove->moveType->Autotune != NULL && chainParams.autotune == YES) MrBayesPrintf (fpMcmc, "\t%.6e", theMove->tuningParam[j][0]); } } if (chainParams.numChains > 1) { for (i=0; i 1) { for (n=0; n strlen(s)) { if ((s = (char *) SafeRealloc ((void *)s, ((size_t)len+5)*sizeof(char))) == NULL) { MrBayesPrint ("%s Problem reallocating %d chars to string 's' on proc 0 in PrintMPISlaves()\n", spacer, len+5); nErrors++; } } /* communicate and print string */ if (nErrors == 0) { if (proc_id == 0) { /* receive string */ ierror = MPI_Recv (s, len+1, MPI_CHAR, i, tag, MPI_COMM_WORLD, &status); if (ierror != MPI_SUCCESS) { MrBayesPrint ("%s Problem receiving printString from proc_id = %d\n", spacer, i); nErrors++; } /* print string */ if (nErrors == 0) MrBayesPrintf (fp, "%s", s); } else if (proc_id == i) { /* send string */ ierror = MPI_Send (printString, len+1, MPI_CHAR, 0, tag, MPI_COMM_WORLD); if (ierror != MPI_SUCCESS) { MrBayesPrint ("%s Problem sending printString from proc_id = %d\n", spacer, i); nErrors++; } } } if (nErrors > 0) break; } if (proc_id == 0) { free (s); s = NULL; } MPI_Allreduce (&nErrors, &sumErrors, 1, MPI_INT, MPI_SUM, MPI_COMM_WORLD); if (sumErrors > 0) { MrBayesPrint ("%s Problem with the printing in PrintMPISlaves().\n", spacer); return (ERROR); } return (NO_ERROR); } #endif /*---------------------------------------------------------------------- | | PrintParamValues: print parameter values and subvalues for param | ----------------------------------------------------------------------*/ void PrintParamValues (Param *p, int chain, char *s) { int j; MrBFlt *value0, *value1; if (p == NULL) MrBayesPrint ("%s %s = NULL\n", spacer, s); else { if (p->nValues > 0) { value0 = GetParamVals (p, chain, 0); value1 = GetParamVals (p, chain, 1); for (j=0; jnValues; j++) MrBayesPrint ("%s hyper [%s] = (%lf %lf)\n", spacer, s, value0[j], value1[j]); } if (p->nSubValues > 0) { value0 = GetParamSubVals (p, chain, 0); value1 = GetParamSubVals (p, chain, 1); for (j=0; jnSubValues; j++) MrBayesPrint ("%s %s = (%lf %lf)\n", spacer, s, value0[j], value1[j]); } } MrBayesPrint ("\n\n"); return; } /*---------------------------------------------------------------------- | | PrintParsMatrix: Print parsimony (bitset) matrix | using hexadecimal representation | | ------------------------------------------------------------------------*/ int PrintParsMatrix (void) { int i, j=0, k, c, d, printWidth, nextColumn, nChars, inputChar; BitsLong x, y, bitsLongOne=1; char ch; ModelInfo *m; printWidth = 79; for (d=0; dnumStates) - 1) / log(16)); for (c=0; cnumChars; c++) { MrBayesPrint ("Parsimony sets for character %d -- \n", (c / m->nParsIntsPerSite)); for (i=0; inParsIntsPerSite; for (nextColumn=13; nextColumn < printWidth; nextColumn+=nChars + 1) { if (j >= m->numChars*m->nParsIntsPerSite) break; x = m->parsSets[i][j]; for (k=8 - nChars; k<8; k++) { y = (x >> (4* (7 - k))) & 15; // if (y > 16) printf ("y is too big %ld\n",y); if (y < 10) ch = (char) y + '0'; else ch = (char) y - 10 + 'A'; MrBayesPrint ("%c", ch); } MrBayesPrint (" "); j++; } MrBayesPrint ("\n"); } MrBayesPrint ("\n"); printf ("Do you want to stop (y/n)?\n"); inputChar = getchar(); if (inputChar == 'y' || inputChar == 'Y') return NO_ERROR; else MrBayesPrint ("\n"); } } /* next division */ return NO_ERROR; } /*------------------------------------------------------------------ | | PrintSiteRates_Gen: general n-state models with rate variation | -------------------------------------------------------------------*/ int PrintSiteRates_Gen (TreeNode *p, int division, int chain) { int c, j, k, nStates, hasPInvar; MrBFlt freq, siteLike, invLike, catLike, pInvar=0.0, *bs, *catRate, baseRate; MrBFlt s01, s10, probOn, probOff, *swr, covBF[40]; CLFlt *lnScaler, *clP, *siteRates, *clInvar=NULL; char *tempStr; int tempStrSize = TEMPSTRSIZE; ModelInfo *m; tempStr = (char *) SafeMalloc((size_t)tempStrSize * sizeof(char)); if (!tempStr) { MrBayesPrint ("%s Problem allocating tempString (%d)\n", spacer, tempStrSize * sizeof(char)); return (ERROR); } /* find model settings and nStates, pInvar, invar cond likes */ m = &modelSettings[division]; nStates = m->numModelStates; if (m->pInvar == NULL) { hasPInvar = NO; } else { hasPInvar = YES; pInvar = *(GetParamVals (m->pInvar, chain, state[chain])); clInvar = m->invCondLikes; } /* find conditional likelihood pointer */ clP = m->condLikes[m->condLikeIndex[chain][p->index]]; /* use scratch space for root node for temporary calculations */ siteRates = m->condLikes[m->condLikeScratchIndex[p->index]]; /* find site scaler */ lnScaler = m->scalers[m->siteScalerIndex[chain]]; /* find base frequencies */ bs = GetParamSubVals (m->stateFreq, chain, state[chain]); /* if covarion model, adjust base frequencies */ if (m->switchRates != NULL) { /* find the stationary frequencies */ swr = GetParamVals(m->switchRates, chain, state[chain]); s01 = swr[0]; s10 = swr[1]; probOn = s01 / (s01 + s10); probOff = 1.0 - probOn; /* now adjust the base frequencies; on-state stored first in cond likes */ for (j=0; jnumRateCats; else freq = (1.0 - pInvar) / m->numRateCats; /* get rate multipliers (for gamma & partition specific rates) */ baseRate = GetRate (division, chain); /* compensate for invariable sites */ if (hasPInvar == YES) baseRate /= (1.0 - pInvar); /* get category rates */ catRate = GetParamSubVals (m->shape, chain, state[chain]); /* loop over characters */ if (hasPInvar == NO) { /* no invariable category */ for (c=0; cnumChars; c++) { siteLike = 0.0; siteRates[c] = 0.0; for (k=0; knumRateCats; k++) { catLike = 0.0; for (j=0; jnumChars; c++) { siteLike = invLike = 0.0; siteRates[c] = 0.0; for (k=0; knumRateCats; k++) { catLike = 0.0; for (j=0; jcompCharStart; SafeSprintf (&tempStr, &tempStrSize, "\t%s", MbPrintNum(siteRates[j])); if (AddToPrintString (tempStr) == ERROR) return (ERROR); } free (tempStr); return NO_ERROR; } /*------------------------------------------------------------------ | | PrintSiteRates_Std: standard model with rate variation | -------------------------------------------------------------------*/ int PrintSiteRates_Std (TreeNode *p, int division, int chain) { int c, j, k, nStates; MrBFlt siteLike, catLike, *bs, *catRate, baseRate; CLFlt *clP, *siteRates; char *tempStr; int tempStrSize = TEMPSTRSIZE; ModelInfo *m; tempStr = (char *) SafeMalloc((size_t)tempStrSize * sizeof(char)); if (!tempStr) { MrBayesPrint ("%s Problem allocating tempString (%d)\n", spacer, tempStrSize * sizeof(char)); return (ERROR); } /* find model settings */ m = &modelSettings[division]; /* find conditional likelihood pointer */ clP = m->condLikes[m->condLikeIndex[chain][p->index]]; /* use scratch space for root node for temporary calculations */ siteRates = m->condLikes[m->condLikeScratchIndex[p->index]]; /* find base frequencies */ bs = GetParamStdStateFreqs (m->stateFreq, chain, state[chain]); /* get rate multiplier */ baseRate = GetRate (division, chain); /* get category rates */ catRate = GetParamSubVals (m->shape, chain, state[chain]); /* loop over characters */ for (c=0; cnumChars; c++) { siteLike = 0.0; siteRates[c] = 0.0; nStates = m->nStates[c]; for (k=0; knumRateCats; k++) { catLike = 0.0; for (j=0; jcompCharStart; SafeSprintf (&tempStr, &tempStrSize, "\t%s", MbPrintNum(siteRates[j])); if (AddToPrintString (tempStr) == ERROR) return (ERROR); } free (tempStr); return NO_ERROR; } /*------------------------------------------------------------------ | | PrintSiteRates_Cont: continuous model with rate variation | -------------------------------------------------------------------*/ int PrintSiteRates_Cont (TreeNode *p, int division, int chain) { int c, k; MrBFlt baseRate, *catRate, lmax, catLike, siteLike, siteRate, lnCatL[MAX_RATE_CATS]; char *tempStr; int tempStrSize = TEMPSTRSIZE; ModelInfo *m; tempStr = (char *) SafeMalloc((size_t)tempStrSize * sizeof(char)); if (!tempStr) { MrBayesPrint ("%s Problem allocating tempString (%d)\n", spacer, tempStrSize * sizeof(char)); return (ERROR); } /* find model settings for this division */ m = &modelSettings[division]; baseRate = GetRate (division, chain); catRate = GetParamSubVals (m->shape, chain, state[chain]); /* print the posterior mean rate of each character */ for (c=0; cnumChars; c++) { lmax = CatLnLike_Cont (division, chain, c, lnCatL); siteRate = siteLike = 0.0; for (k=0; knumRateCats; k++) { catLike = exp(lnCatL[k] - lmax); siteLike += catLike; siteRate += catLike * catRate[k]; } siteRate *= baseRate / siteLike; SafeSprintf (&tempStr, &tempStrSize, "\t%s", MbPrintNum(siteRate)); if (AddToPrintString (tempStr) == ERROR) return (ERROR); } free (tempStr); return NO_ERROR; } int PrintStates (long long curGen, int coldId) { int d, i, j, k, k1, compressedCharPosition, *printedChar=NULL, origAlignmentChars[3], modelIndex; char *partString=NULL, stateString[4]; MrBFlt *st, *sst, sum; Param *p; ModelInfo *m; Tree *tree; TreeNode *node; ModelParams *mp; char *tempStr; int tempStrSize; /* allocate the print string */ printStringSize = tempStrSize = TEMPSTRSIZE; printString = (char *)SafeMalloc((size_t)printStringSize * sizeof(char)); tempStr = (char *) SafeMalloc((size_t)tempStrSize * sizeof(char)); if (!printString) { MrBayesPrint ("%s Problem allocating printString (%d)\n", spacer, printStringSize * sizeof(char)); goto errorExit; } if (!tempStr) { MrBayesPrint ("%s Problem allocating tempString (%d)\n", spacer, (size_t)(tempStrSize) * sizeof(char)); goto errorExit; } *printString = '\0'; *tempStr = '\0'; /* Allocate memory, temporarily, in case we infer positive selection probs, site omegas, or site rates */ if (inferPosSel == YES || inferSiteOmegas == YES) { if (memAllocs[ALLOC_POSSELPROBS] == YES) { MrBayesPrint ("%s posSelProbs not free in PrintStates\n", spacer); goto errorExit; } posSelProbs = (MrBFlt *)SafeMalloc((size_t)numCompressedChars * sizeof(MrBFlt)); if (!posSelProbs) { MrBayesPrint ("%s Problem allocating posSelProbs (%d)\n", spacer, numCompressedChars * sizeof(MrBFlt)); goto errorExit; } for (i=0; iparamType == P_BRLENS) { /* print tree lengths or heights for all trees */ tree = GetTree (p, coldId, state[coldId]); if (tree->isClock == YES) { if (FillRelPartsString(p, &partString) == YES) SafeSprintf (&tempStr, &tempStrSize, "\tTH%s\tTL%s", partString, partString); else SafeSprintf (&tempStr, &tempStrSize, "\tTH\tTL"); if (AddToPrintString (tempStr) == ERROR) goto errorExit; } else { if (FillRelPartsString(p, &partString) == YES) SafeSprintf (&tempStr, &tempStrSize, "\tTL%s", partString); else SafeSprintf (&tempStr, &tempStrSize, "\tTL"); if (AddToPrintString (tempStr) == ERROR) goto errorExit; } /* print # cpp events, or relaxed clock model indicator */ for (j=0; jnSubParams; j++) { if (p->subParams[j]->paramType == P_CPPEVENTS) { if (FillRelPartsString(p->subParams[j], &partString) == YES) SafeSprintf (&tempStr, &tempStrSize, "\tn_CPP%s", partString); else SafeSprintf (&tempStr, &tempStrSize, "\tn_CPP"); if (AddToPrintString (tempStr) == ERROR) goto errorExit; } else if (p->subParams[j]->paramType == P_MIXEDBRCHRATES) { if (FillRelPartsString(p->subParams[j], &partString) == YES) SafeSprintf (&tempStr, &tempStrSize, "\tm_RCl%s", partString); else SafeSprintf (&tempStr, &tempStrSize, "\tm_RCl"); if (AddToPrintString (tempStr) == ERROR) goto errorExit; } } } /* print proportion of ancestral fossils */ else if (p->paramType == P_FOSLRATE) { if (FillRelPartsString(p, &partString) == YES) { SafeSprintf (&tempStr, &tempStrSize, "\tpr_fossil_ancestor%s", partString); if (AddToPrintString (tempStr) == ERROR) goto errorExit; } else { SafeSprintf (&tempStr, &tempStrSize, "\tpr_fossil_ancestor"); if (AddToPrintString (tempStr) == ERROR) goto errorExit; } } } /* print ordinary parameters header */ for (i=0; iparamHeader); if (AddToPrintString (tempStr) == ERROR) goto errorExit; } /* print substitution model parameters header */ if (inferSiteRates == YES) { for (i=0; iprintSiteRates == YES) { if (m->nCharsPerSite == 1) { SafeSprintf (&tempStr, &tempStrSize, "\tr(%d)", i+1); if (AddToPrintString (tempStr) == ERROR) goto errorExit; } else { origAlignmentChars[0] = i; k = 1; for (j=i+1; j m->nCharsPerSite) return (ERROR); origAlignmentChars[k++] = j; printedChar[j] = YES; } } if (k != m->nCharsPerSite) return (ERROR); SafeSprintf (&tempStr, &tempStrSize, "\tr(%d,", origAlignmentChars[0]); if (AddToPrintString (tempStr) == ERROR) goto errorExit; for (j=1; jbrlens, coldId, state[coldId]); if (m->printPosSel == YES) { if (m->PosSelProbs (tree->root->left, d, coldId) == ERROR) goto errorExit; } } /* for (i=0; ibrlens, coldId, state[coldId]); if (m->printSiteOmegas == YES) { if (m->SiteOmegas (tree->root->left, d, coldId) == ERROR) goto errorExit; } } /* for (i=0; i= 0.0 && printedChar[i] == NO && charInfo[i].isExcluded == NO) { for (j=k=0; jprintAncStates != YES) continue; for (i=0; iactiveConstraints[i] == NO || definedConstraintsType[i] != HARD) continue; for (j=0; jdataType == CONTINUOUS) { SafeSprintf (&tempStr, &tempStrSize, "\tm'{%d@%s}", j+1, constraintNames[i]); if (AddToPrintString (tempStr) == ERROR) goto errorExit; } else if (mp->dataType == STANDARD) { for (k=0; knStates[compCharPos[j] - m->compCharStart]; k++) { SafeSprintf (&tempStr, &tempStrSize, "\tp(%c){%d@%s}", m->StateCode(k), j+1, constraintNames[i]); if (AddToPrintString (tempStr) == ERROR) goto errorExit; } } else if ((mp->dataType == DNA || mp->dataType == RNA) && !strcmp(mp->nucModel,"Codon")) { origAlignmentChars[0] = j+1; k1 = 1; for (k=j+1; knumStates; k++) { State_CODON(stateString, k, d); SafeSprintf (&tempStr, &tempStrSize, "\tp(%s){%d,%d,%d@%s}", stateString, origAlignmentChars[0], origAlignmentChars[1], origAlignmentChars[2], constraintNames[i]); if (AddToPrintString (tempStr) == ERROR) goto errorExit; } } else if ((mp->dataType == DNA || mp->dataType == RNA) && !strcmp(mp->nucModel,"Doublet")) { origAlignmentChars[0] = j+1; k1 = 1; for (k=j+1; knumStates; k++) { State_DOUBLET(stateString, k); SafeSprintf (&tempStr, &tempStrSize, "\tp(%s){%d,%d@%s}", stateString, origAlignmentChars[0], origAlignmentChars[1], constraintNames[i]); if (AddToPrintString (tempStr) == ERROR) goto errorExit; } } else if ((mp->dataType == DNA || mp->dataType == RNA) && !strcmp(mp->nucModel,"Protein")) { origAlignmentChars[0] = j+1; k1 = 1; for (k=j+1; knumStates; k++) { SafeSprintf (&tempStr, &tempStrSize, "\tp(%c){%d,%d,%d@%s}", m->StateCode(k), origAlignmentChars[0], origAlignmentChars[1], origAlignmentChars[2], constraintNames[i]); if (AddToPrintString (tempStr) == ERROR) goto errorExit; } } else { for (k=0; knumStates; k++) { SafeSprintf (&tempStr, &tempStrSize, "\tp(%c){%d@%s}", m->StateCode(k), j+1, constraintNames[i]); if (AddToPrintString (tempStr) == ERROR) goto errorExit; } } } } } } SafeSprintf (&tempStr, &tempStrSize, "\n"); if (AddToPrintString (tempStr) == ERROR) goto errorExit; } /* now print parameter values */ SafeSprintf (&tempStr, &tempStrSize, "%lli", curGen); if (AddToPrintString (tempStr) == ERROR) goto errorExit; SafeSprintf (&tempStr, &tempStrSize, "\t%s", MbPrintNum(curLnL[coldId])); if (AddToPrintString (tempStr) == ERROR) goto errorExit; SafeSprintf (&tempStr, &tempStrSize, "\t%s", MbPrintNum(curLnPr[coldId])); if (AddToPrintString (tempStr) == ERROR) goto errorExit; for (i=0; iparamType == P_BRLENS) { /* print tree lengths or heights for all trees */ tree = GetTree (p, coldId, state[coldId]); if (tree->isClock == NO) { SafeSprintf (&tempStr, &tempStrSize, "\t%s", MbPrintNum(TreeLength(p, coldId))); if (AddToPrintString (tempStr) == ERROR) goto errorExit; } else { SafeSprintf (&tempStr, &tempStrSize, "\t%s", MbPrintNum(tree->root->left->nodeDepth)); if (AddToPrintString (tempStr) == ERROR) goto errorExit; SafeSprintf (&tempStr, &tempStrSize, "\t%s", MbPrintNum(TreeLength(p, coldId))); if (AddToPrintString (tempStr) == ERROR) goto errorExit; } /* print # cpp events, or relaxed clock model indicator */ for (j=0; jnSubParams; j++) { if (p->subParams[j]->paramType == P_CPPEVENTS) { SafeSprintf (&tempStr, &tempStrSize, "\t%d", NumCppEvents(p->subParams[j],coldId)); if (AddToPrintString (tempStr) == ERROR) goto errorExit; } else if (p->subParams[j]->paramType == P_MIXEDBRCHRATES) { SafeSprintf (&tempStr, &tempStrSize, "\t%d", *GetParamIntVals(p->subParams[j],coldId,state[coldId])); if (AddToPrintString (tempStr) == ERROR) goto errorExit; } } } else if (p->paramType == P_FOSLRATE) { /* print proportion of ancestral fossils */ SafeSprintf (&tempStr, &tempStrSize, "\t%s", MbPrintNum(PropAncFossil(p, coldId))); if (AddToPrintString (tempStr) == ERROR) goto errorExit; } } /* print ordinary parameters */ for (i=0; irelParts[0]]; m = &modelSettings[p->relParts[0]]; st = GetParamVals (p, coldId, state[coldId]); sst = GetParamSubVals (p, coldId, state[coldId]); if (p->paramId == SYMPI_EXP || p->paramId == SYMPI_UNI || p->paramId == SYMPI_EXP_MS || p->paramId == SYMPI_UNI_MS || p->paramId == SYMPI_FIX_MS) { /* print symmetric dirichlet alpha value if not fixed */ if (p->paramId != SYMPI_FIX_MS) { SafeSprintf (&tempStr, &tempStrSize, "\t%s", MbPrintNum(st[0])); if (AddToPrintString (tempStr) == ERROR) goto errorExit; } /* and then multistate character state frequencies */ sst = GetParamStdStateFreqs (p, coldId, state[coldId]); if (p->hasBinaryStd == YES) sst += 2 * m->numBetaCats; for (j=0; jnSympi; j++) { for (k=0; ksympinStates[j]; k++) { SafeSprintf (&tempStr, &tempStrSize, "\t%s", MbPrintNum(*sst++)); if (AddToPrintString (tempStr) == ERROR) goto errorExit; } } } else if (p->paramType == P_PI || p->paramType == P_MIXTURE_RATES) { /* We print the subvalues if we are dealing with state frequencies (state frequencies are held in subvalues) OR if we are dealing with rates of a siterate mixture (rates held in subvalues). */ for (j=0; jnSubValues; j++) { SafeSprintf (&tempStr, &tempStrSize, "\t%s", MbPrintNum(sst[j])); if (AddToPrintString (tempStr) == ERROR) goto errorExit; } if (!strcmp(mp->statefreqModel, "Mixed")) //SK { if (sst[m->numModelStates] == NOT_APPLICABLE) modelIndex = 0; else modelIndex = 1; SafeSprintf (&tempStr, &tempStrSize, "\t%d", modelIndex); if (AddToPrintString (tempStr) == ERROR) goto errorExit; } } else if (p->paramType == P_TRATIO && !strcmp(mp->tratioFormat,"Dirichlet")) { SafeSprintf (&tempStr, &tempStrSize, "\t%s", MbPrintNum(st[0] / (1.0 + st[0]))); if (AddToPrintString (tempStr) == ERROR) goto errorExit; SafeSprintf (&tempStr, &tempStrSize, "\t%s", MbPrintNum(1.0 / (1.0 + st[0]))); if (AddToPrintString (tempStr) == ERROR) goto errorExit; } else if (p->paramType == P_REVMAT) { if (!strcmp(mp->revmatFormat,"Ratio")) { sum = st[p->nValues-1]; for (j=0; jnValues; j++) { SafeSprintf (&tempStr, &tempStrSize, "\t%s", MbPrintNum(st[j] / sum)); if (AddToPrintString (tempStr) == ERROR) goto errorExit; } } else { /* we already have rate proportions */ for (j=0; jnValues; j++) { SafeSprintf (&tempStr, &tempStrSize, "\t%s", MbPrintNum(st[j])); if (AddToPrintString (tempStr) == ERROR) goto errorExit; } } if (p->paramId == REVMAT_MIX) { /* add model index and k for nst=mixed */ SafeSprintf (&tempStr, &tempStrSize, "\t%d", FromGrowthFxnToIndex(GetParamIntVals(p, coldId, state[coldId]))); if (AddToPrintString (tempStr) == ERROR) goto errorExit; SafeSprintf (&tempStr, &tempStrSize, "\t%d", GetKFromGrowthFxn(GetParamIntVals(p, coldId, state[coldId]))); if (AddToPrintString (tempStr) == ERROR) goto errorExit; } } else if (p->paramType == P_RATEMULT) { if (!strcmp(mp->ratemultFormat,"Ratio")) { for (j=0; jnValues; j++) { SafeSprintf (&tempStr, &tempStrSize, "\t%s", MbPrintNum(st[j] / st[0])); if (AddToPrintString (tempStr) == ERROR) goto errorExit; } } else if (!strcmp(mp->ratemultFormat, "Dirichlet")) { sum = 0.0; for (j=0; jnValues; j++) sum += st[j]; for (j=0; jnValues; j++) { SafeSprintf (&tempStr, &tempStrSize, "\t%s", MbPrintNum(st[j] / sum)); if (AddToPrintString (tempStr) == ERROR) goto errorExit; } } else { for (j=0; jnValues; j++) { SafeSprintf (&tempStr, &tempStrSize, "\t%s", MbPrintNum(st[j])); if (AddToPrintString (tempStr) == ERROR) goto errorExit; } } } else if (p->paramType == P_AAMODEL) { for (j=0; jnValues; j++) { SafeSprintf (&tempStr, &tempStrSize, "\t%d", (int)st[j]); if (AddToPrintString (tempStr) == ERROR) goto errorExit; } } else { /* run of the mill parameter */ for (j=0; jnValues; j++) { SafeSprintf (&tempStr, &tempStrSize, "\t%s", MbPrintNum(st[j])); if (AddToPrintString (tempStr) == ERROR) goto errorExit; } } if (p->paramType == P_OMEGA && p->paramId != OMEGA_DIR && p->paramId != OMEGA_FIX && p->paramId != OMEGA_FFF && p->paramId != OMEGA_FF && p->paramId != OMEGA_10FFF) { /* OK, we also need to print subvalues for the category frequencies in a NY98-like model. */ if (!strcmp(mp->omegaVar, "M10")) { for (j=0; j<4; j++) { SafeSprintf (&tempStr, &tempStrSize, "\t%s", MbPrintNum(sst[mp->numM10BetaCats + mp->numM10GammaCats + 4 + j])); if (AddToPrintString (tempStr) == ERROR) goto errorExit; } for (j=0; j<2; j++) { SafeSprintf (&tempStr, &tempStrSize, "\t%s", MbPrintNum(sst[mp->numM10BetaCats + mp->numM10GammaCats + j])); if (AddToPrintString (tempStr) == ERROR) goto errorExit; } } else { for (j=0; j<3; j++) { SafeSprintf (&tempStr, &tempStrSize, "\t%s", MbPrintNum(sst[j])); if (AddToPrintString (tempStr) == ERROR) goto errorExit; } } } } /* if user wants site rates, we print those here */ if (inferSiteRates == YES) { for (d=0; dprintSiteRates == YES) { tree = GetTree (m->brlens, coldId, state[coldId]); node = tree->root->left; m->PrintSiteRates (node, d, coldId); } } } /* If the user wants to infer sites that are under positive selection, then we need to print out the posterior probability that each site is a positively selected one here. */ if (inferPosSel == YES) { /* loop over the divisions, calculating the probability of being in the positively selected class for each relevant partition */ for (d=0; dbrlens, coldId, state[coldId]); if (m->printPosSel == YES) { if (m->PosSelProbs (tree->root->left, d, coldId) == ERROR) { goto errorExit; } } } /* print the probabilities for the appropriate sites in the original alignment */ for (i=0; i (%3d,%3d,%3d) %1.25le\n", i, origAlignmentChars[0]+1, origAlignmentChars[1]+1, origAlignmentChars[2]+1, posSelProbs[compressedCharPosition]); */ } } } /* If the user wants omega values for each site, we print those here. */ if (inferSiteOmegas == YES) { /* loop over the divisions, calculating the omega value for each site */ for (d=0; dbrlens, coldId, state[coldId]); if (m->printSiteOmegas == YES) { if (m->SiteOmegas (tree->root->left, d, coldId) == ERROR) { goto errorExit; } } } /* print the site omegas for the appropriate sites in the original alignment */ /* note that we use posSelProbs to pass values between SiteOmegas and this function */ for (i=0; i= 0.0 && printedChar[i] == NO && charInfo[i].isExcluded == NO) { for (j=k=0; j (%3d,%3d,%3d) %1.25le\n", i, origAlignmentChars[0]+1, origAlignmentChars[1]+1, origAlignmentChars[2]+1, posSelProbs[compressedCharPosition]); */ } } } /* free memory for positive selection probs or site omegas */ if (inferPosSel == YES || inferSiteOmegas == YES) { if (memAllocs[ALLOC_POSSELPROBS] == YES) free (posSelProbs); memAllocs[ALLOC_POSSELPROBS] = NO; free (printedChar); } /* if user wants ancestral states for constrained nodes, we obtain and print those here */ if (inferAncStates == YES) { for (d=0; dprintAncStates == YES) { mp = &modelParams[d]; tree = GetTree (m->brlens, coldId, state[coldId]); for (i=j=tree->nIntNodes - 1; i>=0; i--) { node = tree->intDownPass[i]; m->CondLikeUp (node, d, coldId); } for (k=0; kactiveConstraints[k] == NO || definedConstraintsType[k] != HARD) continue; for (i=tree->nIntNodes-1; i>=0; i--) { node = tree->intDownPass[i]; if (node->isLocked == YES && k == node->lockID) m->PrintAncStates (node, d, coldId); } } } } } SafeSprintf (&tempStr, &tempStrSize, "\n"); if (AddToPrintString (tempStr) == ERROR) goto errorExit; free (tempStr); SAFEFREE (partString); return (NO_ERROR); errorExit: if (printedChar) free (printedChar); if (memAllocs[ALLOC_POSSELPROBS] == YES) free (posSelProbs); memAllocs[ALLOC_POSSELPROBS] = NO; free (tempStr); SAFEFREE (partString); return (ERROR); } /*---------------------------------------------------------------------- | | PrintStatesToFiles: Print trees and model parameters to files. We | only come into this function if it is the first cycle of the chain | or if we hit a cycle number evenly divisible by the sample frequency, | or this is the last cycle of the chain. | ------------------------------------------------------------------------*/ int PrintStatesToFiles (long long curGen) { int i, j, chn, coldId, runId; MrBFlt clockRate; Tree *tree=NULL; Param *param; # if defined (MPI_ENABLED) int id, x, doesThisProcHaveId, procWithChain, ierror, tag, nErrors, sumErrors; MPI_Status status; # endif # if !defined (MPI_ENABLED) /* print parameter values and trees (single-processor version) */ for (chn=0; chnparamType == P_TOPOLOGY) { if (tree->isClock == YES) clockRate = *GetParamVals(modelSettings[tree->relParts[0]].clockRate, coldId, state[coldId]); else clockRate = 0.0; if (PrintTree (curGen, param, coldId, NO, clockRate) == ERROR) return (ERROR); } else { if (tree->isClock == YES) clockRate = *GetParamVals(modelSettings[tree->relParts[0]].clockRate, coldId, state[coldId]); else clockRate = 0.0; if (PrintTree (curGen, param, coldId, YES, clockRate) == ERROR) return (ERROR); } fprintf (fpTree[runId][i], "%s", printString); fflush (fpTree[runId][i]); free(printString); j = printTreeTopologyIndex[i]; if (j 1) { if (chainParams.relativeBurnin == YES || curGen >= (long long)(chainParams.chainBurnIn) * chainParams.sampleFreq) { if (AddTreeToPartitionCounters (tree, j, runId) == ERROR) return ERROR; if (chainParams.relativeBurnin == YES && chainParams.saveTrees == YES && (noWarn == NO || curGen <= chainParams.stopTreeGen)) { ResetTopologyFromTree (chainParams.dtree, tree); if (AddToTreeList (&chainParams.treeList[numTopologies*runId+j], chainParams.dtree) == ERROR) return (ERROR); } } } } } } } # else /* print parameter values and trees (parallel version) */ /* Wait for all of the processors to get to this point before starting the printing. */ ierror = MPI_Barrier (MPI_COMM_WORLD); if (ierror != MPI_SUCCESS) { MrBayesPrint ("%s Problem at chain barrier.\n", spacer); return ERROR; } tag = nErrors = 0; /* Loop over runs. */ for (runId=0; runId 0) { MrBayesPrint ("%s Problem with PrintStates.\n", spacer); return ERROR; } /* First communication: Send/receive the length of the printString. */ if (proc_id == 0 || proc_id == procWithChain) { if (procWithChain != 0) { if (proc_id == procWithChain) { /* Find out how large the string is, and send the information to proc_id = 0. */ ierror = MPI_Send (&printStringSize, 1, MPI_LONG, 0, tag, MPI_COMM_WORLD); if (ierror != MPI_SUCCESS) nErrors++; } else { /* Receive the length of the string from proc_id = procWithChain, and then allocate printString to be that length. */ ierror = MPI_Recv (&printStringSize, 1, MPI_LONG, procWithChain, tag, MPI_COMM_WORLD, &status); if (ierror != MPI_SUCCESS) { MrBayesPrint ("%s Problem receiving printStringSize from proc_id = %d\n", spacer, procWithChain); nErrors++; } printString = (char *)SafeMalloc((size_t)printStringSize * sizeof(char)); if (!printString) { MrBayesPrint ("%s Problem allocating printString (%d)\n", spacer, printStringSize * sizeof(char)); nErrors++; } strcpy (printString, ""); } } } MPI_Allreduce (&nErrors, &sumErrors, 1, MPI_INT, MPI_SUM, MPI_COMM_WORLD); if (sumErrors > 0) { MrBayesPrint ("%s Problem with first communication (states).\n", spacer); return ERROR; } /* Second communication: Send/receive the printString. */ if (proc_id == 0 || proc_id == procWithChain) { if (procWithChain != 0) { if (proc_id == procWithChain) { /* Send the printString to proc_id = 0. After we send the string to proc_id = 0, we can free it. */ ierror = MPI_Send (&printString[0], (int)printStringSize, MPI_CHAR, 0, tag, MPI_COMM_WORLD); if (ierror != MPI_SUCCESS) nErrors++; free(printString); } else { /* Receive the printString from proc_id = procWithChain. */ ierror = MPI_Recv (&printString[0], (int)printStringSize, MPI_CHAR, procWithChain, tag, MPI_COMM_WORLD, &status); if (ierror != MPI_SUCCESS) { MrBayesPrint ("%s Problem receiving printString from proc_id = %d\n", spacer, procWithChain); nErrors++; } } } } MPI_Allreduce (&nErrors, &sumErrors, 1, MPI_INT, MPI_SUM, MPI_COMM_WORLD); if (sumErrors > 0) { MrBayesPrint ("%s Problem with second communication (states).\n", spacer); return ERROR; } /* Print the string with the parameter information if we are proc_id = 0. */ if (proc_id == 0) { fprintf (fpParm[runId], "%s", printString); fflush (fpParm[runId]); free(printString); } /* ****************************************************************************************************/ /* print trees ****************************************************************************************/ for (i=0; iparamType == P_TOPOLOGY) { if (tree->isClock == YES) clockRate = *GetParamVals(modelSettings[tree->relParts[0]].clockRate, coldId, state[coldId]); else clockRate = 0.0; if (PrintTree (curGen, param, coldId, NO, clockRate) == ERROR) nErrors++; } else { if (tree->isClock == YES) clockRate = *GetParamVals(modelSettings[tree->relParts[0]].clockRate, coldId, state[coldId]); else clockRate = 0.0; if (PrintTree (curGen, param, coldId, YES, clockRate) == ERROR) nErrors++; } } MPI_Allreduce (&nErrors, &sumErrors, 1, MPI_INT, MPI_SUM, MPI_COMM_WORLD); if (sumErrors > 0) { MrBayesPrint ("%s Problem with printing trees.\n", spacer); return ERROR; } /* First communication: Send/receive the length of the printString. */ if (proc_id == 0 || proc_id == procWithChain) { if (procWithChain != 0) { if (proc_id == procWithChain) { /* Find out how large the string is, and send the information to proc_id = 0. */ ierror = MPI_Send (&printStringSize, 1, MPI_LONG, 0, tag, MPI_COMM_WORLD); if (ierror != MPI_SUCCESS) nErrors++; } else { /* Receive the length of the string from proc_id = procWithChain, and then allocate printString to be that length. */ ierror = MPI_Recv (&printStringSize, 1, MPI_LONG, procWithChain, tag, MPI_COMM_WORLD, &status); if (ierror != MPI_SUCCESS) { MrBayesPrint ("%s Problem receiving printStringSize from proc_id = %d\n", spacer, procWithChain); nErrors++; } printString = (char *)SafeMalloc((size_t)printStringSize * sizeof(char)); if (!printString) { MrBayesPrint ("%s Problem allocating printString (%d)\n", spacer, printStringSize * sizeof(char)); nErrors++; } strcpy (printString, ""); } } } MPI_Allreduce (&nErrors, &sumErrors, 1, MPI_INT, MPI_SUM, MPI_COMM_WORLD); if (sumErrors > 0) { MrBayesPrint ("%s Problem with first communication (states).\n", spacer); return ERROR; } /* Second communication: Send/receive the printString. */ if (proc_id == 0 || proc_id == procWithChain) { if (procWithChain != 0) { if (proc_id == procWithChain) { /* Send the printString to proc_id = 0. After we send the string to proc_id = 0, we can free it. */ ierror = MPI_Send (&printString[0], (int)printStringSize, MPI_CHAR, 0, tag, MPI_COMM_WORLD); if (ierror != MPI_SUCCESS) nErrors++; free(printString); } else { /* Receive the printString from proc_id = procWithChain. */ ierror = MPI_Recv (&printString[0], (int)printStringSize, MPI_CHAR, procWithChain, tag, MPI_COMM_WORLD, &status); if (ierror != MPI_SUCCESS) { MrBayesPrint ("%s Problem receiving printString from proc_id = %d\n", spacer, procWithChain); nErrors++; } } } } MPI_Allreduce (&nErrors, &sumErrors, 1, MPI_INT, MPI_SUM, MPI_COMM_WORLD); if (sumErrors > 0) { MrBayesPrint ("%s Problem with second communication (states).\n", spacer); return ERROR; } /* Print the string with the parameter information if we are proc_id = 0. */ if (proc_id == 0) { fprintf (fpTree[runId][i], "%s", printString); fflush (fpTree[runId][i]); j = printTreeTopologyIndex[i]; if (j < numTopologies) { if (chainParams.numRuns > 1 && chainParams.mcmcDiagn == YES) { if (chainParams.relativeBurnin == YES || curGen >= (long long)(chainParams.chainBurnIn) * chainParams.sampleFreq) { char *s = NULL; StripComments (printString); /* if it is the first tree, we strip out the translate block first (twice) */ if (curGen==0) { if (strtok (printString, ";")==NULL) /* get translate lock */ return (ERROR); if (strtok (NULL, ";")==NULL) return (ERROR); if (strtok (NULL, "\n\t\r ")==NULL) /* get 'tree' */ return (ERROR); } else { if (strtok (printString, "\n\t\r ")==NULL) /* get 'tree' */ return (ERROR); } if (strtok (NULL, " =")==NULL) /* get 'rep.xxxx' */ return (ERROR); if ((s = strtok (NULL, " =;"))==NULL) /* get Newick string */ return (ERROR); ResetTopology (chainParams.dtree, s); if (AddTreeToPartitionCounters (chainParams.dtree, j, runId) == ERROR) return ERROR; if (chainParams.relativeBurnin == YES && chainParams.saveTrees == YES && (noWarn == NO || curGen <= chainParams.stopTreeGen)) { if (AddToTreeList (&chainParams.treeList[runId*numTopologies+j], chainParams.dtree) == ERROR) return (ERROR); } } } } free(printString); } } /* Have all of the chains wait here, until the string has been successfully printed on proc_id = 0. */ ierror = MPI_Barrier (MPI_COMM_WORLD); if (ierror != MPI_SUCCESS) { MrBayesPrint ("%s Problem at chain barrier.\n", spacer); return ERROR; } } # endif return (NO_ERROR); } int PrintSwapInfo (void) { int i, j, n, maxNumExchanges, len, maxLen, reweightingChars=0; char *tempStr; int tempStrSize; if (chainParams.numChains == 1) return NO_ERROR; # if defined (MPI_ENABLED) if (ReassembleSwapInfo() == ERROR) return ERROR; if (proc_id != 0) return NO_ERROR; # endif tempStrSize = TEMPSTRSIZE; tempStr = (char *) SafeMalloc((size_t)tempStrSize * sizeof(char)); if (!tempStr) { MrBayesPrint ("%s Problem allocating tempString (%d)\n", spacer, tempStrSize * sizeof(char)); return (ERROR); } for (n=0; n j && swapInfo[n][i][j] > maxNumExchanges) maxNumExchanges = swapInfo[n][i][j]; SafeSprintf (&tempStr, &tempStrSize, "%d", maxNumExchanges); maxLen = (int) strlen(tempStr); if (maxLen < 4) maxLen = 4; reweightingChars = NO; if ((chainParams.weightScheme[0] + chainParams.weightScheme[1]) > 0.00001) reweightingChars = YES; if (chainParams.numRuns == 1) MrBayesPrint ("\n%s Chain swap information:\n\n", spacer); else MrBayesPrint ("\n%s Chain swap information for run %d:\n\n", spacer, n+1); MrBayesPrint ("%s ", spacer); for (j=0; jcovarionModel, "Yes")) nReps = 2; else nReps = 1; nDigits = 1 + (int)(log10(mp->nStates * mp->nStates * nReps)); for (c=m->compCharStart; ccompCharStop; c=j) { for (i=0; i= m->compCharStop) break; MrBayesPrint ("%*d ",nDigits, m->termState[i][j-c]); j++; } MrBayesPrint ("\n"); } MrBayesPrint ("\n"); } } /* next division */ return NO_ERROR; } /*-------------------------------------------------- | | PrintTiProbs: This function is for debugging of | tiProbs; it will print a square matrix of | tiProbs, check row sums, and check for time | reversibility | ---------------------------------------------------*/ void PrintTiProbs (CLFlt *tP, MrBFlt *bs, int nStates) { int i, j; CLFlt *tiP, sum; tiP = tP; printf ("\nTransition matrix\n"); for (i=0; i j && stat->pair[i][j] > maxNumPartitions) maxNumPartitions = stat->pair[i][j]; SafeSprintf (&tempStr, &tempStrSize, "%d", (int) maxNumPartitions); maxLen = (int) strlen(tempStr); if (maxLen < 5) maxLen = 5; if (numTopologies == 1) { if (chainParams.diagnStat == AVGSTDDEV) MrBayesPrint ("%s Pairwise average standard deviation of split frequencies (upper triangle)\n", spacer); else MrBayesPrint ("%s Pairwise maximum standard deviation of split frequencies (upper triangle)\n", spacer); MrBayesPrint ("%s and number of qualifying splits for each comparison (lower triangle):\n\n", spacer); } else { if (chainParams.diagnStat == AVGSTDDEV) MrBayesPrint ("%s Pairwise average standard deviation of split frequencies in topology %d (upper triangle)\n", spacer, n); else MrBayesPrint ("%s Pairwise maximum standard deviation of split frequencies in topology %d (upper triangle)\n", spacer, n); MrBayesPrint ("%s and number of qualifying splits for each comparison (lower triangle):\n\n", spacer); } MrBayesPrint ("%s ", spacer); for (j=0; jpair[i][j]) / (MrBFlt) (stat->pair[j][i])); else /* if (chainParams.diagnStat == MAXSTDDEV) */ SafeSprintf (&tempStr, &tempStrSize, "%1.3lf", stat->pair[i][j]); len = (int) strlen(tempStr); MrBayesPrint ("%*c%s ", maxLen-len+1, ' ', tempStr); } else if (i == j) { MrBayesPrint ("%*c ", maxLen+1, ' '); } else { SafeSprintf (&tempStr, &tempStrSize, "%d", (int) stat->pair[i][j]); len = (int) strlen(tempStr); MrBayesPrint ("%*c%s ", maxLen-len+1, ' ', tempStr); } } MrBayesPrint ("\n"); } MrBayesPrint ("\n"); } free (tempStr); return (NO_ERROR); } void PrintToScreen (long long curGen, long long startGen, time_t endingT, time_t startingT) { int i, chn, nHours, nMins, nSecs; MrBFlt timePerGen; # if defined (MPI_ENABLED) int numLocalColdChains, numFirstAndLastCold; if (curGen == 0) { if (chainParams.isSS == NO && chainParams.mcmcDiagn == YES && chainParams.numRuns > 1) { MrBayesPrint ("\n"); if (chainParams.relativeBurnin == YES) MrBayesPrint ("%s Using a relative burnin of %.1f %% for diagnostics\n", spacer, 100.0*chainParams.burninFraction); else MrBayesPrint ("%s Using an absolute burnin of %d samples for diagnostics\n", spacer, chainParams.chainBurnIn); } MrBayesPrint ("\n"); MrBayesPrint ("%s Chain results (%lli generations requested):\n\n", spacer, chainParams.numGen); } MrBayesPrint ("%s %4lli -- ", spacer, curGen); numLocalColdChains = numFirstAndLastCold = 0; for (chn=0; chn chainParams.printMax) { if (i == chainParams.printMax +1) { i++; if (numLocalColdChains > 0 && numLocalColdChains > numFirstAndLastCold) MrBayesPrint ("[...%d more local chains...] ", numLocalChains - chainParams.printMax); else MrBayesPrint ("(...%d more local chains...) ", numLocalChains - chainParams.printMax); continue; } else continue; } if ((chainId[chn] % chainParams.numChains) == 0) { i++; if (chainParams.printAll == YES) MrBayesPrint ("[%1.3lf] ", curLnL[chn]); else MrBayesPrint ("[%1.3lf] .. ", curLnL[chn]); } else if (chainParams.printAll == YES) { i++; MrBayesPrint ("(%1.3lf) ", curLnL[chn]); } if (chn < numLocalChains - 1 && (chainId[chn] / chainParams.numChains != chainId[chn+1] / chainParams.numChains)) MrBayesPrint ("* "); } if (numLocalColdChains == chainParams.numRuns) MrBayesPrint ("(...%d remote chains...) ", (chainParams.numChains*chainParams.numRuns) - numLocalChains); else MrBayesPrint ("[...%d remote chains...] ", (chainParams.numChains*chainParams.numRuns) - numLocalChains); if (curGen > 0) { timePerGen = (MrBFlt) ((MrBFlt)(endingT-startingT)/(MrBFlt)(curGen-startGen)); nSecs = (int)((chainParams.numGen - curGen) * timePerGen); nHours = nSecs / 3600; nSecs = nSecs % 3600; nMins = nSecs / 60; nSecs = nSecs % 60; MrBayesPrint ("-- %d:%0.2d:%0.2d", nHours, nMins, nSecs); } MrBayesPrint ("\n"); fflush (stdout); # else if (curGen == 0) { if (chainParams.isSS == NO && chainParams.mcmcDiagn == YES && chainParams.numRuns > 1) { MrBayesPrint ("\n"); if (chainParams.relativeBurnin == YES) MrBayesPrint ("%s Using a relative burnin of %.1f %% for diagnostics\n", spacer, 100.0*chainParams.burninFraction); else MrBayesPrint ("%s Using an absolute burnin of %d samples for diagnostics\n", spacer, chainParams.chainBurnIn); } MrBayesPrint ("\n"); MrBayesPrint ("%s Chain results (%lli generations requested):\n\n", spacer, chainParams.numGen); } MrBayesPrint ("%s %5lli -- ", spacer, curGen); if (numLocalChains == 1) MrBayesPrint ("%1.3lf ", curLnL[0]); else { i = 0; for (chn=0; chn= chainParams.printMax) { if (i == chainParams.printMax) MrBayesPrint (".. "); i++; continue; } if (chainParams.numChains == 1) { MrBayesPrint ("%1.3lf ", curLnL[chn]); i++; } else if (chainId[chn] % chainParams.numChains == 0) { if (chainParams.printAll == YES) MrBayesPrint ("[%1.3lf] ", curLnL[chn]); else MrBayesPrint ("[%1.3lf][%d] .. ", curLnL[chn], chn % chainParams.numChains + 1); i++; } else if (chainParams.printAll == YES) { MrBayesPrint ("(%1.3lf) ", curLnL[chn]); i++; } if (chn < numLocalChains - 1 && (chainId[chn] / chainParams.numChains != chainId[chn+1] / chainParams.numChains) && i < chainParams.printMax - 1) MrBayesPrint ("* "); } } if (curGen > 0) { timePerGen = (MrBFlt) ((MrBFlt)(endingT-startingT)/(MrBFlt)(curGen-startGen)); nSecs = (int)((chainParams.numGen - curGen) * timePerGen); nHours = nSecs / 3600; nSecs = nSecs % 3600; nMins = nSecs / 60; nSecs = nSecs % 60; MrBayesPrint ("-- %d:%0.2d:%0.2d", nHours, nMins, nSecs); } MrBayesPrint ("\n"); fflush (stdout); # endif } int PrintTree (long long curGen, Param *treeParam, int chain, int showBrlens, MrBFlt clockRate) { int i, tempStrSize; char *tempStr; Tree *tree; TreeNode *p=NULL, *q; Param *subParm; /* allocate the print string */ printStringSize = 200; printString = (char *)SafeMalloc((size_t)printStringSize * sizeof(char)); if (!printString) { MrBayesPrint ("%s Problem allocating printString (%d)\n", spacer, printStringSize * sizeof(char)); return (ERROR); } *printString = '\0'; tempStrSize = TEMPSTRSIZE; tempStr = (char *) SafeMalloc((size_t)tempStrSize * sizeof(char)); if (!tempStr) { MrBayesPrint ("%s Problem allocating tempString (%d)\n", spacer, tempStrSize * sizeof(char)); return (ERROR); } /* get tree */ tree = GetTree(treeParam, chain, state[chain]); /* order the taxa */ if (chainParams.orderTaxa == YES) { for (i=0; inNodes-1; i++) { p = tree->allDownPass[i]; if (p->left == NULL) { if (p->index == localOutGroup) p->x = -1; else p->x = p->index; } else if (p->left->x < p->right->x) p->x = p->left->x; else p->x = p->right->x; } for (i=0; inIntNodes; i++) { if (p->left->x > p->right->x) { q = p->left; p->left = p->right; p->right = q; } } } /* print the translate block information and the top of the file */ if (curGen == 0) { /* print #NEXUS and translation block information */ SafeSprintf (&tempStr, &tempStrSize, "#NEXUS\n"); if (AddToPrintString (tempStr) == ERROR) return(ERROR); SafeSprintf (&tempStr, &tempStrSize, "[ID: %s]\n", stamp); if (AddToPrintString (tempStr) == ERROR) return(ERROR); SafeSprintf (&tempStr, &tempStrSize, "[Param: tree"); if (AddToPrintString (tempStr) == ERROR) return(ERROR); if (numCurrentDivisions == 1) { SafeSprintf (&tempStr, &tempStrSize, "]\n"); if (AddToPrintString (tempStr) == ERROR) return(ERROR); } else if (numCurrentDivisions == tree->nRelParts) { SafeSprintf (&tempStr, &tempStrSize, "{all}]\n"); if (AddToPrintString (tempStr) == ERROR) return(ERROR); } else { SafeSprintf (&tempStr, &tempStrSize, "{%d", tree->relParts[0]+1); if (AddToPrintString (tempStr) == ERROR) return(ERROR); for (i=1; inRelParts; i++) { SafeSprintf (&tempStr, &tempStrSize, ",%d", tree->relParts[i]+1); if (AddToPrintString (tempStr) == ERROR) return(ERROR); } SafeSprintf (&tempStr, &tempStrSize, "}]\n"); if (AddToPrintString (tempStr) == ERROR) return(ERROR); } SafeSprintf (&tempStr, &tempStrSize, "begin trees;\n"); if (AddToPrintString (tempStr) == ERROR) return(ERROR); SafeSprintf (&tempStr, &tempStrSize, " translate\n"); if (AddToPrintString (tempStr) == ERROR) return(ERROR); if (treeParam->paramType == P_SPECIESTREE) { for (i=0; iparamType == P_BRLENS && treeParam->nSubParams > 0) { for (i=0; inSubParams; i++) { subParm = treeParam->subParams[i]; if (subParm->paramType == P_CPPEVENTS) { if (SafeSprintf (&tempStr, &tempStrSize, " [&E %s]", subParm->name) == ERROR) return (ERROR); if (AddToPrintString (tempStr) == ERROR) return(ERROR); } // if (subParm->paramType == P_MIXEDBRCHRATES) // { // id = *GetParamIntVals(subParm, chain, state[chain]); // if (SafeSprintf (&tempStr, &tempStrSize, " [&B %s %d]", subParm->name, id) == ERROR) return (ERROR); // } else if (SafeSprintf (&tempStr, &tempStrSize, " [&B %s]", subParm->name) == ERROR) return (ERROR); if (AddToPrintString (tempStr) == ERROR) return(ERROR); } } subParm = modelSettings[treeParam->relParts[0]].popSize; if (treeParam->paramType == P_SPECIESTREE && subParm->nValues > 1) { if (SafeSprintf (&tempStr, &tempStrSize, " [&N %s]", subParm->name) == ERROR) return (ERROR); if (AddToPrintString (tempStr) == ERROR) return(ERROR); } /* write the tree in (extended) Newick format */ if (tree->isRooted == YES && tree->isCalibrated == NO) SafeSprintf (&tempStr, &tempStrSize, " = [&R] "); else if (tree->isRooted == YES && tree->isCalibrated == YES) SafeSprintf (&tempStr, &tempStrSize, " = [&R] [&clockrate=%s] ", MbPrintNum(clockRate)); else /* if (tree->isRooted == NO) */ SafeSprintf (&tempStr, &tempStrSize, " = [&U] "); if (AddToPrintString (tempStr) == ERROR) return(ERROR); WriteNoEvtTreeToPrintString (tree->root->left, chain, treeParam, showBrlens, tree->isRooted); SafeSprintf (&tempStr, &tempStrSize, ";\n"); if (AddToPrintString (tempStr) == ERROR) return(ERROR); free (tempStr); return (NO_ERROR); } #if defined (MPI_ENABLED) int ReassembleMoveInfo (void) { int i, n, ierror; double x[7], sum[7]; MCMCMove *mv; for (n=0; nnAccepted[n]; x[1] = mv->nTried[n]; x[2] = mv->nBatches[n]; x[3] = mv->nTotAccepted[n]; x[4] = mv->nTotTried[n]; x[5] = mv->lastAcceptanceRate[n]; if (mv->moveType->Autotune != NULL) x[6]=mv->tuningParam[n][0]; ierror = MPI_Allreduce (&x, &sum, 7, MPI_DOUBLE, MPI_SUM, MPI_COMM_WORLD); if (ierror != MPI_SUCCESS) return (ERROR); if (proc_id == 0) { mv->nAccepted[n] = (int)sum[0]; mv->nTried[n] = (int)sum[1]; mv->nBatches[n] = (int)sum[2]; mv->nTotAccepted[n] = (int)sum[3]; mv->nTotTried[n] = (int)sum[4]; mv->lastAcceptanceRate[n] = (MrBFlt)sum[5]; if (mv->moveType->Autotune != NULL) mv->tuningParam[n][0]=(MrBFlt)sum[6]; } } } return (NO_ERROR); } int ReassembleParamVals (int *curId) { int i, j, k, orderLen, nBrlens, lower, upper, numChainsForProc, proc, ierror, *y, *order, *id, *nEvents; MrBFlt *x, *brlens, **position, **rateMult; MPI_Status status; MPI_Request request; Tree *tree; Param *p; extern MrBFlt *paramValues; extern int paramValsRowSize; extern int intValsRowSize; for (i=0; i 0) lower = upper = numChainsForProc+1; else lower = upper = numChainsForProc; for (proc=1; proc 0) { y = intValues + 2*intValsRowSize*lower; ierror = MPI_Irecv (y, intValsRowSize*2*(upper-lower), MPI_INT, proc, 0, MPI_COMM_WORLD, &request); if (ierror != MPI_SUCCESS) { return (ERROR); } ierror = MPI_Waitall (1, &request, &status); if (ierror != MPI_SUCCESS) { return (ERROR); } } } else if (proc_id == proc) { x = paramValues; ierror = MPI_Isend (x, paramValsRowSize*2*(upper-lower), MPI_DOUBLE, 0, 0, MPI_COMM_WORLD, &request); if (ierror != MPI_SUCCESS) { return (ERROR); } ierror = MPI_Waitall (1, &request, &status); if (ierror != MPI_SUCCESS) { return (ERROR); } if (intValsRowSize > 0) { y = intValues; ierror = MPI_Isend (y, intValsRowSize*2*(upper-lower), MPI_INT, 0, 0, MPI_COMM_WORLD, &request); if (ierror != MPI_SUCCESS) { return (ERROR); } ierror = MPI_Waitall (1, &request, &status); if (ierror != MPI_SUCCESS) { return (ERROR); } } } /* std state frequencies */ if (stdStateFreqsRowSize > 0) { if (proc_id == 0) { x = stdStateFreqs + 2*stdStateFreqsRowSize*lower; ierror = MPI_Irecv (x, stdStateFreqsRowSize*2*(upper-lower), MPI_DOUBLE, proc, 0, MPI_COMM_WORLD, &request); if (ierror != MPI_SUCCESS) { return (ERROR); } ierror = MPI_Waitall (1, &request, &status); if (ierror != MPI_SUCCESS) { return (ERROR); } } else if (proc_id == proc) { x = stdStateFreqs; ierror = MPI_Isend (x, stdStateFreqsRowSize*2*(upper-lower), MPI_DOUBLE, 0, 0, MPI_COMM_WORLD, &request); if (ierror != MPI_SUCCESS) { return (ERROR); } ierror = MPI_Waitall (1, &request, &status); if (ierror != MPI_SUCCESS) { return (ERROR); } } } /* mcmc trees */ brlens = (MrBFlt *) SafeCalloc (2*numLocalTaxa, sizeof(MrBFlt)); order = (int *) SafeCalloc (2*numLocalTaxa, sizeof(int)); for (i=lower; inIntNodes - 1; nBrlens = tree->nNodes - 1; if (proc_id == 0) { tree = GetTreeFromIndex(j,i,state[i]); ierror = MPI_Irecv (order, orderLen, MPI_INT, proc, 0, MPI_COMM_WORLD, &request); if (ierror != MPI_SUCCESS) { return (ERROR); } ierror = MPI_Waitall (1, &request, &status); if (ierror != MPI_SUCCESS) { return (ERROR); } ierror = MPI_Irecv (brlens, nBrlens, MPI_DOUBLE, proc, 0, MPI_COMM_WORLD, &request); if (ierror != MPI_SUCCESS) { return (ERROR); } ierror = MPI_Waitall (1, &request, &status); if (ierror != MPI_SUCCESS) { return (ERROR); } if (tree->isRooted == YES) RetrieveRTreeWithIndices(tree, order, brlens); else { RetrieveUTree(tree, order, brlens); if (localOutGroup!=0) MoveCalculationRoot(tree,localOutGroup); } /* since we only transferred some info, there are additional things we need to consider, like constraints and calibrations; tree names are OK on proc 0 */ InitializeTreeCalibrations(tree); CheckSetConstraints(tree); SetDatedNodeAges(modelSettings[tree->relParts[0]].brlens, i, state[i]); } else if (proc_id == proc) { tree = GetTreeFromIndex(j,i-lower,state[i-lower]); if (tree->isRooted == YES) StoreRTreeWithIndices(tree, order, brlens); else StoreUTree(tree, order, brlens); ierror = MPI_Isend (order, orderLen, MPI_INT, 0, 0, MPI_COMM_WORLD, &request); if (ierror != MPI_SUCCESS) { return (ERROR); } ierror = MPI_Waitall (1, &request, &status); if (ierror != MPI_SUCCESS) { return (ERROR); } ierror = MPI_Isend (brlens, nBrlens, MPI_DOUBLE, 0, 0, MPI_COMM_WORLD, &request); if (ierror != MPI_SUCCESS) { return (ERROR); } ierror = MPI_Waitall (1, &request, &status); if (ierror != MPI_SUCCESS) { return (ERROR); } } } } free (brlens); free (order); /* CPP event parameters */ for (i=lower; iparamType == P_CPPEVENTS) { if (proc_id == proc) { /* get pointers */ nEvents = p->nEvents[2*(i-lower)+state[i-lower]]; position = p->position[2*(i-lower)+state[i-lower]]; rateMult = p->rateMult[2*(i-lower)+state[i-lower]]; /* send number of events */ ierror = MPI_Isend (nEvents, 2*numLocalTaxa, MPI_INT, 0, 0, MPI_COMM_WORLD, &request); if (ierror != MPI_SUCCESS) return (ERROR); ierror = MPI_Waitall (1, &request, &status); if (ierror != MPI_SUCCESS) return (ERROR); /* send events and clear pointers */ for (k=0; k<2*numLocalTaxa; k++) { if (nEvents[k] > 0) { ierror = MPI_Isend (position[k], nEvents[k], MPI_DOUBLE, 0, 0, MPI_COMM_WORLD, &request); if (ierror != MPI_SUCCESS) return (ERROR); ierror = MPI_Waitall (1, &request, &status); if (ierror != MPI_SUCCESS) return (ERROR); ierror = MPI_Isend (rateMult[k], nEvents[k], MPI_DOUBLE, 0, 0, MPI_COMM_WORLD, &request); if (ierror != MPI_SUCCESS) return (ERROR); ierror = MPI_Waitall (1, &request, &status); if (ierror != MPI_SUCCESS) return (ERROR); free(position[k]); free(rateMult[k]); position[k] = NULL; rateMult[k] = NULL; nEvents[k] = 0; } } } else if (proc_id == 0) { /* find pointers */ nEvents = p->nEvents[2*i]; position = p->position[2*i]; rateMult = p->rateMult[2*i]; /* clear previous events */ for (k=0; k<2*numLocalTaxa; k++) { if (nEvents[k] > 0) { free (position[k]); free (rateMult[k]); position[k] = NULL; rateMult[k] = NULL; nEvents[k] = 0; } } /* receive events */ ierror = MPI_Irecv (nEvents, 2*numLocalTaxa, MPI_INT, proc, 0, MPI_COMM_WORLD, &request); if (ierror != MPI_SUCCESS) return (ERROR); ierror = MPI_Waitall (1, &request, &status); if (ierror != MPI_SUCCESS) return (ERROR); for (k=0; k<2*numLocalTaxa; k++) { if (nEvents[k] > 0) { position[k] = (MrBFlt *) SafeCalloc (nEvents[k], sizeof(MrBFlt)); rateMult[k] = (MrBFlt *) SafeCalloc (nEvents[k], sizeof(MrBFlt)); ierror = MPI_Irecv (position[k], nEvents[k], MPI_DOUBLE, proc, 0, MPI_COMM_WORLD, &request); if (ierror != MPI_SUCCESS) return (ERROR); ierror = MPI_Waitall (1, &request, &status); if (ierror != MPI_SUCCESS) return (ERROR); ierror = MPI_Irecv (rateMult[k], nEvents[k], MPI_DOUBLE, proc, 0, MPI_COMM_WORLD, &request); if (ierror != MPI_SUCCESS) return (ERROR); ierror = MPI_Waitall (1, &request, &status); if (ierror != MPI_SUCCESS) return (ERROR); } } } } } } } return (NO_ERROR); } int ReassembleSwapInfo (void) { int i, j, n, x, sum, ierror; for (n=0; nmoveType->numTuningParams > 0) /* we have the tuning parameter of interest */ x[k] = usedMoves[k]->tuningParam[i][0]; else x[k] = 0.0; } ierror = MPI_Allreduce (x, sum, numUsedMoves, MPI_DOUBLE, MPI_SUM, MPI_COMM_WORLD); if (ierror != MPI_SUCCESS) { free (x); return (ERROR); } if (proc_id == 0) { for (k=0; kmoveType->numTuningParams > 0) usedMoves[k]->tuningParam[i][0] = sum[k]; } } } free (x); return (NO_ERROR); } void RedistributeMoveInfo (void) { int i, j, k; MCMCMove *mv; /* Leave if not processor 0, because then we already have the necessary info since it was not deleted in ReassembleMoveInfo */ if (proc_id != 0) return; /* If we are processor 0, simply delete the unnecessary information */ for (i=0; inAccepted[i] = 0; mv->nTried[i] = 0; mv->nBatches[i] = 0; mv->nTotAccepted[i] = 0; mv->nTotTried[i] = 0; mv->lastAcceptanceRate[i] = 0; if (mv->moveType->Autotune != NULL) mv->tuningParam[i][0] = 0.0; } } } } int RedistributeParamVals (void) { int i, j, k, orderLen, nBrlens, lower, upper, numChainsForProc, proc, ierror, *y, *order, *nEvents; MrBFlt *x, *brlens, **position, **rateMult; MPI_Status status; MPI_Request request; Tree *tree; Param *p; extern MrBFlt *paramValues; extern int paramValsRowSize; extern int intValsRowSize; numChainsForProc = numGlobalChains / num_procs; if (numGlobalChains % num_procs > 0) lower = upper = numChainsForProc+1; else lower = upper = numChainsForProc; for (proc=1; proc 0) { y = intValues + 2*intValsRowSize*lower; ierror = MPI_Isend (y, intValsRowSize*2*(upper-lower), MPI_INT, proc, 0, MPI_COMM_WORLD, &request); if (ierror != MPI_SUCCESS) { return (ERROR); } ierror = MPI_Waitall (1, &request, &status); if (ierror != MPI_SUCCESS) { return (ERROR); } } } else if (proc_id == proc) { x = paramValues; ierror = MPI_Irecv (x, paramValsRowSize*2*(upper-lower), MPI_DOUBLE, 0, 0, MPI_COMM_WORLD, &request); if (ierror != MPI_SUCCESS) { return (ERROR); } ierror = MPI_Waitall (1, &request, &status); if (ierror != MPI_SUCCESS) { return (ERROR); } if (intValsRowSize > 0) { y = intValues; ierror = MPI_Irecv (y, intValsRowSize*2*(upper-lower), MPI_INT, 0, 0, MPI_COMM_WORLD, &request); if (ierror != MPI_SUCCESS) { return (ERROR); } ierror = MPI_Waitall (1, &request, &status); if (ierror != MPI_SUCCESS) { return (ERROR); } } } /* mcmc trees */ brlens = (MrBFlt *) SafeCalloc (2*numLocalTaxa, sizeof(MrBFlt)); order = (int *) SafeCalloc (2*numLocalTaxa, sizeof(int)); for (i=lower; inIntNodes - 1; nBrlens = tree->nNodes - 1; if (proc_id == 0) { tree = GetTreeFromIndex(j,i,0); if (tree->isRooted == YES) StoreRTreeWithIndices(tree, order, brlens); else StoreUTree(tree, order, brlens); ierror = MPI_Isend (order, orderLen, MPI_INT, proc, 0, MPI_COMM_WORLD, &request); if (ierror != MPI_SUCCESS) { return (ERROR); } ierror = MPI_Waitall (1, &request, &status); if (ierror != MPI_SUCCESS) { return (ERROR); } ierror = MPI_Isend (brlens, nBrlens, MPI_DOUBLE, proc, 0, MPI_COMM_WORLD, &request); if (ierror != MPI_SUCCESS) { return (ERROR); } ierror = MPI_Waitall (1, &request, &status); if (ierror != MPI_SUCCESS) { return (ERROR); } } else if (proc_id == proc) { tree = GetTreeFromIndex(j,i-lower,0); ierror = MPI_Irecv (order, orderLen, MPI_INT, 0, 0, MPI_COMM_WORLD, &request); if (ierror != MPI_SUCCESS) { return (ERROR); } ierror = MPI_Waitall (1, &request, &status); if (ierror != MPI_SUCCESS) { return (ERROR); } ierror = MPI_Irecv (brlens, nBrlens, MPI_DOUBLE, 0, 0, MPI_COMM_WORLD, &request); if (ierror != MPI_SUCCESS) { return (ERROR); } ierror = MPI_Waitall (1, &request, &status); if (ierror != MPI_SUCCESS) { return (ERROR); } if (tree->isRooted == YES) RetrieveRTreeWithIndices(tree, order, brlens); else { RetrieveUTree(tree, order, brlens); if (localOutGroup != 0) MoveCalculationRoot(tree,localOutGroup); } /* since we only transferred some info, there are additional things we need to consider, like names, constraints and calibrations */ InitializeTreeCalibrations(tree); CheckSetConstraints(tree); SetDatedNodeAges(modelSettings[tree->relParts[0]].brlens, i-lower, 0); strcpy(tree->name, GetTreeFromIndex(j, i, 0)->name); tree = GetTreeFromIndex(j,i-lower,1); strcpy(tree->name, GetTreeFromIndex(j, i, 0)->name); } } } free (brlens); free (order); /* CPP relaxed clock parameters */ for (i=lower; iparamType == P_CPPEVENTS) { if (proc_id == 0) { /* get pointers */ nEvents = p->nEvents[2*i]; position = p->position[2*i]; rateMult = p->rateMult[2*i]; /* send number of events */ ierror = MPI_Isend (nEvents, 2*numLocalTaxa, MPI_INT, proc, 0, MPI_COMM_WORLD, &request); if (ierror != MPI_SUCCESS) return (ERROR); ierror = MPI_Waitall (1, &request, &status); if (ierror != MPI_SUCCESS) return (ERROR); /* send events and clear pointers */ for (k=0; k<2*numLocalTaxa; k++) { if (nEvents[k] > 0) { ierror = MPI_Isend (position[k], nEvents[k], MPI_DOUBLE, proc, 0, MPI_COMM_WORLD, &request); if (ierror != MPI_SUCCESS) return (ERROR); ierror = MPI_Waitall (1, &request, &status); if (ierror != MPI_SUCCESS) return (ERROR); ierror = MPI_Isend (rateMult[k], nEvents[k], MPI_DOUBLE, proc, 0, MPI_COMM_WORLD, &request); if (ierror != MPI_SUCCESS) return (ERROR); ierror = MPI_Waitall (1, &request, &status); if (ierror != MPI_SUCCESS) return (ERROR); free(position[k]); free(rateMult[k]); position[k] = NULL; rateMult[k] = NULL; nEvents[k] = 0; } } } else if (proc_id == proc) { /* find pointers */ nEvents = p->nEvents[2*(i-lower)]; position = p->position[2*(i-lower)]; rateMult = p->rateMult[2*(i-lower)]; /* clear previous events */ for (k=0; k<2*numLocalTaxa; k++) { if (nEvents[k] > 0) { free (position[k]); free (rateMult[k]); position[k] = NULL; rateMult[k] = NULL; nEvents[k] = 0; } } /* receive events */ ierror = MPI_Irecv (nEvents, 2*numLocalTaxa, MPI_INT, 0, 0, MPI_COMM_WORLD, &request); if (ierror != MPI_SUCCESS) return (ERROR); ierror = MPI_Waitall (1, &request, &status); if (ierror != MPI_SUCCESS) return (ERROR); for (k=0; k<2*numLocalTaxa; k++) { if (nEvents[k] > 0) { position[k] = (MrBFlt *) SafeCalloc (nEvents[k], sizeof(MrBFlt)); rateMult[k] = (MrBFlt *) SafeCalloc (nEvents[k], sizeof(MrBFlt)); ierror = MPI_Irecv (position[k], nEvents[k], MPI_DOUBLE, 0, 0, MPI_COMM_WORLD, &request); if (ierror != MPI_SUCCESS) return (ERROR); ierror = MPI_Waitall (1, &request, &status); if (ierror != MPI_SUCCESS) return (ERROR); ierror = MPI_Irecv (rateMult[k], nEvents[k], MPI_DOUBLE, 0, 0, MPI_COMM_WORLD, &request); if (ierror != MPI_SUCCESS) return (ERROR); ierror = MPI_Waitall (1, &request, &status); if (ierror != MPI_SUCCESS) return (ERROR); } } } } } } /* update evolutionary branch lengths or rates (because node indices have changed) */ if (proc_id == proc) { for (i=0; iparamType == P_CPPEVENTS) { tree = GetTree(p, i, 0); UpdateCppEvolLengths (p, tree->root->left, i); } else if (p->paramType == P_TK02BRANCHRATES) { tree = GetTree (p, i, 0); UpdateTK02EvolLengths (p, tree, i); } else if (p->paramType == P_ILNBRANCHRATES || p->paramType == P_IGRBRANCHRATES || p->paramType == P_MIXEDBRCHRATES || p->paramType == P_WNBRANCHRATES) { // *GetParamIntVals(p, i, 0) == RCL_IGR or RCL_ILN tree = GetTree(p, i, 0); UpdateIndBrachLengths (p, tree, i); } } } } } return (NO_ERROR); } int RedistributeTuningParams (void) { int i, j, k, lower, ierror; MrBFlt *x, *sum; x = (MrBFlt *) SafeCalloc (2*numUsedMoves, sizeof(MrBFlt)); sum = x + numUsedMoves; lower = numGlobalChains / num_procs; if (numGlobalChains % num_procs != 0) lower++; if (proc_id != 0) { for (i=0; imoveType->numTuningParams > 0) usedMoves[k]->tuningParam[i][0] = 0.0; } } } for (i=lower; imoveType->numTuningParams > 0) /* we have the tuning parameter of interest */ { x[k] = usedMoves[k]->tuningParam[i][0]; usedMoves[k]->tuningParam[i][0]=0.0; } else x[k] = 0.0; } ierror = MPI_Allreduce (x, sum, numUsedMoves, MPI_DOUBLE, MPI_SUM, MPI_COMM_WORLD); if (ierror != MPI_SUCCESS) { free (x); return (ERROR); } if (j != numLocalChains) /* we have the chain of interest */ { for (k=0; kmoveType->numTuningParams > 0) usedMoves[k]->tuningParam[i][0] = sum[k]; } } } free (x); return (NO_ERROR); } #endif /* RemovePartition: Remove a partition from the tree keeping track of partition frequencies */ int RemovePartition (PFNODE *r, BitsLong *p, int runId) { int i, comp; if (r == NULL) { return (ERROR); } else { for (i=0; ipartition[i] != p[i]) break; } if (i == nLongsNeeded) comp = 0; else if (r->partition[i] < p[i]) comp = -1; else comp = 1; if (comp == 0) /* match */ { if (r->count[runId] == 0) return ERROR; else r->count[runId]--; } else if (comp < 0) /* greater than -> into left subtree */ { if ((RemovePartition (r->left, p, runId)) == ERROR) return ERROR; } else { /* less than -> into right subtree */ if ((RemovePartition (r->right, p, runId)) == ERROR) return ERROR; } } return (NO_ERROR); } /* RemoveTreeFromPartitionCounters: Break a tree into partitions and remove those from counters */ int RemoveTreeFromPartitionCounters (Tree *tree, int treeId, int runId) { int i, j, nTaxa; TreeNode *p; if (tree->isRooted == YES) nTaxa = tree->nNodes - tree->nIntNodes - 1; else nTaxa = tree->nNodes - tree->nIntNodes; for (i=0; inIntNodes-1; i++) { p = tree->intDownPass[i]; assert (p->index >= tree->nNodes - tree->nIntNodes - (tree->isRooted == YES ? 1 : 0)); for (j=0; jindex][j] = partition[p->left->index][j] | partition[p->right->index][j]; } if ((RemovePartition (partFreqTreeRoot[treeId], partition[p->index], runId)) == ERROR) { MrBayesPrint ("%s Could not remove partition %d in RemoveTreeFromPartitionCounters\n", spacer, p->index); ShowParts(stdout,partition[p->index],numLocalTaxa); return ERROR; } } return NO_ERROR; } /* RemoveTreeSamples: Remove tree samples from partition counters */ int RemoveTreeSamples (int from, int to) { int i, j, k, longestLine, line; char c, *s, *lineBuf=0; FILE *fp; Tree *t; TreeList *treeList; char *tempStr; int tempStrSize = TEMPSTRSIZE; # if defined (MPI_ENABLED) if (proc_id != 0) return (NO_ERROR); # endif tempStr = (char *) SafeMalloc((size_t)tempStrSize * sizeof(char)); if (!tempStr) { MrBayesPrint ("%s Problem allocating tempString (%d)\n", spacer, tempStrSize * sizeof(char)); return (ERROR); } if (chainParams.saveTrees == YES) { for (i=0; itree[0]->isRooted == YES) t->isRooted = YES; else t->isRooted = NO; for (j=0; j longestLine) longestLine = line; while ((c = fgetc(fp)) == '\r' || c == '\n') ; } lineBuf = (char *) SafeCalloc (longestLine + 10, sizeof (char)); if (!lineBuf) { SafeFclose (&fp); free (tempStr); return (ERROR); } fsetpos (fp, &chainParams.tFilePos[j*numTopologies+i]); /* The fsetpos and fgetpos pair are affected by writing to the file, at least on Windows systems. The effect is to put the subsequent fsetpos back a few positions. The following code will deal with this problem without affecting systems where this does not happen. */ do { c = fgetc(fp); } while (c != 't'); for (k=from; k<=to; k++) { if (fgets (lineBuf, longestLine + 5, fp) == NULL) { free (tempStr); free (lineBuf); return ERROR; } s = strtok (lineBuf, " "); for (s = strtok (NULL, ";"); *s != '('; s++) ; StripComments (s); if (ResetTopology (t, s) == ERROR) { SafeFclose (&fp); free (tempStr); free (lineBuf); return (ERROR); } if (RemoveTreeFromPartitionCounters (t, i, j) == ERROR) { SafeFclose (&fp); free (tempStr); free (lineBuf); return (ERROR); } } fgetpos (fp, &chainParams.tFilePos[j*numTopologies+i]); free (lineBuf); SafeFclose (&fp); } } } /* remove unnecessary nodes from the tree holding partition counters */ for (i=0; i 1 && chainParams.numRuns == 1) sprintf (fileName, "%s.tree%d.t", localFileName, i+1); else if (numTrees == 1 && chainParams.numRuns > 1) sprintf (fileName, "%s.run%d.t", localFileName, n+1); else sprintf (fileName, "%s.tree%d.run%d.t", localFileName, i+1, n+1); if ((fpTree[n][i] = OpenTextFileA (fileName)) == NULL) return (ERROR); } } /* Reopen the .mcmc file */ if (chainParams.mcmcDiagn == YES) { sprintf (fileName, "%s.mcmc", localFileName); if ((fpMcmc = OpenTextFileA (fileName)) == NULL) return (ERROR); } # if defined (PRINT_DUMP) for (n=0; nnValues; k++) { toVals[k] = fromVals[k]; fromVals[k] = swapVals[k]; } toVals = GetParamSubVals (p, toChn, 0); swapVals = GetParamSubVals (p, toChn, 1); fromVals = GetParamSubVals (p, fromChn, state[fromChn]); for (k=0; knSubValues; k++) { toVals[k] = fromVals[k]; fromVals[k] = swapVals[k]; } if (p->nStdStateFreqs > 0) { toVals = GetParamStdStateFreqs (p, toChn, 0); swapVals = GetParamStdStateFreqs (p, toChn, 1); fromVals = GetParamStdStateFreqs (p, fromChn, state[fromChn]); for (k=0; knStdStateFreqs; k++) { toVals[k] = fromVals[k]; fromVals[k] = swapVals[k]; } } } /* mcmc trees */ for (j=0; jparamType == P_CPPEVENTS) { to = p->nEvents[2*toChn]; swap = p->nEvents[2*toChn+1]; from = p->nEvents[2*fromChn+state[fromChn]]; toPosition = p->position[2*toChn]; swapPosition = p->position[2*toChn+1]; fromPosition = p->position[2*fromChn+state[fromChn]]; toRateMult = p->rateMult[2*toChn]; swapRateMult = p->rateMult[2*toChn+1]; fromRateMult = p->rateMult[2*fromChn+state[fromChn]]; for (k=0; k<2*numLocalTaxa; k++) { if (from[k] > 0) { toPosition[k] = (MrBFlt *) SafeRealloc ((void *)toPosition[k], from[k]*sizeof (MrBFlt)); toRateMult[k] = (MrBFlt *) SafeRealloc ((void *)toRateMult[k], from[k]*sizeof (MrBFlt)); for (k1=0; k1 0) { free (toPosition[k]); toPosition[k] = NULL; free (toRateMult[k]); toRateMult[k] = NULL; } to[k] = from[k]; if (swap[k] > 0) { fromPosition[k] = (MrBFlt *) SafeRealloc ((void *)fromPosition[k], swap[k]*sizeof (MrBFlt)); fromRateMult[k] = (MrBFlt *) SafeRealloc ((void *)fromRateMult[k], swap[k]*sizeof (MrBFlt)); for (k1=0; k1 0) { free (fromPosition[k]); fromPosition[k] = NULL; free (fromRateMult[k]); fromRateMult[k] = NULL; } from[k] = swap[k]; } } } /* reset state of chain */ state[toChn] = 0; /* make sure that the id is correct for the from chain */ tempId = curId[toChn]; curId[fromChn] = curId[toChn]; curId[toChn] = tempId; } free (curId); } /* ResetFlips: Reset flipped cond likes etc after rejection */ void ResetFlips (int chain) { int d, i; ModelInfo *m; TreeNode *p; Tree *tree; #if defined (BEAGLE_ENABLED) int *isScalerNode=NULL; #endif for (d=0; duseBeagle == YES) isScalerNode = m->isScalerNode[chain]; #endif /* skip the following if we have irrelevant model or data types */ if (m->upDateCl != YES || m->parsModelId == YES) continue; #if defined (BEAGLE_ENABLED) if (m->useBeagle == NO || beagleScalingScheme == MB_BEAGLE_SCALE_ALWAYS || m->rescaleBeagleAll == YES) { FlipSiteScalerSpace (m, chain); if (m->useBeagle == YES && m->rescaleBeagleAll == YES) m->rescaleFreq[chain] = m->rescaleFreqOld; } #else if (m->dataType != CONTINUOUS) FlipSiteScalerSpace (m, chain); #endif if (m->upDateCijk == YES && m->nCijkParts > 0) FlipCijkSpace (m, chain); /* cycle over tree */ tree = GetTree (m->brlens, chain, state[chain]); for (i=0; inNodes; i++) { p = tree->allDownPass[i]; if (p->upDateTi == YES || (m->dataType == CONTINUOUS && p->upDateCl == YES)) FlipTiProbsSpace (m, chain, p->index); if (p->right != NULL) { /* do not flip terminals in case these flags are inappropriately set by moves do not consider continuous data using beagle either */ if (p->upDateCl == YES) { FlipCondLikeSpace (m, chain, p->index); #if defined (BEAGLE_ENABLED) if (m->useBeagle == NO || beagleScalingScheme == MB_BEAGLE_SCALE_ALWAYS || (m->rescaleBeagleAll == YES && isScalerNode[p->index] == YES)) FlipNodeScalerSpace (m, chain, p->index); #else if (m->dataType != CONTINUOUS) FlipNodeScalerSpace (m, chain, p->index); #endif } #if defined (BEAGLE_ENABLED) else if (m->rescaleBeagleAll == YES) { FlipCondLikeSpace (m, chain, p->index); if (isScalerNode[p->index] == YES) FlipNodeScalerSpace (m, chain, p->index); } #endif } } /* division flag and tree node flags are reset when trees are copied */ } } /*------------------------------------------------------------------- | | ResetScalersPartition: reset scaler nodes of the given tree by appropriately setting isScalerNode array. | @param isScalerNode is an array which gets set with information about scaler node. | For each internal node isScalerNode[node->index] is set to YES if it has to be scaler node. | Note: Only internal nodes can become scaler nodes thus isScalerNode is set only for elements in interval [numLocalTaxa, numLocalTaxa+t->nIntNodes] | | @param rescaleFreq effectively represent gaps between rescaling, higher number means more sparse choice of rescaling nodes | --------------------------------------------------------------------*/ int ResetScalersPartition (int *isScalerNode, Tree* t, unsigned rescaleFreq) { int n; TreeNode *p; /* set the node depth value of terminal nodes to zero; reset scalerNode */ for (n=0; nnNodes; n++) { p = t->allDownPass[n]; if (p->left == NULL) p->x = 0; } /* loop over interior nodes */ for (n=0; nnIntNodes; n++) { p = t->intDownPass[n]; assert (((p->index - numLocalTaxa) >= 0) && ((p->index - numLocalTaxa) < t->nIntNodes)); p->x = p->left->x + p->right->x + 1; if (p->x > 2 * (int)rescaleFreq) { assert (p->left->left != NULL && p->right->left != NULL); isScalerNode[p->left->index] = YES; p->left->x = 0; isScalerNode[p->right->index] = YES; p->right->x = 0; p->x = 1; } else if (p->x > (int)rescaleFreq) { if (p->left->x > p->right->x) { assert (p->left->left != NULL); isScalerNode[p->left->index] = YES; p->left->x = 0; } else { assert (p->right->left != NULL); isScalerNode[p->right->index] = YES; p->right->x = 0; } p->x = p->left->x + p->right->x + 1; } else isScalerNode[p->index] = NO; } return NO_ERROR; } /*---------------------------------------------------------------------- | | ReusePreviousResults: Save old .p, .t, .ss and .mcmc files with ~ extension, | then prepare new print files with the relevant old values added in | The number of samples is returned in numSamples | ------------------------------------------------------------------------*/ int ReusePreviousResults (int *numSamples, int steps) { int i, n; char localFileName[100], fileName[230], bkupName[230]; (*numSamples) = 0; # if defined (MPI_ENABLED) if (proc_id != 0) return (NO_ERROR); # endif /* Allocate space for file pointers */ if (memAllocs[ALLOC_FILEPOINTERS] == YES) { MrBayesPrint ("%s File pointers already allocated in ReusePreviousResults\n", spacer); return ERROR; } fpMcmc = NULL; fpSS = NULL; fpParm = NULL; fpTree = NULL; fpParm = (FILE **) SafeCalloc (chainParams.numRuns, sizeof (FILE *)); if (fpParm == NULL) { MrBayesPrint ("%s Could not allocate fpParm in ReusePreviousResults\n", spacer); return ERROR; } memAllocs[ALLOC_FILEPOINTERS] = YES; fpTree = (FILE ***) SafeCalloc (chainParams.numRuns, sizeof (FILE **)); if (fpTree == NULL) { MrBayesPrint ("%s Could not allocate fpTree in ReusePreviousResults\n", spacer); return ERROR; } fpTree[0] = (FILE **) SafeCalloc (numTrees*chainParams.numRuns, sizeof (FILE *)); if (fpTree[0] == NULL) { MrBayesPrint ("%s Could not allocate fpTree[0] in ReusePreviousResults\n", spacer); return ERROR; } for (i=1; i 1 && chainParams.numRuns == 1) sprintf (fileName, "%s%s.tree%d.t", workingDir, localFileName, i+1); else if (numTrees == 1 && chainParams.numRuns > 1) sprintf (fileName, "%s%s.run%d.t", workingDir, localFileName, n+1); else sprintf (fileName, "%s%s.tree%d.run%d.t", workingDir, localFileName, i+1, n+1); strcpy(bkupName,fileName); strcat(bkupName,"~"); remove(bkupName); if (rename(fileName,bkupName) != 0) { MrBayesPrint ("%s Could not rename file %s\n", spacer, fileName); return ERROR; } if ((fpTree[n][i] = OpenNewMBPrintFile (fileName+strlen(workingDir))) == NULL) return (ERROR); else if (CopyTreeResults(fpTree[n][i],bkupName+strlen(workingDir),numPreviousGen,numSamples) == ERROR) return (ERROR); } } /* Store old and prepare new .ss file */ if (chainParams.isSS == YES) { sprintf (fileName, "%s%s.ss", workingDir, chainParams.chainFileName); strcpy(bkupName,fileName); strcat(bkupName,"~"); remove(bkupName); if (rename(fileName,bkupName) != 0) { MrBayesPrint ("%s Could not rename file %s\n", spacer, fileName); return ERROR; } if ((fpSS = OpenNewMBPrintFile (fileName+strlen(workingDir))) == NULL) return (ERROR); else if (CopyProcessSsFile(fpSS,bkupName+strlen(workingDir),steps,marginalLnLSS,splitfreqSS)==ERROR) return (ERROR); } /* Store old and prepare new .mcmc file */ if (chainParams.mcmcDiagn == YES) { sprintf (fileName, "%s%s.mcmc", workingDir, chainParams.chainFileName); strcpy(bkupName,fileName); strcat(bkupName,"~"); remove(bkupName); if (rename(fileName,bkupName) != 0) { MrBayesPrint ("%s Could not rename file %s\n", spacer, fileName); return ERROR; } if ((fpMcmc = OpenNewMBPrintFile (fileName+strlen(workingDir))) == NULL) return (ERROR); else if (CopyResults(fpMcmc,bkupName+strlen(workingDir),numPreviousGen)==ERROR) return (ERROR); } # if defined (PRINT_DUMP) fpDump = (FILE **) SafeCalloc (chainParams.numRuns, sizeof (FILE *)); for (n=0; nisRooted == NO) break; if (i < numTrees && numLocalTaxa < 4) { MrBayesPrint ("%s There must be at least four taxa in the analysis\n", spacer); return (ERROR); } else if (i == numTrees && numLocalTaxa < 3) { MrBayesPrint ("%s There must be at least three taxa in the analysis\n", spacer); return (ERROR); } } /* allocate some memory for the chains */ if (memAllocs[ALLOC_CURLNL] == YES) { MrBayesPrint ("%s curLnL is already allocated\n", spacer); nErrors++; } else if ((curLnL = (MrBFlt *)SafeMalloc((size_t)numLocalChains * sizeof(MrBFlt))) == NULL) { MrBayesPrint ("%s Problem allocating curLnL (%d)\n", spacer, numLocalChains * sizeof(MrBFlt)); nErrors++; } else if ((maxLnL0 = (MrBFlt *) SafeCalloc ((size_t)(chainParams.numRuns) * (size_t)(chainParams.numChains), sizeof(MrBFlt))) == NULL) { MrBayesPrint ("%s Problem allocating maxLnL0\n", spacer, numLocalChains * sizeof(MrBFlt)); free (curLnL); nErrors++; } else memAllocs[ALLOC_CURLNL] = YES; # if defined (MPI_ENABLED) MPI_Allreduce (&nErrors, &sumErrors, 1, MPI_INT, MPI_SUM, MPI_COMM_WORLD); if (sumErrors > 0) { MrBayesPrint ("%s Memory allocation error on at least one processor\n", spacer); return ERROR; } # else if (nErrors > 0) return ERROR; # endif if (memAllocs[ALLOC_CURLNPR] == YES) { MrBayesPrint ("%s curLnPr is already allocated\n", spacer); nErrors++; } else if ((curLnPr = (MrBFlt *)SafeMalloc((size_t)numLocalChains * sizeof(MrBFlt))) == NULL) { MrBayesPrint ("%s Problem allocating curLnPr (%d)\n", spacer, numLocalChains * sizeof(MrBFlt)); nErrors++; } else memAllocs[ALLOC_CURLNPR] = YES; # if defined (MPI_ENABLED) MPI_Allreduce (&nErrors, &sumErrors, 1, MPI_INT, MPI_SUM, MPI_COMM_WORLD); if (sumErrors > 0) { MrBayesPrint ("%s Memory allocation error on at least one processor\n", spacer); return ERROR; } # else if (nErrors > 0) return ERROR; # endif if (memAllocs[ALLOC_CHAINID] == YES) { MrBayesPrint ("%s chainId is already allocated\n", spacer); nErrors++; } else if ((chainId = (int *)SafeMalloc((size_t)numLocalChains * sizeof(int))) == NULL) { MrBayesPrint ("%s Problem allocating chainId (%d)\n", spacer, numLocalChains * sizeof(int)); nErrors++; } else memAllocs[ALLOC_CHAINID] = YES; # if defined (MPI_ENABLED) MPI_Allreduce (&nErrors, &sumErrors, 1, MPI_INT, MPI_SUM, MPI_COMM_WORLD); if (sumErrors > 0) { MrBayesPrint ("%s Memory allocation error on at least one processor\n", spacer); return ERROR; } # else if (nErrors > 0) return ERROR; # endif if (memAllocs[ALLOC_SWAPINFO] == YES) { MrBayesPrint ("%s swapInfo is already allocated\n", spacer); nErrors++; } else if ((swapInfo = (int ***) SafeCalloc (chainParams.numRuns, sizeof (int **))) == NULL) { MrBayesPrint ("%s Problem allocating swapInfo\n", spacer); nErrors++; } else { for (k=0; k 0) { MrBayesPrint ("%s Memory allocation error on at least one processor\n", spacer); return ERROR; } # else if (nErrors > 0) return ERROR; # endif for (k=0; k 1) { if (SetUpPartitionCounters () == ERROR) nErrors++; # if defined (MPI_ENABLED) if (proc_id == 0) { # endif if (chainParams.relativeBurnin == YES) { /* we have to remove trees later on */ if (chainParams.saveTrees == YES) { if (chainParams.treeList) nErrors++; else { chainParams.treeList = (TreeList *) SafeCalloc (chainParams.numRuns*numTopologies, sizeof (TreeList)); if (!chainParams.treeList) nErrors++; } if (nErrors == 0) memAllocs[ALLOC_TREELIST] = YES; if (noWarn == YES) chainParams.stopTreeGen = (long long) (chainParams.numGen * chainParams.burninFraction); else chainParams.stopTreeGen = chainParams.numGen; } else /* if (chainParams.saveTrees == NO) */ { chainParams.tFilePos = (fpos_t*) SafeRealloc ((void *)chainParams.tFilePos, chainParams.numRuns*numTopologies*sizeof (fpos_t)); if (!chainParams.tFilePos) nErrors++; else memAllocs[ALLOC_TFILEPOS] = YES; } } # if defined (MPI_ENABLED) } # endif # if defined (MPI_ENABLED) if (proc_id == 0) { if ((chainParams.stat = (STATS *) SafeCalloc (numTopologies, sizeof (STATS))) == NULL) nErrors++; else { memAllocs[ALLOC_STATS] = YES; for (i=0; i 0) { MrBayesPrint ("%s Memory allocation error on at least one processor\n", spacer); return ERROR; } # else if ((chainParams.stat = (STATS *) SafeCalloc (numTopologies, sizeof (STATS))) == NULL) return ERROR; else { memAllocs[ALLOC_STATS] = YES; for (i=0; i 2) MrBayesPrint ("%s There are %d more chains on other processor(s)\n\n", spacer, numGlobalChains - numLocalChains); else if (num_procs==2) MrBayesPrint ("%s There are %d more chains on the other processor\n\n", spacer, numGlobalChains - numLocalChains); # endif /* All steps are assumed to have the same length. */ if (chainParams.isSS == YES) { numGenInStepSS = (chainParams.numGen - (long long)(chainParams.burninSS)*chainParams.sampleFreq)/ chainParams.numStepsSS; numGenInStepSS = chainParams.sampleFreq*(numGenInStepSS/chainParams.sampleFreq); /*make muliple of chainParams.sampleFreq*/ numGenOld = chainParams.numGen; chainParams.numGen = ((long long)(chainParams.burninSS)*chainParams.sampleFreq + chainParams.numStepsSS*numGenInStepSS) ; if (stepRelativeBurninSS==YES) numGenInStepBurninSS = ((long long)(numGenInStepSS*chainParams.burninFraction / chainParams.sampleFreq))*chainParams.sampleFreq; else numGenInStepBurninSS = (long long)(chainParams.chainBurnIn) * chainParams.sampleFreq; MrBayesPrint ("\n"); MrBayesPrint ("%s Starting stepping-stone sampling to estimate marginal likelihood. \n", spacer); MrBayesPrint ("%s %d steps will be used with %lli generations (%d samples) within each step. \n", spacer, chainParams.numStepsSS, numGenInStepSS, (int)(numGenInStepSS/chainParams.sampleFreq)); MrBayesPrint ("%s Total of %lli generations (%d samples) will be collected while first \n", spacer, chainParams.numGen, (int)(chainParams.numGen/chainParams.sampleFreq)); MrBayesPrint ("%s %d generations (%d samples) will be discarded as initial burnin. \n", spacer, chainParams.burninSS*chainParams.sampleFreq, chainParams.burninSS); MrBayesPrint ("%s Additionally at the beginning of each step %lli generations (%d samples) \n", spacer, numGenInStepBurninSS, (int)(numGenInStepBurninSS/chainParams.sampleFreq)); MrBayesPrint ("%s will be discarded as burnin. \n", spacer); if (chainParams.startFromPriorSS==YES) MrBayesPrint ("%s Sampling from prior to posterior, i.e. first step samples from prior. \n", spacer); else { MrBayesPrint ("%s Sampling from posterior to prior, i.e. first step samples from close to \n", spacer); MrBayesPrint ("%s posterior. \n", spacer); } if (numGenOld != chainParams.numGen) { MrBayesPrint ("%s NOTE: Number of generation of each step is reduced to the closest multi-\n", spacer); MrBayesPrint ("%s ple of sampling frequency. That is why, in total it will be taken %lli \n", spacer, chainParams.numGen); MrBayesPrint ("%s generations instead of requested %lli. \n", spacer, numGenOld); } MrBayesPrint ("\n"); if ((numGenInStepSS-numGenInStepBurninSS)/chainParams.sampleFreq < 1) { MrBayesPrint ("%s There is less then one sample in each step of stepping-stone sampling. \n", spacer); MrBayesPrint ("%s Please adjust burnin, number of generations, sampling frequency or \n", spacer); MrBayesPrint ("%s number of step in order to allow at least one sample per step. \n", spacer); return ERROR; /*All MPI run will return here since all of them have the same values*/ } if (numPreviousGen==0 || numPreviousGen < chainParams.burninSS * chainParams.sampleFreq) { lastStepEndSS = (long long)(chainParams.burninSS) * chainParams.sampleFreq; stepIndexSS = chainParams.numStepsSS-1; if (numPreviousGen != 0) removeTo=(int)((numPreviousGen/chainParams.sampleFreq))+1; if (chainParams.startFromPriorSS==YES) { // powerSS = BetaQuantile (chainParams.alphaSS, 1.0, (MrBFlt)(chainParams.numStepsSS-1-stepIndexSS)/(MrBFlt)chainParams.numStepsSS); powerSS = 0.0; stepLengthSS = BetaQuantile (chainParams.alphaSS, 1.0, (MrBFlt)(chainParams.numStepsSS-stepIndexSS)/(MrBFlt)chainParams.numStepsSS)-powerSS; } else { powerSS = BetaQuantile (chainParams.alphaSS, 1.0, (MrBFlt)stepIndexSS/(MrBFlt)chainParams.numStepsSS); stepLengthSS = 1.0-powerSS; } samplesCountSS=0; } else { stepIndexSS = (int)((numPreviousGen-(long long)(chainParams.burninSS) * chainParams.sampleFreq)/numGenInStepSS); /* for now it holds number of steps we fully completed*/ lastStepEndSS = (long long)(chainParams.burninSS) * chainParams.sampleFreq + (long long)(stepIndexSS)*numGenInStepSS; removeTo = chainParams.burninSS + (int)(((long long)(stepIndexSS)*numGenInStepSS+numGenInStepBurninSS)/chainParams.sampleFreq) + 1; if (numPreviousGen < (long long)((removeTo-1))*chainParams.sampleFreq) removeTo=(int)(numPreviousGen/chainParams.sampleFreq+1); stepIndexSS = chainParams.numStepsSS-1-stepIndexSS; if (chainParams.startFromPriorSS==YES) { powerSS = BetaQuantile (chainParams.alphaSS, 1.0, (MrBFlt)(chainParams.numStepsSS-1-stepIndexSS)/(MrBFlt)chainParams.numStepsSS); stepLengthSS = BetaQuantile (chainParams.alphaSS, 1.0, (MrBFlt)(chainParams.numStepsSS-stepIndexSS)/(MrBFlt)chainParams.numStepsSS)-powerSS; } else { powerSS = BetaQuantile (chainParams.alphaSS, 1.0, (MrBFlt)stepIndexSS/(MrBFlt)chainParams.numStepsSS); stepLengthSS = BetaQuantile (chainParams.alphaSS, 1.0, (MrBFlt)(stepIndexSS+1)/(MrBFlt)chainParams.numStepsSS)-powerSS; } # ifdef SAMPLE_ALL_SS samplesCountSS = (int)((numPreviousGen-lastStepEndSS-numGenInStepBurninSS)); # else samplesCountSS = (int)((numPreviousGen-lastStepEndSS-numGenInStepBurninSS)/chainParams.sampleFreq); # endif if (samplesCountSS < 0) samplesCountSS=0; MrBayesPrint ("%s Continue sampling step %d out of %d steps...\n", spacer, chainParams.numStepsSS-stepIndexSS, chainParams.numStepsSS); /*marginalLnLSS will be read from file and distributed to other MPI_proc later. stepScalerSS, stepAcumulatorSS are already read and if (samplesCountSS!=0) they will be redistributed. */ } if (samplesCountSS == 0) /* in appended case it also can happen */ { for (run=0; run 1 && chainParams.mcmcDiagn == YES) /* we potentially need to add tree samples for conv diagn */ { /* Add tree samples to partition counters */ if (chainParams.relativeBurnin == YES) { if (numPreviousGen/(i-1) != chainParams.sampleFreq) { MrBayesPrint ("%s 1. Use the same sampling frequency as in the previous run to use relative burnin.\n", spacer); MrBayesPrint ("%s 2. Check (and modify) the number in [generation: number] at line 3 of the .ckp file\n", spacer); MrBayesPrint ("%s to match the previous number of generations in all the .p and .t files. Such deviations\n", spacer); MrBayesPrint ("%s may happen if checkfreq was smaller than samplefreq.\n", spacer); MrBayesPrint ("%s 3. Rarely, delete the last sample/line in the .p and .t files to achieve 2. above.\n", spacer); MrBayesPrint ("%s Such deviations may happen if ngen was not divisible by samplefreq.\n", spacer); nErrors++; } if (chainParams.isSS == NO) { if (noWarn == YES) { /* We definitely know the final number of generations */ j = (int)((chainParams.numGen/chainParams.sampleFreq)+1); j = (int) (j*chainParams.burninFraction); } else /* User may extend chain so save all trees if saving trees */ j = i; if (j < i) { if (AddTreeSamples(1,j,chainParams.saveTrees) == ERROR) nErrors++; if (AddTreeSamples(j+1,i,NO) == ERROR) nErrors++; /* Since we never need to remove trees from partition counter after total burnin we put NO in the last argument */ } else { if (AddTreeSamples(1,i,chainParams.saveTrees) == ERROR) nErrors++; } } else { if (SetFilePositions(removeTo) == ERROR) nErrors++; if (AddTreeSamples(removeTo+1,i,chainParams.saveTrees) == ERROR) nErrors++; } } else if (chainParams.chainBurnIn < i) { if (AddTreeSamples(chainParams.chainBurnIn,i-1,chainParams.saveTrees) == ERROR) nErrors++; } } if (nErrors == 0) { if (chainParams.isSS == NO && chainParams.mcmcDiagn == YES && chainParams.numRuns > 1) { MrBayesPrint ("\n"); if (chainParams.relativeBurnin == YES) MrBayesPrint ("%s Using a relative burnin of %.1f %% for diagnostics\n", spacer, 100.0*chainParams.burninFraction); else MrBayesPrint ("%s Using an absolute burnin of %d samples for diagnostics\n", spacer, chainParams.chainBurnIn); } MrBayesPrint ("\n"); MrBayesPrint ("%s Chain results (continued from previous run; %lli generations requested):\n\n", spacer, chainParams.numGen); } # if defined (MPI_ENABLED) } # endif if (chainParams.autotune == YES) { for (i=0; icumProposalProb[chainId[i]]; else f = usedMoves[j]->cumProposalProb[chainId[i]] - usedMoves[j-1]->cumProposalProb[chainId[i]]; if (usedMoves[j]->targetRate[chainId[i]] > 0.0) { /* autotuned move; we assume it was perfectly tuned and start tuning from there at appropriate rate */ usedMoves[j]->nBatches[chainId[i]] = (int) (f*numPreviousGen/chainParams.tuneFreq); usedMoves[j]->lastAcceptanceRate[chainId[i]] = usedMoves[j]->targetRate[chainId[i]]; } else { /* not autotuned move; no previous batches will result in acceptance rate calculated from new samples */ usedMoves[j]->nBatches[chainId[i]] = 0; usedMoves[j]->lastAcceptanceRate[chainId[i]] = 0.0; } } } } # if defined (MPI_ENABLED) if (chainParams.isSS == YES) { MPI_Bcast (marginalLnLSS, chainParams.numRuns, MPI_DOUBLE, 0, MPI_COMM_WORLD); if (samplesCountSS != 0) { MPI_Bcast (stepScalerSS, chainParams.numRuns, MPI_DOUBLE, 0, MPI_COMM_WORLD); MPI_Bcast (stepAcumulatorSS, chainParams.numRuns, MPI_DOUBLE, 0, MPI_COMM_WORLD); } /*Set to zero all runs that we are not responsable for*/ for (run=0; run 0) { MrBayesPrint ("%s Error appending to previous run\n", spacer); return ERROR; } # else if (nErrors == 1) { MrBayesPrint ("%s Error appending to previous run\n", spacer); return ERROR; } # endif } else { if (PreparePrintFiles() == ERROR) nErrors++; } # if defined (MPI_ENABLED) MPI_Allreduce (&nErrors, &sumErrors, 1, MPI_INT, MPI_SUM, MPI_COMM_WORLD); if (sumErrors > 0) { MrBayesPrint ("%s Error preparing print files on at least one processor\n", spacer); CloseMBPrintFiles(); return ERROR; } # else if (nErrors > 0) { MrBayesPrint ("%s Error preparing print files\n", spacer); CloseMBPrintFiles(); return ERROR; } # endif stopChain = NO; for (i=0; i 0) { MrBayesPrint ("%s Aborting run.\n"); return ERROR; } # endif if (chainParams.mcmcDiagn == YES) { if (PrintMCMCDiagnosticsToFile (0) == ERROR) { MrBayesPrint ("%s Problem printing MCMC diagnostics headers to file\n", spacer); # if defined (MPI_ENABLED) nErrors++; # else return (ERROR); # endif } # if defined (MPI_ENABLED) MPI_Allreduce (&nErrors, &sumErrors, 1, MPI_INT, MPI_SUM, MPI_COMM_WORLD); if (sumErrors > 0) { MrBayesPrint ("%s Aborting run.\n"); return ERROR; } # endif if (chainParams.isSS == YES && chainParams.burninSS == 0 && chainParams.numRuns > 1) { /* Remove first sample (generation 0) from diagnostics */ removeTo=1; if (RemoveTreeSamples (1,1) == ERROR) { MrBayesPrint ("%s Problem removing tree samples\n"); # if defined (MPI_ENABLED) nErrors++; # else return ERROR; # endif } # if defined (MPI_ENABLED) MPI_Allreduce (&nErrors, &sumErrors, 1, MPI_INT, MPI_SUM, MPI_COMM_WORLD); if (sumErrors > 0) { MrBayesPrint ("%s Aborting run.\n"); return ERROR; } # endif } } if (chainParams.isSS == YES) { if (chainParams.burninSS == 0) MrBayesPrint ("%s Sampling step 1 out of %d steps...\n\n",spacer, chainParams.numStepsSS); /*Printing SS header*/ MrBayesPrintf (fpSS, "[LEGEND: The file contains statistics on the Steppingstone Sampling.]\n"); MrBayesPrintf (fpSS, "[ID: %s]\n", stamp); MrBayesPrintf (fpSS, "[ Step -- Index of the step ]\n"); MrBayesPrintf (fpSS, "[ Power -- At each step we sample from the distribution with density (Likelihood^Power)*Prior ]\n"); MrBayesPrintf (fpSS, "[ runX -- Contribution to the marginal log likelihood of run X, i.e. marginal log likelihood for run X is the sum across all steps in column runX. ]\n"); if (chainParams.diagnStat == AVGSTDDEV) MrBayesPrintf (fpSS, "[ aSplitX -- Average standard deviation of split frequencies of tree X. -2.0 is printed if no diagnostics was requested. -1.0 is printed if there were no splits with frequency above minimum.]\n"); else MrBayesPrintf (fpSS, "[ mSplitX -- Maximal standard deviation of split frequencies of tree X. -2.0 is printed if no diagnostics was requested. -1.0 is printed if there were no splits with frequency above minimum.]\n"); MrBayesPrintf (fpSS, "Step\tPower"); for (j=0; j 10 * CLOCKS_PER_SEC) { CPUTime += (currentCPUTime - previousCPUTime) / (MrBFlt) (CLOCKS_PER_SEC); previousCPUTime = currentCPUTime; } /*! requestAbortRun is set by the signal handler when it receives a CTRL-C (serial version only) */ if (requestAbortRun == YES && ConfirmAbortRun() == 1) return ABORT; // RandLong oldSeed = *seed; /* record the old seed for debugging */ for (chn=0; chnname); # endif # if defined (BEST_MPI_ENABLED) bestCycleGen = n % (numNonTreeMoves + numTreeMoves + numBestMoves); if (bestCycleGen < numNonTreeMoves) PickNonTreeProposal(seed, chainId[chn]); else if (bestCycleGen < numNonTreeMoves + numTreeMoves) PickTreeProposal(seed, chainId[chn]); else PickBestProposal(chainId[chn]); # endif /* set prior and proposal ratios */ lnProposalRatio = 0.0; lnPriorRatio = 0.0; /* reset abort move flag */ abortMove = NO; /* Touch the relevant partitions */ /* as a service to the move functions. */ for (i=0; iparm->nRelParts; i++) modelSettings[theMove->parm->relParts[i]].upDateCl = YES; # ifndef NDEBUG if (IsTreeConsistent(theMove->parm, chn, state[chn]) != YES) { printf ("IsTreeConsistent failed before a move!\n"); return ERROR; } # endif # if defined (DEBUG_CONSTRAINTS) if (theMove->parm->paramType == P_TOPOLOGY && DoesTreeSatisfyConstraints(GetTree (theMove->parm, chn, state[chn]))!=YES) { printf ("DEBUG ERROR: DoesTreeSatisfyConstraints failed before a move\n"); return ERROR; } # endif /* make move */ if ((theMove->moveFxn)(theMove->parm, chn, seed, &lnPriorRatio, &lnProposalRatio, theMove->tuningParam[chainId[chn]]) == ERROR) { printf ("%s Error in move %s\n", spacer, theMove->name); # if defined (MPI_ENABLED) nErrors++; # else return ERROR; # endif } if (theMove->parm->paramType == P_TOPOLOGY && DoesTreeSatisfyConstraints(GetTree (theMove->parm, chn, state[chn])) != YES) { # if defined (DEBUG_CONSTRAINTS) if (DoesTreeSatisfyConstraints(GetTree (theMove->parm, chn, state[chn]))==ABORT) { printf ("DEBUG ERROR: DoesTreeSatisfyConstraints failed after move '%s'\n", theMove->name); } # endif abortMove = YES; } /* abortMove is set to YES if the calculation fails because the likelihood is too small */ if (abortMove == NO) lnLike = LogLike(chn); /* calculate acceptance probability */ if (abortMove == NO) { lnLikelihoodRatio = lnLike - curLnL[chn]; lnPrior = curLnPr[chn] + lnPriorRatio; # ifndef NDEBUG /* We check various aspects of calculations in debug version of code */ if (IsTreeConsistent(theMove->parm, chn, state[chn]) != YES) { printf ("DEBUG ERROR: IsTreeConsistent failed after move '%s'\n", theMove->name); return ERROR; } if (lnPriorRatio != lnPriorRatio) { printf ("DEBUG ERROR: Log prior ratio nan after move '%s'\n", theMove->name); // printf ("Seed: %ld\n", oldSeed); state[chn] ^= 1; PrintCheckPoint (n); return ERROR; } if (fabs((lnPrior-LogPrior(chn))/lnPrior) > 0.0001) { printf ("DEBUG ERROR: Log prior incorrect after move '%s' :%e :%e\n", theMove->name,lnPrior,LogPrior(chn)); // printf ("Seed: %ld\n", oldSeed); state[chn] ^= 1; PrintCheckPoint (n); return ERROR; } if (lnProposalRatio != lnProposalRatio) { printf ("DEBUG ERROR: Log proposal ratio nan after move '%s'\n", theMove->name); // printf ("Seed: %ld\n", oldSeed); state[chn] ^= 1; PrintCheckPoint (n); return ERROR; } if (lnLike != lnLike) { printf ("DEBUG ERROR: Log likelihood nan after move '%s'\n", theMove->name); // printf ("Seed: %ld\n", oldSeed); state[chn] ^= 1; PrintCheckPoint (n); return ERROR; } # if defined (DEBUG_LNLIKELIHOOD) /* slow */ ResetFlips(chn); /* needed to return flags so they point to old state */ TouchEverything(chn); if (fabs((lnLike-LogLike(chn))/lnLike) > 0.0001) { printf ("DEBUG ERROR: Log likelihood incorrect after move '%s'\n", theMove->name); return ERROR; } # endif if (theMove->parm->paramType == P_TOPOLOGY && GetTree (theMove->parm, chn, state[chn])->isClock == YES && IsClockSatisfied (GetTree (theMove->parm, chn, state[chn]),0.001) == NO) { printf ("%s Branch lengths of the tree do not satisfy the requirements of a clock tree.\n", spacer); ShowNodes(GetTree (theMove->parm, chn, state[chn])->root,0,YES); return (ERROR); } # endif /* heat */ lnLikelihoodRatio *= Temperature (chainId[chn]); lnPriorRatio *= Temperature (chainId[chn]); if (chainParams.isSS == YES) lnLikelihoodRatio *= powerSS; /* calculate the acceptance probability */ if (lnLikelihoodRatio + lnPriorRatio + lnProposalRatio < -100.0) r = 0.0; else if (lnLikelihoodRatio + lnPriorRatio + lnProposalRatio > 0.0) r = 1.0; else r = exp(lnLikelihoodRatio + lnPriorRatio + lnProposalRatio); } /* decide to accept or reject the move */ acceptMove = NO; i = chainId[chn]; theMove->nTried[i]++; theMove->nTotTried[i]++; if (abortMove == NO && RandomNumber(seed) < r) { acceptMove = YES; theMove->nAccepted[i]++; theMove->nTotAccepted[i]++; } /* update the chain */ if (acceptMove == NO) { /* the new state did not work out so shift chain back */ if (abortMove == NO) ResetFlips(chn); state[chn] ^= 1; # if defined (BEAGLE_ENABLED) if (recalcScalers == YES) { recalculateScalers(chn); recalcScalers = NO; } # endif } else { /* if the move is accepted then let the chain stay in the new state */ /* store the likelihood and prior of the chain */ curLnL[chn] = lnLike; curLnPr[chn] = lnPrior; } /* check if time to autotune */ if (theMove->nTried[i] >= chainParams.tuneFreq) { theMove->lastAcceptanceRate[i] = (MrBFlt) theMove->nAccepted[i] / (MrBFlt) theMove->nTried[i]; theMove->nTried[i] = 0; theMove->nAccepted[i] = 0; theMove->nBatches[i]++; /* we only autotune at most 10000 times */ if (chainParams.autotune == YES && theMove->moveType->Autotune != NULL && theMove->nBatches[i] < MAXTUNINGPARAM) { theMove->moveType->Autotune(theMove->lastAcceptanceRate[i], theMove->targetRate[i], theMove->nBatches[i], &theMove->tuningParam[i][0], theMove->moveType->minimum[0], theMove->moveType->maximum[0]); } } /* ShowValuesForChain (chn); */ if (curLnL[chn] > maxLnL0[chainId[chn]]) maxLnL0[chainId[chn]] = curLnL[chn]; } /* attempt swap(s) Non-blocking for MPI if no swap with external process. */ if (chainParams.numChains > 1 && n % chainParams.swapFreq == 0) { for (i = 0; i 0) { MrBayesPrint ("%s Aborting run.\n"); return ERROR; } # endif if (PrintStatesToFiles (n) == ERROR) { MrBayesPrint ("%s Error in printing states to files\n"); # if defined (MPI_ENABLED) nErrors++; # else return ERROR; # endif } # if defined (MPI_ENABLED) MPI_Allreduce (&nErrors, &sumErrors, 1, MPI_INT, MPI_SUM, MPI_COMM_WORLD); if (sumErrors > 0) { MrBayesPrint ("%s Aborting run.\n"); return ERROR; } # endif } /* print mcmc diagnostics. Blocking for MPI */ if (chainParams.mcmcDiagn == YES && (n % chainParams.diagnFreq == 0 || n == chainParams.numGen || (chainParams.isSS == YES && (n-lastStepEndSS) % numGenInStepSS == 0))) { if (chainParams.numRuns > 1 && ((n > 0 && chainParams.relativeBurnin == YES && (chainParams.isSS == NO || (n > (long long)(chainParams.burninSS) * chainParams.sampleFreq && (n-lastStepEndSS) > numGenInStepBurninSS))) || (n >= (long long)(chainParams.chainBurnIn) * chainParams.sampleFreq && chainParams.relativeBurnin == NO))) { /* we need some space for coming output */ MrBayesPrint ("\n"); /* remove tree samples if using burninpercentage */ /* the following function returns immediately in MPI if proc_id != 0 */ if (chainParams.relativeBurnin == YES && chainParams.isSS == NO) { removeFrom = removeTo; removeTo = (int)(chainParams.burninFraction * (n/chainParams.sampleFreq+1)); /* (n/chainParams.sampleFreq+1) is the current number of samples */ if (removeFrom < removeTo) { if (RemoveTreeSamples (removeFrom+1, removeTo) == ERROR) { MrBayesPrint ("%s Problem removing tree samples\n"); # if defined (MPI_ENABLED) nErrors++; # else return ERROR; # endif } } # if defined (MPI_ENABLED) MPI_Allreduce (&nErrors, &sumErrors, 1, MPI_INT, MPI_SUM, MPI_COMM_WORLD); if (sumErrors > 0) { MrBayesPrint ("%s Aborting run.\n"); return ERROR; } # endif } lastDiagnostics = (int)(n/chainParams.sampleFreq)+1; /* +1 because we always have start tree sampled*/ if (chainParams.relativeBurnin == YES) { i = lastDiagnostics - removeTo; } else i = lastDiagnostics - chainParams.chainBurnIn; # if defined (MPI_ENABLED) if (proc_id == 0) { # endif /* calculate statistics */ CalcTopoConvDiagn (i); /* output statistics */ if (numTopologies == 1) { f = -1.0; if (chainParams.stat[0].numPartitions == 0) { MrBayesPrint ("%s Average standard deviation of split frequencies: NA (no splits above min. frequency)\n", spacer); } else if (chainParams.diagnStat == AVGSTDDEV) { f = chainParams.stat[0].avgStdDev; MrBayesPrint ("%s Average standard deviation of split frequencies: %.6f\n", spacer, f); } else { f = chainParams.stat[0].max; MrBayesPrint ("%s Max standard deviation of split frequencies: %.6f\n", spacer, f); } if (chainParams.isSS == YES) splitfreqSS[chainParams.numStepsSS-stepIndexSS-1] = f; if (chainParams.stat[0].numPartitions > 0 && f <= chainParams.stopVal) stopChain = YES; if (n < chainParams.numGen - chainParams.printFreq && (chainParams.stopRule == NO || stopChain == NO)) MrBayesPrint ("\n"); } else { stopChain = YES; for (i=0; i chainParams.stopVal) stopChain = NO; } if (n < chainParams.numGen - chainParams.printFreq && (chainParams.stopRule == NO || stopChain == NO)) MrBayesPrint ("\n"); } if (chainParams.allComps == YES) PrintTopConvInfo (); # if defined (MPI_ENABLED) } ierror = MPI_Bcast (&stopChain, 1, MPI_INT, 0, MPI_COMM_WORLD); if (ierror != MPI_SUCCESS) { MrBayesPrint ("%s Problem broadcasting stop value\n", spacer); nErrors++; } # endif } /* part of the following function needs to be performed by all MPI processors. Blocking for MPI. */ if (PrintMCMCDiagnosticsToFile (n) == ERROR) { MrBayesPrint ("%s Problem printing MCMC diagnostics to file\n", spacer); # if defined (MPI_ENABLED) nErrors++; # else return (ERROR); # endif } # if defined (MPI_ENABLED) MPI_Allreduce (&nErrors, &sumErrors, 1, MPI_INT, MPI_SUM, MPI_COMM_WORLD); if (sumErrors > 0) { MrBayesPrint ("%s Aborting run.\n"); return ERROR; } # endif } /* check if time to break because stopVal reached */ if (chainParams.isSS == NO && (chainParams.stopRule == YES && stopChain == YES)) { MrBayesPrint ("\n%s Analysis stopped because convergence diagnostic hit stop value.\n", spacer); break; } /* user may want to extend chain */ if (chainParams.isSS == NO && (n == chainParams.numGen && autoClose == NO)) { stoppingT1 = time(0); currentCPUTime = clock(); CPUTime += (currentCPUTime - previousCPUTime) / (MrBFlt) CLOCKS_PER_SEC; chainParams.numGen += ExtendChainQuery (); stoppingT2 = time(0); startingT += (stoppingT2-stoppingT1); previousCPUTime = clock(); /* timers should not be increased during the wait for a reply */ } /* Do stepping sampling staf if needed */ if (chainParams.isSS == YES && n >= chainParams.burninSS*chainParams.sampleFreq) { # ifndef SAMPLE_ALL_SS if ((n-lastStepEndSS) % chainParams.sampleFreq == 0) # endif { if (n > chainParams.burninSS*chainParams.sampleFreq && (n-lastStepEndSS > numGenInStepBurninSS)) { /* do sampling */ for (chn=0; chn stepScalerSS[run] + 200.0) { // adjust scaler; stepAcumulatorSS[run] /= exp (curLnL[chn]*stepLengthSS - 100.0 - stepScalerSS[run]); stepScalerSS[run]= curLnL[chn]*stepLengthSS - 100.0; } stepAcumulatorSS[run] += exp (curLnL[chn]*stepLengthSS - stepScalerSS[run]); } } samplesCountSS++; } } if ((n-lastStepEndSS) == numGenInStepBurninSS) { /* Remove all previous samples from diagnostics */ if (chainParams.mcmcDiagn == YES && chainParams.numRuns > 1) { removeFrom = removeTo; removeTo = (int)(n/chainParams.sampleFreq); /* (n/chainParams.sampleFreq+1) is the current number of samples including 0 one*/ removeTo++; if (removeFrom < removeTo) { if (RemoveTreeSamples (removeFrom+1, removeTo) == ERROR) { nErrors++; } ERROR_TEST2("Problem removing tree samples",return(ERROR),); } } } if ((n-lastStepEndSS) % numGenInStepSS == 0) /* prepare sample of next step */ { assert (n-lastStepEndSS <= numGenInStepSS); lastStepEndSS=n; if (n > chainParams.burninSS*chainParams.sampleFreq) { /* dump to file current step contribution */ MrBayesPrintf (fpSS, "%3d\t%.4f", chainParams.numStepsSS-stepIndexSS, powerSS); # if defined (MPI_ENABLED) for (j=0; j 1) { for (i=0; i 3600.0) MrBayesPrint ("%s Analysis completed in %d hours %d mins %d seconds\n", spacer, (int) (difftime(endingT, startingT)/3600), ((int) (difftime(endingT, startingT))%3600)/60, (int) (difftime(endingT, startingT))%60); else if (difftime (endingT, startingT) > 60.0) MrBayesPrint ("%s Analysis completed in %d mins %d seconds\n", spacer, (int) (difftime(endingT, startingT)/60), (int) (difftime(endingT, startingT))%60); else if (difftime (endingT, startingT) > 2.0) MrBayesPrint ("%s Analysis completed in %.0f seconds\n", spacer, difftime(endingT, startingT)); else if (difftime (endingT, startingT) >= 1.0) MrBayesPrint ("%s Analysis completed in 1 second\n", spacer); else MrBayesPrint ("%s Analysis completed in less than 1 second\n", spacer); # if defined (DEBUG_TIME) MrBayesPrint ("%s Analysis used %1.2f seconds of total time\n", spacer, (MrBFlt) (1000.0*tw2.tv_sec + 1e-6*tw2.tv_nsec - (1000.0*tw1.tv_sec + 1e-6*tw1.tv_nsec))/1000); # endif # if defined (MPI_ENABLED) MrBayesPrint ("%s Analysis used %1.2f seconds of CPU time on processor 0\n", spacer, (MrBFlt) CPUTime); # else MrBayesPrint ("%s Analysis used %1.2f seconds of CPU time\n", spacer, (MrBFlt) CPUTime); # endif # if defined (MPI_ENABLED) /* find the best likelihoods across all of the processors */ ierror = MPI_Barrier (MPI_COMM_WORLD); if (ierror != MPI_SUCCESS) { MrBayesPrint ("%s Problem at chain barrier\n", spacer); return (ERROR); } for (j=0; j best) best = maxLnL0[j]; } else if (proc_id == i) { ierror = MPI_Send (&maxLnL0[j], 1, MPI_DOUBLE, 0, i, MPI_COMM_WORLD); if (ierror != MPI_SUCCESS) { MrBayesPrint ("%s Problem with MPI_Send\n", spacer); return ERROR; } } } maxLnL0[j] = best; } /* Collecting marginal log likelihoods if SS is used */ if (chainParams.isSS == YES) { for (j=0; j 1) { MeanVarianceLog(marginalLnLSS,chainParams.numRuns,&meanSS,&varSS,NULL); MrBayesPrint ("%s Mean: %9.2f\n\n",spacer,meanSS); //MrBayesPrint ("%s Mean: %9.2lf Scaled variance: %.2f of Marginal log likelihood estimates among runs.\n",spacer,meanSS,varSS-2*meanSS); //MrBayesPrint ("%s Note: Scaled variance is given in log units and calculated as \"variance/mean^2\"\n",spacer); } MrBayesPrint ("%s More statistics on stepping-stone sampling is dumped to %s.ss file.\n", spacer, chainParams.chainFileName); if (chainParams.mcmcDiagn == YES) { if ((tempX = (MrBFlt *) SafeCalloc (chainParams.numStepsSS, sizeof(MrBFlt))) == NULL) { nErrors++; } ERROR_TEST2("Problem allocating memory", return(ERROR),); for (i=0; i 1) { if (chainParams.diagnStat == AVGSTDDEV) MrBayesPrint (" Plots of average standard deviation of split frequencies across steps for different topology."); else MrBayesPrint (" Plots of max standard deviation of split frequencies across steps for different topology."); } else { if (chainParams.diagnStat == AVGSTDDEV) MrBayesPrint (" Plot of average standard deviation of split frequencies across steps."); else MrBayesPrint (" Plot of max standard deviation of split frequencies across steps."); } MrBayesPrint ("\n"); MrBayesPrint (" Points at -1.0 (y-axis) indicate that there were no splits\n"); MrBayesPrint (" above minimum frequency for corresponding step."); if (numTopologies > 1) { for (i=0; i 0) { MrBayesPrint ("%s ReassembleMoveInfo failed\n", spacer); return ERROR; } # endif /* print acceptance rates for the moves */ for (j=0; jnBatches[j] < 1) MrBayesPrint ("%s NA NA %s\n", spacer, mv->name); else MrBayesPrint ("%s %6.1f %% (%3.0f %%) %s\n", spacer, 100.0*mv->nTotAccepted[j]/(MrBFlt)(mv->nTotTried[j]), 100.0*mv->lastAcceptanceRate[j], mv->name); } } } # if defined MPI_ENABLED /* Redistribute move info in case it is needed in a follow-up run */ RedistributeMoveInfo(); # endif /* output information on the success of the chain state swap proposals */ if (PrintSwapInfo () == ERROR) nErrors++; # if defined (MPI_ENABLED) MPI_Allreduce (&nErrors, &sumErrors, 1, MPI_INT, MPI_SUM, MPI_COMM_WORLD); if (sumErrors > 0) { MrBayesPrint ("%s PrintSwapInfo failed\n", spacer); return ERROR; } # else if (nErrors > 1) { MrBayesPrint ("%s PrintSwapInfo failed\n", spacer); return ERROR; } # endif # if defined (MPI_ENABLED) if (proc_id != 0) return (NO_ERROR); # endif if (chainParams.isSS == NO && (chainParams.numRuns > 1 && chainParams.mcmcDiagn == YES)) { f = 0.0; for (i=0; i f) f = chainParams.stat[i].avgStdDev; } if (f > 0.10) { MrBayesPrint ("\n"); MrBayesPrint ("%s ************************* WARNING!! ************************************ \n", spacer); MrBayesPrint ("%s MrBayes suspects that your runs have not converged because the tree \n", spacer); MrBayesPrint ("%s samples are very different (average standard deviation of split frequen- \n", spacer); MrBayesPrint ("%s cies larger than 0.10 (%1.2lf)). MrBayes suggests that you run the ana- \n", spacer, f); MrBayesPrint ("%s lysis longer or try to improve the MCMC sampling efficiency by fine- \n", spacer); MrBayesPrint ("%s tuning MCMC proposal or heating parameters. \n", spacer); MrBayesPrint ("\n"); } } return (NO_ERROR); } int SafeSprintf (char **target, int *targetLen, char *fmt, ...) { va_list argp; int retval; while (1) { /* try to print in the available space */ va_start(argp, fmt); # ifdef VISUAL retval = _vsnprintf (*target, *targetLen, fmt, argp); # else /* With SGI IRIX this function returns 0. Allen Smith suggested using _xpg5_vnsprintf, but this function needs a recent version of IRIX. For now, I just allocate a large buffer for SGI, so there is no need for memory reallocation on this kind of machines */ /* It appears to me that the reason for the bug is that the old version of this function relies on nonstandard return values for vsnprintf. With the current version of the code, SGI should do fine without special treatment. FR */ retval = vsnprintf (*target, *targetLen, fmt, argp); # endif va_end(argp); /* if it worked, retval will be in interval [0,*targetLen-1], else -1 or true length */ if (retval > -1 && retval < *targetLen) return NO_ERROR; /* readjust length */ if (retval > -1) /* some C compilers will return true length in retval */ *targetLen = retval + 1; /* exactly what is needed */ else /* some C compilers will return -1 on buffer overwrite */ *targetLen += TARGETLENDELTA; /* reallocate target */ *target = SafeRealloc ((void *)*target, *targetLen); if (*target == NULL) return ERROR; } } void SetChainIds (void) { /* Fill with the global chain number. Ex. For proc_0, chain[0] = 0; chain[1] = 1; chain[2] = 2; (numchains = 3) For proc_1, chain[0] = 3; chain[1] = 4; (numchains = 2) etc... */ # if defined (MPI_ENABLED) int i, proc, numChainsForProc, numGlobalChains; int id; int remainder; /* calculate global number of chains */ numGlobalChains = chainParams.numChains * chainParams.numRuns; /* there are chains left over after load balancing the chains */ remainder = numGlobalChains % num_procs; /* get the number of chains handled by this proc */ numChainsForProc = (int) (numGlobalChains / num_procs); /* we must distribute the remaining chains (causing the chain load between procs to become unbalanced) */ if (proc_id < remainder) numChainsForProc++; /* NOTE: at this point, procs can have different number of numChainsForProc (one more for chains < remainder, one less for procs larger than or equal to the remainder) */ id = 0; for (proc=0; proc\"\n", spacer, chainParams.chainFileName); } else /* if (chainParams.numRuns > 1) */ { sprintf (comptreeParams.comptFileName1, "%s.run1.t", chainParams.chainFileName); sprintf (comptreeParams.comptFileName2, "%s.run2.t", chainParams.chainFileName); sprintf (plotParams.plotFileName, "%s.run1.p", chainParams.chainFileName); MrBayesPrint ("%s Setting chain output file names to \"%s.run.

\"\n", spacer, chainParams.chainFileName); } } /*---------------------------------------------------------------------------- | | SetLikeFunctions: This function will set up the pointers from each | data division to the relevant likelihood functions. It will also | deal with the settings for SIMD code. | -----------------------------------------------------------------------------*/ int SetLikeFunctions (void) { int i; ModelInfo *m; /* couple divisions with likelihood functions */ for (i=0; iuseVec = VEC_NONE; if (m->dataType == DNA || m->dataType == RNA) { if (m->parsModelId == YES) { m->Likelihood = &Likelihood_Pars; } else { if (m->nucModelId == NUCMODEL_4BY4) { if (m->numModelStates > 4) { /* TODO: allow autocorrelated rates for covarion model */ if (m->gibbsGamma == YES) { m->CondLikeDown = &CondLikeDown_Gen_GibbsGamma; m->CondLikeRoot = &CondLikeRoot_Gen_GibbsGamma; m->CondLikeScaler = &CondLikeScaler_Gen_GibbsGamma; m->Likelihood = &Likelihood_Gen_GibbsGamma; } else { m->CondLikeDown = &CondLikeDown_Gen; m->CondLikeRoot = &CondLikeRoot_Gen; m->CondLikeScaler = &CondLikeScaler_Gen; m->Likelihood = &Likelihood_Gen; } if (m->correlation != NULL) m->Likelihood = &Likelihood_Adgamma; else m->Likelihood = &Likelihood_Gen; if (m->nCijkParts == 1) m->TiProbs = &TiProbs_Gen; else if (m->nCijkParts > 1) m->TiProbs = &TiProbs_GenCov; m->PrintAncStates = &PrintAncStates_NUC4; m->PrintSiteRates = &PrintSiteRates_Gen; } else { # if defined (SSE_ENABLED) if (m->printAncStates == YES || m->printSiteRates == YES) { MrBayesPrint ("%s Non-SSE version of conditional likelihood calculator will be used for division %d\n", spacer, i+1); MrBayesPrint ("%s due to request of reporting 'ancestral states' or 'site rates'.\n", spacer); } m->CondLikeUp = &CondLikeUp_NUC4; m->PrintAncStates = &PrintAncStates_NUC4; m->PrintSiteRates = &PrintSiteRates_Gen; if (m->gibbsGamma == YES) { m->CondLikeDown = &CondLikeDown_NUC4_GibbsGamma; m->CondLikeRoot = &CondLikeRoot_NUC4_GibbsGamma; m->CondLikeScaler = &CondLikeScaler_NUC4_GibbsGamma; } else if (m->correlation != NULL || m->printAncStates == YES || m->printSiteRates == YES) { m->CondLikeDown = &CondLikeDown_NUC4; m->CondLikeRoot = &CondLikeRoot_NUC4; m->CondLikeScaler = &CondLikeScaler_NUC4; } else { m->useVec = VEC_SSE; m->numFloatsPerVec = 4; m->CondLikeDown = &CondLikeDown_NUC4_SSE; m->CondLikeRoot = &CondLikeRoot_NUC4_SSE; m->CondLikeScaler = &CondLikeScaler_NUC4_SSE; #if defined (AVX_ENABLED) // override SSE settings m->useVec = VEC_AVX; m->numFloatsPerVec = 8; m->CondLikeDown = &CondLikeDown_NUC4_AVX; m->CondLikeRoot = &CondLikeRoot_NUC4_AVX; m->CondLikeScaler = &CondLikeScaler_NUC4_AVX; #if defined (FMA_ENABLED) // override AVX settings (CondLikeScaler cannot be improved over AVX) m->useVec = VEC_FMA; m->CondLikeDown = &CondLikeDown_NUC4_FMA; m->CondLikeRoot = &CondLikeRoot_NUC4_FMA; #endif #endif /* Should be sse versions if we want to handle m->printAncStates == YES || inferSiteRates == YES. For now just set to NULL for early error detection if functions anyway got called by mistake */ m->CondLikeUp = NULL; m->PrintAncStates = NULL; m->PrintSiteRates = NULL; } if (m->correlation != NULL) m->Likelihood = &Likelihood_Adgamma; else if (m->gibbsGamma == YES) m->Likelihood = &Likelihood_NUC4_GibbsGamma; else if (m->printAncStates == YES || inferSiteRates == YES) m->Likelihood = &Likelihood_NUC4; else { m->Likelihood = &Likelihood_NUC4_SSE; #if defined (AVX_ENABLED) m->Likelihood = &Likelihood_NUC4_AVX; #if defined (FMA_ENABLED) m->Likelihood = &Likelihood_NUC4_FMA; #endif #endif } # else if (m->gibbsGamma == YES) { m->CondLikeDown = &CondLikeDown_NUC4_GibbsGamma; m->CondLikeRoot = &CondLikeRoot_NUC4_GibbsGamma; m->CondLikeScaler = &CondLikeScaler_NUC4_GibbsGamma; } else { m->CondLikeDown = &CondLikeDown_NUC4; m->CondLikeRoot = &CondLikeRoot_NUC4; m->CondLikeScaler = &CondLikeScaler_NUC4; } if (m->correlation != NULL) m->Likelihood = &Likelihood_Adgamma; else if (m->gibbsGamma == YES) m->Likelihood = &Likelihood_NUC4_GibbsGamma; else m->Likelihood = &Likelihood_NUC4; m->CondLikeUp = &CondLikeUp_NUC4; m->PrintAncStates = &PrintAncStates_NUC4; m->PrintSiteRates = &PrintSiteRates_Gen; # endif if (m->nst == 1) m->TiProbs = &TiProbs_Fels; else if (m->nst == 2) m->TiProbs = &TiProbs_Hky; else m->TiProbs = &TiProbs_Gen; m->StateCode = &StateCode_NUC4; } } else if (m->nucModelId == NUCMODEL_DOUBLET) { if (m->gibbsGamma == YES) { m->CondLikeDown = &CondLikeDown_Gen_GibbsGamma; m->CondLikeRoot = &CondLikeRoot_Gen_GibbsGamma; m->CondLikeScaler = &CondLikeScaler_Gen_GibbsGamma; m->Likelihood = &Likelihood_Gen_GibbsGamma; } else { m->CondLikeDown = &CondLikeDown_Gen; m->CondLikeRoot = &CondLikeRoot_Gen; m->CondLikeScaler = &CondLikeScaler_Gen; m->Likelihood = &Likelihood_Gen; } if (m->nst == 1) m->TiProbs = &TiProbs_Gen; else if (m->nst == 2) m->TiProbs = &TiProbs_Gen; else m->TiProbs = &TiProbs_Gen; m->CondLikeUp = &CondLikeUp_Gen; m->PrintAncStates = &PrintAncStates_Gen; m->PrintSiteRates = &PrintSiteRates_Gen; } else if (m->nucModelId == NUCMODEL_CODON) { /* codon models */ if (m->numOmegaCats == 1) { if (m->gibbsGamma == YES) { m->CondLikeDown = &CondLikeDown_Gen_GibbsGamma; m->CondLikeRoot = &CondLikeRoot_Gen_GibbsGamma; m->CondLikeScaler = &CondLikeScaler_Gen_GibbsGamma; m->Likelihood = &Likelihood_Gen_GibbsGamma; } else { m->CondLikeDown = &CondLikeDown_Gen; m->CondLikeRoot = &CondLikeRoot_Gen; m->CondLikeScaler = &CondLikeScaler_Gen; m->Likelihood = &Likelihood_Gen; # if defined (SSE_ENABLED) if (m->printAncStates == YES || m->printSiteRates == YES) { MrBayesPrint ("%s Non-SSE version of conditional likelihood calculator will be used for division %d\n", spacer, i+1); MrBayesPrint ("%s due to request of reporting 'ancestral states' or 'site rates'.\n", spacer); } else { m->useVec = VEC_SSE; m->numFloatsPerVec = 4; m->CondLikeDown = &CondLikeDown_Gen_SSE; m->CondLikeRoot = &CondLikeRoot_Gen_SSE; m->CondLikeScaler = &CondLikeScaler_Gen_SSE; m->Likelihood = &Likelihood_Gen_SSE; } # endif } } else { m->CondLikeDown = &CondLikeDown_NY98; m->CondLikeRoot = &CondLikeRoot_NY98; m->CondLikeScaler = &CondLikeScaler_NY98; m->Likelihood = &Likelihood_NY98; m->PosSelProbs = &PosSelProbs; m->SiteOmegas = &SiteOmegas; # if defined (SSE_ENABLED) if (m->printAncStates == YES || m->printSiteRates == YES) { MrBayesPrint ("%s Non-SSE version of conditional likelihood calculator will be used for division %d\n", spacer, i+1); MrBayesPrint ("%s due to request of reporting 'ancestral states' or 'site rates'.\n", spacer); } else { m->useVec = VEC_SSE; m->numFloatsPerVec = 4; m->CondLikeDown = &CondLikeDown_NY98_SSE; m->CondLikeRoot = &CondLikeRoot_NY98_SSE; m->CondLikeScaler = &CondLikeScaler_NY98_SSE; m->Likelihood = &Likelihood_NY98_SSE; m->PosSelProbs = &PosSelProbs_SSE; m->SiteOmegas = &SiteOmegas_SSE; } # endif } m->TiProbs = &TiProbs_Gen; if (m->nCijkParts > 1) m->TiProbs = &TiProbs_GenCov; m->CondLikeUp = &CondLikeUp_Gen; m->PrintAncStates = &PrintAncStates_Gen; m->PrintSiteRates = &PrintSiteRates_Gen; } else /* if (m->nucModelId == NUCMODEL_AA) */ { if (m->gibbsGamma == YES) { m->CondLikeDown = &CondLikeDown_Gen_GibbsGamma; m->CondLikeRoot = &CondLikeRoot_Gen_GibbsGamma; m->CondLikeScaler = &CondLikeScaler_Gen_GibbsGamma; m->Likelihood = &Likelihood_Gen_GibbsGamma; } else { m->CondLikeDown = &CondLikeDown_Gen; m->CondLikeRoot = &CondLikeRoot_Gen; m->CondLikeScaler = &CondLikeScaler_Gen; m->Likelihood = &Likelihood_Gen; # if defined (SSE_ENABLED) if (m->printAncStates == YES || m->printSiteRates == YES) { MrBayesPrint ("%s Non-SSE version of conditional likelihood calculator will be used for division %d\n", spacer, i+1); MrBayesPrint ("%s due to request of reporting 'ancestral states' or 'site rates'.\n", spacer); } else { m->useVec = VEC_SSE; m->numFloatsPerVec = 4; m->CondLikeDown = &CondLikeDown_Gen_SSE; m->CondLikeRoot = &CondLikeRoot_Gen_SSE; m->CondLikeScaler = &CondLikeScaler_Gen_SSE; m->Likelihood = &Likelihood_Gen_SSE; } # endif } if (m->nCijkParts > 1) m->TiProbs = &TiProbs_GenCov; else m->TiProbs = &TiProbs_Gen; m->CondLikeUp = &CondLikeUp_Gen; m->StateCode = &StateCode_AA; m->PrintAncStates = &PrintAncStates_Gen; m->PrintSiteRates = &PrintSiteRates_Gen; } } } else if (m->dataType == PROTEIN) { if (m->parsModelId == YES) { m->Likelihood = &Likelihood_Pars; } else { /* TODO: allow autocorrelated rates for covarion model */ if (m->gibbsGamma == YES) { m->CondLikeDown = &CondLikeDown_Gen_GibbsGamma; m->CondLikeRoot = &CondLikeRoot_Gen_GibbsGamma; m->CondLikeScaler = &CondLikeScaler_Gen_GibbsGamma; m->Likelihood = &Likelihood_Gen_GibbsGamma; } else { m->CondLikeDown = &CondLikeDown_Gen; m->CondLikeRoot = &CondLikeRoot_Gen; m->CondLikeScaler = &CondLikeScaler_Gen; m->Likelihood = &Likelihood_Gen; # if defined (SSE_ENABLED) if (m->printAncStates == YES || m->printSiteRates == YES) { MrBayesPrint ("%s Non-SSE version of conditional likelihood calculator will be used for division %d\n", spacer, i+1); MrBayesPrint ("%s due to request of reporting 'ancestral states' or 'site rates'.\n", spacer); } else { m->useVec = VEC_SSE; m->numFloatsPerVec = 4; m->CondLikeDown = &CondLikeDown_Gen_SSE; m->CondLikeRoot = &CondLikeRoot_Gen_SSE; m->CondLikeScaler = &CondLikeScaler_Gen_SSE; m->Likelihood = &Likelihood_Gen_SSE; } # endif } if (m->correlation != NULL) { if (m->gibbsGamma == YES) { MrBayesPrint ("%s Adgamma model cannot be used with Gibbs sampling of rate categories\n", spacer); return (ERROR); } else m->Likelihood = &Likelihood_Adgamma; } if (m->numModelStates > 20 && m->nCijkParts > 1) m->TiProbs = &TiProbs_GenCov; else m->TiProbs = &TiProbs_Gen; m->CondLikeUp = &CondLikeUp_Gen; m->StateCode = &StateCode_AA; m->PrintAncStates = &PrintAncStates_Gen; m->PrintSiteRates = &PrintSiteRates_Gen; } } else if (m->dataType == RESTRICTION) { if (m->parsModelId == YES) { m->Likelihood = &Likelihood_Pars; } else { m->CondLikeDown = &CondLikeDown_Bin; m->CondLikeRoot = &CondLikeRoot_Bin; m->CondLikeScaler = &CondLikeScaler_Gen; m->Likelihood = &Likelihood_Res; m->TiProbs = &TiProbs_Res; m->CondLikeUp = &CondLikeUp_Bin; m->StateCode = &StateCode_Std; m->PrintAncStates = &PrintAncStates_Bin; m->PrintSiteRates = &PrintSiteRates_Gen; } } else if (m->dataType == STANDARD) { if (m->parsModelId == YES) { if (m->numModelStates == 2) { m->Likelihood = &Likelihood_Pars; /* this is much faster if number of states do not vary */ m->numStates = 2; /* this is needed for the parsimony calculator */ } else m->Likelihood = &Likelihood_ParsStd; } else { m->CondLikeDown = &CondLikeDown_Std; m->CondLikeRoot = &CondLikeRoot_Std; m->CondLikeScaler = &CondLikeScaler_Std; m->Likelihood = &Likelihood_Std; m->TiProbs = &TiProbs_Std; m->CondLikeUp = &CondLikeUp_Std; m->StateCode = &StateCode_Std; m->PrintAncStates = &PrintAncStates_Std; m->PrintSiteRates = &PrintSiteRates_Std; } } else if (m->dataType == CONTINUOUS) { if (m->parsModelId == NO) { m->CondLikeDown = &CondLikeDown_Cont; m->CondLikeRoot = &CondLikeRoot_Cont; m->Likelihood = &Likelihood_Cont; m->CondLikeUp = &CondLikeUp_Cont; m->PrintAncStates = &PrintAncStates_Cont; m->PrintSiteRates = &PrintSiteRates_Cont; } } else { MrBayesPrint ("%s ERROR: Data should be one of these types!\n", spacer); return ERROR; } } return NO_ERROR; } /* Determine number of chains and data splits to be handled by MPI processors or threads */ int SetLocalChainsAndDataSplits(void) { # if defined (MPI_ENABLED) /* tell user how many chains each processor has been assigned */ if (num_procs <= numGlobalChains) { if (numGlobalChains % num_procs != 0) { MrBayesPrint ("%s The total number of chains (%d) must be evenly divisible by\n", spacer, numGlobalChains); MrBayesPrint ("%s the number of MPI processors (%d).\n", spacer, num_procs); /* MrBayesPrint ("%s The total number of chains (%d) must be evenly divisible by\n", spacer, numGlobalChains); MrBayesPrint ("%s the number of MPI processors (%d), or the number of MPI\n", spacer, num_procs); MrBayesPrint ("%s processors should be a multiple of the number of chains.\n", spacer, num_procs); */ MrBayesPrint ("%s Please change your MPI settings.\n", spacer, num_procs); return ERROR; } numLocalChains = numGlobalChains / num_procs; MrBayesPrint ("%s Number of chains per MPI processor = %d\n", spacer, numLocalChains); } else { MrBayesPrint ("%s There must be at least as many chains as MPI processors\n", spacer); return (ERROR); } /* { if (num_procs % numGlobalChains != 0) { MrBayesPrint ("%s The number of MPI processors (%d) must be a multiple of the\n", spacer, num_procs); MrBayesPrint ("%s total number of chains (%d), or the total number of chains\n", spacer, numGlobalChains); MrBayesPrint ("%s should be evenly divisible by the number of MPI processsors.\n", spacer); MrBayesPrint ("%s Please change your MPI settings.\n", spacer); return ERROR; } numLocalChains = 1; // numMPIDataSplits = num_procs / numGlobalChains; // MrBayesPrint ("%s Number of MPI data splits per chain = %d\n", spacer, numMPIDataSplits); } */ # else numLocalChains = numGlobalChains; # endif return (NO_ERROR); } /*---------------------------------------------------------------------------- | | ShowMoveSummary: Show summary of moves that will be used in MCMC sampling | -----------------------------------------------------------------------------*/ int ShowMoveSummary (void) { int i, run, chain, areRunsSame, areChainsSame, chainIndex; MCMCMove *mv; MrBFlt prob; /* first find out if the probabilities are different in different runs */ areRunsSame = YES; for (run=1; runrelProposalProb[chainIndex], mv->relProposalProb[chain], 0.000001) == NO) { areRunsSame = NO; break; } } if (areRunsSame == NO) break; } if (areRunsSame == NO) break; } /* now print values */ for (run=0; run= 1) break; /* find out if chains are different within this run */ areChainsSame = YES; for (chain=1; chainrelProposalProb[chainIndex], mv->relProposalProb[chainIndex-chain], 0.000001) == NO) { areChainsSame = NO; break; } } if (areChainsSame == NO) break; } for (chain=0; chain= 1) break; /* now we can print the values */ MrBayesPrint ("\n"); if (areRunsSame == YES && areChainsSame == YES) MrBayesPrint ("%s The MCMC sampler will use the following moves:\n", spacer); else if (areRunsSame == NO && areChainsSame == YES) MrBayesPrint ("%s The MCMC sampler will use the following moves for run %d:\n", spacer, run+1); else if (areRunsSame == YES && areChainsSame == NO) MrBayesPrint ("%s The MCMC sampler will use the following moves for chain %d:\n", spacer, chain+1); else if (areRunsSame == NO && areChainsSame == NO) MrBayesPrint ("%s The MCMC sampler will use the following moves for run %d, chain %d:\n", spacer, run+1, chain+1); chainIndex = run*chainParams.numChains + chain; MrBayesPrint ("%s With prob. Chain will use move\n", spacer); for (i=0; icumProposalProb[chainIndex]; if (i > 0) prob -= usedMoves[i-1]->cumProposalProb[chainIndex]; if (AreDoublesEqual(prob,0.0,0.000001) == YES) continue; MrBayesPrint ("%s %6.2f %% %s\n", spacer, 100*prob, mv->name); } MrBayesPrint ("\n"); } /* next chain */ } /* next run */ return (NO_ERROR); } /*---------------------------------------------------------------------- | | SetUpPartitionCounters: Set up partitions and the root of the | partition frequency tree | |----------------------------------------------------------------------*/ int SetUpPartitionCounters (void) { int i; # if defined (MPI_ENABLED) /* we only keep partition counters on proc 0 in the MPI version */ if (proc_id != 0) return (NO_ERROR); # endif nLongsNeeded = 1 + (numLocalTaxa - 1) / nBitsInALong; if (memAllocs[ALLOC_PFCOUNTERS] == YES) { MrBayesPrint ("%s ERROR: pfcounters not free in SetUpPartitionCounters\n", spacer); return ERROR; } partition = (BitsLong **) SafeCalloc (2*numLocalTaxa, sizeof (BitsLong *)); if (partition == NULL) { MrBayesPrint ("%s Failed to allocate partition in SetUpPartitionCounters\n", spacer); return ERROR; } partition[0] = (BitsLong *) SafeCalloc (2*numLocalTaxa * nLongsNeeded, sizeof(BitsLong)); if (partition[0] == NULL) { free (partition); MrBayesPrint ("%s Failed to allocate partition[0] in SetUpPartitionCounters\n", spacer); return ERROR; } partFreqTreeRoot = (PFNODE **) SafeCalloc (numTopologies, sizeof (PFNODE *)); if (partFreqTreeRoot == NULL) { free (partition[0]); free (partition); MrBayesPrint ("%s Failed to allocate partFreqTreeRoot in SetUpPartitionCounters\n", spacer); return ERROR; } memAllocs[ALLOC_PFCOUNTERS] = YES; for (i=1; i<2*numLocalTaxa; i++) { partition[i] = partition[0] + i*nLongsNeeded; } for (i=0; iuseVec != VEC_NONE ) { m->numVecChars = ((m->numChars - 1) / m->numFloatsPerVec) + 1; numComprChars += m->numVecChars * m->numFloatsPerVec; } else numComprChars += m->numChars; } # else numComprChars = numCompressedChars; # endif termState = (int *) SafeCalloc (numLocalTaxa * numComprChars, sizeof(int)); if (termState) memAllocs[ALLOC_TERMSTATE] = YES; else { MrBayesPrint ("%s Problem allocating termState\n", spacer); return (ERROR); } isPartAmbig = (int *) SafeCalloc (numLocalTaxa*numCurrentDivisions, sizeof(int)); if (isPartAmbig) memAllocs[ALLOC_ISPARTAMBIG] = YES; else { MrBayesPrint ("%s Problem allocating isPartAmbig\n", spacer); return (ERROR); } /*initialize isPartAmbig */ for (i=0; idataType == CONTINUOUS) continue; m->termState = (int **) SafeCalloc (numLocalTaxa, sizeof(int *)); if (!m->termState) { MrBayesPrint ("%s Problems allocating termState pointers for division %d\n", spacer, d+1); return ERROR; } # if defined SSE_ENABLED if (m->dataType != STANDARD && m->gibbsGamma == NO) numComprChars = m->numVecChars * m->numFloatsPerVec; else numComprChars = m->numChars; # else numComprChars = m->numChars; # endif for (i=0; itermState[i] = termStatePtr; termStatePtr += numComprChars; } m->isPartAmbig = isPartAmbig + numLocalTaxa * d; for (i=0; iparsSets[i]; for (c=0; cnumChars; c++) { for (k=n=0; knumStates; k++) { if (IsBitSet(k, p)) { x = k; n++; } } /* find appropriate index */ if (n == 1) m->termState[i][c] = x * m->numModelStates; else if (n == m->numStates) m->termState[i][c] = m->numStates * m->numModelStates; else m->isPartAmbig[i] = YES; p += m->nParsIntsPerSite; } for (; ctermState[i][c] = m->numStates * m->numModelStates; } } } /* print the termState matrix */ # if defined (DEBUG_SETUPTERMSTATE) PrintTermState(); getchar(); # endif return NO_ERROR; } /*---------------------------------------------------------------------------- | | SetUsedMoves: This function will extract an array of pointers to the moves | that will actually be used in the MCMC sampling. It also makes sure | that the parsimonyBased flag is set in the relevant model partitions | if there are moves used that are based on parsimony scores | -----------------------------------------------------------------------------*/ int SetUsedMoves (void) { int i, j, moveIndex, numGlobalChains; MrBFlt prob, sum, cumSum; /* first count moves */ numUsedMoves = 0; numGlobalChains = chainParams.numChains * chainParams.numRuns; for (i=0; irelProposalProb[j] > prob) prob = moves[i]->relProposalProb[j]; } if (prob > 0.000001) numUsedMoves++; } /* allocate space */ if (memAllocs[ALLOC_USEDMOVES] == YES) { MrBayesPrint ("%s Memory problem: usedMoves not free in SetUsedMoves\n", spacer); return (ERROR); } usedMoves = (MCMCMove **) SafeMalloc (numUsedMoves * sizeof (MCMCMove *)); if (!usedMoves) { MrBayesPrint ("%s Problem allocating usedMoves\n", spacer); return (ERROR); } memAllocs[ALLOC_USEDMOVES] = YES; /* set move pointers */ moveIndex = 0; for (i=0; irelProposalProb[j] > prob) prob = moves[i]->relProposalProb[j]; } if (prob > 0.000001) usedMoves[moveIndex++] = moves[i]; } if (moveIndex != numUsedMoves) { MrBayesPrint ("%s Problem finding the used moves\n", spacer); return (ERROR); } /* set parsimony flag if applicable */ for (i=0; imoveType->parsimonyBased == YES) { for (j=0; jparm->nRelParts; j++) modelSettings[usedMoves[i]->parm->relParts[j]].parsimonyBasedMove = YES; } } /* set cumulative proposal probabilities */ for (j=0; jrelProposalProb[j]; } cumSum = 0.0; for (i=0; irelProposalProb[j]; usedMoves[i]->cumProposalProb[j] = cumSum / sum; } } /* reset acceptance probability values */ for (i=0; inAccepted[j] = 0; usedMoves[i]->nTried[j] = 0; usedMoves[i]->nTotAccepted[j] = 0; usedMoves[i]->nTotTried[j] = 0; } } return (NO_ERROR); } void ShowValuesForChain (int chn) { int i; char s[100]; MrBayesPrint ("%s Chain = %d\n", spacer, chn); MrBayesPrint ("%s numParams = %d\n", spacer, numParams); MrBayesPrint ("%s numTrees = %d\n", spacer, numTrees); MrBayesPrint ("%s current state: %d\n", spacer, state[chn]); strcat (spacer, " "); /* tRatio */ for (i=0; iright, i, j); if (q != NULL) { return q; } else if (IsPFNodeEmpty (p) == NO) { *i = j; return p; } else { return SmallestNonemptyPFNode (p->left, i, j); } } /* Talloc: Allocate space for a new node in the tree keeping track of partition frequencies */ PFNODE *Talloc (void) { PFNODE *temp; temp = (PFNODE *) SafeMalloc (sizeof(PFNODE)); if (temp == NULL) return NULL; temp->partition = (BitsLong *) SafeCalloc (nLongsNeeded, sizeof (BitsLong)); if (temp->partition == NULL) { free (temp); return NULL; } temp->count = (int *) SafeCalloc (chainParams.numRuns, sizeof (int)); if (temp->count == NULL) { free (temp->partition); free (temp); return NULL; } return temp; } MrBFlt Temperature (int id) { /* let id be number of chain in run */ id %= chainParams.numChains; if (chainParams.userDefinedTemps == YES) { return (chainParams.userTemps[id]); } else { return (1.0 / (1.0 + chainParams.chainTemp * id)); } } /* Tfree: Free space for partition frequency counter tree */ void Tfree (PFNODE *r) { if (r != NULL) { if (r->left != NULL) Tfree (r->left); if (r->right != NULL) Tfree (r->right); free (r->partition); free (r->count); free (r); } } void TouchAllCijks (int chain) { int i; for (i=0; i= 1) modelSettings[i].upDateCijk = YES; } return; } void TouchAllPartitions (void) { int i; for (i=0; inNodes; j++) { p = t->allDownPass[j]; p->upDateCl = YES; p->upDateTi = YES; } } for (i=0; i= 1) modelSettings[i].upDateCijk = YES; modelSettings[i].upDateCl = YES; modelSettings[i].upDateAll = YES; } for (i=0; inNodes; j++) { p = t->allDownPass[j]; p->upDateCl = YES; p->upDateTi = YES; } } return; } /*------------------------------------------------------------------ | | TreeLength: Calculates the tree length as the sum of the lengths | of all the branches. The tree length is the expected number | of character state transformations per character over the | entire phylogenetic tree. | -------------------------------------------------------------------*/ MrBFlt TreeLength (Param *param, int chain) { int i, j; MrBFlt tl; Tree *t; TreeNode *p; if (param->paramId == BRLENS_PARSIMONY) { tl = 0.0; for (j=0; jnRelParts; j++) tl += modelSettings[param->relParts[j]].parsTreeLength[2*chain+state[chain]]; } else { /* get tree */ t = GetTree (param, chain, state[chain]); /* loop over all branches of the tree */ tl = 0.0; for (i=0; inNodes; i++) { p = t->allDownPass[i]; if (p->anc != NULL) { if (p->anc->anc == NULL && t->isRooted == NO) { tl += p->length; } else { tl += p->length; } } } } return (tl); } /* proportion of ancestral fossils in a FBD tree */ MrBFlt PropAncFossil (Param *param, int chain) { int i, m, k; Tree *t; TreeNode *p; t = GetTree (param, chain, state[chain]); if (t->isRooted == NO) return 0.0; /* count # of tip and ancestral fossils */ m = k = 0; for (i = 0; i < t->nNodes -2; i++) { p = t->allDownPass[i]; if (p->left == NULL && p->right == NULL && p->nodeDepth > 0.0) { if (p->length > 0.0) m++; else k++; } } if (k == 0) return 0.0; else return (MrBFlt)k / (m+k); } NBISweden-MrBayes-ecfd55c/src/mcmc.h000066400000000000000000000040351526212227600172760ustar00rootroot00000000000000#ifndef MCMC_H_ #define MCMC_H_ int AddToPrintString (char *tempStr); void AutotuneDirichlet (MrBFlt acceptanceRate, MrBFlt targetRate, int batch, MrBFlt *alphaPi, MrBFlt minTuning, MrBFlt maxTuning); void AutotuneMultiplier (MrBFlt acceptanceRate, MrBFlt targetRate, int batch, MrBFlt *lambda, MrBFlt minTuning, MrBFlt maxTuning); void AutotuneSlider (MrBFlt acceptanceRate, MrBFlt targetRate, int batch, MrBFlt *width, MrBFlt minTuning, MrBFlt maxTuning); void AutotuneRJClocks (MrBFlt acceptanceRate, MrBFlt targetRate, int batch, MrBFlt *w, MrBFlt minTuning, MrBFlt maxTuning); int DoMcmc (void); int DoMcmcp (void); int DoMcmcParm (char *parmName, char *tkn); int DoSs (void); int DoSsp (void); int DoSsParm (char *parmName, char *tkn); int ExhaustiveParsimonySearch (Tree *t, int chain, TreeInfo *tInfo); MrBFlt GetParsDP (Tree *t, TreeNode *p, int chain); void GetParsFP (Tree *t, TreeNode *p, int chain); int GetParsimonyBrlens (Tree *t, int chain, MrBFlt *brlens); MrBFlt GetParsimonyLength (Tree *t, int chain); void GetParsimonySubtreeRootstate (Tree *t, TreeNode *root, int chain); MrBFlt GetRate (int division, int chain); int LnBirthDeathPriorPr (Tree *t, MrBFlt clockRate, MrBFlt *prob, MrBFlt sR, MrBFlt eR, char *sS, MrBFlt sF); int LnCoalescencePriorPr (Tree *t, MrBFlt *prob, MrBFlt theta, MrBFlt growth); MrBFlt LnUniformPriorPr (Tree *t, MrBFlt clockRate); int LnFossilizationPriorPr (Tree *t, MrBFlt clockRate, MrBFlt *prob, MrBFlt *sR, MrBFlt *eR, MrBFlt *fR, MrBFlt sF, char *sS); int LogClockTreePriorRatio (Param *param, int chain, MrBFlt *lnPriorRatio); MrBFlt LogDirPrior (Tree *t, ModelParams *mp, int PV); MrBFlt LogOmegaPrior (MrBFlt w1, MrBFlt w2, MrBFlt w3); FILE *OpenNewMBPrintFile (char *fileName); int ResetScalersPartition (int *isScalerNode, Tree* t, unsigned rescaleFreq); int SafeSprintf (char **target, int *targetLen, char *fmt, ...); int SetFilePositions (int samplePos); MrBFlt TreeLength (Param *param, int chain); #endif /* MCMC_H_ */ NBISweden-MrBayes-ecfd55c/src/model.c000066400000000000000000045707111526212227600174670ustar00rootroot00000000000000/* * MrBayes 3 * * (c) 2002-2023 * * John P. Huelsenbeck * Dept. Integrative Biology * University of California, Berkeley * Berkeley, CA 94720-3140 * johnh@berkeley.edu * * Fredrik Ronquist * Swedish Museum of Natural History * Box 50007 * SE-10405 Stockholm, SWEDEN * fredrik.ronquist@nrm.se * * With important contributions by * * Paul van der Mark (paulvdm@sc.fsu.edu) * Maxim Teslenko (maxkth@gmail.com) * Chi Zhang (zhangchicool@gmail.com) * * and by many users (run 'acknowledgments' to see more info) * * This program is free software; you can redistribute it and/or * modify it under the terms of the GNU General Public License * as published by the Free Software Foundation; either version 2 * of the License, or (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details (www.gnu.org). * */ #include "bayes.h" #include "best.h" #include "command.h" #include "model.h" #include "mcmc.h" #include "proposal.h" #include "sumpt.h" #include "utils.h" #if defined(__MWERKS__) #include "SIOUX.h" #endif #undef DEBUG_ADDDUMMYCHARS #undef DEBUG_CONSTRAINTS #undef DEBUG_COMPRESSDATA #undef DEBUG_CPP /* local prototypes */ int AddDummyChars (void); void AllocateCppEvents (Param *p); MCMCMove *AllocateMove (MoveType *moveType, Param *param); int AllocateNormalParams (void); int AllocateTreeParams (void); void CheckCharCodingType (Matrix *m, CharInfo *ci); int CheckExpandedModels (void); int CompressData (void); int InitializeChainTrees (Param *p, int from, int to, int isRooted); int FillBrlensSubParams (Param *param, int chn, int state); void FreeCppEvents (Param *p); void FreeMove (MCMCMove *mv); void GetPossibleAAs (int aaCode, int aa[]); void GetPossibleNucs (int nucCode, int nuc[]); void GetPossibleRestrictionSites (int resSiteCode, int *sites); int GetUserTreeFromName (int *index, char *treeName); void InitializeMcmcTrees (Param *p); int IsApplicable (Param *param); int IsApplicable_FiveTaxaOrMore (Param *param); int IsApplicable_FourTaxaOrMore (Param *param); int IsApplicable_ThreeTaxaOrMore (Param *param); int IsApplicable_TreeAgeMove (Param *param); int IsModelSame (int whichParam, int part1, int part2, int *isApplic1, int *isApplic2); int LargestMovableSubtree(Param *treeParam); int NumActiveParts (void); int NumInformativeHardConstraints (ModelParams *mp); int NumNonExcludedChar (void); int NumStates (int part); int PrintCompMatrix (void); int PrintMatrix (void); int ProcessStdChars (RandLong *seed); void SetCode (int part); int SetModelParams (void); int SetPopSizeParam (Param *param, int chn, int state, PolyTree *pt); int SetRelaxedClockParam (Param *param, int chn, int state, PolyTree *pt); int SetUpLinkTable (void); int ShowMoves (int used); int ShowParameters (int showStartVals, int showMoves, int showAllAvailable); int UpdateCppEvolLength (int *nEvents, MrBFlt **pos, MrBFlt **rateMult, MrBFlt *evolLength, TreeNode *p, MrBFlt baseRate); /* globals */ int *activeParams[NUM_LINKED]; /* a table holding the parameter link status */ int localOutGroup; /* outgroup for non-excluded taxa */ Calibration **localTaxonCalibration = NULL; /* stores local taxon calibrations (ages) */ char **localTaxonNames = NULL; /* points to names of non-excluded taxa */ Model *modelParams; /* holds model params */ ModelInfo *modelSettings; /* stores important info on model params */ MCMCMove **moves; /* vector of pointers to applicable moves */ int numApplicableMoves; /* number of moves applicable to model parameters */ int numCurrentDivisions; /* number of partitions of data */ int numGlobalChains; /* number of global chains */ int numLocalTaxa; /* number of non-excluded taxa */ int numLocalChar; /* number of non-excluded characters */ int numParams; /* number of parameters in model */ int numTopologies; /* number of topologies for one chain and state */ int numTrees; /* number of trees for one chain and state */ Param *params; /* vector of parameters */ Param *printParams; /* vector of subst model parameters to print */ ShowmovesParams showmovesParams; /* holds parameters for Showmoves command */ Param *treePrintparams; /* vector of tree parameters to print */ int setUpAnalysisSuccess; /* Set to YES if analysis is set without error */ /* globals used to describe and change the current model; allocated in AllocCharacters and SetPartition */ int *numVars; /* number of variables in setting arrays */ int *activeParts; /* partitions changes should apply to */ int *linkTable[NUM_LINKED]; /* how parameters are linked across parts */ int *tempLinkUnlink[NUM_LINKED]; /* for changing parameter linkage */ int *tempLinkUnlinkVec; /* for changing parameter linkage */ MrBFlt *tempNum; /* vector of numbers used for setting arrays */ /* Aamodel parameters */ MrBFlt aaJones[20][20]; /* rates for Jones model */ MrBFlt aaDayhoff[20][20]; /* rates for Dayhoff model */ MrBFlt aaMtrev24[20][20]; /* rates for mtrev24 model */ MrBFlt aaMtmam[20][20]; /* rates for mtmam model */ MrBFlt aartREV[20][20]; /* rates for rtREV model */ MrBFlt aaWAG[20][20]; /* rates for WAG model */ MrBFlt aacpREV[20][20]; /* rates for aacpREV model */ MrBFlt aaVt[20][20]; /* rates for VT model */ MrBFlt aaBlosum[20][20]; /* rates for Blosum62 model */ MrBFlt aaLG[20][20]; /* rates for LG model */ MrBFlt jonesPi[20]; /* stationary frequencies for Jones model */ MrBFlt dayhoffPi[20]; /* stationary frequencies for Dayhoff model */ MrBFlt mtrev24Pi[20]; /* stationary frequencies for mtrev24 model */ MrBFlt mtmamPi[20]; /* stationary frequencies for mtmam model */ MrBFlt rtrevPi[20]; /* stationary frequencies for rtREV model */ MrBFlt wagPi[20]; /* stationary frequencies for WAG model */ MrBFlt cprevPi[20]; /* stationary frequencies for aacpREV model */ MrBFlt vtPi[20]; /* stationary frequencies for VT model */ MrBFlt blosPi[20]; /* stationary frequencies for Blosum62 model */ MrBFlt lgPi[20]; /* stationary frequencies for LG model */ /* parser flags and variables only used in this file */ int foundComma, foundBeta, foundAaSetting, modelIsFixed, linkNum, foundLeftPar, tempNumStates, foundBSNum[999], foundDSNum[999], foundFSNum[999], foundBSTime[999], foundDSTime[999], foundFSTime[999]; MrBFlt tempStateFreqs[200], tempAaModelPrs[10]; char colonPr[100], clockPr[30]; /* parser flags and variables shared with command.c */ extern int fromI, toJ, foundDash, foundExp, foundEqual, isNegative; /* other local variables (this file) */ MrBFlt empiricalFreqs[200]; /* empirical base frequencies for partition */ int intValsRowSize = 0; /* row size of intValues matrix */ int *intValues = NULL; /* stores int values of chain parameters */ Tree **mcmcTree; /* pointers to trees for mcmc */ int paramValsRowSize = 0; /* row size of paramValues matrix */ MrBFlt *paramValues = NULL; /* stores actual values and subvalues of chain parameters */ int *relevantParts = NULL; /* partitions that are affected by this move */ Param **subParamPtrs; /* pointer to subparams for topology params */ int *stateSize; /* # states for each compressed char */ // int foundCurly; // char *plotTokenP; /* plotToken[CMD_STRING_LENGTH];*/ /*----------------------------------------------------------------------- | | AddDummyChars: Add dummy characters to relevant partitions | ------------------------------------------------------------------------*/ int AddDummyChars (void) { int i, j, k, d, numIncompatible, numDeleted, oldRowSize, newRowSize, numDummyChars, newColumn, newChar, oldColumn, oldChar, isCompat, *tempChar, numIncompatibleChars; BitsLong *tempMatrix, bitsLongOne=1; CLFlt *tempSitesOfPat; ModelInfo *m; ModelParams *mp; CharInfo cinfo; Matrix matrix; /* set pointers to NULL */ tempMatrix = NULL; tempSitesOfPat = NULL; tempChar = NULL; /* check how many dummy characters needed in total */ numDummyChars = 0; // numStdChars = 0; for (d=0; dnumDummyChars = 0; if (mp->dataType == RESTRICTION && !strcmp(mp->parsModel,"No")) { if (mp->coding & NOABSENCESITES) m->numDummyChars++; if (mp->coding & NOPRESENCESITES) m->numDummyChars++; if (mp->coding & NOSINGLETONABSENCE) m->numDummyChars += numLocalTaxa; if (mp->coding & NOSINGLETONPRESENCE) m->numDummyChars += numLocalTaxa; } if (mp->dataType == STANDARD && !strcmp(mp->parsModel,"No")) { if (mp->coding & VARIABLE) m->numDummyChars += 2; if (mp->coding & NOSINGLETONS) m->numDummyChars += 2*numLocalTaxa; // numStdChars += (m->numChars + m->numDummyChars); } numDummyChars += m->numDummyChars; m->numChars += m->numDummyChars; } /* exit if dummy characters not needed */ if (numDummyChars == 0) return NO_ERROR; /* print original compressed matrix */ # if defined (DEBUG_ADDDUMMYCHARS) MrBayesPrint ("Compressed matrix before adding dummy characters...\n"); PrintCompMatrix(); # endif /* set row sizes for old and new matrices */ oldRowSize = compMatrixRowSize; compMatrixRowSize += numDummyChars; newRowSize = compMatrixRowSize; numCompressedChars += numDummyChars; /* allocate space for new data */ tempMatrix = (BitsLong *) SafeCalloc (numLocalTaxa * newRowSize, sizeof(BitsLong)); tempSitesOfPat = (CLFlt *) SafeCalloc (numCompressedChars, sizeof(CLFlt)); tempChar = (int *) SafeCalloc (compMatrixRowSize, sizeof(int)); if (!tempMatrix || !tempSitesOfPat || !tempChar) { MrBayesPrint ("%s Problem allocating temporary variables in AddDummyChars\n", spacer); goto errorExit; } /* initialize indices */ oldChar = newChar = newColumn = numDeleted = 0; /* set up matrix struct */ matrix.origin = compMatrix; matrix.nRows = numLocalTaxa; matrix.rowSize = oldRowSize; /* loop over divisions */ for (d=0; dnumDummyChars > 0) { for (k=0; k<2; k++) { if (((mp->coding & NOSINGLETONPRESENCE) && k == 0) || ((mp->coding & NOSINGLETONABSENCE) && k == 1)) { for (i=0; i< numLocalTaxa; i++) { for (j=0; jcoding & NOABSENCESITES) { for (i=0; icoding & NOPRESENCESITES) { for (i=0; icompMatrixStart; oldColumncompMatrixStop; oldColumn++) { isCompat = YES; /* first check if the character is supposed to be present */ if (m->numDummyChars > 0) { /* set up matrix struct */ matrix.column = oldColumn; /* set up charinfo struct */ cinfo.dType = mp->dataType; cinfo.cType = charInfo[origChar[oldChar]].ctype; cinfo.nStates = charInfo[origChar[oldChar]].numStates; CheckCharCodingType(&matrix, &cinfo); if (mp->coding & VARIABLE) { if((mp->coding & VARIABLE) == VARIABLE && cinfo.variable == NO) { isCompat = NO; } else if((mp->coding & NOABSENCESITES) && cinfo.constant[0] == YES) { isCompat = NO; } else if((mp->coding & NOPRESENCESITES) && cinfo.constant[1] == YES) { isCompat = NO; } } if (mp->coding & NOSINGLETONS) { if((mp->coding & NOSINGLETONS) == NOSINGLETONS && cinfo.informative == NO && cinfo.variable == YES) { isCompat = NO; } else if((mp->coding & NOSINGLETONABSENCE) && cinfo.singleton[0] == YES && cinfo.informative == NO) { isCompat = NO; } else if((mp->coding & NOSINGLETONPRESENCE) && cinfo.singleton[1] == YES && cinfo.informative == NO) { isCompat = NO; } } } if (isCompat == NO) { numIncompatible++; // printf("%d\n", origChar[oldChar]+1); numIncompatibleChars += (int) numSitesOfPat[oldChar]; oldChar++; } else { /* add character */ for (i=0; icompMatrixStart+1) % m->nCharsPerSite == 0) { newChar++; oldChar++; } } } /* print a warning if there are incompatible characters */ if (numIncompatible > 0) { m->numChars -= numIncompatible; m->numUncompressedChars -= numIncompatibleChars; numDeleted += numIncompatible; if (numIncompatibleChars > 1) { MrBayesPrint ("%s WARNING: There are %d characters incompatible with the specified\n", spacer, numIncompatibleChars); MrBayesPrint ("%s coding bias. These characters will be excluded.\n", spacer); } else { MrBayesPrint ("%s WARNING: There is one character incompatible with the specified\n", spacer); MrBayesPrint ("%s coding bias. This character will be excluded.\n", spacer); } } /* update division comp matrix and comp char pointers */ m->compCharStop = newChar; m->compMatrixStop = newColumn; m->compCharStart = newChar - m->numChars; m->compMatrixStart = newColumn - m->nCharsPerSite * m->numChars; } /* next division */ /* compress matrix if necessary */ if (numDeleted > 0) { for (i=k=0; inEvents = (int **) SafeCalloc (2*numGlobalChains, sizeof (int *)); p->nEvents[0] = (int *) SafeCalloc (2*numGlobalChains*(2*numLocalTaxa), sizeof (int)); for (i=1; i<2*numGlobalChains; i++) p->nEvents[i] = p->nEvents[i-1] + (2*numLocalTaxa); p->position = (MrBFlt ***) SafeCalloc (2*numGlobalChains, sizeof (MrBFlt **)); p->position[0] = (MrBFlt **) SafeCalloc (2*numGlobalChains*(2*numLocalTaxa), sizeof (MrBFlt *)); for (i=1; i<2*numGlobalChains; i++) p->position[i] = p->position[i-1] + (2*numLocalTaxa); p->rateMult = (MrBFlt ***) SafeCalloc (2*numGlobalChains, sizeof (MrBFlt **)); p->rateMult[0] = (MrBFlt **) SafeCalloc (2*numGlobalChains*(2*numLocalTaxa), sizeof (MrBFlt *)); for (i=1; i<2*numGlobalChains; i++) p->rateMult[i] = p->rateMult[i-1] + (2*numLocalTaxa); } /*---------------------------------------------------------------------------- | | AllocateMove: Allocate space for and initialize one applicable move | -----------------------------------------------------------------------------*/ MCMCMove *AllocateMove (MoveType *moveType, Param *param) { int i, j, nameLength; char *partitionDescriptor = ""; MCMCMove *temp; if ((temp = (MCMCMove *) SafeCalloc (1, sizeof (MCMCMove))) == NULL) return (NULL); /* calculate length */ if (strcmp (moveType->paramName, "") == 0) nameLength = (int) (strlen (moveType->shortName) + strlen (param->name)) + 10; else { partitionDescriptor = param->name; while (*partitionDescriptor != '\0') { if (*partitionDescriptor == '{') break; partitionDescriptor++; } nameLength = (int) (strlen (moveType->shortName) + strlen (moveType->paramName) + strlen (partitionDescriptor)) + 10; } /* add length of names of subparams */ if (moveType->subParams == YES) { for (i=0; inSubParams; i++) nameLength += (int)(strlen(param->subParams[i]->name)) + 1; } if ((temp->name = (char *) SafeCalloc (nameLength, sizeof (char))) == NULL) { free (temp); return NULL; } if ((temp->nAccepted = (int *) SafeCalloc (5*numGlobalChains, sizeof (int))) == NULL) { free (temp->name); free (temp); return NULL; } temp->nTried = temp->nAccepted + numGlobalChains; temp->nBatches = temp->nAccepted + 2*numGlobalChains; temp->nTotAccepted = temp->nAccepted + 3*numGlobalChains; temp->nTotTried = temp->nAccepted + 4*numGlobalChains; if ((temp->relProposalProb = (MrBFlt *) SafeCalloc (4*numGlobalChains, sizeof (MrBFlt))) == NULL) { free (temp->nAccepted); free (temp->name); free (temp); return NULL; } temp->cumProposalProb = temp->relProposalProb + numGlobalChains; temp->targetRate = temp->relProposalProb + 2*numGlobalChains; temp->lastAcceptanceRate = temp->relProposalProb + 3*numGlobalChains; if ((temp->tuningParam = (MrBFlt **) SafeCalloc (numGlobalChains, sizeof (MrBFlt *))) == NULL) { free (temp->relProposalProb); free (temp->nAccepted); free (temp->name); free (temp); return NULL; } if (moveType->numTuningParams > 0 && (temp->tuningParam[0] = (MrBFlt *) SafeCalloc (moveType->numTuningParams*numGlobalChains, sizeof (MrBFlt))) == NULL) { free (temp->tuningParam); free (temp->relProposalProb); free (temp->nAccepted); free (temp->name); free (temp); return NULL; } for (i=1; ituningParam[i] = temp->tuningParam[0] + (i * moveType->numTuningParams); /* set default values */ if (strcmp(moveType->paramName, "") != 0) sprintf (temp->name, "%s(%s%s)", moveType->shortName, moveType->paramName, partitionDescriptor); else { sprintf (temp->name, "%s(%s", moveType->shortName, param->name); if (moveType->subParams == YES) { for (i=0; inSubParams; i++) { strcat(temp->name,","); strcat(temp->name,param->subParams[i]->name); } } strcat (temp->name,")"); } temp->moveType = moveType; temp->moveFxn = moveType->moveFxn; for (i=0; irelProposalProb[i] = moveType->relProposalProb; temp->cumProposalProb[i] = 0.0; temp->nAccepted[i] = 0; temp->nTried[i] = 0; temp->nBatches[i] = 0; temp->nTotAccepted[i] = 0; temp->nTotTried[i] = 0; temp->targetRate[i] = moveType->targetRate; temp->lastAcceptanceRate[i] = 0.0; for (j=0; jnumTuningParams; j++) temp->tuningParam[i][j] = moveType->tuningParam[j]; } return (temp); } /*---------------------------------------------------------------------- | | AllocateNormalParams: Allocate space for normal parameters | -----------------------------------------------------------------------*/ int AllocateNormalParams (void) { int i, k, nOfParams, nOfIntParams; Param *p; /* Count the number of param values and subvalues */ nOfParams = 0; nOfIntParams = 0; for (k=0; k 1) { paramValues = (MrBFlt *) SafeRealloc ((void *) paramValues, nOfParams * sizeof (MrBFlt)); for (i=0; i 0) intValues = (int *) SafeRealloc ((void *) intValues, nOfIntParams * sizeof(int)); } else { if (nOfParams > 1) paramValues = (MrBFlt *) SafeCalloc (nOfParams, sizeof(MrBFlt)); else paramValues = NULL; if (nOfIntParams > 0) intValues = (int *) SafeCalloc (nOfIntParams, sizeof(int)); else intValues = NULL; } if ((nOfParams > 0 && !paramValues) || (nOfIntParams > 0 && !intValues)) { MrBayesPrint ("%s Problem allocating paramValues\n", spacer); if (paramValues) free (paramValues); if (intValues) free (intValues); return ERROR; } else memAllocs[ALLOC_PARAMVALUES] = YES; /* set pointers to values for chain 1 state 0 */ /* this scheme keeps the chain and state values together */ nOfParams = 0; nOfIntParams = 0; for (k=0; knValues > 0) p->values = paramValues + nOfParams; else p->values = NULL; nOfParams += p->nValues; if (p->nSubValues > 0) p->subValues = paramValues + nOfParams; else p->subValues = NULL; nOfParams += p->nSubValues; if (p->nIntValues > 0) p->intValues = intValues + nOfIntParams; else p->intValues = NULL; nOfIntParams += p->nIntValues; } /* allocate space for cpp events */ for (k=0; kparamType == P_CPPEVENTS) AllocateCppEvents(p); } return (NO_ERROR); } /*---------------------------------------------------------------------- | | AllocateTreeParams: Allocate space for tree parameters | -----------------------------------------------------------------------*/ int AllocateTreeParams (void) { int i, j, k, n, nOfParams, nOfTrees, isRooted, numSubParamPtrs, allModelsStationary; Param *p, *q; /* Count the number of trees and dated trees */ /* based on branch length parameters */ /* One tree is needed for each brlen parameter. A topology may apply to several trees; a topology parameter contains pointers to all trees it applies to */ numTrees = 0; numTopologies = 0; for (k=0; k 0) subParamPtrs = (Param **) SafeCalloc (numSubParamPtrs, sizeof (Param *)); mcmcTree = (Tree **) SafeCalloc (numTrees * 2 * numGlobalChains, sizeof (Tree *)); if ((numSubParamPtrs>0 && !subParamPtrs) || !mcmcTree) { if (subParamPtrs) free (subParamPtrs); if (mcmcTree) free (mcmcTree); subParamPtrs = NULL; mcmcTree = NULL; MrBayesPrint ("%s Problem allocating MCMC trees\n", spacer); printf("subparams: %d -- trees: %d \n", numSubParamPtrs, numTrees); return (ERROR); } else memAllocs[ALLOC_MCMCTREES] = YES; /* Initialize number of subparams, just in case */ for (k=0; krelParts[0]].topology; q->nSubParams++; if (p->printParam == YES) q->nPrintSubParams++; } else if (params[k].paramType == P_TOPOLOGY) { q = modelSettings[p->relParts[0]].speciesTree; if (q != NULL) q->nSubParams++; } } /* Make sure there is also one subparam for a parsimony tree */ for (k=0; knSubParams == 0) p->nSubParams = 1; } /* Count subparams for relaxed clock parameters */ for (k=0; kparamType == P_CPPEVENTS) { q = modelSettings[p->relParts[0]].cppRate; q->nSubParams++; q = modelSettings[p->relParts[0]].cppMultDev; q->nSubParams++; q = modelSettings[p->relParts[0]].brlens; q->nSubParams++; } else if (p->paramType == P_TK02BRANCHRATES) { q = modelSettings[p->relParts[0]].tk02var; q->nSubParams++; q = modelSettings[p->relParts[0]].brlens; q->nSubParams++; } else if (p->paramType == P_WNBRANCHRATES) { q = modelSettings[p->relParts[0]].wnvar; q->nSubParams++; q = modelSettings[p->relParts[0]].brlens; q->nSubParams++; } else if (p->paramType == P_IGRBRANCHRATES) { q = modelSettings[p->relParts[0]].igrvar; q->nSubParams++; q = modelSettings[p->relParts[0]].brlens; q->nSubParams++; } else if (p->paramType == P_ILNBRANCHRATES) { q = modelSettings[p->relParts[0]].ilnvar; q->nSubParams++; q = modelSettings[p->relParts[0]].brlens; q->nSubParams++; } else if (p->paramType == P_MIXEDBRCHRATES) { q = modelSettings[p->relParts[0]].mixedvar; q->nSubParams++; q = modelSettings[p->relParts[0]].brlens; q->nSubParams++; } } /* set pointers to subparams */ nOfParams = 0; for (k=0; knSubParams > 0) { p->subParams = subParamPtrs + nOfParams; nOfParams += p->nSubParams; } } assert (nOfParams == numSubParamPtrs); /* Set brlens param pointers and tree values */ /* the scheme below keeps trees for the same state and chain together */ nOfTrees = 0; for (k=0; kparamType == P_BRLENS) || (p->paramType == P_TOPOLOGY && p->paramId == TOPOLOGY_PARSIMONY_UNIFORM) || (p->paramType == P_TOPOLOGY && p->paramId == TOPOLOGY_PARSIMONY_CONSTRAINED) || (p->paramType == P_SPECIESTREE)) { /* allocate space for trees and initialize trees */ p->treeIndex = nOfTrees; p->tree = mcmcTree + nOfTrees; nOfTrees++; } } /* Set topology params and associated brlen subparams */ for (k=0; kparamType == P_TOPOLOGY) { if (p->paramId == TOPOLOGY_PARSIMONY_UNIFORM || p->paramId == TOPOLOGY_PARSIMONY_CONSTRAINED) /* pure parsimony topology case */ { /* there is no brlen subparam */ /* so let subparam point to the param itself */ q = p->subParams[0] = p; /* p->tree and p->treeIndex have been set above */ } else { /* first set brlens pointers for any parsimony partitions */ for (i=j=0; inRelParts; i++) { if (modelSettings[p->relParts[i]].parsModelId == YES) { modelSettings[p->relParts[i]].brlens = p; } } /* now proceed with pointer assignment */ q = modelSettings[p->relParts[0]].brlens; n = 0; /* number of stored subParams */ i = 0; /* relevant partition number */ while (i < p->nRelParts) { for (j=0; jsubParams[j]) break; if (j == n && q != p) /* a new tree (brlens) for this topology */ { p->subParams[n++] = q; } q = modelSettings[p->relParts[++i]].brlens; } p->tree = p->subParams[0]->tree; p->treeIndex = p->subParams[0]->treeIndex; } } else if (p->paramType == P_SPECIESTREE) { /* now proceed with pointer assignment */ q = modelSettings[p->relParts[0]].topology; n = 0; /* number of stored subParams */ i = 0; /* relevant partition number */ while (i < p->nRelParts) { for (j=0; jsubParams[j]) break; if (j == n && q != p) /* a new topology for this species tree */ { p->subParams[n++] = q; } q = modelSettings[p->relParts[++i]].topology; } } } /* Check for constraints */ for (k=0; kparamType == P_TOPOLOGY) { if (!strcmp(modelParams[p->relParts[0]].topologyPr, "Constraints")) { for (i=0; inSubParams; i++) { q = p->subParams[i]; q->checkConstraints = YES; } } else { for (i=0; inSubParams; i++) { q = p->subParams[i]; q->checkConstraints = NO; } } } } /* update paramId */ for (k=0; kparamType == P_TOPOLOGY) { if (p->nSubParams > 1) { if (p->paramId == TOPOLOGY_NCL_UNIFORM_HOMO) p->paramId = TOPOLOGY_NCL_UNIFORM_HETERO; else if (p->paramId == TOPOLOGY_NCL_CONSTRAINED_HOMO) p->paramId = TOPOLOGY_NCL_CONSTRAINED_HETERO; else if (p->paramId == TOPOLOGY_NCL_FIXED_HOMO) p->paramId = TOPOLOGY_NCL_FIXED_HETERO; else { MrBayesPrint ("%s A clock tree cannot have more than one set of branch lengths\n", spacer); printf ("nparam:%d paramid:%d",p->nSubParams,p->paramId); return (ERROR); } } } } /* finally initialize trees */ for (k=0; kparamType == P_BRLENS) { /* find type of tree */ allModelsStationary = YES; for (i=0; inRelParts; i++) { if (strcmp(modelParams[p->relParts[i]].statefreqModel, "Stationary" )) { allModelsStationary = NO; break; } } if (!strcmp(modelParams[p->relParts[0]].brlensPr,"Clock") || allModelsStationary == NO) isRooted = YES; else isRooted = NO; if (InitializeChainTrees (p, 0, numGlobalChains, isRooted) == ERROR) return (ERROR); } else if (p->paramType == P_SPECIESTREE) { if (InitializeChainTrees (p, 0, numGlobalChains, YES) == ERROR) return (ERROR); } else if (p->paramType == P_TOPOLOGY && p->subParams[0]==p) { if (InitializeChainTrees (p, 0, numGlobalChains, NO) == ERROR) return (ERROR); } } /* now initialize subparam pointers for relaxed clock models */ /* use nSubParams to point to the next available subParam by first resetting all nSubParams to 0 */ for (k=0; kparamType == P_CPPRATE || p->paramType == P_CPPMULTDEV || p->paramType == P_BRLENS || p->paramType == P_TK02VAR || p->paramType == P_WNVAR || p->paramType == P_IGRVAR || p->paramType == P_ILNVAR || p->paramType == P_MIXEDVAR) p->nSubParams = 0; } for (k=0; kparamType == P_CPPEVENTS) { q = modelSettings[p->relParts[0]].cppRate; q->subParams[q->nSubParams++] = p; q = modelSettings[p->relParts[0]].cppMultDev; q->subParams[q->nSubParams++] = p; q = modelSettings[p->relParts[0]].brlens; q->subParams[q->nSubParams++] = p; p->treeIndex = q->treeIndex; p->tree = q->tree; if (p->printParam == YES) q->nPrintSubParams++; } else if (p->paramType == P_TK02BRANCHRATES) { q = modelSettings[p->relParts[0]].tk02var; q->subParams[q->nSubParams++] = p; q = modelSettings[p->relParts[0]].brlens; q->subParams[q->nSubParams++] = p; p->treeIndex = q->treeIndex; p->tree = q->tree; if (p->printParam == YES) q->nPrintSubParams++; } else if (p->paramType == P_WNBRANCHRATES) { q = modelSettings[p->relParts[0]].wnvar; q->subParams[q->nSubParams++] = p; q = modelSettings[p->relParts[0]].brlens; q->subParams[q->nSubParams++] = p; p->treeIndex = q->treeIndex; p->tree = q->tree; if (p->printParam == YES) q->nPrintSubParams++; } else if (p->paramType == P_IGRBRANCHRATES) { q = modelSettings[p->relParts[0]].igrvar; q->subParams[q->nSubParams++] = p; q = modelSettings[p->relParts[0]].brlens; q->subParams[q->nSubParams++] = p; p->treeIndex = q->treeIndex; p->tree = q->tree; if (p->printParam == YES) q->nPrintSubParams++; } else if (p->paramType == P_ILNBRANCHRATES) { q = modelSettings[p->relParts[0]].ilnvar; q->subParams[q->nSubParams++] = p; q = modelSettings[p->relParts[0]].brlens; q->subParams[q->nSubParams++] = p; p->treeIndex = q->treeIndex; p->tree = q->tree; if (p->printParam == YES) q->nPrintSubParams++; } else if (p->paramType == P_MIXEDBRCHRATES) { q = modelSettings[p->relParts[0]].mixedvar; q->subParams[q->nSubParams++] = p; q = modelSettings[p->relParts[0]].brlens; q->subParams[q->nSubParams++] = p; p->treeIndex = q->treeIndex; p->tree = q->tree; if (p->printParam == YES) q->nPrintSubParams++; } } return (NO_ERROR); } int AreDoublesEqual (MrBFlt x, MrBFlt y, MrBFlt tol) { if ((x - y) < -tol || (x - y) > tol) return (NO); else return (YES); } int ChangeNumChains (int from, int to) { int i, i1, j, k, m, st, nRuns, fromIndex, toIndex, run, chn, *tempIntVals, nCppEventParams, *toEvents, *fromEvents; MCMCMove **tempMoves, *fromMove, *toMove; Tree **tempTrees; MrBFlt *tempVals, **toRateMult, **toPosition, **fromRateMult, **fromPosition, *stdStateFreqsOld; Param *p, *q, *cppEventParams = NULL; Tree **oldMcmcTree, *tree; if (from == to) return (NO_ERROR); /* set new number of chains */ chainParams.numChains = to; nRuns = chainParams.numRuns; numGlobalChains = chainParams.numRuns * chainParams.numChains; /* Do the normal parameters */ /* first save old values */ tempVals = paramValues; paramValues = NULL; tempIntVals = intValues; intValues = NULL; memAllocs[ALLOC_PARAMS] = NO; /* .. and old cpp events parameters */ nCppEventParams = 0; for (i=0; iparamType == P_CPPEVENTS) nCppEventParams++; } if (nCppEventParams > 0) { cppEventParams = (Param *) SafeCalloc ((size_t)(nCppEventParams), sizeof(Param)); for (i=0; iparamType == P_CPPEVENTS) { cppEventParams[j].nEvents = p->nEvents; p->nEvents = NULL; cppEventParams[j].position = p->position; p->position = NULL; cppEventParams[j].rateMult = p->rateMult; p->rateMult = NULL; j++; } } } if (AllocateNormalParams () == ERROR) return (ERROR); /* then fill all params */ FillNormalParams (&globalSeed, 0, numGlobalChains); /* finally overwrite with old values if present */ for (run=0; runparamType == P_CPPEVENTS) { fromIndex = 2*(run*from + chn); toIndex = 2*(run*to + chn); fromEvents = cppEventParams[i1].nEvents[fromIndex]; toEvents = p->nEvents[toIndex]; fromPosition = cppEventParams[i1].position[fromIndex]; toPosition = p->position[toIndex]; fromRateMult = cppEventParams[i1].rateMult[fromIndex]; toRateMult = p->rateMult[toIndex]; for (j=0; j<2*numLocalTaxa; j++) { toEvents[j] = fromEvents[j]; toPosition[j] = (MrBFlt *) SafeRealloc ((void *)toPosition[j], toEvents[j]*sizeof(MrBFlt)); toRateMult[j] = (MrBFlt *) SafeRealloc ((void *)toRateMult[j], toEvents[j]*sizeof(MrBFlt)); for (k=0; k 0) free (tempIntVals); for (i=0; i 0) free (cppEventParams); /* then do the trees (they cannot be done before the parameters because otherwise FillTreeParams will overwrite relaxed clock parameters that need to be saved) */ /* reallocate trees */ tempTrees = (Tree **) SafeCalloc (2*nRuns*from*numTrees, sizeof(Tree *)); for (i=0; i<2*nRuns*from*numTrees; i++) tempTrees[i] = mcmcTree[i]; oldMcmcTree = mcmcTree; mcmcTree = (Tree **) SafeRealloc ((void *)(mcmcTree), 2 * (size_t)numGlobalChains * (size_t)numTrees * sizeof(Tree*)); for (i=0; i<2*nRuns*to*numTrees; i++) mcmcTree[i] = NULL; /* move the old trees over */ for (run=0; run= to) continue; /*Here we move only one tree per chain/state?! Should not we move numTrees??*/ fromIndex = 2*(run*from + chn) * numTrees; toIndex = 2*(run*to + chn) * numTrees; for (k=0;k<2*numTrees;k++) { mcmcTree[toIndex+k] = tempTrees[fromIndex+k]; tempTrees[fromIndex+k] = NULL; } } } /* remove any remaining old trees */ for (i=0; i<2*nRuns*from*numTrees; i++) if (tempTrees[i] != NULL) FreeTree (tempTrees[i]); free (tempTrees); /* now fill in the tree parameters */ for (i=0; iparamType == P_TOPOLOGY) { p->tree += (mcmcTree - oldMcmcTree); /* calculate new address */ for (j=0; jnSubParams; j++) { q = p->subParams[j]; assert (q->paramType==P_BRLENS); q->tree += (mcmcTree - oldMcmcTree); /* calculate new address */ if (to > from) for (run=0; run 1) sprintf (tree->name, "mcmc.tree%d_%d", p->treeIndex+1, run*to + m +1); else /* if (numTrees == 1) */ sprintf (tree->name, "mcmc.tree_%d", run*to + m +1); } } InitializeChainTrees (q, run*to + from, run*to + to , GetTree (q, 0, 0)->isRooted); } } } else if (p->paramType == P_CPPEVENTS || p->paramType == P_TK02BRANCHRATES || p->paramType == P_IGRBRANCHRATES || p->paramType == P_ILNBRANCHRATES || p->paramType == P_MIXEDBRCHRATES || p->paramType == P_WNBRANCHRATES) p->tree += (mcmcTree - oldMcmcTree); else assert (p->paramType==P_BRLENS || p->tree==NULL); } /* fill new tree parameters */ if (to > from) { for (run=0; run 0) { assert (memAllocs[ALLOC_STDSTATEFREQS] == YES); stdStateFreqsOld=stdStateFreqs; stdStateFreqs = (MrBFlt *) SafeMalloc (2 * (size_t)stdStateFreqsRowSize * (size_t)numGlobalChains * sizeof (MrBFlt)); if (!stdStateFreqs) { MrBayesPrint ("%s Problem reallocating stdStateFreqs\n", spacer); return (ERROR); } /* set pointers */ for (k=0; kparamType != P_PI || modelParams[p->relParts[0]].dataType != STANDARD) continue; p->stdStateFreqs += stdStateFreqs-stdStateFreqsOld; } for (run=0; run= to) break; fromIndex = 2*(run*from + chn)*stdStateFreqsRowSize; toIndex = 2*(run*to + chn)*stdStateFreqsRowSize; for (k=0;k<2*stdStateFreqsRowSize;k++) { stdStateFreqs[toIndex+k]=stdStateFreqsOld[fromIndex+k]; } } /* set new chains */ FillStdStateFreqs (run*to+from, run*to+to, &globalSeed); } free(stdStateFreqsOld); } /* Do the moves */ /* first allocate space and set up default moves */ tempMoves = moves; moves = NULL; memAllocs[ALLOC_MOVES] = NO; SetMoves (); /* then overwrite with old values if present */ for (i=0; irelProposalProb[toIndex] = fromMove->relProposalProb[fromIndex]; for (j=0; jmoveType->numTuningParams; j++) { toMove->tuningParam[toIndex][j] = fromMove->tuningParam[fromIndex][j]; } } } } } /* and free up space */ for (i=0; iparamType == P_CPPEVENTS) { printf ("Trees before changing number of runs\n"); for (j=0; jnEvents[2*j][k]); for (i1=0; i1nEvents[2*j][k]; i1++) { if (i1 == 0) printf (" (%lf %lf,", p->position[2*j][k], p->rateMult[2*j][k]); else if (i1 == p->nEvents[2*j][k]-1) printf (" %lf %lf)", p->position[2*j][k], p->rateMult[2*j][k]); else printf (" %lf %lf,", p->position[2*j][k], p->rateMult[2*j][k]); } printf ("\n"); } for (k=0; k<2*numLocalTaxa; k++) { printf ("%d -- %d:", k, p->nEvents[2*j][k]); for (i1=0; i1nEvents[2*j+1][k]; i1++) { if (i1 == 0) printf (" (%lf %lf,", p->position[2*j+1][k], p->rateMult[2*j+1][k]); else if (i1 == p->nEvents[2*j][k]-1) printf (" %lf %lf)", p->position[2*j+1][k], p->rateMult[2*j+1][k]); else printf (" %lf %lf,", p->position[2*j+1][k], p->rateMult[2*j+1][k]); } printf ("\n"); } } } } #endif /* set new number of runs */ chainParams.numRuns = to; nChains = chainParams.numChains; numGlobalChains = chainParams.numRuns * chainParams.numChains; /* do the trees, free tree's memory if we reduce number of trees. */ for (i=to*2*nChains*numTrees; iparamType == P_CPPEVENTS) { p->nEvents = (int **) SafeRealloc ((void *)p->nEvents, 2*numGlobalChains*sizeof (int *)); p->nEvents[0] = (int *) SafeRealloc ((void *)p->nEvents[0], 2*numGlobalChains*(2*numLocalTaxa)*sizeof (int)); for (i=1; i<2*numGlobalChains; i++) p->nEvents[i] = p->nEvents[i-1] + (2*numLocalTaxa); if (from > to) { for (j=numGlobalChains; jposition[2*j+0][k]); p->position[2*j+0][k] = NULL; free (p->rateMult[2*j+0][k]); p->rateMult[2*j+0][k] = NULL; } } } p->position = (MrBFlt ***) SafeRealloc ((void *)p->position, 2*numGlobalChains*sizeof (MrBFlt **)); p->position[0] = (MrBFlt **) SafeRealloc ((void *)p->position[0], 2*numGlobalChains*(2*numLocalTaxa)*sizeof (MrBFlt *)); for (i=1; i<2*numGlobalChains; i++) p->position[i] = p->position[i-1] + (2*numLocalTaxa); p->rateMult = (MrBFlt ***) SafeRealloc ((void *)p->rateMult, 2*numGlobalChains*sizeof (MrBFlt **)); p->rateMult[0] = (MrBFlt **) SafeRealloc ((void *)p->rateMult[0], 2*numGlobalChains*(2*numLocalTaxa)*sizeof (MrBFlt *)); for (i=1; i<2*numGlobalChains; i++) p->rateMult[i] = p->rateMult[i-1] + (2*numLocalTaxa); if (to > from) { for (j=from*nChains; jnEvents[2*j+0][k] = 0; p->position[2*j+0][k] = NULL; p->rateMult[2*j+0][k] = NULL; p->nEvents[2*j+1][k] = 0; p->position[2*j+1][k] = NULL; p->rateMult[2*j+1][k] = NULL; } } } } } /* and finally the normal parameters */ oldParamValues = paramValues; if (paramValues != NULL) paramValues = (MrBFlt *) SafeRealloc ((void *) paramValues, (size_t)(paramValsRowSize * 2 * numGlobalChains * sizeof (MrBFlt))); oldintValues = intValues; if (intValues != NULL) intValues = (int *) SafeRealloc ((void *) intValues, (size_t)(intValsRowSize * 2 * numGlobalChains * sizeof (int))); if (paramValues == NULL) { MrBayesPrint ("%s Problem reallocating paramValues\n", spacer); return (ERROR); } if (intValues == NULL && intValsRowSize > 0) { MrBayesPrint ("%s Problem reallocating intValues\n", spacer); return (ERROR); } for (i=0; i from) FillNormalParams (&globalSeed, from*nChains, to*nChains); /* now fill in the tree parameters */ for (i=0; iparamType == P_TOPOLOGY) { p->tree += (mcmcTree - oldMcmcTree); /* calculate new address */ for (j=0; jnSubParams; j++) { q = p->subParams[j]; assert (q->paramType==P_BRLENS); q->tree += (mcmcTree - oldMcmcTree); /* calculate new address */ InitializeChainTrees (q, from*nChains, to*nChains, GetTree (q, 0, 0)->isRooted); } } else if (p->paramType == P_CPPEVENTS || p->paramType == P_TK02BRANCHRATES || p->paramType == P_IGRBRANCHRATES || p->paramType == P_ILNBRANCHRATES || p->paramType == P_MIXEDBRCHRATES || p->paramType == P_WNBRANCHRATES) p->tree += (mcmcTree - oldMcmcTree); } FillTreeParams (&globalSeed, from*nChains, to*nChains); /* fix stationary frequencies for standard data */ if (stdStateFreqsRowSize > 0) { assert (memAllocs[ALLOC_STDSTATEFREQS] == YES); stdStateFreqsOld=stdStateFreqs; stdStateFreqs = (MrBFlt *) SafeRealloc ((void *) stdStateFreqs, stdStateFreqsRowSize * 2 * numGlobalChains * sizeof (MrBFlt)); if (!stdStateFreqs) { MrBayesPrint ("%s Problem reallocating stdStateFreqs\n", spacer); return (ERROR); } /* set pointers */ for (k=n=0; kparamType != P_PI || modelParams[p->relParts[0]].dataType != STANDARD) continue; p->stdStateFreqs += stdStateFreqs-stdStateFreqsOld; } FillStdStateFreqs (from*nChains, to*nChains, &globalSeed); } /* do the moves */ for (i=0; imoveType; moves[i]->tuningParam = (MrBFlt **) SafeRealloc ((void *) moves[i]->tuningParam, (size_t)numGlobalChains * sizeof (MrBFlt *)); if (mvt->numTuningParams > 0) moves[i]->tuningParam[0] = (MrBFlt *) SafeRealloc ((void *) moves[i]->tuningParam[0], (size_t)numGlobalChains * (size_t)(mvt->numTuningParams) * sizeof (MrBFlt)); for (j=1; jtuningParam[j] = moves[i]->tuningParam[0] + j * mvt->numTuningParams; moves[i]->relProposalProb = (MrBFlt *) SafeRealloc ((void *) moves[i]->relProposalProb, 4 * (size_t)numGlobalChains * sizeof (MrBFlt)); moves[i]->cumProposalProb = moves[i]->relProposalProb + numGlobalChains; moves[i]->targetRate = moves[i]->relProposalProb + 2*numGlobalChains; moves[i]->lastAcceptanceRate = moves[i]->relProposalProb + 3*numGlobalChains; moves[i]->nAccepted = (int *) SafeRealloc ((void *) moves[i]->nAccepted, 5* (size_t)numGlobalChains * sizeof (int)); moves[i]->nTried = moves[i]->nAccepted + numGlobalChains; moves[i]->nBatches = moves[i]->nAccepted + 2*numGlobalChains; moves[i]->nTotAccepted = moves[i]->nAccepted + 3*numGlobalChains; moves[i]->nTotTried = moves[i]->nAccepted + 4*numGlobalChains; /* initialize all values to default */ for (j=0; jnAccepted[j] = 0; moves[i]->nTried[j] = 0; moves[i]->nBatches[j] = 0; moves[i]->nTotAccepted[j] = 0; moves[i]->nTotTried[j] = 0; moves[i]->relProposalProb[j] = mvt->relProposalProb; moves[i]->cumProposalProb[j] = 0.0; moves[i]->lastAcceptanceRate[j] = 0.0; for (k=0; knumTuningParams; k++) moves[i]->tuningParam[j][k] = mvt->tuningParam[k]; moves[i]->targetRate[j] = mvt->targetRate; } } #if 0 for (i=0; iparamType == P_CPPEVENTS) { printf ("Trees after changing number of runs\n"); for (j=0; jnEvents[2*j][k]); assert (p->nEvents[2*j] >= 0); for (i1=0; i1nEvents[2*j][k]; i1++) { if (i1 == 0) printf (" (%lf %lf,", p->position[2*j][k], p->rateMult[2*j][k]); else if (i1 == p->nEvents[2*j][k]-1) printf (" %lf %lf)", p->position[2*j][k], p->rateMult[2*j][k]); else printf (" %lf %lf,", p->position[2*j][k], p->rateMult[2*j][k]); } printf ("\n"); } for (k=0; k<2*numLocalTaxa; k++) { printf ("%d -- %d:", k, p->nEvents[2*j+1][k]); assert (p->nEvents[2*j+1] >= 0); for (i1=0; i1nEvents[2*j+1][k]; i1++) { if (i1 == 0) printf (" (%lf %lf,", p->position[2*j+1][k], p->rateMult[2*j+1][k]); else if (i1 == p->nEvents[2*j][k]-1) printf (" %lf %lf)", p->position[2*j+1][k], p->rateMult[2*j+1][k]); else printf (" %lf %lf,", p->position[2*j+1][k], p->rateMult[2*j+1][k]); } printf ("\n"); } } } } #endif return (NO_ERROR); } /*----------------------------------------------------------- | | CheckCharCodingType: check if character is parsimony- | informative, variable, or constant | -----------------------------------------------------------*/ void CheckCharCodingType (Matrix *m, CharInfo *ci) { int i, j, k, x, n1[MAX_STD_STATES], n2[MAX_STD_STATES], largest, smallest, numPartAmbig, numConsidered, numInformative, lastInformative=0, uniqueBits, newPoss, oldPoss; BitsLong combinations[2048], *tempComb, *newComb, *oldComb, bitsLongOne=1; /* set up comb pointers */ oldComb = combinations; newComb = oldComb + 1024; /* set counters to 0 */ numPartAmbig = numConsidered = 0; /* set variable and informative to yes */ ci->variable = ci->informative = YES; /* set constant to no and state counters to 0 for all states */ for (i=0; iconstant[i] = ci->singleton[i] = NO; n1[i] = n2[i] = 0; } for (i=0; inRows; i++) { /* retrieve character */ x = (int) m->origin[m->column + i*m->rowSize]; /* add it to counters if not all ambiguous */ if (NBits(x) < ci->nStates) { numConsidered++; if (NBits(x) > 1) numPartAmbig++; for (j=0; jconstant[i] = YES; ci->variable = ci->informative = NO; } else if (n1[i] == 1) { ci->singleton[i] = YES; } } /* return if variable is no */ if (ci->variable == NO) return; /* the character is either (variable and uninformative) or informative */ /* first consider unambiguous characters */ /* find smallest and largest unambiguous state for this character */ smallest = MAX_STD_STATES-1; largest = 0; for (i=0; i 0) { if (i < smallest) smallest = i; if (i > largest) largest = i; } } /* count the number of informative states in the unambiguous codings */ for (i=numInformative=0; icType == ORD && n2[i] > 0 && i != smallest && i != largest) { numInformative++; lastInformative = i; } else if (n2[i] > 1) { numInformative++; lastInformative = i; } } /* set informative */ if (numInformative > 1) ci->informative = YES; else ci->informative = NO; /* we can return now unless informative is no and numPartAmbig is not 0 */ if (!(numPartAmbig > 0 && ci->informative == NO)) return; /* check if partially ambiguous observations make this character informative after all */ /* first set the bits for the taken states */ x = 0; for (i=0; i 0 && i != lastInformative) x |= (bitsLongOne<nRows; i++) { x = (int) m->origin[m->column + i*m->rowSize]; /* if partambig */ if (NBits(x) > 1 && NBits(x) < ci->nStates) { /* remove lastInformative */ x &= !(bitsLongOne<nRows) ci->informative = YES; return; } /*----------------------------------------------------------- | | CheckModel: check model and warn user if strange things | are discovered. | -------------------------------------------------------------*/ int CheckModel (void) { int ch, i, j, k, answer; Tree *t = NULL; TreeNode *p; MrBFlt treeAge, clockRate; /* there should only be one calibrated tree */ for (i=0; iisCalibrated == YES) break; } if (i < numTrees) { if (!strcmp(modelParams[t->relParts[0]].clockRatePr, "Fixed") && AreDoublesEqual (modelParams[t->relParts[0]].clockRateFix, 1.0, 1E-6) == YES) { MrBayesPrint("%s WARNING: You have calibrated the tree but the clock rate is fixed to 1.0.\n", spacer); MrBayesPrint("%s This means that time is measured in expected changes per time unit. If\n", spacer); MrBayesPrint("%s the calibrations use a different time scale, you need to modify the model\n", spacer); MrBayesPrint("%s by introducing a prior for the clock rate ('prset clockratepr').\n", spacer); if (noWarn == NO) { answer = WantTo("Do you want to continue with the run regardless"); if (answer == YES) { MrBayesPrint("%s Continuing with the run...\n\n", spacer); } else { MrBayesPrint("%s Stopping the run...\n\n", spacer); return (ERROR); } } } } /* * Check that the clock rate is consistent with the tree age prior. We cannot check this earlier * because the clock rate and tree age are set separately, and we do not know in which order they are set. * We need to check all chains because start values are set separately for each chain. */ for (i=0; iisClock == YES && t->isCalibrated == YES) { clockRate = *GetParamVals(modelSettings[t->relParts[0]].clockRate, ch, 0); treeAge = t->root->left->nodeDepth / clockRate; if (!AreDoublesEqual(treeAge, t->root->left->age, 0.000001)) { MrBayesPrint("%s ERROR: The tree age setting is inconsistent with the specified tree age prior.\n", spacer); return (ERROR); } if (modelParams[t->relParts[0]].treeAgePr.prior == fixed) { if (!AreDoublesEqual(treeAge, modelParams[t->relParts[0]].treeAgePr.priorParams[0], 0.000001)) { MrBayesPrint("%s ERROR: The clock rate is inconsistent with the specified tree age prior.\n", spacer); return (ERROR); } } else if (modelParams[t->relParts[0]].treeAgePr.prior == uniform) { if (treeAge < modelParams[t->relParts[0]].treeAgePr.priorParams[0] || treeAge > modelParams[t->relParts[0]].treeAgePr.priorParams[1]) { MrBayesPrint("%s ERROR: The clock rate is inconsistent with the specified tree age prior.\n", spacer); return (ERROR); } } else if (modelParams[t->relParts[0]].treeAgePr.prior == offsetExponential || modelParams[t->relParts[0]].treeAgePr.prior == offsetGamma || modelParams[t->relParts[0]].treeAgePr.prior == truncatedNormal || modelParams[t->relParts[0]].treeAgePr.prior == offsetLogNormal) { if (treeAge < modelParams[t->relParts[0]].treeAgePr.priorParams[0]) { MrBayesPrint("%s ERROR: The clock rate is inconsistent with the specified tree age prior.\n", spacer); return (ERROR); } } } } } /* check coalescence model */ for (i=0; irelParts[0]].clockPr,"Coalescence") || !strcmp(modelParams[t->relParts[0]].clockPr,"Speciestreecoalescence")) && !strcmp(modelParams[t->relParts[0]].clockRatePr, "Fixed") && AreDoublesEqual (modelParams[t->relParts[0]].clockRateFix, 1.0, 1E-6) == YES) { if (i == 0) // We only warn for the first tree { MrBayesPrint("%s WARNING: You are using a coalescent model but the clock rate is fixed to 1.0.\n", spacer); MrBayesPrint("%s This is likely to be incorrect unless you have set the population size prior\n", spacer); MrBayesPrint("%s ('prset popsizepr') to reflect an appropriate prior on theta. Please check that \n", spacer); MrBayesPrint("%s the prior on theta is reasonable for your data.\n", spacer); if (noWarn == NO) { answer = WantTo("Do you want to continue with the run regardless"); if (answer == YES) { MrBayesPrint("%s Continuing with the run...\n\n", spacer); } else { MrBayesPrint("%s Stopping the run...\n\n", spacer); return (ERROR); } } } } } /* Check consistency of best model. First we guarantee that if one topology has a species tree prior, then all topologies have the same prior. Then we make sure that all clock trees have a coalescence prior. */ j = 0; for (i=0; i 0) { if (j != numCurrentDivisions) { MrBayesPrint("%s ERROR: If one gene tree has a speciestree prior then all\n", spacer); MrBayesPrint("%s gene trees must have the same prior.\n", spacer); return (ERROR); } for (i=0; irelParts[0]].clockPr,"Speciestreecoalescence") != 0) { MrBayesPrint("%s ERROR: All gene trees must have a speciestreecoalescence prior\n", spacer); MrBayesPrint("%s if they fold into a species tree.\n", spacer); return (ERROR); } if (t->isCalibrated == YES) { for (k=0; knNodes-1; k++) { p = t->allDownPass[k]; if (p->calibration != NULL) { MrBayesPrint("%s ERROR: Gene trees cannot be individually calibrated\n", spacer); MrBayesPrint("%s if they fold into a species tree.\n", spacer); return (ERROR); } } } } } return NO_ERROR; } /*----------------------------------------------------------- | | CheckExpandedModels: check data partitions that have | the codon or doublet model specified | -------------------------------------------------------------*/ int CheckExpandedModels (void) { int c, d, i, t, s1, s2, s3, whichNuc, uniqueId, numCharsInPart, firstChar, lastChar, contiguousPart, badBreak, badExclusion, nGone, nuc1, nuc2, nuc3, foundStopCodon, posNucs1[4], posNucs2[4], posNucs3[4], oneGoodCodon, foundUnpaired, nPair; char *tempStr; int tempStrSize=100; ModelParams *mp; tempStr = (char *) SafeMalloc((size_t)tempStrSize * sizeof(char)); if (!tempStr) { MrBayesPrint ("%s Problem allocating tempString (%d)\n", spacer, tempStrSize * sizeof(char)); return (ERROR); } /* first, set charId to 0 for all characters */ for (i=0; idataType == DNA || mp->dataType == RNA) { if (!strcmp(mp->nucModel,"Codon") || !strcmp(mp->nucModel,"Protein")) { /* start check that the codon model is appropriate for this partition */ /* find first character in this partition */ for (c=0; c=0; c--) { if (partitionId[c][partitionNum] == d+1) break; } lastChar = c; /*printf (" last character = %d\n", lastChar);*/ /* check that the number of characters in partition is divisible by 3 */ numCharsInPart = 0; for (c=firstChar; c<=lastChar; c++) { if (charInfo[c].isExcluded == YES || partitionId[c][partitionNum] != d+1) continue; numCharsInPart++; } /*printf (" numCharsInPart = %d\n", numCharsInPart);*/ if (numCharsInPart % 3 != 0) { if (numCurrentDivisions == 1) { MrBayesPrint ("%s The number of characters is not divisible by three.\n", spacer); MrBayesPrint ("%s You specified a %s model which requires triplets\n", spacer, mp->nucModel); MrBayesPrint ("%s However, you have %d characters.\n", spacer, numCharsInPart); } else { MrBayesPrint ("%s The number of characters in partition %d is not\n", spacer, d+1); MrBayesPrint ("%s divisible by three. You specified a %s model\n", spacer, mp->nucModel); MrBayesPrint ("%s which requires triplets. \n", spacer); MrBayesPrint ("%s However, you have %d characters in this partition\n", spacer, numCharsInPart); } free (tempStr); return (ERROR); } /* check that all of the characters in the partition are contiguous */ contiguousPart = YES; for (c=firstChar; c<=lastChar; c++) { if (charInfo[c].isExcluded == NO && partitionId[c][partitionNum] != d+1) contiguousPart = NO; } if (contiguousPart == NO) { MrBayesPrint ("%s Partition %d is not contiguous. You specified that\n", spacer, d+1); MrBayesPrint ("%s a %s model be used for this partition. However, there\n", spacer, mp->nucModel); MrBayesPrint ("%s is another partition that is between some of the characters\n", spacer); MrBayesPrint ("%s in this partition. \n", spacer); free (tempStr); return (ERROR); } /* check that there is not a break inside a triplet of characters */ badBreak = NO; whichNuc = 0; for (c=firstChar; c<=lastChar; c++) { if (charInfo[c].isExcluded == NO) continue; whichNuc++; if (charInfo[c].bigBreakAfter == YES && whichNuc != 3) badBreak = YES; if (whichNuc == 3) whichNuc = 0; } if (badBreak == YES) { MrBayesPrint ("%s You specified a databreak inside of a coding triplet.\n", spacer); MrBayesPrint ("%s This is a problem, as you imply that part of the codon\n", spacer); MrBayesPrint ("%s lies in one gene and the remainder in another gene. \n", spacer); free (tempStr); return (ERROR); } /* make certain excluded characters are in triplets but allow frameshift exclusions first in sequence */ badExclusion = NO; whichNuc = nGone = 0; for (c=firstChar; c<=lastChar; c++) if (charInfo[c].isExcluded == NO) break; for (; c<=lastChar; c++) { whichNuc++; if (charInfo[c].isExcluded == YES) nGone++; if (whichNuc == 3) { if (nGone == 1 || nGone == 2) badExclusion = YES; whichNuc = nGone = 0; } } if (badExclusion == YES) { MrBayesPrint ("%s In excluding characters, you failed to remove all of the\n", spacer); MrBayesPrint ("%s sites of at least one codon. If you exclude sites, make \n", spacer); MrBayesPrint ("%s certain to exclude all of the sites in the codon(s). \n", spacer); free (tempStr); return (ERROR); } /* check that there are no stop codons */ foundStopCodon = NO; /* allow frameshifting exclusions in the beginning */ for (c=firstChar; c<=lastChar; c++) if (charInfo[c].isExcluded == NO) break; for (; c<=lastChar; c+=3) { if (charInfo[c].isExcluded == NO) { for (t=0; tcodon[s1*16 + s2*4 + s3] != 21) oneGoodCodon = YES; } // s++; } } } if (oneGoodCodon == NO) { foundStopCodon = YES; MrBayesPrint ("%s Stop codon: taxon %s, sites %d to %d (%c%c%c, %s code)\n", spacer, taxaNames[t], c+1, c+3, WhichNuc (nuc1), WhichNuc (nuc2), WhichNuc (nuc3), mp->geneticCode); } } } } if (foundStopCodon == YES) { MrBayesPrint ("%s At least one stop codon was found. Stop codons are not\n", spacer); MrBayesPrint ("%s allowed under the codon models. \n", spacer); free (tempStr); return (ERROR); } /* everything checks out. Now we can initialize charId */ whichNuc = 0; for (c=firstChar; c<=lastChar; c++) { whichNuc++; charInfo[c].charId = uniqueId; if (whichNuc == 3) { whichNuc = 0; uniqueId++; } } // allCheckedOut++; /* end check that the codon model is appropriate for this partition */ } else if (!strcmp(mp->nucModel,"Doublet")) { /* start check that the doublet model is appropriate for this partition */ /* Check that pairsId does not equal 0 for any of the characters in the partition. If it does, then this means that at least one site was not appropriately paired. Remember, that pairsId is initialized 1, 2, 3, ... for the first pair, second pair, etc. Also, check that every pair is only represented two times. */ foundUnpaired = NO; for (c=0; c= 0 && nuc2 >= 0) { charInfo[nuc1].charId = charInfo[nuc2].charId = uniqueId; uniqueId++; } else { MrBayesPrint ("%s Weird doublet problem in partition %d.\n", spacer, d+1); free (tempStr); return (ERROR); } } } // allCheckedOut++; /* end check that the doublet model is appropriate for this partition */ } } } # if 0 for (c=0; ccompMatrixStart = newColumn; /* set compressed character offset for this division */ m->compCharStart = numCompressedChars; /* set number of compressed characters to 0 for this division */ m->numChars = 0; /* find the number of original characters per model site */ m->nCharsPerSite = 1; if (mp->dataType == DNA || mp->dataType == RNA) { if (!strcmp(mp->nucModel, "Doublet")) m->nCharsPerSite = 2; if (!strcmp(mp->nucModel, "Codon") || !strcmp(mp->nucModel, "Protein")) m->nCharsPerSite = 3; } /* sort and compress the characters for this division */ for (c=0; cnCharsPerSite > 1) { j = 1; if (charInfo[c].charId == 0) { MrBayesPrint ("%s Character %d is not properly defined\n", spacer, c+1); goto errorExit; } for (i=c+1; i= m->nCharsPerSite) { MrBayesPrint ("%s Too many matches in charId (division %d char %d)\n", spacer, d, numCompressedChars); goto errorExit; } else { col[j++] = i; isTaken[i] = YES; } } } if (j != m->nCharsPerSite) { MrBayesPrint ("%s Too few matches in charId (division %d char %d)\n", spacer, d, numCompressedChars); goto errorExit; } } /* add character to temporary matrix in column(s) at newColumn */ for (t=newRow=0; tnCharsPerSite; k++) { tempMatrix[pos(newRow,newColumn+k,numLocalChar)] = matrix[pos(t,col[k],numChar)]; } newRow++; } /* is it unique? */ isSame = NO; if (mp->dataType != CONTINUOUS) { for (i=m->compMatrixStart; inCharsPerSite) { isSame = YES; for (j=0; jnCharsPerSite; k++) if (tempMatrix[pos(j,newColumn+k,numLocalChar)] != tempMatrix[pos(j,i+k,numLocalChar)]) { isSame = NO; break; } if (isSame == NO) break; } if (isSame == YES) break; } } /* if subject to data augmentation, it is always unique */ if (!strcmp(mp->augmentData, "Yes")) { for (k=0; knCharsPerSite; k++) { if (charInfo[col[k]].isMissAmbig == YES) isSame = NO; } } if (isSame == NO) { /* if it is unique then it should be added */ tempSitesOfPat[numCompressedChars] = 1; for (k=0; knCharsPerSite; k++) { compColPos[col[k]] = newColumn + k; compCharPos[col[k]] = numCompressedChars; tempChar[newColumn + k] = col[k]; } newColumn+=m->nCharsPerSite; m->numChars++; numCompressedChars++; } else { /* if it is not unique then simply update tempSitesOfPat */ /* calculate compressed character position and put it into a */ /* (i points to compressed column position) */ a = m->compCharStart + ((i - m->compMatrixStart) / m->nCharsPerSite); tempSitesOfPat[a]++; for (k=0; knCharsPerSite; k++) { compColPos[col[k]] = i; compCharPos[col[k]] = a; /* tempChar (pointing from compressed to uncompressed) */ /* can only be set for first pattern */ } } } /* next character */ /* check that the partition has at least a single character */ if (m->numChars <= 0) { MrBayesPrint ("%s You must have at least one site in a partition. Partition %d\n", spacer, d+1); MrBayesPrint ("%s has %d site patterns.\n", spacer, m->numChars); goto errorExit; } m->compCharStop = m->compCharStart + m->numChars; m->compMatrixStop = newColumn; } /* next division */ compMatrixRowSize = newColumn; /* now we know the size, so we can allocate space for the compressed matrix ... */ if (memAllocs[ALLOC_COMPMATRIX] == YES) { free (compMatrix); compMatrix = NULL; memAllocs[ALLOC_COMPMATRIX] = NO; } compMatrix = (BitsLong *) SafeCalloc (compMatrixRowSize * numLocalTaxa, sizeof(BitsLong)); if (!compMatrix) { MrBayesPrint ("%s Problem allocating compMatrix (%d)\n", spacer, compMatrixRowSize * numLocalTaxa * sizeof(BitsLong)); goto errorExit; } memAllocs[ALLOC_COMPMATRIX] = YES; if (memAllocs[ALLOC_NUMSITESOFPAT] == YES) { free (numSitesOfPat); numSitesOfPat = NULL; memAllocs[ALLOC_NUMSITESOFPAT] = NO; } numSitesOfPat = (CLFlt *) SafeCalloc (numCompressedChars, sizeof(CLFlt)); if (!numSitesOfPat) { MrBayesPrint ("%s Problem allocating numSitesOfPat (%d)\n", spacer, numCompressedChars * sizeof(MrBFlt)); goto errorExit; } memAllocs[ALLOC_NUMSITESOFPAT] = YES; if (memAllocs[ALLOC_ORIGCHAR] == YES) { free (origChar); origChar = NULL; memAllocs[ALLOC_ORIGCHAR] = NO; } origChar = (int *)SafeMalloc((size_t)compMatrixRowSize * sizeof(int)); if (!origChar) { MrBayesPrint ("%s Problem allocating origChar (%d)\n", spacer, numCompressedChars * sizeof(int)); goto errorExit; } memAllocs[ALLOC_ORIGCHAR] = YES; /* ... and copy the data there */ for (i=0; i numCurrentDivisions) { MrBayesPrint ("%s Partition delimiter is too large\n", spacer); return (ERROR); } if (fromI == -1) fromI = tempInt; else if (fromI != -1 && toJ == -1 && foundDash == YES && foundComma == NO) { toJ = tempInt; for (i=fromI-1; i=0; i--) { if (activeParts[i] == YES) { lastActive = i; break; } } /* MrBayesPrint ("\n"); */ if (numCurrentDivisions == 1) MrBayesPrint ("%s Successfully set likelihood model parameters\n", spacer); else { if (nApplied == numCurrentDivisions || nApplied == 0) { MrBayesPrint ("%s Successfully set likelihood model parameters to all\n", spacer); MrBayesPrint ("%s applicable data partitions \n", spacer); } else { MrBayesPrint ("%s Successfully set likelihood model parameters to\n", spacer); if (nApplied == 1) MrBayesPrint ("%s partition", spacer); else MrBayesPrint ("%s partitions", spacer); for (i=0; i 1) MrBayesPrint (" and %d", i+1); else MrBayesPrint (" %d", i+1); if (nApplied > 2 && i != lastActive) MrBayesPrint (","); } } MrBayesPrint (" (if applicable)\n"); } } if (SetUpAnalysis (&globalSeed) == ERROR) return (ERROR); return (NO_ERROR); } int DoLsetParm (char *parmName, char *tkn) { int i, j, tempInt, nApplied; char tempStr[100]; if (defMatrix == NO) { MrBayesPrint ("%s A matrix must be specified before the model can be defined\n", spacer); return (ERROR); } if (inValidCommand == YES) { for (i=0; i numCurrentDivisions) { MrBayesPrint ("%s Partition delimiter is too large\n", spacer); return (ERROR); } if (fromI == -1) fromI = tempInt; else if (fromI != -1 && toJ == -1 && foundDash == YES && foundComma == NO) { toJ = tempInt; for (i=fromI-1; i= 2 && tempInt < MAX_RATE_CATS) { nApplied = NumActiveParts (); for (i=0; i= 2 && tempInt < MAX_RATE_CATS) { nApplied = NumActiveParts (); for (i=0; i= 2 && tempInt < MAX_RATE_CATS) { nApplied = NumActiveParts (); for (i=0; i= 1 && tempInt <= 1000) { nApplied = NumActiveParts (); for (i=0; i= 2 && tempInt < MAX_RATE_CATS) { nApplied = NumActiveParts (); for (i=0; i= 2 && tempInt < MAX_RATE_CATS) { nApplied = NumActiveParts (); for (i=0; i= 2 && tempInt < MAX_RATE_CATS) { nApplied = NumActiveParts (); for (i=0; i)' instead.\n", spacer); return (ERROR); } /* set Parsmodel (useParsModel) *******************************************************/ else if (!strcmp(parmName, "Parsmodel")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(ALPHA); else if (expecting == Expecting(ALPHA)) { if (IsArgValid(tkn, tempStr) == NO_ERROR) { nApplied = NumActiveParts (); for (i=0; i 0) MrBayesPrint ("%s Of the %d lines, %d of them will be summarized (starting at line %d)\n", spacer, fileInfo.numRows+burnin, fileInfo.numRows, fileInfo.firstParamLine); else MrBayesPrint ("%s All %d lines will be summarized (starting at line %d)\n", spacer, fileInfo.numRows, fileInfo.firstParamLine); MrBayesPrint ("%s (Only the last set of lines will be read, in case multiple\n", spacer); MrBayesPrint ("%s parameter blocks are present in the same file.)\n", spacer); /* allocate space to hold parameter information */ if (AllocateParameterSamples (¶meterSamples, 1, fileInfo.numRows, fileInfo.numColumns) == ERROR) goto errorExit; /* Now we read the file for real. First, rewind file pointer to beginning of file... */ if (ReadParamSamples (plotParams.plotFileName, &fileInfo, parameterSamples, 0) == ERROR) goto errorExit; /* get length of longest header */ longestHeader = 9; /* 9 is the length of the word "parameter" (for printing table) */ for (i=0; i longestHeader) longestHeader = len; } /* print x-y plot of parameter vs. generation */ whichIsX = -1; for (i=0; i= 0 && whichIsX != whichIsY) { MrBayesPrint ("\n%s Rough trace plot of parameter %s:\n", spacer, headerNames[whichIsY]); if (PrintPlot (parameterSamples[whichIsX].values[0], parameterSamples[whichIsY].values[0], fileInfo.numRows) == ERROR) goto errorExit; numPlotted++; } } if (numPlotted == 0) { MrBayesPrint ("%s Did not find any parameters matching \"%s\" to plot\n", spacer, plotParams.parameter); } /* free memory */ for (i=0; i 0.50) { MrBayesPrint ("%s Burnin fraction too high (> 0.50)\n", spacer); return (ERROR); } chainParams.burninFraction = tempD; MrBayesPrint ("%s Setting burnin fraction to %.2f\n", spacer, chainParams.burninFraction); expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else { return (ERROR); } } /* set Parameter (plotParams.parameter) *******************************************************/ else if (!strcmp(parmName, "Parameter")) { if (expecting == Expecting(EQUALSIGN)) { expecting = Expecting(ALPHA); readWord = YES; } else if (expecting == Expecting(ALPHA)) { strcpy (plotParams.parameter, tkn); expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); } /* set Parameter (plotParams.match) *******************************************************/ else if (!strcmp(parmName, "Match")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(ALPHA); else if (expecting == Expecting(ALPHA)) { if (IsArgValid(tkn, tempStr) == NO_ERROR) strcpy (plotParams.match, tempStr); else return (ERROR); MrBayesPrint ("%s Setting plot matching to %s\n", spacer, plotParams.match); expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); } else return (ERROR); } return (NO_ERROR); } int DoPropset (void) { MrBayesPrint ("%s Successfully set proposal parameters\n", spacer); return (NO_ERROR); } int DoPropsetParm (char *parmName, char *tkn) { int i, j, k, nMatches, tempInt; MrBFlt tempFloat; static MCMCMove *mv = NULL; static MrBFlt *theValue, theValueMin, theValueMax; static int jump, runIndex, chainIndex; static char *temp=NULL, *localTkn=NULL; /*freed at the end of the call*/ static char *tempName=NULL; /*not freed at the end of the call*/ if (defMatrix == NO) { MrBayesPrint ("%s A matrix must be specified before proposal parameters can be changed\n", spacer); return (ERROR); } if (expecting == Expecting(PARAMETER)) { if (!strcmp(parmName, "Xxxxxxxxxx")) { /* we expect a move name with possible run and chain specification as follows: $(,)= -- apply to run and chain $(,)= -- apply to chain for all runs $(,)= -- apply to all chains of run $prob(,)= -- change relative proposal probability $targetrate(,)= -- change target acc rate for autotuning the parsing is complicated by the fact that the move name can look something like: eTBR(Tau{all}) eTBR(Tau{1,4,5}) so we need to assemble the move name from several tokens that are parsed out separately; here we receive only the first part (before the left parenthesis) */ /* copy to local move name */ SafeStrcpy(&tempName, tkn); mv = NULL; foundComma = foundEqual = NO; expecting = Expecting(LEFTCURL) | Expecting(RIGHTCURL) | Expecting(COMMA) | Expecting(LEFTPAR) | Expecting(RIGHTPAR) | Expecting(NUMBER) | Expecting(ALPHA) | Expecting(DOLLAR); } else return (ERROR); } else if (expecting == Expecting(ALPHA)) { if (mv == NULL) { /* we are still assembling the move name */ SafeStrcat(&tempName, tkn); expecting = Expecting(LEFTCURL) | Expecting(RIGHTCURL) | Expecting(COMMA) | Expecting(LEFTPAR) | Expecting(RIGHTPAR) | Expecting(NUMBER) | Expecting(ALPHA) | Expecting(DOLLAR); } else { /* we have a parameter name; now find the parameter name, case insensitive */ SafeStrcpy(&localTkn, tkn); for (i=0; i<(int)strlen(localTkn); i++) localTkn[i] = tolower(localTkn[i]); nMatches = j = 0; for (i=0; imoveType->numTuningParams; i++) { SafeStrcpy(&temp, mv->moveType->shortTuningName[i]); for (k=0; k<(int)strlen(temp); k++) temp[k] = tolower(temp[k]); if (strncmp(localTkn,temp,strlen(localTkn)) == 0) { j = i; nMatches++; } } if (strncmp(localTkn,"prob",strlen(localTkn)) == 0) { j = -1; nMatches++; } else if (strncmp(localTkn,"targetrate",strlen(localTkn)) == 0) { j = -2; nMatches++; } if (nMatches == 0) { MrBayesPrint ("%s Could not find move parameter to change '%s'\n", spacer, localTkn); return (ERROR); } else if (nMatches > 1) { MrBayesPrint ("%s Several move parameters matched the abbreviated name '%s'\n", spacer, localTkn); return (ERROR); } if (j == -1) { theValue = mv->relProposalProb; theValueMin = 0.0; theValueMax = 1000.0; jump = 1; } else if (j == -2) { theValue = mv->targetRate; theValueMin = 0.10; theValueMax = 0.70; jump = 1; } else { theValue = &mv->tuningParam[0][j]; theValueMin = mv->moveType->minimum[j]; theValueMax = mv->moveType->maximum[j]; jump = mv->moveType->numTuningParams; } chainIndex = -1; runIndex = -1; expecting = Expecting(LEFTPAR) | Expecting(EQUALSIGN); } } else if (expecting == Expecting(LEFTCURL) || expecting == Expecting(RIGHTCURL)) { /* we are still assembling the move name */ SafeStrcat (&tempName, tkn); expecting = Expecting(LEFTCURL) | Expecting(RIGHTCURL) | Expecting(COMMA) | Expecting(LEFTPAR) | Expecting(RIGHTPAR) | Expecting(NUMBER) | Expecting(ALPHA) | Expecting(DOLLAR); } else if (expecting == Expecting(DOLLAR)) { /* we know that the name is complete now; find the move by its name, case insensitive */ SafeStrcpy(&localTkn, tempName); j=(int)strlen(localTkn); for (i=0; iname); for (k=0; k<(int)strlen(temp); k++) temp[k] = tolower(temp[k]); if (strncmp(temp,localTkn,strlen(localTkn)) == 0) { j = i; nMatches++; } } if (nMatches == 0) { MrBayesPrint ("%s Could not find move '%s'\n", spacer, localTkn); return (ERROR); } else if (nMatches > 1) { MrBayesPrint ("%s Several moves matched the abbreviated name '%s'\n", spacer, localTkn); return (ERROR); } else mv = moves[j]; foundComma = foundEqual = NO; expecting = Expecting(ALPHA); } else if (expecting == Expecting(LEFTPAR)) { if (mv == NULL) { /* we are still assembling the move name */ SafeStrcat (&tempName, tkn); expecting = Expecting(LEFTCURL) | Expecting(RIGHTCURL) | Expecting(COMMA) | Expecting(LEFTPAR) | Expecting(RIGHTPAR) | Expecting(NUMBER) | Expecting(ALPHA) | Expecting(DOLLAR); } else /* if (mv != NULL) */ { /* we will be reading in run and chain indices */ expecting = Expecting(NUMBER) | Expecting(COMMA); } } else if (expecting == Expecting(NUMBER)) { if (mv == NULL) { /* we are still assembling the move name */ SafeStrcat (&tempName, tkn); expecting = Expecting(LEFTCURL) | Expecting(RIGHTCURL) | Expecting(COMMA) | Expecting(LEFTPAR) | Expecting(RIGHTPAR) | Expecting(NUMBER) | Expecting(ALPHA) | Expecting(DOLLAR); } else if (foundEqual == YES) { sscanf (tkn, "%lf", &tempFloat); if (tempFloat < theValueMin || tempFloat > theValueMax) { MrBayesPrint ("%s The value is out of range (min = %lf; max = %lf)\n", spacer, theValueMin, theValueMax); return (ERROR); } if (runIndex == -1 && chainIndex == -1) { for (i=0; i= 0) { theValue += chainIndex*jump; for (i=0; i= 0 && chainIndex == -1) { theValue += runIndex*chainParams.numChains*jump; for (i=0; i= 0 && chainIndex >= 0) */ { theValue[runIndex*chainParams.numChains*jump+chainIndex*jump] = tempFloat; } expecting = Expecting (PARAMETER) | Expecting(SEMICOLON); } else /* if (foundEqual == NO) */ { sscanf (tkn, "%d", &tempInt); if (foundComma == NO) { if (tempInt <= 0 || tempInt > chainParams.numRuns) { MrBayesPrint ("%s Run index is out of range (min=1; max=%d)\n", spacer, chainParams.numRuns); return (ERROR); } runIndex = tempInt - 1; expecting = Expecting(COMMA); } else { if (tempInt <= 0 || tempInt > chainParams.numChains) { MrBayesPrint ("%s Chain index is out of range (min=1; max=%d)\n", spacer, chainParams.numChains); return (ERROR); } chainIndex = tempInt - 1; expecting = Expecting(RIGHTPAR); } } } else if (expecting == Expecting(COMMA)) { if (mv == NULL) { /* we are still assembling the move name */ SafeStrcat (&tempName, tkn); expecting = Expecting(LEFTCURL) | Expecting(RIGHTCURL) | Expecting(COMMA) | Expecting(LEFTPAR) | Expecting(RIGHTPAR) | Expecting(NUMBER) | Expecting(ALPHA) | Expecting(DOLLAR); } else { /* we will be reading in chain index, if present */ foundComma = YES; expecting = Expecting(RIGHTPAR) | Expecting(NUMBER); } } else if (expecting == Expecting(RIGHTPAR)) { if (mv == NULL) { /* we are still assembling the move name */ SafeStrcat (&tempName, tkn); expecting = Expecting(LEFTCURL) | Expecting(RIGHTCURL) | Expecting(COMMA) | Expecting(LEFTPAR) | Expecting(RIGHTPAR) | Expecting(NUMBER) | Expecting(ALPHA) | Expecting(DOLLAR); } else expecting = Expecting(EQUALSIGN); } else if (expecting == Expecting(EQUALSIGN)) { foundEqual = YES; expecting = Expecting(NUMBER); } else return (ERROR); SAFEFREE (temp); SAFEFREE (localTkn); return (NO_ERROR); } int DoPrset (void) { int i, nApplied, lastActive=0; nApplied = NumActiveParts (); for (i=numCurrentDivisions-1; i>=0; i--) { if (activeParts[i] == YES) { lastActive = i; break; } } if (numCurrentDivisions == 1) MrBayesPrint ("%s Successfully set prior model parameters\n", spacer); else { if (nApplied == numCurrentDivisions || nApplied == 0) { MrBayesPrint ("%s Successfully set prior model parameters to all\n", spacer); MrBayesPrint ("%s applicable data partitions \n", spacer); } else { MrBayesPrint ("%s Successfully set prior model parameters to\n", spacer); if (nApplied == 1) MrBayesPrint ("%s partition", spacer); else MrBayesPrint ("%s partitions", spacer); for (i=0; i 1) MrBayesPrint (" and %d", i+1); else MrBayesPrint (" %d", i+1); if (nApplied > 2 && i != lastActive) MrBayesPrint (","); } } MrBayesPrint (" (if applicable)\n"); } } if (SetUpAnalysis (&globalSeed) == ERROR) return (ERROR); return (NO_ERROR); } int DoPrsetParm (char *parmName, char *tkn) { int i, j, k, tempInt, nApplied, index, ns, flag=0; MrBFlt tempD, sum; char tempStr[100]; if (defMatrix == NO) { MrBayesPrint ("%s A matrix must be specified before the model can be defined\n", spacer); return (ERROR); } if (inValidCommand == YES) { for (i=0; i numCurrentDivisions) { MrBayesPrint ("%s Partition delimiter is too large\n", spacer); return (ERROR); } if (fromI == -1) fromI = tempInt; else if (fromI != -1 && toJ == -1 && foundDash == YES && foundComma == NO) { toJ = tempInt; for (i=fromI-1; i ALPHA_MAX) { MrBayesPrint ("%s Beta parameter cannot be greater than %1.2lf\n", spacer, ALPHA_MAX); return (ERROR); } if (tempD < ALPHA_MIN) { MrBayesPrint ("%s Beta parameter cannot be less than %1.2lf\n", spacer, ALPHA_MIN); return (ERROR); } modelParams[i].tRatioDir[numVars[i]++] = tempD; if (numVars[i] < 2) expecting = Expecting(COMMA); else { if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting Tratiopr to Beta(%1.2lf,%1.2lf)\n", spacer, modelParams[i].tRatioDir[0], modelParams[i].tRatioDir[1]); else MrBayesPrint ("%s Setting Tratiopr to Beta(%1.2lf,%1.2lf) for partition %d\n", spacer, modelParams[i].tRatioDir[0], modelParams[i].tRatioDir[1], i+1); expecting = Expecting(RIGHTPAR); } } else if (!strcmp(modelParams[i].tRatioPr,"Fixed")) { sscanf (tkn, "%lf", &tempD); modelParams[i].tRatioFix = tempD; if (modelParams[i].tRatioFix > KAPPA_MAX) { MrBayesPrint ("%s Tratio cannot be greater than %1.2lf\n", spacer, KAPPA_MAX); return (ERROR); } if (modelParams[i].tRatioFix < 0.0) { MrBayesPrint ("%s Tratio cannot be less than %1.2lf\n", spacer, 0.0); return (ERROR); } if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting Tratiopr to Fixed(%1.2lf)\n", spacer, modelParams[i].tRatioFix); else MrBayesPrint ("%s Setting Tratiopr to Fixed(%1.2lf) for partition %d\n", spacer, modelParams[i].tRatioFix, i+1); expecting = Expecting(RIGHTPAR); } } } } else if (expecting == Expecting(COMMA)) { expecting = Expecting(NUMBER); } else if (expecting == Expecting(RIGHTPAR)) { expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); } /* set Revmatpr (revMatPr) *********************************************************/ else if (!strcmp(parmName, "Revmatpr")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(ALPHA); else if (expecting == Expecting(ALPHA)) { if (IsArgValid(tkn, tempStr) == NO_ERROR) { nApplied = NumActiveParts (); flag = 0; for (i=0; i ALPHA_MAX) { MrBayesPrint ("%s Dirichlet parameter cannot be greater than %1.2lf\n", spacer, ALPHA_MAX); return (ERROR); } if (tempD < ALPHA_MIN) { MrBayesPrint ("%s Dirichlet parameter cannot be less than %1.2lf\n", spacer, ALPHA_MIN); return (ERROR); } } else if (!strcmp(tempStr,"Fixed")) { if (tempD > RATE_MAX) { MrBayesPrint ("%s Rate value cannot be greater than %1.2le\n", spacer, RATE_MAX); return (ERROR); } if (tempD < 0.0001) { MrBayesPrint ("%s Rate value cannot be less than %1.2le\n", spacer, RATE_MIN); return (ERROR); } } tempNum[tempNumStates++] = tempD; if (tempNumStates == 1 && !strcmp(tempStr,"Dirichlet")) expecting = Expecting(COMMA) | Expecting(RIGHTPAR); else if (tempNumStates < 6) expecting = Expecting(COMMA); else expecting = Expecting(RIGHTPAR); } else if (expecting == Expecting(COMMA)) { expecting = Expecting(NUMBER); } else if (expecting == Expecting(RIGHTPAR)) { nApplied = NumActiveParts (); for (i=0; i ALPHA_MAX) { MrBayesPrint ("%s Dirichlet parameter cannot be greater than %1.2le\n", spacer, ALPHA_MAX); return (ERROR); } if (tempD < ALPHA_MIN) { MrBayesPrint ("%s Dirichlet parameter cannot be less than %1.2le\n", spacer, ALPHA_MIN); return (ERROR); } } else if (!strcmp(tempStr,"Fixed")) { if (tempD > AA_RATE_MAX) { MrBayesPrint ("%s Rate value cannot be greater than %1.2le\n", spacer, AA_RATE_MAX); return (ERROR); } if (tempD < AA_RATE_MIN) { MrBayesPrint ("%s Rate value cannot be less than %1.2le\n", spacer, AA_RATE_MIN); return (ERROR); } } tempStateFreqs[tempNumStates++] = tempD; if (tempNumStates == 1 && !strcmp(tempStr,"Dirichlet")) expecting = Expecting(COMMA) | Expecting(RIGHTPAR); else if (tempNumStates < 190) expecting = Expecting(COMMA); else expecting = Expecting(RIGHTPAR); } else if (expecting == Expecting(COMMA)) { expecting = Expecting(NUMBER); } else if (expecting == Expecting(RIGHTPAR)) { nApplied = NumActiveParts (); for (i=0; i= 10) { MrBayesPrint ("%s Too many arguments in Aamodelpr\n", spacer); return (ERROR); } if (modelIsFixed == NO) { if (foundExp == YES) { if (foundDash == YES) tempAaModelPrs[fromI++] = -tempD; else tempAaModelPrs[fromI++] = tempD; expecting = Expecting(RIGHTPAR); } else { if (foundDash == YES) { MrBayesPrint ("%s Unexpected \"-\" in Aamodelpr\n", spacer); return (ERROR); } else { if (tempD <= 0.000000000001) tempAaModelPrs[fromI++] = -1000000000; else tempAaModelPrs[fromI++] = (MrBFlt) log(tempD); } expecting = Expecting(COMMA) | Expecting(RIGHTPAR); } foundDash = NO; } else { MrBayesPrint ("%s Not expecting a number\n", spacer); return (ERROR); } } else if (expecting == Expecting(LEFTPAR)) { if (modelIsFixed == YES) expecting = Expecting(ALPHA); else { if (foundExp == YES) expecting = Expecting(NUMBER) | Expecting(DASH); else expecting = Expecting(NUMBER) | Expecting(ALPHA); } } else if (expecting == Expecting(RIGHTPAR)) { if (modelIsFixed == YES) expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); else { if (foundExp == YES) { foundExp = NO; expecting = Expecting(COMMA) | Expecting(RIGHTPAR); } else { if (fromI < 10) { MrBayesPrint ("%s Too few arguments in Aamodelpr\n", spacer); return (ERROR); } nApplied = NumActiveParts (); for (i=0; i ALPHA_MAX) { MrBayesPrint ("%s Dirichlet parameter cannot be greater than %1.2lf\n", spacer, ALPHA_MAX); return (ERROR); } if (tempD < ALPHA_MIN) { MrBayesPrint ("%s Dirichlet parameter cannot be less than %1.2lf\n", spacer, ALPHA_MIN); return (ERROR); } modelParams[i].omegaDir[numVars[i]++] = tempD; if (numVars[i] == 1) expecting = Expecting(COMMA); else { if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting Omegapr to Dirichlet(%1.2lf,%1.2lf)\n", spacer, modelParams[i].omegaDir[0], modelParams[i].omegaDir[1]); else MrBayesPrint ("%s Setting Omegapr to Dirichlet(%1.2lf,%1.2lf) for partition %d\n", spacer, modelParams[i].omegaDir[0], modelParams[i].omegaDir[1], i+1); expecting = Expecting(RIGHTPAR); } } else if (!strcmp(modelParams[i].omegaPr,"Fixed")) { sscanf (tkn, "%lf", &tempD); modelParams[i].omegaFix = tempD; if (modelParams[i].omegaFix > KAPPA_MAX) { MrBayesPrint ("%s Omega ratio cannot be greater than %1.2lf\n", spacer, KAPPA_MAX); return (ERROR); } if (modelParams[i].omegaFix < 0.0) { MrBayesPrint ("%s Omega ratio cannot be less than %1.2lf\n", spacer, 0.0); return (ERROR); } if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting Omegapr to Fixed(%1.2lf)\n", spacer, modelParams[i].omegaFix); else MrBayesPrint ("%s Setting Omegapr to Fixed(%1.2lf) for partition %d\n", spacer, modelParams[i].omegaFix, i+1); expecting = Expecting(RIGHTPAR); } } } } else if (expecting == Expecting(COMMA)) { expecting = Expecting(NUMBER); } else if (expecting == Expecting(RIGHTPAR)) { expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); } /* set Ny98omega1pr (ny98omega1pr) *************************************************/ else if (!strcmp(parmName, "Ny98omega1pr")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(ALPHA); else if (expecting == Expecting(ALPHA)) { if (IsArgValid(tkn, tempStr) == NO_ERROR) { nApplied = NumActiveParts (); flag = 0; for (i=0; i= modelParams[i].ny98omega3Uni[1]) { MrBayesPrint ("%s Lower value for uniform should be greater than upper value\n", spacer); return (ERROR); } if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting Ny98omega3pr to Uniform(%1.2lf,%1.2lf)\n", spacer, modelParams[i].ny98omega3Uni[0], modelParams[i].ny98omega3Uni[1]); else MrBayesPrint ("%s Setting Ny98omega3pr to Uniform(%1.2lf,%1.2lf) for partition %d\n", spacer, modelParams[i].ny98omega3Uni[0], modelParams[i].ny98omega3Uni[1], i+1); expecting = Expecting(RIGHTPAR); } } else if (!strcmp(modelParams[i].ny98omega3pr,"Exponential")) { sscanf (tkn, "%lf", &tempD); modelParams[i].ny98omega3Exp = tempD; if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting Ny98omega3pr to Exponential(%1.2lf)\n", spacer, modelParams[i].ny98omega3Exp); else MrBayesPrint ("%s Setting Ny98omega3pr to Exponential(%1.2lf) for partition %d\n", spacer, modelParams[i].ny98omega3Exp, i+1); expecting = Expecting(RIGHTPAR); } else if (!strcmp(modelParams[i].ny98omega3pr,"Fixed")) { sscanf (tkn, "%lf", &tempD); modelParams[i].ny98omega3Fixed = tempD; if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting Ny98omega3pr to Fixed(%1.2lf)\n", spacer, modelParams[i].ny98omega3Fixed); else MrBayesPrint ("%s Setting Ny98omega3pr to Fixed(%1.2lf) for partition %d\n", spacer, modelParams[i].ny98omega3Fixed, i+1); expecting = Expecting(RIGHTPAR); } } } } else if (expecting == Expecting(COMMA)) { expecting = Expecting(NUMBER); } else if (expecting == Expecting(RIGHTPAR)) { expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); } /* set M3omegapr (m3omegapr) *******************************************************/ else if (!strcmp(parmName, "M3omegapr")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(ALPHA); else if (expecting == Expecting(ALPHA)) { if (IsArgValid(tkn, tempStr) == NO_ERROR) { nApplied = NumActiveParts (); flag = 0; for (i=0; i= modelParams[i].m3omegaFixed[1] || modelParams[i].m3omegaFixed[0] >= modelParams[i].m3omegaFixed[2] || modelParams[i].m3omegaFixed[1] >= modelParams[i].m3omegaFixed[2]) { MrBayesPrint ("%s The three omega values must be ordered, such that omega1 < omega2 < omega3\n", spacer); return (ERROR); } if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting M3omegapr to Fixed(%1.2lf,%1.2lf,%1.2lf)\n", spacer, modelParams[i].m3omegaFixed[0], modelParams[i].m3omegaFixed[1], modelParams[i].m3omegaFixed[2]); else MrBayesPrint ("%s Setting M3omegapr to Fixed(%1.2lf,%1.2lf,%1.2lf) for partition %d\n", spacer, modelParams[i].m3omegaFixed[0], modelParams[i].m3omegaFixed[1], modelParams[i].m3omegaFixed[2], i+1); expecting = Expecting(RIGHTPAR); } } } } } else if (expecting == Expecting(COMMA)) { expecting = Expecting(NUMBER); } else if (expecting == Expecting(RIGHTPAR)) { expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); } /* set M10betapr (m10betapr) *******************************************************/ else if (!strcmp(parmName, "M10betapr")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(ALPHA); else if (expecting == Expecting(ALPHA)) { if (IsArgValid(tkn, tempStr) == NO_ERROR) { nApplied = NumActiveParts (); flag = 0; for (i=0; i= modelParams[i].m10betaUni[1]) { MrBayesPrint ("%s Lower value for uniform should be greater than upper value\n", spacer); return (ERROR); } if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting M10betapr to Uniform(%1.2lf,%1.2lf)\n", spacer, modelParams[i].m10betaUni[0], modelParams[i].m10betaUni[1]); else MrBayesPrint ("%s Setting M10betapr to Uniform(%1.2lf,%1.2lf) for partition %d\n", spacer, modelParams[i].m10betaUni[0], modelParams[i].m10betaUni[1], i+1); expecting = Expecting(RIGHTPAR); } } else if (!strcmp(modelParams[i].m10betapr,"Exponential")) { sscanf (tkn, "%lf", &tempD); modelParams[i].m10betaExp = tempD; if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting M10betapr to Exponential(%1.2lf)\n", spacer, modelParams[i].m10betaExp); else MrBayesPrint ("%s Setting M10betapr to Exponential(%1.2lf) for partition %d\n", spacer, modelParams[i].m10betaExp, i+1); expecting = Expecting(RIGHTPAR); } else if (!strcmp(modelParams[i].m10betapr,"Fixed")) { sscanf (tkn, "%lf", &tempD); modelParams[i].m10betaFix[numVars[i]++] = tempD; if (numVars[i] == 1) expecting = Expecting(COMMA); else { if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting M10betapr to Fixed(%1.2lf,%1.2lf)\n", spacer, modelParams[i].m10betaFix[0], modelParams[i].m10betaFix[1]); else MrBayesPrint ("%s Setting M10betapr to Fixed(%1.2lf,%1.2lf) for partition %d\n", spacer, modelParams[i].m10betaFix[0], modelParams[i].m10betaFix[1], i+1); expecting = Expecting(RIGHTPAR); } } } } } else if (expecting == Expecting(COMMA)) { expecting = Expecting(NUMBER); } else if (expecting == Expecting(RIGHTPAR)) { expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); } /* set M10gammapr (m10gammapr) *****************************************************/ else if (!strcmp(parmName, "M10gammapr")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(ALPHA); else if (expecting == Expecting(ALPHA)) { if (IsArgValid(tkn, tempStr) == NO_ERROR) { nApplied = NumActiveParts (); flag = 0; for (i=0; i= modelParams[i].m10gammaUni[1]) { MrBayesPrint ("%s Lower value for uniform should be greater than upper value\n", spacer); return (ERROR); } if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting M10gammapr to Uniform(%1.2lf,%1.2lf)\n", spacer, modelParams[i].m10gammaUni[0], modelParams[i].m10gammaUni[1]); else MrBayesPrint ("%s Setting M10gammapr to Uniform(%1.2lf,%1.2lf) for partition %d\n", spacer, modelParams[i].m10gammaUni[0], modelParams[i].m10gammaUni[1], i+1); expecting = Expecting(RIGHTPAR); } } else if (!strcmp(modelParams[i].m10gammapr,"Exponential")) { sscanf (tkn, "%lf", &tempD); modelParams[i].m10gammaExp = tempD; if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting M10gammapr to Exponential(%1.2lf)\n", spacer, modelParams[i].m10gammaExp); else MrBayesPrint ("%s Setting M10gammapr to Exponential(%1.2lf) for partition %d\n", spacer, modelParams[i].m10gammaExp, i+1); expecting = Expecting(RIGHTPAR); } else if (!strcmp(modelParams[i].m10gammapr,"Fixed")) { sscanf (tkn, "%lf", &tempD); modelParams[i].m10gammaFix[numVars[i]++] = tempD; if (numVars[i] == 1) expecting = Expecting(COMMA); else { if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting M10gammapr to Fixed(%1.2lf,%1.2lf)\n", spacer, modelParams[i].m10gammaFix[0], modelParams[i].m10gammaFix[1]); else MrBayesPrint ("%s Setting M10gammapr to Fixed(%1.2lf,%1.2lf) for partition %d\n", spacer, modelParams[i].m10gammaFix[0], modelParams[i].m10gammaFix[1], i+1); expecting = Expecting(RIGHTPAR); } } } } } else if (expecting == Expecting(COMMA)) { expecting = Expecting(NUMBER); } else if (expecting == Expecting(RIGHTPAR)) { expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); } /* set Codoncatfreqs (codonCatFreqPr) **********************************************/ else if (!strcmp(parmName, "Codoncatfreqs")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(ALPHA); else if (expecting == Expecting(ALPHA)) { if (IsArgValid(tkn, tempStr) == NO_ERROR) { nApplied = NumActiveParts (); flag = 0; for (i=0; i= modelParams[i].shapeUni[1]) { MrBayesPrint ("%s Lower value for uniform should be greater than upper value\n", spacer); return (ERROR); } if (modelParams[i].shapeUni[1] > MAX_SHAPE_PARAM) { MrBayesPrint ("%s Upper value for uniform cannot be greater than %1.2lf\n", spacer, MAX_SHAPE_PARAM); return (ERROR); } if (modelParams[i].shapeUni[0] < MIN_SHAPE_PARAM) { MrBayesPrint ("%s Lower value for uniform cannot be less than %1.2lf\n", spacer, MIN_SHAPE_PARAM); return (ERROR); } if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting Shapepr to Uniform(%1.2lf,%1.2lf)\n", spacer, modelParams[i].shapeUni[0], modelParams[i].shapeUni[1]); else MrBayesPrint ("%s Setting Shapepr to Uniform(%1.2lf,%1.2lf) for partition %d\n", spacer, modelParams[i].shapeUni[0], modelParams[i].shapeUni[1], i+1); expecting = Expecting(RIGHTPAR); } } else if (!strcmp(modelParams[i].shapePr,"Exponential")) { sscanf (tkn, "%lf", &tempD); modelParams[i].shapeExp = tempD; if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting Shapepr to Exponential(%1.2lf)\n", spacer, modelParams[i].shapeExp); else MrBayesPrint ("%s Setting Shapepr to Exponential(%1.2lf) for partition %d\n", spacer, modelParams[i].shapeExp, i+1); expecting = Expecting(RIGHTPAR); } else if (!strcmp(modelParams[i].shapePr,"Fixed")) { sscanf (tkn, "%lf", &tempD); modelParams[i].shapeFix = tempD; if (modelParams[i].shapeFix > MAX_SHAPE_PARAM) { MrBayesPrint ("%s Shape parameter cannot be greater than %1.2lf\n", spacer, MAX_SHAPE_PARAM); return (ERROR); } if (modelParams[i].shapeFix < MIN_SHAPE_PARAM) { MrBayesPrint ("%s Shape parameter cannot be less than %1.2lf\n", spacer, MIN_SHAPE_PARAM); return (ERROR); } if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting Shapepr to Fixed(%1.2lf)\n", spacer, modelParams[i].shapeFix); else MrBayesPrint ("%s Setting Shapepr to Fixed(%1.2lf) for partition %d\n", spacer, modelParams[i].shapeFix, i+1); expecting = Expecting(RIGHTPAR); } } } } else if (expecting == Expecting(COMMA)) { expecting = Expecting(NUMBER); } else if (expecting == Expecting(RIGHTPAR)) { expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); } /* set Pinvarpr (pInvarPr) *********************************************************/ else if (!strcmp(parmName, "Pinvarpr")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(ALPHA); else if (expecting == Expecting(ALPHA)) { if (IsArgValid(tkn, tempStr) == NO_ERROR) { nApplied = NumActiveParts (); flag = 0; for (i=0; i= modelParams[i].pInvarUni[1]) { MrBayesPrint ("%s Lower value for uniform should be greater than upper value\n", spacer); return (ERROR); } if (modelParams[i].pInvarUni[1] > 1.0) { MrBayesPrint ("%s Upper value for uniform should be less than or equal to 1.0\n", spacer); return (ERROR); } if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting Pinvarpr to Uniform(%1.2lf,%1.2lf)\n", spacer, modelParams[i].pInvarUni[0], modelParams[i].pInvarUni[1]); else MrBayesPrint ("%s Setting Pinvarpr to Uniform(%1.2lf,%1.2lf) for partition %d\n", spacer, modelParams[i].pInvarUni[0], modelParams[i].pInvarUni[1], i+1); expecting = Expecting(RIGHTPAR); } } else if (!strcmp(modelParams[i].pInvarPr,"Fixed")) { sscanf (tkn, "%lf", &tempD); if (tempD > 1.0) { MrBayesPrint ("%s Value for Pinvar should be in the interval (0, 1)\n", spacer); return (ERROR); } modelParams[i].pInvarFix = tempD; if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting Pinvarpr to Fixed(%1.2lf)\n", spacer, modelParams[i].pInvarFix); else MrBayesPrint ("%s Setting Pinvarpr to Fixed(%1.2lf) for partition %d\n", spacer, modelParams[i].pInvarFix, i+1); expecting = Expecting(RIGHTPAR); } } } } else if (expecting == Expecting(COMMA)) { expecting = Expecting(NUMBER); } else if (expecting == Expecting(RIGHTPAR)) { expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); } /* set Ratecorrpr (adGammaCorPr) ***************************************************/ else if (!strcmp(parmName, "Ratecorrpr")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(ALPHA); else if (expecting == Expecting(ALPHA)) { if (IsArgValid(tkn, tempStr) == NO_ERROR) { nApplied = NumActiveParts (); flag = 0; for (i=0; i= modelParams[i].adgCorrUni[1]) { MrBayesPrint ("%s Lower value for uniform should be greater than upper value\n", spacer); return (ERROR); } if (modelParams[i].adgCorrUni[1] > 1.0) { MrBayesPrint ("%s Upper value for uniform should be less than or equal to 1.0\n", spacer); return (ERROR); } if (modelParams[i].adgCorrUni[0] < -1.0) { MrBayesPrint ("%s Lower value for uniform should be greater than or equal to -1.0\n", spacer); return (ERROR); } if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting Ratecorrpr to Uniform(%1.2lf,%1.2lf)\n", spacer, modelParams[i].adgCorrUni[0], modelParams[i].adgCorrUni[1]); else MrBayesPrint ("%s Setting Ratecorrpr to Uniform(%1.2lf,%1.2lf) for partition %d\n", spacer, modelParams[i].adgCorrUni[0], modelParams[i].adgCorrUni[1], i+1); expecting = Expecting(RIGHTPAR); } } else if (!strcmp(modelParams[i].adGammaCorPr,"Fixed")) { sscanf (tkn, "%lf", &tempD); if (foundDash == YES) tempD *= -1.0; if (tempD > 1.0 || tempD < -1.0) { MrBayesPrint ("%s Value for Ratecorrpr should be in the interval (-1, +1)\n", spacer); return (ERROR); } modelParams[i].adgCorrFix = tempD; if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting Ratecorrpr to Fixed(%1.2lf)\n", spacer, modelParams[i].adgCorrFix); else MrBayesPrint ("%s Setting Ratecorrpr to Fixed(%1.2lf) for partition %d\n", spacer, modelParams[i].adgCorrFix, i+1); expecting = Expecting(RIGHTPAR); } } } foundDash = NO; } else if (expecting == Expecting(DASH)) { foundDash = YES; expecting = Expecting(NUMBER); } else if (expecting == Expecting(COMMA)) { expecting = Expecting(NUMBER) | Expecting(DASH); } else if (expecting == Expecting(RIGHTPAR)) { expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); } /* set Ratepr (ratePr) *************************************************************/ else if (!strcmp(parmName, "Ratepr")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(ALPHA); else if (expecting == Expecting(ALPHA)) { if (IsArgValid(tkn, tempStr) == NO_ERROR) { nApplied = NumActiveParts (); for (i=0; i ALPHA_MAX) { MrBayesPrint ("%s Ratemultiplier Dirichlet parameter %lf out of range\n", spacer, tempD); return (ERROR); } /* set the parameter */ modelParams[i].ratePrDir = tempD; /* check if all partitions have been filled in */ for (i=0; i 1) MrBayesPrint ("s"); for (i=k=0; i 1) MrBayesPrint (", and %d", i+1); else if (k == 1) MrBayesPrint (" %d", i+1); else MrBayesPrint (", %d", i+1); } } MrBayesPrint ("\n"); } expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); } /* set Generatepr (generatePr) *****************************************************/ else if (!strcmp(parmName, "Generatepr")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(ALPHA); else if (expecting == Expecting(ALPHA)) { if (IsArgValid(tkn, tempStr) == NO_ERROR) { nApplied = NumActiveParts (); for (i=0; i ALPHA_MAX) { MrBayesPrint ("%s Generate multiplier Dirichlet parameter %lf out of range\n", spacer, tempD); return (ERROR); } /* set the parameter */ modelParams[i].generatePrDir = tempD; /* check if all partitions have been filled in */ for (i=0; i 1) MrBayesPrint ("s"); for (i=k=0; i 1) MrBayesPrint (", and %d", i+1); else if (k == 1) MrBayesPrint (" %d", i+1); else MrBayesPrint (", %d", i+1); } } MrBayesPrint ("\n"); } expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); } /* set Covswitchpr (covSwitchPr) ***************************************************/ else if (!strcmp(parmName, "Covswitchpr")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(ALPHA); else if (expecting == Expecting(ALPHA)) { if (IsArgValid(tkn, tempStr) == NO_ERROR) { nApplied = NumActiveParts (); flag = 0; for (i=0; i= modelParams[i].covswitchUni[1]) { MrBayesPrint ("%s Lower value for uniform should be greater than upper value\n", spacer); return (ERROR); } if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting Covswitchpr to Uniform(%1.2lf,%1.2lf)\n", spacer, modelParams[i].covswitchUni[0], modelParams[i].covswitchUni[1]); else MrBayesPrint ("%s Setting Covswitchpr to Uniform(%1.2lf,%1.2lf) for partition %d\n", spacer, modelParams[i].covswitchUni[0], modelParams[i].covswitchUni[1], i+1); expecting = Expecting(RIGHTPAR); } } else if (!strcmp(modelParams[i].covSwitchPr,"Exponential")) { sscanf (tkn, "%lf", &tempD); modelParams[i].covswitchExp = tempD; if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting Covswitchpr to Exponential(%1.2lf)\n", spacer, modelParams[i].covswitchExp); else MrBayesPrint ("%s Setting Covswitchpr to Exponential(%1.2lf) for partition %d\n", spacer, modelParams[i].covswitchExp, i+1); expecting = Expecting(RIGHTPAR); } else if (!strcmp(modelParams[i].covSwitchPr,"Fixed")) { sscanf (tkn, "%lf", &tempD); modelParams[i].covswitchFix[numVars[i]++] = tempD; if (numVars[i] == 1) expecting = Expecting(COMMA); else { if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting Covswitchpr to Fixed(%1.4lf,%1.4lf)\n", spacer, modelParams[i].covswitchFix[0], modelParams[i].covswitchFix[1]); else MrBayesPrint ("%s Setting Covswitchpr to Fixed(%1.4lf,%1.4lf) for partition %d\n", spacer, modelParams[i].covswitchFix[0], modelParams[i].covswitchFix[1], i+1); expecting = Expecting(RIGHTPAR); } } } } } else if (expecting == Expecting(COMMA)) { expecting = Expecting(NUMBER); } else if (expecting == Expecting(RIGHTPAR)) { expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); } /* set Symdirihyperpr (piSymDirPr for standard characters) *************************/ else if (!strcmp(parmName, "Symdirihyperpr")) { if (expecting == Expecting(EQUALSIGN)) { foundBeta = NO; expecting = Expecting(ALPHA); } else if (expecting == Expecting(ALPHA)) { if (foundBeta == NO) { /* expecting to see Uniform, Exponential, or Fixed */ if (IsArgValid(tkn, tempStr) == NO_ERROR) { nApplied = NumActiveParts (); flag = 0; for (i=0; i= modelParams[i].symBetaUni[1]) { MrBayesPrint ("%s Lower value for uniform should be greater than upper value\n", spacer); return (ERROR); } if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting Symdirihyperpr to Uniform(%1.2lf,%1.2lf)\n", spacer, modelParams[i].symBetaUni[0], modelParams[i].symBetaUni[1]); else MrBayesPrint ("%s Setting Symdirihyperpr to Uniform(%1.2lf,%1.2lf) for partition %d\n", spacer, modelParams[i].symBetaUni[0], modelParams[i].symBetaUni[1], i+1); expecting = Expecting(RIGHTPAR); } } else { MrBayesPrint ("%s Problem setting Symdirihyperpr\n", spacer); return (ERROR); } } } } else if (expecting == Expecting(COMMA)) { expecting = Expecting(NUMBER); } else if (expecting == Expecting(RIGHTPAR)) { expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); } /* set Statefreqpr (stateFreqPr) ***************************************************/ else if (!strcmp(parmName, "Statefreqpr")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(ALPHA); else if (expecting == Expecting(ALPHA)) { if (IsSame ("Equal", tkn) == DIFFERENT && IsSame ("Empirical", tkn) == DIFFERENT) { /* the user wants to specify a dirichlet or fixed prior */ if (IsArgValid(tkn, tempStr) == NO_ERROR) { nApplied = NumActiveParts (); for (i=0; i numDefinedConstraints) { MrBayesPrint ("%s Constraint number is too large\n", spacer); return (ERROR); } if (fromI == -1) { if (foundDash == YES) { MrBayesPrint ("%s Unexpected dash\n", spacer); return (ERROR); } fromI = tempInt; tempActiveConstraints[fromI-1] = YES; } else if (fromI != -1 && toJ == -1 && foundDash == YES && foundComma == NO) { toJ = tempInt; //for (i=fromI-1; i numUserTrees) { MrBayesPrint ("%s Tree number is too large\n", spacer); return (ERROR); } if (tempInt < 1) { MrBayesPrint ("%s Tree number is too small\n", spacer); return (ERROR); } fromI = tempInt; expecting = Expecting(RIGHTPAR); /* only one tree number acceptable */ } } else if (expecting == Expecting(COMMA)) { foundComma = YES; expecting = Expecting(NUMBER) | Expecting(ALPHA); } else if (expecting == Expecting(DASH)) { foundDash = YES; expecting = Expecting(NUMBER); } else if (expecting == Expecting(RIGHTPAR)) { /* find out whether we need a tree number or constraint number(s) */ nApplied = NumActiveParts (); for (i=0; i 0.000001) { MrBayesPrint ("%s Lower value for uniform must equal 0.0\n", spacer); return (ERROR); } if (modelParams[i].brlensUni[0] >= modelParams[i].brlensUni[1]) { MrBayesPrint ("%s Lower value for uniform should be greater than upper value\n", spacer); return (ERROR); } if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting Brlenspr to Unconstrained:Uniform(%1.2lf,%1.2lf)\n", spacer, modelParams[i].brlensUni[0], modelParams[i].brlensUni[1]); else MrBayesPrint ("%s Setting Brlenspr to Unconstrained:Uniform(%1.2lf,%1.2lf) for partition %d\n", spacer, modelParams[i].brlensUni[0], modelParams[i].brlensUni[1], i+1); expecting = Expecting(RIGHTPAR); } } else if (!strcmp(modelParams[i].unconstrainedPr,"Exponential")) { sscanf (tkn, "%lf", &tempD); modelParams[i].brlensExp = tempD; if (modelParams[i].brlensExp <= 0.0) { MrBayesPrint ("%s Value for exponential must > 0.0\n", spacer); return (ERROR); } if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting Brlenspr to Unconstrained:Exponential(%1.2lf)\n", spacer, modelParams[i].brlensExp); else MrBayesPrint ("%s Setting Brlenspr to Unconstrained:Exponential(%1.2lf) for partition %d\n", spacer, modelParams[i].brlensExp, i+1); expecting = Expecting(RIGHTPAR); } else if (!strcmp(modelParams[i].unconstrainedPr,"GammaDir")) { sscanf (tkn, "%lf", &tempD); modelParams[i].brlensDir[numVars[i]++] = tempD; if (numVars[i] == 1 || numVars[i] == 2 || numVars[i] == 3) expecting = Expecting(COMMA); else { if (modelParams[i].brlensDir[0] <= 0.0 || modelParams[i].brlensDir[1] <= 0.0 || modelParams[i].brlensDir[2] <= 0.0 || modelParams[i].brlensDir[3] <= 0.0) { MrBayesPrint ("%s alphaT & betaT & a & c for GammaDir prior must > 0.0\n", spacer); return (ERROR); } if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting Brlenspr to Unconstrained:GammaDir(%1.2lf,%1.4lf,%1.2lf,%1.2lf)\n", spacer, modelParams[i].brlensDir[0], modelParams[i].brlensDir[1], modelParams[i].brlensDir[2], modelParams[i].brlensDir[3]); else MrBayesPrint ("%s Setting Brlenspr to Unconstrained:GammaDir(%1.2lf,%1.4lf,%1.2lf,%1.2lf) for partition %d\n", spacer, modelParams[i].brlensDir[0], modelParams[i].brlensDir[1], modelParams[i].brlensDir[2], modelParams[i].brlensDir[3], i+1); expecting = Expecting(RIGHTPAR); } } else if (!strcmp(modelParams[i].unconstrainedPr,"invGamDir")) { sscanf (tkn, "%lf", &tempD); modelParams[i].brlensDir[numVars[i]++] = tempD; if (numVars[i] == 1 || numVars[i] == 2 || numVars[i] == 3) expecting = Expecting(COMMA); else { if (modelParams[i].brlensDir[0] <= 2.0 || modelParams[i].brlensDir[1] <= 0.0 || modelParams[i].brlensDir[2] <= 0.0 || modelParams[i].brlensDir[3] <= 0.0) { MrBayesPrint ("%s alphaT must > 2.0, betaT & a & c for invGamDir prior must > 0.0\n", spacer); return (ERROR); } if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting Brlenspr to Unconstrained:invGamDir(%1.2lf,%1.4lf,%1.2lf,%1.2lf)\n", spacer, modelParams[i].brlensDir[0], modelParams[i].brlensDir[1], modelParams[i].brlensDir[2], modelParams[i].brlensDir[3]); else MrBayesPrint ("%s Setting Brlenspr to Unconstrained:invGamDir(%1.2lf,%1.4lf,%1.2lf,%1.2lf) for partition %d\n", spacer, modelParams[i].brlensDir[0], modelParams[i].brlensDir[1], modelParams[i].brlensDir[2], modelParams[i].brlensDir[3], i+1); expecting = Expecting(RIGHTPAR); } } else if (!strcmp(modelParams[i].unconstrainedPr,"twoExp")) { sscanf (tkn, "%lf", &tempD); modelParams[i].brlens2Exp[numVars[i]++] = tempD; if (numVars[i] == 1) expecting = Expecting(COMMA); else { if (modelParams[i].brlens2Exp[0] <= 0.0 || modelParams[i].brlens2Exp[1] <= 0.0) { MrBayesPrint ("%s Values for the two exponentials must > 0.0\n", spacer); return (ERROR); } if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting Brlenspr to Unconstrained:twoExp(%1.2lf,%1.2lf)\n", spacer, modelParams[i].brlens2Exp[0], modelParams[i].brlens2Exp[1]); else MrBayesPrint ("%s Setting Brlenspr to Unconstrained:twoExp(%1.2lf,%1.2lf) for partition %d\n", spacer, modelParams[i].brlens2Exp[0], modelParams[i].brlens2Exp[1], i+1); expecting = Expecting(RIGHTPAR); } } } } } else if (!strcmp(colonPr,"Fixed") || !strcmp(colonPr,"Clock")) { sscanf (tkn, "%d", &tempInt); if (tempInt < 1 || tempInt > numUserTrees) { MrBayesPrint ("%s Tree needs to be in the range %d to %d\n", spacer, 1, numUserTrees); return (ERROR); } fromI = tempInt; expecting = Expecting(RIGHTPAR); } foundLeftPar = NO; } else if (expecting == Expecting(COLON)) { expecting = Expecting(ALPHA); } else if (expecting == Expecting(COMMA)) { expecting = Expecting(NUMBER); } else if (expecting == Expecting(RIGHTPAR)) { if (!strcmp(colonPr,"Fixed") || (!strcmp(colonPr,"Clock") && !strcmp(clockPr,"Fixed"))) { /* index of a tree which set up branch lengths*/ nApplied = NumActiveParts (); for (i=0; iname); else MrBayesPrint ("%s Setting Brlenspr to Fixed(%s) for partition %d\n", spacer, userTree[fromI-1]->name, i+1); } else { if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting Brlenspr to Fixed(%s)\n", spacer, userTree[fromI-1]->name); else MrBayesPrint ("%s Setting Brlenspr to Fixed(%s) for partition %d\n", spacer, userTree[fromI-1]->name, i+1); } } } expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); } /* set Speciationpr (speciationPr) *************************************************/ else if (!strcmp(parmName, "Speciationpr")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(ALPHA); else if (expecting == Expecting(ALPHA)) { if (IsArgValid(tkn, tempStr) == NO_ERROR) { nApplied = NumActiveParts (); for (i=0; i= modelParams[i].speciationUni[1]) { MrBayesPrint ("%s Lower value for uniform should be greater than upper value\n", spacer); return (ERROR); } if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting Speciationpr to Uniform(%1.2lf,%1.2lf)\n", spacer, modelParams[i].speciationUni[0], modelParams[i].speciationUni[1]); else MrBayesPrint ("%s Setting Speciationpr to Uniform(%1.2lf,%1.2lf) for partition %d\n", spacer, modelParams[i].speciationUni[0], modelParams[i].speciationUni[1], i+1); expecting = Expecting(RIGHTPAR); } } else if (!strcmp(modelParams[i].speciationPr,"Exponential")) { if (tempD <= 0.0) { MrBayesPrint ("%s Exponential parameter must be positive\n", spacer); return (ERROR); } modelParams[i].speciationExp = tempD; if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting Speciationpr to Exponential(%1.2lf)\n", spacer, modelParams[i].speciationExp); else MrBayesPrint ("%s Setting Speciationpr to Exponential(%1.2lf) for partition %d\n", spacer, modelParams[i].speciationExp, i+1); expecting = Expecting(RIGHTPAR); } else if (!strcmp(modelParams[i].speciationPr,"Fixed")) { if (tempD <= 0.0) { MrBayesPrint ("%s Net speciation rate must be positive\n", spacer); return (ERROR); } modelParams[i].speciationFix = tempD; if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting Speciationpr to Fixed(%1.2lf)\n", spacer, modelParams[i].speciationFix); else MrBayesPrint ("%s Setting Speciationpr to Fixed(%1.2lf) for partition %d\n", spacer, modelParams[i].speciationFix, i+1); expecting = Expecting(RIGHTPAR); } } } } else if (expecting == Expecting(COMMA)) { expecting = Expecting(NUMBER); } else if (expecting == Expecting(RIGHTPAR)) { expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); } /* set Extinctionpr (extinctionPr) *************************************************/ else if (!strcmp(parmName, "Extinctionpr")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(ALPHA); else if (expecting == Expecting(ALPHA)) { if (IsArgValid(tkn, tempStr) == NO_ERROR) { nApplied = NumActiveParts (); for (i=0; i= 1.0) { MrBayesPrint ("%s Relative extinction rate must be in range (0,1)\n", spacer); return (ERROR); } modelParams[i].extinctionFix = tempD; if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting Extinctionpr to Fixed(%1.2lf)\n", spacer, modelParams[i].extinctionFix); else MrBayesPrint ("%s Setting Extinctionpr to Fixed(%1.2lf) for partition %d\n", spacer, modelParams[i].extinctionFix, i+1); expecting = Expecting(RIGHTPAR); } } } } else if (expecting == Expecting(COMMA)) { expecting = Expecting(NUMBER); } else if (expecting == Expecting(RIGHTPAR)) { expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); } /* set Fossilizationpr (fossilizationPr) */ else if (!strcmp(parmName, "Fossilizationpr")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(ALPHA); else if (expecting == Expecting(ALPHA)) { if (IsArgValid(tkn, tempStr) == NO_ERROR) { nApplied = NumActiveParts (); for (i=0; i= 1.0) { MrBayesPrint ("%s Relative fossilization rate must be in the range (0,1)\n", spacer); return (ERROR); } modelParams[i].fossilizationFix = tempD; if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting Fossilizationpr to Fixed(%1.2lf)\n", spacer, modelParams[i].fossilizationFix); else MrBayesPrint ("%s Setting Fossilizationpr to Fixed(%1.2lf) for partition %d\n", spacer, modelParams[i].fossilizationFix, i+1); expecting = Expecting(RIGHTPAR); } } } } else if (expecting == Expecting(COMMA)) { expecting = Expecting(NUMBER); } else if (expecting == Expecting(RIGHTPAR)) { expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); } /* set SampleStrat (sampleStrat) ***************************************************/ else if (!strcmp(parmName, "Samplestrat")) // prset samplestrat = random 3: 250 100 60 [fossil sampling], 1: 150 [birth], 2: 200 100 [death]; { if (expecting == Expecting(EQUALSIGN)) { expecting = Expecting(ALPHA); } else if (expecting == Expecting(ALPHA)) { if (IsArgValid(tkn, tempStr) == NO_ERROR) { nApplied = NumActiveParts (); for (i=0; i 99) { MrBayesPrint ("%s Number of fossil sampling rate shift must be between 0 and 100\n", spacer); return (ERROR); } modelParams[i].fossilSamplingNum = tempInt; foundFSNum[i] = YES; if (tempInt == 0) { foundFSTime[i] = YES; expecting = Expecting(COMMA); expecting |= Expecting(PARAMETER); expecting |= Expecting(SEMICOLON); } else expecting = Expecting(COLON); } else if (foundFSTime[i] == NO) { sscanf (tkn, "%lf", &tempD); if (tempD <= 0.0) { MrBayesPrint ("%s Time of fossil sampling rate shift must be > 0.\n", spacer); return (ERROR); } if (numVars[i] > 0 && modelParams[i].fossilSamplingTime[numVars[i]-1] < tempD) { MrBayesPrint ("%s Time of fossil sampling rate shift must be in decreasing order\n", spacer); return (ERROR); } modelParams[i].fossilSamplingTime[numVars[i]] = tempD; if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting %d fossil sampling rate shift time to %1.2lf\n", spacer, numVars[i]+1, modelParams[i].fossilSamplingTime[numVars[i]]); else MrBayesPrint ("%s Setting %d fossil sampling rate shift time to %1.2lf for partition %d\n", spacer, numVars[i]+1, modelParams[i].fossilSamplingTime[numVars[i]], i+1); numVars[i]++; expecting = Expecting(NUMBER); if (numVars[i] == modelParams[i].fossilSamplingNum) { foundFSTime[i] = YES; numVars[i] = 0; expecting = Expecting(COMMA); expecting |= Expecting(PARAMETER); expecting |= Expecting(SEMICOLON); } } else if (foundBSNum[i] == NO) // birth rate shift times { sscanf (tkn, "%d", &tempInt); if (tempInt < 0 || tempInt > 99) { MrBayesPrint ("%s Number of birth rate shift must be between 0 and 100\n", spacer); return (ERROR); } modelParams[i].birthRateShiftNum = tempInt; foundBSNum[i] = YES; if (tempInt == 0) { foundBSTime[i] = YES; expecting = Expecting(COMMA); expecting |= Expecting(PARAMETER); expecting |= Expecting(SEMICOLON); } else expecting = Expecting(COLON); } else if (foundBSTime[i] == NO) { sscanf (tkn, "%lf", &tempD); if (tempD <= 0.0) { MrBayesPrint ("%s Time of birth rate shift must be > 0.\n", spacer); return (ERROR); } if (numVars[i] > 0 && modelParams[i].birthRateShiftTime[numVars[i]-1] < tempD) { MrBayesPrint ("%s Time of birth rate shift must be in decreasing order\n", spacer); return (ERROR); } modelParams[i].birthRateShiftTime[numVars[i]] = tempD; if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting %d birth rate shift time to %1.2lf\n", spacer, numVars[i]+1, modelParams[i].birthRateShiftTime[numVars[i]]); else MrBayesPrint ("%s Setting %d birth rate shift time to %1.2lf for partition %d\n", spacer, numVars[i]+1, modelParams[i].birthRateShiftTime[numVars[i]], i+1); numVars[i]++; expecting = Expecting(NUMBER); if (numVars[i] == modelParams[i].birthRateShiftNum) { foundBSTime[i] = YES; numVars[i] = 0; expecting = Expecting(COMMA); expecting |= Expecting(PARAMETER); expecting |= Expecting(SEMICOLON); } } else if (foundDSNum[i] == NO) // death rate shift times { sscanf (tkn, "%d", &tempInt); if (tempInt < 0 || tempInt > 99) { MrBayesPrint ("%s Number of death rate shift must be between 0 and 100\n", spacer); return (ERROR); } modelParams[i].deathRateShiftNum = tempInt; foundDSNum[i] = YES; if (tempInt == 0) { foundDSTime[i] = YES; expecting = Expecting(COMMA); expecting |= Expecting(PARAMETER); expecting |= Expecting(SEMICOLON); } else expecting = Expecting(COLON); } else if (foundDSTime[i] == NO) { sscanf (tkn, "%lf", &tempD); if (tempD <= 0.0) { MrBayesPrint ("%s Time of death rate shift must be > 0.\n", spacer); return (ERROR); } if (numVars[i] > 0 && modelParams[i].deathRateShiftTime[numVars[i]-1] < tempD) { MrBayesPrint ("%s Time of death rate shift must be in decreasing order\n", spacer); return (ERROR); } modelParams[i].deathRateShiftTime[numVars[i]] = tempD; if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting %d death rate shift time to %1.2lf\n", spacer, numVars[i]+1, modelParams[i].deathRateShiftTime[numVars[i]]); else MrBayesPrint ("%s Setting %d death rate shift time to %1.2lf for partition %d\n", spacer, numVars[i]+1, modelParams[i].deathRateShiftTime[numVars[i]], i+1); numVars[i]++; expecting = Expecting(NUMBER); if (numVars[i] == modelParams[i].deathRateShiftNum) expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } } } } else if (expecting == Expecting(COLON) || expecting == Expecting(COMMA)) { expecting = Expecting(NUMBER); } else { return (ERROR); } } /* set Sampleprob (sampleProb) *****************************************************/ else if (!strcmp(parmName, "Sampleprob")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(NUMBER); else if (expecting == Expecting(NUMBER)) { nApplied = NumActiveParts (); for (i=0; i 1.0) { MrBayesPrint ("%s Sampleprob should be in range [0,1]\n", spacer); return (ERROR); } modelParams[i].sampleProb = tempD; if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting Sampleprob to %1.8lf\n", spacer, modelParams[i].sampleProb); else MrBayesPrint ("%s Setting Sampleprob to %1.8lf for partition %d\n", spacer, modelParams[i].sampleProb, i+1); } } expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); } /* set Treeagepr (treeAgePr) *******************************************************/ else if (!strcmp(parmName, "Treeagepr")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(ALPHA); else if (expecting == Expecting(ALPHA)) { if (IsArgValid(tkn, tempStr) == NO_ERROR) { nApplied = NumActiveParts (); for (i=0; i= modelParams[i].growthUni[1]) { MrBayesPrint ("%s Lower value for uniform should be greater than upper value\n", spacer); return (ERROR); } if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting Growthpr to Uniform(%1.2lf,%1.2lf)\n", spacer, modelParams[i].growthUni[0], modelParams[i].growthUni[1]); else MrBayesPrint ("%s Setting Growthpr to Uniform(%1.2lf,%1.2lf) for partition %d\n", spacer, modelParams[i].growthUni[0], modelParams[i].growthUni[1], i+1); expecting = Expecting(RIGHTPAR); } } else if (!strcmp(modelParams[i].growthPr,"Normal")) { sscanf (tkn, "%lf", &tempD); if (isNegative == YES) tempD *= -1.0; modelParams[i].growthNorm[numVars[i]++] = tempD; if (numVars[i] == 1) expecting = Expecting(COMMA); else { if (modelParams[i].growthNorm[1] < 0.0) { MrBayesPrint ("%s Variance for normal distribution should be greater than zero\n", spacer); return (ERROR); } if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting Growthpr to Normal(%1.2lf,%1.2lf)\n", spacer, modelParams[i].growthNorm[0], modelParams[i].growthNorm[1]); else MrBayesPrint ("%s Setting Growthpr to Normal(%1.2lf,%1.2lf) for partition %d\n", spacer, modelParams[i].growthNorm[0], modelParams[i].growthNorm[1], i+1); expecting = Expecting(RIGHTPAR); } } else if (!strcmp(modelParams[i].growthPr,"Exponential")) { sscanf (tkn, "%lf", &tempD); modelParams[i].growthExp = tempD; if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting Growthpr to Exponential(%1.2lf)\n", spacer, modelParams[i].growthExp); else MrBayesPrint ("%s Setting Growthpr to Exponential(%1.2lf) for partition %d\n", spacer, modelParams[i].growthExp, i+1); expecting = Expecting(RIGHTPAR); } else if (!strcmp(modelParams[i].growthPr,"Fixed")) { sscanf (tkn, "%lf", &tempD); if (isNegative == YES) tempD *= -1.0; modelParams[i].growthFix = tempD; if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting Growthpr to Fixed(%1.2lf)\n", spacer, modelParams[i].growthFix); else MrBayesPrint ("%s Setting Growthpr to Fixed(%1.2lf) for partition %d\n", spacer, modelParams[i].growthFix, i+1); expecting = Expecting(RIGHTPAR); } } } isNegative = NO; } else if (expecting == Expecting(COMMA)) { expecting = Expecting(NUMBER); } else if (expecting == Expecting(RIGHTPAR)) { expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); } /* set Popsizepr (popSizePr) *******************************************************/ else if (!strcmp(parmName, "Popsizepr")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(ALPHA); else if (expecting == Expecting(ALPHA)) { if (IsArgValid(tkn, tempStr) == NO_ERROR) { nApplied = NumActiveParts (); for (i=0; i= modelParams[i].popSizeUni[1]) { MrBayesPrint ("%s Lower value for Uniform should be greater than upper value\n", spacer); return (ERROR); } if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting Popsizepr to Uniform(%1.2lf,%1.2lf)\n", spacer, modelParams[i].popSizeUni[0], modelParams[i].popSizeUni[1]); else MrBayesPrint ("%s Setting Popsizepr to Uniform(%1.2lf,%1.2lf) for partition %d\n", spacer, modelParams[i].popSizeUni[0], modelParams[i].popSizeUni[1], i+1); expecting = Expecting(RIGHTPAR); } } else if (!strcmp(modelParams[i].popSizePr,"Lognormal")) { sscanf (tkn, "%lf", &tempD); modelParams[i].popSizeLognormal[numVars[i]++] = tempD; if (numVars[i] == 1) { expecting = Expecting(COMMA); } else { if (modelParams[i].popSizeLognormal[1] <= 0.0) { MrBayesPrint ("%s Standard deviation of Lognormal must be a positive number\n", spacer); return (ERROR); } if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting Popsizepr to Lognormal(%1.2lf,%1.2lf)\n", spacer, modelParams[i].popSizeLognormal[0], modelParams[i].popSizeLognormal[1]); else MrBayesPrint ("%s Setting Popsizepr to Lognormal(%1.2lf,%1.2lf) for partition %d\n", spacer, modelParams[i].popSizeLognormal[0], modelParams[i].popSizeLognormal[1], i+1); expecting = Expecting(RIGHTPAR); } } else if (!strcmp(modelParams[i].popSizePr,"Normal")) { sscanf (tkn, "%lf", &tempD); modelParams[i].popSizeNormal[numVars[i]++] = tempD; if (numVars[i] == 1) { if (modelParams[i].popSizeNormal[0] <= 0.0) { MrBayesPrint ("%s Mean of Truncated Normal must be a positive number\n", spacer); return (ERROR); } expecting = Expecting(COMMA); } else { if (modelParams[i].popSizeNormal[1] <= 0.0) { MrBayesPrint ("%s Standard deviation of Truncated Normal must be a positive number\n", spacer); return (ERROR); } if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting Popsizepr to Truncated Normal(%1.2lf,%1.2lf)\n", spacer, modelParams[i].popSizeNormal[0], modelParams[i].popSizeNormal[1]); else MrBayesPrint ("%s Setting Popsizepr to Truncated Normal(%1.2lf,%1.2lf) for partition %d\n", spacer, modelParams[i].popSizeNormal[0], modelParams[i].popSizeNormal[1], i+1); expecting = Expecting(RIGHTPAR); } } else if (!strcmp(modelParams[i].popSizePr,"Gamma")) { sscanf (tkn, "%lf", &tempD); modelParams[i].popSizeGamma[numVars[i]++] = tempD; if (numVars[i] == 1) { if (modelParams[i].popSizeGamma[0] <= 0.0) { MrBayesPrint ("%s Shape (alpha) of Gamma must be a positive number\n", spacer); return (ERROR); } expecting = Expecting(COMMA); } else { if (modelParams[i].popSizeGamma[1] <= 0.0) { MrBayesPrint ("%s Rate (beta) of Gamma must be a positive number\n", spacer); return (ERROR); } if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting Popsizepr to Gamma(%1.2lf,%1.2lf)\n", spacer, modelParams[i].popSizeGamma[0], modelParams[i].popSizeGamma[1]); else MrBayesPrint ("%s Setting Popsizepr to Gamma(%1.2lf,%1.2lf) for partition %d\n", spacer, modelParams[i].popSizeGamma[0], modelParams[i].popSizeGamma[1], i+1); expecting = Expecting(RIGHTPAR); } } else if (!strcmp(modelParams[i].popSizePr,"Fixed")) { sscanf (tkn, "%lf", &tempD); if (AreDoublesEqual(tempD, 0.0, ETA)==YES) { MrBayesPrint ("%s Popsizepr cannot be fixed to 0.0\n", spacer); return (ERROR); } modelParams[i].popSizeFix = tempD; if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting Popsizepr to Fixed(%1.5lf)\n", spacer, modelParams[i].popSizeFix); else MrBayesPrint ("%s Setting Popsizepr to Fixed(%1.5lf) for partition %d\n", spacer, modelParams[i].popSizeFix, i+1); expecting = Expecting(RIGHTPAR); } } } } else if (expecting == Expecting(COMMA)) { expecting = Expecting(NUMBER); } else if (expecting == Expecting(RIGHTPAR)) { expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); } /* set Popvarpr (popVarPr) *********************************************************/ else if (!strcmp(parmName, "Popvarpr")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(ALPHA); else if (expecting == Expecting(ALPHA)) { if (IsArgValid(tkn, tempStr) == NO_ERROR) { nApplied = NumActiveParts (); for (i=0; i CPPLAMBDA_MAX) { MrBayesPrint ("%s CPP rate must be in the range %f - %f\n", spacer, CPPLAMBDA_MIN, CPPLAMBDA_MAX); return (ERROR); } modelParams[i].cppRateFix = tempD; if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting Cppratepr to Fixed(%1.2lf)\n", spacer, modelParams[i].cppRateFix); else MrBayesPrint ("%s Setting Cppratepr to Fixed(%1.2lf) for partition %d\n", spacer, modelParams[i].cppRateFix, i+1); expecting = Expecting(RIGHTPAR); } } } } else if (expecting == Expecting(COMMA)) { expecting = Expecting(NUMBER); } else if (expecting == Expecting(RIGHTPAR)) { expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); } /* set Compound Poisson Process rate multiplier standard deviation (log scale) *****/ else if (!strcmp(parmName, "Cppmultdevpr")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(ALPHA); else if (expecting == Expecting(ALPHA)) { if (IsArgValid(tkn, tempStr) == NO_ERROR) { nApplied = NumActiveParts (); for (i=0; i POS_INFINITY) { MrBayesPrint ("%s The standard deviation of rate multipliers must be in the range %f - %f\n", spacer, POS_MIN, POS_INFINITY); return (ERROR); } modelParams[i].cppMultDevFix = tempD; if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting Cppmultdevpr to Fixed(%1.2lf)\n", spacer, modelParams[i].cppMultDevFix); else MrBayesPrint ("%s Setting Cppmultdevpr to Fixed(%1.2lf) for partition %d\n", spacer, modelParams[i].cppMultDevFix, i+1); expecting = Expecting(RIGHTPAR); } } } } else if (expecting == Expecting(COMMA)) { expecting = Expecting(NUMBER); } else if (expecting == Expecting(RIGHTPAR)) { expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); } /* set prior for variance of lognormal of autocorrelated rates (tk02varPr) *********/ else if (!strcmp(parmName, "TK02varpr")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(ALPHA); else if (expecting == Expecting(ALPHA)) { if (IsArgValid(tkn, tempStr) == NO_ERROR) { nApplied = NumActiveParts (); for (i=0; i= modelParams[i].tk02varUni[1]) { MrBayesPrint ("%s Lower value for uniform should be greater than upper value\n", spacer); return (ERROR); } if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting TK02varpr to Uniform(%1.2lf,%1.2lf)\n", spacer, modelParams[i].tk02varUni[0], modelParams[i].tk02varUni[1]); else MrBayesPrint ("%s Setting TK02varpr to Uniform(%1.2lf,%1.2lf) for partition %d\n", spacer, modelParams[i].tk02varUni[0], modelParams[i].tk02varUni[1], i+1); expecting = Expecting(RIGHTPAR); } } else if (!strcmp(modelParams[i].tk02varPr,"Exponential")) { sscanf (tkn, "%lf", &tempD); modelParams[i].tk02varExp = tempD; if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting TK02varpr to Exponential(%1.2lf)\n", spacer, modelParams[i].tk02varExp); else MrBayesPrint ("%s Setting TK02varpr to Exponential(%1.2lf) for partition %d\n", spacer, modelParams[i].tk02varExp, i+1); expecting = Expecting(RIGHTPAR); } else if (!strcmp(modelParams[i].tk02varPr,"Fixed")) { sscanf (tkn, "%lf", &tempD); if (tempD < TK02VAR_MIN || tempD > TK02VAR_MAX) { MrBayesPrint ("%s Ratevar (nu) must be in the range %f - %f\n", spacer, TK02VAR_MIN, TK02VAR_MAX); return (ERROR); } modelParams[i].tk02varFix = tempD; if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting TK02varpr to Fixed(%1.2lf)\n", spacer, modelParams[i].tk02varFix); else MrBayesPrint ("%s Setting TK02varpr to Fixed(%1.2lf) for partition %d\n", spacer, modelParams[i].tk02varFix, i+1); expecting = Expecting(RIGHTPAR); } } } } else if (expecting == Expecting(COMMA)) { expecting = Expecting(NUMBER); } else if (expecting == Expecting(RIGHTPAR)) { expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); } /* set prior for variance of white noise rates (wnvarPr) ***************************/ else if (!strcmp(parmName, "WNvarpr")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(ALPHA); else if (expecting == Expecting(ALPHA)) { if (IsArgValid(tkn, tempStr) == NO_ERROR) { nApplied = NumActiveParts (); for (i=0; i= modelParams[i].wnvarUni[1]) { MrBayesPrint ("%s Lower value for uniform should be greater than upper value\n", spacer); return (ERROR); } if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting WNvarpr to Uniform(%1.2lf,%1.2lf)\n", spacer, modelParams[i].wnvarUni[0], modelParams[i].wnvarUni[1]); else MrBayesPrint ("%s Setting WNvarpr to Uniform(%1.2lf,%1.2lf) for partition %d\n", spacer, modelParams[i].wnvarUni[0], modelParams[i].wnvarUni[1], i+1); expecting = Expecting(RIGHTPAR); } } else if (!strcmp(modelParams[i].wnvarPr,"Exponential")) { sscanf (tkn, "%lf", &tempD); modelParams[i].wnvarExp = tempD; if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting WNvarpr to Exponential(%1.2lf)\n", spacer, modelParams[i].wnvarExp); else MrBayesPrint ("%s Setting WNvarpr to Exponential(%1.2lf) for partition %d\n", spacer, modelParams[i].wnvarExp, i+1); expecting = Expecting(RIGHTPAR); } else if (!strcmp(modelParams[i].wnvarPr,"Fixed")) { sscanf (tkn, "%lf", &tempD); if (tempD < WNVAR_MIN || tempD > WNVAR_MAX) { MrBayesPrint ("%s WNvar must be in the range %f - %f\n", spacer, WNVAR_MIN, WNVAR_MAX); return (ERROR); } modelParams[i].wnvarFix = tempD; if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting WNvarpr to Fixed(%1.2lf)\n", spacer, modelParams[i].wnvarFix); else MrBayesPrint ("%s Setting WNvarpr to Fixed(%1.2lf) for partition %d\n", spacer, modelParams[i].wnvarFix, i+1); expecting = Expecting(RIGHTPAR); } } } } else if (expecting == Expecting(COMMA)) { expecting = Expecting(NUMBER); } else if (expecting == Expecting(RIGHTPAR)) { expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); } /* set prior for variance of lognormal of independent rates (ilnvarPr) *************/ else if (!strcmp(parmName, "ILNvarpr")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(ALPHA); else if (expecting == Expecting(ALPHA)) { if (IsArgValid(tkn, tempStr) == NO_ERROR) { nApplied = NumActiveParts (); for (i=0; i= modelParams[i].ilnvarUni[1]) { MrBayesPrint ("%s Lower value for uniform should be greater than upper value\n", spacer); return (ERROR); } if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting ILNvarpr to Uniform(%1.2lf,%1.2lf)\n", spacer, modelParams[i].ilnvarUni[0], modelParams[i].ilnvarUni[1]); else MrBayesPrint ("%s Setting ILNvarpr to Uniform(%1.2lf,%1.2lf) for partition %d\n", spacer, modelParams[i].ilnvarUni[0], modelParams[i].ilnvarUni[1], i+1); expecting = Expecting(RIGHTPAR); } } else if (!strcmp(modelParams[i].ilnvarPr,"Exponential")) { sscanf (tkn, "%lf", &tempD); modelParams[i].ilnvarExp = tempD; if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting ILNvarpr to Exponential(%1.2lf)\n", spacer, modelParams[i].ilnvarExp); else MrBayesPrint ("%s Setting ILNvarpr to Exponential(%1.2lf) for partition %d\n", spacer, modelParams[i].ilnvarExp, i+1); expecting = Expecting(RIGHTPAR); } else if (!strcmp(modelParams[i].ilnvarPr,"Fixed")) { sscanf (tkn, "%lf", &tempD); if (tempD < INDRVAR_MIN || tempD > INDRVAR_MAX) { MrBayesPrint ("%s ILNvar must be in the range %f - %f\n", spacer, INDRVAR_MIN, INDRVAR_MAX); return (ERROR); } modelParams[i].ilnvarFix = tempD; if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting ILNvarpr to Fixed(%1.2lf)\n", spacer, modelParams[i].ilnvarFix); else MrBayesPrint ("%s Setting ILNvarpr to Fixed(%1.2lf) for partition %d\n", spacer, modelParams[i].ilnvarFix, i+1); expecting = Expecting(RIGHTPAR); } } } } else if (expecting == Expecting(COMMA)) { expecting = Expecting(NUMBER); } else if (expecting == Expecting(RIGHTPAR)) { expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); } /* set prior for variance of independent branch rate gamma distribution (igrvarPr) */ else if (!strcmp(parmName, "IGRvarpr")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(ALPHA); else if (expecting == Expecting(ALPHA)) { if (IsArgValid(tkn, tempStr) == NO_ERROR) { nApplied = NumActiveParts (); for (i=0; i= modelParams[i].igrvarUni[1]) { MrBayesPrint ("%s Lower value for uniform should be greater than upper value\n", spacer); return (ERROR); } if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting IGRvarpr to Uniform(%1.2lf,%1.2lf)\n", spacer, modelParams[i].igrvarUni[0], modelParams[i].igrvarUni[1]); else MrBayesPrint ("%s Setting IGRvarpr to Uniform(%1.2lf,%1.2lf) for partition %d\n", spacer, modelParams[i].igrvarUni[0], modelParams[i].igrvarUni[1], i+1); expecting = Expecting(RIGHTPAR); } } else if (!strcmp(modelParams[i].igrvarPr,"Exponential")) { sscanf (tkn, "%lf", &tempD); modelParams[i].igrvarExp = tempD; if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting IGRvarpr to Exponential(%1.2lf)\n", spacer, modelParams[i].igrvarExp); else MrBayesPrint ("%s Setting IGRvarpr to Exponential(%1.2lf) for partition %d\n", spacer, modelParams[i].igrvarExp, i+1); expecting = Expecting(RIGHTPAR); } else if (!strcmp(modelParams[i].igrvarPr,"Fixed")) { sscanf (tkn, "%lf", &tempD); if (tempD < INDRVAR_MIN || tempD > INDRVAR_MAX) { MrBayesPrint ("%s IGRvar must be in the range %f - %f\n", spacer, INDRVAR_MIN, INDRVAR_MAX); return (ERROR); } modelParams[i].igrvarFix = tempD; if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting IGRvarpr to Fixed(%1.2lf)\n", spacer, modelParams[i].igrvarFix); else MrBayesPrint ("%s Setting IGRvarpr to Fixed(%1.2lf) for partition %d\n", spacer, modelParams[i].igrvarFix, i+1); expecting = Expecting(RIGHTPAR); } } } } else if (expecting == Expecting(COMMA)) { expecting = Expecting(NUMBER); } else if (expecting == Expecting(RIGHTPAR)) { expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); } /* set prior for variance of mixed relaxed clock model (mixedvarPr) ****************/ else if (!strcmp(parmName, "Mixedvarpr")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(ALPHA); else if (expecting == Expecting(ALPHA)) { if (IsArgValid(tkn, tempStr) == NO_ERROR) { nApplied = NumActiveParts (); for (i=0; i= modelParams[i].mixedvarUni[1]) { MrBayesPrint ("%s Lower value for uniform should be greater than upper value\n", spacer); return (ERROR); } if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting Mixedvarpr to Uniform(%1.2lf,%1.2lf)\n", spacer, modelParams[i].mixedvarUni[0], modelParams[i].mixedvarUni[1]); else MrBayesPrint ("%s Setting Mixedvarpr to Uniform(%1.2lf,%1.2lf) for partition %d\n", spacer, modelParams[i].mixedvarUni[0], modelParams[i].mixedvarUni[1], i+1); expecting = Expecting(RIGHTPAR); } } else if (!strcmp(modelParams[i].mixedvarPr,"Exponential")) { sscanf (tkn, "%lf", &tempD); modelParams[i].mixedvarExp = tempD; if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting Mixedvarpr to Exponential(%1.2lf)\n", spacer, modelParams[i].mixedvarExp); else MrBayesPrint ("%s Setting Mixedvarpr to Exponential(%1.2lf) for partition %d\n", spacer, modelParams[i].mixedvarExp, i+1); expecting = Expecting(RIGHTPAR); } else if (!strcmp(modelParams[i].mixedvarPr,"Fixed")) { sscanf (tkn, "%lf", &tempD); if (tempD < INDRVAR_MIN || tempD > INDRVAR_MAX) { MrBayesPrint ("%s Mixedvar must be in the range %f - %f\n", spacer, INDRVAR_MIN, INDRVAR_MAX); return (ERROR); } modelParams[i].mixedvarFix = tempD; if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting Mixedvarpr to Fixed(%1.2lf)\n", spacer, modelParams[i].mixedvarFix); else MrBayesPrint ("%s Setting Mixedvarpr to Fixed(%1.2lf) for partition %d\n", spacer, modelParams[i].mixedvarFix, i+1); expecting = Expecting(RIGHTPAR); } } } } else if (expecting == Expecting(COMMA)) { expecting = Expecting(NUMBER); } else if (expecting == Expecting(RIGHTPAR)) { expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); } /* set Browncorrpr (brownCorrPr) ***************************************************/ else if (!strcmp(parmName, "Browncorrpr")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(ALPHA); else if (expecting == Expecting(ALPHA)) { if (IsArgValid(tkn, tempStr) == NO_ERROR) { nApplied = NumActiveParts (); flag = 0; for (i=0; i= modelParams[i].brownCorrUni[1]) { MrBayesPrint ("%s Lower value for uniform should be greater than upper value\n", spacer); return (ERROR); } if (modelParams[i].brownCorrUni[1] > 1.0) { MrBayesPrint ("%s Upper value for uniform should be less than or equal to 1.0\n", spacer); return (ERROR); } if (modelParams[i].brownCorrUni[0] < -1.0) { MrBayesPrint ("%s Lower value for uniform should be greater than or equal to -1.0\n", spacer); return (ERROR); } if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting Browncorrpr to Uniform(%1.2lf,%1.2lf)\n", spacer, modelParams[i].brownCorrUni[0], modelParams[i].brownCorrUni[1]); else MrBayesPrint ("%s Setting Browncorrpr to Uniform(%1.2lf,%1.2lf) for partition %d\n", spacer, modelParams[i].brownCorrUni[0], modelParams[i].brownCorrUni[1], i+1); expecting = Expecting(RIGHTPAR); } } else if (!strcmp(modelParams[i].brownCorrPr,"Fixed")) { sscanf (tkn, "%lf", &tempD); if (foundDash == YES) tempD *= -1.0; if (tempD > 1.0 || tempD < -1.0) { MrBayesPrint ("%s Value for Browncorrpr should be in the interval (-1, +1)\n", spacer); return (ERROR); } modelParams[i].brownCorrFix = tempD; if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting Browncorrpr to Fixed(%1.2lf)\n", spacer, modelParams[i].brownCorrFix); else MrBayesPrint ("%s Setting Browncorrpr to Fixed(%1.2lf) for partition %d\n", spacer, modelParams[i].brownCorrFix, i+1); expecting = Expecting(RIGHTPAR); } } } foundDash = NO; } else if (expecting == Expecting(DASH)) { foundDash = YES; expecting = Expecting(NUMBER); } else if (expecting == Expecting(COMMA)) { expecting = Expecting(NUMBER) | Expecting(DASH); } else if (expecting == Expecting(RIGHTPAR)) { expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); } /* set Brownscalepr (brownScalePr) *************************************************/ else if (!strcmp(parmName, "Brownscalepr")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(ALPHA); else if (expecting == Expecting(ALPHA)) { if (IsArgValid(tkn, tempStr) == NO_ERROR) { nApplied = NumActiveParts (); flag = 0; for (i=0; i= modelParams[i].brownScaleUni[1]) { MrBayesPrint ("%s Lower value for uniform should be greater than upper value\n", spacer); return (ERROR); } if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting Brownscalepr to Uniform(%1.2lf,%1.2lf)\n", spacer, modelParams[i].brownScaleUni[0], modelParams[i].brownScaleUni[1]); else MrBayesPrint ("%s Setting Brownscalepr to Uniform(%1.2lf,%1.2lf) for partition %d\n", spacer, modelParams[i].brownScaleUni[0], modelParams[i].brownScaleUni[1], i+1); expecting = Expecting(RIGHTPAR); } } else if (!strcmp(modelParams[i].brownScalePr,"Gamma")) { sscanf (tkn, "%lf", &tempD); modelParams[i].brownScaleGamma[numVars[i]++] = tempD; if (numVars[i] == 1) expecting = Expecting(COMMA); else { if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting Brownscalepr to Gamma(%1.2lf,%1.2lf)\n", spacer, modelParams[i].brownScaleGamma[0], modelParams[i].brownScaleGamma[1]); else MrBayesPrint ("%s Setting Brownscalepr to Gamma(%1.2lf,%1.2lf) for partition %d\n", spacer, modelParams[i].brownScaleGamma[0], modelParams[i].brownScaleGamma[1], i+1); expecting = Expecting(RIGHTPAR); } } else if (!strcmp(modelParams[i].brownScalePr,"Fixed")) { sscanf (tkn, "%lf", &tempD); modelParams[i].brownScaleFix = tempD; if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting Brownscalepr to Fixed(%1.2lf)\n", spacer, modelParams[i].brownScaleFix); else MrBayesPrint ("%s Setting Brownscalepr to Fixed(%1.2lf) for partition %d\n", spacer, modelParams[i].brownScaleFix, i+1); expecting = Expecting(RIGHTPAR); } } } } else if (expecting == Expecting(COMMA)) { expecting = Expecting(NUMBER); } else if (expecting == Expecting(RIGHTPAR)) { expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); } else return (ERROR); } return (NO_ERROR); } int DoQuit (void) { int i; char tempName[100]; /* free information for model and matrix */ FreeModel (); FreeMatrix (); SafeFclose (&logFileFp); logToFile = NO; /* check to see if any memory has not been freed */ for (i=0; i numCurrentDivisions) { MrBayesPrint ("%s Partition delimiter is too large\n", spacer); return (ERROR); } if (fromI == -1) fromI = tempInt; else if (fromI != -1 && toJ == -1 && foundDash == YES && foundComma == NO) { toJ = tempInt; for (i=fromI-1; i 1) { strcpy(modelParams[i].ratemultFormat, tempStr); if (nApplied == 0 && numCurrentDivisions == 1) MrBayesPrint ("%s Setting rate multiplier (ratemult) format to %s\n", spacer, modelParams[i].ratemultFormat); else MrBayesPrint ("%s Setting rate multiplier (ratemult) format to %s for partition %d\n", spacer, modelParams[i].ratemultFormat, i+1); } } } else { MrBayesPrint ("%s Invalid rate multiplier (ratemult) format \n", spacer); return (ERROR); } expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); } /* set report format of tree ***************************************************/ else if (!strcmp(parmName, "Tree")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting (ALPHA); else if (expecting == Expecting(ALPHA)) { if (IsArgValid(tkn, tempStr) == NO_ERROR) { nApplied = NumActiveParts (); tempInt = NO; for (i=0; i(,)=(,...) -- apply to run and chain (,)=(,...) -- apply to chain for all runs (,)=(,...) -- apply to run for all chains topology and branch length parameters are specified like (,)=| parameter names will often be followed by partition specifiers like: pi{all} pinvar{1,4,5} so we need to assemble the parameter name from several tokens that are parsed out separately; here we receive only the first part (before the left curly, if present) */ /* copy to local parameter name */ SafeStrcpy (&tempName, tkn); param = NULL; runIndex = chainIndex = -1; useSubvalues = NO; useIntValues = NO; useStdStateFreqs = NO; foundComma = foundEqual = foundName = foundDash = NO; expecting = Expecting(LEFTCURL) | Expecting(LEFTPAR) | Expecting(EQUALSIGN); } else return (ERROR); } else if (expecting == Expecting(ALPHA)) { if (param == NULL) { /* we are still assembling the parameter name */ SafeStrcat (&tempName, tkn); expecting = Expecting(RIGHTCURL) | Expecting(COMMA) | Expecting(LEFTPAR) | Expecting(EQUALSIGN); } else { /* we have a tree name; now find the tree based on name, case insensitive */ if (GetUserTreeFromName (&treeIndex, tkn) == ERROR || treeIndex == -1) { MrBayesPrint ("%s Error in finding user tree\n", spacer); return (ERROR); } /* set the tree parameter */ for (i=0; iparamType != P_POPSIZE) { if (param->paramType == P_TOPOLOGY || param->paramType == P_BRLENS || param->paramType == P_SPECIESTREE) { /* topology or brlens or speciestree params */ theTree = GetTree (param, chainId, 0); usrTree = GetTree (param, chainId, 1); /* use as scratch space */ } else { /* relaxed clock params */ theTree = GetTree (modelSettings[param->relParts[0]].brlens, chainId, 0); usrTree = GetTree (modelSettings[param->relParts[0]].brlens, chainId, 1); } CopyToTreeFromTree (usrTree, theTree); if (param->paramType == P_SPECIESTREE) thePolyTree = AllocatePolyTree(numSpecies); else thePolyTree = AllocatePolyTree (numTaxa); CopyToPolyTreeFromPolyTree (thePolyTree, userTree[treeIndex]); if (param->paramType == P_SPECIESTREE) { ResetIntNodeIndices(thePolyTree); } else { PrunePolyTree (thePolyTree); ResetTipIndices(thePolyTree); } RandResolve (NULL, thePolyTree, &globalSeed, theTree->isRooted); GetPolyDownPass(thePolyTree); ResetIntNodeIndices(thePolyTree); if (param->paramType == P_SPECIESTREE) { ret=CopyToSpeciesTreeFromPolyTree (usrTree, thePolyTree); } else ret=CopyToTreeFromPolyTree (usrTree, thePolyTree); FreePolyTree (thePolyTree); if (ret==ERROR) return ERROR; } else { /* param->paramType == P_POPSIZE */ theTree = GetTree (modelSettings[param->relParts[0]].speciesTree, chainId, 0); usrTree = GetTree (modelSettings[param->relParts[0]].speciesTree, chainId, 1); CopyToTreeFromTree (usrTree, theTree); thePolyTree = AllocatePolyTree(numSpecies); CopyToPolyTreeFromPolyTree (thePolyTree, userTree[treeIndex]); ResetIntNodeIndices(thePolyTree); RandResolve (NULL, thePolyTree, &globalSeed, theTree->isRooted); CopyToSpeciesTreeFromPolyTree (usrTree, thePolyTree); FreePolyTree (thePolyTree); } if (param->paramType == P_TOPOLOGY) { if (theTree->checkConstraints == YES && CheckSetConstraints (usrTree) == ERROR) { MrBayesPrint ("%s Could not set the constraints for topology parameter '%s'\n", spacer, param->name); return (ERROR); } if (ResetTopologyFromTree (theTree, usrTree) == ERROR) { MrBayesPrint ("%s Could not set the topology parameter '%s'\n", spacer, param->name); return (ERROR); } if (theTree->checkConstraints == YES && CheckSetConstraints (theTree)==ERROR) { MrBayesPrint ("%s Could not set the constraints for topology parameter '%s'\n", spacer, param->name); return (ERROR); } FillTopologySubParams (param, chainId, 0, &globalSeed); //MrBayesPrint ("%s Branch lengths and relaxed clock subparameters of a parameter '%s' are reset.\n", spacer, param->name); if (param->paramId == TOPOLOGY_SPECIESTREE) FillSpeciesTreeParams(&globalSeed, chainId, chainId+1); //assert (IsTreeConsistent(param, chainId, 0) == YES); } else if (param->paramType == P_BRLENS) { if (usrTree->allDownPass[0]->length == 0.0 && param->paramId != BRLENS_CLOCK_FOSSIL) { MrBayesPrint ("%s User tree '%s' does not have branch lengths so it cannot be used in setting parameter '%s'\n", spacer, userTree[treeIndex]->name, param->name); return (ERROR); } if (AreTopologiesSame (theTree, usrTree) == NO) { MrBayesPrint ("%s Topology of user tree '%s' wrong in setting parameter '%s'\n", spacer, userTree[treeIndex]->name, param->name); return (ERROR); } //assert (IsTreeConsistent(param, chainId, 0) == YES); /* reset node depths to ensure that non-dated tips have node depth 0.0 */ /* if (usrTree->isClock == YES) SetNodeDepths(usrTree); */ if (ResetBrlensFromTree (theTree, usrTree) == ERROR) { MrBayesPrint ("%s Could not set parameter '%s' from user tree '%s'\n", spacer, param->name, userTree[treeIndex]->name); return (ERROR); } if (theTree->isClock == YES && modelParams[theTree->relParts[0]].treeAgePr.prior == fixed) { // if (!strcmp(modelParams[theTree->relParts[0]].clockPr,"Uniform") || // !strcmp(modelParams[theTree->relParts[0]].clockPr,"Birthdeath") || // !strcmp(modelParams[theTree->relParts[0]].clockPr,"Fossilization")); // We cannot check the consistency of root age and clock rate here because they can be set in any order. // Defer this check to the CheckModel fxn, called just before starting the chain. } /* the test will find suitable clock rate and ages of nodes in theTree */ if (theTree->isClock == YES && IsClockSatisfied (theTree,0.001) == NO) { MrBayesPrint ("%s Non-calibrated tips are not at the same level after setting up starting tree branch lengths(%s) from user tree '%s'.\n", spacer, param->name, userTree[treeIndex]->name); ShowNodes(theTree->root,0,YES); return (ERROR); } if (theTree->isCalibrated == YES && IsCalibratedClockSatisfied (theTree, &minRate,&maxRate, 0.001) == NO) { MrBayesPrint ("%s Problem setting calibrated tree parameters\n", spacer); return (ERROR); } if (theTree->isCalibrated == YES && !strcmp(modelParams[theTree->relParts[0]].clockRatePr, "Fixed")) { clockRate = modelParams[theTree->relParts[0]].clockRateFix; if ((clockRate < minRate && AreDoublesEqual (clockRate, minRate , 0.001) == NO) || (clockRate > maxRate && AreDoublesEqual (clockRate, maxRate , 0.001) == NO)) { MrBayesPrint ("%s Fixed branch lengths do not satisfy fixed clockrate\n", spacer); return (ERROR); } } theTree->fromUserTree=YES; FillBrlensSubParams (param, chainId, 0); //MrBayesPrint ("%s Relaxed clock subparameters of a parameter '%s' are reset.\n", spacer, param->name); //assert (IsTreeConsistent(param, chainId, 0) == YES); if (param->paramId == BRLENS_CLOCK_SPCOAL) FillSpeciesTreeParams(&globalSeed, chainId, chainId+1); //assert (IsTreeConsistent(param, chainId, 0) == YES); } else if (param->paramType == P_CPPEVENTS || param->paramType == P_TK02BRANCHRATES || param->paramType == P_ILNBRANCHRATES || param->paramType == P_IGRBRANCHRATES || param->paramType == P_MIXEDBRCHRATES || param->paramType == P_WNBRANCHRATES) { if (theTree->isCalibrated == YES && theTree->fromUserTree == NO) { /* if theTree is not set from user tree then we can not guarantee that branch lengths will stay the same by the time we start mcmc run because of clockrate adjustment. */ MrBayesPrint ("%s Set starting values for branch lengths first before setting starting values of relaxed parameters!\n", spacer); return (ERROR); } if (theTree->isCalibrated == NO && IsClockSatisfied (usrTree, 0.001) == NO) // user tree is not calibrated so do not check it if calibration is in place { MrBayesPrint ("%s Branch lengths of the user tree '%s' do not satisfy clock in setting parameter '%s'\n", spacer, userTree[treeIndex], param->name); ShowNodes(usrTree->root,0,YES); return (ERROR); } if (AreTopologiesSame (theTree, usrTree) == NO) { MrBayesPrint ("%s Topology of user tree '%s' is wrong in setting parameter '%s'\n", spacer, userTree[treeIndex]->name, param->name); return (ERROR); } if (SetRelaxedClockParam (param, chainId, 0, userTree[treeIndex]) == ERROR) { MrBayesPrint ("%s Could not set parameter '%s' from user tree '%s'\n", spacer, param->name, userTree[treeIndex]->name); return (ERROR); } //assert (IsTreeConsistent(param, chainId, 0) == YES); } else if (param->paramType == P_POPSIZE) { if (AreTopologiesSame (theTree, usrTree) == NO) { MrBayesPrint ("%s Topology of user tree '%s' is wrong in setting parameter '%s'\n", spacer, userTree[treeIndex]->name, param->name); return (ERROR); } if (SetPopSizeParam (param, chainId, 0, userTree[treeIndex]) == ERROR) { MrBayesPrint ("%s Could not set parameter '%s' from user tree '%s'\n", spacer, param->name, userTree[treeIndex]->name); return (ERROR); } } else if (param->paramType == P_SPECIESTREE) { if (IsSpeciesTreeConsistent (usrTree, chainId) == NO) { MrBayesPrint ("%s User-specified species tree '%s' is inconsistent with gene trees\n", spacer, userTree[treeIndex]->name); return (ERROR); } if (CopyToTreeFromTree (theTree, usrTree) == ERROR) { MrBayesPrint ("%s Could not set the species tree parameter '%s'\n", spacer, param->name); return (ERROR); } //assert (IsTreeConsistent(param, chainId, 0) == YES); } } } expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } } else if (expecting == Expecting(LEFTCURL)) { /* we are still assembling the parameter name */ SafeStrcat (&tempName, tkn); expecting = Expecting(NUMBER) | Expecting(ALPHA) | Expecting(LEFTPAR) | Expecting(EQUALSIGN); } else if (expecting == Expecting(RIGHTCURL)) { /* we are still assembling the parameter name */ SafeStrcat (&tempName, tkn); foundComma = NO; /*! if there was a comma in the partition part, we should reset this variable. Otherwise we can't parse something like A{1,2}(3,4) */ expecting = Expecting(LEFTPAR) | Expecting(EQUALSIGN); } else if (expecting == Expecting(LEFTPAR)) { if (foundEqual == NO) { foundName = YES; /* we found the name */ /* we will be reading in run and chain indices */ expecting = Expecting(NUMBER) | Expecting(COMMA); } else { expecting = Expecting(NUMBER); expecting |= Expecting(DASH); } } else if (expecting == Expecting(DASH)) { foundDash = YES; expecting = Expecting(NUMBER); } else if (expecting == Expecting(NUMBER)) { if (foundName == NO) { /* we are still assembling the parameter name */ SafeStrcat (&tempName, tkn); expecting = Expecting(COMMA) | Expecting(LEFTPAR) | Expecting(RIGHTCURL) | Expecting(EQUALSIGN); } else if (foundEqual == YES) { theValueMin = param->min; theValueMax = param->max; /* we are reading in a parameter value */ if (param->paramType==P_OMEGA && nValuesRead==numExpectedValues && useSubvalues == NO) { /* continue with subvalues */ nValuesRead = 0; numExpectedValues = param->nSubValues/2; useSubvalues = YES; theValueMin = ETA; theValueMax = 1.0; } if (param->paramType==P_OMEGA && nValuesRead==numExpectedValues && useSubvalues == NO) { /* continue with subvalues */ nValuesRead = 0; numExpectedValues = param->nSubValues/2; useSubvalues = YES; theValueMin = ETA; theValueMax = 1.0; } if (param->nIntValues > 0 && nValuesRead==numExpectedValues && useIntValues == NO) { /* continue with intValues */ nValuesRead = 0; numExpectedValues = param->nIntValues; useIntValues = YES; } if (param->paramType==P_PI && modelSettings[param->relParts[0]].dataType == STANDARD && param->paramId != SYMPI_EQUAL && nValuesRead==numExpectedValues && useStdStateFreqs == NO) { /* we have read alpha_symdir, continue with multistate char state freqs */ nValuesRead = 0; numExpectedValues = param->nStdStateFreqs; if (param->hasBinaryStd == YES) numExpectedValues -= 2 * modelSettings[param->relParts[0]].numBetaCats; useStdStateFreqs = YES; theValueMin = ETA; theValueMax = 1.0; } nValuesRead++; if (nValuesRead > numExpectedValues) { if (param->paramType == P_OMEGA) MrBayesPrint ("%s Only %d values were expected for parameter '%s'\n", spacer, param->nValues+param->nSubValues/2, param->name); else if (param->nIntValues > 0) MrBayesPrint ("%s Only %d values were expected for parameter '%s'\n", spacer, param->nValues+param->nIntValues, param->name); else MrBayesPrint ("%s Only %d values were expected for parameter '%s'\n", spacer, numExpectedValues, param->name); return (ERROR); } if (useIntValues == YES) sscanf (tkn, "%d", &tempInt); else sscanf (tkn, "%lf", &tempFloat); if (foundDash == YES) { if (useIntValues == YES) tempInt = -tempInt; else tempFloat = -tempFloat; foundDash = NO; } if (useIntValues == NO && (tempFloat < theValueMin || tempFloat > theValueMax)) { MrBayesPrint ("%s The value is out of range (min = %lf; max = %lf)\n", spacer, theValueMin, theValueMax); return (ERROR); } for (i=0; ihasBinaryStd == YES) theValue += 2 * modelSettings[param->relParts[0]].numBetaCats; } else return (ERROR); if (param->paramType == P_CLOCKRATE) { if (UpdateClockRate(tempFloat, i*chainParams.numChains+j) == ERROR) { return (ERROR); } } theValue[nValuesRead-1] = tempFloat; } } } expecting = Expecting (COMMA) | Expecting(RIGHTPAR); } else /* if (foundEqual == NO) */ { sscanf (tkn, "%d", &tempInt); if (foundComma == NO) { if (tempInt <= 0 || tempInt > chainParams.numRuns) { MrBayesPrint ("%s Run index is out of range (min=1; max=%d)\n", spacer, chainParams.numRuns); return (ERROR); } runIndex = tempInt - 1; expecting = Expecting(COMMA); } else { if (tempInt <= 0 || tempInt > chainParams.numChains) { MrBayesPrint ("%s Chain index is out of range (min=1; max=%d)\n", spacer, chainParams.numChains); return (ERROR); } chainIndex = tempInt - 1; foundComma = NO; expecting = Expecting(RIGHTPAR); } } } else if (expecting == Expecting(COMMA)) { if (foundEqual == YES) { /* we expect another parameter value */ expecting = Expecting(NUMBER); } else /* if (foundEqual == NO) */ { /* we will be reading in chain index, if present */ foundComma = YES; expecting = Expecting(RIGHTPAR) | Expecting(NUMBER); /* if the comma is in a list of partitions (so between { and }) we have to add the comma to the parameter name */ if (param == NULL && strchr(tempName, '}')==NULL && strchr(tempName, '{')!=NULL) SafeStrcat (&tempName, ","); } } else if (expecting == Expecting(RIGHTPAR)) { if (foundEqual == NO) { /* this is the end of the run and chain specification */ expecting = Expecting(EQUALSIGN); } else /* if (foundEqual == YES) */ { /* this is the end of the parameter values */ if (nValuesRead != numExpectedValues) { MrBayesPrint ("%s Expected %d values but only found %d values for parameter '%s'\n", spacer, numExpectedValues, nValuesRead, param->name); return (ERROR); } /* Post processing needed for some parameters */ assert (param != NULL); // param is non-NULL here if (param->paramType == P_SHAPE || param->paramType == P_CORREL) { for (i=0; iparamType == P_SHAPE && !strncmp(param->name, "Alpha", 5)) { if (DiscreteGamma (subValue, value[0], value[0], param->nSubValues, 0) == ERROR) return (ERROR); } else if (param->paramType == P_SHAPE && !strncmp(param->name, "Sigma", 5)) { if( DiscreteLogNormal(subValue, value[0], param->nSubValues, 1) == ERROR) return (ERROR); } else if (param->paramType == P_CORREL) { if (AutodGamma (subValue, value[0], (int)(sqrt(param->nSubValues) + 0.5)) == ERROR) return (ERROR); } } } } expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } } else if (expecting == Expecting(EQUALSIGN)) { foundEqual = YES; foundName = YES; /* we now know that the name is complete; try to find the parameter with this name (case insensitive) */ assert (tempName != NULL); // tempName is non-NULL here for (i=0; i<(int)strlen(tempName); i++) tempName[i] = (char)(tolower(tempName[i])); /* first check exact matches */ nMatches = j = 0; for (i=0; iname); for (k=0; k<(int)(strlen(temp)); k++) temp[k] = (char)(tolower(temp[k])); if (strcmp(tempName,temp) == 0) { j = i; nMatches++; } } /* now check unambiguous abbreviation matches */ if (nMatches == 0) { nMatches = j = 0; for (i=0; iname); for (k=0; k<(int)strlen(temp); k++) temp[k] = (char)(tolower(temp[k])); if (strncmp(tempName,temp,strlen(tempName)) == 0) { j = i; nMatches++; } } } if (nMatches == 0) { extern char *tokenP; MrBayesPrint ("%s Could not find parameter '%s': ignoring values\n", spacer, tempName); while (*tokenP && *tokenP++!=')') {}; expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); return (*tokenP ? NO_ERROR:ERROR); } else if (nMatches > 1) { MrBayesPrint ("%s Several parameters matched the abbreviated name '%s'\n", spacer, tempName); return (ERROR); } param = ¶ms[j]; if (param->printParam == NO && !(param->paramType == P_TOPOLOGY && strcmp(modelParams[param->relParts[0]].topologyPr,"Fixed")!=0) && !(param->paramType == P_CPPEVENTS) && !(param->paramType == P_TK02BRANCHRATES) && !(param->paramType == P_WNBRANCHRATES) && !(param->paramType == P_IGRBRANCHRATES) && !(param->paramType == P_ILNBRANCHRATES) && !(param->paramType == P_MIXEDBRCHRATES) && !(param->paramType == P_POPSIZE && param->nValues > 1)) { MrBayesPrint ("%s The parameter '%s' is fixed so the starting value cannot be set\n", spacer, param->name); return (ERROR); } if (param->paramType == P_BRLENS || param->paramType == P_TOPOLOGY || param->paramType == P_CPPEVENTS || param->paramType == P_TK02BRANCHRATES || param->paramType == P_WNBRANCHRATES || param->paramType == P_IGRBRANCHRATES || param->paramType == P_ILNBRANCHRATES || param->paramType == P_MIXEDBRCHRATES || param->paramType == P_SPECIESTREE || (param->paramType == P_POPSIZE && param->nValues > 1)) { /* all these parameters are set from a tree */ expecting = Expecting (ALPHA); } else /* run of the mill character */ { theValueMin = param->min; theValueMax = param->max; if ((param->paramType == P_PI && modelParams[param->relParts[0]].dataType != STANDARD) || param->paramType == P_MIXTURE_RATES) { useSubvalues = YES; useIntValues = NO; numExpectedValues = param->nSubValues; } else if (param->nValues == 0 && param->nIntValues > 0) { useSubvalues = NO; useIntValues = YES; numExpectedValues = param->nIntValues; } else if (param->nValues > 0) { useSubvalues = NO; useIntValues = NO; numExpectedValues = param->nValues; } else { MrBayesPrint ("%s Not expecting any values for parameter '%s'\n", spacer, param->name); return (ERROR); } nValuesRead = 0; expecting = Expecting(LEFTPAR); } } else return (ERROR); SAFEFREE (temp); return (NO_ERROR); } int DoUnlink (void) { int i, j; MrBayesPrint ("%s Unlinking\n", spacer); /* update status of linkTable */ for (j=0; jisRooted == YES) MrBayesPrint ("\n Tree '%s' [rooted]:\n\n", t->name); else MrBayesPrint ("\n Tree '%s' [unrooted]:\n\n", t->name); if (ShowTree (t) == ERROR) return (ERROR); else MrBayesPrint ("\n"); } } } return (NO_ERROR); } int DoShowModel (void) { if (defMatrix == NO) { MrBayesPrint ("%s A matrix must be specified before the model can be defined\n", spacer); return (ERROR); } if (ShowModel() == ERROR) return (ERROR); return (NO_ERROR); } int DoShowMoves (void) { if (defMatrix == NO) { MrBayesPrint ("%s A matrix must be specified before moves can be assigned\n", spacer); return (ERROR); } MrBayesPrint ("%s Moves that will be used by MCMC sampler (rel. proposal prob. > 0.0):\n\n", spacer); if (ShowMoves(YES) == ERROR) return (ERROR); if (showmovesParams.allavailable == YES) { MrBayesPrint ("%s Other available moves (rel. proposal prob. = 0.0):\n\n", spacer); if (ShowMoves(NO) == ERROR) return (ERROR); } else MrBayesPrint ("%s Use 'Showmoves allavailable=yes' to see a list of all available moves\n", spacer); return (NO_ERROR); } int DoShowmovesParm (char *parmName, char *tkn) { char tempStr[100]; if (expecting == Expecting(PARAMETER)) { expecting = Expecting(EQUALSIGN); } else { /* set Allavailable **********************************************************/ if (!strcmp(parmName, "Allavailable")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(ALPHA); else if (expecting == Expecting(ALPHA)) { if (IsArgValid(tkn, tempStr) == NO_ERROR) { if (!strcmp(tempStr, "Yes")) showmovesParams.allavailable = YES; else showmovesParams.allavailable = NO; } else { MrBayesPrint ("%s Invalid argument for allavailable\n", spacer); return (ERROR); } expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); } else return (ERROR); } return (NO_ERROR); } int DoShowParams (void) { if (defMatrix == NO) { MrBayesPrint ("%s A matrix must be specified before model parameters can be shown\n", spacer); return (ERROR); } if (ShowParameters(YES, YES, YES) == ERROR) return (ERROR); return (NO_ERROR); } /*------------------------------------------------------------------------ | | FillNormalParams: Allocate and fill in non-tree parameters | -------------------------------------------------------------------------*/ int FillNormalParams (RandLong *seed, int fromChain, int toChain) { int i, j, k, chn, tempInt, *intValue; MrBFlt *bs, *value, *subValue, scaler; Tree *tree; Param *p; ModelInfo *m; ModelParams *mp; /* fill in values for nontree params for state 0 of chains */ for (chn=fromChain; chnrelParts[0]]; m = &modelSettings[p->relParts[0]]; /* find model settings and nStates, pInvar, invar cond likes */ value = GetParamVals (p, chn, 0); subValue = GetParamSubVals (p, chn, 0); intValue = GetParamIntVals (p, chn, 0); if (p->paramType == P_TRATIO) { /* Fill in tratios **************************************************************************************/ if (p->paramId == TRATIO_DIR) value[0] = 1.0; else if (p->paramId == TRATIO_FIX) value[0] = mp->tRatioFix; } else if (p->paramType == P_REVMAT) { /* Fill in revMat ***************************************************************************************/ /* rates are stored in order, AC or AR first, using the Dirichlet parameterization */ if (p->paramId == REVMAT_DIR) { for (j=0; jnValues; j++) value[j] = 1.0 / (MrBFlt) (p->nValues); } else if (p->paramId == REVMAT_FIX) { scaler = 0.0; if (m->dataType == PROTEIN) { for (j=0; j<190; j++) scaler += (value[j] = mp->aaRevMatFix[j]); for (j=0; j<190; j++) value[j] /= scaler; } else { for (j=0; j<6; j++) scaler += (value[j] = mp->revMatFix[j]); for (j=0; j<6; j++) value[j] /= scaler; } } else if (p->paramId == REVMAT_MIX) { for (j=0; j<6; j++) { value[j] = 1.0 / 6.0; intValue[j] = 0; } } } else if (p->paramType == P_OMEGA) { /* Fill in omega ****************************************************************************************/ if (p->nValues == 1) { if (p->paramId == OMEGA_DIR) value[0] = 1.0; else if (p->paramId == OMEGA_FIX) value[0] = mp->omegaFix; } else { if (!strcmp(mp->omegaVar, "Ny98")) { if (p->paramId == OMEGA_BUD || p->paramId == OMEGA_BUF || p->paramId == OMEGA_BED || p->paramId == OMEGA_BEF || p->paramId == OMEGA_BFD || p->paramId == OMEGA_BFF) value[0] = RandomNumber(seed); else if (p->paramId == OMEGA_FUD || p->paramId == OMEGA_FUF || p->paramId == OMEGA_FED || p->paramId == OMEGA_FEF || p->paramId == OMEGA_FFD || p->paramId == OMEGA_FFF) value[0] = mp->ny98omega1Fixed; value[1] = 1.0; if (p->paramId == OMEGA_BUD || p->paramId == OMEGA_BUF || p->paramId == OMEGA_FUD || p->paramId == OMEGA_FUF) value[2] = mp->ny98omega3Uni[0] + RandomNumber(seed) * (mp->ny98omega3Uni[1] - mp->ny98omega3Uni[0]); else if (p->paramId == OMEGA_BED || p->paramId == OMEGA_BEF || p->paramId == OMEGA_FED || p->paramId == OMEGA_FEF) value[2] = (1.0 - (1.0/mp->ny98omega3Exp) * log(RandomNumber(seed))); else value[2] = mp->ny98omega3Fixed; if (p->paramId == OMEGA_BUD || p->paramId == OMEGA_BED || p->paramId == OMEGA_BFD || p->paramId == OMEGA_FUD || p->paramId == OMEGA_FED || p->paramId == OMEGA_FFD) { subValue[3] = mp->codonCatDir[0]; subValue[4] = mp->codonCatDir[1]; subValue[5] = mp->codonCatDir[2]; DirichletRandomVariable (&subValue[3], &subValue[0], 3, seed); } else { subValue[0] = mp->codonCatFreqFix[0]; subValue[1] = mp->codonCatFreqFix[1]; subValue[2] = mp->codonCatFreqFix[2]; subValue[3] = 0.0; subValue[4] = 0.0; subValue[5] = 0.0; } } else if (!strcmp(mp->omegaVar, "M3")) { if (p->paramId == OMEGA_FD || p->paramId == OMEGA_FF) { value[0] = mp->m3omegaFixed[0]; value[1] = mp->m3omegaFixed[1]; value[2] = mp->m3omegaFixed[2]; } else { value[0] = 0.1; value[1] = 1.0; value[2] = 3.0; } if (p->paramId == OMEGA_ED || p->paramId == OMEGA_FD) { subValue[3] = mp->codonCatDir[0]; subValue[4] = mp->codonCatDir[1]; subValue[5] = mp->codonCatDir[2]; DirichletRandomVariable (&subValue[3], &subValue[0], 3, seed); } else { subValue[0] = mp->codonCatFreqFix[0]; subValue[1] = mp->codonCatFreqFix[1]; subValue[2] = mp->codonCatFreqFix[2]; subValue[3] = 0.0; subValue[4] = 0.0; subValue[5] = 0.0; } } else if (!strcmp(mp->omegaVar, "M10")) { if (p->paramId == OMEGA_10UUB || p->paramId == OMEGA_10UEB || p->paramId == OMEGA_10UFB || p->paramId == OMEGA_10EUB || p->paramId == OMEGA_10EEB || p->paramId == OMEGA_10EFB || p->paramId == OMEGA_10FUB || p->paramId == OMEGA_10FEB || p->paramId == OMEGA_10FFB) { subValue[mp->numM10BetaCats + mp->numM10GammaCats + 2] = mp->codonCatDir[0]; subValue[mp->numM10BetaCats + mp->numM10GammaCats + 3] = mp->codonCatDir[1]; DirichletRandomVariable (&subValue[mp->numM10BetaCats + mp->numM10GammaCats + 2], &subValue[mp->numM10BetaCats + mp->numM10GammaCats + 0], 2, seed); } else { subValue[mp->numM10BetaCats + mp->numM10GammaCats + 0] = mp->codonCatFreqFix[0]; subValue[mp->numM10BetaCats + mp->numM10GammaCats + 1] = mp->codonCatFreqFix[1]; subValue[mp->numM10BetaCats + mp->numM10GammaCats + 2] = 0.0; subValue[mp->numM10BetaCats + mp->numM10GammaCats + 3] = 0.0; } for (i=0; inumM10BetaCats; i++) subValue[i] = subValue[mp->numM10BetaCats + mp->numM10GammaCats + 0] / mp->numM10BetaCats; for (i=mp->numM10BetaCats; inumM10BetaCats+mp->numM10GammaCats; i++) subValue[i] = subValue[mp->numM10BetaCats + mp->numM10GammaCats + 1] / mp->numM10GammaCats; if (p->paramId == OMEGA_10FUB || p->paramId == OMEGA_10FUF || p->paramId == OMEGA_10FEB || p->paramId == OMEGA_10FEF || p->paramId == OMEGA_10FFB || p->paramId == OMEGA_10FFF) { subValue[mp->numM10BetaCats + mp->numM10GammaCats + 4] = mp->m10betaFix[0]; subValue[mp->numM10BetaCats + mp->numM10GammaCats + 5] = mp->m10betaFix[1]; } else { subValue[mp->numM10BetaCats + mp->numM10GammaCats + 4] = 1.0; subValue[mp->numM10BetaCats + mp->numM10GammaCats + 5] = 1.0; } if (p->paramId == OMEGA_10UFB || p->paramId == OMEGA_10UFF || p->paramId == OMEGA_10EFB || p->paramId == OMEGA_10EFF || p->paramId == OMEGA_10FFB || p->paramId == OMEGA_10FFF) { subValue[mp->numM10BetaCats + mp->numM10GammaCats + 6] = mp->m10gammaFix[0]; subValue[mp->numM10BetaCats + mp->numM10GammaCats + 7] = mp->m10gammaFix[1]; } else { subValue[mp->numM10BetaCats + mp->numM10GammaCats + 6] = 1.0; subValue[mp->numM10BetaCats + mp->numM10GammaCats + 7] = 1.0; } BetaBreaks (subValue[mp->numM10BetaCats + mp->numM10GammaCats + 4], subValue[mp->numM10BetaCats + mp->numM10GammaCats + 5], &value[0], mp->numM10BetaCats); if (DiscreteGamma (&value[mp->numM10BetaCats], subValue[mp->numM10BetaCats + mp->numM10GammaCats + 6], subValue[mp->numM10BetaCats + mp->numM10GammaCats + 7], mp->numM10GammaCats, 0) == ERROR) return (ERROR); for (i=0; inumM10GammaCats; i++) value[mp->numM10BetaCats + i] += 1.0; } } } else if (p->paramType == P_PI) { /* Fill in state frequencies ****************************************************************************/ /* note that standard chars are mainly dealt with in ProcessStdChars in mcmc.c */ if (p->paramId == SYMPI_UNI || p->paramId == SYMPI_UNI_MS) value[0] = 1.0; else if (p->paramId == SYMPI_EXP || p->paramId == SYMPI_EXP_MS) value[0] = 1.0; else if (p->paramId == SYMPI_FIX || p->paramId == SYMPI_FIX_MS) value[0] = mp->symBetaFix; else if (p->paramId == PI_DIR) { if (mp->numDirParams != mp->nStates && mp->numDirParams != 0) { MrBayesPrint ("%s Mismatch between number of dirichlet parameters (%d) and the number of states (%d)\n", spacer, mp->numDirParams, m->numStates); return ERROR; } /* if user has not set dirichlet parameters, go with default */ /* overall variance equals number of states */ if (mp->numDirParams == 0) for (i=0; inStates; i++) value[i] = mp->stateFreqsDir[i] = 1.0; else for (i=0; inumStates; i++) value[i] = mp->stateFreqsDir[i]; /* now fill in subvalues */ for (i=0; inumStates; i++) subValue[i] = 1.0 / mp->nStates; } else if (p->paramId == PI_USER) { for (i=0; inumStates; i++) subValue[i] = mp->stateFreqsFix[i]; } else if (p->paramId == PI_FIXED) { if (!strcmp(mp->aaModelPr, "Fixed")) { if (!strcmp(mp->aaModel, "Jones")) { for (i=0; inStates; i++) subValue[i] = jonesPi[i]; } else if (!strcmp(mp->aaModel, "Dayhoff")) { for (i=0; inStates; i++) subValue[i] = dayhoffPi[i]; } else if (!strcmp(mp->aaModel, "Mtrev")) { for (i=0; inStates; i++) subValue[i] = mtrev24Pi[i]; } else if (!strcmp(mp->aaModel, "Mtmam")) { for (i=0; inStates; i++) subValue[i] = mtmamPi[i]; } else if (!strcmp(mp->aaModel, "Wag")) { for (i=0; inStates; i++) subValue[i] = wagPi[i]; } else if (!strcmp(mp->aaModel, "Rtrev")) { for (i=0; inStates; i++) subValue[i] = rtrevPi[i]; } else if (!strcmp(mp->aaModel, "Cprev")) { for (i=0; inStates; i++) subValue[i] = cprevPi[i]; } else if (!strcmp(mp->aaModel, "Vt")) { for (i=0; inStates; i++) subValue[i] = vtPi[i]; } else if (!strcmp(mp->aaModel, "Blosum")) { for (i=0; inStates; i++) subValue[i] = blosPi[i]; } else if (!strcmp(mp->aaModel, "LG")) { for (i=0; inStates; i++) subValue[i] = lgPi[i]; } } } else if (p->paramId == PI_EMPIRICAL) { if (GetEmpiricalFreqs (p->relParts, p->nRelParts) == ERROR) return (ERROR); for (i=0; inStates; i++) subValue[i] = empiricalFreqs[i]; } else if (p->paramId == PI_EQUAL) { for (i=0; inStates; i++) subValue[i] = 1.0 / mp->nStates; } /* fill for the directional model of evolution for state frequencies */ else if (p->paramId == DIRPI_DIRxDIR || p->paramId == DIRPI_DIRxFIXED || p->paramId == DIRPI_FIXEDxDIR || p->paramId == DIRPI_MIX) { /* check if both Dirichlet priors are set correctly, if applicable */ if (p->paramId == DIRPI_DIRxDIR || p->paramId == DIRPI_DIRxFIXED || p->paramId == DIRPI_MIX) { if (mp->numDirParams != mp->nStates && mp->numDirParams != 0) { MrBayesPrint ("%s Mismatch between number of dirichlet parameters for the equillibrium frequencies (%d) and the number of states (%d)\n", spacer, mp->numDirParams, m->numStates); return ERROR; } } if (p->paramId == DIRPI_DIRxDIR || p->paramId == DIRPI_FIXEDxDIR || p->paramId == DIRPI_MIX) { if (mp->numDirParamsRoot != mp->nStates && mp->numDirParamsRoot != 0) { MrBayesPrint ("%s Mismatch between number of dirichlet parameters for the root frequencies (%d) and the number of states (%d)\n", spacer, mp->numDirParamsRoot, m->numStates); return ERROR; } } /* now fill in values and subvalues */ if (p->paramId == DIRPI_DIRxDIR || p->paramId == DIRPI_DIRxFIXED || p->paramId == DIRPI_MIX) { /* if user has not set dirichlet parameters, go with default */ /* overall variance equals number of states */ if (mp->numDirParams == 0) for (i=0; inStates; i++) value[i] = mp->stateFreqsDir[i] = 1.0; else for (i=0; inumStates; i++) value[i] = mp->stateFreqsDir[i]; /* now fill in subvalues for equilibrium frequencies */ for (i=0; inumStates; i++) subValue[i] = (1.0 / mp->nStates); /* and the fixed root freqs, if any */ if (p->paramId == DIRPI_DIRxFIXED) { for (i=0; inumStates; i++) subValue[i+m->numStates] = mp->rootFreqsFix[i]; } } if (p->paramId == DIRPI_DIRxDIR || p->paramId == DIRPI_FIXEDxDIR || p->paramId == DIRPI_MIX) { /* now fill in the root freqs, 2nd half of vector */ if (mp->numDirParamsRoot == 0) for (i=0; inumStates; i++) value[i+m->numStates] = mp->rootFreqsDir[i] = 1.0; else for (i=0; inumStates; i++) value[i+m->numStates] = mp->rootFreqsDir[i]; for (i=0; inumStates; i++) subValue[i+m->numStates] = (1.0 / mp->nStates); if (p->paramId == DIRPI_FIXEDxDIR) { for (i=0; inumStates; i++) subValue[i] = mp->stateFreqsFix[i]; } } } else if (p->paramId == DIRPI_FIXEDxFIXED) { for (i=0; inumStates; i++) { subValue[i] = mp->stateFreqsFix[i]; subValue[i+m->numStates] = mp->rootFreqsFix[i]; } } } else if (p->paramType == P_MIXTURE_RATES) { /* Fill in rates of site rate mixture *******************************************************************/ /* We use value array for dirichlet prior parameters. We use a flat prior, so this will be a series of 1.0 values */ for (i=0; inumRateCats; ++i) value[i] = 1.0; /* Now fill in subvalues by setting them to be equal, Note that we use rates and not rate proportions. */ for (i=0; inumRateCats; ++i) subValue[i] = 1.0; } else if (p->paramType == P_SHAPE) { /* Fill in shape values ********************************************************************************/ /* first get hyperprior */ if (p->paramId == SHAPE_UNI) { value[0] = 1.0; if (value[0] < mp->shapeUni[0] || value[0] > mp->shapeUni[1]) value[0] = mp->shapeUni[0] + (mp->shapeUni[1] - mp->shapeUni[0]) * 0.5; } else if (p->paramId == SHAPE_EXP) value[0] = 1.0; // was 100.0 else if (p->paramId == SHAPE_FIX) value[0] = mp->shapeFix; /* now fill in rates */ if (!strcmp(mp->ratesModel, "LNorm")) { if (DiscreteLogNormal (subValue, value[0], mp->numGammaCats, 1) == ERROR) return (ERROR); } else /* gamma rate */ { if (DiscreteGamma (subValue, value[0], value[0], mp->numGammaCats, 0) == ERROR) return (ERROR); } } else if (p->paramType == P_PINVAR) { /* Fill in pInvar ***************************************************************************************/ if (p->paramId == PINVAR_UNI) value[0] = 0.0; else if (p->paramId == PINVAR_FIX) value[0] = mp->pInvarFix; } else if (p->paramType == P_CORREL) { /* Fill in correlation parameter of adgamma model *******************************************************/ if (p->paramId == CORREL_UNI) value[0] = 0.0; else if (p->paramId == CORREL_FIX) value[0] = mp->adgCorrFix; /* Fill in correlation matrices */ AutodGamma (subValue, value[0], mp->numGammaCats); } else if (p->paramType == P_SWITCH) { /* Fill in switchRates for covarion model ***************************************************************/ for (j=0; j<2; j++) { if (p->paramId == SWITCH_UNI) value[j] = RandomNumber(seed) * (mp->covswitchUni[1] - mp->covswitchUni[0]) + mp->covswitchUni[0]; else if (p->paramId == SWITCH_EXP) value[j] = -(1.0/mp->covswitchExp) * log(RandomNumber(seed)); else if (p->paramId == SWITCH_FIX) value[j] = mp->covswitchFix[j]; } } else if (p->paramType == P_RATEMULT) { /* Fill in division rates *****************************************************************************/ for (j=0; jnValues; j++) { value[j] = 1.0; /* fill in more info about the divisions if this is a true rate multiplier and not a base rate */ if (p->nSubValues > 0) { /* num uncompressed chars */ subValue[j] = (modelSettings[p->relParts[j]].numUncompressedChars); /* Dirichlet parameters */ subValue[p->nValues + j] = modelParams[p->relParts[j]].ratePrDir; } } } else if (p->paramType == P_GENETREERATE) { /* Fill in division rates *****************************************************************************/ for (j=0; jnValues; j++) { value[j] = 1.0; /* Dirichlet parameters fixed to 1.0 for now; ignored if the rate is fixed */ subValue[p->nValues + j] = 1.0; /* Get number of uncompressed chars from tree */ tree = GetTreeFromIndex(j, 0, 0); subValue[j] = 0.0; for (i=0; inRelParts; i++) /* num uncompressed chars */ subValue[j] += modelSettings[tree->relParts[i]].numUncompressedChars; } } else if (p->paramType == P_SPECRATE) { /* Fill in speciation rates *****************************************************************************/ for (j=0; jnValues; j++) { if (p->paramId == SPECRATE_FIX) value[j] = mp->speciationFix; else value[j] = 0.3; } } else if (p->paramType == P_EXTRATE) { /* Fill in extinction rates *****************************************************************************/ for (j=0; jnValues; j++) { if (p->paramId == EXTRATE_FIX) value[j] = mp->extinctionFix; else value[j] = 0.2; } } else if (p->paramType == P_FOSLRATE) { /* Fill in fossilization rates */ for (j=0; jnValues; j++) { if (p->paramId == FOSLRATE_FIX) value[j] = mp->fossilizationFix; else value[j] = 0.1; } } else if (p->paramType == P_GROWTH) { /* Fill in growth rate **********************************************************************************/ if (p->paramId == GROWTH_FIX) value[0] = mp->growthFix; else value[0] = 1.0; } else if (p->paramType == P_POPSIZE) { /* Fill in population size ******************************************************************************/ for (j=0; jnValues; j++) { if (p->paramId == POPSIZE_UNI) value[j] = RandomNumber(seed) * (mp->popSizeUni[1] - mp->popSizeUni[0]) + mp->popSizeUni[0]; else if (p->paramId == POPSIZE_LOGNORMAL) value[j] = exp(mp->popSizeLognormal[0]); else if (p->paramId == POPSIZE_NORMAL) value[j] = mp->popSizeNormal[0]; else if (p->paramId == POPSIZE_GAMMA) value[j] = mp->popSizeGamma[0] / mp->popSizeGamma[1]; else if (p->paramId == POPSIZE_FIX) value[j] = mp->popSizeFix; } } else if (p->paramType == P_BMCORR) { /* Fill in correlation parameter for brownian motion ****************************************************/ if (p->paramId == BMCORR_FIX) value[0] = mp->brownCorrFix; else value[0] = 0.0; } else if (p->paramType == P_BMSIGMA) { /* Fill in sigma parameter for brownian motion **********************************************************/ if (p->paramId == BMSIGMA_FIX) value[0] = mp->brownScaleFix; else if (p->paramId == BMSIGMA_UNI) value[0] = RandomNumber(seed) * (mp->brownScaleUni[1] - mp->brownScaleUni[0]) + mp->brownScaleUni[0]; else if (p->paramId == BMSIGMA_GAMMA) value[0] = mp->brownScaleGamma[0] / mp->brownScaleGamma[1]; } else if (p->paramType == P_AAMODEL) { /* Fill in theta ****************************************************************************************/ if (p->paramId == AAMODEL_MIX) { /* amino acid model ID's AAMODEL_POISSON 0 AAMODEL_JONES 1 AAMODEL_DAY 2 AAMODEL_MTREV 3 AAMODEL_MTMAM 4 AAMODEL_WAG 5 AAMODEL_RTREV 6 AAMODEL_CPREV 7 AAMODEL_VT 8 AAMODEL_BLOSUM 9 */ /* set the amino acid model (the meaning of the numbers is defined) */ tempInt = (int)(RandomNumber(seed) * 10); value[0] = tempInt; /* we need to make certain that the aa frequencies are filled in correctly */ bs = GetParamSubVals (m->stateFreq, chn, 0); if (tempInt == AAMODEL_POISSON) { for (i=0; inStates; i++) bs[i] = (1.0 / 20.0); } else if (tempInt == AAMODEL_JONES) { for (i=0; inStates; i++) bs[i] = jonesPi[i]; } else if (tempInt == AAMODEL_DAY) { for (i=0; inStates; i++) bs[i] = dayhoffPi[i]; } else if (tempInt == AAMODEL_MTREV) { for (i=0; inStates; i++) bs[i] = mtrev24Pi[i]; } else if (tempInt == AAMODEL_MTMAM) { for (i=0; inStates; i++) bs[i] = mtmamPi[i]; } else if (tempInt == AAMODEL_WAG) { for (i=0; inStates; i++) bs[i] = wagPi[i]; } else if (tempInt == AAMODEL_RTREV) { for (i=0; inStates; i++) bs[i] = rtrevPi[i]; } else if (tempInt == AAMODEL_CPREV) { for (i=0; inStates; i++) bs[i] = cprevPi[i]; } else if (tempInt == AAMODEL_VT) { for (i=0; inStates; i++) bs[i] = vtPi[i]; } else if (tempInt == AAMODEL_BLOSUM) { for (i=0; inStates; i++) bs[i] = blosPi[i]; } for (i=0; inSubValues; i++) { subValue[i] = mp->aaModelPrProbs[i]; } } } else if (p->paramType == P_CPPRATE) { /* Fill in lambda (cpp rate) ********************************************************************************************/ if (p->paramId == CPPRATE_EXP) value[0] = -(1.0/mp->cppRateExp) * log(RandomNumber(seed)); else if (p->paramId == CPPRATE_FIX) value[0] = mp->cppRateFix; } else if (p->paramType == P_CPPMULTDEV) { /* Fill in log standard deviation (for relaxed clock rate multiplier) ***********************************************************/ if (p->paramId == CPPMULTDEV_FIX) value[0] = mp->cppMultDevFix; } else if (p->paramType == P_CPPEVENTS) { /* We fill in these when we fill in tree params **************************************************************************/ } else if (p->paramType == P_TK02VAR) { /* Fill in variance of relaxed clock lognormal **************************************************************************/ if (p->paramId == TK02VAR_UNI) value[0] = RandomNumber(seed) * (mp->tk02varUni[1] - mp->tk02varUni[0]) + mp->tk02varUni[0]; else if (p->paramId == TK02VAR_EXP) value[0] = 2.0/(mp->tk02varExp); else if (p->paramId == TK02VAR_FIX) value[0] = mp->tk02varFix; } else if (p->paramType == P_WNVAR) { /* Fill in variance of relaxed clock white noise **************************************************************************/ if (p->paramId == WNVAR_UNI) value[0] = RandomNumber(seed) * (mp->wnvarUni[1] - mp->wnvarUni[0]) + mp->wnvarUni[0]; else if (p->paramId == WNVAR_EXP) value[0] = 1.0/(mp->wnvarExp); else if (p->paramId == WNVAR_FIX) value[0] = mp->wnvarFix; } else if (p->paramType == P_ILNVAR) { /* Fill in variance of relaxed clock lognormal **************************************************************************/ if (p->paramId == ILNVAR_UNI) value[0] = RandomNumber(seed) * (mp->ilnvarUni[1] - mp->ilnvarUni[0]) + mp->ilnvarUni[0]; else if (p->paramId == ILNVAR_EXP) value[0] = 1.0/(mp->ilnvarExp); else if (p->paramId == ILNVAR_FIX) value[0] = mp->ilnvarFix; } else if (p->paramType == P_IGRVAR) { /* Fill in variance of relaxed clock gamma **************************************************************************/ if (p->paramId == IGRVAR_UNI) value[0] = RandomNumber(seed) * (mp->igrvarUni[1] - mp->igrvarUni[0]) + mp->igrvarUni[0]; else if (p->paramId == IGRVAR_EXP) value[0] = 1.0/(mp->igrvarExp); else if (p->paramId == IGRVAR_FIX) value[0] = mp->igrvarFix; } else if (p->paramType == P_MIXEDVAR) { /* Fill in variance of mixed relaxed clock **************************************************************************/ if (p->paramId == MIXEDVAR_UNI) value[0] = RandomNumber(seed) * (mp->mixedvarUni[1] - mp->mixedvarUni[0]) + mp->mixedvarUni[0]; else if (p->paramId == MIXEDVAR_EXP) value[0] = 1.0/(mp->mixedvarExp); else if (p->paramId == MIXEDVAR_FIX) value[0] = mp->mixedvarFix; } else if (p->paramType == P_TK02BRANCHRATES || p->paramType == P_WNBRANCHRATES || p->paramType == P_ILNBRANCHRATES || p->paramType == P_IGRBRANCHRATES) { /* We fill in these when we fill in tree params **************************************************************************/ } else if (p->paramType == P_MIXEDBRCHRATES) { /* initialize the mixed relaxed clock model to TK02 or ILN */ intValue[0] = (RandomNumber(seed) <0.5) ? RCL_IGR : RCL_ILN; /* We fill in the rest when we fill in tree params **************************************************************************/ } else if (p->paramType == P_CLOCKRATE) { /* Fill in base rate of molecular clock **************************************************************************/ if (p->paramId == CLOCKRATE_FIX) value[0] = mp->clockRateFix; else if (p->paramId == CLOCKRATE_NORMAL) value[0] = mp->clockRateNormal[0]; else if (p->paramId == CLOCKRATE_LOGNORMAL) value[0] = exp(mp->clockRateLognormal[0]); else if (p->paramId == CLOCKRATE_EXP) value[0] = 1.0/(mp->clockRateExp); else if (p->paramId == CLOCKRATE_GAMMA) value[0] = mp->clockRateGamma[0]/mp->clockRateGamma[1]; } } /* next param */ } /* next chain */ return NO_ERROR; } int FillRelPartsString (Param *p, char **relPartString) { int i, n, filledString; char *tempStr; int tempStrSize=50; tempStr = (char *) SafeMalloc((size_t)tempStrSize * sizeof(char)); if (!tempStr) { MrBayesPrint ("%s Problem allocating tempString (%d)\n", spacer, tempStrSize * sizeof(char)); return (ERROR); } if (numCurrentDivisions == 1) { filledString = NO; SafeStrcpy (relPartString, ""); } else { filledString = YES; if (p->nRelParts == numCurrentDivisions) { SafeStrcpy (relPartString, "{all}"); } else { SafeStrcpy (relPartString, "{"); for (i=n=0; inRelParts; i++) { n++; SafeSprintf (&tempStr, &tempStrSize, "%d", p->relParts[i] + 1); SafeStrcat (relPartString, tempStr); if (n < p->nRelParts) SafeStrcat (relPartString, ","); } SafeStrcat (relPartString, "}"); } } free (tempStr); return (filledString); } /*-------------------------------------------------------------- | | FillStdStateFreqs: fills stationary frequencies for standard data divisions of chains in range [chfrom, chto) | ---------------------------------------------------------------*/ void FillStdStateFreqs (int chfrom, int chto, RandLong *seed) { int chn, n, i, j, k, b, c, nb, index; MrBFlt *subValue, sum, symDir[MAX_STD_STATES]; Param *p; for (chn=chfrom; chnparamType != P_PI || modelParams[p->relParts[0]].dataType != STANDARD) continue; subValue = GetParamStdStateFreqs (p, chn, 0); if (p->paramId == SYMPI_EQUAL) { for (n = index = 0; n < MAX_STD_STATES-1; n++) { for (i=0; inRelParts; i++) if (modelSettings[p->relParts[i]].isTiNeeded[n] == YES) break; if (i < p->nRelParts) { for (j=0; j<(n+2); j++) { subValue[index++] = 1.0 / (n+2); } } } for (n = MAX_STD_STATES-1; n < 2*MAX_STD_STATES-3; n++) { for (i=0; inRelParts; i++) if (modelSettings[p->relParts[i]].isTiNeeded[n] == YES) break; if (i < p->nRelParts) { for (j = 0; j < n-MAX_STD_STATES+4; j++) { subValue[index++] = 1.0 / (n-MAX_STD_STATES+4); } } } } /* Deal with transition asymmetry for standard characters */ /* First, fill in stationary frequencies for beta categories if needed; */ /* discard category frequencies (assume equal) */ if (p->paramId == SYMPI_FIX || p->paramId == SYMPI_UNI || p->paramId == SYMPI_EXP || p->paramId == SYMPI_FIX_MS || p->paramId == SYMPI_UNI_MS || p->paramId == SYMPI_EXP_MS) { if (p->hasBinaryStd == YES) { nb=modelParams[p->relParts[0]].numBetaCats; BetaBreaks (p->values[0], p->values[0], subValue, nb); b = 2*nb; for (i=b-2; i>0; i-=2) { subValue[i] = subValue[i/2]; } for (i=1; ivalues[0]; for (c=0; cnSympi; c++) { /* now fill in subvalues */ DirichletRandomVariable (symDir, subValue, p->sympinStates[c], seed); sum = 0.0; for (i=0; isympinStates[c]; i++) { if (subValue[i] < 0.0001) subValue[i] = 0.0001; sum += subValue[i]; } for (i=0; irelParts[0]].nStates; i++) subValue[i] /= sum; subValue += p->sympinStates[c]; } } } /* next parameter */ } /* next chain */ } /* FillTopologySubParams: Fill subparams (brlens) for a topology */ int FillTopologySubParams (Param *param, int chn, int state, RandLong *seed) { int i,returnVal; Tree *tree, *tree1; Param *q; MrBFlt clockRate; PolyTree *sourceTree; MrBFlt minRate=0.0, maxRate=0.0; tree = GetTree (param, chn, state); for (i=1; inSubParams; i++) { q = param->subParams[i]; tree1 = GetTree (q, chn, state); if (CopyToTreeFromTree(tree1, tree) == ERROR) return (ERROR); } for (i=0; inSubParams; i++) { q = param->subParams[i]; tree = GetTree (q, chn, state); if (q->paramId == BRLENS_FIXED || q->paramId == BRLENS_CLOCK_FIXED) { if (param->paramId == TOPOLOGY_NCL_FIXED || param->paramId == TOPOLOGY_NCL_FIXED_HOMO || param->paramId == TOPOLOGY_NCL_FIXED_HETERO || param->paramId == TOPOLOGY_CL_FIXED || param->paramId == TOPOLOGY_RCL_FIXED || param->paramId == TOPOLOGY_CCL_FIXED || param->paramId == TOPOLOGY_RCCL_FIXED|| param->paramId == TOPOLOGY_FIXED) { sourceTree = AllocatePolyTree(numTaxa); CopyToPolyTreeFromPolyTree (sourceTree, userTree[modelParams[q->relParts[0]].brlensFix]); PrunePolyTree (sourceTree); ResetTipIndices (sourceTree); ResetIntNodeIndices (sourceTree); if (tree->isRooted != sourceTree->isRooted) { MrBayesPrint ("%s Cannot set fixed branch lengths because of mismatch in rootedness", spacer); FreePolyTree (sourceTree); return (ERROR); } if (CopyToTreeFromPolyTree(tree,sourceTree) == ERROR) { MrBayesPrint ("%s Problem setting fixed branch lengths", spacer); FreePolyTree (sourceTree); return (ERROR); } FreePolyTree (sourceTree); if (tree->isClock == YES && IsClockSatisfied(tree, 0.001) == NO) { MrBayesPrint ("%s Fixed branch lengths do not satisfy clock", spacer); return (ERROR); } if (tree->isCalibrated == YES && IsCalibratedClockSatisfied (tree,&minRate,&maxRate, 0.001) == NO) { MrBayesPrint ("%s Fixed branch lengths do not satisfy calibrations", spacer); return (ERROR); } if (tree->isCalibrated == YES && !strcmp(modelParams[tree->relParts[0]].clockRatePr, "Fixed")) { clockRate = modelParams[tree->relParts[0]].clockRateFix; if ((clockRate < minRate && AreDoublesEqual (clockRate, minRate , 0.001) == NO) || (clockRate > maxRate && AreDoublesEqual (clockRate, maxRate , 0.001) == NO)) { MrBayesPrint ("%s Fixed branch lengths do not satisfy fixed clockrate", spacer); return (ERROR); } } tree->fromUserTree=YES; returnVal = NO_ERROR; } else { MrBayesPrint ("%s Fixed branch lengths can only be used for a fixed topology\n", spacer); return (ERROR); } } else if (tree->isCalibrated == YES || (tree->isClock == YES && (!strcmp(modelParams[tree->relParts[0]].clockPr,"Uniform") || !strcmp(modelParams[tree->relParts[0]].clockPr,"Birthdeath") || !strcmp(modelParams[tree->relParts[0]].clockPr,"Fossilization")))) { assert (tree->isClock == YES); clockRate = *GetParamVals(modelSettings[tree->relParts[0]].clockRate, chn, state); returnVal = InitCalibratedBrlens (tree, clockRate, seed); if (IsClockSatisfied (tree, 0.001) == NO) { MrBayesPrint ("%s Branch lengths of the tree does not satisfy clock\n", spacer); return (ERROR); } tree->fromUserTree=NO; } else if (tree->isClock == YES) returnVal = InitClockBrlens (tree); else returnVal = InitBrlens (tree, 0.02); if (returnVal == ERROR) return (ERROR); if (FillBrlensSubParams (q, chn, state) == ERROR) return (ERROR); } return (NO_ERROR); } /* FillBrlensSubParams: Fill any relaxed clock subparams of a brlens param */ int FillBrlensSubParams (Param *param, int chn, int state) { int i, j, *nEvents; MrBFlt *brlen, *branchRate, **position, **rateMult; Tree *tree; TreeNode *p; Param *q; tree = GetTree (param, chn, state); for (i=0; inSubParams; i++) { q = param->subParams[i]; if (q->paramType == P_CPPEVENTS) { nEvents = q->nEvents[2*chn+state]; position = q->position[2*chn+state]; rateMult = q->rateMult[2*chn+state]; brlen = GetParamSubVals (q, chn, state); for (j=0; jnNodes-1; j++) { p = tree->allDownPass[j]; if (nEvents[p->index] != 0) { free (position[p->index]); position[p->index] = NULL; free (rateMult[p->index]); rateMult[p->index] = NULL; } nEvents[p->index] = 0; // assert (j==tree->nNodes-2 || fabs(p->length - (p->anc->nodeDepth - p->nodeDepth)) < 0.000001); brlen[p->index] = p->length; } } else if (q->paramType == P_TK02BRANCHRATES || q->paramType == P_WNBRANCHRATES || q->paramType == P_IGRBRANCHRATES || q->paramType == P_ILNBRANCHRATES || q->paramType == P_MIXEDBRCHRATES) { branchRate = GetParamVals (q, chn, state); brlen = GetParamSubVals (q, chn, state); for (j=0; jnNodes-1; j++) { p = tree->allDownPass[j]; branchRate[p->index] = 1.0; brlen[p->index] = p->length; } } } return (NO_ERROR); } /* Note: In PruneConstraintPartitions() we can not rely on specific rooting of a tree since different partitions may theoretically have different clock models, while constraints apply to all partitions/trees */ int PruneConstraintPartitions (void) { int i, j, constraintId, nLongsNeeded; nLongsNeeded = (numLocalTaxa - 1) / nBitsInALong + 1; for (constraintId=0; constraintIdcheckConstraints == NO) return YES; /* get some handy numbers */ numTaxa = t->nNodes - t->nIntNodes - (t->isRooted == YES ? 1 : 0); nLongsNeeded = (numTaxa - 1) / nBitsInALong + 1; if (t->bitsets == NULL) { AllocateTreePartitions(t); } else { ResetTreePartitions(t); /*Inefficient function, rewrite faster version*/ } # if defined (DEBUG_CONSTRAINTS) for (i=0; inIntNodes; i++) { p = t->intDownPass[i]; if (p->isLocked == YES) { if (IsUnionEqThird (definedConstraintPruned[p->lockID], definedConstraintPruned[p->lockID], p->partition, nLongsNeeded) == NO && IsUnionEqThird (definedConstraintTwoPruned[p->lockID], definedConstraintTwoPruned[p->lockID], p->partition, nLongsNeeded) == NO) { printf ("DEBUG ERROR: Locked node does not represent right partition. \n"); return ABORT; } else { locks_count++; } } } if (locks_count != t->nLocks) { printf ("DEBUG ERROR: locks_count:%d should be locks_count:%d\n", locks_count, t->nLocks); return ABORT; } # endif for (k=0; kconstraints[k] == YES && definedConstraintsType[k] == HARD) { if (t->isRooted == YES) { CheckFirst = YES; CheckSecond = NO; } else { /*exactly one of next two will be YES*/ CheckFirst = IsBitSet(localOutGroup, definedConstraintPruned[k])==YES ? NO : YES; CheckSecond = IsBitSet(localOutGroup, definedConstraintTwoPruned[k])==YES ? NO : YES; assert ((CheckFirst^CheckSecond)==1); } for (i=0; inIntNodes; i++) { p = t->intDownPass[i]; if (p->anc != NULL) { if (CheckFirst==YES && IsPartNested(definedConstraintPruned[k], p->partition, nLongsNeeded) && IsPartNested(p->partition,definedConstraintPruned[k], nLongsNeeded)) break; if (CheckSecond==YES && IsPartNested(definedConstraintTwoPruned[k], p->partition, nLongsNeeded) && IsPartNested(p->partition, definedConstraintTwoPruned[k], nLongsNeeded)) break; } } if (i==t->nIntNodes) { printf ("DEBUG ERROR: Hard constraint is not satisfied. \n"); return ABORT; //assert (0); } } # endif if (t->constraints[k] == NO || definedConstraintsType[k] == HARD) continue; if (definedConstraintsType[k] == PARTIAL) { /* alternative way if (t->isRooted == NO && !IsBitSet(localOutGroup, definedConstraintPruned[k])) { m = FirstTaxonInPartition (constraintPartition, nLongsNeeded); for (i=0; t->nodes[i].index != m; i++) ; p = &t->nodes[i]; p=p->anc; while (!IsPartNested(definedConstraintPruned[k], p->partition, nLongsNeeded)) p=p->anc; if (IsSectionEmpty(definedConstraintTwoPruned[k], p->partition, nLongsNeeded)) continue; } */ if (t->isRooted == YES) { CheckFirst = YES; CheckSecond = NO; /* In rooted case even if we have a node with partition fully containing second set and not containing the first set it would not satisfy the constraint */ } else { if (NumBits(definedConstraintPruned[k], nLongsNeeded) == 1 || NumBits(definedConstraintTwoPruned[k], nLongsNeeded) == 1) continue; /*one or two of the next two statements will be YES*/ CheckFirst = IsBitSet(localOutGroup, definedConstraintPruned[k])==YES ? NO : YES; CheckSecond = IsBitSet(localOutGroup, definedConstraintTwoPruned[k])==YES ? NO : YES; assert ((CheckFirst|CheckSecond)==1); } for (i=0; inIntNodes; i++) { p = t->intDownPass[i]; if (p->anc != NULL) { if (CheckFirst== YES && IsPartNested(definedConstraintPruned[k], p->partition, nLongsNeeded) && IsSectionEmpty(definedConstraintTwoPruned[k], p->partition, nLongsNeeded)) break; if (CheckSecond==YES && IsPartNested(definedConstraintTwoPruned[k], p->partition, nLongsNeeded) && IsSectionEmpty(definedConstraintPruned[k], p->partition, nLongsNeeded)) break; } } if (i==t->nIntNodes) return NO; } else { assert (definedConstraintsType[k] == NEGATIVE); if (t->isRooted == YES) { CheckFirst = YES; CheckSecond = NO; } else { /*exactly one of next two will be YES*/ CheckFirst = IsBitSet(localOutGroup, definedConstraintPruned[k])==YES ? NO : YES; CheckSecond = IsBitSet(localOutGroup, definedConstraintTwoPruned[k])==YES ? NO : YES; assert ((CheckFirst^CheckSecond)==1); } for (i=0; inIntNodes; i++) { p = t->intDownPass[i]; if (p->anc != NULL) { if (CheckFirst==YES && AreBitfieldsEqual(definedConstraintPruned[k], p->partition, nLongsNeeded)) break; if (CheckSecond==YES && AreBitfieldsEqual(definedConstraintTwoPruned[k], p->partition, nLongsNeeded)) break; } } if (i!=t->nIntNodes) return NO; } } return YES; } /*------------------------------------------------------------------ | | FillTreeParams: Fill in trees and branch lengths | | Note: Should be run after FillNormalParams because | clockrate needs to be set if calibrated tree needs | to be filled. | ------------------------------------------------------------------*/ int FillTreeParams (RandLong *seed, int fromChain, int toChain) { int i, k, chn, nTaxa, tmp; Param *p, *q; Tree *tree; PolyTree *constraintTree; PolyTree *constraintTreeRef; if (PruneConstraintPartitions() == ERROR) return ERROR; /* Build starting trees for state 0 */ for (chn=fromChain; chnparamType == P_TOPOLOGY) { q = p->subParams[0]; tree = GetTree (q, chn, 0); if (tree->isRooted == YES) nTaxa = tree->nNodes - tree->nIntNodes - 1; else nTaxa = tree->nNodes - tree->nIntNodes; /* fixed topology */ if (p->paramId == TOPOLOGY_NCL_FIXED || p->paramId == TOPOLOGY_RNCL_FIXED || p->paramId == TOPOLOGY_NCL_FIXED_HOMO || p->paramId == TOPOLOGY_NCL_FIXED_HETERO || p->paramId == TOPOLOGY_CL_FIXED || p->paramId == TOPOLOGY_RCL_FIXED || p->paramId == TOPOLOGY_CCL_FIXED || p->paramId == TOPOLOGY_RCCL_FIXED|| p->paramId == TOPOLOGY_FIXED || p->paramId == TOPOLOGY_PARSIMONY_FIXED) { constraintTree = AllocatePolyTree (numTaxa); CopyToPolyTreeFromPolyTree (constraintTree, userTree[modelParams[p->relParts[0]].topologyFix]); PrunePolyTree (constraintTree); ResetTipIndices(constraintTree); ResetIntNodeIndices(constraintTree); RandResolve (NULL, constraintTree, seed, constraintTree->isRooted); if (tree->nIntNodes != constraintTree->nIntNodes) { if (tree->isRooted != constraintTree->isRooted) { MrBayesPrint ("%s Could not fix topology because user tree '%s' differs in rootedness with the model tree.\n", spacer, userTree[modelParams[p->relParts[0]].topologyFix]->name); MrBayesPrint ("%s The user tree %s is%srooted, while expected model tree is%srooted.\n", spacer, userTree[modelParams[p->relParts[0]].topologyFix]->name, (constraintTree->isRooted?" ":" not "), (tree->isRooted?" ":" not ")); MrBayesPrint ("%s Check brlenspr is set correctly before fixing topology.\n", spacer); } else MrBayesPrint ("%s Could not fix topology because user tree '%s' is not fully resolved.\n", spacer, userTree[modelParams[p->relParts[0]].topologyFix]->name); FreePolyTree (constraintTree); return (ERROR); } if (CopyToTreeFromPolyTree(tree, constraintTree) == ERROR) { MrBayesPrint ("%s Could not fix topology according to user tree '%s'\n", spacer, userTree[modelParams[p->relParts[0]].topologyFix]->name); FreePolyTree (constraintTree); return (ERROR); } FreePolyTree (constraintTree); } /* constrained topology */ else if (tree->nConstraints > 0) { constraintTreeRef = AllocatePolyTree (nTaxa); if (!constraintTreeRef) return (ERROR); if (BuildConstraintTree (tree, constraintTreeRef, localTaxonNames) == ERROR) { FreePolyTree (constraintTreeRef); return (ERROR); } if (AllocatePolyTreePartitions (constraintTreeRef) == ERROR) return (ERROR); constraintTree = AllocatePolyTree (nTaxa); if (!constraintTree) return (ERROR); if (AllocatePolyTreePartitions (constraintTree) == ERROR) return (ERROR); for (i=0;i<100;i++) { CopyToPolyTreeFromPolyTree(constraintTree,constraintTreeRef); tmp = RandResolve (tree, constraintTree, &globalSeed, tree->isRooted); if (tmp != NO_ERROR) { if (tmp == ERROR) { FreePolyTree (constraintTree); return (ERROR); } else { assert (tmp == ABORT); continue; } } CopyToTreeFromPolyTree(tree, constraintTree); if (DoesTreeSatisfyConstraints(tree)==YES) break; } # if defined (DEBUG_CONSTRAINTS) if (theTree->checkConstraints == YES && CheckConstraints (tree) == ERROR) { printf ("Error in constraints of starting tree\n"); getchar(); } # endif FreePolyTree (constraintTree); FreePolyTree (constraintTreeRef); if (i==100) { MrBayesPrint ("%s Could not build a starting tree satisfying all constraints\n", spacer); return (ERROR); } } /* random topology */ else { if (tree->isRooted == YES) { if (BuildRandomRTopology (tree, &globalSeed) == ERROR) return (ERROR); } else { if (BuildRandomUTopology (tree, &globalSeed) == ERROR) return (ERROR); if (MoveCalculationRoot (tree, localOutGroup) == ERROR) return (ERROR); } } if (LabelTree (tree, localTaxonNames) == ERROR) return (ERROR); if (q == p) continue; /* this is a parsimony tree without branch lengths */ if (InitializeTreeCalibrations (tree) == ERROR) return (ERROR); if (FillTopologySubParams(p, chn, 0, seed)== ERROR) return (ERROR); } } } if (numTopologies > 1 && !strcmp(modelParams[0].topologyPr,"Speciestree")) { if (FillSpeciesTreeParams(seed, fromChain, toChain) == ERROR) return (ERROR); } return (NO_ERROR); } void FreeCppEvents (Param *p) { int i, j; if (p->paramType != P_CPPEVENTS) return; if (p->nEvents != NULL) { free (p->nEvents[0]); free (p->nEvents); for (i=0; iposition[2*i][j]); free (p->rateMult[2*i][j]); free (p->position[2*i+1][j]); free (p->rateMult[2*i+1][j]); } } free (p->position[0]); free (p->position); free (p->rateMult[0]); free (p->rateMult); p->nEvents = NULL; p->position = NULL; p->rateMult = NULL; } } int FreeModel (void) { int i; Param *p; if (memAllocs[ALLOC_MODEL] == YES) { for (i = 0; i < numCurrentDivisions; i++) free (modelParams[i].activeConstraints); free (modelParams); free (modelSettings); memAllocs[ALLOC_MODEL] = NO; } if (memAllocs[ALLOC_MOVES] == YES) { for (i = 0; i < numApplicableMoves; i++) FreeMove (moves[i]); SAFEFREE (moves); numApplicableMoves = 0; memAllocs[ALLOC_MOVES] = NO; } if (memAllocs[ALLOC_COMPMATRIX] == YES) { free (compMatrix); memAllocs[ALLOC_COMPMATRIX] = NO; } if (memAllocs[ALLOC_NUMSITESOFPAT] == YES) { free (numSitesOfPat); memAllocs[ALLOC_NUMSITESOFPAT] = NO; } if (memAllocs[ALLOC_COMPCOLPOS] == YES) { free (compColPos); memAllocs[ALLOC_COMPCOLPOS] = NO; } if (memAllocs[ALLOC_COMPCHARPOS] == YES) { free (compCharPos); memAllocs[ALLOC_COMPCHARPOS] = NO; } if (memAllocs[ALLOC_ORIGCHAR] == YES) { free (origChar); memAllocs[ALLOC_ORIGCHAR] = NO; } if (memAllocs[ALLOC_STDTYPE] == YES) { free (stdType); memAllocs[ALLOC_STDTYPE] = NO; } if (memAllocs[ALLOC_STDSTATEFREQS] == YES) { SAFEFREE (stdStateFreqs); memAllocs[ALLOC_STDSTATEFREQS] = NO; } if (memAllocs[ALLOC_PARAMVALUES] == YES) { for (i = 0; i < numParams; i++) { p = ¶ms[i]; if (p->paramType == P_CPPEVENTS) FreeCppEvents (p); } SAFEFREE (paramValues); SAFEFREE (intValues); paramValsRowSize = intValsRowSize = 0; memAllocs[ALLOC_PARAMVALUES] = NO; } if (memAllocs[ALLOC_PARAMS] == YES) { for (i = 0; i < numParams; i++) { SAFEFREE (params[i].name); if (params[i].paramHeader) SAFEFREE (params[i].paramHeader); } SAFEFREE (params); SAFEFREE (relevantParts); numParams = 0; memAllocs[ALLOC_PARAMS] = NO; } if (memAllocs[ALLOC_MCMCTREES] == YES) { /* mcmcTree holds numTrees * 2 * numGlobalChains pointers; free each tree. Note: params[] is already freed above, so we iterate the flat array directly. */ for (i = 0; i < numTrees * 2 * numGlobalChains; i++) FreeTree (mcmcTree[i]); SAFEFREE (mcmcTree); SAFEFREE (subParamPtrs); memAllocs[ALLOC_MCMCTREES] = NO; } if (memAllocs[ALLOC_SYMPIINDEX] == YES) { free (sympiIndex); memAllocs[ALLOC_SYMPIINDEX] = NO; } if (memAllocs[ALLOC_LOCTAXANAMES] == YES) { free (localTaxonNames); memAllocs[ALLOC_LOCTAXANAMES] = NO; } if (memAllocs[ALLOC_LOCALTAXONCALIBRATION] == YES) { free (localTaxonCalibration); memAllocs[ALLOC_LOCALTAXONCALIBRATION] = NO; } return (NO_ERROR); } void FreeMove (MCMCMove *mv) { free (mv->tuningParam[0]); free (mv->tuningParam); free (mv->relProposalProb); free (mv->nAccepted); free (mv->name); free (mv); } /* Compute empirical state freq are return it in global array empiricalFreqs[] */ int GetEmpiricalFreqs (int *relParts, int nRelParts) { int i, j, k, m, n, thePartition, nuc[20], ns, temp, isDNA, isProtein, firstRel; MrBFlt freqN[20], sum, sumN[20]/*, rawCounts[20]*/; isDNA = isProtein = NO; ns = 0; firstRel = 0; for (i=0; idataType == DNA || m->dataType == RNA) { if (m->nucModelId == NUCMODEL_DOUBLET) n *= 2; else if (m->nucModelId == NUCMODEL_CODON) n *= 3; } m->numUncompressedChars = n; } return (NO_ERROR); } int *GetParamIntVals (Param *parm, int chain, int state) { return parm->intValues + (2 * chain + state) * intValsRowSize; } MrBFlt *GetParamStdStateFreqs (Param *parm, int chain, int state) { return parm->stdStateFreqs + (2 * chain + state) * stdStateFreqsRowSize; } MrBFlt *GetParamSubVals (Param *parm, int chain, int state) { return parm->subValues + (2 * chain + state) * paramValsRowSize; } MrBFlt *GetParamVals (Param *parm, int chain, int state) { return parm->values + (2 * chain + state) * paramValsRowSize; } void GetPossibleAAs (int aaCode, int aa[]) { int m; for (m=0; m<20; m++) aa[m] = 0; if (aaCode > 0 && aaCode <= 20) aa[aaCode-1] = 1; else { for (m=0; m<20; m++) aa[m] = 1; } # if 0 printf ("%2d -- ", aaCode); for (m=0; m<20; m++) printf ("%d", aa[m]); printf ("\n"); # endif } void GetPossibleNucs (int nucCode, int nuc[]) { if (nucCode == 1) { nuc[0] = 1; nuc[1] = 0; nuc[2] = 0; nuc[3] = 0; } else if (nucCode == 2) { nuc[0] = 0; nuc[1] = 1; nuc[2] = 0; nuc[3] = 0; } else if (nucCode == 3) { nuc[0] = 1; nuc[1] = 1; nuc[2] = 0; nuc[3] = 0; } else if (nucCode == 4) { nuc[0] = 0; nuc[1] = 0; nuc[2] = 1; nuc[3] = 0; } else if (nucCode == 5) { nuc[0] = 1; nuc[1] = 0; nuc[2] = 1; nuc[3] = 0; } else if (nucCode == 6) { nuc[0] = 0; nuc[1] = 1; nuc[2] = 1; nuc[3] = 0; } else if (nucCode == 7) { nuc[0] = 1; nuc[1] = 1; nuc[2] = 1; nuc[3] = 0; } else if (nucCode == 8) { nuc[0] = 0; nuc[1] = 0; nuc[2] = 0; nuc[3] = 1; } else if (nucCode == 9) { nuc[0] = 1; nuc[1] = 0; nuc[2] = 0; nuc[3] = 1; } else if (nucCode == 10) { nuc[0] = 0; nuc[1] = 1; nuc[2] = 0; nuc[3] = 1; } else if (nucCode == 11) { nuc[0] = 1; nuc[1] = 1; nuc[2] = 0; nuc[3] = 1; } else if (nucCode == 12) { nuc[0] = 0; nuc[1] = 0; nuc[2] = 1; nuc[3] = 1; } else if (nucCode == 13) { nuc[0] = 1; nuc[1] = 0; nuc[2] = 1; nuc[3] = 1; } else if (nucCode == 14) { nuc[0] = 0; nuc[1] = 1; nuc[2] = 1; nuc[3] = 1; } else { nuc[0] = 1; nuc[1] = 1; nuc[2] = 1; nuc[3] = 1; } } void GetPossibleRestrictionSites (int resSiteCode, int *sites) { int m; for (m=0; m<2; m++) sites[m] = 0; if (resSiteCode == 1) sites[0] = 1; else if (resSiteCode == 2) sites[1] = 1; else sites[0] = sites[1] = 1; # if 0 printf ("%2d -- ", aaCode); for (m=0; m<20; m++) printf ("%d", aa[m]); printf ("\n"); # endif } Tree *GetTree (Param *parm, int chain, int state) { return mcmcTree[parm->treeIndex + ((2 * chain + state) * numTrees)]; } Tree *GetTreeFromIndex (int index, int chain, int state) { return mcmcTree[index + ((2 * chain + state) * numTrees)]; } /*----------------------------------------------------------- | | GetUserTreeFromName: Do case-insensitive search for user | tree, return index if match, -1 and ERROR if error | ------------------------------------------------------------*/ int GetUserTreeFromName (int *index, char *treeName) { int i, j, k, nMatches; char localName[100], temp[100]; (*index) = -1; /* appropriate return if no match */ if ((int)strlen(treeName) > 99) { MrBayesPrint ("%s Too many characters in tree name\n", spacer); return (ERROR); } strcpy (localName, treeName); for (i=0; i<(int)strlen(localName); i++) localName[i] = tolower(localName[i]); nMatches = j = 0; for (i=0; iname); for (k=0; k<(int)strlen(temp); k++) temp[k] = tolower(temp[k]); if (strcmp(localName,temp) == 0) { j = i; nMatches++; } } if (nMatches==0) { for (i=0; iname); for (k=0; k<(int)strlen(temp); k++) temp[k] = tolower(temp[k]); if (strncmp(localName,temp,strlen(localName)) == 0) { j = i; nMatches++; } } } if (nMatches == 0) { MrBayesPrint ("%s Could not find tree '%s'\n", spacer, localName); return (ERROR); } else if (nMatches > 1) { MrBayesPrint ("%s Several trees matched the abbreviated name '%s'\n", spacer, localName); return (ERROR); } else { (*index) = j; return (NO_ERROR); } } /*---------------------------------------------------------------------- | | InitializeChainTrees: 'Constructor' for chain trees | -----------------------------------------------------------------------*/ int InitializeChainTrees (Param *p, int from, int to, int isRooted) { int i, st, isCalibrated, isClock, nTaxa; Tree *tree, **treeHandle; Model *mp; mp = &modelParams[p->relParts[0]]; if (p->paramType == P_SPECIESTREE) nTaxa = numSpecies; else nTaxa = numLocalTaxa; /* figure out whether the trees are clock */ if (!strcmp (mp->brlensPr, "Clock")) isClock = YES; else isClock = NO; /* figure out whether the trees are calibrated */ if (!strcmp (mp->brlensPr, "Clock") && (strcmp (mp->nodeAgePr, "Calibrated") == 0 || strcmp (mp->clockRatePr, "Fixed") != 0 || (strcmp (mp->clockRatePr, "Fixed") == 0 && AreDoublesEqual (mp->clockRateFix, 1.0, 1E-6) == NO))) isCalibrated = YES; else isCalibrated = NO; /* allocate space for and construct the trees */ /* NOTE: The memory allocation scheme used here must match GetTree and GetTreeFromIndex */ for (i = from; i < to; i++) { treeHandle = mcmcTree + p->treeIndex + 2 * i * numTrees; if (*treeHandle) free (*treeHandle); if ((*treeHandle = AllocateTree (nTaxa)) == NULL) { MrBayesPrint ("%s Problem allocating MCMC trees\n", spacer); return (ERROR); } treeHandle = mcmcTree + p->treeIndex + (2 * i + 1) * numTrees; if (*treeHandle) free (*treeHandle); if ((*treeHandle = AllocateTree (nTaxa)) == NULL) { MrBayesPrint ("%s Problem allocating MCMC trees\n", spacer); return (ERROR); } } /* initialize the trees */ for (i = from; i < to; i++) { for (st = 0; st < 2; st++) { tree = GetTree (p, i, st); if (numTrees > 1) sprintf (tree->name, "mcmc.tree%d_%d", p->treeIndex + 1, i + 1); else /* if (numTrees == 1) */ sprintf (tree->name, "mcmc.tree_%d", i + 1); tree->nRelParts = p->nRelParts; tree->relParts = p->relParts; tree->isRooted = isRooted; tree->isClock = isClock; tree->isCalibrated = isCalibrated; if (p->paramType == P_SPECIESTREE) { tree->nNodes = 2 * numSpecies; tree->nIntNodes = numSpecies - 1; } else if (tree->isRooted == YES) { tree->nNodes = 2 * numLocalTaxa; tree->nIntNodes = numLocalTaxa - 1; } else /* if (tree->isRooted == NO) */ { tree->nNodes = 2 * numLocalTaxa - 2; tree->nIntNodes = numLocalTaxa - 2; } if (p->checkConstraints == YES) { tree->checkConstraints = YES; tree->nLocks = NumInformativeHardConstraints (mp); tree->nConstraints = mp->numActiveConstraints; /* nConstraints is number of constraints to check */ tree->constraints = mp->activeConstraints; } else { tree->checkConstraints = NO; tree->nConstraints = tree->nLocks = 0; tree->constraints = NULL; } } } return (NO_ERROR); } int InitializeLinks (void) { int i, j; linkNum = 0; for (i=0; iisCalibrated == NO) return (NO_ERROR); /* Set tip calibrations */ for (i=0; inNodes; i++) { p = t->allDownPass[i]; if (p->left == NULL && p->right == NULL && localTaxonCalibration[p->index]->prior != unconstrained) { p->isDated = YES; p->calibration = localTaxonCalibration[p->index]; p->age = p->calibration->min; } else if (p->left == NULL && p->right == NULL) { p->isDated = NO; p->calibration = NULL; p->age = -1.0; } } /* Initialize interior calibrations */ if (CheckSetConstraints(t) == ERROR) return (ERROR); return (NO_ERROR); } int IsApplicable (Param *param) { if (param == NULL) return NO; return YES; } int IsApplicable_ThreeTaxaOrMore (Param *param) { if (param->paramType != P_TOPOLOGY ) return NO; if (LargestMovableSubtree (param) >= 3) return YES; else return NO; } int IsApplicable_FourTaxaOrMore (Param *param) { if (param->paramType != P_TOPOLOGY ) return NO; if (LargestMovableSubtree (param) >= 4) return YES; else return NO; } int IsApplicable_FiveTaxaOrMore (Param *param) { if (param->paramType != P_TOPOLOGY ) return NO; if (LargestMovableSubtree (param) >= 5) return YES; else return NO; } int IsApplicable_TreeAgeMove (Param *param) { Tree *t; TreeNode *p; if (param == NULL) return NO; if (param->paramType != P_BRLENS) return NO; t = GetTree (param, 0, 0); p = t->root->left; if (p->isDated == NO) return NO; if (p->calibration->prior == fixed) return NO; else return YES; } int IsApplicable_AncestralFossil (Param *param) { ModelParams *mp = &modelParams[param->relParts[0]]; if (!strcmp(mp->sampleStrat, "FossilTip")) return NO; else if (mp->fossilizationFix == 0.0) return NO; else /* fossils may be ancestors of other fossils or extant species */ return YES; } int IsModelSame (int whichParam, int part1, int part2, int *isApplic1, int *isApplic2) { int i, isSame, isFirstNucleotide, isSecondNucleotide, isFirstProtein, isSecondProtein, nDiff, temp1, temp2; isSame = YES; *isApplic1 = YES; *isApplic2 = YES; /* We cannot rely on SetModelInfo being called first so we need to be smart in figuring out model data type by looking at several model params */ isFirstNucleotide = isSecondNucleotide = NO; if ((modelParams[part1].dataType == DNA || modelParams[part1].dataType == RNA) && strcmp(modelParams[part1].nucModel,"Protein") != 0) isFirstNucleotide = YES; if ((modelParams[part2].dataType == DNA || modelParams[part2].dataType == RNA) && strcmp(modelParams[part2].nucModel,"Protein") != 0) isSecondNucleotide = YES; isFirstProtein = isSecondProtein = NO; if (modelParams[part1].dataType == PROTEIN || ((modelParams[part1].dataType == DNA || modelParams[part1].dataType == RNA) && !strcmp(modelParams[part1].nucModel,"Protein"))) isFirstProtein = YES; if (modelParams[part2].dataType == PROTEIN || ((modelParams[part2].dataType == DNA || modelParams[part2].dataType == RNA) && !strcmp(modelParams[part2].nucModel,"Protein"))) isSecondProtein = YES; if (whichParam == P_TRATIO) { /* Check the ti/tv rate ratio for partitions 1 and 2. */ /* Check if the model is parsimony for either partition */ if (!strcmp(modelParams[part1].parsModel, "Yes")) *isApplic1 = NO; /* part1 has a parsimony model and tratio does not apply */ if (!strcmp(modelParams[part2].parsModel, "Yes")) *isApplic2 = NO; /* part2 has a parsimony model and tratio does not apply */ /* Check that the data are nucleotide for both partitions 1 and 2 */ if (isFirstNucleotide == NO) *isApplic1 = NO; /* part1 is not nucleotide data so tratio does not apply */ if (isSecondNucleotide == NO) *isApplic2 = NO; /* part2 is not nucleotide data so tratio does not apply */ /* check that nst=2 for both partitions */ if (strcmp(modelParams[part1].nst, "2")) *isApplic1 = NO; /* part1 does not have nst=2 and tratio does not apply */ if (strcmp(modelParams[part2].nst, "2")) *isApplic2 = NO; /* part2 does not have nst=2 and tratio does not apply */ /* Check if part1 & part2 are restriction */ if (modelParams[part1].dataType == RESTRICTION) *isApplic1 = NO; if (modelParams[part2].dataType == RESTRICTION) *isApplic2 = NO; /* If Nst = 2 for both part1 and part2, we now need to check if the prior is the same for both. */ if (!strcmp(modelParams[part1].tRatioPr,"Beta") && !strcmp(modelParams[part2].tRatioPr,"Beta")) { if (AreDoublesEqual (modelParams[part1].tRatioDir[0], modelParams[part2].tRatioDir[0], (MrBFlt) 0.00001) == NO) isSame = NO; if (AreDoublesEqual (modelParams[part1].tRatioDir[1], modelParams[part2].tRatioDir[1], (MrBFlt) 0.00001) == NO) isSame = NO; } else if (!strcmp(modelParams[part1].tRatioPr,"Fixed") && !strcmp(modelParams[part2].tRatioPr,"Fixed")) { if (AreDoublesEqual (modelParams[part1].tRatioFix, modelParams[part2].tRatioFix, (MrBFlt) 0.00001) == NO) isSame = NO; } else isSame = NO; /* the priors are not the same, so we cannot set the parameter to be equal for both partitions */ /* Check to see if tratio is inapplicable for either partition. */ if ((*isApplic1) == NO || (*isApplic2) == NO) isSame = NO; /* if tratio is inapplicable for either partition, then the parameter cannot be the same */ } else if (whichParam == P_REVMAT) { /* Check the GTR rates for partitions 1 and 2. */ /* Check if the model is parsimony for either partition */ if (!strcmp(modelParams[part1].parsModel, "Yes")) *isApplic1 = NO; /* part1 has a parsimony model and GTR rates do not apply */ if (!strcmp(modelParams[part2].parsModel, "Yes")) *isApplic2 = NO; /* part2 has a parsimony model and GTR rates do not apply */ /* Check that the data are nucleotide or protein for both partitions 1 and 2 */ if (isFirstNucleotide == NO && isFirstProtein == NO) *isApplic1 = NO; /* part1 is not nucleotide or protein data so GTR rates do not apply */ if (isSecondNucleotide == NO && isSecondProtein == NO) *isApplic2 = NO; /* part2 is not nucleotide or protein data so GTR rates do not apply */ /* check that nst=6 or mixed for both partitions if nucleotide */ if (isFirstNucleotide == YES && strcmp(modelParams[part1].nst, "6") && strcmp(modelParams[part1].nst, "Mixed")) *isApplic1 = NO; /* part1 does not have nst=6/Mixed and GTR rates do not apply */ if (isSecondNucleotide == YES && strcmp(modelParams[part2].nst, "6") && strcmp(modelParams[part2].nst, "Mixed")) *isApplic2 = NO; /* part2 does not have nst=6/Mixed and GTR rates do not apply */ /* check that model is GTR for both partitions if protein */ if (isFirstProtein == YES && (strcmp(modelParams[part1].aaModel,"Gtr")!=0 || strcmp(modelParams[part1].aaModelPr,"Fixed")!=0)) *isApplic1 = NO; if (isSecondProtein == YES && (strcmp(modelParams[part2].aaModel,"Gtr")!=0 || strcmp(modelParams[part2].aaModelPr,"Fixed")!=0)) *isApplic2 = NO; /* check that data type is the same for both partitions */ if (isFirstNucleotide == YES && isSecondNucleotide == NO) isSame = NO; if (isFirstProtein == YES && isSecondProtein == NO) isSame = NO; /* GTR applies to both part1 and part2. We now need to check if the prior is the same for both. */ if (isFirstNucleotide == YES) { if (strcmp(modelParams[part1].nst, modelParams[part2].nst) != 0) isSame = NO; if (!strcmp(modelParams[part1].nst,"Mixed")) { if (AreDoublesEqual (modelParams[part1].revMatSymDir, modelParams[part2].revMatSymDir, (MrBFlt) 0.00001) == NO) isSame = NO; } else if (!strcmp(modelParams[part1].nst,"6") && !strcmp(modelParams[part1].revMatPr,"Dirichlet") && !strcmp(modelParams[part2].revMatPr,"Dirichlet")) { for (i=0; i<6; i++) { if (AreDoublesEqual (modelParams[part1].revMatDir[i], modelParams[part2].revMatDir[i], (MrBFlt) 0.00001) == NO) isSame = NO; } } else if (!strcmp(modelParams[part1].nst,"6") && !strcmp(modelParams[part1].revMatPr,"Fixed") && !strcmp(modelParams[part2].revMatPr,"Fixed")) { for (i=0; i<6; i++) { if (AreDoublesEqual (modelParams[part1].revMatFix[i], modelParams[part2].revMatFix[i], (MrBFlt) 0.00001) == NO) isSame = NO; } } else isSame = NO; /* the priors are not the same, so we cannot set the parameter to be equal for both partitions */ } else /* if (isFirstProtein == YES) */ { if (!strcmp(modelParams[part1].aaRevMatPr,"Dirichlet") && !strcmp(modelParams[part2].aaRevMatPr,"Dirichlet")) { for (i=0; i<190; i++) { if (AreDoublesEqual (modelParams[part1].aaRevMatDir[i], modelParams[part2].aaRevMatDir[i], (MrBFlt) 0.00001) == NO) isSame = NO; } } else if (!strcmp(modelParams[part1].aaRevMatPr,"Fixed") && !strcmp(modelParams[part2].aaRevMatPr,"Fixed")) { for (i=0; i<190; i++) { if (AreDoublesEqual (modelParams[part1].aaRevMatFix[i], modelParams[part2].aaRevMatFix[i], (MrBFlt) 0.00001) == NO) isSame = NO; } } else isSame = NO; /* the priors are not the same, so we cannot set the parameter to be equal for both partitions */ } /* Check to see if the GTR rates are inapplicable for either partition. */ if ((*isApplic1) == NO || (*isApplic2) == NO) isSame = NO; /* if GTR rates are inapplicable for either partition, then the parameter cannot be the same */ } else if (whichParam == P_OMEGA) { /* Check the nonsynonymous/synonymous rate ratio for partitions 1 and 2. */ /* Check if the model is parsimony for either partition */ if (!strcmp(modelParams[part1].parsModel, "Yes")) *isApplic1 = NO; /* part1 has a parsimony model and nonsynonymous/synonymous rate ratio does not apply */ if (!strcmp(modelParams[part2].parsModel, "Yes")) *isApplic2 = NO; /* part2 has a parsimony model and nonsynonymous/synonymous rate ratio does not apply */ /* Check that the data are nucleotide for both partitions 1 and 2 */ if (isFirstNucleotide == NO) *isApplic1 = NO; /* part1 is not nucleotide data so a nonsynonymous/synonymous rate ratio does not apply */ if (isSecondNucleotide == NO) *isApplic2 = NO; /* part2 is not nucleotide data so a nonsynonymous/synonymous rate ratio does not apply */ /* Check that the model structure is the same for both. The nucmodel should be "codon". */ if (strcmp(modelParams[part1].nucModel, "Codon")) *isApplic1 = NO; /* part1 does not have Nucmodel = Codon and nonsynonymous/synonymous rate ratio does not apply */ if (strcmp(modelParams[part2].nucModel, "Codon")) *isApplic2 = NO; /* part2 does not have Nucmodel = Codon and nonsynonymous/synonymous rate ratio does not apply */ /* Assuming that Nucmodel = Codon for both part1 and part2, we now need to check if the prior is the same for both. */ if (!strcmp(modelParams[part1].omegaVar, "M3") && !strcmp(modelParams[part2].omegaVar, "M3")) { if (!strcmp(modelParams[part1].m3omegapr, "Exponential") && !strcmp(modelParams[part2].m3omegapr, "Exponential")) { } else if (!strcmp(modelParams[part1].m3omegapr, "Fixed") && !strcmp(modelParams[part1].m3omegapr, "Fixed")) { if (AreDoublesEqual (modelParams[part1].m3omegaFixed[0], modelParams[part2].m3omegaFixed[0], (MrBFlt) 0.00001) == NO) isSame = NO; if (AreDoublesEqual (modelParams[part1].m3omegaFixed[1], modelParams[part2].m3omegaFixed[1], (MrBFlt) 0.00001) == NO) isSame = NO; } else isSame = NO; /* the priors are not the same, so we cannot set the parameter to be equal for both partitions */ if (!strcmp(modelParams[part1].codonCatFreqPr, "Dirichlet") && !strcmp(modelParams[part2].codonCatFreqPr, "Dirichlet")) { if (AreDoublesEqual (modelParams[part1].codonCatDir[0], modelParams[part2].codonCatDir[0], (MrBFlt) 0.00001) == NO) isSame = NO; if (AreDoublesEqual (modelParams[part1].codonCatDir[1], modelParams[part2].codonCatDir[1], (MrBFlt) 0.00001) == NO) isSame = NO; if (AreDoublesEqual (modelParams[part1].codonCatDir[2], modelParams[part2].codonCatDir[2], (MrBFlt) 0.00001) == NO) isSame = NO; } else if (!strcmp(modelParams[part1].codonCatFreqPr, "Fixed") && !strcmp(modelParams[part1].codonCatFreqPr, "Fixed")) { if (AreDoublesEqual (modelParams[part1].codonCatFreqFix[0], modelParams[part2].codonCatFreqFix[0], (MrBFlt) 0.00001) == NO) isSame = NO; if (AreDoublesEqual (modelParams[part1].codonCatFreqFix[1], modelParams[part2].codonCatFreqFix[1], (MrBFlt) 0.00001) == NO) isSame = NO; if (AreDoublesEqual (modelParams[part1].codonCatFreqFix[2], modelParams[part2].codonCatFreqFix[2], (MrBFlt) 0.00001) == NO) isSame = NO; } else isSame = NO; /* the priors are not the same, so we cannot set the parameter to be equal for both partitions */ } else if (!strcmp(modelParams[part1].omegaVar, "M10") && !strcmp(modelParams[part2].omegaVar, "M10")) { if (!strcmp(modelParams[part1].codonCatFreqPr, "Dirichlet") && !strcmp(modelParams[part2].codonCatFreqPr, "Dirichlet")) { if (AreDoublesEqual (modelParams[part1].codonCatDir[0], modelParams[part2].codonCatDir[0], (MrBFlt) 0.00001) == NO) isSame = NO; if (AreDoublesEqual (modelParams[part1].codonCatDir[1], modelParams[part2].codonCatDir[1], (MrBFlt) 0.00001) == NO) isSame = NO; } else if (!strcmp(modelParams[part1].codonCatFreqPr, "Fixed") && !strcmp(modelParams[part1].codonCatFreqPr, "Fixed")) { if (AreDoublesEqual (modelParams[part1].codonCatFreqFix[0], modelParams[part2].codonCatFreqFix[0], (MrBFlt) 0.00001) == NO) isSame = NO; if (AreDoublesEqual (modelParams[part1].codonCatFreqFix[1], modelParams[part2].codonCatFreqFix[1], (MrBFlt) 0.00001) == NO) isSame = NO; } else isSame = NO; /* the priors are not the same, so we cannot set the parameter to be equal for both partitions */ } else if (!strcmp(modelParams[part1].omegaVar, "Ny98") && !strcmp(modelParams[part2].omegaVar, "Ny98")) { if (!strcmp(modelParams[part1].ny98omega1pr, "Beta") && !strcmp(modelParams[part2].ny98omega1pr, "Beta")) { if (AreDoublesEqual (modelParams[part1].ny98omega1Beta[0], modelParams[part2].ny98omega1Beta[0], (MrBFlt) 0.00001) == NO) isSame = NO; if (AreDoublesEqual (modelParams[part1].ny98omega1Beta[1], modelParams[part2].ny98omega1Beta[1], (MrBFlt) 0.00001) == NO) isSame = NO; } else if (!strcmp(modelParams[part1].ny98omega1pr, "Fixed") && !strcmp(modelParams[part1].ny98omega1pr, "Fixed")) { if (AreDoublesEqual (modelParams[part1].ny98omega1Fixed, modelParams[part2].ny98omega1Fixed, (MrBFlt) 0.00001) == NO) isSame = NO; } else isSame = NO; /* the priors are not the same, so we cannot set the parameter to be equal for both partitions */ if (!strcmp(modelParams[part1].ny98omega3pr, "Uniform") && !strcmp(modelParams[part2].ny98omega3pr, "Uniform")) { if (AreDoublesEqual (modelParams[part1].ny98omega3Uni[0], modelParams[part2].ny98omega3Uni[0], (MrBFlt) 0.00001) == NO) isSame = NO; if (AreDoublesEqual (modelParams[part1].ny98omega3Uni[1], modelParams[part2].ny98omega3Uni[1], (MrBFlt) 0.00001) == NO) isSame = NO; } else if (!strcmp(modelParams[part1].ny98omega3pr, "Exponential") && !strcmp(modelParams[part1].ny98omega3pr, "Exponential")) { if (AreDoublesEqual (modelParams[part1].ny98omega3Exp, modelParams[part2].ny98omega3Exp, (MrBFlt) 0.00001) == NO) isSame = NO; } else if (!strcmp(modelParams[part1].ny98omega3pr, "Fixed") && !strcmp(modelParams[part1].ny98omega3pr, "Fixed")) { if (AreDoublesEqual (modelParams[part1].ny98omega3Fixed, modelParams[part2].ny98omega3Fixed, (MrBFlt) 0.00001) == NO) isSame = NO; } else isSame = NO; /* the priors are not the same, so we cannot set the parameter to be equal for both partitions */ if (!strcmp(modelParams[part1].codonCatFreqPr, "Dirichlet") && !strcmp(modelParams[part2].codonCatFreqPr, "Dirichlet")) { if (AreDoublesEqual (modelParams[part1].codonCatDir[0], modelParams[part2].codonCatDir[0], (MrBFlt) 0.00001) == NO) isSame = NO; if (AreDoublesEqual (modelParams[part1].codonCatDir[1], modelParams[part2].codonCatDir[1], (MrBFlt) 0.00001) == NO) isSame = NO; if (AreDoublesEqual (modelParams[part1].codonCatDir[2], modelParams[part2].codonCatDir[2], (MrBFlt) 0.00001) == NO) isSame = NO; } else if (!strcmp(modelParams[part1].codonCatFreqPr, "Fixed") && !strcmp(modelParams[part1].codonCatFreqPr, "Fixed")) { if (AreDoublesEqual (modelParams[part1].codonCatFreqFix[0], modelParams[part2].codonCatFreqFix[0], (MrBFlt) 0.00001) == NO) isSame = NO; if (AreDoublesEqual (modelParams[part1].codonCatFreqFix[1], modelParams[part2].codonCatFreqFix[1], (MrBFlt) 0.00001) == NO) isSame = NO; if (AreDoublesEqual (modelParams[part1].codonCatFreqFix[2], modelParams[part2].codonCatFreqFix[2], (MrBFlt) 0.00001) == NO) isSame = NO; } else isSame = NO; /* the priors are not the same, so we cannot set the parameter to be equal for both partitions */ } else if (!strcmp(modelParams[part1].omegaVar, "Equal") && !strcmp(modelParams[part2].omegaVar, "Equal")) { if (!strcmp(modelParams[part1].omegaPr,"Dirichlet") && !strcmp(modelParams[part2].omegaPr,"Dirichlet")) { if (AreDoublesEqual (modelParams[part1].omegaDir[0], modelParams[part2].omegaDir[0], (MrBFlt) 0.00001) == NO) isSame = NO; if (AreDoublesEqual (modelParams[part1].omegaDir[1], modelParams[part2].omegaDir[1], (MrBFlt) 0.00001) == NO) isSame = NO; } else if (!strcmp(modelParams[part1].omegaPr,"Fixed") && !strcmp(modelParams[part2].omegaPr,"Fixed")) { if (AreDoublesEqual (modelParams[part1].omegaFix, modelParams[part2].omegaFix, (MrBFlt) 0.00001) == NO) isSame = NO; } else isSame = NO; /* the priors are not the same, so we cannot set the parameter to be equal for both partitions */ } else isSame = NO; /* the priors are not the same, so we cannot set the parameter to be equal for both partitions */ /* Check to see if omega is inapplicable for either partition. */ if ((*isApplic1) == NO || (*isApplic2) == NO) isSame = NO; /* if omega is inapplicable for either partition, then the parameter cannot be the same */ } else if (whichParam == P_PI) { /* Check the state frequencies for partitions 1 and 2. */ /* Check if the model is parsimony for either partition */ if (!strcmp(modelParams[part1].parsModel, "Yes")) *isApplic1 = NO; /* part1 has a parsimony model and state frequencies do not apply */ if (!strcmp(modelParams[part2].parsModel, "Yes")) *isApplic2 = NO; /* part2 has a parsimony model and state frequencies do not apply */ /* Check that the data are not CONTINUOUS for partitions 1 and 2 */ if (modelParams[part1].dataType == CONTINUOUS) *isApplic1 = NO; /* state frequencies do not make sense for part1 */ if (modelParams[part2].dataType == CONTINUOUS) *isApplic2 = NO; /* state frequencies do not make sense for part2 */ /* Now, check that the data are the same (i.e., both nucleotide or both amino acid, or whatever). */ if (isFirstNucleotide != isSecondNucleotide) isSame = NO; /* data are not both nucleotide or both not nucleotide */ else if (modelParams[part1].dataType != modelParams[part2].dataType && isFirstNucleotide == NO) isSame = NO; /* data are not the same */ /* Check that the model structure is the same for both partitions */ if (strcmp(modelParams[part1].nucModel, modelParams[part2].nucModel)) isSame = NO; /* the nucleotide models are different */ if (strcmp(modelParams[part1].covarionModel, modelParams[part2].covarionModel) && !(!strcmp(modelParams[part1].nucModel, "Codon") && !strcmp(modelParams[part2].nucModel, "Codon"))) isSame = NO; /* the models have different covarion struture */ /* If both partitions have nucmodel=codon, then we also have to make certain that the same genetic code is used. */ if (!strcmp(modelParams[part1].nucModel, "Codon") && !strcmp(modelParams[part2].nucModel, "Codon")) { if (strcmp(modelParams[part1].geneticCode, modelParams[part2].geneticCode)) isSame = NO; /* the models have different genetic codes */ } /* Let's see if the prior is the same. */ if (modelParams[part1].dataType == STANDARD && modelParams[part2].dataType == STANDARD) { /* The data are morphological (STANDARD). The state frequencies are specified by a symmetric beta distribution, the parameter of which needs to be the same to apply to both partitions. Note that symPiPr = -1 is equivalent to setting the variance to 0.0. */ /* In principle, the two partitions can be linked but if we need to take the possibility of * asymmetry in transition rates into account, then we do not know at this stage whether we * will have to sample from the state frequencies or integrate them out. Since these two * model levels are both represented by the same data structure, we do not allow partitions * to be linked unless symPiPr == -1, that is, if state frequencies are equal so that they * do not have to be sampled under any conditions. */ if (strcmp(modelParams[part1].symPiPr,"Fixed") != 0 || strcmp(modelParams[part2].symPiPr,"Fixed") != 0) { isSame = NO; } else if (modelParams[part1].symBetaFix != -1 || modelParams[part2].symBetaFix != -1) { isSame = NO; } } if (modelSettings[part1].dataType == PROTEIN && modelSettings[part2].dataType == PROTEIN) { /* We are dealing with protein data. */ if (strcmp(modelParams[part1].aaModelPr, modelParams[part2].aaModelPr) == 0) { if (strcmp(modelParams[part1].aaModelPr, "Fixed") == 0) { /* only have a single, fixed, amino acid rate matrix */ if (strcmp(modelParams[part1].aaModel, modelParams[part2].aaModel) != 0) isSame = NO; /* we have different amino acid models, and the state frequencies must be different */ /* if we have an equalin model or Gtr model, then we need to check the prior on the state frequencies */ if (!strcmp(modelParams[part1].aaModel, "Equalin") || !strcmp(modelParams[part1].aaModel, "Gtr")) { if (!strcmp(modelParams[part1].stateFreqPr, modelParams[part2].stateFreqPr)) { /* the prior form is the same */ if (!strcmp(modelParams[part1].stateFreqPr, "Dirichlet")) /* both prior models must be dirichlet */ { for (i=0; irelParts[0]]; if (treeParam->paramType == P_SPECIESTREE) return numLocalTaxa; /* no constraints allowed in species tree; set constraints in gene trees instead */ /* This is difficult because we cannot rely on the tree being initialized. We need to retrieve the bitfields ourselves and figure out what they mean. */ nLongsNeeded = ((numLocalTaxa - 1) / nBitsInALong) + 1; subtreePartition = (BitsLong *) SafeCalloc(3*nLongsNeeded, sizeof(BitsLong)); constraintPartition = (BitsLong **) SafeCalloc (numDefinedConstraints+1, sizeof(BitsLong *)); constraintPartition[0] = (BitsLong *) SafeCalloc ((numDefinedConstraints+1)*nLongsNeeded, sizeof(BitsLong)); for (i=1; iactiveConstraints[a] == NO || definedConstraintsType[a] != HARD) continue; /* set bits in partition under consideration */ ClearBits(constraintPartition[numPartitions], nLongsNeeded); for (i=j=0; ibrlensPr,"Clock") != 0 && IsBitSet(localOutGroup, constraintPartition[numPartitions]) == YES) FlipBits(constraintPartition[numPartitions], nLongsNeeded, mask); /* skip partition if uninformative */ k = NumBits(constraintPartition[numPartitions], nLongsNeeded); if (k == 0 || k == 1) continue; /* record if we hit an all-species partition */ if (k == numLocalTaxa) foundAllSpeciesPartition = YES; numPartitions++; } /* Add all-species partition if not already present */ if (foundAllSpeciesPartition == NO) { ClearBits(constraintPartition[numPartitions], nLongsNeeded); for (i=j=0; ibrlensPr,"Clock") != 0 && NumBits (constraintPartition[i], nLongsNeeded) == numLocalTaxa - 1) k++; if (k > largestSubtree) largestSubtree = k; } free(subtreePartition); free(constraintPartition[0]); free(constraintPartition); return largestSubtree; } int Link (void) { int i, j; for (j=0; jactiveConstraints[a] == NO || definedConstraintsType[a] != HARD) continue; /* set bits in partition to add */ ClearBits(constraintPartition, nLongsNeeded); for (i=j=0; ibrlensPr,"Clock") != 0 && IsBitSet(localOutGroup, constraintPartition) == YES) FlipBits(constraintPartition, nLongsNeeded, mask); /* skip partition if uninformative */ k = NumBits(constraintPartition, nLongsNeeded); if (k == 0 || k == 1) continue; numInformativeHardConstraints++; } return numInformativeHardConstraints; } int NumNonExcludedChar (void) { int i, n; /* count number of non-excluded characters */ n = 0; for (i=0; idataType == PROTEIN) whichChar = &WhichAA; if (mp->dataType == RESTRICTION) whichChar = &WhichRes; if (mp->dataType == STANDARD) whichChar = &WhichStand; else whichChar = &WhichNuc; MrBayesPrint ("\nCompressed matrix for division %d\n\n", d+1); k = 66; if (mp->dataType == CONTINUOUS) k /= 4; for (c=m->compMatrixStart; ccompMatrixStop; c+=k) { for (i=0; i= m->compMatrixStop) break; if (mp->dataType == CONTINUOUS) MrBayesPrint ("%.2lf ", WhichCont((int)compMatrix[pos(i,j,compMatrixRowSize)])); else MrBayesPrint ("%c", whichChar((int)compMatrix[pos(i,j,compMatrixRowSize)])); } MrBayesPrint ("\n"); } MrBayesPrint ("\nNo. sites "); for (j=c; j= m->compMatrixStop) break; i = (int) numSitesOfPat[m->compCharStart + (0*numCompressedChars) + (j - m->compMatrixStart)/m->nCharsPerSite]; /* NOTE: We are printing the unadulterated site pat nums */ if (i>9) i = 'A' + i - 10; else i = '0' + i; if (mp->dataType == CONTINUOUS) MrBayesPrint (" %c ", i); else { if ((j-m->compMatrixStart) % m->nCharsPerSite == 0) MrBayesPrint ("%c", i); else MrBayesPrint (" "); } } MrBayesPrint ("\nOrig. char "); for (j=c; j= m->compMatrixStop) break; i = origChar[j]; if (i>9) i = '0' + (i % 10); else i = '0' + i; if (mp->dataType == CONTINUOUS) MrBayesPrint (" %c ", i); else MrBayesPrint ("%c", i); } if (mp->dataType == STANDARD && m->nStates != NULL) { MrBayesPrint ("\nNo. states "); for (j=c; j= m->compMatrixStop) break; i = m->nStates[j-m->compCharStart]; MrBayesPrint ("%d", i); } MrBayesPrint ("\nCharType "); for (j=c; j= m->compMatrixStop) break; i = m->cType[j-m->compMatrixStart]; if (i == ORD) MrBayesPrint ("%c", 'O'); else if (i == UNORD) MrBayesPrint ("%c", 'U'); else MrBayesPrint ("%c", 'I'); } MrBayesPrint ("\ntiIndex "); for (j=c; j= m->compMatrixStop) break; i = m->tiIndex[j-m->compCharStart]; MrBayesPrint ("%d", i % 10); } MrBayesPrint ("\nbsIndex "); for (j=c; j= m->compMatrixStop) break; i = m->bsIndex[j-m->compCharStart]; MrBayesPrint ("%d", i % 10); } } MrBayesPrint ("\n\n"); } MrBayesPrint ("Press return to continue\n"); getchar(); } /* next division */ return NO_ERROR; } /*---------------------------------------------------------------------- | | PrintMatrix: Print data matrix | | ------------------------------------------------------------------------*/ int PrintMatrix (void) { int i, j=0, c, printWidth, nextColumn; if (!matrix) return ERROR; MrBayesPrint ("\nData matrix\n\n"); printWidth = 79; for (c=0; c= numChar) break; if (charInfo[j].charType == CONTINUOUS && nextColumn < printWidth - 3) break; if (charInfo[j].charType == CONTINUOUS) { MrBayesPrint ("%3d ", matrix[pos(i,j,numChar)]); nextColumn += 3; } else if (charInfo[j].charType == DNA || charInfo[j].charType == RNA) MrBayesPrint ("%c", WhichNuc(matrix[pos(i,j,numChar)])); else if (charInfo[j].charType == PROTEIN) MrBayesPrint ("%c", WhichAA(matrix[pos(i,j,numChar)])); else if (charInfo[j].charType == RESTRICTION) MrBayesPrint ("%c", WhichRes(matrix[pos(i,j,numChar)])); else if (charInfo[j].charType == STANDARD) MrBayesPrint ("%c", WhichStand(matrix[pos(i,j,numChar)])); j++; } MrBayesPrint ("\n"); } MrBayesPrint ("\n"); } return NO_ERROR; } /*-------------------------------------------------------------- | | ProcessStdChars: process standard characters | ---------------------------------------------------------------*/ int ProcessStdChars (RandLong *seed) { int c, d, i, j, k, n, ts, index, numStandardChars, origCharPos, *bsIndex; char piHeader[30]; ModelInfo *m; ModelParams *mp=NULL; Param *p; /* set character type, no. states, ti index and bs index for standard characters */ /* first calculate how many standard characters we have */ numStandardChars = 0; for (d=0; ddataType != STANDARD) continue; numStandardChars += m->numChars; } /* return if there are no standard characters */ if (numStandardChars == 0) return (NO_ERROR); /* we are still here so we have standard characters and need to deal with them */ /* first allocate space for stdType, stateSize, tiIndex, bsIndex */ if (memAllocs[ALLOC_STDTYPE] == YES) { free (stdType); stdType = NULL; memAllocs[ALLOC_STDTYPE] = NO; } stdType = (int *)SafeCalloc(4 * (size_t)numStandardChars, sizeof(int)); if (!stdType) { MrBayesPrint ("%s Problem allocating stdType (%d ints)\n", 4 * numStandardChars); return ERROR; } memAllocs[ALLOC_STDTYPE] = YES; stateSize = stdType + numStandardChars; tiIndex = stateSize + numStandardChars; bsIndex = tiIndex + numStandardChars; /* then fill in stdType and stateSize, set pointers */ /* also fill in isTiNeeded for each division and tiIndex for each character */ for (d=j=0; ddataType != STANDARD) continue; m->cType = stdType + j; m->nStates = stateSize + j; m->tiIndex = tiIndex + j; m->bsIndex = bsIndex + j; m->cijkLength = 0; m->nCijkParts = 0; for (c=0; cnumChars; c++) { if (origChar[c+m->compMatrixStart] < 0) { /* this is a dummy character */ m->cType[c] = UNORD; m->nStates[c] = 2; } else { /* this is an ordinary character */ m->cType[c] = charInfo[origChar[c + m->compMatrixStart]].ctype; m->nStates[c] = charInfo[origChar[c + m->compMatrixStart]].numStates; } /* check ctype settings */ if (m->nStates[c] < 2) { MrBayesPrint ("%s WARNING: Compressed character %d (original character %d) of division %d has \n", spacer, c+m->compCharStart,origChar[c+m->compCharStart]+1, d+1); MrBayesPrint ("%s less than two observed states; it will be assumed to have two states.\n", spacer); m->nStates[c] = 2; } if (m->nStates[c] > MAX_STD_STATES) { MrBayesPrint ("%s Standard characters with more than %d states are not supported\n", spacer, MAX_STD_STATES); return ERROR; } if (m->nStates[c] == 2 && m->cType[c] == ORD) m->cType[c] = UNORD; if (m->cType[c] == IRREV) { MrBayesPrint ("%s Irreversible model not yet supported\n", spacer); return ERROR; } /* find max number of states */ if (m->nStates[c] > m->numModelStates) m->numModelStates = m->nStates[c]; /* update Cijk info */ if (strcmp(mp->symPiPr,"Fixed") != 0 || AreDoublesEqual(mp->symBetaFix, -1.0, 0.00001) == NO) { /* Asymmetry between stationary state frequencies -- we need one cijk and eigenvalue set for each multistate character */ if (m->nStates[c] > 2 && (m->cType[c] == UNORD || m->cType[c] == ORD)) { ts = m->nStates[c]; m->cijkLength += (ts * ts * ts) + (2 * ts); m->nCijkParts++; } } /* set the ti probs needed */ if (m->stateFreq->nValues == 0 || m->nStates[c] == 2) { if (m->cType[c] == UNORD) m->isTiNeeded[m->nStates[c]-2] = YES; if (m->cType[c] == ORD) m->isTiNeeded[m->nStates[c]+MAX_STD_STATES-4] = YES; if (m->cType[c] == IRREV) m->isTiNeeded[m->nStates[c]+2*MAX_STD_STATES-5] = YES; } } /* set ti index for each compressed character first */ /* set bs index later (below) */ /* set base index, valid for binary chars */ for (c=0; cnumChars; c++) m->tiIndex[c] = 0; /* first adjust for unordered characters */ for (k = 0; k < MAX_STD_STATES-1; k++) { if (m->isTiNeeded[k] == NO) continue; for (c=0; cnumChars; c++) { if (m->cType[c] != UNORD || m->nStates[c] > k + 2) { m->tiIndex[c] += (k + 2) * (k + 2) * m->numRateCats; } } } /* second for ordered characters */ for (k = MAX_STD_STATES-1; k < 2*MAX_STD_STATES-3; k++) { if (m->isTiNeeded [k] == NO) continue; for (c=0; cnumChars; c++) { if (m->cType[c] == ORD && m->nStates[c] > k-MAX_STD_STATES+4) { m->tiIndex[c] += (k-MAX_STD_STATES+4) * (k-MAX_STD_STATES+4) * m->numRateCats; } } } /* finally take beta cats into account in tiIndex */ /* the beta cats will only be used for binary characters */ /* multistate characters get their ti indices reset here */ if (m->numBetaCats > 1 && m->isTiNeeded[0] == YES) { k = 4 * m->numBetaCats * m->numRateCats; /* offset for binary character ti probs */ for (c=0; cnumChars; c++) { if (m->nStates[c] > 2) { m->tiIndex[c] = k; k += m->nStates[c] * m->nStates[c] * m->numRateCats; } } } j += m->numChars; } /* deal with bsIndex */ for (k=0; kparamType != P_PI || modelParams[p->relParts[0]].dataType != STANDARD) continue; p->nSympi = 0; p->hasBinaryStd = NO; for (i=0; inRelParts; i++) if (modelSettings[p->relParts[i]].isTiNeeded[0] == YES) break; if (i < p->nRelParts) p->hasBinaryStd = YES; if (p->paramId == SYMPI_EQUAL) { /* calculate the number of state frequencies needed */ /* also set bsIndex appropriately */ for (n = index = 0; n < MAX_STD_STATES-1; n++) { for (i=0; inRelParts; i++) if (modelSettings[p->relParts[i]].isTiNeeded[n] == YES) break; if (i < p->nRelParts) { for (i=0; inRelParts; i++) { m = &modelSettings[p->relParts[i]]; for (c=0; cnumChars; c++) { if (m->cType[c] != UNORD || m->nStates[c] > n + 2) { m->bsIndex[c] += (n + 2); } } } index += (n + 2); } } for (n = MAX_STD_STATES-1; n < 2*MAX_STD_STATES-3; n++) { for (i=0; inRelParts; i++) if (modelSettings[p->relParts[i]].isTiNeeded[n] == YES) break; if (i < p->nRelParts) { for (i=0; inRelParts; i++) { m = &modelSettings[p->relParts[i]]; for (c=0; cnumChars; c++) { if (m->cType[c] == ORD && m->nStates[c] > n-MAX_STD_STATES+4) { m->bsIndex[c] += n-MAX_STD_STATES+4; } } } index += n-MAX_STD_STATES+4; } } p->nStdStateFreqs = index; } else { /* if not equal we need space for beta category frequencies */ index = 0; if (p->hasBinaryStd == YES) index += (2 * modelSettings[p->relParts[0]].numBetaCats); /* as well as one set of frequencies for each multistate character */ for (i=0; inRelParts; i++) { m = &modelSettings[p->relParts[i]]; for (c=0; cnumChars; c++) { if (m->nStates[c] > 2 && (m->cType[c] == UNORD || m->cType[c] == ORD)) { m->bsIndex[c] = index; index += m->nStates[c]; p->nSympi++; } } } p->nStdStateFreqs = index; } } /* allocate space for sympiIndex, stdStateFreqs; then fill */ /* first count number of sympis needed */ for (k=n=i=0; knSympi; /* nsympi calculated above */ } /* then allocate and fill in */ if (n > 0) { if (memAllocs[ALLOC_SYMPIINDEX] == YES) { sympiIndex = (int *) SafeRealloc ((void *) sympiIndex, 3*n * sizeof (int)); for (i=0; i<3*n; i++) sympiIndex[i] = 0; } else sympiIndex = (int *) SafeCalloc (3*n, sizeof (int)); if (!sympiIndex) { MrBayesPrint ("%s Problem allocating sympiIndex\n", spacer); return (ERROR); } else memAllocs[ALLOC_SYMPIINDEX] = YES; /* set up sympi pointers and fill sympiIndex */ for (k=i=0; knSympi > 0) { p->printParam = YES; /* print even if fixed alpha_symdir because we sample state freqs and do not integrate them out */ index = 0; p->sympiBsIndex = sympiIndex + i; p->sympinStates = sympiIndex + i + n; p->sympiCType = sympiIndex + i + (2 * n); for (j=0; jnRelParts; j++) { m = &modelSettings[p->relParts[j]]; for (c=0; cnumChars; c++) { if (m->nStates[c] > 2 && (m->cType[c] == UNORD || m->cType[c] == ORD)) { p->sympinStates[index] = m->nStates[c]; p->sympiBsIndex[index] = m->bsIndex[c]; p->sympiCType[index] = m->cType[c]; origCharPos = origChar[m->compCharStart + c]; for (ts=0; tsnStates[c]; ts++) { sprintf (piHeader, "\tpi_%d(%d)", origCharPos+1, ts); SafeStrcat(&p->paramHeader, piHeader); } index++; } } } assert (index == p->nSympi); i += p->nSympi; } } assert (i == n); } /* count space needed for state frequencies */ for (k=n=0; kparamType != P_PI || modelParams[p->relParts[0]].dataType != STANDARD) continue; n += p->nStdStateFreqs; } stdStateFreqsRowSize = n; /* allocate space */ if (memAllocs[ALLOC_STDSTATEFREQS] == YES) { free (stdStateFreqs); stdStateFreqs = NULL; memAllocs[ALLOC_STDSTATEFREQS] = NO; } stdStateFreqs = (MrBFlt *) SafeCalloc (n * 2 * numGlobalChains, sizeof (MrBFlt)); if (!stdStateFreqs) { MrBayesPrint ("%s Problem allocating stdStateFreqs in ProcessStdChars\n", spacer); return (ERROR); } else memAllocs[ALLOC_STDSTATEFREQS] = YES; /* set pointers */ for (k=n=0; kparamType != P_PI || modelParams[p->relParts[0]].dataType != STANDARD) continue; p->stdStateFreqs = stdStateFreqs + n; n += p->nStdStateFreqs; } FillStdStateFreqs (0 , numGlobalChains, seed); return (NO_ERROR); } int SetAARates (void) { int i, j; MrBFlt diff, sum, scaler; /* A R N D C Q E G H I L K M F P S T W Y V */ /* jones */ aaJones[ 0][ 0] = 0; aaJones[ 0][ 1] = 58; aaJones[ 0][ 2] = 54; aaJones[ 0][ 3] = 81; aaJones[ 0][ 4] = 56; aaJones[ 0][ 5] = 57; aaJones[ 0][ 6] = 105; aaJones[ 0][ 7] = 179; aaJones[ 0][ 8] = 27; aaJones[ 0][ 9] = 36; aaJones[ 0][10] = 30; aaJones[ 0][11] = 35; aaJones[ 0][12] = 54; aaJones[ 0][13] = 15; aaJones[ 0][14] = 194; aaJones[ 0][15] = 378; aaJones[ 0][16] = 475; aaJones[ 0][17] = 9; aaJones[ 0][18] = 11; aaJones[ 0][19] = 298; aaJones[ 1][ 0] = 58; aaJones[ 1][ 1] = 0; aaJones[ 1][ 2] = 45; aaJones[ 1][ 3] = 16; aaJones[ 1][ 4] = 113; aaJones[ 1][ 5] = 310; aaJones[ 1][ 6] = 29; aaJones[ 1][ 7] = 137; aaJones[ 1][ 8] = 328; aaJones[ 1][ 9] = 22; aaJones[ 1][10] = 38; aaJones[ 1][11] = 646; aaJones[ 1][12] = 44; aaJones[ 1][13] = 5; aaJones[ 1][14] = 74; aaJones[ 1][15] = 101; aaJones[ 1][16] = 64; aaJones[ 1][17] = 126; aaJones[ 1][18] = 20; aaJones[ 1][19] = 17; aaJones[ 2][ 0] = 54; aaJones[ 2][ 1] = 45; aaJones[ 2][ 2] = 0; aaJones[ 2][ 3] = 528; aaJones[ 2][ 4] = 34; aaJones[ 2][ 5] = 86; aaJones[ 2][ 6] = 58; aaJones[ 2][ 7] = 81; aaJones[ 2][ 8] = 391; aaJones[ 2][ 9] = 47; aaJones[ 2][10] = 12; aaJones[ 2][11] = 263; aaJones[ 2][12] = 30; aaJones[ 2][13] = 10; aaJones[ 2][14] = 15; aaJones[ 2][15] = 503; aaJones[ 2][16] = 232; aaJones[ 2][17] = 8; aaJones[ 2][18] = 70; aaJones[ 2][19] = 16; aaJones[ 3][ 0] = 81; aaJones[ 3][ 1] = 16; aaJones[ 3][ 2] = 528; aaJones[ 3][ 3] = 0; aaJones[ 3][ 4] = 10; aaJones[ 3][ 5] = 49; aaJones[ 3][ 6] = 767; aaJones[ 3][ 7] = 130; aaJones[ 3][ 8] = 112; aaJones[ 3][ 9] = 11; aaJones[ 3][10] = 7; aaJones[ 3][11] = 26; aaJones[ 3][12] = 15; aaJones[ 3][13] = 4; aaJones[ 3][14] = 15; aaJones[ 3][15] = 59; aaJones[ 3][16] = 38; aaJones[ 3][17] = 4; aaJones[ 3][18] = 46; aaJones[ 3][19] = 31; aaJones[ 4][ 0] = 56; aaJones[ 4][ 1] = 113; aaJones[ 4][ 2] = 34; aaJones[ 4][ 3] = 10; aaJones[ 4][ 4] = 0; aaJones[ 4][ 5] = 9; aaJones[ 4][ 6] = 5; aaJones[ 4][ 7] = 59; aaJones[ 4][ 8] = 69; aaJones[ 4][ 9] = 17; aaJones[ 4][10] = 23; aaJones[ 4][11] = 7; aaJones[ 4][12] = 31; aaJones[ 4][13] = 78; aaJones[ 4][14] = 14; aaJones[ 4][15] = 223; aaJones[ 4][16] = 42; aaJones[ 4][17] = 115; aaJones[ 4][18] = 209; aaJones[ 4][19] = 62; aaJones[ 5][ 0] = 57; aaJones[ 5][ 1] = 310; aaJones[ 5][ 2] = 86; aaJones[ 5][ 3] = 49; aaJones[ 5][ 4] = 9; aaJones[ 5][ 5] = 0; aaJones[ 5][ 6] = 323; aaJones[ 5][ 7] = 26; aaJones[ 5][ 8] = 597; aaJones[ 5][ 9] = 9; aaJones[ 5][10] = 72; aaJones[ 5][11] = 292; aaJones[ 5][12] = 43; aaJones[ 5][13] = 4; aaJones[ 5][14] = 164; aaJones[ 5][15] = 53; aaJones[ 5][16] = 51; aaJones[ 5][17] = 18; aaJones[ 5][18] = 24; aaJones[ 5][19] = 20; aaJones[ 6][ 0] = 105; aaJones[ 6][ 1] = 29; aaJones[ 6][ 2] = 58; aaJones[ 6][ 3] = 767; aaJones[ 6][ 4] = 5; aaJones[ 6][ 5] = 323; aaJones[ 6][ 6] = 0; aaJones[ 6][ 7] = 119; aaJones[ 6][ 8] = 26; aaJones[ 6][ 9] = 12; aaJones[ 6][10] = 9; aaJones[ 6][11] = 181; aaJones[ 6][12] = 18; aaJones[ 6][13] = 5; aaJones[ 6][14] = 18; aaJones[ 6][15] = 30; aaJones[ 6][16] = 32; aaJones[ 6][17] = 10; aaJones[ 6][18] = 7; aaJones[ 6][19] = 45; aaJones[ 7][ 0] = 179; aaJones[ 7][ 1] = 137; aaJones[ 7][ 2] = 81; aaJones[ 7][ 3] = 130; aaJones[ 7][ 4] = 59; aaJones[ 7][ 5] = 26; aaJones[ 7][ 6] = 119; aaJones[ 7][ 7] = 0; aaJones[ 7][ 8] = 23; aaJones[ 7][ 9] = 6; aaJones[ 7][10] = 6; aaJones[ 7][11] = 27; aaJones[ 7][12] = 14; aaJones[ 7][13] = 5; aaJones[ 7][14] = 24; aaJones[ 7][15] = 201; aaJones[ 7][16] = 33; aaJones[ 7][17] = 55; aaJones[ 7][18] = 8; aaJones[ 7][19] = 47; aaJones[ 8][ 0] = 27; aaJones[ 8][ 1] = 328; aaJones[ 8][ 2] = 391; aaJones[ 8][ 3] = 112; aaJones[ 8][ 4] = 69; aaJones[ 8][ 5] = 597; aaJones[ 8][ 6] = 26; aaJones[ 8][ 7] = 23; aaJones[ 8][ 8] = 0; aaJones[ 8][ 9] = 16; aaJones[ 8][10] = 56; aaJones[ 8][11] = 45; aaJones[ 8][12] = 33; aaJones[ 8][13] = 40; aaJones[ 8][14] = 115; aaJones[ 8][15] = 73; aaJones[ 8][16] = 46; aaJones[ 8][17] = 8; aaJones[ 8][18] = 573; aaJones[ 8][19] = 11; aaJones[ 9][ 0] = 36; aaJones[ 9][ 1] = 22; aaJones[ 9][ 2] = 47; aaJones[ 9][ 3] = 11; aaJones[ 9][ 4] = 17; aaJones[ 9][ 5] = 9; aaJones[ 9][ 6] = 12; aaJones[ 9][ 7] = 6; aaJones[ 9][ 8] = 16; aaJones[ 9][ 9] = 0; aaJones[ 9][10] = 229; aaJones[ 9][11] = 21; aaJones[ 9][12] = 479; aaJones[ 9][13] = 89; aaJones[ 9][14] = 10; aaJones[ 9][15] = 40; aaJones[ 9][16] = 245; aaJones[ 9][17] = 9; aaJones[ 9][18] = 32; aaJones[ 9][19] = 961; aaJones[10][ 0] = 30; aaJones[10][ 1] = 38; aaJones[10][ 2] = 12; aaJones[10][ 3] = 7; aaJones[10][ 4] = 23; aaJones[10][ 5] = 72; aaJones[10][ 6] = 9; aaJones[10][ 7] = 6; aaJones[10][ 8] = 56; aaJones[10][ 9] = 229; aaJones[10][10] = 0; aaJones[10][11] = 14; aaJones[10][12] = 388; aaJones[10][13] = 248; aaJones[10][14] = 102; aaJones[10][15] = 59; aaJones[10][16] = 25; aaJones[10][17] = 52; aaJones[10][18] = 24; aaJones[10][19] = 180; aaJones[11][ 0] = 35; aaJones[11][ 1] = 646; aaJones[11][ 2] = 263; aaJones[11][ 3] = 26; aaJones[11][ 4] = 7; aaJones[11][ 5] = 292; aaJones[11][ 6] = 181; aaJones[11][ 7] = 27; aaJones[11][ 8] = 45; aaJones[11][ 9] = 21; aaJones[11][10] = 14; aaJones[11][11] = 0; aaJones[11][12] = 65; aaJones[11][13] = 4; aaJones[11][14] = 21; aaJones[11][15] = 47; aaJones[11][16] = 103; aaJones[11][17] = 10; aaJones[11][18] = 8; aaJones[11][19] = 14; aaJones[12][ 0] = 54; aaJones[12][ 1] = 44; aaJones[12][ 2] = 30; aaJones[12][ 3] = 15; aaJones[12][ 4] = 31; aaJones[12][ 5] = 43; aaJones[12][ 6] = 18; aaJones[12][ 7] = 14; aaJones[12][ 8] = 33; aaJones[12][ 9] = 479; aaJones[12][10] = 388; aaJones[12][11] = 65; aaJones[12][12] = 0; aaJones[12][13] = 43; aaJones[12][14] = 16; aaJones[12][15] = 29; aaJones[12][16] = 226; aaJones[12][17] = 24; aaJones[12][18] = 18; aaJones[12][19] = 323; aaJones[13][ 0] = 15; aaJones[13][ 1] = 5; aaJones[13][ 2] = 10; aaJones[13][ 3] = 4; aaJones[13][ 4] = 78; aaJones[13][ 5] = 4; aaJones[13][ 6] = 5; aaJones[13][ 7] = 5; aaJones[13][ 8] = 40; aaJones[13][ 9] = 89; aaJones[13][10] = 248; aaJones[13][11] = 4; aaJones[13][12] = 43; aaJones[13][13] = 0; aaJones[13][14] = 17; aaJones[13][15] = 92; aaJones[13][16] = 12; aaJones[13][17] = 53; aaJones[13][18] = 536; aaJones[13][19] = 62; aaJones[14][ 0] = 194; aaJones[14][ 1] = 74; aaJones[14][ 2] = 15; aaJones[14][ 3] = 15; aaJones[14][ 4] = 14; aaJones[14][ 5] = 164; aaJones[14][ 6] = 18; aaJones[14][ 7] = 24; aaJones[14][ 8] = 115; aaJones[14][ 9] = 10; aaJones[14][10] = 102; aaJones[14][11] = 21; aaJones[14][12] = 16; aaJones[14][13] = 17; aaJones[14][14] = 0; aaJones[14][15] = 285; aaJones[14][16] = 118; aaJones[14][17] = 6; aaJones[14][18] = 10; aaJones[14][19] = 23; aaJones[15][ 0] = 378; aaJones[15][ 1] = 101; aaJones[15][ 2] = 503; aaJones[15][ 3] = 59; aaJones[15][ 4] = 223; aaJones[15][ 5] = 53; aaJones[15][ 6] = 30; aaJones[15][ 7] = 201; aaJones[15][ 8] = 73; aaJones[15][ 9] = 40; aaJones[15][10] = 59; aaJones[15][11] = 47; aaJones[15][12] = 29; aaJones[15][13] = 92; aaJones[15][14] = 285; aaJones[15][15] = 0; aaJones[15][16] = 477; aaJones[15][17] = 35; aaJones[15][18] = 63; aaJones[15][19] = 38; aaJones[16][ 0] = 475; aaJones[16][ 1] = 64; aaJones[16][ 2] = 232; aaJones[16][ 3] = 38; aaJones[16][ 4] = 42; aaJones[16][ 5] = 51; aaJones[16][ 6] = 32; aaJones[16][ 7] = 33; aaJones[16][ 8] = 46; aaJones[16][ 9] = 245; aaJones[16][10] = 25; aaJones[16][11] = 103; aaJones[16][12] = 226; aaJones[16][13] = 12; aaJones[16][14] = 118; aaJones[16][15] = 477; aaJones[16][16] = 0; aaJones[16][17] = 12; aaJones[16][18] = 21; aaJones[16][19] = 112; aaJones[17][ 0] = 9; aaJones[17][ 1] = 126; aaJones[17][ 2] = 8; aaJones[17][ 3] = 4; aaJones[17][ 4] = 115; aaJones[17][ 5] = 18; aaJones[17][ 6] = 10; aaJones[17][ 7] = 55; aaJones[17][ 8] = 8; aaJones[17][ 9] = 9; aaJones[17][10] = 52; aaJones[17][11] = 10; aaJones[17][12] = 24; aaJones[17][13] = 53; aaJones[17][14] = 6; aaJones[17][15] = 35; aaJones[17][16] = 12; aaJones[17][17] = 0; aaJones[17][18] = 71; aaJones[17][19] = 25; aaJones[18][ 0] = 11; aaJones[18][ 1] = 20; aaJones[18][ 2] = 70; aaJones[18][ 3] = 46; aaJones[18][ 4] = 209; aaJones[18][ 5] = 24; aaJones[18][ 6] = 7; aaJones[18][ 7] = 8; aaJones[18][ 8] = 573; aaJones[18][ 9] = 32; aaJones[18][10] = 24; aaJones[18][11] = 8; aaJones[18][12] = 18; aaJones[18][13] = 536; aaJones[18][14] = 10; aaJones[18][15] = 63; aaJones[18][16] = 21; aaJones[18][17] = 71; aaJones[18][18] = 0; aaJones[18][19] = 16; aaJones[19][ 0] = 298; aaJones[19][ 1] = 17; aaJones[19][ 2] = 16; aaJones[19][ 3] = 31; aaJones[19][ 4] = 62; aaJones[19][ 5] = 20; aaJones[19][ 6] = 45; aaJones[19][ 7] = 47; aaJones[19][ 8] = 11; aaJones[19][ 9] = 961; aaJones[19][10] = 180; aaJones[19][11] = 14; aaJones[19][12] = 323; aaJones[19][13] = 62; aaJones[19][14] = 23; aaJones[19][15] = 38; aaJones[19][16] = 112; aaJones[19][17] = 25; aaJones[19][18] = 16; aaJones[19][19] = 0; jonesPi[ 0] = 0.076748; jonesPi[ 1] = 0.051691; jonesPi[ 2] = 0.042645; jonesPi[ 3] = 0.051544; jonesPi[ 4] = 0.019803; jonesPi[ 5] = 0.040752; jonesPi[ 6] = 0.061830; jonesPi[ 7] = 0.073152; jonesPi[ 8] = 0.022944; jonesPi[ 9] = 0.053761; jonesPi[10] = 0.091904; jonesPi[11] = 0.058676; jonesPi[12] = 0.023826; jonesPi[13] = 0.040126; jonesPi[14] = 0.050901; jonesPi[15] = 0.068765; jonesPi[16] = 0.058565; jonesPi[17] = 0.014261; jonesPi[18] = 0.032102; jonesPi[19] = 0.066005; /* dayhoff */ aaDayhoff[ 0][ 0] = 0; aaDayhoff[ 0][ 1] = 27; aaDayhoff[ 0][ 2] = 98; aaDayhoff[ 0][ 3] = 120; aaDayhoff[ 0][ 4] = 36; aaDayhoff[ 0][ 5] = 89; aaDayhoff[ 0][ 6] = 198; aaDayhoff[ 0][ 7] = 240; aaDayhoff[ 0][ 8] = 23; aaDayhoff[ 0][ 9] = 65; aaDayhoff[ 0][10] = 41; aaDayhoff[ 0][11] = 26; aaDayhoff[ 0][12] = 72; aaDayhoff[ 0][13] = 18; aaDayhoff[ 0][14] = 250; aaDayhoff[ 0][15] = 409; aaDayhoff[ 0][16] = 371; aaDayhoff[ 0][17] = 0; aaDayhoff[ 0][18] = 24; aaDayhoff[ 0][19] = 208; aaDayhoff[ 1][ 0] = 27; aaDayhoff[ 1][ 1] = 0; aaDayhoff[ 1][ 2] = 32; aaDayhoff[ 1][ 3] = 0; aaDayhoff[ 1][ 4] = 23; aaDayhoff[ 1][ 5] = 246; aaDayhoff[ 1][ 6] = 1; aaDayhoff[ 1][ 7] = 9; aaDayhoff[ 1][ 8] = 240; aaDayhoff[ 1][ 9] = 64; aaDayhoff[ 1][10] = 15; aaDayhoff[ 1][11] = 464; aaDayhoff[ 1][12] = 90; aaDayhoff[ 1][13] = 14; aaDayhoff[ 1][14] = 103; aaDayhoff[ 1][15] = 154; aaDayhoff[ 1][16] = 26; aaDayhoff[ 1][17] = 201; aaDayhoff[ 1][18] = 8; aaDayhoff[ 1][19] = 24; aaDayhoff[ 2][ 0] = 98; aaDayhoff[ 2][ 1] = 32; aaDayhoff[ 2][ 2] = 0; aaDayhoff[ 2][ 3] = 905; aaDayhoff[ 2][ 4] = 0; aaDayhoff[ 2][ 5] = 103; aaDayhoff[ 2][ 6] = 148; aaDayhoff[ 2][ 7] = 139; aaDayhoff[ 2][ 8] = 535; aaDayhoff[ 2][ 9] = 77; aaDayhoff[ 2][10] = 34; aaDayhoff[ 2][11] = 318; aaDayhoff[ 2][12] = 1; aaDayhoff[ 2][13] = 14; aaDayhoff[ 2][14] = 42; aaDayhoff[ 2][15] = 495; aaDayhoff[ 2][16] = 229; aaDayhoff[ 2][17] = 23; aaDayhoff[ 2][18] = 95; aaDayhoff[ 2][19] = 15; aaDayhoff[ 3][ 0] = 120; aaDayhoff[ 3][ 1] = 0; aaDayhoff[ 3][ 2] = 905; aaDayhoff[ 3][ 3] = 0; aaDayhoff[ 3][ 4] = 0; aaDayhoff[ 3][ 5] = 134; aaDayhoff[ 3][ 6] = 1153; aaDayhoff[ 3][ 7] = 125; aaDayhoff[ 3][ 8] = 86; aaDayhoff[ 3][ 9] = 24; aaDayhoff[ 3][10] = 0; aaDayhoff[ 3][11] = 71; aaDayhoff[ 3][12] = 0; aaDayhoff[ 3][13] = 0; aaDayhoff[ 3][14] = 13; aaDayhoff[ 3][15] = 95; aaDayhoff[ 3][16] = 66; aaDayhoff[ 3][17] = 0; aaDayhoff[ 3][18] = 0; aaDayhoff[ 3][19] = 18; aaDayhoff[ 4][ 0] = 36; aaDayhoff[ 4][ 1] = 23; aaDayhoff[ 4][ 2] = 0; aaDayhoff[ 4][ 3] = 0; aaDayhoff[ 4][ 4] = 0; aaDayhoff[ 4][ 5] = 0; aaDayhoff[ 4][ 6] = 0; aaDayhoff[ 4][ 7] = 11; aaDayhoff[ 4][ 8] = 28; aaDayhoff[ 4][ 9] = 44; aaDayhoff[ 4][10] = 0; aaDayhoff[ 4][11] = 0; aaDayhoff[ 4][12] = 0; aaDayhoff[ 4][13] = 0; aaDayhoff[ 4][14] = 19; aaDayhoff[ 4][15] = 161; aaDayhoff[ 4][16] = 16; aaDayhoff[ 4][17] = 0; aaDayhoff[ 4][18] = 96; aaDayhoff[ 4][19] = 49; aaDayhoff[ 5][ 0] = 89; aaDayhoff[ 5][ 1] = 246; aaDayhoff[ 5][ 2] = 103; aaDayhoff[ 5][ 3] = 134; aaDayhoff[ 5][ 4] = 0; aaDayhoff[ 5][ 5] = 0; aaDayhoff[ 5][ 6] = 716; aaDayhoff[ 5][ 7] = 28; aaDayhoff[ 5][ 8] = 606; aaDayhoff[ 5][ 9] = 18; aaDayhoff[ 5][10] = 73; aaDayhoff[ 5][11] = 153; aaDayhoff[ 5][12] = 114; aaDayhoff[ 5][13] = 0; aaDayhoff[ 5][14] = 153; aaDayhoff[ 5][15] = 56; aaDayhoff[ 5][16] = 53; aaDayhoff[ 5][17] = 0; aaDayhoff[ 5][18] = 0; aaDayhoff[ 5][19] = 35; aaDayhoff[ 6][ 0] = 198; aaDayhoff[ 6][ 1] = 1; aaDayhoff[ 6][ 2] = 148; aaDayhoff[ 6][ 3] = 1153; aaDayhoff[ 6][ 4] = 0; aaDayhoff[ 6][ 5] = 716; aaDayhoff[ 6][ 6] = 0; aaDayhoff[ 6][ 7] = 81; aaDayhoff[ 6][ 8] = 43; aaDayhoff[ 6][ 9] = 61; aaDayhoff[ 6][10] = 11; aaDayhoff[ 6][11] = 83; aaDayhoff[ 6][12] = 30; aaDayhoff[ 6][13] = 0; aaDayhoff[ 6][14] = 51; aaDayhoff[ 6][15] = 79; aaDayhoff[ 6][16] = 34; aaDayhoff[ 6][17] = 0; aaDayhoff[ 6][18] = 22; aaDayhoff[ 6][19] = 37; aaDayhoff[ 7][ 0] = 240; aaDayhoff[ 7][ 1] = 9; aaDayhoff[ 7][ 2] = 139; aaDayhoff[ 7][ 3] = 125; aaDayhoff[ 7][ 4] = 11; aaDayhoff[ 7][ 5] = 28; aaDayhoff[ 7][ 6] = 81; aaDayhoff[ 7][ 7] = 0; aaDayhoff[ 7][ 8] = 10; aaDayhoff[ 7][ 9] = 0; aaDayhoff[ 7][10] = 7; aaDayhoff[ 7][11] = 27; aaDayhoff[ 7][12] = 17; aaDayhoff[ 7][13] = 15; aaDayhoff[ 7][14] = 34; aaDayhoff[ 7][15] = 234; aaDayhoff[ 7][16] = 30; aaDayhoff[ 7][17] = 0; aaDayhoff[ 7][18] = 0; aaDayhoff[ 7][19] = 54; aaDayhoff[ 8][ 0] = 23; aaDayhoff[ 8][ 1] = 240; aaDayhoff[ 8][ 2] = 535; aaDayhoff[ 8][ 3] = 86; aaDayhoff[ 8][ 4] = 28; aaDayhoff[ 8][ 5] = 606; aaDayhoff[ 8][ 6] = 43; aaDayhoff[ 8][ 7] = 10; aaDayhoff[ 8][ 8] = 0; aaDayhoff[ 8][ 9] = 7; aaDayhoff[ 8][10] = 44; aaDayhoff[ 8][11] = 26; aaDayhoff[ 8][12] = 0; aaDayhoff[ 8][13] = 48; aaDayhoff[ 8][14] = 94; aaDayhoff[ 8][15] = 35; aaDayhoff[ 8][16] = 22; aaDayhoff[ 8][17] = 27; aaDayhoff[ 8][18] = 127; aaDayhoff[ 8][19] = 44; aaDayhoff[ 9][ 0] = 65; aaDayhoff[ 9][ 1] = 64; aaDayhoff[ 9][ 2] = 77; aaDayhoff[ 9][ 3] = 24; aaDayhoff[ 9][ 4] = 44; aaDayhoff[ 9][ 5] = 18; aaDayhoff[ 9][ 6] = 61; aaDayhoff[ 9][ 7] = 0; aaDayhoff[ 9][ 8] = 7; aaDayhoff[ 9][ 9] = 0; aaDayhoff[ 9][10] = 257; aaDayhoff[ 9][11] = 46; aaDayhoff[ 9][12] = 336; aaDayhoff[ 9][13] = 196; aaDayhoff[ 9][14] = 12; aaDayhoff[ 9][15] = 24; aaDayhoff[ 9][16] = 192; aaDayhoff[ 9][17] = 0; aaDayhoff[ 9][18] = 37; aaDayhoff[ 9][19] = 889; aaDayhoff[10][ 0] = 41; aaDayhoff[10][ 1] = 15; aaDayhoff[10][ 2] = 34; aaDayhoff[10][ 3] = 0; aaDayhoff[10][ 4] = 0; aaDayhoff[10][ 5] = 73; aaDayhoff[10][ 6] = 11; aaDayhoff[10][ 7] = 7; aaDayhoff[10][ 8] = 44; aaDayhoff[10][ 9] = 257; aaDayhoff[10][10] = 0; aaDayhoff[10][11] = 18; aaDayhoff[10][12] = 527; aaDayhoff[10][13] = 157; aaDayhoff[10][14] = 32; aaDayhoff[10][15] = 17; aaDayhoff[10][16] = 33; aaDayhoff[10][17] = 46; aaDayhoff[10][18] = 28; aaDayhoff[10][19] = 175; aaDayhoff[11][ 0] = 26; aaDayhoff[11][ 1] = 464; aaDayhoff[11][ 2] = 318; aaDayhoff[11][ 3] = 71; aaDayhoff[11][ 4] = 0; aaDayhoff[11][ 5] = 153; aaDayhoff[11][ 6] = 83; aaDayhoff[11][ 7] = 27; aaDayhoff[11][ 8] = 26; aaDayhoff[11][ 9] = 46; aaDayhoff[11][10] = 18; aaDayhoff[11][11] = 0; aaDayhoff[11][12] = 243; aaDayhoff[11][13] = 0; aaDayhoff[11][14] = 33; aaDayhoff[11][15] = 96; aaDayhoff[11][16] = 136; aaDayhoff[11][17] = 0; aaDayhoff[11][18] = 13; aaDayhoff[11][19] = 10; aaDayhoff[12][ 0] = 72; aaDayhoff[12][ 1] = 90; aaDayhoff[12][ 2] = 1; aaDayhoff[12][ 3] = 0; aaDayhoff[12][ 4] = 0; aaDayhoff[12][ 5] = 114; aaDayhoff[12][ 6] = 30; aaDayhoff[12][ 7] = 17; aaDayhoff[12][ 8] = 0; aaDayhoff[12][ 9] = 336; aaDayhoff[12][10] = 527; aaDayhoff[12][11] = 243; aaDayhoff[12][12] = 0; aaDayhoff[12][13] = 92; aaDayhoff[12][14] = 17; aaDayhoff[12][15] = 62; aaDayhoff[12][16] = 104; aaDayhoff[12][17] = 0; aaDayhoff[12][18] = 0; aaDayhoff[12][19] = 258; aaDayhoff[13][ 0] = 18; aaDayhoff[13][ 1] = 14; aaDayhoff[13][ 2] = 14; aaDayhoff[13][ 3] = 0; aaDayhoff[13][ 4] = 0; aaDayhoff[13][ 5] = 0; aaDayhoff[13][ 6] = 0; aaDayhoff[13][ 7] = 15; aaDayhoff[13][ 8] = 48; aaDayhoff[13][ 9] = 196; aaDayhoff[13][10] = 157; aaDayhoff[13][11] = 0; aaDayhoff[13][12] = 92; aaDayhoff[13][13] = 0; aaDayhoff[13][14] = 11; aaDayhoff[13][15] = 46; aaDayhoff[13][16] = 13; aaDayhoff[13][17] = 76; aaDayhoff[13][18] = 698; aaDayhoff[13][19] = 12; aaDayhoff[14][ 0] = 250; aaDayhoff[14][ 1] = 103; aaDayhoff[14][ 2] = 42; aaDayhoff[14][ 3] = 13; aaDayhoff[14][ 4] = 19; aaDayhoff[14][ 5] = 153; aaDayhoff[14][ 6] = 51; aaDayhoff[14][ 7] = 34; aaDayhoff[14][ 8] = 94; aaDayhoff[14][ 9] = 12; aaDayhoff[14][10] = 32; aaDayhoff[14][11] = 33; aaDayhoff[14][12] = 17; aaDayhoff[14][13] = 11; aaDayhoff[14][14] = 0; aaDayhoff[14][15] = 245; aaDayhoff[14][16] = 78; aaDayhoff[14][17] = 0; aaDayhoff[14][18] = 0; aaDayhoff[14][19] = 48; aaDayhoff[15][ 0] = 409; aaDayhoff[15][ 1] = 154; aaDayhoff[15][ 2] = 495; aaDayhoff[15][ 3] = 95; aaDayhoff[15][ 4] = 161; aaDayhoff[15][ 5] = 56; aaDayhoff[15][ 6] = 79; aaDayhoff[15][ 7] = 234; aaDayhoff[15][ 8] = 35; aaDayhoff[15][ 9] = 24; aaDayhoff[15][10] = 17; aaDayhoff[15][11] = 96; aaDayhoff[15][12] = 62; aaDayhoff[15][13] = 46; aaDayhoff[15][14] = 245; aaDayhoff[15][15] = 0; aaDayhoff[15][16] = 550; aaDayhoff[15][17] = 75; aaDayhoff[15][18] = 34; aaDayhoff[15][19] = 30; aaDayhoff[16][ 0] = 371; aaDayhoff[16][ 1] = 26; aaDayhoff[16][ 2] = 229; aaDayhoff[16][ 3] = 66; aaDayhoff[16][ 4] = 16; aaDayhoff[16][ 5] = 53; aaDayhoff[16][ 6] = 34; aaDayhoff[16][ 7] = 30; aaDayhoff[16][ 8] = 22; aaDayhoff[16][ 9] = 192; aaDayhoff[16][10] = 33; aaDayhoff[16][11] = 136; aaDayhoff[16][12] = 104; aaDayhoff[16][13] = 13; aaDayhoff[16][14] = 78; aaDayhoff[16][15] = 550; aaDayhoff[16][16] = 0; aaDayhoff[16][17] = 0; aaDayhoff[16][18] = 42; aaDayhoff[16][19] = 157; aaDayhoff[17][ 0] = 0; aaDayhoff[17][ 1] = 201; aaDayhoff[17][ 2] = 23; aaDayhoff[17][ 3] = 0; aaDayhoff[17][ 4] = 0; aaDayhoff[17][ 5] = 0; aaDayhoff[17][ 6] = 0; aaDayhoff[17][ 7] = 0; aaDayhoff[17][ 8] = 27; aaDayhoff[17][ 9] = 0; aaDayhoff[17][10] = 46; aaDayhoff[17][11] = 0; aaDayhoff[17][12] = 0; aaDayhoff[17][13] = 76; aaDayhoff[17][14] = 0; aaDayhoff[17][15] = 75; aaDayhoff[17][16] = 0; aaDayhoff[17][17] = 0; aaDayhoff[17][18] = 61; aaDayhoff[17][19] = 0; aaDayhoff[18][ 0] = 24; aaDayhoff[18][ 1] = 8; aaDayhoff[18][ 2] = 95; aaDayhoff[18][ 3] = 0; aaDayhoff[18][ 4] = 96; aaDayhoff[18][ 5] = 0; aaDayhoff[18][ 6] = 22; aaDayhoff[18][ 7] = 0; aaDayhoff[18][ 8] = 127; aaDayhoff[18][ 9] = 37; aaDayhoff[18][10] = 28; aaDayhoff[18][11] = 13; aaDayhoff[18][12] = 0; aaDayhoff[18][13] = 698; aaDayhoff[18][14] = 0; aaDayhoff[18][15] = 34; aaDayhoff[18][16] = 42; aaDayhoff[18][17] = 61; aaDayhoff[18][18] = 0; aaDayhoff[18][19] = 28; aaDayhoff[19][ 0] = 208; aaDayhoff[19][ 1] = 24; aaDayhoff[19][ 2] = 15; aaDayhoff[19][ 3] = 18; aaDayhoff[19][ 4] = 49; aaDayhoff[19][ 5] = 35; aaDayhoff[19][ 6] = 37; aaDayhoff[19][ 7] = 54; aaDayhoff[19][ 8] = 44; aaDayhoff[19][ 9] = 889; aaDayhoff[19][10] = 175; aaDayhoff[19][11] = 10; aaDayhoff[19][12] = 258; aaDayhoff[19][13] = 12; aaDayhoff[19][14] = 48; aaDayhoff[19][15] = 30; aaDayhoff[19][16] = 157; aaDayhoff[19][17] = 0; aaDayhoff[19][18] = 28; aaDayhoff[19][19] = 0; dayhoffPi[ 0] = 0.087127; dayhoffPi[ 1] = 0.040904; dayhoffPi[ 2] = 0.040432; dayhoffPi[ 3] = 0.046872; dayhoffPi[ 4] = 0.033474; dayhoffPi[ 5] = 0.038255; dayhoffPi[ 6] = 0.049530; dayhoffPi[ 7] = 0.088612; dayhoffPi[ 8] = 0.033618; dayhoffPi[ 9] = 0.036886; dayhoffPi[10] = 0.085357; dayhoffPi[11] = 0.080482; dayhoffPi[12] = 0.014753; dayhoffPi[13] = 0.039772; dayhoffPi[14] = 0.050680; dayhoffPi[15] = 0.069577; dayhoffPi[16] = 0.058542; dayhoffPi[17] = 0.010494; dayhoffPi[18] = 0.029916; dayhoffPi[19] = 0.064718; /* mtrev24 */ aaMtrev24[ 0][ 0] = 0.00; aaMtrev24[ 0][ 1] = 23.18; aaMtrev24[ 0][ 2] = 26.95; aaMtrev24[ 0][ 3] = 17.67; aaMtrev24[ 0][ 4] = 59.93; aaMtrev24[ 0][ 5] = 1.90; aaMtrev24[ 0][ 6] = 9.77; aaMtrev24[ 0][ 7] = 120.71; aaMtrev24[ 0][ 8] = 13.90; aaMtrev24[ 0][ 9] = 96.49; aaMtrev24[ 0][10] = 25.46; aaMtrev24[ 0][11] = 8.36; aaMtrev24[ 0][12] = 141.88; aaMtrev24[ 0][13] = 6.37; aaMtrev24[ 0][14] = 54.31; aaMtrev24[ 0][15] = 387.86; aaMtrev24[ 0][16] = 480.72; aaMtrev24[ 0][17] = 1.90; aaMtrev24[ 0][18] = 6.48; aaMtrev24[ 0][19] = 195.06; aaMtrev24[ 1][ 0] = 23.18; aaMtrev24[ 1][ 1] = 0.00; aaMtrev24[ 1][ 2] = 13.24; aaMtrev24[ 1][ 3] = 1.90; aaMtrev24[ 1][ 4] = 103.33; aaMtrev24[ 1][ 5] = 220.99; aaMtrev24[ 1][ 6] = 1.90; aaMtrev24[ 1][ 7] = 23.03; aaMtrev24[ 1][ 8] = 165.23; aaMtrev24[ 1][ 9] = 1.90; aaMtrev24[ 1][10] = 15.58; aaMtrev24[ 1][11] = 141.40; aaMtrev24[ 1][12] = 1.90; aaMtrev24[ 1][13] = 4.69; aaMtrev24[ 1][14] = 23.64; aaMtrev24[ 1][15] = 6.04; aaMtrev24[ 1][16] = 2.08; aaMtrev24[ 1][17] = 21.95; aaMtrev24[ 1][18] = 1.90; aaMtrev24[ 1][19] = 7.64; aaMtrev24[ 2][ 0] = 26.95; aaMtrev24[ 2][ 1] = 13.24; aaMtrev24[ 2][ 2] = 0.00; aaMtrev24[ 2][ 3] = 794.38; aaMtrev24[ 2][ 4] = 58.94; aaMtrev24[ 2][ 5] = 173.56; aaMtrev24[ 2][ 6] = 63.05; aaMtrev24[ 2][ 7] = 53.30; aaMtrev24[ 2][ 8] = 496.13; aaMtrev24[ 2][ 9] = 27.10; aaMtrev24[ 2][10] = 15.16; aaMtrev24[ 2][11] = 608.70; aaMtrev24[ 2][12] = 65.41; aaMtrev24[ 2][13] = 15.20; aaMtrev24[ 2][14] = 73.31; aaMtrev24[ 2][15] = 494.39; aaMtrev24[ 2][16] = 238.46; aaMtrev24[ 2][17] = 10.68; aaMtrev24[ 2][18] = 191.36; aaMtrev24[ 2][19] = 1.90; aaMtrev24[ 3][ 0] = 17.67; aaMtrev24[ 3][ 1] = 1.90; aaMtrev24[ 3][ 2] = 794.38; aaMtrev24[ 3][ 3] = 0.00; aaMtrev24[ 3][ 4] = 1.90; aaMtrev24[ 3][ 5] = 55.28; aaMtrev24[ 3][ 6] = 583.55; aaMtrev24[ 3][ 7] = 56.77; aaMtrev24[ 3][ 8] = 113.99; aaMtrev24[ 3][ 9] = 4.34; aaMtrev24[ 3][10] = 1.90; aaMtrev24[ 3][11] = 2.31; aaMtrev24[ 3][12] = 1.90; aaMtrev24[ 3][13] = 4.98; aaMtrev24[ 3][14] = 13.43; aaMtrev24[ 3][15] = 69.02; aaMtrev24[ 3][16] = 28.01; aaMtrev24[ 3][17] = 19.86; aaMtrev24[ 3][18] = 21.21; aaMtrev24[ 3][19] = 1.90; aaMtrev24[ 4][ 0] = 59.93; aaMtrev24[ 4][ 1] = 103.33; aaMtrev24[ 4][ 2] = 58.94; aaMtrev24[ 4][ 3] = 1.90; aaMtrev24[ 4][ 4] = 0.00; aaMtrev24[ 4][ 5] = 75.24; aaMtrev24[ 4][ 6] = 1.90; aaMtrev24[ 4][ 7] = 30.71; aaMtrev24[ 4][ 8] = 141.49; aaMtrev24[ 4][ 9] = 62.73; aaMtrev24[ 4][10] = 25.65; aaMtrev24[ 4][11] = 1.90; aaMtrev24[ 4][12] = 6.18; aaMtrev24[ 4][13] = 70.80; aaMtrev24[ 4][14] = 31.26; aaMtrev24[ 4][15] = 277.05; aaMtrev24[ 4][16] = 179.97; aaMtrev24[ 4][17] = 33.60; aaMtrev24[ 4][18] = 254.77; aaMtrev24[ 4][19] = 1.90; aaMtrev24[ 5][ 0] = 1.90; aaMtrev24[ 5][ 1] = 220.99; aaMtrev24[ 5][ 2] = 173.56; aaMtrev24[ 5][ 3] = 55.28; aaMtrev24[ 5][ 4] = 75.24; aaMtrev24[ 5][ 5] = 0.00; aaMtrev24[ 5][ 6] = 313.56; aaMtrev24[ 5][ 7] = 6.75; aaMtrev24[ 5][ 8] = 582.40; aaMtrev24[ 5][ 9] = 8.34; aaMtrev24[ 5][10] = 39.70; aaMtrev24[ 5][11] = 465.58; aaMtrev24[ 5][12] = 47.37; aaMtrev24[ 5][13] = 19.11; aaMtrev24[ 5][14] = 137.29; aaMtrev24[ 5][15] = 54.11; aaMtrev24[ 5][16] = 94.93; aaMtrev24[ 5][17] = 1.90; aaMtrev24[ 5][18] = 38.82; aaMtrev24[ 5][19] = 19.00; aaMtrev24[ 6][ 0] = 9.77; aaMtrev24[ 6][ 1] = 1.90; aaMtrev24[ 6][ 2] = 63.05; aaMtrev24[ 6][ 3] = 583.55; aaMtrev24[ 6][ 4] = 1.90; aaMtrev24[ 6][ 5] = 313.56; aaMtrev24[ 6][ 6] = 0.00; aaMtrev24[ 6][ 7] = 28.28; aaMtrev24[ 6][ 8] = 49.12; aaMtrev24[ 6][ 9] = 3.31; aaMtrev24[ 6][10] = 1.90; aaMtrev24[ 6][11] = 313.86; aaMtrev24[ 6][12] = 1.90; aaMtrev24[ 6][13] = 2.67; aaMtrev24[ 6][14] = 12.83; aaMtrev24[ 6][15] = 54.71; aaMtrev24[ 6][16] = 14.82; aaMtrev24[ 6][17] = 1.90; aaMtrev24[ 6][18] = 13.12; aaMtrev24[ 6][19] = 21.14; aaMtrev24[ 7][ 0] = 120.71; aaMtrev24[ 7][ 1] = 23.03; aaMtrev24[ 7][ 2] = 53.30; aaMtrev24[ 7][ 3] = 56.77; aaMtrev24[ 7][ 4] = 30.71; aaMtrev24[ 7][ 5] = 6.75; aaMtrev24[ 7][ 6] = 28.28; aaMtrev24[ 7][ 7] = 0.00; aaMtrev24[ 7][ 8] = 1.90; aaMtrev24[ 7][ 9] = 5.98; aaMtrev24[ 7][10] = 2.41; aaMtrev24[ 7][11] = 22.73; aaMtrev24[ 7][12] = 1.90; aaMtrev24[ 7][13] = 1.90; aaMtrev24[ 7][14] = 1.90; aaMtrev24[ 7][15] = 125.93; aaMtrev24[ 7][16] = 11.17; aaMtrev24[ 7][17] = 10.92; aaMtrev24[ 7][18] = 3.21; aaMtrev24[ 7][19] = 2.53; aaMtrev24[ 8][ 0] = 13.90; aaMtrev24[ 8][ 1] = 165.23; aaMtrev24[ 8][ 2] = 496.13; aaMtrev24[ 8][ 3] = 113.99; aaMtrev24[ 8][ 4] = 141.49; aaMtrev24[ 8][ 5] = 582.40; aaMtrev24[ 8][ 6] = 49.12; aaMtrev24[ 8][ 7] = 1.90; aaMtrev24[ 8][ 8] = 0.00; aaMtrev24[ 8][ 9] = 12.26; aaMtrev24[ 8][10] = 11.49; aaMtrev24[ 8][11] = 127.67; aaMtrev24[ 8][12] = 11.97; aaMtrev24[ 8][13] = 48.16; aaMtrev24[ 8][14] = 60.97; aaMtrev24[ 8][15] = 77.46; aaMtrev24[ 8][16] = 44.78; aaMtrev24[ 8][17] = 7.08; aaMtrev24[ 8][18] = 670.14; aaMtrev24[ 8][19] = 1.90; aaMtrev24[ 9][ 0] = 96.49; aaMtrev24[ 9][ 1] = 1.90; aaMtrev24[ 9][ 2] = 27.10; aaMtrev24[ 9][ 3] = 4.34; aaMtrev24[ 9][ 4] = 62.73; aaMtrev24[ 9][ 5] = 8.34; aaMtrev24[ 9][ 6] = 3.31; aaMtrev24[ 9][ 7] = 5.98; aaMtrev24[ 9][ 8] = 12.26; aaMtrev24[ 9][ 9] = 0.00; aaMtrev24[ 9][10] = 329.09; aaMtrev24[ 9][11] = 19.57; aaMtrev24[ 9][12] = 517.98; aaMtrev24[ 9][13] = 84.67; aaMtrev24[ 9][14] = 20.63; aaMtrev24[ 9][15] = 47.70; aaMtrev24[ 9][16] = 368.43; aaMtrev24[ 9][17] = 1.90; aaMtrev24[ 9][18] = 25.01; aaMtrev24[ 9][19] =1222.94; aaMtrev24[10][ 0] = 25.46; aaMtrev24[10][ 1] = 15.58; aaMtrev24[10][ 2] = 15.16; aaMtrev24[10][ 3] = 1.90; aaMtrev24[10][ 4] = 25.65; aaMtrev24[10][ 5] = 39.70; aaMtrev24[10][ 6] = 1.90; aaMtrev24[10][ 7] = 2.41; aaMtrev24[10][ 8] = 11.49; aaMtrev24[10][ 9] = 329.09; aaMtrev24[10][10] = 0.00; aaMtrev24[10][11] = 14.88; aaMtrev24[10][12] = 537.53; aaMtrev24[10][13] = 216.06; aaMtrev24[10][14] = 40.10; aaMtrev24[10][15] = 73.61; aaMtrev24[10][16] = 126.40; aaMtrev24[10][17] = 32.44; aaMtrev24[10][18] = 44.15; aaMtrev24[10][19] = 91.67; aaMtrev24[11][ 0] = 8.36; aaMtrev24[11][ 1] = 141.40; aaMtrev24[11][ 2] = 608.70; aaMtrev24[11][ 3] = 2.31; aaMtrev24[11][ 4] = 1.90; aaMtrev24[11][ 5] = 465.58; aaMtrev24[11][ 6] = 313.86; aaMtrev24[11][ 7] = 22.73; aaMtrev24[11][ 8] = 127.67; aaMtrev24[11][ 9] = 19.57; aaMtrev24[11][10] = 14.88; aaMtrev24[11][11] = 0.00; aaMtrev24[11][12] = 91.37; aaMtrev24[11][13] = 6.44; aaMtrev24[11][14] = 50.10; aaMtrev24[11][15] = 105.79; aaMtrev24[11][16] = 136.33; aaMtrev24[11][17] = 24.00; aaMtrev24[11][18] = 51.17; aaMtrev24[11][19] = 1.90; aaMtrev24[12][ 0] = 141.88; aaMtrev24[12][ 1] = 1.90; aaMtrev24[12][ 2] = 65.41; aaMtrev24[12][ 3] = 1.90; aaMtrev24[12][ 4] = 6.18; aaMtrev24[12][ 5] = 47.37; aaMtrev24[12][ 6] = 1.90; aaMtrev24[12][ 7] = 1.90; aaMtrev24[12][ 8] = 11.97; aaMtrev24[12][ 9] = 517.98; aaMtrev24[12][10] = 537.53; aaMtrev24[12][11] = 91.37; aaMtrev24[12][12] = 0.00; aaMtrev24[12][13] = 90.82; aaMtrev24[12][14] = 18.84; aaMtrev24[12][15] = 111.16; aaMtrev24[12][16] = 528.17; aaMtrev24[12][17] = 21.71; aaMtrev24[12][18] = 39.96; aaMtrev24[12][19] = 387.54; aaMtrev24[13][ 0] = 6.37; aaMtrev24[13][ 1] = 4.69; aaMtrev24[13][ 2] = 15.20; aaMtrev24[13][ 3] = 4.98; aaMtrev24[13][ 4] = 70.80; aaMtrev24[13][ 5] = 19.11; aaMtrev24[13][ 6] = 2.67; aaMtrev24[13][ 7] = 1.90; aaMtrev24[13][ 8] = 48.16; aaMtrev24[13][ 9] = 84.67; aaMtrev24[13][10] = 216.06; aaMtrev24[13][11] = 6.44; aaMtrev24[13][12] = 90.82; aaMtrev24[13][13] = 0.00; aaMtrev24[13][14] = 17.31; aaMtrev24[13][15] = 64.29; aaMtrev24[13][16] = 33.85; aaMtrev24[13][17] = 7.84; aaMtrev24[13][18] = 465.58; aaMtrev24[13][19] = 6.35; aaMtrev24[14][ 0] = 54.31; aaMtrev24[14][ 1] = 23.64; aaMtrev24[14][ 2] = 73.31; aaMtrev24[14][ 3] = 13.43; aaMtrev24[14][ 4] = 31.26; aaMtrev24[14][ 5] = 137.29; aaMtrev24[14][ 6] = 12.83; aaMtrev24[14][ 7] = 1.90; aaMtrev24[14][ 8] = 60.97; aaMtrev24[14][ 9] = 20.63; aaMtrev24[14][10] = 40.10; aaMtrev24[14][11] = 50.10; aaMtrev24[14][12] = 18.84; aaMtrev24[14][13] = 17.31; aaMtrev24[14][14] = 0.00; aaMtrev24[14][15] = 169.90; aaMtrev24[14][16] = 128.22; aaMtrev24[14][17] = 4.21; aaMtrev24[14][18] = 16.21; aaMtrev24[14][19] = 8.23; aaMtrev24[15][ 0] = 387.86; aaMtrev24[15][ 1] = 6.04; aaMtrev24[15][ 2] = 494.39; aaMtrev24[15][ 3] = 69.02; aaMtrev24[15][ 4] = 277.05; aaMtrev24[15][ 5] = 54.11; aaMtrev24[15][ 6] = 54.71; aaMtrev24[15][ 7] = 125.93; aaMtrev24[15][ 8] = 77.46; aaMtrev24[15][ 9] = 47.70; aaMtrev24[15][10] = 73.61; aaMtrev24[15][11] = 105.79; aaMtrev24[15][12] = 111.16; aaMtrev24[15][13] = 64.29; aaMtrev24[15][14] = 169.90; aaMtrev24[15][15] = 0.00; aaMtrev24[15][16] = 597.21; aaMtrev24[15][17] = 38.58; aaMtrev24[15][18] = 64.92; aaMtrev24[15][19] = 1.90; aaMtrev24[16][ 0] = 480.72; aaMtrev24[16][ 1] = 2.08; aaMtrev24[16][ 2] = 238.46; aaMtrev24[16][ 3] = 28.01; aaMtrev24[16][ 4] = 179.97; aaMtrev24[16][ 5] = 94.93; aaMtrev24[16][ 6] = 14.82; aaMtrev24[16][ 7] = 11.17; aaMtrev24[16][ 8] = 44.78; aaMtrev24[16][ 9] = 368.43; aaMtrev24[16][10] = 126.40; aaMtrev24[16][11] = 136.33; aaMtrev24[16][12] = 528.17; aaMtrev24[16][13] = 33.85; aaMtrev24[16][14] = 128.22; aaMtrev24[16][15] = 597.21; aaMtrev24[16][16] = 0.00; aaMtrev24[16][17] = 9.99; aaMtrev24[16][18] = 38.73; aaMtrev24[16][19] = 204.54; aaMtrev24[17][ 0] = 1.90; aaMtrev24[17][ 1] = 21.95; aaMtrev24[17][ 2] = 10.68; aaMtrev24[17][ 3] = 19.86; aaMtrev24[17][ 4] = 33.60; aaMtrev24[17][ 5] = 1.90; aaMtrev24[17][ 6] = 1.90; aaMtrev24[17][ 7] = 10.92; aaMtrev24[17][ 8] = 7.08; aaMtrev24[17][ 9] = 1.90; aaMtrev24[17][10] = 32.44; aaMtrev24[17][11] = 24.00; aaMtrev24[17][12] = 21.71; aaMtrev24[17][13] = 7.84; aaMtrev24[17][14] = 4.21; aaMtrev24[17][15] = 38.58; aaMtrev24[17][16] = 9.99; aaMtrev24[17][17] = 0.00; aaMtrev24[17][18] = 26.25; aaMtrev24[17][19] = 5.37; aaMtrev24[18][ 0] = 6.48; aaMtrev24[18][ 1] = 1.90; aaMtrev24[18][ 2] = 191.36; aaMtrev24[18][ 3] = 21.21; aaMtrev24[18][ 4] = 254.77; aaMtrev24[18][ 5] = 38.82; aaMtrev24[18][ 6] = 13.12; aaMtrev24[18][ 7] = 3.21; aaMtrev24[18][ 8] = 670.14; aaMtrev24[18][ 9] = 25.01; aaMtrev24[18][10] = 44.15; aaMtrev24[18][11] = 51.17; aaMtrev24[18][12] = 39.96; aaMtrev24[18][13] = 465.58; aaMtrev24[18][14] = 16.21; aaMtrev24[18][15] = 64.92; aaMtrev24[18][16] = 38.73; aaMtrev24[18][17] = 26.25; aaMtrev24[18][18] = 0.00; aaMtrev24[18][19] = 1.90; aaMtrev24[19][ 0] = 195.06; aaMtrev24[19][ 1] = 7.64; aaMtrev24[19][ 2] = 1.90; aaMtrev24[19][ 3] = 1.90; aaMtrev24[19][ 4] = 1.90; aaMtrev24[19][ 5] = 19.00; aaMtrev24[19][ 6] = 21.14; aaMtrev24[19][ 7] = 2.53; aaMtrev24[19][ 8] = 1.90; aaMtrev24[19][ 9] =1222.94; aaMtrev24[19][10] = 91.67; aaMtrev24[19][11] = 1.90; aaMtrev24[19][12] = 387.54; aaMtrev24[19][13] = 6.35; aaMtrev24[19][14] = 8.23; aaMtrev24[19][15] = 1.90; aaMtrev24[19][16] = 204.54; aaMtrev24[19][17] = 5.37; aaMtrev24[19][18] = 1.90; aaMtrev24[19][19] = 0.00; mtrev24Pi[ 0] = 0.072; mtrev24Pi[ 1] = 0.019; mtrev24Pi[ 2] = 0.039; mtrev24Pi[ 3] = 0.019; mtrev24Pi[ 4] = 0.006; mtrev24Pi[ 5] = 0.025; mtrev24Pi[ 6] = 0.024; mtrev24Pi[ 7] = 0.056; mtrev24Pi[ 8] = 0.028; mtrev24Pi[ 9] = 0.088; mtrev24Pi[10] = 0.168; mtrev24Pi[11] = 0.023; mtrev24Pi[12] = 0.054; mtrev24Pi[13] = 0.061; mtrev24Pi[14] = 0.054; mtrev24Pi[15] = 0.072; mtrev24Pi[16] = 0.086; mtrev24Pi[17] = 0.029; mtrev24Pi[18] = 0.033; mtrev24Pi[19] = 0.043; /* mtmam */ aaMtmam[ 0][ 0] = 0; aaMtmam[ 0][ 1] = 32; aaMtmam[ 0][ 2] = 2; aaMtmam[ 0][ 3] = 11; aaMtmam[ 0][ 4] = 0; aaMtmam[ 0][ 5] = 0; aaMtmam[ 0][ 6] = 0; aaMtmam[ 0][ 7] = 78; aaMtmam[ 0][ 8] = 8; aaMtmam[ 0][ 9] = 75; aaMtmam[ 0][10] = 21; aaMtmam[ 0][11] = 0; aaMtmam[ 0][12] = 76; aaMtmam[ 0][13] = 0; aaMtmam[ 0][14] = 53; aaMtmam[ 0][15] = 342; aaMtmam[ 0][16] = 681; aaMtmam[ 0][17] = 5; aaMtmam[ 0][18] = 0; aaMtmam[ 0][19] = 398; aaMtmam[ 1][ 0] = 32; aaMtmam[ 1][ 1] = 0; aaMtmam[ 1][ 2] = 4; aaMtmam[ 1][ 3] = 0; aaMtmam[ 1][ 4] = 186; aaMtmam[ 1][ 5] = 246; aaMtmam[ 1][ 6] = 0; aaMtmam[ 1][ 7] = 18; aaMtmam[ 1][ 8] = 232; aaMtmam[ 1][ 9] = 0; aaMtmam[ 1][10] = 6; aaMtmam[ 1][11] = 50; aaMtmam[ 1][12] = 0; aaMtmam[ 1][13] = 0; aaMtmam[ 1][14] = 9; aaMtmam[ 1][15] = 3; aaMtmam[ 1][16] = 0; aaMtmam[ 1][17] = 16; aaMtmam[ 1][18] = 0; aaMtmam[ 1][19] = 0; aaMtmam[ 2][ 0] = 2; aaMtmam[ 2][ 1] = 4; aaMtmam[ 2][ 2] = 0; aaMtmam[ 2][ 3] = 864; aaMtmam[ 2][ 4] = 0; aaMtmam[ 2][ 5] = 8; aaMtmam[ 2][ 6] = 0; aaMtmam[ 2][ 7] = 47; aaMtmam[ 2][ 8] = 458; aaMtmam[ 2][ 9] = 19; aaMtmam[ 2][10] = 0; aaMtmam[ 2][11] = 408; aaMtmam[ 2][12] = 21; aaMtmam[ 2][13] = 6; aaMtmam[ 2][14] = 33; aaMtmam[ 2][15] = 446; aaMtmam[ 2][16] = 110; aaMtmam[ 2][17] = 6; aaMtmam[ 2][18] = 156; aaMtmam[ 2][19] = 0; aaMtmam[ 3][ 0] = 11; aaMtmam[ 3][ 1] = 0; aaMtmam[ 3][ 2] = 864; aaMtmam[ 3][ 3] = 0; aaMtmam[ 3][ 4] = 0; aaMtmam[ 3][ 5] = 49; aaMtmam[ 3][ 6] = 569; aaMtmam[ 3][ 7] = 79; aaMtmam[ 3][ 8] = 11; aaMtmam[ 3][ 9] = 0; aaMtmam[ 3][10] = 0; aaMtmam[ 3][11] = 0; aaMtmam[ 3][12] = 0; aaMtmam[ 3][13] = 5; aaMtmam[ 3][14] = 2; aaMtmam[ 3][15] = 16; aaMtmam[ 3][16] = 0; aaMtmam[ 3][17] = 0; aaMtmam[ 3][18] = 0; aaMtmam[ 3][19] = 10; aaMtmam[ 4][ 0] = 0; aaMtmam[ 4][ 1] = 186; aaMtmam[ 4][ 2] = 0; aaMtmam[ 4][ 3] = 0; aaMtmam[ 4][ 4] = 0; aaMtmam[ 4][ 5] = 0; aaMtmam[ 4][ 6] = 0; aaMtmam[ 4][ 7] = 0; aaMtmam[ 4][ 8] = 305; aaMtmam[ 4][ 9] = 41; aaMtmam[ 4][10] = 27; aaMtmam[ 4][11] = 0; aaMtmam[ 4][12] = 0; aaMtmam[ 4][13] = 7; aaMtmam[ 4][14] = 0; aaMtmam[ 4][15] = 347; aaMtmam[ 4][16] = 114; aaMtmam[ 4][17] = 65; aaMtmam[ 4][18] = 530; aaMtmam[ 4][19] = 0; aaMtmam[ 5][ 0] = 0; aaMtmam[ 5][ 1] = 246; aaMtmam[ 5][ 2] = 8; aaMtmam[ 5][ 3] = 49; aaMtmam[ 5][ 4] = 0; aaMtmam[ 5][ 5] = 0; aaMtmam[ 5][ 6] = 274; aaMtmam[ 5][ 7] = 0; aaMtmam[ 5][ 8] = 550; aaMtmam[ 5][ 9] = 0; aaMtmam[ 5][10] = 20; aaMtmam[ 5][11] = 242; aaMtmam[ 5][12] = 22; aaMtmam[ 5][13] = 0; aaMtmam[ 5][14] = 51; aaMtmam[ 5][15] = 30; aaMtmam[ 5][16] = 0; aaMtmam[ 5][17] = 0; aaMtmam[ 5][18] = 54; aaMtmam[ 5][19] = 33; aaMtmam[ 6][ 0] = 0; aaMtmam[ 6][ 1] = 0; aaMtmam[ 6][ 2] = 0; aaMtmam[ 6][ 3] = 569; aaMtmam[ 6][ 4] = 0; aaMtmam[ 6][ 5] = 274; aaMtmam[ 6][ 6] = 0; aaMtmam[ 6][ 7] = 22; aaMtmam[ 6][ 8] = 22; aaMtmam[ 6][ 9] = 0; aaMtmam[ 6][10] = 0; aaMtmam[ 6][11] = 215; aaMtmam[ 6][12] = 0; aaMtmam[ 6][13] = 0; aaMtmam[ 6][14] = 0; aaMtmam[ 6][15] = 21; aaMtmam[ 6][16] = 4; aaMtmam[ 6][17] = 0; aaMtmam[ 6][18] = 0; aaMtmam[ 6][19] = 20; aaMtmam[ 7][ 0] = 78; aaMtmam[ 7][ 1] = 18; aaMtmam[ 7][ 2] = 47; aaMtmam[ 7][ 3] = 79; aaMtmam[ 7][ 4] = 0; aaMtmam[ 7][ 5] = 0; aaMtmam[ 7][ 6] = 22; aaMtmam[ 7][ 7] = 0; aaMtmam[ 7][ 8] = 0; aaMtmam[ 7][ 9] = 0; aaMtmam[ 7][10] = 0; aaMtmam[ 7][11] = 0; aaMtmam[ 7][12] = 0; aaMtmam[ 7][13] = 0; aaMtmam[ 7][14] = 0; aaMtmam[ 7][15] = 112; aaMtmam[ 7][16] = 0; aaMtmam[ 7][17] = 0; aaMtmam[ 7][18] = 1; aaMtmam[ 7][19] = 5; aaMtmam[ 8][ 0] = 8; aaMtmam[ 8][ 1] = 232; aaMtmam[ 8][ 2] = 458; aaMtmam[ 8][ 3] = 11; aaMtmam[ 8][ 4] = 305; aaMtmam[ 8][ 5] = 550; aaMtmam[ 8][ 6] = 22; aaMtmam[ 8][ 7] = 0; aaMtmam[ 8][ 8] = 0; aaMtmam[ 8][ 9] = 0; aaMtmam[ 8][10] = 26; aaMtmam[ 8][11] = 0; aaMtmam[ 8][12] = 0; aaMtmam[ 8][13] = 0; aaMtmam[ 8][14] = 53; aaMtmam[ 8][15] = 20; aaMtmam[ 8][16] = 1; aaMtmam[ 8][17] = 0; aaMtmam[ 8][18] =1525; aaMtmam[ 8][19] = 0; aaMtmam[ 9][ 0] = 75; aaMtmam[ 9][ 1] = 0; aaMtmam[ 9][ 2] = 19; aaMtmam[ 9][ 3] = 0; aaMtmam[ 9][ 4] = 41; aaMtmam[ 9][ 5] = 0; aaMtmam[ 9][ 6] = 0; aaMtmam[ 9][ 7] = 0; aaMtmam[ 9][ 8] = 0; aaMtmam[ 9][ 9] = 0; aaMtmam[ 9][10] = 232; aaMtmam[ 9][11] = 6; aaMtmam[ 9][12] = 378; aaMtmam[ 9][13] = 57; aaMtmam[ 9][14] = 5; aaMtmam[ 9][15] = 0; aaMtmam[ 9][16] = 360; aaMtmam[ 9][17] = 0; aaMtmam[ 9][18] = 16; aaMtmam[ 9][19] =2220; aaMtmam[10][ 0] = 21; aaMtmam[10][ 1] = 6; aaMtmam[10][ 2] = 0; aaMtmam[10][ 3] = 0; aaMtmam[10][ 4] = 27; aaMtmam[10][ 5] = 20; aaMtmam[10][ 6] = 0; aaMtmam[10][ 7] = 0; aaMtmam[10][ 8] = 26; aaMtmam[10][ 9] = 232; aaMtmam[10][10] = 0; aaMtmam[10][11] = 4; aaMtmam[10][12] = 609; aaMtmam[10][13] = 246; aaMtmam[10][14] = 43; aaMtmam[10][15] = 74; aaMtmam[10][16] = 34; aaMtmam[10][17] = 12; aaMtmam[10][18] = 25; aaMtmam[10][19] = 100; aaMtmam[11][ 0] = 0; aaMtmam[11][ 1] = 50; aaMtmam[11][ 2] = 408; aaMtmam[11][ 3] = 0; aaMtmam[11][ 4] = 0; aaMtmam[11][ 5] = 242; aaMtmam[11][ 6] = 215; aaMtmam[11][ 7] = 0; aaMtmam[11][ 8] = 0; aaMtmam[11][ 9] = 6; aaMtmam[11][10] = 4; aaMtmam[11][11] = 0; aaMtmam[11][12] = 59; aaMtmam[11][13] = 0; aaMtmam[11][14] = 18; aaMtmam[11][15] = 65; aaMtmam[11][16] = 50; aaMtmam[11][17] = 0; aaMtmam[11][18] = 67; aaMtmam[11][19] = 0; aaMtmam[12][ 0] = 76; aaMtmam[12][ 1] = 0; aaMtmam[12][ 2] = 21; aaMtmam[12][ 3] = 0; aaMtmam[12][ 4] = 0; aaMtmam[12][ 5] = 22; aaMtmam[12][ 6] = 0; aaMtmam[12][ 7] = 0; aaMtmam[12][ 8] = 0; aaMtmam[12][ 9] = 378; aaMtmam[12][10] = 609; aaMtmam[12][11] = 59; aaMtmam[12][12] = 0; aaMtmam[12][13] = 11; aaMtmam[12][14] = 0; aaMtmam[12][15] = 47; aaMtmam[12][16] = 691; aaMtmam[12][17] = 13; aaMtmam[12][18] = 0; aaMtmam[12][19] = 832; aaMtmam[13][ 0] = 0; aaMtmam[13][ 1] = 0; aaMtmam[13][ 2] = 6; aaMtmam[13][ 3] = 5; aaMtmam[13][ 4] = 7; aaMtmam[13][ 5] = 0; aaMtmam[13][ 6] = 0; aaMtmam[13][ 7] = 0; aaMtmam[13][ 8] = 0; aaMtmam[13][ 9] = 57; aaMtmam[13][10] = 246; aaMtmam[13][11] = 0; aaMtmam[13][12] = 11; aaMtmam[13][13] = 0; aaMtmam[13][14] = 17; aaMtmam[13][15] = 90; aaMtmam[13][16] = 8; aaMtmam[13][17] = 0; aaMtmam[13][18] = 682; aaMtmam[13][19] = 6; aaMtmam[14][ 0] = 53; aaMtmam[14][ 1] = 9; aaMtmam[14][ 2] = 33; aaMtmam[14][ 3] = 2; aaMtmam[14][ 4] = 0; aaMtmam[14][ 5] = 51; aaMtmam[14][ 6] = 0; aaMtmam[14][ 7] = 0; aaMtmam[14][ 8] = 53; aaMtmam[14][ 9] = 5; aaMtmam[14][10] = 43; aaMtmam[14][11] = 18; aaMtmam[14][12] = 0; aaMtmam[14][13] = 17; aaMtmam[14][14] = 0; aaMtmam[14][15] = 202; aaMtmam[14][16] = 78; aaMtmam[14][17] = 7; aaMtmam[14][18] = 8; aaMtmam[14][19] = 0; aaMtmam[15][ 0] = 342; aaMtmam[15][ 1] = 3; aaMtmam[15][ 2] = 446; aaMtmam[15][ 3] = 16; aaMtmam[15][ 4] = 347; aaMtmam[15][ 5] = 30; aaMtmam[15][ 6] = 21; aaMtmam[15][ 7] = 112; aaMtmam[15][ 8] = 20; aaMtmam[15][ 9] = 0; aaMtmam[15][10] = 74; aaMtmam[15][11] = 65; aaMtmam[15][12] = 47; aaMtmam[15][13] = 90; aaMtmam[15][14] = 202; aaMtmam[15][15] = 0; aaMtmam[15][16] = 614; aaMtmam[15][17] = 17; aaMtmam[15][18] = 107; aaMtmam[15][19] = 0; aaMtmam[16][ 0] = 681; aaMtmam[16][ 1] = 0; aaMtmam[16][ 2] = 110; aaMtmam[16][ 3] = 0; aaMtmam[16][ 4] = 114; aaMtmam[16][ 5] = 0; aaMtmam[16][ 6] = 4; aaMtmam[16][ 7] = 0; aaMtmam[16][ 8] = 1; aaMtmam[16][ 9] = 360; aaMtmam[16][10] = 34; aaMtmam[16][11] = 50; aaMtmam[16][12] = 691; aaMtmam[16][13] = 8; aaMtmam[16][14] = 78; aaMtmam[16][15] = 614; aaMtmam[16][16] = 0; aaMtmam[16][17] = 0; aaMtmam[16][18] = 0; aaMtmam[16][19] = 237; aaMtmam[17][ 0] = 5; aaMtmam[17][ 1] = 16; aaMtmam[17][ 2] = 6; aaMtmam[17][ 3] = 0; aaMtmam[17][ 4] = 65; aaMtmam[17][ 5] = 0; aaMtmam[17][ 6] = 0; aaMtmam[17][ 7] = 0; aaMtmam[17][ 8] = 0; aaMtmam[17][ 9] = 0; aaMtmam[17][10] = 12; aaMtmam[17][11] = 0; aaMtmam[17][12] = 13; aaMtmam[17][13] = 0; aaMtmam[17][14] = 7; aaMtmam[17][15] = 17; aaMtmam[17][16] = 0; aaMtmam[17][17] = 0; aaMtmam[17][18] = 14; aaMtmam[17][19] = 0; aaMtmam[18][ 0] = 0; aaMtmam[18][ 1] = 0; aaMtmam[18][ 2] = 156; aaMtmam[18][ 3] = 0; aaMtmam[18][ 4] = 530; aaMtmam[18][ 5] = 54; aaMtmam[18][ 6] = 0; aaMtmam[18][ 7] = 1; aaMtmam[18][ 8] =1525; aaMtmam[18][ 9] = 16; aaMtmam[18][10] = 25; aaMtmam[18][11] = 67; aaMtmam[18][12] = 0; aaMtmam[18][13] = 682; aaMtmam[18][14] = 8; aaMtmam[18][15] = 107; aaMtmam[18][16] = 0; aaMtmam[18][17] = 14; aaMtmam[18][18] = 0; aaMtmam[18][19] = 0; aaMtmam[19][ 0] = 398; aaMtmam[19][ 1] = 0; aaMtmam[19][ 2] = 0; aaMtmam[19][ 3] = 10; aaMtmam[19][ 4] = 0; aaMtmam[19][ 5] = 33; aaMtmam[19][ 6] = 20; aaMtmam[19][ 7] = 5; aaMtmam[19][ 8] = 0; aaMtmam[19][ 9] =2220; aaMtmam[19][10] = 100; aaMtmam[19][11] = 0; aaMtmam[19][12] = 832; aaMtmam[19][13] = 6; aaMtmam[19][14] = 0; aaMtmam[19][15] = 0; aaMtmam[19][16] = 237; aaMtmam[19][17] = 0; aaMtmam[19][18] = 0; aaMtmam[19][19] = 0; mtmamPi[ 0] = 0.0692; mtmamPi[ 1] = 0.0184; mtmamPi[ 2] = 0.0400; mtmamPi[ 3] = 0.0186; mtmamPi[ 4] = 0.0065; mtmamPi[ 5] = 0.0238; mtmamPi[ 6] = 0.0236; mtmamPi[ 7] = 0.0557; mtmamPi[ 8] = 0.0277; mtmamPi[ 9] = 0.0905; mtmamPi[10] = 0.1675; mtmamPi[11] = 0.0221; mtmamPi[12] = 0.0561; mtmamPi[13] = 0.0611; mtmamPi[14] = 0.0536; mtmamPi[15] = 0.0725; mtmamPi[16] = 0.0870; mtmamPi[17] = 0.0293; mtmamPi[18] = 0.0340; mtmamPi[19] = 0.0428; /* rtRev */ aartREV[ 0][ 0] = 0; aartREV[ 1][ 0] = 34; aartREV[ 2][ 0] = 51; aartREV[ 3][ 0] = 10; aartREV[ 4][ 0] = 439; aartREV[ 5][ 0] = 32; aartREV[ 6][ 0] = 81; aartREV[ 7][ 0] = 135; aartREV[ 8][ 0] = 30; aartREV[ 9][ 0] = 1; aartREV[10][ 0] = 45; aartREV[11][ 0] = 38; aartREV[12][ 0] = 235; aartREV[13][ 0] = 1; aartREV[14][ 0] = 97; aartREV[15][ 0] = 460; aartREV[16][ 0] = 258; aartREV[17][ 0] = 5; aartREV[18][ 0] = 55; aartREV[19][ 0] = 197; aartREV[ 0][ 1] = 34; aartREV[ 1][ 1] = 0; aartREV[ 2][ 1] = 35; aartREV[ 3][ 1] = 30; aartREV[ 4][ 1] = 92; aartREV[ 5][ 1] = 221; aartREV[ 6][ 1] = 10; aartREV[ 7][ 1] = 41; aartREV[ 8][ 1] = 90; aartREV[ 9][ 1] = 24; aartREV[10][ 1] = 18; aartREV[11][ 1] = 593; aartREV[12][ 1] = 57; aartREV[13][ 1] = 7; aartREV[14][ 1] = 24; aartREV[15][ 1] = 102; aartREV[16][ 1] = 64; aartREV[17][ 1] = 13; aartREV[18][ 1] = 47; aartREV[19][ 1] = 29; aartREV[ 0][ 2] = 51; aartREV[ 1][ 2] = 35; aartREV[ 2][ 2] = 0; aartREV[ 3][ 2] = 384; aartREV[ 4][ 2] = 128; aartREV[ 5][ 2] = 236; aartREV[ 6][ 2] = 79; aartREV[ 7][ 2] = 94; aartREV[ 8][ 2] = 320; aartREV[ 9][ 2] = 35; aartREV[10][ 2] = 15; aartREV[11][ 2] = 123; aartREV[12][ 2] = 1; aartREV[13][ 2] = 49; aartREV[14][ 2] = 33; aartREV[15][ 2] = 294; aartREV[16][ 2] = 148; aartREV[17][ 2] = 16; aartREV[18][ 2] = 28; aartREV[19][ 2] = 21; aartREV[ 0][ 3] = 10; aartREV[ 1][ 3] = 30; aartREV[ 2][ 3] = 384; aartREV[ 3][ 3] = 0; aartREV[ 4][ 3] = 1; aartREV[ 5][ 3] = 78; aartREV[ 6][ 3] = 542; aartREV[ 7][ 3] = 61; aartREV[ 8][ 3] = 91; aartREV[ 9][ 3] = 1; aartREV[10][ 3] = 5; aartREV[11][ 3] = 20; aartREV[12][ 3] = 1; aartREV[13][ 3] = 1; aartREV[14][ 3] = 55; aartREV[15][ 3] = 136; aartREV[16][ 3] = 55; aartREV[17][ 3] = 1; aartREV[18][ 3] = 1; aartREV[19][ 3] = 6; aartREV[ 0][ 4] = 439; aartREV[ 1][ 4] = 92; aartREV[ 2][ 4] = 128; aartREV[ 3][ 4] = 1; aartREV[ 4][ 4] = 0; aartREV[ 5][ 4] = 70; aartREV[ 6][ 4] = 1; aartREV[ 7][ 4] = 48; aartREV[ 8][ 4] = 124; aartREV[ 9][ 4] = 104; aartREV[10][ 4] = 110; aartREV[11][ 4] = 16; aartREV[12][ 4] = 156; aartREV[13][ 4] = 70; aartREV[14][ 4] = 1; aartREV[15][ 4] = 75; aartREV[16][ 4] = 117; aartREV[17][ 4] = 55; aartREV[18][ 4] = 131; aartREV[19][ 4] = 295; aartREV[ 0][ 5] = 32; aartREV[ 1][ 5] = 221; aartREV[ 2][ 5] = 236; aartREV[ 3][ 5] = 78; aartREV[ 4][ 5] = 70; aartREV[ 5][ 5] = 0; aartREV[ 6][ 5] = 372; aartREV[ 7][ 5] = 18; aartREV[ 8][ 5] = 387; aartREV[ 9][ 5] = 33; aartREV[10][ 5] = 54; aartREV[11][ 5] = 309; aartREV[12][ 5] = 158; aartREV[13][ 5] = 1; aartREV[14][ 5] = 68; aartREV[15][ 5] = 225; aartREV[16][ 5] = 146; aartREV[17][ 5] = 10; aartREV[18][ 5] = 45; aartREV[19][ 5] = 36; aartREV[ 0][ 6] = 81; aartREV[ 1][ 6] = 10; aartREV[ 2][ 6] = 79; aartREV[ 3][ 6] = 542; aartREV[ 4][ 6] = 1; aartREV[ 5][ 6] = 372; aartREV[ 6][ 6] = 0; aartREV[ 7][ 6] = 70; aartREV[ 8][ 6] = 34; aartREV[ 9][ 6] = 1; aartREV[10][ 6] = 21; aartREV[11][ 6] = 141; aartREV[12][ 6] = 1; aartREV[13][ 6] = 1; aartREV[14][ 6] = 52; aartREV[15][ 6] = 95; aartREV[16][ 6] = 82; aartREV[17][ 6] = 17; aartREV[18][ 6] = 1; aartREV[19][ 6] = 35; aartREV[ 0][ 7] = 135; aartREV[ 1][ 7] = 41; aartREV[ 2][ 7] = 94; aartREV[ 3][ 7] = 61; aartREV[ 4][ 7] = 48; aartREV[ 5][ 7] = 18; aartREV[ 6][ 7] = 70; aartREV[ 7][ 7] = 0; aartREV[ 8][ 7] = 68; aartREV[ 9][ 7] = 1; aartREV[10][ 7] = 3; aartREV[11][ 7] = 30; aartREV[12][ 7] = 37; aartREV[13][ 7] = 7; aartREV[14][ 7] = 17; aartREV[15][ 7] = 152; aartREV[16][ 7] = 7; aartREV[17][ 7] = 23; aartREV[18][ 7] = 21; aartREV[19][ 7] = 3; aartREV[ 0][ 8] = 30; aartREV[ 1][ 8] = 90; aartREV[ 2][ 8] = 320; aartREV[ 3][ 8] = 91; aartREV[ 4][ 8] = 124; aartREV[ 5][ 8] = 387; aartREV[ 6][ 8] = 34; aartREV[ 7][ 8] = 68; aartREV[ 8][ 8] = 0; aartREV[ 9][ 8] = 34; aartREV[10][ 8] = 51; aartREV[11][ 8] = 76; aartREV[12][ 8] = 116; aartREV[13][ 8] = 141; aartREV[14][ 8] = 44; aartREV[15][ 8] = 183; aartREV[16][ 8] = 49; aartREV[17][ 8] = 48; aartREV[18][ 8] = 307; aartREV[19][ 8] = 1; aartREV[ 0][ 9] = 1; aartREV[ 1][ 9] = 24; aartREV[ 2][ 9] = 35; aartREV[ 3][ 9] = 1; aartREV[ 4][ 9] = 104; aartREV[ 5][ 9] = 33; aartREV[ 6][ 9] = 1; aartREV[ 7][ 9] = 1; aartREV[ 8][ 9] = 34; aartREV[ 9][ 9] = 0; aartREV[10][ 9] = 385; aartREV[11][ 9] = 34; aartREV[12][ 9] = 375; aartREV[13][ 9] = 64; aartREV[14][ 9] = 10; aartREV[15][ 9] = 4; aartREV[16][ 9] = 72; aartREV[17][ 9] = 39; aartREV[18][ 9] = 26; aartREV[19][ 9] =1048; aartREV[ 0][10] = 45; aartREV[ 1][10] = 18; aartREV[ 2][10] = 15; aartREV[ 3][10] = 5; aartREV[ 4][10] = 110; aartREV[ 5][10] = 54; aartREV[ 6][10] = 21; aartREV[ 7][10] = 3; aartREV[ 8][10] = 51; aartREV[ 9][10] = 385; aartREV[10][10] = 0; aartREV[11][10] = 23; aartREV[12][10] = 581; aartREV[13][10] = 179; aartREV[14][10] = 22; aartREV[15][10] = 24; aartREV[16][10] = 25; aartREV[17][10] = 47; aartREV[18][10] = 64; aartREV[19][10] = 112; aartREV[ 0][11] = 38; aartREV[ 1][11] = 593; aartREV[ 2][11] = 123; aartREV[ 3][11] = 20; aartREV[ 4][11] = 16; aartREV[ 5][11] = 309; aartREV[ 6][11] = 141; aartREV[ 7][11] = 30; aartREV[ 8][11] = 76; aartREV[ 9][11] = 34; aartREV[10][11] = 23; aartREV[11][11] = 0; aartREV[12][11] = 134; aartREV[13][11] = 14; aartREV[14][11] = 43; aartREV[15][11] = 77; aartREV[16][11] = 110; aartREV[17][11] = 6; aartREV[18][11] = 1; aartREV[19][11] = 19; aartREV[ 0][12] = 235; aartREV[ 1][12] = 57; aartREV[ 2][12] = 1; aartREV[ 3][12] = 1; aartREV[ 4][12] = 156; aartREV[ 5][12] = 158; aartREV[ 6][12] = 1; aartREV[ 7][12] = 37; aartREV[ 8][12] = 116; aartREV[ 9][12] = 375; aartREV[10][12] = 581; aartREV[11][12] = 134; aartREV[12][12] = 0; aartREV[13][12] = 247; aartREV[14][12] = 1; aartREV[15][12] = 1; aartREV[16][12] = 131; aartREV[17][12] = 111; aartREV[18][12] = 74; aartREV[19][12] = 236; aartREV[ 0][13] = 1; aartREV[ 1][13] = 7; aartREV[ 2][13] = 49; aartREV[ 3][13] = 1; aartREV[ 4][13] = 70; aartREV[ 5][13] = 1; aartREV[ 6][13] = 1; aartREV[ 7][13] = 7; aartREV[ 8][13] = 141; aartREV[ 9][13] = 64; aartREV[10][13] = 179; aartREV[11][13] = 14; aartREV[12][13] = 247; aartREV[13][13] = 0; aartREV[14][13] = 11; aartREV[15][13] = 20; aartREV[16][13] = 69; aartREV[17][13] = 182; aartREV[18][13] =1017; aartREV[19][13] = 92; aartREV[ 0][14] = 97; aartREV[ 1][14] = 24; aartREV[ 2][14] = 33; aartREV[ 3][14] = 55; aartREV[ 4][14] = 1; aartREV[ 5][14] = 68; aartREV[ 6][14] = 52; aartREV[ 7][14] = 17; aartREV[ 8][14] = 44; aartREV[ 9][14] = 10; aartREV[10][14] = 22; aartREV[11][14] = 43; aartREV[12][14] = 1; aartREV[13][14] = 11; aartREV[14][14] = 0; aartREV[15][14] = 134; aartREV[16][14] = 62; aartREV[17][14] = 9; aartREV[18][14] = 14; aartREV[19][14] = 25; aartREV[ 0][15] = 460; aartREV[ 1][15] = 102; aartREV[ 2][15] = 294; aartREV[ 3][15] = 136; aartREV[ 4][15] = 75; aartREV[ 5][15] = 225; aartREV[ 6][15] = 95; aartREV[ 7][15] = 152; aartREV[ 8][15] = 183; aartREV[ 9][15] = 4; aartREV[10][15] = 24; aartREV[11][15] = 77; aartREV[12][15] = 1; aartREV[13][15] = 20; aartREV[14][15] = 134; aartREV[15][15] = 0; aartREV[16][15] = 671; aartREV[17][15] = 14; aartREV[18][15] = 31; aartREV[19][15] = 39; aartREV[ 0][16] = 258; aartREV[ 1][16] = 64; aartREV[ 2][16] = 148; aartREV[ 3][16] = 55; aartREV[ 4][16] = 117; aartREV[ 5][16] = 146; aartREV[ 6][16] = 82; aartREV[ 7][16] = 7; aartREV[ 8][16] = 49; aartREV[ 9][16] = 72; aartREV[10][16] = 25; aartREV[11][16] = 110; aartREV[12][16] = 131; aartREV[13][16] = 69; aartREV[14][16] = 62; aartREV[15][16] = 671; aartREV[16][16] = 0; aartREV[17][16] = 1; aartREV[18][16] = 34; aartREV[19][16] = 196; aartREV[ 0][17] = 5; aartREV[ 1][17] = 13; aartREV[ 2][17] = 16; aartREV[ 3][17] = 1; aartREV[ 4][17] = 55; aartREV[ 5][17] = 10; aartREV[ 6][17] = 17; aartREV[ 7][17] = 23; aartREV[ 8][17] = 48; aartREV[ 9][17] = 39; aartREV[10][17] = 47; aartREV[11][17] = 6; aartREV[12][17] = 111; aartREV[13][17] = 182; aartREV[14][17] = 9; aartREV[15][17] = 14; aartREV[16][17] = 1; aartREV[17][17] = 0; aartREV[18][17] = 176; aartREV[19][17] = 26; aartREV[ 0][18] = 55; aartREV[ 1][18] = 47; aartREV[ 2][18] = 28; aartREV[ 3][18] = 1; aartREV[ 4][18] = 131; aartREV[ 5][18] = 45; aartREV[ 6][18] = 1; aartREV[ 7][18] = 21; aartREV[ 8][18] = 307; aartREV[ 9][18] = 26; aartREV[10][18] = 64; aartREV[11][18] = 1; aartREV[12][18] = 74; aartREV[13][18] =1017; aartREV[14][18] = 14; aartREV[15][18] = 31; aartREV[16][18] = 34; aartREV[17][18] = 176; aartREV[18][18] = 0; aartREV[19][18] = 59; aartREV[ 0][19] = 197; aartREV[ 1][19] = 29; aartREV[ 2][19] = 21; aartREV[ 3][19] = 6; aartREV[ 4][19] = 295; aartREV[ 5][19] = 36; aartREV[ 6][19] = 35; aartREV[ 7][19] = 3; aartREV[ 8][19] = 1; aartREV[ 9][19] =1048; aartREV[10][19] = 112; aartREV[11][19] = 19; aartREV[12][19] = 236; aartREV[13][19] = 92; aartREV[14][19] = 25; aartREV[15][19] = 39; aartREV[16][19] = 196; aartREV[17][19] = 26; aartREV[18][19] = 59; aartREV[19][19] = 0; rtrevPi[ 0] = 0.0646; rtrevPi[ 1] = 0.0453; rtrevPi[ 2] = 0.0376; rtrevPi[ 3] = 0.0422; rtrevPi[ 4] = 0.0114; rtrevPi[ 5] = 0.0606; rtrevPi[ 6] = 0.0607; rtrevPi[ 7] = 0.0639; rtrevPi[ 8] = 0.0273; rtrevPi[ 9] = 0.0679; rtrevPi[10] = 0.1018; rtrevPi[11] = 0.0751; rtrevPi[12] = 0.0150; rtrevPi[13] = 0.0287; rtrevPi[14] = 0.0681; rtrevPi[15] = 0.0488; rtrevPi[16] = 0.0622; rtrevPi[17] = 0.0251; rtrevPi[18] = 0.0318; rtrevPi[19] = 0.0619; /* wag */ aaWAG[ 0][ 0] = 0.0000000; aaWAG[ 1][ 0] = 0.5515710; aaWAG[ 2][ 0] = 0.5098480; aaWAG[ 3][ 0] = 0.7389980; aaWAG[ 4][ 0] = 1.0270400; aaWAG[ 5][ 0] = 0.9085980; aaWAG[ 6][ 0] = 1.5828500; aaWAG[ 7][ 0] = 1.4167200; aaWAG[ 8][ 0] = 0.3169540; aaWAG[ 9][ 0] = 0.1933350; aaWAG[10][ 0] = 0.3979150; aaWAG[11][ 0] = 0.9062650; aaWAG[12][ 0] = 0.8934960; aaWAG[13][ 0] = 0.2104940; aaWAG[14][ 0] = 1.4385500; aaWAG[15][ 0] = 3.3707900; aaWAG[16][ 0] = 2.1211100; aaWAG[17][ 0] = 0.1131330; aaWAG[18][ 0] = 0.2407350; aaWAG[19][ 0] = 2.0060100; aaWAG[ 0][ 1] = 0.5515710; aaWAG[ 1][ 1] = 0.0000000; aaWAG[ 2][ 1] = 0.6353460; aaWAG[ 3][ 1] = 0.1473040; aaWAG[ 4][ 1] = 0.5281910; aaWAG[ 5][ 1] = 3.0355000; aaWAG[ 6][ 1] = 0.4391570; aaWAG[ 7][ 1] = 0.5846650; aaWAG[ 8][ 1] = 2.1371500; aaWAG[ 9][ 1] = 0.1869790; aaWAG[10][ 1] = 0.4976710; aaWAG[11][ 1] = 5.3514200; aaWAG[12][ 1] = 0.6831620; aaWAG[13][ 1] = 0.1027110; aaWAG[14][ 1] = 0.6794890; aaWAG[15][ 1] = 1.2241900; aaWAG[16][ 1] = 0.5544130; aaWAG[17][ 1] = 1.1639200; aaWAG[18][ 1] = 0.3815330; aaWAG[19][ 1] = 0.2518490; aaWAG[ 0][ 2] = 0.5098480; aaWAG[ 1][ 2] = 0.6353460; aaWAG[ 2][ 2] = 0.0000000; aaWAG[ 3][ 2] = 5.4294200; aaWAG[ 4][ 2] = 0.2652560; aaWAG[ 5][ 2] = 1.5436400; aaWAG[ 6][ 2] = 0.9471980; aaWAG[ 7][ 2] = 1.1255600; aaWAG[ 8][ 2] = 3.9562900; aaWAG[ 9][ 2] = 0.5542360; aaWAG[10][ 2] = 0.1315280; aaWAG[11][ 2] = 3.0120100; aaWAG[12][ 2] = 0.1982210; aaWAG[13][ 2] = 0.0961621; aaWAG[14][ 2] = 0.1950810; aaWAG[15][ 2] = 3.9742300; aaWAG[16][ 2] = 2.0300600; aaWAG[17][ 2] = 0.0719167; aaWAG[18][ 2] = 1.0860000; aaWAG[19][ 2] = 0.1962460; aaWAG[ 0][ 3] = 0.7389980; aaWAG[ 1][ 3] = 0.1473040; aaWAG[ 2][ 3] = 5.4294200; aaWAG[ 3][ 3] = 0.0000000; aaWAG[ 4][ 3] = 0.0302949; aaWAG[ 5][ 3] = 0.6167830; aaWAG[ 6][ 3] = 6.1741600; aaWAG[ 7][ 3] = 0.8655840; aaWAG[ 8][ 3] = 0.9306760; aaWAG[ 9][ 3] = 0.0394370; aaWAG[10][ 3] = 0.0848047; aaWAG[11][ 3] = 0.4798550; aaWAG[12][ 3] = 0.1037540; aaWAG[13][ 3] = 0.0467304; aaWAG[14][ 3] = 0.4239840; aaWAG[15][ 3] = 1.0717600; aaWAG[16][ 3] = 0.3748660; aaWAG[17][ 3] = 0.1297670; aaWAG[18][ 3] = 0.3257110; aaWAG[19][ 3] = 0.1523350; aaWAG[ 0][ 4] = 1.0270400; aaWAG[ 1][ 4] = 0.5281910; aaWAG[ 2][ 4] = 0.2652560; aaWAG[ 3][ 4] = 0.0302949; aaWAG[ 4][ 4] = 0.0000000; aaWAG[ 5][ 4] = 0.0988179; aaWAG[ 6][ 4] = 0.0213520; aaWAG[ 7][ 4] = 0.3066740; aaWAG[ 8][ 4] = 0.2489720; aaWAG[ 9][ 4] = 0.1701350; aaWAG[10][ 4] = 0.3842870; aaWAG[11][ 4] = 0.0740339; aaWAG[12][ 4] = 0.3904820; aaWAG[13][ 4] = 0.3980200; aaWAG[14][ 4] = 0.1094040; aaWAG[15][ 4] = 1.4076600; aaWAG[16][ 4] = 0.5129840; aaWAG[17][ 4] = 0.7170700; aaWAG[18][ 4] = 0.5438330; aaWAG[19][ 4] = 1.0021400; aaWAG[ 0][ 5] = 0.9085980; aaWAG[ 1][ 5] = 3.0355000; aaWAG[ 2][ 5] = 1.5436400; aaWAG[ 3][ 5] = 0.6167830; aaWAG[ 4][ 5] = 0.0988179; aaWAG[ 5][ 5] = 0.0000000; aaWAG[ 6][ 5] = 5.4694700; aaWAG[ 7][ 5] = 0.3300520; aaWAG[ 8][ 5] = 4.2941100; aaWAG[ 9][ 5] = 0.1139170; aaWAG[10][ 5] = 0.8694890; aaWAG[11][ 5] = 3.8949000; aaWAG[12][ 5] = 1.5452600; aaWAG[13][ 5] = 0.0999208; aaWAG[14][ 5] = 0.9333720; aaWAG[15][ 5] = 1.0288700; aaWAG[16][ 5] = 0.8579280; aaWAG[17][ 5] = 0.2157370; aaWAG[18][ 5] = 0.2277100; aaWAG[19][ 5] = 0.3012810; aaWAG[ 0][ 6] = 1.5828500; aaWAG[ 1][ 6] = 0.4391570; aaWAG[ 2][ 6] = 0.9471980; aaWAG[ 3][ 6] = 6.1741600; aaWAG[ 4][ 6] = 0.0213520; aaWAG[ 5][ 6] = 5.4694700; aaWAG[ 6][ 6] = 0.0000000; aaWAG[ 7][ 6] = 0.5677170; aaWAG[ 8][ 6] = 0.5700250; aaWAG[ 9][ 6] = 0.1273950; aaWAG[10][ 6] = 0.1542630; aaWAG[11][ 6] = 2.5844300; aaWAG[12][ 6] = 0.3151240; aaWAG[13][ 6] = 0.0811339; aaWAG[14][ 6] = 0.6823550; aaWAG[15][ 6] = 0.7049390; aaWAG[16][ 6] = 0.8227650; aaWAG[17][ 6] = 0.1565570; aaWAG[18][ 6] = 0.1963030; aaWAG[19][ 6] = 0.5887310; aaWAG[ 0][ 7] = 1.4167200; aaWAG[ 1][ 7] = 0.5846650; aaWAG[ 2][ 7] = 1.1255600; aaWAG[ 3][ 7] = 0.8655840; aaWAG[ 4][ 7] = 0.3066740; aaWAG[ 5][ 7] = 0.3300520; aaWAG[ 6][ 7] = 0.5677170; aaWAG[ 7][ 7] = 0.0000000; aaWAG[ 8][ 7] = 0.2494100; aaWAG[ 9][ 7] = 0.0304501; aaWAG[10][ 7] = 0.0613037; aaWAG[11][ 7] = 0.3735580; aaWAG[12][ 7] = 0.1741000; aaWAG[13][ 7] = 0.0499310; aaWAG[14][ 7] = 0.2435700; aaWAG[15][ 7] = 1.3418200; aaWAG[16][ 7] = 0.2258330; aaWAG[17][ 7] = 0.3369830; aaWAG[18][ 7] = 0.1036040; aaWAG[19][ 7] = 0.1872470; aaWAG[ 0][ 8] = 0.3169540; aaWAG[ 1][ 8] = 2.1371500; aaWAG[ 2][ 8] = 3.9562900; aaWAG[ 3][ 8] = 0.9306760; aaWAG[ 4][ 8] = 0.2489720; aaWAG[ 5][ 8] = 4.2941100; aaWAG[ 6][ 8] = 0.5700250; aaWAG[ 7][ 8] = 0.2494100; aaWAG[ 8][ 8] = 0.0000000; aaWAG[ 9][ 8] = 0.1381900; aaWAG[10][ 8] = 0.4994620; aaWAG[11][ 8] = 0.8904320; aaWAG[12][ 8] = 0.4041410; aaWAG[13][ 8] = 0.6793710; aaWAG[14][ 8] = 0.6961980; aaWAG[15][ 8] = 0.7401690; aaWAG[16][ 8] = 0.4733070; aaWAG[17][ 8] = 0.2625690; aaWAG[18][ 8] = 3.8734400; aaWAG[19][ 8] = 0.1183580; aaWAG[ 0][ 9] = 0.1933350; aaWAG[ 1][ 9] = 0.1869790; aaWAG[ 2][ 9] = 0.5542360; aaWAG[ 3][ 9] = 0.0394370; aaWAG[ 4][ 9] = 0.1701350; aaWAG[ 5][ 9] = 0.1139170; aaWAG[ 6][ 9] = 0.1273950; aaWAG[ 7][ 9] = 0.0304501; aaWAG[ 8][ 9] = 0.1381900; aaWAG[ 9][ 9] = 0.0000000; aaWAG[10][ 9] = 3.1709700; aaWAG[11][ 9] = 0.3238320; aaWAG[12][ 9] = 4.2574600; aaWAG[13][ 9] = 1.0594700; aaWAG[14][ 9] = 0.0999288; aaWAG[15][ 9] = 0.3194400; aaWAG[16][ 9] = 1.4581600; aaWAG[17][ 9] = 0.2124830; aaWAG[18][ 9] = 0.4201700; aaWAG[19][ 9] = 7.8213000; aaWAG[ 0][10] = 0.3979150; aaWAG[ 1][10] = 0.4976710; aaWAG[ 2][10] = 0.1315280; aaWAG[ 3][10] = 0.0848047; aaWAG[ 4][10] = 0.3842870; aaWAG[ 5][10] = 0.8694890; aaWAG[ 6][10] = 0.1542630; aaWAG[ 7][10] = 0.0613037; aaWAG[ 8][10] = 0.4994620; aaWAG[ 9][10] = 3.1709700; aaWAG[10][10] = 0.0000000; aaWAG[11][10] = 0.2575550; aaWAG[12][10] = 4.8540200; aaWAG[13][10] = 2.1151700; aaWAG[14][10] = 0.4158440; aaWAG[15][10] = 0.3447390; aaWAG[16][10] = 0.3266220; aaWAG[17][10] = 0.6653090; aaWAG[18][10] = 0.3986180; aaWAG[19][10] = 1.8003400; aaWAG[ 0][11] = 0.9062650; aaWAG[ 1][11] = 5.3514200; aaWAG[ 2][11] = 3.0120100; aaWAG[ 3][11] = 0.4798550; aaWAG[ 4][11] = 0.0740339; aaWAG[ 5][11] = 3.8949000; aaWAG[ 6][11] = 2.5844300; aaWAG[ 7][11] = 0.3735580; aaWAG[ 8][11] = 0.8904320; aaWAG[ 9][11] = 0.3238320; aaWAG[10][11] = 0.2575550; aaWAG[11][11] = 0.0000000; aaWAG[12][11] = 0.9342760; aaWAG[13][11] = 0.0888360; aaWAG[14][11] = 0.5568960; aaWAG[15][11] = 0.9671300; aaWAG[16][11] = 1.3869800; aaWAG[17][11] = 0.1375050; aaWAG[18][11] = 0.1332640; aaWAG[19][11] = 0.3054340; aaWAG[ 0][12] = 0.8934960; aaWAG[ 1][12] = 0.6831620; aaWAG[ 2][12] = 0.1982210; aaWAG[ 3][12] = 0.1037540; aaWAG[ 4][12] = 0.3904820; aaWAG[ 5][12] = 1.5452600; aaWAG[ 6][12] = 0.3151240; aaWAG[ 7][12] = 0.1741000; aaWAG[ 8][12] = 0.4041410; aaWAG[ 9][12] = 4.2574600; aaWAG[10][12] = 4.8540200; aaWAG[11][12] = 0.9342760; aaWAG[12][12] = 0.0000000; aaWAG[13][12] = 1.1906300; aaWAG[14][12] = 0.1713290; aaWAG[15][12] = 0.4939050; aaWAG[16][12] = 1.5161200; aaWAG[17][12] = 0.5157060; aaWAG[18][12] = 0.4284370; aaWAG[19][12] = 2.0584500; aaWAG[ 0][13] = 0.2104940; aaWAG[ 1][13] = 0.1027110; aaWAG[ 2][13] = 0.0961621; aaWAG[ 3][13] = 0.0467304; aaWAG[ 4][13] = 0.3980200; aaWAG[ 5][13] = 0.0999208; aaWAG[ 6][13] = 0.0811339; aaWAG[ 7][13] = 0.0499310; aaWAG[ 8][13] = 0.6793710; aaWAG[ 9][13] = 1.0594700; aaWAG[10][13] = 2.1151700; aaWAG[11][13] = 0.0888360; aaWAG[12][13] = 1.1906300; aaWAG[13][13] = 0.0000000; aaWAG[14][13] = 0.1614440; aaWAG[15][13] = 0.5459310; aaWAG[16][13] = 0.1719030; aaWAG[17][13] = 1.5296400; aaWAG[18][13] = 6.4542800; aaWAG[19][13] = 0.6498920; aaWAG[ 0][14] = 1.4385500; aaWAG[ 1][14] = 0.6794890; aaWAG[ 2][14] = 0.1950810; aaWAG[ 3][14] = 0.4239840; aaWAG[ 4][14] = 0.1094040; aaWAG[ 5][14] = 0.9333720; aaWAG[ 6][14] = 0.6823550; aaWAG[ 7][14] = 0.2435700; aaWAG[ 8][14] = 0.6961980; aaWAG[ 9][14] = 0.0999288; aaWAG[10][14] = 0.4158440; aaWAG[11][14] = 0.5568960; aaWAG[12][14] = 0.1713290; aaWAG[13][14] = 0.1614440; aaWAG[14][14] = 0.0000000; aaWAG[15][14] = 1.6132800; aaWAG[16][14] = 0.7953840; aaWAG[17][14] = 0.1394050; aaWAG[18][14] = 0.2160460; aaWAG[19][14] = 0.3148870; aaWAG[ 0][15] = 3.3707900; aaWAG[ 1][15] = 1.2241900; aaWAG[ 2][15] = 3.9742300; aaWAG[ 3][15] = 1.0717600; aaWAG[ 4][15] = 1.4076600; aaWAG[ 5][15] = 1.0288700; aaWAG[ 6][15] = 0.7049390; aaWAG[ 7][15] = 1.3418200; aaWAG[ 8][15] = 0.7401690; aaWAG[ 9][15] = 0.3194400; aaWAG[10][15] = 0.3447390; aaWAG[11][15] = 0.9671300; aaWAG[12][15] = 0.4939050; aaWAG[13][15] = 0.5459310; aaWAG[14][15] = 1.6132800; aaWAG[15][15] = 0.0000000; aaWAG[16][15] = 4.3780200; aaWAG[17][15] = 0.5237420; aaWAG[18][15] = 0.7869930; aaWAG[19][15] = 0.2327390; aaWAG[ 0][16] = 2.1211100; aaWAG[ 1][16] = 0.5544130; aaWAG[ 2][16] = 2.0300600; aaWAG[ 3][16] = 0.3748660; aaWAG[ 4][16] = 0.5129840; aaWAG[ 5][16] = 0.8579280; aaWAG[ 6][16] = 0.8227650; aaWAG[ 7][16] = 0.2258330; aaWAG[ 8][16] = 0.4733070; aaWAG[ 9][16] = 1.4581600; aaWAG[10][16] = 0.3266220; aaWAG[11][16] = 1.3869800; aaWAG[12][16] = 1.5161200; aaWAG[13][16] = 0.1719030; aaWAG[14][16] = 0.7953840; aaWAG[15][16] = 4.3780200; aaWAG[16][16] = 0.0000000; aaWAG[17][16] = 0.1108640; aaWAG[18][16] = 0.2911480; aaWAG[19][16] = 1.3882300; aaWAG[ 0][17] = 0.1131330; aaWAG[ 1][17] = 1.1639200; aaWAG[ 2][17] = 0.0719167; aaWAG[ 3][17] = 0.1297670; aaWAG[ 4][17] = 0.7170700; aaWAG[ 5][17] = 0.2157370; aaWAG[ 6][17] = 0.1565570; aaWAG[ 7][17] = 0.3369830; aaWAG[ 8][17] = 0.2625690; aaWAG[ 9][17] = 0.2124830; aaWAG[10][17] = 0.6653090; aaWAG[11][17] = 0.1375050; aaWAG[12][17] = 0.5157060; aaWAG[13][17] = 1.5296400; aaWAG[14][17] = 0.1394050; aaWAG[15][17] = 0.5237420; aaWAG[16][17] = 0.1108640; aaWAG[17][17] = 0.0000000; aaWAG[18][17] = 2.4853900; aaWAG[19][17] = 0.3653690; aaWAG[ 0][18] = 0.2407350; aaWAG[ 1][18] = 0.3815330; aaWAG[ 2][18] = 1.0860000; aaWAG[ 3][18] = 0.3257110; aaWAG[ 4][18] = 0.5438330; aaWAG[ 5][18] = 0.2277100; aaWAG[ 6][18] = 0.1963030; aaWAG[ 7][18] = 0.1036040; aaWAG[ 8][18] = 3.8734400; aaWAG[ 9][18] = 0.4201700; aaWAG[10][18] = 0.3986180; aaWAG[11][18] = 0.1332640; aaWAG[12][18] = 0.4284370; aaWAG[13][18] = 6.4542800; aaWAG[14][18] = 0.2160460; aaWAG[15][18] = 0.7869930; aaWAG[16][18] = 0.2911480; aaWAG[17][18] = 2.4853900; aaWAG[18][18] = 0.0000000; aaWAG[19][18] = 0.3147300; aaWAG[ 0][19] = 2.0060100; aaWAG[ 1][19] = 0.2518490; aaWAG[ 2][19] = 0.1962460; aaWAG[ 3][19] = 0.1523350; aaWAG[ 4][19] = 1.0021400; aaWAG[ 5][19] = 0.3012810; aaWAG[ 6][19] = 0.5887310; aaWAG[ 7][19] = 0.1872470; aaWAG[ 8][19] = 0.1183580; aaWAG[ 9][19] = 7.8213000; aaWAG[10][19] = 1.8003400; aaWAG[11][19] = 0.3054340; aaWAG[12][19] = 2.0584500; aaWAG[13][19] = 0.6498920; aaWAG[14][19] = 0.3148870; aaWAG[15][19] = 0.2327390; aaWAG[16][19] = 1.3882300; aaWAG[17][19] = 0.3653690; aaWAG[18][19] = 0.3147300; aaWAG[19][19] = 0.0000000; wagPi[ 0] = 0.08662790; wagPi[ 1] = 0.04397200; wagPi[ 2] = 0.03908940; wagPi[ 3] = 0.05704510; wagPi[ 4] = 0.01930780; wagPi[ 5] = 0.03672810; wagPi[ 6] = 0.05805890; wagPi[ 7] = 0.08325180; wagPi[ 8] = 0.02443130; wagPi[ 9] = 0.04846600; wagPi[10] = 0.08620970; wagPi[11] = 0.06202860; wagPi[12] = 0.01950273; wagPi[13] = 0.03843190; wagPi[14] = 0.04576310; wagPi[15] = 0.06951790; wagPi[16] = 0.06101270; wagPi[17] = 0.01438590; wagPi[18] = 0.03527420; wagPi[19] = 0.07089560; /* cpRev */ aacpREV[ 0][ 0] = 0; aacpREV[ 0][ 1] = 105; aacpREV[ 0][ 2] = 227; aacpREV[ 0][ 3] = 175; aacpREV[ 0][ 4] = 669; aacpREV[ 0][ 5] = 157; aacpREV[ 0][ 6] = 499; aacpREV[ 0][ 7] = 665; aacpREV[ 0][ 8] = 66; aacpREV[ 0][ 9] = 145; aacpREV[ 0][10] = 197; aacpREV[ 0][11] = 236; aacpREV[ 0][12] = 185; aacpREV[ 0][13] = 68; aacpREV[ 0][14] = 490; aacpREV[ 0][15] = 2440; aacpREV[ 0][16] = 1340; aacpREV[ 0][17] = 14; aacpREV[ 0][18] = 56; aacpREV[ 0][19] = 968; aacpREV[ 1][ 0] = 105; aacpREV[ 1][ 1] = 0; aacpREV[ 1][ 2] = 357; aacpREV[ 1][ 3] = 43; aacpREV[ 1][ 4] = 823; aacpREV[ 1][ 5] = 1745; aacpREV[ 1][ 6] = 152; aacpREV[ 1][ 7] = 243; aacpREV[ 1][ 8] = 715; aacpREV[ 1][ 9] = 136; aacpREV[ 1][10] = 203; aacpREV[ 1][11] = 4482; aacpREV[ 1][12] = 125; aacpREV[ 1][13] = 53; aacpREV[ 1][14] = 87; aacpREV[ 1][15] = 385; aacpREV[ 1][16] = 314; aacpREV[ 1][17] = 230; aacpREV[ 1][18] = 323; aacpREV[ 1][19] = 92; aacpREV[ 2][ 0] = 227; aacpREV[ 2][ 1] = 357; aacpREV[ 2][ 2] = 0; aacpREV[ 2][ 3] = 4435; aacpREV[ 2][ 4] = 538; aacpREV[ 2][ 5] = 768; aacpREV[ 2][ 6] = 1055; aacpREV[ 2][ 7] = 653; aacpREV[ 2][ 8] = 1405; aacpREV[ 2][ 9] = 168; aacpREV[ 2][10] = 113; aacpREV[ 2][11] = 2430; aacpREV[ 2][12] = 61; aacpREV[ 2][13] = 97; aacpREV[ 2][14] = 173; aacpREV[ 2][15] = 2085; aacpREV[ 2][16] = 1393; aacpREV[ 2][17] = 40; aacpREV[ 2][18] = 754; aacpREV[ 2][19] = 83; aacpREV[ 3][ 0] = 175; aacpREV[ 3][ 1] = 43; aacpREV[ 3][ 2] = 4435; aacpREV[ 3][ 3] = 0; aacpREV[ 3][ 4] = 10; aacpREV[ 3][ 5] = 400; aacpREV[ 3][ 6] = 3691; aacpREV[ 3][ 7] = 431; aacpREV[ 3][ 8] = 331; aacpREV[ 3][ 9] = 10; aacpREV[ 3][10] = 10; aacpREV[ 3][11] = 412; aacpREV[ 3][12] = 47; aacpREV[ 3][13] = 22; aacpREV[ 3][14] = 170; aacpREV[ 3][15] = 590; aacpREV[ 3][16] = 266; aacpREV[ 3][17] = 18; aacpREV[ 3][18] = 281; aacpREV[ 3][19] = 75; aacpREV[ 4][ 0] = 669; aacpREV[ 4][ 1] = 823; aacpREV[ 4][ 2] = 538; aacpREV[ 4][ 3] = 10; aacpREV[ 4][ 4] = 0; aacpREV[ 4][ 5] = 10; aacpREV[ 4][ 6] = 10; aacpREV[ 4][ 7] = 303; aacpREV[ 4][ 8] = 441; aacpREV[ 4][ 9] = 280; aacpREV[ 4][10] = 396; aacpREV[ 4][11] = 48; aacpREV[ 4][12] = 159; aacpREV[ 4][13] = 726; aacpREV[ 4][14] = 285; aacpREV[ 4][15] = 2331; aacpREV[ 4][16] = 576; aacpREV[ 4][17] = 435; aacpREV[ 4][18] = 1466; aacpREV[ 4][19] = 592; aacpREV[ 5][ 0] = 157; aacpREV[ 5][ 1] = 1745; aacpREV[ 5][ 2] = 768; aacpREV[ 5][ 3] = 400; aacpREV[ 5][ 4] = 10; aacpREV[ 5][ 5] = 0; aacpREV[ 5][ 6] = 3122; aacpREV[ 5][ 7] = 133; aacpREV[ 5][ 8] = 1269; aacpREV[ 5][ 9] = 92; aacpREV[ 5][10] = 286; aacpREV[ 5][11] = 3313; aacpREV[ 5][12] = 202; aacpREV[ 5][13] = 10; aacpREV[ 5][14] = 323; aacpREV[ 5][15] = 396; aacpREV[ 5][16] = 241; aacpREV[ 5][17] = 53; aacpREV[ 5][18] = 391; aacpREV[ 5][19] = 54; aacpREV[ 6][ 0] = 499; aacpREV[ 6][ 1] = 152; aacpREV[ 6][ 2] = 1055; aacpREV[ 6][ 3] = 3691; aacpREV[ 6][ 4] = 10; aacpREV[ 6][ 5] = 3122; aacpREV[ 6][ 6] = 0; aacpREV[ 6][ 7] = 379; aacpREV[ 6][ 8] = 162; aacpREV[ 6][ 9] = 148; aacpREV[ 6][10] = 82; aacpREV[ 6][11] = 2629; aacpREV[ 6][12] = 113; aacpREV[ 6][13] = 145; aacpREV[ 6][14] = 185; aacpREV[ 6][15] = 568; aacpREV[ 6][16] = 369; aacpREV[ 6][17] = 63; aacpREV[ 6][18] = 142; aacpREV[ 6][19] = 200; aacpREV[ 7][ 0] = 665; aacpREV[ 7][ 1] = 243; aacpREV[ 7][ 2] = 653; aacpREV[ 7][ 3] = 431; aacpREV[ 7][ 4] = 303; aacpREV[ 7][ 5] = 133; aacpREV[ 7][ 6] = 379; aacpREV[ 7][ 7] = 0; aacpREV[ 7][ 8] = 19; aacpREV[ 7][ 9] = 40; aacpREV[ 7][10] = 20; aacpREV[ 7][11] = 263; aacpREV[ 7][12] = 21; aacpREV[ 7][13] = 25; aacpREV[ 7][14] = 28; aacpREV[ 7][15] = 691; aacpREV[ 7][16] = 92; aacpREV[ 7][17] = 82; aacpREV[ 7][18] = 10; aacpREV[ 7][19] = 91; aacpREV[ 8][ 0] = 66; aacpREV[ 8][ 1] = 715; aacpREV[ 8][ 2] = 1405; aacpREV[ 8][ 3] = 331; aacpREV[ 8][ 4] = 441; aacpREV[ 8][ 5] = 1269; aacpREV[ 8][ 6] = 162; aacpREV[ 8][ 7] = 19; aacpREV[ 8][ 8] = 0; aacpREV[ 8][ 9] = 29; aacpREV[ 8][10] = 66; aacpREV[ 8][11] = 305; aacpREV[ 8][12] = 10; aacpREV[ 8][13] = 127; aacpREV[ 8][14] = 152; aacpREV[ 8][15] = 303; aacpREV[ 8][16] = 32; aacpREV[ 8][17] = 69; aacpREV[ 8][18] = 1971; aacpREV[ 8][19] = 25; aacpREV[ 9][ 0] = 145; aacpREV[ 9][ 1] = 136; aacpREV[ 9][ 2] = 168; aacpREV[ 9][ 3] = 10; aacpREV[ 9][ 4] = 280; aacpREV[ 9][ 5] = 92; aacpREV[ 9][ 6] = 148; aacpREV[ 9][ 7] = 40; aacpREV[ 9][ 8] = 29; aacpREV[ 9][ 9] = 0; aacpREV[ 9][10] = 1745; aacpREV[ 9][11] = 345; aacpREV[ 9][12] = 1772; aacpREV[ 9][13] = 454; aacpREV[ 9][14] = 117; aacpREV[ 9][15] = 216; aacpREV[ 9][16] = 1040; aacpREV[ 9][17] = 42; aacpREV[ 9][18] = 89; aacpREV[ 9][19] = 4797; aacpREV[10][ 0] = 197; aacpREV[10][ 1] = 203; aacpREV[10][ 2] = 113; aacpREV[10][ 3] = 10; aacpREV[10][ 4] = 396; aacpREV[10][ 5] = 286; aacpREV[10][ 6] = 82; aacpREV[10][ 7] = 20; aacpREV[10][ 8] = 66; aacpREV[10][ 9] = 1745; aacpREV[10][10] = 0; aacpREV[10][11] = 218; aacpREV[10][12] = 1351; aacpREV[10][13] = 1268; aacpREV[10][14] = 219; aacpREV[10][15] = 516; aacpREV[10][16] = 156; aacpREV[10][17] = 159; aacpREV[10][18] = 189; aacpREV[10][19] = 865; aacpREV[11][ 0] = 236; aacpREV[11][ 1] = 4482; aacpREV[11][ 2] = 2430; aacpREV[11][ 3] = 412; aacpREV[11][ 4] = 48; aacpREV[11][ 5] = 3313; aacpREV[11][ 6] = 2629; aacpREV[11][ 7] = 263; aacpREV[11][ 8] = 305; aacpREV[11][ 9] = 345; aacpREV[11][10] = 218; aacpREV[11][11] = 0; aacpREV[11][12] = 193; aacpREV[11][13] = 72; aacpREV[11][14] = 302; aacpREV[11][15] = 868; aacpREV[11][16] = 918; aacpREV[11][17] = 10; aacpREV[11][18] = 247; aacpREV[11][19] = 249; aacpREV[12][ 0] = 185; aacpREV[12][ 1] = 125; aacpREV[12][ 2] = 61; aacpREV[12][ 3] = 47; aacpREV[12][ 4] = 159; aacpREV[12][ 5] = 202; aacpREV[12][ 6] = 113; aacpREV[12][ 7] = 21; aacpREV[12][ 8] = 10; aacpREV[12][ 9] = 1772; aacpREV[12][10] = 1351; aacpREV[12][11] = 193; aacpREV[12][12] = 0; aacpREV[12][13] = 327; aacpREV[12][14] = 100; aacpREV[12][15] = 93; aacpREV[12][16] = 645; aacpREV[12][17] = 86; aacpREV[12][18] = 215; aacpREV[12][19] = 475; aacpREV[13][ 0] = 68; aacpREV[13][ 1] = 53; aacpREV[13][ 2] = 97; aacpREV[13][ 3] = 22; aacpREV[13][ 4] = 726; aacpREV[13][ 5] = 10; aacpREV[13][ 6] = 145; aacpREV[13][ 7] = 25; aacpREV[13][ 8] = 127; aacpREV[13][ 9] = 454; aacpREV[13][10] = 1268; aacpREV[13][11] = 72; aacpREV[13][12] = 327; aacpREV[13][13] = 0; aacpREV[13][14] = 43; aacpREV[13][15] = 487; aacpREV[13][16] = 148; aacpREV[13][17] = 468; aacpREV[13][18] = 2370; aacpREV[13][19] = 317; aacpREV[14][ 0] = 490; aacpREV[14][ 1] = 87; aacpREV[14][ 2] = 173; aacpREV[14][ 3] = 170; aacpREV[14][ 4] = 285; aacpREV[14][ 5] = 323; aacpREV[14][ 6] = 185; aacpREV[14][ 7] = 28; aacpREV[14][ 8] = 152; aacpREV[14][ 9] = 117; aacpREV[14][10] = 219; aacpREV[14][11] = 302; aacpREV[14][12] = 100; aacpREV[14][13] = 43; aacpREV[14][14] = 0; aacpREV[14][15] = 1202; aacpREV[14][16] = 260; aacpREV[14][17] = 49; aacpREV[14][18] = 97; aacpREV[14][19] = 122; aacpREV[15][ 0] = 2440; aacpREV[15][ 1] = 385; aacpREV[15][ 2] = 2085; aacpREV[15][ 3] = 590; aacpREV[15][ 4] = 2331; aacpREV[15][ 5] = 396; aacpREV[15][ 6] = 568; aacpREV[15][ 7] = 691; aacpREV[15][ 8] = 303; aacpREV[15][ 9] = 216; aacpREV[15][10] = 516; aacpREV[15][11] = 868; aacpREV[15][12] = 93; aacpREV[15][13] = 487; aacpREV[15][14] = 1202; aacpREV[15][15] = 0; aacpREV[15][16] = 2151; aacpREV[15][17] = 73; aacpREV[15][18] = 522; aacpREV[15][19] = 167; aacpREV[16][ 0] = 1340; aacpREV[16][ 1] = 314; aacpREV[16][ 2] = 1393; aacpREV[16][ 3] = 266; aacpREV[16][ 4] = 576; aacpREV[16][ 5] = 241; aacpREV[16][ 6] = 369; aacpREV[16][ 7] = 92; aacpREV[16][ 8] = 32; aacpREV[16][ 9] = 1040; aacpREV[16][10] = 156; aacpREV[16][11] = 918; aacpREV[16][12] = 645; aacpREV[16][13] = 148; aacpREV[16][14] = 260; aacpREV[16][15] = 2151; aacpREV[16][16] = 0; aacpREV[16][17] = 29; aacpREV[16][18] = 71; aacpREV[16][19] = 760; aacpREV[17][ 0] = 14; aacpREV[17][ 1] = 230; aacpREV[17][ 2] = 40; aacpREV[17][ 3] = 18; aacpREV[17][ 4] = 435; aacpREV[17][ 5] = 53; aacpREV[17][ 6] = 63; aacpREV[17][ 7] = 82; aacpREV[17][ 8] = 69; aacpREV[17][ 9] = 42; aacpREV[17][10] = 159; aacpREV[17][11] = 10; aacpREV[17][12] = 86; aacpREV[17][13] = 468; aacpREV[17][14] = 49; aacpREV[17][15] = 73; aacpREV[17][16] = 29; aacpREV[17][17] = 0; aacpREV[17][18] = 346; aacpREV[17][19] = 10; aacpREV[18][ 0] = 56; aacpREV[18][ 1] = 323; aacpREV[18][ 2] = 754; aacpREV[18][ 3] = 281; aacpREV[18][ 4] = 1466; aacpREV[18][ 5] = 391; aacpREV[18][ 6] = 142; aacpREV[18][ 7] = 10; aacpREV[18][ 8] = 1971; aacpREV[18][ 9] = 89; aacpREV[18][10] = 189; aacpREV[18][11] = 247; aacpREV[18][12] = 215; aacpREV[18][13] = 2370; aacpREV[18][14] = 97; aacpREV[18][15] = 522; aacpREV[18][16] = 71; aacpREV[18][17] = 346; aacpREV[18][18] = 0; aacpREV[18][19] = 119; aacpREV[19][ 0] = 968; aacpREV[19][ 1] = 92; aacpREV[19][ 2] = 83; aacpREV[19][ 3] = 75; aacpREV[19][ 4] = 592; aacpREV[19][ 5] = 54; aacpREV[19][ 6] = 200; aacpREV[19][ 7] = 91; aacpREV[19][ 8] = 25; aacpREV[19][ 9] = 4797; aacpREV[19][10] = 865; aacpREV[19][11] = 249; aacpREV[19][12] = 475; aacpREV[19][13] = 317; aacpREV[19][14] = 122; aacpREV[19][15] = 167; aacpREV[19][16] = 760; aacpREV[19][17] = 10; aacpREV[19][18] = 119; aacpREV[19][19] = 0; cprevPi[0] = 0.076; cprevPi[1] = 0.062; cprevPi[2] = 0.041; cprevPi[3] = 0.037; cprevPi[4] = 0.009; cprevPi[5] = 0.038; cprevPi[6] = 0.049; cprevPi[7] = 0.084; cprevPi[8] = 0.025; cprevPi[9] = 0.081; cprevPi[10] = 0.101; cprevPi[11] = 0.050; cprevPi[12] = 0.022; cprevPi[13] = 0.051; cprevPi[14] = 0.043; cprevPi[15] = 0.062; cprevPi[16] = 0.054; cprevPi[17] = 0.018; cprevPi[18] = 0.031; cprevPi[19] = 0.066; /* VT model */ aaVt[ 0][ 0] = 0.000000; aaVt[ 0][ 1] = 0.233108; aaVt[ 0][ 2] = 0.199097; aaVt[ 0][ 3] = 0.265145; aaVt[ 0][ 4] = 0.227333; aaVt[ 0][ 5] = 0.310084; aaVt[ 0][ 6] = 0.567957; aaVt[ 0][ 7] = 0.876213; aaVt[ 0][ 8] = 0.078692; aaVt[ 0][ 9] = 0.222972; aaVt[ 0][10] = 0.424630; aaVt[ 0][11] = 0.393245; aaVt[ 0][12] = 0.211550; aaVt[ 0][13] = 0.116646; aaVt[ 0][14] = 0.399143; aaVt[ 0][15] = 1.817198; aaVt[ 0][16] = 0.877877; aaVt[ 0][17] = 0.030309; aaVt[ 0][18] = 0.087061; aaVt[ 0][19] = 1.230985; aaVt[ 1][ 0] = 0.233108; aaVt[ 1][ 1] = 0.000000; aaVt[ 1][ 2] = 0.210797; aaVt[ 1][ 3] = 0.105191; aaVt[ 1][ 4] = 0.031726; aaVt[ 1][ 5] = 0.493763; aaVt[ 1][ 6] = 0.255240; aaVt[ 1][ 7] = 0.156945; aaVt[ 1][ 8] = 0.213164; aaVt[ 1][ 9] = 0.081510; aaVt[ 1][10] = 0.192364; aaVt[ 1][11] = 1.755838; aaVt[ 1][12] = 0.087930; aaVt[ 1][13] = 0.042569; aaVt[ 1][14] = 0.128480; aaVt[ 1][15] = 0.292327; aaVt[ 1][16] = 0.204109; aaVt[ 1][17] = 0.046417; aaVt[ 1][18] = 0.097010; aaVt[ 1][19] = 0.113146; aaVt[ 2][ 0] = 0.199097; aaVt[ 2][ 1] = 0.210797; aaVt[ 2][ 2] = 0.000000; aaVt[ 2][ 3] = 0.883422; aaVt[ 2][ 4] = 0.027495; aaVt[ 2][ 5] = 0.275700; aaVt[ 2][ 6] = 0.270417; aaVt[ 2][ 7] = 0.362028; aaVt[ 2][ 8] = 0.290006; aaVt[ 2][ 9] = 0.087225; aaVt[ 2][10] = 0.069245; aaVt[ 2][11] = 0.503060; aaVt[ 2][12] = 0.057420; aaVt[ 2][13] = 0.039769; aaVt[ 2][14] = 0.083956; aaVt[ 2][15] = 0.847049; aaVt[ 2][16] = 0.471268; aaVt[ 2][17] = 0.010459; aaVt[ 2][18] = 0.093268; aaVt[ 2][19] = 0.049824; aaVt[ 3][ 0] = 0.265145; aaVt[ 3][ 1] = 0.105191; aaVt[ 3][ 2] = 0.883422; aaVt[ 3][ 3] = 0.000000; aaVt[ 3][ 4] = 0.010313; aaVt[ 3][ 5] = 0.205842; aaVt[ 3][ 6] = 1.599461; aaVt[ 3][ 7] = 0.311718; aaVt[ 3][ 8] = 0.134252; aaVt[ 3][ 9] = 0.011720; aaVt[ 3][10] = 0.060863; aaVt[ 3][11] = 0.261101; aaVt[ 3][12] = 0.012182; aaVt[ 3][13] = 0.016577; aaVt[ 3][14] = 0.160063; aaVt[ 3][15] = 0.461519; aaVt[ 3][16] = 0.178197; aaVt[ 3][17] = 0.011393; aaVt[ 3][18] = 0.051664; aaVt[ 3][19] = 0.048769; aaVt[ 4][ 0] = 0.227333; aaVt[ 4][ 1] = 0.031726; aaVt[ 4][ 2] = 0.027495; aaVt[ 4][ 3] = 0.010313; aaVt[ 4][ 4] = 0.000000; aaVt[ 4][ 5] = 0.004315; aaVt[ 4][ 6] = 0.005321; aaVt[ 4][ 7] = 0.050876; aaVt[ 4][ 8] = 0.016695; aaVt[ 4][ 9] = 0.046398; aaVt[ 4][10] = 0.091709; aaVt[ 4][11] = 0.004067; aaVt[ 4][12] = 0.023690; aaVt[ 4][13] = 0.051127; aaVt[ 4][14] = 0.011137; aaVt[ 4][15] = 0.175270; aaVt[ 4][16] = 0.079511; aaVt[ 4][17] = 0.007732; aaVt[ 4][18] = 0.042823; aaVt[ 4][19] = 0.163831; aaVt[ 5][ 0] = 0.310084; aaVt[ 5][ 1] = 0.493763; aaVt[ 5][ 2] = 0.275700; aaVt[ 5][ 3] = 0.205842; aaVt[ 5][ 4] = 0.004315; aaVt[ 5][ 5] = 0.000000; aaVt[ 5][ 6] = 0.960976; aaVt[ 5][ 7] = 0.128660; aaVt[ 5][ 8] = 0.315521; aaVt[ 5][ 9] = 0.054602; aaVt[ 5][10] = 0.243530; aaVt[ 5][11] = 0.738208; aaVt[ 5][12] = 0.120801; aaVt[ 5][13] = 0.026235; aaVt[ 5][14] = 0.156570; aaVt[ 5][15] = 0.358017; aaVt[ 5][16] = 0.248992; aaVt[ 5][17] = 0.021248; aaVt[ 5][18] = 0.062544; aaVt[ 5][19] = 0.112027; aaVt[ 6][ 0] = 0.567957; aaVt[ 6][ 1] = 0.255240; aaVt[ 6][ 2] = 0.270417; aaVt[ 6][ 3] = 1.599461; aaVt[ 6][ 4] = 0.005321; aaVt[ 6][ 5] = 0.960976; aaVt[ 6][ 6] = 0.000000; aaVt[ 6][ 7] = 0.250447; aaVt[ 6][ 8] = 0.104458; aaVt[ 6][ 9] = 0.046589; aaVt[ 6][10] = 0.151924; aaVt[ 6][11] = 0.888630; aaVt[ 6][12] = 0.058643; aaVt[ 6][13] = 0.028168; aaVt[ 6][14] = 0.205134; aaVt[ 6][15] = 0.406035; aaVt[ 6][16] = 0.321028; aaVt[ 6][17] = 0.018844; aaVt[ 6][18] = 0.055200; aaVt[ 6][19] = 0.205868; aaVt[ 7][ 0] = 0.876213; aaVt[ 7][ 1] = 0.156945; aaVt[ 7][ 2] = 0.362028; aaVt[ 7][ 3] = 0.311718; aaVt[ 7][ 4] = 0.050876; aaVt[ 7][ 5] = 0.128660; aaVt[ 7][ 6] = 0.250447; aaVt[ 7][ 7] = 0.000000; aaVt[ 7][ 8] = 0.058131; aaVt[ 7][ 9] = 0.051089; aaVt[ 7][10] = 0.087056; aaVt[ 7][11] = 0.193243; aaVt[ 7][12] = 0.046560; aaVt[ 7][13] = 0.050143; aaVt[ 7][14] = 0.124492; aaVt[ 7][15] = 0.612843; aaVt[ 7][16] = 0.136266; aaVt[ 7][17] = 0.023990; aaVt[ 7][18] = 0.037568; aaVt[ 7][19] = 0.082579; aaVt[ 8][ 0] = 0.078692; aaVt[ 8][ 1] = 0.213164; aaVt[ 8][ 2] = 0.290006; aaVt[ 8][ 3] = 0.134252; aaVt[ 8][ 4] = 0.016695; aaVt[ 8][ 5] = 0.315521; aaVt[ 8][ 6] = 0.104458; aaVt[ 8][ 7] = 0.058131; aaVt[ 8][ 8] = 0.000000; aaVt[ 8][ 9] = 0.020039; aaVt[ 8][10] = 0.103552; aaVt[ 8][11] = 0.153323; aaVt[ 8][12] = 0.021157; aaVt[ 8][13] = 0.079807; aaVt[ 8][14] = 0.078892; aaVt[ 8][15] = 0.167406; aaVt[ 8][16] = 0.101117; aaVt[ 8][17] = 0.020009; aaVt[ 8][18] = 0.286027; aaVt[ 8][19] = 0.068575; aaVt[ 9][ 0] = 0.222972; aaVt[ 9][ 1] = 0.081510; aaVt[ 9][ 2] = 0.087225; aaVt[ 9][ 3] = 0.011720; aaVt[ 9][ 4] = 0.046398; aaVt[ 9][ 5] = 0.054602; aaVt[ 9][ 6] = 0.046589; aaVt[ 9][ 7] = 0.051089; aaVt[ 9][ 8] = 0.020039; aaVt[ 9][ 9] = 0.000000; aaVt[ 9][10] = 2.089890; aaVt[ 9][11] = 0.093181; aaVt[ 9][12] = 0.493845; aaVt[ 9][13] = 0.321020; aaVt[ 9][14] = 0.054797; aaVt[ 9][15] = 0.081567; aaVt[ 9][16] = 0.376588; aaVt[ 9][17] = 0.034954; aaVt[ 9][18] = 0.086237; aaVt[ 9][19] = 3.654430; aaVt[10][ 0] = 0.424630; aaVt[10][ 1] = 0.192364; aaVt[10][ 2] = 0.069245; aaVt[10][ 3] = 0.060863; aaVt[10][ 4] = 0.091709; aaVt[10][ 5] = 0.243530; aaVt[10][ 6] = 0.151924; aaVt[10][ 7] = 0.087056; aaVt[10][ 8] = 0.103552; aaVt[10][ 9] = 2.089890; aaVt[10][10] = 0.000000; aaVt[10][11] = 0.201204; aaVt[10][12] = 1.105667; aaVt[10][13] = 0.946499; aaVt[10][14] = 0.169784; aaVt[10][15] = 0.214977; aaVt[10][16] = 0.243227; aaVt[10][17] = 0.083439; aaVt[10][18] = 0.189842; aaVt[10][19] = 1.337571; aaVt[11][ 0] = 0.393245; aaVt[11][ 1] = 1.755838; aaVt[11][ 2] = 0.503060; aaVt[11][ 3] = 0.261101; aaVt[11][ 4] = 0.004067; aaVt[11][ 5] = 0.738208; aaVt[11][ 6] = 0.888630; aaVt[11][ 7] = 0.193243; aaVt[11][ 8] = 0.153323; aaVt[11][ 9] = 0.093181; aaVt[11][10] = 0.201204; aaVt[11][11] = 0.000000; aaVt[11][12] = 0.096474; aaVt[11][13] = 0.038261; aaVt[11][14] = 0.212302; aaVt[11][15] = 0.400072; aaVt[11][16] = 0.446646; aaVt[11][17] = 0.023321; aaVt[11][18] = 0.068689; aaVt[11][19] = 0.144587; aaVt[12][ 0] = 0.211550; aaVt[12][ 1] = 0.087930; aaVt[12][ 2] = 0.057420; aaVt[12][ 3] = 0.012182; aaVt[12][ 4] = 0.023690; aaVt[12][ 5] = 0.120801; aaVt[12][ 6] = 0.058643; aaVt[12][ 7] = 0.046560; aaVt[12][ 8] = 0.021157; aaVt[12][ 9] = 0.493845; aaVt[12][10] = 1.105667; aaVt[12][11] = 0.096474; aaVt[12][12] = 0.000000; aaVt[12][13] = 0.173052; aaVt[12][14] = 0.010363; aaVt[12][15] = 0.090515; aaVt[12][16] = 0.184609; aaVt[12][17] = 0.022019; aaVt[12][18] = 0.073223; aaVt[12][19] = 0.307309; aaVt[13][ 0] = 0.116646; aaVt[13][ 1] = 0.042569; aaVt[13][ 2] = 0.039769; aaVt[13][ 3] = 0.016577; aaVt[13][ 4] = 0.051127; aaVt[13][ 5] = 0.026235; aaVt[13][ 6] = 0.028168; aaVt[13][ 7] = 0.050143; aaVt[13][ 8] = 0.079807; aaVt[13][ 9] = 0.321020; aaVt[13][10] = 0.946499; aaVt[13][11] = 0.038261; aaVt[13][12] = 0.173052; aaVt[13][13] = 0.000000; aaVt[13][14] = 0.042564; aaVt[13][15] = 0.138119; aaVt[13][16] = 0.085870; aaVt[13][17] = 0.128050; aaVt[13][18] = 0.898663; aaVt[13][19] = 0.247329; aaVt[14][ 0] = 0.399143; aaVt[14][ 1] = 0.128480; aaVt[14][ 2] = 0.083956; aaVt[14][ 3] = 0.160063; aaVt[14][ 4] = 0.011137; aaVt[14][ 5] = 0.156570; aaVt[14][ 6] = 0.205134; aaVt[14][ 7] = 0.124492; aaVt[14][ 8] = 0.078892; aaVt[14][ 9] = 0.054797; aaVt[14][10] = 0.169784; aaVt[14][11] = 0.212302; aaVt[14][12] = 0.010363; aaVt[14][13] = 0.042564; aaVt[14][14] = 0.000000; aaVt[14][15] = 0.430431; aaVt[14][16] = 0.207143; aaVt[14][17] = 0.014584; aaVt[14][18] = 0.032043; aaVt[14][19] = 0.129315; aaVt[15][ 0] = 1.817198; aaVt[15][ 1] = 0.292327; aaVt[15][ 2] = 0.847049; aaVt[15][ 3] = 0.461519; aaVt[15][ 4] = 0.175270; aaVt[15][ 5] = 0.358017; aaVt[15][ 6] = 0.406035; aaVt[15][ 7] = 0.612843; aaVt[15][ 8] = 0.167406; aaVt[15][ 9] = 0.081567; aaVt[15][10] = 0.214977; aaVt[15][11] = 0.400072; aaVt[15][12] = 0.090515; aaVt[15][13] = 0.138119; aaVt[15][14] = 0.430431; aaVt[15][15] = 0.000000; aaVt[15][16] = 1.767766; aaVt[15][17] = 0.035933; aaVt[15][18] = 0.121979; aaVt[15][19] = 0.127700; aaVt[16][ 0] = 0.877877; aaVt[16][ 1] = 0.204109; aaVt[16][ 2] = 0.471268; aaVt[16][ 3] = 0.178197; aaVt[16][ 4] = 0.079511; aaVt[16][ 5] = 0.248992; aaVt[16][ 6] = 0.321028; aaVt[16][ 7] = 0.136266; aaVt[16][ 8] = 0.101117; aaVt[16][ 9] = 0.376588; aaVt[16][10] = 0.243227; aaVt[16][11] = 0.446646; aaVt[16][12] = 0.184609; aaVt[16][13] = 0.085870; aaVt[16][14] = 0.207143; aaVt[16][15] = 1.767766; aaVt[16][16] = 0.000000; aaVt[16][17] = 0.020437; aaVt[16][18] = 0.094617; aaVt[16][19] = 0.740372; aaVt[17][ 0] = 0.030309; aaVt[17][ 1] = 0.046417; aaVt[17][ 2] = 0.010459; aaVt[17][ 3] = 0.011393; aaVt[17][ 4] = 0.007732; aaVt[17][ 5] = 0.021248; aaVt[17][ 6] = 0.018844; aaVt[17][ 7] = 0.023990; aaVt[17][ 8] = 0.020009; aaVt[17][ 9] = 0.034954; aaVt[17][10] = 0.083439; aaVt[17][11] = 0.023321; aaVt[17][12] = 0.022019; aaVt[17][13] = 0.128050; aaVt[17][14] = 0.014584; aaVt[17][15] = 0.035933; aaVt[17][16] = 0.020437; aaVt[17][17] = 0.000000; aaVt[17][18] = 0.124746; aaVt[17][19] = 0.022134; aaVt[18][ 0] = 0.087061; aaVt[18][ 1] = 0.097010; aaVt[18][ 2] = 0.093268; aaVt[18][ 3] = 0.051664; aaVt[18][ 4] = 0.042823; aaVt[18][ 5] = 0.062544; aaVt[18][ 6] = 0.055200; aaVt[18][ 7] = 0.037568; aaVt[18][ 8] = 0.286027; aaVt[18][ 9] = 0.086237; aaVt[18][10] = 0.189842; aaVt[18][11] = 0.068689; aaVt[18][12] = 0.073223; aaVt[18][13] = 0.898663; aaVt[18][14] = 0.032043; aaVt[18][15] = 0.121979; aaVt[18][16] = 0.094617; aaVt[18][17] = 0.124746; aaVt[18][18] = 0.000000; aaVt[18][19] = 0.125733; aaVt[19][ 0] = 1.230985; aaVt[19][ 1] = 0.113146; aaVt[19][ 2] = 0.049824; aaVt[19][ 3] = 0.048769; aaVt[19][ 4] = 0.163831; aaVt[19][ 5] = 0.112027; aaVt[19][ 6] = 0.205868; aaVt[19][ 7] = 0.082579; aaVt[19][ 8] = 0.068575; aaVt[19][ 9] = 3.654430; aaVt[19][10] = 1.337571; aaVt[19][11] = 0.144587; aaVt[19][12] = 0.307309; aaVt[19][13] = 0.247329; aaVt[19][14] = 0.129315; aaVt[19][15] = 0.127700; aaVt[19][16] = 0.740372; aaVt[19][17] = 0.022134; aaVt[19][18] = 0.125733; aaVt[19][19] = 0.000000; vtPi[ 0] = 0.078837; vtPi[ 1] = 0.051238; vtPi[ 2] = 0.042313; vtPi[ 3] = 0.053066; vtPi[ 4] = 0.015175; vtPi[ 5] = 0.036713; vtPi[ 6] = 0.061924; vtPi[ 7] = 0.070852; vtPi[ 8] = 0.023082; vtPi[ 9] = 0.062056; vtPi[10] = 0.096371; vtPi[11] = 0.057324; vtPi[12] = 0.023771; vtPi[13] = 0.043296; vtPi[14] = 0.043911; vtPi[15] = 0.063403; vtPi[16] = 0.055897; vtPi[17] = 0.013272; vtPi[18] = 0.034399; vtPi[19] = 0.073101; /* Blosum62 */ aaBlosum[ 0][ 0] = 0.000000000000; aaBlosum[ 0][ 1] = 0.735790389698; aaBlosum[ 0][ 2] = 0.485391055466; aaBlosum[ 0][ 3] = 0.543161820899; aaBlosum[ 0][ 4] = 1.459995310470; aaBlosum[ 0][ 5] = 1.199705704602; aaBlosum[ 0][ 6] = 1.170949042800; aaBlosum[ 0][ 7] = 1.955883574960; aaBlosum[ 0][ 8] = 0.716241444998; aaBlosum[ 0][ 9] = 0.605899003687; aaBlosum[ 0][10] = 0.800016530518; aaBlosum[ 0][11] = 1.295201266783; aaBlosum[ 0][12] = 1.253758266664; aaBlosum[ 0][13] = 0.492964679748; aaBlosum[ 0][14] = 1.173275900924; aaBlosum[ 0][15] = 4.325092687057; aaBlosum[ 0][16] = 1.729178019485; aaBlosum[ 0][17] = 0.465839367725; aaBlosum[ 0][18] = 0.718206697586; aaBlosum[ 0][19] = 2.187774522005; aaBlosum[ 1][ 0] = 0.735790389698; aaBlosum[ 1][ 1] = 0.000000000000; aaBlosum[ 1][ 2] = 1.297446705134; aaBlosum[ 1][ 3] = 0.500964408555; aaBlosum[ 1][ 4] = 0.227826574209; aaBlosum[ 1][ 5] = 3.020833610064; aaBlosum[ 1][ 6] = 1.360574190420; aaBlosum[ 1][ 7] = 0.418763308518; aaBlosum[ 1][ 8] = 1.456141166336; aaBlosum[ 1][ 9] = 0.232036445142; aaBlosum[ 1][10] = 0.622711669692; aaBlosum[ 1][11] = 5.411115141489; aaBlosum[ 1][12] = 0.983692987457; aaBlosum[ 1][13] = 0.371644693209; aaBlosum[ 1][14] = 0.448133661718; aaBlosum[ 1][15] = 1.122783104210; aaBlosum[ 1][16] = 0.914665954563; aaBlosum[ 1][17] = 0.426382310122; aaBlosum[ 1][18] = 0.720517441216; aaBlosum[ 1][19] = 0.438388343772; aaBlosum[ 2][ 0] = 0.485391055466; aaBlosum[ 2][ 1] = 1.297446705134; aaBlosum[ 2][ 2] = 0.000000000000; aaBlosum[ 2][ 3] = 3.180100048216; aaBlosum[ 2][ 4] = 0.397358949897; aaBlosum[ 2][ 5] = 1.839216146992; aaBlosum[ 2][ 6] = 1.240488508640; aaBlosum[ 2][ 7] = 1.355872344485; aaBlosum[ 2][ 8] = 2.414501434208; aaBlosum[ 2][ 9] = 0.283017326278; aaBlosum[ 2][10] = 0.211888159615; aaBlosum[ 2][11] = 1.593137043457; aaBlosum[ 2][12] = 0.648441278787; aaBlosum[ 2][13] = 0.354861249223; aaBlosum[ 2][14] = 0.494887043702; aaBlosum[ 2][15] = 2.904101656456; aaBlosum[ 2][16] = 1.898173634533; aaBlosum[ 2][17] = 0.191482046247; aaBlosum[ 2][18] = 0.538222519037; aaBlosum[ 2][19] = 0.312858797993; aaBlosum[ 3][ 0] = 0.543161820899; aaBlosum[ 3][ 1] = 0.500964408555; aaBlosum[ 3][ 2] = 3.180100048216; aaBlosum[ 3][ 3] = 0.000000000000; aaBlosum[ 3][ 4] = 0.240836614802; aaBlosum[ 3][ 5] = 1.190945703396; aaBlosum[ 3][ 6] = 3.761625208368; aaBlosum[ 3][ 7] = 0.798473248968; aaBlosum[ 3][ 8] = 0.778142664022; aaBlosum[ 3][ 9] = 0.418555732462; aaBlosum[ 3][10] = 0.218131577594; aaBlosum[ 3][11] = 1.032447924952; aaBlosum[ 3][12] = 0.222621897958; aaBlosum[ 3][13] = 0.281730694207; aaBlosum[ 3][14] = 0.730628272998; aaBlosum[ 3][15] = 1.582754142065; aaBlosum[ 3][16] = 0.934187509431; aaBlosum[ 3][17] = 0.145345046279; aaBlosum[ 3][18] = 0.261422208965; aaBlosum[ 3][19] = 0.258129289418; aaBlosum[ 4][ 0] = 1.459995310470; aaBlosum[ 4][ 1] = 0.227826574209; aaBlosum[ 4][ 2] = 0.397358949897; aaBlosum[ 4][ 3] = 0.240836614802; aaBlosum[ 4][ 4] = 0.000000000000; aaBlosum[ 4][ 5] = 0.329801504630; aaBlosum[ 4][ 6] = 0.140748891814; aaBlosum[ 4][ 7] = 0.418203192284; aaBlosum[ 4][ 8] = 0.354058109831; aaBlosum[ 4][ 9] = 0.774894022794; aaBlosum[ 4][10] = 0.831842640142; aaBlosum[ 4][11] = 0.285078800906; aaBlosum[ 4][12] = 0.767688823480; aaBlosum[ 4][13] = 0.441337471187; aaBlosum[ 4][14] = 0.356008498769; aaBlosum[ 4][15] = 1.197188415094; aaBlosum[ 4][16] = 1.119831358516; aaBlosum[ 4][17] = 0.527664418872; aaBlosum[ 4][18] = 0.470237733696; aaBlosum[ 4][19] = 1.116352478606; aaBlosum[ 5][ 0] = 1.199705704602; aaBlosum[ 5][ 1] = 3.020833610064; aaBlosum[ 5][ 2] = 1.839216146992; aaBlosum[ 5][ 3] = 1.190945703396; aaBlosum[ 5][ 4] = 0.329801504630; aaBlosum[ 5][ 5] = 0.000000000000; aaBlosum[ 5][ 6] = 5.528919177928; aaBlosum[ 5][ 7] = 0.609846305383; aaBlosum[ 5][ 8] = 2.435341131140; aaBlosum[ 5][ 9] = 0.236202451204; aaBlosum[ 5][10] = 0.580737093181; aaBlosum[ 5][11] = 3.945277674515; aaBlosum[ 5][12] = 2.494896077113; aaBlosum[ 5][13] = 0.144356959750; aaBlosum[ 5][14] = 0.858570575674; aaBlosum[ 5][15] = 1.934870924596; aaBlosum[ 5][16] = 1.277480294596; aaBlosum[ 5][17] = 0.758653808642; aaBlosum[ 5][18] = 0.958989742850; aaBlosum[ 5][19] = 0.530785790125; aaBlosum[ 6][ 0] = 1.170949042800; aaBlosum[ 6][ 1] = 1.360574190420; aaBlosum[ 6][ 2] = 1.240488508640; aaBlosum[ 6][ 3] = 3.761625208368; aaBlosum[ 6][ 4] = 0.140748891814; aaBlosum[ 6][ 5] = 5.528919177928; aaBlosum[ 6][ 6] = 0.000000000000; aaBlosum[ 6][ 7] = 0.423579992176; aaBlosum[ 6][ 8] = 1.626891056982; aaBlosum[ 6][ 9] = 0.186848046932; aaBlosum[ 6][10] = 0.372625175087; aaBlosum[ 6][11] = 2.802427151679; aaBlosum[ 6][12] = 0.555415397470; aaBlosum[ 6][13] = 0.291409084165; aaBlosum[ 6][14] = 0.926563934846; aaBlosum[ 6][15] = 1.769893238937; aaBlosum[ 6][16] = 1.071097236007; aaBlosum[ 6][17] = 0.407635648938; aaBlosum[ 6][18] = 0.596719300346; aaBlosum[ 6][19] = 0.524253846338; aaBlosum[ 7][ 0] = 1.955883574960; aaBlosum[ 7][ 1] = 0.418763308518; aaBlosum[ 7][ 2] = 1.355872344485; aaBlosum[ 7][ 3] = 0.798473248968; aaBlosum[ 7][ 4] = 0.418203192284; aaBlosum[ 7][ 5] = 0.609846305383; aaBlosum[ 7][ 6] = 0.423579992176; aaBlosum[ 7][ 7] = 0.000000000000; aaBlosum[ 7][ 8] = 0.539859124954; aaBlosum[ 7][ 9] = 0.189296292376; aaBlosum[ 7][10] = 0.217721159236; aaBlosum[ 7][11] = 0.752042440303; aaBlosum[ 7][12] = 0.459436173579; aaBlosum[ 7][13] = 0.368166464453; aaBlosum[ 7][14] = 0.504086599527; aaBlosum[ 7][15] = 1.509326253224; aaBlosum[ 7][16] = 0.641436011405; aaBlosum[ 7][17] = 0.508358924638; aaBlosum[ 7][18] = 0.308055737035; aaBlosum[ 7][19] = 0.253340790190; aaBlosum[ 8][ 0] = 0.716241444998; aaBlosum[ 8][ 1] = 1.456141166336; aaBlosum[ 8][ 2] = 2.414501434208; aaBlosum[ 8][ 3] = 0.778142664022; aaBlosum[ 8][ 4] = 0.354058109831; aaBlosum[ 8][ 5] = 2.435341131140; aaBlosum[ 8][ 6] = 1.626891056982; aaBlosum[ 8][ 7] = 0.539859124954; aaBlosum[ 8][ 8] = 0.000000000000; aaBlosum[ 8][ 9] = 0.252718447885; aaBlosum[ 8][10] = 0.348072209797; aaBlosum[ 8][11] = 1.022507035889; aaBlosum[ 8][12] = 0.984311525359; aaBlosum[ 8][13] = 0.714533703928; aaBlosum[ 8][14] = 0.527007339151; aaBlosum[ 8][15] = 1.117029762910; aaBlosum[ 8][16] = 0.585407090225; aaBlosum[ 8][17] = 0.301248600780; aaBlosum[ 8][18] = 4.218953969389; aaBlosum[ 8][19] = 0.201555971750; aaBlosum[ 9][ 0] = 0.605899003687; aaBlosum[ 9][ 1] = 0.232036445142; aaBlosum[ 9][ 2] = 0.283017326278; aaBlosum[ 9][ 3] = 0.418555732462; aaBlosum[ 9][ 4] = 0.774894022794; aaBlosum[ 9][ 5] = 0.236202451204; aaBlosum[ 9][ 6] = 0.186848046932; aaBlosum[ 9][ 7] = 0.189296292376; aaBlosum[ 9][ 8] = 0.252718447885; aaBlosum[ 9][ 9] = 0.000000000000; aaBlosum[ 9][10] = 3.890963773304; aaBlosum[ 9][11] = 0.406193586642; aaBlosum[ 9][12] = 3.364797763104; aaBlosum[ 9][13] = 1.517359325954; aaBlosum[ 9][14] = 0.388355409206; aaBlosum[ 9][15] = 0.357544412460; aaBlosum[ 9][16] = 1.179091197260; aaBlosum[ 9][17] = 0.341985787540; aaBlosum[ 9][18] = 0.674617093228; aaBlosum[ 9][19] = 8.311839405458; aaBlosum[10][ 0] = 0.800016530518; aaBlosum[10][ 1] = 0.622711669692; aaBlosum[10][ 2] = 0.211888159615; aaBlosum[10][ 3] = 0.218131577594; aaBlosum[10][ 4] = 0.831842640142; aaBlosum[10][ 5] = 0.580737093181; aaBlosum[10][ 6] = 0.372625175087; aaBlosum[10][ 7] = 0.217721159236; aaBlosum[10][ 8] = 0.348072209797; aaBlosum[10][ 9] = 3.890963773304; aaBlosum[10][10] = 0.000000000000; aaBlosum[10][11] = 0.445570274261; aaBlosum[10][12] = 6.030559379572; aaBlosum[10][13] = 2.064839703237; aaBlosum[10][14] = 0.374555687471; aaBlosum[10][15] = 0.352969184527; aaBlosum[10][16] = 0.915259857694; aaBlosum[10][17] = 0.691474634600; aaBlosum[10][18] = 0.811245856323; aaBlosum[10][19] = 2.231405688913; aaBlosum[11][ 0] = 1.295201266783; aaBlosum[11][ 1] = 5.411115141489; aaBlosum[11][ 2] = 1.593137043457; aaBlosum[11][ 3] = 1.032447924952; aaBlosum[11][ 4] = 0.285078800906; aaBlosum[11][ 5] = 3.945277674515; aaBlosum[11][ 6] = 2.802427151679; aaBlosum[11][ 7] = 0.752042440303; aaBlosum[11][ 8] = 1.022507035889; aaBlosum[11][ 9] = 0.406193586642; aaBlosum[11][10] = 0.445570274261; aaBlosum[11][11] = 0.000000000000; aaBlosum[11][12] = 1.073061184332; aaBlosum[11][13] = 0.266924750511; aaBlosum[11][14] = 1.047383450722; aaBlosum[11][15] = 1.752165917819; aaBlosum[11][16] = 1.303875200799; aaBlosum[11][17] = 0.332243040634; aaBlosum[11][18] = 0.717993486900; aaBlosum[11][19] = 0.498138475304; aaBlosum[12][ 0] = 1.253758266664; aaBlosum[12][ 1] = 0.983692987457; aaBlosum[12][ 2] = 0.648441278787; aaBlosum[12][ 3] = 0.222621897958; aaBlosum[12][ 4] = 0.767688823480; aaBlosum[12][ 5] = 2.494896077113; aaBlosum[12][ 6] = 0.555415397470; aaBlosum[12][ 7] = 0.459436173579; aaBlosum[12][ 8] = 0.984311525359; aaBlosum[12][ 9] = 3.364797763104; aaBlosum[12][10] = 6.030559379572; aaBlosum[12][11] = 1.073061184332; aaBlosum[12][12] = 0.000000000000; aaBlosum[12][13] = 1.773855168830; aaBlosum[12][14] = 0.454123625103; aaBlosum[12][15] = 0.918723415746; aaBlosum[12][16] = 1.488548053722; aaBlosum[12][17] = 0.888101098152; aaBlosum[12][18] = 0.951682162246; aaBlosum[12][19] = 2.575850755315; aaBlosum[13][ 0] = 0.492964679748; aaBlosum[13][ 1] = 0.371644693209; aaBlosum[13][ 2] = 0.354861249223; aaBlosum[13][ 3] = 0.281730694207; aaBlosum[13][ 4] = 0.441337471187; aaBlosum[13][ 5] = 0.144356959750; aaBlosum[13][ 6] = 0.291409084165; aaBlosum[13][ 7] = 0.368166464453; aaBlosum[13][ 8] = 0.714533703928; aaBlosum[13][ 9] = 1.517359325954; aaBlosum[13][10] = 2.064839703237; aaBlosum[13][11] = 0.266924750511; aaBlosum[13][12] = 1.773855168830; aaBlosum[13][13] = 0.000000000000; aaBlosum[13][14] = 0.233597909629; aaBlosum[13][15] = 0.540027644824; aaBlosum[13][16] = 0.488206118793; aaBlosum[13][17] = 2.074324893497; aaBlosum[13][18] = 6.747260430801; aaBlosum[13][19] = 0.838119610178; aaBlosum[14][ 0] = 1.173275900924; aaBlosum[14][ 1] = 0.448133661718; aaBlosum[14][ 2] = 0.494887043702; aaBlosum[14][ 3] = 0.730628272998; aaBlosum[14][ 4] = 0.356008498769; aaBlosum[14][ 5] = 0.858570575674; aaBlosum[14][ 6] = 0.926563934846; aaBlosum[14][ 7] = 0.504086599527; aaBlosum[14][ 8] = 0.527007339151; aaBlosum[14][ 9] = 0.388355409206; aaBlosum[14][10] = 0.374555687471; aaBlosum[14][11] = 1.047383450722; aaBlosum[14][12] = 0.454123625103; aaBlosum[14][13] = 0.233597909629; aaBlosum[14][14] = 0.000000000000; aaBlosum[14][15] = 1.169129577716; aaBlosum[14][16] = 1.005451683149; aaBlosum[14][17] = 0.252214830027; aaBlosum[14][18] = 0.369405319355; aaBlosum[14][19] = 0.496908410676; aaBlosum[15][ 0] = 4.325092687057; aaBlosum[15][ 1] = 1.122783104210; aaBlosum[15][ 2] = 2.904101656456; aaBlosum[15][ 3] = 1.582754142065; aaBlosum[15][ 4] = 1.197188415094; aaBlosum[15][ 5] = 1.934870924596; aaBlosum[15][ 6] = 1.769893238937; aaBlosum[15][ 7] = 1.509326253224; aaBlosum[15][ 8] = 1.117029762910; aaBlosum[15][ 9] = 0.357544412460; aaBlosum[15][10] = 0.352969184527; aaBlosum[15][11] = 1.752165917819; aaBlosum[15][12] = 0.918723415746; aaBlosum[15][13] = 0.540027644824; aaBlosum[15][14] = 1.169129577716; aaBlosum[15][15] = 0.000000000000; aaBlosum[15][16] = 5.151556292270; aaBlosum[15][17] = 0.387925622098; aaBlosum[15][18] = 0.796751520761; aaBlosum[15][19] = 0.561925457442; aaBlosum[16][ 0] = 1.729178019485; aaBlosum[16][ 1] = 0.914665954563; aaBlosum[16][ 2] = 1.898173634533; aaBlosum[16][ 3] = 0.934187509431; aaBlosum[16][ 4] = 1.119831358516; aaBlosum[16][ 5] = 1.277480294596; aaBlosum[16][ 6] = 1.071097236007; aaBlosum[16][ 7] = 0.641436011405; aaBlosum[16][ 8] = 0.585407090225; aaBlosum[16][ 9] = 1.179091197260; aaBlosum[16][10] = 0.915259857694; aaBlosum[16][11] = 1.303875200799; aaBlosum[16][12] = 1.488548053722; aaBlosum[16][13] = 0.488206118793; aaBlosum[16][14] = 1.005451683149; aaBlosum[16][15] = 5.151556292270; aaBlosum[16][16] = 0.000000000000; aaBlosum[16][17] = 0.513128126891; aaBlosum[16][18] = 0.801010243199; aaBlosum[16][19] = 2.253074051176; aaBlosum[17][ 0] = 0.465839367725; aaBlosum[17][ 1] = 0.426382310122; aaBlosum[17][ 2] = 0.191482046247; aaBlosum[17][ 3] = 0.145345046279; aaBlosum[17][ 4] = 0.527664418872; aaBlosum[17][ 5] = 0.758653808642; aaBlosum[17][ 6] = 0.407635648938; aaBlosum[17][ 7] = 0.508358924638; aaBlosum[17][ 8] = 0.301248600780; aaBlosum[17][ 9] = 0.341985787540; aaBlosum[17][10] = 0.691474634600; aaBlosum[17][11] = 0.332243040634; aaBlosum[17][12] = 0.888101098152; aaBlosum[17][13] = 2.074324893497; aaBlosum[17][14] = 0.252214830027; aaBlosum[17][15] = 0.387925622098; aaBlosum[17][16] = 0.513128126891; aaBlosum[17][17] = 0.000000000000; aaBlosum[17][18] = 4.054419006558; aaBlosum[17][19] = 0.266508731426; aaBlosum[18][ 0] = 0.718206697586; aaBlosum[18][ 1] = 0.720517441216; aaBlosum[18][ 2] = 0.538222519037; aaBlosum[18][ 3] = 0.261422208965; aaBlosum[18][ 4] = 0.470237733696; aaBlosum[18][ 5] = 0.958989742850; aaBlosum[18][ 6] = 0.596719300346; aaBlosum[18][ 7] = 0.308055737035; aaBlosum[18][ 8] = 4.218953969389; aaBlosum[18][ 9] = 0.674617093228; aaBlosum[18][10] = 0.811245856323; aaBlosum[18][11] = 0.717993486900; aaBlosum[18][12] = 0.951682162246; aaBlosum[18][13] = 6.747260430801; aaBlosum[18][14] = 0.369405319355; aaBlosum[18][15] = 0.796751520761; aaBlosum[18][16] = 0.801010243199; aaBlosum[18][17] = 4.054419006558; aaBlosum[18][18] = 0.000000000000; aaBlosum[18][19] = 1.000000000000; aaBlosum[19][ 0] = 2.187774522005; aaBlosum[19][ 1] = 0.438388343772; aaBlosum[19][ 2] = 0.312858797993; aaBlosum[19][ 3] = 0.258129289418; aaBlosum[19][ 4] = 1.116352478606; aaBlosum[19][ 5] = 0.530785790125; aaBlosum[19][ 6] = 0.524253846338; aaBlosum[19][ 7] = 0.253340790190; aaBlosum[19][ 8] = 0.201555971750; aaBlosum[19][ 9] = 8.311839405458; aaBlosum[19][10] = 2.231405688913; aaBlosum[19][11] = 0.498138475304; aaBlosum[19][12] = 2.575850755315; aaBlosum[19][13] = 0.838119610178; aaBlosum[19][14] = 0.496908410676; aaBlosum[19][15] = 0.561925457442; aaBlosum[19][16] = 2.253074051176; aaBlosum[19][17] = 0.266508731426; aaBlosum[19][18] = 1.000000000000; aaBlosum[19][19] = 0.000000000000; blosPi[ 0] = 0.074; blosPi[ 1] = 0.052; blosPi[ 2] = 0.045; blosPi[ 3] = 0.054; blosPi[ 4] = 0.025; blosPi[ 5] = 0.034; blosPi[ 6] = 0.054; blosPi[ 7] = 0.074; blosPi[ 8] = 0.026; blosPi[ 9] = 0.068; blosPi[10] = 0.099; blosPi[11] = 0.058; blosPi[12] = 0.025; blosPi[13] = 0.047; blosPi[14] = 0.039; blosPi[15] = 0.057; blosPi[16] = 0.051; blosPi[17] = 0.013; blosPi[18] = 0.032; blosPi[19] = 0.073; /* LG */ aaLG[ 0][ 0] = 0.000000; aaLG[ 0][ 1] = 0.425093; aaLG[ 0][ 2] = 0.276818; aaLG[ 0][ 3] = 0.395144; aaLG[ 0][ 4] = 2.489084; aaLG[ 0][ 5] = 0.969894; aaLG[ 0][ 6] = 1.038545; aaLG[ 0][ 7] = 2.066040; aaLG[ 0][ 8] = 0.358858; aaLG[ 0][ 9] = 0.149830; aaLG[ 0][10] = 0.395337; aaLG[ 0][11] = 0.536518; aaLG[ 0][12] = 1.124035; aaLG[ 0][13] = 0.253701; aaLG[ 0][14] = 1.177651; aaLG[ 0][15] = 4.727182; aaLG[ 0][16] = 2.139501; aaLG[ 0][17] = 0.180717; aaLG[ 0][18] = 0.218959; aaLG[ 0][19] = 2.547870; aaLG[ 1][ 0] = 0.425093; aaLG[ 1][ 1] = 0.000000; aaLG[ 1][ 2] = 0.751878; aaLG[ 1][ 3] = 0.123954; aaLG[ 1][ 4] = 0.534551; aaLG[ 1][ 5] = 2.807908; aaLG[ 1][ 6] = 0.363970; aaLG[ 1][ 7] = 0.390192; aaLG[ 1][ 8] = 2.426601; aaLG[ 1][ 9] = 0.126991; aaLG[ 1][10] = 0.301848; aaLG[ 1][11] = 6.326067; aaLG[ 1][12] = 0.484133; aaLG[ 1][13] = 0.052722; aaLG[ 1][14] = 0.332533; aaLG[ 1][15] = 0.858151; aaLG[ 1][16] = 0.578987; aaLG[ 1][17] = 0.593607; aaLG[ 1][18] = 0.314440; aaLG[ 1][19] = 0.170887; aaLG[ 2][ 0] = 0.276818; aaLG[ 2][ 1] = 0.751878; aaLG[ 2][ 2] = 0.000000; aaLG[ 2][ 3] = 5.076149; aaLG[ 2][ 4] = 0.528768; aaLG[ 2][ 5] = 1.695752; aaLG[ 2][ 6] = 0.541712; aaLG[ 2][ 7] = 1.437645; aaLG[ 2][ 8] = 4.509238; aaLG[ 2][ 9] = 0.191503; aaLG[ 2][10] = 0.068427; aaLG[ 2][11] = 2.145078; aaLG[ 2][12] = 0.371004; aaLG[ 2][13] = 0.089525; aaLG[ 2][14] = 0.161787; aaLG[ 2][15] = 4.008358; aaLG[ 2][16] = 2.000679; aaLG[ 2][17] = 0.045376; aaLG[ 2][18] = 0.612025; aaLG[ 2][19] = 0.083688; aaLG[ 3][ 0] = 0.395144; aaLG[ 3][ 1] = 0.123954; aaLG[ 3][ 2] = 5.076149; aaLG[ 3][ 3] = 0.000000; aaLG[ 3][ 4] = 0.062556; aaLG[ 3][ 5] = 0.523386; aaLG[ 3][ 6] = 5.243870; aaLG[ 3][ 7] = 0.844926; aaLG[ 3][ 8] = 0.927114; aaLG[ 3][ 9] = 0.010690; aaLG[ 3][10] = 0.015076; aaLG[ 3][11] = 0.282959; aaLG[ 3][12] = 0.025548; aaLG[ 3][13] = 0.017416; aaLG[ 3][14] = 0.394456; aaLG[ 3][15] = 1.240275; aaLG[ 3][16] = 0.425860; aaLG[ 3][17] = 0.029890; aaLG[ 3][18] = 0.135107; aaLG[ 3][19] = 0.037967; aaLG[ 4][ 0] = 2.489084; aaLG[ 4][ 1] = 0.534551; aaLG[ 4][ 2] = 0.528768; aaLG[ 4][ 3] = 0.062556; aaLG[ 4][ 4] = 0.000000; aaLG[ 4][ 5] = 0.084808; aaLG[ 4][ 6] = 0.003499; aaLG[ 4][ 7] = 0.569265; aaLG[ 4][ 8] = 0.640543; aaLG[ 4][ 9] = 0.320627; aaLG[ 4][10] = 0.594007; aaLG[ 4][11] = 0.013266; aaLG[ 4][12] = 0.893680; aaLG[ 4][13] = 1.105251; aaLG[ 4][14] = 0.075382; aaLG[ 4][15] = 2.784478; aaLG[ 4][16] = 1.143480; aaLG[ 4][17] = 0.670128; aaLG[ 4][18] = 1.165532; aaLG[ 4][19] = 1.959291; aaLG[ 5][ 0] = 0.969894; aaLG[ 5][ 1] = 2.807908; aaLG[ 5][ 2] = 1.695752; aaLG[ 5][ 3] = 0.523386; aaLG[ 5][ 4] = 0.084808; aaLG[ 5][ 5] = 0.000000; aaLG[ 5][ 6] = 4.128591; aaLG[ 5][ 7] = 0.267959; aaLG[ 5][ 8] = 4.813505; aaLG[ 5][ 9] = 0.072854; aaLG[ 5][10] = 0.582457; aaLG[ 5][11] = 3.234294; aaLG[ 5][12] = 1.672569; aaLG[ 5][13] = 0.035855; aaLG[ 5][14] = 0.624294; aaLG[ 5][15] = 1.223828; aaLG[ 5][16] = 1.080136; aaLG[ 5][17] = 0.236199; aaLG[ 5][18] = 0.257336; aaLG[ 5][19] = 0.210332; aaLG[ 6][ 0] = 1.038545; aaLG[ 6][ 1] = 0.363970; aaLG[ 6][ 2] = 0.541712; aaLG[ 6][ 3] = 5.243870; aaLG[ 6][ 4] = 0.003499; aaLG[ 6][ 5] = 4.128591; aaLG[ 6][ 6] = 0.000000; aaLG[ 6][ 7] = 0.348847; aaLG[ 6][ 8] = 0.423881; aaLG[ 6][ 9] = 0.044265; aaLG[ 6][10] = 0.069673; aaLG[ 6][11] = 1.807177; aaLG[ 6][12] = 0.173735; aaLG[ 6][13] = 0.018811; aaLG[ 6][14] = 0.419409; aaLG[ 6][15] = 0.611973; aaLG[ 6][16] = 0.604545; aaLG[ 6][17] = 0.077852; aaLG[ 6][18] = 0.120037; aaLG[ 6][19] = 0.245034; aaLG[ 7][ 0] = 2.066040; aaLG[ 7][ 1] = 0.390192; aaLG[ 7][ 2] = 1.437645; aaLG[ 7][ 3] = 0.844926; aaLG[ 7][ 4] = 0.569265; aaLG[ 7][ 5] = 0.267959; aaLG[ 7][ 6] = 0.348847; aaLG[ 7][ 7] = 0.000000; aaLG[ 7][ 8] = 0.311484; aaLG[ 7][ 9] = 0.008705; aaLG[ 7][10] = 0.044261; aaLG[ 7][11] = 0.296636; aaLG[ 7][12] = 0.139538; aaLG[ 7][13] = 0.089586; aaLG[ 7][14] = 0.196961; aaLG[ 7][15] = 1.739990; aaLG[ 7][16] = 0.129836; aaLG[ 7][17] = 0.268491; aaLG[ 7][18] = 0.054679; aaLG[ 7][19] = 0.076701; aaLG[ 8][ 0] = 0.358858; aaLG[ 8][ 1] = 2.426601; aaLG[ 8][ 2] = 4.509238; aaLG[ 8][ 3] = 0.927114; aaLG[ 8][ 4] = 0.640543; aaLG[ 8][ 5] = 4.813505; aaLG[ 8][ 6] = 0.423881; aaLG[ 8][ 7] = 0.311484; aaLG[ 8][ 8] = 0.000000; aaLG[ 8][ 9] = 0.108882; aaLG[ 8][10] = 0.366317; aaLG[ 8][11] = 0.697264; aaLG[ 8][12] = 0.442472; aaLG[ 8][13] = 0.682139; aaLG[ 8][14] = 0.508851; aaLG[ 8][15] = 0.990012; aaLG[ 8][16] = 0.584262; aaLG[ 8][17] = 0.597054; aaLG[ 8][18] = 5.306834; aaLG[ 8][19] = 0.119013; aaLG[ 9][ 0] = 0.149830; aaLG[ 9][ 1] = 0.126991; aaLG[ 9][ 2] = 0.191503; aaLG[ 9][ 3] = 0.010690; aaLG[ 9][ 4] = 0.320627; aaLG[ 9][ 5] = 0.072854; aaLG[ 9][ 6] = 0.044265; aaLG[ 9][ 7] = 0.008705; aaLG[ 9][ 8] = 0.108882; aaLG[ 9][ 9] = 0.000000; aaLG[ 9][10] = 4.145067; aaLG[ 9][11] = 0.159069; aaLG[ 9][12] = 4.273607; aaLG[ 9][13] = 1.112727; aaLG[ 9][14] = 0.078281; aaLG[ 9][15] = 0.064105; aaLG[ 9][16] = 1.033739; aaLG[ 9][17] = 0.111660; aaLG[ 9][18] = 0.232523; aaLG[ 9][19] = 10.649107; aaLG[10][ 0] = 0.395337; aaLG[10][ 1] = 0.301848; aaLG[10][ 2] = 0.068427; aaLG[10][ 3] = 0.015076; aaLG[10][ 4] = 0.594007; aaLG[10][ 5] = 0.582457; aaLG[10][ 6] = 0.069673; aaLG[10][ 7] = 0.044261; aaLG[10][ 8] = 0.366317; aaLG[10][ 9] = 4.145067; aaLG[10][10] = 0.000000; aaLG[10][11] = 0.137500; aaLG[10][12] = 6.312358; aaLG[10][13] = 2.592692; aaLG[10][14] = 0.249060; aaLG[10][15] = 0.182287; aaLG[10][16] = 0.302936; aaLG[10][17] = 0.619632; aaLG[10][18] = 0.299648; aaLG[10][19] = 1.702745; aaLG[11][ 0] = 0.536518; aaLG[11][ 1] = 6.326067; aaLG[11][ 2] = 2.145078; aaLG[11][ 3] = 0.282959; aaLG[11][ 4] = 0.013266; aaLG[11][ 5] = 3.234294; aaLG[11][ 6] = 1.807177; aaLG[11][ 7] = 0.296636; aaLG[11][ 8] = 0.697264; aaLG[11][ 9] = 0.159069; aaLG[11][10] = 0.137500; aaLG[11][11] = 0.000000; aaLG[11][12] = 0.656604; aaLG[11][13] = 0.023918; aaLG[11][14] = 0.390322; aaLG[11][15] = 0.748683; aaLG[11][16] = 1.136863; aaLG[11][17] = 0.049906; aaLG[11][18] = 0.131932; aaLG[11][19] = 0.185202; aaLG[12][ 0] = 1.124035; aaLG[12][ 1] = 0.484133; aaLG[12][ 2] = 0.371004; aaLG[12][ 3] = 0.025548; aaLG[12][ 4] = 0.893680; aaLG[12][ 5] = 1.672569; aaLG[12][ 6] = 0.173735; aaLG[12][ 7] = 0.139538; aaLG[12][ 8] = 0.442472; aaLG[12][ 9] = 4.273607; aaLG[12][10] = 6.312358; aaLG[12][11] = 0.656604; aaLG[12][12] = 0.000000; aaLG[12][13] = 1.798853; aaLG[12][14] = 0.099849; aaLG[12][15] = 0.346960; aaLG[12][16] = 2.020366; aaLG[12][17] = 0.696175; aaLG[12][18] = 0.481306; aaLG[12][19] = 1.898718; aaLG[13][ 0] = 0.253701; aaLG[13][ 1] = 0.052722; aaLG[13][ 2] = 0.089525; aaLG[13][ 3] = 0.017416; aaLG[13][ 4] = 1.105251; aaLG[13][ 5] = 0.035855; aaLG[13][ 6] = 0.018811; aaLG[13][ 7] = 0.089586; aaLG[13][ 8] = 0.682139; aaLG[13][ 9] = 1.112727; aaLG[13][10] = 2.592692; aaLG[13][11] = 0.023918; aaLG[13][12] = 1.798853; aaLG[13][13] = 0.000000; aaLG[13][14] = 0.094464; aaLG[13][15] = 0.361819; aaLG[13][16] = 0.165001; aaLG[13][17] = 2.457121; aaLG[13][18] = 7.803902; aaLG[13][19] = 0.654683; aaLG[14][ 0] = 1.177651; aaLG[14][ 1] = 0.332533; aaLG[14][ 2] = 0.161787; aaLG[14][ 3] = 0.394456; aaLG[14][ 4] = 0.075382; aaLG[14][ 5] = 0.624294; aaLG[14][ 6] = 0.419409; aaLG[14][ 7] = 0.196961; aaLG[14][ 8] = 0.508851; aaLG[14][ 9] = 0.078281; aaLG[14][10] = 0.249060; aaLG[14][11] = 0.390322; aaLG[14][12] = 0.099849; aaLG[14][13] = 0.094464; aaLG[14][14] = 0.000000; aaLG[14][15] = 1.338132; aaLG[14][16] = 0.571468; aaLG[14][17] = 0.095131; aaLG[14][18] = 0.089613; aaLG[14][19] = 0.296501; aaLG[15][ 0] = 4.727182; aaLG[15][ 1] = 0.858151; aaLG[15][ 2] = 4.008358; aaLG[15][ 3] = 1.240275; aaLG[15][ 4] = 2.784478; aaLG[15][ 5] = 1.223828; aaLG[15][ 6] = 0.611973; aaLG[15][ 7] = 1.739990; aaLG[15][ 8] = 0.990012; aaLG[15][ 9] = 0.064105; aaLG[15][10] = 0.182287; aaLG[15][11] = 0.748683; aaLG[15][12] = 0.346960; aaLG[15][13] = 0.361819; aaLG[15][14] = 1.338132; aaLG[15][15] = 0.000000; aaLG[15][16] = 6.472279; aaLG[15][17] = 0.248862; aaLG[15][18] = 0.400547; aaLG[15][19] = 0.098369; aaLG[16][ 0] = 2.139501; aaLG[16][ 1] = 0.578987; aaLG[16][ 2] = 2.000679; aaLG[16][ 3] = 0.425860; aaLG[16][ 4] = 1.143480; aaLG[16][ 5] = 1.080136; aaLG[16][ 6] = 0.604545; aaLG[16][ 7] = 0.129836; aaLG[16][ 8] = 0.584262; aaLG[16][ 9] = 1.033739; aaLG[16][10] = 0.302936; aaLG[16][11] = 1.136863; aaLG[16][12] = 2.020366; aaLG[16][13] = 0.165001; aaLG[16][14] = 0.571468; aaLG[16][15] = 6.472279; aaLG[16][16] = 0.000000; aaLG[16][17] = 0.140825; aaLG[16][18] = 0.245841; aaLG[16][19] = 2.188158; aaLG[17][ 0] = 0.180717; aaLG[17][ 1] = 0.593607; aaLG[17][ 2] = 0.045376; aaLG[17][ 3] = 0.029890; aaLG[17][ 4] = 0.670128; aaLG[17][ 5] = 0.236199; aaLG[17][ 6] = 0.077852; aaLG[17][ 7] = 0.268491; aaLG[17][ 8] = 0.597054; aaLG[17][ 9] = 0.111660; aaLG[17][10] = 0.619632; aaLG[17][11] = 0.049906; aaLG[17][12] = 0.696175; aaLG[17][13] = 2.457121; aaLG[17][14] = 0.095131; aaLG[17][15] = 0.248862; aaLG[17][16] = 0.140825; aaLG[17][17] = 0.000000; aaLG[17][18] = 3.151815; aaLG[17][19] = 0.189510; aaLG[18][ 0] = 0.218959; aaLG[18][ 1] = 0.314440; aaLG[18][ 2] = 0.612025; aaLG[18][ 3] = 0.135107; aaLG[18][ 4] = 1.165532; aaLG[18][ 5] = 0.257336; aaLG[18][ 6] = 0.120037; aaLG[18][ 7] = 0.054679; aaLG[18][ 8] = 5.306834; aaLG[18][ 9] = 0.232523; aaLG[18][10] = 0.299648; aaLG[18][11] = 0.131932; aaLG[18][12] = 0.481306; aaLG[18][13] = 7.803902; aaLG[18][14] = 0.089613; aaLG[18][15] = 0.400547; aaLG[18][16] = 0.245841; aaLG[18][17] = 3.151815; aaLG[18][18] = 0.000000; aaLG[18][19] = 0.249313; aaLG[19][ 0] = 2.547870; aaLG[19][ 1] = 0.170887; aaLG[19][ 2] = 0.083688; aaLG[19][ 3] = 0.037967; aaLG[19][ 4] = 1.959291; aaLG[19][ 5] = 0.210332; aaLG[19][ 6] = 0.245034; aaLG[19][ 7] = 0.076701; aaLG[19][ 8] = 0.119013; aaLG[19][ 9] = 10.649107; aaLG[19][10] = 1.702745; aaLG[19][11] = 0.185202; aaLG[19][12] = 1.898718; aaLG[19][13] = 0.654683; aaLG[19][14] = 0.296501; aaLG[19][15] = 0.098369; aaLG[19][16] = 2.188158; aaLG[19][17] = 0.189510; aaLG[19][18] = 0.249313; aaLG[19][19] = 0.000000; lgPi[0] = 0.079066; lgPi[1] = 0.055941; lgPi[2] = 0.041977; lgPi[3] = 0.053052; lgPi[4] = 0.012937; lgPi[5] = 0.040767; lgPi[6] = 0.071586; lgPi[7] = 0.057337; lgPi[8] = 0.022355; lgPi[9] = 0.062157; lgPi[10] = 0.099081; lgPi[11] = 0.064600; lgPi[12] = 0.022951; lgPi[13] = 0.042302; lgPi[14] = 0.044040; lgPi[15] = 0.061197; lgPi[16] = 0.053287; lgPi[17] = 0.012066; lgPi[18] = 0.034155; lgPi[19] = 0.069147; /* now, check that the matrices are symmetrical */ for (i=0; i<20; i++) { for (j=i+1; j<20; j++) { diff = aaJones[i][j] - aaJones[j][i]; if (diff < 0.0) diff = -diff; if (diff > 0.001) { MrBayesPrint ("%s ERROR: Jones model is not symmetrical.\n"); return (ERROR); } } } for (i=0; i<20; i++) { for (j=i+1; j<20; j++) { diff = aaDayhoff[i][j] - aaDayhoff[j][i]; if (diff < 0.0) diff = -diff; if (diff > 0.001) { MrBayesPrint ("%s ERROR: Dayhoff model is not symmetrical.\n"); return (ERROR); } } } for (i=0; i<20; i++) { for (j=i+1; j<20; j++) { diff = aaMtrev24[i][j] - aaMtrev24[j][i]; if (diff < 0.0) diff = -diff; if (diff > 0.001) { MrBayesPrint ("%s ERROR: mtrev24 model is not symmetrical.\n"); return (ERROR); } } } for (i=0; i<20; i++) { for (j=i+1; j<20; j++) { diff = aaMtmam[i][j] - aaMtmam[j][i]; if (diff < 0.0) diff = -diff; if (diff > 0.001) { MrBayesPrint ("%s ERROR: mtmam model is not symmetrical.\n"); return (ERROR); } } } for (i=0; i<20; i++) { for (j=i+1; j<20; j++) { diff = aartREV[i][j] - aartREV[j][i]; if (diff < 0.0) diff = -diff; if (diff > 0.001) { MrBayesPrint ("%s ERROR: aartREV model is not symmetrical.\n"); return (ERROR); } } } for (i=0; i<20; i++) { for (j=i+1; j<20; j++) { diff = aaWAG[i][j] - aaWAG[j][i]; if (diff < 0.0) diff = -diff; if (diff > 0.001) { MrBayesPrint ("%s ERROR: aaWAG model is not symmetrical.\n"); return (ERROR); } } } for (i=0; i<20; i++) { for (j=i+1; j<20; j++) { diff = aacpREV[i][j] - aacpREV[j][i]; if (diff < 0.0) diff = -diff; if (diff > 0.001) { MrBayesPrint ("%s ERROR: cpREV model is not symmetrical.\n"); return (ERROR); } } } for (i=0; i<20; i++) { for (j=i+1; j<20; j++) { diff = aaVt[i][j] - aaVt[j][i]; if (diff < 0.0) diff = -diff; if (diff > 0.001) { MrBayesPrint ("%s ERROR: Vt model is not symmetrical.\n"); return (ERROR); } } } for (i=0; i<20; i++) { for (j=i+1; j<20; j++) { diff = aaBlosum[i][j] - aaBlosum[j][i]; if (diff < 0.0) diff = -diff; if (diff > 0.001) { MrBayesPrint ("%s ERROR: Blosum model is not symmetrical.\n"); return (ERROR); } } } for (i=0; i<20; i++) { for (j=i+1; j<20; j++) { diff = aaLG[i][j] - aaLG[j][i]; if (diff < 0.0) diff = -diff; if (diff > 0.001) { MrBayesPrint ("%s ERROR: LG model is not symmetrical.\n"); return (ERROR); } } } /* rescale stationary frequencies, to make certain they sum to 1.0 */ sum = 0.0; for (i=0; i<20; i++) sum += jonesPi[i]; for (i=0; i<20; i++) jonesPi[i] /= sum; sum = 0.0; for (i=0; i<20; i++) sum += dayhoffPi[i]; for (i=0; i<20; i++) dayhoffPi[i] /= sum; sum = 0.0; for (i=0; i<20; i++) sum += mtrev24Pi[i]; for (i=0; i<20; i++) mtrev24Pi[i] /= sum; sum = 0.0; for (i=0; i<20; i++) sum += mtmamPi[i]; for (i=0; i<20; i++) mtmamPi[i] /= sum; sum = 0.0; for (i=0; i<20; i++) sum += rtrevPi[i]; for (i=0; i<20; i++) rtrevPi[i] /= sum; sum = 0.0; for (i=0; i<20; i++) sum += wagPi[i]; for (i=0; i<20; i++) wagPi[i] /= sum; sum = 0.0; for (i=0; i<20; i++) sum += cprevPi[i]; for (i=0; i<20; i++) cprevPi[i] /= sum; sum = 0.0; for (i=0; i<20; i++) sum += vtPi[i]; for (i=0; i<20; i++) vtPi[i] /= sum; sum = 0.0; for (i=0; i<20; i++) sum += blosPi[i]; for (i=0; i<20; i++) blosPi[i] /= sum; sum = 0.0; for (i=0; i<20; i++) sum += lgPi[i]; for (i=0; i<20; i++) lgPi[i] /= sum; /* multiply entries by amino acid frequencies */ for (i=0; i<20; i++) { for (j=0; j<20; j++) { aaJones[i][j] *= jonesPi[j]; aaDayhoff[i][j] *= dayhoffPi[j]; aaMtrev24[i][j] *= mtrev24Pi[j]; aaMtmam[i][j] *= mtmamPi[j]; aartREV[i][j] *= rtrevPi[j]; aaWAG[i][j] *= wagPi[j]; aacpREV[i][j] *= cprevPi[j]; aaVt[i][j] *= vtPi[j]; aaBlosum[i][j] *= blosPi[j]; aaLG[i][j] *= lgPi[j]; } } /* rescale, so branch lengths are in terms of expected number of amino acid substitutions per site */ scaler = 0.0; for (i=0; i<20; i++) { for (j=i+1; j<20; j++) { scaler += jonesPi[i] * aaJones[i][j]; scaler += jonesPi[j] * aaJones[j][i]; } } scaler = 1.0 / scaler; for (i=0; i<20; i++) for (j=0; j<20; j++) aaJones[i][j] *= scaler; scaler = 0.0; for (i=0; i<20; i++) { for (j=i+1; j<20; j++) { scaler += dayhoffPi[i] * aaDayhoff[i][j]; scaler += dayhoffPi[j] * aaDayhoff[j][i]; } } scaler = 1.0 / scaler; for (i=0; i<20; i++) for (j=0; j<20; j++) aaDayhoff[i][j] *= scaler; scaler = 0.0; for (i=0; i<20; i++) { for (j=i+1; j<20; j++) { scaler += mtrev24Pi[i] * aaMtrev24[i][j]; scaler += mtrev24Pi[j] * aaMtrev24[j][i]; } } scaler = 1.0 / scaler; for (i=0; i<20; i++) for (j=0; j<20; j++) aaMtrev24[i][j] *= scaler; scaler = 0.0; for (i=0; i<20; i++) { for (j=i+1; j<20; j++) { scaler += mtmamPi[i] * aaMtmam[i][j]; scaler += mtmamPi[j] * aaMtmam[j][i]; } } scaler = 1.0 / scaler; for (i=0; i<20; i++) for (j=0; j<20; j++) aaMtmam[i][j] *= scaler; scaler = 0.0; for (i=0; i<20; i++) { for (j=i+1; j<20; j++) { scaler += rtrevPi[i] * aartREV[i][j]; scaler += rtrevPi[j] * aartREV[j][i]; } } scaler = 1.0 / scaler; for (i=0; i<20; i++) for (j=0; j<20; j++) aartREV[i][j] *= scaler; scaler = 0.0; for (i=0; i<20; i++) { for (j=i+1; j<20; j++) { scaler += wagPi[i] * aaWAG[i][j]; scaler += wagPi[j] * aaWAG[j][i]; } } scaler = 1.0 / scaler; for (i=0; i<20; i++) for (j=0; j<20; j++) aaWAG[i][j] *= scaler; scaler = 0.0; for (i=0; i<20; i++) { for (j=i+1; j<20; j++) { scaler += cprevPi[i] * aacpREV[i][j]; scaler += cprevPi[j] * aacpREV[j][i]; } } scaler = 1.0 / scaler; for (i=0; i<20; i++) for (j=0; j<20; j++) aacpREV[i][j] *= scaler; scaler = 0.0; for (i=0; i<20; i++) { for (j=i+1; j<20; j++) { scaler += vtPi[i] * aaVt[i][j]; scaler += vtPi[j] * aaVt[j][i]; } } scaler = 1.0 / scaler; for (i=0; i<20; i++) for (j=0; j<20; j++) aaVt[i][j] *= scaler; scaler = 0.0; for (i=0; i<20; i++) { for (j=i+1; j<20; j++) { scaler += blosPi[i] * aaBlosum[i][j]; scaler += blosPi[j] * aaBlosum[j][i]; } } scaler = 1.0 / scaler; for (i=0; i<20; i++) for (j=0; j<20; j++) aaBlosum[i][j] *= scaler; scaler = 0.0; for (i=0; i<20; i++) { for (j=i+1; j<20; j++) { scaler += lgPi[i] * aaLG[i][j]; scaler += lgPi[j] * aaLG[j][i]; } } scaler = 1.0 / scaler; for (i=0; i<20; i++) for (j=0; j<20; j++) aaLG[i][j] *= scaler; /* set diagonal of matrix */ for (i=0; i<20; i++) { sum = 0.0; for (j=0; j<20; j++) { if (i != j) sum += aaJones[i][j]; } aaJones[i][i] = -sum; } for (i=0; i<20; i++) { sum = 0.0; for (j=0; j<20; j++) { if (i != j) sum += aaDayhoff[i][j]; } aaDayhoff[i][i] = -sum; } for (i=0; i<20; i++) { sum = 0.0; for (j=0; j<20; j++) { if (i != j) sum += aaMtrev24[i][j]; } aaMtrev24[i][i] = -sum; } for (i=0; i<20; i++) { sum = 0.0; for (j=0; j<20; j++) { if (i != j) sum += aaMtmam[i][j]; } aaMtmam[i][i] = -sum; } for (i=0; i<20; i++) { sum = 0.0; for (j=0; j<20; j++) { if (i != j) sum += aartREV[i][j]; } aartREV[i][i] = -sum; } for (i=0; i<20; i++) { sum = 0.0; for (j=0; j<20; j++) { if (i != j) sum += aaWAG[i][j]; } aaWAG[i][i] = -sum; } for (i=0; i<20; i++) { sum = 0.0; for (j=0; j<20; j++) { if (i != j) sum += aacpREV[i][j]; } aacpREV[i][i] = -sum; } for (i=0; i<20; i++) { sum = 0.0; for (j=0; j<20; j++) { if (i != j) sum += aaVt[i][j]; } aaVt[i][i] = -sum; } for (i=0; i<20; i++) { sum = 0.0; for (j=0; j<20; j++) { if (i != j) sum += aaBlosum[i][j]; } aaBlosum[i][i] = -sum; } for (i=0; i<20; i++) { sum = 0.0; for (j=0; j<20; j++) { if (i != j) sum += aaLG[i][j]; } aaLG[i][i] = -sum; } # if 0 for (i=0; i<20; i++) { MrBayesPrint ("%s ", spacer); for (j=0; j<20; j++) { if (aaLG[i][j] < 0.0) MrBayesPrint ("%1.3lf ", aaLG[i][j]); else MrBayesPrint (" %1.3lf ", aaLG[i][j]); } MrBayesPrint ("\n"); } # endif return (NO_ERROR); } void SetCode (int part) { int s, s1, s2, s3; modelParams[part].codon[ 0] = 12; /* AAA Lys */ modelParams[part].codon[ 1] = 3; /* AAC Asn */ modelParams[part].codon[ 2] = 12; /* AAG Lys */ modelParams[part].codon[ 3] = 3; /* AAT Asn */ modelParams[part].codon[ 4] = 17; /* ACA Thr */ modelParams[part].codon[ 5] = 17; /* ACC Thr */ modelParams[part].codon[ 6] = 17; /* ACG Thr */ modelParams[part].codon[ 7] = 17; /* ACT Thr */ modelParams[part].codon[ 8] = 2; /* AGA Arg */ modelParams[part].codon[ 9] = 16; /* AGC Ser */ modelParams[part].codon[10] = 2; /* AGG Arg */ modelParams[part].codon[11] = 16; /* AGT Ser */ modelParams[part].codon[12] = 10; /* ATA Ile */ modelParams[part].codon[13] = 10; /* ATC Ile */ modelParams[part].codon[14] = 13; /* ATG Met */ modelParams[part].codon[15] = 10; /* ATT Ile */ modelParams[part].codon[16] = 6; /* CAA Gln */ modelParams[part].codon[17] = 9; /* CAC His */ modelParams[part].codon[18] = 6; /* CAG Gln */ modelParams[part].codon[19] = 9; /* CAT His */ modelParams[part].codon[20] = 15; /* CCA Pro */ modelParams[part].codon[21] = 15; /* CCC Pro */ modelParams[part].codon[22] = 15; /* CCG Pro */ modelParams[part].codon[23] = 15; /* CCT Pro */ modelParams[part].codon[24] = 2; /* CGA Arg */ modelParams[part].codon[25] = 2; /* CGC Arg */ modelParams[part].codon[26] = 2; /* CGG Arg */ modelParams[part].codon[27] = 2; /* CGT Arg */ modelParams[part].codon[28] = 11; /* CTA Leu */ modelParams[part].codon[29] = 11; /* CTC Leu */ modelParams[part].codon[30] = 11; /* CTG Leu */ modelParams[part].codon[31] = 11; /* CTT Leu */ modelParams[part].codon[32] = 7; /* GAA Glu */ modelParams[part].codon[33] = 4; /* GAC Asp */ modelParams[part].codon[34] = 7; /* GAG Glu */ modelParams[part].codon[35] = 4; /* GAT Asp */ modelParams[part].codon[36] = 1; /* GCA Ala */ modelParams[part].codon[37] = 1; /* GCC Ala */ modelParams[part].codon[38] = 1; /* GCG Ala */ modelParams[part].codon[39] = 1; /* GCT Ala */ modelParams[part].codon[40] = 8; /* GGA Gly */ modelParams[part].codon[41] = 8; /* GGC Gly */ modelParams[part].codon[42] = 8; /* GGG Gly */ modelParams[part].codon[43] = 8; /* GGT Gly */ modelParams[part].codon[44] = 20; /* GTA Val */ modelParams[part].codon[45] = 20; /* GTC Val */ modelParams[part].codon[46] = 20; /* GTG Val */ modelParams[part].codon[47] = 20; /* GTT Val */ modelParams[part].codon[48] = 21; /* TAA Stop*/ modelParams[part].codon[49] = 19; /* TAC Tyr */ modelParams[part].codon[50] = 21; /* TAG Stop*/ modelParams[part].codon[51] = 19; /* TAT Tyr */ modelParams[part].codon[52] = 16; /* TCA Ser */ modelParams[part].codon[53] = 16; /* TCC Ser */ modelParams[part].codon[54] = 16; /* TCG Ser */ modelParams[part].codon[55] = 16; /* TCT Ser */ modelParams[part].codon[56] = 21; /* TGA Stop*/ modelParams[part].codon[57] = 5; /* TGC Cys */ modelParams[part].codon[58] = 18; /* TGG Trp */ modelParams[part].codon[59] = 5; /* TGT Cys */ modelParams[part].codon[60] = 11; /* TTA Leu */ modelParams[part].codon[61] = 14; /* TTC Phe */ modelParams[part].codon[62] = 11; /* TTG Leu */ modelParams[part].codon[63] = 14; /* TTT Phe */ if (!strcmp(modelParams[part].geneticCode, "Vertmt")) { /* UGA: Ter -> Trp AUA: Ile -> Met AGA: Arg -> Ter AGG: Arg -> Ter */ modelParams[part].codon[ 8] = 21; /* AGA Stop */ modelParams[part].codon[10] = 21; /* AGG Stop */ modelParams[part].codon[12] = 13; /* ATA Met */ modelParams[part].codon[56] = 18; /* TGA Trp */ } if (!strcmp(modelParams[part].geneticCode, "Invermt")) { /* UGA: Ter -> Trp AUA: Ile -> Met AGA: Arg -> Ser AGG: Arg -> Ser */ modelParams[part].codon[ 8] = 16; /* AGA Ser */ modelParams[part].codon[10] = 16; /* AGG Ser */ modelParams[part].codon[12] = 13; /* ATA Met */ modelParams[part].codon[56] = 18; /* TGA Trp */ } else if (!strcmp(modelParams[part].geneticCode, "Mycoplasma")) { /* UGA: Ter -> Trp */ modelParams[part].codon[56] = 18; /* TGA Trp */ } else if (!strcmp(modelParams[part].geneticCode, "Yeast")) { /* UGA: Ter -> Trp AUA: Ile -> Met CUA: Leu -> Thr CUC: Leu -> Thr CUG: Leu -> Thr CUU: Leu -> Thr */ modelParams[part].codon[12] = 13; /* ATA Met */ modelParams[part].codon[28] = 17; /* CTA Thr */ modelParams[part].codon[29] = 17; /* CTC Thr */ modelParams[part].codon[30] = 17; /* CTG Thr */ modelParams[part].codon[31] = 17; /* CTT Thr */ modelParams[part].codon[56] = 18; /* TGA Trp */ } else if (!strcmp(modelParams[part].geneticCode, "Ciliate")) { /* UAA: Ter -> Gln UAG: Ter -> Gln */ modelParams[part].codon[48] = 6; /* TAA Gln */ modelParams[part].codon[50] = 6; /* TAG Gln */ } else if (!strcmp(modelParams[part].geneticCode, "Echinoderm")) { /* AAA: Lys -> Asn AGA: Arg -> Ser AGG: Arg -> Ser UGA: Ter -> Trp */ modelParams[part].codon[ 0] = 3; /* AAA Asn */ modelParams[part].codon[ 8] = 16; /* AGA Ser */ modelParams[part].codon[10] = 16; /* AGG Ser */ modelParams[part].codon[56] = 18; /* TGA Trp */ } else if (!strcmp(modelParams[part].geneticCode, "Euplotid")) { /* UGA: Ter -> Cys */ modelParams[part].codon[56] = 5; /* TGA Cys */ } else if (!strcmp(modelParams[part].geneticCode, "Metmt")) { /* UGA: Ter -> Trp AUA: Ile -> Met AGA: Arg -> Ser AGG: Arg -> Ser */ modelParams[part].codon[ 8] = 16; /* AGA Ser */ modelParams[part].codon[10] = 16; /* AGG Ser */ modelParams[part].codon[12] = 13; /* ATA Met */ modelParams[part].codon[56] = 18; /* TGA Trp */ } else { } /* ns = 0; for (i=0; i<64; i++) { if (modelParams[part].codon[i] != 21) ns++; } printf ("ns = %d\n", ns); */ s = 0; for (s1=0; s1<4; s1++) for (s2=0; s2<4; s2++) for (s3=0; s3<4; s3++) if (modelParams[part].codon[s1*16 + s2*4 + s3] != 21) { modelParams[part].codonNucs[s][0] = s1; modelParams[part].codonNucs[s][1] = s2; modelParams[part].codonNucs[s][2] = s3; modelParams[part].codonAAs[s] = modelParams[part].codon[s1*16 + s2*4 + s3]; s++; } } int SetLocalTaxa (void) { int i, j; /* free memory if allocated */ if (memAllocs[ALLOC_LOCTAXANAMES] == YES) { free (localTaxonNames); localTaxonNames = NULL; memAllocs[ALLOC_LOCTAXANAMES] = NO; } if (memAllocs[ALLOC_LOCALTAXONCALIBRATION] == YES) { free (localTaxonCalibration); localTaxonCalibration = NULL; memAllocs[ALLOC_LOCALTAXONCALIBRATION] = NO; } /* count number of non-excluded taxa */ numLocalTaxa = 0; for (i=0; i 0) modelParams[j].activeConstraints = (int *) SafeCalloc((size_t)(numDefinedConstraints), sizeof(int)); /* allocate space for active constraints (yes/no) */ } return (NO_ERROR); } /*---------------------------------------------------------------------------- | | SetModelInfo: This function will set up model info using model | params | -----------------------------------------------------------------------------*/ int SetModelInfo (void) { int i, j, chn, ts; ModelParams *mp; ModelInfo *m; /* wipe all model settings */ inferSiteRates = NO; inferAncStates = NO; inferSiteOmegas = NO; for (i=0; iupDateCijk = YES; m->upDateCl = YES; m->upDateAll = YES; /* make certain that we start with a parsimony branch length of zero */ for (j=0; jparsTreeLength[j*2] = m->parsTreeLength[j*2+1] = 0.0; m->tRatio = NULL; m->revMat = NULL; m->omega = NULL; m->stateFreq = NULL; m->mixtureRates = NULL; m->shape = NULL; m->pInvar = NULL; m->correlation = NULL; m->switchRates = NULL; m->rateMult = NULL; m->topology = NULL; m->brlens = NULL; m->speciationRates = NULL; m->extinctionRates = NULL; m->fossilizationRates = NULL; m->popSize = NULL; m->aaModel = NULL; m->cppRate = NULL; m->cppEvents = NULL; m->cppMultDev = NULL; m->tk02var = NULL; m->tk02BranchRates = NULL; m->wnvar = NULL; m->wnBranchRates = NULL; m->igrvar = NULL; m->igrBranchRates = NULL; m->ilnvar = NULL; m->ilnBranchRates = NULL; m->mixedvar = NULL; m->mixedBrchRates = NULL; m->clockRate = NULL; m->CondLikeDown = NULL; m->CondLikeRoot = NULL; m->CondLikeScaler = NULL; m->Likelihood = NULL; m->TiProbs = NULL; m->CondLikeUp = NULL; m->StateCode = NULL; m->PrintAncStates = NULL; m->PrintSiteRates = NULL; m->printPosSel = NO; m->printAncStates = NO; m->printSiteRates = NO; m->nStates = NULL; m->bsIndex = NULL; m->cType = NULL; m->tiIndex = NULL; m->gibbsGamma = NO; m->gibbsFreq = 0; m->parsimonyBasedMove = NO; # if defined (BEAGLE_ENABLED) m->beagleInstance = -1; /* beagle instance */ m->logLikelihoods = NULL; /* array of log likelihoods from Beagle */ m->inRates = NULL; /* array of category rates for Beagle */ m->branchLengths = NULL; /* array of branch lengths for Beagle */ m->tiProbIndices = NULL; /* array of trans prob indices for Beagle */ m->inWeights = NULL; /* array of weights for Beagle root likelihood */ m->bufferIndices = NULL; /* array of partial indices for root likelihood */ m->childBufferIndices = NULL; /* array of child partial indices (unrooted) */ m->childTiProbIndices = NULL; /* array of child ti prob indices (unrooted) */ m->cumulativeScaleIndices = NULL; /* array of cumulative scale indices */ m->divisionIndex = i; /* division index number */ m->operations = NULL; /* array of operations to be sent to Beagle */ m->opCount = 0; /* partial likelihood operations count */ # if defined (BEAGLE_V3_ENABLED) m->numCharsAll = 0; /* number of compressed chars for all divisions */ m->logLikelihoodsAll = NULL; /* array of log likelihoods for all divisions */ m->cijkIndicesAll = NULL; /* cijk array for all divisions */ m->categoryRateIndicesAll = NULL; /* category rate array for all divisions */ m->operationsAll = NULL; /* array of all operations across divisions */ m->operationsByPartition = NULL; /* array of division operations to be sent to Beagle */ # endif /* BEAGLE_V3_ENABLED */ # endif /* BEAGLE_ENABLED */ /* likelihood calculator flags */ m->useVec = VEC_NONE; /* use SIMD code for this partition? */ m->useBeagle = NO; /* use Beagle for this partition? */ m->useBeagleMultiPartitions = NO; /* use one Beagle instance for all partitions? */ #if defined (SSE_ENABLED) m->numVecChars = 0; m->numFloatsPerVec = 0; m->lnL_Vec = NULL; m->lnLI_Vec = NULL; m->clP_SSE = NULL; #if defined (AVX_ENABLED) m->clP_AVX = NULL; #endif #endif /* set all memory pointers to NULL */ m->parsSets = NULL; m->numParsSets = 0; m->parsNodeLens = NULL; m->numParsNodeLens = 0; m->condLikes = NULL; m->tiProbs = NULL; m->scalers = NULL; m->numCondLikes = 0; m->numTiProbs = 0; m->numScalers = 0; m->condLikeIndex = NULL; m->condLikeScratchIndex = NULL; m->tiProbsIndex = NULL; m->tiProbsScratchIndex = NULL; m->nodeScalerIndex = NULL; m->nodeScalerScratchIndex = NULL; m->siteScalerIndex = NULL; m->siteScalerScratchIndex = -1; m->cijks = NULL; m->nCijkParts = 0; m->cijkIndex = NULL; m->cijkScratchIndex = -1; } /* set state of all chains to zero */ for (chn=0; chnnucModel,"Protein") && (mp->dataType == DNA || mp->dataType == RNA)) m->dataType = PROTEIN; else m->dataType = mp->dataType; /* nuc model structure */ if (!strcmp(mp->nucModel, "4by4")) m->nucModelId = NUCMODEL_4BY4; else if (!strcmp(mp->nucModel, "Doublet")) m->nucModelId = NUCMODEL_DOUBLET; else if (!strcmp(mp->nucModel, "Protein")) m->nucModelId = NUCMODEL_AA; else /* if (!strcmp(mp->nucModelId, "Codon")) */ m->nucModelId = NUCMODEL_CODON; /* model nst */ if (!strcmp(mp->nst, "1")) m->nst = 1; else if (!strcmp(mp->nst, "2")) m->nst = 2; else if (!strcmp(mp->nst, "6")) m->nst = 6; else m->nst = NST_MIXED; /* We set the aa model here. We have two options. First, the model could be fixed, in which case mp->aaModel has been set. We then go ahead and also set the model settings. Second, the model could be mixed. In this case, the amino acid matrix is considered a parameter, and we will deal with it below. It doesn't hurt to set it here anyway (it will be overwritten later). */ if (!strcmp(mp->aaModelPr, "Fixed")) { if (!strcmp(mp->aaModel, "Poisson")) m->aaModelId = AAMODEL_POISSON; else if (!strcmp(mp->aaModel, "Equalin")) m->aaModelId = AAMODEL_EQ; else if (!strcmp(mp->aaModel, "Jones")) m->aaModelId = AAMODEL_JONES; else if (!strcmp(mp->aaModel, "Dayhoff")) m->aaModelId = AAMODEL_DAY; else if (!strcmp(mp->aaModel, "Mtrev")) m->aaModelId = AAMODEL_MTREV; else if (!strcmp(mp->aaModel, "Mtmam")) m->aaModelId = AAMODEL_MTMAM; else if (!strcmp(mp->aaModel, "Wag")) m->aaModelId = AAMODEL_WAG; else if (!strcmp(mp->aaModel, "Rtrev")) m->aaModelId = AAMODEL_RTREV; else if (!strcmp(mp->aaModel, "Cprev")) m->aaModelId = AAMODEL_CPREV; else if (!strcmp(mp->aaModel, "Vt")) m->aaModelId = AAMODEL_VT; else if (!strcmp(mp->aaModel, "Blosum")) m->aaModelId = AAMODEL_BLOSUM; else if (!strcmp(mp->aaModel, "LG")) m->aaModelId = AAMODEL_LG; else if (!strcmp(mp->aaModel, "Gtr")) m->aaModelId = AAMODEL_GTR; else { MrBayesPrint ("%s Uncertain amino acid model\n", spacer); return (ERROR); } } else m->aaModelId = -1; /* parsimony model? */ if (!strcmp(mp->parsModel, "Yes")) m->parsModelId = YES; else m->parsModelId = NO; /* number of rate categories */ if (activeParams[P_SHAPE][i] > 0) { if (!strcmp(mp->ratesModel, "Lnorm")) m->numRateCats = mp->numLnormCats; else m->numRateCats = mp->numGammaCats; } else if (activeParams[P_MIXTURE_RATES][i] > 0) m->numRateCats = mp->numMixtCats; else m->numRateCats = 1; /* number of beta categories */ if (mp->dataType == STANDARD && !(AreDoublesEqual(mp->symBetaFix, -1.0, 0.00001) == YES && !strcmp(mp->symPiPr,"Fixed"))) m->numBetaCats = mp->numBetaCats; else m->numBetaCats = 1; /* number of omega categories */ if ((mp->dataType == DNA || mp->dataType == RNA) && (!strcmp(mp->omegaVar, "Ny98") || !strcmp(mp->omegaVar, "M3")) && !strcmp(mp->nucModel, "Codon")) { m->numOmegaCats = 3; m->numRateCats = 1; /* if we are here, then we cannot have gamma or beta variation */ m->numBetaCats = 1; } else if ((mp->dataType == DNA || mp->dataType == RNA) && !strcmp(mp->omegaVar, "M10") && !strcmp(mp->nucModel, "Codon")) { m->numOmegaCats = mp->numM10BetaCats + mp->numM10GammaCats; m->numRateCats = 1; /* if we are here, then we cannot have gamma or beta variation */ m->numBetaCats = 1; } else m->numOmegaCats = 1; /* number of transition matrices depends on numGammaCats, numBetaCats, and numOmegaCats */ m->numTiCats = m->numRateCats * m->numBetaCats * m->numOmegaCats; /* TODO: check that numStates and numModelStates are set appropriately for codon and doublet models */ /* number of observable states */ if (m->dataType == STANDARD) m->numStates = 0; /* zero, meaning variable */ else if (!strcmp(mp->nucModel,"Protein") && (mp->dataType == DNA || mp->dataType == RNA)) m->numStates = 20; else m->numStates = mp->nStates; /* number of model states including hidden ones */ if ((mp->dataType == DNA || mp->dataType == RNA) && !strcmp (mp->covarionModel, "Yes") && !strcmp(mp->nucModel, "4by4")) m->numModelStates = mp->nStates * 2; else if (m->dataType == PROTEIN && !strcmp (mp->covarionModel, "Yes")) m->numModelStates = mp->nStates * 2; else if ((mp->dataType == DNA || mp->dataType == RNA) && !strcmp(mp->nucModel,"Protein") && !strcmp (mp->covarionModel, "Yes")) m->numModelStates = 20 * 2; else if (mp->dataType == CONTINUOUS) m->numModelStates = 0; else if (mp->dataType == STANDARD) { /* use max possible for now; we don't know what chars will be included */ m->numModelStates = MAX_STD_STATES; } else m->numModelStates = m->numStates; /* Fill in some information for calculating cijk. We will use m->cijkLength to figure out if we need to diagonalize Q to calculate transition probabilities. If cijkLength = 0, then we won't bother. We use cijkLength later in this function. */ m->cijkLength = 0; m->nCijkParts = 1; if (m->dataType == PROTEIN) { ts = m->numModelStates; m->cijkLength = (ts * ts * ts) + (2 * ts); if (!strcmp (mp->covarionModel, "Yes")) { m->cijkLength *= m->numRateCats; m->nCijkParts = m->numRateCats; } } else if (m->dataType == STANDARD) { /* set to 0 for now, update in ProcessStdChars */ m->nCijkParts = 0; } else if (m->dataType == DNA || m->dataType == RNA) { if (m->nucModelId == NUCMODEL_4BY4) { if (!strcmp (mp->covarionModel, "No") && m->nst != 6 && m->nst != 203) m->cijkLength = 0; else { ts = m->numModelStates; m->cijkLength = (ts * ts * ts) + (2 * ts); } if (!strcmp (mp->covarionModel, "Yes")) { m->cijkLength *= m->numRateCats; m->nCijkParts = m->numRateCats; } } else if (m->nucModelId == NUCMODEL_DOUBLET) { ts = m->numModelStates; m->cijkLength = (ts * ts * ts) + (2 * ts); } else if (m->nucModelId == NUCMODEL_CODON) { ts = m->numModelStates; m->cijkLength = (ts * ts * ts) + (2 * ts); m->cijkLength *= m->numOmegaCats; m->nCijkParts = m->numOmegaCats; } else { MrBayesPrint ("%s BUG: Inconsistent model. Please report this as a bug.\n"); return ERROR; } } /* check if we should calculate ancestral states */ if (!strcmp(mp->inferAncStates,"Yes")) { if (m->dataType == PROTEIN && !strcmp(mp->covarionModel, "No")) m->printAncStates = YES; else if (m->dataType == DNA || m->dataType == RNA) { if (!strcmp(mp->nucModel,"4by4") && !strcmp(mp->covarionModel, "No")) m->printAncStates = YES; if (!strcmp(mp->nucModel,"Doublet")) m->printAncStates = YES; if (!strcmp(mp->nucModel,"Codon") && !strcmp(mp->omegaVar,"Equal")) m->printAncStates = YES; } else if (m->dataType == STANDARD || m->dataType == RESTRICTION || m->dataType == CONTINUOUS) m->printAncStates = YES; if (m->printAncStates == YES) inferAncStates = YES; else MrBayesPrint ("%s Print out of ancestral states is not applicable for partition %d.\n",spacer,i); } /* check if we should calculate site rates */ if (!strcmp(mp->inferSiteRates,"Yes")) { if (m->numRateCats > 1) { m->printSiteRates = YES; inferSiteRates = YES; } } /* check if we should calculate positive selection */ if (!strcmp(mp->inferPosSel, "Yes")) { if (m->numOmegaCats > 1) { m->printPosSel = YES; inferPosSel = YES; } } /* check if we should calculate site omegas */ if (!strcmp(mp->inferSiteOmegas, "Yes")) { if (m->numOmegaCats > 1) { m->printSiteOmegas = YES; inferSiteOmegas = YES; } } /* check if we should use gibbs sampling of gamma (don't use it with pinvar or invgamma) */ if (!strcmp(mp->useGibbs,"Yes") && (m->numRateCats > 1)) { if (m->dataType == DNA || m->dataType == RNA || m->dataType == PROTEIN) { if (activeParams[P_CORREL][i] <= 0 && m->printSiteRates == NO && activeParams[P_PINVAR][i] <= 0) { m->gibbsGamma = YES; m->gibbsFreq = mp->gibbsFreq; } } } } return (NO_ERROR); } /*----------------------------------------------------------------- | | SetModelParams: Set up parameter structs for all model | parameters, including trees | |----------------------------------------------------------------*/ int SetModelParams (void) { int c, i, j, k, n, n1, n2, *isPartTouched, numRelParts, nRelParts, areAllPartsParsimony, nClockBrlens, nRelaxedBrlens, nCalibratedBrlens; char tempCodon[15], tempMult[20], *tempStr, temp[30]; char static *partString=NULL; /* mad static to avoid possible memory leak on return ERROR if it would be introduced later */ Param *p; ModelParams *mp; ModelInfo *m; int tempStrSize = 300; tempStr = (char *) SafeMalloc((size_t)tempStrSize * sizeof(char)); isPartTouched = (int *) SafeMalloc ((size_t)numCurrentDivisions * sizeof (int)); if (!tempStr || !isPartTouched) { MrBayesPrint ("%s Problem allocating tempString (%d) or isPartTouched (%d)\n", spacer, tempStrSize * sizeof(char), numCurrentDivisions * sizeof(int)); return (ERROR); } # if defined DEBUG_SETCHAINPARAMS /* only for debugging */ MrBFlt lnPriorRatio = 0.0, lnProposalRatio = 0.0; # endif /* allocate space for parameters */ if (memAllocs[ALLOC_PARAMS] == YES) { for (i=0; i numParams) numParams = activeParams[j][i]; if (activeParams[j][i] > 0) nRelParts++; } } params = (Param *) SafeMalloc (numParams * sizeof(Param)); relevantParts = (int *) SafeMalloc (nRelParts * sizeof(int)); if (!params || !relevantParts) { MrBayesPrint ("%s Problem allocating params and relevantParts\n", spacer); if (params) free (params); if (relevantParts) free (relevantParts); free (tempStr); return ERROR; } else memAllocs[ALLOC_PARAMS] = YES; /* fill in info on each parameter */ nRelParts = 0; /* now cumulative number of relevant partitions */ for (k=0; k 0) break; } /* find pointer to modelParams and modelSettings of first relevant partition */ /* this will be handy later on */ for (i=0; imin = p->max = NEG_INFINITY; /* Parameter nValues and nSubValues, which are needed for memory allocation are calculated for each case in the code below. nSympi, however, is only used for one special type of parameter and it therefore makes sense to initialize it to 0 here. The same applies to hasBinaryStd and nIntValues. To be safe, we set all to 0 here. */ p->nValues = 0; p->nSubValues = 0; p->nIntValues = 0; p->nSympi = 0; p->hasBinaryStd = NO; /* should this parameter be printed to a file? */ p->printParam = NO; /* set print subparams to 0 */ p->nPrintSubParams = 0; /* check constraints for tree parameter ? */ p->checkConstraints = NO; /* set index number of parameter */ p->index = k; /* set prior function to NULL */ p->LnPriorRatio = NULL; /* set prior paramters to NULL */ p->priorParams = NULL; /* set affectsLikelihood to NO */ p->affectsLikelihood = NO; /* set cpp event pointers to NULL */ p->nEvents = NULL; p->position = NULL; p->rateMult = NULL; /* set header and name to NULL */ p->paramHeader = NULL; p->name = NULL; /* set up relevant partitions */ p->nRelParts = numRelParts; p->relParts = relevantParts + nRelParts; nRelParts += numRelParts; for (i=n=0; irelParts[n++] = i; /* get partition descriptor */ SafeStrcat(&partString,""); FillRelPartsString (p, &partString); /* set up information for parameter */ if (j == P_TRATIO) { /* Set up tratio ****************************************************************************************/ p->paramType = P_TRATIO; p->nValues = 1; /* we store only the ratio */ p->nSubValues = 0; p->min = 0.0; p->max = POS_INFINITY; for (i=0; iparamTypeName = "Transition and transversion rates"; SafeStrcat(&p->name, "Tratio"); SafeStrcat(&p->name, partString); /* find the parameter x prior type */ if (!strcmp(mp->tRatioPr,"Beta")) { p->paramId = TRATIO_DIR; } else p->paramId = TRATIO_FIX; if (p->paramId != TRATIO_FIX) p->printParam = YES; if (!strcmp(mp->tratioFormat,"Ratio")) { /* report ti/tv ratio */ SafeStrcat (&p->paramHeader,"kappa"); SafeStrcat (&p->paramHeader, partString); } else { /* report prop. of ratesum (Dirichlet) */ SafeStrcat (&p->paramHeader,"ti"); SafeStrcat (&p->paramHeader, partString); SafeStrcat (&p->paramHeader, "\ttv"); SafeStrcat (&p->paramHeader, partString); } } else if (j == P_REVMAT) { /* Set up revMat ****************************************************************************************/ p->paramType = P_REVMAT; if (m->dataType == PROTEIN) p->nValues = 190; else p->nValues = 6; p->nSubValues = 0; if (!strcmp(mp->nst, "Mixed")) p->nIntValues = 6; else p->nIntValues = 0; p->min = 0.0; p->max = 1.0; /* adjust later for REVMAT_MIX, see a few lines below */ for (i=0; iparamTypeName = "Rates of reversible rate matrix"; SafeStrcat(&p->name, "Revmat"); SafeStrcat(&p->name, partString); /* find the parameter x prior type */ if (m->dataType == PROTEIN) { if (!strcmp(mp->aaRevMatPr,"Dirichlet")) p->paramId = REVMAT_DIR; else p->paramId = REVMAT_FIX; if (p->paramId != REVMAT_FIX) p->printParam = YES; for (n1=0; n1<20; n1++) { for (n2=n1+1; n2<20; n2++) { if (n1==0 && n2==1) { sprintf (temp, "r(%c<->%c)", StateCode_AA(n1), StateCode_AA(n2)); SafeStrcat (&p->paramHeader, temp); SafeStrcat (&p->paramHeader, partString); } else { sprintf (temp, "\tr(%c<->%c)", StateCode_AA(n1), StateCode_AA(n2)); SafeStrcat (&p->paramHeader, temp); SafeStrcat (&p->paramHeader, partString); } } } } else { if (!strcmp(mp->nst, "Mixed")) { p->paramId = REVMAT_MIX; p->max = 1E6; /* some large value */ } else if (!strcmp(mp->revMatPr,"Dirichlet")) p->paramId = REVMAT_DIR; else p->paramId = REVMAT_FIX; if (p->paramId != REVMAT_FIX) p->printParam = YES; for (n1=0; n1<4; n1++) { for (n2=n1+1; n2<4; n2++) { if (n1==0 && n2==1) { sprintf (temp, "r(%c<->%c)", StateCode_NUC4(n1), StateCode_NUC4(n2)); SafeStrcat (&p->paramHeader, temp); SafeStrcat (&p->paramHeader, partString); } else { sprintf (temp, "\tr(%c<->%c)", StateCode_NUC4(n1), StateCode_NUC4(n2)); SafeStrcat (&p->paramHeader, temp); SafeStrcat (&p->paramHeader, partString); } } } if (p->paramId == REVMAT_MIX) { sprintf (temp, "\tgtrsubmodel"); SafeStrcat (&p->paramHeader, temp); SafeStrcat (&p->paramHeader, partString); sprintf (temp, "\tk_revmat"); SafeStrcat (&p->paramHeader, temp); SafeStrcat (&p->paramHeader, partString); } } } else if (j == P_OMEGA) { /* Set up omega *****************************************************************************************/ p->paramType = P_OMEGA; p->min = 0.0; p->max = POS_INFINITY; if (!strcmp(mp->omegaVar, "M3")) { p->nValues = 3; /* omega values */ p->nSubValues = 6; /* category frequencies plus cache */ for (i=0; im3omegapr, "Exponential") && !strcmp(mp->codonCatFreqPr, "Fixed")) p->paramId = OMEGA_EF; else if (!strcmp(mp->m3omegapr, "Exponential") && !strcmp(mp->codonCatFreqPr, "Dirichlet")) p->paramId = OMEGA_ED; else if (!strcmp(mp->m3omegapr, "Fixed") && !strcmp(mp->codonCatFreqPr, "Fixed")) p->paramId = OMEGA_FF; else if (!strcmp(mp->m3omegapr, "Fixed") && !strcmp(mp->codonCatFreqPr, "Dirichlet")) p->paramId = OMEGA_FD; if (p->paramId != OMEGA_FF) p->printParam = YES; SafeStrcat (&p->paramHeader, "omega(1)"); SafeStrcat (&p->paramHeader, partString); SafeStrcat (&p->paramHeader, "\tomega(2)"); SafeStrcat (&p->paramHeader, partString); SafeStrcat (&p->paramHeader, "\tomega(3)"); SafeStrcat (&p->paramHeader, partString); SafeStrcat (&p->paramHeader, "\tpi(1)"); SafeStrcat (&p->paramHeader, partString); SafeStrcat (&p->paramHeader, "\tpi(2)"); SafeStrcat (&p->paramHeader, partString); SafeStrcat (&p->paramHeader, "\tpi(3)"); SafeStrcat (&p->paramHeader, partString); } else if (!strcmp(mp->omegaVar, "M10")) { p->nValues = mp->numM10BetaCats + mp->numM10GammaCats; p->nSubValues = mp->numM10BetaCats + mp->numM10GammaCats + 8; for (i=0; im10betapr, "Uniform") && !strcmp(mp->m10gammapr, "Uniform") && !strcmp(mp->codonCatFreqPr, "Dirichlet")) p->paramId = OMEGA_10UUB; else if (!strcmp(mp->m10betapr, "Uniform") && !strcmp(mp->m10gammapr, "Uniform") && !strcmp(mp->codonCatFreqPr, "Fixed")) p->paramId = OMEGA_10UUF; else if (!strcmp(mp->m10betapr, "Uniform") && !strcmp(mp->m10gammapr, "Exponential") && !strcmp(mp->codonCatFreqPr, "Dirichlet")) p->paramId = OMEGA_10UEB; else if (!strcmp(mp->m10betapr, "Uniform") && !strcmp(mp->m10gammapr, "Exponential") && !strcmp(mp->codonCatFreqPr, "Fixed")) p->paramId = OMEGA_10UEF; else if (!strcmp(mp->m10betapr, "Uniform") && !strcmp(mp->m10gammapr, "Fixed") && !strcmp(mp->codonCatFreqPr, "Dirichlet")) p->paramId = OMEGA_10UFB; else if (!strcmp(mp->m10betapr, "Uniform") && !strcmp(mp->m10gammapr, "Fixed") && !strcmp(mp->codonCatFreqPr, "Fixed")) p->paramId = OMEGA_10UFF; else if (!strcmp(mp->m10betapr, "Exponential") && !strcmp(mp->m10gammapr, "Uniform") && !strcmp(mp->codonCatFreqPr, "Dirichlet")) p->paramId = OMEGA_10EUB; else if (!strcmp(mp->m10betapr, "Exponential") && !strcmp(mp->m10gammapr, "Uniform") && !strcmp(mp->codonCatFreqPr, "Fixed")) p->paramId = OMEGA_10EUF; else if (!strcmp(mp->m10betapr, "Exponential") && !strcmp(mp->m10gammapr, "Exponential") && !strcmp(mp->codonCatFreqPr, "Dirichlet")) p->paramId = OMEGA_10EEB; else if (!strcmp(mp->m10betapr, "Exponential") && !strcmp(mp->m10gammapr, "Exponential") && !strcmp(mp->codonCatFreqPr, "Fixed")) p->paramId = OMEGA_10EEF; else if (!strcmp(mp->m10betapr, "Exponential") && !strcmp(mp->m10gammapr, "Fixed") && !strcmp(mp->codonCatFreqPr, "Dirichlet")) p->paramId = OMEGA_10EFB; else if (!strcmp(mp->m10betapr, "Exponential") && !strcmp(mp->m10gammapr, "Fixed") && !strcmp(mp->codonCatFreqPr, "Fixed")) p->paramId = OMEGA_10EFF; else if (!strcmp(mp->m10betapr, "Fixed") && !strcmp(mp->m10gammapr, "Uniform") && !strcmp(mp->codonCatFreqPr, "Dirichlet")) p->paramId = OMEGA_10FUB; else if (!strcmp(mp->m10betapr, "Fixed") && !strcmp(mp->m10gammapr, "Uniform") && !strcmp(mp->codonCatFreqPr, "Fixed")) p->paramId = OMEGA_10FUF; else if (!strcmp(mp->m10betapr, "Fixed") && !strcmp(mp->m10gammapr, "Exponential") && !strcmp(mp->codonCatFreqPr, "Dirichlet")) p->paramId = OMEGA_10FEB; else if (!strcmp(mp->m10betapr, "Fixed") && !strcmp(mp->m10gammapr, "Exponential") && !strcmp(mp->codonCatFreqPr, "Fixed")) p->paramId = OMEGA_10FEF; else if (!strcmp(mp->m10betapr, "Fixed") && !strcmp(mp->m10gammapr, "Fixed") && !strcmp(mp->codonCatFreqPr, "Dirichlet")) p->paramId = OMEGA_10FFB; else if (!strcmp(mp->m10betapr, "Fixed") && !strcmp(mp->m10gammapr, "Fixed") && !strcmp(mp->codonCatFreqPr, "Fixed")) p->paramId = OMEGA_10FFF; if (p->paramId != OMEGA_10FFF) p->printParam = YES; for (i=0; inValues; i++) { if (i==0) sprintf (temp, "omega(%d)", i+1); else sprintf (temp, "\tomega(%d)", i+1); SafeStrcat (&p->paramHeader, temp); SafeStrcat (&p->paramHeader, partString); } SafeStrcat (&p->paramHeader, "\tbeta(alpha)"); SafeStrcat (&p->paramHeader, partString); SafeStrcat (&p->paramHeader, "\tbeta(beta)"); SafeStrcat (&p->paramHeader, partString); SafeStrcat (&p->paramHeader, "\tgamma(alpha)"); SafeStrcat (&p->paramHeader, partString); SafeStrcat (&p->paramHeader, "\tgamma(beta)"); SafeStrcat (&p->paramHeader, partString); SafeStrcat (&p->paramHeader, "\tpi(1)"); SafeStrcat (&p->paramHeader, partString); SafeStrcat (&p->paramHeader, "\tpi(2)"); SafeStrcat (&p->paramHeader, partString); } else if (!strcmp(mp->omegaVar, "Ny98")) { p->nValues = 3; /* omega values */ p->nSubValues = 6; /* omega category frequencies plus cache */ for (i=0; iny98omega1pr, "Beta") && !strcmp(mp->ny98omega3pr, "Uniform") && !strcmp(mp->codonCatFreqPr, "Dirichlet")) p->paramId = OMEGA_BUD; else if (!strcmp(mp->ny98omega1pr, "Beta") && !strcmp(mp->ny98omega3pr, "Uniform") && !strcmp(mp->codonCatFreqPr, "Fixed")) p->paramId = OMEGA_BUF; else if (!strcmp(mp->ny98omega1pr, "Beta") && !strcmp(mp->ny98omega3pr, "Exponential") && !strcmp(mp->codonCatFreqPr, "Dirichlet")) p->paramId = OMEGA_BED; else if (!strcmp(mp->ny98omega1pr, "Beta") && !strcmp(mp->ny98omega3pr, "Exponential") && !strcmp(mp->codonCatFreqPr, "Fixed")) p->paramId = OMEGA_BEF; else if (!strcmp(mp->ny98omega1pr, "Beta") && !strcmp(mp->ny98omega3pr, "Fixed") && !strcmp(mp->codonCatFreqPr, "Dirichlet")) p->paramId = OMEGA_BFD; else if (!strcmp(mp->ny98omega1pr, "Beta") && !strcmp(mp->ny98omega3pr, "Fixed") && !strcmp(mp->codonCatFreqPr, "Fixed")) p->paramId = OMEGA_BFF; else if (!strcmp(mp->ny98omega1pr, "Fixed") && !strcmp(mp->ny98omega3pr, "Uniform") && !strcmp(mp->codonCatFreqPr, "Dirichlet")) p->paramId = OMEGA_FUD; else if (!strcmp(mp->ny98omega1pr, "Fixed") && !strcmp(mp->ny98omega3pr, "Uniform") && !strcmp(mp->codonCatFreqPr, "Fixed")) p->paramId = OMEGA_FUF; else if (!strcmp(mp->ny98omega1pr, "Fixed") && !strcmp(mp->ny98omega3pr, "Exponential") && !strcmp(mp->codonCatFreqPr, "Dirichlet")) p->paramId = OMEGA_FED; else if (!strcmp(mp->ny98omega1pr, "Fixed") && !strcmp(mp->ny98omega3pr, "Exponential") && !strcmp(mp->codonCatFreqPr, "Fixed")) p->paramId = OMEGA_FEF; else if (!strcmp(mp->ny98omega1pr, "Fixed") && !strcmp(mp->ny98omega3pr, "Fixed") && !strcmp(mp->codonCatFreqPr, "Dirichlet")) p->paramId = OMEGA_FFD; else if (!strcmp(mp->ny98omega1pr, "Fixed") && !strcmp(mp->ny98omega3pr, "Fixed") && !strcmp(mp->codonCatFreqPr, "Fixed")) p->paramId = OMEGA_FFF; if (p->paramId != OMEGA_FFF) p->printParam = YES; SafeStrcat (&p->paramHeader, "omega(-)"); SafeStrcat (&p->paramHeader, partString); SafeStrcat (&p->paramHeader, "\tomega(N)"); SafeStrcat (&p->paramHeader, partString); SafeStrcat (&p->paramHeader, "\tomega(+)"); SafeStrcat (&p->paramHeader, partString); SafeStrcat (&p->paramHeader, "\tpi(-)"); SafeStrcat (&p->paramHeader, partString); SafeStrcat (&p->paramHeader, "\tpi(N)"); SafeStrcat (&p->paramHeader, partString); SafeStrcat (&p->paramHeader, "\tpi(+)"); SafeStrcat (&p->paramHeader, partString); } else { p->nValues = 1; p->nSubValues = 0; for (i=0; iomegaPr,"Dirichlet")) p->paramId = OMEGA_DIR; else p->paramId = OMEGA_FIX; if (p->paramId != OMEGA_FIX) p->printParam = YES; SafeStrcat (&p->paramHeader, "omega"); SafeStrcat (&p->paramHeader, partString); } p->paramTypeName = "Positive selection (omega) model"; SafeStrcat(&p->name, "Omega"); SafeStrcat(&p->name, partString); } else if (j == P_PI) { /* Set up state frequencies *****************************************************************************/ p->paramType = P_PI; p->min = 0.0; p->max = 1.0; for (i=0; idataType == STANDARD) { p->paramTypeName = "Symmetric diricihlet distribution alpha_i parameter"; SafeStrcat(&p->name, "Alpha_symdir"); /* boundaries for alpha_i */ p->min = ETA; p->max = POS_INFINITY; } else { p->paramTypeName = "Stationary state frequencies"; SafeStrcat(&p->name, "Pi"); } SafeStrcat(&p->name, partString); /* find the parameter x prior type */ /* and the number of values and subvalues needed */ if (mp->dataType == STANDARD) { /* find number of model states */ m->numModelStates = 2; for (c=0; cnRelParts; i++) { if (partitionId[c][partitionNum] == p->relParts[i] + 1 && charInfo[c].numStates > m->numModelStates) m->numModelStates = charInfo[c].numStates; } } for (i=0; inRelParts; i++) modelSettings[p->relParts[i]].numModelStates = m->numModelStates; /* symmetric hyperprior with only one variable (0 if equal) */ p->nValues = 1; /* set to 0 below for the SYMPI_EQUAL model */ if (!strcmp(mp->symPiPr,"Uniform")) { if (m->numModelStates > 2) p->paramId = SYMPI_UNI_MS; else p->paramId = SYMPI_UNI; } else if (!strcmp(mp->symPiPr,"Exponential")) { if (m->numModelStates > 2) p->paramId = SYMPI_EXP_MS; else p->paramId = SYMPI_EXP; } else if (!strcmp(mp->symPiPr,"Fixed")) { if (AreDoublesEqual(mp->symBetaFix, -1.0, 0.00001) == YES) { p->paramId = SYMPI_EQUAL; p->nValues = 0; } else { if (m->numModelStates > 2) p->paramId = SYMPI_FIX_MS; else p->paramId = SYMPI_FIX; } } p->nSubValues = 0; /* store state frequencies in p->stdStateFreqs */ if (p->nValues == 1 && strcmp(mp->symPiPr,"Fixed") != 0) { p->printParam = YES; SafeStrcat (&p->paramHeader, "alpha_symdir"); SafeStrcat (&p->paramHeader, partString); } /* further processing done in ProcessStdChars */ } else { /* deal with all models except standard */ /* no hyperprior or fixed to one value, set default to 0 */ p->nValues = 0; /* first check if model is stationary; if not, we process it differently */ if (!strcmp(mp->statefreqModel,"Stationary")) { /* one subvalue for each state */ p->nSubValues = mp->nStates; /* mp->nStates is set to 20 if DNA || RNA && nucmodel==PROTEIN */ if (!strcmp(mp->stateFreqPr, "Dirichlet")) { p->paramId = PI_DIR; p->nValues = mp->nStates; } else if (!strcmp(mp->stateFreqPr, "Fixed") && !strcmp(mp->stateFreqsFixType,"User")) p->paramId = PI_USER; else if (!strcmp(mp->stateFreqPr, "Fixed") && !strcmp(mp->stateFreqsFixType,"Empirical")) p->paramId = PI_EMPIRICAL; else if (!strcmp(mp->stateFreqPr, "Fixed") && !strcmp(mp->stateFreqsFixType,"Equal")) { p->paramId = PI_EQUAL; } if (m->dataType == PROTEIN) { if (!strcmp(mp->aaModelPr, "Fixed")) { if (!strcmp(mp->aaModel, "Poisson")) p->paramId = PI_EQUAL; else if (!strcmp(mp->aaModel, "Equalin") || !strcmp(mp->aaModel, "Gtr")) { /* p->paramId stays to what it was set to above */ } else p->paramId = PI_FIXED; } else p->paramId = PI_FIXED; } if (p->paramId == PI_DIR) p->printParam = YES; if (m->dataType == DNA || m->dataType == RNA) { if (!strcmp(mp->nucModel, "4by4")) { sprintf (temp, "pi(%c)", StateCode_NUC4(0)); SafeStrcat (&p->paramHeader,temp); SafeStrcat (&p->paramHeader,partString); for (n1=1; n1<4; n1++) { sprintf (temp, "\tpi(%c)", StateCode_NUC4(n1)); SafeStrcat (&p->paramHeader,temp); SafeStrcat (&p->paramHeader,partString); } } else if (!strcmp(mp->nucModel, "Doublet")) { State_DOUBLET(tempCodon,0); sprintf (temp, "pi(%s)", tempCodon); SafeStrcat (&p->paramHeader,temp); SafeStrcat (&p->paramHeader,partString); for (n1=1; n1<16; n1++) { State_DOUBLET(tempCodon,n1); sprintf (temp, "\tpi(%s)", tempCodon); SafeStrcat (&p->paramHeader,temp); SafeStrcat (&p->paramHeader,partString); } } else if (!strcmp(mp->nucModel, "Codon")) { for (c=0; cnSubValues; c++) { if (mp->codonNucs[c][0] == 0) strcpy (tempCodon, "pi(A"); else if (mp->codonNucs[c][0] == 1) strcpy (tempCodon, "pi(C"); else if (mp->codonNucs[c][0] == 2) strcpy (tempCodon, "pi(G"); else strcpy (tempCodon, "pi(T"); if (mp->codonNucs[c][1] == 0) strcat (tempCodon, "A"); else if (mp->codonNucs[c][1] == 1) strcat (tempCodon, "C"); else if (mp->codonNucs[c][1] == 2) strcat (tempCodon, "G"); else strcat (tempCodon, "T"); if (mp->codonNucs[c][2] == 0) strcat (tempCodon, "A)"); else if (mp->codonNucs[c][2] == 1) strcat (tempCodon, "C)"); else if (mp->codonNucs[c][2] == 2) strcat (tempCodon, "G)"); else strcat (tempCodon, "T)"); if (c == 0) { SafeStrcat (&p->paramHeader, tempCodon); SafeStrcat (&p->paramHeader, partString); } else { SafeStrcat (&p->paramHeader, "\t"); SafeStrcat (&p->paramHeader, tempCodon); SafeStrcat (&p->paramHeader, partString); } } } } else if (m->dataType == PROTEIN) { if (FillRelPartsString (p, &partString) == YES) { SafeSprintf (&tempStr, &tempStrSize, "pi(Ala)%s\tpi(Arg)%s\tpi(Asn)%s\tpi(Asp)%s\tpi(Cys)%s\tpi(Gln)%s\tpi(Glu)%s\tpi(Gly)%s\tpi(His)%s\tpi(Ile)%s\tpi(Leu)%s\tpi(Lys)%s\tpi(Met)%s\tpi(Phe)%s\tpi(Pro)%s\tpi(Ser)%s\tpi(Thr)%s\tpi(Trp)%s\tpi(Tyr)%s\tpi(Val)%s", partString, partString, partString, partString, partString, partString, partString, partString, partString, partString, partString, partString, partString, partString, partString, partString, partString, partString, partString, partString); SafeStrcat (&p->paramHeader, tempStr); } else SafeStrcat (&p->paramHeader, "pi(Ala)\tpi(Arg)\tpi(Asn)\tpi(Asp)\tpi(Cys)\tpi(Gln)\tpi(Glu)\tpi(Gly)\tpi(His)\tpi(Ile)\tpi(Leu)\tpi(Lys)\tpi(Met)\tpi(Phe)\tpi(Pro)\tpi(Ser)\tpi(Thr)\tpi(Trp)\tpi(Tyr)\tpi(Val)"); } else if (mp->dataType == RESTRICTION) { if (FillRelPartsString (p, &partString) == YES) { SafeSprintf (&tempStr, &tempStrSize, "pi(0)%s\tpi(1)%s", partString, partString); SafeStrcat (&p->paramHeader, tempStr); } else SafeStrcat (&p->paramHeader, "pi(0)\tpi(1)"); } else { MrBayesPrint ("%s Unknown data type in SetModelParams\n", spacer); } } else /* if statefreqmodel != stationary */ { if (mp->dataType != RESTRICTION) { MrBayesPrint ("%s Error in SetModelParams: non-stationary models currently only implemented for RESTRICTION data type\n", spacer); } else { /* two subvalues for each state: stationary and root frequencies */ p->nSubValues = mp->nStates * 2; if (!strcmp(mp->statefreqModel, "Mixed")) { p->paramId = DIRPI_MIX; p->nValues = mp->nStates * 2; } else if (!strcmp(mp->stateFreqPr, "Dirichlet")) { if (!strcmp(mp->rootFreqPr, "Dirichlet")) { p->paramId = DIRPI_DIRxDIR; } else if (!strcmp(mp->rootFreqPr, "Fixed")) { p->paramId = DIRPI_DIRxFIXED; } else { MrBayesPrint ("%s Error in SetModelParams: unknown setting for Rootfreqpr\n", spacer); } p->nValues = m->numModelStates * 2; //SK: use whole nValues vector even if only one of the priors is Dirichlet, start OR root freqs } else if (!strcmp(mp->stateFreqPr, "Fixed")) { if (!strcmp(mp->rootFreqPr, "Dirichlet")) { p->paramId = DIRPI_FIXEDxDIR; p->nValues = mp->nStates * 2; } else if (!strcmp(mp->rootFreqPr, "Fixed")) { p->paramId = DIRPI_FIXEDxFIXED; p->nValues = 0; } else { MrBayesPrint ("%s Error in SetModelParams: unknown setting for Rootfreqpr\n", spacer); } } else { MrBayesPrint ("%s Error in SetModelParams: unknown setting for Statfreqpr, choose either 'Dirichlet' or 'Fixed'. \n", spacer); } if (p->paramId != DIRPI_FIXEDxFIXED) p->printParam = YES; if (FillRelPartsString (p, &partString) == YES) { SafeSprintf(&tempStr, &tempStrSize, "pi(0)%s\tpi(1)%s\trootpi(0)%s\trootpi(1)%s", partString, partString, partString, partString); SafeStrcat (&p->paramHeader, tempStr); if (p->paramId == DIRPI_MIX) { SafeSprintf(&tempStr, &tempStrSize, "\tstatefrmod%s", partString); SafeStrcat (&p->paramHeader, tempStr); } } else { SafeStrcat (&p->paramHeader, "pi(0)\tpi(1)\trootpi(0)\trootpi(1)"); if (p->paramId == DIRPI_MIX) SafeStrcat(&p->paramHeader, "\tstatefrmod"); } } } // end nonstationary model } } else if (j == P_MIXTURE_RATES) { /* Set up mixture of site rates *****************************************************************/ p->paramType = P_MIXTURE_RATES; p->nValues = mp->numMixtCats; /* used for the Dirichlet prior parameters */ p->nSubValues = mp->numMixtCats; p->min = 1E-5; p->max = 100; for (i=0; iparamTypeName = "Rates of site rate mixture"; SafeStrcpy(&p->name, "Mixturerates"); SafeStrcat(&p->name, partString); for (i=0; inValues; ++i) { if (i!=0) SafeStrcat(&p->paramHeader, "\t"); SafeSprintf(&tempStr, &tempStrSize, "mixturerates%s[%d]", partString, i+1); SafeStrcat(&p->paramHeader, tempStr); } /* find the parameter x prior type */ p->paramId = MIXTURE_RATES; /* always print */ p->printParam = YES; } else if (j == P_SHAPE) { /* Set up shape parameter of gamma/lnorm *****************************************************************/ p->paramType = P_SHAPE; p->nValues = 1; if(!strcmp(mp->ratesModel, "Lnorm")) p->nSubValues = mp->numLnormCats; else p->nSubValues = mp->numGammaCats; p->min = 1E-5; p->max = 100; for (i=0; iratesModel, "LNorm")) { p->paramTypeName = "SD of scaled lognormal distribution of site rates"; SafeStrcat(&p->name, "Sigma"); SafeStrcat(&p->paramHeader, "sigma"); } else { p->paramTypeName = "Shape of scaled gamma distribution of site rates"; SafeStrcat(&p->name, "Alpha"); SafeStrcat(&p->paramHeader, "alpha"); } SafeStrcat(&p->name, partString); SafeStrcat(&p->paramHeader, partString); /* find the parameter x prior type */ mp = &modelParams[p->relParts[0]]; if (!strcmp(mp->shapePr,"Uniform")) p->paramId = SHAPE_UNI; else if (!strcmp(mp->shapePr,"Exponential")) p->paramId = SHAPE_EXP; else p->paramId = SHAPE_FIX; if (p->paramId != SHAPE_FIX) p->printParam = YES; } else if (j == P_PINVAR) { /* Set up proportion of invariable sites ****************************************************************/ p->paramType = P_PINVAR; p->nValues = 1; p->nSubValues = 0; p->min = 0.0; p->max = 1.0; for (i=0; iparamTypeName = "Proportion of invariable sites"; SafeStrcat(&p->name, "Pinvar"); SafeStrcat(&p->name, partString); /* find the parameter x prior type */ if (!strcmp(mp->pInvarPr,"Uniform")) p->paramId = PINVAR_UNI; else p->paramId = PINVAR_FIX; if (p->paramId != PINVAR_FIX) p->printParam = YES; SafeStrcat (&p->paramHeader, "pinvar"); SafeStrcat (&p->paramHeader, partString); } else if (j == P_CORREL) { /* Set up correlation parameter of adgamma model ********************************************************/ p->paramType = P_CORREL; p->nValues = 1; p->nSubValues = mp->numGammaCats * mp->numGammaCats; p->min = -1.0; p->max = 1.0; for (i=0; iparamTypeName = "Autocorrelation of gamma distribution of site rates"; SafeStrcat(&p->name, "Rho"); SafeStrcat(&p->name, partString); chainHasAdgamma = YES; /* find the parameter x prior type */ if (!strcmp(mp->adGammaCorPr,"Uniform")) p->paramId = CORREL_UNI; else p->paramId = CORREL_FIX; if (p->paramId != CORREL_FIX) p->printParam = YES; SafeStrcat (&p->paramHeader, "rho"); SafeStrcat (&p->paramHeader, partString); } else if (j == P_SWITCH) { /* Set up switchRates for covarion model ****************************************************************/ p->paramType = P_SWITCH; p->nValues = 2; p->nSubValues = mp->numGammaCats * mp->numGammaCats; p->min = 0.0; p->max = POS_INFINITY; for (i=0; iparamTypeName = "Switch rates of covarion model"; SafeStrcat(&p->name, "s_cov"); SafeStrcat(&p->name, partString); /* find the parameter x prior type */ if (!strcmp(mp->covSwitchPr,"Uniform")) p->paramId = SWITCH_UNI; else if (!strcmp(mp->covSwitchPr,"Exponential")) p->paramId = SWITCH_EXP; else p->paramId = SWITCH_FIX; if (p->paramId != SWITCH_FIX) p->printParam = YES; SafeStrcat (&p->paramHeader, "s(off->on)"); SafeStrcat (&p->paramHeader, partString); SafeStrcat (&p->paramHeader, "\ts(on->off)"); SafeStrcat (&p->paramHeader, partString); } else if (j == P_RATEMULT) { /* Set up rateMult for partition specific rates ***********************************************************/ p->paramType = P_RATEMULT; if (!strcmp(mp->ratePr,"Fixed") || numCurrentDivisions == 1) { p->nValues = 1; p->nSubValues = 0; } else { p->nValues = p->nRelParts = numRelParts; /* keep scaled division rates in value */ p->nSubValues = p->nValues * 2; /* keep number of uncompressed chars for scaling in subValue */ } /* also keep Dirichlet prior parameters here */ p->min = POS_MIN; p->max = POS_INFINITY; for (i=0; iparamTypeName = "Partition-specific rate multiplier"; SafeStrcat(&p->name, "Ratemultiplier"); SafeStrcat(&p->name, partString); /* find the parameter x prior type */ if (p->nSubValues == 0) p->paramId = RATEMULT_FIX; else p->paramId = RATEMULT_DIR; if (p->paramId != RATEMULT_FIX) p->printParam = YES; for (i=0; iparamHeader, tempMult); else { SafeStrcat (&p->paramHeader, "\t"); SafeStrcat (&p->paramHeader, tempMult); } } } } else if (j == P_GENETREERATE) { /* Set up rateMult for gene specific rates ****************************************************************/ p->paramType = P_GENETREERATE; p->nValues = p->nRelParts = numRelParts; /* keep scaled division rates in value */ p->nSubValues = p->nValues * 2; /* keep number of uncompressed chars for scaling in subValue */ /* also keep Dirichlet prior parameters here */ p->min = POS_MIN; p->max = POS_INFINITY; for (i=0; iparamTypeName = "Gene-specific rate multiplier"; SafeStrcat(&p->name, "Generatemult"); SafeStrcat(&p->name, partString); /* find the parameter x prior type */ if (strcmp(mp->generatePr,"Fixed") == 0) p->paramId = GENETREERATEMULT_FIX; else p->paramId = GENETREERATEMULT_DIR; if (p->paramId == GENETREERATEMULT_FIX) p->printParam = NO; else p->printParam = YES; for (i=0; iparamHeader, tempMult); else { SafeStrcat (&p->paramHeader, "\t"); SafeStrcat (&p->paramHeader, tempMult); } } } } else if (j == P_TOPOLOGY) { /* Set up topology **************************************************************************************/ p->paramType = P_TOPOLOGY; p->nValues = 0; p->nSubValues = 0; p->min = NEG_INFINITY; /* NA */ p->max = NEG_INFINITY; /* NA */ for (i=0; iparamTypeName = "Topology"; SafeStrcat(&p->name, "Tau"); SafeStrcat(&p->name, partString); /* check that the model is not parsimony for all of the relevant partitions */ areAllPartsParsimony = YES; for (i=0; inRelParts; i++) { if (modelSettings[p->relParts[i]].parsModelId == NO) areAllPartsParsimony = NO; } /* check if topology is clock or nonclock */ nClockBrlens = 0; nCalibratedBrlens = 0; nRelaxedBrlens = 0; if (areAllPartsParsimony == NO) { for (i=0; inRelParts; i++) { if (!strcmp(modelParams[p->relParts[i]].brlensPr, "Clock")) { nClockBrlens++; if (strcmp(modelParams[p->relParts[i]].clockVarPr,"Strict") != 0) /* not strict */ nRelaxedBrlens++; if (!strcmp(modelParams[p->relParts[i]].nodeAgePr,"Calibrated")) nCalibratedBrlens++; } } } /* now find the parameter x prior type */ if (areAllPartsParsimony == YES) { if (!strcmp(mp->topologyPr, "Uniform")) p->paramId = TOPOLOGY_PARSIMONY_UNIFORM; else if (!strcmp(mp->topologyPr,"Constraints")) p->paramId = TOPOLOGY_PARSIMONY_CONSTRAINED; else p->paramId = TOPOLOGY_PARSIMONY_FIXED; /* For this case, we also need to set the brlens ptr of the relevant partitions so that it points to the topology parameter, since the rest of the program will try to access the tree through this pointer. In FillTreeParams, we will make sure that a pure parsimony topology parameter contains a pointer to the relevant tree (like a brlens parameter would normally) */ for (i=0; inRelParts; i++) modelSettings[p->relParts[i]].brlens = p; } else { /* we assume for now that there is only one branch length; we will correct this later in AllocateTreeParams if there are more than one set of branch lengths, which is only possible for non-clock trees */ if (!strcmp(mp->topologyPr, "Speciestree")) p->paramId = TOPOLOGY_SPECIESTREE; else if (mp->dataType == RESTRICTION && strcmp(mp->statefreqModel, "Stationary") != 0) { /* rooted non-clock trees (directional substitution models) */ if (!strcmp(mp->topologyPr, "Uniform")) p->paramId = TOPOLOGY_RNCL_UNIFORM; else if (!strcmp(mp->topologyPr, "Constraints")) p->paramId = TOPOLOGY_RNCL_CONSTRAINED; else p->paramId = TOPOLOGY_RNCL_FIXED; } else if (!strcmp(mp->topologyPr, "Uniform") && nClockBrlens == 0) p->paramId = TOPOLOGY_NCL_UNIFORM_HOMO; else if (!strcmp(mp->topologyPr,"Constraints") && nClockBrlens == 0) p->paramId = TOPOLOGY_NCL_CONSTRAINED_HOMO; else if (!strcmp(mp->topologyPr,"Fixed") && nClockBrlens == 0) p->paramId = TOPOLOGY_NCL_FIXED_HOMO; /* all below have clock branch lengths */ else if (!strcmp(mp->topologyPr, "Uniform") && nRelaxedBrlens == 0) { if (nCalibratedBrlens >= 1) p->paramId = TOPOLOGY_CCL_UNIFORM; else p->paramId = TOPOLOGY_CL_UNIFORM; } else if (!strcmp(mp->topologyPr,"Constraints") && nRelaxedBrlens == 0) { if (nCalibratedBrlens >= 1) p->paramId = TOPOLOGY_CCL_CONSTRAINED; else p->paramId = TOPOLOGY_CL_CONSTRAINED; } else if (!strcmp(mp->topologyPr,"Fixed") && nRelaxedBrlens == 0) { if (nCalibratedBrlens >= 1) p->paramId = TOPOLOGY_CCL_FIXED; else p->paramId = TOPOLOGY_CL_FIXED; } /* all below have relaxed clock branch lengths */ else if (!strcmp(mp->topologyPr, "Uniform")) { if (nCalibratedBrlens >= 1) p->paramId = TOPOLOGY_RCCL_UNIFORM; else p->paramId = TOPOLOGY_RCL_UNIFORM; } else if (!strcmp(mp->topologyPr,"Constraints")) { if (nCalibratedBrlens >= 1) p->paramId = TOPOLOGY_RCCL_CONSTRAINED; else p->paramId = TOPOLOGY_RCL_CONSTRAINED; } else if (!strcmp(mp->topologyPr,"Fixed")) { if (nCalibratedBrlens >= 1) p->paramId = TOPOLOGY_RCCL_FIXED; else p->paramId = TOPOLOGY_RCL_FIXED; } } /* should we print the topology? */ for (i=0; inRelParts; i++) if (strcmp(modelParams[p->relParts[i]].treeFormat,"Topology") != 0) break; if (i == p->nRelParts) p->printParam = YES; else p->printParam = NO; /* but always print parsimony topology */ if (areAllPartsParsimony == YES) p->printParam = YES; /* and never print fixed topologies */ if (p->paramId == TOPOLOGY_RCL_FIXED || p->paramId == TOPOLOGY_RCCL_FIXED || p->paramId == TOPOLOGY_CL_FIXED || p->paramId == TOPOLOGY_CCL_FIXED || p->paramId == TOPOLOGY_RNCL_FIXED || p->paramId == TOPOLOGY_NCL_FIXED || p->paramId == TOPOLOGY_PARSIMONY_FIXED) p->printParam = NO; } else if (j == P_BRLENS) { /* Set up branch lengths ********************************************************************************/ p->paramType = P_BRLENS; p->nValues = 0; p->nSubValues = 0; p->min = 0.0; p->max = POS_INFINITY; for (i=0; iparamTypeName = "Branch lengths"; SafeStrcat(&p->name, "V"); SafeStrcat(&p->name, partString); /* find the parameter x prior type */ if (modelSettings[p->relParts[0]].parsModelId == YES) p->paramId = BRLENS_PARSIMONY; else if (!strcmp(mp->brlensPr, "Clock")) { if (!strcmp(mp->clockPr,"Uniform")) p->paramId = BRLENS_CLOCK_UNI; else if (!strcmp(mp->clockPr,"Coalescence")) p->paramId = BRLENS_CLOCK_COAL; else if (!strcmp(mp->clockPr,"Birthdeath")) p->paramId = BRLENS_CLOCK_BD; else if (!strcmp(mp->clockPr,"Speciestreecoalescence")) p->paramId = BRLENS_CLOCK_SPCOAL; else if (!strcmp(mp->clockPr,"Fossilization")) p->paramId = BRLENS_CLOCK_FOSSIL; else if (!strcmp(mp->clockPr,"Fixed")) p->paramId = BRLENS_CLOCK_FIXED; } else if (!strcmp(mp->brlensPr, "Unconstrained")) { if (!strcmp(mp->unconstrainedPr,"Uniform")) p->paramId = BRLENS_UNI; else if (!strcmp(mp->unconstrainedPr,"Exponential")) p->paramId = BRLENS_EXP; else if (!strcmp(mp->unconstrainedPr,"GammaDir")) p->paramId = BRLENS_GamDir; else if (!strcmp(mp->unconstrainedPr,"invGamDir")) p->paramId = BRLENS_iGmDir; else if (!strcmp(mp->unconstrainedPr,"twoExp")) p->paramId = BRLENS_twoExp; } else if (!strcmp(mp->brlensPr,"Fixed")) { p->paramId = BRLENS_FIXED; } /* should we print the branch lengths? */ for (i=0; inRelParts; i++) if (strcmp(modelParams[p->relParts[i]].treeFormat,"Topology") != 0) break; if (i < p->nRelParts) p->printParam = YES; else p->printParam = NO; } else if (j == P_SPECIESTREE) { /* Set up species tree **********************************************************************************/ p->paramType = P_SPECIESTREE; p->nValues = 0; p->nSubValues = 0; p->min = NEG_INFINITY; /* NA */ p->max = NEG_INFINITY; /* NA */ for (i=0; iparamTypeName = "Species tree"; SafeStrcat(&p->name, "Spt"); SafeStrcat(&p->name, partString); /* find the parameter x prior type */ p->paramId = SPECIESTREE_UNIFORM; /* should we print the tree? */ p->printParam = YES; } else if (j == P_SPECRATE) { /* Set up speciation rate *******************************************************************************/ p->paramType = P_SPECRATE; if (!strcmp(mp->clockPr,"Fossilization")) p->nValues = mp->birthRateShiftNum +1; // rate in each time interval else p->nValues = 1; p->nSubValues = 0; p->min = POS_MIN; p->max = POS_INFINITY; for (i=0; iparamTypeName = "Speciation rate"; SafeStrcat(&p->name, "Net_speciation"); SafeStrcat(&p->name, partString); /* find the parameter x prior type */ if (!strcmp(mp->speciationPr,"Uniform")) p->paramId = SPECRATE_UNI; else if (!strcmp(mp->speciationPr,"Exponential")) p->paramId = SPECRATE_EXP; else p->paramId = SPECRATE_FIX; if (p->paramId != SPECRATE_FIX) p->printParam = YES; if (p->nValues == 1) { SafeStrcat (&p->paramHeader, "net_speciation"); SafeStrcat (&p->paramHeader, partString); } else for (i = 0; i < p->nValues; i++) { SafeSprintf(&tempStr, &tempStrSize, "\tnet_speciation_%d", i+1); SafeStrcat (&p->paramHeader, tempStr); SafeStrcat (&p->paramHeader, partString); } } else if (j == P_EXTRATE) { /* Set up extinction rates ******************************************************************************/ p->paramType = P_EXTRATE; if (!strcmp(mp->clockPr,"Fossilization")) p->nValues = mp->deathRateShiftNum +1; // rate in each time interval else p->nValues = 1; p->nSubValues = 0; p->min = 0.0; p->max = 1.0; for (i=0; iparamTypeName = "Extinction rate"; SafeStrcat(&p->name, "Relative_extinction"); SafeStrcat(&p->name, partString); /* find the parameter x prior type */ if (!strcmp(mp->extinctionPr,"Beta")) p->paramId = EXTRATE_BETA; else if (!strcmp(mp->extinctionPr,"Exponential")) p->paramId = EXTRATE_EXP; else p->paramId = EXTRATE_FIX; if (p->paramId != EXTRATE_FIX) p->printParam = YES; if (p->nValues == 1) { SafeStrcat (&p->paramHeader, "relative_extinction"); SafeStrcat (&p->paramHeader, partString); } else for (i = 0; i < p->nValues; i++) { SafeSprintf(&tempStr, &tempStrSize, "\trelative_extinction_%d", i+1); SafeStrcat (&p->paramHeader, tempStr); SafeStrcat (&p->paramHeader, partString); } } else if (j == P_FOSLRATE) { /* Set up fossilization rates */ p->paramType = P_FOSLRATE; p->nValues = mp->fossilSamplingNum +1; // rate in each time interval p->nSubValues = 0; p->min = 0.0; p->max = 1.0; for (i=0; iparamTypeName = "Fossilization rate"; SafeStrcat(&p->name, "Relative_fossilization"); SafeStrcat(&p->name, partString); /* find the parameter x prior type */ if (!strcmp(mp->fossilizationPr,"Beta")) p->paramId = FOSLRATE_BETA; else if (!strcmp(mp->fossilizationPr,"Exponential")) p->paramId = FOSLRATE_EXP; else p->paramId = FOSLRATE_FIX; if (p->paramId != FOSLRATE_FIX) p->printParam = YES; if (p->nValues == 1) { SafeStrcat (&p->paramHeader, "relative_fossilization"); SafeStrcat (&p->paramHeader, partString); } else for (i = 0; i < p->nValues; i++) { SafeSprintf(&tempStr, &tempStrSize, "\trelative_fossilization_%d", i+1); SafeStrcat (&p->paramHeader, tempStr); SafeStrcat (&p->paramHeader, partString); } } else if (j == P_POPSIZE) { /* Set up population size *******************************************************************************/ p->paramType = P_POPSIZE; if (!strcmp(mp->topologyPr,"Speciestree") && !strcmp(mp->popVarPr, "Variable")) p->nValues = 2 * numSpecies; else p->nValues = 1; p->nSubValues = 0; p->min = POS_MIN; p->max = POS_INFINITY; for (i=0; iparamTypeName = "Coalescent process population size parameter"; SafeStrcat(&p->name, "Popsize"); SafeStrcat(&p->name, partString); /* find the parameter x prior type */ if (!strcmp(mp->popSizePr,"Uniform")) { p->paramId = POPSIZE_UNI; p->LnPriorRatio = &LnProbRatioUniform; p->priorParams = mp->popSizeUni; p->LnPriorProb = &LnPriorProbUniform; } else if (!strcmp(mp->popSizePr,"Normal")) { p->paramId = POPSIZE_NORMAL; p->LnPriorRatio = &LnProbRatioTruncatedNormal; p->priorParams = mp->popSizeNormal; p->LnPriorProb = &LnPriorProbTruncatedNormal; } else if (!strcmp(mp->popSizePr,"Lognormal")) { p->paramId = POPSIZE_LOGNORMAL; p->LnPriorRatio = &LnProbRatioLognormal; p->priorParams = mp->popSizeLognormal; p->LnPriorProb = &LnPriorProbLognormal; } else if (!strcmp(mp->popSizePr,"Gamma")) { p->paramId = POPSIZE_GAMMA; p->LnPriorRatio = &LnProbRatioGamma; p->priorParams = mp->popSizeGamma; p->LnPriorProb = &LnPriorProbGamma; } else { p->paramId = POPSIZE_FIX; p->LnPriorRatio = NULL; p->priorParams = &mp->popSizeFix; p->LnPriorProb = &LnPriorProbFix; } if (p->paramId != POPSIZE_FIX && p->nValues == 1) p->printParam = YES; SafeStrcat (&p->paramHeader, "popsize"); SafeStrcat (&p->paramHeader, partString); } else if (j == P_GROWTH) { /* Set up growth rate ***********************************************************************************/ p->paramType = P_GROWTH; p->nValues = 1; p->nSubValues = 0; p->min = 1E-6; // TODO: growth rate can be negative (also change Move_Growth_M) p->max = 100.0; for (i=0; iparamTypeName = "Population growth rate"; SafeStrcat(&p->name, "R_pop"); SafeStrcat(&p->name, partString); /* find the parameter x prior type */ if (!strcmp(mp->growthPr,"Uniform")) { p->paramId = GROWTH_UNI; p->priorParams = mp->growthUni; p->min = mp->growthUni[0]; p->max = mp->growthUni[1]; p->LnPriorRatio = &LnProbRatioUniform; p->LnPriorProb = &LnPriorProbUniform; } else if (!strcmp(mp->growthPr,"Exponential")) { p->paramId = GROWTH_EXP; p->priorParams = &mp->growthExp; p->LnPriorRatio = &LnProbRatioExponential; p->LnPriorProb = &LnPriorProbExponential; } else if (!strcmp(mp->growthPr,"Normal")) { p->paramId = GROWTH_NORMAL; p->priorParams = mp->growthNorm; p->LnPriorRatio = &LnProbRatioNormal; p->LnPriorProb = &LnPriorProbNormal; } else { p->paramId = GROWTH_FIX; p->priorParams = &mp->growthFix; p->min = p->max = mp->growthFix; p->LnPriorRatio = &LnProbRatioFix; p->LnPriorProb = &LnPriorProbFix; } if (p->paramId != GROWTH_FIX) p->printParam = YES; SafeStrcat (&p->paramHeader, "growthRate"); SafeStrcat (&p->paramHeader, partString); } else if (j == P_BMCORR) { /* Set up correlation parameter for brownian motion */ p->paramType = P_BMCORR; p->nValues = 1; p->nSubValues = 0; p->min = -1.0; p->max = 1.0; for (i=0; iparamTypeName = "Browian correlation"; SafeStrcat(&p->name, "BM_corr"); SafeStrcat(&p->name, partString); /* find the parameter x prior type */ if (!strcmp(mp->brownCorrPr,"Uniform")) { p->paramId = BMCORR_UNI; p->priorParams = mp->brownCorrUni; p->min = mp->brownCorrUni[0]; p->max = mp->brownCorrUni[1]; p->LnPriorRatio = &LnProbRatioUniform; p->LnPriorProb = &LnPriorProbUniform; } else { p->paramId = BMCORR_FIX; p->priorParams = &mp->brownCorrFix; p->min = p->max = mp->brownCorrFix; p->LnPriorRatio = &LnProbRatioFix; p->LnPriorProb = &LnPriorProbFix; } if (p->paramId != BMCORR_FIX) p->printParam = YES; SafeStrcat (&p->paramHeader, "BM_corr"); SafeStrcat (&p->paramHeader, partString); } else if (j == P_BMSIGMA) { /* Set up sigma parameter for brownian motion */ p->paramType = P_BMSIGMA; p->nValues = 1; p->nSubValues = 0; p->min = 1E-6; p->max = POS_INFINITY; for (i=0; iparamTypeName = "Browian sigma"; SafeStrcat(&p->name, "BM_sigma"); SafeStrcat(&p->name, partString); /* find the parameter x prior type */ if (!strcmp(mp->brownScalePr,"Uniform")) { p->paramId = BMSIGMA_UNI; p->priorParams = mp->brownScaleUni; p->min = mp->brownScaleUni[0]; p->max = mp->brownScaleUni[1]; p->LnPriorRatio = &LnProbRatioUniform; p->LnPriorProb = &LnPriorProbUniform; } else if (!strcmp(mp->brownScalePr,"Gamma")) { p->paramId = BMSIGMA_GAMMA; p->priorParams = mp->brownScaleGamma; p->LnPriorRatio = &LnProbRatioGamma; p->LnPriorProb = &LnPriorProbGamma; } else { p->paramId = BMSIGMA_FIX; p->priorParams = &mp->brownScaleFix; p->min = p->max = mp->brownScaleFix; p->LnPriorRatio = &LnProbRatioFix; p->LnPriorProb = &LnPriorProbFix; } if (p->paramId != BMSIGMA_FIX) p->printParam = YES; SafeStrcat (&p->paramHeader, "BM_sigma"); SafeStrcat (&p->paramHeader, partString); } else if (j == P_AAMODEL) { /* Set up aamodel ***************************************************************************************/ p->paramType = P_AAMODEL; p->nValues = 1; p->nSubValues = 10; p->min = 0; p->max = 9; for (i=0; iparamTypeName = "Aminoacid model"; SafeStrcat(&p->name, "Aamodel"); SafeStrcat(&p->name, partString); /* find the parameter x prior type */ if (!strcmp(mp->aaModelPr,"Mixed")) p->paramId = AAMODEL_MIX; else p->paramId = AAMODEL_FIX; if (p->paramId != AAMODEL_FIX) p->printParam = YES; SafeStrcat (&p->paramHeader, "aamodel"); SafeStrcat (&p->paramHeader, partString); } else if (j == P_CPPRATE) { /* Set up cpprate ***************************************************************************************/ p->paramType = P_CPPRATE; p->nValues = 1; p->nSubValues = 0; p->min = 0.0; p->max = POS_INFINITY; for (i=0; iparamTypeName = "Rate of rate-multiplying compound Poisson process"; SafeStrcat(&p->name, "Lambda_cpp"); SafeStrcat(&p->name, partString); /* find the parameter x prior type */ if (!strcmp(mp->cppRatePr,"Exponential")) p->paramId = CPPRATE_EXP; else p->paramId = CPPRATE_FIX; if (p->paramId != CPPRATE_FIX) p->printParam = YES; SafeStrcat (&p->paramHeader, "cppRate"); SafeStrcat (&p->paramHeader, partString); } else if (j == P_CPPMULTDEV) { /* Set up sigma of cpp rate multipliers *****************************************************************/ p->paramType = P_CPPMULTDEV; p->nValues = 1; p->nSubValues = 0; p->min = 0.0; p->max = POS_INFINITY; for (i=0; iparamTypeName = "Standard deviation (log) of CPP rate multipliers"; SafeStrcat(&p->name, "Sigma_cpp"); SafeStrcat(&p->name, partString); /* find the parameter x prior type */ if (!strcmp(mp->cppMultDevPr,"Fixed")) p->paramId = CPPMULTDEV_FIX; if (p->paramId != CPPMULTDEV_FIX) p->printParam = YES; SafeStrcat (&p->paramHeader, "sigma_cpp"); SafeStrcat (&p->paramHeader, partString); } else if (j == P_CPPEVENTS) { /* Set up cpp events parameter **************************************************************************/ p->paramType = P_CPPEVENTS; p->nValues = 0; p->nSubValues = 2*numLocalTaxa; /* keep effective branch lengths here (for all nodes to be on the safe side) */ p->min = 1E-6; p->max = POS_INFINITY; for (i=0; iparamTypeName = "Events of rate-multiplying compound Poisson process"; SafeStrcat(&p->name, "CppEvents"); SafeStrcat(&p->name, partString); /* find the parameter x prior type */ p->paramId = CPPEVENTS; /* should we print values to .p file? */ p->printParam = NO; SafeStrcat (&p->paramHeader, "cppEvents"); SafeStrcat (&p->paramHeader, partString); } else if (j == P_TK02VAR) { /* Set up tk02 relaxed clock variance parameter *********************************************************/ p->paramType = P_TK02VAR; p->nValues = 1; p->nSubValues = 0; p->min = 1E-6; p->max = POS_INFINITY; for (i=0; iparamTypeName = "Variance of lognormal distribution of branch rates"; SafeStrcat(&p->name, "TK02var"); SafeStrcat(&p->name, partString); /* find the parameter x prior type */ if (!strcmp(mp->tk02varPr,"Uniform")) p->paramId = TK02VAR_UNI; else if (!strcmp(mp->tk02varPr,"Exponential")) p->paramId = TK02VAR_EXP; else p->paramId = TK02VAR_FIX; if (p->paramId != TK02VAR_FIX) p->printParam = YES; SafeStrcat (&p->paramHeader, "tk02var"); SafeStrcat (&p->paramHeader, partString); } else if (j == P_TK02BRANCHRATES) { /* Set up tk02 relaxed clock rates parameter ************************************************************/ p->paramType = P_TK02BRANCHRATES; p->nValues = 2*numLocalTaxa; /* use to hold the branch rates; we need one rate for the root */ p->nSubValues = 2*numLocalTaxa; /* use to hold the effective branch lengths */ p->min = RATE_MIN; p->max = RATE_MAX; for (i=0; iparamTypeName = "Branch rates of TK02 relaxed clock"; SafeStrcat(&p->name, "TK02Brlens"); SafeStrcat(&p->name, partString); /* find the parameter x prior type */ p->paramId = TK02BRANCHRATES; /* should we print values to .p file? */ p->printParam = NO; SafeStrcat (&p->paramHeader, "tk02_brlens"); SafeStrcat (&p->paramHeader, partString); } else if (j == P_WNVAR) { /* Set up white noise relaxed clock variance parameter **************************************************/ p->paramType = P_WNVAR; p->nValues = 1; p->nSubValues = 0; p->min = 1E-6; p->max = POS_INFINITY; for (i=0; iparamTypeName = "Variance of WN model branch rates"; SafeStrcat(&p->name, "WNvar"); SafeStrcat(&p->name, partString); /* find the parameter x prior type */ if (!strcmp(mp->wnvarPr,"Uniform")) p->paramId = WNVAR_UNI; else if (!strcmp(mp->wnvarPr,"Exponential")) p->paramId = WNVAR_EXP; else p->paramId = WNVAR_FIX; if (p->paramId != WNVAR_FIX) p->printParam = YES; SafeStrcat (&p->paramHeader, "wnvar"); SafeStrcat (&p->paramHeader, partString); } else if (j == P_WNBRANCHRATES) { /* Set up white noise relaxed clock rates parameter *****************************************************/ p->paramType = P_WNBRANCHRATES; p->nValues = 2*numLocalTaxa; /* use to hold the branch rates; we need one rate for the root */ p->nSubValues = 2*numLocalTaxa; /* use to hold the effective branch lengths */ p->min = RATE_MIN; p->max = RATE_MAX; for (i=0; iparamTypeName = "Branch lengths of WN relaxed clock"; SafeStrcat(&p->name, "WNBrlens"); SafeStrcat(&p->name, partString); /* find the parameter x prior type */ p->paramId = WNBRANCHRATES; /* should we print values to .p file? */ p->printParam = NO; SafeStrcat (&p->paramHeader, "wn_brlens"); SafeStrcat (&p->paramHeader, partString); } else if (j == P_IGRVAR) { /* Set up igr relaxed clock variance parameter **********************************************************/ p->paramType = P_IGRVAR; p->nValues = 1; p->nSubValues = 0; p->min = 1E-6; p->max = POS_INFINITY; for (i=0; iparamTypeName = "Variance of IGR model branch rates"; SafeStrcat(&p->name, "IGRvar"); SafeStrcat(&p->name, partString); /* find the parameter x prior type */ if (!strcmp(mp->igrvarPr,"Uniform")) p->paramId = IGRVAR_UNI; else if (!strcmp(mp->igrvarPr,"Exponential")) p->paramId = IGRVAR_EXP; else p->paramId = IGRVAR_FIX; if (p->paramId != IGRVAR_FIX) p->printParam = YES; SafeStrcat (&p->paramHeader, "igrvar"); SafeStrcat (&p->paramHeader, partString); } else if (j == P_IGRBRANCHRATES) { /* Set up igr relaxed clock rates parameter *************************************************************/ p->paramType = P_IGRBRANCHRATES; p->nValues = 2*numLocalTaxa; /* use to hold the branch rates; we need one rate for the root */ p->nSubValues = 2*numLocalTaxa; /* use to hold the effective branch lengths */ p->min = RATE_MIN; p->max = RATE_MAX; for (i=0; iparamTypeName = "Branch lengths of IGR relaxed clock"; SafeStrcat(&p->name, "IgrBrlens"); SafeStrcat(&p->name, partString); /* find the parameter x prior type */ p->paramId = IGRBRANCHRATES; /* should we print values to .p file? */ p->printParam = NO; SafeStrcat (&p->paramHeader, "igr_brlens"); SafeStrcat (&p->paramHeader, partString); } else if (j == P_ILNVAR) { /* Set up iln relaxed clock variance parameter **********************************************************/ p->paramType = P_ILNVAR; p->nValues = 1; p->nSubValues = 0; p->min = 1E-6; p->max = POS_INFINITY; for (i=0; iparamTypeName = "Variance of ILN model branch rates"; SafeStrcat(&p->name, "ILNvar"); SafeStrcat(&p->name, partString); /* find the parameter x prior type */ if (!strcmp(mp->ilnvarPr,"Uniform")) p->paramId = ILNVAR_UNI; else if (!strcmp(mp->ilnvarPr,"Exponential")) p->paramId = ILNVAR_EXP; else p->paramId = ILNVAR_FIX; if (p->paramId != ILNVAR_FIX) p->printParam = YES; SafeStrcat (&p->paramHeader, "ilnvar"); SafeStrcat (&p->paramHeader, partString); } else if (j == P_ILNBRANCHRATES) { /* Set up iln relaxed clock rates parameter *************************************************************/ p->paramType = P_ILNBRANCHRATES; p->nValues = 2*numLocalTaxa; /* use to hold the branch rates; we need one rate for the root */ p->nSubValues = 2*numLocalTaxa; /* use to hold the effective branch lengths */ p->min = RATE_MIN; p->max = RATE_MAX; for (i=0; iparamTypeName = "Branch lengths of ILN relaxed clock"; SafeStrcat(&p->name, "IlnBrlens"); SafeStrcat(&p->name, partString); /* find the parameter x prior type */ p->paramId = ILNBRANCHRATES; /* should we print values to .p file? */ p->printParam = NO; SafeStrcat (&p->paramHeader, "iln_brlens"); SafeStrcat (&p->paramHeader, partString); } else if (j == P_MIXEDVAR) { /* Set up mixed relaxed clock variance parameter ********************************************************/ p->paramType = P_MIXEDVAR; p->nValues = 1; p->nSubValues = 0; p->min = 1E-6; p->max = POS_INFINITY; for (i=0; iparamTypeName = "Variance shared for mixed relaxed clock model"; SafeStrcat(&p->name, "Mixedvar"); SafeStrcat(&p->name, partString); /* find the parameter x prior type */ if (!strcmp(mp->mixedvarPr,"Uniform")) p->paramId = MIXEDVAR_UNI; else if (!strcmp(mp->mixedvarPr,"Exponential")) p->paramId = MIXEDVAR_EXP; else p->paramId = MIXEDVAR_FIX; if (p->paramId != MIXEDVAR_FIX) p->printParam = YES; SafeStrcat (&p->paramHeader, "mixedvar"); SafeStrcat (&p->paramHeader, partString); } else if (j == P_MIXEDBRCHRATES) { /* Set up mixed relaxed clock rates parameter ***********************************************************/ p->paramType = P_MIXEDBRCHRATES; p->nValues = 2*numLocalTaxa; /* use to hold the branch rates; we need one rate for the root */ p->nSubValues = 2*numLocalTaxa; /* use to hold the effective branch lengths */ p->nIntValues = 1; /* use to hold the model indicator: IGR or ILN */ p->min = RATE_MIN; p->max = RATE_MAX; for (i=0; iparamTypeName = "Branch rates of mixed relaxed clock"; SafeStrcat(&p->name, "MixedBrlens"); SafeStrcat(&p->name, partString); /* find the parameter x prior type */ p->paramId = MIXEDBRCHRATES; /* how to print model indicator (0 or 1) to .p file? */ p->printParam = NO; SafeStrcat (&p->paramHeader, "mixed_brlens"); SafeStrcat (&p->paramHeader, partString); } else if (j == P_CLOCKRATE) { /* Set up clockRate *************************************************************************************/ p->paramType = P_CLOCKRATE; p->nValues = 1; p->nSubValues = 0; p->min = POS_MIN; p->max = POS_INFINITY; for (i=0; iparamTypeName = "Base rate of clock"; SafeStrcat(&p->name, "Clockrate"); SafeStrcat(&p->name, partString); /* parameter does affect likelihoods */ p->affectsLikelihood = YES; /* find the parameter x prior type */ if (!strcmp(mp->clockRatePr,"Normal")) { p->paramId = CLOCKRATE_NORMAL; p->LnPriorRatio = &LnProbRatioTruncatedNormal; p->priorParams = mp->clockRateNormal; p->LnPriorProb = &LnPriorProbTruncatedNormal; } else if (!strcmp(mp->clockRatePr,"Lognormal")) { p->paramId = CLOCKRATE_LOGNORMAL; p->LnPriorRatio = &LnProbRatioLognormal; p->priorParams = mp->clockRateLognormal; p->LnPriorProb = &LnPriorProbLognormal; } else if (!strcmp(mp->clockRatePr,"Exponential")) { p->paramId = CLOCKRATE_EXP; p->LnPriorRatio = &LnProbRatioExponential; p->priorParams = &mp->clockRateExp; p->LnPriorProb = &LnPriorProbExponential; } else if (!strcmp(mp->clockRatePr,"Gamma")) { p->paramId = CLOCKRATE_GAMMA; p->LnPriorRatio = &LnProbRatioGamma; p->priorParams = mp->clockRateGamma; p->LnPriorProb = &LnPriorProbGamma; } else { p->paramId = CLOCKRATE_FIX; p->LnPriorRatio = NULL; p->priorParams = &mp->clockRateFix; p->LnPriorProb = &LnPriorProbFix; } SafeStrcat (&p->paramHeader, "clockrate"); SafeStrcat (&p->paramHeader, partString); if (p->paramId != CLOCKRATE_FIX) p->printParam = YES; } } free (tempStr); free (isPartTouched); SAFEFREE (partString); return (NO_ERROR); } /*---------------------------------------------------------------------------- | | SetMoves: This function will set up the applicable moves that could | potentially be used in updating the model parameters | -----------------------------------------------------------------------------*/ int SetMoves (void) { int i, j, k, moveIndex; Param *param; /* free up previous moves if any */ if (memAllocs[ALLOC_MOVES] == YES) { for (i=0; i userLevel) continue; if (moveTypes[i].isApplicable(param) == NO) continue; for (j=0; jparamId) { numApplicableMoves++; break; } } } } /* then allocate space for move pointers */ moves = (MCMCMove **) SafeMalloc (numApplicableMoves * sizeof (MCMCMove *)); if (!moves && numApplicableMoves > 0) /* in the middle of defining a model, numApplicableMoves can be 0 */ { MrBayesPrint ("%s Problem allocating moves\n", spacer); return (ERROR); } memAllocs[ALLOC_MOVES] = YES; /* finally allocate space for and set move defaults */ moveIndex = 0; for (k=0; k userLevel) continue; if (moveTypes[i].isApplicable(param) == NO) continue; for (j=0; jparamId) { if ((moves[moveIndex] = AllocateMove (&moveTypes[i], param)) == NULL) break; else { moves[moveIndex]->parm = param; moveIndex++; break; } } } } } if (moveIndex < numApplicableMoves) { for (i=0; inNodes - pt->nIntNodes - 1) / nBitsInALong; /* Get pointer to values to be set */ values = GetParamVals (param, chn, state); /* Get species tree */ speciesTree = GetTree (modelSettings[param->relParts[0]].speciesTree, chn, state); /* Set them based on index of matching partitions */ AllocatePolyTreePartitions(pt); AllocateTreePartitions(speciesTree); for (i=0; inNodes; i++) { pp = pt->allDownPass[i]; for (j=0; jnNodes-1; j++) { p = speciesTree->allDownPass[j]; for (k=0; kpartition[k] != p->partition[k]) break; } if (k == nLongsNeeded) break; } if (j == speciesTree->nNodes - 1) { MrBayesPrint ("%s Non-matching partitions when setting population size parameter", spacer); FreePolyTreePartitions(pt); FreeTreePartitions(speciesTree); return (ERROR); } values[p->index] = pt->popSize[pp->index]; } FreePolyTreePartitions(pt); FreeTreePartitions(speciesTree); return (NO_ERROR); } /* SetRelaxedClockParam: set values for a relaxed clock param from an input tree */ int SetRelaxedClockParam (Param *param, int chn, int state, PolyTree *pt) { int i, j, k, *nEvents=NULL, *nEventsP=NULL, nLongsNeeded, isEventSet; MrBFlt *effectiveBranchLengthP=NULL, *branchRate=NULL, **position=NULL, **rateMult=NULL, **positionP=NULL, **rateMultP=NULL, baseRate; Tree *t; PolyNode *pp; TreeNode *p=NULL, *q; nLongsNeeded = 1 + (numLocalTaxa - 1) / nBitsInALong; /* set pointers to the right set of values */ isEventSet = NO; if (param->paramType == P_CPPEVENTS) { /* find the right event set */ for (i=0; inESets; i++) { if (!strcmp(param->name,pt->eSetName[i])) break; } if (i == pt->nESets) { for (i=0; inBSets; i++) if (!strcmp(param->name,pt->bSetName[i])) break; if (i == pt->nBSets) return (NO_ERROR); else isEventSet = NO; } else isEventSet = YES; if (isEventSet == YES) { nEventsP = pt->nEvents[i]; positionP = pt->position[i]; rateMultP = pt->rateMult[i]; } else effectiveBranchLengthP = pt->effectiveBrLen[i]; nEvents = param->nEvents[2*chn+state]; position = param->position[2*chn+state]; rateMult = param->rateMult[2*chn+state]; } else if (param->paramType == P_TK02BRANCHRATES || param->paramType == P_WNBRANCHRATES || param->paramType == P_IGRBRANCHRATES || param->paramType == P_ILNBRANCHRATES || param->paramType == P_MIXEDBRCHRATES) { /* find the right effective branch length set */ for (i=0; inBSets; i++) if (!strcmp(param->name,pt->bSetName[i])) break; if (i == pt->nBSets) return (NO_ERROR); effectiveBranchLengthP = pt->effectiveBrLen[i]; branchRate = GetParamVals (param, chn, state); } t = GetTree (param, chn, state); AllocatePolyTreePartitions (pt); AllocateTreePartitions (t); for (i=pt->nNodes-1; i>=0; i--) { pp = pt->allDownPass[i]; for (j=0; jnNodes; j++) { p = t->allDownPass[j]; for (k=0; kpartition[k] != pp->partition[k]) break; if (k == nLongsNeeded) break; /* match */ } if (param->paramType == P_CPPEVENTS) { if (isEventSet == NO) { if (nEvents[p->index] != 1) { position[p->index] = (MrBFlt *) SafeRealloc ((void *) position[p->index], 1*sizeof(MrBFlt)); rateMult[p->index] = (MrBFlt *) SafeRealloc ((void *) rateMult[p->index], 1*sizeof(MrBFlt)); nEvents [p->index] = 1; } position[p->index][0] = 0.5; if (p->anc->anc == NULL) rateMult[p->index][0] = 1.0; else { baseRate = 1.0; q = p->anc; while (q->anc != NULL) { baseRate *= rateMult[q->index][0]; q = q->anc; } rateMult[p->index][0] = 2.0 * effectiveBranchLengthP[pp->index] / (p->length * baseRate) - 1.0; } } else { if (nEvents[p->index] != nEventsP[pp->index]) { if (nEventsP[pp->index] == 0) { free (position[p->index]); free (rateMult[p->index]); } else { position[p->index] = (MrBFlt *) SafeRealloc ((void *) position[p->index], nEventsP[pp->index]*sizeof(MrBFlt)); rateMult[p->index] = (MrBFlt *) SafeRealloc ((void *) rateMult[p->index], nEventsP[pp->index]*sizeof(MrBFlt)); } nEvents[p->index] = nEventsP[pp->index]; } for (j=0; jindex]; j++) { position[p->index][j] = positionP[pp->index][j]; rateMult[p->index][j] = rateMultP[pp->index][j]; } } } else if (param->paramType == P_TK02BRANCHRATES) { if (p->anc->anc == NULL) branchRate[p->index] = 1.0; else if (p->length > 0.0) branchRate[p->index] = 2.0 * effectiveBranchLengthP[pp->index] / p->length - branchRate[p->anc->index]; else branchRate[p->index] = branchRate[p->anc->index]; } else if (param->paramType == P_ILNBRANCHRATES || param->paramType == P_IGRBRANCHRATES || param->paramType == P_MIXEDBRCHRATES || param->paramType == P_WNBRANCHRATES) { if (p->length > 0.0) branchRate[p->index] = effectiveBranchLengthP[pp->index] / p->length; else branchRate[p->index] = branchRate[p->anc->index]; } } if (param->paramType == P_CPPEVENTS) { if (UpdateCppEvolLengths (param, t->root->left, chn) == ERROR) return (ERROR); } else if (param->paramType == P_TK02BRANCHRATES) { if (UpdateTK02EvolLengths (param, t, chn) == ERROR) return (ERROR); } else if (param->paramType == P_ILNBRANCHRATES || param->paramType == P_IGRBRANCHRATES || param->paramType == P_MIXEDBRCHRATES || param->paramType == P_WNBRANCHRATES) { if (UpdateIndBrachLengths (param, t, chn) == ERROR) return (ERROR); } FreePolyTreePartitions (pt); FreeTreePartitions (t); return (NO_ERROR); } /*------------------------------------------------------------------------ | | SetUpAnalysis: Set parameters and moves | ------------------------------------------------------------------------*/ int SetUpAnalysis (RandLong *seed) { setUpAnalysisSuccess=NO; /* calculate number of characters and taxa */ numLocalChar = NumNonExcludedChar (); /* we are checking later to make sure no partition is without characters */ SetLocalTaxa (); if (numLocalTaxa <= 2) { MrBayesPrint ("%s There must be at least two included taxa, now there is %s\n", spacer, numLocalTaxa == 0 ? "none" : "only one"); return (ERROR); } /* calculate number of global chains */ numGlobalChains = chainParams.numRuns * chainParams.numChains; /* Set up link table */ if (SetUpLinkTable () == ERROR) return (ERROR); /* Check that the settings for doublet or codon models are correct. */ if (CheckExpandedModels() == ERROR) return (ERROR); /* Set up model info */ if (SetModelInfo() == ERROR) return (ERROR); /* Calculate number of (uncompressed) characters for each division */ if (GetNumDivisionChars() == ERROR) return (ERROR); /* Compress data and calculate some things needed for setting up params. */ if (CompressData() == ERROR) return (ERROR); /* Add dummy characters, if needed. */ if (AddDummyChars() == ERROR) return (ERROR); /* Set up parameters for the chain. */ if (SetModelParams () == ERROR) return (ERROR); /* Allocate normal params */ if (AllocateNormalParams () == ERROR) return (ERROR); /* Allocate tree params */ if (AllocateTreeParams () == ERROR) return (ERROR); /* Set default number of trees for sumt to appropriate number */ sumtParams.numTrees = numTrees; /* Fill in normal parameters */ if (FillNormalParams (seed, 0, numGlobalChains) == ERROR) return (ERROR); /* Process standard characters (calculates bsIndex, tiIndex, and more). */ if (ProcessStdChars(seed) == ERROR) return (ERROR); /* Fill in trees */ if (FillTreeParams (seed, 0, numGlobalChains) == ERROR) return (ERROR); /* Set the applicable moves that could be used by the chain. */ if (SetMoves () == ERROR) return (ERROR); setUpAnalysisSuccess=YES; return (NO_ERROR); } int SetUpLinkTable (void) { int i, j, k, m, paramCount, isApplicable1, isApplicable2, isFirst, isSame; int *check, *modelId; check = (int *) SafeMalloc (2 * (size_t)numCurrentDivisions * sizeof (int)); if (!check) { MrBayesPrint ("%s Problem allocating check (%d)\n", spacer, 2 * numCurrentDivisions * sizeof(int)); return (ERROR); } modelId = check + numCurrentDivisions; for (j=0; j 1) { paramCount = 0; for (j=0; j 0) { m = activeParams[j][i]; paramCount++; for (k=i; klevel = DEVELOPER; mt->numTuningParams = 0; mt->minimum[0] = mt->minimum[1] = -1000000000.0; mt->maximum[0] = mt->maximum[1] = 1000000000.0; mt->tuningParam[0] = mt->tuningParam[1] = 0.0; mt->nApplicable = 0; mt->name = mt->tuningName[0] = mt->tuningName[1] = ""; mt->paramName = ""; mt->subParams = NO; mt->relProposalProb = 0.0; mt->parsimonyBased = NO; mt->isApplicable = &IsApplicable; mt->Autotune = NULL; mt->targetRate = -1.0; } /* Moves are in alphabetic order after parameter name, which matches the name of a move function if there is a separate move function for the parameter. See proposal.h for declaration of move functions. Since 2010-10-04, some parameters use generalized move functions and do not have their own. */ i = 0; /* Move_Aamodel */ mt = &moveTypes[i++]; mt->name = "Uniform random pick"; mt->shortName = "Uniform"; mt->applicableTo[0] = AAMODEL_MIX; mt->nApplicable = 1; mt->moveFxn = &Move_Aamodel; mt->relProposalProb = 1.0; mt->numTuningParams = 0; mt->parsimonyBased = NO; mt->level = STANDARD_USER; /* Move_Adgamma */ mt = &moveTypes[i++]; mt->name = "Sliding window"; mt->shortName = "Slider"; mt->tuningName[0] = "Sliding window size"; mt->shortTuningName[0] = "delta"; mt->applicableTo[0] = CORREL_UNI; mt->nApplicable = 1; mt->moveFxn = &Move_Adgamma; mt->relProposalProb = 1.0; mt->numTuningParams = 1; mt->tuningParam[0] = 0.5; /* window size */ mt->minimum[0] = 0.001; mt->maximum[0] = 1.999; mt->parsimonyBased = NO; mt->level = STANDARD_USER; mt->Autotune = &AutotuneSlider; mt->targetRate = 0.25; /* Move_Beta */ mt = &moveTypes[i++]; mt->name = "Multiplier"; mt->shortName = "Multiplier"; mt->tuningName[0] = "Multiplier tuning parameter"; mt->shortTuningName[0] = "lambda"; mt->applicableTo[0] = SYMPI_UNI; mt->applicableTo[1] = SYMPI_EXP; mt->applicableTo[2] = SYMPI_UNI_MS; mt->applicableTo[3] = SYMPI_EXP_MS; mt->nApplicable = 4; mt->moveFxn = &Move_Beta; mt->relProposalProb = 1.0; mt->numTuningParams = 1; mt->tuningParam[0] = 2.0 * log (1.5); /* so-called lambda */ mt->minimum[0] = 0.0001; mt->maximum[0] = 20.0; mt->parsimonyBased = NO; mt->level = STANDARD_USER; mt->Autotune = &AutotuneMultiplier; mt->targetRate = 0.25; /* Move_BrLen */ mt = &moveTypes[i++]; mt->name = "Random brlen hit with multiplier"; mt->shortName = "Multiplier"; mt->tuningName[0] = "Multiplier tuning parameter"; mt->shortTuningName[0] = "lambda"; mt->applicableTo[0] = BRLENS_UNI; mt->applicableTo[1] = BRLENS_EXP; mt->applicableTo[2] = BRLENS_GamDir; mt->applicableTo[3] = BRLENS_iGmDir; mt->applicableTo[4] = BRLENS_twoExp; mt->nApplicable = 5; // was 2 mt->moveFxn = &Move_BrLen; mt->relProposalProb = 20.0; mt->numTuningParams = 1; mt->tuningParam[0] = 2.0 * log (2.0); /* lambda */ mt->minimum[0] = 0.0001; mt->maximum[0] = 20.0; /* change it smaller to avoid overflow in the exp function, same for following "smaller" */ mt->parsimonyBased = NO; mt->level = STANDARD_USER; mt->Autotune = &AutotuneMultiplier; mt->targetRate = 0.25; /* // Move_BMcorr mt = &moveTypes[i++]; mt->name = "Sliding window"; mt->shortName = "Slider"; mt->tuningName[0] = "Sliding window size"; mt->shortTuningName[0] = "delta"; mt->applicableTo[0] = BMCORR_UNI; mt->nApplicable = 1; mt->moveFxn = &Move_BMcorr; mt->relProposalProb = 1.0; mt->numTuningParams = 1; mt->tuningParam[0] = 0.1; mt->minimum[0] = 0.00001; mt->maximum[0] = 100.0; mt->parsimonyBased = NO; mt->level = STANDARD_USER; mt->Autotune = &AutotuneSlider; mt->targetRate = 0.25; // Move_BMsigma mt = &moveTypes[i++]; mt->name = "Multiplier"; mt->shortName = "Multiplier"; mt->tuningName[0] = "Multiplier tuning parameter"; mt->shortTuningName[0] = "lambda"; mt->applicableTo[0] = BMSIGMA_UNI; mt->applicableTo[1] = BMSIGMA_GAMMA; mt->nApplicable = 2; mt->moveFxn = &Move_BMsigma; mt->relProposalProb = 1.0; mt->numTuningParams = 1; mt->tuningParam[0] = 2.0 * log (1.5); mt->minimum[0] = 0.0001; mt->maximum[0] = 20.0; mt->parsimonyBased = NO; mt->level = STANDARD_USER; mt->Autotune = &AutotuneMultiplier; mt->targetRate = 0.25; */ /* Move_ClockRate_M */ mt = &moveTypes[i++]; mt->name = "Multiplier"; mt->shortName = "Multiplier"; mt->tuningName[0] = "Multiplier tuning parameter"; mt->shortTuningName[0] = "lambda"; mt->applicableTo[0] = CLOCKRATE_NORMAL; mt->applicableTo[1] = CLOCKRATE_LOGNORMAL; mt->applicableTo[2] = CLOCKRATE_GAMMA; mt->applicableTo[3] = CLOCKRATE_EXP; mt->nApplicable = 4; mt->moveFxn = &Move_ClockRate_M; mt->relProposalProb = 4.0; mt->numTuningParams = 1; mt->tuningParam[0] = 2.0 * log (1.5); /* lambda */ mt->minimum[0] = 0.0001; mt->maximum[0] = 20.0; /* smaller */ mt->parsimonyBased = NO; mt->level = STANDARD_USER; mt->Autotune = &AutotuneMultiplier; mt->targetRate = 0.25; /* Move_Extinction */ mt = &moveTypes[i++]; mt->name = "Sliding window"; mt->shortName = "Slider"; mt->tuningName[0] = "Sliding window size"; mt->shortTuningName[0] = "delta"; mt->applicableTo[0] = EXTRATE_BETA; mt->applicableTo[1] = EXTRATE_EXP; mt->nApplicable = 2; mt->moveFxn = &Move_Extinction; mt->relProposalProb = 3.0; mt->numTuningParams = 1; mt->tuningParam[0] = 0.1; /* window size */ mt->minimum[0] = 0.00001; mt->maximum[0] = 100.0; mt->parsimonyBased = NO; mt->level = STANDARD_USER; mt->Autotune = &AutotuneSlider; mt->targetRate = 0.25; /* Move_Fossilization */ mt = &moveTypes[i++]; mt->name = "Sliding window"; mt->shortName = "Slider"; mt->tuningName[0] = "Sliding window size"; mt->shortTuningName[0] = "delta"; mt->applicableTo[0] = FOSLRATE_BETA; mt->applicableTo[1] = FOSLRATE_EXP; mt->nApplicable = 2; mt->moveFxn = &Move_Fossilization; mt->relProposalProb = 3.0; mt->numTuningParams = 1; mt->tuningParam[0] = 0.1; /* window size */ mt->minimum[0] = 0.00001; mt->maximum[0] = 100.0; mt->parsimonyBased = NO; mt->level = STANDARD_USER; mt->Autotune = &AutotuneSlider; mt->targetRate = 0.25; /* Move_AddBranch, for fossilization prior */ mt = &moveTypes[i++]; mt->name = "Add branch for FossilizedBD"; mt->shortName = "AddBranch"; // mt->subParams = YES; mt->applicableTo[0] = BRLENS_CLOCK_FOSSIL; mt->nApplicable = 1; mt->moveFxn = &Move_AddBranch; mt->relProposalProb = 10.0; mt->numTuningParams = 0; mt->parsimonyBased = NO; mt->level =STANDARD_USER; mt->isApplicable = &IsApplicable_AncestralFossil; /* Move_DelBranch, for fossilization prior */ mt = &moveTypes[i++]; mt->name = "Delete branch for FossilizedBD"; mt->shortName = "DelBranch"; // mt->subParams = YES; mt->applicableTo[0] = BRLENS_CLOCK_FOSSIL; mt->nApplicable = 1; mt->moveFxn = &Move_DelBranch; mt->relProposalProb = 10.0; mt->numTuningParams = 0; mt->parsimonyBased = NO; mt->level =STANDARD_USER; mt->isApplicable = &IsApplicable_AncestralFossil; /* Move_ExtSPR */ mt = &moveTypes[i++]; mt->name = "Extending SPR"; mt->shortName = "ExtSPR"; mt->subParams = YES; mt->tuningName[0] = "Extension probability"; mt->shortTuningName[0] = "p_ext"; mt->tuningName[1] = "Multiplier tuning parameter"; mt->shortTuningName[1] = "lambda"; mt->applicableTo[0] = TOPOLOGY_NCL_UNIFORM_HOMO; mt->applicableTo[1] = TOPOLOGY_NCL_CONSTRAINED_HOMO; mt->applicableTo[2] = TOPOLOGY_RNCL_UNIFORM; mt->applicableTo[3] = TOPOLOGY_RNCL_CONSTRAINED; mt->nApplicable = 4; mt->moveFxn = &Move_ExtSPR; mt->relProposalProb = 5.0; mt->numTuningParams = 2; mt->tuningParam[0] = 0.5; /* extension probability */ mt->tuningParam[1] = 2.0 * log (1.05); /* lambda */ mt->minimum[0] = 0.00001; mt->maximum[0] = 0.99999; mt->minimum[1] = 0.00001; mt->maximum[1] = 100.0; mt->parsimonyBased = NO; mt->level = STANDARD_USER; mt->isApplicable = &IsApplicable_FourTaxaOrMore; /* Move_ExtSPR1 */ mt = &moveTypes[i++]; mt->name = "Extending SPR variant 1"; mt->shortName = "ExtSPR1"; mt->subParams = YES; mt->tuningName[0] = "Extension probability"; mt->shortTuningName[0] = "p_ext"; mt->tuningName[1] = "Multiplier tuning parameter"; mt->shortTuningName[1] = "lambda"; mt->applicableTo[0] = TOPOLOGY_NCL_UNIFORM_HOMO; mt->applicableTo[1] = TOPOLOGY_NCL_CONSTRAINED_HOMO; mt->nApplicable = 2; mt->moveFxn = &Move_ExtSPR1; mt->relProposalProb = 0.0; mt->numTuningParams = 2; mt->tuningParam[0] = 0.5; /* extension probability */ mt->tuningParam[1] = 2.0 * log (1.05); /* lambda */ mt->minimum[0] = 0.00001; mt->maximum[0] = 0.99999; mt->minimum[1] = 0.00001; mt->maximum[1] = 100.0; mt->parsimonyBased = NO; mt->level = DEVELOPER; mt->isApplicable = &IsApplicable_FiveTaxaOrMore; /* Move_ExtSPRClock */ mt = &moveTypes[i++]; mt->name = "Extending SPR for clock trees"; mt->shortName = "ExtSPRClock"; mt->subParams = YES; mt->tuningName[0] = "Extension probability"; mt->shortTuningName[0] = "p_ext"; mt->applicableTo[0] = TOPOLOGY_CL_UNIFORM; mt->applicableTo[1] = TOPOLOGY_CCL_UNIFORM; mt->applicableTo[2] = TOPOLOGY_CL_CONSTRAINED; mt->applicableTo[3] = TOPOLOGY_CCL_CONSTRAINED; mt->applicableTo[4] = TOPOLOGY_RCL_UNIFORM; mt->applicableTo[5] = TOPOLOGY_RCL_CONSTRAINED; mt->applicableTo[6] = TOPOLOGY_RCCL_UNIFORM; mt->applicableTo[7] = TOPOLOGY_RCCL_CONSTRAINED; mt->nApplicable = 8; mt->moveFxn = &Move_ExtSPRClock; mt->relProposalProb = 10.0; mt->numTuningParams = 1; mt->tuningParam[0] = 0.5; /* extension probability */ mt->minimum[0] = 0.00001; mt->maximum[0] = 0.99999; mt->parsimonyBased = NO; mt->level = STANDARD_USER; mt->isApplicable = &IsApplicable_ThreeTaxaOrMore; /* Move_ExtSPRClock_Fossil */ mt = &moveTypes[i++]; mt->name = "Extending fossil SPR for clock trees"; mt->shortName = "ExtSPRClockFossil"; mt->subParams = YES; mt->tuningName[0] = "Extension probability"; mt->shortTuningName[0] = "p_ext"; mt->applicableTo[0] = TOPOLOGY_CL_UNIFORM; mt->applicableTo[1] = TOPOLOGY_CCL_UNIFORM; mt->applicableTo[2] = TOPOLOGY_CL_CONSTRAINED; mt->applicableTo[3] = TOPOLOGY_CCL_CONSTRAINED; mt->applicableTo[4] = TOPOLOGY_RCL_UNIFORM; mt->applicableTo[5] = TOPOLOGY_RCL_CONSTRAINED; mt->applicableTo[6] = TOPOLOGY_RCCL_UNIFORM; mt->applicableTo[7] = TOPOLOGY_RCCL_CONSTRAINED; mt->nApplicable = 8; mt->moveFxn = &Move_ExtSPRClock_Fossil; mt->relProposalProb = 0.0; mt->numTuningParams = 1; mt->tuningParam[0] = 0.5; /* extension probability */ mt->minimum[0] = 0.00001; mt->maximum[0] = 0.99999; mt->parsimonyBased = NO; mt->level = DEVELOPER; mt->isApplicable = &IsApplicable_ThreeTaxaOrMore; /* Move_ExtSS */ mt = &moveTypes[i++]; mt->name = "Extending subtree swapper"; mt->shortName = "ExtSS"; mt->subParams = YES; mt->tuningName[0] = "Extension probability"; mt->shortTuningName[0] = "p_ext"; mt->tuningName[1] = "Multiplier tuning parameter"; mt->shortTuningName[1] = "lambda"; mt->applicableTo[0] = TOPOLOGY_NCL_UNIFORM_HOMO; mt->applicableTo[1] = TOPOLOGY_NCL_CONSTRAINED_HOMO; mt->nApplicable = 2; mt->moveFxn = &Move_ExtSS; mt->relProposalProb = 0.0; mt->numTuningParams = 2; mt->tuningParam[0] = 0.5; /* extension probability */ mt->tuningParam[1] = 2.0 * log (1.05); /* lambda */ mt->minimum[0] = 0.00001; mt->maximum[0] = 0.99999; mt->minimum[1] = 0.00001; mt->maximum[1] = 100.0; mt->parsimonyBased = NO; mt->level = STANDARD_USER; mt->isApplicable = &IsApplicable_FourTaxaOrMore; /* Move_ExtSSClock */ mt = &moveTypes[i++]; mt->name = "Extending subtree swapper"; mt->shortName = "ExtSsClock"; mt->subParams = YES; mt->tuningName[0] = "Extension probability"; mt->shortTuningName[0] = "p_ext"; mt->applicableTo[0] = TOPOLOGY_CL_UNIFORM; mt->applicableTo[1] = TOPOLOGY_CCL_UNIFORM; mt->applicableTo[2] = TOPOLOGY_CL_CONSTRAINED; mt->applicableTo[3] = TOPOLOGY_CCL_CONSTRAINED; mt->applicableTo[4] = TOPOLOGY_RCL_UNIFORM; mt->applicableTo[5] = TOPOLOGY_RCL_CONSTRAINED; mt->applicableTo[6] = TOPOLOGY_RCCL_UNIFORM; mt->applicableTo[7] = TOPOLOGY_RCCL_CONSTRAINED; mt->nApplicable = 8; mt->moveFxn = &Move_ExtSSClock; mt->relProposalProb = 0.0; mt->numTuningParams = 1; mt->tuningParam[0] = 0.5; /* extension probability */ mt->minimum[0] = 0.00001; mt->maximum[0] = 0.99999; mt->parsimonyBased = NO; mt->level = DEVELOPER; mt->isApplicable = &IsApplicable_ThreeTaxaOrMore; /* Move_ExtTBR */ mt = &moveTypes[i++]; mt->name = "Extending TBR"; mt->shortName = "ExtTBR"; mt->subParams = YES; mt->tuningName[0] = "Extension probability"; mt->shortTuningName[0] = "p_ext"; mt->tuningName[1] = "Multiplier tuning parameter"; mt->shortTuningName[1] = "lambda"; mt->applicableTo[0] = TOPOLOGY_NCL_UNIFORM_HOMO; mt->applicableTo[1] = TOPOLOGY_NCL_CONSTRAINED_HOMO; mt->applicableTo[2] = TOPOLOGY_RNCL_UNIFORM; mt->applicableTo[3] = TOPOLOGY_RNCL_CONSTRAINED; mt->nApplicable = 4; mt->moveFxn = &Move_ExtTBR; mt->relProposalProb = 5.0; mt->numTuningParams = 2; mt->tuningParam[0] = 0.5; /* extension probability */ mt->tuningParam[1] = 2.0 * log (1.05); /* lambda */ mt->minimum[0] = 0.00001; mt->maximum[0] = 0.99999; mt->minimum[1] = 0.00001; mt->maximum[1] = 100.0; mt->parsimonyBased = NO; mt->level = STANDARD_USER; mt->isApplicable = &IsApplicable_FiveTaxaOrMore; /* Move_GeneRate_Dir */ mt = &moveTypes[i++]; mt->name = "Dirichlet proposal"; mt->shortName = "Dirichlet"; mt->tuningName[0] = "Dirichlet parameter"; mt->shortTuningName[0] = "alpha"; mt->applicableTo[0] = GENETREERATEMULT_DIR; mt->nApplicable = 1; mt->moveFxn = &Move_GeneRate_Dir; mt->relProposalProb = 1.0; mt->numTuningParams = 1; mt->tuningParam[0] = 1000.0; /* alphaPi */ mt->minimum[0] = 0.001; mt->maximum[0] = 1000000.0; mt->parsimonyBased = NO; mt->level = STANDARD_USER; mt->Autotune = &AutotuneDirichlet; mt->targetRate = 0.25; /* Move_GeneTree1 */ mt = &moveTypes[i++]; mt->name = "Extending SPR move for gene trees in species trees"; mt->shortName = "ExtSPRClockGS"; mt->tuningName[0] = "Extension probability"; mt->shortTuningName[0] = "prob"; mt->applicableTo[0] = TOPOLOGY_SPECIESTREE; mt->nApplicable = 1; mt->moveFxn = &Move_GeneTree1; mt->relProposalProb = 10.0; mt->numTuningParams = 1; mt->tuningParam[0] = 0.5; /* Tuning parameter value */ mt->minimum[0] = 0.00001; /* Minimum value of tuning param */ mt->maximum[0] = 0.99999; /* Maximum value of tuning param */ mt->parsimonyBased = NO; /* It does not use parsimony scores */ mt->level = STANDARD_USER; /* Move_GeneTree2 */ mt = &moveTypes[i++]; mt->name = "NNI move for gene trees in species trees"; mt->shortName = "NNIClockGS"; mt->applicableTo[0] = TOPOLOGY_SPECIESTREE; mt->nApplicable = 1; mt->moveFxn = &Move_GeneTree2; mt->relProposalProb = 10.0; mt->numTuningParams = 0; /* No tuning parameters */ mt->parsimonyBased = NO; /* It does not use parsimony scores */ mt->level = STANDARD_USER; /* Move_GeneTree3 */ mt = &moveTypes[i++]; mt->name = "Parsimony-biased SPR for gene trees in species trees"; mt->shortName = "ParsSPRClockGS"; mt->subParams = YES; mt->tuningName[0] = "parsimony warp factor"; mt->shortTuningName[0] = "warp"; mt->tuningName[1] = "reweighting probability"; mt->shortTuningName[1] = "r"; mt->tuningName[2] = "typical branch length"; mt->shortTuningName[2] = "v_t"; mt->applicableTo[0] = TOPOLOGY_SPECIESTREE; mt->nApplicable = 1; mt->moveFxn = &Move_GeneTree3; mt->relProposalProb = 5.0; mt->numTuningParams = 3; mt->tuningParam[0] = 0.1; /* warp */ mt->tuningParam[1] = 0.05; /* upweight and downweight probability */ mt->tuningParam[2] = 0.05; /* typical branch length */ mt->minimum[0] = 0.0; mt->maximum[0] = 1.0; mt->minimum[1] = 0.0; mt->maximum[1] = 0.3; mt->minimum[2] = 0.0001; mt->maximum[2] = 0.5; mt->parsimonyBased = YES; mt->level = STANDARD_USER; /* Move_Growth_M */ mt = &moveTypes[i++]; mt->name = "Multiplier"; mt->shortName = "Multiplier"; mt->tuningName[0] = "Multiplier tuning parameter"; mt->shortTuningName[0] = "lambda"; mt->applicableTo[0] = GROWTH_UNI; mt->applicableTo[1] = GROWTH_EXP; mt->applicableTo[2] = GROWTH_NORMAL; mt->nApplicable = 3; mt->moveFxn = &Move_Growth_M; mt->relProposalProb = 1.0; mt->numTuningParams = 1; mt->tuningParam[0] = 2.0 * log(1.5); /* lambda */ mt->minimum[0] = 0.0001; mt->maximum[0] = 20.0; mt->parsimonyBased = NO; mt->level = STANDARD_USER; mt->Autotune = &AutotuneMultiplier; mt->targetRate = 0.25; /* Move_Local */ mt = &moveTypes[i++]; mt->name = "BAMBE's LOCAL"; mt->shortName = "Local"; mt->subParams = YES; mt->tuningName[0] = "Multiplier tuning parameter"; mt->shortTuningName[0] = "lambda"; mt->applicableTo[0] = TOPOLOGY_NCL_UNIFORM_HOMO; mt->applicableTo[1] = TOPOLOGY_NCL_CONSTRAINED_HOMO; mt->nApplicable = 2; mt->moveFxn = &Move_Local; mt->relProposalProb = 0.0; mt->numTuningParams = 1; mt->tuningParam[0] = 2.0 * log (1.1); /* lambda */ mt->minimum[0] = 0.0001; mt->maximum[0] = 20.0; mt->parsimonyBased = NO; mt->level = STANDARD_USER; mt->isApplicable = &IsApplicable_FourTaxaOrMore; /* Move_LocalClock */ mt = &moveTypes[i++]; mt->name = "Modified LOCAL for clock trees"; mt->shortName = "LocalClock"; mt->subParams = YES; mt->tuningName[0] = "Multiplier tuning parameter"; mt->shortTuningName[0] = "lambda"; mt->applicableTo[0] = TOPOLOGY_CL_UNIFORM; mt->applicableTo[1] = TOPOLOGY_CCL_UNIFORM; mt->applicableTo[2] = TOPOLOGY_CL_CONSTRAINED; mt->applicableTo[3] = TOPOLOGY_CCL_CONSTRAINED; mt->nApplicable = 4; mt->moveFxn = &Move_LocalClock; mt->relProposalProb = 0.0; mt->numTuningParams = 1; mt->tuningParam[0] = 2.0 * log (2.0); /* lambda */ mt->minimum[0] = 0.0001; mt->maximum[0] = 20.0; mt->parsimonyBased = NO; mt->level = DEVELOPER; mt->isApplicable = &IsApplicable_ThreeTaxaOrMore; /* Move_MixtureRates */ mt = &moveTypes[i++]; mt->name = "Dirichlet proposal"; mt->shortName = "Dirichlet"; mt->tuningName[0] = "Dirichlet parameter"; mt->shortTuningName[0] = "alpha"; mt->applicableTo[0] = MIXTURE_RATES; mt->nApplicable = 1; mt->moveFxn = &Move_MixtureRates; mt->relProposalProb = 0.5; mt->numTuningParams = 1; mt->tuningParam[0] = 100.0; /* alphaPi per rate */ mt->minimum[0] = 0.001; mt->maximum[0] = 10000.0; mt->parsimonyBased = NO; mt->level = STANDARD_USER; mt->Autotune = &AutotuneDirichlet; mt->targetRate = 0.25; /* Move_MixtureRates_Slider */ mt = &moveTypes[i++]; mt->name = "Sliding window"; mt->shortName = "Slider"; mt->tuningName[0] = "Sliding window size"; mt->shortTuningName[0] = "delta"; mt->applicableTo[0] = MIXTURE_RATES; mt->nApplicable = 1; mt->moveFxn = &Move_MixtureRates_Slider; mt->relProposalProb = 0.5; mt->numTuningParams = 1; mt->tuningParam[0] = 0.2; /* window size (change in proportions) */ mt->minimum[0] = 0.00001; mt->maximum[0] = 1.0; mt->parsimonyBased = NO; mt->level = STANDARD_USER; mt->Autotune = &AutotuneSlider; mt->targetRate = 0.25; /* Move_NNI */ mt = &moveTypes[i++]; mt->name = "NNI move for parsimony trees"; mt->shortName = "ParsNNI"; mt->applicableTo[0] = TOPOLOGY_PARSIMONY_UNIFORM; mt->applicableTo[1] = TOPOLOGY_PARSIMONY_CONSTRAINED; mt->nApplicable = 2; mt->moveFxn = &Move_NNI; mt->relProposalProb = 10.0; mt->numTuningParams = 0; mt->parsimonyBased = NO; /* no extra parsimony scores are needed */ mt->level = STANDARD_USER; mt->isApplicable = &IsApplicable_FourTaxaOrMore; /* Move_NNIClock */ mt = &moveTypes[i++]; mt->name = "NNI move for clock trees"; mt->shortName = "NNIClock"; mt->subParams = YES; mt->applicableTo[0] = TOPOLOGY_CL_UNIFORM; mt->applicableTo[1] = TOPOLOGY_CCL_UNIFORM; mt->applicableTo[2] = TOPOLOGY_CL_CONSTRAINED; mt->applicableTo[3] = TOPOLOGY_CCL_CONSTRAINED; mt->applicableTo[4] = TOPOLOGY_RCL_UNIFORM; mt->applicableTo[5] = TOPOLOGY_RCL_CONSTRAINED; mt->applicableTo[6] = TOPOLOGY_RCCL_UNIFORM; mt->applicableTo[7] = TOPOLOGY_RCCL_CONSTRAINED; mt->nApplicable = 8; mt->moveFxn = &Move_NNIClock; mt->relProposalProb = 20.0; mt->numTuningParams = 0; mt->parsimonyBased = NO; mt->level = STANDARD_USER; mt->isApplicable = &IsApplicable_ThreeTaxaOrMore; /* Move_NNI */ mt = &moveTypes[i++]; mt->name = "NNI move"; mt->shortName = "NNI"; mt->subParams = YES; mt->applicableTo[0] = TOPOLOGY_NCL_UNIFORM_HOMO; mt->applicableTo[1] = TOPOLOGY_NCL_CONSTRAINED_HOMO; mt->applicableTo[2] = TOPOLOGY_RNCL_UNIFORM; mt->applicableTo[3] = TOPOLOGY_RNCL_CONSTRAINED; mt->nApplicable = 4; mt->moveFxn = &Move_NNI; mt->relProposalProb = 3.0; mt->numTuningParams = 0; mt->parsimonyBased = NO; mt->level = STANDARD_USER; mt->isApplicable = &IsApplicable_FourTaxaOrMore; /* Move_NNI_Hetero */ mt = &moveTypes[i++]; mt->name = "NNI move for trees with independent brlens"; mt->shortName = "MultNNI"; mt->subParams = YES; mt->applicableTo[0] = TOPOLOGY_NCL_UNIFORM_HETERO; mt->applicableTo[1] = TOPOLOGY_NCL_CONSTRAINED_HETERO; mt->nApplicable = 2; /* 3; */ mt->moveFxn = &Move_NNI_Hetero; mt->relProposalProb = 15.0; mt->numTuningParams = 0; mt->parsimonyBased = NO; mt->level = STANDARD_USER; mt->isApplicable = &IsApplicable_FourTaxaOrMore; /* Move_NodeSlider */ mt = &moveTypes[i++]; mt->name = "Node slider (uniform on possible positions)"; mt->shortName = "Nodeslider"; mt->tuningName[0] = "Multiplier tuning parameter"; mt->shortTuningName[0] = "lambda"; mt->applicableTo[0] = BRLENS_UNI; mt->applicableTo[1] = BRLENS_EXP; mt->applicableTo[2] = BRLENS_GamDir; mt->applicableTo[3] = BRLENS_iGmDir; mt->applicableTo[4] = BRLENS_twoExp; mt->nApplicable = 5; mt->moveFxn = &Move_NodeSlider; mt->relProposalProb = 7.0; mt->numTuningParams = 1; mt->tuningParam[0] = 2.0 * log (1.1); /* lambda */ mt->minimum[0] = 0.0001; mt->maximum[0] = 20.0; mt->parsimonyBased = NO; mt->level = STANDARD_USER; /* Move_NodeSliderClock */ mt = &moveTypes[i++]; mt->name = "Node depth window slider (clock-constrained)"; mt->shortName = "NodesliderClock"; mt->tuningName[0] = "Window size"; mt->shortTuningName[0] = "delta"; mt->applicableTo[0] = BRLENS_CLOCK_UNI; mt->applicableTo[1] = BRLENS_CLOCK_COAL; mt->applicableTo[2] = BRLENS_CLOCK_BD; mt->applicableTo[3] = BRLENS_CLOCK_FOSSIL; mt->nApplicable = 4; mt->moveFxn = &Move_NodeSliderClock; mt->relProposalProb = 30.0; mt->numTuningParams = 1; mt->tuningParam[0] = 0.1; /* window size */ mt->minimum[0] = 0.00001; mt->maximum[0] = 100.0; mt->parsimonyBased = NO; mt->level = STANDARD_USER; mt->Autotune = &AutotuneSlider; mt->targetRate = 0.25; /* Move_NodeSliderGeneTree */ mt = &moveTypes[i++]; mt->name = "Node depth slider for gene trees"; mt->shortName = "NodesliderGenetree"; mt->tuningName[0] = "Window size"; mt->shortTuningName[0] = "delta"; mt->applicableTo[0] = BRLENS_CLOCK_SPCOAL; mt->nApplicable = 1; mt->moveFxn = &Move_NodeSliderGeneTree; mt->relProposalProb = 20.0; mt->numTuningParams = 1; mt->tuningParam[0] = 0.05; /* window size */ mt->minimum[0] = 0.00001; mt->maximum[0] = 100.0; mt->parsimonyBased = NO; mt->level = STANDARD_USER; mt->Autotune = &AutotuneSlider; mt->targetRate = 0.25; /* Move_Omega */ mt = &moveTypes[i++]; mt->name = "Sliding window"; mt->shortName = "Slider"; mt->tuningName[0] = "Sliding window size"; mt->shortTuningName[0] = "delta"; mt->applicableTo[0] = OMEGA_DIR; mt->nApplicable = 1; mt->moveFxn = &Move_Omega; mt->relProposalProb = 1.0; mt->numTuningParams = 1; mt->tuningParam[0] = 1.0; /* sliding window size */ mt->minimum[0] = 0.00001; mt->maximum[0] = 100.0; mt->parsimonyBased = NO; mt->level = STANDARD_USER; mt->Autotune = &AutotuneSlider; mt->targetRate = 0.25; /* Move_Omega_M */ mt = &moveTypes[i++]; mt->name = "Multiplier"; mt->shortName = "Multiplier"; mt->tuningName[0] = "Multiplier tuning parameter"; mt->shortTuningName[0] = "lambda"; mt->applicableTo[0] = OMEGA_DIR; mt->nApplicable = 1; mt->moveFxn = &Move_Omega_M; mt->relProposalProb = 0.0; mt->numTuningParams = 1; mt->tuningParam[0] = 2.0 * log (1.5); /* lambda */ mt->minimum[0] = 0.0001; mt->maximum[0] = 20.0; /* smaller */ mt->parsimonyBased = NO; mt->level = STANDARD_USER; mt->Autotune = &AutotuneMultiplier; mt->targetRate = 0.25; /* Move_OmegaBeta_M */ mt = &moveTypes[i++]; mt->name = "Multiplier"; mt->shortName = "Multiplier"; mt->paramName = "Omega_beta_M10"; mt->tuningName[0] = "Multiplier tuning parameter"; mt->shortTuningName[0] = "lambda"; mt->applicableTo[0] = OMEGA_10UUB; mt->applicableTo[1] = OMEGA_10UUF; mt->applicableTo[2] = OMEGA_10UEB; mt->applicableTo[3] = OMEGA_10UEF; mt->applicableTo[4] = OMEGA_10UFB; mt->applicableTo[5] = OMEGA_10UFF; mt->applicableTo[6] = OMEGA_10EUB; mt->applicableTo[7] = OMEGA_10EUF; mt->applicableTo[8] = OMEGA_10EEB; mt->applicableTo[9] = OMEGA_10EEF; mt->applicableTo[10] = OMEGA_10EFB; mt->applicableTo[11] = OMEGA_10EFF; mt->nApplicable = 12; mt->moveFxn = &Move_OmegaBeta_M; mt->relProposalProb = 1.0; mt->numTuningParams = 1; mt->tuningParam[0] = 2.0 * log (1.1); /* lambda */ mt->minimum[0] = 0.0001; mt->maximum[0] = 20.0; /* smaller */ mt->parsimonyBased = NO; mt->level = STANDARD_USER; mt->Autotune = &AutotuneMultiplier; mt->targetRate = 0.25; /* Move_OmegaCat */ mt = &moveTypes[i++]; mt->name = "Dirichlet proposal"; mt->shortName = "Dirichlet"; mt->paramName = "Omega_pi"; mt->tuningName[0] = "Dirichlet parameter"; mt->shortTuningName[0] = "alpha"; mt->applicableTo[0] = OMEGA_BUD; mt->applicableTo[1] = OMEGA_BED; mt->applicableTo[2] = OMEGA_BFD; mt->applicableTo[3] = OMEGA_FUD; mt->applicableTo[4] = OMEGA_FED; mt->applicableTo[5] = OMEGA_FFD; mt->applicableTo[6] = OMEGA_ED; mt->applicableTo[7] = OMEGA_FD; mt->applicableTo[8] = OMEGA_10UUB; mt->applicableTo[9] = OMEGA_10UEB; mt->applicableTo[10] = OMEGA_10UFB; mt->applicableTo[11] = OMEGA_10EUB; mt->applicableTo[12] = OMEGA_10EEB; mt->applicableTo[13] = OMEGA_10EFB; mt->applicableTo[14] = OMEGA_10FUB; mt->applicableTo[15] = OMEGA_10FEB; mt->applicableTo[16] = OMEGA_10FFB; mt->nApplicable = 17; mt->moveFxn = &Move_OmegaCat; mt->relProposalProb = 1.0; mt->numTuningParams = 1; mt->tuningParam[0] = 300.0; /* alpha-pi */ mt->minimum[0] = 0.001; mt->maximum[0] = 10000.0; mt->parsimonyBased = NO; mt->level = STANDARD_USER; mt->Autotune = &AutotuneDirichlet; mt->targetRate = 0.25; /* Move_OmegaGamma_M */ mt = &moveTypes[i++]; mt->name = "Multiplier"; mt->shortName = "Multiplier"; mt->paramName = "Omega_shape_M10"; mt->tuningName[0] = "Multiplier tuning parameter"; mt->shortTuningName[0] = "lambda"; mt->applicableTo[0] = OMEGA_10UUB; mt->applicableTo[1] = OMEGA_10UUF; mt->applicableTo[2] = OMEGA_10UEB; mt->applicableTo[3] = OMEGA_10UEF; mt->applicableTo[4] = OMEGA_10EUB; mt->applicableTo[5] = OMEGA_10EUF; mt->applicableTo[6] = OMEGA_10EEB; mt->applicableTo[7] = OMEGA_10EEF; mt->applicableTo[8] = OMEGA_10FUB; mt->applicableTo[9] = OMEGA_10FUF; mt->applicableTo[10] = OMEGA_10FEB; mt->applicableTo[11] = OMEGA_10FEF; mt->nApplicable = 12; mt->moveFxn = &Move_OmegaGamma_M; mt->relProposalProb = 1.0; mt->numTuningParams = 1; mt->tuningParam[0] = 2.0 * log (1.1); /* lambda */ mt->minimum[0] = 0.0001; mt->maximum[0] = 20.0; /* smaller */ mt->parsimonyBased = NO; mt->level = STANDARD_USER; mt->Autotune = &AutotuneMultiplier; mt->targetRate = 0.25; /* Move_OmegaM3 */ mt = &moveTypes[i++]; mt->name = "Sliding window"; mt->shortName = "Slider"; mt->tuningName[0] = "Sliding window size"; mt->shortTuningName[0] = "delta"; mt->applicableTo[0] = OMEGA_ED; mt->applicableTo[1] = OMEGA_EF; mt->nApplicable = 2; mt->moveFxn = &Move_OmegaM3; mt->relProposalProb = 1.0; mt->numTuningParams = 1; mt->tuningParam[0] = 0.1; /* window size */ mt->minimum[0] = 0.00001; mt->maximum[0] = 1.0; mt->parsimonyBased = NO; mt->level = STANDARD_USER; mt->Autotune = &AutotuneSlider; mt->targetRate = 0.25; /* Move_OmegaPur : Let it be here so that omega moves are listed in logical order! */ mt = &moveTypes[i++]; mt->name = "Sliding window"; mt->shortName = "Slider"; mt->paramName = "Omega_pur"; mt->tuningName[0] = "Sliding window size"; mt->shortTuningName[0] = "delta"; mt->applicableTo[0] = OMEGA_BUD; mt->applicableTo[1] = OMEGA_BUF; mt->applicableTo[2] = OMEGA_BED; mt->applicableTo[3] = OMEGA_BEF; mt->applicableTo[4] = OMEGA_BFD; mt->applicableTo[5] = OMEGA_BFF; mt->nApplicable = 6; mt->moveFxn = &Move_OmegaPur; mt->relProposalProb = 1.0; mt->numTuningParams = 1; mt->tuningParam[0] = 0.1; /* window size */ mt->minimum[0] = 0.00001; mt->maximum[0] = 1.0; mt->parsimonyBased = NO; mt->level = STANDARD_USER; mt->Autotune = &AutotuneSlider; mt->targetRate = 0.25; /* Move_OmegaPos */ mt = &moveTypes[i++]; mt->name = "Sliding window"; mt->shortName = "Slider"; mt->paramName = "Omega_pos"; mt->tuningName[0] = "Sliding window size"; mt->shortTuningName[0] = "delta"; mt->applicableTo[0] = OMEGA_BUD; mt->applicableTo[1] = OMEGA_BUF; mt->applicableTo[2] = OMEGA_BED; mt->applicableTo[3] = OMEGA_BEF; mt->applicableTo[4] = OMEGA_FUD; mt->applicableTo[5] = OMEGA_FUF; mt->applicableTo[6] = OMEGA_FED; mt->applicableTo[7] = OMEGA_FEF; mt->nApplicable = 8; mt->moveFxn = &Move_OmegaPos; mt->relProposalProb = 1.0; mt->numTuningParams = 1; mt->tuningParam[0] = 1.0; /* window size */ mt->minimum[0] = 0.00001; mt->maximum[0] = 100.0; mt->parsimonyBased = NO; mt->level = STANDARD_USER; mt->Autotune = &AutotuneSlider; mt->targetRate = 0.25; /* Move_ParsEraser1 */ mt = &moveTypes[i++]; mt->name = "Parsimony-biased eraser version 1"; mt->shortName = "pEraser1"; mt->subParams = YES; mt->tuningName[0] = "Dirichlet parameter"; mt->shortTuningName[0] = "alpha"; mt->tuningName[1] = "parsimony warp factor"; mt->shortTuningName[1] = "warp"; mt->applicableTo[0] = TOPOLOGY_NCL_UNIFORM_HOMO; mt->nApplicable = 1; mt->moveFxn = &Move_ParsEraser1; mt->relProposalProb = 0.0; mt->numTuningParams = 2; mt->tuningParam[0] = 0.5; /* alphaPi */ mt->tuningParam[1] = 0.1; /* warp */ mt->minimum[0] = 0.00001; mt->maximum[0] = 10000.0; mt->minimum[1] = 0.00001; mt->maximum[1] = 0.99999; mt->parsimonyBased = YES; mt->level = DEVELOPER; mt->isApplicable = &IsApplicable_FiveTaxaOrMore; /* Move_ParsSPR asym */ mt = &moveTypes[i++]; mt->name = "Parsimony-biased SPR variant 3"; mt->shortName = "ParsSPR3"; mt->subParams = YES; mt->tuningName[0] = "parsimony warp factor"; mt->shortTuningName[0] = "warp"; mt->tuningName[1] = "reweighting probability"; mt->shortTuningName[1] = "r"; mt->tuningName[2] = "typical branch length"; mt->shortTuningName[2] = "v_t"; mt->tuningName[3] = "multiplier tuning parameter"; mt->shortTuningName[3] = "lambda"; mt->applicableTo[0] = TOPOLOGY_NCL_UNIFORM_HOMO; mt->applicableTo[1] = TOPOLOGY_NCL_CONSTRAINED_HOMO; mt->nApplicable = 2; mt->moveFxn = &Move_ParsSPR; mt->relProposalProb = 0.0; /* other variants appear to be more efficient */ mt->numTuningParams = 4; mt->tuningParam[0] = 0.1; /* warp */ mt->tuningParam[1] = 0.05; /* upweight and downweight probability */ mt->tuningParam[2] = 0.03; /* typical branch length */ mt->tuningParam[3] = 2.0 * log (1.05); /* multiplier tuning parameter lambda */ mt->minimum[0] = 0.0; mt->maximum[0] = 1.0; mt->minimum[1] = 0.0; mt->maximum[1] = 0.3; mt->minimum[2] = 0.0001; mt->maximum[2] = 0.5; mt->minimum[3] = 0.0001; mt->maximum[3] = 20.0; mt->parsimonyBased = YES; mt->level = DEVELOPER; mt->isApplicable = &IsApplicable_FourTaxaOrMore; /* Move_ParsSPR1 e^{-S} */ mt = &moveTypes[i++]; mt->name = "Parsimony-biased SPR variant 1"; mt->shortName = "ParsSPR"; mt->subParams = YES; mt->tuningName[0] = "parsimony warp factor"; mt->shortTuningName[0] = "warp"; mt->tuningName[1] = "reweighting probability"; mt->shortTuningName[1] = "r"; mt->tuningName[2] = "multiplier tuning parameter"; mt->shortTuningName[2] = "lambda"; mt->tuningName[3] = "moving distance"; mt->shortTuningName[3] = "d"; mt->applicableTo[0] = TOPOLOGY_NCL_UNIFORM_HOMO; mt->applicableTo[1] = TOPOLOGY_NCL_CONSTRAINED_HOMO; mt->nApplicable = 2; mt->moveFxn = &Move_ParsSPR1; mt->relProposalProb = 3.0; mt->numTuningParams = 4; mt->tuningParam[0] = 0.5; /* warp */ mt->tuningParam[1] = 0.05; /* upweight and downweight probability */ mt->tuningParam[2] = 2.0 * log (1.05); /* multiplier tuning parameter lambda */ mt->tuningParam[3] = 10.0; /* distance to move picked branch */ mt->minimum[0] = 0.0; mt->maximum[0] = 5.0; mt->minimum[1] = 0.0; mt->maximum[1] = 0.3; mt->minimum[2] = 0.0001; mt->maximum[2] = 20.0; mt->minimum[3] = 2.0; mt->maximum[3] = 1000.0; mt->parsimonyBased = YES; mt->level = STANDARD_USER; mt->isApplicable = &IsApplicable_FourTaxaOrMore; /* Move_ParsSPR2 S/N */ mt = &moveTypes[i++]; mt->name = "Parsimony-biased SPR variant 2"; mt->shortName = "ParsSPR2"; mt->subParams = YES; mt->tuningName[0] = "parsimony warp factor"; mt->shortTuningName[0] = "warp"; mt->tuningName[1] = "reweighting probability"; mt->shortTuningName[1] = "r"; mt->tuningName[2] = "typical branch length"; mt->shortTuningName[2] = "v_t"; mt->tuningName[3] = "multiplier tuning parameter"; mt->shortTuningName[3] = "lambda"; mt->tuningName[4] = "moving distance"; mt->shortTuningName[4] = "d"; mt->applicableTo[0] = TOPOLOGY_NCL_UNIFORM_HOMO; mt->applicableTo[1] = TOPOLOGY_NCL_CONSTRAINED_HOMO; mt->nApplicable = 2; mt->moveFxn = &Move_ParsSPR2; mt->relProposalProb = 0.0; mt->numTuningParams = 5; mt->tuningParam[0] = 0.1; /* warp */ mt->tuningParam[1] = 0.05; /* upweight and downweight probability */ mt->tuningParam[2] = 0.03; /* typical branch length */ mt->tuningParam[3] = 2.0 * log (1.05); /* multiplier tuning parameter lambda */ mt->tuningParam[4] = 10.0; /* distance to move picked branch */ mt->minimum[0] = 0.0; mt->maximum[0] = 1.0; mt->minimum[1] = 0.0; mt->maximum[1] = 0.3; mt->minimum[2] = 0.0001; mt->maximum[2] = 0.5; mt->minimum[3] = 0.0001; mt->maximum[3] = 20.0; mt->minimum[4] = 2.0; mt->maximum[4] = 1000.0; mt->parsimonyBased = YES; mt->level = DEVELOPER; mt->isApplicable = &IsApplicable_FourTaxaOrMore; /* Move_ParsSPRClock */ mt = &moveTypes[i++]; mt->name = "Parsimony-biased SPR for clock trees"; mt->shortName = "ParsSPRClock"; mt->subParams = YES; mt->tuningName[0] = "parsimony warp factor"; mt->shortTuningName[0] = "warp"; mt->applicableTo[0] = TOPOLOGY_CL_UNIFORM; mt->applicableTo[1] = TOPOLOGY_CCL_UNIFORM; mt->applicableTo[2] = TOPOLOGY_CL_CONSTRAINED; mt->applicableTo[3] = TOPOLOGY_CCL_CONSTRAINED; mt->applicableTo[4] = TOPOLOGY_RCL_UNIFORM; mt->applicableTo[5] = TOPOLOGY_RCL_CONSTRAINED; mt->applicableTo[6] = TOPOLOGY_RCCL_UNIFORM; mt->applicableTo[7] = TOPOLOGY_RCCL_CONSTRAINED; mt->nApplicable = 8; mt->moveFxn = &Move_ParsSPRClock; mt->relProposalProb = 10.0; mt->numTuningParams = 1; mt->tuningParam[0] = 0.1; /* warp */ mt->minimum[0] = 0.0; mt->maximum[0] = 1.0; mt->parsimonyBased = YES; mt->level = STANDARD_USER; mt->isApplicable = &IsApplicable_ThreeTaxaOrMore; /* Move_ParsSPRClock_Fossil */ mt = &moveTypes[i++]; mt->name = "Parsimony-biased fossil SPR for clock trees"; mt->shortName = "ParsSPRClockFossil"; mt->subParams = YES; mt->tuningName[0] = "parsimony warp factor"; mt->shortTuningName[0] = "warp"; mt->applicableTo[0] = TOPOLOGY_CL_UNIFORM; mt->applicableTo[1] = TOPOLOGY_CCL_UNIFORM; mt->applicableTo[2] = TOPOLOGY_CL_CONSTRAINED; mt->applicableTo[3] = TOPOLOGY_CCL_CONSTRAINED; mt->applicableTo[4] = TOPOLOGY_RCL_UNIFORM; mt->applicableTo[5] = TOPOLOGY_RCL_CONSTRAINED; mt->applicableTo[6] = TOPOLOGY_RCCL_UNIFORM; mt->applicableTo[7] = TOPOLOGY_RCCL_CONSTRAINED; mt->nApplicable = 8; mt->moveFxn = &Move_ParsSPRClock_Fossil; mt->relProposalProb = 0.0; mt->numTuningParams = 1; mt->tuningParam[0] = 0.1; /* warp */ mt->minimum[0] = 0.0; mt->maximum[0] = 1.0; mt->parsimonyBased = YES; mt->level = DEVELOPER; mt->isApplicable = &IsApplicable_ThreeTaxaOrMore; /* Move_ParsTBR1 e^{-S} */ mt = &moveTypes[i++]; mt->name = "Parsimony-biased TBR variant 1"; mt->shortName = "ParsTBR"; mt->subParams = YES; mt->tuningName[0] = "parsimony warp factor"; mt->shortTuningName[0] = "warp"; mt->tuningName[1] = "reweighting probability"; mt->shortTuningName[1] = "r"; mt->tuningName[2] = "multiplier tuning parameter"; mt->shortTuningName[2] = "lambda"; mt->tuningName[3] = "moving distance"; mt->shortTuningName[3] = "d"; mt->applicableTo[0] = TOPOLOGY_NCL_UNIFORM_HOMO; mt->applicableTo[1] = TOPOLOGY_NCL_CONSTRAINED_HOMO; mt->nApplicable = 2; mt->moveFxn = &Move_ParsTBR1; mt->relProposalProb = 3.0; mt->numTuningParams = 4; mt->tuningParam[0] = 0.5; /* warp */ mt->tuningParam[1] = 0.05; /* upweight and downweight probability */ mt->tuningParam[2] = 2.0 * log (1.05); /* multiplier tuning parameter lambda */ mt->tuningParam[3] = 5.0; /* distance to move picked branch */ mt->minimum[0] = 0.0; mt->maximum[0] = 5.0; mt->minimum[1] = 0.0; mt->maximum[1] = 0.3; mt->minimum[2] = 0.0001; mt->maximum[2] = 20.0; mt->minimum[3] = 2.0; mt->maximum[3] = 1000.0; mt->parsimonyBased = YES; mt->level = STANDARD_USER; mt->isApplicable = &IsApplicable_FiveTaxaOrMore; /* Move_ParsTBR2 S/N */ mt = &moveTypes[i++]; mt->name = "Parsimony-biased TBR variant 2"; mt->shortName = "ParsTBR2"; mt->subParams = YES; mt->tuningName[0] = "parsimony warp factor"; mt->shortTuningName[0] = "warp"; mt->tuningName[1] = "reweighting probability"; mt->shortTuningName[1] = "r"; mt->tuningName[2] = "typical branch length"; mt->shortTuningName[2] = "v_t"; mt->tuningName[3] = "multiplier tuning parameter"; mt->shortTuningName[3] = "lambda"; mt->tuningName[4] = "moving distance"; mt->shortTuningName[4] = "d"; mt->applicableTo[0] = TOPOLOGY_NCL_UNIFORM_HOMO; mt->applicableTo[1] = TOPOLOGY_NCL_CONSTRAINED_HOMO; mt->nApplicable = 2; mt->moveFxn = &Move_ParsTBR2; mt->relProposalProb = 0.0; mt->numTuningParams = 5; mt->tuningParam[0] = 0.1; /* warp */ mt->tuningParam[1] = 0.05; /* upweight and downweight probability */ mt->tuningParam[2] = 0.05; /* typical branch length */ mt->tuningParam[3] = 2.0 * log (1.05); /* multiplier tuning parameter lambda */ mt->tuningParam[4] = 5.0; /* distance to move picked branch */ mt->minimum[0] = 0.0; mt->maximum[0] = 1.0; mt->minimum[1] = 0.0; mt->maximum[1] = 0.3; mt->minimum[2] = 0.0001; mt->maximum[2] = 0.5; mt->minimum[3] = 0.0001; mt->maximum[3] = 20.0; mt->minimum[4] = 2.0; mt->maximum[4] = 1000.0; mt->parsimonyBased = YES; mt->level = DEVELOPER; mt->isApplicable = &IsApplicable_FiveTaxaOrMore; /* Move_Pinvar */ mt = &moveTypes[i++]; mt->name = "Sliding window"; mt->shortName = "Slider"; mt->tuningName[0] = "Sliding window size"; mt->shortTuningName[0] = "delta"; mt->applicableTo[0] = PINVAR_UNI; mt->nApplicable = 1; mt->moveFxn = &Move_Pinvar; mt->relProposalProb = 1.0; mt->numTuningParams = 1; mt->tuningParam[0] = 0.1; /* window size */ mt->minimum[0] = 0.001; mt->maximum[0] = 0.999; mt->parsimonyBased = NO; mt->level = STANDARD_USER; mt->Autotune = &AutotuneSlider; mt->targetRate = 0.25; /* Move_Popsize_M */ mt = &moveTypes[i++]; mt->name = "Multiplier"; mt->shortName = "Multiplier"; mt->tuningName[0] = "Multiplier tuning parameter"; mt->shortTuningName[0] = "lambda"; mt->applicableTo[0] = POPSIZE_UNI; mt->applicableTo[1] = POPSIZE_LOGNORMAL; mt->applicableTo[2] = POPSIZE_NORMAL; mt->applicableTo[3] = POPSIZE_GAMMA; mt->nApplicable = 4; mt->moveFxn = &Move_PopSize_M; mt->relProposalProb = 1.0; mt->numTuningParams = 1; mt->tuningParam[0] = 2.0 * log(1.5); /* lambda */ mt->minimum[0] = 0.0001; mt->maximum[0] = 20.0; mt->parsimonyBased = NO; mt->level = STANDARD_USER; mt->Autotune = &AutotuneMultiplier; mt->targetRate = 0.25; /* Move_RateMult_Dir */ mt = &moveTypes[i++]; mt->name = "Dirichlet proposal"; mt->shortName = "Dirichlet"; mt->tuningName[0] = "Dirichlet parameter"; mt->shortTuningName[0] = "alpha"; mt->applicableTo[0] = RATEMULT_DIR; mt->nApplicable = 1; mt->moveFxn = &Move_RateMult_Dir; mt->relProposalProb = 0.75; mt->numTuningParams = 1; mt->tuningParam[0] = 50.0; /* alphaPi per site */ mt->minimum[0] = 0.001; mt->maximum[0] = 10000.0; mt->parsimonyBased = NO; mt->level = STANDARD_USER; mt->Autotune = &AutotuneDirichlet; mt->targetRate = 0.25; /* Move_RateMult_Slider */ mt = &moveTypes[i++]; mt->name = "Sliding window"; mt->shortName = "Slider"; mt->tuningName[0] = "Sliding window size"; mt->shortTuningName[0] = "delta"; mt->applicableTo[0] = RATEMULT_DIR; mt->nApplicable = 1; mt->moveFxn = &Move_RateMult_Slider; mt->relProposalProb = 0.75; mt->numTuningParams = 1; mt->tuningParam[0] = 0.05; /* window size */ mt->minimum[0] = 0.00001; mt->maximum[0] = 100.0; mt->parsimonyBased = NO; mt->level = STANDARD_USER; mt->Autotune = &AutotuneSlider; mt->targetRate = 0.25; /* Move_RateShape_M */ mt = &moveTypes[i++]; mt->name = "Multiplier"; mt->shortName = "Multiplier"; mt->tuningName[0] = "Multiplier tuning parameter"; mt->shortTuningName[0] = "lambda"; mt->applicableTo[0] = SHAPE_UNI; mt->applicableTo[1] = SHAPE_EXP; mt->nApplicable = 2; mt->moveFxn = &Move_RateShape_M; mt->relProposalProb = 1.0; mt->numTuningParams = 1; mt->tuningParam[0] = 2.0 * log (1.5); /* lambda */ mt->minimum[0] = 0.0001; mt->maximum[0] = 10.0; /* smaller */ mt->parsimonyBased = NO; mt->level = STANDARD_USER; mt->Autotune = &AutotuneMultiplier; mt->targetRate = 0.25; /* Move_Revmat_Dir */ mt = &moveTypes[i++]; mt->name = "Dirichlet proposal"; mt->shortName = "Dirichlet"; mt->tuningName[0] = "Dirichlet parameter"; mt->shortTuningName[0] = "alpha"; mt->applicableTo[0] = REVMAT_DIR; mt->nApplicable = 1; mt->moveFxn = &Move_Revmat_Dir; mt->relProposalProb = 0.5; mt->numTuningParams = 1; mt->tuningParam[0] = 100.0; /* alphaPi per rate */ mt->minimum[0] = 0.001; mt->maximum[0] = 10000.0; mt->parsimonyBased = NO; mt->level = STANDARD_USER; mt->Autotune = &AutotuneDirichlet; mt->targetRate = 0.25; /* Move_Revmat_Slider */ mt = &moveTypes[i++]; mt->name = "Sliding window"; mt->shortName = "Slider"; mt->tuningName[0] = "Sliding window size"; mt->shortTuningName[0] = "delta"; mt->applicableTo[0] = REVMAT_DIR; mt->nApplicable = 1; mt->moveFxn = &Move_Revmat_Slider; mt->relProposalProb = 0.5; mt->numTuningParams = 1; mt->tuningParam[0] = 0.15; /* window size */ mt->minimum[0] = 0.00001; mt->maximum[0] = 100.0; mt->parsimonyBased = NO; mt->level = STANDARD_USER; mt->Autotune = &AutotuneSlider; mt->targetRate = 0.25; /* Move_Revmat_DirMix */ mt = &moveTypes[i++]; mt->name = "Dirichlet proposal"; mt->shortName = "Dirichlet"; mt->tuningName[0] = "Dirichlet parameter"; mt->shortTuningName[0] = "alpha"; mt->applicableTo[0] = REVMAT_MIX; mt->nApplicable = 1; mt->moveFxn = &Move_Revmat_DirMix; mt->relProposalProb = 1.0; mt->numTuningParams = 1; mt->tuningParam[0] = 100.0; /* alphaPi per rate */ mt->minimum[0] = 0.01; mt->maximum[0] = 10000.0; mt->parsimonyBased = NO; mt->level = STANDARD_USER; mt->Autotune = &AutotuneDirichlet; mt->targetRate = 0.25; /* Move_Revmat_SplitMerge1 */ mt = &moveTypes[i++]; mt->name = "Split-merge move 1"; mt->shortName = "Splitmerge1"; mt->tuningName[0] = "Dirichlet parameter"; mt->shortTuningName[0] = "alpha"; mt->applicableTo[0] = REVMAT_MIX; mt->nApplicable = 1; mt->moveFxn = &Move_Revmat_SplitMerge1; mt->relProposalProb = 1.0; mt->numTuningParams = 1; mt->tuningParam[0] = 10.0; /* alphaPi per rate */ mt->minimum[0] = 0.5; mt->maximum[0] = 100.0; mt->parsimonyBased = NO; mt->level = STANDARD_USER; mt->Autotune = &AutotuneDirichlet; mt->targetRate = 0.25; /* Move_Revmat_SplitMerge2 */ mt = &moveTypes[i++]; mt->name = "Split-merge move 2"; mt->shortName = "Splitmerge2"; mt->tuningName[0] = "Dirichlet parameter"; mt->shortTuningName[0] = "alpha"; mt->applicableTo[0] = REVMAT_MIX; mt->nApplicable = 1; mt->moveFxn = &Move_Revmat_SplitMerge2; mt->relProposalProb = 1.0; mt->numTuningParams = 1; mt->tuningParam[0] = 10.0; /* alphaPi per rate */ mt->minimum[0] = 0.5; mt->maximum[0] = 100.0; mt->parsimonyBased = NO; mt->level = STANDARD_USER; mt->Autotune = &AutotuneDirichlet; mt->targetRate = 0.25; /* Move_Speciation */ mt = &moveTypes[i++]; mt->name = "Sliding window"; mt->shortName = "Slider"; mt->tuningName[0] = "Sliding window size"; mt->shortTuningName[0] = "delta"; mt->applicableTo[0] = SPECRATE_UNI; mt->applicableTo[1] = SPECRATE_EXP; mt->nApplicable = 2; mt->moveFxn = &Move_Speciation; mt->relProposalProb = 3.0; mt->numTuningParams = 1; mt->tuningParam[0] = 0.1; /* window size */ mt->minimum[0] = 0.00001; mt->maximum[0] = 100.0; mt->parsimonyBased = NO; mt->level = STANDARD_USER; mt->Autotune = &AutotuneSlider; mt->targetRate = 0.25; /* Move_Speciation_M */ mt = &moveTypes[i++]; mt->name = "Multiplier"; mt->shortName = "Multiplier"; mt->tuningName[0] = "Multiplier tuning parameter"; mt->shortTuningName[0] = "lambda"; mt->applicableTo[0] = SPECRATE_UNI; mt->applicableTo[1] = SPECRATE_EXP; mt->nApplicable = 2; mt->moveFxn = &Move_Speciation_M; mt->relProposalProb = 0.0; mt->numTuningParams = 1; mt->tuningParam[0] = 2.0 * log (1.5); /* lambda */ mt->minimum[0] = 0.0001; mt->maximum[0] = 20.0; /* smaller */ mt->parsimonyBased = NO; mt->level = STANDARD_USER; mt->Autotune = &AutotuneMultiplier; mt->targetRate = 0.25; /* Move_SpeciesTree */ mt = &moveTypes[i++]; mt->name = "Species tree move"; mt->shortName = "Distmatrixmove"; mt->tuningName[0] = "Divider of rate of truncated exponential"; mt->shortTuningName[0] = "lambdadiv"; mt->applicableTo[0] = SPECIESTREE_UNIFORM; mt->nApplicable = 1; mt->moveFxn = &Move_SpeciesTree; mt->relProposalProb = 10.0; mt->numTuningParams = 1; mt->tuningParam[0] = 1.2; /* Default tuning parameter value */ mt->minimum[0] = 0.0001; /* Minimum value of tuning param */ mt->maximum[0] = 20.0; /* Maximum value of tuning param */ mt->parsimonyBased = NO; /* It does not use parsimony scores */ mt->level = STANDARD_USER; mt->Autotune = &AutotuneMultiplier; /* Autotune this move as a multiplier move (larger is more bold) */ mt->targetRate = 0.25; /* Target acceptance rate */ /* Move_Statefreqs */ mt = &moveTypes[i++]; mt->name = "Dirichlet proposal"; mt->shortName = "Dirichlet"; mt->tuningName[0] = "Dirichlet parameter"; mt->shortTuningName[0] = "alpha"; mt->applicableTo[0] = PI_DIR; mt->applicableTo[1] = DIRPI_DIRxDIR; mt->applicableTo[2] = DIRPI_DIRxFIXED; mt->applicableTo[3] = DIRPI_MIX; mt->nApplicable = 4; mt->moveFxn = &Move_Statefreqs; mt->relProposalProb = 0.5; mt->numTuningParams = 1; mt->tuningParam[0] = 100.0; /* alphaPi per state */ mt->minimum[0] = 0.001; mt->maximum[0] = 10000.0; mt->parsimonyBased = NO; mt->level = STANDARD_USER; mt->Autotune = &AutotuneDirichlet; mt->targetRate = 0.25; /* Move_Statefreqs_Slider */ mt = &moveTypes[i++]; mt->name = "Sliding window"; mt->shortName = "Slider"; mt->tuningName[0] = "Sliding window size"; mt->shortTuningName[0] = "delta"; mt->applicableTo[0] = PI_DIR; mt->applicableTo[1] = DIRPI_DIRxDIR; mt->applicableTo[2] = DIRPI_DIRxFIXED; mt->applicableTo[3] = DIRPI_MIX; mt->nApplicable = 4; mt->moveFxn = &Move_Statefreqs_Slider; mt->relProposalProb = 0.5; mt->numTuningParams = 1; mt->tuningParam[0] = 0.2; /* window size (change in proportions) */ mt->minimum[0] = 0.00001; mt->maximum[0] = 1.0; mt->parsimonyBased = NO; mt->level = STANDARD_USER; mt->Autotune = &AutotuneSlider; mt->targetRate = 0.25; /* Move_StatefreqsRoot */ mt = &moveTypes[i++]; mt->name = "Dirichlet proposal (root)"; mt->shortName = "Dirichlet_root"; mt->tuningName[0] = "Dirichlet parameter"; mt->shortTuningName[0] = "alpha"; mt->applicableTo[0] = DIRPI_DIRxDIR; mt->applicableTo[1] = DIRPI_FIXEDxDIR; mt->applicableTo[2] = DIRPI_MIX; mt->nApplicable = 3; mt->moveFxn = &Move_StatefreqsRoot; mt->relProposalProb = 0.5; mt->numTuningParams = 1; mt->tuningParam[0] = 100.0; /* alphaPi per state */ mt->minimum[0] = 0.001; mt->maximum[0] = 10000.0; mt->parsimonyBased = NO; mt->level = STANDARD_USER; mt->Autotune = &AutotuneDirichlet; mt->targetRate = 0.25; /* Move_StatefreqsRoot_Slider */ mt = &moveTypes[i++]; mt->name = "Sliding window (root)"; mt->shortName = "Slider_root"; mt->tuningName[0] = "Sliding window size"; mt->shortTuningName[0] = "delta"; mt->applicableTo[0] = DIRPI_DIRxDIR; mt->applicableTo[1] = DIRPI_FIXEDxDIR; mt->applicableTo[2] = DIRPI_MIX; mt->nApplicable = 3; mt->moveFxn = &Move_StatefreqsRoot_Slider; mt->relProposalProb = 0.5; mt->numTuningParams = 1; mt->tuningParam[0] = 0.2; /* window size (change in proportions) */ mt->minimum[0] = 0.00001; mt->maximum[0] = 1.0; mt->parsimonyBased = NO; mt->level = STANDARD_USER; mt->Autotune = &AutotuneSlider; mt->targetRate = 0.25; /* Move_Statefreqs_SplitMerge */ mt = &moveTypes[i++]; mt->name = "RJ between stationary and directional model"; mt->shortName = "RJ_Stat-Dir"; mt->paramName = "Statefrmod"; mt->applicableTo[0] = DIRPI_MIX; mt->nApplicable = 1; mt->moveFxn = &Move_Statefreqs_SplitMerge; mt->relProposalProb = 0.5; mt->numTuningParams = 1; mt->tuningParam[0] = 100.0; /* alphaPi per state */ mt->minimum[0] = 0.001; mt->maximum[0] = 10000.0; mt->parsimonyBased = NO; mt->level = STANDARD_USER; mt->Autotune = &AutotuneDirichlet; mt->targetRate = 0.25; /* Move_StatefreqsSymDirMultistate */ mt = &moveTypes[i++]; mt->name = "Dirichlet proposal"; mt->shortName = "Dirichlet"; mt->paramName = "Pi_symdir"; mt->tuningName[0] = "Dirichlet parameter"; mt->shortTuningName[0] = "alpha"; mt->applicableTo[0] = SYMPI_FIX_MS; mt->applicableTo[1] = SYMPI_UNI_MS; mt->applicableTo[2] = SYMPI_EXP_MS; mt->nApplicable = 3; mt->moveFxn = &Move_StatefreqsSymDirMultistate; mt->relProposalProb = 5.0; mt->numTuningParams = 1; mt->tuningParam[0] = 50.0; /* alphaPi */ mt->minimum[0] = 0.001; mt->maximum[0] = 10000.0; mt->parsimonyBased = NO; mt->level = STANDARD_USER; mt->Autotune = &AutotuneDirichlet; mt->targetRate = 0.25; /* Move_SwitchRate */ mt = &moveTypes[i++]; mt->name = "Sliding window"; mt->shortName = "Slider"; mt->tuningName[0] = "Sliding window size"; mt->shortTuningName[0] = "delta"; mt->applicableTo[0] = SWITCH_UNI; mt->applicableTo[1] = SWITCH_EXP; mt->nApplicable = 2; mt->moveFxn = &Move_SwitchRate; mt->relProposalProb = 1.0; mt->numTuningParams = 1; mt->tuningParam[0] = 1.0; /* window size */ mt->minimum[0] = 0.00001; mt->maximum[0] = 100.0; mt->parsimonyBased = NO; mt->level = STANDARD_USER; mt->Autotune = &AutotuneSlider; mt->targetRate = 0.25; /* Move_SwitchRate_M */ mt = &moveTypes[i++]; mt->name = "Multiplier"; mt->shortName = "Multiplier"; mt->tuningName[0] = "Multiplier tuning parameter"; mt->shortTuningName[0] = "lambda"; mt->applicableTo[0] = SWITCH_UNI; mt->applicableTo[1] = SWITCH_EXP; mt->nApplicable = 2; mt->moveFxn = &Move_SwitchRate_M; mt->relProposalProb = 0.0; mt->numTuningParams = 1; mt->tuningParam[0] = 2.0 * log (1.5); /* lambda */ mt->minimum[0] = 0.0001; mt->maximum[0] = 20.0; /* smaller */ mt->parsimonyBased = NO; mt->level = STANDARD_USER; mt->Autotune = &AutotuneMultiplier; mt->targetRate = 0.25; /* Move_Tratio_Dir */ mt = &moveTypes[i++]; mt->name = "Dirichlet proposal"; mt->shortName = "Dirichlet"; mt->tuningName[0] = "Dirichlet parameter"; mt->shortTuningName[0] = "alpha"; mt->applicableTo[0] = TRATIO_DIR; mt->nApplicable = 1; mt->moveFxn = &Move_Tratio_Dir; mt->relProposalProb = 1.0; mt->numTuningParams = 1; mt->tuningParam[0] = 50.0; /* alphaPi */ mt->minimum[0] = 0.001; mt->maximum[0] = 10000.0; mt->parsimonyBased = NO; mt->level = STANDARD_USER; mt->Autotune = &AutotuneDirichlet; mt->targetRate = 0.25; /* Move_TreeStretch */ mt = &moveTypes[i++]; mt->name = "Tree stretch"; mt->shortName = "TreeStretch"; mt->tuningName[0] = "Multiplier tuning parameter"; mt->shortTuningName[0] = "lambda"; mt->applicableTo[0] = BRLENS_CLOCK_UNI; mt->applicableTo[1] = BRLENS_CLOCK_BD; mt->applicableTo[2] = BRLENS_CLOCK_COAL; mt->applicableTo[3] = BRLENS_CLOCK_FOSSIL; mt->nApplicable = 4; mt->moveFxn = &Move_TreeStretch; mt->relProposalProb = 3.0; mt->numTuningParams = 1; mt->tuningParam[0] = 2.0 * log(1.01); /* lambda */ mt->minimum[0] = 0.0001; mt->maximum[0] = 2.0 * log(2.0); mt->parsimonyBased = NO; mt->level = STANDARD_USER; mt->Autotune = &AutotuneMultiplier; mt->targetRate = 0.25; /* Move_TreeLen, by Jeremy Brown */ mt = &moveTypes[i++]; mt->name = "Whole treelength hit with multiplier"; mt->shortName = "TLMultiplier"; mt->tuningName[0] = "Multiplier tuning parameter"; mt->shortTuningName[0] = "lambda"; mt->applicableTo[0] = BRLENS_UNI; mt->applicableTo[1] = BRLENS_EXP; mt->applicableTo[2] = BRLENS_GamDir; mt->applicableTo[3] = BRLENS_iGmDir; mt->applicableTo[4] = BRLENS_twoExp; mt->nApplicable = 5; // was 2 mt->moveFxn = &Move_TreeLen; mt->relProposalProb = 3.0; mt->numTuningParams = 1; mt->tuningParam[0] = 2.0 * log (2.0); /* lambda */ mt->minimum[0] = 0.0001; mt->maximum[0] = 20.0; /* smaller */ mt->parsimonyBased = NO; mt->level = STANDARD_USER; mt->Autotune = &AutotuneMultiplier; mt->targetRate = 0.25; /* Move_AddDeleteCPPEvent */ mt = &moveTypes[i++]; mt->name = "Random addition/deletion of CPP event"; mt->shortName = "Add_delete"; mt->applicableTo[0] = CPPEVENTS; mt->nApplicable = 1; mt->moveFxn = &Move_AddDeleteCPPEvent; mt->relProposalProb = 1.0; mt->numTuningParams = 0; mt->parsimonyBased = NO; mt->level = STANDARD_USER; /* Move_CPPEventPosition */ mt = &moveTypes[i++]; mt->name = "Random draw of CPP event position from prior"; mt->shortName = "Prior_draw_pos"; mt->applicableTo[0] = CPPEVENTS; mt->nApplicable = 1; mt->moveFxn = &Move_CPPEventPosition; mt->relProposalProb = 2.0; mt->numTuningParams = 0; mt->parsimonyBased = NO; mt->level = STANDARD_USER; /* Move_CPPRate */ mt = &moveTypes[i++]; mt->name = "Multiplier"; mt->shortName = "Multiplier"; mt->tuningName[0] = "Multiplier tuning parameter"; mt->shortTuningName[0] = "lambda"; mt->applicableTo[0] = CPPRATE_EXP; mt->nApplicable = 1; mt->moveFxn = &Move_CPPRate; mt->relProposalProb = 2.0; mt->numTuningParams = 1; mt->tuningParam[0] = 2.0 * log (1.1); /* lambda */ mt->minimum[0] = 0.0001; mt->maximum[0] = 20.0; /* smaller */ mt->parsimonyBased = NO; mt->level = STANDARD_USER; mt->Autotune = &AutotuneMultiplier; mt->targetRate = 0.25; /* Move_CPPRateMultiplier_M */ mt = &moveTypes[i++]; mt->name = "Random CPP rate multiplier hit with multiplier"; mt->shortName = "Multiplier"; mt->tuningName[0] = "Multiplier tuning parameter"; mt->shortTuningName[0] = "lambda"; mt->applicableTo[0] = CPPEVENTS; mt->nApplicable = 1; mt->moveFxn = &Move_CPPRateMultiplier_M; mt->relProposalProb = 0.0; mt->numTuningParams = 1; mt->tuningParam[0] = 2.0 * log (1.1); /* lambda */ mt->minimum[0] = 0.0001; mt->maximum[0] = 20.0; /* smaller */ mt->parsimonyBased = NO; mt->level = STANDARD_USER; mt->Autotune = &AutotuneMultiplier; mt->targetRate = 0.25; /* Move_CPPRateMultiplierRnd */ mt = &moveTypes[i++]; mt->name = "Random draw of CPP rate multiplier from prior"; mt->shortName = "Prior_draw_mult"; mt->applicableTo[0] = CPPEVENTS; mt->nApplicable = 1; mt->moveFxn = &Move_CPPRateMultiplierRnd; mt->relProposalProb = 2.0; mt->numTuningParams = 0; mt->parsimonyBased = NO; mt->level = STANDARD_USER; /* Move_Nu */ mt = &moveTypes[i++]; mt->name = "Multiplier"; mt->shortName = "Multiplier"; mt->tuningName[0] = "Multiplier tuning parameter"; mt->shortTuningName[0] = "lambda"; mt->applicableTo[0] = TK02VAR_EXP; mt->applicableTo[1] = TK02VAR_UNI; mt->nApplicable = 2; mt->moveFxn = &Move_Nu; mt->relProposalProb = 2.0; mt->numTuningParams = 1; mt->tuningParam[0] = 2.0 * log (1.5); /* lambda */ mt->minimum[0] = 0.0001; mt->maximum[0] = 20.0; /* smaller */ mt->parsimonyBased = NO; mt->level = STANDARD_USER; mt->Autotune = &AutotuneMultiplier; mt->targetRate = 0.25; /* Move_TK02BranchRate */ mt = &moveTypes[i++]; mt->name = "Multiplier"; mt->shortName = "Multiplier"; mt->tuningName[0] = "Multiplier tuning parameter"; mt->shortTuningName[0] = "lambda"; mt->applicableTo[0] = TK02BRANCHRATES; mt->nApplicable = 1; mt->moveFxn = &Move_TK02BranchRate; mt->relProposalProb = 25.0; mt->numTuningParams = 1; mt->tuningParam[0] = 2.0 * log (1.1); /* lambda */ mt->minimum[0] = 0.0001; mt->maximum[0] = 20.0; mt->parsimonyBased = NO; mt->level = STANDARD_USER; mt->Autotune = &AutotuneMultiplier; mt->targetRate = 0.25; /* Move_WNVar */ mt = &moveTypes[i++]; mt->name = "Multiplier"; mt->shortName = "Multiplier"; mt->tuningName[0] = "Multiplier tuning parameter"; mt->shortTuningName[0] = "lambda"; mt->applicableTo[0] = WNVAR_EXP; mt->applicableTo[1] = WNVAR_UNI; mt->nApplicable = 2; mt->moveFxn = &Move_WNVar; mt->relProposalProb = 2.0; mt->numTuningParams = 1; mt->tuningParam[0] = 2.0 * log (1.5); /* lambda */ mt->minimum[0] = 0.0001; mt->maximum[0] = 20.0; /* smaller */ mt->parsimonyBased = NO; mt->level = STANDARD_USER; mt->Autotune = &AutotuneMultiplier; mt->targetRate = 0.25; /* Move_WNBranchRate */ mt = &moveTypes[i++]; mt->name = "Multiplier"; mt->shortName = "Multiplier"; mt->tuningName[0] = "Multiplier tuning parameter"; mt->shortTuningName[0] = "lambda"; mt->applicableTo[0] = WNBRANCHRATES; mt->nApplicable = 1; mt->moveFxn = &Move_WNBranchRate; mt->relProposalProb = 25.0; mt->numTuningParams = 1; mt->tuningParam[0] = 2.0 * log (1.1); /* lambda */ mt->minimum[0] = 0.0001; mt->maximum[0] = 20.0; mt->parsimonyBased = NO; mt->level = STANDARD_USER; mt->Autotune = &AutotuneMultiplier; mt->targetRate = 0.25; /* Move_IgrVar */ mt = &moveTypes[i++]; mt->name = "Multiplier"; mt->shortName = "Multiplier"; mt->tuningName[0] = "Multiplier tuning parameter"; mt->shortTuningName[0] = "lambda"; mt->applicableTo[0] = IGRVAR_EXP; mt->applicableTo[1] = IGRVAR_UNI; mt->nApplicable = 2; mt->moveFxn = &Move_IgrVar; mt->relProposalProb = 2.0; mt->numTuningParams = 1; mt->tuningParam[0] = 2.0 * log (1.5); /* lambda */ mt->minimum[0] = 0.0001; mt->maximum[0] = 20.0; /* smaller */ mt->parsimonyBased = NO; mt->level = STANDARD_USER; mt->Autotune = &AutotuneMultiplier; mt->targetRate = 0.25; /* Move_IgrBranchRate */ mt = &moveTypes[i++]; mt->name = "Multiplier"; mt->shortName = "Multiplier"; mt->tuningName[0] = "Multiplier tuning parameter"; mt->shortTuningName[0] = "lambda"; mt->applicableTo[0] = IGRBRANCHRATES; mt->nApplicable = 1; mt->moveFxn = &Move_IgrBranchRate; mt->relProposalProb = 25.0; mt->numTuningParams = 1; mt->tuningParam[0] = 2.0 * log (1.1); /* lambda */ mt->minimum[0] = 0.0001; mt->maximum[0] = 20.0; mt->parsimonyBased = NO; mt->level = STANDARD_USER; mt->Autotune = &AutotuneMultiplier; mt->targetRate = 0.25; /* Move_IlnVar */ mt = &moveTypes[i++]; mt->name = "Multiplier"; mt->shortName = "Multiplier"; mt->tuningName[0] = "Multiplier tuning parameter"; mt->shortTuningName[0] = "lambda"; mt->applicableTo[0] = ILNVAR_EXP; mt->applicableTo[1] = ILNVAR_UNI; mt->nApplicable = 2; mt->moveFxn = &Move_IlnVar; mt->relProposalProb = 2.0; mt->numTuningParams = 1; mt->tuningParam[0] = 2.0 * log (1.5); /* lambda */ mt->minimum[0] = 0.0001; mt->maximum[0] = 20.0; /* smaller */ mt->parsimonyBased = NO; mt->level = STANDARD_USER; mt->Autotune = &AutotuneMultiplier; mt->targetRate = 0.25; /* Move_IlnBranchRate */ mt = &moveTypes[i++]; mt->name = "Multiplier"; mt->shortName = "Multiplier"; mt->tuningName[0] = "Multiplier tuning parameter"; mt->shortTuningName[0] = "lambda"; mt->applicableTo[0] = ILNBRANCHRATES; mt->nApplicable = 1; mt->moveFxn = &Move_IlnBranchRate; mt->relProposalProb = 25.0; mt->numTuningParams = 1; mt->tuningParam[0] = 2.0 * log (1.1); /* lambda */ mt->minimum[0] = 0.0001; mt->maximum[0] = 20.0; mt->parsimonyBased = NO; mt->level = STANDARD_USER; mt->Autotune = &AutotuneMultiplier; mt->targetRate = 0.25; /* Move_MixedVar */ mt = &moveTypes[i++]; mt->name = "Multiplier"; mt->shortName = "Multiplier"; mt->tuningName[0] = "Multiplier tuning parameter"; mt->shortTuningName[0] = "lambda"; mt->applicableTo[0] = MIXEDVAR_EXP; mt->applicableTo[1] = MIXEDVAR_UNI; mt->nApplicable = 2; mt->moveFxn = &Move_MixedVar; mt->relProposalProb = 2.0; mt->numTuningParams = 1; mt->tuningParam[0] = 2.0 * log (1.5); /* lambda */ mt->minimum[0] = 0.0001; mt->maximum[0] = 20.0; /* smaller */ mt->parsimonyBased = NO; mt->level = STANDARD_USER; mt->Autotune = &AutotuneMultiplier; mt->targetRate = 0.25; /* Move_MixedBranchRate */ mt = &moveTypes[i++]; mt->name = "Multiplier"; mt->shortName = "Multiplier"; mt->tuningName[0] = "Multiplier tuning parameter"; mt->shortTuningName[0] = "lambda"; mt->applicableTo[0] = MIXEDBRCHRATES; mt->nApplicable = 1; mt->moveFxn = &Move_MixedBranchRate; mt->relProposalProb = 25.0; mt->numTuningParams = 1; mt->tuningParam[0] = 2.0 * log (1.1); /* lambda */ mt->minimum[0] = 0.0001; mt->maximum[0] = 20.0; mt->parsimonyBased = NO; mt->level = STANDARD_USER; mt->Autotune = &AutotuneMultiplier; mt->targetRate = 0.25; /* Move_RelaxedClockModel */ mt = &moveTypes[i++]; mt->name = "rjMCMC among relaxed clock models"; mt->shortName = "RJ_Clocks"; mt->tuningName[0] = "Ratio between ILN and IGR variances"; mt->shortTuningName[0] = "ratio"; // w = sigma_L/sigma_G mt->applicableTo[0] = MIXEDBRCHRATES; mt->nApplicable = 1; mt->moveFxn = &Move_RelaxedClockModel; mt->relProposalProb = 30.0; mt->numTuningParams = 1; mt->tuningParam[0] = 2.0; mt->minimum[0] = 0.01; mt->maximum[0] = 100.0; mt->parsimonyBased = NO; mt->level = STANDARD_USER; mt->Autotune = &AutotuneRJClocks; mt->targetRate = 1.0; numMoveTypes = i; assert(numMoveTypes < NUM_MOVE_TYPES); } /* ShowModel: Display model on screen */ int ShowModel (void) { int i, j, ns; MrBayesPrint ("%s Model settings:\n\n", spacer); for (i=0; i 1) MrBayesPrint ("%s Settings for partition %d --\n", spacer, i+1); else MrBayesPrint ("%s Data not partitioned --\n", spacer); if (modelParams[i].dataType == DNA) { MrBayesPrint ("%s Datatype = DNA\n", spacer); ns = 4; } else if (modelParams[i].dataType == RNA) { MrBayesPrint ("%s Datatype = RNA\n", spacer); ns = 4; } else if (modelParams[i].dataType == PROTEIN) { MrBayesPrint ("%s Datatype = Protein\n", spacer); ns = 20; } else if (modelParams[i].dataType == RESTRICTION) { MrBayesPrint ("%s Datatype = Restriction\n", spacer); ns = 2; } else if (modelParams[i].dataType == STANDARD) { MrBayesPrint ("%s Datatype = Standard\n", spacer); ns = MAX_STD_STATES; } else if (modelParams[i].dataType == CONTINUOUS) { MrBayesPrint ("%s Datatype = Continuous\n", spacer); } if (modelSettings[i].dataType == CONTINUOUS) { /* begin description of continuous models */ if (!strcmp(modelParams[i].nst, "1")) MrBayesPrint ("%s Characters are normalized (between 0 and 1)\n", spacer); else MrBayesPrint ("%s Characters are standardized (mean 0 stdev 1)\n", spacer); if (!strcmp(modelParams[i].brownCorrPr, "Fixed") && AreDoublesEqual(modelParams[i].brownCorrFix, 0.0, ETA)==YES) MrBayesPrint ("%s Model = Independent Brownian motion\n", spacer); else MrBayesPrint ("%s Model = Correlated Brownian motion\n", spacer); MrBayesPrint ("%s Rates = %s\n", spacer, modelParams[i].ratesModel); if (!strcmp(modelParams[i].ratesModel, "Gamma") || !strcmp(modelParams[i].ratesModel, "LNorm")) { /* how many categories is the continuous gamma/lnorm approximated by? */ if (!strcmp(modelParams[i].ratesModel, "Lnorm")) MrBayesPrint ("%s The distribution is approximated using %d categories.\n", spacer, modelParams[i].numLnormCats); else MrBayesPrint ("%s The distribution is approximated using %d categories.\n", spacer, modelParams[i].numGammaCats); /* distribution on shape parameter, if appropriate */ if (!strcmp(modelParams[i].shapePr,"Uniform")) { MrBayesPrint ("%s Shape parameter is uniformly distributed\n", spacer); MrBayesPrint ("%s on the interval (%1.2lf,%1.2lf).\n", spacer, modelParams[i].shapeUni[0], modelParams[i].shapeUni[1]); } else if (!strcmp(modelParams[i].shapePr,"Exponential")) { MrBayesPrint ("%s Shape parameter is exponentially\n", spacer); MrBayesPrint ("%s distributed with parameter (%1.2lf).\n", spacer, modelParams[i].shapeExp); } else { MrBayesPrint ("%s Shape parameter is fixed to %1.2lf.\n", spacer, modelParams[i].shapeFix); } } /* end description of continuous models */ } else { /* begin description of discrete models */ if (!strcmp(modelParams[i].parsModel, "Yes")) { MrBayesPrint ("%s Parsmodel = %s\n", spacer, modelParams[i].parsModel); } else { /* dna characters in this partition */ if (modelSettings[i].dataType == DNA || modelSettings[i].dataType == RNA) { /* general form of the rate matrix */ MrBayesPrint ("%s Nucmodel = %s\n", spacer, modelParams[i].nucModel); /* constraints on rates of substitution */ MrBayesPrint ("%s Nst = %s\n", spacer, modelParams[i].nst); if (!strcmp(modelParams[i].nst, "2")) { if (!strcmp(modelParams[i].tRatioPr,"Beta")) { MrBayesPrint ("%s Transition and transversion rates, expressed\n", spacer); MrBayesPrint ("%s as proportions of the rate sum, have a\n", spacer); MrBayesPrint ("%s Beta(%1.2lf,%1.2lf) prior\n", spacer, modelParams[i].tRatioDir[0], modelParams[i].tRatioDir[1]); } else { MrBayesPrint ("%s Transition/transversion rate ratio is fixed to %1.2lf.\n", spacer, modelParams[i].tRatioFix); } } else if (!strcmp(modelParams[i].nst, "6")) { if (!strcmp(modelParams[i].revMatPr,"Dirichlet")) { MrBayesPrint ("%s Substitution rates, expressed as proportions\n", spacer); MrBayesPrint ("%s of the rate sum, have a Dirichlet prior\n", spacer); MrBayesPrint ("%s (%1.2lf,%1.2lf,%1.2lf,%1.2lf,%1.2lf,%1.2lf)\n", spacer, modelParams[i].revMatDir[0], modelParams[i].revMatDir[1], modelParams[i].revMatDir[2], modelParams[i].revMatDir[3], modelParams[i].revMatDir[4], modelParams[i].revMatDir[5]); } else { MrBayesPrint ("%s Substitution rates are fixed to be \n", spacer); MrBayesPrint ("%s (%1.2lf,%1.2lf,%1.2lf,%1.2lf,%1.2lf,%1.2lf).\n", spacer, modelParams[i].revMatFix[0], modelParams[i].revMatFix[1], modelParams[i].revMatFix[2], modelParams[i].revMatFix[3], modelParams[i].revMatFix[4], modelParams[i].revMatFix[5]); } } else if (!strcmp(modelParams[i].nst, "Mixed")) { if (!strcmp(modelParams[i].revMatPr,"Dirichlet")) { MrBayesPrint ("%s Substitution rates, expressed as proportions\n", spacer); MrBayesPrint ("%s of the rate sum, have a Dirichlet prior\n", spacer); MrBayesPrint ("%s (%1.2lf,%1.2lf,%1.2lf,%1.2lf,%1.2lf,%1.2lf)\n", spacer, modelParams[i].revMatDir[0], modelParams[i].revMatDir[1], modelParams[i].revMatDir[2], modelParams[i].revMatDir[3], modelParams[i].revMatDir[4], modelParams[i].revMatDir[5]); } else { MrBayesPrint ("%s Substitution rates are fixed to be \n", spacer); MrBayesPrint ("%s (%1.2lf,%1.2lf,%1.2lf,%1.2lf,%1.2lf,%1.2lf).\n", spacer, modelParams[i].revMatFix[0], modelParams[i].revMatFix[1], modelParams[i].revMatFix[2], modelParams[i].revMatFix[3], modelParams[i].revMatFix[4], modelParams[i].revMatFix[5]); } } if (!strcmp(modelParams[i].nucModel,"Codon")) { /* what is the distribution on the nonsyn./syn. rate ratio */ if (!strcmp(modelParams[i].omegaVar, "Equal")) { if (!strcmp(modelParams[i].omegaPr,"Dirichlet")) { MrBayesPrint ("%s Nonsynonymous and synonymous rates, expressed\n", spacer); MrBayesPrint ("%s as proportions of the rate sum, have a\n", spacer); MrBayesPrint ("%s Dirichlet(%1.2lf,%1.2lf) prior\n", spacer, modelParams[i].omegaDir[0], modelParams[i].omegaDir[1]); } else { MrBayesPrint ("%s Nonsynonymous/synonymous rate ratio is fixed to %1.2lf.\n", spacer, modelParams[i].omegaFix); } } else if (!strcmp(modelParams[i].omegaVar, "Ny98")) { if (!strcmp(modelParams[i].ny98omega1pr, "Beta")) { MrBayesPrint ("%s Nonsynonymous/synonymous rate ratio for purifying selection\n", spacer); MrBayesPrint ("%s (class 1) has a Beta(%1.2lf,%1.2lf) on the interval (0,1).\n", spacer, modelParams[i].ny98omega1Beta[0], modelParams[i].ny98omega1Beta[1]); } else { MrBayesPrint ("%s Nonsynonymous/synonymous rate ratio for purifying selection\n", spacer); MrBayesPrint ("%s (class 1) is fixed to %1.2lf.\n", spacer, modelParams[i].ny98omega1Fixed); } MrBayesPrint ("%s Nonsynonymous/synonymous rate ratio for neutral selection\n", spacer); MrBayesPrint ("%s (class 2) is fixed to 1.0.\n", spacer); if (!strcmp(modelParams[i].ny98omega3pr, "Uniform")) { MrBayesPrint ("%s Nonsynonymous/synonymous rate ratio for positive selection\n", spacer); MrBayesPrint ("%s is uniformly distributed on the interval (%1.2lf,%1.2lf).\n", spacer, modelParams[i].ny98omega3Uni[0], modelParams[i].ny98omega3Uni[1]); } else if (!strcmp(modelParams[i].ny98omega3pr, "Exponential")) { MrBayesPrint ("%s Nonsynonymous/synonymous rate ratio for positive selection\n", spacer); MrBayesPrint ("%s is exponentially distributed with parameter (%1.2lf).\n", spacer, modelParams[i].ny98omega3Exp); } else { MrBayesPrint ("%s Nonsynonymous/synonymous rate ratio for positive \n", spacer); MrBayesPrint ("%s selection is fixed to %1.2lf.\n", spacer, modelParams[i].ny98omega3Fixed); } } else if (!strcmp(modelParams[i].omegaVar, "M3")) { if (!strcmp(modelParams[i].m3omegapr, "Exponential")) { MrBayesPrint ("%s Nonsynonymous and synonymous rates for the tree classes of\n", spacer); MrBayesPrint ("%s omega are exponentially distributed random variables.\n", spacer); } else if (!strcmp(modelParams[i].m3omegapr, "Fixed")) { MrBayesPrint ("%s Nonsynonymous/synonymous rate ratio for the three omega\n", spacer); MrBayesPrint ("%s are fixed to %1.2lf, %1.2lf, and %1.2lf.\n", spacer, modelParams[i].m3omegaFixed[0], modelParams[i].m3omegaFixed[1], modelParams[i].m3omegaFixed[2]); } } else if (!strcmp(modelParams[i].omegaVar, "M10")) { MrBayesPrint ("%s Nonsynonymous/synonymous rate ratio for purifying \n", spacer); MrBayesPrint ("%s selection (class 1) has a Beta(alpha1,beta1) on the \n", spacer); MrBayesPrint ("%s interval (0,1). Nonsynonymous/synonymous rate ratio \n", spacer); MrBayesPrint ("%s for positive selection (class 2) has an offset \n", spacer); MrBayesPrint ("%s Gamma(alpha2,beta2) on the interval (1,Infinity).\n", spacer); } /* genetic code that is used (if nucmodel=codon) */ MrBayesPrint ("%s Code = %s\n", spacer, modelParams[i].geneticCode); } } /* amino acid characters in this partition */ else if (modelSettings[i].dataType == PROTEIN) { if (modelParams[i].dataType == DNA || modelParams[i].dataType == RNA) MrBayesPrint ("%s Nucmodel = %s\n", spacer, modelParams[i].nucModel); /* constraints on rates of substitution in 20 X 20 matrix */ if (!strcmp(modelParams[i].aaModelPr, "Mixed")) MrBayesPrint ("%s Aamodel = Mixture of models with fixed rate matrices\n", spacer); else MrBayesPrint ("%s Aamodel = %s\n", spacer, modelParams[i].aaModel); /* revmat rates */ if (!strcmp(modelParams[i].aaModelPr, "Mixed")) MrBayesPrint ("%s Substitution rates come from the mixture of models\n", spacer); else if (!strcmp(modelParams[i].aaModelPr, "Fixed") && (!strcmp(modelParams[i].aaModel, "Poisson") || !strcmp(modelParams[i].aaModel, "Equalin"))) MrBayesPrint ("%s Substitution rates are fixed to be equal\n", spacer); else if (!strcmp(modelParams[i].aaModelPr, "Fixed") && strcmp(modelParams[i].aaModel, "Gtr")!=0) MrBayesPrint ("%s Substitution rates are fixed to the %s rates\n", spacer, modelParams[i].aaModel); else if (!strcmp(modelParams[i].aaModelPr, "Fixed") && !strcmp(modelParams[i].aaModel, "Gtr")) { if (!strcmp(modelParams[i].aaRevMatPr,"Dirichlet")) { for (j=0; j<190; j++) if (AreDoublesEqual(modelParams[i].aaRevMatDir[0], modelParams[i].aaRevMatDir[j], 0.00001) == NO) break; if (j == 190) { MrBayesPrint ("%s Substitution rates have a Dirichlet(%1.2lf,%1.2lf,...) prior\n", spacer, modelParams[i].aaRevMatDir[0], modelParams[i].aaRevMatDir[0]); } else { MrBayesPrint ("%s Substitution rates have a Dirichlet(\n", spacer); for (j=0; j<190; j++) { if (j % 10 == 0) MrBayesPrint ("%s ", spacer); MrBayesPrint ("%1.2lf", modelParams[i].aaRevMatDir[j]); if (j == 189) MrBayesPrint (") prior\n"); else if ((j+1) % 10 == 0) MrBayesPrint (",\n"); else MrBayesPrint (","); } } } else /* if (!strcmp(modelParams[i].aaRevMatPr,"Fixed")) */ { for (j=0; j<190; j++) if (AreDoublesEqual(modelParams[i].aaRevMatFix[0], modelParams[i].aaRevMatFix[j], 0.00001) == NO) break; if (j == 190) { MrBayesPrint ("%s Substitution rates are fixed to (%1.1le,%1.1le,...)\n", spacer, modelParams[i].aaRevMatFix[0], modelParams[i].aaRevMatFix[0]); } else { MrBayesPrint ("%s Substitution rates are fixed to (\n", spacer); for (j=0; j<190; j++) { if (j % 10 == 0) MrBayesPrint ("%s ", spacer); MrBayesPrint ("%1.1le", modelParams[i].aaRevMatFix[j]); if (j == 189) MrBayesPrint (")\n"); else if ((j+1) % 10 == 0) MrBayesPrint (",\n"); else MrBayesPrint (","); } } } } } /* restriction site or morphological characters in this partition */ else if (modelSettings[i].dataType == RESTRICTION || modelSettings[i].dataType == STANDARD) { /* what type of characters are sampled? */ MrBayesPrint ("%s Coding = %s\n", spacer, modelParams[i].codingString); } /* is there rate variation in a single site across the tree? */ if (((modelSettings[i].dataType == DNA || modelSettings[i].dataType == RNA) && !strcmp(modelParams[i].nucModel, "4by4")) || modelSettings[i].dataType == PROTEIN) { /* do rates change on tree accoding to covarion model? */ MrBayesPrint ("%s Covarion = %s\n", spacer, modelParams[i].covarionModel); if (!strcmp(modelParams[i].covarionModel, "Yes")) { /* distribution on switching parameters, if appropriate */ if (!strcmp(modelParams[i].covSwitchPr,"Uniform")) { MrBayesPrint ("%s Switching rates have independent uniform dist-\n", spacer); MrBayesPrint ("%s ributions on the interval (%1.2lf,%1.2lf).\n", spacer, modelParams[i].covswitchUni[0], modelParams[i].covswitchUni[1]); } else if (!strcmp(modelParams[i].covSwitchPr,"Exponential")) { MrBayesPrint ("%s Switching rates have independent exponential\n", spacer); MrBayesPrint ("%s distributions with parameters (%1.2lf).\n", spacer, modelParams[i].covswitchExp); } else { MrBayesPrint ("%s Switching rates are fixed to %1.2lf and %1.2lf.\n", spacer, modelParams[i].covswitchFix[0], modelParams[i].covswitchFix[0]); } ns *= 2; } } /* now, let's deal with variation in omega */ if ((modelParams[i].dataType == DNA || modelParams[i].dataType == RNA) && !strcmp(modelParams[i].nucModel,"Codon")) { MrBayesPrint ("%s Omegavar = %s\n", spacer, modelParams[i].omegaVar); if (!strcmp(modelParams[i].geneticCode, "Universal")) ns = 61; else if (!strcmp(modelParams[i].geneticCode, "Vertmt")) ns = 60; else if (!strcmp(modelParams[i].geneticCode, "Invermt")) ns = 62; else if (!strcmp(modelParams[i].geneticCode, "Mycoplasma")) ns = 62; else if (!strcmp(modelParams[i].geneticCode, "Yeast")) ns = 62; else if (!strcmp(modelParams[i].geneticCode, "Ciliate")) ns = 63; else if (!strcmp(modelParams[i].geneticCode, "Echinoderm")) ns = 62; else if (!strcmp(modelParams[i].geneticCode, "Euplotid")) ns = 62; else if (!strcmp(modelParams[i].geneticCode, "Metmt")) ns = 62; } /* what assumptions are made about the state frequencies? */ if (modelParams[i].dataType == STANDARD) MrBayesPrint ("%s # States = Variable, up to %d\n", spacer, MAX_STD_STATES); else if (modelSettings[i].numStates != modelSettings[i].numModelStates) MrBayesPrint ("%s # States = %d (in the model)\n", spacer, modelSettings[i].numModelStates); else MrBayesPrint ("%s # States = %d\n", spacer, ns); if (modelSettings[i].dataType == STANDARD) { if (!strcmp(modelParams[i].symPiPr,"Fixed")) { if (AreDoublesEqual(modelParams[i].symBetaFix, -1.0, ETA)==YES) MrBayesPrint ("%s State frequencies are fixed to be equal\n", spacer); else MrBayesPrint ("%s Symmetric Dirichlet alpha is fixed to %1.2lf\n", spacer, modelParams[i].symBetaFix); } else if (!strcmp(modelParams[i].symPiPr,"Uniform")) { MrBayesPrint ("%s Symmetric Dirichlet alpha has a Uniform(%1.2lf,%1.2lf) prior\n", spacer, modelParams[i].symBetaUni[0], modelParams[i].symBetaUni[1]); } else { MrBayesPrint ("%s Symmetric Dirichlet alpha has a Exponential(%1.2lf) prior\n", spacer, modelParams[i].symBetaExp); } } else if (modelSettings[i].dataType == RESTRICTION) { /* distribution on state frequencies for restriction site model */ if (!strcmp(modelParams[i].stateFreqPr,"Dirichlet")) { MrBayesPrint ("%s State frequencies have a Dirichlet (%1.2lf,%1.2lf) prior\n", spacer, modelParams[i].stateFreqsDir[0], modelParams[i].stateFreqsDir[1]); } else if (!strcmp(modelParams[i].stateFreqPr,"Fixed") && !strcmp(modelParams[i].stateFreqsFixType,"Equal")) { MrBayesPrint ("%s State frequencies are fixed to be equal\n", spacer); } else if (!strcmp(modelParams[i].stateFreqPr,"Fixed") && !strcmp(modelParams[i].stateFreqsFixType,"User")) { MrBayesPrint ("%s State frequencies are fixed(%1.2lf,%1.2lf)\n", spacer, modelParams[i].stateFreqsFix[0], modelParams[i].stateFreqsFix[1]); MrBayesPrint ("%s State frequencies have been fixed by the user\n", spacer); } else if (!strcmp(modelParams[i].stateFreqPr,"Fixed") && !strcmp(modelParams[i].stateFreqsFixType,"Empirical")) { MrBayesPrint ("%s State frequencies are fixed(%1.2lf,%1.2lf)\n", spacer, modelParams[i].stateFreqsFix[0], modelParams[i].stateFreqsFix[1]); MrBayesPrint ("%s State frequencies have been fixed to the empirical frequencies in the data\n", spacer); } if (!strcmp(modelParams[i].statefreqModel,"Directional") || !strcmp(modelParams[i].statefreqModel,"Mixed")) { MrBayesPrint ("%s State frequencies are potentially different for the root (directional model)\n", spacer); if (!strcmp(modelParams[i].statefreqModel,"Directional") && !strcmp(modelParams[i].rootFreqPr,"Fixed")) MrBayesPrint ("%s Root state frequencies are fixed(%1.2lf,%1.2lf)\n", spacer, modelParams[i].rootFreqsFix[0], modelParams[i].rootFreqsFix[1]); else if (!strcmp(modelParams[i].statefreqModel,"Directional") && !strcmp(modelParams[i].rootFreqPr,"Dirichlet")) MrBayesPrint ("%s Root state frequencies have a Dirichlet (%1.2lf,%1.2lf) prior\n", spacer, modelParams[i].rootFreqsDir[0], modelParams[i].rootFreqsDir[1]); } else if (!strcmp(modelParams[i].statefreqModel,"Mixed")) { MrBayesPrint ("%s State frequencies are potentially different for the root\n", spacer); } } else if (modelSettings[i].dataType == PROTEIN) { /* distribution on state frequencies for aminoacid model */ if (!strcmp(modelParams[i].aaModelPr, "Fixed") && (strcmp(modelParams[i].aaModel, "Equalin")==0 || strcmp(modelParams[i].aaModel, "Gtr")==0)) { if (!strcmp(modelParams[i].stateFreqPr,"Dirichlet")) { MrBayesPrint ("%s State frequencies have a Dirichlet prior\n", spacer); MrBayesPrint ("%s (%1.2lf,%1.2lf,%1.2lf,%1.2lf,%1.2lf,", spacer, modelParams[i].stateFreqsDir[0], modelParams[i].stateFreqsDir[1], modelParams[i].stateFreqsDir[2], modelParams[i].stateFreqsDir[3], modelParams[i].stateFreqsDir[4]); MrBayesPrint ("%1.2lf,%1.2lf,%1.2lf,%1.2lf,%1.2lf,\n", modelParams[i].stateFreqsDir[5], modelParams[i].stateFreqsDir[6], modelParams[i].stateFreqsDir[7], modelParams[i].stateFreqsDir[8], modelParams[i].stateFreqsDir[9]); MrBayesPrint ("%s %1.2lf,%1.2lf,%1.2lf,%1.2lf,%1.2lf,", spacer, modelParams[i].stateFreqsDir[10], modelParams[i].stateFreqsDir[11], modelParams[i].stateFreqsDir[12], modelParams[i].stateFreqsDir[13], modelParams[i].stateFreqsDir[14]); MrBayesPrint ("%1.2lf,%1.2lf,%1.2lf,%1.2lf,%1.2lf)\n", modelParams[i].stateFreqsDir[15], modelParams[i].stateFreqsDir[16], modelParams[i].stateFreqsDir[17], modelParams[i].stateFreqsDir[18], modelParams[i].stateFreqsDir[19]); } else if (!strcmp(modelParams[i].stateFreqPr,"Fixed") && !strcmp(modelParams[i].stateFreqsFixType,"Equal")) { MrBayesPrint ("%s State frequencies are fixed to be equal\n", spacer); } else if (!strcmp(modelParams[i].stateFreqPr,"Fixed") && !strcmp(modelParams[i].stateFreqsFixType,"User")) { MrBayesPrint ("%s State frequencies have been fixed by the user\n", spacer); } else if (!strcmp(modelParams[i].stateFreqPr,"Fixed") && !strcmp(modelParams[i].stateFreqsFixType,"Empirical")) { MrBayesPrint ("%s State frequencies have been fixed to the empirical frequencies in the data\n", spacer); } } else if (!strcmp(modelParams[i].aaModelPr, "Fixed") && !strcmp(modelParams[i].aaModel, "Poisson")) { MrBayesPrint ("%s State frequencies are fixed to be equal\n", spacer); } else if (!strcmp(modelParams[i].aaModelPr, "Fixed") && strcmp(modelParams[i].aaModel, "Equalin") && strcmp(modelParams[i].aaModel, "Poisson")) { MrBayesPrint ("%s State frequencies are fixed to the %s frequencies\n", spacer, modelParams[i].aaModel); } else { MrBayesPrint ("%s State frequencies come from the mixture of models\n", spacer); } } else { /* distribution on state frequencies for all other models */ if (!strcmp(modelParams[i].stateFreqPr,"Dirichlet")) { MrBayesPrint ("%s State frequencies have a Dirichlet prior\n", spacer); if (!strcmp(modelParams[i].nucModel, "Doublet")) { MrBayesPrint ("%s (%1.2lf,%1.2lf,%1.2lf,%1.2lf,\n", spacer, modelParams[i].stateFreqsDir[0], modelParams[i].stateFreqsDir[1], modelParams[i].stateFreqsDir[2], modelParams[i].stateFreqsDir[3]); MrBayesPrint ("%s %1.2lf,%1.2lf,%1.2lf,%1.2lf,\n", spacer, modelParams[i].stateFreqsDir[4], modelParams[i].stateFreqsDir[5], modelParams[i].stateFreqsDir[6], modelParams[i].stateFreqsDir[7]); MrBayesPrint ("%s %1.2lf,%1.2lf,%1.2lf,%1.2lf,\n", spacer, modelParams[i].stateFreqsDir[8], modelParams[i].stateFreqsDir[9], modelParams[i].stateFreqsDir[10], modelParams[i].stateFreqsDir[11]); MrBayesPrint ("%s %1.2lf,%1.2lf,%1.2lf,%1.2lf)\n", spacer, modelParams[i].stateFreqsDir[12], modelParams[i].stateFreqsDir[13], modelParams[i].stateFreqsDir[14], modelParams[i].stateFreqsDir[15]); } else if (!strcmp(modelParams[i].nucModel, "4by4")) { MrBayesPrint ("%s (%1.2lf,%1.2lf,%1.2lf,%1.2lf)\n", spacer, modelParams[i].stateFreqsDir[0], modelParams[i].stateFreqsDir[1], modelParams[i].stateFreqsDir[2], modelParams[i].stateFreqsDir[3]); } } else if (!strcmp(modelParams[i].stateFreqPr,"Fixed") && !strcmp(modelParams[i].stateFreqsFixType,"Equal")) { MrBayesPrint ("%s State frequencies are fixed to be equal\n", spacer); } else if (!strcmp(modelParams[i].stateFreqPr,"Fixed") && !strcmp(modelParams[i].stateFreqsFixType,"User")) { MrBayesPrint ("%s State frequencies have been fixed by the user\n", spacer); } else if (!strcmp(modelParams[i].stateFreqPr,"Fixed") && !strcmp(modelParams[i].stateFreqsFixType,"Empirical")) { MrBayesPrint ("%s State frequencies have been fixed to the empirical frequencies in the data\n", spacer); } } /* now, let's deal with rate variation across sites */ if (((modelSettings[i].dataType == DNA || modelSettings[i].dataType == RNA) && strcmp(modelParams[i].nucModel,"Codon")!=0) || modelSettings[i].dataType == PROTEIN || modelSettings[i].dataType == RESTRICTION || modelSettings[i].dataType == STANDARD) { if (!strcmp(modelParams[i].covarionModel, "No")) MrBayesPrint ("%s Rates = %s\n", spacer, modelParams[i].ratesModel); else { if (!strcmp(modelParams[i].ratesModel, "Propinv")) MrBayesPrint ("%s Rates = Equal ", spacer); else if (!strcmp(modelParams[i].ratesModel, "Invgamma")) MrBayesPrint ("%s Rates = Gamma ", spacer); else MrBayesPrint ("%s Rates = %s ", spacer, modelParams[i].ratesModel); MrBayesPrint ("(+ Propinv induced by covarion model)\n"); } if ((modelParams[i].dataType == RESTRICTION || modelParams[i].dataType == STANDARD) && !strcmp(modelParams[i].ratesModel, "Adgamma")) { } else { if (!strcmp(modelParams[i].ratesModel, "Gamma") || !strcmp(modelParams[i].ratesModel, "Invgamma") || !strcmp(modelParams[i].ratesModel, "LNorm") || !strcmp(modelParams[i].ratesModel, "Adgamma") || !strcmp(modelParams[i].ratesModel, "Kmixture")) { /* how many categories is the continuous gamma/lnorm approximated by? or how many components are there in the mixture? */ if (!strcmp(modelParams[i].ratesModel, "Kmixture")) MrBayesPrint ("%s There are %d components in the mixture.\n", spacer, modelParams[i].numMixtCats); else if (!strcmp(modelParams[i].ratesModel, "Lnorm")) MrBayesPrint ("%s The distribution is approximated using %d categories.\n", spacer, modelParams[i].numLnormCats); else MrBayesPrint ("%s The distribution is approximated using %d categories.\n", spacer, modelParams[i].numGammaCats); /* distribution on shape parameter, if appropriate */ if (!strcmp(modelParams[i].shapePr,"Uniform")) { MrBayesPrint ("%s Shape parameter is uniformly distributed\n", spacer); MrBayesPrint ("%s on the interval (%1.2lf,%1.2lf).\n", spacer, modelParams[i].shapeUni[0], modelParams[i].shapeUni[1]); } else if (!strcmp(modelParams[i].shapePr,"Exponential")) { MrBayesPrint ("%s Shape parameter is exponentially\n", spacer); MrBayesPrint ("%s distributed with parameter (%1.2lf).\n", spacer, modelParams[i].shapeExp); } else { MrBayesPrint ("%s Shape parameter is fixed to %1.2lf.\n", spacer, modelParams[i].shapeFix); } } if ((!strcmp(modelParams[i].ratesModel, "Propinv") || !strcmp(modelParams[i].ratesModel, "Invgamma")) && !strcmp(modelParams[i].covarionModel, "No")) { /* distribution on pInvar parameter, if appropriate */ if (!strcmp(modelParams[i].pInvarPr,"Uniform")) { MrBayesPrint ("%s Proportion of invariable sites is uniformly dist-\n", spacer); MrBayesPrint ("%s ributed on the interval (%1.2lf,%1.2lf).\n", spacer, modelParams[i].pInvarUni[0], modelParams[i].pInvarUni[1]); } else { MrBayesPrint ("%s Proportion of invariable sites is fixed to %1.2lf.\n", spacer, modelParams[i].pInvarFix); } } if (!strcmp(modelParams[i].ratesModel, "Adgamma")) { /* distribution on correlation parameter, if appropriate */ if (!strcmp(modelParams[i].adGammaCorPr,"Uniform")) { MrBayesPrint ("%s Rate correlation parameter is uniformly dist-\n", spacer); MrBayesPrint ("%s ributed on the interval (%1.2lf,%1.2lf).\n", spacer, modelParams[i].adgCorrUni[0], modelParams[i].adgCorrUni[1]); } else { MrBayesPrint ("%s Rate correlation parameter is fixed to %1.2lf.\n", spacer, modelParams[i].adgCorrFix); } } } } } /* end description of discrete models */ } if (i != numCurrentDivisions - 1) MrBayesPrint ("\n"); } MrBayesPrint ("\n"); ShowParameters (NO, NO, NO); return (NO_ERROR); } /*------------------------------------------------------------------------------ | | ShowMoves: Show applicable moves | ------------------------------------------------------------------------------*/ int ShowMoves (int used) { int i, k, run, chain, chainIndex, areRunsSame, areChainsSame, numPrintedMoves; MCMCMove *mv; numPrintedMoves = 0; for (i=0; irelProposalProb[k] > 0.000001) break; } if (k == numGlobalChains && used == YES) continue; if (k < numGlobalChains && used == NO) continue; numPrintedMoves++; /* print move number and name */ MrBayesPrint ("%s %4d -- Move = %s\n", spacer, numPrintedMoves, mv->name); /* print move type */ MrBayesPrint ("%s Type = %s\n", spacer, mv->moveType->name); /* print parameter */ if (mv->parm->nSubParams > 0) MrBayesPrint ("%s Parameters = %s [param. %d] (%s)\n", spacer, mv->parm->name, mv->parm->index+1, mv->parm->paramTypeName); else MrBayesPrint ("%s Parameter = %s [param. %d] (%s)\n", spacer, mv->parm->name, mv->parm->index+1, mv->parm->paramTypeName); for (k=0; kparm->nSubParams; k++) MrBayesPrint ("%s %s [param. %d] (%s)\n", spacer, mv->parm->subParams[k]->name, mv->parm->subParams[k]->index+1, mv->parm->subParams[k]->paramTypeName); /* print tuning parameters */ for (k=0; kmoveType->numTuningParams; k++) { if (k==0) MrBayesPrint ("%s Tuningparam = %s (%s)\n", spacer, mv->moveType->shortTuningName[k], mv->moveType->tuningName[k]); else MrBayesPrint ("%s %s (%s)\n", spacer, mv->moveType->shortTuningName[k], mv->moveType->tuningName[k]); } /* loop over tuning parameters */ for (k=0; kmoveType->numTuningParams; k++) { /* find if tuning parameters are different for different runs */ areRunsSame = YES; for (run=1; runtuningParam[chainIndex][k], mv->tuningParam[chain][k], 0.000001) == NO) { areRunsSame = NO; break; } } if (areRunsSame == NO) break; } /* now print values */ for (run=0; run= 1) break; /* find out if chains are different within this run */ areChainsSame = YES; for (chain=1; chaintuningParam[chainIndex][k], mv->tuningParam[chainIndex-chain][k],0.000001) == NO) { areChainsSame = NO; break; } } /* now we can print the values */ for (chain=0; chain= 1) break; if (run == 0 && chain == 0) MrBayesPrint ("%s%22s = %1.3lf", spacer, mv->moveType->shortTuningName[k], mv->tuningParam[chainIndex][k]); else MrBayesPrint ("%s %1.3lf", spacer, mv->tuningParam[chainIndex][k]); if (areChainsSame == NO && areRunsSame == YES) MrBayesPrint (" [chain %d]\n", chain+1); else if (areChainsSame == YES && areRunsSame == NO) MrBayesPrint (" [run %d]\n", run+1); else if (areChainsSame == NO && areRunsSame == NO) MrBayesPrint (" [run %d, chain %d]\n", run+1, chain+1); else MrBayesPrint ("\n"); } } } /* next tuning parameter */ /* print target acceptance rate for autotuning */ if (mv->moveType->targetRate > 0.0 && mv->moveType->targetRate < 1.0) { /* first find out if the targets are different in different runs */ areRunsSame = YES; for (run=1; runtargetRate[chainIndex], mv->targetRate[chain], 0.000001) == NO) { areRunsSame = NO; break; } } if (areRunsSame == NO) break; } /* now print values */ for (run=0; run= 1) break; /* find out if chains are different within this run */ areChainsSame = YES; for (chain=1; chaintargetRate[chainIndex], mv->targetRate[chainIndex-chain], 0.000001) == NO) { areChainsSame = NO; break; } } /* now we can print the values */ for (chain=0; chain= 1) break; if (run == 0 && chain == 0) MrBayesPrint ("%s Targetrate = %1.3lf", spacer, mv->targetRate[chainIndex]); else MrBayesPrint ("%s %1.3lf", spacer, mv->targetRate[chainIndex]); if (areChainsSame == NO && areRunsSame == YES) MrBayesPrint (" [chain %d]\n", chain+1); else if (areChainsSame == YES && areRunsSame == NO) MrBayesPrint (" [run %d]\n", run+1); else if (areChainsSame == NO && areRunsSame == NO) MrBayesPrint (" [run %d, chain %d]\n", run+1, chain+1); else MrBayesPrint ("\n"); } } } /* finally print the relative proposal probability */ /* first find out if the probabilities are different in different runs */ areRunsSame = YES; for (run=1; runrelProposalProb[chainIndex], mv->relProposalProb[chain], 0.000001) == NO) { areRunsSame = NO; break; } } if (areRunsSame == NO) break; } /* now print values */ for (run=0; run= 1) break; /* find out if chains are different within this run */ areChainsSame = YES; for (chain=1; chainrelProposalProb[chainIndex], mv->relProposalProb[chainIndex-chain], 0.000001) == NO) { areChainsSame = NO; break; } } /* now we can print the values */ for (chain=0; chain= 1) break; if (run == 0 && chain == 0) MrBayesPrint ("%s Rel. prob. = %1.1lf", spacer, mv->relProposalProb[chainIndex]); else MrBayesPrint ("%s %1.1lf", spacer, mv->relProposalProb[chainIndex]); if (areChainsSame == NO && areRunsSame == YES) MrBayesPrint (" [chain %d]\n", chain+1); else if (areChainsSame == YES && areRunsSame == NO) MrBayesPrint (" [run %d]\n", run+1); else if (areChainsSame == NO && areRunsSame == NO) MrBayesPrint (" [run %d, chain %d]\n", run+1, chain+1); else MrBayesPrint ("\n"); } } MrBayesPrint ("\n"); } /* next move */ if (numPrintedMoves == 0) { if (used == YES) { MrBayesPrint ("%s No moves currently used for this analysis. All parameters\n", spacer); MrBayesPrint ("%s will be fixed to their starting values.\n\n", spacer); } else { MrBayesPrint ("%s No additional moves available for this model.\n\n", spacer); } } return (NO_ERROR); } /*------------------------------------------------------------------------------ | | ShowParameters: Show parameter table and parameter info | ------------------------------------------------------------------------------*/ int ShowParameters (int showStartVals, int showMoves, int showAllAvailable) { int a, b, d, i, j, k, m, n, run, chain, shouldPrint, isSame, areRunsSame, areChainsSame, nValues, chainIndex, refIndex, numPrinted, numMovedChains, printedCol, screenWidth=100; Param *p; Model *mp; ModelInfo *ms; MrBFlt *value, *refValue, *subValue; MCMCMove *mv; MrBayesPrint ("%s Active parameters: \n\n", spacer); if (numCurrentDivisions > 1) { MrBayesPrint ("%s Partition(s)\n", spacer); MrBayesPrint ("%s Parameters ", spacer); for (i=0; i 1) { MrBayesPrint ("%s ------------------", spacer); for (i=0; i 1) MrBayesPrint ("%s Parameters can be linked or unlinked across partitions using 'link' and 'unlink'\n\n", spacer); for (i=0; iparamType; mp = &modelParams[p->relParts[0]]; ms = &modelSettings[p->relParts[0]]; /* print parameter number and name */ MrBayesPrint ("%s %4d -- Parameter = %s\n", spacer, i+1, p->name); MrBayesPrint ("%s Type = %s\n", spacer, p->paramTypeName); /* print prior */ if (j == P_TRATIO) { if (!strcmp(mp->tRatioPr,"Beta")) MrBayesPrint ("%s Prior = Beta(%1.2lf,%1.2lf)\n", spacer, mp->tRatioDir[0], modelParams[i].tRatioDir[1]); else MrBayesPrint ("%s Prior = Fixed(%1.2lf)\n", spacer, mp->tRatioFix); } else if (j == P_REVMAT) { if (ms->numModelStates != 20) { if (!strcmp(mp->nst,"Mixed")) { MrBayesPrint ("%s Prior = All GTR submodels have equal probability\n", spacer); MrBayesPrint ("%s All revmat rates have a Symmetric Dirichlet(%1.2lf) prior\n", spacer, mp->revMatSymDir); } else if (!strcmp(mp->revMatPr,"Dirichlet")) { MrBayesPrint ("%s Prior = Dirichlet(%1.2lf,%1.2lf,%1.2lf,%1.2lf,%1.2lf,%1.2lf)\n", spacer, mp->revMatDir[0], mp->revMatDir[1], mp->revMatDir[2], mp->revMatDir[3], mp->revMatDir[4], mp->revMatDir[5]); } else MrBayesPrint ("%s Prior = Fixed(%1.2lf,%1.2lf,%1.2lf,%1.2lf,%1.2lf,%1.2lf)\n", spacer, mp->revMatFix[0], mp->revMatFix[1], mp->revMatFix[2], mp->revMatFix[3], mp->revMatFix[4], mp->revMatFix[5]); } else if (ms->numModelStates == 20) { if (!strcmp(mp->aaRevMatPr,"Dirichlet")) MrBayesPrint ("%s Prior = Dirichlet\n", spacer); else MrBayesPrint ("%s Prior = Fixed(user-specified)\n", spacer); } } else if (j == P_OMEGA) { if (!strcmp(mp->omegaVar,"Equal")) { if (!strcmp(mp->omegaPr,"Dirichlet")) MrBayesPrint ("%s Prior = Dirichlet(%1.2lf,%1.2lf)\n", spacer, mp->omegaDir[0], mp->omegaDir[1]); else MrBayesPrint ("%s Prior = Fixed(%1.2lf)\n", spacer, mp->omegaFix); } else if (!strcmp(mp->omegaVar,"Ny98")) { if (!strcmp(mp->ny98omega1pr,"Beta")) MrBayesPrint ("%s Prior = Beta(%1.2lf,%1.2lf) on omega(1)\n", spacer, mp->ny98omega1Beta[0], mp->ny98omega1Beta[1]); else MrBayesPrint ("%s Prior = Fixed(%1.2lf) on omega(1)\n", spacer, mp->ny98omega1Fixed); MrBayesPrint ("%s Fixed(1.00) on omega(2)\n", spacer); if (!strcmp(mp->ny98omega3pr,"Uniform")) MrBayesPrint ("%s Uniform(%1.2lf,%1.2lf) on omega(3)\n", spacer, mp->ny98omega3Uni[0], mp->ny98omega3Uni[1]); else if (!strcmp(mp->ny98omega3pr,"Exponential")) MrBayesPrint ("%s Exponential(%1.2lf) on omega(3)\n", spacer, mp->ny98omega3Exp); else MrBayesPrint ("%s Fixed(%1.2lf) on omega(3)\n", spacer, mp->ny98omega3Fixed); if (!strcmp(mp->codonCatFreqPr,"Dirichlet")) MrBayesPrint ("%s Dirichlet(%1.2lf,%1.2lf,%1.2lf) on pi(-), pi(N), and pi(+)\n", spacer, mp->codonCatDir[0], mp->codonCatDir[1], mp->codonCatDir[2]); else MrBayesPrint ("%s Fixed(%1.2lf,%1.2lf,%1.2lf) on pi(-), pi(N), and pi(+)\n", spacer, mp->codonCatFreqFix[0], mp->codonCatFreqFix[1], mp->codonCatFreqFix[2]); } else if (!strcmp(mp->omegaVar,"M3")) { if (!strcmp(mp->m3omegapr,"Exponential")) MrBayesPrint ("%s dN1, dN2, dN3, and dS are all exponential random variables\n", spacer); else MrBayesPrint ("%s Fixed(%1.2lf,%1.2lf,%1.2lf) for the three selection categories\n", spacer, mp->m3omegaFixed[0], mp->m3omegaFixed[1], mp->m3omegaFixed[2]); if (!strcmp(mp->codonCatFreqPr,"Dirichlet")) MrBayesPrint ("%s Dirichlet(%1.2lf,%1.2lf,%1.2lf) on pi(1), pi(2), and pi(3)\n", spacer, mp->codonCatDir[0], mp->codonCatDir[1], mp->codonCatDir[2]); else MrBayesPrint ("%s Fixed(%1.2lf,%1.2lf,%1.2lf) on pi(1), pi(2), and pi(3)\n", spacer, mp->codonCatFreqFix[0], mp->codonCatFreqFix[1], mp->codonCatFreqFix[2]); } else if (!strcmp(mp->omegaVar,"M10")) { MrBayesPrint ("%s With probability pi(1), omega is drawn from a Beta(alpha1,beta1) \n", spacer); MrBayesPrint ("%s distribution and with probability pi(2), omega is drawn from\n", spacer); MrBayesPrint ("%s a Gamma(alpha2,beta2) distribution.\n", spacer); if (!strcmp(mp->m10betapr,"Uniform")) { MrBayesPrint ("%s The parameters of the beta distribution each follow \n", spacer); MrBayesPrint ("%s independent Uniform(%1.2lf,%1.2lf) priors\n", spacer, mp->m10betaUni[0], mp->m10betaUni[1]); } else if (!strcmp(mp->m10betapr,"Exponential")) { MrBayesPrint ("%s The parameters of the beta distribution each follow \n", spacer); MrBayesPrint ("%s independent Exponential(%1.2lf) priors\n", spacer, mp->m10betaExp); } else { MrBayesPrint ("%s The parameters of the beta distribution are fixed to \n", spacer); MrBayesPrint ("%s %1.2lf and %1.2lf\n", spacer, mp->m10betaFix[0], mp->m10betaFix[0]); } if (!strcmp(mp->m10gammapr,"Uniform")) { MrBayesPrint ("%s The parameters of the gamma distribution each follow \n", spacer); MrBayesPrint ("%s independent Uniform(%1.2lf,%1.2lf) priors\n", spacer, mp->m10gammaUni[0], mp->m10gammaUni[1]); } else if (!strcmp(mp->m10gammapr,"Exponential")) { MrBayesPrint ("%s The parameters of the gamma distribution each follow \n", spacer); MrBayesPrint ("%s independent Exponential(%1.2lf) priors\n", spacer, mp->m10gammaExp); } else { MrBayesPrint ("%s The parameters of the gamma distribution are fixed to \n", spacer); MrBayesPrint ("%s %1.2lf and %1.2lf\n", spacer, mp->m10gammaFix[0], mp->m10gammaFix[0]); } if (!strcmp(mp->codonCatFreqPr,"Dirichlet")) MrBayesPrint ("%s Dirichlet(%1.2lf,%1.2lf) on pi(1) and pi(2)\n", spacer, mp->codonCatDir[0], mp->codonCatDir[1]); else MrBayesPrint ("%s Fixed(%1.2lf,%1.2lf) on pi(1) and pi(2)\n", spacer, mp->codonCatFreqFix[0], mp->codonCatFreqFix[1]); } } else if (j == P_PI) { if (ms->dataType == STANDARD) { if (!strcmp(mp->symPiPr, "Uniform")) MrBayesPrint ("%s Prior = Symmetric dirichlet with uniform(%1.2lf,%1.2lf) variance parameter\n", spacer, mp->symBetaUni[0], mp->symBetaUni[1]); else if (!strcmp(mp->symPiPr, "Exponential")) MrBayesPrint ("%s Prior = Symmetric dirichlet with exponential(%1.2lf) variance parameter\n", spacer, mp->symBetaExp); else { /* mp->symBetaFix == -1 */ if (AreDoublesEqual(mp->symBetaFix, -1.0, ETA)==YES) MrBayesPrint ("%s Prior = Symmetric dirichlet with all parameters fixed to infinity\n", spacer); else MrBayesPrint ("%s Prior = Symmetric dirichlet with all parameters fixed to %1.2lf\n", spacer, mp->symBetaFix); } } else if (ms->dataType == PROTEIN) { if (!strcmp(mp->aaModelPr, "Fixed") && (!strcmp(mp->aaModel, "Equalin") || !strcmp(mp->aaModel, "Gtr"))) { if (!strcmp(mp->stateFreqPr,"Dirichlet")) { MrBayesPrint ("%s Prior = Dirichlet\n", spacer); } else if (!strcmp(mp->stateFreqPr,"Fixed") && !strcmp(mp->stateFreqsFixType,"Equal")) { MrBayesPrint ("%s Prior = Fixed (equal frequencies)\n", spacer); } else if (!strcmp(mp->stateFreqPr,"Fixed") && !strcmp(mp->stateFreqsFixType,"User")) { MrBayesPrint ("%s Prior = Fixed (user-specified)\n", spacer); } else if (!strcmp(mp->stateFreqPr,"Fixed") && !strcmp(mp->stateFreqsFixType,"Empirical")) { MrBayesPrint ("%s Prior = Fixed (empirical frequencies)\n", spacer); } } else if (!strcmp(mp->aaModelPr, "Fixed") && !strcmp(mp->aaModel, "Poisson")) { MrBayesPrint ("%s Prior = Fixed (equal frequencies)\n", spacer); } else if (!strcmp(mp->aaModelPr, "Fixed") && strcmp(mp->aaModel, "Equalin") && strcmp(mp->aaModel, "Poisson")) { MrBayesPrint ("%s Prior = Fixed (%s frequencies)\n", spacer, mp->aaModel); } else { MrBayesPrint ("%s Prior = Fixed (from mixture of models)\n", spacer); } } else { if (!strcmp(mp->stateFreqPr,"Dirichlet")) MrBayesPrint ("%s Prior = Dirichlet\n", spacer); else MrBayesPrint ("%s Prior = Fixed\n", spacer); } } else if (j == P_MIXTURE_RATES) { MrBayesPrint ("%s Prior = Ordered flat Dirichlet distribution\n", spacer); } else if (j == P_SHAPE) { if (!strcmp(mp->shapePr,"Uniform")) MrBayesPrint ("%s Prior = Uniform(%1.2lf,%1.2lf)\n", spacer, mp->shapeUni[0], mp->shapeUni[1]); else if (!strcmp(mp->shapePr,"Exponential")) MrBayesPrint ("%s Prior = Exponential(%1.2lf)\n", spacer, mp->shapeExp); else MrBayesPrint ("%s Prior = Fixed(%1.2lf)\n", spacer, mp->shapeFix); } else if (j == P_PINVAR) { if (!strcmp(mp->pInvarPr,"Uniform")) MrBayesPrint ("%s Prior = Uniform(%1.2lf,%1.2lf)\n", spacer, mp->pInvarUni[0], mp->pInvarUni[1]); else MrBayesPrint ("%s Prior = Fixed(%1.2lf)\n", spacer, mp->pInvarFix); } else if (j == P_CORREL) { if (!strcmp(mp->adGammaCorPr,"Uniform")) MrBayesPrint ("%s Prior = Uniform(%1.2lf,%1.2lf)\n", spacer, mp->adgCorrUni[0], mp->adgCorrUni[1]); else MrBayesPrint ("%s Prior = Fixed(%1.2lf)\n", spacer, mp->adgCorrFix); } else if (j == P_SWITCH) { if (!strcmp(mp->covSwitchPr,"Uniform")) MrBayesPrint ("%s Prior = Uniform(%1.2lf,%1.2lf)\n", spacer, mp->covswitchUni[0], mp->covswitchUni[1]); else if (!strcmp(mp->covSwitchPr,"Exponential")) MrBayesPrint ("%s Prior = Exponential(%1.2lf)\n", spacer, mp->covswitchExp); else MrBayesPrint ("%s Prior = Fixed(%1.2lf,%1.2lf)\n", spacer, mp->covswitchFix[0], mp->covswitchFix[1]); } else if (j == P_RATEMULT) { if (!strcmp(mp->ratePr,"Fixed")) MrBayesPrint ("%s Prior = Fixed(1.0)\n", spacer); else { MrBayesPrint ("%s Prior = Dirichlet(", spacer); for (d=n=0; dgeneratePr,"Fixed")) MrBayesPrint ("%s Prior = Fixed(1.0)\n", spacer); else { MrBayesPrint ("%s Prior = Dirichlet(", spacer); printedCol = (int)(strlen(spacer)) + 25 + 10; for (n=0; n screenWidth) { MrBayesPrint ("\n%s ", spacer); printedCol = (int)(strlen(spacer)) + 25 + 10; } if (n == numTrees-2) MrBayesPrint ("1.00)\n"); else MrBayesPrint ("1.00,"); printedCol += 5; } } } else if (j == P_TOPOLOGY) { if (!strcmp(mp->topologyPr,"Uniform")) MrBayesPrint ("%s Prior = All topologies equally probable a priori\n", spacer); else if (!strcmp(mp->topologyPr,"Speciestree")) MrBayesPrint ("%s Prior = Topology constrained to fold within species tree\n", spacer); else if (!strcmp(mp->topologyPr,"Constraints")) MrBayesPrint ("%s Prior = Prior on topology obeys the following constraints:\n", spacer); else MrBayesPrint ("%s Prior = Prior is fixed to the topology of user tree '%s'\n", spacer, userTree[mp->topologyFix]->name); if (!strcmp(mp->topologyPr,"Constraints")) { for (a=0; aactiveConstraints[a] == NO) continue; if (definedConstraintsType[a] == HARD) MrBayesPrint ("%s -- Hard constraint \"%s\"\n", spacer, constraintNames[a]); else if (definedConstraintsType[a] == PARTIAL) MrBayesPrint ("%s -- Partial constraint \"%s\"\n", spacer, constraintNames[a]); else /* if (true) */ MrBayesPrint ("%s -- Negative constraint \"%s\"\n", spacer, constraintNames[a]); } } } else if (j == P_BRLENS) { if (!strcmp(mp->parsModel, "Yes")) MrBayesPrint ("%s Prior = Reconstructed using parsimony\n", spacer); else { if (!strcmp(mp->brlensPr,"Unconstrained")) { MrBayesPrint ("%s Prior = Unconstrained:%s", spacer, mp->unconstrainedPr); if (!strcmp(mp->unconstrainedPr, "Uniform")) MrBayesPrint ("(%1.1lf,%1.1lf)\n", mp->brlensUni[0], mp->brlensUni[1]); else if (!strcmp(mp->unconstrainedPr, "Exponential")) MrBayesPrint ("(%1.1lf)\n", mp->brlensExp); else if (!strcmp(mp->unconstrainedPr, "twoExp")) MrBayesPrint ("(%1.1lf,%1.1lf)\n", mp->brlens2Exp[0], mp->brlens2Exp[1]); else MrBayesPrint ("(%1.1lf,%1.4lf,%1.1lf,%1.1lf)\n", mp->brlensDir[0], mp->brlensDir[1], mp->brlensDir[2], mp->brlensDir[3]); } else if (!strcmp(mp->brlensPr, "Clock")) { MrBayesPrint ("%s Prior = Clock:%s\n", spacer, mp->clockPr); if ((!strcmp(mp->clockPr, "Uniform") || !strcmp(mp->clockPr, "Birthdeath") || !strcmp(mp->clockPr, "Fossilization")) && !strcmp(mp->nodeAgePr, "Unconstrained")) { MrBayesPrint ("%s Tree age has a %s distribution\n", spacer, mp->treeAgePr.name); } if (!strcmp(mp->nodeAgePr,"Calibrated")) { b = 0; for (a=0; aactiveConstraints[a] == YES && nodeCalibration[a].prior != unconstrained) b++; } if (b > 1) MrBayesPrint ("%s Node depths are constrained by the following age constraints:\n", spacer); else MrBayesPrint ("%s Node depths are calibrated by the following age constraint:\n", spacer); for (a=0; aactiveConstraints[a] == YES && nodeCalibration[a].prior != unconstrained) { MrBayesPrint ("%s -- The age of node '%s' is %s\n", spacer, constraintNames[a], nodeCalibration[a].name); for (k=0; kclockPr,"Uniform") || !strcmp(mp->clockPr, "Birthdeath") || !strcmp(mp->clockPr,"Fossilization")) { MrBayesPrint ("%s -- Tree age has a %s distribution\n", spacer, mp->treeAgePr.name); } } } else MrBayesPrint ("%s Node ages are not constrained\n", spacer); } else { assert (!strcmp(mp->brlensPr, "Fixed")); MrBayesPrint ("%s Prior = Fixed, branch lengths are fixed to the ones of the user tree '%s'\n", spacer, userTree[mp->topologyFix]->name); } } } else if (j == P_SPECRATE) { if (!strcmp(mp->speciationPr,"Uniform")) MrBayesPrint ("%s Prior = Uniform(%1.2lf,%1.2lf)\n", spacer, mp->speciationUni[0], mp->speciationUni[1]); else if (!strcmp(mp->speciationPr,"Exponential")) MrBayesPrint ("%s Prior = Exponential(%1.2lf)\n", spacer, mp->speciationExp); else MrBayesPrint ("%s Prior = Fixed(%1.2lf)\n", spacer, mp->speciationFix); } else if (j == P_EXTRATE) { if (!strcmp(mp->extinctionPr,"Beta")) MrBayesPrint ("%s Prior = Beta(%1.2lf,%1.2lf)\n", spacer, mp->extinctionBeta[0], mp->extinctionBeta[1]); else if (!strcmp(mp->extinctionPr,"Exponential")) MrBayesPrint ("%s Prior = Exponential(%1.2lf)\n", spacer, mp->extinctionExp); else MrBayesPrint ("%s Prior = Fixed(%1.2lf)\n", spacer, mp->extinctionFix); } else if (j == P_FOSLRATE) { if (!strcmp(mp->fossilizationPr,"Beta")) MrBayesPrint ("%s Prior = Beta(%1.2lf,%1.2lf)\n", spacer, mp->fossilizationBeta[0], mp->fossilizationBeta[1]); else if (!strcmp(mp->fossilizationPr,"Exponential")) MrBayesPrint ("%s Prior = Exponential(%1.2lf)\n", spacer, mp->fossilizationExp); else MrBayesPrint ("%s Prior = Fixed(%1.2lf)\n", spacer, mp->fossilizationFix); } else if (j == P_POPSIZE) { if (!strcmp(mp->popSizePr,"Uniform")) MrBayesPrint ("%s Prior = Uniform(%1.2lf,%1.2lf)\n", spacer, mp->popSizeUni[0], mp->popSizeUni[1]); else if (!strcmp(mp->popSizePr,"Lognormal")) MrBayesPrint ("%s Prior = Lognormal(%1.2lf,%1.2lf)\n", spacer, mp->popSizeLognormal[0], mp->popSizeLognormal[1]); else if (!strcmp(mp->popSizePr,"Normal")) MrBayesPrint ("%s Prior = Truncated Normal(%1.2lf,%1.2lf)\n", spacer, mp->popSizeNormal[0], mp->popSizeNormal[1]); else if (!strcmp(mp->popSizePr,"Gamma")) MrBayesPrint ("%s Prior = Gamma(%1.2lf,%1.2lf)\n", spacer, mp->popSizeGamma[0], mp->popSizeGamma[1]); else MrBayesPrint ("%s Prior = Fixed(%1.5lf)\n", spacer, mp->popSizeFix); if (!strcmp(mp->topologyPr,"Speciestree")) { if (!strcmp(mp->popVarPr,"Equal") || !strcmp(mp->popSizePr,"Fixed")) MrBayesPrint ("%s Population size the same across species tree\n", spacer); else MrBayesPrint ("%s Population size varies across branches in species tree\n", spacer); } } else if (j == P_GROWTH) { if (!strcmp(mp->growthPr,"Uniform")) MrBayesPrint ("%s Prior = Uniform(%1.2lf,%1.2lf)\n", spacer, mp->growthUni[0], mp->growthUni[1]); else if (!strcmp(mp->growthPr,"Exponential")) MrBayesPrint ("%s Prior = Exponential(%1.2lf)\n", spacer, mp->growthExp); else if (!strcmp(mp->growthPr,"Normal")) MrBayesPrint ("%s Prior = Normal(%1.2lf,%1.2lf)\n", spacer, mp->growthNorm[0], mp->growthNorm[1]); else MrBayesPrint ("%s Prior = Fixed(%1.2lf)\n", spacer, mp->growthFix); } else if (j == P_AAMODEL) { if (!strcmp(mp->aaModelPr,"Mixed")) { /* check to see if you have a uniform prior */ isSame = YES; for (a=0; a<9; a++) for (b=a+1; b<10; b++) /* mp->aaModelPrProbs[a] != mp->aaModelPrProbs[b] */ if (AreDoublesEqual(mp->aaModelPrProbs[a], mp->aaModelPrProbs[b], ETA)==FALSE) isSame = NO; MrBayesPrint ("%s Prior = Poisson, Jones, Dayhoff, Mtrev, Mtmam, Wag, Rtrev,\n", spacer); if (isSame == YES) MrBayesPrint ("%s Cprev, Vt, and Blosum models have equal prior probability\n", spacer); else MrBayesPrint ("%s Cprev, Vt, and Blosum models have unequal prior probability\n", spacer); } else MrBayesPrint ("%s Prior = Fixed(%s)\n", spacer, mp->aaModel); } else if (j == P_BMCORR) { if (!strcmp(mp->brownCorrPr,"Uniform")) MrBayesPrint ("%s Prior = Uniform(%1.2lf,%1.2lf)\n", spacer, mp->brownCorrUni[0], mp->brownCorrUni[1]); else MrBayesPrint ("%s Prior = Fixed(%1.2lf)\n", spacer, mp->brownCorrFix); } else if (j == P_BMSIGMA) { if (!strcmp(mp->brownScalePr,"Uniform")) MrBayesPrint ("%s Prior = Uniform(%1.2lf,%1.2lf)\n", spacer, mp->brownScaleUni[0], mp->brownScaleUni[1]); else if (!strcmp(mp->brownScalePr,"Gamma")) MrBayesPrint ("%s Prior = Gamma(%1.2lf,%1.2lf)\n", spacer, mp->brownScaleGamma[0], mp->brownScaleGamma[1]); else MrBayesPrint ("%s Prior = Fixed(%1.2lf)\n", spacer, mp->brownScaleFix); } else if (j == P_CPPRATE) { if (!strcmp(mp->cppRatePr,"Exponential")) MrBayesPrint ("%s Prior = Exponential(%1.2lf)\n", spacer, mp->cppRateExp); else MrBayesPrint ("%s Prior = Fixed(%1.2lf)\n", spacer, mp->cppRateFix); } else if (j == P_CPPMULTDEV) { if (!strcmp(mp->cppMultDevPr,"Fixed")) MrBayesPrint ("%s Prior = Fixed(%1.2lf)\n", spacer, mp->cppMultDevFix); } else if (j == P_CPPEVENTS) { MrBayesPrint ("%s Prior = Poisson (%s) [Events]\n", spacer, modelSettings[p->relParts[0]].cppRate->name); MrBayesPrint ("%s Lognormal (0.00,%1.2lf) [Rate multipliers]\n", spacer, mp->cppMultDevFix); } else if (j == P_TK02VAR) { if (!strcmp(mp->tk02varPr,"Uniform")) MrBayesPrint ("%s Prior = Uniform(%1.2lf,%1.2lf)\n", spacer, mp->tk02varUni[0], mp->tk02varUni[1]); else if (!strcmp(mp->tk02varPr,"Exponential")) MrBayesPrint ("%s Prior = Exponential(%1.2lf)\n", spacer, mp->tk02varExp); else MrBayesPrint ("%s Prior = Fixed(%1.2lf)\n", spacer, mp->tk02varFix); } else if (j == P_TK02BRANCHRATES) { MrBayesPrint ("%s Prior = LogNormal (expectation = r_0, variance = %s * v) \n", spacer, modelSettings[p->relParts[0]].tk02var->name); MrBayesPrint ("%s [r_0 is beginning rate of branch, v is branch length]\n", spacer); } else if (j == P_WNVAR) { if (!strcmp(mp->wnvarPr, "Uniform")) MrBayesPrint ("%s Prior = Uniform(%1.2lf,%1.2lf)\n", spacer, mp->wnvarUni[0], mp->wnvarUni[1]); else if (!strcmp(mp->wnvarPr, "Exponential")) MrBayesPrint ("%s Prior = Exponential(%1.2lf)\n", spacer, mp->wnvarExp); else MrBayesPrint ("%s Prior = Fixed(%1.2lf)\n", spacer, mp->wnvarFix); } else if (j == P_WNBRANCHRATES) { MrBayesPrint ("%s Prior = Gamma (expectation = 1.0, variance = %s / v) \n", spacer, modelSettings[p->relParts[0]].wnvar->name); MrBayesPrint ("%s [v is branch length (t * c)]\n", spacer); } else if (j == P_IGRVAR) { if (!strcmp(mp->igrvarPr,"Uniform")) MrBayesPrint ("%s Prior = Uniform(%1.2lf,%1.2lf)\n", spacer, mp->igrvarUni[0], mp->igrvarUni[1]); else if (!strcmp(mp->igrvarPr,"Exponential")) MrBayesPrint ("%s Prior = Exponential(%1.2lf)\n", spacer, mp->igrvarExp); else MrBayesPrint ("%s Prior = Fixed(%1.2lf)\n", spacer, mp->igrvarFix); } else if (j == P_IGRBRANCHRATES) { MrBayesPrint ("%s Prior = Gamma (expectation = 1.0, variance = %s) \n", spacer, modelSettings[p->relParts[0]].igrvar->name); } else if (j == P_ILNVAR) { if (!strcmp(mp->ilnvarPr, "Uniform")) MrBayesPrint ("%s Prior = Uniform(%1.2lf,%1.2lf)\n", spacer, mp->ilnvarUni[0], mp->ilnvarUni[1]); else if (!strcmp(mp->ilnvarPr, "Exponential")) MrBayesPrint ("%s Prior = Exponential(%1.2lf)\n", spacer, mp->ilnvarExp); else MrBayesPrint ("%s Prior = Fixed(%1.2lf)\n", spacer, mp->ilnvarFix); } else if (j == P_ILNBRANCHRATES) { MrBayesPrint ("%s Prior = LogNormal (expectation = 1.0, variance = %s) \n", spacer, modelSettings[p->relParts[0]].ilnvar->name); } else if (j == P_MIXEDVAR) { if (!strcmp(mp->mixedvarPr,"Uniform")) MrBayesPrint ("%s Prior = Uniform(%1.2lf,%1.2lf)\n", spacer, mp->mixedvarUni[0], mp->mixedvarUni[1]); else if (!strcmp(mp->mixedvarPr,"Exponential")) MrBayesPrint ("%s Prior = Exponential(%1.2lf)\n", spacer, mp->mixedvarExp); else MrBayesPrint ("%s Prior = Fixed(%1.2lf)\n", spacer, mp->mixedvarFix); } else if (j == P_MIXEDBRCHRATES) { MrBayesPrint ("%s Prior = Mixed IGR and ILN (mean = 1, variance = %s) \n", spacer, modelSettings[p->relParts[0]].mixedvar->name); MrBayesPrint ("%s Uniform prior on relaxed clock models [Pr(IGR)=Pr(ILN)=0.5]\n", spacer); } else if (j == P_CLOCKRATE) { if (!strcmp(mp->clockRatePr,"Normal")) MrBayesPrint ("%s Prior = Normal(%1.6lf,%1.6lf)\n", spacer, mp->clockRateNormal[0], mp->clockRateNormal[1]); else if (!strcmp(mp->clockRatePr,"Lognormal")) MrBayesPrint ("%s Prior = Lognormal(%1.2lf,%1.2lf)\n", spacer, mp->clockRateLognormal[0], mp->clockRateLognormal[1]); else if (!strcmp(mp->clockRatePr,"Gamma")) MrBayesPrint ("%s Prior = Gamma(%1.2lf,%1.2lf)\n", spacer, mp->clockRateGamma[0], mp->clockRateGamma[1]); else if (!strcmp(mp->clockRatePr,"Exponential")) MrBayesPrint ("%s Prior = Exponential(%1.2lf)\n", spacer, mp->clockRateExp); else MrBayesPrint ("%s Prior = Fixed(%1.6lf)\n", spacer, mp->clockRateFix); if (!strcmp(mp->clockVarPr,"Strict")) MrBayesPrint ("%s The clock rate is constant throughout the tree (strict clock)\n", spacer); else if (!strcmp(mp->clockVarPr,"Cpp")) MrBayesPrint ("%s The clock rate varies according to a CPP model\n", spacer); else if (!strcmp(mp->clockVarPr,"TK02")) MrBayesPrint ("%s The clock rate varies according to a autocorrelated lognormal model\n", spacer); else if (!strcmp(mp->clockVarPr,"WN")) MrBayesPrint ("%s The clock rate varies according to a white noise model\n", spacer); else if (!strcmp(mp->clockVarPr,"IGR")) MrBayesPrint ("%s The clock rate varies according to an independent gamma model\n", spacer); else if (!strcmp(mp->clockVarPr,"ILN")) MrBayesPrint ("%s The clock rate varies according to an independent lognormal model\n", spacer); else /* if (!strcmp(mp->clockVarPr,"Mixed")) */ MrBayesPrint ("%s The clock rate varies according to mixed IGR and ILN models\n", spacer); } else if (j == P_SPECIESTREE) { MrBayesPrint ("%s Prior = Uniform on topologies and branch lengths\n", spacer); } /* print partitions */ if (numCurrentDivisions > 1) { if (p->nRelParts == 1) MrBayesPrint ("%s Partition = %d\n", spacer, p->relParts[0]+1); else if (p->nRelParts == 2) { MrBayesPrint ("%s Partitions = %d and %d\n", spacer, p->relParts[0]+1, p->relParts[1]+1); } else if (p->nRelParts == numCurrentDivisions) { MrBayesPrint ("%s Partitions = All\n", spacer); } else /* if (p->nRelParts > 2) */ { MrBayesPrint ("%s Partitions = ", spacer); for (j=0; jnRelParts; j++) { if (j == p->nRelParts - 2) MrBayesPrint ("%d, and ", p->relParts[j]+1); else if (j == p->nRelParts - 1) MrBayesPrint ("%d\n", p->relParts[j]+1); else MrBayesPrint ("%d, ", p->relParts[j]+1); } } } /* show subparams */ if (p->nSubParams > 0) { if (p->nSubParams == 1) MrBayesPrint ("%s Subparam. = %s\n", spacer, p->subParams[0]->name); else { printedCol = 0; for (k=0; knSubParams; k++) { if (k == 0) { MrBayesPrint ("%s Subparams = %s", spacer, p->subParams[k]->name); printedCol = (int)(strlen(spacer)) + 25 + (int)(strlen(p->subParams[k]->name)); } else if (k == p->nSubParams - 1) { if (printedCol + 5 > screenWidth) MrBayesPrint ("\n%s and ", spacer); else if (printedCol + (int)(strlen(p->subParams[k]->name)) + 5 > screenWidth) MrBayesPrint (" and \n%s ", spacer); else MrBayesPrint (" and "); MrBayesPrint ("%s\n", p->subParams[k]->name); } else { if (printedCol + (int)(strlen(p->subParams[k]->name)) + 2 > screenWidth) { MrBayesPrint (", \n%s ", spacer); printedCol = (int)(strlen(spacer)) + 25; } else { MrBayesPrint (", "); printedCol += 2; } MrBayesPrint ("%s", p->subParams[k]->name); printedCol += (int)strlen(p->subParams[k]->name); } } } } /* show used moves */ if (showMoves == YES && (p->printParam == YES || p->paramType == P_TOPOLOGY || p->paramType == P_BRLENS)) { /* check if runs are same */ areRunsSame = YES; for (run=1; runparm != p) continue; chainIndex = run*chainParams.numChains + chain; refIndex = chain; if (AreDoublesEqual (mv->relProposalProb[chainIndex], mv->relProposalProb[refIndex], 0.000001) == NO) areRunsSame = NO; } } } for (run=0; run 0 && areRunsSame == YES) continue; /* check if chains are same */ areChainsSame = YES; for (chain=1; chainparm != p) continue; chainIndex = run*chainParams.numChains + chain; refIndex = run*chainParams.numChains; if (AreDoublesEqual (mv->relProposalProb[chainIndex], mv->relProposalProb[refIndex], 0.000001) == NO) areChainsSame = NO; } } /* now print moves */ for (chain=0; chain 0 && areChainsSame == YES) continue; if (run==0 && chain==0) MrBayesPrint ("%s Moves = ", spacer); else MrBayesPrint ("%s ", spacer); numPrinted = 0; printedCol = (int)(strlen(spacer)) + 25; for (k=0; kparm != p) continue; chainIndex = run*chainParams.numChains + chain; if (mv->relProposalProb[chainIndex] <= 0.000001) continue; if (numPrinted == 0) { MrBayesPrint ("%s ", mv->name, mv->relProposalProb[chainIndex]); printedCol += 9 + (int)strlen(mv->name) + (int)(log10(mv->relProposalProb[chainIndex])) + 3; } else { if (printedCol + 11 + (int)(strlen(mv->name)) + (int)(log10(mv->relProposalProb[chainIndex])) + 3 > screenWidth) { MrBayesPrint (", \n%s ", spacer); printedCol = 25 + (int)(strlen(spacer)); } else { MrBayesPrint (", "); printedCol += 2; } MrBayesPrint ("%s ", mv->name, mv->relProposalProb[chainIndex]); printedCol += (9 + (int)(strlen(mv->name)) + (int)(log10(mv->relProposalProb[chainIndex])) + 3); } numPrinted++; } if (numPrinted == 0 && p->paramType == P_BRLENS) { for (k=0; ktree) break; MrBayesPrint ("For moves, see param. '%s'", params[k].name); } else if (numPrinted == 0) MrBayesPrint ("WARNING! No moves -- param. fixed to startvals"); if (areRunsSame == YES && areChainsSame == YES) MrBayesPrint ("\n"); else if (areRunsSame == YES && areChainsSame == NO) MrBayesPrint (" [chain %d]\n", chain+1); else if (areRunsSame == NO && areChainsSame == YES) MrBayesPrint (" [run %d]\n", run+1); else /* if (areRunsSame == NO && areChainsSame == NO) */ MrBayesPrint (" [run %d, chain %d]\n", run+1, chain+1); } } } /* show available moves */ if (showAllAvailable == YES && (p->printParam == YES || p->paramType == P_TOPOLOGY || p->paramType == P_BRLENS)) { /* loop over moves */ numPrinted = 0; printedCol = 0; for (k=0; kparm != p) continue; numMovedChains = 0; for (run=0; runrelProposalProb[chainIndex] > 0.000001) numMovedChains++; } } if (numMovedChains == 0) { if (numPrinted == 0) { MrBayesPrint ("%s Not used = ", spacer); printedCol = (int)(strlen(spacer)) + 25; } else if (printedCol + 2 + (int)(strlen(mv->moveType->shortName)) > screenWidth) { MrBayesPrint (", \n%s ", spacer); printedCol = (int)(strlen(spacer)) + 25; } else { MrBayesPrint (", "); printedCol += 2; } MrBayesPrint ("%s", mv->moveType->shortName); printedCol += (int)strlen(mv->moveType->shortName); numPrinted++; } } if (numPrinted > 0) MrBayesPrint ("\n"); } /* show startvals */ if (showStartVals == YES && (p->printParam == YES || p->paramType == P_TOPOLOGY || p->paramType == P_BRLENS || p->paramType == P_SPECIESTREE || p->paramType == P_POPSIZE)) { if (p->paramType == P_TOPOLOGY || p->paramType == P_BRLENS || p->paramType == P_SPECIESTREE) { /* check if they are the same */ areRunsSame = YES; if (p->paramType == P_TOPOLOGY) { for (run=1; runparamType == P_BRLENS) { for (run=1; runparamType == P_SPECIESTREE) { for (run=1; run 0 && areRunsSame == YES) break; for (chain=0; chainname); else MrBayesPrint ("%s tree '%s'", spacer, GetTree (p, run*chainParams.numChains+chain, 0)->name); if (chainParams.numChains > 1 && areRunsSame == YES) MrBayesPrint (" [chain %d]", chain+1); else if (chainParams.numChains == 1 && areRunsSame == NO) MrBayesPrint (" [run %d]", run+1); else if (areRunsSame == NO) MrBayesPrint (" [run %d, chain %d]", run+1, chain+1); MrBayesPrint ("\n"); } } } /* end topology and brlens parameters */ else if (p->paramType == P_OMEGA && p->paramId != OMEGA_DIR && p->paramId != OMEGA_FIX && p->paramId != OMEGA_FFF && p->paramId != OMEGA_FF && p->paramId != OMEGA_10FFF) { /* we need to print values and subvalues for the category frequencies in a NY98-like model. */ areRunsSame = YES; for (run=1; runnValues; for (k=0; krelParts[0]].omegaVar, "M10")) { value += (mp->numM10BetaCats + mp->numM10GammaCats); refValue += (mp->numM10BetaCats + mp->numM10GammaCats); for (k=0; k<4; k++) { if (AreDoublesEqual (value[k + 4], refValue[k + 4], 0.000001) == NO) { areRunsSame = NO; break; } } for (k=0; k<2; k++) { if (AreDoublesEqual (value[k], refValue[k], 0.000001) == NO) { areRunsSame = NO; break; } } } else { for (k=0; k<3; k++) { if (AreDoublesEqual (value[k], refValue[k], 0.000001) == NO) { areRunsSame = NO; break; } } } if (areRunsSame == NO) break; } if (areRunsSame == NO) break; } /* now print values */ for (run=0; runnValues; if (run == 0 && chain == 0) MrBayesPrint ("%s Startvals = (%1.3lf", spacer, value[0]); else MrBayesPrint ("%s (%1.3lf", spacer, value[0]); for (k=1; krelParts[0]].omegaVar, "M10")) { for (k=0; k<4; k++) { MrBayesPrint (",%1.3lf", subValue[k + mp->numM10BetaCats+mp->numM10GammaCats+4]); } for (k=0; k<2; k++) { MrBayesPrint (",%1.3lf", subValue[k + mp->numM10BetaCats+mp->numM10GammaCats]); } } else { for (k=0; k<3; k++) { MrBayesPrint (",%1.3lf", subValue[k]); } } MrBayesPrint (")"); if (chainParams.numChains > 1 && areRunsSame == YES) MrBayesPrint (" [chain %d]\n", chain+1); else if (chainParams.numChains == 1 && areRunsSame == NO) MrBayesPrint (" [run %d]\n", run+1); else if (areRunsSame == NO) MrBayesPrint (" [run %d, chain %d]\n", run+1, chain+1); } } } else { /* run of the mill parameter */ if (p->paramType == P_CLOCKRATE) { for (j=0; jparamType == P_PI && modelParams[p->relParts[0]].dataType != STANDARD)) { nValues = p->nSubValues; value = GetParamSubVals (p, run*chainParams.numChains+chain, 0); refValue = GetParamSubVals (p, chain, 0); } else { nValues = p->nValues; value = GetParamVals (p, run*chainParams.numChains+chain, 0); refValue = GetParamVals (p, chain, 0); } for (k=0; k 0 && areRunsSame == YES) break; areChainsSame = YES; for (chain=1; chainparamType == P_PI && modelParams[p->relParts[0]].dataType != STANDARD)) { nValues = p->nSubValues; value = GetParamSubVals (p, run*chainParams.numChains+chain, 0); refValue = GetParamSubVals (p, run*chainParams.numChains, 0); } else { nValues = p->nValues; value = GetParamVals (p, run*chainParams.numChains+chain, 0); refValue = GetParamVals (p, run*chainParams.numChains, 0); } for (k=0; k 0) continue; if ((p->paramType == P_PI && modelParams[p->relParts[0]].dataType != STANDARD)) { nValues = p->nSubValues; value = GetParamSubVals (p, run*chainParams.numChains+chain, 0); } else { nValues = p->nValues; value = GetParamVals (p, run*chainParams.numChains+chain, 0); } if (run == 0 && chain == 0) MrBayesPrint ("%s Startvals = (%1.3lf", spacer, value[0]); else MrBayesPrint ("%s (%1.3lf", spacer, value[0]); for (k=1; kisCalibrated == NO) continue; if (clockRatep == NULL) { clockRatep = GetParamVals(modelSettings[t->relParts[0]].clockRate, chain, 0); t_calibrated = t; assert (clockRatep); } findAllowedClockrate (t, &mintmp, &maxtmp); if (minClockRate < mintmp) minClockRate = mintmp; if (maxClockRate > maxtmp) maxClockRate = maxtmp; } /* clock rate is the same for all trees of a given chain */ if (clockRatep != NULL) { if (minClockRate > maxClockRate) { MrBayesPrint ("%s ERROR: Calibrated trees require compatible clockrates but they are incompatible for run %d, chain %d.\n", spacer, chain/chainParams.numChains + 1, chain%chainParams.numChains + 1); *clockRatep=0; return (ERROR); } if (!strcmp(modelParams[t_calibrated->relParts[0]].clockRatePr, "Fixed")) { if (clockRate < 0.0 && AreDoublesEqual (*clockRatep, modelParams[t_calibrated->relParts[0]].clockRateFix, 0.0001) == YES) { updateTrees = NO; } else { updateTrees = YES; } *clockRatep = modelParams[t_calibrated->relParts[0]].clockRateFix; if ((*clockRatep < minClockRate && AreDoublesEqual (*clockRatep, minClockRate, 0.0001) == NO) || (*clockRatep > maxClockRate && AreDoublesEqual (*clockRatep, maxClockRate, 0.0001) == NO)) { MrBayesPrint ("%s ERROR: Calibrated trees require clockrate in range from %f to %f, while clockrate prior is fixed to %f for run %d chain %d.\n", spacer, minClockRate, maxClockRate, *clockRatep, chain/chainParams.numChains + 1, chain%chainParams.numChains + 1); *clockRatep=0; return (ERROR); } if (clockRate > 0.0) { if (AreDoublesEqual (*clockRatep, clockRate, 0.0001) == NO) { MrBayesPrint ("%s ERROR: Requested clockrate:%f does not match fixed clockrate prior:%f.\n", spacer, clockRate, *clockRatep); *clockRatep=0; return (ERROR); } } } else { /* clock prior is not fixed */ updateTrees = YES; if (clockRate > 0.0) { *clockRatep = clockRate; if ((*clockRatep < minClockRate && AreDoublesEqual (*clockRatep, minClockRate, 0.0001) == NO) || (*clockRatep > maxClockRate && AreDoublesEqual (*clockRatep, maxClockRate, 0.0001) == NO)) { MrBayesPrint ("%s ERROR: Calibrated trees require clockrate in range from %f to %f, while requested clockrate is %f for run %d chain %d.\n", spacer, minClockRate, maxClockRate, clockRate, chain/chainParams.numChains + 1, chain%chainParams.numChains + 1); *clockRatep=0; return (ERROR); } } else if (clockRate == 0.0) { if ((*clockRatep < minClockRate && AreDoublesEqual (*clockRatep, minClockRate, 0.0001) == NO) || (*clockRatep > maxClockRate && AreDoublesEqual (*clockRatep, maxClockRate, 0.0001) == NO)) { *clockRatep = minClockRate; } } else // if (clockRate < 0.0) { if ((*clockRatep < minClockRate && AreDoublesEqual (*clockRatep, minClockRate, 0.0001) == NO) || (*clockRatep > maxClockRate && AreDoublesEqual (*clockRatep, maxClockRate, 0.0001) == NO)) { *clockRatep = minClockRate; } else { updateTrees = NO; } } } if (updateTrees == YES) { for (i=0; iisCalibrated == NO) continue; UpdateTreeWithClockrate (t,*clockRatep); } } } return (NO_ERROR); } /*---------------------------------------------- | | UpdateCppEvolLength: Recursive function to | update evolLength of one node for Cpp | relaxed clock model | -----------------------------------------------*/ int UpdateCppEvolLength (int *nEvents, MrBFlt **pos, MrBFlt **rateMult, MrBFlt *evolLength, TreeNode *p, MrBFlt baseRate) { int i; MrBFlt endRate; if (p != NULL) { # ifdef DEBUG_CPP if (baseRate < POS_MIN || baseRate > POS_INFINITY) { printf ("baseRate out of bounds (%.15e for node %d\n", baseRate, p->index); return (ERROR); } # endif p->upDateTi = YES; p->upDateCl = YES; if (nEvents[p->index] == 0) { evolLength[p->index] = p->length * baseRate; } else { /* note that event positions are from the top of the branch (more recent) to the bottom of the branch (older) */ /* The algorithm below successively multiplies in the more basal rate multipliers, starting from the top of the branch. The length of the branch is first assumed to be 1.0; at the end we multiply the result with the true length of the branch. */ evolLength[p->index] = pos[p->index][0] * rateMult[p->index][0]; for (i=1; iindex]; i++) { evolLength[p->index] += (pos[p->index][i] - pos[p->index][i-1]); evolLength[p->index] *= rateMult[p->index][i]; } evolLength[p->index] += (1.0 - pos[p->index][nEvents[p->index]-1]); evolLength[p->index] *= baseRate; evolLength[p->index] *= p->length; } /* calculate end rate; we can do this in any order */ endRate = baseRate; for (i=0; iindex]; i++) endRate *= rateMult[p->index][i]; # ifdef DEBUG_CPP if (endRate < POS_MIN || endRate > POS_INFINITY) { printf ("endRate out of bounds (%.15e for node %d)\n", endRate, p->index); return (ERROR); } if (p->anc != NULL && p->anc->anc != NULL && (evolLength[p->index] < POS_MIN || evolLength[p->index] > POS_INFINITY)) { printf ("Effective branch length out of bounds (%.15e for node %d)\n", evolLength[p->index], p->index); return (ERROR); } # endif /* call left and right descendants */ if (UpdateCppEvolLength (nEvents, pos, rateMult, evolLength, p->left, endRate)==ERROR) return (ERROR); if (UpdateCppEvolLength (nEvents, pos, rateMult, evolLength, p->right, endRate)==ERROR) return (ERROR); } return (NO_ERROR); } /*------------------------------------------------- | | UpdateCppEvolLengths: Recalculate effective | evolutionary lengths and set update flags | for Cpp relaxed clock model | --------------------------------------------------*/ int UpdateCppEvolLengths (Param *param, TreeNode *p, int chain) { int i, *nEvents; TreeNode *q; MrBFlt baseRate = 1.0, **pos, **rateMult, *evolLength; i = 2*chain + state[chain]; nEvents = param->nEvents[i]; pos = param->position[i]; rateMult = param->rateMult[i]; evolLength = GetParamSubVals (param, chain, state[chain]); q = p->anc; while (q->anc != NULL) { for (i=0; iindex]; i++) baseRate *= rateMult[q->index][i]; q = q->anc; } if (UpdateCppEvolLength (nEvents, pos, rateMult, evolLength, p, baseRate)==ERROR) return (ERROR); return(NO_ERROR); } /* UpdateTK02EvolLengths: update branch lengths for tk02 model */ int UpdateTK02EvolLengths (Param *param, Tree *t, int chain) { int i; MrBFlt *tk02Rate, *brlens; TreeNode *p; tk02Rate = GetParamVals (param, chain, state[chain]); brlens = GetParamSubVals (param, chain, state[chain]); for (i=0; inNodes-2; i++) { p = t->allDownPass[i]; brlens[p->index] = p->length * (tk02Rate[p->index] + tk02Rate[p->anc->index]) / 2.0; } return (NO_ERROR); } /* UpdateIndBranchLengths: update branch lengths for independent rates model */ int UpdateIndBrachLengths (Param *param, Tree *t, int chain) { int i; MrBFlt *indRate, *brlens; TreeNode *p; indRate = GetParamVals (param, chain, state[chain]); brlens = GetParamSubVals (param, chain, state[chain]); for (i=0; inNodes-2; i++) { p = t->allDownPass[i]; brlens[p->index] = p->length * indRate[p->index]; } return (NO_ERROR); } NBISweden-MrBayes-ecfd55c/src/model.h000066400000000000000000000046051526212227600174620ustar00rootroot00000000000000#ifndef MODEL_H_ #define MODEL_H_ int AreDoublesEqual (MrBFlt x, MrBFlt y, MrBFlt tol); int ChangeNumRuns (int from, int to); int ChangeNumChains (int from, int to); int CheckModel (void); int DoesTreeSatisfyConstraints (Tree *t); int DoLink (void); int DoLinkParm (char *parmName, char *tkn); int DoLset (void); int DoLsetParm (char *parmName, char *tkn); int DoPlot (void); int DoPlotParm (char *parmName, char *tkn); int DoPropset (void); int DoPropsetParm (char *parmName, char *tkn); int DoPrset (void); int DoPrsetParm (char *parmName, char *tkn); int DoQuit (void); int DoReport (void); int DoReportParm (char *parmName, char *tkn); int DoShowMcmcTrees (void); int DoShowModel (void); int DoShowMoves (void); int DoShowmovesParm (char *parmName, char *tkn); int DoShowParams (void); int DoStartvals (void); int DoStartvalsParm (char *parmName, char *tkn); int DoUnlink (void); int FillNormalParams (RandLong *seed, int fromChain, int toChain); int FillRelPartsString (Param *p, char **relPartString); void FillStdStateFreqs (int chfrom, int chto, RandLong *seed); int FillTopologySubParams (Param *param, int chn, int state, RandLong *seed); int FillTreeParams (RandLong *seed, int fromChain, int toChain); int FreeModel (void); int GetEmpiricalFreqs (int *relParts, int nRelParts); int GetNumDivisionChars (void); int *GetParamIntVals (Param *parm, int chain, int state); MrBFlt *GetParamStdStateFreqs (Param *parm, int chain, int state); MrBFlt *GetParamSubVals (Param *parm, int chain, int state); MrBFlt *GetParamVals (Param *parm, int chain, int state); Tree *GetTree (Param *parm, int chain, int state); Tree *GetTreeFromIndex (int index, int chain, int state); int InitializeLinks (void); int InitializeTreeCalibrations (Tree *t); int Link (void); int SetAARates (void); int SetLocalTaxa (void); int SetModelDefaults (void); int SetMoves (void); int SetUpAnalysis (RandLong *seed); void SetUpMoveTypes (void); int ShowModel (void); int Unlink (void); int UpdateClockRate (MrBFlt clockRate, int chain); int UpdateCppEvolLengths (Param *param, TreeNode *p, int chain); int UpdateTK02EvolLengths (Param *param, Tree *t, int chain); int UpdateIndBrachLengths (Param *param, Tree *t, int chain); #endif /* MODEL_H_ */ NBISweden-MrBayes-ecfd55c/src/proposal.c000066400000000000000000022325511526212227600202210ustar00rootroot00000000000000/* * MrBayes 3 * * (c) 2002-2023 * * John P. Huelsenbeck * Dept. Integrative Biology * University of California, Berkeley * Berkeley, CA 94720-3140 * johnh@berkeley.edu * * Fredrik Ronquist * Swedish Museum of Natural History * Box 50007 * SE-10405 Stockholm, SWEDEN * fredrik.ronquist@nrm.se * * With important contributions by * * Paul van der Mark (paulvdm@sc.fsu.edu) * Maxim Teslenko (maxkth@gmail.com) * Chi Zhang (zhangchicool@gmail.com) * * and by many users (run 'acknowledgments' to see more info) * * This program is free software; you can redistribute it and/or * modify it under the terms of the GNU General Public License * as published by the Free Software Foundation; either version 2 * of the License, or (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details (www.gnu.org). * */ #include "bayes.h" #include "best.h" #include "command.h" #include "mcmc.h" #include "model.h" #include "proposal.h" #include "utils.h" /* debugging compiler statements */ #undef DEBUG_LOCAL #undef DEBUG_UNROOTED_SLIDER #undef DEBUG_ParsSPR #undef DEBUG_ExtSS #undef DEBUG_CSLIDER #undef DEBUG_ExtSPR #undef DEBUG_ExtSPRClock #undef DEBUG_ParsSPRClock #undef DEBUG_ExtTBR #undef DEBUG_NNIClock #undef DEBUG_SPLITMERGE #undef DEBUG_FBDPR extern int *chainId; void TouchAllTreeNodes (ModelInfo *m, int chain); int Move_Aamodel (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp) { /* Change amino acid model for model mixing amino acid model ID's AAMODEL_POISSON 0 AAMODEL_JONES 1 AAMODEL_DAY 2 AAMODEL_MTREV 3 AAMODEL_MTMAM 4 AAMODEL_WAG 5 AAMODEL_RTREV 6 AAMODEL_CPREV 7 AAMODEL_VT 8 AAMODEL_BLOSUM 9 */ int i, oldM, newM; MrBFlt *bs, *subValue; ModelParams *mp; /* get model params */ mp = &modelParams[param->relParts[0]]; subValue = GetParamSubVals(param, chain, state[chain]); /* get old value of model */ oldM = (int)*GetParamVals(param, chain, state[chain]); /* get a new model ID */ do { newM = (int)(RandomNumber(seed) * 10); } while (newM == oldM); /* set proposal ratio */ *lnProposalRatio = 0.0; /* set prior ratio */ *lnPriorRatio = subValue[newM] - subValue[oldM]; /* copy new amino acid model ID back */ *GetParamVals(param, chain, state[chain]) = (MrBFlt)newM; /* set amino acid frequencies */ bs = GetParamSubVals (modelSettings[param->relParts[0]].stateFreq, chain, state[chain]); if (newM == AAMODEL_POISSON) { for (i=0; inStates; i++) bs[i] = 1.0 / 20.0; } else if (newM == AAMODEL_JONES) { for (i=0; inStates; i++) bs[i] = jonesPi[i]; } else if (newM == AAMODEL_DAY) { for (i=0; inStates; i++) bs[i] = dayhoffPi[i]; } else if (newM == AAMODEL_MTREV) { for (i=0; inStates; i++) bs[i] = mtrev24Pi[i]; } else if (newM == AAMODEL_MTMAM) { for (i=0; inStates; i++) bs[i] = mtmamPi[i]; } else if (newM == AAMODEL_WAG) { for (i=0; inStates; i++) bs[i] = wagPi[i]; } else if (newM == AAMODEL_RTREV) { for (i=0; inStates; i++) bs[i] = rtrevPi[i]; } else if (newM == AAMODEL_CPREV) { for (i=0; inStates; i++) bs[i] = cprevPi[i]; } else if (newM == AAMODEL_VT) { for (i=0; inStates; i++) bs[i] = vtPi[i]; } else if (newM == AAMODEL_BLOSUM) { for (i=0; inStates; i++) bs[i] = blosPi[i]; } /* Set update flags for all partitions that share this amino acid model. Note that the conditional likelihood update flags have been set before we even call this function. */ for (i=0; inRelParts; i++) TouchAllTreeNodes(&modelSettings[param->relParts[i]],chain); /* Set update flags for cijks for all affected partitions. */ for (i=0; inRelParts; i++) modelSettings[param->relParts[i]].upDateCijk = YES; return (NO_ERROR); } int Move_AddDeleteCPPEvent (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp) { /* add or delete one Poisson process event */ int i, k, addEvent, *nEvents, numEvents; MrBFlt sigma, m, lognormalLnProb, **position, **rateMultiplier, length, pos, rate; TreeNode *p, *q; ModelInfo *model; Tree *t; /* get the model settings */ model = &modelSettings[param->relParts[0]]; /* get cpp rate */ rate = *GetParamVals (model->cppRate, chain, state[chain]); /* get sigma of lognormal of rate multipliers */ sigma = *GetParamVals (model->cppMultDev, chain, state[chain]); /* get the cpp event data */ nEvents = param->nEvents[2*chain+state[chain]]; position = param->position[2*chain+state[chain]]; rateMultiplier = param->rateMult[2*chain+state[chain]]; /* get tree */ t = GetTree (param, chain, state[chain]); /* pick a branch */ do { p = t->allDownPass[(int)(RandomNumber(seed) * (t->nNodes -2))]; } while (p->anc == NULL || (p->anc->anc == NULL)); /* get number of events for convenience */ numEvents = nEvents[p->index]; /* add or delete ? */ addEvent = NO; if (numEvents == 0) addEvent = YES; else if (RandomNumber(seed) < 0.5) addEvent = YES; if (addEvent == NO) { /* delete event */ /* choose random event */ k = (int) (RandomNumber(seed) * numEvents); /* save multiplier to be deleted */ m = rateMultiplier[p->index][k]; /* rearrange and reduce */ for (i=k; iindex][i] = position[p->index][i+1]; rateMultiplier[p->index][i] = rateMultiplier[p->index][i+1]; } if (numEvents-1 > 0) { position[p->index] = (MrBFlt *) SafeRealloc ((void *) position[p->index], (numEvents-1)*sizeof(MrBFlt)); rateMultiplier[p->index] = (MrBFlt *) SafeRealloc ((void *) rateMultiplier[p->index], (numEvents-1)*sizeof(MrBFlt)); assert (position[p->index] != NULL && rateMultiplier[p->index] != NULL); } else { free (position[p->index]); free (rateMultiplier[p->index]); position[p->index] = rateMultiplier[p->index] = NULL; } /* update number of events */ nEvents[p->index]--; } else /* if (addEvent == YES) */ { /* add event */ /* generate new multiplier */ m = LogNormalRandomVariable (0.0, sigma, seed); /* generate new position */ pos = RandomNumber(seed); /* find place in current array */ for (k=0; kindex][k] > pos) break; } /* rearrange and insert */ position[p->index] = (MrBFlt *) SafeRealloc ((void *)position[p->index], (numEvents+1)*sizeof(MrBFlt)); rateMultiplier[p->index] = (MrBFlt *) SafeRealloc ((void *)rateMultiplier[p->index], (numEvents+1)*sizeof(MrBFlt)); assert (position[p->index] != NULL && rateMultiplier[p->index] != NULL); for (i=numEvents; i>k; i--) { position[p->index][i] = position[p->index][i-1]; rateMultiplier[p->index][i] = rateMultiplier[p->index][i-1]; } position[p->index][k] = pos; rateMultiplier[p->index][k] = m; /* update number of events */ nEvents[p->index]++; } /* the CPP process is relative to expected substitutions */ length = p->length; lognormalLnProb = LnProbLogNormal(0.0, sigma, m); if (addEvent == YES) (*lnPriorRatio) = lognormalLnProb + log (rate); else (*lnPriorRatio) = -(lognormalLnProb + log(rate)); if (addEvent == YES) /* note that nEvents[p->index] now contains k+1 after addition */ (*lnProposalRatio) = log (length / ((double) nEvents[p->index])) - lognormalLnProb; else /* note that nEvents[p->index] contains k after deletion */ (*lnProposalRatio) = log ((double)(nEvents[p->index]+1) / length) + lognormalLnProb; /* take care of asymmetric add and delete probabilities around 0 and 1 events */ if (addEvent == YES && nEvents[p->index] == 1) (*lnProposalRatio) += log (0.5); else if (addEvent == NO && nEvents[p->index] == 0) (*lnProposalRatio) += log (2.0); /* update evolLengths in subtree above new event */ if (UpdateCppEvolLengths (param, p, chain)==ERROR) { abortMove=YES; return (NO_ERROR); } /* set update of cond likes down to root */ /* crown tree update flags set in UpdateCppEvolLengths */ q = p->anc; while (q->anc != NULL) { q->upDateCl = YES; q = q->anc; } return (NO_ERROR); } int Move_Adgamma (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp) { /* Change correlation parameter (-1, 1) of adgamma model */ int i, isValidP; MrBFlt oldP, newP, window, minP, maxP, ran, *markovTiValues; ModelParams *mp; /* get size of window, centered on current rho */ window = mvp[0]; /* get model params */ mp = &modelParams[param->relParts[0]]; /* get minimum and maximum values for rho */ minP = mp->adgCorrUni[0]; maxP = mp->adgCorrUni[1]; /* get address of markovTi */ markovTiValues = GetParamSubVals (param, chain, state[chain]); /* get old value of rho */ oldP = *GetParamVals(param, chain, state[chain]); /* change value for rho */ ran = RandomNumber(seed); if (maxP-minP < window) { window = maxP-minP; } newP = oldP + window * (ran - 0.5); /* check that new value is valid */ isValidP = NO; do { if (newP < minP) newP = 2* minP - newP; else if (newP > maxP) newP = 2 * maxP - newP; else isValidP = YES; } while (isValidP == NO); /* get proposal ratio */ *lnProposalRatio = 0.0; /* get prior ratio */ *lnPriorRatio = 0.0; /* copy new rho value back */ *GetParamVals(param, chain, state[chain]) = newP; /* fill in new Markov trans probs */ AutodGamma (markovTiValues, newP, mp->numGammaCats); /* Set update flags for all partitions that share this rho. Note that the conditional likelihood update flags have been set before we even call this function. */ for (i=0; inRelParts; i++) TouchAllTreeNodes(&modelSettings[param->relParts[i]],chain); /* Update flags for divisions already set */ return (NO_ERROR); } int Move_Beta (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp) { /* change symmetric Dirichlet variance using multiplier */ int i, j, k, isValidB, isPriorExp, nStates; MrBFlt oldB, newB, minB, maxB, *bs, ran, factor, tuning, x, y; ModelParams *mp; /* get tuning parameter */ tuning = mvp[0]; /* multiplier tuning parameter lambda */ /* get model paramaters */ mp = &modelParams[param->relParts[0]]; /* get prior, minimum and maximum values for rate */ if (!strcmp(mp->symPiPr,"Uniform")) { isPriorExp = NO; minB = mp->symBetaUni[0]; maxB = mp->symBetaUni[1]; } else { isPriorExp = YES; minB = SYMPI_MIN; maxB = SYMPI_MAX; } /* get old value of symDir */ oldB = *GetParamVals(param, chain, state[chain]); /* change value */ ran = RandomNumber(seed); factor = exp(tuning * (ran - 0.5)); newB = oldB * factor; /* check validity */ isValidB = NO; do { if (newB < minB) newB = minB * minB / newB; else if (newB > maxB) newB = maxB * maxB / newB; else isValidB = YES; } while (isValidB == NO); /* set new value of symDir */ *GetParamVals(param, chain, state[chain]) = newB; /* get proposal ratio */ *lnProposalRatio = log (newB / oldB); /* get prior ratio */ if (isPriorExp == YES) { *lnPriorRatio = (mp->symBetaExp) * (oldB - newB); } else *lnPriorRatio = 0.0; /* fill in the new betacat frequencies */ bs = GetParamStdStateFreqs(param, chain, state[chain]); BetaBreaks (newB, newB, bs, mp->numBetaCats); k = 2 * mp->numBetaCats; for (i=k-2; i>0; i-=2) { bs[i] = bs[i/2]; } for (i=1; inumBetaCats; for (i=0; inSympi; i++) { /* get number of states */ nStates = param->sympinStates[i]; /* get prior ratio update */ x = LnGamma(newB*nStates) - nStates*LnGamma(newB); y = LnGamma(oldB*nStates) - nStates*LnGamma(oldB); for (j=0; jnRelParts; i++) TouchAllTreeNodes(&modelSettings[param->relParts[i]],chain); /* may need to hit update flag for cijks if we have multistate characters */ for (i=0; inRelParts; i++) { if (modelSettings[param->relParts[i]].nCijkParts > 0) modelSettings[param->relParts[i]].upDateCijk = YES; } return (NO_ERROR); } int Move_BrLen (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp) { /* change one branch length */ MrBFlt tuning, maxV, minV, m, newM; TreeNode *p; ModelParams *mp; Tree *t; int isVPriorExp; tuning = mvp[0]; /* Larget & Simon's tuning parameter lambda */ mp = &modelParams[param->relParts[0]]; /* max and min brlen */ if (param->paramId == BRLENS_UNI) { minV = mp->brlensUni[0]; maxV = mp->brlensUni[1]; isVPriorExp = NO; } else if (param->paramId == BRLENS_GamDir) { minV = BRLENS_MIN; maxV = BRLENS_MAX; isVPriorExp = 2; } else if (param->paramId == BRLENS_iGmDir) { minV = BRLENS_MIN; maxV = BRLENS_MAX; isVPriorExp = 3; } else if (param->paramId == BRLENS_twoExp) { minV = BRLENS_MIN; maxV = BRLENS_MAX; isVPriorExp = 4; } else { minV = BRLENS_MIN; maxV = BRLENS_MAX; isVPriorExp = YES; } /* get tree */ t = GetTree (param, chain, state[chain]); /* Dirichlet or twoExp prior */ if (isVPriorExp > 1) (*lnPriorRatio) = -LogDirPrior(t, mp, isVPriorExp); /* pick a branch */ do { p = t->allDownPass[(int)(RandomNumber(seed) * t->nNodes)]; } while (p->anc == NULL || (t->isRooted == YES && p->anc->anc == NULL)); /* determine new length */ m = p->length; newM = m * exp(tuning * (RandomNumber(seed) - 0.5)); /* reflect new length if necessary */ while (newM < minV || newM > maxV) { if (newM < minV) newM = minV * minV / newM; else if (newM > maxV) newM = maxV * maxV / newM; } p->length = newM; /* calculate proposal ratio */ /* must be based on new length after reflection */ (*lnProposalRatio) = log(newM / m); /* set flags for update of transition probabilities at p */ p->upDateTi = YES; /* set the update flag for cond likes if p is connected to root in unrooted */ /* tree, if this is not done, cond likes are not updated in this case */ if (t->isRooted == NO && p->anc->anc == NULL) p->upDateCl = YES; /* set flags for update of cond likes from p->anc and down to root */ while (p->anc->anc != NULL) { p = p->anc; p->upDateCl = YES; } /* update prior if exponential prior on branch lengths */ if (param->paramId == BRLENS_EXP) (*lnPriorRatio) = (mp->brlensExp) * (m - newM); /* Dirichlet or twoExp prior */ else if (isVPriorExp > 1) (*lnPriorRatio) += LogDirPrior(t, mp, isVPriorExp); return (NO_ERROR); } int Move_ClockRate_M (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp) { /* change clock rate using multiplier */ int i, j, k, *nEvents; MrBFlt oldRate, newRate, factor, lambda, nu, var, *brlens, *rate, N, newTheta, oldTheta, growth, newLnPrior, oldLnPrior; Tree *t, *oldT; TreeNode *p, *q; Param *treeParam, *subParm; ModelParams *mp; ModelInfo *m; /* get old value of clock rate */ oldRate = *GetParamVals(param, chain, state[chain]); /* Rely on general algorithm to change the value */ Move_PosRealMultiplier(param, chain, seed, lnPriorRatio, lnProposalRatio, mvp); if (abortMove == YES) return NO_ERROR; /* get new value of clock rate */ newRate = *GetParamVals(param, chain, state[chain]); /* calculate factor */ factor = newRate / oldRate; /* clock rate applies to all clock trees */ for (i = 0; i < numTrees; i++) { t = GetTreeFromIndex(i, chain, state[chain]); if (t->isClock == NO) continue; if (!strcmp(modelParams[t->relParts[0]].clockPr, "Fixed")) continue; oldT = GetTreeFromIndex(i, chain, 1^state[chain]); treeParam = modelSettings[t->relParts[0]].brlens; /* adjust the node depths and lengths */ for (j = 0; j < t->nNodes-1; j++) { p = t->allDownPass[j]; q = oldT->allDownPass[j]; p->nodeDepth *= factor; /* no harm done if nodeDepth==0.0 (undated tips) */ p->length *= factor; /* no harm done if length==0.0 (root or fossil ancestors)*/ if (p->length < 0.0 || (q->length > BRLENS_MIN && p->length < BRLENS_MIN) || (q->length < TIME_MIN && p->length > TIME_MIN)) { /* consider ancestral fossil (brl=0) in fossilized bd tree */ abortMove = YES; return (NO_ERROR); } } /* prior ratio for coalecent tree, as theta is changed */ mp = &modelParams[t->relParts[0]]; if (!strcmp(mp->clockPr,"Coalescence")) { m = &modelSettings[t->relParts[0]]; N = *GetParamVals(m->popSize, chain, state[chain]); if (!strcmp(mp->ploidy, "Diploid")) N *= 4.0; else if (!strcmp(mp->ploidy, "Zlinked")) N *= 3.0; else N *= 2.0; oldTheta = N * oldRate; newTheta = N * newRate; if (!strcmp(mp->growthPr, "Fixed")) growth = mp->growthFix; else growth = *GetParamVals(m->growthRate, chain, state[chain]); if (LnCoalescencePriorPr (oldT, &oldLnPrior, oldTheta, growth) == ERROR) { MrBayesPrint ("%s Problem calculating prior for coalescent process\n", spacer); return (ERROR); } if (LnCoalescencePriorPr (t, &newLnPrior, newTheta, growth) == ERROR) { MrBayesPrint ("%s Problem calculating prior for coalescent process\n", spacer); return (ERROR); } (*lnPriorRatio) += newLnPrior - oldLnPrior; } /* adjust proposal and prior ratio for relaxed clock models */ for (k = 0; k < treeParam->nSubParams; k++) { subParm = treeParam->subParams[k]; if (subParm->paramType == P_CPPEVENTS) { nEvents = subParm->nEvents[2*chain+state[chain]]; lambda = *GetParamVals (modelSettings[subParm->relParts[0]].cppRate, chain, state[chain]); /* proposal ratio */ for (j=0; jnNodes-2; j++) { p = t->allDownPass[j]; q = oldT->allDownPass[j]; (*lnProposalRatio) += nEvents[p->index ] * log (p->length / q->length); } /* prior ratio */ (*lnPriorRatio) += lambda * (TreeLen(oldT) - TreeLen(t)); /* update effective evolutionary lengths */ if (UpdateCppEvolLengths (subParm, t->root->left, chain) == ERROR) { abortMove = YES; return (NO_ERROR); } } else if ( subParm->paramType == P_TK02BRANCHRATES) { if (subParm->paramType == P_TK02BRANCHRATES) nu = *GetParamVals (modelSettings[subParm->relParts[0]].tk02var, chain, state[chain]); else nu = *GetParamVals (modelSettings[subParm->relParts[0]].mixedvar, chain, state[chain]); rate = GetParamVals (subParm, chain, state[chain]); brlens = GetParamSubVals (subParm, chain, state[chain]); /* no proposal ratio effect */ /* prior ratio and update of brlens */ for (j = 0; j < t->nNodes-2; j++) { p = t->allDownPass[j]; q = oldT->allDownPass[j]; if (p->length > 0.0) // not ancestral fossil { (*lnPriorRatio) -= LnProbLogNormal_Mean_LogVar (rate[q->anc->index], nu*q->length, rate[q->index]); (*lnPriorRatio) += LnProbLogNormal_Mean_LogVar (rate[p->anc->index], nu*p->length, rate[p->index]); brlens[p->index] = p->length * (rate[p->anc->index]+rate[p->index])/2.0; } } } else if ( subParm->paramType == P_WNBRANCHRATES) { if (subParm->paramType == P_WNBRANCHRATES) var = *GetParamVals (modelSettings[subParm->relParts[0]].wnvar, chain, state[chain]); else var = *GetParamVals (modelSettings[subParm->relParts[0]].mixedvar, chain, state[chain]); rate = GetParamVals (subParm, chain, state[chain]); brlens = GetParamSubVals (subParm, chain, state[chain]); /* prior ratio and update of brlens */ for (j = 0; j < t->nNodes-2; j++) { p = t->allDownPass[j]; q = oldT->allDownPass[j]; if (p->length > 0.0) // not ancestral fossil { (*lnPriorRatio) -= LnProbGamma (q->length/var, q->length/var, rate[q->index]); (*lnPriorRatio) += LnProbGamma (p->length/var, p->length/var, rate[p->index]); brlens[p->index] = rate[p->index] * p->length; } } } else if ( subParm->paramType == P_ILNBRANCHRATES || (subParm->paramType == P_MIXEDBRCHRATES && *GetParamIntVals(subParm, chain, state[chain]) == RCL_ILN)) { if (subParm->paramType == P_ILNBRANCHRATES) nu = *GetParamVals (modelSettings[subParm->relParts[0]].ilnvar, chain, state[chain]); else nu = *GetParamVals (modelSettings[subParm->relParts[0]].mixedvar, chain, state[chain]); rate = GetParamVals (subParm, chain, state[chain]); brlens = GetParamSubVals (subParm, chain, state[chain]); /* prior ratio and update of brlens */ for (j = 0; j < t->nNodes-2; j++) { p = t->allDownPass[j]; q = oldT->allDownPass[j]; if (p->length > 0.0) // not ancestral fossil { (*lnPriorRatio) -= LnProbLogNormal_Mean_Var (1.0, nu, rate[q->index]); (*lnPriorRatio) += LnProbLogNormal_Mean_Var (1.0, nu, rate[p->index]); brlens[p->index] = rate[p->index] * p->length; } } } else if ( subParm->paramType == P_IGRBRANCHRATES || (subParm->paramType == P_MIXEDBRCHRATES && *GetParamIntVals(subParm, chain, state[chain]) == RCL_IGR)) { if (subParm->paramType == P_IGRBRANCHRATES) var = *GetParamVals (modelSettings[subParm->relParts[0]].igrvar, chain, state[chain]); else var = *GetParamVals (modelSettings[subParm->relParts[0]].mixedvar, chain, state[chain]); rate = GetParamVals (subParm, chain, state[chain]); brlens = GetParamSubVals (subParm, chain, state[chain]); /* prior ratio and update of brlens */ for (j = 0; j < t->nNodes-2; j++) { p = t->allDownPass[j]; q = oldT->allDownPass[j]; if (p->length > 0.0) // not ancestral fossil { (*lnPriorRatio) -= LnProbGamma (1.0/var, 1.0/var, rate[q->index]); (*lnPriorRatio) += LnProbGamma (1.0/var, 1.0/var, rate[p->index]); brlens[p->index] = rate[p->index] * p->length; } } } } } return (NO_ERROR); } int Move_CPPEventPosition (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp) { /* move the position of one CPP event */ int i, j, k, *nEvents; MrBFlt pos, temp, **position, **rateMultiplier; TreeNode *p=NULL, *q; Tree *t; /* get the cpp event data */ nEvents = param->nEvents[2*chain+state[chain]]; position = param->position[2*chain+state[chain]]; rateMultiplier = param->rateMult[2*chain+state[chain]]; /* get tree */ t = GetTree (param, chain, state[chain]); /* pick a branch and an event */ for (i=j=0; inNodes - 2; i++) { p = t->allDownPass[i]; j += nEvents[p->index]; } if (j == 0) { abortMove = YES; return (NO_ERROR); } k = (int) (RandomNumber(seed) * j); for (i=j=0; inNodes - 2; i++) { p = t->allDownPass[i]; j += nEvents[p->index]; if (j > k) break; } if (position[p->index] == NULL) getchar(); /* find local index */ k = k - (j - nEvents[p->index]); /* find new position */ pos = RandomNumber(seed); if (pos < POS_MIN || 1.0 - pos < POS_MIN) { abortMove = YES; return (NO_ERROR); } position[p->index][k] = pos; /* sort events; bubble sort for now */ for (i=0; iindex]; i++) { for (j=i+1; jindex]; j++) { if (position[p->index][j] < position[p->index][i]) { temp = position[p->index][i]; position[p->index][i] = position[p->index][j]; position[p->index][j] = temp; temp = rateMultiplier[p->index][i]; rateMultiplier[p->index][i] = rateMultiplier[p->index][j]; rateMultiplier[p->index][j] = temp; } } } /* calculate prior and proposal ratio */ (*lnPriorRatio) = (*lnProposalRatio) = 0.0; /* update branch evolution lengths */ if (UpdateCppEvolLengths (param, p, chain) == ERROR) { abortMove = YES; return (NO_ERROR); } /* set update of cond likes down to root */ /* update of crowntree set in UpdateCppEvolLengths */ q = p->anc; while (q->anc != NULL) { q->upDateCl = YES; q = q->anc; } return (NO_ERROR); } int Move_CPPRate (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp) { /* move the CPP rate (lambda) using multiplier */ int i, j, *nEvents, sumEvents; MrBFlt oldLambda, newLambda, treeLength, tuning; Model *mp; TreeNode *p; Tree *t; /* get tuning parameter */ tuning = mvp[0]; /* get model params */ mp = &modelParams[param->relParts[0]]; /* get the CPP rate */ oldLambda = *GetParamVals (param, chain, state[chain]); /* set new value */ newLambda = oldLambda * exp ((0.5 - RandomNumber(seed))*tuning); /* reflect if necessary */ while (newLambda < CPPLAMBDA_MIN || newLambda > CPPLAMBDA_MAX) { if (newLambda < CPPLAMBDA_MIN) newLambda = CPPLAMBDA_MIN * CPPLAMBDA_MIN / newLambda; if (newLambda > CPPLAMBDA_MAX) newLambda = CPPLAMBDA_MAX * CPPLAMBDA_MAX / newLambda; } /* store new value */ *GetParamVals (param, chain, state[chain]) = newLambda; /* calculate prior ratio */ (*lnPriorRatio) = 0.0; for (i=0; inSubParams; i++) { nEvents = param->subParams[i]->nEvents[2*chain+state[chain]]; sumEvents = 0; t = GetTree (param->subParams[i], chain, state[chain]); treeLength = 0.0; for (j=0; jnNodes-2; j++) { p = t->allDownPass[j]; sumEvents += nEvents[p->index]; treeLength += p->length; } (*lnPriorRatio) += (oldLambda - newLambda) * treeLength; (*lnPriorRatio) += sumEvents * log (newLambda / oldLambda); } /* adjust for prior on cppRate */ if (!strcmp(mp->cppRatePr,"Exponential")) (*lnPriorRatio) += mp->cppRateExp * (oldLambda - newLambda); /* calculate proposal ratio */ (*lnProposalRatio) = log (newLambda / oldLambda); /* we do not need to update likelihoods */ for (i=0; inRelParts; i++) { modelSettings[param->relParts[i]].upDateCl = NO; } return (NO_ERROR); } int Move_CPPRateMultiplier_M (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp) { /* move one CPP rate multiplier using multiplier */ int i, j, k, *nEvents; MrBFlt newRateMultiplier, oldRateMultiplier, tuning, minM, maxM, sigma, **rateMultiplier; TreeNode *p = NULL; ModelInfo *m; Tree *t; TreeNode *q; /* get the tuning parameter */ tuning = mvp[0]; /* get the model settings */ m = &modelSettings[param->relParts[0]]; /* get tree */ t = GetTree (param, chain, state[chain]); /* get CPP event data */ nEvents = param->nEvents[2*chain+state[chain]]; rateMultiplier = param->rateMult[2*chain+state[chain]]; /* get minimum and maximum of CPP rate multiplier */ minM = param->min; maxM = param->max; /* pick a branch and a rateMultiplier */ for (i=j=0; inNodes - 2; i++) { p = t->allDownPass[i]; j += nEvents[p->index]; } if (j == 0) { abortMove = YES; return (NO_ERROR); } k = (int) (RandomNumber(seed) * j); for (i=j=0; inNodes - 2; i++) { p = t->allDownPass[i]; j += nEvents[p->index]; if (j > k) break; } /* find local index */ k = nEvents[p->index] - (j - k); /* find new rateMultiplier */ oldRateMultiplier = rateMultiplier[p->index][k]; newRateMultiplier = oldRateMultiplier * (exp (0.5 - RandomNumber(seed) * tuning)); /* reflect if necessary */ while (newRateMultiplier < minM || newRateMultiplier > maxM) { if (newRateMultiplier < minM) newRateMultiplier = minM * minM / newRateMultiplier; if (newRateMultiplier > maxM) newRateMultiplier = maxM * maxM / newRateMultiplier; } rateMultiplier[p->index][k] = newRateMultiplier; /* calculate prior ratio */ sigma = *GetParamVals (m->cppMultDev, chain, state[chain]); (*lnPriorRatio) = LnRatioLogNormal (0.0, sigma, newRateMultiplier, oldRateMultiplier); /* calculate proposal ratio */ (*lnProposalRatio) = log (newRateMultiplier / oldRateMultiplier); /* update branch evolution lengths */ if (UpdateCppEvolLengths (param, p, chain)==ERROR) { abortMove = YES; return (NO_ERROR); } /* set update of cond likes down to root */ /* update of crowntree set in UpdateCppEvolLengths */ q = p->anc; while (q->anc != NULL) { q->upDateCl = YES; q = q->anc; } return (NO_ERROR); } int Move_CPPRateMultiplierRnd (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp) { /* move one CPP rate multiplier by redrawing from prior */ int i, j, k, *nEvents; MrBFlt sigma, newRateMultiplier, oldRateMultiplier, **rateMultiplier; TreeNode *p=NULL, *q; ModelInfo *m; Tree *t; /* get the model settings */ m = &modelSettings[param->relParts[0]]; /* get the CPP event data */ nEvents = param->nEvents[2*chain+state[chain]]; rateMultiplier = param->rateMult[2*chain+state[chain]]; /* get tree */ t = GetTree (param, chain, state[chain]); /* pick a branch and a rateMultiplier */ for (i=j=0; inNodes - 2; i++) { p = t->allDownPass[i]; j += nEvents[p->index]; } if (j == 0) { abortMove = YES; return (NO_ERROR); } k = (int) (RandomNumber(seed) * j); for (i=j=0; inNodes - 2; i++) { p = t->allDownPass[i]; j += nEvents[p->index]; if (j > k) break; } /* find local index */ k = nEvents[p->index] - (j - k); /* record old rate multiplier */ oldRateMultiplier = rateMultiplier[p->index][k]; /* find stdev of lognormal */ sigma = *GetParamVals (m->cppMultDev, chain, state[chain]); /* set new value */ do { newRateMultiplier = LogNormalRandomVariable (0.0, sigma, seed); } while (newRateMultiplier < param->min || newRateMultiplier > param->max); rateMultiplier[p->index][k] = newRateMultiplier; /* calculate prior ratio */ (*lnPriorRatio) = LnRatioLogNormal(0.0, sigma, newRateMultiplier, oldRateMultiplier); /* calculate proposal ratio */ (*lnProposalRatio) = LnRatioLogNormal (0.0, sigma, oldRateMultiplier, newRateMultiplier); /* update branch evolution lengths */ if (UpdateCppEvolLengths (param, p, chain) == ERROR) { abortMove = YES; return (NO_ERROR); } /* set update of cond likes down to root */ /* update of crowntree set in UpdateCppEvolLengths */ q = p->anc; while (q->anc != NULL) { q->upDateCl = YES; q = q->anc; } return (NO_ERROR); } int Move_AddBranch (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp) { /* Move an ancestral fossil (brl = 0) to fossil tip (brl > 0) __|__ __|__ | | | | | --> q|___ | --> | | q|___p | |p r| r| 1. Pich a fossil among those with brl = 0 (prob = 1/k) 2. Propose brl from a uniform(0, ?) distribution */ int i, j, k, mFossil, kFossil; MrBFlt minDepth, maxDepth, newLength, clockRate, x, oldQLength, oldRLength, *brlens=NULL, nu=0.0, var=0.0, *rate=NULL; TreeNode *p=NULL, *q=NULL, *r; Tree *t; ModelParams *mp; ModelInfo *m; Param *subParm; Calibration *calibrationPtr = NULL; /* get tree */ t = GetTree (param, chain, state[chain]); /* get number of ancestral and tip fossils */ kFossil = mFossil = 0; for (i = 0; i < t->nNodes -1; i++) { p = t->allDownPass[i]; p->marked = NO; // reset marked node if (p->left == NULL && p->right == NULL && p->nodeDepth > 0.0) { if (p->length > 0.0) { mFossil++; // count tip fossil } else { p->marked = YES; // mark anc fossil kFossil++; // count anc fossil } } } if (kFossil == 0) // no ancestral fossil, nothing to do { abortMove = YES; return (NO_ERROR); } /* get model params and model info */ mp = &modelParams[param->relParts[0]]; m = &modelSettings[param->relParts[0]]; /* get clock rate */ clockRate = *(GetParamVals(m->clockRate, chain, state[chain])); /* pick an ancestral fossil randomly */ j = (int) (RandomNumber(seed) * kFossil); for (i = k = 0; i < t->nNodes -1; i++) { p = t->allDownPass[i]; if (p->marked == YES) k++; if (k > j) break; } /* now p is pointing to the ancestral fossil whose brl needs to be changed to >0. let's do it! */ q = p->anc; if (q->left == p) r = q->right; else r = q->left; /* determine lower and upper bound of forward move, abort if impossible */ minDepth = p->nodeDepth + BRLENS_MIN; if (q->anc->anc == NULL) maxDepth = TREEHEIGHT_MAX; else maxDepth = q->anc->nodeDepth - BRLENS_MIN; if (q->isDated == YES) calibrationPtr = q->calibration; else if (q->anc->anc == NULL) // q is root but not dated calibrationPtr = &mp->treeAgePr; if (calibrationPtr != NULL) { if (calibrationPtr->prior == fixed || calibrationPtr->min * clockRate > minDepth) { abortMove = YES; return (NO_ERROR); } if (calibrationPtr->max * clockRate < maxDepth) maxDepth = calibrationPtr->max * clockRate; } if (minDepth >= maxDepth) { abortMove = YES; return (NO_ERROR); } /* record old lengths and depths */ oldQLength = q->length; oldRLength = r->length; // oldDepth = q->nodeDepth; /* propose the branch length leading to the fossil */ newLength = (RandomNumber(seed)) * (maxDepth - minDepth); /* adjust brls and depths, set flags for update of trans probs */ p->length = newLength; p->upDateTi = YES; q->nodeDepth += newLength; if (q->anc->anc != NULL) { q->length -= newLength; q->upDateTi = YES; } r->length += newLength; r->upDateTi = YES; // newDepth = q->nodeDepth; /* adjust age of q if dated */ if (calibrationPtr != NULL) { q->age = q->nodeDepth / clockRate; } /* set flags for update of cond likes from p/r to root */ r->upDateCl = YES; q = p; while (q->anc != NULL) { q->upDateCl = YES; q = q->anc; } q = p->anc; /* calculate prior ratio */ if (LogClockTreePriorRatio(param, chain, &x) == ERROR) return (ERROR); (*lnPriorRatio) = x; /* calculate proposal ratio; because we abort in the extreme conditions (k == 0 and m == 0), no correction for this needed */ /* note that we assume here that the relative proposal probabilities are the same for the add move and the delete move */ (*lnProposalRatio) = log(kFossil) - log(mFossil +1); /* add the Jacobian term */ (*lnProposalRatio) += log((maxDepth - minDepth) / clockRate); /* adjust proposal and prior ratio for relaxed clock models */ for (i=0; inSubParams; i++) { subParm = param->subParams[i]; if (subParm->paramType == P_CPPEVENTS) { /* CPP is not compatible with FBD ancestral fossils until we have a good way to implement it !! */ MrBayesPrint ("%s CPP clock is not compatible with Fossilization prior currently\n", spacer); return (ERROR); } else if ( subParm->paramType == P_TK02BRANCHRATES) { if (subParm->paramType == P_TK02BRANCHRATES) nu = *GetParamVals (modelSettings[subParm->relParts[0]].tk02var, chain, state[chain]); else nu = *GetParamVals (modelSettings[subParm->relParts[0]].mixedvar, chain, state[chain]); rate = GetParamVals (subParm, chain, state[chain]); brlens = GetParamSubVals (subParm, chain, state[chain]); /* prior ratio */ rate[p->index] = rate[q->index]; (*lnPriorRatio) -= LnProbLogNormal_Mean_LogVar (rate[q->index], nu*oldRLength, rate[r->index]); (*lnPriorRatio) += LnProbLogNormal_Mean_LogVar (rate[q->index], nu* p->length, rate[p->index]); (*lnPriorRatio) += LnProbLogNormal_Mean_LogVar (rate[q->index], nu* r->length, rate[r->index]); if (q->anc->anc != NULL) { (*lnPriorRatio) -= LnProbLogNormal_Mean_LogVar (rate[q->anc->index], nu*oldQLength, rate[q->index]); (*lnPriorRatio) += LnProbLogNormal_Mean_LogVar (rate[q->anc->index], nu* q->length, rate[q->index]); } /* update effective evolutionary lengths */ brlens[p->index] = p->length * (rate[p->index]+rate[q->index])/2.0; brlens[r->index] = r->length * (rate[r->index]+rate[q->index])/2.0; if (q->anc->anc != NULL) { brlens[q->index] = q->length * (rate[q->index]+rate[q->anc->index])/2.0; } } else if ( subParm->paramType == P_WNBRANCHRATES) { if (subParm->paramType == P_WNBRANCHRATES) var = *GetParamVals (modelSettings[subParm->relParts[0]].wnvar, chain, state[chain]); else var = *GetParamVals (modelSettings[subParm->relParts[0]].mixedvar, chain, state[chain]); rate = GetParamVals (subParm, chain, state[chain]); brlens = GetParamSubVals (subParm, chain, state[chain]); /* prior ratio */ rate[p->index] = rate[q->index]; (*lnPriorRatio) -= LnProbGamma (oldRLength/var, oldRLength/var, rate[r->index]); (*lnPriorRatio) += LnProbGamma (p->length /var, p->length /var, rate[p->index]); (*lnPriorRatio) += LnProbGamma (r->length /var, r->length /var, rate[r->index]); if (q->anc->anc != NULL) { (*lnPriorRatio) -= LnProbGamma (oldQLength/var, oldQLength/var, rate[q->index]); (*lnPriorRatio) += LnProbGamma (q->length /var, q->length /var, rate[q->index]); } /* update effective evolutionary lengths */ brlens[p->index] = rate[p->index] * p->length; brlens[r->index] = rate[r->index] * r->length; if (q->anc->anc != NULL) { brlens[q->index] = rate[q->index] * q->length; } } else if ( subParm->paramType == P_ILNBRANCHRATES || (subParm->paramType == P_MIXEDBRCHRATES && *GetParamIntVals(subParm, chain, state[chain]) == RCL_ILN)) { if (subParm->paramType == P_ILNBRANCHRATES) nu = *GetParamVals (modelSettings[subParm->relParts[0]].ilnvar, chain, state[chain]); else nu = *GetParamVals (modelSettings[subParm->relParts[0]].mixedvar, chain, state[chain]); rate = GetParamVals (subParm, chain, state[chain]); brlens = GetParamSubVals (subParm, chain, state[chain]); /* prior ratio */ rate[p->index] = rate[q->index]; (*lnPriorRatio) += LnProbLogNormal_Mean_Var (1.0, nu, rate[p->index]); /* update effective evolutionary lengths */ brlens[p->index] = rate[p->index] * p->length; brlens[r->index] = rate[r->index] * r->length; if (q->anc->anc != NULL) { brlens[q->index] = rate[q->index] * q->length; } } else if ( subParm->paramType == P_IGRBRANCHRATES || (subParm->paramType == P_MIXEDBRCHRATES && *GetParamIntVals(subParm, chain, state[chain]) == RCL_IGR)) { if (subParm->paramType == P_IGRBRANCHRATES) var = *GetParamVals (modelSettings[subParm->relParts[0]].igrvar, chain, state[chain]); else var = *GetParamVals (modelSettings[subParm->relParts[0]].mixedvar, chain, state[chain]); rate = GetParamVals (subParm, chain, state[chain]); brlens = GetParamSubVals (subParm, chain, state[chain]); /* prior ratio */ rate[p->index] = rate[q->index]; (*lnPriorRatio) += LnProbGamma (1.0/var, 1.0/var, rate[p->index]); /* update effective evolutionary lengths */ brlens[p->index] = rate[p->index] * p->length; brlens[r->index] = rate[r->index] * r->length; if (q->anc->anc != NULL) { brlens[q->index] = rate[q->index] * q->length; } } } return (NO_ERROR); } int Move_DelBranch (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp) { /* Move a fossil tip (brl > 0) to be ancestral (brl =0) __|__ __|__ | | | | q|___ --> | | | --> | | |p q|___p r| r| 1. Pich a fossil among those with brl > 0 (prob = 1/m) 2. Set brl = 0 */ int i, j, k, mFossil, kFossil; MrBFlt minDepth, maxDepth, clockRate, x, oldPLength, oldQLength, oldRLength, *brlens=NULL, nu=0.0, var=0.0, *rate=NULL; TreeNode *p=NULL, *q=NULL, *r; Tree *t; ModelParams *mp; ModelInfo *m; Param *subParm; Calibration *calibrationPtr = NULL; /* get tree */ t = GetTree (param, chain, state[chain]); /* get number of ancestral and tip fossils */ kFossil = mFossil = 0; for (i = 0; i < t->nNodes -1; i++) { p = t->allDownPass[i]; p->marked = NO; // reset marked node if (p->left == NULL && p->right == NULL && p->nodeDepth > 0.0) { if (p->length > 0.0) { p->marked = YES; // mark tip fossil mFossil++; // count tip fossil } else { kFossil++; // count anc fossil } } } if (mFossil == 0) // no tip fossil, nothing to do { abortMove = YES; return (NO_ERROR); } /* get model params and model info */ mp = &modelParams[param->relParts[0]]; m = &modelSettings[param->relParts[0]]; /* get clock rate */ clockRate = *(GetParamVals(m->clockRate, chain, state[chain])); /* pick a tip fossil randomly */ j = (int) (RandomNumber(seed) * mFossil); for (i = k = 0; i < t->nNodes -1; i++) { p = t->allDownPass[i]; if (p->marked == YES) k++; if (k > j) break; } /* now p is pointing to the fossil tip whose brl needs to be changed to 0. let's do it */ q = p->anc; if (q->left == p) r = q->right; else r = q->left; /* determine lower and upper bound of backward move, abort if impossible */ minDepth = p->nodeDepth + BRLENS_MIN; if (q->anc->anc == NULL) maxDepth = TREEHEIGHT_MAX; else maxDepth = q->anc->nodeDepth - BRLENS_MIN; if (q->isDated == YES) calibrationPtr = q->calibration; else if (q->anc->anc == NULL) // q is root but not dated calibrationPtr = &mp->treeAgePr; if (calibrationPtr != NULL) { if (calibrationPtr->prior == fixed || calibrationPtr->min * clockRate > minDepth) { abortMove = YES; return (NO_ERROR); } if (calibrationPtr->max * clockRate < maxDepth) maxDepth = calibrationPtr->max * clockRate; } if (r->nodeDepth > p->nodeDepth -BRLENS_MIN || minDepth >= maxDepth) { /* the sister node (another fossil) is older than the current fossil */ abortMove = YES; return (NO_ERROR); } /* record old lengths and depths */ oldPLength = p->length; oldQLength = q->length; oldRLength = r->length; // oldDepth = q->nodeDepth; /* set the brl to 0 for the fossil tip, it becomes an ancestral fossil */ /* set flags for update of transition probabilities too */ q->nodeDepth = p->nodeDepth; if (q->anc->anc != NULL) { q->length += p->length; q->upDateTi = YES; } r->length -= p->length; r->upDateTi = YES; p->length = 0.0; p->upDateTi = YES; // newDepth = q->nodeDepth; /* adjust age of q if dated */ if (calibrationPtr != NULL) { q->age = q->nodeDepth / clockRate; } /* set flags for update of cond likes from p/r to root */ r->upDateCl = YES; q = p; while (q->anc != NULL) { q->upDateCl = YES; q = q->anc; } q = p->anc; /* calculate prior ratio */ if (LogClockTreePriorRatio(param, chain, &x) == ERROR) return (ERROR); (*lnPriorRatio) = x; /* calculate proposal ratio; since we abort in the extreme cases (m == 0 and k == 0), no need to correct for these */ /* note that we assume that the add and del move have the same relative proposal prob */ (*lnProposalRatio) = log(mFossil) - log(kFossil +1); /* add the Jacobian term */ (*lnProposalRatio) -= log((maxDepth - minDepth) / clockRate); /* adjust proposal and prior ratio for relaxed clock models */ for (i=0; inSubParams; i++) { subParm = param->subParams[i]; if (subParm->paramType == P_CPPEVENTS) { /* CPP is not compatible with FBD ancestral fossils until we have a good way to implement it !! */ MrBayesPrint ("%s CPP clock is not compatible with Fossilization prior currently\n", spacer); return (ERROR); } else if ( subParm->paramType == P_TK02BRANCHRATES) { if (subParm->paramType == P_TK02BRANCHRATES) nu = *GetParamVals (modelSettings[subParm->relParts[0]].tk02var, chain, state[chain]); else nu = *GetParamVals (modelSettings[subParm->relParts[0]].mixedvar, chain, state[chain]); rate = GetParamVals (subParm, chain, state[chain]); brlens = GetParamSubVals (subParm, chain, state[chain]); /* prior ratio */ (*lnPriorRatio) -= LnProbLogNormal_Mean_LogVar (rate[q->index], nu*oldPLength, rate[p->index]); (*lnPriorRatio) -= LnProbLogNormal_Mean_LogVar (rate[q->index], nu*oldRLength, rate[r->index]); (*lnPriorRatio) += LnProbLogNormal_Mean_LogVar (rate[q->index], nu* r->length, rate[r->index]); if (q->anc->anc != NULL) { (*lnPriorRatio) -= LnProbLogNormal_Mean_LogVar (rate[q->anc->index], nu*oldQLength, rate[q->index]); (*lnPriorRatio) += LnProbLogNormal_Mean_LogVar (rate[q->anc->index], nu* q->length, rate[q->index]); } /* update effective evolutionary lengths */ brlens[p->index] = 0.0; // rate[p->index] = rate[q->index]; brlens[r->index] = r->length * (rate[r->index]+rate[q->index])/2.0; if (q->anc->anc != NULL) { brlens[q->index] = q->length * (rate[q->index]+rate[q->anc->index])/2.0; } } else if ( subParm->paramType == P_WNBRANCHRATES) { if (subParm->paramType == P_WNBRANCHRATES) var = *GetParamVals (modelSettings[subParm->relParts[0]].wnvar, chain, state[chain]); else var = *GetParamVals (modelSettings[subParm->relParts[0]].mixedvar, chain, state[chain]); rate = GetParamVals (subParm, chain, state[chain]); brlens = GetParamSubVals (subParm, chain, state[chain]); (*lnPriorRatio) -= LnProbGamma (oldPLength/var, oldPLength/var, rate[p->index]); (*lnPriorRatio) -= LnProbGamma (oldRLength/var, oldRLength/var, rate[r->index]); (*lnPriorRatio) += LnProbGamma (r->length /var, r->length /var, rate[r->index]); if (q->anc->anc != NULL) { (*lnPriorRatio) -= LnProbGamma (oldQLength/var, oldQLength/var, rate[q->index]); (*lnPriorRatio) += LnProbGamma (q->length /var, q->length /var, rate[q->index]); } /* update effective evolutionary lengths */ brlens[p->index] = 0.0; // rate[p->index] = rate[q->index]; brlens[r->index] = rate[r->index] * r->length; if (q->anc->anc != NULL) { brlens[q->index] = rate[q->index] * q->length; } } else if ( subParm->paramType == P_ILNBRANCHRATES || (subParm->paramType == P_MIXEDBRCHRATES && *GetParamIntVals(subParm, chain, state[chain]) == RCL_ILN)) { if (subParm->paramType == P_ILNBRANCHRATES) nu = *GetParamVals (modelSettings[subParm->relParts[0]].ilnvar, chain, state[chain]); else nu = *GetParamVals (modelSettings[subParm->relParts[0]].mixedvar, chain, state[chain]); rate = GetParamVals (subParm, chain, state[chain]); brlens = GetParamSubVals (subParm, chain, state[chain]); (*lnPriorRatio) -= LnProbLogNormal_Mean_Var (1.0, nu, rate[p->index]); /* update effective evolutionary lengths */ brlens[p->index] = 0.0; // rate[p->index] = rate[q->index]; brlens[r->index] = rate[r->index] * r->length; if (q->anc->anc != NULL) { brlens[q->index] = rate[q->index] * q->length; } } else if ( subParm->paramType == P_IGRBRANCHRATES || (subParm->paramType == P_MIXEDBRCHRATES && *GetParamIntVals(subParm, chain, state[chain]) == RCL_IGR)) { if (subParm->paramType == P_IGRBRANCHRATES) var = *GetParamVals (modelSettings[subParm->relParts[0]].igrvar, chain, state[chain]); else var = *GetParamVals (modelSettings[subParm->relParts[0]].mixedvar, chain, state[chain]); rate = GetParamVals (subParm, chain, state[chain]); brlens = GetParamSubVals (subParm, chain, state[chain]); (*lnPriorRatio) -= LnProbGamma (1.0/var, 1.0/var, rate[p->index]); /* update effective evolutionary lengths */ brlens[p->index] = 0.0; // rate[p->index] = rate[q->index]; brlens[r->index] = rate[r->index] * r->length; if (q->anc->anc != NULL) { brlens[q->index] = rate[q->index] * q->length; } } } return (NO_ERROR); } int Move_Extinction (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp) { /* change relative extinction rate using sliding window */ int i, isValidM, valIndex; MrBFlt *valPtr, oldM, newM, window, minM, maxM, *sR, *eR, sF, *fR, oldLnPrior, newLnPrior, oldProp[2], newProp[2], x, y, *alphaDir, clockRate; char *sS; ModelParams *mp; ModelInfo *m; Tree *t; /* get size of window, centered on current value */ window = mvp[0]; /* get model params and settings */ mp = &modelParams[param->relParts[0]]; m = &modelSettings[param->relParts[0]]; /* get minimum and maximum values */ if (param->paramId == EXTRATE_BETA) { minM = 0.0; maxM = 0.999999; } else { minM = RATE_MIN; maxM = RATE_MAX; } /* get pointer to value to be changed */ valIndex = (int)(RandomNumber(seed) * param->nValues); valPtr = GetParamVals(param, chain, state[chain]) + valIndex; /* get old value */ oldM = *valPtr; /* change value */ if (window > maxM - minM) window = maxM - minM; newM = oldM + window * (RandomNumber(seed) - 0.5); /* check that new value is valid */ isValidM = NO; do { if (newM < minM) newM = 2 * minM - newM; else if (newM > maxM) newM = 2 * maxM - newM; else isValidM = YES; } while (isValidM == NO); /* get proposal ratio */ *lnProposalRatio = 0.0; /* calculate prior ratio */ t = GetTree(m->brlens, chain, state[chain]); sR = GetParamVals (m->speciationRates, chain, state[chain]); eR = GetParamVals (param, chain, state[chain]); sF = mp->sampleProb; sS = mp->sampleStrat; clockRate = *GetParamVals (m->clockRate, chain, state[chain]); if (!strcmp(mp->clockPr,"Birthdeath")) { if (LnBirthDeathPriorPr (t, clockRate, &oldLnPrior, *sR, *eR, sS, sF) == ERROR) { MrBayesPrint ("%s Problem calculating prior for birth-death process\n", spacer); return (ERROR); } *valPtr = newM; // update with new value if (LnBirthDeathPriorPr (t, clockRate, &newLnPrior, *sR, *eR, sS, sF) == ERROR) { MrBayesPrint ("%s Problem calculating prior for birth-death process\n", spacer); return (ERROR); } } else if (!strcmp(mp->clockPr,"Fossilization")) { fR = GetParamVals (m->fossilizationRates, chain, state[chain]); if (LnFossilizationPriorPr (t, clockRate, &oldLnPrior, sR, eR, fR, sF, sS) == ERROR) { MrBayesPrint ("%s Problem calculating prior for fossilized birth-death process\n", spacer); return (ERROR); } *valPtr = newM; // update with new value if (LnFossilizationPriorPr (t, clockRate, &newLnPrior, sR, eR, fR, sF, sS) == ERROR) { MrBayesPrint ("%s Problem calculating prior for fossilized birth-death process\n", spacer); return (ERROR); } } else { MrBayesPrint ("%s Move_Extinction not applicable\n", spacer); return (ERROR); } /* get proportions */ oldProp[0] = oldM; oldProp[1] = 1.0 - oldM; newProp[0] = newM; newProp[1] = 1.0 - newM; /* adjust prior ratio according to beta distribution */ if (param->paramId == EXTRATE_BETA) { alphaDir = mp->extinctionBeta; x = y = 0.0; for (i=0; i<2; i++) x += (alphaDir[i]-1.0)*log(newProp[i]); for (i=0; i<2; i++) y += (alphaDir[i]-1.0)*log(oldProp[i]); *lnPriorRatio = x - y + newLnPrior - oldLnPrior; } else if (param->paramId == EXTRATE_EXP) *lnPriorRatio = newLnPrior - oldLnPrior - (mp->extinctionExp)*(newM - oldM); return (NO_ERROR); } int Move_Fossilization (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp) { /* change fossilization rate using sliding window */ int i, isValidM, valIndex; MrBFlt *valPtr, oldM, newM, window, minM, maxM, *sR, *eR, sF, *fR, oldLnPrior, newLnPrior, oldProp[2], newProp[2], x, y, *alphaDir, clockRate; char *sS; ModelParams *mp; ModelInfo *m; Tree *t; /* get size of window, centered on current value */ window = mvp[0]; /* get model params and settings */ mp = &modelParams[param->relParts[0]]; m = &modelSettings[param->relParts[0]]; /* get minimum and maximum values */ if (param->paramId == FOSLRATE_BETA) { minM = 0.000001; maxM = 1.0; } else { minM = RATE_MIN; maxM = RATE_MAX; } /* get pointer to value to be changed */ valIndex = (int)(RandomNumber(seed) * param->nValues); valPtr = GetParamVals(param, chain, state[chain]) + valIndex; /* get old value */ oldM = *valPtr; /* change value */ if (window > maxM - minM) window = maxM - minM; newM = oldM + window * (RandomNumber(seed) - 0.5); /* check that new value is valid */ isValidM = NO; do { if (newM < minM) newM = 2 * minM - newM; else if (newM > maxM) newM = 2 * maxM - newM; else isValidM = YES; } while (isValidM == NO); /* get proposal ratio */ *lnProposalRatio = 0.0; /* calculate prior ratio */ t = GetTree(m->brlens, chain, state[chain]); sR = GetParamVals (m->speciationRates, chain, state[chain]); eR = GetParamVals (m->extinctionRates, chain, state[chain]); fR = GetParamVals (param, chain, state[chain]); sF = mp->sampleProb; sS = mp->sampleStrat; clockRate = *GetParamVals(m->clockRate, chain, state[chain]); if (LnFossilizationPriorPr (t, clockRate, &oldLnPrior, sR, eR, fR, sF, sS) == ERROR) { MrBayesPrint ("%s Problem calculating prior for fossilized birth-death process\n", spacer); return (ERROR); } *valPtr = newM; // update with new value if (LnFossilizationPriorPr (t, clockRate, &newLnPrior, sR, eR, fR, sF, sS) == ERROR) { MrBayesPrint ("%s Problem calculating prior for fossilized birth-death process\n", spacer); return (ERROR); } /* get proportions */ oldProp[0] = oldM; oldProp[1] = 1.0 - oldM; newProp[0] = newM; newProp[1] = 1.0 - newM; /* adjust prior ratio according to beta distribution */ if (param->paramId == FOSLRATE_BETA) { alphaDir = mp->fossilizationBeta; x = y = 0.0; for (i=0; i<2; i++) x += (alphaDir[i]-1.0)*log(newProp[i]); for (i=0; i<2; i++) y += (alphaDir[i]-1.0)*log(oldProp[i]); *lnPriorRatio = x - y + newLnPrior - oldLnPrior; } else if (param->paramId == FOSLRATE_EXP) *lnPriorRatio = newLnPrior - oldLnPrior - (mp->fossilizationExp)*(newM - oldM); return (NO_ERROR); } int Move_ExtSPR (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp) { /* Change topology (and branch lengths) using SPR (unrooted) with extension probability. Pick either external or internal branches instead of just internal branches. */ int i, j, topologyHasChanged, nCrownNodes, nRootNodes, directionLeft, directionUp, isVPriorExp, moveInRoot, isStartConstrained, isStopConstrained, tempInt; MrBFlt m, x, y, tuning, maxV, minV, extensionProb; TreeNode *p, *q, *a=NULL, *b=NULL, *c=NULL, *d=NULL, *u, *v, *interiorRoot=NULL; Tree *t; ModelParams *mp; /* these parameters should be possible to set by user */ extensionProb = mvp[0]; /* extension probability */ tuning = mvp[1]; /* Larget & Simon's tuning parameter lambda */ (*lnProposalRatio) = (*lnPriorRatio) = 0.0; /* get tree */ t = GetTree (param, chain, state[chain]); /* for rooted (non-clock) trees, store pointer to current interior root node */ //SK if (t->isRooted) interiorRoot = t->root->left; /* get model params */ mp = &modelParams[param->relParts[0]]; /* max and min brlen */ if (param->subParams[0]->paramId == BRLENS_UNI) { minV = mp->brlensUni[0] > BRLENS_MIN ? mp->brlensUni[0] : BRLENS_MIN; maxV = mp->brlensUni[1] < BRLENS_MAX ? mp->brlensUni[1] : BRLENS_MAX; isVPriorExp = NO; } else if (param->subParams[0]->paramId == BRLENS_GamDir) { minV = BRLENS_MIN; maxV = BRLENS_MAX; isVPriorExp = 2; } else if (param->subParams[0]->paramId == BRLENS_iGmDir) { minV = BRLENS_MIN; maxV = BRLENS_MAX; isVPriorExp = 3; } else if (param->subParams[0]->paramId == BRLENS_twoExp) { minV = BRLENS_MIN; maxV = BRLENS_MAX; isVPriorExp = 4; } else /* (param->subParams[0]->paramId == BRLENS_EXP) */ { minV = BRLENS_MIN; maxV = BRLENS_MAX; isVPriorExp = YES; } /* Dirichlet or twoExp prior */ if (isVPriorExp > 1) (*lnPriorRatio) = -LogDirPrior(t, mp, isVPriorExp); topologyHasChanged = NO; # if defined (DEBUG_ExtSPR) printf ("Before:\n"); ShowNodes (t->root, 2, NO); getchar(); # endif /* pick a random branch */ do { p = t->allDownPass[(int)(RandomNumber(seed) * (t->nNodes -1))]; q = p->anc->right; if (q == p) q = p->anc->left; i = j = 0; if (p->left == NULL) j = 2; if (p->anc->anc == NULL) i = 2; if (p->anc->anc != NULL && (p->anc->isLocked == YES || p->anc->anc->anc == NULL)) i++; if (p->anc->anc != NULL && (q->isLocked == YES || q->left == NULL)) i++; if (p->left != NULL && (p->left->isLocked == YES || p->left->left == NULL)) j++; if (p->left != NULL && (p->right->isLocked == YES || p->right->left == NULL)) j++; } while (i == 2 && j == 2); /* change in root tree ? */ if (j == 2) moveInRoot = YES; else if (i == 2) moveInRoot = NO; else if (RandomNumber(seed) < 0.5) moveInRoot = YES; else moveInRoot = NO; /* determine whether start is constrained on backward move */ isStartConstrained = isStopConstrained = NO; if (moveInRoot == YES && i == 1) isStartConstrained = YES; else if (moveInRoot == NO && j == 1) isStartConstrained = YES; /* set up pointers for nodes around the picked branch */ /* cut the tree into crown, root and attachment part */ /* change the relevant lengths in the attachment part */ v = p; u = p->anc; /* modify length of middle branch */ /* test is needed to deal with rooted nonclock trees */ if (t->isRooted == NO || v->anc->anc != NULL) { m = v->length; x = m * exp(tuning * (RandomNumber(seed) - 0.5)); while (x < minV || x > maxV) { if (x < minV) x = minV * minV / x; if (x > maxV) x = maxV * maxV / x; } v->length = x; v->upDateTi = YES; /* update proposal and prior ratio based on length modification */ (*lnProposalRatio) += log (x / m); if (isVPriorExp == YES) (*lnPriorRatio) += (mp->brlensExp) * (m - x); } /* move around in root subtree */ if (moveInRoot == YES) { /* mark nodes in root part */ /* also determine direction of move in root part */ if (u->left == v) a = u->right; else a = u->left; b = u->anc; if (u->anc->anc == NULL || u->isLocked == YES) directionUp = YES; else if (a->left == NULL || a->isLocked == YES) directionUp = NO; else if (RandomNumber(seed) < 0.5) directionUp = YES; else directionUp = NO; /* cut root part*/ if (directionUp == NO) { b = a; /* switch a and b */ a = u->anc; b->anc = a; if (a->left == u) a->left = b; else a->right = b; } else // if (directionUp == YES) { a->anc = b; if (b->left == u) b->left = a; else b->right = a; y = a->length; a->length = u->length; u->length = y; a->upDateTi = YES; u->upDateTi = YES; } for (nRootNodes=0; RandomNumber(seed)left == NULL || a->isLocked == YES) break; /* can't go further */ b = a; if (RandomNumber(seed) < 0.5) a = a->left; else a = a->right; if (u->isLocked == YES) { b->isLocked = YES; u->isLocked = NO; b->lockID = u->lockID; u->lockID = 0; } } else // directionUp == NO { /* going down tree */ if (a->anc == NULL || u->isLocked == YES) break; /* can't go further */ if (RandomNumber(seed) < 0.5) { directionUp = YES; /* switch direction */ /* find sister of a */ if (a->left == b) { b = a; a = a->right; } else { b = a; a = a->left; } /* as long as we are moving upwards, the cond likes to update will be flagged by the last pass from u to the root */ } else { /* continue down */ b = a; a = a->anc; b->upDateCl = YES; if (b->isLocked == YES) { u->isLocked = YES; b->isLocked = NO; u->lockID = b->lockID; b->lockID = 0; } } } } topologyHasChanged = YES; /* check whether stop is constrained */ if (directionUp == YES && (a->left == NULL || a->isLocked == YES)) isStopConstrained = YES; if (directionUp == NO && (a->anc == NULL || u->isLocked == YES)) isStopConstrained = YES; /* modify branch length */ m = u->length; x = m * exp(tuning * (RandomNumber(seed) - 0.5)); while (x < minV || x > maxV) { if (x < minV) x = minV * minV / x; if (x > maxV) x = maxV * maxV / x; } u->length = x; u->upDateTi = YES; /* update proposal and prior ratio based on length modification */ (*lnProposalRatio) += log (x / m); if (isVPriorExp == YES) (*lnPriorRatio) += (mp->brlensExp) * (m - x); /* combine the subtrees */ if (directionUp == YES) { u->anc = b; if (u->left == v) u->right = a; else u->left = a; a->anc = u; if (b->left == a) b->left = u; else b->right = u; } else // if (directionUp == NO) { u->anc = a; if (u->left == v) u->right = b; else u->left = b; b->anc = u; if (a->left == b) a->left = u; else a->right = u; /* the modified branch contained in u->length will have to be moved to b->length to enable back move BUT if we haven't moved, it is better to keep it in place (necessary for rooted trees) */ y = u->length; u->length = b->length; b->length = y; b->upDateTi = YES; u->upDateTi = YES; } } /* move around in crown subtree */ if (moveInRoot == NO) { /* set up pointers for crown part */ /* also determine direction of move in crown part */ if (v->right->left == NULL || v->right->isLocked == YES) directionLeft = YES; else if (v->left->left == NULL || v->left->isLocked == YES) directionLeft = NO; else if (RandomNumber(seed) < 0.5) directionLeft = YES; else directionLeft = NO; if (directionLeft == YES) { c = v->left; d = v->right; } else { c = v->right; d = v->left; } /* store brlen nodes and brlen to move */ x = c->length; /* cut and reconnect crown part */ c->anc = d; d->anc = c; for (nCrownNodes=0; RandomNumber(seed)left == NULL || c->isLocked == YES) break; /* can't go further */ if (RandomNumber(seed) < 0.5) { /* rotate c anticlockwise - prepare pointers for move left */ c->anc = c->left; /* the root will be in the direction we are heading */ c->left = c->right; c->right = d; } else { /* rotate c clockwise - prepare pointers for move right */ c->anc = c->right; /* the root will be in the direction we are heading */ c->right = c->left; c->left = d; } /* OK - let's move! c->anc points in the right direction don't forget to move the branch lengths as well */ d = c; c = c->anc; d->length = c->length; /* also rotate other info ?? */ d->upDateCl = YES; d->upDateTi = YES; } topologyHasChanged = YES; /* check if stop constrained */ if (c->left == NULL || c->isLocked == YES) isStopConstrained = YES; /* combine the subtrees */ c->anc = v; d->anc = v; if (directionLeft == YES) { v->left = c; v->right = d; } else { v->left = d; v->right = c; } /* the dangling branch is inserted in reverted position such that the back move will be possible if we have moved around in crown subtree otherwise it is left in its original position */ d->length = x; /* modify branch length */ m = d->length; x = m * exp(tuning * (RandomNumber(seed) - 0.5)); while (x < minV || x > maxV) { if (x < minV) x = minV * minV / x; if (x > maxV) x = maxV * maxV / x; } d->length = x; d->upDateTi = YES; /* update proposal and prior ratio based on length modification */ (*lnProposalRatio) += log (x / m); if (isVPriorExp == YES) (*lnPriorRatio) += (mp->brlensExp) * (m - x); } /* adjust proposal ratio for constraints */ if (isStartConstrained == NO && isStopConstrained == YES) (*lnProposalRatio) += log (2.0 * (1.0 - extensionProb)); else if (isStartConstrained == YES && isStopConstrained == NO) (*lnProposalRatio) -= log (2.0 * (1.0 - extensionProb)); /* set flags for update of cond likes from v and down to root */ p = v; while (p->anc != NULL) { p->upDateCl = YES; p = p->anc; } /* if tree is rooted, swap indices of former and current interior root node, if changed */ //SK if (t->isRooted && interiorRoot != t->root->left) { tempInt = interiorRoot->index; interiorRoot->index = t->root->left->index; t->root->left->index = tempInt; } /* get down pass sequence if tree topology has changed */ if (topologyHasChanged == YES) { GetDownPass (t); } /* Dirichlet or twoExp prior */ if (isVPriorExp > 1) (*lnPriorRatio) += LogDirPrior(t, mp, isVPriorExp); # if defined (DEBUG_ExtSPR) printf ("After:\n"); ShowNodes (t->root, 2, NO); getchar(); printf ("Proposal ratio: %f\n",(*lnProposalRatio)); printf ("v: %d u: %d\n", v->index, u->index); if (a) printf ("a: %d\n", a->index); if (b) printf ("b: %d\n", b->index); if (c) printf ("c: %d\n", c->index); if (d) printf ("d: %d\n", d->index); printf ("No. nodes moved in root subtree: %d\n",nRootNodes); printf ("No. nodes moved in crown subtree: %d\n",nCrownNodes); printf ("Has topology changed? %d\n",topologyHasChanged); getchar(); # endif return (NO_ERROR); } int Move_ExtSPR1 (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp) { /* Change topology (and branch lengths) using SPR (unrooted) with extension probability. */ /* Pick only internal branches. For a description, see Lakner et al. (2008). */ int i, j, topologyHasChanged, nCrownNodes, nRootNodes, directionLeft, directionUp, isVPriorExp, moveInRoot, isStartConstrained, isStopConstrained, tempInt; MrBFlt m, x, y, tuning, maxV, minV, extensionProb; TreeNode *p, *a, *b, *c, *d, *u, *v, *brlenNode[7], *interiorRoot=NULL; Tree *t; ModelParams *mp; /* these parameters should be possible to set by user */ extensionProb = mvp[0]; /* extension probability */ tuning = mvp[1]; /* Larget & Simon's tuning parameter lambda */ (*lnProposalRatio) = (*lnPriorRatio) = 0.0; /* get tree */ t = GetTree (param, chain, state[chain]); /* get model params */ mp = &modelParams[param->relParts[0]]; /* for rooted (non-clock) trees, store pointer to current interior root node */ //SK if (t->isRooted) interiorRoot = t->root->left; /* max and min brlen */ if (param->subParams[0]->paramId == BRLENS_UNI) { minV = mp->brlensUni[0] > BRLENS_MIN ? mp->brlensUni[0] : BRLENS_MIN; maxV = mp->brlensUni[1] < BRLENS_MAX ? mp->brlensUni[1] : BRLENS_MAX; isVPriorExp = NO; } else if (param->subParams[0]->paramId == BRLENS_GamDir) { minV = BRLENS_MIN; maxV = BRLENS_MAX; isVPriorExp = 2; } else if (param->subParams[0]->paramId == BRLENS_iGmDir) { minV = BRLENS_MIN; maxV = BRLENS_MAX; isVPriorExp = 3; } else if (param->subParams[0]->paramId == BRLENS_twoExp) { minV = BRLENS_MIN; maxV = BRLENS_MAX; isVPriorExp = 4; } else { minV = BRLENS_MIN; maxV = BRLENS_MAX; isVPriorExp = YES; } /* Dirichlet or twoExp prior */ if (isVPriorExp > 1) (*lnPriorRatio) = -LogDirPrior(t, mp, isVPriorExp); topologyHasChanged = NO; # if defined (DEBUG_ExtSPR) printf ("Before:\n"); ShowNodes (t->root, 2, NO); getchar(); # endif /* pick an internal branch that is free to move in either end (i and j keep track of number of locked directions) */ do { p = t->intDownPass[(int)(RandomNumber(seed) * (t->nIntNodes-1))]; if (p->anc->left == p) a = p->anc->right; else a = p->anc->left; i = j = 0; if (a->isLocked == YES || a->left == NULL) i++; if (p->anc->isLocked == YES || p->anc->anc->anc == NULL) i++; if (p->left->isLocked == YES || p->left->left == NULL) j++; if (p->right->isLocked == YES || p->right->left == NULL) j++; } while (i == 2 && j == 2); /* set up pointers for nodes around the picked branch */ /* cut the tree into crown, root and attachment part */ /* change the relevant lengths in the attachment part */ /* the lengths of a and v are automatically contained in the */ /* "attachment" part but the length of c has to be stored in x */ v = p; u = p->anc; /* store brlen node */ brlenNode[3] = v; /* change in root tree ? */ if (j == 2) moveInRoot = YES; else if (i == 2) moveInRoot = NO; else if (RandomNumber(seed) < 0.5) moveInRoot = YES; else moveInRoot = NO; /* determine whether start is constrained on backward move */ isStartConstrained = isStopConstrained = NO; if (moveInRoot == YES && i == 1) isStartConstrained = YES; else if (moveInRoot == NO && j == 1) isStartConstrained = YES; /* set up pointers for crown part */ /* also determine direction of move in crown part */ if (v->right->left == NULL || v->right->isLocked == YES) directionLeft = YES; else if (v->left->left == NULL || v->left->isLocked == YES) directionLeft = NO; else if (RandomNumber(seed) < 0.5) directionLeft = YES; else directionLeft = NO; if (directionLeft == YES) { c = v->left; d = v->right; } else { c = v->right; d = v->left; } /* store brlen nodes and brlen to move */ brlenNode[0] = d; brlenNode[1] = c; x = c->length; /* cut and reconnect crown part */ c->anc = d; d->anc = c; /* mark nodes in root part */ /* also determine direction of move in root part */ if (u->left == v) a = u->right; else a = u->left; b = u->anc; if (u->anc->anc == NULL || u->isLocked == YES) directionUp = YES; else if (a->left == NULL || a->isLocked == YES) directionUp = NO; else if (RandomNumber(seed) < 0.5) directionUp = YES; else directionUp = NO; if (directionUp == NO) { /* switch a and b */ b = a; a = u->anc; } /* store brlen nodes */ if (directionUp == YES) { brlenNode[4] = u; brlenNode[5] = a; } else { brlenNode[4] = b; brlenNode[5] = u; } /* cut root part*/ if (directionUp == NO) { b->anc = a; if (a->left == u) a->left = b; else a->right = b; } else { a->anc = b; if (b->left == u) b->left = a; else b->right = a; y = a->length; a->length = u->length; u->length = y; a->upDateTi = YES; u->upDateTi = YES; } /* move around in root subtree */ nRootNodes = 0; if (moveInRoot == YES) { for (nRootNodes=0; RandomNumber(seed)left == NULL || a->isLocked == YES) break; /* can't go further */ topologyHasChanged = YES; b = a; if (RandomNumber(seed) < 0.5) a = a->left; else a = a->right; if (u->isLocked == YES) { b->isLocked = YES; u->isLocked = NO; b->lockID = u->lockID; u->lockID = 0; } } else { /* going down tree */ if (a->anc == NULL || u->isLocked == YES) break; /* can't go further */ topologyHasChanged = YES; if (RandomNumber(seed)<0.5) { directionUp = YES; /* switch direction */ /* find sister of a */ if (a->left == b) { b = a; a = a->right; } else { b = a; a = a->left; } /* as long as we are moving upwards the cond likes to update will be flagged by the last pass from u to the root */ } else { /* continue down */ b = a; a = a->anc; b->upDateCl = YES; if (b->isLocked == YES) { u->isLocked = YES; b->isLocked = NO; u->lockID = b->lockID; b->lockID = 0; } } } } /* check whether stop is constrained */ if (directionUp == YES) { if (a->left == NULL || a->isLocked == YES) isStopConstrained = YES; } else { if (a->anc == NULL || u->isLocked == YES) isStopConstrained = YES; } } /* store brlen nodes */ if (nRootNodes > 0) { if (directionUp == YES) { brlenNode[6] = a; brlenNode[5] = u; } else { brlenNode[6] = u; brlenNode[5] = b; } } /* move around in crown subtree */ nCrownNodes = 0; if (moveInRoot == NO) { for (nCrownNodes=0; RandomNumber(seed)left == NULL || c->isLocked == YES) break; /* can't go further */ topologyHasChanged = YES; if (RandomNumber(seed) < 0.5) { /* rotate c anticlockwise - prepare pointers for move left */ c->anc = c->left; /* the root will be in the direction we are heading */ c->left = c->right; c->right = d; } else { /* rotate c clockwise - prepare pointers for move right */ c->anc = c->right; /* the root will be in the direction we are heading */ c->right = c->left; c->left = d; } /* OK - let's move!; c->anc points in the right direction don't forget to move the branch lengths as well */ d = c; c = c->anc; d->length = c->length; d->upDateCl = YES; d->upDateTi = YES; } /* check if stop constrained */ if (c->left == NULL || c->isLocked == YES) isStopConstrained = YES; } /* store brlen nodes */ if (nCrownNodes > 0) { brlenNode[2] = c; brlenNode[1] = d; } /* adjust proposal ratio for constraints */ if (isStartConstrained == NO && isStopConstrained == YES) (*lnProposalRatio) += log (2.0 * (1.0 - extensionProb)); else if (isStartConstrained == YES && isStopConstrained == NO) (*lnProposalRatio) -= log (2.0 * (1.0 - extensionProb)); /* combine the subtrees */ c->anc = v; d->anc = v; if (directionLeft == YES) { v->left = c; v->right = d; } else { v->left = d; v->right = c; } /* the dangling branch is inserted in reverted position such that the back move will be possible if we have moved around in crown subtree otherwise it is left in its original position */ if (nCrownNodes > 0) { d->length = x; d->upDateTi = YES; } else { c->length = x; } if (directionUp == YES) { u->anc = b; if (u->left == v) u->right = a; else u->left = a; a->anc = u; if (b->left == a) b->left = u; else b->right = u; /* the dangling branch is contained in u->length and will automatically be inserted in the right position to enable the back move regardless of whether it was initially directed upwards or downwards BUT if we haven't moved in root subtree, it is advantageous (necessary for rooted trees) to avoid switching branches, which occurs otherwise if directionUp == YES */ if (nRootNodes == 0) { x = u->length; u->length = a->length; a->length = x; a->upDateTi = NO; u->upDateTi = NO; } } else { u->anc = a; if (u->left == v) u->right = b; else u->left = b; b->anc = u; if (a->left == b) a->left = u; else a->right = u; /* the modified branch contained in u->length will have to be moved to b->length to enable back move BUT if we haven't moved, it is better to keep it in place (necessary for rooted trees) */ if (nRootNodes > 0) { x = u->length; u->length = b->length; b->length = x; b->upDateTi = YES; u->upDateTi = YES; } } /* modify branch lengths */ /* first modify length of middle branch */ m = brlenNode[3]->length; x = m * exp(tuning * (RandomNumber(seed) - 0.5)); while (x < minV || x > maxV) { if (x < minV) x = minV * minV / x; else if (x > maxV) x = maxV * maxV / x; } brlenNode[3]->length = x; brlenNode[3]->upDateTi = YES; /* update proposal and prior ratio based on length modification */ (*lnProposalRatio) += log (x / m); if (isVPriorExp == YES) (*lnPriorRatio) += (mp->brlensExp) * (m - x); if (moveInRoot == NO) { /* if no move in crown, then select randomly, otherwise always the moved branch */ if (nCrownNodes == 0 && RandomNumber(seed) < 0.5) p = brlenNode[0]; else p = brlenNode[1]; /* modify branch length */ m = p->length; x = m * exp(tuning * (RandomNumber(seed) - 0.5)); while (x < minV || x > maxV) { if (x < minV) x = minV * minV / x; else if (x > maxV) x = maxV * maxV / x; } p->length = x; p->upDateTi = YES; /* update proposal and prior ratio based on length modification */ (*lnProposalRatio) += log (x / m); if (isVPriorExp == YES) (*lnPriorRatio) += (mp->brlensExp) * (m - x); } if (moveInRoot == YES) { /* if no move in root, then select randomly, otherwise always the moved branch */ if (nRootNodes == 0 && RandomNumber(seed) < 0.5) p = brlenNode[4]; else p = brlenNode[5]; /* modify branch length but not if 'root' branch in rooted tree */ if (t->isRooted == NO || p->anc->anc != NULL) { m = p->length; x = m * exp(tuning * (RandomNumber(seed) - 0.5)); while (x < minV || x > maxV) { if (x < minV) x = minV * minV / x; else if (x > maxV) x = maxV * maxV / x; } p->length = x; p->upDateTi = YES; /* update proposal and prior ratio based on length modification */ (*lnProposalRatio) += log (x / m); if (isVPriorExp == YES) (*lnPriorRatio) += (mp->brlensExp) * (m - x); } } /* set flags for update of cond likes from v and down to root */ p = v; while (p->anc != NULL) { p->upDateCl = YES; p = p->anc; } /* if tree is rooted, swap indices of former and current interior root node, if changed */ //SK if (t->isRooted && interiorRoot != t->root->left) { tempInt = interiorRoot->index; interiorRoot->index = t->root->left->index; t->root->left->index = tempInt; } /* get down pass sequence if tree topology has changed */ if (topologyHasChanged == YES) { GetDownPass (t); } /* Dirichlet or twoExp prior */ if (isVPriorExp > 1) (*lnPriorRatio) += LogDirPrior(t, mp, isVPriorExp); # if defined (DEBUG_ExtSPR) printf ("After:\n"); ShowNodes (t->root, 2, NO); getchar(); printf ("Proposal ratio: %f\n",(*lnProposalRatio)); printf ("v: %d u: %d c: %d d: %d a: %d b: %d\n",v->index, u->index, c->index, d->index, a->index, b->index); printf ("No. nodes moved in root subtree: %d\n",nRootNodes); printf ("No. nodes moved in crown subtree: %d\n",nCrownNodes); printf ("Has topology changed? %d\n",topologyHasChanged); getchar(); # endif return (NO_ERROR); } int Move_ExtSPRClock (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp) { /* Change branch lengths and topology (potentially) using SPR-type move with extension probability (rather than window, attachment rate or similar). The move is Metropolized, which should improve mixing. However, this means that it must be combined with a node slider move to be efficient. The move picks a branch and then moves its lower attachment point from its original position, one node at a time, with a probability determined by the extensionProb parameter. This is done in a way consistent with the clock constraints and any locked nodes there might be in the tree. The lower attachment point is minimally moved one node away. On the ending branch, the attachment point is reinserted randomly along the branch (below the minimum age of the node). */ int i, j, topologyHasChanged=NO, isStartLocked=NO, isStopLocked=NO, nRootNodes, directionUp, n1=0, n2=0, n3=0, n4=0, n5=0, *nEvents, numMovableNodesOld, numMovableNodesNew; MrBFlt x, y=0.0, oldBrlen=0.0, newBrlen=0.0, extensionProb, nu, var, *rate=NULL, v1=0.0, v2=0.0, v3=0.0, v4=0.0, v5=0.0, v3new=0.0, lambda, **position=NULL, **rateMultiplier=NULL, *brlens, minV, clockRate; TreeNode *p, *a, *b, *u, *v, *oldA; Tree *t; ModelInfo *m; Param *subParm; extensionProb = mvp[0]; /* extension probability */ (*lnProposalRatio) = (*lnPriorRatio) = 0.0; /* get tree */ t = GetTree (param, chain, state[chain]); /* get model params and model info */ m = &modelSettings[param->relParts[0]]; /* get clock rate */ clockRate = *GetParamVals (m->clockRate, chain, state[chain]); /* get min and max branch lengths in relative time and substitution units */ minV = BRLENS_MIN; # if defined (DEBUG_ExtSPRClock) printf ("Before:\n"); ShowNodes (t->root, 2, YES); getchar(); # endif numMovableNodesOld = 0; for (i=0; inNodes-2; ++i) { p = t->allDownPass[i]; a = p->anc->left; b = p->anc->right; if (!(p->anc->isLocked == YES || p->anc->anc->anc == NULL || (p == b && a->length < TIME_MIN) || (p == a && b->length < TIME_MIN) || (p->length < TIME_MIN && p->calibration->prior == fixed))) ++numMovableNodesOld; } if (numMovableNodesOld == 0) // we can't move! return NO_ERROR; /* pick a branch */ do { p = t->allDownPass[(int)(RandomNumber(seed) * (t->nNodes - 2))]; a = p->anc->left; b = p->anc->right; } while (p->anc->isLocked == YES || p->anc->anc->anc == NULL || (p == b && a->length < TIME_MIN) || (p == a && b->length < TIME_MIN) || (p->length < TIME_MIN && p->calibration->prior == fixed)); /* skip constraints, siblings of root (and root); and consider ancestral fossils in fbd tree */ /* set up pointers for nodes around the picked branch */ v = p; u = p->anc; if (u->left == v) a = u->right; else a = u->left; b = u->anc; oldA = a; /* record branch length for insertion in back move */ if (v->length > 0.0) /* side branch, not anc fossil */ { if (v->nodeDepth > a->nodeDepth) oldBrlen = b->nodeDepth - v->nodeDepth - 2.0*minV; else oldBrlen = b->nodeDepth - a->nodeDepth - 2.0*minV; } else /* ancestral fossil */ { y = (b->nodeDepth - minV > v->calibration->max * clockRate) ? (v->calibration->max * clockRate) : (b->nodeDepth - minV); x = (a->nodeDepth + minV < v->calibration->min * clockRate) ? (v->calibration->min * clockRate) : (a->nodeDepth + minV); oldBrlen = y - x; } v1 = a->length; v2 = u->length; v3 = v->length; /* reassign events for CPP and adjust prior and proposal ratios for relaxed clock models */ for (i=0; isubParams[0]->nSubParams; i++) { subParm = param->subParams[0]->subParams[i]; if (subParm->paramType == P_CPPEVENTS) { /* get pointers to CPP events */ nEvents = subParm->nEvents[2*chain+state[chain]]; position = subParm->position[2*chain+state[chain]]; rateMultiplier = subParm->rateMult[2*chain+state[chain]]; n1 = nEvents[a->index]; n2 = nEvents[u->index]; n3 = nEvents[v->index]; if (n2 > 0) { position[a->index] = (MrBFlt *) SafeRealloc ((void *) position[a->index], (n1+n2) * sizeof (MrBFlt)); rateMultiplier[a->index] = (MrBFlt *) SafeRealloc ((void *) rateMultiplier[a->index], (n1+n2) * sizeof (MrBFlt)); } for (j=0; jindex][j] *= v1 / (v1+v2); for (j=n1; jindex][j] = (position[u->index][j-n1] * v2 + v1) / (v1+v2); rateMultiplier[a->index][j] = rateMultiplier[u->index][j-n1]; } nEvents[a->index] = n1+n2; nEvents[u->index] = 0; if (n2 > 0) { free (position[u->index]); free (rateMultiplier[u->index]); position[u->index] = rateMultiplier[u->index] = NULL; } } /* end CPP events parm */ else if ( subParm->paramType == P_TK02BRANCHRATES) { /* adjust prior ratio */ if (subParm->paramType == P_TK02BRANCHRATES) nu = *GetParamVals (modelSettings[subParm->relParts[0]].tk02var, chain, state[chain]); else nu = *GetParamVals (modelSettings[subParm->relParts[0]].mixedvar, chain, state[chain]); rate = GetParamVals (subParm, chain, state[chain]); if (v->length > 0.0) (*lnPriorRatio) -= LnProbLogNormal_Mean_LogVar(rate[v->anc->index], nu*v->length, rate[v->index]); (*lnPriorRatio) -= LnProbLogNormal_Mean_LogVar(rate[a->anc->index], nu*a->length, rate[a->index]); (*lnPriorRatio) -= LnProbLogNormal_Mean_LogVar(rate[u->anc->index], nu*u->length, rate[u->index]); (*lnPriorRatio) += LnProbLogNormal_Mean_LogVar(rate[u->anc->index], nu*(a->length+u->length), rate[a->index]); /* adjust effective branch lengths */ brlens = GetParamSubVals (subParm, chain, state[chain]); brlens[a->index] = (rate[a->index] + rate[b->index]) / 2.0 * (a->length + u->length); } /* end tk02 branch rate parameter */ else if ( subParm->paramType == P_WNBRANCHRATES) { if (subParm->paramType == P_WNBRANCHRATES) var = *GetParamVals (modelSettings[subParm->relParts[0]].wnvar, chain, state[chain]); else var = *GetParamVals (modelSettings[subParm->relParts[0]].mixedvar, chain, state[chain]); rate = GetParamVals (subParm, chain, state[chain]); /* adjust prior ratio for old branches */ if (v->length > 0.0) (*lnPriorRatio) -= LnProbGamma(v->length/var, v->length/var, rate[v->index]); (*lnPriorRatio) -= LnProbGamma(a->length/var, a->length/var, rate[a->index]); (*lnPriorRatio) -= LnProbGamma(u->length/var, u->length/var, rate[u->index]); (*lnPriorRatio) += LnProbGamma((a->length+u->length)/var, (a->length+u->length)/var, rate[a->index]); /* adjust effective branch lengths and rates */ brlens = GetParamSubVals (subParm, chain, state[chain]); brlens[a->index] = rate[a->index] * (a->length + u->length); } else if ( subParm->paramType == P_ILNBRANCHRATES || (subParm->paramType == P_MIXEDBRCHRATES && *GetParamIntVals(subParm, chain, state[chain]) == RCL_ILN)) { if (subParm->paramType == P_ILNBRANCHRATES) nu = *GetParamVals (modelSettings[subParm->relParts[0]].ilnvar, chain, state[chain]); else nu = *GetParamVals (modelSettings[subParm->relParts[0]].mixedvar, chain, state[chain]); rate = GetParamVals (subParm, chain, state[chain]); /* adjust prior ratio for old branches */ if (v->length > 0.0) (*lnPriorRatio) -= LnProbLogNormal_Mean_Var(1.0, nu, rate[v->index]); (*lnPriorRatio) -= LnProbLogNormal_Mean_Var(1.0, nu, rate[u->index]); /* adjust effective branch lengths and rates */ brlens = GetParamSubVals (subParm, chain, state[chain]); brlens[a->index] = rate[a->index] * (a->length + u->length); } else if ( subParm->paramType == P_IGRBRANCHRATES || (subParm->paramType == P_MIXEDBRCHRATES && *GetParamIntVals(subParm, chain, state[chain]) == RCL_IGR)) { if (subParm->paramType == P_IGRBRANCHRATES) var = *GetParamVals (modelSettings[subParm->relParts[0]].igrvar, chain, state[chain]); else var = *GetParamVals (modelSettings[subParm->relParts[0]].mixedvar, chain, state[chain]); rate = GetParamVals (subParm, chain, state[chain]); /* adjust prior ratio for old branches */ if (v->length > 0.0) (*lnPriorRatio) -= LnProbGamma(1.0/var, 1.0/var, rate[v->index]); (*lnPriorRatio) -= LnProbGamma(1.0/var, 1.0/var, rate[u->index]); /* adjust effective branch lengths and rates */ brlens = GetParamSubVals (subParm, chain, state[chain]); brlens[a->index] = rate[a->index] * (a->length + u->length); } } /* next subparameter */ /* cut tree */ a->anc = b; if (b->left == u) b->left = a; else b->right = a; a->length += u->length; a->upDateTi = YES; /* determine initial direction of move and whether the reverse move would be stopped by constraints */ if (a->left == NULL || a->isLocked == YES || a->nodeDepth < v->nodeDepth + minV) { isStartLocked = YES; directionUp = NO; } else { isStartLocked = NO; if (RandomNumber(seed) < 0.5) directionUp = YES; else directionUp = NO; } /* move around in root subtree */ for (nRootNodes=0; nRootNodes==0 || RandomNumber(seed)left == NULL || a->isLocked == YES || a->nodeDepth < v->nodeDepth + minV) break; /* can't go farther */ topologyHasChanged = YES; b = a; if (a->left->length < TIME_MIN) a = a->right; else if (a->right->length < TIME_MIN) a = a->left; else if (RandomNumber(seed) < 0.5) a = a->left; else a = a->right; } else { /* going down tree */ topologyHasChanged = YES; if (RandomNumber(seed) < 0.5 || b->anc->anc == NULL || b->isLocked == YES) { directionUp = YES; /* switch direction */ /* find sister of a */ if (b->left == a) a = b->right; else a = b->left; /* as long as we are moving upwards the cond likes to update will be flagged by the last pass from u to the root */ } else { /* continue down */ a = b; b = b->anc; a->upDateCl = YES; } } } /* determine whether the forward move was or would have been stopped by constraints */ isStopLocked = NO; if (directionUp == YES) { if (a->left == NULL || a->isLocked == YES || a->nodeDepth < v->nodeDepth + minV) isStopLocked = YES; } /* reattach u */ if (u->left == v) u->right = a; else u->left = a; a->anc = u; u->anc = b; if (b->left == a) b->left = u; else b->right = u; if (v->length > 0.0) /* side branch, not anc fossil */ { if (a->nodeDepth > v->nodeDepth) newBrlen = b->nodeDepth - a->nodeDepth - 2.0*minV; else newBrlen = b->nodeDepth - v->nodeDepth - 2.0*minV; } else /* ancestral fossil */ { y = (b->nodeDepth - minV > v->calibration->max * clockRate) ? (v->calibration->max * clockRate) : (b->nodeDepth - minV); x = (a->nodeDepth + minV < v->calibration->min * clockRate) ? (v->calibration->min * clockRate) : (a->nodeDepth + minV); newBrlen = y - x; } if (newBrlen <= 0.0) { abortMove = YES; return (NO_ERROR); } /* adjust lengths */ if (v->length > 0.0) /* side branch, not anc fossil */ { u->nodeDepth = b->nodeDepth - minV - RandomNumber(seed) * newBrlen; v->length = u->nodeDepth - v->nodeDepth; } else /* ancestral fossil */ { u->nodeDepth = y - RandomNumber(seed) * newBrlen; v->nodeDepth = u->nodeDepth; v->age = u->age = u->nodeDepth / clockRate; } u->length = b->nodeDepth - u->nodeDepth; a->length = u->nodeDepth - a->nodeDepth; v3new = v->length; v4 = a->length; v5 = u->length; /* adjust events, prior ratio and proposal ratio for relaxed clock models */ for (i=0; isubParams[0]->nSubParams; i++) { subParm = param->subParams[0]->subParams[i]; if (subParm->paramType == P_CPPEVENTS) { /* reassign events for CPP */ nEvents = subParm->nEvents[2*chain+state[chain]]; position = subParm->position[2*chain+state[chain]]; rateMultiplier = subParm->rateMult[2*chain+state[chain]]; for (j=0; jindex]; j++) { if (position[a->index][j] > v4 / (v4+v5)) break; } n4 = j; n5 = nEvents[a->index] - j; nEvents[u->index] = n5; if (n5 > 0) { position[u->index] = (MrBFlt *) SafeRealloc ((void *) position[u->index], n5 * sizeof (MrBFlt)); rateMultiplier[u->index] = (MrBFlt *) SafeRealloc ((void *) rateMultiplier[u->index], n5 * sizeof (MrBFlt)); for (j=n4; jindex]; j++) { position[u->index][j-n4] = (position[a->index][j] * (v4+v5) - v4) / v5; rateMultiplier[u->index][j-n4] = rateMultiplier[a->index][j]; } if (n4 > 0) { position[a->index] = (MrBFlt *) SafeRealloc ((void *) position[a->index], n4 * sizeof (MrBFlt)); rateMultiplier[a->index] = (MrBFlt *) SafeRealloc ((void *) rateMultiplier[a->index], n4 * sizeof (MrBFlt)); for (j=0; jindex][j] *= ((v4+v5) / v4); } else { free (position[a->index]); free (rateMultiplier[a->index]); position[a->index] = rateMultiplier[a->index] = NULL; } nEvents[a->index] = n4; } else { for (j=0; jindex]; j++) position[a->index][j] *= ((v4+v5) / v4); } /* adjust proposal ratio for length change in v branch*/ (*lnProposalRatio) += n3 * log (v3new / v3); /* adjust prior ratio for length change */ lambda = *GetParamVals (modelSettings[subParm->relParts[0]].cppRate, chain, state[chain]); (*lnPriorRatio) += lambda * (v3 - v3new); /* update effective branch lengths */ if (UpdateCppEvolLengths (subParm, oldA, chain) == ERROR) { abortMove = YES; return (NO_ERROR); } if (UpdateCppEvolLengths (subParm, u, chain) == ERROR) { abortMove = YES; return (NO_ERROR); } } /* end cpp events parameter */ else if ( subParm->paramType == P_TK02BRANCHRATES) { /* adjust prior ratio */ if (subParm->paramType == P_TK02BRANCHRATES) nu = *GetParamVals (modelSettings[subParm->relParts[0]].tk02var, chain, state[chain]); else nu = *GetParamVals (modelSettings[subParm->relParts[0]].mixedvar, chain, state[chain]); rate = GetParamVals (subParm, chain, state[chain]); (*lnPriorRatio) -= LnProbLogNormal_Mean_LogVar(rate[u->anc->index], nu*(a->length+u->length), rate[a->index]); (*lnPriorRatio) += LnProbLogNormal_Mean_LogVar(rate[a->anc->index], nu*a->length, rate[a->index]); (*lnPriorRatio) += LnProbLogNormal_Mean_LogVar(rate[u->anc->index], nu*u->length, rate[u->index]); if (v->length > 0.0) (*lnPriorRatio) += LnProbLogNormal_Mean_LogVar(rate[v->anc->index], nu*v->length, rate[v->index]); /* adjust effective branch lengths */ brlens = GetParamSubVals (subParm, chain, state[chain]); brlens[a->index] = a->length * (rate[a->index] + rate[a->anc->index]) / 2.0; brlens[v->index] = v->length * (rate[v->index] + rate[v->anc->index]) / 2.0; brlens[u->index] = u->length * (rate[u->index] + rate[u->anc->index]) / 2.0; } /* end tk02 branch rate parameter */ else if ( subParm->paramType == P_WNBRANCHRATES) { /* adjust prior ratio */ if (subParm->paramType == P_WNBRANCHRATES) var = *GetParamVals (modelSettings[subParm->relParts[0]].wnvar, chain, state[chain]); else var = *GetParamVals (modelSettings[subParm->relParts[0]].mixedvar, chain, state[chain]); rate = GetParamVals (subParm, chain, state[chain]); (*lnPriorRatio) -= LnProbGamma ((a->length+u->length)/var, (a->length+u->length)/var, rate[a->index]); (*lnPriorRatio) += LnProbGamma (a->length/var, a->length/var, rate[a->index]); (*lnPriorRatio) += LnProbGamma (u->length/var, u->length/var, rate[u->index]); if (v->length > 0.0) (*lnPriorRatio) += LnProbGamma (v->length/var, v->length/var, rate[v->index]); /* adjust effective branch lengths */ brlens = GetParamSubVals (subParm, chain, state[chain]); brlens[v->index] = rate[v->index] * v->length; brlens[u->index] = rate[u->index] * u->length; brlens[a->index] = rate[a->index] * a->length; } /* end wn branch rate parameter */ else if ( subParm->paramType == P_ILNBRANCHRATES || (subParm->paramType == P_MIXEDBRCHRATES && *GetParamIntVals(subParm, chain, state[chain]) == RCL_ILN)) { /* adjust prior ratio */ if (subParm->paramType == P_ILNBRANCHRATES) nu = *GetParamVals (modelSettings[subParm->relParts[0]].ilnvar, chain, state[chain]); else nu = *GetParamVals (modelSettings[subParm->relParts[0]].mixedvar, chain, state[chain]); rate = GetParamVals (subParm, chain, state[chain]); (*lnPriorRatio) += LnProbLogNormal_Mean_Var (1.0, nu, rate[u->index]); if (v->length > 0.0) (*lnPriorRatio) += LnProbLogNormal_Mean_Var (1.0, nu, rate[v->index]); /* adjust effective branch lengths */ brlens = GetParamSubVals (subParm, chain, state[chain]); brlens[v->index] = rate[v->index] * v->length; brlens[u->index] = rate[u->index] * u->length; brlens[a->index] = rate[a->index] * a->length; } /* end iln branch rate parameter */ else if ( subParm->paramType == P_IGRBRANCHRATES || (subParm->paramType == P_MIXEDBRCHRATES && *GetParamIntVals(subParm, chain, state[chain]) == RCL_IGR)) { /* adjust prior ratio */ if (subParm->paramType == P_IGRBRANCHRATES) var = *GetParamVals (modelSettings[subParm->relParts[0]].igrvar, chain, state[chain]); else var = *GetParamVals (modelSettings[subParm->relParts[0]].mixedvar, chain, state[chain]); rate = GetParamVals (subParm, chain, state[chain]); (*lnPriorRatio) += LnProbGamma (1.0/var, 1.0/var, rate[u->index]); if (v->length > 0.0) (*lnPriorRatio) += LnProbGamma (1.0/var, 1.0/var, rate[v->index]); /* adjust effective branch lengths */ brlens = GetParamSubVals (subParm, chain, state[chain]); brlens[v->index] = rate[v->index] * v->length; brlens[u->index] = rate[u->index] * u->length; brlens[a->index] = rate[a->index] * a->length; } /* end igr branch rate parameter */ } /* next subparameter */ /* set tiprobs update flags */ a->upDateTi = YES; u->upDateTi = YES; v->upDateTi = YES; /* set flags for update of cond likes from u and down to root */ p = u; while (p->anc != NULL) { p->upDateCl = YES; p = p->anc; } /* adjust proposal ratio for number of movable nodes */ numMovableNodesNew = 0; for (i=0; inNodes-2; ++i) { p = t->allDownPass[i]; a = p->anc->left; b = p->anc->right; if (!(p->anc->isLocked == YES || p->anc->anc->anc == NULL || (p == b && a->length < TIME_MIN) || (p == a && b->length < TIME_MIN) || (p->length < TIME_MIN && p->calibration->prior == fixed))) ++numMovableNodesNew; } if (numMovableNodesOld != numMovableNodesNew) (*lnProposalRatio) += log(numMovableNodesOld/numMovableNodesNew); /* adjust prior ratio for clock tree */ if (LogClockTreePriorRatio(param, chain, &x) == ERROR) return (ERROR); (*lnPriorRatio) += x; if (topologyHasChanged == YES) { /* get down pass sequence if tree topology has changed */ GetDownPass (t); /* calculate proposal ratio for tree change */ (*lnProposalRatio) += log (newBrlen / oldBrlen); if (isStartLocked == NO && isStopLocked == YES) (*lnProposalRatio) += log (2.0 * (1.0 - extensionProb)); else if (isStartLocked == YES && isStopLocked == NO) (*lnProposalRatio) -= log (2.0 * (1.0 - extensionProb)); } # if defined (DEBUG_ExtSPRClock) ShowNodes (t->root, 2, YES); printf ("After\nProposal ratio: %f\n",(*lnProposalRatio)); printf ("v: %d u: %d a: %d b: %d\n",v->index, u->index, a->index, b->index); printf ("No. nodes moved in root subtree: %d\n",nRootNodes); printf ("Has topology changed? %d\n",topologyHasChanged); # endif return (NO_ERROR); } int Move_ExtSPRClock_Fossil (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp) { /* This move is identical to the Move_ExtSPRClock move except that it only moves fossil subtrees. */ int i, j, topologyHasChanged=NO, isStartLocked=NO, isStopLocked=NO, nRootNodes, directionUp, n1=0, n2=0, n3=0, n4=0, n5=0, *nEvents, numMovableNodesOld, numMovableNodesNew; MrBFlt x, y=0.0, oldBrlen=0.0, newBrlen=0.0, extensionProb, nu, var, *rate=NULL, v1=0.0, v2=0.0, v3=0.0, v4=0.0, v5=0.0, v3new=0.0, lambda, **position=NULL, **rateMultiplier=NULL, *brlens, minV, clockRate; TreeNode *p, *a, *b, *u, *v, *oldA; Tree *t; ModelInfo *m; Param *subParm; extensionProb = mvp[0]; /* extension probability */ (*lnProposalRatio) = (*lnPriorRatio) = 0.0; /* get tree */ t = GetTree (param, chain, state[chain]); /* get model params and model info */ m = &modelSettings[param->relParts[0]]; /* get clock rate */ clockRate = *GetParamVals (m->clockRate, chain, state[chain]); /* get min and max branch lengths in relative time and substitution units */ minV = BRLENS_MIN; # if defined (DEBUG_ExtSPRClock) printf ("Before:\n"); ShowNodes (t->root, 2, YES); getchar(); # endif /* mark all nodes that only have fossil children with YES and count number movable nodes in current tree */ numMovableNodesOld = 0; for (i=0; inNodes-2; ++i) { p = t->allDownPass[i]; if (p->left == NULL) { if (p->calibration == NULL) p->x = NO; else { p->x = YES; } } else { if (p->left->x == YES && p->right->x == YES) { p->x = YES; } else p->x = NO; } a = p->anc->left; b = p->anc->right; if (!(p->anc->isLocked == YES || p->anc->anc->anc == NULL || (p == b && a->length < TIME_MIN) || (p == a && b->length < TIME_MIN) || (p->length < TIME_MIN && p->calibration->prior == fixed) || p->x == NO)) numMovableNodesOld++; } if (numMovableNodesOld == 0) return (NO_ERROR); /* pick a branch */ do { p = t->allDownPass[(int)(RandomNumber(seed) * (t->nNodes - 2))]; a = p->anc->left; b = p->anc->right; } while (p->anc->isLocked == YES || p->anc->anc->anc == NULL || (p == b && a->length < TIME_MIN) || (p == a && b->length < TIME_MIN) || (p->length < TIME_MIN && p->calibration->prior == fixed) || p->x == NO); /* skip constraints, siblings of root (and root); and consider ancestral fossils in fbd tree; skip all nodes that subtend extant terminals */ /* set up pointers for nodes around the picked branch */ v = p; u = p->anc; if (u->left == v) a = u->right; else a = u->left; b = u->anc; oldA = a; /* record branch length for insertion in back move */ if (v->length > 0.0) /* side branch, not anc fossil */ { if (v->nodeDepth > a->nodeDepth) oldBrlen = b->nodeDepth - v->nodeDepth - 2.0*minV; else oldBrlen = b->nodeDepth - a->nodeDepth - 2.0*minV; } else /* ancestral fossil */ { y = (b->nodeDepth - minV > v->calibration->max * clockRate) ? (v->calibration->max * clockRate) : (b->nodeDepth - minV); x = (a->nodeDepth + minV < v->calibration->min * clockRate) ? (v->calibration->min * clockRate) : (a->nodeDepth + minV); oldBrlen = y - x; } v1 = a->length; v2 = u->length; v3 = v->length; /* reassign events for CPP and adjust prior and proposal ratios for relaxed clock models */ for (i=0; isubParams[0]->nSubParams; i++) { subParm = param->subParams[0]->subParams[i]; if (subParm->paramType == P_CPPEVENTS) { /* get pointers to CPP events */ nEvents = subParm->nEvents[2*chain+state[chain]]; position = subParm->position[2*chain+state[chain]]; rateMultiplier = subParm->rateMult[2*chain+state[chain]]; n1 = nEvents[a->index]; n2 = nEvents[u->index]; n3 = nEvents[v->index]; if (n2 > 0) { position[a->index] = (MrBFlt *) SafeRealloc ((void *) position[a->index], (n1+n2) * sizeof (MrBFlt)); rateMultiplier[a->index] = (MrBFlt *) SafeRealloc ((void *) rateMultiplier[a->index], (n1+n2) * sizeof (MrBFlt)); } for (j=0; jindex][j] *= v1 / (v1+v2); for (j=n1; jindex][j] = (position[u->index][j-n1] * v2 + v1) / (v1+v2); rateMultiplier[a->index][j] = rateMultiplier[u->index][j-n1]; } nEvents[a->index] = n1+n2; nEvents[u->index] = 0; if (n2 > 0) { free (position[u->index]); free (rateMultiplier[u->index]); position[u->index] = rateMultiplier[u->index] = NULL; } } /* end CPP events parm */ else if ( subParm->paramType == P_TK02BRANCHRATES) { /* adjust prior ratio */ if (subParm->paramType == P_TK02BRANCHRATES) nu = *GetParamVals (modelSettings[subParm->relParts[0]].tk02var, chain, state[chain]); else nu = *GetParamVals (modelSettings[subParm->relParts[0]].mixedvar, chain, state[chain]); rate = GetParamVals (subParm, chain, state[chain]); if (v->length > 0.0) (*lnPriorRatio) -= LnProbLogNormal_Mean_LogVar(rate[v->anc->index], nu*v->length, rate[v->index]); (*lnPriorRatio) -= LnProbLogNormal_Mean_LogVar(rate[a->anc->index], nu*a->length, rate[a->index]); (*lnPriorRatio) -= LnProbLogNormal_Mean_LogVar(rate[u->anc->index], nu*u->length, rate[u->index]); (*lnPriorRatio) += LnProbLogNormal_Mean_LogVar(rate[u->anc->index], nu*(a->length+u->length), rate[a->index]); /* adjust effective branch lengths */ brlens = GetParamSubVals (subParm, chain, state[chain]); brlens[a->index] = (rate[a->index] + rate[b->index]) / 2.0 * (a->length + u->length); } /* end tk02 branch rate parameter */ else if ( subParm->paramType == P_WNBRANCHRATES) { if (subParm->paramType == P_WNBRANCHRATES) var = *GetParamVals (modelSettings[subParm->relParts[0]].wnvar, chain, state[chain]); else var = *GetParamVals (modelSettings[subParm->relParts[0]].mixedvar, chain, state[chain]); rate = GetParamVals (subParm, chain, state[chain]); /* adjust prior ratio for old branches */ if (v->length > 0.0) (*lnPriorRatio) -= LnProbGamma(v->length/var, v->length/var, rate[v->index]); (*lnPriorRatio) -= LnProbGamma(a->length/var, a->length/var, rate[a->index]); (*lnPriorRatio) -= LnProbGamma(u->length/var, u->length/var, rate[u->index]); (*lnPriorRatio) += LnProbGamma((a->length+u->length)/var, (a->length+u->length)/var, rate[a->index]); /* adjust effective branch lengths and rates */ brlens = GetParamSubVals (subParm, chain, state[chain]); brlens[a->index] = rate[a->index] * (a->length + u->length); } else if ( subParm->paramType == P_ILNBRANCHRATES || (subParm->paramType == P_MIXEDBRCHRATES && *GetParamIntVals(subParm, chain, state[chain]) == RCL_ILN)) { if (subParm->paramType == P_ILNBRANCHRATES) nu = *GetParamVals (modelSettings[subParm->relParts[0]].ilnvar, chain, state[chain]); else nu = *GetParamVals (modelSettings[subParm->relParts[0]].mixedvar, chain, state[chain]); rate = GetParamVals (subParm, chain, state[chain]); /* adjust prior ratio for old branches */ if (v->length > 0.0) (*lnPriorRatio) -= LnProbLogNormal_Mean_Var(1.0, nu, rate[v->index]); (*lnPriorRatio) -= LnProbLogNormal_Mean_Var(1.0, nu, rate[u->index]); /* adjust effective branch lengths and rates */ brlens = GetParamSubVals (subParm, chain, state[chain]); brlens[a->index] = rate[a->index] * (a->length + u->length); } else if ( subParm->paramType == P_IGRBRANCHRATES || (subParm->paramType == P_MIXEDBRCHRATES && *GetParamIntVals(subParm, chain, state[chain]) == RCL_IGR)) { if (subParm->paramType == P_IGRBRANCHRATES) var = *GetParamVals (modelSettings[subParm->relParts[0]].igrvar, chain, state[chain]); else var = *GetParamVals (modelSettings[subParm->relParts[0]].mixedvar, chain, state[chain]); rate = GetParamVals (subParm, chain, state[chain]); /* adjust prior ratio for old branches */ if (v->length > 0.0) (*lnPriorRatio) -= LnProbGamma(1.0/var, 1.0/var, rate[v->index]); (*lnPriorRatio) -= LnProbGamma(1.0/var, 1.0/var, rate[u->index]); /* adjust effective branch lengths and rates */ brlens = GetParamSubVals (subParm, chain, state[chain]); brlens[a->index] = rate[a->index] * (a->length + u->length); } } /* next subparameter */ /* cut tree */ a->anc = b; if (b->left == u) b->left = a; else b->right = a; a->length += u->length; a->upDateTi = YES; /* determine initial direction of move and whether the reverse move would be stopped by constraints */ if (a->left == NULL || a->isLocked == YES || a->nodeDepth < v->nodeDepth + minV) { isStartLocked = YES; directionUp = NO; } else { isStartLocked = NO; if (RandomNumber(seed) < 0.5) directionUp = YES; else directionUp = NO; } /* move around in root subtree */ for (nRootNodes=0; nRootNodes==0 || RandomNumber(seed)left == NULL || a->isLocked == YES || a->nodeDepth < v->nodeDepth + minV) break; /* can't go farther */ topologyHasChanged = YES; b = a; if (a->left->length < TIME_MIN) a = a->right; else if (a->right->length < TIME_MIN) a = a->left; else if (RandomNumber(seed) < 0.5) a = a->left; else a = a->right; } else { /* going down tree */ topologyHasChanged = YES; if (RandomNumber(seed) < 0.5 || b->anc->anc == NULL || b->isLocked == YES) { directionUp = YES; /* switch direction */ /* find sister of a */ if (b->left == a) a = b->right; else a = b->left; /* as long as we are moving upwards the cond likes to update will be flagged by the last pass from u to the root */ } else { /* continue down */ a = b; b = b->anc; a->upDateCl = YES; } } } /* determine whether the forward move was or would have been stopped by constraints */ isStopLocked = NO; if (directionUp == YES) { if (a->left == NULL || a->isLocked == YES || a->nodeDepth < v->nodeDepth + minV) isStopLocked = YES; } /* reattach u */ if (u->left == v) u->right = a; else u->left = a; a->anc = u; u->anc = b; if (b->left == a) b->left = u; else b->right = u; if (v->length > 0.0) /* side branch, not anc fossil */ { if (a->nodeDepth > v->nodeDepth) newBrlen = b->nodeDepth - a->nodeDepth - 2.0*minV; else newBrlen = b->nodeDepth - v->nodeDepth - 2.0*minV; } else /* ancestral fossil */ { y = (b->nodeDepth - minV > v->calibration->max * clockRate) ? (v->calibration->max * clockRate) : (b->nodeDepth - minV); x = (a->nodeDepth + minV < v->calibration->min * clockRate) ? (v->calibration->min * clockRate) : (a->nodeDepth + minV); newBrlen = y - x; } if (newBrlen <= 0.0) { abortMove = YES; return (NO_ERROR); } /* adjust lengths */ if (v->length > 0.0) /* side branch, not anc fossil */ { u->nodeDepth = b->nodeDepth - minV - RandomNumber(seed) * newBrlen; v->length = u->nodeDepth - v->nodeDepth; } else /* ancestral fossil */ { u->nodeDepth = y - RandomNumber(seed) * newBrlen; v->nodeDepth = u->nodeDepth; v->age = u->age = u->nodeDepth / clockRate; } u->length = b->nodeDepth - u->nodeDepth; a->length = u->nodeDepth - a->nodeDepth; v3new = v->length; v4 = a->length; v5 = u->length; /* adjust events, prior ratio and proposal ratio for relaxed clock models */ for (i=0; isubParams[0]->nSubParams; i++) { subParm = param->subParams[0]->subParams[i]; if (subParm->paramType == P_CPPEVENTS) { /* reassign events for CPP */ nEvents = subParm->nEvents[2*chain+state[chain]]; position = subParm->position[2*chain+state[chain]]; rateMultiplier = subParm->rateMult[2*chain+state[chain]]; for (j=0; jindex]; j++) { if (position[a->index][j] > v4 / (v4+v5)) break; } n4 = j; n5 = nEvents[a->index] - j; nEvents[u->index] = n5; if (n5 > 0) { position[u->index] = (MrBFlt *) SafeRealloc ((void *) position[u->index], n5 * sizeof (MrBFlt)); rateMultiplier[u->index] = (MrBFlt *) SafeRealloc ((void *) rateMultiplier[u->index], n5 * sizeof (MrBFlt)); for (j=n4; jindex]; j++) { position[u->index][j-n4] = (position[a->index][j] * (v4+v5) - v4) / v5; rateMultiplier[u->index][j-n4] = rateMultiplier[a->index][j]; } if (n4 > 0) { position[a->index] = (MrBFlt *) SafeRealloc ((void *) position[a->index], n4 * sizeof (MrBFlt)); rateMultiplier[a->index] = (MrBFlt *) SafeRealloc ((void *) rateMultiplier[a->index], n4 * sizeof (MrBFlt)); for (j=0; jindex][j] *= ((v4+v5) / v4); } else { free (position[a->index]); free (rateMultiplier[a->index]); position[a->index] = rateMultiplier[a->index] = NULL; } nEvents[a->index] = n4; } else { for (j=0; jindex]; j++) position[a->index][j] *= ((v4+v5) / v4); } /* adjust proposal ratio for length change in v branch*/ (*lnProposalRatio) += n3 * log (v3new / v3); /* adjust prior ratio for length change */ lambda = *GetParamVals (modelSettings[subParm->relParts[0]].cppRate, chain, state[chain]); (*lnPriorRatio) += lambda * (v3 - v3new); /* update effective branch lengths */ if (UpdateCppEvolLengths (subParm, oldA, chain) == ERROR) { abortMove = YES; return (NO_ERROR); } if (UpdateCppEvolLengths (subParm, u, chain) == ERROR) { abortMove = YES; return (NO_ERROR); } } /* end cpp events parameter */ else if ( subParm->paramType == P_TK02BRANCHRATES) { /* adjust prior ratio */ if (subParm->paramType == P_TK02BRANCHRATES) nu = *GetParamVals (modelSettings[subParm->relParts[0]].tk02var, chain, state[chain]); else nu = *GetParamVals (modelSettings[subParm->relParts[0]].mixedvar, chain, state[chain]); rate = GetParamVals (subParm, chain, state[chain]); (*lnPriorRatio) -= LnProbLogNormal_Mean_LogVar(rate[u->anc->index], nu*(a->length+u->length), rate[a->index]); (*lnPriorRatio) += LnProbLogNormal_Mean_LogVar(rate[a->anc->index], nu*a->length, rate[a->index]); (*lnPriorRatio) += LnProbLogNormal_Mean_LogVar(rate[u->anc->index], nu*u->length, rate[u->index]); if (v->length > 0.0) (*lnPriorRatio) += LnProbLogNormal_Mean_LogVar(rate[v->anc->index], nu*v->length, rate[v->index]); /* adjust effective branch lengths */ brlens = GetParamSubVals (subParm, chain, state[chain]); brlens[a->index] = a->length * (rate[a->index] + rate[a->anc->index]) / 2.0; brlens[v->index] = v->length * (rate[v->index] + rate[v->anc->index]) / 2.0; brlens[u->index] = u->length * (rate[u->index] + rate[u->anc->index]) / 2.0; } /* end tk02 branch rate parameter */ else if ( subParm->paramType == P_WNBRANCHRATES) { /* adjust prior ratio */ if (subParm->paramType == P_WNBRANCHRATES) var = *GetParamVals (modelSettings[subParm->relParts[0]].wnvar, chain, state[chain]); else var = *GetParamVals (modelSettings[subParm->relParts[0]].mixedvar, chain, state[chain]); rate = GetParamVals (subParm, chain, state[chain]); (*lnPriorRatio) -= LnProbGamma ((a->length+u->length)/var, (a->length+u->length)/var, rate[a->index]); (*lnPriorRatio) += LnProbGamma (a->length/var, a->length/var, rate[a->index]); (*lnPriorRatio) += LnProbGamma (u->length/var, u->length/var, rate[u->index]); if (v->length > 0.0) (*lnPriorRatio) += LnProbGamma (v->length/var, v->length/var, rate[v->index]); /* adjust effective branch lengths */ brlens = GetParamSubVals (subParm, chain, state[chain]); brlens[v->index] = rate[v->index] * v->length; brlens[u->index] = rate[u->index] * u->length; brlens[a->index] = rate[a->index] * a->length; } /* end wn branch rate parameter */ else if ( subParm->paramType == P_ILNBRANCHRATES || (subParm->paramType == P_MIXEDBRCHRATES && *GetParamIntVals(subParm, chain, state[chain]) == RCL_ILN)) { /* adjust prior ratio */ if (subParm->paramType == P_ILNBRANCHRATES) nu = *GetParamVals (modelSettings[subParm->relParts[0]].ilnvar, chain, state[chain]); else nu = *GetParamVals (modelSettings[subParm->relParts[0]].mixedvar, chain, state[chain]); rate = GetParamVals (subParm, chain, state[chain]); (*lnPriorRatio) += LnProbLogNormal_Mean_Var (1.0, nu, rate[u->index]); if (v->length > 0.0) (*lnPriorRatio) += LnProbLogNormal_Mean_Var (1.0, nu, rate[v->index]); /* adjust effective branch lengths */ brlens = GetParamSubVals (subParm, chain, state[chain]); brlens[v->index] = rate[v->index] * v->length; brlens[u->index] = rate[u->index] * u->length; brlens[a->index] = rate[a->index] * a->length; } /* end iln branch rate parameter */ else if ( subParm->paramType == P_IGRBRANCHRATES || (subParm->paramType == P_MIXEDBRCHRATES && *GetParamIntVals(subParm, chain, state[chain]) == RCL_IGR)) { /* adjust prior ratio */ if (subParm->paramType == P_IGRBRANCHRATES) var = *GetParamVals (modelSettings[subParm->relParts[0]].igrvar, chain, state[chain]); else var = *GetParamVals (modelSettings[subParm->relParts[0]].mixedvar, chain, state[chain]); rate = GetParamVals (subParm, chain, state[chain]); (*lnPriorRatio) += LnProbGamma (1.0/var, 1.0/var, rate[u->index]); if (v->length > 0.0) (*lnPriorRatio) += LnProbGamma (1.0/var, 1.0/var, rate[v->index]); /* adjust effective branch lengths */ brlens = GetParamSubVals (subParm, chain, state[chain]); brlens[v->index] = rate[v->index] * v->length; brlens[u->index] = rate[u->index] * u->length; brlens[a->index] = rate[a->index] * a->length; } /* end igr branch rate parameter */ } /* next subparameter */ /* set tiprobs update flags */ a->upDateTi = YES; u->upDateTi = YES; v->upDateTi = YES; /* set flags for update of cond likes from u and down to root */ p = u; while (p->anc != NULL) { p->upDateCl = YES; p = p->anc; } /* adjust prior ratio for clock tree */ if (LogClockTreePriorRatio(param, chain, &x) == ERROR) return (ERROR); (*lnPriorRatio) += x; if (topologyHasChanged == YES) { /* get down pass sequence if tree topology has changed */ GetDownPass (t); /* calculate proposal ratio for tree change */ (*lnProposalRatio) += log (newBrlen / oldBrlen); if (isStartLocked == NO && isStopLocked == YES) (*lnProposalRatio) += log (2.0 * (1.0 - extensionProb)); else if (isStartLocked == YES && isStopLocked == NO) (*lnProposalRatio) -= log (2.0 * (1.0 - extensionProb)); } /* adjust proposal prob for number movable nodes in new tree */ numMovableNodesNew = 0; for (i=0; inNodes-2; ++i) { p = t->allDownPass[i]; if (p->left == NULL) { if (p->calibration == NULL) p->x = NO; else { p->x = YES; } } else { if (p->left->x == YES && p->right->x == YES) { p->x = YES; } else p->x = NO; } a = p->anc->left; b = p->anc->right; if (!(p->anc->isLocked == YES || p->anc->anc->anc == NULL || (p == b && a->length < TIME_MIN) || (p == a && b->length < TIME_MIN) || (p->length < TIME_MIN && p->calibration->prior == fixed) || p->x == NO)) numMovableNodesNew++; } if (numMovableNodesNew != numMovableNodesOld) (*lnProposalRatio) += log(numMovableNodesOld/numMovableNodesNew); # if defined (DEBUG_ExtSPRClock) ShowNodes (t->root, 2, YES); printf ("After\nProposal ratio: %f\n",(*lnProposalRatio)); printf ("v: %d u: %d a: %d b: %d\n",v->index, u->index, a->index, b->index); printf ("No. nodes moved in root subtree: %d\n",nRootNodes); printf ("Has topology changed? %d\n",topologyHasChanged); # endif return (NO_ERROR); } int Move_ExtSS (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp) { /* Change branch lengths and topology (potentially) using Subtree Swapping (unrooted) with extension probability. This move type picks two subtrees and swaps their position. Like the SPR and TBR, it is a superset of the NNI but there are some interesting differences. With the SPR and TBR, it is not possible to go between all five-tip trees in a single step. For instance, going from ((1,2),3,(4,5)) to ((1,5),3,(4,2)) requires two steps. The SS move can go between all pairs of five-tip trees in a single step. Some six-tip tree pairs will require two steps though. Unlike the published version of the move (Lakner et al, Syst Bio), this version does _not_ multiply all branch lengths between the subtrees. */ int i, numFree, topologyHasChanged, nCrownNodes, nRootNodes, directionLeft, directionUp, isVPriorExp, moveInRoot, tempInt; MrBFlt m, x, tuning, maxV, minV, extensionProb; TreeNode *p, *q, *a, *b, *c, *d, *u, *v, *interiorRoot=NULL; Tree *t; ModelParams *mp; (*lnPriorRatio) = (*lnProposalRatio) = 0.0; /* these parameters should be possible to set by user */ extensionProb = mvp[0]; /* extension probability */ tuning = mvp[1]; /* Larget & Simon's tuning parameter lambda */ /* get tree */ t = GetTree (param, chain, state[chain]); /* get model params */ mp = &modelParams[param->relParts[0]]; /* for rooted (non-clock) trees, store pointer to current interior root node */ //SK if (t->isRooted) interiorRoot = t->root->left; /* max and min brlen */ if (param->subParams[0]->paramId == BRLENS_UNI) { minV = mp->brlensUni[0] > BRLENS_MIN ? mp->brlensUni[0] : BRLENS_MIN; maxV = mp->brlensUni[1]; isVPriorExp = NO; } else if (param->subParams[0]->paramId == BRLENS_GamDir) { minV = BRLENS_MIN; maxV = BRLENS_MAX; isVPriorExp = 2; } else if (param->subParams[0]->paramId == BRLENS_iGmDir) { minV = BRLENS_MIN; maxV = BRLENS_MAX; isVPriorExp = 3; } else if (param->subParams[0]->paramId == BRLENS_twoExp) { minV = BRLENS_MIN; maxV = BRLENS_MAX; isVPriorExp = 4; } else { minV = BRLENS_MIN; maxV = BRLENS_MAX; isVPriorExp = YES; } /* Dirichlet or twoExp prior */ if (isVPriorExp > 1) (*lnPriorRatio) = -LogDirPrior(t, mp, isVPriorExp); topologyHasChanged = NO; /* unmark all tree */ for (i=0; inNodes; i++) { p = t->allDownPass[i]; p->marked = NO; } /* pick a branch */ do { p = t->allDownPass[(int)(RandomNumber(seed) * t->nNodes)]; } while (p->anc == NULL); /* set up pointers for nodes around the picked branch */ v = p; u = p->anc; /* check the possible move directions */ numFree = 0; if (v->left != NULL && v->left->isLocked == NO) numFree ++; if (v->right != NULL && v->right->isLocked == NO) numFree++; if (u->anc != NULL && u->isLocked == NO) numFree++; if (u->left == v) { if (u->right != NULL && u->right->isLocked == NO) numFree++; } else { if (u->left != NULL && u->left->isLocked == NO) numFree++; } /* select one of them randomly */ i = (int) (RandomNumber(seed) * numFree) + 1; numFree = 0; a = b = c = d = p; directionLeft = directionUp = moveInRoot = NO; if (v->left != NULL && v->left->isLocked == NO) { numFree ++; if (i == numFree) { moveInRoot = NO; directionLeft = YES; c = v->left; d = v; } } if (v->right != NULL && v->right->isLocked == NO) { numFree ++; if (i == numFree) { moveInRoot = NO; directionLeft = NO; c = v->right; d = v; } } if (u->anc != NULL && u->isLocked == NO) { numFree ++; if (i == numFree) { moveInRoot = YES; directionUp = NO; a = u->anc; b = u; } } if (u->left == v) { if (u->right != NULL && u->right->isLocked == NO) { numFree ++; if (i == numFree) { moveInRoot = YES; directionUp = YES; a = u->right; b = u; } } } else { if (u->left != NULL && u->left->isLocked == NO) { numFree ++; if (i == numFree) { moveInRoot = YES; directionUp = YES; a = u->left; b = u; } } } # if defined (DEBUG_ExtSS) printf ("Before:\n"); ShowNodes (t->root, 2, NO); printf ("v: %d u: %d c: %d d: %d a: %d b: %d\n",v->index, u->index, c->index, d->index, a->index, b->index); printf ("directionUp = %d -- directionLeft = %d -- moveInRoot = %d\n", directionUp, directionLeft, moveInRoot); getchar(); # endif /* move around and potentially swap in root subtree */ nRootNodes = 0; if (moveInRoot == YES) { for (nRootNodes=0; nRootNodes==0 || RandomNumber(seed)left == NULL || a->isLocked == YES) break; /* can't go further */ topologyHasChanged = YES; b = a; if (RandomNumber(seed) < 0.5) a = a->left; else a = a->right; } else { /* going down tree */ if (a->anc == NULL || a->isLocked == YES) break; /* can't go further */ topologyHasChanged = YES; b->marked = YES; if (RandomNumber(seed) < 0.5) { directionUp = YES; /* switch direction */ /* find sister of a */ if (a->left == b) { b = a; a = a->right; } else { b = a; a = a->left; } } else { /* continue down */ b = a; a = a->anc; } } } /* swap the root subtrees */ if (nRootNodes > 0) { if (directionUp == YES) { v->anc = b; a->anc = u; if (b->left == a) b->left = v; else if (b->right == a) b->right = v; if (u->left == v) u->left = a; else u->right = a; } else { /* rotate the nodes from b to u*/ p = b; q = a; x = b->length; while (p->left->marked == YES || p->right->marked == YES) { if (p->left->marked == YES) { /* rotate p anticlockwise - prepare pointers for move left */ p->anc = p->left; /* the root will be in the direction we are heading */ p->left = p->right; p->right = q; } else { /* rotate p clockwise - prepare pointers for move right */ p->anc = p->right; /* the root will be in the direction we are heading */ p->right = p->left; p->left = q; } /* OK - let's move!; p->anc points in the right direction don't forget to move the branch lengths as well */ q = p; p = p->anc; q->length = p->length; q->upDateTi = YES; } /* rotations finished, take care of u */ if (u->left == v) u->left = u->anc; else u->right = u->anc; u->length = x; /* now swap the subtrees of u and b */ if (a->left == b) a->left = u; else a->right = u; u->anc = a; v->anc = b; if (b->left == a) b->left = v; else b->right = v; } } } /* move around and potentially swap in crown subtree */ nCrownNodes = 0; if (moveInRoot == NO) { x = v->length; /* save v length in case there is a move */ for (nCrownNodes=0; nCrownNodes==0 || RandomNumber(seed)left == NULL || c->isLocked == YES) break; /* can't go further */ topologyHasChanged = YES; /* prepare d for move */ d->anc = c; d->length = c->length; d->upDateTi = YES; d->upDateCl = YES; if (d->isLocked == YES) { c->isLocked = YES; d->isLocked = NO; c->lockID = d->lockID; d->lockID = -1; } /* go left or right with equal probability */ if (RandomNumber(seed) < 0.5) { /* rotate c anticlockwise - prepare pointers for move left */ c->anc = c->left; /* the root will be in the direction we are heading */ c->left = c->right; c->right = d; } else { /* rotate c clockwise - prepare pointers for move right */ c->anc = c->right; /* the root will be in the direction we are heading */ c->right = c->left; c->left = d; } /* OK - let's move!; c->anc points in the right direction */ d = c; c = c->anc; } /* swap the crown subtrees */ if (nCrownNodes > 0) { d->anc = u; d->length = x; if (u->left == v) u->left = d; else u->right = d; c->anc = v; if (directionLeft == YES) v->left = c; else v->right = c; } } /* modify branch lengths */ if (nCrownNodes > 0) { p = c; q = d; } else if (nRootNodes > 0) { if (directionUp == YES) { p = v; q = a; } else { p = v; q = u; } } else { p = v; if (RandomNumber(seed) < 0.5) { if (RandomNumber(seed) < 0.5) q = u; else { if (u->left == v) q = u->right; else q = u->left; } } else { if (RandomNumber(seed) < 0.5) q = v->left; else q = v->right; } } if (p != NULL) { m = p->length; x = m * exp(tuning * (RandomNumber(seed) - 0.5)); while (x < minV || x > maxV) { if (x < minV) x = minV * minV / x; else if (x > maxV) x = maxV * maxV / x; } p->length = x; p->upDateTi = YES; /* update proposal and prior ratio based on length modification */ (*lnProposalRatio) += log (x / m); if (isVPriorExp == YES) (*lnPriorRatio) += (mp->brlensExp) * (m - x); } if (q != NULL && q->anc != NULL) { m = q->length; x = m * exp(tuning * (RandomNumber(seed) - 0.5)); while (x < minV || x > maxV) { if (x < minV) x = minV * minV / x; else if (x > maxV) x = maxV * maxV / x; } q->length = x; q->upDateTi = YES; /* update proposal and prior ratio based on length modification */ (*lnProposalRatio) += log (x / m); if (isVPriorExp == YES) (*lnPriorRatio) += (mp->brlensExp) * (m - x); } /* set flags for update of cond likes from v and down to root */ p = v; while (p->anc != NULL) { p->upDateCl = YES; p = p->anc; } if (topologyHasChanged == YES) { /* set flags for update of cond likes from u and down to root */ p = u; while (p->anc != NULL) { p->upDateCl = YES; p = p->anc; } } /* if tree is rooted, swap indices of former and current interior root node, if changed */ //SK if (t->isRooted && interiorRoot != t->root->left) { tempInt = interiorRoot->index; interiorRoot->index = t->root->left->index; t->root->left->index = tempInt; } /* get down pass sequence if tree topology has changed */ if (topologyHasChanged == YES) { GetDownPass (t); } /* Dirichlet or twoExp prior */ if (isVPriorExp > 1) (*lnPriorRatio) += LogDirPrior(t, mp, isVPriorExp); # if defined (DEBUG_ExtSS) printf ("After:\n"); ShowNodes (t->root, 2, NO); getchar(); printf ("Proposal ratio: %f\n",exp(*lnProposalRatio)); printf ("v: %d u: %d c: %d d: %d a: %d b: %d\n",v->index, u->index, c->index, d->index, a->index, b->index); printf ("No. nodes moved in root subtree: %d\n",nRootNodes); printf ("No. nodes moved in crown subtree: %d\n",nCrownNodes); printf ("Has topology changed? %d\n",topologyHasChanged); printf ("directionUp = %d -- directionLeft = %d\n", directionUp, directionLeft); getchar(); # endif return (NO_ERROR); } int Move_ExtSSClock (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp) { /* Change branch lengths and topology (potentially) using SS-type move with extension probability (rather than window, attachment rate or similar). */ /* This move picks a branch at random. It then moves away from this branch, one one node at a time, with a probability determined by the extensionProb parameter. The process stops when a tip is reached or when a move further upwards would break the clock assumption. When the extension process stops, the subtrees supported by the two chosen branches are swapped. Since 2010-11-01, the move is Metropolized for increased efficiency. */ /* Note: this move is not compatible with fossilized birth-death model with ancestral fossils */ int i, *nEvents, numFreeOld, numFreeNew; MrBFlt x, oldALength, oldCLength, extensionProb, nu, var, *rate, *brlens, ran, cumulativeProb, forwardProb, backwardProb, minV; TreeNode *p, *q, *a, *c; Tree *t; Param *subParm; extensionProb = mvp[0]; /* extension probability */ (*lnProposalRatio) = (*lnPriorRatio) = 0.0; /* get tree */ t = GetTree (param, chain, state[chain]); /* get min and max brlens in relative time and subst units */ minV = BRLENS_MIN; /* calculate the number of free nodes */ numFreeOld = t->nNodes-2; if (t->nConstraints > 1) { numFreeOld = 0; for (i=0; inNodes-2; i++) { p = t->allDownPass[i]; if (p->anc->left == p) q = p->anc->right; else q = p->anc->left; if (p->anc->isLocked == NO || q->isLocked == NO) numFreeOld++; } } /* pick a branch */ do { p = t->allDownPass[(int)(RandomNumber(seed) * (t->nNodes -2))]; if (p->anc->left == p) q = p->anc->right; else q = p->anc->left; } while ((p->anc->isLocked == YES && q->isLocked == YES) || p->length < TIME_MIN || q->length < TIME_MIN); /* choose subtree that can be swapped */ /* set up pointers for nodes around the picked branch */ a = p; if (p->anc->left == p) q = p->anc->right; /* FIXME: Not used? (from clang static analyzer) */ else q = p->anc->left; /* FIXME: Not used? (from clang static analyzer) */ if (p->anc->anc->left == p->anc) c = p->anc->anc->right; /* FIXME: Not used? (from clang static analyzer) */ else c = p->anc->anc->left; /* FIXME: Not used? (from clang static analyzer) */ /* record branch length */ oldALength = a->length; /* reset scratch variables */ for (i=0; inNodes-1; i++) { p = t->allDownPass[i]; p->x = -1; p->y = NO; } /* calculate distance from picked node */ p = a->anc; p->x = 0; while (p->isLocked == NO && p->anc != NULL) { p->anc->x = p->x + 1; p = p->anc; } for (i=t->nIntNodes-2; i>=0; i--) { p = t->intDownPass[i]; if (p->x < 0 && p->anc->x >= 0 && p != a && p->isLocked == NO) p->x = p->anc->x + 1; } /* mark the free nodes and calculate the total score */ cumulativeProb = 0.0; for (i=0; inNodes-2; i++) { p = t->allDownPass[i]; if (p != a && p->anc->x > 0 && a->anc->nodeDepth > p->nodeDepth + minV && p->anc->nodeDepth > a->nodeDepth + minV) { p->y = YES; p->d = pow(0.5 * extensionProb, p->anc->x); cumulativeProb += p->d; } else p->d = 0.0; } /* find the target node */ ran = RandomNumber(seed) * cumulativeProb; x = 0.0; for (i=0; inNodes-2; i++) { p = t->allDownPass[i]; if (p->y == YES) { x += p->d; if (x > ran) break; } } if (i == t->nNodes - 2) { abortMove = YES; return (NO_ERROR); } /* record first forward prob */ forwardProb = p->d / cumulativeProb; /* record partner swap branch */ c = p; oldCLength = c->length; /* calculate second forward prob */ /* reset scratch variables */ for (i=0; inNodes-1; i++) { p = t->allDownPass[i]; p->x = -1; p->y = NO; } /* calculate distance from picked node */ p = c->anc; p->x = 0; while (p->isLocked == NO && p->anc != NULL) { p->anc->x = p->x + 1; p = p->anc; } for (i=t->nIntNodes-1; i>=0; i--) { p = t->intDownPass[i]; if (p->x < 0 && p != c && p->anc->x >= 0 && p->isLocked == NO) p->x = p->anc->x + 1; } /* mark the free nodes and calculate the total score */ cumulativeProb = 0.0; for (i=0; inNodes-2; i++) { p = t->allDownPass[i]; if (p != c && p->anc->x > 0 && c->anc->nodeDepth > p->nodeDepth + minV && p->anc->nodeDepth > c->nodeDepth + minV) { p->y = YES; p->d = pow(0.5 * extensionProb, p->anc->x); cumulativeProb += p->d; } else p->d = 0.0; } /* now we can calculate second forward prob */ forwardProb += a->d / cumulativeProb; /* swap subtrees */ if (a->anc->left == a) a->anc->left = c; else a->anc->right = c; if (c->anc->left == c) c->anc->left = a; else c->anc->right = a; p = a->anc; a->anc = c->anc; c->anc = p; a->length = a->anc->nodeDepth - a->nodeDepth; c->length = c->anc->nodeDepth - c->nodeDepth; /* get down pass sequence */ GetDownPass (t); /* set tiprobs update flags */ a->upDateTi = YES; c->upDateTi = YES; /* set flags for update of cond likes from a->anc and down to root */ p = a->anc; while (p->anc != NULL) { p->upDateCl = YES; p = p->anc; } /* set flags for update of cond likes from c->anc and down to root */ p = c->anc; while (p->anc != NULL) { p->upDateCl = YES; p = p->anc; } /* adjust prior ratio for clock tree */ if (LogClockTreePriorRatio(param, chain, &x) == ERROR) return (ERROR); (*lnPriorRatio) += x; /* calculate first backward prob */ /* reset scratch variables */ for (i=0; inNodes-1; i++) { p = t->allDownPass[i]; p->x = -1; p->y = NO; } /* calculate distance from picked node */ p = a->anc; p->x = 0; while (p->isLocked == NO && p->anc != NULL) { p->anc->x = p->x + 1; p = p->anc; } for (i=t->nIntNodes-1; i>=0; i--) { p = t->intDownPass[i]; if (p->x < 0 && p != a && p->anc->x >= 0 && p->isLocked == NO) p->x = p->anc->x + 1; } /* mark the free nodes and calculate the total score */ cumulativeProb = 0.0; for (i=0; inNodes-2; i++) { p = t->allDownPass[i]; if (p != a && p->anc->x > 0 && a->anc->nodeDepth > p->nodeDepth + minV && p->anc->nodeDepth > a->nodeDepth + minV) { p->y = YES; p->d = pow(0.5 * extensionProb, p->anc->x); cumulativeProb += p->d; } else p->d = 0.0; } /* calculate first backward prob */ backwardProb = c->d / cumulativeProb; /* calculate second backward prob */ /* reset scratch variables */ for (i=0; inNodes-1; i++) { p = t->allDownPass[i]; p->x = -1; p->y = NO; } /* calculate distance from picked node */ p = c->anc; p->x = 0; while (p->isLocked == NO && p->anc != NULL) { p->anc->x = p->x + 1; p = p->anc; } for (i=t->nIntNodes-1; i>=0; i--) { p = t->intDownPass[i]; if (p->x < 0 && p != c && p->anc->x >= 0 && p->isLocked == NO) p->x = p->anc->x + 1; } /* mark the free nodes and calculate the total score */ cumulativeProb = 0.0; for (i=0; inNodes-2; i++) { p = t->allDownPass[i]; if (p != c && p->anc->x > 0 && c->anc->nodeDepth > p->nodeDepth + minV && p->anc->nodeDepth > c->nodeDepth + minV) { p->y = YES; p->d = pow(0.5 * extensionProb, p->anc->x); cumulativeProb += p->d; } else p->d = 0.0; } /* calculate second backward prob */ backwardProb += a->d / cumulativeProb; /* now we can calculate proposal ratio */ (*lnProposalRatio) += log (backwardProb / forwardProb); /* adjust for number of free nodes */ numFreeNew = t->nNodes-2; /* FIXME: Not used? (from clang static analyzer) */ if (t->nConstraints > 1) { numFreeNew = 0; for (i=0; inNodes-2; i++) { p = t->allDownPass[i]; if (p->anc->left == p) q = p->anc->right; else q = p->anc->left; if (p->anc->isLocked == NO || q->isLocked == NO) numFreeNew++; } (*lnProposalRatio) += log(numFreeOld / numFreeNew); } /* adjust proposal and prior ratio for relaxed clock models */ for (i=0; isubParams[0]->nSubParams; i++) { subParm = param->subParams[0]->subParams[i]; if (subParm->paramType == P_CPPEVENTS) { nEvents = subParm->nEvents[2*chain+state[chain]]; /* proposal ratio */ (*lnProposalRatio) += nEvents[a->index] * log (a->length / oldALength); (*lnProposalRatio) += nEvents[c->index] * log (c->length / oldCLength); /* prior ratio: no effect because tree length is the same */ /* update effective evolutionary lengths */ if (UpdateCppEvolLengths (subParm, a, chain) == ERROR || UpdateCppEvolLengths (subParm, c, chain) == ERROR) { abortMove = YES; return (NO_ERROR); } } else if ( subParm->paramType == P_TK02BRANCHRATES) { if (subParm->paramType == P_TK02BRANCHRATES) nu = *GetParamVals (modelSettings[subParm->relParts[0]].tk02var, chain, state[chain]); else nu = *GetParamVals (modelSettings[subParm->relParts[0]].mixedvar, chain, state[chain]); rate = GetParamVals (subParm, chain, state[chain]); brlens = GetParamSubVals (subParm, chain, state[chain]); /* no proposal ratio effect */ /* prior ratio and update of effective evolutionary lengths */ (*lnPriorRatio) -= LnProbLogNormal_Mean_LogVar (rate[c->anc->index], nu*oldALength, rate[a->index]); (*lnPriorRatio) += LnProbLogNormal_Mean_LogVar (rate[a->anc->index], nu* a->length, rate[a->index]); (*lnPriorRatio) -= LnProbLogNormal_Mean_LogVar (rate[a->anc->index], nu*oldCLength, rate[c->index]); (*lnPriorRatio) += LnProbLogNormal_Mean_LogVar (rate[c->anc->index], nu* c->length, rate[c->index]); brlens[a->index] = a->length * (rate[a->index] + rate[a->anc->index])/2.0; brlens[c->index] = c->length * (rate[c->index] + rate[c->anc->index])/2.0; } else if ( subParm->paramType == P_WNBRANCHRATES) { /* get relevant parameters */ if (subParm->paramType == P_WNBRANCHRATES) var = *GetParamVals (modelSettings[subParm->relParts[0]].wnvar, chain, state[chain]); else var = *GetParamVals (modelSettings[subParm->relParts[0]].mixedvar, chain, state[chain]); rate = GetParamVals (subParm, chain, state[chain]); brlens = GetParamSubVals (subParm, chain, state[chain]); /* prior ratio and update of effective evolutionary lengths */ (*lnPriorRatio) -= LnProbGamma (oldALength/var, oldALength/var, rate[a->index]); (*lnPriorRatio) -= LnProbGamma (oldCLength/var, oldCLength/var, rate[c->index]); (*lnPriorRatio) += LnProbGamma (a->length /var, a->length /var, rate[a->index]); (*lnPriorRatio) += LnProbGamma (c->length /var, c->length /var, rate[c->index]); brlens[a->index] = rate[a->index] * a->length; brlens[c->index] = rate[c->index] * c->length; } else if ( subParm->paramType == P_ILNBRANCHRATES || (subParm->paramType == P_MIXEDBRCHRATES && *GetParamIntVals(subParm, chain, state[chain]) == RCL_ILN)) { /* get relevant parameters */ if (subParm->paramType == P_ILNBRANCHRATES) nu = *GetParamVals (modelSettings[subParm->relParts[0]].ilnvar, chain, state[chain]); else nu = *GetParamVals (modelSettings[subParm->relParts[0]].mixedvar, chain, state[chain]); rate = GetParamVals (subParm, chain, state[chain]); brlens = GetParamSubVals (subParm, chain, state[chain]); /* update of effective evolutionary lengths */ brlens[a->index] = rate[a->index] * a->length; brlens[c->index] = rate[c->index] * c->length; } else if ( subParm->paramType == P_IGRBRANCHRATES || (subParm->paramType == P_MIXEDBRCHRATES && *GetParamIntVals(subParm, chain, state[chain]) == RCL_IGR)) { /* get relevant parameters */ if (subParm->paramType == P_IGRBRANCHRATES) var = *GetParamVals (modelSettings[subParm->relParts[0]].igrvar, chain, state[chain]); else var = *GetParamVals (modelSettings[subParm->relParts[0]].mixedvar, chain, state[chain]); rate = GetParamVals (subParm, chain, state[chain]); brlens = GetParamSubVals (subParm, chain, state[chain]); /* update of effective evolutionary lengths */ brlens[a->index] = rate[a->index] * a->length; brlens[c->index] = rate[c->index] * c->length; } } assert (*lnPriorRatio == *lnPriorRatio); return (NO_ERROR); } int Move_ExtTBR (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp) { /* Change topology (and branch lengths) using TBR with extension probability. */ /* This move picks an internal branch and two "danglies", modifies their lengths independently according to the method of Larget & Simon (1999: MBE); it then moves the danglies away from their original position one node at a time with a probability determined by the extensionProb parameter. When the danglies are moved, their direction is changed -- "reflection" necessary to enable the back move. This move type has been tested on all combinations of rooted and unrooted, constrained and unconstrained trees */ int i, j, topologyHasChanged, nCrownNodes, nRootNodes, directionLeft, directionUp, isVPriorExp, alwaysMoveRoot, isCrownStartConstrained, isRootStartConstrained, isStopConstrained, tempInt; MrBFlt m, x, y, tuning, maxV, minV, extensionProb; TreeNode *p, *a, *b, *c, *d, *u, *v, *interiorRoot=NULL; Tree *t; ModelParams *mp; /* these parameters should be possible to set by user */ extensionProb = mvp[0]; /* extension probability */ tuning = mvp[1]; /* Larget & Simon's tuning parameter lambda */ (*lnProposalRatio) = (*lnPriorRatio) = 0.0; /* get tree */ t = GetTree (param, chain, state[chain]); /* get model params */ mp = &modelParams[param->relParts[0]]; /* for rooted (non-clock) trees, store pointer to current interior root node */ //SK if (t->isRooted) interiorRoot = t->root->left; /* max and min brlen */ if (param->subParams[0]->paramId == BRLENS_UNI) { minV = mp->brlensUni[0] > BRLENS_MIN ? mp->brlensUni[0] : BRLENS_MIN; maxV = mp->brlensUni[1] < BRLENS_MAX ? mp->brlensUni[1] : BRLENS_MAX; isVPriorExp = NO; } else if (param->subParams[0]->paramId == BRLENS_GamDir) { minV = BRLENS_MIN; maxV = BRLENS_MAX; isVPriorExp = 2; } else if (param->subParams[0]->paramId == BRLENS_iGmDir) { minV = BRLENS_MIN; maxV = BRLENS_MAX; isVPriorExp = 3; } else if (param->subParams[0]->paramId == BRLENS_twoExp) { minV = BRLENS_MIN; maxV = BRLENS_MAX; isVPriorExp = 4; } else { minV = BRLENS_MIN; maxV = BRLENS_MAX; isVPriorExp = YES; } /* Dirichlet or twoExp prior */ if (isVPriorExp > 1) (*lnPriorRatio) = -LogDirPrior(t, mp, isVPriorExp); topologyHasChanged = NO; # if defined (DEBUG_ExtTBR) printf ("Before:\n"); ShowNodes (t->root, 2, NO); getchar(); # endif /* pick an internal branch */ do { p = t->intDownPass[(int)(RandomNumber(seed) * (t->nIntNodes-1))]; if (p->anc->left == p) a = p->anc->right; else a = p->anc->left; i = j = 0; if (a->isLocked == YES || a->left == NULL) i++; if (p->anc->isLocked == YES || p->anc->anc->anc == NULL) i++; if (p->left->isLocked == YES || p->left->left == NULL) j++; if (p->right->isLocked == YES || p->right->left == NULL) j++; } while (i == 2 && j == 2); /* determine whether to move first step unconditionally in root or in crown */ if (j == 2) alwaysMoveRoot = YES; else if (i == 2) alwaysMoveRoot = NO; else if (RandomNumber(seed) < 0.5) alwaysMoveRoot = YES; else alwaysMoveRoot = NO; /* determine any starting constraints */ isCrownStartConstrained = isRootStartConstrained = NO; if (i >= 1) isRootStartConstrained = YES; if (j >= 1) isCrownStartConstrained = YES; /* set up pointers for nodes around the picked branch */ /* cut the tree into crown, root and attachment part */ /* change the relevant lengths in the attachment part */ /* the lengths of a and v are automatically contained in the */ /* "attachment" part but the length of c has to be stored in x */ v = p; u = p->anc; /* set up pointers for crown part */ /* also determine direction of move in crown part */ if (v->right->left == NULL || v->right->isLocked == YES) directionLeft = YES; else if (v->left->left == NULL || v->left->isLocked == YES) directionLeft = NO; else if (RandomNumber(seed) < 0.5) directionLeft = YES; else directionLeft = NO; if (directionLeft == YES) { c = v->left; d = v->right; } else { c = v->right; d = v->left; } /* cut and reconnect crown part */ c->anc = d; d->anc = c; /* record c length and adjust with multiplier using reflection */ m = c->length; x = c->length * exp(tuning * (RandomNumber(seed) - 0.5)); /* save the modified dangling branch for later use */ while (x < minV || x > maxV) { if (x < minV) x = minV * minV / x; if (x > maxV) x = maxV * maxV / x; } /* calculate proposal and prior ratio based on length modification */ (*lnProposalRatio) = log (x / m); if (isVPriorExp == YES) (*lnPriorRatio) = (mp->brlensExp) * (m - x); /* record v length and adjust with multiplier using reflection*/ m = v->length; v->length *= exp(tuning * (RandomNumber(seed) - 0.5)); while (v->length < minV || v->length > maxV) { if (v->length < minV) v->length = minV * minV / v->length; else if (v->length > maxV) v->length = maxV * maxV / v->length; } v->upDateTi = YES; /* adjust proposal and prior ratio based on length modification */ (*lnProposalRatio) += log (v->length / m); if (isVPriorExp == YES) (*lnPriorRatio) += (mp->brlensExp) * (m - v->length); /* mark nodes in root part */ /* also determine direction of move in root part */ if (u->left == v) a = u->right; else a = u->left; b = u->anc; if (u->anc->anc == NULL || u->isLocked == YES) directionUp = YES; else if (a->left == NULL || a->isLocked == YES) directionUp = NO; else if (RandomNumber(seed) < 0.5) directionUp = YES; else directionUp = NO; if (directionUp == NO) { /* switch a and b */ b = a; a = u->anc; } /* cut root part */ if (directionUp == NO) { b->anc = a; if (a->left == u) a->left = b; else a->right = b; } else { a->anc = b; if (b->left == u) b->left = a; else b->right = a; y = a->length; a->length = u->length; u->length = y; a->upDateTi = YES; } /* adjust length of branch to be modified */ /* if it is not the root branch of a rooted tree */ if (t->isRooted == NO || u->anc->anc != NULL) { m = u->length; u->length *= exp(tuning * (RandomNumber(seed) - 0.5)); while (u->length < minV || u->length > maxV) { if (u->length < minV) u->length = minV * minV / u->length; else if (u->length > maxV) u->length = maxV * maxV / u->length; } /* adjust proposal and prior ratio based on length modification */ (*lnProposalRatio) += log (u->length / m); if (isVPriorExp == YES) (*lnPriorRatio) += (mp->brlensExp) * (m - u->length); } u->upDateTi = YES; /* move around in root subtree */ for (nRootNodes=0; (alwaysMoveRoot == YES && nRootNodes == 0) || RandomNumber(seed) < extensionProb; nRootNodes++) { if (directionUp == YES) { /* going up tree */ if (a->left == NULL || a->isLocked == YES) break; /* can't go further */ topologyHasChanged = YES; b = a; if (RandomNumber(seed) < 0.5) a = a->left; else a = a->right; if (u->isLocked == YES) { b->isLocked = YES; u->isLocked = NO; b->lockID = u->lockID; u->lockID = 0; } } else { /* going down tree */ if (a->anc == NULL || u->isLocked == YES) break; /* can't go further */ topologyHasChanged = YES; if (RandomNumber(seed)<0.5) { directionUp = YES; /* switch direction */ /* find sister of a */ if (a->left == b) { b = a; a = a->right; } else { b = a; a = a->left; } /* as long as we are moving upwards the cond likes to update will be flagged by the last pass from u to the root */ } else { /* continue down */ b = a; a = a->anc; b->upDateCl = YES; if (b->isLocked == YES) { u->isLocked = YES; b->isLocked = NO; u->lockID = b->lockID; b->lockID = 0; } } } } /* adjust proposal ratio for root move if unbalanced */ isStopConstrained = NO; if (directionUp == YES && (a->left == NULL || a->isLocked == YES)) isStopConstrained = YES; if (directionUp == NO && (a->anc == NULL || u->isLocked == YES)) isStopConstrained = YES; if (nRootNodes > 0) { if (isRootStartConstrained == YES && isStopConstrained == NO) (*lnProposalRatio) -= log (2.0 * (1.0 - extensionProb)); else if (isRootStartConstrained == NO && isStopConstrained == YES) (*lnProposalRatio) += log (2.0 * (1.0 - extensionProb)); } /* move around in crown subtree */ for (nCrownNodes=0; (alwaysMoveRoot == NO && nCrownNodes == 0) || RandomNumber(seed) < extensionProb; nCrownNodes++) { if (c->left == NULL || c->isLocked == YES) break; /* can't go further */ topologyHasChanged = YES; if (RandomNumber(seed) < 0.5) { /* rotate c anticlockwise - prepare pointers for move left */ c->anc = c->left; /* the root will be in the direction we are heading */ c->left = c->right; c->right = d; } else { /* rotate c clockwise - prepare pointers for move right */ c->anc = c->right; /* the root will be in the direction we are heading */ c->right = c->left; c->left = d; } /* OK - let's move!; c->anc points in the right direction don't forget to move the branch lengths as well */ d = c; c = c->anc; d->length = c->length; d->upDateCl = YES; d->upDateTi = YES; } /* adjust proposal ratio for crown move if unbalanced */ isStopConstrained = NO; if (c->left == NULL || c->isLocked == YES) isStopConstrained = YES; if (nCrownNodes > 0) { if (isCrownStartConstrained == YES && isStopConstrained == NO) (*lnProposalRatio) -= log (2.0 * (1.0 - extensionProb)); else if (isCrownStartConstrained == NO && isStopConstrained == YES) (*lnProposalRatio) += log (2.0 * (1.0 - extensionProb)); } /* combine the subtrees */ c->anc = v; d->anc = v; if (directionLeft == YES) { v->left = c; v->right = d; } else { v->left = d; v->right = c; } /* the dangling branch is inserted in reverted position such that the back move will be possible if we have moved around in crown subtree otherwise it is left in its original position */ if (nCrownNodes > 0) { d->length = x; d->upDateTi = YES; } else { c->length = x; c->upDateTi = YES; } if (directionUp == YES) { u->anc = b; if (u->left == v) u->right = a; else u->left = a; a->anc = u; if (b->left == a) b->left = u; else b->right = u; /* the dangling branch is contained in u->length and will automatically be inserted in the right position to enable the back move regardless of whether it was initially directed upwards or downwards BUT if we haven't moved in root subtree, it is advantageous (necessary for rooted trees) to avoid switching branches, which occurs otherwise if directionUp == YES */ if (nRootNodes == 0) { y = u->length; u->length = a->length; a->length = y; a->upDateTi = YES; u->upDateTi = NO; /* u retains its old length */ } } else { u->anc = a; if (u->left == v) u->right = b; else u->left = b; b->anc = u; if (a->left == b) a->left = u; else a->right = u; /* the modified branch contained in u->length will have to be moved to b->length to enable back move BUT if we haven't moved, it is better to keep it in place (necessary for rooted trees) */ if (nRootNodes > 0) { y = u->length; u->length = b->length; b->length = y; b->upDateTi = YES; } } /* set flags for update of cond likes from v and down to root */ p = v; while (p->anc != NULL) { p->upDateCl = YES; p = p->anc; } /* if tree is rooted, swap indices of former and current interior root node, if changed */ //SK if (t->isRooted && interiorRoot != t->root->left) { tempInt = interiorRoot->index; interiorRoot->index = t->root->left->index; t->root->left->index = tempInt; } /* get down pass sequence if tree topology has changed */ if (topologyHasChanged == YES) { GetDownPass (t); } /* Dirichlet or twoExp prior */ if (isVPriorExp > 1) (*lnPriorRatio) += LogDirPrior(t, mp, isVPriorExp); # if defined (DEBUG_ExtTBR) printf ("After:\n"); ShowNodes (t->root, 2, NO); getchar(); printf ("Proposal ratio: %f\n",(*lnProposalRatio)); printf ("v: %d u: %d c: %d d: %d a: %d b: %d\n",v->index, u->index, c->index, d->index, a->index, b->index); printf ("No. nodes moved in root subtree: %d\n",nRootNodes); printf ("No. nodes moved in crown subtree: %d\n",nCrownNodes); printf ("Has topology changed? %d\n",topologyHasChanged); getchar(); # endif return (NO_ERROR); } /*---------------------------------------------------------------- | | Move_GeneRate_Dir: Change gene rate multiplier using Dirichlet | proposal. | ----------------------------------------------------------------*/ int Move_GeneRate_Dir (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp) { int i, nRates, isValid; MrBFlt alphaPi, *value, *subValue, numSites, *alphaDir, x, y, sum, rate_pot, *dirParm, *oldRate, *newRate; /* allocate memory */ dirParm = (MrBFlt *) SafeCalloc (3*(numTopologies-1), sizeof(MrBFlt)); oldRate = dirParm + numCurrentDivisions; newRate = dirParm + 2*numCurrentDivisions; /* get number of rates */ nRates = param->nValues; /* get pointer to rates and number of uncompressed chars */ value = GetParamVals(param, chain, state[chain]); subValue = GetParamSubVals(param, chain, state[chain]); /* get Dirichlet parameters */ alphaDir = subValue + nRates; /* calculate old ratesum proportions */ numSites = 0.0; for (i=0; inRelParts; i++) TouchAllTreeNodes(&modelSettings[param->relParts[i]],chain); /* may need to hit update flag for cijks when you have a covarion model */ for (i=0; inRelParts; i++) if (modelSettings[param->relParts[i]].nCijkParts > 1) modelSettings[param->relParts[i]].upDateCijk = YES; free (dirParm); return (NO_ERROR); } int Move_Growth_M (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp) { MrBFlt oldG, newG, lambda, minG, maxG, ran, oldLnPrior, newLnPrior, curTheta; ModelParams *mp; ModelInfo *m; Tree *t; /* get tuning parameter */ lambda = mvp[0]; /* get model params */ m = &modelSettings[param->relParts[0]]; mp = &modelParams[param->relParts[0]]; curTheta = (*GetParamVals(m->popSize, chain, state[chain])) * (*GetParamVals(m->clockRate, chain, state[chain])); if (!strcmp(mp->ploidy, "Diploid")) curTheta *= 4.0; else if (!strcmp(mp->ploidy, "Zlinked")) curTheta *= 3.0; else curTheta *= 2.0; /* get minimum and maximum values for growth */ minG = param->min; maxG = param->max; /* get old value of theta */ newG = oldG = *GetParamVals(param, chain, state[chain]); if (newG < minG) oldG = minG; /* change value of growth */ ran = RandomNumber(seed); newG = oldG * exp (lambda * (ran - 0.5)); /* check that new value is valid */ while (newG < minG || newG > maxG) { if (newG < minG) newG = minG * minG / newG; else if (newG > maxG) newG = maxG * maxG / newG; } /* get proposal ratio */ (*lnProposalRatio) = log (newG / oldG); /* get prior ratio */ t = GetTree(m->brlens, chain, state[chain]); if (LnCoalescencePriorPr (t, &oldLnPrior, curTheta, oldG) == ERROR) { MrBayesPrint ("%s Problem calculating prior for coalescent process\n", spacer); return (ERROR); } if (LnCoalescencePriorPr (t, &newLnPrior, curTheta, newG) == ERROR) { MrBayesPrint ("%s Problem calculating prior for coalescent process\n", spacer); return (ERROR); } (*lnPriorRatio) = newLnPrior - oldLnPrior + param->LnPriorRatio(newG, oldG, param->priorParams); /* copy new growth value back */ *GetParamVals(param, chain, state[chain]) = newG; return (NO_ERROR); } int Move_IgrBranchRate (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp) { /* move one IGR relaxed clock branch rate using multiplier */ int i; MrBFlt newRate, oldRate, tuning, minR, maxR, igrvar, *igrRate, *brlens; TreeNode *p = NULL; ModelInfo *m; Tree *t; TreeNode *q; /* get the tuning parameter */ tuning = mvp[0]; /* get the model settings */ m = &modelSettings[param->relParts[0]]; /* get the IGR branch rate and effective branch length data */ igrRate = GetParamVals (param, chain, state[chain]); brlens = GetParamSubVals (param, chain, state[chain]); /* get tree */ t = GetTree (param, chain, state[chain]); /* get minimum and maximum rate */ minR = RATE_MIN; maxR = RATE_MAX; /* randomly pick a branch */ do { i = (int) (RandomNumber(seed) * (t->nNodes -2)); p = t->allDownPass[i]; } while (p->length < TIME_MIN); // not ancestral fossil /* find new rate using multiplier */ oldRate = igrRate[p->index]; newRate = oldRate * exp ((0.5 - RandomNumber(seed)) * tuning); /* reflect if necessary */ while (newRate < minR || newRate > maxR) { if (newRate < minR) newRate = minR * minR / newRate; if (newRate > maxR) newRate = maxR * maxR / newRate; } igrRate[p->index] = newRate; /* calculate prior ratio */ igrvar = *GetParamVals (m->igrvar, chain, state[chain]); (*lnPriorRatio) = LnProbGamma (1.0/igrvar, 1.0/igrvar, newRate) - LnProbGamma (1.0/igrvar, 1.0/igrvar, oldRate); /* calculate proposal ratio */ (*lnProposalRatio) = log (newRate / oldRate); /* update branch evolution lengths */ brlens[p->index] = newRate * p->length; /* set update of transition probability */ p->upDateTi = YES; /* set update of cond likes down to root */ q = p->anc; while (q->anc != NULL) { q->upDateCl = YES; q = q->anc; } return (NO_ERROR); } int Move_IgrVar (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp) { /* move the variance of the IGR relaxed clock model using multiplier */ int i, j; MrBFlt oldVar, newVar, minVar, maxVar, tuning, *rate; Model *mp; TreeNode *p; Tree *t; /* get tuning parameter */ tuning = mvp[0]; /* get model params */ mp = &modelParams[param->relParts[0]]; /* get the min and max values */ minVar = INDRVAR_MIN; maxVar = INDRVAR_MAX; if (!strcmp(mp->igrvarPr,"Uniform")) { minVar = (mp->igrvarUni[0] < INDRVAR_MIN) ? INDRVAR_MIN : mp->igrvarUni[0]; maxVar = (mp->igrvarUni[1] > INDRVAR_MAX) ? INDRVAR_MAX : mp->igrvarUni[1]; } /* get the igr variance */ oldVar = *GetParamVals (param, chain, state[chain]); /* set new value */ newVar = oldVar * exp ((0.5 - RandomNumber(seed))*tuning); /* reflect if necessary */ while (newVar < minVar || newVar > maxVar) { if (newVar < minVar) newVar = minVar * minVar / newVar; if (newVar > maxVar) newVar = maxVar * maxVar / newVar; } /* store new value */ *GetParamVals (param, chain, state[chain]) = newVar; /* calculate prior ratio */ for (i=0; inSubParams; i++) { rate = GetParamVals (param->subParams[i], chain, state[chain]); t = GetTree (param->subParams[i], chain, state[chain]); for (j=0; jnNodes-2; j++) { p = t->allDownPass[j]; if (p->length > 0.0) // not ancestral fossil { (*lnPriorRatio) -= LnProbGamma (1.0/oldVar, 1.0/oldVar, rate[p->index]); (*lnPriorRatio) += LnProbGamma (1.0/newVar, 1.0/newVar, rate[p->index]); } } } /* take prior on IGRvar into account */ if (!strcmp(mp->igrvarPr, "Exponential")) (*lnPriorRatio) += mp->igrvarExp * (oldVar - newVar); /* calculate proposal ratio */ (*lnProposalRatio) = log (newVar / oldVar); /* we do not need to update likelihoods */ for (i=0; inRelParts; i++) { modelSettings[param->relParts[i]].upDateCl = NO; } return (NO_ERROR); } int Move_IlnBranchRate (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp) { /* move one ILN relaxed clock branch rate using multiplier */ int i; MrBFlt newRate, oldRate, tuning, minR, maxR, ilnvar, *ilnRate, *brlens; TreeNode *p = NULL; ModelInfo *m; Tree *t; TreeNode *q; /* get the tuning parameter */ tuning = mvp[0]; /* get the model settings */ m = &modelSettings[param->relParts[0]]; /* get the ILN branch rate and effective branch length data */ ilnRate = GetParamVals (param, chain, state[chain]); brlens = GetParamSubVals (param, chain, state[chain]); /* get tree */ t = GetTree (param, chain, state[chain]); /* get minimum and maximum rate */ minR = RATE_MIN; maxR = RATE_MAX; /* randomly pick a branch */ do { i = (int) (RandomNumber(seed) * (t->nNodes -2)); p = t->allDownPass[i]; } while (p->length < TIME_MIN); // not ancestral fossil /* find new rate using multiplier */ oldRate = ilnRate[p->index]; newRate = oldRate * exp ((0.5 - RandomNumber(seed)) * tuning); /* reflect if necessary */ while (newRate < minR || newRate > maxR) { if (newRate < minR) newRate = minR * minR / newRate; if (newRate > maxR) newRate = maxR * maxR / newRate; } ilnRate[p->index] = newRate; /* calculate prior ratio */ ilnvar = *GetParamVals (m->ilnvar, chain, state[chain]); (*lnPriorRatio) = LnProbLogNormal_Mean_Var (1.0, ilnvar, newRate) - LnProbLogNormal_Mean_Var (1.0, ilnvar, oldRate); /* calculate proposal ratio */ (*lnProposalRatio) = log (newRate / oldRate); /* update branch evolution lengths */ brlens[p->index] = newRate * p->length; /* set update of transition probability */ p->upDateTi = YES; /* set update of cond likes down to root */ q = p->anc; while (q->anc != NULL) { q->upDateCl = YES; q = q->anc; } return (NO_ERROR); } int Move_IlnVar (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp) { /* move the variance of the ILN relaxed clock model using multiplier */ int i, j; MrBFlt oldVar, newVar, minVar, maxVar, tuning, *ilnRate; Model *mp; TreeNode *p; Tree *t; /* get tuning parameter */ tuning = mvp[0]; /* get model params */ mp = &modelParams[param->relParts[0]]; /* get the min and max values */ minVar = INDRVAR_MIN; maxVar = INDRVAR_MAX; if (!strcmp(mp->ilnvarPr,"Uniform")) { minVar = (mp->ilnvarUni[0] < INDRVAR_MIN) ? INDRVAR_MIN : mp->ilnvarUni[0]; maxVar = (mp->ilnvarUni[1] > INDRVAR_MAX) ? INDRVAR_MAX : mp->ilnvarUni[1]; } /* get the iln variance */ oldVar = *GetParamVals (param, chain, state[chain]); /* set new value */ newVar = oldVar * exp ((0.5 - RandomNumber(seed))*tuning); /* reflect if necessary */ while (newVar < minVar || newVar > maxVar) { if (newVar < minVar) newVar = minVar * minVar / newVar; if (newVar > maxVar) newVar = maxVar * maxVar / newVar; } /* store new value */ *GetParamVals (param, chain, state[chain]) = newVar; /* calculate prior ratio */ for (i=0; inSubParams; i++) { ilnRate = GetParamVals (param->subParams[i], chain, state[chain]); t = GetTree (param->subParams[i], chain, state[chain]); for (j=0; jnNodes-2; j++) { p = t->allDownPass[j]; if (p->length > 0.0) // not ancestral fossil { (*lnPriorRatio) -= LnProbLogNormal_Mean_Var (1.0, oldVar, ilnRate[p->index]); (*lnPriorRatio) += LnProbLogNormal_Mean_Var (1.0, newVar, ilnRate[p->index]); } } } /* take prior on ILNvar into account */ if (!strcmp(mp->ilnvarPr, "Exponential")) (*lnPriorRatio) += mp->ilnvarExp * (oldVar - newVar); /* calculate proposal ratio */ (*lnProposalRatio) = log (newVar / oldVar); /* we do not need to update likelihoods */ for (i=0; inRelParts; i++) { modelSettings[param->relParts[i]].upDateCl = NO; } return (NO_ERROR); } int Move_MixedBranchRate (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp) { /* move one relaxed clock branch rate using multiplier */ int i, *rclModel=NULL; MrBFlt newRate, oldRate, tuning, minR, maxR, mxvar, *mxRate, *brlens; TreeNode *p = NULL, *q; ModelInfo *m; Tree *t; /* get the tuning parameter */ tuning = mvp[0]; /* get the model settings */ m = &modelSettings[param->relParts[0]]; /* get the branch rate and effective branch length data */ mxRate = GetParamVals (param, chain, state[chain]); brlens = GetParamSubVals (param, chain, state[chain]); rclModel = GetParamIntVals(param, chain, state[chain]); /* get tree */ t = GetTree (param, chain, state[chain]); /* get minimum and maximum rate */ minR = RATE_MIN; maxR = RATE_MAX; /* randomly pick a length */ do { i = (int) (RandomNumber(seed) * (t->nNodes -2)); p = t->allDownPass[i]; } while (p->length < TIME_MIN); // not ancestral fossil /* find new rate using multiplier */ oldRate = mxRate[p->index]; newRate = oldRate * exp ((0.5 - RandomNumber(seed)) * tuning); /* reflect if necessary */ while (newRate < minR || newRate > maxR) { if (newRate < minR) newRate = minR * minR / newRate; if (newRate > maxR) newRate = maxR * maxR / newRate; } mxRate[p->index] = newRate; brlens[p->index] = newRate * p->length; /* calculate prior ratio */ mxvar = *GetParamVals (m->mixedvar, chain, state[chain]); if (*rclModel == RCL_IGR) { (*lnPriorRatio) -= LnProbGamma (1.0/mxvar, 1.0/mxvar, oldRate); (*lnPriorRatio) += LnProbGamma (1.0/mxvar, 1.0/mxvar, newRate); } else if (*rclModel == RCL_ILN) { (*lnPriorRatio) -= LnProbLogNormal_Mean_Var (1.0, mxvar, oldRate); (*lnPriorRatio) += LnProbLogNormal_Mean_Var (1.0, mxvar, newRate); } /* calculate proposal ratio */ (*lnProposalRatio) = log (newRate / oldRate); /* set update of transition probability */ p->upDateTi = YES; /* set update of cond likes down to root */ p->upDateCl = YES; q = p->anc; while (q->anc != NULL) { q->upDateCl = YES; q = q->anc; } return (NO_ERROR); } int Move_MixedVar (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp) { /* move the variance of the mixed relaxed clock models using multiplier */ int i, j, *rclModel=NULL; MrBFlt oldVar, newVar, minVar, maxVar, tuning, *rate; Model *mp; TreeNode *p; Tree *t; /* get tuning parameter */ tuning = mvp[0]; /* get model params */ mp = &modelParams[param->relParts[0]]; /* get the min and max values */ minVar = INDRVAR_MIN; maxVar = INDRVAR_MAX; if (!strcmp(mp->mixedvarPr,"Uniform")) { minVar = (mp->mixedvarUni[0] < INDRVAR_MIN) ? INDRVAR_MIN : mp->mixedvarUni[0]; maxVar = (mp->mixedvarUni[1] > INDRVAR_MAX) ? INDRVAR_MAX : mp->mixedvarUni[1]; } /* get the variance */ oldVar = *GetParamVals (param, chain, state[chain]); /* set new value */ newVar = oldVar * exp ((0.5 - RandomNumber(seed))*tuning); /* reflect if necessary */ while (newVar < minVar || newVar > maxVar) { if (newVar < minVar) newVar = minVar * minVar / newVar; if (newVar > maxVar) newVar = maxVar * maxVar / newVar; } /* store new value */ *GetParamVals (param, chain, state[chain]) = newVar; /* calculate prior ratio */ for (i=0; inSubParams; i++) { rclModel = GetParamIntVals (param->subParams[i], chain, state[chain]); rate = GetParamVals (param->subParams[i], chain, state[chain]); t = GetTree (param->subParams[i], chain, state[chain]); for (j=0; jnNodes-2; j++) { p = t->allDownPass[j]; if (p->length > 0.0) { // not ancestral fossil if (*rclModel == RCL_IGR) { (*lnPriorRatio) -= LnProbGamma (1.0/oldVar, 1.0/oldVar, rate[p->index]); (*lnPriorRatio) += LnProbGamma (1.0/newVar, 1.0/newVar, rate[p->index]); } else if (*rclModel == RCL_ILN) { (*lnPriorRatio) -= LnProbLogNormal_Mean_Var (1.0, oldVar, rate[p->index]); (*lnPriorRatio) += LnProbLogNormal_Mean_Var (1.0, newVar, rate[p->index]); } } } } /* take prior on Mixedvar into account */ if (!strcmp(mp->mixedvarPr,"Exponential")) (*lnPriorRatio) += mp->mixedvarExp * (oldVar - newVar); /* calculate proposal ratio */ (*lnProposalRatio) = log (newVar / oldVar); /* we do not need to update likelihoods */ for (i=0; inRelParts; i++) { modelSettings[param->relParts[i]].upDateCl = NO; } return (NO_ERROR); } int Move_RelaxedClockModel (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp) { /* rjMCMC move between IGR (independent gamma) and ILN (independent lognormal) //chi */ int i, *rclModel; MrBFlt *mxvar, *mxRate, /* *brlens, */ igrvar, ilnvar, ratio; Tree *t; TreeNode *p = NULL; ModelInfo *m; Model *mp; /* get model settings and parameters */ m = &modelSettings[param->relParts[0]]; mp = &modelParams[param->relParts[0]]; mxvar = GetParamVals (m->mixedvar, chain, state[chain]); mxRate = GetParamVals (param, chain, state[chain]); //brlens = GetParamSubVals (param, chain, state[chain]); t = GetTree (param, chain, state[chain]); /* get current value of model indicator */ rclModel = GetParamIntVals(param, chain, state[chain]); /* get ilnvar/igrvar ratio */ ratio = mvp[0]; (*lnPriorRatio) = (*lnProposalRatio) = 0.0; /* rjMCMC between models: Pr(IGR) = Pr(ILN) = 1/2 */ /* the current model is IGR, move to ILN */ if ((*rclModel) == RCL_IGR) { /* move the var parameter */ igrvar = (*mxvar); ilnvar = igrvar * ratio; if (ilnvar < INDRVAR_MIN || ilnvar > INDRVAR_MAX) { abortMove = YES; return (NO_ERROR); } /* take prior on Mixedvar into account */ if (!strcmp(mp->mixedvarPr,"Exponential")) (*lnPriorRatio) += mp->mixedvarExp * (igrvar - ilnvar); /* match the rates */ for (i = 0; i < t->nNodes -2; i++) { p = t->allDownPass[i]; if (p->length > 0.0) // not ancestral fossil { (*lnPriorRatio) -= LnProbGamma (1.0/igrvar, 1.0/igrvar, mxRate[p->index]); (*lnPriorRatio) += LnProbLogNormal_Mean_Var (1.0, ilnvar, mxRate[p->index]); } } /* In this move, we simply match the parameters in each model, the dimension is same, the Jacobian is 1. */ (*lnProposalRatio) += log(ratio); /* switch model */ (*rclModel) = RCL_ILN; (*mxvar) = ilnvar; } /* the current model is ILN, move to IGR */ else { /* move the var parameter */ ilnvar = (*mxvar); igrvar = ilnvar / ratio; if (igrvar < INDRVAR_MIN || igrvar > INDRVAR_MAX) { abortMove = YES; return (NO_ERROR); } /* take prior on Mixedvar into account */ if (!strcmp(mp->mixedvarPr,"Exponential")) (*lnPriorRatio) += mp->mixedvarExp * (ilnvar - igrvar); /* match the rates */ for (i = 0; i < t->nNodes -2; i++) { p = t->allDownPass[i]; if (p->length > 0.0) // not ancestral fossil { (*lnPriorRatio) -= LnProbLogNormal_Mean_Var (1.0, ilnvar, mxRate[p->index]); (*lnPriorRatio) += LnProbGamma (1.0/igrvar, 1.0/igrvar, mxRate[p->index]); } } /* In this move, we simply match the parameters in each model, the dimension is same, the Jacobian is 1. */ (*lnProposalRatio) -= log(ratio); /* switch model */ (*rclModel) = RCL_IGR; (*mxvar) = igrvar; } /* since effective branch lengths are updated, we need to update likelihood calculation */ TouchAllTreeNodes(m, chain); return (NO_ERROR); } /*---------------------------------------------------------------- | | Move_Local: This proposal mechanism changes the topology and | branch lengths of an unrooted tree using the LOCAL mech- | anism described by Larget & Simon (1999): | | Larget, B. L., and D. L. Simon. 1999. Markov chain | Monte Carlo algorithms for the Bayesian analysis | of phylogenetic trees. Molecular Biology and | Evolution 16:750-759. | | Programmed by FR 2001-10-14 and partly rewritten 2002-02-21 | for more elegance and the ability to deal with rooted trees. | Support for locked nodes added 2004-01-12 based on mb v2.01. | Calculation of the Hastings ratio corrected 2004-07-01. | Boundary conditions correctly taken care of 2004-09-29. | NB! An alternative to reflection is to skip moves, which might | be better for the LOCAL given the complexity of taking | the boundary conditions into account | ----------------------------------------------------------------*/ int Move_Local (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp) { int topologyHasChanged, isVPriorExp, directionUp, moveX; MrBFlt oldM, newM, x, y, newX, newY, tuning, minV, maxV; TreeNode *v, *u, *a, *b, *c, *d; Tree *t; ModelParams *mp; tuning = mvp[0]; /* Larget & Simon's tuning parameter lambda */ /* get tree */ t = GetTree (param, chain, state[chain]); /* get model params */ mp = &modelParams[param->relParts[0]]; /* max and min brlen */ if (param->subParams[0]->paramId == BRLENS_UNI) { minV = mp->brlensUni[0] > BRLENS_MIN ? mp->brlensUni[0] : BRLENS_MIN; maxV = mp->brlensUni[1]; isVPriorExp = NO; } else if (param->subParams[0]->paramId == BRLENS_GamDir) { minV = BRLENS_MIN; maxV = BRLENS_MAX; isVPriorExp = 2; } else if (param->subParams[0]->paramId == BRLENS_iGmDir) { minV = BRLENS_MIN; maxV = BRLENS_MAX; isVPriorExp = 3; } else if (param->subParams[0]->paramId == BRLENS_twoExp) { minV = BRLENS_MIN; maxV = BRLENS_MAX; isVPriorExp = 4; } else { minV = BRLENS_MIN; maxV = BRLENS_MAX; isVPriorExp = YES; } /* Dirichlet or twoExp prior */ if (isVPriorExp > 1) (*lnPriorRatio) = -LogDirPrior(t, mp, isVPriorExp); topologyHasChanged = NO; # if defined (DEBUG_LOCAL) printf ("Before:\n"); ShowNodes (t->root, 2, NO); getchar(); # endif /* pick an internal branch */ do { v = t->intDownPass[(int)(RandomNumber(seed)*t->nIntNodes)]; } while (v->anc->anc == NULL); /* set up pointers for crown part */ if (RandomNumber(seed) < 0.5) { c = v->left; d = v->right; } else { c = v->right; d = v->left; } /* set up pointers for root part */ u = v->anc; if (RandomNumber(seed) < 0.5 || (t->isRooted == YES && u->anc->anc == NULL)) { directionUp = YES; if (u->left == v) a = u->right; else a = u->left; b = u->anc; } else { directionUp = NO; if (u->left == v) b = u->right; else b = u->left; a = u->anc; } /* store old and new path length as well as old x and y */ oldM = c->length + v->length; if (directionUp == YES) { oldM += a->length; x = a->length; } else { oldM += u->length; x = u->length; } y = x + v->length; /* pick dangly to move */ if (RandomNumber(seed) < 0.5) moveX = YES; else moveX = NO; /* find new m value */ newM = exp(tuning * (RandomNumber(seed) - 0.5)) * oldM; /* pick dangly to move and pick new attachment point */ if (moveX == YES) { /* choose new x */ /* first update y */ newY = y * (newM / oldM); /* find reinsertion point */ if (v->isLocked == YES) { newX = RandomNumber(seed) * newY; } else { newX = RandomNumber(seed) * newM; } } else { /* choose new y */ /* first update x */ newX = x * (newM / oldM); /* find reinsertion point */ if (v->isLocked == YES) { newY = RandomNumber(seed) * (newM - newX) + newX; } else { newY = RandomNumber(seed) * newM; } } /* adjust proposal and prior ratio based on length modification */ /* and insertion mechanism */ (*lnProposalRatio) += 3.0 * log (newM / oldM); if (isVPriorExp == YES) (*lnPriorRatio) = (mp->brlensExp) * (oldM - newM); /* make topology move if necessary and then set branch lengths */ if (newX > newY) { /* check if we need to abort */ if (newY < minV || newY > maxV || (newX-newY) < minV || (newX-newY) > maxV || (newM-newX) < minV || (newM-newX) > maxV) { abortMove = YES; return NO_ERROR; } /* topology has changed */ topologyHasChanged = YES; /* detach v and d */ /* this scheme differs from that used by Larget and Simon but is more convenient because it avoids tree rotations */ if (u->left == v) u->left = c; else u->right = c; c->anc = u; if (directionUp == YES) { /* place v and d below a */ if (v->left == d) v->right = a; else v->left = a; a->anc = v; if (u->left == a) u->left = v; else u->right = v; /* v->anc is already u */ /* adjust lengths */ c->length = newM - newX; v->length = newX - newY; a->length = newY; } else { /* place v and d below u */ if (u->isLocked == YES) { v->isLocked = YES; u->isLocked = NO; v->lockID = u->lockID; u->lockID = 0; } if (v->left == d) v->right = u; else v->left = u; u->anc = v; v->anc = a; if (a->left == u) a->left = v; else a->right = v; /* adjust lengths */ c->length = newM - newX; u->length = newX - newY; v->length = newY; } } else { /* check if we need to abort */ if (newX < minV || newX > maxV || (newY-newX) < minV || (newY-newX) > maxV || (newM-newY) < minV || (newM-newY) > maxV) { abortMove = YES; return NO_ERROR; } /* topology has not changed */ c->length = newM - newY; v->length = newY - newX; if (directionUp == YES) a->length = newX; else u->length = newX; } /* set update of transition probs */ c->upDateTi = YES; v->upDateTi = YES; if (directionUp == YES) a->upDateTi = YES; else u->upDateTi = YES; /* set flags for update of cond likes from v and u down to root */ v->upDateCl = YES; u->upDateCl = YES; if (directionUp == YES) v = b; else v = a; while (v->anc != NULL) { v->upDateCl = YES; v = v->anc; } /* get downpass sequence if tree topology has changed */ if (topologyHasChanged == YES) { GetDownPass (t); } /* Dirichlet or twoExp prior */ if (isVPriorExp > 1) (*lnPriorRatio) += LogDirPrior(t, mp, isVPriorExp); # if defined (DEBUG_LOCAL) printf ("After:\n"); ShowNodes (t->root, 2, NO); getchar(); printf ("Proposal ratio: %f\n",(*lnProposalRatio)); printf ("v: %d u: %d c: %d d: %d a: %d b: %d\n",v->index, u->index, c->index, d->index, a->index, b->index); printf ("Has topology changed? %d\n",topologyHasChanged); getchar(); # endif return (NO_ERROR); } /*---------------------------------------------------------------- | | Move_LocalClock: This proposal mechanism changes the topology and | branch lengths of a rooted tree using the LOCAL (clock) mech- | anism described by Larget & Simon (1999): | | Larget, B. L., and D. L. Simon. 1999. Markov chain | Monte Carlo algorithms for the Bayesian analysis | of phylogenetic trees. Molecular Biology and | Evolution 16:750-759. | | Programmed by JH 2002-07-07 | Modified by FR 2004-05-22 to handle locked and dated trees | Modified by FR 2005-11-09 to take care of erroneous | Hastings ratio. The fix implemented here results in | a move that does not change tree height. | ----------------------------------------------------------------*/ int Move_LocalClock (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp) { int i, topologyHasChanged, vIsRoot, aSister, bSister, cSister, *nEvents; MrBFlt x, y, h1, h2, h3, h[3], tempD, ran, distUv, distCv, oldALength, oldBLength, oldCLength, oldULength, oldVLength, lambda, nu, *tk02Rate=NULL, *brlens, oldTreeLength, newTreeLength; // MrBFlt newDepth, oldDepth, factor, diff; TreeNode *u, *v, *w=NULL, *a, *b, *c, *deepestChild, *p; Tree *t; Param *subParm; /* get tree */ t = GetTree (param, chain, state[chain]); #if defined (DEBUG_LOCAL) /* check branch lengths and node depths */ for (i=0; inNodes-2; i++) { p = t->allDownPass[i]; /* the two checkings don't consider ancestral fossil (brl=0) in fossilized bd tree */ if (p->length < minV) { printf ("%s ERROR when entering LocalClock: node %d has length %lf", spacer, p->index, p->length); return ERROR; } if (p->nodeDepth >= p->anc->nodeDepth) { printf ("%s ERROR when entering LocalClock: node %d has depth %lf larger than its ancestor %d depth %lf", spacer, p->index, p->nodeDepth, p->anc->index, p->anc->nodeDepth); return ERROR; } } #endif # if defined (DEBUG_LOCAL) printf ("Before:\n"); ShowNodes (t->root, 2, YES); # endif /* set up pointers */ do { u = t->intDownPass[(int)(RandomNumber(seed)*(t->nIntNodes-1))]; } while (u->anc->anc == NULL); v = u->anc; a = u->left; b = u->right; if (v->left == u) c = v->right; else c = v->left; vIsRoot = NO; if (v->anc->anc != NULL) w = v->anc; else vIsRoot = YES; oldALength = a->length; oldBLength = b->length; oldCLength = c->length; oldVLength = v->length; oldULength = u->length; oldTreeLength = TreeLength (param, chain); /* get distances from root of move (w or v) to nodes a, b, and c */ if (vIsRoot == NO) h1 = h2 = h3 = v->length; else h1 = h2 = h3 = 0.0; h1 += u->length + a->length; h2 += u->length + b->length; h3 += c->length; h[0] = h1; h[1] = h2; h[2] = h3; /* we also need the distances between u <-> v and c <-> v to calculate the hastings' term */ distUv = u->length; distCv = c->length; /* sort distances (simply make three comparisons and swap values, if necessary) */ if (h[0] > h[1]) { tempD = h[1]; h[1] = h[0]; h[0] = tempD; } if (h[0] > h[2]) { tempD = h[2]; h[2] = h[0]; h[0] = tempD; } if (h[1] > h[2]) { tempD = h[2]; h[2] = h[1]; h[1] = tempD; } /* Find the child node (a, b, or c) that is closest to the root (i.e., has smallest h_i; i=1,2,3). This part deals with the possibility that some of the nodes are at the same nodeDepth and randomly assigns a node to be deepest in case of ties. */ if (AreDoublesEqual (h1, h2, 0.00000001) == YES && AreDoublesEqual (h1, h3, 0.00000001) == YES && AreDoublesEqual (h2, h3, 0.00000001) == YES) { ran = RandomNumber(seed); if (ran < 0.33333333) deepestChild = a; else if (ran > 0.66666666) deepestChild = b; else deepestChild = c; } else if (AreDoublesEqual (h1, h2, 0.00000001) == YES && AreDoublesEqual (h1, h3, 0.00000001) == NO && AreDoublesEqual (h2, h3, 0.00000001) == NO) { if (h1 < h3) { ran = RandomNumber(seed); if (ran < 0.5) deepestChild = a; else deepestChild = b; } else deepestChild = c; } else if (AreDoublesEqual (h1, h2, 0.00000001) == NO && AreDoublesEqual (h1, h3, 0.00000001) == YES && AreDoublesEqual (h2, h3, 0.00000001) == NO) { if (h1 < h2) { ran = RandomNumber(seed); if (ran < 0.5) deepestChild = a; else deepestChild = c; } else deepestChild = b; } else if (AreDoublesEqual (h1, h2, 0.00000001) == NO && AreDoublesEqual (h1, h3, 0.00000001) == NO && AreDoublesEqual (h2, h3, 0.00000001) == YES) { if (h2 < h1) { ran = RandomNumber(seed); if (ran < 0.5) deepestChild = b; else deepestChild = c; } else deepestChild = a; } else { if (h1 < h2 && h1 < h3) deepestChild = a; else if (h2 < h1 && h2 < h3) deepestChild = b; else deepestChild = c; } /* get x and y */ /* for most of the branches, the proposal ratio is 0.0 so it makes sense to set this first */ (*lnProposalRatio) = 0.0; if (u->isDated == YES && v->isDated == YES) { /* this proposal is wasted, change nothing */ if (vIsRoot == NO) { y = v->length; x = y + u->length; } else { y = 0.0; x = u->length; } } else if (u->isDated == YES && v->isDated == NO) { /* we can only change the position of v */ if (vIsRoot == NO) { /* the upper limit of v's height is determined either by u-length + v->length or by c->length + v->length (h[0]) */ x = v->length + u->length; if (x > h[0]) x = h[0]; y = RandomNumber(seed) * x; } else { /* v is root: we leave tree height unchanged so we cannot change anything */ x = u->length; y = 0.0; } } else if (u->isDated == NO && v->isDated == YES) { /* we can only change the position of u */ if (vIsRoot == NO) y = v->length; else y = 0.0; if (u->isLocked == YES) { if (h1 > h2) { x = y + RandomNumber(seed) * (h2 - y); } else { x = y + RandomNumber(seed) * (h1 - y); } } else { x = y + RandomNumber(seed) * (h[1] - y); } } /* if we reach the statements down here, neither u nor v is dated */ else if (u->isLocked == YES) { if (h1 > h2) { y = RandomNumber(seed) * h[0]; x = y + RandomNumber(seed) * (h2 - y); } else { y = RandomNumber(seed) * h[0]; x = y + RandomNumber(seed) * (h1 - y); } } else if (vIsRoot == NO) { /* this is the standard variant for nonroot v */ x = RandomNumber(seed) * h[1]; y = RandomNumber(seed) * h[0]; } else { /* this is the standard variant when v is the root */ /*oldDepth = t->root->left->nodeDepth; factor = exp((RandomNumber(seed) - 0.5) * 2.0 * log(1.2)); t->root->left->nodeDepth = newDepth = factor * h[0] - h[0] + oldDepth; adjust h[0], h[1], and h[2] diff = newDepth - oldDepth; h[0] += diff; h[1] += diff; h[2] += diff;*/ /* set y to 0.0 and select new x */ y = 0.0; x = RandomNumber(seed) * h[1]; /* Adjust proposal ratio. We deal with topology bias below. Note that this proposal ratio is very different from what appeared in Larget and Simon */ /*(*lnProposalRatio) += (t->nIntNodes-1) * log(oldDepth / newDepth);*/ /*(*lnProposalRatio) += 2.0 * log (factor);*/ } /* decide which topology we will construct (cSister is what we started with) */ aSister = bSister = cSister = NO; /* if u is locked then we cannot change topology */ if (u->isLocked == YES) cSister = YES; else if (MaximumValue (x, y) < h[0]) { ran = RandomNumber(seed); if (ran < 0.33333333) aSister = YES; else if (ran > 0.66666666) bSister = YES; else cSister = YES; } else { if (deepestChild == a) aSister = YES; else if (deepestChild == b) bSister = YES; else cSister = YES; } /* adjust lengths of nodes u and v */ u->length = MaximumValue (x, y) - MinimumValue (x, y); v->length = MinimumValue (x, y); if (vIsRoot == NO) v->nodeDepth = w->nodeDepth - v->length; u->nodeDepth = v->nodeDepth - u->length; /* adjust pointers and lengths of nodes a, b, and c */ topologyHasChanged = NO; if (cSister == YES) { if (v->left == u) v->right = c; else v->left = c; u->left = a; u->right = b; a->anc = b->anc = u; c->anc = v; a->length = u->nodeDepth - a->nodeDepth; b->length = u->nodeDepth - b->nodeDepth; c->length = v->nodeDepth - c->nodeDepth; } else if (bSister == YES) { if (v->left == u) v->right = b; else v->left = b; u->left = a; u->right = c; a->anc = c->anc = u; b->anc = v; a->length = u->nodeDepth - a->nodeDepth; b->length = v->nodeDepth - b->nodeDepth; c->length = u->nodeDepth - c->nodeDepth; topologyHasChanged = YES; } else if (aSister == YES) { if (v->left == u) v->right = a; else v->left = a; u->left = b; u->right = c; b->anc = c->anc = u; a->anc = v; a->length = v->nodeDepth - a->nodeDepth; b->length = u->nodeDepth - b->nodeDepth; c->length = u->nodeDepth - c->nodeDepth; topologyHasChanged = YES; } /* check that all branch lengths are good */ if (a->length < 0.0 && b->length < 0.0 && c->length < 0.0 && u->length < 0.0 && v->length < 0.0) { abortMove = YES; return NO_ERROR; } /* calculate the proposal ratio due to asymmetric topology changes */ if (u->isLocked == NO) { if (v->isDated == YES || vIsRoot == YES) { if (distUv > distCv && MaximumValue (x, y) < h[0]) (*lnProposalRatio) += log(3.0); else if (distUv < distCv && MaximumValue (x, y) > h[0]) (*lnProposalRatio) += log(1.0 / 3.0); } else { /* note that Larget and Simon did not have the correct Hastings ratio for this case */ if (distUv > distCv && MaximumValue (x, y) < h[0]) (*lnProposalRatio) += log(3.0 / 2.0); else if (distUv < distCv && MaximumValue (x, y) > h[0]) (*lnProposalRatio) += log(2.0 / 3.0); } } /* set update of transition probs */ a->upDateTi = b->upDateTi = c->upDateTi = u->upDateTi = YES; if (vIsRoot == NO) v->upDateTi = YES; /* set flags for update of cond likes from u down to root */ p = u; while (p->anc != NULL) { p->upDateCl = YES; p = p->anc; } /* get downpass sequence if tree topology has changed */ if (topologyHasChanged == YES) GetDownPass (t); /* adjust proposal and prior ratio for relaxed clock models */ newTreeLength = TreeLength(param, chain); for (i=0; isubParams[0]->nSubParams; i++) { subParm = param->subParams[0]->subParams[i]; if (subParm->paramType == P_CPPEVENTS) { nEvents = subParm->nEvents[2*chain+state[chain]]; lambda = *GetParamVals (modelSettings[subParm->relParts[0]].cppRate, chain, state[chain]); /* proposal ratio */ (*lnProposalRatio) += nEvents[a->index] * log (a->length / oldALength); (*lnProposalRatio) += nEvents[b->index] * log (b->length / oldBLength); (*lnProposalRatio) += nEvents[c->index] * log (c->length / oldCLength); (*lnProposalRatio) += nEvents[u->index] * log (u->length / oldULength); if (v->anc->anc != NULL && v->isDated == NO) (*lnProposalRatio) += nEvents[v->index] * log (v->length / oldVLength); /* prior ratio */ (*lnPriorRatio) += lambda * ((oldTreeLength - newTreeLength)/t->root->left->nodeDepth); /* update effective evolutionary lengths */ if (v->anc->anc == NULL || v->isDated == YES) { if (UpdateCppEvolLengths (subParm, v->left, chain) == ERROR || UpdateCppEvolLengths (subParm, v->right, chain) == ERROR) { abortMove = YES; return (NO_ERROR); } } else { if (UpdateCppEvolLengths (subParm, v, chain) == ERROR) { abortMove = YES; return (NO_ERROR); } } } else if (subParm->paramType == P_TK02BRANCHRATES) { if (subParm->paramType == P_TK02BRANCHRATES) nu = *GetParamVals (modelSettings[subParm->relParts[0]].tk02var, chain, state[chain]); else nu = *GetParamVals (modelSettings[subParm->relParts[0]].mixedvar, chain, state[chain]); nu /= t->root->left->nodeDepth; /* variance increase measured relative to tree height */ tk02Rate = GetParamVals (subParm, chain, state[chain]); brlens = GetParamSubVals (subParm, chain, state[chain]); /* no proposal ratio effect */ /* prior ratio and update of effective evolutionary lengths */ (*lnPriorRatio) -= LnProbLogNormal_Mean_LogVar (tk02Rate[u->index], nu*oldALength, tk02Rate[a->index]); (*lnPriorRatio) += LnProbLogNormal_Mean_LogVar (tk02Rate[u->index], nu*u->left->length, tk02Rate[u->left->index]); (*lnPriorRatio) -= LnProbLogNormal_Mean_LogVar (tk02Rate[u->index], nu*oldBLength, tk02Rate[b->index]); (*lnPriorRatio) += LnProbLogNormal_Mean_LogVar (tk02Rate[u->index], nu*u->right->length, tk02Rate[u->right->index]); (*lnPriorRatio) -= LnProbLogNormal_Mean_LogVar (tk02Rate[v->index], nu*oldCLength, tk02Rate[c->index]); (*lnPriorRatio) -= LnProbLogNormal_Mean_LogVar (tk02Rate[v->index], nu*oldULength, tk02Rate[u->index]); (*lnPriorRatio) += LnProbLogNormal_Mean_LogVar (tk02Rate[v->index], nu*v->left->length, tk02Rate[v->left->index]); (*lnPriorRatio) += LnProbLogNormal_Mean_LogVar (tk02Rate[v->index], nu*v->right->length, tk02Rate[v->right->index]); brlens[a->index] = a->length * (tk02Rate[a->index] + tk02Rate[a->anc->index])/2.0; brlens[b->index] = a->length * (tk02Rate[a->index] + tk02Rate[a->anc->index])/2.0; brlens[c->index] = c->length * (tk02Rate[c->index] + tk02Rate[c->anc->index])/2.0; brlens[u->index] = u->length * (tk02Rate[u->index] + tk02Rate[u->anc->index])/2.0; if (v->anc->anc != NULL && v->isDated == NO) { (*lnPriorRatio) -= LnProbLogNormal_Mean_LogVar (tk02Rate[w->index], nu*oldVLength, tk02Rate[v->index]); (*lnPriorRatio) += LnProbLogNormal_Mean_LogVar (tk02Rate[w->index], nu*v->length, tk02Rate[v->index]); brlens[v->index] = v->length * (tk02Rate[v->index] + tk02Rate[v->anc->index])/2.0; } } else { /* TODO: */ } } /* calculate and adjust prior ratio for clock tree */ if (LogClockTreePriorRatio (param, chain, &x) == ERROR) return (ERROR); (*lnPriorRatio) += x; # if defined (DEBUG_LOCAL) printf ("After:\n"); ShowNodes (t->root, 2, YES); printf ("Has topology changed? %d\n",topologyHasChanged); /* check branch lengths and node depths */ for (i=0; inNodes-2; i++) { p = t->allDownPass[i]; if (p->length < minV) { printf ("%s ERROR when leaving LocalClock: node %d has length %lf", spacer, p->index, p->length); return ERROR; } if (p->nodeDepth >= p->anc->nodeDepth) { printf ("%s ERROR when leaving LocalClock: node %d has depth %lf larger than its ancestor %d depth %lf", spacer, p->index, p->nodeDepth, p->anc->index, p->anc->nodeDepth); return ERROR; } } #endif return (NO_ERROR); } #if 0 /*-------------------------------------------------------------------- | | Move_LSPR: Change topology using move based on likelihood scores | |--------------------------------------------------------------------*/ int Move_LSPR (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp) { /* Change branch lengths and topology (potentially) using SPR-type move biased according to likelihood scores. NOT work for constrained trees. */ int i, j, n, division, topologyHasChanged, isVPriorExp, nNodes; BitsLong *pA, *pV, *pP; MrBFlt x, minV, maxV, brlensExp=0.0, minLength=0.0, curLength=0.0, length=0.0, cumulativeProb, warpFactor, sum, ran, tuning, increaseProb, decreaseProb, divFactor, nStates, rateMult, temp; CLFlt *nSitesOfPat, *globalNSitesOfPat, *tempCondLikes, **tempCondLikePtr; TreeNode *p, *q, *a, *b, *u, *v, *c=NULL, *d, *candidateNodes[20], *vLeft, *vRight; Tree *t; ModelParams *mp; ModelInfo *m = NULL; temp = mvp[0]; /* tuning parameter determining how heavily to weight according to likelihood scores */ var = mvp[1]; /* variance of lognormal for proposing branch lengths */ increaseProb = decreaseProb = mvp[2]; /* reweighting probabilities */ (*lnProposalRatio) = (*lnPriorRatio) = 0.0; /* get model params and model info */ mp = &modelParams[param->relParts[0]]; m = &modelSettings[param->relParts[0]]; /* get tree */ t = GetTree (param, chain, state[chain]); /* max and min brlen */ if (param->subParams[0]->paramId == BRLENS_UNI) { minV = mp->brlensUni[0] > BRLENS_MIN ? mp->brlensUni[0] : BRLENS_MIN; maxV = mp->brlensUni[1]; isVPriorExp = NO; } else { minV = BRLENS_MIN; maxV = BRLENS_MAX; brlensExp = mp->brlensExp; isVPriorExp = YES; } # if defined (DEBUG_MLSPR) printf ("Before:\n"); ShowNodes (t->root, 2, YES); getchar(); # endif /* set topologyHasChanged to NO */ topologyHasChanged = NO; /* reset node variables that will be used */ for (i=0; inNodes; i++) { p = t->allDownPass[i]; p->marked = NO; p->x = 0; p->d = 0.0; } /* pick a random branch */ do { p = t->allDownPass[(int)(RandomNumber(seed)*(t->nNodes - 1))]; } while (p->anc->anc == NULL || p->anc->isLocked == YES); /* set up pointers for nodes around the picked branch */ v = p; u = p->anc; if (u->left == v) a = u->right; else a = u->left; b = u->anc; vLeft = v->left; vRight = vRight; /* store the branch lengths */ aLength = a->length; uLength = u->length; vLength = v->length; if (v->left != NULL) { vLeftLength = v->left->length; vRightLength = v->right->length; } else vLeftLength = vRightLength = 0.0; /* get the ML branch lengths */ /* set initial branch lengths */ /* cycle through using Newton Raphson and reoptimization a fixed number of iterations */ for (i=0; i<5; i++) { } /* get variance of lognormal */ /* clip tree */ a->anc = b; if (b->left == u) b->left = a; else b->right = a; /* count distance to root */ q = b; nNodes = 0; while (q->anc != NULL) { nNodes++; q = q->anc; } /* allocate space for temporary cond likes and condlike pointers */ tempCondLikes = (CLFlt *) SafeCalloc (nNodes*m->numChars*m->numModelStates, sizeof (CLFlt)); tempCondLikePtr = (CLFlt **) SafeCalloc (nNodes, sizeof (CLFlt *)); if (!tempCondLikes || !tempCondLikePtr) { free (tempCondLikes); free (tempCondLikePtr); return (ERROR); } /* shift pointers over */ q = b; j = 0; while (q->anc != NULL) { tempCondLikePtr[j] = m->condLike[chain][q->index][q->clSpace]; m->condLike[chain][q->index][q->clSpace] = tempCondLikes + j*m->numChars*m->numModelStates; j++; q = q->anc; } /* set length to 0.1 for now; test ML brlen later */ aLength = a->length; a->length = 0.1; uLength = u->length; u->length = 0.1; vLength = v->length; v->length = 0.1; /* get downpass cond likes for the root part */ q = b; while (q->anc != NULL) m->condLikeDown (q, division, chain); /* get final pass cond likes for the root part */ GetLikeFPRootPath (a); /* get downpass parsimony states for the crown part */ GetParsDP (t, v, chain); /* mark all nodes in the root part of the tree */ t->root->left->marked = YES; for (i=t->nNodes-3; i>=0; i--) { p = t->allDownPass[i]; if (p->anc->marked == YES && p != u) p->marked = YES; } /* find number of site patterns and modify randomly */ globalNSitesOfPat = numSitesOfPat + (chainId[chain] % chainParams.numChains) * numCompressedChars; nSitesOfPat = (CLFlt *) SafeCalloc (numCompressedChars, sizeof(CLFlt)); if (!nSitesOfPat) { MrBayesPrint ("%s Problem allocating nSitesOfPat in Move_LSPR\n", spacer); free (tempCondLikes); free (tempCondLikePtr); return (ERROR); } for (i=0; i 1.0 - increaseProb) nSitesOfPat[i]++; } } /* cycle through the possibilities and record ln likelihood of each in p->d */ minLength = 0.0; for (i=0; inNodes; i++) { p = t->allDownPass[i]; if (p->marked == NO) continue; /* find the parsimony length */ p->d = 0.0; for (n=0; nnRelParts; n++) { division = t->relParts[n]; /* Find model settings */ m = &modelSettings[division]; nStates = m->numModelStates; if (m->dataType == STANDARD || m->dataType == CONTINUOUS) nStates = 2; rateMult = GetRate(division, chain); divFactor = warpFactor + log(nStates-1) - log (3) - log(rateMult); /* find downpass parsimony sets for the node and its environment */ pP = parsPtr[chain][p->index] + m->parsMatrixStart + Bit(division, p->clSpace) * parsMatrixRowSize; pA = parsPtr[chain][p->anc->index] + m->parsMatrixStart + Bit(division, p->anc->clSpace) * parsMatrixRowSize; pV = parsPtr[chain][v->index] + m->parsMatrixStart + Bit(division, v->clSpace) * parsMatrixRowSize; length = 0.0; for (j=0; jnumChars; j++) { x = (pP[j] | pA[j]) & pV[j]; if (x == 0) length += nSitesOfPat[j]; } p->d += divFactor * length; } if (i == 0) minLength = p->d; else if (p->d < minLength) minLength = p->d; if (p == a) curLength = p->d; } /* find the sum given the warp factor */ sum = 0.0; for (i=0; inNodes; i++) { p = t->allDownPass[i]; if (p->marked == YES) { p->d = exp (minLength - p->d); sum += p->d; } } /* generate a random uniform */ ran = RandomNumber(seed); /* select the appropriate reattachment point */ cumulativeProb = 0.0; for (i=0; inNodes; i++) { p = t->allDownPass[i]; if (p->marked == YES) { c = p; cumulativeProb += p->d / sum; if (cumulativeProb > ran) break; } } if (c->marked != YES) { printf ("Could not select node\n"); getchar(); } /* calculate the proposal ratio */ if (c == a) (*lnProposalRatio) = 0.0; else (*lnProposalRatio) = c->d - curLength; /* reattach */ d = c->anc; c->anc = u; if (u->left == v) u->right = c; else u->left = c; if (d->left == c) d->left = u; else d->right = u; u->anc = d; /* reassign branch lengths */ if (c != a) { topologyHasChanged = YES; if (RandomNumber(seed) < 0.5) { x = u->length; u->length = a->length; a->length = x; } if (RandomNumber(seed) < 0.5) { x = c->length; c->length = u->length; u->length = x; } /* hit c length with multiplier (a and u dealt with below) */ x = c->length * exp(tuning * (RandomNumber(seed) - 0.5)); while (x < minV || x > maxV) { if (x < minV) x = minV * minV / x; else if (x > maxV) x = maxV * maxV / x; } /* calculate proposal and prior ratio based on length modification */ (*lnProposalRatio) += log (x / c->length); if (isVPriorExp == YES) (*lnPriorRatio) += brlensExp * (c->length - x); c->length = x; } /* hit a length with multiplier (even if no topology change was made) */ x = a->length * exp(tuning * (RandomNumber(seed) - 0.5)); while (x < minV || x > maxV) { if (x < minV) x = minV * minV / x; else if (x > maxV) x = maxV * maxV / x; } /* calculate proposal and prior ratio based on length modification */ (*lnProposalRatio) += log (x / a->length); if (isVPriorExp == YES) (*lnPriorRatio) += brlensExp * (a->length - x); a->length = x; /* hit u length with multiplier (even if no topology change was made) */ x = u->length * exp(tuning * (RandomNumber(seed) - 0.5)); while (x < minV || x > maxV) { if (x < minV) x = minV * minV / x; else if (x > maxV) x = maxV * maxV / x; } /* calculate proposal and prior ratio based on length modification */ (*lnProposalRatio) += log (x / u->length); if (isVPriorExp == YES) (*lnPriorRatio) += brlensExp * (u->length - x); u->length = x; /* set tiprobs update flags */ a->upDateTi = YES; u->upDateTi = YES; c->upDateTi = YES; /* could be same as a but that does not matter */ /* set flags for update of cond likes from u and down to root */ p = u; while (p->anc != NULL) { p->upDateCl = YES; p = p->anc; } /* set flags for update of cond likes from b and down to root */ p = b; while (p->anc != NULL && p->upDateCl == NO) { p->upDateCl = YES; p = p->anc; } /* get down pass sequence if tree topology has changed */ if (topologyHasChanged == YES) { GetDownPass (t); } free (nSitesOfPat); # if defined (DEBUG_MLSPR) printf ("After:\n"); ShowNodes (t->root, 2, YES); getchar(); printf ("Proposal ratio: %f\n",(*lnProposalRatio)); printf ("v: %d u: %d a: %d b: %d\n",v->index, u->index, a->index, b->index); printf ("No. nodes moved in root subtree: %d\n",nRootNodes); printf ("Has topology changed? %d\n",topologyHasChanged); getchar(); # endif return (NO_ERROR); } /*-------------------------------------------------------------------- | | Move_LSPR1: Change topology using move based on likelihood scores | |--------------------------------------------------------------------*/ int Move_LSPR1 (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp) { /* Change branch lengths and topology (potentially) using SPR-type move biased according to likelihood scores. NOT work for constrained trees. */ int i, j, n, division, topologyHasChanged, isVPriorExp, nNodes; BitsLong *pA, *pV, *pP; MrBFlt x, minV, maxV, brlensExp=0.0, minLength=0.0, curLength=0.0, length=0.0, cumulativeProb, warpFactor, sum, ran, tuning, increaseProb, decreaseProb, divFactor, nStates, rateMult, temp; CLFlt *nSitesOfPat, *globalNSitesOfPat, *tempCondLikes, **tempCondLikePtr; TreeNode *p, *q, *a, *b, *u, *v, *c=NULL, *d, *candidateNodes[20], *vLeft, *vRight; Tree *t; ModelParams *mp; ModelInfo *m = NULL; temp = mvp[0]; /* tuning parameter determining how heavily to weight according to likelihood scores */ var = mvp[1]; /* variance of lognormal for proposing branch lengths */ increaseProb = decreaseProb = mvp[2]; /* reweighting probabilities */ (*lnProposalRatio) = (*lnPriorRatio) = 0.0; /* get model params and model info */ mp = &modelParams[param->relParts[0]]; m = &modelSettings[param->relParts[0]]; /* get tree */ t = GetTree (param, chain, state[chain]); /* max and min brlen */ if (param->subParams[0]->paramId == BRLENS_UNI) { minV = mp->brlensUni[0] > BRLENS_MIN ? mp->brlensUni[0] : BRLENS_MIN; maxV = mp->brlensUni[1]; isVPriorExp = NO; } else { minV = BRLENS_MIN; maxV = BRLENS_MAX; brlensExp = mp->brlensExp; isVPriorExp = YES; } # if defined (DEBUG_MLSPR) printf ("Before:\n"); ShowNodes (t->root, 2, YES); getchar(); # endif /* set topologyHasChanged to NO */ topologyHasChanged = NO; /* reset node variables that will be used */ for (i=0; inNodes; i++) { p = t->allDownPass[i]; p->marked = NO; p->x = 0; p->d = 0.0; } /* pick a random branch */ do { p = t->allDownPass[(int)(RandomNumber(seed)*(t->nNodes - 1))]; } while (p->anc->anc == NULL || p->anc->isLocked == YES); /* set up pointers for nodes around the picked branch */ v = p; u = p->anc; if (u->left == v) a = u->right; else a = u->left; b = u->anc; vLeft = v->left; vRight = vRight; /* store the branch lengths */ aLength = a->length; uLength = u->length; vLength = v->length; if (v->left != NULL) { vLeftLength = v->left->length; vRightLength = v->right->length; } else vLeftLength = vRightLength = 0.0; /* save DP cond likes */ /* count distance to root */ q = b; nNodes = 0; while (q->anc != NULL) { nNodes++; q = q->anc; } /* allocate space for temporary cond likes and condlike pointers */ tempCondLikes = (CLFlt *) SafeCalloc (nNodes*m->numChars*m->numModelStates, sizeof (CLFlt)); tempCondLikePtr = (CLFlt **) SafeCalloc (nNodes, sizeof (CLFlt *)); if (!tempCondLikes || !tempCondLikePtr) { free (tempCondLikes); free (tempCondLikePtr); return (ERROR); } /* shift pointers over */ q = b; j = 0; while (q->anc != NULL) { tempCondLikePtr[j] = m->condLike[chain][q->index][q->clSpace]; m->condLike[chain][q->index][q->clSpace] = tempCondLikes + j*m->numChars*m->numModelStates; j++; q = q->anc; } /* get cond like uppass up to b */ getLikeUPRootPath (t, b); /* get ML branch lengths */ NRBrlenOptimizer (t, v, 5, 3); /* cycle through using Newton Raphson and reoptimization a fixed number of iterations */ for (i=0; ileft != NULL) { getBaseLikeUpLeft (t, v); /* store instead of DP */ NewtonRaphsonBrlen (t, v->left, chain); getBaseLikeUpRight (t, v); GetNewtonRaphsonBrlen (t, v->right, chain); m->CondLikeDown (v); } if (u->left == v) getBaseLikeUpLeft (t, u); else getBaseLikeUpRight (t, u); NewtonRaphsonBrlen (t, v, chain); if (u->left == v) getBaseLikeUpRight (t, u); else getBaseLikeUpLeft (t, u); NewtonRaphsonBrlen (t, a->length, chain); m->CondLikeDown (t, u); if (b->left == u) getBaseLikeUpLeft (t, b); else getBaseLikeUpRight (t, b); NewtonRaphsonBrlen (t, u->length, chain); getLikeUp(t, u); getLikeUp(t, v); } /* get variance of lognormal for forward move */ f = log (a->length) - log (aLength); fvar = f*f; f = log (v->length) - log (vLength); fvar += f*f; f = log (u->length) - log (uLength); fvar += f*f; if (v->left != NULL) { f = log (v->left->length) - log (vLeftLength); fvar += f*f; f = log (v->right->length) - log (vRightLength); fvar += f*f; fvar /= 5.0; } else fvar /= 3.0; /* clip tree */ a->anc = b; if (b->left == u) b->left = a; else b->right = a; /* get ML branch length for a */ NewtonRaphsonBrlen (t, a, chain, 3); /* propose new length for a */ f = PointNormal(RandomNumber(seed)); f *= fvar; f += log (a->length); a->length = f; /* get downpass cond likes for the root part */ q = b; while (q->anc != NULL) m->condLikeDown (q, division, chain); /* get uppass cond likes for the root part */ GetLikeUp (t, t->root->left); /* cycle through the possibilities and record ln likelihood of each in p->d */ for (i=0; inNodes; i++) { p = t->allDownPass[i]; if (p->marked == NO) continue; /* attach crown tree here */ pLength = p->length; /* find ml branch lengths */ NewtonRaphsonBrlens5 (t, v, chain, 5, 3); /* find score */ m->CondLikeDown (t, v); m->CondLikeRoot (t, u); m->Likelihood (t, u, &lnL); p->d = lnL * warp; if (i == 0) maxLnL = p->d; else if (p->d > maxLnL) maxLnL = p->d; if (p == a) curLnL = p->d; /* detach crown tree */ /* restore p->length */ p->length = pLength; } /* find the sum given the warp factor */ sum = 0.0; for (i=0; inNodes; i++) { p = t->allDownPass[i]; if (p->marked == YES) { p->d = exp (maxLnL - p->d); sum += p->d; } } /* generate a random uniform */ ran = RandomNumber(seed); /* select the appropriate reattachment point */ cumulativeProb = 0.0; for (i=0; inNodes; i++) { p = t->allDownPass[i]; if (p->marked == YES) { c = p; cumulativeProb += p->d / sum; if (cumulativeProb > ran) break; } } if (c->marked != YES) { printf ("Could not select node\n"); getchar(); } /* calculate the proposal ratio based on biased reattachment */ if (c == a) (*lnProposalRatio) = 0.0; else (*lnProposalRatio) = (maxLnL - log(c->d)) - curLnL; /* reattach */ if (c != a) topologyHasChanged = YES; d = c->anc; c->anc = u; if (u->left == v) u->right = c; else u->left = c; if (d->left == c) d->left = u; else d->right = u; u->anc = d; /* optimize branch lengths */ NewtonRaphsonBrlens5 (t, v, chain, 5, 5); /* calculate variance of lognormal for back move */ f = log (a->length) - log (aLength); fvarNew = f*f; f = log (v->length) - log (vLength); fvarNew += f*f; f = log (u->length) - log (uLength); fvarNew += f*f; if (v->left != NULL) { f = log (v->left->length) - log (vLeftLength); fvarNew += f*f; f = log (v->right->length) - log (vRightLength); fvarNew += f*f; fvarNew /= 5.0; } else fvarNew /= 3.0; /* draw new branch lengths */ c->length = fvar * PointNormal(RandomNumber(seed)) + log(c->length); u->length = fvar * PointNormal(RandomNumber(seed)) + log(u->length); v->length = fvar * PointNormal(RandomNumber(seed)) + log(v->length); if (v->left != NULL) { v->left->length = fvar * PointNormal(RandomNumber(seed)) + log(v->left->length); v->right->length = fvar * PointNormal(RandomNumber(seed)) + log(v->right->length); } /* calculate proposal ratio for branch lengths */ /* set tiprobs update flags */ a->upDateTi = YES; u->upDateTi = YES; c->upDateTi = YES; /* could be same as a but that does not matter */ v->upDateTi = YES; if (v->left != NULL) { v->left->upDateTi = YES; v->right->upDateTi = YES; } /* set flags for update of cond likes from v and down to root */ q = v; while (q->anc != NULL) { q->upDateCl = YES; q = q->anc; } /* set flags for update of cond likes from b and down to root */ q = b; while (q->anc != NULL && q->upDateCl == NO) { q->upDateCl = YES; q = q->anc; } /* get down pass sequence if tree topology has changed */ if (topologyHasChanged == YES) { GetDownPass (t); } /* restore old conditional likelihoods */ # if defined (DEBUG_MLSPR) printf ("After:\n"); ShowNodes (t->root, 2, YES); getchar(); printf ("Proposal ratio: %f\n",(*lnProposalRatio)); printf ("v: %d u: %d a: %d b: %d\n",v->index, u->index, a->index, b->index); printf ("No. nodes moved in root subtree: %d\n",nRootNodes); printf ("Has topology changed? %d\n",topologyHasChanged); getchar(); # endif return (NO_ERROR); } #endif /**--------------------------------------------------------------- | | Move_MixtureRates | | Change component rates of site rate mixture using a Dirichlet | proposal. The logic is the same as for the Move_Statefreqs | proposal. | | Added by Fredrik Ronquist 2016-07-15. | ----------------------------------------------------------------*/ int Move_MixtureRates (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp) { /* change mixture rates */ int i, nStates, isValid; MrBFlt dirichletParameters[MAX_RATE_CATS], *newRates, *oldRates, *priorAlpha, sum, alphaPi, x, y, nStatesMrBFlt; /* get the values we need */ nStates = param->nSubValues; nStatesMrBFlt = (MrBFlt)(nStates); priorAlpha = GetParamVals (param, chain, state[chain]); newRates = GetParamSubVals (param, chain, state[chain]); oldRates = GetParamSubVals (param, chain, state[chain] ^ 1); /* tuning parameter */ alphaPi = mvp[0]*nStates; /* multiply old values with some large number to get new values close to the old ones */ /* we normalize the values in the process so we get a standard simplex */ for (i=0; inRelParts; i++) TouchAllTreeNodes(&modelSettings[param->relParts[i]],chain); /* Set update flags for cijks for all affected partitions. If this is a simple 4 X 4 model, we don't take any hit, because we will never go into a general transition probability calculator. However, for many models we do want to update the cijk flag, as the transition probability matrices require diagonalizing the rate matrix. */ for (i=0; inRelParts; i++) modelSettings[param->relParts[i]].upDateCijk = YES; return (NO_ERROR); } /**--------------------------------------------------------------- | | Move_MixtureRates_Slider | | Change component rates of site rate mixture using sliding | window mechanism. The logic is the same as for the | Move_StateFreqs_Slider. See that move for more detailed | description. | | Added by Fredrik Ronquist 2016-07-15. | ----------------------------------------------------------------*/ int Move_MixtureRates_Slider (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp) { int i, j, nStates, isValid; MrBFlt delta, *newRates, *oldRates, *priorAlpha, x, y, sum, min, max, nStatesMrBFlt; /* get the values we need */ nStates = param->nSubValues; nStatesMrBFlt = (MrBFlt)(nStates); priorAlpha = GetParamVals(param, chain, state[chain]); newRates = GetParamSubVals (param, chain, state[chain]); oldRates = GetParamSubVals (param, chain, state[chain] ^ 1); /* get window size */ delta = mvp[0]; /* choose a pair to change */ i = (int) (RandomNumber(seed) * nStates); j = (int) (RandomNumber(seed) * (nStates-1)); if (i == j) j = nStates-1; /* find the sum of these proportions */ sum = oldRates[i] + oldRates[j]; /* reflect */ isValid = NO; min = RATE_MIN; max = 1.0 - min; x = oldRates[i] / sum; if (delta > max-min) /* we do it to avoid following long while loop in case if delta is high */ { delta = max-min; } y = x + delta * (RandomNumber(seed) - 0.5); do { if (y < min) y = 2.0 * min - y; else if (y > max) y = 2.0 * max - y; else isValid = YES; } while (isValid == NO); /* set the new values */ newRates[i] = y * sum; newRates[j] = sum - newRates[i]; /* get proposal ratio */ *lnProposalRatio = 0.0; /* get prior ratio */ /* (the Gamma part of the prior is the same) */ x = (priorAlpha[i]-1.0)*log(newRates[i]/nStatesMrBFlt); x += (priorAlpha[j]-1.0)*log(newRates[j]/nStatesMrBFlt); y = (priorAlpha[i]-1.0)*log(oldRates[i]/nStatesMrBFlt); y += (priorAlpha[j]-1.0)*log(oldRates[j]/nStatesMrBFlt); (*lnPriorRatio) = x - y; /* Set update for entire tree */ for (i=0; inRelParts; i++) TouchAllTreeNodes(&modelSettings[param->relParts[i]],chain); /* Set update flags for cijks for all affected partitions. If this is a simple 4 X 4 model, we don't take any hit, because we will never go into a general transition probability calculator. However, for many models we do want to update the cijk flag, as the transition probability matrices require diagonalizing the rate matrix. */ for (i=0; inRelParts; i++) modelSettings[param->relParts[i]].upDateCijk = YES; return (NO_ERROR); } /* Move_NNI, change topology using NNI move */ int Move_NNI (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp) { TreeNode *p, *u, *v, *a, *b, *c; Tree *t; (*lnProposalRatio) = (*lnPriorRatio) = 0.0; /* get tree */ t = GetTree (param, chain, state[chain]); /* pick an internal branch */ do { p = t->intDownPass[(int)(RandomNumber(seed) * t->nIntNodes)]; } while (p->anc->anc == NULL || p->isLocked == YES); /* set up area of rearrangement */ u = p; v = u->anc; a = u->left; b = u->right; if (v->left == u) c = v->right; else c = v->left; /* change topology */ if (RandomNumber(seed) < 0.5) { if (v->left == u) v->right = b; else v->left = b; u->left = a; u->right = c; a->anc = c->anc = u; b->anc = v; } else { if (v->left == u) v->right = a; else v->left = a; u->left = b; u->right = c; b->anc = c->anc = u; a->anc = v; } /* set update of cond likes */ while (p->anc != NULL) { p->upDateCl = YES; p = p->anc; } GetDownPass (t); return (NO_ERROR); } int Move_NNIClock (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp) { /* Change clock tree using NNI move */ int i, *nEvents, numFreeOld, numFreeNew; MrBFlt x, *brlens, *rate=NULL, var=0.0, nu=0.0, oldALength, oldCLength; TreeNode *p, *q, *a, *c, *u, *v; Tree *t; Param *subParm; /* no tuning parameter */ /* make absolutely sure the proposal ratio and prior ratio are reset */ (*lnProposalRatio) = (*lnPriorRatio) = 0.0; /* get tree */ t = GetTree (param, chain, state[chain]); # if defined (DEBUG_NNIClock) printf ("Before:\n"); ShowNodes (t->root, 2, YES); getchar(); # endif /* count number of free interior branches */ numFreeOld = 0; for (i=0; inIntNodes-1; i++) { p = t->intDownPass[i]; if (p->anc->left == p) q = p->anc->right; else q = p->anc->left; if (p->isLocked == NO && p->nodeDepth >= q->nodeDepth + BRLENS_MIN) numFreeOld++; } /* In extremely constrained trees, it might be impossible to change the tree before nodes have changed in position */ if (numFreeOld == 0) { abortMove = YES; return (NO_ERROR); } /* pick an interior branch, around which it is possible to make an NNI */ do { p = t->intDownPass[(int)(RandomNumber(seed) * (t->nIntNodes-1))]; if (p->anc->left == p) q = p->anc->right; else q = p->anc->left; } while (p->isLocked == YES || p->nodeDepth < q->nodeDepth + BRLENS_MIN); /* set up pointers for nodes around the picked branch */ /* consider ancestral fossil (brl=0) in fossilized bd tree */ if (p->left->length < TIME_MIN) a = p->right; else if (p->right->length < TIME_MIN) a = p->left; else if (RandomNumber(seed) < 0.5) a = p->left; else a = p->right; v = p; u = p->anc; if (u->left == v) c = u->right; else c = u->left; /* record branch lengths */ oldALength = a->length; oldCLength = c->length; /* make topology change */ a->anc = u; c->anc = v; if (v->left == a) v->left = c; else v->right = c; if (u->left == c) u->left = a; else u->right = a; /* adjust branch lengths */ a->length = u->nodeDepth - a->nodeDepth; c->length = v->nodeDepth - c->nodeDepth; assert (a->length > BRLENS_MIN); assert (c->length > BRLENS_MIN); /* no reassignment of CPP events or branch rates necessary */ /* set tiprobs update flags */ a->upDateTi = YES; c->upDateTi = YES; /* set flags for update of cond likes from v and down to root */ p = v; while (p->anc != NULL) { p->upDateCl = YES; p = p->anc; } /* get down pass sequence */ GetDownPass (t); /* count number of free interior branches after the move */ numFreeNew = 0; for (i=0; inIntNodes-1; i++) { p = t->intDownPass[i]; if (p->anc->left == p) q = p->anc->right; else q = p->anc->left; if (p->isLocked == NO && p->nodeDepth >= q->nodeDepth + BRLENS_MIN) numFreeNew++; } /* get proposal ratio if number of free branches has changed */ if (numFreeNew != numFreeOld) (*lnProposalRatio) = log((MrBFlt)numFreeOld / (MrBFlt)numFreeNew); /* calculate and adjust prior ratio for clock trees */ if (LogClockTreePriorRatio(param, chain, &x) == ERROR) return (ERROR); (*lnPriorRatio) += x; /* adjust proposal and prior ratio for relaxed clock models */ for (i=0; isubParams[0]->nSubParams; i++) { subParm = param->subParams[0]->subParams[i]; if (subParm->paramType == P_CPPEVENTS) { nEvents = subParm->nEvents[2*chain+state[chain]]; /* proposal ratio */ (*lnProposalRatio) += nEvents[a->index] * log (a->length / oldALength); (*lnProposalRatio) += nEvents[c->index] * log (c->length / oldCLength); /* prior ratio: no effect because tree length is the same */ /* update effective evolutionary lengths */ if (UpdateCppEvolLengths (subParm, a, chain) == ERROR || UpdateCppEvolLengths (subParm, c, chain) == ERROR) { abortMove = YES; return (NO_ERROR); } } else if ( subParm->paramType == P_TK02BRANCHRATES) { if (subParm->paramType == P_TK02BRANCHRATES) nu = *GetParamVals (modelSettings[subParm->relParts[0]].tk02var, chain, state[chain]); else nu = *GetParamVals (modelSettings[subParm->relParts[0]].mixedvar, chain, state[chain]); rate = GetParamVals (subParm, chain, state[chain]); /* prior ratio and update of effective evolutionary lengths */ (*lnPriorRatio) -= LnProbLogNormal_Mean_LogVar (rate[v->index], nu*oldALength, rate[a->index]); (*lnPriorRatio) += LnProbLogNormal_Mean_LogVar (rate[u->index], nu* a->length, rate[a->index]); (*lnPriorRatio) -= LnProbLogNormal_Mean_LogVar (rate[u->index], nu*oldCLength, rate[c->index]); (*lnPriorRatio) += LnProbLogNormal_Mean_LogVar (rate[v->index], nu* c->length, rate[c->index]); brlens = GetParamSubVals (subParm, chain, state[chain]); brlens[a->index] = a->length * (rate[a->index] + rate[a->anc->index])/2.0; brlens[c->index] = c->length * (rate[c->index] + rate[c->anc->index])/2.0; } else if ( subParm->paramType == P_WNBRANCHRATES) { if (subParm->paramType == P_WNBRANCHRATES) var = *GetParamVals (modelSettings[subParm->relParts[0]].wnvar, chain, state[chain]); else var = *GetParamVals (modelSettings[subParm->relParts[0]].mixedvar, chain, state[chain]); rate = GetParamVals (subParm, chain, state[chain]); /* prior ratio and update of effective evolutionary lengths */ (*lnPriorRatio) -= LnProbGamma (oldALength/var, oldALength/var, rate[a->index]); (*lnPriorRatio) -= LnProbGamma (oldCLength/var, oldCLength/var, rate[c->index]); (*lnPriorRatio) += LnProbGamma (a->length /var, a->length /var, rate[a->index]); (*lnPriorRatio) += LnProbGamma (c->length /var, c->length /var, rate[c->index]); brlens = GetParamSubVals (subParm, chain, state[chain]); brlens[a->index] = rate[a->index] * a->length; brlens[c->index] = rate[c->index] * c->length; } else if ( subParm->paramType == P_ILNBRANCHRATES || (subParm->paramType == P_MIXEDBRCHRATES && *GetParamIntVals(subParm, chain, state[chain]) == RCL_ILN)) { if (subParm->paramType == P_ILNBRANCHRATES) nu = *GetParamVals (modelSettings[subParm->relParts[0]].ilnvar, chain, state[chain]); else nu = *GetParamVals (modelSettings[subParm->relParts[0]].mixedvar, chain, state[chain]); rate = GetParamVals (subParm, chain, state[chain]); /* update of effective evolutionary lengths */ brlens = GetParamSubVals (subParm, chain, state[chain]); brlens[a->index] = rate[a->index] * a->length; brlens[c->index] = rate[c->index] * c->length; } else if ( subParm->paramType == P_IGRBRANCHRATES || (subParm->paramType == P_MIXEDBRCHRATES && *GetParamIntVals(subParm, chain, state[chain]) == RCL_IGR)) { if (subParm->paramType == P_IGRBRANCHRATES) var = *GetParamVals (modelSettings[subParm->relParts[0]].igrvar, chain, state[chain]); else var = *GetParamVals (modelSettings[subParm->relParts[0]].mixedvar, chain, state[chain]); rate = GetParamVals (subParm, chain, state[chain]); /* update of effective evolutionary lengths */ brlens = GetParamSubVals (subParm, chain, state[chain]); brlens[a->index] = rate[a->index] * a->length; brlens[c->index] = rate[c->index] * c->length; } } # if defined (DEBUG_NNIClock) printf ("After:\n"); ShowNodes (t->root, 2, YES); getchar(); printf ("Proposal ratio: %f\n",(*lnProposalRatio)); printf ("v: %d u: %d a: %d b: %d\n",v->index, u->index, a->index, b->index); printf ("Has topology changed? %d\n",topologyHasChanged); getchar(); # endif return (NO_ERROR); } /* Move_NNI_Hetero, change topology with unlinked brlens using NNI */ int Move_NNI_Hetero (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp) { int i, brIndex, moveType; TreeNode *p, *u, *v, *a, *b, *c; Tree *t; (*lnPriorRatio) = (*lnProposalRatio) = 0.0; /* get first tree */ t = GetTree (param, chain, state[chain]); /* pick an internal branch */ do { brIndex = (int) (RandomNumber(seed) * t->nIntNodes); p = t->intDownPass[brIndex]; } while (p->anc->anc == NULL || p->isLocked == YES); /* decide on how to change the tree */ if (RandomNumber(seed) < 0.5) moveType = 0; else moveType = 1; /* cycle through trees */ for (i=0; inSubParams; i++) { /* get tree */ t = GetTree (param->subParams[i], chain, state[chain]); /* find p */ p = t->intDownPass[brIndex]; /* set up area of rearrangement */ u = p; v = u->anc; a = u->left; b = u->right; if (v->left == u) c = v->right; else c = v->left; /* change topology */ if (moveType == 0) { if (v->left == u) v->right = b; else v->left = b; u->left = a; u->right = c; a->anc = c->anc = u; b->anc = v; } else if (moveType == 1) { if (v->left == u) v->right = a; else v->left = a; u->left = b; u->right = c; b->anc = c->anc = u; a->anc = v; } /* set update of ti probs */ a->upDateTi = YES; b->upDateTi = YES; c->upDateTi = YES; u->upDateTi = YES; v->upDateTi = YES; /* set update of conditional likelihoods */ while (p->anc != NULL) { p->upDateCl = YES; p = p->anc; } /* reset tree downpass sequences */ GetDownPass (t); } return (NO_ERROR); } /*----------------------------------------------------------------------------------- | | Move_NodeSlider: move the position of one node without changing topology | -------------------------------------------------------------------------------------*/ int Move_NodeSlider (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp) { MrBFlt tuning, maxV, minV, oldM, newM, newMin, newMax, oldMin, oldMax; TreeNode *p, *q; ModelParams *mp; Tree *t; int isVPriorExp; tuning = mvp[0]; /* Larget & Simon's tuning parameter lambda */ mp = &modelParams[param->relParts[0]]; /* max and min brlen (time) */ if (param->paramId == BRLENS_UNI) { minV = mp->brlensUni[0] > BRLENS_MIN ? mp->brlensUni[0] : BRLENS_MIN; maxV = mp->brlensUni[1]; isVPriorExp = NO; } else if (param->paramId == BRLENS_GamDir) { minV = BRLENS_MIN; maxV = BRLENS_MAX; isVPriorExp = 2; } else if (param->paramId == BRLENS_iGmDir) { minV = BRLENS_MIN; maxV = BRLENS_MAX; isVPriorExp = 3; } else if (param->paramId == BRLENS_twoExp) { minV = BRLENS_MIN; maxV = BRLENS_MAX; isVPriorExp = 4; } else { minV = BRLENS_MIN; maxV = BRLENS_MAX; isVPriorExp = YES; } /* get tree */ t = GetTree (param, chain, state[chain]); /* Dirichlet or twoExp prior */ if (isVPriorExp > 1) (*lnPriorRatio) = -LogDirPrior(t, mp, isVPriorExp); /* pick an interior branch */ do { p = t->intDownPass[(int)(RandomNumber(seed) * t->nIntNodes)]; } while (p->anc == NULL || (t->isRooted == YES && p->anc->anc == NULL)); /* pick one descendant branch */ if (RandomNumber(seed) < 0.5) q = p->left; else q = p->right; /* determine new length */ oldM = (q->length + p->length); newM = oldM * exp(tuning * (RandomNumber(seed) - 0.5)); while (newM < 2.0 * minV || newM > 2.0 * maxV) { if (newM < 2.0 * minV) newM = 4.0 * minV * minV / newM; else if (newM > 2.0 * maxV) newM = 4.0 * maxV * maxV / newM; } /* determine new lengths of p and q */ newMin = minV > newM - maxV ? minV : newM - maxV; newMax = maxV < newM - minV ? maxV : newM - minV; oldMin = minV > oldM - maxV ? minV : oldM - maxV; oldMax = maxV < oldM - minV ? maxV : oldM - minV; q->length = newMin + RandomNumber(seed) * (newMax - newMin); p->length = newM - q->length; /* uniform redraw of q->length within [newMin, newMax] (reverse move within [oldMin, oldMax]) */ (*lnProposalRatio) = log ((newMax - newMin) / (oldMax - oldMin)); /* multiplier on the sum m = p->length + q->length; (p,q) -> (m,q) has unit Jacobian */ (*lnProposalRatio) += log(newM / oldM); /* set flags for update of transition probabilities at p and q */ p->upDateTi = YES; q->upDateTi = YES; p->upDateCl = YES; /* set flags for update of cond likes from p->anc and down to root */ while (p->anc->anc != NULL) { p = p->anc; p->upDateCl = YES; } /* update prior if exponential prior on branch lengths */ if (param->paramId == BRLENS_EXP) (*lnPriorRatio) = (mp->brlensExp) * (oldM - newM); /* Dirichlet or twoExp prior */ else if (isVPriorExp > 1) (*lnPriorRatio) += LogDirPrior(t, mp, isVPriorExp); return (NO_ERROR); } /*----------------------------------------------------------------------------------- | | Move_NodeSliderClock: Move the position of one (root or nonroot) node in clock tree. | In calibrated trees, we need to move also calibrated terminal nodes. | -------------------------------------------------------------------------------------*/ int Move_NodeSliderClock (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp) { int i, *nEvents; MrBFlt window, minDepth, maxDepth, oldDepth, newDepth, minL, minR, oldLeftLength=0.0, oldRightLength=0.0, oldPLength=0.0, x, clockRate, lambda=0.0, nu=0.0, var=0.0, *brlens=NULL, *rate=NULL; TreeNode *p, *q; ModelParams *mp; ModelInfo *m; Tree *t; Param *subParm; Calibration *calibrationPtr; window = mvp[0]; /* window size */ m = &modelSettings[param->relParts[0]]; mp = &modelParams[param->relParts[0]]; /* get tree */ t = GetTree (param, chain, state[chain]); /* get clock rate */ if (m->clockRate == NULL) clockRate = 1.0; else clockRate = *GetParamVals (m->clockRate, chain, state[chain]); /* check whether or not we can change root */ if ((t->root->left->isDated == YES && t->root->left->calibration->prior == fixed) || ((!strcmp(mp->clockPr, "Uniform") || !strcmp(mp->clockPr, "Birthdeath") || !strcmp(mp->clockPr, "Fossilization")) && mp->treeAgePr.prior == fixed)) i = t->nNodes - 2; else i = t->nNodes - 1; /* pick a node that can be changed in position */ do { p = t->allDownPass[(int)(RandomNumber(seed) * i)]; } while ((p->left == NULL && p->isDated == NO) || (p->left == NULL && p->length < TIME_MIN) || (p->isDated == YES && p->calibration->prior == fixed)); #if defined (DEBUG_CSLIDER) printf ("Before node slider (clock):\n"); printf ("Picked branch with index %d and depth %f\n", p->index, p->nodeDepth); if (p->anc->anc == NULL) printf ("Old clock rate: %f\n", clockRate); ShowNodes (t->root, 0, t->isRooted); getchar(); #endif /* store values needed later for prior calculation (relaxed clocks) */ oldPLength = p->length; if (p->left != NULL) { oldLeftLength = p->left->length; oldRightLength = p->right->length; } else oldLeftLength = oldRightLength = 0.0; /* determine lower and upper bound */ if (p->left == NULL) minDepth = 0.0; else // internal node { if (p->left->length > 0.0) minL = p->left->nodeDepth + BRLENS_MIN; else // ancestral fossil { assert (p->left->calibration != NULL); minL = p->left->calibration->min * clockRate; } if (p->right->length > 0.0) minR = p->right->nodeDepth + BRLENS_MIN; else // ancestral fossil { assert (p->right->calibration != NULL); minR = p->right->calibration->min * clockRate; } if (minL > minR) minDepth = minL; else minDepth = minR; } if (p->anc->anc == NULL) maxDepth = TREEHEIGHT_MAX; else maxDepth = p->anc->nodeDepth - BRLENS_MIN; if (p->left != NULL && p->left->length < TIME_MIN) { assert (p->left->calibration != NULL); if (maxDepth > p->left->calibration->max * clockRate) maxDepth = p->left->calibration->max * clockRate; } if (p->right != NULL && p->right->length < TIME_MIN) { assert (p->right->calibration != NULL); if (maxDepth > p->right->calibration->max * clockRate) maxDepth = p->right->calibration->max * clockRate; } if (p->isDated == YES) calibrationPtr = p->calibration; else if (p->anc->anc == NULL && (!strcmp(mp->clockPr, "Uniform") || !strcmp(mp->clockPr, "Birthdeath")|| !strcmp(mp->clockPr, "Fossilization"))) calibrationPtr = &mp->treeAgePr; else calibrationPtr = NULL; if (calibrationPtr != NULL) { if (maxDepth > calibrationPtr->max * clockRate) maxDepth = calibrationPtr->max * clockRate; if (minDepth < calibrationPtr->min * clockRate) minDepth = calibrationPtr->min * clockRate; } /* abort if impossible */ if (minDepth > maxDepth -BRLENS_MIN) { abortMove = YES; return (NO_ERROR); } /* save some reflection time */ if (maxDepth-minDepth < window) { window = maxDepth-minDepth; } /* pick the new node depth */ oldDepth = p->nodeDepth; newDepth = oldDepth + (RandomNumber(seed) - 0.5) * window; /* reflect the new node depth */ while (newDepth < minDepth || newDepth > maxDepth) { if (newDepth < minDepth) newDepth = 2.0 * minDepth - newDepth; if (newDepth > maxDepth) newDepth = 2.0 * maxDepth - newDepth; } p->nodeDepth = newDepth; /* determine new branch lengths around p and set update of transition probabilities */ if (p->left != NULL) { if (p->left->length > 0.0) { p->left->length = p->nodeDepth - p->left->nodeDepth; p->left->upDateTi = YES; } else p->left->nodeDepth = p->nodeDepth; if (p->right->length > 0.0) { p->right->length = p->nodeDepth - p->right->nodeDepth; p->right->upDateTi = YES; } else p->right->nodeDepth = p->nodeDepth; } if (p->anc->anc != NULL) { p->length = p->anc->nodeDepth - p->nodeDepth; p->upDateTi = YES; } /* adjust age of p if dated */ if (calibrationPtr != NULL) p->age = p->nodeDepth / clockRate; if ((p->left != NULL) && (p->left->length < TIME_MIN)) p->left->age = p->nodeDepth / clockRate; if ((p->right != NULL) && (p->right->length < TIME_MIN)) p->right->age = p->nodeDepth / clockRate; /* set flags for update of cond likes from p and down to root */ q = p; while (q->anc != NULL) { q->upDateCl = YES; q = q->anc; } /* calculate proposal ratio */ (*lnProposalRatio) = (*lnPriorRatio) = 0.0; /* calculate and adjust prior ratio for clock tree */ if (LogClockTreePriorRatio (param, chain, &x) == ERROR) return (ERROR); (*lnPriorRatio) += x; /* adjust proposal and prior ratio for relaxed clock models */ for (i=0; inSubParams; i++) { subParm = param->subParams[i]; if (subParm->paramType == P_CPPEVENTS) { nEvents = subParm->nEvents[2*chain+state[chain]]; lambda = *GetParamVals (modelSettings[subParm->relParts[0]].cppRate, chain, state[chain]); /* proposal ratio */ if (p->left != NULL) { (*lnProposalRatio) += nEvents[p->left->index ] * log (p->left->length / oldLeftLength); (*lnProposalRatio) += nEvents[p->right->index] * log (p->right->length / oldRightLength); } if (p->anc->anc != NULL) (*lnProposalRatio) += nEvents[p->index] * log (p->length / oldPLength); /* prior ratio */ if (p->anc->anc == NULL) // two branches changed in same direction (*lnPriorRatio) += lambda * (2.0 * (oldDepth - newDepth)); else if (p->left != NULL) // two branches changed in one direction, one branch in the other direction (*lnPriorRatio) += lambda * (oldDepth - newDepth); else /* if (p->left == NULL) */ // one branch changed (*lnPriorRatio) += lambda * (newDepth - oldDepth); /* update effective evolutionary lengths */ if (UpdateCppEvolLengths (subParm, p, chain) == ERROR) { abortMove = YES; return (NO_ERROR); } } else if ( subParm->paramType == P_TK02BRANCHRATES) { if (subParm->paramType == P_TK02BRANCHRATES) nu = *GetParamVals (modelSettings[subParm->relParts[0]].tk02var, chain, state[chain]); else nu = *GetParamVals (modelSettings[subParm->relParts[0]].mixedvar, chain, state[chain]); rate = GetParamVals (subParm, chain, state[chain]); brlens = GetParamSubVals (subParm, chain, state[chain]); /* prior ratio & update effective evolutionary lengths */ if (p->left != NULL) { if (p->left->length > 0.0) { (*lnPriorRatio) -= LnProbLogNormal_Mean_LogVar (rate[p->index], nu*oldLeftLength, rate[p->left->index]); (*lnPriorRatio) += LnProbLogNormal_Mean_LogVar (rate[p->index], nu*p->left->length, rate[p->left->index]); brlens[p->left->index] = p->left->length * (rate[p->left->index]+rate[p->index])/2.0; } if (p->right->length > 0.0) { (*lnPriorRatio) -= LnProbLogNormal_Mean_LogVar (rate[p->index], nu*oldRightLength, rate[p->right->index]); (*lnPriorRatio) += LnProbLogNormal_Mean_LogVar (rate[p->index], nu*p->right->length, rate[p->right->index]); brlens[p->right->index] = p->right->length * (rate[p->right->index]+rate[p->index])/2.0; } } if (p->anc->anc != NULL) { (*lnPriorRatio) -= LnProbLogNormal_Mean_LogVar (rate[p->anc->index], nu*oldPLength, rate[p->index]); (*lnPriorRatio) += LnProbLogNormal_Mean_LogVar (rate[p->anc->index], nu*p->length, rate[p->index]); brlens[p->index] = p->length * (rate[p->index]+rate[p->anc->index])/2.0; } } else if ( subParm->paramType == P_WNBRANCHRATES) { if (subParm->paramType == P_WNBRANCHRATES) var = *GetParamVals (modelSettings[subParm->relParts[0]].wnvar, chain, state[chain]); else var = *GetParamVals (modelSettings[subParm->relParts[0]].mixedvar, chain, state[chain]); rate = GetParamVals (subParm, chain, state[chain]); brlens = GetParamSubVals (subParm, chain, state[chain]); /* prior ratio & update effective evolutionary lengths */ if (p->left != NULL) { if (p->left->length > 0.0) { (*lnPriorRatio) -= LnProbGamma (oldLeftLength/var, oldLeftLength/var, rate[p->left->index]); (*lnPriorRatio) += LnProbGamma (p->left->length/var, p->left->length/var, rate[p->left->index]); brlens[p->left->index ] = rate[p->left->index ] * p->left->length; } if (p->right->length > 0.0) { (*lnPriorRatio) -= LnProbGamma (oldRightLength/var, oldRightLength/var, rate[p->right->index]); (*lnPriorRatio) += LnProbGamma (p->right->length/var, p->right->length/var, rate[p->right->index]); brlens[p->right->index] = rate[p->right->index] * p->right->length; } } if (p->anc->anc != NULL) { (*lnPriorRatio) -= LnProbGamma (oldPLength/var, oldPLength/var, rate[p->index]); (*lnPriorRatio) += LnProbGamma (p->length /var, p->length /var, rate[p->index]); brlens[p->index] = rate[p->index] * p->length; } } else if ( subParm->paramType == P_ILNBRANCHRATES || (subParm->paramType == P_MIXEDBRCHRATES && *GetParamIntVals(subParm, chain, state[chain]) == RCL_ILN)) { if (subParm->paramType == P_ILNBRANCHRATES) nu = *GetParamVals (modelSettings[subParm->relParts[0]].ilnvar, chain, state[chain]); else nu = *GetParamVals (modelSettings[subParm->relParts[0]].mixedvar, chain, state[chain]); rate = GetParamVals (subParm, chain, state[chain]); brlens = GetParamSubVals (subParm, chain, state[chain]); /* update effective evolutionary lengths */ if (p->left != NULL) { if (p->left->length > 0.0) { brlens[p->left->index ] = rate[p->left->index] * p->left->length; } if (p->right->length > 0.0) { brlens[p->right->index] = rate[p->right->index] * p->right->length; } } if (p->anc->anc != NULL) { brlens[p->index] = rate[p->index] * p->length; } } else if ( subParm->paramType == P_IGRBRANCHRATES || (subParm->paramType == P_MIXEDBRCHRATES && *GetParamIntVals(subParm, chain, state[chain]) == RCL_IGR)) { if (subParm->paramType == P_IGRBRANCHRATES) var = *GetParamVals (modelSettings[subParm->relParts[0]].igrvar, chain, state[chain]); else var = *GetParamVals (modelSettings[subParm->relParts[0]].mixedvar, chain, state[chain]); rate = GetParamVals (subParm, chain, state[chain]); brlens = GetParamSubVals (subParm, chain, state[chain]); /* update effective evolutionary lengths */ if (p->left != NULL) { if (p->left->length > 0.0) { brlens[p->left->index ] = rate[p->left->index] * p->left->length; } if (p->right->length > 0.0) { brlens[p->right->index] = rate[p->right->index] * p->right->length; } } if (p->anc->anc != NULL) { brlens[p->index] = rate[p->index] * p->length; } } } #if defined (DEBUG_CSLIDER) printf ("After node slider (clock):\n"); printf ("Old depth: %f -- New depth: %f -- LnPriorRatio %f -- LnProposalRatio %f\n", oldDepth, newDepth, (*lnPriorRatio), (*lnProposalRatio)); ShowNodes (t->root, 0, t->isRooted); getchar(); #endif return (NO_ERROR); } int Move_Nu (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp) { /* move the variance of the TK02 relaxed clock lognormal using multiplier */ int i, j; MrBFlt oldNu, newNu, minNu, maxNu, tuning, *rate; Model *mp; TreeNode *p; Tree *t; /* get tuning parameter */ tuning = mvp[0]; /* get model params */ mp = &modelParams[param->relParts[0]]; /* get the min and max values */ minNu = TK02VAR_MIN; maxNu = TK02VAR_MAX; if (!strcmp(mp->tk02varPr,"Uniform")) { minNu = (mp->tk02varUni[0] < TK02VAR_MIN) ? TK02VAR_MIN : mp->tk02varUni[0]; maxNu = (mp->tk02varUni[1] > TK02VAR_MAX) ? TK02VAR_MAX : mp->tk02varUni[1]; } /* get the TK02 lognormal variance */ oldNu = *GetParamVals (param, chain, state[chain]); /* set new value */ newNu = oldNu * exp ((0.5 - RandomNumber(seed))*tuning); /* reflect if necessary */ while (newNu < minNu || newNu > maxNu) { if (newNu < minNu) newNu = minNu * minNu / newNu; if (newNu > maxNu) newNu = maxNu * maxNu / newNu; } /* store new value */ *GetParamVals (param, chain, state[chain]) = newNu; /* calculate prior ratio */ for (i=0; inSubParams; i++) { rate = GetParamVals (param->subParams[i], chain, state[chain]); t = GetTree (param->subParams[i], chain, state[chain]); for (j=0; jnNodes-2; j++) { p = t->allDownPass[j]; if (p->length > 0.0) // not ancestral fossil { (*lnPriorRatio) -= LnProbLogNormal_Mean_LogVar (rate[p->anc->index], oldNu*p->length, rate[p->index]); (*lnPriorRatio) += LnProbLogNormal_Mean_LogVar (rate[p->anc->index], newNu*p->length, rate[p->index]); } } } /* take prior on nu into account */ if (!strcmp(mp->tk02varPr,"Exponential")) (*lnPriorRatio) += mp->tk02varExp * (oldNu - newNu); /* calculate proposal ratio */ (*lnProposalRatio) = log (newNu / oldNu); /* we do not need to update likelihoods */ for (i=0; inRelParts; i++) { modelSettings[param->relParts[i]].upDateCl = NO; } return (NO_ERROR); } /*---------------------------------------------------------------- | | Move_Omega: Change the nonysnonymous/synonymous rate ratio | Note that this is appropriate when omegavar=equal | ----------------------------------------------------------------*/ int Move_Omega (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp) { /* change omega using sliding window */ int i, isValidO; MrBFlt oldO, newO, window, minO, maxO, ran, *alphaDir, oldPropRatio, newPropRatio, x, y; ModelParams *mp; /* get size of window, centered on current omega value */ window = mvp[0]; /* get model params */ mp = &modelParams[param->relParts[0]]; /* get minimum and maximum values for omega */ minO = OMEGA_MIN; maxO = OMEGA_MAX; /* get old value of omega */ oldO = *GetParamVals(param, chain, state[chain]); /* get Dirichlet parameters */ alphaDir = mp->tRatioDir; /* change value for omega */ ran = RandomNumber(seed); if (maxO-minO < window) { window = maxO-minO; } newO = oldO + window * (ran - 0.5); /* check that new value is valid */ isValidO = NO; do { if (newO < minO) newO = 2.0 * minO - newO; else if (newO > maxO) newO = 2.0 * maxO - newO; else isValidO = YES; } while (isValidO == NO); /* get proposal ratio */ *lnProposalRatio = 0.0; /* get prior ratio from Dirichlet */ oldPropRatio = oldO / (oldO + 1.0); newPropRatio = newO / (newO + 1.0); x = ((alphaDir[0] - 1.0) * log(newPropRatio)) + ((alphaDir[1] - 1.0) * log (1.0 - newPropRatio)); y = ((alphaDir[0] - 1.0) * log(oldPropRatio)) + ((alphaDir[1] - 1.0) * log (1.0 - oldPropRatio)); (*lnPriorRatio) = x - y; /* copy new omega value back */ *GetParamVals(param, chain, state[chain]) = newO; /* Set update flags for all partitions that share this kappa. Note that the conditional likelihood update flags have been set before we even call this function. */ for (i=0; inRelParts; i++) TouchAllTreeNodes(&modelSettings[param->relParts[i]],chain); /* Set update flags for cijks for all affected partitions. If this is a simple 4 X 4 model, we don't take any hit, because we will never go into a general transition probability calculator. However, for covarion, doublet, and codon models, we do want to update the cijk flag. */ for (i=0; inRelParts; i++) modelSettings[param->relParts[i]].upDateCijk = YES; return (NO_ERROR); } /*---------------------------------------------------------------- | | Move_Omega_M: Change the nonysnonymous/synonymous rate ratio | using multiplier. Note that this is appropriate when | omegavar=equal | ----------------------------------------------------------------*/ int Move_Omega_M (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp) { /* change omega using multiplier */ int i, isValidO; MrBFlt oldO, newO, minO, maxO, tuning, ran, factor, *alphaDir, oldPropRatio, newPropRatio, x, y; ModelParams *mp; /* get model params */ mp = &modelParams[param->relParts[0]]; /* get tuning parameter */ tuning = mvp[0]; /* get minimum and maximum values for omega */ minO = OMEGA_MIN; maxO = OMEGA_MAX; /* get old value of omega */ oldO = *GetParamVals(param, chain, state[chain]); /* get Dirichlet parameters */ alphaDir = mp->omegaDir; /* change value for omega */ ran = RandomNumber(seed); factor = exp(tuning * (ran - 0.5)); newO = oldO * factor; /* check that new value is valid */ isValidO = NO; do { if (newO < minO) newO = minO * minO / newO; else if (newO > maxO) newO = maxO * maxO / newO; else isValidO = YES; } while (isValidO == NO); /* get proposal ratio */ *lnProposalRatio = log(newO / oldO); /* get prior ratio from Dirichlet */ oldPropRatio = oldO / (oldO + 1.0); newPropRatio = newO / (newO + 1.0); x = ((alphaDir[0] - 1.0) * log(newPropRatio)) + ((alphaDir[1] - 1.0) * log (1.0 - newPropRatio)); y = ((alphaDir[0] - 1.0) * log(oldPropRatio)) + ((alphaDir[1] - 1.0) * log (1.0 - oldPropRatio)); (*lnPriorRatio) = x - y; /* copy new omega value back */ *GetParamVals(param, chain, state[chain]) = newO; /* Set update flags for all partitions that share this omega. Note that the conditional likelihood update flags have been set before we even call this function. */ for (i=0; inRelParts; i++) TouchAllTreeNodes(&modelSettings[param->relParts[i]],chain); /* Set update flags for cijks for all affected partitions. If this is a simple 4 X 4 model, we don't take any hit, because we will never go into a general transition probability calculator. However, for covarion, doublet, and codon models, we do want to update the cijk flag. */ for (i=0; inRelParts; i++) modelSettings[param->relParts[i]].upDateCijk = YES; return (NO_ERROR); } /*---------------------------------------------------------------- | | Move_OmegaBeta_M: Change parameters of the beta distribution | using multiplier for the M10 model. Note that this is | appropriate when omegavar=M10 | ----------------------------------------------------------------*/ int Move_OmegaBeta_M (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp) { int i, isValidVal, whichParam; MrBFlt oldVal, newVal, minVal, maxVal, *vals, *subVals, tuning, ran, factor; ModelParams *mp; /* do we pick alpha or beta of the Beta distribution to change */ if (RandomNumber(seed) < 0.5) whichParam = 0; else whichParam = 1; /* get model params */ mp = &modelParams[param->relParts[0]]; /* get tuning parameter */ tuning = mvp[0]; /* get minimum and maximum values for omega */ minVal = 0.05; maxVal = 100.0; /* get old value of omega */ vals = GetParamVals(param, chain, state[chain]); subVals = GetParamSubVals(param, chain, state[chain]); oldVal = subVals[mp->numM10BetaCats + mp->numM10GammaCats + 4 + whichParam]; /* change value for alpha/beta */ ran = RandomNumber(seed); factor = exp(tuning * (ran - 0.5)); newVal = oldVal * factor; /* check that new value is valid */ isValidVal = NO; do { if (newVal < minVal) newVal = minVal * minVal / newVal; else if (newVal > maxVal) newVal = maxVal * maxVal / newVal; else isValidVal = YES; } while (isValidVal == NO); /* get proposal ratio */ *lnProposalRatio = log(newVal / oldVal); /* get prior ratio */ if (!strcmp(mp->m10betapr, "Exponential")) (*lnPriorRatio) = (log(mp->m10betaExp) - newVal * mp->m10betaExp) - (log(mp->m10betaExp) - oldVal * mp->m10betaExp); else (*lnPriorRatio) = 0.0; /* copy new omega value back */ subVals[mp->numM10BetaCats + mp->numM10GammaCats + 4 + whichParam] = newVal; /* update the omega values */ BetaBreaks (subVals[mp->numM10BetaCats + mp->numM10GammaCats + 4], subVals[mp->numM10BetaCats + mp->numM10GammaCats + 5], &vals[0], mp->numM10BetaCats); /* Set update flags for all partitions that share this kappa. Note that the conditional likelihood update flags have been set before we even call this function. */ for (i=0; inRelParts; i++) TouchAllTreeNodes(&modelSettings[param->relParts[i]],chain); /* Set update flags for cijks for all affected partitions. If this is a simple 4 X 4 model, we don't take any hit, because we will never go into a general transition probability calculator. However, for covarion, doublet, and codon models, we do want to update the cijk flag. */ for (i=0; inRelParts; i++) modelSettings[param->relParts[i]].upDateCijk = YES; return (NO_ERROR); } /*---------------------------------------------------------------- | | Move_OmegaGamma_M: Change parameters of the gamma distribution | using multiplier for the M10 model. Note that this is | appropriate whenomegavar=M10 | ----------------------------------------------------------------*/ int Move_OmegaGamma_M (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp) { int i, isValidVal, whichParam; MrBFlt oldVal, newVal, minVal, maxVal, *vals, *subVals, tuning, ran, factor, quantile95; ModelParams *mp; /* do we pick alpha or beta of the Gamma distribution to change */ if (RandomNumber(seed) < 0.5) whichParam = 0; else whichParam = 1; /* get model params */ mp = &modelParams[param->relParts[0]]; /* get tuning parameter */ tuning = mvp[0]; /* get minimum and maximum values for omega */ minVal = 0.05; maxVal = 100.0; /* get values */ vals = GetParamVals(param, chain, state[chain]); subVals = GetParamSubVals(param, chain, state[chain]); oldVal = subVals[mp->numM10BetaCats + mp->numM10GammaCats + 6 + whichParam]; /* change value for alpha/beta */ do { ran = RandomNumber(seed); factor = exp(tuning * (ran - 0.5)); newVal = oldVal * factor; /* check that new value is valid */ isValidVal = NO; do { if (newVal < minVal) newVal = minVal * minVal / newVal; else if (newVal > maxVal) newVal = maxVal * maxVal / newVal; else isValidVal = YES; } while (isValidVal == NO); /* check that the distribution does not go too far to the right */ if (whichParam == 0) quantile95 = QuantileGamma (0.95, newVal, subVals[mp->numM10BetaCats + mp->numM10GammaCats + 7]); else quantile95 = QuantileGamma (0.95, subVals[mp->numM10BetaCats + mp->numM10GammaCats + 6], newVal); } while (quantile95 > 100.0); /* get proposal ratio */ *lnProposalRatio = log(newVal / oldVal); /* get prior ratio */ if (!strcmp(mp->m10gammapr, "Exponential")) (*lnPriorRatio) = (log(mp->m10gammaExp) - newVal * mp->m10gammaExp) - (log(mp->m10gammaExp) - oldVal * mp->m10gammaExp); else (*lnPriorRatio) = 0.0; /* copy new value back */ subVals[mp->numM10BetaCats + mp->numM10GammaCats + 6 + whichParam] = newVal; /* update the omega values */ if (DiscreteGamma (&vals[mp->numM10BetaCats], subVals[mp->numM10BetaCats + mp->numM10GammaCats + 6], subVals[mp->numM10BetaCats + mp->numM10GammaCats + 7], mp->numM10GammaCats, 0) == ERROR) return (ERROR); for (i=0; inumM10GammaCats; i++) vals[mp->numM10BetaCats + i] += 1.0; /* Set update flags for all partitions that share this kappa. Note that the conditional likelihood update flags have been set before we even call this function. */ for (i=0; inRelParts; i++) TouchAllTreeNodes(&modelSettings[param->relParts[i]],chain); /* Set update flags for cijks for all affected partitions. If this is a simple 4 X 4 model, we don't take any hit, because we will never go into a general transition probability calculator. However, for covarion, doublet, and codon models, we do want to update the cijk flag. */ for (i=0; inRelParts; i++) modelSettings[param->relParts[i]].upDateCijk = YES; return (NO_ERROR); } int Move_OmegaCat (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp) { int i, localNumCats, numBetaAndGammaCats; MrBFlt dirichletParameters[3], *newSubVals, *oldSubVals, *newFreqs, *oldFreqs, *priorParams, sum, alpha, x, y; ModelParams *mp; /* get model params */ mp = &modelParams[param->relParts[0]]; /* how many categories are there */ localNumCats = 3; numBetaAndGammaCats = 0; if (!strcmp(mp->omegaVar, "M10")) { localNumCats = 2; numBetaAndGammaCats = mp->numM10BetaCats + mp->numM10GammaCats; } /* get the values we need */ newSubVals = GetParamSubVals (param, chain, state[chain]); oldSubVals = GetParamSubVals (param, chain, state[chain] ^ 1); if (!strcmp(mp->omegaVar, "M10")) { newFreqs = newSubVals + numBetaAndGammaCats; oldFreqs = oldSubVals + numBetaAndGammaCats; priorParams = newSubVals + (numBetaAndGammaCats + 2); } else { newFreqs = newSubVals + 0; oldFreqs = oldSubVals + 0; priorParams = newFreqs + 3; } /* get parameter of proposal mechanism */ alpha = mvp[0]; /* multiply old values with some large number to get new values close to the old ones */ for (i=0; iomegaVar, "M10")) { for (i=0; inumM10BetaCats; i++) newSubVals[i] = newFreqs[0] / mp->numM10BetaCats; for (i=mp->numM10BetaCats; inumM10BetaCats+mp->numM10GammaCats; i++) newSubVals[i] = newFreqs[1] / mp->numM10GammaCats; } /* get proposal ratio */ sum = 0.0; for (i=0; inRelParts; i++) TouchAllTreeNodes(&modelSettings[param->relParts[i]],chain); /* Set update flags for cijks for all affected partitions. */ for (i=0; inRelParts; i++) modelSettings[param->relParts[i]].upDateCijk = YES; return (NO_ERROR); } /*---------------------------------------------------------------- | | Move_OmegaM3: Change the nonysnonymous/synonymous rate ratio | of one class of the M3 model | ----------------------------------------------------------------*/ int Move_OmegaM3 (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp) { int i, isValidO, whichOmega; MrBFlt newO, window, minO, maxO, ran, *value, *oldValue, x, y; /* get size of window, centered on current omega value */ window = mvp[0]; /* get old value of omega */ value = GetParamVals(param, chain, state[chain]); oldValue = GetParamVals(param, chain, state[chain] ^ 1); whichOmega = (int) (RandomNumber(seed)*3.0); /* get minimum and maximum values for omega */ if (whichOmega == 0) minO = 0.0; else minO = value[whichOmega-1]; if (whichOmega == 2) maxO = OMEGA_MAX; else maxO = value[whichOmega+1]; /* change value for omega */ ran = RandomNumber(seed); if (maxO-minO < window) { window = maxO-minO; } newO = oldValue[whichOmega] + window * (ran - 0.5); /* check that new value is valid */ isValidO = NO; do { if (newO < minO) newO = 2* minO - newO; else if (newO > maxO) newO = 2 * maxO - newO; else isValidO = YES; } while (isValidO == NO); /* copy new omega value back */ value[whichOmega] = newO; /* get proposal ratio */ *lnProposalRatio = 0.0; /* get prior ratio */ x = LogOmegaPrior (value[0], value[1], value[2]); y = LogOmegaPrior (oldValue[0], oldValue[1], oldValue[2]); *lnPriorRatio = x - y; /* Set update flags for all partitions that share this omega. Note that the conditional likelihood update flags have been set before we even call this function. */ for (i=0; inRelParts; i++) TouchAllTreeNodes(&modelSettings[param->relParts[i]],chain); /* Set update flags for cijks for all affected partitions. */ for (i=0; inRelParts; i++) modelSettings[param->relParts[i]].upDateCijk = YES; return (NO_ERROR); } /*---------------------------------------------------------------- | | Move_OmegaNeu: Change the nonysnonymous/synonymous rate ratio | for neutral sites | ----------------------------------------------------------------*/ int Move_OmegaNeu (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp) { int i, isOPriorExp, isValidO; MrBFlt oldO, newO, window, minO, maxO, ran, *value, x, y; /* get size of window, centered on current omega value */ window = mvp[0]; /* get old value of omega */ value = GetParamVals(param, chain, state[chain]); oldO = value[1]; /* get minimum and maximum values for omega */ minO = value[0]; maxO = value[2]; /* the only way we would be updating the middle category (omega2) is if we have an exponential prior on all three omegas */ isOPriorExp = YES; /* change value for omega */ ran = RandomNumber(seed); if (maxO-minO < window) { window = maxO-minO; } newO = oldO + window * (ran - 0.5); /* check that new value is valid */ isValidO = NO; do { if (newO < minO) newO = 2 * minO - newO; else if (newO > maxO) newO = 2 * maxO - newO; else isValidO = YES; } while (isValidO == NO); /* get proposal ratio */ *lnProposalRatio = 0.0; /* copy new omega value back */ value[1] = newO; /* get prior ratio */ if (isOPriorExp == NO) { *lnPriorRatio = 0.0; } else { x = LogOmegaPrior (value[0], newO, value[2]); y = LogOmegaPrior (value[0], oldO, value[2]); *lnPriorRatio = x - y; } /* Set update flags for all partitions that share this omega. Note that the conditional likelihood update flags have been set before we even call this function. */ for (i=0; inRelParts; i++) TouchAllTreeNodes(&modelSettings[param->relParts[i]],chain); /* Set update flags for cijks for all affected partitions. */ for (i=0; inRelParts; i++) modelSettings[param->relParts[i]].upDateCijk = YES; return (NO_ERROR); } /*---------------------------------------------------------------- | | Move_OmegaPos: Change the nonysnonymous/synonymous rate ratio | for positively selected sites | ----------------------------------------------------------------*/ int Move_OmegaPos (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp) { int i, isValidO, omegaUni, omegaExp1, omegaExp2; MrBFlt oldO, newO, window, minO=0.0, maxO=0.0, ran, *value, x, y; ModelParams *mp; /* get size of window, centered on current omega value */ window = mvp[0]; /* get model params */ mp = &modelParams[param->relParts[0]]; /* get old value of omega */ value = GetParamVals(param, chain, state[chain]); oldO = value[2]; /* determine prior for omega */ omegaUni = omegaExp1 = omegaExp2 = NO; if (param->paramId == OMEGA_BUD || param->paramId == OMEGA_BUF || param->paramId == OMEGA_FUD || param->paramId == OMEGA_FUF) omegaUni = YES; else if (param->paramId == OMEGA_BED || param->paramId == OMEGA_BEF || param->paramId == OMEGA_FED || param->paramId == OMEGA_FEF) omegaExp1 = YES; else if (param->paramId == OMEGA_ED || param->paramId == OMEGA_EF) omegaExp2 = YES; /* get minimum and maximum values for omega */ if (omegaUni == YES) { minO = mp->ny98omega3Uni[0]; if (minO < value[1]) minO = value[1]; maxO = mp->ny98omega3Uni[1]; if (maxO > KAPPA_MAX) maxO = KAPPA_MAX; } else if (omegaExp1 == YES || omegaExp2 == YES) { minO = value[1]; maxO = KAPPA_MAX; } /* change value for omega */ ran = RandomNumber(seed); if (maxO-minO < window) { window = maxO-minO; } newO = oldO + window * (ran - 0.5); /* check that new value is valid */ isValidO = NO; do { if (newO < minO) newO = 2* minO - newO; else if (newO > maxO) newO = 2 * maxO - newO; else isValidO = YES; } while (isValidO == NO); /* get proposal ratio */ *lnProposalRatio = 0.0; /* copy new omega value back */ value[2] = newO; /* get prior ratio (part 2) */ if (omegaUni == YES) { *lnPriorRatio = 0.0; } else if (omegaExp1 == YES) { *lnPriorRatio = mp->ny98omega3Exp * (oldO - newO); } else if (omegaExp2 == YES) { x = LogOmegaPrior (value[0], value[1], newO); y = LogOmegaPrior (value[0], value[1], oldO); *lnPriorRatio = x - y; } /* Set update flags for all partitions that share this omega. Note that the conditional likelihood update flags have been set before we even call this function. */ for (i=0; inRelParts; i++) TouchAllTreeNodes(&modelSettings[param->relParts[i]],chain); /* Set update flags for cijks for all affected partitions. */ for (i=0; inRelParts; i++) modelSettings[param->relParts[i]].upDateCijk = YES; return (NO_ERROR); } /*---------------------------------------------------------------- | | Move_OmegaPur: Change the nonysnonymous/synonymous rate ratio | for purifying selection sites | ----------------------------------------------------------------*/ int Move_OmegaPur (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp) { int i, isOPriorExp, isValidO; MrBFlt oldO, newO, window, minO, maxO, ran, *value, x, y; /* get size of window, centered on current omega value */ window = mvp[0]; /* get old value of omega */ value = GetParamVals(param, chain, state[chain]); oldO = value[0]; /* get minimum and maximum values for omega */ minO = 0.0; maxO = value[1]; /* get prior for omega */ if (param->paramId == OMEGA_BUD || param->paramId == OMEGA_BUF || param->paramId == OMEGA_BED || param->paramId == OMEGA_BEF || param->paramId == OMEGA_BFD || param->paramId == OMEGA_BFF) isOPriorExp = NO; else isOPriorExp = YES; /* change value for omega */ ran = RandomNumber(seed); if (maxO-minO < window) { window = maxO-minO; } newO = oldO + window * (ran - 0.5); /* check that new value is valid */ isValidO = NO; do { if (newO < minO) newO = 2* minO - newO; else if (newO > maxO) newO = 2 * maxO - newO; else isValidO = YES; } while (isValidO == NO); /* get proposal ratio */ *lnProposalRatio = 0.0; /* copy new omega value back */ value[0] = newO; /* get prior ratio (part 2) */ if (isOPriorExp == NO) { *lnPriorRatio = 0.0; } else { x = LogOmegaPrior (newO, value[1], value[2]); y = LogOmegaPrior (oldO, value[1], value[2]); *lnPriorRatio = x - y; } /* Set update flags for all partitions that share this omega. Note that the conditional likelihood update flags have been set before we even call this function. */ for (i=0; inRelParts; i++) TouchAllTreeNodes(&modelSettings[param->relParts[i]],chain); /* Set update flags for cijks for all affected partitions. */ for (i=0; inRelParts; i++) modelSettings[param->relParts[i]].upDateCijk = YES; return (NO_ERROR); } /*---------------------------------------------------------------- | | Move_ParsEraser1: This proposal mechanism changes the topology and | branch lengths of an unrooted tree. A randomly chosen region of | the tree is erased. Parsimony is used to guide the selection of | a new topology for the erased part of the tree. The parsimony | branch lengths are used to guide the proposal of new branch | lengths. This variant (1) uses exhaustive enumeration. | | Programmed by FR 2004-10-23-- | ----------------------------------------------------------------*/ int Move_ParsEraser1 (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp) { int i, j, isVPriorExp, nSubTerminals, nEmbeddedTrees; MrBFlt alphaPi, warp, minV, maxV, minP, maxP, newM, oldM, maxLen, *brlensCur, *brlensNew, *parslensCur, *parslensNew, curLength, newLength, lnJacobian, lnRandomRatio, alpha[2], prob[2], minLenNew, f; TreeNode *p=NULL; Tree *t, *subtree, *subtree1, memTree[2]; ModelParams *mp; ModelInfo *m; TreeInfo tInfo; /* set pointers to NULL */ subtree = subtree1 = NULL; brlensCur = NULL; for (i=0; i<2; i++) { memTree[i].allDownPass = NULL; memTree[i].intDownPass = NULL; memTree[i].nodes = NULL; } tInfo.leaf = NULL; /* Set alpha Pi for Dirichlet p generator */ alphaPi = mvp[0]; alphaPi = 0.05; /* Set the parsimony warp factor */ warp = mvp[1]; warp = 0.2; /* Set the number of terminals (nSubTerminals, column 3) in erased tree */ /* Erased Nodes => Leaves => Terminals => Embedded trees => Embedded histories => New trees 2 => 3 => 4 => 2 => 2 = 2! => 3 = 1*3 3 => 4 => 5 => 5 => 6 = 3! => 15 = 1*3*5 4 => 5 => 6 => 14 => 24 = 4! => 105 = 1*3*5*7 5 => 6 => 7 => 42 => 120 = 5! => 945 = 1*3*5*7*9 etc */ nSubTerminals = (int) (RandomNumber(seed) * 4) + 4; nSubTerminals = 7; /* initialize log prior and log proposal probabilities */ *lnPriorRatio = *lnProposalRatio = 0.0; /* get tree */ t = GetTree (param, chain, state[chain]); /* get model params */ mp = &modelParams[param->relParts[0]]; /* max and min brlen */ if (param->subParams[0]->paramId == BRLENS_UNI) { minV = mp->brlensUni[0] > BRLENS_MIN ? mp->brlensUni[0] : BRLENS_MIN; maxV = mp->brlensUni[1]; isVPriorExp = NO; } else { minV = BRLENS_MIN; maxV = BRLENS_MAX; isVPriorExp = YES; } minP = 3.0 * ((1.0 / 4.0) - ((1.0 / 4.0) * exp (-4.0 * minV / 3.0))); maxP = 3.0 * ((1.0 / 4.0) - ((1.0 / 4.0) * exp (-4.0 * maxV / 3.0))); /* allocate some memory for this move */ brlensCur = (MrBFlt *) SafeMalloc (8 * nSubTerminals * sizeof (MrBFlt)); if (!brlensCur) { MrBayesPrint ("%s ERROR: Could not allocate brlensCur\n", spacer); goto errorExit; } brlensNew = brlensCur + 2*nSubTerminals; parslensCur = brlensCur + 4 * nSubTerminals; parslensNew = brlensCur + 6 * nSubTerminals; subtree = &memTree[0]; subtree->nNodes = 2 * nSubTerminals - 2; subtree->nIntNodes = nSubTerminals - 2; subtree->nodes = (TreeNode *) SafeCalloc (subtree->nNodes, sizeof (TreeNode)); subtree->allDownPass = (TreeNode **) SafeCalloc (subtree->nNodes, sizeof (TreeNode *)); subtree->intDownPass = (TreeNode **) SafeCalloc (subtree->nIntNodes, sizeof (TreeNode *)); if (!subtree->nodes || !subtree->intDownPass || !subtree->allDownPass) { MrBayesPrint ("%s ERROR: Could not allocate subtree\n", spacer); goto errorExit; } subtree1 = &memTree[1]; subtree1->nNodes = 2 * nSubTerminals - 2; subtree1->nIntNodes = nSubTerminals - 2; subtree1->nodes = (TreeNode *) SafeCalloc (subtree1->nNodes, sizeof (TreeNode)); subtree1->allDownPass = (TreeNode **) SafeCalloc (subtree1->nNodes, sizeof (TreeNode *)); subtree1->intDownPass = (TreeNode **) SafeCalloc (subtree1->nIntNodes, sizeof (TreeNode *)); if (!subtree1->nodes || !subtree1->intDownPass || !subtree1->allDownPass) { MrBayesPrint ("%s ERROR: Could not allocate subtree1\n", spacer); goto errorExit; } tInfo.leaf = (TreeNode **) SafeCalloc (t->nNodes, sizeof(TreeNode *)); if (!tInfo.leaf) { MrBayesPrint ("%s ERROR: Could not allocate tInfo.leaf\n", spacer); goto errorExit; } tInfo.vertex = tInfo.leaf + t->nNodes - t->nIntNodes; /* Select a random embedded subtree with nSubTerminals terminals */ if (GetRandomEmbeddedSubtree (t, nSubTerminals, seed, &nEmbeddedTrees) == ERROR) { MrBayesPrint ("%s ERROR: Could not get subtree\n", spacer); goto errorExit; } /* Set update flags (We'd better do it before the marked nodes disappear) */ for (i=0; inIntNodes; i++) { p = t->intDownPass[i]; if (p->marked == YES) { p->upDateCl = YES; p->upDateTi = YES; } else if (p->left->upDateCl == YES || p->right->upDateCl == YES) p->upDateCl = YES; } /* Fill in subtrees */ CopyTreeToSubtree (t, subtree); CopyTreeToSubtree (t, subtree1); /* Calculate downstates and upstate of root node of subtree */ GetParsDP (t, t->root->left, chain); for (i=0; inIntNodes; i++) { p = t->intDownPass[i]; if (p->marked == YES && p->anc->marked == NO) break; } GetParsimonySubtreeRootstate (t, p->anc, chain); /* Get parsimony length of current tree */ curLength = GetParsimonyLength (subtree, chain); /* Get the Markov and parsimony branch lengths of the current subtree */ GetParsimonyBrlens (subtree, chain, parslensCur); for (i=0; inNodes-1; i++) brlensCur[i] = subtree->allDownPass[i]->length; /* Calculate parsimony score of all trees relative to shortest tree (1.0) */ tInfo.totalScore = 0.0; tInfo.stopScore = -1.0; tInfo.minScore = curLength; tInfo.warp = warp; ExhaustiveParsimonySearch (subtree, chain, &tInfo); /* Choose one of these trees randomly based on its score */ tInfo.stopScore = RandomNumber(seed) * tInfo.totalScore; tInfo.totalScore = 0.0; ExhaustiveParsimonySearch (subtree1, chain, &tInfo); /* ShowNodes (subtree1->root, 0 , NO); */ /* getchar(); */ /* Get length of that tree */ newLength = GetParsimonyLength (subtree1, chain); /* Get the parsimony branch lengths of the new subtree */ GetParsimonyBrlens (subtree1, chain, parslensNew); /* Find the maximum length of a branch */ maxLen = 0.0; for (i=0; inRelParts; i++) { j = t->relParts[i]; m = &modelSettings[j]; maxLen += m->numUncompressedChars; } /* Find the Markov branch lengths of the new subtree */ /* Calculate Jacobian and prob ratio for the Dirichlet random number generator */ lnJacobian = lnRandomRatio = 0.0; minLenNew = 0.0; for (i=0; inNodes-1; i++) { // minLenCur += parslensCur[i]; minLenNew += parslensNew[i]; } for (i=0; inNodes-1; i++) { p = subtree1->allDownPass[i]; f = newLength / minLenNew; alpha[0] = parslensNew[i] * f * alphaPi + 1.0; alpha[1] = (maxLen - parslensNew[i] * f) * alphaPi + 1.0; DirichletRandomVariable (alpha, prob, 2, seed); if (prob[0] >= maxP || prob[0] <= minP) { abortMove = YES; return NO_ERROR; } p->length = (-3.0 / 4.0) * log (1.0 - 4.0 * prob[0] / 3.0); lnJacobian += (-4.0 * brlensCur[i] / 3.0) - log (1.0 - 4.0 * prob[0] / 3.0); lnRandomRatio -= log (pow (prob[0], alpha[0] - 1.0) * pow (prob[1], alpha[1] - 1.0)); f = curLength / minLenNew; alpha[0] = parslensCur[i] * f * alphaPi + 1.0; alpha[1] = (maxLen - parslensCur[i] * f) * alphaPi + 1.0; prob[0] = 3.0 * ((1.0 / 4.0) - ((1.0 / 4.0) * exp (-4.0 * brlensCur[i] / 3.0))); prob[1] = 1.0 - prob[0]; lnRandomRatio += log (pow (prob[0], alpha[0] - 1.0) * pow (prob[1], alpha[1] - 1.0)); } /* Store the new Markov branch lengths */ for (i=0; inNodes-1; i++) brlensNew[i] = subtree1->allDownPass[i]->length; /* Calculate the proposal ratio */ (*lnProposalRatio) = lnJacobian + lnRandomRatio + log (warp/3.0) * (curLength - newLength) + log (1.0-warp) * (newLength - curLength); /* Calculate the prior ratio */ if (isVPriorExp == YES) { newM = oldM = 0.0; for (i=0; inNodes-1; i++) { oldM += brlensCur[i]; newM += brlensNew[i]; } (*lnPriorRatio) += (mp->brlensExp) * (oldM - newM); } /* Copy subtree into tree */ CopySubtreeToTree (subtree1, t); /* ShowNodes (subtree1->root, 0, NO); */ /* ShowNodes (t->root, 0, NO); */ /* Update node sequences */ GetDownPass (t); /* correct for difference in number of embedded subtrees */ if (GetRandomEmbeddedSubtree (t, nSubTerminals, seed, &i) == ERROR) { MrBayesPrint ("%s Could not count number of subtrees in Move_ParsEraser1\n", spacer); goto errorExit; } if (i != nEmbeddedTrees) (*lnProposalRatio) += log ((MrBFlt) nEmbeddedTrees / (MrBFlt) i); /* Free memory allocated for this move */ free (subtree->allDownPass); free (subtree->intDownPass); free (subtree->nodes); free (subtree1->allDownPass); free (subtree1->intDownPass); free (subtree1->nodes); free (brlensCur); free (tInfo.leaf); return (NO_ERROR); errorExit: if (subtree != NULL) { free (subtree->allDownPass); free (subtree->intDownPass); free (subtree->nodes); } if (subtree1 != NULL) { free (subtree1->allDownPass); free (subtree1->intDownPass); free (subtree1->nodes); } free (brlensCur); free (tInfo.leaf); return (ERROR); } int Move_ParsSPR (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp) { /* Change topology (and branch lengths) using SPR (asymmetric) biased according to parsimony scores. */ int i, j, n, division, topologyHasChanged, isVPriorExp, tempInt; BitsLong *pA, *pV, *pP, y[2]; MrBFlt x, minV, maxV, minLength=0.0, length=0.0, cumulativeProb, warpFactor, ran, tuning, increaseProb, decreaseProb, divFactor, nStates, rateMult, v_typical, sum1, sum2, tempsum, tempc, tempy; CLFlt *nSitesOfPat, *nSites, *globalNSitesOfPat; TreeNode *p, *q, *a, *b, *u, *v, *c=NULL, *d, *interiorRoot=NULL; Tree *t; ModelParams *mp; ModelInfo *m = NULL; warpFactor = mvp[0]; /* tuning parameter determining how heavily to weight according to parsimony scores */ increaseProb = decreaseProb = mvp[1]; /* reweighting probabilities */ v_typical = mvp[2]; /* typical branch length for conversion of parsimony score to log prob ratio */ tuning = mvp[3]; /* multiplier tuning parameter */ (*lnProposalRatio) = (*lnPriorRatio) = 0.0; /* get model params and model info */ mp = &modelParams[param->relParts[0]]; m = &modelSettings[param->relParts[0]]; /* get tree */ t = GetTree (param, chain, state[chain]); /* for rooted (non-clock) trees, store pointer to current interior root node */ //SK if (t->isRooted) interiorRoot = t->root->left; /* max and min brlen */ if (param->subParams[0]->paramId == BRLENS_UNI) { minV = mp->brlensUni[0] > BRLENS_MIN ? mp->brlensUni[0] : BRLENS_MIN; maxV = mp->brlensUni[1] < BRLENS_MAX ? mp->brlensUni[1] : BRLENS_MAX; isVPriorExp = NO; } else if (param->subParams[0]->paramId == BRLENS_GamDir) { minV = BRLENS_MIN; maxV = BRLENS_MAX; isVPriorExp = 2; } else if (param->subParams[0]->paramId == BRLENS_iGmDir) { minV = BRLENS_MIN; maxV = BRLENS_MAX; isVPriorExp = 3; } else if (param->subParams[0]->paramId == BRLENS_twoExp) { minV = BRLENS_MIN; maxV = BRLENS_MAX; isVPriorExp = 4; } else { minV = BRLENS_MIN; maxV = BRLENS_MAX; isVPriorExp = YES; } /* Dirichlet or twoExp prior */ if (isVPriorExp > 1) (*lnPriorRatio) = -LogDirPrior(t, mp, isVPriorExp); # if defined (DEBUG_ParsSPR) // WriteTopologyToFile (stdout, t->root->left, t->isRooted); fprintf (stdout, ";\t"); printf ("Before:\n"); ShowNodes (t->root, 2, YES); getchar(); # endif /* set topologyHasChanged to NO */ topologyHasChanged = NO; /* reset node variables that will be used */ for (i=0; inNodes; i++) { p = t->allDownPass[i]; p->marked = NO; p->d = 0; } /* pick a random branch */ p = t->allDownPass[(int)(RandomNumber(seed) * (t->nNodes -2))]; q = p->anc->right; if (q == p) q = p->anc->left; if ((p->anc->anc->anc == NULL || p->anc->isLocked == YES) && (q->left == NULL || q->isLocked == YES)) { abortMove = YES; return (NO_ERROR); } /* set up pointers for nodes around the picked branch */ v = p; u = p->anc; if (u->left == v) a = u->right; else a = u->left; b = u->anc; /* clip tree */ a->anc = b; if (b->left == u) b->left = a; else b->right = a; /* get final parsimony states for the root part */ GetParsDP (t, t->root->left, chain); if (!strcmp(mp->statefreqModel,"Stationary")) { GetParsFP (t, t->root->left, chain); } else //SK: if not stationary model, but directional or RJ { GetParsFP (t, t->root->left->left, chain); GetParsFP (t, t->root->left->right, chain); } /* get downpass parsimony states for the crown part */ GetParsDP (t, v, chain); /* mark all nodes in the root part of the tree, taking constraints into account */ /* first mark a */ a->marked = YES; /* then move down towards root */ if (u->isLocked == NO) { p = a->anc; while (p->anc != NULL) { p->marked = YES; if (p->isLocked == YES) break; p = p->anc; } } /* finally move up */ for (i=t->nNodes-2; i>=0; i--) { p = t->allDownPass[i]; if (p->marked == NO && p->anc->marked == YES && p->anc->isLocked == NO && p != u) p->marked = YES; } /* find number of site patterns and modify randomly */ globalNSitesOfPat = numSitesOfPat + (chainId[chain] % chainParams.numChains) * numCompressedChars; nSitesOfPat = (CLFlt *) SafeCalloc (numCompressedChars, sizeof(CLFlt)); if (!nSitesOfPat) { MrBayesPrint ("%s Problem allocating nSitesOfPat in Move_ParsSPR\n", spacer); return (ERROR); } for (i=0; i 1.0 - increaseProb) nSitesOfPat[i]++; } } /* cycle through the possibilities and record the parsimony length */ for (i=0; inNodes; i++) { p = t->allDownPass[i]; if (p->marked == NO) continue; /* find the parsimony length */ p->d = 0.0; for (n=0; nnRelParts; n++) { division = t->relParts[n]; /* Find model settings */ m = &modelSettings[division]; /* find nSitesOfPat */ nSites = nSitesOfPat + m->compCharStart; /* find downpass parsimony sets for the node and its environment */ pP = m->parsSets[p->index]; pA = m->parsSets[p->anc->index]; pV = m->parsSets[v->index]; length = 0.0; if (m->nParsIntsPerSite == 1) { for (j=0; jnumChars; j++) { y[0] = (pP[j] | pA[j]) & pV[j]; if (y[0] == 0) length += nSites[j]; } } else /* if (m->nParsIntsPerSite == 2) */ { for (j=0; j<2*m->numChars; j+=2) { y[0] = (pP[j] | pA[j]) & pV[j]; y[1] = (pP[j+1] | pA[j+1]) & pV[j+1]; if ((y[0] | y[1]) == 0) length += nSites[j/2]; } } /* find nStates and ratemult */ nStates = m->numModelStates; if (m->dataType == STANDARD || m->dataType == CONTINUOUS) nStates = 2; rateMult = GetRate(division, chain); /* get division warp factor */ divFactor = - warpFactor * log((1.0/nStates) - exp(-nStates/(nStates-1)*v_typical*rateMult)/nStates); p->d += divFactor * length; } } /* find the min length and the sum for the forward move */ minLength = -1.0; for (i=0; inNodes; i++) { p = t->allDownPass[i]; if (p->marked == NO || p == a) continue; if (minLength < 0.0 || p->d < minLength) minLength = p->d; } sum1 = 0.0; tempc = 0.0; for (i=0; inNodes; i++) { p = t->allDownPass[i]; if (p->marked == YES && p != a) { /* Kahan summation to reduce numerical error */ tempy = exp (minLength - p->d) - tempc; tempsum = sum1 + tempy; tempc = (tempsum - sum1) - tempy; sum1 = tempsum; } } /* generate a random uniform */ ran = RandomNumber(seed) * sum1; /* select the appropriate reattachment point */ cumulativeProb = 0.0; tempc = 0.0; for (i=0; inNodes; i++) { p = t->allDownPass[i]; if (p->marked == YES && p != a) { c = p; /* Kahan summation to reduce numerical error */ tempy = exp (minLength - p->d) - tempc; tempsum = cumulativeProb + tempy; tempc = (tempsum - cumulativeProb) - tempy; cumulativeProb = tempsum; if (cumulativeProb > ran) break; } } /* calculate the proposal ratio */ (*lnProposalRatio) = c->d - minLength + log(sum1); /* find the min length and the sum for the backward move */ minLength = -1.0; for (i=0; inNodes; i++) { p = t->allDownPass[i]; if (p->marked == NO || p == c) continue; if (minLength < 0.0 || p->d < minLength) minLength = p->d; } sum2 = 0.0; tempc = 0.0; for (i=0; inNodes; i++) { p = t->allDownPass[i]; if (p->marked == YES && p != c) { /* Kahan summation to reduce numerical error */ tempy = exp (minLength - p->d) - tempc; tempsum = sum2 + tempy; tempc = (tempsum - sum2) - tempy; sum2 = tempsum; } } /* calculate the proposal ratio */ (*lnProposalRatio) += minLength - a->d - log(sum2); /* reattach */ d = c->anc; c->anc = u; if (u->left == v) u->right = c; else u->left = c; if (d->left == c) d->left = u; else d->right = u; u->anc = d; /* c cannot be a, as a is skipped in the selection for reattachment point */ assert (c != a); /* transfer lock if necessary */ /* if u is locked, then we have moved upwards and need to leave the u lock behind */ if (u->isLocked == YES) { u->isLocked = NO; a->isLocked = YES; a->lockID = u->lockID; u->lockID = -1; } /* if c is on root path and locked, we need to transfer lock to u */ p = b; while (p != NULL) { if (p == c) break; p = p->anc; } if (p == c && c->isLocked == YES) { u->isLocked = YES; u->lockID = c->lockID; c->isLocked = NO; c->lockID = -1; } /* reassign branch lengths */ p = c; while (p->anc->anc != NULL) { if (p == a) break; p = p->anc; } if (p == a) { /* c is descendant to a so move a->length and not u->length */ x = u->length; u->length = a->length; a->length = x; } p = a; while (p->anc->anc != NULL) { if (p == c) break; p = p->anc; } if (p == c) { /* c is ancestor to a so insert above instead of below */ x = c->length; c->length = u->length; u->length = x; } topologyHasChanged = YES; /* hit c length with multiplier (a and u dealt with below) */ x = c->length * exp(tuning * (RandomNumber(seed) - 0.5)); while (x < minV || x > maxV) { if (x < minV) x = minV * minV / x; else if (x > maxV) x = maxV * maxV / x; } /* calculate proposal and prior ratio based on length modification */ (*lnProposalRatio) += log (x / c->length); if (isVPriorExp == YES) (*lnPriorRatio) += (mp->brlensExp) * (c->length - x); c->length = x; /* hit a length with multiplier */ x = a->length * exp(tuning * (RandomNumber(seed) - 0.5)); while (x < minV || x > maxV) { if (x < minV) x = minV * minV / x; else if (x > maxV) x = maxV * maxV / x; } /* calculate proposal and prior ratio based on length modification */ (*lnProposalRatio) += log (x / a->length); if (isVPriorExp == YES) (*lnPriorRatio) += (mp->brlensExp) * (a->length - x); a->length = x; /* hit u length with multiplier */ x = u->length * exp(tuning * (RandomNumber(seed) - 0.5)); while (x < minV || x > maxV) { if (x < minV) x = minV * minV / x; else if (x > maxV) x = maxV * maxV / x; } /* calculate proposal and prior ratio based on length modification */ (*lnProposalRatio) += log (x / u->length); if (isVPriorExp == YES) (*lnPriorRatio) += (mp->brlensExp) * (u->length - x); u->length = x; /* set tiprobs update flags */ a->upDateTi = YES; u->upDateTi = YES; c->upDateTi = YES; /* could be same as a but that does not matter */ /* set flags for update of cond likes from u and down to root */ p = u; while (p->anc != NULL) { p->upDateCl = YES; p = p->anc; } /* set flags for update of cond likes from b and down to root */ p = b; while (p->anc != NULL && p->upDateCl == NO) { p->upDateCl = YES; p = p->anc; } /* if tree is rooted, swap indices of former and current interior root node, if changed */ //SK if (t->isRooted && interiorRoot != t->root->left) { tempInt = interiorRoot->index; interiorRoot->index = t->root->left->index; t->root->left->index = tempInt; } /* get down pass sequence if tree topology has changed */ if (topologyHasChanged == YES) { GetDownPass (t); } /* Dirichlet or twoExp prior */ if (isVPriorExp > 1) (*lnPriorRatio) += LogDirPrior(t, mp, isVPriorExp); # if defined (DEBUG_ParsSPR) // WriteTopologyToFile (stdout, t->root->left, t->isRooted); // fprintf (stdout, ";\t"); fprintf (stdout, "%lf\n", *lnProposalRatio); printf ("After:\n"); ShowNodes (t->root, 2, YES); getchar(); printf ("Proposal ratio: %f\n",(*lnProposalRatio)); printf ("v: %d u: %d a: %d b: %d\n",v->index, u->index, a->index, b->index); printf ("Has topology changed? %d\n",topologyHasChanged); getchar(); # endif free (nSitesOfPat); return (NO_ERROR); } int Move_ParsSPR1 (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp) { /* Change topology (and branch lengths) using SPR (symmetric) biased according to parsimony scores, controlled by a window defined by a certain node distance radius. Note: w = e^{-S} */ int i, j, k, n, division, topologyHasChanged, moveInRoot, nNeighbor, nRoot, nCrown, iA, jC, isVPriorExp; BitsLong *pA, *pB, *pP, *pC, *pD, y[2]; MrBFlt x, minV, maxV, minLength=0.0, length=0.0, *parLength=NULL, prob, ran, tuning, warpFactor, sum1, sum2, tempsum, tempc, tempy; CLFlt *nSites, *nSitesOfPat=NULL, *globalNSitesOfPat; TreeNode *p, *q, *r, *a, *b, *u, *v, *c, *d, *newB, *newA, *newC, **pRoot=NULL, **pCrown=NULL; Tree *t; ModelParams *mp; ModelInfo *m=NULL; warpFactor = mvp[0]; /* tuning parameter determining how heavily to weight according to parsimony scores */ // increaseProb = decreaseProb = mvp[1]; /* reweighting probabilities */ tuning = mvp[2]; /* multiplier tuning parameter */ nNeighbor = (int)mvp[3]; /* distance to move picked branch in root and crown part */ (*lnProposalRatio) = (*lnPriorRatio) = 0.0; /* get model params and model info */ mp = &modelParams[param->relParts[0]]; m = &modelSettings[param->relParts[0]]; /* get tree */ t = GetTree (param, chain, state[chain]); /* max and min brlen */ if (param->subParams[0]->paramId == BRLENS_UNI) { minV = mp->brlensUni[0] > BRLENS_MIN ? mp->brlensUni[0] : BRLENS_MIN; maxV = mp->brlensUni[1] < BRLENS_MAX ? mp->brlensUni[1] : BRLENS_MAX; isVPriorExp = NO; } else if (param->subParams[0]->paramId == BRLENS_GamDir) { minV = BRLENS_MIN; maxV = BRLENS_MAX; isVPriorExp = 2; } else if (param->subParams[0]->paramId == BRLENS_iGmDir) { minV = BRLENS_MIN; maxV = BRLENS_MAX; isVPriorExp = 3; } else if (param->subParams[0]->paramId == BRLENS_twoExp) { minV = BRLENS_MIN; maxV = BRLENS_MAX; isVPriorExp = 4; } else { minV = BRLENS_MIN; maxV = BRLENS_MAX; isVPriorExp = YES; } /* Dirichlet or twoExp prior */ if (isVPriorExp > 1) (*lnPriorRatio) = -LogDirPrior(t, mp, isVPriorExp); /* set topologyHasChanged to NO */ topologyHasChanged = NO; /* pick a random branch */ /* do not choose root branch in a rooted non-clock tree */ do { p = t->allDownPass[(int)(RandomNumber(seed) * (t->nNodes-1))]; q = p->anc->right; if (q == p) q = p->anc->left; i = j = 0; if (p->left == NULL) j = 2; if (p->anc->anc == NULL) i = 2; if (p->anc->anc != NULL && (p->anc->isLocked == YES || p->anc->anc->anc == NULL)) i++; if (p->anc->anc != NULL && (q->isLocked == YES || q->left == NULL)) i++; if (p->left != NULL && (p->left->isLocked == YES || p->left->left == NULL)) j++; if (p->left != NULL && (p->right->isLocked == YES || p->right->left == NULL)) j++; } while (i == 2 && j == 2); /* determine moving direction */ if (j == 2) moveInRoot = YES; else if (i == 2) moveInRoot = NO; else if (RandomNumber(seed) < 0.5) moveInRoot = YES; else moveInRoot = NO; /* set up pointers for nodes around the picked branch */ /* should never change u, v, a, b, c, d pointers */ v = p; u = p->anc; if (u->left == v) a = u->right; else a = u->left; b = u->anc; c = v->left; d = v->right; /* reset node variables that will be used */ for (i=0; inNodes; i++) { p = t->allDownPass[i]; p->x = 0; p->marked = NO; } /* mark nodes nNeighbor away in root (negative) or crown (positive) respecting constraints */ nRoot = nCrown = 1; if (moveInRoot == YES) { /* clip root part of tree */ a->anc = b; if (b->left == u) b->left = a; else b->right = a; /* mark the root part */ if (u->isLocked == NO ) { p = a; q = b; n = 0; while (q->anc != NULL) { q->marked = YES; q->x = n; // temporary, for MarkDistance below if (q->left == p) MarkDistance(q->right, YES, nNeighbor, &nRoot); else MarkDistance(q->left, YES, nNeighbor, &nRoot); q->x = --n; // final nRoot++; if (q->isLocked == YES || abs(q->x) >= nNeighbor) break; p = q; q = q->anc; } } if (a->isLocked == NO) { MarkDistance(a->left, YES, nNeighbor, &nRoot); MarkDistance(a->right, YES, nNeighbor, &nRoot); } /* get final parsimony states for the root part */ GetParsDP (t, t->root->left, chain); GetParsFP (t, t->root->left, chain); /* get final parsimony states for the crown part */ GetParsDP (t, v, chain); GetParsFP (t, v, chain); } else /* moveInRoot == NO */ { /* clip crown part of tree */ c->anc = d; d->anc = c; /* mark the crown part */ if (c->isLocked == NO) { MarkDistance(c->left, NO, nNeighbor, &nCrown); MarkDistance(c->right, NO, nNeighbor, &nCrown); } if (d->isLocked == NO) { MarkDistance(d->left, NO, nNeighbor, &nCrown); MarkDistance(d->right, NO, nNeighbor, &nCrown); } /* get final parsimony states for the root part */ if (u->anc != NULL) { a->anc = b; /* clip */ if (b->left == u) b->left = a; else b->right = a; GetParsDP (t, t->root->left, chain); GetParsFP (t, t->root->left, chain); a->anc = u; /* change back */ if (b->left == a) b->left = u; else b->right = u; } /* get final parsimony states for the crown part */ GetParsDP (t, c, chain); GetParsDP (t, d, chain); GetParsFP (t, c, chain); GetParsFP (t, d, chain); } /* find number of site patterns and modify randomly */ globalNSitesOfPat = numSitesOfPat + (chainId[chain] % chainParams.numChains) * numCompressedChars; nSitesOfPat = (CLFlt *) SafeCalloc (numCompressedChars, sizeof(CLFlt)); if (!nSitesOfPat) goto errorExit; for (i=0; i 1.0 - increaseProb) nSitesOfPat[i]++; } // this is slow at the moment */ } /* need to alloc a matrix for parsimony lengths, an array of pointers to crown part, and an array of pointers to root part. */ parLength = (MrBFlt *) SafeCalloc ((size_t)nRoot * (size_t)nCrown, sizeof(MrBFlt)); pRoot = (TreeNode **) SafeCalloc(nRoot, sizeof(TreeNode *)); pCrown = (TreeNode **) SafeCalloc(nCrown, sizeof(TreeNode *)); if (!parLength || !pRoot || !pCrown) goto errorExit; /* starting position */ pRoot[0] = a; pCrown[0] = c; for (i=j=1, n=t->nNodes-2; n>=0; n--) { /* and the rest */ p = t->allDownPass[n]; if (p->marked == YES && p->x < 0) pRoot[i++] = p; if (p->marked == YES && p->x > 0) pCrown[j++] = p; } assert (i==nRoot && j==nCrown); /* cycle through the possibilities and record the parsimony length */ for (j=0; jnRelParts; n++) { division = t->relParts[n]; /* Find model settings */ m = &modelSettings[division]; /* find nSitesOfPat */ nSites = nSitesOfPat + m->compCharStart; /* find parsimony length for each candidate position */ length = 0.0; if (moveInRoot == YES) { pA = m->parsSets[pRoot[i]->index]; pB = m->parsSets[pRoot[i]->anc->index]; pP = m->parsSets[v->index]; if (m->nParsIntsPerSite == 1) { for (k=0; knumChars; k++) { y[0] = (pA[k] | pB[k]) & pP[k]; if (y[0] == 0) length += nSites[k]; } } else /* if (m->nParsIntsPerSite == 2) */ { for (k=0; k<2*m->numChars; k+=2) { y[0] = (pA[k] | pB[k]) & pP[k]; y[1] = (pA[k+1] | pB[k+1]) & pP[k+1]; if ((y[0] | y[1]) == 0) length += nSites[k/2]; } } } else if (u->anc == NULL) { pP = m->parsSets[u->index]; pC = m->parsSets[pCrown[j]->index]; pD = m->parsSets[pCrown[j]->anc->index]; if (m->nParsIntsPerSite == 1) { for (k=0; knumChars; k++) { y[0] = pP[k] & (pC[k] | pD[k]); if (y[0] == 0) length += nSites[k]; } } else /* if (m->nParsIntsPerSite == 2) */ { for (k=0; k<2*m->numChars; k+=2) { y[0] = pP[k] & (pC[k] | pD[k]); y[1] = pP[k+1] & (pC[k+1] | pD[k+1]); if ((y[0] | y[1]) == 0) length += nSites[k/2]; } } } else { pA = m->parsSets[a->index]; pB = m->parsSets[b->index]; pC = m->parsSets[pCrown[j]->index]; pD = m->parsSets[pCrown[j]->anc->index]; if (m->nParsIntsPerSite == 1) { for (k=0; knumChars; k++) { y[0] = (pA[k] | pB[k]) & (pC[k] | pD[k]); if (y[0] == 0) length += nSites[k]; } } else /* if (m->nParsIntsPerSite == 2) */ { for (k=0; k<2*m->numChars; k+=2) { y[0] = (pA[k] | pB[k]) & (pC[k] | pD[k]); y[1] = (pA[k+1] | pB[k+1]) & (pC[k+1] | pD[k+1]); if ((y[0] | y[1]) == 0) length += nSites[k/2]; } } } /* get division warp factor */ parLength[i+j*nRoot] += warpFactor * length; } } } /* find the min length and the sum for the forward move */ minLength = -1.0; for (j=0; j parLength[i+j*nRoot] || minLength < 0.0) minLength = parLength[i+j*nRoot]; } sum1 = 0.0; tempc = 0.0; for (j=0; j ran) { /* proposed new attaching position */ newA = pRoot[i]; newC = pCrown[j]; goto outLoop; } } outLoop:; iA = i; jC = j; /* calculate the proposal ratio */ (*lnProposalRatio) = parLength[i+j*nRoot] - minLength + log(sum1); /* find the min length and the sum for the backward move */ minLength = -1.0; for (j=0; j parLength[i+j*nRoot] || minLength < 0.0) minLength = parLength[i+j*nRoot]; } sum2 = 0.0; tempc = 0.0; for (j=0; janc; newA->anc = u; if (u->left == v) u->right = newA; else u->left = newA; u->anc = newB; if (newB->left == newA) newB->left = u; else newB->right = u; /* if u is locked, then we have moved upwards and need to leave the u lock behind */ if (u->isLocked == YES) { u->isLocked = NO; a->isLocked = YES; a->lockID = u->lockID; u->lockID = -1; } p = newA; while (p->anc != NULL) { if (p == a) break; p = p->anc; } if (p == a) { /* newA is descendant to a so move a->length not u->length */ x = u->length; u->length = a->length; a->length = x; } p = b; while (p->anc != NULL) { if (p == newA) break; p = p->anc; } if (p == newA) { /* newA is ancestor to a so insert above instead of below */ x = newA->length; newA->length = u->length; u->length = x; /* newA is on root path and locked, we need to transfer lock to u */ if (newA->isLocked == YES) { u->isLocked = YES; u->lockID = newA->lockID; newA->isLocked = NO; newA->lockID = -1; } } /* hit a length with multiplier */ x = a->length * exp(tuning * (RandomNumber(seed) - 0.5)); while (x < minV || x > maxV) { if (x < minV) x = minV * minV / x; if (x > maxV) x = maxV * maxV / x; } /* calculate proposal and prior ratio based on length modification */ (*lnProposalRatio) += log (x / a->length); if (isVPriorExp == YES) (*lnPriorRatio) += (mp->brlensExp) * (a->length - x); a->length = x; /* hit u length with multiplier */ x = u->length * exp(tuning * (RandomNumber(seed) - 0.5)); while (x < minV || x > maxV) { if (x < minV) x = minV * minV / x; if (x > maxV) x = maxV * maxV / x; } /* calculate proposal and prior ratio based on length modification */ (*lnProposalRatio) += log (x / u->length); if (isVPriorExp == YES) (*lnPriorRatio) += (mp->brlensExp) * (u->length - x); u->length = x; /* hit newA length with multiplier */ x = newA->length * exp(tuning * (RandomNumber(seed) - 0.5)); while (x < minV || x > maxV) { if (x < minV) x = minV * minV / x; if (x > maxV) x = maxV * maxV / x; } /* calculate proposal and prior ratio based on length modification */ (*lnProposalRatio) += log (x / newA->length); if (isVPriorExp == YES) (*lnPriorRatio) += (mp->brlensExp) * (newA->length - x); newA->length = x; /* set tiprobs update flags */ newA->upDateTi = YES; a->upDateTi = YES; u->upDateTi = YES; /* set flags for update of cond likes */ p = u; while (p->anc != NULL) { p->upDateCl = YES; p = p->anc; } p = b; while (p->anc != NULL) { p->upDateCl = YES; p = p->anc; } } if (moveInRoot == NO) /* crown part has changed */ { r = newC; q = newB = newC->anc; /* rotate nodes from newC to c or d (whichever is closest) */ tempc = r->length; while (r != c && r != d) { p = q->anc; /* rotate pointers of q */ if (q->left == r) q->left = p; else q->right = p; q->anc = r; /* swap q and old */ tempy = q->length; q->length = tempc; q->upDateTi = YES; tempc = tempy; /* make sure we get q and r initialized for next round */ r = q; q = p; } newB->length = tempc; /* hit q length with multiplier while we are at it */ x = q->length * exp(tuning * (RandomNumber(seed) - 0.5)); while (x < minV || x > maxV) { if (x < minV) x = minV * minV / x; if (x > maxV) x = maxV * maxV / x; } /* calculate proposal and prior ratio based on length modification */ (*lnProposalRatio) += log (x / q->length); if (isVPriorExp == YES) (*lnPriorRatio) += (mp->brlensExp) * (q->length - x); q->length = x; q->upDateTi = YES; /* hit newB length with multiplier */ x = newB->length * exp(tuning * (RandomNumber(seed) - 0.5)); while (x < minV || x > maxV) { if (x < minV) x = minV * minV / x; if (x > maxV) x = maxV * maxV / x; } /* calculate proposal and prior ratio based on length modification */ (*lnProposalRatio) += log (x / newB->length); if (isVPriorExp == YES) (*lnPriorRatio) += (mp->brlensExp) * (newB->length - x); newB->length = x; newB->upDateTi = YES; /* hit newC length with multiplier */ x = newC->length * exp(tuning * (RandomNumber(seed) - 0.5)); while (x < minV || x > maxV) { if (x < minV) x = minV * minV / x; if (x > maxV) x = maxV * maxV / x; } /* calculate proposal and prior ratio based on length modification */ (*lnProposalRatio) += log (x / newC->length); if (isVPriorExp == YES) (*lnPriorRatio) += (mp->brlensExp) * (newC->length - x); newC->length = x; newC->upDateTi = YES; /* reattach the crown part */ v->left = newC; v->right = newB; newC->anc = newB->anc = v; /* set flags for update of cond likes */ p = newC; while (p->anc != NULL) { p->upDateCl = YES; p = p->anc; } p = r; while (p->anc != NULL) { p->upDateCl = YES; p = p->anc; } } topologyHasChanged = YES; /* get down pass sequence if tree topology has changed */ if (topologyHasChanged == YES) { GetDownPass (t); } /* Dirichlet or twoExp prior */ if (isVPriorExp > 1) (*lnPriorRatio) += LogDirPrior(t, mp, isVPriorExp); /* free up local memory */ free (parLength); free (pRoot); free (pCrown); free (nSitesOfPat); return (NO_ERROR); errorExit: MrBayesPrint ("%s Problem allocating memory in Move_ParsSPR1\n", spacer); free (parLength); free (pRoot); free (pCrown); free (nSitesOfPat); return (ERROR); } int Move_ParsSPR2 (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp) { /* Change topology (and branch lengths) using SPR (symmetric) biased according to parsimony scores, controlled by a window defined by a certain node distance radius. Note: S/N */ int i, j, k, n, division, topologyHasChanged, moveInRoot, nNeighbor, nRoot, nCrown, iA, jC, isVPriorExp, tempInt; BitsLong *pA, *pB, *pP, *pC, *pD, y[2]; MrBFlt x, minV, maxV, minLength=0.0, length=0.0, *parLength=NULL, prob, ran, tuning, warpFactor, v_typical, divFactor, nStates, sum1, sum2, tempsum, tempc, tempy; CLFlt *nSites, *nSitesOfPat=NULL, *globalNSitesOfPat; TreeNode *p, *q, *r, *a, *b, *u, *v, *c, *d, *newB, *newA, *newC, **pRoot=NULL, **pCrown=NULL, *interiorRoot=NULL; Tree *t; ModelParams *mp; ModelInfo *m=NULL; warpFactor = mvp[0]; /* tuning parameter determining how heavily to weight according to parsimony scores */ // increaseProb = decreaseProb = mvp[1]; /* reweighting probabilities */ v_typical = mvp[2]; /* typical branch length for conversion of parsimony score to log prob ratio */ tuning = mvp[3]; /* multiplier tuning parameter */ nNeighbor = (int)mvp[4]; /* distance to move picked branch in root and crown part */ (*lnProposalRatio) = (*lnPriorRatio) = 0.0; /* get model params and model info */ mp = &modelParams[param->relParts[0]]; m = &modelSettings[param->relParts[0]]; /* get tree */ t = GetTree (param, chain, state[chain]); /* for rooted (non-clock) trees, store pointer to current interior root node */ //SK if (t->isRooted) interiorRoot = t->root->left; /* max and min brlen */ if (param->subParams[0]->paramId == BRLENS_UNI) { minV = mp->brlensUni[0] > BRLENS_MIN ? mp->brlensUni[0] : BRLENS_MIN; maxV = mp->brlensUni[1] < BRLENS_MAX ? mp->brlensUni[1] : BRLENS_MAX; isVPriorExp = NO; } else if (param->subParams[0]->paramId == BRLENS_GamDir) { minV = BRLENS_MIN; maxV = BRLENS_MAX; isVPriorExp = 2; } else if (param->subParams[0]->paramId == BRLENS_iGmDir) { minV = BRLENS_MIN; maxV = BRLENS_MAX; isVPriorExp = 3; } else if (param->subParams[0]->paramId == BRLENS_twoExp) { minV = BRLENS_MIN; maxV = BRLENS_MAX; isVPriorExp = 4; } else { minV = BRLENS_MIN; maxV = BRLENS_MAX; isVPriorExp = YES; } /* Dirichlet or twoExp prior */ if (isVPriorExp > 1) (*lnPriorRatio) = -LogDirPrior(t, mp, isVPriorExp); /* set topologyHasChanged to NO */ topologyHasChanged = NO; /* pick a random branch */ do { p = t->allDownPass[(int)(RandomNumber(seed) * (t->nNodes -1))]; q = p->anc->right; if (q == p) q = p->anc->left; i = j = 0; if (p->left == NULL) j = 2; if (p->anc->anc == NULL) i = 2; if (p->anc->anc != NULL && (p->anc->isLocked == YES || p->anc->anc->anc == NULL)) i++; if (p->anc->anc != NULL && (q->isLocked == YES || q->left == NULL)) i++; if (p->left != NULL && (p->left->isLocked == YES || p->left->left == NULL)) j++; if (p->left != NULL && (p->right->isLocked == YES || p->right->left == NULL)) j++; } while (i == 2 && j == 2); /* pick an internal branch do { p = t->intDownPass[(int)(RandomNumber(seed)*(t->nIntNodes-1))]; q = p->anc->left; if (q == p) q = p->anc->right; i = j = 0; if (q->isLocked == YES || q->left == NULL) i++; if (p->anc->isLocked == YES || p->anc->anc->anc == NULL) i++; if (p->left->isLocked == YES || p->left->left == NULL) j++; if (p->right->isLocked == YES || p->right->left == NULL) j++; } while (i == 2 && j == 2); */ /* determine moving direction */ if (j == 2) moveInRoot = YES; else if (i == 2) moveInRoot = NO; else if (RandomNumber(seed) < 0.5) moveInRoot = YES; else moveInRoot = NO; /* set up pointers for nodes around the picked branch */ /* should never change u, v, a, b, c, d pointers */ v = p; u = p->anc; if (u->left == v) a = u->right; else a = u->left; b = u->anc; c = v->left; d = v->right; /* reset node variables that will be used */ for (i=0; inNodes; i++) { p = t->allDownPass[i]; p->x = 0; p->marked = NO; } /* mark nodes nNeighbor away in root (negative) or crown (positive) respecting constraints */ nRoot = nCrown = 1; if (moveInRoot == YES) { /* clip root part of tree */ a->anc = b; if (b->left == u) b->left = a; else b->right = a; /* mark the root part */ if (u->isLocked == NO ) { p = a; q = b; n = 0; while (q->anc != NULL) { q->marked = YES; q->x = n; // temporary, for MarkDistance below if (q->left == p) MarkDistance(q->right, YES, nNeighbor, &nRoot); else MarkDistance(q->left, YES, nNeighbor, &nRoot); q->x = --n; // final nRoot++; if (q->isLocked == YES || abs(q->x) >= nNeighbor) break; p = q; q = q->anc; } } if (a->isLocked == NO) { MarkDistance(a->left, YES, nNeighbor, &nRoot); MarkDistance(a->right, YES, nNeighbor, &nRoot); } /* get final parsimony states for the root part */ GetParsDP (t, t->root->left, chain); if (!strcmp(mp->statefreqModel,"Stationary")) { GetParsFP (t, t->root->left, chain); } else //SK: if not stationary model, but directional or RJ { GetParsFP (t, t->root->left->left, chain); GetParsFP (t, t->root->left->right, chain); } /* get final parsimony states for the crown part */ GetParsDP (t, v, chain); GetParsFP (t, v, chain); } else /* moveInRoot == NO */ { /* clip crown part of tree */ c->anc = d; d->anc = c; /* mark the crown part */ if (c->isLocked == NO) { MarkDistance(c->left, NO, nNeighbor, &nCrown); MarkDistance(c->right, NO, nNeighbor, &nCrown); } if (d->isLocked == NO) { MarkDistance(d->left, NO, nNeighbor, &nCrown); MarkDistance(d->right, NO, nNeighbor, &nCrown); } /* get final parsimony states for the root part */ if (u->anc != NULL) { a->anc = b; /* clip */ if (b->left == u) b->left = a; else b->right = a; GetParsDP (t, t->root->left, chain); if (!strcmp(mp->statefreqModel,"Stationary")) { GetParsFP (t, t->root->left, chain); } else //SK: if not stationary model, but directional or RJ { GetParsFP (t, t->root->left->left, chain); GetParsFP (t, t->root->left->right, chain); } a->anc = u; /* change back */ if (b->left == a) b->left = u; else b->right = u; } /* get final parsimony states for the crown part */ GetParsDP (t, c, chain); GetParsDP (t, d, chain); GetParsFP (t, c, chain); GetParsFP (t, d, chain); } /* find number of site patterns and modify randomly */ globalNSitesOfPat = numSitesOfPat + (chainId[chain] % chainParams.numChains) * numCompressedChars; nSitesOfPat = (CLFlt *) SafeCalloc (numCompressedChars, sizeof(CLFlt)); if (!nSitesOfPat) goto errorExit; for (i=0; i 1.0 - increaseProb) nSitesOfPat[i]++; } // this is slow at the moment */ } /* need to alloc a matrix for parsimony lengths, an array of pointers to crown part, and an array of pointers to root part. */ parLength = (MrBFlt *) SafeCalloc ((size_t)nRoot * (size_t)nCrown, sizeof(MrBFlt)); pRoot = (TreeNode **) SafeCalloc(nRoot, sizeof(TreeNode *)); pCrown = (TreeNode **) SafeCalloc(nCrown, sizeof(TreeNode *)); if (!parLength || !pRoot || !pCrown) goto errorExit; /* starting position */ pRoot[0] = a; pCrown[0] = c; for (i=j=1, n=t->nNodes-2; n>=0; n--) { /* and the rest */ p = t->allDownPass[n]; if (p->marked == YES && p->x < 0) pRoot[i++] = p; if (p->marked == YES && p->x > 0) pCrown[j++] = p; } assert (i==nRoot && j==nCrown); /* cycle through the possibilities and record the parsimony length */ for (j=0; jnRelParts; n++) { division = t->relParts[n]; /* Find model settings */ m = &modelSettings[division]; /* find nSitesOfPat */ nSites = nSitesOfPat + m->compCharStart; /* find parsimony length for each candidate position */ length = 0.0; if (moveInRoot == YES) { pA = m->parsSets[pRoot[i]->index]; pB = m->parsSets[pRoot[i]->anc->index]; pP = m->parsSets[v->index]; if (m->nParsIntsPerSite == 1) { for (k=0; knumChars; k++) { y[0] = (pA[k] | pB[k]) & pP[k]; if (y[0] == 0) length += nSites[k]; } } else /* if (m->nParsIntsPerSite == 2) */ { for (k=0; k<2*m->numChars; k+=2) { y[0] = (pA[k] | pB[k]) & pP[k]; y[1] = (pA[k+1] | pB[k+1]) & pP[k+1]; if ((y[0] | y[1]) == 0) length += nSites[k/2]; } } } else if (u->anc == NULL) { pP = m->parsSets[u->index]; pC = m->parsSets[pCrown[j]->index]; pD = m->parsSets[pCrown[j]->anc->index]; if (m->nParsIntsPerSite == 1) { for (k=0; knumChars; k++) { y[0] = pP[k] & (pC[k] | pD[k]); if (y[0] == 0) length += nSites[k]; } } else /* if (m->nParsIntsPerSite == 2) */ { for (k=0; k<2*m->numChars; k+=2) { y[0] = pP[k] & (pC[k] | pD[k]); y[1] = pP[k+1] & (pC[k+1] | pD[k+1]); if ((y[0] | y[1]) == 0) length += nSites[k/2]; } } } else { pA = m->parsSets[a->index]; pB = m->parsSets[b->index]; pC = m->parsSets[pCrown[j]->index]; pD = m->parsSets[pCrown[j]->anc->index]; if (m->nParsIntsPerSite == 1) { for (k=0; knumChars; k++) { y[0] = (pA[k] | pB[k]) & (pC[k] | pD[k]); if (y[0] == 0) length += nSites[k]; } } else /* if (m->nParsIntsPerSite == 2) */ { for (k=0; k<2*m->numChars; k+=2) { y[0] = (pA[k] | pB[k]) & (pC[k] | pD[k]); y[1] = (pA[k+1] | pB[k+1]) & (pC[k+1] | pD[k+1]); if ((y[0] | y[1]) == 0) length += nSites[k/2]; } } } /* find nStates and ratemult */ nStates = m->numModelStates; if (m->dataType == STANDARD || m->dataType == CONTINUOUS) nStates = 2; v_typical = length/m->numUncompressedChars + 0.0001; /* get division warp factor (prop. to prob. of change) */ divFactor = - warpFactor * log(1.0/nStates - exp(-nStates/(nStates-1)*v_typical)/nStates); parLength[i+j*nRoot] += divFactor * length; } } } /* find the min length and the sum for the forward move */ minLength = -1.0; for (j=0; j parLength[i+j*nRoot] || minLength < 0.0) minLength = parLength[i+j*nRoot]; } sum1 = 0.0; tempc = 0.0; for (j=0; j ran) { /* proposed new attaching position */ newA = pRoot[i]; newC = pCrown[j]; goto outLoop; } } outLoop:; iA = i; jC = j; /* calculate the proposal ratio */ (*lnProposalRatio) = parLength[i+j*nRoot] - minLength + log(sum1); /* find the min length and the sum for the backward move */ minLength = -1.0; for (j=0; j parLength[i+j*nRoot] || minLength < 0.0) minLength = parLength[i+j*nRoot]; } sum2 = 0.0; tempc = 0.0; for (j=0; janc; newA->anc = u; if (u->left == v) u->right = newA; else u->left = newA; u->anc = newB; if (newB->left == newA) newB->left = u; else newB->right = u; /* if u is locked, then we have moved upwards and need to leave the u lock behind */ if (u->isLocked == YES) { u->isLocked = NO; a->isLocked = YES; a->lockID = u->lockID; u->lockID = -1; } p = newA; while (p->anc != NULL) { if (p == a) break; p = p->anc; } if (p == a) { /* newA is descendant to a so move a->length not u->length */ x = u->length; u->length = a->length; a->length = x; } p = b; while (p->anc != NULL) { if (p == newA) break; p = p->anc; } if (p == newA) { /* newA is ancestor to a so insert above instead of below */ x = newA->length; newA->length = u->length; u->length = x; /* newA is on root path and locked, we need to transfer lock to u */ if (newA->isLocked == YES) { u->isLocked = YES; u->lockID = newA->lockID; newA->isLocked = NO; newA->lockID = -1; } } /* hit a length with multiplier */ x = a->length * exp(tuning * (RandomNumber(seed) - 0.5)); while (x < minV || x > maxV) { if (x < minV) x = minV * minV / x; if (x > maxV) x = maxV * maxV / x; } /* calculate proposal and prior ratio based on length modification */ (*lnProposalRatio) += log (x / a->length); if (isVPriorExp == YES) (*lnPriorRatio) += (mp->brlensExp) * (a->length - x); a->length = x; /* hit u length with multiplier */ x = u->length * exp(tuning * (RandomNumber(seed) - 0.5)); while (x < minV || x > maxV) { if (x < minV) x = minV * minV / x; if (x > maxV) x = maxV * maxV / x; } /* calculate proposal and prior ratio based on length modification */ (*lnProposalRatio) += log (x / u->length); if (isVPriorExp == YES) (*lnPriorRatio) += (mp->brlensExp) * (u->length - x); u->length = x; /* hit newA length with multiplier */ x = newA->length * exp(tuning * (RandomNumber(seed) - 0.5)); while (x < minV || x > maxV) { if (x < minV) x = minV * minV / x; if (x > maxV) x = maxV * maxV / x; } /* calculate proposal and prior ratio based on length modification */ (*lnProposalRatio) += log (x / newA->length); if (isVPriorExp == YES) (*lnPriorRatio) += (mp->brlensExp) * (newA->length - x); newA->length = x; /* set tiprobs update flags */ newA->upDateTi = YES; a->upDateTi = YES; u->upDateTi = YES; /* set flags for update of cond likes */ p = u; while (p->anc != NULL) { p->upDateCl = YES; p = p->anc; } p = b; while (p->anc != NULL) { p->upDateCl = YES; p = p->anc; } } if (moveInRoot == NO) /* crown part has changed */ { r = newC; q = newB = newC->anc; /* rotate nodes from newC to c or d (whichever is closest) */ tempc = r->length; while (r != c && r != d) { p = q->anc; /* rotate pointers of q */ if (q->left == r) q->left = p; else q->right = p; q->anc = r; /* swap q and old */ tempy = q->length; q->length = tempc; q->upDateTi = YES; tempc = tempy; /* make sure we get q and r initialized for next round */ r = q; q = p; } newB->length = tempc; /* hit q length with multiplier while we are at it */ x = q->length * exp(tuning * (RandomNumber(seed) - 0.5)); while (x < minV || x > maxV) { if (x < minV) x = minV * minV / x; if (x > maxV) x = maxV * maxV / x; } /* calculate proposal and prior ratio based on length modification */ (*lnProposalRatio) += log (x / q->length); if (isVPriorExp == YES) (*lnPriorRatio) += (mp->brlensExp) * (q->length - x); q->length = x; q->upDateTi = YES; /* hit newB length with multiplier */ x = newB->length * exp(tuning * (RandomNumber(seed) - 0.5)); while (x < minV || x > maxV) { if (x < minV) x = minV * minV / x; if (x > maxV) x = maxV * maxV / x; } /* calculate proposal and prior ratio based on length modification */ (*lnProposalRatio) += log (x / newB->length); if (isVPriorExp == YES) (*lnPriorRatio) += (mp->brlensExp) * (newB->length - x); newB->length = x; newB->upDateTi = YES; /* hit newC length with multiplier */ x = newC->length * exp(tuning * (RandomNumber(seed) - 0.5)); while (x < minV || x > maxV) { if (x < minV) x = minV * minV / x; if (x > maxV) x = maxV * maxV / x; } /* calculate proposal and prior ratio based on length modification */ (*lnProposalRatio) += log (x / newC->length); if (isVPriorExp == YES) (*lnPriorRatio) += (mp->brlensExp) * (newC->length - x); newC->length = x; newC->upDateTi = YES; /* reattach the crown part */ v->left = newC; v->right = newB; newC->anc = newB->anc = v; /* set flags for update of cond likes */ p = newC; while (p->anc != NULL) { p->upDateCl = YES; p = p->anc; } p = r; while (p->anc != NULL) { p->upDateCl = YES; p = p->anc; } } topologyHasChanged = YES; /* if tree is rooted, swap indices of former and current interior root node, if changed */ //SK if (t->isRooted && interiorRoot != t->root->left) { tempInt = interiorRoot->index; interiorRoot->index = t->root->left->index; t->root->left->index = tempInt; } /* get down pass sequence if tree topology has changed */ if (topologyHasChanged == YES) { GetDownPass (t); } /* Dirichlet or twoExp prior */ if (isVPriorExp > 1) (*lnPriorRatio) += LogDirPrior(t, mp, isVPriorExp); /* free up local memory */ free (parLength); free (pRoot); free (pCrown); free (nSitesOfPat); return (NO_ERROR); errorExit: MrBayesPrint ("%s Problem allocating memory in Move_ParsSPR\n", spacer); free (parLength); free (pRoot); free (pCrown); free (nSitesOfPat); return (ERROR); } int Move_ParsSPRClock (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp) { /* Change branch lengths and topology (potentially) using SPR-type move, parsimony-biased */ /* This move picks a branch and then chooses a reattachment point based on the parsimony score. On the ending branch, the attachment point is reinserted randomly along the branch (below the minimum age of the node). Since 2010-11-02 the move is Metropolized to improve efficiency. */ /* Since 2015-11-06, this move uses the s/n brlen approximation */ int i, j, n, division, n1=0, n2=0, n3=0, n4=0, n5=0, *nEvents, numMovableNodesOld, numMovableNodesNew; BitsLong *pA, *pV, *pP, y[2]; MrBFlt x, oldBrlen=0.0, newBrlen=0.0, v1=0.0, v2=0.0, v3=0.0, v4=0.0, v5=0.0, v3new=0.0, lambda, **position=NULL, **rateMultiplier=NULL, *brlens, nu, var, *rate=NULL, minLength=0.0, length=0.0, cumulativeProb, warpFactor, sum1, sum2, ran, divFactor, nStates, v_approx, minV; CLFlt *nSitesOfPat, *nSites, *globalNSitesOfPat; TreeNode *p, *a, *b, *u, *v, *c=NULL, *d; Tree *t; ModelInfo *m=NULL; Param *subParm; warpFactor = mvp[0]; /* tuning parameter determining how heavily to weight according to parsimony scores */ (*lnProposalRatio) = (*lnPriorRatio) = 0.0; /* get tree */ t = GetTree (param, chain, state[chain]); /* get model params and model info */ m = &modelSettings[param->relParts[0]]; /* get min and max brlen in relative time and subst units */ minV = BRLENS_MIN; # if defined (DEBUG_ParsSPRClock) printf ("Before:\n"); ShowNodes (t->root, 2, YES); getchar(); # endif numMovableNodesOld = 0; for (i=0; inNodes-2; ++i) { p = t->allDownPass[i]; a = p->anc->left; b = p->anc->right; if (!(p->anc->isLocked == YES || p->anc->anc->anc == NULL || (p == b && a->length < TIME_MIN) || (p == a && b->length < TIME_MIN))) ++numMovableNodesOld; } if (numMovableNodesOld == 0) return NO_ERROR; /* pick a branch */ do { p = t->allDownPass[(int)(RandomNumber(seed) * (t->nNodes - 2))]; a = p->anc->left; b = p->anc->right; } while (p->anc->isLocked == YES || p->anc->anc->anc == NULL || (p == b && a->length < TIME_MIN) || (p == a && b->length < TIME_MIN)); /* skip constraints, siblings of root (and root); and consider ancestral fossils in fbd tree */ /* set up pointers for nodes around the picked branch */ v = p; u = p->anc; if (u->left == v) a = u->right; else a = u->left; b = u->anc; /* record branch length for insertion in back move */ if (v->length > 0.0) /* side branch, not anc fossil */ { if (v->nodeDepth > a->nodeDepth) oldBrlen = b->nodeDepth - v->nodeDepth - 2.0*minV; else oldBrlen = b->nodeDepth - a->nodeDepth - 2.0*minV; } v1 = a->length; v2 = u->length; v3 = v->length; /* reassign events for CPP and adjust prior and proposal ratios for relaxed clock models */ for (i=0; isubParams[0]->nSubParams; i++) { subParm = param->subParams[0]->subParams[i]; if (subParm->paramType == P_CPPEVENTS) { nEvents = subParm->nEvents[2*chain+state[chain]]; position = subParm->position[2*chain+state[chain]]; rateMultiplier = subParm->rateMult[2*chain+state[chain]]; n1 = nEvents[a->index]; n2 = nEvents[u->index]; n3 = nEvents[v->index]; if (n2 > 0) { position[a->index] = (MrBFlt *) SafeRealloc ((void *) position[a->index], (n1+n2) * sizeof (MrBFlt)); rateMultiplier[a->index] = (MrBFlt *) SafeRealloc ((void *) rateMultiplier[a->index], (n1+n2) * sizeof (MrBFlt)); } for (j=0; jindex][j] *= v1 / (v1+v2); for (j=n1; jindex][j] = (position[u->index][j-n1] * v2 + v1) / (v1+v2); rateMultiplier[a->index][j] = rateMultiplier[u->index][j-n1]; } nEvents[a->index] = n1+n2; nEvents[u->index] = 0; if (n2 > 0) { free (position[u->index]); free (rateMultiplier[u->index]); position[u->index] = rateMultiplier[u->index] = NULL; } /* adjust effective branch lengths */ brlens = GetParamSubVals (subParm, chain, state[chain]); brlens[a->index] += brlens[u->index]; /* only change in effective branch lengths so far */ } /* end CPP events parm */ else if ( subParm->paramType == P_TK02BRANCHRATES) { /* adjust prior ratio */ if (subParm->paramType == P_TK02BRANCHRATES) nu = *GetParamVals (modelSettings[subParm->relParts[0]].tk02var, chain, state[chain]); else nu = *GetParamVals (modelSettings[subParm->relParts[0]].mixedvar, chain, state[chain]); rate = GetParamVals (subParm, chain, state[chain]); if (v->length > 0.0) (*lnPriorRatio) -= LnProbLogNormal_Mean_LogVar(rate[v->anc->index], nu*v->length, rate[v->index]); (*lnPriorRatio) -= LnProbLogNormal_Mean_LogVar(rate[a->anc->index], nu*a->length, rate[a->index]); (*lnPriorRatio) -= LnProbLogNormal_Mean_LogVar(rate[u->anc->index], nu*u->length, rate[u->index]); (*lnPriorRatio) += LnProbLogNormal_Mean_LogVar(rate[u->anc->index], nu*(a->length+u->length), rate[a->index]); /* adjust effective branch lengths */ brlens = GetParamSubVals (subParm, chain, state[chain]); brlens[a->index] = (rate[a->index] + rate[b->index]) / 2.0 * (a->length + u->length); } /* end tk02 branch rate parameter */ else if ( subParm->paramType == P_WNBRANCHRATES) { if (subParm->paramType == P_WNBRANCHRATES) var = *GetParamVals (modelSettings[subParm->relParts[0]].wnvar, chain, state[chain]); else var = *GetParamVals (modelSettings[subParm->relParts[0]].mixedvar, chain, state[chain]); rate = GetParamVals (subParm, chain, state[chain]); /* adjust prior ratio for old branches */ if (v->length > 0.0) (*lnPriorRatio) -= LnProbGamma(v->length/var, v->length/var, rate[v->index]); (*lnPriorRatio) -= LnProbGamma(a->length/var, a->length/var, rate[a->index]); (*lnPriorRatio) -= LnProbGamma(u->length/var, u->length/var, rate[u->index]); (*lnPriorRatio) += LnProbGamma((a->length+u->length)/var, (a->length+u->length)/var, rate[a->index]); /* adjust effective branch lengths */ brlens = GetParamSubVals (subParm, chain, state[chain]); brlens[a->index] = rate[a->index] * (a->length + u->length); } /* end wn branch rate parameter */ else if ( subParm->paramType == P_ILNBRANCHRATES || (subParm->paramType == P_MIXEDBRCHRATES && *GetParamIntVals(subParm, chain, state[chain]) == RCL_ILN)) { if (subParm->paramType == P_ILNBRANCHRATES) nu = *GetParamVals (modelSettings[subParm->relParts[0]].ilnvar, chain, state[chain]); else nu = *GetParamVals (modelSettings[subParm->relParts[0]].mixedvar, chain, state[chain]); rate = GetParamVals (subParm, chain, state[chain]); /* adjust prior ratio for old branches */ if (v->length > 0.0) (*lnPriorRatio) -= LnProbLogNormal_Mean_Var(1.0, nu, rate[v->index]); (*lnPriorRatio) -= LnProbLogNormal_Mean_Var(1.0, nu, rate[u->index]); /* adjust effective branch lengths */ brlens = GetParamSubVals (subParm, chain, state[chain]); brlens[a->index] = rate[a->index] * (a->length + u->length); } /* end iln branch rate parameter */ else if ( subParm->paramType == P_IGRBRANCHRATES || (subParm->paramType == P_MIXEDBRCHRATES && *GetParamIntVals(subParm, chain, state[chain]) == RCL_IGR)) { if (subParm->paramType == P_IGRBRANCHRATES) var = *GetParamVals (modelSettings[subParm->relParts[0]].igrvar, chain, state[chain]); else var = *GetParamVals (modelSettings[subParm->relParts[0]].mixedvar, chain, state[chain]); rate = GetParamVals (subParm, chain, state[chain]); /* adjust prior ratio for old branches */ if (v->length > 0.0) (*lnPriorRatio) -= LnProbGamma(1.0/var, 1.0/var, rate[v->index]); (*lnPriorRatio) -= LnProbGamma(1.0/var, 1.0/var, rate[u->index]); /* adjust effective branch lengths */ brlens = GetParamSubVals (subParm, chain, state[chain]); brlens[a->index] = rate[a->index] * (a->length + u->length); } /* end igr branch rate parameter */ } /* next subparameter */ /* cut tree */ a->anc = b; if (b->left == u) b->left = a; else b->right = a; a->length += u->length; a->upDateTi = YES; /* get final parsimony states for the root part */ GetParsDP (t, t->root->left, chain); GetParsFP (t, t->root->left->left, chain); GetParsFP (t, t->root->left->right, chain); /* get downpass parsimony states for the crown part */ GetParsDP (t, v, chain); /* reset node variables that will be used */ for (i=0; inNodes; i++) { p = t->allDownPass[i]; p->marked = NO; p->d = 0.0; } /* mark nodes in the root part of the tree, first mark a */ a->marked = YES; /* then move down towards root taking constraints into account */ p = b; while (p->isLocked == NO && p->anc->anc != NULL) { p->marked = YES; p = p->anc; } /* make sure sisters of last node are marked otherwise it will not be marked in the uppass */ p->left->marked = YES; p->right->marked = YES; /* finally move up, skip constraints and ancestral fossil */ for (i=t->nNodes-2; i>=0; i--) { p = t->allDownPass[i]; if (p != u && p->marked == NO && p->anc->marked == YES && p->anc->isLocked == NO && p->anc->nodeDepth > v->nodeDepth + minV && p->length > 0.0) p->marked = YES; } /* unmark nodes if the picked branch is 0 (ancestral fossil) */ if (v->length < TIME_MIN) { n = 0; for (i=0; inNodes-1; i++) { p = t->allDownPass[i]; if (p->nodeDepth > v->nodeDepth - minV || p->anc->nodeDepth < v->nodeDepth + minV) p->marked = NO; if (p->marked == YES) n++; } if (n < 2) /* no new position to move */ { abortMove = YES; return (NO_ERROR); } } /* find number of site patterns and modify randomly */ globalNSitesOfPat = numSitesOfPat + (chainId[chain] % chainParams.numChains) * numCompressedChars; nSitesOfPat = (CLFlt *) SafeCalloc (numCompressedChars, sizeof(CLFlt)); if (!nSitesOfPat) { MrBayesPrint ("%s Problem allocating nSitesOfPat in Move_ParsSPRClock\n", spacer); return (ERROR); } for (i=0; i 1.0 - increaseProb) // nSitesOfPat[i]++; // } } /* cycle through the possibilities and record the parsimony length */ for (i=0; inNodes; i++) { p = t->allDownPass[i]; if (p->marked == NO) continue; /* find the parsimony length */ p->d = 0.0; for (n=0; nnRelParts; n++) { division = t->relParts[n]; /* Find model settings */ m = &modelSettings[division]; /* find nSitesOfPat */ nSites = nSitesOfPat + m->compCharStart; /* find downpass parsimony sets for the node and its environment */ pP = m->parsSets[p->index ]; pA = m->parsSets[p->anc->index]; pV = m->parsSets[v->index ]; length = 0.0; if (m->nParsIntsPerSite == 1) { for (j=0; jnumChars; j++) { y[0] = (pP[j] | pA[j]) & pV[j]; if (y[0] == 0) length += nSites[j]; } } else /* if (m->nParsIntsPerSite == 2) */ { for (j=0; j<2*m->numChars; j+=2) { y[0] = (pP[j] | pA[j]) & pV[j]; y[1] = (pP[j+1] | pA[j+1]) & pV[j+1]; if ((y[0] | y[1]) == 0) length += nSites[j/2]; } } /* find nStates and v approximation using parsimony-based s/n approximation */ nStates = m->numModelStates; if (m->dataType == STANDARD || m->dataType == CONTINUOUS) nStates = 2; v_approx = length/m->numUncompressedChars + 0.0001; /* get division warp factor (prop. to prob. of change) */ divFactor = - warpFactor * log(1.0/nStates - exp(-nStates/(nStates-1)*v_approx)/nStates); p->d += divFactor * length; } } /* find the min length and the sum for the forward move */ minLength = -1.0; for (i=0; inNodes; i++) { p = t->allDownPass[i]; if (p->marked == NO || p == a) continue; if (minLength < 0.0 || p->d < minLength) minLength = p->d; } sum1 = 0.0; for (i=0; inNodes; i++) { p = t->allDownPass[i]; if (p->marked == YES && p != a) sum1 += exp (minLength - p->d); } /* generate a random uniform */ ran = RandomNumber(seed) * sum1; /* select the appropriate reattachment point (not a!) */ cumulativeProb = 0.0; for (i=0; inNodes; i++) { p = t->allDownPass[i]; if (p->marked == YES && p != a) { c = p; cumulativeProb += exp (minLength - p->d); if (cumulativeProb > ran) break; } } /* calculate the proposal ratio */ (*lnProposalRatio) = c->d - minLength + log(sum1); /* find the min length and the sum for the backward move */ minLength = -1.0; for (i=0; inNodes; i++) { p = t->allDownPass[i]; if (p->marked == NO || p == c) continue; if (minLength < 0.0 || p->d < minLength) minLength = p->d; } sum2 = 0.0; for (i=0; inNodes; i++) { p = t->allDownPass[i]; if (p->marked == YES && p != c) sum2 += exp (minLength - p->d); } /* calculate the proposal ratio */ (*lnProposalRatio) += minLength - a->d - log(sum2); /* reattach u */ d = c->anc; c->anc = u; if (u->left == v) u->right = c; else u->left = c; u->anc = d; if (d->left == c) d->left = u; else d->right = u; if (v->length > 0.0) /* side branch, not anc fossil */ { if (c->nodeDepth > v->nodeDepth) newBrlen = d->nodeDepth - c->nodeDepth - 2.0*minV; else newBrlen = d->nodeDepth - v->nodeDepth - 2.0*minV; if (newBrlen <= 0.0) { abortMove = YES; free (nSitesOfPat); return (NO_ERROR); } /* adjust lengths */ u->nodeDepth = d->nodeDepth - minV - RandomNumber(seed) * newBrlen; v->length = u->nodeDepth - v->nodeDepth; /* calculate proposal ratio for tree change */ (*lnProposalRatio) += log (newBrlen / oldBrlen); } u->length = d->nodeDepth - u->nodeDepth; c->length = u->nodeDepth - c->nodeDepth; v3new = v->length; v4 = c->length; v5 = u->length; /* reassign events for CPP and adjust prior and proposal ratios for relaxed clock models */ for (i=0; isubParams[0]->nSubParams; i++) { subParm = param->subParams[0]->subParams[i]; if (subParm->paramType == P_CPPEVENTS) { nEvents = subParm->nEvents[2*chain+state[chain]]; position = subParm->position[2*chain+state[chain]]; rateMultiplier = subParm->rateMult[2*chain+state[chain]]; for (j=0; jindex]; j++) { if (position[c->index][j] > v4 / (v4+v5)) break; } n4 = j; n5 = nEvents[c->index] - j; nEvents[u->index] = n5; if (n5 > 0) { position[u->index] = (MrBFlt *) SafeRealloc ((void *) position[u->index], n5 * sizeof (MrBFlt)); rateMultiplier[u->index] = (MrBFlt *) SafeRealloc ((void *) rateMultiplier[u->index], n5 * sizeof (MrBFlt)); for (j=n4; jindex]; j++) { position[u->index][j-n4] = (position[c->index][j] * (v4+v5) - v4) / v5; rateMultiplier[u->index][j-n4] = rateMultiplier[c->index][j]; } if (n4 > 0) { position[c->index] = (MrBFlt *) SafeRealloc ((void *) position[c->index], n4 * sizeof (MrBFlt)); rateMultiplier[c->index] = (MrBFlt *) SafeRealloc ((void *) rateMultiplier[c->index], n4 * sizeof (MrBFlt)); for (j=0; jindex][j] *= ((v4+v5) / v4); } else { free (position[c->index]); free (rateMultiplier[c->index]); position[c->index] = rateMultiplier[c->index] = NULL; } nEvents[c->index] = n4; } else { for (j=0; jindex]; j++) position[c->index][j] *= ((v4+v5) / v4); } /* adjust proposal ratio */ (*lnProposalRatio) += n3 * log (v3new / v3); /* adjust prior ratio */ lambda = *GetParamVals (modelSettings[subParm->relParts[0]].cppRate, chain, state[chain]); (*lnPriorRatio) += lambda * (v3 - v3new); /* update effective branch lengths */ if (UpdateCppEvolLengths (subParm, a, chain) == ERROR) { abortMove = YES; free (nSitesOfPat); return (NO_ERROR); } if (UpdateCppEvolLengths (subParm, u, chain) == ERROR) { abortMove = YES; free (nSitesOfPat); return (NO_ERROR); } } /* end cpp events parameter */ else if ( subParm->paramType == P_TK02BRANCHRATES) { /* adjust prior ratio */ if (subParm->paramType == P_TK02BRANCHRATES) nu = *GetParamVals (modelSettings[subParm->relParts[0]].tk02var, chain, state[chain]); else nu = *GetParamVals (modelSettings[subParm->relParts[0]].mixedvar, chain, state[chain]); rate = GetParamVals (subParm, chain, state[chain]); (*lnPriorRatio) -= LnProbLogNormal_Mean_LogVar(rate[u->anc->index], nu*(c->length+u->length), rate[c->index]); (*lnPriorRatio) += LnProbLogNormal_Mean_LogVar(rate[c->anc->index], nu*c->length, rate[c->index]); (*lnPriorRatio) += LnProbLogNormal_Mean_LogVar(rate[u->anc->index], nu*u->length, rate[u->index]); if (v->length > 0.0) (*lnPriorRatio) += LnProbLogNormal_Mean_LogVar(rate[v->anc->index], nu*v->length, rate[v->index]); /* adjust effective branch lengths */ brlens = GetParamSubVals (subParm, chain, state[chain]); brlens[c->index] = c->length * (rate[c->index] + rate[c->anc->index]) / 2.0; brlens[v->index] = v->length * (rate[v->index] + rate[v->anc->index]) / 2.0; brlens[u->index] = u->length * (rate[u->index] + rate[u->anc->index]) / 2.0; } /* end tk02 branch rate parameter */ else if ( subParm->paramType == P_WNBRANCHRATES) { /* adjust prior ratio */ if (subParm->paramType == P_WNBRANCHRATES) var = *GetParamVals (modelSettings[subParm->relParts[0]].wnvar, chain, state[chain]); else var = *GetParamVals (modelSettings[subParm->relParts[0]].mixedvar, chain, state[chain]); rate = GetParamVals (subParm, chain, state[chain]); (*lnPriorRatio) -= LnProbGamma ((c->length+u->length)/var, (c->length+u->length)/var, rate[c->index]); (*lnPriorRatio) += LnProbGamma (c->length/var, c->length/var, rate[c->index]); (*lnPriorRatio) += LnProbGamma (u->length/var, u->length/var, rate[u->index]); if (v->length > 0.0) (*lnPriorRatio) += LnProbGamma (v->length/var, v->length/var, rate[v->index]); /* adjust effective branch lengths */ brlens = GetParamSubVals (subParm, chain, state[chain]); brlens[v->index] = rate[v->index] * v->length; brlens[u->index] = rate[u->index] * u->length; brlens[c->index] = rate[c->index] * c->length; } /* end wn branch rate parameter */ else if ( subParm->paramType == P_ILNBRANCHRATES || (subParm->paramType == P_MIXEDBRCHRATES && *GetParamIntVals(subParm, chain, state[chain]) == RCL_ILN)) { /* adjust prior ratio */ if (subParm->paramType == P_ILNBRANCHRATES) nu = *GetParamVals (modelSettings[subParm->relParts[0]].ilnvar, chain, state[chain]); else nu = *GetParamVals (modelSettings[subParm->relParts[0]].mixedvar, chain, state[chain]); rate = GetParamVals (subParm, chain, state[chain]); (*lnPriorRatio) += LnProbLogNormal_Mean_Var (1.0, nu, rate[u->index]); if (v->length > 0.0) (*lnPriorRatio) += LnProbLogNormal_Mean_Var (1.0, nu, rate[v->index]); /* adjust effective branch lengths */ brlens = GetParamSubVals (subParm, chain, state[chain]); brlens[v->index] = rate[v->index] * v->length; brlens[u->index] = rate[u->index] * u->length; brlens[c->index] = rate[c->index] * c->length; } /* end iln branch rate parameter */ else if ( subParm->paramType == P_IGRBRANCHRATES || (subParm->paramType == P_MIXEDBRCHRATES && *GetParamIntVals(subParm, chain, state[chain]) == RCL_IGR)) { /* adjust prior ratio */ if (subParm->paramType == P_IGRBRANCHRATES) var = *GetParamVals (modelSettings[subParm->relParts[0]].igrvar, chain, state[chain]); else var = *GetParamVals (modelSettings[subParm->relParts[0]].mixedvar, chain, state[chain]); rate = GetParamVals (subParm, chain, state[chain]); (*lnPriorRatio) += LnProbGamma (1.0/var, 1.0/var, rate[u->index]); if (v->length > 0.0) (*lnPriorRatio) += LnProbGamma (1.0/var, 1.0/var, rate[v->index]); /* adjust effective branch lengths */ brlens = GetParamSubVals (subParm, chain, state[chain]); brlens[v->index] = rate[v->index] * v->length; brlens[u->index] = rate[u->index] * u->length; brlens[c->index] = rate[c->index] * c->length; } /* end igr branch rate parameter */ } /* next subparameter */ /* set tiprobs update flags */ c->upDateTi = YES; u->upDateTi = YES; v->upDateTi = YES; /* set flags for update of cond likes down to root */ p = u; while (p->anc != NULL) { p->upDateCl = YES; p = p->anc; } p = b; while (p->anc != NULL) { p->upDateCl = YES; p = p->anc; } /* get down pass sequence */ GetDownPass (t); /* adjust proposal ratio for number of movable nodes */ numMovableNodesNew = 0; for (i=0; inNodes-2; ++i) { p = t->allDownPass[i]; a = p->anc->left; b = p->anc->right; if (!(p->anc->isLocked == YES || p->anc->anc->anc == NULL || (p == b && a->length < TIME_MIN) || (p == a && b->length < TIME_MIN))) ++numMovableNodesNew; } if (numMovableNodesOld != numMovableNodesNew) (*lnProposalRatio) += log(numMovableNodesOld/numMovableNodesNew); /* adjust prior ratio for clock tree */ if (LogClockTreePriorRatio (param, chain, &x) == ERROR) { free (nSitesOfPat); return (ERROR); } (*lnPriorRatio) += x; # if defined (DEBUG_ParsSPRClock) ShowNodes (t->root, 2, YES); printf ("After\nProposal ratio: %f\n",(*lnProposalRatio)); printf ("v: %d u: %d a: %d b: %d c: %d\n",v->index, u->index, a->index, b->index, c->index); getchar(); # endif free (nSitesOfPat); return (NO_ERROR); } int Move_ParsSPRClock_Fossil (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp) { /* Change branch lengths and topology (potentially) using SPR-type move, parsimony-biased */ /* This move is identical to ParsSPRClock except that it uses s/n weighting and it only picks fossil subtrees. */ int i, j, n, division, n1=0, n2=0, n3=0, n4=0, n5=0, *nEvents, numMovableNodesOld, numMovableNodesNew; BitsLong *pA, *pV, *pP, y[2]; MrBFlt x, oldBrlen=0.0, newBrlen=0.0, v1=0.0, v2=0.0, v3=0.0, v4=0.0, v5=0.0, v3new=0.0, lambda, **position=NULL, **rateMultiplier=NULL, *brlens, nu, var, *rate=NULL, minLength=0.0, length=0.0, cumulativeProb, warpFactor, sum1, sum2, ran, increaseProb, decreaseProb, divFactor, nStates, v_approx, minV; CLFlt *nSitesOfPat, *nSites, *globalNSitesOfPat; TreeNode *p, *a, *b, *u, *v, *c=NULL, *d; Tree *t; ModelInfo *m=NULL; Param *subParm; warpFactor = mvp[0]; /* tuning parameter determining how heavily to weight according to parsimony scores */ increaseProb = decreaseProb = mvp[1]; /* reweighting probabilities */ (*lnProposalRatio) = (*lnPriorRatio) = 0.0; /* get tree */ t = GetTree (param, chain, state[chain]); /* get model params and model info */ m = &modelSettings[param->relParts[0]]; /* get min and max brlen in relative time and subst units */ minV = BRLENS_MIN; # if defined (DEBUG_ParsSPRClock) printf ("Before:\n"); ShowNodes (t->root, 2, YES); getchar(); # endif /* mark all nodes that only have fossil children with YES and count number movable nodes in current tree */ numMovableNodesOld = 0; for (i=0; inNodes-2; ++i) { p = t->allDownPass[i]; if (p->left == NULL) { if (p->calibration == NULL) p->x = NO; else { p->x = YES; } } else { if (p->left->x == YES && p->right->x == YES) { p->x = YES; } else p->x = NO; } a = p->anc->left; b = p->anc->right; if (!(p->anc->isLocked == YES || p->anc->anc->anc == NULL || (p == b && a->length < TIME_MIN) || (p == a && b->length < TIME_MIN) || p->x == NO)) numMovableNodesOld++; } if (numMovableNodesOld == 0) return (NO_ERROR); /* pick a branch */ do { p = t->allDownPass[(int)(RandomNumber(seed) * (t->nNodes - 2))]; a = p->anc->left; b = p->anc->right; } while (p->anc->isLocked == YES || p->anc->anc->anc == NULL || (p == b && a->length < TIME_MIN) || (p == a && b->length < TIME_MIN) || p->x == NO); /* skip constraints, siblings of root (and root); and consider ancestral fossils in fbd tree; skip all nodes that subtend extant terminals */ /* set up pointers for nodes around the picked branch */ v = p; u = p->anc; if (u->left == v) a = u->right; else a = u->left; b = u->anc; /* record branch length for insertion in back move */ if (v->length > 0.0) /* side branch, not anc fossil */ { if (v->nodeDepth > a->nodeDepth) oldBrlen = b->nodeDepth - v->nodeDepth - 2.0*minV; else oldBrlen = b->nodeDepth - a->nodeDepth - 2.0*minV; } v1 = a->length; v2 = u->length; v3 = v->length; /* reassign events for CPP and adjust prior and proposal ratios for relaxed clock models */ for (i=0; isubParams[0]->nSubParams; i++) { subParm = param->subParams[0]->subParams[i]; if (subParm->paramType == P_CPPEVENTS) { nEvents = subParm->nEvents[2*chain+state[chain]]; position = subParm->position[2*chain+state[chain]]; rateMultiplier = subParm->rateMult[2*chain+state[chain]]; n1 = nEvents[a->index]; n2 = nEvents[u->index]; n3 = nEvents[v->index]; if (n2 > 0) { position[a->index] = (MrBFlt *) SafeRealloc ((void *) position[a->index], (n1+n2) * sizeof (MrBFlt)); rateMultiplier[a->index] = (MrBFlt *) SafeRealloc ((void *) rateMultiplier[a->index], (n1+n2) * sizeof (MrBFlt)); } for (j=0; jindex][j] *= v1 / (v1+v2); for (j=n1; jindex][j] = (position[u->index][j-n1] * v2 + v1) / (v1+v2); rateMultiplier[a->index][j] = rateMultiplier[u->index][j-n1]; } nEvents[a->index] = n1+n2; nEvents[u->index] = 0; if (n2 > 0) { free (position[u->index]); free (rateMultiplier[u->index]); position[u->index] = rateMultiplier[u->index] = NULL; } /* adjust effective branch lengths */ brlens = GetParamSubVals (subParm, chain, state[chain]); brlens[a->index] += brlens[u->index]; /* only change in effective branch lengths so far */ } /* end CPP events parm */ else if ( subParm->paramType == P_TK02BRANCHRATES) { /* adjust prior ratio */ if (subParm->paramType == P_TK02BRANCHRATES) nu = *GetParamVals (modelSettings[subParm->relParts[0]].tk02var, chain, state[chain]); else nu = *GetParamVals (modelSettings[subParm->relParts[0]].mixedvar, chain, state[chain]); rate = GetParamVals (subParm, chain, state[chain]); if (v->length > 0.0) (*lnPriorRatio) -= LnProbLogNormal_Mean_LogVar(rate[v->anc->index], nu*v->length, rate[v->index]); (*lnPriorRatio) -= LnProbLogNormal_Mean_LogVar(rate[a->anc->index], nu*a->length, rate[a->index]); (*lnPriorRatio) -= LnProbLogNormal_Mean_LogVar(rate[u->anc->index], nu*u->length, rate[u->index]); (*lnPriorRatio) += LnProbLogNormal_Mean_LogVar(rate[u->anc->index], nu*(a->length+u->length), rate[a->index]); /* adjust effective branch lengths */ brlens = GetParamSubVals (subParm, chain, state[chain]); brlens[a->index] = (rate[a->index] + rate[b->index]) / 2.0 * (a->length + u->length); } /* end tk02 branch rate parameter */ else if ( subParm->paramType == P_WNBRANCHRATES) { if (subParm->paramType == P_WNBRANCHRATES) var = *GetParamVals (modelSettings[subParm->relParts[0]].wnvar, chain, state[chain]); else var = *GetParamVals (modelSettings[subParm->relParts[0]].mixedvar, chain, state[chain]); rate = GetParamVals (subParm, chain, state[chain]); /* adjust prior ratio for old branches */ if (v->length > 0.0) (*lnPriorRatio) -= LnProbGamma(v->length/var, v->length/var, rate[v->index]); (*lnPriorRatio) -= LnProbGamma(a->length/var, a->length/var, rate[a->index]); (*lnPriorRatio) -= LnProbGamma(u->length/var, u->length/var, rate[u->index]); (*lnPriorRatio) += LnProbGamma((a->length+u->length)/var, (a->length+u->length)/var, rate[a->index]); /* adjust effective branch lengths */ brlens = GetParamSubVals (subParm, chain, state[chain]); brlens[a->index] = rate[a->index] * (a->length + u->length); } /* end wn branch rate parameter */ else if ( subParm->paramType == P_ILNBRANCHRATES || (subParm->paramType == P_MIXEDBRCHRATES && *GetParamIntVals(subParm, chain, state[chain]) == RCL_ILN)) { if (subParm->paramType == P_ILNBRANCHRATES) nu = *GetParamVals (modelSettings[subParm->relParts[0]].ilnvar, chain, state[chain]); else nu = *GetParamVals (modelSettings[subParm->relParts[0]].mixedvar, chain, state[chain]); rate = GetParamVals (subParm, chain, state[chain]); /* adjust prior ratio for old branches */ if (v->length > 0.0) (*lnPriorRatio) -= LnProbLogNormal_Mean_Var(1.0, nu, rate[v->index]); (*lnPriorRatio) -= LnProbLogNormal_Mean_Var(1.0, nu, rate[u->index]); /* adjust effective branch lengths */ brlens = GetParamSubVals (subParm, chain, state[chain]); brlens[a->index] = rate[a->index] * (a->length + u->length); } /* end iln branch rate parameter */ else if ( subParm->paramType == P_IGRBRANCHRATES || (subParm->paramType == P_MIXEDBRCHRATES && *GetParamIntVals(subParm, chain, state[chain]) == RCL_IGR)) { if (subParm->paramType == P_IGRBRANCHRATES) var = *GetParamVals (modelSettings[subParm->relParts[0]].igrvar, chain, state[chain]); else var = *GetParamVals (modelSettings[subParm->relParts[0]].mixedvar, chain, state[chain]); rate = GetParamVals (subParm, chain, state[chain]); /* adjust prior ratio for old branches */ if (v->length > 0.0) (*lnPriorRatio) -= LnProbGamma(1.0/var, 1.0/var, rate[v->index]); (*lnPriorRatio) -= LnProbGamma(1.0/var, 1.0/var, rate[u->index]); /* adjust effective branch lengths */ brlens = GetParamSubVals (subParm, chain, state[chain]); brlens[a->index] = rate[a->index] * (a->length + u->length); } /* end igr branch rate parameter */ } /* next subparameter */ /* cut tree */ a->anc = b; if (b->left == u) b->left = a; else b->right = a; a->length += u->length; a->upDateTi = YES; /* get final parsimony states for the root part */ GetParsDP (t, t->root->left, chain); GetParsFP (t, t->root->left->left, chain); GetParsFP (t, t->root->left->right, chain); /* get downpass parsimony states for the crown part */ GetParsDP (t, v, chain); /* reset node variables that will be used */ for (i=0; inNodes; i++) { p = t->allDownPass[i]; p->marked = NO; p->d = 0.0; } /* mark nodes in the root part of the tree, first mark a */ a->marked = YES; /* then move down towards root taking constraints into account */ p = b; while (p->isLocked == NO && p->anc->anc != NULL) { p->marked = YES; p = p->anc; } /* make sure sisters of last node are marked otherwise it will not be marked in the uppass */ p->left->marked = YES; p->right->marked = YES; /* finally move up, skip constraints and ancestral fossil */ for (i=t->nNodes-2; i>=0; i--) { p = t->allDownPass[i]; if (p != u && p->marked == NO && p->anc->marked == YES && p->anc->isLocked == NO && p->anc->nodeDepth > v->nodeDepth + minV && p->length > 0.0) p->marked = YES; } /* unmark nodes if the picked branch is 0 (ancestral fossil) */ if (v->length < TIME_MIN) { n = 0; for (i=0; inNodes-1; i++) { p = t->allDownPass[i]; if (p->nodeDepth > v->nodeDepth - minV || p->anc->nodeDepth < v->nodeDepth + minV) p->marked = NO; if (p->marked == YES) n++; } if (n < 2) /* no new position to move */ { abortMove = YES; return (NO_ERROR); } } /* find number of site patterns and modify randomly */ globalNSitesOfPat = numSitesOfPat + ((chainId[chain] % chainParams.numChains) * numCompressedChars) + m->compCharStart; nSitesOfPat = (CLFlt *) SafeCalloc (numCompressedChars, sizeof(CLFlt)); if (!nSitesOfPat) { MrBayesPrint ("%s Problem allocating nSitesOfPat in Move_ParsSPRClock\n", spacer); return (ERROR); } for (i=0; i 1.0 - increaseProb) nSitesOfPat[i]++; } } /* cycle through the possibilities and record the parsimony length */ for (i=0; inNodes; i++) { p = t->allDownPass[i]; if (p->marked == NO) continue; /* find the parsimony length */ p->d = 0.0; for (n=0; nnRelParts; n++) { division = t->relParts[n]; /* Find model settings */ m = &modelSettings[division]; /* find nSitesOfPat */ nSites = nSitesOfPat + m->compCharStart; /* find downpass parsimony sets for the node and its environment */ pP = m->parsSets[p->index ]; pA = m->parsSets[p->anc->index]; pV = m->parsSets[v->index ]; length = 0.0; if (m->nParsIntsPerSite == 1) { for (j=0; jnumChars; j++) { y[0] = (pP[j] | pA[j]) & pV[j]; if (y[0] == 0) length += nSites[j]; } } else /* if (m->nParsIntsPerSite == 2) */ { for (j=0; j<2*m->numChars; j+=2) { y[0] = (pP[j] | pA[j]) & pV[j]; y[1] = (pP[j+1] | pA[j+1]) & pV[j+1]; if ((y[0] | y[1]) == 0) length += nSites[j/2]; } } /* find nStates and v approximation using parsimony-based s/n approximation */ nStates = m->numModelStates; if (m->dataType == STANDARD || m->dataType == CONTINUOUS) nStates = 2; v_approx = length/m->numUncompressedChars + 0.0001; /* get division warp factor (prop. to prob. of change) */ divFactor = - warpFactor * log(1.0/nStates - exp(-nStates/(nStates-1)*v_approx)/nStates); p->d += divFactor * length; } } /* find the min length and the sum for the forward move */ minLength = -1.0; for (i=0; inNodes; i++) { p = t->allDownPass[i]; if (p->marked == NO || p == a) continue; if (minLength < 0.0 || p->d < minLength) minLength = p->d; } sum1 = 0.0; for (i=0; inNodes; i++) { p = t->allDownPass[i]; if (p->marked == YES && p != a) sum1 += exp (minLength - p->d); } /* generate a random uniform */ ran = RandomNumber(seed) * sum1; /* select the appropriate reattachment point (not a!) */ cumulativeProb = 0.0; for (i=0; inNodes; i++) { p = t->allDownPass[i]; if (p->marked == YES && p != a) { c = p; cumulativeProb += exp (minLength - p->d); if (cumulativeProb > ran) break; } } /* calculate the proposal ratio */ (*lnProposalRatio) = c->d - minLength + log(sum1); /* find the min length and the sum for the backward move */ minLength = -1.0; for (i=0; inNodes; i++) { p = t->allDownPass[i]; if (p->marked == NO || p == c) continue; if (minLength < 0.0 || p->d < minLength) minLength = p->d; } sum2 = 0.0; for (i=0; inNodes; i++) { p = t->allDownPass[i]; if (p->marked == YES && p != c) sum2 += exp (minLength - p->d); } /* calculate the proposal ratio */ (*lnProposalRatio) += minLength - a->d - log(sum2); /* reattach u */ d = c->anc; c->anc = u; if (u->left == v) u->right = c; else u->left = c; u->anc = d; if (d->left == c) d->left = u; else d->right = u; if (v->length > 0.0) /* side branch, not anc fossil */ { if (c->nodeDepth > v->nodeDepth) newBrlen = d->nodeDepth - c->nodeDepth - 2.0*minV; else newBrlen = d->nodeDepth - v->nodeDepth - 2.0*minV; if (newBrlen <= 0.0) { abortMove = YES; free (nSitesOfPat); return (NO_ERROR); } /* adjust lengths */ u->nodeDepth = d->nodeDepth - minV - RandomNumber(seed) * newBrlen; v->length = u->nodeDepth - v->nodeDepth; /* calculate proposal ratio for tree change */ (*lnProposalRatio) += log (newBrlen / oldBrlen); } u->length = d->nodeDepth - u->nodeDepth; c->length = u->nodeDepth - c->nodeDepth; v3new = v->length; v4 = c->length; v5 = u->length; /* reassign events for CPP and adjust prior and proposal ratios for relaxed clock models */ for (i=0; isubParams[0]->nSubParams; i++) { subParm = param->subParams[0]->subParams[i]; if (subParm->paramType == P_CPPEVENTS) { nEvents = subParm->nEvents[2*chain+state[chain]]; position = subParm->position[2*chain+state[chain]]; rateMultiplier = subParm->rateMult[2*chain+state[chain]]; for (j=0; jindex]; j++) { if (position[c->index][j] > v4 / (v4+v5)) break; } n4 = j; n5 = nEvents[c->index] - j; nEvents[u->index] = n5; if (n5 > 0) { position[u->index] = (MrBFlt *) SafeRealloc ((void *) position[u->index], n5 * sizeof (MrBFlt)); rateMultiplier[u->index] = (MrBFlt *) SafeRealloc ((void *) rateMultiplier[u->index], n5 * sizeof (MrBFlt)); for (j=n4; jindex]; j++) { position[u->index][j-n4] = (position[c->index][j] * (v4+v5) - v4) / v5; rateMultiplier[u->index][j-n4] = rateMultiplier[c->index][j]; } if (n4 > 0) { position[c->index] = (MrBFlt *) SafeRealloc ((void *) position[c->index], n4 * sizeof (MrBFlt)); rateMultiplier[c->index] = (MrBFlt *) SafeRealloc ((void *) rateMultiplier[c->index], n4 * sizeof (MrBFlt)); for (j=0; jindex][j] *= ((v4+v5) / v4); } else { free (position[c->index]); free (rateMultiplier[c->index]); position[c->index] = rateMultiplier[c->index] = NULL; } nEvents[c->index] = n4; } else { for (j=0; jindex]; j++) position[c->index][j] *= ((v4+v5) / v4); } /* adjust proposal ratio */ (*lnProposalRatio) += n3 * log (v3new / v3); /* adjust prior ratio */ lambda = *GetParamVals (modelSettings[subParm->relParts[0]].cppRate, chain, state[chain]); (*lnPriorRatio) += lambda * (v3 - v3new); /* update effective branch lengths */ if (UpdateCppEvolLengths (subParm, a, chain) == ERROR) { abortMove = YES; free (nSitesOfPat); return (NO_ERROR); } if (UpdateCppEvolLengths (subParm, u, chain) == ERROR) { abortMove = YES; free (nSitesOfPat); return (NO_ERROR); } } /* end cpp events parameter */ else if ( subParm->paramType == P_TK02BRANCHRATES) { /* adjust prior ratio */ if (subParm->paramType == P_TK02BRANCHRATES) nu = *GetParamVals (modelSettings[subParm->relParts[0]].tk02var, chain, state[chain]); else nu = *GetParamVals (modelSettings[subParm->relParts[0]].mixedvar, chain, state[chain]); rate = GetParamVals (subParm, chain, state[chain]); (*lnPriorRatio) -= LnProbLogNormal_Mean_LogVar(rate[u->anc->index], nu*(c->length+u->length), rate[c->index]); (*lnPriorRatio) += LnProbLogNormal_Mean_LogVar(rate[c->anc->index], nu*c->length, rate[c->index]); (*lnPriorRatio) += LnProbLogNormal_Mean_LogVar(rate[u->anc->index], nu*u->length, rate[u->index]); if (v->length > 0.0) (*lnPriorRatio) += LnProbLogNormal_Mean_LogVar(rate[v->anc->index], nu*v->length, rate[v->index]); /* adjust effective branch lengths */ brlens = GetParamSubVals (subParm, chain, state[chain]); brlens[c->index] = c->length * (rate[c->index] + rate[c->anc->index]) / 2.0; brlens[v->index] = v->length * (rate[v->index] + rate[v->anc->index]) / 2.0; brlens[u->index] = u->length * (rate[u->index] + rate[u->anc->index]) / 2.0; } /* end tk02 branch rate parameter */ else if ( subParm->paramType == P_WNBRANCHRATES) { /* adjust prior ratio */ if (subParm->paramType == P_WNBRANCHRATES) var = *GetParamVals (modelSettings[subParm->relParts[0]].wnvar, chain, state[chain]); else var = *GetParamVals (modelSettings[subParm->relParts[0]].mixedvar, chain, state[chain]); rate = GetParamVals (subParm, chain, state[chain]); (*lnPriorRatio) -= LnProbGamma ((c->length+u->length)/var, (c->length+u->length)/var, rate[c->index]); (*lnPriorRatio) += LnProbGamma (c->length/var, c->length/var, rate[c->index]); (*lnPriorRatio) += LnProbGamma (u->length/var, u->length/var, rate[u->index]); if (v->length > 0.0) (*lnPriorRatio) += LnProbGamma (v->length/var, v->length/var, rate[v->index]); /* adjust effective branch lengths */ brlens = GetParamSubVals (subParm, chain, state[chain]); brlens[v->index] = rate[v->index] * v->length; brlens[u->index] = rate[u->index] * u->length; brlens[c->index] = rate[c->index] * c->length; } /* end wn branch rate parameter */ else if ( subParm->paramType == P_ILNBRANCHRATES || (subParm->paramType == P_MIXEDBRCHRATES && *GetParamIntVals(subParm, chain, state[chain]) == RCL_ILN)) { /* adjust prior ratio */ if (subParm->paramType == P_ILNBRANCHRATES) nu = *GetParamVals (modelSettings[subParm->relParts[0]].ilnvar, chain, state[chain]); else nu = *GetParamVals (modelSettings[subParm->relParts[0]].mixedvar, chain, state[chain]); rate = GetParamVals (subParm, chain, state[chain]); (*lnPriorRatio) += LnProbLogNormal_Mean_Var (1.0, nu, rate[u->index]); if (v->length > 0.0) (*lnPriorRatio) += LnProbLogNormal_Mean_Var (1.0, nu, rate[v->index]); /* adjust effective branch lengths */ brlens = GetParamSubVals (subParm, chain, state[chain]); brlens[v->index] = rate[v->index] * v->length; brlens[u->index] = rate[u->index] * u->length; brlens[c->index] = rate[c->index] * c->length; } /* end iln branch rate parameter */ else if ( subParm->paramType == P_IGRBRANCHRATES || (subParm->paramType == P_MIXEDBRCHRATES && *GetParamIntVals(subParm, chain, state[chain]) == RCL_IGR)) { /* adjust prior ratio */ if (subParm->paramType == P_IGRBRANCHRATES) var = *GetParamVals (modelSettings[subParm->relParts[0]].igrvar, chain, state[chain]); else var = *GetParamVals (modelSettings[subParm->relParts[0]].mixedvar, chain, state[chain]); rate = GetParamVals (subParm, chain, state[chain]); (*lnPriorRatio) += LnProbGamma (1.0/var, 1.0/var, rate[u->index]); if (v->length > 0.0) (*lnPriorRatio) += LnProbGamma (1.0/var, 1.0/var, rate[v->index]); /* adjust effective branch lengths */ brlens = GetParamSubVals (subParm, chain, state[chain]); brlens[v->index] = rate[v->index] * v->length; brlens[u->index] = rate[u->index] * u->length; brlens[c->index] = rate[c->index] * c->length; } /* end igr branch rate parameter */ } /* next subparameter */ /* set tiprobs update flags */ c->upDateTi = YES; u->upDateTi = YES; v->upDateTi = YES; /* set flags for update of cond likes down to root */ p = u; while (p->anc != NULL) { p->upDateCl = YES; p = p->anc; } p = b; while (p->anc != NULL) { p->upDateCl = YES; p = p->anc; } /* get down pass sequence */ GetDownPass (t); /* adjust prior ratio for clock tree */ if (LogClockTreePriorRatio (param, chain, &x) == ERROR) { free (nSitesOfPat); return (ERROR); } (*lnPriorRatio) += x; /* adjust proposal prob for number movable nodes in new tree */ numMovableNodesNew = 0; for (i=0; inNodes-2; ++i) { p = t->allDownPass[i]; if (p->left == NULL) { if (p->calibration == NULL) p->x = NO; else { p->x = YES; } } else { if (p->left->x == YES && p->right->x == YES) { p->x = YES; } else p->x = NO; } a = p->anc->left; b = p->anc->right; if (!(p->anc->isLocked == YES || p->anc->anc->anc == NULL || (p == b && a->length < TIME_MIN) || (p == a && b->length < TIME_MIN) || p->x == NO)) numMovableNodesNew++; } if (numMovableNodesNew != numMovableNodesOld) (*lnProposalRatio) += log(numMovableNodesOld/numMovableNodesNew); # if defined (DEBUG_ParsSPRClock) ShowNodes (t->root, 2, YES); printf ("After\nProposal ratio: %f\n",(*lnProposalRatio)); printf ("v: %d u: %d a: %d b: %d c: %d\n",v->index, u->index, a->index, b->index, c->index); getchar(); # endif free (nSitesOfPat); return (NO_ERROR); } int Move_ParsTBR1 (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp) { /* Change topology and map branch lengths using TBR-type move biased according to parsimony scores, controlled by a window defined by a certain node distance radius. */ int i, j, k, n, division, topologyHasChanged, nNeighbor, nRoot, nCrown, iA, jC, isVPriorExp, tempInt; BitsLong *pA, *pB, *pC, *pD, y[2]; MrBFlt x, minV, maxV, minLength=0.0, length=0.0, *parLength=NULL, prob, ran, tuning, warpFactor, sum1, sum2, tempsum, tempc, tempy; CLFlt *nSites, *nSitesOfPat=NULL, *globalNSitesOfPat; TreeNode *p, *q, *r, *a, *b, *u, *v, *c, *d, *newB, *newA, *newC, **pRoot=NULL, **pCrown=NULL, *interiorRoot=NULL; Tree *t; ModelParams *mp; ModelInfo *m=NULL; warpFactor = mvp[0]; /* tuning parameter determining how heavily to weight according to parsimony scores */ // increaseProb = decreaseProb = mvp[1]; /* reweighting probabilities */ tuning = mvp[2]; /* multiplier tuning parameter */ nNeighbor = (int)mvp[3]; /* distance to move picked branch in root and crown part */ (*lnProposalRatio) = (*lnPriorRatio) = 0.0; /* get model params and model info */ mp = &modelParams[param->relParts[0]]; m = &modelSettings[param->relParts[0]]; /* get tree */ t = GetTree (param, chain, state[chain]); /* for rooted (non-clock) trees, store pointer to current interior root node */ //SK if (t->isRooted) interiorRoot = t->root->left; /* max and min brlen */ if (param->subParams[0]->paramId == BRLENS_UNI) { minV = mp->brlensUni[0] > BRLENS_MIN ? mp->brlensUni[0] : BRLENS_MIN; maxV = mp->brlensUni[1] < BRLENS_MAX ? mp->brlensUni[1] : BRLENS_MAX; isVPriorExp = NO; } else if (param->subParams[0]->paramId == BRLENS_GamDir) { minV = BRLENS_MIN; maxV = BRLENS_MAX; isVPriorExp = 2; } else if (param->subParams[0]->paramId == BRLENS_iGmDir) { minV = BRLENS_MIN; maxV = BRLENS_MAX; isVPriorExp = 3; } else if (param->subParams[0]->paramId == BRLENS_twoExp) { minV = BRLENS_MIN; maxV = BRLENS_MAX; isVPriorExp = 4; } else { minV = BRLENS_MIN; maxV = BRLENS_MAX; isVPriorExp = YES; } /* Dirichlet or twoExp prior */ if (isVPriorExp > 1) (*lnPriorRatio) = -LogDirPrior(t, mp, isVPriorExp); /* set topologyHasChanged to NO */ topologyHasChanged = NO; /* reset node variables that will be used */ for (i=0; inNodes; i++) { p = t->allDownPass[i]; p->x = 0; p->marked = NO; } /* pick an internal branch */ do { p = t->intDownPass[(int)(RandomNumber(seed)*(t->nIntNodes-1))]; q = p->anc->left; if (q == p) q = p->anc->right; i = j = 0; if (q->isLocked == YES || q->left == NULL) i++; if (p->anc->isLocked == YES || p->anc->anc->anc == NULL) i++; if (p->left->isLocked == YES || p->left->left == NULL) j++; if (p->right->isLocked == YES || p->right->left == NULL) j++; } while (i == 2 && j == 2); /* set up pointers for nodes around the picked branch */ v = p; u = p->anc; c = p->left; d = p->right; if (u->left == v) a = u->right; else a = u->left; b = u->anc; /* clip root part of tree */ a->anc = b; if (b->left == u) b->left = a; else b->right = a; /* clip crown part of tree */ c->anc = d; d->anc = c; /* should never change u, v, a, b, c, d pointers */ /* mark nodes nNeighbor away in root (negative) and crown (positive) respecting constraints */ /* first move down towards root */ nRoot = nCrown = 0; if (u->isLocked == NO) { p = a; q = b; n = 0; while (q->anc != NULL) { q->marked = YES; q->x = n; // temporary, for MarkDistance below if (q->left == p) MarkDistance(q->right, YES, nNeighbor, &nRoot); else MarkDistance(q->left, YES, nNeighbor, &nRoot); q->x = --n; // final nRoot++; if (q->isLocked == YES || abs(q->x) >= nNeighbor) break; p = q; q = q->anc; } } /* then move up in root part */ a->marked = YES; nRoot++; if (a->isLocked == NO) { MarkDistance(a->left, YES, nNeighbor, &nRoot); MarkDistance(a->right, YES, nNeighbor, &nRoot); } /* finally in crown part */ c->marked = YES; nCrown++; if (c->isLocked == NO) { MarkDistance(c->left, NO, nNeighbor, &nCrown); MarkDistance(c->right, NO, nNeighbor, &nCrown); } if (d->isLocked == NO) { MarkDistance(d->left, NO, nNeighbor, &nCrown); MarkDistance(d->right, NO, nNeighbor, &nCrown); } /* need to alloc a matrix for parsimony lengths, an array of pointers to crown part, and an array of pointers to root part. */ parLength = (MrBFlt *) SafeCalloc ((size_t)nRoot * (size_t)nCrown, sizeof(MrBFlt)); pRoot = (TreeNode **) SafeCalloc(nRoot, sizeof(TreeNode *)); pCrown = (TreeNode **) SafeCalloc(nCrown, sizeof(TreeNode *)); if (!parLength || !pRoot || !pCrown) goto errorExit; /* starting position */ pRoot[0] = a; pCrown[0] = c; for (i=j=1, n=t->nNodes-2; n>=0; n--) { /* and the rest */ p = t->allDownPass[n]; if (p->marked == YES && p->x < 0) pRoot[i++] = p; if (p->marked == YES && p->x > 0) pCrown[j++] = p; } assert (i==nRoot && j==nCrown); /* get final parsimony state sets for the root part */ GetParsDP (t, t->root->left, chain); if (!strcmp(mp->statefreqModel,"Stationary")) { GetParsFP (t, t->root->left, chain); } else //SK: if not stationary model, but directional or RJ { GetParsFP (t, t->root->left->left, chain); GetParsFP (t, t->root->left->right, chain); } /* get final parsimony state sets for the crown part */ GetParsDP (t, c, chain); GetParsDP (t, d, chain); GetParsFP (t, c, chain); GetParsFP (t, d, chain); /* find number of site patterns and modify randomly */ globalNSitesOfPat = numSitesOfPat + (chainId[chain] % chainParams.numChains) * numCompressedChars; nSitesOfPat = (CLFlt *) SafeCalloc (numCompressedChars, sizeof(CLFlt)); if (!nSitesOfPat) goto errorExit; for (i=0; i 1.0 - increaseProb) nSitesOfPat[i]++; } // this is not used at the moment */ } /* cycle through the possibilities and record the parsimony length */ for (j=0; jnRelParts; n++) { division = t->relParts[n]; /* Find model settings */ m = &modelSettings[division]; /* find nSitesOfPat */ nSites = nSitesOfPat + m->compCharStart; /* find downpass parsimony sets for the potential new connection nodes and their environment */ pA = m->parsSets[pRoot[i]->index]; pB = m->parsSets[pRoot[i]->anc->index]; pC = m->parsSets[pCrown[j]->index]; pD = m->parsSets[pCrown[j]->anc->index]; length = 0.0; if (m->nParsIntsPerSite == 1) { for (k=0; knumChars; k++) { y[0] = (pC[k] | pD[k]) & (pA[k] | pB[k]); if (y[0] == 0) length += nSites[k]; } } else /* if (m->nParsIntsPerSite == 2) */ { for (k=0; k<2*m->numChars; k+=2) { y[0] = (pC[k] | pD[k]) & (pA[k] | pB[k]); y[1] = (pC[k+1] | pD[k+1]) & (pA[k+1] | pB[k+1]);; if ((y[0] | y[1]) == 0) length += nSites[k/2]; } } parLength[i+j*nRoot] += warpFactor * length; } } } /* find the min length and the sum for the forward move */ minLength = -1.0; for (j=0; j parLength[i+j*nRoot] || minLength < 0.0) minLength = parLength[i+j*nRoot]; } sum1 = 0.0; tempc = 0.0; for (j=0; j ran) { /* proposed new attaching position */ newA = pRoot[i]; newC = pCrown[j]; goto outLoop; } } outLoop:; iA = i; jC = j; /* calculate the proposal ratio */ (*lnProposalRatio) = parLength[i+j*nRoot] - minLength + log(sum1); /* find the min length and the sum for the backward move */ minLength = -1.0; for (j=0; j parLength[i+j*nRoot] || minLength < 0.0) minLength = parLength[i+j*nRoot]; } sum2 = 0.0; tempc = 0.0; for (j=0; janc; newA->anc = u; if (u->left == v) u->right = newA; else u->left = newA; u->anc = newB; if (newB->left == newA) newB->left = u; else newB->right = u; /* transfer lock and reassign branch lengths, if necessary */ if (newA != a) { /* if u is locked, then we have moved upwards and need to leave the u lock behind */ if (u->isLocked == YES) { u->isLocked = NO; a->isLocked = YES; a->lockID = u->lockID; u->lockID = -1; } p = newA; while (p->anc != NULL) { if (p == a) break; p = p->anc; } if (p == a) { /* newA is descendant to a so move a->length not u->length */ x = u->length; u->length = a->length; a->length = x; } p = b; while (p->anc != NULL) { if (p == newA) break; p = p->anc; } if (p == newA) { /* newA is ancestor to a so insert above instead of below */ x = newA->length; newA->length = u->length; u->length = x; /* newA is on root path and locked, we need to transfer lock to u */ if (newA->isLocked == YES) { u->isLocked = YES; u->lockID = newA->lockID; newA->isLocked = NO; newA->lockID = -1; } } /* hit u length with multiplier */ x = u->length * exp(tuning * (RandomNumber(seed) - 0.5)); while (x < minV || x > maxV) { if (x < minV) x = minV * minV / x; if (x > maxV) x = maxV * maxV / x; } /* calculate proposal and prior ratio based on length modification */ (*lnProposalRatio) += log (x / u->length); if (isVPriorExp == YES) (*lnPriorRatio) += (mp->brlensExp) * (u->length - x); u->length = x; /* set tiprobs update flags */ u->upDateTi = YES; newA->upDateTi = YES; a->upDateTi = YES; } r = newC; q = newB = newC->anc; if (newC != c) // crown part has changed { /* rotate nodes from newC to c or d (whichever is closest) */ tempc = r->length; while (r != c && r != d) { p = q->anc; /* rotate pointers of q */ if (q->left == r) q->left = p; else q->right = p; q->anc = r; /* swap q and old */ tempy = q->length; q->length = tempc; q->upDateTi = YES; tempc = tempy; /* make sure we get q and r initialized for next round */ r = q; q = p; } newB->length = tempc; /* hit newB length with multiplier */ x = newB->length * exp(tuning * (RandomNumber(seed) - 0.5)); while (x < minV || x > maxV) { if (x < minV) x = minV * minV / x; if (x > maxV) x = maxV * maxV / x; } /* calculate proposal and prior ratio based on length modification */ (*lnProposalRatio) += log (x / newB->length); if (isVPriorExp == YES) (*lnPriorRatio) += (mp->brlensExp) * (newB->length - x); newB->length = x; newB->upDateTi = YES; } /* reattach the crown part */ v->left = newC; v->right = newB; newC->anc = newB->anc = v; topologyHasChanged = YES; /* hit v length with multiplier */ x = v->length * exp(tuning * (RandomNumber(seed) - 0.5)); while (x < minV || x > maxV) { if (x < minV) x = minV * minV / x; if (x > maxV) x = maxV * maxV / x; } /* calculate proposal and prior ratio based on length modification */ (*lnProposalRatio) += log (x / v->length); if (isVPriorExp == YES) (*lnPriorRatio) += (mp->brlensExp) * (v->length - x); v->length = x; v->upDateTi = YES; /* set flags for update of cond likes */ p = u; while (p->anc != NULL) { p->upDateCl = YES; p = p->anc; } p = b; while (p->anc != NULL) { p->upDateCl = YES; p = p->anc; } p = newC; while (p->anc != NULL) { p->upDateCl = YES; p = p->anc; } p = r; while (p->anc != NULL) { p->upDateCl = YES; p = p->anc; } /* if tree is rooted, swap indices of former and current interior root node, if changed */ //SK if (t->isRooted && interiorRoot != t->root->left) { tempInt = interiorRoot->index; interiorRoot->index = t->root->left->index; t->root->left->index = tempInt; } /* get down pass sequence if tree topology has changed */ if (topologyHasChanged == YES) { GetDownPass (t); } /* Dirichlet or twoExp prior */ if (isVPriorExp > 1) (*lnPriorRatio) += LogDirPrior(t, mp, isVPriorExp); /* free up local memory */ free (parLength); free (pRoot); free (pCrown); free (nSitesOfPat); return (NO_ERROR); errorExit: MrBayesPrint ("%s Problem allocating memory in Move_ParsTBR\n", spacer); free (parLength); free (pRoot); free (pCrown); free (nSitesOfPat); return (ERROR); } int Move_ParsTBR2 (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp) { /* Change topology and map branch lengths using TBR-type move biased according to parsimony scores, controlled by a window defined by a certain node distance radius. */ int i, j, k, n, division, topologyHasChanged, nNeighbor, nRoot, nCrown, iA, jC, isVPriorExp, tempInt; BitsLong *pA, *pB, *pC, *pD, y[2]; MrBFlt x, minV, maxV, minLength=0.0, length=0.0, *parLength=NULL, prob, ran, tuning, warpFactor, v_typical, divFactor, nStates, sum1, sum2, tempsum, tempc, tempy; CLFlt *nSites, *nSitesOfPat=NULL, *globalNSitesOfPat; TreeNode *p, *q, *r, *a, *b, *u, *v, *c, *d, *newB, *newA, *newC, **pRoot=NULL, **pCrown=NULL, *interiorRoot=NULL; Tree *t; ModelParams *mp; ModelInfo *m=NULL; warpFactor = mvp[0]; /* tuning parameter determining how heavily to weight according to parsimony scores */ // increaseProb = decreaseProb = mvp[1]; /* reweighting probabilities */ v_typical = mvp[2]; /* typical branch length for conversion of parsimony score to log prob ratio */ tuning = mvp[3]; /* multiplier tuning parameter */ nNeighbor = (int)mvp[4]; /* distance to move picked branch in root and crown part */ (*lnProposalRatio) = (*lnPriorRatio) = 0.0; /* get model params and model info */ mp = &modelParams[param->relParts[0]]; m = &modelSettings[param->relParts[0]]; /* get tree */ t = GetTree (param, chain, state[chain]); /* for rooted (non-clock) trees, store pointer to current interior root node */ //SK if (t->isRooted) interiorRoot = t->root->left; /* max and min brlen */ if (param->subParams[0]->paramId == BRLENS_UNI) { minV = mp->brlensUni[0] > BRLENS_MIN ? mp->brlensUni[0] : BRLENS_MIN; maxV = mp->brlensUni[1] < BRLENS_MAX ? mp->brlensUni[1] : BRLENS_MAX; isVPriorExp = NO; } else if (param->subParams[0]->paramId == BRLENS_GamDir) { minV = BRLENS_MIN; maxV = BRLENS_MAX; isVPriorExp = 2; } else if (param->subParams[0]->paramId == BRLENS_iGmDir) { minV = BRLENS_MIN; maxV = BRLENS_MAX; isVPriorExp = 3; } else if (param->subParams[0]->paramId == BRLENS_twoExp) { minV = BRLENS_MIN; maxV = BRLENS_MAX; isVPriorExp = 4; } else { minV = BRLENS_MIN; maxV = BRLENS_MAX; isVPriorExp = YES; } /* Dirichlet or twoExp prior */ if (isVPriorExp > 1) (*lnPriorRatio) = -LogDirPrior(t, mp, isVPriorExp); /* set topologyHasChanged to NO */ topologyHasChanged = NO; /* reset node variables that will be used */ for (i=0; inNodes; i++) { p = t->allDownPass[i]; p->x = 0; p->marked = NO; } /* pick an internal branch */ do { p = t->intDownPass[(int)(RandomNumber(seed)*(t->nIntNodes-1))]; q = p->anc->left; if (q == p) q = p->anc->right; i = j = 0; if (q->isLocked == YES || q->left == NULL) i++; if (p->anc->isLocked == YES || p->anc->anc->anc == NULL) i++; if (p->left->isLocked == YES || p->left->left == NULL) j++; if (p->right->isLocked == YES || p->right->left == NULL) j++; } while (i == 2 && j == 2); /* set up pointers for nodes around the picked branch */ v = p; u = p->anc; c = p->left; d = p->right; if (u->left == v) a = u->right; else a = u->left; b = u->anc; /* clip root part of tree */ a->anc = b; if (b->left == u) b->left = a; else b->right = a; /* clip crown part of tree */ c->anc = d; d->anc = c; /* should never change u, v, a, b, c, d pointers */ /* mark nodes nNeighbor away in root (negative) and crown (positive) respecting constraints */ /* first move down towards root */ nRoot = nCrown = 0; if (u->isLocked == NO) { p = a; q = b; n = 0; while (q->anc != NULL) { q->marked = YES; q->x = n; // temporary, for MarkDistance below if (q->left == p) MarkDistance(q->right, YES, nNeighbor, &nRoot); else MarkDistance(q->left, YES, nNeighbor, &nRoot); q->x = --n; // final nRoot++; if (q->isLocked == YES || abs(q->x) >= nNeighbor) break; p = q; q = q->anc; } } /* then move up in root part */ a->marked = YES; nRoot++; if (a->isLocked == NO) { MarkDistance(a->left, YES, nNeighbor, &nRoot); MarkDistance(a->right, YES, nNeighbor, &nRoot); } /* finally in crown part */ c->marked = YES; nCrown++; if (c->isLocked == NO) { MarkDistance(c->left, NO, nNeighbor, &nCrown); MarkDistance(c->right, NO, nNeighbor, &nCrown); } if (d->isLocked == NO) { MarkDistance(d->left, NO, nNeighbor, &nCrown); MarkDistance(d->right, NO, nNeighbor, &nCrown); } /* need to alloc a matrix for parsimony lengths, an array of pointers to crown part, and an array of pointers to root part. */ parLength = (MrBFlt *) SafeCalloc ((size_t)nRoot * (size_t)nCrown, sizeof(MrBFlt)); pRoot = (TreeNode **) SafeCalloc(nRoot, sizeof(TreeNode *)); pCrown = (TreeNode **) SafeCalloc(nCrown, sizeof(TreeNode *)); if (!parLength || !pRoot || !pCrown) goto errorExit; /* starting position */ pRoot[0] = a; pCrown[0] = c; for (i=j=1, n=t->nNodes-2; n>=0; n--) { /* and the rest */ p = t->allDownPass[n]; if (p->marked == YES && p->x < 0) pRoot[i++] = p; if (p->marked == YES && p->x > 0) pCrown[j++] = p; } assert (i==nRoot && j==nCrown); /* get final parsimony state sets for the root part */ GetParsDP (t, t->root->left, chain); if (!strcmp(mp->statefreqModel,"Stationary")) { GetParsFP (t, t->root->left, chain); } else //SK: if not stationary model, but directional or RJ { GetParsFP (t, t->root->left->left, chain); GetParsFP (t, t->root->left->right, chain); } /* get final parsimony state sets for the crown part */ GetParsDP (t, c, chain); GetParsDP (t, d, chain); GetParsFP (t, c, chain); GetParsFP (t, d, chain); /* find number of site patterns and modify randomly */ globalNSitesOfPat = numSitesOfPat + (chainId[chain] % chainParams.numChains) * numCompressedChars; nSitesOfPat = (CLFlt *) SafeCalloc (numCompressedChars, sizeof(CLFlt)); if (!nSitesOfPat) goto errorExit; for (i=0; i 1.0 - increaseProb) nSitesOfPat[i]++; } // this is not used at the moment */ } /* cycle through the possibilities and record the parsimony length */ for (j=0; jnRelParts; n++) { division = t->relParts[n]; /* Find model settings */ m = &modelSettings[division]; /* find nSitesOfPat */ nSites = nSitesOfPat + m->compCharStart; /* find downpass parsimony sets for the potential new connection nodes and their environment */ pA = m->parsSets[pRoot[i]->index]; pB = m->parsSets[pRoot[i]->anc->index]; pC = m->parsSets[pCrown[j]->index]; pD = m->parsSets[pCrown[j]->anc->index]; length = 0.0; if (m->nParsIntsPerSite == 1) { for (k=0; knumChars; k++) { y[0] = (pC[k] | pD[k]) & (pA[k] | pB[k]); if (y[0] == 0) length += nSites[k]; } } else /* if (m->nParsIntsPerSite == 2) */ { for (k=0; k<2*m->numChars; k+=2) { y[0] = (pC[k] | pD[k]) & (pA[k] | pB[k]); y[1] = (pC[k+1] | pD[k+1]) & (pA[k+1] | pB[k+1]);; if ((y[0] | y[1]) == 0) length += nSites[k/2]; } } /* find nStates and ratemult */ nStates = m->numModelStates; if (m->dataType == STANDARD || m->dataType == CONTINUOUS) nStates = 2; v_typical = length/m->numUncompressedChars + 0.0001; /* get division warp factor (prop. to prob. of change) */ divFactor = - warpFactor * log(1.0/nStates - exp(-nStates/(nStates-1)*v_typical)/nStates); parLength[i+j*nRoot] += divFactor * length; } } } /* find the min length and the sum for the forward move */ minLength = -1.0; for (j=0; j parLength[i+j*nRoot] || minLength < 0.0) minLength = parLength[i+j*nRoot]; } sum1 = 0.0; tempc = 0.0; for (j=0; j ran) { /* proposed new attaching position */ newA = pRoot[i]; newC = pCrown[j]; goto outLoop; } } outLoop:; iA = i; jC = j; /* calculate the proposal ratio */ (*lnProposalRatio) = parLength[i+j*nRoot] - minLength + log(sum1); /* find the min length and the sum for the backward move */ minLength = -1.0; for (j=0; j parLength[i+j*nRoot] || minLength < 0.0) minLength = parLength[i+j*nRoot]; } sum2 = 0.0; tempc = 0.0; for (j=0; janc; newA->anc = u; if (u->left == v) u->right = newA; else u->left = newA; u->anc = newB; if (newB->left == newA) newB->left = u; else newB->right = u; /* transfer lock and reassign branch lengths, if necessary */ if (newA != a) { /* if u is locked, then we have moved upwards and need to leave the u lock behind */ if (u->isLocked == YES) { u->isLocked = NO; a->isLocked = YES; a->lockID = u->lockID; u->lockID = -1; } p = newA; while (p->anc != NULL) { if (p == a) break; p = p->anc; } if (p == a) { /* newA is descendant to a so move a->length not u->length */ x = u->length; u->length = a->length; a->length = x; } p = b; while (p->anc != NULL) { if (p == newA) break; p = p->anc; } if (p == newA) { /* newA is ancestor to a so insert above instead of below */ x = newA->length; newA->length = u->length; u->length = x; /* newA is on root path and locked, we need to transfer lock to u */ if (newA->isLocked == YES) { u->isLocked = YES; u->lockID = newA->lockID; newA->isLocked = NO; newA->lockID = -1; } } /* hit u length with multiplier */ x = u->length * exp(tuning * (RandomNumber(seed) - 0.5)); while (x < minV || x > maxV) { if (x < minV) x = minV * minV / x; if (x > maxV) x = maxV * maxV / x; } /* calculate proposal and prior ratio based on length modification */ (*lnProposalRatio) += log (x / u->length); if (isVPriorExp == YES) (*lnPriorRatio) += (mp->brlensExp) * (u->length - x); u->length = x; /* set tiprobs update flags */ u->upDateTi = YES; newA->upDateTi = YES; a->upDateTi = YES; } r = newC; q = newB = newC->anc; if (newC != c) // crown part has changed { /* rotate nodes from newC to c or d (whichever is closest) */ tempc = r->length; while (r != c && r != d) { p = q->anc; /* rotate pointers of q */ if (q->left == r) q->left = p; else q->right = p; q->anc = r; /* swap q and old */ tempy = q->length; q->length = tempc; q->upDateTi = YES; tempc = tempy; /* make sure we get q and r initialized for next round */ r = q; q = p; } newB->length = tempc; /* hit newB length with multiplier */ x = newB->length * exp(tuning * (RandomNumber(seed) - 0.5)); while (x < minV || x > maxV) { if (x < minV) x = minV * minV / x; if (x > maxV) x = maxV * maxV / x; } /* calculate proposal and prior ratio based on length modification */ (*lnProposalRatio) += log (x / newB->length); if (isVPriorExp == YES) (*lnPriorRatio) += (mp->brlensExp) * (newB->length - x); newB->length = x; newB->upDateTi = YES; } /* reattach the crown part */ v->left = newC; v->right = newB; newC->anc = newB->anc = v; topologyHasChanged = YES; /* hit v length with multiplier */ x = v->length * exp(tuning * (RandomNumber(seed) - 0.5)); while (x < minV || x > maxV) { if (x < minV) x = minV * minV / x; if (x > maxV) x = maxV * maxV / x; } /* calculate proposal and prior ratio based on length modification */ (*lnProposalRatio) += log (x / v->length); if (isVPriorExp == YES) (*lnPriorRatio) += (mp->brlensExp) * (v->length - x); v->length = x; v->upDateTi = YES; /* set flags for update of cond likes */ p = u; while (p->anc != NULL) { p->upDateCl = YES; p = p->anc; } p = b; while (p->anc != NULL) { p->upDateCl = YES; p = p->anc; } p = newC; while (p->anc != NULL) { p->upDateCl = YES; p = p->anc; } p = r; while (p->anc != NULL) { p->upDateCl = YES; p = p->anc; } /* if tree is rooted, swap indices of former and current interior root node, if changed */ //SK if (t->isRooted && interiorRoot != t->root->left) { tempInt = interiorRoot->index; interiorRoot->index = t->root->left->index; t->root->left->index = tempInt; } /* get down pass sequence if tree topology has changed */ if (topologyHasChanged == YES) { GetDownPass (t); } /* Dirichlet or twoExp prior */ if (isVPriorExp > 1) (*lnPriorRatio) += LogDirPrior(t, mp, isVPriorExp); /* free up local memory */ free (parLength); free (pRoot); free (pCrown); free (nSitesOfPat); return (NO_ERROR); errorExit: MrBayesPrint ("%s Problem allocating memory in Move_ParsTBR\n", spacer); free (parLength); free (pRoot); free (pCrown); free (nSitesOfPat); return (ERROR); } int Move_Pinvar (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp) { /* change proportion of invariable sites (pInvar) */ int i, c, isValidP, *rateCat, nGammaCats; MrBFlt oldP, newP, window, minP, maxP, ran, lnInvarRatio, lnVarRatio; CLFlt *nSitesOfPat; ModelParams *mp; ModelInfo *m; /* get size of window, centered on current pInvar value */ window = mvp[0]; /* get model params */ mp = &modelParams[param->relParts[0]]; /* get minimum and maximum values for pInvar */ minP = mp->pInvarUni[0]; maxP = mp->pInvarUni[1]; /* get old value of pInvar */ oldP = *GetParamVals(param, chain, state[chain]); /* change value for pInvar */ ran = RandomNumber(seed); if (maxP-minP < window) { window = maxP-minP; } newP = oldP + window * (ran - 0.5); /* check validity */ isValidP = NO; do { if (newP < minP) newP = 2* minP - newP; else if (newP > maxP) newP = 2 * maxP - newP; else isValidP = YES; } while (isValidP == NO); /* get proposal ratio */ *lnProposalRatio = 0.0; /* get prior ratio */ *lnPriorRatio = 0.0; lnInvarRatio = log(newP) - log(oldP); lnVarRatio = log(1.0-newP) - log(1.0-oldP); for (i=0; inRelParts; i++) { m = &modelSettings[param->relParts[i]]; if (m->gibbsGamma == YES) { /* find rate category index and number of gamma categories */ rateCat = m->tiIndex + chain * m->numChars; nGammaCats = m->numRateCats; /* find nSitesOfPat */ nSitesOfPat = numSitesOfPat + (chainId[chain] % chainParams.numChains)*numCompressedChars + m->compCharStart; /* loop over characters */ for (c=0; cnumChars; c++) { if (rateCat[c] < nGammaCats) *lnPriorRatio += lnVarRatio * nSitesOfPat[c]; else *lnPriorRatio += lnInvarRatio * nSitesOfPat[c]; } } } /* copy new pInvar value back */ *GetParamVals(param, chain, state[chain]) = newP; /* Set update flags for all partitions that share this pInvar. Note that the conditional likelihood update flags for divisions have been set before we even call this function. */ for (i=0; inRelParts; i++) TouchAllTreeNodes(&modelSettings[param->relParts[i]],chain); /* TODO: However, you do need to update cijk flags if this is a covarion model */ return (NO_ERROR); } int Move_PopSize_M (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp) { int isValidN, valIndex; MrBFlt *valPtr, oldN, newN, tuning, minN, maxN, ran, oldLnPrior=0.0, newLnPrior=0.0, growth, oldT, newT, clockRate; ModelParams *mp; ModelInfo *m; Tree *t; /* get multiplier tuning parameter */ tuning = mvp[0]; /* get model params */ mp = &modelParams[param->relParts[0]]; /* get model settings */ m = &modelSettings[param->relParts[0]]; /* get minimum and maximum values for population size */ if (param->paramId == POPSIZE_UNI) { minN = mp->popSizeUni[0]; maxN = mp->popSizeUni[1]; } else { minN = 0.00000001; maxN = 100000000; } /* get pointer to value to be changed */ valIndex = (int)(RandomNumber(seed) * param->nValues); valPtr = GetParamVals(param, chain, state[chain]) + valIndex; /* get old value of population size */ oldN = *valPtr; /* get old prior for species tree coalescence */ if (m->brlens->paramId == BRLENS_CLOCK_SPCOAL) { oldLnPrior = LnSpeciesTreeProb(chain); } /* change value for theta */ ran = RandomNumber(seed); newN = oldN * exp(tuning * (ran - 0.5)); /* check that new value is valid */ isValidN = NO; do { if (newN < minN) newN = 2* minN - newN; else if (newN > maxN) newN = 2 * maxN - newN; else isValidN = YES; } while (isValidN == NO); /* copy new population size value back */ (*valPtr) = newN; /* get proposal ratio */ *lnProposalRatio = log (newN / oldN); /* get prior ratio */ if (m->brlens->paramId == BRLENS_CLOCK_SPCOAL) { newLnPrior = LnSpeciesTreeProb(chain); } else { t = GetTree(m->brlens, chain, state[chain]); clockRate = *GetParamVals(m->clockRate, chain, state[chain]); if (!strcmp(mp->ploidy, "Diploid")) clockRate *= 4.0; else if (!strcmp(mp->ploidy, "Zlinked")) clockRate *= 3.0; else clockRate *= 2.0; newT = newN * clockRate; oldT = oldN * clockRate; if (!strcmp(mp->growthPr, "Fixed")) growth = mp->growthFix; else growth = *(GetParamVals (m->growthRate, chain, state[chain])); if (LnCoalescencePriorPr (t, &oldLnPrior, oldT, growth) == ERROR) { MrBayesPrint ("%s Problem calculating prior for coalescent process\n", spacer); return (ERROR); } if (LnCoalescencePriorPr (t, &newLnPrior, newT, growth) == ERROR) { MrBayesPrint ("%s Problem calculating prior for coalescent process\n", spacer); return (ERROR); } } (*lnPriorRatio) = param->LnPriorRatio(newN, oldN, param->priorParams); (*lnPriorRatio) += newLnPrior - oldLnPrior; return (NO_ERROR); } /* Generalized lognormal move for positive real random variables */ int Move_PosRealLognormal (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp) { int i; MrBFlt oldX, newX, minX, maxX, tuning, u, z; /* get tuning parameter */ tuning = mvp[0]; /* get minimum and maximum values for X */ minX = param->min; maxX = param->max; /* get old value of X */ oldX = *GetParamVals(param, chain, state[chain]); /* change value of X */ u = RandomNumber(seed); z = PointNormal(u); newX = exp (log(oldX) + z * tuning); /* check that new value is valid */ if (newX < minX || newX > maxX) { abortMove = YES; return (NO_ERROR); } /* get proposal ratio */ (*lnProposalRatio) = log (newX / oldX); /* get prior ratio */ (*lnPriorRatio) = param->LnPriorRatio(newX, oldX, param->priorParams); /* copy new value back */ *GetParamVals(param, chain, state[chain]) = newX; /* Set update flags for tree nodes if relevant */ if (param->affectsLikelihood == YES) { for (i=0; inRelParts; i++) TouchAllTreeNodes(&modelSettings[param->relParts[i]],chain); } return (NO_ERROR); } /* Generalized multiplier move for positive real random variables */ int Move_PosRealMultiplier (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp) { int i, isValid; MrBFlt oldX, newX, minX, maxX, tuning, ran, factor; /* get tuning parameter */ tuning = mvp[0]; /* get minimum and maximum values for X */ minX = param->min; maxX = param->max; /* get old value of X */ oldX = *GetParamVals(param, chain, state[chain]); /* change value of X */ ran = RandomNumber(seed); factor = exp(tuning * (ran - 0.5)); newX = oldX * factor; /* check that new value is valid */ isValid = NO; do { if (newX < minX) newX = minX * minX / newX; else if (newX > maxX) newX = maxX * maxX / newX; else isValid = YES; } while (isValid == NO); /* get proposal ratio */ (*lnProposalRatio) = log (newX / oldX); /* get prior ratio */ (*lnPriorRatio) = param->LnPriorRatio(newX, oldX, param->priorParams); /* copy new value back */ *(GetParamVals(param, chain, state[chain])) = newX; /* Set update flags for tree nodes if relevant */ if (param->affectsLikelihood == YES) { for (i=0; inRelParts; i++) TouchAllTreeNodes(&modelSettings[param->relParts[i]],chain); } return (NO_ERROR); } /* Generalized normal move for real random variables */ int Move_RealNormal (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp) { int i; MrBFlt oldX, newX, tuning, minX, maxX, u, z; /* get tuning parameter */ tuning = mvp[0]; /* get minimum and maximum values for X */ minX = param->min; maxX = param->max; /* get old value of X */ oldX = *GetParamVals(param, chain, state[chain]); /* change value */ u = RandomNumber(seed); z = PointNormal(u); newX = oldX + z * tuning; /* check that new value is valid */ if (newX < minX || newX > maxX) { abortMove = YES; return (NO_ERROR); } /* get proposal ratio */ (*lnProposalRatio) = 0.0; /* get prior ratio */ (*lnPriorRatio) = param->LnPriorRatio(newX, oldX, param->priorParams); /* copy new value back */ *GetParamVals(param, chain, state[chain]) = newX; /* Set update flags for tree nodes if relevant */ if (param->affectsLikelihood == YES) { for (i=0; inRelParts; i++) TouchAllTreeNodes(&modelSettings[param->relParts[i]],chain); } return (NO_ERROR); } /* Generalized slider move for real random variables */ int Move_RealSlider (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp) { int i, isValid; MrBFlt oldX, newX, window, minX, maxX, u; /* get size of window, centered on current value */ window = mvp[0]; /* get minimum and maximum values for X */ minX = param->min; maxX = param->max; /* get old value of X */ oldX = *GetParamVals(param, chain, state[chain]); /* change value */ u = RandomNumber(seed); newX = oldX + window * (u - 0.5); /* check that new value is valid */ isValid = NO; do { if (newX < minX) newX = 2* minX - newX; else if (newX > maxX) newX = 2 * maxX - newX; else isValid = YES; } while (isValid == NO); /* get proposal ratio */ (*lnProposalRatio) = 0.0; /* get prior ratio */ (*lnPriorRatio) = param->LnPriorRatio(newX, oldX, param->priorParams); /* copy new value back */ *GetParamVals(param, chain, state[chain]) = newX; /* Set update flags for tree nodes if relevant */ if (param->affectsLikelihood == YES) { for (i=0; inRelParts; i++) TouchAllTreeNodes(&modelSettings[param->relParts[i]],chain); } return (NO_ERROR); } /*---------------------------------------------------------------- | | Move_RateMult_Dir: Change rate multiplier using Dirichlet | proposal. | ----------------------------------------------------------------*/ int Move_RateMult_Dir (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp) { int i, nRates, isValid; MrBFlt alphaPi, *value, *subValue, numSites, *alphaDir, x, y, sum, rate_pot, *dirParm, *oldRate, *newRate; /* allocate memory */ dirParm = (MrBFlt *) SafeCalloc (3*numCurrentDivisions, sizeof(MrBFlt)); oldRate = dirParm + numCurrentDivisions; newRate = dirParm + 2*numCurrentDivisions; /* get number of rates */ nRates = param->nValues; /* get pointer to rates and number of uncompressed chars */ value = GetParamVals(param, chain, state[chain]); subValue = GetParamSubVals(param, chain, state[chain]); /* get Dirichlet parameters */ alphaDir = subValue + nRates; /* calculate old ratesum proportions */ numSites = 0.0; for (i=0; inRelParts; i++) TouchAllTreeNodes(&modelSettings[param->relParts[i]],chain); /* may need to hit update flag for cijks when you have a covarion model */ for (i=0; inRelParts; i++) if (modelSettings[param->relParts[i]].nCijkParts > 1) modelSettings[param->relParts[i]].upDateCijk = YES; free (dirParm); return (NO_ERROR); } /*---------------------------------------------------------------- | | Move_RateMult_Slider: Change rate multiplier using slider | proposal. | ----------------------------------------------------------------*/ int Move_RateMult_Slider (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp) { int i, indexI, indexJ, nRates; MrBFlt delta, *value, *subValue, sum, numSites, *alphaDir, x, y, oldRate[2], newRate[2], min, max; /* get number of rates */ nRates = param->nValues; /* get pointer to rates and number of uncompressed chars */ value = GetParamVals(param, chain, state[chain]); subValue = GetParamSubVals(param, chain, state[chain]); /* get Dirichlet prior parameters */ alphaDir = subValue + nRates; /* randomly select two rates */ indexI = (int) (RandomNumber(seed) * nRates); indexJ = (int) (RandomNumber(seed) * (nRates - 1)); if (indexJ == indexI) indexJ = nRates - 1; /* calculate old ratesum proportions */ numSites = 0.0; for (i=0; i max-min) /* we do it to avoid following long while loop in case if delta is high */ delta = max-min; x = oldRate[0] / sum; y = x + delta * (RandomNumber(seed) - 0.5); while (y < min || y > max) { if (y < min) y = 2.0 * min - y; if (y > max) y = 2.0 * max - y; } /* set the new values */ newRate[0] = y * sum; newRate[1] = sum - newRate[0]; value[indexI] = newRate[0] * numSites / subValue[indexI]; value[indexJ] = newRate[1] * numSites / subValue[indexJ]; /* get proposal ratio */ (*lnProposalRatio) = 0.0; /* get prior ratio */ (*lnPriorRatio) = (alphaDir[indexI]-1.0) * (log(newRate[0]) - log(oldRate[0])); (*lnPriorRatio) += (alphaDir[indexJ]-1.0) * (log(newRate[1]) - log(oldRate[1])); /* Set update flags for all partitions that share the rate multiplier. Note that the conditional likelihood update flags have been set before we even call this function. */ for (i=0; inRelParts; i++) TouchAllTreeNodes(&modelSettings[param->relParts[i]],chain); /* may need to hit update flag for cijks when you have a covarion model */ for (i=0; inRelParts; i++) if (modelSettings[param->relParts[i]].nCijkParts > 1) modelSettings[param->relParts[i]].upDateCijk = YES; return (NO_ERROR); } int Move_RateShape_M (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp) { /* change gamma/lnorm shape parameter using multiplier */ int i, isAPriorExp, isValidA; MrBFlt oldA, newA, minA, maxA, ran, factor, tuning, *rates; ModelParams *mp; /* get tuning parameter */ tuning = mvp[0]; /* get model params */ mp = &modelParams[param->relParts[0]]; /* get minimum and maximum values for alpha */ if (param->paramId == SHAPE_UNI) { minA = mp->shapeUni[0]; maxA = mp->shapeUni[1]; if (minA < MIN_SHAPE_PARAM) minA = MIN_SHAPE_PARAM; if (maxA > MAX_SHAPE_PARAM) maxA = MAX_SHAPE_PARAM; isAPriorExp = NO; } else { minA = MIN_SHAPE_PARAM; maxA = MAX_SHAPE_PARAM; isAPriorExp = YES; } /* get old value of alpha */ oldA = *GetParamVals(param, chain, state[chain]); /* change value for alpha */ ran = RandomNumber(seed); factor = exp(tuning * (ran - 0.5)); newA = oldA * factor; /* check validity */ isValidA = NO; do { if (newA < minA) newA = minA * minA / newA; else if (newA > maxA) newA = maxA * maxA / newA; else isValidA = YES; } while (isValidA == NO); /* get proposal ratio */ *lnProposalRatio = log(newA / oldA); /* get prior ratio */ if (isAPriorExp == NO) *lnPriorRatio = 0.0; else *lnPriorRatio = -(mp->shapeExp) * (newA - oldA); /* copy new alpha value back */ *GetParamVals(param, chain, state[chain]) = newA; /* now, update rate category information */ rates = GetParamSubVals (param, chain, state[chain]); if(!strcmp(mp->ratesModel, "LNorm")) { if (DiscreteLogNormal (rates, newA, mp->numGammaCats, 1) == ERROR) return (ERROR); } else /* gamma rate */ { if (DiscreteGamma (rates, newA, newA, mp->numGammaCats, 0) == ERROR) return (ERROR); } /* Set update flags for all partitions that share this alpha. Note that the conditional likelihood update flags have been set before we even call this function. */ for (i=0; inRelParts; i++) TouchAllTreeNodes(&modelSettings[param->relParts[i]],chain); /* We need to update flags when we have a covarion model */ for (i=0; inRelParts; i++) if (modelSettings[param->relParts[i]].nCijkParts > 1) modelSettings[param->relParts[i]].upDateCijk = YES; return (NO_ERROR); } /*---------------------------------------------------------------- | | Move_Revmat_Dir: Change rate matrix using Dirichlet proposal | mechanism. | ----------------------------------------------------------------*/ int Move_Revmat_Dir (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp) { /* change revMat using Dirichlet proposal */ int i, nRates,isValid; MrBFlt oldRate[200], newRate[200], dirParm[200], *value, sum, x, y, rate_pot, *alphaDir, alphaPi; ModelParams *mp; ModelInfo *m; /* get model params and settings */ mp = &modelParams[param->relParts[0]]; m = &modelSettings[param->relParts[0]]; /* get rates and nRates */ value = GetParamVals(param, chain, state[chain]); nRates = param->nValues; /* get so called alpha_pi parameter and adjust for number of components */ alphaPi = mvp[0] * nRates; /* get Dirichlet parameters */ if (m->dataType == PROTEIN) alphaDir = mp->aaRevMatDir; else alphaDir = mp->revMatDir; /* copy old rates */ for (i=0; inRelParts; i++) TouchAllTreeNodes(&modelSettings[param->relParts[i]],chain); /* Set update flags for cijks for all affected partitions */ for (i=0; inRelParts; i++) modelSettings[param->relParts[i]].upDateCijk = YES; return (NO_ERROR); } /*---------------------------------------------------------------- | | Move_Revmat_DirMix: Dirichlet proposal for REVMAT_MIX. From | Huelsenbeck et al. (2004), but note that the prior density | is different in that paper because they set the rate sum | to 6, not to 1. | ----------------------------------------------------------------*/ int Move_Revmat_DirMix (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp) { int i, j, k, isValid, *growthFxn, nRates, groupSize[6]; MrBFlt *value, dirParm[6], newRate[6], oldRate[6], alphaPi, symDir, sum, rate_pot, x, y; ModelParams *mp; /* get model params and settings */ mp = &modelParams[param->relParts[0]]; /* get growthFunction and nRates */ value = GetParamVals (param, chain, state[chain]); growthFxn = GetParamIntVals (param, chain, state[chain]); nRates = GetKFromGrowthFxn(growthFxn); /* we can't do anything if there is only one rate */ if (nRates == 1) { abortMove = YES; return (NO_ERROR); } /* extract unique rates from value vector */ for (i=0; irevMatSymDir; /* multiply old ratesum props with some large number to get new values close to the old ones */ for (i=0; inRelParts; i++) TouchAllTreeNodes(&modelSettings[param->relParts[i]],chain); /* Set update flags for cijks for all affected partitions */ for (i=0; inRelParts; i++) modelSettings[param->relParts[i]].upDateCijk = YES; return (NO_ERROR); } /*---------------------------------------------------------------- | | Move_Revmat_Slider: Change rate matrix using sliding window | move. Choose a pair of rates (e.g. r(A<>C), and r(A<>G)) at | random and denote them rA, and rB. Let oldProp = rA/(rA + rB) | and newProp = oldProp + delta(U - 0.5), where U is a uniform | random variable on the interval (0, 1] and delta is a tuning | parameter. Values that fall outside the boundaries are reflected | back in. Then set new_rA = newProp*(rA+rB) and new_rB = | (1-newProp)*(piA+piB). The Hastings ratio of this move is 1.0. | ----------------------------------------------------------------*/ int Move_Revmat_Slider (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp) { int i, j, nRates; MrBFlt delta, *newRate, *oldRate, *priorAlpha, x, y, sum, min, max; ModelParams *mp; ModelInfo *m; /* get model params and settings */ mp = &modelParams[param->relParts[0]]; m = &modelSettings[param->relParts[0]]; /* get Dirichlet parameters */ if (m->dataType == PROTEIN) priorAlpha = mp->aaRevMatDir; else priorAlpha = mp->revMatDir; /* get the values we need */ nRates = param->nValues; newRate = GetParamVals (param, chain, state[chain]); oldRate = GetParamVals (param, chain, state[chain] ^ 1); /* choose a pair to change */ i = (int) (RandomNumber(seed) * nRates); j = (int) (RandomNumber(seed) * (nRates-1)); if (i == j) j = nRates-1; /* find new proportion */ sum = oldRate[i] + oldRate[j]; /* get window size */ delta = mvp[0]; /* reflect */ min = RATE_MIN / sum; max = 1.0 - min; if (delta > max-min) /* we do it to avoid following long while loop in case if delta is high */ delta = max-min; x = oldRate[i] / sum; y = x + delta * (RandomNumber(seed) - 0.5); while (y < min || y > max) { if (y < min) y = 2.0 * min - y; if (y > max) y = 2.0 * max - y; } /* set the new values */ newRate[i] = y * sum; newRate[j] = sum - newRate[i]; /* get proposal ratio */ (*lnProposalRatio) = 0.0; /* get prior ratio */ (*lnPriorRatio) = (priorAlpha[i]-1.0) * (log(newRate[i]) - log(oldRate[i])); (*lnPriorRatio) += (priorAlpha[j]-1.0) * (log(newRate[j]) - log(oldRate[j])); /* Set update for entire tree */ for (i=0; inRelParts; i++) TouchAllTreeNodes(&modelSettings[param->relParts[i]],chain); /* Set update flags for cijks for all affected partitions. If this is a simple 4 X 4 model, we don't take any hit, because we will never go into a general transition probability calculator. However, for many models we do want to update the cijk flag, as the transition probability matrices require diagonalizing the rate matrix. */ for (i=0; inRelParts; i++) modelSettings[param->relParts[i]].upDateCijk = YES; return (NO_ERROR); } /*---------------------------------------------------------------- | | Move_Revmat_SplitMerge1: Split or merge rates of rate matrix. | See Huelsenbeck et al. (2004). Note that the prior used | here is different from theirs. Also, a Beta proposal is | used instead of a uniform to propose new rate proportions. | ----------------------------------------------------------------*/ int Move_Revmat_SplitMerge1 (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp) { int i, j, k, index_i, index_j, n_i, n_j, foundFirstI, foundFirstJ, *newGrowthFxn, *oldGrowthFxn, nOldRates, nNewRates, merge, groupSize[6], nCompositeRates; MrBFlt R, R_i, R_j, *newValue, *oldValue, newRate[6], oldRate[6], symDir, prob_split, prob_merge, dirParm[2], rateProps[2], x, alphaPi; ModelParams *mp; /* get model params and settings */ mp = &modelParams[param->relParts[0]]; /* get the values we need */ oldValue = GetParamVals(param, chain, state[chain] ^ 1); newValue = GetParamVals(param, chain, state[chain]); oldGrowthFxn = GetParamIntVals(param, chain, state[chain] ^ 1); newGrowthFxn = GetParamIntVals (param, chain, state[chain]); nOldRates = GetKFromGrowthFxn(oldGrowthFxn); symDir = mp->revMatSymDir; alphaPi = mvp[0]; /* tuning parameter alpha */ /* get the old rates */ for (i=0; i index_j) { i = index_i; index_i = index_j; index_j = i; } /* find group sizes */ n_i = n_j = 0; for (i=0; i<6; i++) { if (oldGrowthFxn[i] == index_i) n_i++; else if (oldGrowthFxn[i] == index_j) n_j++; } /* adjust growth function */ for (i=0; i<6; i++) { if (oldGrowthFxn[i] == index_j) newGrowthFxn[i] = index_i; else if (oldGrowthFxn[i] > index_j) newGrowthFxn[i] = oldGrowthFxn[i] - 1; else newGrowthFxn[i] = oldGrowthFxn[i]; } /* find the new rates */ for (i=0; i= index_j) newRate[i] = oldRate[i+1]; } /* copy new unique rate values back into the value array */ for (i=0; i 1) nCompositeRates++; } /* calculate prior ratio (different in the paper) */ (*lnPriorRatio) = LnGamma(n_i * symDir) + LnGamma(n_j * symDir) - LnGamma ((n_i + n_j) * symDir); (*lnPriorRatio) += ((n_i + n_j) * symDir - 1.0) * log(R) - (n_i * symDir - 1.0) * log(R_i) - (n_j * symDir - 1.0) * log(R_j); /* calculate proposal ratio */ (*lnProposalRatio) = log ((prob_split / prob_merge) * ((nOldRates * (nOldRates - 1)) / (2.0 * nCompositeRates)) * (1.0 / ((pow(2, n_i + n_j - 1) - 1)))); /* adjust for Beta proposal in back move */ dirParm[0] = alphaPi * n_i; dirParm[1] = alphaPi * n_j; rateProps[0] = R_i / R; rateProps[1] = R_j / R; x = LnGamma(dirParm[0] + dirParm[1]); x -= LnGamma(dirParm[0]); x -= LnGamma(dirParm[1]); x += (dirParm[0] - 1.0) * log(rateProps[0]); x += (dirParm[1] - 1.0) * log(rateProps[1]); (*lnProposalRatio) += x; /* Jacobian for the rate proportion */ (*lnProposalRatio) -= log(R); } else { /* split two rates */ nNewRates = nOldRates + 1; /* determine split and merge probs */ if (nNewRates == 6) prob_merge = 1.0; else prob_merge = 0.5; if (nOldRates == 1) prob_split = 1.0; else prob_split = 0.5; /* check group sizes before split */ for (i=0; i 1) nCompositeRates++; } /* randomly select a rate with two or more components to split */ k = (int) (RandomNumber(seed) * nCompositeRates); for (i=j=0; i 1) { if (k == j) break; j++; } } assert (i < nOldRates && groupSize[i] > 1); index_i = i; /* adjust growth function */ do { foundFirstI = foundFirstJ = NO; k = 0; index_j = -1; for (i=0; i<6; i++) { if (oldGrowthFxn[i] == index_i) { if (foundFirstI == NO) { newGrowthFxn[i] = index_i; foundFirstI = YES; } else { if (RandomNumber(seed) < 0.5) { if (foundFirstJ == NO) { foundFirstJ = YES; index_j = k + 1; /* one more than previous max */ newGrowthFxn[i] = index_j; } else { newGrowthFxn[i] = index_j; } } else newGrowthFxn[i] = index_i; } } else if (foundFirstJ == YES && oldGrowthFxn[i] >= index_j) newGrowthFxn[i] = oldGrowthFxn[i] + 1; else newGrowthFxn[i] = oldGrowthFxn[i]; if (foundFirstJ == NO && oldGrowthFxn[i] > k) k = oldGrowthFxn[i]; } } while (foundFirstJ == NO); /* find group sizes */ n_i = n_j = 0; for (i=0; i<6; i++) { if (newGrowthFxn[i] == index_i) n_i++; else if (newGrowthFxn[i] == index_j) n_j++; } /* find old rate */ R = oldRate[index_i]; /* propose new rates */ dirParm[0] = alphaPi * n_i; dirParm[1] = alphaPi * n_j; DirichletRandomVariable(dirParm, rateProps, 2, seed); R_i = rateProps[0] * R; R_j = rateProps[1] * R; if (R_i/n_i < RATE_MIN) { R_i = RATE_MIN*n_i; rateProps[0] = R_i/R; rateProps[1] = 1-rateProps[0]; R_j = rateProps[1] * R; assert (R_j/n_j < RATE_MIN); } else if (R_j/n_j < RATE_MIN) { R_j = RATE_MIN*n_j; rateProps[1] = R_j/R; rateProps[0] = 1-rateProps[1]; R_i = rateProps[0] * R; assert (R_i/n_i < RATE_MIN); } /* set the new rates */ for (i=0; i index_j) newRate[i] = oldRate[i-1]; else newRate[i] = oldRate[i]; } /* copy new unique rate values back into the value array */ for (i=0; inRelParts; i++) TouchAllTreeNodes(&modelSettings[param->relParts[i]],chain); /* Set update flags for cijks for all affected partitions */ for (i=0; inRelParts; i++) modelSettings[param->relParts[i]].upDateCijk = YES; return (NO_ERROR); } /*---------------------------------------------------------------- | | Move_Revmat_SplitMerge2: Componentwise split or merge move. | ----------------------------------------------------------------*/ int Move_Revmat_SplitMerge2 (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp) { int i, k, n_i, n_j, index_i, index_j, groupIndex_i, groupIndex_j, *newGrowthFxn, *oldGrowthFxn; MrBFlt R_i, R_j, r_j, alphaPi, *newValue, *oldValue, symDir, dirParm[2], rateProps[2], x; ModelParams *mp; /* get model params and settings */ mp = &modelParams[param->relParts[0]]; /* get the values we need */ oldValue = GetParamVals(param, chain, state[chain] ^ 1); newValue = GetParamVals(param, chain, state[chain]); oldGrowthFxn = GetParamIntVals(param, chain, state[chain] ^ 1); newGrowthFxn = GetParamIntVals (param, chain, state[chain]); symDir = mp->revMatSymDir; alphaPi = mvp[0]; /* tuning parameter */ /* pick two component rates at random without replacement */ index_i = (int) (RandomNumber(seed) * 6); index_j = (int) (RandomNumber(seed) * 5); if (index_j == index_i) index_j = 5; groupIndex_i = oldGrowthFxn[index_i]; groupIndex_j = oldGrowthFxn[index_j]; if (oldGrowthFxn[index_i] != oldGrowthFxn[index_j]) { /* the rates are different, so merge them */ /* calculate n_i, n_j, R_i and R_j before merge */ n_i = n_j = 0; R_i = R_j = 0.0; for (i=0; i<6; i++) { if (oldGrowthFxn[i] == groupIndex_i) { n_i++; R_i += oldValue[i]; } if (oldGrowthFxn[i] == groupIndex_j) { n_j++; R_j += oldValue[i]; } } /* merge component rates by adding j to i */ newGrowthFxn[index_j] = oldGrowthFxn[index_i]; /* select a new rate for r_j */ if (n_j == 1) r_j = oldValue[index_j]; else { dirParm[0] = alphaPi * 1; dirParm[1] = alphaPi * (n_j - 1); DirichletRandomVariable(dirParm, rateProps, 2, seed); r_j = rateProps[0] * R_j; if (R_j - r_j < RATE_MIN) { r_j = R_j - RATE_MIN; rateProps[0] = r_j/R_j; rateProps[1] = 1 - rateProps[0]; } } /* update new growth function */ UpdateGrowthFxn(newGrowthFxn); /* we divide R_i + r_j equally among components of merged group, and R_j - r_j equally among split group */ for (i=0; i<6; i++) { if (oldGrowthFxn[i] == oldGrowthFxn[index_i] || i == index_j) newValue[i] = (R_i + r_j) / (MrBFlt)(n_i + 1); else if (oldGrowthFxn[i] == oldGrowthFxn[index_j]) newValue[i] = (R_j - r_j) / (MrBFlt)(n_j - 1); else newValue[i] = oldValue[i]; } /* calculate prior ratio */ if (n_j > 1) { /* no category disappeared */ (*lnPriorRatio) += LnGamma (n_i * symDir) + LnGamma(n_j * symDir); (*lnPriorRatio) -= LnGamma((n_i +1)* symDir) + LnGamma((n_j-1) * symDir); (*lnPriorRatio) += ((n_i + 1) * symDir - 1.0) * log(R_i + r_j) + ((n_j - 1) * symDir - 1.0) * log(R_j - r_j); (*lnPriorRatio) -= (n_i * symDir - 1.0) * log(R_i) + (n_j * symDir - 1.0) * log(R_j); } else { /* j category disappeared */ (*lnPriorRatio) += LnGamma (n_i * symDir) + LnGamma(n_j * symDir); (*lnPriorRatio) -= LnGamma((n_i +1)* symDir); (*lnPriorRatio) += ((n_i + 1) * symDir - 1.0) * log(R_i + r_j); (*lnPriorRatio) -= (n_i * symDir - 1.0) * log(R_i) + (n_j * symDir - 1.0) * log(R_j); } /* calculate proposal ratio; this is the probability of choosing the right category for rate j when splitting */ k = GetKFromGrowthFxn(newGrowthFxn); (*lnProposalRatio) = log (1.0 / k); /* adjust for Beta proposal in back move */ dirParm[0] = alphaPi * 1; dirParm[1] = alphaPi * n_i; rateProps[0] = r_j / (R_i + r_j); rateProps[1] = 1.0 - rateProps[0]; x = LnGamma(dirParm[0] + dirParm[1]); x -= LnGamma(dirParm[0]); x -= LnGamma(dirParm[1]); x += (dirParm[0] - 1.0) * log(rateProps[0]); x += (dirParm[1] - 1.0) * log(rateProps[1]); (*lnProposalRatio) += x; /* adjust for Beta proposal in forward move */ if (n_j > 1) { dirParm[0] = alphaPi * 1; dirParm[1] = alphaPi * n_j; rateProps[0] = r_j / R_j; rateProps[1] = 1.0 - rateProps[0]; x = LnGamma(dirParm[0] + dirParm[1]); x -= LnGamma(dirParm[0]); x -= LnGamma(dirParm[1]); x += (dirParm[0] - 1.0) * log(rateProps[0]); x += (dirParm[1] - 1.0) * log(rateProps[1]); (*lnProposalRatio) -= x; } /* Jacobian */ (*lnProposalRatio) -= log (R_i + r_j); if (n_j > 1) (*lnProposalRatio) += log (R_j); } else { /* split component rates because they are the same */ /* split component rates by selecting new group for j from (0,K), with j starting a new group if index becomes the same */ k = GetKFromGrowthFxn(oldGrowthFxn); newGrowthFxn[index_j] = (int) (RandomNumber(seed) * k); if (newGrowthFxn[index_j] == oldGrowthFxn[index_j]) newGrowthFxn[index_j] = k + 1; /* update growth function and group indices */ UpdateGrowthFxn(newGrowthFxn); groupIndex_i = newGrowthFxn[index_i]; groupIndex_j = newGrowthFxn[index_j]; /* calculate n_i, n_j, R_i and R_j after split */ n_i = n_j = 0; R_i = R_j = 0.0; for (i=0; i<6; i++) { if (i == index_j) { R_i += oldValue[i]; n_i++; } else if (newGrowthFxn[i] == groupIndex_i) { n_i++; R_i += oldValue[i]; } else if (newGrowthFxn[i] == groupIndex_j) { n_j++; R_j += oldValue[i]; } } /* select a new rate for r_j */ dirParm[0] = alphaPi * 1; dirParm[1] = alphaPi * (n_i - 1); DirichletRandomVariable(dirParm, rateProps, 2, seed); r_j = rateProps[0] * R_i; if (R_i-r_j < RATE_MIN) { r_j = R_i - RATE_MIN; rateProps[0] = r_j/R_i; rateProps[1] = 1 - rateProps[0]; } /* update n_i, n_j, R_i and R_j after split */ n_i -= 1; n_j += 1; R_i -= r_j; R_j += r_j; /* we divide R_i equally among remaining components of split group, and R_j equally among new or expanded group */ for (i=0; i<6; i++) { if (newGrowthFxn[i] == groupIndex_i) newValue[i] = R_i / (MrBFlt)(n_i); else if (newGrowthFxn[i] == groupIndex_j) newValue[i] = R_j / (MrBFlt)(n_j); else newValue[i] = oldValue[i]; } /* calculate prior ratio */ if (n_j > 1) { /* no new category created by split */ (*lnPriorRatio) += LnGamma((n_i +1)* symDir) + LnGamma((n_j-1) * symDir); (*lnPriorRatio) -= LnGamma (n_i * symDir) + LnGamma(n_j * symDir); (*lnPriorRatio) += (n_i * symDir - 1.0) * log(R_i) + (n_j * symDir - 1.0) * log(R_j); (*lnPriorRatio) -= ((n_i + 1) * symDir - 1.0) * log(R_i + r_j) + ((n_j - 1) * symDir - 1.0) * log(R_j - r_j); } else { /* new category created by split */ (*lnPriorRatio) += LnGamma((n_i +1)* symDir); (*lnPriorRatio) -= LnGamma (n_i * symDir) + LnGamma(n_j * symDir); (*lnPriorRatio) += (n_i * symDir - 1.0) * log(R_i) + (n_j * symDir - 1.0) * log(R_j); (*lnPriorRatio) -= ((n_i + 1) * symDir - 1.0) * log(R_i + r_j); } /* calculate proposal ratio; this is one over the probability of choosing the right category for rate j when splitting */ k = GetKFromGrowthFxn(oldGrowthFxn); (*lnProposalRatio) = log (k); /* adjust for Beta proposal in back move */ if (n_j > 1) { dirParm[0] = alphaPi * 1; dirParm[1] = alphaPi * (n_j - 1); rateProps[0] = r_j / R_j; rateProps[1] = 1.0 - rateProps[0]; x = LnGamma(dirParm[0] + dirParm[1]); x -= LnGamma(dirParm[0]); x -= LnGamma(dirParm[1]); x += (dirParm[0] - 1.0) * log(rateProps[0]); x += (dirParm[1] - 1.0) * log(rateProps[1]); (*lnProposalRatio) += x; } /* adjust for Beta proposal in forward move */ dirParm[0] = alphaPi * 1; dirParm[1] = alphaPi * n_i; rateProps[0] = r_j / (R_i + r_j); rateProps[1] = 1.0 - rateProps[0]; x = LnGamma(dirParm[0] + dirParm[1]); x -= LnGamma(dirParm[0]); x -= LnGamma(dirParm[1]); x += (dirParm[0] - 1.0) * log(rateProps[0]); x += (dirParm[1] - 1.0) * log(rateProps[1]); (*lnProposalRatio) -= x; /* Jacobian */ (*lnProposalRatio) += log (R_i + r_j); if (n_j > 1) (*lnProposalRatio) -= log (R_j); } /* Set update flags for all partitions that share this revmat. Note that the conditional likelihood update flags have been set before we even call this function. */ for (i=0; inRelParts; i++) TouchAllTreeNodes(&modelSettings[param->relParts[i]],chain); /* Set update flags for cijks for all affected partitions */ for (i=0; inRelParts; i++) modelSettings[param->relParts[i]].upDateCijk = YES; return (NO_ERROR); } int Move_Speciation (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp) { /* change speciation rate using sliding window */ int isLPriorExp, isValidL, valIndex; MrBFlt *valPtr, oldL, newL, minL, maxL, *sR, *eR, sF, *fR, oldLnPrior, newLnPrior, window, clockRate; char *sS; ModelParams *mp; ModelInfo *m; Tree *t; /* get size of window, centered on current value */ window = mvp[0]; /* get model params and settings */ mp = &modelParams[param->relParts[0]]; m = &modelSettings[param->relParts[0]]; /* get minimum and maximum values */ if (param->paramId == SPECRATE_UNI) { minL = mp->speciationUni[0]; maxL = mp->speciationUni[1]; isLPriorExp = NO; } else { minL = RATE_MIN; maxL = RATE_MAX; isLPriorExp = YES; } /* get pointer to value to be changed */ valIndex = (int)(RandomNumber(seed) * param->nValues); valPtr = GetParamVals(param, chain, state[chain]) + valIndex; /* get old value */ oldL = *valPtr; /* change value */ if (maxL-minL < window) window = maxL-minL; newL = oldL + window * (RandomNumber(seed) - 0.5); /* check that new value is valid */ isValidL = NO; do { if (newL < minL) newL = 2 * minL - newL; else if (newL > maxL) newL = 2 * maxL - newL; else isValidL = YES; } while (isValidL == NO); /* get proposal ratio */ *lnProposalRatio = 0.0; /* calculate prior ratio */ t = GetTree(m->brlens, chain, state[chain]); sR = GetParamVals (param, chain, state[chain]); eR = GetParamVals (m->extinctionRates, chain, state[chain]); sF = mp->sampleProb; sS = mp->sampleStrat; clockRate = *GetParamVals (m->clockRate, chain, state[chain]); if (!strcmp(mp->clockPr,"Birthdeath")) { if (LnBirthDeathPriorPr (t, clockRate, &oldLnPrior, *sR, *eR, sS, sF) == ERROR) { MrBayesPrint ("%s Problem calculating prior for birth-death process\n", spacer); return (ERROR); } *valPtr = newL; // update with new value if (LnBirthDeathPriorPr (t, clockRate, &newLnPrior, *sR, *eR, sS, sF) == ERROR) { MrBayesPrint ("%s Problem calculating prior for birth-death process\n", spacer); return (ERROR); } } else if (!strcmp(mp->clockPr,"Fossilization")) { fR = GetParamVals (m->fossilizationRates, chain, state[chain]); if (LnFossilizationPriorPr (t, clockRate, &oldLnPrior, sR, eR, fR, sF, sS) == ERROR) { MrBayesPrint ("%s Problem calculating prior for fossilized birth-death process\n", spacer); return (ERROR); } *valPtr = newL; // update with new value if (LnFossilizationPriorPr (t, clockRate, &newLnPrior, sR, eR, fR, sF, sS) == ERROR) { MrBayesPrint ("%s Problem calculating prior for fossilized birth-death process\n", spacer); return (ERROR); } } else { MrBayesPrint ("%s Move_Speciation not applicable\n", spacer); return (ERROR); } if (isLPriorExp == NO) *lnPriorRatio = newLnPrior - oldLnPrior; else *lnPriorRatio = newLnPrior - oldLnPrior - (mp->speciationExp)*(newL - oldL); return (NO_ERROR); } int Move_Speciation_M (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp) { /* change speciation rate using multiplier */ int isLPriorExp, isValidL, valIndex; MrBFlt *valPtr, oldL, newL, minL, maxL, *sR, *eR, sF, *fR, oldLnPrior, newLnPrior, tuning, clockRate; char *sS; ModelParams *mp; ModelInfo *m; Tree *t; /* get tuning parameter */ tuning = mvp[0]; /* get model params and settings */ mp = &modelParams[param->relParts[0]]; m = &modelSettings[param->relParts[0]]; /* get minimum and maximum values */ if (param->paramId == SPECRATE_UNI) { minL = mp->speciationUni[0]; maxL = mp->speciationUni[1]; isLPriorExp = NO; } else { minL = RATE_MIN; maxL = RATE_MAX; isLPriorExp = YES; } /* get pointer to value to be changed */ valIndex = (int)(RandomNumber(seed) * param->nValues); valPtr = GetParamVals(param, chain, state[chain]) + valIndex; /* get old value */ oldL = *valPtr; /* change value */ newL = oldL * exp(tuning * (RandomNumber(seed) - 0.5)); /* check that new value is valid */ isValidL = NO; do { if (newL < minL) newL = minL * minL / newL; else if (newL > maxL) newL = maxL * maxL / newL; else isValidL = YES; } while (isValidL == NO); /* get proposal ratio */ *lnProposalRatio = log (newL / oldL); /* calculate prior ratio */ t = GetTree(m->brlens, chain, state[chain]); sR = GetParamVals (param, chain, state[chain]); eR = GetParamVals (m->extinctionRates, chain, state[chain]); sF = mp->sampleProb; sS = mp->sampleStrat; clockRate = *GetParamVals(m->clockRate, chain, state[chain]); if (!strcmp(mp->clockPr,"Birthdeath")) { if (LnBirthDeathPriorPr (t, clockRate, &oldLnPrior, *sR, *eR, sS, sF) == ERROR) { MrBayesPrint ("%s Problem calculating prior for birth-death process\n", spacer); return (ERROR); } *valPtr = newL; // update with new value if (LnBirthDeathPriorPr (t, clockRate, &newLnPrior, *sR, *eR, sS, sF) == ERROR) { MrBayesPrint ("%s Problem calculating prior for birth-death process\n", spacer); return (ERROR); } } else if (!strcmp(mp->clockPr,"Fossilization")) { fR = GetParamVals (m->fossilizationRates, chain, state[chain]); if (LnFossilizationPriorPr (t, clockRate, &oldLnPrior, sR, eR, fR, sF, sS) == ERROR) { MrBayesPrint ("%s Problem calculating prior for fossilized birth-death process\n", spacer); return (ERROR); } *valPtr = newL; // update with new value if (LnFossilizationPriorPr (t, clockRate, &newLnPrior, sR, eR, fR, sF, sS) == ERROR) { MrBayesPrint ("%s Problem calculating prior for fossilized birth-death process\n", spacer); return (ERROR); } } else { MrBayesPrint ("%s Move_Speciation_M not applicable\n", spacer); return (ERROR); } if (isLPriorExp == NO) *lnPriorRatio = newLnPrior - oldLnPrior; else *lnPriorRatio = newLnPrior - oldLnPrior - (mp->speciationExp)*(newL - oldL); return (NO_ERROR); } int Move_Statefreqs (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp) { /* change pi */ int i, nStates, isValid; MrBFlt dirichletParameters[64], *newPi, *oldPi, *priorAlpha, sum, alphaPi, x, y; ModelInfo *m; //added by SK to account for DIRPI parameters m = &modelSettings[param->relParts[0]]; /* get the values we need */ nStates = m->numModelStates; priorAlpha = GetParamVals(param, chain, state[chain]); newPi = GetParamSubVals (param, chain, state[chain]); oldPi = GetParamSubVals (param, chain, state[chain] ^ 1); /* tuning parameter */ alphaPi = mvp[0]*nStates; /* multiply old values with some large number to get new values close to the old ones */ for (i=0; inRelParts; i++) TouchAllTreeNodes(&modelSettings[param->relParts[i]],chain); /* Set update flags for cijks for all affected partitions. If this is a simple 4 X 4 model, we don't take any hit, because we will never go into a general transition probability calculator. However, for many models we do want to update the cijk flag, as the transition probability matrices require diagonalizing the rate matrix. */ for (i=0; inRelParts; i++) modelSettings[param->relParts[i]].upDateCijk = YES; return (NO_ERROR); } /*---------------------------------------------------------------- | | Move_Statefreqs_Slider: Change state frequencies using Slider proposal | mechanism. | Choose pairs of the parameter values (e.g. pi(A), and pi(G)) at | random and denote them piA, and piB. Let oldProp = piA/(piA + piB) | and newProp = oldProp + delta(U - 0.5), where U is a uniform random variable | on the interval (0, 1] and delta is a tuning parameter. Values | that fall outside the boundaries are reflected back in. Then | set newPiA = newProp*(piA+piB) and newPiB = (1-newProp)*(piA+piB). | The Hastings ratio of this move is 1.0. | ----------------------------------------------------------------*/ int Move_Statefreqs_Slider (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp) { int i, j, nStates, isValid; MrBFlt delta, *newPi, *oldPi, *priorAlpha, x, y, sum, min, max; ModelInfo *m; //added by SK to account for DIRPI parameters m = &modelSettings[param->relParts[0]]; /* get the values we need */ nStates = m->numModelStates; priorAlpha = GetParamVals(param, chain, state[chain]); newPi = GetParamSubVals (param, chain, state[chain]); oldPi = GetParamSubVals (param, chain, state[chain] ^ 1); /* get window size */ delta = mvp[0]; /* choose a pair to change */ i = (int) (RandomNumber(seed) * nStates); j = (int) (RandomNumber(seed) * (nStates-1)); if (i == j) j = nStates-1; /* find new proportion */ sum = oldPi[i] + oldPi[j]; /* reflect */ isValid = NO; min = PI_MIN / sum; max = 1.0 - min; x = oldPi[i] / sum; if (delta > max-min) /* we do it to avoid following long while loop in case if delta is high */ { delta = max-min; } y = x + delta * (RandomNumber(seed) - 0.5); do { if (y < min) y = 2.0 * min - y; else if (y > max) y = 2.0 * max - y; else isValid = YES; } while (isValid == NO); /* set the new values */ newPi[i] = y * sum; newPi[j] = sum - newPi[i]; /* get proposal ratio */ *lnProposalRatio = 0.0; /* get prior ratio */ /* (the Gamma part of the prior is the same) */ x = (priorAlpha[i]-1.0)*log(newPi[i]); x += (priorAlpha[j]-1.0)*log(newPi[j]); y = (priorAlpha[i]-1.0)*log(oldPi[i]); y += (priorAlpha[j]-1.0)*log(oldPi[j]); (*lnPriorRatio) = x - y; /* Set update for entire tree */ for (i=0; inRelParts; i++) TouchAllTreeNodes(&modelSettings[param->relParts[i]],chain); /* Set update flags for cijks for all affected partitions. If this is a simple 4 X 4 model, we don't take any hit, because we will never go into a general transition probability calculator. However, for many models we do want to update the cijk flag, as the transition probability matrices require diagonalizing the rate matrix. */ for (i=0; inRelParts; i++) modelSettings[param->relParts[i]].upDateCijk = YES; return (NO_ERROR); } /*---------------------------------------------------------------- | | Move_StatefreqsRoot: Change the frequencies at the root in a | directional model of evolution. Move mechanism is the same | as for stationary state frequencies. //SK | ----------------------------------------------------------------*/ int Move_StatefreqsRoot (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp) { /* change pi */ int i, nStates, isValid; MrBFlt dirichletParameters[64], *newPi, *oldPi, *priorAlpha, *temp, alphaPi, x, y; ModelInfo *m; m = &modelSettings[param->relParts[0]]; /* get the values we need */ nStates = m->numModelStates; /* get pointers to root frequencies */ priorAlpha = GetParamVals(param, chain, state[chain]) +nStates; newPi = GetParamSubVals (param, chain, state[chain]) +nStates; oldPi = GetParamSubVals (param, chain, state[chain] ^ 1) +nStates; if (param->paramId == DIRPI_MIX && oldPi[0] < 0.0) /* abort move if we are in RJ mode and currently in stationary model */ { abortMove = YES; return (NO_ERROR); } temp = (MrBFlt *) SafeCalloc(nStates, sizeof (MrBFlt)); if (!temp) { free (temp); return (ERROR); } /* tuning parameter */ alphaPi = mvp[0]*nStates; /* multiply old values with some large number to get Dirichlet parameters that result in new values close to the old ones */ for (i=0; irelParts[0]]; /* get the values we need */ nStates = m->numModelStates; priorAlpha = GetParamVals(param, chain, state[chain]) + nStates; newPi = GetParamSubVals (param, chain, state[chain]) + nStates; oldPi = GetParamSubVals (param, chain, state[chain] ^ 1) + nStates; if (param->paramId == DIRPI_MIX && oldPi[0] < 0.0) /* abort move if we are in RJ mode and currently in stationary model */ { abortMove = YES; return (NO_ERROR); } /* get window size */ delta = mvp[0]; /* choose a pair to change */ i = (int) (RandomNumber(seed) * nStates); j = (int) (RandomNumber(seed) * (nStates-1)); if (i == j) j = nStates-1; /* find new proportion */ sum = oldPi[i] + oldPi[j]; /* reflect */ isValid = NO; min = PI_MIN / sum; max = 1.0 - min; x = oldPi[i] / sum; if( delta > max-min ) /* we do it to avoid following long while loop in case if delta is high */ { delta = max-min; } y = x + delta * (RandomNumber(seed) - 0.5); do { if (y < min) y = 2.0 * min - y; else if (y > max) y = 2.0 * max - y; else isValid = YES; } while (isValid == NO); /* set the new values */ newPi[i] = y * sum; newPi[j] = sum - newPi[i]; /* get proposal ratio */ *lnProposalRatio = 0.0; /* get prior ratio */ /* (the Gamma part of the prior is the same) */ x = (priorAlpha[i] - 1.0) * log(newPi[i]); x += (priorAlpha[j] - 1.0) * log(newPi[j]); y = (priorAlpha[i] - 1.0) * log(oldPi[i]); y += (priorAlpha[j] - 1.0) * log(oldPi[j]); (*lnPriorRatio) = x - y; /* no flags need to be set up for this move */ return (NO_ERROR); } /*---------------------------------------------------------------- | | Move_Statefreqs_SplitMerge: Jumps between the stationary and | directional model of evolution. | The move either reduces the model to a stationary one by | removing the root frequencies and drawing the new stationary | freqs from a Beta (or Dirichlet) distribution centered on | the average of the previous stationary and root frequencies | (merge move). In the split move, it expands to the | directional model by picking the new values from a | Beta centered on the previous stationary frequency. | The Jacobian of both split and merge moves is 1. | We assume equal prior probabilities of the two models. //SK | ----------------------------------------------------------------*/ int Move_Statefreqs_SplitMerge (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp) { /* switch between stationary and directional model of evolution */ int i, nStates, isValid, inStationary; MrBFlt dirichletParameters[64], *newPi, *oldPi, *priorAlpha, *newPiRoot, *oldPiRoot, *priorAlphaRoot, *temp, alphaPi, x, y; ModelInfo *m; m = &modelSettings[param->relParts[0]]; /* get the values and pointers we need */ nStates = m->numModelStates; /* get pointers to stationary and root frequencies */ priorAlpha = GetParamVals(param, chain, state[chain]); newPi = GetParamSubVals (param, chain, state[chain]); oldPi = GetParamSubVals (param, chain, state[chain] ^ 1); priorAlphaRoot = GetParamVals(param, chain, state[chain]) +nStates; newPiRoot = GetParamSubVals (param, chain, state[chain]) +nStates; oldPiRoot = GetParamSubVals (param, chain, state[chain] ^ 1) +nStates; /* check which model we are currently in */ if (oldPiRoot[0] == NOT_APPLICABLE) //SK: root PI are set to NOT_APPLICABLE (-9999) when in stationary model inStationary = 1; else inStationary = 0; /* allocate vec to hold state freqs * tuning parameter */ temp = (MrBFlt *) SafeCalloc(nStates, sizeof (MrBFlt)); if (!temp) { free (temp); return (ERROR); } /* tuning parameter */ alphaPi = mvp[0]*nStates; /* merge move */ if (!inStationary) { /* multiply old values with some large number to get Dirichlet parameters that result in new values close to the old ones */ for (i=0; i restriction data type /* multiply old values with some large number to get Dirichlet parameters that result in new values close to the old ones */ for (i=0; inRelParts; i++) TouchAllTreeNodes(&modelSettings[param->relParts[i]],chain); /* Set update flags for cijks for all affected partitions. If this is a simple 4 X 4 model, we don't take any hit, because we will never go into a general transition probability calculator. However, for many models we do want to update the cijk flag, as the transition probability matrices require diagonalizing the rate matrix. */ for (i=0; inRelParts; i++) modelSettings[param->relParts[i]].upDateCijk = YES; free (temp); return (NO_ERROR); } int Move_StatefreqsSymDirMultistate (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp) { /* change state freqs of multistate characters */ /* ideally, we would let the likelihood calculator deal with only the affected character but we do not have the mechanism for doing that in the current version of mrbayes, so take the hit of updating all chars of the morph partition(s). */ int i, nStates, charIndex; MrBFlt dirichletParameters[MAX_STD_STATES], symDirAlphai, *newPi, *oldPi, sum, alphaPi, x, y; Model *mp; /* tuning parameters */ alphaPi = mvp[0]; /* get model paramaters */ mp = &modelParams[param->relParts[0]]; /* select one character at random */ charIndex = (int) (RandomNumber(seed) * param->nSympi); /* get the values we need */ symDirAlphai = *GetParamVals(param, chain, state[chain]); newPi = GetParamStdStateFreqs (param, chain, state[chain]); oldPi = GetParamStdStateFreqs (param, chain, state[chain] ^ 1); newPi += 2 * mp->numBetaCats; oldPi += 2 * mp->numBetaCats; for (i=0; isympinStates[i]; newPi += param->sympinStates[i]; } nStates = param->sympinStates[charIndex]; /* multiply old values with some large number to get new values close to the old ones */ for (i=0; inRelParts; i++) TouchAllTreeNodes(&modelSettings[param->relParts[i]],chain); /* Set update flags for cijks for all affected partitions. Only cijks for the changed character actually need to be updated but we can't do that in the current version of the program. */ for (i=0; inRelParts; i++) modelSettings[param->relParts[i]].upDateCijk = YES; return (NO_ERROR); } int Move_SwitchRate (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp) { /* change switch rate of covarion model using sliding window */ int i, isSPriorExp, isValidS, whichRate; MrBFlt oldS, newS, window, minS, maxS, ran, *value; ModelParams *mp; /* decide which switching rate to change */ if (RandomNumber(seed) < 0.5) whichRate = 0; else whichRate = 1; /* get size of window, centered on current switching rates value */ window = mvp[0]; /* get model params */ mp = &modelParams[param->relParts[0]]; /* get minimum and maximum values for switching rate */ if (param->paramId == SWITCH_UNI) { minS = mp->covswitchUni[0]; maxS = mp->covswitchUni[1]; isSPriorExp = NO; } else { minS = 0.01; maxS = KAPPA_MAX; isSPriorExp = YES; } /* get old value of switching rate */ value = GetParamVals(param, chain, state[chain]); oldS = value[whichRate]; /* change value for switching rate */ ran = RandomNumber(seed); if (maxS-minS < window) { window = maxS-minS; } newS = oldS + window * (ran - 0.5); /* check that new value is valid */ isValidS = NO; do { if (newS < minS) newS = 2* minS - newS; else if (newS > maxS) newS = 2 * maxS - newS; else isValidS = YES; } while (isValidS == NO); /* get proposal ratio */ *lnProposalRatio = 0.0; /* get prior ratio */ if (isSPriorExp == NO) *lnPriorRatio = 0.0; else *lnPriorRatio = -(mp->covswitchExp) * (newS - oldS); /* copy new switching rate value back */ value[whichRate] = newS; /* Set update flags for all partitions that share this switching rate. Note that the conditional likelihood update flags have been set before we even call this function. */ for (i=0; inRelParts; i++) TouchAllTreeNodes(&modelSettings[param->relParts[i]],chain); /* Set update flags for cijks for all affected partitions. If this is a simple 4 X 4 model, we don't take any hit, because we will never go into a general transition probability calculator. However, for covarion, doublet, and codon models, we do want to update the cijk flag. */ for (i=0; inRelParts; i++) modelSettings[param->relParts[i]].upDateCijk = YES; return (NO_ERROR); } int Move_SwitchRate_M (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp) { /* change switch rate of covarion model using multiplier */ int i, isSPriorExp, isValidS, whichRate; MrBFlt oldS, newS, minS, maxS, tuning, ran, factor, *value; ModelParams *mp; /* decide which switching rate to change */ if (RandomNumber(seed) < 0.5) whichRate = 0; else whichRate = 1; /* get tuning parameter */ tuning = mvp[0]; /* get model params */ mp = &modelParams[param->relParts[0]]; /* get minimum and maximum values for switching rate */ if (param->paramId == SWITCH_UNI) { minS = mp->covswitchUni[0]; maxS = mp->covswitchUni[1]; isSPriorExp = NO; } else { minS = 0.01; maxS = KAPPA_MAX; isSPriorExp = YES; } /* get old value of switching rate */ value = GetParamVals(param, chain, state[chain]); oldS = value[whichRate]; /* change value for switching rate */ ran = RandomNumber(seed); factor = exp(tuning * (ran - 0.5)); newS = oldS * factor; /* check that new value is valid */ isValidS = NO; do { if (newS < minS) newS = minS * minS / newS; else if (newS > maxS) newS = maxS * maxS / newS; else isValidS = YES; } while (isValidS == NO); /* get proposal ratio */ *lnProposalRatio = log (newS / oldS); /* get prior ratio */ if (isSPriorExp == NO) *lnPriorRatio = 0.0; else *lnPriorRatio = -(mp->covswitchExp) * (newS - oldS); /* copy new switching rate value back */ value[whichRate] = newS; /* Set update flags for all partitions that share this switching rate. Note that the conditional likelihood update flags have been set before we even call this function. */ for (i=0; inRelParts; i++) TouchAllTreeNodes(&modelSettings[param->relParts[i]],chain); /* Set update flags for cijks for all affected partitions. If this is a simple 4 X 4 model, we don't take any hit, because we will never go into a general transition probability calculator. However, for covarion, doublet, and codon models, we do want to update the cijk flag. */ for (i=0; inRelParts; i++) modelSettings[param->relParts[i]].upDateCijk = YES; return (NO_ERROR); } int Move_TK02BranchRate (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp) { /* move one TK02 relaxed clock branch rate using multiplier */ int i; MrBFlt newRate, oldRate, tuning, minR, maxR, nu, *tk02Rate, *brlens; TreeNode *p = NULL; ModelInfo *m; Tree *t; TreeNode *q; /* get model settings */ m = &modelSettings[param->relParts[0]]; /* get the tuning parameter */ tuning = mvp[0]; /* get the TK02 branch rate and effective branch length data */ tk02Rate = GetParamVals (param, chain, state[chain]); brlens = GetParamSubVals (param, chain, state[chain]); /* get tree */ t = GetTree (param, chain, state[chain]); /* get minimum and maximum rate */ minR = RATE_MIN; maxR = RATE_MAX; /* randomly pick a rate */ do { i = (int) (RandomNumber(seed) * (t->nNodes -2)); p = t->allDownPass[i]; } while (p->length < TIME_MIN); // not ancestral fossil /* find new rate */ oldRate = tk02Rate[p->index]; newRate = oldRate * exp ((0.5 - RandomNumber(seed)) * tuning); /* reflect if necessary */ while (newRate < minR || newRate > maxR) { if (newRate < minR) newRate = minR * minR / newRate; if (newRate > maxR) newRate = maxR * maxR / newRate; } tk02Rate[p->index] = newRate; /* calculate prior ratio */ nu = *GetParamVals (m->tk02var, chain, state[chain]); (*lnPriorRatio) -= LnProbLogNormal_Mean_LogVar (tk02Rate[p->anc->index], nu*p->length, oldRate); (*lnPriorRatio) += LnProbLogNormal_Mean_LogVar (tk02Rate[p->anc->index], nu*p->length, newRate); if (p->left != NULL) { if (p->left->length > 0.0) { (*lnPriorRatio) -= LnProbLogNormal_Mean_LogVar (oldRate, nu*p->left->length, tk02Rate[p->left->index ]); (*lnPriorRatio) += LnProbLogNormal_Mean_LogVar (newRate, nu*p->left->length, tk02Rate[p->left->index ]); } if (p->right->length > 0.0) { (*lnPriorRatio) -= LnProbLogNormal_Mean_LogVar (oldRate, nu*p->right->length, tk02Rate[p->right->index]); (*lnPriorRatio) += LnProbLogNormal_Mean_LogVar (newRate, nu*p->right->length, tk02Rate[p->right->index]); } } /* calculate proposal ratio */ (*lnProposalRatio) = log (newRate / oldRate); /* update branch evolution lengths */ brlens[p->index] = p->length * (newRate + tk02Rate[p->anc->index]) / 2.0; if (p->left != NULL) { if (p->left->length > 0.0) { brlens[p->left->index] = p->left->length * (tk02Rate[p->left->index] + newRate) / 2.0; } if (p->right->length > 0.0) { brlens[p->right->index] = p->right->length * (tk02Rate[p->right->index] + newRate) / 2.0; } } /* set update of ti probs */ p->upDateTi = YES; if (p->left != NULL) { p->left ->upDateTi = YES; p->right->upDateTi = YES; } /* set update of cond likes down to root */ /* update of crowntree set in UpdateCppEvolLengths */ p->upDateCl = YES; q = p->anc; while (q->anc != NULL) { q->upDateCl = YES; q = q->anc; } return (NO_ERROR); } int Move_Tratio_Dir (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp) { /* change tratio using Dirichlet proposal */ int i; MrBFlt oldK, alphaPi, *alphaDir, oldProp[2], newProp[2], dirParm[2], sum, x, y; ModelParams *mp; /* get model params */ mp = &modelParams[param->relParts[0]]; /* get so called alphaPi parameter */ alphaPi = mvp[0]; /* get old value of kappa */ oldK = *GetParamVals(param, chain, state[chain]); /* get Dirichlet parameters */ alphaDir = mp->tRatioDir; /* calculate old ratesum proportions */ oldProp[0] = oldK / (oldK + 1.0); oldProp[1] = 1.0 - oldProp[0]; /* multiply old ratesum props with some large number to get new values close to the old ones */ dirParm[0] = oldProp[0] * alphaPi; dirParm[1] = oldProp[1] * alphaPi; /* get new values */ DirichletRandomVariable (dirParm, newProp, 2, seed); if (newProp[0] < DIR_MIN) { newProp[0] = DIR_MIN; newProp[1] = 1.0-DIR_MIN; } else if (newProp[1] < DIR_MIN) { newProp[1] = DIR_MIN; newProp[0] = 1.0-DIR_MIN; } /* calculate and copy new kappa value back */ *GetParamVals(param, chain, state[chain]) = newProp[0] / newProp[1]; /* get proposal ratio */ sum = 0.0; for (i=0; i<2; i++) sum += newProp[i]*alphaPi; x = LnGamma(sum); for (i=0; i<2; i++) x -= LnGamma(newProp[i]*alphaPi); for (i=0; i<2; i++) x += (newProp[i]*alphaPi-1.0)*log(oldProp[i]); sum = 0.0; for (i=0; i<2; i++) sum += oldProp[i]*alphaPi; y = LnGamma(sum); for (i=0; i<2; i++) y -= LnGamma(oldProp[i]*alphaPi); for (i=0; i<2; i++) y += (oldProp[i]*alphaPi-1.0)*log(newProp[i]); (*lnProposalRatio) = x - y; /* get prior ratio */ x = y = 0.0; for (i=0; i<2; i++) x += (alphaDir[i]-1.0)*log(newProp[i]); for (i=0; i<2; i++) y += (alphaDir[i]-1.0)*log(oldProp[i]); (*lnPriorRatio) = x - y; /* Set update flags for all partitions that share this kappa. Note that the conditional likelihood update flags have been set before we even call this function. */ for (i=0; inRelParts; i++) TouchAllTreeNodes(&modelSettings[param->relParts[i]],chain); /* Set update flags for cijks for all affected partitions. If this is a simple 4 X 4 model, we don't take any hit, because we will never go into a general transition probability calculator. However, for covarion, doublet, and codon models, we do want to update the cijk flag. */ for (i=0; inRelParts; i++) modelSettings[param->relParts[i]].upDateCijk = YES; return (NO_ERROR); } /* Code added by Jeremy Brown and modified by Maxim Teslenko */ int Move_TreeLen (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp) { /* change all branch lengths */ MrBFlt begin_tl, treescaler, tuning, maxV, minV; TreeNode *p; ModelParams *mp; Tree *t; int i,branch_counter, isVPriorExp; tuning = mvp[0]; /* Larget & Simon's tuning parameter lambda */ mp = &modelParams[param->relParts[0]]; /* max and min brlen */ if (param->paramId == BRLENS_UNI) { minV = mp->brlensUni[0]; maxV = mp->brlensUni[1]; isVPriorExp = NO; } else if (param->paramId == BRLENS_GamDir) { minV = BRLENS_MIN; maxV = BRLENS_MAX; isVPriorExp = 2; } else if (param->paramId == BRLENS_iGmDir) { minV = BRLENS_MIN; maxV = BRLENS_MAX; isVPriorExp = 3; } else if (param->paramId == BRLENS_twoExp) { minV = BRLENS_MIN; maxV = BRLENS_MAX; isVPriorExp = 4; } else { minV = BRLENS_MIN; maxV = BRLENS_MAX; isVPriorExp = YES; } /* get tree */ t = GetTree (param, chain, state[chain]); assert (t->isClock == NO); /* Dirichlet or twoExp prior */ if (isVPriorExp > 1) (*lnPriorRatio) = -LogDirPrior(t, mp, isVPriorExp); treescaler = exp(tuning * (RandomNumber(seed) - 0.5)); begin_tl = 0.0; branch_counter=0; for (i=0; inNodes; i++) { p = t->allDownPass[i]; if (p->anc != NULL && (t->isRooted == NO || p->anc->anc != NULL)) //SK: additional condition for rooted trees { if (p->length*treescaler < minV || p->length*treescaler > maxV) { abortMove = YES; return NO_ERROR; } begin_tl += p->length; branch_counter++; } } if (t->isRooted == NO) assert(branch_counter==t->nNodes-1); else assert(branch_counter==t->nNodes-2); //SK: added assert for rooted tree /* iterate scaling over all branches */ for (i=0; i < t->nNodes; i++) { p = t->allDownPass[i]; if (p->anc != NULL && (t->isRooted == NO || p->anc->anc != NULL)) //SK: additional condition for rooted trees { /* set new length */ p->length *= treescaler; /* set flags for update of transition probabilities at p */ p->upDateTi = YES; p->anc->upDateCl = YES; } } /* calculate proposal ratio */ (*lnProposalRatio) = branch_counter * log(treescaler); /* update prior if exponential prior on branch lengths */ if (param->paramId == BRLENS_EXP) (*lnPriorRatio) = (mp->brlensExp) * begin_tl * (1 - treescaler); /* Dirichlet or twoExp prior */ else if (isVPriorExp > 1) (*lnPriorRatio) += LogDirPrior(t, mp, isVPriorExp); return (NO_ERROR); } /*----------------------------------------------------------------------------------- | | Move_TreeStretch: Shrink or grow a clock tree | -------------------------------------------------------------------------------------*/ int Move_TreeStretch (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp) { int i, j, *nEvents, numChangedNodes; MrBFlt minV, maxV, tuning, factor, lambda=0.0, x, *brlens=NULL, nu=0.0, var=0.0, *rate=NULL; TreeNode *p, *q; ModelParams *mp; ModelInfo *m; Tree *t, *oldT; Param *subParm; Calibration *calibrationPtr; tuning = mvp[0]; /* Larget & Simon tuning parameter lambda */ (*lnProposalRatio) = (*lnPriorRatio) = 0.0; m = &modelSettings[param->relParts[0]]; mp = &modelParams[param->relParts[0]]; /* get trees */ t = GetTree (param, chain, state[chain]); oldT = GetTree (param, chain, 1^state[chain]); /* min and max branch lengths in relative time and substitution units */ minV = BRLENS_MIN; maxV = BRLENS_MAX; /* determine multiplication factor */ factor = exp(tuning * (RandomNumber(seed) - 0.5)); /* multiply all changeable ages and node depths by this factor */ numChangedNodes = 0; for (i=0; inNodes-1; i++) { p = t->allDownPass[i]; /* skip extant tip and fixed calibration */ if (p->left == NULL && p->isDated == NO) continue; if (p->isDated == YES) calibrationPtr = p->calibration; else if (p->anc->anc == NULL && (!strcmp(mp->clockPr, "Uniform") || !strcmp(mp->clockPr, "Birthdeath") || !strcmp(mp->clockPr, "Fossilization"))) calibrationPtr = &mp->treeAgePr; else calibrationPtr = NULL; if (calibrationPtr != NULL && calibrationPtr->prior == fixed) continue; /* now stretch the node */ if (calibrationPtr != NULL) { p->age *= factor; if (p->age < calibrationPtr->min || p->age > calibrationPtr->max) { abortMove = YES; return (NO_ERROR); } } p->nodeDepth *= factor; numChangedNodes++; /* deal with ancestral fossils */ if (p->left != NULL) { if (p->left->length < TIME_MIN) { p->left->length = 0.0; p->nodeDepth = p->left->nodeDepth; if (calibrationPtr != NULL) { assert (p->left->calibration != NULL); p->age = p->left->age; if (p->age < calibrationPtr->min || p->age > calibrationPtr->max) { abortMove = YES; return (NO_ERROR); } } numChangedNodes--; } if (p->right->length < TIME_MIN) { p->right->length = 0.0; p->nodeDepth = p->right->nodeDepth; if (calibrationPtr != NULL) { assert (p->right->calibration != NULL); p->age = p->right->age; if (p->age < calibrationPtr->min || p->age > calibrationPtr->max) { abortMove = YES; return (NO_ERROR); } } numChangedNodes--; } assert (!(p->left->length == 0.0 && p->right->length == 0.0)); } } /* update brls */ for (i=0; inNodes-1; i++) { p = t->allDownPass[i]; if (p->left != NULL) { if (p->left->length > 0.0) p->left->length = p->nodeDepth - p->left->nodeDepth; if (p->right->length > 0.0) p->right->length = p->nodeDepth - p->right->nodeDepth; } } /* check that all branch lengths are proper, which need not be the case */ for (i = 0; i < t->nNodes -2; i++) { p = t->allDownPass[i]; q = oldT->allDownPass[i]; if (p->length < 0.0 || p->length > maxV || (q->length > minV && p->length < minV) || (q->length < TIME_MIN && p->length > TIME_MIN)) { /* consider ancestral fossil (brl=0) in fossilized bd tree */ abortMove = YES; return NO_ERROR; } } /* calculate proposal ratio */ (*lnProposalRatio) = numChangedNodes * log(factor); /* calculate prior ratio */ if (LogClockTreePriorRatio(param, chain, &x) == ERROR) return ERROR; (*lnPriorRatio) += x; /* adjust proposal and prior ratio for relaxed clock models */ for (i=0; inSubParams; i++) { subParm = param->subParams[i]; if (subParm->paramType == P_CPPEVENTS) { nEvents = subParm->nEvents[2*chain+state[chain]]; lambda = *GetParamVals (modelSettings[subParm->relParts[0]].cppRate, chain, state[chain]); /* proposal ratio */ for (j=0; jnNodes-2; j++) { p = t->allDownPass[j]; q = oldT->allDownPass[j]; (*lnProposalRatio) += nEvents[p->index ] * log (p->length / q->length); } /* prior ratio */ (*lnPriorRatio) += lambda * (TreeLen(oldT) - TreeLen(t)); /* update effective evolutionary lengths */ if (UpdateCppEvolLengths (subParm, t->root->left, chain) == ERROR) { abortMove = YES; return (NO_ERROR); } } else if ( subParm->paramType == P_TK02BRANCHRATES) { if (subParm->paramType == P_TK02BRANCHRATES) nu = *GetParamVals (modelSettings[subParm->relParts[0]].tk02var, chain, state[chain]); else nu = *GetParamVals (modelSettings[subParm->relParts[0]].mixedvar, chain, state[chain]); rate = GetParamVals (subParm, chain, state[chain]); brlens = GetParamSubVals (subParm, chain, state[chain]); /* prior ratio and update of brlens */ for (j=0; jnNodes-2; j++) { p = t->allDownPass[j]; q = oldT->allDownPass[j]; if (p->length > 0.0) // not ancestral fossil { (*lnPriorRatio) -= LnProbLogNormal_Mean_LogVar (rate[q->anc->index], nu*q->length, rate[q->index]); (*lnPriorRatio) += LnProbLogNormal_Mean_LogVar (rate[p->anc->index], nu*p->length, rate[p->index]); brlens[p->index] = p->length * (rate[p->anc->index]+rate[p->index])/2.0; if (brlens[p->index] < RELBRLENS_MIN || brlens[p->index] > RELBRLENS_MAX) { abortMove = YES; return (NO_ERROR); } } } } else if ( subParm->paramType == P_WNBRANCHRATES) { if (subParm->paramType == P_WNBRANCHRATES) var = *GetParamVals (modelSettings[subParm->relParts[0]].wnvar, chain, state[chain]); else var = *GetParamVals (modelSettings[subParm->relParts[0]].mixedvar, chain, state[chain]); rate = GetParamVals (subParm, chain, state[chain]); brlens = GetParamSubVals (subParm, chain, state[chain]); /* prior ratio and update of branch lengths and rates (stretched in the same way as tree) */ for (j=0; jnNodes-2; j++) { p = t->allDownPass[j]; q = oldT->allDownPass[j]; if (p->length > 0.0) // not ancestral fossil { (*lnPriorRatio) -= LnProbGamma (q->length/var, q->length/var, rate[q->index]); (*lnPriorRatio) += LnProbGamma (p->length/var, p->length/var, rate[p->index]); brlens[p->index] = p->length * rate[p->index]; if (brlens[p->index] < RELBRLENS_MIN || brlens[p->index] > RELBRLENS_MAX) { abortMove = YES; return (NO_ERROR); } } } } else if ( subParm->paramType == P_ILNBRANCHRATES || (subParm->paramType == P_MIXEDBRCHRATES && *GetParamIntVals(subParm, chain, state[chain]) == RCL_ILN)) { if (subParm->paramType == P_ILNBRANCHRATES) nu = *GetParamVals (modelSettings[subParm->relParts[0]].ilnvar, chain, state[chain]); else nu = *GetParamVals (modelSettings[subParm->relParts[0]].mixedvar, chain, state[chain]); rate = GetParamVals (subParm, chain, state[chain]); brlens = GetParamSubVals (subParm, chain, state[chain]); /* prior ratio and update of iln branch lengths and rates (stretched in the same way as tree) */ for (j=0; jnNodes-2; j++) { p = t->allDownPass[j]; q = oldT->allDownPass[j]; if (p->length > 0.0) // not ancestral fossil { brlens[p->index] = p->length * rate[p->index]; if (brlens[p->index] < RELBRLENS_MIN || brlens[p->index] > RELBRLENS_MAX) { abortMove = YES; return (NO_ERROR); } } } } else if ( subParm->paramType == P_IGRBRANCHRATES || (subParm->paramType == P_MIXEDBRCHRATES && *GetParamIntVals(subParm, chain, state[chain]) == RCL_IGR)) { if (subParm->paramType == P_IGRBRANCHRATES) var = *GetParamVals (modelSettings[subParm->relParts[0]].igrvar, chain, state[chain]); else var = *GetParamVals (modelSettings[subParm->relParts[0]].mixedvar, chain, state[chain]); rate = GetParamVals (subParm, chain, state[chain]); brlens = GetParamSubVals (subParm, chain, state[chain]); /* prior ratio and update of branch lengths and rates (stretched in the same way as tree) */ for (j=0; jnNodes-2; j++) { p = t->allDownPass[j]; q = oldT->allDownPass[j]; if (p->length > 0.0) // not ancestral fossil { brlens[p->index] = p->length * rate[p->index]; if (brlens[p->index] < RELBRLENS_MIN || brlens[p->index] > RELBRLENS_MAX) { abortMove = YES; return (NO_ERROR); } } } } } TouchAllTreeNodes(m, chain); #if defined (DEBUG_TREESTRETCH) printf ("After treestretch:\n"); printf ("Old tree height: %f -- New tree height: %f -- lnPriorRatio = %f -- lnProposalRatio = %f\n", oldT->root->left->nodeDepth, t->root->left->nodeDepth, (*lnPriorRatio), (*lnProposalRatio)); #endif return (NO_ERROR); } int Move_WNBranchRate (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp) { /* move one WN relaxed clock branch rate using multiplier */ int i; MrBFlt newRate, oldRate, tuning, minR, maxR, wnvar, *wnRate, *brlens; TreeNode *p = NULL; ModelInfo *m; Tree *t; TreeNode *q; /* get the tuning parameter */ tuning = mvp[0]; /* get the model settings */ m = &modelSettings[param->relParts[0]]; /* get the WN branch rate and effective branch length data */ wnRate = GetParamVals (param, chain, state[chain]); brlens = GetParamSubVals (param, chain, state[chain]); /* get tree */ t = GetTree (param, chain, state[chain]); /* get minimum and maximum rate */ minR = RATE_MIN; maxR = RATE_MAX; /* randomly pick a branch */ do { i = (int) (RandomNumber(seed) * (t->nNodes -2)); p = t->allDownPass[i]; } while (p->length < TIME_MIN); // not ancestral fossil /* find new rate using multiplier */ oldRate = wnRate[p->index]; newRate = oldRate * exp ((0.5 - RandomNumber(seed)) * tuning); /* reflect if necessary */ while (newRate < minR || newRate > maxR) { if (newRate < minR) newRate = minR * minR / newRate; if (newRate > maxR) newRate = maxR * maxR / newRate; } wnRate[p->index] = newRate; /* calculate prior ratio */ wnvar = *GetParamVals (m->wnvar, chain, state[chain]); (*lnPriorRatio) = LnProbGamma (p->length/wnvar, p->length/wnvar, newRate) - LnProbGamma (p->length/wnvar, p->length/wnvar, oldRate); /* calculate proposal ratio */ (*lnProposalRatio) = log (newRate / oldRate); /* update branch evolution lengths */ brlens[p->index] = newRate * p->length; /* set update of transition probability */ p->upDateTi = YES; /* set update of cond likes down to root */ q = p->anc; while (q->anc != NULL) { q->upDateCl = YES; q = q->anc; } return (NO_ERROR); } int Move_WNVar (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp) { /* move the variance of the WN relaxed clock model using multiplier */ int i, j; MrBFlt oldVar, newVar, minVar, maxVar, tuning, *rate; Model *mp; TreeNode *p; Tree *t; /* get tuning parameter */ tuning = mvp[0]; /* get model params */ mp = &modelParams[param->relParts[0]]; /* get the min and max values */ minVar = WNVAR_MIN; maxVar = WNVAR_MAX; if (!strcmp(mp->wnvarPr,"Uniform")) { minVar = (mp->wnvarUni[0] < WNVAR_MIN) ? WNVAR_MIN : mp->wnvarUni[0]; maxVar = (mp->wnvarUni[1] > WNVAR_MAX) ? WNVAR_MAX : mp->wnvarUni[1]; } /* get the wn variance */ oldVar = *GetParamVals (param, chain, state[chain]); /* set new value */ newVar = oldVar * exp ((0.5 - RandomNumber(seed))*tuning); /* reflect if necessary */ while (newVar < minVar || newVar > maxVar) { if (newVar < minVar) newVar = minVar * minVar / newVar; if (newVar > maxVar) newVar = maxVar * maxVar / newVar; } /* store new value */ *GetParamVals (param, chain, state[chain]) = newVar; /* calculate prior ratio */ for (i=0; inSubParams; i++) { rate = GetParamVals (param->subParams[i], chain, state[chain]); t = GetTree (param->subParams[i], chain, state[chain]); for (j=0; jnNodes-2; j++) { p = t->allDownPass[j]; if (p->length > 0.0) // not ancestral fossil { (*lnPriorRatio) -= LnProbGamma (p->length/oldVar, p->length/oldVar, rate[p->index]); (*lnPriorRatio) += LnProbGamma (p->length/newVar, p->length/newVar, rate[p->index]); } } } /* take prior on WNvar into account */ if (!strcmp(mp->wnvarPr,"Exponential")) (*lnPriorRatio) += mp->wnvarExp * (oldVar - newVar); /* calculate proposal ratio */ (*lnProposalRatio) = log (newVar / oldVar); /* we do not need to update likelihoods */ for (i=0; inRelParts; i++) { modelSettings[param->relParts[i]].upDateCl = NO; } return (NO_ERROR); } void TouchAllTreeNodes (ModelInfo *m, int chain) { int i; Tree *t; TreeNode *p; t = GetTree(m->brlens, chain, state[chain]); for (i=0; inNodes; i++) { p = t->allDownPass[i]; p->upDateCl = YES; p->upDateTi = YES; } m->upDateAll = YES; } NBISweden-MrBayes-ecfd55c/src/proposal.h000066400000000000000000000262701526212227600202230ustar00rootroot00000000000000#ifndef PROPOSAL_H_ #define PROPOSAL_H_ int Move_Aamodel (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp); int Move_AddBranch (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp); int Move_Adgamma (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp); int Move_AddDeleteCPPEvent (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp); int Move_Beta (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp); int Move_Beta_M (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp); int Move_BrLen (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp); int Move_ClockRate_M (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp); int Move_CPPEventPosition (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp); int Move_CPPRate (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp); int Move_CPPRateMultiplier_M (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp); int Move_CPPRateMultiplierRnd (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp); int Move_DelBranch (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp); int Move_Extinction (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp); int Move_ExtSPR (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp); int Move_ExtSPR1 (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp); int Move_ExtSPRClock (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp); int Move_ExtSPRClock_Fossil (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp); int Move_ExtSS (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp); int Move_ExtSSClock (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp); int Move_ExtTBR (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp); int Move_Fossilization (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp); int Move_GeneRate_Dir (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp); int Move_Growth_M (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp); int Move_IgrVar (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp); int Move_IgrBranchRate (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp); int Move_IlnVar (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp); int Move_IlnBranchRate (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp); int Move_Local (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp); int Move_LocalClock (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp); int Move_MixedVar (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp); int Move_MixedBranchRate (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp); int Move_MixtureRates (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp); int Move_MixtureRates_Slider (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp); int Move_NNI (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp); int Move_NNIClock (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp); int Move_NNI_Hetero (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp); int Move_NodeSlider (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp); int Move_NodeSliderClock (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp); int Move_Nu (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp); int Move_Omega (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp); int Move_Omega_M (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp); int Move_OmegaBeta_M (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp); int Move_OmegaCat (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp); int Move_OmegaGamma_M (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp); int Move_OmegaM3 (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp); int Move_OmegaNeu (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp); int Move_OmegaPos (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp); int Move_OmegaPur (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp); int Move_ParsEraser1 (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp); int Move_ParsSPR (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp); int Move_ParsSPR1 (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp); int Move_ParsSPR2 (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp); int Move_ParsSPRClock (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp); int Move_ParsSPRClock_Fossil (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp); int Move_ParsTBR1 (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp); int Move_ParsTBR2 (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp); int Move_PopSize (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp); int Move_PosRealLognormal (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp); int Move_PosRealMultiplier (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp); int Move_PopSize_M (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp); int Move_Pinvar (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp); int Move_RateMult_Dir (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp); int Move_RateMult_Slider (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp); int Move_RateShape_M (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp); int Move_RealNormal (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp); int Move_RealSlider (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp); int Move_RelaxedClockModel (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp); int Move_Revmat_Dir (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp); int Move_Revmat_DirMix (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp); int Move_Revmat_Slider (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp); int Move_Revmat_SplitMerge1 (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp); int Move_Revmat_SplitMerge2 (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp); int Move_Speciation (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp); int Move_Speciation_M (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp); int Move_Statefreqs (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp); int Move_Statefreqs_Slider (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp); int Move_StatefreqsRoot (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp); int Move_StatefreqsRoot_Slider (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp); int Move_Statefreqs_SplitMerge (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp); int Move_StatefreqsSymDirMultistate (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp); int Move_SwitchRate (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp); int Move_SwitchRate_M (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp); int Move_TK02BranchRate (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp); int Move_Tratio_Dir (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp); int Move_TreeStretch (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp); int Move_TreeLen (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp); int Move_WNVar (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp); int Move_WNBranchRate (Param *param, int chain, RandLong *seed, MrBFlt *lnPriorRatio, MrBFlt *lnProposalRatio, MrBFlt *mvp); #endif /* PROPOSAL_H_ */ NBISweden-MrBayes-ecfd55c/src/sumpt.c000066400000000000000000012303171526212227600175270ustar00rootroot00000000000000/* * MrBayes 3 * * (c) 2002-2023 * * John P. Huelsenbeck * Dept. Integrative Biology * University of California, Berkeley * Berkeley, CA 94720-3140 * johnh@berkeley.edu * * Fredrik Ronquist * Swedish Museum of Natural History * Box 50007 * SE-10405 Stockholm, SWEDEN * fredrik.ronquist@nrm.se * * With important contributions by * * Paul van der Mark (paulvdm@sc.fsu.edu) * Maxim Teslenko (maxkth@gmail.com) * Chi Zhang (zhangchicool@gmail.com) * * and by many users (run 'acknowledgments' to see more info) * * This program is free software; you can redistribute it and/or * modify it under the terms of the GNU General Public License * as published by the Free Software Foundation; either version 2 * of the License, or (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details (www.gnu.org). * */ #include "bayes.h" #include "command.h" #include "likelihood.h" #include "mcmc.h" #include "sumpt.h" #include "utils.h" #if defined(__MWERKS__) #include "SIOUX.h" #endif typedef struct partctr { struct partctr *left, *right; BitsLong *partition; int totCount; int *count; MrBFlt **length; MrBFlt **height; MrBFlt **age; int ***nEvents; /* nEvents[0,nESets][0,numRuns][0,count[RunID]] */ MrBFlt ***bRate; /* bRate [0,nBSets][0,numRuns][0,count[RunID]] */ MrBFlt ***bLen; /* bLen [0,nBSets][0,numRuns][0,count[RunID]] */ MrBFlt **popSize; /* popSize[0,numRuns][0,count[RunID]] */ } PartCtr; typedef struct treectr { struct treectr *left, *right; int count; int *order; } TreeCtr; typedef struct { int longestLineLength; int numTreeBlocks; int lastTreeBlockBegin; int lastTreeBlockEnd; int numTreesInLastBlock; } SumtFileInfo; #define ALLOC_LEN 100 /* number of values to allocate each time in partition counter nodes */ #if defined (PRINT_RATEMUL_CPP) FILE *rateMultfp=NULL; #endif #undef DEBUG_CONTREE extern int inSumtCommand; extern int inComparetreeCommand; extern int DoUserTree (void); extern int DoUserTreeParm (char *parmName, char *tkn); extern int SafeFclose(FILE **); extern int SetUpPartitionCounters (void); extern int AddTreeToPartitionCounters (Tree *tree, int treeId, int runId); extern void CalcTopoConvDiagn2 (int *nTrees); extern void FreeChainMemory (void); /* local prototypes */ int CompareModelProbs (const void *x, const void *y); int PrintModelStats (char *fileName, char **headerNames, int nHeaders, ParameterSample *parameterSamples, int nRuns, int nSamples); int PrintOverlayPlot (MrBFlt **xVals, MrBFlt **yVals, int nRows, int startingFrom, int nSamples); int PrintParamStats (char *fileName, char **headerNames, int nHeaders, ParameterSample *parameterSamples, int nRuns, int nSamples); void PrintPlotHeader (void); PartCtr *AddSumtPartition (PartCtr *r, PolyTree *t, PolyNode *p, int runId); TreeCtr *AddSumtTree (TreeCtr *r, int *order); PartCtr *AllocPartCtr (void); TreeCtr *AllocTreeCtr (void); void CalculateTreeToTreeDistance (Tree *tree1, Tree *tree2, MrBFlt *d1, MrBFlt *d2, MrBFlt *d3); int ConTree (PartCtr **treeParts, int numTreeParts); MrBFlt CppEvolRate (PolyTree *t, PolyNode *p, int eSet); int ExamineSumtFile (char *fileName, SumtFileInfo *sumtFileInfo, char **treeName, int *brlensDef); void FreePartCtr (PartCtr *r); void FreeSumtParams (void); void FreeTreeCtr (TreeCtr *r); int Label (PolyNode *p, int addIndex, char *label, int maxLength); int OpenBrlensFile (int treeNo); int OpenComptFiles (void); int OpenSumtFiles (int treeNo); void PartCtrUppass (PartCtr *r, PartCtr **uppass, int *index); int PrintBrParamsToFile (PartCtr **treeParts, int numTreeParts, int treeNo, char *divString); void PrintConTree (FILE *fp, PolyTree *t); void PrintFigTreeConTree (FILE *fp, PolyTree *t, PartCtr **treeParts); void PrintFigTreeNodeInfo (FILE *fp, PartCtr *x, MrBFlt length); void PrintSumtTableLine (int numRuns, int *rowCount, Stat *theStats, MrBFlt *numPSRFSamples, MrBFlt *maxPSRF, MrBFlt *sumPSRF); void PrintSumtTaxaInfo (void); void Range (MrBFlt *vals, int nVals, MrBFlt *min, MrBFlt *max); void ResetTaxonSet (void); int ShowConPhylogram (FILE *fp, PolyTree *t, int screenWidth); void ShowSomeParts (FILE *fp, BitsLong *p, int offset, int nTaxaToShow); void SortPartCtr (PartCtr **item, int left, int right); void SortTerminalPartCtr (PartCtr **item, int len); void SortTreeCtr (TreeCtr **item, int left, int right); int StoreSumtTree (PackedTree *treeList, int index, PolyTree *t); void TreeCtrUppass (TreeCtr *r, TreeCtr **uppass, int *index); int TreeProb (void); void WriteConTree (PolyNode *p, FILE *fp, int showSupport); void WriteFigTreeConTree (PolyNode *p, FILE *fp, PartCtr **treeParts); /* local (to this file) */ static int numUniqueSplitsFound, numUniqueTreesFound, numPackedTrees[2], numAsterices; /* length of local to this file variables */ static FILE *fpParts=NULL, *fpTstat=NULL, *fpVstat, *fpCon=NULL, *fpTrees=NULL, *fpDists=NULL; /* file pointers */ static PartCtr *partCtrRoot = NULL; /* binary tree for holding splits info */ static TreeCtr *treeCtrRoot = NULL; /* binary tree for holding unique tree info */ static PackedTree *packedTreeList[2]; /* list of trees in packed format */ /* AllocateParameterSamples: Allocate space for parameter samples */ int AllocateParameterSamples (ParameterSample **parameterSamples, int numRuns, int numRows, int numColumns) { int i, j; (*parameterSamples) = (ParameterSample *) SafeCalloc (numColumns, sizeof(ParameterSample)); if (!(*parameterSamples)) return ERROR; (*parameterSamples)[0].values = (MrBFlt **) SafeCalloc (numColumns * numRuns, sizeof (MrBFlt *)); if (!((*parameterSamples)[0].values)) { FreeParameterSamples(*parameterSamples); return ERROR; } (*parameterSamples)[0].values[0] = (MrBFlt *) SafeCalloc (numColumns * numRuns * numRows, sizeof (MrBFlt)); for (i=1; i (*((ModelProb *)(y))).prob) return -1; else if ((*((ModelProb *)(x))).prob < (*((ModelProb *)(y))).prob) return 1; else return 0; } int DoSump (void) { int i, n, nHeaders=0, numRows, numColumns, numRuns, whichIsX, whichIsY, unreliable, oneUnreliable, burnin, longestHeader, len; MrBFlt mean, harm_mean; char **headerNames=NULL, temp[130]; SumpFileInfo fileInfo, firstFileInfo; ParameterSample *parameterSamples=NULL; FILE *fpLstat=NULL; # if defined (MPI_ENABLED) if (proc_id != 0) return NO_ERROR; # endif /* tell user we are ready to go */ if (sumpParams.numRuns == 1) MrBayesPrint ("%s Summarizing parameters in file %s.p\n", spacer, sumpParams.sumpFileName); else if (sumpParams.numRuns == 2) MrBayesPrint ("%s Summarizing parameters in files %s.run1.p and %s.run2.p\n", spacer, sumpParams.sumpFileName, sumpParams.sumpFileName); else /* if (sumpParams.numRuns > 2) */ { MrBayesPrint ("%s Summarizing parameters in %d files (%s.run1.p,\n", spacer, sumpParams.numRuns, sumpParams.sumpFileName); MrBayesPrint ("%s %s.run2.p, etc)\n", spacer, sumpParams.sumpFileName); } MrBayesPrint ("%s Writing summary statistics to file %s.pstat\n", spacer, sumpParams.sumpFileName); if (chainParams.relativeBurnin == YES) MrBayesPrint ("%s Using relative burnin ('relburnin=yes'), discarding the first %.0f %% of samples\n", spacer, chainParams.burninFraction*100.0, chainParams.burninFraction); else MrBayesPrint ("%s Using absolute burnin ('relburnin=no'), discarding the first %d samples\n", spacer, chainParams.chainBurnIn, chainParams.chainBurnIn); /* Initialize to silence warning. */ firstFileInfo.numRows = 0; firstFileInfo.numColumns = 0; /* examine input file(s) */ for (i=0; i longestHeader) longestHeader = len; } /* Print header */ PrintPlotHeader (); /* Print trace plots */ if (FindHeader("Gen", headerNames, nHeaders, &whichIsX) == ERROR) { MrBayesPrint ("%s Could not find the 'Gen' column\n", spacer); return ERROR; } if (FindHeader("lnLike", headerNames, nHeaders, &whichIsY) == ERROR) { MrBayesPrint ("%s Could not find the 'lnLike' column\n", spacer); return ERROR; } if (sumpParams.numRuns > 1) { if (sumpParams.allRuns == YES) { for (i=0; i 1) MrBayesPrintf (fpLstat, "[ID: %s]\n", stamp); /* print header */ if (sumpParams.numRuns == 1) MrBayesPrintf (fpLstat, "arithmetic_mean\tharmonic_mean\tvalues_discarded\n"); else MrBayesPrintf (fpLstat, "run\tarithmetic_mean\tharmonic_mean\tvalues_discarded\n"); oneUnreliable = NO; for (n=0; n 2) MrBayesPrint ("%s \"%s.run1.p\", \"%s.run2.p\", etc:\n", spacer, sumpParams.sumpFileName, sumpParams.sumpFileName); else /* if (sumpParams.numRuns == 2) */ MrBayesPrint ("%s \"%s.run1.p\" and \"%s.run2.p\":\n", spacer, sumpParams.sumpFileName, sumpParams.sumpFileName); MrBayesPrint ("%s (Use the harmonic mean for Bayes factor comparisons of models)\n\n", spacer, sumpParams.sumpFileName); MrBayesPrint ("%s (Values are saved to the file %s.lstat)\n\n", spacer, sumpParams.sumpOutfile); MrBayesPrint ("%s Run Arithmetic mean Harmonic mean\n", spacer); MrBayesPrint ("%s --------------------------------------\n", spacer); } if (unreliable == NO) MrBayesPrint ("%s %3d %9.2lf %9.2lf\n", spacer, n+1, mean, harm_mean); else MrBayesPrint ("%s %3d %9.2lf * %9.2lf *\n", spacer, n+1, mean, harm_mean); /* print to file */ MrBayesPrintf (fpLstat, "%d\t", n+1); MrBayesPrintf (fpLstat, "%s\t", MbPrintNum(mean)); MrBayesPrintf (fpLstat, "%s\t", MbPrintNum(harm_mean)); if (unreliable == YES) MrBayesPrintf (fpLstat, "yes\n"); else MrBayesPrintf (fpLstat, "no\n"); } } /* next run */ if (sumpParams.numRuns == 1) { MrBayesPrint ("%s --------------------------------\n", spacer); } else { if (HarmonicArithmeticMeanOnLogs (parameterSamples[whichIsY].values[0], sumpParams.numRuns*numRows, &mean, &harm_mean) == ERROR) { unreliable = YES; oneUnreliable = YES; } else unreliable = NO; MrBayesPrint ("%s --------------------------------------\n", spacer); if (unreliable == YES) MrBayesPrint ("%s TOTAL %9.2lf * %9.2lf *\n", spacer, mean, harm_mean); else MrBayesPrint ("%s TOTAL %9.2lf %9.2lf\n", spacer, mean, harm_mean); MrBayesPrint ("%s --------------------------------------\n", spacer); /* print total to file */ MrBayesPrintf (fpLstat, "all\t"); MrBayesPrintf (fpLstat, "%s\t", MbPrintNum(mean)); MrBayesPrintf (fpLstat, "%s\t", MbPrintNum(harm_mean)); if (unreliable == YES) MrBayesPrintf (fpLstat, "yes\n"); else MrBayesPrintf (fpLstat, "no\n"); } if (oneUnreliable == YES) { MrBayesPrint ("%s * These estimates may be unreliable because \n", spacer); MrBayesPrint ("%s some extreme values were excluded\n\n", spacer); } else { MrBayesPrint ("\n"); } SafeFclose(&fpLstat); /* Only print parameter summaries if we have any sampled paramters (except Gen, lnLike and lnPrior) */ if (nHeaders > 3) { /* Calculate burnin */ burnin = fileInfo.firstParamLine - fileInfo.headerLine; /* Print parameter information to screen and to file. */ if (sumpParams.numRuns > 1 && sumpParams.allRuns == YES) { for (i=0; i 2) */ { MrBayesPrint ("%s Summarizing parameters in %d files (%s.run1.p,\n", spacer, sumssParams.numRuns, sumpParams.sumpFileName); MrBayesPrint ("%s %s.run2.p, etc)\n", spacer, sumpParams.sumpFileName); } //MrBayesPrint ("%s Writing summary statistics to file %s.pstat\n", spacer, sumpParams.sumpFileName); if (chainParams.relativeBurnin == YES) MrBayesPrint ("%s Using relative burnin ('relburnin=yes'), discarding the first %.0f %% of samples within each step.\n", spacer, chainParams.burninFraction*100.0, chainParams.burninFraction); else MrBayesPrint ("%s Using absolute burnin ('relburnin=no'), discarding the first %d samples within each step.\n", spacer, chainParams.chainBurnIn, chainParams.chainBurnIn); /* Initialize to silence warning. */ firstFileInfo.numRows = 0; firstFileInfo.numColumns = 0; /* examine input file(s) */ for (i=0; i longestHeader) longestHeader = len; } /* Print trace plots */ if (FindHeader("Gen", headerNames, nHeaders, &whichIsX) == ERROR) { MrBayesPrint ("%s Could not find the 'Gen' column\n", spacer); goto errorExit; } if (FindHeader("lnLike", headerNames, nHeaders, &whichIsY) == ERROR) { MrBayesPrint ("%s Could not find the 'lnLike' column\n", spacer); goto errorExit; } if (chainParams.burninSS > 0) { stepBeginSS = chainParams.burninSS + 1; } else { numSamplesInStepSS = (numRows-1)/(chainParams.numStepsSS-chainParams.burninSS); stepBeginSS = numSamplesInStepSS + 1; } numSamplesInStepSS = (numRows - stepBeginSS)/chainParams.numStepsSS; if ((numRows - stepBeginSS)%chainParams.numStepsSS!=0) { MrBayesPrint ("%s Error: Number of samples could not be evenly divided among steps (%d samples among %d steps). \n", spacer,(numRows - stepBeginSS),chainParams.numStepsSS); goto errorExit; } if (chainParams.relativeBurnin == YES) { stepBurnin = (int)(numSamplesInStepSS*chainParams.burninFraction); } else { stepBurnin = chainParams.chainBurnIn; if (stepBurnin >= numSamplesInStepSS) { MrBayesPrint ("%s Error: Burnin in each step(%d) is longer then the step itself(%d). \n", spacer,stepBurnin, numSamplesInStepSS); goto errorExit; } } marginalLnLSS = (MrBFlt *) SafeCalloc (sumssParams.numRuns, sizeof(MrBFlt)); /*Preparing and printing joined plot.*/ plotArrayY = (MrBFlt **) SafeCalloc (sumssParams.numRuns+1, sizeof(MrBFlt*)); for (i=0; i=0; stepIndexSS--) { if (chainParams.startFromPriorSS==YES) { stepLengthSS = BetaQuantile(chainParams.alphaSS, 1.0, (MrBFlt)(chainParams.numStepsSS-stepIndexSS)/(MrBFlt)chainParams.numStepsSS) - BetaQuantile(chainParams.alphaSS, 1.0, (MrBFlt)(chainParams.numStepsSS-1-stepIndexSS)/(MrBFlt)chainParams.numStepsSS); } else { stepLengthSS = BetaQuantile(chainParams.alphaSS, 1.0, (MrBFlt)(stepIndexSS+1)/(MrBFlt)chainParams.numStepsSS) - BetaQuantile(chainParams.alphaSS, 1.0, (MrBFlt)stepIndexSS/(MrBFlt)chainParams.numStepsSS); } MrBayesPrint (" %3d ", chainParams.numStepsSS-stepIndexSS); for (i=0; i stepScalerSS + 200.0) { // adjust scaler; stepAcumulatorSS /= exp(*lnlp*stepLengthSS - 100.0 - stepScalerSS); stepScalerSS= *lnlp*stepLengthSS - 100.0; } stepAcumulatorSS += exp(*lnlp*stepLengthSS - stepScalerSS); lnlp++; } tmpMfl = (log(stepAcumulatorSS/(numSamplesInStepSS-stepBurnin)) + stepScalerSS); MrBayesPrint (" %10.3lf", tmpMfl); marginalLnLSS[i] += tmpMfl; } MrBayesPrint ("\n"); //MrBayesPrint ("%s %3d %9.2f \n", spacer, i+1, marginalLnLSS); } MrBayesPrint (" "); for (j=0; j 1) { MrBayesPrint ("%s Below are rough plots of the generations (x-axis) during burn in \n", spacer); MrBayesPrint ("%s phase versus the log probability of observing the data (y-axis). \n", spacer); MrBayesPrint ("%s You can use these graphs to determine if the burn in for your SS \n", spacer); MrBayesPrint ("%s analysis was sufficient. The log probability suppose to plateau \n", spacer); MrBayesPrint ("%s indicating that you may be at stationarity by the time you finish \n", spacer); MrBayesPrint ("%s burn in phase. This burn in, unlike burn in within each step, is \n", spacer); MrBayesPrint ("%s fixed and can not be changed. \n", spacer); } else { MrBayesPrint ("%s Below is a rough plot of the generations (x-axis) during burn in \n", spacer); MrBayesPrint ("%s phase versus the log probability of observing the data (y-axis). \n", spacer); MrBayesPrint ("%s You can use these graph to determine if the burn in for your SS \n", spacer); MrBayesPrint ("%s analysis was sufficient. The log probability suppose to plateau \n", spacer); MrBayesPrint ("%s indicating that you may be at stationarity by the time you finish \n", spacer); MrBayesPrint ("%s burn in phase. This burn in, unlike burn in within each step, is \n", spacer); MrBayesPrint ("%s fixed and can not be changed. \n", spacer); } */ if (firstPass == NO) goto sumssExitOptions; sumssStepPlot: MrBayesPrint ("\n\n%s Step plot(s).\n",spacer); while (1) { MrBayesPrint ("\n"); if (sumssParams.stepToPlot == 0) { beginPrint=(int)(sumssParams.discardFraction*stepBeginSS); countPrint=stepBeginSS-beginPrint; MrBayesPrint ("%s Plotting step 0, i.e initial burn-in phase consisting of %d samples.\n", spacer,stepBeginSS); MrBayesPrint ("%s According to 'Discardfrac=%.2f', first %d samples are not plotted.\n", spacer,sumssParams.discardFraction,beginPrint); } else { if (sumssParams.stepToPlot > chainParams.numStepsSS) { MrBayesPrint ("%s Chosen index of step to print %d is out of range of step indices[0,...,%d].\n", spacer,sumssParams.stepToPlot,chainParams.numStepsSS); goto errorExit; } beginPrint=stepBeginSS+(sumssParams.stepToPlot-1)*numSamplesInStepSS + (int)(sumssParams.discardFraction*numSamplesInStepSS); countPrint=numSamplesInStepSS-(int)(sumssParams.discardFraction*numSamplesInStepSS); MrBayesPrint ("%s Plotting step %d consisting of %d samples.\n", spacer,sumssParams.stepToPlot,numSamplesInStepSS); MrBayesPrint ("%s According to 'Discardfrac=%.2f', first %d samples are not plotted.\n", spacer,sumssParams.discardFraction,(int)(sumssParams.discardFraction*numSamplesInStepSS)); } if (sumssParams.numRuns > 1) { if (sumpParams.allRuns == YES) { for (i=0; i chainParams.numStepsSS) { do { MrBayesPrint (" Enter new value for 'Discardfrac', should be in range 0.0 to 1.0:"); k = scanf("%f",&tmpf); sumssParams.discardFraction = (MrBFlt)tmpf; } while (sumssParams.discardFraction < 0.0 || sumssParams.discardFraction > 1.0); } else sumssParams.stepToPlot=j; } if (firstPass == NO) goto sumssExitOptions; sumssJoinedPlot: MrBayesPrint ("\n\n%s Joined plot(s).\n",spacer); while (1) { MrBayesPrint ("\n"); MrBayesPrint ("%s Joined plot of %d samples of all steps together. 'smoothing' is set to:%d\n", spacer,numSamplesInStepSS,sumssParams.smoothing); MrBayesPrint ("%s According to step burn-in, first %d samples are not plotted.\n", spacer,stepBurnin); for (i=0; i0; stepIndexSS--) { firstlnlp=plotArrayY[sumssParams.numRuns] + stepBurnin; lnlp+=stepBurnin; nextSteplnlp += numSamplesInStepSS; while (lnlp < nextSteplnlp) { *firstlnlp+=*lnlp; firstlnlp++; lnlp++; } } for (j=stepBurnin;j=stepBurnin && k 1) { if (sumpParams.allRuns == YES) { for (i=0; i= numSamplesInStepSS) { MrBayesPrint (" Enter new value for 'Smoothing':"); k = scanf("%d",&j); sumssParams.smoothing = abs(j); } else stepBurnin=j; } firstPass = NO; sumssExitOptions: if (sumssParams.askForMorePlots == YES) { MrBayesPrint ("\n"); MrBayesPrint (" Sumss is interactive, because of parameter 'Askmore=YES' setting. \n"); MrBayesPrint (" What would you like to do next?\n"); MrBayesPrint (" 1) Print updated table according to new step burn-in.\n"); MrBayesPrint (" 2) Print Step plot(s).\n"); MrBayesPrint (" 3) Print Joined plot(s).\n"); MrBayesPrint (" 4) Exit 'sumss'.\n"); MrBayesPrint (" Enter a number that corresponds to one of the options:"); do { k = scanf("%d",&j); }while (j<1 || j>4); if (j == 1) { MrBayesPrint (" Allowed range of step burn-in values are from 0 to %d\n", numSamplesInStepSS-1); MrBayesPrint (" Current step burn-in value is:%d\n", stepBurnin); MrBayesPrint (" Enter new step burn-in:"); do { k = scanf("%d",&stepBurnin); } while (stepBurnin < 0 || stepBurnin > numSamplesInStepSS-1); MrBayesPrint ("\n"); goto sumssTable; } else if (j == 2) { goto sumssStepPlot; } else if (j == 3) goto sumssJoinedPlot; } /* free memory */ FreeParameterSamples(parameterSamples); for (i=0; i99 && (strchr(tkn,' ')-tkn) > 99) { MrBayesPrint ("%s Maximum allowed length of file name is 99 characters. The given name:\n", spacer); MrBayesPrint ("%s '%s'\n", spacer,tkn); return (ERROR); } sscanf (tkn, "%s", tempStr); strcpy (sumpParams.sumpFileName, tempStr); strcpy (sumpParams.sumpOutfile, tempStr); MrBayesPrint ("%s Setting sump filename and output file name to %s\n", spacer, sumpParams.sumpFileName); expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); } /* set Outputname (sumpParams.sumpOutfile) *******************************************************/ else if (!strcmp(parmName, "Outputname")) { if (expecting == Expecting(EQUALSIGN)) { expecting = Expecting(ALPHA); readWord = YES; } else if (expecting == Expecting(ALPHA)) { if (strlen(tkn)>99 && (strchr(tkn,' ')-tkn) > 99) { MrBayesPrint ("%s Maximum allowed length of file name is 99 characters. The given name:\n", spacer); MrBayesPrint ("%s '%s'\n", spacer,tkn); return (ERROR); } sscanf (tkn, "%s", tempStr); strcpy (sumpParams.sumpOutfile, tempStr); MrBayesPrint ("%s Setting sump output file name to \"%s\"\n", spacer, sumpParams.sumpOutfile); expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); } /* set Relburnin (chainParams.relativeBurnin) ********************************************************/ else if (!strcmp(parmName, "Relburnin")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(ALPHA); else if (expecting == Expecting(ALPHA)) { if (IsArgValid(tkn, tempStr) == NO_ERROR) { if (!strcmp(tempStr, "Yes")) chainParams.relativeBurnin = YES; else chainParams.relativeBurnin = NO; } else { MrBayesPrint ("%s Invalid argument for Relburnin\n", spacer); return (ERROR); } if (chainParams.relativeBurnin == YES) MrBayesPrint ("%s Using relative burnin (a fraction of samples discarded).\n", spacer); else MrBayesPrint ("%s Using absolute burnin (a fixed number of samples discarded).\n", spacer); expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else { return (ERROR); } } /* set Burnin (chainParams.chainBurnIn) ***********************************************************/ else if (!strcmp(parmName, "Burnin")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(NUMBER); else if (expecting == Expecting(NUMBER)) { sscanf (tkn, "%d", &tempI); chainParams.chainBurnIn = tempI; MrBayesPrint ("%s Setting burn-in to %d\n", spacer, chainParams.chainBurnIn); expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else { return (ERROR); } } /* set Burninfrac (chainParams.burninFraction) ************************************************************/ else if (!strcmp(parmName, "Burninfrac")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(NUMBER); else if (expecting == Expecting(NUMBER)) { sscanf (tkn, "%lf", &tempD); if (tempD < 0.01) { MrBayesPrint ("%s Burnin fraction too low (< 0.01)\n", spacer); return (ERROR); } if (tempD > 0.50) { MrBayesPrint ("%s Burnin fraction too high (> 0.50)\n", spacer); return (ERROR); } chainParams.burninFraction = tempD; MrBayesPrint ("%s Setting burnin fraction to %.2f\n", spacer, chainParams.burninFraction); expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else { return (ERROR); } } /* set Minprob (sumpParams.minProb) ************************************************************/ else if (!strcmp(parmName, "Minprob")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(NUMBER); else if (expecting == Expecting(NUMBER)) { sscanf (tkn, "%lf", &tempD); if (tempD > 0.50) { MrBayesPrint ("%s Minprob too high (it should be smaller than 0.50)\n", spacer); return (ERROR); } sumpParams.minProb = tempD; MrBayesPrint ("%s Setting minprob to %1.3f\n", spacer, sumpParams.minProb); expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else { return (ERROR); } } /* set Nruns (sumpParams.numRuns) *******************************************************/ else if (!strcmp(parmName, "Nruns")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(NUMBER); else if (expecting == Expecting(NUMBER)) { sscanf (tkn, "%d", &tempI); if (tempI < 1) { MrBayesPrint ("%s Nruns must be at least 1\n", spacer); return (ERROR); } else { sumpParams.numRuns = tempI; MrBayesPrint ("%s Setting sump nruns to %d\n", spacer, sumpParams.numRuns); expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } } else return (ERROR); } /* set Hpd (sumpParams.HPD) ********************************************************/ else if (!strcmp(parmName, "Hpd")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(ALPHA); else if (expecting == Expecting(ALPHA)) { if (IsArgValid(tkn, tempStr) == NO_ERROR) { if (!strcmp(tempStr, "Yes")) sumpParams.HPD = YES; else sumpParams.HPD = NO; } else { MrBayesPrint ("%s Invalid argument for Hpd\n", spacer); return (ERROR); } if (sumpParams.HPD == YES) MrBayesPrint ("%s Reporting 95 %% region of Highest Posterior Density (HPD).\n", spacer); else MrBayesPrint ("%s Reporting median interval containing 95 %% of values.\n", spacer); expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else { return (ERROR); } } /* set Allruns (sumpParams.allRuns) ********************************************************/ else if (!strcmp(parmName, "Allruns")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(ALPHA); else if (expecting == Expecting(ALPHA)) { if (IsArgValid(tkn, tempStr) == NO_ERROR) { if (!strcmp(tempStr, "Yes")) sumpParams.allRuns = YES; else sumpParams.allRuns = NO; } else { MrBayesPrint ("%s Invalid argument for allruns (valid arguments are 'yes' and 'no')\n", spacer); return (ERROR); } if (sumpParams.allRuns == YES) MrBayesPrint ("%s Setting sump to print information for each run\n", spacer); else MrBayesPrint ("%s Setting sump to print only summary information for all runs\n", spacer); expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); } else return (ERROR); } return (NO_ERROR); } int DoSumSsParm (char *parmName, char *tkn) { int tempI; MrBFlt tempD; char tempStr[100]; if (expecting == Expecting(PARAMETER)) { expecting = Expecting(EQUALSIGN); } else { if (!strcmp(parmName, "Xxxxxxxxxx")) { expecting = Expecting(PARAMETER); expecting |= Expecting(SEMICOLON); } /* set Filename (sumpParams.sumpFileName) ***************************************************/ else if (!strcmp(parmName, "Filename")) { if (expecting == Expecting(EQUALSIGN)) { expecting = Expecting(ALPHA); readWord = YES; } else if (expecting == Expecting(ALPHA)) { if (strlen(tkn)>99 && (strchr(tkn,' ')-tkn) > 99) { MrBayesPrint ("%s Maximum allowed length of file name is 99 characters. The given name:\n", spacer); MrBayesPrint ("%s '%s'\n", spacer,tkn); return (ERROR); } sscanf (tkn, "%s", tempStr); strcpy (sumpParams.sumpFileName, tempStr); strcpy (sumpParams.sumpOutfile, tempStr); MrBayesPrint ("%s Setting sump filename and output file name to %s\n", spacer, sumpParams.sumpFileName); expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); } /* set Outputname (sumpParams.sumpOutfile) *******************************************************/ /*else if (!strcmp(parmName, "Outputname")) { if (expecting == Expecting(EQUALSIGN)) { expecting = Expecting(ALPHA); readWord = YES; } else if (expecting == Expecting(ALPHA)) { if (strlen(tkn)>99 && (strchr(tkn,' ')-tkn) > 99) { MrBayesPrint ("%s Maximum allowed length of file name is 99 characters. The given name:\n", spacer); MrBayesPrint ("%s '%s'\n", spacer,tkn); return (ERROR); } sscanf (tkn, "%s", tempStr); strcpy (sumpParams.sumpOutfile, tempStr); MrBayesPrint ("%s Setting sump output file name to \"%s\"\n", spacer, sumpParams.sumpOutfile); expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); }*/ /* set Relburnin (chainParams.relativeBurnin) ********************************************************/ else if (!strcmp(parmName, "Relburnin")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(ALPHA); else if (expecting == Expecting(ALPHA)) { if (IsArgValid(tkn, tempStr) == NO_ERROR) { if (!strcmp(tempStr, "Yes")) chainParams.relativeBurnin = YES; else chainParams.relativeBurnin = NO; } else { MrBayesPrint ("%s Invalid argument for Relburnin\n", spacer); return (ERROR); } if (chainParams.relativeBurnin == YES) MrBayesPrint ("%s Using relative burnin (a fraction of samples discarded).\n", spacer); else MrBayesPrint ("%s Using absolute burnin (a fixed number of samples discarded).\n", spacer); expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else { return (ERROR); } } /* set Burnin (chainParams.chainBurnIn) ***********************************************************/ else if (!strcmp(parmName, "Burnin")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(NUMBER); else if (expecting == Expecting(NUMBER)) { sscanf (tkn, "%d", &tempI); chainParams.chainBurnIn = tempI; MrBayesPrint ("%s Setting burn-in to %d\n", spacer, chainParams.chainBurnIn); expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else { return (ERROR); } } /* set Burninfrac (chainParams.burninFraction) ************************************************************/ else if (!strcmp(parmName, "Burninfrac")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(NUMBER); else if (expecting == Expecting(NUMBER)) { sscanf (tkn, "%lf", &tempD); if (tempD < 0.01) { MrBayesPrint ("%s Burnin fraction too low (< 0.01)\n", spacer); return (ERROR); } if (tempD > 0.50) { MrBayesPrint ("%s Burnin fraction too high (> 0.50)\n", spacer); return (ERROR); } chainParams.burninFraction = tempD; MrBayesPrint ("%s Setting burnin fraction to %.2f\n", spacer, chainParams.burninFraction); expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else { return (ERROR); } } /* set Nruns (sumssParams.numRuns) *******************************************************/ else if (!strcmp(parmName, "Nruns")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(NUMBER); else if (expecting == Expecting(NUMBER)) { sscanf (tkn, "%d", &tempI); if (tempI < 1) { MrBayesPrint ("%s Nruns must be at least 1\n", spacer); return (ERROR); } else { sumssParams.numRuns = tempI; MrBayesPrint ("%s Setting sumss nruns to %d\n", spacer, sumssParams.numRuns); expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } } else return (ERROR); } /* set Allruns (sumssParams.allRuns) ********************************************************/ else if (!strcmp(parmName, "Allruns")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(ALPHA); else if (expecting == Expecting(ALPHA)) { if (IsArgValid(tkn, tempStr) == NO_ERROR) { if (!strcmp(tempStr, "Yes")) sumssParams.allRuns = YES; else sumssParams.allRuns = NO; } else { MrBayesPrint ("%s Invalid argument for allruns (valid arguments are 'yes' and 'no')\n", spacer); return (ERROR); } if (sumssParams.allRuns == YES) MrBayesPrint ("%s Setting sump to print information for each run\n", spacer); else MrBayesPrint ("%s Setting sump to print only summary information for all runs\n", spacer); expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); } /* set Steptoplot (sumssParams.stepToPlot) *******************************************************/ else if (!strcmp(parmName, "Steptoplot")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(NUMBER); else if (expecting == Expecting(NUMBER)) { sscanf (tkn, "%d", &tempI); if (tempI < 0) { MrBayesPrint ("%s Steptoplot must be at least 0\n", spacer); return (ERROR); } else { sumssParams.stepToPlot = tempI; MrBayesPrint ("%s Setting sumss steptoplot to %d\n", spacer, sumssParams.stepToPlot); expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } } else return (ERROR); } /* set Smoothing (sumssParams.smoothing) *******************************************************/ else if (!strcmp(parmName, "Smoothing")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(NUMBER); else if (expecting == Expecting(NUMBER)) { sscanf (tkn, "%d", &tempI); if (tempI < 0) { MrBayesPrint ("%s Smoothing must be at least 0\n", spacer); return (ERROR); } else { sumssParams.smoothing = tempI; MrBayesPrint ("%s Setting sumss smoothing to %d\n", spacer, sumssParams.smoothing); expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } } else return (ERROR); } /* set Allruns (sumssParams.askForMorePlots) ********************************************************/ else if (!strcmp(parmName, "Askmore")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(ALPHA); else if (expecting == Expecting(ALPHA)) { if (IsArgValid(tkn, tempStr) == NO_ERROR) { if (!strcmp(tempStr, "Yes")) sumssParams.askForMorePlots = YES; else sumssParams.askForMorePlots = NO; } else { MrBayesPrint ("%s Invalid argument for askmore (valid arguments are 'yes' and 'no')\n", spacer); return (ERROR); } if (sumssParams.askForMorePlots == YES) MrBayesPrint ("%s Setting sumss to be interactive by asking for more plots of burn-in or individual steps.\n", spacer); else MrBayesPrint ("%s Setting sumss not to be interactive. It will not ask to print more plots.\n", spacer); expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); } /* set Discardfrac (sumssParams.discardFraction) ************************************************************/ else if (!strcmp(parmName, "Discardfrac")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(NUMBER); else if (expecting == Expecting(NUMBER)) { sscanf (tkn, "%lf", &tempD); if (tempD < 0.00) { MrBayesPrint ("%s Discard fraction too low (< 0.00)\n", spacer); return (ERROR); } if (tempD > 1.00) { MrBayesPrint ("%s Discard fraction too high (> 1.00)\n", spacer); return (ERROR); } sumssParams.discardFraction = tempD; MrBayesPrint ("%s Setting discard fraction to %.2f\n", spacer, sumssParams.discardFraction); expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else { return (ERROR); } } else return (ERROR); } return (NO_ERROR); } /* ExamineSumpFile: Collect info on the parameter samples in the file */ int ExamineSumpFile (char *fileName, SumpFileInfo *fileInfo, char ***headerNames, int *nHeaders) { char *sumpTokenP, sumpToken[CMD_STRING_LENGTH], *s=NULL, *headerLine, *t; int i, lineTerm, inSumpComment, lineNum, lastNonDigitLine, numParamLines, allDigitLine, lastTokenWasDash, nNumbersOnThisLine, tokenType, burnin, nLines, firstNumCols; MrBFlt tempD; FILE *fp = NULL; /* open binary file */ if ((fp = OpenBinaryFileR(fileName)) == NULL) { /* test for some simple errors */ if (strlen(sumpParams.sumpFileName) > 2) { s = sumpParams.sumpFileName + (int) strlen(sumpParams.sumpFileName) - 2; if (strcmp(s, ".p") == 0) { MrBayesPrint ("%s It appears that you need to remove '.p' from the 'Filename' parameter\n", spacer); MrBayesPrint ("%s Also make sure that 'Nruns' is set correctly\n", spacer); return ERROR; } } MrBayesPrint ("%s Make sure that 'Nruns' is set correctly\n", spacer); return ERROR; } /* find out what type of line termination is used */ lineTerm = LineTermType (fp); if (lineTerm != LINETERM_MAC && lineTerm != LINETERM_DOS && lineTerm != LINETERM_UNIX) { MrBayesPrint ("%s Unknown line termination\n", spacer); goto errorExit; } /* find length of longest line */ fileInfo->longestLineLength = LongestLine (fp); fileInfo->longestLineLength += 10; /* better safe than sorry; if you fgets with raw longestLineLength, you run into problems */ /* allocate string long enough to hold a line */ s = (char *)SafeMalloc(2 * ((size_t)(fileInfo->longestLineLength) +10) * sizeof(char)); if (!s) { MrBayesPrint ("%s Problem allocating string for reading sump file\n", spacer); goto errorExit; } headerLine = s + fileInfo->longestLineLength + 10; /* close binary file */ SafeFclose (&fp); /* open text file */ if ((fp = OpenTextFileR(fileName)) == NULL) goto errorExit; /* Check file for appropriate blocks. We want to find the last block in the file and start from there. */ inSumpComment = NO; lineNum = lastNonDigitLine = numParamLines = 0; while (fgets (s, fileInfo->longestLineLength + 2, fp) != NULL) { sumpTokenP = &s[0]; allDigitLine = YES; lastTokenWasDash = NO; nNumbersOnThisLine = 0; do { if (GetToken (sumpToken, &tokenType, &sumpTokenP)) goto errorExit; /* printf ("%s (%d)\n", sumpToken, tokenType); */ if (IsSame("[", sumpToken) == SAME) inSumpComment = YES; if (IsSame("]", sumpToken) == SAME) inSumpComment = NO; if (inSumpComment == NO) { if (tokenType == NUMBER) { sscanf (sumpToken, "%lf", &tempD); if (lastTokenWasDash == YES) tempD *= -1.0; nNumbersOnThisLine++; lastTokenWasDash = NO; } else if (tokenType == DASH) { lastTokenWasDash = YES; } else if (tokenType != UNKNOWN_TOKEN_TYPE) { allDigitLine = NO; lastTokenWasDash = NO; } } } while (*sumpToken); lineNum++; if (allDigitLine == NO) { lastNonDigitLine = lineNum; numParamLines = 0; } else { if (nNumbersOnThisLine > 0) numParamLines++; } } /* Now, check some aspects of the .p file. */ if (inSumpComment == YES) { MrBayesPrint ("%s Unterminated comment in file \"%s\"\n", spacer, fileName); goto errorExit; } if (numParamLines <= 0) { MrBayesPrint ("%s No parameters were found in file or there characters not representing a number in last string of the file.\"%s\"\n", spacer, fileName); goto errorExit; } /* calculate burnin */ if (chainParams.isSS == YES) { burnin = 0; } else { if (chainParams.relativeBurnin == YES) burnin = (int) (chainParams.burninFraction * numParamLines); else burnin = chainParams.chainBurnIn; } /* check against burnin */ if (burnin > numParamLines) { MrBayesPrint ("%s No parameters can be sampled from file %s as the burnin (%d) exceeds the number of lines in last block (%d)\n", spacer, fileName, burnin, numParamLines); MrBayesPrint ("%s Try setting burnin to a number less than %d\n", spacer, numParamLines); goto errorExit; } /* Set some info in fileInfo */ fileInfo->firstParamLine = lastNonDigitLine + burnin; fileInfo->headerLine = lastNonDigitLine; /* Calculate and check the number of columns and rows for the file; get header line at the same time */ (void)fseek(fp, 0L, 0); for (lineNum=0; lineNumlongestLineLength + 2, fp)==NULL) goto errorExit; strcpy(headerLine, s); for (; lineNum < lastNonDigitLine+burnin; lineNum++) if (fgets (s, fileInfo->longestLineLength + 2, fp)==NULL) goto errorExit; inSumpComment = NO; nLines = 0; firstNumCols = 0; while (fgets (s, fileInfo->longestLineLength + 2, fp) != NULL) { sumpTokenP = &s[0]; allDigitLine = YES; lastTokenWasDash = NO; nNumbersOnThisLine = 0; do { if (GetToken (sumpToken, &tokenType, &sumpTokenP)) goto errorExit; if (IsSame("[", sumpToken) == SAME) inSumpComment = YES; if (IsSame("]", sumpToken) == SAME) inSumpComment = NO; if (inSumpComment == NO) { if (tokenType == NUMBER) { nNumbersOnThisLine++; lastTokenWasDash = NO; } else if (tokenType == DASH) { lastTokenWasDash = YES; } else if (tokenType != UNKNOWN_TOKEN_TYPE) { allDigitLine = NO; lastTokenWasDash = NO; } } } while (*sumpToken); lineNum++; if (allDigitLine == NO) { MrBayesPrint ("%s Found a line with non-digit characters (line %d) in file %s\n", spacer, lineNum, fileName); goto errorExit; } else { if (nNumbersOnThisLine > 0) { nLines++; if (nLines == 1) firstNumCols = nNumbersOnThisLine; else { if (nNumbersOnThisLine != firstNumCols) { MrBayesPrint ("%s Number of columns is not even (%d in first line and %d in line %d of file %s)\n", spacer, firstNumCols, nNumbersOnThisLine, lineNum, fileName); goto errorExit; } } } } } fileInfo->numRows = nLines; fileInfo->numColumns = firstNumCols; /* set or check headers */ if ((*headerNames) == NULL) { GetHeaders (headerNames, headerLine, nHeaders); if (*nHeaders != fileInfo->numColumns) { MrBayesPrint ("%s Expected %d headers but found %d headers\n", spacer, fileInfo->numColumns, *nHeaders); for (i=0; i<*nHeaders; i++) SAFEFREE ((*headerNames)[i]); SAFEFREE (*headerNames); *nHeaders=0; goto errorExit; } } else { if (*nHeaders != fileInfo->numColumns) { MrBayesPrint ("%s Expected %d columns but found %d columns\n", spacer, *nHeaders, fileInfo->numColumns); goto errorExit; } for (i=0, t=strtok(headerLine,"\t\n\r"); t!=NULL; t=strtok(NULL,"\t\n\r"), i++) { if (i == *nHeaders) { MrBayesPrint ("%s Expected %d headers but found more headers.\n", spacer, fileInfo->numColumns); goto errorExit; } if (strcmp(t,(*headerNames)[i])!=0) { MrBayesPrint ("%s Expected header '%s' for column %d but the header for this column was '%s' in file '%s'\n", spacer, (*headerNames)[i], i+1, t, fileName); MrBayesPrint ("%s It could be that some parameter values are not numbers and the whole string containing \n",spacer); MrBayesPrint ("%s this wrongly formatted parameter is treated as a header.\n",spacer); goto errorExit; } } if (t != NULL) { MrBayesPrint ("%s Expected %d headers but found more headers.\n",spacer, fileInfo->numColumns); goto errorExit; } if (i < *nHeaders) { MrBayesPrint ("%s Expected header '%s' for column %d but the header for this column was '%s' in file '%s'\n", spacer, (*headerNames)[i], i+1, t, fileName); goto errorExit; } } free (s); fclose(fp); return (NO_ERROR); errorExit: free(s); fclose(fp); return (ERROR); } /*************************************************** | | FindHeader: Find token in list | ----------------------------------------------------*/ int FindHeader (char *token, char **headerNames, int nHeaders, int *index) { int i, match=0, nMatches; *index = -1; nMatches = 0; for (i=0; i longestHeader) longestHeader = len; } /* open output file */ strncpy (temp, fileName, 99); strcat (temp, ".pstat"); fp = OpenNewMBPrintFile (temp); if (!fp) return ERROR; /* print unique identifier to the output file */ if (strlen(stamp) > 1) MrBayesPrintf (fp, "[ID: %s]\n", stamp); /* allocate and set nSamples */ sampleCounts = (int *) SafeCalloc (nRuns, sizeof(int)); if (!sampleCounts) { fclose(fp); return ERROR; } for (i=0; i 1) MrBayesPrint (" PSRF+ "); MrBayesPrint ("\n"); MrBayesPrint ("%s ", spacer); for (j=0; j 1) MrBayesPrint ("----------"); MrBayesPrint ("\n"); if (nRuns > 1) MrBayesPrintf (fp, "Parameter\tMean\tVariance\tLower\tUpper\tMedian\tPSRF\n"); else MrBayesPrintf (fp, "Parameter\tMean\tVariance\tLower\tUpper\tMedian\n"); /* print table values */ for (i=0; i 1) { if (theStats.PSRF < 0.0) { MrBayesPrint (" NA "); MrBayesPrintf (fp, "NA"); } else { MrBayesPrint (" %7.3lf", theStats.PSRF); MrBayesPrintf (fp, "\t%s", MbPrintNum(theStats.PSRF)); } } MrBayesPrint ("\n"); MrBayesPrintf (fp, "\n"); } MrBayesPrint ("%s ", spacer); for (j=0; j 1) MrBayesPrint ("----------"); MrBayesPrint ("\n"); if (nRuns > 1) { MrBayesPrint ("%s + Convergence diagnostic (PSRF = Potential Scale Reduction Factor; Gelman\n", spacer); MrBayesPrint ("%s and Rubin, 1992) should approach 1.0 as runs converge.\n", spacer); } fclose (fp); free (sampleCounts); return (NO_ERROR); } /* PrintModelStats: Print model stats to screen and to .mstat file */ int PrintModelStats (char *fileName, char **headerNames, int nHeaders, ParameterSample *parameterSamples, int nRuns, int nSamples) { int i, j, j1, j2, k, longestName, nElements, *modelCounts=NULL; MrBFlt f, *prob=NULL, *sum=NULL, *ssq=NULL, *min=NULL, *max=NULL, *stddev=NULL; char temp[130]; FILE *fp; ModelProb *elem = NULL; /* nHeaders - is a convenient synonym for number of column headers */ /* check if we have any model indicator variables and also check for longest header */ k = 0; longestName = 0; for (i=0; i longestName) longestName = j2; } break; } } } /* return if nothing to do */ if (k==0) return NO_ERROR; /* open output file */ MrBayesPrint ("%s Model probabilities above %1.3lf\n", spacer, sumpParams.minProb); MrBayesPrint ("%s Estimates saved to file \"%s.mstat\".\n", spacer, sumpParams.sumpOutfile); strncpy (temp, fileName, 99); strcat (temp, ".mstat"); fp = OpenNewMBPrintFile(temp); if (!fp) return ERROR; MrBayesPrint ("\n"); /* print unique identifier to the output file */ if (strlen(stamp) > 1) MrBayesPrintf (fp, "[ID: %s]\n", stamp); /* print header */ MrBayesPrintf (fp, "\n\n"); if (nRuns == 1) { MrBayesPrint ("%s %*c Posterior\n", spacer, longestName-5, ' '); MrBayesPrint ("%s Model%*c Probability\n", spacer, longestName-5, ' '); MrBayesPrint ("%s -----", spacer); for (i=0; i max[j2]) max[j2] = f; } } for (j1=0; j1 1) */ { sprintf (temp, "%s[%s]", headerNames[i], modelElementNames[j][elem[j1].index]); MrBayesPrint ("%s %-*s %1.3lf %1.3lf %1.3lf %1.3lf\n", spacer, longestName, temp, prob[elem[j1].index], stddev[elem[j1].index], min[elem[j1].index], max[elem[j1].index]); MrBayesPrintf (fp, "%s", temp); MrBayesPrintf (fp, "\t%s", MbPrintNum(prob[elem[j1].index])); MrBayesPrintf (fp, "\t%s", MbPrintNum(stddev[elem[j1].index])); MrBayesPrintf (fp, "\t%s", MbPrintNum(min[elem[j1].index])); MrBayesPrintf (fp, "\t%s", MbPrintNum(max[elem[j1].index])); MrBayesPrintf (fp, "\n"); } } free(elem); elem = NULL; free(modelCounts); modelCounts = NULL; free (prob); prob = NULL; } /* print footer */ if (nRuns == 1) { MrBayesPrint ("%s -----", spacer); for (i=0; i 9) MrBayesPrint ("%s (1 = Run number 1; 2 = Run number 2 etc.; x = Run number 10 or above; * = Several runs)\n", spacer); else if (nRuns > 2) MrBayesPrint ("%s (1 = Run number 1; 2 = Run number 2 etc.; * = Several runs)\n", spacer); else MrBayesPrint ("%s (1 = Run number 1; 2 = Run number 2; * = Both runs)\n", spacer); /* print overlay x-y plot of log likelihood vs. generation for all runs */ screenWidth = 60; /* don't change this without changing numY, meanY, and plotSymbol declared above */ screenHeight = 15; /* find minX, minY, maxX, and maxY over all runs */ minX = minY = 1000000000.0; maxX = maxY = -1000000000.0; for (n=0; n maxX) maxX = x; } } for (n=0; n= screenWidth) k = screenWidth - 1; if (k == j) { y += yVals[n][i]; k2 ++; } else { y /= k2; if (y < minY) minY = y; if (y > maxY) maxY = y; k2 = 1; y = yVals[n][i]; j++; } } if (k2 > 0) { y /= k2; if (y < minY) minY = y; if (y > maxY) maxY = y; } } /* initialize the plot symbols */ for (i=0; i= screenWidth) k = screenWidth - 1; if (k <= 0) k = 0; meanY[k] += y; numY[k]++; } /* transfer these points to the overlay */ for (i=0; i 0) { k = (int) ((((meanY[i] / numY[i]) - minY)/ (maxY - minY)) * screenHeight); if (k < 0) k = 0; else if (k >= screenHeight) k = screenHeight - 1; if (plotSymbol[k][i] == ' ') { if (n <= 8) plotSymbol[k][i] = '1' + n; else plotSymbol[k][i] = 'x'; } else plotSymbol[k][i] = '*'; } } } /* next run */ /* now print the overlay plot */ MrBayesPrint ("\n%s +", spacer); for (i=0; i=0; i--) { MrBayesPrint ("%s |", spacer); for (j=0; j0) width=(int)(log10(minX)); else if ((int)minX==0) width=1; else width=(int)(log10(-minX))+1; for (i=0; i longestHeader) longestHeader = len; } /* open output file */ strncpy (tempf, fileName, 99); strcat (tempf, ".pstat"); fp = OpenNewMBPrintFile (tempf); if (!fp) return ERROR; /* print unique identifier to the output file */ if (strlen(stamp) > 1) MrBayesPrintf (fp, "[ID: %s]\n", stamp); /* allocate and set nSamples */ sampleCounts = (int *) SafeCalloc (nRuns, sizeof(int)); validSampleCounts = (int *) SafeCalloc (nRuns, sizeof(int)); // for removing NAs if (!sampleCounts || !validSampleCounts) { fclose(fp); return ERROR; } for (i=0; i 1) MrBayesPrint ("%s Parameter%*c Mean Variance Lower Upper Median min ESS* avg ESS PSRF+ ", spacer, longestHeader-9, ' '); else MrBayesPrint ("%s Parameter%*c Mean Variance Lower Upper Median ESS*", spacer, longestHeader-9, ' '); MrBayesPrint ("\n"); MrBayesPrint ("%s ", spacer); for (j=0; j 1) MrBayesPrint ("-------------------"); MrBayesPrint ("\n"); if (nRuns > 1) MrBayesPrintf (fp, "Parameter\tMean\tVariance\tLower\tUpper\tMedian\tminESS\tavgESS\tPSRF\n"); else MrBayesPrintf (fp, "Parameter\tMean\tVariance\tLower\tUpper\tMedian\tESS\n"); /* print table values */ for (i=0; i 1) { if (nValidTotal != 0 && theStats.minESS == theStats.minESS) { MrBayesPrint (" %8.2lf", theStats.avrESS); MrBayesPrintf (fp, "\t%s", MbPrintNum(theStats.avrESS)); } else { MrBayesPrint (" NA "); MrBayesPrintf (fp, "\tNA"); } if (nValidTotal == 0 || theStats.PSRF < 0.0) { MrBayesPrint (" NA "); MrBayesPrintf (fp, "\tNA"); } else { MrBayesPrint (" %7.3lf", theStats.PSRF); MrBayesPrintf (fp, "\t%s", MbPrintNum(theStats.PSRF)); } } MrBayesPrint ("\n"); MrBayesPrintf (fp, "\n"); } MrBayesPrint ("%s ", spacer); for (j=0; j 1) MrBayesPrint ("-------------------"); MrBayesPrint ("\n"); if (nRuns > 1) { MrBayesPrint ("%s * Convergence diagnostic (ESS = Estimated Sample Size); min and avg values\n", spacer); MrBayesPrint ("%s correspond to minimal and average ESS among runs. \n", spacer); MrBayesPrint ("%s ESS value below 100 may indicate that the parameter is undersampled. \n", spacer); MrBayesPrint ("%s + Convergence diagnostic (PSRF = Potential Scale Reduction Factor; Gelman\n", spacer); MrBayesPrint ("%s and Rubin, 1992) should approach 1.0 as runs converge.\n", spacer); } else { MrBayesPrint ("%s * Convergence diagnostic (ESS = Estimated Sample Size); ESS value \n", spacer); MrBayesPrint ("%s below 100 may indicate that the parameter is undersampled. \n", spacer); } MrBayesPrint ("\n\n"); fclose (fp); free (sampleCounts); for (i=0; i maxX) maxX = x; } /* collect Y data */ for (i=0; i= screenWidth) k = screenWidth - 1; if (k < 0) k = 0; meanY[k] += y; numY[k]++; } /* find minY and maxY */ minY = maxY = meanY[0] / numY[0]; for (i=0; i maxY) maxY = meanY[i]; } /* find difference */ diff = maxY - minY; /* print plot */ MrBayesPrint ("\n +"); for (i=0; i=0; j--) { MrBayesPrint (" |"); for (i=0; i 0) { if ((meanY[i] > ((diff/screenHeight)*j)+minY && meanY[i] <= ((diff/screenHeight)*(j+1))+minY) || (j == 0 && meanY[i] <= minY)) MrBayesPrint ("*"); else MrBayesPrint (" "); } else { MrBayesPrint (" "); } } MrBayesPrint ("|\n"); } MrBayesPrint (" +"); for (i=0; i 1) { MrBayesPrint ("%s Below are rough plots of the generation (x-axis) versus the log \n", spacer); MrBayesPrint ("%s probability of observing the data (y-axis). You can use these \n", spacer); MrBayesPrint ("%s graphs to determine what the burn in for your analysis should be. \n", spacer); MrBayesPrint ("%s When the log probability starts to plateau you may be at station- \n", spacer); MrBayesPrint ("%s arity. Sample trees and parameters after the log probability \n", spacer); MrBayesPrint ("%s plateaus. Of course, this is not a guarantee that you are at sta- \n", spacer); MrBayesPrint ("%s tionarity. Also examine the convergence diagnostics provided by \n", spacer); MrBayesPrint ("%s the 'sump' and 'sumt' commands for all the parameters in your \n", spacer); MrBayesPrint ("%s model. Remember that the burn in is the number of samples to dis- \n", spacer); MrBayesPrint ("%s card. There are a total of ngen / samplefreq samples taken during \n", spacer); MrBayesPrint ("%s a MCMC analysis. \n", spacer); } else { MrBayesPrint ("%s Below is a rough plot of the generation (x-axis) versus the log \n", spacer); MrBayesPrint ("%s probability of observing the data (y-axis). You can use this \n", spacer); MrBayesPrint ("%s graph to determine what the burn in for your analysis should be. \n", spacer); MrBayesPrint ("%s When the log probability starts to plateau you may be at station- \n", spacer); MrBayesPrint ("%s arity. Sample trees and parameters after the log probability \n", spacer); MrBayesPrint ("%s plateaus. Of course, this is not a guarantee that you are at sta- \n", spacer); MrBayesPrint ("%s analysis should be. When the log probability starts to plateau \n", spacer); MrBayesPrint ("%s tionarity. When possible, run multiple analyses starting from dif-\n", spacer); MrBayesPrint ("%s ferent random trees; if the inferences you make for independent \n", spacer); MrBayesPrint ("%s analyses are the same, this is reasonable evidence that the chains\n", spacer); MrBayesPrint ("%s have converged. You can use MrBayes to run several independent \n", spacer); MrBayesPrint ("%s analyses simultaneously. During such a run, MrBayes will monitor \n", spacer); MrBayesPrint ("%s the convergence of topologies. After the run has been completed, \n", spacer); MrBayesPrint ("%s the 'sumt' and 'sump' functions will provide additional conver- \n", spacer); MrBayesPrint ("%s gence diagnostics for all the parameters in your model. Remember \n", spacer); MrBayesPrint ("%s that the burn in is the number of samples to discard. There are \n", spacer); MrBayesPrint ("%s a total of ngen / samplefreq samples taken during a MCMC analysis.\n", spacer); } } /* ReadParamSamples: Read parameter samples from .p file */ int ReadParamSamples (char *fileName, SumpFileInfo *fileInfo, ParameterSample *parameterSamples, int runNo) { char sumpToken[CMD_STRING_LENGTH], *s=NULL, *p; int inSumpComment, lineNum, numLinesRead, numLinesToRead, column, lastTokenWasDash, tokenType; MrBFlt tempD; FILE *fp; /* open file */ if ((fp = OpenTextFileR (fileName)) == NULL) return ERROR; /* allocate space for reading lines */ s = (char *) SafeCalloc (fileInfo->longestLineLength + 10, sizeof(char)); /* fast forward to beginning of last unburned parameter line. */ for (lineNum=0; lineNumfirstParamLine; lineNum++) if (fgets (s, fileInfo->longestLineLength + 5, fp) == 0) goto errorExit; /* parse file, line-by-line. We are only parsing lines that have digits that should be read. */ inSumpComment = NO; numLinesToRead = fileInfo->numRows; numLinesRead = 0; while (fgets (s, fileInfo->longestLineLength + 1, fp) != NULL) { lastTokenWasDash = NO; column = 0; p = s; do { if (GetToken (sumpToken, &tokenType, &p)) goto errorExit; if (IsSame("[", sumpToken) == SAME) inSumpComment = YES; if (IsSame("]", sumpToken) == SAME) inSumpComment = NO; if (inSumpComment == NO) { if (tokenType == NUMBER) { /* read the number */ if (column >= fileInfo->numColumns) { MrBayesPrint ("%s Too many values read on line %d of file %s\n", spacer, lineNum, fileName); goto errorExit; } sscanf (sumpToken, "%lf", &tempD); if (lastTokenWasDash == YES) tempD *= -1.0; parameterSamples[column].values[runNo][numLinesRead] = tempD; column++; lastTokenWasDash = NO; } else if (tokenType == DASH) { lastTokenWasDash = YES; } else if (tokenType != UNKNOWN_TOKEN_TYPE) { /* we have a problem */ MrBayesPrint ("%s Line %d of file %s has non-digit characters\n", spacer, lineNum, fileName); goto errorExit; } } } while (*sumpToken); lineNum++; if (column == fileInfo->numColumns) numLinesRead++; else if (column != 0) { MrBayesPrint ("%s Too few values on line %d of file %s\n", spacer, lineNum, fileName); goto errorExit; } } /* Check how many parameter line was read in. */ if (numLinesRead != numLinesToRead) { MrBayesPrint ("%s Unable to read all lines that should contain parameter samples\n", spacer); goto errorExit; } fclose (fp); free (s); return (NO_ERROR); errorExit: fclose (fp); free (s); return ERROR; } /* the following are moved from sumt.c to combine with sump.c */ PartCtr *AddSumtPartition (PartCtr *r, PolyTree *t, PolyNode *p, int runId) { int i, n, comp, nLongsNeeded = sumtParams.BitsLongsNeeded; if (r == NULL) { /* new partition */ /* create a new node */ r = AllocPartCtr (); if (r == NULL) return NULL; numUniqueSplitsFound++; for (i=0; ipartition[i] = p->partition[i]; for (i=0; icount[i] = 0; r->left = r->right = NULL; /* record values */ if (sumtParams.brlensDef == YES) r->length[runId][0]= p->length; if (sumtParams.isClock == YES) r->height[runId][0]= p->depth; if (sumtParams.isCalibrated == YES) r->age[runId][0]= p->age; for (i=0; inEvents[i][runId][0] = t->nEvents[i][p->index]; for (i=0; ibLen [i][runId][0] = t->effectiveBrLen[i][p->index]; r->bRate[i][runId][0] = t->effectiveBrLen[i][p->index] / p->length; if (p->length == 0.0) r->bRate[i][runId][0] = 1.0; // deal with zero brl } if (t->popSizeSet == YES) r->popSize[runId][0] = t->popSize[p->index]; r->count[runId] ++; r->totCount++; } else { for (i=0; ipartition[i] != p->partition[i]) break; } if (i == nLongsNeeded) comp = 0; else if (r->partition[i] < p->partition[i]) comp = -1; else comp = 1; if (comp == 0) /* repeated partition */ { n = r->count[runId]; /* check if we need to allocate more space */ if (n % ALLOC_LEN == 0) { /* allocate more space */ if (sumtParams.brlensDef == YES) r->length[runId] = (MrBFlt *) SafeRealloc ((void *)r->length[runId], ((size_t)n+ALLOC_LEN)*sizeof(MrBFlt)); if (sumtParams.isClock == YES) r->height[runId] = (MrBFlt *) SafeRealloc ((void *)r->height[runId], ((size_t)n+ALLOC_LEN)*sizeof(MrBFlt)); if (sumtParams.isCalibrated == YES) r->age[runId] = (MrBFlt *) SafeRealloc ((void *)r->age[runId], ((size_t)n+ALLOC_LEN)*sizeof(MrBFlt)); if (sumtParams.nESets > 0) { for (i=0; inEvents[i][runId] = (int *) SafeRealloc ((void *)r->nEvents[i][runId], ((size_t)n+ALLOC_LEN)*sizeof(int)); } if (sumtParams.nBSets > 0) { for (i=0; ibRate[i][runId] = (MrBFlt *) SafeRealloc ((void *)r->bRate[i][runId], ((size_t)n+ALLOC_LEN)*sizeof(MrBFlt)); r->bLen [i][runId] = (MrBFlt *) SafeRealloc ((void *)r->bLen [i][runId], ((size_t)n+ALLOC_LEN)*sizeof(MrBFlt)); } } if (sumtParams.popSizeSet == YES) r->popSize[runId] = (MrBFlt *) SafeRealloc ((void *)r->popSize[runId], ((size_t)n+ALLOC_LEN)*sizeof(MrBFlt)); } /* record values */ r->count[runId]++; r->totCount++; if (sumtParams.brlensDef == YES) r->length[runId][n]= p->length; if (sumtParams.isClock == YES) r->height[runId][n]= p->depth; if (sumtParams.isCalibrated == YES) r->age[runId][n]= p->age; if (sumtParams.nESets > 0) { for (i=0; inEvents[i][runId][n] = t->nEvents[i][p->index]; } if (sumtParams.nBSets > 0) { for (i=0; ibLen [i][runId][n] = t->effectiveBrLen[i][p->index]; r->bRate[i][runId][n] = t->effectiveBrLen[i][p->index] / p->length; if (p->length == 0.0) r->bRate[i][runId][n] = 1.0; // deal with zero length } } if (sumtParams.popSizeSet == YES) r->popSize[runId][n] = t->popSize[p->index]; } else if (comp < 0) /* greater than -> into left subtree */ { if ((r->left = AddSumtPartition (r->left, t, p, runId)) == NULL) { FreePartCtr (r); return NULL; } } else { /* smaller than -> into right subtree */ if ((r->right = AddSumtPartition (r->right, t, p, runId)) == NULL) { FreePartCtr (r); return NULL; } } } return r; } TreeCtr *AddSumtTree (TreeCtr *r, int *order) { int i, comp; if (r == NULL) { /* new tree */ /* create a new node */ r = AllocTreeCtr(); if (!r) return NULL; numUniqueTreesFound++; for (i=0; iorder[i] = order[i]; r->count = 1; } else { for (i=0; iorder[i] != order[i]) break; if (i==sumtParams.orderLen) comp = 0; else if (order[i] < r->order[i]) comp = 1; else comp = -1; if (comp == 0) /* repeated partition */ r->count++; else if (comp < 0) /* greater than -> into left subtree */ { if ((r->left = AddSumtTree (r->left, order)) == NULL) { FreeTreeCtr (r); return NULL; } } else { /* smaller than -> into right subtree */ if ((r->right = AddSumtTree (r->right, order)) == NULL) { FreeTreeCtr (r); return NULL; } } } return r; } /* AllocPartCtr: Allocate space for one partition counter node using info in sumtParams */ PartCtr *AllocPartCtr (void) { int i, j; PartCtr *r; /* allocate basic stuff */ r = (PartCtr *) SafeCalloc (1, sizeof(PartCtr)); r->left = r->right = NULL; r->partition = (BitsLong *) SafeCalloc ((size_t)(sumtParams.BitsLongsNeeded), sizeof(BitsLong)); r->count = (int *) SafeCalloc ((size_t)(sumtParams.numRuns), sizeof (int)); if (sumtParams.brlensDef) { r->length = (MrBFlt **) SafeCalloc ((size_t)(sumtParams.numRuns), sizeof (MrBFlt *)); for (i=0; ilength[i] = (MrBFlt *) SafeCalloc ((size_t)ALLOC_LEN, sizeof(MrBFlt)); } if (sumtParams.isClock) { r->height = (MrBFlt **) SafeCalloc ((size_t)(sumtParams.numRuns), sizeof (MrBFlt *)); for (i=0; iheight[i] = (MrBFlt *) SafeCalloc ((size_t)ALLOC_LEN, sizeof(MrBFlt)); r->age = (MrBFlt **) SafeCalloc ((size_t)(sumtParams.numRuns), sizeof (MrBFlt *)); for (i=0; iage[i] = (MrBFlt *) SafeCalloc ((size_t)ALLOC_LEN, sizeof(MrBFlt)); } /* allocate relaxed clock parameters: eRate, nEvents, bRate */ if (sumtParams.nESets > 0) r->nEvents = (int ***) SafeCalloc ((size_t)(sumtParams.nESets), sizeof(int **)); for (i=0; inEvents[i] = (int **) SafeCalloc ((size_t)(sumtParams.numRuns), sizeof(int *)); for (j=0; jnEvents[i][j] = (int *) SafeCalloc ((size_t) ALLOC_LEN, sizeof(int)); } if (sumtParams.nBSets > 0) { r->bLen = (MrBFlt ***) SafeCalloc ((size_t)(sumtParams.nBSets), sizeof(MrBFlt **)); r->bRate = (MrBFlt ***) SafeCalloc ((size_t)(sumtParams.nBSets), sizeof(MrBFlt **)); } for (i=0; ibLen[i] = (MrBFlt **) SafeCalloc ((size_t)(sumtParams.numRuns), sizeof(MrBFlt *)); r->bRate[i] = (MrBFlt **) SafeCalloc ((size_t)(sumtParams.numRuns), sizeof(MrBFlt *)); for (j=0; jbLen[i][j] = (MrBFlt *) SafeCalloc ((size_t)ALLOC_LEN, sizeof(MrBFlt)); r->bRate[i][j] = (MrBFlt *) SafeCalloc ((size_t)ALLOC_LEN, sizeof(MrBFlt)); } } if (sumtParams.popSizeSet == YES) { r->popSize = (MrBFlt **) SafeCalloc ((size_t)(sumtParams.numRuns), sizeof (MrBFlt *)); for (i=0; ipopSize[i] = (MrBFlt *) SafeCalloc ((size_t)ALLOC_LEN, sizeof(MrBFlt)); } return r; } /* AllocTreeCtr: Allocate space for a tree counter node using info in sumtParams struct*/ TreeCtr *AllocTreeCtr (void) { TreeCtr *r; r = (TreeCtr *) SafeCalloc (1, sizeof(TreeCtr)); r->left = r->right = NULL; r->order = (int *) SafeCalloc ((size_t)(sumtParams.orderLen), sizeof(int)); return r; } void CalculateTreeToTreeDistance (Tree *tree1, Tree *tree2, MrBFlt *d1, MrBFlt *d2, MrBFlt *d3) { int i, j, k; MrBFlt treeLen1=0.0, treeLen2=0.0; TreeNode *p, *q=NULL; (*d1) = (*d2) = (*d3) = 0.0; /* set distance-based measures to max value */ if (sumtParams.brlensDef == YES) { treeLen1 = TreeLen(tree1); treeLen2 = TreeLen(tree2); (*d2) = treeLen1 + treeLen2; (*d3) = 2.0; } /* now we can get distances in a single pass */ for (i=0; inNodes; i++) { p = tree1->allDownPass[i]; for (j=0; jnNodes; j++) { q = tree2->allDownPass[j]; for (k=0; kpartition[k] != q->partition[k]) break; if (k == sumtParams.BitsLongsNeeded) break; } if (j < tree2->nNodes) { /* match */ if (sumtParams.brlensDef == YES) { (*d2) -= (p->length + q->length - fabs(p->length - q->length)); (*d3) -= (p->length/treeLen1 + q->length/treeLen2 - fabs(p->length/treeLen1 - q->length/treeLen2)); } } else /* if (k < sumtParams.BitsLongsNeeded) */ { /* no match */ (*d1) += 2.0; } } # if 0 printf ("DISTANCES: %lf %lf %lf (%lf %lf)\n", *d1, *d2, *d3, tl1, tl2); for (i=0; iisRooted = sumtParams.isRooted; t->isClock = sumtParams.isClock; t->isRelaxed = sumtParams.isRelaxed; /* initialize consensus tree nodes */ for (i=0; inodes[i].left = NULL; t->nodes[i].sib = NULL; t->nodes[i].index = i; t->nodes[i].partitionIndex = -1; /* partition ID */ t->nodes[i].age = 0.0; /* temporally set to minimum value to allow any insertion in front of the terminal before actual values of age and depth are available */ strcpy(t->nodes[i].label, sumtParams.taxaNames[i]); t->nodes[i].depth = 0.0; } for (; imemNodes; i++) { t->nodes[i].left = NULL; t->nodes[i].sib = NULL; t->nodes[i].index = i; t->nodes[i].partitionIndex = -1; /* partition ID */ strcpy (t->nodes[i].label, ""); } /* create bush ->x counts number of subtended terminals make sure t->root->left is in outgroup */ p = t->root = &t->nodes[sumtParams.numTaxa]; p->anc = p->sib = NULL; p->x = sumtParams.numTaxa; p->age = MRBFLT_MAX; /* temporarily set to maximum value to allow any insertion in front of the root before actual values of age and depth are available */ p->depth = MRBFLT_MAX; j = localOutGroup; q = &t->nodes[j]; p->left = q; q->anc = p; q->x = 1; for (i=0; isib = &t->nodes[i]; q = q->sib; q->anc = p; q->x = 1; } } q->sib = NULL; /* Resolve bush according to partitions. Partitions may include incompatible ones. Partitions must be sorted from most frequent to least frequent for quit test to work when a 50% majority rule tree is requested and in general for consensus tree to be correct. */ t->nNodes = sumtParams.numTaxa + 1; t->nIntNodes = 1; if (sumtParams.isRooted == YES) targetNode = 2 * sumtParams.numTaxa - 2; else targetNode = 2 * sumtParams.numTaxa - 3; numTerminalsEncountered = 0; for (i=0; inNodes > targetNode && numTerminalsEncountered == sumtParams.numTaxa) break; freq = (MrBFlt)(part->totCount) / (MrBFlt)(sumtParams.numTreesSampled); if (freq < 0.50 && !strcmp(sumtParams.sumtConType, "Halfcompat")) break; /* get partition */ partition = part->partition; /* count bits in this partition */ for (j=nBits=0; jroot; q->partitionIndex = i; if (sumtParams.isClock == YES) { GetSummary(part->height, sumtParams.numRuns, part->count, &theStats, sumtParams.HPD); q->depth = theStats.median; for (p = q->left; p!=NULL; p = p->sib) { if (q->depth <= p->depth) break; } assert (p==NULL); /* Root always has 100% freq and it should be older than any other node that has 100% freq. */ } if (sumtParams.isCalibrated == YES) { GetSummary(part->age, sumtParams.numRuns, part->count, &theStats, sumtParams.HPD); q->age = theStats.median; for (p = q->left; p!=NULL; p = p->sib) { if (q->age <= p->age) break; } assert (p==NULL); /* Root always has 100% freq and it should be older than any other node that has 100% freq. */ } } else if (nBits > 1 && !(nBits == sumtParams.numTaxa - 1 && sumtParams.isRooted == NO)) { /* this is an informative partition */ /* find anc of partition */ j = FirstTaxonInPartition (partition, sumtParams.BitsLongsNeeded); for (p = &t->nodes[j]; p!=NULL; p = p->anc) if (p->x > nBits) break; /* do not include if incompatible with ancestor or any of descendants do not check terminals or root because it is redundant and partitions have not necessarily been set for those */ isCompat = YES; if (p->anc != NULL && IsPartNested(partition, p->partition, sumtParams.BitsLongsNeeded)==NO) isCompat = NO; else { for (q=p->left; q!=NULL; q=q->sib) { if (q->x > 1 && IsPartCompatible(q->partition, partition, sumtParams.BitsLongsNeeded)==NO) break; } if (q!=NULL) isCompat = NO; } if (isCompat == NO) continue; /* set new node */ q = &t->nodes[t->nNodes]; q->partitionIndex = i; q->x = nBits; q->partition = partition; if (part->length != NULL) GetSummary(part->length, sumtParams.numRuns, part->count, &theStats, sumtParams.HPD); q->support = freq; q->length = theStats.median; r=NULL; if (sumtParams.isClock == YES) { GetSummary(part->height, sumtParams.numRuns, part->count, &theStats, sumtParams.HPD); q->depth = theStats.median; if (freq < 1.00) { for (r = p->left; r!=NULL; r = r->sib) { if (IsPartNested(r->partition, partition, sumtParams.BitsLongsNeeded) && r->depth >= q->depth) break; /* child is older then the node we try to add. Not good.*/ } if (p->depth <= q->depth) { /* New node older than the parent. Not good.*/ r = p; /* Just to make r!=NULL*/ } } } if (sumtParams.isCalibrated == YES) { GetSummary(part->age, sumtParams.numRuns, part->count, &theStats, sumtParams.HPD); q->age = theStats.median; if (freq < 1.00) { for (r = p->left; r!=NULL; r = r->sib) { if (freq < 1.00 && IsPartNested(r->partition, partition, sumtParams.BitsLongsNeeded) && r->age >= q->age) break; /* child is older then the node we try to add. Not good.*/ } if (p->age <= q->age) { /* New node older than the parent. Not good.*/ r = p; /* Just to make r!=NULL*/ } } } if (r!=NULL && isFirstLoop) { /* cancel the addition of the new node*/ isInterapted =1; freqInterapted=freq; break; /* Finish creating the polytree */ } t->nNodes++; t->nIntNodes++; /* go through descendants of anc */ ql = pl = NULL; for (r=p->left; r!=NULL; r=r ->sib) { /* test if r is in the new partition or not */ if ((r->x > 1 && IsPartNested(r->partition, partition, sumtParams.BitsLongsNeeded)) || (r->x == 1 && (partition[r->index / nBitsInALong] & (bitsLongOne << (r->index % nBitsInALong))) != 0)) { /* r is in the partition */ if (ql == NULL) q->left = r; else ql->sib = r; ql = r; r->anc = q; } else { /* r is not in the partition */ if (pl == NULL) p->left = r; else pl->sib = r; pl = r; } } /* terminate new sib-nod chain */ ql->sib = NULL; /* new node is last in old sib-node chain */ pl->sib = q; q->sib = NULL; q->anc = p; } else /* singleton partition */ { if (nBits == sumtParams.numTaxa - 1) j = localOutGroup; else j = FirstTaxonInPartition(partition, sumtParams.BitsLongsNeeded); /* nbits == 1 */ q = &t->nodes[j]; q->partitionIndex = i; q->partition = partition; numTerminalsEncountered++; if (part->length!=NULL) GetSummary(part->length, sumtParams.numRuns, part->count, &theStats, sumtParams.HPD); q->length = theStats.median; if (sumtParams.isClock == YES) { GetSummary(part->height, sumtParams.numRuns, part->count, &theStats, sumtParams.HPD); q->depth = theStats.median; if (q->anc->depth < q->depth) { /* We never should get here because terminals always have 100% freq and they are younger than any other node that has 100% freq. */ /* We should be careful with the trees with 0-brl generated under the fossilized birth-death prior! (<= is changed to <) */ assert (0); } } if (sumtParams.isCalibrated == YES) { GetSummary(part->age, sumtParams.numRuns, part->count, &theStats, sumtParams.HPD); q->age = theStats.median; if (q->anc->age < q->age) { /* We never should get here because terminals always have 100% freq and they are younger than any other node that has 100% freq. */ /* We should be careful with the trees with 0-brl generated under the fossilized birth-death prior! (<= is changed to <) */ assert (0); } } } } if (isFirstLoop) { t2 = t; if (isInterapted) { isFirstLoop = 0; goto treeConstruction; } } /* get downpass arrays */ GetPolyDownPass(t); /* order tips */ if (sumtParams.orderTaxa == YES) OrderTips (t); if (t!=t2) { /* get downpass arrays */ GetPolyDownPass(t2); /* order tips */ if (sumtParams.orderTaxa == YES) OrderTips (t2); } /* draw tree to stdout and fp */ MrBayesPrint ("\n%s Clade credibility values:\n\n", spacer); ShowConTree (stdout, t, 80, YES); if (logToFile == YES) ShowConTree (logFileFp, t, 80, YES); if (sumtParams.brlensDef == YES) { MrBayesPrint ("\n"); if (sumtParams.isClock == YES) MrBayesPrint ("%s Phylogram (based on median node depths):\n", spacer); else MrBayesPrint ("%s Phylogram (based on average branch lengths):\n", spacer); if (isInterapted) { MrBayesPrint ("%s Warning. Phylogram containing all nodes with credibility values exceeding\n",spacer); MrBayesPrint ("%s the level set by Contype could not be constructed.\n",spacer); MrBayesPrint ("%s Only nodes with credibility values exceeding %.2f%% (percentage of trees\n", spacer, freqInterapted*100); MrBayesPrint ("%s where the node is present) were included in the phylogram.\n", spacer); } MrBayesPrint ("\n"); ShowConPhylogram (stdout, t2, 80); if (logToFile == YES) ShowConPhylogram (logFileFp, t2, 80); } /* print taxa block */ MrBayesPrintf (fpCon, "begin taxa;\n"); MrBayesPrintf (fpCon, "\tdimensions ntax=%d;\n", sumtParams.numTaxa); MrBayesPrintf (fpCon, "\ttaxlabels\n", sumtParams.numTaxa); for (i=0; inNodes; j++) if (t2->nodes[j].index == i) break; MrBayesPrintf (fpCon, "\t\t%s\n", t2->nodes[j].label); } MrBayesPrintf (fpCon, "\t\t;\nend;\n"); MrBayesPrintf (fpCon, "begin trees;\n"); MrBayesPrintf (fpCon, "\ttranslate\n"); for (i=0; inNodes; j++) if (t2->nodes[j].index == i) break; if (i == sumtParams.numTaxa-1) MrBayesPrintf (fpCon, "\t\t%d\t%s\n", t2->nodes[i].index+1, t2->nodes[i].label); else MrBayesPrintf (fpCon, "\t\t%d\t%s,\n", t2->nodes[i].index+1, t2->nodes[i].label); } MrBayesPrintf (fpCon, "\t\t;\n"); if (sumtParams.consensusFormat == SIMPLE) PrintConTree(fpCon, t2); else if (sumtParams.consensusFormat == FIGTREE) PrintFigTreeConTree(fpCon, t2, treeParts); MrBayesPrintf (fpCon, "end;\n"); if (t!=t2) { FreePolyTree (t2); } /* free memory */ FreePolyTree (t); return (NO_ERROR); } MrBFlt CppEvolRate (PolyTree *t, PolyNode *p, int eSet) { int i, nEvents; MrBFlt ancRate, branchRate, *rate, *pos; PolyNode *q; nEvents = t->nEvents[eSet][p->index]; pos = t->position[eSet][p->index]; rate = t->rateMult[eSet][p->index]; /* note that event positions are from top of branch (more recent, descendant tip) */ ancRate = 1.0; // if (t->eType[eSet] == CPPm) // { for (q=p; q->anc != NULL; q=q->anc) { for (i=0; inEvents[eSet][p->index]; i++) ancRate *= t->rateMult[eSet][p->index][i]; } if (nEvents > 0) { branchRate = rate[0] * pos[0]; for (i=1; ieType[eSet] == CPPi) { for (q=p; q->anc != NULL; q=q->anc) { if (t->nEvents[eSet][p->index]>0) { ancRate = t->rateMult[eSet][p->index][0]; break; } } if (nEvents > 0) { branchRate = ancRate * (1.0 - pos[nEvents-1]); for (i=nEvents-2; i>=0; i--) { branchRate += (rate[i+1] * (pos[i+1] - pos[i])); } branchRate += (rate[0] * pos[0]); } else branchRate = ancRate; } */ return branchRate; } int DoCompareTree (void) { int i, j, k, n, longestLineLength, brlensDef[2], numTreesInLastBlock[2], lastTreeBlockBegin[2], lastTreeBlockEnd[2], xaxis, yaxis, starHolder[80], minNumTrees, screenWidth, screenHeigth, numY[60], nSamples; RandLong temporarySeed; BitsLong *mask; PartCtr *x; MrBFlt xProb, yProb, xInc, yInc, xUpper, xLower, yUpper, yLower, *dT1=NULL, *dT2=NULL, *dT3=NULL, d1, d2, d3, meanY[60], xVal, yVal, minX, minY, maxX, maxY, sums[3]; char *s=NULL, prCh, *treeName[2]; FILE *fp; time_t curTime; PartCtr **treeParts=NULL; Tree *tree1=NULL, *tree2=NULL; SumtFileInfo sumtFileInfo; # if defined (MPI_ENABLED) if (proc_id == 0) { # endif /* Make sure we read trees using DoSumtTree() code instead of with the user tree code */ inComparetreeCommand = YES; /* set file pointer to NULL */ fp = NULL; /* set default tree names, in case parameter name is not specified in the .t file(s) */ treeName[0] = (char*)SafeCalloc(5,sizeof(char)); treeName[1] = (char*)SafeCalloc(5,sizeof(char)); if (!treeName[0] || !treeName[1]) goto errorExit; strcpy(treeName[0],"tree"); strcpy(treeName[1],"tree"); /* Check that a data set has been read in. We check taxon names against those read in. */ if (isTaxsetDef == NO) { MrBayesPrint ("%s A matrix or set of taxon labels must be specified before comparetree can be used\n", spacer); goto errorExit; } /* open output files for summary information (two files); check if we want to overwrite previous results */ if (OpenComptFiles () == ERROR) goto errorExit; MrBayesPrint ("%s Examining files ...\n", spacer); /* Examine first file */ if (ExamineSumtFile(comptreeParams.comptFileName1, &sumtFileInfo, &treeName[0], &(brlensDef[0])) == ERROR) return ERROR; /* Capture info */ longestLineLength = sumtFileInfo.longestLineLength; numTreesInLastBlock[0] = sumtFileInfo.numTreesInLastBlock; lastTreeBlockBegin[0] = sumtFileInfo.lastTreeBlockBegin; lastTreeBlockEnd[0] = sumtFileInfo.lastTreeBlockEnd; /* Examine second file */ if (ExamineSumtFile(comptreeParams.comptFileName2, &sumtFileInfo, &treeName[1], &brlensDef[1]) == ERROR) return ERROR; /* Capture info */ if (longestLineLength < sumtFileInfo.longestLineLength) longestLineLength = sumtFileInfo.longestLineLength; numTreesInLastBlock[1] = sumtFileInfo.numTreesInLastBlock; lastTreeBlockBegin[1] = sumtFileInfo.lastTreeBlockBegin; lastTreeBlockEnd[1] = sumtFileInfo.lastTreeBlockEnd; /* Check whether we should work with brlens */ if (brlensDef[0] == YES && brlensDef[1] == YES) sumtParams.brlensDef = YES; else sumtParams.brlensDef = NO; /* Allocate space for command string */ longestLineLength += 10; s = (char *)SafeMalloc((size_t)longestLineLength * sizeof(char)); if (!s) { MrBayesPrint ("%s Problem allocating string for reading tree file\n", spacer); goto errorExit; } /* Allocate space for packed trees */ if (chainParams.relativeBurnin == YES) { numPackedTrees[0] = numTreesInLastBlock[0] - (int)(chainParams.burninFraction * numTreesInLastBlock[0]); numPackedTrees[1] = numTreesInLastBlock[1] - (int)(chainParams.burninFraction * numTreesInLastBlock[1]); } else { numPackedTrees[0] = numTreesInLastBlock[0] - chainParams.chainBurnIn; numPackedTrees[1] = numTreesInLastBlock[1] - chainParams.chainBurnIn; } if (memAllocs[ALLOC_PACKEDTREES] == YES) { MrBayesPrint ("%s packedTreeList is already allocated\n", spacer); goto errorExit; } packedTreeList[0] = (PackedTree *) SafeCalloc(numPackedTrees[0]+numPackedTrees[1], sizeof(PackedTree)); packedTreeList[1] = packedTreeList[0] + numPackedTrees[0]; if (!packedTreeList[0]) { MrBayesPrint ("%s Problem allocating packed tree list\n", spacer); goto errorExit; } memAllocs[ALLOC_PACKEDTREES] = YES; /* Tell user we are ready to go */ MrBayesPrint ("%s Summarizing trees in files \"%s\" and \"%s\"\n", spacer, comptreeParams.comptFileName1, comptreeParams.comptFileName2); if (chainParams.relativeBurnin == YES) MrBayesPrint ("%s Using relative burnin ('relburnin=yes'), discarding the first %.0f %% ('burninfrac=%1.2f') of sampled trees\n", spacer, chainParams.burninFraction*100.0, chainParams.burninFraction); else MrBayesPrint ("%s Using absolute burnin ('relburnin=no'), discarding the first %d ('burnin=%d') sampled trees\n", spacer, chainParams.chainBurnIn, chainParams.chainBurnIn); MrBayesPrint ("%s Writing statistics to file %s.\n", spacer, comptreeParams.comptOutfile); /* Set up cheap status bar. */ MrBayesPrint ("\n%s Tree reading status:\n\n", spacer); MrBayesPrint ("%s 0 10 20 30 40 50 60 70 80 90 100\n", spacer); MrBayesPrint ("%s v-------v-------v-------v-------v-------v-------v-------v-------v-------v-------v\n", spacer); MrBayesPrint ("%s *", spacer); numAsterices = 0; /* Read file 1 for real */ if ((fp = OpenTextFileR(comptreeParams.comptFileName1)) == NULL) goto errorExit; /* ...and fast forward to beginning of last tree block (skipping begin trees). */ for (i=0; ipartition) == YES && sumtParams.isRooted == NO) FlipBits(x->partition, sumtParams.BitsLongsNeeded, mask); if ((MrBFlt)x->totCount/(MrBFlt)sumtParams.numTreesSampled >= comptreeParams.minPartFreq) { MrBayesPrint ("%s %4d -- ", spacer, i+1); ShowParts (stdout, x->partition, sumtParams.numTaxa); j = (int)(log10(sumtParams.numTreesSampled)) + 1; if (j < 3) j = 3; MrBayesPrint (" %*d %*d %1.3lf %1.3lf\n", j, x->count[0], j, x->count[1], (MrBFlt)x->count[0]/(MrBFlt)sumtParams.numFileTreesSampled[0], (MrBFlt)x->count[1]/(MrBFlt)sumtParams.numFileTreesSampled[1]); MrBayesPrintf (fpParts, "%d\t%d\t%d\t%1.3lf\t%1.3lf\n", i+1, x->count[0], x->count[1], (MrBFlt)x->count[0]/(MrBFlt)sumtParams.numFileTreesSampled[0], (MrBFlt)x->count[1]/(MrBFlt)sumtParams.numFileTreesSampled[1]); } } free (mask); /* make a nifty graph plotting frequencies of clades found in the two tree files */ MrBayesPrint (" \n"); MrBayesPrint ("%s Bivariate plot of clade probabilities: \n", spacer); MrBayesPrint (" \n"); MrBayesPrint ("%s This graph plots the probabilities of clades found in file 1 (the x-axis) \n", spacer); MrBayesPrint ("%s against the probabilities of the same clades found in file 2 (the y-axis). \n", spacer); MrBayesPrint (" \n"); xInc = (MrBFlt) (1.0 / 80.0); yInc = (MrBFlt) (1.0 / 40.0); yUpper = 1.0; yLower = yUpper - yInc; for (yaxis=39; yaxis>=0; yaxis--) { xLower = 0.0; xUpper = xLower + xInc; for (xaxis=0; xaxis<80; xaxis++) { starHolder[xaxis] = 0; for (i=0; icount[0]/(MrBFlt)sumtParams.numFileTreesSampled[0]; yProb = (MrBFlt)x->count[1]/(MrBFlt)sumtParams.numFileTreesSampled[1]; if ((xProb > xLower || (xProb == 0.0 && xaxis == 0)) && (xProb <= xUpper || (xProb == 1.0 && xaxis == 79)) && (yProb > yLower || (yProb == 0.0 && yaxis == 0)) && (yProb <= yUpper || (yProb == 1.0 && yaxis == 39))) starHolder[xaxis] = 1; } xLower += xInc; xUpper = xLower + xInc; } MrBayesPrint ("%s ", spacer); for (xaxis=0; xaxis<80; xaxis++) { prCh = ' '; if ((xaxis == 0 && yaxis == 0) || (xaxis == 79 && yaxis == 39)) prCh = '+'; else if ((xaxis == 0 && yaxis == 39) || (xaxis == 79 && yaxis == 0)) prCh = '+'; else if ((yaxis == 0 || yaxis == 39) && xaxis > 0 && xaxis < 79) prCh = '-'; else if ((xaxis == 0 || xaxis == 79) && yaxis > 0 && yaxis < 39) prCh = '|'; if (starHolder[xaxis] == 1) prCh = '*'; MrBayesPrint ("%c", prCh); } if (yaxis == 39) MrBayesPrint (" 1.00\n"); else if (yaxis == 0) MrBayesPrint (" 0.00\n"); else MrBayesPrint ("\n"); yUpper -= yInc; yLower = yUpper - yInc; } MrBayesPrint ("%s ^ ^\n", spacer); MrBayesPrint ("%s 0.00 1.00\n", spacer); /* get tree-to-tree distances: first allocate some space */ minNumTrees = sumtParams.numFileTreesSampled[0]; if (sumtParams.numFileTreesSampled[1] < minNumTrees) minNumTrees = sumtParams.numFileTreesSampled[1]; dT1 = (MrBFlt *) SafeMalloc (3 * (size_t)minNumTrees * sizeof(MrBFlt)); tree1 = AllocateFixedTree (sumtParams.numTaxa, sumtParams.isRooted); tree2 = AllocateFixedTree (sumtParams.numTaxa, sumtParams.isRooted); if (!dT1 || !tree1 || !tree2) { MrBayesPrint ("%s Problem allocating topological distances\n", spacer); goto errorExit; } dT2 = dT1 + minNumTrees; dT3 = dT2 + minNumTrees; for (i=0; i maxX) maxX = xVal; if (yVal > maxY) maxY = yVal; } for (i=0; i= screenWidth) k = screenWidth - 1; meanY[k] += yVal; numY[k]++; } MrBayesPrint ("\n%s +", spacer); for (i=0; i=0; j--) { MrBayesPrint ("%s |", spacer); for (i=0; i 0) { if (meanY[i] / numY[i] > (((maxY - minY)/screenHeigth)*j)+minY && meanY[i] / numY[i] <= (((maxY - minY)/screenHeigth)*(j+1))+minY) MrBayesPrint ("*"); else MrBayesPrint (" "); } else { MrBayesPrint (" "); } } MrBayesPrint ("|\n"); } MrBayesPrint ("%s +", spacer); for (i=0; i 0 && meanY[i] / numY[i] <= minY) MrBayesPrint ("*"); else if (i % (screenWidth/10) == 0 && i != 0) MrBayesPrint ("+"); else MrBayesPrint ("-"); } MrBayesPrint ("+ %1.2lf\n", minY); MrBayesPrint ("%s ^", spacer); for (i=0; i maxX) maxX = xVal; if (yVal > maxY) maxY = yVal; } for (i=0; i= screenWidth) k = screenWidth - 1; meanY[k] += yVal; numY[k]++; } MrBayesPrint ("\n%s +", spacer); for (i=0; i=0; j--) { MrBayesPrint ("%s |", spacer); for (i=0; i 0) { if (meanY[i] / numY[i] > (((maxY - minY)/screenHeigth)*j)+minY && meanY[i] / numY[i] <= (((maxY - minY)/screenHeigth)*(j+1))+minY) MrBayesPrint ("*"); else MrBayesPrint (" "); } else { MrBayesPrint (" "); } } MrBayesPrint ("|\n"); } MrBayesPrint ("%s +", spacer); for (i=0; i 0 && meanY[i] / numY[i] <= minY) MrBayesPrint ("*"); else if (i % (screenWidth/10) == 0 && i != 0) MrBayesPrint ("+"); else MrBayesPrint ("-"); } MrBayesPrint ("+ %1.2lf\n", minY); MrBayesPrint ("%s ^", spacer); for (i=0; i 0.50) { MrBayesPrint ("%s Burnin fraction too high (> 0.50)\n", spacer); return (ERROR); } chainParams.burninFraction = tempD; MrBayesPrint ("%s Setting burnin fraction to %.2f\n", spacer, chainParams.burninFraction); expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else { return (ERROR); } } /* set Minpartfreq (comptreeParams.minPartFreq) *******************************************************/ else if (!strcmp(parmName, "Minpartfreq")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(NUMBER); else if (expecting == Expecting(NUMBER)) { sscanf (tkn, "%lf", &tempD); comptreeParams.minPartFreq = tempD; MrBayesPrint ("%s Including partitions with probability greater than or equal to %lf in summary statistics\n", spacer, comptreeParams.minPartFreq); expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); } else return (ERROR); } return (NO_ERROR); } int DoCompRefTree (void) { /* Compare a tree file with the reference tree files to generate the SDSFs. Use parameters in CompareTree and MCMCP (lazy option) */ char outName[130], inName[130], inRefName[140], *treeName=NULL, *lineBuf=NULL, *s; FILE *fpTre=NULL, *fpOut=NULL; int i, n, longestL=0, burnin, gen, nRefRun, nTre[2]={0}, skip; SumtFileInfo tFileInfo; Tree *t; # if defined (MPI_ENABLED) if (proc_id != 0) return NO_ERROR; # endif /* Check that a data set has been read in. We check taxon names against those read in. */ if (isTaxsetDef == NO) { MrBayesPrint ("%s A matrix or set of taxon labels must be specified before compareref can be used\n", spacer); return (ERROR); } /* this is a hack to account for the comparing (0) and the reference (1) tree samples chainParams.numRuns is used in AddTreeToPartitionCounters to alloc mem correctly */ nRefRun = chainParams.numRuns; chainParams.numRuns = 2; /* initialize */ if ((t = AllocateTree (numLocalTaxa)) == NULL) { MrBayesPrint ("%s Problem allocating diagn tree\n", spacer); goto errorExit; } if (SetUpPartitionCounters () == ERROR) goto errorExit; if ((chainParams.stat = (STATS *) SafeCalloc (numTopologies, sizeof (STATS))) == NULL) goto errorExit; else memAllocs[ALLOC_STATS] = YES; treeName = (char*)SafeCalloc(5,sizeof(char)); if (!treeName) { MrBayesPrint ("%s Problem allocating treeName\n", spacer); goto errorExit; } strcpy(treeName,"tree"); /* deal with the reference tree files */ // for (k=0; k= tFileInfo.numTreesInLastBlock) { MrBayesPrint ("%s Burnin should be smaller than the total number of trees\n", spacer); goto errorExit; } /* open the ref tree file */ if ((fpTre = OpenTextFileR (inRefName)) == NULL) goto errorExit; /* ...and fast forward to beginning in last tree block */ for (i=0; i <= tFileInfo.lastTreeBlockBegin; i++) { if (fgets(lineBuf, longestL-2, fpTre) == NULL) goto errorExit; } /* process each ref tree */ for (i=0; i < tFileInfo.numTreesInLastBlock; i++) { do { if (fgets (lineBuf, longestL-2, fpTre) == NULL) goto errorExit; s = strtok (lineBuf, " "); } while (strcmp (s, "tree") != 0); /* add reference trees to partition counters, discarding burnin */ if (i < burnin) continue; s = strtok (NULL, ";"); while (*s != '(') s++; StripComments(s); if (ResetTopology (t, s) == ERROR) goto errorExit; if (AddTreeToPartitionCounters (t, 0, 0) == ERROR) goto errorExit; nTre[0]++; } MrBayesPrint ("%s \t%d trees processed, %d trees discarded as burnin\n", spacer, tFileInfo.numTreesInLastBlock, burnin); /* close the tree file */ SafeFclose (&fpTre); } /* end reference tree files */ MrBayesPrint ("%s \t%d reference trees in total from %d runs\n", spacer, nTre[0], nRefRun); MrBayesPrint ("%s Processing the test run ...\n", spacer, n+1); /* open output file */ strcpy (outName, comptreeParams.comptOutfile); strcat (outName, ".sdsf"); if ((fpOut = OpenNewMBPrintFile (outName)) == NULL) goto errorExit; /* print stamp and header */ if ((int)strlen(stamp) > 1) MrBayesPrintf (fpOut, "[ID: %s]\n", stamp); if (chainParams.diagnStat == AVGSTDDEV) MrBayesPrintf (fpOut, "Gen\tASDSF\n"); else // MAXSTDDEV MrBayesPrintf (fpOut, "Gen\tMSDSF\n"); /* Examine the tree file to be compared, save info to tFileInfo */ strcpy(inName, comptreeParams.comptFileName1); if (ExamineSumtFile(inName, &tFileInfo, &treeName, &sumtParams.brlensDef) == ERROR) goto errorExit; if (longestL < tFileInfo.longestLineLength) { longestL = tFileInfo.longestLineLength; lineBuf = (char *) SafeRealloc (lineBuf, (size_t)longestL * sizeof(char)); if (!lineBuf) { MrBayesPrint ("%s Problem allocating string for reading tree file\n", spacer); goto errorExit; } } /* open the tree file to be compared */ if ((fpTre = OpenTextFileR (inName)) == NULL) goto errorExit; /* ...and fast forward to beginning in last tree block */ for (i=0; i <= tFileInfo.lastTreeBlockBegin; i++) { if (fgets(lineBuf, longestL-2, fpTre) == NULL) goto errorExit; } /* process each tree to be compared and print SDSF to file */ skip = 1; // skip the first few trees (default 1) // skip = (int) (0.5 * tFileInfo.numTreesInLastBlock); for (i=0; i < tFileInfo.numTreesInLastBlock; i++) { do { if (fgets (lineBuf, longestL-2, fpTre) == NULL) goto errorExit; s = strtok (lineBuf, " "); } while (strcmp (s, "tree") != 0); if (i < skip) continue; s = strtok (NULL, ";"); gen = atoi(s+4); // 4 is offset to get rid of "rep." in tree name while (*s != '(') s++; StripComments(s); /* add the tree to partition counters */ if (ResetTopology (t, s) == ERROR) goto errorExit; if (AddTreeToPartitionCounters (t, 0, 1) == ERROR) goto errorExit; nTre[1]++; /* calculate and write stdev of split freq */ CalcTopoConvDiagn2 (nTre); if (chainParams.stat[0].numPartitions == 0) { MrBayesPrintf (fpOut, "%d\tNA\n", gen); } else if (chainParams.diagnStat == AVGSTDDEV) { MrBayesPrintf (fpOut, "%d\t%lf\n", gen, chainParams.stat[0].avgStdDev); } else // MAXSTDDEV { MrBayesPrintf (fpOut, "%d\t%lf\n", gen, chainParams.stat[0].max); } } MrBayesPrint ("%s \t%d trees discarded, the last %d trees compared to the reference\n", spacer, skip, nTre[1]); /* change back to the actual numRuns, end of hack */ chainParams.numRuns = nRefRun; /* close tree file */ SafeFclose (&fpTre); /* close output file */ SafeFclose (&fpOut); /* free memory */ free(lineBuf); free(treeName); FreeTree (t); FreeChainMemory(); return (NO_ERROR); /* error exit */ errorExit: MrBayesPrint ("%s Error in DoCompRefTree\n", spacer); chainParams.numRuns = nRefRun; SafeFclose (&fpTre); SafeFclose (&fpOut); FreeTree (t); FreeChainMemory(); free(lineBuf); free(treeName); return (ERROR); } #if defined (PRINT_RATEMUL_CPP) int DELETE_ME_count_taxa(PolyNode *p) { int sum=0; if (p->left==NULL) { if (p->depth > 0.1) return 1; else return 0; } p=p->left; while (p != NULL) { sum+=DELETE_ME_count_taxa(p); p=p->sib; } return sum; } void DELETE_ME_dump_depth(PolyNode *p) { /*print depth of two taxa clade*/ /* if (p->left != NULL && p->left->left == NULL && p->left->sib != NULL && p->left->sib->left == NULL){ fprintf (rateMultfp,"%f\n",p->depth); } if (p->left != NULL && p->left->left == NULL && p->left->sib != NULL && p->left->sib->left == NULL){ if (p->left->depth > 0.1 && p->left->sib->depth > 0.1) fprintf (rateMultfp,"%f\n",p->depth); } */ /* print depth of three taxa clade */ if (((p->left != NULL && p->left->left == NULL) && p->left->sib != NULL && p->left->sib->left != NULL && p->left->sib->left->left == NULL && p->left->sib->left->sib->left == NULL) || (p->left != NULL && p->left->left != NULL && p->left->left->left == NULL && p->left->left->sib->left == NULL && (p->left->sib->left == NULL))) { if (DELETE_ME_count_taxa(p)==2) fprintf (rateMultfp,"%f\n",p->depth); } p=p->left; while (p != NULL) { DELETE_ME_dump_depth(p); p=p->sib; } } #endif int DoSumt (void) { int i, j=0, k, n, len, longestName, numTreePartsToPrint, maxWidthID, maxWidthNumberPartitions, maxNumTaxa, tableWidth=0, unreliable, oneUnreliable, longestHeader; MrBFlt f, var_s, sum_s, stddev_s=0.0, sumsq_s, sumStdDev=0.0, maxStdDev=0.0, sumPSRF=0.0, maxPSRF=0.0, avgStdDev=0.0, avgPSRF=0.0, min_s=0.0, max_s=0.0, numPSRFSamples=0, min; PartCtr *x; char *s=NULL, tempName[137], fileName[120], *treeName=NULL, *divString=NULL; char *tempStr=NULL; /*not static because error ext is handled*/ int tempStrLength; FILE *fp=NULL; PartCtr **treeParts=NULL,*tmp; SumtFileInfo sumtFileInfo; Stat theStats; BitsLong *mask; #define SCREEN_WIDTH 80 # if defined (MPI_ENABLED) if (proc_id == 0) { # endif /* Ensure that we read trees with sumt code and not user tree code */ inSumtCommand = YES; /* set file pointers to NULL */ fp = fpParts = fpTstat = fpVstat = fpCon = fpTrees = NULL; /* allocate initial space for tree name and set a default name and empty divString */ treeName = (char*)SafeCalloc(5,sizeof(char)); if (!treeName) { MrBayesPrint ("%s Problems allocating treeName\n", spacer); goto errorExit; } strcpy(treeName,"tree"); divString = (char*)SafeCalloc(1,sizeof(char)); if (!divString) { MrBayesPrint ("%s Problems allocating divString\n", spacer); goto errorExit; } strcpy(divString,""); /* Check if there is anything to do */ if (sumtParams.table == NO && sumtParams.summary == NO && sumtParams.showConsensus == NO) { MrBayesPrint ("%s Nothing to do, all output parameters (Table, Summary, Consensus) set to 'NO'\n", spacer); goto errorExit; } SafeStrcpy(&tempStr, " "); /* Initialize sumtParams struct */ sumtParams.numTaxa = 0; sumtParams.taxaNames = NULL; sumtParams.BitsLongsNeeded = 0; sumtParams.tree = AllocatePolyTree (numTaxa); sumtParams.treeNo = 0; sumtParams.nESets = 0; sumtParams.nBSets = 0; sumtParams.eSetName = NULL; sumtParams.bSetName = NULL; sumtParams.popSizeSet = NO; sumtParams.popSizeSetName = NULL; AllocatePolyTreePartitions (sumtParams.tree); sumtParams.numFileTrees = (int *) SafeCalloc (2*sumtParams.numRuns+2*numTaxa, sizeof(int)); sumtParams.numFileTreesSampled = sumtParams.numFileTrees + sumtParams.numRuns; sumtParams.order = sumtParams.numFileTrees + 2*sumtParams.numRuns; sumtParams.absentTaxa = sumtParams.numFileTrees + 2*sumtParams.numRuns + numTaxa; if (!sumtParams.numFileTrees) { MrBayesPrint ("%s Problems allocating sumtParams.numFileTrees in DoSumt()\n", spacer); goto errorExit; } else memAllocs[ALLOC_SUMTPARAMS] = YES; /* Make sure outgroup is set correctly */ for (sumtParams.treeNo = 0; sumtParams.treeNo < sumtParams.numTrees; sumtParams.treeNo++) { /* initialize across-file tree and partition counters */ sumtParams.numTreesSampled = sumtParams.numTreesEncountered = 0; numUniqueSplitsFound = numUniqueTreesFound = 0; /* initialize oneUnreliable && unreliable */ oneUnreliable = unreliable = NO; /* initialize summary statistics */ sumStdDev = 0.0; sumPSRF = 0.0; numPSRFSamples = 0; maxStdDev = 0.0; maxPSRF = 0.0; /* tell user we are ready to go */ if (sumtParams.numTrees > 1) sprintf (fileName,"%s.tree%d", sumtParams.sumtFileName, sumtParams.treeNo+1); else strcpy (fileName, sumtParams.sumtFileName); if (sumtParams.numRuns == 1) MrBayesPrint ("%s Summarizing trees in file \"%s.t\"\n", spacer, fileName); else if (sumtParams.numRuns == 2) MrBayesPrint ("%s Summarizing trees in files \"%s.run1.t\" and \"%s.run2.t\"\n", spacer, fileName, fileName); else if (sumtParams.numRuns > 2) MrBayesPrint ("%s Summarizing trees in files \"%s.run1.t\", \"%s.run2.t\",...,\"%s.run%d.t\"\n", spacer, fileName, fileName,fileName,sumtParams.numRuns); if (chainParams.relativeBurnin == YES) MrBayesPrint ("%s Using relative burnin ('relburnin=yes'), discarding the first %.0f %% of sampled trees\n", spacer, chainParams.burninFraction*100.0, chainParams.burninFraction); else MrBayesPrint ("%s Using absolute burnin ('relburnin=no'), discarding the first %d sampled trees\n", spacer, chainParams.chainBurnIn, chainParams.chainBurnIn); MrBayesPrint ("%s Writing statistics to files %s.\n", spacer, sumtParams.sumtOutfile); for (sumtParams.runId=0; sumtParams.runId < sumtParams.numRuns; sumtParams.runId++) { /* initialize tree counters */ sumtParams.numFileTrees[sumtParams.runId] = sumtParams.numFileTreesSampled[sumtParams.runId] = 0; /* open binary file */ if (sumtParams.numRuns == 1) sprintf (tempName, "%s.t", fileName); else sprintf (tempName, "%s.run%d.t", fileName, sumtParams.runId+1); strcpy(sumtParams.curFileName, tempName); /* tell user we are examining files if for the first run */ if (sumtParams.runId == 0) { if (sumtParams.numRuns > 1 && sumtParams.numTrees > 1) MrBayesPrint ("%s Examining first file for tree %d ...\n", spacer, sumtParams.treeNo); else if (sumtParams.numRuns > 1 && sumtParams.numTrees == 1) MrBayesPrint ("%s Examining first file ...\n", spacer); else if (sumtParams.numRuns == 1 && sumtParams.numTrees > 1) MrBayesPrint ("%s Examining file for tree %d ...\n", spacer, sumtParams.treeNo); else MrBayesPrint ("%s Examining file ...\n", spacer); } /* examine file */ if (ExamineSumtFile(tempName, &sumtFileInfo, &treeName, &sumtParams.brlensDef) == ERROR) goto errorExit; /* make sure divString can hold string with relevant partitions/divisions */ if (strlen(treeName) > strlen(divString)) { divString=(char*)SafeRealloc(divString,strlen(treeName)*sizeof(char)); if (!divString) { MrBayesPrint ("%s Problem allocating divString\n", spacer); goto errorExit; } } /* capture values */ if (sumtParams.runId == 0) /* catch lack of sampled trees */ if (chainParams.relativeBurnin == NO && chainParams.chainBurnIn > sumtFileInfo.numTreesInLastBlock) { MrBayesPrint ("%s No trees are sampled as the burnin exceeds the number of trees in last block\n", spacer); MrBayesPrint ("%s Try setting burnin to a number less than %d\n", spacer, sumtFileInfo.numTreesInLastBlock); goto errorExit; } /* tell the user that everything is fine */ if (sumtParams.runId == 0) { if (sumtFileInfo.numTreeBlocks == 1) MrBayesPrint ("%s Found one tree block in file \"%s\" with %d trees in last block\n", spacer, tempName, sumtFileInfo.numTreesInLastBlock); else { MrBayesPrint ("%s Found %d tree blocks in file \"%s\" with %d trees in last block\n", spacer, sumtFileInfo.numTreeBlocks, tempName, sumtFileInfo.numTreesInLastBlock); MrBayesPrint ("%s Only the %d trees in last tree block will be summarized\n", spacer, sumtFileInfo.numTreesInLastBlock); } sumtParams.numTreesInLastBlock = sumtFileInfo.numTreesInLastBlock; if (sumtParams.numRuns > 1) MrBayesPrint ("%s Expecting the same number of trees in the last tree block of all files\n", spacer); if (chainParams.relativeBurnin == NO) sumtParams.burnin = chainParams.chainBurnIn; else sumtParams.burnin = (int) (sumtFileInfo.numTreesInLastBlock * chainParams.burninFraction); } else { if (sumtFileInfo.numTreesInLastBlock != sumtParams.numFileTrees[0]) { MrBayesPrint ("%s Found %d trees in first file but %d trees in file \"%s\"\n", spacer, sumtParams.numFileTrees[0], sumtFileInfo.numTreesInLastBlock, tempName); goto errorExit; } } /* Now we read the file for real. First, allocate a string for reading the file... */ if (sumtParams.runId == 0 && sumtParams.treeNo == 0) { s = (char *) SafeMalloc ((size_t)(sumtFileInfo.longestLineLength) * sizeof(char)); if (!s) { MrBayesPrint ("%s Problem allocating string for reading sumt file\n", spacer); goto errorExit; } } else { free (s); s = (char *) SafeMalloc ((size_t)(sumtFileInfo.longestLineLength) * sizeof (char)); if (!s) { MrBayesPrint ("%s Problem reallocating string for reading sumt file\n", spacer); goto errorExit; } } /* ... open the file ... */ if ((fp = OpenTextFileR(tempName)) == NULL) goto errorExit; /* ...and fast forward to beginning of last tree block. */ for (i=0; i 1) MrBayesPrint ("\n%s Tree reading status for tree %d:\n\n", spacer, sumtParams.treeNo+1); else MrBayesPrint ("\n%s Tree reading status:\n\n", spacer); MrBayesPrint ("%s 0 10 20 30 40 50 60 70 80 90 100\n", spacer); MrBayesPrint ("%s v-------v-------v-------v-------v-------v-------v-------v-------v-------v-------v\n", spacer); MrBayesPrint ("%s *", spacer); numAsterices = 0; } /* ...and parse file, tree-by-tree. We are only parsing lines between the "begin trees" and "end" statements. We don't actually get those lines, however, but rather the lines between those statements. */ expecting = Expecting(COMMAND); /* We skip the begin trees statement so we need to set up some variables here */ inTreesBlock = YES; ResetTranslateTable(); for (i=0; i 1) { if (sumtParams.runId != 0 && sumtParams.numFileTreesSampled[sumtParams.runId]!=sumtParams.numFileTreesSampled[0]) { if (sumtParams.runId == sumtParams.numRuns - 1) MrBayesPrint ("\n\n"); MrBayesPrint ("%s Found %d post-burnin trees in the first file but %d post-burnin trees in file %d\n", spacer, sumtParams.numFileTreesSampled[0], sumtParams.numFileTreesSampled[sumtParams.runId], sumtParams.runId+1); goto errorExit; } if (sumtParams.runId == sumtParams.numRuns - 1) { MrBayesPrint ("%s Read a total of %d trees in %d files (sampling %d of them)\n", spacer, sumtParams.numTreesEncountered, sumtParams.numRuns, sumtParams.numTreesSampled); MrBayesPrint ("%s (Each file contained %d trees of which %d were sampled)\n", spacer, sumtParams.numFileTrees[0], sumtParams.numFileTreesSampled[0]); } } /* Check that at least one tree was read in. */ if (sumtParams.numTreesSampled <= 0) { MrBayesPrint ("%s No trees read in\n", spacer); goto errorExit; } SafeFclose (&fp); } /* next run for this tree */ /* Extract partition counter pointers */ treeParts = (PartCtr **) SafeCalloc ((size_t)numUniqueSplitsFound, sizeof(PartCtr *)); i = 0; PartCtrUppass(partCtrRoot, treeParts, &i); min = (sumtParams.minPartFreq * (sumtParams.numTreesSampled/sumtParams.numRuns)); numTreePartsToPrint=numUniqueSplitsFound; for (i=0; icount[j]>=min) break; } if (j==sumtParams.numRuns) { numTreePartsToPrint--; tmp=treeParts[numTreePartsToPrint]; treeParts[numTreePartsToPrint]=treeParts[i]; treeParts[i]=tmp; } else { i++; } } /* Sort taxon partitions (clades, splits) ... */ SortPartCtr (treeParts, 0, numTreePartsToPrint-1); /* Sort root and tips among those splits always present */ SortTerminalPartCtr (treeParts, numUniqueSplitsFound); /* open output files for summary information (three files) */ if (OpenSumtFiles (sumtParams.treeNo) == ERROR) goto errorExit; /* Print partitions to screen. */ if (sumtParams.treeNo == 0) { longestName = 0; for (k=0; k longestName) longestName = len; } if (sumtParams.table == YES) { MrBayesPrint (" \n"); MrBayesPrint ("%s General explanation: \n", spacer); MrBayesPrint (" \n"); MrBayesPrint ("%s In an unrooted tree, a taxon bipartition (split) is specified by removing a \n", spacer); MrBayesPrint ("%s branch, thereby dividing the species into those to the left and those to the \n", spacer); MrBayesPrint ("%s right of the branch. Here, taxa to one side of the removed branch are denoted \n", spacer); MrBayesPrint ("%s '.' and those to the other side are denoted '*'. Specifically, the '.' symbol \n", spacer); MrBayesPrint ("%s is used for the taxa on the same side as the outgroup. \n", spacer); MrBayesPrint (" \n"); MrBayesPrint ("%s In a rooted or clock tree, the tree is rooted using the model and not by \n", spacer); MrBayesPrint ("%s reference to an outgroup. Each bipartition therefore corresponds to a clade, \n", spacer); MrBayesPrint ("%s that is, a group that includes all the descendants of a particular branch in \n", spacer); MrBayesPrint ("%s the tree. Taxa that are included in each clade are denoted using '*', and \n", spacer); MrBayesPrint ("%s taxa that are not included are denoted using the '.' symbol. \n", spacer); MrBayesPrint (" \n"); MrBayesPrint ("%s The output first includes a key to all the bipartitions with frequency larger \n", spacer); MrBayesPrint ("%s or equal to (Minpartfreq) in at least one run. Minpartfreq is a parameter to \n", spacer); MrBayesPrint ("%s sumt command and currently it is set to %1.2lf. This is followed by a table \n", spacer, sumtParams.minPartFreq); MrBayesPrint ("%s with statistics for the informative bipartitions (those including at least \n", spacer); MrBayesPrint ("%s two taxa), sorted from highest to lowest probability. For each bipartition, \n", spacer); MrBayesPrint ("%s the table gives the number of times the partition or split was observed in all\n", spacer); MrBayesPrint ("%s runs (#obs) and the posterior probability of the bipartition (Probab.), which \n", spacer); MrBayesPrint ("%s is the same as the split frequency. If several runs are summarized, this is \n", spacer); MrBayesPrint ("%s followed by the minimum split frequency (Min(s)), the maximum frequency \n", spacer); MrBayesPrint ("%s (Max(s)), and the standard deviation of frequencies (Stddev(s)) across runs. \n", spacer); MrBayesPrint ("%s The latter value should approach 0 for all bipartitions as MCMC runs converge.\n", spacer); MrBayesPrint (" \n"); MrBayesPrint ("%s This is followed by a table summarizing branch lengths, node heights (if a \n", spacer); MrBayesPrint ("%s clock model was used) and relaxed clock parameters (if a relaxed clock model \n", spacer); MrBayesPrint ("%s was used). The mean, variance, and 95 %% credible interval are given for each \n", spacer); MrBayesPrint ("%s of these parameters. If several runs are summarized, the potential scale \n", spacer); MrBayesPrint ("%s reduction factor (PSRF) is also given; it should approach 1 as runs converge. \n", spacer); MrBayesPrint ("%s Node heights will take calibration points into account, if such points were \n", spacer); MrBayesPrint ("%s used in the analysis. \n", spacer); MrBayesPrint ("%s \n", spacer); MrBayesPrint ("%s Note that Stddev may be unreliable if the partition is not present in all \n", spacer); MrBayesPrint ("%s runs (the last column indicates the number of runs that sampled the partition \n", spacer); MrBayesPrint ("%s if more than one run is summarized). The PSRF is not calculated at all if \n", spacer); MrBayesPrint ("%s the partition is not present in all runs.The PSRF is also sensitive to small \n", spacer); MrBayesPrint ("%s sample sizes and it should only be considered a rough guide to convergence \n", spacer); MrBayesPrint ("%s since some of the assumptions allowing one to interpret it as a true potential\n", spacer); MrBayesPrint ("%s scale reduction factor are violated in MrBayes. \n", spacer); MrBayesPrint ("%s \n", spacer); MrBayesPrint ("%s List of taxa in bipartitions: \n", spacer); MrBayesPrint (" \n"); j = 1; for (k=0; k 1 && (sumtParams.table == YES || sumtParams.summary == YES)) { MrBayesPrint ("\n\n"); if (strcmp(modelParams[0].topologyPr,"Speciestree")==0) { if (sumtParams.treeNototCount/(MrBFlt)sumtParams.numTreesSampled >= sumtParams.minPartFreq) numTreePartsToPrint++; } */ maxWidthID = (int) (log10 (numTreePartsToPrint)) + 1; if (maxWidthID < 2) maxWidthID = 2; maxNumTaxa = SCREEN_WIDTH - 9; for (j=0; j SCREEN_WIDTH) tableWidth = SCREEN_WIDTH; MrBayesPrint ("%s ", spacer); for (i=0; ipartition) == YES && sumtParams.isRooted == NO) FlipBits(x->partition, sumtParams.BitsLongsNeeded, mask); if ((NumBits(x->partition, sumtParams.BitsLongsNeeded) == numLocalTaxa || NumBits(x->partition, sumtParams.BitsLongsNeeded) == 0) && sumtParams.isClock == NO) continue; MrBayesPrint ("%s %*d -- ", spacer, maxWidthID, i); if (sumtParams.numTaxa <= maxNumTaxa) ShowParts (stdout, x->partition, sumtParams.numTaxa); else { if (sumtParams.numTaxa - j > maxNumTaxa) ShowSomeParts (stdout, x->partition, j, maxNumTaxa); else ShowSomeParts (stdout, x->partition, j, sumtParams.numTaxa - j); } fflush(stdout); MrBayesPrint ("\n"); MrBayesPrintf (fpParts, "%d\t", i); ShowParts (fpParts, x->partition, sumtParams.numTaxa); MrBayesPrintf (fpParts, "\n"); } free (mask); /* finish screen table */ if (sumtParams.table == YES) { MrBayesPrint ("%s ", spacer); for (i=0; itotCount/(MrBFlt)sumtParams.numTreesSampled >= sumtParams.minPartFreq) numTreePartsToPrint++; } */ maxWidthID = (int) (log10 (numTreePartsToPrint)) + 1; if (maxWidthID < 2) maxWidthID = 2; maxWidthNumberPartitions = (int) (log10 (treeParts[0]->totCount)) + 1; if (maxWidthNumberPartitions < 4) maxWidthNumberPartitions = 4; /* print header to screen and to parts file simultaneously */ if (sumtParams.table == YES) { /* first print header to screen */ MrBayesPrint ("%s ", spacer); MrBayesPrint ("%*s ", maxWidthID, "ID"); tableWidth = maxWidthID + 3; MrBayesPrint ("#obs"); tableWidth += 4; for (i=4; i 1) { MrBayesPrint (" Sd(s)+ "); MrBayesPrint (" Min(s) Max(s) "); tableWidth += 36; MrBayesPrint (" Nruns "); tableWidth += 8; } MrBayesPrint ("\n%s ", spacer); for (i=0; i 1) MrBayesPrintf (fpTstat, "ID\t#obs\tProbability(=s)\tStddev(s)\tMin(s)\tMax(s)\tNruns\n"); else MrBayesPrintf (fpTstat, "ID\t#obs\tProbability(=s)\n"); } /* now, show informative partitions that were found on screen; print to .tstat file simultaneously */ for (i=0; ipartition, sumtParams.BitsLongsNeeded) <= 1 || NumBits(x->partition, sumtParams.BitsLongsNeeded) == sumtParams.numTaxa) continue; if (NumBits(x->partition, sumtParams.BitsLongsNeeded) == sumtParams.numTaxa - 1 && sumtParams.isRooted == NO) continue; if (sumtParams.table == YES) { MrBayesPrint ("%s %*d", spacer, maxWidthID, i); fflush(stdout); MrBayesPrintf (fpTstat, "%d\t", i); } if (sumtParams.numRuns > 1) { sum_s = 0.0; sumsq_s = 0.0; min_s = 1.0; max_s = 0.0; for (n=j=0; ncount[n] > 0) j++; f = (MrBFlt) x->count[n] / (MrBFlt) sumtParams.numFileTreesSampled[n]; sum_s += f; sumsq_s += f * f; if (f < min_s) min_s = f; if (f > max_s) max_s = f; } var_s = sumsq_s - sum_s * sum_s / (MrBFlt) sumtParams.numRuns; var_s /= (sumtParams.numRuns - 1); if (var_s > 0.0) stddev_s = sqrt (var_s); else stddev_s = 0.0; if (j == sumtParams.numRuns) unreliable = NO; else { unreliable = YES; oneUnreliable = YES; } } if (sumtParams.table == YES) { f = (MrBFlt) x->totCount / (MrBFlt) sumtParams.numTreesSampled; MrBayesPrint (" %*d %1.6lf", maxWidthNumberPartitions, x->totCount, f); MrBayesPrintf (fpTstat, "\t%d\t%s", x->totCount, MbPrintNum(f)); if (sumtParams.numRuns > 1) { MrBayesPrint (" %1.6lf %1.6lf %1.6lf", stddev_s, min_s, max_s); MrBayesPrint (" %3d", j); MrBayesPrintf (fpTstat, "\t%s", MbPrintNum(stddev_s)); MrBayesPrintf (fpTstat, "\t%s", MbPrintNum(min_s)); MrBayesPrintf (fpTstat, "\t%s", MbPrintNum(max_s)); MrBayesPrintf (fpTstat, "\t%d", j); } MrBayesPrintf (fpTstat, "\n"); if (unreliable == YES) MrBayesPrint (" *\n"); else MrBayesPrint ("\n"); sumStdDev += stddev_s; if (stddev_s > maxStdDev) maxStdDev = stddev_s; } } /* finish screen table */ if (sumtParams.table == YES) { MrBayesPrint ("%s ", spacer); for (i=0; i 1) { MrBayesPrint ("%s + Convergence diagnostic (standard deviation of split frequencies)\n", spacer); MrBayesPrint ("%s should approach 0.0 as runs converge.\n\n", spacer); } if (oneUnreliable == YES) MrBayesPrint ("%s * The partition was not found in all runs so the values are unreliable\n", spacer); } /* Third, print statitistics for branch and node parameters */ /* check if we have at least lengths before printing this table; we might have parsimony trees */ if (sumtParams.table == YES && treeParts[1]->length != NULL) { MrBayesPrint ("\n"); MrBayesPrint ("%s Summary statistics for branch and node parameters\n", spacer); MrBayesPrint ("%s (saved to file \"%s.vstat\"):\n", spacer, sumtParams.sumtOutfile); } /* check if we have at least lengths before printing this table; we might have parsimony trees */ if (sumtParams.table == YES && treeParts[1]->length != NULL) { /* calculate longest header */ longestHeader = 9; /* length of 'parameter' */ i = (int)(log10(numTreePartsToPrint)) + 3; /* length of partition specifier including [] */ len = i + (int)(strlen(treeName)) + 2; /* length of length{m}[n] or height{m}[n] */ if (len > longestHeader) longestHeader = len; for (j=0; jbSetName[j]) + 7 + i; if (len > longestHeader) longestHeader = len; } for (j=0; jeSetName[j]) + 8 + i; if (len > longestHeader) longestHeader = len; } if (sumtParams.popSizeSet == YES) { len = (int) strlen(sumtParams.tree->popSizeSetName) + i; if (len > longestHeader) longestHeader = len; } /* print the header rows */ MrBayesPrint ("\n"); if (sumtParams.HPD == NO) MrBayesPrint ("%s %*c 95%% Cred. Interval\n", spacer, longestHeader, ' '); else MrBayesPrint ("%s %*c 95%% HPD Interval\n", spacer, longestHeader, ' '); MrBayesPrint ("%s %*c --------------------\n", spacer, longestHeader, ' '); MrBayesPrint ("%s Parameter%*c Mean Variance Lower Upper Median", spacer, longestHeader-9, ' '); tableWidth = 68 + longestHeader - 9; if (sumtParams.HPD == YES) MrBayesPrintf (fpVstat, "Parameter\tMean\tVariance\tCredInt_Lower\tCredInt_Upper\tMedian", spacer, longestHeader-9, ' '); else MrBayesPrintf (fpVstat, "Parameter\tMean\tVariance\tHPD_Lower\tHPD_Upper\tMedian", spacer, longestHeader-9, ' '); if (sumtParams.numRuns > 1) { MrBayesPrint (" PSRF+ Nruns"); tableWidth += 17; MrBayesPrintf (fpVstat, "\tPSRF\tNruns"); } MrBayesPrint ("\n"); MrBayesPrintf (fpVstat, "\n"); MrBayesPrint ("%s ", spacer); for (j=0; jlength, sumtParams.numRuns, x->count, &theStats, sumtParams.HPD); MrBayesPrint ("%s %-*s ", spacer, longestHeader, tempStr); MrBayesPrintf (fpVstat, "%s", tempStr); PrintSumtTableLine(sumtParams.numRuns, x->count, &theStats, &numPSRFSamples, &maxPSRF, &sumPSRF); } /* print heights */ if (sumtParams.isClock == YES) { strcpy (divString, treeName+4); for (i=0; iheight, sumtParams.numRuns, x->count, &theStats, sumtParams.HPD); MrBayesPrint ("%s %-*s ", spacer, longestHeader, tempStr); MrBayesPrintf (fpVstat, "%s", tempStr); PrintSumtTableLine(sumtParams.numRuns, x->count, &theStats, &numPSRFSamples, &maxPSRF, &sumPSRF); } } /* print ages */ if (sumtParams.isCalibrated == YES) { strcpy (divString, treeName+4); for (i=0; iage, sumtParams.numRuns, x->count, &theStats, sumtParams.HPD); MrBayesPrint ("%s %-*s ", spacer, longestHeader, tempStr); MrBayesPrintf (fpVstat, "%s", tempStr); PrintSumtTableLine(sumtParams.numRuns, x->count, &theStats, &numPSRFSamples, &maxPSRF, &sumPSRF); } } /* print effective branch lengths */ if (sumtParams.isRelaxed == YES) { for (i=0; ibLen[i], sumtParams.numRuns, x->count, &theStats, sumtParams.HPD); MrBayesPrint ("%s %-*s ", spacer, longestHeader, tempStr); MrBayesPrintf (fpVstat, "%s", tempStr); PrintSumtTableLine(sumtParams.numRuns, x->count, &theStats, &numPSRFSamples, &maxPSRF, &sumPSRF); } for (j=1; jbRate[i], sumtParams.numRuns, x->count, &theStats, sumtParams.HPD); MrBayesPrint ("%s %-*s ", spacer, longestHeader, tempStr); MrBayesPrintf (fpVstat, "%s", tempStr); PrintSumtTableLine(sumtParams.numRuns, x->count, &theStats, &numPSRFSamples, &maxPSRF, &sumPSRF); } } for (i=0; inEvents[i], sumtParams.numRuns, x->count, &theStats, sumtParams.HPD); MrBayesPrint ("%s %-*s ", spacer, longestHeader, tempStr); MrBayesPrintf (fpVstat, "%s", tempStr); PrintSumtTableLine(sumtParams.numRuns, x->count, &theStats, &numPSRFSamples, &maxPSRF, &sumPSRF); } } } /* print population size sets */ if (sumtParams.popSizeSet == YES) { for (j=1; jpopSize, sumtParams.numRuns, x->count, &theStats, sumtParams.HPD); MrBayesPrint ("%s %-*s ", spacer, longestHeader, tempStr); MrBayesPrintf (fpVstat, "%s", tempStr); PrintSumtTableLine(sumtParams.numRuns, x->count, &theStats, &numPSRFSamples, &maxPSRF, &sumPSRF); } } /* finish table */ MrBayesPrint ("%s ", spacer); for (j=0; j 1) { MrBayesPrint ("%s + Convergence diagnostic (PSRF = Potential Scale Reduction Factor; Gelman\n", spacer); MrBayesPrint ("%s and Rubin, 1992) should approach 1.0 as runs converge. NA is reported when\n", spacer); MrBayesPrint ("%s deviation of parameter values within all runs is 0 or when a parameter\n", spacer); MrBayesPrint ("%s value (a branch length, for instance) is not sampled in all runs.\n", spacer); } if (oneUnreliable == YES) { MrBayesPrint ("%s * The partition was not found in all runs so the values are unreliable.\n", spacer); } MrBayesPrint ("\n\n"); } /* Print branch parameters to file if requested */ strcpy( divString, treeName+4); if (sumtParams.saveBrParams == YES && PrintBrParamsToFile (treeParts, numUniqueSplitsFound, sumtParams.treeNo, divString) == ERROR) goto errorExit; /* Exclude trivial splits when calculating average standard deviation of split frequencies. */ avgStdDev = sumStdDev / (numTreePartsToPrint-sumtParams.numTaxa-1); avgPSRF = sumPSRF / numPSRFSamples; if (sumtParams.numRuns > 1 && sumtParams.summary == YES) { MrBayesPrint ("%s Summary statistics for partitions with frequency >= %1.2lf in at least one run:\n", spacer, sumtParams.minPartFreq); MrBayesPrint ("%s Average standard deviation of split frequencies = %1.6lf\n", spacer, avgStdDev); MrBayesPrint ("%s Maximum standard deviation of split frequencies = %1.6lf\n", spacer, maxStdDev); } if (sumtParams.brlensDef == YES && sumtParams.numRuns > 1 && sumtParams.summary == YES) { MrBayesPrint ("%s Average PSRF for parameter values (excluding NA and >10.0) = %1.3lf\n", spacer, avgPSRF); if (maxPSRF == 10) MrBayesPrint ("%s Maximum PSRF for parameter values = NA\n", spacer); else MrBayesPrint ("%s Maximum PSRF for parameter values = %1.3lf\n", spacer, maxPSRF); } MrBayesPrint ("\n"); SortPartCtr (treeParts, 0, numUniqueSplitsFound-1); /* We sort again but this time we sort all partitions instead of just first numTreePartsToPrintNow */ /* make the majority rule consensus tree */ if (sumtParams.showConsensus == YES && ConTree (treeParts, numUniqueSplitsFound) == ERROR) goto errorExit; /* get probabilities of individual trees */ if (TreeProb () == ERROR) goto errorExit; /* close files */ SafeFclose (&fpParts); SafeFclose (&fpTstat); SafeFclose (&fpVstat); SafeFclose (&fpCon); SafeFclose (&fpTrees); # if defined (PRINT_RATEMUL_CPP) SafeFclose (&rateMultfp); # endif /* free pointer array to partitions */ free (treeParts); treeParts = NULL; FreePartCtr (partCtrRoot); partCtrRoot = NULL; FreeTreeCtr (treeCtrRoot); treeCtrRoot = NULL; } /* next tree */ /* free memory and file pointers */ if (s) free(s); free(treeName); free(divString); FreeSumtParams(); /* reset numLocalTaxa and localOutGroup */ ResetTaxonSet(); # if defined (MPI_ENABLED) } # endif expecting = Expecting(COMMAND); inSumtCommand = NO; SAFEFREE (tempStr); return (NO_ERROR); /* error exit */ errorExit: /* free sumtParams */ if (s) free(s); free(treeName); free(divString); FreeSumtParams(); /* close files in case they are open*/ SafeFclose (&fp); SafeFclose (&fpParts); SafeFclose (&fpTstat); SafeFclose (&fpVstat); SafeFclose (&fpCon); SafeFclose (&fpTrees); # if defined (PRINT_RATEMUL_CPP) SafeFclose (&rateMultfp); # endif /* free pointer array to partitions, part and tree counters */ free (treeParts); FreePartCtr (partCtrRoot); FreeTreeCtr (treeCtrRoot); partCtrRoot = NULL; treeCtrRoot = NULL; /* reset taxon set */ ResetTaxonSet(); expecting = Expecting(COMMAND); inSumtCommand = NO; SAFEFREE (tempStr); return (ERROR); } int DoSumtParm (char *parmName, char *tkn) { int tempI; MrBFlt tempD; char tempStr[100]; if (defMatrix == NO) { MrBayesPrint ("%s A matrix must be specified before sumt can be used\n", spacer); return (ERROR); } if (expecting == Expecting(PARAMETER)) { expecting = Expecting(EQUALSIGN); } else { if (!strcmp(parmName, "Xxxxxxxxxx")) { expecting = Expecting(PARAMETER); expecting |= Expecting(SEMICOLON); } /* set Filename (sumtParams.sumtFileName) ***************************************************/ else if (!strcmp(parmName, "Filename")) { if (expecting == Expecting(EQUALSIGN)) { expecting = Expecting(ALPHA); readWord = YES; } else if (expecting == Expecting(ALPHA)) { if (strlen(tkn)>99) { MrBayesPrint ("%s Maximum allowed length of file name is 99 characters. The given name:\n", spacer); MrBayesPrint ("%s '%s'\n", spacer,tkn); MrBayesPrint ("%s has %d characters.\n", spacer,strlen(tkn)); return (ERROR); } strcpy (sumtParams.sumtFileName, tkn); strcpy(sumtParams.sumtOutfile, tkn); MrBayesPrint ("%s Setting sumt filename and outputname to %s\n", spacer, sumtParams.sumtFileName); expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); } /* set Relburnin (chainParams.relativeBurnin) ********************************************************/ else if (!strcmp(parmName, "Relburnin")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(ALPHA); else if (expecting == Expecting(ALPHA)) { if (IsArgValid(tkn, tempStr) == NO_ERROR) { if (!strcmp(tempStr, "Yes")) chainParams.relativeBurnin = YES; else chainParams.relativeBurnin = NO; } else { MrBayesPrint ("%s Invalid argument for Relburnin\n", spacer); return (ERROR); } if (chainParams.relativeBurnin == YES) MrBayesPrint ("%s Using relative burnin (a fraction of samples discarded).\n", spacer); else MrBayesPrint ("%s Using absolute burnin (a fixed number of samples discarded).\n", spacer); expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else { return (ERROR); } } /* set Burnin (chainParams.chainBurnIn) ***********************************************************/ else if (!strcmp(parmName, "Burnin")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(NUMBER); else if (expecting == Expecting(NUMBER)) { sscanf (tkn, "%d", &tempI); chainParams.chainBurnIn = tempI; MrBayesPrint ("%s Setting urn-in to %d\n", spacer, chainParams.chainBurnIn); expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else { return (ERROR); } } /* set Burninfrac (chainParams.burninFraction) ************************************************************/ else if (!strcmp(parmName, "Burninfrac")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(NUMBER); else if (expecting == Expecting(NUMBER)) { sscanf (tkn, "%lf", &tempD); if (tempD < 0.01) { MrBayesPrint ("%s Burnin fraction too low (< 0.01)\n", spacer); return (ERROR); } if (tempD > 0.50) { MrBayesPrint ("%s Burnin fraction too high (> 0.50)\n", spacer); return (ERROR); } chainParams.burninFraction = tempD; MrBayesPrint ("%s Setting burnin fraction to %.2f\n", spacer, chainParams.burninFraction); expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else { return (ERROR); } } /* set Nruns (sumtParams.numRuns) *******************************************************/ else if (!strcmp(parmName, "Nruns")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(NUMBER); else if (expecting == Expecting(NUMBER)) { sscanf (tkn, "%d", &tempI); if (tempI < 1) { MrBayesPrint ("%s Nruns must be at least 1\n", spacer); return (ERROR); } else { sumtParams.numRuns = tempI; MrBayesPrint ("%s Setting sumt nruns to %d\n", spacer, sumtParams.numRuns); expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } } else return (ERROR); } /* set Ntrees (sumtParams.numTrees) *******************************************************/ else if (!strcmp(parmName, "Ntrees")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(NUMBER); else if (expecting == Expecting(NUMBER)) { sscanf (tkn, "%d", &tempI); if (tempI < 1) { MrBayesPrint ("%s Ntrees must be at least 1\n", spacer); return (ERROR); } else { sumtParams.numTrees = tempI; MrBayesPrint ("%s Setting sumt ntrees to %d\n", spacer, sumtParams.numTrees); expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } } else return (ERROR); } /* set Contype (sumtParams.sumtConType) *****************************************************/ else if (!strcmp(parmName, "Contype")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(ALPHA); else if (expecting == Expecting(ALPHA)) { if (IsArgValid(tkn, tempStr) == NO_ERROR) { strcpy (sumtParams.sumtConType, tempStr); MrBayesPrint ("%s Setting sumt contype to %s\n", spacer, sumtParams.sumtConType); } expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); } /* set Conformat (sumtParams.consensusFormat) *****************************************************/ else if (!strcmp(parmName, "Conformat")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(ALPHA); else if (expecting == Expecting(ALPHA)) { if (IsArgValid(tkn, tempStr) == NO_ERROR) { if (!strcmp(tempStr,"Figtree")) { MrBayesPrint ("%s Setting sumt conformat to Figtree\n", spacer); sumtParams.consensusFormat = FIGTREE; } else { MrBayesPrint ("%s Setting sumt conformat to Simple\n", spacer); sumtParams.consensusFormat = SIMPLE; } } else { MrBayesPrint ("%s Invalid argument for calctreeprobs\n", spacer); return (ERROR); } expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); } /* set Calctreeprobs (sumtParams.calcTreeprobs) *********************************************/ else if (!strcmp(parmName, "Calctreeprobs")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(ALPHA); else if (expecting == Expecting(ALPHA)) { if (IsArgValid(tkn, tempStr) == NO_ERROR) { if (!strcmp(tempStr, "Yes")) sumtParams.calcTreeprobs = YES; else sumtParams.calcTreeprobs = NO; } else { MrBayesPrint ("%s Invalid argument for calctreeprobs\n", spacer); return (ERROR); } if (sumtParams.calcTreeprobs == YES) MrBayesPrint ("%s Setting calctreeprobs to yes\n", spacer); else MrBayesPrint ("%s Setting calctreeprobs to no\n", spacer); expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); } /* set Showtreeprobs (sumtParams.showSumtTrees) *********************************************/ else if (!strcmp(parmName, "Showtreeprobs")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(ALPHA); else if (expecting == Expecting(ALPHA)) { if (IsArgValid(tkn, tempStr) == NO_ERROR) { if (!strcmp(tempStr, "Yes")) sumtParams.showSumtTrees = YES; else sumtParams.showSumtTrees = NO; } else { MrBayesPrint ("%s Invalid argument for showtreeprobs\n", spacer); return (ERROR); } if (sumtParams.showSumtTrees == YES) MrBayesPrint ("%s Setting showtreeprobs to yes\n", spacer); else MrBayesPrint ("%s Setting showtreeprobs to no\n", spacer); expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); } /* set Hpd (sumpParams.HPD) ********************************************************/ else if (!strcmp(parmName, "Hpd")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(ALPHA); else if (expecting == Expecting(ALPHA)) { if (IsArgValid(tkn, tempStr) == NO_ERROR) { if (!strcmp(tempStr, "Yes")) sumtParams.HPD = YES; else sumtParams.HPD = NO; } else { MrBayesPrint ("%s Invalid argument for Hpd\n", spacer); return (ERROR); } if (sumtParams.HPD == YES) MrBayesPrint ("%s Reporting 95 %% region of Highest Posterior Density (HPD).\n", spacer); else MrBayesPrint ("%s Reporting median interval containing 95 %% of values.\n", spacer); expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); } /* set Savebrparams (sumtParams.saveBrParams) *********************************************/ else if (!strcmp(parmName, "Savebrparams")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(ALPHA); else if (expecting == Expecting(ALPHA)) { if (IsArgValid(tkn, tempStr) == NO_ERROR) { if (!strcmp(tempStr, "Yes")) sumtParams.saveBrParams = YES; else sumtParams.saveBrParams = NO; } else { MrBayesPrint ("%s Invalid argument for savebrparams\n", spacer); return (ERROR); } if (sumtParams.saveBrParams == YES) MrBayesPrint ("%s Setting savebrparams to yes\n", spacer); else MrBayesPrint ("%s Setting savebrparams to no\n", spacer); expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); } /* set Minbrparamfreq (sumtParams.minBrParamFreq) *******************************************************/ else if (!strcmp(parmName, "Minbrparamfreq")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(NUMBER); else if (expecting == Expecting(NUMBER)) { sscanf (tkn, "%lf", &tempD); sumtParams.minBrParamFreq = tempD; MrBayesPrint ("%s Printing branch parameters to file for partitions with probability >= %lf\n", spacer, sumtParams.minBrParamFreq); expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); } /* set Ordertaxa (sumtParams.orderTaxa) *********************************************/ else if (!strcmp(parmName, "Ordertaxa")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(ALPHA); else if (expecting == Expecting(ALPHA)) { if (IsArgValid(tkn, tempStr) == NO_ERROR) { if (!strcmp(tempStr, "Yes")) sumtParams.orderTaxa = YES; else sumtParams.orderTaxa = NO; } else { MrBayesPrint ("%s Invalid argument for ordertaxa\n", spacer); return (ERROR); } if (sumtParams.orderTaxa == YES) MrBayesPrint ("%s Setting ordertaxa to yes\n", spacer); else MrBayesPrint ("%s Setting ordertaxa to no\n", spacer); expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); } /* set Outputname (sumtParams.sumtOutfile) *******************************************************/ else if (!strcmp(parmName, "Outputname")) { if (expecting == Expecting(EQUALSIGN)) { expecting = Expecting(ALPHA); readWord = YES; } else if (expecting == Expecting(ALPHA)) { sscanf (tkn, "%s", tempStr); strcpy (sumtParams.sumtOutfile, tempStr); MrBayesPrint ("%s Setting sumt output file name to \"%s\"\n", spacer, sumtParams.sumtOutfile); expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); } /* set Table (sumtParams.table) ********************************************************/ else if (!strcmp(parmName, "Table")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(ALPHA); else if (expecting == Expecting(ALPHA)) { if (IsArgValid(tkn, tempStr) == NO_ERROR) { if (!strcmp(tempStr, "Yes")) sumtParams.table = YES; else sumtParams.table = NO; } else { MrBayesPrint ("%s Invalid argument for Table (valid arguments are 'yes' and 'no')\n", spacer); return (ERROR); } if (sumtParams.table == YES) MrBayesPrint ("%s Setting sumt to compute table of partition frequencies\n", spacer); else MrBayesPrint ("%s Setting sumt not to compute table of partition frequencies\n", spacer); expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); } /* set Summary (sumtParams.summary) ********************************************************/ else if (!strcmp(parmName, "Summary")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(ALPHA); else if (expecting == Expecting(ALPHA)) { if (IsArgValid(tkn, tempStr) == NO_ERROR) { if (!strcmp(tempStr, "Yes")) sumtParams.summary = YES; else sumtParams.summary = NO; } else { MrBayesPrint ("%s Invalid argument for 'Summary' (valid arguments are 'yes' and 'no')\n", spacer); return (ERROR); } if (sumtParams.summary == YES) MrBayesPrint ("%s Setting sumt to summary statistics\n", spacer); else MrBayesPrint ("%s Setting sumt not to compute summary statistics\n", spacer); expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); } /* set Consensus (sumtParams.showConsensus) ********************************************************/ else if (!strcmp(parmName, "Consensus")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(ALPHA); else if (expecting == Expecting(ALPHA)) { if (IsArgValid(tkn, tempStr) == NO_ERROR) { if (!strcmp(tempStr, "Yes")) sumtParams.showConsensus = YES; else sumtParams.showConsensus = NO; } else { MrBayesPrint ("%s Invalid argument for Consensus (valid arguments are 'yes' and 'no')\n", spacer); return (ERROR); } if (sumtParams.showConsensus == YES) MrBayesPrint ("%s Setting sumt to show consensus trees\n", spacer); else MrBayesPrint ("%s Setting sumt not to show consensus trees\n", spacer); expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); } /* set Minpartfreq (sumtParams.minPartFreq) *******************************************************/ else if (!strcmp(parmName, "Minpartfreq")) { if (expecting == Expecting(EQUALSIGN)) expecting = Expecting(NUMBER); else if (expecting == Expecting(NUMBER)) { sscanf (tkn, "%lf", &tempD); sumtParams.minPartFreq = tempD; MrBayesPrint ("%s Including partitions with probability greater than or equal to %lf in summary statistics\n", spacer, sumtParams.minPartFreq); expecting = Expecting(PARAMETER) | Expecting(SEMICOLON); } else return (ERROR); } else return (ERROR); } return (NO_ERROR); } int DoSumtTree (void) { int i, j, z, printEvery, nAstPerPrint, burnin; MrBFlt x, y; PolyTree *t; PolyNode *p; # if defined (PRINT_RATEMUL_CPP) /* get depths if relevant */ if (sumtParams.tree->isClock) GetPolyDepths (sumtParams.tree); if (rateMultfp !=NULL && sumtParams.tree->root !=NULL) DELETE_ME_dump_depth(sumtParams.tree->root); # endif /* increment number of trees read in */ sumtParams.numFileTrees[sumtParams.runId]++; sumtParams.numTreesEncountered++; /* update status bar */ if (sumtParams.numTreesInLastBlock * sumtParams.numRuns < 80) { printEvery = 1; nAstPerPrint = 80 / (sumtParams.numTreesInLastBlock * sumtParams.numRuns); if (sumtParams.numTreesEncountered % printEvery == 0) { for (i=0; i burnin) { /* increment the number of trees sampled */ sumtParams.numFileTreesSampled[sumtParams.runId]++; sumtParams.numTreesSampled++; /* get the tree we just read in */ t = sumtParams.tree; /* check taxon set and outgroup */ if (sumtParams.runId == 0 && sumtParams.numFileTreesSampled[0] == 1) { if (isTranslateDef == YES && isTranslateDiff == YES) { /* we are using a translate block with different taxa set (not all taxa) */ if (t->nNodes - t->nIntNodes != numTranslates) { MrBayesPrint ("%s ERROR: Expected %d taxa; found %d taxa\n", spacer, numTranslates, sumtParams.numTaxa); return (ERROR); } /* figure out which taxa are absent */ for (i=0; inNodes; i++) { p = t->allDownPass[i]; if (p->left == NULL) sumtParams.absentTaxa[p->index] = NO; } localOutGroup = 0; for (i=j=0; inNodes - t->nIntNodes; numLocalTaxa = sumtParams.numTaxa; sumtParams.BitsLongsNeeded = ((numLocalTaxa-1) / nBitsInALong) + 1; if (t->isRooted == YES) sumtParams.orderLen = numLocalTaxa - 2; else sumtParams.orderLen = numLocalTaxa - 3; } else { /* check whether we have some taxa that should not be in the tree */ for (i=0; inNodes; i++) { p = t->allDownPass[i]; if (p->left == NULL && taxaInfo[p->index].isDeleted == NO && sumtParams.absentTaxa[p->index] == YES) { MrBayesPrint ("%s Taxon %d should not be in sampled tree\n", spacer, p->index + 1); return (ERROR); } } /* now we can safely prune the tree based on taxaInfo[].isDeleted */ PrunePolyTree (t); /* reset tip and int node indices in case some taxa deleted */ ResetTipIndices (t); ResetIntNodeIndices(t); /* check that all taxa are included */ if (t->nNodes - t->nIntNodes != sumtParams.numTaxa) { MrBayesPrint ("%s Expecting %d taxa but tree '%s' in file '%s' has %d taxa\n", spacer, sumtParams.numTaxa, t->name, sumtParams.curFileName, t->nNodes-t->nIntNodes); return ERROR; } } if (sumtParams.runId == 0 && sumtParams.numFileTreesSampled[0] == 1) { /* harvest labels (can only be done safely after pruning) */ for (i=0; inNodes; j++) { p = t->allDownPass[j]; if (p->index == i) { if (strlen(p->label)>99) { MrBayesPrint ("%s Taxon name %s is too long. Maximum 99 characters is allowed.\n", spacer, p->label); return (ERROR); } AddString(&sumtParams.taxaNames, i, p->label); } } } } /* check that tree agrees with template */ if (sumtParams.numTreesSampled == 1) { sumtParams.brlensDef = t->brlensDef; sumtParams.isRooted = t->isRooted; sumtParams.isClock = t->isClock; sumtParams.isCalibrated = t->isCalibrated; sumtParams.isRelaxed = t->isRelaxed; sumtParams.nBSets = 0; sumtParams.nESets = 0; for (i=0; inBSets; i++) AddString(&sumtParams.bSetName,sumtParams.nBSets++,t->bSetName[i]); for (i=0; inESets; i++) AddString(&sumtParams.eSetName,sumtParams.nESets++,t->eSetName[i]); if (t->popSizeSet == YES) { sumtParams.popSizeSet = YES; sumtParams.popSizeSetName = (char *) SafeCalloc (strlen(t->popSizeSetName)+1, sizeof(char)); strcpy(sumtParams.popSizeSetName, t->popSizeSetName); } else sumtParams.popSizeSet = NO; } else /* if (sumtParams.numTreesSampled > 1) */ { if (sumtParams.brlensDef != t->brlensDef) { MrBayesPrint ("%s Trees with and without branch lengths mixed\n", spacer); return ERROR; } if (sumtParams.isRooted != t->isRooted) { if (sumtParams.isRooted == YES) MrBayesPrint ("%s Expected rooted tree but tree '%s' in file '%s' is not rooted\n", spacer, t->name, sumtParams.curFileName); else if (sumtParams.isRooted == NO) MrBayesPrint ("%s Expected unrooted tree but tree '%s' in file '%s' is rooted\n", spacer, t->name, sumtParams.curFileName); return ERROR; } if (sumtParams.isClock != t->isClock) { if (sumtParams.isClock == YES) MrBayesPrint ("%s Expected clock tree but tree '%s' in file '%s' is not clock\n", spacer, t->name, sumtParams.curFileName); else if (sumtParams.isClock == NO) MrBayesPrint ("%s Expected nonclock tree but tree '%s' in file '%s' is clock\n", spacer, t->name, sumtParams.curFileName); return ERROR; } if (sumtParams.isCalibrated != t->isCalibrated) { if (sumtParams.isCalibrated == YES) MrBayesPrint ("%s Expected calibrated tree but tree '%s' in file '%s' is not calibrated\n", spacer, t->name, sumtParams.curFileName); else if (sumtParams.isCalibrated == NO) MrBayesPrint ("%s Expected noncalibrated tree but tree '%s' in file '%s' is calibrated\n", spacer, t->name, sumtParams.curFileName); return ERROR; } if (inComparetreeCommand == NO && sumtParams.isRelaxed != t->isRelaxed) { if (sumtParams.isRelaxed == YES) MrBayesPrint ("%s Expected relaxed clock tree but tree '%s' in file '%s' is not relaxed\n", spacer, t->name, sumtParams.curFileName); else if (sumtParams.isRelaxed == NO) MrBayesPrint ("%s Expected unrooted tree but tree '%s' in file '%s' is rooted\n", spacer, t->name, sumtParams.curFileName); return ERROR; } if (inComparetreeCommand == NO && (sumtParams.nESets != t->nESets || sumtParams.nBSets != t->nBSets)) { MrBayesPrint ("%s Tree '%s' in file '%s' does not have the expected relaxed clock parameters\n", spacer, t->name, sumtParams.curFileName); return ERROR; } } /* set partitions for tree */ ResetPolyTreePartitions(t); /* get depths if relevant */ if (t->isClock) GetPolyDepths (t); /* get ages if relevant */ if (t->isCalibrated) GetPolyAges (t); /* add partitions to counters */ for (i=0; inNodes; i++) { p = t->allDownPass[i]; partCtrRoot = AddSumtPartition (partCtrRoot, t, p, sumtParams.runId); } /* add the tree to relevant tree list */ if (inSumtCommand == YES) { if (t->isRooted == YES) StoreRPolyTopology (t, sumtParams.order); else /* if (sumtParams.isRooted == NO) */ StoreUPolyTopology (t, sumtParams.order); treeCtrRoot = AddSumtTree (treeCtrRoot, sumtParams.order); } else { i = sumtParams.numFileTreesSampled[sumtParams.runId] - 1; if (StoreSumtTree (packedTreeList[sumtParams.runId], i, t) == ERROR) return (ERROR); } /* Display the tree nodes. */ # if 0 ShowPolyNodes(t); # endif } return (NO_ERROR); } int ExamineSumtFile (char *fileName, SumtFileInfo *sumtFileInfo, char **treeName, int *brlensDef) { int i, foundBegin, lineTerm, inTreeBlock, blockErrors, inSumtComment, lineNum, numTreesInBlock, tokenType; char sumtToken[100], *s, *sumtTokenP; FILE *fp; /* open binary file */ if ((fp = OpenBinaryFileR(fileName)) == NULL) return ERROR; /* find out what type of line termination is used for file 1 */ lineTerm = LineTermType (fp); if (lineTerm != LINETERM_MAC && lineTerm != LINETERM_DOS && lineTerm != LINETERM_UNIX) { MrBayesPrint ("%s Unknown line termination for file \"%s\"\n", spacer, fileName); return ERROR; } /* find length of longest line in either file */ sumtFileInfo->longestLineLength = LongestLine (fp); sumtFileInfo->longestLineLength += 10; /* allocate a string long enough to hold a line */ s = (char *)SafeMalloc((size_t)(sumtFileInfo->longestLineLength) * sizeof(char)); if (!s) { MrBayesPrint ("%s Problem allocating string for examining file \"%s\"\n", spacer, fileName); return (ERROR); } /* close binary file */ SafeFclose (&fp); foundBegin = inTreeBlock = blockErrors = inSumtComment = NO; lineNum = numTreesInBlock = 0; sumtFileInfo->numTreeBlocks = 0; sumtFileInfo->lastTreeBlockBegin = 0; sumtFileInfo->lastTreeBlockEnd = 0; sumtFileInfo->numTreesInLastBlock = 0; /* open text file */ if ((fp = OpenTextFileR(fileName))==NULL) { MrBayesPrint ("%s Could not read file \"%s\" in text mode \n", spacer, fileName); return (ERROR); } /* read file */ while (fgets (s, sumtFileInfo->longestLineLength-2, fp) != NULL) { sumtTokenP = &s[0]; do { if (GetToken (sumtToken, &tokenType, &sumtTokenP)) goto errorExit; if (IsSame("[", sumtToken) == SAME) inSumtComment = YES; if (IsSame("]", sumtToken) == SAME) inSumtComment = NO; if (inSumtComment == YES) { if (IsSame ("Param", sumtToken) == SAME) { /* extract the tree name */ if (GetToken (sumtToken, &tokenType, &sumtTokenP)) /* get the colon */ goto errorExit; if (GetToken (sumtToken, &tokenType, &sumtTokenP)) /* get the tree name */ goto errorExit; /* reallocate space for tree name if too small */ if (strlen(s) > strlen(*treeName)) (*treeName) = (char*)SafeRealloc((*treeName), (strlen(s)+1)*sizeof(char)); strcpy ((*treeName), sumtToken); if (GetToken (sumtToken, &tokenType, &sumtTokenP)) goto errorExit; while (IsSame("]", sumtToken) != SAME) { strcat ((*treeName), sumtToken); if (GetToken (sumtToken, &tokenType, &sumtTokenP)) goto errorExit; } inSumtComment = NO; } } else /* if (inSumtComment == NO) */ { if (foundBegin == YES) { if (IsSame("Trees", sumtToken) == SAME) { numTreesInBlock = 0; inTreeBlock = YES; foundBegin = NO; sumtFileInfo->lastTreeBlockBegin = lineNum; } } else { if (IsSame("Begin", sumtToken) == SAME) { if (foundBegin == YES) { MrBayesPrint ("%s Found inappropriate \"Begin\" statement in file\n", spacer); blockErrors = YES; } foundBegin = YES; } else if (IsSame("End", sumtToken) == SAME) { if (inTreeBlock == YES) { sumtFileInfo->numTreeBlocks++; inTreeBlock = NO; sumtFileInfo->lastTreeBlockEnd = lineNum; } else { MrBayesPrint ("%s Found inappropriate \"End\" statement in file\n", spacer); blockErrors = YES; } sumtFileInfo->numTreesInLastBlock = numTreesInBlock; } else if (IsSame("Tree", sumtToken) == SAME) { if (inTreeBlock == YES) { numTreesInBlock++; if (numTreesInBlock == 1) { *brlensDef = NO; for (i=0; s[i]!='\0'; i++) { if (s[i] == ':') { *brlensDef = YES; break; } } } } else { MrBayesPrint ("%s Found a \"Tree\" statement that is not in a tree block\n", spacer); blockErrors = YES; } } } } } while (*sumtToken); lineNum++; } /* Now, check some aspects of the tree file, such as the number of tree blocks and whether they are properly terminated. */ if (inTreeBlock == YES) { MrBayesPrint ("%s Unterminated tree block in file %s. You probably need to\n", spacer, fileName); MrBayesPrint ("%s add a new line to the end of the file with \"End;\" on it.\n", spacer); goto errorExit; } if (inSumtComment == YES) { MrBayesPrint ("%s Unterminated comment in file %s\n", spacer, fileName); goto errorExit; } if (blockErrors == YES) { MrBayesPrint ("%s Found formatting errors in file %s\n", spacer, fileName); goto errorExit; } if (sumtFileInfo->lastTreeBlockEnd < sumtFileInfo->lastTreeBlockBegin) { MrBayesPrint ("%s Problem reading tree file %s\n", spacer, fileName); goto errorExit; } if (sumtFileInfo->numTreesInLastBlock <= 0) { MrBayesPrint ("%s No trees were found in last tree block of file %s\n", spacer, fileName); goto errorExit; } free (s); return (NO_ERROR); errorExit: free (s); return (ERROR); } /* FreePartCtr: Recursively free partition counter nodes */ void FreePartCtr (PartCtr *r) { int i, j; if (r==NULL) return; FreePartCtr (r->left); FreePartCtr (r->right); /* free relaxed clock parameters: eRate, nEvents, bRate */ if (sumtParams.nESets > 0) { for (i=0; inEvents[i][j]); free (r->nEvents[i]); } free (r->nEvents); } if (sumtParams.nBSets > 0) { for (i=0; ibLen [i][j]); free (r->bRate[i][j]); } free (r->bLen [i]); free (r->bRate[i]); } free (r->bLen); free (r->bRate); } if (r->length) { for (i=0; ilength[i]); free (r->length); } /* free basic parameters */ free (r->count); free (r->partition); free (r); numUniqueSplitsFound--; r = NULL; } /* FreeSumtParams: Free parameters allocated in sumtParams struct */ void FreeSumtParams(void) { int i; if (memAllocs[ALLOC_SUMTPARAMS] == YES) { for (i=0; i 0) { for (i=0; i 0) { for (i=0; ileft); FreeTreeCtr (r->right); free (r->order); free (r); numUniqueTreesFound--; r = NULL; } /* Label: Calculate length of label and fill in char *label if not NULL */ int Label (PolyNode *p, int addIndex, char *label, int maxLength) { int i, j0, j1, k, n, length, nameLength, index; if (p == NULL) return 0; /* first calculate length */ if (inSumtCommand == YES && isTranslateDiff == NO) { for (index=i=0; indexindex == i) break; else i++; } } else index = p->index; if (addIndex != NO) length = (int)(strlen(p->label)) + 4 + (int)(log10(index+1)); else length = (int)(strlen(p->label)); length = (length > maxLength ? maxLength : length); /* fill in label if label != NULL */ if (label != NULL) { if (addIndex != NO) nameLength = length - 4 - (int)(log10(index+1)); else nameLength = length; for (i=0; ilabel[i]; if ((int)strlen(p->label) > nameLength) label[i] = '~'; else label[i] = p->label[i]; if (addIndex != NO) { label[++i] = ' '; label[++i] = '('; n = index + 1; k = (int)(log10(n)) + 1; while (n != 0) { j0 = (int)(log10(n)); j1 = (int)(pow(10,j0)); label[++i] = '0' + n/j1; n = n % j1; k--; } while (k!=0) { label[++i] = '0'; k--; } label[++i] = ')'; } label[++i] = '\0'; } return length; } int OpenComptFiles (void) { int len, previousFiles, oldNoWarn, oldAutoOverwrite; char pFilename[120], dFilename[120]; FILE *fpTemp; oldNoWarn = noWarn; oldAutoOverwrite = autoOverwrite; /* set file names */ strcpy (pFilename, comptreeParams.comptOutfile); strcpy (dFilename, comptreeParams.comptOutfile); strcat (pFilename, ".pairs"); strcat (dFilename, ".dists"); /* one overwrite check for both files */ previousFiles = NO; if (noWarn == NO) { if ((fpTemp = OpenTextFileR(pFilename)) != NULL) { previousFiles = YES; fclose(fpTemp); } if ((fpTemp = OpenTextFileR(dFilename)) != NULL) { previousFiles = YES; fclose(fpTemp); } if (previousFiles == YES) { MrBayesPrint("%s There are previous compare results saved using the same filenames.\n", spacer); if (WantTo("Do you want to overwrite these results") == YES) { MrBayesPrint("\n"); noWarn = YES; autoOverwrite = YES; } else { MrBayesPrint("\n"); MrBayesPrint("%s Please specify a different output file name before running the comparetree command.\n", spacer); MrBayesPrint("%s You can do that using 'comparetree outputfile='. You can also move or\n", spacer); MrBayesPrint("%s rename the old result files.\n", spacer); return ERROR; } } } if ((fpParts = OpenNewMBPrintFile (pFilename)) == NULL) { noWarn = oldNoWarn; autoOverwrite = oldAutoOverwrite; return ERROR; } if ((fpDists = OpenNewMBPrintFile (dFilename)) == NULL) { noWarn = oldNoWarn; autoOverwrite = oldAutoOverwrite; return ERROR; } /* Reset file flags */ noWarn = oldNoWarn; autoOverwrite = oldAutoOverwrite; /* print unique identifiers to each file */ len = (int) strlen (stamp); if (len > 1) { MrBayesPrintf (fpParts, "[ID: %s]\n", stamp); MrBayesPrintf (fpDists, "[ID: %s]\n", stamp); } return (NO_ERROR); } int OpenSumtFiles (int treeNo) { int i, len, oldNoWarn, oldAutoOverwrite, previousFiles; char pFilename[145], sFilename[145], vFilename[145], cFilename[145], tFilename[145]; FILE *fpTemp; oldNoWarn = noWarn; oldAutoOverwrite = autoOverwrite; /* one overwrite check for all files */ if (noWarn == NO && treeNo == 0) { previousFiles = NO; for (i=0; i 1) { sprintf (pFilename, "%s.tree%d.parts", sumtParams.sumtOutfile, i+1); sprintf (sFilename, "%s.tree%d.tstat", sumtParams.sumtOutfile, i+1); sprintf (vFilename, "%s.tree%d.vstat", sumtParams.sumtOutfile, i+1); sprintf (cFilename, "%s.tree%d.con.tre", sumtParams.sumtOutfile, i+1); sprintf (tFilename, "%s.tree%d.trprobs", sumtParams.sumtOutfile, i+1); } else { sprintf (pFilename, "%s.parts", sumtParams.sumtOutfile); sprintf (sFilename, "%s.tstat", sumtParams.sumtOutfile); sprintf (vFilename, "%s.vstat", sumtParams.sumtOutfile); sprintf (cFilename, "%s.con.tre", sumtParams.sumtOutfile); sprintf (tFilename, "%s.trprobs", sumtParams.sumtOutfile); } if ((fpTemp = TestOpenTextFileR(pFilename)) != NULL) { previousFiles = YES; fclose(fpTemp); } if ((fpTemp = TestOpenTextFileR(sFilename)) != NULL) { previousFiles = YES; fclose(fpTemp); } if ((fpTemp = TestOpenTextFileR(vFilename)) != NULL) { previousFiles = YES; fclose(fpTemp); } if ((fpTemp = TestOpenTextFileR(cFilename)) != NULL) { previousFiles = YES; fclose(fpTemp); } if ((fpTemp = TestOpenTextFileR(tFilename)) != NULL) { previousFiles = YES; fclose(fpTemp); } if (previousFiles == YES) { MrBayesPrint("\n"); MrBayesPrint("%s There are previous tree sample summaries saved using the same filenames.\n", spacer); if (WantTo("Do you want to overwrite these results") == YES) { MrBayesPrint("\n"); noWarn = YES; autoOverwrite = YES; } else { MrBayesPrint("\n"); MrBayesPrint("%s Please specify a different output file name before running the sumt command.\n", spacer); MrBayesPrint("%s You can do that using 'sumt outputfile='. You can also move or\n", spacer); MrBayesPrint("%s rename the old result files.\n", spacer); return ERROR; } } } } /* set file names */ if (sumtParams.numTrees > 1) { sprintf (pFilename, "%s.tree%d.parts", sumtParams.sumtOutfile, treeNo+1); sprintf (sFilename, "%s.tree%d.tstat", sumtParams.sumtOutfile, treeNo+1); sprintf (vFilename, "%s.tree%d.vstat", sumtParams.sumtOutfile, treeNo+1); sprintf (cFilename, "%s.tree%d.con.tre", sumtParams.sumtOutfile, treeNo+1); sprintf (tFilename, "%s.tree%d.trprobs", sumtParams.sumtOutfile, treeNo+1); } else { sprintf (pFilename, "%s.parts", sumtParams.sumtOutfile); sprintf (sFilename, "%s.tstat", sumtParams.sumtOutfile); sprintf (vFilename, "%s.vstat", sumtParams.sumtOutfile); sprintf (cFilename, "%s.con.tre", sumtParams.sumtOutfile); sprintf (tFilename, "%s.trprobs", sumtParams.sumtOutfile); } /* open files checking for over-write as appropriate */ if ((fpParts = OpenNewMBPrintFile(pFilename)) == NULL) return ERROR; if ((fpTstat = OpenNewMBPrintFile(sFilename)) == NULL) { SafeFclose (&fpParts); return ERROR; } if ((fpVstat = OpenNewMBPrintFile(vFilename)) == NULL) { SafeFclose (&fpParts); SafeFclose (&fpTstat); return ERROR; } if ((fpCon = OpenNewMBPrintFile(cFilename)) == NULL) { SafeFclose (&fpParts); SafeFclose (&fpTstat); SafeFclose (&fpVstat); return ERROR; } if (sumtParams.calcTreeprobs == YES) { if ((fpTrees = OpenNewMBPrintFile(tFilename)) == NULL) { SafeFclose (&fpParts); SafeFclose (&fpTstat); SafeFclose (&fpVstat); SafeFclose (&fpCon); return ERROR; } } /* print #NEXUS if appropriate */ MrBayesPrintf (fpCon, "#NEXUS\n"); if (sumtParams.calcTreeprobs == YES) MrBayesPrintf (fpTrees, "#NEXUS\n"); /* print unique identifiers to each file */ len = (int) strlen (stamp); if (len > 1) { MrBayesPrintf (fpParts, "[ID: %s]\n", stamp); MrBayesPrintf (fpTstat, "[ID: %s]\n", stamp); MrBayesPrintf (fpVstat, "[ID: %s]\n", stamp); MrBayesPrintf (fpCon, "[ID: %s]\n", stamp); if (sumtParams.calcTreeprobs == YES) MrBayesPrintf (fpTrees, "[ID: %s]\n", stamp); } /* Reset noWarn and autoOverwrite */ if (treeNo == sumtParams.numTrees - 1) { noWarn = oldNoWarn; autoOverwrite = oldAutoOverwrite; } return (NO_ERROR); } void PartCtrUppass (PartCtr *r, PartCtr **uppass, int *index) { if (r != NULL) { uppass[(*index)++] = r; PartCtrUppass (r->left, uppass, index); PartCtrUppass (r->right, uppass, index); } } /* PrintBrParamsToFile: Print branch parameters to file */ int PrintBrParamsToFile (PartCtr **treeParts, int numTreeParts, int treeNo, char *divString) { int i, j, numPartitions, min, treeSample, runNo; char filename[145]; PartCtr *x; FILE *fp; /* set file name */ if (sumtParams.numTrees > 1) sprintf (filename, "%s.tree%d.brparams", sumtParams.sumtOutfile, treeNo+1); else sprintf (filename, "%s.brparams", sumtParams.sumtOutfile); MrBayesPrint ( "%s Saving branch parameters to file \"%s\"\n", spacer, filename); /* Open file checking for over-write as appropriate */ if ((fp = OpenNewMBPrintFile(filename)) == NULL) { MrBayesPrint ("\n"); return ERROR; } /* count number of branch params to print */ min = (sumtParams.minBrParamFreq * (sumtParams.numTreesSampled/sumtParams.numRuns)); for (i=0; itotCount < min) break; } numPartitions = i; /* print header */ /* -- header for branch lengths; NB! We skip the root branch length (i starts at 1) */ for (i=1; icount[runNo] > treeSample) MrBayesPrintf (fp, "%s", MbPrintNum (x->length[runNo][treeSample])); else MrBayesPrintf (fp, "NA"); if (i!=numPartitions-1) MrBayesPrintf (fp, "\t"); } /* print node heights */ if (sumtParams.isClock == YES) { MrBayesPrintf (fp, "\t"); for (i=0; icount[runNo] > treeSample ) MrBayesPrintf (fp, "%s", MbPrintNum (x->height[runNo][treeSample])); else MrBayesPrintf (fp, "NA"); if (i!=numPartitions-1) MrBayesPrintf (fp, "\t"); } } /* print node ages */ if (sumtParams.isCalibrated == YES) { MrBayesPrintf (fp, "\t"); for (i=0; icount[runNo] > treeSample ) MrBayesPrintf (fp, "%s", MbPrintNum (x->age[runNo][treeSample])); else MrBayesPrintf (fp, "NA"); if (i!=numPartitions-1) MrBayesPrintf (fp, "\t"); } } /* print effective branch lengths */ if (sumtParams.isRelaxed) { for (i=0; icount[runNo] > treeSample ) MrBayesPrintf (fp, "%s", MbPrintNum (x->bLen[i][runNo][treeSample])); else MrBayesPrintf (fp, "NA"); MrBayesPrintf (fp, "\t"); } for (j=1; jcount[runNo] > treeSample ) MrBayesPrintf (fp, "%s", MbPrintNum (x->bRate[i][runNo][treeSample])); else MrBayesPrintf (fp, "NA"); if (j!=numPartitions-1) MrBayesPrintf (fp, "\t"); } } for (i=0; icount[runNo] > treeSample ) MrBayesPrintf (fp, "%d", x->nEvents[i][runNo][treeSample]); else MrBayesPrintf (fp, "NA"); if (j!=numPartitions-1) MrBayesPrintf (fp, "\t"); } } } MrBayesPrintf (fp, "\n"); } // next tree sample } // next run MrBayesPrint ("\n"); return NO_ERROR; } /* PrintConTree: Print consensus tree in standard format readable by TreeView, Paup etc */ void PrintConTree (FILE *fp, PolyTree *t) { MrBayesPrintf (fp, " [Note: This tree contains information on the topology, \n"); MrBayesPrintf (fp, " branch lengths (if present), and the probability\n"); MrBayesPrintf (fp, " of the partition indicated by the branch.]\n"); if (!strcmp(sumtParams.sumtConType, "Halfcompat")) MrBayesPrintf (fp, " tree con_50_majrule = "); else MrBayesPrintf (fp, " tree con_all_compat = "); WriteConTree (t->root, fp, YES); MrBayesPrintf (fp, ";\n"); if (sumtParams.brlensDef == YES) { MrBayesPrintf (fp, "\n"); MrBayesPrintf (fp, " [Note: This tree contains information only on the topology\n"); MrBayesPrintf (fp, " and branch lengths (median of the posterior probability density).]\n"); if (!strcmp(sumtParams.sumtConType, "Halfcompat")) MrBayesPrintf (fp, " tree con_50_majrule = "); else MrBayesPrintf (fp, " tree con_all_compat = "); WriteConTree (t->root, fp, NO); MrBayesPrintf (fp, ";\n"); } } /* PrintFigTreeConTree: Print consensus tree in rich format for FigTree */ void PrintFigTreeConTree (FILE *fp, PolyTree *t, PartCtr **treeParts) { if (!strcmp(sumtParams.sumtConType, "Halfcompat")) MrBayesPrintf (fp, " tree con_50_majrule = "); else MrBayesPrintf (fp, " tree con_all_compat = "); if (t->isRooted == YES) MrBayesPrintf (fp, "[&R] "); else MrBayesPrintf (fp, "[&U] "); WriteFigTreeConTree (t->root, fp, treeParts); MrBayesPrintf (fp, ";\n"); } void PrintFigTreeNodeInfo (FILE *fp, PartCtr *x, MrBFlt length) { int i, postProbPercent, postProbSdPercent; MrBFlt *support, mean, var, min, max; Stat theStats; support = SafeCalloc (sumtParams.numRuns, sizeof(MrBFlt)); for (i=0; icount[i] / (MrBFlt) sumtParams.numFileTreesSampled[i]; } if (sumtParams.numRuns > 1) { MeanVariance (support, sumtParams.numRuns, &mean, &var); Range (support, sumtParams.numRuns, &min, &max); postProbPercent = (int) (100.0*mean + 0.5); postProbSdPercent = (int) (100.0 * sqrt(var) + 0.5); fprintf (fp, "[&prob=%.8le,prob_stddev=%.8le,prob_range={%.8le,%.8le},prob(percent)=\"%d\",prob+-sd=\"%d+-%d\"", mean, sqrt(var), min, max, postProbPercent, postProbPercent, postProbSdPercent); } else { postProbPercent = (int) (100.0*support[0] + 0.5); fprintf (fp, "[&prob=%.8le,prob(percent)=\"%d\"", support[0], postProbPercent); } if (sumtParams.isClock == YES) { GetSummary (x->height, sumtParams.numRuns, x->count, &theStats, sumtParams.HPD); if (sumtParams.HPD == YES) fprintf (fp, ",height_mean=%.8le,height_median=%.8le,height_95%%HPD={%.8le,%.8le}", theStats.mean, theStats.median, theStats.lower, theStats.upper); else fprintf (fp, ",height_mean=%.8le,height_median=%.8le,height_95%%CredInt={%.8le,%.8le}", theStats.mean, theStats.median, theStats.lower, theStats.upper); } if (sumtParams.isCalibrated == YES) { GetSummary (x->age, sumtParams.numRuns, x->count, &theStats, sumtParams.HPD); if (sumtParams.HPD == YES) fprintf (fp, ",age_mean=%.8le,age_median=%.8le,age_95%%HPD={%.8le,%.8le}", theStats.mean, theStats.median, theStats.lower, theStats.upper); else fprintf (fp, ",age_mean=%.8le,age_median=%.8le,age_95%%CredInt={%.8le,%.8le}", theStats.mean, theStats.median, theStats.lower, theStats.upper); } fprintf (fp, "]"); if (length >= 0.0) fprintf (fp, ":%s", MbPrintNum(length)); if (sumtParams.brlensDef == YES) { GetSummary (x->length, sumtParams.numRuns, x->count, &theStats, sumtParams.HPD); if (sumtParams.HPD == YES) fprintf (fp, "[&length_mean=%.8le,length_median=%.8le,length_95%%HPD={%.8le,%.8le}", theStats.mean, theStats.median, theStats.lower, theStats.upper); else fprintf (fp, "[&length_mean=%.8le,length_median=%.8le,length_95%%CredInt={%.8le,%.8le}", theStats.mean, theStats.median, theStats.lower, theStats.upper); } if (sumtParams.isClock == YES && sumtParams.isRelaxed == YES) { for (i=0; ibLen[i], sumtParams.numRuns, x->count, &theStats, sumtParams.HPD); if (sumtParams.HPD == YES) fprintf (fp, ",brlen%s_mean=%lf,brlen%s_median=%lf,brlen%s_95%%HPD={%lf,%lf}", sumtParams.tree->bSetName[i], theStats.mean, sumtParams.tree->bSetName[i], theStats.median, sumtParams.tree->bSetName[i], theStats.lower, theStats.upper); else fprintf (fp, ",brlen%s_mean=%lf,brlen%s_median=%lf,brlen%s_95%%CredInt={%lf,%lf}", sumtParams.tree->bSetName[i], theStats.mean, sumtParams.tree->bSetName[i], theStats.median, sumtParams.tree->bSetName[i], theStats.lower, theStats.upper); GetSummary (x->bRate[i], sumtParams.numRuns, x->count, &theStats, sumtParams.HPD); if (sumtParams.HPD == YES) fprintf (fp, ",rate%s_mean=%lf,rate%s_median=%lf,rate%s_95%%HPD={%lf,%lf}", sumtParams.tree->bSetName[i], theStats.mean, sumtParams.tree->bSetName[i], theStats.median, sumtParams.tree->bSetName[i], theStats.lower, theStats.upper); else fprintf (fp, ",rate%s_mean=%lf,rate%s_median=%lf,rate%s_95%%CredInt={%lf,%lf}", sumtParams.tree->bSetName[i], theStats.mean, sumtParams.tree->bSetName[i], theStats.median, sumtParams.tree->bSetName[i], theStats.lower, theStats.upper); } for (i=0; inEvents[i], sumtParams.numRuns, x->count, &theStats, sumtParams.HPD); if (sumtParams.HPD == YES) fprintf (fp, ",nEvents%s_mean=%lf,nEvents%s_median=%lf,nEvents%s_95%%HPD={%lf,%lf}", sumtParams.tree->eSetName[i], theStats.mean, sumtParams.tree->eSetName[i], theStats.median, sumtParams.tree->eSetName[i], theStats.lower, theStats.upper); else fprintf (fp, ",nEvents%s_mean=%lf,nEvents%s_median=%lf,nEvents%s_95%%CredInt={%lf,%lf}", sumtParams.tree->eSetName[i], theStats.mean, sumtParams.tree->eSetName[i], theStats.median, sumtParams.tree->eSetName[i], theStats.lower, theStats.upper); } } if (sumtParams.brlensDef == YES) fprintf (fp, "]"); free (support); } void PrintSumtTableLine(int numRuns, int *rowCount, Stat *theStats, MrBFlt *numPSRFSamples, MrBFlt *maxPSRF, MrBFlt *sumPSRF) { int j,k; MrBayesPrint ("%10.6lf %10.6lf %10.6lf %10.6lf %10.6lf", theStats->mean, theStats->var, theStats->lower, theStats->upper, theStats->median); MrBayesPrintf (fpVstat, "\t%s", MbPrintNum(theStats->mean)); MrBayesPrintf (fpVstat, "\t%s", MbPrintNum(theStats->var)); MrBayesPrintf (fpVstat, "\t%s", MbPrintNum(theStats->lower)); MrBayesPrintf (fpVstat, "\t%s", MbPrintNum(theStats->upper)); MrBayesPrintf (fpVstat, "\t%s", MbPrintNum(theStats->median)); if (numRuns > 1) { for (j=k=0; j 0) k++; if (theStats->PSRF < 0.0) { MrBayesPrint (" NA %3d", k); MrBayesPrintf (fpVstat, "\tNA\t%d", k); } else { if (theStats->PSRF > 10.0) { MrBayesPrint (" >10.0 %3d", k); MrBayesPrintf (fpVstat, "\tNA\t%d", k); (*maxPSRF) = 10.0; } else { MrBayesPrint (" %7.3lf %3d", theStats->PSRF, k); MrBayesPrintf (fpVstat, "\t%s\t%d", MbPrintNum(theStats->PSRF), k); (*sumPSRF) += theStats->PSRF; (*numPSRFSamples)++; if (theStats->PSRF > *maxPSRF) (*maxPSRF) = theStats->PSRF; } } if (k != numRuns) MrBayesPrint (" *"); } MrBayesPrint ("\n"); MrBayesPrintf (fpVstat, "\n"); } /* PrintSumtTaxaInfo: Print information on pruned and absent taxa */ void PrintSumtTaxaInfo (void) { int i, j, lineWidth, numExcludedTaxa, len; char tempStr[100]; /* print out information on absent taxa */ numExcludedTaxa = 0; for (i=0; i 0) { if (numExcludedTaxa == 1) MrBayesPrint ("%s The following taxon was absent from trees:\n", spacer); else MrBayesPrint ("%s The following %d taxa were absent from trees:\n", spacer, numExcludedTaxa); MrBayesPrint ("%s ", spacer); j = lineWidth = 0; for (i=0; i 60) { MrBayesPrint ("\n%s ", spacer); lineWidth = 0; } if (numExcludedTaxa == 1) MrBayesPrint ("%s\n", tempStr); else if (numExcludedTaxa == 2 && j == 1) MrBayesPrint ("%s ", tempStr); else if (j == numExcludedTaxa) MrBayesPrint ("and %s\n", tempStr); else MrBayesPrint ("%s, ", tempStr); } } MrBayesPrint ("\n"); } /* print out information on pruned taxa */ numExcludedTaxa = 0; for (i=0; i 0) { if (numExcludedTaxa == 1) MrBayesPrint ("%s The following taxon was pruned from trees:\n", spacer); else MrBayesPrint ("%s The following %d taxa were pruned from trees:\n", spacer, numExcludedTaxa); MrBayesPrint ("%s ", spacer); j = lineWidth = 0; for (i=0; i 60) { MrBayesPrint ("\n%s ", spacer); lineWidth = 0; } if (numExcludedTaxa == 1) MrBayesPrint ("%s\n", tempStr); else if (numExcludedTaxa == 2 && j == 1) MrBayesPrint ("%s ", tempStr); else if (j == numExcludedTaxa) MrBayesPrint ("and %s\n", tempStr); else MrBayesPrint ("%s, ", tempStr); } } MrBayesPrint ("\n"); } } /* Range: Determine range for a vector of MrBFlt values */ void Range (MrBFlt *vals, int nVals, MrBFlt *min, MrBFlt *max) { SortMrBFlt_Asc (vals, 0, nVals-1); *min = vals[0]; *max = vals[nVals-1]; } /* ResetTaxonSet: Reset included taxa and local outgroup number */ void ResetTaxonSet (void) { int i, j; /* reset numLocalTaxa and localOutGroup */ localOutGroup = 0; numLocalTaxa = 0; for (i=j=0; iroot->f = 0.0; for (i=t->nNodes-2; i>=0; i--) { p = t->allDownPass[i]; /* find distance to root in relevant units */ if (sumtParams.isClock == YES && sumtParams.isCalibrated == NO) p->f = t->root->depth - p->depth; else if (sumtParams.isClock == YES && sumtParams.isCalibrated == YES) p->f = t->root->age - p->age; else p->f = p->anc->f + p->length; if (p->left == NULL) { f = p->f / (screenWidth - Label(p,YES,NULL,maxLabelLength) - 2); if (f > scale) { scale = f; treeWidth = screenWidth - Label(p,YES,NULL,maxLabelLength) - 2; } } } /* calculate x coordinates */ for (i=0; inNodes; i++) { p = t->allDownPass[i]; p->x = (int) (0.5 + (p->f / scale)); } /* calculate y coordinates and lines to print */ for (i=nLines=0; inNodes; i++) { p = t->allDownPass[i]; if (p->left != NULL) { /* internal node */ for (q=p->left->sib; q->sib!=NULL; q=q->sib) ; p->y = (int) (0.5 + ((p->left->y + q->y) / 2.0)); } else { /* terminal node */ p->y = nLines; nLines += 2; } } /* print tree line by line */ for (i=0; inNodes; j++) { p = t->allDownPass[j]; if (p->y != i) continue; /* this branch should be printed */ /* add branch */ if (p->anc == NULL) { /* this is the root of the whole tree */ printLine[p->x] = '+'; } else { /* this is an ordinary branch */ to = p->x; from = p->anc->x; for (k=from+1; k<=to; k++) printLine[k] = '-'; if (p == p->anc->left) { if (markLine[from] == 0) printLine[from] = '/'; else printLine[from] = '|'; markLine[from] ++; } else if (p->sib == NULL) { if (markLine[from] == 1) printLine[from] = '\\'; else printLine[from] = '|'; markLine[from] --; } if (p->left!=NULL) { if (from != to) printLine[to] = '+'; else printLine[to] = '|'; } else { /* add label if the branch is terminal */ Label(p,YES,label,maxLabelLength); sprintf (printLine+to+2,"%s", label); } } } /* check for cross branches */ for (j=0; j= 1 && printLine[j] == ' ') printLine[j] = '|'; } MrBayesPrintf (fp, "%s\n",printLine); } /* print scale */ k = (int) (floor (log10 (scale * 80))); scaleBar = pow (10, k); barLength = (int) (scaleBar / scale); if (barLength > 80) { barLength /= 10; scaleBar /= 10.0; } else if (barLength > 40) { barLength /= 5; scaleBar /= 5.0; } else if (barLength > 16) { barLength /= 2; scaleBar /= 2.0; } if (t->isClock == YES) { MrBayesPrint ("%s ", spacer); for (i=0; i= 1000.0 || f < 0.10) { printExponential = YES; precision = 0; } else { printExponential = NO; precision = 2 - (int) (log10 (f)); } curPos = 0; f = nTimes * scaleBar; while (curPos < treeWidth) { /* print the number */ if (printExponential == YES) sprintf (temp, "%.2e", f); else sprintf (temp, "%.*lf", precision, f); /* room to print ? if so, print */ width = (int) strlen (temp); newPos = treeWidth - (int) (nTimes * (scaleBar / scale)); numSpaces = newPos - width / 2 - curPos; if (numSpaces >= 0 || (numSpaces >= -2 && curPos == 0)) { while (numSpaces > 0) { printLine[curPos++] = ' '; numSpaces--; } for (i=0; temp[i]!='\0'; i++) printLine[curPos++] = temp[i]; } /* get new number */ f -= scaleBar; nTimes--; } MrBayesPrint ("%s\n", printLine); if (sumtParams.isCalibrated == YES) MrBayesPrint ("\n%s [User-defined time units]\n\n", spacer); else MrBayesPrint ("\n%s [Expected changes per site]\n\n", spacer); } else { MrBayesPrintf (fp, "%s |", spacer); for (i=0; inNodes - t->nIntNodes; for (i=0; inIntNodes; i++) { p = t->intDownPass[i]; p->index = k++; } /* calculate max length of labels including taxon index number */ maxLabelLength = 0; for (i=0; inNodes; i++) { p = t->allDownPass[i]; if (p->left == NULL) { j = Label(p,YES,NULL,maxLength); if (j > maxLabelLength) maxLabelLength = j; } } /* make sure label can hold an interior node index number */ j = (int) (3.0 + log10((MrBFlt)t->nNodes)); maxLabelLength = (maxLabelLength > j ? maxLabelLength : j); /* calculate remaining screen width for tree and maxWidth in terms of branches */ treeWidth = screenWidth - maxLabelLength - 1; maxWidth = treeWidth / minBranchLength; /* unmark whole tree */ for (i=0; inNodes; i++) t->allDownPass[i]->mark = 0; nodesToBePrinted = t->nNodes; while (nodesToBePrinted > 0) { /* count depth of nodes in unprinted tree */ for (i=0; inNodes; i++) { p = t->allDownPass[i]; if (p->mark == 0) /* the node has not been printed yet */ { p->x = 0; /* if it is an interior node in the tree that will be printed compute the depth of the node */ if (p->left != NULL && p->left->mark == 0) { for (q = p->left; q!=NULL; q=q->sib) { if (q->x > p->x) p->x = q->x; } p->x++; /* break when root of print subtree has been found */ if (p->x >= maxWidth) break; } } } /* if internal node then find largest nonprinted subtree among descendant nodes */ if (p->anc != NULL) { for (q=p->left; q!=NULL; q=q->sib) { if (q->x == p->x - 1 && q->mark == 0) p = q; } MrBayesPrintf (fp, "%s Subtree rooted at node %d:\n\n", spacer, p->index); isTreeDivided = YES; } else if (isTreeDivided == YES) MrBayesPrintf (fp, "%s Root part of tree:\n\n", spacer); /* mark subtree for printing and translate x coordinates from depth to position */ if (p->anc == NULL) printWidth = p->x; else printWidth = p->x + 1; p->mark = 1; p->x = (int) (treeWidth - 0.5 - ((treeWidth - 1) * (p->x / (MrBFlt) printWidth))); for (i=t->nNodes-2; i>=0; i--) { p = t->allDownPass[i]; if (p->mark == 0 && p->anc->mark == 1) { p->mark = 1; p->x = (int) (treeWidth - 0.5 - ((treeWidth - 1) * (p->x / (MrBFlt) printWidth))); } } /* calculate y coordinates of nodes to be printed and lines to print */ for (i=nLines=0; inNodes; i++) { p = t->allDownPass[i]; if (p->mark == 1) { if (p->left != NULL && p->left->mark == 1) { /* internal node */ for (q=p->left->sib; q->sib!=NULL; q=q->sib) ; p->y = (int) (0.5 + ((p->left->y + q->y) / 2.0)); } else { /* terminal node */ p->y = nLines; nLines += 2; } } } /* print subtree line by line */ for (i=0; inNodes; j++) { p = t->allDownPass[j]; if (p->mark != 1 || p->y != i) continue; /* this branch should be printed add label if the branch is terminal in tree to be printed */ if (p->left == NULL) { Label (p,YES,label,maxLength); sprintf (printLine+treeWidth+1,"%s", label); } else if (p->left->mark == 2) sprintf (printLine+treeWidth+1,"(%d)", p->index); /* add branch */ if (p->anc == NULL) { /* this is the root of the whole tree */ printLine[p->x] = '+'; nodesToBePrinted--; } else if (p->anc->mark == 0) { /* this is a root of a subtree this branch will have to be printed again so do not decrease nodesToBePrinted */ to = p->x; from = 0; for (k=from; ksupport*100.0 + 0.5)); else *temp='\0'; from = (int)(from + 1.5 + ((to - from - 1 - strlen(temp)) / 2.0)); for (k=0; temp[k]!='\0'; k++) printLine[from++] = temp[k]; } else { /* this is an ordinary branch */ to = p->x; from = p->anc->x; for (k=from+1; k<=to; k++) printLine[k] = '-'; if (p == p->anc->left) { printLine[from] = '/'; markLine[from] = 1; } else if (p->sib == NULL) { printLine[from] = '\\'; markLine[from] = 0; } if (p->left!=NULL && p->left->mark!=2) { printLine[to] = '+'; if (showSupport == YES) sprintf (temp, "%d", (int) (p->support*100.0 + 0.5)); else *temp='\0'; from = (int)(from + 1.5 + ((to - from - 1 - strlen(temp)) / 2.0)); for (k=0; temp[k]!='\0'; k++) printLine[from++] = temp[k]; } nodesToBePrinted--; } } /* check for cross branches */ for (j=0; jnNodes; i++) { p = t->allDownPass[i]; if (p->mark == 1) { if (p->anc == NULL) p->mark = 2; else if (p->anc->mark == 0) p->mark = 0; /* this branch will have to be printed again */ else p->mark = 2; } } } /* next subtree */ free (printLine); return NO_ERROR; } void ShowParts (FILE *fp, BitsLong *p, int nTaxaToShow) { int i; BitsLong x, y, bitsLongOne=1; for (i=0; i= 0); assert (right >= 0); i = left; j = right; x = item[(left+right)/2]->totCount; do { while (item[i]->totCount > x && i < right) i++; while (x > item[j]->totCount && j > left) j--; if (i <= j) { tempPartCtr = item[i]; item[i] = item[j]; item[j] = tempPartCtr; i++; j--; } } while (i <= j); if (left < j) SortPartCtr (item, left, j); if (i < right) SortPartCtr (item, i, right); } void SortTerminalPartCtr (PartCtr **item, int len) { register int i, j, maxCount; PartCtr *temp; maxCount = item[0]->totCount; /* put root first */ for (i=0; item[i]->totCount == maxCount; i++) if (NumBits(item[i]->partition, sumtParams.BitsLongsNeeded) == sumtParams.numTaxa) break; if (i!=0) { temp = item[0]; item[0] = item[i]; item[i] = temp; } /* then find terminals in index order */ for (i=1; i<=sumtParams.numTaxa; i++) { for (j=i; item[j]->totCount == maxCount && jpartition, sumtParams.BitsLongsNeeded) == 1 && FirstTaxonInPartition(item[j]->partition, sumtParams.BitsLongsNeeded) == i-1) break; if (j!=i) { temp = item[i]; item[i] = item[j]; item[j] = temp; } } } void SortTreeCtr (TreeCtr **item, int left, int right) { register int i, j; TreeCtr *tempTreeCtr; int x; i = left; j = right; x = item[(left+right)/2]->count; do { while (item[i]->count > x && i < right) i++; while (x > item[j]->count && j > left) j--; if (i <= j) { tempTreeCtr = item[i]; item[i] = item[j]; item[j] = tempTreeCtr; i++; j--; } } while (i <= j); if (left < j) SortTreeCtr (item, left, j); if (i < right) SortTreeCtr (item, i, right); } /* StoreSumtTree: Store tree in treeList in packed format */ int StoreSumtTree (PackedTree *treeList, int index, PolyTree *t) { int orderLen, numBrlens; assert (treeList[index].brlens == NULL); assert (treeList[index].order == NULL); /* get tree dimensions */ numBrlens = t->nNodes - 1; orderLen = t->nIntNodes - 1; /* allocate space */ treeList[index].brlens = (MrBFlt *) SafeCalloc (numBrlens, sizeof(MrBFlt)); treeList[index].order = (int *) SafeCalloc (orderLen, sizeof(MrBFlt)); if (!treeList[index].order || !treeList[index].brlens) { MrBayesPrint ("%s Could not store packed representation of tree '%s'\n", spacer, t->name); return (ERROR); } /* store tree */ if (t->isRooted == YES) StoreRPolyTree (t, treeList[index].order, treeList[index].brlens); else StoreUPolyTree (t, treeList[index].order, treeList[index].brlens); return (NO_ERROR); } /* TreeCtrUppass: extract TreeCtr nodes in uppass sequence */ void TreeCtrUppass (TreeCtr *r, TreeCtr **uppass, int *index) { if (r != NULL) { uppass[(*index)++] = r; TreeCtrUppass (r->left, uppass, index); TreeCtrUppass (r->right, uppass, index); } } int TreeProb (void) { int i, num, nInSets[5]; MrBFlt treeProb, cumTreeProb; TreeCtr **trees; Tree *theTree; /* check if we need to do this */ if (sumtParams.calcTreeprobs == NO) return (NO_ERROR); MrBayesPrint ("%s Calculating tree probabilities...\n\n", spacer); /* allocate space for tree counters and trees */ trees = (TreeCtr **) SafeCalloc ((size_t)numUniqueTreesFound, sizeof(TreeCtr *)); theTree = AllocateTree (sumtParams.numTaxa); if (!trees || !theTree) { MrBayesPrint ("%s Problem allocating trees or theTree in TreeProb\n", spacer); return (ERROR); } /* extract trees */ i = 0; TreeCtrUppass (treeCtrRoot, trees, &i); /* sort trees */ SortTreeCtr (trees, 0, numUniqueTreesFound-1); /* set basic params in receiving tree */ theTree->isRooted = sumtParams.isRooted; if (theTree->isRooted) { theTree->nNodes = 2 * sumtParams.numTaxa; theTree->nIntNodes = sumtParams.numTaxa - 1; } else { theTree->nNodes = 2 * sumtParams.numTaxa - 2; theTree->nIntNodes = sumtParams.numTaxa - 2; } /* show tree data */ cumTreeProb = 0.0; nInSets[0] = nInSets[1] = nInSets[2] = nInSets[3] = nInSets[4] = 0; for (num=0; numcount / (MrBFlt)sumtParams.numTreesSampled; cumTreeProb += treeProb; if (cumTreeProb >= 0.0 && cumTreeProb < 0.5) nInSets[0]++; else if (cumTreeProb >= 0.5 && cumTreeProb < 0.9) nInSets[1]++; else if (cumTreeProb >= 0.9 && cumTreeProb < 0.95) nInSets[2]++; else if (cumTreeProb >= 0.95 && cumTreeProb < 0.99) nInSets[3]++; else nInSets[4]++; /* draw tree to stdout */ if (theTree->isRooted == YES) RetrieveRTopology (theTree, trees[num]->order); else RetrieveUTopology (theTree, trees[num]->order); if (sumtParams.showSumtTrees == YES) { MrBayesPrint ("\n%s Tree %d (p = %1.3lf, P = %1.3lf):\n\n", spacer, num+1, treeProb, cumTreeProb); ShowTree (theTree); } /* draw tree to file */ if (num == 0) { MrBayesPrintf (fpTrees, "[This file contains the trees that were found during the MCMC\n"); MrBayesPrintf (fpTrees, "search, sorted by posterior probability. \"p\" indicates the\n"); MrBayesPrintf (fpTrees, "posterior probability of the tree whereas \"P\" indicates the\n"); MrBayesPrintf (fpTrees, "cumulative posterior probability.]\n\n"); MrBayesPrintf (fpTrees, "begin trees;\n"); MrBayesPrintf (fpTrees, " translate\n"); for (i=0; iroot->left, theTree->isRooted); MrBayesPrintf (fpTrees, ";\n"); if (num == numUniqueTreesFound - 1) MrBayesPrintf (fpTrees, "end;\n"); } /* print out general information on credible sets of trees */ i = nInSets[0] + nInSets[1] + nInSets[2] + nInSets[3] + nInSets[4]; MrBayesPrint ("%s Credible sets of trees (%d tree%s sampled):\n", spacer, i, i > 1 ? "s" : ""); i = nInSets[0] + 1; if (i > 1) MrBayesPrint ("%s 50 %% credible set contains %d trees\n", spacer, i); i += nInSets[1]; if (i > 1) MrBayesPrint ("%s 90 %% credible set contains %d trees\n", spacer, i); i += nInSets[2]; if (i > 1) MrBayesPrint ("%s 95 %% credible set contains %d trees\n", spacer, i); i += nInSets[3]; MrBayesPrint ("%s 99 %% credible set contains %d tree%s\n\n", spacer, i, i > 1 ? "s" : ""); /* free memory */ free (trees); return (NO_ERROR); } void WriteConTree (PolyNode *p, FILE *fp, int showSupport) { PolyNode *q; if (p->anc != NULL) if (p->anc->left == p) fprintf (fp, "("); for (q = p->left; q != NULL; q = q->sib) { if (q->anc->left != q) /* Note that q->anc always exists (it is p) */ fprintf (fp, ","); WriteConTree (q, fp, showSupport); } if (p->left == NULL) { if (sumtParams.brlensDef == YES) { if (sumtParams.isClock == NO) fprintf (fp, "%d:%s", p->index+1, MbPrintNum(p->length)); else fprintf (fp, "%d:%s", p->index+1, MbPrintNum(p->anc->depth - p->depth)); } else fprintf (fp, "%d", p->index+1); } if (p->sib == NULL && p->anc != NULL) { if (p->anc->anc != NULL) { if (sumtParams.brlensDef == YES && showSupport == NO) { if (sumtParams.isClock == NO) fprintf (fp, "):%s", MbPrintNum(p->anc->length)); else fprintf (fp, "):%s", MbPrintNum(p->anc->anc->depth - p->anc->depth)); } else if (sumtParams.brlensDef == NO && showSupport == YES) fprintf (fp, ")%1.3lf", p->anc->support); else if (sumtParams.brlensDef == YES && showSupport == YES) { if (sumtParams.isClock == NO) fprintf (fp, ")%1.3lf:%s", p->anc->support, MbPrintNum(p->anc->length)); else fprintf (fp, ")%1.3lf:%s", p->anc->support, MbPrintNum(p->anc->anc->depth - p->anc->depth)); } else fprintf (fp, ")"); } else fprintf (fp, ")"); } } /* WriteFigTreeConTree: Include rich information for each node in a consensus tree */ void WriteFigTreeConTree (PolyNode *p, FILE *fp, PartCtr **treeParts) { PolyNode *q; if (p->left == NULL) { fprintf (fp, "%d", p->index+1); if (sumtParams.isClock == NO) PrintFigTreeNodeInfo(fp, treeParts[p->partitionIndex], p->length); else if (sumtParams.isCalibrated == YES) PrintFigTreeNodeInfo(fp, treeParts[p->partitionIndex], p->anc->age - p->age); else PrintFigTreeNodeInfo(fp, treeParts[p->partitionIndex], p->anc->depth - p->depth); } else { fprintf (fp, "("); for (q = p->left; q != NULL; q = q->sib) { WriteFigTreeConTree (q, fp, treeParts); if (q->sib != NULL) fprintf (fp, ","); } fprintf (fp, ")"); if (p->partitionIndex >= 0 && p->partitionIndex < numUniqueSplitsFound) { if (p->anc == NULL) { if (sumtParams.isClock == YES) PrintFigTreeNodeInfo(fp,treeParts[p->partitionIndex], -1.0); } else if (sumtParams.isClock == NO) PrintFigTreeNodeInfo(fp, treeParts[p->partitionIndex], p->length); else if (sumtParams.isCalibrated == YES) PrintFigTreeNodeInfo(fp, treeParts[p->partitionIndex], p->anc->age - p->age); else PrintFigTreeNodeInfo(fp, treeParts[p->partitionIndex], p->anc->depth - p->depth); } } } NBISweden-MrBayes-ecfd55c/src/sumpt.h000066400000000000000000000033411526212227600175260ustar00rootroot00000000000000#ifndef SUMPT_H_ #define SUMPT_H_ /* struct to hold info about a .p file */ typedef struct { int longestLineLength; int headerLine; int firstParamLine; int numRows; int numColumns; } SumpFileInfo; /* struct to hold info about a model probability */ typedef struct { int index; double prob; } ModelProb; /* struct to hold a parameter sample, possibly from multiple files */ typedef struct { MrBFlt **values; } ParameterSample; /* function declarations */ int AllocateParameterSamples (ParameterSample **parameterSamples, int numRuns, int numRows, int numColumns); int DoSump (void); int DoSumpParm (char *parmName, char *tkn); int DoSumSs (void); int DoSumSsParm (char *parmName, char *tkn); int ExamineSumpFile (char *fileName, SumpFileInfo *fileInfo, char ***headerNames, int *nHeaders); int FindHeader (char *token, char **headerNames, int nHeaders, int *index); void FreeParameterSamples (ParameterSample *parameterSamples); int GetHeaders (char ***headerNames, char *headerLine, int *nHeaders); int PrintPlot (MrBFlt *xVals, MrBFlt *yVals, int nSamples); int ReadParamSamples (char *fileName, SumpFileInfo *fileInfo, ParameterSample *parameterSamples, int runNo); int DoCompareTree (void); int DoCompareTreeParm (char *parmName, char *tkn); int DoCompRefTree (void); int DoSumt (void); int DoSumtParm (char *parmName, char *tkn); int DoSumtTree (void); int DoSumtTreeParm (char *parmName, char *tkn); void ResetTranslateTable (void); int ShowConTree (FILE *fp, PolyTree *t, int screenWidth, int showSupport); void ShowParts (FILE *fp, BitsLong *p, int nTaxaToShow); #endif /* SUMPT_H_ */ NBISweden-MrBayes-ecfd55c/src/utils.c000066400000000000000000014440071526212227600175220ustar00rootroot00000000000000/* * MrBayes 3 * * (c) 2002-2023 * * John P. Huelsenbeck * Dept. Integrative Biology * University of California, Berkeley * Berkeley, CA 94720-3140 * johnh@berkeley.edu * * Fredrik Ronquist * Swedish Museum of Natural History * Box 50007 * SE-10405 Stockholm, SWEDEN * fredrik.ronquist@nrm.se * * With important contributions by * * Paul van der Mark (paulvdm@sc.fsu.edu) * Maxim Teslenko (maxkth@gmail.com) * Chi Zhang (zhangchicool@gmail.com) * * and by many users (run 'acknowledgments' to see more info) * * This program is free software; you can redistribute it and/or * modify it under the terms of the GNU General Public License * as published by the Free Software Foundation; either version 2 * of the License, or (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details (www.gnu.org). * */ #include "bayes.h" #include "best.h" #include "command.h" #include "mcmc.h" #include "model.h" #include "utils.h" #define MAX_RATE_CATS 20 #define POINTGAMMA(prob,alpha,beta) PointChi2(prob,2.0*(alpha))/(2.0*(beta)) #define PAI2 6.283185307 #define TINY 1.0e-20 #define EVALUATE_COMPLEX_NUMBERS 2 #if !defined(MAX) #define MAX(a,b) (((a) > (b)) ? (a) : (b)) #endif #if !defined(MIN) #define MIN(a,b) (((a) < (b)) ? (a) : (b)) #endif #define SQUARE(a) ((a)*(a)) /* local global variable */ char noLabel[] = ""; /* local prototypes */ void DatedNodeDepths (TreeNode *p, MrBFlt *nodeDepths, int *index); void DatedNodes (TreeNode *p, TreeNode **datedTips, int *index); int NConstrainedTips (TreeNode *p); int NDatedTips (TreeNode *p); void PrintNode (char **s, int *len, TreeNode *p, int isRooted); void ResetPolyNode (PolyNode *p); void ResetTreeNode (TreeNode *p); void SetNodeDepths (Tree *t); void AddTwoMatrices (int dim, MrBFlt **a, MrBFlt **b, MrBFlt **result); void BackSubstitutionRow (int dim, MrBFlt **u, MrBFlt *b); void Balanc (int dim, MrBFlt **a, int *low, int *high, MrBFlt *scale); void BalBak (int dim, int low, int high, MrBFlt *scale, int m, MrBFlt **z); MrBFlt BetaCf (MrBFlt a, MrBFlt b, MrBFlt x); MrBFlt BetaQuantile (MrBFlt alpha, MrBFlt beta, MrBFlt x); MrBFlt CdfBinormal (MrBFlt h1, MrBFlt h2, MrBFlt r); MrBFlt CdfNormal (MrBFlt x); MrBComplex Complex (MrBFlt a, MrBFlt b); MrBFlt ComplexAbsoluteValue (MrBComplex a); MrBComplex ComplexAddition (MrBComplex a, MrBComplex b); MrBComplex ComplexConjugate (MrBComplex a); MrBComplex ComplexDivision (MrBComplex a, MrBComplex b); void ComplexDivision2 (MrBFlt ar, MrBFlt ai, MrBFlt br, MrBFlt bi, MrBFlt *cr, MrBFlt *ci); MrBComplex ComplexExponentiation (MrBComplex a); int ComplexInvertMatrix (int dim, MrBComplex **a, MrBFlt *dwork, int *indx, MrBComplex **aInverse, MrBComplex *col); MrBComplex ComplexLog (MrBComplex a); void ComplexLUBackSubstitution (int dim, MrBComplex **a, int *indx, MrBComplex *b); int ComplexLUDecompose (int dim, MrBComplex **a, MrBFlt *vv, int *indx, MrBFlt *pd); MrBComplex ComplexMultiplication (MrBComplex a, MrBComplex b); MrBComplex ComplexSquareRoot (MrBComplex a); MrBComplex ComplexSubtraction (MrBComplex a, MrBComplex b); int ComputeEigenSystem (int dim, MrBFlt **a, MrBFlt *v, MrBFlt *vi, MrBFlt **u, int *iwork, MrBFlt *dwork); void ComputeLandU (int dim, MrBFlt **aMat, MrBFlt **lMat, MrBFlt **uMat); void ComputeMatrixExponential (int dim, MrBFlt **a, int qValue, MrBFlt **f); void DivideByTwos (int dim, MrBFlt **a, int power); MrBFlt D_sign (MrBFlt a, MrBFlt b); int EigensForRealMatrix (int dim, MrBFlt **a, MrBFlt *wr, MrBFlt *wi, MrBFlt **z, int *iv1, MrBFlt *fv1); void ElmHes (int dim, int low, int high, MrBFlt **a, int *interchanged); void ElTran (int dim, int low, int high, MrBFlt **a, int *interchanged, MrBFlt **z); void Exchange (int j, int k, int l, int m, int n, MrBFlt **a, MrBFlt *scale); MrBFlt Factorial (int x); void ForwardSubstitutionRow (int dim, MrBFlt **L, MrBFlt *b); MrBFlt GammaRandomVariable (MrBFlt a, MrBFlt b, RandLong *seed); void GaussianElimination (int dim, MrBFlt **a, MrBFlt **bMat, MrBFlt **xMat); int Hqr2 (int dim, int low, int high, MrBFlt **h, MrBFlt *wr, MrBFlt *wi, MrBFlt **z); MrBFlt IncompleteBetaFunction (MrBFlt alpha, MrBFlt beta, MrBFlt x); MrBFlt IncompleteGamma (MrBFlt x, MrBFlt alpha, MrBFlt LnGamma_alpha); int InvertMatrix (int dim, MrBFlt **a, MrBFlt *col, int *indx, MrBFlt **aInv); MrBFlt LBinormal (MrBFlt h1, MrBFlt h2, MrBFlt r); int LogBase2Plus1 (MrBFlt x); void LUBackSubstitution (int dim, MrBFlt **a, int *indx, MrBFlt *b); int LUDecompose (int dim, MrBFlt **a, MrBFlt *vv, int *indx, MrBFlt *pd); void MultiplyMatrixByScalar (int dim, MrBFlt **a, MrBFlt scalar, MrBFlt **result); MrBFlt PointChi2 (MrBFlt prob, MrBFlt v); void PrintComplexVector (int dim, MrBComplex *vec); void PrintSquareComplexMatrix (int dim, MrBComplex **m); void PrintSquareDoubleMatrix (int dim, MrBFlt **matrix); void PrintSquareIntegerMatrix (int dim, int **matrix); MrBComplex ProductOfRealAndComplex (MrBFlt a, MrBComplex b); MrBFlt RndGamma (MrBFlt s, RandLong *seed); MrBFlt RndGamma1 (MrBFlt s, RandLong *seed); MrBFlt RndGamma2 (MrBFlt s, RandLong *seed); int SetQvalue (MrBFlt tol); void SetToIdentity (int dim, MrBFlt **matrix); MrBFlt Tha (MrBFlt h1, MrBFlt h2, MrBFlt a1, MrBFlt a2); void TiProbsUsingEigens (int dim, MrBFlt *cijk, MrBFlt *eigenVals, MrBFlt v, MrBFlt r, MrBFlt **tMat, MrBFlt **fMat, MrBFlt **sMat); void TiProbsUsingPadeApprox (int dim, MrBFlt **qMat, MrBFlt v, MrBFlt r, MrBFlt **tMat, MrBFlt **fMat, MrBFlt **sMat); MrBFlt QuantileLogNormal (MrBFlt prob, MrBFlt mu, MrBFlt sigma); int DiscreteLogNormal (MrBFlt *rK, MrBFlt sigma, int K, int median); MrBFlt LogNormalPoint (MrBFlt x, MrBFlt mu, MrBFlt sigma); #if defined (BEAGLE_V3_ENABLED) int Height(TreeNode *p); void ReverseLevelOrder(Tree *t, TreeNode *p, int *i); void StoreGivenLevel(Tree *t, TreeNode *p, int level, int *i); #endif /* qsort compare function for MrBFlt */ int cmpMrBFlt(const void *a, const void *b) { MrBFlt x = *((MrBFlt *)(a)); MrBFlt y = *((MrBFlt *)(b)); if ( x < y ) return -1; else if ( x == y ) return 0; else return 1; } /* AddBitfield: Add bitfield to list of bitfields. The function uses global variable nLongsNeeded. */ int AddBitfield (BitsLong ***list, int listLen, int *set, int setLen) { int i, nLongsNeeded; nLongsNeeded = (setLen - 1) / nBitsInALong + 1; (*list) = (BitsLong **) SafeRealloc ((void *)(*list), ((size_t)listLen+1)*sizeof(BitsLong *)); if (!(*list)) return ERROR; (*list)[listLen] = (BitsLong *) SafeMalloc ((size_t)nLongsNeeded*sizeof(BitsLong)); if (!(*list)[listLen]) return ERROR; ClearBits ((*list)[listLen], nLongsNeeded); for (i=0; ilastGen) break; fprintf(toFile,"%s",strBuf); fflush (toFile); } SafeFclose(&fromFile); free(strBuf); free(strCpy); return (NO_ERROR); } /* CopyProcessSsFile: copy results from one file to another up to lastStep. Also marginalLnLSS is collected for processed steps*/ int CopyProcessSsFile (FILE *toFile, char *fromFileName, int lastStep, MrBFlt *marginalLnLSS, MrBFlt * splitfreqSS) { int longestLine, run, curStep, i; double tmp; char *strBuf, *strCpy, *word, *tmpcp; FILE *fromFile; if ((fromFile = OpenBinaryFileR(fromFileName)) == NULL) return ERROR; longestLine = LongestLine(fromFile)+10; SafeFclose(&fromFile); if ((fromFile = OpenTextFileR(fromFileName)) == NULL) return ERROR; strBuf = (char *)SafeCalloc(longestLine, sizeof(char)); strCpy = (char *)SafeCalloc(longestLine, sizeof(char)); while (fgets(strBuf,longestLine,fromFile)!=NULL) { strncpy (strCpy,strBuf,longestLine); word = strtok(strCpy," \t\n"); /* atoi returns 0 when word is not integer number */ if (atoi(word)>lastStep) break; fprintf(toFile,"%s",strBuf); fflush (toFile); curStep = atoi(word); if (curStep > 0) { strtok(NULL,"\t\n"); /*skip power*/ for (run=0; runlastGen) break; (*numTrees)++; fprintf(toFile,"%s",strBuf); } else if (*numTrees == 0) /* do not print the end statement */ fprintf(toFile,"%s",strBuf); fflush (toFile); } SafeFclose(&fromFile); free(strBuf); free(strCpy); return (NO_ERROR); } /* FirstTaxonInPartition: Find index of first taxon in partition */ int FirstTaxonInPartition (BitsLong *partition, int length) { int i, j, nBits, taxon; BitsLong x, bitsLongOne=1; nBits = sizeof(BitsLong) * 8; taxon = 0; for (i=0; i=0; k--) { for (i=0; i<6; i++) { if (fxn[i] != growthFxn[i]) break; } if (i == 6) break; /* get next growth fxn */ for (i=5; i>=0; i--) { fxn[i]--; if (fxn[i] >= 0) break; } if (i < 0) return -1; /* error */ else if (i < 5) { max = 0; for (j=0; j<=i; j++) { if (fxn[j] > max) max = fxn[j]; } fxn[++i] = max + 1; for (++i; i<6; i++) fxn[i] = fxn[i-1] + 1; } } return k; } /* Convert from model index to 0-based growth function over six states */ void FromIndexToGrowthFxn(int index, int *growthFxn) { int i, j, max, k; /* set growth fxn to lexicographical max */ for (i=0; i<6; i++) growthFxn[i] = i; /* decrease until we reach index */ for (k=202; k>index; k--) { for (i=5; i>=0; i--) { growthFxn[i]--; if (growthFxn[i] >= 0) break; } if (i < 0) return; /* ERROR */ else if (i < 5) { max = 0; for (j=0; j<=i; j++) { if (growthFxn[j] > max) max = growthFxn[j]; } growthFxn[++i] = max + 1; for (++i; i<6; i++) growthFxn[i] = growthFxn[i-1] + 1; } } } /* GetIntSummary: Get summary statistics for a number of runs (int version) */ void GetIntSummary (int **vals, int nRows, int *rowCount, Stat *theStats, int HPD) { int i, j, nVals; MrBFlt *theValues, *p; nVals = 0; for (i=0; imean), &(theStats->var)); if (HPD == YES) LowerUpperMedian (theValues, nVals, &(theStats->lower), &(theStats->upper), &(theStats->median)); else LowerUpperMedian (theValues, nVals, &(theStats->lower), &(theStats->upper), &(theStats->median)); free (theValues); } /* Get k from 0-based growth function */ int GetKFromGrowthFxn(int *growthFxn) { int i, k=0; for (i=0; i<6; i++) if (growthFxn[i] > k) k = growthFxn[i]; return k+1; } /* GetSummary: Get summary statistics for a number of runs */ void GetSummary (MrBFlt **vals, int nRows, int *rowCount, Stat *theStats, int HPD) { int i, nVals; MrBFlt *theValues, *p, *ESS; nVals = 0; for (i=0; imean), &(theStats->var)); if (HPD == YES) LowerUpperMedianHPD (theValues, nVals, &(theStats->lower), &(theStats->upper), &(theStats->median)); else LowerUpperMedian (theValues, nVals, &(theStats->lower), &(theStats->upper), &(theStats->median)); if (nRows > 1) theStats->PSRF = PotentialScaleReduction (vals, nRows, rowCount); ESS = (MrBFlt *) SafeMalloc ((size_t)nRows * sizeof(MrBFlt)); EstimatedSampleSize (vals, nRows, rowCount, ESS); theStats->avrESS = theStats->minESS = ESS[0]; for (i=1; iavrESS += ESS[i]; if (theStats->minESS > ESS[i]) { theStats->minESS = ESS[i]; } } theStats->avrESS /=nRows; free (ESS); free (theValues); } /* HarmonicArithmeticMean: Calculate harmonic and arithmetic mean from log values */ int HarmonicArithmeticMeanOnLogs (MrBFlt *vals, int nVals, MrBFlt *mean, MrBFlt *harm_mean) { int i, reliable; MrBFlt a, x, y, scaler, n; reliable = YES; scaler = vals[nVals-1]; a = n = 0.0; for (i=0; i 400.0) { if (y > 5000.0) { reliable = NO; continue; } a /= exp(y - 100.0); scaler += y - 100.0; y = 100.0; } x = (MrBFlt) exp(y); if (n < 0.5) a = x; else { a += x; } n += 1.0; } /* arithmetic mean */ (*mean) = (MrBFlt) log(a/n) + scaler; scaler = (MrBFlt) (0.0 - vals[nVals-1]); a = n = 0.0; for (i=0; i 400.0) { if (y > 5000.0) { reliable = NO; continue; } a /= exp(y - 100.0); scaler += y - 100.0; y = 100.0; } x = (MrBFlt) exp(y); if (n < 0.5) a = x; else { a += x; } n += (MrBFlt) 1.0; } /* harmonic mean */ (*harm_mean) = - (MrBFlt) log(a/n) - scaler; if (reliable == YES) return (NO_ERROR); else return (ERROR); } /* IsBitSet: Is bit i set in BitsLong *bits ? */ int IsBitSet (int i, BitsLong *bits) { BitsLong x, bitsLongOne=1; bits += i / nBitsInALong; x = bitsLongOne << (i % nBitsInALong); if ((*bits) & x) return (YES); else return (NO); } /* IsConsistentWith: Is token consistent with expected word, case insensitive ? */ int IsConsistentWith (const char *token, const char *expected) { int i, len; if (strlen(token) > strlen(expected)) return NO; len = (int) strlen (token); for (i=0; i longest) longest = lineLength; lineLength = 0; /* if ((ch == '\n') || (ch == '\r')) { if (lineLength > longest) longest = lineLength; lineLength = 0; } else lineLength++; */ } rewind (fp); /* rewind */ return (longest+1); /*+1 to accommodate last character*/ } /* LowerUpperMedian: Determine median and 95 % credible interval */ void LowerUpperMedian (MrBFlt *vals, int nVals, MrBFlt *lower, MrBFlt *upper, MrBFlt *median) { SortMrBFlt_Asc (vals, 0, nVals-1); *lower = vals[(int)(0.025*nVals)]; *upper = vals[(int)(0.975*nVals)]; *median = vals[nVals/2]; } /* LowerUpperMedianHPD: Use a simple way to determine HPD */ void LowerUpperMedianHPD (MrBFlt *vals, int nVals, MrBFlt *lower, MrBFlt *upper, MrBFlt *median) { int i, width, theStart; MrBFlt f, g, interval; SortMrBFlt_Asc (vals, 0, nVals-1); width = (int)(nVals * 0.95 + 0.5); theStart = 0; interval = vals[width-1] - vals[0]; for (i=1; i y) return (x); else return (y); } MrBFlt MinimumValue (MrBFlt x, MrBFlt y) { if (x < y) return (x); else return (y); } /* NOTE!!!! The result of this function should be used before consecutive call to it again. It means NEVER use it like this: printf ("%s %s", MbPrintNum (a),MbPrintNum (b)) */ char *MbPrintNum (MrBFlt num) { static char s[40]; if (scientific == YES) sprintf (s,"%.*le", precision, num); else sprintf (s,"%.*lf", precision, num); return s; } void MeanVariance (MrBFlt *vals, int nVals, MrBFlt *mean, MrBFlt *var) { int i; MrBFlt a, aOld, s, x; a = s = 0.0; for (i=0; i 200.0) { a /= exp(y - 100.0); s /= exp(2*(y - 100)); scaler += y - 100.0; y = 100.0; } x=(MrBFlt)exp(y); aOld = a; a += (x - a) / (MrBFlt) (i + 1); s += (x - a) * (x - aOld); } /* mean */ (*mean) = log(a) + scaler; /* variance */ if (var!=NULL) { if (nVals <= 1) (*var) = 0.0; else (*var) = log(s / nVals) + 2*scaler; } /* variance */ if (varEst!=NULL) { if (nVals <= 1) (*varEst) = 0.0; else (*varEst) = log(s / (nVals+1)) + 2*scaler; } } void MrBayesPrint (char *format, ...) { va_list ptr; # if defined (MPI_ENABLED) if (proc_id == 0) { if (echoMB == YES) { va_start (ptr, format); vprintf (format, ptr); va_end(ptr); fflush (stdout); } if (logToFile == YES) { if (logFileFp == NULL) printf ("%s Could not print log output to file\n", spacer); else { va_start (ptr, format); vfprintf (logFileFp, format, ptr); va_end(ptr); fflush (logFileFp); } } } # else if (chainParams.redirect == NO) { if (echoMB == YES) { va_start (ptr, format); vprintf (format, ptr); va_end(ptr); fflush (stdout); } if (logToFile == YES) { if (logFileFp == NULL) { printf ("%s Could not print log output to file\n", spacer); logToFile = NO; } else { va_start (ptr, format); vfprintf (logFileFp, format, ptr); va_end(ptr); fflush (logFileFp); } } } # endif } void MrBayesPrintf (FILE *f, char *format, ...) { va_list ptr; # if defined (MPI_ENABLED) if (proc_id == 0) { va_start (ptr, format); vfprintf (f, format, ptr); va_end(ptr); fflush(f); } # else va_start (ptr, format); vfprintf (f, format, ptr); va_end(ptr); fflush(f); # endif } /** Next taxon in partition, for cycling over set bits in bit fields */ int NextTaxonInPartition(int currentTaxon, BitsLong *partition, int length) { int i, j, taxon; BitsLong x, bitsLongOne=1; taxon = currentTaxon + 1; i = taxon / nBitsInALong; x = (bitsLongOne << taxon % nBitsInALong); for (j=taxon%nBitsInALong; j 0.0) { R2 = ((weight - 1.0) / weight) + ((MrBFlt)(nRuns + 1) / (MrBFlt) (nRuns)) * (sB / sW); return sqrt(R2); } else return -1.0; } /*! \param vals [0..nRuns][count[]] All records for all runs \param nRuns Number of runs \param count [0..nRuns] Number of records in each run \param returnESS [0..nRuns] Is an array in which the routine returns ESS values for each run. */ void EstimatedSampleSize (MrBFlt **vals, int nRuns, int *count, MrBFlt *returnESS) { int i, j, lag, maxLag, samples; MrBFlt *values, mean, del1, del2, varStat=0.0; MrBFlt gammaStat[2000]; for (i=0; i 2000)?2000:(samples - 1); for (lag = 0; lag < maxLag; lag++) { gammaStat[lag]=0; for (j = 0; j < samples - lag; j++) { del1 = values[j] - mean; del2 = values[j + lag] - mean; gammaStat[lag] += (del1 * del2); } gammaStat[lag] /= ((MrBFlt) (samples - lag)); if (lag == 0) { varStat = gammaStat[0]; } else if (lag % 2 == 0) { if (gammaStat[lag - 1] + gammaStat[lag] > 0) { varStat += 2.0 * (gammaStat[lag - 1] + gammaStat[lag]); } else maxLag = lag; } } returnESS[i] = (gammaStat[0] * samples) / varStat; } } int SafeFclose(FILE **fp) { int retval=-1; # if defined MPI_ENABLED if (proc_id == 0) { # endif if (fp!=NULL && (*fp)!=NULL) retval=fclose(*fp); *fp = NULL; # if defined MPI_ENABLED } # endif return retval; } /* SafeFree: Set pointer to freed space to NULL */ /* Calls to be made like this: * * ptr = SafeFree(ptr); * * or using the SAFEFREE() macro defined in utils.h: * * SAFEFREE(ptr); * * See http://stackoverflow.com/questions/38569628/calling-a-free-wrapper-dereferencing-type-punned-pointer-will-break-strict-al * */ void *SafeFree (void *ptr) { free (ptr); ptr = NULL; return ptr; } /* SafeMalloc: Print error if out of memory; clear memory */ void *SafeMalloc (size_t s) { void *ptr; if (s == 0) { MrBayesPrint ("%s WARNING: Allocation of zero size attempted. This is probably a bug. Problems may follow.\n", spacer); return NULL; } ptr = calloc (1, s); if (ptr == NULL && s > 0) { MrBayesPrint ("%s Out of memory. Most probable cause for the problem is that MrBayes reached \n", spacer); MrBayesPrint ("%s the limit of allowed memory for a process in your Operating System. Consult\n", spacer); MrBayesPrint ("%s the documentation of your OS on how to extend the limit. \n", spacer); MrBayesPrint ("%s Segmentation fault may follow. \n", spacer); } return ptr; } /* SafeCalloc: Print error if out of memory */ void *SafeCalloc (size_t n, size_t s) { void *ptr; if (s * n == 0) { MrBayesPrint ("%s WARNING: Allocation of zero size attempted. This is probably a bug; problems may follow.\n", spacer); return NULL; } ptr = calloc (n, s); if (ptr == NULL && n * s > 0) { MrBayesPrint ("%s Out of memory. Most probable cause for the problem is that MrBayes reached \n", spacer); MrBayesPrint ("%s the limit of allowed memory for a process in your Operating System. Consult\n", spacer); MrBayesPrint ("%s the documentation of your OS on how to extend the limit. \n", spacer); MrBayesPrint ("%s Segmentation fault may follow. \n", spacer); } return ptr; } /* SafeRealloc: Print error if out of memory */ void *SafeRealloc (void *ptr, size_t s) { void *tmp; if (s == 0) { MrBayesPrint ("%s WARNING: Reallocation of zero size attempted. This is probably a bug. Problems may follow.\n", spacer); free (ptr); return NULL; } if (ptr == NULL) tmp = calloc (1, s); else tmp = realloc (ptr, s); if (tmp == NULL) { MrBayesPrint ("%s Out of memory. Most probable cause for the problem is that MrBayes reached \n", spacer); MrBayesPrint ("%s the limit of allowed memory for a process in your Operating System. Consult\n", spacer); MrBayesPrint ("%s the documentation of your OS on how to extend the limit. \n", spacer); MrBayesPrint ("%s Segmentation fault may follow. \n", spacer); } return tmp; } /* SafeStrcat: Allocate or reallocate target to fit result; assumes ptr is NULL if not allocated */ char *SafeStrcat (char **target, const char *source) { if (*target == NULL) *target = (char *) SafeCalloc (strlen (source) + 1, sizeof (char)); else *target = (char *) SafeRealloc ((void *) *target, (strlen (source) + strlen (*target) + 1) * sizeof (char)); if (*target) strcat (*target, source); return (*target); } /* SafeStrcpy: Allocate or reallocate target to fit result; assumes ptr is NULL if not allocated */ char *SafeStrcpy (char **target, const char *source) { if (*target == NULL) *target = (char *) SafeCalloc (strlen (source) + 1, sizeof (char)); else *target = (char *) SafeRealloc ((void *) *target, (strlen (source) + 1) * sizeof (char)); if (*target) strcpy (*target, source); return (*target); } /* SetBit: Set a particular bit in a series of longs */ void SetBit (int i, BitsLong *bits) { BitsLong x, bitsLongOne=1; bits += i / nBitsInALong; x = bitsLongOne << (i % nBitsInALong); (*bits) |= x; } int MrBFlt_cmpAsc (const void *a, const void *b) { MrBFlt x = * (MrBFlt *) a; MrBFlt y = * (MrBFlt *) b; if (x < y) return -1; else if (x > y) return 1; return 0; } int MrBFlt_cmpDes (const void *a, const void *b) { MrBFlt x = * (MrBFlt *) a; MrBFlt y = * (MrBFlt *) b; if (x > y) return -1; else if (x < y) return 1; return 0; } /* Sort MrBFlt in increasing order */ void SortMrBFlt_Asc (MrBFlt *item, int left, int right) { qsort ((void *) item, right - left + 1, sizeof (MrBFlt), &MrBFlt_cmpAsc); } /* Sort MrBFlt in decreasing order */ void SortMrBFlt_Des (MrBFlt *item, int left, int right) { qsort ((void *) item, right - left + 1, sizeof (MrBFlt), &MrBFlt_cmpDes); } /* StrCmpCaseInsensitiveLen: Case insensitive string comparison (with maximum * string length restriction). */ int StrCmpCaseInsensitiveLen (const char *s, const char *t, size_t len) { char sc, tc; if (len == 0) return 0; while (len-- > 0) { sc = tolower (s[0]); s++; tc = tolower (t[0]); t++; if (sc > tc) return 1; else if (sc < tc) return -1; else if (sc == '\0') break; } return 0; } /* StrCmpCaseInsensitive: Case insensitive string comparison */ int StrCmpCaseInsensitive (char *s, char *t) { int i, minLen; if (strlen(s) < strlen(t)) minLen = (int) strlen(s); else minLen = (int) strlen(t); for (i=0; i tolower(t[i])) return 1; else return -1; } /* StripComments: Strip possibly nested comments from the string s. Example: s="text1[text2[text3]]"-> s="text1" */ void StripComments (char *s) { char *t; int inComment; inComment = 0; for (t=s; *s != '\0'; s++) { if (inComment == 0) { if (*s == '[') inComment++; else *t++ = *s; } else { if (*s == ']') inComment--; else if (*s == '[') inComment++; } } *t = '\0'; } FILE *TestOpenTextFileR (char *name) { char fileName[100]; strcpy(fileName, workingDir); strncat(fileName, name, 99 - strlen(fileName)); return fopen (fileName, "r"); } /*--------- | | UpdateGrowthFxn: We expect a set of unique indexes from 0 to 5 | indicating a partition of 6 rates into sets. We make sure | the indices correspond to a restricted growth function here. | -----------------------*/ void UpdateGrowthFxn(int *growthFxn) { int i, j, max, fxn[6]; for (i=0; i<6; i++) fxn[i] = -1; max = 0; for (i=0; i<6; i++) { if (fxn[i] != -1) continue; for (j=i; j<6; j++) { if (growthFxn[j] == growthFxn[i]) fxn[j] = max; } max++; } for (i=0; i<6; i++) growthFxn[i] = fxn[i]; } int UpperTriangIndex(int i, int j, int size) { if (i < j) return (2*size - i - 3) * i / 2 + j - 1; else return (2*size - j - 3) * j / 2 + i - 1; } int WantTo (const char *msg) { char s[100]; int i; MrBayesPrint ("%s %s? (yes/no): ", spacer, msg); for (i=0; i<10; i++) { if (fgets (s, 98, stdin) == NULL) { MrBayesPrint ("%s Failed to retrieve answer; will take that as a no\n", spacer); return NO; } /* Strip away the newline */ s[strlen(s)-1] = '\0'; /* Check answer */ if (IsConsistentWith (s, "yes") == YES) return YES; else if (IsConsistentWith (s, "no") == YES) return NO; MrBayesPrint ("%s Enter yes or no: ", spacer); } MrBayesPrint ("%s MrBayes does not understand; will take that as a no\n", spacer); return NO; } /* the following are moved from tree.c */ /* AddToTreeList: Add tree at end of tree list */ int AddToTreeList (TreeList *treeList, Tree *tree) { TreeListElement *listElement = (TreeListElement *) SafeCalloc (1, sizeof(TreeListElement)); if (!listElement) return (ERROR); listElement->order = (int *) SafeCalloc (tree->nIntNodes-1, sizeof(int)); if (!listElement->order) { SAFEFREE (listElement); return (ERROR); } listElement->next = NULL; if (treeList->last == NULL) treeList->last = treeList->first = listElement; else { treeList->last->next = listElement; treeList->last = listElement; } if (tree->isRooted) StoreRTopology (tree, listElement->order); else StoreUTopology (tree, listElement->order); return (NO_ERROR); } /* AllocatePolyTree: Allocate memory space for a polytomous tree */ PolyTree *AllocatePolyTree (int numTaxa) { int i; PolyTree *pt; pt = (PolyTree *) SafeCalloc (1, sizeof (PolyTree)); if (!pt) return (NULL); pt->memNodes = 2*numTaxa; pt->nodes = (PolyNode *) SafeCalloc (2*numTaxa, sizeof(PolyNode)); pt->allDownPass = (PolyNode **) SafeCalloc (3*numTaxa, sizeof (PolyNode *)); pt->intDownPass = pt->allDownPass + 2*numTaxa; if (pt->nodes == NULL || pt->allDownPass == NULL) { free (pt->nodes); free (pt->allDownPass); free (pt); return (NULL); } /* initialize nodes and set index and memoryIndex */ for (i=0; i<2*numTaxa; i++) { ResetPolyNode(&pt->nodes[i]); pt->nodes[i].memoryIndex = i; pt->nodes[i].index = i; } /* initialize tree properties */ pt->nNodes = pt->nIntNodes = 0; pt->root = NULL; pt->brlensDef = NO; pt->isRooted = NO; pt->isClock = NO; pt->isCalibrated = NO; pt->isRelaxed = NO; pt->clockRate = 0.0; strcpy(pt->name,""); /* initialize bitsets */ pt->bitsets = NULL; /* initialize relaxed clock parameters */ pt->nESets = 0; pt->nEvents = NULL; pt->position = NULL; pt->rateMult = NULL; pt->eSetName = NULL; pt->nBSets = 0; pt->effectiveBrLen = NULL; pt->bSetName = NULL; /* initialize population size set parameters */ pt->popSizeSet = NO; pt->popSize = NULL; pt->popSizeSetName = NULL; return (pt); } /* AllocatePolyTreeRelClockParams: Allocate space for relaxed clock parameters */ int AllocatePolyTreeRelClockParams (PolyTree *pt, int nBSets, int nESets) { int i; /* free previous clock params if any */ FreePolyTreeRelClockParams (pt); /* set number of sets */ pt->nBSets = nBSets; pt->nESets = nESets; /* we do not allocate space for the actual names here; these will be NULL pointers */ /* take care of branch params */ if (pt->nBSets > 0) { pt->bSetName = (char **) SafeCalloc (pt->nBSets, sizeof (char *)); pt->effectiveBrLen = (MrBFlt **) SafeCalloc (pt->nBSets, sizeof (MrBFlt *)); for (i=0; inBSets; i++) pt->effectiveBrLen[i] = (MrBFlt *) SafeCalloc (pt->memNodes, sizeof(MrBFlt)); } /* take care of breakpoint params */ if (pt->nESets > 0) { pt->eSetName = (char **) SafeCalloc (pt->nESets, sizeof(char *)); pt->nEvents = (int **) SafeCalloc (pt->nESets, sizeof(int *)); pt->position = (MrBFlt ***) SafeCalloc (pt->nESets, sizeof(MrBFlt **)); pt->rateMult = (MrBFlt ***) SafeCalloc (pt->nESets, sizeof(MrBFlt **)); for (i=0; inESets; i++) { pt->nEvents[i] = (int *) SafeCalloc (pt->memNodes, sizeof(int)); pt->position[i] = (MrBFlt **) SafeCalloc (pt->memNodes, sizeof(MrBFlt *)); pt->rateMult[i] = (MrBFlt **) SafeCalloc (pt->memNodes, sizeof(MrBFlt *)); } } return (NO_ERROR); } /* AllocatePolyTreePartitions: Allocate space for and set partitions for polytomous tree */ int AllocatePolyTreePartitions (PolyTree *pt) { int i, nLongsNeeded, numTaxa; /* get some handy numbers */ numTaxa = pt->memNodes/2; nLongsNeeded = (numTaxa -1) / nBitsInALong + 1; /* allocate space */ pt->bitsets = (BitsLong *) SafeRealloc ((void *)pt->bitsets, pt->memNodes*nLongsNeeded*sizeof(BitsLong)); if (pt->bitsets == NULL) return (ERROR); for (i=0; imemNodes*nLongsNeeded; i++) pt->bitsets[i] = 0; /* set node partition pointers */ for (i=0; imemNodes; i++) pt->nodes[i].partition = pt->bitsets + i*nLongsNeeded; /* clear and set partitions; if the tree is empty, nothing is set */ ResetPolyTreePartitions(pt); return (NO_ERROR); } /* AllocateTree: Allocate memory space for a tree (unrooted or rooted) */ Tree *AllocateTree (int numTaxa) { int i; Tree *t; t = (Tree *) SafeCalloc (1, sizeof (Tree)); if (t == NULL) return NULL; /* initialize basic tree properties */ t->memNodes = 2 * numTaxa; strcpy (t->name, ""); t->isRooted = NO; t->isClock = NO; t->checkConstraints = NO; t->nConstraints = 0; t->nLocks = 0; t->isCalibrated = NO; t->nNodes = t->nIntNodes = 0; t->nRelParts = 0; t->relParts = NULL; /* initialize pointers */ t->bitsets = NULL; t->flags = NULL; t->constraints = NULL; /* allocate and initialize nodes and node arrays (enough for both rooted and unrooted trees) */ t->nNodes = 0; t->nIntNodes = 0; if ((t->nodes = (TreeNode *) SafeCalloc (2 * numTaxa, sizeof (TreeNode))) == NULL) { free (t); return NULL; } if ((t->allDownPass = (TreeNode **) SafeCalloc (3 * numTaxa, sizeof (TreeNode *))) == NULL) { free (t->nodes); free (t); return NULL; } t->intDownPass = t->allDownPass + t->memNodes; #if defined (BEAGLE_V3_ENABLED) t->levelPassEnabled = 0; #endif /* initialize nodes and set index and memoryIndex */ for (i = 0; i < t->memNodes; i++) { ResetTreeNode (&t->nodes[i]); t->nodes[i].memoryIndex = i; t->nodes[i].index = i; } return t; } /* AllocateFixedTree: Allocate memory space for a fixed unrooted or rooted tree */ Tree *AllocateFixedTree (int numTaxa, int isRooted) { int i; Tree *t; t = (Tree *) SafeCalloc (1, sizeof (Tree)); if (t == NULL) return NULL; /* initialize basic tree properties */ if (isRooted == YES) t->memNodes = 2*numTaxa; else t->memNodes = 2*numTaxa - 2; strcpy (t->name, ""); t->isRooted = isRooted; t->isClock = NO; t->checkConstraints = NO; t->nConstraints = 0; t->nLocks = 0; t->isCalibrated = NO; t->nNodes = t->nIntNodes = 0; t->nRelParts = 0; t->relParts = NULL; /* initialize pointers */ t->bitsets = NULL; t->flags = NULL; t->constraints = NULL; /* allocate and initialize nodes and node arrays (enough for both rooted and unrooted trees) */ if (t->isRooted) { t->nNodes = 2*numTaxa; t->nIntNodes = numTaxa - 1; } else { t->nNodes = 2*numTaxa - 2; t->nIntNodes = numTaxa - 2; } if ((t->nodes = (TreeNode *) SafeCalloc (t->nNodes, sizeof (TreeNode))) == NULL) { free (t); return NULL; } if ((t->allDownPass = (TreeNode **) SafeCalloc (t->nNodes + t->nIntNodes, sizeof (TreeNode *))) == NULL) { free (t->nodes); free (t); return NULL; } t->intDownPass = t->allDownPass + t->nNodes; #if defined (BEAGLE_V3_ENABLED) t->levelPassEnabled = 0; #endif /* initialize nodes and set index and memoryIndex */ for (i=0; imemNodes; i++) { ResetTreeNode(&t->nodes[i]); t->nodes[i].memoryIndex = i; t->nodes[i].index = i; } return t; } /* AllocateTreePartitions: Allocate space for and set partitions for tree */ int AllocateTreePartitions (Tree *t) { int i, nLongsNeeded, numTaxa; TreeNode *p; /* get some handy numbers */ if (t->isRooted == YES) numTaxa = t->nNodes - t->nIntNodes - 1; else numTaxa = t->nNodes - t->nIntNodes; nLongsNeeded = (numTaxa - 1) / nBitsInALong + 1; /* reallocate space */ t->bitsets = (BitsLong *) SafeRealloc ((void *)(t->bitsets), (size_t)(t->nNodes) * (size_t)nLongsNeeded * sizeof(BitsLong)); if (!t->bitsets) return (ERROR); /* clear bit fields */ for (i=0; inNodes*nLongsNeeded; i++) t->bitsets[i] = 0; /* set node pointers to bit fields */ for (i=0; inNodes; i++) { p = t->allDownPass[i]; p->partition = t->bitsets + i*nLongsNeeded; } /* set partition specifiers for terminals */ ResetTreePartitions(t); return (NO_ERROR); } int AreTopologiesSame (Tree *t1, Tree *t2) { int i, j, k, nLongsNeeded, nTaxa; BitsLong *mask; TreeNode *p, *q; if (t1->nNodes != t2->nNodes) return (NO); if (t1->nIntNodes != t2->nIntNodes) return (NO); if (t1->isRooted == YES) nTaxa = t1->nNodes - t1->nIntNodes - 1; else nTaxa = t1->nNodes - t1->nIntNodes; /* allocate space for mask */ nLongsNeeded = (nTaxa - 1) / nBitsInALong + 1; mask = (BitsLong *) SafeCalloc (nLongsNeeded, sizeof(BitsLong)); /* set mask */ for (i=0; inIntNodes; i++) { p = t1->intDownPass[i]; if (t1->isRooted == NO && IsBitSet(t2->root->index,p->partition)) FlipBits(p->partition,nLongsNeeded, mask); for (j=0; jnIntNodes; j++) { q = t2->intDownPass[j]; for (k=0; kpartition[k] != q->partition[k]) break; } if (k == nLongsNeeded) break; } if (j == t2->nIntNodes) { FreeTreePartitions (t1); FreeTreePartitions (t2); free (mask); return (NO); } } FreeTreePartitions (t1); FreeTreePartitions (t2); free (mask); return (YES); } int AreTreesSame (Tree *t1, Tree *t2) { int i, j, k, nLongsNeeded, nTaxa; BitsLong *mask; TreeNode *p, *q; extern void ShowNodes(TreeNode*, int, int); if (t1->nNodes != t2->nNodes) return (NO); if (t1->nIntNodes != t2->nIntNodes) return (NO); if (t1->isRooted == YES) nTaxa = t1->nNodes - t1->nIntNodes - 1; else nTaxa = t1->nNodes - t1->nIntNodes; /* allocate space for mask */ nLongsNeeded = (nTaxa - 1) / nBitsInALong + 1; mask = (BitsLong *) SafeCalloc (nLongsNeeded, sizeof(BitsLong)); /* set mask */ for (i=0; inNodes; i++) { p = t1->allDownPass[i]; if (p->anc == NULL && t1->isRooted == YES) continue; if (t1->isRooted == NO && IsBitSet(t2->root->index,p->partition)) FlipBits(p->partition,nLongsNeeded, mask); for (j=0; jnNodes; j++) { q = t2->allDownPass[j]; for (k=0; kpartition[k] != q->partition[k]) break; } if (k == nLongsNeeded && AreDoublesEqual (p->length, q->length, 0.000001) == YES) break; else if (k == nLongsNeeded) { FreeTreePartitions (t1); FreeTreePartitions (t2); free (mask); return (NO); } } if (j == t2->nNodes) { FreeTreePartitions (t1); FreeTreePartitions (t2); free (mask); return (NO); } } FreeTreePartitions (t1); FreeTreePartitions (t2); free (mask); return (YES); } /*---------------------------------------------------------------- | | BuildConstraintTree: Build constraint tree. The tree t is | needed only to hold information about constraints and | included taxa. | ----------------------------------------------------------------*/ int BuildConstraintTree (Tree *t, PolyTree *pt, char **localTaxonNames) { int i, j, k, constraintId, nLongsNeeded, nextNode; BitsLong *constraintPartition, *mask; PolyNode *pp, *qq, *rr, *ss, *tt; pt->isRooted = t->isRooted; nLongsNeeded = (numLocalTaxa - 1) / nBitsInALong + 1; constraintPartition = (BitsLong *) SafeCalloc (2*nLongsNeeded, sizeof(BitsLong)); if (!constraintPartition) { MrBayesPrint ("%s Problems allocating constraintPartition in BuildConstraintTree", spacer); return (ERROR); } mask = constraintPartition + nLongsNeeded; /* calculate mask (needed to take care of unused bits when flipping partitions) */ for (i=0; inodes[i]; pp->isDated = NO; pp->calibration = NULL; pp->age = -1.0; pp->isLocked = NO; pp->lockID = -1; pp->index = i; } /* build a bush */ pt->root = &pt->nodes[numLocalTaxa]; for (i=0; inodes[i]; pp->index = i; pp->left = NULL; if (i == numLocalTaxa - 1) pp->sib = NULL; else pp->sib = &pt->nodes[i+1]; pp->anc = pt->root; } pp = pt->root; pp->left = &pt->nodes[0]; pp->anc = pp->sib = NULL; pt->nNodes = numLocalTaxa + 1; pt->nIntNodes = 1; /* make sure the outgroup is the right-most node */ pt->nodes[localOutGroup].index = numLocalTaxa - 1; pt->nodes[numLocalTaxa - 1].index = localOutGroup; /* allocate and set partition specifiers in bush */ GetPolyDownPass(pt); AllocatePolyTreePartitions(pt); /* set terminal taxon labels */ for (i=0; inNodes; i++) { pp = pt->allDownPass[i]; if (pp->index < numLocalTaxa) strcpy (pp->label, localTaxonNames[pp->index]); } /* resolve the bush according to constraints */ /* for now, satisfy all constraints */ /* for now, bail out if constraints are not compatible */ /* Eventually, we might want to be build a parsimony (WAB) or compatibility (WIB) matrix and draw a starting tree from the universe according to the score of the tree. A simple way of accomplishing approximately this is to use sequential addition, with probabilities in each step determined by the parsimony or compatibility score of the different possibilities. */ nextNode = numLocalTaxa + 1; t->nLocks=0; for (constraintId=0; constraintIdconstraints[constraintId] == NO || definedConstraintsType[constraintId] != HARD) continue; /* initialize bits in partition to add; get rid of deleted taxa in the process */ ClearBits(constraintPartition, nLongsNeeded); for (i=j=0; iisRooted == NO && IsBitSet(localOutGroup, constraintPartition)) FlipBits(constraintPartition, nLongsNeeded, mask); /* check that partition should be included */ k = NumBits(constraintPartition, nLongsNeeded); if (k == 0) { MrBayesPrint ("%s WARNING: Constraint '%s' refers only to deleted taxa\n", spacer, constraintNames[constraintId]); MrBayesPrint ("%s and will be disregarded\n", spacer); t->constraints[constraintId] = NO; continue; } if (k == 1) { MrBayesPrint ("%s WARNING: Constraint '%s' refers to a single tip and\n", spacer, constraintNames[constraintId]); MrBayesPrint ("%s will be disregarded\n", spacer); t->constraints[constraintId] = NO; continue; } /* check if root in rooted tree (we allow this to enable inference of ancestral states) */ if (k == numLocalTaxa && t->isRooted == YES) { if (pt->root->isLocked == YES) { MrBayesPrint ("%s WARNING: Constraint '%s' is a duplicate of another constraint\n", spacer, constraintNames[constraintId]); MrBayesPrint ("%s and will be ignored\n", spacer); t->constraints[constraintId] = NO; continue; } pt->root->isLocked = YES; pt->root->lockID = constraintId; t->nLocks++; continue; } /* check if interior root in unrooted tree (we allow this to enable inference of ancestral states) */ if ((k == numLocalTaxa - 1 || k == numLocalTaxa) && t->isRooted == NO) { if (pt->root->isLocked == YES) { MrBayesPrint ("%s WARNING: Constraint '%s' is a duplicate of another constraint\n", spacer, constraintNames[constraintId]); MrBayesPrint ("%s and will be ignored\n", spacer); t->constraints[constraintId] = NO; continue; } pt->root->isLocked = YES; pt->root->lockID = constraintId; t->nLocks++; continue; } /* find first included terminal */ k = FirstTaxonInPartition (constraintPartition, nLongsNeeded); for (i=0; pt->nodes[i].index != k; i++) ; pp = &pt->nodes[i]; /* go down until node is not included in constraint */ do { qq = pp; pp = pp->anc; } while (IsPartNested(pp->partition, constraintPartition, nLongsNeeded)); /* check that the node has not yet been included */ for (i=0; ipartition[i] != constraintPartition[i]) break; } if (i==nLongsNeeded) { MrBayesPrint ("%s WARNING: Constraint '%s' is a duplicate of another constraint\n", spacer, constraintNames[constraintId]); MrBayesPrint ("%s and will be ignored\n", spacer); t->constraints[constraintId] = NO; continue; } /* create a new node */ tt = &pt->nodes[nextNode++]; tt->anc = pp; tt->isLocked = YES; tt->lockID = constraintId; t->nLocks++; for (i=0; ipartition[i] = constraintPartition[i]; pt->nIntNodes++; pt->nNodes++; /* sort descendant nodes in two connected groups: included and excluded */ /* if there is a descendant that overlaps (incompatible) then return error */ rr = ss = NULL; qq = pp->left; do { if (IsPartNested(qq->partition, constraintPartition, nLongsNeeded)) { if (ss != NULL) ss->sib = qq; else tt->left = qq; ss = qq; qq->anc = tt; } else if (IsPartCompatible(qq->partition, constraintPartition, nLongsNeeded)) { if (rr != NULL) rr->sib = qq; else tt->sib = qq; rr = qq; } else { free (constraintPartition); return (ERROR); } qq = qq->sib; } while (qq != NULL); pp->left = tt; rr->sib = ss->sib = NULL; } /* relabel interior nodes */ GetPolyDownPass(pt); for (i=0; inIntNodes; i++) pt->intDownPass[i]->index = i + numLocalTaxa; /* exit */ free (constraintPartition); FreePolyTreePartitions(pt); return (NO_ERROR); } /*---------------------------------------------- | | BuildRandomRTopology: Builds a random rooted | topology. Will set indices in t->nodes | such that they are from 0 to n-1 for the n tips | and from n to 2n-2 for the n-1 interior | nodes. Last is root. Does not touch labels | of tips. | ----------------------------------------------*/ int BuildRandomRTopology (Tree *t, RandLong *seed) { int i, j, nTips; TreeNode *p, *q, *r; nTips = t->nNodes - t->nIntNodes - 1; for (i=0; inNodes; i++) { p = &t->nodes[i]; p->index = i; p->left = p->right = p->anc = NULL; } /* connect the first two tip nodes */ q = &t->nodes[0]; r = &t->nodes[1]; p = &t->nodes[nTips]; q->anc = r->anc = p; p->left = q; p->right = r; q = &t->nodes[2*nTips-1]; p->anc = q; q->left = p; for (i=2; inodes[i]; r = &t->nodes[i-2+nTips+1]; q->anc = r; r->left = q; j = (int) (RandomNumber(seed) * (2 * i - 1)); if (j < i) p = &t->nodes[j]; else p = &t->nodes[j-i + nTips]; r->right = p; r->anc = p->anc; if (p->anc != NULL) { if (p->anc->left == p) p->anc->left = r; else p->anc->right = r; } p->anc = r; } /* set root and get downpass */ t->root = &t->nodes[2*nTips-1]; GetDownPass (t); /* relabel interior nodes */ for (i=0; inIntNodes; i++) t->intDownPass[i]->index = i+nTips; return (NO_ERROR); } /*---------------------------------------------- | | BuildRandomUTopology: Builds a random unrooted | topology. Assumes that indices are set | in t->nodes from 0 to n-1 for the n tips | and from n to 2n-3 for the n-2 interior | nodes. Move the calculation root after | this routine to get the right root. | ----------------------------------------------*/ int BuildRandomUTopology (Tree *t, RandLong *seed) { int i, j, nTips; TreeNode *p, *q, *r; nTips = t->nNodes - t->nIntNodes; for (i=0; inNodes; i++) { p = &t->nodes[i]; p->index = i; p->left = p->right = p->anc = NULL; } /* connect the first three nodes, assuming 0 is calc root */ q = &t->nodes[1]; r = &t->nodes[2]; p = &t->nodes[nTips]; q->anc = r->anc = p; p->left = q; p->right = r; q = &t->nodes[0]; p->anc = q; q->left = p; for (i=3; inodes[i]; r = &t->nodes[i - 3 + nTips + 1]; q->anc = r; r->left = q; j = (int) (RandomNumber(seed) * (2 * i - 3)); if (j < i - 1) p = &t->nodes[j+1]; else p = &t->nodes[j+1-i + nTips]; r->right = p; r->anc = p->anc; if (p->anc->left == p) p->anc->left = r; else p->anc->right = r; p->anc = r; } t->root = &t->nodes[0]; /* get downpass */ GetDownPass (t); /* relabel interior nodes */ for (i=0; inIntNodes; i++) t->intDownPass[i]->index = i+nTips; return (NO_ERROR); } /*---------------------------------------------------------------- | | CheckConstraints: Check that tree complies with constraints | ----------------------------------------------------------------*/ int CheckConstraints (Tree *t) { int a, i, j, k, nLongsNeeded; BitsLong *constraintPartition, *mask; TreeNode *p=NULL; if (t->checkConstraints == NO) return (NO_ERROR); /* allocate space */ nLongsNeeded = (numLocalTaxa - 1) / nBitsInALong + 1; constraintPartition = (BitsLong *) SafeCalloc (2*nLongsNeeded, sizeof(BitsLong)); if (!constraintPartition) { MrBayesPrint ("%s Problems allocating constraintPartition in CheckConstraints", spacer); return (ERROR); } mask = constraintPartition + nLongsNeeded; /* set mask (needed to reset unused bits when flipping partitions) */ for (i=0; iconstraints[a] == NO || definedConstraintsType[a] != HARD) continue; /* set bits in partition to check */ ClearBits(constraintPartition, nLongsNeeded); for (j=k=0; jisRooted == NO && IsBitSet(localOutGroup, constraintPartition)) FlipBits(constraintPartition, nLongsNeeded, mask); /* find the locked node */ for (i=j=0; inNodes; i++) { if (t->allDownPass[i]->isLocked == YES && t->allDownPass[i]->lockID == a) { p = t->allDownPass[i]; j++; } } if (j != 1) { MrBayesPrint ("%s Tree has %d locks with id %d identifying constraint '%s'\n", spacer, j, a, constraintNames[a]); free (constraintPartition); FreeTreePartitions(t); return (ERROR); } /* check that locked node is correct */ for (i=0; ipartition[i] != constraintPartition[i]) { MrBayesPrint ("%s Lock %d is set for the wrong node [this is a bug]\n", spacer, a); free (constraintPartition); FreeTreePartitions(t); return (ERROR); } } } FreeTreePartitions (t); free (constraintPartition); return (NO_ERROR); } /*---------------------------------------------------------------- | | CheckSetConstraints: Check and set tree constraints | ----------------------------------------------------------------*/ int CheckSetConstraints (Tree *t) { int a, i, j, k, nLongsNeeded, foundIt, numLocks; BitsLong *constraintPartition, *mask; TreeNode *p; if (t->checkConstraints == NO) return (NO_ERROR); /* reset all existing locks, if any */ for (i=0; inNodes; i++) { p = t->allDownPass[i]; p->isLocked = NO; p->lockID = -1; if (p->left != NULL) { p->calibration = NULL; p->isDated = NO; p->age = -1.0; } } /* allocate space */ if (AllocateTreePartitions (t) == ERROR) { MrBayesPrint ("%s Problems allocating tree bitsets", spacer); return ERROR; } nLongsNeeded = ((numLocalTaxa - 1) / nBitsInALong) + 1; constraintPartition = (BitsLong *) SafeCalloc (2*nLongsNeeded, sizeof(BitsLong)); if (!constraintPartition) { MrBayesPrint ("%s Problems allocating constraintPartition", spacer); FreeTreePartitions(t); return ERROR; } mask = constraintPartition + nLongsNeeded; /* set mask (needed to take care of unused bits when flipping partitions) */ for (i=0; irelParts[0]].activeConstraints[a] == NO || definedConstraintsType[a] != HARD) continue; /* set bits in partition to add */ ClearBits(constraintPartition, nLongsNeeded); for (i=j=0; iisRooted == NO && IsBitSet(localOutGroup, constraintPartition) == YES) FlipBits(constraintPartition, nLongsNeeded, mask); /* skip partition if uninformative */ k = NumBits(constraintPartition, nLongsNeeded); if (k == 0 || k == 1) continue; /* find the node that should be locked */ foundIt = NO; for (i=0; inIntNodes; i++) { p = t->intDownPass[i]; for (j=0; jpartition[j] != constraintPartition[j]) break; } if (j == nLongsNeeded) { foundIt = YES; p->isLocked = YES; p->lockID = a; if (nodeCalibration[a].prior != unconstrained) { p->isDated = YES; p->calibration = &nodeCalibration[a]; } numLocks++; break; } } if (foundIt == NO) { MrBayesPrint ("%s Tree breaks constraint '%s'\n", spacer, constraintNames[a]); FreeTreePartitions (t); free (constraintPartition); return (ERROR); } } if (numLocks != t->nLocks) { MrBayesPrint ("%s Inconsistent lock settings. This is a bug, please report it.\n", spacer); FreeTreePartitions (t); free (constraintPartition); return (ERROR); } /* exit */ FreeTreePartitions(t); free (constraintPartition); return (NO_ERROR); } /*----------------------------------------------------------------------- | | ColorClusters: Recursive function to color the clusters in a tree by | assigning numbers to them in their variable x | ------------------------------------------------------------------------*/ void ColorClusters (TreeNode *p, int *index) { if (p!=NULL) { if (p->isLocked == YES || p->anc == NULL || p->anc->anc == NULL) p->x = (++(*index)); else p->x = p->anc->x; ColorClusters(p->left, index); ColorClusters(p->right, index); } } /* CopyPolyNodes: Copies everything except pointers and memoryIndex */ void CopyPolyNodes (PolyNode *p, PolyNode *q, int nLongsNeeded) { p->index = q->index; p->mark = q->mark; p->length = q->length; p->x = q->x; p->y = q->y; p->isDated = q->isDated; p->calibration = q->calibration; p->age = q->age; p->isLocked = q->isLocked; p->lockID = q->lockID; strcpy (p->label, q->label); if (nLongsNeeded!=0) { assert (p->partition); assert (q->partition); memcpy (p->partition,q->partition, nLongsNeeded*sizeof(BitsLong)); } p->support = q->support; p->f = q->f; } void CopySubtreeToTree (Tree *subtree, Tree *t) { int i, /*j,*/ k; TreeNode *p, *q=NULL, *r; for (i=/*j=*/0; inNodes - 1; i++) { p = subtree->allDownPass[i]; for (k=0; knNodes; k++) { q = t->allDownPass[k]; if (q->index == p->index) break; } q->length = p->length; q->marked = YES; if (p->left != NULL && p->right != NULL) { for (k=0; knNodes; k++) { r = t->allDownPass[k]; if (r->index == p->left->index) { q->left = r; r->anc = q; } else if (r->index == p->right->index) { q->right = r; r->anc = q; } } } } p = subtree->root; for (k=0; knNodes; k++) { q = t->allDownPass[k]; if (q->index == p->index) break; } if (q->left->marked == YES) { for (k=0; knIntNodes; k++) { r = t->intDownPass[k]; if (r->index == p->left->index) { q->left = r; r->anc = q; } } } else if (q->right->marked == YES) { for (k=0; knIntNodes; k++) { r = t->intDownPass[k]; if (r->index == p->left->index) { q->right = r; r->anc = q; } } } } /*----------------------------------------------------------------- | | CopyToPolyTreeFromPolyTree: copies second tree to first tree | -----------------------------------------------------------------*/ int CopyToPolyTreeFromPolyTree (PolyTree *to, PolyTree *from) { int i, j, k, nLongsNeeded; PolyNode *p, *q; /* check we have enough memory */ assert (to->memNodes >= from->nNodes); if (from->bitsets==NULL || to->bitsets==NULL) { nLongsNeeded=0; } else { assert (to->memNodes >= from->memNodes);/*Otherwise partition length would not be long enough for nodes in "to" */ nLongsNeeded = (from->memNodes/2 - 1) / nBitsInALong + 1; } /* copy nodes */ for (i=0; inNodes; i++) { /* copy pointers */ p = from->nodes + i; q = to->nodes + i; if (p->anc != NULL) q->anc = to->nodes + p->anc->memoryIndex; else { q->anc = NULL; to->root = q; } if (p->left != NULL) q->left = to->nodes + p->left->memoryIndex; else q->left = NULL; if (p->sib != NULL) q->sib = to->nodes + p->sib->memoryIndex; else q->sib = NULL; /* Copy everything else except memoryIndex */ CopyPolyNodes (q, p, nLongsNeeded); } /* fill node arrays */ /* copy tree properties */ to->nNodes = from->nNodes; to->nIntNodes = from->nIntNodes; to->isRooted = from->isRooted; to->isClock = from->isClock; to->isRelaxed = from->isRelaxed; to->clockRate = from->clockRate; strcpy (to->name, from->name); GetPolyDownPass (to); /* copy partitions */ if (from->bitsets) { if (!to->bitsets) AllocatePolyTreePartitions(to); else ResetPolyTreePartitions(to); } /* copy relaxed clock parameters */ FreePolyTreeRelClockParams (to); if (from->nBSets + from->nESets > 0) AllocatePolyTreeRelClockParams (to, from->nBSets, from->nESets); for (i=0; inBSets; i++) { to->bSetName[i] = (char *) SafeCalloc (strlen(from->bSetName[i])+2, sizeof(char)); strcpy (to->bSetName[i], from->bSetName[i]); for (j=0; jnNodes; j++) to->effectiveBrLen[i][j] = from->effectiveBrLen[i][j]; } for (i=0; inESets; i++) { to->eSetName[i] = (char *) SafeCalloc (strlen(from->eSetName[i])+2, sizeof(char)); strcpy (to->eSetName[i], from->eSetName[i]); for (j=0; jnNodes; j++) { to->nEvents[i][j] = from->nEvents[i][j]; if (to->nEvents[i][j] > 0) { to->position[i][j] = (MrBFlt *) SafeCalloc (to->nEvents[i][j], sizeof (MrBFlt)); to->rateMult[i][j] = (MrBFlt *) SafeCalloc (to->nEvents[i][j], sizeof (MrBFlt)); for (k=0; knEvents[i][j]; k++) { to->position[i][j][k] = from->position[i][j][k]; to->rateMult[i][j][k] = from->rateMult[i][j][k]; } } } } /* copy population size parameters */ FreePolyTreePopSizeParams(to); to->popSizeSet = from->popSizeSet; if (to->popSizeSet == YES) { to->popSize = (MrBFlt *) SafeCalloc (to->nNodes, sizeof(MrBFlt)); for (i=0; inNodes; i++) to->popSize[i] = from->popSize[i]; to->popSizeSetName = (char *) SafeCalloc (strlen(from->popSizeSetName) + 1, sizeof(char)); strcpy (to->popSizeSetName, from->popSizeSetName); } return (NO_ERROR); } /*----------------------------------------------------------------- | | CopyToSpeciesTreeFromPolyTree: copies second tree (polytomous) to | first tree, which is a species tree. The species tree needs to | be allocated enough space first to hold the resulting tree. | -----------------------------------------------------------------*/ int CopyToSpeciesTreeFromPolyTree (Tree *to, PolyTree *from) { int i; PolyNode *p; TreeNode *q, *q1; # if defined (DEBUG_SPECIESTREE) int j; # endif /* make sure assumptions are correct */ assert (from->isRooted == YES); assert (from->isClock == YES); assert (from->nNodes - from->nIntNodes == numSpecies); assert (to->memNodes == 2*numSpecies); assert (to->nIntNodes == from->nIntNodes); assert (to->nNodes == from->nNodes + 1); /* make sure indices are set correctly for from nodes */ # if defined (DEBUG_SPECIESTREE) for (i=0; inNodes; i++) { for (j=0; jnNodes; j++) { p = from->allDownPass[j]; if (p->index == i) break; } assert (j != from->nNodes); assert (!(p->left == NULL && p->index >= numSpecies)); } # endif /* copy nodes */ for (i=0; inNodes; i++) { /* copy pointers */ p = from->allDownPass[i]; q = to->nodes + p->index; if (p->anc != NULL) q->anc = to->nodes + p->anc->index; else q->anc = NULL; if (p->left != NULL) q->left = to->nodes + p->left->index; else q->left = NULL; if (p->left != NULL) q->right = to->nodes + p->left->sib->index; else q->right = NULL; q->nodeDepth = p->depth; q->age = p->age; q->length = p->length; q->index = p->index; if (q->index < numSpecies) q->label = speciesNameSets[speciespartitionNum].names[q->index]; else q->label = noLabel; } /* fix root */ p = from->root; q = to->nodes + p->index; q1 = to->nodes + from->nNodes; /* get the 'extra' root node that polytomous trees do not use */ q->anc = q1; q1->index = from->nNodes; q1->left = q; q1->right = q1->anc = NULL; q1->isLocked = NO; q1->lockID = -1; q1->isDated = NO; q1->calibration = NULL; q1->age = -1.0; to->root = q1; /* get downpass */ GetDownPass (to); /* a user tree might not come with node depths set */ if (to->root->left->nodeDepth == 0.0) SetNodeDepths(to); /* set partitions */ if (to->bitsets) ResetTreePartitions(to); return (NO_ERROR); } /*----------------------------------------------------------------- | | CopyToTreeFromPolyTree: copies second tree (polytomous) to first | tree (used to initialize constrained starting trees, e.g.). | An unrooted source tree will be rooted on outgroup | An unrooted source tree that needs to be copied to | a rooted target tree will be randomly rooted on a node below | all defined constraints. The to tree needs to be allocated | enough space first to hold the resulting tree. | -----------------------------------------------------------------*/ int CopyToTreeFromPolyTree (Tree *to, PolyTree *from) { int i, j; PolyNode *p=NULL; TreeNode *q, *q1; /* refuse to arbitrarily root an input tree */ assert (!(from->isRooted == NO && to->isRooted == YES)); if ((from->isRooted == NO) && (to->isRooted == YES)) { MrBayesPrint ("%s Failed to copy trees due to difference in rootedness of source and destination. \n", spacer); return (ERROR); } /* make sure assumptions are in order */ assert (to->memNodes >= from->nNodes + (to->isRooted == NO ? 0 : 1)); assert (localOutGroup >= 0 && localOutGroup < numLocalTaxa); assert (numLocalTaxa == from->nNodes - from->nIntNodes); assert (!(from->isRooted == YES && from->nNodes != 2*from->nIntNodes + 1)); assert (!(from->isRooted == NO && from->nNodes != 2*from->nIntNodes + 2)); /* make sure indices are set correctly for from nodes */ for (i=0; inNodes; i++) { for (j=0; jnNodes; j++) { p = from->allDownPass[j]; if (p->index == i) break; } assert (j != from->nNodes); assert (!(p->left == NULL && p->index >= numLocalTaxa)); } /* deal with root */ if (to->isRooted == NO && from->isRooted == YES) Deroot(from); /* make sure calculation root is set correctly */ if (to->isRooted == NO && MovePolyCalculationRoot (from, localOutGroup) == ERROR) return ERROR; /* copy nodes */ for (i=0; inNodes; i++) { /* copy pointers */ p = from->allDownPass[i]; q = to->nodes + p->index; if (p->anc != NULL) q->anc = to->nodes + p->anc->index; else q->anc = NULL; if (p->left != NULL) q->left = to->nodes + p->left->index; else q->left = NULL; if (p->left != NULL) q->right = to->nodes + p->left->sib->index; else q->right = NULL; q->isLocked = p->isLocked; q->lockID = p->lockID; q->isDated = p->isDated; q->calibration = p->calibration; q->age = p->age; q->nodeDepth = p->depth; q->length = p->length; q->index = p->index; if (q->index < numLocalTaxa) q->label = localTaxonNames[q->index]; else q->label = noLabel; } /* fix root */ if (to->isRooted == NO) { p = from->root; q = to->nodes + p->index; q->anc = to->root = to->nodes + p->left->sib->sib->index; q->length = to->root->length; to->root->length = 0.0; to->root->left = q; to->root->right = to->root->anc = NULL; } else { p = from->root; q = to->nodes + p->index; q1 = to->nodes + from->nNodes; /* get the 'extra' root node that polytomous trees do not use */ q->anc = q1; q1->index = from->nNodes; q1->left = q; q1->right = q1->anc = NULL; q1->isLocked = NO; q1->lockID = -1; q1->isDated = NO; q1->calibration = NULL; q1->age = -1.0; to->root = q1; } /* get downpass */ GetDownPass (to); /* set node depths */ if (to->isRooted == YES && to->root->left->nodeDepth == 0.0) SetNodeDepths(to); /* set partitions */ if (to->bitsets) ResetTreePartitions(to); /* relaxed clock parameters are not stored in binary trees but in separate parameters */ return (NO_ERROR); } /*----------------------------------------------------------------- | | CopyToTreeFromTree: copies second tree to first tree | (used to initialize brlen sets for same topology) | Note: partition information of nodes are not copied if | either "from" or "to" tree does not have bitsets allocated | -----------------------------------------------------------------*/ int CopyToTreeFromTree (Tree *to, Tree *from) { int i, numTaxa, nLongsNeeded; TreeNode *p, *q; numTaxa = from->nNodes - from->nIntNodes - (from->isRooted == YES ? 1 : 0); nLongsNeeded = (numTaxa - 1) / nBitsInALong + 1; if (from->bitsets==NULL || to->bitsets==NULL) nLongsNeeded=0; /* check that there is enough memory */ assert (to->memNodes >= from->nNodes); /* copy nodes (use index of p as memoryIndex for q) */ for (i=0; inNodes; i++) { /* copy pointers */ p = from->nodes + i; q = to->nodes + p->index; if (p->anc != NULL) q->anc = to->nodes + p->anc->index; else { q->anc = NULL; to->root = q; } if (p->left != NULL) q->left = to->nodes + p->left->index; else q->left = NULL; if (p->right != NULL) q->right = to->nodes + p->right->index; else q->right = NULL; CopyTreeNodes (q, p, nLongsNeeded); } /* create new node arrays */ to->nNodes = from->nNodes; to->nIntNodes = from->nIntNodes; GetDownPass (to); /* copy tree properties (these should be constant most of them) */ strcpy (to->name, from->name); to->isRooted = from->isRooted; to->isClock = from->isClock; to->isCalibrated = from->isCalibrated; to->checkConstraints = from->checkConstraints; to->nConstraints = from->nConstraints; to->constraints = from->constraints; to->nLocks = from->nLocks; to->nRelParts = from->nRelParts; to->relParts = from->relParts; /* copy partitions */ if (from->bitsets) { if (!to->bitsets) AllocateTreePartitions(to); else ResetTreePartitions(to); } return (NO_ERROR); } /* Copy node q to node p */ void CopyTreeNodes (TreeNode *p, TreeNode *q, int nLongsNeeded) { /* copies everything except pointers and memoryIndex */ p->label = q->label; p->index = q->index; p->upDateCl = q->upDateCl; p->upDateTi = q->upDateTi; p->isLocked = q->isLocked; p->lockID = q->lockID; p->isDated = q->isDated; p->marked = q->marked; p->x = q->x; p->y = q->y; p->d = q->d; p->length = q->length; p->nodeDepth = q->nodeDepth; p->calibration = q->calibration; p->age = q->age; if (nLongsNeeded != 0) { assert (p->partition); assert (q->partition); memcpy (p->partition, q->partition, nLongsNeeded*sizeof(BitsLong)); } } void CopyTreeToSubtree (Tree *t, Tree *subtree) { int i, j, k; TreeNode *p, *q, *r; for (i=j=0; inNodes; i++) { p = t->allDownPass[i]; if (p->marked == NO) continue; q = &subtree->nodes[j++]; q->index = p->index; q->length = p->length; if (p->left == NULL || p->left->marked == NO) q->left = q->right = NULL; else { for (k=0; knodes[k]; if (r->index == p->left->index) { q->left = r; r->anc = q; } else if (r->index == p->right->index) { q->right = r; r->anc = q; } } } if (p->anc->marked == NO) { r = &subtree->nodes[j++]; subtree->root = r; r->anc = r->right = NULL; r->left = q; q->anc = r; r->length = 0.0; r->index = p->anc->index; } } GetDownPass (subtree); subtree->isRooted = t->isRooted; subtree->nRelParts = t->nRelParts; subtree->relParts = t->relParts; } /* DatedNodeDepths: Recursive function to get node depths */ void DatedNodeDepths (TreeNode *p, MrBFlt *nodeDepths, int *index) { if (p != NULL) { if (p->left == NULL || p->isDated == YES) nodeDepths[(*index)++] = p->nodeDepth; else { DatedNodeDepths (p->left, nodeDepths, index); DatedNodeDepths (p->right, nodeDepths, index); } } } /* DatedNodes: Recursive function to get dated tips or interior nodes */ void DatedNodes (TreeNode *p, TreeNode **datedNodes, int *index) { if (p != NULL) { if (p->left != NULL && p->isDated == NO) { DatedNodes (p->left, datedNodes, index); DatedNodes (p->right, datedNodes, index); } datedNodes[(*index)++] = p; } } /* Deroot: Deroot a rooted polytomous tree with branch lengths */ int Deroot (PolyTree *pt) { PolyNode *p, *q, *r, tempNode; int i; p = pt->root; if (p->left->sib->sib != NULL) return (ERROR); /* tree is not rooted or it is polytomous */ if (p != &pt->nodes[pt->nNodes-1]) { q = &pt->nodes[pt->nNodes-1]; /* now swap content of p and q including pointers */ tempNode = *q; *q = *p; *p = tempNode; /* swap back memoryindex */ p->memoryIndex = q->memoryIndex; q->memoryIndex = tempNode.memoryIndex; /* all pointers to q should be pointers to p */ for (i=0; inNodes; i++) { r = &pt->nodes[i]; if (r->left == q) r->left = p; if (r->anc == q) r->anc = p; if (r->sib == q) r->sib = p; } /* all pointers to p should be pointers to q; all these are anc pointers from the descendants of the root */ pt->root = q; for (r=q->left; r!=NULL; r=r->sib) r->anc = q; /* finally set p to the new root */ p = pt->root; } /* make sure the left of the old root is interior and can be used as new root */ if (p->left->left == NULL) { q = p->left; r = q->sib; p->left = r; r->sib = q; q->sib = NULL; } pt->root = p->left; pt->root->left->sib->sib = p->left->sib; p->left->sib->length += pt->root->length; pt->root->length = 0.0; pt->root->sib = NULL; pt->root->anc = NULL; pt->nNodes--; pt->nIntNodes--; GetPolyDownPass(pt); return (NO_ERROR); } /* EraseTreeList: Erase all trees in treeList */ void EraseTreeList (TreeList *treeList) { TreeListElement *listElement; TreeListElement *previous; listElement = treeList->first; if (listElement != NULL) do { free (listElement->order); previous = listElement; listElement = listElement->next; free (previous); } while (listElement != NULL); treeList->first = treeList->last = NULL; } void UpdateTreeWithClockrate (Tree *t, MrBFlt clockRate) { int i; TreeNode *p; if (t->fromUserTree == NO) { /*Set nodeDepth*/ for (i=0; inNodes; i++) { p = t->allDownPass[i]; p->nodeDepth = p->age * clockRate; } /* calculate branch lengths */ for (i=0; inNodes; i++) { p = t->allDownPass[i]; if (p->anc != NULL) { if (p->anc->anc != NULL) { p->length = p->anc->nodeDepth - p->nodeDepth; } else p->length = 0.0; //not a problem for root node. } } } else { for (i=0; inNodes-1; i++) { p = t->allDownPass[i]; p->age = p->nodeDepth / clockRate; } } } /*---------------------------------------------------------------- | | findAllowedClockrate: Finds the range of clock rates allowed for the tree. | | @param t - tree to check (IN) | @minClockRate - address where minimum allowed clock rate is stored (OUT) | @maxClockRate - address where maximum allowed clock rate is stored (OUT) | ----------------------------------------------------------------*/ void findAllowedClockrate (Tree *t, MrBFlt *minClockRate, MrBFlt *maxClockRate) { int i; TreeNode *p; MrBFlt min, max, tmp, ageDelta; min=0.0; max=MRBFLT_MAX; *minClockRate = 2.0; *maxClockRate = 1.0; if (t->fromUserTree == NO) { for (i=0; inNodes-1; i++) { p = t->allDownPass[i]; if (p->anc->anc != NULL) { ageDelta = p->anc->age - p->age; /* fbd trees can legitimately contain sampled ancestors with (near) zero branch length */ if (ageDelta <= TIME_MIN) continue; tmp = BRLENS_MIN / ageDelta; if (tmp > min) min = tmp; tmp = BRLENS_MAX / ageDelta; if (tmp > max) max = tmp; } } *minClockRate= min; *maxClockRate= max; } else { IsCalibratedClockSatisfied (t,minClockRate,maxClockRate, 0.001); } } /* FreePolyTree: Free memory space for a polytomous tree (unrooted or rooted) */ void FreePolyTree (PolyTree *pt) { if (pt != NULL) { FreePolyTreePartitions(pt); FreePolyTreeRelClockParams(pt); FreePolyTreePopSizeParams(pt); free (pt->allDownPass); free (pt->nodes); free (pt); } } /* FreePolyTreePartitions: Free memory space for polytomous tree partitions */ void FreePolyTreePartitions (PolyTree *pt) { int i; if (pt != NULL && pt->bitsets != NULL) { for (i=0; imemNodes; i++) pt->nodes[i].partition = NULL; free (pt->bitsets); pt->bitsets = NULL; } } /* FreePolyTreePopSizeParams: Free population size set parameters of polytree */ void FreePolyTreePopSizeParams (PolyTree *pt) { if (pt->popSizeSet == YES) { free (pt->popSize); free (pt->popSizeSetName); } pt->popSizeSet = NO; pt->popSize = NULL; pt->popSizeSetName = NULL; } /* FreePolyTreeRelClockParams: Free relaxed clock parameters of polytree */ void FreePolyTreeRelClockParams (PolyTree *pt) { int i, j; /* free breakpoint clock parameters */ for (i=0; inESets; i++) { for (j=0; jmemNodes; j++) { if (pt->nEvents[i][j] > 0) { free (pt->position[i][j]); free (pt->rateMult[i][j]); } } free (pt->eSetName[i]); free (pt->nEvents[i]); free (pt->position[i]); free (pt->rateMult[i]); } free (pt->nEvents); free (pt->position); free (pt->rateMult); free (pt->eSetName); pt->nESets = 0; pt->nEvents = NULL; pt->position = NULL; pt->rateMult = NULL; pt->eSetName = NULL; /* free branch clock parameters */ for (i=0; inBSets; i++) { free (pt->bSetName[i]); free (pt->effectiveBrLen[i]); } free (pt->effectiveBrLen); free (pt->bSetName); pt->nBSets = 0; pt->effectiveBrLen = NULL; pt->bSetName = NULL; } /* FreeTree: Free memory space for a tree (unrooted or rooted) */ void FreeTree (Tree *t) { if (t != NULL) { free (t->bitsets); free (t->flags); free (t->allDownPass); #if defined (BEAGLE_V3_ENABLED) free (t->intDownPassLevel); #endif free (t->nodes); free (t); } } /* FreeTreePartitions: Free memory space for tree partitions */ void FreeTreePartitions (Tree *t) { int i; if (t != NULL && t->bitsets != NULL) { free (t->bitsets); t->bitsets = NULL; for (i=0; imemNodes; i++) t->nodes[i].partition = NULL; } } /*------------------------------------------------------------------------------------------- | | GetDatedNodeDepths: Get an array containing the node depths of the dated tips, | internal or external, plus dated root | ---------------------------------------------------------------------------------------------*/ void GetDatedNodeDepths (TreeNode *p, MrBFlt *nodeDepths) { int index = 0; assert (p != NULL); nodeDepths[index++] = p->nodeDepth; /* include root node depth */ if (p->left != NULL) { DatedNodeDepths (p->left, nodeDepths, &index); DatedNodeDepths (p->right, nodeDepths, &index); } } /*------------------------------------------------------------------------------------------- | | GetDatedNodes: Get an array containing the dated tips, | internal or external, and all interior nodes in the same subtree | ---------------------------------------------------------------------------------------------*/ void GetDatedNodes (TreeNode *p, TreeNode **datedNodes) { int index = 0; assert (p != NULL); if (p->left!= NULL) { DatedNodes (p->left, datedNodes, &index); DatedNodes (p->right, datedNodes, &index); } } /* get down pass for tree t (wrapper function) */ void GetDownPass (Tree *t) { int i, j; i = j = 0; GetNodeDownPass (t, t->root, &i, &j); #if defined (BEAGLE_V3_ENABLED) if (t->levelPassEnabled) { i = 0; ReverseLevelOrder (t, t->root, &i); } #endif } /* get the actual down pass sequences */ void GetNodeDownPass (Tree *t, TreeNode *p, int *i, int *j) { if (p != NULL) { GetNodeDownPass (t, p->left, i, j); GetNodeDownPass (t, p->right, i, j); if (p->left != NULL && p->right != NULL && p->anc != NULL) { t->intDownPass[(*i)++] = p; t->allDownPass[(*j)++] = p; } else if (p->left == NULL && p->right == NULL && p->anc != NULL) { t->allDownPass[(*j)++] = p; } else if (p->left != NULL && p->right == NULL && p->anc == NULL) { t->allDownPass[(*j)++] = p; } } } #if defined (BEAGLE_V3_ENABLED) /* Compute the "height" of a tree -- the number of nodes along the longest path from the root node down to the farthest leaf node.*/ int Height(TreeNode *p) { if (p==NULL) return 0; else { /* compute the height of each subtree */ int lheight = Height(p->left); int rheight = Height(p->right); /* use the larger one */ if (lheight > rheight) return(lheight+1); else return(rheight+1); } } /* Function to perform reverse level order traversal a tree*/ void ReverseLevelOrder(Tree *t, TreeNode *p, int *i) { int h = Height(p); int l; for (l=h; l>=1; l--) StoreGivenLevel(t, p, l, i); } /* Store nodes at a given level */ void StoreGivenLevel(Tree *t, TreeNode *p, int level, int *i) { if (p == NULL) return; if (level == 1) { if (p->left != NULL && p->right != NULL && p->anc != NULL) { t->intDownPassLevel[(*i)++] = p; } } else if (level > 1) { StoreGivenLevel(t, p->left, level-1, i); StoreGivenLevel(t, p->right, level-1, i); } } #endif /* GetPolyAges: Get PolyTree node ages */ void GetPolyAges (PolyTree *t) { int i; PolyNode *p; GetPolyDepths (t); /* just to make sure... */ for (i=0; inNodes; i++) { p = t->allDownPass[i]; p->age = p->depth / t->clockRate; } } /* GetPolyDepths: Get PolyTree node depths */ void GetPolyDepths (PolyTree *t) { int i; MrBFlt maxDepth; PolyNode *p; maxDepth = t->root->depth = 0.0; for (i=t->nNodes-2; i>=0; i--) { p = t->allDownPass[i]; p->depth = p->anc->depth + p->length; if (p->depth > maxDepth) maxDepth = p->depth; } for (i=0; inNodes; i++) { p = t->allDownPass[i]; p->depth = maxDepth - p->depth; } } /* get down pass for polytomous tree t (wrapper function) */ void GetPolyDownPass (PolyTree *t) { int i, j; i = j = 0; GetPolyNodeDownPass (t, t->root, &i, &j); assert (t->nIntNodes==j); } /* get the actual down pass sequences for a polytomous tree */ void GetPolyNodeDownPass (PolyTree *t, PolyNode *p, int *i, int *j) { PolyNode *q; if (p->left != NULL) { for (q=p->left; q!=NULL; q=q->sib) GetPolyNodeDownPass(t, q, i, j); } t->allDownPass[(*i)++] = p; if (p->left != NULL) t->intDownPass[(*j)++] = p; } /* GetFromTreeList: Get first tree from a tree list and remove it from the list*/ int GetFromTreeList (TreeList *treeList, Tree *tree) { TreeListElement *listElement; if (treeList->first == NULL) { MrBayesPrint ("%s Tree list empty\n", spacer); return (ERROR); } if (tree->isRooted == YES) RetrieveRTopology (tree, treeList->first->order); else { RetrieveUTopology (tree, treeList->first->order); if (localOutGroup != 0) MoveCalculationRoot (tree, localOutGroup); } listElement = treeList->first; treeList->first = listElement->next; free (listElement->order); free (listElement); return (NO_ERROR); } /*------------------------------------------------------------------ | | InitBrlens: This routine will set all branch lengths of a | nonclock tree to the value given by 'v'. | ------------------------------------------------------------------*/ int InitBrlens (Tree *t, MrBFlt v) { int i; TreeNode *p; for (i=0; inNodes; i++) { p = t->allDownPass[i]; if (p->anc != NULL && !(t->isRooted == YES && p->anc->anc == NULL)) p->length = v; else p->length = 0.0; } return (NO_ERROR); } /* @param levUp is the number of edges between the "node" and the most resent calibrated predecessor +1, for the calibrated nodes it should be 1 @param calibrUp is the age of the most resent calibrated predecessor @return age of the node */ MrBFlt SetNodeCalibratedAge(TreeNode *node, unsigned levUp, MrBFlt calibrUp) { MrBFlt r,l; if (node->age != -1.0) { if (node->right != NULL) SetNodeCalibratedAge (node->right, 2, node->age); if (node->left != NULL) SetNodeCalibratedAge (node->left, 2, node->age); return node->age; } r = SetNodeCalibratedAge (node->right, levUp+1, calibrUp); l = SetNodeCalibratedAge (node->left, levUp+1, calibrUp); if (r > l) { assert (calibrUp - r > 0.0); return node->age = r + (calibrUp - r)/levUp; } else { assert (calibrUp - l > 0.0); return node->age = l + (calibrUp - l)/levUp; } } /*------------------------------------------------------------------- | | InitCalibratedBrlens: This routine will build a clock tree | consistent with calibration constraints on terminal | taxa and/or constrained interior nodes. At least one | node should be calibrated. | If not possible to build such a tree, ERROR | is returned. | --------------------------------------------------------------------*/ int InitCalibratedBrlens (Tree *t, MrBFlt clockRate, RandLong *seed) { int i; TreeNode *p; Model *mp; MrBFlt treeAgeMin, treeAgeMax; Calibration *calibrationPtr; # ifdef DEBUG_CALIBRATION printf ("Before initializing calibrated brlens\n"); ShowNodes(t->root, 0, YES); # endif if (t->isRooted == NO) { MrBayesPrint ("%s Tree is unrooted\n", spacer); return (ERROR); } /* Check whether root has age constraints */ mp = &modelParams[t->relParts[0]]; treeAgeMin = 0.0; treeAgeMax = POS_INFINITY; if (t->root->left->isDated == YES) { treeAgeMin = t->root->left->calibration->min; treeAgeMax = t->root->left->calibration->max; } else if (!strcmp(mp->clockPr, "Uniform") || !strcmp(mp->clockPr, "Birthdeath") || !strcmp(mp->clockPr, "Fossilization")) { if (mp->treeAgePr.min > treeAgeMin) treeAgeMin = mp->treeAgePr.min; if (mp->treeAgePr.max < treeAgeMax) treeAgeMax = mp->treeAgePr.max; } /* date all nodes from top to bottom with min. age as nodeDepth*/ for (i=0; inNodes; i++) { p = t->allDownPass[i]; if (p->anc != NULL) { if (p->left == NULL && p->right == NULL) { if (p->isDated == NO) { p->nodeDepth = 0.0; p->age = 0.0; } else { if (p->calibration->prior == fixed) p->nodeDepth = p->age = p->calibration->priorParams[0]; else p->nodeDepth = p->age = p->calibration->min; } } else { if (p->left->nodeDepth > p->right->nodeDepth) p->nodeDepth = p->left->nodeDepth; else p->nodeDepth = p->right->nodeDepth; if (p->isDated == YES || (p->anc->anc == NULL && (!strcmp(mp->clockPr, "Uniform") || !strcmp(mp->clockPr, "Birthdeath") || !strcmp(mp->clockPr, "Fossilization")))) { if (p->isDated == NO) calibrationPtr = &mp->treeAgePr; else calibrationPtr = p->calibration; if (calibrationPtr->max <= p->nodeDepth) { if (p->isDated == NO) MrBayesPrint ("%s Calibration inconsistency for root node\n", spacer); else MrBayesPrint ("%s Calibration inconsistency for node '%s'\n", spacer, constraintNames[p->lockID]); MrBayesPrint ("%s Cannot make a tree where the node is %s\n", spacer, calibrationPtr->name); return (ERROR); } else { if (calibrationPtr->min < p->nodeDepth) p->age = p->nodeDepth; else p->age = p->nodeDepth = calibrationPtr->min; } } else p->age = -1.0; } } } /* try to make root node deeper than minimum age */ p = t->root->left; if (p->nodeDepth==0.0) p->nodeDepth = 1.0; if (p->nodeDepth * 1.5 < treeAgeMax) p->nodeDepth = p->age = 1.5 * p->nodeDepth; else p->nodeDepth = p->age = treeAgeMax; SetNodeCalibratedAge (p, 1, p->age); /* Setup node depths */ for (i=0; inNodes; i++) { p = t->allDownPass[i]; p->nodeDepth = p->age * clockRate; assert (!(p->left == NULL && p->calibration == NULL && p->nodeDepth != 0.0)); } /* calculate branch lengths */ for (i=0; inNodes; i++) { p = t->allDownPass[i]; if (p->anc != NULL) { if (p->anc->anc != NULL) { p->length = p->anc->nodeDepth - p->nodeDepth; // no need to check p->length < BRLENS_MIN or p->length > BRLENS_MAX } else p->length = 0.0; //not a problem for root node. } } # ifdef DEBUG_CALIBRATION printf ("after\n"); ShowNodes (t->root, 0, YES); getchar(); # endif return (NO_ERROR); } /*------------------------------------------------------- | | InitClockBrlens: This routine will initialize | a clock tree by setting the root to depth 1.0 | and then assigning node depths according to | the relative node depth measured in terms of the | maximum number of branches to the tip from each | node. | --------------------------------------------------------*/ int InitClockBrlens (Tree *t) { int i, maxBrSegments=0; TreeNode *p; if (t->isRooted == NO) { MrBayesPrint ("%s Tree is unrooted\n", spacer); return (ERROR); } /* calculate maximum number of branch segments above root */ for (i=0; inNodes; i++) { p = t->allDownPass[i]; if (p->anc != NULL) { if (p->left == NULL && p->right == NULL) { p->x = 0; } else { if (p->left->x > p->right->x) p->x = p->left->x + 1; else p->x = p->right->x + 1; } if (p->anc->anc == NULL) maxBrSegments = p->x; } } /* assign node depths */ for (i=0; inNodes; i++) { p = t->allDownPass[i]; if (p->anc != NULL) p->nodeDepth = (MrBFlt) (p->x) / (MrBFlt) maxBrSegments; else p->nodeDepth = 0.0; } /* calculate branch lengths */ for (i=0; inNodes; i++) { p = t->allDownPass[i]; if (p->anc != NULL) { if (p->anc->anc != NULL) p->length = p->anc->nodeDepth - p->nodeDepth; else p->length = 0.0; } } return (NO_ERROR); } int GetRandomEmbeddedSubtree (Tree *t, int nTerminals, RandLong *seed, int *nEmbeddedTrees) { int i, j, k, n, ran, *pP, *pL, *pR, nLeaves, *nSubTrees; TreeNode *p=NULL, **leaf; /* Calculate number of leaves in subtree (number of terminals minus the root) */ nLeaves = nTerminals - 1; /* Initialize all flags */ for (i=0; inNodes; i++) { p = t->allDownPass[i]; p->marked = NO; p->x = 0; p->y = 0; } /* Allocate memory */ nSubTrees = (int *) SafeCalloc (nTerminals * t->nNodes, sizeof(int)); if (!nSubTrees) return (ERROR); leaf = (TreeNode **) SafeMalloc (nLeaves * sizeof (TreeNode *)); if (!leaf) { free (nSubTrees); return (ERROR); } /* Calculate how many embedded trees rooted at each node */ (*nEmbeddedTrees) = 0; for (i=0; inNodes-1; i++) { p = t->allDownPass[i]; if (p->left == NULL) { p->x = 0; nSubTrees[p->index*nTerminals + 1] = 1; } else { pL = nSubTrees + p->left->index*nTerminals; pR = nSubTrees + p->right->index*nTerminals; pP = nSubTrees + p->index*nTerminals; pP[1] = 1; for (j=2; j<=nLeaves; j++) { for (k=1; kx = pP[nLeaves]; (*nEmbeddedTrees) += p->x; } } /* Randomly select one embedded tree of the right size */ ran = (int) (RandomNumber(seed) * (*nEmbeddedTrees)); /* Find the interior root corresponding to this tree */ for (i=j=0; inIntNodes; i++) { p = t->intDownPass[i]; j += p->x; if (j>ran) break; } /* Find one random embedded tree with this root */ p->y = nLeaves; p->marked = YES; leaf[0] = p; n = 1; while (n < nLeaves) { /* select a node with more than one descendant */ for (i=0; iy > 1) break; } /* break it into descendants */ pL = nSubTrees + p->left->index*nTerminals; pR = nSubTrees + p->right->index*nTerminals; pP = nSubTrees + p->index*nTerminals; ran = (int) (RandomNumber(seed) * pP[p->y]); k = 0; for (j=1; jy; j++) { k += pL[j] * pR[p->y-j]; if (k > ran) break; } p->left->y = j; p->right->y = p->y - j; p->left->marked = YES; p->right->marked = YES; leaf[i] = p->left; leaf[n++] = p->right; } free (nSubTrees); free (leaf); return (NO_ERROR); } /*----------------------------------------------------------------------------- | | IsCalibratedClockSatisfied: This routine SETS (not just checks as name suggested) calibrated clock tree nodes age, depth. based on branch lengths | and checks that user defined brlens satisfy the specified calibration(s) up to tolerance tol | TODO: clock rate is divided here and used to set ages but clockrate parameter is not updated here (make sure that it does not produce inconsistency) | |------------------------------------------------------------------------------*/ int IsCalibratedClockSatisfied (Tree *t,MrBFlt *minClockRate,MrBFlt *maxClockRate , MrBFlt tol) { int i, j, maxRateConstrained, minRateConstrained, isViolated; MrBFlt f, maxHeight, minRate=0, maxRate=0, ageToAdd, *x, *y, clockRate; TreeNode *p, *q, *r, *s; /* By default assume the tree does not have allowed range of clockrate */ *minClockRate = 2.0; *maxClockRate = 1.0; if (t->isRooted == NO) return (NO); x = (MrBFlt *) SafeCalloc (2*t->nNodes, sizeof (MrBFlt)); if (x == NULL) { MrBayesPrint ("%s Out of memory in IsCalibratedClockSatisfied\n", spacer); free (x); return (NO); } y = x + t->nNodes; /* reset node depth and age, and set minimum (x) and maximum (y) age of each node */ for (i=0; inNodes; i++) { p = t->allDownPass[i]; p->age = -1.0; p->nodeDepth = -1.0; if (p->isDated == YES) { assert (p->calibration->prior != unconstrained); x[p->index] = p->calibration->min; y[p->index] = p->calibration->max; } else if (p->left == NULL && p->right == NULL) x[p->index] = y[p->index] = 0.0; else { x[p->index] = y[p->index] = -1.0; } } /* calculate node heights in branch length units */ /* node depth will be set from the root for now */ p = t->root->left; p->nodeDepth = 0.0; for (i=t->nNodes-3; i>=0; i--) { p = t->allDownPass[i]; p->nodeDepth = p->anc->nodeDepth + p->length; } /* find maximum height of tree */ maxHeight = -1.0; for (i=0; inNodes-1; i++) { p = t->allDownPass[i]; if (p->left == NULL && p->right == NULL) { if (p->nodeDepth > maxHeight) { maxHeight = p->nodeDepth; } } } /* calculate node depth from tip of tree */ for (i=0; inNodes-1; i++) { p = t->allDownPass[i]; p->nodeDepth = maxHeight - p->nodeDepth; } /* check potentially constraining calibrations */ /* and find minimum and maximum possible rate */ maxRateConstrained = NO; minRateConstrained = NO; isViolated = NO; for (i=0; inNodes-1; i++) { p = t->allDownPass[i]; if (x[p->index] < 0.0 && y[p->index] < 0.0) continue; for (j=i+1; jnNodes-1; j++) { q = t->allDownPass[j]; if (x[q->index] < 0.0 && y[q->index] < 0.0) continue; if (p->nodeDepth == q->nodeDepth) // because clock rate could be as low as possible we can not take approximate equality. { /* same depth so they must share a possible age */ if ((x[p->index] != -1.0 && y[q->index] != -1.0 && AreDoublesEqual (x[p->index], y[q->index], tol) == NO && x[p->index] > y[q->index]) || (y[p->index] != -1.0 && x[q->index] != -1.0 && AreDoublesEqual (y[p->index], x[q->index], tol) == NO && y[p->index] < x[q->index])) { isViolated = YES; break; } } else { if (p->nodeDepth > q->nodeDepth) { r = p; s = q; } else { r = q; s = p; } if (x[r->index] >= 0.0 && y[s->index] >= 0.0) { f = (r->nodeDepth - s->nodeDepth) / (x[r->index] - y[s->index]); if (f <= 0.0 || x[r->index] == y[s->index]) { if (AreDoublesEqual (r->nodeDepth, s->nodeDepth, tol*0.1) == YES) continue; if ((r->calibration != NULL && r->calibration->prior != fixed) || (s->calibration != NULL && s->calibration->prior != fixed)) continue; isViolated = YES; break; } if (maxRateConstrained == NO) { maxRateConstrained = YES; maxRate = f; } else if (f < maxRate) maxRate = f; } if (y[r->index] >= 0.0 && x[s->index] >= 0.0) { f = (r->nodeDepth - s->nodeDepth) / (y[r->index] - x[s->index]); if (f <= 0.0 || y[r->index] == x[s->index]) { if (AreDoublesEqual (r->nodeDepth, s->nodeDepth, tol*0.1) == YES) continue; isViolated = YES; break; } if (minRateConstrained == NO) { minRateConstrained = YES; minRate = f; } else if (f > minRate) minRate = f; } } } if (isViolated == YES) break; } /* check if outright violation */ if (isViolated == YES) { MrBayesPrint ("%s Branch lengths do not satisfy the calibration(s)\n", spacer); free (x); return (NO); } /* Allow tolerance */ if (minRateConstrained == YES && maxRateConstrained == YES && AreDoublesEqual (minRate, maxRate, tol) == YES && minRate > maxRate) { maxRate = minRate; } if (minRateConstrained == YES) *minClockRate = minRate; else *minClockRate = 0.0; if (maxRateConstrained == YES) *maxClockRate = maxRate; else *maxClockRate = MRBFLT_MAX; /* check that minimum and maximum rates are consistent */ if (minRateConstrained == YES && maxRateConstrained == YES && minRate > maxRate) { MrBayesPrint ("%s Branch lengths do not satisfy the calibration(s)\n", spacer); free (x); return (NO); } /* date all nodes based on a suitable rate */ if (minRateConstrained == YES) clockRate = minRate; else if (maxRateConstrained == YES) clockRate = 0.5 * maxRate; else clockRate = 1.0; for (i=0; inNodes-1; i++) { p = t->allDownPass[i]; p->age = p->nodeDepth / clockRate; } /* check if there is an age to add (I guess this is here because when max rate is close to minrate and we have numerical precision inaccuracy) */ ageToAdd = 0.0; for (i=0; inNodes-1; i++) { p = t->allDownPass[i]; if (x[p->index] > 0.0 && x[p->index] > p->age) { f = x[p->index] - p->age; if (f > ageToAdd) ageToAdd = f; } } /* add extra length if any */ if (AreDoublesEqual (ageToAdd, 0.0, 0.00000001) == NO) { for (i=0; inNodes-1; i++) { p = t->allDownPass[i]; p->age += ageToAdd; } } free (x); /* reset node depths to ensure that non-dated tips have node depth 0.0 */ SetNodeDepths(t); return (YES); } int IsClockSatisfied (Tree *t, MrBFlt tol) { int i, foundFirstLength, isClockLike; MrBFlt firstLength=0.0, length; TreeNode *p, *q; if (t->isRooted == NO) return (NO); foundFirstLength = NO; isClockLike = YES; for (i=0; inNodes; i++) { p = t->allDownPass[i]; if (p->left == NULL && p->right == NULL) { if (p->isDated == YES) { length = p->nodeDepth; } else length = 0.0; q = p; while (q->anc != NULL) { if (q->anc->anc != NULL) length += q->length; q = q->anc; } if (foundFirstLength == NO) { firstLength = length; foundFirstLength = YES; } else { if (AreDoublesEqual (firstLength, length, tol) == NO) { MrBayesPrint ("%s Node (%s) is not at the same depth as some other tip taking calibration into account. \n", spacer, p->label); isClockLike = NO; } } } } if (firstLength < BRLENS_MIN) isClockLike = NO; return (isClockLike); } /* Check that tree obeys topology constraints and that node depths and ages are consistent */ int IsTreeConsistent (Param *param, int chain, int state) { Tree *tree; TreeNode *p; int i, j; MrBFlt b, r, rAnc, clockRate; Param *subParm; if (param->paramType != P_TOPOLOGY && param->paramType != P_BRLENS && param->paramType != P_SPECIESTREE) return YES; tree = GetTree(param, chain, state); if (modelSettings[param->relParts[0]].clockRate != NULL) clockRate = *GetParamVals(modelSettings[param->relParts[0]].clockRate, chain, state); else clockRate = 1.0; if (CheckConstraints(tree)==ERROR) { printf ("Tree does not obey constraints\n"); return NO; } /* check that the last few indices are not taken in a rooted tree */ if (tree->isRooted == YES && tree->root->index != tree->nNodes - 1) { printf ("Problem with root index\n"); return NO; } if (tree->isRooted == YES && tree->root->left->index != tree->nNodes - 2) { printf ("Problem with interior root index\n"); return NO; } if (tree->isRooted == NO) { if (modelSettings[param->relParts[0]].parsModelId == YES) { for (i=0; inNodes-1; i++) { p = tree->allDownPass[i]; if (p->length != 0.0) { printf ("Node %d does has non-zero branch length %f for parsimony tree\n", p->index, p->length); return NO; } } return YES; } else { for (i=0; inNodes-1; i++) { p = tree->allDownPass[i]; if (p->length <= 0.0) { if (p->length == 0.0) printf ("Node %d has zero branch length %f\n", p->index, p->length); else printf ("Node %d has negative branch length %f\n", p->index, p->length); return NO; } } return YES; } } if (tree->isRooted == YES && tree->isClock == NO) { for (i=0; inNodes-2; i++) { p = tree->allDownPass[i]; if (p->length <= 0.0) { if (p->length == 0.0) printf ("Node %d has zero branch length %f\n", p->index, p->length); else printf ("Node %d has negative branch length %f\n", p->index, p->length); return NO; } } return YES; } /* Clock trees */ /* tree->isRooted == YES && tree->isClock == YES */ /* Check that lengths and depths are consistent */ for (i=0; inNodes-2; i++) { p = tree->allDownPass[i]; if (p->length < 0.0) { printf ("Node %d has negative branch length %f\n", p->index, p->length); return NO; } if (fabs(p->anc->nodeDepth - p->nodeDepth - p->length) > 0.000001) { printf ("Node %d has length %f but nodeDepth %f and ancNodeDepth %f\n", p->index, p->length, p->nodeDepth, p->anc->nodeDepth); return NO; } if (p->left == NULL && p->isDated == NO && p->nodeDepth != 0.0) { printf ("Node %d is an autodated tip (0.0) but has node depth %lf\n", p->index, p->nodeDepth); return NO; } } /* Check that ages and calibrations are consistent */ if (tree->isCalibrated == YES) { for (i=0; inNodes-1; i++) { p = tree->allDownPass[i]; if (p->isDated == YES) { if (fabs((p->age - p->nodeDepth/clockRate)/p->age) > 0.000001) { printf ("Node %d has age %f but nodeDepth %f when clock rate is %f\n", p->index, p->age, p->nodeDepth, clockRate); return NO; } if (p->calibration->prior == fixed && fabs((p->age - p->calibration->priorParams[0])/p->age) > 0.000001) { printf ("Node %d has age %f but should be fixed to age %f\n", p->index, p->age, p->calibration->priorParams[0]); return NO; } else if (p->calibration->prior == uniform && ((p->age - p->calibration->min)/p->age < -0.000001 || (p->age - p->calibration->max)/p->age > 0.000001)) { printf ("Node %d has age %f but should be in the interval [%f,%f]\n", p->index, p->age, p->calibration->min, p->calibration->max); return NO; } else if ((p->age - p->calibration->min)/p->age < -0.000001) { printf ("Node %d has age %f but should be at least of age %f\n", p->index, p->age, p->calibration->min); return NO; } else if ((p->age - p->calibration->max)/p->age > 0.000001) { printf ("Node %d has age %f but should be no older than %f\n", p->index, p->age, p->calibration->max); return NO; } } } } for (i=0; inSubParams; i++) { subParm = param->subParams[i]; if (subParm->paramId == TK02BRANCHRATES) { rAnc = GetParamVals(subParm, chain, state)[tree->root->left->index]; if (fabs(rAnc - 1.0) > 1E-6) { printf ("%s TK02 relaxed clock mismatch in root rate, which is %e\n", spacer, rAnc); return NO; } for (j=0; jnNodes-2; j++) { p = tree->allDownPass[j]; b = GetParamSubVals(subParm, chain, state)[p->index]; r = GetParamVals(subParm, chain, state)[p->index]; rAnc = GetParamVals(subParm, chain, state)[p->anc->index]; if (fabs(p->length * (r + rAnc) / 2.0 - b) > 0.000001) { printf ("%s TK02 relaxed clock mismatch in branch %d\n", spacer, p->index); return NO; } } } else if (subParm->paramId == ILNBRANCHRATES || subParm->paramId == IGRBRANCHRATES || subParm->paramId == MIXEDBRCHRATES || subParm->paramId == WNBRANCHRATES) { // *GetParamIntVals(subParm, chain, state) == RCL_ILN or RCL_IGR for (j=0; jnNodes-2; j++) { p = tree->allDownPass[j]; b = GetParamSubVals(subParm, chain, state)[p->index]; r = GetParamVals(subParm, chain, state)[p->index]; if (fabs(p->length * r - b) > 0.000001) { printf ("%s Independent/uncorrelated relaxed clock mismatch in branch %d\n", spacer, p->index); return NO; } } } } if (param->paramType == P_SPECIESTREE) return (IsSpeciesTreeConsistent(GetTree(param, chain, state), chain)); return YES; } /* LabelTree: Label tree; remove previous labels if any */ int LabelTree (Tree *t, char **taxonNames) { int i, nTaxa; TreeNode *p = NULL; nTaxa = t->nNodes - t->nIntNodes; if (t->isRooted == YES) nTaxa--; /* erase previous labels, if any */ for (i=0; inNodes; i++) { p = t->allDownPass[i]; p->marked = NO; t->nodes[i].label = noLabel; } /* add labels */ for (i=0; inNodes; i++) { p = &t->nodes[i]; if (p->left == NULL || (t->isRooted == NO && p->anc == NULL)) { if (p->marked == YES || p->index < 0 || p->index >= nTaxa) { MrBayesPrint ("%s Taxon node index repeated or out of range\n", spacer); return (ERROR); } else p->label = taxonNames[p->index]; p->marked = YES; } else if (p->index > 0 && p->index < nTaxa) { MrBayesPrint ("%s Terminal taxon index set for interior node\n", spacer); return (ERROR); } } return (NO_ERROR); } /*------------------------------------------------------------------------------------------- | | Mark: This routine will mark up a subtree rooted at p | ---------------------------------------------------------------------------------------------*/ void Mark (TreeNode *p) { if (p != NULL) { p->marked = YES; Mark (p->left); Mark (p->right); } } /*------------------------------------------------------------------------------------------- | | MarkDistance: This routine will mark up an unconstrained subtree rooted at p within dist | The distance will be positive in the crown part and negative in the root part. | ---------------------------------------------------------------------------------------------*/ void MarkDistance (TreeNode *p, int YESorNO, int dist, int *n) { if (p == NULL || p->anc == NULL) return; p->marked = YES; if (YESorNO == YES) // in root part p->x = p->anc->x -1; else // in crown part p->x = p->anc->x +1; (*n)++; if (p->isLocked == NO && abs(p->x) < dist) { MarkDistance (p->left, YESorNO, dist, n); MarkDistance (p->right,YESorNO, dist, n); } } /*------------------------------------------------------------------------------------------- | | MarkUnconstrained: This routine will mark up an unconstrained subtree rooted at p | ---------------------------------------------------------------------------------------------*/ void MarkUnconstrained (TreeNode *p) { if (p != NULL) { p->marked = YES; if (p->isLocked == NO) { MarkUnconstrained (p->left); MarkUnconstrained (p->right); } } } /*------------------------------------------------------------------------------------------- | | MoveCalculationRoot: This routine will move the calculation root to the terminal with | index outgroup | ---------------------------------------------------------------------------------------------*/ int MoveCalculationRoot (Tree *t, int outgroup) { int i; TreeNode *p, *q, *r; if (t->isRooted == YES || outgroup < 0 || outgroup > t->nNodes - t->nIntNodes - (t->isRooted == YES ? 1 : 0)) { MrBayesPrint ("%s Problem moving calculation root\n", spacer); return (ERROR); } if (t->root->index == outgroup) return (NO_ERROR); /* nothing to do */ /* mark the path to the new calculation root */ for (i=0; inNodes; i++) { p = t->allDownPass[i]; if (p->left == NULL && p->right == NULL) { if (p->index == outgroup) p->marked = YES; else p->marked = NO; } else { if (p->left->marked == YES || p->right->marked == YES) p->marked = YES; else p->marked = NO; } } /* rotate the tree to use the specified calculation root */ p = t->root->left; q = t->root; q->anc = p; q->left = q->right = NULL; q->length = p->length; while (p->left != NULL && p->right != NULL) { if (p->left->marked == YES) { r = p->left; p->anc = r; p->left = q; p->length = r->length; q = p; p = r; } else /* if (p->right->marked == YES) */ { r = p->right; p->anc = r; p->right = q; p->length = r->length; q = p; p = r; } } p->left = p->anc; p->right = p->anc = NULL; t->root = p; p->length = 0.0; GetDownPass (t); return (NO_ERROR); } /*------------------------------------------------------------------------------------------- | | MovePolyCalculationRoot: This routine will move the calculation root to the terminal with | index outgroup and place it as the right-most descendant of the root node | ---------------------------------------------------------------------------------------------*/ int MovePolyCalculationRoot (PolyTree *t, int outgroup) { int i; PolyNode *p = NULL, *q, *r; /* check if tree is rooted, in which case calculation root is irrelevant */ if (t->root->left->sib->sib == NULL) return (NO_ERROR); if (outgroup < 0 || outgroup > t->nNodes - t->nIntNodes) { MrBayesPrint ("%s Outgroup index is out of range\n", spacer); return (ERROR); } if (t->root->left->sib->sib->sib != NULL) { MrBayesPrint ("%s Root has more than three descendants\n", spacer); return (ERROR); } /* check if rerooting actually necessary */ if (t->root->left->sib->sib->index == outgroup) return (NO_ERROR); /* mark the path to the new calculation root */ for (i=0; inNodes; i++) { p = t->allDownPass[i]; if (p->index == outgroup) break; } if (p->left != NULL) { MrBayesPrint ("%s Outgroup index set for internal node\n", spacer); for (i=0; inNodes; i++) printf ("%d -- %d\n", i, t->allDownPass[i]->index); getchar(); return (ERROR); } /* mark path to current root */ for (i=0; inNodes; i++) t->allDownPass[i]->mark = NO; q = p; while (q != NULL) { q->mark = YES; q = q->anc; } /* rotate the tree to use the specified calculation root */ p = t->root; for (;;) { /* find marked descendant */ for (q=p->left; q->mark == NO; q=q->sib) ; if (q->index == outgroup) break; /* add old root to descendants of that node */ for (r=q->left; r->sib!=NULL; r=r->sib) ; r->sib = p; p->sib = NULL; /* should not be needed */ p->anc = q; p->length = q->length; /* remove that node from descendants of old root node */ if (p->left == q) p->left = q->sib; else { for (r=p->left; r->sib!=q; r=r->sib) ; r->sib = r->sib->sib; } /* make new node root */ q->sib = NULL; q->anc = NULL; q->length = 0.0; p = q; } /* p is now the new root */ t->root = p; /* finally make sure calculation root is last node among root's descendants */ for (q=p->left; q->sib!=NULL; q=q->sib) ; if (q->index != outgroup) { if (p->left->index == outgroup) { q->sib = p->left; p->left = p->left->sib; q->sib->sib = NULL; } else { for (r=p->left; r->sib->index!=outgroup; r=r->sib) ; q->sib = r->sib; r->sib = r->sib->sib; q->sib->sib = NULL; } } GetPolyDownPass (t); return (NO_ERROR); } /* @return the number of levels for the tree rooted at the "node" */ int NrSubTreeLevels(TreeNode *node) { int r,l; if (node == NULL) { return -1; } r = NrSubTreeLevels (node->right); l = NrSubTreeLevels (node->left); return ((r>l)?(r):(l))+1; } /*------------------------------------------------------------------------------------------- | | NumConstrainedTips: This routine will return the number of constrained tips, internal or external | ---------------------------------------------------------------------------------------------*/ int NumConstrainedTips (TreeNode *p) { int i = 0; if (p == NULL) return i; if (p->left == NULL) return 1; i += NConstrainedTips (p->left); i += NConstrainedTips (p->right); return i; } /* NConstrainedTips: Recursive function to get the number of constrained tips */ int NConstrainedTips (TreeNode *p) { int i=0; if (p!=NULL) { if (p->left == NULL || p->isLocked == YES) return 1; else { i += NConstrainedTips (p->left); i += NConstrainedTips (p->right); } } return i; } /*------------------------------------------------------------------------------------------- | | NumDatedTips: This routine will return the number of dated tips, internal or external | ---------------------------------------------------------------------------------------------*/ int NumDatedTips (TreeNode *p) { int i = 0; assert (p != NULL && p->left != NULL); i += NDatedTips (p->left); i += NDatedTips (p->right); return i; } /* NDatedTips: recursive function to get the number of dated tips */ int NDatedTips (TreeNode *p) { int i=0; assert (p!=NULL); if (p->left == NULL || p->isDated == YES) return 1; else { i += NDatedTips (p->left); i += NDatedTips (p->right); return i; } } /* OrderTips: Order tips in a polytomous tree */ void OrderTips (PolyTree *t) { int i, j; PolyNode *p, *q, *r, *pl, *ql, *rl; /* label by minimum index */ for (i=0; inNodes; i++) { p = t->allDownPass[i]; if (p->left == NULL) { if (t->isRooted == NO && p->index == localOutGroup) p->x = -1; else p->x = p->index; } else { j = t->nNodes; for (q=p->left; q!=NULL; q=q->sib) { if (q->x < j) j = q->x; } p->x = j; } } /* and rearrange */ for (i=0; inNodes; i++) { p = t->allDownPass[i]; if (p->left == NULL || p->anc == NULL) continue; for (ql=NULL, q=p->left; q->sib!=NULL; ql=q, q=q->sib) { for (rl=q, r=q->sib; r!=NULL; rl=r, r=r->sib) { if (r->x < q->x) { if (ql == NULL) p->left = r; if (r == q->sib) /* swap adjacent q and r */ { if (ql != NULL) ql->sib = r; pl = r->sib; r->sib = q; q->sib = pl; } else /* swap separated q and r */ { if (ql != NULL) ql->sib = r; pl = r->sib; r->sib = q->sib; rl->sib = q; q->sib = pl; } pl = q; q = r; r = pl; } } } } GetPolyDownPass(t); } /* PrintNodes: Print a list of tree nodes, pointers and length */ void PrintNodes (Tree *t) { int i; TreeNode *p; printf ("Node\tleft\tright\tanc\tlength\n"); for (i=0; inNodes; i++) { p = &t->nodes[i]; printf ("%d\t%d\t%d\t%d\t%f\t%f\n", p->index, p->left == NULL ? -1 : p->left->index, p->right == NULL ? -1 : p->right->index, p->anc == NULL ? -1 : p->anc->index, p->length, p->nodeDepth); } if (t->root == NULL) printf ("root: NULL\n"); else printf ("root: %d\n", t->root->index); printf ("allDownPass:"); for (i=0; inNodes; i++) { p = t->allDownPass[i]; if (p!=NULL) printf (" %d", p->index); else printf (" NULL"); } printf ("\nintDownPass: "); for (i=0; inIntNodes; i++) { p = t->intDownPass[i]; if (p!=NULL) printf (" %d\t", p->index); else printf (" NULL\t"); } printf ("\n"); } /* PrintPolyNodes: Print a list of polytomous tree nodes, pointers and length */ void PrintPolyNodes (PolyTree *pt) { int i, j, k; PolyNode *p; printf ("Node\tleft\tsib\tanc\tlength\tlabel\n"); for (i=0; imemNodes; i++) { p = &pt->nodes[i]; printf ("%d\t%d\t%d\t%d\t%f\t%s\n", p->index, p->left == NULL ? -1 : p->left->index, p->sib == NULL ? -1 : p->sib->index, p->anc == NULL ? -1 : p->anc->index, p->length, p->label); } printf ("root: %d\n", pt->root->index); fflush(stdout); if (pt->nBSets > 0) { for (i=0; inBSets; i++) { printf ("Effective branch length set '%s'\n", pt->bSetName[i]); for (j=0; jnNodes; j++) { printf ("%d:%f", j, pt->effectiveBrLen[pt->nBSets][j]); if (j != pt->nNodes-1) printf (", "); } printf ("\n"); } } if (pt->nESets > 0) { for (i=0; inESets; i++) { printf ("Cpp event set '%s'\n", pt->eSetName[i]); for (j=0; jnNodes; j++) { if (pt->nEvents[i][j] > 0) { printf ("\tNode %d -- %d:(", j, pt->nEvents[i][j]); for (k=0; knEvents[i][j]; k++) { printf ("%f %f", pt->position[i][j][k], pt->rateMult[i][j][k]); if (k != pt->nEvents[i][j]-1) printf (", "); } printf (")\n"); } } printf ("\n"); } } fflush(stdout); } /* PrintTranslateBlock: Print a translate block to file fp for tree t */ void PrintTranslateBlock (FILE *fp, Tree *t) { int i, j, nTaxa; if (t->isRooted == NO) nTaxa = t->nNodes - t->nIntNodes; else nTaxa = t->nNodes - t->nIntNodes - 1; fprintf (fp, "\ttranslate\n"); for (i=0; inNodes; j++) if (t->allDownPass[j]->index == i) break; if (i == nTaxa-1) fprintf (fp, "\t\t%d\t%s;\n", i+1, t->allDownPass[j]->label); else fprintf (fp, "\t\t%d\t%s,\n", i+1, t->allDownPass[j]->label); } } /** Update relaxed clock parameter of the branch of a node with index "b" after node with index "a" is removed. i.e. make branch of node with index "b" be a concatenation of its original branch and the branch of node with index "a" Relaxed clock parameter of node with index "a" become invalid in the process. Note: For Non-clock models the routine has no effect. | | | | a | | -> | | | | b b */ void AppendRelaxedBranch (int a,int b,PolyTree *t) { int i,len; for (i=0; inBSets; i++) { t->effectiveBrLen[i][b] += t->effectiveBrLen[i][a]; } for (i=0; inESets; i++) { len=t->nEvents[i][a]+t->nEvents[i][b]; t->position[i][a] = (MrBFlt *) SafeRealloc ((void *)t->position[i][a], len*sizeof(MrBFlt)); t->rateMult[i][a] = (MrBFlt *) SafeRealloc ((void *)t->rateMult[i][a], len*sizeof(MrBFlt)); memcpy (t->position[i][a]+t->nEvents[i][a], t->position[i][b], t->nEvents[i][b]*sizeof(MrBFlt)); memcpy (t->rateMult[i][a]+t->nEvents[i][a], t->rateMult[i][b], t->nEvents[i][b]*sizeof(MrBFlt)); free(t->position[i][b]); free(t->rateMult[i][b]); t->position[i][b] = t->position[i][a]; t->rateMult[i][b] = t->rateMult[i][a]; t->position[i][a] = NULL; t->rateMult[i][a] = NULL; t->nEvents[i][a] = 0; t->nEvents[i][b] = len; } } /** Swap relaxed clock parameters of the branch of nodes with index "a" and "b". */ void SwapRelaxedBranchInfo (int a,int b,PolyTree *t) { int i,j; MrBFlt tmp, *tmpp; for (i=0; inBSets; i++) { tmp = t->effectiveBrLen[i][a]; t->effectiveBrLen[i][a] = t->effectiveBrLen[i][b]; t->effectiveBrLen[i][b] = tmp; } if (t->popSizeSet == YES) { tmp = t->popSize[a]; t->popSize[a]=t->popSize[b]; t->popSize[b] = tmp; } for (i=0; inESets; i++) { tmpp = t->position[i][a]; t->position[i][a] = t->position[i][b]; t->position[i][b] = tmpp; tmpp = t->rateMult[i][a]; t->rateMult[i][a] = t->rateMult[i][b]; t->rateMult[i][b] = tmpp; j = t->nEvents[i][a]; t->nEvents[i][a] = t->nEvents[i][b]; t->nEvents[i][b] = j; } } /*------------------------------------------------------------------------------------------- | | PrunePolyTree: This routine will prune a polytomous tree according to the currently | included taxa. NB! All tree nodes cannot be accessed by cycling over the | pt->nodes array after the deletion, because some spaces will be occupied by deleted | nodes and pt->nNodes is no longer the length of this array | (if proper rearrangement of pt->nodes needed then remove "#if 0" and make changes to p->x, see below). | ---------------------------------------------------------------------------------------------*/ int PrunePolyTree (PolyTree *pt) { int i, j, numDeleted, numTermPruned, numIntPruned, index; PolyNode *p = NULL, *q=NULL, *r=NULL, *qa; numDeleted = 0; for (i=0; inNodes; i++) { p = pt->allDownPass[i]; CheckString (taxaNames, numTaxa, p->label, &index); if (p->left == NULL && taxaInfo[index].isDeleted == YES) numDeleted++; } if (numDeleted == 0) { /* nothing to do */ return (NO_ERROR); } if (pt->nNodes - pt->nIntNodes - numDeleted < 3) { MrBayesPrint ("%s Pruned tree has less than three taxa in it\n", spacer); return (ERROR); } if (pt->nNodes - pt->nIntNodes < numLocalTaxa) { MrBayesPrint ("%s Tree to be pruned does not include all taxa\n", spacer); return (ERROR); } /* prune away one node at a time */ numIntPruned = 0; numTermPruned = 0; for (i=0; inNodes; i++) { p = pt->allDownPass[i]; if (p->left != NULL) continue; CheckString (taxaNames, numTaxa, p->label, &index); if (taxaInfo[index].isDeleted == YES) { numTermPruned++; for (q=p->anc->left; q!=NULL; q=q->sib) { if (q->sib == p) break; } if (q == NULL) { /* p is the left of its ancestor */ assert (p->anc->left == p); p->anc->left = p->sib; } else { /* p is q->sib; this also works if p->sib is NULL */ q->sib = p->sib; } /* if only one child left, delete ancestral node */ j = 0; for (q=p->anc->left; q!=NULL; q=q->sib) j++; if (j == 1) { /* p->anc->left is only child left; make p->anc be p->anc->left and accommodate its length */ numIntPruned++; qa= p->anc; q = qa->left; if (q->left == NULL) { AppendRelaxedBranch (qa->index, q->index, pt); qa->index = q->index; qa->length += q->length; strcpy(qa->label, q->label); qa->left = NULL; /* To make sure that q is not treated as the representer of the tip it represented before. i.e. make condition if (p->left != NULL) true */ q->left = (struct pNode*)1; } else { if (qa->anc != NULL) { AppendRelaxedBranch (qa->index, q->index, pt); qa->length += q->length; } qa->index = q->index; qa->left = q->left; for (r=q->left; r!= NULL; r=r->sib) r->anc = qa; } } /* if unrooted, then root node has to have more then 2 children, thus the following check */ if (j == 2 && pt->isRooted == NO && p->anc->anc == NULL) { numIntPruned++; r=p->anc; /*r is the root with only 2 children*/ if (r->left->left != NULL) {/* Make r->left new root by attaching "right" child of r to children of r->left */ for (q=r->left->left; q->sib!=NULL; q=q->sib) ; q->sib = r->left->sib; r->left->sib->anc = q->anc; r->left->sib->length += q->anc->length; r->left->sib = NULL; r->left->anc = NULL; pt->root = r->left; } else {/* Make "right" child of r (r->left->sib) the new root by attaching r->left to children of r->"right" */ for (q=r->left->sib->left; q->sib!=NULL; q=q->sib) ; q->sib = r->left; r->left->anc = q->anc; r->left->length += q->anc->length; r->left->sib = NULL; q->anc->anc = NULL; pt->root = q->anc; } } } } #if 0 /* place unused space at the end of pt->nodes array. If activated this code p->x has to be set to non 0 value for all p that are deleted. */ for (i=0; inNodes; i++) { p = &pt->nodes[i]; if (p->x != 0) { for (j=i+1; jnNodes; j++) { q = &pt->nodes[j]; if (q->x == 0) break; } if (j != pt->nNodes) { /* swap nodes; quite difficult! */ CopyPolyNodes (p, q, nLongsNeeded); p->left = q->left; p->sib = q->sib; p->anc = q->anc; for (k=0; knNodes; k++) { r = &pt->nodes[k]; if (r->left == q) r->left = p; if (r->sib == q) r->sib = p; if (r->anc == q) r->anc = p; } } } } #endif /* correct number of nodes */ pt->nNodes -= (numTermPruned + numIntPruned); pt->nIntNodes -= numIntPruned; /* get downpass; note that the deletion procedure does not change the root in rooted case */ i=j=0; GetPolyNodeDownPass (pt, pt->root, &i, &j); assert (i==pt->nNodes); assert (j==pt->nIntNodes); return (NO_ERROR); } /*-------------------------------------------------------------------- | | RandPerturb: Randomly perturb a tree by nPert NNIs | ---------------------------------------------------------------------*/ int RandPerturb (Tree *t, int nPert, RandLong *seed) { int i, whichNode; TreeNode *p, *q, *a, *b, *c; if (t->nConstraints >= t->nIntNodes) { MrBayesPrint ("%s User tree cannot be perturbed because all nodes are locked\n", spacer); return (ERROR); } for (i=0; inIntNodes - 1)); p = t->intDownPass[whichNode]; } while (p->isLocked == YES); q = p->anc; a = p->left; b = p->right; if (q->left == p) c = q->right; else c = q->left; if (RandomNumber(seed) < 0.5) { /* swap b and c */ p->right = c; c->anc = p; if (q->left == c) q->left = b; else q->right = b; b->anc = q; } else { /* swap a and c */ p->left = c; c->anc = p; if (q->left == c) q->left = a; else q->right = a; a->anc = q; } if (t->isCalibrated == YES) InitCalibratedBrlens (t, 0.0001, seed); else if (t->isClock == YES) InitClockBrlens (t); } GetDownPass (t); if (t->checkConstraints == YES && CheckConstraints (t) == NO_ERROR) { MrBayesPrint ("%s Broke constraints when perturbing tree\n", spacer); return (ERROR); } return (NO_ERROR); } /* | Reorder array of nodes "nodeArray" such that first nodes in it could be paired with "w" to create immediate common ancestor and this ancestor node would not violate any constraint. | | @param w Reference node as described | @param nodeArray A set of node to order as described | @param activeConstraints Array containing indices of active constraints in the set of defined constraints | @param nLongsNeeded Length of partition information (in BitsLong) in a node and constraint definition. | @param isRooted Do constraints apply to rooted tree YES or NO | | @return Number of nodes in "nodeArray" that could be paired with "w" to create immediate common ancestor and this ancestor node would not violate any constraint */ int ConstraintAllowedSet(PolyNode *w, PolyNode **nodeArray, int nodeArraySize, int *activeConstraints, int activeConstraintsSize, int nLongsNeeded, int isRooted) { int i, j, k, FirstEmpty; BitsLong **constraintPartition; PolyNode *tmp; for (j=0; jpartition, nLongsNeeded) == YES) || (isRooted == NO && IsPartNested(definedConstraintTwoPruned[k], w->partition, nLongsNeeded) == YES)) continue;/* all nodes are comparable because condition of the constraint has to be satisfied in the subtree rooted at w*/ FirstEmpty = IsSectionEmpty(definedConstraintPruned[k], w->partition, nLongsNeeded); if (FirstEmpty == YES && IsSectionEmpty(definedConstraintTwoPruned[k], w->partition, nLongsNeeded) == YES) continue; /* all nodes are comparable because w does not contain any constraint taxa*/ assert (FirstEmpty^IsSectionEmpty(definedConstraintTwoPruned[k], w->partition, nLongsNeeded)); if (FirstEmpty == YES) {/*w->partition has intersection with definedConstraintTwoPruned[k], thus remove all nodes from nodeArray that intersect with definedConstraintPruned[k]*/ constraintPartition=definedConstraintPruned; } else {/*w->partition has intersection with definedConstraintPruned[k], thus remove all nodes from nodeArray that intersect with definedConstraintTwoPruned[k]*/ constraintPartition=definedConstraintTwoPruned; } for (i=0;ipartition, nLongsNeeded) == NO && ((FirstEmpty == NO && isRooted== YES) || IsPartNested(constraintPartition[k], nodeArray[i]->partition, nLongsNeeded) == NO)) /*second part of if statement is to bail out "nodeArray[i]" when "w" contains nodes for example from definedConstraintPruned and "nodeArray[i]" have definedConstraintTwoPruned fully nested in it This bail out not applicable if t->isRooted== YES Since we should create a rooting node for the first set of taxa in the constraint. Note that such case possible because we may have hard constraint node that fully nest definedConstraintTwoPruned but also having taxa from definedConstraintPruned keeping constraint active.*/ { tmp = nodeArray[i]; nodeArray[i]=nodeArray[--nodeArraySize]; nodeArray[nodeArraySize]=tmp; i--; } } }/*end if PARTIAL*/ else { assert (definedConstraintsType[k] == NEGATIVE); if (isRooted == YES || IsBitSet(localOutGroup, definedConstraintPruned[k])==NO) constraintPartition=definedConstraintPruned; else constraintPartition=definedConstraintTwoPruned; if (IsSectionEmpty(constraintPartition[k], w->partition, nLongsNeeded)==YES) continue; for (i=0;ipartition, nodeArray[i]->partition, constraintPartition[k], nLongsNeeded) == YES) { tmp = nodeArray[i]; nodeArray[i]=nodeArray[--nodeArraySize]; nodeArray[nodeArraySize]=tmp; i--; } } }/*end if NEGATIVE*/ } return nodeArraySize; } /* | Check if "partition" violate any constraint. | | @param partiton Partition to check | @param activeConstraints Array containing indices of active constraints in the set of defined constraints | @param nLongsNeeded Length of partition information (in BitsLong) in a node and constraint definition | @param isRooted Do constraints apply to rooted tree YES or NO | | @return Index of first violated constraint in activeConstraints array, -1 if no constraint is violated. */ int ViolatedConstraint(BitsLong *partition, int *activeConstraints, int activeConstraintsSize, int nLongsNeeded, int isRooted) { int j, k; BitsLong **constraintPartition; for (j=0; jpartition, nLongsNeeded) == YES && IsSectionEmpty(definedConstraintTwoPruned[k], w->partition, nLongsNeeded)) || (isRooted == NO && IsPartNested(definedConstraintTwoPruned[k], w->partition, nLongsNeeded) == YES && IsSectionEmpty(definedConstraintPruned[k], w->partition, nLongsNeeded))) { //tmp = activeConstraints[j]; activeConstraints[j]=activeConstraints[--activeConstraintsSize]; //activeConstraints[activeConstraintsSize]=tmp; j--; } }/*end if PARTIAL*/ else { assert (definedConstraintsType[k] == NEGATIVE); if (isRooted == YES || IsBitSet(localOutGroup, definedConstraintPruned[k])==NO) constraintPartition=definedConstraintPruned; else constraintPartition=definedConstraintTwoPruned; if (IsPartNested(constraintPartition[k], w->partition, nLongsNeeded)==NO && IsSectionEmpty(constraintPartition[k], w->partition, nLongsNeeded)==NO) { //tmp = activeConstraints[j]; activeConstraints[j]=activeConstraints[--activeConstraintsSize]; //activeConstraints[activeConstraintsSize]=tmp; j--; } }/*end if NEGATIVE*/ } return activeConstraintsSize; } /*-------------------------------------------------------------------- | | RandResolve: Randomly resolve a polytomous tree | | @param tt is a tree which contains information about applicable constraints. If it is set to NULL then no constraints will be used. | If t!=NULL then partitions of nodes of polytree should be allocated for example by AllocatePolyTreePartitions (t); | @return NO_ERROR on success, ABORT if could not resolve a tree without violating some constraint, ERROR if any other error occur ---------------------------------------------------------------------*/ int RandResolve (Tree *tt, PolyTree *t, RandLong *seed, int destinationIsRooted) { int i, j, k, nextNode, stopNode, rand1, rand2, nTaxa, nLongsNeeded, tmp; PolyNode *p=NULL, *q, *r, *u, *w1, *w2; int nodeArrayAllowedSize, nodeArraySize, activeConstraintsSize; PolyNode **nodeArray; int *activeConstraints; assert (tt==NULL || t->bitsets!=NULL); /* partition fields of t nodes need to be allocated if constraints are used*/ nTaxa = t->nNodes - t->nIntNodes; /* different from numLocalTaxa potentially if a species tree */ assert (nTaxa <= t->memNodes/2); /* allocated tree has to be big enough*/ nLongsNeeded = (nTaxa - 1) / nBitsInALong + 1; /* allocated length of partitions is t->memNodes/2 bits but only first nTaxa bits are used */ nodeArray = t->allDownPass; /*temporary use t->allDownPass for different purpose. It get properly reset at the end. */ activeConstraints = tempActiveConstraints; activeConstraintsSize = 0; /* collect constraints to consider if applicable*/ if (tt!=NULL && tt->constraints!=NULL) { for (k=0; kconstraints[k] == YES && definedConstraintsType[k] != HARD) activeConstraints[activeConstraintsSize++]=k; } } /* count immediate descendants */ GetPolyDownPass(t); for (i=0; inIntNodes; i++) { p = t->intDownPass[i]; tmp=ViolatedConstraint(p->partition, activeConstraints, activeConstraintsSize, nLongsNeeded, t->isRooted); if (tmp != -1) { assert (p->isLocked == YES); MrBayesPrint ("%s Could not build a constraint tree since hard constraint \"%s\" and constraint \"%s\" are incompatible\n", spacer, constraintNames[p->lockID], constraintNames[activeConstraints[tmp]]); return (ERROR); } activeConstraintsSize = PruneActiveConstraints (p, activeConstraints, activeConstraintsSize, nLongsNeeded, t->isRooted); j = 0; for (q=p->left; q!=NULL; q=q->sib) j++; p->x = j; } /* add one node at a time */ if (destinationIsRooted == NO) stopNode = 2*nTaxa - 2; else stopNode = 2*nTaxa - 1; for (nextNode=t->nNodes; nextNode < stopNode; nextNode++) { /* find a polytomy to break */ for (i=0; inIntNodes; i++) { p = t->intDownPass[i]; if (destinationIsRooted == YES && p->x > 2) break; if (destinationIsRooted == NO && ((p->anc != NULL && p->x > 2) || (p->anc == NULL && p->x > 3))) break; } /* if we can't find one, there's an error */ if (i == t->nIntNodes) { return ERROR; } nodeArraySize=0; /*Collect initial list of candidate nodes to join*/ for (q = p->left; q!= NULL; q = q->sib) { nodeArray[nodeArraySize++]=q; } assert (nodeArraySize==p->x); /* identify two descendants randomly */ /* make sure we do not select outgroup if it is an unrooted tree */ if (p->anc == NULL && destinationIsRooted == NO) nodeArraySize--; do { /* Pick first node */ rand1 = (int) (RandomNumber(seed) * nodeArraySize); w1 = nodeArray[rand1]; nodeArray[rand1] = nodeArray[--nodeArraySize]; if (nodeArraySize==0) return ABORT; /* Potentially here we could instead revert by removing last added node and try again. */ /* Move all nodes in nodeArray which can be paired with w to the beginning of array */ nodeArrayAllowedSize=ConstraintAllowedSet(w1, nodeArray, nodeArraySize, activeConstraints, activeConstraintsSize, nLongsNeeded, t->isRooted); /* TODO optimization for Maxim (if not Maxim remove it if you still see it): if nodeArrayAllowedSize==0 then set w1->y */ } while (nodeArrayAllowedSize == 0); rand2 = (int) (RandomNumber(seed) *nodeArrayAllowedSize); w2 = nodeArray[rand2]; /* create a new node */ u = &t->nodes[nextNode]; u->anc = p; u->x = 2; p->x--; if (tt != NULL) { for (j=0; jpartition[j] = w1->partition[j] | w2->partition[j] ; activeConstraintsSize = PruneActiveConstraints (u, activeConstraints, activeConstraintsSize, nLongsNeeded, t->isRooted); } u->left = w1; t->nNodes++; t->nIntNodes++; /* connect tree together */ r = u; for (q = p->left; q!= NULL; q = q->sib) { if (q != w1 && q != w2) { r->sib=q; r = q; } } r->sib = NULL; w1->sib = w2; w2->sib = NULL; w1->anc = u; w2->anc = u; p->left = u; /* update tree */ GetPolyDownPass (t); } /* relabel interior nodes (important that last indices are at the bottom!) */ for (i=0; inIntNodes; i++) { p = t->intDownPass[i]; p->index = nTaxa + i; } return NO_ERROR; } /* ResetTreeNode: Reset tree node except for memory index */ void ResetTreeNode (TreeNode *p) { /* do not change memoryIndex; that is set once and for all when tree is allocated */ p->index = 0; p->upDateCl = NO; p->upDateTi = NO; p->marked = NO; p->length = 0.0; p->nodeDepth = 0.0; p->index = 0; p->isDated = NO; p->calibration = NULL; p->age = -1.0; p->isLocked = NO; p->lockID = -1; p->label = noLabel; p->x = 0; p->y = 0; p->d = 0.0; p->partition = NULL; } /* ResetPolyNode: Reset all values of one node in a polytree */ void ResetPolyNode (PolyNode *p) { /* we reset everything here except memoryIndex, which should be immutable */ p->length = 0.0; p->depth = 0.0; p->age = 0.0; p->anc = p->left = p->sib = NULL; p->calibration = NULL; p->f = 0.0; p->index = 0; p->isDated = NO; p->isLocked = NO; strcpy (p->label,""); p->lockID = 0; p->partition = NULL; p->partitionIndex = 0; p->support = 0.0; p->x = p->y = 0; } /* ResetPolyTree: Reset polytomous tree to pristine state but keep relevant memory. */ void ResetPolyTree (PolyTree *pt) { int i, maxTaxa, nLongsNeeded; /* clear nodes */ for (i=0; imemNodes; i++) ResetPolyNode (&pt->nodes[i]); /* empty node arrays and tree properties but keep space */ for (i=0; inNodes; i++) pt->allDownPass[i] = NULL; for (i=0; inIntNodes; i++) pt->intDownPass[i] = NULL; pt->nNodes = 0; pt->nIntNodes = 0; pt->root = NULL; pt->brlensDef = NO; pt->isRooted = NO; pt->isClock = NO; pt->isRelaxed = NO; pt->clockRate = 0.0; /* empty bitsets but keep space and pointers */ if (pt->bitsets) { maxTaxa = pt->memNodes / 2; nLongsNeeded = (maxTaxa - 1) / nBitsInALong + 1; for (i=0; imemNodes*nLongsNeeded; i++) pt->bitsets[i] = 0; for (i=0; imemNodes; i++) pt->nodes[i].partition = pt->bitsets + i*nLongsNeeded; } /* empty relaxed clock parameters */ FreePolyTreeRelClockParams (pt); /* empty population size set parameters */ FreePolyTreePopSizeParams (pt); } /* ResetPolyTreePartitions: Reset and set bit patterns describing partitions */ void ResetPolyTreePartitions (PolyTree *pt) { int i, j, numTaxa, nLongsNeeded; PolyNode *pp; /* get some handy numbers */ numTaxa = pt->memNodes/2; nLongsNeeded = (numTaxa - 1) / nBitsInALong + 1; /* reset bits describing partitions */ for (i=0; imemNodes*nLongsNeeded; i++) { pt->bitsets[i] = 0; } /* set bits describing partitions */ for (i=0; inNodes; i++) { assert (pt->allDownPass != NULL && pt->allDownPass[i] != NULL); assert (pt->allDownPass[i]->partition != NULL); pp = pt->allDownPass[i]; if (pp->left == NULL) { SetBit (pp->index, pp->partition); } if (pp->anc != NULL) { for (j=0; janc->partition[j] |= pp->partition[j]; } } } /*---------------------------------------------- | | ResetRootHeight: Reset node heights in a clock | tree to fit a new root height. Assumes | node depths and lengths set correctly. | -----------------------------------------------*/ int ResetRootHeight (Tree *t, MrBFlt rootHeight) { int i; TreeNode *p; MrBFlt factor, x, y; if (t->isClock == NO) return ERROR; /* make sure node depths are set */ for (i=0; inNodes-1; i++) { p = t->allDownPass[i]; if (p->left == NULL) p->nodeDepth = 0.0; else { x = p->left->nodeDepth + p->left->length; y = p->right->nodeDepth + p->right->length; if (x > y) p->nodeDepth = x; else p->nodeDepth = y; } } for (i=t->nNodes-3; i>=0; i--) { p = t->allDownPass[i]; p->nodeDepth = p->anc->nodeDepth - p->length; } /* now reset node depths and branch lengths */ factor = rootHeight / t->root->left->nodeDepth; t->root->left->nodeDepth = rootHeight; for (i=t->nNodes-2; i>=0; i--) { p = t->allDownPass[i]; p->nodeDepth *= factor; p->length *= factor; } return NO_ERROR; } /*---------------------------------------------- | | ResetTipIndices: reset tip indices to be from | 0 to number of included taxa, in same order | as in the original taxon set. | -----------------------------------------------*/ void ResetTipIndices(PolyTree *pt) { int i, j, k, m; PolyNode *p = NULL; for (i=j=0; inNodes; k++) { p = pt->allDownPass[k]; if (StrCmpCaseInsensitive(p->label,taxaNames[i]) == 0) break; } if (k < pt->nNodes) { assert (p->left == NULL); if (p->index!=j) { SwapRelaxedBranchInfo (p->index, j, pt); for (m=0; mnNodes; m++) { if (pt->allDownPass[m]->index==j) { pt->allDownPass[m]->index=p->index; break; } } p->index = j; } j++; } } } /*---------------------------------------------- | | ResetTopology: rebuild the tree t to fit the | Newick string s. Everything except topology | is left in the same state in t. | -----------------------------------------------*/ int ResetTopology (Tree *t, char *s) { TreeNode *p, *q; int i, j, k, inLength; char temp[30]; /* set all pointers to NULL */ for (i=0; imemNodes; i++) { p = &t->nodes[i]; p->anc = p->right = p->left = NULL; p->index = -1; } p = &t->nodes[0]; /* start out assuming that the tree is rooted; we will detect below if it is not */ t->isRooted = YES; inLength = NO; for (i=0, j=1; *s!='\0'; s++) { if (*s == ',' || *s == ')' || *s == ':') { if (p->right == NULL && inLength == NO) { temp[i] = '\0'; k = atoi (temp); p->index = k-1; i = 0; } else inLength = NO; } if (*s == '(') { q = p; p = &t->nodes[j++]; q->left = p; p->anc = q; } else if (*s == ',') { if (p->anc->right == NULL) { q = p->anc; p = &t->nodes[j++]; p->anc = q; q->right = p; } else /* if p->anc->right == p (near 'root' of unrooted trees) */ { q = p->anc; p = &t->nodes[j++]; q->anc = p; p->left = q; t->isRooted = NO; } } else if (*s == ')') { p = p->anc; } else if (*s == ':') { inLength = YES; } else if (inLength == NO) { temp[i++] = *s; } } /* attach root to rooted tree */ if (t->isRooted == YES) { p = &t->nodes[0]; q = &t->nodes[j++]; q->left = p; p->anc = q; } /* relabel interior nodes, find number of nodes and root */ t->nNodes = j; t->nIntNodes = t->nNodes/2 - 1; if (t->isRooted == NO) j = t->nNodes - t->nIntNodes; else j = t->nNodes - t->nIntNodes - 1; for (i=0; inNodes; i++) { p = &t->nodes[i]; if (p->index == -1) p->index = j++; if (p->anc == NULL) t->root = p; } GetDownPass (t); return NO_ERROR; } /*----------------------------------------------------------------- | | ResetBrlensFromTree: copies brlens and depths from second tree (vTree) to | first tree (used to initialize brlen sets for same topology) | -----------------------------------------------------------------*/ int ResetBrlensFromTree (Tree *tree, Tree *vTree) { int i, j, k, nLongsNeeded, numTips; MrBFlt d1, d2; TreeNode *p, *q; if (tree->isRooted != vTree->isRooted) return (ERROR); if (AreTopologiesSame (tree, vTree) == NO) return (ERROR); /* allocate and set up partitions */ AllocateTreePartitions (vTree); AllocateTreePartitions (tree); numTips = tree->nNodes - tree->nIntNodes - (tree->isRooted == YES ? 1 : 0); nLongsNeeded = (int) ((numTips - 1) / nBitsInALong) + 1; /*copy lengths and nodeDepthes*/ for (i=0; inNodes; i++) { p = vTree->allDownPass[i]; for (j=0; jnNodes; j++) { q = tree->allDownPass[j]; for (k=0; kpartition[k] != q->partition[k]) break; if (k==nLongsNeeded) { q->length = p->length; if (tree->isRooted == YES) q->nodeDepth = p->nodeDepth; } } } if (tree->isRooted == YES) { /*Next compute height for the root. */ for (i=0; inNodes-1; i++) { p = tree->allDownPass[i]; if (p->left == NULL) p->nodeDepth = 0.0; else { d1 = p->left->nodeDepth + p->left->length; d2 = p->right->nodeDepth + p->right->length; if (d1 > d2) p->nodeDepth = d1; else p->nodeDepth = d2; } } for (i=tree->nNodes-3; i>=0; i--) { p = tree->allDownPass[i]; if (p->left==NULL && p->calibration==NULL) continue; /* leave at 0.0 */ p->nodeDepth = p->anc->nodeDepth - p->length; } } FreeTreePartitions(tree); FreeTreePartitions(vTree); return (NO_ERROR); } /* ResetIntNodeIndices: Set int node indices in downpass order from numTaxa to 2*numTaxa-2 */ void ResetIntNodeIndices (PolyTree *t) { int i, m, index; index = t->nNodes - t->nIntNodes; for (i=0; inIntNodes; i++) { if (t->intDownPass[i]->index != index) { SwapRelaxedBranchInfo (t->intDownPass[i]->index, index, t); for (m=0; mnIntNodes; m++) { if (t->intDownPass[m]->index==index) { t->intDownPass[m]->index=t->intDownPass[i]->index; break; } } t->intDownPass[i]->index = index; } index++; } } /* ResetTopologyFromTree: use top to set topology in tree */ int ResetTopologyFromTree (Tree *tree, Tree *top) { int i, j, k; TreeNode *p, *q, *r, *p1; /* adopt rooting */ tree->isRooted = top->isRooted; tree->nNodes = top->nNodes; tree->nIntNodes = top->nIntNodes; /* set all pointers to NULL */ for (i=0; inNodes; i++) { p = &tree->nodes[i]; p->anc = p->right = p->left = NULL; } /* now copy topology */ for (i=0; inIntNodes; i++) { p1 = top->intDownPass[i]; k = p1->index; for (j=0; jnNodes; j++) if (tree->nodes[j].index == k) break; p = &tree->nodes[j]; k = p1->left->index; for (j=0; jnNodes; j++) if (tree->nodes[j].index == k) break; q = &tree->nodes[j]; k = p1->right->index; for (j=0; jnNodes; j++) if (tree->nodes[j].index == k) break; r = &tree->nodes[j]; p->left = q; p->right= r; q->anc = r->anc = p; } /* arrange the root */ k = top->root->index; for (j=0; jnNodes; j++) if (tree->nodes[j].index == k) break; p = &tree->nodes[j]; k = top->root->left->index; for (j=0; jnNodes; j++) if (tree->nodes[j].index == k) break; q = &tree->nodes[j]; p->left = q; q->anc = p; p->right = p->anc = NULL; tree->root = p; GetDownPass (tree); return (NO_ERROR); } /* ResetTopologyFromPolyTree: use polytree top to set topology in tree */ int ResetTopologyFromPolyTree (Tree *tree, PolyTree *top) { int i, j, k; TreeNode *p, *q, *r; PolyNode *p1; if (tree->isRooted != top->isRooted) return (ERROR); /* set all pointers to NULL */ for (i=0; inNodes; i++) { p = &tree->nodes[i]; p->anc = p->right = p->left = NULL; } /* now copy topology */ for (i=0; inIntNodes; i++) { p1 = top->intDownPass[i]; k = p1->index; for (j=0; jnNodes; j++) if (tree->nodes[j].index == k) break; p = &tree->nodes[j]; k = p1->left->index; for (j=0; jnNodes; j++) if (tree->nodes[j].index == k) break; q = &tree->nodes[j]; k = p1->left->sib->index; for (j=0; jnNodes; j++) if (tree->nodes[j].index == k) break; r = &tree->nodes[j]; p->left = q; p->right= r; q->anc = r->anc = p; } /* arrange the root */ if (top->isRooted == YES) { k = top->root->index; for (j=0; jnNodes; j++) if (tree->nodes[j].index == k) break; p = &tree->nodes[j]; k = top->nNodes; for (j=0; jnNodes; j++) if (tree->nodes[j].index == k) break; q = &tree->nodes[j]; q->left = p; q->anc = NULL; q->right = NULL; tree->root = q; } else /* if (top->isRooted == NO) */ { k = top->root->index; for (j=0; jnNodes; j++) if (tree->nodes[j].index == k) break; p = &tree->nodes[j]; k = localOutGroup; for (p1=top->root->left; p1!=NULL; p1=p1->sib) if (p1->index == k) break; assert (p1 != NULL); if (p1 == NULL) return (ERROR); q = &tree->nodes[p1->index]; k = p1->anc->left->sib->sib->index; /* index of missing child */ if (p->left == q) p->left = &tree->nodes[k]; else if (p->right == q) p->right = &tree->nodes[k]; q->anc = q->right = NULL; p->anc = q; q->left = p; } GetDownPass (tree); return (NO_ERROR); } /* ResetTreePartitions: Reset bitsets describing tree partitions */ void ResetTreePartitions (Tree *t) { int i, j, numTaxa, nLongsNeeded; TreeNode *p; /* get some handy numbers */ numTaxa = t->nNodes - t->nIntNodes - (t->isRooted == YES ? 1 : 0); nLongsNeeded = (numTaxa - 1) / nBitsInALong + 1; /* reset bits describing partitions */ for (i=0; inNodes; i++) { assert (t->allDownPass != NULL && t->allDownPass[i] != NULL); assert (t->allDownPass[i]->partition != NULL); p = t->allDownPass[i]; for (j=0; jpartition[j] = 0; } /* set bits describing partitions */ for (i=0; inNodes; i++) { p = t->allDownPass[i]; if (p->left == NULL || (p->anc == NULL && t->isRooted == NO)) SetBit (p->index, p->partition); else if (p->anc != NULL) { for (j=0; jpartition[j] = p->left->partition[j] | p->right->partition[j]; } } } /*------------------------------------------------------- | | RetrieveRTopology: This routine will rebuild a rooted | tree from the order array created by StoreRTopology. | All tree information except the structure will | remain unaltered. | --------------------------------------------------------*/ int RetrieveRTopology (Tree *t, int *order) { int i, numTaxa; TreeNode *p, *q, *r; numTaxa = t->nNodes - t->nIntNodes - 1; /* sort the tips in the t->allDownPass array */ p = t->nodes; for (i=0; inNodes; i++, p++) t->allDownPass[p->index] = p; /* make sure the root has index 2*numTaxa-1 */ q = t->allDownPass[t->nNodes-1]; q->anc = q->right = NULL; t->root = q; /* connect the first two tips */ p = t->allDownPass[numTaxa]; p->anc = q; q->left = p; p->length = 0.0; q = t->allDownPass[0]; r = t->allDownPass[1]; p->left = q; p->right = r; q->anc = r->anc = p; /* add one tip at a time */ for (i=2; iallDownPass[i]; q = t->allDownPass[numTaxa-1+i]; r = t->allDownPass[*(order++)]; p->anc = q; q->left = p; q->right = r; q->anc = r->anc; if (r->anc->left == r) r->anc->left = q; else r->anc->right = q; r->anc = q; } /* get downpass */ GetDownPass (t); /* relabel interior nodes (root is correctly labeled already) */ for (i=0; inIntNodes; i++) t->intDownPass[i]->index = i+numTaxa; return (NO_ERROR); } /*------------------------------------------------------- | | RetrieveRTree: This routine will rebuild a rooted | tree from the arrays created by StoreRTree. | All tree information except the structure and | branch lengths will remain unaltered. | --------------------------------------------------------*/ int RetrieveRTree (Tree *t, int *order, MrBFlt *brlens) { int i, numTaxa; TreeNode *p, *q, *r; numTaxa = t->nNodes - t->nIntNodes - 1; /* sort the tips in the t->allDownPass array */ p = t->nodes; for (i=0; inNodes; i++, p++) t->allDownPass[p->index] = p; /* make sure that root has index 2*numTaxa-1 */ q = t->allDownPass[t->nNodes-1]; q->anc = q->right = NULL; q->length = 0.0; t->root = q; /* connect the first three tips */ p = t->allDownPass[numTaxa]; p->anc = q; q->left = p; p->length = 0.0; q = t->allDownPass[0]; r = t->allDownPass[1]; p->left = q; p->right = r; q->anc = r->anc = p; q->length = *(brlens++); r->length = *(brlens++); /* add one tip at a time */ for (i=2; iallDownPass[i]; q = t->allDownPass[numTaxa-1+i]; r = t->allDownPass[*(order++)]; p->anc = q; q->left = p; q->right = r; q->anc = r->anc; if (r->anc->left == r) r->anc->left = q; else r->anc->right = q; r->anc = q; if (q->anc->anc != NULL) q->length = *(brlens++); else { r->length = *(brlens++); q->length = 0.0; } p->length = *(brlens++); } /* get downpass */ GetDownPass (t); /* relabel interior nodes (root is correctly labeled already) */ for (i=0; inIntNodes; i++) t->intDownPass[i]->index = i+numTaxa; /* set the node depths */ SetNodeDepths (t); return (NO_ERROR); } /*------------------------------------------------------- | | RetrieveRTreeWithIndices: This routine will rebuild a rooted | tree from the arrays created by StoreRTreeWithIndices. | All tree information except the structure, branch lengths | and node indices will remain unaltered. | --------------------------------------------------------*/ int RetrieveRTreeWithIndices (Tree *t, int *order, MrBFlt *brlens) { int i, numTaxa; TreeNode *p, *q, *r; extern void ShowNodes (TreeNode *, int, int); numTaxa = t->nNodes - t->nIntNodes - 1; /* sort the tips in the t->allDownPass array */ p = t->nodes; for (i=0; inNodes; i++, p++) t->allDownPass[p->index] = p; /* make sure that root has index 2*numTaxa-1 */ q = t->allDownPass[t->nNodes-1]; q->anc = q->right = NULL; q->length = 0.0; t->root = q; /* connect the first three 'tips' with interior node, index from order array */ p = t->allDownPass[numTaxa]; p->x = *(order++); p->anc = q; q->left = p; p->length = 0.0; q = t->allDownPass[0]; r = t->allDownPass[1]; p->left = q; p->right = r; q->anc = r->anc = p; q->length = *(brlens++); r->length = *(brlens++); /* add one tip at a time */ for (i=2; iallDownPass[i]; assert (*order >= numTaxa && *order < 2*numTaxa - 1); q = t->allDownPass[numTaxa-1+i]; q->x = *(order++); r = t->allDownPass[*(order++)]; p->anc = q; q->left = p; q->right = r; q->anc = r->anc; if (r->anc->left == r) r->anc->left = q; else r->anc->right = q; r->anc = q; if (q->anc->anc != NULL) q->length = *(brlens++); else { r->length = *(brlens++); q->length = 0.0; } p->length = *(brlens++); } /* get downpass */ GetDownPass (t); /* relabel interior nodes using labels in scratch variable x */ for (i=0; inIntNodes; i++) { p = t->intDownPass[i]; p->index = p->x; } /* set the node depths */ SetNodeDepths (t); return (NO_ERROR); } /*------------------------------------------------------- | | RetrieveUTopology: This routine will rebuild an unrooted | tree from the order array created by StoreUTopology. | All tree information except the structure | will remain unaltered. | --------------------------------------------------------*/ int RetrieveUTopology (Tree *t, int *order) { int i, numTips; TreeNode *p, *q, *r; /* preliminaries */ numTips = t->nNodes - t->nIntNodes; for (i=0; inNodes; i++) t->nodes[i].left = t->nodes[i].right = t->nodes[i].anc = NULL; /* sort the tips in the t->allDownPass array */ p = t->nodes; for (i=0; inNodes; i++, p++) t->allDownPass[p->index] = p; /* make sure root has index 0 */ q = t->allDownPass[0]; q->anc = q->right = NULL; t->root = q; /* connect the first three tips */ p = t->allDownPass[numTips]; p->anc = q; q->left = p; q = t->allDownPass[1]; r = t->allDownPass[2]; p->left = q; p->right = r; q->anc = r->anc = p; /* add one tip at a time */ for (i=3; iallDownPass[i]; q = t->allDownPass[numTips-2+i]; r = t->allDownPass[order[i-3]]; p->anc = q; q->left = p; q->right = r; q->anc = r->anc; if (r->anc->left == r) r->anc->left = q; else r->anc->right = q; r->anc = q; } /* get downpass */ GetDownPass (t); /* relabel interior nodes (root is correctly labeled already) */ for (i=0; inIntNodes; i++) t->intDownPass[i]->index = i+numTips; return (NO_ERROR); } /*------------------------------------------------------- | | RetrieveUTree: This routine will rebuild an unrooted | tree from the arrays created by StoreUTree. | All tree information except the structure and | branch lengths will remain unaltered. | --------------------------------------------------------*/ int RetrieveUTree (Tree *t, int *order, MrBFlt *brlens) { int i, numTips; TreeNode *p, *q, *r; /* preliminaries */ numTips = t->nNodes - t->nIntNodes; for (i=0; inNodes; i++) t->nodes[i].left = t->nodes[i].right = t->nodes[i].anc = NULL; /* sort the tips in the t->allDownPass array */ p = t->nodes; for (i=0; inNodes; i++, p++) t->allDownPass[p->index] = p; /* make sure that root has index 0 */ q = t->allDownPass[0]; q->anc = q->right = NULL; t->root = q; /* connect the first three tips */ p = t->allDownPass[numTips]; p->anc = q; q->left = p; p->length = *(brlens++); q = t->allDownPass[1]; r = t->allDownPass[2]; p->left = q; p->right = r; q->anc = r->anc = p; q->length = *(brlens++); r->length = *(brlens++); /* add one tip at a time */ for (i=3; iallDownPass[i]; q = t->allDownPass[numTips-2+i]; r = t->allDownPass[order[i-3]]; p->anc = q; q->left = p; q->right = r; q->anc = r->anc; if (r->anc->left == r) r->anc->left = q; else r->anc->right = q; r->anc = q; q->length = *(brlens++); p->length = *(brlens++); } /* get downpass */ GetDownPass (t); /* relabel interior nodes (root is correctly labeled already) */ for (i=0; inIntNodes; i++) t->intDownPass[i]->index = i+numTips; return (NO_ERROR); } void SetDatedNodeAges (Param *param, int chain, int state) { int i; MrBFlt clockRate; ModelInfo *m; TreeNode *p; Tree *t; extern void ShowNodes(TreeNode *,int,int); t = GetTree (param, chain, state); m = &modelSettings[t->relParts[0]]; if (m->clockRate == NULL) clockRate = 1.0; else clockRate = *GetParamVals(m->clockRate, chain, state); for (i=0; inNodes-1; i++) { p = t->allDownPass[i]; if (p->isDated == YES) p->age = p->nodeDepth / clockRate; else p->age = -1.0; } } void SetNodeDepths (Tree *t) { int i; MrBFlt d1, d2; TreeNode *p; extern void ShowNodes(TreeNode *,int,int); for (i=0; inNodes-1; i++) { p = t->allDownPass[i]; if (p->left == NULL) p->nodeDepth = 0.0; else { d1 = p->left->nodeDepth + p->left->length; d2 = p->right->nodeDepth + p->right->length; //assert (!(t->isCalibrated == NO && AreDoublesEqual(d1,d2,0.00001)==NO)); // may not work if we set startval topology of strict clock tree by non clock tree. if (d1 > d2) p->nodeDepth = d1; else p->nodeDepth = d2; } } for (i=t->nNodes-3; i>=0; i--) { p = t->allDownPass[i]; if (p->left == NULL && p->calibration == NULL) p->nodeDepth = 0.0; else p->nodeDepth = p->anc->nodeDepth - p->length; } } /* Set ages of a clock tree according to depth and clockrate. Check that resulting ages are consistent with calibration. | return YES if tree is age consistent, No otherwise. */ int SetTreeNodeAges (Param *param, int chain, int state) { Tree *tree; TreeNode *p; int i; MrBFlt clockRate; if (param->paramType != P_TOPOLOGY && param->paramType != P_BRLENS && param->paramType != P_SPECIESTREE) return YES; tree = GetTree(param, chain, state); if (modelSettings[param->relParts[0]].clockRate != NULL) clockRate = *GetParamVals(modelSettings[param->relParts[0]].clockRate, chain, state); else return YES; /* Clock trees */ /* Check that lengths and depths are consistent. That would work for the case when we set up branch length from starting tree */ for (i=0; inNodes-1; i++) { p = tree->allDownPass[i]; p->age = p->nodeDepth / clockRate; } /* Check that ages and calibrations are consistent */ if (tree->isCalibrated == YES) { for (i=0; inNodes-1; i++) { p = tree->allDownPass[i]; if (p->isDated == YES) { if (p->calibration->prior == fixed && fabs((p->age - p->calibration->priorParams[0])/p->age) > 0.000001) { printf ("Node %d has age %f but should be fixed to age %f\n", p->index, p->age, p->calibration->priorParams[0]); return NO; } else if (p->calibration->prior == uniform && (p->age < p->calibration->min || p->age > p->calibration->max)) { printf ("Node %d has age %f but should be in the interval [%f,%f]\n", p->index, p->age, p->calibration->min, p->calibration->max); return NO; } else if (p->age < p->calibration->min) { printf ("Node %d has age %f but should be minimally of age %f\n", p->index, p->age, p->calibration->min); return NO; } else if (p->age > p->calibration->max) { printf ("Node %d has age %f but should be maximally of age %f\n", p->index, p->age, p->calibration->max); return NO; } } } } return YES; } int ShowPolyNodes (PolyTree *pt) { int i; PolyNode *p; /* this is the tree, on a node-by-node basis */ printf (" memnodes = %d nNodes = %d nIntNodes = %d root = %d\n", pt->memNodes, pt->nNodes, pt->nIntNodes, pt->root->index); printf (" isRooted = %d\n", pt->isRooted); printf (" no. index (left sib anc) -- locked/free -- label (p->x)\n"); for (i=0; imemNodes; i++) { p = &pt->nodes[i]; if (!(p->left == NULL && p->sib == NULL && p->anc == NULL)) { printf ("%4d -- %4d ", i, p->index); if (p->left != NULL) printf ("(%4d ", p->left->index); else printf ("(null "); if (p->sib != NULL) printf ("%4d ", p->sib->index); else printf ("null "); if (p->anc != NULL) printf ("%4d)", p->anc->index); else printf ("null)"); if (p->isLocked == YES) printf ("-- locked -- "); else printf ("-- free --"); if (p->left == NULL && p->anc != NULL) printf (" \"%s\" (%d)\n", p->label, p->x); else printf (" \"\" (%d)\n", p->x); } } return NO_ERROR; } /* ShowTree: Show tree on screen */ int ShowTree (Tree *t) { int i, j, k, x, nLines, nLevels, levelDepth, from, to; char treeLine[SCREENWIDTH2], labelLine[100]; TreeNode *p; /* get coordinates */ x = 0; nLines = 0; for (i=0; inNodes; i++) { p = t->allDownPass[i]; if (p->left == NULL && p->right == NULL) { p->x = x; x += 2; p->y = 0; nLines += 2; } else if (p->left != NULL && p->right != NULL && p->anc != NULL) { p->x = p->left->x + (p->right->x - p->left->x) / 2; if (p->left->y > p->right->y) p->y = p->left->y + 1; else p->y = p->right->y + 1; } else { p->x = x; x += 2; p->y = 0; } } /* print tree out, line-by-line */ levelDepth = SCREENWIDTH / t->root->left->y; nLevels = t->root->left->y; for (j=0; j<=nLines-2; j++) { for (i=0; inNodes; i++) { p = t->allDownPass[i]; if (p->left == NULL && p->x == j) { strcpy (labelLine, p->label); } } } for (i=0; inNodes; i++) { p = t->allDownPass[i]; if (p->anc != NULL) { if (p->anc->anc != NULL) { if (p->x == j) { from = (nLevels - p->anc->y) * levelDepth; to = (nLevels - p->y) * levelDepth; if (p->y == 0) to = SCREENWIDTH-1; if (to >= SCREENWIDTH) to = SCREENWIDTH-1; for (k=from; kanc->left == p) treeLine[from] = '/'; else treeLine[from] = '\\'; if (p->left != NULL) { treeLine[to] = '+'; } if (p->anc->anc == t->root && p->anc->right == p) { if (t->isRooted == NO) treeLine[to] = '+'; else treeLine[from] = '\\'; } } else { if (p->left != NULL && p->right != NULL) { if (j < p->x && j > p->left->x) { from = (nLevels - p->y) * levelDepth; treeLine[from] = '|'; } else if (j > p->x && j < p->right->x && p->left != NULL) { from = (nLevels - p->y) * levelDepth; treeLine[from] = '|'; } } } } else { if (p->x == j) { treeLine[0] = '|'; /* temp */ } else if (j < p->x && j > p->left->x) { treeLine[0] = '|'; } else if (j > p->x && j < p->right->x) { treeLine[0] = '|'; } if (t->isRooted == NO) { if (j > p->x && j <= nLines-2) treeLine[0] = '|'; if (j == p->right->x) treeLine[0] = '+'; } else { if (j == p->x) treeLine[0] = '+'; } } } } treeLine[SCREENWIDTH-1] = '\0'; if (j % 2 == 0) MrBayesPrint (" %s %s\n", treeLine, labelLine); else MrBayesPrint (" %s \n", treeLine); } if (t->isRooted == NO) { for (i=0; iroot->label); labelLine[19] = '\0'; MrBayesPrint (" %s %s\n", treeLine, labelLine); } # if defined (DEBUG_CONSTRAINTS) for (i=0; inNodes; i++) printf ("%d -- %s\n", t->allDownPass[i]->index + 1, t->allDownPass[i]->isLocked == YES ? "locked" : "free"); # endif return (NO_ERROR); } /*------------------------------------------------------- | | StoreRPolyTopology: Same as StoreRTopology but for | binary polytree source trees. | --------------------------------------------------------*/ int StoreRPolyTopology (PolyTree *t, int *order) { int i, numTaxa; PolyNode *p, *q; /* find number of taxa */ numTaxa = t->nNodes - t->nIntNodes; /* first get the terminal taxon positions and store them in the order array. */ for (i=0; inNodes; i++) { p = t->allDownPass[i]; /* we do not need to worry about the first two taxa */ if (p->index > 1 && p->index < numTaxa) order[p->index-2] = i; } /* label the interior nodes with the correct index */ for (i=0; inNodes; i++) { p = t->allDownPass[i]; if (p->left == NULL) p->x = p->y = p->index; else { if (p->left->y < p->left->sib->y) { p->y = p->left->y; p->x = p->left->sib->y + numTaxa - 1; } else { p->y = p->left->sib->y; p->x = p->left->y + numTaxa - 1; } } } /* break the tree into pieces */ for (i=0; iallDownPass[order[numTaxa-3-i]]; q = p->anc; if (q->left == p) { order[numTaxa-3-i] = q->left->sib->x; p->sib->anc = q->anc; if (q->anc == NULL) { p->sib->left->sib->sib = p->sib->sib; p->sib->sib = NULL; } else if (q->anc->left == q) { q->anc->left = q->left->sib; p->sib->sib = q->sib; } else q->anc->left->sib = q->left->sib; } else { order[numTaxa-3-i] = q->left->x; q->left->anc = q->anc; if (q->anc == NULL) { q->left->left->sib->sib = p->sib; q->left->sib = NULL; } else if (q->anc->left == q) { q->anc->left = q->left; q->anc->left->sib = q->sib; } else { q->anc->left->sib = q->left; q->left->sib = NULL; } } } return (NO_ERROR); } /*------------------------------------------------------- | | StoreRPolyTree: Same as StoreRTree but for | binary rooted polytree source trees. | --------------------------------------------------------*/ int StoreRPolyTree (PolyTree *t, int *order, MrBFlt *brlens) { int i, j, numTaxa; PolyNode *p, *q; /* find number of taxa */ numTaxa = t->nNodes - t->nIntNodes; /* first get the terminal taxon positions and store them in the order array. */ for (i=0; inNodes; i++) { p = t->allDownPass[i]; /* we do not need to worry about the first two taxa */ if (p->index > 1 && p->index < numTaxa) order[p->index-2] = i; } /* label the interior nodes with the correct index */ for (i=0; inNodes; i++) { p = t->allDownPass[i]; if (p->left == NULL) p->x = p->y = p->index; else { if (p->left->y < p->left->sib->y) { p->y = p->left->y; p->x = p->left->sib->y + numTaxa - 1; } else { p->y = p->left->sib->y; p->x = p->left->y + numTaxa - 1; } } } /* break the tree into pieces */ j = t->nNodes - 2; /* index of first branch length */ for (i=0; iallDownPass[order[numTaxa-3-i]]; q = p->anc; brlens[j--] = p->length; brlens[j--] = q->length; if (q->left == p) { order[numTaxa-3-i] = q->left->sib->x; p->sib->anc = q->anc; if (q->anc == NULL) { p->sib->left->sib->sib = p->sib->sib; p->sib->sib = NULL; } else if (q->anc->left == q) { q->anc->left = q->left->sib; p->sib->sib = q->sib; } else q->anc->left->sib = q->left->sib; } else { order[numTaxa-3-i] = q->left->x; q->left->anc = q->anc; if (q->anc == NULL) { q->left->left->sib->sib = p->sib; q->left->sib = NULL; } else if (q->anc->left == q) { q->anc->left = q->left; q->anc->left->sib = q->sib; } else { q->anc->left->sib = q->left; q->left->sib = NULL; } } } /* store the last two lengths; index 0 and 1 */ p = t->root; brlens[p->left->index] = p->left->length; brlens[p->left->sib->index] = p->left->sib->length; return (NO_ERROR); } /*------------------------------------------------------- | | StoreRTopology: This routine will break a rooted tree | into an array of ints describing the structure | of the tree. The tree will be destroyed | in the process (the node pointers, that is). | However, the tree is not deleted. | --------------------------------------------------------*/ int StoreRTopology (Tree *t, int *order) { int i, numTaxa; TreeNode *p, *q; /* find number of taxa */ numTaxa = t->nNodes - t->nIntNodes - 1; /* first get the terminal taxon positions and store them in the order array. */ for (i=0; inNodes; i++) { p = t->allDownPass[i]; /* we do not need to worry about the first two taxa */ if (p->index > 1 && p->index < numTaxa) order[p->index-2] = i; } /* label the interior nodes with the correct index */ for (i=0; inNodes; i++) { p = t->allDownPass[i]; if (p->left == NULL) p->x = p->y = p->index; else if (p->right != NULL) { if (p->left->y < p->right->y) { p->y = p->left->y; p->x = p->right->y + numTaxa - 1; } else { p->y = p->right->y; p->x = p->left->y + numTaxa - 1; } } } /* break the tree into pieces */ for (i=0; iallDownPass[order[numTaxa-3-i]]; q = p->anc; if (q->left == p) { order[numTaxa-3-i] = q->right->x; q->right->anc = q->anc; if (q->anc->left == q) q->anc->left = q->right; else q->anc->right = q->right; } else { order[numTaxa-3-i] = q->left->x; q->left->anc = q->anc; if (q->anc->left == q) q->anc->left = q->left; else q->anc->right = q->left; } } return (NO_ERROR); } /*------------------------------------------------------- | | StoreRTree: This routine will break a rooted tree | into an array of ints describing the structure | of the tree and an array of doubles storing | the branch lengths. The tree will be | destroyed in the process (the node pointers, | that is). However, the tree is not deleted. | --------------------------------------------------------*/ int StoreRTree (Tree *t, int *order, MrBFlt *brlens) { int i, j, numTaxa; TreeNode *p, *q; extern void ShowNodes (TreeNode *p, int indent, int isRooted); /* find number of taxa */ numTaxa = t->nNodes - t->nIntNodes - 1; /* first get the terminal taxon positions and store them in the order array. */ for (i=0; inNodes; i++) { p = t->allDownPass[i]; /* we do not need to worry about the first two taxa */ if (p->index > 1 && p->index < numTaxa) order[p->index-2] = i; } /* label the interior nodes with the correct index */ for (i=0; inNodes; i++) { p = t->allDownPass[i]; if (p->left == NULL) p->x = p->y = p->index; else if (p->right != NULL) { if (p->left->y < p->right->y) { p->y = p->left->y; p->x = p->right->y + numTaxa - 1; } else { p->y = p->right->y; p->x = p->left->y + numTaxa - 1; } } } /* break the tree into pieces */ j = 2 * numTaxa - 3; for (i=0; iallDownPass[order[numTaxa-3-i]]; q = p->anc; brlens[j--] = p->length; if (q->left == p) { if (q->anc->anc != NULL) brlens[j--] = q->length; else brlens[j--] = q->right->length; order[numTaxa-3-i] = q->right->x; q->right->anc = q->anc; if (q->anc->left == q) q->anc->left = q->right; else q->anc->right = q->right; } else { if (q->anc->anc != NULL) brlens[j--] = q->length; else brlens[j--] = q->left->length; order[numTaxa-3-i] = q->left->x; q->left->anc = q->anc; if (q->anc->left == q) q->anc->left = q->left; else q->anc->right = q->left; } } /* store the final two branch lengths in the right order; they have indices 0 and 1 */ p = t->root->left; brlens[p->left->index] = p->left->length; brlens[p->right->index] = p->right->length; return (NO_ERROR); } /*------------------------------------------------------- | | StoreRTreeWithIndices: This routine will break a rooted | tree into an array of ints describing the structure | of the tree and the interior node indices, and an array | of doubles storing the branch lengths. The tree will be | destroyed in the process (the node pointers, | that is). However, the tree is not deleted. | --------------------------------------------------------*/ int StoreRTreeWithIndices (Tree *t, int *order, MrBFlt *brlens) { int i, j, k, numTaxa; TreeNode *p, *q; extern void ShowNodes (TreeNode *p, int indent, int isRooted); /* find number of taxa */ numTaxa = t->nNodes - t->nIntNodes - 1; /* first get the terminal taxon positions and store them in the order array. */ for (i=0; inNodes; i++) { p = t->allDownPass[i]; /* we do not need to worry about the first two taxa */ if (p->index > 1 && p->index < numTaxa) order[p->index-2] = i; } /* label the interior nodes with the correct index */ for (i=0; inNodes; i++) { p = t->allDownPass[i]; if (p->left == NULL) p->x = p->y = p->index; else if (p->right != NULL) { if (p->left->y < p->right->y) { p->y = p->left->y; p->x = p->right->y + numTaxa - 1; } else { p->y = p->right->y; p->x = p->left->y + numTaxa - 1; } } } /* break the tree into pieces */ j = 2 * numTaxa - 3; k = 2*(numTaxa - 2); for (i=0; iallDownPass[order[numTaxa-3-i]]; q = p->anc; brlens[j--] = p->length; if (q->left == p) { if (q->anc->anc != NULL) brlens[j--] = q->length; else brlens[j--] = q->right->length; order[k--] = q->right->x; order[k--] = q->index; q->right->anc = q->anc; if (q->anc->left == q) q->anc->left = q->right; else q->anc->right = q->right; } else { if (q->anc->anc != NULL) brlens[j--] = q->length; else brlens[j--] = q->left->length; order[k--] = q->left->x; order[k--] = q->index; q->left->anc = q->anc; if (q->anc->left == q) q->anc->left = q->left; else q->anc->right = q->left; } } /* store the final two branch lengths in the right order; they have indices 0 and 1 */ p = t->root->left; order[k] = p->index; brlens[p->left->index] = p->left->length; brlens[p->right->index] = p->right->length; return (NO_ERROR); } /*------------------------------------------------------- | | StoreUPolyTopology: Same as StoreUTopology but for | binary polytree source. | --------------------------------------------------------*/ int StoreUPolyTopology (PolyTree *t, int *order) { int i, numTips; PolyNode *p, *q; /* check if the tree is rooted on taxon 0 */ if (t->root->left->sib->sib->index != 0) MovePolyCalculationRoot (t, 0); /* rearrange the root */ t->root->anc = t->root->left->sib->sib; t->root->left->sib->sib = NULL; t->root->anc->left = t->root; t->root->anc->sib = NULL; t->root->anc->anc = NULL; t->root = t->root->anc; /* find number of tips */ numTips = t->nNodes - t->nIntNodes; /* first get the terminal taxon positions and store them in the order array. */ for (i=0; inNodes; i++) { p = t->allDownPass[i]; /* we do not need to worry about the first three taxa */ if (p->index > 2 && p->index < numTips) order[p->index-3] = i; } /* label the interior nodes with the correct index */ for (i=0; inNodes; i++) { p = t->allDownPass[i]; if (p->left == NULL || p->anc == NULL) p->x = p->y = p->index; else { if (p->left->y < p->left->sib->y) { p->y = p->left->y; p->x = p->left->sib->y + numTips - 2; } else { p->y = p->left->sib->y; p->x = p->left->y + numTips - 2; } } } /* break the tree into pieces */ for (i=0; iallDownPass[order[numTips-4-i]]; q = p->anc; if (q->left == p) { order[numTips-4-i] = q->left->sib->x; p->sib->anc = q->anc; if (q->anc->left == q) { q->anc->left = p->sib; p->sib->sib = q->sib; } else { q->anc->left->sib = p->sib; p->sib->sib = q->sib; } } else { order[numTips-4-i] = q->left->x; q->left->anc = q->anc; if (q->anc->left == q) { q->anc->left = q->left; q->left->sib = q->sib; } else { q->anc->left->sib = q->left; q->left->sib = q->sib; } } } return (NO_ERROR); } /*------------------------------------------------------- | | StoreUPolyTree: Same as StoreUTopology but for | binary polytree source. | --------------------------------------------------------*/ int StoreUPolyTree (PolyTree *t, int *order, MrBFlt *brlens) { int i, j, numTips; PolyNode *p, *q; /* check if the tree is rooted on taxon 0 */ if (t->root->left->sib->sib->index != 0) MovePolyCalculationRoot (t, 0); /* rearrange the root */ t->root->anc = t->root->left->sib->sib; t->root->left->sib->sib = NULL; t->root->anc->left = t->root; t->root->anc->sib = NULL; t->root->anc->anc = NULL; t->root = t->root->anc; /* find number of tips */ numTips = t->nNodes - t->nIntNodes; /* first get the terminal taxon positions and store them in the order array. */ for (i=0; inNodes; i++) { p = t->allDownPass[i]; /* we do not need to worry about the first three taxa */ if (p->index > 2 && p->index < numTips) order[p->index-3] = i; } /* label the interior nodes with the correct index */ for (i=0; inNodes; i++) { p = t->allDownPass[i]; if (p->left == NULL || p->anc == NULL) p->x = p->y = p->index; else { if (p->left->y < p->left->sib->y) { p->y = p->left->y; p->x = p->left->sib->y + numTips - 2; } else { p->y = p->left->sib->y; p->x = p->left->y + numTips - 2; } } } /* break the tree into pieces */ j = 2*numTips - 4; for (i=0; iallDownPass[order[numTips-4-i]]; assert (p->index > 2 && p->index < numTips); assert (p->anc->anc != NULL); q = p->anc; brlens[j--] = p->length; brlens[j--] = q->length; if (q->left == p) { order[numTips-4-i] = q->left->sib->x; p->sib->anc = q->anc; if (q->anc->left == q) { q->anc->left = p->sib; p->sib->sib = q->sib; } else { q->anc->left->sib = p->sib; p->sib->sib = q->sib; } } else { order[numTips-4-i] = q->left->x; q->left->anc = q->anc; if (q->anc->left == q) { q->anc->left = q->left; q->left->sib = q->sib; } else { q->anc->left->sib = q->left; q->left->sib = q->sib; } } } /* store last three branch lengths, index 0, 1, 2 */ q = t->root; assert (q->index == 0); brlens[q->index] = q->length; q = q->left->left; assert (q->index == 1 || q->index == 2); brlens[q->index] = q->length; q = q->sib; assert (q->index == 1 || q->index == 2); brlens[q->index] = q->length; return (NO_ERROR); } /*------------------------------------------------------- | | StoreUTopology: This routine will break an unrooted tree | into an array of ints describing the structure | of the tree. The tree will be destroyed | in the process (the node pointers, that is). | However, the tree is not deleted. | --------------------------------------------------------*/ int StoreUTopology (Tree *t, int *order) { int i, numTips; TreeNode *p, *q; /* check if the tree is rooted on taxon 0 */ if (t->root->index != 0) MoveCalculationRoot (t, 0); /* find number of tips */ numTips = t->nNodes - t->nIntNodes; /* first get the terminal taxon positions and store them in the order array. */ for (i=0; inNodes; i++) { p = t->allDownPass[i]; /* we do not need to worry about the first three taxa */ if (p->index > 2 && p->index < numTips) order[p->index-3] = i; } /* label the interior nodes with the correct index */ for (i=0; inNodes; i++) { p = t->allDownPass[i]; if (p->left == NULL) p->x = p->y = p->index; else if (p->right != NULL) { if (p->left->y < p->right->y) { p->y = p->left->y; p->x = p->right->y + numTips - 2; } else { p->y = p->right->y; p->x = p->left->y + numTips - 2; } } } /* break the tree into pieces */ for (i=0; iallDownPass[order[numTips-4-i]]; q = p->anc; if (q->left == p) { order[numTips-4-i] = q->right->x; q->right->anc = q->anc; if (q->anc->left == q) q->anc->left = q->right; else q->anc->right = q->right; } else { order[numTips-4-i] = q->left->x; q->left->anc = q->anc; if (q->anc->left == q) q->anc->left = q->left; else q->anc->right = q->left; } } return (NO_ERROR); } /*------------------------------------------------------- | | StoreUTree: This routine will break an unrooted tree | into an array of ints describing the structure | of the tree and an array of doubles storing | the branch lengths. The tree will be | destroyed in the process (the node pointers, | that is). However, the tree is not deleted. | --------------------------------------------------------*/ int StoreUTree (Tree *t, int *order, MrBFlt *brlens) { int i, j, numTips; TreeNode *p, *q; /* check if the tree is rooted on taxon 0 */ if (t->root->index != 0) MoveCalculationRoot(t, 0); /* find number of tips */ numTips = t->nNodes - t->nIntNodes; /* first get the terminal taxon positions and store them in the order array. */ for (i=0; inNodes; i++) { p = t->allDownPass[i]; /* we do not need to worry about the first three taxa */ if (p->index > 2 && p->index < numTips) order[p->index-3] = i; } /* label the interior nodes with the correct index */ for (i=0; inNodes; i++) { p = t->allDownPass[i]; if (p->left == NULL) p->x = p->y = p->index; else if (p->right != NULL) { if (p->left->y < p->right->y) { p->y = p->left->y; p->x = p->right->y + numTips - 2; } else { p->y = p->right->y; p->x = p->left->y + numTips - 2; } } } /* break the tree into pieces */ j = 2 * numTips - 4; for (i=0; iallDownPass[order[numTips-4-i]]; q = p->anc; brlens[j--] = p->length; brlens[j--] = q->length; if (q->left == p) { order[numTips-4-i] = q->right->x; q->right->anc = q->anc; if (q->anc->left == q) q->anc->left = q->right; else q->anc->right = q->right; } else { order[numTips-4-i] = q->left->x; q->left->anc = q->anc; if (q->anc->left == q) q->anc->left = q->left; else q->anc->right = q->left; } } /* store the final three branch lengths */ /* we need to check the rotation of the tree to store the brlens in the right order (after node index) */ p = t->root->left; if (p->right->index == 2) { brlens[j--] = p->right->length; brlens[j--] = p->left->length; } else { brlens[j--] = p->left->length; brlens[j--] = p->right->length; } brlens[j--] = p->length; return (NO_ERROR); } /* TreeLength: Calculate tree length */ MrBFlt TreeLen (Tree *t) { int i, numLenNodes; MrBFlt len = 0.0; if (t->isRooted == NO) numLenNodes = t->nNodes - 1; else numLenNodes = t->nNodes - 2; for (i=0; iallDownPass[i]->length; return len; } /*------------------------------------------------------------------------------------------- | | Unmark: This routine will unmark a subtree rooted at p | ---------------------------------------------------------------------------------------------*/ void Unmark (TreeNode *p) { if (p != NULL) { p->marked = NO; Unmark (p->left); Unmark (p->right); } } void WriteEventTree (TreeNode *p, int chain, Param *param) { int j, nEvents; MrBFlt brlen, *position, *rateMult; if (p != NULL) { if (p->left == NULL && p->right == NULL) { printf ("%d:%s", p->index + 1, MbPrintNum(p->length)); if (param->paramType == P_CPPEVENTS) { nEvents = param->nEvents[2*chain+state[chain]][p->index]; if (nEvents > 0) { printf ("[&E %s %d: (", param->name, nEvents); position = param->position[2*chain+state[chain]][p->index]; rateMult = param->rateMult[2*chain+state[chain]][p->index]; for (j=0; jname); } brlen = GetParamSubVals (param, chain, state[chain])[p->index]; // brlen = (GetParamSubVals (param, chain, state[chain])[p->index] + GetParamVals (param, chain, state[chain])[p->anc->index]) / 2.0; printf ("[&B %s %s]", param->name, MbPrintNum(brlen)); } else { if (p->anc != NULL) printf ("("); WriteEventTree(p->left, chain, param); printf (","); WriteEventTree(p->right, chain, param); if (p->anc != NULL) { if (p->anc->anc != NULL) { printf ("):%s", MbPrintNum(p->length)); if (param->paramType == P_CPPEVENTS) { nEvents = param->nEvents[2*chain+state[chain]][p->index]; if (nEvents > 0) { printf ("[&E %s %d: (", param->name, nEvents); position = param->position[2*chain+state[chain]][p->index]; rateMult = param->rateMult[2*chain+state[chain]][p->index]; for (j=0; jname); } brlen = GetParamSubVals (param, chain, state[chain])[p->index]; // brlen = (GetParamSubVals (param, chain, state[chain])[p->index] + GetParamVals (param, chain, state[chain])[p->anc->index]) / 2.0; printf ("[&B %s %s]", param->name, MbPrintNum(brlen)); } else printf (")"); } } } } void WriteEventTreeToPrintString (TreeNode *p, int chain, Param *param, int printAll) { char *tempStr; int i, j, nEvents, tempStrSize = TEMPSTRSIZE; MrBFlt brlen, *position, *rateMult; tempStr = (char *) SafeMalloc((size_t)tempStrSize * sizeof(char)); if (!tempStr) MrBayesPrint ("%s Problem allocating tempString (%d)\n", spacer, tempStrSize * sizeof(char)); if (p != NULL) { if (p->left == NULL && p->right == NULL) { SafeSprintf (&tempStr, &tempStrSize, "%d:%s", p->index + 1, MbPrintNum(p->length)); AddToPrintString (tempStr); for (i=0; inSubParams; i++) { if (param->subParams[i]->paramType == P_CPPEVENTS) { nEvents = param->subParams[i]->nEvents[2*chain+state[chain]][p->index]; if (nEvents > 0) { SafeSprintf (&tempStr, &tempStrSize, "[&E %s %d", param->subParams[i]->name, nEvents); AddToPrintString (tempStr); position = param->subParams[i]->position[2*chain+state[chain]][p->index]; rateMult = param->subParams[i]->rateMult[2*chain+state[chain]][p->index]; if (printAll == YES) { SafeSprintf (&tempStr, &tempStrSize, ": ("); AddToPrintString (tempStr); for (j=0; jsubParams[i]->name); AddToPrintString (tempStr); } } else if (param->subParams[i]->paramType != P_CPPEVENTS) { /* other relaxed clock models */ brlen = GetParamSubVals (param->subParams[i], chain, state[chain])[p->index]; SafeSprintf (&tempStr, &tempStrSize, "[&B %s %s]", param->subParams[i]->name, MbPrintNum(brlen)); AddToPrintString (tempStr); } } } else { if (p->anc != NULL) AddToPrintString ("("); WriteEventTreeToPrintString (p->left, chain, param, printAll); AddToPrintString (","); WriteEventTreeToPrintString (p->right, chain, param, printAll); if (p->anc != NULL) { if (p->anc->anc != NULL) { SafeSprintf (&tempStr, &tempStrSize, "):%s", MbPrintNum(p->length)); AddToPrintString (tempStr); for (i=0; inSubParams; i++) { if (param->subParams[i]->paramType == P_CPPEVENTS) { nEvents = param->subParams[i]->nEvents[2*chain+state[chain]][p->index]; if (nEvents > 0) { SafeSprintf (&tempStr, &tempStrSize, "[&E %s %d", param->subParams[i]->name, nEvents); AddToPrintString (tempStr); position = param->subParams[i]->position[2*chain+state[chain]][p->index]; rateMult = param->subParams[i]->rateMult[2*chain+state[chain]][p->index]; if (printAll == YES) { SafeSprintf (&tempStr, &tempStrSize, ": ("); AddToPrintString (tempStr); for (j=0; jsubParams[i]->name); AddToPrintString (tempStr); } } else if (param->subParams[i]->paramType != P_CPPEVENTS) { /* other relaxed clock models */ brlen = GetParamSubVals (param->subParams[i], chain, state[chain])[p->index]; SafeSprintf (&tempStr, &tempStrSize, "[&B %s %s]", param->subParams[i]->name, MbPrintNum(brlen)); AddToPrintString (tempStr); } } } else AddToPrintString(")"); } } } free (tempStr); } void WriteEvolTree (TreeNode *p, int chain, Param *param) { MrBFlt *length; if (p != NULL) { length = GetParamSubVals(param, chain, state[chain]); if (p->left == NULL && p->right == NULL) { printf ("%d:%s", p->index + 1, MbPrintNum(length[p->index])); } else { if (p->anc != NULL) printf ("("); WriteEvolTree(p->left, chain, param); printf (","); WriteEvolTree(p->right, chain, param); if (p->anc != NULL) { if (p->anc->anc != NULL) printf ("):%s", MbPrintNum(length[p->index])); else printf (")"); } } } } void WriteNoEvtTreeToPrintString (TreeNode *p, int chain, Param *param, int showBrlens, int isRooted) { char *tempStr; int i, tempStrSize = TEMPSTRSIZE, nEvents; MrBFlt brlen, N; tempStr = (char *) SafeMalloc((size_t)tempStrSize * sizeof(char)); if (!tempStr) MrBayesPrint ("%s Problem allocating tempString (%d)\n", spacer, tempStrSize * sizeof(char)); if (p != NULL) { if (p->left == NULL && p->right == NULL) { if (showBrlens == YES) { SafeSprintf (&tempStr, &tempStrSize, "%d:%s", p->index + 1, MbPrintNum(p->length)); } else SafeSprintf (&tempStr, &tempStrSize, "%d", p->index + 1); AddToPrintString (tempStr); if (param->paramType == P_BRLENS) { for (i=0; inSubParams; i++) { if (param->subParams[i]->paramType == P_CPPEVENTS) { nEvents = param->subParams[i]->nEvents[2*chain+state[chain]][p->index]; SafeSprintf (&tempStr, &tempStrSize, "[&E %s %d]", param->subParams[i]->name, nEvents); AddToPrintString (tempStr); } brlen = GetParamSubVals (param->subParams[i], chain, state[chain])[p->index]; SafeSprintf (&tempStr, &tempStrSize, "[&B %s %s]", param->subParams[i]->name, MbPrintNum(brlen)); AddToPrintString (tempStr); } } else if (param->paramType == P_SPECIESTREE && modelSettings[param->relParts[0]].popSize->nValues > 1) { N = GetParamVals (modelSettings[param->relParts[0]].popSize, chain, state[chain])[p->index]; SafeSprintf (&tempStr, &tempStrSize, "[&N %s %s]", modelSettings[param->relParts[0]].popSize->name, MbPrintNum(N)); AddToPrintString (tempStr); } } else { if (p->anc != NULL) AddToPrintString ("("); WriteNoEvtTreeToPrintString (p->left, chain, param, showBrlens, isRooted); if (p->anc != NULL) AddToPrintString (","); WriteNoEvtTreeToPrintString (p->right, chain, param, showBrlens, isRooted); if (p->anc != NULL) { if (p->anc->anc == NULL && isRooted == NO) { if (showBrlens == YES) SafeSprintf (&tempStr, &tempStrSize, ",%d:%s)", p->anc->index + 1, MbPrintNum(p->length)); else SafeSprintf (&tempStr, &tempStrSize, ",%d)", p->anc->index + 1); AddToPrintString (tempStr); } else if (p->anc->anc != NULL) { if (showBrlens == YES) SafeSprintf (&tempStr, &tempStrSize, "):%s", MbPrintNum(p->length)); else SafeSprintf (&tempStr, &tempStrSize, ")"); AddToPrintString (tempStr); if (param->paramType == P_BRLENS) { for (i=0; inSubParams; i++) { if (param->subParams[i]->paramType == P_CPPEVENTS) { nEvents = param->subParams[i]->nEvents[2*chain+state[chain]][p->index]; SafeSprintf (&tempStr, &tempStrSize, "[&E %s %d]", param->subParams[i]->name, nEvents); AddToPrintString (tempStr); } brlen = GetParamSubVals (param->subParams[i], chain, state[chain])[p->index]; SafeSprintf (&tempStr, &tempStrSize, "[&B %s %s]", param->subParams[i]->name, MbPrintNum(brlen)); AddToPrintString (tempStr); } } else if (param->paramType == P_SPECIESTREE && modelSettings[param->relParts[0]].popSize->nValues > 1) { N = GetParamVals (modelSettings[param->relParts[0]].popSize, chain, state[chain])[p->index]; SafeSprintf (&tempStr, &tempStrSize, "[&N %s %s]", modelSettings[param->relParts[0]].popSize->name, MbPrintNum(N)); AddToPrintString (tempStr); } } else if (param->paramType == P_SPECIESTREE && modelSettings[param->relParts[0]].popSize->nValues > 1) { N = GetParamVals (modelSettings[param->relParts[0]].popSize, chain, state[chain])[p->index]; SafeSprintf (&tempStr, &tempStrSize, ")[&N %s %s]", modelSettings[param->relParts[0]].popSize->name, MbPrintNum(N)); AddToPrintString (tempStr); } else AddToPrintString(")"); } } } free (tempStr); } /* WriteTopologyToFile: Simply write topology to file */ void WriteTopologyToFile (FILE *fp, TreeNode *p, int isRooted) { if (p != NULL) { if (p->left == NULL && p->right == NULL) fprintf (fp, "%d", p->index + 1); else { if (p->anc != NULL) fprintf (fp, "("); WriteTopologyToFile (fp, p->left, isRooted); if (p->anc != NULL) fprintf (fp, ","); WriteTopologyToFile (fp, p->right, isRooted); if (p->anc != NULL) { if (p->anc->anc == NULL && isRooted == NO) fprintf (fp, ",%d", p->anc->index + 1); fprintf (fp, ")"); } } } } /* the following are moved from mbmath.c */ /*--------------------------------------------------------------------------------- | | AddTwoMatrices | | Takes the sum of two matrices, "a" and "b", and puts the results in a matrix | called "result". | ---------------------------------------------------------------------------------*/ void AddTwoMatrices (int dim, MrBFlt **a, MrBFlt **b, MrBFlt **result) { int row, col; for (row=0; row 0) y -= M[(i-1)*K+j]; if (j > 0) y -= M[i*K+(j-1)]; if (i > 0 && j > 0) y += M[(i-1)*K+(j-1)]; M[i*K+j] = (M[i*K+j] + y) * K; } } for (i=0; i=0; i--) { dotProduct = 0.0; for (j=i+1; j=0; j--) { for (i=0; i<=l; i++) { if (i != j) { if (AreDoublesEqual(a[j][i],0.0, ETA)==NO) goto next_j1; } } /* bug that DLS caught */ /*m = l; Exchange(j, k, l, m, dim, a, scale); if (l < 0) goto leave; else j = --l;*/ m = l; Exchange(j, k, l, m, dim, a, scale); if (--l < 0) goto leave; next_j1: ; } for (j=k; j<=l; j++) { for (i=k; i<=l; i++) { if (i != j) { if (AreDoublesEqual(a[i][j], 0.0, ETA)==NO) goto next_j; } } m = k; Exchange(j, k, l, m, dim, a, scale); k++; next_j: ; } for (i=k; i<=l; i++) scale[i] = 1.0; do { noconv = FALSE; for (i=k; i<=l; i++) { c = 0.0; r = 0.0; for (j=k; j<=l; j++) { if (j != i) { c += fabs(a[j][i]); r += fabs(a[i][j]); } } if (AreDoublesEqual(c,0.0,ETA)==NO && AreDoublesEqual(r,0.0,ETA)==NO) { g = r / FLT_RADIX; f = 1.0; s = c + r; while (c < g) { f *= FLT_RADIX; c *= b2; } g = r * FLT_RADIX; while (c >= g) { f /= FLT_RADIX; c /= b2; } if ((c + r) / f < s * .95) { g = 1.0 / f; scale[i] *= f; noconv = TRUE; for (j=k; j= g) goto L220; f *= radix; c__ *= b2; goto L210; L220: g = r__ * radix; L230: if (c__ < g) goto L240; f /= radix; c__ /= b2; goto L230; /* .......... now balance .......... */ L240: if ((c__ + r__) / f >= s * .95) goto L270; g = 1.0 / f; scale[i__] *= f; noconv = TRUE; i__2 = *n; for (j = k; j <= i__2; ++j) { /* L250: */ a[i__ + j * a_dim1] *= g; } i__2 = l; for (j = 1; j <= i__2; ++j) { /* L260: */ a[j + i__ * a_dim1] *= f; } L270: ; } if (noconv) goto L190; L280: *low = k; *igh = l; return 0; } /* end f2c version of code */ # endif } /*--------------------------------------------------------------------------------- | | BalBak | | This subroutine forms the eigenvectors of a real general | matrix by back transforming those of the corresponding | balanced matrix determined by balance. | | On input: | | * dim is the order of the matrix | | * low and high are integers determined by balance | | * scale contains information determining the permutations | and scaling factors used by balance | | * m is the number of columns of z to be back transformed | | * z contains the real and imaginary parts of the eigen- | vectors to be back transformed in its first m columns | | On output: | | * z contains the real and imaginary parts of the | transformed eigenvectors in its first m columns | | This routine is a translation of the Algol procedure from | Handbook for Automatic Computation, vol. II, Linear Algebra, | by Wilkinson and Reinsch, Springer-Verlag. | ---------------------------------------------------------------------------------*/ void BalBak (int dim, int low, int high, MrBFlt *scale, int m, MrBFlt **z) { int i, j, k, ii; MrBFlt s; if (m != 0) /* change "==" to "!=" to eliminate a goto statement */ { if (high != low) /* change "==" to "!=" to eliminate a goto statement */ { for (i=low; i<=high; i++) { s = scale[i]; for (j=0; j high)) /* was (i >= lo) && (i<= hi) but this */ { /* eliminates a goto statement */ if (i < low) i = low - ii; k = (int)scale[i]; if (k != i) /* change "==" to "!=" to eliminate a goto statement */ { for (j = 0; j < m; j++) { s = z[i][j]; z[i][j] = z[k][j]; z[k][j] = s; } } } } } #if 0 /* begin f2c version of code: balbak.f -- translated by f2c (version 19971204) */ int balbak (int *nm, int *n, int *low, int *igh, MrBFlt *scale, int *m, MrBFlt *z__) { /* system generated locals */ int z_dim1, z_offset, i__1, i__2; /* Local variables */ static int i__, j, k; static MrBFlt s; static int ii; /* parameter adjustments */ --scale; z_dim1 = *nm; z_offset = z_dim1 + 1; z__ -= z_offset; /* function Body */ if (*m == 0) goto L200; if (*igh == *low) goto L120; i__1 = *igh; for (i__ = *low; i__ <= i__1; ++i__) { s = scale[i__]; /* .......... left hand eigenvectors are back transformed */ /* if the foregoing statement is replaced by */ /* s=1.0d0/scale(i) ........... */ i__2 = *m; for (j = 1; j <= i__2; ++j) { /* L100: */ z__[i__ + j * z_dim1] *= s; } /* L110: */ } /* .........for i=low-1 step -1 until 1, igh+1 step 1 until n do -- .......... */ L120: i__1 = *n; for (ii = 1; ii <= i__1; ++ii) { i__ = ii; if (i__ >= *low && i__ <= *igh) goto L140; if (i__ < *low) i__ = *low - ii; k = (integer) scale[i__]; if (k == i__) goto L140; i__2 = *m; for (j = 1; j <= i__2; ++j) { s = z__[i__ + j * z_dim1]; z__[i__ + j * z_dim1] = z__[k + j * z_dim1]; z__[k + j * z_dim1] = s; /* L130: */ } L140: ; } L200: return 0; } /* end f2c version of code */ #endif } void BetaBreaks (MrBFlt alpha, MrBFlt beta, MrBFlt *values, int K) { int i; MrBFlt r, quantile, lower, upper; lower = 0.0; upper = (1.0 / K); r = (upper - lower) * 0.5 + lower; for (i=0; i 100) { MrBayesPrint ("%s Error in BetaCf.\n", spacer); exit(0); } return (h); } MrBFlt BetaQuantile (MrBFlt alpha, MrBFlt beta, MrBFlt x) { int i, stopIter, direction, nswitches; MrBFlt curPos, curFraction, increment; i = nswitches = 0; curPos = 0.5; stopIter = NO; increment = 0.25; curFraction = IncompleteBetaFunction (alpha, beta, curPos); if (curFraction > x) direction = DOWN; else direction = UP; while (stopIter == NO) { curFraction = IncompleteBetaFunction (alpha, beta, curPos); if (curFraction > x && direction == DOWN) { /* continue going down */ while (curPos - increment <= 0.0) { increment /= 2.0; } curPos -= increment; } else if (curFraction > x && direction == UP) { /* switch directions, and go down */ nswitches++; direction = DOWN; while (curPos - increment <= 0.0) { increment /= 2.0; } increment /= 2.0; curPos -= increment; } else if (curFraction < x && direction == UP) { /* continue going up */ while (curPos + increment >= 1.0) { increment /= 2.0; } curPos += increment; } else if (curFraction < x && direction == DOWN) { /* switch directions, and go up */ nswitches++; direction = UP; while (curPos + increment >= 1.0) { increment /= 2.0; } increment /= 2.0; curPos += increment; } else { stopIter = YES; } if (i > 1000 || nswitches > 20) stopIter = YES; i++; } return (curPos); } /*--------------------------------------------------------------------------------- | | CalcCijk | | This function precalculates the product of the eigenvectors and their | inverse for faster calculation of transition probabilities. The output | is a vector of precalculated values. The input is the eigenvectors (u) and | the inverse of the eigenvector matrix (v). | ---------------------------------------------------------------------------------*/ void CalcCijk (int dim, MrBFlt *c_ijk, MrBFlt **u, MrBFlt **v) { register int i, j, k; MrBFlt *pc; pc = c_ijk; for (i=0; i limit) return (invers ? 0 : 1); if (x < t) p = 0.5 - x * (0.398942280444 - 0.399903438504 * y / (y + 5.75885480458 - 29.8213557808 / (y + 2.62433121679 + 48.6959930692 / (y + 5.92885724438)))); else p = 0.398942280385 * exp(-y) / (x - 3.8052e-8 + 1.00000615302 / (x + 3.98064794e-4 + 1.98615381364 / (x - 0.151679116635 + 5.29330324926 / (x + 4.8385912808 - 15.1508972451 / (x + 0.742380924027 + 30.789933034 / (x + 3.99019417011)))))); return (invers ? p : 1-p); } /*--------------------------------------------------------------------------------- | | Complex | | Returns a complex number with specified real and imaginary parts. | ---------------------------------------------------------------------------------*/ MrBComplex Complex (MrBFlt a, MrBFlt b) { MrBComplex c; c.re = a; c.im = b; return (c); } /*--------------------------------------------------------------------------------- | | ComplexAbsoluteValue | | Returns the complex absolute value (modulus) of a complex number. | ---------------------------------------------------------------------------------*/ MrBFlt ComplexAbsoluteValue (MrBComplex a) { MrBFlt x, y, answer, temp; x = fabs(a.re); y = fabs(a.im); if (AreDoublesEqual(x, 0.0, ETA)==YES) /* x == 0.0 */ answer = y; else if (AreDoublesEqual(y, 0.0, ETA)==YES) /* y == 0.0 */ answer = x; else if (x > y) { temp = y / x; answer = x * sqrt(1.0 + temp * temp); } else { temp = x / y; answer = y * sqrt(1.0 + temp * temp); } return (answer); } /*--------------------------------------------------------------------------------- | | ComplexAddition | | Returns the complex sum of two complex numbers. | ---------------------------------------------------------------------------------*/ MrBComplex ComplexAddition (MrBComplex a, MrBComplex b) { MrBComplex c; c.re = a.re + b.re; c.im = a.im + b.im; return (c); } /*--------------------------------------------------------------------------------- | | ComplexConjugate | | Returns the complex conjugate of a complex number. | ---------------------------------------------------------------------------------*/ MrBComplex ComplexConjugate (MrBComplex a) { MrBComplex c; c.re = a.re; c.im = -a.im; return (c); } /*--------------------------------------------------------------------------------- | | ComplexDivision | | Returns the complex quotient of two complex numbers. | ---------------------------------------------------------------------------------*/ MrBComplex ComplexDivision (MrBComplex a, MrBComplex b) { MrBComplex c; MrBFlt r, den; if (fabs(b.re) >= fabs(b.im)) { r = b.im / b.re; den = b.re + r * b.im; c.re = (a.re + r * a.im) / den; c.im = (a.im - r * a.re) / den; } else { r = b.re / b.im; den = b.im + r * b.re; c.re = (a.re * r + a.im) / den; c.im = (a.im * r - a.re) / den; } return (c); } /*--------------------------------------------------------------------------------- | | ComplexDivision2 | | Returns the complex quotient of two complex numbers. It does not require that | the numbers be in a complex structure. | ---------------------------------------------------------------------------------*/ void ComplexDivision2 (MrBFlt ar, MrBFlt ai, MrBFlt br, MrBFlt bi, MrBFlt *cr, MrBFlt *ci) { MrBFlt s, ais, bis, ars, brs; s = fabs(br) + fabs(bi); ars = ar / s; ais = ai / s; brs = br / s; bis = bi / s; s = brs*brs + bis*bis; *cr = (ars*brs + ais*bis) / s; *ci = (ais*brs - ars*bis) / s; } /*--------------------------------------------------------------------------------- | | ComplexExponentiation | | Returns the complex exponential of a complex number. | ---------------------------------------------------------------------------------*/ MrBComplex ComplexExponentiation (MrBComplex a) { MrBComplex c; c.re = exp(a.re); if (AreDoublesEqual(a.im,0.0, ETA)==YES) /* == 0 */ c.im = 0; else { c.im = c.re*sin(a.im); c.re *= cos(a.im); } return (c); } /*--------------------------------------------------------------------------------- | | ComplexInvertMatrix | | Inverts a matrix of complex numbers using the LU-decomposition method. | The program has the following variables: | | a -- the matrix to be inverted | aInverse -- the results of the matrix inversion | dim -- the dimension of the square matrix a and its inverse | dwork -- a work vector of doubles | indx -- a work vector of integers | col -- carries the results of the back substitution | | The function returns YES (1) or NO (0) if the results are singular. | ---------------------------------------------------------------------------------*/ int ComplexInvertMatrix (int dim, MrBComplex **a, MrBFlt *dwork, int *indx, MrBComplex **aInverse, MrBComplex *col) { int isSingular, i, j; isSingular = ComplexLUDecompose (dim, a, dwork, indx, (MrBFlt *)NULL); if (isSingular == 0) { for (j=0; j= 0) { for (j = ii; j <= i - 1; j++) sum = ComplexSubtraction (sum, ComplexMultiplication (a[i][j], b[j])); } else if (AreDoublesEqual(sum.re,0.0,ETA)==NO || AreDoublesEqual(sum.im, 0.0, ETA)==NO) /* 2x != 0.0 */ ii = i; b[i] = sum; } for (i = dim - 1; i >= 0; i--) { sum = b[i]; for (j = i + 1; j < dim; j++) sum = ComplexSubtraction (sum, ComplexMultiplication (a[i][j], b[j])); b[i] = ComplexDivision (sum, a[i][i]); } } /*--------------------------------------------------------------------------------- | | ComplexLUDecompose | | Replaces the matrix a with its LU-decomposition. | The program has the following variables: | | a -- the matrix | dim -- the dimension of the square matrix a and its inverse | vv -- a work vector of doubles | indx -- row permutation according to partial pivoting sequence | pd -- 1 if number of row interchanges was even, -1 if number of | row interchanges was odd. Can be NULL. | | The function returns YES (1) or NO (0) if the results are singular. | ---------------------------------------------------------------------------------*/ int ComplexLUDecompose (int dim, MrBComplex **a, MrBFlt *vv, int *indx, MrBFlt *pd) { int i, imax, j, k; MrBFlt big, dum, temp, d; MrBComplex sum, cdum; d = 1.0; imax = 0; for (i = 0; i < dim; i++) { big = 0.0; for (j = 0; j < dim; j++) { if ((temp = ComplexAbsoluteValue (a[i][j])) > big) big = temp; } if (AreDoublesEqual(big, 0.0, ETA)==YES) /* == 0.0 */ { MrBayesPrint ("%s Error: Problem in ComplexLUDecompose\n", spacer); return (1); } vv[i] = 1.0 / big; } for (j = 0; j < dim; j++) { for (i = 0; i < j; i++) { sum = a[i][j]; for (k = 0; k < i; k++) sum = ComplexSubtraction (sum, ComplexMultiplication (a[i][k], a[k][j])); a[i][j] = sum; } big = 0.0; for (i = j; i < dim; i++) { sum = a[i][j]; for (k = 0; k < j; k++) sum = ComplexSubtraction (sum, ComplexMultiplication (a[i][k], a[k][j])); a[i][j] = sum; dum = vv[i] * ComplexAbsoluteValue (sum); if (dum >= big) { big = dum; imax = i; } } if (j != imax) { for (k = 0; k < dim; k++) { cdum = a[imax][k]; a[imax][k] = a[j][k]; a[j][k] = cdum; } d = -d; vv[imax] = vv[j]; } indx[j] = imax; if (AreDoublesEqual(a[j][j].re, 0.0, ETA)==YES && AreDoublesEqual(a[j][j].im, 0.0, ETA)==YES) /* 2x == 0.0 */ a[j][j] = Complex (1.0e-20, 1.0e-20); if (j != dim - 1) { cdum = ComplexDivision (Complex(1.0, 0.0), a[j][j]); for (i = j + 1; i < dim; i++) a[i][j] = ComplexMultiplication (a[i][j], cdum); } } if (pd != NULL) *pd = d; return (0); } /*--------------------------------------------------------------------------------- | | ComplexMultiplication | | Returns the complex product of two complex numbers. | ---------------------------------------------------------------------------------*/ MrBComplex ComplexMultiplication (MrBComplex a, MrBComplex b) { MrBComplex c; c.re = a.re * b.re - a.im * b.im; c.im = a.im * b.re + a.re * b.im; return (c); } /*--------------------------------------------------------------------------------- | | ComplexSquareRoot | | Returns the complex square root of a complex number. | ---------------------------------------------------------------------------------*/ MrBComplex ComplexSquareRoot (MrBComplex a) { MrBComplex c; MrBFlt x, y, w, r; if (AreDoublesEqual(a.re, 0.0, ETA)==YES && AreDoublesEqual(a.im, 0.0, ETA)==YES) /* 2x == 0.0 */ { c.re = 0.0; c.im = 0.0; return (c); } else { x = fabs(a.re); y = fabs(a.im); if (x >= y) { r = y / x; w = sqrt(x) * sqrt(0.5 * (1.0 + sqrt(1.0 + r * r))); } else { r = x / y; w = sqrt(y) * sqrt(0.5 * (r + sqrt(1.0 + r * r))); } if (a.re >= 0.0) { c.re = w; c.im = a.im / (2.0 * w); } else { c.im = (a.im >= 0.0) ? w : -w; c.re = a.im / (2.0 * c.im); } return (c); } } /*--------------------------------------------------------------------------------- | | ComplexSubtraction | | Returns the complex difference of two complex numbers. | ---------------------------------------------------------------------------------*/ MrBComplex ComplexSubtraction (MrBComplex a, MrBComplex b) { MrBComplex c; c.re = a.re - b.re; c.im = a.im - b.im; return (c); } /*--------------------------------------------------------------------------------- | | ComputeEigenSystem | | Calculates the eigenvalues, eigenvectors, and the inverse of the eigenvectors | for a matrix of real numbers. | ---------------------------------------------------------------------------------*/ int ComputeEigenSystem (int dim, MrBFlt **a, MrBFlt *v, MrBFlt *vi, MrBFlt **u, int *iwork, MrBFlt *dwork) { int i, rc; rc = EigensForRealMatrix (dim, a, v, vi, u, iwork, dwork); if (rc != NO_ERROR) { MrBayesPrint ("%s Error in ComputeEigenSystem.\n", spacer); return (ERROR); } for (i=0; i0; i--) { rK[i] -= rK[i-1]; rK[i] *= factor; } rK[0] *= factor; } return (NO_ERROR); } /*--------------------------------------------------------------------------------- | | DiscreteLogNormal | | Discretization of lognormal distribution with equal proportions in each | category. | | LBH Notes: K = # of rate classes | *rK = pointer to output rate class matrix | median = flag to use media or not (1 = use median, 0 = mean?) | ---------------------------------------------------------------------------------*/ int DiscreteLogNormal (MrBFlt *rK, MrBFlt sigma, int K, int median) { int i; MrBFlt t, factor; MrBFlt mu = -0.5*sigma*sigma; if (median) { for (i=0; i0; i--) { rK[i] -= rK[i-1]; rK[i] *= factor; } rK[0] *= factor; } return (NO_ERROR); } /* LogNormal Quantile Function */ MrBFlt QuantileLogNormal (MrBFlt prob, MrBFlt mu, MrBFlt sigma) { MrBFlt a = 0.0, b = 0.0; a = PointNormal((0.5*(2.0*prob-1.0))+0.5) / sqrt(2.0); b = mu+(sqrt(2.0)* sigma * a); return exp(b); } /* LogNormal Point Function */ MrBFlt LogNormalPoint (MrBFlt x, MrBFlt mu, MrBFlt sigma) { if(x <= 0.0) return(0.0); MrBFlt a = LnProbLogNormal(mu, sigma, x); return exp(a); } /*--------------------------------------------------------------------------------- | | DivideByTwos | | Divides all of the elements of the matrix a by 2^power. | ---------------------------------------------------------------------------------*/ void DivideByTwos (int dim, MrBFlt **a, int power) { int divisor = 1, i, row, col; for (i=0; i= 0 ? a : -a); return (b >= 0 ? x : -x); } /*--------------------------------------------------------------------------------- | | Eigens | | The matrix of interest is a. The output is the real and imaginary parts of the | eigenvalues (wr and wi). z contains the real and imaginary parts of the | eigenvectors. iv2 and fv1 are working vectors. | ---------------------------------------------------------------------------------*/ int EigensForRealMatrix (int dim, MrBFlt **a, MrBFlt *wr, MrBFlt *wi, MrBFlt **z, int *iv1, MrBFlt *fv1) { static int is1, is2; int ierr; Balanc (dim, a, &is1, &is2, fv1); ElmHes (dim, is1, is2, a, iv1); ElTran (dim, is1, is2, a, iv1, z); ierr = Hqr2 (dim, is1, is2, a, wr, wi, z); if (ierr == 0) BalBak (dim, is1, is2, fv1, dim, z); return (ierr); } /*--------------------------------------------------------------------------------- | | ElmHes | | Given a real general matrix, this subroutine | reduces a submatrix situated in rows and columns | low through high to upper Hessenberg form by | stabilized elementary similarity transformations. | | On input: | | * dim is the order of the matrix | | * low and high are integers determined by the balancing | subroutine balanc. if balanc has not been used, | set low=1, high=dim. | | * a contains the input matrix. | | On output: | | * a contains the hessenberg matrix. The multipliers | which were used in the reduction are stored in the | remaining triangle under the hessenberg matrix. | | * interchanged contains information on the rows and columns | interchanged in the reduction. | | Only elements low through high are used. | ---------------------------------------------------------------------------------*/ void ElmHes (int dim, int low, int high, MrBFlt **a, int *interchanged) { int i, j, m, la, mm1, kp1, mp1; MrBFlt x, y; la = high - 1; kp1 = low + 1; if (la < kp1) return; /* remove goto statement, which exits at bottom of function */ for (m=kp1; m<=la; m++) { mm1 = m - 1; x = 0.0; i = m; for (j=m; j<=high; j++) { if (fabs(a[j][mm1]) > fabs(x)) /* change direction of inequality */ { /* remove goto statement */ x = a[j][mm1]; i = j; } } interchanged[m] = i; if (i != m) /* change "==" to "!=", eliminating goto statement */ { /* interchange rows and columns of a */ for (j=mm1; j 0) { for (j=0; j high)) { wr[i] = h[i][i]; wi[i] = 0.0; } } en = high; t = 0.0; itn = dim * 30; /* search for next eigenvalues */ while (en >= low) /* changed from an "if (en < lo)" to eliminate a goto statement */ { its = 0; na = en - 1; enm2 = na - 1; twoRoots = FALSE; for (;;) { for (l=en; l>low; l--) /* changed indexing, got rid of lo, ll */ { s = fabs(h[l-1][l-1]) + fabs(h[l][l]); if (AreDoublesEqual(s, 0.0, ETA)==YES) /* == 0.0 */ s = norm; tst1 = s; tst2 = tst1 + fabs(h[l][l-1]); if (fabs(tst2 - tst1) < ETA) /* tst2 == tst1 */ break; /* changed to break to remove a goto statement */ } /* form shift */ x = h[en][en]; if (l == en) /* changed to break to remove a goto statement */ break; y = h[na][na]; w = h[en][na] * h[na][en]; if (l == na) /* used to return to other parts of the code */ { twoRoots = TRUE; break; } if (itn == 0) return (en); /* form exceptional shift */ if ((its == 10) || (its == 20)) /* changed to remove a goto statement */ { t += x; for (i = low; i <= en; i++) h[i][i] -= x; s = fabs(h[en][na]) + fabs(h[na][enm2]); x = 0.75 * s; y = x; w = -0.4375 * s * s; } its++; itn--; /* look for two consecutive small sub-diagonal elements */ for (m=enm2; m>=l; m--) { /* removed m = enm2 + l - mm and above loop to remove variables */ zz = h[m][m]; r = x - zz; s = y - zz; p = (r * s - w) / h[m+1][m] + h[m][m+1]; q = h[m+1][m+1] - zz - r - s; r = h[m+2][m+1]; s = fabs(p) + fabs(q) + fabs(r); p /= s; q /= s; r /= s; if (m == l) break; /* changed to break to remove a goto statement */ tst1 = fabs(p) * (fabs(h[m-1][m-1]) + fabs(zz) + fabs(h[m+1][m+1])); tst2 = tst1 + fabs(h[m][m-1]) * (fabs(q) + fabs(r)); if (fabs(tst2 - tst1) < ETA) /* tst2 == tst1 */ break; /* changed to break to remove a goto statement */ } mp2 = m + 2; for (i = mp2; i <= en; i++) { h[i][i-2] = 0.0; if (i != mp2) /* changed "==" to "!=" to remove a goto statement */ h[i][i-3] = 0.0; } /* MrBFlt QR step involving rows l to en and columns m to en */ for (k=m; k<=na; k++) { notlas = (k != na); if (k != m) /* changed "==" to "!=" to remove a goto statement */ { p = h[k][k-1]; q = h[k+1][k-1]; r = 0.0; if (notlas) r = h[k+2][k-1]; x = fabs(p) + fabs(q) + fabs(r); if (x < ETA) /* == 0.0 */ continue; /* changed to continue remove a goto statement */ p /= x; q /= x; r /= x; } /*s = sqrt(p*p+q*q+r*r); sgn = (p<0)?-1:(p>0); s = sgn*sqrt(p*p+q*q+r*r);*/ s = D_sign(sqrt(p*p + q*q + r*r), p); if (k != m) /* changed "==" to "!=" to remove a goto statement */ h[k][k-1] = -s * x; else if (l != m) /* else if gets rid of another goto statement */ h[k][k-1] = -h[k][k-1]; p += s; x = p / s; y = q / s; zz = r / s; q /= p; r /= p; if (!notlas) /* changed to !notlas to remove goto statement (see **) */ { /* row modification */ for (j=k; j= -1e-12) /* change "<" to ">=", and also change "0.0" to */ { /* a small number (Swofford's change) */ /* real pair */ zz = p + D_sign(zz, p); wr[na] = x + zz; wr[en] = wr[na]; if (fabs(zz) > ETA) /* != 0.0 */ wr[en] = x - w/zz; wi[na] = 0.0; wi[en] = 0.0; x = h[en][na]; s = fabs(x) + fabs(zz); p = x / s; q = zz / s; r = sqrt(p*p + q*q); p /= r; q /= r; /* row modification */ for (j=na; j=0; en--) { /*en = n - nn - 1; and change for loop */ p = wr[en]; q = wi[en]; na = en - 1; if (q < -1e-12) { /* last vector component chosen imaginary so that eigenvector matrix is triangular */ m = na; if (fabs(h[en][na]) > fabs(h[na][en])) { h[na][na] = q / h[en][na]; h[na][en] = -(h[en][en] - p) / h[en][na]; } else ComplexDivision2 (0.0, -h[na][en], h[na][na] - p, q, &h[na][na], &h[na][en]); h[en][na] = 0.0; h[en][en] = 1.0; enm2 = na - 1; if (enm2 >= 0) /* changed direction to remove goto statement */ { for (i=enm2; i>=0; i--) { w = h[i][i] - p; ra = 0.0; sa = 0.0; for (j=m; j<=en; j++) { ra += h[i][j] * h[j][na]; sa += h[i][j] * h[j][en]; } if (wi[i] < 0.0) /* changed direction to remove goto statement */ { zz = w; r = ra; s = sa; } else { m = i; if (fabs(wi[i]) tst1); /* made into a do/while loop */ } ComplexDivision2 (x * r - zz * ra + q * sa, x * s - zz * sa - q * ra, vr, vi, &h[i][na], &h[i][en]); if (fabs(x) > fabs(zz) + fabs(q)) /* changed direction to remove goto statement */ { h[i+1][na] = (-ra - w * h[i][na] + q * h[i][en]) / x; h[i+1][en] = (-sa - w * h[i][en] - q * h[i][na]) / x; } else ComplexDivision2 (-r - y * h[i][na], -s - y * h[i][en], zz, q, &h[i+1][na], &h[i+1][en]); } /* overflow control */ tst1 = fabs(h[i][na]); tst2 = fabs(h[i][en]); t = MAX(tst1, tst2); if (t > ETA) /* t != 0.0 */ { tst1 = t; tst2 = tst1 + 1.0 / tst1; if (tst2 <= tst1) { for (j = i; j <= en; j++) { h[j][na] /= t; h[j][en] /= t; } } } } } } } else if (fabs(q)= 0) { for (i=na; i>=0; i--) { w = h[i][i] - p; r = 0.0; for (j = m; j <= en; j++) r += h[i][j] * h[j][en]; if (wi[i] < 0.0) /* changed direction to remove goto statement */ { zz = w; s = r; continue; /* changed to continue to remove goto statement */ } else { m = i; if (fabs(wi[i]) tst1); } h[i][en] = -r / t; } else { /* solve real equations */ x = h[i][i+1]; y = h[i+1][i]; q = (wr[i] - p) * (wr[i] - p) + wi[i] * wi[i]; t = (x * s - zz * r) / q; h[i][en] = t; if (fabs(x) > fabs(zz)) /* changed direction to remove goto statement */ h[i+1][en] = (-r - w * t) / x; else h[i+1][en] = (-s - y * t) / zz; } /* overflow control */ t = fabs(h[i][en]); if (t > ETA) { tst1 = t; tst2 = tst1 + 1. / tst1; if (tst2 <= tst1) { for (j = i; j <= en; j++) h[j][en] /= t; } } } } } } } for (i=0; i high)) /* changed to rid goto statement */ { for (j=i; j=low; j--) { m = MIN(j, high); for (i=low; i<=high; i++) { zz = 0.0; for (k = low; k <= m; k++) zz += z[i][k] * h[k][j]; z[i][j] = zz; } } return (0); #if 0 int hqr2 (int *nm, int *n, int *low, int *igh, MrBFlt *h__, MrBFlt *wr, MrBFlt *wi, MrBFlt *z__, int *ierr) { /* system generated locals */ int h_dim1, h_offset, z_dim1, z_offset, i__1, i__2, i__3; MrBFlt d__1, d__2, d__3, d__4; /* builtin functions */ MrBFlt sqrt(doublereal), d_sign(doublereal *, doublereal *); /* Local variables */ static MrBFlt norm; static int i__, j, k, l, m; static MrBFlt p, q, r__, s, t, w, x, y; static int na, ii, en, jj; static MrBFlt ra, sa; static int ll, mm, nn; static MrBFlt vi, vr, zz; static logical notlas; static int mp2, itn, its, enm2; static MrBFlt tst1, tst2; /* parameter adjustments */ z_dim1 = *nm; z_offset = z_dim1 + 1; z__ -= z_offset; --wi; --wr; h_dim1 = *nm; h_offset = h_dim1 + 1; h__ -= h_offset; /* function Body */ *ierr = 0; norm = 0.; k = 1; /* .......... store roots isolated by balanc and compute matrix norm .......... */ i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { i__2 = *n; for (j = k; j <= i__2; ++j) { /* L40: */ norm += (d__1 = h__[i__ + j * h_dim1], abs(d__1)); } k = i__; if (i__ >= *low && i__ <= *igh) goto L50; wr[i__] = h__[i__ + i__ * h_dim1]; wi[i__] = 0.; L50: ; } en = *igh; t = 0.; itn = *n * 30; /* ..........search for next eigenvalues.......... */ L60: if (en < *low) goto L340; its = 0; na = en - 1; enm2 = na - 1; /* ..........look for single small sub-diagonal element for l=en step -1 until low do -- .......... */ L70: i__1 = en; for (ll = *low; ll <= i__1; ++ll) { l = en + *low - ll; if (l == *low) goto L100; s = (d__1 = h__[l - 1 + (l - 1) * h_dim1], abs(d__1)) + (d__2 = h__[l + l * h_dim1], abs(d__2)); if (s == 0.0) s = norm; tst1 = s; tst2 = tst1 + (d__1 = h__[l + (l - 1) * h_dim1], abs(d__1)); if (tst2 == tst1) goto L100; /* L80: */ } /* .......... form shift .......... */ L100: x = h__[en + en * h_dim1]; if (l == en) goto L270; y = h__[na + na * h_dim1]; w = h__[en + na * h_dim1] * h__[na + en * h_dim1]; if (l == na) goto L280; if (itn == 0) goto L1000; if (its != 10 && its != 20) goto L130; /* .......... form exceptional shift .......... */ t += x; i__1 = en; for (i__ = *low; i__ <= i__1; ++i__) { /* L120: */ h__[i__ + i__ * h_dim1] -= x; } s = (d__1 = h__[en + na * h_dim1], abs(d__1)) + (d__2 = h__[na + enm2 * h_dim1], abs(d__2)); x = s * 0.75; y = x; w = s * -0.4375 * s; L130: ++its; --itn; /* .......... look for two consecutive small sub-diagonal elements for m=en-2 step -1 until l do -- .......... */ i__1 = enm2; for (mm = l; mm <= i__1; ++mm) { m = enm2 + l - mm; zz = h__[m + m * h_dim1]; r__ = x - zz; s = y - zz; p = (r__ * s - w) / h__[m + 1 + m * h_dim1] + h__[m + (m + 1) * h_dim1]; q = h__[m + 1 + (m + 1) * h_dim1] - zz - r__ - s; r__ = h__[m + 2 + (m + 1) * h_dim1]; s = abs(p) + abs(q) + abs(r__); p /= s; q /= s; r__ /= s; if (m == l) goto L150; tst1 = abs(p) * ((d__1 = h__[m - 1 + (m - 1) * h_dim1], abs(d__1)) + abs(zz) + (d__2 = h__[m + 1 + (m + 1) * h_dim1], abs(d__2))); tst2 = tst1 + (d__1 = h__[m + (m - 1) * h_dim1], abs(d__1)) * (abs(q) + abs(r__)); if (tst2 == tst1) goto L150; /* L140: */ } L150: mp2 = m + 2; i__1 = en; for (i__ = mp2; i__ <= i__1; ++i__) { h__[i__ + (i__ - 2) * h_dim1] = 0.0; if (i__ == mp2) goto L160; h__[i__ + (i__ - 3) * h_dim1] = 0.; L160: ; } /* .......... MrBFlt qr step involving rows l to en and columns m to en .......... */ i__1 = na; for (k = m; k <= i__1; ++k) { notlas = k != na; if (k == m) goto L170; p = h__[k + (k - 1) * h_dim1]; q = h__[k + 1 + (k - 1) * h_dim1]; r__ = 0.; if (notlas) r__ = h__[k + 2 + (k - 1) * h_dim1]; x = abs(p) + abs(q) + abs(r__); if (x == 0.) goto L260; p /= x; q /= x; r__ /= x; L170: d__1 = sqrt(p * p + q * q + r__ * r__); s = d_sign(&d__1, &p); if (k == m) goto L180; h__[k + (k - 1) * h_dim1] = -s * x; goto L190; L180: if (l != m) { h__[k + (k - 1) * h_dim1] = -h__[k + (k - 1) * h_dim1]; } L190: p += s; x = p / s; y = q / s; zz = r__ / s; q /= p; r__ /= p; if (notlas) goto L225; /* .......... row modification .......... */ i__2 = *n; for (j = k; j <= i__2; ++j) { p = h__[k + j * h_dim1] + q * h__[k + 1 + j * h_dim1]; h__[k + j * h_dim1] -= p * x; h__[k + 1 + j * h_dim1] -= p * y; /* L200: */ } /* computing MIN */ i__2 = en, i__3 = k + 3; j = min(i__2,i__3); /* .......... column modification .......... */ i__2 = j; for (i__ = 1; i__ <= i__2; ++i__) { p = x * h__[i__ + k * h_dim1] + y * h__[i__ + (k + 1) * h_dim1]; h__[i__ + k * h_dim1] -= p; h__[i__ + (k + 1) * h_dim1] -= p * q; /* L210: */ } /* .......... accumulate transformations .......... */ i__2 = *igh; for (i__ = *low; i__ <= i__2; ++i__) { p = x * z__[i__ + k * z_dim1] + y * z__[i__ + (k + 1) * z_dim1]; z__[i__ + k * z_dim1] -= p; z__[i__ + (k + 1) * z_dim1] -= p * q; /* L220: */ } goto L255; L225: /* .......... row modification .......... */ i__2 = *n; for (j = k; j <= i__2; ++j) { p = h__[k + j * h_dim1] + q * h__[k + 1 + j * h_dim1] + r__ * h__[k + 2 + j * h_dim1]; h__[k + j * h_dim1] -= p * x; h__[k + 1 + j * h_dim1] -= p * y; h__[k + 2 + j * h_dim1] -= p * zz; /* L230: */ } /* computing MIN */ i__2 = en, i__3 = k + 3; j = min(i__2,i__3); /* .......... column modification .......... */ i__2 = j; for (i__ = 1; i__ <= i__2; ++i__) { p = x * h__[i__ + k * h_dim1] + y * h__[i__ + (k + 1) * h_dim1] + zz * h__[i__ + (k + 2) * h_dim1]; h__[i__ + k * h_dim1] -= p; h__[i__ + (k + 1) * h_dim1] -= p * q; h__[i__ + (k + 2) * h_dim1] -= p * r__; /* L240: */ } /* .......... accumulate transformations .......... */ i__2 = *igh; for (i__ = *low; i__ <= i__2; ++i__) { p = x * z__[i__ + k * z_dim1] + y * z__[i__ + (k + 1) * z_dim1] + zz * z__[i__ + (k + 2) * z_dim1]; z__[i__ + k * z_dim1] -= p; z__[i__ + (k + 1) * z_dim1] -= p * q; z__[i__ + (k + 2) * z_dim1] -= p * r__; /* L250: */ } L255: L260: ; } goto L70; /* .......... one root found .......... */ L270: h__[en + en * h_dim1] = x + t; wr[en] = h__[en + en * h_dim1]; wi[en] = 0.; en = na; goto L60; /* .......... two roots found .......... */ L280: p = (y - x) / 2.; q = p * p + w; zz = sqrt((abs(q))); h__[en + en * h_dim1] = x + t; x = h__[en + en * h_dim1]; h__[na + na * h_dim1] = y + t; if (q < 0.) goto L320; /* .......... real pair .......... */ zz = p + d_sign(&zz, &p); wr[na] = x + zz; wr[en] = wr[na]; if (zz != 0.) { wr[en] = x - w / zz; } wi[na] = 0.0; wi[en] = 0.0; x = h__[en + na * h_dim1]; s = abs(x) + abs(zz); p = x / s; q = zz / s; r__ = sqrt(p * p + q * q); p /= r__; q /= r__; /* .......... row modification .......... */ i__1 = *n; for (j = na; j <= i__1; ++j) { zz = h__[na + j * h_dim1]; h__[na + j * h_dim1] = q * zz + p * h__[en + j * h_dim1]; h__[en + j * h_dim1] = q * h__[en + j * h_dim1] - p * zz; /* L290: */ } /* .......... column modification .......... */ i__1 = en; for (i__ = 1; i__ <= i__1; ++i__) { zz = h__[i__ + na * h_dim1]; h__[i__ + na * h_dim1] = q * zz + p * h__[i__ + en * h_dim1]; h__[i__ + en * h_dim1] = q * h__[i__ + en * h_dim1] - p * zz; /* L300: */ } /* .......... accumulate transformations .......... */ i__1 = *igh; for (i__ = *low; i__ <= i__1; ++i__) { zz = z__[i__ + na * z_dim1]; z__[i__ + na * z_dim1] = q * zz + p * z__[i__ + en * z_dim1]; z__[i__ + en * z_dim1] = q * z__[i__ + en * z_dim1] - p * zz; /* L310: */ } goto L330; /* .......... complex pair .......... */ L320: wr[na] = x + p; wr[en] = x + p; wi[na] = zz; wi[en] = -zz; L330: en = enm2; goto L60; /* .......... all roots found. backsubstitute to find vectors of upper triangular form .......... */ L340: if (norm == 0.0) goto L1001; /* .......... for en=n step -1 until 1 do -- .......... */ i__1 = *n; for (nn = 1; nn <= i__1; ++nn) { en = *n + 1 - nn; p = wr[en]; q = wi[en]; na = en - 1; if (q < 0.) goto L710; else if (q == 0) goto L600; else goto L800; /* .......... real vector .......... */ L600: m = en; h__[en + en * h_dim1] = 1.0; if (na == 0) goto L800; /* .......... for i=en-1 step -1 until 1 do -- .......... */ i__2 = na; for (ii = 1; ii <= i__2; ++ii) { i__ = en - ii; w = h__[i__ + i__ * h_dim1] - p; r__ = 0.0; i__3 = en; for (j = m; j <= i__3; ++j) { /* L610: */ r__ += h__[i__ + j * h_dim1] * h__[j + en * h_dim1]; } if (wi[i__] >= 0.0) goto L630; zz = w; s = r__; goto L700; L630: m = i__; if (wi[i__] != 0.0) goto L640; t = w; if (t != 0.0) goto L635; tst1 = norm; t = tst1; L632: t *= 0.01; tst2 = norm + t; if (tst2 > tst1) goto L632; L635: h__[i__ + en * h_dim1] = -r__ / t; goto L680; /* .......... solve real equations .......... */ L640: x = h__[i__ + (i__ + 1) * h_dim1]; y = h__[i__ + 1 + i__ * h_dim1]; q = (wr[i__] - p) * (wr[i__] - p) + wi[i__] * wi[i__]; t = (x * s - zz * r__) / q; h__[i__ + en * h_dim1] = t; if (abs(x) <= abs(zz)) goto L650; h__[i__ + 1 + en * h_dim1] = (-r__ - w * t) / x; goto L680; L650: h__[i__ + 1 + en * h_dim1] = (-s - y * t) / zz; /* .......... overflow control .......... */ L680: t = (d__1 = h__[i__ + en * h_dim1], abs(d__1)); if (t == 0.0) goto L700; tst1 = t; tst2 = tst1 + 1.0 / tst1; if (tst2 > tst1) goto L700; i__3 = en; for (j = i__; j <= i__3; ++j) { h__[j + en * h_dim1] /= t; /* L690: */ } L700: ; } /* .......... end real vector .......... */ goto L800; /* .......... complex vector .......... */ L710: m = na; /* .......... last vector component chosen imaginary so that eigenvector matrix is triangular .......... */ if ((d__1 = h__[en + na * h_dim1], abs(d__1)) <= (d__2 = h__[na + en * h_dim1], abs(d__2))) goto L720; h__[na + na * h_dim1] = q / h__[en + na * h_dim1]; h__[na + en * h_dim1] = -(h__[en + en * h_dim1] - p) / h__[en + na * h_dim1]; goto L730; L720: d__1 = -h__[na + en * h_dim1]; d__2 = h__[na + na * h_dim1] - p; cdiv_(&c_b49, &d__1, &d__2, &q, &h__[na + na * h_dim1], &h__[na + en * h_dim1]); L730: h__[en + na * h_dim1] = 0.0; h__[en + en * h_dim1] = 1.0; enm2 = na - 1; if (enm2 == 0) goto L800; /* .......... for i=en-2 step -1 until 1 do -- .......... */ i__2 = enm2; for (ii = 1; ii <= i__2; ++ii) { i__ = na - ii; w = h__[i__ + i__ * h_dim1] - p; ra = 0.0; sa = 0.0; i__3 = en; for (j = m; j <= i__3; ++j) { ra += h__[i__ + j * h_dim1] * h__[j + na * h_dim1]; sa += h__[i__ + j * h_dim1] * h__[j + en * h_dim1]; /* L760: */ } if (wi[i__] >= 0.0) goto L770; zz = w; r__ = ra; s = sa; goto L795; L770: m = i__; if (wi[i__] != 0.0) goto L780; d__1 = -ra; d__2 = -sa; cdiv_(&d__1, &d__2, &w, &q, &h__[i__ + na * h_dim1], &h__[i__ + en * h_dim1]); goto L790; /* .......... solve complex equations .......... */ L780: x = h__[i__ + (i__ + 1) * h_dim1]; y = h__[i__ + 1 + i__ * h_dim1]; vr = (wr[i__] - p) * (wr[i__] - p) + wi[i__] * wi[i__] - q * q; vi = (wr[i__] - p) * 2.0 * q; if (vr != 0.0 || vi != 0.0) goto L784; tst1 = norm * (abs(w) + abs(q) + abs(x) + abs(y) + abs(zz)); vr = tst1; L783: vr *= 0.01; tst2 = tst1 + vr; if (tst2 > tst1) goto L783; L784: d__1 = x * r__ - zz * ra + q * sa; d__2 = x * s - zz * sa - q * ra; cdiv_(&d__1, &d__2, &vr, &vi, &h__[i__ + na * h_dim1], &h__[i__ + en * h_dim1]); if (abs(x) <= abs(zz) + abs(q)) goto L785; h__[i__ + 1 + na * h_dim1] = (-ra - w * h__[i__ + na * h_dim1] + q * h__[i__ + en * h_dim1]) / x; h__[i__ + 1 + en * h_dim1] = (-sa - w * h__[i__ + en * h_dim1] - q * h__[i__ + na * h_dim1]) / x; goto L790; L785: d__1 = -r__ - y * h__[i__ + na * h_dim1]; d__2 = -s - y * h__[i__ + en * h_dim1]; cdiv_(&d__1, &d__2, &zz, &q, &h__[i__ + 1 + na * h_dim1], &h__[i__ + 1 + en * h_dim1]); /* .......... overflow control .......... */ L790: /* Computing MAX */ d__3 = (d__1 = h__[i__ + na * h_dim1], abs(d__1)), d__4 = (d__2 = h__[i__ + en * h_dim1], abs(d__2)); t = max(d__3,d__4); if (t == 0.0) goto L795; tst1 = t; tst2 = tst1 + 1.0 / tst1; if (tst2 > tst1) goto L795; i__3 = en; for (j = i__; j <= i__3; ++j) { h__[j + na * h_dim1] /= t; h__[j + en * h_dim1] /= t; /* L792: */ } L795: ; } /* .......... end complex vector .......... */ L800: ; } /* .......... end back substitution vectors of isolated roots .......... */ i__1 = *n; for (i__ = 1; i__ <= i__1; ++i__) { if (i__ >= *low && i__ <= *igh) goto L840; i__2 = *n; for (j = i__; j <= i__2; ++j) { /* L820: */ z__[i__ + j * z_dim1] = h__[i__ + j * h_dim1]; } L840: ; } /* .......... multiply by transformation matrix to give vectors of original full matrix. */ /* for j=n step -1 until low do -- .......... */ i__1 = *n; for (jj = *low; jj <= i__1; ++jj) { j = *n + *low - jj; m = min(j,*igh); i__2 = *igh; for (i__ = *low; i__ <= i__2; ++i__) { zz = 0.0; i__3 = m; for (k = *low; k <= i__3; ++k) { /* L860: */ zz += z__[i__ + k * z_dim1] * h__[k + j * h_dim1]; } z__[i__ + j * z_dim1] = zz; /* L880: */ } } goto L1001; /* .......... set error -- all eigenvalues have not converged after 30*n iterations .......... */ L1000: *ierr = en; L1001: return 0; } /* end f2c version of code */ #endif } MrBFlt IncompleteBetaFunction (MrBFlt alpha, MrBFlt beta, MrBFlt x) { MrBFlt bt, gm1, gm2, gm3, temp; if (x < 0.0 || x > 1.0) { MrBayesPrint ("%s Error: Problem in IncompleteBetaFunction.\n", spacer); exit (0); } if (fabs(x) < ETA || fabs(x-1.0)1 && x>=p) goto l30; gin = 1.0; term = 1.0; rn = p; l20: rn++; term *= x/rn; gin += term; if (term > accurate) goto l20; gin *= factor/p; goto l50; l30: a = 1.0-p; b = a+x+1.0; term = 0.0; pn[0] = 1.0; pn[1] = x; pn[2] = x+1; pn[3] = x*b; gin = pn[2]/pn[3]; l32: a++; b += 2.0; term++; an = a*term; for (i=0; i<2; i++) pn[i+4] = b*pn[i+2]-an*pn[i]; if (fabs(pn[5]) < ETA) goto l35; rn = pn[4]/pn[5]; dif = fabs(gin-rn); if (dif>accurate) goto l34; if (dif<=accurate*rn) goto l42; l34: gin = rn; l35: for (i=0; i<4; i++) pn[i] = pn[i+2]; if (fabs(pn[4]) < overflow) goto l32; for (i=0; i<4; i++) pn[i] /= overflow; goto l32; l42: gin = 1.0-factor*gin; l50: return (gin); } /*--------------------------------------------------------------------------------- | | InvertMatrix | | Calculates aInv = a^{-1} using LU-decomposition. The input matrix a is | destroyed in the process. The program returns an error if the matrix is | singular. col and indx are work vectors. | ---------------------------------------------------------------------------------*/ int InvertMatrix (int dim, MrBFlt **a, MrBFlt *col, int *indx, MrBFlt **aInv) { int rc, i, j; rc = LUDecompose (dim, a, col, indx, (MrBFlt *)NULL); if (rc == FALSE) { for (j = 0; j < dim; j++) { for (i = 0; i < dim; i++) col[i] = 0.0; col[j] = 1.0; LUBackSubstitution (dim, a, indx, col); for (i = 0; i < dim; i++) aInv[i][j] = col[i]; } } return (rc); } /*--------------------------------------------------------------------------------- | | LBinormal | | L(h1,h2,r) = prob(x>h1, y>h2), where x and y are standard binormal, | with r=corr(x,y), error < 2e-7. | | Drezner Z., and G.O. Wesolowsky (1990) On the computation of the | bivariate normal integral. J. Statist. Comput. Simul. 35:101-107. | ---------------------------------------------------------------------------------*/ MrBFlt LBinormal (MrBFlt h1, MrBFlt h2, MrBFlt r) { int i; MrBFlt x[]={0.04691008, 0.23076534, 0.5, 0.76923466, 0.95308992}; MrBFlt w[]={0.018854042, 0.038088059, 0.0452707394,0.038088059,0.018854042}; MrBFlt Lh=0.0, r1, r2, r3, rr, aa, ab, h3, h5, h6, h7, h12, temp1, temp2, exp1, exp2; h12 = (h1 * h1 + h2 * h2) / 2.0; if (fabs(r) >= 0.7) { r2 = 1.0 - r * r; r3 = sqrt(r2); if (r < 0) h2 *= -1; h3 = h1 * h2; h7 = exp(-h3 / 2.0); if (fabs(r-1.0)>ETA) /* fabs(r) != 1.0 */ { h6 = fabs(h1-h2); h5 = h6 * h6 / 2.0; h6 /= r3; aa = 0.5 - h3 / 8; ab = 3.0 - 2.0 * aa * h5; temp1 = -h5 / r2; if (temp1 < -100.0) exp1 = 0.0; else exp1 = exp(temp1); Lh = 0.13298076 * h6 * ab * (1.0 - CdfNormal(h6)) - exp1 * (ab + aa * r2) * 0.053051647; for (i=0; i<5; i++) { r1 = r3 * x[i]; rr = r1 * r1; r2 = sqrt(1.0 - rr); temp1 = -h5 / rr; if (temp1 < -100.0) exp1 = 0.0; else exp1 = exp(temp1); temp2 = -h3 / (1.0 + r2); if (temp2 < -100.0) exp2 = 0.0; else exp2 = exp(temp2); Lh -= w[i] * exp1 * (exp2 / r2 / h7 - 1.0 - aa * rr); } } if (r > 0) Lh = Lh * r3 * h7 + (1.0 - CdfNormal(MAX(h1, h2))); else if (r<0) Lh = (h1 < h2 ? CdfNormal(h2) - CdfNormal(h1) : 0) - Lh * r3 * h7; } else { h3 = h1 * h2; if (fabs(r)>ETA) { for (i=0; i<5; i++) { r1 = r * x[i]; r2 = 1.0 - r1 * r1; temp1 = (r1 * h3 - h12) / r2; if (temp1 < -100.0) exp1 = 0.0; else exp1 = exp(temp1); Lh += w[i] * exp1 / sqrt(r2); } } Lh = (1.0 - CdfNormal(h1)) * (1.0 - CdfNormal(h2)) + r * Lh; } return (Lh); } /*--------------------------------------------------------------------------------- | | LnFactorial: Calculates the log of the factorial for an integer | ---------------------------------------------------------------------------------*/ MrBFlt LnFactorial (int value) { int i; MrBFlt result; result = 0.0; for (i = 2; i<=value; i++) result += log(i); return result; } /*--------------------------------------------------------------------------------- | | LnGamma | | Calculates the log of the gamma function. The Gamma function is equal | to: | | Gamma(alp) = {integral from 0 to infinity} t^{alp-1} e^-t dt | | The result is accurate to 10 decimal places. Stirling's formula is used | for the central polynomial part of the procedure. | | Pike, M. C. and I. D. Hill. 1966. Algorithm 291: Logarithm of the gamma | function. Communications of the Association for Computing | Machinery, 9:684. | ---------------------------------------------------------------------------------*/ MrBFlt LnGamma (MrBFlt alp) { MrBFlt x = alp, f = 0.0, z; if (x < 7) { f = 1.0; z = x-1.0; while (++z < 7.0) f *= z; x = z; f = -log(f); } z = 1.0 / (x*x); return (f + (x-0.5)*log(x) - x + 0.918938533204673 + (((-0.000595238095238*z+0.000793650793651)*z-0.002777777777778)*z +0.083333333333333)/x); } /* Calculate probability of a realization for exponential random variable */ MrBFlt LnPriorProbExponential (MrBFlt val, MrBFlt *params) { return log(params[0]) - params[0] * val; } /* Calculate probability of a realization for exponential random variable; parameter mean and not rate */ MrBFlt LnPriorProbExponential_Param_Mean (MrBFlt val, MrBFlt *params) { return - log(params[0]) - val / params[0]; } /* Calculate probability of a realization for a fixed variable */ MrBFlt LnPriorProbFix (MrBFlt val, MrBFlt *params) { if (fabs((val - params[0])/val) < 1E-5) return 0.0; else return NEG_INFINITY; } /* Calculate probability of a realization for gamma random variable */ MrBFlt LnPriorProbGamma (MrBFlt val, MrBFlt *params) { return (params[0] - 1) * log(val) + params[0] * log(params[1]) - params[1] * val - LnGamma(params[0]); } /* Calculate probability of a realization for gamma random variable; parameters mean and sd */ MrBFlt LnPriorProbGamma_Param_Mean_Sd (MrBFlt val, MrBFlt *params) { MrBFlt alpha, beta; beta = params[0] / (params[1]*params[1]); alpha = params[0] * beta; return (alpha - 1) * log(val) + alpha * log(beta) - beta * val - LnGamma(alpha); } /* Calculate probability of a realization for lognormal random variable */ MrBFlt LnPriorProbLognormal (MrBFlt val, MrBFlt *params) { MrBFlt z; z = (log(val) - params[0]) / params[1]; return - log(params[1] * val * sqrt(2.0 * M_PI)) - z * z / 2.0; } /* Calculate probability of a realization for lognormal random variable; parameters mean and sd on linear scale */ MrBFlt LnPriorProbLognormal_Param_Mean_Sd (MrBFlt val, MrBFlt *params) { MrBFlt z, mean_log, sd_log; sd_log = sqrt (log((params[1]*params[1])/(params[0]*params[0]) + 1)); mean_log = log(params[0]) - sd_log * sd_log / 2.0; z= (log(val) - mean_log) / sd_log; return - log(sd_log * val * sqrt(2.0 * M_PI)) - z * z / 2.0; } /* Calculate probability of a realization for normal random variable */ MrBFlt LnPriorProbNormal (MrBFlt val, MrBFlt *params) { MrBFlt z; z = (val - params[0]) / params[1]; return - log(params[1] * sqrt(2.0 * M_PI)) - z * z / 2.0; } /* Calculate probability of a realization for an offset exponential random variable */ MrBFlt LnPriorProbOffsetExponential (MrBFlt val, MrBFlt *params) { return log(params[1]) - params[1] * (val - params[0]); } /* Calculate probability of a realization for an offset exponential random variable; parameters offset and mean */ MrBFlt LnPriorProbOffsetExponential_Param_Offset_Mean (MrBFlt val, MrBFlt *params) { MrBFlt x, rate; x = val - params[0]; rate = 1.0 / (params[1] - params[0]); return log(rate) - rate * x; } /* Calculate probability of a realization for an offset gamma random variable */ MrBFlt LnPriorProbOffsetGamma (MrBFlt val, MrBFlt *params) { MrBFlt x, alpha, beta; x = val - params[0]; alpha = params[1]; beta = params[2]; return (alpha - 1) * log(x) + alpha * log(beta) - beta * x - LnGamma(alpha); } /* Calculate probability of a realization for an offset gamma random variable; parameters offset, mean and sd */ MrBFlt LnPriorProbOffsetGamma_Param_Offset_Mean_Sd (MrBFlt val, MrBFlt *params) { MrBFlt x, mean, sd, alpha, beta; x = val - params[0]; mean = params[1] - params[0]; sd = params[2]; beta = mean / (sd*sd); alpha = mean * beta; return (alpha - 1) * log(x) + alpha * log(beta) - beta * x - LnGamma(alpha); } /* Calculate probability of a realization for an offset lognormal random variable */ MrBFlt LnPriorProbOffsetLognormal (MrBFlt val, MrBFlt *params) { MrBFlt x, mean_log, sd_log, z; x = val - params[0]; mean_log = params[1] - params[0]; sd_log = params[2]; z = (log(x) - mean_log) / sd_log; return - log(sd_log * x * sqrt(2.0 * M_PI)) - z * z / 2.0; } /* Calculate probability of a realization for an offset lognormal random variable; parameters offset, mean and sd */ MrBFlt LnPriorProbOffsetLognormal_Param_Offset_Mean_Sd (MrBFlt val, MrBFlt *params) { MrBFlt x, mean, sd, mean_log, sd_log, z; x = val - params[0]; mean = params[1] - params[0]; sd = params[2]; sd_log = sqrt (log((sd*sd)/(mean*mean) + 1)); mean_log = log(mean) - sd_log * sd_log / 2.0; z = (log(x) - mean_log) / sd_log; return - log(sd_log * x * sqrt(2.0 * M_PI)) - z * z / 2.0; } /* Calculate probability of a realization for truncated (only positive values) normal random variable */ MrBFlt LnPriorProbTruncatedNormal (MrBFlt val, MrBFlt *params) { MrBFlt z, z_0, normConst; z = (val - params[0]) / params[1]; z_0 = (0.0 - params[0]) / params[1]; normConst = CdfNormal(z_0); return - log(params[1] * sqrt(2.0 * M_PI)) - z * z / 2.0 - log(1.0 - normConst); } /* Calculate probability of a realization for arbitrarily truncated normal random variable; parameters truncation point, mean and sd */ MrBFlt LnPriorProbTruncatedNormal_Param_Trunc_Mean_Sd (MrBFlt val, MrBFlt *params) { MrBFlt z, z_trunc, normConst; z = (val - params[1]) / params[2]; z_trunc = (params[0] - params[1]) / params[2]; normConst = CdfNormal(z_trunc); return - log(params[2] * sqrt(2.0 * M_PI)) - z * z / 2.0 - log(1.0 - normConst); } /* Calculate probability of a realization for uniform random variable */ MrBFlt LnPriorProbUniform (MrBFlt val, MrBFlt *params) { return - log(params[1] - params[0]); } /* Calculate probability ratio of realizations for exponential random variable */ MrBFlt LnProbRatioExponential (MrBFlt newX, MrBFlt oldX, MrBFlt *params) { return params[0] * (oldX - newX); } /* Calculate probability ratio of realizations for exponential random variable; parameter mean and not rate */ MrBFlt LnProbRatioExponential_Param_Mean (MrBFlt newX, MrBFlt oldX, MrBFlt *params) { return (oldX - newX) / params[0]; } /* Calculate probability of a realization for a fixed variable */ MrBFlt LnProbRatioFix (MrBFlt newX, MrBFlt oldX, MrBFlt *params) { if (fabs((newX - params[0])/newX) < 1E-5 && fabs((oldX - params[0])/oldX) < 1E-5) return 0.0; else return NEG_INFINITY; } /* Calculate probability ratio of realizations for gamma random variable */ MrBFlt LnProbRatioGamma (MrBFlt newX, MrBFlt oldX, MrBFlt *params) { MrBFlt alpha, beta; alpha = params[0]; beta = params[1]; return (alpha - 1.0) * (log(newX) - log(oldX)) - beta * (newX - oldX); } /* Calculate probability ratio of realizations for gamma random variable; parameters mean and sd */ MrBFlt LnProbRatioGamma_Param_Mean_Sd (MrBFlt newX, MrBFlt oldX, MrBFlt *params) { MrBFlt alpha, beta; beta = params[0] / (params[1]*params[1]); alpha = params[0] * beta; return (alpha - 1.0) * (log(newX) - log(oldX)) - beta * (newX - oldX); } /* Calculate probability ratio of realizations for log normal random variable */ MrBFlt LnProbRatioLognormal (MrBFlt newX, MrBFlt oldX, MrBFlt *params) { MrBFlt newZ, oldZ; newZ = (log(newX) - params[0]) / params[1]; oldZ = (log(oldX) - params[0]) / params[1]; return (oldZ * oldZ - newZ * newZ) / 2.0 + log(oldX) - log(newX); } /* Calculate probability ratio of realizations for log normal random variable; parameters mean and sd */ MrBFlt LnProbRatioLognormal_Param_Mean_Sd (MrBFlt newX, MrBFlt oldX, MrBFlt *params) { MrBFlt newZ, oldZ, mean_log, sd_log; sd_log = sqrt (log((params[1]*params[1])/(params[0]*params[0]) + 1)); mean_log = log(params[0]) - sd_log * sd_log / 2.0; newZ = (log(newX) - mean_log) / sd_log; oldZ = (log(oldX) - mean_log) / sd_log; return (oldZ * oldZ - newZ * newZ) / 2.0 + log(oldX) - log(newX); } /* Calculate probability ratio of realizations for normal random variable */ MrBFlt LnProbRatioNormal (MrBFlt newX, MrBFlt oldX, MrBFlt *params) { MrBFlt newZ, oldZ; newZ = (newX - params[0]) / params[1]; oldZ = (oldX - params[0]) / params[1]; return (oldZ * oldZ - newZ * newZ) / 2.0; } /* Calculate probability ratio of realizations for offset exponential random variable */ MrBFlt LnProbRatioOffsetExponential (MrBFlt newX, MrBFlt oldX, MrBFlt *params) { return params[1] * (oldX - newX); } /* Calculate probability ratio of realizations for offset exponential random variable; parameters offset and mean */ MrBFlt LnProbRatioOffsetExponential_Param_Offset_Mean (MrBFlt newX, MrBFlt oldX, MrBFlt *params) { return (oldX - newX) / (params[1] - params[0]); } /* Calculate probability ratio of realizations for offset gamma random variable */ MrBFlt LnProbRatioOffsetGamma (MrBFlt newX, MrBFlt oldX, MrBFlt *params) { MrBFlt alpha, beta, newZ, oldZ; alpha = params[1]; beta = params[2]; newZ = newX - params[0]; oldZ = oldX - params[0]; return (alpha - 1.0) * (log(newZ) - log(oldZ)) - beta * (newZ - oldZ); } /* Calculate probability ratio of realizations for offset gamma random variable; parameters offset, mean and sd */ MrBFlt LnProbRatioOffsetGamma_Param_Offset_Mean_Sd (MrBFlt newX, MrBFlt oldX, MrBFlt *params) { MrBFlt mean, sd, alpha, beta; mean = params[1] - params[0]; sd = params[2]; beta = mean / (sd*sd); alpha = mean * beta; newX -= params[0]; oldX -= params[0]; return (alpha - 1.0) * (log(newX) - log(oldX)) - beta * (newX - oldX); } /* Calculate probability ratio of realizations for offset lognormal random variable */ MrBFlt LnProbRatioOffsetLognormal (MrBFlt newX, MrBFlt oldX, MrBFlt *params) { MrBFlt newZ, oldZ, mean_log, sd_log; sd_log = params[2]; mean_log = params[1]; newZ = (log(newX-params[0]) - mean_log) / sd_log; oldZ = (log(oldX-params[0]) - mean_log) / sd_log; return (oldZ * oldZ - newZ * newZ) / 2.0 + log(oldX-params[0]) - log(newX-params[0]); } /* Calculate probability ratio of realizations for offset lognormal random variable; parameters offset, mean and sd */ MrBFlt LnProbRatioOffsetLognormal_Param_Offset_Mean_Sd (MrBFlt newX, MrBFlt oldX, MrBFlt *params) { MrBFlt newZ, oldZ, mean, sd, mean_log, sd_log; mean = params[1] - params[0]; sd = params[2]; sd_log = sqrt (log((sd*sd)/(mean*mean) + 1)); mean_log = log(mean) - sd_log * sd_log / 2.0; newX -= params[0]; oldX -= params[0]; newZ = (log(newX) - mean_log) / sd_log; oldZ = (log(oldX) - mean_log) / sd_log; return (oldZ * oldZ - newZ * newZ) / 2.0 - log(newX / oldX); } /* Calculate probability ratio of realizations for truncated normal random variable */ MrBFlt LnProbRatioTruncatedNormal (MrBFlt newX, MrBFlt oldX, MrBFlt *params) { MrBFlt newZ, oldZ; newZ = (newX - params[0]) / params[1]; oldZ = (oldX - params[0]) / params[1]; return (oldZ * oldZ - newZ * newZ) / 2.0; } /* Calculate probability ratio of realizations for arbitrarily truncated normal random variable; parameters truncation point, mean and sd */ MrBFlt LnProbRatioTruncatedNormal_Param_Trunc_Mean_Sd (MrBFlt newX, MrBFlt oldX, MrBFlt *params) { MrBFlt newZ, oldZ; newZ = (newX - params[1]) / params[2]; oldZ = (oldX - params[1]) / params[2]; return (oldZ * oldZ - newZ * newZ) / 2.0; } /* Calculate probability ratio of realizations for uniform random variable */ MrBFlt LnProbRatioUniform (MrBFlt newX, MrBFlt oldX, MrBFlt *params) { return 0.0; } /* Log probability for a value drawn from a gamma distribution */ MrBFlt LnProbGamma (MrBFlt alpha, MrBFlt beta, MrBFlt x) { MrBFlt lnProb; lnProb = (alpha-1.0)*log(x) + alpha*log(beta) - x*beta - LnGamma(alpha); return lnProb; } /* Log probability for a value drawn from a truncated gamma distribution */ MrBFlt LnProbTruncGamma (MrBFlt alpha, MrBFlt beta, MrBFlt x, MrBFlt min, MrBFlt max) { MrBFlt lnProb; lnProb = (alpha-1.0)*log(x) + alpha*log(beta) - x*beta - LnGamma(alpha); lnProb -= log (IncompleteGamma (max*beta, alpha, LnGamma(alpha)) - IncompleteGamma (min*beta, alpha, LnGamma(alpha))); return lnProb; } /* Log probability for a value drawn from a lognormal distribution */ /* mu and sigma are on the log scale */ MrBFlt LnProbLogNormal (MrBFlt mu, MrBFlt sigma, MrBFlt x) { MrBFlt lnProb, z; z = (log(x) - mu) / sigma; lnProb = - log (x * sigma * sqrt (2.0 * M_PI)) - (z * z / 2.0); return lnProb; } /* Log ratio for two values drawn from a lognormal distribution */ /* mu and sigma are on the log scale */ MrBFlt LnRatioLogNormal (MrBFlt mu, MrBFlt sigma, MrBFlt xNew, MrBFlt xOld) { MrBFlt newZ, oldZ; newZ = (log(xNew) - mu) / sigma; oldZ = (log(xOld) - mu) / sigma; return (oldZ * oldZ - newZ * newZ) / 2.0 + log(xOld) - log(xNew); } /* Log probability for a value drawn from a lognormal distribution; parameters are natural mean (not of log value) and variance on log scale */ MrBFlt LnProbLogNormal_Mean_LogVar (MrBFlt mean, MrBFlt sigma2, MrBFlt x) { MrBFlt mu, sigma; mu = log(mean) - sigma2 / 2.0; sigma = sqrt(sigma2); return LnProbLogNormal(mu, sigma, x); } /* Log probability for a value drawn from a lognormal distribution; parameters are mean and variance of value (not of log value) */ MrBFlt LnProbLogNormal_Mean_Var (MrBFlt mean, MrBFlt var, MrBFlt x) { MrBFlt mu, sigma; sigma = sqrt(log(1.0 + var / (mean*mean))); mu = log(mean) - sigma * sigma / 2.0; return LnProbLogNormal(mu, sigma, x); } /* Log probability for a value drawn from a normal distribution */ MrBFlt LnProbNormal (MrBFlt mu, MrBFlt sigma, MrBFlt x) { MrBFlt z; z = (x - mu) / sigma; return - log(sigma * sqrt(2.0 * M_PI)) - z * z / 2.0; } /*--------------------------------------------------------------------------------- | | LogBase2Plus1 | | This function is called from ComputeMatrixExponential. | ---------------------------------------------------------------------------------*/ int LogBase2Plus1 (MrBFlt x) { int j = 0; while (x > 1.0 - 1.0e-07) { x /= 2.0; j++; } return (j); } /*--------------------------------------------------------------------------------- | | LogNormalRandomVariable | | Draw a random variable from a lognormal distribution. | ---------------------------------------------------------------------------------*/ MrBFlt LogNormalRandomVariable (MrBFlt mean, MrBFlt sd, RandLong *seed) { MrBFlt x; x = PointNormal(RandomNumber(seed)); x *= sd; x += mean; return exp(x); } /*--------------------------------------------------------------------------------- | | LUBackSubstitution | | Back substitute into an LU-decomposed matrix. | ---------------------------------------------------------------------------------*/ void LUBackSubstitution (int dim, MrBFlt **a, int *indx, MrBFlt *b) { int i, ip, j, ii = -1; MrBFlt sum; for (i=0; i= 0) { for (j=ii; j<=i-1; j++) sum -= a[i][j] * b[j]; } else if (fabs(sum)>ETA) ii = i; b[i] = sum; } for (i=dim-1; i>=0; i--) { sum = b[i]; for (j=i+1; j big) big = temp; } if (fabs(big)= big) { big = dum; imax = i; } } if (j != imax) { for (k=0; k 0.999998 || v <= 0.0) return (-1.0); g = LnGamma (v/2.0); xx = v/2.0; c = xx - 1.0; if (v >= -1.24*log(p)) goto l1; ch = pow((p*xx*exp(g+xx*aa)), 1.0/xx); if (ch-e<0) return (ch); goto l4; l1: if (v > 0.32) goto l3; ch = 0.4; a = log(1.0-p); l2: q = ch; p1 = 1.0+ch*(4.67+ch); p2 = ch*(6.73+ch*(6.66+ch)); t = -0.5+(4.67+2.0*ch)/p1 - (6.73+ch*(13.32+3.0*ch))/p2; ch -= (1.0-exp(a+g+0.5*ch+c*aa)*p2/p1)/t; if (fabs(q/ch-1.0)-0.01 <= 0.0) goto l4; else goto l2; l3: x = PointNormal (p); p1 = 0.222222/v; tmp = (x*sqrt(p1)+1.0-p1); ch = v*tmp*tmp*tmp; if (ch > 2.2*v+6.0) ch = -2.0*(log(1.0-p)-c*log(0.5*ch)+g); l4: q = ch; p1 = 0.5*ch; if ((t = IncompleteGamma (p1, xx, g)) < 0.0) { MrBayesPrint ("%s Error: Problem in PointChi2", spacer); return (-1.0); } p2 = p-t; t = p2*exp(xx*aa+g+p1-c*log(ch)); b = t/ch; a = 0.5*t-b*c; s1 = (210.0+a*(140.0+a*(105.0+a*(84.0+a*(70.0+60.0*a))))) / 420.0; s2 = (420.0+a*(735.0+a*(966.0+a*(1141.0+1278.0*a))))/2520.0; s3 = (210.0+a*(462.0+a*(707.0+932.0*a)))/2520.0; s4 = (252.0+a*(672.0+1182.0*a)+c*(294.0+a*(889.0+1740.0*a)))/5040.0; s5 = (84.0+264.0*a+c*(175.0+606.0*a)) / 2520.0; s6 = (120.0+c*(346.0+127.0*c)) / 5040.0; ch += t*(1+0.5*t*s1-b*c*(s1-b*(s2-b*(s3-b*(s4-b*(s5-b*s6)))))); if (fabs(q/ch-1.0) > e) goto l4; return (ch); } /*--------------------------------------------------------------------------------- | | PointNormal | | Returns z so That Prob{x 0) *seed = test; else *seed = test + 2147483647; return ((MrBFlt)(*seed) / (MrBFlt)2147483647); } /*--------------------------------------------------------------------------------- | | RndGamma | ---------------------------------------------------------------------------------*/ MrBFlt RndGamma (MrBFlt s, RandLong *seed) { MrBFlt r=0.0; if (s <= 0.0) puts ("Gamma parameter less than zero\n"); else if (s < 1.0) r = RndGamma1 (s, seed); else if (s > 1.0) r = RndGamma2 (s, seed); else /* 0-log() == -1 * log(), but =- looks confusing */ r -= log(RandomNumber(seed)); return (r); } /*--------------------------------------------------------------------------------- | | RndGamma1 | ---------------------------------------------------------------------------------*/ MrBFlt RndGamma1 (MrBFlt s, RandLong *seed) { MrBFlt r, x=0.0, tiny=1e-37, w; static MrBFlt a, p, uf, ss=10.0, d; if (fabs(s-ss)>ETA) /* s != ss */ { a = 1.0 - s; p = a / (a + s * exp(-a)); uf = p * pow(tiny / a, s); d = a * log(a); ss = s; } for (;;) { r = RandomNumber(seed); if (r > p) { x = a - log((1.0 - r) / (1.0 - p)); w = a * log(x) - d; } else if (r > uf) { x = a * pow(r / p, 1.0 / s); w = x; } else return (0.0); r = RandomNumber(seed); if (1.0 - r <= w && r > 0.0) if (r*(w + 1.0) >= 1.0 || -log(r) <= w) continue; break; } return (x); } /*--------------------------------------------------------------------------------- | | RndGamma2 | ---------------------------------------------------------------------------------*/ MrBFlt RndGamma2 (MrBFlt s, RandLong *seed) { MrBFlt r , d, f, g, x; static MrBFlt b, h, ss=0.0; if (fabs(s-ss)>ETA) /* s != ss */ { b = s - 1.0; h = sqrt(3.0 * s - 0.75); ss = s; } for (;;) { r = RandomNumber(seed); g = r - r * r; f = (r - 0.5) * h / sqrt(g); x = b + f; if (x <= 0.0) continue; r = RandomNumber(seed); d = 64 * r * r * g * g * g; if (d * x < x - 2.0 * f * f || log(d) < 2.0 * (b * log(x / b) - f)) break; } return (x); } /*--------------------------------------------------------------------------------- | | SetQvalue | | The Pade method for calculating the matrix exponential, tMat = e^{qMat * v}, | has an error, e(p,q), that can be controlled by setting p and q to appropriate | values. The error is: | | e(p,q) = 2^(3-(p+q)) * ((p!*q!) / (p+q)! * (p+q+1)!) | | Setting p = q will minimize the error for a given amount of work. This function | assumes that p = q. The function takes in as a parameter the desired tolerance | for the accuracy of the matrix exponentiation, and returns qV = p = q, that | will achieve the tolerance. The Pade approximation method is described in: | | Golub, G. H., and C. F. Van Loan. 1996. Matrix Computations, Third Edition. | The Johns Hopkins University Press, Baltimore, Maryland. | | The function is called from TiProbsUsingPadeApprox. | ---------------------------------------------------------------------------------*/ int SetQvalue (MrBFlt tol) { int qV; MrBFlt x; /* x = pow(2.0, 3.0 - (0 + 0)) * Factorial(0) * Factorial (0) / (Factorial(0+0) * Factorial (0+0+1)); */ x = 8.0; qV = 0; while (x > tol) { qV++; x = pow(2.0, 3.0 - (qV + qV)) * Factorial(qV) * Factorial (qV) / (Factorial(qV+qV) * Factorial (qV+qV+1)); } return (qV); } /*--------------------------------------------------------------------------------- | | SetToIdentity | | Make a dim X dim identity matrix. | ---------------------------------------------------------------------------------*/ void SetToIdentity (int dim, MrBFlt **matrix) { int row, col; for (row=0; row= 0.0) t = (1.0 - t) / 2.0; else t /= 2.0; return (t*(a1 >= 0.0 ? 1.0 : -1.0)); } a = a1 / a2; if (a < 0.0) sign = -1.0; a = fabs(a); h = fabs(h); k = h*a; if (h > tv2 || a < tv1) return (0.0); if (h < tv1) return (atan(a)/pai2*sign); if (h < 0.3 && a > 7.0) /* (Boys RJ, 1989) */ { x1 = exp(-k*k/2.0)/k; x2 = (CdfNormal(k)-0.5)*sqrt(pai2); t = 0.25 - (x1+x2)/pai2*h + ((1.0+2.0/(k*k))*x1+x2)/(6.0*pai2)*h*h*h; return (MAX(t,0)*sign); } t = -h*h / 2.0; x2 = a; s = a*a; if (log(1.0+s)-t*s >= tv3) { x1 = a/2; s /= 4.0; for (;;) /* truncation point by Newton iteration */ { x2 = x1 + (t*s+tv3-log(s+1.0)) / (2.0*x1*(1.0/(s+1.0)-t)); s = x2*x2; if (fabs(x2-x1) < tv4) break; x1 = x2; } } for (i=0,rt=0; i 1.0001 || sum < 0.9999) { MrBayesPrint ("%s Warning: Transition probabilities do not sum to 1.0 (%lf)\n", spacer, sum); } } # endif if (fMat != NULL && sMat != NULL) { ptr = cijk; for (i=0; i&2; \ exit 1;; \ esac; \ has_opt=no; \ sane_makeflags=$$MAKEFLAGS; \ if $(am__is_gnu_make); then \ sane_makeflags=$$MFLAGS; \ else \ case $$MAKEFLAGS in \ *\\[\ \ ]*) \ bs=\\; \ sane_makeflags=`printf '%s\n' "$$MAKEFLAGS" \ | sed "s/$$bs$$bs[$$bs $$bs ]*//g"`;; 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%s\n' "$1" exit 1 } case "@TESTS_USE_MPI@" in yes) set -- mpirun -np 2 -H localhost:2 "@abs_top_builddir@/src/mb" ;; no) set -- "@abs_top_builddir@/src/mb" ;; *) echo 'Do not know whether to use MPI or not' >&2 exit 1 esac tmpdir=$(mktemp -d ./${nex_file##*/}.XXXXXXXX) t=$(( t + 1 )); m='Setup' if cp "@top_srcdir@/examples/primates.nex" "$tmpdir" && cp "$nex_file" "$tmpdir" then ok; else error 'Could not copy needed files...'; fi trap 'rm -rf "$tmpdir"' EXIT TERM INT pathname=${nex_file##*/} t=$(( t + 1 )); m='Ran MrBayes' if (cd "$tmpdir" && "$@" "$pathname") >"$tmpdir/$pathname.out" then ok; else error 'Could not create output...'; fi t=$(( t + 1 )); m='Analysis completed' # Analysis completed in N seconds # ... should occur once (N is integer). awk ' /Analysis completed in [[:digit:]]+ seconds/ { count++ } END { if (count != 1) { printf "Did find expected text (count = %d != 1)\n", count err = 1 } exit err }' "$tmpdir/$pathname".out if [ "$?" -eq 0 ]; then ok; else fail; fi t=$(( t + 1 )); m='Cold state likelihood in range [-5715,-5700]' # Likelihood of best state for "cold" chain of run 1 was N # ... should occur once, with N in the range [-5715,-5700] (N is a "simple" float). awk ' /Likelihood of best state for "cold" chain of run 1 was [-[:digit:]]+\.[[:digit:]]+/ { count++ if ($NF < -5715 || $NF > -5700) { printf "%g not in range [-5715,-5700] on line %d\n", $NF, NR err = 1 } } END { if (count != 1) { printf "Did find expected text (count = %d != 1)\n", count err = 1 } exit err }' "$tmpdir/$pathname".out if [ "$?" -eq 0 ]; then ok; else xfail; fi t=$(( t + 1 )); m='TL{all} in range [0.95,1.2]' # There should be one line with "TL{all}" in the first field. # The value in the last field on that lines should be in the range [0.95,1.2]. awk ' $1 == "TL{all}" { count++ if ($NF < 0.95 || $NF > 1.2) { printf "%g not in range [0.95,1.2] on line %d\n", $NF, NR err = 1 } } END { if (count != 1) { printf "Did find expected text (count = %d != 1)\n", count err = 1 } exit err }' "$tmpdir/$pathname".out if [ "$?" -eq 0 ]; then ok; else xfail; fi t=$(( t + 1 )); m='Avg. stdv. of split freqs. < 0.05' # Average standard deviation of split frequencies = N # ... should occur one times, where N is a "simple" float. # N should be below 0.05. awk ' /Average standard deviation of split frequencies = [[:digit:]]+\.[[:digit:]]+/ { count++ if ($NF >= 0.05) { printf "%g not below 0.05 on line %d\n", $NF, NR err = 1 } } END { if (count != 1) { printf "Did find expected text (count = %d != 1)\n", count err = 1 } exit err }' "$tmpdir/$pathname".out if [ "$?" -eq 0 ]; then ok; else xfail; fi t=$(( t + 1 )); m='Avg. PSRF in range [0.95,1.2]' # Average PSRF for parameter values (excluding NA and >10.0) = N # ... should occur once and N (a "simple" float) should lie in the range [0.95,1.2]. awk ' /Average PSRF for parameter values \(excluding NA and >10.0\) = [[:digit:]]+\.[[:digit:]]+/ { count++ if ($NF < 0.95 || $NF > 1.2) { printf "%g not in range [0.95,1.2] on line %d\n", $NF, NR err = 1 } } END { if (count != 1) { printf "Did find expected text (count = %d != 2)\n", count err = 1 } exit err }' "$tmpdir/$pathname".out if [ "$?" -eq 0 ]; then ok; else xfail; fi NBISweden-MrBayes-ecfd55c/testing/test1.nex000066400000000000000000000005471526212227600206540ustar00rootroot00000000000000#NEXUS [Test 1. Mixed model with partitions.] begin mrbayes; set autoclose=yes nowarnings=yes; execute "primates.nex"; partition test = 2: 1-400, 401-.; set partition=test; lset nst=mixed rates=invgamma; unlink statefr=(all) revmat=(all) pinvar=(all) shape=(all); prset ratepr=variable; mcmc ng=20000; sump; sumt; end; NBISweden-MrBayes-ecfd55c/testing/test2.nex000066400000000000000000000006411526212227600206500ustar00rootroot00000000000000#NEXUS [Test 2. Mixed model with partitions. IGR-clock model.] begin mrbayes; set autoclose=yes nowarnings=yes; execute "primates.nex"; partition test = 2: 1-400, 401-.; set partition=test; lset nst=mixed rates=invgamma; unlink statefr=(all) revmat=(all) pinvar=(all) shape=(all); prset brlenspr=clock:uniform clockratepr=exp(1) clockvarpr=igr; mcmc ng=20000; sump; sumt; end;