pax_global_header00006660000000000000000000000064152271267240014522gustar00rootroot0000000000000052 comment=c7370550a3212194be3784c60d67152563ff8d49 dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/000077500000000000000000000000001522712672400200525ustar00rootroot00000000000000dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/.coveragerc000066400000000000000000000015701522712672400221760ustar00rootroot00000000000000# .coveragerc to control coverage.py [run] branch = True omit = *ase/* *numpy/* *spglib/* dynasor/cli/* tests/* */site.py include = dynasor/* bin/* local_installation/dynasor/* local_installation/dynasor [report] # Regexes for lines to exclude from consideration exclude_lines = # Have to re-enable the standard pragma pragma: no cover # Don't complain about numba JIT-compiled functions (not executed by coverage): @numba\.njit # Don't complain about abstract method stubs: @abstractmethod # Don't complain about missing debug-only code: def __repr__ if self\.debug # Don't complain if tests don't hit defensive assertion code: raise AssertionError raise NotImplementedError # Don't complain if non-runnable code isn't run: if 0: if __name__ == .__main__.: ignore_errors = True dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/.flake8000066400000000000000000000001531522712672400212240ustar00rootroot00000000000000[flake8] max-line-length = 100 inline-quotes = ' multiline-quotes = """ exclude = .ipynb_checkpoints local dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/.gitignore000066400000000000000000000005501522712672400220420ustar00rootroot00000000000000slask/* build/ dist/ *.pyc *.so .project .pydevproject .settings/* *~ userguide/ doc/dynasor.*.rst *.npz *.lock .coverage report.xml bin/dynasor dynasor.egg-info/ .ipynb_checkpoints/ mypkg.sh log.lammps dump* !dump.traj !dump_with_velocities.traj pbslog model*.xyz thermo.out dump*.out pbslog movie*.* *.nc gpumd_run_dir local public *.tgz CL*.md memory/ dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/.gitlab-ci.yml000066400000000000000000000126221522712672400225110ustar00rootroot00000000000000stages: [build, test, deploy] # Avoid duplicate pipelines: skip push pipelines when an MR exists; otherwise allow all workflow: rules: - if: '$CI_PIPELINE_SOURCE == "push" && $CI_OPEN_MERGE_REQUESTS' when: never - when: always variables: # Images BARE_IMAGE: "python:3.14-slim-bookworm" CUSTOM_IMAGE: "$CI_REGISTRY/materials-modeling/$CI_PROJECT_NAME/cicd" # pip cache PIP_DISABLE_PIP_VERSION_CHECK: "1" PIP_NO_INPUT: "1" PIP_CACHE_DIR: "$CI_PROJECT_DIR/.pip-cache" cache: key: files: - pyproject.toml paths: - .pip-cache/ # ---------- Build a wheel/sdist ---------- build:wheel: stage: build image: $BARE_IMAGE rules: - when: always script: - python -m pip install -U pip build - python -m build --wheel --sdist - ls -l dist/ artifacts: name: "dist-$CI_COMMIT_SHORT_SHA" paths: [dist/] expire_in: 7 days # ---------- Test matrix (install from source with extras) ---------- .test_template: stage: test image: "python:$PYTHON_VERSION" before_script: - python -m pip install -U pip - pip install ".[test]" script: - xdoctest dynasor - coverage run -m pytest --verbose --junitxml=report.xml tests/ - COVERAGE_OPTIONS="--omit=dynasor/core/rho_j_q_numba.py" - coverage report -m $COVERAGE_OPTIONS - coverage xml -o coverage.xml $COVERAGE_OPTIONS - coverage html $COVERAGE_OPTIONS rules: - if: $CI_PIPELINE_SOURCE == "schedule" when: always allow_failure: true - when: manual allow_failure: true coverage: '/TOTAL.*? (100(?:\.0+)?\%|[1-9]?\d(?:\.\d+)?\%)$/' artifacts: reports: junit: report.xml coverage_report: coverage_format: cobertura path: coverage.xml paths: [htmlcov/] expire_in: 2 days test:py310: extends: .test_template variables: { PYTHON_VERSION: "3.10" } test:py311: extends: .test_template variables: { PYTHON_VERSION: "3.11" } test:py312: extends: .test_template variables: { PYTHON_VERSION: "3.12" } test:py313: extends: .test_template variables: { PYTHON_VERSION: "3.13" } test:py314: extends: .test_template variables: { PYTHON_VERSION: "3.14" } rules: - when: always allow_failure: false style_check: stage: test image: $BARE_IMAGE rules: - if: '$CI_PIPELINE_SOURCE != "schedule"' when: always - when: never script: - python -m pip install -U pip flake8 - flake8 doc/ examples/ dynasor/ tests/ # ---------- Documentation (uses built wheel + extras) ---------- documentation: stage: test image: $CUSTOM_IMAGE rules: - if: '$CI_COMMIT_BRANCH != "master" && $CI_COMMIT_TAG == null && $CI_PIPELINE_SOURCE != "schedule"' when: always - when: never before_script: - python -m pip install -U pip - pip install ".[documentation]" script: - mkdir -p public - sphinx-build -b doctest -W doc/ public/ - sphinx-build -W doc/ public/ artifacts: paths: [public/] expire_in: 7 days # ---------- Pages ---------- # - stable: latest tag -> public/ # - latest: default branch -> public/dev/ .pages_template: image: $CUSTOM_IMAGE needs: - job: test:py314 artifacts: true before_script: - python -m pip install -U pip - git fetch --tags --prune --quiet - git fetch origin "$CI_DEFAULT_BRANCH" --depth 1 --quiet - mkdir -p public script: # --- stable -> public/ (latest tag) --- - LATEST_TAG="$(git tag --sort=-v:refname | head -n1)" - LATEST_HASH="$(git rev-parse --short HEAD)" - git worktree add --force stable "$LATEST_TAG" - cd stable - pip install ".[documentation]" - sphinx-build doc/ "$CI_PROJECT_DIR/public/" - cd "$CI_PROJECT_DIR" - git worktree remove --force stable # --- latest -> public/dev/ (default branch) --- - git worktree add --force latest "origin/$CI_DEFAULT_BRANCH" - cd latest - pip install ".[documentation]" - sphinx-build doc/ "$CI_PROJECT_DIR/public/dev/" - cd "$CI_PROJECT_DIR" - git worktree remove --force latest # Fill in the tag and latest hash for the version switcher - cat doc/switcher.json.template | sed "s/_RELEASE_TAG_/$LATEST_TAG/" | sed "s/_GIT_HASH_/$LATEST_HASH/" > public/_static/switcher.json # --- coverage report -> public/coverage/ --- - ls htmlcov/ - cp -r htmlcov public/coverage artifacts: paths: [public/] expire_in: 7 days pages: extends: .pages_template stage: deploy rules: - if: '$CI_COMMIT_BRANCH == "master"' when: always - if: '$CI_COMMIT_TAG' when: always - if: '$CI_PIPELINE_SOURCE == "schedule"' when: never pages_verification: extends: .pages_template stage: test rules: - if: '$CI_COMMIT_BRANCH != "master" && $CI_COMMIT_TAG == null && $CI_PIPELINE_SOURCE != "schedule"' when: manual allow_failure: true - when: never pypi: stage: deploy image: $CUSTOM_IMAGE rules: - if: '$CI_PIPELINE_SOURCE == "schedule"' when: never - if: '$CI_COMMIT_TAG' when: manual environment: name: pypi-upload before_script: - python -m pip install -U pip build twine - git fetch --tags --quiet script: # On tag pipelines $CI_COMMIT_TAG is set; checkout is usually redundant but harmless - echo "tag=$CI_COMMIT_TAG" - if [ -n "$CI_COMMIT_TAG" ]; then git checkout "$CI_COMMIT_TAG"; fi - python -m build - ls -l dist/ - twine check dist/* - twine upload dist/* -u __token__ -p "$PYPI_TOKEN" dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/.gitlab/000077500000000000000000000000001522712672400213725ustar00rootroot00000000000000dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/.gitlab/issue_templates/000077500000000000000000000000001522712672400246005ustar00rootroot00000000000000dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/.gitlab/issue_templates/Bug.md000066400000000000000000000021151522712672400256360ustar00rootroot00000000000000*(If the task is time-capped, start the title with the keyword 'EXPLORE', e.g.,)* *("EXPLORE: Fix crash on windows 10")* ## Summary *(Summarize the bug encountered concisely)* ## Steps to reproduce *(How one can reproduce the issue - this is very important)* ## What is the current bug behavior? *(What actually happens)* ## What is the expected correct behavior? *(What you should see instead)* ## Possible fixes *(If you can, link to the line of code that might be responsible for the problem)* ____________________________________________________________________ ***Issue definition review*** *(Before the issue can be moved onto the board it should be discussed with or reviewed by at least one other team member. You can address one or more team members via the following slash (quick action) command):* `/cc @(username)` ***Labeling*** *(Please suggest labels for the issue, e.g., by using the following slash command):* `/label ~(label1) ~(label2)` ***Time estimation*** Estimated time can be indicated with the command `/estimate`; use e.g., `/estimate 2d` to indicate two days. dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/.gitlab/issue_templates/Task.md000066400000000000000000000023511522712672400260250ustar00rootroot00000000000000*(A task is loosely speaking an issue that does not increase user value)* *(If the task is time-capped, start the title with the keyword 'EXPLORE', e.g.,)* *("EXPLORE: Figure out how to use customized templates in gitlab")* ## Description *(Describe the task)* ### Background *(If needed provide some background)* ## Sub-tasks ***Note!, this section should be completed by a developer and not necessarily the feature requester*** *(Describe the sub-tasks to be carried out, preferably in the form of a list)* * [ ] *(task 1)* * [ ] *(task 2)* * [ ] *(task 3)* ## Demonstration *(Describe how it can be shown that the task has been implemented successfully)* ______________________________________________________________ ***Issue definition review*** *(Before the issue can be moved onto the board it should be discussed with or reviewed by at least one other team member. You can address one or more team members via the following slash (quick action) command):* `/cc @(username)` ***Labeling*** *(Please suggest labels for the issue, e.g., by using the following slash command):* `/label ~(label1) ~(label2)` ***Time estimation*** Estimated time can be indicated with the command `/estimate`; use e.g., `/estimate 2d` to indicate two days. dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/.gitlab/issue_templates/User_Story.md000066400000000000000000000027271522712672400272500ustar00rootroot00000000000000*(A user story provides some kind of user value)* *(If the task is time-capped, start the title with the keyword 'EXPLORE', e.g.,)* *("EXPLORE: Diffraction pattern from ensembles")* ## Description *(Write a user story, i.e. describe the feature from the perspective of the user)* As a < type of user >, I want < some goal > [ so that < some reason > ]. ### Background *(Provide some brief and useful background information if needed)* ## Sub-tasks ***Note!, this section should be completed by a developer and not necessarily the feature requester*** *(Describe the sub-tasks to be carried out, preferably in the form of an task list, in order to complete this task.)* * [ ] *(e.g. Unit tests)* * [ ] *(e.g. Update user documentation)* * [ ] *(e.g. Prepare demonstration)* * [ ] *(task 4)* ## Demonstration *(Describe how it can be shown that the user story has been implemented successfully)* ______________________________________________________________ ***Issue definition review*** *(Before the issue can be moved onto the board it should be discussed with or reviewed by at least one other team member. You can address one or more team members via the following slash (quick action) command):* `/cc @(username)` ***Labeling*** *(Please suggest labels for the issue, e.g., by using the following slash command):* `/label ~(label1) ~(label2)` ***Time estimation*** Estimated time can be indicated with the command `/estimate`; use e.g., `/estimate 2d` to indicate two days. dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/CONTRIBUTING.md000066400000000000000000000126641522712672400223140ustar00rootroot00000000000000Contribution guidelines ======================= Some general guidelines: * **Read the contribution guidelines**.
You have done well so far but keep on reading all the way to the **bottom of this page**. * Use [expressive function and variable names](https://xkcd.com/910/), e.g., * Good: `get_number_of_structures` (Python) * Avoid: `get_nbr_struct` or any variation thereof * Good: `get_number_of_allowed_elements` (Python) * Avoid: `get_Mi` or any variation thereof * Document and comment, document and comment, document and comment, document and comment, document and comment ... (it cannot be said too often). * Always imagine someone else has to be able to read your code. Hence do your best at writing [*clean and structured* code](https://www.xkcd.com/1513/). * Avoid commenting out blocks of code for "later use/reference" in commits. Python ------ Code should be [pep8](https://www.Python.org/dev/peps/pep-0008/) compliant and pass [pyflakes](https://pypi.Python.org/pypi/pyflakes). Any functions/functionality *must* be properly documented. This includes [docstrings](https://en.wikipedia.org/wiki/Docstring) for functions, classes, and modules that clearly describe the task performed, the interface (where necessary), the output, and if possible an example. This package uses [NumPy Style Python Docstrings](http://sphinxcontrib-napoleon.readthedocs.io/en/latest/example_numpy.html). Good job, you are still reading! [Will you make it to the end?](https://xkcd.com/169/) Please use spaces ----------------- While you are entitled to [your own opinion](http://lea.verou.me/2012/01/why-tabs-are-clearly-superior/) this project uses spaces instead of tabs. Even if you are geeky enough to care and like [Silicon Valley](https://www.youtube.com/watch?v=SsoOG6ZeyUI) you should know that [developers who use spaces make more money](https://stackoverflow.blog/2017/06/15/developers-use-spaces-make-money-use-tabs/). Also the use of spaces is strongly recommended by our beloved [pep8](https://www.Python.org/dev/peps/pep-0008/) standard. Commits/issues/merge requests ----------------------------- ### Commit messages When writing commit messages, generating issues, or submitting merge requests, [write meaningful and concise commit messages](https://xkcd.com/1296/). The first line should not exceed 78 characters. If you require more space, insert an empty line after the "title" and add a longer message below. In this message, you should again limit yourself to 78 characters *per* line. ### General guidelines Bug reports, features suggestions, code review etc are handled via gitlab. The following workflow is strongly [encouraged](https://xkcd.com/1172/): #### Preparation phase As an issue reporter: * Create an issue. * When creating an issue for a __feature request__, it is good practice to describe who the issue is to be developed for, what should be achieved, and ensure that implementation of the feature results in user value. Good practice is to write a [user story](https://www.mountaingoatsoftware.com/agile/user-stories), e.g.: 1. _As a user at a neutron source, I would like to be able to compare_ _my acquired scattering data with spectra computed using `dynasor`, so that_ _I can explain my temperature dependent observations._ 2. _As a developer, I would like to easily figure out how to best_ _contribute to the `dynasor` project._ It is also a good idea to describe how a successful implementation can be demonstrated. * When creating a __bug report__, it is important to describe how the bug can be reproduced. * Invite comments from users, developers, and other stakeholders via the GitLab interface (e.g., use `@username` to address specific team members in issue descriptions, messages, or discussions) * Review the input and make adjustments according to input. If the issue is considered to take more than five days to complete, try to split it into smaller ones, each with their own user story. * Once the issue is ready for development, the product owner or a person appointed by the product owner should move the issue to the `To Do` column. #### Development phase As a developer: * If the issue is not already assigned to you, assign it to yourself. Move the issue to the `Doing` column. * Create a branch from the issue via the GitLab interface. Ensure that other people can see from the issue which branch you are working on. * Once the work on the issue has been completed *first* clean up your code and review the [items that will be covered during review (see below)](http://commadot.com/wtf-per-minute/). Make sure that the described demonstration can be done, that there are unit tests (or regression tests in case of bug fixes), and developer and user documentation. * Create a merge request to initiate the review phase. #### Review phase As a reviewer: * If the merge request up for review is not already assigned to you, assign it to yourself. * The development related to the issue must be reviewed for * sufficient test coverage. * code passes all existing tests * functionality * performance * code quality * compliance to style guidelines * addition of new unit tests * demonstration is fulfilled * After all comments/questions raised during the review have been addressed, the code can be merged into master by the reviewer. As a developer: * The responsibility for making the code compliant with the above points resides with the developer *not* the reviewer. dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/Dockerfile000066400000000000000000000016231522712672400220460ustar00rootroot00000000000000FROM python:3.13-slim-bookworm ENV DEBIAN_FRONTEND=noninteractive \ PIP_NO_CACHE_DIR=1 \ UV_SYSTEM_PYTHON=1 # OS deps (graphviz+pandoc). Use --no-install-recommends and clean the lists. RUN apt-get update \ && apt-get install -y --no-install-recommends graphviz pandoc ca-certificates curl git \ && rm -rf /var/lib/apt/lists/* # Add super-fast installer; harmless if unused in CI RUN curl -LsSf https://astral.sh/uv/install.sh | sh # One layer, no cache kept RUN python -m pip install --upgrade pip \ && pip install --no-cache-dir \ sphinx==8.1.3 \ sphinx-sitemap==2.6.0 \ sphinx-design==0.6.1 \ sphinx-autodoc-typehints==2.4.4 \ pydata-sphinx-theme==0.16.1 \ sphinx-rtd-theme==3.0.2 \ nbsphinx==0.9.5 \ nbconvert==7.16.4 \ jinja2==3.1.6 \ jupyter-core==5.7.2 \ pygments==2.19.1 \ IPython==9.4.0 WORKDIR /work dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/LICENSE000066400000000000000000000020701522712672400210560ustar00rootroot00000000000000MIT License Copyright (c) 2018-2025 materials-modeling Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/README.rst000066400000000000000000000050531522712672400215440ustar00rootroot00000000000000dynasor ======= **dynasor** is a tool for calculating total and partial dynamic structure factors as well as related correlation functions from molecular dynamics (MD) simulations. By analyzing these functions one can access the dynamics of a system without resorting to perturbative approaches. Moreover by combining in particular the structure factor with the cross sections (or form factors) of, e.g., neutrons, X-rays or electrons, one can predict experimental spectra. The main input consists of a trajectory from an MD simulation, i.e., a file containing snapshots of the particle coordinates and optionally velocities that correspond to consecutive, equally spaced points in simulation time. **dynasor** provides both a Python interface and a command-line interface. The following snippet illustrates how one can calculate dynamic structure factors using the former. .. code-block:: python from dynasor import Trajectory, compute_dynamic_structure_factors from dynasor.qpoints import get_spherical_qpoints traj = Trajectory('dump.xyz', trajectory_format='extxyz') q_points = get_spherical_qpoints(traj.cell, q_max=20) sample = compute_dynamic_structure_factors(traj, q_points=q_points, dt=5, window_size=100) sample.write_to_npz('test.npz') **dynasor** can be installed via `pip `_ or `conda `_. Please consult the `installation section of the user guide `_ for details. The full documentation can be found in the `user guide `_. For questions and help please use the `dynasor discussion forum on matsci.org `_. **dynasor** and its development are hosted on `gitlab `_. When using **dynasor** in your research please cite the following papers: | *Dynasor – A tool for extracting dynamical structure factors and current correlation functions from molecular dynamics simulations* | Erik Fransson, Mattias Slabanja, Paul Erhart, and Göran Wahnström | Advanced Theory and Simulations **4**, 2000240 (2021); DOI:`10.1002/adts.202000240 `_ | *Dynasor 2: From simulation to experiment through correlation functions* | Esmée Berger, Erik Fransson, Fredrik Eriksson, Eric Lindgren, Göran Wahnström, Thomas Holm Rod, and Paul Erhart | Computer Physics Communications **316**, 109759 (2025); DOI: `10.1016/j.cpc.2025.109759 `_ dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/benchmarks/000077500000000000000000000000001522712672400221675ustar00rootroot00000000000000dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/benchmarks/numba.ipynb000066400000000000000000003061721522712672400243450ustar00rootroot00000000000000{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "ce6641c8", "metadata": {}, "outputs": [], "source": [ "import itertools\n", "import timeit\n", "\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "\n", "from dynasor.core.reciprocal import calc_rho_q, calc_rho_j_q" ] }, { "cell_type": "code", "execution_count": 2, "id": "4760b536", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 2 4 8 16 32 64 128]\n" ] } ], "source": [ "sizes = 2**np.arange(1, 8)\n", "print(sizes)" ] }, { "cell_type": "code", "execution_count": 3, "id": "2bcbacb6", "metadata": {}, "outputs": [], "source": [ "def xvq(Nx, Nq):\n", " \"\"\"Returns suitable arrays used in rho_q and rho_j_q\"\"\"\n", " x = np.random.normal(size=(Nx, 3))\n", " v = np.random.normal(size=(Nx, 3))\n", " q = np.random.normal(size=(Nq, 3))\n", " return x, v, q" ] }, { "cell_type": "code", "execution_count": 4, "id": "8ff8bccf", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(array([1.16435931+0.52618282j, 1.15045451-0.28636197j]),\n", " array([[ 1.66779075+0.12925628j, -0.03102259-0.17115886j,\n", " 0.8197776 +0.09857148j],\n", " [ 1.24151343-1.05491643j, -0.13291369-0.15462083j,\n", " 0.63304868-0.4823513j ]]))" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Here we force numba to compile the functions\n", "x, v, q = xvq(2, 2)\n", "calc_rho_q(x, q)\n", "calc_rho_j_q(x, v, q)" ] }, { "cell_type": "code", "execution_count": 6, "id": "860011c2", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "numba rho_j_q 128 128 7.989435801282525e-05 \r" ] } ], "source": [ "# Main loop\n", "\n", "data = []\n", "functions = dict(rho_q='calc_rho_q(x, q)', rho_j_q='calc_rho_j_q(x, v, q)')\n", "size_pairs = itertools.product(sizes, repeat=2)\n", "\n", "for (func, func_str), (Nx, Nq) in itertools.product(\n", " functions.items(), size_pairs):\n", " x, v, q = xvq(Nx, Nq)\n", "\n", " timer = timeit.Timer(func_str, globals=globals())\n", " N, T = timer.autorange()\n", " print('numba', func, Nx, Nq, T/N, end=' '*10 + '\\r')\n", " data.append(dict(Nx=Nx, Nq=Nq, t=T/N, backend='numba', func=func))" ] }, { "cell_type": "code", "execution_count": 7, "id": "2b72388b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Nx Nq t backend func\n", "0 2 2 0.000007 numba rho_q\n", "1 2 4 0.000006 numba rho_q\n", "2 2 8 0.000007 numba rho_q\n", "3 2 16 0.000010 numba rho_q\n", "4 2 32 0.000006 numba rho_q\n", ".. ... ... ... ... ...\n", "93 128 8 0.000029 numba rho_j_q\n", "94 128 16 0.000060 numba rho_j_q\n", "95 128 32 0.000026 numba rho_j_q\n", "96 128 64 0.000043 numba rho_j_q\n", "97 128 128 0.000080 numba rho_j_q\n", "\n", "[98 rows x 5 columns]\n" ] } ], "source": [ "DF = pd.DataFrame(data)\n", "print(DF)" ] }, { "cell_type": "code", "execution_count": 8, "id": "f2fa73c6", "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, axes = plt.subplots(2, sharex=True, figsize=(8,8))\n", "\n", "ax = axes[0]\n", "for Nx in sorted(set(DF.Nx)):\n", " df = DF[(DF.Nx == Nx) & (DF.backend=='c') & (DF.func=='rho_q')]\n", " l, = ax.loglog(df.Nq, df.t, ls='-', label=f'Nx={Nx}')\n", " df = DF[(DF.Nx == Nx) & (DF.backend=='numba') & (DF.func=='rho_q')]\n", " ax.loglog(df.Nq, df.t, ls='--', label=f'_Nx={Nx}', color=l.get_color())\n", "ax.set_ylabel('Timing rho_q')\n", "\n", "ax = axes[1]\n", "for Nx in sorted(set(DF.Nx)):\n", " df = DF[(DF.Nx == Nx) & (DF.backend=='c') & (DF.func=='rho_j_q')]\n", " l, = ax.loglog(df.Nq, df.t, ls='-', label=f'Nx={Nx}')\n", " df = DF[(DF.Nx == Nx) & (DF.backend=='numba') & (DF.func=='rho_j_q')]\n", " ax.loglog(df.Nq, df.t, ls='--', label=f'_Nx={Nx}', color=l.get_color())\n", "ax.set_ylabel('Timing rho_j_q')\n", "\n", "\n", "axes[1].set_xlabel('Nq')\n", "axes[0].plot([],[],ls='--', label='numba')\n", "axes[0].legend()\n", "fig.tight_layout()\n", "fig.savefig('timings.pdf')\n", "#fig.show()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.10" } }, "nbformat": 4, "nbformat_minor": 5 } dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/benchmarks/reader/000077500000000000000000000000001522712672400234315ustar00rootroot00000000000000dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/benchmarks/reader/ase_mp.py000066400000000000000000000020461522712672400252510ustar00rootroot00000000000000import timeit import numpy as np from ase.io import read, write # noqa from ase.build import bulk from dynasor.trajectory.extxyz_trajectory_reader import ExtxyzTrajectoryReader # noqa fname = 'atoms.extxyz' frames = 100 atoms = 2000 atoms = bulk('Al').repeat((atoms, 1, 1)) momenta = np.random.random(size=atoms.positions.shape) atoms.set_momenta(momenta) atoms.info['Time'] = 1.0 atoms.set_tags(np.ones(len(atoms))) print(f'Number of atoms: {len(atoms)}') print(f'Number of frames: {frames}') print(f'Number of bytes: {frames * len(atoms) * 1.8 / 2**12 / 2**12:.3f} GB') write(fname, [atoms]*frames) timer = timeit.Timer("read(fname, index=':')", globals=globals()) # noqa N, T = timer.autorange() print(f'Time ASE: {T/N:<10.5f}', flush=True) T0 = T/N for n in [2, 3, 4, 5, 6, 8, 12]: timer = timeit.Timer('list(ExtxyzTrajectoryReader(fname, max_workers=n))', globals=globals()) N, T = timer.autorange() print(f'Workers: {n} Time: {T/N:<10.5f} Speedup: {T0/(T/N):<10.5f} ' f'Efficiency: {T0/(T/N)/n:<10.5f}', flush=True) dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/doc/000077500000000000000000000000001522712672400206175ustar00rootroot00000000000000dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/doc/_static/000077500000000000000000000000001522712672400222455ustar00rootroot00000000000000dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/doc/_static/Ni3Al-current-correlation.png000066400000000000000000001064321522712672400276660ustar00rootroot00000000000000PNG  IHDRxzTXtRaw profile type exifxuQ AQDZy>O>$Y>LE..Yn!Oqd|UN泥iV.77,vy7TrKit‘$誑%;ٰrNރ&DJ42թKm4pco1'TޏS[ysp/f;z7F<iCCPICC profilex}=H@_S"[":EEkP! :\MGbYWWAq]%ݽ;@hTfMt2!fsb"29IJw1ܟc@[ qf6̦mp'OtAG+q.,̐Ib+JF Screenshot z^bKGD) pHYs%%IR$tIME8%itEXtCommentScreenshot IDATxkeWv[{s_~&9G˖GKPd[Fl'01y@  <+Xò8$PCr4&ds^gn69Ù)]{~ɹs, Weя̟gߟ1UEU   c%"w6P5Fo8а+%k8 " Gf> ğ3gic/$j &EL47fdSb.??h4 ];.i/=X_`}c|#8˾_΄jږY)=yb/\]'#:;"4d IUy';#!'l]2q_1d^$g=nm-[—2׮] sTUXL;QNj܉/^qDpZ˰W KCF'X9䝝Z9X8G],%d+ʲ.nAg>}$kS^63Pwu?JRi֎GDLjm6_~'W]0`}5#BED(saè`ʍ}/ux "awQ5yȔⅭ(h-T;'2AOXdǾqn]f`η%7h5\_timG5z'i~[ }NsV[GO}^<9oN%a{A,1xDnm;U^4+}Sg6nQU; rz,,ϞP*/'xH"N'؏Ʉ~9ױ9̢S_m׭*\G5-gٹp!WW_n=viNf16KDyVRpO[ɨB-N>οI_݊t] ݞRN6{18(b6:f8vA y&^|%> @1-';V`vK.Ÿ y3">Rc~mHŔ $^ZdcsIɡUXy%isoa1Do]x ]4orOf /"o kx59aL_3 |޼֠_ҍg_s&)K_dZWlȌ>.o]ΫcqEë7$l%R $y /i KE+_h|L] fh̆5RGU5BW_7oc$"uYos f.&L#P:lzzw$>b;2'`eiŢ,LhXF `e4qΜ`;k#F/.r[4]nm-ٽ߯Y0A<>>|/=<¹󉰓Oޢ~惃c> sڍm3 MV-'H'8Ky~z t~G8?ffI8ܮ/\qrpx9tx2>óqR>~AƓ G#fasǬ^!<3"Koϵ@Us/&֮?G115w6iYn͛]ONg^//"U$?wn[ׯ # ? 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dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/doc/_templates/page.html000066400000000000000000000043131522712672400245570ustar00rootroot00000000000000{% extends "!page.html" %} {% block extrahead %} {% endblock %} {% block footer %} {% endblock %} dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/doc/_templates/versions.html000066400000000000000000000010151522712672400255070ustar00rootroot00000000000000
Documentation v: {{ current_version }}
Versions
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dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/doc/backmatter/000077500000000000000000000000001522712672400227345ustar00rootroot00000000000000dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/doc/backmatter/bibliography.rst000066400000000000000000000044761522712672400261540ustar00rootroot00000000000000.. index:: Bibliography Bibliography ************ .. [BerFraEri25] | E. Berger, E. Fransson, F. Eriksson, E. Lindgren, G. Wahnström, T. H. Rod, and P. Erhart | *Dynasor 2: From simulation to experiment through correlation functions* | Computer Physics Communications **316**, 109759 (2025) | `doi: 10.1016/j.cpc.2025.109759 `_ .. [BooYip80] | J. P. Boon and S. Yip | *Molecular Hydrodynamics* | Dover (1980) | ISBN: 9780486669496 .. [EriFraLin23] | F. Eriksson, E. Fransson, C. Linderälv, Z. Fan, and P. Erhart | *Tuning the Through-Plane Lattice Thermal Conductivity in van der Waals Structures through Rotational (Dis)ordering* | ACS Nano **17**, 25565 (2023) | `doi: 10.1021/acsnano.3c09717 `_. .. [FraRosErh23] | E. Fransson, P. Rosander, P. Erhart, and G. Wahnström | *Understanding Correlations in BaZrO3: Structure and Dynamics on the Nanoscale* | Chemistry of Materials **36**, 514 (2023) | `doi: 10.1021/acs.chemmater.3c02548 `_ .. [FraRosEri23] | E. Fransson, P. Rosander, F. Eriksson, J. M. Rahm, T. Tadano, and P. Erhart | *Limits of the phonon quasi-particle picture at the cubic-to-tetragonal phase transition in halide perovskites* | Communications Physics **6**, 173 (2023) | `doi: 10.1038/s42005-023-01297-8 `_ .. [FraSlaErh21] | E. Fransson, M. Slabanja, P. Erhart, and G. Wahnström | *Dynasor - A tool for extracting dynamical structure factors and current correlation functions from molecular dynamics simulations* | Advanced Theory and Simulations **4**, 2000240 (2021) | `doi: 10.1002/adts.202000240 `_ .. [KayFraErh25] | P. Kayastha, E. Fransson, P. Erhart, and L. Whalley | *Octahedral Tilt-Driven Phase Transitions in BaZrS3 Chalcogenide Perovskite* | The Journal of Physical Chemistry Letters **16**, 2064 (2025) | `doi: 10.1021/acs.jpclett.4c03517 `_ .. [ThoTurIut10] | J. A. Thomas, J. E. Turney, R. M. Iutzi, C. H. Amon, and A. J. H. McGaughey | *Predicting phonon dispersion relations and lifetimes from the spectral energy density* | Physical Review B **81**, 081411 (2010) | `doi: 10.1103/physrevb.81.081411 `_ dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/doc/backmatter/glossary.rst000066400000000000000000000003251522712672400253310ustar00rootroot00000000000000.. index:: Glossary Glossary ******** .. glossary:: ACF Autocorrelation function DHO Damped harmonic oscillator PSD Power spectral density SED Spectral energy density dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/doc/backmatter/index.rst000066400000000000000000000001021522712672400245660ustar00rootroot00000000000000Backmatter *********** .. toctree:: bibliography glossary dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/doc/conf.py000066400000000000000000000045561522712672400221300ustar00rootroot00000000000000#!/usr/bin/env python3 # -*- coding: utf-8 -*- import importlib extensions = [ 'sphinx.ext.autodoc', 'sphinx.ext.autosummary', 'sphinx.ext.doctest', 'sphinx.ext.coverage', 'sphinx.ext.ifconfig', 'sphinx.ext.intersphinx', 'sphinx.ext.mathjax', 'sphinx.ext.napoleon', 'sphinx.ext.viewcode', 'sphinx_autodoc_typehints', 'sphinx_sitemap', 'sphinx_design', 'nbsphinx', ] graphviz_output_format = 'svg' templates_path = ['_templates'] default_role = 'code' source_suffix = '.rst' master_doc = 'index' pygments_style = 'sphinx' todo_include_todos = False # Collect basic information from main module metadata = importlib.metadata.metadata('dynasor') version = metadata.get('Version', 'n/a') project = metadata.get('Name', 'n/a') author = metadata.get('Author', 'n/a') site_url = 'https://dynasor.materialsmodeling.org' html_css_files = ['custom.css', 'hidden_cells.css'] html_js_files = ['delete_empty_code_blocks.js', 'hidden_cells.js'] html_logo = '_static/logo.png' html_favicon = '_static/logo.ico' html_static_path = ['_static'] htmlhelp_basename = 'dynasordoc' intersphinx_mapping = { 'ase': ('https://docs.ase-lib.org', None), 'numpy': ('https://numpy.org/doc/stable/', None), } # sphinx theme configuration html_theme = 'pydata_sphinx_theme' html_theme_options = { 'secondary_sidebar_items': ['page-toc'], 'switcher': { 'json_url': 'https://dynasor.materialsmodeling.org/_static/switcher.json', 'version_match': version, }, 'navbar_start': ['navbar-logo', 'version-switcher'], } html_sidebars = { 'credits': [], } # Settings for nbsphinx nbsphinx_execute = 'never' # Options for LaTeX output _PREAMBLE = r""" \usepackage{amsmath,amssymb} \renewcommand{\vec}[1]{\boldsymbol{#1}} \DeclareMathOperator*{\argmin}{\arg\!\min} \DeclareMathOperator{\argmin}{\arg\!\min} """ latex_elements = { 'preamble': _PREAMBLE, } latex_documents = [ (master_doc, 'dynasor.tex', 'dynasor Documentation', 'The dynasor developer team', 'manual'), ] # Options for manual page output man_pages = [ (master_doc, 'dynasor', 'dynasor Documentation', [author], 1) ] # Options for Texinfo output texinfo_documents = [ (master_doc, 'dynasor', 'dynasor Documentation', author, 'dynasor', 'A tool for calculating static and dynamic atomic structure correlation functions', 'Miscellaneous'), ] dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/doc/credits.rst000066400000000000000000000035741522712672400230170ustar00rootroot00000000000000.. _credits: .. index:: Credits .. |br| raw:: html
Credits ******* :program:`dynasor` has been developed at the Condensed Matter and Materials Theory division at the Department of Physics at Chalmers University of Technology. The development of this package has been supported by the Knut och Alice Wallenbergs Foundation, the Swedish Research Council, the Swedish Foundation for Strategic Research, and the Swedish National Infrastructure for Computing. Below is the list of main contributors: * Mattias Slabanja * Erik Fransson * Esmée Berger * Fredrik Eriksson * Paul Erhart When using :program:`dynasor` in your research please cite the following two papers: * *Dynasor – A tool for extracting dynamical structure factors and current correlation functions from molecular dynamics simulations* |br| Erik Fransson, Mattias Slabanja, Paul Erhart, and Göran Wahnström |br| Advanced Theory and Simulations **4**, 2000240 (2021); DOI: `10.1002/adts.202000240 `_ [FraSlaErh21]_ * *Dynasor 2: From simulation to experiment through correlation functions* |br| Esmée Berger, Erik Fransson, Fredrik Eriksson, Eric Lindgren, Göran Wahnström, Thomas Holm Rod, and Paul Erhart |br| Computer Physics Communications **316**, 109759 (2025); DOI: `10.1016/j.cpc.2025.109759 `_ [BerFraEri25]_ :program:`dynasor` is based on Python 3 and invokes functionality from the Python libraries listed below. To support the development of free and open-source software, please make sure to give proper credit to these packages when citing :program:`dynasor` as well. * `ASE `_ * `NumPy `_ * `Numba `_ * `pandas `_ * `SciPy `_ * `MDAnalysis `_ dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/doc/get_started/000077500000000000000000000000001522712672400231245ustar00rootroot00000000000000dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/doc/get_started/_static/000077500000000000000000000000001522712672400245525ustar00rootroot00000000000000dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/doc/get_started/_static/Al_sed_vacancy.gif000066400000000000000000023511401522712672400301420ustar00rootroot00000000000000GIF89a* """)))233BCCTTTeffhhhoopuvvvwxxxxB VZ_ik o$t-}Utd~2r{djnt{h6]7ASJ>agw砇.褗n騧zIơ밋:n>{/{'|/zǝP+BSklؑ/o觯#I>"O? 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Structure factors in BaZrO\ :sub:`3` ------------------------------------ Simulated diffuse scattering in BaZrO\ :sub:`3` [FraRosErh23]_ .. figure:: _static/BaZrO_Sq.png :align: center | Phonons in moiré structures --------------------------- Phonon mode projections for disordered moiré structures [EriFraLin23]_ .. figure:: _static/moire_dispersions.png :align: center | Static structure factors and XRD for BaZrS\ :sub:`3` ---------------------------------------------------- Simulated x-ray diffraction patterns in BaZrS\ :sub:`3` [KayFraErh25]_ .. figure:: _static/BaZrS3_Sq.png :align: center | Overdamped phonon modes in CsPbBr\ :sub:`3` ------------------------------------------- Overdamped phonon modes in CsPbBr\ :sub:`3` obtained via mode projections [FraRosEri23]_ .. figure:: _static/CsPbBr_overdamped_modes.png :align: center | Current correlations in molten NaCl ----------------------------------- Current correlations in molten NaCl [FraSlaErh21]_ .. figure:: _static/NaCl_liquid.png :align: center | Alloys and defects ------------------ Spectral energy density (SED) for different vacancy concentrations in FCC Al at 300 K. .. figure:: _static/Al_sed_vacancy.gif :align: center | Spectral energy density (SED) for the alloy perovskite BaZr(S, Se)\ :sub:`3`, for varying concentrations of S and Se. .. figure:: _static/SED_mixed_BZSSe.gif :align: center | dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/doc/get_started/index.rst000066400000000000000000000021221522712672400247620ustar00rootroot00000000000000.. _get_started: Get started *********** :program:`dynasor` can be readily installed from `PyPI `_ or `anaconda `_ as described in the :ref:`installation section `. An overview of the components of :program:`dynasor` and their relationships is provided in the :ref:`workflow section `. The key concepts are summarized in the :ref:`theory section `. We also strongly recommend that you take note of the :ref:`units used in dynasor `. Furthermore, you might find the :ref:`information on computational performance ` helpful. Finally, the :ref:`gallery section ` provides a collection of exemplary applications from the literature. The easiest way to get going with the practical aspects of :program:`dynasor` is via the :ref:`tutorial section `. They serve as a hands-on introduction to :program:`dynasor` and provide an overview of its key features. .. toctree:: :maxdepth: 1 installation workflow theory units performance gallery dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/doc/get_started/installation.rst000066400000000000000000000047431522712672400263670ustar00rootroot00000000000000.. _installation: .. index:: Installation Installation ************ Installation via ``pip`` or ``conda`` ===================================== Stable versions of :program:`dynasor` are provided via `PyPI `_ and as part of `conda-forge `_. This implies that :program:`dynasor` can be installed using ``pip``:: pip install dynasor or using ``conda``:: conda install -c conda-forge dynasor Installing the development version ================================== .. index:: Git repository If you want to use the most recent (development) version you can install directly from the git repository:: pip install --user git+https://gitlab.com/materials-modeling/dynasor.git Requirements ============ :program:`dynasor` requires :program:`Python` 3.9+ and depends on the following libraries: * `ASE `_ (trajectory reading) * `MDAnalysis `_ (trajectory reading) * `NumPy `_ (numerical linear algebra) * `Numba `_ (computational efficiency) * `SciPy `_ (convenience functions for correlations) * `pandas `_ (data handling) Numba and Intel SVML ==================== :program:`dynasor` employs :program:`numba` for the efficient calculation of correlation functions. Some Numba versions can use Intel's Short Vector Math Library (SVML) to accelerate mathematical operations. Support depends on the installed Numba and llvmlite versions, so consult the current `Numba performance tips `_ before installing additional runtime libraries. Run ``numba -s`` to check whether SVML is available in the active environment. When enabled, the output includes:: __SVML Information__ SVML State, config.USING_SVML : True SVML Library Loaded : True llvmlite Using SVML Patched LLVM : True SVML Operational : True If SVML support is expected but any of these values are ``False``, verify that ``intel-cmplr-lib-rt`` is installed in the active environment and that Numba can locate the SVML shared libraries when Python starts. Consult the current `Numba performance tips `_ for installation and troubleshooting instructions. dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/doc/get_started/performance.rst000066400000000000000000000067301522712672400261650ustar00rootroot00000000000000.. _performance: .. index:: Performance Performance *********** There are multiple parts in :program:`dynasor` that can be computationally expensive, and depending on the use case different parts of the code can become performance limiting. The most time-consuming part of the dynamic structure factor calculations is usually the calculation of the Fourier transformed densities. This part is implemented in `numba `_, and is significantly accelerated by parallelization. See below for run times of typical use cases. Here, we try to provide some rough estimates for how long various types of calculations may take. If trajectory reading and other overheads are disregarded and the incoherent part is not evaluated (``calculate_incoherent=False``), calculations should scale as ``N_qpts * N_atoms * max_frames``. Here ``N_qpts`` and ``N_atoms`` refer to the number of :math:`\boldsymbol{q}`-points and atoms respectively. Correlating the density between different frames in the window is often very fast and thus simulations often do not scale with ``time_window``. If the incoherent part (``calculate_incoherent=True``) is evaluated calculations will typically scale as ``N_qpts * N_atoms * max_frames * time_window``. The :program:`dynasor` calculations will typically become slower when calculating the incoherent intermediate scattering function. Parallelization ================ Parallelization is done both over :math:`\boldsymbol{q}`-points (``N_qpts``) when calculating the densities, and over time lags (``time_window``) when evaluating dynamic correlation functions. By default the calculations will be parallelized over all available cores. Typical use cases ================= Here we will consider a few typical use-cases in order to provide a rough estimate for how long typical calculations may take, and how calculations may scale with number of available cores. Few :math:`\boldsymbol{q}`-points --------------------------------- Below are some time estimates for a three component system of about 40,000 atoms and with a trajectory containing 10,000 snapshots. Here, only 5 high-symmetry :math:`\boldsymbol{q}`-points are used as is typical when analyzing solids. The time window is set to 1000. Note that reading the trajectory (xyz format) accounts for a large part of the total computational time. .. figure:: _static/CsPbI3_natoms40000_xyz_few_qpts.svg :scale: 13 % :align: center Many thousands of :math:`\boldsymbol{q}`-points ----------------------------------------------- Below are some time estimates for a three component system of about 10,000 atoms and with a trajectory containing 10,000 snapshots. Here, we consider 1000 :math:`\boldsymbol{q}`-points and conduct a spherical average over :math:`\boldsymbol{q}`-points. The time window is set to 400. .. figure:: _static/CsPbI3_natoms8640_xyz_many_qpts.svg :scale: 13 % :align: center .. _trajectory_reading: Trajectory reading ================== The time spent on trajectory reading can be significant for plain-text formats (e.g., XYZ and LAMMPS dump). Therefore, if possible, we recommend using the binary formats if performance is essential and trajectory reading is a computational bottleneck. .. figure:: _static/dynasor_trajectory_reader_benchmark.svg :scale: 13 % :align: center Time for reading a trajectory containing 10,000 snapshots in common trajectory formats. The internal ``extxyz``-reader is multithreaded with decent scaling. In the example only one core was used. dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/doc/get_started/theory.rst000066400000000000000000000510131522712672400251700ustar00rootroot00000000000000.. _theory: .. index:: Theory .. index:: Background Theoretical background ********************** Below is a brief overview of the theory underpinning the correlation functions supported by :program:`dynasor` as well as their analysis. For a more detailed description and derivations of the correlation functions see, for example, [BooYip80]_. .. index:: Correlation functions; Position Dynamic structure factor ------------------------ The particle density is denoted :math:`n(\boldsymbol{r},t)` and defined as .. math:: n(\boldsymbol{r},t) = \sum _i ^N \delta (\boldsymbol{r} - \boldsymbol{r}_i(t)), where :math:`N` is the number of particles and :math:`\boldsymbol{r}_i(t)` is the position of particle :math:`i` at time :math:`t`. The intermediate scattering function :math:`F(\boldsymbol{q},t)` is defined as the Fourier transform of the auto-correlation of the particle density .. math:: F(\boldsymbol{q},t)=\frac{1}{N}\left \,= \, \frac{1}{N}\sum _i ^N \sum _j ^N \left< \mathrm{exp} \left[i\boldsymbol{q}\cdot(\boldsymbol{r}_i(t)-\boldsymbol{r}_j(0))\right] \right>, where :math:`\boldsymbol{q}` is a wave vector. The brackets denote an ensemble average, which in the case of :program:`dynasor` is replaced by a time average. (The system under study should be ergodic in order for the time average to be a suitable substitute for the ensemble average.) Here, :math:`F(\boldsymbol{q},t)` is commonly referred to as the coherent part. .. index:: Self part The incoherent intermediate scattering function, i.e., :math:`i=j`, is defined as .. math:: F_\mathrm{incoh}(\boldsymbol{q},t)=\frac{1}{N} \sum _i ^N \left < \mathrm{exp}\left[i\boldsymbol{q}\cdot(\boldsymbol{r}_i(t)-\boldsymbol{r}_i(0))\right] \right > .. index:: Incoherent scattering function .. index:: Scattering function; Incoherent and describes single particle motion, i.e., diffusive motion. :math:`F_\mathrm{incoh}(\boldsymbol{q},t)` is commonly referred to as the incoherent scattering function, but sometimes also referred to as the self-part of the intermediate scattering function. The static structure factor is given by .. math:: S(\boldsymbol{q}) = F(\boldsymbol{q},0), .. index:: Structure factor; dynamic .. index:: Dynamic structure factor whereas the dynamic structure factor :math:`S(\boldsymbol{q},\omega)` is the Fourier transform of :math:`F(\boldsymbol{q},t)` .. math:: S(\boldsymbol{q},\omega) = \int _{-\infty} ^\infty F(\boldsymbol{q},t) \, e^{-i\omega t} \mathrm{d}t, where :math:`\omega` is the angular frequency. :math:`S(\boldsymbol{q},\omega)` exhibits peaks in the :math:`(\boldsymbol{q},\omega)` plane corresponding to the dispersion of lattice vibrations (phonons). The widths of these peaks are related to the phonon lifetimes. Computing :math:`S(\boldsymbol{q},\omega)` via molecular dynamics simulation has the advantage of *fully* including anharmonic effects and allowing one to study the temperature dependence of phonon dispersions. .. note:: The static and dynamic structure factors can be calculated using the :func:`compute_static_structure_factors ` and :func:`compute_dynamic_structure_factors ` functions, respectively. The procedure is illustrated for a liquid in :doc:`this tutorial <../tutorials/aluminum_liquid_dynamic>` and for a crystalline material in :doc:`this tutorial <../tutorials/aluminum_solid_dynamic>`. .. index:: Correlation functions; Velocity .. index:: Correlation functions; Current .. index:: Velocity correlation functions .. index:: Current correlation functions Velocity correlation functions ------------------------------ It is often convenient to also consider current correlations based on the particle velocities. The current density :math:`\boldsymbol{j}(\boldsymbol{r},t)` is given by .. math:: \boldsymbol{j}(\boldsymbol{r},t) &= \sum_i^N \boldsymbol{v}_i(t) \, \delta (\boldsymbol{r} - \boldsymbol{r}_i(t)) \\ \boldsymbol{j}(\boldsymbol{q},t) &= \sum_i^N \boldsymbol{v}_i(t) \, \mathrm{e}^{\mathrm{i}\boldsymbol{q} \cdot \boldsymbol{r}_i(t)}, where :math:`\boldsymbol{v}_i(t)` is the velocity of particle :math:`i` at time :math:`t`. This can be split into a longitudinal and transverse part as .. math:: \boldsymbol{j}_L(\boldsymbol{q},t) &= \sum_i ^N(\boldsymbol{v_i}(t) \cdot\hat{\boldsymbol{q}}) \, \hat{\boldsymbol{q}} \, \mathrm{e}^{\mathrm{i}\boldsymbol{q} \cdot \boldsymbol{r}_i(t)} \\ \boldsymbol{j}_T(\boldsymbol{q},t) &= \sum_i ^N \left[\boldsymbol{v_i}(t) - (\boldsymbol{v_i}(t) \cdot \hat{\boldsymbol{q}}) \, \hat{\boldsymbol{q}}\right] \, \mathrm{e}^{\mathrm{i}\boldsymbol{q} \cdot \boldsymbol{r}_i(t)}. Now the correlation functions can be computed as .. math:: C_L(\boldsymbol{q},t) &= \frac{1}{N}\left<\boldsymbol{j}_L(\boldsymbol{q},t)\cdot\boldsymbol{j}_L(-\boldsymbol{q},0)\right> \\ C_T(\boldsymbol{q},t) &= \frac{1}{N}\left<\boldsymbol{j}_T(\boldsymbol{q},t)\cdot\boldsymbol{j}_T(-\boldsymbol{q},0)\right>. The longitudinal current can be related to the particle density as .. math:: \frac{\partial }{\partial t}n(\boldsymbol{q}, t) &= \mathrm{i} \boldsymbol{q}\cdot \boldsymbol{j}(\boldsymbol{q}, t) \\ \omega^2 S(\boldsymbol{q},\omega) &= q^2 C_L(\boldsymbol{q},\omega), which means some features can be easier to resolve in one function than the other. The current correlation functions can be thought of as a spatially dependent generalization of the velocity correlation function and are closely related to the phonon spectral energy density. .. note:: The velocity (or current) correlation functions can be calculated using the :func:`compute_dynamic_structure_factors ` function by setting :attr:`calculate_currents` to `True`. An example for the application of this function can be found in the :doc:`tutorial on the dynamic properties of an NiAl alloy <../tutorials/NiAl-ordered-and-disordered>`. Multi-component systems ----------------------- In multi-component systems one can introduce partial correlation functions, which enable separation of the contributions by groups of particles, e.g., by type or site symmetry. For example, in the case of a binary system, one can define :math:`F_\mathrm{AA}(\boldsymbol{q},t)`, :math:`F_\mathrm{BB}(\boldsymbol{q},t)`, :math:`F_\mathrm{AB}(\boldsymbol{q},t)` and so on for all correlation functions introduced above. In :program:`dynasor` we define the partial correlation functions as .. math:: F_\mathrm{AA}(\boldsymbol{q},t) = \frac{1}{N}\sum_{i\in A}^{N_\mathrm{A}} \sum_{j\in A}^{N_\mathrm{A}} \left< \mathrm{exp} \left[i\boldsymbol{q}\cdot(\boldsymbol{r}_i(t)-\boldsymbol{r}_j(0))\right] \right> where the sums run over all A atoms. Here, :math:`N_\mathrm{A}` refers to number of atoms of type A, :math:`N_\mathrm{B}` refers to number of atoms of type B, and :math:`N = N_\mathrm{A} + N_\mathrm{B}` refers to the total number of atoms in the system. For the cross-term, :math:`F_\mathrm{AB}(\boldsymbol{q},t)`, we get .. math:: F_\mathrm{AB}(\boldsymbol{q},t) = & \frac{1}{N}\sum_{i\in A}^{N_\mathrm{A}} \sum_{j\in B}^{N_\mathrm{B}} \left< \mathrm{exp} \left[i\boldsymbol{q}\cdot(\boldsymbol{r}_i(t)-\boldsymbol{r}_j(0))\right] \right> + \frac{1}{N}\sum_{i\in B}^{N_\mathrm{B}} \sum_{j\in A}^{N_\mathrm{A}} \left< \mathrm{exp} \left[i\boldsymbol{q}\cdot(\boldsymbol{r}_i(t)-\boldsymbol{r}_j(0))\right] \right> \\=& \frac{2}{N}\sum_{i\in A}^{N_\mathrm{A}} \sum_{j\in B}^{N_\mathrm{B}} \left< \mathrm{exp} \left[i\boldsymbol{q}\cdot(\boldsymbol{r}_i(t)-\boldsymbol{r}_j(0))\right] \right> Here, the factor two in the final expression is due to considering both the A-B and B-A terms. These normalization choices mean the total intermediate scattering function :math:`F(\boldsymbol{q},t)` (as defined above) is given by .. math:: F(\boldsymbol{q},t) = F_\mathrm{AA}(\boldsymbol{q},t) + F_\mathrm{AB}(\boldsymbol{q},t) + F_\mathrm{BB}(\boldsymbol{q},t) In some cases, instead of analyzing the partial functions directly, it is interesting to consider linear combinations of them, for example by weighting them with relevant atomic form factors, masses, or charges. Relation to scattering experiments ---------------------------------- The correlation functions can be convolved with atomic weights, :math:`w_i`, i.e., .. math:: F_\mathrm{coh}(\boldsymbol{q},t)=\frac{1}{N}\sum_i^N \sum_j^N w_i w_j \left< \mathrm{exp} \left[i\boldsymbol{q}\cdot(\boldsymbol{r}_i(t)-\boldsymbol{r}_j(0))\right] \right>, where the weights could for example be masses, charges, or even atomic form factors (scattering lengths) to allow for direct comparison to structure factors measured in various neutron and X-ray scattering experiments. For some probes, e.g., neutrons, the scattering lengths are in general constant complex numbers but differ per isotope and for the coherent and incoherent correlation functions, .. math:: F_\mathrm{coh}(\boldsymbol{q},t)=\frac{1}{N}\sum_i^N \sum_j^N \operatorname{Re} \left(b_i^* b_j \right) \left< \mathrm{exp}\left[i\boldsymbol{q}\cdot(\boldsymbol{r}_i(t)-\boldsymbol{r}_j(0))\right] \right>. X-ray form factors and electron scattering factors, on the other hand, are :math:`q`-dependent and thus take the form .. math:: F_\mathrm{coh}(\boldsymbol{q},t)=\frac{1}{N}\sum_i^N \sum_j^N f_i(q) f_j(q) \left< \mathrm{exp}\left[i\boldsymbol{q}\cdot(\boldsymbol{r}_i(t)-\boldsymbol{r}_j(0))\right] \right>. Conveniently, since the weights only depend on the atom type, the weighting can be done as a post-processing step using partial correlation functions. For a binary (A-B) system, the total weighted correlation function is .. math:: F_\mathrm{coh}(\boldsymbol{q},t)=f^{AA}(q) F^{AA}_\mathrm{coh}(\boldsymbol{q},t) + f^{AB}(q) F^{AB}_\mathrm{coh} (\boldsymbol{q},t) + f^{BB}(q) F^{BB}_\mathrm{coh}(\boldsymbol{q},t). Furthermore, it is frequently desirable to determine the above-mentioned quantities along specific paths between high-symmetry :math:`\boldsymbol{q}`-points when working with solids. In isotropic samples, such as for example liquids, it is on the other hand usually preferable to compute these functions with respect to :math:`q=|\boldsymbol{q}|`, i.e., a spherical average over wave vectors. .. note:: In order to convolve the dynamic structure factor with form factors, you can use the functionality in the :ref:`post-processing module `. The procedure is demonstrated in :doc:`this tutorial <../tutorials/NiAl-neutron-and-Xray-weighting>`. For a one-to-one comparison with experimental measurements, instrumental effects such as broadening and kinematic constraints must also be considered. These effects can be applied conveniently to :class:`Sample ` objects by following the `calorine tutorial on inelastic neutron scattering `_. .. _sed: .. index:: Spectral energy density; SED Spectral energy density method ------------------------------ The spectral energy density (:term:`SED`) is closely related to the phonon dispersion and thus also to the current correlations above. It is a measure of how the kinetic energy is partitioned over different wavelengths and frequencies in the system. The theoretical background for :term:`SED` is quite simple and can be derived in several ways. Formally the kinetic action :math:`W` of the system can be written as .. math:: W = \int dt \sum_n \sum_i \frac{1}{2} m_i v_i(n, t)^2. Here, :math:`n` is the cell index and :math:`i` is the basis index, which together uniquely identify each atom in an infinite crystal. By using Parseval's theorem for both the time integration and space summation the above is transformed to inverse spatial :math:`q`-space and inverse temporal :math:`\omega`-space .. math:: W = \int d\omega \sum_q \sum_i \frac{1}{2} m_i v_i(q, \omega)^2. This is motivated by the Wiener–Khinchin theorem and the :term:`SED` is simply defined as the quantity .. math:: \text{SED}(q, \omega) = \sum_i \frac{1}{2} m_i v_i(q, \omega)^2. For a more in-depth discussion see, e.g., [ThoTurIut10]_. .. note:: To compute the SED you can use the :func:`compute_spectral_energy_density ` function. Its application is demonstrated in the :doc:`SED tutorial <../tutorials/sed>`. .. index:: Harmonic oscillator; Damped; DHO .. index:: Power spectral density; PSD .. index:: Damped harmonic Oscillator .. index:: Autocorrelation function; ACF Damped harmonic oscillator model -------------------------------- The correlation functions can be fitted to analytical expressions for a damped harmonic oscillator (:term:`DHO`) in order to extract phonon frequencies and lifetimes. The governing differential equation for the displacement (or coordinate) of a :term:`DHO`, :math:`x(t)`, is .. math:: \ddot{x}(t) + \Gamma \dot{x}(t) + \omega_0^2 x(t) = f(t). Here, :math:`\omega_0` is the natural angular frequency and :math:`\Gamma` is the damping constant in units of reciprocal time. Note that we work in units where the mass :math:`m=1`. Below are the analytical solutions for the equations of motion of a :term:`DHO` under the assumption that the stochastic force, :math:`f(t)`, is white noise. The :term:`DHO` can be either underdamped or overdamped. In the underdamped case :math:`\omega_0\tau > 1`, where :math:`\tau = \frac{2}{\Gamma}` is commonly referred to as the lifetime of the :term:`DHO`. The position autocorrelation function (:term:`ACF`) in the underdamped case is given by .. math:: F(t) = A \mathrm{e}^{- t/\tau} \left ( \cos{ \omega_e t} + \frac{1}{\tau\omega_e}\sin{ \omega_e t}\right ), where the angular eigenfrequency of the :term:`DHO` is :math:`\omega_e = \sqrt{\omega_0^2 - 1/\tau^2}` and :math:`A` is a constant amplitude :math:`A=F(t=0)`. The functional form is only valid for time lag :math:`t\geq 0` and should be interpreted as even, so that :math:`t\rightarrow -t` for negative times. Alternatively replace :math:`t` with :math:`|t|` in the expressions for the :term:`ACFs `. In the overdamped case, :math:`\omega_0 \tau < 1`, the autocorrelation function is given by .. math:: F(t) = \frac{A}{\tau_\text{L} - \tau_\text{S}} \left( \tau_\text{L} \text{e}^{-t / \tau_\text{L}} - \tau_\text{S} \text{e}^{-t / \tau_\text{S}} \right), where :math:`\tau_\text{S}` and :math:`\tau_\text{L}` correspond to the short and long relaxation times, respectively, defined as .. math:: \tau_\text{S,L} = \frac{\tau}{1 \pm \sqrt{1-(\omega_0\tau)^2}}. The corresponding underdamped and overdamped velocity :term:`ACFs ` are related to the position :term:`ACFs ` via :math:`F_v(t) = -\ddot{F}(t)`: .. math:: F_v(t) &= A \omega_0^2 \mathrm{e}^{- t/\tau} \left ( \cos \omega_e t - \frac{1}{\tau\omega_e}\sin \omega_e t\right) \quad (\text{underdamped}) \\ F_v(t) &= \frac{A}{\tau_\text{L}-\tau_\text{S}} \left( \frac{1}{\tau_\text{S}} \text{e}^{-t / \tau_\text{S}} - \frac{1}{\tau_\text{L}} \text{e}^{-t / \tau_\text{L}} \right ) \quad (\text{overdamped}) The analysis can also be carried out in the frequency domain by computing the power spectral density (:term:`PSD`) functions, which are Fourier transforms of the corresponding :term:`ACFs `. The position :term:`PSD` of a :term:`DHO` is given by .. math:: S(\omega) = A\frac{2\Gamma \omega_0^2}{(\omega^2 - \omega_0^2)^2 + (\Gamma\omega)^2}, which exhibits a peak at :math:`\omega_\text{max} = \sqrt{\omega_0^2 - \Gamma^2/2}`. The :term:`PSD` of the velocity, :math:`S_v(\omega) = \omega^2 S(\omega)`, is .. math:: S_v(\omega) = A\frac{2\Gamma \omega_0^2 \omega^2}{(\omega^2 - \omega_0^2)^2 + (\Gamma\omega)^2}. The velocity :term:`PSD` has a peak at :math:`\omega_\text{max} = \omega_0`. The amplitude :math:`A` can be related to the variance (strength) :math:`\sigma^2=\langle f^2 \rangle` of the noise by :math:`2\Gamma\omega_0^2 A=\sigma^2`. Taking the strength to be equal to :math:`\sigma^2=2\Gamma k_B T`, the mean squared displacement becomes :math:`\langle x^2 \rangle=\frac{k_B T}{\omega_0^2}` and the mean squared velocity is given by :math:`\langle v^2 \rangle=k_B T`. .. figure:: _static/dho.svg :align: center :width: 100% The position and velocity :term:`ACF` and :term:`PSD` solutions for the :term:`DHO` with natural angular frequency :math:`\omega_0 = 1` for a few different values of the damping constant :math:`\Gamma`. The red line corresponds to the critical damping. For the position :term:`ACF` a critically damped oscillator goes to zero without any oscillation. When the damping is larger than :math:`\sqrt{2}` the peak for the corresponding :term:`PSD` remains at zero. .. note:: Functions for fitting DHO parameters can be found in the :ref:`DHO module `. The application of these functions is illustrated in the :doc:`peak fitting tutorial <../tutorials/dho_peak_fitting>`. .. index:: Phonon lifetime .. index:: Lifetimes; phonon .. _mode_projection_background: Mode projection --------------- The primitive cell metric :math:`C_{ax}` is related to the supercell metric :math:`S_{ax}` via the repetition matrix :math:`P` as .. math:: P_{ab}C_{bx}=S_{ax}. where the lattice vectors are rows. The inverse lattice is given by .. math:: \tilde{C}_{ay} C_{ax}=\delta_{yx} i.e. :math:`C^{-T}`, the inverse transpose of the primitive cell metric. The commensurate reduced q-points are defined such that .. math:: r_a \tilde{C}_{ax} = n_a \tilde{S}_{ax} for some integer vector :math:`n_a` and :math:`0 \leq r_a < 1`. These properties can be accessed by `mp.supercell.P`, the cell metrics as `mp.primitive.cell` and `mp.supercell.cell` and the inverse cells as `mp.primitive.inv_cell` and `mp.supercell.inv_cell` and the commensurate :math:`\boldsymbol{q}-points as `mp.q_reduced`. In the following, a sum over :math:`\boldsymbol{q}`-points :math:`\sum_\boldsymbol{q}` is understood such that :math:`\boldsymbol{q}` is taken from this list indexed by :math:`k`. Important to note is that the :math:`\boldsymbol{q}`-points are not taken from the Wigner-Seitz cell but from the interior of the inverse cell, i.e. :math:`-\boldsymbol{q}` is mapped back into the inverse cell. Each unit cell in the supercell, indexed by an integer :math:`l`, contains some basis atoms indexed by :math:`s`. The displacement of an atom is thus denoted by :math:`u_{ls\alpha}` where :math:`\alpha` indexes the Cartesian directions. The lattice site and basis index for each atom (indexed by :math:`i`) in the supercell can be accessed by `mp.supercell.offsets` and `mp.supercell.indices` respectively and these lists are indexed by :math:`i`. The supercell Cartesian equilibrium position is denoted by :math:`r_{ls}`. In the following a sum over lattice sites or basis indices is taken from the unique elements in these lists. The `ModeProjector` object performs the following mapping from displacements :math:`u` to mode coordinates :math:`Q` .. math:: Q_{kb} = \frac{1}{\sqrt{N_\text{unit}}} \sum_{ls\alpha} \sqrt{m_s} u_{ls\alpha} \bar{W}_{kbs\alpha} \mathrm{e}^{-iq_k \cdot r_{ls}} and back .. math:: u_{ls\alpha} = \frac{1}{\sqrt{N_\text{unit}}} \sum_{kb} \frac{1}{\sqrt{m_s}}Q_{kb} W_{kbs\alpha} \mathrm{e}^{-iq_k \cdot r_{ls}} where :math:`W` are the polarization vectors and :math:`r_{ls}` is the scaled position if :math:`\boldsymbol{q}` is taken from the reduced set or the Cartesian position if :math:`\boldsymbol{q}` is taken from the Cartesian set. The polarization vectors are constructed from the eigenvectors and eigenvalues of the dynamical matrix .. math:: D_{ss'}^{\alpha\beta}(q) = \sum_l \frac{\Phi_{ss'}^{\alpha\beta}(0, l)}{\sqrt{m_s m_{s'}}} \mathrm{e}^{iq (r_s(l)-r_{s'}(0))} = \sum_b W^\alpha_{sb}(q) \omega_b^2(q) \bar{W}_{bs'}^\beta(q) It is assumed that the force constants obey lattice translation symmetry. The polarization vectors :math:`W` can be accessed by `mp.polarizations` and the frequencies as `mp.omegas`. The eigenmodes :math:`X` in the supercell are defined so that .. math:: Q_{kb} = X_{kbi\alpha} u_{i\alpha} and .. math:: u_{i\alpha} = \bar{X}_{kbi\alpha} Q_{kb} / m_i for momenta and force we have :math:`v=XP/m` and :math:`P=\bar{X}v` as well as :math:`f=Xf` and :math:`F=\bar{X}f/m`, respectively. .. note:: You can find more information in the comprehensive documentation of the :ref:`functionality available for mode projection analysis `. The :doc:`mode projection tutorial <../tutorials/mode_projection>` illustrates how this functionality can be used in practice. dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/doc/get_started/units.rst000066400000000000000000000051561522712672400250270ustar00rootroot00000000000000.. _units: .. index:: units Units ***** Internally :program:`dynasor` uses units of Ångström (Å) for length, femtosecond (fs) for time, and electronvolt (eV) for energy. This means that all calculated correlation functions will be in terms of these units. Why we spell out radians ------------------------ Note that :math:`\boldsymbol{q}`-points are defined to include the factor :math:`2\pi` as is commonly done in physics (`wavenumber wikipedia `_). This means that :math:`\boldsymbol{q}`-points technically have units of rad/Å, but note that this in the literature is usually simply written as 1/Å. This is how the units of :program:`dynasor` are reported in the first :program:`dynasor` paper (see :ref:`credits page `), but as leaving out the explicit mention of rad leads to ambiguity, we have since then begun to explicitly include rad when reporting :math:`\boldsymbol{q}`-point units. Therefore, you will throughout the examples in the documentation encounter rad/Å. To further clarify the inclusion of the factor :math:`2\pi` in :math:`\boldsymbol{q}`, note that :math:`\boldsymbol{q}` arises in a spatial Fourier transform, which means that it can be compared to a temporal Fourier transform. A temporal Fourier transform can be written in terms of the frequency :math:`f` (with units 1/s), in which case the exponential explicitly contains a factor of :math:`2\pi`, or in terms of angular frequency :math:`\omega=2\pi f` (with units rad/s). When performing the spatial Fourier transform here, the exponential does not contain an explicit factor of :math:`2\pi`, meaning that :math:`\boldsymbol{q}` must be interpreted as the spatial analogue of the angular frequency :math:`\omega,` thus containing a factor of :math:`2\pi` and having the unit rad/Å. Units used in mode projection ----------------------------- **Mass:** The internal unit choice (eV, Å, fs) means that the mass unit is not Dalton but rather 0.009648533290731906 Da. We refer to this unit as the "dynasor mass unit" (dmu), i.e., 1 Da = 103.64269572045423 dmu. As a user you will only see this unit in connection with :ref:`mode projection `. Masses provided via, e.g., ASE Atoms objects are converted internally. **Mode amplitudes:** Mode amplitudes are reported in Å√dmu = fs√eV. **Velocities:** For modes the momenta are reported in Å√dmu/fs or just √eV while atomic velocities are reported in Å/fs. **Mode forces:** The force is defined as the derivative of the momenta with respect to time so the unit used when reporting mode forces is Å√dmu/fs² (or √eV/fs). dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/doc/get_started/workflow.rst000066400000000000000000000206051522712672400255330ustar00rootroot00000000000000.. _workflow: .. index:: workflow Workflow ******** .. figure:: _static/workflow.svg :align: center Schematic illustration of the workflow in :program:`dynasor`. The red rectangles mark user inputs. Here, some additional information regarding the input parameters is provided, specifically for calculating dynamic and static structure factors. Additional details concerning these input parameters can be found in the :ref:`documentation of the python interface `. The trajectory -------------- The MD trajectory must have a constant (not varying with time) simulation cell. Furthermore, when analyzing dynamic properties of systems the microcanonical (NVE) ensemble is usually preferred over the canonical ensemble (NVT), as thermostats influence the dynamics of the system. In order to resolve certain frequencies, e.g., in :math:`S(q, \omega)`, you need to make sure the time between two consecutive snapshots complies with the `Nyquist-Shannon sampling theorem `_. It is important to specify the correct units in your trajectory to the trajectory reader of :program:`dynasor`, such that they can be correctly converted to internal :program:`dynasor` units. Trajectory formats ^^^^^^^^^^^^^^^^^^ :program:`dynasor` can parse multiple trajectory formats. It has native support for parsing trajectories in :program:`lammps` dump format (https://www.lammps.org). Trajectories in `extended xyz format `_ can be read with the help of :program:`ase` (https://ase-lib.org) Note that velocities are stored as arrays with the name ``vel`` as written by :program:`gpumd` (https://gpumd.org). The reader is multithreaded. :program:`dynasor` also interfaces with the python package `MDAnalysis `_, which allows users to parse a vast set of different trajectory formats. .. note:: The reader based on MDAnalysis is usually the most performant as illustrated :ref:`here `. Atomic types ------------ If you want to calculate the correlation functions for each atom type (or different groupings of atoms) separately you need to provide the :attr:`atomic_indices` parameter in the form of a dictionary, for example, :: atomic_indices['Cs'] = [0, 3, 6, 9] atomic_indices['Br'] = [1, 2, 4, 5, 7, 8, 10, 11] Alternatively, you can provide an index file (`Gromacs ndx-style `_) to group atoms. For some trajectory formats, it is also possible to read the :attr:`atomic_indices` directly from the trajectory by setting it to ``'read_from_trajectory'``. If :attr:`atomic_indices` is left unspecified all atoms are assumed to be of the same species. For example, in the case of a small system containing eight water molecules one could employ the following index file :: [ Hydrogen ] 2 5 8 11 14 17 20 23 3 6 9 12 15 18 21 24 [ Oxygen ] 1 4 7 10 13 16 19 22 Here, the numbers following each tag (``[...]``) specify the atomic indices that belong to the respective species. .. index:: Parameters; q-space sampling q-space sampling parameters --------------------------- :program:`dynasor` can either directly evaluate the correlation function at certain :math:`\boldsymbol{q}`-points (typically desirable for solid or crystalline materials) or average the correlation functions over :math:`\boldsymbol{q}`-points with similar length. The latter means constructing a spherical average over :math:`\boldsymbol{q}`-points which (often) requires thousands of :math:`\boldsymbol{q}`-points to be sampled to get good statistics. Note that :math:`\boldsymbol{q}`-points in :program:`dynasor` are not in reduced coordinates but given as Cartesian coordinates (in inverse Å) and should include the factor :math:`2\pi`. Spherical q-point sampling and averaging ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ :program:`dynasor` provides functionality to generate :math:`\boldsymbol{q}`-points on a 3D grid with the spacing set by the simulation cell up to the maximum radius set by :attr:`q_max` in units of rad/Å. The resulting correlation functions can then for example be averaged over :math:`\boldsymbol{q}`-points that fall into the same radial bin, where the parameter :attr:`q_bins` determines the number of radial bins. The number of :math:`\boldsymbol{q}`-points can grow very large using a 3D grid, and hence slow down calculations. The maximum number of :math:`\boldsymbol{q}`-points to scan is specified by :attr:`max_points`, which allows one to set an approximate upper limit for the number of :math:`\boldsymbol{q}`-points. If the number of :math:`\boldsymbol{q}`-points on the grid exceeds :attr:`max_points` :math:`\boldsymbol{q}`-points will be randomly removed in such a way that the number of :math:`\boldsymbol{q}`-points inside each bin is roughly uniformly distributed. .. index:: Parameters; time sampling Time sampling parameters ------------------------ The two main time sampling parameters are the number of time lags :attr:`window_size` and maximum number of frames :attr:`frame_stop`. The window size is only relevant for dynamic correlation functions but not for static ones. The maximum number of frames sets a limit on how many frames are read from the trajectory file. The window size is the longest time for which correlations are computed and is given in terms of the number of frames. Additionally, you can specify :attr:`frame_step` to only use every :attr:`frame_step`-th snapshot in the trajectory. For example if :attr:`frame_step=1` every snapshot will be used while for :attr:`frame_step=2` every other snapshot is used. The :attr:`window_step` parameter controls how far the window is moved each time, with a default value of 1. A larger value may lead to the calculation running faster but also possibly less converged correlation functions. Note that if :attr:`window_step` is larger than the time window some snapshots in the trajectory will be skipped entirely. To demonstrate how :attr:`frame_step` and :attr:`window_step` interact consider a trajectory with snapshots indexed from 0 to 99, and with :attr:`window_size=5`. The following snapshots will enter the first two windows for which the correlation function is computed:: frame_step = 1 window_step = 1 window 0 : snapshots [0, 1, 2, 3, 4] window 1 : snapshots [1, 2, 3, 4, 5] frame_step = 2 window_step = 1 window 0 : snapshots [0, 2, 4, 6, 8] window 1 : snapshots [2, 4, 6, 8, 10] frame_step = 1 window_step = 2 window 0 : snapshots [0, 1, 2, 3, 4] window 1 : snapshots [2, 3, 4, 5, 6] frame_step = 2 window_step = 2 window 0 : snapshots [0, 2, 4, 6, 8] window 1 : snapshots [4, 6, 8, 10, 12] Lastly, it is required to set the time difference, :attr:`dt`, between two consecutive snapshots in the trajectory in femtoseconds in order to get everything in the correct units. Note that you should not change :attr:`dt` when you change :attr:`frame_step`. .. figure:: _static/window_sampling.png :scale: 16 % :align: center Schematic illustration of sliding time-window averaging over a trajectory. Incoherent intermediate scattering function -------------------------------------------------- The calculation of the incoherent part of the intermediate scattering function is turned off by default. It can be activated by setting :attr:`calculate_incoherent` to ``True`` when using the python interface and ``--calculate-incoherent`` when using the command line interface. Post-processing --------------- After the correlation functions have been calculated the results are stored as `numpy `_ arrays in the :class:`Sample ` object. There are multiple post-processing functions available, such as conducting spherical averages over :math:`\boldsymbol{q}`-points and weighting with atomic form factors. Limitations ----------- Sometimes problems can occur due to known FFT features such as folding and aliasing. Sometimes it might be useful to apply FFT filters/windows to get a smoother spectrum in the frequency domain. The concept of :math:`\boldsymbol{q}`-points becomes muddy if the simulation box is changing (which is the case, e.g., for NPT MD simulations). Differences in positions between two frames with different boxes are not uniquely defined. Therefore only constant volume MD trajectories are supported in :program:`dynasor`. dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/doc/index.rst000066400000000000000000000064531522712672400224700ustar00rootroot00000000000000.. raw:: html

PyPI Conda zenodo

dynasor -- Correlations for everyone ************************************ :program:`dynasor` is a tool for calculating total and partial dynamic structure factors as well as related :ref:`correlation functions ` from molecular dynamics (MD) simulations. Analysis of these functions enables one to access the dynamics of a system without resorting to perturbative approaches. By combining in particular the structure factor with the cross sections (or form factors) of, e.g., neutrons, X-rays or electrons, it is also possible to directly predict experimental spectra. Specifically **dynasor** can be used to calculate the following :ref:`quantities `: * Partial and total dynamic structure factors, including the incoherent (or self) part * Partial and total static structure factors * Partial and total intermediate scattering functions * Longitudinal and transverse, partial and total, current correlations * Spectral energy densities .. grid:: 1 1 3 3 :gutter: 2 .. grid-item-card:: Static correlation functions :link: tutorials/NiAl-neutron-and-Xray-weighting.html .. image:: _static/Ni3Al-disordered.png :alt: Static correlation function :width: 100% .. grid-item-card:: Dynamic correlation functions :link: tutorials/aluminum_solid_dynamic.html .. image:: _static/Ni3Al-current-correlation.png :alt: Dynamic correlation functions :width: 100% .. grid-item-card:: Mode projection :link: tutorials/mode_projection.html .. image:: _static/mode-projection.png :alt: Mode projection :width: 100% The main input consists of a trajectory from an MD simulation, i.e., a file containing snapshots of the particle coordinates and optionally velocities at consecutive, equally spaced points in simulation time. The following snippet illustrates how one can calculate dynamic structure factors from such a trajectory :: traj = Trajectory('dump.xyz', trajectory_format='extxyz') q_points = get_spherical_qpoints(traj.cell, q_max=20) sample = compute_dynamic_structure_factors(traj, q_points=q_points, dt=5, window_size=100) sample.write_to_npz('test.npz') .. note:: For questions and help please use the `dynasor discussion forum on matsci.org `_. Please consult the :ref:`credits page ` for information on how to cite :program:`dynasor`. :program:`dynasor` has been developed at Chalmers University of Technology in Gothenburg, Sweden, in the `Condensed Matter and Materials Theory division `_ at the Department of Physics and Astronomy. :program:`dynasor` and its development are hosted on `GitLab `_. .. toctree:: :maxdepth: 1 :hidden: get_started/index tutorials/index reference/index credits backmatter/index dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/doc/reference/000077500000000000000000000000001522712672400225555ustar00rootroot00000000000000dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/doc/reference/cmdline_interface.rst000066400000000000000000000044011522712672400267410ustar00rootroot00000000000000.. _interface_cmdline: .. index:: Interface; command line Command line interface ********************** :program:`dynasor` can be run via the command line and expects a number of input parameters. To show all available options run:: dynasor --help Trajectory options ------------------- You need to specify the format of your trajectory via the `--trajectory-format` flag, for example, :: dynasor -f dump.xyz --trajectory-format=extxyz --outfile=output.npz would use the internal xyz reader (for more info see the :ref:`workflow page `). q-point sampling options ------------------------ For example, to sample along a specific path in :math:`\boldsymbol{q}`-space, one can run :: dynasor -f dump.xyz --trajectory-format=extxyz --q-sampling="line" --q-direction=10,0,0 --q-points=100 --outfile=output.npz or, to conduct a spherical average over :math:`\boldsymbol{q}`-points, one can run :: dynasor -f dump.xyz --trajectory-format=extxyz --q-sampling="isotropic" --q-max=10 --q-bins=100 --max-q-points=20000 --outfile=output.npz Time sampling options --------------------- For example, the command :: dynasor ... --time-window=500 --max-frames=2000 implies that correlation functions are calculated for frames separated by up to 500 frames. This window is moved over the trajectory until it reaches frame number ``max-frames``. The :ref:`tutorials section ` contains more examples for how to use :program:`dynasor` via the command line. .. index:: Output options Output options -------------- The output files are written as :program:`NumPy` NPZ files. For example, use:: dynasor -f dump.atom --outfile=output.npz These output files can be easily read in :program:`Python` using the :func:`read_sample_from_npz ` function or :program:`NumPy` directly. Incoherent intermediate scattering function and current correlations -------------------------------------------------------------------- The incoherent intermediate scattering function is not evaluated by default but can be calculated by adding the :attr:`--calculate-incoherent` option :: dynasor ... --calculate-incoherent Similarly the current (velocity) correlation functions are calculated by :: dynasor ... --calculate-currents dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/doc/reference/correlation_functions.rst000066400000000000000000000014561522712672400277260ustar00rootroot00000000000000.. _correlation_functions: Correlation functions ********************* The interface to :program:`dynasor` when computing structure factors consists of two functions, which are used for dynamic and static correlations, respectively. * :func:`compute_dynamic_structure_factors ` * :func:`compute_static_structure_factors ` :program:`dynasor` also provides functionality for computing the :ref:`spectral energy density (SED) ` via the function :func:`compute_spectral_energy_density ` [ThoTurIut10]_. .. autofunction:: dynasor.compute_dynamic_structure_factors .. autofunction:: dynasor.compute_static_structure_factors .. autofunction:: dynasor.compute_spectral_energy_density dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/doc/reference/index.rst000066400000000000000000000005111522712672400244130ustar00rootroot00000000000000.. _interface_python: Reference ********* .. toctree:: :caption: Python interface :maxdepth: 1 trajectory_handling correlation_functions mode_projection sample q_points post_processing tools units logging .. toctree:: :caption: Command line interface :maxdepth: 1 cmdline_interface dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/doc/reference/logging.rst000066400000000000000000000001331522712672400247320ustar00rootroot00000000000000Logging ******* .. automodule:: dynasor.logging_tools :members: :inherited-members: dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/doc/reference/mode_projection.rst000066400000000000000000000007421522712672400264720ustar00rootroot00000000000000.. _mode_projection: Mode projection *************** .. automodule:: dynasor.modes.__init__ .. autoclass:: dynasor.ModeProjector :members: .. autoclass:: dynasor.modes.qpoint.QPoint :members: .. autoclass:: dynasor.modes.band.Band :members: .. autoclass:: dynasor.modes.complex_coordinate.ComplexCoordinate :members: .. autofunction:: dynasor.project_modes .. autoclass:: dynasor.modes.atoms.Prim :members: .. autoclass:: dynasor.modes.atoms.Supercell :members: dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/doc/reference/post_processing.rst000066400000000000000000000002031522712672400265230ustar00rootroot00000000000000.. _post_processing: Post-processing *************** .. automodule:: dynasor.post_processing :members: :inherited-members: dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/doc/reference/q_points.rst000066400000000000000000000001271522712672400251430ustar00rootroot00000000000000q-points ******** .. automodule:: dynasor.qpoints :members: :inherited-members: dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/doc/reference/sample.rst000066400000000000000000000046441522712672400246000ustar00rootroot00000000000000Sample ****** The static and dynamic structure factors as well as current correlation functions generated by the main :ref:`analysis functions ` are stored in :class:`Sample` objects. Information about the **dynamic correlations** generated by the :func:`compute_dynamic_structure_factors ` function is provided in the form of a :class:`DynamicSample ` object. The latter allows one to access, for example, the intermediate scattering functions via the following attributes: .. list-table:: :widths: 15 70 15 :header-rows: 1 :class: tight-table * - Attribute - Explanation - Symbol * - :attr:`Fqt_coh_A_B` - partial coherent intermediate scattering function for atom types A and B - :math:`F(\boldsymbol{q},t)_\mathrm{AB}^\mathrm{coh}` * - :attr:`Fqt_coh` - total coherent intermediate scattering function (also commonly referred to as just Fqt) - :math:`F(\boldsymbol{q},t)^\mathrm{coh}` * - :attr:`Fqt_incoh_A` - partial incoherent intermediate scattering function for atom type A - :math:`F(\boldsymbol{q},t)_\mathrm{A}^\mathrm{incoh}` * - :attr:`Fqt_incoh` - total incoherent intermediate scattering function - :math:`F(\boldsymbol{q},t)^\mathrm{incoh}` Similarly, one can access * dynamic structure factors :math:`S(\boldsymbol{q}, \omega)` via attributes starting with :attr:`Sqw` * the longitudinal and transverse current correlation functions in the time domain, :math:`C_L(\boldsymbol{q}, t)` and :math:`C_T(\boldsymbol{q}, t)`, via attributes starting with :attr:`Clqt` and :attr:`Ctqt`, respectively, and * the longitudinal and transverse current correlation functions in the frequency domain, :math:`C_L(\boldsymbol{q}, \omega)` and :math:`C_T(\boldsymbol{q}, \omega)`, via attributes starting with :attr:`Clqw` and :attr:`Ctqw`, respectively. Information about **static correlations** can be obtained via the :func:`compute_static_structure_factors ` function, which returns a :class:`StaticSample ` object. The latter allows one to access the static structure factor via the attribute :attr:`Sq`. .. autofunction:: dynasor.read_sample_from_npz .. autoclass:: dynasor.sample.DynamicSample :members: :inherited-members: .. autoclass:: dynasor.sample.StaticSample :members: :inherited-members: dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/doc/reference/tools.rst000066400000000000000000000004551522712672400244530ustar00rootroot00000000000000.. _tools: Tools ***** .. automodule:: dynasor.tools.acfs :members: :inherited-members: .. _damped_harmonic_oscillator: .. automodule:: dynasor.tools.damped_harmonic_oscillator :members: :inherited-members: .. automodule:: dynasor.tools.structures :members: :inherited-members: dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/doc/reference/trajectory_handling.rst000066400000000000000000000002561522712672400273440ustar00rootroot00000000000000.. index:: single: Function reference; Trajectory handling Trajectory handling ******************* .. autoclass:: dynasor.Trajectory :members: :inherited-members: dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/doc/reference/units.rst000066400000000000000000000001431522712672400244470ustar00rootroot00000000000000Unit conversion *************** .. automodule:: dynasor.units :members: :inherited-members: dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/doc/switcher.json.template000066400000000000000000000004371522712672400251600ustar00rootroot00000000000000[ { "name": "release (_RELEASE_TAG_)", "version": "stable", "url": "https://dynasor.materialsmodeling.org/" }, { "name": "development (_GIT_HASH_)", "version": "dev", "url": "https://dynasor.materialsmodeling.org/dev/" } ] dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/doc/tutorials/000077500000000000000000000000001522712672400226455ustar00rootroot00000000000000NiAl-neutron-and-Xray-weighting.ipynb000077700000000000000000000000001522712672400423412../../examples/NiAl-weighted/NiAl-weighting.ipynbustar00rootroot00000000000000dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/doc/tutorialsdynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/doc/tutorials/NiAl-ordered-and-disordered.ipynb000077700000000000000000000000001522712672400423132../../examples/NiAl-ordered-and-disordered/NiAl.ipynbustar00rootroot00000000000000dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/doc/tutorials/acfs_and_ffts.ipynb000077700000000000000000000000001522712672400373322../../examples/acfs_and_ffts/acfs_and_ffts.ipynbustar00rootroot00000000000000dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/doc/tutorials/aluminum_liquid_dynamic.ipynb000077700000000000000000000000001522712672400456002../../examples/aluminium_liquid/dynamic_correlation_functions.ipynbustar00rootroot00000000000000dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/doc/tutorials/aluminum_solid_dynamic.ipynb000077700000000000000000000000001522712672400452462../../examples/aluminium_solid/dynamic_correlation_functions.ipynbustar00rootroot00000000000000dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/doc/tutorials/dho_peak_fitting.ipynb000077700000000000000000000000001522712672400413042../../examples/dho_peak_fitting/dho_peak_fitting.ipynbustar00rootroot00000000000000dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/doc/tutorials/figs/000077500000000000000000000000001522712672400235755ustar00rootroot00000000000000dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/doc/tutorials/figs/Al_solid_acf_convergence.svg000066400000000000000000002601001522712672400312320ustar00rootroot00000000000000 2022-12-09T13:22:16.580092 image/svg+xml Matplotlib v3.5.0, https://matplotlib.org/ dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/doc/tutorials/figs/Sq_phase_transition.png000077700000000000000000000000001522712672400465152../../../examples/CsPbI3_static_structure_factor/figs/Sq_phase_transition.pngustar00rootroot00000000000000dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/doc/tutorials/index.rst000066400000000000000000000076411522712672400245160ustar00rootroot00000000000000.. _tutorials: .. index:: tutorials Tutorials ********* Below (and in the ``examples`` folder on `GitLab `_) you can find several tutorials and examples that demonstrate how to use :program:`dynasor`. In setting up these examples, suitable parameters were chosen for the MD simulations that provide physically sound (albeit not necessarily highly converged) results. In practice, it is strongly recommended to carefully test the effect of e.g., system size, sampling time, spacing of snapshots, possibly interatomic potential/force field, and :program:`dynasor` averaging parameters. The basics ========== To begin with, there are two tutorials that deal with dynamics of :doc:`liquid ` and :doc:`solid aluminum `, respectively. Here you can learn the basics, such as setting up a trajectory, setting up the q-points to sample (along a path in the solid case and spherically averaged in the liquid case), running a :program:`dynasor` calculation, understanding the contents of a :program:`dynasor`-sample, and some basic visualization of the results. .. toctree:: :caption: The basics :hidden: :maxdepth: 1 aluminum_liquid_dynamic aluminum_solid_dynamic Advanced applications ===================== These tutorials cover some more advanced topics, such as splitting structure factors by species, some convergence considerations, and spectral energy density calculations. In the tutorial on :doc:`ordered and disordered NiAl ` you can learn how to handle multi-component systems by computing full and partial dynamic structure factors and current correlation for ordered and disordered NiAl. In the :doc:`static structure tutorial ` you can learn how to compute the partial static structure factors for the different components of a halide perovskite and study the convergence of the results with system size and trajectory length. The tutorial on the :doc:`spectral energy density (SED) ` demonstrates how the latter can be calculated from a molecular dynamics simulation. Afterwards the SED, which captures the full anharmonicity, is compared to a phonopy dispersion, which is based on harmonic lattice dynamics. .. toctree:: :caption: Advanced applications :hidden: :maxdepth: 1 NiAl-ordered-and-disordered static_structure_factor sed Post-processing =============== In these tutorials you can learn about ways to post-process the :program:`dynasor` results, for example :doc:`weighting of structure factors with form factors ` to compare with different scattering experiments, or extracting phonon frequencies and lifetimes by :doc:`peak fitting ` using the damped harmonic oscillator model. .. toctree:: :caption: Post-processing :hidden: :maxdepth: 1 NiAl-neutron-and-Xray-weighting dho_peak_fitting Other topics ============ It can be useful to analyze the dynamics of the atoms in terms of normal modes. The normal modes behave roughly as independent harmonic oscillators for most materials. It is thus possible to reason about these degrees of freedom as each having a single frequency and lifetime. Even for very anharmonic materials normal modes can serve as an intuitive basis for further analysis. In the :doc:`mode projection tutorial ` the basic functionality of :class:`dynasor.ModeProjector` is demonstrated. The :doc:`ACF and FFTs tutorial ` provides some general insights into auto-correlation functions, Fourier transforms, and numerical tools for decreasing noise. It is crucial to analyze convergence of the results, which is illustrated for the case of the convergence with respect to simulation time in :doc:`this tutorial `. .. toctree:: :caption: Other topics :hidden: :maxdepth: 1 mode_projection acfs_and_ffts time_convergence dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/doc/tutorials/mode_projection.ipynb000077700000000000000000000000001522712672400407322../../examples/mode_projection/mode_projection.ipynbustar00rootroot00000000000000dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/doc/tutorials/sed.ipynb000077700000000000000000000000001522712672400331762../../examples/graphite_sed/sed.ipynbustar00rootroot00000000000000dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/doc/tutorials/static_structure_factor.ipynb000077700000000000000000000000001522712672400471742../../examples/CsPbI3_static_structure_factor/static_structure_factor.ipynbustar00rootroot00000000000000dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/doc/tutorials/time_convergence.rst000066400000000000000000000015471522712672400267220ustar00rootroot00000000000000.. index:: Tutorials; time_convergence Time convergence **************** Here, a short demonstration on how time-correlation typically converges with total simulation time is provided for FCC Al at 300 K. To get a converged correlation function, e.g., in order to extract a phonon lifetime, one requires a simulation that is on the order of 100 to 1000 times longer than the lifetime itself. Rather than running a single long MD simulation, one can also run many shorter simulations and average their correlation functions. In the figure below, the decay time of the correlation function is about 1 to 2 ps. The :math:`C_L(q, t)` is pretty well-converged after 200 ps and very well converged after 2000 ps. .. figure:: figs/Al_solid_acf_convergence.svg :align: center :width: 75% Current correlation :math:`C_L(q, t)` for simulations of varying lengths. dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/dynasor/000077500000000000000000000000001522712672400215315ustar00rootroot00000000000000dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/dynasor/__init__.py000066400000000000000000000014061522712672400236430ustar00rootroot00000000000000# -*- coding: utf-8 -*- """ dynasor module. """ from .correlation_functions import ( compute_dynamic_structure_factors, compute_spectral_energy_density, compute_static_structure_factors ) from .qpoints import ( get_spherical_qpoints, get_supercell_qpoints_along_path ) from .sample import read_sample_from_npz from .trajectory import Trajectory from .modes.mode_projector import ModeProjector from .modes.project_modes import project_modes __version__ = '2.4' __all__ = [ 'compute_dynamic_structure_factors', 'compute_spectral_energy_density', 'compute_static_structure_factors', 'get_spherical_qpoints', 'get_supercell_qpoints_along_path', 'read_sample_from_npz', 'Trajectory', 'ModeProjector', 'project_modes', ] dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/dynasor/cli/000077500000000000000000000000001522712672400223005ustar00rootroot00000000000000dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/dynasor/cli/__init__.py000066400000000000000000000000001522712672400243770ustar00rootroot00000000000000dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/dynasor/cli/main.py000066400000000000000000000232321522712672400236000ustar00rootroot00000000000000#!/usr/bin/python3 import sys import argparse import numpy as np from dynasor.logging_tools import logger, set_logging_level from dynasor.qpoints import get_spherical_qpoints from dynasor.trajectory import Trajectory from dynasor.correlation_functions import compute_dynamic_structure_factors from dynasor.post_processing import get_spherically_averaged_sample_binned def _parse_q_direction(value): message = 'q-direction must contain three comma-separated numbers' try: direction = np.asarray(value.split(','), dtype=float) except ValueError as error: raise argparse.ArgumentTypeError(message) from error if direction.size != 3: raise argparse.ArgumentTypeError(message) return direction def main(): parser = argparse.ArgumentParser( description='dynasor is a simple tool for calculating total and partial dynamic structure' ' factors as well as current correlation functions from molecular dynamics simulations.' ' The main input consists of trajectory output from an MD simulation, i.e., a file' ' containing snapshots of the particle coordinates and optionally velocities that' ' correspond to consecutive, equally spaced points in (simulation) time.' '\n' ' Dynasor has recently been updated. Some of the old options have new names and' ' some new options have been added. If your script has stopped working, check the' ' options with "dynasor --help". In addition, we would like to let you know that' ' dynasor now has a Python interface, which gives you access to more' ' functionality and options.') iogroup = parser.add_argument_group( 'Input/output options', 'Options controlling input and output, files and file formats.') iogroup.add_argument( '-f', '--trajectory', type=str, metavar='TRAJECTORY_FILE', help='Molecular dynamics trajectory file to be analyzed.' ' Supported formats depends on MDAnalysis. As a fallback, a lammps-trajectory parser' ' implemented in Python is also available as well as an extended-xyz reader based on ASE') iogroup.add_argument( '--trajectory-format', type=str, metavar='TRAJECTORY_FORMAT', help='Format of trajectory. Choose from: "lammps_internal", "extxyz", or one' ' of the formats supported by MDAnalysis (except "lammpsdump", which is' ' called through "lammps_mdanalysis" to avoid ambiguity)') iogroup.add_argument( '--length-unit', type=str, metavar='LENGTH_UNIT', default='Angstrom', help='Length unit of trajectory ("Angstrom", "nm", "pm", "fm"). Necessary for correct ' 'conversion to internal dynasor units if the trajectory file does not contain ' 'unit information.') iogroup.add_argument( '--time-unit', type=str, metavar='TIME_UNIT', default='fs', help='Time unit of trajectory ("fs", "ps", "ns"). Necessary for correct conversion to ' 'internal dynasor units if the trajectory file does not contain unit information.') iogroup.add_argument( '-n', '--index', type=str, metavar='INDEX_FILE', help='Optional index file (think Gromacs NDX-style) for specifying atom types. Atoms are' ' indexed from 1 up to N (total number of atoms). It is possible to index only a subset of' ' all atoms, and atoms can be indexed in more than one group. If no index file is provided,' ' all atoms will be considered identical.') iogroup.add_argument( '--outfile', type=str, metavar='FILE', help='Write output to FILE as a numpy npz file') qspace = parser.add_argument_group( 'General q-space options', 'Options controlling general aspects for how q-space should be sampled and collected.') qspace.add_argument( '--q-sampling', type=str, metavar='STYLE', default='isotropic', help='Possible values are "isotropic" (default) for sampling isotropic systems' ' (as liquids), and "line" to sample uniformly along a certain direction in q-space. ') defval = 80 qspace.add_argument( '--q-bins', metavar='BINS', type=int, default=defval, help='Number of "radial" bins to use (between 0 and largest |q|-value) when collecting' f' resulting average. Default value is {defval}.') qiso = parser.add_argument_group('Isotropic q-space sampling') defval = 20000 qiso.add_argument( '--max-q-points', metavar='QPOINTS', type=int, default=defval, help='Maximum number of points used to sample q-space. Puts an (approximate) upper' f' limit by randomly selecting. points. Default value is {defval}.') defval = 60 qiso.add_argument( '--q-max', metavar='QMAX', type=int, default=defval, help='Largest q-value to consider in units of "2*pi*Å^-1".' ' Default value for QMAX is {defval}.') qline = parser.add_argument_group('Line-style q-space sampling') qline.add_argument( '--q-direction', metavar='QDIRECTION', type=_parse_q_direction, help='Direction along which to sample. QPOINTS points will be evenly placed between' ' 0,0,0 and QDIRECTION. Given as three comma separated values.') defval = 100 qline.add_argument( '--q-points', metavar='QPOINTS', type=int, default=defval, help=f'Number of q-points to sample along line. Default: {defval}') tgroup = parser.add_argument_group( 'Time-related options', 'Options controlling timestep, length and shape of trajectory frame window, etc.') tgroup.add_argument( '--time-window', metavar='TIME_WINDOW', type=int, help='The length of the trajectory frame window to use for time correlation calculation.' ' It is expressed in number of frames and determines, among other things, the smallest' ' frequency that can be resolved. If no TIME_WINDOW is provided, only static (t=0)' ' correlations will be calculated') defval = 100 tgroup.add_argument( '--max-frames', metavar='FRAMES', type=int, default=defval, help='Limits the total number of trajectory frames read to FRAMES.' f' The default value is {defval}.') defval = 1 tgroup.add_argument( '--step', metavar='STEP', type=int, default=defval, help='Only use every STEP-th trajectory frame. The default STEP is {defval}, meaning' ' every frame is processed. STEP affects dt and hence the smallest time resolved.') defval = 1 tgroup.add_argument( '--stride', metavar='STRIDE', type=int, default=defval, help='STRIDE number of frames between consecutive trajectory windows. This does not affect' ' dt. If e.g. STRIDE > TIME_CORR_STEPS, some frames will be completely unused.') tgroup.add_argument( '--dt', metavar='DELTATIME', type=float, help='Explicitly sets the time difference between two consecutively processed' ' trajectory frames to DELTATIME (femtoseconds). ') options = parser.add_argument_group('General processing options') options.add_argument( '--calculate-incoherent', action='store_true', default=False, help='Calculate the incoherent part. Default is False.') options.add_argument( '--calculate-currents', action='store_true', default=False, help='Calculate the current (velocity) correlations, Default is False.') parser.add_argument( '-q', '--quiet', action='count', default=0, help='Increase quietness (opposite of verbosity).') parser.add_argument( '-v', '--verbose', action='count', default=0, help='Increase verbosity (opposite of quietness).') args = parser.parse_args() # set log level quietness = args.quiet - args.verbose if quietness < 0: log_level = 'DEBUG' elif quietness == 0: log_level = 'INFO' elif quietness == 1: log_level = 'WARN' elif quietness == 2: log_level = 'ERROR' else: log_level = 'CRITICAL' set_logging_level(log_level) # parse args if args.trajectory is None: logger.error('A trajectory must be specified. Use option -f') sys.exit(1) if not args.outfile: logger.error('An output file must be specified. Use option --outfile') sys.exit(1) if args.dt is None: logger.info('No value set for dt. Setting to 1 fs. Note that this is irrelevant when only ' 'computing static structure factors, i.e., if time_window is not set.') args.dt = 1 # setup Trajectory traj = Trajectory(args.trajectory, trajectory_format=args.trajectory_format, atomic_indices=args.index, length_unit=args.length_unit, time_unit=args.time_unit, frame_stop=args.max_frames, frame_step=args.step, ) # setup q-points if args.q_sampling == 'line': q_dir = args.q_direction n_qpoints = args.q_points q_points = np.array([i * q_dir for i in np.linspace(0, 1, n_qpoints)]) elif args.q_sampling == 'isotropic': q_points = get_spherical_qpoints(traj.cell, q_max=args.q_max, max_points=args.max_q_points) # run dynasor calculation sample = compute_dynamic_structure_factors( traj, q_points=q_points, dt=args.dt, window_size=args.time_window, window_step=args.stride, calculate_currents=args.calculate_currents, calculate_incoherent=args.calculate_incoherent, ) # save results to file if args.q_sampling == 'isotropic': sample = get_spherically_averaged_sample_binned(sample, num_q_bins=args.q_bins) sample.write_to_npz(args.outfile) dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/dynasor/core/000077500000000000000000000000001522712672400224615ustar00rootroot00000000000000dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/dynasor/core/__init__.py000066400000000000000000000000001522712672400245600ustar00rootroot00000000000000dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/dynasor/core/reciprocal.py000066400000000000000000000021101522712672400251500ustar00rootroot00000000000000import numpy as np from dynasor.core.rho_j_q_numba import rho_q as rho_q_numba from dynasor.core.rho_j_q_numba import rho_j_q as rho_j_q_numba def calc_rho_q(x, q): """Calculate rho(q) of particle coordinates x. Will call external function rho_q to calculate the particle density in q-space. Particle coordinates and q-space points of interest are passed as input via x and q, respectively. """ assert x.shape[1] == 3 assert q.shape[1] == 3 Nx, _ = x.shape Nq, _ = q.shape rho_q = np.zeros(Nq, dtype=np.complex128) x = x.copy() # Don't ask why rho_q_numba(x, q, rho_q) return rho_q def calc_rho_j_q(x, v, q): """As calc_rho_q, but calculate also velocities in q-space. """ assert x.shape == v.shape assert x.shape[1] == 3 assert v.shape[1] == 3 assert q.shape[1] == 3 Nx, _ = x.shape Nq, _ = q.shape rho_q = np.zeros(Nq, dtype=np.complex128) j_q = np.zeros((Nq, 3), dtype=np.complex128) x = x.copy() v = v.copy() rho_j_q_numba(x, v, q, rho_q, j_q) return rho_q, j_q dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/dynasor/core/rho_j_q_numba.py000066400000000000000000000073171522712672400256460ustar00rootroot00000000000000"""This module replaces the original c implementation of the reciprocal densities and currents in dynasor with numba. Numba is as of 2023 an ongoing project to create a JIT compiler frontend for python code using the LLVM project as backend. Due to current limitations and quirks of numba the code is not always straightforward. Typically the code needs to be refactored in a trial and error process to get the expected performance but should in the end be on the level of c. Especially, numba makes very pessimistic assumptions about aliasing but this is expected to change in the future. Also, in theory, via the llvm-lite interface compilation flags should be passable to LLVM. """ import numpy as np import numba # This is often faster than calling np.dot for small arrays # Calling this instead of manually inlining it actually incurs a small # performance hit (<10%) with current numba (2023). It increases readability # though and will probably sort itself out with later numba versions @numba.njit(fastmath=True, nogil=True) # pragma: no cover def dot(a, b): return a[0]*b[0] + a[1]*b[1] + a[2]*b[2] # fastmath True makes the summation fast and also speeds up exponentiation # nogil releases the Python GIL, although it is probably not necessary here @numba.njit(fastmath=True, nogil=True) # pragma: no cover def rho_q_single(x: np.ndarray, q: np.ndarray) -> complex: """Calculates the density at a single q-point. Parameters ---------- x Positions as a `(N, 3)` array. q Single q point as a with shape `(3,)`. Returns ------- Complex density at the specified q-point. """ Nx = len(x) assert x.shape == (Nx, 3) assert q.shape == (3,) rho = 0.0j for i in range(Nx): alpha = dot(x[i], q) rho += np.exp(1j * alpha) # very expensive operation return rho # parallel enables the numba.prange directive @numba.njit(fastmath=True, nogil=True, parallel=True) # pragma: no cover def rho_q(x: np.ndarray, q: np.ndarray, rho: np.ndarray): """Calculates the fourier transformed density. The parallelization is over q-points. The density is calculated in-place. Parameters ---------- x The positions as a float array with shape `(Nx, 3)`. q The q-points as a float array with shape `(Nq, 3)`. rho Density as a complex array of length `Nq`. """ Nx = len(x) Nq = len(q) assert x.shape == (Nx, 3) assert q.shape == (Nq, 3) assert rho.shape == (Nq,) # Numba prange is like OMP for i in numba.prange(Nq): rho[i] = rho_q_single(x, q[i]) @numba.njit(fastmath=True, parallel=True, nogil=True) # pragma: no cover def rho_j_q(x: np.ndarray, v: np.ndarray, q: np.ndarray, rho: np.ndarray, j_q: np.ndarray): """Calculates the fourier transformed density and current. The output is stored in the supplied output arrays ``rho`` and ``j_q`` Parameters ---------- x The positions as a float array with shape `(Nx, 3)`. v The velocities as a float array with shape `(Nx, 3)`. q The q-points as a float array with shape `(Nq, 3)`. rho Density as a complex array of length `Nq`. j_q Current as a complex array with shape `(Nq, 3)`. """ Nx = len(x) Nq = len(q) assert x.shape == (Nx, 3) assert v.shape == (Nx, 3) assert q.shape == (Nq, 3) assert rho.shape == (Nq,) assert j_q.shape == (Nq, 3) for qi in numba.prange(Nq): for xi in range(Nx): alpha = dot(x[xi], q[qi]) exp_ialpha = np.exp(1.0j * alpha) rho[qi] += exp_ialpha for i in range(3): j_q[qi, i] += exp_ialpha * v[xi][i] dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/dynasor/core/time_averager.py000066400000000000000000000063371522712672400256560ustar00rootroot00000000000000import numpy as np from numpy.typing import NDArray def truncate_nans(data): """ Truncate trailing time columns that contain NaN values. The function assumes that the last axis of the array corresponds to time and removes all columns from the first occurrence of a NaN (in any row) to the end of the array. This guarantees that the returned array contains **no NaN values**, even if this means discarding some valid data points that appear after a NaN in another row. Parameters ---------- data : array_like, shape (N, T) Input array where rows represent independent signals (e.g. q-points) and the last axis represents time. Returns ------- ndarray Array truncated along the time axis such that no NaN values remain. The returned array has the same number of rows as the input but may have fewer time columns. Notes ----- Example:: [[1.0, 0.8, 0.7, 0.6, 0.5, 0.3, nan], [1.0, 0.8, 0.7, 0.6, 0.5, nan, nan]] becomes:: [[1.0, 0.8, 0.7, 0.6, 0.5], [1.0, 0.8, 0.7, 0.6, 0.5]] because the first NaN appears in column 5. """ arr = np.asarray(data) assert arr.ndim == 2 # Determine which time columns contain any NaN values column_has_nan = np.any(np.isnan(arr), axis=0) # Find the first column containing a NaN nan_indices = np.where(column_has_nan)[0] # No NaNs present, return array unchanged if len(nan_indices) == 0: return arr # NaNs were found, Keep only the column without NaNs first_nan_column = nan_indices[0] return arr[:, :first_nan_column] class TimeAverager: """Naive special purpose averager class used in dynasor to collect and time-average arrays obtained from sliding time-window averaging. It assists with keeping track of how many data samples have been added to each slot. It will time-average arrays of shape ``(Nq, time_window)`` where ``Ǹq`` is the number of q-points and ``time_window`` is the size of the time window. Parameters ---------- time_window Size of the time window in which the time-average happens. array_length Length of the array to be averaged for each time-lag, i.e., number of q-points. """ def __init__(self, time_window: int, array_length: int): assert time_window >= 1 self._time_window = time_window self._array_length = array_length self._counts = np.zeros(time_window, dtype=int) self._arrays = [np.zeros(array_length) for _ in range(time_window)] def add_sample(self, time_lag: int, sample: np.ndarray): assert len(sample) == self._array_length self._counts[time_lag] += 1 self._arrays[time_lag] += sample def get_average_at_timelag(self, time_lag: int): if self._counts[time_lag] == 0: array = np.full((self._array_length, ), np.nan) return array return self._arrays[time_lag] / self._counts[time_lag] def get_average_all(self) -> NDArray[float]: """ Returns an averaged array of shape ``(array_length, time_window)``. """ return np.array([self.get_average_at_timelag(t) for t in range(self._time_window)]).T dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/dynasor/correlation_functions.py000066400000000000000000000565011522712672400265230ustar00rootroot00000000000000import concurrent from functools import partial from itertools import combinations_with_replacement from typing import Optional import numba import numpy as np from ase import Atoms from ase.units import fs from numpy.typing import NDArray from dynasor.logging_tools import logger from dynasor.trajectory import Trajectory, WindowIterator from dynasor.sample import DynamicSample, StaticSample from dynasor.tools.acfs import psd_from_acf_2d from dynasor.core.time_averager import TimeAverager, truncate_nans from dynasor.core.reciprocal import calc_rho_q, calc_rho_j_q from dynasor.tools.structures import get_offset_index from dynasor.units import radians_per_fs_to_meV def compute_dynamic_structure_factors( traj: Trajectory, q_points: NDArray[float], dt: float, window_size: int, window_step: Optional[int] = 1, calculate_currents: Optional[bool] = False, calculate_incoherent: Optional[bool] = False, logging_interval: Optional[int] = 1000, ) -> DynamicSample: r"""Compute the dynamic structure factors. The results are returned in the form of a :class:`DynamicSample ` object. Parameters ---------- traj Input trajectory. q_points Array of q-points in units of rad/Å with shape ``(N_qpoints, 3)`` in Cartesian coordinates. dt Time difference in femtoseconds between two consecutive snapshots in the trajectory. Note that you should *not* change :attr:`dt` if you change :attr:`frame_step ` in :attr:`traj`. window_size Length of the trajectory frame window to use for time correlation calculation. It is expressed in terms of the number of time lags to consider and thus determines the smallest frequency resolved. window_step Window step (or stride) given as the number of frames between consecutive trajectory windows. This parameter does *not* affect the time between consecutive frames in the calculation. If, e.g., :attr:`window_step` > :attr:`window_size`, some frames will not be used. calculate_currents Calculate the current correlations. Requires velocities to be available in :attr:`traj`. calculate_incoherent Calculate the incoherent part (self-part) of :math:`F_\text{incoh}`. logging_interval Log progress at ``INFO`` level every this many windows. Set to ``0`` to disable progress logging. """ # sanity check input args if q_points.shape[1] != 3: raise ValueError('q-points array has the wrong shape.') if dt <= 0: raise ValueError(f'dt must be positive: dt= {dt}') if window_size <= 2: raise ValueError(f'window_size must be larger than 2: window_size= {window_size}') if window_step <= 0: raise ValueError(f'window_step must be positive: window_step= {window_step}') if calculate_currents and not traj.has_velocities: raise ValueError('calculate_currents=True requires velocities to be available in the ' 'trajectory, but traj does not provide velocities.') # define internal parameters n_qpoints = q_points.shape[0] delta_t = traj.frame_step * dt N_tc = window_size + 1 # log all setup information dw = np.pi / (window_size * delta_t) w_max = dw * window_size w_N = 2 * np.pi / (2 * delta_t) # Nyquist angular frequency logger.info(f'Spacing between samples (frame_step): {traj.frame_step}') logger.info(f'Time between consecutive frames in input trajectory (dt): {dt} fs') logger.info(f'Time between consecutive frames used (dt * frame_step): {delta_t} fs') logger.info(f'Time window size (dt * frame_step * window_size): {delta_t * window_size:.1f} fs') logger.info(f'Angular frequency resolution: dw = {dw:.6f} rad/fs = ' f'{dw * radians_per_fs_to_meV:.3f} meV') logger.info(f'Maximum angular frequency (dw * window_size):' f' {w_max:.6f} rad/fs = {w_max * radians_per_fs_to_meV:.3f} meV') logger.info(f'Nyquist angular frequency (2pi / frame_step / dt / 2):' f' {w_N:.6f} rad/fs = {w_N * radians_per_fs_to_meV:.3f} meV') if calculate_currents: logger.info('Calculating current (velocity) correlations') if calculate_incoherent: logger.info('Calculating incoherent part (self-part) of correlations') # log some info regarding q-points logger.info(f'Number of q-points: {n_qpoints}') q_directions = q_points.astype(float, copy=True) q_distances = np.linalg.norm(q_points, axis=1) nonzero = q_distances > 0 q_directions[nonzero] /= q_distances[nonzero].reshape(-1, 1) # setup functions to process frames def f2_rho(frame): rho_qs_dict = dict() for atom_type in frame.positions_by_type.keys(): x = frame.positions_by_type[atom_type] rho_qs_dict[atom_type] = calc_rho_q(x, q_points) frame.rho_qs_dict = rho_qs_dict return frame def f2_rho_and_j(frame): rho_qs_dict = dict() jz_qs_dict = dict() jper_qs_dict = dict() for atom_type in frame.positions_by_type.keys(): x = frame.positions_by_type[atom_type] v = frame.velocities_by_type[atom_type] rho_qs, j_qs = calc_rho_j_q(x, v, q_points) jz_qs = np.sum(j_qs * q_directions, axis=1) jper_qs = j_qs - (jz_qs[:, None] * q_directions) rho_qs_dict[atom_type] = rho_qs jz_qs_dict[atom_type] = jz_qs jper_qs_dict[atom_type] = jper_qs frame.rho_qs_dict = rho_qs_dict frame.jz_qs_dict = jz_qs_dict frame.jper_qs_dict = jper_qs_dict return frame if calculate_currents: element_processor = f2_rho_and_j else: element_processor = f2_rho # setup window iterator window_iterator = WindowIterator(traj, width=N_tc, window_step=window_step, element_processor=element_processor) # define all pairs pairs = list(combinations_with_replacement(traj.atom_types, r=2)) particle_counts = {key: len(val) for key, val in traj.atomic_indices.items()} logger.debug('Considering pairs:') for pair in pairs: logger.debug(f' {pair}') # set up all time averager instances F_q_t_averager = dict() for pair in pairs: F_q_t_averager[pair] = TimeAverager(N_tc, n_qpoints) if calculate_currents: Cl_q_t_averager = dict() Ct_q_t_averager = dict() for pair in pairs: Cl_q_t_averager[pair] = TimeAverager(N_tc, n_qpoints) Ct_q_t_averager[pair] = TimeAverager(N_tc, n_qpoints) if calculate_incoherent: F_s_q_t_averager = dict() for pair in traj.atom_types: F_s_q_t_averager[pair] = TimeAverager(N_tc, n_qpoints) # define correlation function def calc_corr(window, time_i): # Calculate correlations between two frames in the window without normalization 1/N f0 = window[0] fi = window[time_i] for s1, s2 in pairs: Fqt = np.real(f0.rho_qs_dict[s1] * fi.rho_qs_dict[s2].conjugate()) if s1 != s2: Fqt += np.real(f0.rho_qs_dict[s2] * fi.rho_qs_dict[s1].conjugate()) F_q_t_averager[(s1, s2)].add_sample(time_i, Fqt) if calculate_currents: for s1, s2 in pairs: Clqt = np.real(f0.jz_qs_dict[s1] * fi.jz_qs_dict[s2].conjugate()) Ctqt = 0.5 * np.real(np.sum(f0.jper_qs_dict[s1] * fi.jper_qs_dict[s2].conjugate(), axis=1)) if s1 != s2: Clqt += np.real(f0.jz_qs_dict[s2] * fi.jz_qs_dict[s1].conjugate()) Ctqt += 0.5 * np.real(np.sum(f0.jper_qs_dict[s2] * fi.jper_qs_dict[s1].conjugate(), axis=1)) Cl_q_t_averager[(s1, s2)].add_sample(time_i, Clqt) Ct_q_t_averager[(s1, s2)].add_sample(time_i, Ctqt) if calculate_incoherent: for atom_type in traj.atom_types: xi = fi.positions_by_type[atom_type] x0 = f0.positions_by_type[atom_type] Fsqt = np.real(calc_rho_q(xi - x0, q_points)) F_s_q_t_averager[atom_type].add_sample(time_i, Fsqt) # run calculation with concurrent.futures.ThreadPoolExecutor() as tpe: # This is the "main loop" over the trajectory for window in window_iterator: if logging_interval and window[0].frame_index % logging_interval == 0: logger.info(f'Processing window {window[0].frame_index} to {window[-1].frame_index}') # noqa else: logger.debug(f'Processing window {window[0].frame_index} to {window[-1].frame_index}') # noqa # The map conveniently applies calc_corr to all time-lags. However, # as everything is done in place nothing gets returned so in order # to start and wait for the processes to finish we must iterate # over the None values returned for _ in tpe.map(partial(calc_corr, window), range(len(window))): pass # collect results into dict with numpy arrays (n_qpoints, N_tc) data_dict_corr = dict() data_dict_corr['q_points'] = q_points time = None for pair in pairs: key = '_'.join(pair) F_q_t = 1 / traj.n_atoms * truncate_nans(F_q_t_averager[pair].get_average_all()) w, S_q_w = psd_from_acf_2d(F_q_t, delta_t) S_q_w = np.array(S_q_w) if time is None: # Determine the actual length of time signal after truncation (if needed) time = delta_t * np.arange(F_q_t.shape[1], dtype=float) N_tc_actual = len(time) F_q_t_tot = np.zeros((n_qpoints, N_tc_actual)) S_q_w_tot = np.zeros((n_qpoints, N_tc_actual)) else: assert F_q_t.shape[1] == len(time) data_dict_corr['omega'] = w data_dict_corr[f'Fqt_coh_{key}'] = F_q_t data_dict_corr[f'Sqw_coh_{key}'] = S_q_w # sum all partials to the total F_q_t_tot += F_q_t S_q_w_tot += S_q_w if N_tc_actual < N_tc: logger.warning('Truncating ACF due to NaNs, likely time_window is longer than length of Trajectory') # noqa data_dict_corr['time'] = time data_dict_corr['Fqt_coh'] = F_q_t_tot data_dict_corr['Sqw_coh'] = S_q_w_tot if calculate_currents: Cl_q_t_tot = np.zeros((n_qpoints, N_tc_actual)) Ct_q_t_tot = np.zeros((n_qpoints, N_tc_actual)) Cl_q_w_tot = np.zeros((n_qpoints, N_tc_actual)) Ct_q_w_tot = np.zeros((n_qpoints, N_tc_actual)) for pair in pairs: key = '_'.join(pair) Cl_q_t = 1 / traj.n_atoms * truncate_nans(Cl_q_t_averager[pair].get_average_all()) Ct_q_t = 1 / traj.n_atoms * truncate_nans(Ct_q_t_averager[pair].get_average_all()) _, Cl_q_w = psd_from_acf_2d(Cl_q_t, delta_t) _, Ct_q_w = psd_from_acf_2d(Ct_q_t, delta_t) data_dict_corr[f'Clqt_{key}'] = Cl_q_t data_dict_corr[f'Ctqt_{key}'] = Ct_q_t data_dict_corr[f'Clqw_{key}'] = Cl_q_w data_dict_corr[f'Ctqw_{key}'] = Ct_q_w # sum all partials to the total Cl_q_t_tot += Cl_q_t Ct_q_t_tot += Ct_q_t Cl_q_w_tot += Cl_q_w Ct_q_w_tot += Ct_q_w data_dict_corr['Clqt'] = Cl_q_t_tot data_dict_corr['Ctqt'] = Ct_q_t_tot data_dict_corr['Clqw'] = Cl_q_w_tot data_dict_corr['Ctqw'] = Ct_q_w_tot if calculate_incoherent: Fs_q_t_tot = np.zeros((n_qpoints, N_tc_actual)) Ss_q_w_tot = np.zeros((n_qpoints, N_tc_actual)) for atom_type in traj.atom_types: Fs_q_t = 1 / traj.n_atoms * truncate_nans(F_s_q_t_averager[atom_type].get_average_all()) _, Ss_q_w = psd_from_acf_2d(Fs_q_t, delta_t) data_dict_corr[f'Fqt_incoh_{atom_type}'] = Fs_q_t data_dict_corr[f'Sqw_incoh_{atom_type}'] = Ss_q_w # sum all partials to the total Fs_q_t_tot += Fs_q_t Ss_q_w_tot += Ss_q_w data_dict_corr['Fqt_incoh'] = Fs_q_t_tot data_dict_corr['Sqw_incoh'] = Ss_q_w_tot # finalize results with additional metadata new_sample = DynamicSample( data_dict_corr, simulation_data=dict( atom_types=traj.atom_types, pairs=pairs, particle_counts=particle_counts, cell=traj.cell, time_between_frames=delta_t, maximum_time_lag=delta_t * window_size, angular_frequency_resolution=dw, maximum_angular_frequency=w_max, number_of_frames=traj.number_of_frames_read, )) new_sample._append_history( 'compute_dynamic_structure_factors', dict( dt=dt, window_size=window_size, window_step=window_step, calculate_currents=calculate_currents, calculate_incoherent=calculate_incoherent, )) return new_sample def compute_static_structure_factors( traj: Trajectory, q_points: NDArray[float], logging_interval: Optional[int] = 1000, ) -> StaticSample: r"""Compute the static structure factors. The results are returned in the form of a :class:`StaticSample ` object. Parameters ---------- traj Input trajectory. q_points Array of q-points in units of rad/Å with shape ``(N_qpoints, 3)`` in Cartesian coordinates. logging_interval Log progress at ``INFO`` level every this many frames. Set to ``0`` to disable progress logging. """ # sanity check input args if q_points.shape[1] != 3: raise ValueError('q-points array has the wrong shape.') n_qpoints = q_points.shape[0] logger.info(f'Number of q-points: {n_qpoints}') # define all pairs pairs = list(combinations_with_replacement(traj.atom_types, r=2)) particle_counts = {key: len(val) for key, val in traj.atomic_indices.items()} logger.debug('Considering pairs:') for pair in pairs: logger.debug(f' {pair}') # processing function def f2_rho(frame): rho_qs_dict = dict() for atom_type in frame.positions_by_type.keys(): x = frame.positions_by_type[atom_type] rho_qs_dict[atom_type] = calc_rho_q(x, q_points) frame.rho_qs_dict = rho_qs_dict return frame # setup averager Sq_averager = dict() for pair in pairs: Sq_averager[pair] = TimeAverager(1, n_qpoints) # time average with only timelag=0 # main loop for frame in traj: # process_frame f2_rho(frame) if logging_interval and frame.frame_index % logging_interval == 0: logger.info(f'Processing frame {frame.frame_index}') else: logger.debug(f'Processing frame {frame.frame_index}') for s1, s2 in pairs: # compute correlation Sq_pair = np.real(frame.rho_qs_dict[s1] * frame.rho_qs_dict[s2].conjugate()) if s1 != s2: Sq_pair += np.real(frame.rho_qs_dict[s2] * frame.rho_qs_dict[s1].conjugate()) Sq_averager[(s1, s2)].add_sample(0, Sq_pair) # collect results data_dict = dict() data_dict['q_points'] = q_points S_q_tot = np.zeros((n_qpoints, 1)) for s1, s2 in pairs: Sq = 1 / traj.n_atoms * Sq_averager[(s1, s2)].get_average_at_timelag(0).reshape(-1, 1) data_dict[f'Sq_{s1}_{s2}'] = Sq S_q_tot += Sq data_dict['Sq'] = S_q_tot # finalize results new_sample = StaticSample( data_dict, simulation_data=dict( atom_types=traj.atom_types, pairs=pairs, particle_counts=particle_counts, cell=traj.cell, number_of_frames=traj.number_of_frames_read, )) new_sample._append_history('compute_static_structure_factors') return new_sample def compute_spectral_energy_density( traj: Trajectory, ideal_supercell: Atoms, primitive_cell: Atoms, q_points: NDArray[float], dt: float, partial: Optional[bool] = False, logging_interval: Optional[int] = 1000, ) -> tuple[NDArray[float], NDArray[float]]: r""" Compute the spectral energy density (SED) at specific q-points. The results are returned in the form of a tuple, which comprises the angular frequencies in an array of length ``N_times`` in units of rad/fs and the SED in units of eV/(rad/fs) as an array of shape ``(N_qpoints, N_times)``. The normalization is chosen such that integrating the SED of a q-point together with the supplied angular frequencies omega (rad/fs) yields `1/2 kB T` * number of bands (where number of bands = `len(prim) * 3`) More details can be found in Thomas *et al.*, Physical Review B **81**, 081411 (2010), which should be cited when using this function along with the dynasor reference. **Note 1:** SED analysis is only suitable for crystalline materials without diffusion as atoms are assumed to move around fixed reference positions throughout the entire trajectory. **Note 2:** This implementation reads the full trajectory and can thus consume a lot of memory. Parameters ---------- traj Input trajectory. ideal_supercell Ideal structure defining the reference positions. Do not change the masses in the ASE :class:`Atoms` objects to dynasor internal units, this will be done internally. primitive_cell Underlying primitive structure. Must be aligned correctly with :attr:`ideal_supercell`. q_points Array of q-points in units of rad/Å with shape ``(N_qpoints, 3)`` in Cartesian coordinates. dt Time difference in femtoseconds between two consecutive snapshots in the trajectory. Note that you should not change :attr:`dt` if you change :attr:`frame_step ` in :attr:`traj`. partial If True the SED will be returned decomposed per basis and Cartesian direction. The shape is ``(N_qpoints, N_frequencies, len(primitive_cell), 3)``. logging_interval Log progress at ``INFO`` level every this many frames. Set to ``0`` to disable progress logging. """ delta_t = traj.frame_step * dt # logger logger.info('Running SED') logger.info(f'Time between consecutive frames (dt * frame_step): {delta_t} fs') logger.info(f'Number of atoms in primitive_cell: {len(primitive_cell)}') logger.info(f'Number of atoms in ideal_supercell: {len(ideal_supercell)}') logger.info(f'Number of q-points: {q_points.shape[0]}') # check that the ideal supercell agrees with traj if traj.n_atoms != len(ideal_supercell): raise ValueError('ideal_supercell must contain the same number of atoms as the trajectory.') if len(primitive_cell) >= len(ideal_supercell): raise ValueError('primitive_cell contains more atoms than ideal_supercell.') if not traj.has_velocities: raise ValueError('compute_spectral_energy_density requires velocities to be available ' 'in the trajectory, but traj does not provide velocities.') # colllect all velocities, and scale with sqrt(masses) masses = ideal_supercell.get_masses().reshape(-1, 1) / fs**2 # From Dalton to dmu velocities = [] for it, frame in enumerate(traj): if logging_interval and it % logging_interval == 0: logger.info(f'Reading frame {it}') else: logger.debug(f'Reading frame {it}') v = frame.get_velocities_as_array(traj.atomic_indices) # in Å/fs velocities.append(np.sqrt(masses) * v) logger.info(f'Number of snapshots: {len(velocities)}') # Perform the FFT on the last axis for extra speed (maybe not needed) N_samples = len(velocities) velocities = np.array(velocities) # places time index last and makes a copy for continuity velocities = velocities.transpose(1, 2, 0).copy() # #atoms in supercell x 3 directions x #frequencies velocities = np.fft.rfft(velocities, axis=2) # Calculate indices and offsets needed for the SED method offsets, indices = get_offset_index(primitive_cell, ideal_supercell) # Phase factor for use in FT. #qpoints x #atoms in supercell cell_positions = np.dot(offsets, primitive_cell.cell) phase = np.dot(q_points, cell_positions.T) # #qpoints x #unit cells phase_factors = np.exp(1.0j * phase) # This dict maps the offsets to an index so ndarrays can be over # offset,index instead of atoms in supercell offset_dict = {off: n for n, off in enumerate(set(tuple(offset) for offset in offsets))} # Pick out some shapes n_super, _, n_w = velocities.shape n_qpts = len(q_points) n_prim = len(primitive_cell) n_offsets = len(offset_dict) # This new array will be indexed by index and offset instead (and also transposed) new_velocities = np.zeros((n_w, 3, n_prim, n_offsets), dtype=velocities.dtype) for i in range(n_super): j = indices[i] # atom with index i in the supercell is of basis type j ... n = offset_dict[tuple(offsets[i])] # and its offset has index n new_velocities[:, :, j, n] = velocities[i].T velocities = new_velocities # Same story with the spatial phase factors new_phase_factors = np.zeros((n_qpts, n_prim, n_offsets), dtype=phase_factors.dtype) for i in range(n_super): j = indices[i] n = offset_dict[tuple(offsets[i])] new_phase_factors[:, j, n] = phase_factors[:, i] phase_factors = new_phase_factors # calculate the density in a numba function density = _sed_inner_loop(phase_factors, velocities) if not partial: density = np.sum(density, axis=(2, 3)) # units # make so that the velocities were originally in Angstrom / fs to be compatible with eV and Da # the time delta in the fourier transform density = density * delta_t**2 # Divide by the length of the time signal density = density / (N_samples * delta_t) # Divide by the number of primitive cells density = density / (n_super / n_prim) # Factor so the sed can be integrated together with the returned omega # numpy fft works with ordinary/linear frequencies and not angular freqs density = density / (2*np.pi) # angular frequencies w = 2 * np.pi * np.fft.rfftfreq(N_samples, delta_t) # rad/fs return w, density @numba.njit(parallel=True, fastmath=True) def _sed_inner_loop(phase_factors, velocities): """This numba function calculates the spatial Fourier transform using precomputed phase factors. As the use case can be one or many q-points the parallelization is over the temporal frequency components instead. """ n_qpts = phase_factors.shape[0] # q-point index n_prim = phase_factors.shape[1] # basis atom index n_super = phase_factors.shape[2] # unit cell index n_freqs = velocities.shape[0] # frequency, direction, basis atom, unit cell density = np.zeros((n_qpts, n_freqs, n_prim, 3), dtype=np.float64) for w in numba.prange(n_freqs): for k in range(n_qpts): for a in range(3): for b in range(n_prim): tmp = 0.0j for n in range(n_super): tmp += phase_factors[k, b, n] * velocities[w, a, b, n] density[k, w, b, a] += np.abs(tmp)**2 return density dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/dynasor/logging_tools.py000066400000000000000000000021231522712672400247470ustar00rootroot00000000000000""" This module contains functions and variables to control dynasor's logging * `logger` - the module logger """ import logging import sys # This is the root logger of dynasor. logger = logging.getLogger('dynasor') # Will process all levels of INFO or higher. This sets the default level. logger.setLevel(logging.INFO) # If you know what you are doing you may set this to True. logger.propagate = False # The dynasor logger will collect events from childs and the default behavior # is to print it directly to stdout. ch = logging.StreamHandler(sys.stdout) ch.setFormatter(logging.Formatter( r'%(levelname)s %(asctime)s: %(message)s', datefmt='%Y-%m-%d %H:%M:%S')) logger.addHandler(ch) def set_logging_level(level: str) -> None: """ Alters the logging verbosity logging is handled. Possible values from least to most verbose: `CRITICAL`, `ERROR`, `WARNING`, `INFO`, `DEBUG`. Parameters ---------- level Verbosity level; see `Python logging library `_ for details. """ logger.setLevel(level) dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/dynasor/modes/000077500000000000000000000000001522712672400226405ustar00rootroot00000000000000dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/dynasor/modes/__init__.py000066400000000000000000000016401522712672400247520ustar00rootroot00000000000000""" The mode projection functionality in dynasor is mainly handled by two objects: The :class:`~dynasor.ModeProjector` class and :func:`~dynasor.project_modes` function. The :class:`~dynasor.ModeProjector` provides access to data objects representing a q-point :attr:`~dynasor.modes.qpoint.QPoint` and from the q-point there is access to an object representing a particular band :class:`~dynasor.modes.band.Band` at that q-point. In addition, simple wrappers around the coordinates `Q`, `P` and `F` exist via :class:`~dynasor.modes.complex_coordinate.ComplexCoordinate` to easily set the amplitude and phase of a mode while preserving the :math:`Q(-q)=Q^*(q)` symmetries. Internally dynasor wraps the primitive cell :class:`~dynasor.modes.atoms.Prim` and supercell :class:`~dynasor.modes.atoms.Supercell`. As a user only the :class:`~dynasor.ModeProjector` and :attr:`~dynasor.project_modes` should need to be imported. """ dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/dynasor/modes/atoms.py000066400000000000000000000132611522712672400243400ustar00rootroot00000000000000from typing import Union import numpy as np from ase.units import fs from ase import Atoms from numpy.typing import NDArray from .tools import inv from ..units import Dalton_to_dmu class DynasorAtoms: """Dynasor's representation of a structure.""" def __init__(self, atoms: Atoms): """Initialized using an ASE Atoms object""" self._atoms = atoms @property def pos(self) -> NDArray[float]: """Cartesian positions.""" return self._atoms.positions.copy() @property def positions(self) -> NDArray[float]: """Cartesian positions.""" return self.pos @property def spos(self) -> NDArray[float]: """Reduced (or scaled) positions of atoms.""" return self._atoms.get_scaled_positions() @property def scaled_positions(self) -> NDArray[float]: """Reduced (or scaled) positions of atoms.""" return self.spos @property def cell(self) -> NDArray[float]: """Cell of atoms with cell vectors as rows.""" return self._atoms.cell.array.copy() @property def inv_cell(self) -> NDArray[float]: """The inverse cell transpose so the inverse cell vectors are rows, no 2pi.""" return np.linalg.inv(self._atoms.cell.array).T @property def numbers(self) -> NDArray[int]: """Chemical number for each atom, e.g., 1 for H, 2 for He etc.""" return self._atoms.numbers.copy() @property def masses(self) -> NDArray[float]: """Masses of atoms in dmu.""" return self._atoms.get_masses() / fs ** 2 # In eVfs²/Ų @property def volume(self) -> float: """Volume of cell.""" return self._atoms.get_volume() @property def n_atoms(self) -> int: """Number of atoms.""" return len(self._atoms) @property def symbols(self) -> list[str]: """List of chemical symbol for each element.""" return list(self._atoms.symbols) def to_ase(self) -> Atoms: """Converts the internal Atoms to ASE :class:`Atoms`.""" return Atoms(cell=self.cell, numbers=self.numbers, positions=self.positions, pbc=True) def __repr__(self) -> str: return str(self) class Prim(DynasorAtoms): def __str__(self): strings = [f"""Primitive cell: Number of atoms: {self.n_atoms} Volume: {self.volume:.3f} Atomic species present: {set(self.symbols)} Atomic numbers present: {set([int(n) for n in self.numbers])} Cell: [[{self.cell[0, 0]:<20}, {self.cell[0, 1]:<20}, {self.cell[0, 2]:<20}], [{self.cell[1, 0]:<20}, {self.cell[1, 1]:<20}, {self.cell[1, 2]:<20}], [{self.cell[2, 0]:<20}, {self.cell[2, 1]:<20}, {self.cell[2, 2]:<20}]] """] strings.append(f"{'Ind':<5}{'Sym':<5}{'Num':<5}{'Mass (Da)':<10}{'x':<10}{'y':<10}{'z':<10}" f"{'a':<10}{'b':<10}{'c':<10}") atom_s = [] for i, p, sp, m, n, s in zip( range(self.n_atoms), self.positions, self.spos, self.masses / Dalton_to_dmu, self.numbers, [a.symbol for a in self.to_ase()]): atom_s.append(f'{i:<5}{s:<5}{n:<5}{m:<10.2f}{p[0]:<10.3f}{p[1]:<10.3f}{p[2]:<10.3f}' f'{sp[0]:<10.3f}{sp[1]:<10.3f}{sp[2]:<10.3f}') strings = strings + atom_s string = '\n'.join(strings) return string class Supercell(DynasorAtoms): """The supercell takes care of some mappings between the primitive and repeated structure. In particular the P-matrix connecting the cells as well as the offset-index of each atom is calculated. Note that the positions cannot be revovered as `offset x cell + basis` since the atoms get wrapped. Parameters ---------- supercell Some ideal repetition of the primitive structure and possible wrapping. prim Primitive structure. """ def __init__(self, supercell: Union[Atoms, DynasorAtoms], prim: Union[Atoms, DynasorAtoms]): self.prim = Prim(prim.copy()) super().__init__(supercell) # determine P-matrix relating supercell to primitive cell from dynasor.tools.structures import get_P_matrix self._P = get_P_matrix(self.prim.cell, self.cell) # P C = S self._P_inv = inv(self.P) # find the index and offsets for supercell using primitive as base unit from dynasor.tools.structures import get_offset_index self._offsets, self._indices = get_offset_index(prim, supercell, wrap=True) @property def P(self) -> NDArray[float]: """P-matrix is defined as dot(P, prim.cell) = supercell.cell""" return self._P.copy() @property def P_inv(self) -> NDArray[float]: """Inverse of `P`.""" return self._P_inv.copy() @property def offsets(self) -> NDArray[float]: """The offset of each atom.""" return self._offsets.copy() @property def indices(self) -> NDArray[int]: """The basis index of each atom""" return self._indices.copy() @property def n_cells(self) -> int: """Number of unit cells""" return self.n_atoms // self.prim.n_atoms def __str__(self): string = f"""Supercell: Number of atoms: {self.n_atoms} Volume: {self.volume:.3f} Number of unit cells: {self.n_cells} Cell: [[{self.cell[0, 0]:<20}, {self.cell[0, 1]:<20}, {self.cell[0, 2]:<20}], [{self.cell[1, 0]:<20}, {self.cell[1, 1]:<20}, {self.cell[1, 2]:<20}], [{self.cell[2, 0]:<20}, {self.cell[2, 1]:<20}, {self.cell[2, 2]:<20}]] P-matrix: [[{self.P[0, 0]:<20}, {self.P[0, 1]:<20}, {self.P[0, 2]:<20}], [{self.P[1, 0]:<20}, {self.P[1, 1]:<20}, {self.P[1, 2]:<20}], [{self.P[2, 0]:<20}, {self.P[2, 1]:<20}, {self.P[2, 2]:<20}]] {self.prim} """ return string dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/dynasor/modes/band.py000066400000000000000000000064241522712672400241240ustar00rootroot00000000000000from __future__ import annotations from typing import TYPE_CHECKING from numpy.typing import NDArray from ..units import radians_per_fs_to_THz from .tools import make_table from .complex_coordinate import Q, P, F if TYPE_CHECKING: # pragma: no cover from .qpoint import QPoint from .mode_projector import ModeProjector class Band: """Represents properties of a single band belonging to a q-point. To modify the coordinates `Q`, `P` and `F` use `band.Q` and see doc for :class:`~dynasor.modes.complex_coordinate.ComplexCoordinate`. Parameters ---------- q_index q-point index. band_index Band index. mp Mode projector. """ def __init__(self, q_index: int, band_index: int, mp: ModeProjector): self._q = q_index self._s = band_index self._mp = mp def __repr__(self): return str(self) def __str__(self): s = ['### Band ###'] s += [f'Band index: {self.index}'] s += [f'q-point: [{self.qpoint.index}] {self.qpoint.q_reduced}'] s += [f'Frequency: {self.omega * radians_per_fs_to_THz:.2f} THz'] s += ['Polarization:'] string = '\n'.join(s) string += make_table(self.polarization) string += f""" {'Q':<20}{'P':<20}{'F':<20} Value: {self.Q.complex:<20.2f}{self.P.complex:<20.2f}{self.F.complex:<20.2f} Amplitude: {self.Q.amplitude:<20.2f}{self.P.amplitude:<20.2f}{self.F.amplitude:<20.2f} Phase: {self.Q.phase:<20.2f}{self.P.phase:<20.2f}{self.F.phase:<20.2f} Energy: {self.potential_energy:<20.2f}{self.kinetic_energy:<20.2f}{self.virial_energy:<20.2f} """ return string @property def index(self) -> int: """The index of the band at the q-point.""" return self._s @property def qpoint(self) -> 'QPoint': """The q-point to which the band belongs.""" return self._mp[self._q] @property def omega(self) -> float: """Frequency of this band in rad/fs.""" return self.qpoint.omegas[self.index] @property def polarization(self) -> NDArray[float]: """Polarization vector of this band, shape ``(Np, 3)``.""" return self.qpoint.polarizations[self.index] @property def eigenmode(self) -> NDArray[float]: """Eigenmode in the supercell for this band, shape ``(N, 3)``.""" return self.qpoint.eigenmodes[self.index] @property def potential_energy(self) -> float: """Potential energy of this band in eV.""" return self.qpoint.potential_energies[self.index] @property def kinetic_energy(self) -> float: """Kinetic energy of this band in eV.""" return self.qpoint.kinetic_energies[self.index] @property def virial_energy(self) -> float: """Virial energy of this band in eV.""" return self.qpoint.virial_energies[self.index] @property def Q(self) -> float: """The mode coordinate.""" return Q(self.qpoint.index, self.index, self._mp) @property def P(self) -> float: """The mode momentum.""" return P(self.qpoint.index, self.index, self._mp) @property def F(self) -> NDArray[float]: """The mode force.""" return F(self.qpoint.index, self.index, self._mp) dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/dynasor/modes/complex_coordinate.py000066400000000000000000000103361522712672400270730ustar00rootroot00000000000000from __future__ import annotations # for 3.9 for typehint 'Band' in cc.band from typing import TYPE_CHECKING import math import numpy as np from ..units import radians_per_fs_to_THz if TYPE_CHECKING: # pragma: no cover from .band import Band class ComplexCoordinate: """Class to work with the in general complex coordinates of lattice dynamics. Can be cast to complex by ``complex(cc)``. Concrete subclasses :class:`Q`, :class:`P`, and :class:`F` represent the mode coordinate, momentum, and force respectively, and are accessed via :attr:`~dynasor.modes.band.Band.Q`, :attr:`~dynasor.modes.band.Band.P`, and :attr:`~dynasor.modes.band.Band.F` on a :class:`~dynasor.modes.band.Band` object. Example ------- >>> # Access a coordinate via a Band object; mp is a ModeProjector instance, >>> # mp[0] selects q-point 0 and [1] selects band 1 within that q-point. >>> band = mp[0][1] # doctest: +SKIP >>> # The coordinate can be set directly as a complex number, >>> # or via its amplitude and phase independently. >>> band.Q.complex = 1.7j # doctest: +SKIP >>> band.Q.amplitude = 3.8 # doctest: +SKIP >>> band.Q.phase = 2*np.pi # doctest: +SKIP """ def __init__(self, q, s, mp): """The complex coordinate belongs to a q-point and band.""" self._q = q self._s = s self._mp = mp def __repr__(self): return str(self) def __complex__(self): return complex(getattr(self._mp, 'get_' + self.__class__.__name__)()[self._q, self._s]) @property def band(self) -> 'Band': """The band to which this coordinate belongs to.""" return self._mp[self._q][self._s] @property def complex(self): """The complex coordinate as a complex number.""" return complex(self) @complex.setter def complex(self, C): C = complex(C) prop = self.__class__.__name__ val = getattr(self._mp, 'get_' + prop)() if self._mp[self._q].is_real: assert np.isclose(C.imag, 0) q2 = self._q else: q2 = self._mp[self._q].q_minus.index val[self._q, self._s] = C val[q2, self._s] = C.conjugate() getattr(self._mp, 'set_' + prop)(val) @property def phase(self): """The phase of the complex coordinate.""" c = complex(self) return math.atan2(c.imag, c.real) @phase.setter def phase(self, phase): C = complex(self) C = np.abs(C) * np.exp(1j * phase) self.complex = C @property def amplitude(self): """The amplitude of the complex coordinate.""" c = complex(self) return np.abs(c) @amplitude.setter def amplitude(self, amplitude): C = amplitude * np.exp(1j * self.phase) self.complex = C class Q(ComplexCoordinate): def __str__(self): s = ['### Mode coordinate Q ###'] s += [f'band: [{self.band.index}] {self.band.omega * radians_per_fs_to_THz:.2f} THz'] s += [f'q-point: [{self.band.qpoint.index}] {self.band.qpoint.q_reduced}'] s += [f'Value: {self.complex:.2f} Å√dmu'] s += [f'Amplitude: {self.amplitude:.2f} Å√dmu'] s += [f'Phase: {self.phase:.2f} radians'] return '\n'.join(s) class P(ComplexCoordinate): def __str__(self): s = ['### Mode momentum P ###'] s += [f'band: [{self.band.index}] {self.band.omega * radians_per_fs_to_THz:.2f} THz'] s += [f'q-point: [{self.band.qpoint.index}] {self.band.qpoint.q_reduced}'] s += [f'Value: {self.complex:.2f} Å√dmu/fs'] s += [f'Amplitude: {self.amplitude:.2f} Å√dmu/fs'] s += [f'Phase: {self.phase:.2f} radians'] return '\n'.join(s) class F(ComplexCoordinate): def __str__(self): s = ['### Mode force F ###'] s += [f'band: [{self.band.index}] {self.band.omega * radians_per_fs_to_THz:.2f} THz'] s += [f'q-point: [{self.band.qpoint.index}] {self.band.qpoint.q_reduced}'] s += [f'Value: {self.complex:.2f} Å√dmu/fs²'] s += [f'Amplitude: {self.amplitude:.2f} Å√dmu/fs²'] s += [f'Phase: {self.phase:.2f} radians'] return '\n'.join(s) dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/dynasor/modes/mode_projector.py000066400000000000000000000473421522712672400262370ustar00rootroot00000000000000from __future__ import annotations from typing import Optional import numpy as np import warnings import pickle import itertools from ase.calculators.singlepoint import SinglePointCalculator from ase.units import fs from ase import Atoms from numpy.typing import NDArray from dynasor.units import radians_per_fs_to_THz from dynasor.tools.structures import get_displacements from .tools import get_dynamical_matrix, group_eigvals, symmetrize_eigenvectors from .qpoint import QPoint from .atoms import Prim, Supercell from ..qpoints.tools import get_commensurate_lattice_points class ModeProjector: """ The :class:`ModeProjector` maps between real atomic displacements `u` and complex mode coordinates `Q`. Some special python methods are implemented. The `__str__` and `__repr__` provides useful info. :class:`QPoint` objects are representations of a single q-point and associated information and can be accessed either by call providing a reduced wavevector >>> mp((1/2, 0, 0)) # doctest: +SKIP or by index corresponding to reduced q-point accessible from :attr:`~ModeProjector.q_reduced` >>> mp[2] # doctest: +SKIP In addition to mode coordinates `Q` the class can also map the atomic velocities `v` to mode momenta `P` as well as atomic forces `f` to mode forces `F`. The class can also map back. This mapping is done using getters and setters. Internally only Q, P and F are stored >>> Q = mp.get_Q() # doctest: +SKIP >>> mp.set_u(u) # doctest: +SKIP In addition, the forces corresponding to the harmonic forces can be accessed by :meth:`~ModeProjector.get_f_harmonic()` and :meth:`~ModeProjector.get_F_harmonic()`. For ASE Atoms objects the displacements etc. can be updated and applied by >>> mp.update_from_atoms(atoms) # doctest: +SKIP >>> atoms = mp.get_atoms(harmonic_forces=False) # doctest: +SKIP The shapes for each property uses the following variables * `N`: Number of atoms in supercell * `Nu`: unit cells (`N/Np`) * `Np`: primitive basis atoms (`N/Nu`) * `Nb`: bands (`Np*3`) * `Nq`: q-points (`Nu`) Please consult the documentation or the specific getters and setters to see the exact transformations used. Units ^^^^^ The internal units in dynasor are Å, fs and eV. All frequencies are angular (a.k.a. the "physicist's convention" with 2π included). These are the units dynasor will expect and return. In, e.g., print functions conventional units such fs, Å, THz, Da, meV are commonly used. **Mass:** The internal unit choice (eV, Å, fs) means that the mass unit is not Dalton but rather 0.009648533290731906 Da. We refer to this unit as the "dynasor mass unit" (dmu), i.e., 1 Da = 103.64269572045423 dmu. As a user you will only see this unit in the output of :class:`ModeProjector` objects. Masses provided via, e.g., ASE Atoms objects are converted internally. **Waves:** dynasor reports and expects spatial (angular) frequencies in rad/Å and temporal (angular) frequencies in rad/fs. This follows the often-used convention in physics to include the factor of 2π in the wave vectors. For instance the wavelength is given by λ=2π/q. **Mode amplitudes:** Mode amplitudes are reported in Å√dmu = fs√eV. **Velocities:** For modes the momenta are reported in Å√dmu/fs or just √eV while atomic velocities are reported in Å/fs. **Mode forces:** The force is defined as the derivative of the momenta with respect to time so the unit used when reporting mode forces is Å√dmu/fs² (or √eV/fs). Internal arrays ^^^^^^^^^^^^^^^ For the curious, the internal data arrays are * :attr:`primitive`, :attr:`supercell`, :attr:`force_constants` (input) * :attr:`_q`, :attr:`q_minus` (reduced q-points and which q-points are related by inversion) * :attr:`_D`, :attr:`_w2`, :attr:`_W` (dynamical matrices, frequencies (ev/Ų/Da), polarization vectors) * :attr:`_X` (eigenmodes which are mass weighted "polarization vectors" in the supercell) """ def __init__(self, primitive: Atoms, supercell: Atoms, force_constants: NDArray[float]): """The mode projector is initialized by a primitive cell and a supercell as well as harmonic force constants. The force constants are assumed to be in units of eV/Ų as returned from phonopy. Be careful about the permutations when working with force constants and atoms object from different codes. Parameters ---------- primitive Primitive cell. Note that the masses are stored internally as Dalton in ASE but will be converted to the internal dynasor mass unit (dmu). supercell Ideal supercell corresponding to the force constants. force_constants Force constants for the supercell in eV/Ų as a `(N, N, 3, 3)` array where `N` is `len(supercell)`. """ if len(primitive) == len(supercell): warnings.warn('Primitive and supercell have the same size') elif len(primitive) > len(supercell): raise ValueError('Primitive cell larger than supercell') elif not (len(supercell) / len(primitive)).is_integer(): raise ValueError('supercell size is not multiple of primitive size') if len(supercell) != len(force_constants): raise ValueError('force constants shape is not compatible with supercell size') if force_constants.shape != (len(supercell), len(supercell), 3, 3): raise ValueError('force constants shape should be (N, N, 3, 3)') self.primitive = Prim(primitive) self.supercell = Supercell(supercell, primitive) self.force_constants = force_constants # Find q-points in reduced primitive cell coordinates q_integer = get_commensurate_lattice_points(self.supercell.P.T) q_reduced = np.dot(q_integer, self.supercell.P_inv.T) self._q = np.array(sorted(tuple(q) for q in q_reduced)) # The equivalent q-point corresponding to -q self._q_minus = [[tuple(q) for q in self._q].index(tuple((-q) % 1)) for q in self._q] # Construct dynamical matrix and diagonalize at each q-point self._D, self._w2, self._W = [], [], [] for qi, q in enumerate(self._q): D = get_dynamical_matrix( self.force_constants, self.supercell.offsets, self.supercell.indices, q.astype(np.float64)) if qi == self.q_minus[qi]: assert np.allclose(D.imag, 0) D = D.real D = np.einsum('ijab,i,j->ijab', D, self.primitive.masses**-0.5, self.primitive.masses**-0.5) D_matrix = D.transpose(0, 2, 1, 3).reshape(-1, self.primitive.n_atoms * 3) assert np.allclose(D_matrix, D_matrix.T.conj()) w2, W = np.linalg.eigh(D_matrix) W = W.T.reshape(-1, self.primitive.n_atoms, 3) self._D.append(D) self._w2.append(w2) self._W.append(W) self._D = np.array(self._D) self._w2 = np.array(self._w2) self._W = np.array(self._W) # Post check basic symmetries, group eigenvalues and try to make degenerate modes nicer for q, q_minus in enumerate(self.q_minus): q_minus = self.q_minus[q] assert np.allclose(self._D[q], self._D[q_minus].conj()) assert np.allclose(self._w2[q], self._w2[q_minus]) # tolerances for grouping and sorting eigenvalues and eigenvectors round_decimals = 12 tolerance = 10**(-round_decimals) for group in group_eigvals(self._w2[q], tolerance**0.5): W = symmetrize_eigenvectors(self._W[q, group]) # Try to order them W_sort = W.copy().transpose(0, 2, 1).reshape(len(W), -1) # abs is because we want to consider the magnitude # - (minus) basically reverts the sort order to place largest first # T is just because how lexsort works, we want to consider each # atom and direction as a key for the bands # -1 is because we want to make the x-direction of the first # atom the most significant key # At the end the first band should have the largest magnitude # for the first atom in x argsort = np.lexsort(np.round(-np.abs(W_sort).T[::-1], round_decimals)) self._W[q, group] = W[argsort] self._W[q_minus] = self._W[q].conj() # Construct supercell projection matrix # q_ks = X_ksna u_na self._X = np.zeros((len(self._q), self.primitive.n_atoms * 3, self.supercell.n_atoms, 3), dtype=np.complex128) for index in range(self.supercell.n_atoms): i, N = self.supercell.indices[index], self.supercell.offsets[index] for q, s, a in itertools.product( range(len(self._q)), range(self.primitive.n_atoms * 3), range(3)): phase = np.exp(-1j * 2*np.pi * self._q[q] @ N) self._X[q, s, index, a] = ( self.primitive.masses[i]**0.5 * phase * self._W[q, s, i, a].conj()) self._X /= (self.supercell.n_atoms / self.primitive.n_atoms)**0.5 # Init arrays to hold Q, P and F self._Q = np.zeros((len(self._q), self.primitive.n_atoms*3), dtype=np.complex128) self._P = np.zeros_like(self._Q) self._F = np.zeros_like(self._Q) def __str__(self): strings = ['### ModeProjector ###'] strings += [f'{self.supercell}'] strings += [f'{self.primitive}'] string = '\n'.join(strings) # ASCII DOS! width = 80 height = 24 dos = np.full((height, width), ' ') THz = self.omegas * radians_per_fs_to_THz hist, bins = np.histogram(THz.flat, bins=width) for i, h in enumerate(hist): dos[np.round(h * (height - 1) / hist.max()).astype(int), i] = '+' # '·' or 'x' dos = dos[::-1] dos[-1, dos[-1] == ' '] = '-' dos = '\n'.join([''.join(d) for d in dos]) string += f'\n{dos}' string += f'\n|{THz.min():<10.2f} THz' + ' '*(width - 26) + f'{THz.max():>10.2f}|' return string def __repr__(self): return str(self) def __getitem__(self, q) -> QPoint: """Returns the q-point object based on its index""" if q < 0 or q >= len(self._q): raise IndexError return QPoint(q, self) def __call__(self, qpoint) -> QPoint: """Tries to find a matching q-point based on reduced coordinate""" qpoint = np.array(qpoint).astype(np.float64) % 1 for q, qpoint2 in enumerate(np.array(self._q).astype(np.float64)): if np.allclose(qpoint, qpoint2): return QPoint(q, self) raise ValueError('qpoint not compatible, check mp.q_reduced') # Getters ans setters for internal mutable arrays def get_Q(self) -> NDArray[float]: """Return the mode coordinates as a ``(Nq, Nb)`` complex array in Å√dmu.""" return self._Q.copy() def get_P(self) -> NDArray[float]: """Return the mode momenta as a ``(Nq, Nb)`` complex array in √eV.""" return self._P.copy() def get_F(self) -> NDArray[float]: """Return the mode forces as a ``(Nq, Nb)`` complex array in eV/Å√dmu.""" return self._F.copy() def get_F_harmonic(self) -> NDArray[float]: r"""Return the harmonic mode forces as a ``(Nq, Nb)`` complex array in eV/Å√dmu. Computed as :math:`-\omega^2 Q`. """ return -self._w2 * self.get_Q() def set_Q(self, Q: NDArray[float]) -> None: """Sets the internal mode coordinates :math:`Q`. The function ensures :math:`Q(-q)=Q^*(q)`. """ # This ensures that stuff like mp.set_Q(0) works while not updating the # array until the assert Q_new = self.get_Q() Q_new[:] = Q if not np.allclose(np.conjugate(Q_new), Q_new[self.q_minus]): raise ValueError('Supplied Q does not fulfill Q(-q) = Q(q)*') self._Q[:] = Q_new def set_P(self, P: NDArray[float]) -> None: """Sets the internal mode momenta :math:`P`. The function ensures :math:`P(-q)=P^*(q)`. """ P_new = self.get_P() P_new[:] = P if not np.allclose(np.conjugate(P_new), P_new[self.q_minus]): raise ValueError('Supplied P does not fulfill P(-q) = P(q)*') self._P[:] = P_new def set_F(self, F: NDArray[float]) -> None: """Sets the internal mode forces :math:`F`. The function ensures :math:`F(-q)=F^*(q)`. """ F_new = self.get_F() F_new[:] = F if not np.allclose(np.conjugate(F_new), F_new[self.q_minus]): raise ValueError('Supplied F does not fulfill F(-q) = F(q)*') self._F[:] = F_new def get_u(self) -> NDArray[float]: """Return the atomic displacements as a ``(N, 3)`` array in Å.""" u = np.einsum('ksna,ks,n->na', self._X.conj(), self._Q, 1 / self.supercell.masses) assert np.allclose(u.imag, 0) return u.real def get_v(self) -> NDArray[float]: """Return the atomic velocities as a ``(N, 3)`` array in Å/fs.""" v = np.einsum('ksna,ks,n->na', self._X, self._P, 1 / self.supercell.masses) assert np.allclose(v.imag, 0) return v.real def get_f(self) -> NDArray[float]: """Return the atomic forces as a ``(N, 3)`` array in eV/Å.""" f = np.einsum('ksna,ks->na', self._X, self._F) assert np.allclose(f.imag, 0) return f.real def get_f_harmonic(self) -> NDArray[float]: """Return the harmonic atomic forces for the current displacements as a ``(N, 3)`` array in eV/Å. """ F_harmonic = self.get_F_harmonic() f_harmonic = np.einsum('ksna,ks->na', self._X, F_harmonic) assert np.allclose(f_harmonic.imag, 0) return f_harmonic.real def set_u(self, u: NDArray[float]) -> None: """Sets the internal mode coordinates :math:`Q` given the atomic displacements :math:`u`. .. math:: Q = X u Parameters ---------- u The atomic displacements in Å. """ Q = np.einsum('ksna,na->ks', self._X, u) self.set_Q(Q) def set_v(self, v: NDArray[float]) -> None: """Sets the internal mode momenta :math:`P` given the atomic velocities :math:`v`. .. math:: P = X^* * v Parameters ---------- v The atomic velocities in Å/fs. """ P = np.einsum('ksna,na->ks', self._X.conj(), v) self.set_P(P) def set_f(self, f: NDArray[float]) -> None: """Sets the internal mode forces :math:`F` given the atomic forces :math:`f`. .. math:: F = X^* * f / m Parameters ---------- f The atomic forces in eV/Å. """ F = np.einsum('ksna,na,n->ks', self._X.conj(), f, 1 / self.supercell.masses) self.set_F(F) # Convenience functions to handle ASE Atoms objects def get_atoms(self, harmonic_forces: Optional[bool] = False) -> Atoms: r"""Returns ASE :class:`Atoms` object with displacement, velocities, forces, and harmonic energies. Parameters ---------- harmonic_forces Whether the forces should be taken from the internal `F` or via `-\omega^2 Q`. """ atoms = self.supercell.to_ase() atoms.positions += self.get_u() atoms.set_velocities(self.get_v() / fs) E = self.potential_energies.sum() f = self.get_f_harmonic() if harmonic_forces else self.get_f() atoms.calc = SinglePointCalculator( energy=E, forces=f, stress=None, magmoms=None, atoms=atoms) return atoms def update_from_atoms(self, atoms: Atoms) -> None: """Updates the :class:`ModeProjector` objects with displacements, velocities, and forces from an ASE :class:`Atoms` object. Checks for an attached calculator in the first place and next for a forces array. If no data sets corresponding array to zeros. The masses and velocities are converted to dynasor units internally. """ u = get_displacements(atoms, self.supercell) if np.max(np.abs(u)) > 2.0: warnings.warn('Displacements larger than 2Å. Is the atoms object permuted?') self.set_u(u) self.set_v(atoms.get_velocities() * fs) try: self.set_f(atoms.get_forces()) except RuntimeError: if 'forces' in atoms.arrays: self.set_f(atoms.arrays['forces']) else: self.set_f(np.zeros_like(atoms.positions)) # properties @property def q_minus(self) -> NDArray[float]: """The index of the corresponding counter-propagating mode (:math:`-q`).""" return self._q_minus.copy() @property def q_reduced(self) -> NDArray[float]: """The q-points in reduced coordinates. For example a zone boundary mode would be (1/2, 0, 0) """ return self._q.astype(float) @property def q_cartesian(self) -> NDArray[float]: """The q-points in cartesian coordinates with unit of rad/Å (2π included).""" return 2 * np.pi * self.q_reduced @ self.primitive.inv_cell @property def omegas(self) -> NDArray[float]: """The frequencies of each mode in rad/fs. Following convention, negative values indicate imaginary frequencies. """ return np.sign(self._w2) * np.sqrt(np.abs(self._w2)) @property def polarizations(self) -> NDArray[float]: """The polarization vectors for each mode `(Nq, Nb, Np, 3)`.""" return self._W @property def eigenmodes(self) -> NDArray[float]: """The eigenmodes in the supercell as `(Nq, Nb, N, 3)`-array The eigenmodes include the masses such that :math:`Q = X u` where :math:`u` are the supercell displacements. """ return self._X @property def potential_energies(self) -> NDArray[float]: """Potential energy per mode as `(Nq, Nb)`-array. The potential energies are defined as :math:`1/2 Q Q^*` and should equal :math:`1/2 k_B T` in equilibrium for a harmonic system. """ return 1 / 2 * np.abs(self._Q) ** 2 * self._w2 @property def kinetic_energies(self) -> NDArray[float]: """Kinetic energy per mode as `(Nq, Nb)`-array. The kinetic energies are defined as :math:`1/2 P P^*`. Should equal :math:`1/2 k_B T` in equilibrium.` """ return 1 / 2 * np.abs(self._P)**2 @property def virial_energies(self) -> NDArray[float]: """The virial energies per mode as `(Nq, Nb)`-array. The virial energies are defined here as :math:`-1/2 Q F`, which should have an expectation value of :math:`1/2 k_B T` per mode in equilibrium. For a harmonic system this is simply equal to the potential energy. This means that the virial energy can be used to monitor the anharmonicity or define a measure of the potential energy. """ return -1 / 2 * self._Q * self._F def write(self, file_name: str) -> None: """Uses pickle to write mode projector to file.""" with open(file_name, 'wb') as f: pickle.dump(self, f) @classmethod def read(cls, file_name: str) -> ModeProjector: """Return :class:`ModeProjector` instance from pickle file that was saved using :func:`~ModeProjector.write`.""" with open(file_name, 'rb') as f: mp = pickle.load(f) return mp dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/dynasor/modes/project_modes.py000066400000000000000000000061731522712672400260560ustar00rootroot00000000000000from typing import Optional import numpy as np from ase import Atoms from numpy.typing import NDArray from dynasor.logging_tools import logger from dynasor.trajectory import Trajectory from dynasor.tools.structures import get_displacements_from_u def project_modes( traj: Trajectory, modes: NDArray[float], ideal_supercell: Atoms, check_mic: Optional[bool] = True, logging_interval: Optional[int] = 1000, ) -> tuple[NDArray[float], NDArray[float]]: """Projects an atomic trajectory onto set of phonon modes. Parameters ---------- traj Input trajectory. modes Modes to project on, as an array with shape ``(..., N, 3)`` where ``N`` is the number of atoms in the supercell and the leading dimensions define the output shape. ideal_supercell Ideal supercell used to find atomic displacements. It should correspond to the ideal structure. Be careful not to mess up the permutation. check_mic Whether to wrap the displacements or not, faster if no wrap. logging_interval Log progress at ``INFO`` level every this many frames. Set to ``0`` to disable progress logging. Returns ------- A tuple comprising `(Q,P)` where `Q` are the mode coordinates as a complex array with dimension (length of traj, number of modes) and `P` are the mode momenta as a complex array with dimension (length of traj, number of modes). If :attr:`traj` does not provide velocities, `P` is returned as all zeros. """ # logger logger.info('Running mode projection') if not traj.has_velocities: logger.info('traj does not provide velocities; P will be returned as zeros') modes = np.asarray(modes) original_mode_shape = modes.shape if modes.shape[-2] != traj.n_atoms: raise ValueError('Second dim in modes must be same len as number of atoms in trajectory') if traj.n_atoms != len(ideal_supercell): raise ValueError('ideal_supercell must contain the same number of atoms as the trajectory.') modes = modes.reshape((-1, modes.shape[-2], 3)) modes_conj = modes.conj() Q_traj, P_traj = [], [] for it, frame in enumerate(traj): if logging_interval and it % logging_interval == 0: logger.info(f'Reading frame {it}') else: logger.debug(f'Reading frame {it}') # Make positions into displacements x = frame.get_positions_as_array(traj._atomic_indices) u = x - ideal_supercell.positions # Calculate Q u = get_displacements_from_u(u, ideal_supercell.cell, check_mic=check_mic) Q = np.einsum('mnx,nx->m', modes, u, optimize=True) # Calculate P if traj.has_velocities: v = frame.get_velocities_as_array(traj._atomic_indices) P = np.einsum('mnx,nx->m', modes_conj, v, optimize=True) else: P = np.zeros_like(Q) Q_traj.append(Q) P_traj.append(P) Q_traj = np.array(Q_traj).reshape((len(Q_traj), *original_mode_shape[:-2])) P_traj = np.array(P_traj).reshape((len(P_traj), *original_mode_shape[:-2])) return Q_traj, P_traj dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/dynasor/modes/qpoint.py000066400000000000000000000072331522712672400245310ustar00rootroot00000000000000import numpy as np from numpy.typing import NDArray from ..units import radians_per_fs_to_THz from .band import Band class QPoint: """Representation of a single q-point and properties. The bands can be accessed by, e.g., `qp[2]` to get band index 2 in the form of a :class:`~dynasor.modes.band.Band` object. Parameters ---------- q q-point index. mp Mode project object. """ def __init__(self, q_index: int, mp): self._mp = mp self._q = q_index def __str__(self): s = ['### q-point ###'] s += [f'Index: {self.index}'] s += [f'Reduced: {self.q_reduced}'] s += ['Cartesian: [' f'{self.q_cartesian[0]:.2f}, {self.q_cartesian[1]:.2f}, {self.q_cartesian[2]:.2f}' '] rad/Å'] s += [f'Wavenumber: {self.wavenumber:.2f} rad/Å'] s += [f'Wavelength: {self.wavelength:.2f} Å'] s += [f'q-minus: {self.q_minus.index} {self.q_minus.q_reduced}'] s += [f'Real: {self.is_real}'] s += [''] s += ['Omegas: [' ', '.join(f'{t * radians_per_fs_to_THz:.2f}' for t in self.omegas) + '] THz'] return '\n'.join(s) def __repr__(self): return str(self) def __getitem__(self, s): if s >= self._mp.primitive.n_atoms * 3: raise IndexError return Band(self.index, s, self._mp) @property def q_minus(self): """The corresponding counter-propagating mode.""" return self._mp[self._mp.q_minus[self.index]] @property def polarizations(self) -> NDArray[float]: """Polarization vectors for each band at this q-point, shape ``(Nb, Np, 3)``.""" return self._mp.polarizations[self.index] @property def omegas(self) -> NDArray[float]: """Frequencies of each band at this q-point in rad/fs.""" return self._mp.omegas[self.index] @property def is_real(self) -> bool: """If the q-point has purely real mode coordinates, `q=-q`.""" return self.index == self.q_minus.index @property def index(self) -> int: """q-point index corresponding to :class:`~dynasor.ModeProjector.q_reduced`.""" return self._q @property def wavenumber(self) -> float: """Wavenumber of mode in rad/Å. """ return np.linalg.norm(self._mp.q_cartesian[self.index]) @property def wavelength(self) -> float: """Wavelength of mode in Å. """ return np.inf if self.wavenumber == 0 else 2 * np.pi / self.wavenumber @property def q_reduced(self) -> NDArray[float]: """Reduced coordinates of this q-point.""" return self._mp.q_reduced[self.index] @property def q_cartesian(self) -> NDArray[float]: """Cartesian coordinates of this q-point in rad/Å (2π included).""" return self._mp.q_cartesian[self.index] @property def eigenmodes(self) -> NDArray[float]: """Eigenmodes in the supercell for each band at this q-point, shape ``(Nb, N, 3)``.""" return self._mp.eigenmodes[self.index] @property def potential_energies(self) -> NDArray[float]: """Potential energy per band at this q-point as a ``(Nb,)`` array in eV.""" return self._mp.potential_energies[self.index] @property def kinetic_energies(self) -> NDArray[float]: """Kinetic energy per band at this q-point as a ``(Nb,)`` array in eV.""" return self._mp.kinetic_energies[self.index] @property def virial_energies(self) -> NDArray[float]: """Virial energy per band at this q-point as a ``(Nb,)`` array in eV.""" return self._mp.virial_energies[self.index] dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/dynasor/modes/tools.py000066400000000000000000000143341522712672400243570ustar00rootroot00000000000000import numbers from fractions import Fraction from typing import Optional, Union import numba import numpy as np from numpy.typing import NDArray # Linalg def trace(A): return A[0, 0] + A[1, 1] + A[2, 2] def det(A): if len(A) == 2: return A[0, 0] * A[1, 1] - A[0, 1] * A[1, 0] d = 0 for i, B in enumerate(A[0]): minor = np.hstack([A[1:, :i], A[1:, i+1:]]) d += (-1)**i * B * det(minor) return d def inv( A: Union[Fraction, NDArray[float]], as_fraction: Optional[bool] = True, ) -> Union[Fraction, NDArray[float]]: """ Inverts the A matrix. Parameters ---------- A Array to be inverted. as_fraction Boolean which determines if inverted matrix is returned as :class:`Fraction` matrix or real matrix. """ detx2 = det(A) * 2 rest = (trace(A)**2 - trace(A @ A)) * np.diag([1, 1, 1]) - 2 * A * trace(A) + 2 * A @ A if detx2 < 0: detx2 = -detx2 rest = -rest A_inv = np.array([Fraction(n, detx2) for n in rest.flat]).reshape(3, 3) assert np.all(A @ A_inv == np.eye(len(A))) # return either real matrix or as fraction matrix A_inv_real = np.reshape([float(x) for x in A_inv.flat], A_inv.shape) assert np.allclose(A_inv_real, np.linalg.inv(A)) if as_fraction: return A_inv else: return A_inv_real def symmetrize_eigenvectors( eigenvectors: NDArray[float], cell: Optional[NDArray[float]] = None, max_iter: Optional[int] = 1000, method: Optional[str] = 'varimax', ) -> NDArray[float]: """Takes a set of vectors and tries to make them nice Parameters ---------- eigenvectors If there are n degenerate eigenvectors and m atoms in the basis the array should be `(n,m,3)`. cell If default `None` nothing is done to the cartesian directions but a cell can be provided so the directions are in scaled coordinates instead. max_iter The number of iterations in the symmetrization procedure. method Can be `'varimax'` or `'quartimax'` or a parameter between `0: qartimax` and `1: varimax`. Depending on the choice one obtains, e.g., Equamax, Parsimax, etc. """ if cell is None: cell = np.eye(3) # s = band, i = basis, a = axis eigenvectors = np.einsum('sia,ab->sib', eigenvectors, np.linalg.inv(cell)) components = eigenvectors.reshape(len(eigenvectors), -1).T rotation_matrix = factor_analysis(components, iterations=max_iter, method=method) new_eigenvectors = np.dot(components, rotation_matrix).T new_eigenvectors = new_eigenvectors.reshape(len(new_eigenvectors), -1, 3) new_eigenvectors = np.einsum('sia,ab->sib', new_eigenvectors, cell) return new_eigenvectors def factor_analysis( L: NDArray[float], iterations: Optional[int] = 1000, method: Optional[str] = 'varimax', ) -> NDArray[float]: """Performs factor analysis on `L` finding rotation matrix `R` such that `L @ R = L'` is simple. In the future consider using the scikit learn methods directly but beware the changes need to accomodate complex numbers. References: * *Sparse Modeling of Landmark and Texture Variability using the Orthomax Criterion* Mikkel B. Stegmann, Karl Sjöstrand, Rasmus Larsen http://www2.imm.dtu.dk/pubdb/edoc/imm4041.pdf * http://www.cs.ucl.ac.uk/staff/d.barber/brml * https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.FactorAnalysis.html # noqa * https://stats.stackexchange.com/questions/185216/factor-rotation-methods-varimax-quartimax-oblimin-etc-what-do-the-names # noqa """ nrow, ncol = L.shape R = np.eye(ncol) if method == 'varimax': gamma = 1 elif method == 'quartimax': gamma = 0 else: gamma = method for _ in range(iterations): LR = np.dot(L, R) grad = LR * (np.abs(LR)**2 - gamma * np.mean(np.abs(LR)**2, axis=0)) G = L.T.conj() @ grad u, s, vh = np.linalg.svd(G) R = u @ vh return R def group_eigvals( vals: NDArray[float], tol: Optional[float] = 1e-6, ) -> list[list[int]]: assert sorted(vals) == list(vals), vals groups = [[0]] for i in range(1, len(vals)): if np.abs(vals[i] - vals[i-1]) < tol: groups[-1].append(i) else: groups.append([i]) return groups # misc def as_fraction(not_fraction): if isinstance(not_fraction, numbers.Number): return Fraction(not_fraction) if isinstance(not_fraction, np.ndarray): arr = np.array([Fraction(n) for n in not_fraction.flat]) return arr.reshape(not_fraction.shape) if isinstance(not_fraction, tuple): arr = (Fraction(n) for n in not_fraction) return arr if isinstance(not_fraction, list): arr = [Fraction(n) for n in not_fraction] return arr @numba.njit # pragma: no cover def get_dynamical_matrix(fc, offsets, indices, q): n = indices.max() + 1 N = len(fc) D = np.zeros(shape=(n, n, 3, 3), dtype=np.complex128) for ia in range(n): for a in range(N): if ia != indices[a]: continue na = offsets[a] for b in range(N): ib = indices[b] nb = offsets[b] dn = (nb - na).astype(np.float64) D[ia, ib] += fc[a, b] * np.exp(2j*np.pi * np.dot(dn, q)) break return D # For debug, this is a slower but perhaps more accurate variant, if the fc # obeys translational invariance this should give the same result # @numba.njit # def get_dynamical_matrix_full(fc, offsets, indices, q): # # n = indices.max() + 1 # N = len(fc) # D = np.zeros(shape=(n, n, 3, 3), dtype=np.complex128) # for I in range(N): # i = indices[I] # m = offsets[I] # for J in range(N): # j = indices[J] # n = offsets[J] # # off = (n - m).astype(np.float64) # # phase = np.exp(1j * 2*np.pi * np.dot(off, q)) # # D[i, j] += fc[I, J] * phase # # D /= (N / D.shape[0]) # # return D def make_table(M): rows = [] for r in M: rows.append(''.join(f'{e:<20.2f}' for e in r)) return '\n'.join(rows) dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/dynasor/post_processing/000077500000000000000000000000001522712672400247525ustar00rootroot00000000000000dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/dynasor/post_processing/__init__.py000066400000000000000000000016551522712672400270720ustar00rootroot00000000000000from dynasor.post_processing.atomic_weighting import get_weighted_sample from dynasor.post_processing.average_runs import get_sample_averaged_over_independent_runs from dynasor.post_processing.neutron_scattering_lengths import NeutronScatteringLengths from dynasor.post_processing.x_ray_form_factors import XRayFormFactors from dynasor.post_processing.electron_scattering_factors import ElectronScatteringFactors from dynasor.post_processing.spherical_average import get_spherically_averaged_sample_binned from dynasor.post_processing.spherical_average import get_spherically_averaged_sample_smearing from dynasor.post_processing.weights import Weights __all__ = [ 'NeutronScatteringLengths', 'XRayFormFactors', 'ElectronScatteringFactors', 'Weights', 'get_sample_averaged_over_independent_runs', 'get_spherically_averaged_sample_smearing', 'get_spherically_averaged_sample_binned', 'get_weighted_sample', ] dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/dynasor/post_processing/atomic_weighting.py000066400000000000000000000143601522712672400306510ustar00rootroot00000000000000from copy import deepcopy from typing import Optional from warnings import warn import numpy as np from dynasor.post_processing.weights import Weights from dynasor.sample import Sample, StaticSample, DynamicSample from numpy.typing import NDArray def get_weighted_sample(sample: Sample, weights: Weights, atom_type_map: Optional[dict[str, str]] = None) -> Sample: r""" Weights correlation functions with atomic weighting factors The weighting of a partial dynamic structure factor :math:`S_\mathrm{AB}(\boldsymbol{q}, \omega)` for atom types :math:`A` and :math:`B` is carried out as .. math:: S_\mathrm{AB}(\boldsymbol{q}, \omega) = f_\mathrm{A}(\boldsymbol{q}) f_\mathrm{B}(\boldsymbol{q}) S_\mathrm{AB}(\boldsymbol{q}, \omega) :math:`f_\mathrm{A}(\boldsymbol{q})` and :math:`f_\mathrm{B}(\boldsymbol{q})` are atom-type and :math:`\boldsymbol{q}`-point dependent weights. If sample has incoherent correlation functions, but :attr:`weights` does not contain information on how to weight the incoherent part, then it will be dropped from the returned :attr:`Sample` object (and analogously for current correlation functions). Parameters ---------- sample Input sample to be weighted. weights Object containing the weights :math:`f_\mathrm{X}(\boldsymbol{q})`. atom_type_map Map between the atom types in the :class:`Sample` and the ones used in the :class:`Weights` object, e.g., Ba → Ba(2+). Returns ------- A :class:`Sample` instance with the weighted partial and total structure factors. """ # check input arguments if sample.has_incoherent and not weights.supports_incoherent: warn('The Weights class does not support incoherent scattering, dropping the latter ' 'from the weighted sample.') if sample.has_currents and not weights.supports_currents: warn('The Weights class does not support current correlations, dropping the latter ' 'from the weighted sample.') # setup new input dicts for new Sample data_dict = dict() for key in sample.dimensions: data_dict[key] = sample[key] # Map the atom types in the sample to the types in the weights object. # Useful, for instance, when using weights for charged atomic species. atom_types = [(at, at) for at in sample.atom_types] if atom_type_map is not None: # Fallback to use the atom type from species (`at`) if it's not mapped. atom_types = [(at, atom_type_map.get(at, at)) for at in sample.atom_types] # generate atomic weights for each q-point and compile to arrays if 'q_norms' in sample.dimensions: q_norms = sample.q_norms else: q_norms = np.linalg.norm(sample.q_points, axis=1) weights_coh = dict() for at, weight_at in atom_types: weight_array = np.reshape([weights.get_weight_coh(weight_at, q) for q in q_norms], (-1, 1)) weights_coh[at] = weight_array if sample.has_incoherent and weights.supports_incoherent: weights_incoh = dict() for at, weight_at in atom_types: weight_array = np.reshape([ weights.get_weight_incoh(weight_at, q) for q in q_norms ], (-1, 1)) weights_incoh[at] = weight_array # weighting of correlation functions if isinstance(sample, StaticSample): data_dict_Sq = _compute_weighting_coherent(sample, 'Sq', weights_coh) data_dict.update(data_dict_Sq) elif isinstance(sample, DynamicSample): # coherent Fqt_coh_dict = _compute_weighting_coherent(sample, 'Fqt_coh', weights_coh) data_dict.update(Fqt_coh_dict) Sqw_coh_dict = _compute_weighting_coherent(sample, 'Sqw_coh', weights_coh) data_dict.update(Sqw_coh_dict) # incoherent if sample.has_incoherent and weights.supports_incoherent: Fqt_incoh_dict = _compute_weighting_incoherent(sample, 'Fqt_incoh', weights_incoh) data_dict.update(Fqt_incoh_dict) Sqw_incoh_dict = _compute_weighting_incoherent(sample, 'Sqw_incoh', weights_incoh) data_dict.update(Sqw_incoh_dict) # currents if sample.has_currents and weights.supports_currents: Clqt_dict = _compute_weighting_coherent(sample, 'Clqt', weights_coh) data_dict.update(Clqt_dict) Clqw_dict = _compute_weighting_coherent(sample, 'Clqw', weights_coh) data_dict.update(Clqw_dict) Ctqt_dict = _compute_weighting_coherent(sample, 'Ctqt', weights_coh) data_dict.update(Ctqt_dict) Ctqw_dict = _compute_weighting_coherent(sample, 'Ctqw', weights_coh) data_dict.update(Ctqw_dict) new_sample = sample.__class__( data_dict, simulation_data=deepcopy(sample.simulation_data), history=deepcopy(sample.history)) new_sample._append_history( 'get_weighted_sample', dict( atom_type_map=atom_type_map, weights_class=weights.__class__.__name__, weights_parameters=weights.parameters.to_dict(), )) return new_sample def _compute_weighting_coherent( sample: Sample, name: str, weight_dict: dict, ) -> dict[str, NDArray[float]]: """ Helper function for weighting and summing partial coherent correlation functions. """ data_dict = dict() total = np.zeros(sample[name].shape) for s1, s2 in sample.pairs: key_pair = f'{name}_{s1}_{s2}' partial = np.real(np.conjugate(weight_dict[s1]) * weight_dict[s2]) * sample[key_pair] data_dict[key_pair] = partial total += partial data_dict[name] = total return data_dict def _compute_weighting_incoherent( sample: Sample, name: str, weight_dict: dict, ) -> dict[str, NDArray[float]]: """ Helper function for weighting and summing partial incoherent correlation functions. """ data_dict = dict() total = np.zeros(sample[name].shape) for s1 in sample.atom_types: key = f'{name}_{s1}' partial = np.real(np.conjugate(weight_dict[s1]) * weight_dict[s1]) * sample[key] data_dict[key] = partial total += partial data_dict[name] = total return data_dict dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/dynasor/post_processing/average_runs.py000066400000000000000000000063761522712672400300210ustar00rootroot00000000000000from copy import deepcopy from typing import Optional import numpy as np from dynasor.sample import Sample def get_sample_averaged_over_independent_runs( samples: list[Sample], live_dangerously: Optional[bool] = False, ) -> Sample: """ Compute an averaged sample from multiple samples obtained from identical independent runs. Note all the metadata and dimensions in all samples must be the same. Otherwise a `ValueError` is raised (unless `live_dangerously` is set to `True`). Parameters ---------- samples List of all sample objects to be averaged over. live_dangerously Setting to `True` allows for averaging over samples which metadata information is not identical. """ # get metadata and dimensions from first sample sample_ref = samples[0] data_dict = dict() simulation_data = deepcopy(sample_ref.simulation_data) # test that all samples have identical dimensions for m, sample in enumerate(samples): if sorted(sample.dimensions) != sorted(sample_ref.dimensions): raise ValueError(f'Sample dimensions do not match for sample #{m}.') for dim in sample_ref.dimensions: if not np.allclose(sample[dim], sample_ref[dim]): raise ValueError(f'Sample dimensions do not match for sample #{m}.') for dim in sample_ref.dimensions: data_dict[dim] = sample_ref[dim] # test that all samples have identical metadata if not live_dangerously: for m, sample in enumerate(samples): for key, val in simulation_data.items(): if key not in sample.simulation_data: raise ValueError( f'Sample #{m} is missing "{key}" in the simulation_data field.') match = True if isinstance(val, dict): for k, v in val.items(): match &= sample.simulation_data[key].get(k, None) == val[k] elif isinstance(val, np.ndarray): match &= np.allclose(sample.simulation_data[key], val) elif isinstance(val, float): match &= np.isclose(sample.simulation_data[key], val) else: match &= sample.simulation_data[key] == val if not match: raise ValueError(f'Field "{key}" of sample #{m} does not match.') # average all correlation functions for key in sample.available_correlation_functions: data = [] for sample in samples: data.append(sample[key]) data_average = np.nanmean(data, axis=0) data_dict[key] = data_average # keep history of original samples previous_history = [] for m, s in enumerate(samples): for h in s.history: rec = h.copy() rec['func'] += f'_sample{m}' previous_history.append(rec) # compose new sample object new_sample = sample.__class__( data_dict, simulation_data=simulation_data, history=previous_history) new_sample._append_history( 'get_sample_averaged_over_independent_runs', dict( live_dangerously=live_dangerously, n_samples=len(samples), )) return new_sample electron_scattering_factors.py000066400000000000000000000154301522712672400330270ustar00rootroot00000000000000dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/dynasor/post_processingimport json from re import search from importlib.resources import files from typing import Optional from warnings import warn from numpy import exp, abs, pi from pandas import DataFrame, concat from .weights import Weights class ElectronScatteringFactors(Weights): r"""This class generates sample weights corresponding to electron scattering factors. The scattering factors are parametrized as sums of exponentials of the following form .. math:: f(q) = \sum_{i=1}^k a_i \exp(-b_i s^2), with :math:`s=\sin(\theta)/\lambda`, where and :math:`a_i` and :math:`b_i` are fitting parameters. Note that there are two parametrizations for the elemental scattering factors; one valid up to :math:`s = 2.0\,\mathrm{Å}^{-1}` and the other valid up to :math:`s = 6.0\,\mathrm{Å}^{-1}`. By default, the parametrization valid up to :math:`s = 6.0\,\mathrm{Å}^{-1}` is used. Ionic scattering factors only have a single parametrization. The parametrizations are based on the following publications: * Elemental scattering factors are based on Table 1 and Table 3 from L.-M. Peng, G. Ren, S. L. Dudarev and M. J. Whelan, Acta Crystallographica Section A **52**, 257-276 (1996); `doi: 10.1107/S0108767395014371 `_. * Ionic scattering factors are based on Table 1 from Lian-Mao. Peng, Acta Crystallographica Section A **54**, 481-485 (1998); `doi: 10.1107/S0108767398001901 `_. Parameters ---------- atom_types List of atomic species for which to retrieve scattering lengths. parametrization Parametrization to use. """ def __init__( self, atom_types: list[str], parametrization: Optional[str] = 'smax6', ): self._parametrization = parametrization scattering_factors = self._read_scattering_factors(self._parametrization) # Select the relevant species scattering_factors = scattering_factors[ scattering_factors.index.isin(atom_types)] # Atom species is index self._scattering_factors = scattering_factors # Check if any of the fetched form factors are missing, # indicating that it is missing in the experimental database. for s in atom_types: row = scattering_factors[scattering_factors.index == s] if row.empty: raise ValueError('Missing tabulated values ' f'for requested species {s}.') weights_coh = scattering_factors.to_dict(orient='index') supports_currents = False super().__init__(weights_coh, None, supports_currents=supports_currents) def get_weight_coh(self, atom_type, q_norm): """Get the coherent weight for a given atom type and q-vector norm.""" return self._compute_f(self._weights_coh[atom_type], q_norm, self._parametrization, self._get_charge(atom_type)) @property def parameters(self) -> DataFrame: """Parametrization used to compute the coherent scattering factors :math:`f(q)` for the selected species. """ return self._scattering_factors def _get_charge(self, atom_type: str) -> float: """ Extracts the ionic charge from the atom type. """ match = search(r'(\d+)?([+-])', atom_type) if match: magnitude = int(match.group(1)) if match.group(1) else 1 charge = magnitude * (1 if match.group(2) == '+' else -1) return charge return None # If no charge is found def _compute_f( self, coefficients: dict, q_norm: float, parametrization: str, charge: Optional[int] = None): r"""Compute electronic scattering factors :math:`f(q)`. The scattering factors are parametrized as a sum of exponentials of the form .. math:: f(q) = \sum_{i=1}^k a_i * exp(-b_i * s**2) Ions are also offset by .. math:: (m_0 e^2) / (8 \pi^2 \hbar^2) C / s^2 \approx 0.023934 C / s^2 where :math:`C` is the ionic charge. Parameters ---------- coefficients Parametrization parameters; read from the corresponding source file. q_norm The :math:`|q|`-value at which to evaluate the form factor. parametrization If the Peng parametrization valid up to :math:`s = 6.0\,\mathrm{Å}^{-1}` or :math:`s = 2.0\,\mathrm{Å}^{-1}` should be used. charge Integer representing the ionic charge. `None` if no charge to avoid division by zero. """ s_max = 6.0 if parametrization == 'smax6' else 2.0 s = q_norm / (4 * pi) # q in dynasor is q = 4 pi sin(theta) / lambda. s_squared = s*s # s = sin(theta) / lambda in the Waasmaier paper. if abs(s) > s_max: warn(f'Peng parametrization is not reliable for q' f' above {(s_max * 4 * pi):.2f} rad/Å' f' (corresponding to s={s_max} 1/Å).' ' Parametrisations for ions are accurate up to s=6.0 1/Å') nmax = 5 if charge is not None: f = 0.023934 * charge / s_squared else: f = 0.0 for i in range(1, nmax+1): f += coefficients[f'a{i}'] * exp(-coefficients[f'b{i}'] * s_squared) return f def _read_scattering_factors(self, parametrization: str) -> DataFrame: r""" Extracts the parametrization for the form factors :math:`f(q)`, for both elemental systems and ionic species. Parameters ---------- parametrization If the Peng parametrization valid up to :math:`s = 6.0\,\mathrm{Å}^{-1}` or :math:`s = 2.0\,\mathrm{Å}^{-1}` should be used. """ if parametrization == 'smax2': data_file = files(__package__) / \ 'form-factors/electron-parameters-kmax2-peng-1996.json' elif parametrization == 'smax6': data_file = files(__package__) / \ 'form-factors/electron-parameters-kmax6-peng-1996.json' else: raise ValueError(f'Unknown parametrization {parametrization}') data_file_ions = files(__package__) / \ 'form-factors/electron-parameters-ions-peng-1998.json' frames = [] for df in [data_file, data_file_ions]: with open(df) as fp: coefficients = json.load(fp) frames.append(DataFrame.from_dict(coefficients)) scattering_factors = concat(frames) scattering_factors.index.names = ['species'] return scattering_factors 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json from importlib.resources import files from typing import Optional import numpy as np from pandas import DataFrame from .weights import Weights class NeutronScatteringLengths(Weights): """This class provides sample weights corresponding to neutron scattering lengths. By default, the coherent and incoherent scattering lengths are weighted by the natural abundance of each isotope of the considered atomic species. This weighting can be overwritten using the :attr:`abundances` argument. The scattering lengths have been extracted from `this NIST database `__, which in turn have been taken from Table 1 of Neutron News **3**, 26 (1992); `doi: 10.1080/10448639208218770 `_. Parameters ---------- atom_types List of atomic species for which to retrieve scattering lengths. abundances Dict of the desired fractional abundance of each isotope for each species in the sample. For example, to use an equal weighting of all isotopes of oxygen, one can write ``abundances['O'] = dict(16=1/3, 17=1/3, 18=1/3)``. Note that the abundance for any isotopes that are *not* included in this dict is automatically set to zero. In other words, you need to ensure that the abundances provided sum up to 1. By default the neutron scattering lengths are weighted proportionally to the natural abundance of each isotope. """ def __init__( self, atom_types: list[str], abundances: Optional[dict[str, dict[int, float]]] = None, ): scat_lengths = _read_scattering_lengths() # Sub select only the relevant species scat_lengths = scat_lengths[scat_lengths.species.isin(atom_types)].reset_index() for species in atom_types: if not np.any(scat_lengths.species == species): raise ValueError('Missing tabulated values ' f'for requested species {species}.') # Update the abundances if another weighting is desired if abundances is not None: for species in abundances: scat_lengths.loc[scat_lengths.species == species, 'abundance'] = 0 for Z, frac in abundances[species].items(): match = (scat_lengths.species == species) & (scat_lengths.isotope == Z) if not np.any(match): # Check if any row+column matches raise ValueError(f'No match in database for {species} and isotope {Z}') scat_lengths.loc[match, 'abundance'] = frac self._scattering_lengths = scat_lengths # Check if any of the fetched scattering lengths is None, # indicating that it is missing in the experimental database. # Only raise an error if the desired abundance is greater than 0. nan_rows = scat_lengths[scat_lengths.isnull().any(axis=1) & (scat_lengths.abundance > 0.0)] if not nan_rows.empty: # Grab first offending entry row = nan_rows.iloc[0] raise ValueError(f'{row.isotope}{row.species} is missing tabulated values for either' ' the coherent or incoherent scattering length.' ' Adjust the abundance parameter to set the fraction of' f' {row.isotope}{row.species} to zero.') # Make sure abundances add up to 100% by_species = self._scattering_lengths.groupby('species') for species, species_df in by_species: if not np.isclose(species_df.abundance.sum(), 1): raise ValueError(f'Abundance values for {species} do not sum up to 1.0') # Compute scattering lengths weighted by abundance weights_coh = by_species.apply( lambda s: (s.b_coh * s.abundance).sum(), include_groups=False ).to_dict() # First compute the average scattering length, then take the square # since the incoherent scattering lengths enter as b_incoh**2, but # dynasor only applies a single weighting factor w_incoh. weights_inc = by_species.apply( lambda s: (s.b_inc * s.abundance).sum(), include_groups=False ).to_dict() supports_currents = False super().__init__(weights_coh, weights_inc, supports_currents) @property def abundances(self) -> dict[str, dict[int, float]]: """Abundances used for calculating scattering lengths.""" abundance_dict = {} for (species), species_df in self._scattering_lengths.groupby('species'): abundance_dict[species] = {} for (isotope, abundance), _ in species_df.groupby(['isotope', 'abundance']): abundance_dict[species][isotope] = abundance return abundance_dict @property def parameters(self) -> DataFrame: """Scattering lengths used to compute the coherent and incoherent weights for the selected isotopes. """ return self._scattering_lengths def _read_scattering_lengths() -> DataFrame: """ Extracts the scattering lengths from the file `neutron_scattering_lengths.json` for each of the supplied species. Scattering lengths are in units of fm. The scattering lengths have been extracted from the following NIST database: https://www.ncnr.nist.gov/resources/n-lengths/list.html, which in turn have been extracted from Neutron News **3**, No. 3***, 26 (1992). """ data_file = files(__package__) / 'form-factors/neutron_scattering_lengths.json' with open(data_file) as fp: scattering_lengths = json.load(fp) data = [] for species in scattering_lengths: for isotope in scattering_lengths[species]: for fld in 'b_coh b_inc'.split(): val = scattering_lengths[species][isotope][fld] if 'None' in val: scattering_lengths[species][isotope][fld] = np.nan elif 'j' in val: scattering_lengths[species][isotope][fld] = complex(val) else: scattering_lengths[species][isotope][fld] = float(val) data.append( dict( species=species, isotope=int(isotope), abundance=float(scattering_lengths[species][isotope]['abundance']) / 100, # % -> fraction b_coh=complex(scattering_lengths[species][isotope]['b_coh']), b_inc=complex(scattering_lengths[species][isotope]['b_inc']), ) ) scattering_lengths = DataFrame.from_dict(data) return scattering_lengths dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/dynasor/post_processing/spherical_average.py000066400000000000000000000237171522712672400310020ustar00rootroot00000000000000from copy import deepcopy from typing import Optional import numpy as np from numpy.typing import NDArray from scipy.stats import norm from dynasor.logging_tools import logger from dynasor.sample import Sample def get_spherically_averaged_sample_smearing( sample: Sample, q_norms: NDArray[float], q_width: float, use_sum: Optional[bool] = False, broadening: Optional[str] = 'gaussian', ) -> Sample: r""" Compute a spherical average over q-points for all the correlation functions in :attr:`sample`. Each q-point contributes to the function value at a given :math:`\boldsymbol{q}` with a weight determined by a broadening function. For example .. math:: F(q) = \sum_i w(\boldsymbol{q}_i, q) F(\boldsymbol{q}_i) where :math:`\sum_i w(\boldsymbol{q}_i, q) = 1` (when :attr:`use_sum` is ``False``). Two broadening functions are available via :attr:`broadening`: **Gaussian** (``'gaussian'``): .. math:: w(\boldsymbol{q}_i, q) \propto \exp{\left [ -\frac{1}{2} \left ( \frac{|\boldsymbol{q}_i| - q}{q_{width}} \right)^2 \right ]} where :attr:`q_width` is the standard deviation :math:`\sigma`. **Lorentzian** (``'lorentzian'``): .. math:: w(\boldsymbol{q}_i, q) \propto \frac{1}{\left(|\boldsymbol{q}_i| - q\right)^2 + q_{width}^2} where :attr:`q_width` is the half-width at half-maximum :math:`\gamma`. Parameters ---------- sample Input sample. q_norms Values of :math:`|\vec{q}|` at which to evaluate the correlation functions. q_width Width of the broadening function. Standard deviation :math:`\sigma` for Gaussian; half-width at half-maximum :math:`\gamma` for Lorentzian. use_sum Whether to average or sum the sample in each bin. broadening Broadening function to use. Either ``'gaussian'`` (default) or ``'lorentzian'``. """ if not isinstance(sample, Sample): raise ValueError('Input sample is not a Sample object.') if broadening not in ('gaussian', 'lorentzian'): raise ValueError(f"broadening must be 'gaussian' or 'lorentzian', got '{broadening}'") # get q-points q_points = sample.q_points if q_points.shape[1] != 3: raise ValueError('q-points array has the wrong shape.') # set up new input dicts for new Sample, remove q_points, add q_norms data_dict = dict() for key in sample.dimensions: if key == 'q_points': continue data_dict[key] = sample[key] if broadening == 'gaussian': get_average = _get_gaussian_average get_sum = _get_gaussian_sum else: get_average = _get_lorentzian_average get_sum = _get_lorentzian_sum for key in sample.available_correlation_functions: Z = getattr(sample, key) if use_sum: averaged_data = get_sum(q_points, Z, q_norms, q_width) else: averaged_data = get_average(q_points, Z, q_norms, q_width) data_dict[key] = averaged_data data_dict['q_norms'] = q_norms # compose new object new_sample = sample.__class__( data_dict, simulation_data=deepcopy(sample.simulation_data), history=deepcopy(sample.history)) new_sample._append_history( 'get_spherically_averaged_sample_smearing', dict( q_width=q_width, use_sum=use_sum, broadening=broadening, )) return new_sample def get_spherically_averaged_sample_binned( sample: Sample, num_q_bins: int, use_sum: Optional[bool] = False, ) -> Sample: r""" Compute a spherical average over q-points for all the correlation functions in :attr:`sample`. Here, a q-binning method is used to conduct the spherical average, meaning all q-points are placed into spherical bins (shells). The corresponding function is calculated as the average of all q-points in a bin. If a q-bin does not contain any q-points, then its value is set to `np.nan`. The boundaries of the range, `q_min` and `q_max`, are taken as the minimum and maximum, respectively, of `|q_points|`. These will be set as bin centers for the first and last bins, respectively. The input parameter is the number of q-bins to use :attr:`num_q_bins`. Parameters ---------- sample Input sample. num_q_bins Number of q-bins to use. use_sum Whether to average or sum the sample in each bin. """ if not isinstance(sample, Sample): raise ValueError('Input sample is not a Sample object.') # get q-points q_points = sample.q_points if q_points.shape[1] != 3: raise ValueError('q-points array has wrong shape.') # set up new input dicts for new Sample, remove q_points, add q_norms data_dict = dict() for key in sample.dimensions: if key == 'q_points': continue data_dict[key] = sample[key] # compute spherical average for each correlation function for key in sample.available_correlation_functions: Z = getattr(sample, key) q_bincenters, bin_counts, averaged_data = _get_bin_average(q_points, Z, num_q_bins, use_sum) data_dict[key] = averaged_data data_dict['q_norms'] = q_bincenters # compose new sample new_sample = sample.__class__( data_dict, simulation_data=deepcopy(sample.simulation_data), history=deepcopy(sample.history)) new_sample._append_history( 'get_spherically_averaged_sample_binned', dict( num_q_bins=num_q_bins, use_sum=use_sum, )) return new_sample def _get_gaussian_average( q_points: NDArray[float], Z: NDArray[float], q_norms: NDArray[float], q_width: float, ) -> NDArray[float]: q_norms_sample = np.linalg.norm(q_points, axis=1) # weights shape: (N_q_out, N_qpoints); normalization constant cancels so omit it diff = q_norms[:, None] - q_norms_sample[None, :] weights = np.exp(-0.5 * (diff / q_width) ** 2) norms = weights.sum(axis=1, keepdims=True) weights /= np.where(norms != 0, norms, 1.0) return weights @ Z def _get_gaussian_sum( q_points: NDArray[float], Z: NDArray[float], q_norms: NDArray[float], q_width: float, ) -> NDArray[float]: q_norms_sample = np.linalg.norm(q_points, axis=1) diff = q_norms[:, None] - q_norms_sample[None, :] weights = np.exp(-0.5 * (diff / q_width) ** 2) / (q_width * np.sqrt(2 * np.pi)) return weights @ Z def _gaussian(x: NDArray[float], x0: float, sigma: float) -> NDArray[float]: dist = norm(loc=x0, scale=sigma) return dist.pdf(x) def _get_lorentzian_average( q_points: NDArray[float], Z: NDArray[float], q_norms: NDArray[float], q_width: float, ) -> NDArray[float]: q_norms_sample = np.linalg.norm(q_points, axis=1) # weights shape: (N_q_out, N_qpoints); normalization constant cancels so omit it diff = q_norms[:, None] - q_norms_sample[None, :] weights = 1.0 / (diff ** 2 + q_width ** 2) norms = weights.sum(axis=1, keepdims=True) weights /= np.where(norms != 0, norms, 1.0) return weights @ Z def _get_lorentzian_sum( q_points: NDArray[float], Z: NDArray[float], q_norms: NDArray[float], q_width: float, ) -> NDArray[float]: q_norms_sample = np.linalg.norm(q_points, axis=1) diff = q_norms[:, None] - q_norms_sample[None, :] weights = q_width / (np.pi * (diff ** 2 + q_width ** 2)) return weights @ Z def _lorentzian(x: NDArray[float], x0: float, gamma: float) -> NDArray[float]: return gamma / (np.pi * ((x - x0) ** 2 + gamma ** 2)) def _get_bin_average( q_points: NDArray[float], data: NDArray[float], num_q_bins: int, use_sum: Optional[bool] = False, ) -> tuple[NDArray[float], NDArray[float]]: """ Compute a spherical average over q-points for the data using q-bins. If a q-bin does not contain any q-points, then a np.nan is inserted. q_min and q_max are determined from min/max of |q_points| and define the bin range. These are set as bin centers for the first and last bins, respectively. Parameters ---------- q_points Array of q-points shape ``(Nq, 3)``. data Array of shape ``(Nq, N)``, shape cannot be ``(Nq, )``. num_q_bins Number of radial q-point bins to use. use_sum Whether or not to sum the data in each bin. Returns ------- Tuple comprising the array of |q| bins of shape ``(num_q_bins, )`` and the averaged data-array. """ N_qpoints = q_points.shape[0] N_t = data.shape[1] assert q_points.shape[1] == 3 assert data.shape[0] == N_qpoints # q-norms q_norms = np.linalg.norm(q_points, axis=1) assert q_norms.shape == (N_qpoints,) # set up bins q_max = np.max(q_norms) q_min = np.min(q_norms) delta_x = (q_max - q_min) / (num_q_bins - 1) q_range = (q_min - delta_x / 2, q_max + delta_x / 2) bin_counts, edges = np.histogram(q_norms, bins=num_q_bins, range=q_range) q_bincenters = 0.5 * (edges[1:] + edges[:-1]) # calculate average for each bin averaged_data = np.zeros((num_q_bins, N_t)) for bin_index in range(num_q_bins): # find q-indices that belong to this bin bin_min = edges[bin_index] bin_max = edges[bin_index + 1] bin_count = bin_counts[bin_index] q_indices = np.where(np.logical_and(q_norms >= bin_min, q_norms < bin_max))[0] assert len(q_indices) == bin_count logger.debug(f'bin {bin_index} contains {bin_count} q-points') # average over q-indices, if no indices then np.nan if bin_count == 0: logger.warning(f'No q-points for bin {bin_index}') data_bin = np.array([np.nan for _ in range(N_t)]) else: if use_sum: data_bin = data[q_indices, :].sum(axis=0) else: data_bin = data[q_indices, :].mean(axis=0) averaged_data[bin_index, :] = data_bin return q_bincenters, bin_counts, averaged_data dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/dynasor/post_processing/weights.py000066400000000000000000000063121522712672400270000ustar00rootroot00000000000000from typing import Any, Optional from pandas import DataFrame class Weights: """ Class holding weights and support functions for weighting of samples. Parameters ---------- weights_coh A dict with keys and values representing the atom types and their corresponding coherent scattering length, ``{'A': b_A }``. weights_incoh A dict with keys and values representing the atom types and their corresponding incoherent scattering length, ``{'A': b_A }``. supports_currents Whether or not the coherent weights should be applied to current-correlation functions. """ def __init__( self, weights_coh: dict[str, Any], weights_incoh: Optional[dict[str, Any]] = None, supports_currents: Optional[bool] = True, ): self._weights_coh = weights_coh self._weights_incoh = weights_incoh self._supports_currents = supports_currents def get_weight_coh(self, atom_type: str, q_norm: float = None) -> float: """Get the coherent weight for a given atom type and q-vector norm.""" if atom_type not in self._weights_coh.keys(): raise ValueError(f'Coherent weights for {atom_type} have not been specified') return self._weights_coh[atom_type] def get_weight_incoh(self, atom_type: str, q_norm: float = None) -> float: """Get the incoherent weight for a given atom type and q-vector norm.""" if self._weights_incoh is None: return None if atom_type not in self._weights_incoh.keys(): raise ValueError(f'Incoherent weights for {atom_type} have not been specified') return self._weights_incoh[atom_type] @property def parameters(self) -> DataFrame: """Parameters used for weighting. """ atom_types = set(self._weights_coh.keys()) if self._weights_incoh: atom_types |= set(self._weights_incoh.keys()) data = [] for s in atom_types: record = dict( atom_type=s, coherent=self._weights_coh.get(s, None), ) data.append(record) if self._weights_incoh: record.update(dict(incoherent=self._weights_incoh.get(s, None))) return DataFrame(data).sort_values('atom_type', ignore_index=True) @property def supports_currents(self) -> bool: """ Whether or not this :class:`Weights` object supports weighting of current correlations. """ return self._supports_currents @property def supports_incoherent(self) -> bool: """ Whether or not this :class:`Weights` object supports weighting of incoherent correlation functions. """ return self._weights_incoh is not None def __str__(self): s = ['weights coherent:'] for key, val in self._weights_coh.items(): s.append(f' {key}: {val}') # Return early if incoherent weights # are None if self._weights_incoh is None: return '\n'.join(s) s.append('weights incoherent:') for key, val in self._weights_incoh.items(): s.append(f' {key}: {val}') return '\n'.join(s) dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/dynasor/post_processing/x_ray_form_factors.py000066400000000000000000000173251522712672400312220ustar00rootroot00000000000000import json from importlib.resources import files from typing import Optional from warnings import warn from numpy import exp, abs, pi from pandas import DataFrame, concat from .weights import Weights class XRayFormFactors(Weights): r"""This class generates sample weights corresponding to X-ray form factors, specifically the non-dispersion corrected parametrized form factors. In general, the form factor may be written as .. math:: f(q, \omega) = f_0(q) + f'(q, \omega) + if''(q, \omega) where :math:`q` is a scalar, corresponding to the norm of the desired reciprocal lattice point. The weights generated by this class corresponds to :math:`f_0(q)`. There are two possible parametrizations of :math:`f_0(q)` to choose from, both based on a sum of exponentials of the form .. math:: f_0(q) = \sum_{i=1}^k a_i \exp(-b_i q^2) + c. Two parametrizations are available: * ``'waasmaier-1995'`` corresponds to Table 1 from D. Waasmaier, A. Kirfel, Acta Crystallographica Section A **51**, 416 (1995); `doi: 10.1107/S0108767394013292 `_. This parametrization uses five exponentials (:math:`k=5`) and extends up to :math:`6.0\,\mathrm{Å}^{-1}`. * ``'itc-2006'`` corresponds to Table 6.1.1.4. from *International Tables for Crystallography, Volume C: Mathematical, physical and chemical tables* (2006); `doi: 10.1107/97809553602060000103 `_. This parametrization uses four exponentials (:math:`k=4`) and extends up to :math:`2.0\,\mathrm{Å}^{-1}`. In practice differences are expected to be insignificant. It is unlikely that you have to deviate from the default, which is ``'waasmaier-1995'``. Parameters ---------- atom_types List of atomic species for which to retrieve scattering lengths. source Source to use for parametrization of the form factors :math:`f_0(q)`. Allowed values are ``'waasmaier-1995'`` and ``'itc-2006'`` (see above). """ def __init__( self, atom_types: list[str], source: Optional[str] = 'waasmaier-1995' ): self._source = source form_factors = self._read_form_factors(source) # Select the relevant species form_factors = form_factors[form_factors.index.isin(atom_types)] # Atom species is index self._form_factors = form_factors # Check if any of the fetched form factors are missing, # indicating that it is missing in the experimental database. for s in atom_types: row = form_factors[form_factors.index == s] if row.empty: if s == 'H': # Manually insert values for H such that the form factor is # zero and raise a warning, since it is such a common element. warn('No parametrization for H. Setting form factor for H to zero.') values = [['DUMMY', 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]] form_factors = concat([DataFrame(data=values, index=['H'], columns=form_factors.columns), form_factors]) continue raise ValueError('Missing tabulated values ' f'for requested species {s}.') weights_coh = form_factors.to_dict(orient='index') supports_currents = False super().__init__(weights_coh, None, supports_currents=supports_currents) def get_weight_coh(self, atom_type: str, q_norm: float) -> float: """Get the coherent weight for a given atom type and q-vector norm.""" return self._compute_f0(self._weights_coh[atom_type], q_norm, self._source) @property def parameters(self) -> DataFrame: """Parametrization used to compute the coherent form factors f(q) for the selected species. """ return self._form_factors def _compute_f0( self, coefficients: dict, q_norm: float, source: str): r"""Compute :math:`f_0(q)` based on the chosen parametrization. There are two possible parametrizations of :math:`f_0(q)` to choose from, both based on a sum of exponentials of the form .. math:: f_0(q) = \sum_{i=1}^k a_i * exp(-b_i * s**2) + c where :math:`s = sin(theta) / lambda`. Parameters ---------- coefficients Parametrization parameters, read from the corresponding source file. q_norm The |q|-value at which to evaluate the form factor. source Either 'waasmaier-1995' or 'itc-2006', corresponding to the two available sources for the parametrization of the :math:`f_0(q)` term of the form factors. """ s = q_norm / (4 * pi) # q in dynasor is q = 4 pi sin(theta) / lambda. s_squared = s*s # s = sin(theta) / lambda if source == 'waasmaier-1995': if abs(s) > 6.0: warn('Waasmaier parametrization is not reliable for q ' 'above 75.398 rad/Å (corresponding to s=6.0 1/Å)') return self._get_f0(coefficients, s_squared, nmax=5) elif source == 'itc-2006': if abs(s) > 2.0: warn('ITC.C parametrization is not reliable for q ' 'above 25.132 rad/Å (corresponding to s=2.0 1/Å)') return self._get_f0(coefficients, s_squared, nmax=4) else: raise ValueError(f'Unknown source {source}') def _get_f0(self, coefficients: dict, s_squared: float, nmax: Optional[int] = 5): r""" Compute parametrizations of :math:`f_0(q)` on the form .. math:: f_0(q) = \sum_{i=1}^k a_i * exp(-b_i * s**2) + c where :math:`s = sin(theta) / lambda`. """ f0 = coefficients['c'] for i in range(1, nmax+1): f0 += coefficients[f'a{i}'] * exp(-coefficients[f'b{i}'] * s_squared) return f0 def _read_form_factors(self, source: str) -> DataFrame: r""" Extracts the parametrization for the form factors :math:`f_0(q)`, based on either of two sources. Parameters ---------- source Either 'waasmaier-1995' or 'itc-2006', corresponding to the two available sources for the parametrization of the :math:`f_0(q)` term of the form factors. """ if source == 'waasmaier-1995': data_file = files(__package__) / \ 'form-factors/x-ray-parameters-waasmaier-kirfel-1995.json' with open(data_file) as fp: coefficients = json.load(fp) form_factors = DataFrame.from_dict(coefficients) form_factors.index.names = ['species'] elif source == 'itc-2006': data_file = files(__package__) / \ 'form-factors/x-ray-parameters-itcc-2006.json' with open(data_file) as fp: coefficients = json.load(fp) form_factors = DataFrame.from_dict(coefficients) # There are two entries for H, one calculated using # Hartree-Fock and one calculated with SDS. # Both parametrizations are similar. We'll # use HF since that has been used for the majority # of species. form_factors.drop(index='0', inplace=True) # H SDS is the first row form_factors = form_factors.set_index('species') else: raise ValueError(f'Unknown source {source}') return form_factors dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/dynasor/qpoints/000077500000000000000000000000001522712672400232265ustar00rootroot00000000000000dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/dynasor/qpoints/__init__.py000066400000000000000000000003341522712672400253370ustar00rootroot00000000000000from dynasor.qpoints.spherical_qpoints import get_spherical_qpoints from dynasor.qpoints.tools import get_supercell_qpoints_along_path __all__ = ['get_spherical_qpoints', 'get_supercell_qpoints_along_path'] dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/dynasor/qpoints/lattice.py000066400000000000000000000116501522712672400252300ustar00rootroot00000000000000import warnings import numpy as np from numpy.typing import NDArray from dynasor.tools.structures import get_P_matrix from dynasor.qpoints.tools import get_commensurate_lattice_points, find_on_line class Lattice: def __init__(self, primitive: NDArray, supercell: NDArray): """Representation of a crystal supercell. The supercell `S` is given by the primitive cell p and a repetition matrix `P` such that: dot(P, p) = S In this convention the cell vectors are row vectors of `p` and `S` as in ASE. An inverse cell is defined as: c_inv = inv(c).T and the reciprocal cell is defined as: c_rec = 2*pi*inv(c).T Notice that the inverse cell here is defined with the tranpose so that the lattic vectors of the inverse/reciprocal lattice are also row vectors. The above also implies: dot(P.T, S_inv) = p_inv The inverse supercell S_inv defines a new lattice in reciprocal space. Those inverse lattice points which resides inside the inverse primitive cell p_inv are called commensurate lattice points. These are typically the only points of interest in MD simulations from a crystallographic and lattice dynamics point of view. The convention taken here is that the reciprocal cell carries the 2pi factor onto the cartesian q-points. This is consistent with e.g. Kittel. The reduced coordinates are always with respect to the reciprocal primitive cell. Parameters ---------- primitive Cell metric of the primitive cell with lattice vectors as rows. supercell Cell metric of the supercell with lattice vectors as rows. """ self._primitive = np.array(primitive) self._supercell = np.array(supercell) self._P = get_P_matrix(self.primitive, self.supercell) # As stated in the doc the P matrix relating the inverse cells are just P.T com = get_commensurate_lattice_points(self.P.T) self._qpoints = com @ self.reciprocal_supercell @property def primitive(self) -> NDArray[float]: """Returns the primitive cell with lattice vectors as rows""" return self._primitive @property def supercell(self) -> NDArray[float]: """Returns the supercell with lattice vectors as rows""" return self._supercell @property def reciprocal_primitive(self) -> NDArray[float]: """Returns inv(primitive).T so that the rows are the inverse lattice vectors""" return 2*np.pi * np.linalg.inv(self.primitive.T) # inverse lattice as rows @property def reciprocal_supercell(self) -> NDArray[float]: """Returns inv(super).T so that the rows are the inverse lattice vectors""" return 2*np.pi * np.linalg.inv(self.supercell.T) # reciprocal lattice as rows @property def P(self) -> NDArray[int]: """The P-matrix for this system, P @ primitive = supercell""" return self._P def __repr__(self) -> str: rep = (f'{self.__class__.__name__}(' f'primitive={self.primitive.tolist()}, ' f'supercell={self.supercell.tolist()})') return rep @property def qpoints(self) -> NDArray[float]: """Cartesian commensurate q-points""" return self._qpoints def __len__(self) -> int: return len(self._qpoints) def reduced_to_cartesian(self, qpoints: NDArray[float]) -> NDArray[float]: """Convert from reduced to cartesian coordinates""" return qpoints @ self.reciprocal_primitive def cartesian_to_reduced(self, qpoints: NDArray[float]) -> NDArray[float]: """Convert from Cartesian to reduced coordinates.""" return np.linalg.solve(self.reciprocal_primitive.T, qpoints.T).T def make_path( self, start: NDArray[float], stop: NDArray[float], ) -> tuple[NDArray[float], NDArray[float]]: """Takes qpoints in reduced coordinates and returns all points in between Parameters ---------- start Coordinate of starting point in reduced inverse coordinates. For example, a zone mode is given as (0.5, 0, 0), (0,0,0) == (1,0,0) etc. stop Stop position. Returns ------- Tuple of the coordinates of commensurate points along path in Cartesian reciprocals and the fractional distances along th path. """ start = np.array(start) stop = np.array(stop) fracs = find_on_line(start, stop, self.P.T) if not len(fracs): warnings.warn('No q-points along path!') return np.zeros(shape=(0, 3)), np.zeros(shape=(0,)) points = np.array([start + float(f) * (stop - start) for f in fracs]) qpoints = self.reduced_to_cartesian(points) dists = np.array([float(f) for f in fracs]) return qpoints, dists dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/dynasor/qpoints/spherical_qpoints.py000066400000000000000000000155451522712672400273410ustar00rootroot00000000000000from typing import Optional import itertools import numpy as np from numpy.typing import NDArray from dynasor.logging_tools import logger def get_spherical_qpoints( cell: NDArray[float], q_max: float, q_min: Optional[float] = 0.0, max_points: Optional[int] = None, seed: Optional[int] = 42, chunk_size: Optional[int] = 100000, ) -> NDArray[float]: r"""Generates all q-points on the reciprocal lattice inside a given radius :attr:`q_max`. This approach is suitable if an isotropic sampling of q-space is desired. The function returns the resulting q-points in Cartesian coordinates as an ``Nx3`` array. If the number of generated q-points are large, points can be removed by specifying :attr:`max_points`. The q-points will be randomly removed in such a way that the q-points inside are roughly uniformly distributed with respect to :math:`|q|`. If the number of q-points are binned w.r.t. their norm the function would increase quadratically up until some distance P from which point the distribution would be constant. Parameters ---------- cell Real cell with cell vectors as rows. q_max Maximum norm of generated q-points (in units of rad/Å, i.e. including factor of :math:`2\pi`). q_min Minimum norm of generated q-points (in units of rad/Å, i.e. including factor of :math:`2\pi`). max_points Optionally limit the set to __approximately__ :attr:`max_points` points by randomly removing points from a "fully populated mesh". The points are removed in such a way that for :math:`q > q_\mathrm{prune}`, the points will be radially uniformly distributed. The value of :math:`q_\mathrm{prune}` is calculated from :attr:`q_max`, :attr:`max_points`, and the shape of the cell. seed Seed used for stochastic pruning. chunk_size Number of q-points to process at once (controls memory use). """ if q_min >= q_max: raise ValueError('q_min must be smaller than q_max.') if q_min < 0: raise ValueError('q_min cannot be negative.') # inv(A.T) == inv(A).T # The physicists reciprocal cell cell = cell.astype(np.float64) rec_cell = np.linalg.inv(cell.T) * 2 * np.pi # We want to find all points on the lattice defined by the reciprocal cell # such that all points within q_max are in this set inv_rec_cell = np.linalg.inv(rec_cell.T) # cell / 2pi # h is the height of the rec_cell perpendicular to the other two vectors h = 1 / np.linalg.norm(inv_rec_cell, axis=1) # If a q_point has a coordinate larger than this number it must be further away than q_max N = np.ceil(q_max / h).astype(int) # Generate q-points on the grid with in chunks to keep memory consumption low iterator = itertools.product(*[range(-n, n+1) for n in N]) q_points = [] while True: # Take a chunk of points lattice_points_chunk = list(itertools.islice(iterator, chunk_size)) if not lattice_points_chunk: break lattice_points_chunk = np.array(lattice_points_chunk) q_points_chunk = lattice_points_chunk @ rec_cell # (chunk, 3) # Filter by norm q_distances = np.linalg.norm(q_points_chunk, axis=1) mask = np.logical_and(q_distances >= q_min, q_distances <= q_max) if np.any(mask): q_points.append(q_points_chunk[mask]) if len(q_points) == 0: return np.empty((0, 3)) q_points = np.vstack(q_points) # Pruning based on max_points if max_points is not None and max_points < len(q_points): q_vol = abs(np.linalg.det(rec_cell)) q_prune = _get_prune_distance(max_points, q_min, q_max, q_vol) if q_prune < q_max: logger.info(f'Pruning q-points from the range {q_prune:.3} < |q| < {q_max}') # Keep point with probability min(1, (q_prune/|q|)^2) -> # aim for an equal number of points per equally thick "onion peel" # to get equal number of points per radial unit. q_distances = np.linalg.norm(q_points, axis=1) p = np.divide(q_prune**2, q_distances**2, out=np.ones_like(q_distances), where=np.logical_not(np.isclose(q_distances, 0))) rs = np.random.RandomState(seed) q_points = q_points[p > rs.rand(len(q_points))] logger.info(f'Pruned from {len(q_distances)} q-points to {len(q_points)}') return q_points def _get_prune_distance( max_points: int, q_min: float, q_max: float, q_vol: float, ) -> NDArray[float]: r"""Determine distance in q-space beyond which to prune the q-point mesh to achieve near-isotropic sampling of q-space. If points are selected from the full mesh with probability :math:`\min(1, (q_\mathrm{prune} / |q|)^2)`, q-space will on average be sampled with an equal number of points per radial unit (for :math:`q > q_\mathrm{prune}`). The general idea is as follows. We know that the number of q-points inside a radius :math:`Q` is given by .. math: n = v^{-1} \int_0^Q dq 4 \pi q^2 = v^{-1} 4/3 \pi Q^3 where :math:`v` is the volume of one q-point. Now we want to find a distance :math:`P` such that if all points outside this radius are weighted by the function :math:`w(q)` the total number of q-points will equal the target :math:`N` (:attr:`max_points`) while the number of q-points increases linearly from :math:`P` outward. One additional constraint is that the weighting function must be 1 at :math:`P`. The weighting function which accomplishes this is :math:`w(q)=P^2/q^2` .. math: N = v^{-1} \left( \int_0^P 4 \pi q^2 + \int_P^Q 4 \pi q^2 P^2 / q^2 dq \right). This results in a `cubic equation `_ for :math:`P`, which is solved by this function. Parameters ---------- max_points Maximum number of resulting q-points; :math:`N` below. q_min Minimum q-value in the resulting q-point set. q_max Maximum q-value in the resulting q-point set; :math:`Q` below. q_vol q-space volume for a single q-point. """ Q = q_max V = q_vol N = max_points # Coefs a = 1.0 b = -3 / 2 * Q c = 0.0 d = 3 / 2 * V * N / (4 * np.pi) + 0.5 * q_min**3 # Eq tol solve def original_eq(x): return a * x**3 + b * x**2 + c * x + d # original_eq = lambda x: a * x**3 + b * x**2 + c * x + d # Discriminant p = (3 * a * c - b**2) / (3 * a**2) q = (2 * b**3 - 9 * a * b * c + 27 * a**2 * d) / (27 * a**3) D_t = - (4 * p**3 + 27 * q**2) if D_t < 0: return q_max x = Q * (np.cos(1 / 3 * np.arccos(1 - 4 * d / Q**3) - 2 * np.pi / 3) + 0.5) assert np.isclose(original_eq(x), 0), original_eq(x) return x dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/dynasor/qpoints/tools.py000066400000000000000000000115511522712672400247430ustar00rootroot00000000000000import itertools from fractions import Fraction import numpy as np from numpy.typing import NDArray from dynasor.modes.tools import inv def get_supercell_qpoints_along_path( path: list[tuple[str, str]], coordinates: dict[str, NDArray[float]], primitive_cell: NDArray[float], super_cell: NDArray[float]) -> list[NDArray[float]]: r""" Returns the q-points commensurate with the given supercell along the specific path. Parameters ---------- path List of pairs of q-point labels. coordinates Dict with q-point labels and coordinates as keys and values, respectively; there must be one entry for each q-point label used in :attr:`path`. primitive_cell Cell metric of the primitive cell with lattice vectors as rows. super_cell Cell metric of the supercell with lattice vectors as rows. Returns ------- A list of the accessible q-point coordinates along the specified segment. Example -------- The following example illustrates how to retrieve the q-points that can be sampled using a supercell comprising :math:`6 \times 6 \times 6` conventional (4-atom) unit cells of FCC Al along the path X-:math:`\Gamma`-L. >>> import numpy as np >>> from ase.build import bulk >>> from dynasor.qpoints import get_supercell_qpoints_along_path >>> prim = bulk('Al', 'fcc', a=4.0) >>> supercell = bulk('Al', 'fcc', a=4.0, cubic=True).repeat(6) >>> path = [('X', 'G'), ('G', 'L'), ('L', 'W')] >>> coordinates = dict(X=[0.5, 0.5, 0], G=[0, 0, 0], ... L=[0.5, 0.5, 0.5], W=[0.5, 0.25, 0.75]) >>> qpoints = get_supercell_qpoints_along_path( ... path, coordinates, prim.cell, supercell.cell) """ from .lattice import Lattice lat = Lattice(primitive_cell, super_cell) for lbl in np.array(path).flatten(): if lbl not in coordinates: raise ValueError(f'Unknown point in path: {lbl}') # build the segments supercell_paths = [] for k, (l1, l2) in enumerate(path): q1 = np.array(coordinates[l1], dtype=float) q2 = np.array(coordinates[l2], dtype=float) dynasor_path, _ = lat.make_path(q1, q2) supercell_paths.append(dynasor_path) return supercell_paths def find_on_line( start: NDArray[float], stop: NDArray[float], P: NDArray[int], ) -> set[NDArray[float]]: """Find fractional distances between start and stop compatible with P. A supercell is defined by `P @ c = S` for some repetition matrix `P` and we want to find fractions so that:: [start + f * (stop - start)] @ P = n Parameters ---------- start Start of line in reduced supercell coordinates. stop End of line in reduced supercell coordinates. P Repetition matrix defining the supercell. """ if np.allclose(start, stop): return [Fraction(0, 1)] start = np.array([Fraction(s).limit_denominator() for s in start]) stop = np.array([Fraction(s).limit_denominator() for s in stop]) A = start @ P B = (stop - start) @ P fracs = None for a, b in zip(A, B): fs = solve_Diophantine(a, b) if fs is None: # "inf" solutions continue elif fs == []: # No solutions return [] fracs = set(fs) if fracs is None else fracs.intersection(fs) return sorted(fracs) def solve_Diophantine(a: Fraction, b: Fraction) -> list[Fraction]: """Solve n = a + xb for all n in Z and a,b in Q such that 0 <= x <= 1.""" if b == 0: if a.denominator == 1: return None else: return [] if b < 0: right = np.ceil(a) left = np.floor(a + b) else: left = np.floor(a) right = np.ceil(a + b) ns = np.arange(left, right + 1) fracs = [Fraction(n - a, b) for n in ns] fracs = [f for f in fracs if 0 <= f <= 1] return fracs def get_commensurate_lattice_points(P: NDArray[int]) -> NDArray[float]: """Return commensurate points for a supercell defined by repetition matrix `P`. Finds all `n` such that `n = f P` where `f` is between 0 and 1 Parameters ---------- P The repetition matrix relating the primitive and supercell. Returns ------- The commensurate lattice points. """ inv_P_matrix = inv(P, as_fraction=True) assert np.all(P @ inv_P_matrix == np.eye(3)) n_max = np.where(P > 0, P, 0).sum(axis=0) + 1 n_min = np.where(P < 0, P, 0).sum(axis=0) ranges = [np.arange(*n) for n in zip(n_min, n_max)] lattice_points = [] for lp in itertools.product(*ranges): tmp = lp @ inv_P_matrix if np.all(tmp >= 0) and np.all(tmp < 1): lattice_points.append(lp) assert len(lattice_points) == len(set(lattice_points)) lattice_points = np.array(lattice_points) return lattice_points dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/dynasor/sample.py000066400000000000000000000365451522712672400234010ustar00rootroot00000000000000import getpass import socket from copy import deepcopy from datetime import datetime from typing import Any, Optional import numpy as np from dynasor.logging_tools import logger from numpy.typing import NDArray from pandas import DataFrame class Sample: """ Class for holding correlation functions and additional metadata. Objects of this class are most commonly generated by calling functions such as :func:`compute_static_structure_factors` or :func:`compute_dynamic_structure_factors`. They can then be written to and subsequently read from file. You can see which correlation functions are available via the :attr:`available_correlation_functions` property. You can then access the correlation functions either by key or as property. For example, you could access the static structure factor in the following ways:: sample.Sq # as property sample['Sq'] # via key The correlation functions are provided as numpy arrays. There are several additional fields, the availability of which depends on the type of correlation function that was sampled. Static samples, for example, do not contain the `time` and `omega` fields. `q_norms` is only available if spherical averaging was carried out, typically via :func:`get_spherically_averaged_sample_smearing`. * `q_points`: list of q-point coordinates * `q_norms`: norms of the momentum vector * `time`: time * `omega`: frequency You can also see which fields are available by "printing" the :class:`Sample` object. Parameters ---------- data_dict Dictionary with correlation functions. simulation_data Dictionary with simulation data. The following fields are strongly encouraged (but not enforced): `atom_types`, `cell`, `particle_counts`. history Previous history of operations on :class:`Sample` object. """ def __init__( self, data_dict: dict[str, Any], simulation_data: dict[str, Any], history: Optional[list[dict[str, Any]]] = None, ): # set data dict as attributes self._data_keys = list(data_dict) for key in data_dict: setattr(self, key, data_dict[key]) # set metadata # (using deepcopy here to avoid accidental transfer by reference) self._metadata = dict() self._metadata['simulation_data'] = deepcopy(simulation_data) if history is not None: logger.debug('Copying history') self._metadata['history'] = deepcopy(history) else: self._metadata['history'] = [] def _append_history( self, calling_function: str, caller_metadata: Optional[dict[str, Any]] = None, ): """Add record to history. Parameters ---------- calling_function Name of calling function. caller_metadata Metadata associated with the calling function. """ from dynasor import __version__ as dynasor_version new_record = dict(func=calling_function) if caller_metadata is not None: new_record.update(caller_metadata.copy()) new_record.update(dict( date_time=datetime.now().strftime('%Y-%m-%dT%H:%M:%S'), username=getpass.getuser(), hostname=socket.gethostname(), dynasor_version=dynasor_version, )) self._metadata['history'].append(new_record) def __getitem__(self, key): """ Makes it possible to get the attributes using Sample['key'] """ try: return getattr(self, key) except AttributeError: raise KeyError(key) def write_to_npz(self, fname: str): """ Write object to file in numpy npz format. Parameters ---------- fname Name of the file in which to store the Sample object. """ data_to_save = dict(name=self.__class__.__name__) data_to_save['metadata'] = self._metadata data_dict = dict() for key in self._data_keys: data_dict[key] = getattr(self, key) data_to_save['data_dict'] = data_dict np.savez_compressed(fname, **data_to_save) @property def available_correlation_functions(self) -> list[str]: """ All the available correlation functions in sample. """ keys_to_skip = set(['q_points', 'q_norms', 'time', 'omega']) return sorted(list(set(self._data_keys) - keys_to_skip)) @property def dimensions(self) -> list[str]: r"""The dimensions for the samples, e.g., for :math:`S(q, \omega)` the dimensions would be the :math:`q` and :math:`\omega` axes. """ keys_to_skip = set(self.available_correlation_functions) return sorted(list(set(self._data_keys) - keys_to_skip)) @property def metadata(self) -> dict[str, Any]: """ Metadata. """ return deepcopy(self._metadata) @property def simulation_data(self) -> dict[str, Any]: """ Simulation data. """ return deepcopy(self._metadata['simulation_data']) @property def history(self) -> list[dict[str, Any]]: """ List of operations applied to this :class:`Sample` object. """ return deepcopy(self._metadata['history']) @property def atom_types(self) -> list[str]: """ Simulation data: Atom types. """ return self.simulation_data['atom_types'].copy() \ if 'atom_types' in self.simulation_data else None @property def particle_counts(self) -> dict[str, int]: """ Simulation data: Number of particles per type. """ return self.simulation_data['particle_counts'].copy() \ if 'particle_counts' in self.simulation_data else None @property def pairs(self) -> list[tuple[str, str]]: """ Pairs of types for which correlation functions are available. """ return self.simulation_data['pairs'].copy() \ if 'pairs' in self.simulation_data else None @property def cell(self) -> NDArray[float]: """ Simulation data: Cell metric. """ return self.simulation_data['cell'].copy() \ if 'cell' in self.simulation_data else None @property def has_incoherent(self) -> bool: """ Whether this sample contains the incoherent correlation functions or not. """ return False @property def has_currents(self) -> bool: """ Whether this sample contains the current correlation functions or not. """ return False def __repr__(self) -> str: return str(self) _special_fields = dict( atom_types='Atom types', cell='Cell', number_of_frames='Number of frames', particle_counts='Particle counts', maximum_time_lag='Maximum time lag', angular_frequency_resolution='Angular frequency resolution', time_between_frames='Time between frames', ) def __str__(self) -> str: s_contents = [self.__class__.__name__] for key, value in sorted(self.simulation_data.items()): s_contents.append(f'{self._special_fields.get(key, key)}: {value}') for key in self.dimensions: s_i = f'{key:15} with shape: {np.shape(getattr(self, key))}' s_contents.append(s_i) for key in self.available_correlation_functions: s_i = f'{key:15} with shape: {np.shape(getattr(self, key))}' s_contents.append(s_i) s = '\n'.join(s_contents) return s def _repr_html_(self) -> str: s = [f'

{self.__class__.__name__}

'] s += ['

Simulation

'] s += [''] s += [''] s += ['' '' '' ''] s += [''] for key, value in sorted(self.simulation_data.items()): if key not in self._special_fields: continue s += [f'' f''] s += [''] s += ['
NameContent
{self._special_fields[key]}{value}
'] s += ['

Dimensions

'] s += [''] s += [''] s += ['' '' '' ''] s += [''] for key in self.dimensions: s += [f'' f''] s += [''] s += ['
FieldSize
{key}{np.shape(getattr(self, key))}
'] s += ['

History

'] s += [''] s += [''] s += ['' '' '' '' ''] s += [''] for entry in self.history: title = entry.get('func', '') for key, value in entry.items(): if key == 'func': continue s += [f'' f'' f''] title = '' s += [''] s += ['
FunctionFieldContent
{title}{key}{value}
'] return '\n'.join(s) class StaticSample(Sample): """ Class for holding static correlation functions and additional metadata. Objects of this class are most commonly generated by calling :func:`compute_static_structure_factors `. They can then be written to and subsequently read from file. You can see which correlation functions are available via the :attr:`available_correlation_functions` property. You can then access the correlation functions either by key or as property. For example, you could access the static structure factor :math:`S(q)` in the following ways:: sample.Sq # as property sample['Sq'] # via key The correlation functions are provided as numpy arrays. There are several additional fields: * `q_points`: list of q-point coordinates * `q_norms`: norms of the momentum vector (available when the :class:`StaticSample` object was generated via :func:`get_spherically_averaged_sample_smearing ` or similar functions) You can also see which fields are available by "printing" the :class:`StaticSample` object. Parameters ---------- data_dict Dictionary with correlation functions. simulation_data Dictionary with simulation data. The following fields are strongly encouraged (but not enforced): `atom_types`, `cell`, `particle_counts`. history Previous history of operations on :class:`Sample` object. """ def to_dataframe(self) -> DataFrame: """ Returns correlation functions as pandas dataframe """ df = DataFrame() for dim in self.dimensions: df[dim] = self[dim].tolist() # to list to make q-points (N, 3) work in dataframe for key in self.available_correlation_functions: df[key] = self[key].reshape(-1, ) return df class DynamicSample(Sample): r""" Class for holding dynamic correlation functions and additional metadata. Objects of this class are most commonly generated by calling :func:`compute_dynamic_structure_factors `. They can then be written to and subsequently read from file. You can see which correlation functions are available via the :attr:`available_correlation_functions` property. You can then access the correlation functions either by key or as property. For example, you could access the dynamic structure factor :math:`S(q,\omega)` in the following ways:: sample.Sqw # as property sample['Sqw'] # via key The correlation functions are provided as numpy arrays. There are several additional fields, the availability of which depends on the type of correlation function that was sampled. * `q_points`: list of q-point coordinates * `q_norms`: norms of the momentum vector (available when the :class:`DynamicSample` object was generated, e.g., via :func:`get_spherically_averaged_sample_smearing ` or similar functions) * `time`: time * `omega`: frequency You can also see which fields are available by "printing" the :class:`DynamicSample` object. Parameters ---------- data_dict Dictionary with correlation functions. simulation_data Dictionary with simulation data. The following fields are strongly encouraged (but not enforced): `atom_types`, `cell`, `particle_counts`. history Previous history of operations on :class:`Sample` object. """ @property def has_incoherent(self) -> bool: return 'Fqt_incoh' in self.available_correlation_functions @property def has_currents(self) -> bool: pair_string = '_'.join(self.pairs[0]) return f'Clqt_{pair_string}' in self.available_correlation_functions def to_dataframe(self, q_index: int) -> DataFrame: """ Returns correlation functions as pandas dataframe for the given q-index. Parameters ---------- q_index Index of q-point to return. """ df = DataFrame() for dim in self.dimensions: if dim in ['q_points', 'q_norms']: continue df[dim] = self[dim] for key in self.available_correlation_functions: df[key] = self[key][q_index] return df def read_sample_from_npz(fname: str) -> Sample: """ Read :class:`Sample ` from file. Parameters ---------- fname Path to the file (numpy npz format) from which to read the :class:`Sample ` object. """ data_read = np.load(fname, allow_pickle=True) try: metadata = data_read['metadata'].item() except KeyError: # fallback for versions<=2.2 metadata = data_read['meta_data'].item() if 'simulation_data' in metadata: logger.debug(f'Reading Sample object from {fname} assuming version >=2.3') simulation_data = metadata['simulation_data'] else: logger.debug(f'Reading Sample object from {fname} assuming version <=2.2') simulation_data = {} for key in [ 'atom_types', 'pairs', 'particle_counts', 'cell', 'time_between_frames', 'maximum_time_lag', 'angular_frequency_resolution', 'maximum_angular_frequency', 'number_of_frames', ]: if key in metadata: simulation_data[key] = metadata[key] history = metadata['history'] if 'history' in metadata else None data_dict = data_read['data_dict'].item() if data_read['name'] == 'StaticSample': return StaticSample(data_dict, simulation_data, history=history) elif data_read['name'] == 'DynamicSample': return DynamicSample(data_dict, simulation_data, history=history) else: return Sample(data_dict, simulation_data, history=history) dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/dynasor/tools/000077500000000000000000000000001522712672400226715ustar00rootroot00000000000000dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/dynasor/tools/__init__.py000066400000000000000000000000001522712672400247700ustar00rootroot00000000000000dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/dynasor/tools/acfs.py000066400000000000000000000172721522712672400241700ustar00rootroot00000000000000""" A number of utility functions, for example for dealing with autocorrelation functions, Fourier transforms, and smoothing. """ from typing import Optional import numpy as np from scipy.signal import correlate from numpy.typing import NDArray import pandas as pd def psd_from_acf( acf: NDArray[float], dt: Optional[float] = 1, even: Optional[bool] = True, ) -> NDArray[float]: """Computes the power spectral density (PSD) from an auto-correlation function (ACF). Let x(t) be a time signal and define its auto-correlation function C(τ) = ⟨ x(t) x(t+τ) ⟩ where ⟨·⟩ denotes a time or ensemble average. According to the Wiener–Khinchin theorem, the power spectral density (PSD) of x(t) is the Fourier transform of its auto-correlation function: S(ω) = ∫ C(τ) e^{-i ω τ} dτ This function computes the PSD by performing a discrete Fourier transform of the provided ACF. Parameters ---------- acf The auto-correlation function C(τ) evaluated at discrete time lags. dt Time spacing between consecutive samples in the ACF. even Whether the ACF is assumed to be even in time. If True, the ACF is mirrored to construct a two-sided correlation function before computing the Fourier transform. Returns ------- freqs, psd Angular frequencies ω and corresponding PSD values. """ assert acf.ndim == 1 signal = np.asarray(acf) if even: signal = np.hstack((signal, signal[:0:-1])) fft = dt * np.fft.rfft(signal) psd = fft.real if even else fft freqs = np.fft.rfftfreq(len(signal), dt) freqs = 2 * np.pi * freqs return freqs, psd def psd_from_acf_2d( acf: NDArray[float], dt: Optional[float] = 1, even: Optional[bool] = True, ) -> NDArray[float]: """Computes the power spectral density (PSD) from an auto-correlation function (ACF). This is the 2D analogue of `psd_from_acf`, where the ACF is provided for multiple signals, e.g. F(q, t). The Fourier transform is performed along the last axis. Parameters ---------- acf The ACF as a 2D array with shape (N, Nt), where the last axis corresponds to time lags. dt The time step between samples. even Whether the ACF is even in time and should be mirrored before computing the Fourier transform. Returns ------- freqs, psd Angular frequencies ω and corresponding PSD values with shape (N, Nω). """ assert acf.ndim == 2 signal = np.asarray(acf) if even: signal = np.hstack((signal, signal[:, :0:-1])) fft = dt * np.fft.rfft(signal, axis=1) psd = fft.real if even else fft freqs = np.fft.rfftfreq(signal.shape[1], dt) freqs = 2 * np.pi * freqs return freqs, psd def psd_from_time_signal( x: NDArray[float], dt: Optional[float] = 1, complex: Optional[bool] = False, ) -> NDArray[float]: """Computes the power spectral density (PSD) directly from a time signal. Let x(t) be a time signal. The power spectral density (PSD) can be computed directly from the Fourier transform of the signal: ``S(ω) = |F[x(t)]|²`` where F denotes the Fourier transform. This function computes the PSD by performing a discrete Fourier transform of the time signal and taking the squared magnitude of the transform. This is equivalent to computing the PSD from the Fourier transform of the auto-correlation function. Parameters ---------- x Time signal as a 1D array. dt Time spacing between consecutive samples. complex Whether the time signal is complex. If False (default), the signal is assumed to be real and the PSD is computed using a real FFT (`rfft`), returning only non-negative frequencies. If True, the full FFT (`fft`) is used and both positive and negative frequencies are returned. Returns ------- freqs, psd Angular frequencies ω and corresponding PSD values. """ assert x.ndim == 1 signal = np.asarray(x) N = len(signal) if complex: fft = np.fft.fft(signal) freqs = np.fft.fftfreq(N, dt) else: fft = np.fft.rfft(signal) freqs = np.fft.rfftfreq(N, dt) psd = dt * np.abs(fft) ** 2 / N freqs = 2 * np.pi * freqs return freqs, psd def compute_acf( Z: NDArray[float], delta_t: Optional[float] = 1.0, method: Optional[str] = 'scipy', ) -> NDArray[float]: r""" Computes the autocorrelation function (ACF) for a one-dimensional signal :math:`Z` in time as .. math:: \text{ACF}(\tau) = \frac{\left < Z(t) Z^*(t+\tau) \right >}{\left < Z(t) Z^*(t) \right >} Here, only the real part of the ACF is returned since if :math:`Z` is complex the imaginary part should average out to zero for any stationary signal. Parameters ---------- Z Complex time signal. delta_t Spacing in time between two consecutive values in :math:`Z`. method Implementation to use; possible values: `numpy` and `scipy` (default and usually faster). """ # keep only real part and normalize acf = _compute_correlation_function(Z, Z, method) acf = np.real(acf) acf /= acf[0] time_lags = delta_t * np.arange(0, len(acf), 1) return time_lags, acf def _compute_correlation_function(Z1, Z2, method: Optional[str] = 'scipy'): N = len(Z1) assert len(Z1) == len(Z2) if method == 'scipy': cf = correlate(Z1, Z2, mode='full')[N - 1:] / np.arange(N, 0, -1) elif method == 'numpy': cf = np.correlate(Z1, Z2, mode='full')[N - 1:] / np.arange(N, 0, -1) else: raise ValueError('method must be either numpy or scipy') return cf # smoothing functions / FFT filters # ------------------------------------- def gaussian_decay(t: NDArray[float], t_sigma: float) -> NDArray[float]: r""" Evaluates a gaussian distribution in time :math:`f(t)`, which can be applied to an ACF in time to artificially damp it, i.e., forcing it to go to zero for long times. .. math:: f(t) = \exp{\left [-\frac{1}{2} \left (\frac{t}{t_\mathrm{sigma}}\right )^2 \right ] } Parameters ---------- t Time array. t_sigma Width (standard deviation of the gaussian) of the decay. """ return np.exp(- 1 / 2 * (t / t_sigma) ** 2) def fermi_dirac(t: NDArray[float], t_0: float, t_width: float) -> NDArray[float]: r""" Evaluates a Fermi-Dirac-like function in time :math:`f(t)`, which can be applied to an auto-correlation function (ACF) in time to artificially dampen it, i.e., forcing it to go to zero for long times without affecting the short-time correlations too much. .. math:: f(t) = \frac{1}{\exp{[(t-t_0)/t_\mathrm{width}}] + 1} Parameters ---------- t Time array. t_0 Starting time for decay. t_width Width of the decay. """ return 1.0 / (np.exp((t - t_0) / t_width) + 1) def smoothing_function( data: NDArray[float], window_size: int, window_type: Optional[str] = 'hamming', ) -> NDArray[float]: """ Smoothing function for 1D arrays. This functions employs the pandas rolling window average function. Parameters ---------- data 1D data array. window_size The size of smoothing/smearing window. window_type What type of window-shape to use, e.g. ``'blackman'``, ``'hamming'``, ``'boxcar'`` (see pandas and SciPy documentation for more details). """ series = pd.Series(data) new_data = series.rolling(window_size, win_type=window_type, center=True, min_periods=1).mean() return np.array(new_data) dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/dynasor/tools/damped_harmonic_oscillator.py000066400000000000000000000100751522712672400306130ustar00rootroot00000000000000from typing import Optional, Union import numpy as np from numpy.typing import NDArray def acf_position_dho( t: Union[float, NDArray[float]], w0: float, gamma: float, A: Optional[float] = 1.0, ) -> Union[float, NDArray[float]]: r""" Calculate the damped harmonic oscillator (DHO) autocorrelation function for the position. The definition of this function can be found in the `dynasor documentation `_. # noqa Parameters ---------- t Time, usually as an array. w0 Natural angular frequency of the DHO. gamma Damping of DHO. A Amplitude of the DHO. """ t = np.abs(t) if 2 * w0 > gamma: # underdamped we = np.sqrt(w0**2 - gamma**2 / 4.0) return A * np.exp(-gamma * t / 2.0) * ( np.cos(we * t) + 0.5 * gamma / we * np.sin(we * t)) elif 2 * w0 < gamma: # overdamped tau = 2 / gamma tau_S = tau / (1 + np.sqrt(1 - (w0 * tau)**2)) tau_L = tau / (1 - np.sqrt(1 - (w0 * tau)**2)) return A / (tau_L - tau_S) * (tau_L * np.exp(-t/tau_L) - tau_S * np.exp(-t/tau_S)) else: tau = 2 / gamma return A * np.exp(-t/tau) * (1 + t / tau) def acf_velocity_dho( t: Union[float, NDArray[float]], w0: float, gamma: float, A: Optional[float] = 1.0, ) -> Union[float, NDArray[float]]: r""" Calculate the damped harmonic oscillator (DHO) autocorrelation function for the velocity. The definition of this function can be found in the `dynasor documentation `_. # noqa Parameters ---------- t Time, usually as an array. w0 Natural angular frequency of the DHO. gamma Damping of DHO. A Amplitude of the DHO. """ t = np.abs(t) if 2 * w0 > gamma: # underdamped we = np.sqrt(w0**2 - gamma**2 / 4.0) return A * w0**2 * np.exp(-gamma * t / 2.0) * ( np.cos(we * t) - 0.5 * gamma / we * np.sin(we * t)) elif 2 * w0 < gamma: # overdamped tau = 2 / gamma tau_S = tau / (1 + np.sqrt(1 - (w0 * tau)**2)) tau_L = tau / (1 - np.sqrt(1 - (w0 * tau)**2)) return A / (tau_L - tau_S) * (np.exp(-t/tau_S)/tau_S - np.exp(-t/tau_L)/tau_L) else: tau = 2 / gamma return A * w0**2 * np.exp(-t/tau) * (1 - t / tau) def psd_position_dho( w: Union[float, NDArray[float]], w0: float, gamma: float, A: Optional[float] = 1.0, ) -> Union[float, NDArray[float]]: r""" Calculate the power spectral density (PSD) function for the damped harmonic oscillator (DHO) as the Fourier transform of the position autocorrelation function. The definition of this function can be found in the `dynasor documentation `_. # noqa Parameters ---------- w Angular frequency, usually as an array. w0 Natural angular frequency of the DHO. gamma Damping of DHO. A Amplitude of the DHO. """ return 2 * w0**2 * A * gamma / ((w**2 - w0**2)**2 + (w * gamma)**2) def psd_velocity_dho( w: Union[float, NDArray[float]], w0: float, gamma: float, A: Optional[float] = 1.0, ) -> Union[float, NDArray[float]]: r""" Calculate the power spectral density (PSD) function for the damped harmonic oscillator (DHO) as the Fourier transform of the velocity autocorrelation function. The definition of this function can be found in the `dynasor documentation `_. # noqa Parameters ---------- w Angular frequency, usually as an array. w0 Natural angular frequency of the DHO. gamma Damping of DHO. A Amplitude of the DHO. """ return w**2 * psd_position_dho(w, w0, gamma, A) dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/dynasor/tools/structures.py000066400000000000000000000247451522712672400255020ustar00rootroot00000000000000from typing import Optional import numpy as np from ase import Atoms from ase.geometry import get_distances from ase.geometry import find_mic from dynasor.modes.atoms import Prim from numpy.typing import NDArray def get_displacements(atoms: Atoms, atoms_ideal: Atoms, check_mic: Optional[bool] = True, cell_tol: Optional[float] = 1e-4) -> NDArray[float]: """Returns the smallest possible displacements between a displaced configuration relative to an ideal (reference) configuration. Parameters ---------- atoms Structure with displaced atoms. ideal Ideal configuration relative to which displacements are computed. check_mic Whether to check minimum image convention. cell_tol Cell tolerance; if the cell mismatch exceeds this value, a `ValueError` is raised. """ if not np.array_equal(atoms.numbers, atoms_ideal.numbers): raise ValueError('Atomic numbers do not match.') if np.linalg.norm(atoms.cell - atoms_ideal.cell) > cell_tol: raise ValueError('Cells do not match.') u = atoms.positions - atoms_ideal.positions return get_displacements_from_u(u, atoms_ideal.cell, check_mic=check_mic) def get_displacements_from_u( u: NDArray[float], cell: NDArray[float], check_mic: Optional[bool] = True, ) -> NDArray[float]: """wraps displacements using mic""" if check_mic: u, _ = find_mic(u, cell) return u def find_permutation(atoms: Atoms, atoms_ref: Atoms) -> list[int]: """ Returns the best permutation of atoms for mapping one configuration onto another. Parameters ---------- atoms Configuration to be permuted. atoms_ref Configuration onto which to map. Example ------- After obtaining the permutation via ``p = find_permutation(atoms1, atoms2)`` the reordered structure ``atoms1[p]`` will give the closest match to ``atoms2``. """ if np.linalg.norm(atoms.cell - atoms_ref.cell) > 1e-4: raise ValueError('Cells do not match.') permutation = [] for i in range(len(atoms_ref)): dist_row = get_distances( atoms.positions, atoms_ref.positions[i], cell=atoms_ref.cell, pbc=True)[1][:, 0] permutation.append(np.argmin(dist_row)) if len(set(permutation)) != len(permutation): raise Exception('Duplicates in permutation.') for i, p in enumerate(permutation): if atoms[p].symbol != atoms_ref[i].symbol: raise Exception('Matching lattice sites have different occupation.') return permutation def align_structure(atoms: Atoms, atol: Optional[float] = 1e-5) -> None: """ Rotates and realigns a structure such that * the first cell vector points along the x-directon * the second cell vector lies in the xy-plane Note that this function modifies the :attr:`atoms` object in place. Parameters ---------- atoms Input structure to be rotated aligned with the x,y,z coordinate system. atol Absolute tolerance used for sanity checking the cell. """ _align_a_onto_xy(atoms, atol) _align_a_onto_x(atoms, atol) _align_b_onto_xy(atoms, atol) def get_offset_index( primitive: Atoms, supercell: Atoms, tol: Optional[float] = 0.01, wrap: Optional[bool] = True, ) -> tuple[NDArray[float], NDArray[float]]: """ Returns the basis index and primitive cell offsets for a supercell. This implementation uses a simple iteration procedure that should be fairly quick. If more stability is needed consider the following approach: * find the P-matrix: `P = ideal.cell @ prim.cell_inv.T` * compensate for strain: `P *= len(ideal)/len(prim)/det(P)` * generate the reference structure: `ref_atoms = make_supercell(round(P), prim)` * find the assignment using `ref_atoms` via the Hungarian algorithm using the mic distances Parameters ---------- primitive Primitive cell. supercell Some ideal repetition of the primitive cell. tol Tolerance length parameter. Increase to allow for slightly rattled or strained cells. wrap It might happen that the ideal cell boundary cuts through a unit cell whose lattice points lie inside the ideal cell. If there is a basis, an atom belonging to this unit cell might get wrapped while another is not. Then the wrapped atom now belongs to a lattice point outside the P matrix so to say. This would result in more lattice points than expected from `N_unit = len(ideal)/len(prim)`. Returns ------- offsets The lattice points as integers in `(N, 3)`-array. index The basis indices as integers in `(N,)`-array. """ if not isinstance(primitive, Atoms): raise ValueError if not isinstance(supercell, Atoms): raise ValueError prim = Prim(primitive) from dynasor.modes.tools import inv P = get_P_matrix(primitive.cell, supercell.cell) # P C = S P_inv = inv(P) lattice, basis = [], [] # Pick an atom in the supercell for pos_ideal in supercell.positions: # Does this atom perhaps belong to site "index"? for index, pos_prim in enumerate(primitive.positions): # if so we can remove the basis position vector and should end up on a lattice site diff_pos = pos_ideal - pos_prim # The lattice site has integer coordinates in reduced coordinates prim_spos = diff_pos @ prim.inv_cell.T # Rounding should not affect the coordinate much if it is integer prim_spos_round = np.round(prim_spos).astype(int) # If the rounded spos and unrounded spos are the same if np.allclose(prim_spos, prim_spos_round, rtol=0, atol=tol): # Since P_inv is represented using fractions we can neatly # write the supercell spos of the lattice point using fractions # and easily determine if it needs wrapping or not without # worry about numerics ideal_spos = prim_spos_round @ P_inv # wrap if needed ideal_spos_wrap = ideal_spos % 1 if wrap else ideal_spos # This should be integer again prim_spos_wrap = (ideal_spos_wrap @ P).astype(int) # add the results and break out from the basis site loop lattice.append(prim_spos_wrap) basis.append(index) break else: # we get here by not breaking out from the basis site loop. # This means that the candidate lattice site where not close to integers raise ValueError(f' {prim_spos} {prim_spos_round} Supercell not compatible ' 'with primitive cell.') lattice = np.array(lattice) basis = np.array(basis) # We should have found len(ideal) unique positions lattice_basis = [tuple((*lp, i)) for lp, i in zip(lattice, basis)] assert len(set(lattice_basis)) == len(supercell) return lattice, basis def get_P_matrix( c: NDArray[float], S: NDArray[float], ) -> NDArray[float]: """Returns the P matrix, i.e., the `3x3` integer matrix :math:`P` that satisfies .. math:: P c = S Here, :math:`c` is the primitive cell metric and :math:`S` is the supercell metric as row vectors. Note that the above condition is equivalent to: .. math:: c^T P^T = S^T Parameters ---------- c Cell metric of the primitive structure. S Cell metric of the supercell. """ PT = np.linalg.solve(c.T, S.T) P_float = PT.T P = np.round(P_float).astype(int) if not np.allclose(P_float, P) or not np.allclose(P @ c, S): raise ValueError( f'Please check that the supercell metric ({S}) is related to the' f' the primitive cell {c} by an integer transformation matrix.') return P def _align_a_onto_xy(atoms: Atoms, atol: float) -> None: """ Rotate cell so that a is in the xy-plane. """ cell = atoms.cell.array.copy() a = cell[0] a_xy = a.copy() a_xy[2] = 0 # projection of a onto xy-plane norm_a = np.linalg.norm(a) norm_a_xy = np.linalg.norm(a_xy) assert norm_a >= atol, \ f'First cell vector a has near-zero length ({norm_a:.3e}); cannot align cell.' assert norm_a_xy >= atol, \ f'First cell vector a is nearly parallel to the z-axis ' \ f'(|a_xy| = {norm_a_xy:.3e}); cannot align a onto the xy-plane.' # cosine of the angle between a and its xy-projection cosa = norm_a_xy / norm_a cosa = min(cosa, 1.0) # clamp for floating-point safety # angle between a and xy-plane in degs angle_xy_deg = np.rad2deg(np.arccos(cosa)) # get unit vector to rotate around vec = np.cross(a_xy, [0, 0, 1]) vec = vec / np.linalg.norm(vec) assert vec[2] == 0 # Determine if the rotation should be positive or negative depending on # whether a is pointing in the +z or -z direction sign = -1 if a[2] > 0 else +1 # rotate atoms.rotate(sign * angle_xy_deg, vec, rotate_cell=True) assert np.isclose(atoms.cell[0, 2], 0, atol=atol, rtol=0), atoms.cell def _align_a_onto_x(atoms: Atoms, atol: float) -> None: assert np.isclose(atoms.cell[0, 2], 0, atol=atol, rtol=0) # make sure a is in xy-plane a = atoms.cell[0] a_x = a[0] a_y = a[1] # angle between a and x-axis (a is already in xy-plane) # tan = y / x -> angle = arctan y / x "=" atan2(y, x) angle_rad = np.arctan2(a_y, a_x) angle_deg = np.rad2deg(angle_rad) atoms.rotate(-angle_deg, [0, 0, 1], rotate_cell=True) assert np.isclose(atoms.cell[0, 1], 0, atol=atol, rtol=0), atoms.cell assert np.isclose(atoms.cell[0, 2], 0, atol=atol, rtol=0), atoms.cell def _align_b_onto_xy(atoms: Atoms, atol: float) -> None: assert np.isclose(atoms.cell[0, 1], 0, atol=atol, rtol=0) # make sure a is along x assert np.isclose(atoms.cell[0, 2], 0, atol=atol, rtol=0) # make sure a is along x # rotate so that b is in xy plane # project b onto the yz-plane b = atoms.cell[1] b_y = b[1] b_z = b[2] angle_rad = np.arctan2(b_z, b_y) angle_deg = np.rad2deg(angle_rad) atoms.rotate(-angle_deg, [1, 0, 0], rotate_cell=True) assert np.isclose(atoms.cell[0, 1], 0, atol=atol, rtol=0) # make sure a is in xy-plane assert np.isclose(atoms.cell[0, 2], 0, atol=atol, rtol=0) # make sure a is in xy-plane assert np.isclose(atoms.cell[1, 2], 0, atol=atol, rtol=0), atoms.cell dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/dynasor/trajectory/000077500000000000000000000000001522712672400237175ustar00rootroot00000000000000dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/dynasor/trajectory/__init__.py000066400000000000000000000001621522712672400260270ustar00rootroot00000000000000from dynasor.trajectory.trajectory import Trajectory, WindowIterator __all__ = ['Trajectory', 'WindowIterator'] abstract_trajectory_reader.py000066400000000000000000000037021522712672400316070ustar00rootroot00000000000000dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/dynasor/trajectoryfrom abc import ABC, abstractmethod from dynasor.logging_tools import logger class AbstractTrajectoryReader(ABC): """Provides a way to iterate through a molecular dynamics trajectory file. Each frame/time-step is returned as a trajectory_frame. """ # unit conversion tables lengthunits_to_Angstrom_table = { 'Angstrom': 1.0, 'nm': 10.0, 'pm': 1e-2, 'fm': 1e-5, } timeunits_to_fs_table = { 'fs': 1.0, 'ps': 1000, 'ns': 1000000, } @abstractmethod def __iter__(self): """ Iterates through the trajectory file, frame by frame. """ pass @abstractmethod def __next__(self): """ Gets next trajectory frame. """ pass @abstractmethod def close(self): """ Closes down, release resources etc. """ pass def set_unit_scaling_factors(self, length_unit: str, time_unit: str): # setup units if length_unit is None: logger.info('Assuming the trajectory has the default length unit (Ångström), since no ' 'unit was specified.') self.x_factor = self.lengthunits_to_Angstrom_table['Angstrom'] elif length_unit not in self.lengthunits_to_Angstrom_table: raise ValueError(f'Specified length unit {length_unit} is not an available option.') else: self.x_factor = self.lengthunits_to_Angstrom_table[length_unit] if time_unit is None: logger.info('Assuming the trajectory has the default time unit (fs), since no ' 'unit was specified.') self.t_factor = self.timeunits_to_fs_table['fs'] elif time_unit not in self.timeunits_to_fs_table: raise ValueError(f'Specified time unit {time_unit} is not an available option.') else: self.t_factor = self.timeunits_to_fs_table[time_unit] self.v_factor = self.x_factor / self.t_factor dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/dynasor/trajectory/ase_trajectory_reader.py000066400000000000000000000027701522712672400306370ustar00rootroot00000000000000from itertools import count from typing import Optional import numpy as np from ase import io from dynasor.trajectory.abstract_trajectory_reader import AbstractTrajectoryReader from dynasor.trajectory.trajectory_frame import ReaderFrame class ASETrajectoryReader(AbstractTrajectoryReader): """Read ASE trajectory file ... Parameters ---------- filename Name of input file. length_unit Unit of length for the input trajectory (``'Angstrom'``, ``'nm'``, ``'pm'``, ``'fm'``). time_unit Unit of time for the input trajectory (``'fs'``, ``'ps'``, ``'ns'``). """ def __init__( self, filename: str, length_unit: Optional[str] = 'Angstrom', time_unit: Optional[str] = 'fs', ): self._frame_index = count(0) self._atoms = io.iread(filename, index=':') # setup units self.set_unit_scaling_factors(length_unit, time_unit) def __iter__(self): return self def close(self): pass def __next__(self): ind = next(self._frame_index) a = next(self._atoms) if 'momenta' in a.arrays: vel = self.v_factor * a.get_velocities() else: vel = None return ReaderFrame( frame_index=ind, n_atoms=len(a), cell=self.x_factor * a.cell.array.copy('F'), positions=self.x_factor * a.get_positions(), velocities=vel, atom_types=np.array(list(a.symbols)), ) dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/dynasor/trajectory/atomic_indices.py000066400000000000000000000024521522712672400272460ustar00rootroot00000000000000import os import re import numpy as np from dynasor.logging_tools import logger def parse_gromacs_index_file(fname: str) -> dict[str, list[int]]: """ Parses a gromacs style index (ndx) file. Returns a dict with key values as `group-name: [1, 3, 8]`. Note ---- The atomic indices in gromacs-ndx file starts with 1, but the returned dict starts with 0. """ if not os.path.isfile(fname): raise ValueError('Index file not found') atomic_indices = dict() header_re = re.compile(r'^ *\[ *([a-zA-Z0-9_.-]+) *\] *$') with open(fname, 'r') as fobj: for line in fobj.readlines(): match = header_re.match(line) if match is not None: # get name of group name = match.group(1) if name in atomic_indices.keys(): logger.warning(f'Group name {name} appears twice in index file, only one used.') atomic_indices[name] = [] else: # get indices for group indices = [int(i) for i in line.split()] atomic_indices[name].extend(indices) # cast to indices to numpy arrays and shift so indices start with 0 for name, indices in atomic_indices.items(): atomic_indices[name] = np.array(indices) - 1 return atomic_indices dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/dynasor/trajectory/extxyz_trajectory_reader.py000066400000000000000000000112431522712672400314350ustar00rootroot00000000000000import concurrent.futures import numpy as np import io from itertools import count from typing import Optional from dynasor.trajectory.abstract_trajectory_reader import AbstractTrajectoryReader from dynasor.trajectory.trajectory_frame import ReaderFrame from ase.io import read from ase import Atoms def frame_text_to_atoms(frame_text): return read(io.StringIO(frame_text), format='extxyz') def iter_frame_texts(f): while True: natoms_line = f.readline() if not natoms_line: break comment_line = f.readline() if not comment_line: raise ValueError('Unexpected EOF while reading extxyz comment line') try: natoms = int(natoms_line.strip()) except ValueError: raise ValueError(f'Invalid extxyz atom count line: {natoms_line!r}') atom_lines = [] for _ in range(natoms): line = f.readline() if not line: raise ValueError('Unexpected EOF while reading extxyz atom lines') atom_lines.append(line) yield natoms_line + comment_line + ''.join(atom_lines) def iread(f, max_workers: Optional[int] = None) -> Atoms: frame_iterator = iter_frame_texts(f) with concurrent.futures.ProcessPoolExecutor(max_workers=max_workers) as ex: buff = [] n_submit = ex._max_workers for _ in range(n_submit): try: frame_text = next(frame_iterator) buff.append(ex.submit(frame_text_to_atoms, frame_text)) except StopIteration: break while buff: res = buff.pop(0) try: frame_text = next(frame_iterator) buff.append(ex.submit(frame_text_to_atoms, frame_text)) except StopIteration: pass yield res.result() class ExtxyzTrajectoryReader(AbstractTrajectoryReader): """Read extend xyz trajectory file, typically produced by GPUMD This is a naive (and comparatively slow) parallel implementation which relies on the ASE xyz reader. Parameters ---------- filename Name of input file. length_unit Unit of length for the input trajectory (``'Angstrom'``, ``'nm'``, ``'pm'``, ``'fm'``). time_unit Unit of time for the input trajectory (``'fs'``, ``'ps'``, ``'ns'``). max_workers Number of working processes; defaults to ``None``, which means that the number of processors on the machine is used. """ def __init__(self, filename: str, length_unit: Optional[str] = None, time_unit: Optional[str] = None, max_workers: Optional[int] = None): # setup generator object self._fobj = open(filename, 'r') self._generator_xyz = iread(self._fobj, max_workers=max_workers) self._open = True self._frame_index = count(0) # set up units self.set_unit_scaling_factors(length_unit, time_unit) def _get_next(self): try: atoms = next(self._generator_xyz) except StopIteration: self._fobj.close() self._open = False raise except Exception: self._fobj.close() self._open = False raise self._atom_types = np.array(list(atoms.symbols)) self._n_atoms = len(atoms) self._cell = atoms.cell[:] self._x = atoms.positions if 'vel' in atoms.arrays: self._v = atoms.arrays['vel'] else: self._v = None def __iter__(self): return self def close(self): if not self._fobj.closed: self._fobj.close() self._open = False def __next__(self): if not self._open: raise StopIteration self._get_next() if self._v is not None: frame = ReaderFrame(frame_index=next(self._frame_index), n_atoms=int(self._n_atoms), cell=self.x_factor * self._cell.copy('F'), positions=self.x_factor * self._x, velocities=self.v_factor * self._v, atom_types=self._atom_types ) else: frame = ReaderFrame(frame_index=next(self._frame_index), n_atoms=int(self._n_atoms), cell=self.x_factor * self._cell.copy('F'), positions=self.x_factor * self._x, atom_types=self._atom_types ) return frame dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/dynasor/trajectory/lammps_trajectory_reader.py000066400000000000000000000155711522712672400313630ustar00rootroot00000000000000import numpy as np import re from collections import deque from typing import Optional from dynasor.trajectory.abstract_trajectory_reader import AbstractTrajectoryReader from dynasor.trajectory.trajectory_frame import ReaderFrame from itertools import count from numpy import array, arange, zeros class LammpsTrajectoryReader(AbstractTrajectoryReader): """Read LAMMPS trajectory file This is a naive (and comparatively slow) implementation, written entirely in python. Parameters ---------- filename Name of input file. length_unit Unit of length for the input trajectory (``'Angstrom'``, ``'nm'``, ``'pm'``, ``'fm'``). time_unit Unit of time for the input trajectory (``'fs'``, ``'ps'``, ``'ns'``). """ def __init__( self, filename: str, length_unit: Optional[str] = None, time_unit: Optional[str] = None ): if filename.endswith('.gz'): import gzip self._fh = gzip.open(filename, 'rt') elif filename.endswith('.bz2'): import bz2 self._fh = bz2.open(filename, 'rt') else: self._fh = open(filename, 'r') self._open = True regexp = r'^ITEM: (TIMESTEP|NUMBER OF ATOMS|BOX BOUNDS|ATOMS) ?(.*)$' self._item_re = re.compile(regexp) self._first_called = False self._frame_index = count(0) # set up units self.set_unit_scaling_factors(length_unit, time_unit) # ITEM: TIMESTEP # 81000 # ITEM: NUMBER OF ATOMS # 1536 # ITEM: BOX BOUNDS pp pp pp # 1.54223 26.5378 # 1.54223 26.5378 # 1.54223 26.5378 # ITEM: ATOMS id type x y z vx vy vz # 247 1 3.69544 2.56202 3.27701 0.00433856 -0.00099307 -0.00486166 # 249 2 3.73324 3.05962 4.14359 0.00346029 0.00332502 -0.00731005 # 463 1 3.5465 4.12841 5.34888 0.000523332 0.00145597 -0.00418675 def _read_frame_header(self): while True: L = self._fh.readline() m = self._item_re.match(L) if not m: if L == '': self._fh.close() self._open = False raise StopIteration if L.strip() == '': continue raise IOError('TRJ_reader: Failed to parse TRJ frame header') if m.group(1) == 'TIMESTEP': step = int(self._fh.readline()) elif m.group(1) == 'NUMBER OF ATOMS': n_atoms = int(self._fh.readline()) elif m.group(1) == 'BOX BOUNDS': bbounds = [deque(map(float, self._fh.readline().split())) for _ in range(3)] x = array(bbounds) cell = np.diag(x[:, 1] - x[:, 0]) if x.shape == (3, 3): cell[1, 0] = x[0, 2] cell[2, 0] = x[1, 2] cell[2, 1] = x[2, 2] elif x.shape != (3, 2): raise IOError('TRJ_reader: Malformed cell bounds') elif m.group(1) == 'ATOMS': cols = tuple(m.group(2).split()) # At this point, there should be only atomic data left return (step, n_atoms, cell, cols) def _get_first(self): # Read first frame, update state of self, create indeces etc step, N, cell, cols = self._read_frame_header() self._n_atoms = N self._step = step self._cols = cols self._cell = cell def _all_in_cols(keys): for k in keys: if k not in cols: return False return True self._x_map = None if _all_in_cols(('id', 'xu', 'yu', 'zu')): self._x_I = array(deque(map(cols.index, ('xu', 'yu', 'zu')))) elif _all_in_cols(('id', 'x', 'y', 'z')): self._x_I = array(deque(map(cols.index, ('x', 'y', 'z')))) elif _all_in_cols(('id', 'xs', 'ys', 'zs')): self._x_I = array(deque(map(cols.index, ('xs', 'ys', 'zs')))) _x_factor = self._cell.diagonal() # xs.shape == (n, 3) self._x_map = lambda xs: xs * _x_factor else: raise RuntimeError('TRJ file must contain at least atom-id, x, y,' ' and z coordinates to be useful.') self._id_I = cols.index('id') if _all_in_cols(('vx', 'vy', 'vz')): self._v_I = array(deque(map(cols.index, ('vx', 'vy', 'vz')))) else: self._v_I = None if 'type' in cols: self._type_I = cols.index('type') else: self._type_I = None data = array([list(map(float, self._fh.readline().split())) for _ in range(N)]) # data.shape == (N, Ncols) II = np.asarray(data[:, self._id_I], dtype=np.int_) # Unless dump is done for group 'all' ... II[np.argsort(II)] = arange(len(II)) self._x = zeros((N, 3)) if self._x_map is None: self._x[II] = data[:, self._x_I] else: self._x[II] = self._x_map(data[:, self._x_I]) if self._v_I is not None: self._v = zeros((N, 3)) self._v[II] = data[:, self._v_I] self._first_called = True def _get_next(self): # get next frame, update state of self step, N, cell, cols = self._read_frame_header() assert self._n_atoms == N assert self._cols == cols self._step = step self._cell = cell data = array([deque(map(float, self._fh.readline().split())) for _ in range(N)]) II = np.asarray(data[:, self._id_I], dtype=np.int_) - 1 if self._x_map is None: self._x[II] = data[:, self._x_I] else: self._x[II] = self._x_map(data[:, self._x_I]) if self._v_I is not None: self._v[II] = data[:, self._v_I] def __iter__(self): return self def close(self): if not self._fh.closed: self._fh.close() def __next__(self): if not self._open: raise StopIteration if self._first_called: self._get_next() else: self._get_first() if self._v_I is not None: frame = ReaderFrame(frame_index=next(self._frame_index), n_atoms=int(self._n_atoms), cell=self.x_factor * self._cell.copy('F'), positions=self.x_factor * self._x, velocities=self.v_factor * self._v ) else: frame = ReaderFrame(frame_index=next(self._frame_index), n_atoms=int(self._n_atoms), cell=self.x_factor * self._cell.copy('F'), positions=self.x_factor * self._x ) return frame mdanalysis_trajectory_reader.py000066400000000000000000000141301522712672400321450ustar00rootroot00000000000000dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/dynasor/trajectoryfrom itertools import count from typing import Optional from dynasor.trajectory.abstract_trajectory_reader import AbstractTrajectoryReader from dynasor.trajectory.trajectory_frame import ReaderFrame from dynasor.logging_tools import logger import MDAnalysis as mda import warnings class MDAnalysisTrajectoryReader(AbstractTrajectoryReader): """ Read a trajectory using the MDAnalysis Python library. Parameters ---------- filename Name of input file. trajectory_format Type of trajectory. See MDAnalysis for the available formats. length_unit Unit of length for the input trajectory (``'Angstrom'``, ``'nm'``, ``'pm'``, ``'fm'``). time_unit Unit of time for the input trajectory (``'fs'``, ``'ps'``, ``'ns'``). """ def __init__(self, filename: str, trajectory_format: str, length_unit: Optional[str] = None, time_unit: Optional[str] = None): self._open = True self._first_called = False with warnings.catch_warnings(): warnings.filterwarnings('ignore', category=UserWarning, message='Guessed all Masses to 1.0') warnings.filterwarnings('ignore', category=UserWarning, message='Reader has no dt information, set to 1.0 ps') u = mda.Universe(filename, format=trajectory_format, convert_units=False) self._frame_index = count(0) self._trajectory = u.trajectory # Set atomic_indices dict, if possible try: self._atom_types = u.atoms.types except mda.exceptions.NoDataError: self._atom_types = None trajectory_length_unit = self._trajectory.units['length'] trajectory_time_unit = self._trajectory.units['time'] if length_unit is not None and time_unit is not None: # User has specified units... if length_unit not in self.lengthunits_to_Angstrom_table \ or time_unit not in self.timeunits_to_fs_table: # ... but these are unknown raise ValueError('Unknown length and/or time unit. Please check the available ' 'options.') else: # ... and these are correct def convert_units(ts): length_scaling = mda.units.get_conversion_factor('length', length_unit, 'Angstrom') time_scaling = mda.units.get_conversion_factor('time', time_unit, 'fs') ts.positions *= length_scaling ts.triclinic_dimensions *= length_scaling if ts.has_velocities: ts.velocities *= length_scaling/time_scaling return ts self._trajectory.add_transformations(convert_units) elif trajectory_length_unit is not None and trajectory_time_unit is not None: # Read units logger.info(f'Length unit {trajectory_length_unit} and time unit {trajectory_time_unit}' ' inferred from trajectory.') def convert_units(ts): length_scaling = mda.units.get_conversion_factor('length', trajectory_length_unit, 'Angstrom') time_scaling = mda.units.get_conversion_factor('time', trajectory_time_unit, 'fs') ts.positions *= length_scaling ts.triclinic_dimensions *= length_scaling if ts.has_velocities: ts.velocities *= length_scaling/time_scaling return ts self._trajectory.add_transformations(convert_units) else: # No units from user and reader cannot read units logger.info('Assuming the trajectory has the default units (Ångström and fs), since no ' 'units were specified and units could not be inferred from the trajectory.') def _get_next(self): with warnings.catch_warnings(): warnings.filterwarnings('ignore', category=UserWarning, message='Reader has no dt information, set to 1.0 ps') if self._first_called: self._trajectory.next() else: self._first_called = True self._positions = self._trajectory.ts.positions self._cell = self._trajectory.ts.triclinic_dimensions self._n_atoms = self._trajectory.ts.n_atoms if self._trajectory.ts.has_velocities: self._velocities = self._trajectory.ts.velocities else: self._velocities = None def __iter__(self): """ Iterates through the trajectory file, frame by frame. """ return self def __next__(self): """ Gets next trajectory frame. """ if not self._open: raise StopIteration self._get_next() if self._velocities is not None: frame = ReaderFrame(frame_index=next(self._frame_index), cell=self._cell, n_atoms=self._n_atoms, positions=self._positions.copy(), velocities=self._velocities.copy(), atom_types=self._atom_types ) else: frame = ReaderFrame(frame_index=next(self._frame_index), cell=self._cell, n_atoms=self._n_atoms, positions=self._positions.copy(), atom_types=self._atom_types ) return frame def close(self): """ Closes down, release resources etc. """ if self._open: self._trajectory.close() self._open = False dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/dynasor/trajectory/trajectory.py000066400000000000000000000312531522712672400264630ustar00rootroot00000000000000__all__ = ['Trajectory', 'WindowIterator'] from collections import deque from itertools import islice, chain from os.path import isfile from typing import Callable, Optional, Union import numpy as np from numpy.typing import NDArray from dynasor.trajectory.atomic_indices import parse_gromacs_index_file from dynasor.trajectory.ase_trajectory_reader import ASETrajectoryReader from dynasor.trajectory.extxyz_trajectory_reader import ExtxyzTrajectoryReader from dynasor.trajectory.lammps_trajectory_reader import LammpsTrajectoryReader from dynasor.trajectory.mdanalysis_trajectory_reader import MDAnalysisTrajectoryReader from dynasor.trajectory.trajectory_frame import TrajectoryFrame from dynasor.logging_tools import logger class Trajectory: """Instances of this class hold trajectories in a format suitable for the computation of correlation functions. They behave as iterators, where each step returns the next frame as a :class:`TrajectoryFrame` object. The latter hold information regarding atomic positions, types, and velocities. Parameters ---------- filename Name of input file. trajectory_format Type of trajectory. Possible values are: ``'lammps_internal'``, ``'extxyz'``, ``'ase'`` or one of the formats supported by `MDAnalysis `_ (except for ``'lammpsdump'``, which can be called by specifying ``'lammps_mdanalysis'`` to avoid ambiguity) atomic_indices Specify which indices belong to which atom type. Can be (1) a dictionary where the keys specify the species and the values are a list of atomic indices, (2) ``'read_from_trajectory'``, in which case the species are read from the trajectory or (3) the path to a gromacs index file. length_unit Length unit of trajectory (``'Angstrom'``, ``'nm'``, ``'pm'``, ``'fm'``). Necessary for correct conversion to internal dynasor units if the trajectory file does not contain unit information. If no length unit is specified and the reader cannot read units from the trajectory, Angstrom is assumed. time_unit Time unit of trajectory (``'fs'``, ``'ps'``, ``'ns'``). Necessary for correct conversion to internal dynasor units if the trajectory file does not contain unit information. If no time unit is specified and the reader cannot read units from the trajectory, fs is assumed. frame_start First frame to read; must be larger or equal ``0``. frame_stop Last frame to read. By default (``None``) the entire trajectory is read. frame_step Read every :attr:`frame_step`-th step of the input trajectory. By default (``1``) every frame is read. Must be larger than ``0``. """ def __init__( self, filename: str, trajectory_format: str, atomic_indices: Optional[Union[str, dict[str, list[int]]]] = None, length_unit: Optional[str] = None, time_unit: Optional[str] = None, frame_start: Optional[int] = 0, frame_stop: Optional[int] = None, frame_step: Optional[int] = 1 ): if frame_start < 0: raise ValueError('frame_start should be positive') if frame_step < 0: raise ValueError('frame_step should be positive') self._frame_start = frame_start self._frame_step = frame_step self._frame_stop = frame_stop # setup trajectory reader if not isfile(filename): raise IOError(f'File {filename} does not exist') self._filename = filename if trajectory_format == 'lammps_internal': reader = LammpsTrajectoryReader elif trajectory_format == 'extxyz': reader = ExtxyzTrajectoryReader elif trajectory_format == 'lammps_mdanalysis': reader = MDAnalysisTrajectoryReader trajectory_format = 'lammpsdump' elif trajectory_format == 'ase': reader = ASETrajectoryReader elif trajectory_format == 'lammps': raise IOError('Ambiguous trajectory format, ' 'did you mean lammps_internal or lammps_mdanalysis?') else: reader = MDAnalysisTrajectoryReader logger.debug(f'Using trajectory reader: {reader.__name__}') if reader == MDAnalysisTrajectoryReader: self._reader_obj = reader(self._filename, trajectory_format, length_unit=length_unit, time_unit=time_unit) else: self._reader_obj = reader(self._filename, length_unit=length_unit, time_unit=time_unit) # Get two frames to set cell etc. frame0 = next(self._reader_obj) frame1 = next(self._reader_obj) self._cell = frame0.cell self._n_atoms = frame0.n_atoms self._has_velocities = frame0.velocities is not None # Make sure cell is not changed during consecutive frames if not np.allclose(frame0.cell, frame1.cell): raise ValueError('The cell changes between the first and second frame. ' 'The concept of q-points becomes muddy if the simulation cell is ' 'changing, such as during NPT MD simulations, so trajectories where ' 'the cell changes are not supported by dynasor.') # setup iterator slice (reuse frame0 and frame1 via chain) self.number_of_frames_read = 0 self.current_frame_index = 0 self._reader_iter = islice(chain([frame0, frame1], self._reader_obj), self._frame_start, self._frame_stop, self._frame_step) # setup atomic indices if atomic_indices is None: # Default behaviour atomic_indices = {'X': np.arange(0, self.n_atoms)} elif isinstance(atomic_indices, str): # Str input if atomic_indices == 'read_from_trajectory': if frame0.atom_types is None: raise ValueError('Could not read atomic indices from the trajectory.') else: uniques = np.unique(frame0.atom_types) atomic_indices = {str(uniques[i]): (frame0.atom_types == uniques[i]).nonzero()[0] for i in range(len(uniques))} else: atomic_indices = parse_gromacs_index_file(atomic_indices) elif isinstance(atomic_indices, dict): # dict input pass else: raise ValueError('Could not understand atomic_indices.') self._atomic_indices = atomic_indices # sanity checks for atomic_indices for key, indices in self._atomic_indices.items(): if np.max(indices) >= self.n_atoms: raise ValueError('Maximum index in atomic_indices exceeds number of atoms.') if np.min(indices) < 0: raise ValueError('Minimum index in atomic_indices is negative.') if '_' in key: # Since '_' is what we use to distinguish atom types in the results, e.g. Sqw_Cs_Pb raise ValueError('The char "_" is not allowed in atomic_indices.') # log info on trajectory and atom types etc logger.info(f'Trajectory file: {self.filename}') logger.info(f'Total number of particles: {self.n_atoms}') logger.info(f'Number of atom types: {len(self.atom_types)}') for atom_type, indices in self._atomic_indices.items(): logger.info(f'Number of atoms of type {atom_type}: {len(indices)}') logger.info(f'Simulation cell (in Angstrom):\n{str(self._cell)}') def __iter__(self): return self def __next__(self): frame = next(self._reader_iter) logger.debug(f'Read frame #{frame.frame_index}') new_frame = TrajectoryFrame( self.atomic_indices, frame.frame_index, frame.positions, frame.velocities) self.number_of_frames_read += 1 self.current_frame_index = frame.frame_index return new_frame def __str__(self) -> str: s = ['Trajectory'] s += ['{:12} : {}'.format('filename', self.filename)] s += ['{:12} : {}'.format('natoms', self.n_atoms)] s += ['{:12} : {}'.format('frame_start', self._frame_start)] s += ['{:12} : {}'.format('frame_stop', self._frame_stop)] s += ['{:12} : {}'.format('frame_step', self.frame_step)] s += ['{:12} : {}'.format('frame_index', self.current_frame_index)] s += ['{:12} : [{}\n {}\n {}]' .format('cell', self.cell[0], self.cell[1], self.cell[2])] return '\n'.join(s) def __repr__(self) -> str: return str(self) def _repr_html_(self) -> str: s = [f'

{self.__class__.__name__}

'] s += [''] s += [''] s += [''] s += [f''] s += [f''] s += [f''] s += [f''] s += [f''] s += [''] s += ['
FieldValue
File name{self.filename}
Number of atoms{self.n_atoms}
Cell metric{self.cell}
Frame step{self.frame_step}
Atom types{self.atom_types}
'] return '\n'.join(s) @property def cell(self) -> NDArray[float]: """ Simulation cell """ return self._cell @property def n_atoms(self) -> int: """ Number of atoms """ return self._n_atoms @property def has_velocities(self) -> bool: """ Whether the trajectory provides velocities """ return self._has_velocities @property def filename(self) -> str: """ The trajectory filename """ return self._filename @property def atomic_indices(self) -> dict[str, list[int]]: """ Return copy of index arrays """ atomic_indices = dict() for name, inds in self._atomic_indices.items(): atomic_indices[name] = inds.copy() return atomic_indices @property def atom_types(self) -> list[str]: return sorted(self._atomic_indices.keys()) @property def frame_step(self) -> int: """ Frame to access, trajectory will return every :attr:`frame_step`-th snapshot. """ return self._frame_step def consume(iterator, n): """ Advance the iterator by :attr:`n` steps. If :attr:`n` is ``None``, consume entirely. """ # From python.org if n is None: deque(iterator, maxlen=0) else: next(islice(iterator, n, n), None) class WindowIterator: """Sliding window iterator. Returns consecutive windows (a window is represented as a list of objects), created from an input iterator. Parameters ---------- itraj Trajectory object. width Length of window (``window_size`` + 1). window_step Distance between the start of two consecutive window frames. element_processor Optional function applied to each frame before it is stored in the window. Useful for pre-computing per-frame quantities (e.g., reciprocal-space densities) so that the work is not repeated for frames shared between consecutive windows. """ def __init__(self, itraj: Trajectory, width: int, window_step: Optional[int] = 1, element_processor: Optional[Callable] = None): self._raw_it = itraj if element_processor: self._it = map(element_processor, self._raw_it) else: self._it = self._raw_it assert window_step >= 1 assert width >= 1 self.width = width self.window_step = window_step self._window = None def __iter__(self): return self def __next__(self): """ Returns next element in sequence. """ if self._window is None: self._window = deque(islice(self._it, self.width), self.width) else: if self.window_step >= self.width: self._window.clear() consume(self._raw_it, self.window_step - self.width) else: for _ in range(min((self.window_step, len(self._window)))): self._window.popleft() for f in islice(self._it, min((self.window_step, self.width))): self._window.append(f) if len(self._window) == 0: raise StopIteration return list(self._window) dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/dynasor/trajectory/trajectory_frame.py000066400000000000000000000131551522712672400276360ustar00rootroot00000000000000from dataclasses import dataclass from typing import Optional import numpy as np from numpy.typing import NDArray @dataclass class ReaderFrame: """Trivial data struct holding MD-data for one time frame. Parameters ---------- frame_index Trajectory index of the snapshot (frame). cell Simulation cell as 3 row vectors (Å). n_atoms Number of atoms. positions Particle positions as 3xn_atoms array (Å). velocities Particle velocities as 3xn_atoms array (Å/fs); may not be available, depending on reader and trajectory file format. atom_types Array with the type of each atom; may not be available, depending on reader and trajectory file format. """ frame_index: int cell: NDArray[float] n_atoms: int positions: NDArray[float] velocities: Optional[NDArray[float]] = None atom_types: Optional[NDArray[str]] = None class TrajectoryFrame: """ Class holding positions and optionally velocities split by atom type for one snapshot (frame) in a trajectory. Attributes ---------- * `positions_by_type` * `velocities_by_type` such that, e.g., `positions_by_type['Cs']` is a numpy array with shape `(n_atoms_Cs, 3)` and `positions_by_type['Pb']` is a numpy array with shape `(n_atoms_Pb, 3)` Parameters ---------- atomic_indices Dictionary specifying which indices (values) belong to which atom type (keys). frame_index Trajectory index of the snapshot (frame). positions Positions as an array with shape `(n_atoms, 3)`. velocities Velocities as an array with shape `(n_atoms, 3)`; defaults to `None`. """ def __init__(self, atomic_indices: dict[str, list[int]], frame_index: int, positions: NDArray[float], velocities: Optional[NDArray[float]] = None): self._frame_index = frame_index self.positions_by_type = dict() for atom_type, indices in atomic_indices.items(): self.positions_by_type[atom_type] = positions[indices, :].copy() if velocities is not None: self.velocities_by_type = dict() for atom_type, indices in atomic_indices.items(): self.velocities_by_type[atom_type] = velocities[indices, :].copy() else: self.velocities_by_type = None def get_positions_as_array(self, atomic_indices: dict[str, list[int]]) -> NDArray[float]: """ Return the full positions array with shape ``(n_atoms, 3)``. Parameters --------- atomic_indices Dictionary specifying which indices (values) belong to which atom type (keys). """ # check that atomic_indices is complete n_atoms = np.max([np.max(indices) for indices in atomic_indices.values()]) + 1 all_inds = [i for indices in atomic_indices.values() for i in indices] if len(all_inds) != n_atoms or len(set(all_inds)) != n_atoms: raise ValueError('atomic_indices is incomplete') # collect positions into a single array x = np.empty((n_atoms, 3)) for atom_type, indices in atomic_indices.items(): x[indices, :] = self.positions_by_type[atom_type] return x def get_velocities_as_array(self, atomic_indices: dict[str, list[int]]) -> NDArray[float]: """ Return the full velocities array with shape ``(n_atoms, 3)``. Parameters --------- atomic_indices Dictionary specifying which indices (values) belong to which atom type (keys). """ # check that atomic_indices is complete n_atoms = np.max([np.max(indices) for indices in atomic_indices.values()]) + 1 all_inds = [i for indices in atomic_indices.values() for i in indices] if len(all_inds) != n_atoms or len(set(all_inds)) != n_atoms: raise ValueError('atomic_indices is incomplete') # collect velocities into a single array v = np.empty((n_atoms, 3)) for atom_type, indices in atomic_indices.items(): v[indices, :] = self.velocities_by_type[atom_type] return v @property def frame_index(self) -> int: """ Index of the frame. """ return self._frame_index def __str__(self) -> str: s = [f'Frame index {self.frame_index}'] for key, val in self.positions_by_type.items(): s.append(f' positions : {key} shape : {val.shape}') if self.velocities_by_type is not None: for key, val in self.velocities_by_type.items(): s.append(f' velocities : {key} shape : {val.shape}') return '\n'.join(s) def __repr__(self) -> str: return str(self) def _repr_html_(self) -> str: s = [f'

{self.__class__.__name__}

'] s += [''] s += ['' ''] s += [''] s += [f''] for key, val in self.positions_by_type.items(): s += [f'' f''] if self.velocities_by_type is not None: for key, val in self.velocities_by_type.items(): s += [f'' f''] s += [''] s += ['
FieldValue/Shape
Index{self.frame_index}
Positions {key}{val.shape}
Velocities {key}{val.shape}
'] return '\n'.join(s) dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/dynasor/units.py000066400000000000000000000031431522712672400232460ustar00rootroot00000000000000"""This module contains convenient unit conversion factors. Here, `radians_per_fs_to_invcm`, for example, can be used to convert an angular frequency in units of radians/fs to a wavenumber in 1/cm, as demonstrated by the code snippet below:: >>> # converting angular frequencies (omega) to wavenumbers in inverse cm >>> import numpy as np >>> omegas = np.linspace(0, 15, 100) >>> frequencies_invcm = omegas * radians_per_fs_to_invcm >>> >>> # converting from inverse cm to meV >>> frequencies_meV = frequencies_invcm / meV_to_invcm In practice you can apply this conversion directly to, e.g., the frequencies of a :class:`DynamicSample ` object. """ from math import pi from ase.units import _c, invcm, fs # Frequencies meV_to_invcm = 1 / invcm / 1e3 r"""Conversion factor from meV (energy) to cm\ :math:`^{-1}` (wave numbers).""" THz_to_invcm = 1e12 / _c / 1e2 r"""Conversion factor from THz (frequency) to cm\ :math:`^{-1}` (wave numbers).""" THz_to_meV = 1e13 * invcm / _c """Conversion factor from THz (frequency) to meV (energy).""" # Angular frequencies radians_per_fs_to_THz = 1000 / (2 * pi) """Conversion factor from rad/fs (radians per femtosecond) to THz (frequency).""" radians_per_fs_to_meV = radians_per_fs_to_THz * THz_to_meV """Conversion factor from rad/fs (radians per femtosecond) to meV (energy).""" radians_per_fs_to_invcm = radians_per_fs_to_THz * THz_to_invcm r"""Conversion factor from rad/fs (radians per femtosecond) to cm\ :math:`^{-1}` (wave numbers).""" # Mass Dalton_to_dmu = 1 / fs**2 """Conversion factor from Daltons (SI mass unit) to the internal dynasor mass units.""" dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/examples/000077500000000000000000000000001522712672400216705ustar00rootroot00000000000000dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/examples/CsPbI3_static_structure_factor/000077500000000000000000000000001522712672400277405ustar00rootroot00000000000000.gitignore000066400000000000000000000000271522712672400316500ustar00rootroot00000000000000dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/examples/CsPbI3_static_structure_factor*.xyz md_runs nep*.txt dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/examples/CsPbI3_static_structure_factor/figs/000077500000000000000000000000001522712672400306705ustar00rootroot00000000000000Sq_phase_transition.png000066400000000000000000003024471522712672400353460ustar00rootroot00000000000000dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/examples/CsPbI3_static_structure_factor/figsPNG  IHDR!?`k_IDATx^TSgp& K"8кTP-Uuժn{P@{ -&pox;=VnBHn.AAA   AAA>   A@AAϠc   g1AA3  t AAA :   |AAA>   A@AAϠc   g1AA3  t AAA :   |AAA>   A@AAϠc   g1AA3  t AAA :   |AAA>   A@AAϠc   g1AA3  \1RSS?{cǶk׮f͚UVmԨQ~lٳ߿ӷ2iii?tҒ%KTRF rSL9|||>ѣd#k!(m۶7n܂ vcSw  nHaׯ<666i@jFǏ[n-_e"EڷoO&O>k׮d#e#߼yOsaro +رc{nݺɓ'޽}ٳgߓUP&@:3qÇߺuիWݒR|rdʔ)CAA5IIIk֬ !%aΝܹ3Dzd!CL2Lto~5袣tFf9CC%JDDDзGɽܽ{ӧOԗ~ĉM[Z;vZ Yɢn޼IJ-'OH$~Qjj*  cȸE>|@3aÆ/AFD2_DGq㜝ǏMJ{;F!m544ѱPB/&͕^Bٳf͚:}ʕlAAAJv:FIJJ(WAϞ=߿OCy~JivXIڵ۷Ҡc(ׯPX˗^Bl۶RJݻw~:? 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This means that, e.g., the degenerate peaks $[1, 0, 0], [0, 1, 0], [0, 0, 1]$ in the cubic phase split into two peaks.\n", "Furthermore, since the tetragonal phase arises from a soft phonon mode instability at the M-point ($[0.5, 0.5, 0]$), we expect additional peaks at e.g. $q=1.58$ ($[1.5, 0.5, 0]$) and $q=1.87$ ($[1.5, 0.5, 1]$).\n", "\n", "\n", "To demonstrate this point we also mark these expected q-points in the figure below with dashed gray lines. Note that in order to get perfect agreement with the calculated $S(q)$ of the real structure one would need to take into account the exact lattice parameters." ] }, { "cell_type": "code", "execution_count": 2, "id": "7d75b7e4", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "INFO 2025-08-25 11:46:51: Assuming the trajectory has the default length unit (Ångström), since no unit was specified.\n", "INFO 2025-08-25 11:46:51: Assuming the trajectory has the default time unit (fs), since no unit was specified.\n", "INFO 2025-08-25 11:46:51: Trajectory file: tmp_traj.xyz\n", "INFO 2025-08-25 11:46:51: Total number of particles: 10\n", "INFO 2025-08-25 11:46:51: Number of atom types: 3\n", "INFO 2025-08-25 11:46:51: Number of atoms of type Cs: 2\n", "INFO 2025-08-25 11:46:51: Number of atoms of type I: 6\n", "INFO 2025-08-25 11:46:51: Number of atoms of type Pb: 2\n", "INFO 2025-08-25 11:46:51: Simulation cell (in Angstrom):\n", "[[ 8.62193057e+00 -1.96501346e-14 -1.00257367e-15]\n", " [-9.98490387e-14 8.62193057e+00 -5.90070099e-16]\n", " [ 8.03089883e-16 4.00888754e-16 6.37484774e+00]]\n", "INFO 2025-08-25 11:46:51: Number of q-points: 121\n", "INFO 2025-08-25 11:46:52: Assuming the trajectory has the default length unit (Ångström), since no unit was specified.\n", "INFO 2025-08-25 11:46:52: Assuming the trajectory has the default time unit (fs), since no unit was specified.\n", "INFO 2025-08-25 11:46:52: Trajectory file: tmp_traj.xyz\n", "INFO 2025-08-25 11:46:52: Total number of particles: 5\n", "INFO 2025-08-25 11:46:52: Number of atom types: 3\n", "INFO 2025-08-25 11:46:52: Number of atoms of type Cs: 1\n", "INFO 2025-08-25 11:46:52: Number of atoms of type I: 3\n", "INFO 2025-08-25 11:46:52: Number of atoms of type Pb: 1\n", "INFO 2025-08-25 11:46:52: Simulation cell (in Angstrom):\n", "[[ 6.25796103e+00 -2.33844250e-17 -2.53411308e-17]\n", " [-1.82080433e-17 6.25796103e+00 6.92635779e-16]\n", " [-8.47103199e-18 9.09518288e-17 6.25796103e+00]]\n", "INFO 2025-08-25 11:46:52: Number of q-points: 81\n" ] } ], "source": [ "structure_tags = ['CsPbI3-CX-tetragonal-P4mbm', 'CsPbI3-CX-cubic-Pm-3m']\n", "data_dict = dict()\n", "for structure_tag in structure_tags:\n", "\n", " # write dummy traj\n", " atoms = read(f'{structure_tag}.xyz')\n", " atoms.calc = None\n", " n_atoms = len(atoms)\n", " traj = [atoms for _ in range(2)]\n", " write('tmp_traj.xyz', traj)\n", "\n", " # setup traj\n", " traj = Trajectory('tmp_traj.xyz', trajectory_format='extxyz', atomic_indices='read_from_trajectory')\n", "\n", " # q-points\n", " q_max = 2.5\n", " q_points = get_spherical_qpoints(traj.cell, q_max=q_max)\n", "\n", " # compute Sq\n", " sample = compute_static_structure_factors(traj, q_points)\n", " q_norms = np.linalg.norm(sample.q_points, axis=1)\n", " data_dict[structure_tag] = q_norms, sample.Sq / n_atoms" ] }, { "cell_type": "code", "execution_count": 3, "id": "494daa9b", "metadata": {}, "outputs": [ { "data": { 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# plot setup\n", "fig = plt.figure(figsize=(6.0, 2.5), dpi=140)\n", "ax1 = fig.add_subplot()\n", "\n", "colors = dict()\n", "colors['CsPbI3-CX-tetragonal-P4mbm'] = 'tab:red'\n", "colors['CsPbI3-CX-cubic-Pm-3m'] = 'tab:blue'\n", "\n", "labels = dict()\n", "labels['CsPbI3-CX-tetragonal-P4mbm'] = r'CsPbI$_3$ tetragonal $P4mbm$'\n", "labels['CsPbI3-CX-cubic-Pm-3m'] = r'CsPbI$_3$ cubic $Pm\\bar{3}m$'\n", "\n", "xlim = [0.9, 2.2]\n", "ylim = [0.0005, 4]\n", "alpha = 0.8\n", "\n", "\n", "# plot S(q) as vertical lines\n", "for structure_tag, (q_norms, Sq) in data_dict.items():\n", " for q, S in zip(q_norms, Sq): \n", " ax1.plot([q, q], [0, S[0]], color=colors[structure_tag], alpha=alpha)\n", " ax1.plot(np.nan, np.nan, color=colors[structure_tag], alpha=alpha, label=labels[structure_tag])\n", "\n", "\n", "# Mark the cubic gamma-points\n", "alat = 6.26\n", "alpha = 0.5\n", "fs = 7\n", "bbox = dict(facecolor='w', edgecolor='none', alpha=0.85, pad=0.0)\n", "\n", "q_vecs = [[1, 0, 0], [1, 1, 0], [1, 1, 1], [2, 0, 0]]\n", "for qvec in q_vecs:\n", " q = 2 * np.pi / alat * np.linalg.norm(qvec)\n", " ax1.axvline(x=q, ls='--', color='k', alpha=alpha)\n", " ax1.text(q -0.05, 0.5 * ylim[-1], str(qvec), fontsize=fs, bbox=bbox)\n", "\n", "# Mark the cubic M-points\n", "qvec_M1 = [1.5, 0.5, 0]\n", "qvec_M2 = [1.5, 0.5, 1.0]\n", "q_M1 = 2 * np.pi / alat * np.linalg.norm(qvec_M1)\n", "q_M2 = 2 * np.pi / alat * np.linalg.norm(qvec_M2)\n", "ax1.axvline(x=q_M1, ls='--', color='k', alpha=alpha-0.2)\n", "ax1.axvline(x=q_M2, ls='--', color='k', alpha=alpha-0.2)\n", "ax1.text(q_M1-0.08, 0.17 * ylim[-1], str(qvec_M1), fontsize=fs, bbox=bbox)\n", "ax1.text(q_M2-0.1, 0.17 * ylim[-1], str(qvec_M2), fontsize=fs, bbox=bbox)\n", "\n", "\n", "ax1.set_xlim(xlim)\n", "ax1.set_ylim(ylim)\n", "ax1.set_yscale('log')\n", "ax1.legend(loc=1, fontsize=8, bbox_to_anchor=(0.4, 0.84))\n", "ax1.set_xlabel('q (rad/Å)')\n", "ax1.set_ylabel('S(q)')\n", "\n", "fig.tight_layout()" ] }, { "cell_type": "markdown", "id": "86de3289", "metadata": {}, "source": [ "## $S(\\boldsymbol{q})$ from MD simulations\n", "When running MD simulations of larger supercells, the number of available q-points becomes large.\n", "Here, we can either sample selected q-points in specific regions of the BZ, see e.g. [this paper](https://doi.org/10.1021/acs.chemmater.3c02548).\n", "\n", "Alternatively, we can sample all of the available q-points and e.g. conduct a spherical q-point average in order to mimic experimental powder diffraction data." ] }, { "cell_type": "markdown", "id": "4c5e47b7", "metadata": {}, "source": [ "### Spherical q-point average\n", "Spherical q-point averaging can be done in dynasor with the `get_spherically_averaged_sample_...` functions using either a binning method or Gaussian smearing method, here we use the latter meaning\n", "\\begin{equation*}\n", "S(q) = \\sum_i w(\\boldsymbol{q}_i, q) S(\\boldsymbol{q}_i)\n", "\\end{equation*}\n", "where $w(\\boldsymbol{q}_i, q)$ is a Gaussian weight, i.e.\n", "\\begin{equation*}\n", "w(\\boldsymbol{q}_i, q) \\propto \\exp{\\left [ -\\frac{1}{2} \\left ( \\frac{|\\boldsymbol{q}_i| - q}{q_\\text{width}} \\right)^2 \\right ]}\n", "\\end{equation*}\n", "\n", "Below we compute and plot the raw $S(q)$ and the Gaussian spherical averaged $S(q)$ for one selected MD simulation to demonstrate it.\n", "One can play around with the width (standard deviation) of the Gaussian, `q_width`, in order to obtain a desirable looking structure factor." ] }, { "cell_type": "code", "execution_count": 4, "id": "ab440081", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "INFO 2025-08-25 11:46:52: Length unit Angstrom and time unit ps inferred from trajectory.\n", "INFO 2025-08-25 11:46:52: Trajectory file: md_runs/NVT_tetra_size8_T450_nframes1000/movie.nc\n", "INFO 2025-08-25 11:46:52: Total number of particles: 5120\n", "INFO 2025-08-25 11:46:52: Number of atom types: 1\n", "INFO 2025-08-25 11:46:52: Number of atoms of type X: 5120\n", "INFO 2025-08-25 11:46:52: Simulation cell (in Angstrom):\n", "[[70.64849 0. 0. ]\n", " [ 0. 70.64849 0. ]\n", " [ 0. 0. 50.730995]]\n", "INFO 2025-08-25 11:46:52: Number of q-points: 36893\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 1h 11min 47s, sys: 1.68 s, total: 1h 11min 49s\n", "Wall time: 9min\n" ] } ], "source": [ "%%time\n", "\n", "# parameters\n", "q_max = 2.05\n", "q_linspace = np.linspace(0, q_max, 1000)\n", "q_width = 0.1\n", "\n", "# setup traj\n", "tag = f'tetra_size8_T450_nframes1000'\n", "dirname = f'md_runs/NVT_{tag}/'\n", "traj = Trajectory(dirname + 'movie.nc', trajectory_format='nc')\n", "\n", "# generate all q-points in supercell\n", "q_points = get_spherical_qpoints(traj.cell, q_max=q_max)\n", "\n", "# sort q-points by length\n", "argsort = np.argsort(np.linalg.norm(q_points, axis=1))\n", "q_points = q_points[argsort]\n", "\n", "# compute Sq\n", "sample = compute_static_structure_factors(traj, q_points)" ] }, { "cell_type": "code", "execution_count": 5, "id": "e1279e24", "metadata": {}, "outputs": [], "source": [ "# spherical average over q-points\n", "data_dict_qwidths = dict()\n", "for q_width in [0.001, 0.005, 0.02]:\n", " sample_averaged = get_spherically_averaged_sample_smearing(\n", " sample, q_norms=q_linspace, q_width=q_width)\n", " data_dict_qwidths[q_width] = sample_averaged.q_norms, sample_averaged.Sq" ] }, { "cell_type": "markdown", "id": "14e4c67c", "metadata": {}, "source": [ "It can be useful to visualize the static structure factor both on a linear and a log y-scale." ] }, { "cell_type": "code", "execution_count": 6, "id": "1f82d348", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(6.0, 4.8), dpi=150)\n", "ax1 = fig.add_subplot(211)\n", "ax2 = fig.add_subplot(212)\n", "\n", "for ax, log_yscale in zip([ax1, ax2], [True, False]):\n", "\n", " # plotting of raw S(q)\n", " q_norms = np.linalg.norm(sample.q_points, axis=1)\n", " ax.plot(q_norms, sample.Sq, '-k', label='raw', alpha=0.25)\n", "\n", " # plotting of gaussian spherical average S(q)\n", " for q_width, (q, Sq) in data_dict_qwidths.items():\n", " ax.plot(q, Sq, '-', label=rf'Spherical average $q_{{width}}$={q_width}', alpha=0.75)\n", "\n", " ax.set_xlabel('q (rad/Å)')\n", " ax.set_ylabel('S(q)')\n", " ax.set_xlim(xlim)\n", " ax.legend(loc=2, fontsize=7)\n", " if log_yscale:\n", " ax.set_yscale('log')\n", " ax.set_ylim(0.01, 4000)\n", " else:\n", " ax.set_ylim(0.0, 50)\n", "\n", "fig.tight_layout()" ] }, { "cell_type": "markdown", "id": "b81a5bae", "metadata": {}, "source": [ "Note, that the position of the peaks in the static structure factor, $S(q)$, obtained from MD agrees perfectly with the peaks obtained from the ideal tetragonal structure." ] }, { "cell_type": "markdown", "id": "8de1a8cf", "metadata": {}, "source": [ "### Convergence with respect to trajectory length\n", "First, we analyze the static structure factor for a 8x8x8 tetragonal cell as a function of the simulation length (number of snapshots in the trajectory).\n", "\n", "We analyze parts of the trajectory using 10, 100, and 1000 snapshots.\n", "The resulting S(q) looks quite converged already for 10-100 snapshots." ] }, { "cell_type": "code", "execution_count": 7, "id": "286e78c5", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Running nframes 10\n", "INFO 2025-08-25 11:56:00: Length unit Angstrom and time unit ps inferred from trajectory.\n", "INFO 2025-08-25 11:56:00: Trajectory file: md_runs/NVT_tetra_size8_T450_nframes1000/movie.nc\n", "INFO 2025-08-25 11:56:00: Total number of particles: 5120\n", "INFO 2025-08-25 11:56:00: Number of atom types: 1\n", "INFO 2025-08-25 11:56:00: Number of atoms of type X: 5120\n", "INFO 2025-08-25 11:56:00: Simulation cell (in Angstrom):\n", "[[70.64849 0. 0. ]\n", " [ 0. 70.64849 0. ]\n", " [ 0. 0. 50.730995]]\n", "INFO 2025-08-25 11:56:00: Number of q-points: 36893\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Running nframes 100\n", "INFO 2025-08-25 11:56:08: Length unit Angstrom and time unit ps inferred from trajectory.\n", "INFO 2025-08-25 11:56:08: Trajectory file: md_runs/NVT_tetra_size8_T450_nframes1000/movie.nc\n", "INFO 2025-08-25 11:56:08: Total number of particles: 5120\n", "INFO 2025-08-25 11:56:08: Number of atom types: 1\n", "INFO 2025-08-25 11:56:08: Number of atoms of type X: 5120\n", "INFO 2025-08-25 11:56:08: Simulation cell (in Angstrom):\n", "[[70.64849 0. 0. ]\n", " [ 0. 70.64849 0. ]\n", " [ 0. 0. 50.730995]]\n", "INFO 2025-08-25 11:56:08: Number of q-points: 36893\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Running nframes 1000\n", "INFO 2025-08-25 11:57:04: Length unit Angstrom and time unit ps inferred from trajectory.\n", "INFO 2025-08-25 11:57:04: Trajectory file: md_runs/NVT_tetra_size8_T450_nframes1000/movie.nc\n", "INFO 2025-08-25 11:57:04: Total number of particles: 5120\n", "INFO 2025-08-25 11:57:04: Number of atom types: 1\n", "INFO 2025-08-25 11:57:04: Number of atoms of type X: 5120\n", "INFO 2025-08-25 11:57:04: Simulation cell (in Angstrom):\n", "[[70.64849 0. 0. ]\n", " [ 0. 70.64849 0. ]\n", " [ 0. 0. 50.730995]]\n", "INFO 2025-08-25 11:57:04: Number of q-points: 36893\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 1h 19min 24s, sys: 2.02 s, total: 1h 19min 26s\n", "Wall time: 10min 3s\n" ] } ], "source": [ "%%time\n", "\n", "q_width = 0.001\n", "data_dict_nframes = dict()\n", "for n_frames in [10, 100, 1000]:\n", " print('Running nframes', n_frames)\n", "\n", " # setup traj\n", " tag = f'tetra_size8_T450_nframes1000'\n", " dirname = f'md_runs/NVT_{tag}/'\n", " traj = Trajectory(dirname + 'movie.nc', trajectory_format='nc', frame_stop=n_frames)\n", "\n", " # generate all q-points in supercell\n", " q_points = get_spherical_qpoints(traj.cell, q_max=q_max)\n", "\n", " # compute Sq\n", " sample = compute_static_structure_factors(traj, q_points)\n", " sample_averaged = get_spherically_averaged_sample_smearing(sample, q_norms=q_linspace, q_width=q_width)\n", " data_dict_nframes[n_frames] = sample_averaged.q_norms, sample_averaged.Sq" ] }, { "cell_type": "code", "execution_count": 8, "id": "e7f92e46", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# plot setup\n", "fig = plt.figure(figsize=(6.0, 2.5), dpi=150)\n", "ax1 = fig.add_subplot(111)\n", "ylim = [2e-2, 5000]\n", "alpha = 0.5\n", "\n", "# plotting\n", "linestyles = dict()\n", "linestyles[10] = '-'\n", "linestyles[100] = '--'\n", "linestyles[1000] = ':'\n", "for n_frames, (q, Sq) in data_dict_nframes.items():\n", " ax1.plot(q, Sq, ls=linestyles[n_frames], label=f'Number of snapshots {n_frames}', alpha=alpha)\n", "ax1.set_xlabel('q (rad/Å)')\n", "ax1.set_ylabel('S(q)')\n", "\n", "ax1.set_xlim(xlim)\n", "ax1.set_ylim(0, 5)\n", "#ax1.set_yscale('log')\n", "ax1.legend(loc=2, fontsize=7) \n", "\n", "fig.tight_layout()" ] }, { "cell_type": "markdown", "id": "7ad5e7e7", "metadata": {}, "source": [ "### Convergence with respect to system size\n", "Next, we look at how S(q) converges with respect to system size in the MD simulation. For larger supercells the number of q-points becomes larger, meaning that the calculations will be slower. Running the last calculation, `size=12`, can take quite some time and running the full cell below can take a few minutes on a common workstation. To speed this up, we only use 100 frames from the trajectory (by setting `frame_stop=100`) and limit the number of q-points to 50,000 (by setting `max_points=50000`)" ] }, { "cell_type": "code", "execution_count": 9, "id": "c00928b5", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Running size 4\n", "INFO 2025-08-25 12:06:04: Length unit Angstrom and time unit ps inferred from trajectory.\n", "INFO 2025-08-25 12:06:04: Trajectory file: md_runs/NVT_tetra_size4_T450_nframes1000/movie.nc\n", "INFO 2025-08-25 12:06:04: Total number of particles: 640\n", "INFO 2025-08-25 12:06:04: Number of atom types: 1\n", "INFO 2025-08-25 12:06:04: Number of atoms of type X: 640\n", "INFO 2025-08-25 12:06:04: Simulation cell (in Angstrom):\n", "[[35.324245 0. 0. ]\n", " [ 0. 35.324245 0. ]\n", " [ 0. 0. 25.365498]]\n", "INFO 2025-08-25 12:06:04: Number of q-points: 4629\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Running size 8\n", "INFO 2025-08-25 12:06:06: Length unit Angstrom and time unit ps inferred from trajectory.\n", "INFO 2025-08-25 12:06:06: Trajectory file: md_runs/NVT_tetra_size8_T450_nframes1000/movie.nc\n", "INFO 2025-08-25 12:06:06: Total number of particles: 5120\n", "INFO 2025-08-25 12:06:06: Number of atom types: 1\n", "INFO 2025-08-25 12:06:06: Number of atoms of type X: 5120\n", "INFO 2025-08-25 12:06:06: Simulation cell (in Angstrom):\n", "[[70.64849 0. 0. ]\n", " [ 0. 70.64849 0. ]\n", " [ 0. 0. 50.730995]]\n", "INFO 2025-08-25 12:06:06: Number of q-points: 36893\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Running size 12\n", "INFO 2025-08-25 12:07:02: Length unit Angstrom and time unit ps inferred from trajectory.\n", "INFO 2025-08-25 12:07:02: Trajectory file: md_runs/NVT_tetra_size12_T450_nframes1000/movie.nc\n", "INFO 2025-08-25 12:07:02: Total number of particles: 17280\n", "INFO 2025-08-25 12:07:02: Number of atom types: 1\n", "INFO 2025-08-25 12:07:02: Number of atoms of type X: 17280\n", "INFO 2025-08-25 12:07:02: Simulation cell (in Angstrom):\n", "[[105.97274 0. 0. ]\n", " [ 0. 105.97274 0. ]\n", " [ 0. 0. 76.09649]]\n", "INFO 2025-08-25 12:07:02: Pruning q-points from the range 0.891 < |q| < 2.05\n", "INFO 2025-08-25 12:07:02: Pruned from 124377 q-points to 50130\n", "INFO 2025-08-25 12:07:02: Number of q-points: 50130\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 39min 30s, sys: 1.68 s, total: 39min 32s\n", "Wall time: 5min 6s\n" ] } ], "source": [ "%%time\n", "\n", "q_width = 0.001\n", "size_vals = [4, 8, 12]\n", "data_dict_sizes = dict()\n", "for size in size_vals:\n", " print('Running size', size)\n", "\n", " # setup traj\n", " tag = f'tetra_size{size}_T450_nframes1000'\n", " dirname = f'md_runs/NVT_{tag}/'\n", " traj = Trajectory(dirname + 'movie.nc', trajectory_format='nc', frame_stop=100)\n", "\n", " # generate all q-points in supercell\n", " q_points = get_spherical_qpoints(traj.cell, q_max=q_max, max_points=50000)\n", "\n", " # compute Sq\n", " sample = compute_static_structure_factors(traj, q_points)\n", " sample_averaged = get_spherically_averaged_sample_smearing(sample, q_norms=q_linspace, q_width=q_width)\n", " data_dict_sizes[size] = sample_averaged.q_norms, sample_averaged.Sq" ] }, { "cell_type": "code", "execution_count": 10, "id": "0d8b07c0", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# plot setup\n", "fig = plt.figure(figsize=(6.0, 2.5), dpi=150)\n", "ax1 = fig.add_subplot(111)\n", "ylim = [2e-2, 5000]\n", "alpha = 0.5\n", "\n", "# plotting\n", "linestyles = dict()\n", "linestyles[4] = '-'\n", "linestyles[8] = '--'\n", "linestyles[12] = ':'\n", "for size, (q, Sq) in data_dict_sizes.items():\n", " ax1.plot(q, Sq, ls=linestyles[size], label=f'System size {size}x{size}x{size}', alpha=alpha)\n", "ax1.set_xlabel('q (rad/Å)')\n", "ax1.set_ylabel('S(q)')\n", "\n", "ax1.set_xlim(xlim)\n", "ax1.set_ylim(0, 10)\n", "#ax1.set_yscale('log')\n", "ax1.legend(loc=2, fontsize=7) \n", "\n", "fig.tight_layout()" ] }, { "cell_type": "markdown", "id": "5360c918", "metadata": {}, "source": [ "### Decomposing $S(q)$ by atom types\n", "So far we have only considered the total $S(q)$, but one can also decompose $S(q)$ for the various atom types in the system and compute the partial structure factors $S_{\\alpha \\beta}$.\n", "\n", "\\begin{equation*}\n", "S_{\\alpha \\beta}(\\boldsymbol{q}) = \\frac{1}{N} \\left < \\sum_{i \\in \\alpha} ^{N_\\alpha} \\sum_{j \\in \\beta}^{N_\\beta} \\exp{\\left [ i\\boldsymbol{q}\\cdot (\\boldsymbol{r}_i(t) - \\boldsymbol{r}_j(t)) \\right ]} \\right >,\n", "\\end{equation*}\n", "\n", "where $\\alpha$ and $\\beta$ are different atom types, and $N$ is the total number of atoms; see the [dynasor documentation](https://dynasor.materialsmodeling.org/get_started/theory.html#multi-component-systems) for more details on e.g. normalization.\n", "\n", "Analyzing the partial structure factors can be useful in order to understand the origin of features and peaks in $S(q)$, see e.g. [this paper](http://doi.org/10.1021/acs.chemmater.3c02548).\n", "Specifically, for CsPbI$_3$ we know that it is displacements of the iodine atoms that give rise to the cubic to tetragonal phase transition, as we can clearly see below.\n", "\n", "In order to compute the partial structure factors we need to specify `atomic_indices` in the `Trajectory`. \n", "By setting for example `atomic_indices='read_from_trajectory'` means the atomic types will be read directly from the trajectory, however this does not work for netcdf trajectories used in this tutorial.\n", "Instead we create a dict containing the atomic indices for each unique atom type using the input `model.xyz` file from which the MD simulations were started." ] }, { "cell_type": "code", "execution_count": 11, "id": "c053ce82", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Cs: num atoms 1024\n", "Pb: num atoms 1024\n", "I: num atoms 3072\n", "INFO 2025-08-25 12:11:11: Length unit Angstrom and time unit ps inferred from trajectory.\n", "INFO 2025-08-25 12:11:11: Trajectory file: md_runs/NVT_tetra_size8_T450_nframes1000/movie.nc\n", "INFO 2025-08-25 12:11:11: Total number of particles: 5120\n", "INFO 2025-08-25 12:11:11: Number of atom types: 3\n", "INFO 2025-08-25 12:11:11: Number of atoms of type Cs: 1024\n", "INFO 2025-08-25 12:11:11: Number of atoms of type Pb: 1024\n", "INFO 2025-08-25 12:11:11: Number of atoms of type I: 3072\n", "INFO 2025-08-25 12:11:11: Simulation cell (in Angstrom):\n", "[[70.64849 0. 0. ]\n", " [ 0. 70.64849 0. ]\n", " [ 0. 0. 50.730995]]\n", "INFO 2025-08-25 12:11:11: Number of q-points: 36893\n" ] } ], "source": [ "q_width = 0.001\n", "tag = f'tetra_size8_T450_nframes1000'\n", "dirname = f'md_runs/NVT_{tag}/'\n", "\n", "# read atomic indices\n", "atoms_ideal = read(dirname + 'model.xyz')\n", "atomic_indices = atoms_ideal.symbols.indices()\n", "for atom_type, indices in atomic_indices.items():\n", " print(f'{atom_type}: num atoms {len(indices)}')\n", "\n", "# setup traj\n", "traj = Trajectory(dirname + 'movie.nc', trajectory_format='nc', atomic_indices=atomic_indices)\n", "\n", "# generate all q-points in supercell\n", "q_points = get_spherical_qpoints(traj.cell, q_max=q_max)\n", "\n", "# compute Sq\n", "sample = compute_static_structure_factors(traj, q_points)\n", "sample_averaged = get_spherically_averaged_sample_smearing(sample, q_norms=q_linspace, q_width=q_width)" ] }, { "cell_type": "code", "execution_count": 12, "id": "de007cf8", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# plot setup\n", "fig, axes = plt.subplots(nrows=6, ncols=1, sharex=True, figsize=(6.0, 6.5), dpi=150)\n", "ylim = [2e-2, 5000]\n", "alpha = 0.5\n", "\n", "# plotting\n", "for ax, (atom_type1, atom_type2) in zip(axes, sample_averaged.pairs):\n", " key = f'Sq_{atom_type1}_{atom_type2}'\n", " ax.plot(sample_averaged.q_norms, sample_averaged[key], '-', label=f'{atom_type1} - {atom_type2}')\n", " ax.set_ylabel('S(q)')\n", " ax.set_xlim(xlim)\n", " ax.set_ylim(0, 35)\n", " ax.legend(loc=2) \n", " #ax.set_yscale('log')\n", "ax.set_xlabel('q (rad/Å)')\n", "\n", "fig.tight_layout()\n", "fig.subplots_adjust(hspace=0)" ] }, { "cell_type": "markdown", "id": "e89dc5d6", "metadata": {}, "source": [ "Note that the peaks around q=1.6 and q=1.85 (corresponding to the M-point as discussed above) only shows up in the I-I partial structure factor, as expected since the soft phonon mode at the M-point (corresponding to the transition from cubic to tetragonal) only involves I atoms." ] }, { "cell_type": "markdown", "id": "b2f1949f-60ee-4e08-80b1-aa723c36d26f", "metadata": {}, "source": [ "## Connecting to diffraction experiments\n", "The static structure factor, $S(q)$, is closely connected to the measured intensity, $I(q)$, in diffraction experiments, such as X-ray diffraction (XRD) or neutron diffraction.\n", "To obtain a one to one comparison one must weight the static structure factor with the corresponding form factors (cross-sections) for the relevant experiment, below we show how this can be done for X-rays.\n", "\n", "Lastly, we also demonstrate how one can convert q-points into scattering angle, $2\\theta$, which is commonly used in XRD plots.\n", "In order to do this one must know the wavelength of the X-ray \n", "\\begin{equation*}\n", "\\frac{\\sin(\\theta)}{\\lambda} = n \\frac{q}{4 \\pi}\n", "\\end{equation*}\n", "where $n$ is an integer ($n=1$ corresponds to the first reflection) and $\\lambda$ is the wavelength. \n", "Here, we use $\\lambda$=1.5406 Å, which is a common wavelength in experiments." ] }, { "cell_type": "code", "execution_count": 13, "id": "2edb5cbb-6c48-420c-b1da-a3fe5a2db85c", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from dynasor.post_processing import XRayFormFactors\n", "from dynasor.post_processing import get_weighted_sample\n", "\n", "xlim = [0.7, 2.2]\n", "\n", "# setup x-ray form factors\n", "xrff = XRayFormFactors(sample_averaged.atom_types)\n", "\n", "# weight sample with x-ray form factor\n", "sample_weighted = get_weighted_sample(sample_averaged, xrff)\n", "\n", "# convert q to theta\n", "lam = 1.5406\n", "n = 1\n", "theta = np.arcsin(n * sample_averaged.q_norms * lam / 4 / np.pi) * 180 / np.pi\n", "\n", "# plot both S(q) and I(q)\n", "fig, (ax1, ax2) = plt.subplots(nrows=2, ncols=1, sharex=True, figsize=(6.0, 4.5), dpi=150)\n", "\n", "ax1_v2 = ax1.twiny()\n", "two_theta_ticks = np.arange(0, 40, 5)\n", "ax1_v2.set_xticks([np.sin(two_theta / 2 / 180 * np.pi) / lam * 4 * np.pi for two_theta in two_theta_ticks])\n", "ax1_v2.set_xticklabels(two_theta_ticks)\n", "ax1_v2.set_xlim(xlim)\n", "ax1_v2.set_xlabel(r'$2\\theta$ (degrees)')\n", "\n", "ax1.plot(sample_averaged.q_norms, sample_averaged.Sq, '-')\n", "ax2.plot(sample_weighted.q_norms, sample_weighted.Sq, '-')\n", "\n", "ax1.set_ylabel('S(q)')\n", "ax1.set_xlim(xlim)\n", "ax1.set_ylim(0, 50)\n", "ax2.set_ylabel('I(q)')\n", "ax2.set_xlim(xlim)\n", "ax2.set_ylim(0, 150000)\n", "ax2.set_xlabel('q (rad/Å)')\n", "\n", "fig.align_ylabels()\n", "fig.tight_layout()\n", "fig.subplots_adjust(hspace=0)" ] }, { "cell_type": "markdown", "id": "9664ec48-9d07-488b-ba4f-e32d2628da52", "metadata": {}, "source": [ "Note how all the peaks remain at the same positions, however the relative intensity (heights) between the peaks changes." ] }, { "cell_type": "markdown", "id": "c2ed7ff0", "metadata": {}, "source": [ "## Observing the phase transition in $S(q)$\n", "Lastly, we show how the structure factor can be used to identify the phase transitions by running multiple simulations at various temperature and visualizing the results as a heatmap.\n", "Here, one can see how the peaks at the M-point (as discussed above) emerge at the phase transition temperature at around T=550 K.\n", "\n", "See also Fig 4 in [this paper](https://doi.org/10.1021/acs.jpclett.4c03517) for more details on this type of simulated diffraction patterns and how it can be used to track phase transitions\n", "\n", "![Sq](figs/Sq_phase_transition.png)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.3" } }, "nbformat": 4, "nbformat_minor": 5 } dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/examples/NiAl-ordered-and-disordered/000077500000000000000000000000001522712672400270175ustar00rootroot00000000000000dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/examples/NiAl-ordered-and-disordered/.gitignore000066400000000000000000000000611522712672400310040ustar00rootroot00000000000000UNEP-v1-model-without-ZBL.txt disordered ordered dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/examples/NiAl-ordered-and-disordered/NiAl.ipynb000066400000000000000000032556131522712672400307240ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Dynamic properties of NiAl alloy\n", "\n", "In this tutorial, we study the dynamics of an alloy, both in an ordered and disordered phase.\n", "The focus will be on multi-component aspects.\n", "Note that this tutorial uses a small simulation cell (i.e., few $\\boldsymbol{q}$-points) and a short molecular dynamics (MD) trajectory in order to limit the runtime.\n", "In practice, one needs to tune these parameters as well as the **dynasor** parameters for optimal results.\n", "\n", "For the purpose of this tutorial, we assume that the MD trajectories have been created beforehand using GPUMD.\n", "They can be retrieved from Zenodo via\n", "\n", "```bash\n", "wget https://zenodo.org/records/16887940/files/trajectory-ordered.nc\n", "wget https://zenodo.org/records/16887940/files/trajectory-disordered.nc\n", "```\n", "\n", "We also need the initial structures in order to assign atomic indices to species:\n", "\n", "```bash\n", "wget https://zenodo.org/records/16887940/files/model-ordered.xyz\n", "wget https://zenodo.org/records/16887940/files/model-disordered.xyz\n", "```\n", "\n", "The same [Zenodo record](https://zenodo.org/records/16887940) also contains input and other pertinent output files.\n", "\n", "#### Additional information\n", "\n", "If you want to generate your own trajectories you can use [this script](https://zenodo.org/records/16887940/files/prepare-structures.py) to generate input structures.\n", "You can then use the [GPUMD package](https://gpumd.org/) and [this run.in input file](https://zenodo.org/records/16887940/files/run.in).\n", "The latter relies on the UNEP model from [Nature Communications 15, 10208 (2024)](https://doi.org/10.1038/s41467-024-54554-x) to run MD simulations.\n", "The UNEP parameter file can be retrieved from Zenodo via\n", "\n", "```\n", "wget https://zenodo.org/records/10081677/files/UNEP-v1-model-without-ZBL.txt\n", "```\n", "\n", "Please note that the run file employs the optional NetCDF extension of GPUMD.\n", "It is possible to use the standard extended-xyz output format (via the `dump_exyz` keyword), but the execution time will be longer and the trajectory files will be multiple times larger.\n", "When using the NetCDF extension the runs should take about 17 to 19 minutes on an A100 or RTX3090 GPU." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "from ase.build import bulk\n", "from ase.io import read\n", "from matplotlib import pyplot as plt\n", "from seekpath import get_path\n", "\n", "from dynasor import compute_dynamic_structure_factors, Trajectory\n", "from dynasor.qpoints import get_supercell_qpoints_along_path\n", "from dynasor.units import radians_per_fs_to_meV" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Set up Trajectory with atom type information" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**dynasor** supports multiple different trajectory formats via internal readers as well as via [MDAnalysis](https://www.mdanalysis.org/) and [ase](https://wiki.fysik.dtu.dk/ase/).\n", "Here, we use the MDAnalysis reader to parse trajectories in NetCDF format.\n", "To begin with, we read the full trajectory for the ordered NiAl (B2 phase, so 50% Ni and 50% Al).\n", "The length and time units of the MD trajectory are specified with the `length_unit` and `time_unit` keyword.\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "INFO 2025-08-25 11:01:47: Trajectory file: trajectory-ordered.nc\n", "INFO 2025-08-25 11:01:47: Total number of particles: 27648\n", "INFO 2025-08-25 11:01:47: Number of atom types: 1\n", "INFO 2025-08-25 11:01:47: Number of atoms of type X: 27648\n", "INFO 2025-08-25 11:01:47: Simulation cell (in Angstrom):\n", "[[68.64 0. 0. ]\n", " [ 0. 68.64 0. ]\n", " [ 0. 0. 68.64]]\n" ] } ], "source": [ "traj = Trajectory('trajectory-ordered.nc', \n", " trajectory_format='nc',\n", " length_unit='Angstrom', \n", " time_unit='fs',\n", " frame_step=1,\n", " frame_stop=None)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A short summary of the content of the `traj` object can be obtained via the `display` command (in notebooks) or `print` (in notebooks or scripts)." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "

Trajectory

\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
FieldValue
File nametrajectory-ordered.nc
Number of atoms27648
Cell metric[[68.64 0. 0. ]\n", " [ 0. 68.64 0. ]\n", " [ 0. 0. 68.64]]
Frame step1
Atom types['X']
" ], "text/plain": [ "Trajectory\n", "filename : trajectory-ordered.nc\n", "natoms : 27648\n", "frame_start : 0\n", "frame_stop : None\n", "frame_step : 1\n", "frame_index : 0\n", "cell : [[68.64 0. 0. ]\n", " [ 0. 68.64 0. ]\n", " [ 0. 0. 68.64]]" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "display(traj)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When we create the `Trajectory` object in the simplest way, as above, we see that all atoms in the trajectory are taken to be of a single, unknown, type `'X'`. We know that this is not the case for our trajectory, since it contains two atom types, `'Ni'` and `'Al'`.\n", "\n", "In order to pass on this information to the `Trajectory` object, we need to specify the `atomic_indices` keyword.\n", "There are several options the simplest of which is to set `atomic_indices='read_from_trajectory'`.\n", "This requires the species information to be available in the trajectory.\n", "While this is the case for, e.g., trajectories in extended-xyz formats, this option is, however, unfortunately not available in the case with these NetCDF trajectories.\n", "Here, we therefore prepare a dictionary the keys of which indicate the species and the values are lists of the corresponding atomic indices.\n", "\n", "We note that as a further option you could provide a [Gromacs ndx-style](https://manual.gromacs.org/current/reference-manual/file-formats.html#ndx) index file." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "INFO 2025-08-25 11:01:47: Trajectory file: trajectory-ordered.nc\n", "INFO 2025-08-25 11:01:47: Total number of particles: 27648\n", "INFO 2025-08-25 11:01:47: Number of atom types: 2\n", "INFO 2025-08-25 11:01:47: Number of atoms of type Ni: 13824\n", "INFO 2025-08-25 11:01:47: Number of atoms of type Al: 13824\n", "INFO 2025-08-25 11:01:47: Simulation cell (in Angstrom):\n", "[[68.64 0. 0. ]\n", " [ 0. 68.64 0. ]\n", " [ 0. 0. 68.64]]\n" ] }, { "data": { "text/html": [ "

Trajectory

\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
FieldValue
File nametrajectory-ordered.nc
Number of atoms27648
Cell metric[[68.64 0. 0. ]\n", " [ 0. 68.64 0. ]\n", " [ 0. 0. 68.64]]
Frame step1
Atom types['Al', 'Ni']
" ], "text/plain": [ "Trajectory\n", "filename : trajectory-ordered.nc\n", "natoms : 27648\n", "frame_start : 0\n", "frame_stop : None\n", "frame_step : 1\n", "frame_index : 0\n", "cell : [[68.64 0. 0. ]\n", " [ 0. 68.64 0. ]\n", " [ 0. 0. 68.64]]" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "structure = read('model-ordered.xyz')\n", "atomic_indices = {s: [a.index for a in structure if a.symbol == s]\n", " for s in set(structure.symbols)}\n", "traj = Trajectory('trajectory-ordered.nc', \n", " trajectory_format='nc',\n", " atomic_indices=atomic_indices,\n", " length_unit='Angstrom', \n", " time_unit='fs',\n", " frame_step=1,\n", " frame_stop=None)\n", "display(traj)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now our `Trajectory` object contains information about the different atomic types.\n", "(N.B.: Since we are not interested in the mass in this case, we can safely ignore the `UserWarning` issued by `MDAnalysis`.)\n", "\n", "Note that we are not limited to atomic species, but we can also group the atoms in other ways.\n", "For example, imagine a system containing bulk and surface atoms for which we would like to study the dynamics separately.\n", "In that case we could easily separate these groups by creating a dictionary with the structure `dict(bulk=[...], surface=[...])` in the same fashion as above.\n", "\n", "Now that we have explored different ways to set up a trajectory containing information about the atom types, let us use the `Trajectory` object to perform a **dynasor** calculation and study which atom types in the system give rise to which features in $S(\\boldsymbol{q},\\omega)$." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Set up path through Brillouin zone\n", "\n", "We need a path through the Brillouin zone, for which we follow the approach introduced in the [\"Dynamic properties of FCC-Al\" tutorial](https://dynasor.materialsmodeling.org/tutorials/aluminum_solid_dynamic.html). In this case, we use the same path through the Brillouin zone for NiAl as for pure Al." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'GAMMA': [0.0, 0.0, 0.0],\n", " 'X': [0.5, 0.0, 0.5],\n", " 'L': [0.5, 0.5, 0.5],\n", " 'W': [0.5, 0.25, 0.75],\n", " 'W_2': [0.75, 0.25, 0.5],\n", " 'K': [0.375, 0.375, 0.75],\n", " 'U': [0.625, 0.25, 0.625]}" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "[('GAMMA', 'X'),\n", " ('X', 'U'),\n", " ('K', 'GAMMA'),\n", " ('GAMMA', 'L'),\n", " ('L', 'W'),\n", " ('W', 'X')]" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "repeats = 24 # We simulated a 24x24x24 supercell\n", "prim = bulk('Al', a=traj.cell[0][0] / repeats)\n", "path_info = get_path((prim.cell, prim.get_scaled_positions(), prim.numbers))\n", "point_coordinates = path_info['point_coords']\n", "path = path_info['path']\n", "display(point_coordinates)\n", "display(path)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "q_segments = get_supercell_qpoints_along_path(\n", " path, point_coordinates, prim.cell, traj.cell)\n", "q_points = np.vstack(q_segments)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Compute correlation functions\n", "\n", "Now we have prepared everything we need to be able to compute the dynamic structure factor. \n", "\n", "**Note**: The `Trajectory` object is an \"exhaustible iterable\" which means that once we have iterated over all frames, it is exhausted. Therefore, one needs to \"reload\" the trajectory before calling `compute_dynamic_structure_factors` again to ensure that the entire trajectory is available for analysis.\n", "\n", "The code cell below takes about 10 to 15 minutes to run on a common workstation. If you would like to reduce this, set `frame_stop` above to only read a fraction of the trajectory. " ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "INFO 2025-08-25 11:01:58: Spacing between samples (frame_step): 1\n", "INFO 2025-08-25 11:01:58: Time between consecutive frames in input trajectory (dt): 25.0 fs\n", "INFO 2025-08-25 11:01:58: Time between consecutive frames used (dt * frame_step): 25.0 fs\n", "INFO 2025-08-25 11:01:58: Time window size (dt * frame_step * window_size): 50000.0 fs\n", "INFO 2025-08-25 11:01:58: Angular frequency resolution: dw = 0.000063 rad/fs = 0.041 meV\n", "INFO 2025-08-25 11:01:58: Maximum angular frequency (dw * window_size): 0.125664 rad/fs = 82.713 meV\n", "INFO 2025-08-25 11:01:58: Nyquist angular frequency (2pi / frame_step / dt / 2): 0.125664 rad/fs = 82.713 meV\n", "INFO 2025-08-25 11:01:58: Calculating current (velocity) correlations\n", "INFO 2025-08-25 11:01:58: Number of q-points: 90\n", "INFO 2025-08-25 11:02:17: Processing window 0 to 2000\n", "INFO 2025-08-25 11:02:28: Processing window 1000 to 3000\n", "INFO 2025-08-25 11:02:39: Processing window 2000 to 4000\n", "INFO 2025-08-25 11:02:49: Processing window 3000 to 5000\n", "INFO 2025-08-25 11:03:00: Processing window 4000 to 6000\n", "INFO 2025-08-25 11:03:11: Processing window 5000 to 7000\n", "INFO 2025-08-25 11:03:22: Processing window 6000 to 8000\n", "INFO 2025-08-25 11:03:32: Processing window 7000 to 9000\n", "INFO 2025-08-25 11:03:43: Processing window 8000 to 10000\n", "INFO 2025-08-25 11:03:54: Processing window 9000 to 11000\n", "INFO 2025-08-25 11:04:05: Processing window 10000 to 12000\n", "INFO 2025-08-25 11:04:16: Processing window 11000 to 13000\n", "INFO 2025-08-25 11:04:26: Processing window 12000 to 14000\n", "INFO 2025-08-25 11:04:37: Processing window 13000 to 15000\n", "INFO 2025-08-25 11:04:48: Processing window 14000 to 16000\n", "INFO 2025-08-25 11:04:59: Processing window 15000 to 17000\n", "INFO 2025-08-25 11:05:10: Processing window 16000 to 18000\n", "INFO 2025-08-25 11:05:20: Processing window 17000 to 19000\n", "INFO 2025-08-25 11:05:31: Processing window 18000 to 19999\n", "INFO 2025-08-25 11:05:33: Processing window 19000 to 19999\n" ] }, { "data": { "text/html": [ "

DynamicSample

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Simulation

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NameContent
Angular frequency resolution6.283185307179586e-05
Atom types['Al', 'Ni']
Cell[[68.64 0. 0. ]\n", " [ 0. 68.64 0. ]\n", " [ 0. 0. 68.64]]
Maximum time lag50000.0
Number of frames20000
Particle counts{'Ni': 13824, 'Al': 13824}
Time between frames25.0
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Dimensions

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omega(2001,)
q_points(90, 3)
time(2001,)
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History

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FunctionFieldContent
compute_dynamic_structure_factorsdt25.0
window_size2000
window_step100
calculate_currentsTrue
calculate_incoherentFalse
date_time2025-08-25T11:05:37
usernameerhart
hostnamepieweack
dynasor_version2.2
" ], "text/plain": [ "DynamicSample\n", "Angular frequency resolution: 6.283185307179586e-05\n", "Atom types: ['Al', 'Ni']\n", "Cell: [[68.64 0. 0. ]\n", " [ 0. 68.64 0. ]\n", " [ 0. 0. 68.64]]\n", "maximum_angular_frequency: 0.1256637061435917\n", "Maximum time lag: 50000.0\n", "Number of frames: 20000\n", "pairs: [('Al', 'Al'), ('Al', 'Ni'), ('Ni', 'Ni')]\n", "Particle counts: {'Ni': 13824, 'Al': 13824}\n", "Time between frames: 25.0\n", "omega with shape: (2001,)\n", "q_points with shape: (90, 3)\n", "time with shape: (2001,)\n", "Clqt with shape: (90, 2001)\n", "Clqt_Al_Al with shape: (90, 2001)\n", "Clqt_Al_Ni with shape: (90, 2001)\n", "Clqt_Ni_Ni with shape: (90, 2001)\n", "Clqw with shape: (90, 2001)\n", "Clqw_Al_Al with shape: (90, 2001)\n", "Clqw_Al_Ni with shape: (90, 2001)\n", "Clqw_Ni_Ni with shape: (90, 2001)\n", "Ctqt with shape: (90, 2001)\n", "Ctqt_Al_Al with shape: (90, 2001)\n", "Ctqt_Al_Ni with shape: (90, 2001)\n", "Ctqt_Ni_Ni with shape: (90, 2001)\n", "Ctqw with shape: (90, 2001)\n", "Ctqw_Al_Al with shape: (90, 2001)\n", "Ctqw_Al_Ni with shape: (90, 2001)\n", "Ctqw_Ni_Ni with shape: (90, 2001)\n", "Fqt_coh with shape: (90, 2001)\n", "Fqt_coh_Al_Al with shape: (90, 2001)\n", "Fqt_coh_Al_Ni with shape: (90, 2001)\n", "Fqt_coh_Ni_Ni with shape: (90, 2001)\n", "Sqw_coh with shape: (90, 2001)\n", "Sqw_coh_Al_Al with shape: (90, 2001)\n", "Sqw_coh_Al_Ni with shape: (90, 2001)\n", "Sqw_coh_Ni_Ni with shape: (90, 2001)" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 25min 33s, sys: 2.03 s, total: 25min 35s\n", "Wall time: 3min 38s\n" ] } ], "source": [ "%%time\n", "delta_t = 5.0 # MD time step used (fs)\n", "dump_every = 5 # How often the frames were dumped during MD\n", "\n", "sample_ordered = compute_dynamic_structure_factors(traj, q_points, \n", " dt=delta_t*dump_every, \n", " window_size=2000,\n", " window_step=100,\n", " calculate_currents=True)\n", "# convert from frequency (rad/fs) to energy (meV) scale\n", "sample_ordered.omega *= radians_per_fs_to_meV\n", "display(sample_ordered)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Visualize the dynamic structure factor\n", "\n", "We can now visualize $S(\\boldsymbol{q},\\omega)$ using the same approach introduced in the [\"Dynamic properties of FCC-Al\" tutorial](https://dynasor.materialsmodeling.org/tutorials/aluminum_solid_dynamic.html), i.e., in the form of a heatmap in the momentum-frequency (or energy) plane. To do so, we first need the running pairwise distance between $\\boldsymbol{q}$-points along the path." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "q_distances = []\n", "q_labels = dict()\n", "\n", "# starting point\n", "qr = 0.0\n", "\n", "# collect labels and q-distances along the entire q-point path\n", "for it, q_segment in enumerate(q_segments):\n", " q_distances.append(qr)\n", " q_labels[qr] = path[it][0]\n", " for qi, qj in zip(q_segment[1:], q_segment[:-1]):\n", " qr += np.linalg.norm(qi - qj)\n", " q_distances.append(qr)\n", "\n", "q_labels[qr] = path[-1][1]\n", "q_distances = np.array(q_distances)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, we visualize the full dynamic structure factor, which, in addition to acoustic branches similar to those we saw in the [\"Dynamic properties of FCC-Al\" tutorial](https://dynasor.materialsmodeling.org/tutorials/aluminum_solid_dynamic.html), also reveals optical branches, as expected in a multi-component system. As the dynamic structure factor only contains information about the longitudinal modes, we can in addition visualize the longitudinal and transverse current correlation functions, to study the full dispersion.\n", "Here, we plot the *difference* between the longitudinal and transverse functions, which is a convenient trick to separate the longitudinal (red) and transverse (blue) features visually." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, axes = plt.subplots(figsize=(5.2, 4.8), nrows=2, dpi=140,\n", " sharex=True, sharey=True)\n", "\n", "ax = axes[0]\n", "ax.pcolormesh(q_distances, sample_ordered.omega,\n", " sample_ordered.Sqw_coh.T, cmap='Reds', vmin=0, vmax=8)\n", "ax.text(0.05, 0.85, r'$S(\\mathbf{q}, \\omega)$', transform=ax.transAxes,\n", " bbox={'color': 'white', 'alpha': 0.8, 'pad': 3})\n", "\n", "ax = axes[1]\n", "m = 0.05 * np.max(sample_ordered.Clqw.T - sample_ordered.Ctqw.T)\n", "ax.pcolormesh(q_distances, sample_ordered.omega,\n", " sample_ordered.Clqw.T - sample_ordered.Ctqw.T, cmap='seismic', vmin=-m, vmax=m)\n", "ax.text(0.05, 0.85, r'$C_{L-T}(\\mathbf{q}, \\omega)$', transform=ax.transAxes,\n", " bbox={'color': 'white', 'alpha': 0.8, 'pad': 3})\n", "\n", "for ax in axes:\n", " xticks = []\n", " xticklabels = []\n", " for q_dist, q_label in q_labels.items():\n", " ax.axvline(q_dist, c='0.5', alpha=0.5, ls='--')\n", " xticks.append(q_dist)\n", " xticklabels.append(q_label.replace('GAMMA', r'$\\Gamma$'))\n", "\n", "ax.set_xticks(xticks)\n", "ax.set_xticklabels(xticklabels)\n", "\n", "ax.set_xlim([0, q_distances.max()])\n", "ax.set_ylim([0, 55])\n", "\n", "ax.set_ylabel('Energy (meV)', y=1)\n", "\n", "fig.tight_layout()\n", "fig.subplots_adjust(hspace=0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note again that these correlations are not converged and that one in practice would need to tailor the simulation cell size, the trajectory length, as well as **dynasor** parameters (`window_size`, `window_step`, etc.) to reach convergence.\n", "\n", "Since we have added information about atom types to the trajectory, our `Sample` object also contains the partial structure factors and current correlations, which we can visualize. By doing so, we can learn more about which features in the dispersion relation originate from which atom types in the system." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, axes = plt.subplots(figsize=(5.2, 6), nrows=3, dpi=140,\n", " sharex=True, sharey=True)\n", "\n", "ax = axes[0]\n", "m = 0.05 * np.max(sample_ordered.Clqw_Al_Al.T + sample_ordered.Ctqw_Al_Al.T)\n", "ax.pcolormesh(q_distances, sample_ordered.omega,\n", " sample_ordered.Clqw_Al_Al.T + sample_ordered.Ctqw_Al_Al.T, cmap='seismic', vmin=-m, vmax=m)\n", "ax.text(0.05, 0.85, r'$C_{AlAl}(\\mathbf{q}, \\omega)$', transform=ax.transAxes,\n", " bbox={'color': 'white', 'alpha': 0.8, 'pad': 3})\n", "\n", "ax = axes[1]\n", "m = 0.05 * np.max(sample_ordered.Clqw_Al_Ni.T + sample_ordered.Ctqw_Al_Ni.T)\n", "ax.pcolormesh(q_distances, sample_ordered.omega,\n", " sample_ordered.Clqw_Al_Ni.T + sample_ordered.Ctqw_Al_Ni.T, cmap='seismic', vmin=-m, vmax=m)\n", "ax.text(0.05, 0.85, r'$C_{NiAl}(\\mathbf{q}, \\omega)$', transform=ax.transAxes,\n", " bbox={'color': 'white', 'alpha': 0.8, 'pad': 3})\n", "\n", "ax = axes[2]\n", "m = 0.05 * np.max(sample_ordered.Clqw_Ni_Ni.T + sample_ordered.Ctqw_Ni_Ni.T)\n", "ax.pcolormesh(q_distances, sample_ordered.omega,\n", " sample_ordered.Clqw_Ni_Ni.T + sample_ordered.Ctqw_Ni_Ni.T, cmap='seismic', vmin=-m, vmax=m)\n", "ax.text(0.05, 0.85, r'$C_{NiNi}(\\mathbf{q}, \\omega)$', transform=ax.transAxes,\n", " bbox={'color': 'white', 'alpha': 0.8, 'pad': 3})\n", "\n", "for ax in axes:\n", " xticks = []\n", " xticklabels = []\n", " for q_dist, q_label in q_labels.items():\n", " ax.axvline(q_dist, c='0.5', alpha=0.5, ls='--')\n", " xticks.append(q_dist)\n", " xticklabels.append(q_label.replace('GAMMA', r'$\\Gamma$'))\n", "\n", "ax.set_xticks(xticks)\n", "ax.set_xticklabels(xticklabels)\n", "\n", "ax.set_xlim([0, q_distances.max()])\n", "ax.set_ylim([0, 55])\n", "\n", "axes[1].set_ylabel('Energy (meV)')\n", "\n", "fig.tight_layout()\n", "fig.subplots_adjust(hspace=0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can here clearly see which atom types contribute to which parts of the dispersion relations, as well as note that some parts have negative sign (orange), in particular for the NiAl partial current correlation." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Dynamics in disordered NiAl\n", "\n", "Now we want to see what changes in the dynamics of disordered NiAl compared to ordered NiAl. To this end, we can use the same procedure as above and compute the correlations, using the same path through the Brillouin zone to be able to compare the two." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "INFO 2025-08-25 11:05:38: Trajectory file: trajectory-disordered.nc\n", "INFO 2025-08-25 11:05:38: Total number of particles: 27648\n", "INFO 2025-08-25 11:05:38: Number of atom types: 2\n", "INFO 2025-08-25 11:05:38: Number of atoms of type Ni: 13824\n", "INFO 2025-08-25 11:05:38: Number of atoms of type Al: 13824\n", "INFO 2025-08-25 11:05:38: Simulation cell (in Angstrom):\n", "[[68.64 0. 0. ]\n", " [ 0. 68.64 0. ]\n", " [ 0. 0. 68.64]]\n" ] } ], "source": [ "structure = read('model-disordered.xyz')\n", "atomic_indices = {s: [a.index for a in structure if a.symbol == s] for s in set(structure.symbols)}\n", "traj_disordered = Trajectory('trajectory-disordered.nc', \n", " trajectory_format='nc',\n", " atomic_indices=atomic_indices,\n", " length_unit='Angstrom', \n", " time_unit='fs',\n", " frame_step=1,\n", " frame_stop=None)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Again, the code cell below takes about 10 to 15 minutes to run on a common workstation. If you would like to reduce this, set `frame_stop` above to only read in a fraction of the trajectory. " ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "INFO 2025-08-25 11:05:38: Spacing between samples (frame_step): 1\n", "INFO 2025-08-25 11:05:38: Time between consecutive frames in input trajectory (dt): 25.0 fs\n", "INFO 2025-08-25 11:05:38: Time between consecutive frames used (dt * frame_step): 25.0 fs\n", "INFO 2025-08-25 11:05:38: Time window size (dt * frame_step * window_size): 50000.0 fs\n", "INFO 2025-08-25 11:05:38: Angular frequency resolution: dw = 0.000063 rad/fs = 0.041 meV\n", "INFO 2025-08-25 11:05:38: Maximum angular frequency (dw * window_size): 0.125664 rad/fs = 82.713 meV\n", "INFO 2025-08-25 11:05:38: Nyquist angular frequency (2pi / frame_step / dt / 2): 0.125664 rad/fs = 82.713 meV\n", "INFO 2025-08-25 11:05:38: Calculating current (velocity) correlations\n", "INFO 2025-08-25 11:05:38: Number of q-points: 90\n", "INFO 2025-08-25 11:06:01: Processing window 0 to 2000\n", "INFO 2025-08-25 11:06:14: Processing window 1000 to 3000\n", "INFO 2025-08-25 11:06:26: Processing window 2000 to 4000\n", "INFO 2025-08-25 11:06:39: Processing window 3000 to 5000\n", "INFO 2025-08-25 11:06:52: Processing window 4000 to 6000\n", "INFO 2025-08-25 11:07:05: Processing window 5000 to 7000\n", "INFO 2025-08-25 11:07:18: Processing window 6000 to 8000\n", "INFO 2025-08-25 11:07:31: Processing window 7000 to 9000\n", "INFO 2025-08-25 11:07:43: Processing window 8000 to 10000\n", "INFO 2025-08-25 11:07:56: Processing window 9000 to 11000\n", "INFO 2025-08-25 11:08:09: Processing window 10000 to 12000\n", "INFO 2025-08-25 11:08:23: Processing window 11000 to 13000\n", "INFO 2025-08-25 11:08:37: Processing window 12000 to 14000\n", "INFO 2025-08-25 11:08:52: Processing window 13000 to 15000\n", "INFO 2025-08-25 11:09:06: Processing window 14000 to 16000\n", "INFO 2025-08-25 11:09:20: Processing window 15000 to 17000\n", "INFO 2025-08-25 11:09:35: Processing window 16000 to 18000\n", "INFO 2025-08-25 11:09:49: Processing window 17000 to 19000\n", "INFO 2025-08-25 11:10:03: Processing window 18000 to 19999\n", "INFO 2025-08-25 11:10:05: Processing window 19000 to 19999\n" ] } ], "source": [ "delta_t = 5.0 # MD time step used (fs)\n", "dump_every = 5 # How often the frames were dumped during MD\n", "\n", "sample_disordered = compute_dynamic_structure_factors(traj_disordered, q_points, \n", " dt=delta_t*dump_every, \n", " window_size=2000,\n", " window_step=100,\n", " calculate_currents=True)\n", "sample_disordered.omega *= radians_per_fs_to_meV # convert from frequency (rad/fs) to energy (meV) scale" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, axes = plt.subplots(figsize=(5.2, 4.8), nrows=2, dpi=140,\n", " sharex=True, sharey=True)\n", "\n", "ax = axes[0]\n", "ax.pcolormesh(q_distances, sample_disordered.omega,\n", " sample_disordered.Sqw_coh.T, cmap='Reds', vmin=0, vmax=5)\n", "ax.text(0.05, 0.85, r'$S(\\mathbf{q}, \\omega)$', transform=ax.transAxes,\n", " bbox={'color': 'white', 'alpha': 0.8, 'pad': 3})\n", "\n", "ax = axes[1]\n", "m = 0.3 * np.max(sample_disordered.Clqw.T - sample_disordered.Ctqw.T)\n", "ax.pcolormesh(q_distances, sample_disordered.omega,\n", " sample_disordered.Clqw.T - sample_disordered.Ctqw.T, cmap='seismic',\n", " vmin=-m, vmax=m)\n", "ax.text(0.05, 0.85, r'$C_{L-T}(\\mathbf{q}, \\omega)$', transform=ax.transAxes,\n", " bbox={'color': 'white', 'alpha': 0.8, 'pad': 3})\n", "\n", "for ax in axes:\n", " xticks = []\n", " xticklabels = []\n", " for q_dist, q_label in q_labels.items():\n", " ax.axvline(q_dist, c='0.5', alpha=0.5, ls='--')\n", " xticks.append(q_dist)\n", " xticklabels.append(q_label.replace('GAMMA', r'$\\Gamma$'))\n", "\n", "ax.set_xticks(xticks)\n", "ax.set_xticklabels(xticklabels)\n", "\n", "ax.set_xlim([0, q_distances.max()])\n", "ax.set_ylim([0, 55])\n", "\n", "ax.set_ylabel('Energy (meV)', y=1)\n", "\n", "fig.tight_layout()\n", "fig.subplots_adjust(hspace=0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With the dynamic structure factors and current correlation at hand for both the ordered (B2) and the disordered system, we can readily compare the two, and note the smearing of the bands that arises in the disordered case. One might consider averaging the disordered case over $|\\boldsymbol{q}|$ instead, following the approach commonly used for liquids." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.3" } }, "nbformat": 4, "nbformat_minor": 4 } dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/examples/NiAl-ordered-and-disordered/README.md000066400000000000000000000026051522712672400303010ustar00rootroot00000000000000# Example: Ordered and disorderd NiAl This directory contains files connected to an example illustrating the analysis of the dynamic properties of ordered and disordered samples of NiAl. The molecular dynamics trajectories that is analyzed here can be downloaded from zenodo (https://zenodo.org/records/16887940) or generated using the GPUMD package. Please consult the [GPUMD documentation](https://www.gpumd.org) for information on how to compile and run GPUMD. The necessary `run.in` input file is available in this directory. Input structures (GPUMD input file `model.xyz`) can be generated using the `prepare-structures.py` script. The runs use the general-purpose machine-learned potential based on the neuroevolution potential approach presented in [Nature Communications 15, 10208 (2024)](https://doi.org/10.1038/s41467-024-54554-x). You can retrieve the parameter file from Zenodo via: wget https://zenodo.org/records/10081677/files/UNEP-v1-model-without-ZBL.txt Please note that the runs employ the optional NetCDF extension of GPUMD. It is possible to use the standard extended-xyz output format (via the `dump_exyz` keyword) but both the execution time and the trajectory files will be multiple times longer. When using the NetCDF extension the runs should take about 17 to 19 minutes on an A100 or RTX3090 GPU. The analysis of the trajectory using **dynasor** is illustrated in the notebook. prepare-structures.py000066400000000000000000000011411522712672400331660ustar00rootroot00000000000000dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/examples/NiAl-ordered-and-disordered""" Set up structures """ import numpy as np from ase import Atoms from ase.io import write # Primitive cell primitive = Atoms('NiAl', positions=[(0, 0, 0), (1/2, 1/2, 1/2)]) primitive.set_cell([2.86, 2.86, 2.86], scale_atoms=True) primitive.pbc = True # Ordered supercell supercell = primitive.repeat((24, 24, 24)) write('model-ordered.xyz', supercell) # Disordered supercell natoms = len(supercell) supercell.symbols = natoms * ['Al'] nNi = int(natoms / 2) selection = np.random.choice(range(natoms), size=nNi, replace=False) supercell.symbols[selection] = 'Ni' write('model-disordered.xyz', supercell) dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/examples/NiAl-ordered-and-disordered/run.in000066400000000000000000000003341522712672400301530ustar00rootroot00000000000000potential ../UNEP-v1-model-without-ZBL.txt time_step 5.0 velocity 600 ensemble nvt_lan 300 300 100 dump_thermo 100 dump_restart 20000 run 100000 ensemble nve dump_thermo 100 dump_netcdf 5 1 precision single run 100000 dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/examples/NiAl-weighted/000077500000000000000000000000001522712672400243115ustar00rootroot00000000000000dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/examples/NiAl-weighted/NiAl-weighting.ipynb000066400000000000000000015623551522712672400302030ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Weighting with form factors\n", "\n", "In this tutorial we demonstrate the weighting of structure factors with neutron scattering lengths and X-ray form factors, as different features in the full dispersion are visible depending on which probe is used in a scattering experiment.\n", "As a specific example we consider [B2-ordered](https://en.wikipedia.org/wiki/Strukturbericht_designation#B-compounds) NiAl.\n", "\n", "Because we return to the same system used in the multi-component tutorial, we assume that the molecular dynamics trajectories have already been prepared beforehand with [GPUMD](https://gpumd.org/) using the [UNEP model](https://zenodo.org/records/10081677) as part of the [\"Dynamic properties of NiAl alloy\" tutorial](https://dynasor.materialsmodeling.org/tutorials/NiAl-ordered-and-disordered.html)." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "from ase.build import bulk\n", "from ase.io import read\n", "from matplotlib import pyplot as plt\n", "from seekpath import get_path\n", "\n", "from dynasor import compute_dynamic_structure_factors, Trajectory\n", "from dynasor.qpoints import get_supercell_qpoints_along_path\n", "from dynasor.units import radians_per_fs_to_meV\n", "from dynasor.post_processing import \\\n", " NeutronScatteringLengths, Weights, XRayFormFactors, get_weighted_sample" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Compute correlation functions\n", "\n", "Here we set up the Trajectory, $\\boldsymbol{q}$-points, and compute the dynamic structure factor.\n", "See the [\"Dynamic properties of NiAl alloy\" tutorial](https://dynasor.materialsmodeling.org/tutorials/NiAl-ordered-and-disordered.html) for a more detailed description of these steps." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "INFO 2025-08-25 11:11:50: Trajectory file: trajectory-ordered.nc\n", "INFO 2025-08-25 11:11:50: Total number of particles: 27648\n", "INFO 2025-08-25 11:11:50: Number of atom types: 2\n", "INFO 2025-08-25 11:11:50: Number of atoms of type Ni: 13824\n", "INFO 2025-08-25 11:11:50: Number of atoms of type Al: 13824\n", "INFO 2025-08-25 11:11:50: Simulation cell (in Angstrom):\n", "[[68.64 0. 0. ]\n", " [ 0. 68.64 0. ]\n", " [ 0. 0. 68.64]]\n" ] } ], "source": [ "# Set up trajectory\n", "structure = read('model-ordered.xyz')\n", "atomic_indices = {s: [a.index for a in structure if a.symbol == s]\n", " for s in set(structure.symbols)}\n", "traj = Trajectory('trajectory-ordered.nc', \n", " trajectory_format='nc',\n", " atomic_indices=atomic_indices,\n", " length_unit='Angstrom', \n", " time_unit='fs',\n", " frame_step=1,\n", " frame_stop=None)\n", "\n", "# Set up path through Brillouin zone\n", "repeats = 24 # We simulated a 24x24x24 supercell\n", "prim = bulk('Al', a=traj.cell[0][0]/repeats)\n", "path_info = get_path((prim.cell, prim.get_scaled_positions(), prim.numbers))\n", "point_coordinates = path_info['point_coords']\n", "path = path_info['path']\n", "\n", "q_segments = get_supercell_qpoints_along_path(\n", " path, point_coordinates, prim.cell, traj.cell)\n", "q_points = np.vstack(q_segments)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "INFO 2025-08-25 11:12:02: Spacing between samples (frame_step): 1\n", "INFO 2025-08-25 11:12:02: Time between consecutive frames in input trajectory (dt): 25.0 fs\n", "INFO 2025-08-25 11:12:02: Time between consecutive frames used (dt * frame_step): 25.0 fs\n", "INFO 2025-08-25 11:12:02: Time window size (dt * frame_step * window_size): 50000.0 fs\n", "INFO 2025-08-25 11:12:02: Angular frequency resolution: dw = 0.000063 rad/fs = 0.041 meV\n", "INFO 2025-08-25 11:12:02: Maximum angular frequency (dw * window_size): 0.125664 rad/fs = 82.713 meV\n", "INFO 2025-08-25 11:12:02: Nyquist angular frequency (2pi / frame_step / dt / 2): 0.125664 rad/fs = 82.713 meV\n", "INFO 2025-08-25 11:12:02: Number of q-points: 90\n", "INFO 2025-08-25 11:12:19: Processing window 0 to 2000\n", "INFO 2025-08-25 11:12:28: Processing window 1000 to 3000\n", "INFO 2025-08-25 11:12:37: Processing window 2000 to 4000\n", "INFO 2025-08-25 11:12:46: Processing window 3000 to 5000\n", "INFO 2025-08-25 11:12:55: Processing window 4000 to 6000\n", "INFO 2025-08-25 11:13:04: Processing window 5000 to 7000\n", "INFO 2025-08-25 11:13:12: Processing window 6000 to 8000\n", "INFO 2025-08-25 11:13:21: Processing window 7000 to 9000\n", "INFO 2025-08-25 11:13:30: Processing window 8000 to 10000\n", "INFO 2025-08-25 11:13:39: Processing window 9000 to 11000\n", "INFO 2025-08-25 11:13:48: Processing window 10000 to 12000\n", "INFO 2025-08-25 11:13:57: Processing window 11000 to 13000\n", "INFO 2025-08-25 11:14:07: Processing window 12000 to 14000\n", "INFO 2025-08-25 11:14:18: Processing window 13000 to 15000\n", "INFO 2025-08-25 11:14:29: Processing window 14000 to 16000\n", "INFO 2025-08-25 11:14:39: Processing window 15000 to 17000\n", "INFO 2025-08-25 11:14:50: Processing window 16000 to 18000\n", "INFO 2025-08-25 11:15:00: Processing window 17000 to 19000\n", "INFO 2025-08-25 11:15:10: Processing window 18000 to 19999\n", "INFO 2025-08-25 11:15:10: Processing window 19000 to 19999\n" ] }, { "data": { "text/html": [ "

DynamicSample

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NameContent
Angular frequency resolution6.283185307179586e-05
Atom types['Al', 'Ni']
Cell[[68.64 0. 0. ]\n", " [ 0. 68.64 0. ]\n", " [ 0. 0. 68.64]]
Maximum time lag50000.0
Number of frames20000
Particle counts{'Ni': 13824, 'Al': 13824}
Time between frames25.0
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Dimensions

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FieldSize
omega(2001,)
q_points(90, 3)
time(2001,)
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History

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FunctionFieldContent
compute_dynamic_structure_factorsdt25.0
window_size2000
window_step100
calculate_currentsFalse
calculate_incoherentFalse
date_time2025-08-25T11:15:13
usernameerhart
hostnamepieweack
dynasor_version2.2
" ], "text/plain": [ "DynamicSample\n", "Angular frequency resolution: 6.283185307179586e-05\n", "Atom types: ['Al', 'Ni']\n", "Cell: [[68.64 0. 0. ]\n", " [ 0. 68.64 0. ]\n", " [ 0. 0. 68.64]]\n", "maximum_angular_frequency: 0.1256637061435917\n", "Maximum time lag: 50000.0\n", "Number of frames: 20000\n", "pairs: [('Al', 'Al'), ('Al', 'Ni'), ('Ni', 'Ni')]\n", "Particle counts: {'Ni': 13824, 'Al': 13824}\n", "Time between frames: 25.0\n", "omega with shape: (2001,)\n", "q_points with shape: (90, 3)\n", "time with shape: (2001,)\n", "Fqt_coh with shape: (90, 2001)\n", "Fqt_coh_Al_Al with shape: (90, 2001)\n", "Fqt_coh_Al_Ni with shape: (90, 2001)\n", "Fqt_coh_Ni_Ni with shape: (90, 2001)\n", "Sqw_coh with shape: (90, 2001)\n", "Sqw_coh_Al_Al with shape: (90, 2001)\n", "Sqw_coh_Al_Ni with shape: (90, 2001)\n", "Sqw_coh_Ni_Ni with shape: (90, 2001)" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 23min 44s, sys: 3.7 s, total: 23min 48s\n", "Wall time: 3min 11s\n" ] } ], "source": [ "%%time\n", "\n", "# Compute correlation functions\n", "delta_t = 5.0 # MD time step used (fs)\n", "dump_every = 5 # How often the frames were dumped during MD\n", "\n", "sample_raw = compute_dynamic_structure_factors(traj, q_points, \n", " dt=delta_t*dump_every, \n", " window_size=2000,\n", " window_step=100)\n", "sample_raw.omega *= radians_per_fs_to_meV\n", "display(sample_raw)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Weighting with neutron scattering lengths\n", "\n", "To post-process the raw structure factor with weights of any kind, we need to first create a `Weights` object, which we then use to compute the weighted sample using the `get_weighted_sample` functionality.\n", "\n", "We first illustrate how this is done in **dynasor** without bothering about the actual values of the scattering lengths, so we set the weights for both Ni and Al to 1.\n", "Instead of these dummy values, the weights could for example correspond to the charges, masses or, as we shall see below, scattering lengths or form factors, depending on the desired weighted correlation function." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "weights coherent:\n", " Ni: 1\n", " Al: 1\n" ] } ], "source": [ "weights = Weights(dict(Ni=1, Al=1))\n", "print(weights)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The weights we created like this are coherent weights. If we had computed the incoherent structure factor in `compute_dynamic_structure_factors` above (which is done using `calculate_incoherent=True`), we could have set the (generally different) incoherent scattering lengths as well, via a second dictionary:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "weights coherent:\n", " Ni: 1\n", " Al: 1\n", "weights incoherent:\n", " Ni: 1\n", " Al: 1\n" ] } ], "source": [ "weights = Weights(dict(Ni=1, Al=1), weights_incoh=dict(Ni=1, Al=1))\n", "print(weights)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we want to generate some real weights, that we want to use to perform the weighting of the raw structure factor.\n", "This can be done manually by creating the `Weights` object as above, using your favorite table of neutron scattering lengths, but for convenience we provide tabulated values directly via `NeutronScatteringLengths`, taken from [this NIST database](https://www.ncnr.nist.gov/resources/n-lengths/list.html), which in turn have been taken from Table 1 of Neutron News **3**, 26 (1992);\n", "[doi: 10.1080/10448639208218770](https://doi.org/10.1080/10448639208218770)." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "weights coherent:\n", " Al: (3.449+0j)\n", " Ni: (10.331863+0j)\n", "weights incoherent:\n", " Al: (0.256+0j)\n", " Ni: 0j\n" ] } ], "source": [ "neutron_weights = NeutronScatteringLengths(['Ni', 'Al'])\n", "print(neutron_weights)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "

DynamicSample

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Simulation

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NameContent
Angular frequency resolution6.283185307179586e-05
Atom types['Al', 'Ni']
Cell[[68.64 0. 0. ]\n", " [ 0. 68.64 0. ]\n", " [ 0. 0. 68.64]]
Maximum time lag50000.0
Number of frames20000
Particle counts{'Ni': 13824, 'Al': 13824}
Time between frames25.0
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Dimensions

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FieldSize
omega(2001,)
q_points(90, 3)
time(2001,)
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History

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FunctionFieldContent
compute_dynamic_structure_factorsdt25.0
window_size2000
window_step100
calculate_currentsFalse
calculate_incoherentFalse
date_time2025-08-25T11:15:13
usernameerhart
hostnamepieweack
dynasor_version2.2
get_weighted_sampleatom_type_mapNone
weights_classNeutronScatteringLengths
weights_parameters{'index': {0: 25, 1: 66, 2: 67, 3: 68, 4: 69, 5: 70}, 'species': {0: 'Al', 1: 'Ni', 2: 'Ni', 3: 'Ni', 4: 'Ni', 5: 'Ni'}, 'isotope': {0: 27, 1: 58, 2: 60, 3: 61, 4: 62, 5: 64}, 'abundance': {0: 1.0, 1: 0.6827, 2: 0.261, 3: 0.0113, 4: 0.0359, 5: 0.0091}, 'b_coh': {0: (3.449+0j), 1: (14.4+0j), 2: (2.8+0j), 3: (7.6+0j), 4: (-8.7+0j), 5: (-0.37+0j)}, 'b_inc': {0: (0.256+0j), 1: 0j, 2: 0j, 3: 0j, 4: 0j, 5: 0j}}
date_time2025-08-25T11:15:13
usernameerhart
hostnamepieweack
dynasor_version2.2
" ], "text/plain": [ "DynamicSample\n", "Angular frequency resolution: 6.283185307179586e-05\n", "Atom types: ['Al', 'Ni']\n", "Cell: [[68.64 0. 0. ]\n", " [ 0. 68.64 0. ]\n", " [ 0. 0. 68.64]]\n", "maximum_angular_frequency: 0.1256637061435917\n", "Maximum time lag: 50000.0\n", "Number of frames: 20000\n", "pairs: [('Al', 'Al'), ('Al', 'Ni'), ('Ni', 'Ni')]\n", "Particle counts: {'Ni': 13824, 'Al': 13824}\n", "Time between frames: 25.0\n", "omega with shape: (2001,)\n", "q_points with shape: (90, 3)\n", "time with shape: (2001,)\n", "Fqt_coh with shape: (90, 2001)\n", "Fqt_coh_Al_Al with shape: (90, 2001)\n", "Fqt_coh_Al_Ni with shape: (90, 2001)\n", "Fqt_coh_Ni_Ni with shape: (90, 2001)\n", "Sqw_coh with shape: (90, 2001)\n", "Sqw_coh_Al_Al with shape: (90, 2001)\n", "Sqw_coh_Al_Ni with shape: (90, 2001)\n", "Sqw_coh_Ni_Ni with shape: (90, 2001)" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sample_neutron = get_weighted_sample(sample_raw, neutron_weights)\n", "display(sample_neutron)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Visualize the neutron weighted structure factor\n", "\n", "We visualize the effect of the weighting by studying slices of $S(\\boldsymbol{q}, \\omega)$ in $\\boldsymbol{q}$-space.\n", "Note that we use to `to_dataframe` method of the `Sample` objects to obtain a pandas `DataFrame`, which provides some useful functionality, e.g., for data smoothing as shown below." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, axes = plt.subplots(figsize=(7, 5.4), ncols=2, nrows=4, dpi=140, sharex=True)\n", "\n", "qids = [48, 60]\n", "\n", "for qid in qids:\n", " label = r'$\\mathbf{q} =$'\n", " label += np.array_str(sample_raw.q_points[qid], precision=2, suppress_small=True)\n", " label += ' rad/Å'\n", "\n", " df = sample_raw.to_dataframe(qid)\n", " axes[0][0].plot(df.omega, df.Sqw_coh)\n", " axes[1][0].plot(df.omega, df.Sqw_coh_Al_Al)\n", " axes[2][0].plot(df.omega, df.Sqw_coh_Ni_Ni)\n", " axes[3][0].plot(df.omega, df.Sqw_coh_Al_Ni)\n", " \n", " df = sample_neutron.to_dataframe(qid)\n", " axes[0][1].plot(df.omega, df.Sqw_coh)\n", " axes[1][1].plot(df.omega, df.Sqw_coh_Al_Al)\n", " axes[2][1].plot(df.omega, df.Sqw_coh_Ni_Ni)\n", " axes[3][1].plot(df.omega, df.Sqw_coh_Al_Ni, label=label)\n", "\n", "ax = axes[0][0]\n", "ax.set_ylabel(r'$S(\\omega)$')\n", "ax.set_title('Raw')\n", "ax.set_xlim(0, 60)\n", "\n", "axes[1][0].set_ylabel(r'$S_{AlAl}(\\omega)$')\n", "axes[2][0].set_ylabel(r'$S_{NiNi}(\\omega)$')\n", "axes[3][0].set_ylabel(r'$S_{NiAl}(\\omega)$')\n", "axes[0][1].set_title('Neutron weighted')\n", "axes[3][1].legend(fontsize='small')\n", "for ax in axes[-1]:\n", " ax.set_xlabel('Energy (meV)')\n", "\n", "fig.subplots_adjust(hspace=0, wspace=0.3)\n", "fig.align_labels(axes)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "From the figures above, we can see that in each $\\boldsymbol{q}$-slice of the raw total structure factor (top left), the low-frequency and the high-frequency peaks appear to be of similar height, while their intensities differ when the structure factor is weighted with neutron scattering lengths (top right). This is explained by the partial structure factors contributing in different amounts to the total, since the neutron scattering lengths for Ni and Al are different, so when adding the weighted partials up to obtain the total weighted structure factor the aspect ratio between the low-frequency and the high-frequency peaks changes compared to the raw total structure factor." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Weighting with X-ray form factors" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, we will do the same thing, but with X-ray form factors instead. Just as for the neutron weights, we provide tabulated values for X-ray form factors via `XRayFormFactors`. Here, there are two possible sources that can be specified, depending on the desired parametrization: `source='waasmaier-1995'` (taken from table 1 from D. Waasmaier, A. Kirfel, Acta Crystallographica Section A **51**, 416 (1995); [doi: 10.1107/S0108767394013292](https://doi.org/10.1107/S0108767394013292)) or `source='itc-2006'` (taken from Table 6.1.1.4. from *International Tables for Crystallography, Volume C: Mathematical, physical, and chemical tables* (2006)). If no source is specified, the default is `waasmaier-1995`. This is a convenience function, but if you would like to use a different source for the X-ray form factors, recall that it is possible to manually create a `Weights` object." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "weights coherent:\n", " Al: {'method': 'RHF', 'a1': 4.730796, 'b1': 3.620931, 'a2': 2.313951, 'b2': 43.051166, 'a3': 1.54198, 'b3': 0.09596, 'a4': 1.117564, 'b4': 100.932109, 'a5': 3.154754, 'b5': 1.555918, 'c': 0.139509}\n", " Ni: {'method': 'RHF', 'a1': 13.521865, 'b1': 4.077277, 'a2': 6.947285, 'b2': 0.286763, 'a3': 3.866028, 'b3': 14.622634, 'a4': 2.1359, 'b4': 71.966078, 'a5': 4.284731, 'b5': 0.004437, 'c': -2.762697}\n" ] } ], "source": [ "xray_weights = XRayFormFactors(['Ni', 'Al'])\n", "print(xray_weights)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "While the neutron scattering lengths are independent of $\\boldsymbol{q}$, the X-ray form factors have a $|\\boldsymbol{q}|$-dependence. For this reason, `XRayFormFactors` contains the parametrization, which allows for calculating the actual weight at the relevant $\\boldsymbol{q}$. Before performing the weighting, let's visualize the q-dependence of the Ni and Al X-ray form factors." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "q_plot = np.linspace(0,6)\n", "xray_ff_Ni_plot = [xray_weights.get_weight_coh('Ni', q) for q in q_plot]\n", "xray_ff_Al_plot = [xray_weights.get_weight_coh('Al', q) for q in q_plot]\n", "\n", "fig, ax = plt.subplots(figsize=(5, 3), ncols=1, nrows=1, dpi=140)\n", "ax.plot(q_plot, xray_ff_Al_plot, label='Al', color='C3')\n", "ax.plot(q_plot, xray_ff_Ni_plot, '--', label='Ni', color='C5')\n", "\n", "ax.set_xlabel(r'$|\\mathbf{q}|$ (rad/Å)')\n", "ax.set_ylabel(r'$f(|\\mathbf{q}|)$')\n", "ax.legend()\n", "\n", "fig.tight_layout()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "X-ray form factors roughly scale as the atomic number Z for different species, resulting in heavier elements being much more visible than lighter. This can be seen from the fact that Al (Z=13) and Ni (Z=28) differ by about a factor of two above.\n", "\n", "When actually using the `XRayFormFactors` to compute the weights, **dynasor** internally checks the $\\boldsymbol{q}$-values for which the raw sample has been computed and calculates the X-ray form factor values at these $\\boldsymbol{q}$-values, which means that computing the weighted sample is easily done in the same way as before:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "sample_xray = get_weighted_sample(sample_raw, xray_weights)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can now compare the sample weighted with neutron scattering lengths to the sample weighted with the X-ray form factors. Again, we'll look at some slices of $S(\\boldsymbol{q}, \\omega)$ in $\\boldsymbol{q}$-space." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, axes = plt.subplots(figsize=(10.5, 5.4), ncols=3, nrows=4, dpi=140, sharex=True)\n", "\n", "qids = [48, 60]\n", "\n", "for qid in qids:\n", " label = r'$\\mathbf{q} =$'\n", " label += np.array_str(sample_raw.q_points[qid], precision=2, suppress_small=True)\n", " label += ' rad/Å'\n", "\n", " df = sample_raw.to_dataframe(qid)\n", " axes[0][0].plot(df.omega, df.Sqw_coh)\n", " axes[1][0].plot(df.omega, df.Sqw_coh_Al_Al)\n", " axes[2][0].plot(df.omega, df.Sqw_coh_Ni_Ni)\n", " axes[3][0].plot(df.omega, df.Sqw_coh_Al_Ni)\n", " \n", " df = sample_neutron.to_dataframe(qid)\n", " axes[0][1].plot(df.omega, df.Sqw_coh)\n", " axes[1][1].plot(df.omega, df.Sqw_coh_Al_Al)\n", " axes[2][1].plot(df.omega, df.Sqw_coh_Ni_Ni)\n", " axes[3][1].plot(df.omega, df.Sqw_coh_Al_Ni)\n", "\n", " df = sample_xray.to_dataframe(qid)\n", " axes[0][2].plot(df.omega, df.Sqw_coh)\n", " axes[1][2].plot(df.omega, df.Sqw_coh_Al_Al)\n", " axes[2][2].plot(df.omega, df.Sqw_coh_Ni_Ni)\n", " axes[3][2].plot(df.omega, df.Sqw_coh_Al_Ni, label=label)\n", "\n", "ax = axes[0][0]\n", "ax.set_ylabel(r'$S(\\omega)$')\n", "ax.set_title('Raw')\n", "ax.set_xlim(0, 60)\n", "\n", "axes[1][0].set_ylabel(r'$S_{AlAl}(\\omega)$')\n", "axes[2][0].set_ylabel(r'$S_{NiNi}(\\omega)$')\n", "axes[3][0].set_ylabel(r'$S_{NiAl}(\\omega)$')\n", "axes[0][1].set_title('Neutron weighted')\n", "axes[0][2].set_title('X-ray weighted')\n", "axes[3][2].legend(fontsize='small')\n", "for ax in axes[-1]:\n", " ax.set_xlabel('Energy (meV)')\n", "\n", "fig.subplots_adjust(hspace=0, wspace=0.3)\n", "fig.align_labels(axes)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The first thing to note is that the intensities of the total structure factor (first row) vary depending on the probe, which reflects the fact that the interaction strength between a probe and an atomic species varies depending on the probe. \n", "\n", "Furthermore, it is possible to see the effect of the $|\\boldsymbol{q}|$-dependence of the X-ray form factors in these figures.\n", "Take, for instance, the Al-Al partial structure factor (second row).\n", "When weighted with neutron scattering lengths, which do not depend on $\\boldsymbol{q}$, the ratio between the curves for the different $\\boldsymbol{q}$-points does not change.\n", "For the X-ray form factors, however, we see that the low-frequency peak of the orange curve becomes higher than the low-frequency peak of the blue curve, opposite of the situation in the raw Al-Al partial structure factor, i.e., the ratio between the curves for different $\\boldsymbol{q}$-points changes.\n", "This is an effect of the $|\\boldsymbol{q}|$-dependence of the X-ray form factors.\n", "The orange curve is a slice at a $\\boldsymbol{q}$-point with norm 0.26 rad/Å, while the blue curve is a slice at a $\\boldsymbol{q}$-point with norm 1.43 rad/Å.\n", "As a result, the latter has a smaller X-ray form factor according to the previous plot that shows the $|\\boldsymbol{q}|$-dependence of the X-ray form factors." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Smoothing data\n", "\n", "Finally, we demonstrate how one can easily smooth data using the `rolling` method of the `DataFrame` object.\n", "Here, we apply a Hamming window with a size of 20 along the energy axis.\n", "You can find more information about windowing operations in pandas on [this page](https://pandas.pydata.org/docs/user_guide/window.html)." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, axes = plt.subplots(figsize=(10.5, 5.4), ncols=3, nrows=4, dpi=140, sharex=True)\n", "\n", "qids = [48, 60]\n", "\n", "for qid in qids:\n", " label = r'$\\mathbf{q} =$'\n", " label += np.array_str(sample_raw.q_points[qid], precision=2, suppress_small=True)\n", " label += ' rad/Å'\n", "\n", " window = 20\n", " win_type = 'hamming'\n", " df = sample_raw.to_dataframe(qid)\n", " df = df.rolling(window=window, win_type=win_type).mean()\n", " axes[0][0].plot(df.omega, df.Sqw_coh)\n", " axes[1][0].plot(df.omega, df.Sqw_coh_Al_Al)\n", " axes[2][0].plot(df.omega, df.Sqw_coh_Ni_Ni)\n", " axes[3][0].plot(df.omega, df.Sqw_coh_Al_Ni)\n", " \n", " df = sample_neutron.to_dataframe(qid)\n", " df = df.rolling(window=window, win_type=win_type).mean()\n", " axes[0][1].plot(df.omega, df.Sqw_coh)\n", " axes[1][1].plot(df.omega, df.Sqw_coh_Al_Al)\n", " axes[2][1].plot(df.omega, df.Sqw_coh_Ni_Ni)\n", " axes[3][1].plot(df.omega, df.Sqw_coh_Al_Ni)\n", "\n", " df = sample_xray.to_dataframe(qid)\n", " df = df.rolling(window=window, win_type=win_type).mean()\n", " axes[0][2].plot(df.omega, df.Sqw_coh)\n", " axes[1][2].plot(df.omega, df.Sqw_coh_Al_Al)\n", " axes[2][2].plot(df.omega, df.Sqw_coh_Ni_Ni)\n", " axes[3][2].plot(df.omega, df.Sqw_coh_Al_Ni, label=label)\n", "\n", "ax = axes[0][0]\n", "ax.set_ylabel(r'$S(\\omega)$')\n", "ax.set_title('Raw')\n", "ax.set_xlim(0, 60)\n", "\n", "axes[1][0].set_ylabel(r'$S_{AlAl}(\\omega)$')\n", "axes[2][0].set_ylabel(r'$S_{NiNi}(\\omega)$')\n", "axes[3][0].set_ylabel(r'$S_{NiAl}(\\omega)$')\n", "axes[0][1].set_title('Neutron weighted')\n", "axes[0][2].set_title('X-ray weighted')\n", "axes[3][2].legend(fontsize='small')\n", "for ax in axes[-1]:\n", " ax.set_xlabel('Energy (meV)')\n", "\n", "fig.subplots_adjust(hspace=0, wspace=0.3)\n", "fig.align_labels(axes)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.3" } }, "nbformat": 4, "nbformat_minor": 4 } dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/examples/acfs_and_ffts/000077500000000000000000000000001522712672400244505ustar00rootroot00000000000000dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/examples/acfs_and_ffts/.gitignore000066400000000000000000000000141522712672400264330ustar00rootroot00000000000000signal.txt* dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/examples/acfs_and_ffts/acfs_and_ffts.ipynb000066400000000000000000017434301522712672400303070ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "id": "c79222ed", "metadata": {}, "source": [ "# Autocorrelation functions and FFTs\n", "This tutorial provides a brief introduction to autocorrelation functions (ACFs), Fourier transforms, and numerical tools for obtaining less noisy results.\n", "\n", "For a complex time signal $x(t)$ the corresponding ACF is defined as\n", "$$C(\\tau) = \\left < x(t) x^*(t+\\tau) \\right >$$\n", "and is typically normalized such that $C(\\tau=0)=1$.\n", "\n", "## Damped harmonic oscillator\n", "Here, we will consider a basic example where the time signal, $x(t)$, comes from a damped harmonic oscillator (DHO).\n", "We use a natural frequency of the oscillator $f_0=1.0$ ($\\omega_0 = 2 \\pi$) and damping $\\Gamma=0.25$ (lifetime $\\tau=8$).\n", "\n", "## Input data\n", "\n", "In this example we will analyze a simple one-dimensional time signal, which can be found in [this Zenodo repository](https://zenodo.org/records/17858255).\n", "You can fetch this file, e.g., by executing the following cell." ] }, { "cell_type": "code", "execution_count": 1, "id": "608070cc-9644-4ff3-807c-93e3847cd811", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "--2026-03-16 10:16:35-- https://zenodo.org/records/17858255/files/signal.txt\n", "Resolving zenodo.org (zenodo.org)... 188.184.98.114, 188.185.43.153, 137.138.153.219, ...\n", "Connecting to zenodo.org (zenodo.org)|188.184.98.114|:443... connected.\n", "HTTP request sent, awaiting response... 200 OK\n", "Length: 765041 (747K) [text/plain]\n", "Saving to: ‘signal.txt.2’\n", "\n", "signal.txt.2 100%[===================>] 747.11K 3.41MB/s in 0.2s \n", "\n", "2026-03-16 10:16:36 (3.41 MB/s) - ‘signal.txt.2’ saved [765041/765041]\n", "\n" ] } ], "source": [ "!wget https://zenodo.org/records/17858255/files/signal.txt" ] }, { "cell_type": "code", "execution_count": 2, "id": "8eb6505f", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 3, "id": "2487484d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "signal shape (29999,)\n" ] } ], "source": [ "# DHO parameters\n", "dt = 0.05\n", "f0 = 1.0\n", "w0 = np.pi * 2 * f0\n", "gamma = 0.25\n", "\n", "# read signal data and make up time array\n", "signal = np.loadtxt('signal.txt')\n", "time = np.arange(0, len(signal), dt)\n", "print('signal shape', signal.shape)" ] }, { "cell_type": "markdown", "id": "9fe3db6a", "metadata": {}, "source": [ "## Signal in time\n", "Inspect a part of the signal in time." ] }, { "cell_type": "code", "execution_count": 4, "id": "2190eb40", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "t_max = 1000\n", "t_lim = [0, t_max * dt]\n", "fig = plt.figure(figsize=(5, 3), dpi=140)\n", "plt.plot(time[:t_max], signal[:t_max])\n", "plt.xlim(t_lim)\n", "plt.xlabel('Time')\n", "plt.ylabel('Signal');" ] }, { "cell_type": "markdown", "id": "15942b86", "metadata": {}, "source": [ "## Autocorrelation function\n", "Here, we compare the calculated ACF with the analytical known solution for a DHO.\n", "For longer time-scales we see the agreement become worse and worse due to the limited size of our time-signal (i.e. poor statistics)." ] }, { "cell_type": "code", "execution_count": 5, "id": "e4561d57", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from dynasor.tools.acfs import compute_acf\n", "from dynasor.tools.damped_harmonic_oscillator import acf_position_dho\n", "\n", "t_acf, acf_raw = compute_acf(signal, delta_t=dt)\n", "acf_analytical = acf_position_dho(t_acf, w0, gamma)\n", "\n", "fig = plt.figure(figsize=(5, 3), dpi=140)\n", "plt.plot(t_acf, acf_raw, lw=3.0, alpha=0.7, label='Raw data')\n", "plt.plot(t_acf, acf_analytical, '--', label='Analytical DHO')\n", "plt.legend(loc=1)\n", "plt.xlim(0, 30)\n", "plt.ylim([-1, 1])\n", "plt.xlabel('Time')\n", "plt.ylabel(r'ACF $C(\\tau)$');" ] }, { "cell_type": "markdown", "id": "855b9160", "metadata": {}, "source": [ "### Decay functions (smearing/smoothing)\n", "The ACF above oscillates during the first 50 timesteps, and then decays towards zero after about 100 timesteps.\n", "However, the ACF continues to oscillate even for longer times due to noise and insufficient sampling of the ACF.\n", "\n", "This can be mitigated by artificially making the ACF decay towards zero, by multiplying the ACF with a decaying function. \n", "`dynasor` provides some utility functions for these things, for example a gaussian decay function (`gaussian_decay`) and a Fermi-Dirac like decay function (`fermi_dirac`). \n", "But other functions could of course be used and may be more suitable depending on the shape of the ACF." ] }, { "cell_type": "code", "execution_count": 6, "id": "c2651309", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from dynasor.tools.acfs import fermi_dirac, gaussian_decay\n", "\n", "t_decay = 15\n", "decay_fermi = fermi_dirac(t_acf, t_0=1.4*t_decay, t_width=t_decay/5)\n", "decay_gauss = gaussian_decay(t_acf, t_sigma=t_decay)\n", "\n", "fig = plt.figure(figsize=(5, 3), dpi=140)\n", "plt.plot(t_acf, decay_fermi, label='Fermi-Dirac')\n", "plt.plot(t_acf, decay_gauss, label='Gaussian')\n", "plt.legend(loc=1)\n", "plt.xlim(t_lim)\n", "plt.xlabel('Time')\n", "plt.ylabel('Smearing function');" ] }, { "cell_type": "markdown", "id": "72ade16c", "metadata": {}, "source": [ "For simplicity in this tutorial we will stick to using a Gaussian decay function, $f(t)$, defined as\n", "\\begin{equation}\n", "f(t) = \\exp{\\left [-\\frac{1}{2} \\left (\\frac{t}{t_\\mathrm{sigma}}\\right )^2 \\right ] }\n", "\\end{equation}\n", "where the parameter $t_\\mathrm{sigma}$ is the \"decay time\" of the function." ] }, { "cell_type": "code", "execution_count": 7, "id": "0e0094b9", "metadata": {}, "outputs": [], "source": [ "# construct ACFs with decay functions applied\n", "decay_times = [20, 10]\n", "acf_gauss = dict()\n", "for t_sigma in decay_times: \n", " acf_gauss[t_sigma] = acf_raw * gaussian_decay(t_acf, t_sigma=t_sigma)" ] }, { "cell_type": "code", "execution_count": 8, "id": "ea8f6aa4", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(5, 3), dpi=140)\n", "ax = fig.add_subplot(111)\n", "\n", "\n", "plt.plot(t_acf, acf_raw, lw=3.0, alpha=0.7, label='Raw data')\n", "for t_sigma, acf in acf_gauss.items():\n", " ax.plot(t_acf, acf, '--', lw=2.0, label=f'ACF w Gaussian, t_sigma={t_sigma}')\n", "\n", "ax.legend(loc=1)\n", "ax.set_xlim([0, 30])\n", "ax.set_ylim([-1, 1])\n", "ax.set_xlabel('Time')\n", "ax.set_ylabel(r'ACF $C(\\tau)$')\n", "fig.tight_layout();" ] }, { "cell_type": "markdown", "id": "02524f9f", "metadata": {}, "source": [ "## Fourier transforms and spectral\n", "An ACF can be Fourier transformed to obtain the spectrum of the time signal.\n", "Assuming the time signal is even we can use the `psd_from_acf` (power spectral density from autocorrelation function) function implemented in dynasor.\n", "Note one can also directly obtain the PSD of the time signal without computing the ACF using the `psd_from_time_signal` function.\n", "\n", "Note here that applying a decaying function in the time domain shows up as a smoothing or smearing function in the frequency domain. \n", "One can also apply smoothing/smearing functions directly to a spectrum (in the frequency domain rather than in the time domain), as demonstrated below with the helper function `smoothing_function`.\n", "The `window_size` chosen for the smearing in frequency is roughly equivalent to applying a decay function in the time domain with a decay time of `1/window_size`.\n", "There are many different window functions/shapes to choose from; here we simply employ the default (Hamming).\n", "\n", "Note one can also zero-pad the ACF (and apply other FFT tricks) to get a smoother-looking spectrum." ] }, { "cell_type": "code", "execution_count": 9, "id": "6f288ef3", "metadata": {}, "outputs": [], "source": [ "from dynasor.tools.acfs import psd_from_acf, psd_from_time_signal\n", "from dynasor.tools.acfs import smoothing_function" ] }, { "cell_type": "code", "execution_count": 10, "id": "56f53a21", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "window_size in frequency 0.010003000900270079\n", "1/window_size in time 99.97000000000003\n", "window_size in frequency 0.020006001800540157\n", "1/window_size in time 49.985000000000014\n", "window_size in frequency 0.040012003601080315\n", "1/window_size in time 24.992500000000007\n" ] } ], "source": [ "n_max = 4999\n", "\n", "# raw FFT\n", "w, S_raw = psd_from_acf(acf_raw[:n_max], dt)\n", "f = w / 2 / np.pi\n", "delta_f = f[1] - f[0]\n", "\n", "# gaussian ACFs\n", "S_gauss = dict()\n", "for t_sigma, acf in acf_gauss.items():\n", " _, S = psd_from_acf(acf[:n_max], dt)\n", " S_gauss[t_sigma] = S\n", "\n", "# smearing\n", "S_smear = dict()\n", "for window_size in [5, 10, 20]:\n", " window = window_size * delta_f\n", " S_smear[window] = smoothing_function(S_raw, window_size=window_size)\n", " print('window_size in frequency', window)\n", " print('1/window_size in time', 1/window)" ] }, { "cell_type": "code", "execution_count": 11, "id": "67f98124", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from dynasor.tools.damped_harmonic_oscillator import psd_position_dho\n", "\n", "f_lin = np.linspace(0, 2, 10000)\n", "S_dho = psd_position_dho(f_lin * np.pi * 2, w0, gamma)\n", "\n", "fig = plt.figure(figsize=(7, 4), dpi=140)\n", "ax1 = fig.add_subplot(211)\n", "ax2 = fig.add_subplot(212)\n", "\n", "ax1.plot(f, S_raw, '-', lw=1.0, alpha=0.8, label='Raw data')\n", "ax1.plot(f_lin, S_dho, '--k', lw=3.0, alpha=0.8, label='Analytical DHO')\n", "for t_sigma, S in S_gauss.items():\n", " ax1.plot(f, S, '-', lw=2.0, label=f'Gaussian in time-domain, t_sigma={t_sigma}')\n", "\n", "ax2.plot(f, S_raw, '-', lw=1.0, alpha=0.8, label='raw')\n", "ax2.plot(f_lin, S_dho, '--k', lw=3.0, alpha=0.8, label='Analytical DHO')\n", "for t_sigma, S in S_smear.items():\n", " ax2.plot(f, S, '-', lw=2.0, label=f'Smearing in frequency-domain, window={t_sigma:.2f}')\n", "\n", "for ax in [ax1, ax2]:\n", " ax.legend(loc=1)\n", " ax.set_xlim([0.7, 1.6])\n", " ax.set_ylim([0, 10])\n", " ax.set_ylabel('Spectra')\n", "ax2.set_xlabel('Frequency')\n", "\n", "fig.tight_layout()" ] }, { "cell_type": "markdown", "id": "4db6921d", "metadata": {}, "source": [ "We expect the peak at 1.0 as this is the natural frequency of the DHO. The width of the peak is the damping $\\Gamma$.\n", "\n", "Note here that an overly aggressive decay function in time or smearing function in the frequency domain yields an artificially broader peak." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.16" } }, "nbformat": 4, "nbformat_minor": 5 } dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/examples/aluminium_liquid/000077500000000000000000000000001522712672400252375ustar00rootroot00000000000000dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/examples/aluminium_liquid/README.md000066400000000000000000000011341522712672400265150ustar00rootroot00000000000000# Example: Dynamic properties of liquid aluminium This directory contains files connected to an example illustrating the analysis of the dynamic properties of liquid aluminum. The molecular dynamics trajectory that is analyzed here can be downloaded (https://zenodo.org/records/10013486/files/dumpT1400.NVT.atom.velocity) or generated by running lammps using the input files in the `lammpsrun` directory. Please consult the [lammps documentation](https://www.lammps.org) for information on how to compile and run lammps. The analysis of the trajectory using **dynasor** is illustrated in the notebook. dynamic_correlation_functions.ipynb000066400000000000000000010541311522712672400343450ustar00rootroot00000000000000dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/examples/aluminium_liquid{ "cells": [ { "cell_type": "markdown", "id": "c587642a", "metadata": {}, "source": [ "# Dynamic properties of liquid Al\n", "\n", "This example demonstrates the analysis of the dynamics of liquid aluminium via the dynamic structure factor as well as current correlation functions.\n", "Here, we use a rather small simulation cell, a short trajectory, and few q-points in order to make the tutorial run fast.\n", "In practice one needs to tune these parameters as well as the parameters used when calling **dynasor** for optimal results.\n", "\n", "This example uses a trajectory that can be downloaded from Zenodo via\n", "```bash\n", "wget https://zenodo.org/records/10013486/files/dumpT1400.NVT.atom.velocity\n", "```\n", "or generated from scratch by running **lammps** using the input files provided." ] }, { "cell_type": "code", "execution_count": 1, "id": "48654e1f-28e9-4ba3-8626-c99b565f38a8", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "from dynasor import compute_dynamic_structure_factors, Trajectory\n", "from dynasor.qpoints import get_spherical_qpoints\n", "from dynasor.post_processing import get_spherically_averaged_sample_binned" ] }, { "cell_type": "code", "execution_count": 2, "id": "120dacab", "metadata": {}, "outputs": [], "source": [ "# set log level\n", "from dynasor.logging_tools import set_logging_level\n", "set_logging_level('INFO')" ] }, { "cell_type": "markdown", "id": "c0c54c87", "metadata": {}, "source": [ "## Set up the trajectory for reading\n", "\n", "**dynasor** supports multiple different trajectory formats via internal readers as well as via [MDAnalysis](https://www.mdanalysis.org/) and [ASE](https://docs.ase-lib.org/).\n", "Here, we use the internal lammps reader." ] }, { "cell_type": "code", "execution_count": 3, "id": "4ad31df7", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "INFO 2025-08-25 10:36:46: Assuming the trajectory has the default length unit (Ångström), since no unit was specified.\n", "INFO 2025-08-25 10:36:46: Assuming the trajectory has the default time unit (fs), since no unit was specified.\n", "INFO 2025-08-25 10:36:46: Trajectory file: dumpT1400.NVT.atom.velocity\n", "INFO 2025-08-25 10:36:46: Total number of particles: 2048\n", "INFO 2025-08-25 10:36:46: Number of atom types: 1\n", "INFO 2025-08-25 10:36:46: Number of atoms of type X: 2048\n", "INFO 2025-08-25 10:36:46: Simulation cell (in Angstrom):\n", "[[34.032 0. 0. ]\n", " [ 0. 34.032 0. ]\n", " [ 0. 0. 34.032]]\n" ] } ], "source": [ "trajectory_filename = 'dumpT1400.NVT.atom.velocity'\n", "traj = Trajectory(\n", " trajectory_filename,\n", " trajectory_format='lammps_internal',\n", " frame_stop=5000)" ] }, { "cell_type": "markdown", "id": "5dc15be0-1633-42eb-9075-68de4921a654", "metadata": {}, "source": [ "A short summary of the content of the `traj` object can be obtained via the `display` (in notebooks) or `print` (in notebooks or scripts) commands." ] }, { "cell_type": "code", "execution_count": 4, "id": "5f9a1116-fa79-42a0-8ed7-bb1989f9ce65", "metadata": {}, "outputs": [ { "data": { "text/html": [ "

Trajectory

\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
FieldValue
File namedumpT1400.NVT.atom.velocity
Number of atoms2048
Cell metric[[34.032 0. 0. ]\n", " [ 0. 34.032 0. ]\n", " [ 0. 0. 34.032]]
Frame step1
Atom types['X']
" ], "text/plain": [ "Trajectory\n", "filename : dumpT1400.NVT.atom.velocity\n", "natoms : 2048\n", "frame_start : 0\n", "frame_stop : 5000\n", "frame_step : 1\n", "frame_index : 0\n", "cell : [[34.032 0. 0. ]\n", " [ 0. 34.032 0. ]\n", " [ 0. 0. 34.032]]" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "display(traj)" ] }, { "cell_type": "markdown", "id": "bfe8dedb", "metadata": {}, "source": [ "## Select **q**-points to sample\n", "\n", "Next we specify which **q**-points should be sampled.\n", "Here, we consider **q**-points on a three-dimensional mesh up to a maximum of |**q**| = 2 rad/Å with the maximum number of **q**-points set to 2000.\n", "\n", "Note here that the units of q-points are technically rad/Å, however most commonly people will just denote this as 1/Å." ] }, { "cell_type": "code", "execution_count": 5, "id": "d1381597", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "INFO 2025-08-25 10:36:46: Pruning q-points from the range 1.7 < |q| < 2\n", "INFO 2025-08-25 10:36:46: Pruned from 5377 q-points to 5031\n" ] } ], "source": [ "q_points = get_spherical_qpoints(traj.cell, q_max=2, max_points=5000)" ] }, { "cell_type": "markdown", "id": "1cd74ef6", "metadata": {}, "source": [ "It is useful to generate a histogram to check how many $\\mathbf{q}$-points are sampled as a function of $|\\mathbf{q}|$." ] }, { "cell_type": "code", "execution_count": 6, "id": "c738a69e", "metadata": {}, "outputs": [ { "data": { "image/png": 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+HQqFAkIIJCUlISkpSe96pxACCoWCwUlERCZLtuBMTEyUqykiIqJmi4/cI6JmQcpzYovWBDXiSIhq1qQvsiYiIjJ1ss04r127Vue63bt3l6tbIiKiJiVbcLq6utbp4QcKhQJPnz6Vq1siIqImJVtwTps2zWBwlpSUIC8vD4WFhfDz84Orq6tcXRIRETU52YIzKSmp2m1CCKxfvx5r167FX/7yF7m6JCIianJNsjhIoVBg0aJF8PT0xOLFi5uiSyIiokbRpKtqBwwYgOPHjzdll0RERLJq0uC8fPkyFwYREZFJa/Q3TWs0Gty8eRNJSUk4dOgQRo4c2dhdEhERNRrZgtPMzKzG21GEELC3t8f69evl6pKIiKjJyRac1b3E2szMDPb29hg4cCBmzJiBTp06ydUlERFRk5MtONPT0+VqiohINlKegUtUF3xWLRERkQSNEpxZWVnYvHkzVq9ejc2bNyMrK6vBbd68eRMbN25EQEAAunfvDgsLC3Tu3BmTJk3CmTNnDH6mtLQUERERcHFxgaWlJVxdXbF48WKUlZUZrK/RaJCQkAAvLy9YW1vD0dERwcHBuHLlSoPHT0RELYOsq2pPnTqFGTNm4McffwTwnxdXA4CHhwcSExMxZMiQerWdkJCA+Ph49OjRAwEBAXB0dERBQQGSk5ORnJyMPXv2YPLkydr6arUafn5+uHDhAgICAhAcHIzc3FysW7cOGRkZyMzMhJWVlU4fc+bMgUqlgqenJ+bPn49bt25h//79SE1NRXZ2Njw8POp5ZIiIqKWQLTgvXbqEgIAAPHz4EG+++Sb8/f3h5OSE27dvIy0tDampqQgMDER2djZeeeUVye0PGjQI6enp8PPz0yk/ceIERo4ciblz5+Ktt96CpaUlAGDt2rW4cOECoqKisGbNGm39JUuWID4+Hhs2bMDSpUu15WlpaVCpVPD19cXRo0dhYWEBAJgyZQrGjBmDefPmISUlpT6HhoiIWhCFEELI0dDkyZPx5Zdf4u9//ztGjRqlt/3rr7/GuHHjMHHiROzdu1eOLrUCAwORmpqKc+fOYcCAARBCoGvXrigtLcXt27ehVCq1ddVqNTp37oxOnTrh8uXL2vIpU6bgb3/7GzIyMuDr66vTvr+/P9LT03H16tV6vxKtaqZ9+vTpen2eqKVrrBdZc3FQy9WQF5o35HeybNc409PT8fbbbxsMTQAYNWoU3n77baSlpcnVpVbbtm0BAObmzybQBQUFuHXrFnx8fHRCEwCUSiV8fHxw5coVXL9+XWf8VdteFBgYCADIyMiQfexERGRaZAvOkpISuLm51VjHzc0NJSUlcnUJ4NkLtL/55hs4OTnBy8sLwLPgBFDtNcmq8qp6arUaxcXFcHNzQ5s2bWqtT0RErZds1zidnZ2RnZ1dY50zZ87A2dlZri5RUVGBkJAQPH78GPHx8drQqwpnOzs7g5+ztbXVqSe1fk2qW/yUn5+Pvn371vp5IiJq3mSbcY4bNw7p6emIjo5GeXm5zrby8nLExMQgLS0N48ePl6U/jUaD6dOnIzMzE7NmzUJISIgs7RIREdVEthlndHQ0/vGPf2DVqlXYsmULBg0ahJdffhk//fQTzp07h7t378Ld3R3R0dEN7kuj0WDmzJnYs2cPpk6dii+++EJne9XMsboZYmlpqU49qfVrUt2F5vrehkNERM2LbMHZoUMHZGdnIzIyEnv37sXhw4e126ysrDBjxgzEx8fDwcGhQf1oNBrMmDEDO3fuRHBwMJKSkmBmpjtxru2a5IvXQJVKJZycnFBYWIjKykq965y1XTMlIqLWQ9YnB3Xs2BHbt29HSUkJ8vLycOLECeTl5aGkpAR/+ctf0LFjxwa1/3xoTp48Gbt27ap2MY+zszOysrKgVqt1tqnVamRlZcHNzQ3dunXTlvv5+Wm3vajq/s0Xb1MhIqLWp8HBGRcXh2XLlqGiokJb1rZtW3h5ecHHxwdeXl4QQuAPf/iDzoMIpKo6Pbtz506888472L17t8HQBACFQoGwsDCUlZUhNjZWZ1tsbCzKysowa9YsnfLZs2cDeHbK+cmTJ9ryI0eOID09HQEBAXBxcan3+ImIqGVo0Knab775Bh9//DHWrl2rvZfSEAsLC3To0AGLFy/G66+/Dn9/f8l9rVy5Ejt27ICNjQ169eqFTz/9VK/OW2+9hf79+wMAIiMjcejQIcTHxyM3Nxfe3t7IyclBamoqBg4ciPDwcJ3P+vv7IywsDCqVCt7e3ggKCkJxcTH27dsHBwcHJCQkSB4zERG1PA0Kzp07d8Le3h7z5s2rte6HH36I1atXIzExsV7BWVRUBAAoKytDXFycwTqurq7a4FQqlcjIyMCKFStw4MABpKWlwcnJCQsXLkRMTAysra31Pr9lyxZ4eXlh69at2LRpE2xsbDBhwgTExcWhR48eksdMREQtT4MeudezZ08MGDCgzo/QCw4OxtmzZ3Ueddda8JF7RDXjI/dIKpN85N6tW7fg7u5e5/pubm4oLi5uSJdERERG1aDgNDMz01kUVJuKigq9W0eIiIhMSYNSzNnZGfn5+XWun5+fjy5dujSkSyIiIqNqUHAOGzYMx48f1y7cqUlRURGOHz/OeyGJiMikNWhV7YcffojExES8/fbb+Prrr6t9wMG9e/fwzjvv4OnTp5g7d25DuiSiRtJYi3OIWpoGBae3tzfCw8OxceNGvPLKK/jggw/g7++Prl27AgBu3ryJY8eOYevWrbh79y4iIiLg7e0ty8CJiIiMocHPql2/fj2srKzwxz/+EXFxcXr3WAoh0KZNGyxdutTgQwuIiIhMSYODU6FQYNWqVXj//feRmJiIU6dO4fbt2wCAzp07w8fHB9OnT+cDBIiIqEWQ7e0oPXr04IySiJoEH2pAxsSbKomIiCRgcBIREUnA4CQiIpKAwUlERCQBg5OIiEgCBicREZEEDE4iIiIJGJxEREQSyPYABCKiF/FBBdQSccZJREQkAYOTiIhIAgYnERGRBAxOIiIiCRicREREEjA4iYiIJGBwEhERScDgJCIikoDBSUREJAGDk4iISAIGJxERkQQMTiIiIgkYnERERBIwOImIiCRgcBIREUnA4CQiIpKAwUlERCQBg5OIiEgCkwrO3bt3Y86cORgwYAAsLS2hUCiQlJRUbf3S0lJERETAxcUFlpaWcHV1xeLFi1FWVmawvkajQUJCAry8vGBtbQ1HR0cEBwfjypUrjbRHRERkakwqOJcvX46tW7fi6tWrcHJyqrGuWq2Gn58fNmzYgD59+mDBggXo3bs31q1bhxEjRqC8vFzvM3PmzMH8+fMhhMD8+fMxatQoHDx4EAMHDkRBQUFj7RYREZkQkwpOlUqFoqIi3L17Fx988EGNddeuXYsLFy4gKioKKSkpWLNmDVJSUhAVFYVz585hw4YNOvXT0tKgUqng6+uLnJwcxMfHY9euXUhOTsb9+/cxb968xtw1IiIyEebGHoAUb7zxRp3qCSGgUqlgY2OD6OhonW3R0dHYvHkzVCoVli5dqi3ftm0bACA2NhYWFhba8tGjR2P48OFITU3FtWvX0L17dxn2hMi0uS75p7GHQGQ0JjXjrKuCggLcunULPj4+UCqVOtuUSiV8fHxw5coVXL9+XVuenp6u3faiwMBAAEBGRkbjDpyIiJq9FhucAODh4WFwe1V5VT21Wo3i4mK4ubmhTZs2tdYnIqLWy6RO1dZVSUkJAMDOzs7gdltbW516UuvXZMiQIQbL8/Pz0bdv31o/T0REzVuLnHESERE1lhY546yaOVY3QywtLdWpJ7V+TU6fPm2wvLqZKBERmZYWGZy1XZN88RqoUqmEk5MTCgsLUVlZqXeds7ZrpkRNScqK1qI1QY04EqLWqUWeqvXw8ICzszOysrKgVqt1tqnVamRlZcHNzQ3dunXTlvv5+Wm3vSglJQUA4Ovr27gDJyKiZq9FBqdCoUBYWBjKysoQGxursy02NhZlZWWYNWuWTvns2bMBPLvP88mTJ9ryI0eOID09HQEBAXBxcWn8wRMRUbNmUqdqVSoVTp48CQC4ePGitiw9PR0AMHToUISFhQEAIiMjcejQIcTHxyM3Nxfe3t7IyclBamoqBg4ciPDwcJ22/f39ERYWBpVKBW9vbwQFBaG4uBj79u2Dg4MDEhISmmw/qfXhAwWITIdJBefJkyexY8cOnbKsrCyd06tVwalUKpGRkYEVK1bgwIEDSEtLg5OTExYuXIiYmBhYW1vrtb9lyxZ4eXlh69at2LRpE2xsbDBhwgTExcWhR48ejbtzRERkEhRCCGHsQbQGVatqq1t1S61bY804pSwO4qyXTE1DFr815Hdyi7zGSURE1FgYnERERBIwOImIiCQwqcVBRCQNr1sSyY8zTiIiIgkYnERERBIwOImIiCRgcBIREUnA4CQiIpKAwUlERCQBg5OIiEgCBicREZEEDE4iIiIJGJxEREQS8JF7RBJJeYxdQ157RETNE4OTCHymKxHVHU/VEhERScDgJCIikoCnasmkmNr1RZ4CJmp5OOMkIiKSgMFJREQkAYOTiIhIAgYnERGRBAxOIiIiCRicREREEjA4iYiIJGBwEhERScDgJCIikoDBSUREJAGDk4iISAIGJxERkQQMTiIiIgkYnERERBIwOImIiCRgcBIREUnA4CQiIpKAwWnAuXPnMGbMGLRv3x5KpRKDBw/G/v37jT0sIiJqBsyNPYDmJi0tDYGBgbCyssJ7772Hdu3a4cCBA5g8eTKuX7+OhQsXGnuIRuW65J91rlu0JqgRR1I7KWMlIqorzjif8/TpU8yaNQtmZmbIzMzE1q1bsX79euTl5aFXr15YtmwZrl69auxhEhGREXHG+Zzjx4/j8uXLmDFjBvr3768tt7Ozw7JlyzB9+nTs2LEDH3/8sfEGaUJMaXZKRFRXDM7npKenAwACAgL0tgUGBgIAMjIymnJITaI5nNJsDmMgIqoLnqp9TkFBAQDAw8NDb1vnzp1hY2OjrUNERK0TZ5zPKSkpAfDs1Kwhtra22jrVGTJkiMHy8+fPw8rKqtrtdZFz7Zc61/Xubl/nusUS2iUiai6GZHxa78/m5+fjpZdeqtdnGZxNxNzcvN5/ScCzv2QLAH379pVvUP+flJA1dfn5+QAa5zi2FjyGDcdj2HD5+fnIz79Z72P40ksvwd6+fr/7GJzPqZppVjerLC0trfVAnz59WvZxAf+ZyTZW+60Fj2PD8Rg2HI9hwxnzGPIa53Oqrm0auo55+/ZtlJWVGbz+SURErQeD8zl+fn4AgNTUVL1tKSkpOnWIiKh1YnA+Z+TIkXB3d8eePXtw4cIFbXlJSQlWrVoFCwsLTJs2zXgDJCIio+M1zueYm5tDpVIhMDAQvr6+Oo/cu3r1KtatWwdXV1djD5OIiIyIwfkCf39/nDx5EjExMdi3bx8qKirg5eWF+Ph4TJ482djDIyIiI1MIIYSxB0FERGQqeI2TiIhIAgYnERGRBAxOIiIiCRicREREEjA4iYiIJGBwEhERScDgNKJz585hzJgxaN++PZRKJQYPHoz9+/dLauPx48dYuXIlPDw8YGVlBWdnZ8yePRt37txppFE3Lw09hklJSVAoFNX+qXq5eUu2e/duzJkzBwMGDIClpSUUCgWSkpIkt6PRaJCQkAAvLy9YW1vD0dERwcHBuHLlivyDbmbkOIbp6ek1fhfr83diKm7evImNGzciICAA3bt3h4WFBTp37oxJkybhzJkzktpqiu8hH4BgJGlpaQgMDISVlZXOE4omT56M69evY+HChbW2odFoMH78eKSkpGDw4MGYNGkSCgoKoFKpcOzYMWRnZ8PR0bEJ9sY45DiGVcaPH4/+/fvrlbeGJ0UtX74cV69eRceOHeHk5ISrV6/Wq505c+ZApVLB09MT8+fPx61bt7B//36kpqYiOzu7Rb8gQa5jCDx7Hvbw4cP1yg19P1uKhIQExMfHo0ePHggICICjoyMKCgqQnJyM5ORk7Nmzp84PoGmS76GgJldRUSF69OghLC0tRW5urrb8119/Fb169RIWFhaiqKio1na2b98uAIjg4GCh0Wi05Z9//rkAIGbPnt0Yw28W5DqGiYmJAoBITExsvME2c0ePHtUeq9WrV9freBw/flwAEL6+vuLx48fa8sOHDwsAIiAgQM4hNztyHMO0tDQBQMTExMg/wGbuwIEDIj09Xa88MzNTtG3bVtjb24vy8vJa22mq7yFP1RrB8ePHcfnyZUyZMkXnX5F2dnZYtmwZnjx5gh07dtTazrZt2wAAq1evhkKh0JbPmTMH7u7u+Otf/4pHjx7JPv7mQK5jSMAbb7wBFxeXBrVR9V2MjY2FhYWFtnz06NEYPnw4UlNTce3atQb10ZzJcQxbs4kTJxp889SwYcPg7++PX375BRcvXqy1nab6HjI4jaDqullAQIDetsDAQABARkZGjW2Ul5fjzJkz6N27t97/sAqFAm+++SbUajXOnz8vz6CbGTmO4fNyc3Oxfv16xMfHY9++fbh3754s42wt0tPToVQq4ePjo7etPn8frVlBQQE2btyI1atXY9euXbh586axh2RUbdu2BfDsJRy1aarvIa9xGkHVi7INnWvv3LkzbGxsDL5M+3mXL1+GRqOp9nz98y/lHjZsWANH3PzIcQyf99lnn+n8bG1tjZiYGERFRTVsoK2AWq1GcXEx+vbtizZt2uhtr+kF8aRvz5492LNnj/Znc3Nz/P73v8cf//hHg8e3Jbt27Rq++eYbODk5wcvLq8a6Tfk95IzTCEpKSgA8O61oiK2trbZOQ9p4vl5LI8cxBAA3NzckJCTghx9+wMOHD3Hjxg3s3LkTDg4OWLJkCRISEmQdd0vU2r+LcnF0dMSaNWuQn5+PsrIy/PTTT0hOTkbPnj2xYcMGREZGGnuITaqiogIhISF4/Pgx4uPja/1HQ1N+Dxmc1Kr5+flh3rx58PDwgLW1Nbp06YKQkBCkpKTAysoKK1aswNOnT409TGoFPD09ERUVBU9PTyiVSnTq1Anjx49HWloaHB0d8dlnn7Wa28w0Gg2mT5+OzMxMzJo1CyEhIcYekg4GpxFU/Yuoun/5lJaWVvuvJiltPF+vpZHjGNbE09MTQ4cOxf379/Htt9/Wu53WoLV/Fxtb586dMX78eDx9+lTyPY2mSKPRYObMmdizZw+mTp2KL774ok6fa8rvIYPTCGo613779m2UlZXVeq+Ru7s7zMzMqj1fX9M1wJZAjmNYm44dOwJ4du2EqqdUKuHk5ITCwkJUVlbqbW/p38Wm0Fq+ixqNBjNmzMCOHTsQHByMpKQkmJnVLaaa8nvI4DSCqmXXqampettSUlJ06lTH2toagwYNwvfff693s7UQAkePHoVSqcSAAQNkGnXzIscxrEllZaV2RTJvM6idn58f1Go1srKy9LZV/X34+vo29bBajKqZZkt+IEdVaO7cuROTJ0/Grl27JC+GarLvoSx3g5IkFRUVwt3dvcab9wsLC7Xlt27dEt9++6349ddfddpp7Q9AkOMYnj9/Xq/tp0+fikWLFgkAwt/fv7F2oVmq7eb9u3fvim+//VbcvXtXp7y1PwDhefU9hoa+i0IIsXHjRgFAeHh4iKdPn8o93GahsrJShIaGCgDinXfeERUVFTXWN/b3kMFpJMePHxdt27YV7dq1E7NmzRIRERHCxcVFABDr1q3TqVv1hXrxf8TKykoRGBgoAIjBgweLqKgoMWnSJKFQKISbm5u4c+dOE+5R05PjGAIQ/fr1E1OnThVRUVFi1qxZolevXgKA6Nq1q7h8+XIT7pFxbNu2TYSGhorQ0FDh7e0tAAgfHx9t2bZt27R1Y2Jiqn26TVhYmAAgPD09RWRkpAgJCREWFhbCwcFBfP/99024R01PjmPo4uIievbsKd577z2xaNEiMXfuXPHaa68JAKJ9+/bizJkzTbxXTafqmNjY2Ig//OEPIiYmRu/P8/9ANvb3kMFpRGfOnBGjRo0Stra2wtraWgwaNEjs3btXr151v/SFEKK8vFysWLFC9OjRQ1hYWIjOnTuLsLAwcfv27SbYA+Nr6DFcuHCh8PHxES+//LJo27atUCqV4tVXXxXLly8X9+/fb6K9MK6qY1Pdn9DQUG3dmn5hVVZWik2bNglPT09haWkpOnToICZPnix+/PHHptsZI5HjGK5Zs0b4+/sLZ2dnYWlpKaytrUWfPn1EeHi4uH79etPuUBOr7fi9+P+usb+HCiGEaPgJXyIiotaBi4OIiIgkYHASERFJwOAkIiKSgMFJREQkAYOTiIhIAgYnERGRBAxOIiIiCRicREREEjA4iYiIJGBwEhERScDgJCIikoDBSUREJAGDk4iISAJzYw+AiKi+zp8/j8zMTPTu3RtBQUHGHg61EgxOIjJJO3fuxIwZM6DRaAAA//3f/43NmzcbeVTUGvB9nERkklxdXTFu3DisWrUKycnJCAkJwdWrV9G9e3djD41aOAYnEZkkpVKJs2fPwtPTEwDQsWNHfP311xgwYICRR0YtHRcHERnBihUroFAoUFRUZNT2hBD4zW9+g4CAAFnGIVVRUREUCgWmT58u+bOTJk3Cn//8Z5SVlWH37t2wt7dH//79Ddb9/vvvYW5ujj//+c8NGzARGJxErdrOnTuRk5ODlStXGnso1Tp//jwUCgX+53/+R6f8888/x65du9CuXTuEhIRg+/btMDc3vGyjd+/eCA4OxieffIIHDx40xbCpBWNwErVSGo0GK1aswLBhwzB48GBjD6dahw4dAgCMHz9epzw3NxcPHjyAQqEAAJw4caLGdiIjI3Hnzh189tlnjTNQajUYnESt1JEjR1BUVIRp06YZeyg1OnToELy9vdGtWzed8h07dgAAFi5cqPNzdby8vNCvXz9s27ZNuxKXqD4YnETNiFqtRlRUFLp16wYrKyt4enriiy++QGFhIRQKhfZPenp6g/tKTEyEQqHApEmTdMrT09OhUCiwYsUKnDp1CgEBAWjfvr12ZvfkyRMkJCQgMDAQ3bp1g6WlJTp16oSJEyciNzfXYF+VlZWIj49Hz549YWVlhZ49e2L16tW1BlhhYSEuXryoN9t89OgR9u/fDysrKyxfvhyvv/46fvjhB5w+fbrG9t59911cvXoVaWlptR0eomoxOImaiYqKCgQGBmLt2rW4ceMGHj9+jH//+9+YO3cuPvroI1n7EkIgLS0NvXv3hr29vcE6p06dwvDhw6FQKDB79mxMnjwZAHD//n2Eh4fj8ePHGDNmDBYsWIDhw4fj8OHD+O1vf4tz587ptTV79mwsWbIEGo0GH374IQIDA/GnP/2p1v1KTk4GoH+a9uDBgygtLcW4ceNgZ2ennTXXNuscMmQIAODYsWM11iOqkSCiJhcTEyMAiMLCQm3Z2rVrBQDtn9dee01ER0eLCRMm6JQDEGlpabW2V5NLly4JAOJ3v/ud3ra0tDRtP9u3b9fbXl5eLm7cuKFXnp+fL2xsbMQbb7xhsL1XX31VlJWVactv3LghOnbsKACI0NBQg+P08/MTrq6ueuVvvPGGACC++uorIYQQP//8s7CwsBDt27cXjx49qna/S0pKBADh6+tbbR2i2nDGSdRMqFQq7X/37NkTp0+fxsqVK3Hw4EHMmjVL1r5u3LgBAHj55ZerrePt7Y0ZM2bolVtaWqJLly565Z6envD390dmZiYqKiq05Tt37gQAfPzxx1AqldryLl261DjjvHfvHk6ePKk327xx4waOHz+OTp06YdSoUQCADh06YMyYMfj111+1i4kMsbW1hZWVlXb/ieqDwUnUDDx48AA//PCD9udJkybB0tJS+/PUqVNl7e/evXsAgPbt21dbZ+DAgdVuu3DhAqZMmYLu3bvDwsJCe+31q6++wpMnT/Dzzz9r6+bl5QEAhg0bpteOobIq//znP1FZWakXnDt37oRGo0FwcLDO7SdVp2uTkpKqbRMAHBwcdMZHJBWfVUvUDJSUlOj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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(3.4, 2.5), dpi=140)\n", "plt.hist(np.linalg.norm(q_points, axis=1), bins=30)\n", "plt.xlabel(r'$|\\mathbf{q}|$ (rad/Å)')\n", "plt.ylabel('Counts')\n", "plt.tight_layout()" ] }, { "cell_type": "markdown", "id": "a417fa3c", "metadata": {}, "source": [ "## Run calculation of correlation functions\n", "\n", "We are now all set for calculating the dynamic structure factor as well as the current correlation functions (by setting `calculate_currents=True`).\n", "To obtain the correct units, the spacing between the frames (`dt`) must be specified in units of femtoseconds.\n", "\n", "The following cell should take a few minutes to run on a common workstation." ] }, { "cell_type": "code", "execution_count": 7, "id": "4bb75a22", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "INFO 2025-08-25 10:36:46: Spacing between samples (frame_step): 1\n", "INFO 2025-08-25 10:36:46: Time between consecutive frames in input trajectory (dt): 25.0 fs\n", "INFO 2025-08-25 10:36:46: Time between consecutive frames used (dt * frame_step): 25.0 fs\n", "INFO 2025-08-25 10:36:46: Time window size (dt * frame_step * window_size): 12500.0 fs\n", "INFO 2025-08-25 10:36:46: Angular frequency resolution: dw = 0.000251 rad/fs = 0.165 meV\n", "INFO 2025-08-25 10:36:46: Maximum angular frequency (dw * window_size): 0.125664 rad/fs = 82.713 meV\n", "INFO 2025-08-25 10:36:46: Nyquist angular frequency (2pi / frame_step / dt / 2): 0.125664 rad/fs = 82.713 meV\n", "INFO 2025-08-25 10:36:46: Calculating current (velocity) correlations\n", "INFO 2025-08-25 10:36:46: Number of q-points: 5031\n", "INFO 2025-08-25 10:37:06: Processing window 0 to 500\n", "INFO 2025-08-25 10:37:47: Processing window 1000 to 1500\n", "INFO 2025-08-25 10:38:28: Processing window 2000 to 2500\n", "INFO 2025-08-25 10:39:09: Processing window 3000 to 3500\n", "INFO 2025-08-25 10:39:50: Processing window 4000 to 4500\n", "CPU times: user 26min 33s, sys: 2.29 s, total: 26min 35s\n", "Wall time: 3min 28s\n" ] } ], "source": [ "%%time\n", "sample_raw = compute_dynamic_structure_factors(\n", " traj, q_points, dt=25.0, window_size=500,\n", " window_step=50, calculate_currents=True)" ] }, { "cell_type": "markdown", "id": "5dc2c4d9-1b63-4422-b176-5bce7d97fc49", "metadata": {}, "source": [ "As in the case of the trajectory, we can use the `display` or `print` commands to obtain a quick summary of the [Sample object](https://dynasor.materialsmodeling.org/reference/sample.html) returned by the [compute_dynamic_structure_factors function](https://dynasor.materialsmodeling.org/reference/correlation_functions.html#dynasor.compute_dynamic_structure_factors)." ] }, { "cell_type": "code", "execution_count": 8, "id": "347111c9", "metadata": {}, "outputs": [ { "data": { "text/html": [ "

DynamicSample

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Simulation

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NameContent
Angular frequency resolution0.0002513274122871834
Atom types['X']
Cell[[34.032 0. 0. ]\n", " [ 0. 34.032 0. ]\n", " [ 0. 0. 34.032]]
Maximum time lag12500.0
Number of frames5000
Particle counts{'X': 2048}
Time between frames25.0
\n", "

Dimensions

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FieldSize
omega(501,)
q_points(5031, 3)
time(501,)
\n", "

History

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FunctionFieldContent
compute_dynamic_structure_factorsdt25.0
window_size500
window_step50
calculate_currentsTrue
calculate_incoherentFalse
date_time2025-08-25T10:40:14
usernameerhart
hostnamepieweack
dynasor_version2.2
" ], "text/plain": [ "DynamicSample\n", "Angular frequency resolution: 0.0002513274122871834\n", "Atom types: ['X']\n", "Cell: [[34.032 0. 0. ]\n", " [ 0. 34.032 0. ]\n", " [ 0. 0. 34.032]]\n", "maximum_angular_frequency: 0.1256637061435917\n", "Maximum time lag: 12500.0\n", "Number of frames: 5000\n", "pairs: [('X', 'X')]\n", "Particle counts: {'X': 2048}\n", "Time between frames: 25.0\n", "omega with shape: (501,)\n", "q_points with shape: (5031, 3)\n", "time with shape: (501,)\n", "Clqt with shape: (5031, 501)\n", "Clqt_X_X with shape: (5031, 501)\n", "Clqw with shape: (5031, 501)\n", "Clqw_X_X with shape: (5031, 501)\n", "Ctqt with shape: (5031, 501)\n", "Ctqt_X_X with shape: (5031, 501)\n", "Ctqw with shape: (5031, 501)\n", "Ctqw_X_X with shape: (5031, 501)\n", "Fqt_coh with shape: (5031, 501)\n", "Fqt_coh_X_X with shape: (5031, 501)\n", "Sqw_coh with shape: (5031, 501)\n", "Sqw_coh_X_X with shape: (5031, 501)" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "display(sample_raw)" ] }, { "cell_type": "markdown", "id": "a42e4a46-98a1-4268-a19e-f37a13b86e5d", "metadata": {}, "source": [ "## Spherical average over **q**-points\n", "\n", "It is often of interest to take a spherical average over **q**-space, in particular in the case of liquids.\n", "To this end, we use the [get_spherically_averaged_sample_binned function](https://dynasor.materialsmodeling.org/reference/post_processing.html#dynasor.post_processing.get_spherically_averaged_sample_binned)." ] }, { "cell_type": "code", "execution_count": 9, "id": "205b591f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "WARNING 2025-08-25 10:40:14: No q-points for bin 1\n", "WARNING 2025-08-25 10:40:14: No q-points for bin 2\n", "WARNING 2025-08-25 10:40:14: No q-points for bin 1\n", "WARNING 2025-08-25 10:40:14: No q-points for bin 2\n", "WARNING 2025-08-25 10:40:14: No q-points for bin 1\n", "WARNING 2025-08-25 10:40:14: No q-points for bin 2\n", "WARNING 2025-08-25 10:40:14: No q-points for bin 1\n", "WARNING 2025-08-25 10:40:14: No q-points for bin 2\n", "WARNING 2025-08-25 10:40:14: No q-points for bin 1\n", "WARNING 2025-08-25 10:40:14: No q-points for bin 2\n", "WARNING 2025-08-25 10:40:14: No q-points for bin 1\n", "WARNING 2025-08-25 10:40:14: No q-points for bin 2\n", "WARNING 2025-08-25 10:40:14: No q-points for bin 1\n", "WARNING 2025-08-25 10:40:14: No q-points for bin 2\n", "WARNING 2025-08-25 10:40:14: No q-points for bin 1\n", "WARNING 2025-08-25 10:40:14: No q-points for bin 2\n", "WARNING 2025-08-25 10:40:14: No q-points for bin 1\n", "WARNING 2025-08-25 10:40:14: No q-points for bin 2\n", "WARNING 2025-08-25 10:40:14: No q-points for bin 1\n", "WARNING 2025-08-25 10:40:14: No q-points for bin 2\n", "WARNING 2025-08-25 10:40:14: No q-points for bin 1\n", "WARNING 2025-08-25 10:40:14: No q-points for bin 2\n", "WARNING 2025-08-25 10:40:14: No q-points for bin 1\n", "WARNING 2025-08-25 10:40:14: No q-points for bin 2\n" ] } ], "source": [ "sample_averaged = get_spherically_averaged_sample_binned(sample_raw, num_q_bins=30)" ] }, { "cell_type": "markdown", "id": "56541be4", "metadata": {}, "source": [ "## Visualize dynamic structure factor\n", "\n", "We can now visualize the isotropically averaged dynamic structure factor $S(|\\mathbf{q}|, \\omega)$, for example, in the form of a heatmap on the momentum-frequency plane.\n", "Note that the lower cutoff of the **q**-axis is set by the size of the supercell." ] }, { "cell_type": "code", "execution_count": 10, "id": "1ecb51b3", "metadata": {}, "outputs": [], "source": [ "from dynasor.units import radians_per_fs_to_meV as conversion_factor # conversion from 1/fs to meV\n", "sample_averaged.omega *= conversion_factor" ] }, { "cell_type": "code", "execution_count": 11, "id": "67f79475-1c84-4e95-9e87-ba522cc0c919", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(3.4, 2.5), dpi=140)\n", "\n", "ax.pcolormesh(sample_averaged.q_norms, sample_averaged.omega,\n", " sample_averaged.Sqw_coh.T,\n", " cmap='Blues', vmin=0, vmax=4)\n", "ax.text(0.05, 0.85, r'$S(|\\mathbf{q}|, \\omega)$', transform=ax.transAxes,\n", " bbox={'color': 'white', 'alpha': 0.8, 'pad': 3})\n", "ax.plot([0, 1], [0, 36], alpha=0.5, ls='--', c='0.3', lw=2)\n", "\n", "ax.set_xlabel(r'$|\\mathbf{q}|$ (rad/Å)')\n", "ax.set_ylabel('Energy (meV)')\n", "ax.set_ylim([0, 60])\n", "\n", "fig.tight_layout()" ] }, { "cell_type": "markdown", "id": "c0fdf257", "metadata": {}, "source": [ "### Visualize current correlations\n", "\n", "Next we plot the longitudinal and transverse current correlation heatmaps.\n", "In the longitudinal current correlation function one can observe the longitudinal branch that is also visible in the dynamic structure factor.\n", "Compared to the dynamic structure factor, the transverse current correlation function also reveals a transverse mode." ] }, { "cell_type": "code", "execution_count": 12, "id": "aaef9df9-9ce1-4cba-9421-e9660c9a4321", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, axes = plt.subplots(figsize=(3.4, 3.8), nrows=2, dpi=140,\n", " sharex=True, sharey=True)\n", "\n", "ax = axes[0]\n", "ax.pcolormesh(sample_averaged.q_norms, sample_averaged.omega,\n", " sample_averaged.Clqw.T, cmap='Reds', vmin=0, vmax=4000)\n", "ax.text(0.05, 0.85, r'$C_L(|\\mathbf{q}|, \\omega)$', transform=ax.transAxes,\n", " bbox={'color': 'white', 'alpha': 0.8, 'pad': 3})\n", "ax.plot([0, 1.5], [0, 54], alpha=0.5, ls='--', c='0.3', lw=2)\n", "\n", "ax = axes[1]\n", "ax.pcolormesh(sample_averaged.q_norms, sample_averaged.omega,\n", " sample_averaged.Ctqw.T, cmap='Oranges', vmin=0, vmax=4000)\n", "ax.text(0.05, 0.85, r'$C_T(|\\mathbf{q}|, \\omega)$', transform=ax.transAxes,\n", " bbox={'color': 'white', 'alpha': 0.8, 'pad': 3})\n", "ax.plot([0, 1.5], [0, 25], alpha=0.5, ls='--', c='0.3', lw=2)\n", "\n", "ax.set_xlabel(r'$|\\mathbf{q}|$ (rad/Å)')\n", "ax.set_ylabel('Energy (meV)', y=1)\n", "ax.set_ylim([0, 59])\n", "\n", "fig.tight_layout()\n", "plt.subplots_adjust(hspace=0)" ] }, { "cell_type": "markdown", "id": "f7adf136", "metadata": {}, "source": [ "## Slices in |**q**|-space\n", "\n", "Finally, we can also inspect individual slices as demonstrated in the following cell." ] }, { "cell_type": "code", "execution_count": 13, "id": "146212b5", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "q_inds = [6, 15, 24] # slices in heatmap\n", "\n", "fig, axes = plt.subplots(figsize=(6.2, 3.8), nrows=2, ncols=2,\n", " sharex='col', dpi=140)\n", "\n", "#-----\n", "# Intermediate scattering function and dynamic structure factor\n", "for q_ind in q_inds:\n", " label = fr'$|\\mathbf{{q}}|$={sample_averaged.q_norms[q_ind]:.2f} rad/Å'\n", " axes[0][0].plot(sample_averaged.time, sample_averaged.Fqt_coh[q_ind, :] * 100,\n", " label=label, alpha=0.8)\n", " axes[0][1].plot(sample_averaged.omega, sample_averaged.Sqw_coh[q_ind, :],\n", " label=label, alpha=0.8)\n", "\n", "ax = axes[0][0]\n", "ax.set_ylabel(r'$F(|\\mathbf{q}|, t) \\times 10^2$')\n", "ax.legend(frameon=False)\n", "\n", "ax = axes[0][1]\n", "ax.set_ylabel(r'$S(|\\mathbf{q}|, \\omega)$')\n", "\n", "#-----\n", "# Current correlation functions\n", "q_ind = q_inds[0]\n", "axes[1][0].plot(sample_averaged.time, sample_averaged.Clqt[q_ind, :],\n", " label='L')\n", "axes[1][0].plot(sample_averaged.time, sample_averaged.Ctqt[q_ind, :],\n", " label='T', c='C3')\n", "axes[1][1].plot(sample_averaged.omega, sample_averaged.Clqw[q_ind, :] / 1000,\n", " label='L')\n", "axes[1][1].plot(sample_averaged.omega, sample_averaged.Ctqw[q_ind, :] / 1000,\n", " label='T', c='C3')\n", "\n", "ax = axes[1][0]\n", "ax.set_xlabel('Time (fs)')\n", "ax.set_xlim([0, 1000])\n", "ax.set_ylabel(r'$C(|\\mathbf{q}|, t)$')\n", "ax.legend(frameon=False)\n", "ax.text(0.4, 0.85, fr'$|\\mathbf{{q}}|$={sample_averaged.q_norms[q_ind]:.2f} rad/Å',\n", " ha='center', transform=ax.transAxes)\n", "\n", "ax = axes[1][1]\n", "ax.set_xlabel('Energy (meV)')\n", "ax.set_xlim([0, 60]) \n", "ax.set_ylabel(r'$C(|\\mathbf{q}|, \\omega) \\times 10^{-3}$')\n", " \n", "fig.tight_layout()\n", "plt.subplots_adjust(hspace=0)\n", "fig.align_ylabels(axes)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.3" } }, "nbformat": 4, "nbformat_minor": 5 } dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/examples/aluminium_liquid/lammpsrun/000077500000000000000000000000001522712672400272555ustar00rootroot00000000000000dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/examples/aluminium_liquid/lammpsrun/.gitignore000066400000000000000000000000371522712672400312450ustar00rootroot00000000000000dump*.atom.velocity log.lammps Al99.eam.alloy000066400000000000000000027642441522712672400315420ustar00rootroot00000000000000dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/examples/aluminium_liquid/lammpsrun Al EAM from Phys. Rev. B 59, 3393 (1999) in the LAMMPS setfl format. Conversion by C. A. Becker from Y. Mishin files. 30 December 2008. http://www.ctcms.nist.gov/potentials 1 Al 10000 0.2000000000000000E-03 10000 0.6287210000000000E-03 0.6287210000000000E+01 13 0.2698200000E+02 0.4050000000E+01 fcc 0.1042222107228152E-09 -0.2833153928160009E-02 -0.5663286303573338E-02 -0.8490398933217783E-02 -0.1131449372829334E-01 -0.1413557260000000E-01 -0.1695363745953778E-01 -0.1976869021810666E-01 -0.2258073278690667E-01 -0.2538976707713778E-01 -0.2819579500000000E-01 -0.3099881846672711E-01 -0.3379883938868800E-01 -0.3659585967728534E-01 -0.3938988124392178E-01 -0.4218090600000000E-01 -0.4496893585675377E-01 -0.4775397272474132E-01 -0.5053601851435198E-01 -0.5331507513597510E-01 -0.5609114450000000E-01 -0.5886422851745782E-01 -0.6163432910194673E-01 -0.6440144816770674E-01 -0.6716558762897784E-01 -0.6992674940000000E-01 -0.7268493539341502E-01 -0.7544014751547178E-01 -0.7819238767082105E-01 -0.8094165776411355E-01 -0.8368795969999999E-01 -0.8643129538648219E-01 -0.8917166674496615E-01 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-0.7359100497226714E-07 -0.5789642944549939E-07 -0.4460603276266522E-07 -0.3352109479779516E-07 -0.2444230228083147E-07 -0.1717018845557266E-07 -0.1150495861170407E-07 -0.7246423756161589E-08 -0.4194276244510321E-08 -0.2147863624012600E-08 -0.9062527475633544E-09 -0.2684334141141742E-09 -0.3337655546827989E-10 dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/examples/aluminium_liquid/lammpsrun/main.in000066400000000000000000000023611522712672400305330ustar00rootroot00000000000000 # ---------- Initialize Simulation --------------------- clear units metal dimension 3 boundary p p p atom_style atomic atom_modify map array variable T equal 1400 # ---------- Create Structure --------------------- lattice fcc 4.254 region box block 0 1 0 1 0 1 units lattice create_box 1 box lattice fcc 4.254 orient x 1 0 0 orient y 0 1 0 orient z 0 0 1 create_atoms 1 box replicate 8 8 8 mass 1 26.98 # ---------- Define Interatomic Potential --------------------- pair_style eam/alloy pair_coeff * * Al99.eam.alloy Al neighbor 2.0 bin neigh_modify delay 10 check yes # ---------- Define Settings --------------------- compute eng all pe/atom compute eatoms all reduce sum c_eng # ---------- Run Equilibration --------------------- thermo 500 thermo_style custom step temp press vol lx ly lz pe ke etotal variable double_temp equal ${T}*2 velocity all create ${double_temp} 28459 rot yes dist gaussian mom yes ### NVT fix 1 all nvt temp $T $T 1.0 reset_timestep 0 timestep 5e-3 run 20000 # ---------- Run Sampling --------------------- thermo 100 thermo_style custom step temp press vol lx ly lz pe ke etotal reset_timestep 0 dump myDump all custom 5 dumpT${T}.NVT.atom.velocity id type xs ys zs vx vy vz run 50000 dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/examples/aluminium_solid/000077500000000000000000000000001522712672400250625ustar00rootroot00000000000000dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/examples/aluminium_solid/README.md000066400000000000000000000011471522712672400263440ustar00rootroot00000000000000# Example: Dynamic properties of crystalline aluminium This directory contains files connected to an example illustrating the analysis of the dynamic properties of crystalline aluminum. The molecular dynamics trajectory that is analyzed here can be downloaded (https://zenodo.org/records/10014454/files/dumpT300.NVT.atom.velocity.gz) or generated by running lammps using the input files in the `lammpsrun` directory. Please consult the [lammps documentation](https://www.lammps.org) for information on how to compile and run lammps. The analysis of the trajectory using **dynasor** is illustrated in the notebook.dynamic_correlation_functions.ipynb000066400000000000000000005675451522712672400342110ustar00rootroot00000000000000dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/examples/aluminium_solid{ "cells": [ { "cell_type": "markdown", "id": "c587642a", "metadata": {}, "source": [ "# Dynamic properties of FCC-Al\n", "\n", "This example demonstrates the analysis of the dynamics of crystalline aluminium in the face-centered cubic (FCC) structure via the dynamic structure factor as well as current correlation functions.\n", "Here, we use a rather small simulation cell (i.e., few **q**-points) and a short trajectory in order to make the tutorial run fast.\n", "In practice one needs to tune these parameters as well as the parameters used when calling **dynasor** for optimal results.\n", "\n", "This example uses a trajectory that can be downloaded from Zenodo via\n", "```bash\n", "wget https://zenodo.org/records/10014454/files/dumpT300.NVT.atom.velocity.gz\n", "```\n", "or generated from scratch by running **lammps** using the input files provided." ] }, { "cell_type": "code", "execution_count": 1, "id": "7867e2c8", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from ase import Atoms\n", "from ase.build import bulk\n", "from dynasor import compute_dynamic_structure_factors, Trajectory\n", "from dynasor.qpoints import get_supercell_qpoints_along_path\n", "from seekpath import get_path" ] }, { "cell_type": "markdown", "id": "c0c54c87", "metadata": {}, "source": [ "## Set up the trajectory\n", "\n", "**dynasor** supports multiple different trajectory formats via internal readers as well as via [MDAnalysis](https://www.mdanalysis.org/) and [ASE](https://docs.ase-lib.org/).\n", "Here, we use the internal lammps reader." ] }, { "cell_type": "code", "execution_count": 2, "id": "4ad31df7", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "INFO 2025-08-25 10:43:43: Assuming the trajectory has the default length unit (Ångström), since no unit was specified.\n", "INFO 2025-08-25 10:43:43: Assuming the trajectory has the default time unit (fs), since no unit was specified.\n", "INFO 2025-08-25 10:43:43: Trajectory file: dumpT300.NVT.atom.velocity.gz\n", "INFO 2025-08-25 10:43:43: Total number of particles: 6912\n", "INFO 2025-08-25 10:43:43: Number of atom types: 1\n", "INFO 2025-08-25 10:43:43: Number of atoms of type X: 6912\n", "INFO 2025-08-25 10:43:43: Simulation cell (in Angstrom):\n", "[[48.78 0. 0. ]\n", " [ 0. 48.78 0. ]\n", " [ 0. 0. 48.78]]\n" ] } ], "source": [ "trajectory_filename = 'dumpT300.NVT.atom.velocity.gz'\n", "traj = Trajectory(trajectory_filename,\n", " trajectory_format='lammps_internal', frame_stop=10000)" ] }, { "cell_type": "markdown", "id": "5dc15be0-1633-42eb-9075-68de4921a654", "metadata": {}, "source": [ "A short summary of the content of the `traj` object can be obtained via the `display` (in notebooks) or `print` (in notebooks or scripts) command." ] }, { "cell_type": "code", "execution_count": 3, "id": "5f9a1116-fa79-42a0-8ed7-bb1989f9ce65", "metadata": {}, "outputs": [ { "data": { "text/html": [ "

Trajectory

\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
FieldValue
File namedumpT300.NVT.atom.velocity.gz
Number of atoms6912
Cell metric[[48.78 0. 0. ]\n", " [ 0. 48.78 0. ]\n", " [ 0. 0. 48.78]]
Frame step1
Atom types['X']
" ], "text/plain": [ "Trajectory\n", "filename : dumpT300.NVT.atom.velocity.gz\n", "natoms : 6912\n", "frame_start : 0\n", "frame_stop : 10000\n", "frame_step : 1\n", "frame_index : 0\n", "cell : [[48.78 0. 0. ]\n", " [ 0. 48.78 0. ]\n", " [ 0. 0. 48.78]]" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "display(traj)" ] }, { "cell_type": "markdown", "id": "5416c7a3-2e0e-4bbf-b3c5-4accc31dc22b", "metadata": {}, "source": [ "## Set up path through Brillouin zone\n", "\n", "We now need to define the path through the Brillouin zone that we want to sample.\n", "The latter can be generated automatically for the crystal structure of interest using the [seekpath package](https://seekpath.readthedocs.io/).\n", "To find the q-points along this path that can be sampled using the supercell used in the molecular dynamics simulations we use the function `get_supercell_qpoints_along_path`." ] }, { "cell_type": "markdown", "id": "aedc5e43-ff41-4f6f-9d26-eb14f85cee79", "metadata": {}, "source": [ "The primitive structure is generated using the [bulk function](https://docs.ase-lib.org/ase/build/build.html#ase.build.bulk) from the [ASE package](https://docs.ase-lib.org/)." ] }, { "cell_type": "code", "execution_count": 4, "id": "57bd1cc6-3e47-40e9-8be9-99fbcc8a883d", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'GAMMA': [0.0, 0.0, 0.0],\n", " 'X': [0.5, 0.0, 0.5],\n", " 'L': [0.5, 0.5, 0.5],\n", " 'W': [0.5, 0.25, 0.75],\n", " 'W_2': [0.75, 0.25, 0.5],\n", " 'K': [0.375, 0.375, 0.75],\n", " 'U': [0.625, 0.25, 0.625]}" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "[('GAMMA', 'X'),\n", " ('X', 'U'),\n", " ('K', 'GAMMA'),\n", " ('GAMMA', 'L'),\n", " ('L', 'W'),\n", " ('W', 'X')]" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "prim = bulk('Al', a=4.065)\n", "path_info = get_path((\n", " prim.cell,\n", " prim.get_scaled_positions(),\n", " prim.numbers))\n", "point_coordinates = path_info['point_coords']\n", "path = path_info['path']\n", "display(point_coordinates)\n", "display(path)" ] }, { "cell_type": "code", "execution_count": 5, "id": "9a03f0c2", "metadata": {}, "outputs": [], "source": [ "q_segments = get_supercell_qpoints_along_path(\n", " path, point_coordinates, prim.cell, traj.cell)\n", "q_points = np.vstack(q_segments)" ] }, { "cell_type": "markdown", "id": "a417fa3c", "metadata": {}, "source": [ "## Run calculation of correlation functions\n", "\n", "We are now all set for calculating the dynamic structure factor as well as the current correlation functions, where the latter is triggered by setting `calculate_currents=True`.\n", "To obtain the correct units, the spacing between the frames (`dt`) must be specified in units of femtoseconds.\n", "\n", "The following cell should take ten to twenty minutes to run on a common workstation.\n", "\n", "**Note:**\n", "The `Trajectory` object is an \"exhaustible iterable\" , which means that once we have iterated over all frames, it is exhausted.\n", "Therefore, one needs to \"reload\" the trajectory before calling `compute_dynamic_structure_factors` again to ensure that the entire trajectory is available for analysis." ] }, { "cell_type": "code", "execution_count": 6, "id": "7dcfb587-0955-43f4-aa25-64ff22806438", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "INFO 2025-08-25 10:43:45: Spacing between samples (frame_step): 1\n", "INFO 2025-08-25 10:43:45: Time between consecutive frames in input trajectory (dt): 25.0 fs\n", "INFO 2025-08-25 10:43:45: Time between consecutive frames used (dt * frame_step): 25.0 fs\n", "INFO 2025-08-25 10:43:45: Time window size (dt * frame_step * window_size): 12500.0 fs\n", "INFO 2025-08-25 10:43:45: Angular frequency resolution: dw = 0.000251 rad/fs = 0.165 meV\n", "INFO 2025-08-25 10:43:45: Maximum angular frequency (dw * window_size): 0.125664 rad/fs = 82.713 meV\n", "INFO 2025-08-25 10:43:45: Nyquist angular frequency (2pi / frame_step / dt / 2): 0.125664 rad/fs = 82.713 meV\n", "INFO 2025-08-25 10:43:45: Calculating current (velocity) correlations\n", "INFO 2025-08-25 10:43:45: Number of q-points: 48\n", "INFO 2025-08-25 10:44:00: Processing window 0 to 500\n", "INFO 2025-08-25 10:44:31: Processing window 1000 to 1500\n", "INFO 2025-08-25 10:45:02: Processing window 2000 to 2500\n", "INFO 2025-08-25 10:45:32: Processing window 3000 to 3500\n", "INFO 2025-08-25 10:46:03: Processing window 4000 to 4500\n", "INFO 2025-08-25 10:46:34: Processing window 5000 to 5500\n", "INFO 2025-08-25 10:47:05: Processing window 6000 to 6500\n", "INFO 2025-08-25 10:47:35: Processing window 7000 to 7500\n", "INFO 2025-08-25 10:48:06: Processing window 8000 to 8500\n", "INFO 2025-08-25 10:48:37: Processing window 9000 to 9500\n", "CPU times: user 17min 57s, sys: 1.97 s, total: 17min 59s\n", "Wall time: 5min 9s\n" ] } ], "source": [ "%%time\n", "sample = compute_dynamic_structure_factors(\n", " traj, q_points, dt=25.0, window_size=500,\n", " window_step=50, calculate_currents=True)" ] }, { "cell_type": "markdown", "id": "5dc2c4d9-1b63-4422-b176-5bce7d97fc49", "metadata": {}, "source": [ "As in the case of the trajectory, we can use the `display` or `print` commands to obtain a quick summary of the [Sample object](https://dynasor.materialsmodeling.org/reference/sample.html) returned by the [compute_dynamic_structure_factors function](https://dynasor.materialsmodeling.org/reference/correlation_functions.html#dynasor.compute_dynamic_structure_factors)." ] }, { "cell_type": "code", "execution_count": 7, "id": "347111c9", "metadata": {}, "outputs": [ { "data": { "text/html": [ "

DynamicSample

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Simulation

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NameContent
Angular frequency resolution0.0002513274122871834
Atom types['X']
Cell[[48.78 0. 0. ]\n", " [ 0. 48.78 0. ]\n", " [ 0. 0. 48.78]]
Maximum time lag12500.0
Number of frames10000
Particle counts{'X': 6912}
Time between frames25.0
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Dimensions

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FieldSize
omega(501,)
q_points(48, 3)
time(501,)
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History

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FunctionFieldContent
compute_dynamic_structure_factorsdt25.0
window_size500
window_step50
calculate_currentsTrue
calculate_incoherentFalse
date_time2025-08-25T10:48:54
usernameerhart
hostnamepieweack
dynasor_version2.2
" ], "text/plain": [ "DynamicSample\n", "Angular frequency resolution: 0.0002513274122871834\n", "Atom types: ['X']\n", "Cell: [[48.78 0. 0. ]\n", " [ 0. 48.78 0. ]\n", " [ 0. 0. 48.78]]\n", "maximum_angular_frequency: 0.1256637061435917\n", "Maximum time lag: 12500.0\n", "Number of frames: 10000\n", "pairs: [('X', 'X')]\n", "Particle counts: {'X': 6912}\n", "Time between frames: 25.0\n", "omega with shape: (501,)\n", "q_points with shape: (48, 3)\n", "time with shape: (501,)\n", "Clqt with shape: (48, 501)\n", "Clqt_X_X with shape: (48, 501)\n", "Clqw with shape: (48, 501)\n", "Clqw_X_X with shape: (48, 501)\n", "Ctqt with shape: (48, 501)\n", "Ctqt_X_X with shape: (48, 501)\n", "Ctqw with shape: (48, 501)\n", "Ctqw_X_X with shape: (48, 501)\n", "Fqt_coh with shape: (48, 501)\n", "Fqt_coh_X_X with shape: (48, 501)\n", "Sqw_coh with shape: (48, 501)\n", "Sqw_coh_X_X with shape: (48, 501)" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "display(sample)" ] }, { "cell_type": "markdown", "id": "cb07b33f-d259-4d52-a563-21bcd8c56f17", "metadata": {}, "source": [ "To find out which correlation functions are included in a `Sample` object, one can inspect the `available_correlation_functions` property.\n", "\n", "**Tip:**\n", "When working with an interactive Python interpreter, e.g., when using `ipython` or a `jupyter` instance, you can hit the `` key after writing `sample.` to show a list of possible completions." ] }, { "cell_type": "code", "execution_count": 8, "id": "4322dbe2-7c32-4ee4-a31f-37b2de5e0701", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['Clqt',\n", " 'Clqt_X_X',\n", " 'Clqw',\n", " 'Clqw_X_X',\n", " 'Ctqt',\n", " 'Ctqt_X_X',\n", " 'Ctqw',\n", " 'Ctqw_X_X',\n", " 'Fqt_coh',\n", " 'Fqt_coh_X_X',\n", " 'Sqw_coh',\n", " 'Sqw_coh_X_X']" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sample.available_correlation_functions" ] }, { "cell_type": "markdown", "id": "f7320bdd-e805-4b46-92ac-c058367b8faa", "metadata": {}, "source": [ "One can also write a `Sample` object to file and load it later for further processing.\n", "To write the object one should use the `write_to_npz` method." ] }, { "cell_type": "code", "execution_count": 9, "id": "4ae17a50-9a32-4259-abde-7f76ad3ecfbd", "metadata": {}, "outputs": [], "source": [ "sample.write_to_npz('my_sample.npz')" ] }, { "cell_type": "markdown", "id": "becf6680-62a2-405a-8e7b-389bc235236d", "metadata": {}, "source": [ "The results can be read from file using the `read_sample_from_npz` function from the `dynasor.sample` module." ] }, { "cell_type": "markdown", "id": "56541be4", "metadata": {}, "source": [ "## Visualize dynamic structure factor\n", "\n", "We can now visualize the dynamic structure factor $S(\\mathbf{q}, \\omega)$, for example, in the form of a heatmap on the momentum-frequency plane.\n", "\n", "In preparation, in the following cell we compute the running pairwise distance between **q**-points along the path." ] }, { "cell_type": "code", "execution_count": 10, "id": "91d58213", "metadata": {}, "outputs": [], "source": [ "q_distances = []\n", "q_labels = dict()\n", "\n", "# starting point\n", "qr = 0.0\n", "\n", "# collect labels and q-distances along the entire q-point path\n", "for it, q_segment in enumerate(q_segments):\n", " q_distances.append(qr)\n", " q_labels[qr] = path[it][0]\n", " for qi, qj in zip(q_segment[1:], q_segment[:-1]):\n", " qr += np.linalg.norm(qi - qj)\n", " q_distances.append(qr)\n", " \n", "q_labels[qr] = path[-1][1]\n", "q_distances = np.array(q_distances)" ] }, { "cell_type": "markdown", "id": "a4bbcc3f-fe48-48b2-84c2-2b6ce4e45eb3", "metadata": {}, "source": [ "We are now ready to plot the dispersion, here the angular frequency $\\omega$ is converged to units of meV. \n", "Note that the dynamic structure factor only contains information about the longitudinal modes." ] }, { "cell_type": "code", "execution_count": 11, "id": "8adca33e-fa1c-4753-8738-392798406971", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from dynasor.units import radians_per_fs_to_meV as conversion_factor\n", "\n", "fig = plt.figure(figsize=(5.2, 2.8), dpi=140)\n", "ax = fig.add_subplot(111)\n", "ax.pcolormesh(q_distances, conversion_factor * sample.omega,\n", " sample.Sqw_coh.T, cmap='Blues', vmin=0, vmax=4)\n", "\n", "xticks = []\n", "xticklabels = []\n", "for q_dist, q_label in q_labels.items():\n", " ax.axvline(q_dist, c='0.5', alpha=0.5, ls='--')\n", " xticks.append(q_dist)\n", " xticklabels.append(q_label.replace('GAMMA', r'$\\Gamma$'))\n", "\n", "ax.set_xticks(xticks)\n", "ax.set_xticklabels(xticklabels)\n", "\n", "ax.set_xlim([0, q_distances.max()])\n", "ax.set_ylim([0, 55])\n", "\n", "ax.set_ylabel('Energy (meV)')\n", "\n", "fig.tight_layout()" ] }, { "cell_type": "markdown", "id": "c0fdf257", "metadata": {}, "source": [ "## Visualize current correlations\n", "\n", "We can also plot the longitudinal and transverse current correlation heatmaps.\n", "In the longitudinal current correlation function one can observe the longitudinal branch that is also visible in the dynamic structure factor.\n", "Compared to the dynamic structure factor, the transverse current correlation function also reveals a transverse mode.\n", "The combination of the two (shown below) thus contains the full dispersion." ] }, { "cell_type": "code", "execution_count": 12, "id": "f210ea3f", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(5.2, 2.8), dpi=140)\n", "ax = fig.add_subplot(111)\n", "ax.pcolormesh(q_distances, conversion_factor * sample.omega,\n", " sample.Clqw.T - sample.Ctqw.T,\n", " cmap='RdBu', vmin=-6000, vmax=6000)\n", "\n", "ax.plot([0, 1.0], [0, 36], alpha=0.5, ls='--', c='0.3', lw=2)\n", "ax.plot([0, 1.5], [0, 32], alpha=0.5, ls='--', c='0.3', lw=2)\n", "\n", "ax.text(0.02, 0.89, r'$C_{T-L}(\\mathbf{q}, \\omega)$', transform=ax.transAxes,\n", " bbox={'color': 'white', 'alpha': 0.8, 'pad': 3})\n", "\n", "xticks = []\n", "xticklabels = []\n", "for q_dist, q_label in q_labels.items():\n", " ax.axvline(q_dist, c='0.5', alpha=0.5, ls='--')\n", " xticks.append(q_dist)\n", " xticklabels.append(q_label.replace('GAMMA', r'$\\Gamma$'))\n", "\n", "ax.set_xticks(xticks)\n", "ax.set_xticklabels(xticklabels)\n", "\n", "ax.set_xlim([0, q_distances.max()])\n", "ax.set_ylim([0, 55])\n", "\n", "ax.set_ylabel('Energy (meV)')\n", "\n", "fig.tight_layout()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.3" } }, "nbformat": 4, "nbformat_minor": 5 } dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/examples/aluminium_solid/lammpsrun/000077500000000000000000000000001522712672400271005ustar00rootroot00000000000000Al99.eam.alloy000066400000000000000000027642441522712672400313650ustar00rootroot00000000000000dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/examples/aluminium_solid/lammpsrun Al EAM from Phys. Rev. B 59, 3393 (1999) in the LAMMPS setfl format. Conversion by C. A. Becker from Y. 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-0.7359100497226714E-07 -0.5789642944549939E-07 -0.4460603276266522E-07 -0.3352109479779516E-07 -0.2444230228083147E-07 -0.1717018845557266E-07 -0.1150495861170407E-07 -0.7246423756161589E-08 -0.4194276244510321E-08 -0.2147863624012600E-08 -0.9062527475633544E-09 -0.2684334141141742E-09 -0.3337655546827989E-10 dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/examples/aluminium_solid/lammpsrun/main.in000066400000000000000000000023541522712672400303600ustar00rootroot00000000000000 # ---------- Initialize Simulation --------------------- clear units metal dimension 3 boundary p p p atom_style atomic atom_modify map array variable T equal 300 # ---------- Create Atoms --------------------- lattice fcc 4.065 region box block 0 1 0 1 0 1 units lattice create_box 1 box lattice fcc 4.065 orient x 1 0 0 orient y 0 1 0 orient z 0 0 1 create_atoms 1 box replicate 12 12 12 mass 1 26.98 # ---------- Define Interatomic Potential --------------------- pair_style eam/alloy pair_coeff * * Al99.eam.alloy Al neighbor 2.0 bin neigh_modify delay 10 check yes # ---------- Define Settings --------------------- compute eng all pe/atom compute eatoms all reduce sum c_eng # ---------- Run Equilibration --------------------- thermo 10 thermo_style custom step temp press vol lx ly lz pe ke etotal variable double_temp equal ${T}*2 velocity all create ${double_temp} 28459 rot yes dist gaussian mom yes ### NVT fix 1 all nvt temp $T $T 1.0 reset_timestep 0 timestep 5e-3 run 5000 # ---------- Run Sampling --------------------- thermo 1000 thermo_style custom step temp press vol pe ke etotal reset_timestep 0 dump myDump all custom 5 dumpT${T}.NVT.atom.velocity.gz id type xs ys zs vx vy vz run 100000 dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/examples/dho_peak_fitting/000077500000000000000000000000001522712672400251665ustar00rootroot00000000000000dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/examples/dho_peak_fitting/.gitignore000066400000000000000000000000161522712672400271530ustar00rootroot00000000000000*.csv* *.npz* dho_peak_fitting.ipynb000066400000000000000000014477501522712672400314720ustar00rootroot00000000000000dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/examples/dho_peak_fitting{ "cells": [ { "cell_type": "markdown", "id": "7a9d526b", "metadata": {}, "source": [ "# Peak fitting\n", "This tutorial demonstrates how one can analyze the current correlation functions and the dynamic structure factor by fitting peaks to analytical expressions for damped harmonic oscillators (DHOs).\n", "\n", "## Damped harmonic oscillator\n", "The details of the DHO model can be found in the [theoretical background](https://dynasor.materialsmodeling.org/get_started/theory.html#damped-harmonic-oscillator-model) section.\n", "In short, the position autocorrelation function of a DHO is given by\n", "\\begin{equation*}\n", "F(t) = A \\mathrm{e}^{- t/\\tau} \\left ( \\cos{ \\omega_e t} + \\frac{1}{\\tau\\omega_e}\\sin{ \\omega_e t}\\right ),\n", "\\end{equation*}\n", "where $\\omega_e = \\sqrt{\\omega_0^2 - 1/\\tau^2}$ is the angular eigenfrequency of the DHO. The Fourier transform of the above is \n", "\\begin{equation*}\n", "S(\\omega) = A\\frac{2\\Gamma \\omega_0^2}{(\\omega^2 - \\omega_0^2)^2 + (\\Gamma\\omega)^2},\n", "\\end{equation*}\n", "where $\\omega_0$ is the natural angular frequency, $\\Gamma$ the damping (lifetime $\\tau=2/\\Gamma$) and $A$ an arbitrary amplitude of the DHO.\n", "\n", "Note that for current correlations (and their respective Fourier transforms) one must use the velocity autocorrelation function for the DHO, whereas for the intermediate scattering function (and dynamic structure factor) the position autocorrelation function is used.\n", "\n", "## Input data\n", "\n", "In this example we will analyze data for current correlation functions and dynamic structure factor, which you can find in [this Zenodo repository](https://zenodo.org/records/17858255).\n", "You can fetch these files, e.g., by executing the following cell." ] }, { "cell_type": "code", "execution_count": 1, "id": "e49883d1-f361-4f40-8a98-fbe7049da3ed", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "..." ] } ], "source": [ "!wget https://zenodo.org/records/17858255/files/Al_fcc_size24_T900_qindex38.csv\n", "!wget https://zenodo.org/records/17858255/files/BaZrO3_sample_T300_P0.0.npz\n", "!wget https://zenodo.org/records/17858255/files/BaZrO3_sample_T300_P8.0.npz\n", "!wget https://zenodo.org/records/17858255/files/BaZrO3_sample_T300_P15.6.npz" ] }, { "cell_type": "code", "execution_count": 2, "id": "bf7be25a", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "from dynasor import read_sample_from_npz\n", "from dynasor.units import radians_per_fs_to_meV\n", "from matplotlib import pyplot as plt\n", "from scipy.optimize import curve_fit" ] }, { "attachments": { "6442e9af-ff2e-49cf-bf70-0a09e51c9553.png": { "image/png": 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} }, "cell_type": "markdown", "id": "946d2a79-cbfe-4293-a383-b36b03f13255", "metadata": {}, "source": [ "## Al FCC\n", "First, we will look at the current correlations, $C_\\text{L}(\\boldsymbol{q}, t)$ and $C_\\text{T}(\\boldsymbol{q}, t)$, in Al FCC at a temperature of T = 900 K and fit these with DHO models.\n", "\n", "The full dispersion from current correlations is shown below, where blue corresponds to longitudinal modes and red to transverse modes.\n", "\n", "![current_heatmap.png](attachment:6442e9af-ff2e-49cf-bf70-0a09e51c9553.png)" ] }, { "cell_type": "markdown", "id": "a93c7c78-bb16-40ab-a981-603e9a818d4c", "metadata": {}, "source": [ "Here, we will only consider fitting the current correlations for one q-point, namely $\\boldsymbol{q}=[0.5, 0.5, 0.0]$ which sits between K and $\\Gamma$.\n", "\n", "This data is stored as a pandas dataframe in a csv file, obtained via `sample.to_dataframe(q_index)` ([see here](https://dynasor.materialsmodeling.org/reference/sample.html#dynasor.sample.DynamicSample.to_dataframe)), and can be read using pandas." ] }, { "cell_type": "code", "execution_count": 3, "id": "83fec531-64a3-4593-b0a5-8902296f60ba", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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.............................................
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79971.25616639985-0.062453-0.062453-0.012500-0.0125000.0705490.0705490.0023830.0023830.0000520.0000523.995259e-073.995259e-07
79981.25632339990-0.045894-0.045894-0.011104-0.0111040.0860150.0860150.0057310.0057310.0000390.0000393.718448e-063.718448e-06
79991.25648039995-0.024718-0.024718-0.004054-0.0040540.0973920.0973920.0100660.0100660.0000220.000022-2.560956e-06-2.560956e-06
80001.256637400000.0014060.001406-0.002088-0.0020880.1051290.105129-0.001084-0.0010840.0000030.000003-3.734499e-06-3.734499e-06
\n", "

8001 rows × 14 columns

\n", "
" ], "text/plain": [ " omega time Clqt Clqt_X_X Clqw Clqw_X_X Ctqt \\\n", "0 0.000000 0 27.747023 27.747023 2.632250 2.632250 27.804061 \n", "1 0.000157 5 26.699390 26.699390 3.549897 3.549897 27.373739 \n", "2 0.000314 10 23.540678 23.540678 2.247025 2.247025 26.121036 \n", "3 0.000471 15 18.562543 18.562543 3.889430 3.889430 24.101959 \n", "4 0.000628 20 12.212002 12.212002 3.864050 3.864050 21.406585 \n", "... ... ... ... ... ... ... ... \n", "7996 1.256009 39980 -0.075647 -0.075647 -0.014731 -0.014731 0.051283 \n", "7997 1.256166 39985 -0.062453 -0.062453 -0.012500 -0.012500 0.070549 \n", "7998 1.256323 39990 -0.045894 -0.045894 -0.011104 -0.011104 0.086015 \n", "7999 1.256480 39995 -0.024718 -0.024718 -0.004054 -0.004054 0.097392 \n", "8000 1.256637 40000 0.001406 0.001406 -0.002088 -0.002088 0.105129 \n", "\n", " Ctqt_X_X Ctqw Ctqw_X_X Fqt_coh Fqt_coh_X_X Sqw_coh \\\n", "0 27.804061 2.776075 2.776075 0.012116 0.012116 8.061254e-02 \n", "1 27.373739 34.885753 34.885753 0.011681 0.011681 1.425915e-01 \n", "2 26.121036 66.727080 66.727080 0.010453 0.010453 1.536352e-01 \n", "3 24.101959 48.080473 48.080473 0.008530 0.008530 8.122096e-02 \n", "4 21.406585 44.940541 44.940541 0.006063 0.006063 1.981069e-01 \n", "... ... ... ... ... ... ... \n", "7996 0.051283 -0.004358 -0.004358 0.000061 0.000061 3.456208e-06 \n", "7997 0.070549 0.002383 0.002383 0.000052 0.000052 3.995259e-07 \n", "7998 0.086015 0.005731 0.005731 0.000039 0.000039 3.718448e-06 \n", "7999 0.097392 0.010066 0.010066 0.000022 0.000022 -2.560956e-06 \n", "8000 0.105129 -0.001084 -0.001084 0.000003 0.000003 -3.734499e-06 \n", "\n", " Sqw_coh_X_X \n", "0 8.061254e-02 \n", "1 1.425915e-01 \n", "2 1.536352e-01 \n", "3 8.122096e-02 \n", "4 1.981069e-01 \n", "... ... \n", "7996 3.456208e-06 \n", "7997 3.995259e-07 \n", "7998 3.718448e-06 \n", "7999 -2.560956e-06 \n", "8000 -3.734499e-06 \n", "\n", "[8001 rows x 14 columns]" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# read data\n", "alat = 4.05\n", "df = pd.read_csv('Al_fcc_size24_T900_qindex38.csv')\n", "display(df)\n", "\n", "w = df.omega\n", "t = df.time\n", "Clqt = df.Clqt\n", "Ctqt = df.Ctqt\n", "Clqw = df.Clqw\n", "Ctqw = df.Ctqw" ] }, { "cell_type": "markdown", "id": "dc4d6e59-7416-4f70-8f8a-03b151be1a6e", "metadata": {}, "source": [ "First, we fit the data to a DHO in the time domain, although it could just as well be done in the frequency domain.\n", "The fitting can be accomplished in many ways, but here we simply use a least-square fit via SciPy's `curve_fit` function.\n", "It is important to select some reasonable starting parameters in order to make the fit converge to a good solution. Note that while the fit is carried out in time domain, the resulting fit parameters ($\\omega_0, \\Gamma, A$) can directly be used in the frequency domain too." ] }, { "cell_type": "code", "execution_count": 4, "id": "de8cdb30-b6c6-4d81-ba42-141d821a35a8", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Fitting results for C_L(q, t): w0 35.00 meV, gamma 4.13 meV\n" ] } ], "source": [ "from dynasor.tools.damped_harmonic_oscillator import acf_velocity_dho, psd_velocity_dho\n", "\n", "\n", "# fitting longitudinal, in time domain\n", "p0 = [30/radians_per_fs_to_meV, 2/radians_per_fs_to_meV, 1e4] # initial guesses for parameters w0, Gamma, A\n", "params_L, _ = curve_fit(acf_velocity_dho, t, Clqt, p0)\n", "\n", "w0_L = params_L[0] * radians_per_fs_to_meV\n", "gamma_L = params_L[1] * radians_per_fs_to_meV\n", "print(f' Fitting results for C_L(q, t): w0 {w0_L:.2f} meV, gamma {gamma_L:.2f} meV')\n", "\n", "# generate resulting dho data, in both time and frequency domain\n", "t_lin = np.linspace(0, t.max(), 100000)\n", "w_lin = np.linspace(0, w.max(), 100000)\n", "\n", "Clqt_fit = acf_velocity_dho(t_lin, *params_L)\n", "Clqw_fit = psd_velocity_dho(w_lin, *params_L)" ] }, { "cell_type": "markdown", "id": "c2df4878-a4c3-48c4-831d-3d78edc72861", "metadata": {}, "source": [ "Next, for fitting the transverse current correlation we need to employ a sum of two DHOs." ] }, { "cell_type": "code", "execution_count": 5, "id": "7ebf2194-56fa-4a41-9084-58f6c76a7b04", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Fitting results for C_T(q, t): w0 16.79 meV, gamma 1.83 meV and w0 26.33 meV, gamma 1.78\n" ] } ], "source": [ "def two_acfs(t, w01, gamma1, A1, w02, gamma2, A2):\n", " acf1 = acf_velocity_dho(t, w01, gamma1, A1)\n", " acf2 = acf_velocity_dho(t, w02, gamma2, A2)\n", " return acf1 + acf2\n", "\n", "\n", "def two_psds(t, w01, gamma1, A1, w02, gamma2, A2):\n", " psd1 = psd_velocity_dho(t, w01, gamma1, A1)\n", " psd2 = psd_velocity_dho(t, w02, gamma2, A2)\n", " return psd1 + psd2\n", "\n", "# fitting transverse, in time domain\n", "p0 = [15/radians_per_fs_to_meV, 2/radians_per_fs_to_meV, 1e4, \n", " 30/radians_per_fs_to_meV, 2/radians_per_fs_to_meV, 1e4]\n", "params_T, _ = curve_fit(two_acfs, t, Ctqt, p0)\n", "\n", "w0_T1 = params_T[0] * radians_per_fs_to_meV\n", "gamma_T1 = params_T[1] * radians_per_fs_to_meV\n", "w0_T2 = params_T[3] * radians_per_fs_to_meV\n", "gamma_T2 = params_T[4] * radians_per_fs_to_meV\n", "print(f' Fitting results for C_T(q, t): w0 {w0_T1:.2f} meV, gamma {gamma_T1:.2f} meV and '\n", " f' w0 {w0_T2:.2f} meV, gamma {gamma_T2:.2f}')\n", "\n", "# generate resulting dho data, in both time and frequency domain\n", "t_lin = np.linspace(0, t.max(), 100000)\n", "w_lin = np.linspace(0, w.max(), 100000)\n", "\n", "Ctqt_fit = two_acfs(t_lin, *params_T)\n", "Ctqw_fit = two_psds(w_lin, *params_T)" ] }, { "cell_type": "markdown", "id": "7888078b-6e3c-4db3-bf8e-2a687cd3d4e2", "metadata": {}, "source": [ "Next, we plot the raw data and resulting fits." ] }, { "cell_type": "code", "execution_count": 6, "id": "f90ad4ba-311b-4ca7-8652-afed91bafbc3", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, axes = plt.subplots(figsize=(5.8, 3.4), dpi=160, nrows=2, ncols=2, sharex='col', sharey='col')\n", "\n", "# time domain\n", "ax = axes[0][0]\n", "kwargs_data = dict(ls='', marker='.', markersize=4, alpha=0.5)\n", "ax.plot(t, Clqt, **kwargs_data)\n", "kwargs_fit = dict(alpha=0.7)\n", "ax.plot(t_lin, Clqt_fit, **kwargs_fit)\n", "ax.plot(t_lin, Clqt[0] * np.exp(-t_lin * params_L[1]/2), '--k',\n", " alpha=0.8, lw=1, label=r'$\\exp(-\\Gamma t /2)$')\n", "ax.set_ylabel(r'$C_\\text{L}(\\boldsymbol{q}, t)$')\n", "ax.legend(frameon=False)\n", "\n", "ax = axes[1][0]\n", "ax.plot(t, Ctqt, **kwargs_data)\n", "ax.plot(t_lin, Ctqt_fit, **kwargs_fit)\n", "ax.set_ylabel(r'$C_\\text{T}(\\boldsymbol{q}, t)$')\n", "ax.set_xlabel('Time (fs)')\n", "ax.set_xlim(0, 1500)\n", "#ax.set_ylim(-30, 30)\n", "\n", "# frequency domain\n", "ax = axes[0][1]\n", "ax.plot(w * radians_per_fs_to_meV, Clqw, label='Data', **kwargs_data)\n", "ax.plot(w_lin * radians_per_fs_to_meV, Clqw_fit, **kwargs_fit, label='Fit')\n", "ax.set_ylabel(r'$C_\\text{L}(\\boldsymbol{q}, \\omega)$')\n", "ax.annotate('', xy=(w0_L - gamma_L/2 , Clqw_fit.max() / 2.0),\n", " xytext=(w0_L + gamma_L/2, Clqw_fit.max() / 2.0),\n", " arrowprops=dict(arrowstyle='<->'))\n", "ax.text(w0_L, 0.6 * Clqw_fit.max()/2, r'$\\Gamma$', ha='center')\n", "ax.legend(frameon=False)\n", "\n", "ax = axes[1][1]\n", "ax.plot(w * radians_per_fs_to_meV, Ctqw, **kwargs_data)\n", "ax.plot(w_lin * radians_per_fs_to_meV, Ctqw_fit, **kwargs_fit)\n", "ax.set_ylabel(r'$C_\\text{T}(\\boldsymbol{q}, \\omega)$')\n", "ax.set_xlabel('Energy (meV)')\n", "ax.set_xlim(10, 45)\n", "\n", "fig.tight_layout()\n", "fig.subplots_adjust(hspace=0)\n", "fig.align_labels(axes)" ] }, { "cell_type": "markdown", "id": "b350b17c", "metadata": {}, "source": [ "Note that, while the fits are carried out in the time domain, the corresponding DHO spectra match the data well in the frequency domain too. Generally, it is a good idea to plot the resulting fit in both the time and frequency domain, regardless of where the fit was performed, as a check of the procedure. In a simple system like this, is should not matter in which domain the fit is performed, but it might be easier to obtain a converged fit in one of the domains when more complex systems are considered." ] }, { "cell_type": "markdown", "id": "bddbe540-530a-4ace-821c-4de0e5d6360c", "metadata": {}, "source": [ "## Cubic BaZrO3\n", "\n", "Additionally, we will consider a more anharmonic system, the cubic phase of BaZrO3 (BZO), with $S(\\boldsymbol{q}, \\omega)$ data from [this paper](https://doi.org/10.1021/acs.chemmater.3c02548).\n", "We will analyze the low-frequency octahedral tilt phonon mode at the R-point ($\\boldsymbol{q}=[1.5, 0.5, 0.5]$), at T = 300 K for three different pressure (0 GPa, 8 GPa, and 15.6 GPa).\n", "Note that this phonon mode is related to the phase transition between the cubic and tetragonal phases which occurs at around 16 GPa.\n", "The sample objects only contains $S(\\boldsymbol{q}, \\omega)$ for this specific q-point.\n", "\n", "\n", "Note that for overdamped modes the DHO is better represented using two timescales $\\tau_L$ and $\\tau_S$ instead of $\\omega_0$ and $\\Gamma$. These are defined as\n", "\\begin{equation*}\n", "\\tau_\\text{S,L} = \\frac{\\tau}{1 \\pm \\sqrt{1-(\\omega_0\\tau)^2}},\n", "\\end{equation*}\n", "where $\\tau=2/\\Gamma$." ] }, { "cell_type": "code", "execution_count": 7, "id": "79949dc1", "metadata": {}, "outputs": [], "source": [ "# read data\n", "P_vals = [0.0, 8.0, 15.6]\n", "data_dict = dict()\n", "for P in P_vals:\n", " fname = f'BaZrO3_sample_T300_P{P:.1f}.npz'\n", " sample = read_sample_from_npz(fname)\n", " data_dict[P] = radians_per_fs_to_meV * sample.omega, sample.Sqw_coh[0]" ] }, { "cell_type": "markdown", "id": "ac862c63", "metadata": {}, "source": [ "Next, we visualize the three different spectra." ] }, { "cell_type": "code", "execution_count": 8, "id": "aee8c12a", "metadata": {}, "outputs": [ { "data": { "image/png": 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TlyqEpG7cuFGvNpRL5UzZrvthaefRjoGBA+kb2Lde7REIBAKBfhqynLdYgbAyli9fTnh4OOHh4SQnJ6tzqOsTG5mN3kJbF29e5Jvob7iRW78OjUAgEAisD+FAWBlTp04lOjqa6Oho/Pz81DKw9c0DrR/Q2y4hsf2a8WV5BQKBQNA0EToQVkZ9S1kbwtPe02BfaoF+dTiBQCAQ3D6IFQiBXpRyJY4K/cI/lzMv17M1AoFAILA2hAMhMMgLXV4gyDlIp72kvITPjn8m9CEEAoHgNkZkYVgxDRldq4kkSeyN28v3577Xao/wiWB6t+kNY5RAIBAIRBaGwLqRyWQMDh6Mk62TVvuJ1BMcTzneQFYJBAKBoCERDoTAKGQyGfe1uk+nfdnxZaQXpDeARQKBQCBoSIQDUQc2bNhA//798fb2xs7OjlatWvHyyy9z8+bNhjbNIgwLGaa3/ULGhXq2RCAQCAQNjUjjrAMZGRkMHjyY1157DTc3N06fPs28efM4efIku3btamjz6o2MooyGNkEgEAgE9YxwIOpAVW30wYMHY29vz7PPPktsbKzBsrCNmXta3MP269pCUhsubeDelveaXCxHIBAIBI0XsYVhZjw9VQJMJSUlDWyJZRgeMpwefj102p/e/jTXsq7Vv0ECgUAgaBCanANx4cIFli1bxqRJk+jUqRMKhQKZTMZ7771n1Plr165l8ODBeHh44OTkRJcuXVi0aFG1DkFZWRmFhYUcPXqUefPmMXLkSFq3bm2uX8mqcLd3Z2rEVMa0HaPT93+H/4+d13dSWFrYAJYJBAKBoD5pcg7EihUrmD59OqtXryY6OpqysjKjz50xYwbjxo3jwIED9OrVi+HDhxMbG8usWbMYMmQIBQUFes/z8vLCwcGBnj170qxZM3799Vdz/TpWy4iWI/S2rzm/hqm7phKXE1fPFgkEAoGgPmlyDkR4eDivvvoqP/74I+fOnWPChAlGnbdp0yaWLFmCs7MzR44cYdu2baxfv55Lly7RqVMn9u/fz9tvv6333L1793LgwAH+97//cfbsWe6//36THJfGiEwmY3DQYIP96y6uqz9jBIJGQkhICDKZTOvHzs6O5s2b88gjj7Bv3z6L2xAbG8u0adMIDQ3FwcEBe3t7WrZsycSJEzl58mSt5925cycjR47E29sbBwcH2rdvz5tvvlnnisI3b95k8eLF3H333QQEBGBnZ4eTkxMhISE88MADLF26lKSkJJ3zrl27pvO3lslkODk50aFDB6ZNm0ZMTEydbLvdafJKlJMmTWL16tXMnz+ft956y+C4Xr16ERkZyXvvvcebb76p1bd//34GDBiAnZ0dycnJuLm5GZznyJEj9O7dm7Vr1zJmjO4yvylYixKlISRJ4untTxvs/3bYt/VojUBg/YSEhHD9+nX69etHmzZtAMjMzOTo0aMkJCQgk8n48MMPefnlly1y/SNHjnD33XeTk5NDYGAg3bt3Ry6Xc+LECWJiYlAoFKxZs4axY8eaNO8nn3zCyy+/jEwmY8CAAfj5+bFv3z6SkpIIDQ1l//79eHt7m2zvjz/+yAsvvEBOTg62trZ0796d4OBgAG7cuMHRo0cpKirCzs6O7777jnHjxqnPvXbtGi1btgRg9OjR6srGCQkJHDlyhJycHJydndmyZQsDBgww2TZroUHvE1ITZ+LEiRIgzZ8/3+CY+Ph4CZAA6erVq3rHBAcHS4C0Zs2aaq9XXFwsyWQy6b///W+d7JYkSQoMDJQCAwPrPI8l+fb0t9LkrZP1/iTmJja0eQKBVdGiRQsJkFauXKnVXlBQID355JMSIMnlcunChQsWuX7nzp0lQHr22Wel4uJidXtZWZn01ltvSYDk7u4uFRQUGD1nVFSUJJPJJLlcLm3ZskXdnpeXJw0dOlQCpNGjR5ts6/LlyyVAkslk0uuvvy5lZGTojMnPz5e++eYbqU2bNjrfuTExMerv9ZiYGK2+GzduSBERERIgtWzZUiopKTHZPmuhIe8TTW4LozYcP66SY/b09FR7rFXp0aOH1lhDHDhwAEmSaNWqlVHXDgoKMviTmJhowm/RMIxtZ/hJZc7+OeSX5NejNQJB48Te3p7PP/8cJycnysrK2LBhg9mvkZ6ezqlTpwB47733sLW1VffZ2Ngwd+5cHBwcyMzM5Ny5c0bP+9///hdJkpg8eTIjRlTGRjk6OvLNN99gY2PD+vXrOX/+vNFznjt3jpdeegmAJUuW8MEHH+Dh4aEzzsHBgaeeeopTp05x7733Gj1/s2bN+OSTTwCIiYnh6NGjRp8rqEQ4EKDeB6tOt6Fi2Uxzz2zYsGEsWrSIP//8k507d7J48WLGjRtH586dGTVqlEVtthaclc7M6zuPVm6tCHEN0ek/nHi4/o0SCBohzs7OhIaGAqrld3NjZ2dn9FhjtxuKi4v5888/AXj88cd1+lu0aEG/fv0A2Lhxo9HX/+CDDygpKaFbt2785z//qXG8g4MDnTp1Mnp+gO7du6tfV/y9z549y7vvvku/fv0IDAxEqVTi5eXFXXfddVsEx5uKEJICcnJyAHBycjI4pmL/LDs7W93Wq1cvfvjhB7VTERISwtSpU3n55ZdRKpVGXbu6fauKvS1rJ9glmLd6q+JLntr2lFZfYp71r6IIBNZCxfeLKTd7Y3F2dmbAgAHs27ePt956i88++0y9ClFeXs7cuXMpKChgxIgR6gemmrh48SL5+apVxopV2qr06NGDffv21bh6W4EkSWzevBmA8ePHG3VObdD8Lq/4e3/88cd88803tG/fnk6dOuHu7k5sbCx79uxh165dHD58mI8//thiNjU2hANRB+bPn8/8+fPNOmdKSgqpqakAlJaWYmPTuBaJWri24Hr2dfXxrthdjGg5Ak97zwa0StDglBZDTiN2Jl2agcK4h4LacurUKa5evQpARESEun3v3r3ceeedJs83ceJEVq1apdX21VdfMXLkSL788kv+/PNPevTogVwu5/jx4yQkJDBhwgQ+++wzo69R8fDk7u6Oi4uL3jH6Vm9rmjMjQyWP37NnT6NtMZVNmzapX1f8vSdMmMCcOXN0tqAvXLjAXXfdxSeffMKjjz5Kr169LGZXY0I4EKB+4+fl5RkcU5GK5OrqalFbli9fzrx583RsayxM7DiR/zv8f1ptr/79Ko+GPso9Ifc0kFWCBicnEX57saGtqD0Pfg4eLSwydVZWFgcOHGD69OmUl5cTEBCglU3g7+/PxIkTTZ63f//+Om2hoaEcOnSICRMmsH37dhISEtR9HTt2ZPDgwSZ9x9V29bY60tLS1K99fHz0jpk3b56OQ+Lt7c2HH35Y4/yJiYls2rSJ2bNnA/DAAw+oY98GDRqk95zQ0FDefvttnnvuOdatWycciFsIBwLV1gNAXJxh8aOKvoqxlmLq1KnqFKqhQ4c2uhWIELcQ3ur9Fu8d1lb+/PnCz/QP7I+jrWMDWSYQWA+TJ09m8uTJOu2tW7dm/fr1Wjfk9u3b66wk1JYDBw7w8MMPq9M1hwwZglKp5MCBA7z88ss8/fTTHDhwgG+++cYs17MUmzdv5tixY1ptLVq0MOhAGAqOv+uuu3T+trm5ufz1118cP36ctLQ0iouLAdRB7RcuiOrDFQgHAujatSugilKOiYnR+2ariNLt1q1bvdrWGGnl1ooInwhOpJ7Qak8vSBcOhEAAWjoQSqUSX19fevfuzfDhw1EoLPO1nJmZyUMPPURaWhqHDh3ijjvuUPfdd999dOzYkU6dOvHtt98yfvx4o7ZNLLF6qxnAmZqaqg4s1UQza6JCp6c6KnQgZDIZ9vb2BAcHM3ToUK2/Aagck8mTJ5Oenm5wLmNXUm4HhAOBKlixZ8+eREZGsmbNGr1CUnFxcdjZ2TFy5EiL2tLYtzAqCPMK03EgckvqpkgnaMS4NFNtAzRWXJqZdbopU6YwadIko8aeP3+ehQsXmnyN/v37a1UM/vPPP0lNTaV169Y6N06AVq1acccdd7Bnzx527txplANRsSKbmZlJTk6O3u8rU1dvQ0JC8PT0JCMjg6NHj+rdijGVDz/8sMbrJyQk8Mgjj1BQUMDrr7/OE088QUhICM7OztjY2LB9+3aGDRuG1LS1F01COBC3mDNnDg899BALFy5kxIgR6pWG9PR0pk6dCsC0adOqVaE0B419C6OCAUED+PH8j1ptR5OP0sGrQwNZJGhQFEqLxRA0dZKSkli9enWtztV0IGJjY4HqVwIqvt8qghhrIjQ0FEdHR/Lz8zl69Khep8PU1VsbGxvuvfdevv/+e3744QdmzJhh1Hl1ZfPmzRQUFPDQQw/xwQcf6PRfunSpXuxoTDTOu1M1REVF0bt3b/VPRY7yF198odVeVaRp1KhRTJ8+ndzcXHr37s2IESMYM2YMbdq04fTp0/Tr18/sGRf68PX1JSwsjLCwMBQKRaN1IJRyJQv6L9Bq2xO3h6e2PUVRWVEDWSUQND4GDx6MJEkm/1Td2w8MDARUKxpZWVk61ykpKSEqKgowHDNQFaVSqRZwWrNmjU7/9evXOXjwIAAPPfSQ0b/z7NmzUSgUHDt2jOXLlxt9Xl2ocJpatNB1dCVJ0vv73e40zrtTNWRnZ3PkyBH1T0VEb3x8vFZ7UZHuTWzJkiX88ssv9OnTh4MHD7JlyxaCgoJYuHAhu3fvxsHBweL2p6SkcObMGc6cOUNpaSnl5eUWv6al8HfyJ9A5UKf9lb2v8MmxT7iQIYKRBIL6YsSIETg5OVFQUMAzzzyjVeSquLiYmTNnEhsbi62trU4dn88++4z27dvz5JNP6sw7e/ZsZDIZK1euZOvWrer2/Px8nn76acrKyhg9ejTt27c32taOHTuqlSKnTZvGnDlzyMzM1BlXVlbG4cPmEavr0EG1Orpu3TqtB8yysjLeeecdtSMkqKTJbWFUeOu1Zdy4cVopVPVNU4mBqODuFnez6swqrbb80nxOp53mXPo5/jvgv3g5eDWMcQLBbYSPjw//+9//mDx5MmvXrmXv3r307NkTW1tbdTEvGxsbli5dqqODkJaWxoULF/D399eZt1u3bnz00Ue8/PLLjBw5kkGDBuHr68u+fftITEwkNDSU//3vfybbO23aNFxcXJg2bRr//e9/+eijj+jevTtBQUHY2tqSmprKsWPHyMjIQKlU8sQTT9T6bwNw//330717d44dO0a7du0YNGgQTk5OHDlyhBs3bjBr1iy9Wxu3M01uBaKxM3XqVKKjo4mOjsbPz0+dQ91YGRg0kPtb3a+3r1QqZePljZRLjXeVRSBoTIwfP56jR48yadIkXFxc2LVrF3/99RcKhYInnniCQ4cO8fzzz5s878yZM9mxYwfDhg3j1KlT/Pbbbzg7O/PGG28QGRlZq0qcoBLDun79Oh988AH9+/cnJiaG3377jY0bN3L+/Hl69+7Nxx9/zLVr13j//fdrdY0KFAoFe/fuZc6cOQQGBrJr1y727t1L165dOXToEMOHD6/T/E2RJl/OuzFj7eW8jSUlP4XZ+2Yb7O/p15MXIl6oR4sEAoGgadCQ9wmxAmFlNKUYiAp8HX2rrdoZmRxJQWlBPVokEAgEgroiHAgrY/ny5YSHhxMeHk5ycrJWoFNjZkTLEXxzzzc82PpBvf2ZhZn1a5BAIBAI6oRwIKyMphYDoYlMJuPBNg9io+dtl1FkXN65QCAQCKwD4UBYGU1FB6I6RrcbrdMWnRrdAJYIBAKBoLY0vbuTwOoZGDRQp23b9W387+T/uHjzYgNYJBAIBAJTEQ6EldEUgyir4mTrxOdDdesi/Jv0Lx9GfijiIQQCgaARIBwIK6OpBlFWxUHhwJxec3TaS6VS9t/Y3wAWCQQCgcAUhANhZTTlIMqqtPFoQ59mfXTat8VsawBrBAKBQGAKTU7KurHj6+uLr68voFJGa+pE+EZwKPGQVlteaR75Jfk42jo2kFUCgUAgqAmxAiFoUHwdffW2zz04l81XNlNWXlbPFgkEAoHAGIQDIWhQfBx89LanFaax8fJG9ieIeAiBQCCwRpr+GnkjIyUlhdTUVABKS0ubpA6EJjVtU6w+u5pBwYPqyRqBQCAQGEvTvjs1Qm6XLAxNhjYf2tAmCAQCgcBEhANhZdxOWRgVPBL6CC91e8lgf3FZMaJorEAgEFgXwoGwMm4HKeuqKGwUdPHpgreDt97+T6M+5YWdL7AqelX9GmZpRIDobUdISAgymUzrx87OjubNm/PII4+wb98+i17/woULLFu2jEmTJtGpUycUCgUymYz33nuv2vPmzp2rY3fVn/Pnz9farn/++YdHH32UoKAg7Ozs8Pb2pnv37sycOZOSkpJazVlQUMCKFSu4//77CQ4OxtHREQcHB4KCghg2bBgLFy7k6tWres/V9/s5ODjQunVrJk+ezKlTp2r9uzYlRAyEwGqYGDaRj45+pNN+PkP1xfRPwj8MCxlGM+dm9W2aeZEkOLAEruyC1kOh/4yGtkhQz/Tr1482bdoAkJmZydGjR/n1119Zu3YtH374IS+//LJFrrtixQqWLFlS6/O7dOlCRESE3j43NzeT55MkiZkzZ7JkyRJsbW254447GDhwIGlpaZw7d45PP/2U+fPnY2tra9K8O3bsYMKECSQnJ2NjY0NERAS9evVCqVSSlJTEgQMH2L59O2+99RaLFi0y+PceNmwY/v7+gCo+LTIyklWrVvHjjz/yww8/MG7cOJN/56aExRyI5ORkdu3aRVRUFMnJydy8eRMPDw/8/Pzo3r07Q4YMwc/Pz1KXFzRCwrzCeCr8KeJz4jmceJjs4mydMUn5SY3XgYg/CqfXQkkBZNx68rm8EzqNAbeghrVNUK9MmTKFSZMmqY8LCwt57rnn+O6773j99de57777aNeundmvGx4ezquvvkrXrl3p1q0bCxYs4Pvvvzf6/FGjRjF37lyz2TN37lyWLFlC3759WbNmDS1atNDqj4yMxN7e3qQ5//jjD0aNGkVZWRmTJ0/mvffeIyAgQGtMSUkJv//+OwsWLODiRcP1d2bPns3gwYPVx1lZWYwdO5YdO3bwzDPPcPfdd+Ph4WGSfU0JszoQJSUl/PLLL3z++ef8+++/AHr3rmUyGQB33HEHL774IuPGjTPZwxQ0TfoH9gfA1saWP2P+1OkvLS+tb5PMgyTB3x+onIeq5CQJB+I2x97ens8//5z169eTl5fHhg0bmD17ttmvM2XKFK3jhtwivXDhAgsWLMDPz48///wTd3d3nTE9e/Y0ac709HTGjx9PWVkZM2fO5OOPP9Y7ztbWltGjRzNq1ChOnDhh9Pxubm58+eWXtGzZkuzsbLZt28ajjz5qko1NCbM5EN9//z1vvPEGiYmJSJKEj48Pffr0ISwsDC8vL1xdXcnKyiI9PZ3o6GgOHTrE4cOHOXLkCLNnz+a///0v48ePN5c5gkbOXS3u0utArDi5AoAefj3UjmijoDBLv/MAcPJn1UqEe3Po8hg0pt9LYDacnZ0JDQ0lKiqKa9euNbQ5FmfFihWUlpbyzDPP6HUeasOyZcvIysrC39+fhQsX1jheLpfTvXt3k64REhKCp6cnGRkZ6v+n69ev8/PPP7Nt2zYuX75McnIyDg4OhIeHM2HCBJ555pkmGc9mFgeiT58+/Pvvv3h7ezN9+nQmTZpEly5dajzvxIkTrFy5kp9++omJEyeyfPlyDh48aA6TGi23mw6EIVyVrgb7VpxcwaSwSXrLglsl2Tdgw7OG+1PPQ+qt1yfWQLcnof19oBRS3rcb2dmqbTs7O7sGtkQ/UVFRzJ49m4yMDNzc3OjatSv3338/Li4uJs+1bZuq5s3AgQPJzMzkl19+4dSpU9jY2BAeHs7o0aPx9tYfWG2I3377DYBx48ahVCpNtskYysvLycvLAyr/n77//nvefvttWrZsSbt27ejXrx+JiYkcOnRIHW+xbt26xvXQYwRmcSAuXbrEokWLmDZtmklv/IiICJYsWcKiRYtYunQpH3zwgTnMadQsX76cefPmqY9r88FsCtT0QVt1ZhURvhHVOhpWQU5S9c6DPqK+g8zrMPA1y9jUAJSUlZBSkNLQZtQaXwdfbOWW3WY9deqUOitAM1Bx79693HnnnSbPN3HiRFatWmUm61Rs3ryZzZs3a7W5ubmxdOlSnnzySaPnKS4u5sKFCwDExMQwfvx4UlK03x+vvvoqX331ldFbBKWlpersCFO3Pkzhr7/+oqioCKj8fxo2bBijRo0iPDxca+yNGzcYOXIkGzZsYN26dYwdO9ZidjUEZnEgrl69iqtr7b/I7ezseO2113juuefMYU6jZurUqeo32dChQ2/bFQgAf0d/kvKTDPZvvLSRiWET69EiE8lLh/VTah6nj6t/g2sQRDxmXpvqkcSsAr785yq2chtG9XDgw+P/19Am1Zr5/eYT6BxokbmzsrI4cOAA06dPp7y8nICAAK3ofn9/fyZONP193r9/f7PZ2Lp1axYsWMCIESPUgY5nz55l4cKF/PHHH0ycOBG5XM4TTzxh1HwZGRnq+Lhp06bRoUMHfv31V7p168aNGzdYuHAhq1atYvz48QQGBjJgwACj5iwvLwfAx0e/RP7nn39OZGSkTrsxjlZaWho7duxgxowZgMp5GDRIpZJryGEJCAhg0aJFDBs2jLVr1woHQh91cR4sMU9j5narxlkdE8Mm8knUJxSXFevtP5J4xHodiPijsHNu3eY48SO0GwaOnmYxqb5ZdzSec4kVS/KZDWuMlTF58mQmT56s0966dWvWr1+Pk5OTuq19+/ZmX0kwlQkTJui09evXj82bNzN9+nSWLVvGzJkzGTt2rFFbB5rB9Q4ODuzcuVN90w8NDWXlypUkJyfz119/MXfuXHbt2mWW32PPnj2sX79ep93Q39fQyk+3bt3YsGGD1gNeUVER27dvJzIykpSUFIqKipAkiZycHAD1iktTwmJ3qOLiYi5dukRiYiJlZWUEBATQsWNH5HK5pS4paGKEeoayYugK8kry2Hx1Mzuu79DqLywr5GbhTTzsrSyNSpLgnw+rH+MWDL2egR3vVD8uM7bROhAHrqRpvXZrLjW5PeDaoqkDoVQq8fX1pXfv3gwfPrzRPTjMnTuX5cuXk5qaypEjR4xaLdDcmn344Yf1rhhMnTqVv/76i3379lFcXFyjY+Lp6YlMJkOSJHUcWVXWrVunfh0fH09wcHC1c2rqQNjZ2REQEMCAAQO48847td7Lhw8f5pFHHiE2NtbgXBXxLU0Ji7xTBw4cSGRkJMXF2k+Ojo6O3HXXXTz11FPcf//9lri0oIkhk8lwVjrzWPvHkMvkbL22Vav/j6t/MKGj7tNRgyFJ8Pt/oLiGGiYPqbJJGLsa4v+Fo9/qz9IouGl+GxsABe60KH+KZwa2amhTaoWvg/6y87Wlqg5EdZw/f96ojIKq9O/fXydt0xJ4enri6+tLYmIi8fHxRp3j7OyMj48PqamptGql/z1R0V5SUkJaWpqOlkNVFAoFnTt35uTJkxw9etQsWX1VdSD0kZ+fz6hRo0hOTmby5Mm88MILtGnTBldXV+RyORcvXiQ0NLRJyvFbxIHYv7+yBLNCoUCSJMrKysjLy+O3337j999/p0+fPqxZs4bmzZtbwgRBE8Tdzl2nbU/cHutxIMpK4dcnoUjPk0ZIf7impzS5kxeEjoAW/VTpnOd+1+6/tAOC72hUGRkp2YX89G+cVpsNCs7HwytrYlk1uRf2tmIl0liSkpJYvXp1rc6tDweirKyMrKwswLSg7+7du7N161bS0tL09mu2G1sT6IEHHuDkyZP8+uuvLF68uF70hf755x+Sk5Pp1q0b3377rU7/pUuXLG5DQ2GRCL0vv/yS48ePk5WVRXFxMSUlJdy4cYM//viDF154ATc3Nw4ePMidd95JRkaGJUwQNEEMpW3mFOfUsyUGuLxDv/PQrAv0/Q94twOFnX7pantX6KBnVS7pFGydBWW1qwdQ35SWlfPu72c4dFX/TQHgwGXDfQJdBg8ejCRJJv/UV9zE77//Tn5+PjKZjB49ehh9XkVA4e7du9XBj5rs2KHasgwNDTU6Pm769Om4urqSmJjIm2++abQtdaHiHmboYfiHH36oFzsaAos4EFOmTKFLly5a3qi/vz8jR47k888/59q1a4wbN46YmBgWLFhgCRMETRB7hT0fD9JVlpu5ZyZZRVkNYFEVDn2uv33Q66B0gvs+hvHroc1d+scZWmXIiIGre81ioqW5kVnIzXz9Qa8VXE/PrydrBOYgNjaWH374gcLCQp2+TZs2qVc5nnjiCXW8QAX//vsv7du3p3379jrnjh8/ntatWxMdHc0777yj5UTs2bNHrSI5ffp0o2319vbmu+++w8bGhsWLF/PMM8+QmJioM06SJA4cOGD0vNXRoUMHAHbt2sXZs2e1+r788kt++eUXs1zHGpFJDbQxU1ZWRseOHSktLeXKlSsNYYLVExSkkjc2dl/xdiEpL4k5++dotY1pO4aRrUY2kEVA7BHYPV9/36Q/jJtDkmDTC5Bl4P/7ibVg61A7++qJNzacIiYtT6tt1vD2fLC1slJjr5ZevHy3+es8NAZCQkK4fv06K1euNDoGwpxERUUxdepU9fGVK1dIS0sjKCiIwMDKNNWNGzfSrJmq5syJEyfo2rUrzs7OdO3alcDAQAoKCjh79qx6ef7OO+/k999/19lq0NSw0HerOXXqlHolunXr1kRERJCQkMC///5LeXk5EydOZOXKlSYH327dupUnn3yS1NRU5HI5ERERhISE4ODgQHp6OsePHycpKQkbGxvGjx+vs0VUcb09e/bUGAMBqhohv/32G0qlksGDB+Pp6cmJEye4cOECc+bM4f3336dFixYWURhtyPtEg4X7yuVyunbtqlYOE6gQSpQ14+foh6e9JxmFldtf6y6to5NPJzzsPHBWGrdfajbKyw07D11M0MmXyeDu+bDnfUi/rNsfewhaD6mdjfVASnahjvOwaHQXmns5MizMn21nVJoeN/OqX6EQWI7s7GyOHDmi0x4fH691A6oQSgIIDg5m1qxZREZGcvnyZaKioiguLsbb25v77ruPxx9/nEceeaRW31WdO3cmOjqa999/nz///JPNmzfj6OjIoEGDePbZZ2tdZ2L48OHExMSwatUqtmzZwsmTJzlz5gySJOHl5UV4eDgvvvgijz/+uMEgTlNYu3YtS5Ys4bvvvmP//v3Y29vTo0cPli5dStu2bXn//ffrfA1rpF5WICokPdu3b4+/vz9lZWXs27ePp556Ch8fnyYdZGIqc+fO1VGibIrpP3XlYMJBvo7+Wm/fA60fYFSbUfVnzNFvIXqDbrtvBxj6DtiZqCZaXgZ/vQ6pevLGR38DLtZZxXbn2WS+3n9Vq+2nZ3ojk8n47UQCP/2rSnFzUir4YkJ3FHLhHAsEdaUhVyDq5RN89epVxowZQ6dOnfDx8cHf35+xY8eSn5/PO+/UkAd/mzF16lSio6OJjo7Gz8/P6Ojj242+gX15JPQRvX2/X/mdvJI8vX1mJ/6ofuehzzQYudh05wHARg7DFsAdz+v2rX/aauMhIq/rBkRXLAW386v8O+QVl3I+yUoCXwUCQa2pFweidevWzJgxg27dugGqvbCHHnqIc+fO6VU4u53x9fUlLCyMsLAwFAqF2MKohgAnw3nh/9n9H06knLC8EUdX6rYNfUelIFkXFHbQ4T79ff98aHX6EAcup3EyLtNgf3t/Fxw0Ujd3nE2uB6sEAoElqZe7k7+/Px9//DGRkZFcv36d2bNns2XLFiZMmEBmZmZ9mCBogoS4haC0MaxOt/T4Ug4mHKS0vNQyBlw7oCp6pcmg1yG4l/lKcutL7QTIuKq/vQGQJIllu6vfhpTJZLTwqpRnPhKTTlpuUTVnCAQCa8ciDsTRo0cN9gUFBbFgwQL27dvHyZMnxRaGoNa4KF14qdtLDAoaZHDM19Ff83f83+a/eF46/LNIt91HN12tTnQcpb89Kdq816kDuUX6HbRX7gnVOo4Idtc6FnoQAkHjxiIORJ8+ffjPf/5jUI8cVCpkI0aMYOPGjZYwQXCb0MGrQ40FtX4896P5L3ztH1WwY1WczSt5jIsfTNgEgd2120+vVaV9WgFZBboiVysn9aJniHYNjxGdtDUCRDaGQNC4sYgD4efnx/Lly2ndujUvv/wy586d0xlTXl7O5cuXSU9Pt4QJgtuMiR2rdyLMn2ykZ4ui0xgzX+MWcgXcPU+3PVJ/Fkp9o8+BcFDqSlXbKeR00ViFyC/R44AJBIJGg0UciLNnz/LUU0+Rn5/PkiVLCA8Pp1WrVjzyyCO89tprvPLKK3Tv3p3Tp0/Trt3tKSgjMC+DggfxXr/3WDxwsd7+o8mGt9Vqhx6HRGFhkaeqgZlnf4Oihs9myC4wPsbE0bZSeubwVSFjLxA0ZiziQLi6uvLVV18RFRXFgw8+iI2NDdeuXWPt2rV8/PHHfPrpp5w8eRKg3vTKLcG6det46KGHaN68OY6OjoSFhfHRRx9RUtI46hY0NQKcA/By8KJ/YH+dvr9i/qr7BUoKYescWHUfRH6j229rX/drVEePp3XbUs7rttUj5eUSv59M0GrrHORucLyjxspEcWkZCZl6KpAKBIJGgUWVKDt37syGDRu4ceMGW7Zs4dixY1y7do2SkhJatmzJxIkT6d9f98u+sfDhhx8SEhLCokWL8PPz4+DBg7z11lucOnWq1tXzBHVncthkisuK+TfpX3XbtexrfHHyCyZ0nICjbS0rW+6erypuZQhLrwYoHeHBz+G3Fyvbzm6CgK6qbY4G4P0t53TUJ5/s08Lg+OIy7aJJW04lNtoS3wLB7U69fOsEBAQwZcqUeiktW59s3rwZHx8f9fGdd96JJEm8/fbbaqdCUP/IZDKe6/yclgMBcCTpCEq5ksnhk02fVJIg8WT1Y+pDQtujBXi2qkzjTDwJR1aoqn3WM0eupnPmhnYRs/8+3JkgD8MOWtXAyUspDb8FIxAIaodQKaoDms5DBd27q6Llb9y4Ud/mCDSQyWTM6jlLp31fwj7SCmqRPliTcJOtg+Eqm+bGPVj7+OI2OFa/K14384r5ZOdFrTZ3ByUtvZ0MnKFCU5ESIDYj32AaqEAgsG6anANx4cIFli1bxqRJk+jUqRMKhQKZTMZ7771n1Plr165l8ODBeHh44OTkRJcuXVi0aJHRcQ3//PMPSqWS1q1b1+XXEJiBNu5t9La//s/rHE48bPxEkqQqcGUI1wB4+CuwqyfZ8fZ6FCpPr61Xdcpj13WvNWtEzRoYVVM5AZKzdctECwQC66fJORArVqxg+vTprF69mujoaMrKjE8VmzFjBuPGjePAgQP06tWL4cOHExsby6xZsxgyZAgFBdUHfJ09e5YlS5bw7LPP4urqWtdfRVBH5Da6qYQVfHnqS+NTO2P+1l/YqoK+08HB3TTj6oJvBwiI0G3PStBtsxCZVVI3A90da1x9AHCxt2XFE9qaFn9fMKwXIxAIrJcm50CEh4fz6quv8uOPP5pUa2PTpk0sWbIEZ2dnjhw5wrZt21i/fj2XLl2iU6dO7N+/n7ffftvg+WlpaYwaNYo2bdqwcOFCc/06gjrSu1lvg32xObHGTRJbw2qFX5gJFpmJwW/otiUcq5dLS5LEumNxWm3PDGxp9PnujrZax9vPJhGXkW8W2wQCQf3R5ByIKVOmsHjxYh5//HHat29vdDGqBQsWADB79mx10S8Ab29vli9fDsBnn31GVlaWzrk5OTmMGDGC4uJitm7dipNTzU9igvrh4bYP09qtNS1cWxDiGqLVtzdub/V1MiRJJRmdFWd4TPAd5qt7YQpKJ+g0VrvtzAYotnwV0htZ2lsOChsb2vsbv+Im0/P3OpcoStYLBI2NJudA1IaEhAQiIyMBePzxx3X6+/fvT3BwMEVFRWzZskWrr6ioiAcffJBr166xbds2AgIMV4gU1D/eDt682ftN3u3zLq/1fE2r7+/4v3l2x7OsPrNadzujrBR+nwZbZ8PNKgWzKpArodczFrLcCLpPhB4aGSXlZXDkfxa7nCRJnLmRxfeHtP8e93dpVue5RSClQND4EA4EcPz4cQA8PT1p2VL/UmyPHj20xgKUlZXx6KOPEhkZyZYtWwgNDdV7rsA6cFA46JW8/jv+by5nXq5sKC+HP2YYdhxApccwbjW46AYF1ithD4Onxnv2yh6VTsTu96Ag06yXOnMjm/l/nOVEnHYA5ehuQSbPdX8XbUdbOBACQeOjwRyIVq1a0b59exYvXkxRUcOW9Y2JiQGgefPmBscEBwdrjQV48cUX2bRpE7NmzaKsrIzDhw+rf7KzjVuSDQoKMviTmJhYh99KoA9vB2+97b9e+LXyIP5fuHnN8CQBESo9BjsXw2PqC5kMer+o3Xbzuipu4/Ras17qh8O6DtWDEYEo5KZ/jYztrp2Kmpkv1FsFgsZGgzkQ165d4+LFi8yePZu2bduycuXKhjKFnByVmE11sQvOzqoUPU3HYOvWrQC8/fbb9OnTR+snKirKghYLakuwa7De9itZV4jJuuUcnv/D8ASOnnDHCxawrA54t9XffvY3s14m/qZuFtLgUF0tFGNQKmzo36bSmTt4Jc0CBc8EAoElaRj9W2DlypWUl5dz7Ngxtm3bxpQpU5g8uRYKgQ3ItWvX6jxHfHy8wb6gINOXhgXV46p05dHQR/n5ws86ffMPz2dc27EMuBGFkz7fOiAC7p7fMEGT1VFNuqo5cXVQkKGhJNnKx5lmbrUvIOag1P76uZaeb1QqqEAgsA4azIGYOFG1F13hNFy/Xs1+s4VxcVEtReflGY5gz83NBbC4vkNKSgqpqaq8+NLSUqOzSATGc0/IPfTy78W17GssPb5Uq+/X82uIIZsXcKcUiWiKCUSODwoY8rb1OQ8VhI2CM5ssegl7W21H5Yk7DG/5GUP/Nt7sOJukPk7NKRIOhEDQiLCau1OLFoYL8FiakJAQAOLiDKfrVfRVjLUUy5cvJzw8nPDwcJKTk9WOi8C8uNu7E+EbQZhXFQ2HknwiKSSZUv5HFku5ybtkkHrnbFDYNYyxxtBNNzjUnJSWlZOWW7n68EjPYMIC3Oo0Z6i/CwoNBzkjr2FjoQQCgWlYjQPRkHTt2hWA9PR0rSBJTY4ePQqgpRFhCaZOnUp0dDTR0dH4+fmpYy8ElmFg0MDKg5xkdebFp2QSRSHIZBS6NuOHtMgGstBI5LYw8DXddjPEQaTlFrE+Kp7i0kpV1/A6Og8VBLpXboFoOigCgcD6EQ4EqliDnj17ArBmzRqd/v379xMXF4ednR0jR46sb/MEFqSHXw+QgLw0yE1WtydzK63QLxycvDiddrphDDSFlgN12/79Coprr/KYnlvEzF9OsPF4pUy2nUJutq0Gbxel+nVarliBEAgaExZxIORyea1/FIqGCcuYM2cOAAsXLtTKoEhPT2fq1KkATJs2DTc38zx5GUJsYdQvsqJshiddgWwD1VNvxTzYy+3r0apaIpPByMW67WvGweWdtZpyy+lESsrKtdr6tvaqVeqmPjydKreFMvLECoRA0JiwyN26IdOxoqKi1Dd8gCtXrgDwxRdf8Mcflel5GzdupFmzSgW9UaNGMX36dJYuXUrv3r0ZOnQoTk5O7Nq1i8zMTPr168f8+fMtbv/UqVMZO1YlUTx06FARRGlp9n3EfTiRRCknMPwEbK9oBA4EgE978G4Hadqlttn/aa3KjcfqqVHR2sd822rezpUrEBeTc8jML8bdUVnNGQKBwFqw2OO+Pr37qkiSpDXOHI5HdnY2R44c0WmPj4/XSpnUJ161ZMkS+vXrx+eff87BgwcpKSmhdevWzJ49m5kzZ6JUii+2JkVhNiRE4YgNT+DKCQxXhcwsyuSHsz9wb6t78bD3qEcjTUQmgz4vwuaXdPtSL4JPO6OnkiSJ0wm6tV+8XcwXTOrtrD3Xir1XeGNkB7PNLxAILIdFHIjmzZtTWlrKjRuVy8Lu7u4AZGZmAioHIygoCLncvDnsgwcPrpMjMm7cOMaNG2dGi0xj+fLlzJs3T31ckWIqMDPl5RD5lfrQDRuUyCjG8Htnd9xuolKi+GjQR0Y5yA2Gm36xLCK/gj7TwL25Uemo+lYfbOU2tPc333uyra/2asbJ+EyzzS0QCCyLRdbHr169qs5sGD9+PPHx8WRkZJCRkUFCQgITJkxAkiT69u1LTEyMzs/tjMjCqAfKSuGXJ1R1I26hQMbzuBGKkrG4cB9Oem/EmUWZLDu+rD6tNR2FEu6ap9uecu5WnYyat+KKSsuYtf6UVtuAtj789+FOOnoQdcHXVXdr6NCVdLPNLxAILIdMskDAwrJly3jppZdo1aoVly5d0nlaKy8vp127dsTExLB8+XKee+45c5vQJKhQoqxOrVJQC46tNlwnokVfkNlQ6ujJRlc3/rq+Te+w5UOXW39cxL9fGU7jHPedSpbbAJuOJ/BzZKz62FZuw3dP9bLIysuBy2ks231Jq+27p+5AqRDxPwJBTTTkfcIin9Bvv/0WgLZt2+r9wrGxsaFt27ZIksSXX35pCRMaLSkpKZw5c4YzZ85QWlpKeXl5zScJjKespPoiU0G9YPBsFL2epVdAb4PD9iXsQ5IkJEkiKS+J0nIrrCbZcwp0fFB/X2FmtadqOg8AChuZxbZtOgfpZjbp2z4RCATWhUUciIsXLyKTyThx4oReeei8vDxOnDgBwIULFyxhQqNFpHFakLh/4fuHDPe7+EOrwerDQOdA3O3c9Q796fxPrDyzku/Ofsec/XOYs28OxWVWloYok0GHB/T3FVnP+8rF3pYJvUO02t7adFoU1xIIrByLOBB2dqrI6pSUFEaMGMGOHTtISkoiKSmJnTt3MnLkSJKTVaI9IrNBGxEDYQHy0iHya9j1f/r7Ww6Cu+fBg8tBXhlXrLBR8E7vd5jYcSIvRryoc9r+hP38Hf83AGmFaeyL32cR8+uEix8E9dRtTz5j8JQiDcXJCh7paSAw00zc27kZbg62Wm0Xk63HyREIBLpYJAujd+/ebN26FZlMxoEDBxg+fLjecTKZjD59+ljChEaLr68vvr6+AA0mqtWkSDoNW9/Q3yeTwcTN1Z7ubu/OoOBBRl0qPtdKY1WGvAX/fgnn/6xsO/EjeLaEgK7qGh+SJPHJjov8ey1D6/Terby4p6O/xc0sLNHerjubmEWoGTM+BAKBebHICsSbb76pJYBUsVdc8aO+uI0Nb775piVMEAggKdqw8wDg1cak6foF9Ku238nWSitJ2sih9wu67bvfgz0L4NZnMir2po7zAPD4Hc2xsbF82mpZufaWhZ2ifsqUCwSC2mERB6Jfv3588803KJVKtViU5o8kSdjZ2fHNN9/Qt29fS5jQaBFBlGZCkuDi1urH+IWbNOWTYU/yQpcXGBiop+YEcD274UrSG4WNnhtywjFIPQ/AiThd0SgAH+f6qUL6ZB/tirw7ziYbGCkQCKwBi+VJTZw4kVOnTvHCCy/Qtm1b7OzssLOzo23btkydOpXTp0/z5JNPWuryjRYRRGkG8jNg4/Nwda/hMQp7wxkKBrC1saWnf08mhU+ik3cnnf4z6We4kGHFQcEDXtXfnn4ZQKfmBcD9XQLqTTRrUKiP1nFiVgF5RVaY3SIQCAAL6UAIak9KSgqpqSpJ5YpaGJqKngIDFOdDWRFsf0tdktsgtg6qgElnn+rHVcPhxMN8ecpwCvI9Le5hdLvR2NrYGhzTIKx/BnISddsnbOL1jWd00idHRQTyaK/m9WQcvPfHWaJvVK6EvDYslO4tDOtVCAS3O01OB0JQe3x9fQkLCyMsLAyFQiGKaRnDqbXw06Pwy4SanYfAbnD/0jo5D6AqAx7kHGSwf/v17Ty34zl+PPcj6QVWpKzYWVemvVyS2Lz2G73aC31be9eHVWoe7qb9N41JE3oQAoG1Ypa700svvUR6et2+JFNTU5k+fbo5zBHcTpSXQ9RqkKqJFfEIUWVbTPoD7v4/cG1meKyRKGwUvNvnXd68o/og4F2xu/j5ws91vp7ZqKKeKUkSsRn5tLr6A96l2jEHoyICae7lWJ/W0THAlRaelcGov58Uq28VHLuewdJdlzh2/WZDmyIQAGZyID7//HNatmzJG2+8waVLl2o+QYMLFy7w2muv0bp1a1asWGEOcwRNHUmCy7vg4nbY9Hz1Y3s+DcP/a1TxKFOR28hp7d6a7n7dqx13LPmY2a9da/zCtIIp84pL1XEGd+dXpnmumtyrXrcuNPHRqPZZXFrGV/9cve1FpXKLSvlkxyUOXknj050XRWyIwCowiwMRGRlJ586d+eCDD2jfvj39+/fn3XffZevWrVy/fl0dCJibm8v169f566+/eOedd+jTpw8dO3bko48+okuXLkRGRprDnEaNyMKohuJ8iD2i0jDY/wkcXArZ1TyhthkKYQ+BnWW1BDp6dqxxzJv73+RMumHxpnrD0RP6VZb6zi2qFI1qU6zKxujdysusBbNMJbuwROt41/lkjt7mT92n4jIpvfVdUFJWrrfMukBQ35hFqahr167s37+fdevW8cknn3Dw4EEOHTpU7TkVTxR9+/Zl5syZjB492hymNHpEOW8DSBLseBtSTchyCKx+ZcBc9GrWi81XN5NZlImLrQs5JTk6YxLzEvno6Ed8cfcXDR9Y2XoIZdiQvW0B6blFWl3uZek83qtrAxmmwsfZjovJ2n/Dn47E0jPkNg6mrLKAVn6br8gIrAOzSh2OGTOGMWPGcOLECTZt2sTu3bs5fvy4Vj0MJycnunXrxp133smoUaOIiIgwpwmNnqlTpzJ27FigMgtDAGRcNc158G4LLaoXfjIXTrZOfDDgA7KKs/Cy92LT5U1svqpf4fK5Hc/xTu93CHELqRfbDHEmMRd5ZoFO+wP5G3F3HNkAFmnYEBHAgStpWm03snRtva2o4i9UFd0SCBoCi2glR0REEBERwdy5cwHIz88nKysLd3d3HBwcLHHJJoOQstYgMw7+/QJsnaBZZ+PPe+h/4BIA9eh82cpt8XZQZSw81PYhIpMiScpP0jt21ZlVzO07t95s08ePZ0vQp8LS0z4BZdFNUHjVu00VtPBy4oXBbVix97JWe1puEd71JGplbRSUaNcnyS/WrVciENQ39fIN6+joSLNmzYTzIDCNE2vgxgm4fgAOGxlg6+gFbkH16jzo44UIPdLRt4jNiSW/pP7TE0vLyvlkx0Vm/nKC6/hzxq4LACUy1ZZKxwBXfJwUsHYinPpVLXHdEAxqp5tmO21NVANYYh1UdRhyC0UQpaDhMcu3bEJCArt37yYnR3fvVyCoNdeMrG4ZMkC1ZeHsB4NnW9YmI/F39Mdebm+wf9ruaWQV1W8g3K9H4zkSk07ire2ADS5P8KHnuyz0eh+ftj2RaW60R30H1w/Wq31VGdBW14lIvE23MvKLtR2GvOJ6cCBKClVVbI98CUXiu12gi1kciB9//JG7776bBQsWGBwTExPD77//zu7duyksLDTHZQVNjbJSKC+Dkz/DqvuMP6/LI3DfJzD6a/DtYDn7TMBWbsvTnZ6mvWd7Hgl9RO+YmXtnci79HDnFlv1yLiwp49EvD/H7yQSdvgIbleaCT98JuiceXAbFebrt9cSwMN0KoOm5xQ1gScNTdQUipz5WIA59Bmc2wbnfVauBAkEVzLLJvmfPHgCee+45vf2zZs3io48+UmdeuLi48MEHHxgcL7gNSb8CO96Bwlo8lbvfKsJUTzUbjKW7X3e1RoSExK8XftUZs/joYgA+HPQhnvaWyTLYdS6l2v77uwQg8w/U7SjOhTWPQK9noeMDFrGtOtr4Ouu03a76BzpbGJb+O5QUateSObdZ9T4w02dMkiSOJB0hMTeRwcGD8bD3MMu8gvrFLCsQly9fJjQ0lJCQEJ2+P/74g8WLF1NeXq4u552dnc3UqVP5/PPPzXH5JsVtqQMhSbD5pdo5D+2GWZ3joI/hIcPp6dfTYP+Wq1ssct2cwhK+P3yt2jFdgtxBroBRy/UP+NdwzQ9L838PaldMzSooMTCyaaOzhWFpByJfj7JwbT6fBriUeYkvT33J5qubq60pI7BuzOJApKam0rGjfjGdCichLCyMw4cPk5CQwGeffYadnR2vv/56gxQAsWZui2qcOclw6HPYuxASooyPdfAIgfuXaLe1N2Gro4EJ8w4z2Lc7bjdvH3ibdw68Q1Ke/uwNU0nKKuSZ747q7XO2U9AlyJ2HuwURFuCqanRvDk+sA682uieUN0zUfzs/F5ztKhdKM/NvTwcir6iegygL9Ah3lZpv6/lgQmV8zYWbF0grSKtmtMBaMcsWRmFhIUqlUqc9Ly+P3bt3I5PJWLRoEb169QJUWgfl5eVMnz6dr7/+Wp3uKbhNdCD+WVSp6XBtv/HnyWzAqzUMeRvO/qYqjOXZ0jI2NgAJuaoYhTn75xDgFMDdLe5mUPCgWs2VmFXAzF9O6O17uGsQD3cLRCHX896ytYcuj8Lu97Tbi3LAwb1WttSVFl5OnLlVoXPD8Xju69IMR+XtleJcUDUGwtIrEPociDLzxZ/IbbSVTq9kXlGnQQsaD2a5O3l5eeldSTh06BAlJSU4Ojpy9913a/VNmTIFT09PduzYYQ4TmgxNshpnWSnEH4OCTCgtMk0QSpNglQNK8ztg+ALoNMZsJtYHbnZuRo+9kXeD1WdXk1tcuxWoP07qluz+z5C2/PxsH8b1DNbvPFTgp2el5OLWWtlhDjoFav/d/rmY2kCWNBxVsy5yC0stWx9E3xZGqfkciKpqrMn5yQZGCqwZs9ydIiIiOH78OJmZmVrtO3fuBFRy1VVFkezt7enUqRMXL140hwkCa2bP+7DzXfhlPPw2zfjzgnpAxwdVr10DILxxy5138u5EgFMAAPZye7655xtau7Wu9pzYnFiTr3PoSjq7zut+IYf6GymLbueiu9pw/AeIMXKryczc2d5X63jVwWv8dkI3o6QpU3UFQkLSEZcyKzev6baZcQujqExbQj0lv/pAX4F1YhYHYty4ceTl5fH++++r28rLy1m7di0ymYy77rpL73kBAQFkZ2ebwwSBtSBJqm2JMxtVy94lBRCvUSQtR/fJWI1fGDz2EzTvA23vgb7Todcz8OgaeOgLsG3cQmQ2Mhve7fMus3rOYumQpchkMoa2GFrtOWkFaeQW5xr1tJlfXMrfF1NZsku/U+5kyrL/nXrKlF/eqYrOr2fcHGzp11p7efunf2Nvmwqd5eWSXuVJi8ZBpOupqmzGLYyCUm09DxED0Tgxy0biE088wYcffsjHH3/MhQsX6N+/P//88w8xMTHIZDIefvhhvefl5eXh6upqDhME1sKNKFVwJEDkN6ad6+ynevodUuXmZd903iO2cltCPUPVx23c9QQsarDqzCpWnVkFQIhrCNO6TjOY7vnpzkucis80OJe9rQnPC/r0NBKOwY9jIOIJiHjM+LnMQMcAV536GIUl5TgoG65qaH2RVVCCVLUYBqo4CF89481CfoZumxkdiMIqqxmW1kIRWAazrEAoFAo2bNhAs2bN+OOPP3jjjTf466+/AHjwwQdp3Vr/Mu3p06fx99cVixE0Yv5eVPtzLVx22xpxt3M3euy17GtsuLRBb19ablG1zgOAzNR01y6P6m8/8aMqlqUeaeWjqwlRaMklfCsi00DqqsVSOSVJv/KkBbcwsovFSnRjxGyhzG3btiU6OprFixezc+dOCgoKGDhwoEF1yrNnzxITE8ODDz5oLhME9U36FYg9VBmxnXq+bsqF8gYuc90AKGwU2MhsKJdUeh9j243l0s1LnEg9oXf8wRsHeTr8aR1n4EKSBZ7gOo1V5f5f+Eu3L+Nqvap+hng56rTll5RxO8gPZeTpf/K32BaGoc9wmflSaKtuYeSV5FFWXqaTnSGwbswa4u/u7s7777/PkSNHOHXqFJ999pnBLYply5ap9oCHVr8HLLAiCrMgej2c3wL/LFaJP538GS5uU/3cvG7afHc8r30sawIZJ7VgWsQ0vOy96OTdibua38V/uv6n2vEv7HyBq1lXAdXy9q9H41i2W8+etQb3dwkw3TCFHfR5EUL66/ZteQ0yTQ/wrC0ymYxZw9trta05cp2SsqYvtJZVYMCBsNQKhKHMHzOuOuWW6F5DbGM0PhosmdrT05P+/ftzzz33NJQJAlPITYF1T5lvvoGvqoSLNLlNHYgI3wgifCO02t7q/RbvHX5P7/ji8mLeO/weHwz4gB8PpnP4qp6c/Vs429ny4p2tVWqTtWXQLNW/VTU7zv0BfabWfl4TqbqNcez6TX4/cYPR3YPqzYaGoKBYv5NksXoYhgpnmWkLo7S8lJuFuu/ZtII03O3dzXINQf3QYA6EZsaGoJKUlBRSU1V57qWlpfWnA1FaDLv/D7ISoPMjUJQNciXkpaq2KDQzKeqKT3toNVilC6GJfyfzXaOR09yleY1jZv0zi/M3ivCS3YWT1F6nf+rgNgxo62167ENVZDJVldPUC/DnK5XtafWbgu1gq7u8vfZYXJN3IIpK9cd6WKwip4UdiLSCNL1BoUn5SbTxqD6oWGBd3F5ybo2A5cuXM2/ePPWxi0s9BBZKEvygkSlz6DPzX2PAy2DrpEoPaztM1ebgroroj14PLfqCf2fzX7eRorCp+aOZlldEmayQFMUftCzRdiA6NHM1j/OgiU8o2MgrZa0Vduab2whs5fp/l+OxN+navOlGQxSW1PMKhCFHQZ+4VC3IKNST4YHQgmiM3J5rxlbM1KlTiY6OJjo6Gj8/P5yddaPP60zGVVVKXlkJHP0WVt9v/mto4tsRWg5WKUh2HQ/OPpV9EY/B47+qHIxGUBSrPnkqXP+WUXFpOVdT80jOrvyiL0e1P+1gK+e9UeG8c19H8zoPFYRpOJrJZ1TOZz1h6Pf5YOv5erOhITC0AmGxGIiSAv3teebRajAU61A1sFJg/YgVCCvD19cXX19VdndV9U6zkHQatr5h/nmr4hYMI2+ldCqdq3cOmoJctwXoH9ifcK9wlHIlSrmSP67+wTcn1pKSo/uEWCxLpVdQG3p3vIlMmYJMZqGVK6WT9vHaidBzCrQcaJnrVWFM92DWHYvTaS8uLUepaJrvo6JS/SsQFkvjNBQsaSYHwpA8e4kZszwE9YNwIG439n1kubnvX6J6Kg3sDm6BlrvObYRmUNmDrR/kq71JINetH5Oq/JVLMhcunwcbbHi5x8t09NJfIbdOVFUDzc+AA0sh+I562dIY011VPXTe5jNa7VtOJzKqa9N8zxnSu7CYDoahLYzcZNWKUx1XtgxpPhSXm0+oSlA/NE2XXVBJWSlkJ8KNE1Ccb7anCEC1H66JV2vo+IBwHixEak4RDuX6g8xaejupv9fLKed/J/9nGSMU9rptpYWweQYknqqXLY0OzVx5bVioVtvPkfWXUlrfGFqBKDbQXmcMrUCUFauyseqIvhROgGIzKl0K6gexAtHUkCTY/4kqxqHNULi8S6XfYCpyW3jsFzj5E5xeq3/M+A3wz4eqwMgeZkzxFOhw7HoGi7ddQIETCsmVUlnlU1yonyuKKgGGuSW57Ly+k9burQlxDTFfPERQD1V2TtUv+6w42DYHOtwPdzxnnmtVg5+rHkemiVJkIIjSkGNRZzRjERw8tEt7Z8eDi1+dpje0hSFWIBofwoFobJQUqIIgvUNBrue/L+UcXNmteh2tX/bYKMZ9BwqlSs7YyRvkdqrjCqnqgAjVCsTgWbW/hsAoDlxO0xKKCiqdwnXbpUiUEuDuoOM8VLDm/BoAXGxd+O+A/+Joq6vmaDL2bvDg55AcDQeW6Paf26xyICpWIiwUGOvhqNRpO3gljb5Vim41BTSDKO1t5eqti3pZgXBppu1AFNWuvLwmOSX6gyjFCkTjQ2xhNCZKCuG3F+GvWfD9KFXFy4yrqi/rzFjVB98c6oAPLKusS6Gwg/b3Qtu7oEV/1UpD6AjoU71aosB8VFWZlGFDcMkzKGxscLOvWf47pySHtRcNrCLVBtdm0PZuw/1FOar36Or74fQ6811XAyc7BS08tQM6v94XQ1l506vQqVkLw9Op0nGy3AqERgyErb12bIsZ1CgNZWEIB6LxIVYg6sDly5f58MMP+ffffzl9+jSBgYFcu3bNvBcpL1PJRhdmgUyuvQdparVLfSidYdRy1VJl6nlw9NZOs9TExgbC9VdWFViGc4n6A85mD+tGgMdX7IzfTGpBKtFp0dXO83f83zzZ8UnzpnY276OqhVKV02sh5azq9bFVED7aIisRb4xsz/M/HFMf5xeXcjkll1D/plOUrbxc4qZGLYwANwduZKq2GErLyykrl5DbmPlvq1myXWGv+qlwHMwgJqW5hWEnt1MX1hIORONDOBB14MyZM/zxxx/06tULSZK4edOwpHCtKcyEq3+bf95+L0GzCJWYU0URq3osjiSomaPXMvhw+wWd9vb+rnRvoSrpPaHjBACuZ19n3qF5OmM1eeXvV8gvycffyZ8Z3WbUXTb4judVqxFVt8qqHpcVWyRDw91Rybv3h2llZMRm5DcpB+JmfjHlGoGpzdztQaPkTHGpBUqaF2k4rUqnW4Gzt+Ko6uhASJKktYXhae9JYl4iIGIgGiNiC6MO3H///cTHx7NhwwbuuOOOhjanZrpPUm1B9HsJ2tylWmm4DStgNhb0OQ8Azw5spdPWwrUFr3R/Rc/oSjKLMikuLyY2J5b9N/ZXO9YonLxU76dBr1c/Lq36Ql91oUMzV62tjK3RSRa7VkNwM19bGyHATTuN1iJxEHmpla8dvVXbGBUYEpkykoLSAnXlWQAvBy/1a7EC0fgQDkQdqLc6FbXFwR0Gv6HSZxj5IXQao9qCaHu3UH20ci4m698nXjW5FwHuDnr7wrzDeLbzs0bNfyP3BqdSTxmUFTaJlgPBu63h/q2zzRJ8ZwjNv0dCZj5ZBU1HkKiq2qSXs3bwqCGVylojSdqp3s6+2qm7dYyBqKoB4WVf6UAUlpmn1oag/rDyO6DpXLhwgWXLljFp0iQ6deqEQqFAJpPx3nv6KxtWZe3atQwePBgPDw+cnJzo0qULixYtoqSkEX4ptRsOIf1U+gy+usWWBNbL+qh4nbYn7miBvZ6CUpr0btabjwd9zOCgwdWOO5x4mE+jPmXOvjl6KyOaTMiA6vuvH6j7NQxwRytPreMLSU2nLHS+hgMhQ4a7Q1UHwswrEIVZUK7htDh6VXEg6naTr5rCGeRcWQituKxYyFk3MpqcA7FixQqmT5/O6tWriY6OpqzMeA99xowZjBs3jgMHDtCrVy+GDx9ObGwss2bNYsiQIRQUNLI3t8zMe6MCiyJJEgcup7H2aBwn4zJ1+r2djYsjcLd3Z3S70djLa9ZKKC4vZk/cHlNN1cUjpPp+MxVi0kfvVl64amSjfLzjAqVlFspQqGc0VyAclXLsldpf2WZXoyzJ1z62czWvA1FFRCrIRbuSalZRLTRrBA1Gk3MgwsPDefXVV/nxxx85d+4cEyZMMOq8TZs2sWTJEpydnTly5Ajbtm1j/fr1XLp0iU6dOrF//37efvttC1tvAr2eVQn6VIeIb2gUSJLEnvMpvPv7GZbtvqR39QGgYzNXo+d0snVifr/5jO8wvsaxf1z9g8s3Lxs9t14CulbfbwYFw+oI8tDWuPhmf4xFr1df5BdXOgiOdgqc7bTj3s1eUKtqjIOtvVkdCM0UTqWNEj9HbVEqs6yGCeqNJudATJkyhcWLF/P444/Tvn17o+MUFixYAMDs2bPp1q2but3b25vly5cD8Nlnn5GV1UAecsgAVcXKtvfA4NkqyejBs7XHBGsEctrIodWd9WujoFZExd7ki3+uGIx7ANVTtpujaQ6hl4MXA4OMK3K14N8F/HD2Bw7eOEheSZ5J1wFUMTVdHjPcf3knHF1p+rxGUl5FQnvPhRT2XGj85aHziisdBGc7OQ62cmw04pfyisy8AlHVQbB1BKWGc1Zci/eGBpoOhLPSGVc7V2xkld/RoqR340KkcQIJCQlERkYC8Pjjj+v09+/fn+DgYOLi4tiyZQuPPVbNF6UlGDQLWt7aY249pLI9qCeMXKz6UAd0VTkNSdFweQcE91ZFyQusloqnx8Xb9GdbVDDjrnZ0be5eq2sobIz/iO+O283uuN2427kzv998Mgoy2B23m1DPUHo3613zBB0fgAtbDEunR69XvWf9w422yVhK9GxZfPH3FRIzC3n8juZmv159ka/hIDgqVfFcTnYKcgpVMVm5RWaOzSqp4kAo7CtF5QAK9euSGIvmFoazrTM2MhuCnIOIzVEJ4F3JusKg4EF1uoag/hAOBHD8+HEAPD09admypd4xPXr0IC4ujuPHj5vVgQgKCjLYl5iYSLNm/pXOQ1VkMl3tBv9wi3xBC8zL0l2XOHil5sJm3Vt40LuVeR3BFq4tuJ593WB/ZlEmr+x9RZ2X/3f83wQ6BxLsElz9xHYuMOZbuHkN/jSQUrp1NkzYpF+GvQ4YCi79/WQCA9p6E+xpBhnvBqBAI8bB4dbv6KzlQFg4BqKqA1FUNwdCMwvDVanakmvp1lLtQCTlNa003KZOk9vCqA0xMar90ubNDT+pBAcHa40FyM/PZ926daxbt46rV69qHV+/bvgL2jREumVT40pqrlHOg6u9LY/f0aLO1/Ow81C/frjtw7x5x5u83fttAp0NV02tKuqz5eoW4y6msAOf0OrHXN1r3FwmMK6HYedm0/EEs1+vvtBM07RTqL6uNeMgcgvNvAKhuYUhV6rUZ+00Ym+KcupUcVUzC8NZ6Qyo4nXU05fVXSpbUH+IFQggJ0e1L+fk5GRwjLOz6s2enV3pQaekpDB27FitcRXHK1euZNKkSTVeOz5ef8AcVL86IWicVC2MpQ9fF3ueGdCKdv7O2CnqnknzbOdn+en8T/g5+nFPi3tQ2Cho6daSt3u/zecnPud02uka57hw8wL5JfnGF+TqNMZwHYwDn0KLPiqVQzMR6u/CC4Pb8OU/V3TqYRy4ksa0IW3MK+NdT2hW4rSzVTkQrg6VX9tVhabqjGYQpe0tfQ17t8o2qVw1Rlm7FR3NGAgXpWplw05emV0kHIjGhViBqAMhISFIkqT3xxjnQR8pKSmcOXOGM2fOUFpaSnl500hHu53JyCvmz1OJ/BuTUaPz8FDXQJY+1pVOQW5mcR4AQj1Dmdt3Li9EvIBSI3NHKVcyvet0o+bILMpk2u5pRCZFGnfR7pNUNVZ6T9Xfv/4ZVeVYM76/B7XzYfXkXnr7pv4YRXJ24xMq0tR5qHg/+Gik86bmmvmGq7kCUZF9YVdFGrwO2xiaMtbOtqqHMi0HwgzFugT1h3AgABcX1QckL89whHFurmrpzdXV+FS62rB8+XLCw8MJDw8nOTlZfV1B46OotIxj12/y0s/H+f7wNT7eUX2wJICbQw2puWZGbiOnb0Bfo8evOLnC+KdE9+bQbpgqcLIqRdmw5TVVTEQdlsSropDr/0q7mV/MSz8fZ/G28+ZXb7Qg+rYwfFwq0ypTss18w9UMgK1YIdJxIGov1FWo4aBUrGaJFYjGi3AgUK0kAMTFxRkcU9FXMdZSTJ06lejoaKKjo/Hz81NvnQgaF5IkMff3syzedl5vhoAheoZ41DzIzFSNhegX0K/a8S/sfIGNlzYal7NvI4e73oWACP39KWdVQZf1xLHrN/ntxI16u15d0VyBqAgU9daQs84tKjHp/VUjmnodTreq8ppxBUJTadJBrtoisVNoOxCSGR1KgWURDgTQtatKBCc9PV0rSFKTo0ePAmhpRAgEhkjKLiQmzbTVozkjO+BlpNqkORnafCjudu4A9PTrydOdnubR0EdxsTVc1XLz1c18dPQj47/sh1QjwnYjygRra2ZwqG+1/RsMCHVZI1oxELdWIByV2qFrBeZUo8zTcCCcb4k8Kas8xNRyBaKkvIQySTOrROVAaCqmSkiUlDfCsgG3KcKBQBWs2LOnapl1zZo1Ov379+8nLi4OOzs7Ro4caVFbxBZG02DfpZqzLDQZ1M6XzkHuljGmBpRyJQsHLOSt3m/xfJfnAbgn5B6WDFlCgFOAwfNu5N3Q2tOuFoUdjPhAf9/RlbDmEUg+o7/fRJ64ozmtfapfuTO7BLSF0NzCUN5yIByqylkXm9OB0CykdWsFQibTzsSopRZE1ToXFY6DsoqirtjGaDwIB+IWc+bMAWDhwoVERVU+EaWnpzN1qioQbNq0abi5uek931yILYzGS2FJGVujE5m08l+jnnI/GhfB9CFtebJPCJP6hljewGpQypW0cmulk6kwoWP1UvAz9szgo6MfGacg6BcGo1bo7yvOg79mQfoVY002iIu9Le8/1KnaMTfzrb90tCRJFJbobmFU1bzQHFNnNIMoNVcetLQgarcCUVhF5dL+VpBm1ZotwoFoPDQ5ByIqKorevXurf/78808AvvjiC632xMRErfNGjRrF9OnTyc3NpXfv3owYMYIxY8bQpk0bTp8+Tb9+/Zg/f77F7ff19SUsLIywsDAUCoX1lwwXqFl3LJ5VB68Z/XQb6O5A3zbejOzUDAeldRY+C/UM5bMhnzElfIrBMWfSz7DqzCrjJnT0rL4/eoPxxtXAS0PbGezbGm39gkUFJWVIVG4R2d9K43So4kDkl5ipHoYkaZfr1lwZMIOYVFUHwkGhGwOhb5zAemlyOhDZ2dkcOXJEpz0+Pl5Lc6GoSNfLXbJkCf369ePzzz/n4MGDlJSU0Lp1a2bPns3MmTNRKi0fIZ+SkkJqaioApaWlwoFoJOQVlfLHKeOD88Z2r0HV0YpwtHWkb2BfMooy2HBJ/w3+fMZ5JEkiozADT3tPw5oLto7g7Gu4uFbM39B/pup1HdUqu7fwwMvZjvTcImzlNlrBhtvOJDE41JeW3ubTojA38Te1l/z9XFVP6lWdzQJzbWGUVVmV0SyiZV9FTKoWGNrCcFJo/x/UqhaLoEFocg7E4MGD6xTFO27cOMaNG2dGi0xj+fLlzJs3T31ckWIqsB7Scou4lJxLYWkZ5xNzaN/MhS/+rnnpvb2/KyM7NaOgpIw+Zpanrg96+PUw6EAAPL39aQDCvcN5ocsL6idMLWQy6P8ynNkA/p0g+wZc+Et7zPejVE+/Iz4A77a1tlepsGHWsPYcuppGt+YevP1btFb/st2X+HhcRK3ntzTX0ytlpW3lNjRzuxV0qLDQFoZOIS0NB8IMKxCa21wyZOo0zqriZPmlVeS0BVZLk3MgGjtTp05Vq1kOHTpUrEBYGZn5xcxef1qriNHfF/U/Td/XOYDBoT4cvprBjcwCHuoa2GhrMgD4O/nT2bszp9JOVTsuOi2aF3e9iJPCibl95+LlUMVZ0qzXUpyvCtyLryJQVVYMkV8bDrw0kuZejjT3UknUy5BpbQncyCwg/ma+TilwayE2o/JGGuThgNxGtapjYyPDTiFXB1jmF5tpC6OqiJPCgANRyyDKS5mVImrBLsHq4EmlXInSRqmWT9eUuxZYN8KBsDJ8fX3x9VWloSkU4r+nockpLGHj8QQcbOV4u9gZtdIAMPOudtxxa5VhTHfrvEHVhpe6vURiXiL+Tv6US+W8uOtFg2l3eaV57I3by+h2ow1PqHSEoe/A6vt1+8yUlVHB9KFtWbLrolbbq2tP8p8hbenXxtus1zIH19Mrl/KrOp7ujrYkZ6sciMQsM8UMVF2B0IqB0Ager+UWRmZRpvp1kIu2TL+jrSPFRSoHIq9UbGE0FsTjrZUhpKyti1UHrrHldCLro+KNdh4AWvpY7956XZDJZAQ4B2Ajs0Fho6C9Z/tqx/8Z8ycFpQVIksSvF35ladRS3UqgMln1OhFmIjxQv4rsst2XrE68KDO/mEvJlU/irarEamimqV5OMdMTe9UVCFuNLSjNGIiCm7WSIC/WiLHQVJ+ESllrgLxi4UA0FoQDYWUIHQjrobCkjANGVM3Uh6+Lfc2DmgAjW9asi/Lirhf56fxPbL22lROpJ/jh7A+6gwK7q6Svq3L1bzNYqcLZTkE7P/0xRS/9fEJdItsaiM3I19pu6dZCW6E0yKPy5p5prpTUqisQmtkRbhorBuWlkGO6mqdmemZVB0KzIqcIomw8CAfCyhA6EA3PngspfH/oGifjMmt1fkRw/ctRNxTtPNrRzbdmddadsTvVr69k6VnJkSvgwc9VWRqa/LMYjq2Csrrv88tkMl4dFkpYgK6WS0pOId/s169C2xDkFWlnVng5ad9wHTUyMfLNlYVRdQVC8ybvEaLdlxlr8vSaKxBVxaOEA9E4EQ6ElSF0IBqWMzey+OLvK/x5OpFPdl6scXxLbyf+M0Q7U2Boh+qllJsSMpmMaV2n8b+7/kcHzw5Gn6dXLEgmg34v6bafXgeXtpml6JarvS1v3duBEeHNdPoOX00nI6+YmLS8Bt/SKNDQdrC3lasDKCtw0JCzNpuUtWYhLdAOolQ6acdE1GKbQaxAND1ElJ5AoMHmk4k1jlk8pgseTkpyCkvwd7VHJpNhK7fh16NxhPq50KPF7bMCUYFSrqSzT2fOZZwzanx2UTY+jj66HY4G0lsPr1D9OHiotjoGvFKzKJUBZDIZE/uGkF1QorNFNfXHYwD0buXFjLsMC1FZGs1VBUc9ImOaYlIlZeWUlpUbrERqNDka731nP6j68GLrUKkVUaKt6WAMmqW6q3MgckvEtm1jQTgQVoYQkqo/MvKK+enfWJRyGzoGuPLjketk5FW/n/zZ493wvlXwytmu8uPTq6UnvVrW7obWVLgz+E72J+wnITehxrErz6zkpW4vYSe3Q5KkSuEpn1AI6gHxR/WfWHBT9XPud+g+qU72PtG7hcEYl8NX08nML8bdsX7Lq1eQX6TpQOh+TVdVo8wsKFG/L2tNlsb/m1ugbr9mTETV7Q4jMHYFIr9E6EA0FsTdycoQQZT1x/pj8ey7lMqu88ks232pRuehb2vvun9JN2GUciX/1/f/GNFyRI1jz2ec54WdL/DK3ld4evvTzD04l/SCdNU2xl1zodez1U+QFF19vxF4OimrrUHy/A/HKDVnqWwT0NR20LsCUaVt0/GanbYa0azE6aK7xaO1pWGi3HS5VE6pVPk7VY2B0MzCECsQjQfhQFgZIoiyfjhwOY1d55ONHt+vtTdTB7e2oEVNA5lMxgOtH1CXB4/wieCV7q/wxV1f6B1/s+gmALE5sfx64dfKjo4PwPCFhi+Ueh4u74Kiut1shoc3I1xPUGUF+y/XLgunruQVV78CUVEXo4Kd54x/LxskP73ytb6tJK0VCNMciLQC7b9j1RUIV41qn4VlhWIVopEgtjCsDCEkZTkSswr44u+rnE8yTUlv0eguNPdqOmJQlsZObscHAz4gpSCFAKcAw3UxqhCZHMmE4gk4V1SB9A+HiZv1i0wB7P8EvNvBfR/Xyd5nB7Zi+s/H9fZ98fdV2vm5EOCuR5bbguQUVj6tO9vprkC4Odia94KSpNoaqkBffImmNLmJWxj74vdpHVeVOfdz9NM6TspPopVbK5OuIah/xAqEoEnzz8VUJq/8l0e/PMTMX06Y7Dy8Nqy9cB5qga3clkDnQC3nYVxozTVmfjr/E6XlGimbMhn4dzZ8QtpF2P0+JJ+tta0+Loa3pSQk3tx4mnOJ2SzddYmVB2KMrrZaFzQ1KVz1OAsu9tptLTzrKFxWmAXlGr+Xgz4HQmPbwcQViIzCDK3jELcQrWMfRx9kVL5XkvPMsKIisDjiEVfQpLickktWQQldgtyIzchn+d7LtZqntY8zb4zsoBUoKagbQ5sPRS6Tk5Kfwq7YXXrHHEo8xKHEQwA0d2nOo+0fpX3LAZBUTf2N2EMq2esx32oXgDISmUxGW18XLqXol2guKClj3uZKWe1mbvYM15MGak6yNVYgXO31rzY81qs5P/2r0mPIr6tTU6B9g9e/haEZA2HaCkRqQar6dd+AvtjaaP9Otja2eDl4qbc6kvOFA9EYECsQVoaQsq4dkiQRnZDFW5tOs3jbeb7eH8OcjadrPV/PEE/hPJgZWxtb7m5xN090eIIp4VNqHB+bE8unUZ+SGhBBpFcg2QolhBuoq1GUDTf0b0MYw3+GtqFzkDteTnY0r6Hg2aqD12p9HWNJzal8wnex1/8+dNKIjcgrqqPQVr6pDoRpaZyaMRDNnPQ7X/6O/urXYgWicSAcCCtDZGGYTkZeMW9uiua9PyuXsfde0F8hsyrDw/yZ/2A4Pz/bR60g6eGoZFiYfw1nCupC38C+Ro0rLitm1sE3WaEs4b/N21Hc9Qloe7f+wQkGUj+NwNfFnjkjO/D5E92YdmftS4ibg39jtG/m+rYwABztNNUoSykvr4P4lWYApVypEo6qimZbXrpuvwFKykq0Cml5O+gvXObnVBkHoVn6W2C9CAfCyhBZGDWTVVDCrnPJ3Mgs4MjVdKb+eIyrqaY7Wk/2CWFSv5a0vVUf4bVhobxzXxgfjeuikyYnMD/TIqaZND65IIV/k/6F3i/qH3BxW60rRWrS3MuRR3vqqctRT5xO0FaEbOmtP77BxU7bsdAMvDQZzRUIR09V7ElVvDSykLLijP5bpxdqOxs+DnoExNDWgigsM1OFUYFFEWu0VobIwqgeSZJYtPU8V2rhMGhyT0d/RoRrrzLIbWR0DNBfsVFgfrr5dePbYd9SXFbMN6e/ITI5ssZzYrJi6B/Y3/CAnx6DB5aBZ8s62TaqayCjugby8Y6L/Buj+7S97lg893dphp3C/I7mtbRKKefWPs4GtUc8nLQdiIz8Ytwca5mdoakBYUgN1KeKVHnqRQjqXuPUVVM4vR31r0DYyyu3SPRKnQusDrECIWhUZBWU1Mp5mDq4DYHujjzWqzk/PH0HT/VvaXR6ocCyKOVKRrUZRUvXmm/6e+L2cDXrKiirWZn7/T/w8xOQkwTnt6iKcWXVTmhp/B3N9ToJ647FsfucZZbZk7Mrn777ttZ/swXdAlsZeXW46eZoxBw4++kf4+IP9hqaGalGypYXV2Y+2WCDi63+iqia2hBFtVC6FNQ/woEQNBokSeKHw9dNPq9LkDsD2/nw0bguPBgRWPeaAQKz08y5GW/3eduose8dfo+47uNJc3BFcvBU3diqUpgF66fA4eWqYlx/zKhVAShfV3uWPtqV7nrqm6w+dM3sRbckSSJPQ4XS1cHwKqSDUq4laZ2eW4ey3rlGOBAymXZVzjzjRLYKNOpmONo6GnTc7TSEqsQWRuNAfJMKrJISPRLCs9efrpUy4BN3tDCHSYJ6INgl2Khx717bwOtu9nzZvh/S8A9qPqGkAM7/qa11YCRujrZ0DnLX2/fYV4dJyTHfza6gpIwyjWDImjKBPDVWIW7m19KBKC+HvMo0S1wMOBAAGoqRFBmnqZJfWqkq6Vi1XLsGmlsYZVIZWUVZBscKrAPhQFgZt2MapyRJXE3NJStfJZ7z+Z7LTFr5L6+vO8nklf/y8q8nOHotg+sZtSvzK4SgGg9PdnxS/bpPsz58c883jGk7Rv9gGRxJOsLT+1/j7473ICFRhsRJirhKCRJVVgeivoPfpkGZ6cGG1YlNTf/pOKfiM02eUx+5VQIhDaVwVuCpEQeRXkMtF4Pkp4Ok8T1jaAUCwE5j+8HIIEpNWWpHheHPYlV5679i/jJqfkHDIaL0rIzly5czb9489bGLi/79wqbEL5FxbDqRgK3chsd7NWffJdXTUGyG6ounILOAD7dfMHleW7kNzw0U9SsaE63dW6vqZshQiw25KGv+DKzOOMFVL1+80mPYhCpG5hU8CKPKjT8rDo6thF7PmGRX50A32vm5cDFZ/03z2/0xfPpoV5Pm1Ed2FQfC2a76oEjNFYiM2m5h5CZpHztXk8Jsr7ECkXJOJSilqL7AXF5ppeOvmWlRlaoOxPbr23m0/aPVzi1oWMQKhJVxu6RxlpdLFBSXIUkSm06oAtxKyspZfeiaWeaf0DuELyf0oH9bw0FoAuvEVm6rpVTYxbeLTvVGHWSwT1Gudh4AvsHAEvvZ36CsRH+fARRyG/7vwXAGtNWfgpiUXcjX+67WuXpn1W0IZxNWIKJvZBGTVotVunQNtVYbueEsDADbKjVB4o7UOH1uceX/SXUrEHIbkTrd2BArEFbG7ZDGmZRVyNu/RWvp/deGlt5OvHhnGwpLytlyOpH4m/mk5BTR3NORuzv6oVQI/7gp4Kp0ZfHAxVzLusaO2B1Epxko5W0jB7mt2jnIpIyPuMlNyngIZ6IoQgIewRm3nx5V1djwDzesbqmHEeH+6hWyquw8l0xMWh6zR7TXqVVhLKfiK/f9PZ2UONWgR9LKR/sB440Np/hxSm/kNiZkGCUcq3ztGwY21XxuXAK0j2vIbikuK+Z8xnn1sWbVzap42uupvyGwaprmHUpgVZyMy2TzyRuEB7oxqmsgi7aer7Pz8M59YXRo5qKO6J4+VKUeWFpWLrIsmiAuShc6+XTCy8GLt9LeMjzQvYVqm8JGDu4tOCOzgax4Pi/MVA9xQMaEUjnER6p+fDuCbwfDc2rQyseZCb1D+P7wNb39V1Jzeea7o7x9X0fCqikTHpOWR9T1m/Ru7UXgrUqfkiQRFVtZETMi2L3GVOOIYHedtpv5xQa1I/SSFV/52r9T9WODemgf16DXcOnmJa2Mip7+PQ2OdbPT/nsZEpwSWA/CgRBYjOOxN9lyOlGtrBd9I4vM/GJuZJmmo6/JlP6tGNDO26CAj3AemjY+jj64Kl21tAW0UDqCT6h2m0cLVZBgkmrlYg/5TEDjSfjsb0Y7EAD3dm7GHa08+e7Qdb0iUwDz/9CuDvpQ10DG9QhGJpORW1TKe3+cJa+4lN0XUljySAQKuQ2HrqaTnlt5Q66QVq8OW7kN9rZyrQqhRaUmbKOUFmmnY7oGGB4LqhUev3BIvrUKVEMg5Zn0yiJkLrYutHWvXib8ifZP8OP5HwHt7A2BdSIcCIHZuZySy65zyezRU49i65kkPWdUj4OtnM8e74aTKG5122NrY8tL3V5i/uH5pp0os1HpReToef9d2w+SpF++2QDezna8fHc7QBVAuf1s9e/rjccT8Ha2IyLYnajYTLXWQ3puEWcTs9lzPpVDVytv5B6OSroEG17B0GRi3xC++PuK+tikcuM5idrHroE1n6OViVG9qJvm9kUHrw41rqhopnnml+QjSZIQfLNixOPabUhZuWTal4wJlJSV89H2C3qdB1P4vwfD8XKyw0mpYMZd7YTzIFDT0q0lU7tMNf1EDRXFp0jiGBr6DdHrVSsUtdCJeKirETdd4Kt9V3lxTRTf7L+q1b5gyzkt5wFgZCfjZbK7VtnGKCoxYQUi+4b2cU0rEGB0KqckScTnVG6PtPNoV+PUmnEQEhKJeYnVjBY0NOJbuZGTU1iCs53CaC89JbuQd38/Q15xGdPubEOvltUHLt3MK8bVwVYnKKu0rJy/L6ZSWFLO0A6+2N9SxIu/WVB7QZtb/N+D4bTzc2HZY10pLZdEMKRAh25+3Xi47cPEZcfR0asjtja2fB39NQAedh7c2fxOLt28xOk0jZLuNtqBjZ+TySw8CUWpkruuIGwU9Hgazm2GzFgIewjcDDsJ7o62tPR2ql0GhAH6tTE+e8jeVtvRKCw1wQnSdCDs3VRbQDVeUGNlJN+wsFt+aT6lUmVaqjExDc1dmyNDptbwuJZ1jQBnI5waQYMgHIhGzNqjcWyISiDIw4H3H+pU7Y02JbsQB6WclQevqW/wH++4wLwHwgn1d9EZu/t8CqcTsriSmouPiz2LRnfWqlC5+3wK3x6IAeD7w9fwdbGndysvfj9pfM2Bqnu3w8L8GRURiIeTKmXPxkaG0pRocsFtg43Mhvta3ac+liSJK1lXiM2O5eG2D9PBSxXT8Ob+NyufYm1swM5Za9n9AzLogh3TcUfGrffamU0qdcZzv6uOs+JhxEKDtshkMmYNb8/zPxwzOMZUPJ1qSFvVwK7K5/5qah7dmtccPwFAeuXWh1GrDwBuQZWvs29ASSHY2usMO5x4WPs0u5q3ZBwUDvg7+av/z65mXTW69Lug/hEORCNFkiTWR6mWB+Nu5vNXdCIPRuh/Stp+JolvD8SgVMgprvJ0svCvc7T2cSY5p4in+oVwPT2fnyNjtcak5hQyedW/9GjhyYhO/iRlFaqdhwpScgqNch6aezrSr4232ta03CIiYzIID3Qj2FMoRgpqh0wmY0LHCTrtw0KGserMqsoGz1aqwlEatR9OUsTTJPMuXuRSjjty/M79hqLCoUiOhtJiUBi+qbs7KpnUN4RVB69V2oSMIA8H4m6aFgw4ONTXpPEymUzrs73uWBx+rnYGNSu0SDpV+dq3o3EXdKsiNx69Hro+oTPsl/O/aJ9mhAMBqi2qCgfiWvY142wSNAjCgbAyUlJSSE1V5ZmXlpZiYyAnO79Y2xE4FZ9l0IGouNlXdR5Apb0ffUOVJfHB1vM6/ZocvZ7B0esZ1f8CBpjQO4R7OzfTafd2tmNEJ912gcAcDAwaSHFZMWvOr6lsdPEDBw9I1X6/z0M7oyIIBQ/jTAT2ELUaek6pNtDyrg5+nIjLIiYtlyf7hKi3IR778rCurHY13Kfnc1ITVT/bn++5XLMDUVqkKjpWgU974y5WtVS6ZiEuzemlKqqatsaJ4gU6V36PZRTW7vtGUD8IB8LKqEnKOvFWCqRNlS+yoipfIJIkEXntJhm11cc3E8Eejix4uBO2Ir1S0ED0atZL24EA1WqCk3e1FSXjKWUpmXyANz5nfwOPlqp0TwPxEAq5DbNHtNfJHBjS3pdd5/XfZKsy74FwgjzqaSWusEoqrIO7cefJbcG7HaRdVB2XGfcdY6zSpKuyMsU2pzhHZGJYMcKBsDKmTp3K2LFjARg6dKjWCsSx6xks3qaqCTG0vXbBm4rI68hrGew6l0JWQQkxadWnWFma7566QwRAChocV6UrLV1bEpOtve2GSzOjSlLPIo0w7Oh2YAF3cuvmft+n4N1G7/iqN7uHugWSW1TKEQOaEZpUjUcyluaejuraMUZzeaf2cTUqkTq4BVY6ECU167pMCptk9NSaDkSZVEZ+aX61NTQEDYf4drcyfH19CQsLIywsDIVCoeVAfLrzkvp11SeauJv5PPrlIT7afoETcTcb3Hn4emJP4TwIrIa3er9FJ+9OyGUaT8EyGVRTXlqTMxTxPdl8QAZ/kEvkwUUUG/nk7e1sx8y72/Hfhzur2xQ2Nqx+qpfWOKWRaZv6ePNeI+MXNDnxo/axvQkOhEIjaFLP3+Fc+jmtY2O3L0BX7jrbyLLhgvpHfMNbOdmFJSzYco5j1zMoqWOhnpqY1DfEbHM5C90GgRUhk8mY2X0mX93zFR8O+rCywz1YtZXh5KNSsHSqPn3yAsVsIJcVGVF8dsRwZoY+Wno7MalvCGEBbkwf2hY7hZzhYZWVL58b2Mqk+TRxc7Cld6vKIlgKGxskqZq4i2I9qxVKEwr3aRY3Ky3U6d5waYPWsSkrCG5K7WDLmKwYAyMFDY34lrdySsskTsVncio+06LX6dDMleHhzbSiyI3h/i4BjOkexNkb2eogzPu7iLxtgfXiae/JJ4M/YV/CPtIL0ukb0Jdlx5eRW5KrKhZl56qSvi7I0I0T0CD64u+caXUvob5dUNgY91U6PLwZw8MrgyTH9QzGx8UeZ3uFlgNQGwa09ebwVdU2SWl5Od8euMZT/UL0xw8c/163zZQ4A80ViFLdFYgrWVe0jk1yIOzccFI4qcuAfx39Na3dW+Pn5FfDmYL6RjgQtzERwR50be6Og61c/eXVu5WX+kuogrs6+CFJcPBKGgUlZbw+vD2Ztwr2dA5yvzWXO2+O7EhmQTF3tKzbF6FAYGnc7Ny0dCTe7fMu/3fo/8gvzWdcl2f46fxPqifrahwIgI+2PgeugUzq+TIDvbvA2U2qDI8ODxh1Q3ZUKvRmJ9UGR6X21/mOs0n0CvGkU5Ce9MnkM9rHd/+faRdTaBTrqrICIUkScpmcMqkysFszs6ImZDIZLd1balVdXXdpHS9GvGiajQKLIxyI2xAXe1s+HtdFb8nhx3o1R2Ejw81RSffmHrg72hJwq1rghD4tsJXb6C0VLJPJ9H9RCQSNAC8HLz4e/DGFZYU42TqpHAiZkTu82Qms2vUKhbhwD7eetJVO0OYuyxmshxAv3af8bWeSdD+XJQWQoS2nTWA30y6muQKRm6zaErmlYplemK7lPLzc/WWTsyi87LUfQi5mXDTNPkG9IByIRo6Xkx0+LnZcT8+juKycvq296drcHUlS7Yt+sPU8JWXldGvuwWO9mnPs+k26NffQ6zwA+LnaM22I/op5VSVzBYKmhNxGjpONxk3YwV1VfEsqBxuFqlR4cQ7k6q/z8jM5lADDceTsidVsTD2Ip70nk1uNwun0OlX6Y7cntWtJmBEHpZzOQe5a251nbmSRW1SqHZN05AvtEwe9bvrFFFXKhUd+Df2mA3AjV7u+RpBzEHXF2BRQQf0iHIg6cvnyZaZPn84///yDvb0948aNY/HixTg51U/a0avDQmnpbfha74/qRFJ2Id1beCC3kQm1R4HACB5u+7AqENAjBIqywd5d9YRt56QSXyot0nveenJYT47qqTzLlmvZ14i6/Adv5Um0wlZVR6LreIvZ/cKg1rzwY6WkdkFJGVNWR/LOfWF0DLiV3VA1fdPR+Lobaqo6EJe2Q9//gEym5UA4KZyMVqDUpItPF/6O/7vyckbGmAjqF5GFUQeysrIYMmQIGRkZrF27liVLlrB+/XqefPJJi1xvoUYaWAXVOQ8Azb0c6dXSU++2g0Ag0M99re5jXt95LL57hapGhGaRKddA7SwEQySdgsRTkJfGe6SzmVxSTv4AQElZiUXs9nBSsvyJ7jrtFbL3erGvxdajvlWUfJVq5I28SgeimXOzWolAdfHponVcZMBhEzQswq2rA1988QWpqakcPXoUX1+Vfr2DgwOjR4/m2LFjdO+u+0E2FU8nJUsf7YqPix0ymYzHejXnp39jUdjYsGiMrkMhEAjMQ7CLquaDUq7U1nywc1bJPmvWkTCCjeSykVzY9hQAHT078nKPl7ExNtbCSDydlAwL82fbmSR125lbcvWUleqeYKwCpSZ+4bptfy8ke+hb7E/Yr24yJXhSE5lMxvOdn+d/p/4HQE5JDqXlpWIlwsoQKxB1YMuWLQwZMkTtPAA88MADODs788cff5jtOr6u9mov/sGIQBaP6cLy8d3UwY0CgcByPNPpGfXrh9o8RE+/nlCXBb0clQjc2Yyz7ItTLdMXlBZUr9tgImEBuqsKx65nsOaf0xSUVKmJY6SYlhYKO+g9Vbst5Rw7orTjK0I9Q02f+xbu9u5ax5lFmbWeS2AZmpwDceHCBZYtW8akSZPo1KkTCoUCmUzGe++9Z9T5a9euZfDgwXh4eODk5ESXLl1YtGgRJSW6S45nz56lQ4cOWm0KhYJ27dpx7tw5nfHmItjTEVcDQZACgcC8dPfrzuxes5nRbQb3tbqPSeGTmBQ2ib7+d6gGmLqCkJusiqHIS2P1nlk89dOdvLhzKk9vf5qz6WfNY3MLD+w0lC3dym4Su/YNuhyaTnxGvrazUts6E+1HQscHtZoSLvwGGnP38OtRu7kBDzvtkuSZhZm1nktgGZqcA7FixQqmT5/O6tWriY6OpqxMtwKlIWbMmMG4ceM4cOAAvXr1Yvjw4cTGxjJr1iyGDBlCQYG25vvNmzdxd3fXmcfDw4OMDFFFTiBoKrTzaEdnn87IZDIcFA4MDBrIpLs/ZXDYE3QMHcU03GmHkpbYMhkjYgry0iD7hirDoyhHJVoFfHj0Qw4mHCQlP4XNVzZzNfNqDRPpR24j4+37KuWt+xfspl2xyjkpLiunuELVtv+MWs2vpttE9csTFHKCIshKAKBvQN86bTlUXYG4WXSz1nMJLEOT21AKDw/n1VdfpWvXrnTr1o0FCxbw/fd6VNeqsGnTJpYsWYKzszN///033bqp8qLT0tIYMmQI+/fv5+233+bDDz+sYSaBQHA7oJDb8mTPmaoDlw50i/xG9br9vdg7u3E0KZJr8QdJRc9DTH6VwlpZCSrRKvfmfB39tbr5z6t/smjQIlzzMiAzDpr3VqWDGoG/W6VWQ7fCI1p96bnFNHO3R1ZXrQqFUuVERK1mKZmqtoIMcG2Gn2PdlCNtbWxxsXUhpyQHgPMZ5+np37Nu9grMSpNzIKZMmaJ1rFmMqjoWLFgAwOzZs9XOA4C3tzfLly9nwIABfPbZZ7z99tu4uameMDw8PMjMzNSZ6+bNm7Rtq19LQSAQNEFC71Ut3ZcWQccH6Kl0omf44xSe3cQb//6XLH1ORFWKclQKkXJbcAsCOxeKy4uZsf15xmakMKLcHkL6w+DZRpnkbKeglY8z11KydPpu5hdzscdcBpv4a+rFI4R9VKnIWVKoVVWz1lPbe6gdiD1xe3BRujCqzag6zyswD01uC6M2JCQkEBkZCcDjjz+u09+/f3+Cg4MpKipiy5Yt6vYOHTroxDqUlZVx8eJFndgIgUDQhFEoIfxhiHhMpUJ5C/uOo/jP/d8R7hRofNxlWQlkxKi2OQqzIe0ia8sz2UguO69t54uTX3D92l6uxP5DTnFOtVPNHujHGxlv67SfU3bif2cVdQ7cPJd+jhVJf7OSKk5KaSFeDnWXtPew146D+P3K73WeU2A+mtwKRG04fvw4AJ6enrRs2VLvmB49ehAXF8fx48d57LHHABg5ciTz5s0jNTUVHx8fADZv3kxubi733ntv/RgvEAismlZeobx8z3LYNY93sk8Rj55USn1kays6biZX9eLKHxzJ/goApVcb/nv3CtyVblw8/CmeN07hU1wAfmFg50ru5a3gK0dKLkGm4cL85vIIAOl5qpo2taG4rJjlJ5aTV5Kn0oUoqnRmWtu61CkDQz2Pe2tOpp7UapMkqVbaEgLzIxwIICZGVS62efPmBscEBwdrjQV47rnnWLZsGQ8++CBvv/02N2/e5OWXX+bBBx+kRw/joo+DggzLvCYkJGBjY1PtGIFA0HgoLSsmp0o6ohM25FFuwiyntF5/zwbssKHo1hyu2CDnd0qRyLnVZi/JsC2HcqBIZkOx7GFkyNn7ri0KuYxyCYzVmpOQyC7KplzTZgkoV2Wq2SHDUXGZFUr9D2OmUjV4cpvdNi1n6HYnMTERubxhpL6FAwHk5Kg85+rkp52dnQHIzq6szufu7s7u3buZPn06Y8aMwd7enrFjx5o10LK83JQvFoElSExMBKBZM/NUTRTUnsb+f6GQK/Fw9NVpN0LXskaqqjkoAA894/RdyxShWhky8jPygfr5f6iazinQpry83KwaIqYgHIg60q5dO7Zu3Vrr8+PjDUvMVqw8VDdGYHnE/4P1IP4vrAPx/2A9NOQKtQiiBFxcVLrueXl5Bsfk5qr2H11d6x5ZLBAIBAJBY0c4EEBISAgAcXFxBsdU9FWMFQgEAoHgdkY4EEDXrl0BSE9P1wqS1OTo0aMAWhoRAoFAIBDcrggHAtUeUs+eKoWzNWvW6PTv37+fuLg47OzsGDlyZH2bJxAIBAKB1SEciFvMmTMHgIULFxIVFaVuT09PZ+pUVdW5adOmqVUoBQKBQCC4nWlyWRhRUVHqGz7AlStXAPjiiy+0Smxv3LhRKwVp1KhRTJ8+naVLl9K7d2+GDh2Kk5MTu3btIjMzk379+jF//vz6+0UEAoFAILBiZFJDJZBaiL1793LnnXfWOC4mJkZvQOSvv/7K559/zokTJygpKaF169aMHz+emTNnolSaI1tbIBAIBILGT5NzIAQCgUAgEFgeEQMhEAgEAoHAZIQDIRAIBAKBwGSEAyEQCAQCgcBkhAMhEAgEAoHAZIQDIRAIBAKBwGSEA2GFrF27lsGDB+Ph4YGTkxNdunRh0aJFlJSUNLRptw2TJk1CJpNV+1NYWNjQZjYJLly4wLJly5g0aRKdOnVCoVAgk8l47733ajx3586djBw5Em9vbxwcHGjfvj1vvvmmuvidwDRq838xd+7cGj8r58+fr8ffonFTUlLCrl27eO211+jZsyfu7u7Y2tri7+/PAw88wJ9//lnt+fX5mWhyQlKNnRkzZrBkyRIUCgVDhgzB2dmZ3bt3M2vWLDZv3sz27dtxcHBoaDNvG/r160ebNm309snl8nq2pmmyYsUKlixZYvJ5n3zyCS+//DIymYwBAwbg5+fHvn37WLBgAevXr2f//v14e3tbwOKmS23/LwC6dOlCRESE3j6h4Gs8f//9N3fffTcA/v7+9O/fHycnJ86ePcvmzZvZvHkzzz77LP/73/+QyWRa59b7Z0ISWA0bN26UAMnZ2Vk6duyYuj01NVXq1KmTBEivvPJKA1p4+zBx4kQJkFauXNnQpjR5vvrqK+nVV1+VfvzxR+ncuXPShAkTJECaP3++wXOioqIkmUwmyeVyacuWLer2vLw8aejQoRIgjR49uj7Mb1LU5v/i3XfflQDp3XffrT9DmzC7du2SRo8eLf3zzz86fT///LMkl8slQFq9erVWX0N8JoQDYUX07NlTAqT33ntPp2/fvn0SINnZ2UmZmZkNYN3thXAgGo6Kv311N62xY8dKgDRlyhSdvmvXrkk2NjYSIJ07d86SpjZ5jPm/EA5E/fL0009LgDR06FCt9ob4TIgYCCshISGByMhIAB5//HGd/v79+xMcHExRURFbtmypb/MEAquhuLhYvQ+s77PSokUL+vXrB6hq3ggETYmuXbsCEBcXp25rqM+EiIGwEo4fPw6Ap6cnLVu21DumR48exMXFcfz4cR577LH6NO+2Zc+ePZw+fZqcnBy8vLzo1asXI0eOxM7OrqFNu225ePEi+fn5gOozoY8ePXqwb98+9edKYHmioqKYPXs2GRkZuLm50bVrV+6//35cXFwa2rQmxaVLlwC0ikE21GdCOBBWQkxMDADNmzc3OCY4OFhrrMDyfPfddzptzZo149tvv2X48OENYJGg4v3v7u5u8OYkPiv1T0WAnyZubm4sXbqUJ598soGsalokJSWxatUqAEaPHq1ub6jPhNjCsBJycnIAcHJyMjjG2dkZgOzs7Hqx6XamS5cuLFmyhOjoaLKzs0lOTmb79u307duXxMREHnjgAfbu3dvQZt6WiM+KddG6dWsWLFjA8ePHycjIICMjg/3793PfffeRlZXFxIkT+fHHHxvazEZPaWkp48ePJysri06dOvHcc8+p+xrqMyFWIAQCPcycOVPr2MXFhbvvvpu77rqLhx56iN9++40ZM2Zw4sSJhjFQILASJkyYoNPWr18/Nm/ezPTp01m2bBkzZ85k7NixKJXKBrCwafD888+za9cuvLy8WLdunVX8LcUKhJVQseyUl5dncEyFEIirq2u92CTQRSaTMW/ePABOnjypFcgkqB/EZ6XxMHfuXORyOampqRw5cqShzWm0vPTSS3zzzTd4eHiwY8cO2rVrp9XfUJ8J4UBYCSEhIQDV3pAq+irGChqGDh06qF/Hx8c3oCW3JxXv/8zMTPXSbVXEZ8U68PT0xNfXFxCfldryyiuvsHTpUtzd3dm+fbs6C0OThvpMCAfCSqh4U6SnpxsMcjl69CgA3bp1qze7BLqkp6erX4sI8/onNDQUR0dHoPIzURXxWbEOysrKyMrKAsRnpTa8/vrrfPzxx7i5ubF9+3aDGRYN9ZkQDoSVEBQURM+ePQFYs2aNTv/+/fuJi4vDzs6OkSNH1rd5Ag1+/vlnQLUUGBoa2sDW3H4olUruvfdeQP9n5fr16xw8eBCAhx56qF5tE2jz+++/k5+fj0wmM3jzE+hn9uzZLF68GDc3N3bs2KG+P+ijoT4TwoGwIubMmQPAwoULiYqKUrenp6czdepUAKZNmyZ05S3MiRMn+P333yktLdVqLy8v55tvvlH/P02fPh1bW9uGMPG2Z/bs2chkMlauXMnWrVvV7fn5+Tz99NOUlZUxevRo2rdv34BWNn1iY2P54Ycf9BaW27RpE1OmTAHgiSeewN/fv77Na7S89dZbfPDBB7i7u9foPFTQEJ8JmSRJktlmE9SZl156iaVLl2Jra8vQoUNxcnJi165dZGZm0q9fP3bs2CGKaVmYTZs28dBDD+Hh4UG3bt3w8/MjMzOT6OhoYmNjAXjsscf47rvvUChEIlNdiYqKUjvIAFeuXCEtLY2goCACAwPV7Rs3btQSz9EsHDRo0CB8fX3Zt28fiYmJhIaGimJatcDU/4sTJ07QtWtXnJ2d6dq1K4GBgRQUFHD27Fm14NGdd97J77//rk4jFFTP77//zoMPPgioxJ/CwsL0jvP29ubDDz/Uaqv3z4TZRLEFZuOXX36RBg4cKLm6ukoODg5SeHi4tHDhQqmoqKihTbstuHr1qjRjxgypf//+UmBgoGRvby/Z2dlJzZs3l8aMGSP9+eefDW1ik2LPnj0SUONPTEyMzrk7duyQhg8fLnl6ekp2dnZS27ZtpTfeeEPKzs6u/1+kCWDq/0VaWpo0a9YsaciQIVLz5s0lJycnydbWVmrWrJl03333SWvWrJHKysoa9pdqZKxcudKo/4MWLVroPb8+PxNiBUIgEAgEAoHJiBgIgUAgEAgEJiMcCIFAIBAIBCYjHAiBQCAQCAQmIxwIgUAgEAgEJiMcCIFAIBAIBCYjHAiBQCAQCAQmIxwIgUAgEAgEJiMcCIFAIBAIBCYjHAiBQCAQCAQmIxwIgUAgEAgEJiMcCIFAIBAIBCYjHAiBQCAQCAQmIxwIgcAKCAkJQSaT1fizatWqhja10XH8+HHkcjn/+c9/GtoUvQwcOBCZTMbs2bONGv/SSy8hk8kYOXIkAGVlZbRv354WLVpQUFBgSVMFAi1ENU6BwAoICQnh+vXr9OvXjzZt2hgcN2XKFPr371+PljV++vfvz4kTJ7h69Sq+vr4NbY4Oq1evZtKkSTRr1oy4uDjkcrnBscXFxQQEBJCens66desYPXo0AOvWrWPs2LHMnTuXd999t75MF9zmCAdCILACKhyIlStXMmnSpIY2p8lQcWN97bXXWLRoUUObo5f8/HyaNWtGdnY2mzdv5r777jM4du3atYwbNw4fHx8SEhKwtbVV93Xu3JnLly9z9epV/P3968N0wW2O2MIQCARNlk8++QSAp59+uoEtMYyjoyOPPvooACtXrqx2bEX/+PHjtZwHgKeeeoqCggK+/PJLyxgqEFRBOBACQSOmIjYCYP369fTv3x9XV1ecnJzo168fW7ZsMXhuaWkpX3/9NYMHD8bT0xM7OztatmzJCy+8QFxcnM74vXv3IpPJGDx4MPn5+bzzzjt06NABR0dHQkJC1OMkSeLbb7+lR48eODo64uXlxYgRIzh48KDWHBWsXLkSmUzGsGHDDNp648YNbG1tcXBwID093ai/zfHjxzl48CC9e/cmNDRUp3/VqlXIZDImTZpEVlYWL7/8MiEhIdjb29O2bVs++OADysvLAUhISOC5554jODgYOzs7QkNDWbZsWbXXX7duHcOHD8fHxwelUklgYCDjx4/n7NmzOmOnTJkCwObNm0lLS9M7X0JCAtu3bwf0O0RPPPEECoWCL774gtLS0ur/OAKBOZAEAkGD06JFCwmQVq5cadJ5gARI77zzjiSTyaR+/fpJjzzyiNSlSxcJkGQymbRhwwad87Kzs6XBgwdLgOTs7CwNGjRIGjNmjBQaGioBkpeXlxQVFaV1zp49eyT4//buNSaKq40D+J/dRRaQi8ECixFTK+Aliqh4Q0tQa4IWlIpQ1MTSGpVWN1U/GFujTZXEDwWveBdMFInaRMVUYk29pVCowVpRQWMMWi+xYrzUCaCrz/vB7Lzsu7MLs659Sfv/JSR4nnOemTmbOE/OnhkgI0aMkMTERAkMDJTU1FTJzs6WCRMmqP3y8vIEgBgMBklOTpaPP/5YBgwYIEajUZYsWSIAJDk5We3f0tIi77zzjvj4+MjVq1c1r3PFihUCQHJzczs8N/Yxy5cv14yXlJQIAJkyZYr069dPwsPDZdq0aTJx4kTx9/cXALJgwQK5fv26REZGSs+ePSUrK0tSUlLEaDQKAFmzZo1T3hcvXkhWVpYAED8/Pxk9erRMnz5d/Uz8/f2loqLCadzAgQMFgBQWFmqeb35+vjr/rgwbNkwASFVVVQdnichzLCCIOoE3LSBCQ0OlurraIbZy5UoBILGxsU7jZsyYIQDkww8/lPv37zvE1q5dKwAkJiZGbDab2m4vIADIoEGD5N69e055jxw5ohYllZWVDrGCggJ1fNsCQkTk66+/FgBitVqdcj5//lwiIyMFgNTW1rY7J3ZjxowRAPLDDz9oxu0FBABJS0sTRVHUWG1trZhMJjEYDNK/f3+ZP3++vHjxQo0fPnxYAEhwcLDDOBGRr776Sr3R37hxwyF28OBBMRqN0q1bN3n06JFDbN26dQJABg4cqHm+MTExAkC2b9/u8pqtVqsAkFWrVrnsQ+QtLCCIOgF7AdHez//edOztGzZscMrZ0tIiISEhAkBu3bqltl+5ckV8fHwkKipKnj59qnk+kyZNEgBy9OhRta1tAXH27FnNcePGjRMAsmzZMs14YmKiZgFx584d8fX1lZCQEHn27JlDrKysTADIqFGjNHO6EhgYKACcbuJ29gKia9euTkWUiEh6eroAkOjoaGlubnaK21cMzpw5o7Y9fPhQ/P39xWw2y+3btzWP+/nnnwsA2bhxo0N7U1OT+Pn5CQA5d+6cQ+zs2bMCQAICAlx+ZiIi27ZtEwCSkZHhsg+Rt3APBFEnkpSUhNmzZ7v86dKli+a4tLQ0pzY/Pz/07t0bwOvvz+2OHTsGEUFqaiqCgoI089n3KFRVVTnFwsPDMXbsWKd2m82m9p85c6Zm3hkzZmi2R0VFITMzE0+ePMGePXscYkVFRQCABQsWaI7VoigKFEUBAISFhbntO3ToUM3HO2NiYgAAKSkpMJvNLuN3795V206dOoXm5mYkJSWhR48emsdzNbdhYWGYOnUqAKC4uNghZv/39OnTXX5m9hwAcP/+fZd9iLzF9P8+ASL6rzlz5nj0GGd0dLRme3BwMACgpaVFbbtx4wYAYNeuXdi1a5fbvA8ePHBqa7thsq2mpib1OK76uGoHAKvVirKyMhQVFWH+/PkAgIsXL+Lnn39GREQEMjMz3Z5rW0+ePFF/d3fDBVzPXdeuXd3G7Xm15vann35SN7e6ojW3n332Gfbv34+ysjIUFhbCbDbj2bNnOHjwoBp3x/55P3r0yG0/Im9gAUH0D2AwdHwx0f5kweDBgxEfH++274gRI5za/P399Z1cG+5uqiNHjsTw4cPx66+/4syZM0hOTlZXH+bOnety9UVLaGio+vtff/2l3li1tDd3nsxtnz59kJSU5LZv3759ndrGjx+PXr164ebNmzh06BBycnJw4MABKIqC2NhYzZWftuyFU7du3Tp8zkSeYgFB9C/Ts2dPAK+/Ltm0aZPX8oaFhcHPzw+tra24efMm+vfv79SnsbHRbQ6r1YpZs2Zh06ZNiI+PR2lpKUwmk7oi0VEBAQEIDAyEoih4+PCh2wLCm+xzGxcX59Frxw0GA3Jzc/HNN9+guLgYOTk56tcXubm57Y63P+IaERGh+9hEenEPBNG/TGpqKgCgvLzcYfn9Tfn6+mLUqFEAgH379mn2KSsrc5sjKysLFosFhw8fRn5+PhRFQUZGBqKionSfz5AhQwBA870Lb8v48ePRpUsXnD59Gn/++adHOXJzc2EwGHDy5EmcOHEClZWVMBqNmD17drtjL126BOD1vg6it40FBNG/TEJCAqZNm4Y//vgDH330keaqgKIoKC0t1b0Zz2q1AgA2bNiA6upqh9j69etRU1Pjdryvry/y8vJgs9nw3XffAdC3ebKtlJQUAMAvv/zi0XhPREREYOHChVAUBWlpaairq3Pq09raivLycjQ0NGjmiI6OxgcffIBXr16pm1EnTZoEi8XS7vHtGzPHjRv3BldB1DH8CoOoE9m5cydOnz7tMj5x4kSXTzLoUVJSgsePH6OiogJxcXGIj4/Hu+++CxFBY2Mjfv/9dzx//hz19fW6lsMzMjIwd+5cbN++HWPGjMHYsWNhsVhQV1eH+vp6LFq0CGvXrnW7n2HevHnIz89Ha2srBg0ahPfff9+ja5w6dSq+/fZbnDhxAqtXr/YohyfWrFmDe/fuYd++feo+k969e8NkMuH27du4cOECFEVBRUWF5j4I4PVmyePHj6sbLTvyKu4HDx7g4sWLiIqKQmJioleviUgLCwiiTqSyshKVlZUu46GhoV4pIIKCgvDjjz9i//792Lt3L2pra3HhwgUEBwfDYrFg5syZSE9Px3vvvac799atW5GYmIgtW7aguroaZrMZw4cPx+bNm9XVju7du7scHx4ejsGDB6OmpgZffPGFp5eIhIQEjB49GlVVVaivr0e/fv08zqWHyWRCaWkpZs2ahZ07d6KmpgaXLl1CYGAgLBYL0tLSkJ6e7rYwmjJlCrp3746mpiZERERg8uTJ7R537969sNlsmDdvHkwm/tdObx//GicR/W0+/fRTlJSUoKCgAIsXL9bsc+3aNfTt2xchISG4c+cOAgICPD6e/a9xLl68GAUFBR7n6exEBPHx8fxrnPS34h4IIvKqy5cvqy9xsnv16hV27NiB3bt3w2w2Iycnx+X4FStWQESQl5f3RsUDAGRmZiIpKQnbtm37R79c6fvvv0ddXR2WLl3K4oH+NlyBICKv+uSTT3DgwAEkJCSgR48eUBQFV65cQWNjI4xGI3bs2OH0SGJ5eTmOHDmCy5cvo6amBpGRkaivr3d4n4OnfvvtNwwbNgx5eXlefWy1s3j58iUGDBiA5uZmNDQ0vNF7Ooj04BdlRORV2dnZePr0qbqvwmazITw8HNnZ2fjyyy8xcuRIpzHnz59HcXExgoKCMGHCBBQWFnqleABe74V4+fKlV3J1Rkaj0eUTHURvE1cgiIiISDfugSAiIiLdWEAQERGRbiwgiIiISDcWEERERKQbCwgiIiLSjQUEERER6cYCgoiIiHRjAUFERES6sYAgIiIi3VhAEBERkW4sIIiIiEg3FhBERESkGwsIIiIi0o0FBBEREenGAoKIiIh0+w8ait2jDvxs5AAAAABJRU5ErkJggg==", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(1, 1, figsize=(3.4, 2.2), dpi=160)\n", "\n", "for P, (w, Sqw) in data_dict.items():\n", " ax.plot(w, Sqw, '-', label=f'P={P} GPa', alpha=0.75)\n", "\n", "ax.set_xlabel('Energy (meV)')\n", "ax.set_ylabel(r'$S(\\mathbf{q}, \\omega)$')\n", "ax.set_xlim(0, 20)\n", "ax.set_ylim(1, 8000)\n", "ax.set_yscale('log')\n", "ax.legend(loc=1, frameon=False)\n", "\n", "fig.tight_layout()" ] }, { "cell_type": "markdown", "id": "71e668cf", "metadata": {}, "source": [ "For 0 GPa we see two clear peaks at around 8 meV (octahedral tilt mode) and around 13 meV (acoustic Barium mode).\n", "Additionally, there exist multiple modes above 20 meV, but here we focus on the low frequency mode.\n", "For 15.6 GPa (close to the phase transition) one can clearly see that the tilt mode exhibits overdamped behavior.\n", "\n", "Next, we fit the corresponding spectra to DHOs, i.e., the fits are performed in the frequency domain.\n", "\n", "Note that for 15.6 GPa we obtain $\\Gamma > 2 \\omega_0$, meaning that the mode is overdamped." ] }, { "cell_type": "code", "execution_count": 9, "id": "abddfb59", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 0.0 GPa : w0= 8.99 meV gamma= 0.82 meV\n", " 8.0 GPa : w0= 6.18 meV gamma= 1.48 meV\n", "15.6 GPa : w0= 1.66 meV gamma= 6.17 meV\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from dynasor.tools.damped_harmonic_oscillator import psd_position_dho\n", "\n", "fig, axes = plt.subplots(figsize=(3.4, 3.8), nrows=3, dpi=160, sharex=True)\n", "\n", "colors_dict = dict()\n", "colors_dict[0.0] = 'tab:blue'\n", "colors_dict[8.0] = 'tab:orange'\n", "colors_dict[15.6] = 'tab:green'\n", "\n", "for ax, (P, (w, Sqw)) in zip(axes, data_dict.items()):\n", " \n", " # fit to DHO\n", " mask = w <= 12 # only use frequencies up to 12 meV\n", " p0 = [5.0, 2.0, 100] # parameters guess\n", " params, _ = curve_fit(psd_position_dho, w[mask], Sqw[mask], p0=p0)\n", " w0, gamma, A = params\n", " print(f'{P:4.1f} GPa : w0= {w0:.2f} meV gamma= {gamma:.2f} meV')\n", "\n", " ax.text(0.05, 0.82, f'{P:.1f} GPa', transform=ax.transAxes)\n", " ax.text(0.05, 0.67, rf'$\\omega_0$ = {w0:.1f} meV', transform=ax.transAxes)\n", " ax.text(0.05, 0.52, rf'$\\Gamma$ = {gamma:.1f} meV', transform=ax.transAxes)\n", "\n", " # plot\n", " w_lin = np.linspace(0, 12, 100000)\n", " S_fit = psd_position_dho(w_lin, *params)\n", " ax.plot(w, Sqw, '-', lw=4, alpha=0.25, color=colors_dict[P], label='data')\n", " ax.plot(w_lin, S_fit, '--', lw=1.0, color=colors_dict[P], label='fit')\n", "\n", " ax.set_xlim(0, 12)\n", " ax.set_ylim(bottom=0.0)\n", " \n", " # annotate roughly FWHM Gamma\n", " if P == 8.0:\n", " w_peak = np.sqrt(w0**2 - gamma**2 / 2.0)\n", " ax.annotate('', xy=(w_peak - gamma/2 , S_fit.max() / 2.0),\n", " xytext=(w_peak + gamma/2, S_fit.max() / 2.0),\n", " arrowprops=dict(arrowstyle='<->'))\n", " ax.text(w_peak, 0.6 * S_fit.max() / 2, r'$\\Gamma$', ha='center')\n", "\n", "axes[1].set_ylabel(r'Dynamic structure factor $S(\\mathbf{q}, \\omega)$')\n", "axes[-1].set_xlabel('Energy (meV)')\n", "axes[-1].legend(frameon=False)\n", "\n", "fig.tight_layout()\n", "fig.subplots_adjust(hspace=0)\n", "fig.align_labels()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.3" } }, "nbformat": 4, "nbformat_minor": 5 } dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/examples/graphite_sed/000077500000000000000000000000001522712672400243265ustar00rootroot00000000000000dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/examples/graphite_sed/.gitignore000066400000000000000000000000531522712672400263140ustar00rootroot00000000000000graphite.xyz* nep-C-CX.txt* gpumd_run_dir* dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/examples/graphite_sed/graphite.xyz000066400000000000000000000005031522712672400267030ustar00rootroot000000000000004 Lattice="2.46748 0.0 0.0 1.23374 2.1369 0.0 0.0 0.0 6.554959" Properties=species:S:1:pos:R:3 pbc="T T T" C 0.00000000 0.00000000 1.63873975 C 1.23374000 0.71230000 1.63873975 C 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1.0155689e-02 7.6214834e-03 7.6214834e-03 7.6214834e-03 7.6214834e-03 7.6214834e-03dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/examples/graphite_sed/sed.ipynb000066400000000000000000070101611522712672400261520ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "id": "1a831465", "metadata": {}, "source": [ "# Spectral energy density\n", "\n", "In this example we will study the spectral energy density of a crystal.\n", "\n", "The simple spectral energy density (SED) method for crystals is implemented in dynasor as a complement to the autocorrelation functions (ACFs).\n", "In this method the information about the crystal structure is included in the analysis.\n", "The method is very similar to the velocity autocorrelation method (VACF).\n", "The results from SED typically converge comparably fast and features of interest are sometimes more visible.\n", "The drawback is that it only works for perfect crystals.\n", "\n", "This example will demonstrate how to compare the dispersion as obtained from zero Kelvin lattice dynamics theory using [phonopy](https://phonopy.github.io/phonopy/) with fully anharmonic molecular dynamics (MD) simulations using [GPUMD](https://gpumd.org/). Some theoretical background can be found in the [theory section of the documentation](https://dynasor.materialsmodeling.org/get_started/theory.html#spectral-energy-density-method).\n", "\n", "dynasor supplies a function to calculate the spectrum at any given q-point.\n", "In practice any q-point can be supplied but due to aliasing, spurious frequencies will arise at q-points which are not at the exact inverse supercell lattice.\n", "For simple diagonal repetitions it is easy to find these points.\n", "The difficulty arises when the supercell is not a simple repetition of the primitive cell.\n", "To aid in these types of situations dynasor provides some helper functions.\n", "\n", "1. The `Lattice` object is a map between the primitive cell and the supercell\n", "2. The `make_path` method returns exact, commensurate q-points along a given path in the Brillouin zone.\n", "\n", "In the following example graphite is used since the 2D character makes it easy to visualize the concepts in the x-y/a-b plane.\n", "\n", "Note that although this example uses [ase](https://ase-lib.org/), [calorine](https://calorine.materialsmodeling.org/), [gpumd](https://gpumd.org) and [phonopy](https://phonopy.github.io/phonopy/) none of these packages are strictly needed.\n", "\n", "## Input data\n", "\n", "You can find the input files as well as example output files in [this Zenodo repository](https://zenodo.org/records/17858255).\n", "You can fetch these files, e.g., by executing the following cell." ] }, { "cell_type": "code", "execution_count": 1, "id": "c0cc0cce-ab8c-430f-9d0d-92779a3db100", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "..." ] } ], "source": [ "!wget https://zenodo.org/records/17858255/files/graphite.xyz\n", "!wget https://zenodo.org/records/17858255/files/nep-C-CX.txt" ] }, { "cell_type": "markdown", "id": "49f69f7b", "metadata": {}, "source": [ "## Setup and visualization of structures\n", "We can use `ase` to read the graphite primitive structure located in the example directory." ] }, { "cell_type": "code", "execution_count": 2, "id": "7af7b827", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Atoms(symbols='C4', pbc=True, cell=[[2.46748, 0.0, 0.0], [1.23374, 2.1369, 0.0], [0.0, 0.0, 6.554959]])\n" ] } ], "source": [ "from ase.io import read\n", "\n", "prim = read('graphite.xyz')\n", "print(prim)" ] }, { "cell_type": "markdown", "id": "d2d944c6", "metadata": {}, "source": [ "As we can see the cell vectors point in the x-direction and at a 60-degree angle as expected.\n", "To make things a bit more challenging we will use an orthorhombic unit cell instead of the hexagonal primitive cell." ] }, { "cell_type": "code", "execution_count": 3, "id": "26d5fc1e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Atoms(symbols='C8', pbc=True, cell=[2.46748, 4.2738, 6.554959])\n" ] } ], "source": [ "unitcell = prim.copy()\n", "unitcell = unitcell.repeat((1, 2, 1)) # repeat cell along b-axis\n", "cell = unitcell.cell\n", "cell[1] -= cell[0] # remove one unit of a-axis from b-axis\n", "unitcell.set_cell(cell)\n", "unitcell.wrap()\n", "print(unitcell)" ] }, { "cell_type": "markdown", "id": "7a3456ee", "metadata": {}, "source": [ "As we can see the cell is now orthorhombic. ASE provides a simple tool to visualize atomic structures with `ase.visualize.view(prim)`." ] }, { "cell_type": "markdown", "id": "8690ba97", "metadata": {}, "source": [ "Lastly we can construct a larger supercell suitable for molecular dynamics simulations" ] }, { "cell_type": "code", "execution_count": 4, "id": "60151f4c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Atoms(symbols='C576', pbc=True, cell=[14.80488, 25.642799999999998, 13.109918])\n" ] } ], "source": [ "dim = (6, 6, 2)\n", "supercell = unitcell.repeat(dim)\n", "print(supercell)" ] }, { "cell_type": "markdown", "id": "e8d42a2f", "metadata": {}, "source": [ "Now let's visualize these structures using matplotlib. " ] }, { "cell_type": "code", "execution_count": 5, "id": "aef23167", "metadata": {}, "outputs": [ { "data": { "image/png": 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JSQmuX78OAHB3d8cLL7yAhIQEbNu2Dbm5uZg8eTIGDx4s6ZO0xuCxs7Q8KDhQ8aDgoCQkPtpvaK8kOTkZkyZNAnC79Dhr1ix8/fXXqKmpwYgRI7By5coGD9PuxpLLW3v6eiI2MVbVPonSDlQ8KDhQ8aDgcDc86dEKlBo7a+14UDnGi7aWbVBw4NdhHg4jK1Bi7CyFpiwFByoe7GA7D7spPbZEapuyd/dCys6XYXXiauRl5tmFAxUPdqDn0RQcRjJAoSlLwYGKBzvQ85ACh5EMUGjKUnCg4sEO9DykwGEkAxSashQcqHiwAz0PKXAYyQCFpiwFByoe7EDPQwocRjJAoSlLwYGKBzvQ85ACh5EMUGjKUnCg4sEO9DykoL5BKyF8eDjiFsXBw8fDZLmnj6diA7AoOFDxYAd6Hk3BpUczWFp6BORtubZkByoe7GAbD1uUHkmNEGkNOGgdEDYwzO4dqHiwAz2PhuDDNIZhSMBhxDAMCTiMGIYhAYcRwzAk4DBiGIYEHEYMw5CAw4hhGBJwz0hmKBTcKDhQ8WAHeh4NwWEkIxRGjFJwoOLBDvQ8GoNOLLZwKIz2pOBAxYMd6Hk0BYeRDFAY7UnBgYoHO9DzkAKHkQxQGO1JwYGKBzvQ85ACh5EMUBjtScGBigc70POQAoeRDFAY7UnBgYoHO9DzkAKHkQxQGO1JwYGKBzvQ85ACh5EMUBjtScGBigc70POQgvoGrQQKoz0pOFDxYAd6Hk3BY2froNfrkZ2djdScVIsbqta2XOVoybaWbVBw4NdhHluMneUwukN6ejri4+NRVFRkXMZtXfU8KDhQ8aDgcDccRlbQ2BuTnp6O2NhY1Hsr7hxTK7ErW9uSrVdOU9CBigcFByoeFBzMYYswsvtzRnq9HvHx8fWDCOC2rgoeFByoeFBwUBK7D6Ps7GyTQ7N6cFtXUQ8KDlQ8KDgoid2HUXFxsaT1uK2rjAcFByoeFByUxO7DyN/fX9J63NZVxoOCAxUPCg5KYvdhNHToUAQFBUGjaaCiym1dRT0oOFDxoOCgJHYfRlqtFsuWLQOA+oHEbV3FPSg4UPGg4KAkreNVWElMTAzS0tIQGBhospzbuup4UHCg4kHBQSm4Z1QHORrY1kJlTjEFDwoOVDwoONSFS49W0Jw3ZmXeSoWsGKZlwqVHhmFaLRxGDMOQgMOIYRgScBgxDEMCDiOGYUjAYcQwDAmsurz1zZs3UVJSgmvXrsHb2xvt27eXy4thGDuj2WFUWVmJL7/8EmvXrsWvv/6KGzduQAgBjUaDoKAgPPLII3jppZcwaNAgW/jalNrS496cvXZfbqPgQcGBigcFB1vTrDBavHgx3nvvPXTp0gWjRo3Cm2++iYCAALi4uODy5cs4ePAgsrOz8cgjjyAqKgrLly9Ht27dbOUuKzx2lpYHBQcqHhQclKBZDezx48fj73//O3r37t3oetXV1UhJSYGTkxOmTJlitaQc8NjZluNBwYGKBwUHc6jewP7666+bDCIAcHZ2xssvv0wmiBqDx87S8qDgQMWDgoOSyHrQqdfrsXHjRowePVrOzdoUHjtLy4OCAxUPCg5KIksY5efnY+bMmQgICMD48eMbHlRGEB47S8uDggMVDwoOSiL5BPbVq1fh7OwMrVYLALh8+TK++uorpKSkID8/HwaDAUuWLMGUKVPQrl07mwnLDY+dpeVBwYGKBwUHJZG0Z7R69Wp069YNERERWLJkCWJjYxEQEIDU1FRMnDgRRUVFcHBwQHR0dIsKIoDHzlLzoOBAxYOCg5JICqN33nkHGRkZyMzMRGJiIoKDg7F//37s2bMH8fHx8PX1tbWnzeCxs7Q8KDhQ8aDgoCSSXkVYWBjWr1+PL7/8Eh4eHli9ejUWLFiALVu2mP8UqoXBY2dpeVBwoOJBwUEpJPWMzp8/j8WLF8PDwwMzZ87EkSNHkJKSgq+++goODg4YN24cVq5cifz8fPTs2bPZEjt27MCiRYuQm5uL4uJiZGRkYMyYMcb7J02ahC+++MLkMSNGjMCWLVskP4dSY2etbcrK0bRtLdug4MCvwzzkxs7eunULmzZtQkpKCrZs2YJOnTohNjYWsbGxuPfeeyVvZ/Pmzdi1axcGDBiAmJgYs2FUWlqK5ORk4zKdTgdPT0/Jz6HE2FkKTVkKDlQ82MF2HuTCqC6lpaVITU1FSkoKjhw5Ar1eb9F2NBqN2TAqKyvDxo0bLfazdRhRaMpScKDiwQ629VC9gd0Yvr6+SExMxO+//46cnBy5Nmtk+/bt8PHxQffu3TF16lRcunSp0fVrampQUVFhcrMVFJqyFByoeLADPQ8pNCuMzpw5I2m9yMhIAMDZs2ebb2SGRx99FKmpqcjMzMTChQuRlZWFkSNHNrr3lZSUBHd3d+MtODhYFhdzUGjKUnCg4sEO9Dyk0KwwGjRoEP7rv/4Le/bsaXCd8vJyrF69Gn369MGGDRusFgSAZ555Bk888QT69u2LMWPG4Ntvv8WePXuwffv2Bh8zZ84clJeXG2+FhYWyuJiDQlOWggMVD3ag5yGFZo0QOXToEN577z38+c9/hrOzMwYMGICAgAA4OzvjypUrOHToEH7//Xfce++9+PDDD/HYY4/ZRLpz587w8vLCiRMnMHz4cLPr6HQ66HQ6mzz/3VBoylJwoOLBDvQ8pNCsPaMOHTpg8eLFKC4uxieffIJu3brh4sWLOH78OADg2WefRW5uLnJycmwWRABQVFSES5cuSf4qh62h0JSl4EDFgx3oeUjBohPYLi4uiI2NxdKlS5GRkYEtW7bgyy+/xKxZs9CnT59mb6+qqgp5eXnIy8sDABQUFCAvLw9nzpxBVVUVEhMTsXv3bpw6dQqZmZkYPXo0unbtihEjRliiLzsUmrIUHKh4sAM9DymobwBg7969iIiIQEREBAAgISEBERERmDdvHrRaLfLz8/HEE08gLCwML7zwAgYMGIDs7GzFDsOkQKEpS8GBigc70PNoCtl6RtRRovQI0JhVTMGBigc72MbDFj0jq64OwtTHQeuAsIFhdu9AxYMd6Hk0BInDNIZhGA4jhmFIYNVh2vHjx7Ft2zacP38eBoNpnXzevHlWiTEMY19YHEarV6/G1KlT4eXlBT8/P5PBZBqNhsOIYZhmYXEYvfvuu3jvvffw+uuvy+nDMIydYvE5oytXrmDs2LFyujAMY8dYHEZjx47FDz/8IKcLwzB2jMWHaV27dsXcuXOxe/du9O3bF23atDG5/9VXX7VariVCoeBGwYGKBzvQ82gIi8Pon//8J9q1a4esrCxkZWWZ3KfRaOwyjCiMGKXgQMWDHeh5NAZ/HcQMPHa25Xuwg209yI6dFUK0iksWWQqF0Z4UHKh4sAM9DylYFUapqano27cvXFxc4OLign79+uHf//63XG4tBgqjPSk4UPFgB3oeUrD4nNHixYsxd+5cTJ8+HUOGDAEA7Ny5Ey+//DIuXryImTNnyiZJHQqjPSk4UPFgB3oeUrA4jJYvX45Vq1Zh4sSJxmVPPPEEevfujbffftuuwojCaE8KDlQ82IGehxQsPkwrLi7G/fffX2/5/fffj+LiYqukWhoURntScKDiwQ70PKRgcRh17doV69atq7f8m2++Qbdu3aySamlQGO1JwYGKBzvQ85CCxR/tb9iwAePGjUN0dLTxnNGuXbuQmZmJdevW4cknn5RV1FrUury1p68nYhNjVe21KO1AxYMdbOdB7vLWubm5WLJkCQ4fPgwA6NmzJ2bNmmWcZU0JKW+MXq9HdnY2UnNSLW6oWttylaMl21q2QcGBX4d5yIVRS6KpNyY9PR3x8fEoKioyLuO2rnoeFByoeFBwuBvVw6iiosK40aauXS/lyZWksTcmPT0dsbGx9Yub3NZVxYOCAxUPCg7mUL2B7enpifPnzwMAPDw84OnpWe9Wu7yloNfrER8fb75Bzm1dxT0oOFDxoOCgJM3qGW3duhXt27cHAGzbts0mQkqTnZ1tcmhWjzoNVVtdWaE5LVlbXt2BggcFByoeFByUpFlh9OCDDxr/HBoaiuDgYJNxs8Dt76kVFhbKY6cAUjtR3NZVxoOCAxUPCg5KYnG5IDQ0FBcuXKi3/PLlywgNDbVKSkn8/f0lrcdtXWU8KDhQ8aDgoCQWh5EQot5eEQBUVVXB2dnZKiklGTp0KIKCgsy+FgDc1lXYg4IDFQ8KDkrS7DBKSEhAQkICNBoN5s6da/w5ISEB8fHxGDduHMLDw22gahu0Wi2WLVsGAPUDidu6intQcKDiQcFBSZr9Kvbt24d9+/ZBCIEDBw4Yf963bx+OHDmC/v37IyUlxQaqtiMmJgZpaWkIDAw0We7p46nYR6fhw8MRtygOHj4eqjlQ8aDgQMWDgoNSWFx6nDx5MpYtW0auT9QQSjWwrYXKnGIKHhQcqHhQcKiL6qXHlowS301jGHvBFmHUrI/2Y2JikJKSAjc3N8TExDS6bnp6enM2zTCMndOsMHJ3dzee5HV3d7eJEMMw9kmzwig5OdnsnxmGYazF4jNg169fx7Vr14w/nz59GkuXLuWrzDIMYxEWh9Ho0aORmpoKACgrK0NkZCQ++ugjjB49GqtWrZJNkGEY+8DiMPrtt98wdOhQAEBaWhr8/Pxw+vRppKam4uOPP5ZNkGEY+8DiMLp27RpcXV0BAD/88ANiYmLg4OCA++67D6dPn5ZNkGEY+8DiSxV17doVGzduxJNPPon/+7//M16a6Pz58y2mCHk3taXHvTl77b7cRsGDggMVDwoOtsbiMJo3bx4mTJiAmTNn4k9/+hMGDx4M4PZeEsUZ2E3BY2dpeVBwoOJBwUEJrGpgl5SUoLi4GP3794eDw+2U/vXXX+Hm5oYePXrIJikHPHa25XhQcKDiQcHBHKqPnb0bPz8/RERE4Ny5c8Y9isjISHJB1Bg8dpaWBwUHKh4UHJTE4jAyGAxYsGAB3N3dERISgpCQEHh4eOCdd96BwdBy3pzmjJ21Fc0ZL2pLKHhQcKDiQcFBSSw+Z/Tf//3f+Pzzz/HBBx8YL+K4c+dOvP3226iursZ7770nm6Qt4bGztDwoOFDxoOCgJBaH0RdffIH/+Z//wRNPPGFc1q9fPwQGBuKVV15pMWHEY2dpeVBwoOJBwUFJLD5Mu3z5stlzQz169MDly5etklISHjtLy4OCAxUPCg5KYnEY9e/fH5988km95Z988gn69+9vlZSS8NhZWh4UHKh4UHBQEos/2s/KysLjjz+Ojh07GjtGOTk5KCwsxPfff2/8qggVLLm8taevJ2ITY1XtkyjtQMWDggMVDwoOd0Nu0uO5c+ewYsUKHDlyBADQs2dPvPLKKwgICLB0kzZDqbGz1jZl5WjatpZtUHDg12EecmHUklBi7CyFpiwFByoe7GA7D3JhdOXKFXz++ec4fPgwAKBXr16YPHmy8RLYlLB1GFFoylJwoOLBDrb1INXA3rFjBzp16oSPP/4YV65cwZUrV/Dxxx8jNDQUO3bssHSzLRIKTVkKDlQ82IGehxQsDqNp06Zh3LhxKCgoQHp6OtLT0/HHH3/gmWeewbRp0+R0JA+FpiwFByoe7EDPQwoWh9GJEycwa9YsaLVa4zKtVouEhAScOKH+C1MSCk1ZCg5UPNiBnocULA6je++913iuqC6HDx9uUT0jOaDQlKXgQMWDHeh5SMHir4O8+uqriI+Px4kTJ3DfffcBAHbv3o0VK1bggw8+QH5+vnHdfv36WW9KmNqmbNn5MvPH5prblyNWoq2rpgMVD3ag5yEFi/eMxo8fj8LCQrz22msYNmwYhg0bhtdeew2nT5/G+PHjERERgfDw8BY5aK25UGjKUnCg4sEO9DykYLFBQUFBo7c//vjD+N+m2LFjB0aNGoWAgABoNBps3LjR5H4hBObNmwd/f3+4uLggOjoax48ft1TdJoQPD0fcojh4+HiYLPf08VTsI1wKDlQ82IGeR1OQKD1u3rwZu3btwoABAxATE4OMjAyMGTPGeP/ChQuRlJSEL774AqGhoZg7dy4OHDiAQ4cOwdnZWdJzKFF6BGjMKqbgQMWDHWzjQa70aAs0Go1JGAkhEBAQgFmzZmH27NkAgPLycvj6+iIlJQXPPPOMpO0qFUYMYw+QKj0qRUFBAUpKShAdHW1c5u7ujqioKOTk5KhoxjCMnFj8aZpSlJSUAAB8fX1Nlvv6+hrvM0dNTQ1qamqMP1dUqN+jYBimYcjvGVlKUlIS3N3djbfg4GC1lRiGaQSLw+j5559X5Dtofn5+AIDS0lKT5aWlpcb7zDFnzhyUl5cbb4WFhTb1ZBjGOiwOo/LyckRHR6Nbt254//33cfbsWTm9jISGhsLPzw+ZmZnGZRUVFfjll1+MQ93ModPp4ObmZnJjGIYuFofRxo0bcfbsWUydOhXffPMNOnXqhJEjRyItLQ03b95s1raqqqqQl5eHvLw8ALdPWufl5eHMmTPQaDSYMWMG3n33XWzatAkHDhzAxIkTERAQYPLxP8MwLRurzhl5e3sjISEB+/fvxy+//IKuXbviueeeQ0BAAGbOnCm5mLh3715EREQY29oJCQmIiIjAvHnzAACvvfYa/va3v+Gll17CoEGDUFVVhS1btkjuGDEMQx9ZPk0rLi7Gjz/+iB9//BFarRaPPfYYDhw4gF69euHDDz/EzJkzG338Qw89ZP6KrnfQaDRYsGABFixYIIeuTaFQcKPgQMWDHeh5NITFYXTz5k1s2rQJycnJ+OGHH9CvXz/MmDEDEyZMMJ6fycjIwJQpU5oMo9YChRGjFByoeLADPY/GsLiB7eXlBYPBgPHjxyMuLg7h4eH11ikrK0NERAQKCgqs9bQaHjvbssecsgMtD1IN7CVLlhivDmIuiADAw8ODRBDZGgqjPSk4UPFgB3oeUrA4jJ577jk+gXwHCqM9KThQ8WAHeh5SoHP2qgVDYbQnBQcqHuxAz0MKHEYyQGG0JwUHKh7sQM9DChxGMlA72rPeJL1aNLcvR6zEmFM1Hah4sAM9DylwGMkAhdGeFByoeLADPQ8pkBuuZiukfMyo1+uRnZ2N1JxUi0ph5rocnr6eiE2MlfzRqbXFNDkcqHhQcLDWg4KDnB612MWkR1vR1BuTnp6O+Ph4FBUVGZdZUgqT+xdXaQcqHhQc5PKg4CCHR104jKygsTcmPT0dsbGx9b+SwgU5VTwoOFDxoOBgDlKlx9aCXq9HfHy8+e/GcUFOcQ8KDlQ8KDgoid2HUXZ2tsmhWT24IKeoBwUHKh4UHJTE7sOouLhY0npckFPGg4IDFQ8KDkpi92Hk7+8vaT0uyCnjQcGBigcFByWx+zAaOnQogoKCoNE00ArjgpyiHhQcqHhQcFASuw8jrVaLZcuWAUD9QOKCnOIeFByoeFBwUJLW8SqsJCYmBmlpaQgMDDRZztdlV8eDggMVDwoOSsE9ozpY28CWAyqjQSl4UHCg4kHBoS5cerQCW096ZBh7gkuPDMO0WjiMGIYhAYcRwzAk4DBiGIYEHEYMw5CAw4hhGBJwGDEMQwIOI4ZhSOCotgAlahvYe3P22n3TloIHBQcqHhQcbA2H0R3kmoFtDXLNOm4NHhQcqHhQcFAC/joIeAY2NQ8KDlQ8KDiYg78OYgN4BjYtDwoOVDwoOCiJ3YcRz8Cm5UHBgYoHBQclsfsw4hnYtDwoOFDxoOCgJHYfRjwDm5YHBQcqHhQclMTuw4hnYNPyoOBAxYOCg5LYfRjxDGxaHhQcqHhQcFAS/mj/DuZ6Rp6+nohNjFX1+vJKO1DxoOAglwcFBzk86sJjZ61AiRnYcpTTbPGLa0lBjoIHBQdrPSg4yOlRC4eRFdh6BjaFchoFByoe7GBbDy49EoVCOY2CAxUPdqDnIQUOIxmgUE6j4EDFgx3oeUiBw0gGKJTTKDhQ8WAHeh5S4DCSAQrlNAoOVDzYgZ6HFDiMZIBCOY2CAxUPdqDnIQUOIxmgUE6j4EDFgx3oeUhBfYNWQvjwcMQtioOHj4fJck8fT8U+wqXgQMWDHeh5NAX3jMxgSc+oFgrjQSk4UPFgB9t42KJnxGNnZcZB64CwgWF270DFgx3oeTQEH6YxDEMCDiOGYUjAYcQwDAk4jBiGIQGHEcMwJOAwYhiGBBxGDMOQgMOIYRgStIjS49tvv4358+ebLOvevTuOHDmiklHDUGjbUnCg4sEO9DwaokWEEQD07t0bP/30k/FnR0d66nLPGW6pDlQ82IGeR2PQicUmcHR0hJ+fn/Hm5eWltpIJtXOG756qV3a+DKsTVyMvM88uHKh4sAM9j6ZoMWF0/PhxBAQEoHPnznj22Wdx5swZtZWMUJgzTMGBigc70POQQosIo6ioKKSkpGDLli1YtWoVCgoKMHToUFRWVjb4mJqaGlRUVJjcbAWFOcMUHKh4sAM9DynQO/FihpEjRxr/3K9fP0RFRSEkJATr1q3DCy+8YPYxSUlJ9U562woKc4YpOFDxYAd6HlJoEXtGd+Ph4YGwsDCcONFwms+ZMwfl5eXGW2Fhoc18KMwZpuBAxYMd6HlIoUWGUVVVFU6ePAl/f/8G19HpdHBzczO52QoKc4YpOFDxYAd6HlJoEWE0e/ZsZGVl4dSpU/j555/x5JNPQqvVYvz48WqrAaAxZ5iCAxUPdqDnIQX1DSRQVFSE8ePHo3v37nj66afRoUMH7N69G97e3rI+j16vx/bt27F3814c23usWZ8wyDVn2KA34NjeY6o6UPGg4GCNBwUHuT1sCc/AvkN6ejri4+NRVFRkXGZJKcyalqtcxTRrm7YUPCg4yOVBwUEOj7rYYgY2hxFuB1FsbCzqvRV3dmOV+NejtphWrw+ioAMVDwoOVDwoOJjDFmHUIg7TbIler0d8fHz9IAK4IKeCBwUHKh4UHJTE7sMoOzvb5NCsHlyQU9SDggMVDwoOStIiSo+2pLi4WNJ6Xvt3YVBb23wF5fJ+aR0oeyjIUXCg4kHBQUnsfs+osa5SXbw8nG3mIHXb9lCQo+BAxYOCg5LYfRgNHToUQUFB0GgaaoUBvh1cEN7DdlMCwnt4wad9I4FkRwU5Cg5UPCg4KIndh5FWq8WyZcsAoMFASniuL7QODYeV1Q4OGsya2M/8nXZWkKPgQMWDgoOStI5XYSUxMTFIS0tDYGCgyXLfDi5YOCMSD0cGNvBI+Xg4MhALZ0TW20NSuphGoSBHwYGKBwUHpeCeUR30ej2ys7Oxc0MivDycEd7Dy6Z7RGYdDAJ5Ry7i13u62f2IUgoOVDwoONSFS49W0Jw3Zs/XMQpZNeLQM1ptBYZpEC49MgzTauEwYhiGBBxGDMOQgMOIYRgScBgxDEMCDiOGYUjAYcQwDAk4jBiGIYHdjxCpi7GB/XOh6g3svaf2cuuYgAMVDwoOtobD6A7mZmD7tHfGrIn9FPluGgBs+/UsPkrNx/nL1QB2ArBs1rG1yDVzuaU7UPGg4KAE/HUQNDID+w5KfFl2269n8frSX+vfwXOfVXGg4kHBwRz8dRAb0OgM7Dss/vcB6A22y2y9QeCj1Hzzd/LcZ8UdqHhQcFASuw+jJmdgAyi9dB15Ry7azCHvyMU7h2YNwHOfFXWg4kHBQUnsPoykzsC+WNZIWFiJ1G3z3GdlHKh4UHBQErsPI56B3fzt28PcZwoeFByUxO7DiGdg/wcKM5cpOFDxoOCgJHYfRjwD+z9QmLlMwYGKBwUHJeGP9u9grmfk28EFCc/1bdbH+rWlxYtl1c0uTpr2jG7j6euJ2MRY1a8vr7QHBQe5PCg4yOFRFx47awVKzMA2FybNLU5aOwNbroKcLf4CNdeDgoO1HhQc5PSohcPICmw9A7vB0uIdmluctGQGNpWCHAUPdrCtB5ceidJoafEOti5OUinIUfBgB3oeUuAwkoEmS4uwfXGSSkGOggc70POQAn9RVgaklhb3FLcFgoIlrXvynEuzHI4fuy5pPS4L2o8DJQ8pcBjJgNTSYpUuACevSesrFV9yapZDtaO07XJZ0H4cKHlIgQ/TZKDJ0iIAVw83BHUOsZmDd/cecGnfvuEVuCxodw6UPKTAYSQDjZYW7/DwmMfg4GDDgpyDAwY8+5z5O7ksaJcOlDykoL5BK+HhyEA89+JotHM33d119XDDE5OeQVi/XjZ3CB40CCNem377X8I6ePp4Kjr3Jnx4OOIWxcHDRz0PdqDn0RTcMzKDJT2jomp3/FzWGQaDAUV/nMbVikq0dXNFUOcQi/aIznp1bvZjHDRAeLdKODveIjGilMKoVHawjYctekZ8AlsG9EKD/ZVBAG4fLnXsGqqKh1+HGtyjMwBwQNjAMFUc6uKgVd+DHeh5NAQfpsnAsas+uKpv3qdfctPGUaCjj+1mLjGMreEwspLr+jY4fNVPbQ109K2Go1ZtC4axHA4jK8mvCsAtoe7b2NZFDz/PG6o6MIy1cBhZwaWb9+DM9Ua6PQrR2e86GpkNxzAtAg4jCxECyKsIMvv9QyXxcr8J93Z6lS0Yxno4jCzkTLUnLt1sq6qDgwPQyU/ad9IYhjocRhZw0+CA/CplrjLbGIFeNXB2UnvfjGHkgcPIAo5c9cV1fRtVHZzaGBDkxR/lM60HLj02k6t6Jxy75tvg/XI1sJuik281tA18lN8aG7/s0Ho8GoLDqJnsrwyEXpj/6OpY/iFszfgeVeX/mQ3Tzt0Nf3ryMVm/m+Z6jx7eHjfN3if3rGNLoeDBDvQ8GoNOLLYAzt9oh6JqD7P3Hcs/hE0pa02CCACqyiuwKWUtjuUfksVBowE6+5v/KL921vHdk/3KzpdhdeJq5GXmyeLQFBQ82IGeR1NwGEmk9qN8cxgMBmzN+L7Rx2/b+D0MBuvnDHt73IDrPfU/yqcy65iCBzvQ85ACh5FE/rjuhbJb5kfBFv1xut4e0d1UllWg6I/TVjloHQRCfM2ftKYy65iCBzvQ85ACh5EEbhi0OFjl3+D9VysqJW1H6noNEeRdA10b8x/lU5l1TMGDHeh5SIHDSAK/V/mhxtDwuf62bq6StiN1PXM4OxkQ6FXT4P1UZh1T8GAHeh5S4DBqgopbOpy87t3oOkGdQ+pNeLwba2dgh/pdR2MNASqzjil4sAM9DylwGDXB/sogGBr4KL8WBwcH/OnJxxpdx5oZ2B7tbqGD+63GHYjMOqbgwQ70PKTAY2froNfrkZ2djZ0bEuHl4Qzf0FD8XN5N8nOY6xm5erjh4THSe0a1pclC4QIXDw/49OiBe8Ouoq2ztE87zPVJPH09EZsYq/q13ZvrQcHBWg8KDnJ61GKLsbMtKoxWrFiBRYsWoaSkBP3798fy5csRGRkp6bFNvTHp6emIj49HUVGRcZmbhyseGvN4swqL1jSwzZYmvTwx/g31f3EtKcjJ/ZdYaQe5PCg4yOFRF7sOo2+++QYTJ07Ep59+iqioKCxduhTr16/H0aNH4ePj0+TjG3tj0tPTERsbi4beCiWu7lFbmjSLBopdxaG2IFevl3Jnl14JDwoOVDwoOJjDFmGk/oGiRBYvXoy4uDhMnjwZvXr1wqeffop77rkH//rXv6zarl6vR3x8fINBBMhXWGwIKaVJeynIUXCg4kHBQUlaRBjduHEDubm5iI6ONi5zcHBAdHQ0cnJyzD6mpqYGFRUVJjdzZGdnmxyamUOOwmJjNFmatKOCHAUHKh4UHJSkRXxR9uLFi9Dr9fD1Nf22vK+vL44cOWL2MUlJSZg/f36T2y4uLpbkMKDXEDw2JlbSus3l+41pWIfkJteLahuF8eHjbeIAAF8f/lrSerb0oOBAxYOCg5K0iDCyhDlz5iAhIcH4c0VFBYKDg+ut5+/fcLO6LpG9u6BPoLtsfnW52LuLpPWkulqK1O3b0oOCAxUPCg6KIloANTU1QqvVioyMDJPlEydOFE888YSkbZSXlwsAory83GT5rVu3RFBQkNBoNAK3j8RNbhqNRgQHB4tbt27J9XLqQcGBigcFByoeFBysoaG/cw3RIs4ZOTk5YcCAAcjMzDQuMxgMyMzMxODBg63atlarxbJlywAAmrvmctT+vHTpUmgbmmQmAxQcqHhQcKDiQcFBUWwcjrKxdu1aodPpREpKijh06JB46aWXhIeHhygpKZH0+KZSesOGDSIoKMjkX57g4GCxYcMGOV9Go1BwoOJBwYGKBwUHS2junlGL6RkBwCeffGIsPYaHh+Pjjz9GVFSUpMc2p4FdXFwMf39/DB06VPF/dSg4UPGg4EDFg4JDc2m1pUdrae4bwzCMdbTa0iPDMK0bDiOGYUjAYcQwDAk4jBiGIQGHEcMwJOAwYhiGBBxGDMOQgMOIYRgScBgxDEMCDiOGYUjQaucZ3U3tt14amvjIMIy81P5dk/qNM7sJo8rK25eWNjdgjWEY21FZWQl396YHE9rNF2UNBgPOnTsHV1fXerNh6lI7EbKwsJC/UAt+P+rC74UpTb0fQghUVlYiICBA0uW67GbPyMHBAUFBQZLXd3Nz41+4OvD78R/4vTClsfdDyh5RLXwCm2EYEnAYMQxDAg6ju9DpdHjrrbeg0+nUViEBvx//gd8LU+R+P+zmBDbDMLThPSOGYUjAYcQwDAk4jBiGIQGHUR1WrFiBTp06wdnZGVFRUfj111/VVlKFt99+GxqNxuTWo0cPtbUUY8eOHRg1ahQCAgKg0WiwceNGk/uFEJg3bx78/f3h4uKC6OhoHD9+XB1ZBWjq/Zg0aVK935dHH3202c/DYXSHb775BgkJCXjrrbfw22+/oX///hgxYgTOnz+vtpoq9O7dG8XFxcbbzp071VZSjKtXr6J///5YsWKF2fs//PBDfPzxx/j000/xyy+/oG3bthgxYgSqq6sVNlWGpt4PAHj00UdNfl++/vrr5j+RDS4k2SKJjIwU06ZNM/6s1+tFQECASEpKUtFKHd566y3Rv39/tTVIAEBkZGQYfzYYDMLPz08sWrTIuKysrEzodDrx9ddfq2CoLHe/H0II8fzzz4vRo0dbvW3eMwJw48YN5ObmIjo62rjMwcEB0dHRyMnJUdFMPY4fP46AgAB07twZzz77LM6cOaO2EgkKCgpQUlJi8rvi7u6OqKgou/1dAYDt27fDx8cH3bt3x9SpU3Hp0qVmb4PDCMDFixeh1+vh6+trstzX1xclJSUqWalHVFQUUlJSsGXLFqxatQoFBQUYOnSocfKBPVP7+8C/K//h0UcfRWpqKjIzM7Fw4UJkZWVh5MiR0Ov1zdqO3XxRlpHOyJEjjX/u168foqKiEBISgnXr1uGFF15Q0YyhyDPPPGP8c9++fdGvXz906dIF27dvx/DhwyVvh/eMAHh5eUGr1aK0tNRkeWlpKfz8/FSyooOHhwfCwsJw4sQJtVVUp/b3gX9XGqZz587w8vJq9u8LhxEAJycnDBgwAJmZmcZlBoMBmZmZGDx4sIpmNKiqqsLJkyfh7++vtorqhIaGws/Pz+R3paKiAr/88gv/rtyhqKgIly5davbvCx+m3SEhIQHPP/88Bg4ciMjISCxduhRXr17F5MmT1VZTnNmzZ2PUqFEICQnBuXPn8NZbb0Gr1WL8+PFqqylCVVWVyb/qBQUFyMvLQ/v27dGxY0fMmDED7777Lrp164bQ0FDMnTsXAQEBGDNmjHrSNqSx96N9+/aYP38+nnrqKfj5+eHkyZN47bXX0LVrV4wYMaJ5T2T153GtiOXLl4uOHTsKJycnERkZKXbv3q22kiqMGzdO+Pv7CycnJxEYGCjGjRsnTpw4obaWYmzbtk0AqHd7/vnnhRC3P96fO3eu8PX1FTqdTgwfPlwcPXpUXWkb0tj7ce3aNfHII48Ib29v0aZNGxESEiLi4uJESUlJs5+Hv7XPMAwJ+JwRwzAk4DBiGIYEHEYMw5CAw4hhGBJwGDEMQwIOI4ZhSMBhxDAMCTiMGIYhAYcRw0iktLQUw4cPR1BQED777DO1dVod3MBmGIm8/vrruHnzJsaOHYuYmBgcO3YMrq6uamu1GnjPiFGdSZMmNfkl0+3bt0Oj0aCsrMzmPpcuXYKPjw9OnTplstzd3R2BgYHo2rUrOnToAGdnZ5P7n3nmGXz00Uc292ut8J4Rozrl5eUQQsDDwwMA8NBDDyE8PBxLly41rnPjxg1cvnwZvr6+0Gg0NvVJSEhAZWUlVq9ebbK8uroaQUFBuHLlCj788EPMmjXL5P6DBw9i2LBhKCgogLu7u00dWyO8Z8Sojru7uzGIGsLJyQl+fn42D6Jr167h888/NzvR8ujRo7h27RqmT5+On3/+ud79ffr0QZcuXfDll1/a1LG1wmFkR1y4cAF+fn54//33jct+/vlnODk5mQwLq8upU6eg0Wiwdu1a3H///XB2dkafPn2QlZVlsl5WVhYiIyOh0+ng7++PN954A7du3TLen5aWhr59+8LFxQUdOnRAdHQ0rl69CsD0MG3SpEnIysrCsmXLjNfgOnXqlNnDtA0bNqB3797Q6XTo1KlTvUOkTp064f3338eUKVPg6uqKjh074p///Gej79H3338PnU6H++67r959ycnJGD16NKZOnYpvv/0WFy9erLfOqFGjsHbt2kafg2kAOeeeMPT57rvvRJs2bcSePXtERUWF6Ny5s5g5c2aD6xcUFAgAIigoSKSlpYlDhw6JF198Ubi6uoqLFy8KIYQoKioS99xzj3jllVfE4cOHRUZGhvDy8hJvvfWWEEKIc+fOCUdHR7F48WJRUFAg8vPzxYoVK0RlZaUQwvRSN2VlZWLw4MEiLi5OFBcXi+LiYnHr1i3jTJ0rV64IIYTYu3evcHBwEAsWLBBHjx4VycnJwsXFRSQnJxvdQ0JCRPv27cWKFSvE8ePHRVJSknBwcBBHjhxp8PW++uqr4tFHH623/MaNG8LLy0t8++23QgghBgwYIJYuXVpvvc2bNwsnJydRXV3d4HMw5uEwskNeeeUVERYWJiZMmCD69u3b6F+c2jD64IMPjMtu3rwpgoKCxMKFC4UQQrz55puie/fuwmAwGNdZsWKFaNeundDr9SI3N1cAEKdOnTL7HHdfd+vBBx8U8fHxJuvcHUYTJkwQf/7zn03WSUxMFL169TL+HBISIv76178afzYYDMLHx0esWrWqwdc7evRoMWXKlHrLN2zYILy9vcXNmzeFEEIsWbJEhIeH11tv//79jb5WpmH4MM0O+cc//oFbt25h/fr1+Oqrr6DT6Zp8TN35zo6Ojhg4cCAOHz4MADh8+DAGDx5scj5nyJAhqKqqQlFREfr374/hw4ejb9++GDt2LFavXo0rV65Y9RoOHz6MIUOGmCwbMmQIjh8/bnKJnH79+hn/rNFo4Ofn1+hVgq9fv17vUzLg9iHauHHj4Oh4e1Lz+PHjceDAAeTl5Zms5+LiAuD2uSemeXAY2SEnT57EuXPnYDAY6n18bQu0Wi1+/PFHbN68Gb169cLy5cvRvXt3FBQU2Py527RpY/KzRqOBwWBocH0vL696QVlSUoItW7Zg5cqVcHR0hKOjIwIDA6HX65GcnGyy7uXLlwEA3t7eMr0C+4HDyM64ceMG/vrXv2LcuHF455138OKLLza6p1DL7t27jX++desWcnNz0bNnTwBAz549kZOTA1GnJbJr1y64uroiKCgIwO0QGDJkCObPn499+/bByckJGRkZZp/LycmpyQsA9uzZE7t27TJZtmvXLoSFhUGr1Tb5ehoiIiIChw4dMln273//GyEhIdi/fz/y8vKMt6VLl2LNmjW4efOmcd2DBw8iKCgIXl5eFjvYLWofJzLKMnv2bNGpUydRXl4u9Hq9eOCBB8Tjjz/e4Pq154w6duwo0tPTxeHDh8VLL70k2rVrJy5cuCCE+M8J7GnTponDhw+LjRs3mpzA3r17t3jvvffEnj17xOnTp8W6deuEk5OT+P7774UQ9c8ZxcXFiUGDBomCggJx4cIFodfr650zys3NNTmBnZKSYvYE9pIlS0xeT//+/Y1e5sjPzxeOjo7i8uXLxmU9e/YUr7/+er11KyoqhE6nExs2bDAue/75582ec2KahsPIjti2bZtwdHQU2dnZxmUFBQXCzc1NrFy50uxjasNozZo1IjIyUjg5OYlevXqJrVu3mqy3fft2MWjQIOHk5CT8/PzE66+/bjzZe+jQITFixAjh7e0tdDqdCAsLE8uXLzc+9u4wOnr0qLjvvvuEi4uLACAKCgrqhZEQQqSlpYlevXqJNm3aiI4dO4pFixaZOFkSRkIIERkZKT799FMhxO0gBSB+/fVXs+uOGjVK/OUvfxFCCHH9+nXh7u4ucnJyGt0+Yx5uYDONcurUKYSGhmLfvn0IDw9XW0cRvvvuOyQmJuLgwYNwcJB+JmPVqlXIyMjADz/8YEO71gtfxJFh7uLxxx/H8ePHcfbsWQQHB0t+XJs2bbB8+XIbmrVueM+IaRR73DNi1IHDiGEYEvBH+wzDkIDDiGEYEnAYMQxDAg4jhmFIwGHEMAwJOIwYhiEBhxHDMCTgMGIYhgQcRgzDkOD/AZ/D6NyvMZnZAAAAAElFTkSuQmCC", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from matplotlib import pyplot as plt\n", "import numpy as np\n", "\n", "fig, ax = plt.subplots()\n", "\n", "ax.set_title('Real')\n", "ax.set_xlabel('x position (Å)')\n", "ax.set_ylabel('y position (Å)')\n", "ax.set_aspect('equal')\n", "\n", "# Finds corners of cell\n", "make_box = lambda cell:([[0,0],[1,0],[1,1],[0,1],[0,0]] @ cell).T\n", "\n", "x, y = make_box(supercell.cell[:2, :2])\n", "ax.fill(x, y, facecolor='tab:green', alpha=0.5)\n", "\n", "x, y = make_box(unitcell.cell[:2, :2])\n", "ax.fill(x, y, facecolor='tab:orange', alpha=0.5)\n", "\n", "x, y = make_box(prim.cell[:2, :2])\n", "ax.fill(x, y, facecolor='tab:blue', alpha=0.5)\n", "\n", "# plot the atoms in the bottom graphene layer as black dots\n", "x, y, z = supercell.positions.T\n", "bottom_layer = np.isclose(z, z.min())\n", "x, y = x[bottom_layer], y[bottom_layer]\n", "ax.plot(x, y, 'o', color='k')\n", "\n", "fig.tight_layout()" ] }, { "cell_type": "markdown", "id": "1a4a9508", "metadata": {}, "source": [ "The above figure uses an equal aspect ratio so we can see the perfect honeycomb pattern of the bottommost graphene layer.\n", "The blue cell indicates the primitive cell, the orange cell indicates the unit cell, and the green cell indicates the supercell." ] }, { "cell_type": "markdown", "id": "6d93f756", "metadata": {}, "source": [ "## Phonopy calculations\n", "Next we use phonopy and calorine to generate the phonon dispersion of graphite.\n", "First we use calorine to set up an ase calculator." ] }, { "cell_type": "code", "execution_count": 6, "id": "500f4360", "metadata": {}, "outputs": [], "source": [ "from calorine.calculators import CPUNEP\n", "\n", "model_fname = 'nep-C-CX.txt'\n", "calc = CPUNEP(model_fname)\n", "prim.calc = calc" ] }, { "cell_type": "markdown", "id": "31b32469", "metadata": {}, "source": [ "Next we use calorine to set up the `phonon` object.\n", "Note that we will use the same dimensions as the supercell but in principle as long as the there are no periodic image interactions any dimension can be used." ] }, { "cell_type": "code", "execution_count": 7, "id": "82324e85", "metadata": {}, "outputs": [], "source": [ "from calorine.tools import get_force_constants, relax_structure\n", "\n", "phonon = get_force_constants(prim, calc, [6, 6, 2])" ] }, { "cell_type": "markdown", "id": "ede70c8d", "metadata": {}, "source": [ "We are now ready to generate the dispersion.\n", "We will generate our own path in the Brillouin zone.\n", "Remember that the coordinates correspond to scaled positions of the inverse primitive cell.\n", "For example, `(1/2, 0, 0)` would correspond to a wave at the Brillouin zone boundary traveling in the direction perpendicular to the `b` and `c` axes of the real cell." ] }, { "cell_type": "code", "execution_count": 8, "id": "863fc78b", "metadata": {}, "outputs": [], "source": [ "path = 'AGMKG'\n", "special_points = dict(\n", " A = [0, 0, 1/2],\n", " G = [0, 0, 0 ], # Gamma point\n", " M = [0, 1/2, 0 ],\n", " K = [1/3, 2/3, 0 ])\n", "\n", "path_list = []\n", "for start, stop in zip(path[:-1], path[1:]):\n", " start = special_points[start]\n", " stop = special_points[stop]\n", " path_list.append(np.linspace(start, stop, 100))\n", "\n", "phonon.run_band_structure(path_list)\n", "\n", "band = phonon.get_band_structure_dict()\n", "phonopy_freqs = band['frequencies']\n", "phonopy_paths = band['qpoints']\n", "phonopy_dists = band['distances']" ] }, { "cell_type": "markdown", "id": "96e28f7e", "metadata": {}, "source": [ "We are now ready to plot the dispersion from phonopy." ] }, { "cell_type": "code", "execution_count": 9, "id": "9c0b6f06", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig_phonopy_dispersion, ax = plt.subplots()\n", "\n", "ax.plot(np.hstack(phonopy_dists), np.vstack(phonopy_freqs), color='tab:red', alpha=0.5)\n", "\n", "ax.set_title('Phonopy dispersion')\n", "ax.set_xlabel('Reciprocal path')\n", "ax.set_ylabel('Frequency (THz)')\n", "\n", "xticks = [d[0] for d in phonopy_dists] + [phonopy_dists[-1][-1]]\n", "ax.set_xticks(xticks)\n", "ax.set_xticklabels(list(path))\n", "ax.set_xlim(xticks[0], xticks[-1])\n", "ylim = (0, 50)\n", "ax.set_ylim(ylim)\n", "\n", "fig_phonopy_dispersion.tight_layout()" ] }, { "cell_type": "markdown", "id": "854bb3f9", "metadata": {}, "source": [ "Now we want to create the same dispersion using MD.\n", "The problem is that not all of the q-points in the above dispersion are supported by our choice of supercell.\n", "All points between are in some sense interpolated.\n", "q-points at exact inverse supercell lattice points are always exact and points between are exact only if the force constants decay to zero within the supercell (a.k.a. the L/2 criterion).\n", "\n", "dynasor supplies two helper functions to find these exact q-points.\n", "The `Lattice` object represents the map between the primitive cell and the supercell.\n", "It will provide us with the `P` matrix.\n", "The `make_path` method can find exact points along a straight path in the Brillouin zone.\n", "It returns those points and fractional distances along the path.\n", "This approach is a step by step way to generate the q-points, which we here use for clarity, but **dynasor** also has a helper function `get_supercell_qpoints_along_path` that can be used instead to obtain the q-points in the supercell (see, e.g., the [FCC-Al tutorial](https://dynasor.materialsmodeling.org/tutorials/aluminum_solid_dynamic.html#Set-up-path-through-Brillouin-zone)).\n" ] }, { "cell_type": "code", "execution_count": 10, "id": "04f9de07", "metadata": {}, "outputs": [], "source": [ "# get qpoints\n", "from dynasor.qpoints.lattice import Lattice\n", "\n", "lat = Lattice(prim.cell, supercell.cell)\n", "\n", "dyna_paths, dyna_dists = [], []\n", "for p, d in zip(phonopy_paths, phonopy_dists):\n", " p0, p1 = p[[0, -1]]\n", " \n", " dyna_path, dyna_dist = lat.make_path(p0, p1)\n", " dyna_paths.append(dyna_path)\n", " \n", " d0, d1 = d[[0, -1]]\n", " dyna_dists.append(d0 + (d1 - d0) * dyna_dist)" ] }, { "cell_type": "markdown", "id": "4ae244c1", "metadata": {}, "source": [ "We ca now update the dispersion with the exact q-points." ] }, { "cell_type": "code", "execution_count": 11, "id": "2a563610", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ax = fig_phonopy_dispersion.get_axes()[0]\n", "for x0 in np.hstack(dyna_dists):\n", " ax.axvline(x0, 0, 1, ls=':', color='gray', alpha=0.4)\n", "fig_phonopy_dispersion" ] }, { "cell_type": "markdown", "id": "b850caaf", "metadata": {}, "source": [ "To get a better feel for how the real and inverse/reciprocal cells relate we can plot them as we did for the real space case.\n", "\n", "First we need to convert our paths and inverse lattice points to Cartesian coordinates." ] }, { "cell_type": "code", "execution_count": 12, "id": "3d278bbd", "metadata": {}, "outputs": [], "source": [ "qpoints_supercell = lat.qpoints\n", "phonopy_path = lat.reduced_to_cartesian(np.vstack(phonopy_paths))\n", "dynasor_path = np.vstack(dyna_paths)" ] }, { "cell_type": "markdown", "id": "8c4cf2fe", "metadata": {}, "source": [ "Now let us plot them " ] }, { "cell_type": "code", "execution_count": 13, "id": "e403b335", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "\n", "# plot the blue Brillouin zone\n", "ax.fill(*make_box(lat.reciprocal_primitive[:2, :2]), facecolor='tab:blue', alpha=0.3)\n", "\n", "# plot red phonopy path\n", "ax.plot(phonopy_path[:, 0], phonopy_path[:, 1], '-', color='tab:red')\n", "\n", "# plot all supercell lattice points\n", "ax.plot(qpoints_supercell[:, 0], qpoints_supercell[:, 1], 'o', color='tab:green')\n", "\n", "# plot all points along path\n", "ax.plot(dynasor_path[:, 0], dynasor_path[:, 1], 'o', color='tab:red')\n", "\n", "ax.set_aspect('equal')\n", "ax.set_title('Reciprocal')\n", "ax.set_xlabel('x direction (rad/Å)')\n", "ax.set_ylabel('y direction (rad/Å)')\n", "\n", "# Annotate the special points\n", "for label in path:\n", " lp = special_points[label]\n", " if not np.isclose(lp[2], 0): # skip the A-point\n", " continue\n", " q = lat.reduced_to_cartesian(lp)\n", " ax.annotate(label, q[:2])\n", "\n", "fig.tight_layout()" ] }, { "cell_type": "markdown", "id": "dea496e8", "metadata": {}, "source": [ "The blue cell is now the inverse primitive cell.\n", "Notice how the reciprocal a-axis is perpendicular to the real b- and c-axes (the c-axis is directed towards us with the A point above the gamma point G).\n", "The green dots indicate all q-points which are spanned by our supercell.\n", "They represent all supported waves and, as expected since the real supercell is shorter in the x/a direction, the inverse supercell lattice is sparser in that direction.\n", "The red path is the infinite limit taken by phonopy and the red circles are the inverse supercell lattice points along this path.\n", "Evidently as we change the supercell we can make the sampling denser or sparser or completely miss the special high-symmetry points.\n", "It is also possible to make use of lattice symmetries to combine points from different but symmetrically equivalent paths to further increase the statistics and/or density of points along a path of interest.\n", "Notice for example if we went along the symmetrically equivalent inverse a-axis we would only get two points instead of five points between the gamma point and the zone boundary.\n", "On the other hand we would get three points for the (K-G) path instead of one.\n", "This optimization is left as an exercise." ] }, { "cell_type": "markdown", "id": "b4a03b1f", "metadata": {}, "source": [ "## Molecular dynamics simulations\n", "\n", "We are now (finally) ready to make the dispersion using MD and the SED method.\n", "\n", "We will use gpumd for this purpose and the first step is to write a suitable input file.\n", "The most important parameters are `dump` and `time_step`.\n", "They control the maximum frequency we can resolve in the simulation, which should be higher than the maximum frequency in the dispersion.\n", "GPUMD uses femtoseconds as the time unit, and with a dump frequency of once every 10 timesteps the maximum frequency is given by `0.5 / (dt * dump)` petahertz, which equals 50 terahertz, just above our maximum frequency from phonopy.\n", "\n", "Remember to remove any old trajectories before starting a new simulation.\n", "\n", "If you have gpumd installed the following cell will take less than a minute to run on common consumer GPUs." ] }, { "cell_type": "code", "execution_count": 14, "id": "a1a293a6", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 8.59 ms, sys: 17.2 ms, total: 25.8 ms\n", "Wall time: 38.7 s\n" ] } ], "source": [ "%%time\n", "\n", "from calorine.calculators import GPUNEP\n", "\n", "dt = 1.0 # time step in fs\n", "dump = 10 # number of steps between configuration is written\n", "temperature = 300 # target temperature in K\n", "steps = 10 ** 4 # number of steps to run\n", "\n", "calc = GPUNEP(model_fname, directory='gpumd_run_dir')\n", "supercell.calc = calc\n", "\n", "parameters = [('time_step', dt),\n", " # equilibration\n", " ('velocity', 2 * temperature),\n", " ('ensemble', ('nvt_lan', temperature, temperature, 100)),\n", " ('run', steps // 10),\n", " # production\n", " ('ensemble', 'nve'),\n", " ('dump_exyz', (dump, 1)), # the \"1\" requests to also write the velocities\n", " ('run', steps)]\n", "calc.run_custom_md(parameters)" ] }, { "cell_type": "markdown", "id": "c76a03d9", "metadata": {}, "source": [ "The SED function is simply invoked as follows. The ideal and primitive structures must be provided in order to correctly map atoms to primitive cells. Also notice that the q-points are given in angular units and the resulting frequency vector `w` is an angular frequency." ] }, { "cell_type": "code", "execution_count": 15, "id": "216c7213", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "INFO 2025-12-08 18:00:25: Assuming the trajectory has the default length unit (Ångström), since no unit was specified.\n", "INFO 2025-12-08 18:00:25: Assuming the trajectory has the default time unit (fs), since no unit was specified.\n", "INFO 2025-12-08 18:00:25: Trajectory file: gpumd_run_dir/dump.xyz\n", "INFO 2025-12-08 18:00:25: Total number of particles: 576\n", "INFO 2025-12-08 18:00:25: Number of atom types: 1\n", "INFO 2025-12-08 18:00:25: Number of atoms of type X: 576\n", "INFO 2025-12-08 18:00:25: Simulation cell (in Angstrom):\n", "[[14.80488 0. 0. ]\n", " [ 0. 25.6428 0. ]\n", " [ 0. 0. 13.109918]]\n", "INFO 2025-12-08 18:00:25: Running SED\n", "INFO 2025-12-08 18:00:25: Time between consecutive frames (dt * frame_step): 10.0 fs\n", "INFO 2025-12-08 18:00:25: Number of atoms in primitive_cell: 4\n", "INFO 2025-12-08 18:00:25: Number of atoms in ideal_supercell: 576\n", "INFO 2025-12-08 18:00:25: Number of q-points: 15\n", "INFO 2025-12-08 18:00:25: Number of snapshots: 1000\n", "CPU times: user 2.09 s, sys: 795 ms, total: 2.89 s\n", "Wall time: 2.12 s\n" ] } ], "source": [ "%%time\n", "\n", "from dynasor import Trajectory, compute_spectral_energy_density\n", "from dynasor.units import radians_per_fs_to_THz\n", "\n", "traj = Trajectory(f'{calc.directory}/dump.xyz', trajectory_format='extxyz')\n", "\n", "w, sed = compute_spectral_energy_density(\n", " traj,\n", " ideal_supercell=supercell,\n", " primitive_cell=prim,\n", " q_points=dynasor_path,\n", " dt=dt*dump)\n", "\n", "freqs = w * radians_per_fs_to_THz # radians/fs to cycles/ps (THz)" ] }, { "cell_type": "markdown", "id": "65f8f9b0", "metadata": {}, "source": [ "Now we can plot the dispersion along with the one from phonopy." ] }, { "cell_type": "code", "execution_count": 16, "id": "49bd42ea", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot \n", "fig, ax = plt.subplots()\n", "\n", "# dynasor\n", "ax.pcolormesh(np.hstack(dyna_dists), freqs, np.log(sed).T,\n", " shading='auto', rasterized=True)\n", "\n", "# phonopy\n", "ax.plot(np.hstack(phonopy_dists), np.vstack(phonopy_freqs), color='tab:red', alpha=0.4)\n", "\n", "# indicate q-points that were sampled\n", "ax.vlines(np.hstack(dyna_dists), 0, 50, color='gray', alpha=0.5)\n", "\n", "ax.set_xlabel('Reciprocal path')\n", "ax.set_ylabel('Frequency (THz)')\n", "ax.set_xticks(xticks)\n", "ax.set_xticklabels(list(path))\n", "ax.set_ylim(ylim)\n", "\n", "fig.tight_layout()" ] }, { "attachments": { "dispersion-3.png": { "image/png": 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" } }, "cell_type": "markdown", "id": "29468c4b", "metadata": {}, "source": [ "The red line is the \"exact\" 0 K phonopy dispersion.\n", "The gray vertical lines are exact supercell inverse lattice points and the only consistent points to consider.\n", "The vertical stripes are the results from SED and only carry meaning at the gray lines.\n", "As we can see where the red and gray lines meet we have a high intensity from SED which is as expected.\n", "\n", "Below is a dispersion for a 24x24x8 system at 900 K simulated for 1 ns,\n", "\n", "![dispersion-3.png](attachment:dispersion-3.png \"24x24x8 ortho graphite at 900K simulated for 1ns\")\n", "\n", "It is also possible to plot slices at different q-points separately." ] }, { "cell_type": "code", "execution_count": 17, "id": "1507f521", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "special_indices = np.cumsum([0] + [len(d) for d in dyna_dists])[:-1]\n", "\n", "fig, ax = plt.subplots()\n", "\n", "for label, spectrum in zip('AGMK', sed[special_indices]):\n", " ax.semilogy(freqs, spectrum, label=label, alpha=0.7)\n", "ax.set_xlim(0, 50)\n", "ax.set_xlabel('Frequency (THz)')\n", "ax.set_ylabel('Spectral energy density (eV / (rad/fs))')\n", "ax.legend()\n", "\n", "fig.tight_layout()" ] }, { "attachments": { "e097e80b-ae94-4386-90ae-ef7a8867ebc4.png": { "image/png": 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" } }, "cell_type": "markdown", "id": "a4f24e9d", "metadata": {}, "source": [ "Below follow the equivalent converged spectra.\n", "
\n", "\n", "
" ] }, { "cell_type": "markdown", "id": "6a4c3a28-091f-4cdc-9381-b32612b2faa5", "metadata": {}, "source": [ "The SED is normalized in such a way that the integral over angular frequencies for a single q-point yields `1/2 * n_bands * kB * T` in units of eV, as you can see below. \n", "Note that for the $\\Gamma$-point (G) there are three modes missing and thus the integrated temperature is off by about a factor of 3/4." ] }, { "cell_type": "code", "execution_count": 18, "id": "91deaacc-b35b-4889-929f-a391314bb930", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of bands 12\n", "q-point A: SED temperature 350.07 K\n", "q-point G: SED temperature 215.15 K\n", "q-point M: SED temperature 295.22 K\n", "q-point K: SED temperature 241.17 K\n" ] } ], "source": [ "# SED normalization test\n", "from ase.units import kB\n", "\n", "n_bands = len(prim) * 3\n", "print('Number of bands', n_bands) \n", "for label, spectrum in zip('AGMK', sed[special_indices]):\n", " I = np.trapz(spectrum, w)\n", " T = I / (1 / 2 * kB * n_bands)\n", " print(f'q-point {label}: SED temperature {T:.2f} K')" ] }, { "attachments": { "19df3e56-23e2-4846-9326-e596e30e267a.png": { "image/png": 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" } }, "cell_type": "markdown", "id": "bd8df5b2-2e9a-4bb3-8078-e23c0b0458bd", "metadata": {}, "source": [ "## Partial spectral energy density\n", "It is also possible to decompose the resulting SED by atom type and by Cartesian direction.\n", "This is achieved by setting `partial=True` in the `compute_spectral_energy_density` function.\n", "\n", "Here, as an example the resulting partial SED for the SrTiO3 cubic perovskite is shown.\n", "The top panel shows the full SED, the middle panel shows the SED decomposed by atom type, and the final row shows how motion in the different Cartesian directions contributes to the SED for Sr atoms.\n", "\n", "![STO_partial_SEDs.png](attachment:19df3e56-23e2-4846-9326-e596e30e267a.png)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.3" } }, "nbformat": 4, "nbformat_minor": 5 } dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/examples/mode_projection/000077500000000000000000000000001522712672400250505ustar00rootroot00000000000000dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/examples/mode_projection/mode_projection.ipynb000066400000000000000000002057261522712672400313070ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "id": "18a776b8-3883-4a82-b2fb-aa3fcbf2f3dd", "metadata": {}, "source": [ "# Mode projection" ] }, { "cell_type": "markdown", "id": "ba804ff5-2dbe-488b-9a2c-dfe573f2731e", "metadata": {}, "source": [ "In this tutorial we demonstrate how to use **dynasor** to study the dynamics of phonon modes. We will explore the `ModeProjector` object, which can be used together with force constants to obtain information about the dynamics in the system based on the harmonic approximation. The projector maps between real space atomic displacements u and inverse lattice phonon coordinates Q." ] }, { "cell_type": "markdown", "id": "c617134f-7e9b-4733-97f4-713bae8c9d7d", "metadata": {}, "source": [ "First, we construct the non-cubic primitive cell and cubic supercell using **ase**" ] }, { "cell_type": "code", "execution_count": 1, "id": "387a4b43-249d-421f-9285-95f6a4eb9036", "metadata": {}, "outputs": [], "source": [ "from ase.build import bulk\n", "\n", "prim = bulk('Al', cubic=False)\n", "repeat = 2\n", "unit = bulk('Al', cubic=True)\n", "supercell = unit.repeat(repeat)" ] }, { "cell_type": "markdown", "id": "c4609293-9cd6-4e2c-b500-5f63247e810d", "metadata": {}, "source": [ "Next, we need to set up **phonopy** and for that we need the repetition matrix P connecting the cell of the primitive structure to the supercell. This matrix can be found using a utility function in **dynasor**. Be careful, however, because **phonopy** requires the transpose of this matrix." ] }, { "cell_type": "code", "execution_count": 2, "id": "dcf5135e-bbc7-464b-9a80-8f69e6c873b4", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[-2 2 2]\n", " [ 2 -2 2]\n", " [ 2 2 -2]]\n" ] } ], "source": [ "from dynasor.tools.structures import get_P_matrix\n", "\n", "P_matrix = get_P_matrix(prim.cell, supercell.cell)\n", "\n", "print(P_matrix)" ] }, { "cell_type": "markdown", "id": "221c4dc1-9b4f-4635-a467-c9e35c700f09", "metadata": {}, "source": [ "Now we are ready to create the **phonopy** object and generate displaced supercells to create the force constant matrix.\n", "We use the built in emt calculator from **ase** for simplicity and utility functions from **calorine**." ] }, { "cell_type": "code", "execution_count": 3, "id": "47d5b5cf-76f5-4724-9fd3-ebe5d7d7d07d", "metadata": {}, "outputs": [], "source": [ "from calorine.tools.phonons import phonopy_to_ase, get_force_constants\n", "from phonopy import Phonopy\n", "from ase.calculators.emt import EMT\n", "\n", "phonon = get_force_constants(prim, EMT(), supercell_matrix=P_matrix.T)\n", "fc2_phonopy = phonon.force_constants" ] }, { "cell_type": "markdown", "id": "851d7406-bf32-46a7-aed4-7bfb3c5868e7", "metadata": {}, "source": [ "The internal **phonopy** supercell object is permuted relative to our original supercell. Either the supercell of phonopy can be used for subsequent calculations or the force constants need to be permuted. For this there is again a utility function in **dynasor**" ] }, { "cell_type": "code", "execution_count": 4, "id": "2dd3550f-3bd7-4ba6-b5c5-de00234d0b01", "metadata": {}, "outputs": [], "source": [ "from dynasor.tools.structures import find_permutation\n", "\n", "supercell_phonopy = phonopy_to_ase(phonon.supercell)\n", "\n", "p = find_permutation(supercell_phonopy, supercell)\n", "\n", "fc2 = fc2_phonopy[p][:,p]" ] }, { "cell_type": "markdown", "id": "495abc54-8722-443c-a59d-a5fd03012ff0", "metadata": {}, "source": [ "We are now ready with the setup and can construct the `ModeProjector`" ] }, { "cell_type": "code", "execution_count": 5, "id": "2d7b1a8f-4b65-479d-be1b-f612e8f3ac2b", "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "### ModeProjector ###\n", "Supercell:\n", "Number of atoms: 32\n", "Volume: 531.441\n", "Number of unit cells: 32\n", "Cell:\n", "[[8.1 , 0.0 , 0.0 ],\n", " [0.0 , 8.1 , 0.0 ],\n", " [0.0 , 0.0 , 8.1 ]]\n", "P-matrix:\n", "[[-2 , 2 , 2 ],\n", " [2 , -2 , 2 ],\n", " [2 , 2 , -2 ]]\n", "Primitive cell:\n", "Number of atoms: 1\n", "Volume: 16.608\n", "Atomic species present: {'Al'}\n", "Atomic numbers present: {13}\n", "Cell:\n", "[[0.0 , 2.025 , 2.025 ],\n", " [2.025 , 0.0 , 2.025 ],\n", " [2.025 , 2.025 , 0.0 ]]\n", "\n", "Ind Sym Num Mass (Da) x y z a b c \n", "0 Al 13 26.98 0.000 0.000 0.000 0.000 0.000 0.000 \n", "\n", "Primitive cell:\n", "Number of atoms: 1\n", "Volume: 16.608\n", "Atomic species present: {'Al'}\n", "Atomic numbers present: {13}\n", "Cell:\n", "[[0.0 , 2.025 , 2.025 ],\n", " [2.025 , 0.0 , 2.025 ],\n", " [2.025 , 2.025 , 0.0 ]]\n", "\n", "Ind Sym Num Mass (Da) x y z a b c \n", "0 Al 13 26.98 0.000 0.000 0.000 0.000 0.000 0.000 \n", " + \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " + + + + + \n", " \n", " \n", " \n", " \n", " \n", " +\n", " + \n", " \n", " \n", " \n", "+ \n", " \n", " \n", "-++++++++++++++++++++++++++++++++-+++-+++++++++++++--++-++++++++++-+-++++++++++-\n", "|-0.00 THz 7.99|\n" ] } ], "source": [ "from dynasor import ModeProjector\n", "\n", "mp = ModeProjector(prim, supercell, fc2)\n", "\n", "print(mp)" ] }, { "cell_type": "markdown", "id": "3284e698-9e4f-49e4-ad6b-7e3d2b7b7a75", "metadata": {}, "source": [ "Printing the `ModeProjector` reveals some basic information about the primitive and supercell as well as an ASCII representation of the density of states. Under the hood the dynamical matrix has been constructed and diagonalized at each commensurate lattice point. The information about which q-points are supported is available in the `reduced` and `cartesian` arrays. Let's print the first three points." ] }, { "cell_type": "code", "execution_count": 6, "id": "d29a7bb4-a83b-499d-8373-c4f12877cef9", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0 [0. 0. 0.]\n", "1 [0. 0. 0.5]\n", "2 [0. 0.25 0.25]\n" ] } ], "source": [ "for index, qp in enumerate(mp.q_reduced[:3]):\n", " print(f'{index:<5}{qp}')" ] }, { "cell_type": "markdown", "id": "3b21d014-034a-41b4-9004-b70d78ed6e90", "metadata": {}, "source": [ "From the above, we can note that the q-points are stored as reduced coordinates relative to the inverse primitive cell. Information about the primitive and supercell can be accessed by `primitive` and `supercell` respectively." ] }, { "cell_type": "code", "execution_count": 7, "id": "68b159ba-b34d-4e84-b9ee-895584d9b279", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Primitive cell:\n", "Number of atoms: 1\n", "Volume: 16.608\n", "Atomic species present: {'Al'}\n", "Atomic numbers present: {13}\n", "Cell:\n", "[[0.0 , 2.025 , 2.025 ],\n", " [2.025 , 0.0 , 2.025 ],\n", " [2.025 , 2.025 , 0.0 ]]\n", "\n", "Ind Sym Num Mass (Da) x y z a b c \n", "0 Al 13 26.98 0.000 0.000 0.000 0.000 0.000 0.000 \n" ] } ], "source": [ "print(mp.primitive)" ] }, { "cell_type": "code", "execution_count": 8, "id": "27de830c-5f99-448a-a34b-4d30203c6f2f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Supercell:\n", "Number of atoms: 32\n", "Volume: 531.441\n", "Number of unit cells: 32\n", "Cell:\n", "[[8.1 , 0.0 , 0.0 ],\n", " [0.0 , 8.1 , 0.0 ],\n", " [0.0 , 0.0 , 8.1 ]]\n", "P-matrix:\n", "[[-2 , 2 , 2 ],\n", " [2 , -2 , 2 ],\n", " [2 , 2 , -2 ]]\n", "Primitive cell:\n", "Number of atoms: 1\n", "Volume: 16.608\n", "Atomic species present: {'Al'}\n", "Atomic numbers present: {13}\n", "Cell:\n", "[[0.0 , 2.025 , 2.025 ],\n", " [2.025 , 0.0 , 2.025 ],\n", " [2.025 , 2.025 , 0.0 ]]\n", "\n", "Ind Sym Num Mass (Da) x y z a b c \n", "0 Al 13 26.98 0.000 0.000 0.000 0.000 0.000 0.000 \n", "\n" ] } ], "source": [ "print(mp.supercell)" ] }, { "cell_type": "markdown", "id": "c72d3b56-bf6b-420f-972a-8fb27e8592fc", "metadata": {}, "source": [ "In particular, the Cartesian q-vector can be constructed by" ] }, { "cell_type": "code", "execution_count": 9, "id": "6ceaec0b-1991-467b-8242-d5923bdd8d23", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0 [0. 0. 0.] [0. 0. 0.]\n", "1 [0. 0. 0.5] [ 0.77570189 0.77570189 -0.77570189]\n", "2 [0. 0.25 0.25] [0.77570189 0. 0. ]\n" ] } ], "source": [ "import numpy as np\n", "\n", "for index, q in enumerate(mp.q_reduced[:3]):\n", " q_cart = q @ (2*np.pi*mp.primitive.inv_cell)\n", " print(index, q, q_cart)" ] }, { "cell_type": "markdown", "id": "d421c5e7-0044-4647-abda-8e0976452095", "metadata": {}, "source": [ "For convenience these can also be accessed through" ] }, { "cell_type": "code", "execution_count": 10, "id": "1b83ef53-3184-494b-993e-4e977dcae640", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 0.00000000e+00 0.00000000e+00 0.00000000e+00]\n", " [ 7.75701890e-01 7.75701890e-01 -7.75701890e-01]\n", " [ 7.75701890e-01 2.27536266e-17 -2.27536266e-17]]\n" ] } ], "source": [ "print(mp.q_cartesian[:3])" ] }, { "cell_type": "markdown", "id": "5c0d3cc2-284c-49c6-b6d3-69e41ed5ac1e", "metadata": {}, "source": [ "In addition, q-points can also be accessed by index from the `ModeProjector`, or by call providing the reduced q-vector." ] }, { "cell_type": "code", "execution_count": 11, "id": "dd04c5f8-f27d-42cc-93be-07c7108cc2dc", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "### q-point ###\n", "Index: 1\n", "Reduced: [0. 0. 0.5]\n", "Cartesian: [0.78, 0.78, -0.78] rad/Å\n", "Wavenumber: 1.34 rad/Å\n", "Wavelength: 4.68 Å\n", "q-minus: 1 [0. 0. 0.5]\n", "Real: True\n", "Omegas: [3.30, 3.30, 7.92] THz\n", "### q-point ###\n", "Index: 2\n", "Reduced: [0. 0.25 0.25]\n", "Cartesian: [0.78, 0.00, -0.00] rad/Å\n", "Wavenumber: 0.78 rad/Å\n", "Wavelength: 8.10 Å\n", "q-minus: 7 [0. 0.75 0.75]\n", "Real: False\n", "Omegas: [3.78, 3.78, 5.18] THz\n" ] } ], "source": [ "qp_1 = mp[1]\n", "qp_2 = mp((0, 1/4, 1/4))\n", "\n", "print(qp_1)\n", "print(qp_2)" ] }, { "cell_type": "markdown", "id": "ccf0d912-305f-424c-9e11-e3c78fd5eb23", "metadata": {}, "source": [ "Printing the q-point reveals more detailed information. Notice that `(0, 1/4, 1/4)` is not real since its counterpropagating wave exists at another point in our supercell. This means that the amplitudes of these two modes are not independent. To access the q-point of the counterpropagating wave we can simply do" ] }, { "cell_type": "code", "execution_count": 12, "id": "440f09e5-e604-440a-a035-95ed925226c8", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "### q-point ###\n", "Index: 7\n", "Reduced: [0. 0.75 0.75]\n", "Cartesian: [2.33, -0.00, 0.00] rad/Å\n", "Wavenumber: 2.33 rad/Å\n", "Wavelength: 2.70 Å\n", "q-minus: 2 [0. 0.25 0.25]\n", "Real: False\n", "Omegas: [3.78, 3.78, 5.18] THz\n" ] } ], "source": [ "print(qp_2.q_minus)" ] }, { "cell_type": "markdown", "id": "34c2c19c-e50c-4d94-a920-d1d021dffd71", "metadata": {}, "source": [ "or" ] }, { "cell_type": "code", "execution_count": 13, "id": "2256ed07-bb04-4701-95eb-3b17644e2bb6", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "### q-point ###\n", "Index: 7\n", "Reduced: [0. 0.75 0.75]\n", "Cartesian: [2.33, -0.00, 0.00] rad/Å\n", "Wavenumber: 2.33 rad/Å\n", "Wavelength: 2.70 Å\n", "q-minus: 2 [0. 0.25 0.25]\n", "Real: False\n", "Omegas: [3.78, 3.78, 5.18] THz\n" ] } ], "source": [ "print(mp(-qp_2.q_reduced))" ] }, { "cell_type": "markdown", "id": "84ac9641-5874-473b-89d8-ebcccb62e0cd", "metadata": {}, "source": [ "This information is also contained in the `q_minus` array of the `ModeProjector`" ] }, { "cell_type": "code", "execution_count": 14, "id": "1ba07649-cc34-48ea-b4cc-afd187c383da", "metadata": {}, "outputs": [], "source": [ "assert mp.q_minus[qp_2.index] == qp_2.q_minus.index" ] }, { "cell_type": "markdown", "id": "31eb3018-c884-42eb-9fa3-0c1e822bdb9e", "metadata": {}, "source": [ "From the q-point we can access detailed information about each band by indexing the q-point object." ] }, { "cell_type": "code", "execution_count": 15, "id": "2302a2eb-568f-40d5-8095-2abc7c02ad18", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "### Band ###\n", "Band index: 2\n", "q-point: [1] [0. 0. 0.5]\n", "Frequency: 7.92 THz\n", "Polarization:\n", "0.58-0.00j 0.58-0.00j -0.58-0.00j \n", " Q P F \n", "Value: 0.00+0.00j 0.00+0.00j 0.00+0.00j \n", "Amplitude: 0.00 0.00 0.00 \n", "Phase: 0.00 0.00 0.00 \n", "Energy: 0.00 0.00 -0.00+0.00j \n", "\n" ] } ], "source": [ "band_2 = qp_1[2]\n", "print(band_2)" ] }, { "cell_type": "markdown", "id": "c9cc2eee-2c34-41f8-9193-7cc0e97dba0a", "metadata": {}, "source": [ "As expected from a zone boundary mode the polarization vector is purely real. The polarization `W` can be accessed from both the `ModeProjector` itself or from the `QPoint` as well as the `Band` objects." ] }, { "cell_type": "code", "execution_count": 16, "id": "fd06a8c9-a7a7-4f33-b77d-cc55dbc8f08d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 0.57735027-0.j 0.57735027-0.j -0.57735027-0.j]]\n", "[[ 0.57735027-0.j 0.57735027-0.j -0.57735027-0.j]]\n", "[[ 0.57735027-0.j 0.57735027-0.j -0.57735027-0.j]]\n" ] } ], "source": [ "print(mp.polarizations[band_2.qpoint.index, band_2.index])\n", "print(qp_1.polarizations[band_2.index])\n", "print(band_2.polarization)" ] }, { "cell_type": "markdown", "id": "8d7d973b-9b5b-40da-8f7b-4ce8f059e821", "metadata": {}, "source": [ "By printing the band we also see information about the current values of the amplitude (Q) and momentum (P) of the mode, as well as the force (F) acting on the mode. All of these properties can be accessed from the `ModeProjector`, `QPoint` and `Band` objects.\n", "\n", "It is also possible to modify the mode amplitudes directly. The `Q`, `P` and `F` coordinates can be accessed by, e.g.," ] }, { "cell_type": "code", "execution_count": 17, "id": "12c4e02e-9fc4-4c49-b782-d89208b4224c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "### Mode coordinate Q ###\n", "band: [2] 7.92 THz\n", "q-point: [1] [0. 0. 0.5]\n", "Value: 0.00+0.00j Å√dmu\n", "Amplitude: 0.00 Å√dmu\n", "Phase: 0.00 radians\n", "\n" ] } ], "source": [ "print(band_2.Q)" ] }, { "cell_type": "markdown", "id": "fd789966-974c-46db-acb0-4ab9d76f06a0", "metadata": {}, "source": [ "As a final exercise, let's map out the 0K energy landscape for the current band and compare with the harmonic approximation. The mode amplitudes should follow a normal distribution with deviation $k_BT/\\omega^2$" ] }, { "cell_type": "code", "execution_count": 18, "id": "913702ab-6fe8-497f-b8aa-44130e7594b5", "metadata": {}, "outputs": [], "source": [ "from ase.units import kB\n", "import numpy as np\n", "max_amp = 1 * (kB * 300 / np.abs(band_2.omega)**2)\n", "amplitudes = np.linspace(-max_amp, max_amp)\n", "\n", "harmonic_energies = []\n", "emt_energies = []\n", "\n", "ideal = mp.supercell.to_ase()\n", "ideal.calc = EMT()\n", "E0 = ideal.get_potential_energy()\n", "\n", "mp.set_Q(0)\n", "\n", "for amp in amplitudes:\n", " band_2.Q.amplitude = amp\n", " atoms = mp.get_atoms()\n", " atoms.calc = EMT()\n", " \n", " harmonic_energies.append(mp.potential_energies.sum())\n", " emt_energies.append(atoms.get_potential_energy()- E0)" ] }, { "cell_type": "code", "execution_count": 19, "id": "0ec29da1-2afd-465a-8106-3871bc82c2f2", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from matplotlib import pyplot as plt\n", "\n", "fig, ax = plt.subplots()\n", "\n", "ax.plot(amplitudes, emt_energies, label='EMT energy')\n", "ax.plot(amplitudes, harmonic_energies, ls='--', label='Harmonic energy')\n", "\n", "ax.set_ylabel('Energy (eV)')\n", "ax.set_xlabel('Mode amplitude (Å√Da)')\n", "\n", "ax.legend()\n", "\n", "fig.tight_layout()" ] }, { "cell_type": "markdown", "id": "eb57ea48-8c59-4695-a758-e4e5075e7b22", "metadata": {}, "source": [ "As demonstrated the harmonic approximation works well for aluminium for this mode.\n", "\n", "There are many more functions and properties accessible via the `ModeProjector`, `QPoint`, `Band` and `ComplexCoordinate` objects respectively. You can consult the corresponding documentation pages for more information." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.3" } }, "nbformat": 4, "nbformat_minor": 5 } dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/pyproject.toml000066400000000000000000000036351522712672400227750ustar00rootroot00000000000000[build-system] requires = ["setuptools>=61.0"] build-backend = "setuptools.build_meta" [project] name = "dynasor" description = "Dynamical structure factors and correlation functions from molecular dynamics trajectories" dynamic = ["version"] dependencies = [ "ase", "mdanalysis", "numba>=0.55", "numpy>=1.18", "pandas>=2.2", "scipy", ] requires-python = ">=3.9" authors = [ {name = "The dynasor developer team"} ] maintainers = [ {name = "The dynasor developer team"} ] license = "MIT" license-files = ["LICENSE"] readme = "README.rst" keywords = ["chemistry", "physics", "scientific"] classifiers = [ "Development Status :: 5 - Production/Stable", "Operating System :: OS Independent", "Programming Language :: Python :: 3 :: Only", "Programming Language :: Python :: 3", "Programming Language :: Python :: 3.9", "Programming Language :: Python :: 3.10", "Programming Language :: Python :: 3.11", "Programming Language :: Python :: 3.12", "Programming Language :: Python :: 3.13", "Intended Audience :: Education", "Intended Audience :: Science/Research", "Programming Language :: Python", "Topic :: Scientific/Engineering :: Chemistry", "Topic :: Scientific/Engineering :: Physics" ] [project.scripts] dynasor = "dynasor.cli.main:main" [project.optional-dependencies] test = [ "coverage", "flake8", "phonopy", "pytest", "xdoctest", ] documentation = [ "pydata-sphinx-theme", "nbsphinx", "sphinx-autodoc-typehints", "sphinx-design", "sphinx-sitemap", ] [project.urls] Documentation = "https://dynasor.materialsmodeling.org" Repository = "https://gitlab.com/materials-modeling/dynasor/" [tool.setuptools.dynamic] version = {attr = "dynasor.__version__"} [tool.setuptools.packages.find] where = ["."] include = ["dynasor*"] [tool.setuptools.package-data] "*" = ["*.json"] [tool.pytest.ini_options] filterwarnings = [ "ignore::UserWarning", ]dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/tests/000077500000000000000000000000001522712672400212145ustar00rootroot00000000000000dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/tests/conftest.py000066400000000000000000000164101522712672400234150ustar00rootroot00000000000000import os from itertools import combinations_with_replacement import numpy as np import pytest from dynasor.sample import Sample, DynamicSample, StaticSample # parameters N_qpoints = 20 window_size = 100 @pytest.fixture def q_points(): return np.array([np.array([1, 0, 0]) * amp for amp in np.linspace(0, 1, N_qpoints)]) @pytest.fixture def time(): return np.linspace(0, 10, window_size) @pytest.fixture def omega(): return np.linspace(0, 5, window_size) @pytest.fixture def traj_fname_xyz_long(): this_dir = os.path.dirname(__file__) traj_fname = os.path.join(this_dir, 'trajectory_reader/trajectory_files/dump_long_with_velocities.xyz') return traj_fname @pytest.fixture def simple_data_dict(q_points, time, omega): # setup data dict data_dict = dict() data_dict['q_points'] = q_points data_dict['time'] = time data_dict['omega'] = omega size = (N_qpoints, window_size) data_dict['Fqt_coh_A_A'] = np.linspace(-1, 1, N_qpoints * window_size).reshape(size) data_dict['Fqt_coh_B_B'] = np.linspace(-0.4, 0.4, N_qpoints * window_size).reshape(size) data_dict['Fqt_coh_A_B'] = np.linspace(-0.3, 0.3, N_qpoints * window_size).reshape(size) data_dict['Sqw_coh_A_A'] = np.linspace(-10, 10, N_qpoints * window_size).reshape(size) data_dict['Sqw_coh_B_B'] = np.linspace(-4, 4, N_qpoints * window_size).reshape(size) data_dict['Sqw_coh_A_B'] = np.linspace(-3, 3, N_qpoints * window_size).reshape(size) return data_dict @pytest.fixture def data_dict_with_incoh(q_points, time, omega): # setup data dict data_dict = dict() data_dict['q_points'] = q_points data_dict['time'] = time data_dict['omega'] = omega # coherent and currents names = ['Fqt_coh', 'Sqw_coh', 'Clqt', 'Clqw', 'Ctqt', 'Ctqw'] pairs = ['A_A', 'B_B', 'A_B'] size = (N_qpoints, window_size) for it, name in enumerate(names): total = np.zeros(size) for jt, pair in enumerate(pairs): # some determinstic values minval = 0.5 * it ** 2 - 4.8 * jt maxval = 1.5 * it + 14.8*jt + 25.0 F = np.linspace(minval, maxval, N_qpoints * window_size).reshape(size) total += F data_dict[f'{name}_{pair}'] = F data_dict[name] = total # incoherent names = ['Fqt_incoh', 'Sqw_incoh'] atom_types = ['A', 'B'] for it, name in enumerate(names): total = np.zeros(size) for jt, atom_type in enumerate(atom_types): # some determinstic values minval = 0.331 * it ** 2.2 - 51.8 * jt maxval = 3.1 * it + 84.8*jt + 121.2 F = np.linspace(minval, maxval, N_qpoints * window_size).reshape(size) total += F data_dict[f'{name}_{atom_type}'] = F data_dict[name] = total return data_dict @pytest.fixture def data_dict_without_incoh(q_points, time, omega): # setup data dict data_dict = dict() data_dict['q_points'] = q_points data_dict['time'] = time data_dict['omega'] = omega # coherent and currents names = ['Fqt_coh', 'Sqw_coh', 'Clqt', 'Clqw', 'Ctqt', 'Ctqw'] pairs = ['A_A', 'B_B', 'A_B'] size = (N_qpoints, window_size) for it, name in enumerate(names): total = np.zeros(size) for jt, pair in enumerate(pairs): # some determinstic values minval = -0.225 * it ** 2 - 14.8 * jt maxval = 15.7 * it + 2.38 * jt + 225.2 F = np.linspace(minval, maxval, N_qpoints * window_size).reshape(size) total += F data_dict[f'{name}_{pair}'] = F data_dict[name] = total return data_dict @pytest.fixture def simulation_parameters(): simulation_parameters = dict() simulation_parameters['atom_types'] = ['A', 'B'] simulation_parameters['pairs'] = [('A', 'A'), ('A', 'B'), ('B', 'B')] simulation_parameters['particle_counts'] = dict(A=2050, B=1020) simulation_parameters['cell'] = np.diag([1.5, 2.0, 3.0]) return simulation_parameters @pytest.fixture def simple_sample(data_dict, simulation_parameters): sample = Sample(data_dict, simulation_parameters) sample._metadata['type'] = 'data_dict' return sample @pytest.fixture def dynamic_sample_with_incoh(data_dict_with_incoh, simulation_parameters): sample = DynamicSample(data_dict_with_incoh, simulation_parameters) sample._metadata['type'] = 'data_dict_with_incoh' return sample @pytest.fixture def dynamic_sample_without_incoh(data_dict_without_incoh, simulation_parameters): sample = DynamicSample(data_dict_without_incoh, simulation_parameters) sample._metadata['type'] = 'data_dict_without_incoh' return sample @pytest.fixture def static_sample(q_points, simulation_parameters): data_dict = dict() data_dict['q_points'] = q_points data_dict['Sq_A_A'] = np.linspace(-1.1, 1, N_qpoints).reshape((N_qpoints, 1)) data_dict['Sq_A_B'] = np.linspace(0, 1.5, N_qpoints).reshape((N_qpoints, 1)) data_dict['Sq_B_B'] = np.linspace(5.1, 9.0, N_qpoints).reshape((N_qpoints, 1)) data_dict['Sq'] = data_dict['Sq_A_A'] + data_dict['Sq_A_B'] + data_dict['Sq_B_B'] sample = StaticSample(data_dict, simulation_parameters) return sample @pytest.fixture(scope='function') def sample_with_species(request, q_points, time, omega): """ Returns a dynamic sample with coherent, incoherent and currents for the requested species. """ species = request.param # Simulation simulation_parameters simulation_parameters = dict() simulation_parameters['atom_types'] = species pairs = list(combinations_with_replacement(species, 2)) simulation_parameters['pairs'] = pairs simulation_parameters['particle_counts'] = {} for i, s in enumerate(species): simulation_parameters['particle_counts'][s] = i*1000 + 100 simulation_parameters['cell'] = np.diag([1.5, 2.0, 3.0]) # Correlation functions names = ['Fqt_coh', 'Sqw_coh', 'Clqt', 'Clqw', 'Ctqt', 'Ctqw'] data_dict = dict() data_dict['q_points'] = q_points data_dict['time'] = time data_dict['omega'] = omega size = (N_qpoints, window_size) # list of the form [A_A, B_B, A_B, ...] pair_strings = ['_'.join(combination) for combination in pairs] for it, name in enumerate(names): total = np.zeros(size) for jt, pair in enumerate(pair_strings): # some determinstic values minval = 0.5 * it ** 2 - 4.8 * jt maxval = 1.5 * it + 14.8 * jt + 25.0 F = np.linspace(minval, maxval, N_qpoints * window_size).reshape(size) total += F data_dict[f'{name}_{pair}'] = F data_dict[name] = total # incoherent names = ['Fqt_incoh', 'Sqw_incoh'] for it, name in enumerate(names): total = np.zeros(size) for jt, atom_type in enumerate(species): # some determinstic values minval = 0.331 * it ** 2.2 - 51.8 * jt maxval = 3.1 * it + 84.8 * jt + 121.2 F = np.linspace(minval, maxval, N_qpoints * window_size).reshape(size) total += F data_dict[f'{name}_{atom_type}'] = F data_dict[name] = total # Create the DynamicSample sample = DynamicSample(data_dict, simulation_parameters) sample._metadata['type'] = 'data_dict' return sample dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/tests/dynasor_old_cmdline.pickle000066400000000000000000001525141522712672400264250ustar00rootroot00000000000000A](numpy.core.multiarray _reconstructnumpyndarrayKCbR(KKhdtypef8R(Ka?H @VOe?uSn?i<=?Lyq@2}?b&?0c??9#n?1?r??+:?tb F_k_t_0_0:Partial intermediate scattering function [time, k] (Cs-Cs)hhKhR(KKKhB`P@U݋"5x=r徭2 ؐkj,r?UIk^ @_NMR?MP\b?-f#2?ӳ?"L4?(CJ]bگ?:}6ѿ,Y)?P@+*d !|7P3r;C3 Id?a @LPM?8{kp`?dF(ΠL?Y70ְ?+M{>?Z_JL<_?&iE9ѿmῂ}6?P@`xz\q&G"lw6P68X?+d @GsJ?-28BX4_?$kRXh"?Iߝw?qͣJ?" 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>9韋߀=A]C<C8FژOogl> -!9>W۬>MTS:>{&Q.z>$3 1=fnԶ>)Wͽ -V>X= >$Pf ]Rb% >3wP41=lC'}LGE=HWJ\>Xi7>Yqx="9>w"(S >4n^5@{x=OLQԯ~=xv>޹)0 >Í4֪ >lD0&<=]PQafd?b[=[qa !>؃'(6>CC;=:7>]v3)aŅ>ĩJ`R'>ǫ4U##=e>zԧI >LP*C>">E;=4>@\=9}3l 5>݈)ڍ=[5"}Ij' >HTSzM=7/ ><.c >}2=U9<> ؘEW$ =]3&&=6I W>u1>)7j3>dՃV*~=oȅڡ>]/,[GEe=-|A >TZ>H׉T $3>i5>A\?8='X9p ^=:NzHT>tb Cl_k_t_0_12Longitudinal current correlation [time, k] (Cs-Pb)hhKhR(KKKhB`yb ,C>Ӟ9>K3 4>OHndT2_0>@ &> 9O>ɟժt=^Sg1=N fB.xYжrȽCr(*(= ~z"䷺ >Fӽ+_:~F>T:>95>)S2Q" >uU">?pi>FKܹ=)QmXI> # a*.~PTC}zOpܵ=ڦ'=EFo >,K`7<׆I>1o@;>#7>M#f2NF=ws>(0M >Jn>?ҽ`xFm ƜTTvZC=D= S L=׾>i مX޽r5}M>`fpX<>#Y:8><3"j>#ű> Y J>g)A>+^`>u,d= Sw'a xwA$^X=_n=Ϻ2h`3>\ǽIXDS=>{O5;9>82 Ml؄?{> >e5 >pkf >kو|`=5ϱwG$ Jq=jq޽3Ǽa>jl] w=z(S>=>|A9>kI2)y$[G=W8B&">[M >ɿ#I >kj >YsA InAjG%)G=T o b򽲋YBaU#V>㽣.9%>ݜ}U>L㧱=> #:>W[2v/+RS E>AQ#>6E> >1;>,J>WO8 R*VFw}=k^o Im3>uxܿOٽq5w >tb Cl_k_t_0_22Longitudinal current correlation [time, k] (Cs-Br)hhKhR(KKKhB`sQ>PAoM>n^(Q>HM>B)\Q>k>}DR>#S>˱oO>#{nQ>hU'R>;0WQ>3=6R>$P>RwQ>P+֜]Q>JM|Q>ճQ>l&‹Q>R@2Q>M% Q>4OQ8"M>㶢qP>m6)M>jpX̊MQ>b+> R>?R>XjrVN>/Q>pƢ4Q>%jBQ>xDQ>h3MP>P>L'2Q>S)sUatQ>sm uQ>uS:Q>aG2P>R: P>yUK>5\O>z}K>1 MP>Dt*Q>Fqá=R>9!1L>|P>'2zQ> P>C{aQ>i$)O>h?$P>)kP>?3P>V2@rhP> j)rP>󵤟P>x~M>οI>A%2SM>ng6J>:|O>YBP>4,EQ>qJ>S 7O>5P>&O>SGrP>L>-M>IN>⳯_qO>(ݝwO>WKhN>{M> K>~F>I>xadG>ύM>ZTO>BO>kH>&L>M>>nM>=kwN>_(J>䠏J>ڙkL>lfL>H,L>?gkK>#dJ>LPJG>S C>SBnE>6_E> ,[K>LǂL>b?EL>GID>h_I>=$^J>/UQJ>[aK>6[IXF>zzB`\bG>D qH>4AI>g|v9I>%G>fG>eD>*?>(œ@> yB>V@ ,H>dT J>8xZI>q HA>1{E>Kt-FG>-ݖG>7H[H>]7#&C>ƮQCE>F>DtE>$C>_`FRC>tb Cl_k_t_1_12Longitudinal current correlation [time, k] (Pb-Pb)hhKhR(KKKhB`}4.,0`;Ҝ!>WsRapMHU%ϡoQ>/M:Q > Ө۽?s~x ˔mX>/*K >?S&Ⱥs=y~.=nz>U&=4ۨR%Q tH85>[^o=کp I$lJz0>+4  C O>UO8tƽN҂\Afe棈 >]Ja >#ѽ_zm=\n?ݨ&=*'[=[,|Zq.1s]Cl>;=Uxr㔢f$DQ>Yv 1 >로C= 74нMA?x6 ׼>X.>^ɁQɮ3=h ⽳U=)=E)A>Yv V >9 f=7MT \%n47>1^E=R;լ=z .=}:wX>UgZ>rs ']G=RCѷp%+i=M~u%>c7>*E:">_1&=v>&c{'u&U+f}Ja2 m&=EK>NJv=;vi ><>O{kW=x,R 8:<=Yq_04>dxO.>^%>L>cRys ,'u8*]ɝ!$pr^{; S94>;Ջ>h[¦>u,!|$>6>AE:H=EQ ʣнP(=>Ӻrv;>fJ%(>T Պ>eJM1~r6?-Jx:-gifeE&>boƅ >$>Kp'>Ll qI>&Lƈ/G=@5:p0EhX %꽔tb Cl_k_t_1_22Longitudinal current correlation [time, k] (Pb-Br)hhKhR(KKKhB`7Ru9q>b#f>1yl>MKo>53h>4U5{h>k f> Aɹj>/]֓l>pq%$h>gZi>-VOYMNj>61k>z0?i>gwi>K1h>ZMTi> k>o$Pq>f>CQk> n>#ig>*h>NTe>#Ij>m!l>t !h>-ȞVNi>!EUg>y豆i>0ۗj>:ܟXi>ݽI8%i>!"ph>󽏏Ah>gl^j>Sߜp>أTޘd>]i>6Em>Jf>uc g>d>O%i>Sk>EGg>0"H!h>3e!f>:!H;q3(i> "g>,g>j+f>g>2߁h>iƂ;o>.Nb>_L+g>@=l>B_e>3kxe>S\c>;`bg>e04Qi> 0Y^e>egTf>jMwPd>^f>a0Vf>V[e>MJ/ie>AS_d>Ód>u Le>5ʝm>=G^>55k'3j>;bSc>@*'!mc>T~a>նe>ڡ C*g>)iFc>r&t d>xybPb>ɉP,5c>ڼ-ed>|c>!96b>ߢa>8a>~y>8b>ҷ\(k>4aTX>&]>2eC˵[g>?`>>a>Q ^>iCb>7){?d>$h`>@+dla>Q"_>ff``>S`b`>-6_>[L^>cڒ,]>\>\E|\>n%h>[ Q>\L(T>C od>) \>x\> +Y>٫Dh^_>fb>UZj\><^D]>F %HY>g/}GY>@8aZ>ޑY># O3nGV>n)-U>XwjmT>tb Cl_k_t_2_22Longitudinal current correlation [time, k] (Br-Br)hhKhR(KKKhB`uO>gd>E Ab>/Em[>`>tWM'`>ٵbtp`>c:`>PÓw_>}$?`>=Q_> rL{X`>AuMa>K6.6J`>^k`>DͰa`>g94`>ߏFM`>m`>]v`>3N> i'd>Zb>W-ק[>(0`>@X!`>Y'`>=`>i0pGh_>n>8`>盽_>]!`>xFa>C?`>X`>G)Be`>S"(`>wǥ *`>ojN\`> -`>uR.N> ^d>UKtb>ۥ8[>]ň`>;@`>>R`>ɢS`>܌_>5q`>k_>.Co `>r4`>4`>Uԭۜ`>5h_>+_>4H}`>wtbԸ_>)B`>7pM>Fc>b[b>Z>pT`>ܢ`>,6U`>㊎O`>xl꥜^>7_>^>_{``>Hh `>t_>Y5nI`>].b_>aJ~=_>7r*`>(2^>Mzr*8`>L_WK>dl]c>o|ta>Y>X0[`>yG)X`>5[55'_>ǚ?_>%I ]>$qV^>Pl4^>W븀_>0`>*z^> B_>nv^>ۉ{I^>U D_>#]>wlj_>GB:J>_+b>V2/a>K X>}(E_>⁈_>_^>n[=^>al\>b#?t^>}Q|,]>^>GV_>Kj3]>9xґ^>;w]>"N]>]>OnZ\>Oh!&^>z.H> )@@7b>ـ[`>bW>/[ S^>!gÎ_>>%-\>c] ]> \a[> &7]>cp4\>(raz]>2^>ns8\> $0WE]>vZ[>Ax\j[>k!m\>:H?1[>`ū\>tb Ct_k_t_0_01Transversal current correlation [time, k] (Cs-Cs)hhKhR(KKKhB`3ؘ0>`H H9h(+ ]i>G=O*xFa\=Zks>Ɂ s=eޘ3=b;p_>yA뽊\5=ۙi=; C' =~Y,fe<<0>Kek!# =BBYyhqH>Qj=U*"\=sr>J ,W ŞO ?=="vn>}!^9=T%.ǹ=p;7jVF/>1+K=mr4o!}9Q>|w=pDrͲ=4׶N>&w6a"̽v=k̑h>#ze=N◨5!8ƽy+|0->em]crZX >i:c$<.J>v=Ro(eöǝB=_)>G;WBڽ?иʵ=}5B>OQ>, 6 ZB=5)l ޽.J9L` .ۭ+>{Z~ZocV>[e&ڊ`0>j6 =BWkT=Ɓ>:ثMΰ}Q 㽻ndYl--˽ Q') ܇!>GC={:E٦𽂰I齮*7)>Gl &I+>3b) b>s=W8[tc>>$F6>S::Av $ཙ͢ #~K>xo]j*z='o!뽓\:_a\iR'>uXðyq>;Q*aU">ml@=RjNfi'>'߼C5>ul:z LJ1Mabjm=Qf:\I>9J=xs`A!D>+Q!>s>fu"b^&&ЖtW > 0I>ؚƩ >hTwD>cG* Tt)2}V( v36L%#o}t>^Э9"-L>He >m >A\CB#RjKw6Uｅ,W>UHG&Gy"H o>F05>E w KXTW2o *M~BK^54"d;A&܆ ?>#Cm\t8)|CE>$5"&k9>Ku>:Ovȼv`Zh_<>*1fd#v%->uޚ>]DlIco S%aL2>J]*ɖ٧1!2&]>B)p+ķ>>5>{"{Fޓ9*Ә>`! hqz>}5>be3ǀRP|u1,)+C)".S&I>Q2Ã8c(GI>}v>FYX`>*tH\1YBGM轭;>}TNgD(q²Bj5 >| ݩ>Mc^QeR07 i*,Aϥ)5b&Pdf>~'Ƃڐg6=!e >ЩJ>,!>\]v#-%i]-㽻Z>(R@-MͥN >`>꽦nF)PW-#*En$)m%RN2x>sNtĩ}-%d j>8>,E>ߍTY>+m#}wٽm>D%9Q1𽹆 >iv >8ýtb Ct_k_t_0_21Transversal current correlation [time, k] (Cs-Br)hhKhR(KKKhB`ö+(zc>u,R>,[KQ>Վ6 S>[rxV>(BQ>yQ>h CU>0JQ>I3S>@6׃S>fNR>լ{Z?S>mC=g'R>LXR>bS> 6R>myR>JS>ZeS>:d>\R> Q>tR>9C|$V>+RQ>&Q>NN U>CUQ>AeR>]JS>vR>Q S>B>LR>7qqR>[fS>9R>2R>_xS>qqS>һڑc>g\aQ>yP>R>r3V>kP>i+6Q>eT>k*P>9,%8R>Wk*R>릗3R>޶ݫxR>t+R> R>w!R>:R> pO؂Q>eĕ^R>b R>6-c>2M:Q>M$O>[UQ>ݬJU>6?O>/RP>;+ s_S>O>2HQ>3ͨձQ>?>OQ>_[Q>._VQ> JQ>*_Q>Q>P>%TQ>_Q>&"c>*Q>vvqL>RO>Oo8T>ê"=%M>,MDN>ZQ>+yyM>S P>n_5aP>Z wP>̺~LP>ʉ2xdO>"忖O>١mqP>,O>kO>?O>!lP>1Wxb>ϸP>ړI>$qzL>dR>UJ>-zcK>ʁVQP>9)gJ>z!M>l5M>TnVM>M>b{'4L>Q{L>.)M>j?SsL>|K>/pL>{Z.M>k&a>+ܩO>P)&dF>˚H>naQ>ƂG>XnH>gL>kIbG>/I> ‘5I>m];) J>C8 J>NAB\H>8|H>?J>SKH> +7H> H>6v%KQJ>tb Ct_k_t_1_11Transversal current correlation [time, k] (Pb-Pb)hhKhR(KKKhB`ro.x!A86m9R1!P >?9 3o> j! ɵ>2'jͽ[3 TC7nC6=l['XusN=ִ6t=dq=s*s彋C3>.5o_ B3e:ٔ0{H>a=w: ol@ >m%z=]"F Tk5#\O=Hat4=`v=f?aXD=B0e޽Eb>b{oz=.ԛZv-)B.{i}>*(nr.Q9 >[(3=glIwy/%*v =.hx?T+jǽ$(==:{gQ/ҽ{T% >/ʞB!)B)65>Uy;bI7K!>-J;!8b=[lG_34&&<"C.w=]44z ｢K"ٽ?7|= =ކ&Rf >uXmu0p#G1##xfx!>PGo/=lʠR` cRcP{ T>P!>5D[xTh1>ҲR6uk=3L佢d$Aq,-=߂1=.НNQP >ll&\>d&*?>ӾxBR -I$>"@p` >$y23tw >3se > APGe$=T)˽_0PnC-=/ =T 4̥=T7E>j;>*>YM~ ~Fܸ'>_go>ȁ+Tn-YS# >?`]CI#L>Ȯg ;P=z35=5.zE< ܡҽ;jt= jA=)j@ >tb Ct_k_t_1_21Transversal current correlation [time, k] (Pb-Br)hhKhR(KKKhB`.|>h?c>f>;Ni>'h>)g>g$_k>y%j>l<:/i>Ii>j>W:(Fi>Dh>Ȗ j> h.i>\Ij>{$i>1R=$j><6Ui>Zݥ@j>s|>%" Lc>S*&f>mwi>Ph>qwog>kOk>UǮi>o{h>k[Ni>a/=Z~j>X&Dh>(h>!,?@j>+)h>v~Vi>Tj2h>,i>jQh>>ei>cs|>kg b>je>:fh>)d Hh>$f>{qj>uh>`Gg>f h>kUyi>Nh>_́ g>5i>g>h>Y¯g>焦h>r.*ɸg>Uxɼ{>:db>cYVd>% h>"H6*f>pljbiie>7l] i>ٽ]f>;_Nn8f>N/>g>Ϭ7g> f>FZ4Jf>&hg>"f>fqg>c f>f>2Z af>t*/f>\ Iz>9 b(a>A`b>^4f>Ø:̊e>5osrc>-7g>b-d>3.d>=+e> 5e>l:d> :Qgd>-9e>m_mc>/d>qI.c>:qd>='Kیc>Q,fd>ґry>`>| Pa>m8Ve>x c>4va>$/: e>0zga>Raa>ϤjO+b>G;c>5uURb>oC63b>#FNc>hbxa>{>*Wb>c0oToa>`b>[JJa>Q',a> w>u]>;g{^>?0ʠc>3va>rD_>p3vb>4^>rSc^>qͽs|>/`>*Gh$`>n_>D`>K7r]>JPSE_>q]>í^> ]>hRN;]>tb Ct_k_t_2_21Transversal current correlation [time, k] (Br-Br)hhKhR(KKKhB`?????????????????????Th?( 4??ZE?. ?Ȋch:?я3?3?W)? ;?WES?:N? ???t??Ӳ? F?MQ$??ު?#BY?q6ܒ{?E?Nu7?_? Ƀ?o? Z?#3D?-?d;???Ep?3?4Ī?CG?#ˤ?Aww??'gٵ?I ?# m?bÇu?m\?G]??ۉ ?OST?P!?Z?aГ?Ns??H6\\ ?I&C?p~?]Z?ty?͒?(V U?2c?y?UzbY?[ y:?L?x~L?j(7?wL?<)}e?Z3?D?x-h?tb F_s_k_t_06Self part of intermediate scattring function [time, k]hhKhR(KKKhB`?????????????????????O?+V?Hd?`?Mh??a ?a?2[o?>=?,?Y6h?P*?w:?.1?S?%3n?-fx?Hwl?E???i!L?*?2?-? ?qiB4?8zp?Ye?@%?s_?W<‰-?%;7?Jm?en纐?Q"?V9:?b?GW??%?O>?tc??=M]? 3?Ҳ?-G<2?3?9 k?uXXa?y!|-?X$?a@9?#(R?j?A?i1w?@(??03k:?O^d?3lV?“y?cU_?e?\q?c?.?3>ȷ?ޮD?<>?*?T宦?[(u?,ԋ?Vc,}?:m?ČW`??B?J ?4N_?̒2?65&?׋}?\b?Yg?TV?h?5hU/?fٛ?G?K~5w?Dc?]R>N?k>7?EC؛?"# ??#XS?(6?_&U[???>t??q]M/?![6T?gH/?f??䅈?Nor?JZ?S??c"}i>#?su4&?5?Νw;?ԗ9?tb F_s_k_t_1hׇhhKhR(KKKhB`?????????????????????.M?#,?8rQ?/د?ׁ?;^(?Nn?Kt?kiT?'ۤF?*ƃ???-fq?X?3Ȣ?tN??j` ?E??7t?]+?K?,?9Z?{=? ?F*4?(8J ?d*r?U~?B?«;i??g{{ ?shﯨ?gb牝? y?!!??/r?`;M?J*J?#l?/?"?tZ?R\6?F?mH `?&x?1@(?jR9?I&k?U?2x"??n&?AE ? ??K? G?ۺ;x?(lk?c?qn?6?{Ī?@';?Kz?Y:_?#WFcB?2 4"?"[kވ?hj?iJl;@? ??6&|Z?ʱ6??R3ʼ?Hc?y#??wCY}?PVi?M?3}?`ՇmZ?y#Q3?xJ8 ?J#??hP=x?ֻx>?GL+?YJq?/m?;L??aJe?>q2?;?8?6Yx?Z&?P#ir?yl,H?XgĔ?)dw?ed?טTk?ӜP'?NZ?ơ?X1_?ڑ?r?KmH?m;? b???̾!3?REs-?s>l?TЃA?Ɨ?[~qY?}K?tb G_r_t_0_06Calculated partial van Hove function [time, r] (Cs-Cs)hhKhR(KKKhBhFr?suO@(kU?p"6z$.?|Vvwƿ6l?-Q@)&T?o[{ ?Gƿq巹v?PHS@$5R?V*?Paƿ.և#?U@]Q??p,<$3ǿk R2?nX@= 'P?H$}w?&}ǿёB?UZ@D?wP?$Rp?Nǿ6DR?pHs]@qD̩P?(?_+ȿtb G_r_t_0_16Calculated partial van Hove function [time, r] (Cs-Pb)hhKhR(KKKhBr8oRv?*!?e?דTG??q ?Oq~?:#%?B?㔭٪?-I ??#f69?QlJG?vV*?,?@ߍ?|?K? vѬ?,-?dt?=~?0x;3?tb G_r_t_1_26Calculated partial van Hove function [time, r] (Pb-Br)hhKhR(KKKhBЕ?H`?i,M?!&?N?xA[??E)?2މ?\~ y?Ao`?(,?}*?G/?>sڼ?ǡW$?e|mh8? ?gS8o?o@??uOrD?XfP?XS?=?Y?0N{?>?c,?}ͬ?=B??JU z)?yO?e X=?$%?tb G_r_t_2_26Calculated partial van Hove function [time, r] (Br-Br)hhKhR(KKhC8xTr?xTr?'?xTr?{Ϊ?'?tbw omega [fs^-1]hhKhR(KKKhB`@8:kc @+zW4@;59@ήeS@FE~@: f@ ư@xRn@'A@pli@ ]&"@^C ƺ@.@PRIE@w@ՎlX@W5i@[MQ@FM0@ky=q́$χI#w˿ϙп~]آؿhfW2NO-*,,#+il [!A'x &Ȕ*s tu 3|`M:w*g]"hފ=[q? HIðmk#,R^ĿjfȿjC}Ϳ8Կ`8$ԿLxGR1ݿlH(ݿOU+Aj٘c)hI+쿊4 fHp/' 촿s NzMêd R¿o%Ŀ0~ǿ/]oɿL;T̿!Kοw7=Xg[>Jg8aA+$phӨ{bWvW%4CِaG梿0kq"!?5@ky=r[Uj B^a? qx?ti?ǰ"zbb][if?`-eL?7- @k? ^:ȿFHƷ?Kϱ$ Ep?}Iƿ:'W?p5XT,5zyi`M:w~kjTB p?eedռKu?1Y0: %2ËY?,}ľ6#DaD?xBԩMV w^K?MXZ[t&Yq/V?QK?1L}-Be\m?8+fYs ?D^O{OۑyFt}?Ȫsqʤhe3¸M5_`SQ?S?t @";Y1ۨ1\N%t0(@Cy(@(0ĕߗj cx{Bf//7 @D ]v*S/&@~F= _,=u@OII=WZ*#||B?HBhvD9ӳ?^- ?>>=?"{Q-m*?} R^Cy?K4d?[?bQKz?L:?@!FRut$_ِh?[\|M'N7fœ?ܟ?4}2P~?/ᔿ-z?@JT>Od?4bպi6YOcP+1?GQ ? 0:` ?P7sz?m-՝5cGsjJfI5MViXFL'>r< ?W}?4j?8P;g?P)ocR?\!Pl_Gl?j? ;Xe^B?W=h?!„-t=բ4C]< p\Ze.|zs?=z%q?"y\?Gȃu^[?vqΘssd(K?K#b[3t'D _?M{?ꏷ,xb@)`҂?pչ>`?|Dtg]IC=}-C=2#R)Oz[HnRi?~e?Ȍ[ S?9Ʈ'kؤ,;Q?߯Bi; =?b|@iX݆9"ʇ>TT?^Ogq?~n0)4Fx?MR?)&r*:@]rJf)6MVIGL'>R@#< a?W]?rJ?ඉb];G?)oaJ2?h\a!0@l_GL?jh?;Xd^Bp?W=H?!i-Ttb S_k_w_0_25Partial dynamical structure factor [omega, k] (Cs-Br)hhKhR(KKKhB`@XfH@k@P^S@gr1@B:l^@Q*S@l눭@n8R?N@26ù@5g nnơ@z5@G0}@8djh@moys@lLg@VNd@"9Ԓ@C-ش@ς-@ky=3]Pĉ5*િ.~0Ͷn3BƍnĿ<)9п ؿ )ٿW´=濳utbf~0ʤn^㸮u"('̃b>}3EƠ `M:wK.λejj JohT? 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mp.supercell.positions, u) assert np.allclose(atoms.get_velocities() * fs, v) assert np.allclose(atoms.get_forces(), f) assert np.allclose(atoms.get_potential_energy(), mp.potential_energies.sum()) assert np.isclose(1/2 * np.einsum('ijab,ia,jb', fc2, u, u), mp.potential_energies.sum()) assert np.isclose(atoms.get_kinetic_energy(), mp.kinetic_energies.sum()) assert np.isclose(-1/2 * np.sum(u * f), mp.virial_energies.sum()) # Test qpoints qp = mp[0] print(qp) repr(qp) assert np.allclose(qp.q_reduced, mp.q_reduced[qp.index]) assert np.allclose(qp.q_cartesian, mp.q_cartesian[qp.index]) assert np.allclose(qp.omegas, mp.omegas[qp.index]) assert np.allclose(qp.polarizations, mp.polarizations[qp.index]) assert np.allclose(qp.eigenmodes, mp.eigenmodes[qp.index]) assert np.allclose(qp.potential_energies, mp.potential_energies[qp.index]) assert np.allclose(qp.kinetic_energies, mp.kinetic_energies[qp.index]) assert np.allclose(qp.virial_energies, mp.virial_energies[qp.index]) # ------------------------------------------------------------------ shared fixture --- @pytest.fixture def mp(primitive, supercell, fc2): return ModeProjector(primitive, supercell, fc2) # --------------------------------------------------------- constructor validation --- def test_mode_projector_primitive_larger_than_supercell(primitive, supercell, fc2): """Swapping primitive and supercell (primitive > supercell) must raise.""" with pytest.raises(ValueError, match='Primitive cell larger than supercell'): ModeProjector(supercell, primitive, np.zeros((len(primitive), len(primitive), 3, 3))) def test_mode_projector_fc2_wrong_n(primitive, supercell): """Force constants with incompatible N must raise.""" wrong_fc2 = np.zeros((10, 10, 3, 3)) with pytest.raises(ValueError, match='not compatible with supercell size'): ModeProjector(primitive, supercell, wrong_fc2) def test_mode_projector_fc2_wrong_shape(primitive, supercell): """Force constants with wrong trailing dimensions must raise.""" N = len(supercell) wrong_fc2 = np.zeros((N, N, 3)) with pytest.raises(ValueError, match='shape should be'): ModeProjector(primitive, supercell, wrong_fc2) # --------------------------------------------------------- __getitem__ bounds --- def test_mode_projector_getitem_out_of_bounds(mp): with pytest.raises(IndexError): mp[1000] def test_qpoint_band_out_of_bounds(mp): qp = mp[0] with pytest.raises(IndexError): qp[1000] # ----------------------------------------------- harmonic force accessors --- def test_get_F_harmonic(mp): """get_F_harmonic returns -omega^2 * Q.""" np.random.seed(5) N = mp.supercell.n_atoms mp.set_u(np.random.normal(scale=0.01, size=(N, 3))) F_harm = mp.get_F_harmonic() assert F_harm.shape == mp.get_Q().shape assert np.allclose(F_harm, -mp._w2 * mp.get_Q()) def test_get_f_harmonic(mp): """get_f_harmonic returns real forces consistent with get_atoms(harmonic_forces=True).""" np.random.seed(6) N = mp.supercell.n_atoms mp.set_u(np.random.normal(scale=0.01, size=(N, 3))) f_harm = mp.get_f_harmonic() assert f_harm.shape == (N, 3) assert f_harm.dtype == float # Must match the forces attached to the atoms object when harmonic_forces=True atoms = mp.get_atoms(harmonic_forces=True) assert np.allclose(f_harm, atoms.get_forces()) def test_get_atoms_harmonic_forces(mp): """get_atoms with harmonic_forces=True uses the harmonic force field.""" np.random.seed(7) N = mp.supercell.n_atoms mp.set_u(np.random.normal(scale=0.01, size=(N, 3))) atoms = mp.get_atoms(harmonic_forces=True) assert np.allclose(atoms.get_forces(), mp.get_f_harmonic()) # -------------------------------------------------- set_Q/P/F symmetry guard --- def test_set_Q_violates_conjugate_symmetry(mp): """set_Q must reject Q that does not satisfy Q(-q) = Q(q)*.""" Q = mp.get_Q() Q[0, 0] = 1j # imaginary part violates Q=Q* for a self-conjugate q-point with pytest.raises(ValueError, match='Q\\(-q\\)'): mp.set_Q(Q) def test_set_P_violates_conjugate_symmetry(mp): P = mp.get_P() P[0, 0] = 1j with pytest.raises(ValueError, match='P\\(-q\\)'): mp.set_P(P) def test_set_F_violates_conjugate_symmetry(mp): F = mp.get_F() F[0, 0] = 1j with pytest.raises(ValueError, match='F\\(-q\\)'): mp.set_F(F) # ------------------------------------------------------------ write / read --- def test_write_and_read(mp, tmp_path): """ModeProjector survives a round-trip through pickle via write/read.""" path = tmp_path / 'mp.pkl' mp.write(str(path)) mp2 = ModeProjector.read(str(path)) assert np.allclose(mp2.q_reduced, mp.q_reduced) assert np.allclose(mp2.omegas, mp.omegas) assert np.allclose(mp2._X, mp._X) # ------------------------------------------------- update_from_atoms edge cases --- def test_update_from_atoms_large_displacement_warns(mp): """Displacements > 2 Å trigger a warning about possible permutation error.""" N = mp.supercell.n_atoms atoms = mp.supercell.to_ase() atoms.positions[0, 0] += 2.5 # exceeds the 2 Å threshold atoms.set_velocities(np.zeros((N, 3))) with pytest.warns(UserWarning, match='Displacements larger than 2'): mp.update_from_atoms(atoms) def test_update_from_atoms_forces_in_arrays(mp): """Forces stored in atoms.arrays (no calculator) are read correctly.""" N = mp.supercell.n_atoms atoms = mp.supercell.to_ase() atoms.set_velocities(np.zeros((N, 3))) np.random.seed(8) forces = np.random.normal(size=(N, 3)) atoms.arrays['forces'] = forces # no calculator attached mp.update_from_atoms(atoms) assert np.allclose(mp.get_f(), forces) def test_update_from_atoms_no_forces_uses_zeros(mp): """When no calculator and no forces array are present, forces default to zero.""" N = mp.supercell.n_atoms atoms = mp.supercell.to_ase() atoms.set_velocities(np.zeros((N, 3))) # No calculator, no 'forces' in arrays mp.update_from_atoms(atoms) assert np.allclose(mp.get_f(), 0) # --------------------------------------------------- Band object --- def test_band_str_and_repr(mp): """Band __str__ and __repr__ execute without error.""" np.random.seed(9) N = mp.supercell.n_atoms mp.set_u(np.random.normal(scale=0.01, size=(N, 3))) mp.set_v(np.random.normal(scale=0.1, size=(N, 3))) mp.set_f(np.random.normal(scale=0.01, size=(N, 3))) band = mp[0][0] s = str(band) assert 'Band' in s assert str(band) == repr(band) def test_band_properties(mp): """All Band property accessors return values with correct shapes/types.""" Np = mp.primitive.n_atoms N = mp.supercell.n_atoms band = mp[0][0] assert isinstance(band.index, int) assert band.index == 0 assert isinstance(band.omega, (float, np.floating)) assert band.polarization.shape == (Np, 3) assert band.eigenmode.shape == (N, 3) assert isinstance(band.potential_energy, (float, np.floating)) assert isinstance(band.kinetic_energy, (float, np.floating)) assert np.isscalar(band.virial_energy) or band.virial_energy.ndim == 0 assert band.qpoint.index == 0 # --------------------------------------- ComplexCoordinate accessors and setters --- def _first_non_gamma_q(mp): """Return (q_index, band_index) for the first non-Gamma q-point.""" for i, q in enumerate(mp.q_reduced): if not np.allclose(q, 0): return i, 0 raise RuntimeError('No non-Gamma q-point found') def test_complex_coordinate_accessors(mp): """Q, P, F complex coordinates expose amplitude, phase, and complex value.""" np.random.seed(10) N = mp.supercell.n_atoms mp.set_u(np.random.normal(scale=0.01, size=(N, 3))) mp.set_v(np.random.normal(scale=0.1, size=(N, 3))) mp.set_f(np.random.normal(scale=0.01, size=(N, 3))) q_idx, s_idx = _first_non_gamma_q(mp) band = mp[q_idx][s_idx] for cc in (band.Q, band.P, band.F): assert isinstance(complex(cc), complex) assert isinstance(cc.complex, complex) assert isinstance(cc.amplitude, (float, np.floating)) assert isinstance(cc.phase, float) # str of Q, P, F subclasses assert 'Q' in str(band.Q) assert 'P' in str(band.P) assert 'F' in str(band.F) def test_complex_coordinate_amplitude_setter(mp): """Setting amplitude scales the complex value while preserving the phase.""" np.random.seed(11) N = mp.supercell.n_atoms mp.set_u(np.random.normal(scale=0.01, size=(N, 3))) q_idx, s_idx = _first_non_gamma_q(mp) band = mp[q_idx][s_idx] old_phase = band.Q.phase band.Q.amplitude = 0.5 assert np.isclose(band.Q.amplitude, 0.5) assert np.isclose(np.exp(1j * band.Q.phase), np.exp(1j * old_phase)) def test_complex_coordinate_phase_setter(mp): """Setting phase rotates the complex value while preserving the amplitude.""" np.random.seed(12) N = mp.supercell.n_atoms mp.set_u(np.random.normal(scale=0.01, size=(N, 3))) q_idx, s_idx = _first_non_gamma_q(mp) band = mp[q_idx][s_idx] old_amplitude = band.Q.amplitude band.Q.phase = 0.0 assert np.isclose(band.Q.amplitude, old_amplitude) assert np.isclose(band.Q.phase, 0.0) def test_complex_coordinate_complex_setter(mp): """Setting the complex value directly updates Q and respects Q(-q)=Q(q)*.""" q_idx, s_idx = _first_non_gamma_q(mp) # All q-points in a 2×2×2 Al supercell are self-conjugate; Q must be real band = mp[q_idx][s_idx] band.Q.complex = 0.3 # purely real value is valid for a self-conjugate q-point assert np.isclose(band.Q.complex, 0.3) # Conjugate symmetry must still hold globally assert np.allclose(mp.get_Q().conj(), mp.get_Q()[mp.q_minus]) # ----------------------------------------------- QPoint wavenumber / wavelength --- def test_qpoint_gamma_wavelength_is_infinite(mp): """The Gamma point (q=0) has infinite wavelength.""" gamma_idx = [tuple(q) for q in mp.q_reduced].index((0.0, 0.0, 0.0)) qp = mp[gamma_idx] assert qp.wavenumber == pytest.approx(0.0) assert qp.wavelength == np.inf def test_qpoint_nonzero_wavelength(mp): """A non-Gamma q-point has a finite, positive wavelength.""" # Find a non-Gamma point for i, q in enumerate(mp.q_reduced): if not np.allclose(q, 0): qp = mp[i] break assert qp.wavenumber > 0 assert np.isfinite(qp.wavelength) assert np.isclose(qp.wavelength, 2 * np.pi / qp.wavenumber) # ----------------------------------------------- atoms.scaled_positions --- def test_atoms_scaled_positions(mp): """Prim and Supercell expose scaled_positions (spos alias).""" spos = mp.primitive.scaled_positions assert spos.shape == (mp.primitive.n_atoms, 3) assert spos.dtype == float # ------------------------------------------------- constructor edge cases --- def test_mode_projector_nonmultiple_supercell_raises(primitive): """A supercell whose size is not a multiple of the primitive must raise.""" # 3-atom supercell vs 1-atom primitive → 3/1=3, OK; use 2-atom fake primitive prim2 = Atoms('Al2', positions=[[0, 0, 0], [2, 0, 0]], cell=[4, 4, 4], pbc=True) sc3 = Atoms('Al3', positions=[[0, 0, 0], [2, 0, 0], [4, 0, 0]], cell=[6, 4, 4], pbc=True) fc2 = np.zeros((3, 3, 3, 3)) with pytest.raises(ValueError, match='not multiple of primitive size'): ModeProjector(prim2, sc3, fc2) def test_mode_projector_call_invalid_qpoint_raises(mp): """Calling mp with a q-point not in the mesh must raise ValueError.""" with pytest.raises(ValueError, match='qpoint not compatible'): mp((0.123, 0.456, 0.789)) # ------------------------------------------------- project_modes --- class FakeTraj: """Minimal trajectory stub for testing project_modes.""" def __init__(self, frames, n_atoms, has_velocities=True): self._atomic_indices = {'Al': list(range(n_atoms))} self.n_atoms = n_atoms self.has_velocities = has_velocities self._frames = frames def __iter__(self): return iter(self._frames) @pytest.fixture def fake_traj_with_velocities(supercell): N = len(supercell) atomic_indices = {'Al': list(range(N))} np.random.seed(42) n_frames = 3 frames = [ TrajectoryFrame( atomic_indices, i, supercell.positions + np.random.normal(scale=0.01, size=(N, 3)), np.random.normal(size=(N, 3)), ) for i in range(n_frames) ] return FakeTraj(frames, N, has_velocities=True) @pytest.fixture def fake_traj_no_velocities(supercell): N = len(supercell) atomic_indices = {'Al': list(range(N))} np.random.seed(43) frames = [ TrajectoryFrame( atomic_indices, 0, supercell.positions + np.random.normal(scale=0.01, size=(N, 3)), velocities=None, ) ] return FakeTraj(frames, N, has_velocities=False) def test_project_modes_with_velocities(mp, supercell, fake_traj_with_velocities): """project_modes returns Q and P arrays of the correct shape.""" modes = mp.eigenmodes # shape: (Nk, Ns, N, 3) Q_traj, P_traj = project_modes(fake_traj_with_velocities, modes, supercell) Nk = len(mp.q_reduced) Ns = mp.primitive.n_atoms * 3 n_frames = len(fake_traj_with_velocities._frames) assert Q_traj.shape == (n_frames, Nk, Ns) assert P_traj.shape == (n_frames, Nk, Ns) def test_project_modes_no_velocities(mp, supercell, fake_traj_no_velocities): """project_modes with frames lacking velocities sets P to zero.""" modes = mp.eigenmodes Q_traj, P_traj = project_modes(fake_traj_no_velocities, modes, supercell) assert np.allclose(P_traj, 0) def test_project_modes_modes_n_atoms_mismatch_raises(supercell): """project_modes raises if modes[-2] != traj.n_atoms.""" N = len(supercell) traj = FakeTraj([], N) modes = np.zeros((1, N + 1, 3)) # modes has wrong atom count with pytest.raises(ValueError, match='Second dim in modes'): project_modes(traj, modes, supercell) def test_project_modes_supercell_n_atoms_mismatch_raises(supercell): """project_modes raises if traj.n_atoms != len(ideal_supercell).""" N = len(supercell) traj = FakeTraj([], N + 1) modes = np.zeros((1, N + 1, 3)) with pytest.raises(ValueError, match='same number of atoms'): project_modes(traj, modes, supercell) dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/tests/modes/test_modes_tools.py000066400000000000000000000120771522712672400262720ustar00rootroot00000000000000from fractions import Fraction import numpy as np import pytest from dynasor.modes.tools import ( as_fraction, det, factor_analysis, group_eigvals, inv, make_table, symmetrize_eigenvectors, trace, ) # ------------------------------------------------------------------ trace --- def test_trace_identity(): A = np.eye(3) assert trace(A) == pytest.approx(3.0) def test_trace_arbitrary(): A = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=float) assert trace(A) == pytest.approx(15.0) # ------------------------------------------------------------------- det ---- def test_det_2x2(): A = np.array([[3, 4], [2, 5]], dtype=float) assert det(A) == pytest.approx(np.linalg.det(A)) def test_det_3x3_identity(): A = np.eye(3) assert det(A) == pytest.approx(1.0) def test_det_3x3_arbitrary(): np.random.seed(0) A = np.array([[2, 1, 0], [0, 3, 1], [1, 0, 4]], dtype=float) assert det(A) == pytest.approx(np.linalg.det(A)) def test_det_singular(): A = np.zeros((3, 3)) assert det(A) == pytest.approx(0.0) # ------------------------------------------------------------------- inv ---- def test_inv_returns_fraction_by_default(): A = np.array([[2, 0, 0], [0, 3, 0], [0, 0, 6]], dtype=int) A_inv = inv(A) assert A_inv.dtype == object assert isinstance(A_inv[0, 0], Fraction) def test_inv_returns_float_when_requested(): A = np.array([[2, 0, 0], [0, 4, 0], [0, 0, 1]], dtype=int) A_inv = inv(A, as_fraction=False) assert np.allclose(A_inv, np.linalg.inv(A)) def test_inv_correctness(): A = np.array([[1, 2, 3], [0, 1, 4], [5, 6, 0]], dtype=int) A_inv = inv(A, as_fraction=False) product = A @ A_inv assert np.allclose(product, np.eye(3)) # -------------------------------------------------------------- group_eigvals def test_group_eigvals_no_degeneracy(): vals = [1.0, 2.0, 3.0] groups = group_eigvals(vals) assert groups == [[0], [1], [2]] def test_group_eigvals_all_degenerate(): vals = [1.0, 1.0, 1.0] groups = group_eigvals(vals) assert groups == [[0, 1, 2]] def test_group_eigvals_partial_degeneracy(): vals = [1.0, 1.0, 2.0, 3.0, 3.0] groups = group_eigvals(vals) assert groups == [[0, 1], [2], [3, 4]] def test_group_eigvals_unsorted_raises(): with pytest.raises(AssertionError): group_eigvals([3.0, 1.0, 2.0]) # ----------------------------------------------------------- factor_analysis - def test_factor_analysis_returns_unitary(): np.random.seed(7) L = np.random.normal(size=(6, 3)) R = factor_analysis(L, iterations=50) assert R.shape == (3, 3) # R should be orthogonal (rotation) assert np.allclose(R @ R.T, np.eye(3), atol=1e-10) def test_factor_analysis_quartimax(): np.random.seed(7) L = np.random.normal(size=(6, 3)) R = factor_analysis(L, iterations=50, method='quartimax') assert np.allclose(R @ R.T, np.eye(3), atol=1e-10) def test_factor_analysis_numeric_gamma(): np.random.seed(7) L = np.random.normal(size=(6, 3)) R = factor_analysis(L, iterations=50, method=0.5) assert np.allclose(R @ R.T, np.eye(3), atol=1e-10) # ------------------------------------------------------- symmetrize_eigvecs -- def test_symmetrize_eigenvectors_shape(): np.random.seed(1) eigvecs = np.random.normal(size=(3, 4, 3)) result = symmetrize_eigenvectors(eigvecs) assert result.shape == eigvecs.shape def test_symmetrize_eigenvectors_with_cell(): np.random.seed(2) eigvecs = np.random.normal(size=(2, 2, 3)) cell = np.diag([3.0, 3.0, 3.0]) result = symmetrize_eigenvectors(eigvecs, cell=cell) assert result.shape == eigvecs.shape # ---------------------------------------------------------------- as_fraction - def test_as_fraction_scalar(): result = as_fraction(3) assert isinstance(result, Fraction) assert result == Fraction(3) def test_as_fraction_array(): arr = np.array([1, 2, 3]) result = as_fraction(arr) assert result.shape == arr.shape assert all(isinstance(x, Fraction) for x in result) def test_as_fraction_tuple(): t = (1, 2, 3) result = list(as_fraction(t)) assert all(isinstance(x, Fraction) for x in result) def test_as_fraction_list(): lst = [4, 5, 6] result = as_fraction(lst) assert all(isinstance(x, Fraction) for x in result) # ------------------------------------------------------------------- inv (negative det branch) --- def test_inv_negative_determinant(): # Odd permutation matrix has det = -1, triggering the detx2 < 0 branch in inv() A = np.array([[0, 1, 0], [1, 0, 0], [0, 0, 1]], dtype=int) assert np.linalg.det(A) < 0 A_inv = inv(A, as_fraction=False) assert np.allclose(A_inv, np.linalg.inv(A)) assert np.allclose(A @ A_inv, np.eye(3)) # ----------------------------------------------------------------- make_table --- def test_make_table(): M = np.array([[1.0, 2.5], [3.0, 4.75]]) result = make_table(M) assert '1.00' in result assert '4.75' in result # Each row on its own line lines = result.splitlines() assert len(lines) == 2 dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/tests/modes/test_project_modes.py000066400000000000000000000162671522712672400266050ustar00rootroot00000000000000import os import tempfile import numpy as np import pytest from ase.build import bulk from ase import Atoms from ase.calculators.emt import EMT from ase.io import write as ase_write from phonopy import Phonopy from phonopy.structure.atoms import PhonopyAtoms from dynasor import ModeProjector, Trajectory from dynasor.tools.structures import get_P_matrix, find_permutation from dynasor.modes.project_modes import project_modes # ── Helpers ─────────────────────────────────────────────────────────────────── def _write_traj(tmp_dir, snapshots, name): """Write snapshots to extxyz and return the filename (not a Trajectory). A fresh Trajectory must be constructed per test since dynasor iterates lazily.""" fname = os.path.join(tmp_dir, name) ase_write(fname, snapshots, format='extxyz') return fname def _traj(fname): return Trajectory(fname, trajectory_format='extxyz') # ── Fixtures ────────────────────────────────────────────────────────────────── @pytest.fixture(scope='module') def tmp_dir(): with tempfile.TemporaryDirectory() as d: yield d @pytest.fixture(scope='module') def primitive(): return bulk('Al', cubic=False) @pytest.fixture(scope='module') def supercell(): return bulk('Al', cubic=True).repeat(2) @pytest.fixture(scope='module') def mp(primitive, supercell): """ModeProjector built with EMT force constants.""" P = get_P_matrix(primitive.cell, supercell.cell) primitive_phonopy = PhonopyAtoms(numbers=primitive.numbers, cell=primitive.cell, positions=primitive.positions) phonon = Phonopy(primitive_phonopy, P.T) phonon.generate_displacements() forces = [] for s in phonon.supercells_with_displacements: atoms = Atoms(symbols=s.symbols, cell=s.cell, scaled_positions=s.scaled_positions, pbc=True) atoms.calc = EMT() forces.append(atoms.get_forces()) phonon.forces = forces phonon.produce_force_constants() sc = Atoms(cell=phonon.supercell.cell, numbers=phonon.supercell.numbers, positions=phonon.supercell.positions, pbc=True) p = find_permutation(sc, supercell) fc2 = phonon.force_constants[p][:, p] return ModeProjector(primitive, supercell, fc2) @pytest.fixture(scope='module') def fname_with_velocities(mp, tmp_dir): """5-snapshot trajectory file with random displacements and velocities.""" rng = np.random.default_rng(42) N = len(mp.supercell.to_ase()) snapshots = [] for _ in range(5): atoms = mp.supercell.to_ase().copy() atoms.positions += rng.normal(scale=0.01, size=(N, 3)) atoms.set_velocities(rng.normal(scale=1.0, size=(N, 3))) snapshots.append(atoms) return _write_traj(tmp_dir, snapshots, name='traj_with_velocities.extxyz') @pytest.fixture(scope='module') def fname_no_velocities(mp, tmp_dir): """5-snapshot trajectory file with random displacements but no velocities.""" rng = np.random.default_rng(42) N = len(mp.supercell.to_ase()) snapshots = [] for _ in range(5): atoms = mp.supercell.to_ase().copy() atoms.positions += rng.normal(scale=0.01, size=(N, 3)) snapshots.append(atoms) return _write_traj(tmp_dir, snapshots, name='traj_no_velocities.extxyz') # ── Regression test ─────────────────────────────────────────────────────────── def test_project_modes_no_zeros_as_bug(mp, fname_with_velocities): """Regression: np.zeros_as (invalid) was used instead of np.zeros_like in dynasor's project_modes. This test FAILS with unfixed dynasor and PASSES once the bug is fixed.""" Q, P = project_modes(_traj(fname_with_velocities), mp.eigenmodes, mp.supercell.to_ase()) # ── Shape / dtype tests ─────────────────────────────────────────────────────── def test_project_modes_output_shapes(mp, fname_with_velocities): """Q and P should have shape (n_frames, n_qpoints, n_modes) and match each other.""" Q, P = project_modes(_traj(fname_with_velocities), mp.eigenmodes, mp.supercell.to_ase()) assert Q.ndim == 3, "Q should be 3D (n_frames, n_qpoints, n_modes)" assert Q.shape == P.shape, "Q and P should have the same shape" def test_project_modes_output_complex(mp, fname_with_velocities): """Q and P should be complex-valued.""" Q, P = project_modes(_traj(fname_with_velocities), mp.eigenmodes, mp.supercell.to_ase()) assert np.iscomplexobj(Q), "Q should be complex" assert np.iscomplexobj(P), "P should be complex" def test_project_modes_output_finite(mp, fname_with_velocities): """Q and P should contain no NaN or Inf.""" Q, P = project_modes(_traj(fname_with_velocities), mp.eigenmodes, mp.supercell.to_ase()) assert np.all(np.isfinite(Q)), "Q contains NaN or Inf" assert np.all(np.isfinite(P)), "P contains NaN or Inf" # ── No-velocity tests ───────────────────────────────────────────────────────── def test_project_modes_no_velocities_P_is_zero(mp, fname_no_velocities): """Without velocities in the trajectory, P should be zero.""" Q, P = project_modes(_traj(fname_no_velocities), mp.eigenmodes, mp.supercell.to_ase()) assert np.allclose(P, 0.0), "P should be zero when trajectory has no velocities" def test_project_modes_no_velocities_Q_nonzero(mp, fname_no_velocities): """Without velocities, Q should still be computed from displacements.""" Q, P = project_modes(_traj(fname_no_velocities), mp.eigenmodes, mp.supercell.to_ase()) assert np.any(np.abs(Q) > 1e-10), "Q should be nonzero for nonzero displacements" # ── Consistency test ────────────────────────────────────────────────────────── def test_project_modes_consistent_with_mp_get_Q_get_P(mp, tmp_dir): """project_modes and mp.get_Q/get_P should give the same result for the same snapshot.""" rng = np.random.default_rng(0) N = len(mp.supercell.to_ase()) u = rng.normal(scale=0.01, size=(N, 3)) v = rng.normal(scale=1.0, size=(N, 3)) snapshots = [] for _ in range(5): atoms = mp.supercell.to_ase().copy() atoms.positions += u atoms.set_array('vel', v) snapshots.append(atoms) fname = _write_traj(tmp_dir, snapshots, name='traj_consistency.extxyz') Q_pm, P_pm = project_modes(_traj(fname), mp.eigenmodes, mp.supercell.to_ase()) mp.set_u(u) mp.set_v(v) Q_mp = mp.get_Q() P_mp = mp.get_P() assert np.allclose(Q_pm[0], Q_mp, atol=1e-10), "Q mismatch between project_modes and mp.get_Q" assert np.allclose(P_pm[0], P_mp, atol=1e-10), "P mismatch between project_modes and mp.get_P" dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/tests/modes/test_regression.py000066400000000000000000000360401522712672400261170ustar00rootroot00000000000000from ase import Atoms from dynasor import ModeProjector import numpy as np def test_mode_projection_regression(): # parameters dim = (1, 1, 1) alat = 3.9 # build SrTiO3 primitive spos_A = np.array([0, 0, 0]) spos_B = np.array([0.5, 0.5, 0.5]) spos_X = np.array([[0.5, 0.5, 0], [0.5, 0, 0.5], [0, 0.5, 0.5]]) scaled_positions = np.vstack((spos_A, spos_B, spos_X)) cell = alat * np.eye(3) symbols = ['Sr', 'Ti', 'O', 'O', 'O'] prim = Atoms(pbc=True, cell=cell, scaled_positions=scaled_positions, symbols=symbols) # build fc2 if False: from calorine.tools.phonons import phonopy_to_ase from calorine.tools import get_force_constants from calorine.calculators import CPUNEP potential_fname = 'nep.txt' calc = CPUNEP(potential_fname) phonon = get_force_constants(prim, calc, dim) supercell = phonopy_to_ase(phonon.supercell) phonon.symmetrize_force_constants() fc2 = phonon.force_constants else: supercell = prim.copy() fc2 = np.array([[[[ 3.85092533e+00, 1.35585468e-30, -8.80834636e-18], # noqa [ 1.35585468e-30, 3.85092533e+00, -1.54283009e-17], # noqa [-8.80834636e-18, -1.54283009e-17, 3.85092533e+00]], # noqa [[-2.91966416e+00, 1.26208183e-18, -7.96578567e-18], # noqa [-1.26208183e-18, -2.91966416e+00, -1.68875459e-17], # noqa [-7.96578567e-18, -1.45064273e-17, -2.91966416e+00]], # noqa [[ 1.57798366e-01, -4.06210770e-18, 1.53545873e-17], # noqa [ 4.06210770e-18, 1.57798366e-01, 5.95836334e-17], # noqa [ 1.19704597e-17, 3.53065617e-17, -1.24685790e+00]], # noqa [[ 1.57798366e-01, 3.30111014e-17, -1.05509150e-17], # noqa [ 3.02110755e-17, -1.24685790e+00, -7.37503191e-18], # noqa [-1.05509150e-17, 7.67502859e-18, 1.57798366e-01]], # noqa [[-1.24685790e+00, -3.02110755e-17, 1.19704597e-17], # noqa [-3.30111014e-17, 1.57798366e-01, -1.98927547e-17], # noqa [ 1.53545873e-17, -1.30468620e-17, 1.57798366e-01]]], # noqa [[[-2.91966416e+00, -1.26208183e-18, -7.96578567e-18], # noqa [ 1.26208183e-18, -2.91966416e+00, -1.45064273e-17], # noqa [-7.96578567e-18, -1.68875459e-17, -2.91966416e+00]], # noqa [[ 3.12572516e+00, -1.14015053e-30, -7.12322498e-18], # noqa [-1.14015053e-30, 3.12572516e+00, -1.59656722e-17], # noqa [-7.12322498e-18, -1.59656722e-17, 3.12572516e+00]], # noqa [[ 3.65583053e-01, -5.32418953e-18, 2.31987472e-17], # noqa [ 5.32418953e-18, 3.65583053e-01, 6.71105416e-17], # noqa [ 1.59861775e-18, 3.41419422e-17, -9.37227103e-01]], # noqa [[ 3.65583053e-01, 3.83540541e-17, -9.70835432e-18], # noqa [ 3.17677828e-17, -9.37227103e-01, -1.76675609e-17], # noqa [-9.70835432e-18, 1.32173829e-17, 3.65583053e-01]], # noqa [[-9.37227103e-01, -3.17677828e-17, 1.59861775e-18], # noqa [-3.83540541e-17, 3.65583053e-01, -1.89708811e-17], # noqa [ 2.31987472e-17, -1.45061070e-17, 3.65583053e-01]]], # noqa [[[ 1.57798366e-01, 4.06210770e-18, 1.19704597e-17], # noqa [-4.06210770e-18, 1.57798366e-01, 3.53065617e-17], # noqa [ 1.53545873e-17, 5.95836334e-17, -1.24685790e+00]], # noqa [[ 3.65583053e-01, 5.32418953e-18, 1.59861775e-18], # noqa [-5.32418953e-18, 3.65583053e-01, 3.41419422e-17], # noqa [ 2.31987472e-17, 6.71105416e-17, -9.37227103e-01]], # noqa [[ 4.04675964e+00, -3.36498480e-30, 1.12303427e-16], # noqa [-3.36498480e-30, 4.04675964e+00, -5.20378916e-17], # noqa [ 1.12303427e-16, -5.20378916e-17, 1.26445763e+01]], # noqa [[ 6.60104601e-01, 2.77690553e-17, 7.62485029e-18], # noqa [ 3.71553525e-17, -5.23024566e+00, -5.92591049e-17], # noqa [ 2.49279027e-17, -1.27317202e-16, -5.23024566e+00]], # noqa [[-5.23024566e+00, -3.71553525e-17, -1.33497355e-16], # noqa [-2.77690553e-17, 6.60104601e-01, 4.18484927e-17], # noqa [-1.75784665e-16, 5.26609186e-17, -5.23024566e+00]]], # noqa [[[ 1.57798366e-01, 3.02110755e-17, -1.05509150e-17], # noqa [ 3.30111014e-17, -1.24685790e+00, 7.67502859e-18], # noqa [-1.05509150e-17, -7.37503191e-18, 1.57798366e-01]], # noqa [[ 3.65583053e-01, 3.17677828e-17, -9.70835432e-18], # noqa [ 3.83540541e-17, -9.37227103e-01, 1.32173829e-17], # noqa [-9.70835432e-18, -1.76675609e-17, 3.65583053e-01]], # noqa [[ 6.60104601e-01, 3.71553525e-17, 2.49279027e-17], # noqa [ 2.77690553e-17, -5.23024566e+00, -1.27317202e-16], # noqa [ 7.62485029e-18, -5.92591049e-17, -5.23024566e+00]], # noqa [[ 4.04675964e+00, -9.91342108e-17, -1.22934837e-17], # noqa [-9.91342108e-17, 1.26445763e+01, 9.18983316e-17], # noqa [-1.22934837e-17, 9.18983316e-17, 4.04675964e+00]], # noqa [[-5.23024566e+00, 6.28623534e-31, 7.62485029e-18], # noqa [-1.83656679e-30, -5.23024566e+00, 1.45264589e-17], # noqa [ 2.49279027e-17, -7.59663378e-18, 6.60104601e-01]]], # noqa [[[-1.24685790e+00, -3.30111014e-17, 1.53545873e-17], # noqa [-3.02110755e-17, 1.57798366e-01, -1.30468620e-17], # noqa [ 1.19704597e-17, -1.98927547e-17, 1.57798366e-01]], # noqa [[-9.37227103e-01, -3.83540541e-17, 2.31987472e-17], # noqa [-3.17677828e-17, 3.65583053e-01, -1.45061070e-17], # noqa [ 1.59861775e-18, -1.89708811e-17, 3.65583053e-01]], # noqa [[-5.23024566e+00, -2.77690553e-17, -1.75784665e-16], # noqa [-3.71553525e-17, 6.60104601e-01, 5.26609186e-17], # noqa [-1.33497355e-16, 4.18484927e-17, -5.23024566e+00]], # noqa [[-5.23024566e+00, -1.83656679e-30, 2.49279027e-17], # noqa [ 6.28623534e-31, -5.23024566e+00, -7.59663378e-18], # noqa [ 7.62485029e-18, 1.45264589e-17, 6.60104601e-01]], # noqa [[ 1.26445763e+01, 9.91342108e-17, 1.12303427e-16], # noqa [ 9.91342108e-17, 4.04675964e+00, -1.75113158e-17], # noqa [ 1.12303427e-16, -1.75113158e-17, 4.04675964e+00]]]]) # noqa # setup MP mp = ModeProjector(prim, supercell, fc2) # regression test frequencies omega_target = np.array([[-9.72899743e-07, -9.72899178e-07, -9.72895352e-07, 1.49435536e-02, 1.49435536e-02, 1.49435536e-02, 3.12694254e-02, 3.12694254e-02, 3.12694254e-02, 4.51928481e-02, 4.51928481e-02, 4.51928481e-02, 1.01573478e-01, 1.01573478e-01, 1.01573478e-01]]) assert np.allclose(mp.omegas, omega_target) # regression test eigenmodes eigenmodes_target = np.array([[[[ 6.58526866e+01+0.j, -7.01313685e-15+0.j, # noqa 1.17565644e-14+0.j], # noqa [ 3.59754684e+01+0.j, 2.75670320e-15+0.j, # noqa 1.04944320e-14+0.j], # noqa [ 1.20243912e+01+0.j, 1.94836223e-15+0.j, # noqa 5.52161429e-15+0.j], # noqa [ 1.20243912e+01+0.j, 1.42164396e-15+0.j, # noqa 6.46119042e-15+0.j], # noqa [ 1.20243911e+01+0.j, 1.81975998e-15+0.j, # noqa 6.25532188e-15+0.j]], # noqa [[ 5.01111914e-14+0.j, 6.58526866e+01+0.j, # noqa -8.76899421e-14+0.j], # noqa [ 2.69434970e-14+0.j, 3.59754684e+01+0.j, # noqa -5.34556878e-14+0.j], # noqa [-2.60964639e-14+0.j, 1.20243912e+01+0.j, # noqa 1.72828079e-14+0.j], # noqa [-4.25810506e-14+0.j, 1.20243911e+01+0.j, # noqa 1.95690765e-14+0.j], # noqa [-2.66004082e-14+0.j, 1.20243912e+01+0.j, # noqa 2.26766462e-14+0.j]], # noqa [[ 1.18660361e-14+0.j, 1.14097795e-14+0.j, # noqa 6.58526866e+01+0.j], # noqa [ 2.54514107e-14+0.j, -7.14403465e-14+0.j, # noqa 3.59754684e+01+0.j], # noqa [-4.21030245e-14+0.j, 6.74114597e-14+0.j, # noqa 1.20243911e+01+0.j], # noqa [-1.75703040e-14+0.j, 5.48266335e-14+0.j, # noqa 1.20243912e+01+0.j], # noqa [-2.45985161e-14+0.j, 6.87884806e-14+0.j, # noqa 1.20243912e+01+0.j]], # noqa [[ 3.37720509e+01+0.j, -1.05955689e-13+0.j, # noqa -6.03226027e-14+0.j], # noqa [ 2.63427273e+01+0.j, -1.02570912e-13+0.j, # noqa -4.99839047e-14+0.j], # noqa [-2.15317661e+01+0.j, -7.15141021e-15+0.j, # noqa -8.96743679e-15+0.j], # noqa [-2.15317661e+01+0.j, -9.95390352e-15+0.j, # noqa -7.50033592e-15+0.j], # noqa [-1.70512448e+01+0.j, -6.59284527e-15+0.j, # noqa -8.63252848e-15+0.j]], # noqa [[-3.47565314e-14+0.j, -3.37720509e+01+0.j, # noqa -2.02763278e-13+0.j], # noqa [-2.29505694e-14+0.j, -2.63427273e+01+0.j, # noqa -8.04099352e-14+0.j], # noqa [ 2.02546976e-14+0.j, 2.15317661e+01+0.j, # noqa -2.23029292e-14+0.j], # noqa [ 1.95483052e-14+0.j, 1.70512448e+01+0.j, # noqa -2.19778006e-14+0.j], # noqa [ 1.42350896e-14+0.j, 2.15317661e+01+0.j, # noqa -2.08541673e-14+0.j]], # noqa [[-1.12887481e-14+0.j, -5.68331627e-14+0.j, # noqa -3.37720509e+01+0.j], # noqa [-1.51628174e-14+0.j, -2.62604075e-14+0.j, # noqa -2.63427273e+01+0.j], # noqa [ 5.44041732e-15+0.j, -2.60031878e-14+0.j, # noqa 1.70512448e+01+0.j], # noqa [ 6.42936670e-15+0.j, -3.18882422e-14+0.j, # noqa 2.15317661e+01+0.j], # noqa [ 3.46572101e-15+0.j, -4.40351735e-14+0.j, # noqa 2.15317661e+01+0.j]], # noqa [[ 5.99692117e+01+0.j, 2.17807534e-14+0.j, # noqa 5.14159805e-15+0.j], # noqa [-5.44614116e+01+0.j, -9.40684656e-15+0.j, # noqa 3.68166465e-15+0.j], # noqa [-1.76290136e+00+0.j, -1.62844315e-16+0.j, # noqa 4.48264504e-16+0.j], # noqa [-1.76290136e+00+0.j, -6.23858424e-16+0.j, # noqa 2.18975185e-18+0.j], # noqa [-1.98199738e+00+0.j, -4.05508629e-16+0.j, # noqa 1.18769775e-16+0.j]], # noqa [[ 2.69624844e-14+0.j, -5.99692117e+01+0.j, # noqa 1.38006061e-14+0.j], # noqa [ 1.27961896e-14+0.j, 5.44614116e+01+0.j, # noqa 4.88317880e-14+0.j], # noqa [-3.15144984e-14+0.j, 1.76290136e+00+0.j, # noqa 2.32602130e-15+0.j], # noqa [ 7.62334848e-16+0.j, 1.98199738e+00+0.j, # noqa 3.13700818e-16+0.j], # noqa [-1.65048671e-14+0.j, 1.76290136e+00+0.j, # noqa 2.28673256e-14+0.j]], # noqa [[-4.88818898e-15+0.j, 3.09068816e-14+0.j, # noqa 5.99692117e+01+0.j], # noqa [-2.59902592e-15+0.j, -2.06811181e-14+0.j, # noqa -5.44614116e+01+0.j], # noqa [-1.99276861e-14+0.j, 6.25066304e-15+0.j, # noqa -1.98199738e+00+0.j], # noqa [ 1.83066954e-14+0.j, 1.07727683e-15+0.j, # noqa -1.76290136e+00+0.j], # noqa [-5.17933316e-16+0.j, 1.23765871e-14+0.j, # noqa -1.76290136e+00+0.j]], # noqa [[-4.66463833e-17+0.j, 4.57221585e-14+0.j, # noqa -7.49033493e-14+0.j], # noqa [ 2.14636681e-15+0.j, -2.14611702e-14+0.j, # noqa -1.10121123e-14+0.j], # noqa [-2.87939185e+01+0.j, 3.17176528e-15+0.j, # noqa -4.14491731e-15+0.j], # noqa [ 2.87939185e+01+0.j, -6.75652026e-16+0.j, # noqa 3.04129003e-15+0.j], # noqa [-1.15651224e-15+0.j, 2.99376019e-16+0.j, # noqa -4.23626604e-15+0.j]], # noqa [[-1.14436510e-16+0.j, -9.50416608e-15+0.j, # noqa -2.61821782e-14+0.j], # noqa [-1.97422120e-17+0.j, 4.09055676e-14+0.j, # noqa -2.71102266e-15+0.j], # noqa [ 3.11993040e-16+0.j, -2.87939185e+01+0.j, # noqa -1.54279073e-15+0.j], # noqa [ 2.70393491e-15+0.j, 9.09993240e-15+0.j, # noqa 6.69167456e-15+0.j], # noqa [ 3.11116765e-17+0.j, 2.87939185e+01+0.j, # noqa 4.24180939e-15+0.j]], # noqa [[ 2.52913304e-16+0.j, 1.40290340e-14+0.j, # noqa 1.57013538e-14+0.j], # noqa [ 5.13645998e-16+0.j, -1.79615036e-14+0.j, # noqa -1.58889460e-14+0.j], # noqa [ 3.68814467e-16+0.j, 6.11650916e-15+0.j, # noqa 4.79662561e-15+0.j], # noqa [-3.19799137e-16+0.j, -3.43986202e-15+0.j, # noqa -2.87939185e+01+0.j], # noqa [-4.44222988e-16+0.j, -1.52083058e-14+0.j, # noqa 2.87939185e+01+0.j]], # noqa [[-2.78188710e+00+0.j, -1.99296089e-14+0.j, # noqa 2.09143434e-15+0.j], # noqa [-2.61647768e+00+0.j, 8.52327631e-16+0.j, # noqa -8.55759848e-17+0.j], # noqa [-1.47573368e+01+0.j, 8.29721002e-16+0.j, # noqa -6.41410824e-17+0.j], # noqa [-1.47573368e+01+0.j, -1.98092481e-15+0.j, # noqa -6.33688470e-17+0.j], # noqa [ 3.49130383e+01+0.j, 1.23586274e-15+0.j, # noqa 2.72153989e-16+0.j]], # noqa [[ 3.31922523e-17+0.j, 2.78188710e+00+0.j, # noqa -1.14542092e-15+0.j], # noqa [-8.81462706e-16+0.j, 2.61647768e+00+0.j, # noqa -2.71102718e-15+0.j], # noqa [ 2.88965659e-15+0.j, 1.47573368e+01+0.j, # noqa -2.33056695e-15+0.j], # noqa [-7.83583951e-16+0.j, -3.49130383e+01+0.j, # noqa -1.57962499e-15+0.j], # noqa [-1.97933898e-15+0.j, 1.47573368e+01+0.j, # noqa 5.20044708e-15+0.j]], # noqa [[ 1.22424699e-17+0.j, 9.35380069e-17+0.j, # noqa -2.78188710e+00+0.j], # noqa [ 2.06454380e-17+0.j, -1.33009559e-14+0.j, # noqa -2.61647768e+00+0.j], # noqa [-4.99193443e-16+0.j, -2.80096822e-15+0.j, # noqa 3.49130383e+01+0.j], # noqa [ 8.42952139e-16+0.j, 5.05203364e-16+0.j, # noqa -1.47573368e+01+0.j], # noqa [ 6.45956326e-17+0.j, -2.53896340e-15+0.j, # noqa -1.47573368e+01+0.j]]]]) # noqa # assert np.allclose(mp.eigenmodes, eigenmodes_target) # This assert doesnt work because eigenmodes can randomly change between + and -, hence # we test the abs values assert np.allclose(np.abs(mp.eigenmodes), np.abs(eigenmodes_target)) dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/tests/regression_tests/000077500000000000000000000000001522712672400246165ustar00rootroot00000000000000NaClI_size4x3x2_100frames_with_velocity.xyz000066400000000000000000017616101522712672400346470ustar00rootroot00000000000000dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/tests/regression_tests48 Lattice="0.0 10.8 10.8 8.100000000000001 0.0 8.100000000000001 5.4 5.4 0.0" Properties=species:S:1:pos:R:3:vel:R:3 pbc="T T T" Na 0.35281047 0.08003144 0.19574760 1.86907241 -2.52379494 -1.31398331 Cl 3.14817864 0.37351160 -0.19545558 3.72881182 0.18423288 7.35518014 Na 2.89001768 2.66972856 -0.02064377 8.55652894 6.79502163 4.66184318 Cl 5.48211970 2.72880871 0.29085470 0.00502758 3.45146441 0.25716614 Na 2.85220755 0.02433500 2.78877265 3.18803834 1.64293569 8.98386405 Cl 5.46673487 0.29881581 2.65896835 4.74209431 -5.27040902 0.82282782 Na 5.46261354 2.52918085 2.18940204 -6.98856590 1.68654962 9.48899344 Cl 8.23072372 2.87288724 2.55156700 4.27513928 5.09391021 5.00085083 Na 5.85395092 -0.29087313 5.40915170 3.65771390 2.98254315 -0.19429988 Cl 8.06256323 0.30655584 5.69387175 -1.19517853 -3.48340120 0.78801437 Na 8.13098949 2.77563250 5.22244285 1.32028426 -8.30294594 8.44715858 Cl 10.40384071 2.63041757 5.43126979 5.43717291 1.66519323 -2.86099316 Na 0.24605814 2.94047597 2.62253464 -3.19837570 -2.19128856 1.18253916 Cl 2.63953945 2.49028941 2.41599641 -4.53384776 0.56263591 0.52867031 Na 2.35874596 5.79015508 2.59806956 3.65721767 -7.75181236 1.88717185 Cl 5.31238514 5.14944093 2.85549807 -1.59655496 -4.29460321 -3.42750461 Na 2.37722043 2.65745194 5.22090669 -6.33631178 -1.44952964 -5.21162255 Cl 5.47738050 2.59783897 5.16387356 -10.56738991 4.29509014 -3.11536908 Na 5.39436355 5.48566637 5.41330344 0.43183979 1.46136712 -14.95882129 Cl 8.16049438 5.27313558 5.32745177 3.15078659 4.63274038 10.34730903 Na 5.26550791 2.62808937 7.93737074 7.90358018 -0.27014384 0.83484945 Cl 7.75474348 2.73548523 8.01964381 3.06216637 -9.83480640 2.43990285 Na 7.77396033 5.49255645 7.91854033 -1.26065406 -3.07282243 -6.11624610 Cl 10.81038908 5.54581811 8.12579658 11.50373402 -2.71252682 6.10158124 Na 0.22788014 5.15303484 5.48046833 -1.13856333 -3.86306069 -1.25988430 Cl 2.56303798 5.22584057 5.28423007 3.16916843 -1.86692865 2.16860478 Na 2.63768949 8.11123307 5.16697003 0.57658435 -6.42943611 2.74103894 Cl 5.58016530 8.19313249 5.09275126 4.13910688 8.59214682 6.62244900 Na 2.99765044 5.77917784 8.33575591 -4.79677715 0.58743455 1.29993192 Cl 5.36401503 5.18584948 8.31089035 1.96251194 -8.09636563 4.14113789 Na 5.31936461 8.34448901 8.14165500 -0.24883143 -8.72008713 -6.73379068 Cl 8.29532781 8.17127328 8.24131463 2.20997565 -2.89255015 -0.37626324 Na 5.40210000 5.75717410 10.82538242 -2.94904855 -1.87478057 -1.41727382 Cl 8.18039787 5.77663014 10.53044819 -7.64240717 -0.25508201 -8.05409031 Na 7.84590300 8.29387934 10.56537532 -3.62301392 -2.38846069 3.02796478 Cl 11.18872424 8.01727620 10.65050904 -6.76262200 -5.59827253 -0.19022724 Na 0.38458841 8.39610296 8.47351179 0.67820716 0.82153360 3.24849406 Cl 2.88120893 7.92775486 8.48201299 -2.12203837 -2.85570146 5.22829406 Na 2.64639933 10.96049128 8.28945039 -11.74694343 7.80113268 1.33087489 Cl 5.36899798 10.92281587 8.28444133 2.08622500 5.67568328 4.96643147 Na 2.77528511 7.88011984 10.85964763 5.36052622 -5.55726494 2.51658543 Cl 5.66527718 7.96108643 10.77007309 -0.88990104 3.23452163 0.97967683 Na 5.31296929 11.16985275 10.93445895 -2.94945943 7.61986496 -0.31471461 Cl 8.18149237 10.64601679 10.90784984 0.81826522 7.28834682 0.05016605 Na 5.26513347 8.10636611 13.37283078 -2.18222400 6.72620714 4.99733316 Cl 8.23528666 8.21531816 13.45834025 -2.36548846 -3.03666004 -0.39469853 Na 8.17920134 10.58138770 13.20174848 -1.40582878 3.22041584 -4.80739658 I 10.88787834 10.83333470 13.62700629 3.44213765 -4.41978954 -2.29534313 48 Lattice="0.0 10.8 10.8 8.100000000000001 0.0 8.100000000000001 5.4 5.4 0.0" Properties=species:S:1:pos:R:3:vel:R:3 pbc="T T T" Na 0.54136997 0.12562654 0.18159389 5.22719218 -0.75763743 4.80767576 Cl 2.80076515 0.13022359 -0.06386361 0.22964054 2.57280466 4.39066418 Na 2.53038460 2.82119307 -0.40363365 7.12565247 -1.70046615 -1.60522823 Cl 5.54802441 2.80576270 -0.11780011 1.50610805 6.46864081 -2.87191820 Na 2.73773906 -0.15177441 2.51335256 -0.15097358 -1.54388743 0.12720894 Cl 5.59101130 0.03815886 3.09575146 4.71319246 0.31758265 -1.63529153 Na 5.92119346 2.83670178 2.76053309 3.69229114 0.13613505 -2.05263975 Cl 8.43874459 2.35878281 2.46817612 -11.03673388 -3.67221602 -3.58712705 Na 5.37303186 0.07810557 5.43338093 -2.24908735 -2.62346601 1.65832438 Cl 8.13690037 0.16154118 5.41459194 5.57880336 5.70810026 -13.78268498 Na 8.22775740 2.76592926 5.30057920 5.86196096 -7.24003374 -0.06593243 Cl 10.64918606 2.51131872 5.49695033 3.33000316 -3.40732448 -2.16828268 Na -0.02335466 3.08035096 2.74762539 -2.13273545 -2.67512049 -2.87265134 Cl 3.09933046 2.50134730 2.73935990 -9.48396893 0.25385870 1.82707780 Na 2.47267108 5.40007330 2.90519683 -6.40350777 3.17842196 1.34203568 Cl 5.36868042 5.39368417 2.82996517 -1.20871721 -4.34140461 6.27083609 Na 3.13096929 2.57794823 5.24893493 1.19431941 -1.56089135 1.98424080 Cl 5.33071630 2.72940535 5.30411039 -6.06239905 -3.29433924 -1.75895514 Na 5.51175388 5.60496206 5.21482515 -3.85274706 2.10249989 -5.67056442 Cl 8.47257283 5.17323657 5.52209558 -3.12508537 3.94167618 6.77640844 Na 5.47720606 3.11680371 8.02469626 -8.72119881 -2.60453867 -0.40747062 Cl 8.14606727 2.83624186 8.30702501 4.66411883 -0.62581066 -4.88016233 Na 8.09376790 5.78798646 7.89896262 4.55585599 -4.75035537 -6.58516987 Cl 10.65164206 5.43742490 7.95343097 1.41041481 -8.59025051 -0.96642790 Na -0.27658402 5.69649910 5.59229163 0.00248612 -0.82790650 1.74616832 Cl 2.27175754 5.59851469 5.63844813 -1.67248596 -7.68795647 9.60142146 Na 2.49064409 8.35855292 5.10649720 12.79826029 2.19672370 -5.00635579 Cl 5.30118093 8.06749305 5.49716175 0.27188615 4.04712364 2.87821529 Na 2.77849776 5.44429814 7.92896079 7.09603302 3.09127751 5.88323232 Cl 5.70839808 5.53326386 7.99235307 -2.83291898 -7.97462061 -1.13863593 Na 5.28628373 8.38146765 8.22836110 -5.67883103 -3.53348497 -1.98641254 Cl 7.91898002 8.02176867 8.30565863 5.92653323 -0.94463244 5.82366048 Na 5.00547898 5.22662299 10.94415752 -0.87699906 -9.24879524 -6.95803032 Cl 7.85538359 5.72135597 10.57685801 -1.07075020 4.52940259 0.63141800 Na 7.82292429 7.83406799 10.80829199 1.31224649 -0.01799894 2.38342439 Cl 10.71778892 7.94573428 10.82209548 6.98420432 -9.65536968 -0.65543700 Na -0.16093030 8.15070961 8.22982955 -10.94457219 4.79097498 -3.10123355 Cl 2.77178811 7.88390571 8.28047955 -2.07609172 8.90109428 2.70440194 Na 2.73235624 10.96660586 8.29514394 1.54848744 4.40057628 -1.53856516 Cl 5.32235226 10.95666321 7.95820925 -2.67380497 -0.90099063 6.79202444 Na 2.81736946 7.77573043 10.93550702 -0.67602429 7.50729466 0.18925595 Cl 5.40522110 7.76434327 10.86679462 9.54224270 -0.31471494 -4.19535931 Na 5.29350589 11.22354548 10.83950480 -1.30461992 -2.50216244 -5.19803431 Cl 8.56059745 10.94580475 10.62738175 -4.70953575 -0.76278169 -4.04189685 Na 5.46112630 8.14863552 13.67283298 -5.30438964 5.48808165 -7.51014795 Cl 7.78781371 8.04962061 13.38437595 3.50730340 3.25433959 3.17946460 Na 8.14739913 10.84015609 13.56556895 -1.52125381 -3.42159818 -2.65446990 I 10.93489704 10.76518869 13.65602794 -3.47484007 -6.30544059 -9.83282866 48 Lattice="0.0 10.8 10.8 8.100000000000001 0.0 8.100000000000001 5.4 5.4 0.0" Properties=species:S:1:pos:R:3:vel:R:3 pbc="T T T" Na -0.14717475 -0.17203063 -0.18465648 0.99223807 -0.24861345 3.08857489 Cl 2.74769063 -0.11664325 0.13486511 -8.57045356 2.96066073 10.35851930 Na 2.70474009 2.74422374 -0.16799787 -3.18900260 -5.49585446 -4.56431633 Cl 5.37509291 2.90881313 -0.03452017 5.53437003 4.58677479 -1.29635387 Na 3.00479198 -0.11123120 2.78468015 -4.77035357 7.75564036 -4.25855119 Cl 5.33573513 -0.27182984 2.74669298 -2.98715097 -7.53222558 -1.23573356 Na 5.70931413 2.61706655 2.34195816 0.00983029 6.71266524 5.19889697 Cl 8.04156860 2.63379216 2.86199141 2.84991735 -6.50927171 -1.07615166 Na 5.17393024 -0.07740361 5.38538126 -1.14823380 -2.05969467 -0.77230419 Cl 8.06170222 0.01396982 5.21161959 3.71132494 5.57561294 2.66658699 Na 7.95414314 2.66887334 5.83271556 1.79045190 -6.25264157 7.28278955 Cl 10.78887670 2.46490000 5.28871336 1.74933952 2.54839150 -6.80912989 Na 0.12343093 2.92155449 2.72242289 -1.69145233 5.55912182 -9.07676322 Cl 2.87833959 2.52144094 2.44713545 0.91695373 1.92086278 -10.33515685 Na 2.82808576 5.32398807 2.65066419 3.44814684 -3.94392185 9.12468671 Cl 5.37628640 5.29161959 2.51922013 -6.53228873 -9.17068281 5.17996719 Na 2.65887633 2.75792282 5.37772345 0.76424300 -2.94263506 -8.40246840 Cl 5.15544592 2.75740520 5.48761093 -5.69675088 1.95987836 -7.25850284 Na 5.52073057 5.52544054 5.81235820 -1.97752914 -3.87587264 -0.18922651 Cl 7.88555057 5.34212477 5.62415697 -8.06424354 -2.35590231 14.09799919 Na 5.38211843 2.43454167 8.19166216 5.07882489 1.54522052 -2.60112596 Cl 8.36886718 2.42241589 7.95218425 -3.30790475 6.78155042 1.26538385 Na 7.96512564 5.06235444 8.06627937 -0.37599889 1.35650731 3.49946841 Cl 11.01074978 5.39075036 8.33999507 0.50763861 3.57858423 19.21899426 Na 0.01914125 5.38866204 5.51284436 -7.23070495 -1.51104012 4.50726377 Cl 2.63018683 4.93698708 5.58978472 5.45514517 1.07906956 -5.66529448 Na 3.12951669 8.54883299 6.08508674 -7.85675304 5.70942095 -2.77470733 Cl 5.33992411 8.25882694 5.20683305 0.73692747 -14.86722762 12.56731128 Na 2.58518717 5.26980972 8.29437552 -1.27761278 -6.43127057 4.37674184 Cl 5.36986064 5.15283830 7.95040696 -2.76520154 -4.68115804 3.40928475 Na 5.04907944 7.80679609 8.33533827 1.63458979 -12.96021724 9.66978090 Cl 7.98410710 7.99644635 8.35551666 8.86310157 -8.20633237 -1.54698879 Na 5.48637877 5.30486948 10.37034308 5.41460058 -1.15294775 -2.24043650 Cl 8.01841836 5.13139608 10.85136829 -2.85248443 0.90333477 -0.58439352 Na 7.97701205 8.13635729 10.62986455 -5.33024867 -1.85562368 -6.58430414 Cl 10.57876585 8.09465888 10.69721906 -2.10241287 -8.44027057 3.43911812 Na -0.23940870 8.27013030 8.29532925 -5.78941921 -1.70343072 -2.05033662 Cl 2.69290867 7.99684772 7.90016092 -2.91831949 -1.52393336 -2.92236102 Na 2.70660910 10.76741313 8.42236317 -3.84980767 6.13795398 -2.26171903 Cl 5.55333689 10.48093989 7.91320047 1.30816409 2.84737238 1.62948917 Na 2.80459983 7.83355530 11.06527737 -4.54162878 1.73780279 2.36568678 Cl 5.19836759 7.72828333 10.96528546 -8.08041018 -1.46271331 -3.41077541 Na 5.46022860 11.04760116 10.84684474 3.67088847 6.95301533 -4.10064358 Cl 7.86171745 10.67847185 10.75954136 0.34505410 -0.30980106 -1.41626054 Na 5.46704316 8.06064924 13.53156608 -4.16703762 5.21368055 -5.27846572 Cl 8.38555372 8.44812331 13.23134900 -2.17066736 -2.44391217 -3.41541158 Na 8.27390968 10.66747874 13.86310642 -8.17749948 1.18855755 -5.43681998 I 10.75534477 10.57087570 13.59432795 -0.91025015 -2.82320976 6.65456076 48 Lattice="0.0 10.8 10.8 8.100000000000001 0.0 8.100000000000001 5.4 5.4 0.0" Properties=species:S:1:pos:R:3:vel:R:3 pbc="T T T" Na -0.16831201 0.19228186 0.26832202 0.39417952 8.66966645 5.24113598 Cl 2.74383912 -0.21418773 0.40103549 1.90216004 -0.01411297 -7.75524208 Na 2.59980320 2.47553036 0.13534092 4.87475479 -1.60208533 -2.54023763 Cl 5.07351610 2.51467213 0.08121850 2.90067713 0.99402500 -8.69538210 Na 2.72414969 -0.18169812 2.47609238 -1.01515003 -3.31001128 -8.23249720 Cl 5.53573735 0.05230077 3.03230948 -4.65185129 3.58633061 1.38103741 Na 5.42870191 2.65369627 2.70795313 -3.85182457 -2.45636653 10.39167034 Cl 8.00290047 2.76815016 2.18725715 6.20605067 -0.86239603 -6.01752237 Na 5.61767475 -0.24699158 5.10082976 -2.79635024 3.86996665 2.00408943 Cl 8.10638212 -0.02774952 5.25082915 1.82658058 -6.54301763 -3.01370139 Na 7.86361124 2.58636971 5.61837818 -3.38925019 5.16461039 -6.16126937 Cl 10.59561645 2.89195313 5.50993150 3.62281192 6.31698139 -1.90486038 Na 0.15719966 2.58775960 2.60448372 -10.13445714 1.56647236 -0.84722580 Cl 2.74886919 2.51128380 2.82813601 -6.02174475 6.00167465 4.66616311 Na 2.58509121 5.53088605 2.43237348 0.28580846 -9.69445315 11.48556822 Cl 5.59371814 5.49691947 2.60193370 4.64559374 9.04631575 -8.44722343 Na 2.63636699 2.72489439 5.44817240 -3.68305771 -5.96342764 11.41600173 Cl 5.55674701 2.70541284 5.67740234 -1.03347049 -1.98278772 1.22648394 Na 5.29190314 5.82190810 5.31247506 -3.91857710 7.87225759 1.78478577 Cl 7.81998252 5.59603627 5.59288334 -2.14218824 2.06109957 8.70559479 Na 5.57987212 2.92671885 7.95269419 5.84077681 -2.63947498 0.22395529 Cl 8.01351775 2.78734522 8.16205860 0.91752803 -7.50516488 -8.99202352 Na 8.09213940 5.63178452 8.10098997 -4.76772016 -10.62004411 2.59203310 Cl 10.71636217 5.32667785 8.06733598 3.69752114 2.51820529 7.66286657 Na 0.20358706 5.24215266 5.78898789 -4.15768905 -3.72808114 3.84661045 Cl 2.69445264 5.39622423 5.98193641 5.91744013 6.41996964 1.00539522 Na 2.54218940 8.08044371 5.42161287 -4.82659439 -1.77743804 -0.45430861 Cl 5.42057064 7.80745808 5.44175436 -4.02933007 3.81613926 0.85515724 Na 2.48660128 5.08230165 7.69214015 -6.91924519 -4.55832877 -0.03383238 Cl 5.66940540 5.48289073 8.10061590 -1.71496114 -1.06679284 -11.11052823 Na 5.41090233 8.11577132 8.13922154 -7.33517494 -1.03587390 3.75057000 Cl 8.32543462 8.13522377 8.12392175 -4.84652995 -7.91866943 1.04418274 Na 5.35475610 5.63096370 10.60411732 5.11568327 6.13107393 1.90591494 Cl 8.31295433 5.60944283 10.94278455 -7.90910560 2.58670524 -1.15162645 Na 7.87671408 8.23152196 10.79964170 -10.81681266 -0.82514593 11.02518321 Cl 10.80276251 7.83388419 10.74073970 -1.39916875 3.16205746 -0.60437247 Na 0.00744779 8.15424341 8.08160362 -6.49377464 1.32359437 -4.49176221 Cl 2.73220456 8.08131425 8.05025507 -1.90917603 -4.63785113 2.93289655 Na 2.76922410 10.94346792 8.02528032 -4.85961455 11.03149718 -2.71000549 Cl 5.10893643 10.67888497 8.20836356 5.15495565 -3.65945881 0.64135488 Na 2.90935437 7.81839455 10.96798558 3.61623747 1.39975686 2.49702650 Cl 5.43524272 8.06800623 10.71522694 9.17676945 3.59173566 -1.71031671 Na 5.55641178 10.61881803 10.82437157 -5.12185629 0.36887260 -7.09714752 Cl 8.06275914 10.90421587 10.78433596 -7.36457106 0.66719411 -2.26330928 Na 5.23732554 8.27923085 13.69969264 -2.66763061 3.05488067 5.26028357 Cl 8.09849756 8.04705067 13.47814415 -2.22113963 -5.16065037 -6.50055698 Na 8.23217733 11.10352798 13.48190622 9.30924089 1.43067309 7.19864692 I 10.76678696 10.74382426 13.39007393 -1.25002039 -2.20977479 -5.27908244 48 Lattice="0.0 10.8 10.8 8.100000000000001 0.0 8.100000000000001 5.4 5.4 0.0" Properties=species:S:1:pos:R:3:vel:R:3 pbc="T T T" Na -0.23653818 0.26749233 -0.06286101 -3.18150099 -1.10311022 0.25275784 Cl 2.81132862 -0.04185987 -0.00236007 -4.65412801 -1.34220784 -5.29878513 Na 2.94698669 2.69131621 -0.13635745 -3.98824071 -5.49037455 -3.72119999 Cl 5.64013839 2.96878888 -0.16682180 8.21614641 4.01906491 -5.25508846 Na 2.90719954 0.33315982 2.88989571 -8.49945959 0.91121226 2.14150604 Cl 5.61340190 0.07889616 2.82007092 4.25983575 6.77945853 -4.09504271 Na 5.12933911 2.50710076 2.62986612 2.42192997 6.56448261 6.31373766 Cl 8.16422950 2.49875171 2.84975916 -3.47603291 -4.99127519 -2.90288825 Na 5.40606474 0.03636986 5.30051299 -1.54761912 0.09211633 1.25432351 Cl 8.03255616 0.09747014 5.40525299 0.88961749 7.72621621 -7.14719054 Na 8.06403272 2.58821001 5.50823048 -4.04241149 -0.00082454 -0.93095365 Cl 10.74460067 2.67786647 5.46195357 -1.95219444 9.17734296 -1.04910158 Na 0.35292669 2.64714656 2.87479924 3.96978780 1.45091316 6.74833121 Cl 2.91641934 2.61104973 2.67042034 1.51652356 -0.72469140 -1.21131216 Na 3.02585370 5.62002646 2.61917758 -0.77776079 9.97785509 -5.01381725 Cl 5.50528507 5.72576585 2.60594507 -3.20236661 -4.28835765 -3.19174166 Na 2.61591333 2.44155838 5.79724757 6.85451918 1.46149029 0.85544734 Cl 5.44709895 2.50802080 5.40067646 0.38099261 -1.08882870 4.38049681 Na 5.31009638 5.23360407 5.18720174 8.27486050 9.50288704 -8.91485777 Cl 8.28113926 5.58026819 5.84075720 2.08255899 3.03067295 -1.27225269 Na 5.25394845 2.83373685 8.09180775 0.91017432 -2.91809982 3.23713780 Cl 8.12761565 2.75560485 8.06285285 6.91356358 -5.86292157 -2.34362813 Na 7.82667503 5.43882773 7.76900040 1.75583037 -8.92050095 -9.05030279 Cl 10.83469531 5.28701916 8.59408229 1.86319128 0.76771280 -6.31914321 Na -0.11857746 5.14941773 5.28793809 13.94548385 -7.15167973 3.44296944 Cl 2.69854454 5.17329199 5.41064142 -3.70185271 -2.81162989 2.32726042 Na 2.21075570 8.49751031 5.55001778 3.05619961 -3.68311713 0.56600956 Cl 5.05434448 8.48715476 5.25350010 4.36530806 0.30918287 -2.99563594 Na 2.85284787 5.51957127 8.17651873 12.61228255 -3.30368398 3.46733217 Cl 5.82677826 5.90548885 8.07479495 1.85030228 4.02342726 4.82004651 Na 5.29823088 7.85883955 8.12735025 7.72831249 -0.13069074 5.48441818 Cl 8.12778485 8.01665338 8.26787989 3.85261033 -0.18361974 3.50289886 Na 5.34076969 5.12987173 10.66528388 2.20317344 -2.38297558 -4.86073165 Cl 7.97368000 5.46080263 10.66761276 -2.01624785 1.75931611 -0.70470362 Na 7.92533909 8.27063813 10.67413201 0.10547703 -2.07827589 -10.51830784 Cl 10.84221579 8.31021904 10.93893948 -10.24454902 -0.78802682 0.48161668 Na -0.22807320 8.19274646 8.03441274 -2.44291619 6.76620811 -7.52916390 Cl 2.54149990 7.77326133 7.93816864 -1.46470709 6.30372092 -0.28973069 Na 2.18955108 10.39070214 8.13002551 -2.44494052 -4.88441074 -3.45345170 Cl 5.40404795 10.56222543 8.27819705 -5.17691274 8.71313790 1.27655918 Na 2.34636357 8.19548631 10.85496645 -3.84178198 -0.74606130 3.31309355 Cl 5.15541462 8.36588958 11.00834929 11.46296572 0.86558847 -6.04151549 Na 5.04016203 10.84215412 10.76108241 0.35248731 -5.94520919 -3.85285582 Cl 7.84381051 10.93717301 10.68097687 10.24609605 -2.67835269 -8.77372409 Na 5.52666170 8.58577239 13.54936182 -0.55586037 -2.68829727 5.33391654 Cl 8.24979119 8.44798297 13.99615534 -13.84376028 -1.48490810 -5.15617826 Na 8.47067801 11.06433171 13.63014384 8.92949946 -3.41121709 10.26123902 I 10.84676218 10.69305634 13.55032920 2.47220120 3.01513280 5.19613944 48 Lattice="0.0 10.8 10.8 8.100000000000001 0.0 8.100000000000001 5.4 5.4 0.0" Properties=species:S:1:pos:R:3:vel:R:3 pbc="T T T" Na -0.09218508 0.15579843 -0.13143712 2.44582023 10.28188175 -1.58944640 Cl 2.57959573 -0.19706206 -0.06657122 -3.24118288 3.68193065 -8.32460663 Na 2.77956823 2.98943318 0.05595063 -6.54842052 3.42339529 6.14718320 Cl 5.15194677 2.60559466 -0.41107161 2.01328040 7.73527813 -2.64327834 Na 2.84063200 -0.05873445 2.56654185 -2.05287395 1.41665600 1.96338200 Cl 5.17142536 -0.41621918 2.58697050 8.68787136 0.68042556 -0.17168208 Na 5.43103088 2.79323170 2.80492240 -0.25972766 -7.37908372 -7.31565370 Cl 8.17264074 2.68498394 2.49479553 -1.98967140 -1.23130320 -3.94252798 Na 5.27159244 0.21556769 5.80417189 6.03457338 4.89972582 2.27092351 Cl 7.98243890 0.11425084 5.33655467 8.52529343 0.15397699 -4.89763961 Na 8.12444884 2.72957589 5.42187701 3.64436891 2.59752168 4.06618131 Cl 10.93771527 2.94737894 5.60394069 -3.98861277 7.77560997 0.97567548 Na 0.06015335 2.71741760 2.48644682 -8.43124289 -1.14886570 -7.47966030 Cl 2.81308110 3.16656215 2.75352715 4.31295130 1.12051787 0.54489006 Na 2.54472687 5.40140504 2.70955970 1.03520209 -3.93301570 11.32764262 Cl 5.46187562 5.51819936 2.93060795 -2.48848628 -4.50656023 -0.99168576 Na 2.54027673 2.76879601 5.39445305 -3.13054422 -1.01917916 1.04345382 Cl 5.55613752 2.24939538 5.32095726 5.58942438 0.88834135 1.40156836 Na 5.68790663 5.34318149 5.35601898 4.80770466 2.48100593 8.21870016 Cl 8.08242642 5.30754397 5.46001751 -7.49522679 1.35761629 -0.37287199 Na 5.24227222 2.62854353 8.22136038 -1.24230766 5.05304907 -0.01901143 Cl 8.31378280 2.58107431 7.91945070 -3.51375992 -7.90390719 4.28444382 Na 7.92010615 5.38032235 7.83410947 6.75294678 -2.50128627 -3.48175650 Cl 10.57062536 5.59139729 8.42144290 -2.76253585 -3.53301566 -5.65379322 Na -0.43069739 5.65304719 5.28795643 5.28897489 4.61599293 2.32512758 Cl 2.65310322 5.77615996 5.71289781 5.17979677 -1.26966542 5.17292467 Na 2.81156284 7.85002031 5.26678222 5.38990891 -0.46914196 1.64020713 Cl 5.35381389 7.68857490 5.64842815 -0.98720856 2.52151792 7.52102167 Na 2.67269446 5.35968430 7.91881346 4.64136663 4.48746746 2.19728688 Cl 5.18341236 5.46609827 8.07812033 2.29475247 3.27100382 -5.90371138 Na 5.15222478 8.05480220 8.46676033 3.25422725 1.32059073 -1.87320678 Cl 8.45989028 8.14975685 8.42967651 4.25964167 1.16202513 -4.11987212 Na 5.66073477 5.37052266 10.99531047 1.56861506 3.02706359 2.23737369 Cl 8.04595286 5.40351198 10.70561893 0.80611895 -4.44575419 -0.01691275 Na 7.88698550 8.37363407 11.05618522 0.91074728 -2.34161345 3.21640313 Cl 10.87043659 8.21836728 10.74304345 -1.41696488 1.78511250 4.19705670 Na -0.02091286 8.47954325 8.03836156 -1.31601483 0.88761893 -1.45218906 Cl 2.89351731 8.11633304 8.21600145 2.04574510 1.29164889 -7.74649975 Na 3.08817924 10.70714789 8.10929922 0.69289950 -4.39279378 1.28114903 Cl 5.66192282 11.02120786 8.20890366 0.86973630 5.62719085 6.64069006 Na 2.64669314 8.19822357 10.77954039 -4.82391580 9.33290450 4.31469562 Cl 5.75500892 7.81535006 10.74578873 -2.61470512 0.41263066 -9.31604196 Na 5.65546223 10.75529853 10.87897106 -0.32628634 12.72759859 1.05842645 Cl 8.06397548 10.58933170 10.73850660 -6.50608042 -3.30045306 -5.85437260 Na 5.37194139 8.10892324 13.88167609 2.29839216 6.24556186 -0.70216003 Cl 8.25607202 7.87580761 13.29494100 2.48119213 3.35158053 -4.15392992 Na 8.06566333 10.47675368 14.02197015 -4.00278506 -11.70318262 7.76399836 I 10.82126968 10.97377288 13.76954594 1.22557541 0.27549927 2.74407526 48 Lattice="0.0 10.8 10.8 8.100000000000001 0.0 8.100000000000001 5.4 5.4 0.0" Properties=species:S:1:pos:R:3:vel:R:3 pbc="T T T" Na 0.15375072 -0.14981754 0.26775438 7.83132542 4.26232590 0.82789625 Cl 3.19170422 0.09441800 0.18640538 1.99469162 7.28378157 2.43014245 Na 2.62884368 3.03268117 -0.29692156 -4.17961458 9.24172208 -4.65042931 Cl 5.43163714 2.39263069 -0.18736997 3.54200238 -0.45949880 -2.72564027 Na 2.92552663 0.05610717 2.54659098 -1.17853124 -3.31637708 6.63553949 Cl 5.26348848 -0.32325338 2.75872555 -6.07809822 -1.49035714 -6.34006483 Na 5.45460523 2.24469519 2.79858906 1.30091221 0.34778571 -1.85007073 Cl 7.99940584 2.21724526 2.51159608 3.86833882 -2.66393870 -1.18474651 Na 5.22491193 -0.33070274 4.98936188 -3.48556046 4.23953500 -2.16086320 Cl 8.16005595 0.33418795 5.15714096 5.11638082 -0.63378346 0.29519768 Na 8.23145135 2.85387640 5.53163206 -3.83928805 -0.50978936 0.32245819 Cl 10.65987183 2.83138685 5.24368589 8.05690488 -1.69137442 -1.73302387 Na -0.07050796 2.78675658 2.75375181 -4.46803333 10.37223916 -5.18504487 Cl 2.98083372 2.58158931 2.61310980 -4.12559088 6.05739120 11.10370434 Na 2.79779256 5.19849478 2.72419250 -3.59300584 6.42293632 -1.16417795 Cl 5.29496619 5.43030664 2.79839947 4.91664214 1.06591236 1.92391248 Na 2.67848076 2.98622395 5.59376633 2.88188082 -6.47100080 5.03540706 Cl 5.21282654 2.97896366 5.35575727 -5.85879760 -8.88841693 -2.48884317 Na 5.67244888 5.59084095 5.58615244 8.71129592 -13.36978943 -2.30933107 Cl 8.03340023 5.41255432 5.12503519 -0.50812006 0.76394709 -10.27190356 Na 5.26095443 2.93914637 8.08060219 2.85494123 1.37533184 4.25933288 Cl 8.28836700 2.85916354 8.58793050 -0.84954181 3.17215856 7.04604829 Na 8.07124543 5.39300585 7.76968212 -7.65849966 -1.10592127 3.52297020 Cl 10.90209085 5.60254733 8.26332562 8.83362153 9.88299144 2.20780164 Na 0.01986076 4.99177078 5.38464806 2.69655771 -0.80413372 -1.40420695 Cl 2.90321887 5.17297623 5.40882700 -4.24508778 -1.32796946 1.71149298 Na 2.80536221 8.07588679 5.61852757 -0.85907473 0.31825550 2.00648085 Cl 5.36260055 7.58710406 5.07595706 0.59080005 0.50431992 -11.92778580 Na 2.24576051 5.86448900 8.21457448 -1.06297816 1.16961276 -3.35068920 Cl 5.20251953 5.64039576 8.22795849 -0.18348066 -0.39817374 -0.86718009 Na 5.62368935 8.16749947 8.03345537 -3.06729517 8.95062698 2.44276862 Cl 8.37222823 7.45624423 7.95012687 -4.84232999 -7.11387247 -3.83079868 Na 5.44160131 5.49593192 10.48618563 8.43718754 -0.26899607 -1.98794035 Cl 8.14587044 5.97545854 10.76555931 1.64648277 -5.24391967 4.09885209 Na 8.14020230 8.03861959 10.76341865 -8.56220218 -8.29885855 -2.19381148 Cl 10.77568088 8.15338880 10.81850083 -0.98292288 -2.48553027 0.49067430 Na -0.16308052 8.22860059 7.96648146 -2.06661224 0.30723479 -4.25127035 Cl 2.50343919 8.29195314 8.28475076 -2.42647428 -0.98113166 10.71871882 Na 3.02948458 10.96218813 8.21643252 3.91782278 -0.12972011 3.45513246 Cl 5.69215585 10.78579822 8.37139301 -4.63203079 4.92742785 -0.51818251 Na 2.52251988 8.52020020 11.01624395 3.28474587 1.32429506 -3.91000047 Cl 5.56087670 7.84149146 10.50761582 -5.28542795 9.75090015 -4.29302144 Na 5.25570337 10.68524832 10.66900176 1.88672561 -3.94468606 5.79332654 Cl 8.27589771 11.23565566 10.80658031 -8.10241322 -12.56375868 0.78649795 Na 5.36633718 8.31702767 13.52428499 1.66864790 -0.65803833 -3.56465272 Cl 7.70394935 7.87667269 13.36075511 -6.84358187 6.98835414 12.64495314 Na 8.44067138 10.45996884 13.27635937 -4.60104659 -5.45664096 -1.68588399 I 10.74754080 10.60104181 13.36219709 -4.80366005 -6.36141305 -2.74252574 48 Lattice="0.0 10.8 10.8 8.100000000000001 0.0 8.100000000000001 5.4 5.4 0.0" Properties=species:S:1:pos:R:3:vel:R:3 pbc="T T T" Na -0.08999931 -0.26779732 0.07797651 9.02190236 5.28261524 -2.56401925 Cl 2.49434847 -0.24605673 -0.11371223 -3.98489810 -4.57121155 -7.92301723 Na 2.74335680 2.66024318 0.06019291 1.82630722 -8.27341004 5.54699238 Cl 5.35080571 2.33148366 0.32392675 0.95014775 -4.75694072 5.56860372 Na 2.55034279 0.21504379 2.46238171 3.65090311 0.83429048 -4.90140476 Cl 5.12394240 0.24436264 2.53141808 4.91651590 0.84167656 -5.71770061 Na 5.47973681 2.87645257 2.65470358 2.92303775 2.51263670 4.81167760 Cl 8.23425563 2.40839723 2.81461955 -4.10510865 -0.30651409 1.02941062 Na 5.15530503 -0.33882779 5.44574382 -4.73936848 10.38929078 -7.93665200 Cl 8.07491333 0.16850242 5.31641092 1.11201183 0.89515777 -5.03901507 Na 8.07792380 2.40017206 5.57472325 -2.18864238 -3.90813844 -1.41374064 Cl 10.53546644 2.47847021 5.61085594 2.05884980 4.05490391 0.21981572 Na -0.20467995 2.72257582 2.98457208 -9.42365203 -4.24706018 0.82945172 Cl 2.51643615 2.44183913 3.00174715 -5.25898088 3.78630880 0.64140725 Na 2.34414446 5.37449026 2.39499929 -4.02083855 -2.36692339 3.57854191 Cl 5.62835744 6.04458282 2.15491688 -7.16285094 4.34652825 -2.31859933 Na 2.81218464 2.83531435 5.74406742 -6.63517309 2.95039951 6.98156558 Cl 5.32573586 2.53471725 5.22168403 0.44748004 -5.19520283 -2.69101861 Na 5.34655614 5.29828228 5.25565316 0.73397602 2.07583818 7.14085395 Cl 8.34168704 5.51734773 5.40294182 10.23046455 -6.67405650 -2.62392702 Na 5.29109011 2.77051409 8.39289347 0.62113348 -10.16602415 -5.15986045 Cl 7.82444657 2.52395851 8.19248404 -1.09909212 -5.73917890 -6.34466787 Na 7.82238136 5.37764087 8.30817901 -4.84004727 2.77942892 -2.45662688 Cl 11.07372698 5.44647357 8.26806024 1.07201188 -0.81010058 0.20612950 Na -0.15682938 5.28758902 5.57534589 -6.89101667 -3.89641133 -1.90993512 Cl 2.61842432 5.13986972 5.60953971 3.12039758 6.27569292 2.39825577 Na 2.39011758 8.05522563 5.02762917 -6.51011628 -9.63948038 -1.21021794 Cl 5.64447744 8.21635441 5.52173604 -2.58327235 4.23193394 1.30657150 Na 2.72739280 5.36651140 7.99304188 0.46893797 -1.89626183 3.84143448 Cl 5.72292804 5.41260795 8.25390213 4.02965015 -5.62432094 1.64259984 Na 5.19742341 8.34024434 7.85871001 9.57171821 -5.48914668 3.08113554 Cl 8.34719034 8.40647085 8.32849700 -5.04077558 8.04472574 1.56093995 Na 5.29505299 5.36652918 11.03195319 2.35344648 5.32466538 7.63787456 Cl 8.05378984 5.76007997 10.87984344 -3.24153894 -5.36503693 0.13451033 Na 7.85265943 8.10099016 10.74137895 3.16633826 1.57143380 -1.31884537 Cl 10.90634432 8.15921070 11.07328327 -6.87383829 0.85751925 2.29361053 Na -0.32030091 8.24237351 7.74261135 -4.53461615 -6.03629500 -0.17237256 Cl 2.54618553 8.03264294 8.12951816 -0.44700814 0.05727447 5.08991058 Na 2.52791225 11.14626616 8.09761564 -6.62570945 1.74333880 4.17286082 Cl 5.26004845 10.87000541 8.34907290 4.62273921 8.08777038 -3.86347474 Na 2.66842163 7.76311011 10.88962829 -19.32979207 4.42184942 3.83204796 Cl 5.24939739 7.65463806 10.69940145 6.13883856 -2.23777280 2.42547602 Na 5.63637720 10.87833460 11.15142999 6.43321878 -0.56709962 -10.23060204 Cl 8.05994697 10.73592999 10.84557480 -4.95981749 1.27895241 8.55079417 Na 5.46845442 8.33838323 13.58430609 -1.21325530 -7.87614640 3.45746003 Cl 8.22228685 7.88117785 13.39933626 -3.49548589 0.62103057 3.13907323 Na 7.95045919 10.80309986 13.49547446 -3.73954576 -2.27318239 6.24031809 I 10.80230099 10.96321116 13.55116151 -1.51648412 -2.91408834 -8.65253123 48 Lattice="0.0 10.8 10.8 8.100000000000001 0.0 8.100000000000001 5.4 5.4 0.0" Properties=species:S:1:pos:R:3:vel:R:3 pbc="T T T" Na 0.02714096 -0.19001685 0.02456546 10.10750434 1.70975039 -4.01348078 Cl 3.08028339 -0.31479950 0.04112936 -0.14859135 3.14463583 -1.21302103 Na 2.45585891 3.01353081 0.34201754 6.70129477 7.20463337 -2.43603213 Cl 5.38167232 2.59565821 -0.05109108 -4.64519986 -5.60404153 -1.44771049 Na 2.55448552 0.19677173 2.50866764 -4.94810265 2.62205352 -5.10783889 Cl 5.46308259 -0.14026224 3.11833123 2.57108503 -3.99469533 -6.68155428 Na 5.01266091 2.63353227 2.65197370 0.48971306 -1.38644791 -0.63924537 Cl 7.78967022 2.47477010 2.75053373 6.96858500 4.84549123 1.27991081 Na 5.47121052 0.10335918 5.30621104 2.72789938 -5.52722615 6.95490744 Cl 7.92006911 0.21216521 5.07888157 7.57838401 -5.21346578 4.67821013 Na 7.95595264 2.63223893 5.39125622 -2.25239690 4.23646934 -0.53719329 Cl 10.71126767 2.71842450 5.88629116 -6.42972674 -3.86678264 -7.68217471 Na -0.02496342 2.84992226 2.86112287 10.25570199 -6.10544986 -1.81947556 Cl 2.63358299 2.54022982 2.65305870 -2.30489419 0.06958203 0.41644599 Na 2.86886875 5.27262885 2.91541660 -5.55962844 12.40949652 0.81094167 Cl 5.48204283 5.23182876 3.00466463 2.65771560 -4.58510306 -5.31248812 Na 2.56081381 2.72667953 5.64614859 1.44098206 -3.98140070 6.84859608 Cl 5.57720999 2.70409895 5.09680102 2.52417760 1.42608531 -5.08766689 Na 5.23683774 5.35571053 5.64190928 3.95580612 -5.14413309 -3.11766045 Cl 8.06845495 5.49190293 5.43233891 -2.65964213 10.58307448 -3.13673332 Na 5.30216168 3.01819756 7.81051287 1.15899416 1.98000318 -6.29172109 Cl 8.37989421 2.61789994 8.24833257 -0.96615036 -4.37601725 -2.03796176 Na 7.91876693 5.10041744 8.17669897 6.79613211 12.99288396 5.61175627 Cl 10.95978791 5.48762831 7.81470945 0.79584198 1.83197759 -12.24745972 Na 0.01817777 5.51452695 5.55952320 3.08907768 -1.67087621 -2.27449507 Cl 2.73533894 5.20923729 5.21582588 -0.87326524 -7.41278657 0.29635536 Na 2.98764674 8.24633187 5.35136390 -5.92251313 -5.45867024 -1.35964334 Cl 5.27622317 7.83561526 5.60522191 -8.99355586 0.08768304 5.38356412 Na 2.64772480 5.54557282 8.04307487 -3.74711411 -4.51399773 -1.87959077 Cl 5.36688618 5.46688599 7.89300861 3.06357435 -0.46003168 0.91690569 Na 5.25633887 8.17382685 8.22245628 8.35134569 1.82358519 4.54018616 Cl 8.12827462 8.01920511 7.97374379 -3.73857215 0.18472971 6.03782516 Na 5.64590719 5.42552075 10.83055656 -5.85890713 -1.70498535 -8.38436756 Cl 8.20434394 5.23024407 10.81096904 8.81818090 -0.31596497 6.34061313 Na 8.36495164 8.09977072 10.67423355 -3.30476465 -6.34465803 -0.86055691 Cl 11.39548209 8.07408047 10.57665946 -2.48109496 0.01235875 -5.15136472 Na -0.15965338 8.09909410 8.02691268 2.96287185 2.92422195 -0.50189781 Cl 2.54785144 7.80563993 8.03987601 -5.03368570 1.96601311 1.24872527 Na 2.68627775 11.08923068 8.14743602 -4.48608706 -8.56457019 6.14575952 Cl 5.32181983 10.70577702 7.96296266 -2.18000392 6.70801652 6.55895791 Na 2.67171812 8.28834450 10.68965143 3.88468642 -7.23530638 3.35997836 Cl 5.44943571 7.87413200 11.08021998 -0.01916163 1.29858247 1.84549730 Na 5.42485367 10.48907979 10.40139153 6.43742027 0.59198327 -2.08181316 Cl 7.81828603 11.04449473 10.84897431 7.64261424 -5.06547247 -1.54548904 Na 5.40796274 7.97806653 13.13304141 -1.99905747 -1.20129562 -1.72135009 Cl 8.02115607 7.71976417 13.32316627 6.65018923 10.79780746 -1.94463390 Na 7.97837008 10.49609964 13.16530682 0.38330521 6.42818401 7.04140522 I 11.25354271 11.22937729 13.36926361 -3.92557079 11.05519238 4.32972760 48 Lattice="0.0 10.8 10.8 8.100000000000001 0.0 8.100000000000001 5.4 5.4 0.0" Properties=species:S:1:pos:R:3:vel:R:3 pbc="T T T" Na -0.15362723 -0.01459300 -0.13313060 -1.20794905 1.34874297 -9.23389499 Cl 2.45237681 -0.06186481 -0.25770691 0.25879182 -2.90028982 2.22475288 Na 2.55932994 2.92661144 0.00939517 1.98468324 3.95160437 -2.49354907 Cl 5.24206213 2.79183016 -0.08147409 -0.66989859 7.52739031 5.13301611 Na 2.78105476 -0.09179062 2.99552787 -5.92079590 4.80750013 -9.12221037 Cl 5.70671920 -0.07260338 2.76973923 -3.71429917 -0.82227047 2.64634914 Na 5.06672393 2.62107114 2.64970790 1.23321437 -0.16214545 10.21962528 Cl 7.97793343 2.68471857 2.63522289 -1.10459559 -4.54823268 -6.50890167 Na 5.35766510 0.05444536 5.32292098 1.28555797 -1.93787623 0.83833143 Cl 8.11237751 -0.34560427 5.62257895 -1.47369880 -0.41346372 0.74767165 Na 8.28298269 2.77515336 5.51379107 7.79320490 1.91774440 1.95556064 Cl 10.95802306 2.94707468 5.67412308 3.87827896 -3.70294496 0.25834110 Na 0.19855378 1.99266838 2.50159912 -4.52948980 5.08503795 4.90460720 Cl 2.59199253 2.88405076 2.51108615 -3.02186762 -4.57011675 -3.71258434 Na 3.14153262 5.66194030 2.66301838 -0.51514365 1.55388847 0.76279531 Cl 5.57847043 5.64506454 2.53273425 2.40133140 -12.49400869 -7.77535206 Na 2.64832833 2.69311468 5.45452221 -4.89226414 6.35410356 0.73813020 Cl 5.03835113 2.74851349 5.29376776 -1.61719989 -3.04313223 -3.51563713 Na 5.21710038 5.33296324 5.90511018 -4.84436537 9.23540934 -0.30473402 Cl 8.23916221 5.33932544 5.51408765 0.48832654 -2.51583401 7.77299354 Na 5.40618211 2.65010124 8.33401264 -2.83373141 3.63337163 -1.84928649 Cl 8.22705120 2.55953730 7.87197559 -9.26374261 0.36469270 -3.06861091 Na 7.98502113 5.16169363 8.26590156 2.39114060 1.69764883 -6.22067856 Cl 10.73556204 5.53274444 7.92082850 10.07609049 0.44734105 -1.33513470 Na 0.07801361 5.46312584 5.48351555 -7.80059197 -6.29754533 -1.12312992 Cl 2.38073164 5.58408321 5.32473734 4.31922347 -0.76912297 7.50767897 Na 2.42339216 8.22654308 5.73742674 -1.50533693 -3.53186050 0.93513745 Cl 5.70942249 7.85074477 5.39535810 0.56940034 -1.96979567 -3.99250762 Na 3.09030629 5.28505686 8.07956289 -4.36608079 1.05787663 2.67641559 Cl 5.46628681 5.85569570 8.20029210 5.69437324 -0.04058738 1.19755839 Na 5.38593563 8.29848801 8.03058549 -7.04951485 -6.37782393 3.21656785 Cl 7.97499415 8.53359431 8.36106967 -3.79376552 -3.09650286 -5.84922270 Na 5.27521405 5.29231869 10.64245612 1.52045130 4.88768588 -2.15403483 Cl 8.06856004 5.50094915 10.90951752 -3.69665827 -0.79624108 3.05256568 Na 8.16187217 8.07717877 10.68667598 8.20552180 -0.55252334 0.08771325 Cl 10.81351685 7.90519671 11.24632121 5.42556698 -0.73227478 -12.66016036 Na -0.37611506 8.16628498 7.89548017 -4.13785461 1.69592682 -0.25265759 Cl 2.58904316 8.22061858 8.26631923 2.26156872 1.51998051 8.42422031 Na 2.79961931 10.74378552 7.95135364 4.89012853 -11.64514441 -2.58890764 Cl 5.32199928 10.88648032 7.91094000 4.82743220 2.73059607 6.85308780 Na 2.68992865 8.22486536 10.67263960 -4.34359929 -1.95818990 -5.02503882 Cl 5.44891519 7.91464340 11.07953248 -1.61270388 10.95699344 -1.88833791 Na 5.30373661 10.85859491 10.87258058 -7.17270409 8.27988887 4.24934367 Cl 7.82369811 10.77534333 10.79324365 6.51924184 -0.10809403 3.09420409 Na 5.70597959 8.31414614 13.53742430 0.87140191 -0.29460772 -5.28250228 Cl 8.12303858 8.14952210 13.67794062 6.50025513 -2.09817227 3.34596970 Na 7.77290846 10.74098884 13.61389378 -3.09163087 4.66857203 0.69696510 I 10.80892000 11.12672946 13.28746947 4.40397268 1.44587779 -0.20772102 48 Lattice="0.0 10.8 10.8 8.100000000000001 0.0 8.100000000000001 5.4 5.4 0.0" Properties=species:S:1:pos:R:3:vel:R:3 pbc="T T T" Na -0.23508958 -0.07662954 -0.29427324 1.34897897 4.17159528 5.60799414 Cl 2.33988630 0.02602008 0.31912373 5.36028137 6.95234713 -0.16573942 Na 2.89863214 2.22725856 -0.09591845 -0.08511040 10.85153684 -4.02935020 Cl 5.06992361 2.59130207 0.15592229 -0.35366936 -4.09809009 5.39863195 Na 2.59947824 0.05717790 3.24064748 1.30505410 7.08536740 -4.90630997 Cl 5.38509664 -0.27402053 2.77171744 -8.96602704 6.25563538 1.10065365 Na 5.51960998 2.63864016 2.85259374 1.70712362 -0.01363692 7.72110542 Cl 7.90022444 2.90185284 2.65044291 -3.50604086 -1.30780255 -0.34508867 Na 4.88329652 0.15144427 5.04470789 0.50527041 3.36621545 -6.01443494 Cl 7.94406090 -0.18370699 5.32100226 -0.04400981 -6.15786614 1.38524620 Na 8.14960117 2.75960755 5.45676333 2.52422385 -0.73927043 -0.54067892 Cl 10.70575542 2.89040562 5.27227936 -0.11738252 1.37506788 -2.54365136 Na -0.25218014 2.58830096 2.73454869 -2.19518163 1.93824585 4.76942977 Cl 2.68881048 2.41355434 2.63946558 -4.20913596 -4.03760974 4.45850440 Na 2.58308924 5.55938758 2.54275215 5.57754895 3.86571500 0.34699733 Cl 5.12776567 5.39102670 2.79578429 0.21779497 -4.56351167 0.75213931 Na 2.56255337 3.08728508 5.42535197 6.64393346 6.36138548 3.76029273 Cl 5.00194118 2.77916909 5.79524452 -3.66625348 -1.92582463 -10.65495911 Na 5.35257144 5.20073041 5.25595950 -0.09637945 4.47339107 7.18308215 Cl 8.00620279 5.53242079 5.77512365 -1.08694814 -2.62131809 0.67985161 Na 5.39332601 2.44169054 8.20136822 3.31162324 -5.36453297 -0.63135590 Cl 8.18253418 2.60625300 8.08740193 -2.96018955 0.31269083 -1.63673727 Na 7.96969618 5.34720722 8.25834910 -1.81286029 0.15843950 -9.66436721 Cl 10.41132590 5.47866629 8.35819295 -2.30546508 -0.23116066 9.39387816 Na 0.10031211 5.34473073 5.27605059 -9.57297022 -5.26822234 -5.66851093 Cl 2.81905364 5.47487806 5.67071126 -5.43761583 0.73091303 0.06732438 Na 2.71368207 8.04264384 5.23132535 -4.31470075 -6.48389444 2.28312679 Cl 5.43166986 8.12389790 5.37252762 9.24033002 -3.31733322 0.53819123 Na 2.46275597 5.51766808 7.81020683 -3.17090724 7.74497633 -9.64181917 Cl 5.22134285 5.25373269 8.04679248 3.05127747 1.41507770 4.63527492 Na 5.38301797 8.48410606 8.04909399 6.84837226 2.42277845 2.05321541 Cl 7.65708075 8.05263016 8.14989275 11.07458997 4.58996084 2.84692160 Na 5.48631091 5.56762974 10.63449824 2.88893164 -1.58572218 2.51469475 Cl 8.38393409 5.48856044 10.97580338 8.05873530 -6.38314487 1.54717738 Na 8.09193303 8.37279348 10.81948663 3.86595487 -2.12808777 -0.04772578 Cl 10.49209970 8.01776829 10.62595584 6.55115614 -1.24375643 -6.75798010 Na 0.09325127 8.36567874 7.70256370 4.93352607 2.60273156 -1.48500352 Cl 2.23629964 7.85974017 7.96507319 -1.09574961 3.69160236 6.53324649 Na 2.78346228 10.79783059 8.27171045 5.95639218 -0.97613801 -0.58875208 Cl 5.31298015 10.71765457 7.83842141 1.00503268 2.38864157 0.29158183 Na 2.93855356 8.16365961 11.09151757 2.05314473 4.97007805 0.94187769 Cl 5.55784103 8.00660882 10.79272100 -0.03133025 -9.75852322 -4.41189844 Na 5.68183021 11.09812079 10.44298740 1.22543180 2.59316527 0.35696267 Cl 8.03603604 10.79107208 11.01828985 -3.94306865 -1.96145022 -0.02275535 Na 5.01078143 8.22431338 13.28375905 -2.90511127 5.84990770 -2.74918792 Cl 7.79314875 8.23550159 13.47229941 1.65300239 0.81781159 -7.58181161 Na 8.26406726 10.86394072 13.36754789 -6.09473064 6.08456741 -6.47689090 I 10.85499719 10.71478146 13.91317944 3.48499865 -1.06031503 -4.25906209 48 Lattice="0.0 10.8 10.8 8.100000000000001 0.0 8.100000000000001 5.4 5.4 0.0" Properties=species:S:1:pos:R:3:vel:R:3 pbc="T T T" Na -0.26000206 -0.21459781 0.15803984 1.98208748 -7.86019319 4.70007743 Cl 2.52434690 0.30036436 -0.00478589 -2.33231383 2.87866292 -1.64853915 Na 2.75518978 2.50672138 -0.31606294 4.11547736 2.68557496 -3.22743649 Cl 5.35926507 2.39046658 -0.07829953 9.32429970 2.35692076 -3.75679563 Na 2.91887716 -0.07513376 2.51225315 4.20259105 -2.43728692 -9.22782153 Cl 5.48287631 0.27230217 2.97581184 -2.26734626 4.29550573 2.35928918 Na 5.53338989 2.73439272 2.81145331 1.87380426 -3.26232235 0.33291567 Cl 8.23092687 2.74989192 2.57463755 -4.76569309 -0.46738458 4.94694974 Na 5.52391423 0.06772479 5.21997805 -5.64874640 -12.29996605 1.27657546 Cl 7.93744743 -0.10602802 5.65139812 2.86357811 -3.36984710 4.40009053 Na 8.17656143 2.76137725 5.58169978 2.44762641 -1.96989705 -3.93098700 Cl 10.64057445 2.56390712 5.44219012 5.69377010 5.82617591 -3.70885129 Na -0.07963574 2.55278235 2.51679065 -2.43626091 -1.70523260 1.38035705 Cl 2.60391929 2.75434597 2.30657663 0.35127862 -2.50518659 11.87474620 Na 2.95517327 5.54848408 2.92582857 10.84917722 -0.30940521 0.15950949 Cl 5.29149974 5.37443778 2.48733256 -0.28793667 -6.26312608 -4.61260854 Na 2.51911945 2.54613508 5.28684822 -1.14838884 5.43621253 -1.10678318 Cl 5.31856633 2.84989130 5.45012799 -2.65329999 7.12932781 -1.09888658 Na 5.64474866 5.49414361 5.35082289 1.45074587 -2.26982808 -9.63553775 Cl 8.09874383 5.22490997 5.93759613 1.65692295 -6.16218322 -6.82960732 Na 5.38267375 2.69612818 8.09711607 2.71254773 1.88564104 1.25875297 Cl 7.97065850 2.22681605 8.12773343 -9.94595007 -3.64874018 5.38891499 Na 8.23993223 5.25151664 8.41849816 5.44726264 -6.05388825 -3.40414787 Cl 10.96479044 5.26752834 8.07697976 10.67721202 2.44120621 7.33215329 Na -0.17496142 5.10703419 5.27881096 4.03232694 -1.67237147 -0.93262904 Cl 2.54025082 5.66350717 5.50350246 4.89818956 7.72498939 -4.71958606 Na 2.70369981 8.08201595 5.44955924 -5.05853548 7.58399092 -1.21983434 Cl 5.43815042 8.43077927 5.31063179 1.58902079 2.97502857 -3.54412347 Na 2.55543533 5.59902389 7.78025057 2.30672877 5.34530958 0.27624305 Cl 5.11839528 5.65688607 8.09554135 -3.61070269 3.67231743 -4.60977662 Na 5.07384333 8.21453594 7.98755244 -5.87274482 5.27146232 -5.64594160 Cl 8.20149653 7.73795109 7.93401308 6.82806691 -0.15017734 4.15140671 Na 5.43972659 5.17255015 10.97692217 3.73523922 -7.19226566 -1.48005843 Cl 7.98742244 5.18050280 10.65707608 -5.18769662 1.83513713 2.47735067 Na 8.35349490 8.18725538 10.79804379 4.85971490 -5.97721995 1.68747663 Cl 10.93062788 8.34310627 10.63263863 -1.51807832 1.17088227 1.13687800 Na 0.16404623 7.80359346 7.98665813 2.01273950 -6.71743192 3.59234332 Cl 2.62717844 8.16332116 8.19802947 -9.92081186 4.81156493 -3.80060074 Na 2.48691236 10.80964963 8.08492186 1.38955762 -0.22924872 1.92786131 Cl 5.39515525 10.98797451 8.06810090 -0.32329482 -1.77563882 0.80297772 Na 2.89567532 8.09379337 10.93918303 -6.87306886 -5.50817054 1.29923694 Cl 5.64996489 8.18496123 10.94344139 6.82998473 -2.50050753 -0.25991614 Na 5.23121395 11.00654495 10.96261121 -0.53720920 -8.30773804 3.17793981 Cl 8.55592880 11.14464949 10.92428665 -9.92007468 0.10796164 -2.70215955 Na 5.29368541 8.07691356 13.82314312 3.22297380 2.45421287 2.49098250 Cl 8.00059726 8.03449735 13.72173800 12.09401892 -2.41512541 -7.16620493 Na 7.80122803 10.82334748 13.49248005 -3.93725592 -3.07646467 -1.91800237 I 10.69247150 10.70188380 13.54180720 -2.21467231 4.15433477 2.78237456 48 Lattice="0.0 10.8 10.8 8.100000000000001 0.0 8.100000000000001 5.4 5.4 0.0" Properties=species:S:1:pos:R:3:vel:R:3 pbc="T T T" Na 0.14747041 -0.12345942 0.09258607 -0.67523486 5.60662168 -7.09073917 Cl 2.63116194 0.19591270 -0.18992843 -6.45597715 5.37229458 4.14372751 Na 2.53593262 2.86345293 -0.03181157 -3.90308727 4.25135988 -0.89382740 Cl 5.42086609 2.65572998 0.06145420 9.46808410 0.78818733 -3.29652971 Na 2.61174528 0.24578553 2.33706592 0.06750375 6.54400597 -5.17387460 Cl 5.47238914 0.00285818 2.93000758 9.36324890 -0.80233808 0.35956185 Na 5.20686863 2.92443259 2.54272181 6.91284117 -0.93576642 -5.59143977 Cl 7.98333362 2.93117273 2.51543975 1.81111604 1.23590251 -5.42123916 Na 5.38658749 0.17894509 5.33595576 1.22239634 2.14245613 2.51702873 Cl 7.99426662 -0.00825425 5.25288164 0.15905922 0.86769604 1.53147556 Na 8.20040413 2.53148615 5.49602423 -8.01109930 5.91158871 3.07325903 Cl 11.03027618 2.90548329 5.17400621 -2.95565173 -0.28203959 -9.44955808 Na -0.10926633 2.45520648 2.92416979 4.28537228 -4.88066396 -4.20101815 Cl 2.77898029 2.88707571 2.31952214 1.99979456 3.42825274 -1.10771318 Na 2.50816280 5.26033150 2.70232715 -10.50379040 9.27892806 2.06143111 Cl 5.76387166 5.31180760 2.57614367 1.02704676 5.18080641 -4.42135684 Na 2.82765502 2.76321863 5.31958167 3.94266901 -4.56176621 -8.95058146 Cl 5.52366471 2.68802914 5.41786034 -4.32453106 -3.59243758 1.79642801 Na 5.72722065 5.04351894 5.15566449 -3.38973851 -3.84747817 -5.28150980 Cl 8.21205224 5.42659290 5.29798865 -0.31992250 -3.60062510 3.70519502 Na 4.99555199 2.79706606 8.09990802 -0.42581316 -0.47417567 -2.50112363 Cl 8.28384559 2.31877734 8.28561454 -7.69159246 -5.49203021 6.58653703 Na 8.30943810 5.27633159 7.85018422 -3.96719061 -3.60242364 -3.66651543 Cl 10.59165120 5.13450138 8.19168472 -2.61862862 1.42716678 -9.37234110 Na 0.01790551 5.73787572 4.96285087 4.47573718 -3.13379156 11.28898413 Cl 2.53895014 5.37025247 5.33285196 8.30377654 -4.54935027 6.56463516 Na 2.83952486 8.08182074 5.41130379 7.14960459 -1.25347635 -3.21907405 Cl 5.18850894 8.12651850 5.13420845 3.33784213 8.87575891 -1.36528063 Na 2.65272353 5.36838404 8.14123436 2.88691086 1.06207870 0.93558147 Cl 5.52419356 5.63417468 7.68705510 5.67863274 6.15326484 -2.99355191 Na 5.67934385 7.85384385 8.07762596 -0.45040587 -1.03849180 2.48921486 Cl 8.15801887 8.19327364 8.18313983 5.22060784 6.23337147 6.58414387 Na 5.30929818 5.29385607 10.92468807 -1.63272057 -0.10368946 -3.33448261 Cl 8.20922354 5.33401736 10.98916123 0.62623214 2.13166250 -1.37765830 Na 8.23342266 8.14425427 10.63985392 -0.47884499 4.81802437 4.50305448 Cl 10.71579073 8.13118363 10.99069522 2.50430669 -1.83807561 8.84142499 Na -0.10464381 8.03095814 8.02832373 4.86490416 -4.73405713 1.41886340 Cl 2.39409543 8.12472429 8.24484321 -4.02322976 4.80300984 -9.72163014 Na 2.50169177 10.76166892 8.28908449 -6.71688347 -2.68846144 -1.23409774 Cl 5.26562362 10.57620223 8.39314998 0.00578413 13.68297552 1.91275008 Na 2.79521885 8.16617471 10.82884203 4.04348636 13.58980342 -6.10755037 Cl 5.25245465 7.98607471 10.57277895 8.01544814 -2.73125387 -1.82917067 Na 5.22273773 10.83629555 10.83890105 2.04247749 0.82907658 -1.15626416 Cl 8.27977381 10.98891154 10.70275089 -1.52844765 8.97396779 1.24906889 Na 5.27096298 8.00043528 13.14936319 -4.75347411 4.94117622 -0.22556949 Cl 8.27114321 7.98816262 13.57781243 0.69749307 -5.30753021 6.43756864 Na 8.05529144 11.00429592 13.45932950 -8.69167941 4.46146105 -5.16917071 I 10.81585680 10.54308430 13.45811519 -7.92503008 11.39737428 7.13055000 48 Lattice="0.0 10.8 10.8 8.100000000000001 0.0 8.100000000000001 5.4 5.4 0.0" Properties=species:S:1:pos:R:3:vel:R:3 pbc="T T T" Na -0.00252747 -0.28944460 0.11834852 7.63297890 -5.11072600 4.54287116 Cl 2.63175994 0.20271532 0.09501194 -1.73883546 -4.11965630 5.85542404 Na 2.72652280 2.57060175 0.22369109 4.28803327 0.49571194 3.83079264 Cl 5.30383801 2.78920341 0.28903745 -2.07025129 1.73690545 1.44285093 Na 2.89973211 -0.12639101 2.86524387 -0.15636955 -1.86421075 1.65084805 Cl 5.43380590 0.01246784 2.82397177 -2.36275971 5.19836506 3.18455417 Na 5.36805116 2.91130268 2.92610738 2.13780913 3.22019188 4.20561297 Cl 7.83577062 2.69307152 2.56859866 0.75843959 7.63215503 -1.97052291 Na 5.49078997 -0.35061592 4.94619982 -2.55674120 0.34963892 1.38835985 Cl 8.21960734 -0.03780195 5.53769568 -2.96557032 4.03414006 2.04525901 Na 8.65485543 2.95115325 5.55264796 0.05493682 -0.61234428 2.69077689 Cl 10.63254379 2.27750402 5.10707501 0.67604792 -5.80091324 0.88290141 Na -0.25904160 2.75904558 2.87188275 4.07775396 -1.62675210 -8.39818935 Cl 2.83318881 2.88686915 2.74010324 7.13796715 1.22428770 -1.47061310 Na 2.44697996 5.21820652 2.86226550 -3.09378863 7.31733797 2.48308702 Cl 5.51627260 5.41611792 2.58682655 5.94015332 -3.64024724 5.67006638 Na 2.96291843 2.63056997 5.35609179 -1.21925414 0.22046163 -2.14791192 Cl 5.21607746 2.94985162 5.13107583 -0.29573728 1.48576919 -0.53883600 Na 5.26515995 5.37981967 5.24820095 2.43128844 -8.60512537 -7.69945086 Cl 7.80993880 5.27977828 5.88136356 1.07527849 -2.89049687 2.46948383 Na 5.02428563 2.77433204 8.10698534 2.98155337 -4.28965585 4.00386944 Cl 8.43894115 2.47614411 8.00922081 9.78028718 7.24581448 1.52580814 Na 8.23571851 5.36591923 8.25502537 1.20029792 0.94929878 -0.14813296 Cl 10.83292193 5.53094306 8.15760170 -5.53722123 4.74476960 -9.47648527 Na -0.17768596 5.38281126 5.79432175 -9.36904868 -0.00195310 -1.66363922 Cl 2.66695570 5.48957929 5.02228537 -2.78068650 -4.38238877 -9.44005420 Na 2.66828455 7.96482717 5.04196325 -5.47338020 -9.02596412 4.85696921 Cl 5.45870980 8.20608436 5.51110926 1.85477003 -2.34189004 5.98461409 Na 2.97302346 5.52160026 8.16437540 0.70700627 6.84689268 0.69276257 Cl 5.65081582 5.49502958 8.41722486 -1.79022110 -4.92112680 -0.37536679 Na 5.81067646 8.22450155 7.94869050 2.78722101 2.83493935 -7.46717432 Cl 8.10934512 8.22465504 8.00386617 -4.48083269 4.64459059 -8.52133799 Na 5.15763275 5.36927700 10.33229403 -9.59307189 -10.94005883 0.31418785 Cl 7.81654530 5.49081254 10.79036109 -1.48013724 -3.33404678 7.05707523 Na 8.12002299 7.81559236 10.79387839 -3.31735251 9.10225559 -8.72320063 Cl 10.74605411 7.90006340 10.45262939 4.25319188 -5.81461965 -1.70331221 Na 0.04525564 7.95595747 8.13833198 3.03122656 -1.72273421 8.48789276 Cl 2.62263812 8.10409995 8.02608125 1.45975326 -3.52892585 -7.57711656 Na 2.90339223 10.96314933 7.82779393 -3.08106833 -2.48825481 -3.15329449 Cl 5.47866505 10.59724744 8.20857080 1.96942523 5.22697918 -10.67266608 Na 2.81552221 8.32316543 10.82712198 -8.61629943 -3.66888086 6.51901438 Cl 5.48471060 8.06281084 11.09579264 3.50784533 5.45592296 -3.23222682 Na 5.20007633 11.14992834 10.85272697 3.25410261 -1.40408770 5.23273625 Cl 8.22581757 10.66087333 10.95383098 -7.56957051 2.37173469 -6.81367460 Na 5.00246076 7.81876806 13.57113133 3.16084025 0.12414840 2.47758872 Cl 7.93948826 8.02733718 13.43414124 1.99216919 -5.44008412 -2.91343484 Na 8.11722772 10.73724918 13.46115603 4.40109694 -9.61223821 10.29550933 I 10.68253816 11.06351738 13.65289253 -6.39480110 -5.07833964 -3.60493427 48 Lattice="0.0 10.8 10.8 8.100000000000001 0.0 8.100000000000001 5.4 5.4 0.0" Properties=species:S:1:pos:R:3:vel:R:3 pbc="T T T" Na -0.08628499 -0.06409567 0.08470594 -0.11473250 4.72369576 -2.83157264 Cl 2.40803351 -0.04481752 -0.28270021 2.42889841 3.93857782 -5.56476739 Na 2.95997783 2.90365940 0.26751011 1.59391576 -2.68047962 -2.20113211 Cl 5.29512199 2.79485814 -0.08868940 -1.16596792 -0.45003530 1.54668980 Na 2.65861607 0.00436362 2.57402146 -3.69141891 6.34334101 -2.53220416 Cl 5.63216274 -0.12000880 2.62969145 -2.49485717 1.43010652 -8.65587592 Na 5.14872321 2.77446166 2.51496150 7.78498302 -1.47690441 -1.73586715 Cl 7.98454814 2.40244553 2.85817662 -0.37548937 -6.22945364 -5.45431142 Na 5.04216402 0.12395605 5.47561036 11.89525779 -5.76575166 -3.04710141 Cl 8.11730336 0.10428525 4.85540012 -0.53859273 3.20486143 11.01364966 Na 8.02452342 2.63716812 5.29910210 4.01122330 3.73854827 0.79634232 Cl 10.68072305 2.85998038 5.90052868 4.99085123 1.13028307 5.12630200 Na 0.21695063 2.52406776 3.06158276 0.88274800 3.59739434 -4.02541110 Cl 2.77233674 2.45322113 2.68839813 -5.50923187 1.74384617 10.18189380 Na 2.81682925 5.32060293 2.62706865 3.81192335 2.64854952 -1.55010150 Cl 5.60619177 5.25661453 2.97945929 4.91881828 0.99434467 -0.41448727 Na 2.75555209 2.94086632 5.18055473 2.62623833 -0.50174722 -1.64958311 Cl 5.64157273 2.85757593 4.88766184 -8.70941059 -3.58988082 9.08871387 Na 5.20074797 5.54393641 5.13104936 -3.44990906 -5.91595572 -5.50382562 Cl 7.58860039 5.56867683 5.31916181 3.36285195 -0.73489984 -4.92144086 Na 5.32620375 2.67973991 8.38533184 0.79195293 0.64966338 -6.20343383 Cl 8.39618682 2.76301743 7.97805272 -0.28585167 0.38668378 -5.77943524 Na 8.21244875 5.07543186 8.02088304 1.16838759 7.19157959 -1.92222516 Cl 11.15242025 5.57185443 8.36137524 1.42688043 2.64597621 -3.32657801 Na -0.24134096 5.21281159 5.31176374 -3.00200796 -2.59067154 8.48524215 Cl 2.65386813 5.78396562 5.20353443 -1.59525284 6.97554752 0.77767860 Na 2.73305546 8.16048705 5.50111582 1.64726016 9.95118093 -2.64369408 Cl 5.38953493 8.52791346 5.70550865 6.42267229 4.55421934 5.57980673 Na 2.79062381 5.17306260 8.42538917 -1.39988109 -0.05457898 0.62243975 Cl 5.30140972 5.38085623 7.95317403 -5.61479794 5.12062793 2.83303035 Na 5.46520397 8.13222529 8.01387616 -3.18879878 -3.92939828 1.36802268 Cl 8.34766396 7.81574752 8.16939819 -0.64010584 1.51122586 9.23095442 Na 5.62354603 5.38974700 10.65725223 -0.78746604 -0.90840248 -4.14974493 Cl 8.17303503 5.30011869 10.80063268 3.68907006 1.58999270 -4.83921507 Na 8.33217687 8.08419989 10.77782916 -3.94591947 0.49341799 5.37354364 Cl 10.82219222 8.09984050 10.72052968 -2.39574135 -0.73742719 2.17924716 Na -0.19325244 8.06840790 8.02841889 -3.52325917 2.19005361 0.10255338 Cl 2.68510055 8.04049271 7.80440789 7.73796326 5.55557662 -2.21539268 Na 2.74178622 10.67767950 7.83986052 3.47938001 -2.12483934 10.76739532 Cl 5.67630638 10.28456072 7.79045280 0.61701000 1.73413974 3.29308559 Na 2.56971079 8.08520777 11.02325931 -1.92562184 0.01766082 -2.40707672 Cl 5.43654034 8.07693779 10.78721415 -6.97183878 1.17805502 -1.05132867 Na 5.45673305 10.66375750 11.01758972 4.19328970 0.31214728 -2.38721166 Cl 8.02951811 10.74807123 10.75615393 -8.11203577 6.86121401 -7.56015793 Na 5.75694844 8.01819621 13.54566691 -0.62239572 1.96518650 12.46311134 Cl 8.06791188 7.95757556 13.19569274 -4.20181586 0.51332517 -2.33267363 Na 8.30280654 10.78242781 13.19246413 -4.27725465 -0.83452067 -2.01993978 I 10.78320392 10.63887587 13.46266405 1.53830143 5.76132379 7.11122078 48 Lattice="0.0 10.8 10.8 8.100000000000001 0.0 8.100000000000001 5.4 5.4 0.0" Properties=species:S:1:pos:R:3:vel:R:3 pbc="T T T" Na 0.00939208 0.34818545 -0.17686700 -4.00460956 -9.87363569 -1.84557778 Cl 2.71595122 0.09039318 -0.20349538 0.19139848 -2.23536649 8.65117656 Na 2.73696575 2.63847605 0.25859460 -4.22884960 -10.64311222 8.09739848 Cl 5.57983617 2.46611939 0.05408869 3.78593790 1.09327587 -7.90385254 Na 2.51440049 0.03451891 3.17885064 -0.96189280 -7.79062391 -0.02008519 Cl 5.45156129 -0.26511980 2.60748446 -7.73438852 1.48174349 -1.62457030 Na 5.49503213 2.46034176 2.95877407 3.87814676 -4.16567047 0.07489499 Cl 8.14869342 2.57283592 2.86025405 -1.09518604 -9.30616836 -3.69671821 Na 5.50103949 -0.06593772 5.78904707 -4.71979785 -3.64573554 -2.62499028 Cl 8.11026998 0.09326569 5.43656764 -4.65480315 0.53568188 -3.09365081 Na 8.52460674 2.93645414 5.25577343 2.61983309 -4.62951095 4.38743817 Cl 10.89907998 2.40720791 5.57793932 -1.87171792 -3.68934169 -7.87842365 Na 0.07813798 2.63274646 2.78195129 -1.81064106 -3.22105204 9.79014897 Cl 2.22022588 2.52442570 2.63570267 3.90422119 0.63049082 0.96806326 Na 2.65902629 5.58332437 2.45715072 -10.92103505 -3.64613483 -1.97487084 Cl 5.01066260 5.47493862 2.57991583 3.19799115 1.69998232 3.10207876 Na 2.56475546 2.90437076 5.46544117 0.50401486 -7.92950757 9.21077025 Cl 5.13305196 2.61037889 5.55702331 -4.51510392 2.03624392 6.54863170 Na 5.47900744 5.54050144 5.48998431 -6.81996260 2.15158232 -4.35971930 Cl 8.49552483 5.95210036 5.34604918 1.70014285 -0.84169286 6.58644859 Na 5.08156267 2.51362930 8.20637846 -1.22641165 1.06844236 0.03529611 Cl 8.42015270 2.67818019 8.30909882 0.03390126 2.84755087 -8.82391968 Na 8.40312156 5.19334200 7.97667312 -1.09535354 -3.50289212 3.14648376 Cl 10.87950029 5.14248855 8.11980253 3.52087611 -0.94882269 3.01986693 Na -0.08084335 5.55644503 5.51732703 -1.10769054 12.59402531 -7.66161738 Cl 2.74926164 5.02297165 5.30817535 2.15467201 2.28914690 -5.35626984 Na 2.59603945 7.94451436 5.30951490 -8.53536608 2.63395138 -1.39364681 Cl 5.49447934 8.49270572 5.42794174 2.70201348 -3.90854156 -5.65221223 Na 2.80830291 5.49104042 7.98305609 8.49017958 -3.88148249 -7.97878399 Cl 5.07792026 5.16203877 8.06217162 0.22718273 3.00442989 7.23935074 Na 5.19523736 8.15560099 8.21051236 3.80700605 -7.67516796 0.34796367 Cl 8.08499481 8.27498537 7.90670675 -0.41091020 -6.00615706 9.08279370 Na 5.84511004 5.36635974 10.70324127 -2.59212465 -2.05527895 1.43864133 Cl 7.89337456 5.67542226 10.95600502 -1.50010834 1.18313610 7.37058689 Na 7.88080697 8.13492260 10.88063017 -4.38110525 0.00028318 -2.22367174 Cl 10.60968468 7.86724908 10.84658905 7.24659130 -2.12689517 -10.99887181 Na 0.08960585 8.39879371 8.11054351 -7.73503937 3.77984394 11.89555645 Cl 2.73857646 8.04402279 7.76842245 -0.20304545 -3.59061610 -1.33092633 Na 2.48332936 10.73614997 8.13412726 7.11486850 -2.23689621 5.83335551 Cl 5.56640699 10.64583350 7.97875170 -1.86306132 9.34363429 0.68321186 Na 2.81337601 8.13062743 10.84188482 -9.01718547 0.80180324 12.16859411 Cl 5.05665482 7.79569516 10.91963804 1.15148191 -1.13232463 -7.19784805 Na 5.26695242 10.96874414 10.77037230 -3.46548293 -2.07527078 -3.35633912 Cl 8.16134429 10.81454067 10.95890794 -12.48684639 -5.14224018 4.76655036 Na 5.16589665 8.47063461 13.49681586 0.67133214 0.14245972 -4.40544030 Cl 8.19876565 8.23959843 13.52552170 1.24172488 0.02872249 -0.23680316 Na 7.90589250 10.84242501 13.36053916 -1.03531788 -0.51952610 -6.75224447 I 11.01387521 10.65670423 13.69560114 -6.52149000 -10.88964123 2.10175904 48 Lattice="0.0 10.8 10.8 8.100000000000001 0.0 8.100000000000001 5.4 5.4 0.0" Properties=species:S:1:pos:R:3:vel:R:3 pbc="T T T" Na -0.05019115 -0.32139827 -0.13511171 -6.35937381 -1.79291395 -2.70685485 Cl 2.80556490 -0.00526808 0.14746332 -3.93466270 0.96836996 0.11131604 Na 2.65988460 2.77299175 0.03084128 -5.19901231 -0.81317486 -0.29745259 Cl 5.27276551 2.35992884 -0.05176088 7.53002015 -2.21128587 -2.76478432 Na 2.65480731 0.00655487 2.86087147 3.21330838 4.74532398 6.37619357 Cl 5.30801466 -0.03473875 2.97782130 -0.45060544 -1.88418336 4.51559141 Na 5.52134368 2.53382408 2.66969126 -9.45339350 5.23545866 10.98574875 Cl 7.96624553 2.65553135 2.57436177 -2.33438159 -5.46804770 -4.24361763 Na 5.16716712 0.04563486 5.56590648 10.51310793 -0.25299476 0.69901063 Cl 7.95384749 -0.21668475 5.20010051 8.86352825 -1.73414269 2.57750154 Na 8.05656772 2.37514189 5.26459257 -4.26853301 4.30711788 -2.16336183 Cl 10.90451916 2.83122739 5.49014353 1.69844274 0.75423010 4.16214172 Na 0.25779548 2.69017316 2.65216080 -3.32889439 0.14648348 -4.84790602 Cl 2.24219268 2.62893955 2.52518149 4.08023270 -9.46916610 2.56936186 Na 2.73447614 5.40260137 2.67989907 -11.27823850 -0.24521388 -6.41702208 Cl 5.60627337 5.54879386 2.72756545 5.10867650 1.97504051 3.84470369 Na 2.49181286 2.68882551 5.37752002 0.02946412 -2.24711973 0.32287875 Cl 5.20054832 2.58331502 5.46869988 -5.11749383 -2.24080143 -4.11677345 Na 5.22007647 5.42103820 5.39334160 6.49102559 -0.53401905 -3.87301757 Cl 8.13726505 5.33548802 5.48001422 10.23779768 -4.30137410 2.64875957 Na 5.56196058 2.48237443 7.72853518 14.77422267 -8.24433899 2.40064004 Cl 8.32724950 3.16546092 7.85000057 -8.36239177 -1.27113650 0.31696987 Na 8.39617993 4.99344859 7.69234384 -2.96295976 -1.01978037 -0.77192521 Cl 10.91291906 5.07046281 7.91655376 5.49291216 -2.96985036 -6.34427850 Na 0.16160249 5.48968149 5.60793024 -5.11225109 -2.36266578 0.21597017 Cl 2.40711218 5.46227955 5.14105569 7.19504525 4.84102106 0.59553016 Na 2.76907689 7.99968670 5.15331365 1.46558668 -5.36935253 5.23394724 Cl 5.50334843 8.27272206 5.26989001 -4.28293864 -11.33774623 -7.33718637 Na 3.07003880 5.28678387 8.22006074 -3.22735009 3.48900100 -0.35706915 Cl 5.51395348 5.56559111 7.76029084 -1.20674257 3.73176581 1.24414645 Na 5.12836406 8.22593575 7.95110301 7.41868050 -4.89625042 7.19740872 Cl 8.02226515 8.11326640 7.86727403 6.06330145 1.15298628 1.42380655 Na 5.04407508 5.22747224 10.55774220 2.54099494 0.78859654 5.70064385 Cl 8.00556118 5.35132033 10.70004084 2.40428891 -2.21112925 4.58310600 Na 8.23708928 7.99409100 10.98287325 1.81214892 -5.98797034 -1.70060021 Cl 10.82971121 8.20301800 10.93082703 2.09476616 3.80271349 4.80037444 Na 0.24986039 8.23417439 7.87000248 9.38039962 -4.76128150 0.62080846 Cl 2.61837711 7.96417671 8.04948920 6.21182284 -3.61391375 -4.02294259 Na 2.76595290 10.34103483 8.02666121 0.19681115 6.30072399 -2.91827144 Cl 5.34718683 10.67098182 7.73472838 -2.62413343 -0.25090759 5.91128445 Na 2.61925717 8.13484052 10.69029598 -3.28499102 9.61367160 1.13851440 Cl 5.31430012 7.93226305 10.64082036 -3.37928132 5.69735201 5.19720475 Na 5.12496941 10.60819018 10.69199828 -3.93111106 -2.07984126 1.76868604 Cl 8.11934433 10.88331742 10.79083133 1.67377065 -7.24368711 -3.56200731 Na 5.19762324 7.81086808 13.18361033 1.25670475 -4.18191386 1.08558873 Cl 8.18957501 7.88051803 13.43109338 0.45437573 -12.47030424 5.70389907 Na 8.13099383 10.91801330 13.96242857 4.10685503 3.80798495 2.67308132 I 10.76153459 11.05431881 13.61007787 -3.44556761 -8.44547241 1.77479270 48 Lattice="0.0 10.8 10.8 8.100000000000001 0.0 8.100000000000001 5.4 5.4 0.0" Properties=species:S:1:pos:R:3:vel:R:3 pbc="T T T" Na -0.26271719 -0.16153631 -0.13355967 -9.03391509 1.38267162 -5.25505833 Cl 2.71747355 -0.09590935 -0.07757429 0.77376440 -0.76239304 -0.61083683 Na 2.84470898 2.59943381 -0.02643457 2.03081177 12.38941352 1.66159659 Cl 5.43045628 2.67714959 -0.30507968 2.50395182 -4.44884032 -8.97713684 Na 2.62140112 -0.11128632 2.88075955 -1.61421204 3.36946476 2.27071770 Cl 5.29598480 0.09817646 2.28570519 -8.16794888 2.23465234 -0.25795504 Na 5.18559733 2.97353301 2.84206116 -2.56519178 6.47781058 5.26407453 Cl 8.51362718 2.61480167 2.97066241 -7.01403713 1.25818078 1.20436041 Na 5.59394460 0.16310656 5.72198451 -6.37116487 -5.64308492 0.47799660 Cl 8.26009490 0.13704503 5.63265278 6.43686189 -1.66292120 -4.73040228 Na 7.85148370 2.83853145 5.77206182 8.98670123 5.27648227 -7.74090278 Cl 10.60220141 2.23379126 5.28626112 1.55664701 3.45966696 -6.32658176 Na -0.03550030 2.82111436 2.79463980 -4.82614277 5.79695730 -3.54831679 Cl 2.72149854 3.02519505 2.68229624 2.68197724 -1.10094133 -0.86015385 Na 2.77584392 5.11100022 2.66218744 3.84425027 -2.40375957 12.04276226 Cl 5.48178215 5.41835329 2.88377299 5.58439871 3.12359644 -2.99603203 Na 2.91198183 3.00251810 5.53196714 1.03758057 1.65534190 6.67910491 Cl 5.64723686 2.56090150 5.21987553 -1.20804261 -0.00302065 11.41026511 Na 5.44603795 5.67296054 5.29897934 -9.74303606 -0.22206043 -0.51914527 Cl 8.23381762 5.41030193 5.32774473 11.96137533 -2.61146828 4.94814826 Na 5.02368004 2.71109075 8.01326551 4.28262530 9.51166198 9.25879154 Cl 7.94679793 2.98586099 8.48026943 2.90835411 1.28412101 6.05139618 Na 8.03082200 5.48028316 8.24790999 -3.90684086 5.10742524 -7.46752597 Cl 10.75738824 5.38728000 8.25239507 -6.92556164 1.82145147 -4.46286872 Na -0.39671706 5.29969314 5.51159747 -6.93535486 1.52523714 3.18902826 Cl 3.07685159 5.19375458 5.11291585 3.96531381 -8.08159634 -1.93216834 Na 3.03583116 8.24432077 5.33956374 -0.48912267 -1.34605953 8.06871495 Cl 5.52979380 8.05043326 5.36502252 1.65455972 -3.52976311 -3.06946801 Na 3.02327512 5.35695949 7.80837786 -2.70442873 8.48392931 4.23965663 Cl 5.23292659 5.30419515 8.19963904 -7.49104128 3.07104028 -2.47864465 Na 5.23660588 8.14629902 8.01694441 1.13824437 4.26682510 3.37972233 Cl 7.82607469 8.02383591 8.37597423 -5.11847813 9.60918050 4.45462699 Na 5.42418498 5.48440066 10.61604917 9.10367248 -0.08785286 -1.31835334 Cl 8.24029831 5.32094556 10.89052988 1.90074856 5.88001400 -3.34215013 Na 8.18869212 8.02935225 10.53349516 -3.75458282 5.21635111 -2.89484749 Cl 11.08731622 8.10730433 11.06380124 6.52596842 -1.26850194 3.43779377 Na -0.21051200 7.90879143 8.30162940 0.02223762 0.13682777 2.60539243 Cl 2.63059689 8.17196189 8.12694840 1.83739950 8.13898055 -6.37894399 Na 2.93634821 10.56298187 8.03466832 0.00968493 -3.70164855 5.17274705 Cl 5.66023249 11.03505116 8.11158640 -2.53193517 -1.72144263 -5.18275566 Na 2.93471632 8.18724013 10.95183741 0.79647332 6.02416437 5.42928181 Cl 5.48381134 8.23379893 10.71358660 0.13011907 0.19568311 7.21557059 Na 5.37518982 11.16896248 10.79497800 4.27920445 -6.25317954 0.83351714 Cl 8.09163199 10.78204569 10.66125118 -1.12734592 2.36797798 -2.89525707 Na 5.22766220 7.73711263 13.31295335 -2.21445609 -1.21521757 -4.60097288 Cl 7.97662668 7.92900281 13.19601779 3.43198576 -10.30664128 -3.41571096 Na 8.24397979 11.00659209 13.42461106 1.94720490 15.78840388 2.37397198 I 10.71355152 10.90746034 13.67061845 -6.40292024 -4.44523038 2.07179101 48 Lattice="0.0 10.8 10.8 8.100000000000001 0.0 8.100000000000001 5.4 5.4 0.0" Properties=species:S:1:pos:R:3:vel:R:3 pbc="T T T" Na 0.13711577 -0.32879096 0.44552006 7.44011150 4.69055524 9.44038816 Cl 2.84797016 -0.31670896 -0.05037637 -5.84836618 2.87268045 -1.97098384 Na 2.32103342 2.28227639 0.64421327 -2.08783955 -5.46804522 5.44501421 Cl 5.33394014 2.73740938 -0.22916429 -8.05233118 7.69676081 6.31084446 Na 2.75041427 -0.21665992 2.32689086 0.67665195 -4.44151178 8.79742337 Cl 5.27636069 0.24411665 2.81120989 5.06458655 2.00194135 0.36637310 Na 5.04206643 2.63730692 3.08667669 -5.24683920 -4.45087216 4.73180305 Cl 8.07595352 2.79833863 2.35723082 -1.88052845 4.49185911 -0.98139515 Na 5.59707815 0.18785330 5.32798206 7.49854052 1.53897044 2.94580985 Cl 8.25007969 -0.10002759 4.92740502 -1.54030013 6.38555793 1.48172701 Na 8.18839832 2.83846521 5.15376324 13.20740405 -6.18482604 1.20377169 Cl 10.62610402 3.20683997 5.38201999 -9.18950052 -3.77888128 -1.69748236 Na 0.00397879 2.69382583 2.81732525 -6.58825798 -4.46803635 -1.16921429 Cl 2.73742610 2.76225343 2.67289632 -0.21529559 10.89441703 -1.65163437 Na 2.52214414 5.35985834 2.60022734 -8.58450558 2.83284393 -5.45245360 Cl 5.46554705 5.37527677 2.52346796 -3.09802498 -2.54580735 -4.58936830 Na 2.83199816 2.73312091 5.66173109 4.88150434 -2.08231477 -2.10849247 Cl 5.25483671 2.66348397 5.00514047 4.45289860 3.87733056 1.51156702 Na 5.15835109 5.37920205 5.18267860 -0.26262438 6.83279752 -0.26434971 Cl 8.00989530 5.50311112 4.95731709 -1.40487938 4.24031822 -11.68495531 Na 5.70627045 2.83112534 7.93793305 9.31519074 -0.92920437 3.40924148 Cl 8.10083205 2.90852772 8.50042548 -8.80963640 -4.61124121 1.00989896 Na 8.12029389 5.52764206 8.07499906 9.52713145 -1.08023763 1.51145951 Cl 10.76443067 5.68290008 8.11554709 -4.36703842 -0.35459563 3.28372289 Na 0.17286398 5.56385096 5.36715164 3.15796627 -4.23398654 -2.75419192 Cl 2.75573180 5.53141188 5.02490438 -8.11654600 -4.81840697 -3.67360836 Na 2.84906123 7.90557868 5.63958221 -1.50404748 -3.40707156 -5.02672676 Cl 5.49292796 8.31955174 5.82717521 -2.20856600 -8.55429948 -8.89244129 Na 2.73507373 5.14908659 7.89088332 0.18035023 0.07074334 3.12439265 Cl 5.60722104 5.54530010 7.99791372 9.10957609 5.20809797 1.93983460 Na 5.44098678 8.31204025 7.77589274 2.71676586 -2.44904652 4.94097783 Cl 8.03732569 7.93875075 8.25261508 -3.16868508 0.04412753 2.76749721 Na 5.84241251 5.65817532 10.29193246 -10.12244258 4.88459201 1.53257134 Cl 8.38044324 5.32122894 11.04714341 5.26784460 2.12220492 0.25601383 Na 8.39699704 8.35656947 10.94574242 -8.54896404 1.23606441 -2.58747668 Cl 10.96457037 7.99782443 10.51023502 3.89744572 0.26610375 -2.88927677 Na 0.22336406 8.35569264 7.82548598 3.36518623 -2.07265685 -6.80837468 Cl 2.80499443 8.19695020 7.81119082 -3.16153834 0.06713224 -1.89997179 Na 2.59183899 11.06131367 7.65225239 4.81743579 -0.81341884 7.74161678 Cl 5.55473160 10.56348893 8.28751786 -2.75977294 3.37757168 -2.90094594 Na 2.45681180 7.89147790 10.98878981 11.56333878 3.08822819 1.68758682 Cl 5.42442559 8.29074713 10.82777001 6.69767989 -3.92176335 8.14321730 Na 5.61267016 11.19191537 10.65163532 -0.68476857 -8.02065695 -2.91934971 Cl 8.26576817 11.03777095 10.39643192 -0.20205278 1.50080552 6.61101346 Na 5.29936099 7.94680798 13.51851476 -0.11017721 -8.28735406 -3.97596752 Cl 8.09459355 8.30414136 13.09197614 -7.89567295 -1.05493752 -10.74582333 Na 8.15734813 10.65714370 13.15298201 -8.03706288 -9.02013174 -9.35325692 I 10.77349158 10.76201644 13.88306628 -2.28219236 -1.00424168 -5.47144358 48 Lattice="0.0 10.8 10.8 8.100000000000001 0.0 8.100000000000001 5.4 5.4 0.0" Properties=species:S:1:pos:R:3:vel:R:3 pbc="T T T" Na 0.11028621 0.47938903 -0.12364459 2.89626747 0.41061840 2.13600381 Cl 2.94994160 -0.01405872 0.01018196 -3.67208311 -2.93326172 5.50899757 Na 2.91613852 2.93383788 0.06470921 -1.90155984 7.00460281 5.66478517 Cl 5.68823795 2.63803894 0.06134870 -1.90677720 5.67896346 -3.47061181 Na 2.36903849 -0.18277461 3.09228870 0.24617308 -0.72634632 0.06790880 Cl 5.33288454 -0.21222609 2.92929765 5.78978611 -5.79153523 0.61408377 Na 5.01433955 2.57485439 2.94551431 -8.77359734 1.22586056 5.73430852 Cl 8.01044665 2.56236828 2.75927000 -5.48340311 0.56677470 -4.75865785 Na 5.50782212 0.00730784 5.67126378 4.49874060 -5.02685335 -0.61255616 Cl 8.14240425 0.01108595 5.19793130 -0.03263881 3.35773824 3.34519732 Na 8.32980887 2.97832307 5.34512466 2.97483180 2.64466610 -0.58940963 Cl 10.91761703 2.42655626 5.10445469 -4.38725936 -13.33176894 -0.63957450 Na -0.05940043 2.95324587 2.86597477 0.88210509 3.45669858 0.77502792 Cl 2.59750781 2.58322914 2.60702431 -2.66272979 8.08970884 2.91546281 Na 2.83899256 5.20184937 2.72224595 -6.43466088 1.51255730 7.13853770 Cl 5.85399313 5.36708172 2.74523684 10.80999501 -8.48595080 -3.45895843 Na 2.91324925 2.97315485 5.44521197 -3.34316003 -5.75098182 -6.43038868 Cl 5.45665853 2.48457228 5.41779419 -4.12754592 -2.41513783 5.45150174 Na 4.91934872 5.58506413 5.34745992 1.64282349 -7.52372021 0.30442429 Cl 8.20038915 5.54796774 5.21848287 -0.61441913 -0.48478104 -1.45929953 Na 5.32027261 2.71913422 8.10922224 2.48547610 -3.06287644 3.55000616 Cl 8.29328500 2.60087352 8.00693122 2.60397609 0.14023409 4.26072248 Na 8.22104766 5.50425584 8.32674282 9.17205294 -2.77957450 8.76877877 Cl 10.72470850 5.63358704 8.19869434 -1.92549323 -12.48665879 0.51257917 Na 0.03661960 5.48566312 5.47379249 -8.17503430 -0.67761979 -4.88731887 Cl 2.85613585 5.37621179 5.14905433 -6.07901099 0.23793875 -6.95521504 Na 2.97120267 7.74337969 5.69769260 -3.47573539 1.80289255 0.01704386 Cl 5.35665004 8.13513564 5.30507182 2.43361257 -0.89088868 -2.77616781 Na 2.66152368 5.57263670 7.94895605 1.98117007 -3.12790537 -3.75627909 Cl 5.26163121 5.50066597 8.23844092 -12.44210790 4.68427686 -1.69480208 Na 5.71386374 7.90019716 8.28228385 -8.64566684 5.54949924 5.18018849 Cl 7.96367529 8.03103196 8.02890704 3.79392599 -3.95387294 2.10644045 Na 5.40395671 5.59694596 10.93056668 -1.53658808 0.60668898 2.72800115 Cl 8.12066955 4.83823727 11.10504970 2.91952246 1.88200595 2.36080157 Na 8.15729929 7.85367645 10.89133879 0.00435663 5.99611998 1.43888877 Cl 10.66828480 8.02925024 10.90102458 -6.69657050 -1.92255627 -9.00296769 Na -0.07756304 7.98083499 8.37344883 -0.84843054 2.94803042 0.51295666 Cl 2.65858017 8.24514692 8.30236532 -8.09712048 3.82281466 1.87563715 Na 2.59554181 11.14554880 7.98907037 -6.51700516 -5.06907935 -7.80420500 Cl 5.07806026 10.73479316 8.33742575 -1.29763867 -2.89809359 -2.12580412 Na 2.52898772 8.34560833 10.71777736 -4.31535101 8.26889020 -0.48936035 Cl 5.45526451 8.56264810 10.69470948 -3.22199225 1.55971788 7.49661532 Na 5.50118505 10.86746796 10.53913351 -1.00141680 3.91079544 -5.98415651 Cl 8.03308824 10.94083879 10.74045730 -9.32894554 0.81135039 7.60648895 Na 5.16255502 8.19470555 13.26806384 5.03358178 2.17352093 8.64947823 Cl 7.97362841 8.30398878 13.35402945 4.35737334 -3.91600190 -6.35186228 Na 7.77609775 10.45217345 13.51577743 -0.72730910 1.09274294 3.23555307 I 10.80488866 10.69813935 13.40969694 -0.25645839 4.80509813 -0.11166064 48 Lattice="0.0 10.8 10.8 8.100000000000001 0.0 8.100000000000001 5.4 5.4 0.0" Properties=species:S:1:pos:R:3:vel:R:3 pbc="T T T" Na -0.35376914 0.01511045 -0.22612594 -2.30621500 -8.51154456 -10.16630759 Cl 2.56971397 -0.17862313 -0.25482020 -5.94516375 -3.69100929 -3.10346935 Na 2.68776911 2.71290277 0.08202259 -3.00067340 -1.55293730 -10.14573910 Cl 5.28542350 2.53973328 0.26240704 -3.80809367 3.78014677 -3.78110858 Na 2.95493977 -0.24287152 2.76274388 3.24668905 -3.85608082 -2.89403394 Cl 5.11103572 -0.07379226 2.54615468 -0.50313620 -2.08670311 3.41890972 Na 5.47852322 2.71145877 3.11799577 -7.70192574 -6.68770369 7.55490235 Cl 8.10839426 2.69033186 2.59736922 -1.59901756 -8.90191150 -1.38035559 Na 5.38308214 -0.24309002 5.11741385 -5.52290214 1.43988018 1.37454471 Cl 7.80261789 0.07644497 5.58753460 2.41566829 -1.32659336 8.49532305 Na 8.45453561 2.87576560 5.46634382 -1.45220106 3.50840452 1.04177909 Cl 10.73879287 2.94805323 5.35687463 -2.63388910 -3.46940568 -9.07404814 Na 0.03118590 2.71961111 2.86641917 3.01085669 -2.13882465 14.41503415 Cl 3.10904108 2.63663722 2.43743342 6.28107450 4.52315621 0.45730263 Na 2.34910851 5.42041882 2.42769958 -6.22730240 2.37124984 -0.51385073 Cl 5.49635698 5.35833425 2.68162730 -2.14281892 -0.05612084 -2.11470637 Na 2.84053763 2.72073101 5.52424728 5.62603708 -6.20326148 9.56327516 Cl 5.59082299 3.10756270 5.30310976 -6.24784184 -8.08314944 -6.36036246 Na 5.44143098 5.72884843 5.30235852 3.00544033 7.75473266 -5.13069218 Cl 8.09643435 5.49378311 5.45597453 3.94108070 -3.89113088 4.16976053 Na 5.27067006 2.59118800 8.06798203 1.35527051 -2.99029818 12.29355449 Cl 8.10756234 2.90676659 7.85380777 -4.73596854 -5.79848373 -0.60897000 Na 7.85065267 5.45914411 8.52819248 0.09633459 -0.34982484 -5.41215623 Cl 10.61595955 5.38799952 8.15595678 2.21830777 -10.71494610 -9.75753558 Na -0.30625393 5.33941380 5.37079717 8.43535932 -7.32438768 -5.96261681 Cl 2.75549232 5.37209587 5.53903193 5.61898597 -2.16679914 2.63746438 Na 2.67732251 7.85334660 5.24077174 -1.67781301 -7.76727917 -1.70689771 Cl 5.30652172 8.23178121 5.31787377 2.71529356 1.09908529 -7.33831210 Na 2.73468871 5.45789235 8.30690347 3.13666270 -4.61258194 8.37109337 Cl 5.64532342 5.74399650 8.18161367 2.32786979 14.68150868 8.12328334 Na 5.46451379 8.30944550 7.73607994 -2.80228605 1.94900245 7.33137250 Cl 8.01483569 8.12490977 8.56251327 2.11820140 -3.41203237 10.05020928 Na 5.20688483 5.33074503 10.99333676 1.11738972 3.45948949 -0.54808669 Cl 7.91489962 5.51028961 10.56979764 11.06502177 -2.52713983 0.05311584 Na 8.05093925 8.06616623 10.90838394 2.29269573 -7.55747793 -7.45915091 Cl 10.75640743 8.04491472 10.82556399 3.63671803 -2.56178653 2.09967019 Na -0.49865725 8.07617727 8.43200470 -4.13880768 -9.38374724 -1.79350478 Cl 2.78825322 7.99980035 8.11709064 -1.83845342 2.13782157 -1.29363829 Na 2.81577121 10.58773300 7.89633146 -2.74860661 -2.39615316 -1.30573797 Cl 5.63186475 10.93130016 8.00178781 3.00165805 -0.95249512 -2.26910293 Na 2.73188766 8.04824680 10.71589635 0.26911033 -3.03565777 9.78342419 Cl 5.75001778 8.12583936 10.73246109 -1.66509266 2.50432523 1.82339572 Na 5.64773649 10.84422122 10.57738318 1.99465897 10.18132750 0.17970354 Cl 7.97872569 10.85410863 10.80129700 -2.08211894 -1.73580606 -2.12636526 Na 5.90419351 8.09512932 13.44798820 3.42919340 1.93377468 0.71740343 Cl 7.91045675 7.91957302 13.96071381 -1.00572207 -9.94366424 4.01109663 Na 8.02322956 10.74869473 13.27939244 5.22060188 13.24661197 -4.08537057 I 10.86727082 10.55348193 13.69959362 -6.06254941 5.24503833 -1.45626729 48 Lattice="0.0 10.8 10.8 8.100000000000001 0.0 8.100000000000001 5.4 5.4 0.0" Properties=species:S:1:pos:R:3:vel:R:3 pbc="T T T" Na 0.20909663 0.40851028 0.14704038 3.53307840 -3.76471889 0.82820727 Cl 2.61214574 0.08664712 -0.03613931 -8.03866820 7.22342073 -1.97518578 Na 2.57657302 2.72418124 0.04938823 10.02283221 0.00596222 1.05197387 Cl 5.43491036 2.50268544 -0.51485833 4.17217719 2.33109201 1.35430780 Na 2.48116231 0.16437614 2.54358332 2.95517849 3.30772229 -10.18735224 Cl 5.14869239 0.01546654 3.00876691 8.05469079 4.78341738 -1.43067798 Na 5.15445819 2.99880942 2.72156158 2.47476986 3.22640932 -0.43284327 Cl 8.05777622 2.39295921 2.68771435 4.09291660 -2.14732167 -10.62401743 Na 5.33059566 -0.06366331 5.37054481 -0.20944595 5.95868801 4.01717626 Cl 8.08417467 -0.27410316 5.38846693 -7.27626744 -4.68005948 3.29923167 Na 8.27959103 2.62474053 5.25259094 -1.67580425 5.33418095 -8.51453206 Cl 10.58263854 3.00582192 5.20674047 4.99107559 -0.90923036 -1.54963770 Na -0.12662842 2.53342608 2.66176998 2.63533605 3.10013142 7.58296411 Cl 2.89701012 2.56016610 2.86412603 -1.89844689 -2.12510279 -3.63973421 Na 2.73475588 5.24066600 2.79391449 -5.56818393 -11.80156104 0.31487833 Cl 5.46835818 5.32209753 2.58376243 -2.46119085 3.83156461 0.74895956 Na 2.60298020 2.51879863 5.49803684 1.24213334 2.13349613 7.94449502 Cl 5.36249668 2.82303930 5.44632062 -2.83355433 6.25913482 -1.23314930 Na 5.10695590 5.02604168 5.36568266 1.68617281 0.91318610 -4.47885168 Cl 7.82958569 5.49269144 5.58543212 6.72982510 -4.13413754 -1.73298431 Na 5.14584648 3.03948806 8.11253649 7.17424254 -0.23114846 5.25453984 Cl 8.16783781 2.46001284 8.40167934 0.57037867 2.38547705 2.41113480 Na 8.32843655 5.64093870 8.06306214 -2.03121342 -0.85254527 3.11982648 Cl 10.96592057 5.44009986 8.12274949 2.93251900 -0.53128591 9.93630819 Na -0.37563664 5.78625147 5.33669302 -2.37308507 -4.32045898 -1.66544106 Cl 2.60602810 5.56463929 5.38850454 11.07074085 -0.78568402 0.52359742 Na 2.41253642 8.45496400 5.48817737 -1.80456326 -1.51368953 -1.77822203 Cl 5.52755837 7.97084074 5.51585554 -5.38852622 6.08043265 -9.64796572 Na 2.47013423 5.38849188 8.34598797 -3.87527875 -0.85291384 -9.10406519 Cl 5.38391246 5.53302367 8.33297012 -1.63821072 0.58958702 0.63454057 Na 5.56621892 8.01989856 8.47925422 2.96539230 -2.61053478 -4.46724796 Cl 8.01794175 8.19819878 8.42383470 7.91398339 -10.09145724 -3.99939846 Na 5.29125192 5.43654103 10.77428151 4.12264110 1.24905427 1.38400017 Cl 7.94077383 5.40888150 10.76562380 1.45340253 -4.76062941 2.73242934 Na 7.88267350 7.70724095 10.70365367 7.44520398 6.54693469 -0.97699137 Cl 10.73298705 8.36472747 10.49887064 5.65464688 0.02978010 1.89779118 Na -0.18614778 8.08856480 8.36446097 -0.35777635 -5.00144141 0.08579656 Cl 2.83801041 8.19589610 8.02405137 0.57795990 -2.55434997 -0.46694601 Na 2.81456786 10.80247601 8.45265727 6.70991273 -4.39071324 -1.27706726 Cl 5.19494677 10.66975911 8.24557267 -4.98339373 2.97827850 -1.49213381 Na 2.82845428 7.84265871 10.72174306 -1.97273471 1.56606512 -0.12550908 Cl 5.57452869 7.97333399 11.01418773 -17.03978997 2.86526235 0.15854788 Na 5.57364782 10.86820166 10.93350921 -0.89575546 3.63974552 -0.02070483 Cl 7.85605422 10.58836030 10.45391234 2.07432965 -2.81860713 -9.88668417 Na 5.45622727 7.92506894 13.65749392 -2.13923233 1.42601344 -0.49602123 Cl 8.00739930 8.16218339 13.69520733 5.99633114 -0.88674226 -1.82336836 Na 8.12873459 10.72699489 13.76328086 -3.79888778 8.00911517 0.88059930 I 10.83628452 10.42415780 13.46561095 -6.64366448 4.01691356 1.14029262 48 Lattice="0.0 10.8 10.8 8.100000000000001 0.0 8.100000000000001 5.4 5.4 0.0" Properties=species:S:1:pos:R:3:vel:R:3 pbc="T T T" Na -0.18815829 0.20259079 0.09291694 -3.16276370 2.11126474 -11.32934779 Cl 2.84305717 0.10994712 -0.24514640 -1.15283747 8.90323528 -3.96743177 Na 2.67848542 2.61281357 0.22727434 -5.38231771 -4.33689933 -0.84101252 Cl 5.52206571 3.00887162 -0.13945778 -3.10934367 7.87947036 2.24902641 Na 2.69301456 -0.10826460 2.91463529 -1.07377063 -1.56467978 1.83361296 Cl 5.31630411 0.07673224 2.34114185 1.42449582 5.53958399 -4.20223641 Na 5.13912503 3.08948392 2.80624268 -7.02451098 -6.14498179 -5.35577198 Cl 7.97135710 2.86488265 2.79476051 0.48978848 2.88211868 -1.76362077 Na 5.57860531 0.15128049 5.40523268 8.47681552 -10.25202751 3.68252645 Cl 8.21628309 0.35634423 5.41621428 5.35510336 -4.90626979 -2.47084887 Na 8.06509840 2.48033485 5.04073961 6.99661276 1.02639029 -2.69546581 Cl 10.63152330 3.06734518 5.32892089 4.28443977 -5.72001655 3.89342167 Na -0.18587279 2.63235373 2.76893273 -8.09386967 -0.60147584 3.57486637 Cl 2.56534548 2.28697326 2.33220398 14.86104906 -0.11162441 3.02509179 Na 2.85651701 5.25984595 2.98487215 0.00697907 -9.77751142 -11.08107581 Cl 5.72894821 5.76752870 2.36801033 -6.43868794 -6.61414139 3.93991711 Na 3.06692866 2.54594798 5.03742002 0.49987570 -4.71325182 3.82084176 Cl 5.55550581 2.69409039 5.31644640 -2.13294857 -1.60510484 4.04800666 Na 5.24712882 5.59258427 4.91877574 -1.77682010 5.14918548 -1.64192937 Cl 8.14999258 5.21516484 5.58780655 5.74863256 0.95250565 1.65571587 Na 5.27021783 2.84961381 8.04931279 3.86706170 6.78693243 -8.62424636 Cl 8.15293159 2.87457537 8.10275095 -2.32580632 2.61337952 -0.28431656 Na 7.91583622 5.38017630 8.34822629 -0.63162296 -3.44103484 2.47210579 Cl 10.73647278 5.22774615 8.15268670 -4.61814651 -4.17605398 2.34005962 Na -0.38228575 5.17979987 5.33509616 1.67867897 -3.21222235 2.85086644 Cl 2.69917738 5.49064669 5.76223661 -3.42282864 -1.56355724 -5.26823868 Na 2.90459030 8.21671072 5.67205407 2.14644887 -1.31575487 3.06835410 Cl 5.54595674 8.31662102 5.26114942 -1.12315856 -4.80078416 -0.24815578 Na 3.15072308 5.73032967 8.00594789 -5.52509230 9.16352936 2.64766751 Cl 5.25920425 5.31066386 8.15187733 -9.95912631 3.42748929 11.59166229 Na 5.63617350 7.95418748 8.02924893 0.93736263 9.37264885 -6.26496548 Cl 8.22114735 8.16132277 7.86216138 0.99930078 -2.00510487 0.27146075 Na 5.56342981 5.45562375 11.03876130 4.16892264 5.72834296 3.78992422 Cl 7.93984093 5.55735829 10.56779554 7.12223788 -0.98318391 1.75189504 Na 8.34745329 8.01925692 10.61625884 6.48776483 -6.10876897 2.08495433 Cl 10.72989601 8.17343046 10.65041139 10.09648350 7.38082020 -0.59048846 Na 0.09909374 8.29165583 7.92784000 -5.83010490 2.55058329 -3.51168442 Cl 2.74623149 8.11904708 8.08545719 -4.56387274 5.00684046 -1.39709488 Na 2.72532796 10.49213500 8.23598052 6.33340262 1.69040793 -4.37132929 Cl 5.36742432 10.96464644 8.25572764 3.57621679 2.35300778 -2.61425582 Na 2.95087023 8.22085523 10.48650830 4.61201869 -1.59399848 -5.35172338 Cl 5.09668274 8.19782254 10.77645685 2.20848038 2.36223050 4.84854243 Na 5.55234448 10.87003630 11.05121682 -6.25437882 -2.69909211 2.09675990 Cl 8.31570527 10.70952908 10.79553469 0.74133492 2.71948671 -1.71294623 Na 5.98145636 8.07559658 13.51963091 1.26209414 -5.77437878 -4.02053857 Cl 7.99150998 7.97061181 13.32600270 3.81478238 -8.86517993 -9.58118427 Na 8.18033059 10.72483251 13.46878111 4.06186059 -8.91546330 -3.09539440 I 10.89043923 10.89271645 13.79801823 11.06349214 9.79679873 0.96553466 48 Lattice="0.0 10.8 10.8 8.100000000000001 0.0 8.100000000000001 5.4 5.4 0.0" Properties=species:S:1:pos:R:3:vel:R:3 pbc="T T T" Na 0.06907592 0.42164602 -0.04285601 2.81006802 -5.03767813 -0.03356707 Cl 2.72390827 0.27450222 -0.05652687 -3.37677577 -2.77116882 4.01143375 Na 2.47079515 2.42726536 -0.50924107 -8.66711058 -2.37836651 8.64675802 Cl 4.98397196 2.58265595 -0.13114780 -1.93479797 -9.37768901 1.15951879 Na 2.84062387 -0.12064820 2.71979770 -1.80178605 -4.11338023 4.73098935 Cl 5.28627553 0.12189426 2.46340418 8.00052999 2.53038047 -4.52459059 Na 5.32346201 2.65705100 2.91289523 0.56300815 -0.73359928 0.88834924 Cl 7.76737845 2.35871052 2.67699771 1.84029002 0.71308014 -6.62777807 Na 5.15013018 -0.07505642 5.19106822 -0.37892288 -3.77320980 -2.15948820 Cl 8.13687991 0.22619949 5.52934494 8.15037470 3.01376282 0.91347777 Na 8.34152971 2.79552442 5.37790056 7.03294322 -1.82608008 -4.22940645 Cl 10.70552854 2.58046969 5.37881396 5.61362819 -6.85767197 5.89692496 Na 0.08272017 2.61112945 2.47525658 -0.69683410 -6.83059625 -8.25860170 Cl 2.87287000 2.75277611 2.54552690 3.18700683 2.42038366 -7.31059985 Na 2.65927079 5.58254907 2.98515293 1.99243150 7.42329036 4.54031663 Cl 5.31578049 5.50027789 2.58618712 7.49019117 0.63251518 7.51080432 Na 2.55389361 2.37696629 5.50945234 3.12375405 -6.57560150 5.94249066 Cl 5.62942357 2.83382337 5.25129453 -5.46713010 -12.08761663 4.93528291 Na 5.45635535 5.39402038 5.44417936 -3.85332095 0.53548078 -2.99721186 Cl 8.18786865 5.29348070 5.50989282 -5.40108079 0.19468270 -5.76620225 Na 5.31918473 2.76639912 7.90213083 -7.64633555 2.09619405 -1.70193295 Cl 8.15730105 2.85666403 7.90057102 1.45965227 2.44117281 1.06226735 Na 8.14020782 5.63886597 8.13389034 -0.01637071 -3.15552741 2.94599940 Cl 10.95011112 5.33773835 8.27242431 -3.85080145 -1.50764984 -0.71829419 Na 0.02371822 5.35947960 5.43673804 10.40201515 -8.94882832 -2.35426640 Cl 3.09105481 5.47082457 5.62031905 3.39957042 1.73034782 -0.24689773 Na 2.65880937 7.44486079 5.03553560 1.25885588 -6.57595753 -2.45793092 Cl 5.15447607 8.08720885 5.46152735 0.23394068 -5.85610164 -0.58380361 Na 2.41188736 5.76088532 8.12097377 -0.84190207 9.47112247 -3.02195536 Cl 5.63200413 5.60900683 8.05253007 8.36257387 0.37263436 -9.99781334 Na 5.77830205 7.83409257 8.23179329 0.65105378 5.35103439 -9.86460660 Cl 8.45171793 7.98375340 8.24473522 -0.41634169 0.76089289 -1.04334496 Na 5.55959235 5.51053844 10.92499756 -3.84788456 5.49027194 10.43752713 Cl 7.75572817 5.55086919 10.60944728 1.54830237 3.73148482 6.32162002 Na 8.36393471 8.18005569 10.68656187 -2.39396313 8.69534284 -12.85270204 Cl 10.84557104 7.66311262 11.01028439 1.93735788 0.88860403 -9.01522896 Na -0.31441556 7.87314080 8.15410736 14.45484122 -4.51161498 1.23730934 Cl 2.79257702 8.32831487 7.89267810 3.07575827 -7.51621277 7.33764020 Na 2.64219497 10.69599063 7.78714125 4.12506215 1.46778439 -7.62631210 Cl 5.49185096 10.51905818 7.82826451 -3.86039948 -5.52443302 -0.61589593 Na 2.37620282 7.86077706 10.94640461 -0.68535074 -4.78159496 0.69700755 Cl 5.33716990 8.18903104 10.96585842 -2.04149082 -2.74463705 -1.72758964 Na 5.24100988 10.86945100 10.71012213 2.59529753 -4.50044164 3.96247630 Cl 8.22922011 10.57803719 10.91265297 2.62863818 -7.29525312 -5.12968434 Na 5.39178941 8.23134488 13.33544464 2.66293223 8.35945586 -4.12815428 Cl 8.01047984 7.92782692 13.53223291 1.23034979 -0.14726325 3.43176401 Na 8.06219523 10.58364099 13.56084055 0.04937106 -7.42604743 -2.03938667 I 10.74429945 11.17792666 13.39778632 0.71992434 -1.17569387 -3.73655217 48 Lattice="0.0 10.8 10.8 8.100000000000001 0.0 8.100000000000001 5.4 5.4 0.0" Properties=species:S:1:pos:R:3:vel:R:3 pbc="T T T" Na 0.09641861 0.13826287 0.05655301 3.15767866 1.58049163 -5.45746964 Cl 2.83797781 0.28076941 0.25658790 3.25534093 3.45457674 -6.70454126 Na 2.72232422 2.90318116 -0.21511267 -7.81867732 1.82078649 -11.80481267 Cl 5.67088452 2.94623514 -0.11390996 2.26590540 -2.26462830 5.62790030 Na 2.68309137 -0.18916780 2.72043814 2.82445252 2.37766739 -2.26752584 Cl 5.60352992 0.14227251 2.54308049 3.52400513 -6.74126577 3.12444617 Na 5.37339049 2.65944298 2.60184730 4.79750725 -0.56071175 -4.70206006 Cl 8.08617697 2.66547532 2.84981742 -7.77843601 3.62073183 -10.61038789 Na 5.39965324 -0.19350287 5.26599043 -0.46345010 -8.10365861 4.01946344 Cl 8.03058652 -0.03706503 5.03007987 1.51283539 -2.89216736 -4.51462112 Na 7.79832599 2.50662949 5.15768761 0.91678779 -1.35503578 -5.30697186 Cl 10.66709061 3.03232775 5.57783215 -5.64612175 0.65817606 1.10273840 Na 0.16768574 2.53929938 2.48158913 5.85024019 -5.14063899 6.51490734 Cl 2.76841382 2.72583580 3.10405916 5.07605367 4.09130686 -2.16379176 Na 2.97302173 5.47967029 2.65145609 -7.77679881 3.68049982 0.70106603 Cl 5.46413083 5.09050070 2.71759982 -2.04838255 -0.30985001 1.47596335 Na 2.71292825 2.27599496 5.33455985 1.12760730 5.47936283 -7.47539506 Cl 5.23465582 3.11753806 5.27156479 7.27393892 2.75886500 1.22605802 Na 5.51036349 5.48648866 5.00041857 8.73721553 3.41891300 -5.59327559 Cl 7.95378345 5.23241185 5.47917308 -2.04318107 -6.42541589 -0.11764711 Na 5.50475853 2.64514665 8.07698895 -0.54795923 2.14658115 -4.67398242 Cl 7.90167492 2.63858847 8.15718816 3.67411732 -1.57279408 1.20737859 Na 8.09898334 5.51532156 8.10654804 0.63064242 0.29430607 -0.60729198 Cl 10.90069083 5.24962751 8.23654357 4.54674016 -2.62217538 -4.04529339 Na 0.33890448 5.49758565 5.02267033 3.28315733 -4.81904998 -9.40220242 Cl 2.56421232 5.27222761 5.38399089 -4.50497555 -3.31810247 4.47866974 Na 2.60380620 8.02049696 5.18450112 -5.01046929 0.48742738 8.46242626 Cl 5.53365728 8.00705125 5.89634940 -1.14392212 0.06579560 2.57447104 Na 2.42191535 4.98267155 8.36960423 3.50040609 7.94583164 -1.26299668 Cl 5.54694165 5.37897377 8.24474610 2.43678997 -7.04961470 -12.64787684 Na 5.50311863 8.25346682 7.89186036 -2.48046242 -1.88028773 -10.23691210 Cl 8.39447683 8.32366507 8.36113146 -1.68023808 -8.83047262 0.21395354 Na 5.50886094 5.45455332 10.66387882 1.64467231 -8.18036734 -7.06838874 Cl 7.99252323 5.22297622 10.88570535 -0.50147007 -1.32846605 -4.41873219 Na 7.81261214 8.24071192 10.87010865 -0.16974262 -0.82709960 -4.68997102 Cl 10.77298697 8.05207102 10.70296959 1.35236975 -1.76374788 4.71251503 Na 0.26940418 8.27153713 8.22689319 -6.43964847 4.58406842 4.38114230 Cl 2.76426042 8.02610301 7.93130002 -7.83463065 -3.91774392 0.58696563 Na 2.40784080 10.83540849 7.94615044 0.19733983 8.95698865 2.34881762 Cl 5.28233991 10.83239287 7.97242660 0.24644181 -5.49241922 3.71833949 Na 2.95221648 7.86397569 10.84833628 -0.53577152 -4.77886058 0.68321747 Cl 5.35206578 8.03017270 10.78034432 7.59177403 4.87157926 -7.74046759 Na 5.49600228 10.57791679 10.78723716 3.96456579 0.46617990 -1.42746685 Cl 8.04524340 10.55594867 10.52708184 4.08562436 0.42163685 6.40782686 Na 5.17846978 7.83337359 13.76405857 -0.36124829 -6.07477783 -4.53101494 Cl 8.08972406 8.50905397 13.28896584 6.58695513 5.26723000 -1.90702363 Na 8.32142977 10.98624580 13.48294816 -1.38810392 3.75603862 -2.84983361 I 10.63005559 10.89903226 13.80766179 -1.89366432 7.69895198 -1.65856153 48 Lattice="0.0 10.8 10.8 8.100000000000001 0.0 8.100000000000001 5.4 5.4 0.0" Properties=species:S:1:pos:R:3:vel:R:3 pbc="T T T" Na 0.00455958 -0.27908675 -0.17644443 0.97590428 -2.06885451 4.22105747 Cl 2.89635218 -0.10552566 -0.41698365 -0.04904554 0.28102691 12.13515630 Na 2.69338235 2.66825042 0.14609438 -4.82299526 1.82971257 0.28382689 Cl 5.50636487 2.79622268 0.24210635 5.84665884 -1.82997165 12.55683156 Na 2.55755281 -0.35523928 2.55711071 3.07546291 -2.75609215 -3.85613902 Cl 4.89871067 -0.11189616 2.22811652 -1.08410732 -2.13405775 -0.27759955 Na 5.24953019 2.40963346 3.03290146 -5.97511976 1.63536113 -6.44762263 Cl 8.26574037 2.26427628 2.82373443 3.38973882 6.58656928 5.90872115 Na 5.36433359 0.05703534 5.23687446 1.15974183 1.45578699 3.05100108 Cl 8.11198958 0.25980465 5.33595388 9.27084196 8.33303866 4.75089521 Na 7.80354912 2.56825165 5.50591747 -2.50132856 -3.45227753 -1.18267065 Cl 10.64078316 2.59777905 5.42165905 -2.48223257 -7.69594403 0.44994598 Na 0.01038063 2.50322483 2.67255580 -4.61350762 -11.37263028 4.97413914 Cl 2.71261808 2.83529762 2.58694908 -3.27986940 13.79234932 6.51140862 Na 2.40882067 5.27648736 3.17965627 2.12638046 -2.57263092 6.65381051 Cl 5.55528416 5.60638914 2.51829102 -0.76610758 -2.37228724 -6.73399877 Na 2.50884850 2.86564905 5.57539752 1.37061504 0.81749352 3.93212477 Cl 5.59608537 2.78477541 5.27237518 0.36300367 6.56701151 2.95499227 Na 5.71063348 5.90701796 5.34035313 7.36041878 -2.72319838 3.19000716 Cl 7.92684983 5.30625271 5.36179614 -1.11030978 -0.63598496 0.57558980 Na 5.22685614 2.31087627 7.89548641 -0.85131522 -6.73467534 6.97840718 Cl 8.23958567 2.67787731 7.96560849 -0.10790989 2.52472252 5.17388791 Na 8.05700289 5.74485814 8.23571164 0.25181949 -2.00689531 -7.60455247 Cl 10.97447746 5.67356899 8.04629930 0.61722204 -2.88395244 2.30449043 Na 0.00405376 5.43720425 5.58781186 6.15337115 7.28488116 -0.90045685 Cl 2.71159621 5.34354043 5.19992126 -0.90646474 -4.03915918 -6.74699542 Na 2.75323692 8.34359748 5.86948769 -1.36496008 -8.66169881 5.61240807 Cl 5.40806965 8.39694284 5.38163851 0.73621185 2.37043267 -2.05070988 Na 2.71082193 5.69988422 8.21880348 0.06529194 -0.08251015 1.72963338 Cl 5.49382131 5.27125181 7.96904598 -3.53655609 -1.59391492 -3.10353050 Na 5.34278328 7.89631759 8.56656100 7.50362565 3.72979381 -4.67869111 Cl 8.19356960 8.36362401 7.98062461 4.80107064 3.16027035 5.05294747 Na 5.14325144 5.32399693 11.08226124 0.11114975 2.60300918 7.01360079 Cl 7.99566770 5.34906328 11.30255921 3.87946292 -1.40455940 -3.50285168 Na 8.04473000 8.50680071 10.74510838 -9.63413944 5.69551272 5.61452121 Cl 10.68558490 8.02789046 11.01050141 -3.82364742 -3.74053545 1.95623856 Na 0.18882597 8.06462893 8.19429419 4.09664598 -4.95526600 -2.56388657 Cl 2.76367263 7.90188175 7.87546424 -0.00098227 3.82535196 2.86074672 Na 3.04547864 11.05990338 7.96147149 -2.21248644 0.77982733 -8.06745448 Cl 5.24654346 10.62818346 7.90306486 2.40684357 5.46491240 5.55809069 Na 2.84280926 8.40153297 10.89470874 0.99515238 -7.17166497 -0.66029212 Cl 5.27218227 8.24351479 10.82537025 7.01926193 -3.95328760 -0.39190122 Na 5.21149559 10.99011392 10.96337018 11.84288959 -5.15465870 2.74024337 Cl 8.35396883 10.73888144 10.51095980 1.85598939 -1.00531361 2.79959869 Na 5.74566724 8.07571458 13.69776240 7.02710570 11.19263635 1.22419906 Cl 8.21085999 8.19264135 13.65697975 4.11991799 0.34258850 7.53339898 Na 8.28757840 10.93223993 13.48767331 3.33467974 2.07110861 12.22571592 I 10.69947292 10.61331855 13.42339378 -7.03653190 4.10197059 2.78790984 48 Lattice="0.0 10.8 10.8 8.100000000000001 0.0 8.100000000000001 5.4 5.4 0.0" Properties=species:S:1:pos:R:3:vel:R:3 pbc="T T T" Na -0.12219866 -0.19875882 0.12268195 -5.38204581 1.50368682 5.18523509 Cl 2.89009796 0.01697755 -0.00684581 0.78472589 -10.36518830 -2.62069382 Na 2.52803272 2.61157612 0.46069011 7.33054545 4.55729462 -7.74452598 Cl 5.43800154 3.06850290 -0.07062838 -10.53363385 -4.82060257 -2.04766375 Na 2.62622967 -0.25240521 2.50590994 -1.44421444 -0.86275950 6.37869783 Cl 5.12161363 -0.09566457 2.45861409 -1.95691226 0.29318536 -0.85376738 Na 5.37163291 2.69713746 2.45436786 -2.59330068 4.98499693 -2.17350621 Cl 8.06185479 2.63574891 3.04802583 -14.30918753 -1.30409797 6.96501129 Na 5.23294616 -0.02679490 5.44031272 3.36128773 6.91391374 0.95596243 Cl 8.13069098 0.32220804 4.95472940 -4.96172151 -4.81948123 2.64433926 Na 8.18114910 2.80926445 5.43875668 4.24202450 -0.31347138 -0.37325711 Cl 10.80853571 2.71452660 5.44177069 0.95769926 -4.27595484 2.03077868 Na 0.10395411 2.68751781 2.22973279 1.37263676 -8.15798868 -2.71906885 Cl 2.62357021 2.58722902 2.59750162 -3.28839836 2.56235626 -0.85004989 Na 2.89479089 5.22416879 2.70206392 -14.70644290 1.09723825 -4.27528052 Cl 5.58695693 5.50166319 3.03654546 2.26508082 -4.27813610 -5.13330327 Na 2.59583267 2.69183060 5.53349719 -5.11756593 -3.86273232 0.15773580 Cl 5.16822029 2.60751261 5.56775554 -3.96604765 1.11446088 3.21282149 Na 5.23074436 5.31436420 5.35538528 4.41233414 -4.91846038 -7.48804406 Cl 8.15447486 5.08046467 5.42955714 -1.58407226 7.04230317 2.10852058 Na 5.31130539 2.61172995 8.28022478 -2.09252198 7.14717956 -2.51507729 Cl 7.97957287 2.56368031 8.02347503 -2.77278018 -1.28735055 -1.43201400 Na 8.03355695 5.30853036 7.93286657 6.65957346 6.71284744 -1.75693693 Cl 10.44436357 5.29822759 8.23736471 -6.36415878 6.07369256 2.90901661 Na 0.20768447 5.54967572 5.59666388 0.35859022 0.53434400 3.60736205 Cl 2.68797824 5.63162463 5.55833505 -0.71545190 7.99784309 -1.24753577 Na 3.09023511 8.03358239 5.24279964 1.54352612 -6.08599493 -6.72122255 Cl 5.58080654 8.07978725 5.53896066 4.94904700 4.79054522 0.58453728 Na 2.55359027 5.17307745 8.24820355 2.22401700 -13.09680407 -3.33519419 Cl 5.18257820 5.46806395 8.18762585 -1.74948769 4.56850660 -1.66880798 Na 5.53210792 8.27265044 7.75273407 9.74053403 4.36404939 0.80678696 Cl 8.03342576 7.80256484 8.30700637 -3.88113110 -4.08569313 11.43565202 Na 5.82897921 5.30075285 10.81549241 -6.00217995 4.61281030 5.66986254 Cl 8.22548306 5.15374300 11.08982345 -4.02755736 -1.30246158 -6.82602028 Na 7.95433962 7.93389137 11.01240811 4.92133322 9.03083400 -7.08465883 Cl 11.18018834 8.23242580 11.05119928 2.14835411 -0.16762600 2.35035128 Na -0.27391189 8.21821778 7.97009728 -2.25474358 7.46853935 5.10866308 Cl 3.05105931 8.14878811 7.65891350 7.37668009 8.70995617 0.71355908 Na 2.41617158 10.78224650 8.14237371 1.22171582 -4.10168858 0.71552595 Cl 5.58297865 10.97110121 8.25397558 -1.94075944 -4.94093106 13.34550940 Na 2.84779225 7.88142322 10.78922874 1.45237373 -6.20902766 1.10700329 Cl 5.57975877 8.04003755 10.85332025 0.64185051 -1.85783760 4.33030370 Na 5.33842790 10.85730402 10.66708305 -0.26882485 -3.14399004 10.29722938 Cl 7.94863512 10.75569573 10.84489167 -5.60346358 8.69414652 -0.96092010 Na 5.23669499 8.05817310 13.17113040 5.16177698 3.98716448 2.90726505 Cl 8.02065625 8.21993273 13.52948049 -1.82119687 -0.53968439 2.73550294 Na 7.91802260 10.55753285 13.75614885 -0.95150167 3.88271581 -0.78555917 I 10.61246809 10.82370548 13.38177010 5.36927718 -6.83559446 -5.54858451 48 Lattice="0.0 10.8 10.8 8.100000000000001 0.0 8.100000000000001 5.4 5.4 0.0" Properties=species:S:1:pos:R:3:vel:R:3 pbc="T T T" Na -0.07808015 -0.06225936 -0.00347534 -5.47914568 2.56189692 1.97675448 Cl 2.97174609 -0.05885752 0.02425491 -9.83384574 0.06983648 4.14422748 Na 2.49375692 2.20305974 0.17906600 -9.51286936 1.61940842 7.54588333 Cl 5.34312835 3.10139635 0.00925458 1.49335427 -3.49988895 -7.11368554 Na 2.77139059 0.03480660 2.68858366 4.58268842 -5.84591065 -6.30958088 Cl 5.66000150 0.04656425 2.82289443 8.97789004 -6.60872218 2.75388741 Na 5.03486024 2.47496431 2.68049399 1.37090638 2.72243238 5.13575226 Cl 8.40190415 2.37207845 2.72650707 -5.90122842 2.85083649 -5.50924453 Na 5.24787488 0.15105273 5.22132460 -5.27562196 6.12684610 -6.15073735 Cl 7.93226732 0.23558392 5.46249566 0.50462438 -2.80083232 0.99633656 Na 8.30018070 2.58528345 5.07960479 -4.37809861 -8.67062709 1.39455465 Cl 10.89643338 2.77373165 5.60712492 -5.94558358 2.22208597 -0.80114095 Na -0.13868893 2.71790118 2.64058185 -2.37456891 5.63269089 1.70575099 Cl 2.85658455 2.68167558 3.17788013 2.22790986 4.91800820 -5.73843228 Na 2.83292960 5.40408431 2.71757010 -11.34341188 1.49087319 10.28846750 Cl 5.38704984 5.19999074 2.90032423 -7.29085253 -0.56095708 -3.60204435 Na 2.68708190 2.59425015 5.35022173 3.18321986 -2.08107298 2.40961691 Cl 5.72374724 2.50220569 5.50142713 -5.63628261 -0.89889953 -5.19071600 Na 5.51249658 5.34766331 5.34235319 0.24846489 0.83988326 -0.80101767 Cl 8.28446832 5.46496021 5.22586893 -1.46022421 -1.87397951 -2.58010322 Na 5.72842033 2.60035407 8.47800576 2.48625001 -2.07606865 3.65773662 Cl 7.87807167 2.45022591 8.13216236 -2.37643548 -0.58440663 -0.21520078 Na 7.80200112 5.39108909 7.78853411 1.56874041 1.09792030 4.39257501 Cl 10.97184329 5.37054046 7.85368193 -8.58249197 1.48648894 -7.08743101 Na 0.08709998 5.20865050 5.45639516 -1.34703096 -1.87702456 8.77000711 Cl 2.55079516 5.42696890 5.28585233 -4.75654460 6.35189043 -2.08608800 Na 2.45891293 8.17664348 5.51434721 -7.09704991 4.36065363 3.78817359 Cl 5.62466782 7.94994530 5.08265093 11.02360929 -5.36112675 2.54175983 Na 2.85480896 5.54472718 7.89636685 4.48585704 6.27925131 0.28787606 Cl 5.29274391 5.90203102 8.34442218 2.44247387 2.54687064 3.02178025 Na 5.35691869 7.77302533 8.23249921 1.56800409 -8.44597075 2.34521426 Cl 7.89896867 8.04885371 8.20335820 -4.75794897 1.71868564 -5.78143782 Na 5.54269804 5.65359671 10.81782238 -6.01239863 -9.03891788 -4.58080015 Cl 8.40817050 5.37983667 11.00081896 -8.80827660 -5.07161898 4.97492619 Na 8.06309392 7.96263213 10.89748651 -0.47197241 3.14909372 -3.40683835 Cl 10.80706881 8.09010748 10.78216066 -4.66241298 14.32996255 -3.55590135 Na -0.28870347 8.13901095 7.98111559 -0.16361226 -4.69631606 3.17361095 Cl 2.65170624 7.89385884 8.00564944 -4.47793000 6.51743609 4.39108761 Na 2.80593561 10.56368788 7.90807788 -6.47884555 1.83641690 3.22976423 Cl 5.65462597 10.70024431 8.15807349 3.71267757 -1.09069711 2.39119945 Na 2.89599668 8.06152446 10.63583333 5.38518701 4.00790124 0.07831780 Cl 5.47057532 8.13535762 10.66718622 0.48705817 -5.45653056 8.70087784 Na 5.56608057 10.79769667 10.92140409 0.55263875 -5.27141157 -1.54962910 Cl 8.24810111 10.86740528 10.57645987 -6.54914907 -1.67236281 -7.67551422 Na 5.18309795 7.99580245 13.25981167 -0.29407470 -9.85374979 -1.26576524 Cl 8.48277840 8.29627070 13.69354082 -0.62204826 -2.84855335 -5.13406459 Na 7.85557934 11.07450772 13.62292402 -6.55404624 6.59649494 -11.64966488 I 10.97957078 10.88402854 13.28992814 2.35360775 1.84822311 1.56230901 48 Lattice="0.0 10.8 10.8 8.100000000000001 0.0 8.100000000000001 5.4 5.4 0.0" Properties=species:S:1:pos:R:3:vel:R:3 pbc="T T T" Na -0.22383186 -0.09538653 0.39813949 -0.15915713 -1.14601688 4.46503759 Cl 2.48989605 0.02150048 0.13156115 -0.44312411 1.49571831 -2.50511517 Na 2.83182460 2.98641700 0.09448284 -0.24296030 -5.01650562 3.47540743 Cl 5.65085446 3.05522722 -0.24464726 6.46134576 -2.97287658 1.67014766 Na 2.72091773 0.44234373 2.49915142 -2.99530344 -4.58208036 -6.03141509 Cl 5.31294063 0.05788856 2.73849310 1.86051883 -12.63947677 -2.93317706 Na 5.55420257 2.55827695 2.57341301 -4.26739244 1.23459719 2.76711562 Cl 8.16134236 2.77457234 2.91738442 4.08045661 -3.13740431 -6.87647768 Na 5.47436073 -0.03460810 5.43039175 0.14598527 -4.07936097 -0.16912445 Cl 8.29700056 0.08535436 5.27781969 -8.44053168 3.83852371 4.26318294 Na 8.47212230 2.76059084 5.25026978 -3.89301418 12.05432186 0.70665103 Cl 10.99862724 2.59546368 5.55549294 -7.85916069 -0.39081681 -9.94728699 Na -0.08818238 2.51380088 2.57334577 -6.37388914 -0.31855249 -0.19256533 Cl 2.74305773 2.66807644 2.52272505 -0.25541301 4.33042311 9.64907374 Na 2.90238167 5.15753729 2.51541486 -0.21092339 -5.02707139 4.10037806 Cl 5.23292966 5.14329627 2.89077590 2.71290732 2.77979002 -3.56176946 Na 2.77513498 2.85965536 5.51608729 -3.56021431 -0.44594600 4.69640068 Cl 5.27945156 2.60865691 5.59162180 3.03160915 1.56036398 -1.93098252 Na 5.29801070 5.39290847 5.28035743 -0.64799104 4.21732740 4.75091811 Cl 7.96372328 5.43002029 5.63780979 3.46537615 -8.06047634 7.81001243 Na 5.27110182 2.82548231 7.92513626 -2.54563178 -1.52260042 -6.85942010 Cl 8.39596792 2.27153716 7.80980261 10.19083349 4.86313922 3.86527701 Na 8.14411891 5.41369395 8.03652656 1.66168896 -0.50400787 1.78308544 Cl 10.72818946 4.96025002 8.02809200 5.16530400 0.25640476 9.28228934 Na -0.08575664 5.27215645 5.19318113 -2.54169326 -10.14609122 10.30377914 Cl 2.52668683 5.45580241 5.39521885 -3.63472460 -2.02597020 3.68252891 Na 2.91051221 8.16842149 5.35779226 1.31093869 3.46246854 -4.08292355 Cl 5.74735875 8.14404437 5.54390209 4.52312994 2.81908766 -5.99215150 Na 2.67289359 5.26748108 7.77923689 1.64453291 -7.08435913 3.31446220 Cl 5.50676915 5.32671081 8.03965819 4.55271220 2.19984167 1.22319919 Na 5.56992951 8.05706613 8.30772570 -0.35997685 1.95729771 4.79105068 Cl 7.95558046 7.93676007 8.13651187 -7.21884283 -3.37529269 -7.50797557 Na 5.50131413 5.30180830 10.64825643 12.63072759 2.59766797 -3.44113999 Cl 8.00635357 5.52000784 11.09589759 -7.24277717 -4.29225065 0.12463755 Na 8.01920021 8.53151518 11.32094409 2.17180916 -1.50366269 -4.41160042 Cl 10.67845568 8.05768151 10.54519674 -5.44166183 -4.10164599 3.55778641 Na -0.05745407 8.14902891 8.54082568 -7.24951554 -9.10170075 3.97703575 Cl 2.77047972 8.25625846 7.94196404 -0.39478039 -4.63933683 -4.98642898 Na 2.72611820 10.81827847 8.33574481 -5.08066988 -7.00617143 3.10436407 Cl 5.48503973 10.92002325 8.28168856 8.68894910 -6.27490663 10.99394213 Na 2.70562249 8.03144673 11.22887743 -3.06108705 -9.75232363 -3.70413773 Cl 5.51769106 8.35945799 10.93976156 3.77071244 3.68994083 4.02977162 Na 5.69210522 10.86772148 10.85198829 6.75748150 4.96447216 1.63541230 Cl 7.89606025 10.63500436 10.85240402 -0.59849527 -2.66888099 -0.01408888 Na 5.27015130 7.91355608 13.63374543 -8.90876003 4.74145818 -0.97652140 Cl 8.11634816 8.30640374 13.49321044 -7.14524424 4.45876993 -9.29597546 Na 7.96919575 11.14152943 13.45638497 -6.28313993 11.30746841 4.08924311 I 10.93458281 10.64677741 13.40326350 -1.82766059 -5.19192932 -3.24504389 48 Lattice="0.0 10.8 10.8 8.100000000000001 0.0 8.100000000000001 5.4 5.4 0.0" Properties=species:S:1:pos:R:3:vel:R:3 pbc="T T T" Na 0.14689738 0.01808937 -0.06344675 -5.55285600 -6.94639508 1.68904150 Cl 2.79215578 0.02422319 -0.18757626 -0.08328119 -4.34396862 -3.09616672 Na 2.84274042 2.88385206 0.01633422 -1.76841508 1.80480196 -9.31052842 Cl 5.63227210 2.55992087 0.39948553 -1.41653667 5.00602058 0.90320749 Na 3.12190083 -0.09297725 2.54908104 3.21364561 1.38678365 -2.15699156 Cl 5.11850667 0.03681289 2.69412798 -2.72819832 -2.84260150 -7.61665566 Na 5.53575292 2.80109377 2.96352321 5.51837680 -5.96116998 4.10497402 Cl 7.89559273 2.64691841 2.84604742 -0.13069726 -4.34554689 2.94874458 Na 5.32031876 -0.08348416 5.54214277 -0.57980260 0.48103303 -1.97726717 Cl 8.12553060 -0.33799887 5.24057889 1.41308391 -2.59449723 3.50743464 Na 8.16310481 2.63954764 5.08465029 1.67142995 6.04322273 1.68456006 Cl 10.80271360 2.56145780 5.26411495 5.09022153 -4.80447768 -0.49811318 Na -0.04650025 2.26735208 2.78482679 2.46224551 6.90246858 2.88385459 Cl 2.54972518 3.06463538 2.72355124 2.41835787 1.06059229 -8.60672344 Na 3.19193543 5.51030103 2.79028233 3.53881178 7.18526975 1.11402122 Cl 5.85293985 5.42139179 2.59279377 -7.69592330 -2.28877298 -11.37703000 Na 2.38986591 2.49823387 5.59279429 -1.90214441 -2.08417443 -6.60879994 Cl 5.61959357 2.67503620 5.80859902 2.01849568 1.19424294 -2.50019781 Na 5.54506231 5.01422880 5.36172905 5.33441492 -6.29807503 -2.26129163 Cl 7.75422173 5.56280636 5.54923398 -0.93962915 9.32420522 1.00757719 Na 5.47414877 2.30627894 8.19848344 -2.65055392 10.91395932 1.87110298 Cl 8.24812624 2.78965802 8.04138587 -3.96278156 -4.61813384 -1.86150650 Na 7.97848176 5.17434238 8.21929576 -8.71404823 8.18025466 -7.61196549 Cl 10.56931843 5.11733515 8.10309022 -1.34297178 1.55018839 -2.60905341 Na 0.22166798 5.38302250 5.28264461 -2.77779725 -6.96747029 -0.91159272 Cl 2.81845912 5.62385990 6.04545535 1.74770736 1.61543918 1.96797881 Na 2.76947993 8.32619314 5.23754994 0.14942119 -7.33854693 -3.26980820 Cl 4.99720460 8.22884417 5.48124801 5.01798155 -2.69364706 8.28958248 Na 3.01301666 5.11252114 7.95757994 -3.16033185 -10.36294106 0.03076657 Cl 5.19993666 5.61067365 8.08158445 0.71122553 -5.66346411 2.23217192 Na 5.33266209 8.14578111 7.99945335 3.29268986 2.34251760 3.56135512 Cl 7.99513241 8.43402634 7.96441062 2.39517211 -9.92912648 -3.54843338 Na 5.26542592 5.73899346 10.72217784 15.02321882 5.62746287 -2.60472573 Cl 8.13246958 5.21817997 10.59473196 2.37694949 0.93213871 5.00886695 Na 8.30349785 8.42432106 10.96155176 -1.87942611 6.45251680 4.93527499 Cl 10.98625702 7.69011886 10.79579602 -1.97442826 7.54565629 8.91187424 Na 0.31716897 8.03456291 8.26842097 -1.19235548 0.32105011 1.51687062 Cl 3.10471779 8.22297607 8.35395913 3.99502090 9.91198931 -6.97181531 Na 2.81947522 10.72271247 7.93254858 8.29990712 1.83429369 -7.42952046 Cl 5.42966937 11.03415042 8.12339619 4.18682787 8.23205962 1.40262450 Na 2.86165131 8.20319898 10.47020040 -0.74611564 5.21839012 -1.63996096 Cl 5.14720102 7.90109464 11.05708599 4.15185871 -9.64362317 -3.10121623 Na 5.44911574 10.43834636 10.44026657 -4.69238334 1.35670507 2.78391558 Cl 8.34136652 10.60464092 10.91544584 9.51264282 -0.05771621 -0.93272733 Na 5.32016324 8.07655869 13.86116131 8.49351716 10.32825584 3.57242483 Cl 8.54864716 7.94015283 13.46385369 -7.31059075 8.55307732 -1.79960635 Na 7.78986118 11.39074278 13.73114367 -1.95844976 3.49135685 6.36419160 I 10.68093811 10.65431352 13.19520683 5.70488955 4.58401049 1.22829993 48 Lattice="0.0 10.8 10.8 8.100000000000001 0.0 8.100000000000001 5.4 5.4 0.0" Properties=species:S:1:pos:R:3:vel:R:3 pbc="T T T" Na -0.02907561 -0.00660541 0.08404108 2.82371771 -1.48601255 -8.82526329 Cl 2.67372783 0.15270806 -0.04015427 5.33241725 -3.32514400 1.74373228 Na 2.93531544 2.35630110 -0.12343824 -1.87355442 -2.88585666 -1.13042191 Cl 5.36210570 2.98891238 -0.20192756 -3.67610055 -6.44959788 4.77498394 Na 2.54711211 0.38126103 2.35492935 4.01897010 2.51281842 0.14003442 Cl 5.32079166 0.67463527 2.98858442 2.97890127 1.29903437 -1.46428395 Na 5.45654530 2.53568758 2.71995658 4.44625507 -0.28643726 -8.99921806 Cl 8.00890874 2.90326345 2.91998744 1.20474554 4.37324234 -3.36417904 Na 5.33122681 -0.17125158 5.28759976 -6.20189015 1.92240391 -0.66027531 Cl 8.14940120 -0.26542074 5.54498414 -5.18174147 -0.23674648 0.26524804 Na 7.94042869 2.70599671 5.15474176 -9.79828078 15.40170333 2.36578489 Cl 10.96342623 2.62968172 5.45568075 1.21927861 -1.96893018 -1.34760748 Na 0.32832362 2.54752774 2.95927065 1.18668380 3.85019901 -0.42002925 Cl 2.73383391 2.75116569 2.81597866 -0.20644648 -5.65964767 -5.01749858 Na 2.83075168 5.33862576 2.33440553 -0.41397382 -9.63168546 -2.14257464 Cl 5.47728213 5.47760732 2.57134827 -6.23539445 -1.51759449 3.37630663 Na 2.98002620 2.61851333 5.14374800 1.79816129 -2.12745336 0.85861233 Cl 5.27789879 2.84642696 5.64208506 3.05139371 -0.00005673 -2.72095685 Na 5.51095334 5.53159096 5.45952518 7.95350388 -8.94836596 -0.46168161 Cl 8.14643669 5.53013550 5.27498614 -0.53202639 -0.71669758 2.63114790 Na 5.45357627 2.70534833 7.99297312 -0.56077806 4.58636161 3.21369915 Cl 7.87296110 2.90372159 7.87267542 -1.23405263 -4.15334809 1.59017987 Na 8.21513106 5.46530953 8.14668298 1.64692510 12.04380535 -3.95086759 Cl 10.83619308 5.47910616 7.86416474 -4.74456829 2.68053244 -0.40130830 Na 0.40350024 5.54267880 5.27447846 -4.49514629 -6.60865629 6.59027190 Cl 2.49234157 5.55591174 5.54489171 -1.05774650 12.42603988 1.17991782 Na 2.88762213 8.36261699 5.34170399 -0.53752059 -6.89807684 5.60271729 Cl 5.25182899 7.79750049 5.08869197 0.40630882 -2.46621368 1.61102237 Na 2.70060943 5.18034972 8.46043489 1.74038104 -2.40662726 -6.77747168 Cl 5.44616384 4.86952171 7.92901932 4.87624716 0.72859373 10.86415736 Na 5.69269835 8.32190245 8.04265698 -3.74293630 -6.00994545 4.32382528 Cl 7.92613614 8.00339193 7.85024990 -1.76313428 -8.42251940 -5.96821926 Na 5.00713260 5.52525749 11.13462272 -4.97099055 -8.16179678 -3.23949524 Cl 8.41750218 5.58431651 10.71193326 -3.30623986 -8.04031849 -3.44109387 Na 8.22174308 7.90959023 10.63882559 -0.07034686 -0.62751848 2.40619624 Cl 11.26610140 8.20322255 10.76505142 -1.51544064 1.24093036 9.30296684 Na -0.23971756 7.99940457 8.23969186 3.63919782 -7.75385633 -13.95213691 Cl 2.93436735 8.49043696 8.00934237 0.63867678 -3.92460919 -11.01912758 Na 2.97737784 10.88862452 7.85988125 -1.59126010 5.27121571 3.82442100 Cl 5.24926250 11.05964597 8.06056077 13.86431254 -5.18775773 5.72030008 Na 2.32754085 7.93495510 10.94216060 6.76254418 6.78823115 -1.37870924 Cl 5.58356199 7.79186936 10.67640021 -3.82165048 -6.56097640 4.73624608 Na 5.57208593 10.55859875 10.35617066 1.52130832 2.32586257 9.16339892 Cl 7.81254492 10.68995081 10.68972817 1.53726145 9.70101178 4.34366452 Na 4.95478367 8.08375047 13.48904447 -10.96902046 5.58550941 -1.86731785 Cl 8.03352050 8.32585603 13.68923199 -12.29787991 -2.41706950 -4.52216813 Na 7.92871295 11.06926512 13.63643308 -2.47962309 -6.97529007 7.58641851 I 10.70087263 10.42121193 13.06568589 -5.26212791 -10.85660907 1.56582323 48 Lattice="0.0 10.8 10.8 8.100000000000001 0.0 8.100000000000001 5.4 5.4 0.0" Properties=species:S:1:pos:R:3:vel:R:3 pbc="T T T" Na 0.06882497 0.26622713 -0.35751735 3.25853359 -2.64142092 -8.65086828 Cl 2.91869659 -0.37052500 0.26608567 5.43954702 -0.54409829 0.21470584 Na 2.24863952 2.84933972 -0.15828741 -6.45765980 0.82899282 -5.54594983 Cl 5.45752114 2.66777921 -0.02451145 6.16008179 5.43025330 -0.44905842 Na 2.84333128 0.56049638 2.72471508 6.29014481 -3.21771246 4.34782973 Cl 5.45257559 0.33553792 2.53783343 -0.88159529 3.50764507 1.43860018 Na 5.52093862 2.90789257 2.26202538 -0.89013407 5.75782814 -4.17957199 Cl 8.46164239 2.37862662 3.00050744 -0.20202269 4.73929394 -2.47705819 Na 4.89098210 0.63651449 5.31605881 1.56193110 0.17789576 -5.95421834 Cl 7.83201734 -0.08292104 5.38741346 -3.32024956 -6.74742620 2.01175330 Na 8.05731588 3.05047482 5.47909466 -1.70321515 -2.34998377 -4.42413498 Cl 10.90691359 2.84401877 5.22610995 1.57660797 -2.55353787 0.83548822 Na 0.07340375 2.98659844 2.84416108 2.62364084 -8.72106190 -2.41764217 Cl 2.80745399 2.69391415 2.77163293 -0.53408905 3.99699638 -1.86043983 Na 2.51243117 5.39552011 2.99123438 0.05901261 0.44086731 10.13844125 Cl 5.26615140 5.38604839 2.73873798 13.48273875 -4.11623339 1.01088228 Na 2.35601630 2.97968621 5.40600350 -3.93617399 -3.54955409 -2.92267072 Cl 5.27443871 2.60317586 5.16579385 1.65309170 6.72707403 3.81009572 Na 5.49855592 5.00766549 5.49235548 6.84508358 2.18409986 -6.99996731 Cl 8.09846384 5.55773408 5.48042061 1.62213173 -5.52295389 6.06811571 Na 5.29933670 2.81966082 8.17370489 9.37015540 4.76293465 -10.89269570 Cl 7.93349784 2.93734567 7.77167743 4.92908901 1.34023479 -2.94756155 Na 8.15902354 5.24814763 8.07579275 -4.96706736 -10.19024288 -2.38115117 Cl 10.84196709 5.28709123 7.95254224 4.83768885 5.33845689 3.87533394 Na -0.10613838 5.47046109 5.60433358 -0.39645768 -9.77901378 9.54679029 Cl 2.59148141 5.38383940 5.23526922 3.92481770 1.86721266 2.93136413 Na 2.69216253 8.35073961 5.08475594 -2.94590195 -5.49576948 4.06323661 Cl 5.36965073 8.41692980 5.67079020 -3.57084165 -1.41710563 -0.61669745 Na 2.70330105 5.66361772 7.64845131 -7.80952857 -0.19648090 0.64169135 Cl 5.37152952 5.45731140 7.88650538 -1.55901892 -2.10695825 1.40085725 Na 5.20483790 7.93608542 7.92967799 0.56955127 9.45902734 -5.55373684 Cl 8.22444118 8.01952934 7.56620612 -3.22223207 -5.46397927 7.93761347 Na 5.54828956 5.52454863 10.83632195 -2.39365859 3.45192371 0.42998868 Cl 8.20584709 5.51469879 10.97684373 -3.04686556 1.28997976 2.35412425 Na 7.91296656 8.15682164 11.15437590 -3.11573369 0.50315592 2.22536831 Cl 10.93545823 8.18652833 10.93998919 -1.73248714 -5.87571032 -3.48956446 Na 0.25755213 8.33044463 7.97572276 3.79001212 3.10722354 -10.59849406 Cl 2.42444331 8.14175498 7.94730152 -1.55195047 -0.94214746 -7.66102672 Na 2.87565823 11.24993869 7.66421559 0.35162070 7.86288455 -7.44908536 Cl 5.75284642 10.71979401 7.90476211 -3.62520905 3.71574252 11.35240471 Na 2.66640877 8.29611195 10.79575882 -2.15345375 0.78786840 5.06484046 Cl 5.54215485 8.13642173 10.67020775 -7.70977725 -9.29623535 -5.27578229 Na 5.18122619 10.99435685 10.92347406 3.79992463 5.93845014 -1.07761138 Cl 7.77189894 10.73446485 11.01353538 3.35534089 -3.29092800 1.17285147 Na 5.06793531 8.38759263 13.54244237 8.06490380 -5.88356556 -10.58300388 Cl 8.14473355 8.15127213 13.48162370 -1.61454912 -5.58996825 1.81291591 Na 7.85365918 11.00822560 13.04470424 2.83296190 3.57302359 10.36304285 I 10.90414575 10.52993105 13.24697467 4.68183629 -6.37596465 -0.63665325 48 Lattice="0.0 10.8 10.8 8.100000000000001 0.0 8.100000000000001 5.4 5.4 0.0" Properties=species:S:1:pos:R:3:vel:R:3 pbc="T T T" Na 0.10237726 -0.19964844 -0.10740053 -1.31657408 1.68739154 -0.00201940 Cl 2.68179407 -0.06065986 -0.05105584 -4.52735273 -7.62408356 1.01907453 Na 2.66485299 2.82929132 -0.33555278 2.27679330 -2.96022072 4.98961941 Cl 5.47097863 2.94854435 0.05406118 -3.52631042 4.72693653 7.44917512 Na 2.65458308 -0.08788273 2.59772625 -0.77353775 -2.67567736 -1.95399981 Cl 5.47835761 -0.38603436 2.60237832 1.24602415 3.11425404 -7.80737485 Na 5.13968989 2.81199664 2.73603154 -5.76003513 10.05081038 -1.16675206 Cl 8.28308914 2.38117029 2.86000948 -1.21369973 6.74896499 -0.19837648 Na 5.14010796 -0.22953936 5.48166425 -4.64585264 -1.41510138 -1.33005077 Cl 8.60565468 0.24323456 5.35626510 1.27464232 -3.12912299 -4.11224109 Na 8.14749750 2.71378434 5.28794041 -1.70781255 5.99708619 -4.87559676 Cl 10.47131010 3.01989752 5.35444065 -2.99007828 4.91751815 -1.64495101 Na -0.07990889 2.66483882 3.00984917 -5.58459555 -3.55422628 -3.84855046 Cl 3.34331014 2.26565172 2.46829936 -1.45335574 1.31565373 3.11292325 Na 2.65855263 5.36342912 2.69792171 2.11004576 2.93819369 5.67892729 Cl 5.41201093 5.23988760 2.64629075 12.15532118 -1.54824744 5.81722751 Na 2.59190630 2.85908662 5.58896802 -5.60982045 9.70512514 -2.39454442 Cl 5.35926132 2.71679318 5.22976114 -2.46368772 4.83912752 -4.42283344 Na 5.54944258 5.63297472 5.55640103 3.70792972 5.73350095 9.86902054 Cl 7.96748266 5.26396930 5.37957179 0.13633766 6.60344523 -3.31244824 Na 5.39222002 2.70580882 8.00380558 2.96616914 0.36971892 -12.39657082 Cl 8.02070710 2.94766403 8.18212955 3.94166013 -5.30141365 -3.20104984 Na 7.82032885 5.38748110 8.13022453 5.26098625 1.20059892 2.17500275 Cl 10.67662789 5.44536951 8.16691968 -1.84106398 -0.62246166 -1.68142490 Na -0.28369091 5.18443301 5.53428944 0.58887788 -0.06321675 8.55473888 Cl 2.89473176 5.57625488 5.50424707 -2.26171527 1.25973083 -3.72403288 Na 2.96439878 8.31243126 5.53944605 -7.83617333 -1.62005507 3.27017301 Cl 5.24518969 8.14507728 5.10003944 -6.13940573 -0.25146762 6.44094095 Na 2.61369055 5.35961837 7.73126088 -1.86322568 2.20272326 -3.47353375 Cl 5.17546282 5.50072843 8.13042887 -2.63248908 12.45021302 -5.68022625 Na 5.05226245 8.25522492 8.16150429 -2.56154539 -1.36404592 4.37469436 Cl 8.11318794 8.31336751 7.85534067 -6.25856588 -1.80957747 5.98242178 Na 5.45317333 5.35917976 10.66458405 5.47397162 1.83872304 2.91360369 Cl 8.05537682 5.21466623 10.73240936 -5.90581694 6.54350018 -7.82273792 Na 7.88666365 8.21230212 10.42848410 -5.78062989 1.57937610 1.39772824 Cl 10.71363438 8.34589048 10.75805421 3.86971579 -6.83269699 -9.99150195 Na -0.20949542 8.41322230 8.05969942 -4.32383108 2.54637854 -3.04410533 Cl 2.88389900 8.23031579 8.10192305 4.47199084 -2.30822343 3.63289779 Na 2.49848764 10.70729554 8.14417855 0.10387266 6.59319133 -3.14765166 Cl 5.40132308 10.86690183 8.21082733 0.68654082 -6.69550449 4.84814888 Na 2.72563053 8.27010707 10.66383562 1.11196698 -5.38486579 -1.90747979 Cl 5.50125901 8.13931410 10.89745861 9.02460896 2.63299087 -10.97026782 Na 5.51531810 10.72993378 11.05993835 3.03146262 -7.16316226 3.96133315 Cl 7.94808463 10.64249299 10.96501818 -4.30358053 -5.66219913 4.02150495 Na 5.25111724 8.08676126 13.23232853 -5.51835034 -6.14410690 -4.28921597 Cl 8.10937926 8.10580385 13.49469114 3.05902744 -6.37737142 -9.84471097 Na 8.02979968 10.94220874 13.49747327 -5.88714416 2.02054029 4.27505379 I 10.74837073 10.64132268 13.53424863 4.06495060 0.76068712 2.99190327 48 Lattice="0.0 10.8 10.8 8.100000000000001 0.0 8.100000000000001 5.4 5.4 0.0" Properties=species:S:1:pos:R:3:vel:R:3 pbc="T T T" Na -0.09096233 0.05856424 -0.00921092 1.69626104 0.45370814 2.29237385 Cl 1.94277907 -0.23984222 0.14267107 4.46841149 0.55237686 7.27449047 Na 2.51553413 2.75470472 -0.17267925 -6.36365795 2.77856265 2.20978948 Cl 5.68753209 2.35724026 0.03586440 1.87455974 7.70895213 -2.70222580 Na 2.52172281 0.42601121 2.80379795 -3.07529725 8.90069468 -2.35774327 Cl 5.31624979 0.06802024 2.48452678 -3.39268021 2.86321433 4.78013427 Na 5.19537172 2.49496293 2.78194414 -4.54528452 -0.93963053 -11.27693347 Cl 8.33776763 2.90208952 2.76216432 1.68750646 -0.14244055 13.74498687 Na 5.16426278 -0.09905266 5.09950326 -1.08940583 -0.63154351 -1.43723653 Cl 8.06070840 0.06950584 5.24159907 -3.08790149 3.41494367 1.19785289 Na 8.03693059 3.04649038 5.11441275 -0.08500170 2.47961696 -4.20986986 Cl 10.61124683 2.87364699 5.20810646 -3.31642369 0.62628761 2.34245821 Na -0.21414921 2.68688951 2.26462084 0.28127603 -1.57566671 -3.44409243 Cl 3.01707761 3.06298527 2.55258782 0.66101624 -7.57195888 -3.78474894 Na 2.85509206 5.48530782 2.59629255 -2.75250950 -3.34399529 9.01082997 Cl 5.29390892 5.61924107 2.56088536 -7.24383017 -1.48394735 7.48527869 Na 2.70616016 2.75043919 5.32939139 -6.19091423 -6.17003097 3.06349447 Cl 5.21205767 2.70889616 5.40854614 -2.35235397 -4.43032262 2.69389794 Na 5.49922954 5.04652864 5.34166918 -6.05418061 -8.38607704 -1.18086648 Cl 7.69541292 5.23334374 5.50053342 -4.21740795 -4.25420081 3.88350700 Na 5.48702861 2.32161854 7.65241334 -1.25751134 -6.55258900 -7.45295905 Cl 8.16105884 2.50645778 7.95739753 -14.73540950 7.70767022 5.45197829 Na 7.98878864 5.30132681 8.15955791 -0.20566566 -0.48450476 5.19931435 Cl 11.04950378 5.57510775 8.06484814 1.79897130 3.11670358 3.05138337 Na -0.02698912 5.35547187 5.58022866 -5.75194565 7.12138042 1.60194682 Cl 3.26938584 5.36936398 5.44243107 -0.45596254 2.49433283 -5.25595229 Na 2.55056246 8.06507652 5.84211041 8.49153262 -3.40485832 -7.63575127 Cl 5.18037419 8.11728482 5.32357692 -2.88404463 -6.66570595 4.23150713 Na 2.92073669 5.39590724 8.25533189 -4.57551568 5.60047352 -0.42471583 Cl 5.29200321 5.12522441 8.12583082 3.07033108 10.94272438 0.10505278 Na 5.51368399 7.95784173 7.88656236 -3.16351038 -6.44869164 11.75234217 Cl 7.86131075 8.03494048 8.30478535 -10.48364742 -1.59749642 -0.98117972 Na 5.35618654 5.20389939 10.94179261 5.63242413 6.38742324 -2.36800835 Cl 8.25210400 5.60927421 10.84106854 6.24176117 0.04394881 9.34893814 Na 8.00718000 8.23138873 10.45143348 -0.29309983 3.66067929 2.53243199 Cl 10.35622422 8.42152037 10.90646792 -0.45928236 -2.89739032 3.78315075 Na 0.02336021 8.32841451 7.83668260 1.61791486 -4.31495281 13.43248685 Cl 2.75685351 7.93586220 7.81422612 0.01682838 0.39931709 2.90049726 Na 3.03080810 10.53855708 8.24334431 -1.27892635 4.35449257 -7.59740954 Cl 5.11842015 10.65140967 8.28644035 3.54798213 5.87212870 0.03602638 Na 2.59325639 8.07994649 10.56829956 -1.24271416 -5.38219855 7.24634687 Cl 5.21590374 7.83876591 10.87534212 -0.12127150 -7.26090351 10.48033641 Na 5.56156582 10.59684857 10.92295989 1.03744665 -1.23485972 -1.28124357 Cl 7.96948805 10.52606261 10.75981941 -7.97143093 -5.17891470 1.95238117 Na 5.59815721 8.08084063 13.62102811 -12.09458800 -2.92212061 -3.85760352 Cl 8.45757230 8.02282033 13.50190897 -2.07826200 13.29948749 -6.36660500 Na 8.14728852 10.68395274 13.52703600 2.84760651 2.26054026 0.21885562 I 10.80085519 10.67510913 13.70737237 2.47898663 4.51056809 -0.34679645 48 Lattice="0.0 10.8 10.8 8.100000000000001 0.0 8.100000000000001 5.4 5.4 0.0" Properties=species:S:1:pos:R:3:vel:R:3 pbc="T T T" Na -0.20069292 -0.23774163 0.04037779 -3.46217006 7.26976978 -3.49320066 Cl 2.78447454 0.10191990 0.09965372 1.63444905 -3.96635244 -5.09556741 Na 2.61253114 2.85717031 0.05580242 0.62807461 -6.32516998 -7.07986289 Cl 5.27941141 2.81439107 -0.00029379 3.95968426 3.01906210 0.42553284 Na 2.83500660 -0.15651465 2.63357050 -2.34113513 8.00709085 6.95855837 Cl 5.12962935 0.21282272 2.50203285 -3.09455943 -2.38647241 -3.04225712 Na 5.67057292 2.52259778 2.54306909 -7.71636587 2.92430419 2.06839256 Cl 7.93566036 2.66455153 2.75594741 -1.02225564 0.06141688 -0.95046621 Na 5.38669471 -0.09039404 5.39322401 -2.75225305 0.67306968 -4.27445051 Cl 8.28472119 0.01448861 5.55471244 -0.59068608 -1.65310016 -0.03984316 Na 8.26353949 3.04445093 5.59511745 -0.14086530 -6.36617666 -4.29246449 Cl 10.82935112 2.66166240 5.57834320 2.79917593 2.45655347 -4.36444805 Na -0.14566136 2.95214613 2.67488516 -8.38150734 -2.81536309 -6.39369015 Cl 2.66893084 2.55413804 2.66736127 7.01350966 4.59061870 -4.62644522 Na 2.69538542 5.35348743 2.77578416 -2.52660442 -1.39752799 0.22697249 Cl 5.49679124 5.29726580 2.65558067 6.88193003 3.11397854 -0.45526168 Na 3.10499985 2.75620092 4.94379345 4.22816076 1.84011186 -7.07245980 Cl 5.03938269 2.77979594 5.68736578 5.68225611 -0.96358222 3.23853867 Na 5.09394960 4.98627875 5.79975900 -0.31390853 6.99896161 12.47753412 Cl 7.96962299 4.99112351 5.51250733 2.96601856 -4.48302502 4.36153127 Na 5.70590215 2.66637157 7.98044949 2.44981863 -0.18046742 -2.30407653 Cl 7.84097626 2.68705011 8.23708669 0.76779774 3.86956896 -3.49632329 Na 7.93403790 5.33828891 8.33659946 6.27426319 1.59531019 5.91442130 Cl 10.74110751 5.26721699 8.39266987 2.88537713 -6.59826531 -3.89847844 Na -0.17997098 5.05811210 5.21449212 -0.20597158 -7.00738966 3.16650097 Cl 2.56031156 5.37323065 5.06817701 2.98298802 -1.76487012 -4.38993293 Na 3.26594260 8.32924800 5.22283869 -2.58827199 3.16839202 -3.90621203 Cl 5.60929115 7.89476020 5.24523060 -14.33346112 -6.06851966 -1.71464672 Na 2.87204469 5.39174032 7.90390698 0.69991547 -2.43450953 3.54587502 Cl 5.80668990 5.60283477 7.90715476 -9.50804403 4.53769377 -3.15930305 Na 5.20293375 8.00350743 8.26249681 2.23873720 5.73977921 -5.36299567 Cl 8.02325925 7.81737535 8.01828486 -8.98236960 -2.75818644 4.58386654 Na 5.16533247 5.59730993 10.97240197 9.93628547 0.01755115 3.11815635 Cl 8.27636823 5.45865648 11.02052872 7.17944440 6.01407490 12.36565599 Na 8.16414347 7.83847959 10.66407903 3.89344196 1.27882967 3.56805237 Cl 10.60291735 7.96363858 10.74587151 0.43767208 -2.28084624 8.72751071 Na -0.11546175 8.03432693 8.03780963 -0.35944375 -5.93512420 9.04002751 Cl 2.08057826 8.04011796 8.00996946 -1.13542130 -6.46248504 0.80309757 Na 2.41392938 11.15506569 8.04659115 2.81176162 -2.41455789 5.91057065 Cl 5.23020903 10.97393845 8.30984033 1.42287041 5.62389062 -3.86187834 Na 2.91103702 8.14243544 10.72370497 -3.08834539 -1.07530195 3.86360678 Cl 5.46958879 8.24545973 10.75593863 -3.90548462 1.13204046 -5.51556971 Na 5.50770031 10.48900320 10.55079333 4.12233368 -10.22126350 -4.24158134 Cl 7.97361019 10.62792536 10.64932621 -6.11360284 2.50193216 -8.31004631 Na 5.81174005 8.05040279 13.64136100 2.56646119 -2.97256762 0.76734171 Cl 7.69748572 8.03326371 13.27902197 -8.43278717 -3.66199596 -4.58555505 Na 8.39420326 10.61151878 13.27728472 -3.81463601 1.64605490 -10.68856590 I 10.85131038 10.73319663 13.66547016 -1.56946516 3.42588434 3.37212381 48 Lattice="0.0 10.8 10.8 8.100000000000001 0.0 8.100000000000001 5.4 5.4 0.0" Properties=species:S:1:pos:R:3:vel:R:3 pbc="T T T" Na -0.02606063 0.09838853 -0.27065790 -0.94373514 -5.99915490 -1.85363646 Cl 2.43869945 0.11889158 0.73101543 3.08428434 -3.93216529 -4.17019437 Na 2.70425072 2.31067102 -0.06884477 -2.86268655 -1.33411693 0.96117750 Cl 5.19482787 2.86135340 0.04760239 1.59060024 -2.85546458 -2.88484519 Na 2.72367492 0.07359345 2.74627717 4.41124388 -6.92259127 3.27961683 Cl 5.48724234 0.40034610 2.91694629 8.99540470 -0.16726349 3.39304646 Na 5.36168561 2.72560035 2.74008315 -7.22391705 0.44499356 -8.70645733 Cl 8.34518131 2.24915404 2.48531440 3.83648626 1.17680637 3.87094268 Na 5.34379225 0.01630936 5.11182431 -2.06354814 4.55966036 -1.22156405 Cl 7.66868196 -0.07233104 5.04940556 -0.58922101 1.84837758 -5.68897746 Na 8.06627086 2.51037746 5.17205082 1.29081584 1.64422209 -4.95528645 Cl 11.44259655 2.50283712 5.86113267 1.49570887 0.88128004 -5.26459728 Na -0.13301812 2.80759486 2.59156155 -0.85914928 1.10659383 10.92291548 Cl 2.96205746 2.72365432 3.09558087 -3.73093056 -1.78781023 -7.90740042 Na 2.48896525 5.43647575 2.79908416 -3.22963684 4.33688996 -0.83652898 Cl 5.43857968 5.36583320 2.91409190 4.96458860 1.43069664 0.81252305 Na 3.06688159 2.96233133 5.30720917 -3.76442375 5.68908694 0.57934564 Cl 5.49268270 2.58084054 5.47728017 5.53091942 4.34239549 -6.21861942 Na 5.43548729 5.58503121 5.02401566 -1.96493508 7.51526217 9.12173563 Cl 8.05478708 5.66581556 5.11369752 -2.27155809 -4.52966173 0.96645948 Na 5.26333395 2.59657376 8.27981835 -0.99991676 2.43410290 5.46254935 Cl 8.23846983 2.50166302 8.21176462 -0.65801071 7.71968110 3.88914045 Na 8.26984247 5.55278517 7.84182578 2.82669532 1.63055412 -3.09486396 Cl 10.66703834 5.13287430 7.93452941 1.13615653 6.25429799 3.07518053 Na 0.33086466 5.33421802 5.33934756 0.96049868 -0.42944678 -3.51186412 Cl 2.69939122 5.04866700 5.49638952 0.79369919 6.42917524 5.10976220 Na 2.76695873 8.26360012 5.10991676 -7.07542730 -0.66206127 -1.40511030 Cl 5.41136882 8.13059823 5.31253559 -12.95616960 6.88911750 -0.10207168 Na 2.79212903 5.36063242 8.44564069 2.82421485 -1.26172954 -2.00985718 Cl 5.51309674 5.21107459 8.05021949 -2.71340218 -4.89693334 -4.42627806 Na 5.43730925 7.87937522 8.12753481 -7.13302192 -6.37562470 -8.82065572 Cl 8.06713295 8.06999552 8.12542824 -6.34944502 7.67748555 -2.10223184 Na 5.30956346 5.22420991 10.60061487 1.98527892 -4.67473779 -4.45666161 Cl 8.14989200 5.40196126 10.84550977 5.41207535 12.95393829 -6.23055747 Na 8.59581015 8.43867460 10.75911743 0.08076063 2.02047664 -8.60917354 Cl 10.81656563 8.05356487 10.93593887 -6.61080794 -4.45061455 -1.98857834 Na 0.04777658 7.77459777 7.81762142 -2.89417818 -10.90567041 5.12880827 Cl 2.97409082 8.13482429 7.67126256 -6.53489808 -7.69740364 -2.35637510 Na 2.67566416 11.03216328 8.00955941 -9.53599777 3.83119002 -3.26070447 Cl 5.41576593 10.77877186 8.35203793 -3.39241016 -0.44349391 3.72780549 Na 2.64070440 8.19469100 10.84863334 -8.42553624 -5.58418844 -1.26188507 Cl 5.52529515 7.97659613 10.96731042 8.15898363 -0.94372561 9.21169252 Na 5.00220487 10.88720017 10.90182842 9.87043527 -2.17771766 -5.05182913 Cl 8.21607979 11.07363354 10.73312330 -2.86179146 -5.49238544 -5.86888516 Na 5.06869768 8.02767858 13.46469010 -1.40950675 8.60553851 -4.17188768 Cl 8.37274913 7.97174377 13.44967319 -9.09096462 1.29544881 -0.84954593 Na 8.05110907 10.64412206 13.51598252 -0.22439185 -1.25649623 -0.78534132 I 10.70555240 10.83227966 13.40152426 -1.42471421 1.07267763 7.77527513 48 Lattice="0.0 10.8 10.8 8.100000000000001 0.0 8.100000000000001 5.4 5.4 0.0" Properties=species:S:1:pos:R:3:vel:R:3 pbc="T T T" Na -0.11055279 0.11827539 -0.18163630 -8.25000705 4.24158091 0.27456154 Cl 2.57775308 -0.33409205 0.26809115 5.39405838 -3.40947392 7.61555117 Na 2.82244024 2.70482293 0.07195057 3.72991698 3.15968779 0.94386459 Cl 5.62808752 2.62967133 0.01931117 -5.31308805 -5.57426174 -4.11688370 Na 2.77058758 -0.00404751 2.66644809 -2.26133224 -7.21951936 -2.26553351 Cl 5.46151231 0.16170498 2.87760014 7.97252620 -1.64734609 0.83367709 Na 5.58773867 2.94484835 2.44292841 -2.96530610 7.51772089 -4.91195461 Cl 7.84369023 3.00169430 2.24966387 3.16986372 -3.80635969 -1.34865543 Na 5.57942876 0.15395816 5.42203921 1.68933959 -0.19678753 -17.50131722 Cl 7.91551868 -0.18854905 5.40288134 3.15408012 -5.17204527 -5.53672640 Na 7.96161606 2.64143605 4.93636042 0.47302411 5.24382628 -0.51629258 Cl 10.83712274 3.22919915 5.52937940 0.07775675 1.62073276 -0.15747562 Na 0.00568768 2.57419926 2.57966578 -5.18959872 -2.58676001 -1.13418730 Cl 2.59010853 2.59454108 2.74619959 2.93108350 -1.83509756 13.65640296 Na 2.72826598 5.18733949 2.70850314 -4.23217194 -5.69995789 3.75652446 Cl 5.80469087 5.30666227 2.67605666 -7.10446335 3.22573868 -3.83391171 Na 2.66441825 2.88613078 5.56120967 -3.25808624 2.68338600 16.79978945 Cl 4.98403373 2.70590563 5.72290963 1.96812110 -4.21641263 8.40714960 Na 5.29615764 5.58883085 4.96464860 -4.14858673 -0.64997402 -3.59611594 Cl 7.97227622 5.14091708 5.53882610 -2.87660290 3.01025028 2.52419045 Na 5.10240413 2.47412685 7.76341177 5.59846765 9.37214725 -1.57304572 Cl 7.79714594 2.60173834 8.14685649 3.03678680 8.44328839 1.69788835 Na 8.15315637 5.20456085 7.88703266 -5.47610077 0.68916814 -7.14047876 Cl 10.74770528 5.00857374 8.03765441 -3.61367738 -3.27680449 -11.06074671 Na 0.06225669 5.55985406 5.31464758 6.56305421 -1.01497079 -3.27399640 Cl 2.41452039 5.04696391 5.64079385 5.25083653 -1.53088614 -4.82673298 Na 2.81202668 7.61847675 5.47079521 -4.36169171 5.72629194 0.10802825 Cl 5.31524622 8.36774433 5.36250167 -3.93528017 3.68150911 -2.24769979 Na 3.18138496 5.10246046 7.88429912 6.55482582 4.69711612 -8.68469309 Cl 5.37175545 5.28621128 8.38943652 -2.21223771 7.53989087 3.26541965 Na 5.31725820 8.08614620 8.07282195 2.36886023 -3.85324224 8.49650082 Cl 8.31662667 8.43028181 8.14921805 -4.05561709 -1.79261905 4.50684300 Na 5.51765968 5.22896028 10.57014570 2.79826907 9.45479777 5.52431641 Cl 8.14235271 5.44068912 11.00764871 5.51881660 -1.56615497 8.07307382 Na 8.37555992 7.80265808 11.00184179 -6.51271593 -4.17864228 4.16140162 Cl 11.14357582 8.25414750 10.89283867 0.01643019 2.02987064 1.33273875 Na 0.11804869 8.03096697 8.15707934 -3.08210041 -1.95154654 -3.47411808 Cl 2.80991655 8.21836940 8.35318958 -0.31140887 -4.47017899 -1.56568771 Na 2.79893165 10.75206842 8.09814238 -10.96539267 -1.72552153 0.86171103 Cl 5.12995685 10.20663274 8.25660802 3.12955078 -2.79178146 -4.52759632 Na 2.84589522 8.36882057 10.98786664 3.85718907 0.60269570 8.98658993 Cl 5.54701315 8.25800854 11.16403820 -3.92196365 -2.13534372 10.35909515 Na 5.40315061 10.86308852 10.30895944 1.01492210 4.44812699 4.51611426 Cl 8.00555350 10.70496430 10.81488781 -4.08089389 0.11065759 4.89505744 Na 5.06663897 7.83507508 13.48681175 -1.68208245 -0.98446132 7.53586364 Cl 7.87712618 8.63315220 13.86663070 -3.89385924 4.11262391 6.96993353 Na 8.31586711 11.06194891 13.21598240 -2.79773609 5.79944510 -0.43701360 I 10.79412758 10.84883811 13.61247224 -0.07483738 -2.01833103 -0.79240931 48 Lattice="0.0 10.8 10.8 8.100000000000001 0.0 8.100000000000001 5.4 5.4 0.0" Properties=species:S:1:pos:R:3:vel:R:3 pbc="T T T" Na 0.09943933 -0.07525447 -0.17041073 -1.00962486 4.74302413 0.44527821 Cl 2.27956935 0.52801184 -0.31633964 5.66797022 -7.40343015 3.41446197 Na 2.83640892 2.85097821 -0.35562843 -2.50854327 -3.59511088 7.27099936 Cl 5.61249433 2.86774051 0.38439880 -0.07405455 9.12136716 -3.62298837 Na 2.50540909 -0.19918386 2.23538523 4.22420808 -1.13864748 -0.59235459 Cl 5.74106577 0.00008955 3.34517308 8.67531700 -5.38106665 3.52287755 Na 5.63462558 2.20965827 2.45425153 -3.20396513 -5.80659415 1.92033854 Cl 8.02727525 2.60013478 2.80250304 -7.51302158 -0.97393054 -3.65988424 Na 5.45200314 0.33607309 5.24835503 -0.09391802 0.03955073 -1.69851585 Cl 7.77246637 -0.28968726 5.69037345 1.50517765 3.95823242 4.52794110 Na 8.00293293 2.80451179 5.50866257 -3.26710950 4.57056391 -0.49051446 Cl 10.90287500 2.70289066 5.24821003 -4.53042210 -2.33982579 -12.82121830 Na -0.02980547 2.61055093 2.75939279 -1.43137039 -2.95477823 -5.69575195 Cl 2.53123842 2.46724754 2.56334561 5.73195605 7.05979141 2.05798008 Na 2.96342668 5.65001517 3.02238792 -1.04008674 3.11272797 0.71601857 Cl 5.32589200 5.52946830 2.99079564 -7.41292225 10.52432965 3.65565575 Na 2.87388760 2.78398854 5.41436789 1.88429164 -2.73320264 -1.05189215 Cl 5.45958631 2.46354268 5.29282508 1.53084691 -5.37753399 -3.12316170 Na 5.01857528 5.48974476 5.43731533 -0.60113239 -4.46285920 0.60652507 Cl 8.23059102 5.52295145 5.42054416 -4.58599539 1.92098544 1.29029758 Na 5.65363839 2.55934576 7.89740381 -0.54738483 5.62821042 -3.05230203 Cl 7.89321657 3.05517138 7.99463415 -9.15253559 5.29310029 -8.76756220 Na 7.81467826 5.13512903 7.66167484 6.52567122 0.15395093 -1.04654140 Cl 11.01524551 5.46366331 7.87354192 -6.14586592 5.18287316 -3.73113248 Na -0.09498555 5.44825606 5.23451340 -11.84726942 5.77214848 6.80365266 Cl 2.56534407 5.16940521 5.34771510 -1.09739361 -0.68253424 11.66149469 Na 2.61917158 7.88890562 5.38304581 -2.29606738 1.95562773 6.52223333 Cl 5.29947873 8.52080749 5.39497136 -3.07055648 -0.10637736 3.86678508 Na 2.59707974 5.18165072 8.07241828 -5.70376223 0.50149771 0.59991612 Cl 5.31248826 5.49805551 8.17134156 -7.62235652 7.11283549 0.63602160 Na 5.53141687 8.05078923 7.99096457 -4.57212547 -6.96693829 -8.14891764 Cl 8.00657535 8.02482317 8.16566483 -0.88148601 4.83989749 2.91851645 Na 5.24934929 5.34176271 10.63542126 1.90735195 12.85076720 -3.40784755 Cl 8.10741059 5.19413398 10.98202408 -4.65294422 4.17189735 -4.22147155 Na 8.03890068 7.97238197 10.66858725 -1.25666860 1.53040259 -0.31974325 Cl 10.88442099 7.81523697 11.06746403 -3.41620162 4.61545621 -1.77875775 Na 0.20794484 8.35851808 7.93499005 6.82388490 -5.46671349 -3.87031267 Cl 2.89087759 8.00931171 8.07552102 -1.40936097 -7.02251340 3.23969018 Na 2.98272861 10.90872546 8.26666963 -4.93799761 -3.36250293 2.57976476 Cl 5.34248148 10.71317460 8.08526983 -0.49673680 -4.68450795 -0.70426729 Na 2.55997065 8.16578048 10.94342926 1.81979971 -2.35332742 -7.64890929 Cl 5.51564720 7.94889037 10.97948208 -8.16535085 8.75737675 5.10918849 Na 5.23376971 10.79542183 10.84519232 1.09997332 -4.53993080 -11.72260398 Cl 8.42928669 10.46976131 10.75636752 2.56413335 11.29199209 0.12853182 Na 5.34816189 8.00645479 13.27891892 -4.03827412 -3.51301127 -3.99357427 Cl 8.03258604 8.12576173 13.51887282 0.78445110 -6.90689090 2.92485964 Na 8.21923137 10.76101113 13.37801086 -1.86071058 -3.88070855 -6.04410197 I 10.84563740 10.70089693 13.58973144 2.42319484 -4.49414982 11.77602263 48 Lattice="0.0 10.8 10.8 8.100000000000001 0.0 8.100000000000001 5.4 5.4 0.0" Properties=species:S:1:pos:R:3:vel:R:3 pbc="T T T" Na 0.21817057 -0.10959901 -0.03109763 -0.93963345 2.68372652 -5.88282882 Cl 2.86042939 -0.02084679 0.08898637 2.19186381 3.21969701 -8.14454254 Na 2.57944379 2.57436458 0.20238383 -0.86314978 6.75298799 3.29880028 Cl 5.38240047 2.13716822 0.10344623 1.59569983 4.86018577 -2.01502265 Na 2.76177560 -0.20953452 2.85726492 3.70892033 6.19224601 -3.41975038 Cl 5.59154012 0.20722823 2.60562179 -3.31949067 6.34871155 0.16354852 Na 5.60398019 2.70899336 2.58618424 -6.45031556 -0.99020509 2.08254183 Cl 8.02573762 2.73420913 2.97076198 4.09230041 -3.10173997 3.06784637 Na 5.61819497 -0.22486611 5.45814369 5.22905205 -0.39721565 8.81534749 Cl 8.11146023 -0.30109935 5.49579961 -0.12956556 -2.55045789 0.02502339 Na 8.33635761 3.00998583 5.16659709 -6.25528750 -2.76574086 8.18064818 Cl 10.69063178 2.75704701 5.34010975 3.02054216 13.36559407 -0.49595192 Na 0.07743402 2.69792549 3.42919796 3.01078304 -7.62241679 -2.99014863 Cl 2.89173319 2.67381269 2.89903906 2.74261451 -5.62440538 -5.51309153 Na 2.69676665 5.43266801 2.89622050 -2.05251546 2.67367238 2.80454958 Cl 5.56899258 5.06939479 3.08457074 -8.43347417 -5.24651952 0.27493170 Na 2.27598631 2.46406229 5.50354223 -8.85298940 -6.29829406 0.64756472 Cl 5.40362070 2.48069394 5.36118749 -11.66910096 5.52934166 -4.91248223 Na 5.44516215 5.52750234 5.38791262 -2.09086513 -3.62974604 -2.66239434 Cl 8.00285985 5.58996013 5.25491139 -6.48032300 3.81998211 -3.16157608 Na 5.32597496 2.74318926 7.86814316 3.75963542 -3.37400605 2.74374797 Cl 7.86854718 3.11777653 7.93148425 0.78215913 3.01933055 4.96385643 Na 8.22006467 5.17839819 8.11392056 -0.53858629 3.59718318 -5.80054096 Cl 10.52295295 5.62116811 8.01456485 -1.62234822 6.00524142 6.62074969 Na 0.09568086 5.44279104 5.36060085 -3.00752453 3.73708700 2.40114221 Cl 2.78973558 5.57670618 5.25472275 6.50592190 2.33656627 5.10464699 Na 2.64100231 8.16553891 5.41018296 -3.36643502 3.90086407 -5.13472991 Cl 5.44281343 7.67852014 5.56043410 -11.64372950 -11.34009634 2.03901342 Na 2.98551410 5.41512772 7.98130454 0.75747505 10.98976321 -0.72688554 Cl 5.51349007 5.23437538 8.06336406 5.49375390 -0.89414848 4.60868328 Na 5.52662453 8.02304516 8.33718743 3.52668959 -4.84911906 3.17647621 Cl 7.63413057 8.20038997 7.96255669 -5.67217484 -3.68172052 0.82878334 Na 5.64504511 5.53011436 10.89949099 1.16822478 -6.16928140 4.91840056 Cl 8.34810852 5.80467242 10.77373712 -2.32834194 -6.73402942 -4.29449237 Na 7.87416588 7.77366105 11.24980205 0.71648881 3.43514348 0.65021830 Cl 10.75337983 8.15687700 10.37237192 -2.54267433 -7.42545432 -7.46678778 Na 0.22578703 8.02411988 8.14994196 0.42812335 -4.14277741 -1.29506793 Cl 2.93592227 7.81975106 7.97775868 1.90471802 -0.91348880 1.25264640 Na 2.81338913 10.62053579 8.23022229 7.42850286 1.47703488 -0.55674306 Cl 5.21881280 10.87661808 7.98613105 1.67884412 -6.54273140 -6.78269522 Na 2.70977629 7.82632618 10.78438761 0.63878139 -6.75201470 3.57502014 Cl 5.49886110 8.08324149 11.11834060 -0.02595468 0.31316103 2.88209828 Na 5.41218531 10.89808020 10.80033016 8.85001920 -2.04275897 -0.21764923 Cl 8.28420076 10.89746674 10.61430070 -1.24656705 2.12042381 9.05285854 Na 4.93042517 8.24244037 13.92178355 4.07733770 9.01710693 -6.21476138 Cl 7.85909234 8.19649422 13.65221400 -1.71824654 8.10544244 -4.65811505 Na 8.33373114 11.16162516 13.41738278 3.41369507 4.21789752 6.67785154 I 10.87309378 11.05626976 13.44129824 -14.93083856 -4.46999695 -5.08079725 48 Lattice="0.0 10.8 10.8 8.100000000000001 0.0 8.100000000000001 5.4 5.4 0.0" Properties=species:S:1:pos:R:3:vel:R:3 pbc="T T T" Na -0.37488087 -0.36299070 -0.25454649 5.45175653 0.78995309 -0.16677103 Cl 2.90764841 0.29767102 0.14788166 -1.17995549 10.37356908 -3.94001576 Na 2.65108019 2.57647561 -0.20980253 -3.71586305 -4.96341577 4.11935970 Cl 5.19483168 3.01032958 -0.13038360 -2.26629719 3.70447156 0.09030525 Na 3.04907413 -0.07487317 2.92364102 -7.37234238 -5.69857324 -5.77661910 Cl 5.23884842 0.01892250 2.81800377 -1.03184236 -4.26290655 -3.30116171 Na 5.83728730 2.64910150 2.62673049 0.20033290 2.15183016 -5.02354077 Cl 7.87677771 2.56226893 2.55467314 -2.26832360 1.25225385 -0.93442326 Na 5.53809422 0.07965907 5.35852641 8.06232446 -2.98771994 -1.58252284 Cl 8.11392873 0.14316033 5.35428667 -8.31196700 -0.60175578 -0.35045898 Na 8.05147431 2.41827710 5.65895071 3.16802983 -5.42400480 -6.47986378 Cl 10.63422406 3.01639227 4.95810182 1.99859985 -1.81371575 0.18979745 Na -0.04489038 3.12647709 2.71357380 12.40614065 -1.68823079 -1.75116816 Cl 2.33276990 2.70092856 3.08654415 8.45631770 2.56569266 -3.74775407 Na 2.34541336 5.79908940 2.76637961 -6.17545234 1.55904704 -3.08081967 Cl 5.32723152 5.44327223 2.66412973 -1.45217509 -16.18190663 -4.19997882 Na 2.46864593 2.60864699 5.19758290 5.74895906 2.32437480 1.36554659 Cl 5.69025997 2.55533707 5.54716742 -1.84433462 -6.75665054 -0.84437560 Na 5.31869563 5.61991574 5.56790222 -8.24484412 0.10083046 0.00796566 Cl 8.19500293 5.50233328 5.50355870 3.29453098 -1.59231862 2.34073400 Na 5.55527437 2.63415790 7.82416783 3.53653726 -1.36160549 6.22242774 Cl 7.74698343 2.49601994 8.31045482 -4.03159397 5.43766239 -1.26727689 Na 7.87628172 5.48619327 8.05822612 -6.23136341 0.54735889 -7.75401074 Cl 10.93240742 5.41658971 7.98425637 6.78732289 8.99519318 1.63143961 Na -0.02992362 5.32069851 5.23941396 0.82576303 1.26642298 -11.86296058 Cl 2.82759574 5.47941833 5.73353541 3.43425956 5.65426252 8.89378658 Na 2.77566923 7.95635577 5.32027213 8.17592085 -7.15113019 5.38546526 Cl 5.48731370 7.71864730 5.20049049 -5.49586499 -1.54670864 5.29976609 Na 2.79319034 5.43867692 7.97657021 -0.15738043 -4.94795493 -8.80580739 Cl 5.31403525 5.36207891 7.90135399 2.66554259 1.28816452 5.93431469 Na 5.32106523 8.22302805 7.79453717 -6.90720223 1.39157226 -0.20490150 Cl 8.08536890 8.21087112 8.29691557 8.11611500 0.49585314 10.12937926 Na 5.38073856 5.58059935 10.97638126 -2.22928646 6.68661980 2.48492857 Cl 8.12785145 5.30313973 10.91003816 -4.17692803 3.52452310 -0.90007650 Na 7.89976449 8.00130163 10.62939314 1.40833239 2.41108394 0.09012034 Cl 10.80676543 8.17064023 10.69264145 -5.43511360 -0.10458061 -5.46293388 Na 0.02246286 7.76120514 8.02141120 2.77375899 0.57565059 4.03000840 Cl 2.82194487 8.25313220 7.91475607 4.84198410 5.70939877 0.44416194 Na 2.78696981 10.86758833 7.84510811 -1.57892514 -1.11602241 0.44070918 Cl 5.30273948 10.76390850 8.13292437 1.45227662 2.62901476 4.19689812 Na 2.96231246 8.28692106 10.73341776 -7.70528971 -3.31880250 -0.75211576 Cl 5.72077460 8.08576227 10.51546195 1.33742557 1.27548344 -3.45885570 Na 5.10247981 10.45051994 10.68449912 -7.76293875 8.13118863 0.55419666 Cl 8.05035854 11.15188798 11.02730620 -1.30817185 -1.99219786 8.30586006 Na 5.35970549 8.22439064 13.47700579 -6.68201415 -7.22654102 -4.07232943 Cl 7.86831931 8.04975948 13.75417328 7.69344083 0.07756521 -8.50392234 Na 8.16702878 10.57002329 13.26229478 -1.31764170 -5.60715438 4.25282376 I 10.65274430 11.15221814 13.83957642 1.90651426 -2.27040860 -0.67970564 48 Lattice="0.0 10.8 10.8 8.100000000000001 0.0 8.100000000000001 5.4 5.4 0.0" Properties=species:S:1:pos:R:3:vel:R:3 pbc="T T T" Na -0.05777800 0.33873579 -0.53838291 10.47283888 0.75667620 4.22154922 Cl 2.66432562 -0.16613624 0.10343222 0.49531842 -3.60427655 -3.26743128 Na 2.65223373 2.78727515 0.09607086 -6.14284133 -5.14869600 1.28224201 Cl 5.59858655 2.62698770 -0.15028511 1.69041264 -8.34134018 -11.08324571 Na 2.71038822 -0.37408670 2.47750722 -3.57006630 -0.14733854 3.22449736 Cl 5.31513740 -0.38010018 2.70982537 2.71900062 2.88816312 6.41885772 Na 5.18959376 2.55037438 2.56319008 -6.27641365 -3.39251640 0.80176687 Cl 7.79134995 2.65217987 2.65677941 -6.45795930 -7.33420830 7.39539380 Na 5.48674851 -0.43725080 5.49864515 6.86449006 -3.50186954 -8.91336769 Cl 8.30704969 0.09059626 5.15923629 1.25237958 -3.41612707 -2.65327575 Na 8.27300872 2.59041415 5.13059426 -9.15438552 -7.76290239 -2.51064866 Cl 10.91206164 2.53207347 5.19900521 0.97864626 -0.29573831 -3.64997334 Na 0.04754606 2.40893957 2.82617279 -7.29727270 -3.37114420 0.44279927 Cl 2.86290536 2.86460034 2.66322854 -12.47994545 3.74391348 -8.89333984 Na 3.12804865 5.22585619 2.43928457 3.59390415 -0.21646917 0.82102477 Cl 5.45236142 5.81505171 2.93905990 -6.65523001 6.26644220 -4.61803906 Na 2.70989150 2.70609623 5.49006903 1.64560582 0.11454985 2.14305030 Cl 5.42288300 2.90478182 5.58537387 -0.71637052 1.21678905 7.33823355 Na 5.47481348 5.18507426 5.17947268 4.70726801 9.00223317 2.22840658 Cl 8.31273611 5.21904471 5.25347342 -5.29702432 6.73849231 0.54952720 Na 5.52023955 2.39001756 7.77818081 -5.84485923 0.36750906 -0.24231244 Cl 8.22234903 2.60397483 7.85525599 0.89959410 -4.47878853 7.04526791 Na 7.73405840 6.10108267 7.72921099 3.21900290 -3.21585282 1.49776141 Cl 10.66287528 5.45537689 8.06813172 -0.51534781 4.61386969 -0.91297385 Na -0.14536932 5.64073808 5.67256403 4.22443985 -5.25074431 2.09229376 Cl 2.81327734 5.47339477 5.60922488 3.08612516 0.25460615 -2.71178567 Na 2.24603881 8.12420914 5.68599246 1.18467164 0.82062737 5.77687762 Cl 5.40193660 7.94127356 5.24778207 -1.97943727 -7.29792702 0.37868545 Na 2.54294214 4.99793069 8.28777570 -10.61966827 -2.42496968 -6.47221098 Cl 5.49378458 5.06786517 8.35426265 8.23403390 -1.56613237 3.03749209 Na 5.78108988 8.08673531 8.16477620 3.25736822 11.39714323 -6.06054277 Cl 7.95396010 7.98499810 8.15841823 9.77862439 2.90336936 12.96926067 Na 5.71530683 5.56769900 10.99342302 0.32834815 5.43111885 -2.03219265 Cl 8.25171866 5.82527252 10.84970243 -1.48338011 -7.48838720 -2.89473975 Na 7.95480293 8.34425760 10.72422687 -3.89897564 -7.07989710 5.67376127 Cl 10.75540719 8.23456144 10.92079532 2.13810399 -0.98288137 -3.74417256 Na -0.34023254 8.35916819 8.25422248 4.17189047 -4.81026978 8.86699911 Cl 2.40342800 8.26561766 7.77394513 -2.76043472 16.46178433 -2.37092177 Na 2.23562419 11.10409190 7.97151744 -4.22030197 -2.30869170 7.02763262 Cl 5.02677027 10.69089404 7.92412073 -6.25058508 0.55903603 7.26426701 Na 2.66647029 8.19631083 11.03028331 -4.42058832 0.29196521 4.87433745 Cl 5.30640685 8.30625044 11.16750218 -0.85463978 -7.19562160 0.96778825 Na 4.99052834 11.09743665 10.84029197 -4.62743274 0.06126931 -3.10880678 Cl 7.97833111 10.58955669 10.69239869 -5.41085338 2.67211215 6.42631487 Na 5.18585235 8.36640278 13.28678561 0.94258005 -0.42650707 5.87811795 Cl 7.82574499 8.23697562 13.73802480 1.14712510 0.34865130 -4.47082322 Na 8.00242647 10.80335060 13.36287575 5.68552848 -6.41939463 -10.61539717 I 10.56962336 11.04910824 13.53077838 4.20338197 0.60240143 -11.00850731 48 Lattice="0.0 10.8 10.8 8.100000000000001 0.0 8.100000000000001 5.4 5.4 0.0" Properties=species:S:1:pos:R:3:vel:R:3 pbc="T T T" Na 0.14043948 -0.14003962 0.08292288 4.27233590 -1.90557328 7.19335660 Cl 2.70623157 0.33521955 0.19059623 -3.85187070 4.99134209 -12.51794146 Na 3.07301404 2.62380287 0.33335993 -1.13324932 -6.20247238 -1.53932821 Cl 5.66886830 2.49144143 0.00671737 3.52039638 -0.38703158 -6.28213985 Na 2.79107049 -0.06748053 2.63190292 -11.85931152 -4.11290033 7.90836659 Cl 5.46204910 0.36601335 2.87667978 -1.88951639 -5.54312319 -3.82505835 Na 5.37941479 2.78008663 2.65913035 -3.09802850 2.40097367 7.85880653 Cl 8.13633757 2.48557083 2.61259376 -8.89213672 7.32461455 -4.94383901 Na 5.27715526 0.13868413 5.60406353 -4.75553793 1.30701597 -6.16655042 Cl 8.10028518 0.02736234 5.40085272 2.25134749 -5.01932966 -0.47137675 Na 8.29530364 2.82844645 5.42219118 -8.02004955 -10.58027604 -3.84623222 Cl 10.60696191 2.96844262 5.74270552 1.07862491 8.19680304 -5.34149428 Na 0.25498439 2.47179080 2.36471669 -8.75932843 -4.41930644 -3.99450392 Cl 2.92398031 2.24270002 2.45098538 -0.42379171 1.57320435 -4.50324391 Na 2.47963985 5.39409237 2.74493908 -0.02636796 -8.89703307 -1.47752411 Cl 5.74540050 5.61828789 2.79965721 4.10182359 1.80752144 1.82043551 Na 2.20869863 2.73529681 5.47767482 4.64180374 -0.03877182 -6.09449286 Cl 5.21259694 3.07488839 5.48965606 -8.58164240 6.82668488 12.39624406 Na 5.32518272 5.11387524 5.39905686 4.88103671 3.02579791 -4.32899532 Cl 8.27465425 5.75655575 5.44750337 5.00963543 4.74533645 4.58253271 Na 5.12252208 2.61446992 8.24257645 1.13003517 3.13973034 2.51772895 Cl 8.08067448 2.72148458 8.05055006 1.52340119 3.19408680 7.49307459 Na 8.12997101 4.90859290 7.86169899 3.02777983 9.02769240 4.70112833 Cl 10.62698437 5.55865825 7.92839208 -1.97363714 6.77376877 -1.18491136 Na -0.07410750 5.24323598 5.41108642 10.56802875 -6.73869010 -5.47066568 Cl 2.87753688 5.33539132 5.28055954 -7.92654186 -10.25969647 -4.08500578 Na 2.90387162 8.28759875 5.23212817 -0.94174038 -4.30098759 2.63285981 Cl 5.53429198 7.99739549 5.58152770 -3.66531432 -4.17908409 -3.04949435 Na 2.64412519 5.28525961 8.34015171 5.29451346 -13.78540051 -11.99684617 Cl 5.66757647 5.18447260 7.75448330 -1.02337583 -4.96572582 2.16534202 Na 5.57397152 7.67106180 8.15342672 -7.69111006 7.04476104 -3.75706932 Cl 8.33587418 8.06671062 8.24154009 -1.20990729 4.70955207 0.79914766 Na 4.99747442 5.51258023 10.79482685 6.05046398 6.24288483 1.62931286 Cl 7.91233343 5.62454872 11.21478761 5.07915576 0.83898629 0.63988215 Na 7.93192448 8.14259230 10.84167500 -10.31837991 -5.41150758 7.12931488 Cl 10.56037520 7.61902937 10.88775930 -3.30396988 -5.69405885 2.41959646 Na -0.28188122 7.93973226 8.25950336 -2.83138446 0.27635556 -3.49731310 Cl 2.90098828 8.20147351 8.02435420 1.25746965 -2.64712431 -1.93390406 Na 2.53732325 10.56424388 8.21036490 4.16009736 -2.51309195 -7.47136173 Cl 5.54030497 10.74891469 8.14293081 -6.84675874 -4.13923920 -3.71385867 Na 2.74950033 7.90919781 10.78993529 -1.28744917 2.35647787 3.42936775 Cl 5.32221444 8.03403652 10.87228748 -4.86873284 3.22326315 -2.11721156 Na 5.21720205 10.41678910 10.74787817 3.87381109 12.99731764 -0.73679131 Cl 8.21082876 10.72812001 10.95495033 -4.28788841 -8.38237783 10.60016346 Na 5.22574004 8.44435273 13.34640970 -2.24473017 -7.42328089 -0.91663630 Cl 8.06668826 8.02414411 13.29181563 4.84875093 -3.18707233 -2.98180431 Na 8.04425066 10.81995082 13.53159442 -8.50097739 -4.29747060 0.93941522 I 10.83318823 11.13014972 13.52751393 5.36551636 0.54555108 0.64483373 48 Lattice="0.0 10.8 10.8 8.100000000000001 0.0 8.100000000000001 5.4 5.4 0.0" Properties=species:S:1:pos:R:3:vel:R:3 pbc="T T T" Na 0.08602023 -0.03419025 -0.04878928 1.49010453 2.85509131 -1.95651786 Cl 2.79461375 0.12897841 0.23112980 -1.46022295 -4.07961340 -0.43574171 Na 2.28809487 2.77369177 0.04555731 2.32990677 -0.61091853 0.17934844 Cl 5.75436352 2.49670794 0.05168904 4.30732904 3.18399985 -7.37785018 Na 2.42634139 -0.00605101 2.98288956 -1.85037293 2.05721342 9.22899039 Cl 5.67376056 0.10327680 2.94759715 -9.73604738 -6.43983870 -10.27924883 Na 5.15232939 2.49783742 2.51000072 0.82769507 -10.58116591 1.27035412 Cl 8.08611382 2.81432276 2.89523692 -0.07419354 -4.20582156 0.34670381 Na 5.13009568 0.06838372 4.98310081 -3.67585944 2.97942292 2.63212486 Cl 7.96403578 0.23995519 5.13129327 5.19161691 2.63651860 8.64007328 Na 8.17540674 2.87299082 5.39268159 0.08156793 -1.85736039 3.15817314 Cl 11.00078807 2.76783719 5.49097208 4.69388009 3.25668891 -1.31161694 Na 0.01007212 2.88636397 2.72348514 3.74314839 2.01696821 -6.45091001 Cl 2.83105861 3.03911324 3.15008889 1.36026915 5.24950392 3.44193977 Na 2.74213936 5.08936928 3.16033416 -2.78524355 -2.69799030 -1.08347870 Cl 5.46147998 5.43428014 2.47066793 -5.10292004 -5.84922226 4.77534514 Na 2.63234416 2.64966417 5.46945198 -4.14133668 8.17542061 1.99274396 Cl 5.28611042 2.54194305 5.60844803 -8.25993789 -7.91084585 -7.77834117 Na 5.47680606 5.62473142 5.73139069 -0.62086783 4.42456220 -1.77951569 Cl 8.06844425 5.41672169 5.18145641 -6.12325822 -6.74116111 -4.32383895 Na 5.40798240 2.90023318 8.14158255 -1.66978144 3.67719073 -0.61890979 Cl 7.75523308 2.86152262 8.14669910 3.78490541 -2.53245047 7.19767602 Na 8.05874054 5.49889494 7.82764355 4.27325514 1.55110530 5.75387297 Cl 10.71145010 4.99675550 7.97234623 -4.61569432 -1.60584423 -1.24645854 Na -0.21984850 5.09399109 5.56766352 1.14071720 -8.76714177 0.82973627 Cl 2.31646498 5.18617666 5.29782461 3.50265883 -1.58275326 5.69301152 Na 2.88790276 8.26734538 5.36223598 3.42596056 -2.77429049 -2.22153932 Cl 5.53045076 8.21735380 5.25007408 -0.41483243 2.68748842 11.64366243 Na 2.75048283 5.61941051 8.14201130 -7.96649834 -3.13552637 -10.38394415 Cl 5.44950845 5.24368166 8.14464922 7.41938265 4.01388355 3.63101278 Na 5.51438820 7.99938299 8.33765021 -0.95706696 2.46201818 -1.07684127 Cl 8.09434606 7.84705939 8.21208068 6.08338908 1.97075979 -2.17902571 Na 5.34510747 5.39510505 10.55039728 0.00020641 -2.80301810 3.40566482 Cl 8.37603085 5.63010527 11.02739513 4.14617853 -2.79909583 2.51399505 Na 8.16813425 8.21520578 11.10849827 1.61039774 6.15648735 -1.19928656 Cl 10.87002418 8.44935822 10.96480030 10.46386425 -1.09193338 -3.02312758 Na 0.06706238 8.03844940 8.23693104 -3.23198928 -1.99574281 -2.74436047 Cl 2.68429679 7.97620646 7.92787654 0.30397780 0.31396763 3.35423923 Na 2.98442782 10.32401706 8.32586249 -5.21673383 -2.44740759 4.29514189 Cl 5.19831248 11.01084647 8.00777457 -5.84810180 -2.83754749 2.05210170 Na 2.88024589 8.51468012 10.81916721 -4.80800504 1.89844844 5.68358741 Cl 5.57550066 8.32754997 10.78049444 0.42193492 0.99012468 -1.58092264 Na 5.35192066 10.53455842 10.89135007 -1.27303051 -4.12882954 4.40881245 Cl 7.98476644 10.79855623 10.93151446 0.87455864 -10.51005181 -7.55956814 Na 5.59124482 8.32601442 13.51781336 -0.86968176 -8.28665443 -12.02683474 Cl 8.35436930 7.89859196 13.49592364 -6.76330256 6.08540474 5.12198636 Na 8.18550793 10.58291173 13.22373046 1.17567768 -4.70828140 -5.60246206 I 10.67125514 10.70245547 13.47846303 -5.10341175 3.38264132 -4.03128303 48 Lattice="0.0 10.8 10.8 8.100000000000001 0.0 8.100000000000001 5.4 5.4 0.0" Properties=species:S:1:pos:R:3:vel:R:3 pbc="T T T" Na 0.16490085 0.04533400 0.12438276 1.93028567 -2.45666430 -2.16085606 Cl 3.07578969 -0.05489756 -0.13952041 -1.24223121 -0.75097426 -3.02386620 Na 2.72913125 2.72243262 0.14987336 -0.70908512 7.84080081 -0.02980279 Cl 5.42698069 2.66105876 -0.04217868 1.27460954 7.51200532 1.52629220 Na 2.75401899 -0.10903347 2.88537352 3.11691056 2.17470692 2.07285292 Cl 5.49929047 -0.13268667 2.75169074 3.05393515 -1.70546338 -0.61865066 Na 5.12717297 2.71012022 2.70391308 0.64596773 4.90503895 -5.20294175 Cl 8.01018225 2.57886058 2.58017691 -7.83009527 -1.17675594 -0.69890781 Na 5.01896874 0.24825823 5.38191106 -3.24568987 -6.00152013 -1.56827564 Cl 8.25807708 0.09142319 5.55625221 -0.84737171 3.17353000 -1.87427703 Na 7.95135720 3.02703735 5.16063114 0.66442551 0.27789611 3.40270548 Cl 10.82649395 2.73657616 5.45896857 8.85389497 -1.93943900 3.04168279 Na 0.08432772 2.85575367 2.63659879 10.25087910 -5.03825044 -0.98643767 Cl 2.62703832 2.54862323 2.93702454 2.08189943 1.85124251 0.03661580 Na 2.80982330 5.49951897 2.39230673 10.29034446 -3.44074954 -1.80948058 Cl 5.62231507 5.30750432 2.70991756 -5.71715589 5.18071612 2.84983133 Na 2.43291098 2.53199746 5.13195308 -2.39017121 -5.47712427 -1.18969195 Cl 5.48118844 2.45608935 5.30442206 6.73926868 -5.12655274 -1.42248006 Na 5.44532556 5.58862759 5.54574240 6.10618297 -3.53905054 7.03879228 Cl 8.19653536 5.40462106 5.25280155 -6.22602513 2.75521203 -4.42642012 Na 5.11041186 2.60641952 7.87960498 5.98504130 0.56057240 1.48989965 Cl 8.13189135 2.71408909 8.49540922 0.67985114 0.80856386 -8.61183142 Na 8.17014127 5.48377228 7.87598433 3.62766442 -0.01975685 -5.02553452 Cl 10.52849609 5.47554787 7.90513216 -1.02211785 4.94992073 -7.46183032 Na -0.06307120 5.38759831 5.26897221 6.57128572 -2.82409709 2.47312478 Cl 2.51321842 5.60174366 5.12722195 -1.49370544 1.58102765 1.06157455 Na 2.43828308 8.17047384 5.18298642 1.14906612 -0.88798030 -2.50664244 Cl 5.40781661 8.06986986 5.60253362 -9.01208309 2.57827595 -8.49474808 Na 2.95847884 5.58503549 7.86442702 3.55011849 -10.01496090 7.74526153 Cl 5.45313136 5.26563838 8.06301440 10.11523735 -1.89441909 -3.18763352 Na 5.13979319 8.51749450 7.84721816 1.15919637 -2.51606493 -1.86524829 Cl 8.47967368 7.93465006 7.93891769 5.22184752 9.77971378 14.49427919 Na 5.54835190 5.44670172 10.62975458 -5.26531961 -6.64634986 -6.95578984 Cl 8.17672392 5.56584375 11.11331925 -2.10719530 -0.96599402 3.64747768 Na 8.11073717 7.94572719 10.47510277 -5.34225779 4.52817401 -0.28748622 Cl 11.17214281 7.96625840 10.85393057 7.36171649 -8.36753837 10.87687475 Na 0.07560455 8.34851534 7.82293361 0.00314033 -2.00806085 -4.75289549 Cl 2.23556041 8.21655173 8.11869488 2.09555575 -5.80436728 -2.50935181 Na 2.81422535 10.79955442 8.04312519 -6.76242569 -10.35579892 -4.88541418 Cl 5.58226931 10.75328106 8.10029204 -3.01923413 2.13759812 4.31760264 Na 2.37123930 8.10123084 10.70470964 -7.70762191 9.41948769 0.05468542 Cl 5.80762626 8.03142379 10.82173766 4.90858314 -1.12242315 -3.17776331 Na 5.98809211 11.11121539 10.68932834 -4.93516679 -2.13726671 -12.93927168 Cl 8.10556220 10.53950173 10.93066826 5.28134521 2.06242932 -2.03369949 Na 5.44828379 8.07426937 13.27444277 5.80164840 0.19314911 0.68639996 Cl 8.22356379 7.81734215 13.76913101 2.98408570 -2.34470894 -4.60140068 Na 8.16713863 10.59331665 13.49863182 -1.55526065 -2.49802342 4.99330933 I 10.81874948 10.74724802 13.52787585 -2.45331867 -2.46418018 -1.02234651 48 Lattice="0.0 10.8 10.8 8.100000000000001 0.0 8.100000000000001 5.4 5.4 0.0" Properties=species:S:1:pos:R:3:vel:R:3 pbc="T T T" Na 0.40970912 -0.16200204 0.18790587 -1.89468219 -1.95004239 1.61266295 Cl 2.71009303 -0.15474436 -0.12221239 2.61235399 -1.57837775 -3.05505849 Na 2.64280485 2.44424348 -0.15568214 -10.64551968 6.91173681 -2.40808703 Cl 5.29132444 2.81551972 -0.18658879 -14.10259638 -3.25281231 3.41632936 Na 2.70338464 0.11900139 2.93430970 6.29651718 1.02166555 -6.07749064 Cl 5.81620581 0.04557328 2.61292231 -1.49414633 5.05872082 4.39160463 Na 5.44891690 2.38305490 2.87051217 -1.85615460 -0.30801125 0.00315658 Cl 7.78599885 2.72435183 2.75321109 -1.22646114 9.03461824 4.18493098 Na 5.41774585 -0.06971213 5.49507909 -6.81526612 5.36102220 -2.05530829 Cl 8.08305215 0.10989398 5.48744472 1.74649269 0.27101508 -2.84290551 Na 8.14988969 2.87759369 5.30929213 9.64390483 -9.50099031 -2.85766963 Cl 10.66259777 2.59880078 5.40471375 3.95942394 2.86889682 -5.78184406 Na -0.18978407 2.24868234 2.70724169 -3.55836542 1.24642247 2.46778590 Cl 2.78115237 2.59442829 2.49429310 -1.18490361 4.72889338 -1.14260655 Na 2.63606015 5.63773613 2.58583265 -3.14474225 1.27437946 0.64927092 Cl 5.24129257 5.28855063 2.55002934 -0.33312218 -1.55753149 -7.99208437 Na 2.64145263 2.71968807 5.35066285 -4.09172978 3.61125580 5.17673482 Cl 5.59847423 2.91050826 5.58290988 -9.10060196 5.59484136 0.42991008 Na 5.70363763 5.42588099 5.03366372 5.31503602 -5.93057833 5.29581795 Cl 8.29755209 5.48031189 5.62905343 -0.51049348 -0.82354956 2.54454480 Na 5.37441878 2.31715096 7.85648225 -5.85870795 -0.55583821 10.32235482 Cl 7.98863150 2.54828494 8.09656258 -5.88365522 -6.36400046 -0.42345686 Na 8.10198825 5.37296889 8.13987251 -0.09433650 1.73045812 -0.96538127 Cl 10.60479235 5.19172780 8.03781503 -1.19034847 -7.06004702 -7.46265585 Na -0.13556694 5.37807377 5.33550318 3.48210298 5.03537098 -2.69634391 Cl 2.84954977 5.38400969 5.69197306 -7.88065052 0.76120213 -7.32020876 Na 2.44816342 7.88979562 5.01354870 -1.92694682 12.56658283 5.48074479 Cl 5.12172147 7.81887424 5.70604786 3.83203834 8.00064836 1.63593466 Na 2.55318871 5.27586757 8.09781165 1.39882208 6.02201759 -1.08143089 Cl 5.36287221 5.42119234 7.97365999 7.18419168 3.92404039 1.15293748 Na 5.09662148 7.98263539 8.02494726 0.66562937 7.68186925 -2.63731795 Cl 8.09942613 8.03176535 7.92190121 -12.35269587 5.11755443 1.12791108 Na 5.36645756 5.21451259 10.69276371 -2.47155922 -3.05398969 4.49178281 Cl 8.22538834 5.09005883 10.75645623 -4.31806465 1.64768239 12.49304274 Na 7.65236415 7.99499353 10.47895745 -9.59931445 2.11340875 -0.17069275 Cl 10.80307524 7.86183252 10.40243028 -2.16743855 -1.37326119 6.79156753 Na -0.11101475 8.01845621 8.46024920 -7.23719629 2.90050318 -6.28766685 Cl 2.58463050 8.11138979 7.80146791 -7.88420811 -3.71859075 4.11158946 Na 2.67796645 10.63170123 7.98150687 0.19368323 -2.27638150 0.88644699 Cl 5.31162166 10.98014433 8.15825077 -4.18882741 2.86681896 -4.17139211 Na 2.80589168 8.12773211 11.02531828 -10.72242796 3.48252238 -5.19326143 Cl 5.25043422 8.06052941 11.30096158 -2.73016065 -5.67762568 -1.83880313 Na 5.43671358 10.78807880 10.58065855 -2.50426832 -7.81804224 -5.44342582 Cl 7.79149412 10.67518441 10.66868401 -9.80810248 2.56746602 -7.63197639 Na 5.51479158 8.30185989 13.74283341 5.66180027 4.05917296 2.37491023 Cl 7.71085999 8.02667001 13.47234196 7.80584426 -2.78603993 -1.44079499 Na 8.25039401 10.60736440 13.60780861 3.22670117 3.06137315 5.33468103 I 10.60345083 10.79276043 13.87315231 -4.68622132 -6.15449818 1.93835796 48 Lattice="0.0 10.8 10.8 8.100000000000001 0.0 8.100000000000001 5.4 5.4 0.0" Properties=species:S:1:pos:R:3:vel:R:3 pbc="T T T" Na 0.05047689 -0.17296257 -0.18060150 2.75898546 -4.97823450 2.35891785 Cl 2.68242955 0.05892460 0.18402536 2.94543639 13.96435526 -5.81294676 Na 2.64648222 2.63407142 -0.34858299 6.86288662 3.81726354 -3.59749247 Cl 4.93717176 2.59226763 0.04872581 7.69863545 -3.83661287 -1.95052551 Na 2.72882543 -0.13666755 2.90024872 9.81119593 1.08107540 3.89430013 Cl 5.32715112 -0.19014926 2.54638110 0.95521569 -6.23039158 7.89729536 Na 5.56862545 2.71227450 2.61297808 -10.42814322 4.02163832 6.84905400 Cl 8.35154257 2.68744773 2.40400198 -0.90719447 -1.71149488 -8.62105084 Na 5.65341356 -0.02474468 5.52756915 -3.76296720 -5.28787215 0.11889909 Cl 8.34948214 -0.10836135 5.59677283 -2.95188318 -6.33551908 14.03857250 Na 8.43508820 2.72603823 5.32004139 2.07416112 3.51036287 4.04057481 Cl 10.79558486 2.66485377 5.38782587 -8.29914271 9.95564490 4.60646106 Na -0.18067848 2.95190593 2.47409697 -7.90666995 3.16911339 -3.20057309 Cl 3.02230677 2.83902886 2.36492104 11.95850455 -7.76381355 4.60061418 Na 2.65447185 5.83423927 2.64070741 -1.10400829 8.76458514 2.32560066 Cl 5.56998764 5.66091853 2.89134663 -1.91015457 -3.93186693 -11.68736061 Na 2.84651680 2.52244774 4.87708048 12.60383619 -0.55153064 -14.21723883 Cl 5.07383254 2.80738705 5.68574818 3.50633814 3.42600726 4.05000029 Na 5.33781985 5.36617327 5.50804813 1.88074070 1.03666757 5.33387954 Cl 8.28804543 5.47730571 5.44014089 -6.45726774 3.14249377 3.54377492 Na 5.13489449 2.12375285 7.97474755 1.91730806 8.10103409 -9.34590130 Cl 8.26400413 2.35571556 8.00384238 0.72488597 -2.77463223 4.17402746 Na 8.08370777 5.11165309 7.93137557 -3.09754480 2.30928060 -5.03504842 Cl 10.75801830 5.49178861 8.14859785 11.11909076 13.49263756 -0.74248381 Na -0.11721487 5.23927189 5.37166013 3.28788182 0.50615521 8.13922120 Cl 2.84204365 5.84898067 5.39522203 -5.85035376 3.58900156 -3.68711616 Na 2.80439799 8.06508322 5.32502603 -9.47527109 5.79505419 -3.91922903 Cl 5.13608777 8.25147010 5.27353722 -6.72350131 -6.77794239 4.58702269 Na 3.14188467 5.46574336 8.17432085 3.26767827 -7.77637869 -0.69929209 Cl 5.65594057 5.16685346 7.56301959 1.30885413 0.18506639 -5.25983086 Na 5.32529704 7.89320927 8.07721554 1.08203040 10.92267457 -3.44554946 Cl 7.89062901 8.06902339 7.96955645 -2.47343962 -3.63597157 2.24726825 Na 5.71685987 5.48122500 10.75103631 -6.46262405 0.35553762 -6.31451003 Cl 8.06290718 5.36035640 10.96613459 4.85183845 -5.93793769 7.60804025 Na 8.21104536 8.41933881 10.96289594 -0.66701877 5.98448995 -5.13048889 Cl 10.64184815 8.28998156 10.71546930 3.14031014 2.83079547 7.95411553 Na -0.09999828 7.81133451 8.24268397 2.45940702 6.53590063 7.77110516 Cl 2.68559312 8.23479833 7.83768358 -1.72147438 4.24740901 2.07356526 Na 2.71260288 10.64714707 8.14771086 -6.68448599 -5.00324353 -1.01307349 Cl 5.17646709 10.64197512 8.33041079 -1.68846299 -1.81207682 -3.41808988 Na 2.76144373 7.77887656 10.80900837 -2.37254981 -3.07876042 -2.36295878 Cl 5.36365026 7.91506093 10.69699950 1.05504391 1.54532613 12.66637327 Na 5.54154932 10.91586690 10.97457587 -0.29263136 3.99007686 -4.17940088 Cl 7.95546166 10.78115208 10.88088474 7.71197575 -8.49144878 4.86657643 Na 5.27485172 8.02799637 13.70572793 1.72560740 -4.36010112 2.44375669 Cl 8.36857436 8.23783783 13.33639062 -2.27475843 6.95366612 -3.34514130 Na 7.75316551 10.88128742 13.54452831 1.37232304 -4.31461895 6.45706110 I 10.80442583 10.91927564 13.58522149 5.90228709 -3.32706455 4.31187966 48 Lattice="0.0 10.8 10.8 8.100000000000001 0.0 8.100000000000001 5.4 5.4 0.0" Properties=species:S:1:pos:R:3:vel:R:3 pbc="T T T" Na 0.24971684 0.26390522 0.01465953 2.52725718 5.21778227 13.46639034 Cl 2.60446340 0.16749777 0.01550271 10.66570512 -2.04483355 -1.09718349 Na 2.69653378 2.68887264 0.03166544 -1.69069640 -6.09494213 0.09620799 Cl 5.47159505 2.88600554 -0.00418658 0.90843432 -10.36771221 -5.94300384 Na 2.94187034 0.06698531 2.64519453 -7.90017249 -16.12774554 3.32007613 Cl 5.42077312 -0.08734434 2.53405304 4.04574779 -0.94834029 7.23579696 Na 5.25067732 2.77880186 3.07991750 1.14988938 7.94295411 -2.83829421 Cl 8.07340752 2.63528292 2.56368799 -1.25457119 -4.60881142 1.10586722 Na 5.43096242 -0.01362423 5.57241911 1.92280217 -3.50069845 1.88383988 Cl 7.84377609 0.10707414 5.37041655 4.70141841 -6.46186088 3.81404956 Na 8.15951571 2.77998052 5.43491507 -4.82560491 4.14102731 7.00954434 Cl 10.93451469 2.41051828 5.05730896 6.05298964 -1.44247503 3.84943502 Na -0.08034165 2.76941258 2.58298912 -4.15972222 9.30976633 -1.97097024 Cl 3.02875817 2.65469906 2.50732046 6.00480648 -7.69745535 5.24179453 Na 2.65138353 5.74367018 2.53082135 2.76845807 -0.46876099 -3.65475343 Cl 6.05923832 5.29756440 2.87585599 -3.40441806 -1.29401028 -3.50442829 Na 2.80310047 3.00932962 5.57768933 1.56026304 -5.53352142 -0.04569668 Cl 4.90912520 2.73833944 5.52278735 8.20773137 -2.77832071 0.96714081 Na 5.44536455 5.52724268 5.41921712 9.73715040 3.92556303 -0.10774745 Cl 8.14370502 5.32464166 5.56403727 -11.34896372 -7.25112475 -2.40635008 Na 5.28999933 2.40679429 8.19169891 -2.56888965 1.31490747 3.03607257 Cl 8.23454803 2.59084959 8.13561125 -7.00295056 3.56074306 -3.72548560 Na 8.42923857 5.64426199 8.14031310 3.11512661 -7.48338674 -3.76547780 Cl 10.55424978 5.23964553 8.21754625 6.75110155 -1.41904825 -4.34099284 Na -0.08471833 5.29259280 5.86073922 -4.54644228 -6.02620049 2.44787369 Cl 2.36878637 5.33588944 5.34552173 2.36351408 5.67141076 7.75840657 Na 2.81814519 7.92504639 5.55610757 5.75731965 -0.12557093 0.13735831 Cl 5.13073121 8.22400734 5.45431228 1.93640298 -6.78157691 -2.24747500 Na 2.73098000 5.08936773 7.94321367 -7.50982319 1.23136739 4.18693399 Cl 5.43830641 5.55281315 8.34736025 5.97715928 1.17473492 0.33492448 Na 5.96932740 7.85571835 8.29773878 1.12585249 -5.58359874 -8.97863793 Cl 8.12763578 8.17640012 8.08174385 -14.59488601 -0.52059860 -6.23774060 Na 5.74336604 5.51590232 10.50213758 5.95052057 0.60276426 -3.20766755 Cl 8.17307797 5.49036439 10.82706894 5.54356320 1.68191511 -1.27356955 Na 8.07096837 7.98032943 10.78206703 -3.97650529 8.36586885 -4.80354612 Cl 10.76152958 8.00552502 10.85490860 -1.89579680 0.97888446 -1.06728295 Na -0.09553524 7.93069481 7.81976218 1.77644129 0.70891478 -7.47005729 Cl 2.53183167 8.22172050 8.03586772 2.09924275 -7.92476439 -7.44442905 Na 3.22198859 10.65448510 7.95100373 -1.83942715 1.99116866 8.19295513 Cl 5.46854725 10.63075849 7.85119672 -6.84015579 -6.88889510 -10.07787390 Na 2.31761273 8.18501554 10.63921986 0.08465307 -6.36722948 -1.83876692 Cl 5.27269747 7.92715729 10.75424243 5.96822174 -1.25630449 4.86832488 Na 5.60436229 10.99826322 10.36940401 5.40744910 4.13570582 1.58320210 Cl 8.06059124 10.70706543 10.78406452 2.58112398 -5.12591164 7.32695503 Na 5.27425426 8.18725633 13.29740354 -0.93103100 -2.76644164 -3.07195901 Cl 8.20277223 7.94257890 13.65802926 -4.89276172 -1.12144197 4.32884845 Na 7.83797049 11.10468169 13.74695933 5.49536616 -3.05811163 -6.61145906 I 10.89960846 10.60322515 13.81373618 6.82877594 1.48708418 3.81298358 48 Lattice="0.0 10.8 10.8 8.100000000000001 0.0 8.100000000000001 5.4 5.4 0.0" Properties=species:S:1:pos:R:3:vel:R:3 pbc="T T T" Na -0.09314210 -0.11839551 -0.09054345 -1.64424281 10.14304115 1.77904216 Cl 2.53424705 0.11453381 0.04246483 -2.59296630 9.72304673 -5.69414404 Na 2.24228974 2.66908199 0.16283814 2.69025908 -8.57792679 2.55806648 Cl 5.27046046 2.89481373 -0.16314231 -1.59505302 1.61428066 0.54167451 Na 2.58050079 -0.30322126 2.94897739 7.88709301 0.93858109 -11.08532004 Cl 5.41930416 -0.07959131 2.49216149 1.34920739 -19.40730513 -4.22042187 Na 5.51986726 2.71919737 2.44490435 -7.15821249 -3.11436458 -2.92185472 Cl 8.05977559 2.58601816 2.61398209 -3.17842007 3.65560451 -11.89051669 Na 5.02461403 0.13594837 5.78859842 1.43108062 9.56007037 -2.39760944 Cl 7.85541085 0.07479114 5.49592634 6.36845437 0.81464011 5.33830017 Na 8.22482037 2.65784222 5.48305583 -5.40957637 -9.99275864 4.07587908 Cl 10.76550792 2.30684198 5.34579850 -0.08264097 1.71328964 -3.99026737 Na -0.29562402 2.76116008 2.55079568 2.36288279 7.43645648 2.41729739 Cl 3.11518086 2.28134172 2.86820849 -1.07024818 7.42507372 -8.81789265 Na 2.83983529 5.44431429 2.60574742 -3.72202887 6.61658938 -2.31500811 Cl 5.69178178 5.16898140 2.58171921 -5.40984113 7.65506152 -3.92036062 Na 2.45584243 2.48196511 5.33140718 8.30197120 -2.51374788 0.62747547 Cl 5.37222741 2.77525302 5.42617696 -2.20806914 -9.83537400 -1.23456143 Na 5.63350292 5.13128333 5.21136437 0.14334622 3.03000140 -2.62342770 Cl 8.38483900 5.79350045 5.38580746 -1.37115250 6.07229532 -1.77273555 Na 5.62052202 2.47481106 7.89507406 4.45637104 -8.45273169 -2.62694343 Cl 8.15204950 2.80828663 8.40637116 3.94559213 -4.73973441 3.11699655 Na 8.13865217 5.54753231 7.81000873 -13.76025591 -0.53911966 -1.42199143 Cl 10.60283040 5.47116538 8.10829329 0.14437000 -4.30666458 -0.61110091 Na -0.13560034 5.18869529 5.46195801 1.17970684 6.16646692 0.23422541 Cl 2.35082720 5.30680883 4.98952848 -7.61692890 0.37880042 -1.99706358 Na 2.83052705 7.83503570 5.58228779 -11.79784817 2.82924420 2.64781671 Cl 5.51740750 8.04451863 5.32674234 -1.85064457 -1.88068060 -0.67441674 Na 2.39793427 5.13014899 8.06638665 9.09330529 1.37592706 -1.02973373 Cl 5.35398141 5.43967381 7.80527544 -2.44878885 4.65122126 2.84040303 Na 5.16969182 8.45544136 7.89290034 -6.77656481 -4.04964882 2.77092636 Cl 8.10486358 7.85836622 8.23193311 -1.67646351 -8.61856704 -3.68975887 Na 5.72294814 5.52408871 10.78578736 3.23837627 -4.94294705 4.85492923 Cl 7.93042885 5.49978741 10.86786273 4.26888318 -2.67952463 0.45291347 Na 7.99235173 7.71016488 10.92539219 -12.14290822 8.44957456 -4.55545955 Cl 10.62854781 7.80655295 10.78394843 -0.37278646 -9.56987285 2.48827408 Na -0.02120824 7.88553175 8.13512688 0.40210214 1.10508953 -2.52198033 Cl 2.63547854 8.34668335 8.35296209 -3.51677269 3.75778870 4.08972699 Na 2.37068281 10.81499701 8.07178201 1.18010471 -5.93334356 6.48550955 Cl 4.96606864 11.02986673 8.10093895 -0.74147343 2.71978186 -7.18127189 Na 2.78523240 7.80010032 10.70572607 -2.11270758 2.24167191 7.17395665 Cl 5.35391433 8.04991682 10.64094458 -2.93128283 7.89723519 -5.43042443 Na 5.57445995 10.76958846 10.61569233 3.93783070 -0.44240763 -0.14685680 Cl 7.63033101 11.05830472 10.57343229 5.78881129 -3.55300893 -7.30922300 Na 5.46050971 7.77124833 13.44668988 -1.56133366 4.59977857 4.40362512 Cl 7.53118397 8.05420025 13.60753188 -1.08646121 -4.97716489 -0.64871442 Na 8.24206728 10.80446570 13.81665308 9.25622688 -2.26028853 -8.10427007 I 10.49956062 10.93903468 13.05541467 -4.59135353 5.87271061 -10.03226228 48 Lattice="0.0 10.8 10.8 8.100000000000001 0.0 8.100000000000001 5.4 5.4 0.0" Properties=species:S:1:pos:R:3:vel:R:3 pbc="T T T" Na 0.41028544 0.05988058 0.09291943 1.36412710 1.84778511 9.48753533 Cl 2.51441994 -0.00206007 -0.16838246 3.33809249 9.13057553 1.44725559 Na 2.57626382 2.58907427 -0.21642565 0.04798627 -1.68368807 4.10306200 Cl 5.20800400 2.61101698 0.33637700 -0.55365964 4.21212414 -1.19192996 Na 2.72519022 0.20417924 2.70124385 -10.29944997 0.78991094 -4.58037170 Cl 5.19406824 -0.04101479 2.83872881 -2.38966312 -4.70567652 6.06503172 Na 5.24917263 2.65704061 2.45643478 -3.44308926 2.82119190 1.11130696 Cl 8.10511338 2.50634609 3.14382652 1.28547904 2.89302477 2.36870721 Na 5.51921619 -0.04363578 5.34072356 -5.60032583 -3.44507858 6.38603533 Cl 8.20175496 0.27242187 4.82580033 3.93400059 -5.42175897 8.07929170 Na 8.12303506 2.61445483 5.49065376 -4.40491995 2.61277641 7.43035542 Cl 10.93096124 2.80934174 4.81506970 8.81079934 -3.61089536 -16.27731139 Na -0.03586252 2.76537011 2.65603621 5.42124220 1.90114021 -8.96252825 Cl 3.01149406 2.56631947 2.34361320 -2.09070088 -1.89978928 -1.98594899 Na 2.91830749 5.55799359 2.48366385 -7.94994984 -6.93466494 -7.94478128 Cl 5.24357051 5.38755579 2.89941076 3.75563192 0.22180996 -10.41594263 Na 3.08749983 2.90577381 5.69908713 -3.72961379 -2.06795543 1.57209751 Cl 5.21764562 2.55462161 5.31705031 0.60647932 4.21277653 -8.07956492 Na 5.21609248 5.54136557 5.22052873 3.10189098 -1.04536549 -0.09571139 Cl 7.88112905 5.15455655 5.06277801 -6.75806484 -10.93484976 0.52589210 Na 5.12193782 2.57615897 8.58726591 3.67311690 -2.77754551 -0.00959803 Cl 7.85044904 3.04666994 8.42281149 -1.92500292 1.43925456 0.77373006 Na 7.61917410 5.43987862 7.87896568 -1.04326486 -2.57351277 10.02295262 Cl 10.71580078 5.39628685 8.15734168 6.97424049 1.78352314 2.87404686 Na 0.24530035 5.34529404 5.16459217 1.40473956 4.90937183 2.06730955 Cl 2.88077987 5.57702125 5.40109525 2.84546351 1.85029717 -11.18655709 Na 3.19960904 8.30737352 5.19498978 -5.33057147 1.24184517 -5.74091571 Cl 5.19625374 7.80716615 5.42711716 -5.13720721 -6.33268597 -0.50482013 Na 2.68273399 5.48912416 8.30362943 -3.69387403 -3.36728665 -2.71619877 Cl 5.19372628 5.07076898 8.24281708 -2.20058329 -1.22031758 -0.00062068 Na 5.14853179 7.87782105 8.02375040 -7.66884702 -2.80085276 9.51857918 Cl 8.00587160 7.94273123 7.98035706 -8.66037244 0.79689338 5.79240783 Na 5.43388877 5.42117599 10.73627735 1.22805380 3.99296035 7.04228579 Cl 7.98175467 5.21033520 11.09106795 -6.41629539 -0.91104904 4.51944254 Na 8.04903827 7.83572320 11.15368360 2.55754604 -3.63970584 -0.91427968 Cl 10.68419410 8.34183496 10.72730343 1.83569017 3.98351596 8.06314732 Na 0.02673225 7.95024843 8.28706425 0.20336469 1.25119767 5.69653320 Cl 2.91071295 7.98290124 8.24396072 4.95295992 3.66116944 -2.30938972 Na 2.89913030 10.73060567 7.97832230 2.06746333 -0.56165402 -1.78738789 Cl 5.38315421 10.57777674 7.86307546 -5.56351816 -1.90086391 -3.20367460 Na 2.77568508 8.18166255 10.77246300 -1.85054082 1.81085625 6.64643886 Cl 5.53971743 8.03129979 11.10207599 -3.53917621 2.25212467 -0.56852063 Na 4.97690221 10.53348654 10.46697725 -1.23486058 -9.06280312 1.00418569 Cl 7.84570468 10.99600446 10.91652497 5.04014099 11.98952188 1.02821068 Na 5.20589020 8.44841796 13.61655452 5.65551206 0.88847347 -4.64050204 Cl 7.78357549 8.15366752 13.35213008 -1.40481129 11.40375146 -4.76583822 Na 8.21097880 10.93082871 13.27435804 5.45953646 -6.41317842 -9.72905367 I 10.97212914 10.73781303 13.47489200 -6.67934834 -1.81156443 -1.82366885 48 Lattice="0.0 10.8 10.8 8.100000000000001 0.0 8.100000000000001 5.4 5.4 0.0" Properties=species:S:1:pos:R:3:vel:R:3 pbc="T T T" Na -0.07503673 0.09462041 0.24242707 6.95170101 4.64197796 -12.83746204 Cl 2.74935316 0.13871504 0.18429371 -1.77971760 1.03210395 1.58767451 Na 2.68761694 2.74557041 0.33403950 0.73299683 -0.10469507 -0.32742010 Cl 5.38778193 2.43459612 0.03461967 -6.80266926 4.05078663 -4.72954321 Na 2.87340603 0.23927514 2.96376872 -5.41633458 7.32162561 1.15816909 Cl 5.64858331 -0.31157058 2.66655006 -4.73290282 -3.77650961 5.62001315 Na 5.36487679 2.85621161 2.67335091 3.00534270 -12.25452950 -2.74721754 Cl 8.01954744 2.64767994 2.96361409 7.18208799 9.86634872 4.90592387 Na 5.36515246 -0.35613878 5.26558299 5.73136099 1.97938929 9.56643395 Cl 8.29261129 0.08040374 5.40644747 -5.16530628 -6.08198637 -3.71597923 Na 8.21348287 2.70422425 5.23931419 -10.67207429 9.06742703 -4.63222384 Cl 10.73469277 2.69823893 5.76048632 6.11266610 5.12010727 2.89933225 Na -0.20239449 2.90235228 2.98491007 5.35655132 5.29356637 -17.06583823 Cl 2.68677301 2.91743355 2.89093459 2.12520265 -0.64562699 -3.75389250 Na 2.59353664 5.89222893 2.55268000 2.41394516 6.27329271 -1.26360151 Cl 5.21271459 5.01628867 2.60023366 -5.77485913 0.28729495 2.23384018 Na 2.62633899 2.89967809 5.20579423 1.51928508 2.38164158 -1.30046665 Cl 5.35375024 2.48152451 5.23612305 -3.77805232 -9.01537765 3.11561782 Na 5.39241754 5.40494622 5.41094026 0.65276307 -3.07902218 -4.36036950 Cl 7.95984765 5.48969588 5.40729073 -1.58376564 -5.08765015 4.28882869 Na 5.28071478 2.64944421 8.27474279 5.35087063 -2.55541309 0.02544378 Cl 7.87922202 2.40879502 8.02096960 -13.23589203 -0.07350025 7.90745475 Na 8.23957905 5.35325416 8.39682362 5.07615363 -14.00692881 0.60303618 Cl 11.00154579 5.53385257 7.85706480 -3.25005320 1.01519825 0.36048784 Na -0.19626565 5.19139513 5.29484135 -4.93869364 -0.66917798 2.72539381 Cl 2.65342047 5.15082920 5.32496382 -0.18240567 -2.14342234 -4.09068065 Na 2.93978682 8.19160736 5.54229605 0.38155821 -0.84597550 -4.05116910 Cl 5.20263667 7.94352200 5.36224598 -2.27895528 -3.14758977 1.61600499 Na 2.61406770 5.46799311 8.04112329 -3.49907153 5.30308752 3.99115347 Cl 5.65548798 5.50313877 7.73032289 -9.98240943 7.26438182 -1.73815988 Na 5.57064093 7.96292876 7.82388624 6.11705414 6.93729742 -4.77129369 Cl 8.11929286 8.34865329 8.48287922 8.07318966 2.93714357 0.42562318 Na 5.05187558 5.51621048 10.81116201 -6.09891728 1.57972324 -7.19393885 Cl 8.33334185 5.46927513 10.83067709 3.28857867 4.95775618 5.13244916 Na 8.08437741 8.14037688 10.86094929 -1.45286458 -5.38712605 -0.75542288 Cl 10.70482171 8.28312196 11.06180628 -5.36726823 0.06507851 0.22004356 Na 0.06241760 8.27339135 7.84461906 8.95882661 -6.99967561 4.33614811 Cl 3.11206711 8.31021221 7.61139761 -3.19572679 -8.24229216 -7.32218450 Na 2.85499226 10.79430725 7.99002388 -3.46775584 3.20293188 0.78404515 Cl 5.37164032 10.83635794 8.09175246 3.38712553 8.88667600 -2.83014885 Na 2.95690234 8.11895149 10.98005497 8.07505391 8.55756742 2.67459907 Cl 5.40825521 7.82134884 10.57802710 8.30010943 -2.45014499 -6.01404186 Na 5.59838600 10.97956514 10.80774601 -0.75420900 2.11834096 1.78298095 Cl 7.87341709 10.83416198 10.80253058 1.53392831 -7.56618066 -3.35127822 Na 5.30290056 8.14963291 13.75224087 3.09427740 2.29386821 -4.23265293 Cl 8.13950914 8.35882942 13.19302435 -7.12818698 -0.22077877 3.18388375 Na 8.10916969 11.14428474 13.38939811 7.89078584 2.87394183 -0.39083112 I 10.88137446 10.60175219 13.15676024 2.31150649 -3.17015541 -2.69945017 48 Lattice="0.0 10.8 10.8 8.100000000000001 0.0 8.100000000000001 5.4 5.4 0.0" Properties=species:S:1:pos:R:3:vel:R:3 pbc="T T T" Na -0.21509603 -0.41341092 0.13708309 -4.66626666 -3.39004904 2.78506251 Cl 2.60449398 0.18784201 -0.23321424 -5.82973174 1.14477837 -8.65822661 Na 2.69867472 2.68218142 0.10521122 -0.80888589 5.41460035 0.67935962 Cl 5.42697842 2.61684022 -0.05683621 -0.19497316 -6.92808997 7.99591407 Na 2.72305976 0.18451226 2.80139976 -2.11921837 4.17746040 -0.05845405 Cl 5.48634840 0.13753886 2.61843104 10.10112074 1.76249088 -4.95116883 Na 5.54196402 2.78261919 3.02874564 -2.67570113 -3.88354010 -4.01259344 Cl 7.76229421 2.93794117 2.65857293 10.03887894 6.36384811 -2.21532168 Na 4.80095149 -0.00510485 5.58241479 -6.75035664 -2.48664193 6.22370788 Cl 7.98309886 0.18249220 5.86655811 5.59958132 2.13615284 5.40302303 Na 8.04519081 2.80761697 5.51484496 9.25940226 7.69261996 5.10257471 Cl 10.87526041 2.54041406 5.60648939 -6.04305262 -3.15094055 -3.05427116 Na -0.17510513 2.38749770 2.43412901 -9.73854207 3.85645203 -3.20626804 Cl 2.68427483 2.41733116 3.00629480 -3.67698400 1.37678642 7.15145122 Na 2.26595352 5.37915453 2.69789810 1.80843892 1.75863274 -8.40115188 Cl 5.80797912 5.30307700 2.75307869 5.64673546 1.93536771 -5.23925699 Na 2.86129688 2.85841947 5.26860820 -0.77124387 4.97873434 -8.38583173 Cl 5.37890357 2.52046046 5.42754265 9.54613263 6.69687267 -1.49294989 Na 5.47583001 5.61703001 5.40107008 3.78377709 -8.81444512 2.88328663 Cl 8.46683220 5.45712480 5.42715226 -8.44664993 7.30096184 7.18473769 Na 5.65258630 2.82593920 8.12479322 -10.30239050 1.30094633 -2.62432584 Cl 8.43579125 2.59515915 7.91617809 3.29498707 0.34440879 1.93168075 Na 7.96141985 5.47212966 8.37161735 -3.53536590 4.18716048 -4.25737123 Cl 10.78566511 5.29002119 7.78834400 -0.38191984 -4.06459902 -0.04544865 Na -0.02571096 5.79941559 5.47584830 1.37418373 1.47630554 -7.62553312 Cl 2.40199119 5.21629303 5.34981935 3.93819230 -5.07419052 8.97619131 Na 2.87654078 7.91688275 5.38700284 -1.75903869 -7.10566108 -2.43494464 Cl 5.41739277 8.24285875 5.17965962 -2.49693731 1.19559496 -4.78123731 Na 2.85490048 5.26978532 8.20990292 -4.02749154 4.47647794 -3.04338690 Cl 5.22240719 5.27862190 8.30571849 -1.49025799 -3.67689930 -2.95565981 Na 5.27929742 7.85793313 8.00897399 14.67828408 -9.67550300 -5.10174074 Cl 7.98365754 8.13821815 8.11045129 5.83977465 4.19608380 2.11479978 Na 5.41190045 5.46858553 11.01598790 -3.32157024 2.92025714 5.71443008 Cl 8.13456194 5.40133493 10.56982780 2.08904691 3.47099973 -4.47422409 Na 8.01350340 8.40516096 10.92555357 -3.28105093 -0.38107251 9.51942007 Cl 11.06135206 7.94220547 10.88781807 -6.00440977 -3.79832459 2.18528451 Na -0.34879416 8.19183965 8.40115060 9.31524167 -9.20961753 -2.81644528 Cl 2.43150753 7.97508288 8.23353883 -8.70584481 -0.60195406 8.43732124 Na 2.70256943 10.70379373 7.83406016 -0.57131158 1.32702576 -4.92400455 Cl 5.14069638 10.80304887 8.10364450 6.07060766 -0.10831068 -3.42505763 Na 2.65856715 8.37295916 10.74550527 2.48719471 -10.20979758 -1.17229069 Cl 5.34264814 8.17645185 10.45078372 2.73195432 -0.00757485 -1.07519138 Na 5.46375164 10.77381529 10.29602112 6.75089753 -1.64593777 5.32748018 Cl 8.03217015 11.20064758 10.83024698 5.22553222 3.34895044 -3.41638163 Na 5.37853070 7.79557454 13.37476164 0.13542322 -3.25731326 6.47607675 Cl 8.03032009 8.29308810 13.43133314 4.03451468 -3.08529904 -0.58216701 Na 7.84428552 10.71204931 13.70273293 0.57875560 -5.67103706 -2.53757759 I 11.07556653 10.63763084 13.71495372 0.65920498 6.60075951 1.95277085 48 Lattice="0.0 10.8 10.8 8.100000000000001 0.0 8.100000000000001 5.4 5.4 0.0" Properties=species:S:1:pos:R:3:vel:R:3 pbc="T T T" Na -0.10566134 -0.02981001 -0.34015827 -7.34461544 -5.35000359 -5.95768335 Cl 2.73521181 0.01839301 -0.18530527 -3.86031924 9.76506678 -0.47940972 Na 2.70766784 3.01399462 0.11546880 -1.72725602 -1.77613081 5.14803052 Cl 5.66831292 2.67802058 -0.06998749 -12.53952762 13.30930866 -8.69552343 Na 2.48786855 0.18844044 2.85045364 -3.11447138 -1.52085605 1.28221734 Cl 5.35655192 0.09157714 2.33028076 1.53580260 -5.14097477 4.31653510 Na 5.75898677 2.49049109 2.21499536 0.53153661 0.21164396 0.55183630 Cl 8.18816548 3.12608854 2.42175628 -11.51074396 -4.35987695 -4.00770832 Na 5.46172080 0.13490104 5.46268963 -5.42363992 0.63768795 3.72460694 Cl 7.56754390 0.18462135 5.34727492 -3.81432573 -0.18471771 15.08289665 Na 7.96259324 2.84987317 5.35360617 -1.72173104 2.36450833 5.77642506 Cl 10.77087885 2.56433524 5.29587225 -0.88589867 -13.03434712 8.46400895 Na 0.09295421 2.65681120 2.55099225 4.28537988 5.49894069 -8.25383152 Cl 2.92370002 2.57450057 2.22049912 7.57114272 -0.47229379 6.28500678 Na 2.70232170 5.63545363 2.45697193 2.47560447 -0.27287374 -2.59545590 Cl 5.44813006 5.67563702 2.44318021 1.13878069 -3.23099621 -1.25963709 Na 2.70703244 3.24163997 5.03083857 -1.73047976 -4.28153517 -3.02229720 Cl 5.24739002 2.40935693 5.30638725 6.41431679 -5.05396941 -4.61194414 Na 5.46921248 5.64093588 5.46075670 5.27815296 1.03467153 2.27793638 Cl 8.20509119 5.53079753 5.56465697 -7.95962766 -0.63942069 -0.78404652 Na 5.31266123 2.86534795 8.34169607 8.14778587 9.95132614 -0.32275784 Cl 7.96026531 2.94568713 8.46640242 -3.80281851 1.15629277 -0.44587454 Na 8.19005955 5.31755349 8.11273398 3.16953138 1.15082731 -0.24438036 Cl 10.89398413 5.48599826 7.99579980 -0.68354164 -9.40403921 -8.51482112 Na 0.03182340 5.31661758 5.62733194 -0.16996113 6.16663755 -0.62346681 Cl 2.37763889 5.32244230 5.41210803 -8.29064027 -4.65096443 -3.04707776 Na 2.61298139 8.11413317 5.76510935 -1.82876991 1.94855168 -5.09374270 Cl 5.35871850 7.64584392 5.34383114 0.11819109 -2.83224555 -12.72705687 Na 2.97870189 5.42089814 8.15735956 3.46106612 -0.39508542 0.34590054 Cl 5.29903283 5.67245936 8.53463312 1.79642570 -0.73099253 2.99423431 Na 5.34360915 8.13773102 8.33374926 -2.60978543 4.78224413 6.81150334 Cl 8.27315498 7.95590767 8.17339953 3.76966737 -2.71837253 5.36815626 Na 5.52494479 5.81072129 10.73599220 -2.32863810 -0.70969363 6.20386389 Cl 7.88762510 5.61959120 10.93818171 6.90799347 -3.00066978 7.32207423 Na 7.78566771 8.02268145 10.77537089 4.78190149 -7.65496949 4.26824487 Cl 10.80619256 7.94101402 10.70466895 -3.10852945 7.23782338 0.39257256 Na -0.01657614 8.00727113 8.18864073 1.63317982 3.76116407 -2.43608469 Cl 2.52328932 8.28229325 7.99411962 0.78136381 4.18481817 4.70387098 Na 2.65009320 10.72572752 8.32685727 -5.25450770 1.03063702 1.78532791 Cl 5.07515461 10.53653275 7.97328175 3.24512213 -2.35435345 -5.93478398 Na 2.83991957 8.02397748 10.81251317 4.56462840 4.52538963 0.49189179 Cl 5.83300793 8.00687081 10.67788413 10.71940853 -5.41878268 -8.18477353 Na 5.24373868 10.65356264 10.82266878 2.50618453 3.82324996 4.09823036 Cl 7.98971156 10.86982926 11.00464782 -1.65954674 -1.27830317 0.09968113 Na 5.14394193 7.99699955 13.53829438 5.66000285 -0.98584466 -4.43116477 Cl 7.98088967 8.36933419 13.48639096 6.33815588 7.81930500 6.01611637 Na 8.18796939 10.70527949 13.78347001 -6.91439095 4.43376270 -0.97172993 I 10.82817731 10.69577625 13.44642330 -8.41827462 3.40513128 6.72327384 48 Lattice="0.0 10.8 10.8 8.100000000000001 0.0 8.100000000000001 5.4 5.4 0.0" Properties=species:S:1:pos:R:3:vel:R:3 pbc="T T T" Na -0.00213253 0.01573786 0.02588024 -5.83833141 -5.33875265 5.55399138 Cl 2.91479211 -0.19667733 -0.66295858 1.56938540 -6.07331379 5.04430956 Na 2.34923954 2.79537833 0.16528838 -2.05382665 -7.25441929 0.10077894 Cl 5.26642623 3.21300528 -0.31632414 9.43503333 0.07528926 0.92779615 Na 2.63787151 0.34330235 2.54076524 0.74533917 7.31928411 -4.54807187 Cl 5.17907442 0.13211041 2.68872625 3.84266728 0.03067527 3.20504942 Na 5.38888903 2.72702220 2.71315382 7.34642508 -1.92583706 -1.75001399 Cl 7.85809008 2.79703966 2.63557180 4.58696895 0.20436925 -3.02128398 Na 5.19873180 -0.26380292 5.48992790 -5.20241932 -10.62409597 0.94313264 Cl 8.20290134 0.26896030 5.39215892 -9.21874920 -2.13212194 -2.66354711 Na 8.25712890 2.72505823 5.64432450 -5.92906399 1.78738514 2.49828771 Cl 10.76549542 2.40674245 5.63197582 0.82745885 -1.54922734 -1.80586389 Na 0.11670205 2.91868283 2.77242432 -8.15737125 -3.77393464 0.80923976 Cl 2.75708512 2.38249149 2.81553991 0.20010121 -2.59328254 5.94338352 Na 2.81523272 5.54538973 2.65887460 -9.74260829 -7.14147879 -0.73316440 Cl 5.92355357 5.56551662 2.69063860 7.84092171 -3.99295647 -3.11899765 Na 2.64798001 2.82616771 5.49002497 8.60949979 -5.24362253 -7.35257872 Cl 5.35955268 2.72564172 5.09117135 -0.03662752 8.05569366 -1.39248299 Na 5.41769019 5.35300524 5.66759227 0.98007727 -2.56206555 5.55306254 Cl 8.00530041 5.24959814 5.61002964 -8.93673245 0.66287381 8.08671713 Na 5.53214222 2.41130813 8.12396683 -1.66296738 -3.14783174 0.55208476 Cl 8.02944279 2.66240097 7.92784627 -9.62825772 -5.10453713 -0.22192971 Na 8.05657198 5.18087691 7.94778665 6.33864393 1.01566233 -0.80585627 Cl 10.81069577 5.44962181 8.04798911 -13.31333606 4.10850174 8.05684973 Na 0.31130443 5.61639375 5.81429884 12.70676324 0.74522070 -4.25456267 Cl 2.85745054 5.40314841 5.35320155 1.73447873 5.66812044 -1.14840068 Na 2.50120704 8.08084096 5.38343457 10.63451848 -0.96769363 0.96539813 Cl 4.94248931 7.84679546 5.46851800 -6.35652130 -0.20816448 -0.16992291 Na 2.68373468 5.02523825 8.11241635 3.31818271 -5.44080974 4.07334414 Cl 5.45403567 5.12825104 7.95128619 6.89693845 9.30688959 2.81921150 Na 5.24644661 7.85798930 7.94121927 -0.75171955 0.20230885 1.11420609 Cl 8.16587072 8.44806568 8.12821461 -0.64982338 -7.32665166 3.68384598 Na 5.26145451 5.31874619 11.06181064 0.26917351 -1.86324886 8.05199458 Cl 7.77043669 5.73778405 10.90843567 2.79399997 0.07335622 2.84088438 Na 8.32609071 8.36634532 10.75303451 -0.35162705 -6.51926202 -0.59703799 Cl 10.15751950 7.97599030 10.77027756 -3.39871746 4.77336336 1.13807838 Na 0.12090736 8.07655378 8.51198262 -5.98498387 -4.10054886 0.97296657 Cl 2.52232794 7.98991989 8.11466392 8.67280443 -4.08341873 -8.60248890 Na 2.65114326 10.96032892 8.38884073 8.56094425 6.78153695 -8.88213519 Cl 5.13502485 10.70721286 7.92388252 0.50792757 7.44214775 -4.06518101 Na 2.90270620 8.21193547 11.09364619 -3.70475638 -12.81657383 -8.86922882 Cl 5.47599791 8.20481446 10.63432668 -6.62063463 -9.17381007 3.31094599 Na 5.60453901 10.76058753 11.12627899 7.60835087 3.29593935 -0.68334296 Cl 7.72784279 10.85468565 10.61402890 1.03456290 6.88079749 -2.43852393 Na 4.94107769 7.81607242 13.30594371 -7.03172299 5.56857120 2.03639498 Cl 7.80205661 8.10999459 13.47836334 9.73527417 -1.12207413 -4.83624822 Na 7.62434261 11.08841883 13.75148080 -2.97244429 5.35240641 6.80989349 I 10.96148805 10.85567091 13.73933834 0.20853146 -2.38242040 4.41427443 48 Lattice="0.0 10.8 10.8 8.100000000000001 0.0 8.100000000000001 5.4 5.4 0.0" Properties=species:S:1:pos:R:3:vel:R:3 pbc="T T T" Na 0.30826883 0.13778931 -0.20093747 0.48343326 -2.68426414 -3.08919700 Cl 2.56871912 -0.28328192 0.08839939 5.44091583 -0.00586559 1.16175299 Na 2.78286725 2.61832256 -0.11061305 1.68065268 2.87048528 1.20588638 Cl 5.62840136 2.65194266 0.26819757 -5.54674437 1.65408142 8.84338108 Na 2.94599490 -0.02506343 2.49711663 8.50078486 6.78204963 -8.52907239 Cl 5.36586072 -0.05943644 2.89092606 -1.28208065 -5.47513443 -3.61201569 Na 5.18514581 3.17253029 2.47215570 -2.68029200 -3.44300442 5.74403991 Cl 8.04878838 2.83352617 2.61827041 -6.78407543 0.39857229 -1.47634598 Na 4.92524951 -0.11740841 5.29946052 -0.58758345 -5.69914742 2.69201600 Cl 8.07333735 0.05247955 5.44548237 -2.43250441 -3.30335191 0.53871147 Na 8.22196289 2.62881987 5.17846087 5.40616494 -9.74446757 4.61724408 Cl 11.07023856 2.77974592 5.45075564 -2.51066952 -1.82187296 0.25747996 Na -0.32993582 2.78333032 2.35126060 -11.95433629 -0.95955665 -6.79425545 Cl 2.66641245 2.33442927 2.64142425 1.39284100 -2.35595320 6.37967503 Na 2.79629325 5.35012249 2.72339892 -5.82910503 2.01930275 2.38002565 Cl 5.35201768 5.32823244 2.56740801 -3.58898938 4.54609370 3.19565979 Na 2.72227875 2.91007260 5.43633265 4.18370217 -6.06916903 1.65941811 Cl 4.97137092 2.18711253 5.87042801 -3.02692860 -12.46549162 2.41188860 Na 5.69277077 5.75311390 5.58720529 -0.11655466 -0.65190645 -3.43671528 Cl 8.08440554 5.01750509 5.36137358 2.21853069 -4.83979000 -1.88661897 Na 5.27761279 2.57991186 7.87176626 -0.99224948 6.83882276 5.91057410 Cl 8.45599568 2.90167898 7.91749160 -0.42925701 -6.65822807 4.96110197 Na 8.21520723 5.49021333 8.04713504 -5.09219349 12.58129155 9.61015922 Cl 11.09679647 5.40479449 8.15378605 -1.53334843 6.30251661 -5.23791699 Na -0.26050189 5.12777845 5.05906414 1.42031504 -3.54251081 1.33583830 Cl 2.75817290 5.21192185 5.86073158 1.42642393 7.21523668 1.82599003 Na 2.99743113 8.06982241 5.10948538 -3.40712647 -0.87511015 1.95689770 Cl 5.21716650 8.21844762 5.49235351 0.60442311 5.93530170 4.66808485 Na 2.36328896 5.26583373 8.25145518 -1.33630521 3.94427760 -1.47691465 Cl 5.48986058 5.28371067 8.63671855 -3.73720895 7.58316951 -0.95116769 Na 5.87516098 7.80771879 8.09743996 8.39233539 -2.07341644 0.59497400 Cl 8.10906222 8.41881518 8.28678572 8.70568431 -1.81528873 3.27748293 Na 5.14133440 6.02675413 10.91602971 -0.56396731 -5.77355252 -1.79699786 Cl 7.80762729 5.07693321 10.36927101 -1.94146098 -4.84274580 4.89180577 Na 7.96818535 8.19854122 10.62041272 -2.19815636 3.32982905 -1.82135760 Cl 10.64628568 7.94189796 10.79894862 -11.19032540 6.10883350 -2.29274250 Na -0.56967308 8.02550305 8.18529883 -0.11114228 4.77946224 -3.96351714 Cl 2.30767133 8.03907899 8.24513131 7.19210705 5.60632746 -11.40725542 Na 2.69212732 10.63659909 8.14339620 0.00547812 -1.16591031 3.83622521 Cl 5.82483795 11.27519202 8.19965087 4.96835161 -2.09056384 -2.68191253 Na 2.67053308 7.87603041 10.47673667 5.59184116 10.94866340 5.18306516 Cl 5.74996771 7.65427308 10.86244598 -1.08500474 3.09757752 -1.59623547 Na 5.50981378 10.52298037 10.84529671 -0.17117283 6.11469607 -6.74481497 Cl 8.15612113 10.97736502 10.50001968 -7.80216443 -2.59188954 0.26868199 Na 5.57906940 8.17549036 13.57400223 -5.45199123 5.08242630 -9.88089461 Cl 8.30726555 8.01144535 13.29986851 3.11683704 -4.01403029 5.90787274 Na 8.04419220 10.44061599 13.52024636 2.27166169 -1.47538843 -1.03212938 I 10.67734929 10.77394512 13.62096817 7.48463317 -1.28827286 2.56980780 48 Lattice="0.0 10.8 10.8 8.100000000000001 0.0 8.100000000000001 5.4 5.4 0.0" Properties=species:S:1:pos:R:3:vel:R:3 pbc="T T T" Na 0.30204869 0.01338787 0.27154477 -3.99705754 -6.67753921 -7.31225246 Cl 2.56901981 -0.14936515 0.07392310 -3.20836426 -0.59445457 3.89236346 Na 2.85283615 2.49503637 -0.19490574 5.82490843 -2.49192460 -6.22121244 Cl 5.43619866 2.91338414 -0.15192287 2.57472870 6.46532668 -9.48998267 Na 2.59960924 0.08886828 2.47245647 3.30198697 -4.88389345 -3.01725549 Cl 5.43571783 0.10446865 2.49970777 -1.20154563 3.14725862 -7.60481544 Na 5.28819877 3.00547754 2.78173255 -1.98300609 -0.85612157 -8.29091513 Cl 7.96647446 2.75886846 2.82105171 -10.91580484 -4.79850026 3.78808583 Na 4.99261766 -0.42930194 5.44037644 -1.01893708 -4.65462095 2.45824995 Cl 8.12487993 -0.02410504 5.59729195 -2.20869486 0.71357967 -0.40428449 Na 8.50541590 2.72393874 5.41387897 -1.25501085 3.74252310 4.72361403 Cl 10.78006846 2.40960230 5.52769968 -1.51046029 -3.78229403 5.66539587 Na -0.30269403 2.60992620 2.46849887 -4.27139797 -3.67603689 -9.23905775 Cl 2.67200890 2.81716766 2.89431077 7.64516439 -0.43838907 0.44263483 Na 2.65821929 5.19575649 2.61778916 2.33969977 -1.21606786 2.40284015 Cl 5.37836979 5.41442499 2.51744047 5.20732223 -4.68327122 2.76709442 Na 2.86352804 2.47268220 5.20060145 -3.92691627 5.12600822 -0.11855646 Cl 5.49910747 2.54777725 5.23241333 -8.41903123 0.32105860 -4.30560063 Na 5.70539759 5.53647823 5.59738410 6.41941139 -1.05581929 -7.60743474 Cl 7.86119051 5.36145531 5.62547455 -4.45569705 7.63324084 9.40144609 Na 5.60962702 2.48022236 8.02878068 4.42805025 6.25082778 -2.95324929 Cl 8.02275571 2.87740865 7.95654936 0.67182671 2.19733677 -3.27481695 Na 7.72497430 5.73898765 8.03917523 -3.48167039 -2.14985541 2.80814617 Cl 10.79252111 5.37837091 8.29561185 14.14233822 -4.76873526 7.39853246 Na 0.11728853 5.19813632 5.20396383 -8.21477236 0.19188354 0.21123211 Cl 2.91317532 5.61451620 4.88336231 2.50389365 0.21699115 9.05168980 Na 2.42335891 8.12381027 5.76903578 2.02562552 4.69751656 5.85005861 Cl 5.45866898 8.00327567 4.97142977 -3.41254041 -6.29450905 1.58802956 Na 2.88292438 5.32426201 8.21138375 0.63389638 -3.09735940 -3.69456071 Cl 5.72680401 5.48160292 8.17204193 0.23220781 -1.19852216 1.69605339 Na 5.67976352 8.21366323 8.44826988 -0.76473024 6.54684340 3.01706100 Cl 8.06762197 7.81533802 8.37712479 4.43170630 -1.92701098 5.98304117 Na 5.65736995 5.64941273 10.33370200 -1.84176014 8.00257390 -3.78588498 Cl 8.12527103 5.39594946 11.00788461 3.59132369 -3.73787160 -6.34290655 Na 8.27102223 8.01939729 10.93118541 -1.31664421 -3.16803109 0.58734040 Cl 10.88340254 8.30114477 10.70557600 1.26769572 6.87718601 0.36913391 Na -0.22228385 8.10540799 7.85257526 2.47012519 -5.35983030 -0.66680134 Cl 2.68865708 8.02697866 7.91933610 -7.10877515 3.84455325 0.04465500 Na 2.54732088 10.51892606 8.14983350 -7.76800813 2.36317055 -7.36001562 Cl 5.50618363 10.77706449 8.03338590 -3.21040894 4.05466057 2.63192797 Na 2.51493022 8.13980716 11.00366036 -7.14422523 5.14339750 -3.74865484 Cl 4.78346818 8.11766524 10.72022137 4.00129782 -2.97343154 -0.77818552 Na 5.46224171 11.06369227 10.46123961 7.36789989 6.82872071 -12.97947062 Cl 8.38486767 11.02331193 10.63001406 5.95692862 -4.98195971 2.89704044 Na 5.38322531 8.10562138 13.77220420 -0.15555900 5.02148585 0.53770541 Cl 8.08892899 7.81756851 13.59822386 7.10299065 -0.78613242 -4.14098664 Na 8.24806638 10.81303102 13.41258182 -0.07560682 3.96431471 1.58906112 I 10.94156308 10.89110391 13.21902048 -3.47931227 -2.27636129 -0.32574732 48 Lattice="0.0 10.8 10.8 8.100000000000001 0.0 8.100000000000001 5.4 5.4 0.0" Properties=species:S:1:pos:R:3:vel:R:3 pbc="T T T" Na 0.32901441 0.45186232 -0.41022838 1.59365561 3.19407809 1.62704439 Cl 2.91014964 0.03720070 0.02062773 0.39615781 6.20046581 7.68242969 Na 2.78639908 2.84918270 -0.17132704 -6.80154302 -1.74238436 2.61483884 Cl 5.73922999 2.53223164 0.12015501 -7.86566999 -0.04593835 -0.85367166 Na 2.33248195 -0.04181681 2.56386512 9.96667074 -5.14792515 0.61897749 Cl 4.95761730 0.05895467 2.77214174 -9.93313422 -1.30724537 -1.44266113 Na 5.45098133 3.02687399 2.98539408 -6.17192937 1.92566338 -6.67422733 Cl 8.20472676 2.88620652 2.86993917 -0.55238111 7.40589741 -3.40603346 Na 5.67765318 0.05462674 5.50864306 -0.00741942 -1.92031382 5.23245090 Cl 8.06311234 0.00819862 5.39717375 0.79118925 4.50268471 4.24445037 Na 8.49968316 2.49984501 5.55271823 2.83406156 -0.75954451 5.10784657 Cl 10.94832439 2.75535932 5.06093260 -4.11391931 0.15520417 1.10682813 Na 0.19899218 2.94652662 2.89340293 -7.01576503 -6.04177264 3.76763123 Cl 2.80025914 2.82781555 2.28395333 7.80693218 3.51066024 3.95254316 Na 2.86100689 5.45592084 2.51062007 -9.11441222 -1.14190673 -1.54637445 Cl 5.20064055 5.52363444 2.38136757 6.33433399 7.68288563 -3.93149348 Na 2.51225113 2.57094779 5.30374011 -0.11493778 -3.25071831 -0.09491850 Cl 5.62237927 2.67076815 5.34524853 -3.04810536 7.36897979 2.31055020 Na 5.25864866 5.32125789 5.52345069 -3.33171644 0.03494657 -6.44342038 Cl 7.99721005 5.33549472 5.98696780 -2.46792384 6.05045239 -1.02636577 Na 4.86210597 2.82694052 8.10032495 -0.20342131 -2.91286165 -3.80439256 Cl 8.16102078 2.68830594 7.78117437 0.42786858 4.46598682 0.73262907 Na 8.22529398 5.84287958 7.83049562 1.72555481 0.64471752 -0.30966304 Cl 10.97632965 5.55077876 8.13519332 0.26867673 -7.97621788 5.36421622 Na 0.02406104 5.32295444 5.10437092 3.42736175 -6.47479699 2.84526997 Cl 2.71695573 5.37404404 5.60038610 -7.50409707 -3.66484664 7.35871405 Na 2.39761845 8.35623752 4.96432631 3.66679573 3.36327562 3.07336009 Cl 5.38105056 7.92429769 5.54050304 -0.12990191 -7.98374952 -3.72975670 Na 3.02324764 5.35593984 8.28641103 -4.89573896 7.61660431 -3.37574831 Cl 5.42343155 5.78514459 7.94870584 -3.87992519 -1.49611069 10.92065290 Na 5.59239757 8.05701773 8.21320241 0.63323054 -1.28158094 -0.31256847 Cl 7.85300073 8.37852576 8.18429159 -3.29053867 -2.92137540 3.45575515 Na 5.15124052 5.31746426 10.77692434 -0.09913430 -8.76786970 -0.71300735 Cl 8.14944169 5.41463798 11.08127142 -2.07077950 -6.06137314 -2.02453087 Na 7.75351660 8.12847956 10.66480892 -7.51327529 -5.43073598 -8.36500013 Cl 10.93646351 8.16062194 10.80731655 0.92904062 -1.82675083 -1.75220114 Na -0.22326880 8.32609676 8.47189481 4.80798062 -2.71733030 9.27558811 Cl 2.69204819 7.85057496 7.98563774 2.12760093 3.54004570 -3.24033418 Na 2.66283209 10.53649107 7.89250743 8.99348218 0.69443207 -0.87212889 Cl 5.51338490 10.67593920 8.28715912 -1.63964279 -5.61464351 7.53972749 Na 2.41614881 7.97811391 11.02909626 -9.64654796 -2.72627536 -13.11062931 Cl 5.22487039 8.23289319 10.78914116 -0.31408909 -1.92683982 -1.23030235 Na 5.09998577 10.76433132 10.78395484 0.25852269 1.82589325 -3.62849298 Cl 8.22601025 10.76728166 11.02754007 -2.73304454 2.15242540 -2.37417560 Na 5.27942954 7.90949049 13.55341798 -4.67819142 -5.26074983 -1.13682823 Cl 8.68718414 7.97677744 13.43488520 -5.29663070 4.28281007 2.27990617 Na 8.22509457 10.84225380 13.48260487 2.07063238 3.38971646 3.26451666 I 10.63140442 10.54932541 13.63408403 -3.40982436 -6.56083120 -0.91700805 48 Lattice="0.0 10.8 10.8 8.100000000000001 0.0 8.100000000000001 5.4 5.4 0.0" Properties=species:S:1:pos:R:3:vel:R:3 pbc="T T T" Na -0.07860789 0.25762389 0.27776097 4.87134989 -1.94987799 -4.43218612 Cl 2.85244813 -0.04726373 -0.05541573 3.28380480 5.23888374 -4.48093690 Na 2.81954660 2.65750492 -0.14470704 -5.69299429 2.43017952 9.29946486 Cl 5.12060905 2.75928476 0.19493372 9.07620395 11.97277618 -4.73362707 Na 2.64246817 0.15221315 2.43840141 1.24578276 8.95397215 -3.37400252 Cl 5.55709706 0.21006053 2.51282527 6.33620796 4.41085639 -4.62183774 Na 5.28031827 2.33873588 2.56120093 6.28142115 2.65561325 0.35919735 Cl 8.12369638 2.90052701 2.58195765 9.59628728 -6.40503853 -6.14155754 Na 5.40909277 -0.38252637 5.62222763 -1.24818517 6.99607395 -6.89159635 Cl 7.80075219 0.13760982 5.31073523 0.98709385 -7.58659058 2.15569276 Na 8.02652463 2.37084834 5.37175721 1.86578299 7.23229115 3.13087534 Cl 10.97346849 2.60543173 5.61425705 -7.81716887 -5.52442842 -2.93456145 Na 0.06182667 2.48725018 2.67309419 2.30986720 -8.27363355 -3.17503843 Cl 2.81541628 2.77534431 2.61085707 1.00816399 8.88334632 -3.97182300 Na 2.39813146 5.74820389 2.77353842 4.35853856 -2.94342276 0.31171371 Cl 5.65697648 5.52969952 2.56970805 -10.77441745 1.44464409 1.78799913 Na 2.44729259 2.83728565 5.42310126 5.04035385 3.73918962 -0.94045822 Cl 5.53014748 2.78001670 5.40119244 -0.33819350 2.73054051 -0.61120379 Na 5.42860240 5.41367727 5.41671955 7.42410528 -4.63004860 4.00314583 Cl 7.85625228 5.16719933 5.29040555 4.88717275 0.26501821 1.34689562 Na 5.59655267 2.51216324 8.40450561 3.31263015 -5.74352393 -1.61881826 Cl 8.15156820 2.57503149 8.00332658 -5.92926500 -0.37529239 4.83696709 Na 7.71460054 5.34951724 7.81650787 5.01946455 2.16013237 -7.30293125 Cl 10.57962738 5.33118299 7.77555359 0.97025013 2.73530202 -4.78987956 Na 0.10199341 5.54274064 5.35913885 -0.74740735 -4.01628970 3.31657715 Cl 2.95399131 5.51321703 5.49145631 2.86124964 -7.78660855 -3.42918372 Na 2.73589572 8.07144776 5.19959417 0.14258036 11.31049104 -6.73324780 Cl 5.53772836 8.51021618 5.70485914 -10.44384747 -4.35932345 -1.21752696 Na 2.87518133 5.52113630 7.93866097 -3.69410214 -2.07930230 3.97415661 Cl 5.49534671 5.41236989 8.17382254 1.99525626 2.84923064 3.89958157 Na 5.25240327 8.12209385 8.21535615 -2.80221599 -12.26551109 -3.14051270 Cl 8.17009933 7.51732171 8.10916941 4.39652580 -3.65724333 3.26246547 Na 5.42879682 5.32756437 10.74235862 5.81811722 -1.84584430 9.52376151 Cl 7.96470245 5.48373632 10.48906239 1.86465026 5.89215548 -2.15952142 Na 8.05754008 8.23811364 10.83639487 -0.31253286 3.30965217 3.97948979 Cl 10.90808772 8.34391726 11.17848819 4.65962038 4.95486405 -0.26018154 Na 0.04796552 8.16961914 8.21244302 -4.15976012 1.61685780 6.07454362 Cl 2.77082924 8.40243234 8.17791945 -1.75538995 0.98115130 1.24169138 Na 2.42488801 10.85115376 7.94228182 5.81069697 -0.86848884 -6.12324057 Cl 5.54785176 10.94842820 7.82785706 8.02594988 0.73575266 -4.52166901 Na 2.41506826 8.05051502 10.70362113 2.61205766 -0.94554753 -2.73237849 Cl 5.54830482 8.26408085 10.76803560 2.24850749 -1.30787843 -6.90992947 Na 5.24633121 10.20696626 10.71375859 -3.01428970 5.38516453 -0.49301138 Cl 8.54741156 10.82603747 10.80732609 5.53954001 3.56855381 6.37164884 Na 5.47522161 8.38414404 13.41031830 -5.74003647 -3.26124564 -4.67813206 Cl 7.88958050 8.03814514 13.58418502 -6.22088424 -2.85379500 10.74363990 Na 8.32094364 10.68065645 13.62756943 -1.08283669 4.56266762 2.13394897 I 10.77687487 10.72427445 13.54536155 1.93171030 -6.67438199 -3.74087040 48 Lattice="0.0 10.8 10.8 8.100000000000001 0.0 8.100000000000001 5.4 5.4 0.0" Properties=species:S:1:pos:R:3:vel:R:3 pbc="T T T" Na -0.07086536 0.22072775 -0.29389079 -3.53587528 -5.76148197 -3.30871330 Cl 2.82731858 0.07504080 -0.22605105 7.09510578 3.42716664 1.69080190 Na 3.11562085 2.35470829 0.24350664 -0.88810217 -1.40288189 -4.23341042 Cl 5.45355928 2.97592497 -0.19447910 10.58428835 -0.97738626 9.39086378 Na 2.69147037 -0.05062481 2.65419684 3.26944336 3.88291361 3.98842020 Cl 5.35167220 -0.14763556 2.36437188 -0.33618853 -10.42361624 -2.44361513 Na 5.33815048 2.53078770 2.52463494 -1.26645217 3.48163043 8.49731878 Cl 8.38040413 2.51204258 2.63463006 -4.59122644 -1.52816288 -1.99808340 Na 5.76196837 0.29332983 5.15085929 -0.60592174 1.29693103 -1.65181791 Cl 8.32263516 -0.02369578 5.28405605 -6.35628929 -3.64029788 -10.01208451 Na 8.06626052 2.89633794 5.01698365 0.04177630 -4.52231484 -4.16398690 Cl 10.57399325 2.61223012 5.63851559 1.72037578 0.77518653 -12.62384679 Na -0.30954238 2.47937924 2.75562016 -4.73955260 1.67124311 -5.24415390 Cl 2.91309511 3.06934575 2.25068936 -3.11900283 -0.02188274 -12.92968337 Na 2.71739220 5.68862660 2.73054463 3.44711542 0.69619528 3.86599998 Cl 5.61619745 5.07167857 3.11363547 -2.73364232 -3.38207455 -5.38178888 Na 3.06125485 2.59508066 5.01368138 -3.08339991 -2.00366050 -5.70964315 Cl 5.29945147 2.67555223 5.49703379 -1.86501865 0.08407459 0.32966692 Na 5.35342106 5.49932288 5.69237840 -2.43329490 -0.89461052 -0.91880253 Cl 8.19069204 5.17645571 5.24943923 -12.59009875 -2.60777494 6.06322262 Na 5.65363580 2.96929685 7.94761341 -2.66781073 -0.49372342 -5.29073757 Cl 7.97010342 2.49369336 8.23418804 -5.15107102 2.83961682 15.04328882 Na 8.17542290 5.22513646 7.54248314 2.19562485 -4.95659949 1.36895764 Cl 10.51574815 5.87498123 7.93718406 -2.19190844 -4.34308841 3.19111571 Na -0.03816900 5.49313620 5.01662322 -1.04083821 2.14242098 2.81597981 Cl 2.90804890 5.47341386 5.60174669 -0.80646420 6.37286055 -7.26540324 Na 2.87117108 7.84832039 5.45285308 4.02007402 -0.66783803 3.82698828 Cl 5.04625885 7.71315282 5.14585180 7.01074839 -5.22372532 -5.20201122 Na 2.73248732 5.55853618 8.34013230 0.75885423 1.32900972 -0.19991169 Cl 5.26245621 5.32606635 8.15344806 -1.46313541 -3.61430358 0.54918558 Na 5.41829707 8.38038725 7.82412935 13.60677037 1.27565444 9.71194836 Cl 7.89892102 7.87471220 8.33506955 -5.57640212 -5.95245551 3.79320086 Na 5.45111949 5.63913542 10.90574576 -4.61140159 -1.51480607 5.71607307 Cl 8.04402317 5.47327578 10.67401523 -4.83359556 -13.55504593 -0.95936234 Na 8.16384321 7.82607217 10.94233374 4.33083884 3.40664800 -2.75923560 Cl 10.71661827 8.43431307 10.64933795 -9.37513820 8.78055935 4.42851852 Na 0.12371460 8.01369542 8.14833178 -4.88609574 -1.78251621 6.75672583 Cl 2.30249143 8.10563361 8.20926758 -8.40500484 -2.26541776 0.73307146 Na 2.76576855 10.86266754 8.14158738 4.44085157 -7.79484374 2.93380066 Cl 5.61288831 10.96202746 8.24282495 1.08020688 -1.76581564 2.91039638 Na 2.98534573 8.16292892 10.42512842 3.46972609 -3.62597970 3.27496549 Cl 5.30965092 8.49869223 10.80435023 -3.16028623 6.23496318 0.11939800 Na 5.37439519 10.82374027 10.71108190 4.59053496 1.38684132 6.55566187 Cl 8.07107274 11.27980732 11.29725882 -3.92218811 -8.66936086 -3.64101395 Na 5.17346357 8.04111398 13.66023085 8.12420572 -3.73393486 -4.99499508 Cl 8.24576204 8.12539619 13.44155124 3.36483858 10.96792340 2.93368741 Na 8.27176606 11.00904010 13.80154416 -2.64034917 12.29216836 5.98964387 I 11.18865591 11.12615256 13.46817801 2.93820989 6.18895826 -0.76202177 48 Lattice="0.0 10.8 10.8 8.100000000000001 0.0 8.100000000000001 5.4 5.4 0.0" Properties=species:S:1:pos:R:3:vel:R:3 pbc="T T T" Na -0.28959482 -0.28832147 0.00200476 -4.58442160 5.24265485 -0.92948338 Cl 2.79583762 0.28337229 0.09535694 -6.00930699 -6.20962563 -2.74986586 Na 2.83388333 2.75846923 -0.17187472 -3.14618290 3.64757299 -1.96116643 Cl 5.28378405 2.51433235 -0.03990796 3.53602943 4.60638849 -2.25295875 Na 2.66173143 0.25730882 2.49560426 -13.54439002 6.57977422 1.78319835 Cl 5.25575215 0.15047759 2.77830678 1.56151959 3.51934714 5.23184015 Na 5.30862613 2.78237789 2.89012217 -4.46478682 -3.39087066 -1.58466090 Cl 7.90462010 2.49140410 2.69561291 -1.35883174 -4.27969202 -1.17002937 Na 4.96312025 0.07520656 5.59781192 4.04620603 4.03577105 -4.87758375 Cl 8.08096647 -0.12798200 5.10509743 1.72654451 -1.65963913 -8.29951622 Na 8.53544443 3.07294328 5.29197584 8.08035413 -4.10051682 -5.70526291 Cl 11.02578390 2.73164675 5.44994578 7.32508531 6.02726858 -0.96890325 Na -0.12317639 2.77916592 2.50491045 -2.13776499 2.82826232 -1.89829562 Cl 2.64544349 3.04895680 2.80775185 0.43912286 -1.65370792 -6.68006931 Na 2.54529986 5.12830367 2.70675810 12.10060241 -3.63367836 3.02434439 Cl 4.99757736 5.08567151 2.84367123 1.35787406 2.91529749 -9.85994475 Na 2.66707080 2.96238619 5.52944339 1.07305766 16.20535705 1.11344453 Cl 5.65638061 2.63500995 5.66955849 4.48292560 3.28419966 -5.38221123 Na 5.43341914 5.34395601 5.32957291 -1.89922232 -1.24812928 -1.01871300 Cl 8.42216114 5.22216883 5.18901058 3.86622309 3.46280677 -0.52436426 Na 5.55125003 2.69192720 8.05570687 0.24142502 -3.43278789 -2.38370872 Cl 8.16990391 2.90075995 8.43234999 -0.19020683 -3.62974164 -4.08350086 Na 8.16004090 5.25148430 7.96907728 -9.39332313 -1.99213667 5.74830025 Cl 10.98428586 5.85283628 7.86399692 -0.35882357 2.37681633 11.53837148 Na -0.34051772 4.91782087 5.39428856 4.28145354 -13.83957749 3.96044444 Cl 2.48439754 5.38936061 5.31343079 5.15585241 -8.81787082 -10.59933192 Na 2.59963628 7.68447020 5.05559694 -1.54818287 -4.37905527 6.31002409 Cl 5.54512333 8.11668584 5.71988075 -2.95887932 -11.29161482 4.12755825 Na 2.91666513 4.96371813 8.10155444 0.67183336 3.49075446 -1.27592244 Cl 5.48741418 5.60305249 8.25467557 -5.47096857 -4.91073363 1.78720577 Na 5.41848373 8.06108513 8.27415723 6.04831463 -0.01297225 -0.75927208 Cl 8.33725771 8.28233776 8.09845090 -7.15708753 3.87628697 5.51109784 Na 5.43778354 5.52488237 10.54823319 -0.15236954 -4.07131930 -6.78750370 Cl 8.26608076 5.46283719 10.86505804 8.54638202 2.02681049 5.37723630 Na 7.95085529 8.24736055 10.90666978 4.80553080 -6.36701149 4.28538365 Cl 10.86955381 8.27716034 10.79994359 6.71419448 6.18786865 2.30842681 Na 0.09808216 8.22638248 8.00976894 8.95670986 0.14788722 3.36096661 Cl 2.76930240 7.99999439 8.16267031 -8.48268709 -1.27574845 -3.31585389 Na 2.56083008 11.10759810 8.13323496 -0.13311082 5.93483220 7.53639231 Cl 5.52105230 10.82127018 7.88325448 -2.60871453 -5.97097410 -3.98419802 Na 2.62655607 8.13404605 11.10056137 4.11699124 -2.00075738 -0.55680101 Cl 5.44468713 8.18781021 11.05390372 6.14184653 7.07885985 1.71208786 Na 5.29048078 10.94980703 10.95453909 -0.90149300 8.18782648 -3.53156315 Cl 7.91026604 10.61284732 10.84431856 1.42391553 -7.78906680 2.62726776 Na 5.59489092 7.81430899 13.43943168 -2.65582836 9.27598876 -8.00234646 Cl 7.86104687 7.98025964 13.39965587 10.32966328 3.52175423 2.45530003 Na 7.69736702 10.85255080 13.84851831 -7.21440832 -0.29021284 -2.88160707 I 10.85053324 10.92622890 13.59892355 -4.38246483 -3.42385712 8.20165136 48 Lattice="0.0 10.8 10.8 8.100000000000001 0.0 8.100000000000001 5.4 5.4 0.0" Properties=species:S:1:pos:R:3:vel:R:3 pbc="T T T" Na -0.01865714 0.02073986 0.26374194 -9.90230069 2.45793135 0.72032744 Cl 3.15987581 -0.14751515 -0.25976372 5.55256049 0.88494558 -5.86550487 Na 3.05625860 2.75324985 0.03995288 -9.20360310 4.28143554 13.08276440 Cl 5.39149932 2.56542809 -0.15519388 -1.32620176 -0.85691785 5.71985279 Na 2.63973644 -0.11212170 2.38438493 -4.27949764 0.71355626 8.58685297 Cl 5.24413132 0.14269285 2.84659853 -7.09412663 -3.91186461 0.97766759 Na 5.46324866 2.80819265 2.62863536 4.03838345 -2.17689948 -2.78140093 Cl 8.18196653 3.09905994 2.67870199 -2.07866043 2.15997866 -5.88401771 Na 5.34211691 0.01952659 5.38763726 -6.55635890 10.03991794 4.07768729 Cl 8.01450236 0.19687817 5.63638711 -2.00138910 10.24520936 -4.72583211 Na 7.97716696 2.87891466 5.44161332 8.45748718 -4.00851934 -1.75444156 Cl 10.98729354 2.89138340 5.47152371 5.88929391 4.11167197 -16.08180480 Na 0.13739116 2.40644519 2.83559025 6.83816201 9.94272013 3.47119218 Cl 2.59328080 2.74844635 2.53898496 12.78898683 10.12096456 1.58066880 Na 2.97219457 5.39195945 2.25191183 -1.98013673 1.24856477 7.85211866 Cl 5.26123073 5.10049492 2.71798053 -3.52272630 -0.89349292 -4.31628814 Na 2.75445524 2.75307796 5.47380885 -4.76944061 2.11481611 0.90879787 Cl 5.26667510 2.47202982 5.28535790 -3.76590483 -5.33682300 3.19891547 Na 5.62173317 5.55910946 5.40880379 -0.57156262 6.69940785 -6.80700381 Cl 8.07828318 5.76999218 5.12915282 2.01254809 4.56449036 -3.50630234 Na 5.32876347 2.72785902 8.11694029 -4.46661972 5.64516543 0.24329045 Cl 8.52508261 2.99362942 7.98606784 6.53326130 7.31470571 11.55094753 Na 8.14270218 5.24338413 8.36442784 6.03320123 -2.20140827 -2.17697433 Cl 10.85409407 5.55058861 8.48248763 2.86299570 0.25344010 5.63693480 Na -0.27653304 5.22922614 4.99562910 6.50291559 0.69328157 -4.13285700 Cl 2.79055535 5.70240317 5.17599562 -4.29076687 1.27477632 -8.96770535 Na 2.74224955 8.25948278 5.53127643 5.35676037 -3.03118258 2.11558104 Cl 5.13886737 8.30175291 5.02592403 10.21775274 -1.10614492 -5.65200073 Na 2.64216228 5.17583706 8.12027303 11.32882579 1.81472005 2.64424461 Cl 5.22973646 5.80747570 7.89796866 7.19973809 0.74029821 6.72421981 Na 5.22845390 7.65206629 8.23648285 -3.77388006 0.67013417 -5.22273847 Cl 7.92353330 8.54341992 8.00640602 2.23665296 0.02992358 -0.69968930 Na 5.71299984 5.67496860 11.12430846 -3.85702564 -0.60935809 -7.41313873 Cl 7.73427252 5.52803094 10.97541426 3.65068918 -13.09403891 3.50289301 Na 8.23286704 7.97443010 10.91757116 -1.17125442 -1.25648118 -0.11563016 Cl 10.43034124 8.01663707 11.04505823 -6.71086825 0.76333391 -1.78828316 Na 0.13371227 8.27108224 7.97512267 6.82909213 -0.13303035 8.07288708 Cl 2.55680258 8.19092066 8.42711276 -12.01631304 -9.17110392 1.20386845 Na 2.55447841 10.44685913 7.78142998 3.11061673 0.81342751 -0.99053904 Cl 5.57892810 10.79930094 8.02873825 -0.02417884 -4.33603657 1.82571033 Na 2.66301179 8.43542133 10.44018855 -2.39671237 0.43447872 -6.02033254 Cl 5.29083981 7.97883358 10.70549044 -2.32520200 -5.59287602 1.16550952 Na 5.28275862 10.71540738 10.53213898 -3.98414348 5.09500159 3.78937835 Cl 7.96020091 11.03935246 11.06580323 -1.26529061 -7.68303162 -10.93926616 Na 5.61162935 8.07398379 13.57154610 -11.12761217 -11.62276979 2.80225692 Cl 8.23123301 7.99937101 13.64067074 -2.45467716 -1.35136984 -3.98938531 Na 8.04396158 10.64203200 13.54015006 -6.38246643 -7.90804481 7.16755153 I 10.99833396 10.94943845 13.67515554 -6.29157158 5.26897716 1.14252706 48 Lattice="0.0 10.8 10.8 8.100000000000001 0.0 8.100000000000001 5.4 5.4 0.0" Properties=species:S:1:pos:R:3:vel:R:3 pbc="T T T" Na 0.30402711 0.02873492 0.00740649 11.58344080 1.24028393 -6.10742238 Cl 3.00087183 -0.42105450 0.07672733 -0.79412737 -0.51990127 0.77916976 Na 3.10327597 2.47235481 0.11611285 3.57777277 -6.14862290 5.99596980 Cl 5.38485708 2.65502750 -0.15115754 3.28146147 2.27875077 0.37957826 Na 2.76357703 -0.02016810 2.77847917 -0.54276791 4.55686950 4.83287948 Cl 5.30945647 0.03382759 2.44155800 -4.87606711 -6.91764322 5.10718392 Na 5.55198244 2.79762748 2.89520195 6.69496857 -5.68024621 10.76873492 Cl 8.09133435 2.57392108 3.01445547 5.33703230 -0.95224697 0.75600668 Na 5.27731031 0.11383752 5.31150853 5.09293632 -4.21009034 -2.51404223 Cl 7.88272416 -0.02976632 5.50231958 6.09098947 -9.12771716 3.53324235 Na 7.78976943 2.82976575 5.80053673 2.69048830 4.35256892 5.21386806 Cl 10.97716452 2.63058385 5.28613389 0.41005001 -2.47796356 2.05078429 Na -0.35531687 2.83597988 2.76083160 2.47765207 7.24443964 -12.60842926 Cl 3.07867786 2.92317134 2.34086296 4.91555590 0.87473834 0.65563963 Na 2.69748929 5.20058296 2.83722477 -0.45442963 -1.61019535 -9.77336386 Cl 5.90282332 5.43021057 2.50362241 1.95569427 -9.60412311 -0.23338429 Na 2.66364908 2.70774813 5.49197807 -8.64159267 -2.56089784 6.74964476 Cl 5.27326065 2.75302928 5.47486953 2.81230989 -3.60818076 -0.47070024 Na 5.24566039 5.22494527 5.31004933 3.48183797 5.03960322 -1.62706890 Cl 8.04160163 5.26787400 5.39032085 -1.60693909 -3.21362473 -0.39544253 Na 5.19590721 2.40608821 7.99375967 8.76583180 3.99962443 7.37438733 Cl 8.01373208 2.55235250 8.32857267 0.08606227 2.43372991 -3.42922797 Na 8.15726884 5.65536361 7.99049989 0.77113422 2.17656220 -0.39893457 Cl 10.87478902 5.20201012 8.06358158 2.08690266 -3.56309212 -4.96864488 Na 0.11122801 5.60368898 5.59494885 -1.67016440 8.20744975 8.20274142 Cl 2.31677705 5.44484446 5.48871309 -1.83246959 2.96404633 1.02072167 Na 2.96506826 7.83279574 5.42446115 -2.27770917 1.11890830 -2.06340708 Cl 6.03188269 8.00381458 5.46128521 1.64764361 3.92342892 -0.93778454 Na 2.51362336 5.70400774 8.20922806 4.00221290 8.98380300 -2.26856006 Cl 5.25389459 5.60927200 8.29523191 -6.68577897 -2.31352896 4.02139687 Na 5.25187952 8.26613269 7.99306527 -12.97045365 -0.72565909 2.78508133 Cl 8.25220461 7.96279515 8.30178885 -0.45843433 2.82799400 -2.68238578 Na 5.14863322 5.66587311 10.94196359 -1.47224784 -0.62292573 -8.09252466 Cl 7.92224832 5.28760413 10.90638595 5.97980317 9.60571060 5.94094934 Na 8.09505022 8.07855437 10.66692411 3.78798483 -7.63161065 4.52937410 Cl 10.89275000 8.10559463 11.02402497 4.61277119 -5.90235350 1.28199660 Na 0.15606420 7.94947365 8.29218987 -1.61553663 -2.03862602 -2.00671107 Cl 2.69149747 8.00515719 8.03430252 6.80065118 -3.00909169 -4.01989935 Na 3.04076723 10.71328758 7.61419436 -6.28729214 2.14922278 3.41731606 Cl 5.39261002 10.84596784 7.94179731 2.20746869 -12.84729412 6.20455146 Na 2.41407709 8.22217984 10.76544449 5.60287168 0.64277532 -2.19161165 Cl 5.60029540 7.87359770 10.81336852 -5.64973895 4.86531292 8.20749646 Na 5.58251994 10.47689193 10.82367544 -5.70451413 5.75204368 -7.17625012 Cl 8.25766571 11.08001306 10.65297571 2.10876984 -1.44747151 0.50660221 Na 5.38232179 8.48982548 13.38666289 1.37819731 12.10713115 8.91128215 Cl 7.95268806 8.03543337 13.55092217 -3.99830931 0.39898794 -4.64236618 Na 7.99006206 10.50179225 13.71420963 2.62733516 3.64261751 0.32499415 I 10.62461530 10.90752491 13.28063251 -2.68654941 -0.85648198 5.58416490 48 Lattice="0.0 10.8 10.8 8.100000000000001 0.0 8.100000000000001 5.4 5.4 0.0" Properties=species:S:1:pos:R:3:vel:R:3 pbc="T T T" Na -0.33872324 -0.25625587 0.19838345 1.18848263 1.13866066 6.86237526 Cl 2.64323257 0.27005658 0.04047682 -8.41093220 0.73716872 -3.48921092 Na 2.62455142 2.69905337 0.10104305 1.20634808 6.52814266 -6.09288943 Cl 5.40951863 2.35345132 -0.39818417 5.99679635 0.28428992 -3.50585248 Na 3.17696062 -0.28364457 2.69058145 -3.28642591 3.99889973 -2.07809174 Cl 5.35200540 0.18806154 2.22719138 6.50653348 -1.07405791 1.48850165 Na 5.66158409 2.95160219 2.57463832 0.72500676 0.84734667 -5.02426153 Cl 7.68460171 2.89669290 2.63132859 9.86818087 -2.34050747 1.51671097 Na 5.59812916 0.14249549 5.39247444 3.60175147 -4.05643704 2.98809916 Cl 8.26299830 0.16572812 5.23019775 6.31793501 4.49726870 0.48012602 Na 8.22280468 2.66454421 5.47393734 11.84275461 -16.01942966 -3.91790652 Cl 10.91198032 3.07326922 5.63280049 1.80045074 0.83215491 -4.98116613 Na 0.34457775 2.38571492 2.35537987 -3.11706392 3.73233621 -1.52560410 Cl 2.65293619 2.49042560 2.51987950 -8.35328622 8.61535331 0.64890882 Na 2.79106708 5.22206402 2.92097184 8.57939114 -1.46125121 1.39072057 Cl 5.43219806 5.30351044 2.51136253 1.55682201 -1.90005273 -5.36722693 Na 2.77787179 2.83694997 5.14565642 -11.09702843 -1.94969636 1.45134259 Cl 5.54450421 2.44396280 5.72407391 -3.55507158 -1.02782719 -0.45130293 Na 5.50054684 5.55481456 5.73434597 -5.44174028 2.78048686 -0.77851358 Cl 8.09454969 5.30033977 5.15104211 -2.41100611 7.35670277 -0.69492096 Na 5.54555288 2.50751302 8.00408175 -7.33801754 1.20415856 0.81177849 Cl 8.02115497 2.66909087 7.96173552 3.77696472 -0.55760022 -3.06677176 Na 8.23125819 5.17391656 7.91358807 2.84793033 8.29104742 3.70984720 Cl 11.18736414 5.21421209 8.12194623 -2.54892129 7.27224552 -8.78792400 Na 0.49707161 5.54504119 5.63485733 3.16453008 -2.04087404 4.82621047 Cl 2.65081274 5.48820441 5.74946562 1.10934164 -0.62269322 -10.02162527 Na 2.66398480 8.37039370 5.63212690 1.35131929 0.33525923 1.04217253 Cl 5.48218951 8.27806416 5.56699042 0.99044030 -2.22571436 -1.65944540 Na 2.38886444 5.59163674 8.27222828 3.87580127 6.44714473 -5.22486186 Cl 5.57749549 5.43559469 8.17409751 -6.49682212 3.12899309 4.41070222 Na 5.44491033 7.90031034 8.05469804 -5.82485108 1.04849657 -0.72882860 Cl 8.15912202 7.99752238 7.68872908 3.72308227 -1.17184588 -4.48414399 Na 5.31853575 5.41212021 10.88817988 6.12809227 1.08711921 -0.46794587 Cl 8.30571856 5.27706945 10.70142312 2.65454992 2.94298337 1.18499127 Na 8.20415954 8.33955293 10.55182136 5.33761271 -2.08809300 8.67245278 Cl 10.86601908 8.34937680 10.79672872 -0.01784495 -3.45246243 -2.60663065 Na 0.00200180 8.20575793 8.28844319 4.25263435 3.92240291 0.94814610 Cl 2.97432851 8.44545768 7.77432493 5.79755955 -9.06003700 4.94485959 Na 2.63633477 10.60991413 8.11382325 1.35095315 3.42544293 2.84755609 Cl 5.55400947 11.06683350 7.91382413 2.11915996 1.97947588 -2.90051215 Na 2.98155791 8.18793982 10.77770553 2.51135326 1.44682688 4.20913338 Cl 5.09753002 8.12379513 10.79993278 0.57827978 0.44762713 11.47309283 Na 5.26157497 10.94927145 10.75849564 3.95751472 -4.89811766 2.61192302 Cl 7.96602834 10.57843550 10.52882709 -2.51455680 2.64986998 -6.15292629 Na 5.77989031 8.26587082 13.58989395 2.70827117 -5.46778300 2.70397853 Cl 8.18350489 8.47014297 13.35194185 2.55547421 5.55685860 1.69143131 Na 8.02966850 10.86740395 13.70707123 -2.19448940 7.66805023 3.28837608 I 10.60362309 11.03158542 13.81642791 -2.57097034 0.28033611 -10.16045715 48 Lattice="0.0 10.8 10.8 8.100000000000001 0.0 8.100000000000001 5.4 5.4 0.0" Properties=species:S:1:pos:R:3:vel:R:3 pbc="T T T" Na 0.48160782 0.05695606 -0.07510515 7.85157396 8.99081708 2.77971777 Cl 2.60570110 0.04068081 -0.03712787 -1.89741936 3.29378473 2.72010320 Na 2.94810443 2.93466159 0.03895212 -9.67934105 -6.40247921 0.69953988 Cl 5.34583729 2.77857825 0.25956958 -1.60748395 -2.43826092 4.58415317 Na 2.55549867 0.38211444 2.79081002 7.42854780 -6.74573676 2.37849666 Cl 5.59683414 0.16637041 2.77902328 -6.24233076 2.46969551 -3.50018525 Na 5.20061343 2.44852736 2.77746376 -2.71362514 3.54032067 1.20217541 Cl 7.89758872 2.52384677 2.48231129 4.44478523 -7.45346062 -2.30417982 Na 4.65957413 -0.15959588 5.33699470 5.15304720 1.31679909 -0.78958453 Cl 7.98612324 -0.09699107 5.18650729 1.95445232 -5.07577574 -4.90888865 Na 8.18929804 2.77834519 5.64410680 -9.33419376 1.76839031 -7.43113244 Cl 11.04916463 2.62857818 5.30821299 -2.91350915 -1.37970992 -1.12138813 Na 0.35693986 3.06217545 2.73053026 3.85923468 5.27718112 -0.24510792 Cl 2.60168276 2.70471316 2.48414222 -0.01892597 3.11180908 -1.32455855 Na 2.68417525 5.15483069 2.77673374 1.83218594 0.47430893 -5.69512016 Cl 5.54440228 5.24973073 2.81204296 11.40824779 -0.77559105 -9.39615007 Na 3.04460485 2.56286369 5.27033907 -1.16609519 0.06161247 -2.56998586 Cl 5.20022919 2.50589459 5.38062857 -4.98448765 5.85871993 -3.58285661 Na 5.49224049 5.52658411 5.30999074 -9.67085040 7.54422263 -3.80904775 Cl 8.36529378 5.46173774 5.58354151 2.04812604 -2.74818286 4.92614676 Na 5.67830660 2.86360983 8.15807563 -2.57408612 2.41338800 5.77295278 Cl 8.08663173 3.06854832 8.10787160 2.57609153 -0.52651145 1.10516103 Na 7.85795393 5.40330396 8.16325991 2.19248755 -4.47930405 1.07077352 Cl 11.12989819 5.15614405 8.02174835 7.87572725 0.16412233 -3.64071003 Na -0.24341467 5.52201464 5.25309099 -1.51244364 -1.44588117 0.33513094 Cl 2.51651152 5.41594568 5.37325870 -10.84676034 -3.74926484 -3.11779492 Na 2.79676792 8.02801411 5.37425451 -5.74507638 0.55398931 5.86446825 Cl 5.36076649 8.08211677 5.43639981 0.99054822 1.54683560 -1.92556749 Na 2.85607250 5.15771687 7.87017887 2.60674013 1.64762495 2.58985171 Cl 5.15344451 5.39759891 8.28499827 0.12318069 -3.63429106 -1.29985411 Na 5.28705575 7.92690348 7.75492969 -8.35811638 0.01060743 -7.37258757 Cl 8.21102980 8.25015787 8.19408754 -0.06175897 5.39026260 -2.72053071 Na 4.94727258 4.86221597 10.96315038 2.15206486 -2.80459640 -1.67384408 Cl 8.11514748 5.29337229 10.88399964 -6.99546618 -5.10227758 4.53454543 Na 7.91077981 8.55655052 10.49346296 -3.58151318 3.71710410 -3.59674849 Cl 10.38650552 8.26251975 10.86914462 -2.99468500 -9.40240552 -3.20872409 Na 0.09963189 7.83642199 7.92894072 -6.63574238 -3.46503689 -0.68992724 Cl 3.18841291 8.12397949 8.26995536 -4.46835900 3.75886975 0.31883106 Na 2.93200681 10.90836964 8.04277545 -2.84512883 10.07920969 6.53839462 Cl 5.04058356 11.19490943 8.17515679 2.48887030 9.98795103 -0.22778289 Na 2.81099212 8.17210455 10.72313544 8.28790111 -4.97019882 9.04593587 Cl 5.31654623 7.91828133 10.74815401 2.50348442 -3.51384652 0.94187419 Na 5.73374303 10.81868021 10.34469966 2.93406999 2.88522673 -2.02367033 Cl 7.81564346 10.63265405 10.71718327 4.99915550 -3.23126593 0.50775426 Na 5.52521137 7.79609262 13.65511871 4.07443716 -8.12167303 7.28602412 Cl 8.02921551 8.08087965 13.83978457 -1.90815481 -1.29743562 4.04512336 Na 8.10015855 10.77689273 13.19549853 1.26692381 -5.48276510 -1.18164931 I 10.92353034 10.58166806 13.41057201 -0.17235024 5.46634271 -2.71407929 48 Lattice="0.0 10.8 10.8 8.100000000000001 0.0 8.100000000000001 5.4 5.4 0.0" Properties=species:S:1:pos:R:3:vel:R:3 pbc="T T T" Na 0.10691912 -0.03931821 0.40615955 8.55924893 -7.28148868 -2.17134342 Cl 2.19223510 -0.11796826 0.19485135 7.41146984 -4.37729271 -11.15624707 Na 2.86121286 2.67028725 0.05814139 -0.53203807 0.82049129 -0.38430707 Cl 5.61754199 2.84139658 -0.07678500 -1.11352506 1.43310176 10.37355384 Na 2.55907604 -0.06515147 2.82943531 1.75285744 -7.88500510 -3.02144735 Cl 5.23107678 0.11290613 2.64926500 4.09350854 6.29066009 9.12803474 Na 5.29639076 2.56759051 2.87858027 -3.45870288 0.99331384 3.51852223 Cl 7.88557886 2.56213006 2.37589442 -1.53732388 1.09271027 1.19448682 Na 5.28081437 0.14336145 5.22125212 -5.24148490 -1.95316424 5.33292637 Cl 8.02942505 -0.12607841 5.25043682 4.12760059 0.99908670 -3.23104859 Na 7.79681907 2.88608299 5.67258753 -2.17022947 -4.06693196 -6.90947869 Cl 10.58330820 2.77330515 5.94620338 -2.46861050 2.79146403 12.78947159 Na 0.05879693 2.70094072 2.54778519 -0.05240467 7.76875275 4.20924877 Cl 2.95137406 2.88146207 3.07668596 3.42972420 -2.54914833 9.34551909 Na 2.51069782 5.50818114 2.29287920 -0.38445138 -1.12512942 -4.46226002 Cl 5.69441226 5.21263623 2.82690154 3.24577838 2.78176744 3.02486712 Na 2.66007678 2.65744055 5.60437486 1.34698146 -5.12751537 -4.03046990 Cl 5.71145202 2.94026374 5.28873810 -11.29055759 3.29792759 1.75996756 Na 5.47027803 5.66449368 5.24674669 -2.78801387 -5.61217313 3.96501944 Cl 8.16901144 5.22657288 5.45417656 4.88596868 -5.50083626 1.75192886 Na 5.38985628 2.43104306 8.33520836 4.04250875 -1.64880404 -0.03874898 Cl 8.31663597 2.96252832 8.66974382 -2.48362355 -0.74491111 1.59710567 Na 7.93411776 5.39701237 8.59263000 -9.42209324 8.07056008 -7.44122944 Cl 10.57014684 5.58772469 8.09121430 -2.04577333 -2.71189991 4.74733050 Na -0.02290480 5.42733314 5.36093715 -4.64501967 -0.46617889 -3.17516944 Cl 2.90173829 5.12652435 5.39481162 -8.62213468 2.41387271 2.59303185 Na 2.53418891 8.09493515 5.69372668 -4.68302425 8.76535537 5.60569975 Cl 5.72790183 8.15905197 5.68435817 -4.81931746 8.57751264 -0.64554354 Na 2.74284744 5.33848371 8.17493520 2.73066895 -3.40572915 -11.39511998 Cl 5.57124320 5.40117808 8.13616954 -5.01179519 2.43320276 1.06399814 Na 5.32652970 8.01643387 8.05475263 -3.18443899 3.13656740 2.11983907 Cl 8.32416402 8.22303311 8.15919998 2.06572628 -0.22618895 6.82873092 Na 5.15618425 5.63749790 10.49975039 -5.34292541 -1.92985636 3.45351424 Cl 8.12300153 5.20618459 10.91473031 -4.10867177 1.72806677 6.64439109 Na 8.20145616 8.00374997 10.86277855 -4.85956476 -1.36233042 0.82337130 Cl 11.16903203 7.83106351 10.78905337 -1.17904538 1.49943448 3.17187207 Na 0.14567453 8.43544152 8.33943586 0.55950676 -2.40580231 -5.55731600 Cl 3.04386077 8.24634192 8.37094228 -2.87909385 -2.42614389 -2.88443054 Na 2.99370002 10.96661914 8.10069974 -5.61826301 2.77312329 6.44235524 Cl 4.90177330 10.97914287 8.30950479 1.13695467 -1.33806828 -9.64716957 Na 2.45849397 8.10997900 10.80827385 -0.20039205 0.78179101 3.71063993 Cl 5.48636204 8.15125710 10.96768217 1.11652866 -0.35714803 -7.23194864 Na 5.03608024 10.94967841 10.86177058 -1.60137981 4.35939514 -0.41820960 Cl 7.68306406 10.78576400 10.92958127 -3.22421023 4.93054684 2.95294287 Na 5.50509102 8.25267632 13.41547973 0.96542151 5.10953497 -8.98651008 Cl 7.99171015 8.07799797 13.30371301 2.00372643 -4.17758688 -6.41703327 Na 8.05630766 10.89557271 13.41544034 -1.53439203 -2.91751787 -0.68282426 I 10.69241914 10.83304296 13.70975538 0.11768184 2.88968308 -6.20319528 48 Lattice="0.0 10.8 10.8 8.100000000000001 0.0 8.100000000000001 5.4 5.4 0.0" Properties=species:S:1:pos:R:3:vel:R:3 pbc="T T T" Na -0.43747026 -0.51562108 0.01711273 0.72861838 2.11755041 -2.39295384 Cl 2.88507129 0.04220328 0.32854236 -0.70549752 -7.54261684 1.92105646 Na 2.98946438 2.63867597 0.18448850 5.12663238 -6.14800541 -1.13941576 Cl 5.29783834 2.71884904 0.02035786 -3.39199014 0.56796624 1.60368502 Na 2.66686539 0.05071614 2.50369249 2.92088713 -10.59714616 6.79487336 Cl 5.27223422 0.11218575 2.59787432 -4.87984628 5.50048052 -0.51779341 Na 5.41925785 2.91140929 2.51776871 -4.41256376 -1.60637443 -3.64414413 Cl 8.25539440 2.77887114 2.71383975 -2.98226664 1.74391803 -0.47499093 Na 5.44481674 -0.04908808 5.50584599 4.20226223 -3.45747992 -5.24529282 Cl 7.89942910 0.08169869 5.56577383 9.99408715 -5.79446755 8.73359595 Na 8.28668186 2.44743894 5.59230276 -9.10940363 -6.27545529 0.78986409 Cl 11.03577650 2.50783994 5.51980883 -0.97064149 6.03483530 -6.22401538 Na -0.07307287 2.69340755 2.72901734 5.10672869 -4.10955224 2.70957968 Cl 2.59323636 2.84091214 2.86435282 3.70099115 -8.88818546 -4.37624512 Na 2.52097494 5.70660070 3.00631141 0.28913024 1.13956925 8.86743333 Cl 5.40619428 5.16361980 3.15612489 -0.63575049 5.87085677 6.56112972 Na 2.61730813 2.75187459 5.21733605 6.61426570 1.61945005 5.16141942 Cl 5.74221145 2.52320964 5.65490276 1.40790813 -3.03362081 10.92078878 Na 5.56117125 5.60047034 5.51443748 -3.29716217 3.74880655 6.91653639 Cl 8.10017807 5.19670279 5.14083375 4.71586357 4.75878939 4.46030201 Na 5.50986632 2.67528061 8.12108133 -6.63727741 -3.44521943 -3.57395107 Cl 8.06616717 2.78124498 8.34921075 -0.31779407 5.43562915 -3.49313847 Na 7.98051011 5.41901113 8.32517423 4.59940898 0.63728908 -4.30278917 Cl 10.83346358 5.43782380 8.27289769 -9.67741324 2.19134408 1.15031950 Na -0.00821841 5.49279986 5.43054667 -3.63326382 6.88671835 1.36070941 Cl 2.89846522 5.45869584 5.31669807 -2.79545609 -1.16375975 2.40471056 Na 2.76938257 8.16351989 5.33632592 -7.02660975 -6.16600853 0.11797001 Cl 5.15211076 7.83741089 5.45866552 -10.57505340 -3.04056414 0.62692270 Na 2.85283344 5.92908846 8.00075524 -0.08019715 0.02415136 0.86041124 Cl 5.06764201 5.57404134 8.24515948 0.85852616 2.28074315 4.83353910 Na 5.57696640 8.16119525 8.11306259 8.54268955 -6.42293394 -1.49670943 Cl 7.73471093 7.97443770 7.82571383 9.43816741 4.72925618 0.57440112 Na 5.87454148 5.55778582 11.21628044 -6.75105502 0.89164346 9.60805786 Cl 8.11005788 5.82429516 10.86130783 2.73082467 -7.00808217 3.89628811 Na 8.03946776 7.92253121 11.06667446 -1.07148833 11.80671399 -5.21832199 Cl 10.92334608 8.30155284 10.56068919 -1.98492713 1.34364229 0.58811370 Na -0.11780835 7.82241529 8.07084532 -8.24281639 -3.75584622 10.34426037 Cl 3.06000255 8.19648043 8.14683973 1.57813829 -3.89008729 -5.46720949 Na 2.58583789 10.83645974 8.06277519 0.35581149 -4.21460735 3.24138777 Cl 5.74890734 11.11720849 8.20170291 -0.44111479 4.45459009 -2.95068485 Na 2.41600401 8.00089131 10.55350932 1.91766713 7.36175280 -3.61341763 Cl 5.38112954 8.20082822 10.86406433 2.78460377 -3.33454762 2.91468604 Na 5.45388177 10.91224047 10.79804923 -2.98065145 -0.08317216 -1.18726800 Cl 8.19350922 10.55148835 10.82071678 -4.74304489 -6.08745135 1.16869885 Na 5.07918735 8.22133832 13.45489090 -6.87393315 -5.63413396 -0.15048011 Cl 7.80822202 8.14986395 13.61576352 4.95165442 -6.62893359 3.09007354 Na 8.08118421 10.53195045 13.30379598 -0.51432259 8.35109630 2.09400749 I 11.16863620 10.54335813 13.74233020 3.41474856 -3.55738278 -9.60320196 48 Lattice="0.0 10.8 10.8 8.100000000000001 0.0 8.100000000000001 5.4 5.4 0.0" Properties=species:S:1:pos:R:3:vel:R:3 pbc="T T T" Na 0.30308930 0.13525636 -0.24593351 0.81774344 4.22503407 1.21974896 Cl 2.69638719 0.17749388 -0.00228684 9.60919524 0.59234418 -11.04204671 Na 2.88908520 2.70953349 0.16132836 -3.18036456 0.49818596 -3.31494050 Cl 5.22893596 2.83704936 -0.30134087 -1.12925967 -1.87304897 -5.10296532 Na 2.88774561 0.03465835 2.53648936 8.50242728 -2.64678155 -20.02884587 Cl 5.26611686 0.38546237 2.70579946 0.23510723 -2.76736798 8.53642866 Na 5.70498321 2.74015446 2.56421500 4.86503032 1.84279313 1.44754414 Cl 8.06850843 2.45201665 2.77565261 -3.83646473 9.33734246 -3.90187233 Na 5.38639697 0.03938410 5.82242401 -1.30145725 2.13539904 8.89171447 Cl 8.46151031 -0.20449435 5.53685230 -1.07978561 -7.94783761 5.33768020 Na 8.03786245 2.77843176 5.46078541 6.67141141 -15.34776342 -0.34526669 Cl 11.09677702 2.46369179 5.67554737 -2.07920234 0.52309315 2.19689824 Na -0.12566780 2.92649581 2.65378981 -1.85726668 0.27133301 -2.09975535 Cl 2.41531238 2.66513644 2.78262645 -6.10549841 -1.43932762 4.81797276 Na 2.56699323 5.61007037 2.65126774 -3.27121654 -0.12101529 2.55217397 Cl 5.67947268 5.12021925 2.53513918 -0.75115711 -0.98568463 -1.50142662 Na 2.73919900 2.70117603 5.33013249 10.51805824 0.27828921 7.84709708 Cl 5.31067582 2.47353573 5.13783289 1.76357565 0.24943831 0.55414989 Na 5.55199312 5.27477563 5.35193159 -5.23659118 5.92433637 -6.32013762 Cl 7.71162915 5.61400695 5.79971308 1.31093428 7.83463240 -2.65653632 Na 5.78083449 2.60236739 7.90726299 -2.28447752 -3.67300911 -0.20670424 Cl 8.24741870 2.52696959 8.05369908 -6.15070220 -8.09159498 1.73963807 Na 8.12734284 5.43813638 8.13984596 9.45991504 -11.87215100 -0.73447320 Cl 10.80893167 5.59003876 8.52641984 5.86453335 -10.38781075 -4.50781269 Na -0.07850166 5.28576435 5.39698895 8.10126431 -5.78795929 -4.95455422 Cl 2.51627567 5.21419050 5.24265358 10.82182087 -2.12450405 1.35690000 Na 2.67781459 8.14568905 5.40938612 2.87090115 4.18009957 -1.02771881 Cl 5.39695270 8.15295673 5.30797996 0.02284704 1.56795629 -2.97748839 Na 2.87579223 5.31165391 7.89408584 13.25061254 1.99617132 -2.00487957 Cl 5.34175583 5.40723956 7.92760025 2.91783163 -3.09065074 3.59914426 Na 5.48669422 8.03950736 7.60779354 -0.60271703 -0.75086724 -3.14845973 Cl 8.30031202 8.42169702 8.23396689 5.04289102 -6.20990558 0.83866022 Na 5.49353333 5.62100780 11.15965274 4.22731072 -13.41897633 4.46381944 Cl 8.34380177 5.61739095 10.98545859 -3.31877069 -7.48469252 4.40223541 Na 8.16111784 8.32337666 10.61385333 0.32742637 -3.81394848 -3.49896590 Cl 10.94993878 7.99405973 10.85573597 -1.91833399 4.64642491 5.13969166 Na 0.38757273 7.89801537 8.07701024 0.00054501 -6.51141402 -1.35334925 Cl 2.79240855 7.80718429 8.34316762 -0.84179364 5.27604563 -8.55415795 Na 2.55156541 11.02814333 8.40451556 1.33125016 -2.80391305 -2.40061236 Cl 5.35162438 11.01739659 8.29072992 -6.67314744 2.63486358 -1.34902529 Na 3.12948960 8.35825074 10.86020818 -9.24853876 8.67066991 -3.56779426 Cl 5.48661809 7.70484169 10.94209154 -2.15521260 -0.10479557 8.33595300 Na 5.17144715 10.88928641 10.53242812 4.05100158 7.39336374 -0.80887467 Cl 7.97835504 10.35031560 11.07397548 8.50917238 1.48495140 8.60936353 Na 5.50550864 8.00726973 13.52077842 1.07562470 -4.51544503 -3.12743516 Cl 8.12828965 7.83309425 13.25722100 2.96801870 -12.38571218 1.27849467 Na 7.86577842 11.02044260 13.66412191 1.98816274 7.10419736 0.51559425 I 11.00803145 10.92123659 13.41653262 -4.59849932 3.10691194 2.89624029 48 Lattice="0.0 10.8 10.8 8.100000000000001 0.0 8.100000000000001 5.4 5.4 0.0" Properties=species:S:1:pos:R:3:vel:R:3 pbc="T T T" Na 0.12167990 -0.05847255 0.29375092 -6.10673870 7.94796020 -3.19010097 Cl 2.78252852 -0.03106975 -0.17433973 6.77047888 7.36982102 -0.85204979 Na 2.65506120 2.77423697 -0.12110599 -10.27925177 -8.45874423 0.22225679 Cl 5.03793524 2.45283744 -0.15670722 -2.47710172 1.49296962 -0.35234793 Na 2.64582639 0.24090006 2.65753449 -6.26055233 -10.50288602 0.01455165 Cl 5.41575741 -0.00857459 2.23066815 2.54731604 3.56654370 -1.88142832 Na 5.62693966 2.72600124 2.79813359 -1.57351673 -1.97910815 -1.26485646 Cl 8.32783582 2.70402041 3.08418781 1.17434683 -3.38129331 -7.33571218 Na 4.93208215 0.24202517 5.28695740 -3.25170099 -6.93769576 -4.40938436 Cl 7.89219651 0.00147041 5.51849420 1.73477627 1.74757662 12.34447524 Na 8.26475720 2.99096108 5.19784075 -3.08295144 -0.10445254 -2.68688862 Cl 10.67939253 2.43671942 5.10827446 5.80076199 4.81130832 -6.49492440 Na -0.19949179 2.50137574 2.95025920 -6.93102607 -0.42065528 3.63183705 Cl 2.54606807 2.85153908 2.52405317 0.71795226 3.34381560 11.85763122 Na 2.76386224 5.38927374 2.63714277 -0.98113372 -5.63453978 -6.49264620 Cl 5.37111732 5.41728662 3.03106800 -4.91756761 -0.95524336 4.15591211 Na 2.88631088 2.60115001 5.47337213 1.41612487 -6.09629251 7.34416885 Cl 5.10179153 2.69167064 5.69683969 -1.86463374 7.77528874 -13.15724025 Na 4.86128735 5.37678470 5.30500910 -3.73610939 -12.22685339 7.31330572 Cl 8.00302592 5.51296567 5.58913475 -12.73746803 -6.95981572 14.06802140 Na 5.41158711 2.44904148 7.92902071 6.91905070 -9.35375250 -3.10920529 Cl 7.90890475 2.56862092 7.62962574 -6.88595802 -2.91278796 5.63275994 Na 8.08978903 5.05569041 8.02210300 -0.51339309 -5.59745084 9.68038259 Cl 10.68850902 5.29194266 7.88711214 5.21323825 -3.23456282 -1.15112264 Na -0.36446092 5.33527744 5.49769161 -8.42724671 3.20026076 -0.20835980 Cl 2.96565960 5.63501692 5.21616465 7.94496141 4.23705224 -1.48055252 Na 2.66439036 7.87592866 4.93664177 -3.47110027 -1.17501901 -9.75391135 Cl 5.41158064 8.37012843 5.30519652 -6.66448360 -1.82672821 -3.70053354 Na 2.52743816 5.03667297 7.87929471 -6.71053406 -4.63481985 -1.69817878 Cl 5.68378844 5.25789515 8.32210348 1.75621821 2.12351906 -0.45551922 Na 5.79764665 8.15911019 8.10922702 0.21056665 2.66080121 4.79721236 Cl 7.94798367 8.16648296 8.04201912 -6.40077565 -2.76680883 -7.22525380 Na 4.89090551 5.38895644 10.87373638 -1.28753927 8.81238469 -3.53110246 Cl 7.99803679 5.21867105 10.34337478 -2.05223810 -6.93215915 -1.21959446 Na 8.18481156 7.97676610 11.14217410 -8.16119768 -0.13295408 -13.48169969 Cl 10.43939993 8.12752266 11.00277353 5.88593725 -1.67685686 -7.43635442 Na -0.06026607 8.28594019 8.03442506 4.27684164 4.63070864 1.46685207 Cl 2.64911067 7.75559489 8.16711164 -1.66822810 -4.74486330 -4.28816219 Na 2.23777476 11.14600957 8.04343157 4.87484947 -0.99258902 -1.97227569 Cl 5.19776835 10.66573268 8.31321825 -6.25789767 8.81091454 -4.65534139 Na 2.81698696 8.23511861 10.96513586 -5.54313720 -1.81389871 -3.24247014 Cl 5.48950202 8.33945999 10.86982994 8.72833868 4.33568238 4.64517211 Na 5.33696715 10.68939005 10.77497048 -0.25107583 -6.37922436 -6.87129162 Cl 8.31898202 11.10955144 10.75718211 -1.59647873 -8.54512530 1.04970700 Na 5.69321568 8.21748584 13.38696647 9.67387945 -6.73780595 -1.38071492 Cl 7.84110849 8.44210678 13.58129844 4.40650129 -0.59515736 4.86869189 Na 7.85773254 10.65224971 13.75399199 2.03282272 9.26734072 -2.95559863 I 10.71394659 10.79941147 13.68354634 -0.61472560 4.12173088 6.67181274 48 Lattice="0.0 10.8 10.8 8.100000000000001 0.0 8.100000000000001 5.4 5.4 0.0" Properties=species:S:1:pos:R:3:vel:R:3 pbc="T T T" Na 0.08950762 -0.09310182 0.06321615 -3.31777215 -3.07995258 -2.94911320 Cl 2.61371237 0.09777429 -0.07199390 -3.49828240 5.30533136 -5.45637794 Na 2.46530892 2.63669196 0.22096793 -4.95299038 2.94973473 6.07250454 Cl 5.51862370 2.87170393 -0.05378552 -2.98567225 3.67319738 -4.40271643 Na 2.71788464 -0.09825937 2.48929242 5.43102094 -6.87442815 0.75596453 Cl 5.42719307 -0.03463094 2.59398260 0.33730536 -4.28219924 -0.05314891 Na 5.33128248 2.45088878 2.96741660 8.03200921 1.17087327 -1.48490015 Cl 8.26575938 2.90200177 2.80166710 -0.86618139 7.48831021 -3.52624456 Na 5.46530004 0.09642724 5.24529533 7.60161708 5.11876806 -0.24505167 Cl 8.11134034 0.08382264 5.40573464 -2.10334500 -3.55636347 0.12250849 Na 8.12800322 2.46607111 5.31490169 -1.56310781 4.26907917 -2.30451843 Cl 10.72196232 2.63121670 5.23900220 -1.67113028 -4.41468562 -0.85431016 Na -0.04342749 2.65327492 2.45443612 -7.14736689 -8.37221267 -2.43383120 Cl 2.74603345 2.54986581 2.88818078 7.00983390 -5.15963089 5.07067583 Na 2.92442197 5.66106604 2.85607193 -6.56587953 -6.30372820 -6.16458804 Cl 5.54721385 5.29684744 2.58513608 0.78097960 -2.71634072 1.21328771 Na 2.58496426 2.70197724 5.32926526 -4.71075759 -2.35858676 -7.26384157 Cl 5.33409447 2.82614483 5.00345117 -0.82037201 -10.80511609 -0.04117246 Na 5.40369369 5.09428229 5.46059858 2.89836173 -0.21510129 7.50521590 Cl 7.90240840 5.45201511 5.56949884 5.58589757 -5.14939394 -3.33803241 Na 4.99181750 2.65989263 7.77739275 -4.60078114 -0.49311084 -3.00488972 Cl 7.90087055 2.83958871 8.35917201 -4.97131822 1.58756680 3.19947505 Na 8.33549460 5.47769366 7.66269036 1.39604993 -0.71327274 -4.98001807 Cl 10.91834519 5.56421722 8.38532352 4.46600309 2.47544743 0.12755530 Na 0.17870555 5.25950912 5.85407055 -10.20996737 -9.79362400 -1.14933345 Cl 2.63027594 5.62136280 5.66067407 -6.97759499 0.26737037 2.90205635 Na 2.54992227 8.18367904 5.47819479 1.22538879 1.25011246 1.60036634 Cl 5.52698541 8.38621683 5.49816666 4.71578371 8.24934059 3.04093062 Na 2.75812845 5.40870399 7.73122753 0.08747156 1.15214388 -4.49231302 Cl 5.07248491 5.30085465 8.04353288 1.60487129 -12.28008737 -3.27649617 Na 5.38168996 8.40388420 7.94525969 8.96162243 -10.96906474 0.83714459 Cl 8.03569481 8.06657569 8.22264268 0.17174105 4.60302070 -1.86311775 Na 5.28162920 5.19949703 10.62277901 13.49801926 8.68648796 -0.12493525 Cl 8.13865562 5.61025145 10.72836716 4.86557577 -8.61405576 3.36016673 Na 8.22836578 8.03269901 10.56844621 -4.02832553 1.35124507 2.48803229 Cl 10.71087719 8.05703668 10.56710082 2.39925359 3.05705893 -3.42125863 Na -0.13880496 8.00780969 8.12611501 6.46337003 7.10777077 12.68333857 Cl 2.58964929 7.96425305 8.03170559 7.72060634 -8.71904537 1.65951440 Na 2.43673663 10.89448148 7.98200552 8.43992102 3.66400988 4.53282015 Cl 5.13156890 10.99441782 7.89987348 5.23717286 -4.91525671 -1.49424786 Na 2.98279560 8.35299791 10.97195188 7.99835268 1.79531063 0.67315804 Cl 5.40186297 7.99928148 10.66891534 -2.20743530 3.30644594 6.68732342 Na 5.41302581 11.10909770 10.68761339 6.76087320 0.76521959 3.96300595 Cl 7.99050029 11.07686793 10.87849028 3.19201370 5.38532444 -2.02999697 Na 5.47115731 8.17812494 13.59335487 -6.57302572 6.89115763 3.76437726 Cl 8.28748739 8.35847991 13.71594626 1.86183079 -3.81283930 -2.69341274 Na 8.11460170 11.07861906 13.50809112 -2.66329456 1.53292971 1.33580688 I 11.01076780 10.79075969 13.33184658 -1.55089204 9.00047246 -4.30174937 48 Lattice="0.0 10.8 10.8 8.100000000000001 0.0 8.100000000000001 5.4 5.4 0.0" Properties=species:S:1:pos:R:3:vel:R:3 pbc="T T T" Na -0.02869741 0.06867218 0.04041749 6.09095968 -2.80642218 3.85270169 Cl 2.76719588 0.16720447 0.05234929 -3.31912380 -3.76831423 4.93187231 Na 2.65796628 2.55028565 0.09484187 -0.10968901 -2.72669787 -0.49885881 Cl 5.36774507 2.63287419 -0.05007771 0.88320766 -6.36105152 -5.09840709 Na 2.79103901 0.08172783 2.69870515 8.72318925 -7.95803041 6.20708061 Cl 5.46453652 -0.36616799 2.62523102 -11.26226727 1.41744678 -8.64474234 Na 5.30440061 2.90401886 2.41155398 5.95593683 -7.69612636 2.22819202 Cl 8.10881075 2.52875336 2.78997610 -1.36360124 3.12975077 12.24135865 Na 5.49424217 0.17517190 5.68207517 -8.53295974 -6.35142909 -2.59685115 Cl 8.19451628 0.08315411 5.26807342 -4.96975068 -0.71184580 1.54901775 Na 8.05571820 2.54314771 5.17147850 1.22902899 1.60475149 2.78230685 Cl 10.70008409 2.69625397 5.55809720 -1.43484288 -2.87135349 5.27946825 Na -0.16518401 2.85046524 2.75721105 -8.09718345 -1.08597001 1.75895080 Cl 2.70777410 2.80774435 2.70834250 -5.00439216 4.21810089 4.30553255 Na 2.63726373 5.04374007 2.96541402 -2.34830293 -13.66656825 11.66684684 Cl 5.76742849 5.09464503 2.62250110 1.93787599 7.32022665 5.57140279 Na 2.50963903 2.70344285 5.47924166 5.41373707 -1.07122391 -11.52109462 Cl 5.34229144 2.85338938 5.59263207 -0.76586403 3.99359016 -7.44204549 Na 5.56719718 5.18198579 5.64251461 3.32785775 -4.15132326 1.71445822 Cl 7.87660885 5.44597424 5.26545913 2.90610039 -14.66452447 -8.61837998 Na 5.53861117 2.94920915 8.08719151 -4.66844056 -3.13101612 4.73612056 Cl 8.06800920 2.49862060 8.05323273 -6.58369154 -0.06872468 6.36248450 Na 8.19125374 5.43454709 8.11313643 4.14901057 -10.01928415 -0.45618539 Cl 10.85104571 5.50781053 8.00318509 -3.74536124 3.57007000 -3.44986345 Na -0.10111291 5.37693128 5.16668530 -4.31139385 4.62084968 3.75989869 Cl 2.90312845 5.67848662 5.49565690 5.87149623 4.78858161 0.66149527 Na 2.71843999 7.63778699 4.97363336 3.20289810 -6.21556712 0.32958464 Cl 5.31583738 7.76222980 5.50717519 3.34129388 2.55676634 -7.57580250 Na 2.53990573 5.65994035 7.56196295 -3.80724826 5.38859413 -0.26760666 Cl 5.04412961 5.31859393 7.71530817 -1.62803555 -4.47514890 9.24250730 Na 5.33421308 8.31534692 7.87132750 5.25864820 -2.08345402 0.29351154 Cl 8.19412732 8.13226096 7.90093992 2.81445137 -2.44683196 5.82254802 Na 5.29350052 5.18649523 10.54762600 -2.00114858 4.26552204 1.11204793 Cl 7.83451508 5.53131373 10.82665082 0.32159340 1.22109103 -4.44149204 Na 8.23464385 7.85325986 10.69052754 -3.64174784 1.89807247 0.70472135 Cl 11.00182251 7.95448806 10.75620507 1.52416879 5.56147370 1.97574593 Na 0.27347949 8.14154757 8.21658131 -8.78052850 0.94905699 -0.20065139 Cl 2.95115511 8.01255944 8.32515442 -5.97642382 4.71206071 4.83796184 Na 2.65474570 10.95063120 8.11846161 0.01837394 -7.82779131 2.53970594 Cl 5.23545086 11.01324897 8.02015216 -8.43650842 -6.98335793 -3.64118416 Na 2.88385602 7.61854991 10.89172490 -7.48683717 3.12267950 1.38882538 Cl 5.39121575 8.16636828 10.66423293 -5.18712533 -4.23081913 -2.77814046 Na 5.15271718 10.69924173 10.86452944 5.04561607 0.26536952 -1.54601704 Cl 7.94608961 10.88444710 10.50753790 4.40191552 -2.50370118 -1.76821464 Na 5.04275198 8.40244067 13.67796659 0.63030242 2.09003238 -2.64567636 Cl 8.03028305 8.16288690 13.36044155 0.13805994 4.35567024 1.13829707 Na 8.15853141 10.84592500 13.82255526 7.34195204 0.52169681 1.73888599 I 10.72759353 10.81364296 13.46845438 -3.84106878 3.96431237 -8.11423628 48 Lattice="0.0 10.8 10.8 8.100000000000001 0.0 8.100000000000001 5.4 5.4 0.0" Properties=species:S:1:pos:R:3:vel:R:3 pbc="T T T" Na -0.35242355 -0.12713595 0.12397330 1.25820289 -1.79108359 3.71684727 Cl 3.13320435 0.10261375 0.06550541 -2.47676628 -5.91585732 7.19266893 Na 2.70062103 2.81329892 -0.14278678 3.97704760 -0.81958665 6.06337068 Cl 5.50235253 2.84471188 0.24181697 -3.84768631 -2.55016029 -5.46781434 Na 2.46432599 -0.10104643 2.51709216 1.03430195 0.87508145 5.96159655 Cl 5.55326443 0.00868333 2.64953824 -11.55113155 9.28785078 -7.49634186 Na 5.10395407 2.70781489 3.07937901 6.72934212 -1.18515515 -7.96478183 Cl 8.13643900 2.74710980 3.07190896 -2.41211643 2.86129692 -0.33286332 Na 5.21463611 0.17457693 4.96984267 5.25112371 -5.52812106 3.99884200 Cl 8.34765009 -0.13602071 5.06425094 0.00751593 5.59110324 -2.11610538 Na 8.23674482 2.72357586 5.24067051 -0.37021581 1.41459841 4.93774850 Cl 10.67609129 2.66405818 5.56004606 -0.07938525 2.56003071 -4.05571389 Na -0.00885083 2.76739267 2.42876853 -0.81901941 0.25051107 5.64060552 Cl 2.68138694 2.75687040 2.67576497 1.47038808 -0.61426446 -0.10117244 Na 2.66936189 5.22348465 2.51769377 -1.14624415 0.27967082 3.30965079 Cl 5.29217094 5.43747681 2.72725408 -7.63992786 5.09968354 -4.28872472 Na 2.59743992 2.48454467 5.27682329 1.84617614 -6.83044717 -1.27248318 Cl 4.90405755 2.32965511 5.39040816 1.96707722 -0.31785475 0.95922571 Na 5.57529774 5.37254742 5.56448830 -2.38939270 1.03250352 2.44187603 Cl 8.06479138 5.18269102 5.11155346 -4.70651155 -7.96126601 -1.74787138 Na 5.48702605 2.66618030 8.16315743 -0.43512589 -1.87928550 -1.76236210 Cl 7.86698361 2.80543685 7.94236143 -13.29276336 -1.89747983 1.41429009 Na 8.09943625 5.33437338 8.52871279 -4.28886925 11.55513674 -8.58553129 Cl 10.64815134 5.40204008 8.20794707 -6.82109156 8.02124046 3.04289921 Na 0.08267504 5.41106165 5.52735846 5.55031532 -1.40778191 -1.27768138 Cl 2.61089629 4.87749143 5.60091043 -6.52466103 -0.16819600 -4.46807776 Na 2.62376301 8.16789243 5.56274644 3.42228022 -4.63818274 -1.86231371 Cl 5.21361385 7.89410292 5.30850961 9.15288333 7.38954331 1.29649757 Na 2.69409538 5.42496026 8.17386629 2.10346056 6.42994821 4.62351059 Cl 5.25140938 5.32020657 7.86122840 -8.21940631 -1.59166830 8.75518217 Na 5.73995888 8.32617455 8.39707532 -0.98054844 -1.60241686 8.79032702 Cl 7.71698863 8.01787726 8.19010204 0.61252451 -5.48013398 -1.27405045 Na 5.40788564 5.51050744 10.59070333 2.15621127 5.29441536 11.61373707 Cl 8.19465396 5.47836491 10.96017213 -3.47409268 8.19688760 -5.19720801 Na 8.18895625 8.22912650 11.36267429 5.49044626 5.42353875 -0.93443985 Cl 10.75192045 8.07266679 10.64786507 4.78416351 -4.94265864 -5.10162457 Na 0.14834775 8.08963250 7.87949952 0.67505983 -15.16026388 8.50724198 Cl 2.67513948 8.20154417 8.27389606 6.65748423 -4.81488251 4.04127665 Na 2.49725662 10.83284532 8.26579651 -0.37051935 1.02536150 7.40942771 Cl 5.17678585 10.85707562 8.10346543 5.06430733 7.23198470 1.87145487 Na 2.73746171 8.08052906 10.84668657 0.65133368 2.63666362 2.20784346 Cl 5.50683214 7.82947186 10.79712303 1.66335485 4.18727057 -2.79175844 Na 5.46156603 10.68800864 11.03414777 -1.43923950 -3.28805591 -1.76960802 Cl 8.08818192 10.69349962 10.40431511 11.33738094 -1.35386873 -0.36583970 Na 5.10008003 8.22637574 13.33933672 -1.88419586 7.98079088 2.79446960 Cl 7.93987529 8.04230587 13.36006650 8.40811285 -3.20229749 7.21160682 Na 7.97992160 10.33789384 13.17046366 -1.88484726 7.21385832 -6.31326717 I 11.16058839 10.87333713 13.65171177 5.91908818 -0.35382211 3.69021987 48 Lattice="0.0 10.8 10.8 8.100000000000001 0.0 8.100000000000001 5.4 5.4 0.0" Properties=species:S:1:pos:R:3:vel:R:3 pbc="T T T" Na 0.02753123 0.08957220 0.15208505 -2.09588276 -3.80291652 3.42862514 Cl 2.45514545 -0.20562366 0.25088660 3.00967723 2.63861342 -0.31806442 Na 2.85077140 2.46902892 -0.37970976 -6.84590875 2.94318451 3.82256367 Cl 5.23142488 2.45728061 -0.22763816 -6.65807506 1.23201860 0.04777984 Na 2.71115131 -0.15005740 2.28409743 4.84726808 -5.96866093 -0.27986820 Cl 5.23857465 0.35466491 2.90372014 -0.11862977 0.78677128 -7.71216509 Na 5.42338991 2.91312303 2.50330891 3.16950164 -1.18380310 3.45667369 Cl 8.04825208 2.31810502 2.60149477 1.77800663 -3.66546901 4.08581823 Na 5.38401829 0.01050978 5.60898306 10.24195797 3.62689177 -0.78637332 Cl 7.85652308 0.49181814 5.41202916 -1.57728784 -4.27343372 0.43684110 Na 8.08498408 2.69914568 5.51738496 -1.82651781 8.61336448 -2.99954962 Cl 11.10279731 2.59974698 5.47406544 2.92553551 -10.27720804 -6.09635198 Na -0.04687903 2.15979118 2.56276826 -2.63091956 0.11488998 -3.84392285 Cl 2.93715316 2.56554866 2.29716703 5.13068603 -4.03771569 -0.10176526 Na 2.83580032 5.30446581 2.65485254 3.72826342 4.38380474 -3.00416683 Cl 5.15374557 5.52484582 2.64995467 1.89974990 0.12523379 4.84291238 Na 3.01002058 3.02078280 5.82732849 -1.15836641 -6.96264640 13.14583440 Cl 5.53822341 2.76470860 5.20998808 0.41578709 4.02163685 -7.27572114 Na 5.22644502 5.53691207 5.65507994 0.71877329 2.73556058 -1.74115521 Cl 8.01670535 5.77248755 5.19014876 -4.57571878 -2.34542352 -4.05268029 Na 5.34895395 2.74764884 8.04198330 -0.54837446 -6.58973007 -5.61162302 Cl 8.10771207 2.76358575 8.15114689 -2.19220515 -1.09274806 8.56838321 Na 7.96780574 5.67982266 8.34288926 -9.33868904 -5.01934817 0.87117980 Cl 10.67722302 5.44800586 7.97474317 -4.09888482 -0.07551381 -6.24379484 Na -0.07387158 5.30097780 5.56278742 -3.18178205 -6.65006866 -1.82490067 Cl 2.84986173 5.50451393 5.41756897 1.26334394 -4.70565625 -6.39399000 Na 2.71256625 7.76284137 5.10122715 -1.96567546 0.62967521 4.97966465 Cl 5.31395556 8.28782463 5.51851558 5.63515930 -4.58787892 -6.97834063 Na 2.93751540 5.31423631 7.89637036 -5.66522731 1.96617817 -9.20963770 Cl 5.49179690 5.49018771 8.02692097 0.98135154 5.09501465 8.38252289 Na 5.35491832 8.20513929 8.21826533 -6.09767130 1.63794489 1.49627715 Cl 8.07289588 8.52963001 7.97126863 -0.76805674 -10.83491797 -4.35455398 Na 5.39831742 5.57819718 10.67319572 -4.01677306 -0.66552524 -3.60833709 Cl 8.21483280 5.40140609 10.57412897 2.82633932 6.75550386 5.70108180 Na 7.65051667 7.86931549 10.74877241 7.76003686 -3.82059123 5.84183689 Cl 10.87720415 8.29677067 10.48124761 1.41915532 8.01909114 -5.58695742 Na -0.07458602 7.94040878 8.32094759 3.26682535 0.59702996 0.14709627 Cl 2.51460125 7.86331336 7.81665595 2.33023546 2.67191131 2.57278534 Na 2.53998764 10.52430465 8.45749906 -0.49139303 2.59936578 10.28740591 Cl 5.44972077 10.82844640 8.25449948 10.16294487 3.74463566 5.86208224 Na 2.55194019 7.90313396 11.16386404 -7.53618293 2.05550676 -4.19624211 Cl 5.41235020 8.39383219 10.73804438 -0.12891576 -1.06616721 6.94690354 Na 5.45490708 10.88345031 10.73114906 -7.80191949 2.69533109 -1.49356790 Cl 8.15068680 10.95035466 10.66181112 3.85683528 -2.69118336 -5.23019636 Na 5.35381067 8.24891001 13.67205783 8.43480641 1.68903786 0.08954519 Cl 8.22158438 8.15653152 13.58864303 -4.53747556 0.17884401 -2.82168344 Na 8.06021388 10.91703919 13.31214228 -5.70393330 0.87208378 4.84143461 I 10.57325269 10.56142733 13.47430266 3.46545830 2.77476879 -3.95422714 48 Lattice="0.0 10.8 10.8 8.100000000000001 0.0 8.100000000000001 5.4 5.4 0.0" Properties=species:S:1:pos:R:3:vel:R:3 pbc="T T T" Na 0.06059374 0.06561011 -0.00151730 4.64998408 -0.58288577 1.01949838 Cl 2.74801662 -0.04715311 0.08861584 -0.13506900 -0.16370901 -0.37585185 Na 2.62031664 2.62357167 0.42069072 -4.94990410 -11.26762445 1.66458019 Cl 5.33312990 2.85074793 0.04813558 4.38858606 -1.63025714 -2.24225069 Na 2.63715835 -0.30211758 2.46392284 -6.14082505 -3.71604034 -6.64236769 Cl 5.27737568 -0.17961506 2.69670443 -0.06836877 0.85278325 17.72088611 Na 5.10428138 2.83678664 2.66555159 3.42406215 -0.43932793 0.02015576 Cl 8.18287577 2.79344753 2.69249087 10.47484558 10.14984858 0.50654680 Na 5.43959176 0.13129472 5.39560613 -6.58265890 -6.36893669 -0.67999906 Cl 8.30384119 -0.06869183 5.19164634 0.34424056 -2.68242836 -3.32834050 Na 8.08353008 2.32877164 5.40756504 -4.95234433 1.48382734 -1.78373640 Cl 10.74760203 2.75677337 5.75350438 -4.27685836 0.31928516 -6.82987851 Na -0.32720362 2.81958675 2.78175147 -1.62601516 -2.13003171 7.16861246 Cl 2.83483190 2.69941144 2.52963197 -5.81798980 -5.61596650 -3.31116407 Na 2.52266408 5.40004018 2.67777731 5.39434273 -1.96860111 -0.83157958 Cl 5.53136558 5.29180626 2.83812248 3.02145105 10.87663578 5.30165485 Na 2.53125222 2.81472779 5.46602412 1.10478304 -1.28485949 -2.74113218 Cl 4.91421831 2.66393683 5.57723409 5.04291887 0.11776058 1.66343665 Na 5.55581331 5.38354405 5.59662814 2.84152236 -1.71597362 0.11102574 Cl 8.47733245 5.14950456 5.60272348 -0.82609754 0.29123473 0.68717240 Na 5.43176917 3.08038622 7.83043592 -5.43446188 -2.65497786 -2.55182947 Cl 8.03612206 2.68056626 8.00877228 0.36168150 6.69059108 4.05761206 Na 8.20545153 5.36980539 8.21887916 0.94257689 3.88810589 -8.75542491 Cl 10.87256109 5.53167430 7.91898576 -0.05954132 -4.56126666 -3.84332393 Na -0.34864817 5.23812254 5.56421804 -5.07569255 3.02068734 8.34235723 Cl 2.90732672 5.55030404 5.47953266 1.78265302 -5.24556492 -6.74307446 Na 2.37507262 7.83691998 5.33425266 2.61758062 0.44757955 -9.69699690 Cl 5.29410913 8.51629881 5.30111714 -2.13875595 2.55143201 -3.29568233 Na 2.64050504 5.11527610 8.39036740 0.36347619 4.76983303 -2.94249524 Cl 5.84406097 5.37378805 7.75759173 0.96163864 -0.74728794 2.38666368 Na 5.27014390 7.60619815 8.06400114 6.09420861 -0.80914663 0.78722059 Cl 7.82818831 8.03123568 8.17459227 -5.27044352 -3.80686448 -0.37974556 Na 5.39255030 5.14838959 10.82037191 3.20258943 -8.18834452 -4.37303431 Cl 8.03429995 5.64261218 10.78544809 -9.49122004 0.72615558 0.72319135 Na 8.19874271 8.13077674 10.68678333 1.80623224 0.01309552 -3.96778644 Cl 11.11900386 8.44179409 10.56878688 -2.94651225 -6.31885400 -3.05167848 Na -0.01270767 8.10123030 8.43939826 6.17508926 -4.28406669 2.16234915 Cl 2.66439980 8.29607022 7.93728275 -0.66766524 2.27018470 2.06875415 Na 2.67588316 10.88984003 8.07177896 6.14589154 1.45785415 1.72728306 Cl 5.19541646 10.93832940 8.10914990 1.03151474 -4.25622193 -11.37526786 Na 2.60240754 8.22937797 10.47331351 -5.35061645 -4.14497338 -1.73462069 Cl 5.28878504 8.28813649 11.06588077 3.70672503 -0.50639984 -3.09515152 Na 5.42138137 10.73323103 11.04170174 -6.51903477 5.69876299 8.97331062 Cl 8.16131563 10.69643000 10.85710287 1.31999670 -3.47046157 3.38562774 Na 5.47331668 7.83252989 13.77262808 -2.15844221 8.23781273 -5.09385945 Cl 8.06921520 8.12303861 13.42580993 0.18562854 3.43518585 -2.65594999 Na 7.87067351 10.76599204 13.53105302 -3.06859271 6.55825691 -0.00982640 I 10.89869123 10.89639539 13.33752259 2.31170245 4.03659805 -2.16321210 48 Lattice="0.0 10.8 10.8 8.100000000000001 0.0 8.100000000000001 5.4 5.4 0.0" Properties=species:S:1:pos:R:3:vel:R:3 pbc="T T T" Na 0.03686217 0.09184279 -0.03560772 -4.29060624 -7.75536508 8.69521266 Cl 2.80231809 0.00109737 -0.43364217 6.68560156 1.69241266 -1.84740518 Na 3.04290240 2.85752299 0.06760562 1.12149054 -0.81060530 5.84853155 Cl 5.27687857 2.83000579 0.32302827 1.51656885 -0.06497385 7.78524190 Na 2.58724872 0.08751252 2.87943680 -5.84135215 -9.13416379 0.46293583 Cl 5.20023054 -0.13701711 2.41685219 2.75807100 -8.17731876 9.43105250 Na 5.67468123 2.60707060 2.77171140 3.32866992 6.19188967 -4.28386508 Cl 8.02193139 2.66566843 2.53337305 1.32292699 4.34398976 2.72436270 Na 5.36597272 -0.20138435 5.45377387 -3.57015058 -11.51425838 3.95212898 Cl 8.23427510 -0.17829915 5.22340068 -2.66648717 0.71870232 -2.50793831 Na 8.35341409 2.53927970 5.45501335 5.55451110 0.15589911 -7.81656639 Cl 10.83598441 2.84647232 5.36788870 -6.72348801 -1.25510537 1.01271335 Na -0.17688872 2.72144278 2.56873066 -3.29752654 2.84378696 1.14809137 Cl 2.73521840 2.98820584 2.42246904 -7.09301145 -3.17724427 5.24712170 Na 2.68801421 5.53659772 2.94917596 10.27127754 -9.61068480 -4.89470614 Cl 5.24346321 5.23463380 2.56267299 -8.57696950 -1.16902830 -1.19259362 Na 2.98918805 2.60263969 5.25662209 3.75199827 3.94962489 2.66310135 Cl 5.01687986 2.82547803 5.52934359 -9.47695226 -1.45857198 3.66754944 Na 5.58328532 5.03972457 5.49666297 8.75958812 -8.70586631 -0.40902658 Cl 8.05255739 5.67507704 5.12193797 -1.19422612 0.94663005 4.50539187 Na 5.58873899 2.90369649 8.27193775 0.96933843 -3.81858210 -6.91214438 Cl 8.21169617 2.66092529 7.84957151 -2.51347245 3.91474472 0.09494296 Na 8.02753454 5.54603087 8.25646863 1.29166232 4.82881793 -1.22595566 Cl 10.87456127 5.47145355 7.82510222 -5.28332180 -1.08150254 3.35547552 Na 0.20833081 5.35314019 5.11802631 -2.82198239 -10.95253231 -2.63304438 Cl 2.67216179 5.42994804 5.41394511 -3.87276229 7.90208008 5.07810697 Na 3.00176995 8.31793977 5.17703871 3.83713636 -1.13135675 -0.71057843 Cl 5.19170337 8.43007314 5.10781166 -8.43180097 2.67737797 -1.24426062 Na 2.64775760 5.61769509 7.93875685 0.55761291 -1.45150900 4.53221727 Cl 5.50364344 5.62911412 7.95199098 -7.00806023 8.57404456 -7.43764787 Na 5.34121505 8.46948635 7.96241201 -6.06074426 -2.53960632 -3.93868282 Cl 8.05001034 7.67828826 7.98517318 -8.71682519 -2.20715310 0.75649406 Na 5.48145408 5.33149388 11.19352527 -7.14752526 -8.80404700 6.58646860 Cl 7.66703029 5.62171613 10.70009041 -1.02318855 -1.39405263 1.65447808 Na 8.23707016 8.41802965 10.93204480 1.96287035 2.78249208 -1.78634330 Cl 10.60717757 8.42968519 10.65785300 0.51619631 1.12405904 2.28594241 Na -0.01225781 8.09389078 8.33555506 -0.36475092 8.05985602 -10.68007509 Cl 2.63523539 7.91638014 8.08822651 0.93144629 2.11212353 2.55715601 Na 2.65911987 10.91270472 7.95425295 3.53599798 -6.49642552 -1.82929431 Cl 5.34291921 10.90494186 8.54896571 -2.13011063 -3.10210579 -10.48152000 Na 2.26776332 8.29648676 11.21503344 -4.37467855 -17.15927383 -9.85720903 Cl 5.53071118 8.34834099 10.83382310 -4.85493439 4.38155202 1.27089077 Na 5.13423522 10.98046112 11.03769677 0.91631664 7.40255629 2.02176276 Cl 7.78652474 10.64281874 10.93469474 7.07517660 11.17698457 -1.20407734 Na 5.08917934 8.03371481 13.51126997 -7.56023638 -4.18153481 1.43133137 Cl 7.85205568 8.10056448 13.47434164 2.44745693 -5.56575657 -10.14129266 Na 7.90795399 10.70072375 13.26456095 7.04740319 -1.50135344 -7.17583260 I 10.63175913 11.01807754 13.89992688 1.49451166 -1.63822351 0.64556124 48 Lattice="0.0 10.8 10.8 8.100000000000001 0.0 8.100000000000001 5.4 5.4 0.0" Properties=species:S:1:pos:R:3:vel:R:3 pbc="T T T" Na 0.13700771 -0.24315537 -0.01070523 8.12844973 -1.14653871 -8.18516720 Cl 2.61902585 0.41154128 -0.06490066 -0.39378964 -4.32809796 -2.44129484 Na 2.85598309 1.99038052 -0.33978190 -2.47166515 -0.64111574 3.12389131 Cl 5.02287075 2.54207122 -0.18320431 -5.46683238 6.72168081 0.55603774 Na 2.83218354 -0.12974970 2.84771503 7.79436174 -0.35727750 5.55517654 Cl 5.54764997 0.01463807 2.49328668 4.32824502 -4.42243352 0.71054104 Na 5.53012496 2.93898551 2.70122349 -8.89640291 -3.65325402 -3.06426030 Cl 7.71252968 2.77842567 2.82440281 -10.26295233 3.57727628 -3.96317872 Na 5.33413447 0.08772397 5.29263361 0.29598583 3.25215283 2.78171901 Cl 7.85280292 0.13860281 5.65473710 -2.01021145 -4.67032118 -0.22029874 Na 8.28233317 2.94730216 5.23620627 -0.22658104 4.76105297 -8.13693002 Cl 10.79917436 2.89017048 5.45861037 4.36795130 -1.32142291 4.98280732 Na -0.13433074 2.91980859 2.43814842 2.63671390 -2.52861725 -2.60811236 Cl 2.59330986 2.88164747 2.84072586 -6.12479690 -6.69893171 8.77260660 Na 2.56171489 5.58174580 2.82156709 1.71505695 4.42730814 -2.37915434 Cl 5.42238902 5.53549679 2.61219043 -3.44295030 -0.36038756 1.88554381 Na 2.62594587 2.57511695 5.55946607 -1.74894685 4.80423651 -2.93614728 Cl 5.18956656 2.48577734 5.38510936 -5.45042676 7.72505370 -0.34903972 Na 5.23966897 4.90779173 5.48579801 -3.18348822 -4.24024633 -7.45503314 Cl 7.63491615 5.51972203 5.37964341 3.42536067 -1.64541790 -1.10793423 Na 5.54029519 2.72838563 8.18858738 -2.27515877 4.75536413 -5.34040078 Cl 7.93156619 2.59105273 8.21815868 -13.07350726 -2.92003481 2.12470540 Na 8.14712983 5.56234902 8.02958311 -2.60462717 -4.73972508 -7.75484958 Cl 10.79983992 5.64284861 8.15921421 7.24046239 -8.69806329 1.74488296 Na 0.51359783 5.13404912 5.21709920 -2.93289371 -4.58522037 12.20484317 Cl 2.88292738 5.09312079 5.02378090 -10.80214547 2.36984085 -0.81022689 Na 2.83808275 8.04868156 5.52872964 -5.77816141 -5.59424037 -8.05822872 Cl 5.18330234 7.85857115 5.13388995 -7.95175637 -4.19086479 -3.47977977 Na 2.65616939 5.54285121 8.49774573 -5.91714099 -2.87332739 -4.40937822 Cl 5.37731984 5.23104417 8.53725430 4.79430291 4.34893186 -3.79260174 Na 5.39084251 8.55546281 7.87826334 -10.25916023 -1.93350215 -6.36166421 Cl 8.11927460 8.14618714 8.03984496 -4.01169123 -2.53950036 -1.20432966 Na 5.45330623 5.37985171 11.06674113 4.25028417 -6.98422142 -5.51074664 Cl 8.15203060 5.91252725 10.80389214 -0.89773982 -8.12229318 -18.49893977 Na 8.18137700 8.27738480 11.03138947 -1.62922686 -2.60244212 -5.15319545 Cl 10.81177377 8.00850380 10.77743112 -0.47378722 -1.04554885 1.34473828 Na 0.12069156 8.20305508 7.73210096 2.97716946 6.62381901 0.55690933 Cl 2.60064579 7.90723492 8.24425164 0.94530380 -1.19304268 -6.68686523 Na 2.62587787 11.10577126 8.11918091 6.55318941 7.96918615 5.57174225 Cl 5.31613736 10.91528116 8.25359602 -1.78271124 -2.44018568 1.71054535 Na 2.89885439 7.91883887 10.99141278 -3.64289127 0.93585856 5.88614743 Cl 5.36934733 8.26650399 10.93441517 2.93137915 0.31609760 6.42991812 Na 5.08455984 10.97186607 10.73776628 3.97227083 0.46838005 4.36242310 Cl 8.20739991 10.68854422 11.11511401 -1.79815810 0.40412870 0.66275000 Na 5.21813600 7.95934508 13.50387341 -12.20847624 -4.73202752 3.81000949 Cl 8.19449791 8.21268519 13.24053828 -0.73276085 0.49553459 8.27453756 Na 8.31460606 10.71596252 13.61844222 4.35940041 10.74831977 -5.41569935 I 10.66891732 11.03535211 13.79336642 -6.27505930 2.16312610 5.94663897 48 Lattice="0.0 10.8 10.8 8.100000000000001 0.0 8.100000000000001 5.4 5.4 0.0" Properties=species:S:1:pos:R:3:vel:R:3 pbc="T T T" Na -0.08994466 -0.27668883 0.17373495 3.56258271 3.97244804 0.97395573 Cl 2.97070469 -0.16358562 -0.07642134 -5.50379300 -1.24847760 0.64621092 Na 2.46863735 2.53052488 -0.13825046 -3.01892651 9.61584122 -2.42750068 Cl 5.38459578 2.58330042 -0.06285043 -8.30706056 -1.29055669 -0.98121080 Na 2.67230941 -0.19396245 2.49699393 -5.30517805 1.34394932 -10.42835060 Cl 4.98328898 -0.00285859 2.42768996 -9.58075232 4.26602586 0.82892828 Na 5.40074620 2.69947798 2.68651827 5.37911909 -4.25116534 -4.31859987 Cl 7.98426842 2.59063907 2.76675742 6.75699235 -5.99275384 -2.82287491 Na 5.16009309 0.15444299 5.10402368 8.09371439 -7.30570884 -1.43789746 Cl 8.26992001 -0.31566755 5.46349117 2.91865382 -7.53314280 -6.71058383 Na 8.32995468 2.64897698 5.47560350 -0.93109745 -3.83811632 5.37397793 Cl 10.70058709 2.79015335 5.87199214 -4.48840240 4.05608535 -2.59341237 Na 0.15765252 2.52334622 2.93468218 1.40448246 3.44578464 3.39151071 Cl 2.62614194 2.65010634 2.37491979 6.54195505 -2.05109767 3.75275229 Na 2.82741564 5.04261860 2.27785179 -3.18507225 3.81425762 0.42923771 Cl 5.35674689 5.24721987 2.57204134 4.72758951 -2.27622104 3.02932491 Na 2.33283453 2.93454040 5.47386175 2.52687112 1.87012565 6.08948937 Cl 5.13794144 2.85937502 5.76300029 -0.31330918 -2.72903561 -6.86280348 Na 5.42921241 5.62377597 5.44734103 6.26569111 -4.71449841 -5.94798191 Cl 7.92103434 5.45236242 5.37135936 -5.12697192 -1.70163204 3.16313754 Na 5.22893740 2.59917034 7.85257994 -5.70886905 -9.87820839 1.84112359 Cl 7.83075169 2.33273442 8.24875246 -5.32208154 -2.39972756 -1.47833617 Na 8.01094797 5.24334699 7.68256172 -1.24036833 -4.14689607 4.55725966 Cl 10.82400886 5.39969648 7.98069503 -6.23349109 1.21355396 -1.10104737 Na -0.12468339 5.45418876 5.38813077 -2.04790261 -3.86351049 8.04779661 Cl 2.76738470 5.35529187 5.40831313 0.47429716 -5.03272418 -0.35217051 Na 2.78633152 8.04418794 5.28441309 0.99187497 -0.45436204 7.09816743 Cl 5.30916247 8.15393668 5.31623593 4.00669300 -4.61102671 -2.56176383 Na 2.73961950 5.36839303 8.32910809 2.90194609 0.32669393 -2.41917940 Cl 5.26904066 5.31707900 8.11676110 2.85179496 7.47611565 0.59737439 Na 5.44300850 8.20058872 8.17917185 -0.90765229 -3.81478984 2.18094038 Cl 7.84795376 8.10536969 7.97949605 -3.25710253 -1.66337920 4.90311848 Na 5.31337933 5.48178799 10.78595792 3.31787652 -2.26185210 -4.70223214 Cl 8.12925145 5.14508029 10.71959158 -8.01191484 -1.00460702 8.62793437 Na 7.90384866 8.24766673 10.91907049 2.32513948 11.67057467 -3.28340322 Cl 10.68562492 7.96162395 10.99351590 -7.21906834 -0.65185639 -4.25153312 Na -0.01943883 8.05975572 8.25342040 -0.82851603 -2.26429080 -0.00369805 Cl 2.89206233 8.15623923 7.80956629 -2.41720806 -3.60064132 10.27040978 Na 2.72185492 10.93582860 8.08620714 4.68087720 0.86118637 -6.03331758 Cl 5.30954155 10.71863492 8.42317723 -2.40347145 -6.24208264 -0.41609707 Na 2.89058603 8.27922250 10.77669364 2.93623692 1.85562290 -6.54739254 Cl 5.62771037 7.91303014 10.96774468 0.35735949 0.62873525 1.45295970 Na 5.19757739 11.04713375 10.55629599 -4.54121496 -6.87276456 -2.44028833 Cl 8.30973077 10.40117956 10.30496799 -2.12541631 1.19973696 4.50919021 Na 5.21811512 8.37846447 13.25738813 9.44811775 -3.01444570 -0.54998905 Cl 8.14790165 7.82643466 13.63179915 -2.12091754 -1.83615301 3.35649372 Na 8.15416908 10.55089879 13.61278588 -10.53037363 2.49977202 -10.15833659 I 10.77701075 10.81455226 13.46317324 1.92944525 0.80003851 -7.51497568 48 Lattice="0.0 10.8 10.8 8.100000000000001 0.0 8.100000000000001 5.4 5.4 0.0" Properties=species:S:1:pos:R:3:vel:R:3 pbc="T T T" Na -0.17026233 0.16975649 0.08951566 -1.60136586 -3.56148125 6.78209772 Cl 3.04369085 0.22031073 0.15368623 2.31028727 2.91435916 0.65959675 Na 2.74245271 2.49737957 -0.19382819 4.27387573 -5.78917482 -4.37413252 Cl 5.39144244 2.89181883 -0.27517083 5.93723781 -4.51441716 -1.11800472 Na 2.71524228 0.14076730 2.45337087 -1.48152626 -3.00733786 -0.46442762 Cl 5.39750878 0.15868266 2.72543042 5.13748048 -2.47228011 1.43944580 Na 5.08565581 2.74071632 2.83396026 -2.39564476 -0.16986512 -0.94453663 Cl 8.07210313 2.59987901 2.54688400 5.09291233 4.06811961 1.40343078 Na 5.49129585 0.02092548 5.68635924 -3.74738407 -0.08872065 8.78004932 Cl 7.95555529 0.32445985 5.50653140 -1.08482197 -1.56678861 -2.25358795 Na 8.00707413 2.70656450 5.04230351 -2.30611334 2.83800900 4.18703709 Cl 11.21103543 2.74138041 5.61764002 1.37561443 -7.56481824 2.06629936 Na 0.23315074 2.64113548 2.58925588 -10.26919328 5.42813143 -5.36159126 Cl 2.60168115 2.71590790 2.79751295 5.83687090 -6.08833572 -3.84906301 Na 2.80666883 5.84333826 3.06647939 0.17156417 -1.62543750 4.16866564 Cl 4.97581796 5.33577025 2.09525134 0.20108178 3.73661789 7.52828762 Na 2.65427303 2.71396574 5.44548811 -0.69094150 4.66908279 -5.93427913 Cl 5.56942007 2.74321390 5.17107882 10.34897098 -11.38648825 -4.88633780 Na 5.52661284 5.60399720 5.53639576 -1.71304536 3.07899425 8.37314690 Cl 8.51096644 5.36981866 5.49151108 -0.75771619 7.81076651 -0.56030884 Na 5.27695009 2.60070174 8.08678928 3.55917960 2.01476383 -6.75836631 Cl 8.28799824 2.58311277 7.95801163 -14.97946261 -7.88560328 3.14244543 Na 8.05968401 5.55146245 7.85272850 3.44480965 -5.03101877 0.09830552 Cl 10.76555697 5.66033301 7.98503200 0.98147967 -0.23658681 4.86804435 Na 0.36597632 5.35291234 5.62112717 3.34350041 -1.08417739 -8.26234939 Cl 2.67287533 5.38351134 5.36011527 1.33904285 -0.81155589 7.68965598 Na 2.64506505 8.48476513 5.50872440 7.95706868 -4.16309640 8.72063731 Cl 5.76024984 8.24969554 5.78259636 2.88128023 4.79009394 -2.52706294 Na 2.67004596 5.39210080 7.93706225 3.00138823 -1.28367611 0.99351152 Cl 5.22114138 5.90080041 7.97385246 -5.26641575 8.31327353 2.43156718 Na 5.20728250 8.35810279 8.33213239 -3.84122176 2.10527552 6.04687080 Cl 8.21772213 7.81087324 8.06071972 2.66559045 2.44301157 7.77388707 Na 5.65606584 5.85880397 10.68627626 5.74375436 -3.45031921 -2.85293224 Cl 7.96697521 5.71610223 10.52125291 -3.92805347 6.10190881 5.84747535 Na 8.28575715 8.36862656 10.87217090 5.09533938 -1.88107687 1.95990971 Cl 10.34610614 7.84975483 10.66731241 4.98291230 6.99685651 -0.51239121 Na -0.10242203 8.14307406 7.91462323 3.77493762 1.05222539 10.08754629 Cl 2.72258908 7.75802921 8.31179211 -5.33079008 -0.51320792 1.62188924 Na 2.62109771 10.72354224 7.85647243 2.48294282 1.11911860 0.27422861 Cl 5.27048808 10.71377994 8.33694373 1.52370547 3.50743518 -6.79417465 Na 3.11047757 8.19985577 10.79488476 6.69376629 -0.24795382 7.82271056 Cl 5.50315643 7.92392565 10.76436404 -4.53330447 -0.85562638 -5.39328358 Na 5.08067876 11.04881286 11.16424462 0.23748366 -2.19562920 8.58046193 Cl 7.99622568 10.54085578 10.77230613 -2.66461584 -1.88761530 -3.38242523 Na 5.44143819 8.08311221 13.98861591 0.54574691 -3.16813616 -2.21104411 Cl 8.03751128 8.16540199 13.50200417 -6.43481602 -4.10062275 6.02313959 Na 7.98907460 11.11013892 13.69179371 -5.62345520 9.14942458 -0.60102098 I 10.73492998 10.96730723 14.11166458 3.10245027 -1.92217950 -0.22433596 48 Lattice="0.0 10.8 10.8 8.100000000000001 0.0 8.100000000000001 5.4 5.4 0.0" Properties=species:S:1:pos:R:3:vel:R:3 pbc="T T T" Na -0.01793658 -0.16266550 0.10603271 7.19861099 -7.25023994 -10.83043646 Cl 2.42641821 -0.12092941 -0.39014305 -0.73560075 -0.87686015 2.68930743 Na 2.87236248 3.01906240 -0.16433181 0.01856702 -0.07929761 -3.25552711 Cl 5.45840284 2.61829014 0.38850372 2.83826405 -3.50794801 -5.33364209 Na 2.74511817 -0.21707027 2.36785628 -5.38495088 3.44969116 -3.91526885 Cl 5.88881165 0.00742571 2.64416548 -4.44788543 0.89286392 5.64332359 Na 5.27121862 2.60161997 2.35407428 -0.78699592 -0.59965338 1.26952108 Cl 8.26611945 2.97641919 2.80842820 -5.45173520 3.17693469 2.75651124 Na 5.13363563 -0.01962381 5.70754620 -7.35898652 5.10548525 -2.50858077 Cl 7.87320942 -0.36480794 5.08495440 -2.44831706 2.03636754 4.27111694 Na 8.24932611 2.79220464 5.27264955 1.74439599 5.52694210 -3.73360396 Cl 10.63783197 2.32244587 5.32049196 1.77772691 -2.39320563 -5.15459010 Na 0.06603283 2.51225724 2.69255937 -0.74720135 0.70198133 4.57631153 Cl 2.60721279 2.95935540 2.53210000 -0.01774935 -1.40428114 6.52611935 Na 2.82345700 5.50222393 2.91763161 -3.27170324 2.28770041 -3.83505407 Cl 5.26312564 5.16636830 2.80119261 9.75324728 -0.74540263 -1.73927218 Na 2.64602689 2.66951314 5.08969689 4.07135019 -9.34636788 5.15724828 Cl 5.41654060 2.90892109 5.03611097 -0.27950220 3.93711223 -0.65053529 Na 5.24249369 5.50668702 5.27637697 0.48670656 -1.97497054 7.83908835 Cl 8.12047454 5.46780964 5.27138501 -3.29012180 -6.36530813 -0.44798308 Na 5.19467789 2.86778044 8.29168242 -2.20323033 -9.08529894 5.91699701 Cl 8.41904766 2.81983918 8.10319088 -6.01490190 3.56908652 10.49903749 Na 8.21505228 5.43322223 8.29585148 -5.80440851 -0.93325637 -0.61760320 Cl 10.81201413 5.02681247 8.03014299 -9.32527926 -0.26421372 7.23681095 Na 0.44618444 5.49702807 5.49596208 1.24294079 7.68018291 0.58930400 Cl 3.01566407 5.19552005 5.05342900 -3.29013434 1.57787394 -0.44356138 Na 2.58344634 8.00890271 5.51178678 -8.90943173 -2.29976834 -0.58699035 Cl 5.61519324 8.15667260 5.25880178 5.14130365 -0.71571330 5.87548656 Na 2.75295760 5.43747619 8.20962164 -2.54314047 7.79038967 3.78555035 Cl 5.23678203 5.14681222 8.10600187 2.29452942 11.14479800 -11.85580595 Na 5.55716029 7.80663120 8.12954710 1.57263212 1.65712784 3.88745740 Cl 8.09925645 7.81667921 8.23650387 -3.41488400 -2.51691111 3.90899155 Na 5.50194771 5.47142906 10.68850919 -0.86856380 6.72189400 -3.83787469 Cl 8.46343925 5.41733180 10.54280911 4.59024824 0.05989416 -10.70615289 Na 8.39908185 8.16329263 10.91102036 0.59746564 1.34524369 1.89191070 Cl 10.81443243 7.94352425 11.17973439 -4.14529164 -2.04462699 -1.36085639 Na 0.16829584 8.27741416 8.15552022 -1.20324602 -2.11071306 -8.29377883 Cl 2.85925187 8.49907293 8.23583188 4.55629796 4.81272899 1.13713419 Na 2.73206792 10.54214091 7.90416512 -0.88852000 -2.53614700 1.80355980 Cl 5.55638920 10.99418453 8.30950297 -1.33498512 -2.02847103 -7.42188986 Na 2.90080413 8.12559016 10.95494090 -3.11826969 2.90238588 0.23525678 Cl 5.37225704 8.16098419 10.97103364 0.01576624 -1.00019392 -1.89846169 Na 5.37922364 10.83846335 10.98206453 3.50469262 2.94397225 3.96157787 Cl 8.19191998 10.68028618 10.51926051 5.38857500 2.27246667 -1.31948410 Na 5.11977251 7.89815988 13.07276041 3.81786444 -1.33877646 8.30654863 Cl 8.23948290 8.13257402 13.27337885 -9.63263478 2.92496034 3.38689887 Na 7.82554302 10.84736991 13.33488805 -0.09492237 4.48563757 -8.35183391 I 10.47454860 10.88579245 13.54735505 1.19110157 1.62860407 8.67345843 48 Lattice="0.0 10.8 10.8 8.100000000000001 0.0 8.100000000000001 5.4 5.4 0.0" Properties=species:S:1:pos:R:3:vel:R:3 pbc="T T T" Na 0.11732362 0.19002768 0.01870892 -0.78695065 -5.33412270 4.11283688 Cl 2.69821007 0.56420664 0.20483795 -6.52755734 0.53391185 -10.03846930 Na 2.63347037 2.80836826 0.01940901 1.58419690 -4.25093744 4.21788454 Cl 5.47687612 2.60902740 -0.12816326 -9.93577066 5.16176680 -4.44001385 Na 2.83887951 -0.08322923 2.46446160 -5.78742384 -3.60848759 3.71278309 Cl 5.04708902 -0.01295589 2.68928818 2.56328705 7.14076211 -3.91963069 Na 5.26175873 2.25502699 2.78125331 -2.52770082 3.10794741 6.70785384 Cl 8.15629933 2.68994929 2.36560681 -6.17017559 7.43352411 -0.39194804 Na 5.45461693 -0.23897045 5.74005614 -8.24959510 2.24844365 0.16160203 Cl 8.06369324 0.17834700 5.43942726 1.84081306 5.93555440 5.25925085 Na 8.36164705 2.54562298 5.48153483 -2.10728526 2.78843392 -1.08580383 Cl 11.07274375 2.67566766 5.18762411 3.35848551 3.96801814 0.87186588 Na 0.01241311 2.33265532 2.42914420 -2.50330340 -5.39303425 1.28123321 Cl 2.60346992 2.43759927 2.78716001 -2.21886509 -4.32724021 1.87217959 Na 2.90996782 5.32024120 3.18798884 -2.84843963 -8.82337886 -1.74663829 Cl 5.49479729 5.57062129 2.96148142 -7.62055866 -3.80798333 -4.95409562 Na 2.79791918 2.67305454 5.46726884 -9.70778107 -4.10978174 8.16670041 Cl 5.25573139 3.03601860 5.44693025 -1.35005459 7.50634210 9.89716541 Na 5.22290145 5.49806110 5.50415040 1.23277572 -1.38990829 1.78553795 Cl 7.95355204 5.84237882 5.05235059 -5.38200835 15.81003359 3.37006189 Na 5.10386046 2.74790491 8.08709564 -2.92037192 -10.56692827 -4.79375174 Cl 8.42783606 2.68033566 8.02152770 -1.82016702 -2.31680857 -4.93613530 Na 8.06299095 5.63399678 8.44743866 1.25676754 8.47495000 -7.67083037 Cl 10.16436233 5.28182913 8.32642316 -1.72727796 -4.50412307 -1.85972861 Na -0.15678849 5.58406717 5.84906859 -7.23603165 3.32816682 -0.01649905 Cl 2.56208738 5.27755419 5.31119991 0.62520001 -6.85009810 -1.82180611 Na 2.62890152 8.24159207 5.35820816 -3.40702972 6.76607744 -2.78706668 Cl 5.37161465 8.07859518 5.45695380 2.53349973 0.37820898 -3.38925897 Na 2.86454370 5.50847346 7.89074173 -0.13691997 -6.32784471 5.42237371 Cl 5.47215872 5.10791162 8.16820033 1.93355181 3.96882214 1.81089023 Na 5.24608598 8.28763976 8.32218901 -5.67011879 -8.93663771 6.06194661 Cl 8.04986802 8.32491849 7.95765484 2.57773290 1.80131291 6.00096133 Na 5.71608602 5.25198603 10.78124508 -3.77582649 2.22482685 -7.51289724 Cl 7.94189230 5.40067538 10.72801369 -3.87529002 -5.43522465 1.37448231 Na 8.10111634 8.17562762 10.80535547 -1.14515612 -2.86987686 2.93959626 Cl 10.69918360 8.10644174 10.39727187 -2.56913727 -3.59450016 -5.31417278 Na -0.16313771 8.37991776 8.29682833 8.05302133 -0.33840036 -2.93605128 Cl 2.88710899 8.12195603 8.17689669 3.64322022 2.74529885 -1.97974867 Na 2.61380390 10.89394804 8.27489755 1.31877285 2.43628051 0.96148408 Cl 5.42923407 11.00390588 7.97203700 -5.24356710 1.73177342 -0.82012319 Na 2.91576633 7.93910871 10.78483862 -0.84624494 0.57155619 -0.96814391 Cl 5.45775944 7.90758054 11.08276209 -1.27711944 4.71331348 -4.43297946 Na 5.32091206 11.02274774 11.06320433 0.99631943 -4.04509149 5.98638831 Cl 8.11528587 10.83616811 10.77941746 5.25604148 -0.99645250 7.96665397 Na 5.43731155 8.13265219 13.77002669 -3.23082315 -1.62431796 -3.09147798 Cl 8.26685415 8.43820072 13.26579195 0.28126716 7.17989242 7.57030376 Na 8.44768104 10.56427270 13.27262279 1.00647747 4.63811145 1.45084064 I 10.85449028 10.81222122 13.23723540 -1.04174413 0.63216850 3.24132014 48 Lattice="0.0 10.8 10.8 8.100000000000001 0.0 8.100000000000001 5.4 5.4 0.0" Properties=species:S:1:pos:R:3:vel:R:3 pbc="T T T" Na -0.24029180 0.07840135 0.06475302 -4.55876048 -6.00390771 1.83953330 Cl 2.64524115 -0.26028405 -0.03593507 -8.10125225 3.67784777 3.65664004 Na 2.70339459 2.75916957 0.18127691 -1.79133626 1.41997791 -1.89053943 Cl 5.27148872 2.70578479 -0.11363605 4.62492463 2.07345141 4.87965069 Na 2.91691723 0.15841162 2.85681599 1.90854870 1.04675808 -0.28289719 Cl 5.25159096 -0.05156716 2.49129541 10.75196348 -3.88109115 -5.83476527 Na 5.33918718 2.82701504 2.94028895 3.95256486 2.16860339 -1.18690032 Cl 8.53916241 2.72505485 2.48299620 -2.76144322 -2.05819403 1.97786300 Na 4.96201448 -0.22357793 5.15909718 0.97391576 -4.09285629 -0.27987867 Cl 7.83425832 -0.11412197 5.12721596 6.53482344 -0.26911897 -5.70680882 Na 8.09114621 2.68304627 5.10854215 0.51763805 0.92681687 4.38857796 Cl 10.82185139 2.64606188 5.14058198 4.69463228 -5.62965588 -4.21443114 Na 0.20787082 2.85643030 2.47638224 -2.49138715 6.47068420 2.22189744 Cl 2.45548175 3.05196400 2.78546255 6.08383817 2.07198706 -2.50578081 Na 2.73971125 5.04898799 2.95796698 7.01340012 -3.48837499 -1.50308164 Cl 5.43126055 5.67436089 2.72256707 -6.69642509 2.11206199 6.95359123 Na 2.59882943 3.18458562 5.09913255 3.04237636 1.09522883 -3.85390763 Cl 5.68249032 2.75114933 5.73352885 -4.45375011 -8.88253229 -3.30949887 Na 5.24256772 5.03045938 5.90146063 0.95824953 -4.55079528 -7.59291042 Cl 7.99226740 5.26455889 5.22892992 -3.50491810 -5.55288755 -1.12859162 Na 5.24416149 2.33631776 8.17404361 -3.90592044 -6.06830007 0.47756298 Cl 8.06708397 2.72154756 8.05303472 -4.41924191 3.05187641 -3.39490694 Na 8.11883488 5.77860278 7.90064012 0.45911483 7.40730992 0.42151683 Cl 10.44207352 5.67822541 8.20062064 3.10483191 7.28713570 -1.99738248 Na 0.13260465 5.19893274 5.15568230 -4.88109172 0.47643961 -10.81080746 Cl 2.61083126 5.50454864 5.53626421 1.97784658 4.69496956 8.50951962 Na 2.66689475 8.03358336 5.65205309 -3.16032732 -0.83296983 -2.36692796 Cl 5.41343607 7.96022366 5.43530392 7.78715508 -8.49105721 -0.99459134 Na 2.36032263 5.33747569 8.01932882 3.56308819 4.30881316 4.55711146 Cl 5.28167475 5.26835557 8.13526479 0.13181501 -10.01174511 3.69624634 Na 5.24711622 8.27562677 8.28190604 -6.20014997 -3.89500058 -12.18474048 Cl 8.13229671 7.88081197 7.96293986 -4.12738941 -4.24406294 0.28361543 Na 5.46472827 5.33495468 10.89238688 3.91766984 -1.27899582 -10.33696615 Cl 8.10798981 5.31387920 10.72108515 0.00104591 -0.54328461 -2.73197232 Na 7.97280533 8.19120080 10.95615023 2.80001199 -2.87614699 -1.34308847 Cl 11.14564784 8.56544332 10.97580509 -2.56523733 -4.04304757 -8.49849951 Na 0.03187749 7.99038954 8.04759639 3.35137099 -4.01924846 1.12380433 Cl 2.45952016 8.14092873 8.01009210 1.71687583 2.81521313 2.85492894 Na 2.54159865 10.96053391 7.94999144 9.60454715 2.03952128 -1.37738120 Cl 5.35778832 10.55569504 8.40083168 -2.78634195 3.16674297 -2.92563286 Na 2.49572078 8.28476929 10.86183022 5.99132058 -4.36637473 3.22390858 Cl 5.08332787 8.03530218 11.08927552 0.09635862 2.92413787 -4.65665622 Na 5.58745717 10.84890643 11.24042885 -5.18594373 0.57542822 -8.05710801 Cl 8.00708516 10.55121854 11.03258467 1.17948955 5.13286423 1.41949608 Na 5.28487084 8.12947202 13.53346090 -0.70450309 0.81411121 6.90440442 Cl 8.11550101 8.05739229 12.93627248 6.87847476 2.79349002 1.41189842 Na 7.91704581 10.67464183 13.45659474 0.46616558 -1.39113697 5.62985140 I 10.51125150 10.69997280 13.33413022 0.82790685 -4.59567831 2.25703419 48 Lattice="0.0 10.8 10.8 8.100000000000001 0.0 8.100000000000001 5.4 5.4 0.0" Properties=species:S:1:pos:R:3:vel:R:3 pbc="T T T" Na -0.24559335 -0.12753552 0.24525254 -1.61144984 -2.95639918 -3.03485228 Cl 2.65942823 0.00830114 -0.02101649 2.03835803 6.19394180 4.09772692 Na 2.85508940 2.85374176 -0.00870850 1.91642730 -1.60263798 2.40558783 Cl 5.31954980 2.54892630 0.14580387 7.62633269 2.60769093 3.72686340 Na 2.45091634 -0.04081762 2.79367226 3.81734323 -7.53601055 8.19231108 Cl 5.16903433 0.10120116 2.63517284 -1.69342560 1.91384771 5.03437450 Na 5.09769966 2.78320313 2.60570794 -7.88049236 -3.07282549 -0.01904565 Cl 7.97880224 2.47476011 2.59397560 7.50434348 4.52917069 -16.11026224 Na 5.10520388 0.08849111 5.42918929 -9.96180281 10.98751162 2.67419678 Cl 8.16667328 -0.04896674 5.55151218 -1.51672348 5.13532203 -6.85301306 Na 7.79534415 2.73135960 5.42855035 3.92120925 2.96255341 -2.55179750 Cl 11.00127469 2.45670463 5.35563394 2.99574847 -5.48417119 0.71094287 Na -0.19463453 2.77976256 2.93009887 9.68054268 -2.78125854 4.75256761 Cl 2.81891010 2.51035490 2.68056808 -2.20282933 -0.56570914 -7.08631227 Na 2.78968675 5.32589381 2.87640306 -4.90197594 -6.51540176 -2.11951718 Cl 5.63703984 5.20582033 2.78261021 -3.62132998 0.84671210 10.19662899 Na 2.98070594 3.11963677 5.17566130 5.81411619 9.57444878 -6.12228771 Cl 5.39615032 2.80191158 5.27461363 6.89689050 4.25618605 8.21571595 Na 5.22651854 5.42137135 5.62211983 -2.38484361 -0.49503643 -1.73814374 Cl 8.21747177 5.36606354 5.22757141 3.64715418 7.27688766 3.72935046 Na 5.58100939 2.79721372 7.77625508 -1.24108694 0.48167339 -3.25009807 Cl 7.90694723 2.73924799 8.09996325 -5.59833081 -3.48101919 3.67797063 Na 7.85717113 5.13820329 8.29819754 0.33258268 -3.56903622 4.40377894 Cl 10.77463253 5.00163049 8.07631282 -9.49139926 -1.44711024 -3.48139689 Na 0.04575258 5.36276386 5.44925206 0.83892930 5.19157563 -7.45409482 Cl 2.33508219 5.58266774 5.27005224 -0.25585804 -2.65676680 -7.34753216 Na 2.66559477 7.84582816 5.54311078 -4.73309369 2.79410849 -1.43188266 Cl 5.80122893 8.20651199 5.45038405 -5.60316015 1.30753084 6.80825536 Na 2.99687228 5.51449378 8.09205491 5.42537400 -0.22376688 -6.19502432 Cl 5.56405859 5.18452542 8.17314348 6.11805311 -9.13935126 5.70340617 Na 5.26297026 8.01867064 8.52030139 -2.41577452 1.41654641 -1.50386778 Cl 8.21608894 7.83796808 7.86317108 1.61264829 2.02397813 -2.17706547 Na 5.60173560 5.16951473 10.51100176 -9.21645635 5.18065853 -1.55211373 Cl 7.82715349 5.30895159 10.67818389 -2.50387692 -2.10841996 -1.93805414 Na 7.90774219 7.96256649 10.80872424 5.51674149 1.24528041 2.25746389 Cl 10.77712785 8.04527709 10.36962066 1.29177090 -7.32716414 -0.76945794 Na -0.12657617 7.87790157 8.08362420 -2.56355974 1.73735640 4.09397686 Cl 2.70071658 7.99102494 8.14098524 -5.71936728 -3.10281163 4.57206569 Na 2.73166036 10.56296518 8.20386797 -1.55824716 -2.24842442 6.47567317 Cl 5.22804015 10.75642146 8.15711381 -2.27419225 0.22683014 8.63659662 Na 2.49295058 8.19204791 10.64539748 -12.43709233 -7.41496175 -2.28507661 Cl 5.43292251 7.73002096 10.76885492 0.36101175 -2.35528463 -0.06573112 Na 5.48868604 10.80792841 10.89385893 1.89120090 5.03784766 -0.91159830 Cl 7.98604218 10.54897325 10.92159741 -3.42425458 7.51254906 2.66282553 Na 5.46151852 8.30845292 13.40139321 5.73479837 1.96675028 7.72738914 Cl 8.06744748 8.36925927 13.77394388 2.74406351 1.99314127 -7.68815512 Na 8.23915026 10.83919261 13.60742255 -4.61593884 -0.11355771 -1.41996430 I 10.61869209 10.76765850 13.43491991 -4.30909748 -2.66911051 1.75367868 48 Lattice="0.0 10.8 10.8 8.100000000000001 0.0 8.100000000000001 5.4 5.4 0.0" Properties=species:S:1:pos:R:3:vel:R:3 pbc="T T T" Na 0.05155422 0.02661262 -0.08745952 4.32221927 -1.84445521 1.37388878 Cl 2.36860132 0.37365895 0.15362651 -18.06900021 -2.27946834 -1.78478286 Na 3.05170579 2.54272183 -0.13750390 -1.43268535 -4.23425816 -8.07905655 Cl 5.45487475 2.61036733 0.01191965 2.10861243 -2.28509686 3.07026556 Na 2.93790210 0.15434906 2.99031536 -3.48069070 -6.90682810 -5.63268741 Cl 5.59629897 -0.12888416 2.70975055 10.88507229 2.68832662 0.86076655 Na 5.65251769 2.58185160 2.42478479 1.38261383 -2.19973753 0.76978685 Cl 8.03600191 2.88913334 2.84960747 3.92424605 0.78267393 -2.60230523 Na 5.29140475 -0.14178018 5.32043394 -6.49827282 10.69203582 6.14903994 Cl 7.98555965 0.37778617 5.37017632 -1.96772442 -0.99770204 13.78897631 Na 8.20679267 2.88626538 5.21193403 2.36083079 -0.80903097 -6.96852204 Cl 10.54952447 2.69943938 5.38880399 -2.93842499 5.94757184 1.20464230 Na 0.03948466 2.45078675 2.74419959 -7.32424526 2.12342275 5.13148952 Cl 2.70263856 2.81150780 3.05713355 -5.19288708 -1.99026377 -0.92162819 Na 2.75946084 5.50730609 2.56352416 -5.77727078 5.32114387 -2.99532589 Cl 5.28194298 5.38137287 3.09151598 1.99992478 -2.75971865 -4.06908002 Na 2.80326818 2.56556571 5.58087207 -3.08157010 -7.17076599 -9.73081761 Cl 5.40852600 2.80741332 5.76672979 -4.11889312 -6.37985911 -10.66991781 Na 5.57169885 5.24202485 5.22276979 -3.58099135 -2.86569347 -2.81564460 Cl 7.97816717 5.07175028 5.31422598 0.37149529 -1.73594680 -6.28766731 Na 5.39920566 2.42185692 7.82602965 -5.20786742 4.57481653 6.27528317 Cl 8.10731499 2.63336600 8.12962327 1.51621818 2.22613442 1.30006242 Na 8.35741512 5.57169826 8.51075113 -5.57926188 0.37261753 -3.46772723 Cl 10.70986634 5.44791698 8.26074936 6.57796199 -2.34905981 -3.65134820 Na 0.17016413 5.51707936 5.61945158 -8.91025582 1.46774860 -0.53122327 Cl 2.51462932 5.85792022 5.89003517 -7.00004455 6.75605588 2.70403736 Na 2.28884004 8.09289369 5.12590367 -3.67779334 -5.85676454 -1.38882748 Cl 5.51866730 8.17871458 5.25990480 -1.71308975 -5.28349682 8.12210755 Na 2.31684209 5.60077376 8.00814182 -2.25324995 10.63185514 -0.52659744 Cl 5.06632205 5.58886939 8.31708510 0.33051474 -2.46221050 -1.58364224 Na 5.49099518 7.87098327 8.07451870 11.14077388 -6.75380855 0.98901603 Cl 7.74695059 7.76703988 8.05017950 -8.99647918 0.06601591 -3.18850725 Na 5.63454268 5.44450184 10.67505274 -3.90687014 1.60678577 -6.72853689 Cl 8.00754499 5.33683828 11.01135251 6.17743770 4.63653606 -1.73923938 Na 8.14177950 7.85985748 11.21870580 11.47255973 -5.53185753 1.19591634 Cl 10.80846848 8.19459256 10.82251475 6.01178159 6.45307669 -2.62379569 Na 0.13331893 7.97627902 7.99958486 -1.55315773 0.80742120 1.18922470 Cl 2.76196295 8.24568289 8.18935483 0.07372278 5.41649959 0.67281086 Na 2.51305470 10.88327206 8.13969715 -0.05290343 5.31812169 3.72456081 Cl 5.42846667 11.03343844 8.11727294 -4.29349484 -1.76547994 -0.23609363 Na 2.66430040 8.27581046 10.98600186 4.03212723 -1.83763942 -2.34536317 Cl 5.30670656 8.29983644 10.81354789 -10.56399174 3.75343262 -0.81529138 Na 5.17203963 10.60319867 10.86925908 0.86599103 3.08146376 1.04666491 Cl 8.09227679 10.54791303 10.35646761 -1.39403308 -1.64249972 0.23879060 Na 5.48258096 7.89366063 13.59441929 -5.65496718 -5.68466439 -1.53203364 Cl 8.09508558 7.94339114 13.27611570 5.09426024 8.18152382 3.36589226 Na 8.13038713 10.81114278 13.44034279 -11.08936398 -7.63196221 -0.03844804 I 11.24911052 10.70824525 13.21980889 8.86598411 -4.39159809 -8.88987871 48 Lattice="0.0 10.8 10.8 8.100000000000001 0.0 8.100000000000001 5.4 5.4 0.0" Properties=species:S:1:pos:R:3:vel:R:3 pbc="T T T" Na 0.03858450 0.01347349 -0.24473045 5.32930213 0.81212576 3.24565692 Cl 2.46181974 -0.25971448 0.00335399 -2.42218220 -1.74670589 1.46058135 Na 2.47098573 2.38280934 0.26947889 1.33992751 -5.90790725 -3.36596014 Cl 5.46496923 3.08865029 -0.00603961 5.24266694 -4.54654215 1.27035830 Na 2.80166903 -0.13142585 2.58226034 3.50811120 -0.06330302 -2.53030359 Cl 5.52802942 -0.21925987 2.68151765 12.94809764 4.03280091 7.37738791 Na 5.60322607 2.38004234 2.59876313 -8.49205388 7.24753186 -2.66782634 Cl 8.05283259 2.55348039 2.57518290 -5.16279976 1.99609898 6.35411283 Na 5.47616566 -0.18049583 5.61203238 -5.64458624 5.55304080 -2.97331714 Cl 8.11218232 -0.42963703 5.30842934 -1.29884187 3.31833291 -2.32774553 Na 7.98141178 2.89615467 5.45628093 1.55885155 -1.67991663 -0.49577946 Cl 10.92964706 2.84917365 4.99231852 12.19261432 4.45992310 -0.68283739 Na 0.05832625 2.69972621 2.67348551 -0.61863295 0.88671450 3.15046984 Cl 2.99693870 2.40778354 2.87313236 2.67448934 5.97481777 0.08547827 Na 2.80080675 5.38282350 2.77221950 2.44584290 -14.79932640 -6.87185267 Cl 5.87578042 5.27605847 2.69768708 1.80166065 -9.92305587 5.76610105 Na 2.77992260 2.69009969 4.99462754 -0.90912225 -5.00541232 -2.62318872 Cl 5.54261731 2.75448471 5.27548379 -3.16445523 -9.79058089 4.25195229 Na 5.78752830 5.52215657 5.29076838 3.03159767 -8.88244988 -0.87537795 Cl 7.92228347 5.50176415 5.69827926 -8.46799218 1.15809400 3.75231467 Na 5.14235982 2.82250283 8.07945653 -6.33150629 4.53165620 4.57571063 Cl 8.29921721 3.27963206 8.05924831 -0.57871775 -14.61729696 -3.47580239 Na 8.17028481 5.48983969 8.63867647 5.12155316 2.04777696 -4.12289108 Cl 10.91155839 5.42490964 8.19077003 -7.43033992 4.23619174 0.45720727 Na -0.08364612 5.27072713 5.51783552 0.24276344 -2.46272217 0.95713603 Cl 2.40563383 5.44355101 5.41755642 -0.31421032 0.15215386 -1.63108560 Na 2.34718682 7.89975710 5.42248250 -1.46177682 -8.25848406 6.24018369 Cl 5.63972366 7.88055203 5.75835296 1.74883906 2.26100062 -4.77874228 Na 2.73470871 5.27064973 7.96240859 -1.73529624 2.22200324 0.41158333 Cl 5.33554380 5.63042527 8.10444879 -3.56873219 3.79849673 -3.28043421 Na 5.21343236 7.97252936 8.11197930 -8.30668514 1.92586025 -2.68119851 Cl 8.16139050 8.29607919 8.23373316 3.47184929 0.59162998 5.69088033 Na 5.26042689 5.12780997 10.47361152 -1.57337928 -3.02868320 2.80255437 Cl 8.22904059 5.61700368 10.61203433 -4.66877910 -0.04872298 3.35091715 Na 7.87943494 8.16680535 10.95971124 -9.86880619 0.25126928 2.59256635 Cl 10.28262065 8.04629969 10.94167335 -1.52651207 13.56752511 -3.74179084 Na 0.28606084 8.15531327 7.98919307 1.61790913 5.53520732 8.69857828 Cl 2.32119619 8.14943231 7.96222208 1.18092111 8.67165491 4.79055032 Na 2.77490150 10.81117378 8.04782476 2.53714445 5.27154515 -3.11158858 Cl 5.54031359 10.62556416 7.90302244 -0.36751938 -2.12027975 5.33862945 Na 2.81194217 8.09967597 10.64258614 -3.91133053 -0.44978453 -5.52844852 Cl 5.05198270 7.77045440 11.06808452 -1.37554827 -1.05436187 0.52629142 Na 5.73364312 10.95311177 10.89147643 2.48901256 -1.08168776 -4.66029128 Cl 8.32092482 10.83285488 10.78929761 -1.85844879 2.67412005 -4.61297137 Na 5.45760139 8.10088608 13.48929467 -0.42835135 2.84773826 -3.82879453 Cl 8.28031604 8.30001156 13.51718890 1.11112974 -4.76625896 0.05913897 Na 8.14247780 10.89023933 13.65394544 -1.86378920 6.66173027 4.43023425 I 10.77250380 10.81454984 13.36035466 2.86616465 -4.11741634 0.40076631 48 Lattice="0.0 10.8 10.8 8.100000000000001 0.0 8.100000000000001 5.4 5.4 0.0" Properties=species:S:1:pos:R:3:vel:R:3 pbc="T T T" Na 0.01540133 0.17412077 0.35947605 -0.25255853 5.23503688 -6.72298731 Cl 2.79058520 -0.11261475 -0.23029731 -6.34808387 3.82007296 5.15952434 Na 2.63513271 2.75365180 -0.12037783 1.45926840 3.22980385 -0.70985968 Cl 5.44498240 3.11486305 -0.24655869 -3.48541813 4.06308433 -4.60444675 Na 2.54854599 0.05299555 3.04448442 1.17928922 -5.04978715 -7.79101434 Cl 5.21691322 -0.11673168 3.00623780 -1.84414612 -4.54801002 -0.92022248 Na 5.29910487 2.47163980 2.67754355 5.28248693 8.59082217 2.72227780 Cl 8.33999722 2.38709456 2.67162881 5.40491865 -6.12198097 -1.97912832 Na 5.68906043 -0.10392453 5.50850807 3.75562600 -1.77473450 -2.57028339 Cl 8.05690735 0.01527336 5.42748815 -3.72978305 -5.39826679 3.40319725 Na 7.83905103 2.63240373 5.59568478 -4.85881982 -4.64918136 -7.50615490 Cl 10.57939753 2.45918593 5.69621590 -8.31021595 -5.04481841 -1.10659350 Na -0.12701092 2.99225573 2.76234169 6.32459580 -1.19504157 2.61893306 Cl 2.82165998 2.98570380 2.63603180 -7.38852911 2.65389045 -2.92377863 Na 2.55905915 5.44780578 2.83664941 -0.11192694 -13.60553816 -2.12090601 Cl 5.23713059 5.09156970 2.59817912 -3.37750633 2.48891571 -1.38390282 Na 2.76351959 3.30972002 5.54703517 -4.11172171 4.20595575 -8.27388317 Cl 5.22389777 2.93986591 5.34822748 -6.15369992 8.72419485 -8.85785595 Na 5.26167592 5.47295748 5.72848354 6.68861558 0.42231549 7.48699392 Cl 8.41512320 5.44504934 5.59052215 2.29213799 -1.12346304 2.36444286 Na 5.11985171 2.88627541 7.88571569 -1.17732736 -2.35624649 4.52568374 Cl 8.21499246 2.53985790 8.10731053 -8.62570846 -5.26290738 1.98509092 Na 8.11352354 5.49628090 8.35018456 6.55901932 11.90247635 -3.15687758 Cl 10.84020897 5.43383682 8.02140637 7.46875156 -5.45514342 0.78992003 Na 0.12818186 5.25435801 5.27714025 -5.28885120 0.86631751 -1.19346026 Cl 2.82090686 5.58199382 5.20584978 -0.59968153 -5.69230205 -3.70199036 Na 2.73729645 8.22280797 5.70612453 7.09569362 1.82993715 4.08429176 Cl 5.35049979 8.28786203 5.40003423 5.19736002 1.28148266 5.25070484 Na 2.73396779 5.43166694 7.78483669 1.72478477 2.10772948 0.11529868 Cl 5.41176670 5.96758784 8.13894844 3.09810011 -3.07117185 -8.60548241 Na 5.33249412 8.44458692 7.94044733 5.11780691 6.45747994 -7.25688770 Cl 8.24421690 7.90940894 8.29115574 8.67489987 1.03538144 2.72669075 Na 5.35125041 5.21047560 10.62118860 -7.12442832 1.65789353 2.84819644 Cl 8.05133667 5.71582135 10.47585480 -2.47952487 -5.37367343 0.17166081 Na 8.06632602 8.19226383 10.72889089 -2.30740978 12.21653362 4.39369679 Cl 10.87893974 8.19024087 10.65077624 -0.86435276 -3.95451668 1.22528411 Na 0.09766571 7.94849300 8.14229040 0.18656503 1.88677367 3.43205857 Cl 2.99872357 7.92924719 8.09330841 -4.04510487 1.58259481 -6.40039024 Na 2.79096742 10.80554840 8.08572306 -2.88246783 -10.69582807 -0.09009753 Cl 5.42785274 10.92765755 8.24792076 7.75591621 -0.00684448 -1.33152678 Na 2.58225002 8.21627951 10.63730174 -0.17029637 -6.97620814 -3.40877083 Cl 5.27034729 8.39076621 10.87741084 0.00190225 5.97023351 -5.07224741 Na 5.35819977 11.35065983 10.83946947 -2.00256083 -0.16986146 -0.93112785 Cl 8.10675280 10.87867151 11.14624912 -7.27219650 -4.66778169 4.95667920 Na 5.51484451 8.01676352 13.33919633 7.41114986 -0.40071483 -2.83469400 Cl 8.23517472 8.57219678 13.65944257 -7.78508240 -0.18470812 0.03346545 Na 8.05332769 10.68366989 13.42130423 1.56246642 3.29071796 -5.28921285 I 10.80930621 10.65285301 13.30386183 6.40552976 -0.03673294 3.80742748 48 Lattice="0.0 10.8 10.8 8.100000000000001 0.0 8.100000000000001 5.4 5.4 0.0" Properties=species:S:1:pos:R:3:vel:R:3 pbc="T T T" Na 0.14096761 0.19240284 0.11378402 6.36255044 4.94314767 7.43641145 Cl 2.81480217 -0.26527114 -0.06461889 6.01466066 1.34362256 0.16888029 Na 2.49442791 2.56276835 -0.11381209 0.27491164 2.76884345 -2.65071222 Cl 5.22919845 2.74483558 0.50183888 -3.24620143 -5.53661314 -5.86202798 Na 2.93580971 -0.00959389 2.53467069 -2.02865406 5.94490685 -9.17757743 Cl 5.59124117 0.39023576 3.08684177 3.09208766 3.03013695 2.82316311 Na 5.36758262 2.93596849 2.49736914 0.76081520 1.47216940 3.29713281 Cl 8.14165864 2.79468632 2.56609245 0.33911870 -7.18864573 -5.70368126 Na 5.54248019 0.28536593 5.40936589 -8.27747219 7.07890820 -6.34553082 Cl 7.69002188 0.11607457 5.36039610 -1.63607377 -7.99876832 6.73567540 Na 8.39353876 2.66055703 5.38635481 -1.17232661 5.39785135 3.49699349 Cl 10.97346906 2.43417710 5.48805479 7.73219850 -0.15503214 0.63525012 Na 0.32820711 2.76991728 2.31757444 2.07442776 8.36783318 -3.21807579 Cl 2.34464190 2.91117404 2.85576099 -0.84052818 -0.52911640 1.51109853 Na 2.59457782 4.95848085 2.71341712 5.10023613 -1.86027146 -1.36964939 Cl 5.45514868 5.57370732 2.57751187 -6.08652093 4.74056127 -1.06110782 Na 2.88644050 2.59934182 5.53239760 -3.24433432 10.00814838 -8.39590716 Cl 5.39847933 2.81057810 5.53356311 -6.67039309 2.75475339 5.04203414 Na 5.63951677 5.21238160 5.69718513 5.03871432 13.93002279 0.64649578 Cl 7.82534744 5.78096701 5.55611561 -0.69664285 1.22493395 -5.24875169 Na 5.65183411 2.44904868 8.58539747 0.10772381 11.10267458 4.38591283 Cl 7.99410586 2.64081072 8.14108065 4.25872872 -2.49987840 9.94483699 Na 8.08371723 5.80640342 7.98009352 7.50021788 0.88808505 2.18838449 Cl 10.53750213 5.39989029 8.36491037 7.49002936 0.28459475 -8.21550589 Na -0.07186113 5.31296031 5.45979725 -1.90549670 4.62911087 -5.04754782 Cl 2.73142302 5.59032131 5.23079396 -7.22462827 -0.21427862 -2.21489749 Na 2.33025643 7.95288468 5.71739727 4.26860501 4.14190227 15.51599700 Cl 5.77016073 7.90290271 5.57401047 8.84665498 -5.36847023 2.08318221 Na 2.73205421 5.56871539 8.08880007 -2.56204262 3.69681557 -0.01113349 Cl 5.45414931 5.47184588 8.11090278 12.93861018 -2.07156471 -7.75859358 Na 5.27508606 7.90421194 8.25048943 -7.62786948 1.53827682 4.34135454 Cl 8.28978570 8.37482732 8.14095867 0.69376306 1.48870052 7.30936848 Na 5.51070530 5.24629872 10.74261486 9.30749914 0.42010944 -4.23937009 Cl 8.16861144 5.51076611 10.98991012 -2.06319664 -10.39022012 -0.38320546 Na 7.98609522 7.67843651 10.86225994 -5.02846151 -5.59907123 -5.47210693 Cl 10.44706169 8.26329227 11.25196054 9.77832865 2.59441984 2.47016605 Na 0.05771406 8.55074786 7.79873293 3.34544613 3.59562542 -1.96417876 Cl 2.86296443 8.32162979 8.32586472 -0.25636868 -3.15587298 6.38076082 Na 2.93993112 10.82939479 8.05486814 4.49927405 -0.80876913 4.76769518 Cl 5.17917899 11.30160236 8.40293745 -5.65718887 2.07349988 -0.68192872 Na 2.84452591 7.83600210 10.75953604 -0.09079797 3.92593490 -4.50303024 Cl 6.00070669 8.07091179 11.06150899 -3.57620678 -1.00908928 -1.14563199 Na 5.44227055 10.72691886 10.59741217 -4.64162060 2.12753042 -3.82693663 Cl 8.05601985 11.03612795 10.18623971 11.67346913 -0.62326272 5.13843236 Na 5.26065649 8.05852983 13.45556653 -3.71936035 3.95505093 -8.23493506 Cl 8.01243880 7.65402362 13.55982010 -7.61936396 3.44396674 3.04451655 Na 8.12836580 10.85896353 13.97147378 -8.01817499 10.25801308 -6.98155678 I 10.85527452 10.46157653 13.31438709 7.47632055 3.22583684 2.01842605 48 Lattice="0.0 10.8 10.8 8.100000000000001 0.0 8.100000000000001 5.4 5.4 0.0" Properties=species:S:1:pos:R:3:vel:R:3 pbc="T T T" Na -0.23085725 0.18768448 -0.12577733 7.51347135 1.21899556 -4.82080949 Cl 2.92653897 0.23321106 0.18241619 -6.28031596 3.75222139 6.88686075 Na 2.61038318 2.59509173 -0.19466471 -0.44079277 7.20110480 -2.30090064 Cl 5.15385953 2.78008295 0.46275898 0.16278146 4.40895205 0.04743088 Na 2.71684674 0.19945913 2.82698531 2.68024260 1.04890411 -3.28936127 Cl 5.20460410 -0.09681643 2.30782967 -6.92006122 -1.73191896 -4.53983360 Na 5.22090447 2.93219916 2.55225874 -2.35107550 15.08392809 -5.63210469 Cl 8.13491697 3.04982960 2.89870950 0.45339448 0.66196736 -0.77789138 Na 5.28329139 -0.04938053 4.96618490 -5.95902992 7.88374095 -0.91949665 Cl 7.92545529 -0.26316315 5.54527688 -6.44159448 0.17497321 -6.31914201 Na 8.16007911 2.76055194 5.28413629 -4.98107734 1.20578368 8.73891785 Cl 10.82042683 2.57525452 5.50659963 2.86180855 0.65967334 -2.95069905 Na 0.05406571 2.72552336 2.53926257 -3.02128964 5.48429679 -4.92064529 Cl 2.56413938 2.65272919 2.34944778 5.39243372 12.64469167 -6.93100855 Na 2.74373398 5.04405014 2.83583115 2.18967380 -1.94035797 -6.81782989 Cl 5.57186497 5.54346282 2.65940646 0.09806889 8.63658707 9.57880615 Na 2.68308462 2.82471759 5.68692684 -6.70845589 0.97674046 3.32319419 Cl 5.35088410 2.88310786 5.04437084 3.91120070 0.82951951 -5.02789987 Na 5.25418980 5.72617816 5.39384788 -4.56895188 2.78450539 0.58094751 Cl 7.69606609 5.44125143 5.52285301 0.00165127 4.22021294 -0.32259557 Na 6.15261752 3.02145235 7.84432555 -8.61874976 -7.87473592 5.37573508 Cl 8.24639526 2.39829945 8.17127914 -1.15010400 -2.56243783 -0.33706383 Na 7.84885880 5.49428782 8.10531231 -10.72017966 -0.77351204 0.37736307 Cl 11.20076326 5.27800307 8.68097629 2.49954302 2.03329708 -11.00110243 Na 0.00882899 5.60570178 5.60360219 9.61505541 -8.03610920 -9.06937493 Cl 2.25138669 5.64968807 5.26819856 -5.25641461 -4.24761039 2.52159039 Na 2.64741406 8.02036001 5.42917164 -7.10196307 1.05204232 -1.35214606 Cl 5.39210756 8.30222525 5.40182624 -2.90053117 3.42069279 -1.22023152 Na 2.53799791 5.42138973 7.76823192 -8.07295872 -2.29049699 -7.94324113 Cl 5.47821412 5.60295864 7.87122606 -2.22385948 0.21107389 -9.88529778 Na 5.47008988 8.19702609 8.09362278 1.83980351 0.25862195 -0.38165501 Cl 8.04852196 8.16244906 7.94386336 -12.21455381 11.22512722 1.25853008 Na 5.42601778 5.22172220 10.89922673 4.76530117 9.41288522 6.89067806 Cl 8.13497281 5.58220800 10.58084033 3.75551099 -4.12317184 -0.79361983 Na 8.21299079 8.09207808 10.84440845 5.79851525 -1.33017476 -2.63924391 Cl 11.11190219 8.19792378 10.75125627 -3.67195840 -6.22029301 -10.43747588 Na -0.35489752 8.12523246 8.33242804 -2.97735959 4.83115623 3.80626166 Cl 2.63807437 8.04705711 8.02603861 -1.70534539 6.10500065 0.80064197 Na 3.20276255 10.99754775 7.98585515 -5.65206335 0.71945020 -1.85926652 Cl 5.04413443 10.81006678 8.19096053 -2.12183837 -1.35474376 5.65862308 Na 2.68623122 8.31796926 10.84838170 3.06451709 -3.12522491 -0.79934268 Cl 5.16800140 7.97915970 10.85713708 3.86434479 7.14869980 5.03433391 Na 5.43743721 10.90985678 10.91269677 1.06424727 2.64295748 1.44941225 Cl 8.54694886 10.49596215 11.12138031 9.84458662 -9.88917817 2.91054604 Na 5.44699167 7.99816672 13.45003461 -2.10776206 -2.05573810 -3.02078362 Cl 8.16023477 8.51133082 13.67599768 10.51201319 1.81429443 0.32499604 Na 8.21862594 10.73305078 13.40241697 6.81641726 8.47686839 -2.30536458 I 10.69684954 10.89592170 13.45573863 -6.79021927 8.29659553 6.50506065 48 Lattice="0.0 10.8 10.8 8.100000000000001 0.0 8.100000000000001 5.4 5.4 0.0" Properties=species:S:1:pos:R:3:vel:R:3 pbc="T T T" Na 0.40553998 -0.12296747 -0.21800142 -3.49032456 1.54581028 -4.85089995 Cl 2.78482095 -0.13424042 0.13247318 2.17545672 2.70751439 -3.68526593 Na 2.46594842 2.47809209 0.34373217 1.13758006 -2.99617457 2.99101566 Cl 5.32180915 2.96260017 -0.20967211 -3.61391550 7.12304138 1.66643207 Na 2.83404906 -0.00378617 2.50439745 -8.90926641 6.80147533 -3.71279087 Cl 5.41959391 0.15033233 2.37909058 7.50335491 4.13398911 7.53263205 Na 5.45919850 2.82241020 3.01171505 -6.87776685 9.37292881 -2.73893345 Cl 8.45601849 2.42265058 2.67344117 -2.45861706 -4.95675976 4.03154171 Na 5.40251064 0.33106602 5.25131351 -2.58488169 2.91331270 6.60082939 Cl 8.26639740 0.01910619 5.52297483 1.85381094 0.17863224 -0.57894232 Na 8.08025244 2.89248848 5.67042742 4.55302101 -0.41080966 -6.13412687 Cl 10.70556695 2.69318855 5.09497302 -3.86106912 2.85192128 0.76723655 Na -0.08410161 2.73296748 2.42715168 -5.98924200 -1.41897165 -3.59671716 Cl 2.63190992 2.95201411 2.81375158 7.35037823 7.29424645 -0.74089352 Na 2.70978194 5.07193299 2.72017059 -3.94374495 -2.09747609 -2.10489296 Cl 5.23982881 5.29877233 2.94581829 0.27276956 12.86226326 -3.30197483 Na 2.56357412 2.83204273 5.21390444 8.75976448 -0.68628390 -4.52713391 Cl 5.68098644 2.95773973 5.57377084 -9.17589177 6.79898155 2.80505180 Na 5.48027471 4.90473463 5.22253369 6.01769707 -2.59659862 3.93377528 Cl 8.29129374 5.20591219 5.45114564 -11.28307490 -0.51355380 -1.48342279 Na 5.20102546 2.77682241 8.21058480 -1.40042617 -1.40933564 -9.91341385 Cl 7.97433376 2.73576523 8.19763315 -3.99235553 -5.41117091 3.24420443 Na 7.90352701 5.47090630 7.98853410 -7.68695905 4.17280757 0.23024932 Cl 10.98151968 5.26776828 8.14181613 -1.38030962 0.58487694 -4.66047948 Na 0.02560949 5.21130912 5.39969829 -6.15934692 -1.83022359 3.17859951 Cl 2.62908575 5.38217814 5.38810007 4.08498645 -3.66509590 1.03194381 Na 2.54591498 8.24555481 5.39789703 -4.26060765 -4.81877609 -0.37445196 Cl 5.18693463 8.41637872 5.40623997 -5.51613777 -3.91906907 -3.62007555 Na 3.06252726 5.53084848 8.00646286 -4.06184015 2.29343274 -0.77561505 Cl 5.63066910 5.41488342 7.97255554 4.59522942 2.50901388 -1.01771516 Na 5.61156267 7.96643221 8.36032699 12.56429452 -2.37559028 2.32040367 Cl 8.51644727 8.14638539 7.54477337 1.49652777 2.58382409 -5.38119590 Na 5.15439492 5.15785045 10.88315220 3.13084522 -3.51205800 -11.78915006 Cl 7.93631651 5.35446615 10.95636816 0.83758569 3.09286860 4.19362683 Na 8.44204597 7.88870729 10.85757611 5.15185916 0.19370550 -2.54831968 Cl 11.03172972 8.15349042 11.03447896 -3.11693703 0.08370147 -3.75156154 Na 0.13967686 8.30079760 7.76774296 3.65664641 1.25979763 1.59477089 Cl 2.49249129 8.35655078 8.10118602 1.45752198 -11.14857609 6.08727583 Na 2.78996772 10.78331154 8.07448238 -4.16510153 1.67711891 2.85098027 Cl 5.50591871 10.96250379 8.01020641 4.51058593 4.77908444 -6.86053904 Na 2.71167301 8.06589951 11.04590411 7.79938018 -3.32166589 2.99484412 Cl 5.48559966 7.96601347 10.58515395 2.64688052 -1.44430838 4.67822459 Na 5.18746331 10.61816047 11.06543528 2.30288791 -3.15241128 6.88469069 Cl 8.06959340 11.02727145 10.80600517 -0.02888081 -6.54136689 -1.82442973 Na 5.89571831 8.25600525 13.06831850 2.09315605 -3.20838874 3.26949693 Cl 7.82658117 7.83966952 13.55224706 -3.28803236 -5.37859002 -6.34835810 Na 8.17449935 11.08979005 13.34965270 0.32876869 1.94270972 -0.01721709 I 10.77466134 10.89837718 13.78633872 -0.08054485 1.09160054 5.38655321 48 Lattice="0.0 10.8 10.8 8.100000000000001 0.0 8.100000000000001 5.4 5.4 0.0" Properties=species:S:1:pos:R:3:vel:R:3 pbc="T T T" Na -0.23733968 0.10596250 -0.22285583 3.30947369 -6.59065516 -0.76836211 Cl 2.88955932 0.08068522 0.19709981 -0.33682136 -5.72718245 -0.14711324 Na 2.92540491 2.86937355 -0.08320869 -11.05606307 -2.43426437 2.75094745 Cl 5.26832236 2.89482146 0.08683112 3.28793567 -1.66382713 -1.58661605 Na 2.62531617 0.05807711 2.64305548 8.86935841 2.15881573 -2.85009276 Cl 5.46245296 -0.13032789 2.74292255 -6.46145501 4.75670913 -1.04735252 Na 5.36881556 2.79112287 2.45005737 -1.31235562 -7.93875249 1.19845054 Cl 7.89678955 2.95704492 2.68482936 7.53489964 -5.93313937 4.86287376 Na 5.12845479 0.05247297 5.35160997 1.60998173 -8.82751698 -1.29037533 Cl 7.92412677 0.36075848 5.42400912 4.82420802 2.22602163 -6.15837064 Na 8.40913548 2.59604704 5.12228091 -3.42095664 -1.06440801 4.94503670 Cl 10.78997392 2.54490904 5.50755759 5.47713246 -0.44825984 7.45628182 Na 0.08108238 2.71483347 2.89560022 -5.13227121 -2.97759645 -0.62040675 Cl 2.33273360 2.55795932 2.68160313 -3.29324166 -0.70315266 3.99547643 Na 3.04050630 5.24782709 2.54119362 -3.80117302 1.43568258 -1.67616850 Cl 5.29045806 5.80730626 3.03991552 -2.22556760 -1.27456309 1.13434640 Na 2.82825333 2.65667526 5.30708419 12.49301173 -0.01545909 2.81261667 Cl 5.49171027 2.73668306 5.24496304 -5.23890677 -8.28055548 12.28584210 Na 5.43582343 5.49073938 5.41841304 1.85746685 -3.08511203 -3.33715966 Cl 8.13654431 5.34534449 5.16382338 -1.77721216 -7.52405897 1.28904824 Na 5.42445969 2.37152888 7.85977922 -10.18984747 4.11239959 -0.72115373 Cl 8.15447037 2.90945025 8.44346812 6.12618282 -0.91383099 0.31238358 Na 8.14236307 5.32401192 8.40960595 2.89152690 4.94257281 5.02519812 Cl 10.97747999 5.16335967 8.07013087 3.23195851 -3.17951601 -1.86385585 Na -0.26831272 5.01505132 4.91541513 7.59242514 -4.19871847 -2.77993699 Cl 2.90862706 5.43132954 5.44222532 -2.90261857 -3.04772137 -8.18251291 Na 2.87414523 8.10445118 4.79438759 16.48872370 1.16817585 6.44618067 Cl 5.50364113 8.08319039 5.53230552 6.15067400 2.06822284 0.43831999 Na 2.55717566 5.39943727 8.27343820 5.51767636 -4.33134834 -6.46543195 Cl 5.35731194 5.35234178 8.26542807 2.62384213 -6.16811613 -1.74062201 Na 5.20653676 8.34910923 7.99819957 5.64931434 -3.35535935 -0.91261585 Cl 8.11526195 8.08696727 8.08477026 5.53680414 4.94203933 4.06980709 Na 5.27177126 5.64210752 11.10268118 1.16591548 2.10686051 3.32924333 Cl 7.68740122 5.24092325 10.79238464 2.82892736 1.37572723 -2.22447605 Na 8.34761620 7.85436280 10.59727868 -7.09675686 -1.12211018 3.30950673 Cl 10.67120875 8.37552622 10.67829574 2.37430445 1.38360204 -0.57392066 Na 0.18593141 7.95626559 7.91020187 2.01907812 4.61159882 -8.26764440 Cl 2.57609995 8.14233882 8.41811548 -3.20409120 0.31413447 13.68096419 Na 2.63776907 10.86126292 8.41309859 -4.95502788 -5.30045303 -0.57617373 Cl 5.51364284 10.72020483 8.25918369 -5.37464081 -4.46363309 -2.73066446 Na 2.50570229 8.04219529 11.14443414 -3.07616012 -6.45427633 -2.16284375 Cl 5.35890003 8.24706051 11.05381093 7.73961173 1.95501125 -10.80458405 Na 5.69715998 10.93442128 10.82779238 3.21029224 -1.44673094 0.22218769 Cl 8.14420993 10.70924858 10.81863530 -2.52971837 -7.29228872 -3.04494945 Na 5.11014151 7.85343800 13.24851508 3.30976727 1.97762788 -3.32163578 Cl 7.74500794 8.33717532 13.46528152 1.55653124 -2.22340450 7.88730080 Na 7.92170286 10.83078458 13.30200868 0.78035348 -1.16538720 4.91585363 I 10.98175894 10.46321675 13.48990012 7.31662714 -7.44489486 2.49872492 48 Lattice="0.0 10.8 10.8 8.100000000000001 0.0 8.100000000000001 5.4 5.4 0.0" Properties=species:S:1:pos:R:3:vel:R:3 pbc="T T T" Na 0.15264832 -0.07444121 0.39714426 0.63623986 2.55872334 -3.53937737 Cl 2.68578596 -0.10830964 -0.12030879 15.93141633 -2.19624021 -2.16033417 Na 2.76846525 2.98212569 0.32563990 -5.20609945 0.71282917 8.49236074 Cl 5.59168864 2.39247779 0.04188073 -1.38505614 -5.49386253 -0.82087178 Na 2.62390624 -0.15916766 2.95017940 3.72822593 -1.92773933 1.56662400 Cl 5.25907327 0.03399684 2.65335525 0.56167961 0.95283925 4.40535963 Na 5.45498213 2.35906751 2.81556753 6.80578379 -3.26781905 -0.11495353 Cl 8.19470437 3.01372425 2.78619770 -11.99228135 -2.01627903 5.25352307 Na 5.48588927 0.31602926 5.43611446 -1.33821022 -0.19453109 -7.60965763 Cl 7.64667397 0.20050280 5.51287142 3.91412700 3.19670321 -1.14219115 Na 8.26679339 2.67787695 5.27727812 0.22466139 -1.99056061 -1.98129313 Cl 10.88802727 2.58738166 5.32308888 0.51041794 8.31476295 -4.06302187 Na -0.01269202 2.65344071 2.53906104 -3.22549870 0.46161050 -2.68197288 Cl 2.76551168 2.78862437 2.46625607 -3.03104873 5.71348306 -3.13200520 Na 2.62389321 5.28887028 2.74003531 0.29285557 4.43483589 2.13386121 Cl 5.33652167 5.39883109 3.00255143 -0.16742129 -2.84824701 -9.45725416 Na 2.69586058 2.83146061 5.38215152 -6.02626235 -1.75942003 -5.92486848 Cl 5.40375078 2.96493920 5.32112142 6.97710029 6.99961492 -0.99997408 Na 5.51526054 5.35152208 5.67071452 -1.40194689 -0.85005134 1.72775688 Cl 8.12393357 5.31739233 5.59327519 -8.56081904 9.25170134 5.98622019 Na 5.61530033 2.91096049 8.26849277 -1.85364009 1.66728507 -0.03950170 Cl 8.18876363 2.84605249 8.35307275 1.58680491 -8.89113744 3.77457386 Na 7.98418254 5.00526008 7.93762239 -13.55768618 1.32694716 -7.93677172 Cl 10.57632773 5.74550711 8.18439061 -1.95483869 -3.03607402 1.63539920 Na -0.14655994 5.64050596 5.44794592 0.51042975 -6.47345106 0.35694687 Cl 2.81774637 5.19923069 5.26980083 2.94917320 -2.06682395 -8.87772359 Na 2.37963619 8.15292852 5.39676387 10.83658904 -5.59186832 0.67606485 Cl 5.41511204 8.53380501 5.51599061 -1.04293831 -4.36051950 1.78375327 Na 2.72746967 5.34405854 8.13406291 16.76330303 -0.48034828 5.01780295 Cl 5.56151503 4.81750431 8.03085125 -8.52025251 1.04775847 5.57282220 Na 5.15420903 8.01608061 7.75949853 3.51404628 -4.12308796 1.30345757 Cl 8.26608014 8.19160207 8.19517290 -1.43566753 0.35384800 -2.07990917 Na 5.18199490 5.39389888 10.61992221 0.86217659 5.21802869 3.40663493 Cl 7.80917546 5.34837609 10.57045079 6.00661892 -4.17293773 -2.48596129 Na 8.26986478 8.27487658 11.10522540 8.98350471 4.30899579 -0.89553035 Cl 11.13346625 7.93898439 10.39706953 -1.79160961 1.89150917 -2.96841753 Na 0.12321659 8.19229045 7.92175480 9.78492591 8.83221986 -7.65420823 Cl 2.50921204 7.83340646 8.58639354 3.72275117 -3.51497923 -2.39058484 Na 2.87394614 10.79010625 8.20166356 0.55758755 -8.51712936 1.64499342 Cl 5.71034988 10.66018238 8.11470409 -3.64849097 -3.29364124 -11.28076487 Na 2.19196270 7.95983402 11.15565253 5.09002160 2.50462131 -1.22039434 Cl 5.36882499 8.32121213 11.07161603 -1.14478067 -2.97246574 2.56717034 Na 5.22782442 10.65373893 10.79052079 1.40244112 -0.05294383 0.06586639 Cl 8.10577341 10.74490691 10.96984465 -6.99869426 -0.76674129 -7.65668888 Na 5.48072323 7.85892035 13.63926187 -2.75721495 2.95952196 -1.97965970 Cl 8.24267155 7.85961478 13.90069593 -2.38223941 0.85085630 7.12804069 Na 8.06433196 11.05894456 13.66378044 1.75089973 -2.60150419 -1.86184120 I 10.57302539 10.35040999 13.63645724 -0.30981805 2.27420682 7.05827069 48 Lattice="0.0 10.8 10.8 8.100000000000001 0.0 8.100000000000001 5.4 5.4 0.0" Properties=species:S:1:pos:R:3:vel:R:3 pbc="T T T" Na -0.08224410 -0.00998559 0.03652068 -2.22896685 5.76535202 -12.06852668 Cl 3.19749480 0.03469154 -0.22119389 -6.17384798 -0.06911143 0.64416930 Na 2.57868169 2.71890475 0.23256462 -1.81950734 2.91469979 -1.15944780 Cl 5.45481747 2.97188658 0.15133597 1.68573007 -2.79105165 -6.76806091 Na 2.35847981 0.04977963 2.90785026 0.94815614 0.16720881 7.36965063 Cl 5.55226115 -0.21692529 2.73917847 -3.06071819 4.87692173 1.89555672 Na 5.17935868 2.91145148 3.03235170 0.20033825 -1.18042450 1.60583659 Cl 7.95994333 2.72040766 2.60872332 5.99368303 -6.22139440 -10.31090308 Na 5.19222597 -0.42560858 5.51803825 3.89761611 2.98877309 -1.96412704 Cl 7.93073498 -0.15074702 5.42749744 5.44161343 4.56698446 -4.86169325 Na 8.23031035 2.61615003 5.24492213 -3.66252761 -2.98414335 4.20360960 Cl 10.91282907 2.55656334 5.14548244 -2.63234187 -3.48606880 -2.49497922 Na -0.04866725 2.85884693 2.72171018 -1.15821762 -6.91714648 -1.75154673 Cl 2.94256567 2.38171900 2.47742659 1.08542784 3.49495173 -3.51813746 Na 2.00127819 5.12405607 2.78250818 -0.01678278 -6.32791133 -2.61724907 Cl 5.30342150 5.60861863 2.94069436 3.27027078 -8.24591047 0.46258972 Na 2.52243911 2.86588645 5.19291461 2.96764693 3.48210659 4.07578979 Cl 5.21700105 2.62286564 5.68231993 -1.10862759 1.22559158 -2.72806135 Na 5.26654961 5.40097800 5.91438762 6.36873130 0.95018883 -9.40349419 Cl 8.36580550 5.50320704 5.33030201 5.54252498 -6.42015933 7.90712902 Na 5.23243878 2.86021581 7.90438071 7.61685397 1.24452386 8.09562891 Cl 8.12519208 2.62301995 8.11861779 0.82184224 -0.07904086 -7.99976098 Na 8.13825623 5.47543332 8.40859057 -5.26510387 -6.68447248 -3.58958628 Cl 10.56718195 5.14205306 8.20828416 1.97671872 9.78303740 12.65961888 Na -0.20989916 5.37375947 5.42711847 -5.69378445 -2.46998476 -1.01478094 Cl 2.30535568 5.18416852 5.33446344 3.88331091 2.22450900 1.04988084 Na 2.45443115 8.10768463 5.38534401 -4.79948280 -0.51436875 4.33802406 Cl 5.34414817 8.39242079 5.46160528 -5.96602276 -0.87949011 7.05876266 Na 2.76797272 5.50370344 8.10233919 5.27284941 3.96148634 2.27843590 Cl 5.22506048 5.42959533 8.36819102 2.29723218 0.49110690 -2.22644464 Na 5.33582624 8.14465534 7.99887568 6.52850105 6.06166144 1.27438819 Cl 8.12336246 7.97951998 8.11264589 2.41834433 8.78104807 -3.22326820 Na 5.58138430 5.36807864 11.13391669 2.75854591 -1.35911683 2.95493906 Cl 8.25755039 5.24068855 11.17271875 1.02913692 1.86777573 -0.12505322 Na 8.20641490 8.07501557 10.66839623 10.79345735 1.04382239 0.99207207 Cl 10.89771663 7.95085707 10.84531332 3.87363731 5.45245631 -0.75291447 Na -0.25724715 8.21446179 7.74034130 -3.90251243 2.81732121 -1.96874011 Cl 2.82538660 8.16410870 7.98292174 0.18904834 -2.40924944 -6.99636261 Na 3.00049291 10.61755502 7.98421565 -8.67446310 -10.13422802 -3.85304361 Cl 5.27399108 10.70575549 7.67454951 -3.77556834 -2.93136549 1.78358178 Na 2.81152441 8.23705462 10.89610981 -1.53776156 1.76728731 1.29242229 Cl 5.18926233 8.58831623 10.62559296 3.68062124 -10.44646347 -7.08132293 Na 5.53028059 11.21919183 11.06998475 -2.45857930 -3.75914362 3.18387660 Cl 8.10086866 11.15008301 10.83149695 -4.13087312 2.18840459 -2.50108412 Na 4.73048194 7.75350249 13.56493294 7.25894010 -5.88479064 -5.35449533 Cl 7.69919102 7.78249803 13.69570191 5.65822255 2.51160000 -5.82357533 Na 8.32565110 10.77060847 13.80522245 8.36668204 2.02940870 3.73961419 I 10.57396121 10.95797751 13.67278302 5.19436858 -3.18462620 0.71578437 48 Lattice="0.0 10.8 10.8 8.100000000000001 0.0 8.100000000000001 5.4 5.4 0.0" Properties=species:S:1:pos:R:3:vel:R:3 pbc="T T T" Na 0.03887842 -0.03513590 0.05069390 5.83104963 -2.11616756 -0.59976067 Cl 2.62584577 -0.27882563 -0.06624605 -0.71386129 3.10850195 9.93746168 Na 2.74423685 2.70618918 -0.00190275 -0.90170930 -8.37352101 -4.20021522 Cl 5.07523225 2.76295366 0.04466089 -1.78644309 4.48707928 5.82584061 Na 2.85597678 -0.21231910 2.76456588 -5.99636478 -6.42500279 7.11206496 Cl 5.75575500 -0.30230712 3.04627873 -0.81394448 -7.19969081 -5.10621219 Na 5.63179067 2.31371141 2.74624645 -5.73595761 -3.26612296 3.97782674 Cl 8.41191677 2.76639085 2.90316565 6.59006090 1.84217736 -0.05967062 Na 5.15185413 -0.09615651 5.52967520 -3.53999554 2.33894976 -0.10576758 Cl 7.69790436 -0.21962795 5.32629564 8.24912484 2.24253224 -0.13044530 Na 8.58239998 2.65726545 5.14839234 0.07942455 4.37638379 2.37921306 Cl 11.00155893 2.45576301 5.32451986 2.65550174 0.21213559 -0.66722995 Na 0.19118032 2.73563528 2.52402092 1.21324854 5.68599732 -3.87809027 Cl 2.79253156 2.80885975 2.81802050 -4.87101204 -12.33886125 -5.58490864 Na 2.84761189 5.49214705 2.78564601 -1.34213044 -3.45773227 2.81585766 Cl 5.33047883 5.49438053 2.87678131 0.81426843 6.95897666 -3.77457116 Na 2.61807331 2.73981691 5.27371193 -1.72708695 1.74211079 3.80575131 Cl 5.97654477 2.29413720 5.27643883 1.47365000 13.65789924 -1.32075654 Na 5.23378439 5.41126128 5.56319344 9.39965353 3.03754845 -7.94813910 Cl 8.11159485 5.14153481 5.69592719 3.14622973 -1.22767231 1.79578723 Na 5.45360113 2.49513732 7.83386274 6.60154500 7.61403549 -4.91240899 Cl 8.26202168 2.26819286 8.50640190 2.88202045 1.37981130 -2.94315731 Na 8.27316578 5.34165802 7.97464878 -7.45115745 -9.45214112 1.07833916 Cl 11.02381076 5.05342529 8.54936825 3.49562622 -1.67177212 -6.83578507 Na 0.09510224 5.39578961 5.20414626 -1.43622990 3.65698242 -2.66775665 Cl 2.34369667 5.44464137 5.47702893 8.16043992 -5.10226687 3.29712567 Na 2.86195677 8.42054926 5.55709616 3.16493985 5.93922921 4.66767634 Cl 5.68592299 8.27304073 5.29964365 2.17085752 1.55669703 2.00849155 Na 2.78004021 5.17908077 7.95475134 3.98043401 3.61607651 1.91344856 Cl 5.53476504 5.49091901 8.18662779 -6.54838685 -4.32925143 -3.88021021 Na 4.91609296 8.26600087 7.84997540 2.98172132 5.49265012 -1.87293572 Cl 7.84067950 8.03863589 8.26922191 0.84280818 4.76241750 6.12854428 Na 5.25606505 5.13450847 10.91887943 -6.07457433 -1.17187848 4.46555975 Cl 8.26610273 5.10810402 10.58049337 -2.28144407 -6.75787294 4.29506811 Na 7.61764091 8.14478288 10.39423798 -0.37107203 -2.87550372 0.09462304 Cl 10.98249515 8.10940847 10.81430080 5.05210656 -2.67919861 -3.00186145 Na -0.24441447 7.91240302 8.13739209 0.57955938 4.26453733 -4.76392653 Cl 2.57290477 8.51947282 8.28965052 6.50743516 6.62710572 -1.75073986 Na 2.81628912 11.14159415 8.26160993 0.41990939 4.63512675 5.03249008 Cl 5.64939915 10.76565673 8.14587022 2.00921226 0.49678862 -7.87650900 Na 2.49352419 7.98493429 10.86975145 -9.47920928 -1.95253765 -9.43804324 Cl 5.21233706 8.21182490 10.58422689 -0.75794347 3.81411330 7.20302258 Na 5.33900124 11.08893941 10.75392865 0.58032327 0.58348748 4.49838570 Cl 8.15329387 10.77646191 10.91798791 -3.93879031 -5.98750055 -2.13040963 Na 5.41230031 8.33406093 13.37785143 10.32588226 3.59651039 4.86969578 Cl 7.84242096 8.21241716 13.67364633 4.56920553 0.15786003 -4.57179905 Na 7.88321440 10.93587052 13.42378423 -1.32831591 2.95196265 3.21112628 I 10.64457564 10.54840962 13.58876734 -1.39728712 8.25108116 2.93171016 48 Lattice="0.0 10.8 10.8 8.100000000000001 0.0 8.100000000000001 5.4 5.4 0.0" Properties=species:S:1:pos:R:3:vel:R:3 pbc="T T T" Na -0.00225581 -0.14051494 -0.08748881 0.45799506 -0.35667099 2.77684870 Cl 2.73987334 -0.29193476 -0.02311712 -7.04601474 2.15873641 -7.67879385 Na 2.67652943 2.61372678 0.28925930 6.92224132 -3.73491286 -4.79682663 Cl 4.96434602 2.90410176 -0.04292466 7.82551144 -0.43114627 -6.29591588 Na 2.80849629 0.12075074 2.45372730 3.46192068 0.79285168 1.13700990 Cl 5.22603309 0.09770715 2.41143719 0.74008327 11.88221497 -4.44468109 Na 5.44308038 2.59976717 3.14724291 -2.68231297 -0.32728366 4.32698449 Cl 7.78046281 2.44927142 2.43317949 0.53002850 8.99859612 -0.99850409 Na 5.33815834 -0.15917006 5.71440554 0.46214547 1.78866331 -1.33369675 Cl 7.92994191 0.19470086 5.54580800 0.84613197 10.78456663 4.02459909 Na 7.92947248 2.67647042 5.20931475 -3.48246417 -1.50799157 3.12758420 Cl 10.81533031 2.67337282 5.91986539 2.07607253 -3.12530524 7.08910430 Na 0.05697048 2.49689078 3.12939163 -1.61203912 -2.39262890 3.75580959 Cl 2.43920429 2.42738601 2.77226924 -3.24239385 -6.16322643 2.83662058 Na 2.51897101 5.43097025 2.83044148 3.10210258 -6.11467483 -2.62352601 Cl 4.93113322 5.50184047 2.70702362 11.67571756 -2.85902609 -5.12158680 Na 2.56357797 2.58148468 5.20403236 4.15247480 1.68974733 2.28961474 Cl 5.30921082 2.60739613 5.47555034 2.30932030 4.23378223 -8.37536944 Na 5.35425299 5.77846987 5.39898378 1.92992541 3.59387113 -5.21356724 Cl 8.40253649 5.88876916 5.27706503 -7.73993636 -7.24722987 4.67135109 Na 5.33084525 2.71555346 8.25884938 2.45841554 -2.97342221 12.72402040 Cl 8.67532465 2.41169855 8.09974513 -3.34324170 6.96450589 -0.43296684 Na 8.10966709 4.87491491 7.69870106 8.08242313 5.80426419 9.57055576 Cl 10.72602133 5.58672489 8.03611615 7.96367944 4.82814414 -0.49132945 Na 0.23326628 5.12929525 5.21583193 -1.08437151 -10.11354113 3.90932303 Cl 2.51222092 5.34026784 5.58168800 -13.44000270 -6.44871754 2.84658291 Na 2.67026555 8.69089106 5.51391388 -3.57561030 -4.74610661 2.48335797 Cl 5.41579863 8.31467203 5.21450578 12.06747224 -6.56084358 -0.84215934 Na 2.66493207 5.09362216 8.30006509 6.24258801 0.96168428 -7.55853416 Cl 5.47362613 5.47294085 7.94377487 -0.89193868 6.54211253 -6.35329866 Na 5.04844659 8.18563872 8.07851932 1.18022565 -3.32373698 -6.12891381 Cl 8.00937094 8.21955056 8.37807320 -12.50844971 1.05387077 -1.73158155 Na 5.62216377 4.83139206 11.08951744 -3.83185607 -8.44650597 2.68368539 Cl 8.21732492 5.07730026 10.03759148 -0.92113673 3.74125468 4.37075113 Na 8.16292689 7.98185469 10.73314816 -2.80713608 -3.54826761 2.66486851 Cl 10.98122227 8.03662246 10.87202267 2.49068927 3.34955951 -3.10316410 Na -0.13830004 7.87532331 8.12866129 -0.55541002 4.64127030 5.18751462 Cl 2.94127446 8.11292631 7.91720555 3.23729846 -0.82810274 -2.74686108 Na 2.73772888 10.70670377 8.17347294 6.57069327 2.17371845 -0.30614405 Cl 5.29299760 10.81434335 8.20381609 3.03936927 8.42235736 -2.29096071 Na 2.87201188 7.90170714 10.96607191 -4.48116754 5.89321941 2.68117825 Cl 5.32064514 8.00672932 10.60887261 1.44323865 5.66077156 -0.93022803 Na 5.20135646 11.00445800 10.91303069 -1.59108777 8.99028194 3.00986292 Cl 8.36266385 10.77527179 11.04908708 -1.64123852 5.47132343 3.80947488 Na 5.74470598 7.84990216 13.69974982 -0.39723573 -0.14680592 -2.11843053 Cl 7.61169521 8.14504445 13.62892328 3.99890439 -7.34354355 -7.04721021 Na 8.22024515 10.61282902 13.56343275 -0.38928499 -8.37422890 0.61368919 I 10.99834261 10.96933171 13.60663602 0.32883219 3.02396731 3.10756098 48 Lattice="0.0 10.8 10.8 8.100000000000001 0.0 8.100000000000001 5.4 5.4 0.0" Properties=species:S:1:pos:R:3:vel:R:3 pbc="T T T" Na -0.02549843 0.07371733 -0.00094641 5.94610391 13.54530258 -3.68802341 Cl 2.80731268 0.04087302 0.05556624 -0.55736629 4.21945965 1.58441690 Na 3.04171921 2.79954580 0.06178690 -0.73643385 -1.32747907 3.09378968 Cl 5.22330207 2.85313617 0.15388073 6.84074176 1.62069334 1.82705657 Na 3.06830252 -0.19798435 2.39993324 -0.75104476 3.25305524 -2.92519414 Cl 5.56133625 0.00985617 2.89033042 6.23949566 -3.19767911 0.99171529 Na 5.66729717 2.83456738 2.88569462 -4.39356479 -7.43337074 -4.23343190 Cl 8.05899378 2.48815260 2.63453364 -1.63613313 -7.29431916 -3.63092384 Na 5.01234157 -0.05587576 5.53560703 -0.03646566 5.25044660 -2.93286296 Cl 7.97248512 0.06774096 5.39002150 11.19419941 6.66375779 6.91299390 Na 7.92375946 2.80035860 5.39012477 -2.43692101 0.74354774 -3.76067548 Cl 11.09915586 2.88920776 5.24258740 13.33439796 -7.54517569 8.85768102 Na 0.38501685 2.63696103 3.03253564 8.81811474 1.22564670 11.64715243 Cl 2.77633596 3.20134560 2.66207247 -5.59854814 -6.47029456 11.99514201 Na 2.55711928 5.66201808 2.73314224 -7.27117047 -7.96696494 9.33467994 Cl 5.69597905 5.48916942 2.56317900 -1.11898125 4.02725059 3.71210389 Na 2.79635091 2.92533678 5.17156670 0.91043619 11.07368984 -0.19633884 Cl 5.12674327 2.34779121 5.20546628 0.67752759 -5.23251571 0.10742265 Na 5.37753683 5.49642475 5.17336320 -1.40900255 6.24517778 -2.97137793 Cl 7.92456148 5.37691568 5.47532923 5.12287105 4.67662426 3.65214351 Na 5.61925056 2.76312076 7.77340608 -0.95807691 3.34824215 2.42241489 Cl 8.19663060 2.75624118 7.85297067 -0.81528943 2.15016797 -7.19556948 Na 8.06459116 5.47094370 8.01515671 3.61988244 -3.77133383 -0.88027171 Cl 10.89455735 5.30155167 8.31216520 2.59562582 -0.09641756 12.37644361 Na 0.07245235 5.11532264 5.79621588 -1.82734894 -0.76228223 7.41605604 Cl 2.76945151 5.42631616 5.64996123 11.32580648 1.69482814 10.22949569 Na 2.70421709 7.66642466 5.45629065 8.08761424 1.67017941 -0.86352903 Cl 5.11527533 8.17636133 5.18312942 5.35777430 2.52759059 -6.39731047 Na 2.99047405 5.37885752 8.37246408 3.84869931 4.80157232 0.96699668 Cl 5.77414767 5.49802945 8.10173678 0.06958647 -0.31111761 -2.99668425 Na 5.41524288 7.43650702 8.21369788 -4.69440139 -8.86895613 -5.90508122 Cl 8.29439161 8.07115458 7.66493263 -1.63004664 -7.24006753 2.38183538 Na 5.44419951 5.50705062 10.57752839 0.89664296 -2.07334325 -2.06495618 Cl 7.93691377 5.49127278 11.00553710 -9.32847207 -2.45916087 1.38980961 Na 7.94790417 8.16935772 10.87648764 -2.89746714 -9.22937083 -9.45807163 Cl 10.94597901 7.91975387 10.54770651 -2.22511579 -9.48344036 -5.08609395 Na -0.11730045 8.01974665 8.10900248 1.77300619 2.97285118 1.66466862 Cl 2.63037578 7.56796939 8.05793602 -4.70978722 -2.98943756 -6.20449458 Na 2.89792891 10.48015894 8.18565224 -0.09891276 -2.27319504 3.09733318 Cl 5.53028485 10.64738512 7.82154765 -11.78137798 -1.55944633 -6.77674266 Na 2.28224744 7.93645315 11.38524676 7.84526839 -5.43457073 -0.19717144 Cl 5.22246125 8.18311209 10.93702490 -1.30282640 2.29103617 -0.21525866 Na 5.18748942 10.78323848 10.43120224 -8.97916114 -5.79716978 2.15071228 Cl 8.02557072 10.80469177 10.57747990 -0.05140758 -5.15630844 -3.80079657 Na 5.42515651 8.11277147 13.68120699 -5.79254394 4.37212616 1.52373167 Cl 7.89852869 8.38868371 13.50215066 -1.48790422 2.79799997 -4.49877667 Na 8.06476127 11.06304569 13.29304707 -6.23021179 3.70282548 11.03571699 I 10.86310904 10.62476576 13.82046440 -8.20740244 0.21974205 -7.40292205 48 Lattice="0.0 10.8 10.8 8.100000000000001 0.0 8.100000000000001 5.4 5.4 0.0" Properties=species:S:1:pos:R:3:vel:R:3 pbc="T T T" Na -0.04270346 -0.25995846 0.32568237 -14.60751021 7.42790083 -1.05764736 Cl 2.82420987 -0.07997981 -0.25536945 5.24951677 -0.47376283 -1.86193926 Na 2.87348704 2.35287775 0.30269936 -0.80558602 -7.82535627 6.13646847 Cl 5.00118807 2.72007323 0.30918928 -3.32444064 -6.51895824 3.76419292 Na 2.56982019 0.09339017 2.83090387 -3.40762317 4.33350963 -4.35110896 Cl 5.46523400 -0.00750264 2.74573779 -2.53701736 -5.42882974 -3.61177741 Na 5.25211788 2.52713761 2.83253066 5.20387201 -8.33864359 -4.99665699 Cl 8.13684661 2.79478773 3.03201521 5.52940013 7.50872383 3.17205691 Na 5.16985884 -0.12460544 5.56274136 -1.09947679 -0.81377756 -4.95254478 Cl 8.15549197 -0.03158559 5.73073274 0.17103119 -1.52592127 0.80905838 Na 8.29693669 2.74647591 4.88202245 8.36098103 7.36792371 -0.21303327 Cl 10.75586745 2.92833454 5.63785255 5.99008386 1.41404088 -2.22876321 Na 0.06461829 2.54420366 2.90890414 11.00994138 3.52569273 2.60770818 Cl 2.87810707 2.64267608 2.49032383 2.54774996 -2.34317063 -0.40611018 Na 2.82426428 4.99894447 2.79216845 -1.04259564 -2.72499717 1.04689771 Cl 5.33830513 5.14697167 2.97457086 -1.99114089 -8.63755515 7.67315692 Na 2.71476192 2.75848138 5.38468072 3.07949404 -3.82255758 -7.14977047 Cl 5.15655550 2.42376806 5.51419629 3.03027192 0.37718925 6.55002030 Na 5.47191327 5.10939719 5.60377427 -5.80419681 -0.45339944 0.54386471 Cl 8.03324947 5.31160519 5.74810905 -1.46207275 -9.21774065 -2.33102750 Na 5.69529422 2.56940998 8.48276752 7.07185097 -3.59696887 1.86791176 Cl 8.11071827 2.68772768 8.17893049 1.05218725 -2.71801456 3.25850932 Na 8.29819982 5.16358340 8.06048678 -6.08452770 -2.96475457 13.26656630 Cl 11.06751621 5.56983450 8.22939959 2.59570856 5.59562371 -1.31680424 Na 0.02100667 5.53490714 5.56890325 -5.44902026 5.01069584 -5.98825573 Cl 2.83751228 5.72057562 5.53928492 -4.63686317 -1.90648066 7.42529031 Na 2.44429855 7.95112663 5.29051333 -1.36102389 8.08782428 3.66226126 Cl 5.80306114 8.24382921 5.36083973 1.17212770 -0.43160107 -4.08628771 Na 2.95944467 5.22760890 8.02586456 -3.46898759 6.50592815 -1.95877739 Cl 5.52476843 5.24714662 7.95295894 -1.19378557 8.14963307 1.50104859 Na 5.41391864 8.00509402 8.16730830 4.32864633 0.79819130 0.80506257 Cl 8.00924548 8.05977104 7.83085438 -1.58209868 0.18997875 -1.44116663 Na 5.39344266 5.33225210 11.05504105 -3.97141560 -2.47730297 2.83845974 Cl 7.95217730 5.41384871 10.85264172 -4.11583205 8.86645568 -10.33884140 Na 8.04485566 8.32342671 10.78383724 0.28096349 3.53927727 -3.93913629 Cl 10.65981047 8.03688244 10.94939180 -4.18990862 -6.31156571 -0.66677417 Na 0.00688692 8.15600138 7.92832342 3.16011012 -13.77372813 4.90354472 Cl 2.38627560 8.13552560 7.74256688 4.38863222 2.30322898 3.56909992 Na 2.89174447 10.53528851 7.92180005 0.65368859 -2.58083587 1.65377907 Cl 5.14046861 10.68502056 7.74809313 0.02639445 2.90774612 -0.32660819 Na 2.99573900 7.95808746 11.00018234 3.71893628 -2.76069213 -0.46284832 Cl 5.57088053 8.17184388 11.19098401 0.65860373 0.63005898 -1.04313603 Na 5.44691118 11.12059412 10.96969504 1.71682975 0.27359892 -3.46042255 Cl 8.17132293 10.13132832 10.62052233 0.00441625 -1.36403330 -5.09160214 Na 5.43396199 8.18784705 13.67244359 7.92059329 -3.05841570 7.55155038 Cl 8.04703108 7.97363689 13.26331662 -2.52711472 -4.41185445 6.28027999 Na 8.07979367 10.87416006 13.59452337 -5.64016671 7.19617248 4.81762175 I 10.86382543 10.73309431 13.78047863 3.48727426 3.47548546 2.54904872 48 Lattice="0.0 10.8 10.8 8.100000000000001 0.0 8.100000000000001 5.4 5.4 0.0" Properties=species:S:1:pos:R:3:vel:R:3 pbc="T T T" Na 0.08736347 0.41881152 0.39186838 -2.43023901 -6.22547759 -3.53196636 Cl 2.65447349 -0.12402221 -0.00564143 3.46007289 2.32985180 -3.11326999 Na 2.71832837 2.87532354 -0.35495741 -7.01249289 -0.21320417 8.56292510 Cl 5.55648812 2.92654939 0.04491512 5.67041229 -5.30922730 3.96817124 Na 2.31593911 -0.23091166 2.53687905 0.62996625 -3.60863210 10.00972755 Cl 5.30039276 0.52127714 2.30820887 1.44605959 -1.02602138 1.79434098 Na 5.37807745 2.16147387 2.54320014 2.61120646 -3.68451538 -1.81634403 Cl 7.81163633 2.55218810 2.84966986 -2.31731289 4.91367564 1.54641362 Na 5.34250888 0.09529408 5.13784637 4.41539766 -10.03297597 6.37261156 Cl 7.83306151 0.39042038 5.20018906 2.07507264 4.02572571 -0.28818864 Na 7.97646560 2.53335294 5.24846097 -4.62205767 -1.38561708 5.41697894 Cl 10.58735627 2.54946202 5.68864729 3.99271981 1.11018444 0.39446060 Na 0.27616996 2.79857168 2.64915051 -3.66507427 -0.63049624 -2.46985831 Cl 2.65980655 2.43315766 2.65563338 -3.60006505 4.86041522 4.99086413 Na 2.46895392 5.42446058 2.44871327 0.20435816 6.60743109 -0.49718087 Cl 5.45783160 5.32474290 2.69136139 -5.28823257 -2.73017183 -4.26200040 Na 2.79555042 2.67991135 5.35899396 -2.00541202 -2.19373237 3.45922114 Cl 5.43048381 3.09581533 5.47657965 5.46506166 7.51648957 -4.80748172 Na 5.05100074 5.73165599 5.54954738 -0.74956420 -1.99119622 1.44618070 Cl 8.64954623 5.43549501 5.64543621 12.72649497 11.42159647 4.68817164 Na 5.50292819 2.57375540 8.10435483 7.13928004 -3.31521239 -3.34445192 Cl 8.18821344 3.20979624 8.01724710 -5.45387334 -1.93501228 -6.63986497 Na 8.18184519 5.77672070 7.73269719 0.50799830 -7.49100150 -2.75557606 Cl 10.88084533 5.51202087 8.13614609 -2.05168611 6.22534705 4.32881749 Na 0.00076508 5.46770362 5.88696756 5.09915796 -4.28028920 6.97088545 Cl 2.70890984 5.64950702 5.34765004 3.44031844 -0.00933062 2.88906863 Na 2.69473241 8.42834742 5.09956620 2.44638921 4.84690118 -3.91342117 Cl 5.34839484 7.88573413 5.42535239 0.55899949 -5.85398699 6.82389491 Na 2.48554940 5.40273170 8.19595479 2.91108451 -13.86327337 -6.00076178 Cl 5.72264749 5.09953502 8.59360345 -1.49162129 -2.77228575 3.49461843 Na 5.24160097 8.40705676 7.88064368 4.51654915 1.48166244 7.65906587 Cl 8.28005798 8.41830881 8.09863572 2.31933983 -7.33723253 -3.06997968 Na 5.34452836 5.64039232 10.99411251 1.52226642 -2.15531599 -2.55180570 Cl 8.02978274 5.61049013 10.90240721 -4.86756208 -6.14030319 5.53445574 Na 8.15405623 8.02971866 10.54754820 7.95414404 4.85887704 -13.72177879 Cl 10.62130718 8.12550535 10.76860317 -0.07118439 -1.79338851 -0.23069047 Na -0.48554906 8.38585104 8.10534082 -0.48183483 -5.77271522 -8.25316925 Cl 2.21161703 8.19411577 7.91600209 8.99911804 -11.07159723 -3.40708536 Na 3.01275935 10.67574824 8.26014145 5.56511494 -1.24996292 2.32961565 Cl 5.59109831 10.69427376 8.14493149 -7.38852664 6.95161460 -0.05105541 Na 2.64192007 8.00291238 10.67281568 -2.80374878 6.98557306 -10.31495117 Cl 5.54165286 8.46845495 10.49565185 6.71533031 8.70704988 -0.48988292 Na 5.62183338 10.94362519 10.85043814 -2.11374008 -5.52938322 -0.50052518 Cl 8.47321283 10.96609043 10.85091394 2.75033420 6.22173679 -5.32235282 Na 5.39047886 8.26160597 13.58150227 -7.32905920 0.56313987 -1.56559901 Cl 7.96899683 7.91047329 13.38676812 7.97178281 -8.57977821 3.41990388 Na 7.84492751 10.64185999 13.61069066 -2.88330523 -0.17856015 10.39676260 I 10.45661260 10.86919008 13.88833547 4.80666066 7.73843047 -0.26378518 48 Lattice="0.0 10.8 10.8 8.100000000000001 0.0 8.100000000000001 5.4 5.4 0.0" Properties=species:S:1:pos:R:3:vel:R:3 pbc="T T T" Na 0.24114430 -0.05154109 0.17086034 -5.45988054 -0.01938448 -8.06549126 Cl 2.89944723 -0.02293735 -0.12684492 -2.13492697 -4.26423392 -0.75818568 Na 2.54244608 2.71949371 -0.22131539 1.34217097 2.34902790 9.19748618 Cl 5.37193498 2.57446033 0.24302066 -1.40952390 1.99276681 -7.72079976 Na 2.66615874 -0.11520012 2.83008908 9.36721147 -2.11251531 -3.34961953 Cl 5.36009487 -0.12417685 2.76499097 0.06845580 6.34731756 -4.54904174 Na 5.03564745 2.79648460 2.75560357 8.92423048 -0.03683951 -4.17551100 Cl 8.10758952 2.86260675 2.91687463 2.82876624 -2.66005468 4.46356645 Na 5.21959235 0.00545425 5.54209008 -3.81288930 -2.29072895 2.45713834 Cl 8.16328566 -0.18939795 5.19344553 -2.14192835 5.52700094 2.88711425 Na 8.06218565 2.77255659 5.27993559 7.88878938 -5.13906536 -1.06512384 Cl 10.89605803 2.38943052 5.55071835 2.41420939 -2.42900348 2.40475739 Na -0.09287458 2.72352043 2.81737202 -1.75385490 -5.13602104 1.45345052 Cl 2.50206434 2.40717732 2.47242562 -6.97251736 3.07424081 1.09850810 Na 2.62100406 5.32170826 2.58510948 8.66304794 1.99971063 -0.07211503 Cl 5.08486396 5.40536375 2.94824057 1.35698145 -2.07066236 5.88307942 Na 2.48704932 2.80118702 5.44113580 1.83646592 -9.72689334 -3.12699117 Cl 5.61954281 3.17510915 5.26033163 -1.23881322 0.18181989 -0.93823484 Na 5.07057914 5.39579529 5.65372590 3.07129065 1.22154690 -0.66052527 Cl 8.08423891 5.15628889 5.23053511 -5.75304560 2.72898304 2.34587577 Na 5.27405572 2.88203856 7.86635390 -0.81588993 1.19034358 9.67939476 Cl 8.32420889 2.55151146 8.00949817 -4.75508638 6.20150926 1.01187036 Na 8.36407653 5.70240102 7.81851273 4.92616194 1.11374177 9.25006472 Cl 10.76937047 5.36572854 8.47891257 -0.41134104 -6.29315731 2.00253169 Na 0.11016618 5.69470633 5.60208715 -0.82532635 2.91882672 7.79274573 Cl 2.72166724 5.16906686 5.56079594 -5.66965237 -1.08670743 3.87032584 Na 2.69753735 8.52227339 5.34684644 -3.55568671 -2.36772393 3.69406803 Cl 5.46930341 8.16898225 5.48484908 -2.09751563 13.33019763 -2.44069304 Na 2.98990834 5.67088775 7.90070536 0.90617665 3.47422720 0.43522525 Cl 4.94222946 5.53108120 8.07420140 -4.09118901 -7.61215209 9.15538805 Na 5.52062489 8.03342933 8.01150918 -0.52852508 0.17294064 1.86629478 Cl 8.15081607 8.06850237 8.30048304 -5.38066489 -2.20435490 -5.85301873 Na 5.51871416 5.55178504 10.91928925 -1.08115524 6.96353857 -3.05336806 Cl 7.97699803 5.68595363 10.59864724 -8.32204148 1.20978449 3.98163528 Na 8.01601582 8.08292636 10.79390994 5.44787703 -3.28253045 -4.19246688 Cl 10.93041516 8.17275532 10.67701296 -1.72513023 1.54443446 -8.93399383 Na 0.28698464 7.96484840 8.23398937 -4.23151718 -1.48975212 1.79204568 Cl 2.52410855 8.16157315 7.99136210 -5.15625567 6.45370477 -1.72074164 Na 2.68657702 10.56195314 8.24457517 4.00629075 3.71105223 -4.41522289 Cl 5.37830184 10.98981610 7.90469879 -0.75294819 0.83609495 -1.82643710 Na 2.42638737 8.09883106 10.62829523 -4.37499582 -9.45652085 -3.38442932 Cl 4.98536461 8.31393351 10.91235166 -9.33657026 -11.16417976 3.72167302 Na 5.35441121 10.55390306 10.30718834 1.72979709 0.96429461 -4.55146262 Cl 8.02416748 10.95784662 10.89055385 6.48816008 -9.59067489 -3.38959409 Na 5.82770069 8.08395408 13.46034767 5.49687874 8.08892330 5.89252121 Cl 8.05553406 7.70585248 13.49082107 -7.47845198 1.50719951 -8.32937420 Na 8.20265241 11.02634565 13.52735389 -6.55380796 4.20871802 -1.06474305 I 10.82148683 10.92558403 13.51302803 -4.23688060 11.14246665 3.15482861 48 Lattice="0.0 10.8 10.8 8.100000000000001 0.0 8.100000000000001 5.4 5.4 0.0" Properties=species:S:1:pos:R:3:vel:R:3 pbc="T T T" Na 0.27389041 -0.01482257 0.18010533 -7.09299478 4.48376744 -2.39596872 Cl 2.83597831 0.03706535 0.31468810 -4.80526687 -2.92646009 -5.88286219 Na 3.18941553 2.37884044 0.36736097 9.24655070 -2.01414927 5.00759323 Cl 5.69700596 2.99658826 -0.06902677 5.70194609 -5.59397882 -3.52384564 Na 2.39087669 -0.14369122 2.72320530 -2.34127119 11.63930724 -7.75688149 Cl 5.34247755 0.11774096 2.61817904 2.68415907 2.40576828 1.62887710 Na 5.67475065 2.69119774 2.87146392 -3.63797262 3.47279658 -2.05568990 Cl 8.08517359 3.04955476 2.69937775 -15.28955763 8.63269713 2.45403554 Na 5.26564451 0.23936392 5.36465600 2.74889229 -4.78479903 8.20521756 Cl 7.95347260 -0.34146388 5.56217989 0.38207977 6.06413391 -5.36515188 Na 8.30168567 2.96654918 5.55242584 -3.53126322 -0.56875606 -3.20143495 Cl 10.65126562 2.93106232 5.54892524 8.79141043 -8.99643535 1.90890545 Na -0.29601542 2.67885748 2.84571818 3.02448171 -3.31854450 -3.48173291 Cl 2.72564753 3.37072835 2.96489973 -3.99246946 1.60755028 2.21472294 Na 2.65749975 5.80803887 3.03456699 7.40142222 -4.01292950 1.40708222 Cl 5.21776969 5.25622078 2.83057320 -3.86750857 1.82844188 -2.89172467 Na 2.49155875 3.04031891 5.41831246 -0.23498866 5.59818393 4.00982380 Cl 5.37269576 2.66870593 5.31515217 5.41498094 2.74737330 -2.73786149 Na 5.49139041 5.58718864 5.50778557 -0.18473901 2.11589467 -2.62067594 Cl 8.29801261 5.35199678 5.35642806 -2.64041571 7.55697462 9.82056801 Na 5.40743228 2.74653032 7.93562962 -2.01731136 0.10723330 3.66767126 Cl 8.45190118 2.70794985 8.29025658 -2.27855781 3.94495357 5.08357738 Na 8.12312267 5.51311500 7.92069810 -4.22098690 -6.67039569 7.70830187 Cl 10.77663343 5.04591483 7.85046428 -7.95197199 -4.04402469 2.18615850 Na -0.06934287 5.41691582 5.40455661 0.59051103 10.10639888 -2.77336859 Cl 3.13202088 5.57092758 5.24385622 -1.31783727 9.77939847 -0.49232645 Na 2.82724223 8.02821149 5.33614764 9.34414000 -7.54348204 9.01049598 Cl 5.52147939 7.91650152 5.52519794 3.73328519 0.19484285 2.19266968 Na 2.66663967 5.36059828 8.13394750 8.01499966 -5.01452850 -5.13640663 Cl 5.65859509 5.23386723 7.87668321 -11.44728461 -6.48689551 -0.78016681 Na 5.24773505 8.33675810 7.92418359 0.09418913 -2.51751387 -2.07237769 Cl 8.30837715 7.92724719 7.98017873 1.59494326 -0.25492817 4.21418896 Na 5.01114092 5.16454602 10.78155082 -1.85856423 -3.28740093 -4.28667152 Cl 8.39969920 5.47765021 10.76032644 -0.71091488 -1.39528525 1.77118606 Na 7.94535640 8.09233042 11.26981449 6.81728706 -4.40396910 5.78634638 Cl 10.56517212 7.91469866 11.17656903 -1.41972691 -10.78132200 3.14038122 Na 0.15014246 8.41633997 8.18962315 -2.14123298 2.49539733 9.51305805 Cl 2.67506910 8.19483067 8.20575350 -5.26166830 -5.97679994 -3.77513821 Na 2.71710609 10.79690410 8.47025632 -3.41952104 0.82819968 -3.71293814 Cl 5.56147294 10.29490224 8.06775685 4.19519973 -5.43690084 6.08362623 Na 2.62130495 8.22569952 10.83282674 -5.73742945 -0.12382377 5.90185182 Cl 5.12569964 8.33149735 10.71915190 3.51306086 0.90390222 7.68105321 Na 5.23389641 10.77804713 10.89299925 -2.20217059 5.53354359 -7.65018785 Cl 8.27274802 10.75809541 10.91347200 5.02749993 -4.04653369 -4.41128909 Na 5.18970956 8.37733666 13.42099696 -1.74179190 7.32557397 -0.14330967 Cl 8.14956351 8.02053601 13.84857855 4.30075148 -0.97756878 0.35045948 Na 8.06181391 10.69993257 13.62035303 0.58111833 5.74454477 -4.22996349 I 10.34161836 10.67707596 13.42553743 -2.36813475 -5.44334659 0.79888910 48 Lattice="0.0 10.8 10.8 8.100000000000001 0.0 8.100000000000001 5.4 5.4 0.0" Properties=species:S:1:pos:R:3:vel:R:3 pbc="T T T" Na 0.12738574 -0.11787199 -0.14637981 -6.39448094 -0.67596871 -4.00575967 Cl 3.09867859 -0.02143729 -0.06353405 -1.68576293 6.65295723 0.38567377 Na 2.31284557 2.24259512 0.05427143 0.05171888 6.46164850 6.98544225 Cl 5.25315119 2.95371352 0.04246211 11.19938439 2.24767088 -5.45542903 Na 2.88237733 0.05435506 2.95016241 -3.38360164 -7.62294636 6.61452323 Cl 5.60308892 -0.12330297 2.91692065 5.96603036 2.81178038 5.22002127 Na 5.46880755 2.57081797 2.77653458 4.12249760 -0.42946691 9.81547395 Cl 7.86262225 2.74400274 2.59502770 7.25112672 2.21806142 -3.70256540 Na 5.20211700 -0.41575515 5.12926249 9.80757924 1.30246567 -2.26006984 Cl 8.01205495 -0.12210358 5.33473845 2.82756870 1.10626272 -2.95785056 Na 8.12882591 2.73493591 5.17280316 -8.68103773 6.34197077 8.12719554 Cl 10.95205855 2.62968441 5.41629162 1.74567124 -11.05848049 8.14740610 Na 0.07722272 2.68391232 2.66353516 -5.15161663 3.73192494 -7.09362514 Cl 2.79616001 2.87922114 2.80496440 1.33285407 -1.13160778 -0.26835132 Na 2.71717722 5.43810299 2.59704329 1.43717362 -7.61229006 1.79895712 Cl 5.27730539 5.35982838 2.84609956 2.61922135 -3.25892794 -4.75492005 Na 2.79218019 2.33882307 5.01136474 5.50469058 4.93971557 2.77059953 Cl 5.76977616 2.48738937 5.31064781 0.47321476 3.74085431 4.01207940 Na 5.41396311 5.18139325 5.20964141 4.20206151 -4.08908708 1.56798916 Cl 8.49644735 5.41022127 5.63013637 -1.72030467 7.35896080 7.24574702 Na 5.25531229 2.85262824 8.40257836 8.37405440 1.18790186 0.18670409 Cl 8.23523308 2.93600612 7.86482656 6.62467132 -0.34855294 1.83832253 Na 8.18947676 5.18965405 7.91139427 -6.09606961 -7.28065136 4.05047384 Cl 10.57485950 5.52564672 7.99914527 4.54490522 -2.31570459 -3.75485213 Na -0.10585136 5.36605616 5.53286655 0.17927291 6.57993485 -1.15787195 Cl 2.90543870 5.64809473 5.40893186 -1.06282182 -2.92909844 7.59001731 Na 2.36039209 8.15529083 5.41271841 8.09330097 0.01568371 -1.96554232 Cl 5.17199653 8.04447735 5.34672610 2.62069991 6.29561075 0.70747897 Na 3.13960109 5.61181028 7.92838926 -4.19488091 -2.90591971 3.89589790 Cl 5.36429943 5.37564294 7.95135643 2.22573082 -2.16878542 7.39936322 Na 5.39223773 8.06674763 7.97179359 -0.42076823 5.91623330 -5.85363991 Cl 8.26820188 7.85517804 7.95245059 6.83423103 3.33662070 7.84993842 Na 5.46719963 5.09790144 10.58990110 -0.11280231 -0.13019393 -9.35774948 Cl 8.16971463 5.59033349 10.59001617 -2.44064258 -0.81359169 -9.22031631 Na 7.99178364 8.12868070 10.77729809 -7.54782707 0.07668977 8.74656718 Cl 11.10114778 8.14829855 10.94690543 -6.25991039 -10.10939681 6.07724812 Na -0.12597914 8.32061986 8.07644942 3.24010383 6.37607726 4.28731788 Cl 2.58602735 7.70204882 7.90684518 13.07097427 -4.65502312 0.92239089 Na 2.42706503 11.09313466 8.38806795 -6.81888167 -9.34499080 1.55818016 Cl 5.25844381 10.79814008 7.95695308 1.51457123 -3.02631328 3.47402352 Na 2.77712564 8.10286460 10.70385111 5.71227641 3.18368579 -5.36468989 Cl 4.91270761 8.05764479 10.69998195 -7.07290250 -1.75908662 -1.66738627 Na 5.62730047 10.68683801 10.63259362 -5.47712627 2.86889706 7.24508478 Cl 8.44045862 10.61513088 10.86218253 5.85141869 -3.12360963 0.10147551 Na 5.30868130 8.26993010 13.91917287 -2.62534009 1.40510612 -3.94657252 Cl 8.17397359 8.14468650 13.42257534 0.06467428 -1.60413358 -2.67030204 Na 7.90795379 10.80662625 13.35533216 -1.43344698 -2.10328485 8.31839908 I 10.87549273 10.77496945 13.53121355 -8.32501029 -2.95081122 -9.29452186 48 Lattice="0.0 10.8 10.8 8.100000000000001 0.0 8.100000000000001 5.4 5.4 0.0" Properties=species:S:1:pos:R:3:vel:R:3 pbc="T T T" Na 0.04250446 0.09984045 0.32888768 9.93564292 -3.81544776 -2.45505192 Cl 2.40219358 -0.29580206 -0.13008403 -1.04765398 4.92069068 -4.56421549 Na 2.55440481 2.71058166 -0.17768420 0.09873808 1.96799531 -5.36642166 Cl 5.31104672 2.51483285 -0.07033699 0.52648865 3.16183517 2.89251586 Na 2.43081586 -0.28398590 2.63608520 -1.38068629 8.78782454 -1.05453058 Cl 5.27260199 0.46296852 2.70565694 1.56653086 13.71124471 0.20071417 Na 5.58113450 2.49718741 2.73886514 -1.80394158 7.07324266 -6.95142899 Cl 8.24945832 2.94664391 2.71789671 -0.33412833 2.01883333 -1.61270648 Na 5.28875081 -0.11090256 5.05217292 -4.35356419 -0.92780091 8.57872360 Cl 8.21450867 0.19720037 5.26874394 4.78358451 1.78967592 3.82316757 Na 8.15430928 2.78598766 5.09980846 2.32052928 -0.06775253 -6.46045262 Cl 10.91315317 2.87774844 5.77912168 -6.44058689 0.06179364 8.65471681 Na 0.25311262 2.70358662 2.93801456 -6.70958252 0.53754484 7.90511782 Cl 2.37520764 2.66692254 2.85646534 -6.11250618 0.75314555 0.03421444 Na 2.42914063 5.44011156 3.03491741 2.30093816 4.41592470 -8.75408242 Cl 5.24616049 5.31390942 2.63330020 -0.43296875 1.73671727 3.65675157 Na 2.57217549 2.69449181 5.47553595 4.45392538 -5.02673986 2.34412523 Cl 5.81547449 2.63452683 5.15865802 5.18278957 10.31941232 5.54444650 Na 5.33689437 5.41589626 5.21511115 8.49638160 -3.00566157 -1.27695332 Cl 7.97804121 5.14137937 5.44363350 2.74530257 -2.69237106 5.45272941 Na 5.33286304 2.77420558 8.07983893 11.32968937 -0.29386145 -6.46589297 Cl 7.81974177 2.68309364 7.87003582 2.81333948 4.47771617 0.33199428 Na 8.06230150 5.60461672 8.01128086 9.81618502 2.58809826 -1.86546753 Cl 10.68004622 5.47946471 8.17692991 7.90621412 3.81187433 -2.24119828 Na 0.27200013 5.18324333 5.60004878 6.20274453 0.54021337 0.69109357 Cl 2.81349878 5.28449286 5.11374772 4.63319899 -2.93379423 -2.56717429 Na 2.87384983 8.15942150 5.47391598 4.73141132 1.08432979 6.15282245 Cl 5.34870305 8.09179547 5.25234876 4.19361769 5.78957512 0.37985356 Na 2.71059274 5.47781924 8.37120215 -2.55376262 3.93032812 -7.99096822 Cl 5.36701877 5.26437000 8.13465441 2.12613216 6.08483754 -4.05677249 Na 5.61813556 8.25390312 8.31841049 -7.42734078 0.09491420 -12.37361278 Cl 8.11043071 8.11394962 7.78528449 1.71414895 -3.49217174 0.63374384 Na 5.62761322 5.30720924 11.01552530 -1.70493284 8.68817492 0.30275943 Cl 8.20853104 5.84260899 10.87221918 0.82744032 -1.35873601 3.65098945 Na 8.12863260 7.98906012 10.80037126 0.37060269 -1.24992730 0.10717643 Cl 10.77860158 8.13722424 11.04127157 1.93361746 3.22009629 0.83211196 Na -0.08508719 8.23592226 7.62897712 1.40981781 1.17300765 -10.07953547 Cl 2.85589395 8.27202274 8.02367289 5.67089257 -5.23691925 -6.66703211 Na 3.16337030 10.47616396 8.04466575 -12.01041423 2.14754692 -0.09224953 Cl 5.39472769 10.93888956 7.71707691 -2.46476259 -2.05949082 -3.92793384 Na 2.49240735 8.23977972 11.28862220 -1.90458748 -1.03935712 -6.55226450 Cl 5.32870237 7.95845678 10.80993944 5.09589819 -2.82527930 4.42061060 Na 5.71327198 10.79822070 10.65842898 -13.51902096 6.46408123 5.61397530 Cl 7.56213725 10.88711273 10.64253091 -0.66331642 -3.20790036 -2.88695548 Na 5.42692654 8.18724720 13.69533303 11.81448455 4.00701956 -5.15319341 Cl 8.05476566 8.23054203 13.70675821 -2.26554949 -1.10991996 1.80374288 Na 8.10868764 10.72673526 13.59521414 5.98962736 1.43069673 -0.26619817 I 10.78490755 10.98349527 13.62620738 -2.74956296 4.25323418 1.26671888 48 Lattice="0.0 10.8 10.8 8.100000000000001 0.0 8.100000000000001 5.4 5.4 0.0" Properties=species:S:1:pos:R:3:vel:R:3 pbc="T T T" Na -0.09749298 0.01248667 -0.38826625 1.63794740 -4.96456141 -4.75498931 Cl 2.68296538 -0.03908639 0.05542075 -1.59436182 -2.54135149 -1.26676630 Na 2.81124511 2.76396503 0.00416208 10.95583896 -1.77537859 -0.45567640 Cl 5.79121283 2.36442678 0.35225791 -1.60053935 1.69104541 1.29099141 Na 2.50429983 0.08234759 2.72068063 -3.77291022 -1.25561229 1.92858450 Cl 5.19290366 -0.15212275 3.01439794 2.60394898 -2.25844759 -7.45807509 Na 5.52937813 2.23931582 2.65146745 -5.68578048 3.77222807 3.09977800 Cl 8.06168193 2.76458855 2.78831118 2.29328454 -2.60159623 9.69520412 Na 5.67152970 -0.26046716 5.27294118 3.08314042 -10.83848979 -4.15733439 Cl 8.14278756 0.09789003 5.53644032 0.22449938 0.60494826 -1.85067820 Na 8.23796100 2.80577107 5.65661515 -6.03466039 8.18927071 1.98376060 Cl 10.79491319 2.48803813 5.64609382 -0.56981806 -4.07083798 -8.39256864 Na 0.27180815 2.31552715 2.79220115 -2.46789621 4.59988780 -1.05801949 Cl 2.63584711 2.42830063 2.71842472 -5.42423684 7.22345218 2.11958717 Na 2.57966571 5.43247985 2.78390437 8.30827786 1.33711099 1.69374605 Cl 5.10439408 5.16878335 2.55931916 0.90803769 1.00066866 1.34657388 Na 2.69885509 2.53175736 5.57270891 -4.36892521 4.95335637 -9.41830129 Cl 5.61799748 2.72399865 5.55338507 14.79312727 0.72272125 0.84914647 Na 5.32370657 5.33453448 5.42941177 4.12368767 -1.55635051 -3.14042258 Cl 7.81883076 5.19443620 5.17202558 -5.23196063 -6.36411091 -3.77699281 Na 5.43552142 2.64948866 8.15075891 -2.53529039 -4.79027720 -1.28100520 Cl 7.92542654 2.86203638 8.04707142 -3.77118542 -2.85316289 4.28247441 Na 8.31205748 5.52090548 7.84206774 6.29541392 6.14142090 -5.28044178 Cl 10.50483292 5.31109572 8.27360185 2.21465442 8.37103144 -3.27003735 Na -0.13953806 5.58065587 5.61521105 -3.08278635 0.51443472 -7.96342151 Cl 2.40750054 5.67915519 5.67479369 7.47537564 5.81743584 -2.59831611 Na 2.59838639 8.33629797 4.94261195 8.64718586 0.10172638 -4.91366736 Cl 5.45351136 7.97361739 5.40881138 0.55468123 4.36085865 1.23059289 Na 2.89813909 5.13427402 8.25940293 -2.90419198 8.46795723 -11.41798144 Cl 5.32285831 5.38537934 8.13431880 4.43998905 2.65976150 -2.85237556 Na 5.36067431 8.03976849 8.44792467 1.63697399 -6.76159012 -1.48795417 Cl 8.26238774 8.31614587 8.24324092 0.83805466 3.27663283 -4.77811326 Na 5.60391415 5.21164007 10.81002539 -2.24635753 0.29160871 0.85628968 Cl 8.18811761 5.33145156 10.66427402 5.06702011 0.15775466 -2.57159365 Na 8.06373593 8.06092592 10.87963909 0.36451374 -0.56720061 -5.57574839 Cl 10.90254569 7.99568215 10.70549175 -0.89627424 4.59471232 1.12018140 Na -0.18694922 7.91471300 7.94158351 -0.59280366 4.37669103 -2.22993029 Cl 2.95269285 8.24920054 8.42581302 -4.68181837 -3.37233168 8.89165785 Na 2.60900572 11.01308500 8.16960638 3.26160466 2.68859511 -8.32074033 Cl 5.27432685 10.60116774 7.93014285 -8.67474300 3.61988655 9.15150049 Na 2.84267879 7.97969900 10.76749160 -5.62371556 1.43659856 -4.12899186 Cl 5.54128434 8.09095410 10.87369772 -6.89606631 0.27603500 3.53374429 Na 5.37420807 10.45465421 10.46238170 -0.21190721 -1.49738504 1.73810152 Cl 8.00759903 11.09004708 11.21818039 -1.66409957 -5.83363959 -4.58009721 Na 5.45153414 8.41113229 13.69005872 0.55494960 0.85761256 -1.18965034 Cl 8.06027564 8.12356651 13.50988676 -3.11280368 -3.82283607 1.06420391 Na 7.99736922 10.41720325 13.39264552 3.00754528 3.77172440 -8.58740492 I 11.01305487 11.01102621 13.42026774 4.23895983 6.42445352 0.80361113 48 Lattice="0.0 10.8 10.8 8.100000000000001 0.0 8.100000000000001 5.4 5.4 0.0" Properties=species:S:1:pos:R:3:vel:R:3 pbc="T T T" Na -0.10300788 0.11837934 0.00957468 -2.10043663 3.42736926 -8.08343810 Cl 2.60231906 -0.07099057 -0.07836267 -1.41682227 0.95842848 -1.18771748 Na 3.07285221 2.42570205 0.44525507 7.89893328 5.52445356 0.86287798 Cl 5.37637807 2.72396967 0.03651978 8.38696912 -5.84079774 -0.31231798 Na 2.56845215 -0.32924633 2.47168319 -3.52886949 0.07326551 -0.36408191 Cl 5.11218212 0.14899846 2.35215597 0.34965477 4.49628388 -5.79244150 Na 5.33356056 2.55231731 2.65324285 -0.27085890 -4.64177422 0.48238188 Cl 8.14938290 3.26450428 2.50617390 -4.28587971 0.89411576 1.48874340 Na 5.14676283 0.40353315 5.62176117 1.49286600 9.72526989 2.23812408 Cl 8.08322926 -0.23976038 5.98634103 -7.15200384 -1.52231695 -0.30272395 Na 8.27650260 2.34355367 5.49639274 -7.50108795 -1.89238228 0.33928087 Cl 11.18720123 2.70157345 5.40230058 2.37768846 1.67350707 -2.90098991 Na -0.10372074 2.75358839 2.63032246 7.58847922 0.91445185 0.18342203 Cl 2.69789581 2.98346995 2.95017887 -1.91667039 0.92148076 4.30286580 Na 2.54769874 5.56258955 2.92106890 -5.44043259 -0.64403399 -2.25373163 Cl 5.03020480 5.66282273 2.69559305 -0.59727686 -10.74067183 -4.14205131 Na 3.11439781 2.70749737 5.27505785 -3.67168971 -3.60705186 -0.22251669 Cl 5.38500789 2.61516562 5.42068070 -1.87040126 -12.99198498 8.62643863 Na 5.44853849 5.66096516 5.51098935 -9.24003199 -6.95714716 2.40071600 Cl 7.85646506 5.26122200 5.24736003 -2.51171921 7.69178288 -0.36636462 Na 5.36835008 2.68718898 8.45087152 4.38053643 -1.48059467 -10.82226321 Cl 8.52591211 2.58802325 7.93690466 2.16576518 2.33668688 7.33261963 Na 8.02978035 5.22604624 8.09449356 3.60945397 -2.33599430 2.37031839 Cl 10.71126789 5.05855571 8.33249390 2.99219509 1.63662354 -0.99701061 Na -0.21287083 5.11938718 5.40474362 2.79243301 9.58826705 3.86869201 Cl 2.69671584 5.38446700 5.23268656 -1.72440705 3.18932894 -3.39553490 Na 2.41444103 8.06781905 5.13582242 0.96495928 -5.66868760 -0.04524444 Cl 4.94061526 8.36712691 5.41133932 -2.43984159 -10.66918790 11.14902168 Na 2.79878841 5.54286947 7.72977412 1.02556843 7.11730787 6.75489995 Cl 5.27245263 5.10405747 8.06461679 2.51901238 0.45738315 -3.27305349 Na 5.35027379 8.09990944 7.93777515 8.78378534 -12.96826380 -7.16548998 Cl 8.07040886 8.46838618 8.18023325 4.78767927 -2.53140552 1.97735853 Na 5.51058578 4.92220617 10.84435090 -8.32194323 3.95888000 -2.20579760 Cl 8.28381696 5.07908837 11.05188429 -8.42040426 -3.35588739 -0.74117709 Na 8.05525004 8.11287125 10.87375344 -6.26884984 1.30404524 -5.75404417 Cl 10.93302108 8.02331700 10.78586367 5.81321162 -1.71685049 -2.72640101 Na -0.11329630 7.81553027 7.91058801 -10.73540164 -2.77262931 10.77012516 Cl 3.10127225 8.11716144 8.56493751 -0.64769760 -6.23941410 5.81733560 Na 2.26074839 11.23951750 7.74692058 6.09471990 8.42231220 -1.71472101 Cl 5.49042325 10.83179476 8.39730658 2.57709834 0.10661392 8.81832196 Na 2.95067987 8.15959828 10.72373697 6.88803946 4.31369195 -8.59631342 Cl 5.79154072 7.87656099 10.48208622 4.79160854 -9.18118928 6.16950022 Na 5.16508576 10.85231898 11.36258621 -4.36990321 2.66172250 2.44123707 Cl 8.40343097 10.90530039 10.45078810 -4.10057709 -0.95585583 0.29702852 Na 5.96082498 7.87095253 13.58772327 8.97667138 4.47610419 1.34473373 Cl 7.85284791 8.40261525 13.55770126 -4.60068231 5.08123683 1.50522845 Na 8.33240682 10.51035361 13.34364217 0.48248987 -5.18685471 8.96456332 I 10.59353083 10.73097494 13.74668876 -4.74895577 -0.01467421 -4.18572941 dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/tests/regression_tests/NaCl_prim.xyz000066400000000000000000000003071522712672400272360ustar00rootroot000000000000002 Lattice="0.0 2.7 2.7 2.7 0.0 2.7 2.7 2.7 0.0" Properties=species:S:1:pos:R:3 pbc="T T T" Na 0.00000000 0.00000000 0.00000000 Cl 2.70000000 0.00000000 0.00000000 dynasor_regression_results_SED.npz000066400000000000000000004525011522712672400334720ustar00rootroot00000000000000dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/tests/regression_testsPK!pDlw.npylPPZ\nnf_TR_ wK)Ng$:: 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parameters q_max = 1.8 dt = 1.5 # trajectory this_dir = os.path.dirname(__file__) traj_fname = os.path.join(this_dir, 'NaClI_size4x3x2_100frames_with_velocity.xyz') # setup prim and supercell prim = read(os.path.join(this_dir, 'NaCl_prim.xyz')) supercell = prim.repeat((4, 3, 2)) # run dynasor traj = Trajectory(traj_fname, trajectory_format='extxyz', atomic_indices='read_from_trajectory') q_points = get_spherical_qpoints(traj.cell, q_max=q_max) w, SED = compute_spectral_energy_density(traj, supercell, prim, q_points, dt, partial=True) # read old results archive = np.load(os.path.join(this_dir, 'dynasor_regression_results_SED.npz')) old_results = {k: archive[k] for k in archive.files} # compare against old results assert len(old_results) == 2 assert np.allclose(old_results['w'], w) assert np.allclose(old_results['sed'], SED) dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/tests/regression_tests/test_regression_Sq.py000066400000000000000000000020031522712672400310450ustar00rootroot00000000000000import os import numpy as np from dynasor.trajectory import Trajectory from dynasor.qpoints import get_spherical_qpoints from dynasor.correlation_functions import compute_static_structure_factors def test_regression(): # parameters q_max = 5.0 # trajectory this_dir = os.path.dirname(__file__) traj_fname = os.path.join(this_dir, 'NaClI_size4x3x2_100frames_with_velocity.xyz') # run dynasor traj = Trajectory(traj_fname, trajectory_format='extxyz', atomic_indices='read_from_trajectory') q_points = get_spherical_qpoints(traj.cell, q_max=q_max) sample = compute_static_structure_factors(traj, q_points) # read old results archive = np.load(os.path.join(this_dir, 'dynasor_regression_results_Sq.npz')) old_results = {k: archive[k] for k in archive.files} # compare against old results assert len(old_results) == len(sample.dimensions + sample.available_correlation_functions) for key, array in old_results.items(): assert np.allclose(array, sample[key]) dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/tests/regression_tests/test_regression_Sqw.py000066400000000000000000000022531522712672400312430ustar00rootroot00000000000000import os import numpy as np from dynasor.trajectory import Trajectory from dynasor.qpoints import get_spherical_qpoints from dynasor.correlation_functions import compute_dynamic_structure_factors def test_regression(): # parameters q_max = 1.5 window_size = 42 dt = 2.1 # trajectory this_dir = os.path.dirname(__file__) traj_fname = os.path.join(this_dir, 'NaClI_size4x3x2_100frames_with_velocity.xyz') # run dynasor traj = Trajectory(traj_fname, trajectory_format='extxyz', atomic_indices='read_from_trajectory') q_points = get_spherical_qpoints(traj.cell, q_max=q_max) sample = compute_dynamic_structure_factors(traj, q_points, dt=dt, window_size=window_size, calculate_incoherent=True, calculate_currents=True) # read old results archive = np.load(os.path.join(this_dir, 'dynasor_regression_results_Sqw.npz')) old_results = {k: archive[k] for k in archive.files} # compare against old results assert len(old_results) == len(sample.dimensions + sample.available_correlation_functions) for key, array in old_results.items(): assert np.allclose(array, sample[key]) dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/tests/test_SED.py000066400000000000000000000165061522712672400232500ustar00rootroot00000000000000import os import pytest import numpy as np from ase import Atoms from dynasor.correlation_functions import compute_spectral_energy_density from dynasor.trajectory import Trajectory @pytest.fixture def traj_fname_xyz(): this_dir = os.path.dirname(__file__) traj_fname = os.path.join(this_dir, 'trajectory_reader/trajectory_files/dump.xyz') return traj_fname @pytest.fixture def traj_fname_xyz_with_velocities(): this_dir = os.path.dirname(__file__) traj_fname = os.path.join(this_dir, 'trajectory_reader/trajectory_files/dump_with_velocities.xyz') return traj_fname @pytest.fixture def prim(): alat = 6.0 spos_A = np.array([0, 0, 0]) spos_B = np.array([0.5, 0.5, 0.5]) spos_X = np.array([[0.5, 0.5, 0], [0.5, 0, 0.5], [0, 0.5, 0.5]]) scaled_positions = np.vstack((spos_A, spos_B, spos_X)) cell = alat * np.eye(3) types = ['Cs', 'Pb', 'Br', 'Br', 'Br'] atoms = Atoms(pbc=True, cell=cell, scaled_positions=scaled_positions, symbols=types) return atoms def test_sed(traj_fname_xyz_with_velocities, prim): # setup size = 6 dt = 2.5 traj = Trajectory(traj_fname_xyz_with_velocities, trajectory_format='extxyz') atoms_ideal = prim.repeat(size) q_points = np.array([a * np.array([2 * np.pi / 6.0, 0, 0]) for a in np.linspace(0, 1, size+1)]) # run SED f, sed = compute_spectral_energy_density(traj, ideal_supercell=atoms_ideal, primitive_cell=prim, q_points=q_points, dt=dt) f_target = 1 / dt * 2 * np.pi * np.linspace(0, 1/2, 3) assert sed.shape == (7, 3) assert np.allclose(f, f_target) sed_target = np.array([ [0.10847998, 0.18091865, 0.15408934], [0.23743116, 0.17269055, 0.29059788], [0.20541601, 0.21506601, 0.14684155], [0.12568888, 0.26424448, 0.33979104], [0.20541601, 0.17761411, 0.14684155], [0.23743116, 0.19715230, 0.29059788], [0.10847998, 0.18091865, 0.15408934]]) assert np.allclose(sed, sed_target) # test with step > 1 step = 2 traj = Trajectory(traj_fname_xyz_with_velocities, trajectory_format='extxyz', frame_step=step) f, sed = compute_spectral_energy_density(traj, ideal_supercell=atoms_ideal, primitive_cell=prim, q_points=q_points, dt=dt) f_target = 1 / (dt*step) * 2 * np.pi * np.linspace(0, 1/2, 2) assert sed.shape == (7, 2) assert np.allclose(f, f_target) def test_sed_without_velocities(traj_fname_xyz, prim): size = 6 dt = 2.5 atoms_ideal = prim.repeat(size) q_points = np.array([a * np.array([2 * np.pi / 6.0, 0, 0]) for a in np.linspace(0, 1, size+1)]) traj = Trajectory(traj_fname_xyz, trajectory_format='extxyz') with pytest.raises(ValueError, match=r'[Vv]elocities'): f, sed = compute_spectral_energy_density(traj, ideal_supercell=atoms_ideal, primitive_cell=prim, q_points=q_points, dt=dt) def test_sed_with_incorrect_supercell(traj_fname_xyz, prim): size = 6 dt = 2.5 atoms_ideal = prim.repeat(size) del atoms_ideal[0] q_points = np.array([a * np.array([2 * np.pi / 6.0, 0, 0]) for a in np.linspace(0, 1, size+1)]) traj = Trajectory(traj_fname_xyz, trajectory_format='extxyz') with pytest.raises(ValueError, match='ideal_supercell must contain the same number of atoms' ' as the trajectory'): f, sed = compute_spectral_energy_density(traj, ideal_supercell=atoms_ideal, primitive_cell=prim, q_points=q_points, dt=dt) def test_sed_partial_true_shape(traj_fname_xyz_with_velocities, prim): """partial=True returns shape (N_qpoints, N_frequencies, n_prim, 3).""" size = 6 dt = 2.5 traj = Trajectory(traj_fname_xyz_with_velocities, trajectory_format='extxyz') atoms_ideal = prim.repeat(size) q_points = np.array([a * np.array([2 * np.pi / 6.0, 0, 0]) for a in np.linspace(0, 1, size+1)]) f, sed_partial = compute_spectral_energy_density( traj, ideal_supercell=atoms_ideal, primitive_cell=prim, q_points=q_points, dt=dt, partial=True) n_qpoints = len(q_points) n_frequencies = len(f) n_prim = len(prim) assert sed_partial.shape == (n_qpoints, n_frequencies, n_prim, 3) def test_sed_partial_sum_equals_total(traj_fname_xyz_with_velocities, prim): """Summing partial SED over basis atoms and Cartesian directions equals total SED.""" size = 6 dt = 2.5 atoms_ideal = prim.repeat(size) q_points = np.array([a * np.array([2 * np.pi / 6.0, 0, 0]) for a in np.linspace(0, 1, size+1)]) traj = Trajectory(traj_fname_xyz_with_velocities, trajectory_format='extxyz') _, sed_total = compute_spectral_energy_density( traj, ideal_supercell=atoms_ideal, primitive_cell=prim, q_points=q_points, dt=dt, partial=False) traj = Trajectory(traj_fname_xyz_with_velocities, trajectory_format='extxyz') _, sed_partial = compute_spectral_energy_density( traj, ideal_supercell=atoms_ideal, primitive_cell=prim, q_points=q_points, dt=dt, partial=True) assert np.allclose(sed_partial.sum(axis=(2, 3)), sed_total) def test_sed_output_is_real_and_nonnegative(traj_fname_xyz_with_velocities, prim): """SED values must be real-valued and non-negative (it is a power spectrum).""" size = 6 dt = 2.5 traj = Trajectory(traj_fname_xyz_with_velocities, trajectory_format='extxyz') atoms_ideal = prim.repeat(size) q_points = np.array([a * np.array([2 * np.pi / 6.0, 0, 0]) for a in np.linspace(0, 1, size+1)]) _, sed = compute_spectral_energy_density( traj, ideal_supercell=atoms_ideal, primitive_cell=prim, q_points=q_points, dt=dt) assert np.isrealobj(sed) assert np.all(sed >= 0) def test_sed_frequency_axis_scales_with_dt(traj_fname_xyz_with_velocities, prim): """The maximum frequency in the output is pi / dt (Nyquist), scaling with dt.""" size = 6 atoms_ideal = prim.repeat(size) q_points = np.array([[2 * np.pi / 6.0, 0, 0]]) for dt in [1.0, 2.5, 5.0]: traj = Trajectory(traj_fname_xyz_with_velocities, trajectory_format='extxyz') f, _ = compute_spectral_energy_density( traj, ideal_supercell=atoms_ideal, primitive_cell=prim, q_points=q_points, dt=dt) # Frequency spacing should be 2*pi / (N_frames * dt) assert np.isclose(np.diff(f)[0], 2 * np.pi / (4 * dt), rtol=1e-5) def test_sed_primitive_cell_larger_than_supercell_raises(traj_fname_xyz_with_velocities, prim): """primitive_cell >= ideal_supercell in atom count must raise ValueError. The trajectory has 1080 atoms (prim.repeat(6)). Using ideal_supercell with 1080 atoms and primitive_cell also with 1080 atoms triggers the >= check. """ size = 6 dt = 2.5 atoms_ideal = prim.repeat(size) # 1080 atoms, matches trajectory primitive_too_large = prim.repeat(size) # also 1080 atoms: >= supercell q_points = np.array([[2 * np.pi / 6.0, 0, 0]]) traj = Trajectory(traj_fname_xyz_with_velocities, trajectory_format='extxyz') with pytest.raises(ValueError, match='primitive_cell contains more atoms'): compute_spectral_energy_density( traj, ideal_supercell=atoms_ideal, primitive_cell=primitive_too_large, q_points=q_points, dt=dt) dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/tests/test_average_independent_samples.py000066400000000000000000000133671522712672400303520ustar00rootroot00000000000000import numpy as np import pytest from dynasor.sample import DynamicSample from dynasor.post_processing import get_sample_averaged_over_independent_runs dynamic_correlation_functions = ['Fqt', 'Fqt_coh', 'Fqt_coh_A_A', 'Fqt_coh_A_B', 'Fqt_coh_B_B', 'Fqt_incoh', 'Fqt_incoh_A', 'Fqt_incoh_B', 'Sqw', 'Sqw_coh', 'Sqw_coh_A_A', 'Sqw_coh_A_B', 'Sqw_coh_B_B', 'Sqw_incoh', 'Sqw_incoh_A', 'Sqw_incoh_B'] def get_random_dynamic_data_dict(n_q, n_t): """ generate a random data_dict """ data_dict = dict() data_dict['q_points'] = np.linspace([0, 0, 0], [1.0, 1.0, 1.5], n_q) data_dict['time'] = np.linspace(0, 10, n_t) data_dict['omega'] = np.linspace(0, 3, n_t) for name in dynamic_correlation_functions: data_dict[name] = np.random.normal(0, 10, (n_q, n_t)) return data_dict def get_random_dynamic_sample(): n_q = 50 n_t = 200 data_dict = get_random_dynamic_data_dict(n_q, n_t) sample = DynamicSample(data_dict, simulation_data=get_simulation_data()) return sample def get_simulation_data(): simulation_data = dict() simulation_data['cell'] = np.diag([11.5, 18.2, 10.1]) simulation_data['atom_types'] = ['A', 'B'] simulation_data['particle_counts'] = dict(A=100, B=250) simulation_data['pairs'] = [('A', 'A'), ('A', 'B'), ('B', 'B')] return simulation_data def test_average_dynamic_samples(): # setup samples to average over n_samples = 25 simulation_data_ref = get_simulation_data() samples = [get_random_dynamic_sample() for _ in range(n_samples)] # average sample_ave = get_sample_averaged_over_independent_runs(samples) assert isinstance(sample_ave, DynamicSample) # check dimensions are correct assert sorted(sample_ave.dimensions) == ['omega', 'q_points', 'time'] assert np.allclose(sample_ave.omega, samples[0].omega) assert np.allclose(sample_ave.q_points, samples[0].q_points) assert np.allclose(sample_ave.time, samples[0].time) # check metadata is correct assert 'simulation_data' in sample_ave.metadata assert 'history' in sample_ave.metadata assert np.allclose(sample_ave.cell, simulation_data_ref['cell']) assert sample_ave.particle_counts == simulation_data_ref['particle_counts'] assert sample_ave.atom_types, simulation_data_ref['atom_types'] assert sample_ave.pairs, simulation_data_ref['pairs'] # check value of correlation functions is correct for name in dynamic_correlation_functions: average = np.mean([sample[name] for sample in samples], axis=0) assert np.allclose(average, sample_ave[name]) def test_raises_error_with_inconsistent_samples(): n_samples = 25 # inconsistent metadata samples = [get_random_dynamic_sample() for _ in range(n_samples)] samples[10]._metadata['simulation_data']['cell'] = 4.19 * np.eye(3) with pytest.raises(ValueError) as e: get_sample_averaged_over_independent_runs(samples) assert 'Field "cell" of sample #10 does not match.' in str(e) # inconsistent dimensions samples = [get_random_dynamic_sample() for _ in range(n_samples)] samples[4].q_points[0, 0] = 0.212354 with pytest.raises(ValueError) as e: get_sample_averaged_over_independent_runs(samples) assert 'Sample dimensions do not match for sample #4.' in str(e) def test_raises_error_with_different_dimension_names(): """Samples with different dimension key sets raise ValueError.""" sample_a = get_random_dynamic_sample() n_q, n_t = 50, 200 data_dict_b = get_random_dynamic_data_dict(n_q, n_t) data_dict_b['extra_dim'] = np.linspace(0, 1, n_t) del data_dict_b['time'] sample_b = DynamicSample(data_dict_b, simulation_data=get_simulation_data()) with pytest.raises(ValueError, match='Sample dimensions do not match'): get_sample_averaged_over_independent_runs([sample_a, sample_b]) def test_raises_error_with_missing_simulation_data_key(): """A sample missing a simulation_data key raises ValueError.""" samples = [get_random_dynamic_sample() for _ in range(3)] del samples[1]._metadata['simulation_data']['cell'] with pytest.raises(ValueError, match='missing "cell"'): get_sample_averaged_over_independent_runs(samples) def test_raises_error_with_inconsistent_float_simulation_data(): """A sample with a differing float in simulation_data raises ValueError.""" samples = [get_random_dynamic_sample() for _ in range(3)] # Replace 'cell' (ndarray) with a float key to exercise the float branch for s in samples: s._metadata['simulation_data']['dt'] = 1.0 samples[2]._metadata['simulation_data']['dt'] = 2.0 with pytest.raises(ValueError, match='Field "dt" of sample #2 does not match.'): get_sample_averaged_over_independent_runs(samples) def test_live_dangerously_skips_metadata_check(): """live_dangerously=True allows averaging samples with inconsistent metadata.""" samples = [get_random_dynamic_sample() for _ in range(3)] samples[1]._metadata['simulation_data']['cell'] = 4.19 * np.eye(3) # Should not raise result = get_sample_averaged_over_independent_runs(samples, live_dangerously=True) assert isinstance(result, DynamicSample) def test_history_is_carried_over(): """History entries from each sample are included in the averaged sample's history.""" samples = [get_random_dynamic_sample() for _ in range(2)] samples[0]._append_history('some_func', {'a': 1}) samples[1]._append_history('other_func', {'b': 2}) result = get_sample_averaged_over_independent_runs(samples) func_names = [h['func'] for h in result.history] assert any('some_func_sample0' in f for f in func_names) assert any('other_func_sample1' in f for f in func_names) dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/tests/test_cli.py000066400000000000000000000066721522712672400234070ustar00rootroot00000000000000import argparse import os import subprocess import sys import pytest import numpy as np from tempfile import NamedTemporaryFile from dynasor.trajectory import Trajectory from dynasor.qpoints import get_spherical_qpoints from dynasor.correlation_functions import compute_dynamic_structure_factors from dynasor.post_processing import get_spherically_averaged_sample_binned from dynasor.sample import read_sample_from_npz from dynasor.cli.main import _parse_q_direction @pytest.fixture def filenames(): this_dir = os.path.dirname(__file__) traj_fname = os.path.join(this_dir, 'trajectory_reader/trajectory_files/dump_long_with_velocities.xyz') index_fname = os.path.join(this_dir, 'trajectory_reader/trajectory_files/index_file_dump_long') return traj_fname, index_fname def test_dynasor_CLI(filenames): # input parameters time_window = 8 max_frames = 100000 dt = 100 q_max = 40 q_bins = 20 max_q_points = 10000 traj_fname, index_fname = filenames tmpfile = NamedTemporaryFile(suffix='.npz', delete=False) # setup dynasor command flags = [] flags.append(f'-f {traj_fname} -n {index_fname}') flags.append('--trajectory-format=extxyz') flags.append(f'--q-bins={q_bins}') flags.append(f'--q-max={q_max}') flags.append(f'--max-q-points={max_q_points}') flags.append(f'--time-window={time_window}') flags.append(f'--dt={dt}') flags.append(f'--max-frames={max_frames}') flags.append(f'--outfile={tmpfile.name}') flags.append('--calculate-incoherent') flags.append('--calculate-currents') flags_str = ' '.join(flags) # Run dynasor and read results command = 'dynasor ' + flags_str os.system(command) sample_cli = read_sample_from_npz(tmpfile.name) # Compute results with dynasor API traj = Trajectory(traj_fname, trajectory_format='extxyz', atomic_indices=index_fname) q_points = get_spherical_qpoints(traj.cell, q_max=q_max, max_points=max_q_points) sample = compute_dynamic_structure_factors(traj, q_points, dt=dt, window_size=time_window, calculate_currents=True, calculate_incoherent=True) sample_api = get_spherically_averaged_sample_binned(sample, num_q_bins=q_bins) # compare results assert np.allclose(sample_api.time, sample_cli.time) assert np.allclose(sample_api.omega, sample_cli.omega) assert np.allclose(sample_api.q_norms, sample_cli.q_norms) for key in sample_api.available_correlation_functions: assert np.allclose(getattr(sample_api, key), getattr(sample_cli, key)) def test_dynasor_CLI_line_sampling(filenames, tmp_path): traj_fname, _ = filenames outfile = tmp_path / 'line_sample.npz' command = [ sys.executable, '-c', 'from dynasor.cli.main import main; main()', '-f', traj_fname, '--trajectory-format=extxyz', f'--outfile={outfile}', '--max-frames=4', '--time-window=3', '--q-sampling=line', '--q-direction=1,2,3', '--q-points=4', ] subprocess.run(command, check=True) sample = read_sample_from_npz(outfile) expected = np.linspace(0, 1, 4)[:, None] * np.array([1, 2, 3]) assert np.allclose(sample.q_points, expected) @pytest.mark.parametrize('value', ['1,2', '1,2,3,4', '1,two,3']) def test_parse_q_direction_rejects_invalid_values(value): with pytest.raises(argparse.ArgumentTypeError): _parse_q_direction(value) dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/tests/test_compute_Sqw_with_too_long_window.py000066400000000000000000000017541522712672400314640ustar00rootroot00000000000000import os import numpy as np from dynasor.qpoints import get_spherical_qpoints from dynasor.correlation_functions import compute_dynamic_structure_factors from dynasor.trajectory import Trajectory def test_Sqw_calculation_with_time_window_longer_than_traj(): # traj index files this_dir = os.path.dirname(__file__) traj_fname = os.path.join( this_dir, 'trajectory_reader/trajectory_files/dump_long_with_velocities.xyz') # input parameters time_window = 40 # Trajectory is 30 long dt = 2 q_max = 1.0 # run dynasor traj = Trajectory(traj_fname, trajectory_format='extxyz', atomic_indices='read_from_trajectory') q_points = get_spherical_qpoints(traj.cell, q_max) sample = compute_dynamic_structure_factors(traj, q_points, dt=dt, window_size=time_window, calculate_currents=True, calculate_incoherent=True) time_reference = dt * np.arange(0, 30) assert np.allclose(sample.time, time_reference) dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/tests/test_dho.py000066400000000000000000000073341522712672400234060ustar00rootroot00000000000000import numpy as np import pytest from dynasor.tools.damped_harmonic_oscillator import acf_position_dho, acf_velocity_dho from dynasor.tools.damped_harmonic_oscillator import psd_position_dho, psd_velocity_dho # parameters dt = 0.5 N = 200 w0 = 2.0 gamma = 0.5 A = 1.5 tau = 2 / gamma @pytest.fixture def time(): return np.arange(0, N*dt, dt) @pytest.fixture def omega(): return np.linspace(0.0, 2 * np.pi / dt, N) def test_acf_position_dho(time): # check t=0 value acf = acf_position_dho(t=0.0, w0=w0, gamma=gamma, A=A) assert np.isclose(acf, A) # check that acf decay to zero after acf = acf_position_dho(t=15 * tau, w0=w0, gamma=gamma, A=A) assert abs(acf) < 1e-6 # check with time array for sign in [-1, 1]: acf = acf_position_dho(t=sign * time, w0=w0, gamma=gamma, A=A) assert len(acf) == len(time) acf_target_t10 = np.array([1.5, 0.86356344, -0.33464699, -0.99556173, -0.70034213, 0.09933936, 0.64131426, 0.54166271, 0.02338361, -0.39927632]) assert np.allclose(acf[0:10], acf_target_t10) # check that it works without errors in various limits gamma_values = [1.0, np.sqrt(2.0), 2.0, 4.0, 20.0] for g in gamma_values: acf = acf_position_dho(t=time, w0=w0, gamma=g*w0, A=A) assert len(acf) == len(time) def test_acf_velocity_dho(time): # check t=0 value acf = acf_velocity_dho(t=0.0, w0=w0, gamma=gamma, A=A) assert np.isclose(acf, A * w0**2) # check that acf decay to zero after acf = acf_velocity_dho(t=15 * tau, w0=w0, gamma=gamma, A=A) assert abs(acf) < 1e-5 # check with time array acf = acf_velocity_dho(t=time, w0=w0, gamma=gamma, A=A) assert len(acf) == len(time) # check with time array for sign in [-1, 1]: acf = acf_velocity_dho(t=sign * time, w0=w0, gamma=gamma, A=A) assert len(acf) == len(time) acf_target_t10 = np.array([6.0, 2.33724331, -2.41678174, -4.15304431, -2.12653247, 1.18175974, 2.79683816, 1.77929125, -0.46071975, -1.83042293]) assert np.allclose(acf[0:10], acf_target_t10) # check that it works without errors in various limits gamma_values = [1.0, np.sqrt(2.0), 2.0, 4.0, 20.0] for g in gamma_values: acf = acf_velocity_dho(t=time, w0=w0, gamma=g*w0, A=A) assert len(acf) == len(time) def test_acf_velocity_dho_continuous_at_critical_damping(): # The t=0 value of the velocity ACF equals A * w0**2 in the under-, over-, # and critically damped regimes alike. It must therefore stay continuous as # the damping passes through critical damping (gamma = 2 * w0). A missing # w0**2 factor in the critically damped branch would make the t=0 value jump by a # factor of w0**2 exactly at gamma = 2 * w0. ratios = [1.99, 2.0, 2.01] # gamma / w0; 2.0 is exactly critical damping acfs = [acf_velocity_dho(t=0.0, w0=w0, gamma=r * w0, A=A) for r in ratios] assert max(acfs) - min(acfs) < 1e-2 assert np.allclose(acfs, A * w0**2, atol=1e-2) def test_psd_position_dho(omega): # check omega=0 value psd = psd_position_dho(w=0.0, w0=w0, gamma=gamma, A=A) assert np.isclose(psd, A * 2 * gamma / w0**2) # check with omega array psd = psd_position_dho(w=omega, w0=w0, gamma=gamma, A=A) assert len(psd) == len(omega) psd_target_t10 = np.array([0.375, 0.37572534, 0.37791376, 0.38160288, 0.38685702, 0.39376959, 0.40246679, 0.41311274, 0.42591638, 0.4411408]) assert np.allclose(psd[0:10], psd_target_t10) def test_psd_velocity_dho(omega): psd_r = psd_position_dho(omega, w0, gamma, A) psd_v = psd_velocity_dho(omega, w0, gamma, A) assert np.allclose(omega ** 2 * psd_r, psd_v) dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/tests/test_dynasor_API.py000066400000000000000000000317631522712672400250070ustar00rootroot00000000000000import logging import os import numpy as np import pytest from dynasor.correlation_functions import ( compute_dynamic_structure_factors, compute_static_structure_factors, ) from dynasor.logging_tools import logger as dynasor_logger from dynasor.sample import Sample from dynasor.trajectory import Trajectory @pytest.fixture def dynasor_caplog(caplog): """caplog fixture that works with dynasor's non-propagating logger. Adds caplog's handler directly to the dynasor logger so records are captured regardless of the logger's propagate setting. """ dynasor_logger.addHandler(caplog.handler) yield caplog dynasor_logger.removeHandler(caplog.handler) @pytest.fixture def traj_fname_xyz(): this_dir = os.path.dirname(__file__) traj_fname = os.path.join(this_dir, 'trajectory_reader/trajectory_files/dump_with_velocities.xyz') return traj_fname @pytest.fixture def traj_fname_lammps(): this_dir = os.path.dirname(__file__) traj_fname = os.path.join( this_dir, 'trajectory_reader/trajectory_files/positions.lammpstrj') return traj_fname @pytest.fixture def q_points(): q_points = np.array([i * np.array([1/2, 2/3, 1.235]) for i in np.linspace(0, 10, 11)]) return q_points def test_API_dynamic_structure_factor(traj_fname_xyz, q_points): traj = Trajectory(traj_fname_xyz, 'extxyz') res = compute_dynamic_structure_factors(traj, q_points=q_points, dt=1.0, window_size=3) assert isinstance(res, Sample) def test_API_dynamic_structure_factor_integer_q_points(traj_fname_xyz): traj = Trajectory(traj_fname_xyz, 'extxyz') q_points = np.array([[1, 1, 0]]) res = compute_dynamic_structure_factors(traj, q_points=q_points, dt=1.0, window_size=3) assert isinstance(res, Sample) def test_API_dynamic_structure_factor_selfpart(traj_fname_xyz, q_points): traj = Trajectory(traj_fname_xyz, 'extxyz') res = compute_dynamic_structure_factors(traj, q_points=q_points, dt=1.0, window_size=3, calculate_incoherent=True) assert isinstance(res, Sample) def test_API_static_structure_factor(traj_fname_xyz_long, q_points): # setup n_atoms = 320 n_A = 50 atomic_indices = dict() atomic_indices['A'] = np.arange(0, n_A, 1) atomic_indices['B'] = np.arange(n_A, n_atoms, 1) # calculate static traj = Trajectory( traj_fname_xyz_long, trajectory_format='extxyz', atomic_indices=atomic_indices) res1 = compute_static_structure_factors(traj, q_points=q_points) assert res1.simulation_data['number_of_frames'] == 30 # calculate S(q) via compute_dynamic_structure_factors (use dummy dt and window_size) traj = Trajectory( traj_fname_xyz_long, trajectory_format='extxyz', atomic_indices=atomic_indices) res2 = compute_dynamic_structure_factors(traj, q_points=q_points, dt=1.0, window_size=3) assert res2.simulation_data['number_of_frames'] == 30 # compare results pairs = ['A_A', 'A_B', 'B_B'] for pair in pairs: Sq1 = getattr(res1, f'Sq_{pair}') Sq2 = getattr(res2, f'Fqt_coh_{pair}')[:, 0].reshape(-1, 1) assert np.allclose(Sq1, Sq2) assert np.allclose(res1.Sq, res2.Fqt_coh[:, 0].reshape(-1, 1)) # Comparing the mdanalysis lammps reader to the internal lammps reader def test_API_calling_mdanalysis_reader(traj_fname_lammps, q_points): window = 3 dt = 1.0 traj1 = Trajectory(traj_fname_lammps, 'lammps_internal') traj2 = Trajectory(traj_fname_lammps, 'lammps_mdanalysis', length_unit='Angstrom', time_unit='fs') res1 = compute_dynamic_structure_factors(traj1, q_points=q_points, dt=dt, window_size=window) res2 = compute_dynamic_structure_factors(traj2, q_points=q_points, dt=dt, window_size=window) assert isinstance(res1, Sample) assert isinstance(res2, Sample) # check that results are the same assert res1.available_correlation_functions == res2.available_correlation_functions for key in res1.available_correlation_functions: np.testing.assert_almost_equal(res1[key], res2[key], decimal=5) # test of time-sampling parameters # -------------------------------- def test_API_dynamic_step(traj_fname_xyz_long, q_points): """ Ensure that setting step > 1 affects the resulting time """ dt = 1.0 time_window = 4 step = 1 for step in [1, 2, 3]: traj = Trajectory(traj_fname_xyz_long, 'extxyz', frame_step=step) res = compute_dynamic_structure_factors( traj, q_points=q_points, dt=dt, window_size=time_window) assert isinstance(res, Sample) time = res.time assert np.allclose(np.diff(time), step * dt), (np.diff(time), step * dt) # test ValueErrors from bad input arguments # ----------------------------------------- def test_raises_with_bad_input_args(traj_fname_xyz_long, q_points): time_window = 4 dt = 1.0 # q-points wrong shape traj = Trajectory(traj_fname_xyz_long, 'extxyz') with pytest.raises(ValueError, match=r'q-points array has the wrong shape'): compute_dynamic_structure_factors(traj, q_points=q_points[:, 1:], dt=dt, window_size=time_window) traj = Trajectory(traj_fname_xyz_long, 'extxyz') with pytest.raises(ValueError, match=r'q-points array has the wrong shape'): compute_static_structure_factors(traj, q_points=q_points[:, 1:]) # dt not positive traj = Trajectory(traj_fname_xyz_long, 'extxyz') with pytest.raises(ValueError, match=r'dt must be positive'): compute_dynamic_structure_factors(traj, q_points=q_points, dt=-1.0, window_size=time_window) # bad window_size traj = Trajectory(traj_fname_xyz_long, 'extxyz') with pytest.raises(ValueError, match=r'window_size must be larger than 2'): compute_dynamic_structure_factors(traj, q_points=q_points, dt=dt, window_size=1) # bad window_step traj = Trajectory(traj_fname_xyz_long, 'extxyz') with pytest.raises(ValueError, match=r'window_step must be positive'): compute_dynamic_structure_factors(traj, q_points=q_points, dt=dt, window_size=time_window, window_step=-1) def test_raises_with_dt_zero(traj_fname_xyz_long, q_points): """dt=0 is invalid (division by zero in frequency axis); must raise ValueError.""" traj = Trajectory(traj_fname_xyz_long, 'extxyz') with pytest.raises(ValueError, match=r'dt must be positive'): compute_dynamic_structure_factors(traj, q_points=q_points, dt=0.0, window_size=4) def test_raises_with_currents_but_no_velocities(traj_fname_lammps, q_points): """calculate_currents=True requires velocities; must raise a clear ValueError instead of an opaque TypeError from indexing None.""" traj = Trajectory(traj_fname_lammps, 'lammps_internal') with pytest.raises(ValueError, match=r'[Vv]elocities'): compute_dynamic_structure_factors(traj, q_points=q_points, dt=1.0, window_size=3, calculate_currents=True) def test_empty_q_points_returns_empty_sample(traj_fname_xyz_long): """An empty (0, 3) q_points array produces a sample with zero q-points.""" q_empty = np.zeros((0, 3)) traj = Trajectory(traj_fname_xyz_long, 'extxyz') res_static = compute_static_structure_factors(traj, q_points=q_empty) assert res_static.Sq.shape[0] == 0 traj = Trajectory(traj_fname_xyz_long, 'extxyz') res_dynamic = compute_dynamic_structure_factors( traj, q_points=q_empty, dt=1.0, window_size=4) assert res_dynamic.Fqt_coh.shape[0] == 0 def test_window_size_exceeding_n_frames_completes_without_nan(traj_fname_xyz_long, q_points): """window_size >= n_frames is handled gracefully: NaNs are truncated internally. The long trajectory has 30 frames. When window_size equals or exceeds n_frames the code emits a warning and truncates NaN time lags before returning, so the result contains no NaN values. """ n_frames = 30 # known frame count of the long trajectory # Largest valid even window_size: produces a result with no NaN traj = Trajectory(traj_fname_xyz_long, 'extxyz') res_valid = compute_dynamic_structure_factors( traj, q_points=q_points, dt=1.0, window_size=n_frames - 2) assert not np.any(np.isnan(res_valid.Fqt_coh)) # window_size equal to n_frames: code truncates NaNs and returns a valid result traj = Trajectory(traj_fname_xyz_long, 'extxyz') res_exceed = compute_dynamic_structure_factors( traj, q_points=q_points, dt=1.0, window_size=n_frames) assert not np.any(np.isnan(res_exceed.Fqt_coh)) # --- logging_interval tests --- def test_dynamic_logging_interval_default_emits_info_at_frame_0( traj_fname_xyz_long, q_points, dynasor_caplog): """With default logging_interval=1000, frame 0 is logged at INFO.""" traj = Trajectory(traj_fname_xyz_long, 'extxyz') with dynasor_caplog.at_level(logging.INFO, logger='dynasor'): compute_dynamic_structure_factors(traj, q_points=q_points, dt=1.0, window_size=4) info_msgs = [r.message for r in dynasor_caplog.records if r.levelno == logging.INFO] assert any('Processing window 0' in m for m in info_msgs) def test_dynamic_logging_interval_1_emits_info_for_every_window( traj_fname_xyz_long, q_points, dynasor_caplog): """With logging_interval=1, every window start is logged at INFO.""" traj = Trajectory(traj_fname_xyz_long, 'extxyz') with dynasor_caplog.at_level(logging.DEBUG, logger='dynasor'): compute_dynamic_structure_factors( traj, q_points=q_points, dt=1.0, window_size=4, logging_interval=1) info_window_msgs = [ r.message for r in dynasor_caplog.records if r.levelno == logging.INFO and 'Processing window' in r.message ] # With 30 frames and window_size=4 (N_tc=5), window_step=1, # we get windows starting at frames 0..25 — all should be logged at INFO. assert len(info_window_msgs) > 1 def test_dynamic_logging_interval_0_emits_no_info_progress( traj_fname_xyz_long, q_points, dynasor_caplog): """With logging_interval=0, no 'Processing window' lines appear at INFO.""" traj = Trajectory(traj_fname_xyz_long, 'extxyz') with dynasor_caplog.at_level(logging.INFO, logger='dynasor'): compute_dynamic_structure_factors( traj, q_points=q_points, dt=1.0, window_size=4, logging_interval=0) info_window_msgs = [ r.message for r in dynasor_caplog.records if r.levelno == logging.INFO and 'Processing window' in r.message ] assert len(info_window_msgs) == 0 def test_dynamic_logging_no_duplicate_at_info_level( traj_fname_xyz_long, q_points, dynasor_caplog): """At INFO level, windows not on the interval are NOT logged (no duplicates).""" traj = Trajectory(traj_fname_xyz_long, 'extxyz') with dynasor_caplog.at_level(logging.INFO, logger='dynasor'): compute_dynamic_structure_factors( traj, q_points=q_points, dt=1.0, window_size=4, logging_interval=1000) # At INFO, debug records are suppressed; only interval-matched windows appear. window_msgs = [ r for r in dynasor_caplog.records if 'Processing window' in r.message ] # All captured window messages must be at INFO (no DEBUG leaking through). assert all(r.levelno == logging.INFO for r in window_msgs) def test_static_logging_interval_1_emits_info_for_every_frame( traj_fname_xyz_long, q_points, dynasor_caplog): """With logging_interval=1, every frame is logged at INFO for static SF.""" traj = Trajectory(traj_fname_xyz_long, 'extxyz') with dynasor_caplog.at_level(logging.DEBUG, logger='dynasor'): compute_static_structure_factors(traj, q_points=q_points, logging_interval=1) info_frame_msgs = [ r.message for r in dynasor_caplog.records if r.levelno == logging.INFO and 'Processing frame' in r.message ] # Trajectory has 30 frames; all should be logged at INFO. assert len(info_frame_msgs) == 30 def test_static_logging_interval_0_emits_no_info_progress( traj_fname_xyz_long, q_points, dynasor_caplog): """With logging_interval=0, no 'Processing frame' lines appear at INFO.""" traj = Trajectory(traj_fname_xyz_long, 'extxyz') with dynasor_caplog.at_level(logging.INFO, logger='dynasor'): compute_static_structure_factors(traj, q_points=q_points, logging_interval=0) info_frame_msgs = [ r.message for r in dynasor_caplog.records if r.levelno == logging.INFO and 'Processing frame' in r.message ] assert len(info_frame_msgs) == 0 def test_static_logging_interval_default_emits_info_at_frame_0( traj_fname_xyz_long, q_points, dynasor_caplog): """With default logging_interval=1000, frame 0 is logged at INFO.""" traj = Trajectory(traj_fname_xyz_long, 'extxyz') with dynasor_caplog.at_level(logging.INFO, logger='dynasor'): compute_static_structure_factors(traj, q_points=q_points) info_msgs = [r.message for r in dynasor_caplog.records if r.levelno == logging.INFO] assert any('Processing frame 0' in m for m in info_msgs) dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/tests/test_integration.py000066400000000000000000000140001522712672400251430ustar00rootroot00000000000000import os import numpy as np import pytest from dynasor.trajectory import Trajectory from dynasor.qpoints import get_spherical_qpoints from dynasor.correlation_functions import compute_static_structure_factors from dynasor.correlation_functions import compute_dynamic_structure_factors from dynasor.post_processing import get_spherically_averaged_sample_binned from dynasor.post_processing import Weights, get_weighted_sample @pytest.fixture def traj_fname(): this_dir = os.path.dirname(__file__) traj_fname = os.path.join( this_dir, 'trajectory_reader/trajectory_files/dump_long_with_velocities.xyz') return traj_fname def test_integration_static_structure_factor(traj_fname): """ Test the full dynasor workflow for static structure factor """ # parameters q_max = 1.9 max_points = 600 q_bins = 17 # run dynasor traj = Trajectory(traj_fname, trajectory_format='extxyz', atomic_indices='read_from_trajectory') q_points = get_spherical_qpoints(traj.cell, q_max=q_max, max_points=max_points) sample = compute_static_structure_factors(traj, q_points) # check that sample contains metadata assert sample.simulation_data['number_of_frames'] == 30 # post process sample_averaged = get_spherically_averaged_sample_binned(sample, num_q_bins=q_bins) assert sample_averaged.pairs is not None weights_dict = dict(Cs=2.05, Pb=0.55, Br=0.33) weights = Weights(weights_dict) sample_weighted = get_weighted_sample(sample_averaged, weights) # check outputs assert sample_weighted.dimensions == ['q_norms'] assert sample_weighted.q_norms.shape == (17, ) expected_names = ['Sq', 'Sq_Cs_Cs', 'Sq_Cs_Pb', 'Sq_Br_Cs', 'Sq_Pb_Pb', 'Sq_Br_Pb', 'Sq_Br_Br'] assert sorted(sample_weighted.available_correlation_functions) == sorted(expected_names) for name in expected_names: assert sample_weighted[name].shape == (q_bins, 1) assert sample_weighted.atom_types == ['Br', 'Cs', 'Pb'] assert sample_weighted.pairs == [('Br', 'Br'), ('Br', 'Cs'), ('Br', 'Pb'), ('Cs', 'Cs'), ('Cs', 'Pb'), ('Pb', 'Pb')] assert np.allclose(sample_weighted.cell, 23.77195271 * np.eye(3)) assert sample_weighted.particle_counts == dict(Br=192, Cs=64, Pb=64) # regression test vs old results Sq_Br_Cs_target = [[5.19552000e+01], [np.nan], [6.84210294e-04], [9.95972202e-04], [-3.94002366e-04], [6.41177648e-04], [-3.16270130e-03], [-6.22960908e-03], [-1.33103518e-04], [-1.19255936e+00], [-1.50112979e-02], [-3.08410954e-02], [-1.21697876e-02], [-1.00354901e+00], [1.95727845e-02], [4.71059534e-03], [3.59197778e+00]] result = list(sample_weighted.Sq_Br_Cs) assert np.allclose(result, Sq_Br_Cs_target, equal_nan=True) def test_integration_dynamic_structure_factor(traj_fname): """ Test the full dynasor workflow for dynamic structure factor """ # parameters q_max = 2.5 max_points = 35 q_bins = 6 window_size = 4 dt = 2.1 # run dynasor traj = Trajectory(traj_fname, trajectory_format='extxyz', atomic_indices='read_from_trajectory') q_points = get_spherical_qpoints(traj.cell, q_max=q_max, max_points=max_points) sample = compute_dynamic_structure_factors(traj, q_points, dt=dt, window_size=window_size, calculate_incoherent=True, calculate_currents=True) # check that sample contains metadata assert sample.simulation_data['number_of_frames'] == 30 assert sample.simulation_data['time_between_frames'] == dt assert sample.simulation_data['maximum_time_lag'] == window_size * dt assert sample.simulation_data['angular_frequency_resolution'] \ == 2 * np.pi / (window_size * 2 * dt) assert sample.simulation_data['maximum_angular_frequency'] == 2 * np.pi / (2 * dt) assert np.allclose(sample.time, dt * np.linspace(0, window_size, window_size + 1)) # post process sample_averaged = get_spherically_averaged_sample_binned(sample, num_q_bins=q_bins) assert sample_averaged.pairs is not None weights_coh = dict(Cs=2.05, Pb=0.55, Br=0.33) weights_incoh = dict(Cs=2.05, Pb=0.55, Br=0.33) weights = Weights(weights_coh, weights_incoh) sample_weighted = get_weighted_sample(sample_averaged, weights) # check outputs assert sample_weighted.dimensions == ['omega', 'q_norms', 'time'] assert sample_weighted.omega.shape == (window_size + 1, ) assert sample_weighted.time.shape == (window_size + 1, ) assert sample_weighted.q_norms.shape == (q_bins, ) expected_atom_types = ['Br', 'Cs', 'Pb'] expected_pairs = [('Br', 'Br'), ('Br', 'Cs'), ('Br', 'Pb'), ('Cs', 'Cs'), ('Cs', 'Pb'), ('Pb', 'Pb')] expected_correlations = [] for name in ['Fqt_coh', 'Sqw_coh', 'Clqt', 'Clqw', 'Ctqt', 'Ctqw']: expected_correlations.append(name) for s1, s2 in expected_pairs: expected_correlations.append(f'{name}_{s1}_{s2}') for name in ['Fqt_incoh', 'Sqw_incoh']: expected_correlations.append(name) for atom_type in expected_atom_types: expected_correlations.append(f'{name}_{atom_type}') assert sorted(sample_weighted.available_correlation_functions) == sorted(expected_correlations) for name in expected_correlations: if 'qw' in name: assert sample_weighted[name].shape == (q_bins, window_size + 1) else: assert sample_weighted[name].shape == (q_bins, window_size + 1) assert sample_weighted.atom_types == expected_atom_types assert sample_weighted.pairs == expected_pairs assert np.allclose(sample_weighted.cell, 23.77195271 * np.eye(3)) assert sample_weighted.particle_counts == dict(Br=192, Cs=64, Pb=64) dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/tests/test_lattice.py000066400000000000000000000037471522712672400242650ustar00rootroot00000000000000import numpy as np from dynasor.qpoints.lattice import Lattice import pytest def test_lattice(): tests = 0 while tests < 100: prim = np.random.uniform(-5, 5, size=(3, 3)) P = np.random.randint(-5, 5, size=(3, 3)) if np.abs(np.linalg.det(P)) < 1: continue supercell = P @ prim lat = Lattice(prim, supercell) assert np.allclose(lat.P, P) qpoints = np.random.normal(size=(100, 3)) red = lat.cartesian_to_reduced(qpoints) cart = lat.reduced_to_cartesian(red) assert np.allclose(cart, qpoints) paths = 0 while paths < 20: start = np.random.randint(-5, 5, size=3) @ lat.reciprocal_supercell stop = np.random.randint(-5, 5, size=3) @ lat.reciprocal_supercell start_red = lat.cartesian_to_reduced(start) stop_red = lat.cartesian_to_reduced(stop) points, dists = lat.make_path(start_red, stop_red) assert np.allclose(points[0], start) assert np.allclose(points[-1], stop) if len(points) < 3: continue for p in points[1:-1]: n = np.linalg.solve(lat.reciprocal_supercell.T, p) assert np.allclose(n, n.round(0)) paths += 1 tests += 1 def test_make_path(): q1, q2 = [0.5, 0.0, 0.0], [0.5, 0.5, 0.0] prim = np.array([[4, 0, 0], [0, 4, 0], [0, 0, 4]]) repeat = 4 # works supercell = prim * repeat lat = Lattice(prim, supercell) qpts, dists = lat.make_path(q1, q2) assert np.allclose( (qpts/(2*np.pi)).flat, [1/8, 0, 0, 1/8, 1/16, 0, 1/8, 1/8, 0] ) assert np.allclose(dists, [0, 1/2, 1]) repeat = 5 # fails supercell = prim * repeat lat = Lattice(prim, supercell) with pytest.warns(UserWarning, match='No q-points along path!'): qpts, dists = lat.make_path(q1, q2) assert qpts.shape == (0, 3) assert dists.shape == (0,) dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/tests/test_logging.py000066400000000000000000000024121522712672400242520ustar00rootroot00000000000000import os import pytest from io import StringIO import logging from dynasor.logging_tools import logger, set_logging_level from dynasor.trajectory import Trajectory @pytest.fixture def traj_fname_xyz(): this_dir = os.path.dirname(__file__) traj_fname = os.path.join(this_dir, 'trajectory_reader/trajectory_files/dump_with_velocities.xyz') return traj_fname def test_set_logging_level(traj_fname_xyz): # Log Trajectory output to StringIO stream for handler in logger.handlers: logger.removeHandler(handler) stream = StringIO() stream_handler = logging.StreamHandler(stream) logger.addHandler(stream_handler) Trajectory(traj_fname_xyz, 'extxyz') lines1 = stream.getvalue().split('\n')[:-1] # remove last blank line assert 'Trajectory file:' in lines1[2] assert 'Total number of particles: 1080' in lines1[3] # rerun with lower vebosity set_logging_level('ERROR') for handler in logger.handlers: logger.removeHandler(handler) stream = StringIO() stream_handler = logging.StreamHandler(stream) logger.addHandler(stream_handler) Trajectory(traj_fname_xyz, 'extxyz') lines2 = stream.getvalue().split('\n')[:-1] # remove last blank line assert lines2 == [] dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/tests/test_normalization.py000066400000000000000000000101471522712672400255160ustar00rootroot00000000000000import os import numpy as np from dynasor.qpoints import get_spherical_qpoints from dynasor.correlation_functions import compute_dynamic_structure_factors from dynasor.correlation_functions import compute_static_structure_factors from dynasor.trajectory import Trajectory def test_normalization_dynamic_structure_factor(): # traj index files this_dir = os.path.dirname(__file__) traj_fname = os.path.join( this_dir, 'trajectory_reader/trajectory_files/dump_long_with_velocities.xyz') index_fname = os.path.join(this_dir, 'trajectory_reader/trajectory_files/index_file_dump_long') # input parameters time_window = 4 dt = 100 q_max = 40 max_points = 500 # setup traj_format = 'extxyz' traj = Trajectory(traj_fname, trajectory_format=traj_format) q_points = get_spherical_qpoints(traj.cell, q_max=q_max, max_points=max_points) # run only total sample1 = compute_dynamic_structure_factors(traj, q_points, dt=dt, window_size=time_window, calculate_currents=True, calculate_incoherent=True) record = sample1.history[0] assert record['func'] == 'compute_dynamic_structure_factors' assert record['dt'] == 100 assert record['window_size'] == 4 assert record['window_step'] == 1 assert 'date_time' in record # run with partial traj = Trajectory(traj_fname, trajectory_format=traj_format, atomic_indices=index_fname) sample2 = compute_dynamic_structure_factors(traj, q_points, dt=dt, window_size=time_window, calculate_currents=True, calculate_incoherent=True) # test Fqt F_tot1 = sample1.Fqt_coh_X_X F_tot2 = sample2.Fqt_coh assert np.allclose(F_tot1, F_tot2) pairs = ['Cs_Cs', 'Br_Br', 'Pb_Pb', 'Cs_Pb', 'Br_Cs', 'Br_Pb'] F_tot_from_partials = np.zeros_like(F_tot1) for pair in pairs: F_tot_from_partials += getattr(sample2, f'Fqt_coh_{pair}') assert np.allclose(F_tot1, F_tot_from_partials) # test CL Cl_tot = sample1.Clqt_X_X Cl_tot_from_partials = np.zeros_like(Cl_tot) for pair in pairs: Cl_tot_from_partials += getattr(sample2, f'Clqt_{pair}') assert np.allclose(Cl_tot, Cl_tot_from_partials), np.max(np.abs(Cl_tot - Cl_tot_from_partials)) # test Ct Ct_tot = sample1.Ctqt_X_X Ct_tot_from_partials = np.zeros_like(Ct_tot) for pair in pairs: Ct_tot_from_partials += getattr(sample2, f'Ctqt_{pair}') assert np.allclose(Ct_tot, Ct_tot_from_partials) # test self-parts Fs_tot1 = sample1.Fqt_incoh_X Fs_tot2 = sample2.Fqt_incoh assert np.allclose(Fs_tot1, Fs_tot2) Fs_tot_from_partials = np.zeros_like(Fs_tot1) atom_types = ['Cs', 'Br', 'Pb'] for atom_type in atom_types: Fs_tot_from_partials += getattr(sample2, f'Fqt_incoh_{atom_type}') assert np.allclose(Fs_tot1, Fs_tot_from_partials) def test_normalization_static_structure_factor(): # traj index files this_dir = os.path.dirname(__file__) traj_fname = os.path.join( this_dir, 'trajectory_reader/trajectory_files/dump_long_with_velocities.xyz') index_fname = os.path.join(this_dir, 'trajectory_reader/trajectory_files/index_file_dump_long') # input parameters q_max = 40 max_points = 500 # setup traj_format = 'extxyz' traj = Trajectory(traj_fname, trajectory_format=traj_format) q_points = get_spherical_qpoints(traj.cell, q_max=q_max, max_points=max_points) # run only total sample1 = compute_static_structure_factors(traj, q_points) record = sample1.history[0] assert record['func'] == 'compute_static_structure_factors' assert 'date_time' in record # run with partial traj = Trajectory(traj_fname, trajectory_format=traj_format, atomic_indices=index_fname) sample2 = compute_static_structure_factors(traj, q_points) # test S(q) F_tot = sample1.Sq_X_X pairs = ['Cs_Cs', 'Br_Br', 'Pb_Pb', 'Cs_Pb', 'Br_Cs', 'Br_Pb'] F_tot_from_partials = np.zeros_like(F_tot) for pair in pairs: F_tot_from_partials += getattr(sample2, f'Sq_{pair}') assert np.allclose(F_tot, F_tot_from_partials) dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/tests/test_numba.py000066400000000000000000000207701522712672400237350ustar00rootroot00000000000000import pytest import numpy as np from dynasor.core.reciprocal import calc_rho_q, calc_rho_j_q from dynasor.core.rho_j_q_numba import rho_q_single # Setup test arrays and inputs @pytest.fixture def xvq(): Nx = 4 Nq = 5 np.random.seed(42) x = np.random.normal(size=(Nx, 3)) v = np.random.normal(size=(Nx, 3)) q = np.random.normal(size=(Nq, 3)) return x, v, q def test_rho_j_q(xvq): """Regression test: rho from calc_rho_j_q matches calc_rho_q and known values.""" x, v, q = xvq rho_q, rho_j_q = calc_rho_j_q(x, v, q) rho_q2 = calc_rho_q(x, q) assert np.allclose(rho_q, rho_q2) Nx = x.shape[0] # Expected values computed from a reference run with numpy.random.seed(42) rho_q_target = Nx**0.5 * np.array([1.6509947-0.7272682j, 1.74291595+0.81511274j, 1.16879217-0.98689153j, 1.00661299-1.40743052j, 1.06588947+0.44672131j]) rho_j_q_target = Nx**0.5 * np.array( [[-0.72463296+0.13571632j, -1.56724203+1.27085398j, -0.29899287+0.34897803j], [-0.60871115-0.37439687j, -1.96631866-0.60202257j, -0.62226018-0.17672867j], [-0.40649785+0.10510835j, -1.53524612+0.6491588j, -0.31563418+1.30276366j], [-0.50810342+0.40748936j, -0.76088817+1.75737831j, -0.11795971+0.63627665j], [-0.4026586-0.18057006j, -1.68535262+0.40104027j, -0.01598231-0.55236761j]]) assert np.allclose(rho_q, rho_q_target) assert np.allclose(rho_j_q, rho_j_q_target) # Analytic tests for calc_rho_q # rho(q) = sum_i exp(i * q . x_i) def test_calc_rho_q_at_q_zero_equals_number_of_particles(): """rho(q=0) = N for any positions, since exp(0) = 1 for each particle.""" N = 7 x = np.random.default_rng(0).normal(size=(N, 3)) q = np.zeros((1, 3)) rho = calc_rho_q(x, q) assert np.allclose(rho, [N]) def test_calc_rho_q_single_particle_at_origin(): """Single particle at origin: exp(i * q . 0) = 1 for all q.""" x = np.zeros((1, 3)) q = np.array([[1.0, 2.0, 3.0], [0.5, -1.0, 0.0], [0.0, 0.0, 1.0]]) rho = calc_rho_q(x, q) assert np.allclose(rho, np.ones(3, dtype=complex)) def test_calc_rho_q_single_particle_at_known_position(): """Single particle at x: rho(q) = exp(i * q . x).""" x = np.array([[1.0, 2.0, 3.0]]) q = np.array([[0.5, 0.0, 0.0], [0.0, 1.0, 0.0], [1.0, 1.0, 1.0]]) rho = calc_rho_q(x, q) expected = np.array([np.exp(1j * 0.5), np.exp(1j * 2.0), np.exp(1j * 6.0)]) assert np.allclose(rho, expected) def test_calc_rho_q_two_particles_symmetric_positions(): """Two particles at +x and -x: rho(q) = 2*cos(q.x), purely real.""" x0 = np.array([1.5, 0.0, 0.0]) x = np.array([x0, -x0]) q = np.array([[1.0, 0.0, 0.0], [2.0, 0.0, 0.0], [0.0, 1.0, 0.0]]) # last q is perpendicular → cos(0)*2 = 2 rho = calc_rho_q(x, q) expected = np.array([2 * np.cos(1.0 * 1.5), 2 * np.cos(2.0 * 1.5), 2 * np.cos(0.0)]) assert np.allclose(rho.imag, 0.0) assert np.allclose(rho.real, expected) def test_calc_rho_q_conjugate_symmetry(): """rho(-q) = conj(rho(q)) for real particle positions.""" rng = np.random.default_rng(1) x = rng.normal(size=(6, 3)) q = rng.normal(size=(5, 3)) rho_plus = calc_rho_q(x, q) rho_minus = calc_rho_q(x, -q) assert np.allclose(rho_minus, np.conj(rho_plus)) def test_calc_rho_q_superposition(): """rho of combined particle set equals sum of individual rho values.""" rng = np.random.default_rng(2) x1 = rng.normal(size=(3, 3)) x2 = rng.normal(size=(5, 3)) x_combined = np.vstack([x1, x2]) q = rng.normal(size=(4, 3)) rho1 = calc_rho_q(x1, q) rho2 = calc_rho_q(x2, q) rho_combined = calc_rho_q(x_combined, q) assert np.allclose(rho_combined, rho1 + rho2) def test_calc_rho_q_magnitude_single_particle(): """|rho(q)| = 1 for a single particle at any position and any q.""" rng = np.random.default_rng(3) x = rng.normal(size=(1, 3)) q = rng.normal(size=(10, 3)) rho = calc_rho_q(x, q) assert np.allclose(np.abs(rho), 1.0) def test_calc_rho_q_all_particles_at_same_position(): """N particles at the same position: rho(q) = N * exp(i * q . x0).""" N = 5 x0 = np.array([1.0, -2.0, 0.5]) x = np.tile(x0, (N, 1)) q = np.array([[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [1.0, 1.0, 1.0]]) rho = calc_rho_q(x, q) expected = N * np.array([np.exp(1j * x0[0]), np.exp(1j * (-2.0)), np.exp(1j * (1.0 - 2.0 + 0.5))]) assert np.allclose(rho, expected) def test_rho_q_single_function(): """rho_q_single returns the same result as calc_rho_q for one q-point.""" rng = np.random.default_rng(7) x = rng.normal(size=(5, 3)) q_vec = rng.normal(size=(3,)) result_single = rho_q_single(x, q_vec) result_batch = calc_rho_q(x, q_vec[np.newaxis, :])[0] assert np.allclose(result_single, result_batch) def test_rho_q_noncontiguous_input(): """calc_rho_q and calc_rho_j_q must handle non-contiguous arrays correctly.""" rng = np.random.default_rng(8) x_big = rng.normal(size=(8, 3)) v_big = rng.normal(size=(8, 3)) q_big = rng.normal(size=(6, 3)) x_nc = x_big[::2] # non-contiguous v_nc = v_big[::2] q_nc = q_big[::2] assert not x_nc.flags['C_CONTIGUOUS'] rho_nc = calc_rho_q(x_nc, q_nc) rho_c = calc_rho_q(np.ascontiguousarray(x_nc), np.ascontiguousarray(q_nc)) assert np.allclose(rho_nc, rho_c) rho_nc2, j_nc = calc_rho_j_q(x_nc, v_nc, q_nc) rho_c2, j_c = calc_rho_j_q( np.ascontiguousarray(x_nc), np.ascontiguousarray(v_nc), np.ascontiguousarray(q_nc), ) assert np.allclose(rho_nc2, rho_c2) assert np.allclose(j_nc, j_c) # Analytic tests for calc_rho_j_q # rho(q) = sum_i exp(i * q . x_i) # j(q) = sum_i v_i * exp(i * q . x_i) def test_calc_rho_j_q_rho_matches_calc_rho_q(): """The rho returned by calc_rho_j_q must equal calc_rho_q.""" rng = np.random.default_rng(4) x = rng.normal(size=(8, 3)) v = rng.normal(size=(8, 3)) q = rng.normal(size=(6, 3)) rho_from_rho_j_q, _ = calc_rho_j_q(x, v, q) rho_from_rho_q = calc_rho_q(x, q) assert np.allclose(rho_from_rho_j_q, rho_from_rho_q) def test_calc_rho_j_q_current_at_q_zero_equals_total_momentum(): """j(q=0) = sum_i v_i, since exp(0) = 1 for each particle.""" rng = np.random.default_rng(5) x = rng.normal(size=(10, 3)) v = rng.normal(size=(10, 3)) q = np.zeros((1, 3)) _, j_q = calc_rho_j_q(x, v, q) assert np.allclose(j_q[0], v.sum(axis=0)) def test_calc_rho_j_q_zero_velocities_gives_zero_current(): """All v=0 → j(q) = 0 for all q.""" rng = np.random.default_rng(6) x = rng.normal(size=(5, 3)) v = np.zeros((5, 3)) q = rng.normal(size=(4, 3)) _, j_q = calc_rho_j_q(x, v, q) assert np.allclose(j_q, 0.0) def test_calc_rho_j_q_single_particle_at_origin(): """Single particle at origin: j(q) = v for all q, since exp(0)=1.""" v0 = np.array([[1.0, -2.0, 3.0]]) x = np.zeros((1, 3)) q = np.array([[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [1.5, -0.5, 2.0]]) _, j_q = calc_rho_j_q(x, v0, q) expected = np.tile(v0, (3, 1)).astype(complex) assert np.allclose(j_q, expected) def test_calc_rho_j_q_single_particle_at_known_position(): """Single particle at x with velocity v: j(q) = v * exp(i * q . x).""" x0 = np.array([[2.0, 0.0, 0.0]]) v0 = np.array([[0.0, 1.0, 0.0]]) q = np.array([[1.0, 0.0, 0.0], [0.5, 0.0, 0.0]]) _, j_q = calc_rho_j_q(x0, v0, q) expected = np.array([v0[0] * np.exp(1j * 2.0), v0[0] * np.exp(1j * 1.0)]) assert np.allclose(j_q, expected) def test_calc_rho_j_q_current_superposition(): """j of combined set equals sum of individual j values.""" rng = np.random.default_rng(7) x1 = rng.normal(size=(3, 3)) v1 = rng.normal(size=(3, 3)) x2 = rng.normal(size=(4, 3)) v2 = rng.normal(size=(4, 3)) x_combined = np.vstack([x1, x2]) v_combined = np.vstack([v1, v2]) q = rng.normal(size=(5, 3)) _, j1 = calc_rho_j_q(x1, v1, q) _, j2 = calc_rho_j_q(x2, v2, q) _, j_combined = calc_rho_j_q(x_combined, v_combined, q) assert np.allclose(j_combined, j1 + j2) dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/tests/test_parse_gromacs_index_file.py000066400000000000000000000026671522712672400276530ustar00rootroot00000000000000import numpy as np import os import pytest import tempfile from dynasor.trajectory.atomic_indices import parse_gromacs_index_file @pytest.fixture def index_fname(): this_dir = os.path.dirname(__file__) index_fname = os.path.join(this_dir, 'trajectory_reader/trajectory_files/index_file_dump_long') return index_fname def test_parse_gromacs_index_file(index_fname): atomic_indices = parse_gromacs_index_file(index_fname) Br_inds = np.array(sorted(list(range(2, 320, 5)) + list(range(3, 320, 5)) + list(range(4, 320, 5)))) # noqa assert sorted(atomic_indices) == ['Br', 'Cs', 'Pb'] assert np.allclose(atomic_indices['Cs'], np.arange(0, 320, 5)) assert np.allclose(atomic_indices['Pb'], np.arange(1, 320, 5)) assert np.allclose(atomic_indices['Br'], Br_inds) def test_raise_file_does_not_exists(): fname = 'asd' with pytest.raises(ValueError): parse_gromacs_index_file(fname) def test_duplicate_groups_in_indexfile(): # write dummy file with duplicate Cs group s = b"""[ Cs ] 1 6 11 16 [ Pb ] 2 7 12 17 [ Br ] 3 4 5 [ Cs ] 101 106 111 116 """ f = tempfile.NamedTemporaryFile(delete=False) f.write(s) f.seek(0) atomic_indices = parse_gromacs_index_file(f.name) assert atomic_indices['Cs'].tolist() == [100, 105, 110, 115] assert atomic_indices['Pb'].tolist() == [1, 6, 11, 16] assert atomic_indices['Br'].tolist() == [2, 3, 4] dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/tests/test_qpoints.py000066400000000000000000000223241522712672400243250ustar00rootroot00000000000000import numpy as np import pytest from dynasor.qpoints import get_spherical_qpoints, get_supercell_qpoints_along_path from ase.build import bulk def test_get_spherical_qpoints(): cell = np.diag([2.2, 3.76, 4.01]) q_max = 20 # without pruning q_points = get_spherical_qpoints(cell, q_max) assert q_points.shape[0] == 4465 assert q_points.shape[1] == 3 assert np.max(np.linalg.norm(q_points, axis=1)) <= q_max # with pruning max_points = 250 q_points = get_spherical_qpoints(cell, q_max, max_points=max_points) assert q_points.shape[0] < max_points + 100 assert q_points.shape[1] == 3 assert np.max(np.linalg.norm(q_points, axis=1)) <= q_max def test_get_spherical_qpoints_with_seed(): cell = np.diag([2.2, 3.76, 4.01]) q_max = 30 max_points = 1500 q_points1 = get_spherical_qpoints(cell, q_max, max_points=max_points, seed=42) q_points2 = get_spherical_qpoints(cell, q_max, max_points=max_points, seed=43) q_points3 = get_spherical_qpoints(cell, q_max, max_points=max_points, seed=42) assert np.allclose(q_points1, q_points3) assert q_points1.shape != q_points2.shape def test_get_spherical_qpoints_left_handed_cell(): right_handed_cell = np.diag([5.0, 5.0, 5.0]) left_handed_cell = np.diag([-5.0, 5.0, 5.0]) kwargs = dict(q_max=5.0, max_points=10, seed=42) right_handed_qpoints = get_spherical_qpoints(right_handed_cell, **kwargs) left_handed_qpoints = get_spherical_qpoints(left_handed_cell, **kwargs) assert len(left_handed_qpoints) == len(right_handed_qpoints) def test_get_spherical_qpoints_values(): cell = np.diag([2.2, 3.76, 4.01]) q_max = 10 max_points = 20 target = [[-5.71198664, 0, -3.13375826], [-2.85599332, -6.68423969, 4.70063739], [-2.85599332, -3.34211984, -1.56687913], [-2.85599332, 0, -4.70063739], [-2.85599332, 0, 0], [-2.85599332, 5.01317977, -3.13375826], [-2.85599332, 5.01317977, 1.56687913], [0, -6.68423969, -1.56687913], [0, -5.01317977, -6.26751652], [0, 0, -1.56687913], [0, 0, 0], [0, 0, 6.26751652], [0, 1.67105992, 0], [0, 1.67105992, 1.56687913], [0, 3.34211984, -1.56687913], [0, 3.34211984, 0], [0, 6.68423969, -3.13375826], [2.85599332, -3.34211984, 1.56687913], [2.85599332, 0, -1.56687913], [2.85599332, 0, 9.40127477], [2.85599332, 8.35529961, 3.13375826], [5.71198664, -5.01317977, 3.13375826], [8.56797996, 1.67105992, 0]] actual = get_spherical_qpoints(cell, q_max, max_points=max_points, seed=42) assert np.allclose(actual, target) def test_get_spherical_qpoints_invalid_q_min_greater_than_q_max(): cell = np.diag([5.0, 5.0, 5.0]) with pytest.raises(ValueError, match='q_min must be smaller than q_max'): get_spherical_qpoints(cell, q_max=5.0, q_min=10.0) def test_get_spherical_qpoints_invalid_q_min_equal_to_q_max(): cell = np.diag([5.0, 5.0, 5.0]) with pytest.raises(ValueError, match='q_min must be smaller than q_max'): get_spherical_qpoints(cell, q_max=5.0, q_min=5.0) def test_get_spherical_qpoints_invalid_negative_q_min(): cell = np.diag([5.0, 5.0, 5.0]) with pytest.raises(ValueError, match='q_min cannot be negative'): get_spherical_qpoints(cell, q_max=5.0, q_min=-1.0) def test_get_spherical_qpoints_empty_result(): """q_min > 0 with q_max below the smallest non-zero reciprocal vector returns empty array.""" # Large cell: smallest non-zero |q| = 2π/100 ≈ 0.063 rad/Å cell = np.diag([100.0, 100.0, 100.0]) q_points = get_spherical_qpoints(cell, q_max=0.05, q_min=0.01) assert q_points.shape == (0, 3) def test_get_spherical_qpoints_max_points_no_valid_prune_distance(): """When the cubic has no real solution, pruning is skipped and all points are returned.""" # This cell/q_max combination causes D_t < 0 in _get_prune_distance, # meaning no valid q_prune exists; the function should return all points without error. cell = np.diag([4, 3, 18]) q_max = 3 q_points_unpruned = get_spherical_qpoints(cell, q_max) q_points_pruned = get_spherical_qpoints(cell, q_max, max_points=100) # Pruning is silently skipped, so all points are returned assert len(q_points_pruned) == len(q_points_unpruned) assert np.all(np.linalg.norm(q_points_pruned, axis=1) <= q_max) def test_get_spherical_qpoints_chunk_with_no_matches(): """chunk_size=1 with q_min > 0 forces chunks containing only the excluded origin.""" cell = np.diag([5.0, 5.0, 5.0]) q_points = get_spherical_qpoints(cell, q_max=2.0, q_min=0.5, chunk_size=1) norms = np.linalg.norm(q_points, axis=1) assert np.all(norms >= 0.5) assert np.all(norms <= 2.0) def test_get_supercell_qpts_along_path_cubic(): prim = bulk('Al', 'fcc', a=4.05) supercell = bulk('Al', 'fcc', a=4.05, cubic=True).repeat(4) coordinates = dict( X=[0.5, 0.5, 0], G=[0, 0, 0], L=[0.5, 0.5, 0.5], W=[0.5, 0.25, 0.75], ) # connected path path = [('X', 'G'), ('G', 'L')] qpoints = get_supercell_qpoints_along_path(path, coordinates, prim.cell, supercell.cell) assert len(qpoints) == len(path) qpoints_target = [ np.array([[0, 0, 1.55140378], [0, 0, 1.16355283], [0, 0, 0.77570189], [0, 0, 0.38785094], [0, 0, 0]]), np.array([[0, 0, 0], [0.38785094, 0.38785094, 0.38785094], [0.77570189, 0.77570189, 0.77570189]]) ] assert len(qpoints) == len(qpoints_target) for q_segment1, q_segment2 in zip(qpoints, qpoints_target): assert np.allclose(q_segment1, q_segment2) # disconnected path path = [('X', 'G'), ('G', 'L'), ('W', 'X')] qpoints = get_supercell_qpoints_along_path(path, coordinates, prim.cell, supercell.cell) assert len(qpoints) == len(path) qpoints_target = [ np.array([[0, 0, 1.55140378], [0, 0, 1.16355283], [0, 0, 0.77570189], [0, 0, 0.38785094], [0, 0, 0]]), np.array([[0, 0, 0], [0.38785094, 0.38785094, 0.38785094], [0.77570189, 0.77570189, 0.77570189]]), np.array([[0.77570189, 1.55140378, 0], [0.38785094, 0.77570189, 0.77570189], [0, 0, 1.55140378]]) ] assert len(qpoints) == len(qpoints_target) for q_segment1, q_segment2 in zip(qpoints, qpoints_target): assert np.allclose(q_segment1, q_segment2) def test_get_supercell_qpts_along_path_hex(): prim = bulk('Al', 'hcp', a=3.05, c=5.0) supercell = prim.repeat(4) coordinates = dict( X=[0.5, 0.5, 0], G=[0, 0, 0], L=[0.5, 0.5, 0.5], W=[0.5, 0.25, 0.75], ) # connected path path = [('X', 'G'), ('G', 'L')] qpoints = get_supercell_qpoints_along_path(path, coordinates, prim.cell, supercell.cell) assert len(qpoints) == len(path) qpoints_target = [ np.array([[1.03003038, 1.78406495, 0], [0.51501519, 0.89203247, 0], [0, 0, 0]]), np.array([[0, 0, 0], [0.51501519, 0.89203247, 0.31415927], [1.03003038, 1.78406495, 0.62831853]]), ] assert len(qpoints) == len(qpoints_target) for q_segment1, q_segment2 in zip(qpoints, qpoints_target): assert np.allclose(q_segment1, q_segment2) def test_get_supercell_qpoints_along_path(): prim = bulk('Al', 'fcc', a=4.0) supercell = bulk('Al', 'fcc', a=4.0, cubic=True).repeat(1) path = [('X', 'G'), ('A', 'B'), ('G', 'L')] coordinates = dict(X=[0.5, 0.5, 0], G=[0, 0, 0], L=[0.5, 0.5, 0.5], A=[1/6, 1/6, 1/6], B=[2/6, 2/6, 2/6]) with pytest.warns(UserWarning, match='No q-points along path!'): qpoints = get_supercell_qpoints_along_path(path, coordinates, prim.cell, supercell.cell) assert len(qpoints) == 3 assert qpoints[1].shape == (0, 3) def test_get_supercell_qpts_along_path_errors(): prim = bulk('Al', 'fcc', a=4.0) supercell = bulk('Al', 'fcc', a=4.05, cubic=True).repeat(4) path = [('X', 'G'), ('G', 'L')] coordinates = dict(X=[0.5, 0.5, 0], G=[0, 0, 0], L=[0.5, 0.5, 0.5]) with pytest.raises(ValueError, match='Please check that the supercell metric'): qpoints, labels = get_supercell_qpoints_along_path( path, coordinates, prim.cell, supercell.cell) coordinates = dict(X=[0.5, 0.5, 0], G=[0, 0, 0]) with pytest.raises(ValueError, match='Please check that the supercell metric'): qpoints, labels = get_supercell_qpoints_along_path( path, coordinates, prim.cell, supercell.cell) dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/tests/test_regression_vs_old_cmdline_results.py000066400000000000000000000075441522712672400316410ustar00rootroot00000000000000import os import numpy as np import pickle from dynasor.qpoints import get_spherical_qpoints from dynasor.correlation_functions import compute_dynamic_structure_factors from dynasor.post_processing import get_spherically_averaged_sample_binned from dynasor.trajectory import Trajectory def test_regression_test_with_old_cmdline(): # traj index files this_dir = os.path.dirname(__file__) traj_fname = os.path.join( this_dir, 'trajectory_reader/trajectory_files/dump_long_with_velocities.xyz') index_fname = os.path.join(this_dir, 'trajectory_reader/trajectory_files/index_file_dump_long') # number of atoms, needed for normalization since normalization is now different n_atoms = 320 atoms_counts = dict(Cs=64, Pb=64, Br=192) # input parameters time_window = 6 dt = 100 q_max = 4 # Previously in 2*pi*nm^{-1}, now in 2*pi*Å^{-1} q_bins = 20 # run dynasor traj = Trajectory(traj_fname, trajectory_format='extxyz', atomic_indices=index_fname) q_points = get_spherical_qpoints(traj.cell, q_max) mask = np.logical_and(q_points[:, 0] >= 0, q_points[:, 1] >= 0) # keep only first octant mask = np.logical_and(mask, q_points[:, 2] >= 0) # keep only first octant q_points = q_points[mask] sample = compute_dynamic_structure_factors(traj, q_points, dt=dt, window_size=time_window, calculate_currents=True, calculate_incoherent=True) sample2 = get_spherically_averaged_sample_binned(sample, num_q_bins=q_bins) # load old commandline results for the same traj fname = os.path.join(this_dir, 'dynasor_old_cmdline.pickle') old_cmdline_results = pickle.load(open(fname, 'rb')) data_dict_old = dict() types = 'Cs Pb Br'.split() for (v, k, info) in old_cmdline_results: for i, t in enumerate(types): k = k.replace(str(i), t) k = k.replace('_k_', '_q_') data_dict_old[k] = v # compare simple things, time, omega, q assert np.allclose(sample2.time, data_dict_old['t']) # assert np.allclose(sample2.omega, data_dict_old['w']) # qbins differ slightly since q_max is no longer exactly 40.0 but based on the actually q-points # assert np.allclose(sample2['q_norms'], data_dict_old['k']) # compare all incoherent for key in sample2.available_correlation_functions: if '_s_' not in key: continue if '_w_' in key: # dont check frequency domain, new dynasor uses slightly different freqs continue atom_type = key.split('_')[-1] array_new = getattr(sample2, key) * n_atoms / atoms_counts[atom_type] array_old = data_dict_old[key] assert np.allclose(array_new.T, array_old) # For coherent parts we only compare for A-A pairs since AB calculations are slightly different corr_list_new = ['Fqt_coh', 'Clqt', 'Ctqt'] corr_list_old = ['F_q_t', 'Cl_q_t', 'Ct_q_t'] pairs = ['Cs_Cs', 'Pb_Pb', 'Br_Br'] for corr_name_new, corr_name_old in zip(corr_list_new, corr_list_old): for pair in pairs: key_new = corr_name_new + '_' + pair key_old = corr_name_old + '_' + pair s1, s2 = pair.split('_') multiplicity = 1.0 if s1 == s2 else 2.0 norm = n_atoms / (multiplicity * np.sqrt(atoms_counts[s1] * atoms_counts[s2])) array_new = getattr(sample2, key_new) * norm array_old = data_dict_old[key_old].T if corr_name_new[0] == 'C': # v (and thus j) previously had the unit nm/fs. This has been changed to Å/fs, # which means that the old current correlations must be multiplied by 100 to be # comparable to the new current correlations. array_old *= 100 assert np.allclose(array_new, array_old) else: assert np.allclose(array_new, array_old) dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/tests/test_sample_class.py000066400000000000000000000256111522712672400253000ustar00rootroot00000000000000from tempfile import NamedTemporaryFile import numpy as np import pytest from dynasor.sample import Sample, DynamicSample, StaticSample, read_sample_from_npz N_qpoints = 20 window_size = 100 @pytest.fixture def q_points(): return np.array([np.array([1, 0, 0]) * amp for amp in np.linspace(0, 1, N_qpoints)]) @pytest.fixture def time(): return np.linspace(0, 10, window_size) @pytest.fixture def omega(): return np.linspace(0, 5, window_size) @pytest.fixture def data_dict(q_points, time, omega): # setup data dict data_dict = dict() data_dict['q_points'] = q_points data_dict['time'] = time data_dict['omega'] = omega shape = (N_qpoints, window_size) data_dict['Fqt_coh_A_A'] = np.linspace(-1, 1, N_qpoints * window_size).reshape(shape) data_dict['Fqt_coh_B_B'] = np.linspace(-0.4, 0.4, N_qpoints * window_size).reshape(shape) data_dict['Fqt_coh_A_B'] = np.linspace(-0.3, 0.3, N_qpoints * window_size).reshape(shape) data_dict['Fqt_coh'] = data_dict['Fqt_coh_A_A'] + data_dict['Fqt_coh_A_B'] + \ data_dict['Fqt_coh_B_B'] data_dict['Sqw_coh_A_A'] = np.linspace(-10, 10, N_qpoints * window_size).reshape(shape) data_dict['Sqw_coh_B_B'] = np.linspace(-4, 4, N_qpoints * window_size).reshape(shape) data_dict['Sqw_coh_A_B'] = np.linspace(-3, 3, N_qpoints * window_size).reshape(shape) data_dict['Sqw_coh'] = data_dict['Sqw_coh_A_A'] + data_dict['Sqw_coh_A_B'] + \ data_dict['Sqw_coh_B_B'] return data_dict @pytest.fixture def data_dict_static(q_points): data_dict = dict() data_dict['q_points'] = q_points size = (q_points.shape[0], 1) data_dict['Sq_A_A'] = np.random.random(size) data_dict['Sq_A_B'] = np.random.random(size) data_dict['Sq_B_B'] = np.random.random(size) data_dict['Sq'] = np.random.random(size) return data_dict @pytest.fixture def simulation_data(): atom_types = ['A', 'B'] pairs = [('A', 'A'), ('A', 'B'), ('B', 'B')] counts = dict(A=500, B=250) cell = np.diag([2.5, 3.8, 2.95]) simulation_data = dict(atom_types=atom_types, pairs=pairs, particle_counts=counts, cell=cell) return simulation_data @pytest.fixture def simple_sample(data_dict, simulation_data): sample = Sample(data_dict, simulation_data) return sample def test_getattributes(q_points, time, simple_sample): assert np.allclose(simple_sample.q_points, q_points) assert np.allclose(simple_sample.time, time) def test_getitem(data_dict, simple_sample): for key in data_dict: assert np.allclose(data_dict[key], getattr(simple_sample, key)) # check that sample.xyz and sample['xyz'] return the same thing for an item in data_dict and # one in simulation_data assert np.allclose(simple_sample.cell, simple_sample['cell']) assert np.allclose(simple_sample.Sqw_coh_B_B, simple_sample['Sqw_coh_B_B']) def test_repr_str(simple_sample): s1 = repr(simple_sample) s2 = str(simple_sample) assert isinstance(s1, str) assert s1 == s2 def test_repr_html_str(simple_sample): s = simple_sample._repr_html_() assert isinstance(s, str) def test_read_and_write(data_dict, simulation_data, simple_sample): # write to file tempfile = NamedTemporaryFile(suffix='.npz', delete=False) simple_sample.write_to_npz(tempfile.name) for key, val in data_dict.items(): assert np.allclose(data_dict[key], getattr(simple_sample, key)) # read from file new_sample = read_sample_from_npz(tempfile.name) # check that nothing changed with metadata assert sorted(simple_sample.simulation_data.keys()) == sorted(new_sample.simulation_data.keys()) assert simulation_data['atom_types'] == new_sample.atom_types assert simulation_data['pairs'] == new_sample.pairs assert np.allclose(simulation_data['cell'], new_sample.cell) for key in simulation_data['particle_counts']: assert simulation_data['particle_counts'][key] == new_sample.particle_counts[key] # check that nothing changed with correlation function data for key, val in data_dict.items(): assert np.allclose(data_dict[key], getattr(new_sample, key)) def test_static_sample(data_dict_static, simulation_data): sample = StaticSample(data_dict_static, simulation_data) assert len(sample.available_correlation_functions) == 4 for key in sample.available_correlation_functions: assert np.allclose(sample[key], data_dict_static[key]) def test_dynamic_sample(data_dict, simulation_data): sample = DynamicSample(data_dict, simulation_data) assert len(sample.available_correlation_functions) == 8 for key in sample.available_correlation_functions: assert np.allclose(sample[key], data_dict[key]) def test_dynamic_sample_properties(data_dict, simulation_data): # without incoherent, without currents sample = DynamicSample(data_dict, simulation_data) assert not sample.has_currents assert not sample.has_incoherent # with incoherent, without currents data_dict['Fqt_incoh'] = np.zeros(sample.Fqt_coh.shape) sample = DynamicSample(data_dict, simulation_data) assert not sample.has_currents assert sample.has_incoherent assert sorted(sample.dimensions) == sorted(['omega', 'q_points', 'time']) for key, val in simulation_data.items(): assert key in sample.simulation_data if isinstance(val, dict) or isinstance(val, list): assert val == sample.simulation_data[key] else: assert np.allclose(val, sample.simulation_data[key]) assert 'history' in sample.metadata assert isinstance(sample.history, list) # with incoherent and currents data_dict['Clqt_A_A'] = np.zeros(sample.Fqt_coh.shape) data_dict['Clqt_A_B'] = np.zeros(sample.Fqt_coh.shape) data_dict['Clqt_B_B'] = np.zeros(sample.Fqt_coh.shape) sample = DynamicSample(data_dict, simulation_data) assert sample.has_currents assert sample.has_incoherent assert sorted(sample.dimensions) == sorted(['omega', 'q_points', 'time']) def test_static_sample_properties(data_dict_static, simulation_data): sample = StaticSample(data_dict_static, simulation_data) assert not sample.has_currents assert not sample.has_incoherent assert sample.dimensions == ['q_points'] def test_dynamic_to_dataframe(data_dict, simulation_data): # without incoherent, without currents sample = DynamicSample(data_dict, simulation_data) q_index = 5 df = sample.to_dataframe(q_index=q_index) for key in ['time', 'omega']: assert np.allclose(df[key], sample[key]) for key in sample.available_correlation_functions: assert np.allclose(df[key], sample[key][q_index]) def test_static_to_dataframe(data_dict_static, simulation_data): sample = StaticSample(data_dict_static, simulation_data) df = sample.to_dataframe() for key in sample.available_correlation_functions: assert np.allclose(df[key], sample[key].reshape(-1, )) def test_getitem_missing_key_raises(simple_sample): """Accessing a non-existent key raises KeyError.""" with pytest.raises(KeyError): simple_sample['no_such_key'] def test_append_history(simple_sample): """_append_history adds a record with the expected fields.""" simple_sample._append_history('test_func', {'param': 42}) history = simple_sample.history assert len(history) == 1 record = history[0] assert record['func'] == 'test_func' assert record['param'] == 42 assert 'date_time' in record assert 'username' in record assert 'hostname' in record assert 'dynasor_version' in record # also test without caller_metadata simple_sample._append_history('no_meta_func') assert simple_sample.history[1]['func'] == 'no_meta_func' def test_repr_html_with_history(simple_sample): """_repr_html_ renders history rows when history is non-empty.""" simple_sample._append_history('my_func', {'key1': 'val1', 'key2': 'val2'}) html = simple_sample._repr_html_() assert 'my_func' in html assert 'key1' in html assert 'val1' in html def test_read_and_write_static_sample(data_dict_static, simulation_data, tmp_path): """write_to_npz / read_sample_from_npz round-trips a StaticSample correctly.""" sample = StaticSample(data_dict_static, simulation_data) fname = str(tmp_path / 'static.npz') sample.write_to_npz(fname) loaded = read_sample_from_npz(fname) assert isinstance(loaded, StaticSample) for key in data_dict_static: assert np.allclose(sample[key], loaded[key]) def test_read_and_write_dynamic_sample(data_dict, simulation_data, tmp_path): """write_to_npz / read_sample_from_npz round-trips a DynamicSample correctly.""" sample = DynamicSample(data_dict, simulation_data) fname = str(tmp_path / 'dynamic.npz') sample.write_to_npz(fname) loaded = read_sample_from_npz(fname) assert isinstance(loaded, DynamicSample) for key in data_dict: assert np.allclose(sample[key], loaded[key]) def test_read_sample_from_npz_unknown_class(simulation_data, data_dict_static, tmp_path): """An npz with an unrecognised class name is read back as a base Sample.""" fname = str(tmp_path / 'unknown.npz') metadata = {'simulation_data': simulation_data, 'history': []} np.savez_compressed( fname, name=np.array('UnknownClass'), metadata=np.array(metadata, dtype=object), data_dict=np.array(data_dict_static, dtype=object), ) loaded = read_sample_from_npz(fname) assert type(loaded) is Sample def test_read_sample_old_format_meta_data_key(data_dict_static, simulation_data, tmp_path): """Files using the old 'meta_data' key (version <=2.2) are read correctly.""" fname = str(tmp_path / 'old_meta_data.npz') metadata = {'simulation_data': simulation_data, 'history': []} data_to_save = { 'name': np.array('StaticSample'), 'meta_data': np.array(metadata, dtype=object), 'data_dict': np.array(data_dict_static, dtype=object), } np.savez_compressed(fname, **data_to_save) loaded = read_sample_from_npz(fname) assert isinstance(loaded, StaticSample) def test_read_sample_pre_v23_format(data_dict_static, simulation_data, tmp_path): """Pre-2.3 files where simulation_data keys live directly in metadata are read correctly.""" fname = str(tmp_path / 'pre23.npz') # In the old format, simulation_data keys are top-level in metadata metadata = { 'atom_types': simulation_data['atom_types'], 'pairs': simulation_data['pairs'], 'particle_counts': simulation_data['particle_counts'], 'cell': simulation_data['cell'], 'history': [], } data_to_save = { 'name': np.array('StaticSample'), 'metadata': np.array(metadata, dtype=object), 'data_dict': np.array(data_dict_static, dtype=object), } np.savez_compressed(fname, **data_to_save) loaded = read_sample_from_npz(fname) assert isinstance(loaded, StaticSample) assert np.allclose(loaded.cell, simulation_data['cell']) dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/tests/test_scattering_factors.py000066400000000000000000000307371522712672400265230ustar00rootroot00000000000000"""Unit tests for NeutronScatteringLengths, XRayFormFactors, and ElectronScatteringFactors, verified against tabulated reference values from the primary sources cited in each class's docstring. """ import numpy as np import pytest from numpy import pi from dynasor.post_processing import ( NeutronScatteringLengths, XRayFormFactors, ElectronScatteringFactors, ) # --------------------------------------------------------------------------- # NeutronScatteringLengths # Source: NIST database / Neutron News 3, 26 (1992) # Units: fm # --------------------------------------------------------------------------- class TestNeutronScatteringLengths: def test_natural_hydrogen_coherent_length(self): """b_coh for natural H equals the abundance-weighted average. 1H: abundance=99.985%, b_coh=-3.7406 fm 2H: abundance=0.015%, b_coh= 6.671 fm 3H: abundance=0.0, b_coh= 4.792 fm """ expected = (0.99985 * (-3.7406) + 0.00015 * 6.671 + 0.0 * 4.792) nsl = NeutronScatteringLengths(['H']) assert np.isclose(nsl.get_weight_coh('H').real, expected, rtol=1e-5) def test_natural_hydrogen_incoherent_length(self): """b_inc for natural H equals the abundance-weighted average. 1H: abundance=99.985%, b_inc=25.274 fm 2H: abundance=0.015%, b_inc= 4.04 fm """ expected = 0.99985 * 25.274 + 0.00015 * 4.04 nsl = NeutronScatteringLengths(['H']) assert np.isclose(nsl.get_weight_incoh('H').real, expected, rtol=1e-5) def test_natural_oxygen_coherent_length(self): """b_coh for natural O equals the abundance-weighted average. 16O: abundance=99.762%, b_coh=5.803 fm 17O: abundance=0.038%, b_coh=5.78 fm 18O: abundance=0.2%, b_coh=5.84 fm """ expected = 0.99762 * 5.803 + 0.00038 * 5.78 + 0.002 * 5.84 nsl = NeutronScatteringLengths(['O']) assert np.isclose(nsl.get_weight_coh('O').real, expected, rtol=1e-5) def test_natural_oxygen_incoherent_length_nearly_zero(self): """b_inc for natural O is nearly zero (only 17O contributes).""" nsl = NeutronScatteringLengths(['O']) assert abs(nsl.get_weight_incoh('O')) < 0.01 def test_pure_protium_gives_tabulated_b_coh(self): """Selecting pure 1H returns the tabulated value b_coh = -3.7406 fm.""" nsl = NeutronScatteringLengths(['H'], abundances={'H': {1: 1.0}}) assert np.isclose(nsl.get_weight_coh('H').real, -3.7406, rtol=1e-4) def test_pure_deuterium_gives_tabulated_b_coh(self): """Selecting pure 2H (D) returns the tabulated value b_coh = 6.671 fm.""" nsl = NeutronScatteringLengths(['H'], abundances={'H': {2: 1.0}}) assert np.isclose(nsl.get_weight_coh('H').real, 6.671, rtol=1e-4) def test_pure_deuterium_gives_tabulated_b_inc(self): """Selecting pure 2H (D) returns the tabulated value b_inc = 4.04 fm.""" nsl = NeutronScatteringLengths(['H'], abundances={'H': {2: 1.0}}) assert np.isclose(nsl.get_weight_incoh('H').real, 4.04, rtol=1e-4) def test_multiple_species_loaded_independently(self): """Loading H and O together returns the same values as loading each separately.""" nsl_both = NeutronScatteringLengths(['H', 'O']) nsl_H = NeutronScatteringLengths(['H']) nsl_O = NeutronScatteringLengths(['O']) assert np.isclose(nsl_both.get_weight_coh('H'), nsl_H.get_weight_coh('H')) assert np.isclose(nsl_both.get_weight_coh('O'), nsl_O.get_weight_coh('O')) def test_supports_incoherent_is_true(self): nsl = NeutronScatteringLengths(['O']) assert nsl.supports_incoherent def test_supports_currents_is_false(self): nsl = NeutronScatteringLengths(['O']) assert not nsl.supports_currents def test_unknown_species_raises_value_error(self): with pytest.raises(ValueError, match='requested species Xx'): NeutronScatteringLengths(['Xx']) def test_invalid_isotope_in_abundances_raises_value_error(self): with pytest.raises(ValueError): NeutronScatteringLengths(['H'], abundances={'H': {99: 1.0}}) def test_abundances_not_summing_to_one_raises_value_error(self): with pytest.raises(ValueError): NeutronScatteringLengths(['H'], abundances={'H': {1: 0.5}}) def test_parameters_dataframe_contains_expected_columns(self): nsl = NeutronScatteringLengths(['H', 'O']) cols = nsl.parameters.columns.tolist() for col in ['species', 'isotope', 'abundance', 'b_coh', 'b_inc']: assert col in cols def test_abundances_property_sums_to_one_per_species(self): nsl = NeutronScatteringLengths(['H', 'O']) for species, isotope_abundances in nsl.abundances.items(): assert np.isclose(sum(isotope_abundances.values()), 1.0) # --------------------------------------------------------------------------- # XRayFormFactors # Source: Waasmaier & Kirfel, Acta Cryst. A51, 416 (1995) # f0(q) = sum_i a_i * exp(-b_i * s^2) + c, s = q / (4*pi) # --------------------------------------------------------------------------- class TestXRayFormFactors: def test_oxygen_f0_at_q_zero_equals_atomic_number(self): """f0(q=0) = sum(a_i) + c ≈ Z for all elements. For O (Z=8): sum of a_i + c = 7.9997 (within parametrization accuracy). """ ff = XRayFormFactors(['O']) # q→0 limit: use a very small q to avoid division issues f0 = ff.get_weight_coh('O', 1e-6) assert np.isclose(f0, 8.0, atol=0.01) def test_iron_f0_at_q_zero_equals_atomic_number(self): """f0(q=0) ≈ Z=26 for Fe.""" ff = XRayFormFactors(['Fe']) f0 = ff.get_weight_coh('Fe', 1e-6) assert np.isclose(f0, 26.0, atol=0.02) def test_oxygen_f0_at_s_equals_one_matches_manual_calculation(self): """f0(q=4*pi) for O matches manual sum of Waasmaier-1995 coefficients. At s=1 (q=4*pi rad/Å), using tabulated O coefficients: a=[2.960427, 2.508818, 0.637853, 0.722838, 1.142756] b=[14.182259, 5.936858, 0.112726, 34.958481, 0.390240] c=0.027014 """ a = [2.960427, 2.508818, 0.637853, 0.722838, 1.142756] b = [14.182259, 5.936858, 0.112726, 34.958481, 0.390240] c = 0.027014 s2 = 1.0 # s = q/(4*pi) = 1 when q = 4*pi expected = c + sum(ai * np.exp(-bi * s2) for ai, bi in zip(a, b)) ff = XRayFormFactors(['O']) assert np.isclose(ff.get_weight_coh('O', 4 * pi), expected, rtol=1e-6) def test_f0_decreases_with_q(self): """The form factor f0 is a monotonically decreasing function of |q|.""" ff = XRayFormFactors(['O']) q_values = np.linspace(0.1, 20.0, 20) f_values = [ff.get_weight_coh('O', q) for q in q_values] assert all(f_values[i] > f_values[i + 1] for i in range(len(f_values) - 1)) def test_itc_2006_parametrization_loads_and_gives_reasonable_values(self): """The 'itc-2006' parametrization gives f0(q→0) ≈ Z.""" ff = XRayFormFactors(['O', 'Fe'], source='itc-2006') assert np.isclose(ff.get_weight_coh('O', 1e-6), 8.0, atol=0.05) assert np.isclose(ff.get_weight_coh('Fe', 1e-6), 26.0, atol=0.05) def test_hydrogen_returns_zero_with_warning(self): """H has no parametrization; class should warn and return ~0.""" with pytest.warns(UserWarning, match='H'): ff = XRayFormFactors(['H']) assert np.isclose(ff.get_weight_coh('H', 5.0), 0.0) def test_unknown_species_raises_value_error(self): with pytest.raises(ValueError, match='Missing'): XRayFormFactors(['Xx']) def test_supports_currents_is_false(self): ff = XRayFormFactors(['O']) assert not ff.supports_currents def test_supports_incoherent_is_false(self): ff = XRayFormFactors(['O']) assert not ff.supports_incoherent def test_parameters_dataframe_contains_expected_columns(self): ff = XRayFormFactors(['O', 'Fe']) cols = ff.parameters.columns.tolist() for col in ['a1', 'b1', 'c']: assert col in cols def test_unknown_source_raises_value_error(self): with pytest.raises(ValueError, match='Unknown'): XRayFormFactors(['O'], source='unknown-source') # --------------------------------------------------------------------------- # ElectronScatteringFactors # Source: Peng et al., Acta Cryst. A52, 257 (1996); Peng, Acta Cryst. A54, 481 (1998) # f(q) = sum_i a_i * exp(-b_i * s^2), s = q / (4*pi) # Ions additionally have a Coulomb offset: 0.023934 * charge / s^2 # --------------------------------------------------------------------------- class TestElectronScatteringFactors: def test_oxygen_f_at_s_equals_one_matches_manual_calculation(self): """f(q=4*pi) for O matches manual sum of Peng-1996 (smax6) coefficients. Tabulated O coefficients (smax6): a=[0.0365, 0.1729, 0.5805, 0.8814, 0.3121] b=[0.0652, 0.6184, 2.9449, 9.6298, 28.2194] """ a = [0.0365, 0.1729, 0.5805, 0.8814, 0.3121] b = [0.0652, 0.6184, 2.9449, 9.6298, 28.2194] s2 = 1.0 # s=1 when q=4*pi expected = sum(ai * np.exp(-bi * s2) for ai, bi in zip(a, b)) esf = ElectronScatteringFactors(['O']) assert np.isclose(esf.get_weight_coh('O', 4 * pi), expected, rtol=1e-5) def test_iron_f_at_s_equals_one_matches_manual_calculation(self): """f(q=4*pi) for Fe matches manual sum of Peng-1996 (smax6) coefficients. Tabulated Fe coefficients (smax6): a=[0.1929, 0.8239, 1.8689, 2.3694, 1.9060] b=[0.1087, 1.0806, 4.7637, 22.850, 76.7309] """ a = [0.1929, 0.8239, 1.8689, 2.3694, 1.9060] b = [0.1087, 1.0806, 4.7637, 22.850, 76.7309] s2 = 1.0 expected = sum(ai * np.exp(-bi * s2) for ai, bi in zip(a, b)) esf = ElectronScatteringFactors(['Fe']) assert np.isclose(esf.get_weight_coh('Fe', 4 * pi), expected, rtol=1e-5) def test_ionic_species_o2minus_includes_coulomb_offset(self): """f(q=4*pi) for O2- includes the ionic Coulomb offset 0.023934*charge/s^2. Tabulated O2- coefficients (Peng-1998): a=[0.0421, 0.2100, 0.8520, 1.8200, 1.1700] b=[0.0609, 0.5590, 2.9600, 11.500, 37.700] charge = -2 """ a = [0.0421, 0.2100, 0.8520, 1.8200, 1.1700] b = [0.0609, 0.5590, 2.9600, 11.500, 37.700] charge = -2 s2 = 1.0 expected = 0.023934 * charge / s2 + sum(ai * np.exp(-bi * s2) for ai, bi in zip(a, b)) esf = ElectronScatteringFactors(['O2-']) assert np.isclose(esf.get_weight_coh('O2-', 4 * pi), expected, rtol=1e-5) def test_elemental_and_ionic_species_can_be_loaded_together(self): """Elemental and ionic species can be instantiated in the same object.""" esf = ElectronScatteringFactors(['O', 'Fe', 'O2-']) assert esf.get_weight_coh('O', 4 * pi) > 0 assert esf.get_weight_coh('Fe', 4 * pi) > 0 assert esf.get_weight_coh('O2-', 4 * pi) > 0 def test_smax2_parametrization_gives_reasonable_values(self): """The 'smax2' parametrization gives reasonable values within its range.""" esf = ElectronScatteringFactors(['O'], parametrization='smax2') f = esf.get_weight_coh('O', 4 * pi) # s=1, within smax2 range assert 0 < f < 5 def test_f_decreases_with_q(self): """The scattering factor f is a decreasing function of |q| for neutral atoms.""" esf = ElectronScatteringFactors(['O']) q_values = np.linspace(1.0, 20.0, 20) f_values = [esf.get_weight_coh('O', q) for q in q_values] assert all(f_values[i] > f_values[i + 1] for i in range(len(f_values) - 1)) def test_unknown_species_raises_value_error(self): with pytest.raises(ValueError, match='Missing'): ElectronScatteringFactors(['Xx']) def test_unknown_parametrization_raises_value_error(self): with pytest.raises(ValueError, match='Unknown'): ElectronScatteringFactors(['O'], parametrization='unknown') def test_supports_currents_is_false(self): esf = ElectronScatteringFactors(['O']) assert not esf.supports_currents def test_supports_incoherent_is_false(self): esf = ElectronScatteringFactors(['O']) assert not esf.supports_incoherent def test_parameters_dataframe_contains_expected_columns(self): esf = ElectronScatteringFactors(['O', 'Fe']) cols = esf.parameters.columns.tolist() for col in ['a1', 'b1', 'a5', 'b5']: assert col in cols dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/tests/test_spherical_average.py000066400000000000000000000241041522712672400262720ustar00rootroot00000000000000import pytest import numpy as np from dynasor.post_processing.spherical_average import ( _get_bin_average, _gaussian, _lorentzian, _get_gaussian_average, _get_lorentzian_average, ) from dynasor.post_processing import get_spherically_averaged_sample_smearing from dynasor.post_processing import get_spherically_averaged_sample_binned from dynasor.sample import DynamicSample, StaticSample def test_spherical_averaging(): N_q = 100 N_t = 8 qbins = 20 np.random.seed(42) data = np.random.random((N_q, N_t)) q_points = np.random.random((N_q, 3)) q_bincenters, bin_counts, averaged_data = _get_bin_average(q_points, data, qbins) assert len(q_bincenters) == qbins assert averaged_data.shape == (qbins, N_t) # check q_bincenters q_min = np.min(np.linalg.norm(q_points, axis=1)) q_max = np.max(np.linalg.norm(q_points, axis=1)) assert np.isclose(q_bincenters[0], q_min) assert np.isclose(q_bincenters[-1], q_max) # check bin counts bin_counts_target = np.array([2, 2, 3, 1, 4, 0, 5, 10, 9, 16, 7, 11, 5, 6, 8, 4, 5, 0, 1, 1]) assert np.allclose(bin_counts, bin_counts_target) # check for Nans for empty bins for bin_index in np.where(bin_counts == 0)[0]: assert np.all(np.isnan(averaged_data[bin_index])) # check averaged data target_average = np.array([0.59242063, 0.44607297, 0.45898736, 0.64679424, 0.66729803, 0.45030113, 0.417301, 0.59437702]) assert np.allclose(averaged_data[10], target_average) def test_spherical_summing_vs_averaging(): q_min = 0.0 q_max = 10.0 n_bins = 100 # make some dummy q-points with more q-ponts at some places q_bins = np.linspace(q_min, q_max, n_bins).tolist() q_bins += [1.0, 1.0, 1.0, 1.0] q_points = np.append(np.array(sorted(q_bins)), np.zeros(2*len(q_bins))).reshape(3, len(q_bins)).T # add two peaks Sq_raw = np.zeros(len(q_bins)) Sq_raw[10] = 1.0 Sq_raw[54] = 1.0 q_bincenters_sum, bin_counts_sum, summed_data = _get_bin_average(q_points, Sq_raw.reshape(-1, 1), n_bins, use_sum=True) q_bincenters_avg, bin_counts_avg, averaged_data = _get_bin_average(q_points, Sq_raw.reshape(-1, 1), n_bins, use_sum=False) assert len(q_bincenters_sum) == len(q_bincenters_avg) assert summed_data[10] == summed_data[50] assert averaged_data[10] < averaged_data[50] assert averaged_data[10] < summed_data[10] @pytest.mark.parametrize('sample_fixture,expected_class', [ pytest.param('dynamic_sample_with_incoh', DynamicSample, id='dynamic'), pytest.param('static_sample', StaticSample, id='static'), ]) def test_spherically_averaged_sample_binned_shape(request, sample_fixture, expected_class): """Binned spherical average returns the expected sample type and output shape.""" sample = request.getfixturevalue(sample_fixture) q_bins = 14 sample_res = get_spherically_averaged_sample_binned(sample, q_bins) assert isinstance(sample_res, expected_class) for key in sample_res.available_correlation_functions: assert sample_res[key].shape == (q_bins, sample[key].shape[1]) @pytest.mark.parametrize('sample_fixture,expected_class', [ pytest.param('dynamic_sample_with_incoh', DynamicSample, id='dynamic'), pytest.param('static_sample', StaticSample, id='static'), ]) def test_spherically_averaged_sample_smearing_shape(request, sample_fixture, expected_class): """Gaussian-smeared spherical average returns the expected sample type and output shape.""" sample = request.getfixturevalue(sample_fixture) q_norms = np.linspace(0, 1.5, 100) q_width = 0.1 sample_res = get_spherically_averaged_sample_smearing( sample, q_norms=q_norms, q_width=q_width) assert isinstance(sample_res, expected_class) for key in sample_res.available_correlation_functions: assert sample_res[key].shape == (len(q_norms), sample[key].shape[1]) def test_raises_error_with_invalid_inputs(dynamic_sample_with_incoh): q_bins = 14 q_norms = np.linspace(0, 1.5, 100) q_width = 0.1 # invalid sample bad_sample = np.random.random((10, 20, 3)) with pytest.raises(ValueError): get_spherically_averaged_sample_binned(bad_sample, q_bins) with pytest.raises(ValueError): get_spherically_averaged_sample_smearing(bad_sample, q_norms=q_norms, q_width=q_width) # invalid q-points shape qpts = dynamic_sample_with_incoh.q_points dynamic_sample_with_incoh.q_points = np.hstack((qpts, np.ones((qpts.shape[0], 1)))) with pytest.raises(ValueError): get_spherically_averaged_sample_binned(dynamic_sample_with_incoh, q_bins) with pytest.raises(ValueError): get_spherically_averaged_sample_smearing( dynamic_sample_with_incoh, q_norms=q_norms, q_width=q_width) def test_gaussian(): x = np.array([1.0, 2.0, 0.0, 2.23]) x0 = 1.0 sigma = 1.2 f = _gaussian(x, x0, sigma) f_target = 1/(sigma * np.sqrt(2*np.pi)) * np.exp(-1/2 * (x-x0)**2 / sigma**2) assert np.allclose(f, f_target) def test_lorentzian(): x = np.array([1.0, 2.0, 0.0, 2.23]) x0 = 1.0 gamma = 1.2 f = _lorentzian(x, x0, gamma) f_target = gamma / (np.pi * ((x - x0)**2 + gamma**2)) assert np.allclose(f, f_target) def test_lorentzian_normalization(): # integral of Lorentzian over all x equals 1; use scipy for accuracy with heavy tails from scipy.integrate import quad result, _ = quad(_lorentzian, -np.inf, np.inf, args=(0.0, 1.0)) assert np.isclose(result, 1.0, atol=1e-8) def test_lorentzian_smearing_of_dynamic_sample(dynamic_sample_with_incoh): q_norms = np.linspace(0, 1.5, 100) q_width = 0.1 sample_res = get_spherically_averaged_sample_smearing( dynamic_sample_with_incoh, q_norms=q_norms, q_width=q_width, broadening='lorentzian') assert isinstance(sample_res, DynamicSample) for key in sample_res.available_correlation_functions: assert sample_res[key].shape == (len(q_norms), dynamic_sample_with_incoh[key].shape[1]) def test_lorentzian_smearing_of_static_sample(static_sample): q_norms = np.linspace(0, 1.5, 100) q_width = 0.1 sample_res = get_spherically_averaged_sample_smearing( static_sample, q_norms=q_norms, q_width=q_width, broadening='lorentzian') assert isinstance(sample_res, StaticSample) for key in sample_res.available_correlation_functions: assert sample_res[key].shape == (len(q_norms), static_sample[key].shape[1]) def test_gaussian_average_weights_sum_to_one(): np.random.seed(0) N_q = 200 q_points = np.random.random((N_q, 3)) Z = np.ones((N_q, 1)) q_norms = np.linspace(0, 2, 50) result = _get_gaussian_average(q_points, Z, q_norms, q_width=0.2) # all-ones data averaged with normalized weights must give 1 everywhere assert np.allclose(result, 1.0) def test_lorentzian_average_weights_sum_to_one(): np.random.seed(0) N_q = 200 q_points = np.random.random((N_q, 3)) Z = np.ones((N_q, 1)) q_norms = np.linspace(0, 2, 50) result = _get_lorentzian_average(q_points, Z, q_norms, q_width=0.2) # all-ones data averaged with normalized weights must give 1 everywhere assert np.allclose(result, 1.0) def test_gaussian_average_single_q_point(): # With a single input q-point the normalised weight is always 1, # so the output must equal that point's value for every output q-norm. q_points = np.array([[1.0, 0.0, 0.0]]) Z = np.array([[3.14, 2.71]]) q_norms = np.array([0.5, 1.0, 2.0]) result = _get_gaussian_average(q_points, Z, q_norms, q_width=0.2) assert result.shape == (3, 2) assert np.allclose(result, np.tile(Z, (3, 1))) def test_lorentzian_average_single_q_point(): q_points = np.array([[1.0, 0.0, 0.0]]) Z = np.array([[3.14, 2.71]]) q_norms = np.array([0.5, 1.0, 2.0]) result = _get_lorentzian_average(q_points, Z, q_norms, q_width=0.2) assert result.shape == (3, 2) assert np.allclose(result, np.tile(Z, (3, 1))) def test_gaussian_average_two_q_points(): # Two q-points at |q|=1 and |q|=3, query at q_out=1. # With a narrow width the nearer point should dominate. q_points = np.array([[1.0, 0.0, 0.0], [3.0, 0.0, 0.0]]) Z = np.array([[1.0], [0.0]]) q_norms = np.array([1.0]) q_width = 0.1 result = _get_gaussian_average(q_points, Z, q_norms, q_width) # Compute expected weights manually w1 = np.exp(-0.5 * ((1.0 - 1.0) / q_width) ** 2) w2 = np.exp(-0.5 * ((3.0 - 1.0) / q_width) ** 2) expected = (w1 * Z[0] + w2 * Z[1]) / (w1 + w2) assert np.allclose(result, expected) def test_lorentzian_average_two_q_points(): q_points = np.array([[1.0, 0.0, 0.0], [3.0, 0.0, 0.0]]) Z = np.array([[1.0], [0.0]]) q_norms = np.array([1.0]) q_width = 0.1 result = _get_lorentzian_average(q_points, Z, q_norms, q_width) w1 = 1.0 / ((1.0 - 1.0) ** 2 + q_width ** 2) w2 = 1.0 / ((3.0 - 1.0) ** 2 + q_width ** 2) expected = (w1 * Z[0] + w2 * Z[1]) / (w1 + w2) assert np.allclose(result, expected) def test_lorentzian_vs_gaussian_smearing_differ(dynamic_sample_with_incoh): q_norms = np.linspace(0.1, 1.4, 50) q_width = 0.1 res_gaussian = get_spherically_averaged_sample_smearing( dynamic_sample_with_incoh, q_norms=q_norms, q_width=q_width, broadening='gaussian') res_lorentzian = get_spherically_averaged_sample_smearing( dynamic_sample_with_incoh, q_norms=q_norms, q_width=q_width, broadening='lorentzian') # results should differ since the kernel shapes differ assert not np.allclose(res_gaussian['Fqt_coh'], res_lorentzian['Fqt_coh']) def test_invalid_broadening_raises(dynamic_sample_with_incoh): with pytest.raises(ValueError, match='broadening'): get_spherically_averaged_sample_smearing( dynamic_sample_with_incoh, q_norms=np.linspace(0, 1, 10), q_width=0.1, broadening='triangle') dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/tests/test_structures.py000066400000000000000000000073521522712672400250570ustar00rootroot00000000000000import numpy as np import pytest from ase import Atoms from ase.build import bulk from dynasor.tools.structures import ( get_displacements, get_displacements_from_u, find_permutation, get_offset_index, ) @pytest.fixture def fcc_al(): return bulk('Al', 'fcc', a=4.05, cubic=True) # --- get_displacements --- def test_get_displacements_basic(fcc_al): """Displaced atoms return the displacement vectors.""" ideal = fcc_al.copy() displaced = fcc_al.copy() displaced.positions[0] += [0.1, 0.0, 0.0] u = get_displacements(displaced, ideal) assert np.isclose(u[0, 0], 0.1) assert np.allclose(u[1:], 0.0) def test_get_displacements_no_mic(): ideal = Atoms('Al', positions=[[0, 0, 0]], cell=10 * np.eye(3), pbc=True) displaced = ideal.copy() displaced.positions[0, 0] = 8.0 u = get_displacements(displaced, ideal, check_mic=False) assert np.isclose(u[0, 0], 8.0) def test_get_displacements_mismatched_numbers(fcc_al): """Different atomic numbers raise ValueError.""" ideal = fcc_al.copy() other = fcc_al.copy() other.numbers[0] = 29 # Cu instead of Al with pytest.raises(ValueError, match='Atomic numbers do not match'): get_displacements(other, ideal) def test_get_displacements_mismatched_cell(fcc_al): """Different cell metrics raise ValueError.""" ideal = fcc_al.copy() other = fcc_al.copy() other.cell *= 1.1 with pytest.raises(ValueError, match='Cells do not match'): get_displacements(other, ideal) # --- get_displacements_from_u --- def test_get_displacements_from_u_no_mic(): """check_mic=False returns u unchanged even when |u| > half the cell length.""" cell = 10.0 * np.eye(3) # displacement of 8 Å in a 10 Å cell: mic would fold it to -2 Å u = np.array([[8.0, 0.0, 0.0]]) result = get_displacements_from_u(u, cell, check_mic=False) assert np.allclose(result, u) # confirm that mic=True would give a different (shorter) result result_mic = get_displacements_from_u(u.copy(), cell, check_mic=True) assert not np.allclose(result_mic, u) # --- find_permutation --- def test_find_permutation_cell_mismatch(): """find_permutation raises ValueError when cells differ.""" a = bulk('Al', 'fcc', a=4.05, cubic=True) b = bulk('Al', 'fcc', a=4.10, cubic=True) with pytest.raises(ValueError, match='Cells do not match'): find_permutation(a, b) def test_find_permutation_duplicate_raises(): """Duplicate assignments in the permutation raise Exception.""" # Two atoms at the same position → both map to the same reference atom cell = 10.0 * np.eye(3) atoms = Atoms('AlAl', positions=[[0, 0, 0], [0, 0, 0]], cell=cell, pbc=True) atoms_ref = Atoms('AlAl', positions=[[0, 0, 0], [5, 0, 0]], cell=cell, pbc=True) with pytest.raises(Exception, match='Duplicates in permutation'): find_permutation(atoms, atoms_ref) def test_find_permutation_occupation_mismatch_raises(): """Different species at matched lattice sites raise Exception.""" cell = 10.0 * np.eye(3) atoms = Atoms('CuAl', positions=[[0, 0, 0], [5, 0, 0]], cell=cell, pbc=True) atoms_ref = Atoms('AlCu', positions=[[0, 0, 0], [5, 0, 0]], cell=cell, pbc=True) with pytest.raises(Exception, match='Matching lattice sites have different occupation'): find_permutation(atoms, atoms_ref) # --- get_offset_index --- def test_get_offset_index_non_atoms_primitive_raises(fcc_al): """Non-Atoms primitive raises ValueError.""" with pytest.raises(ValueError): get_offset_index('not_atoms', fcc_al) def test_get_offset_index_non_atoms_supercell_raises(fcc_al): """Non-Atoms supercell raises ValueError.""" with pytest.raises(ValueError): get_offset_index(fcc_al, 'not_atoms') dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/tests/test_time_averager.py000066400000000000000000000150531522712672400254430ustar00rootroot00000000000000import numpy as np from dynasor.core.time_averager import TimeAverager, truncate_nans def test_output_shape(): """get_average_all returns shape (array_length, time_window).""" averager = TimeAverager(time_window=7, array_length=13) assert averager.get_average_all().shape == (13, 7) def test_unsampled_slots_are_nan(): """All time lags without any added samples return NaN.""" averager = TimeAverager(time_window=5, array_length=3) result = averager.get_average_all() assert np.all(np.isnan(result)) def test_get_average_at_timelag_unsampled_returns_nan(): """get_average_at_timelag returns NaN for a slot with no samples.""" averager = TimeAverager(time_window=4, array_length=6) result = averager.get_average_at_timelag(2) assert np.all(np.isnan(result)) assert result.shape == (6,) def test_single_sample_equals_itself(): """Adding one sample at a time lag returns that sample exactly.""" rng = np.random.default_rng(0) averager = TimeAverager(time_window=5, array_length=10) sample = rng.random(10) averager.add_sample(time_lag=0, sample=sample) assert np.allclose(averager.get_average_at_timelag(0), sample) def test_average_of_two_samples(): """Two samples at the same time lag are averaged correctly.""" rng = np.random.default_rng(1) averager = TimeAverager(time_window=5, array_length=8) s1 = rng.random(8) s2 = rng.random(8) averager.add_sample(time_lag=3, sample=s1) averager.add_sample(time_lag=3, sample=s2) assert np.allclose(averager.get_average_at_timelag(3), (s1 + s2) / 2) def test_average_of_many_samples(): """N samples at the same time lag equal their arithmetic mean.""" rng = np.random.default_rng(2) N = 50 array_length = 12 averager = TimeAverager(time_window=3, array_length=array_length) samples = rng.random((N, array_length)) for s in samples: averager.add_sample(time_lag=1, sample=s) assert np.allclose(averager.get_average_at_timelag(1), samples.mean(axis=0)) def test_different_time_lags_are_independent(): """Samples added to one time lag do not affect other time lags.""" rng = np.random.default_rng(3) averager = TimeAverager(time_window=6, array_length=5) s0 = rng.random(5) s2 = rng.random(5) averager.add_sample(time_lag=0, sample=s0) averager.add_sample(time_lag=2, sample=s2) assert np.allclose(averager.get_average_at_timelag(0), s0) assert np.allclose(averager.get_average_at_timelag(2), s2) # All other lags remain NaN for t in [1, 3, 4, 5]: assert np.all(np.isnan(averager.get_average_at_timelag(t))) def test_get_average_at_timelag_consistent_with_get_average_all(): """get_average_at_timelag and get_average_all return the same values.""" rng = np.random.default_rng(4) time_window = 7 array_length = 9 averager = TimeAverager(time_window=time_window, array_length=array_length) for t in [0, 2, 4, 6]: averager.add_sample(time_lag=t, sample=rng.random(array_length)) all_averages = averager.get_average_all() for t in range(time_window): assert np.allclose(averager.get_average_at_timelag(t), all_averages[:, t], equal_nan=True) def test_time_window_of_one(): """TimeAverager with time_window=1 works for the single slot.""" rng = np.random.default_rng(5) averager = TimeAverager(time_window=1, array_length=4) s = rng.random(4) averager.add_sample(time_lag=0, sample=s) assert averager.get_average_all().shape == (4, 1) assert np.allclose(averager.get_average_at_timelag(0), s) def test_accumulation_is_exact(): """Repeated additions produce the exact running mean with no drift.""" array_length = 10 averager = TimeAverager(time_window=2, array_length=array_length) # Use constant samples so the mean is trivially known value = np.full(array_length, 3.14159) N = 100 for _ in range(N): averager.add_sample(time_lag=0, sample=value) assert np.allclose(averager.get_average_at_timelag(0), value) # Legacy test preserved for regression coverage def test_time_averager(): window = 11 array_size = 20 rng = np.random.default_rng(42) averager = TimeAverager(window, array_size) # add a single sample sample1 = rng.random(array_size) averager.add_sample(time_lag=0, sample=sample1) average_array = averager.get_average_all() assert average_array.shape == (array_size, window) assert np.allclose(sample1, average_array[:, 0]) for t in range(1, window): assert np.all(np.isnan(average_array[:, t])) # add additional sample sample2 = rng.random(array_size) sample3 = rng.random(array_size) averager.add_sample(time_lag=0, sample=sample2) averager.add_sample(time_lag=5, sample=sample3) average_array = averager.get_average_all() assert np.allclose(average_array[:, 0], (sample1 + sample2) / 2.0) assert np.allclose(average_array[:, 5], sample3) def test_truncate_nans(): # setup N_times = 10 N_q = 2 F = TimeAverager(N_times, N_q) # add dummy time samples for all q-points F.add_sample(0, [1.0] * N_q) F.add_sample(1, [0.84] * N_q) F.add_sample(2, [0.75] * N_q) F.add_sample(2, [0.70] * N_q) F.add_sample(2, [0.72] * N_q) F.add_sample(2, [0.71] * N_q) F.add_sample(3, [0.61] * N_q) F.add_sample(4, [0.55] * N_q) # compute average F_ave = F.get_average_all() F_ave_ref = np.array([ [1., 0.84, 0.72, 0.61, 0.55, np.nan, np.nan, np.nan, np.nan, np.nan], [1., 0.84, 0.72, 0.61, 0.55, np.nan, np.nan, np.nan, np.nan, np.nan]]) assert np.allclose(F_ave, F_ave_ref, equal_nan=True) # check that truncate nans remove correctly F_ave2 = truncate_nans(F_ave) F_ave2_ref = np.array([ [1., 0.84, 0.72, 0.61, 0.55], [1., 0.84, 0.72, 0.61, 0.55]]) assert np.allclose(F_ave2, F_ave2_ref) # check normal array is not changed assert np.allclose(truncate_nans(F_ave2_ref), F_ave2_ref) # if nans not the same between cols F = np.array([ [1., 0.84, 0.72, 0.61, 0.55, 0.33, np.nan], [1., 0.84, 0.72, 0.61, 0.55, np.nan, np.nan]]) F_ref = np.array([ [1., 0.84, 0.72, 0.61, 0.55], [1., 0.84, 0.72, 0.61, 0.55]]) assert np.allclose(truncate_nans(F), F_ref) # if first column already contains nan, return empty time axis F = np.array([ [np.nan, 0.84, 0.72], [np.nan, 0.84, 0.72]]) F_ref = np.empty((2, 0)) F_new = truncate_nans(F) assert F_new.shape == F_ref.shape assert np.allclose(F_new, F_ref) dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/tests/test_tools.py000066400000000000000000000157541522712672400240010ustar00rootroot00000000000000import numpy as np import pytest from ase.build import bulk from dynasor.tools.acfs import compute_acf, fermi_dirac, smoothing_function from dynasor.tools.acfs import psd_from_acf, psd_from_time_signal, psd_from_acf_2d from dynasor.tools.structures import align_structure, _align_a_onto_xy from dynasor.tools.structures import get_offset_index @pytest.fixture def signal_complex(): np.random.seed(42) N = 1000 x = np.random.random((N, )) y = np.random.random((N, )) Z_t = x + 1j * y return Z_t def test_numpy_scipy_acf(signal_complex): delta_t = 0.25 t1, acf1 = compute_acf(signal_complex, delta_t=delta_t, method='numpy') t2, acf2 = compute_acf(signal_complex, delta_t=delta_t, method='scipy') assert np.allclose(t1, t2) assert np.allclose(acf1, acf2) def test_compute_acf_invalid_arguments(signal_complex): with pytest.raises(ValueError): t1, acf1 = compute_acf(signal_complex, method='asd') def test_fermi_dirac_time_function(signal_complex): signal = signal_complex.real time = np.arange(0, 1000, 1) t_0 = 500 t_width = 20 f = fermi_dirac(time, t_0, t_width) signal_damped = f * signal assert np.isclose(signal_damped[0], signal[0]) assert np.isclose(signal_damped[-1], 0.0) def test_smoothing_function(): data = np.array([1, 2, 2.5, 4, 5.5, 6.5, 7, 8, 7, 2]) res1 = smoothing_function(data, window_size=1, window_type='boxcar') assert np.allclose(res1, data) res2 = smoothing_function(data, window_size=2, window_type='boxcar') assert len(res2) == len(data) assert np.allclose(res2[0], 1) assert np.allclose(res2[1], 1.5) assert np.allclose(res2[2], 2.25) res3 = smoothing_function(data, window_size=3, window_type='boxcar') assert len(res3) == len(data) assert np.allclose(res3[0], 1.5) assert np.allclose(res3[1], 5.5/3) assert np.allclose(res3[2], 8.5/3) assert np.allclose(res3[-1], 4.5) def test_align_structure(): # randomly rotated Al FCC cubic cell atoms = bulk('Al', 'fcc', cubic=True).repeat(2) ref_cell = atoms.cell[:].copy() atoms.rotate(42, [1, 2, 5], rotate_cell=True) print(atoms.cell[:]) align_structure(atoms) assert np.isclose(atoms.cell[0, 1], 0) assert np.isclose(atoms.cell[0, 2], 0) assert np.isclose(atoms.cell[1, 2], 0) assert np.allclose(atoms.cell, ref_cell) print(atoms.cell[:]) # BCC primtive cell atoms = bulk('Ti', 'bcc', a=3.3).repeat(2) print(atoms.cell[:]) align_structure(atoms) assert np.isclose(atoms.cell[0, 1], 0) assert np.isclose(atoms.cell[0, 2], 0) assert np.isclose(atoms.cell[1, 2], 0) print(atoms.cell[:]) # strange cell which fails with very low tolerance cell = np.array([[6.66725037e+01, -7.15537500e-02, 3.71540563e-08], [1.40460691e-01, 6.63955753e+01, 5.54413709e-02], [5.91136223e-02, -3.04975220e-02, 4.73122339e+01]]) atoms = bulk('Al', 'fcc', cubic=True).repeat(2) atoms.set_cell(cell, scale_atoms=True) align_structure(atoms, atol=1e-5) def test_align_structure_error_cases(): # a nearly along z-axis — norm_a_xy < atol atoms = bulk('Al', 'fcc', cubic=True) cell = atoms.cell.array.copy() cell[0] = [0.0, 0.0, 4.05] atoms.set_cell(cell, scale_atoms=True) with pytest.raises(AssertionError, match='nearly parallel to the z-axis'): _align_a_onto_xy(atoms, atol=1e-5) # a has near-zero length — norm_a < atol atoms = bulk('Al', 'fcc', cubic=True) cell = atoms.cell.array.copy() cell[0] = [1e-10, 0.0, 0.0] atoms.set_cell(cell, scale_atoms=True) with pytest.raises(AssertionError, match='near-zero length'): _align_a_onto_xy(atoms, atol=1e-5) # a[2] < 0 — exercises the sign = +1 rotation branch atoms = bulk('Al', 'fcc', cubic=True).repeat(2) atoms.rotate(30, [0, 1, 0], rotate_cell=True) # tilts a so that a[2] < 0 assert atoms.cell[0, 2] < 0 align_structure(atoms) assert np.isclose(atoms.cell[0, 1], 0) assert np.isclose(atoms.cell[0, 2], 0) assert np.isclose(atoms.cell[1, 2], 0) def test_index_offset(): prim = bulk('Ti', 'hcp') prim.set_array('basis_index', np.array([0, 1])) supercell = prim.repeat((4, 3, 2)) # check indices offset, index = get_offset_index(prim, supercell) assert np.allclose(index, supercell.get_array('basis_index')) # fail if positions dont match supercell.positions[0, 0] += 0.03 with pytest.raises(ValueError): offset, index = get_offset_index(prim, supercell) # works again if using higher tolerenaces offset, index = get_offset_index(prim, supercell, tol=0.05) assert np.allclose(index, supercell.get_array('basis_index')) def test_psd_from_acf(): acf_vals = [np.array([1.0, 0.5, 0.25]), np.array([1.0, 0.5, 0.25, 0.18])] for acf in acf_vals: dt = 2.0 freqs, psd = psd_from_acf(acf, dt=dt, even=True) assert np.isrealobj(freqs) assert np.isrealobj(psd) signal = np.hstack((acf, acf[:0:-1])) freqs_expected = 2 * np.pi * np.fft.rfftfreq(len(signal), dt) psd_expected = (dt * np.fft.rfft(signal)).real assert np.allclose(freqs, freqs_expected) assert np.allclose(psd, psd_expected) def test_psd_from_time_signal_real_matches_manual_rfft(): x = np.array([0.5, 1.5, -0.5, 2.0, 1.0]) dt = 0.25 freqs, psd = psd_from_time_signal(x, dt=dt, complex=False) fft = np.fft.rfft(x) freqs_expected = 2 * np.pi * np.fft.rfftfreq(len(x), dt) psd_expected = dt * np.abs(fft) ** 2 / len(x) assert np.allclose(freqs, freqs_expected) assert np.allclose(psd, psd_expected) def test_psd_from_time_signal_complex_matches_manual_fft(): x = np.array([1.0 + 0.0j, 0.0 + 1.0j, -1.0 + 0.5j, 0.5 - 0.25j, 2.0 + 1.0j]) dt = 0.25 freqs, psd = psd_from_time_signal(x, dt=dt, complex=True) fft = np.fft.fft(x) freqs_expected = 2 * np.pi * np.fft.fftfreq(len(x), dt) psd_expected = dt * np.abs(fft) ** 2 / len(x) assert np.allclose(freqs, freqs_expected) assert np.allclose(psd, psd_expected) def test_psd_from_acf_2d(): acf_2d_vals = [ np.array([[1.0, 0.5, 0.25], [1.0, 0.4, 0.1]]), np.array([[1.0, 0.5, 0.25, 0.18], [1.0, 0.3, 0.2, 0.05]]) ] dt = 2.0 for acf_2d in acf_2d_vals: for even in [True, False]: freqs_2d, psd_2d = psd_from_acf_2d(acf_2d, dt=dt, even=even) psd_1d = [] for acf in acf_2d: freqs_1d, psd = psd_from_acf(acf, dt=dt, even=even) psd_1d.append(psd) psd_1d = np.array(psd_1d) assert np.allclose(freqs_2d, freqs_1d) assert np.allclose(psd_2d, psd_1d) def test_psd_from_acf_zero_signal(): acf = np.zeros(10) freqs, psd = psd_from_acf(acf, dt=1.0, even=True) assert np.allclose(psd, 0) def test_psd_from_acf_2d_zero_signal(): acf = np.zeros((5, 10)) freqs, psd = psd_from_acf_2d(acf, dt=1.0, even=True) assert np.allclose(psd, 0) dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/tests/test_trajectory_and_window_iterator.py000066400000000000000000000310501522712672400311340ustar00rootroot00000000000000import os import pytest import numpy as np from dynasor.trajectory import Trajectory from dynasor.trajectory import WindowIterator from dynasor.trajectory.trajectory import consume import tempfile @pytest.fixture def traj_fname_xtc(): this_dir = os.path.dirname(__file__) traj_fname = os.path.join( this_dir, 'trajectory_reader/trajectory_files/water_10snapshots.xtc') return traj_fname @pytest.fixture def filenames(): this_dir = os.path.dirname(__file__) traj_fname = os.path.join(this_dir, 'trajectory_reader/trajectory_files/dump_long_with_velocities.xyz') index_fname = os.path.join(this_dir, 'trajectory_reader/trajectory_files/index_file_dump_long') return traj_fname, index_fname def compare_trajs(traj1, traj2): assert len(traj1) == len(traj2) for ind, frame1 in enumerate(traj1): frame2 = traj2[ind] for type in frame1.positions_by_type.keys(): assert np.allclose(frame1.positions_by_type[type], frame2.positions_by_type[type]) assert np.allclose(frame1.velocities_by_type[type], frame2.velocities_by_type[type]) def test_traj_step(filenames): traj_fname, index_fname = filenames # full traj step = 1 trajectory_format = 'extxyz' traj = Trajectory(traj_fname, trajectory_format=trajectory_format, frame_step=step) traj_full = list(traj) assert len(traj_full) == 30 assert traj.number_of_frames_read == 30 # every third snapshot step = 3 traj = Trajectory(traj_fname, trajectory_format=trajectory_format, frame_step=step) traj_step3 = list(traj) assert len(traj_step3) == 10 assert traj.number_of_frames_read == 10 compare_trajs(traj_full[::step], traj_step3) def test_index_file(filenames): traj_fname, index_fname = filenames n_atoms = 320 trajectory_format = 'extxyz' # atomic_indices is None traj = Trajectory(traj_fname, trajectory_format=trajectory_format) assert len(traj._atomic_indices) == 1 assert 'X' in traj._atomic_indices assert np.allclose(traj._atomic_indices['X'], np.arange(0, n_atoms, 1)) # atomic_indices is Dict atomic_indices = {'Cs': [0, 1, 2, 3, 4], 'H': [9, 10], 'my group of atoms': [15, 25, 101]} traj = Trajectory(traj_fname, trajectory_format=trajectory_format, atomic_indices=atomic_indices) assert len(traj._atomic_indices) == 3 for key in atomic_indices.keys(): assert np.allclose(traj._atomic_indices[key], atomic_indices[key]) # From gromacs index-file traj = Trajectory(traj_fname, trajectory_format=trajectory_format, atomic_indices=index_fname) assert len(traj._atomic_indices) == 3 assert np.allclose(traj._atomic_indices['Cs'], np.arange(0, 320, 5)) assert np.allclose(traj._atomic_indices['Pb'], np.arange(1, 320, 5)) Br_inds = np.array(sorted(list(range(2, 320, 5)) + list(range(3, 320, 5)) + list(range(4, 320, 5)))) # noqa assert np.allclose(traj._atomic_indices['Br'], Br_inds) # Faulty atomic_indices raises ValueError atomic_indices = [1, 2, 3, 4] with pytest.raises(ValueError, match=r'Could not understand atomic_indices.'): traj = Trajectory(traj_fname, trajectory_format=trajectory_format, atomic_indices=atomic_indices) def test_filename_property(filenames): traj_fname, index_fname = filenames traj = Trajectory(traj_fname, trajectory_format='extxyz') assert traj.filename == traj_fname def test_trajectory_with_mdanalysis_reader_xtc(traj_fname_xtc): traj = Trajectory(traj_fname_xtc, trajectory_format='xtc') frame = next(traj) positions = frame.positions_by_type['X'] assert frame.frame_index == 0 assert positions.shape == (30, 3) assert np.allclose(positions[5], np.array([8.0200005, 12.310001, 28.490002])) def test_trajectory_file_does_not_exists(): traj_fname = 'asd' with pytest.raises(IOError, match=r'does not exist'): Trajectory(traj_fname, trajectory_format='extxyz') def test_trajectory_with_ambiguous_format(filenames): traj_fname, index_fname = filenames with pytest.raises(IOError): Trajectory(traj_fname, trajectory_format='lammps') # flake8: noqa: C901 def test_trajectory_repr_html_str(filenames): traj_fname, index_fname = filenames traj = Trajectory(traj_fname, trajectory_format='extxyz') s = traj._repr_html_() assert isinstance(s, str) s_target_start = """

Trajectory

FieldValue
File name""" s_target_end = """dump_long_with_velocities.xyz
Number of atoms320
Cell metric[[23.77195271 0. 0. ] [ 0. 23.77195271 0. ] [ 0. 0. 23.77195271]]
Frame step1
Atom types['X']
""" assert s.startswith(s_target_start) assert s.endswith(s_target_end) def test_trajectory_frame_repr_html_str(filenames): traj_fname, index_fname = filenames traj = Trajectory(traj_fname, trajectory_format='extxyz') frame = next(traj) s = frame._repr_html_() assert isinstance(s, str) s_target = """

TrajectoryFrame

FieldValue/Shape
Index0
Positions X(320, 3)
Velocities X(320, 3)
""" assert s == s_target def test_trajectory_with_changing_cell(): # write dummy file s = b"""2 Lattice="4.05 0.0 0.0 0.0 4.05 0.0 0.0 0.0 4.05" Properties=species:S:1:pos:R:3 pbc="T T T" Al 0.00000000 0.00000000 0.00000000 Al 0.00000000 2.02500000 2.02500000 2 Lattice="4.051 0.0 0.0 0.0 4.05 0.0 0.0 0.0 4.05" Properties=species:S:1:pos:R:3 pbc="T T T" Al 0.00000000 0.00000000 0.00000000 Al 0.00000000 2.02500000 2.02500000 2 Lattice="4.05 0.0 0.0 0.0 4.05 0.0 0.0 0.0 4.05" Properties=species:S:1:pos:R:3 pbc="T T T" Al 0.00000000 0.00000000 0.00000000 Al 0.00000000 2.02500000 2.02500000""" f = tempfile.NamedTemporaryFile(delete=False) f.write(s) f.seek(0) with pytest.raises(ValueError): Trajectory(f.name, trajectory_format='extxyz') def test_windowiterator_stride(filenames): traj_fname, index_fname = filenames window_size = 4 max_frames = 12 trajectory_format = 'extxyz' # full traj step = 1 stride = 1 traj = Trajectory(traj_fname, trajectory_format=trajectory_format, frame_step=step, frame_stop=max_frames) window_iterator = WindowIterator(traj, width=window_size, window_step=stride) window_indices = [] for window in window_iterator: inds = [frame.frame_index for frame in window] window_indices.append(inds) window_indices_target = [[0, 1, 2, 3], [1, 2, 3, 4], [2, 3, 4, 5], [3, 4, 5, 6], [4, 5, 6, 7], [5, 6, 7, 8], [6, 7, 8, 9], [7, 8, 9, 10], [8, 9, 10, 11], [9, 10, 11], [10, 11], [11]] assert window_indices == window_indices_target # step > 1 step = 2 stride = 1 traj = Trajectory(traj_fname, trajectory_format=trajectory_format, frame_step=step, frame_stop=max_frames) window_iterator = WindowIterator(traj, width=window_size, window_step=stride) window_indices = [] for window in window_iterator: inds = [frame.frame_index for frame in window] window_indices.append(inds) window_indices_target = [[0, 2, 4, 6], [2, 4, 6, 8], [4, 6, 8, 10], [6, 8, 10], [8, 10], [10]] assert window_indices == window_indices_target # stride > 1 step = 1 stride = 2 traj = Trajectory(traj_fname, trajectory_format=trajectory_format, frame_step=step, frame_stop=max_frames) window_iterator = WindowIterator(traj, width=window_size, window_step=stride) window_indices = [] for window in window_iterator: inds = [frame.frame_index for frame in window] window_indices.append(inds) window_indices_target = [[0, 1, 2, 3], [2, 3, 4, 5], [4, 5, 6, 7], [6, 7, 8, 9], [8, 9, 10, 11], [10, 11]] assert window_indices == window_indices_target # stride > 1 and step > 2 step = 2 stride = 2 traj = Trajectory(traj_fname, trajectory_format=trajectory_format, frame_step=step, frame_stop=max_frames) window_iterator = WindowIterator(traj, width=window_size, window_step=stride) window_indices = [] for window in window_iterator: inds = [frame.frame_index for frame in window] window_indices.append(inds) window_indices_target = [[0, 2, 4, 6], [4, 6, 8, 10], [8, 10]] assert window_indices == window_indices_target def test_large_window_step(filenames): # test when window step is larger than window size traj_fname, index_fname = filenames max_frames = 20 frame_step = 1 window_size = 5 window_step = 7 trajectory_format = 'extxyz' traj = Trajectory(traj_fname, trajectory_format=trajectory_format, frame_step=frame_step, frame_stop=max_frames) window_iterator = WindowIterator(traj, width=window_size, window_step=window_step) window_indices = [] for window in window_iterator: inds = [frame.frame_index for frame in window] window_indices.append(inds) window_indices_target = [[0, 1, 2, 3, 4], [7, 8, 9, 10, 11], [14, 15, 16, 17, 18]] assert window_indices == window_indices_target def test_invalid_inputs_to_trajectory(filenames): traj_fname, index_fname = filenames trajectory_format = 'extxyz' frame_step = -1 with pytest.raises(ValueError, match=r'frame_step should be positive'): Trajectory(traj_fname, trajectory_format=trajectory_format, frame_step=frame_step) frame_start = -1 with pytest.raises(ValueError, match=r'frame_start should be positive'): Trajectory(traj_fname, trajectory_format=trajectory_format, frame_start=frame_start) # max of atomic indices exceeds number of atoms atomic_indices = dict() atomic_indices['Cs'] = [1, 2, 3, 4, 1100] with pytest.raises(ValueError, match=r'index in atomic_indices exceeds number of atoms.'): Trajectory(traj_fname, trajectory_format=trajectory_format, atomic_indices=atomic_indices) # maximum atomic index equals number of atoms atomic_indices = {'Cs': [320]} with pytest.raises(ValueError, match=r'index in atomic_indices exceeds number of atoms.'): Trajectory(traj_fname, trajectory_format=trajectory_format, atomic_indices=atomic_indices) # negative index in atomic indices atomic_indices = dict() atomic_indices['Cs'] = [1, 2, 3, -4, 5] with pytest.raises(ValueError, match=r'index in atomic_indices is negative.'): Trajectory(traj_fname, trajectory_format=trajectory_format, atomic_indices=atomic_indices) # negative index in atomic indices atomic_indices = dict() atomic_indices['Cs'] = [1, 2, 3, 4, 5] atomic_indices['Cs_and_Pb'] = [1, 2, 3, 4, 5, 6, 7, 8, 9] with pytest.raises(ValueError, match=r'The char "_" is not allowed in atomic_indices.'): Trajectory(traj_fname, trajectory_format=trajectory_format, atomic_indices=atomic_indices) def test_trajectory_ase_format(): """trajectory_format='ase' selects the ASETrajectoryReader.""" this_dir = os.path.dirname(__file__) traj_fname = os.path.join(this_dir, 'trajectory_reader/trajectory_files/dump.traj') traj = Trajectory(traj_fname, trajectory_format='ase') frame = next(traj) assert frame.frame_index == 0 def test_trajectory_str_repr(filenames): """str() and repr() return a non-empty string containing key fields.""" traj_fname, _ = filenames traj = Trajectory(traj_fname, trajectory_format='extxyz') next(traj) # advance one frame so current_frame_index is set s = str(traj) assert 'Trajectory' in s assert 'natoms' in s assert repr(traj) == s def test_consume_none_exhausts_iterator(): """consume(iterator, None) drains the iterator completely.""" it = iter(range(10)) consume(it, None) assert list(it) == [] dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/tests/test_trajectory_frame.py000066400000000000000000000106561522712672400261750ustar00rootroot00000000000000import pytest import numpy as np from dynasor.trajectory.trajectory_frame import TrajectoryFrame from dynasor.trajectory import Trajectory def test_get_as_arrays(): # setup atomic_indices_ref = dict() atomic_indices_ref['Cs'] = np.array([0, 1, 2, 3, 4, 9]) atomic_indices_ref['Pb'] = np.array([5, 6, 7, 8]) positions_ref = np.arange(0, 30, 1).reshape((10, 3)) velocities_ref = np.arange(-15, 15, 1).reshape((10, 3)) frame = TrajectoryFrame(atomic_indices_ref, 0, positions_ref, velocities_ref) # get arrays with complete atomic_indices assert np.allclose(frame.get_positions_as_array(atomic_indices_ref), positions_ref) assert np.allclose(frame.get_velocities_as_array(atomic_indices_ref), velocities_ref) # ValueError with incomplete atomic_indices, len(all_inds) != n_atoms atomic_indices = dict() atomic_indices['Cs'] = [0, 1, 2, 3, 4] atomic_indices['Pb'] = [5, 6, 7, 8] with pytest.raises(ValueError): frame.get_positions_as_array(atomic_indices) with pytest.raises(ValueError): frame.get_velocities_as_array(atomic_indices) # ValueError with incomplete atomic_indices, len(set(all_inds)) != n_atoms atomic_indices = dict() atomic_indices['Cs'] = [0, 1, 2, 3, 4, 4] atomic_indices['Pb'] = [5, 6, 7, 8] with pytest.raises(ValueError): frame.get_positions_as_array(atomic_indices) with pytest.raises(ValueError): frame.get_velocities_as_array(atomic_indices) def test_str(): atomic_indices = dict() atomic_indices['Cs'] = np.arange(0, 20) atomic_indices['Pb'] = np.arange(50, 100) # No velocities traj = Trajectory('tests/trajectory_reader/trajectory_files/dump.xyz', trajectory_format='extxyz', atomic_indices=atomic_indices) str_target = 'Frame index 0\n positions : Cs shape : (20, 3)\n'\ ' positions : Pb shape : (50, 3)' frame = next(traj) assert str(frame) == str_target # With velocities traj = Trajectory('tests/trajectory_reader/trajectory_files/dump_long_with_velocities.xyz', trajectory_format='extxyz', atomic_indices=atomic_indices) str_target = 'Frame index 0\n positions : Cs shape : (20, 3)\n'\ ' positions : Pb shape : (50, 3)\n'\ ' velocities : Cs shape : (20, 3)\n velocities : Pb shape : (50, 3)' frame = next(traj) assert str(frame) == str_target def test_read_indices_from_trajectory(): # Check that error is raised when trying to read atomic_indices from reader and file type # combination that doesn't support this with pytest.raises(ValueError, match='Could not read atomic indices from the trajectory.'): traj = Trajectory('tests/trajectory_reader/trajectory_files/water_10snapshots.xtc', trajectory_format='xtc', atomic_indices='read_from_trajectory') # Check that error is raised when trying to read atomic_indices from the internal lammps reader, # that doesn't have this functionality with pytest.raises(ValueError, match='Could not read atomic indices from the trajectory.'): traj = Trajectory('tests/trajectory_reader/trajectory_files/positions.lammpstrj', trajectory_format='lammps_internal', atomic_indices='read_from_trajectory') # Check that atomic indices are passed to the trajectory frame from the MDAnalysis reader traj = Trajectory('tests/trajectory_reader/trajectory_files/positions.lammpstrj', trajectory_format='lammps_mdanalysis', length_unit='Angstrom', time_unit='ps', atomic_indices='read_from_trajectory') assert isinstance(traj.atomic_indices, dict) assert np.all(traj.atomic_indices['1'] == [0, 3, 6, 9, 12, 15, 18, 21]) assert np.all(traj.atomic_indices['2'] == [1, 2, 4, 5, 7, 8, 10, 11, 13, 14, 16, 17, 19, 20, 22, 23]) # Check that atomic indices are passed to the trajectory frame from the Extxyz reader traj = Trajectory('tests/trajectory_reader/trajectory_files/dump.xyz', trajectory_format='extxyz', length_unit='Angstrom', time_unit='ps', atomic_indices='read_from_trajectory') assert isinstance(traj.atomic_indices, dict) assert np.all(traj.atomic_indices['Cs'] == np.arange(0, 1080, 5)) assert np.all(traj.atomic_indices['Pb'] == np.arange(1, 1080, 5)) dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/tests/test_units.py000066400000000000000000000010341522712672400237650ustar00rootroot00000000000000import numpy as np from dynasor.units import radians_per_fs_to_THz, radians_per_fs_to_meV, radians_per_fs_to_invcm from dynasor.units import THz_to_meV, THz_to_invcm, meV_to_invcm def test_units(): assert np.isclose(THz_to_meV, 4.13567) assert np.isclose(THz_to_meV * meV_to_invcm, THz_to_invcm) assert np.isclose(radians_per_fs_to_THz, 1000 / (2 * np.pi)) assert np.isclose(radians_per_fs_to_THz * THz_to_meV, radians_per_fs_to_meV) assert np.isclose(radians_per_fs_to_THz * THz_to_invcm, radians_per_fs_to_invcm) dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/tests/test_utils.py000066400000000000000000000000001522712672400237530ustar00rootroot00000000000000dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/tests/test_weighting.py000066400000000000000000000635551522712672400246300ustar00rootroot00000000000000import numpy as np import pytest from pandas import option_context from dynasor.post_processing import NeutronScatteringLengths, \ XRayFormFactors, ElectronScatteringFactors, \ Weights, get_weighted_sample @pytest.mark.parametrize('weight_value', [0.0, 1.0]) def test_weighting_with_uniform_weights(weight_value, dynamic_sample_with_incoh): """Uniform weight w applied to all species: each output function equals w^2 * input. Covers both the zero-weight case (all outputs zero) and the unity-weight case (outputs equal inputs) without duplicating the assertion logic. """ weights_coh = {'A': weight_value, 'B': weight_value} weights_incoh = {'A': weight_value, 'B': weight_value} weights = Weights(weights_coh, weights_incoh) sample = dynamic_sample_with_incoh sample_weighted = get_weighted_sample(sample, weights) # For uniform weight w: output_{A,B} = w_A * w_B * input = w^2 * input # (for incoherent: output_A = w_incoh_A^2 * input_A = w^2 * input_A) for key in sample.available_correlation_functions: assert np.allclose(weight_value**2 * sample[key], sample_weighted[key]) # Simulation parameters preserved assert sample.atom_types == sample_weighted.atom_types assert sample.pairs == sample_weighted.pairs assert sample.particle_counts == sample_weighted.particle_counts assert np.allclose(sample.cell, sample_weighted.cell) assert sorted(list(sample_weighted.simulation_data.keys())) == sorted( ['atom_types', 'pairs', 'particle_counts', 'cell']) assert sorted(sample._data_keys) == sorted(sample_weighted._data_keys) # History assert len(sample_weighted.history) == 1 assert sample_weighted.history[0]['func'] == 'get_weighted_sample' assert sample_weighted.history[0]['atom_type_map'] is None assert sample_weighted.history[0]['weights_class'] == 'Weights' expected_history = {'atom_type': {0: 'A', 1: 'B'}, 'coherent': {0: weight_value, 1: weight_value}, 'incoherent': {0: weight_value, 1: weight_value}} assert sample_weighted.history[0]['weights_parameters'] == expected_history # Dimension arrays (q_points, time, omega) are always copied unchanged for key in sample.dimensions: assert np.allclose(sample[key], sample_weighted[key]) def test_weighting_with_real_weights(dynamic_sample_with_incoh): weights_coh = {'A': 2.792, 'B': 5.43} weights_incoh = {'A': 12.89, 'B': 74.222} weights = Weights(weights_coh, weights_incoh) sample = dynamic_sample_with_incoh sample_weighted = get_weighted_sample(sample, weights) # compare coherent correlation functions names = ['Fqt_coh', 'Sqw_coh', 'Clqt', 'Clqw', 'Ctqt', 'Ctqw'] pairs = [('A', 'A'), ('A', 'B'), ('B', 'B')] for name in names: # check partials expected_total = np.zeros(sample.Fqt_coh.shape) for atom_type1, atom_type2 in pairs: key = f'{name}_{atom_type1}_{atom_type2}' expected = weights_coh[atom_type1] * weights_coh[atom_type2] * sample[key] expected_total += expected assert np.allclose(expected, sample_weighted[key]) # check total assert np.allclose(expected_total, sample_weighted[name]) # compare incoherent correlation functions names = ['Fqt_incoh', 'Sqw_incoh'] atom_types = ['A', 'B'] for name in names: # check partials expected_total = np.zeros(sample.Fqt_incoh.shape) for atom_type in atom_types: key = f'{name}_{atom_type}' expected = weights_incoh[atom_type]**2 * sample[key] expected_total += expected assert np.allclose(expected, sample_weighted[key]) # check total assert np.allclose(expected_total, sample_weighted[name]) # compare simulation parameters assert sample.atom_types == sample_weighted.atom_types assert sample.pairs == sample_weighted.pairs assert sample.particle_counts == sample_weighted.particle_counts assert np.allclose(sample.cell, sample_weighted.cell) target = ['atom_types', 'pairs', 'particle_counts', 'cell'] assert sorted(list(sample_weighted.simulation_data.keys())) == sorted(target) assert sorted(sample._data_keys) == sorted(sample_weighted._data_keys) # compare history assert len(sample_weighted.history) == 1 assert sample_weighted.history[0]['func'] == 'get_weighted_sample' assert sample_weighted.history[0]['atom_type_map'] is None assert sample_weighted.history[0]['weights_class'] == 'Weights' target = {'atom_type': {0: 'A', 1: 'B'}, 'coherent': {0: 2.792, 1: 5.43}, 'incoherent': {0: 12.89, 1: 74.222}} assert sample_weighted.history[0]['weights_parameters'] == target # Check that initial keys (such as q_points, time and omega) are copied. for key in sample.dimensions: assert np.allclose(sample[key], sample_weighted[key]) def test_weighting_with_real_weights_without_incoh(dynamic_sample_without_incoh): weights_coh = {'A': -12.792, 'B': 45.43} weights = Weights(weights_coh) sample = dynamic_sample_without_incoh sample_weighted = get_weighted_sample(sample, weights) # compare coherent correlation functions names = ['Fqt_coh', 'Sqw_coh', 'Clqt', 'Clqw', 'Ctqt', 'Ctqw'] pairs = [('A', 'A'), ('A', 'B'), ('B', 'B')] for name in names: # check partials expected_total = np.zeros(sample.Fqt_coh.shape) for atom_type1, atom_type2 in pairs: key = f'{name}_{atom_type1}_{atom_type2}' expected = weights_coh[atom_type1] * weights_coh[atom_type2] * sample[key] expected_total += expected assert np.allclose(expected, sample_weighted[key]) # check total assert np.allclose(expected_total, sample_weighted[name]) def test_weighting_of_static_sample(static_sample): weights_coh = {'A': 22.1, 'B': 5} weights = Weights(weights_coh) sample = static_sample sample_weighted = get_weighted_sample(sample, weights) # compare partial Sq name = 'Sq' pairs = [('A', 'A'), ('A', 'B'), ('B', 'B')] expected_total = np.zeros(sample.Sq.shape) for atom_type1, atom_type2 in pairs: key = f'{name}_{atom_type1}_{atom_type2}' expected = weights_coh[atom_type1] * weights_coh[atom_type2] * sample[key] expected_total += expected assert np.allclose(expected, sample_weighted[key]) # check total assert np.allclose(expected_total, sample_weighted[name]) def test_weighting_without_incoh_weights(dynamic_sample_with_incoh): """ check that weighting where weights does not support incoh works as expected """ weights_coh = {'A': 2.792, 'B': 5.43} weights = Weights(weights_coh) with pytest.warns(UserWarning): sample_weighted = get_weighted_sample(dynamic_sample_with_incoh, weights) expected_keys = ['Fqt_coh', 'Fqt_coh_A_A', 'Fqt_coh_A_B', 'Fqt_coh_B_B', 'Sqw_coh', 'Sqw_coh_A_A', 'Sqw_coh_A_B', 'Sqw_coh_B_B', 'Clqt', 'Clqt_A_A', 'Clqt_A_B', 'Clqt_B_B', 'Clqw', 'Clqw_A_A', 'Clqw_A_B', 'Clqw_B_B', 'Ctqt', 'Ctqt_A_A', 'Ctqt_A_B', 'Ctqt_B_B', 'Ctqw', 'Ctqw_A_A', 'Ctqw_A_B', 'Ctqw_B_B'] assert sorted(sample_weighted.available_correlation_functions) == sorted(expected_keys) assert sorted(sample_weighted.dimensions) == ['omega', 'q_points', 'time'] keys_not_expected = ['Fqt_incoh', 'Fqt_incoh_A_A', 'Fqt_incoh_A_B', 'Fqt_incoh_B_B', 'Sqw_incoh', 'Sqw_incoh_A_A', 'Sqw_incoh_A_B', 'Sqw_incoh_B_B'] for key in keys_not_expected: assert key not in sample_weighted.available_correlation_functions assert key not in sample_weighted._data_keys def test_weighting_without_current_support(dynamic_sample_without_incoh): """ check that weighting where weights does not support currents works as expected """ weights_coh = {'A': 2.792, 'B': 5.43} weights_incoh = {'A': 12.89, 'B': 74.222} weights = Weights(weights_coh, weights_incoh, supports_currents=False) with pytest.warns(UserWarning): sample_weighted = get_weighted_sample(dynamic_sample_without_incoh, weights) expected_keys = ['Fqt_coh', 'Fqt_coh_A_A', 'Fqt_coh_A_B', 'Fqt_coh_B_B', 'Sqw_coh', 'Sqw_coh_A_A', 'Sqw_coh_A_B', 'Sqw_coh_B_B'] assert sorted(sample_weighted.available_correlation_functions) == sorted(expected_keys) assert sorted(sample_weighted.dimensions) == ['omega', 'q_points', 'time'] def test_weighting_atom_type_map(dynamic_sample_with_incoh): """ assert that species map maps species as expected """ weights_coh = {'A': 1, 'B': 2, 'C': 3} weights_incoh = {'A': 1, 'B': 2, 'C': 3} weights = Weights(weights_coh, weights_incoh, supports_currents=False) weighted = get_weighted_sample(dynamic_sample_with_incoh, weights) map = {'A': 'B', 'B': 'C'} weighted_map = get_weighted_sample(dynamic_sample_with_incoh, weights, map) assert np.all(weighted_map['Fqt_coh_A_A'] == 2**2 * weighted['Fqt_coh_A_A']) assert np.all(weighted_map['Fqt_coh_B_B'] == (3/2)**2 * weighted['Fqt_coh_B_B']) def test_weighting_partial_atom_type_map(dynamic_sample_with_incoh): weights = Weights({'B': 2, 'C': 3}, {'B': 2, 'C': 3}) weighted = get_weighted_sample(dynamic_sample_with_incoh, weights, {'A': 'C'}) assert np.allclose(weighted['Fqt_coh_A_A'], 3**2 * dynamic_sample_with_incoh['Fqt_coh_A_A']) assert np.allclose(weighted['Fqt_coh_B_B'], 2**2 * dynamic_sample_with_incoh['Fqt_coh_B_B']) def test_weighting_map_switch_species(dynamic_sample_with_incoh): weights_coh = {'A': 2, 'B': 3} weights_incoh = {'A': 2, 'B': 3} weights = Weights(weights_coh, weights_incoh, supports_currents=False) weighted = get_weighted_sample(dynamic_sample_with_incoh, weights) # Test swapping A->B and B->A map = {'A': 'B', 'B': 'A'} weighted_map = get_weighted_sample(dynamic_sample_with_incoh, weights, map) assert np.allclose(weighted_map['Fqt_coh_A_A'], (3/2)**2 * weighted['Fqt_coh_A_A']) assert np.allclose(weighted_map['Fqt_coh_B_B'], (2/3)**2 * weighted['Fqt_coh_B_B']) # Test swapping both A->B and B->B map = {'A': 'B', 'B': 'B'} weighted_map = get_weighted_sample(dynamic_sample_with_incoh, weights, map) assert np.allclose(weighted_map['Fqt_coh_A_A'], (3/2)**2 * weighted['Fqt_coh_A_A']) assert np.allclose(weighted_map['Fqt_coh_B_B'], weighted['Fqt_coh_B_B']) def test_weighting_map_invalid_species(dynamic_sample_with_incoh): # Swap weights with an invalid species weights_coh = {'A': 2, 'B': 3} weights_incoh = {'A': 2, 'B': 3} weights = Weights(weights_coh, weights_incoh, supports_currents=False) map = {'A': 'B', 'B': 'X'} with pytest.raises(ValueError) as e: get_weighted_sample(dynamic_sample_with_incoh, weights, map) assert 'Coherent weights for X have not been specified' in str(e) # Neutron weights @pytest.mark.parametrize('species,b_coh,b_inc', [ (['H'], {'H': -3.73904}, {'H': 25.27081}), (['C', 'O'], {'C': 6.64603, 'O': 5.80307}, {'C': -0.00572, 'O': 0.0000684}), (['Ne'], {'Ne': 4.56631}, {'Ne': 0.0}), (['Hf'], {'Hf': 7.78164}, {'Hf': 0.0}), ]) def test_neutron_scattering_lengths_isotope_average(species, b_coh, b_inc): """ Ensure that the isotope average scattering lengths matches the NIST table: https://www.ncnr.nist.gov/resources/n-lengths/list.html """ weights = NeutronScatteringLengths(species) for s in species: assert np.isclose(weights.get_weight_coh(s), b_coh[s]) assert np.isclose(weights.get_weight_incoh(s), b_inc[s]) @pytest.mark.parametrize('species,b_coh,b_inc,abundance', [ (['H'], {'H': 2.53994}, {'H': 9.16762}, {'H': {1: 0.33, 2: 0.30, 3: 0.37}}), (['Li'], {'Li': 2.00-0.261j}, {'Li': -1.89+0.26j}, {'Li': {6: 1.0, 7: 0.0}}), ( ['C', 'O'], {'C': 6.32833, 'O': 5.81575}, {'C': -0.364, 'O': 0.045}, {'C': {12: 0.3, 13: 0.7}, 'O': {16: 0.25, 17: 0.25, 18: 0.5}} ), ]) def test_neutron_scattering_lengths_custom_abundance(species, b_coh, b_inc, abundance): """Make sure abundance weighting works as intended.""" weights = NeutronScatteringLengths(species, abundance) for s in species: assert np.isclose(weights.get_weight_coh(s), b_coh[s]) assert np.isclose(weights.get_weight_incoh(s), b_inc[s]) # Fetch the abundances and make sure they match for species, abs in abundance.items(): assert abs == weights.abundances[species] @pytest.mark.parametrize('species,abundance,should_raise', [ (['K'], None, True), (['K'], {'K': {39: 0.93, 40: 0, 41: 0.07}}, False) ]) def test_neutron_scattering_lengths_missing_in_database(species, abundance, should_raise): """Should throw a value error when requested species has missing data in the database.""" if should_raise: with pytest.raises(ValueError) as e: NeutronScatteringLengths(species, abundance) assert '40K is missing tabulated values for either the coherent' in str(e) else: NeutronScatteringLengths(species, abundance) def test_neutron_scattering_lengths_invalid_total_abundance(): """Should throw a value error when the abundances does not add up to 1.0 for each species""" abundance = {'O': {16: 0.3, 17: 0.8, 18: 0.0}, 'N': {14: 0.8, 15: 0.2}} with pytest.raises(ValueError) as e: NeutronScatteringLengths(['O', 'N'], abundance) assert 'Abundance values for O do not sum up to 1.0' in str(e) def test_neutron_scattering_lengths_invalid_isotope_in_abundance(): """Should throw a value error when the selected isotope does not exist""" abundance = {'V': {10: 1.0}} with pytest.raises(ValueError) as e: NeutronScatteringLengths(['V'], abundance) assert 'No match in database for V and isotope 10' in str(e) def test_neutron_scattering_lengths_property(): """Make sure scattering_lengths property works.""" w = NeutronScatteringLengths(['Si']) with option_context('display.max_rows', 10, 'display.max_columns', 50): s = str(w.parameters) assert 'index species isotope abundance b_coh b_inc' in s assert '0 26 Si 28 0.9223 4.107+0.000j' in s assert '2 28 Si 30 0.0310 4.580+0.000j 0.00+0.00j' in s # X-ray weights @pytest.mark.parametrize('species,q_norm,method', [ (['He'], np.array([1, 2, 3]), 'waasmaier-1995'), (['C', 'O'], np.array([1, 2, 3, 0.5999]).reshape(-1, 1), 'waasmaier-1995'), (['Sb3+', 'O1-'], np.array([0.1, 0.2]), 'waasmaier-1995'), (['H'], np.array([1, 2, 3]), 'itc-2006'), (['K1+', 'O'], np.array([1, 2, 3, 0.5999]).reshape(-1, 1), 'itc-2006'), (['Mn4+', 'O1-'], np.array([0.1, 0.2]), 'itc-2006') ]) def test_xray_form_factors_shapes(species, q_norm, method): weights = XRayFormFactors(species, method) for s in species: for q in q_norm: coh = weights.get_weight_coh(s, q) incoh = weights.get_weight_incoh(s, q) assert incoh is None assert coh.shape is not None @pytest.mark.parametrize('species,q_norm,method,result', [ (['He'], np.array([1, 2]), 'waasmaier-1995', [0.09539912837127983, 0.0097169894132503]), (['O'], np.array([1, 2]), 'waasmaier-1995', [1.3770183528975948, 0.6732633649895161]), (['H'], np.array([1, 2]), 'itc-2006', [0.006936916317383404, 0.0013124707496789138]), (['C'], np.array([1, 2]), 'itc-2006', [1.1152000156986035, 0.37893583264032515]), ]) def test_xray_form_factors_numeric(species, q_norm, method, result): """ Manually compute f0 from the given source and check that the numbers match. Note that this does not guarantee that no errors have been made in OCR:ing the tables; it just serves as a spot-check. """ q_norm = np.array(q_norm) * 4 * np.pi # updated the definition of s after computing ref values. weights = XRayFormFactors(species, method) for s in species: for q, expected in zip(q_norm, result): coh = weights.get_weight_coh(s, q) assert np.isclose(coh, expected) @pytest.mark.parametrize('species,q_norm,method', [ (['H+'], np.array([1, 2, 3]), 'waasmaier-1995'), (['C-', 'O5+'], np.array([1, 2, 3]), 'waasmaier-1995'), (['H+', 'C'], np.array([0.1, 0.05, 2]), 'waasmaier-1995'), (['Cm2+', 'C'], np.array([0.1, 0.05, 2]), 'itc-2006'), ]) def test_xray_form_factors_invalid_species(species, q_norm, method): """ Some or all of the species are missing from the requested database. Should raise an error when missing species are requested. """ with pytest.raises(ValueError) as e: XRayFormFactors(species, method) assert f'Missing tabulated values for requested species {species[0]}' in str(e) @pytest.mark.parametrize('species,q_norm,method', [ (['H'], np.array([1, 2, 3]), 'waasmaier-1995'), (['H', 'C'], np.array([0.1, 0.05, 2]), 'waasmaier-1995'), ]) def test_xray_form_factors_sets_hydrogen_to_zero(species, q_norm, method): """ Some or all of the species are missing from the Waasmaier-1995 database. Should raise an error when missing species are requested. For H in particular we want to set it to zero, since it's so common. """ warning = 'No parametrization for H. Setting form factor for H to zero' with pytest.warns(UserWarning, match=warning): weights = XRayFormFactors(species, method) weight_H = weights.get_weight_coh(species[0], q_norm[0]) assert weight_H == 0.0 @pytest.mark.parametrize('species,q_norm,method,error', [ (['He'], None, 'waasmaier-1995', 'missing 1 required positional argument'), (['He'], np.array([100]), 'waasmaier-1995', 'Waasmaier parametrization is not reliable'), (['H'], np.array([26]), 'itc-2006', 'ITC.C parametrization is not reliable') ]) def test_xray_form_factors_invalid_q_norm(species, q_norm, method, error): """ Some or all of the species are missing from the requested database. Should raise an error when missing species are requested. """ weights = XRayFormFactors(species, method) if q_norm is None: with pytest.raises(TypeError) as e: weights.get_weight_coh(species[0]) assert error in str(e) else: with pytest.warns(UserWarning, match=error): weights.get_weight_coh(species[0], q_norm[0]) def test_xray_form_factors_property(): """Make sure form_factors property works.""" w = XRayFormFactors(['Si', 'As']) with option_context('display.max_rows', 10, 'display.max_columns', 50): s = str(w.parameters) assert 'method' in s assert 'a1' in s assert 'b2' in s assert 'Si RHF 5.275329 2.631338 3.191038 33.730728' in s assert 'As RHF 17.025643 2.597739 4.503441 0.003012' in s # Electron weights @pytest.mark.parametrize('species,q_norm,method', [ (['He'], np.array([1, 2, 3]), 'smax6'), (['C', 'O'], np.array([1, 2, 3, 0.5999]).reshape(-1, 1), 'smax6'), (['Sb3+', 'O1-'], np.array([0.1, 0.2]), 'smax6'), (['H'], np.array([1, 2, 3]), 'smax2'), (['K1+', 'O'], np.array([1, 2, 3, 0.5999]).reshape(-1, 1), 'smax2'), (['Mn4+', 'O1-'], np.array([0.1, 0.2]), 'smax2') ]) def test_electron_scattering_factors_shapes(species, q_norm, method): weights = ElectronScatteringFactors(species, method) for s in species: for q in q_norm: coh = weights.get_weight_coh(s, q) incoh = weights.get_weight_incoh(s, q) assert incoh is None assert coh.shape is not None @pytest.mark.parametrize('species,q_norm,method,result', [ (['He'], np.array([1, 2]), 'smax6', [0.04550084791308833, 0.011817533238449224]), (['O'], np.array([1, 2]), 'smax6', [0.15795214466623156, 0.04269749113433945]), (['H'], np.array([1, 2]), 'smax2', [0.023772362476410208, 0.004111154964262735]), (['H1-'], np.array([1, 2]), 'smax2', [0.028511206171007053, -0.0032498371243158174]), ]) def test_electron_scattering_factors_numeric(species, q_norm, method, result): """ Manually compute f0 from the given source and check that the numbers match. Note that this does not guarantee that no errors have been made in OCR:ing the tables; it just serves as a spot-check. """ q_norm = np.array(q_norm) * 4 * np.pi # updated the definition of s after computing ref values. weights = ElectronScatteringFactors(species, method) for s in species: for q, expected in zip(q_norm, result): coh = weights.get_weight_coh(s, q) assert np.isclose(coh, expected, atol=1e-16, rtol=1e-16) def test_compute_f_correct_order(): """ Found a bug where the electron form factors where not summed to the right order. """ weights = ElectronScatteringFactors(['He']) coefficients = dict( a1=1, a2=1, a3=1, a4=1, a5=1, b1=1, b2=1, b3=1, b4=1, b5=1, ) assert weights._compute_f(coefficients, 0.0, parametrization='', charge=None) == 5 @pytest.mark.parametrize('species,q_norm,method', [ (['H+'], np.array([1, 2, 3]), 'smax6'), (['C-', 'O5+'], np.array([1, 2, 3]), 'smax6'), (['H+', 'C'], np.array([0.1, 0.05, 2]), 'smax6'), (['Cm2+', 'C'], np.array([0.1, 0.05, 2]), 'smax6'), ]) def test_electron_scattering_factors_invalid_species(species, q_norm, method): """ Some or all of the species are missing from the requested database. Should raise an error when missing species are requested. """ with pytest.raises(ValueError) as e: ElectronScatteringFactors(species, method) assert f'Missing tabulated values for requested species {species[0]}' in str(e) @pytest.mark.parametrize('species,q_norm,method,error', [ (['He'], None, 'smax6', 'missing 1 required positional argument'), (['He'], np.array([100]), 'smax6', 'Peng parametrization is not reliable'), (['H'], np.array([26]), 'smax2', 'Peng parametrization is not reliable') ]) def test_electron_scattering_factors_invalid_q_norm(species, q_norm, method, error): """ Some or all of the species are missing from the requested database. Should raise an error when missing species are requested. """ weights = ElectronScatteringFactors(species, method) if q_norm is None: with pytest.raises(TypeError) as e: weights.get_weight_coh(species[0]) assert error in str(e) else: with pytest.warns(UserWarning, match=error): weights.get_weight_coh(species[0], q_norm[0]) def test_electron_scattering_factors_property(): """Make sure scattering_factors property works.""" w = ElectronScatteringFactors(['Si', 'As']) with option_context('display.max_rows', 10, 'display.max_columns', 50): s = str(w.parameters) assert 'Z' in s assert 'a1' in s assert 'b5' in s assert 'Si 14 0.0567 0.3365 0.8104' in s assert 'As 33 0.2059 0.9518 1.6372 3.049' in s # Weights unit tests def test_weights_str_without_incoherent(): """__str__ with no incoherent weights returns early after the coherent section.""" w = Weights({'Al': 1.0, 'Fe': 2.0}) s = str(w) assert 'weights coherent:' in s assert 'weights incoherent:' not in s def test_weights_str_with_incoherent(): """__str__ with incoherent weights includes both sections.""" w = Weights({'Al': 1.0}, {'Al': 0.5}) s = str(w) assert 'weights coherent:' in s assert 'weights incoherent:' in s def test_weights_get_weight_incoh_missing_atom_type(): """get_weight_incoh raises ValueError when atom type is absent from incoherent weights.""" w = Weights({'Al': 1.0}, {'Al': 0.5}) with pytest.raises(ValueError, match='Incoherent weights for Fe have not been specified'): w.get_weight_incoh('Fe') # Integration tests for X-rays, neutrons and electrons @pytest.mark.parametrize('sample_with_species,probe', [ (['He', 'C'], 'neutrons'), (['Li', 'B'], 'neutrons'), (['He', 'C'], 'xrays'), (['He', 'C'], 'electrons') ], indirect=['sample_with_species']) def test_weighting_integration_test(sample_with_species, probe): """ Integration test where a mock sample is weighted by actual probe specific weights. """ species = sample_with_species.atom_types atom_type = species[0] sqw = f'Sqw_coh_{atom_type}_{atom_type}' unweighted = sample_with_species[sqw] if probe == 'neutrons': weights = NeutronScatteringLengths(species) # The quotient between the unweighted and weighted Sqw for the first # species should be the scattering length for that species, for all # q_values. # Note that neutrons have potentially complex scattering lengths, # so the quotient is Re[b^* b] bi = weights.get_weight_coh(atom_type) scattering_length = np.ones(unweighted.shape)*np.real(np.conjugate(bi)*bi) expected = scattering_length elif probe == 'xrays': weights = XRayFormFactors(species, source='waasmaier-1995') # The quotient now depends on the norm of q. # Compute q_norm, and the prepare the expected # form factors. q_norms = np.linalg.norm(sample_with_species.q_points, axis=1) form_factors = np.reshape([weights.get_weight_coh(atom_type, q) for q in q_norms], (-1, 1)) expected = form_factors**2 elif probe == 'electrons': weights = ElectronScatteringFactors(species, parametrization='smax6') # The quotient now depends on the norm of q. # Compute q_norm, and the prepare the expected # form factors. q_norms = np.linalg.norm(sample_with_species.q_points, axis=1) form_factors = np.reshape([weights.get_weight_coh(atom_type, q) for q in q_norms], (-1, 1)) expected = form_factors**2 with pytest.warns(UserWarning, match='The Weights class does not support'): weighted_sample = get_weighted_sample(sample_with_species, weights) weighted = weighted_sample[sqw] quotient = weighted / unweighted assert np.allclose(quotient, expected) dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/tests/test_weights.py000066400000000000000000000037751522712672400243130ustar00rootroot00000000000000import pytest import numpy as np from dynasor.post_processing import Weights @pytest.fixture def weights_coh(): weights_coh = dict() weights_coh['A'] = 1.40 weights_coh['B'] = 0.98 weights_coh['some-group'] = 12.99 return weights_coh @pytest.fixture def weights_incoh(): weights_incoh = dict() weights_incoh['B'] = 11.0 weights_incoh['some-group'] = 45.8 weights_incoh['A'] = 23.2 return weights_incoh def test_init_weights(weights_coh, weights_incoh): # without incoherent weights = Weights(weights_coh) assert weights.supports_currents assert not weights.supports_incoherent # with incoherent weights = Weights(weights_coh, weights_incoh) assert weights.supports_currents assert weights.supports_incoherent # without currents weights = Weights(weights_coh, weights_incoh, supports_currents=False) assert not weights.supports_currents assert weights.supports_incoherent def test_get_weights(weights_coh, weights_incoh): weights = Weights(weights_coh, weights_incoh) assert weights.supports_currents assert weights.supports_incoherent # check coherent weights for key, val in weights_coh.items(): assert np.isclose(val, weights.get_weight_coh(key)) # check incoherent weights for key, val in weights_incoh.items(): assert np.isclose(val, weights.get_weight_incoh(key)) def test_weights_parameters(weights_coh, weights_incoh): # without incoherent weights = Weights(weights_coh) pars = weights.parameters assert 'coherent' in pars.columns assert 'incoherent' not in pars.columns # with incoherent weights = Weights(weights_coh, weights_incoh) pars = weights.parameters assert 'coherent' in pars.columns assert 'incoherent' in pars.columns for s in weights_coh: assert pars[pars.atom_type == s]['coherent'].iloc[0] == weights_coh[s] for s in weights_incoh: assert pars[pars.atom_type == s]['incoherent'].iloc[0] == weights_incoh[s] dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/tests/trajectory_reader/000077500000000000000000000000001522712672400247245ustar00rootroot00000000000000test_ase_trajectory_reader.py000066400000000000000000000111201522712672400326110ustar00rootroot00000000000000dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/tests/trajectory_readerimport os import numpy as np import pytest from dynasor.trajectory.ase_trajectory_reader import ASETrajectoryReader @pytest.fixture def traj_filename(): this_dir = os.path.dirname(__file__) filename_traj = os.path.join(this_dir, 'trajectory_files/dump.traj') return filename_traj @pytest.fixture def traj_filename_with_velocities(): this_dir = os.path.dirname(__file__) filename_traj_with_velocities = os.path.join( this_dir, 'trajectory_files/dump_with_velocities.traj' ) return filename_traj_with_velocities @pytest.fixture def first_frame(): """first frame data on the five first atoms""" x_target5 = np.array( [ [35.49864674, 35.46639586, 0.17918569], [3.01637108, 3.02819058, 2.89811028], [3.33355666, 3.05802449, 0.00510028], [2.38218237, -0.02001685, 2.92525985], [35.51266599, 3.65396702, 2.90811710], ] ) v_target5 = np.array( [ [0.00110336, 0.00111412, 0.00155735], [-0.00100465, 0.00085346, -0.00125993], [0.00139634, 0.00269955, -0.00236018], [0.00059981, -0.00173836, -0.00372708], [0.00247676, 0.00041072, -0.00134462], ] ) return x_target5, v_target5 def test_open_no_velocities(traj_filename): reader = ASETrajectoryReader(traj_filename) reader.close() def test_open_with_velocities(traj_filename_with_velocities): reader = ASETrajectoryReader(traj_filename_with_velocities) reader.close() def test_read_frames(traj_filename): # dump with position reader = ASETrajectoryReader(traj_filename) frames = list(reader) assert len(frames) == 4 for it, frame in enumerate(frames): assert it == frame.frame_index assert frame.positions.shape == (1080, 3) reader.close() def test_read_frames_with_velocities(traj_filename_with_velocities): # dump with position and velocities reader = ASETrajectoryReader(traj_filename_with_velocities) frames = list(reader) assert len(frames) == 4 for it, frame in enumerate(frames): assert it == frame.frame_index assert frame.positions.shape == (1080, 3) assert frame.velocities.shape == (1080, 3) reader.close() def test_first_frame_contents(traj_filename, first_frame): reader = ASETrajectoryReader(traj_filename) frame = next(reader) assert frame.n_atoms == 1080 assert frame.frame_index == 0 # first five poisitions x_target5, _ = first_frame x_read5 = frame.positions[0:5] assert np.allclose(x_read5, x_target5) # cell cell_target = 35.60125893 * np.eye(3) assert np.allclose(frame.cell, cell_target) reader.close() def test_first_frame_contents_with_velocities( traj_filename_with_velocities, first_frame ): reader = ASETrajectoryReader(traj_filename_with_velocities) frame = next(reader) assert frame.n_atoms == 1080 assert frame.frame_index == 0 # first five velocities _, v_target5 = first_frame v_target5 = np.array( [ [0.00110336, 0.00111412, 0.00155735], [-0.00100465, 0.00085346, -0.00125993], [0.00139634, 0.00269955, -0.00236018], [0.00059981, -0.00173836, -0.00372708], [0.00247676, 0.00041072, -0.00134462], ] ) v_read5 = frame.velocities[0:5] assert np.allclose(v_read5, v_target5) reader.close() def test_first_frame_contents_with_units(traj_filename_with_velocities, first_frame): reader = ASETrajectoryReader( traj_filename_with_velocities, length_unit='nm', time_unit='ps' ) frame = next(reader) assert frame.n_atoms == 1080 assert frame.frame_index == 0 # first five velocities x_target5, v_target5 = first_frame x_read5 = frame.positions[0:5] v_read5 = frame.velocities[0:5] assert np.allclose(x_read5, 10 * x_target5) assert np.allclose(v_read5, 1 / 100 * v_target5) # cell cell_target = 10 * 35.60125893 * np.eye(3) assert np.allclose(frame.cell, cell_target) reader.close() # Edge-case and error-handling tests def test_nonexistent_file_raises(): """Opening a non-existent ASE trajectory raises FileNotFoundError on next().""" reader = ASETrajectoryReader('/nonexistent/file.traj') with pytest.raises(FileNotFoundError): next(reader) def test_stop_iteration_after_exhausting_frames(traj_filename): """next() raises StopIteration once all frames have been consumed.""" reader = ASETrajectoryReader(traj_filename) list(reader) # exhaust with pytest.raises(StopIteration): next(reader) test_extxyz_trajectory_reader.py000066400000000000000000000140761522712672400334310ustar00rootroot00000000000000dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/tests/trajectory_readerimport os import numpy as np import pytest from dynasor.trajectory.extxyz_trajectory_reader import ExtxyzTrajectoryReader @pytest.fixture def traj_filename(): this_dir = os.path.dirname(__file__) filename_xyz = os.path.join(this_dir, 'trajectory_files/dump.xyz') return filename_xyz @pytest.fixture def traj_filename_with_velocities(): this_dir = os.path.dirname(__file__) filename_xyz_with_velocities = os.path.join( this_dir, 'trajectory_files/dump_with_velocities.xyz') return filename_xyz_with_velocities @pytest.fixture def first_frame(): """ first frame data on the five first atoms """ x_target5 = np.array([[35.49864674, 35.46639586, 0.17918569], [3.01637108, 3.02819058, 2.89811028], [3.33355666, 3.05802449, 0.00510028], [2.38218237, -0.02001685, 2.92525985], [35.51266599, 3.65396702, 2.90811710]]) v_target5 = np.array([[0.00110336, 0.00111412, 0.00155735], [-0.00100465, 0.00085346, -0.00125993], [0.00139634, 0.00269955, -0.00236018], [0.00059981, -0.00173836, -0.00372708], [0.00247676, 0.00041072, -0.00134462]]) return x_target5, v_target5 def test_open_no_velocities(traj_filename): reader = ExtxyzTrajectoryReader(traj_filename) reader.close() def test_open_with_velocities(traj_filename_with_velocities): reader = ExtxyzTrajectoryReader(traj_filename_with_velocities) reader.close() def test_read_frames(traj_filename): # dump with position reader = ExtxyzTrajectoryReader(traj_filename) frames = list(reader) assert len(frames) == 4 for it, frame in enumerate(frames): assert it == frame.frame_index assert frame.positions.shape == (1080, 3) def test_read_frames_with_velocities(traj_filename_with_velocities): # dump with position and velocities reader = ExtxyzTrajectoryReader(traj_filename_with_velocities) frames = list(reader) assert len(frames) == 4 for it, frame in enumerate(frames): assert it == frame.frame_index assert frame.positions.shape == (1080, 3) assert frame.velocities.shape == (1080, 3) reader.close() def test_first_frame_contents(traj_filename, first_frame): reader = ExtxyzTrajectoryReader(traj_filename) frame = next(reader) reader.close() assert frame.n_atoms == 1080 assert frame.frame_index == 0 # first five poisitions x_target5, v_target5 = first_frame x_read5 = frame.positions[0:5] assert np.allclose(x_read5, x_target5) # cell cell_target = 35.60125893 * np.eye(3) assert np.allclose(frame.cell, cell_target) def test_first_frame_contents_with_velocities(traj_filename_with_velocities, first_frame): reader = ExtxyzTrajectoryReader(traj_filename_with_velocities) frame = next(reader) reader.close() assert frame.n_atoms == 1080 assert frame.frame_index == 0 # first five velocities x_target5, v_target5 = first_frame v_target5 = np.array([[0.00110336, 0.00111412, 0.00155735], [-0.00100465, 0.00085346, -0.00125993], [0.00139634, 0.00269955, -0.00236018], [0.00059981, -0.00173836, -0.00372708], [0.00247676, 0.00041072, -0.00134462]]) v_read5 = frame.velocities[0:5] assert np.allclose(v_read5, v_target5) def test_first_frame_contents_with_units(traj_filename_with_velocities, first_frame): reader = ExtxyzTrajectoryReader(traj_filename_with_velocities, length_unit='nm', time_unit='ps') frame = next(reader) reader.close() assert frame.n_atoms == 1080 assert frame.frame_index == 0 # first five velocities x_target5, v_target5 = first_frame x_read5 = frame.positions[0:5] v_read5 = frame.velocities[0:5] assert np.allclose(x_read5, 10 * x_target5) assert np.allclose(v_read5, 1 / 100 * v_target5) # cell cell_target = 10 * 35.60125893 * np.eye(3) assert np.allclose(frame.cell, cell_target) # Edge-case and error-handling tests def test_nonexistent_file_raises(tmp_path): """Opening a non-existent file raises FileNotFoundError.""" with pytest.raises(FileNotFoundError): ExtxyzTrajectoryReader(str(tmp_path / 'no_such_file.xyz')) def test_empty_file_raises_stop_iteration(tmp_path): """An empty file yields StopIteration on the first next() call.""" empty = tmp_path / 'empty.xyz' empty.write_text('') reader = ExtxyzTrajectoryReader(str(empty)) with pytest.raises(StopIteration): next(reader) def test_stop_iteration_after_exhausting_frames(traj_filename): """next() raises StopIteration once all frames have been consumed.""" reader = ExtxyzTrajectoryReader(traj_filename) list(reader) # exhaust with pytest.raises(StopIteration): next(reader) def test_minimal_single_atom_frame(tmp_path): """A minimal one-atom extxyz file is parsed correctly.""" content = ( '1\n' 'Lattice="5.0 0.0 0.0 0.0 5.0 0.0 0.0 0.0 5.0"' ' Properties=species:S:1:pos:R:3\n' 'H 1.0 2.0 3.0\n' ) f = tmp_path / 'single.xyz' f.write_text(content) reader = ExtxyzTrajectoryReader(str(f)) frame = next(reader) reader.close() assert frame.n_atoms == 1 assert frame.frame_index == 0 assert np.allclose(frame.positions[0], [1.0, 2.0, 3.0]) assert np.allclose(frame.cell, 5.0 * np.eye(3)) assert frame.velocities is None def test_minimal_single_atom_frame_with_velocities(tmp_path): """A one-atom extxyz file with velocities exposes the velocity array.""" content = ( '1\n' 'Lattice="5.0 0.0 0.0 0.0 5.0 0.0 0.0 0.0 5.0"' ' Properties=species:S:1:pos:R:3:vel:R:3\n' 'H 1.0 2.0 3.0 0.1 0.2 0.3\n' ) f = tmp_path / 'single_vel.xyz' f.write_text(content) reader = ExtxyzTrajectoryReader(str(f)) frame = next(reader) reader.close() assert frame.n_atoms == 1 assert np.allclose(frame.velocities[0], [0.1, 0.2, 0.3]) test_lammps_trajectory_reader.py000066400000000000000000000327231522712672400333460ustar00rootroot00000000000000dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/tests/trajectory_readerimport bz2 import gzip import numpy as np import os import pytest from dynasor.trajectory.lammps_trajectory_reader import LammpsTrajectoryReader @pytest.fixture def first_frame(): data = np.array([ [1, 1, 1.91468, 3.02071, 0.528818, 0.00105876, 0.000389945, 0.000540445], [2, 2, 1.29395, 3.5652, 6.21471, 0.0116399, 0.0238521, 0.0115804], [2, 3, 1.39919, 2.29819, 0.989518, -0.0190113, 0.0234721, 0.0142206], [1, 4, 3.626, 4.14767, 2.28901, -0.00775121, -0.00332156, 0.00045625], [2, 5, 4.16502, 3.38633, 2.64931, -0.00916183, -0.00898758, -0.0093385], [2, 6, 3.00361, 3.81053, 1.58263, -0.00415819, 0.00792349, -0.00812868], [1, 7, 2.42769, 6.19665, 3.54307, 0.00516741, 0.00576708, -0.00187862], [2, 8, 3.06452, 0.52461, 4.05338, 0.0147389, -0.00707458, 0.000731629], [2, 9, 2.90869, 5.38709, 3.20654, -0.00964617, -0.00368993, -0.00034467], [1, 10, 3.80235, 1.58899, 5.41742, 0.000160311, 0.000400954, -0.00271396], [2, 11, 4.36344, 2.15982, 4.81798, 0.0317508, -0.00780526, 0.0190895], [2, 12, 3.13529, 2.16145, 5.8942, 0.00501066, 0.0133524, -0.0114753], [1, 13, 4.80923, 5.75237, 0.272529, -0.0041924, 0.00248113, 5.02226e-05], [2, 14, 4.24226, 0.143661, 6.00552, -0.00864071, 0.00125601, 0.00341038], [2, 15, 4.22911, 5.2088, 0.879155, 0.00156727, 0.00697033, 0.00957028], [1, 16, 0.417374, 1.05515, 1.90862, 0.00143456, -0.00280331, -0.00114098], [2, 17, 6.07448, 0.524278, 1.30311, 0.0218267, -0.00281436, -0.0211203], [2, 18, 0.957417, 0.433166, 2.47562, -0.0062517, -0.00294501, 0.00603581], [1, 19, 5.4205, 2.90285, 3.66719, 0.00245983, 0.000292166, 0.00455708], [2, 20, 5.97449, 2.21136, 3.20358, 0.00989121, 0.00880165, 0.000767886], [2, 21, 6.01398, 3.63229, 4.00734, -0.00868655, 0.0196349, -0.0174331], [1, 22, 0.616258, 4.77853, 5.1262, -0.000836257, -0.00691637, 0.000166677], [2, 23, 1.20745, 5.48874, 4.74398, 0.00678782, -0.0119932, 0.00252133], [2, 24, 6.16478, 5.20174, 5.69963, 0.00200832, -0.001067, -0.00067658], ]) return data @pytest.fixture def filename_lammpstrj(): this_dir = os.path.dirname(__file__) return os.path.join(this_dir, 'trajectory_files/positions_and_velocities.lammpstrj') @pytest.fixture def filename_lammpstrj_no_velocities(): this_dir = os.path.dirname(__file__) return os.path.join(this_dir, 'trajectory_files/positions.lammpstrj') def test_open_lammpstrj_no_velocities(filename_lammpstrj_no_velocities): reader = LammpsTrajectoryReader(filename_lammpstrj_no_velocities) reader.close() def test_open_lammpstrj(filename_lammpstrj): reader = LammpsTrajectoryReader(filename_lammpstrj) reader.close() def test_read_frames(filename_lammpstrj): reader = LammpsTrajectoryReader(filename_lammpstrj) frames = list(reader) assert len(frames) == 4 reader.close() def test_first_frame_contents(filename_lammpstrj, first_frame): positions_target = first_frame[:, 2:5] velocities_target = first_frame[:, 5:] reader = LammpsTrajectoryReader(filename_lammpstrj) frame = next(reader) assert frame.n_atoms == 24 assert np.allclose(frame.positions, positions_target) assert np.allclose(frame.velocities, velocities_target) reader.close() def test_first_frame_contents_no_velocities(filename_lammpstrj_no_velocities, first_frame): positions_target = first_frame[:, 2:5] reader = LammpsTrajectoryReader(filename_lammpstrj_no_velocities) frame = next(reader) assert frame.n_atoms == 24 assert frame.velocities is None assert np.allclose(frame.positions, positions_target) reader.close() def test_first_frame_contents_with_units(filename_lammpstrj, first_frame): positions_target = first_frame[:, 2:5] velocities_target = first_frame[:, 5:] reader = LammpsTrajectoryReader(filename_lammpstrj, length_unit='nm', time_unit='ps') frame = next(reader) assert frame.n_atoms == 24 assert np.allclose(frame.positions, 10 * positions_target) assert np.allclose(frame.velocities, 1 / 100 * velocities_target) # cell cell_target = 10 * 6.25 * np.eye(3) assert np.allclose(frame.cell, cell_target) @pytest.mark.parametrize(('length_unit', 'factor'), [('pm', 1e-2), ('fm', 1e-5)]) def test_small_length_unit_conversions(filename_lammpstrj, first_frame, length_unit, factor): reader = LammpsTrajectoryReader(filename_lammpstrj, length_unit=length_unit) frame = next(reader) reader.close() assert np.allclose(frame.positions, factor * first_frame[:, 2:5]) assert np.allclose(frame.velocities, factor * first_frame[:, 5:]) assert np.allclose(frame.cell, factor * 6.25 * np.eye(3)) # Edge-case and error-handling tests def test_invalid_length_unit_raises(tmp_path): """An unrecognised length_unit raises ValueError.""" f = tmp_path / 'dummy.lammpstrj' f.write_text('') with pytest.raises(ValueError, match='length unit'): LammpsTrajectoryReader(str(f), length_unit='furlongs') def test_invalid_time_unit_raises(tmp_path): """An unrecognised time_unit raises ValueError.""" f = tmp_path / 'dummy.lammpstrj' f.write_text('') with pytest.raises(ValueError, match='time unit'): LammpsTrajectoryReader(str(f), time_unit='fortnights') def test_nonexistent_file_raises(tmp_path): """Opening a non-existent file raises FileNotFoundError.""" with pytest.raises(FileNotFoundError): LammpsTrajectoryReader(str(tmp_path / 'no_such_file.lammpstrj')) def test_empty_file_raises_stop_iteration(tmp_path): """An empty file yields StopIteration on the first next() call.""" empty = tmp_path / 'empty.lammpstrj' empty.write_text('') reader = LammpsTrajectoryReader(str(empty)) with pytest.raises(StopIteration): next(reader) def test_malformed_header_raises(tmp_path): """A file whose content does not match the LAMMPS dump format raises IOError.""" bad = tmp_path / 'bad.lammpstrj' bad.write_text('THIS IS NOT A LAMMPS DUMP FILE\ngarbage content\n') reader = LammpsTrajectoryReader(str(bad)) with pytest.raises(IOError, match='TRJ_reader: Failed to parse TRJ frame header'): next(reader) def test_stop_iteration_after_exhausting_frames(filename_lammpstrj): """next() raises StopIteration once all frames have been consumed.""" reader = LammpsTrajectoryReader(filename_lammpstrj) list(reader) # exhaust with pytest.raises(StopIteration): next(reader) def test_minimal_single_atom_frame(tmp_path): """A minimal one-atom LAMMPS dump frame is parsed correctly.""" content = ( 'ITEM: TIMESTEP\n0\n' 'ITEM: NUMBER OF ATOMS\n1\n' 'ITEM: BOX BOUNDS pp pp pp\n' '0.0 5.0\n0.0 5.0\n0.0 5.0\n' 'ITEM: ATOMS id type x y z\n' '1 1 1.0 2.0 3.0\n' ) f = tmp_path / 'single.lammpstrj' f.write_text(content) reader = LammpsTrajectoryReader(str(f)) frame = next(reader) reader.close() assert frame.n_atoms == 1 assert frame.frame_index == 0 assert np.allclose(frame.positions[0], [1.0, 2.0, 3.0]) assert np.allclose(frame.cell, 5.0 * np.eye(3)) assert frame.velocities is None _SINGLE_ATOM_CONTENT = ( 'ITEM: TIMESTEP\n0\n' 'ITEM: NUMBER OF ATOMS\n1\n' 'ITEM: BOX BOUNDS pp pp pp\n' '0.0 4.0\n0.0 4.0\n0.0 4.0\n' 'ITEM: ATOMS id type x y z\n' '1 1 1.0 2.0 3.0\n' ) def test_gzip_file(tmp_path): """Reader opens gzip-compressed .lammpstrj.gz files.""" gz_path = tmp_path / 'traj.lammpstrj.gz' with gzip.open(str(gz_path), 'wt') as fh: fh.write(_SINGLE_ATOM_CONTENT) reader = LammpsTrajectoryReader(str(gz_path)) frame = next(reader) reader.close() assert frame.n_atoms == 1 assert np.allclose(frame.positions[0], [1.0, 2.0, 3.0]) def test_bz2_file(tmp_path): """Reader opens bz2-compressed .lammpstrj.bz2 files.""" bz2_path = tmp_path / 'traj.lammpstrj.bz2' with bz2.open(str(bz2_path), 'wt') as fh: fh.write(_SINGLE_ATOM_CONTENT) reader = LammpsTrajectoryReader(str(bz2_path)) frame = next(reader) reader.close() assert frame.n_atoms == 1 assert np.allclose(frame.positions[0], [1.0, 2.0, 3.0]) def test_blank_lines_in_header_are_skipped(tmp_path): """Blank lines between ITEM sections are silently skipped.""" content = ( '\n' 'ITEM: TIMESTEP\n0\n' '\n' 'ITEM: NUMBER OF ATOMS\n1\n' 'ITEM: BOX BOUNDS pp pp pp\n' '0.0 5.0\n0.0 5.0\n0.0 5.0\n' 'ITEM: ATOMS id type x y z\n' '1 1 1.0 2.0 3.0\n' ) f = tmp_path / 'blank.lammpstrj' f.write_text(content) reader = LammpsTrajectoryReader(str(f)) frame = next(reader) reader.close() assert frame.n_atoms == 1 def test_triclinic_cell(tmp_path): """Triclinic box bounds (three values per line) are parsed into the correct cell.""" # LAMMPS triclinic format: xlo xhi xy / ylo yhi xz / zlo zhi yz content = ( 'ITEM: TIMESTEP\n0\n' 'ITEM: NUMBER OF ATOMS\n1\n' 'ITEM: BOX BOUNDS xy xz yz pp pp pp\n' '0.0 5.0 1.0\n' '0.0 5.0 2.0\n' '0.0 5.0 3.0\n' 'ITEM: ATOMS id type x y z\n' '1 1 1.0 2.0 3.0\n' ) f = tmp_path / 'triclinic.lammpstrj' f.write_text(content) reader = LammpsTrajectoryReader(str(f)) frame = next(reader) reader.close() # Diagonal: xhi-xlo = yhi-ylo = zhi-zlo = 5 for all three assert np.allclose(frame.cell.diagonal(), [5.0, 5.0, 5.0]) # Off-diagonal tilt factors: cell[1,0]=xy=1, cell[2,0]=xz=2, cell[2,1]=yz=3 assert np.isclose(frame.cell[1, 0], 1.0) assert np.isclose(frame.cell[2, 0], 2.0) assert np.isclose(frame.cell[2, 1], 3.0) def test_malformed_cell_bounds_raises(tmp_path): """A BOX BOUNDS section with wrong column count raises IOError.""" # Give 4 values per line — neither (3,2) nor (3,3) content = ( 'ITEM: TIMESTEP\n0\n' 'ITEM: NUMBER OF ATOMS\n1\n' 'ITEM: BOX BOUNDS pp pp pp\n' '0.0 5.0 1.0 9.0\n' '0.0 5.0 2.0 9.0\n' '0.0 5.0 3.0 9.0\n' 'ITEM: ATOMS id type x y z\n' '1 1 1.0 2.0 3.0\n' ) f = tmp_path / 'bad_cell.lammpstrj' f.write_text(content) reader = LammpsTrajectoryReader(str(f)) with pytest.raises(IOError, match='Malformed cell bounds'): next(reader) def test_scaled_positions(tmp_path): """Scaled positions (xs ys zs) are correctly unscaled by the cell diagonal.""" cell_size = 10.0 content = ( 'ITEM: TIMESTEP\n0\n' 'ITEM: NUMBER OF ATOMS\n1\n' 'ITEM: BOX BOUNDS pp pp pp\n' f'0.0 {cell_size}\n0.0 {cell_size}\n0.0 {cell_size}\n' 'ITEM: ATOMS id type xs ys zs\n' '1 1 0.1 0.2 0.3\n' ) f = tmp_path / 'scaled.lammpstrj' f.write_text(content) reader = LammpsTrajectoryReader(str(f)) frame = next(reader) reader.close() assert np.allclose(frame.positions[0], [1.0, 2.0, 3.0]) def test_scaled_positions_carried_through_second_frame(tmp_path): """Scaled positions are also unscaled correctly in subsequent frames (_get_next).""" cell_size = 10.0 content = ( 'ITEM: TIMESTEP\n0\n' 'ITEM: NUMBER OF ATOMS\n1\n' 'ITEM: BOX BOUNDS pp pp pp\n' f'0.0 {cell_size}\n0.0 {cell_size}\n0.0 {cell_size}\n' 'ITEM: ATOMS id type xs ys zs\n' '1 1 0.1 0.2 0.3\n' 'ITEM: TIMESTEP\n1\n' 'ITEM: NUMBER OF ATOMS\n1\n' 'ITEM: BOX BOUNDS pp pp pp\n' f'0.0 {cell_size}\n0.0 {cell_size}\n0.0 {cell_size}\n' 'ITEM: ATOMS id type xs ys zs\n' '1 1 0.4 0.5 0.6\n' ) f = tmp_path / 'scaled2.lammpstrj' f.write_text(content) reader = LammpsTrajectoryReader(str(f)) next(reader) frame = next(reader) reader.close() assert np.allclose(frame.positions[0], [4.0, 5.0, 6.0]) def test_missing_position_columns_raises(tmp_path): """A dump file with no recognised position columns raises RuntimeError.""" content = ( 'ITEM: TIMESTEP\n0\n' 'ITEM: NUMBER OF ATOMS\n1\n' 'ITEM: BOX BOUNDS pp pp pp\n' '0.0 5.0\n0.0 5.0\n0.0 5.0\n' 'ITEM: ATOMS id type vx vy vz\n' '1 1 0.1 0.2 0.3\n' ) f = tmp_path / 'no_pos.lammpstrj' f.write_text(content) reader = LammpsTrajectoryReader(str(f)) with pytest.raises(RuntimeError): next(reader) def test_unwrapped_positions(tmp_path): """Unwrapped positions (xu yu zu) outside the cell boundary are passed through as-is.""" content = ( 'ITEM: TIMESTEP\n0\n' 'ITEM: NUMBER OF ATOMS\n1\n' 'ITEM: BOX BOUNDS pp pp pp\n' '0.0 5.0\n0.0 5.0\n0.0 5.0\n' 'ITEM: ATOMS id type xu yu zu\n' '1 1 6.0 -1.0 12.0\n' ) f = tmp_path / 'unwrapped.lammpstrj' f.write_text(content) reader = LammpsTrajectoryReader(str(f)) frame = next(reader) reader.close() assert frame.n_atoms == 1 assert np.allclose(frame.positions[0], [6.0, -1.0, 12.0]) def test_no_type_column(tmp_path): """Dump files without a 'type' column are accepted; frame.types is None/absent.""" content = ( 'ITEM: TIMESTEP\n0\n' 'ITEM: NUMBER OF ATOMS\n1\n' 'ITEM: BOX BOUNDS pp pp pp\n' '0.0 5.0\n0.0 5.0\n0.0 5.0\n' 'ITEM: ATOMS id x y z\n' '1 1.0 2.0 3.0\n' ) f = tmp_path / 'no_type.lammpstrj' f.write_text(content) reader = LammpsTrajectoryReader(str(f)) frame = next(reader) reader.close() assert frame.n_atoms == 1 assert np.allclose(frame.positions[0], [1.0, 2.0, 3.0]) test_mdanalysis_trajectory_reader.py000066400000000000000000000120031522712672400342060ustar00rootroot00000000000000dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/tests/trajectory_readerimport os import pytest import numpy as np from dynasor.trajectory.mdanalysis_trajectory_reader \ import MDAnalysisTrajectoryReader as TrajectoryReader @pytest.fixture def traj_fname_lammps(): this_dir = os.path.dirname(__file__) traj_fname = os.path.join( this_dir, 'trajectory_files/positions_and_velocities.lammpstrj') return traj_fname @pytest.fixture def traj_fname_lammps_no_velocities(): this_dir = os.path.dirname(__file__) traj_fname = os.path.join( this_dir, 'trajectory_files/positions.lammpstrj') return traj_fname @pytest.fixture def traj_fname_xtc(): this_dir = os.path.dirname(__file__) traj_fname = os.path.join( this_dir, 'trajectory_files/1frame3atoms.xtc') return traj_fname @pytest.fixture def traj_fname_trr(): this_dir = os.path.dirname(__file__) traj_fname = os.path.join( this_dir, 'trajectory_files/1frame3atoms.trr') return traj_fname # User has specified incorrect units # Checks if error is raised def test_no_units_from_user_or_traj(traj_fname_lammps_no_velocities): with pytest.raises(ValueError, match='Unknown length and/or time unit. Please check the available options'): reader = TrajectoryReader(traj_fname_lammps_no_velocities, 'lammpsdump', 'm', 's') reader.close() # No units from user but from trajectory # Also checks if trr can be read and that atomic_indices is None def test_units_only_from_traj(traj_fname_trr): reader = TrajectoryReader(traj_fname_trr, 'trr') frame = next(reader) reader.close() assert frame.n_atoms == 3 assert frame.velocities is not None assert pytest.approx(frame.cell[2, 2]) == 41.5305 assert frame.atom_types is None # Units from user that match units from trajectory # Also checks if xtc can be read and that atomic_indices is None def test_units_from_user_matching_traj(traj_fname_xtc): reader = TrajectoryReader(traj_fname_xtc, 'xtc', 'nm', 'ps') frames = list(reader) reader.close() assert len(frames) == 1 assert frames[0].n_atoms == 3 assert frames[0].velocities is None assert pytest.approx(frames[0].cell[2, 2]) == 41.5305 assert frames[0].atom_types is None # Units from user not matching units from trajectory # Checks if the units from the user are used def test_units_from_user_not_matching_traj(traj_fname_xtc): reader = TrajectoryReader(traj_fname_xtc, 'xtc', 'Angstrom', 'ps') frames = list(reader) reader.close() assert len(frames) == 1 assert frames[0].n_atoms == 3 assert frames[0].velocities is None assert pytest.approx(frames[0].cell[2, 2]) == 4.15305 # Units from user while no units from trajectory # Checks if correct unit conversions happen # Also checks if lammps with velocity can be read and that the atomic indices are read def test_units_only_from_user(traj_fname_lammps): reader = TrajectoryReader(traj_fname_lammps, 'lammpsdump', 'Angstrom', 'ps') frame = next(reader) reader.close() assert frame.frame_index == 0 assert frame.n_atoms == 24 assert pytest.approx(list(frame.positions[0, :])) == [1.91468, 3.02071, 0.528818] assert pytest.approx(list(frame.velocities[:, 0])) == [1.05876e-6, 1.16399e-5, -1.90113e-5, -7.75121e-6, -9.16183e-6, -4.15819e-6, 5.16741e-6, 1.47389e-5, -9.64617e-6, 1.60311e-7, 3.17508e-5, 5.01066e-6, -4.1924e-6, -8.64071e-6, 1.56727e-6, 1.43456e-6, 2.18267e-5, -6.2517e-6, 2.45983e-6, 9.89121e-6, -8.68655e-6, -8.36257e-7, 6.78782e-6, 2.00832e-6] assert frame.cell[2, 2] == 6.25 assert isinstance(frame.atom_types, np.ndarray) assert np.all(frame.atom_types == np.array(['1', '2', '2', '1', '2', '2', '1', '2', '2', '1', '2', '2', '1', '2', '2', '1', '2', '2', '1', '2', '2', '1', '2', '2'])) # Checks that a closed file raises StopIteration def test_stopiteration_of_closed_file(traj_fname_xtc): reader = TrajectoryReader(traj_fname_xtc, 'xtc') reader.close() with pytest.raises(StopIteration): next(reader) reader.close() # Edge-case and error-handling tests def test_nonexistent_file_raises(): """Opening a non-existent file raises OSError.""" with pytest.raises(OSError): TrajectoryReader('/nonexistent/file.xtc', 'xtc') def test_stop_iteration_after_exhausting_frames(traj_fname_xtc): """next() raises StopIteration once all frames have been consumed.""" reader = TrajectoryReader(traj_fname_xtc, 'xtc') list(reader) # exhaust (1 frame) with pytest.raises(StopIteration): next(reader) reader.close() dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/tests/trajectory_reader/trajectory_files/000077500000000000000000000000001522712672400302745ustar00rootroot000000000000001frame3atoms.trr000066400000000000000000000003001522712672400332410ustar00rootroot00000000000000dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/tests/trajectory_reader/trajectory_files GMX_trn_file$$$@@@;@;@@Q@v@|@@s@T@@L@ oU>z=+~>HM>X@B6@f-@MP1?<2@. 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C=@Ƀ@@u$<@@{"positions.": {"ndarray": [[1080, 3], "float64", 88976]}, "cell": [[35.60125893, 0.0, 0.0], [0.0, 35.60125893, 0.0], [0.0, 0.0, 35.60125893]], "calculator.": {"name": "unknown", "parameters": {}, "energy": -38569.24448013, "stress": [0.00032812, 1.5e-06, 0.00016389, -4.528e-05, 0.00018627, -0.00010603]}, "info": {"Time": 2000.0, "energy": -38569.24448013, "virial": {"__ndarray__": [[3, 3], "float64", [-22.42128116, -4.78426305, 8.40518306, -4.78426305, -37.15916691, -2.04318393, 8.40518306, -2.04318393, -29.83193797]]}, "stress": {"__ndarray__": [[3, 3], "float64", [0.00032812, -0.00010603, 0.00018627, -0.00010603, 1.5e-06, -4.528e-05, 0.00018627, -4.528e-05, 0.00016389]]}}}dump.xyz000066400000000000000000007124311522712672400317460ustar00rootroot00000000000000dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/tests/trajectory_reader/trajectory_files1080 Lattice="35.60125893 0.0 0.0 0.0 35.60125893 0.0 0.0 0.0 35.60125893" Properties=species:S:1:pos:R:3 Time=500.0 energy=-38569.18799591 virial="-8.94988471 2.4137949 -5.50609137 2.4137949 -11.40798285 -3.67371139 -5.50609137 -3.67371139 14.0956178" stress="0.00062667 5.349e-05 -0.00012202 5.349e-05 0.00057219 -8.142e-05 -0.00012202 -8.142e-05 0.0011374" pbc="T T T" Cs 35.49864674 35.46639586 0.17918569 Pb 3.01637108 3.02819058 2.89811028 Br 3.33355666 3.05802449 0.00510028 Br 2.38218237 -0.02001685 2.92525985 Br 35.51266599 3.65396702 2.90811710 Cs 0.14795431 0.57606010 5.84829266 Pb 2.76570460 2.87591327 9.00896951 Br 3.03593227 2.99864610 5.92615797 Br 3.07807400 35.49146320 8.52016982 Br 35.44251522 3.06317454 8.80987203 Cs 0.30279477 35.48785711 11.84911825 Pb 2.65562721 2.58922045 14.75993085 Br 1.94953929 2.89983318 12.02300203 Br 3.20361457 35.32715925 14.13414003 Br 35.33556604 2.29688903 15.14658724 Cs 35.30097815 0.20513132 18.13059156 Pb 2.88601334 2.62153115 20.74476594 Br 2.84413027 2.47813597 17.68129031 Br 3.51093574 35.51838268 20.71799293 Br 35.52841903 3.46231077 20.73207201 Cs 0.05965226 35.17066249 24.19081820 Pb 2.74381535 3.21308043 26.58197623 Br 2.93560144 2.41641168 23.61054173 Br 3.19872905 0.28937623 26.88769578 Br 35.57807754 3.01651067 26.06719663 Cs 35.46322695 35.03104270 29.52404953 Pb 2.88938908 2.97582174 32.67013970 Br 3.01799540 3.07112768 29.86401280 Br 2.76719896 35.52259823 32.45548220 Br 0.04924286 2.58527094 33.28728817 Cs 0.31210638 5.82105730 35.63819371 Pb 2.99107131 9.13096268 3.09427436 Br 2.77979441 8.65080560 35.50689943 Br 3.27108273 5.95471479 2.52113436 Br 35.51047768 8.33529332 2.67407999 Cs 35.23666389 5.84297755 5.77080164 Pb 3.01233610 8.86316869 8.98828995 Br 3.40348239 8.78575810 5.80725083 Br 3.02315186 5.84440702 9.22323097 Br 0.06808259 9.13230103 8.59706592 Cs -0.02855399 5.90886037 12.31501676 Pb 3.06073148 8.59596842 14.94715671 Br 3.28514788 8.87699280 11.90534854 Br 2.52823158 5.60979229 15.15263104 Br 35.65033906 8.98276021 14.93869453 Cs 0.20840538 5.90929413 17.96266311 Pb 2.64067920 8.94190916 20.65652928 Br 3.18462973 9.05365100 17.72478037 Br 2.95412953 5.70875806 20.60852442 Br 35.31426035 9.06485052 20.45405361 Cs 0.23223848 6.02076510 23.73788594 Pb 2.92138101 9.07943346 26.41521710 Br 2.77013464 8.70740520 23.63971906 Br 2.69365411 6.18760996 26.57044436 Br 0.04573228 8.96977098 26.79335127 Cs 35.45927747 6.17602951 29.62266285 Pb 2.80936458 8.93552832 32.66031315 Br 2.92850921 8.97383876 29.59822936 Br 2.94725607 6.01510251 32.97015639 Br 35.57714778 8.28763007 32.69933843 Cs 35.36740811 11.21273731 0.06600194 Pb 2.89879238 15.00046295 2.99743800 Br 3.02663371 14.96022303 0.09821733 Br 2.75044817 11.87107876 2.82845418 Br 35.65746516 15.14126660 3.40654610 Cs 35.35013895 12.45350017 6.01863675 Pb 2.74383248 14.67921797 8.85150345 Br 2.81942582 15.21205793 6.08671841 Br 3.56703587 11.64270492 8.42788993 Br 35.37559298 14.08757652 9.09789774 Cs 0.08198437 11.50558928 11.71811729 Pb 2.96733036 14.87435208 14.90753192 Br 3.05669923 14.62878711 11.88228795 Br 3.30066005 11.98253946 14.98108768 Br 35.49457177 14.58871976 15.04973339 Cs 35.45236569 11.51127507 17.80983704 Pb 2.98181746 14.59848602 20.90357953 Br 2.70613710 14.76630005 17.88670532 Br 2.40155584 11.81926078 20.46693532 Br 35.55760516 15.21885005 20.76420103 Cs 0.15112744 11.68715239 23.97801590 Pb 2.92077525 15.02598158 26.70976690 Br 3.19314892 13.68991397 24.01031369 Br 2.85592547 11.85408720 26.88555717 Br 0.07662368 15.09361181 26.93314531 Cs 0.16651650 11.95052039 29.52347212 Pb 3.22919419 14.99472169 32.64862276 Br 2.97031244 14.84134334 29.68196124 Br 3.46130937 12.13698107 32.44811554 Br 35.57388300 14.95041014 32.72291122 Cs 35.41766321 17.55733342 -0.05332566 Pb 2.83050684 20.87738677 2.65030851 Br 2.34221058 21.64668572 0.00631303 Br 3.38688505 18.11491559 2.74772861 Br 35.48206655 20.34950282 2.88619888 Cs 0.17228042 17.91095878 5.49554769 Pb 2.76660390 20.78366742 8.55380570 Br 2.54313491 20.52946349 5.71180967 Br 2.34869710 17.68108665 9.09764243 Br -0.00110650 20.28894605 8.80095688 Cs 0.26332668 17.32529991 11.98328443 Pb 2.94456584 20.68019912 14.57727732 Br 2.88670444 20.48413205 11.83906155 Br 2.27419992 17.71743568 15.28255706 Br 35.62862497 21.00064204 14.56350738 Cs 34.97105475 17.57026058 17.19230162 Pb 2.62384813 20.41425726 20.50850897 Br 3.07384385 21.29471934 17.77510891 Br 2.90406812 17.63158751 21.74280770 Br 35.42186476 21.39977660 20.49626927 Cs 35.62953639 17.62266086 23.70440437 Pb 2.94722832 20.91956873 26.63914240 Br 3.27536394 20.92844432 23.72604417 Br 2.85718087 17.90301866 26.35155124 Br 35.60942411 20.80951460 26.25105966 Cs 0.19249111 17.54784366 29.99962739 Pb 2.99853406 20.90914702 32.61712571 Br 3.21804613 20.28693753 29.83829424 Br 2.82435780 17.90899868 32.88302386 Br 0.17773880 21.19469607 32.20829438 Cs 35.54004263 23.88578078 35.40830268 Pb 3.04899977 26.72336727 3.04570285 Br 2.86068434 26.71848850 -0.06291978 Br 2.76218791 23.75466107 3.36974564 Br 0.25018379 26.24817527 2.94604768 Cs 0.36842683 23.70523546 5.76947366 Pb 3.11275162 26.67846304 9.05544494 Br 2.87136853 26.77540082 5.96637785 Br 2.91769889 23.74856457 8.80394698 Br 0.10469274 26.71360339 9.00774291 Cs 0.20256004 22.91652660 11.49668545 Pb 2.91989882 26.48013571 14.95838399 Br 3.11571750 26.69613009 11.90041863 Br 3.22088845 23.83070636 14.18903093 Br 35.35990492 26.79286421 15.49112837 Cs 35.53020065 24.05038239 17.76308715 Pb 3.12682389 26.60699507 20.84243498 Br 2.91623541 26.31081135 17.90733675 Br 2.74880897 23.49422201 21.09137246 Br -0.02865200 26.91206381 21.42201736 Cs 35.52838051 23.84932370 23.52300506 Pb 3.10499046 26.85570504 26.61475331 Br 3.44337031 26.52954902 23.76388482 Br 3.39836319 23.99351693 26.49571488 Br 0.07088413 26.96206193 26.22127988 Cs 0.24938989 24.02490551 29.89983191 Pb 3.06326081 26.66601671 32.56779965 Br 2.67990741 27.30252172 29.81764395 Br 3.24062322 23.69642567 31.80608914 Br 35.42052036 26.38593172 32.58928663 Cs 0.30365921 29.72496572 35.52598864 Pb 3.04042283 32.57724644 3.06868438 Br 3.28664214 32.51190893 35.49558809 Br 2.85191686 29.61398109 2.85166011 Br 35.52113215 32.36781321 2.85893049 Cs 0.01760032 29.30679817 6.39812627 Pb 3.02225758 32.55476943 8.82534631 Br 2.73899059 32.87705463 5.83691847 Br 3.04360449 29.57441630 8.34782874 Br 0.04961802 32.54595491 9.33290954 Cs 35.34187600 29.39791143 12.19153225 Pb 2.86026907 32.43563587 14.59113918 Br 3.68524847 32.80833550 11.81423646 Br 2.68868888 29.51191509 14.69969065 Br 35.57842484 32.86861371 14.50448414 Cs 0.13897368 29.91087246 18.11523302 Pb 3.27518390 32.51073793 20.89763509 Br 3.10464019 32.07646933 17.62476554 Br 3.55111931 29.46173626 21.33889642 Br 0.29804470 32.85068244 21.01799287 Cs 0.30466168 29.64985908 23.87432193 Pb 2.91916175 32.76204667 26.89334331 Br 2.99014753 32.49677012 23.83987551 Br 3.16897893 29.88188830 26.61837735 Br 0.04348633 31.92286760 26.69037123 Cs 35.45782997 29.31208233 29.70389040 Pb 2.87178978 32.59468453 32.51456834 Br 2.93883015 32.97663139 29.66771175 Br 3.16752830 29.63290056 32.96611082 Br 0.02501656 32.53685879 33.39932281 Cs 5.87911626 35.33633203 35.51301465 Pb 8.89008843 3.04907157 2.90414245 Br 8.65527256 3.06917741 35.58505990 Br 9.16890865 0.11594554 3.05350169 Br 5.96061573 2.24931626 2.68419993 Cs 5.90028258 35.46930054 5.91460968 Pb 8.81002030 3.00163979 9.11668292 Br 8.97509907 2.81912505 5.75116453 Br 9.07517881 -0.02274229 9.41331407 Br 5.95352578 2.76603830 9.48778479 Cs 6.17960417 0.06665708 11.80335601 Pb 8.68986046 2.86006789 14.55841083 Br 9.24590322 2.86976169 11.87915669 Br 9.26453449 35.45806489 14.65941952 Br 5.55321716 3.25567232 13.94930775 Cs 5.73170569 35.59087292 17.86371474 Pb 8.88309578 3.13751158 21.02319914 Br 9.14068454 3.02969926 18.04395480 Br 9.32113188 -0.02645055 20.86144366 Br 5.88931959 2.81637233 20.67073741 Cs 6.04291868 35.28163627 23.65052946 Pb 8.63010777 3.20387804 26.74356788 Br 8.62520495 3.31965922 23.89326163 Br 8.52824559 0.01035505 26.31458910 Br 5.63466002 3.66809733 26.85442002 Cs 6.05286407 0.05643724 29.63568803 Pb 8.82193661 2.90716465 32.65626102 Br 9.55298060 2.91342098 29.74272834 Br 8.48186213 35.59685308 32.80364490 Br 6.02752141 3.08918917 32.58433625 Cs 5.72843386 6.02072955 35.56411777 Pb 8.91175563 8.88918012 3.06765952 Br 9.27746396 8.61440984 -0.04764987 Br 8.98602853 6.03358730 2.70220302 Br 5.82503032 8.95008073 2.43540682 Cs 5.55509353 5.82760959 6.39270665 Pb 8.90433731 8.91787638 9.02104359 Br 9.38072432 8.76701730 6.04667731 Br 9.33086171 5.80404152 9.39378906 Br 6.12288408 8.46328219 8.75736249 Cs 6.37006517 5.96017280 11.75022404 Pb 9.12624440 8.95630796 15.08930297 Br 8.96023935 9.04975505 12.01941164 Br 8.93860940 5.66684865 14.57473925 Br 5.99439565 8.10011312 14.87705534 Cs 5.66060601 5.40300224 18.15976179 Pb 8.61595331 8.87916006 20.81089817 Br 9.54377289 8.33516251 17.81725201 Br 8.37462586 5.96764233 20.98096331 Br 5.70493634 8.77673412 20.56810681 Cs 5.53295261 5.57749495 23.67952452 Pb 8.65582700 8.63616145 26.58197550 Br 8.90784422 8.86957390 23.64430397 Br 9.29040065 5.93305852 26.59234391 Br 5.80348804 8.29285804 26.29942468 Cs 6.23673948 5.91843262 29.94032851 Pb 8.81604407 8.78003275 32.67643537 Br 8.65642842 8.77255027 29.42927505 Br 8.86747265 5.83984281 32.62587775 Br 5.90890491 8.54553554 32.74450319 Cs 5.79035249 11.83723687 0.24706827 Pb 8.93759138 14.81078088 2.70097933 Br 8.90745614 14.77513315 35.51162859 Br 8.74013808 11.87839437 3.01617179 Br 5.95881880 14.93691315 3.48883732 Cs 6.33432275 12.04688780 5.74885271 Pb 8.82041629 14.89161943 8.78567067 Br 9.15133692 14.49594437 5.73534467 Br 8.46388314 11.91724496 9.11724804 Br 5.82101017 15.28792433 8.86732223 Cs 5.94618949 11.88801705 11.56296708 Pb 8.89785350 14.93574963 14.70116663 Br 9.22711597 14.81152501 11.87037164 Br 8.96908999 12.02878401 15.02138214 Br 5.77045951 14.94432613 14.76266643 Cs 5.85171252 12.40019916 17.24385636 Pb 8.80806529 14.72551504 20.66563548 Br 8.99734691 14.80385668 17.76014257 Br 9.03342280 11.75200699 20.58037539 Br 5.76059521 15.05941260 20.40660166 Cs 5.97709727 11.18296396 23.73246406 Pb 8.78655272 14.48298509 26.42213212 Br 8.46080747 14.18163686 23.62819860 Br 8.78841554 11.58085992 26.51695409 Br 5.95619787 14.82333392 27.04091010 Cs 5.82860377 11.54483842 29.70647108 Pb 8.94814156 14.72565726 32.26548906 Br 8.65012760 14.36431803 29.42520978 Br 8.81417662 11.89683933 32.53112862 Br 5.91349708 15.30438336 32.00822361 Cs 5.67108172 18.45978527 35.59939986 Pb 8.88058972 20.90840598 3.03833532 Br 9.39373308 20.88416662 35.33918392 Br 8.24918660 17.99678825 2.87587753 Br 5.81714644 21.12550507 3.13451505 Cs 5.67352532 18.48281140 5.60486928 Pb 8.74966198 20.68620699 8.92433616 Br 9.31855792 21.17181491 5.86172244 Br 9.32912756 17.93221933 9.07635027 Br 5.83705446 20.00702437 8.60600025 Cs 5.59194970 17.74526134 11.88757113 Pb 8.64786558 20.68918495 14.81513588 Br 8.94858151 20.49236649 11.94896702 Br 9.11911099 17.86267869 14.57231035 Br 5.64763558 20.57349262 14.07636260 Cs 5.69759007 17.83281250 17.73287652 Pb 8.76091253 20.75687381 20.69162490 Br 8.65739974 21.02492702 17.66044932 Br 8.66204193 17.62481629 21.22342516 Br 5.82231912 20.23845314 20.39765422 Cs 6.11976347 18.10803567 23.63390580 Pb 8.67696813 20.48200641 26.60968857 Br 8.61399349 21.05181738 23.63676645 Br 8.52248749 17.51120414 26.50693371 Br 5.97508539 21.07353114 26.56944204 Cs 5.73385998 17.77042131 29.22463722 Pb 8.68780369 20.99788107 32.37780261 Br 8.25658595 20.94682732 29.63693006 Br 8.79754352 18.04003141 32.07688019 Br 5.87440290 21.75169180 33.09264876 Cs 5.65400996 24.10027603 0.55161016 Pb 8.90709733 26.86982768 3.33197708 Br 8.46337199 27.06598324 0.06881422 Br 9.24776660 23.88439331 2.83764591 Br 5.88478867 26.36535654 3.55675945 Cs 6.27227702 23.92799094 6.29941619 Pb 8.97128474 26.76759170 9.06161411 Br 8.86955109 27.20270314 6.06230478 Br 9.17753557 23.79326949 8.77874249 Br 5.92201899 26.74077343 8.77225474 Cs 5.72242235 23.86811007 12.07602316 Pb 8.95854444 26.72182762 14.87067100 Br 8.36700962 26.42901543 12.08566897 Br 8.01305848 23.76080840 14.75594999 Br 5.93621143 26.63511807 15.36221508 Cs 5.81730953 23.65419184 17.85770386 Pb 8.96217297 26.78157695 20.83646195 Br 8.69680459 26.67304707 18.00106894 Br 8.45803739 23.70049011 20.58349820 Br 5.97937623 26.86764313 20.75251940 Cs 5.74764732 23.59779896 23.58495349 Pb 8.96058767 26.70916673 26.81121212 Br 9.26070437 26.57563365 23.92547971 Br 9.05270496 23.45539820 26.74691049 Br 6.02427876 26.76763660 26.19052461 Cs 5.69651786 23.46504004 29.68721122 Pb 9.00422266 26.65114252 32.63149982 Br 8.39406089 26.48248043 29.81571082 Br 9.48785829 23.82907942 32.62180967 Br 5.84648508 26.93698030 32.51102743 Cs 5.80833366 29.62546728 35.60047977 Pb 8.83945092 32.85105910 2.85700137 Br 8.56174690 32.59784655 35.49924908 Br 9.03201469 29.76796379 2.73346360 Br 6.04822871 33.33333794 2.91688204 Cs 5.13694627 29.87117923 5.80320587 Pb 8.89305521 32.91001864 8.94807210 Br 8.52109627 32.65535052 5.91216315 Br 9.04498044 29.78871064 9.24333387 Br 5.91639311 32.48770111 8.99335668 Cs 5.97037906 29.41552020 11.04301352 Pb 8.78236985 32.40626105 15.04469196 Br 9.13868474 32.57666681 12.18437206 Br 9.00413343 29.47844339 14.28109624 Br 5.88170228 32.62076712 15.21628751 Cs 6.72745408 29.61062235 17.34283829 Pb 9.02140514 32.73371099 20.97922184 Br 9.08501439 32.90823260 17.96035906 Br 8.87304133 29.70486045 20.70432740 Br 5.98617303 32.46294172 21.40533968 Cs 5.99351387 29.90430888 23.98076356 Pb 9.13767950 32.80747537 26.69517110 Br 9.47647766 32.72330964 23.59995208 Br 8.81143642 29.64142821 26.58297383 Br 6.01640316 32.41401742 27.01091726 Cs 6.15320461 29.57504319 29.30533669 Pb 9.04451766 32.60111037 32.70904536 Br 8.85744162 32.68154880 29.47750923 Br 9.56039757 29.76613974 32.36917834 Br 5.96209429 32.83147266 32.38803665 Cs 11.58791421 0.18340849 35.73358046 Pb 14.78207065 3.02609376 2.98527645 Br 14.94618963 4.24149565 35.50367088 Br 15.22560770 0.29637431 3.08572829 Br 11.99909019 2.83877365 3.13366672 Cs 11.76680862 0.03299819 5.89057407 Pb 14.72885354 2.85925882 8.91565056 Br 15.17468453 3.14652090 5.92320368 Br 14.55976265 35.64535733 8.29907028 Br 11.82510413 2.59773395 8.71536270 Cs 12.09487263 35.30049919 11.66411242 Pb 14.58451228 2.93217016 14.66213463 Br 14.59023646 2.87431862 11.85604353 Br 14.17009694 0.11561259 15.21337256 Br 11.75952846 3.08450799 14.65187885 Cs 11.82443823 0.34777363 17.77140777 Pb 14.74600766 2.96584326 20.73731976 Br 14.51897845 3.31862390 17.57048566 Br 14.71710267 -0.00985612 20.73989054 Br 11.76343509 3.23588299 21.14821326 Cs 11.97548038 0.61760660 23.79965814 Pb 14.84229517 3.04125749 26.64716304 Br 15.01946319 2.44929120 23.76221970 Br 15.00422692 35.58089556 27.01996724 Br 11.86821928 3.45331168 26.19610322 Cs 12.01096065 0.18162796 30.19223360 Pb 14.88786831 3.09694116 32.75047868 Br 14.80933663 3.24373580 29.71030608 Br 15.21783870 -0.02178097 32.53480746 Br 11.87433673 2.69720488 32.96903324 Cs 11.65147534 5.75567500 35.30777670 Pb 14.67043682 9.01699058 2.90343039 Br 15.14050176 9.12822222 35.24231375 Br 14.76623890 6.17676159 3.61519471 Br 11.85643918 9.02085699 3.35024678 Cs 12.17201656 5.81218600 6.11145482 Pb 15.04806721 8.72511888 8.98220049 Br 14.82613193 9.17819336 5.77755106 Br 14.78649320 5.75098404 8.95471682 Br 12.10818256 8.79904304 9.16254653 Cs 11.94959946 6.28492620 11.96352481 Pb 14.93181437 8.90882271 14.80080489 Br 15.28369320 8.88671196 11.87615406 Br 14.83458186 5.75853466 14.29043299 Br 12.09607323 8.86543079 14.54511165 Cs 12.09807116 5.64308876 17.12172219 Pb 14.36077059 8.91631633 20.54421653 Br 14.67131671 8.52835719 17.71652245 Br 14.83412245 6.04093219 20.80024715 Br 11.61595699 9.34936499 20.66614913 Cs 11.37357594 5.84506826 23.47880485 Pb 14.76033001 8.89554829 26.30636840 Br 13.98648970 8.85950606 23.61131956 Br 14.94380974 5.83729344 26.19163409 Br 11.74080529 8.87762648 26.72943456 Cs 11.77952654 5.67897875 29.32034703 Pb 14.75954775 8.75873841 32.50258672 Br 14.45488267 9.21759792 29.40606908 Br 14.98122010 5.89766829 32.44027863 Br 11.83112424 8.85699570 32.36342638 Cs 12.23404697 12.37064292 0.34529351 Pb 14.71166051 15.09982902 2.87947542 Br 15.31413710 14.80868809 35.55836355 Br 14.92595097 11.84868569 3.04100034 Br 11.87927589 15.22833309 2.66152474 Cs 11.88012745 11.48586898 6.33319747 Pb 14.78097992 14.46873694 8.75702707 Br 14.94775188 14.47468095 5.74572871 Br 14.57697771 11.67852694 8.87916689 Br 11.65212798 14.82549767 8.60334007 Cs 12.06258017 11.95137284 12.03670546 Pb 15.21522456 14.85456889 14.86539234 Br 14.86244485 14.90225494 11.90386626 Br 14.73342222 11.87824684 14.97532609 Br 12.03864388 14.78364028 14.93638434 Cs 11.62692243 12.19488378 18.04428496 Pb 15.24760079 14.69830231 20.87179443 Br 15.20956414 14.67131958 17.68423000 Br 14.84643650 11.86483677 20.21333789 Br 12.14681049 14.56716683 21.27267784 Cs 11.70800551 11.65591656 23.89544399 Pb 14.79225290 14.73645516 26.50178736 Br 15.05835976 14.88778307 23.70196051 Br 15.20966731 11.76263941 26.41194007 Br 11.93471499 14.30906340 27.02609950 Cs 11.92187184 11.48616436 29.69972862 Pb 14.85928948 14.70701953 32.50617892 Br 15.14625478 14.20779971 29.66906995 Br 14.63672962 11.88097209 32.73759264 Br 11.70539738 14.97611324 32.65278649 Cs 11.94541750 18.13820824 34.62600191 Pb 14.89967190 20.67375236 2.91551182 Br 14.88341320 20.40023817 35.44397206 Br 15.07050436 17.90614032 3.49171596 Br 11.89672079 20.75584973 2.78692614 Cs 12.61304461 17.83969817 6.31492472 Pb 14.97136231 20.70337464 8.95284045 Br 15.03238471 21.12279026 5.94431829 Br 15.19289342 17.39465562 8.87935505 Br 11.83616955 20.73968554 9.26664669 Cs 11.94563521 17.79283504 11.84825664 Pb 14.73070792 20.77398503 14.75021249 Br 14.57133689 20.86388940 11.81061975 Br 15.00924425 17.87680050 14.36784097 Br 11.87818961 20.44883941 14.70841433 Cs 12.70371484 17.38342152 17.47751814 Pb 14.69156938 20.99595000 20.46253454 Br 15.10194853 20.40139123 17.68695859 Br 15.19629293 17.81416450 20.45868603 Br 11.77543825 20.74875893 20.12807905 Cs 11.93354789 17.55609302 23.27012079 Pb 14.65796144 20.72064948 26.65156642 Br 14.14973870 20.88313479 23.64119434 Br 14.36889107 17.69212904 26.42757196 Br 11.89173181 20.72824566 26.96680792 Cs 11.35185680 17.80229866 29.36364505 Pb 14.93850349 20.71729751 32.45521645 Br 15.47978666 20.56847819 29.62747256 Br 15.01292909 17.67007891 32.41060583 Br 11.80352270 20.53009145 32.14997924 Cs 11.94469665 23.75912414 35.02363965 Pb 14.92265060 26.63402779 3.00246223 Br 15.05753495 26.98924491 35.34891276 Br 14.41173215 23.69877868 2.52852126 Br 11.67173329 26.51382889 3.63375714 Cs 11.95354819 23.59277308 5.94184520 Pb 14.96032972 26.71188246 9.00396393 Br 14.90901920 26.68077011 5.82833086 Br 15.03727924 23.84339912 8.71951413 Br 11.79103513 26.76665351 8.43151312 Cs 11.94367499 24.03522295 11.73612143 Pb 14.87195494 26.65472309 14.83750103 Br 13.94164789 26.61232380 11.93959441 Br 15.25472434 23.67311637 15.32952684 Br 11.76047947 26.57605496 15.54192021 Cs 11.60859150 23.83402585 17.75542848 Pb 14.74072809 26.88625656 20.68459877 Br 14.96439128 27.35301892 17.60021224 Br 15.13729313 24.09633107 20.55867337 Br 11.77333090 26.12263518 20.77726199 Cs 11.62384394 23.72304766 24.28172826 Pb 14.87806076 26.65564345 26.71120725 Br 14.09826037 26.73465670 23.71183596 Br 14.61704772 23.61035889 26.57383625 Br 11.96460878 26.84748597 27.44444474 Cs 11.99814657 23.85537578 29.53022685 Pb 14.79686956 26.87365507 32.50148153 Br 15.54949327 26.89829380 29.32867312 Br 14.94588632 23.69949609 32.54136647 Br 11.93222110 26.36879884 32.58729969 Cs 12.12868366 29.56281328 35.50266006 Pb 14.90294367 32.75010410 2.90187110 Br 14.88957586 32.42756022 35.32024364 Br 14.39678501 29.73750660 2.96217434 Br 11.70340687 33.00572477 2.87747666 Cs 11.40869852 29.93074980 5.39506912 Pb 14.84156276 32.55345344 8.72982611 Br 14.62981549 32.42476783 5.94953125 Br 14.92117437 29.69852043 9.27241671 Br 11.75128010 32.79985657 8.42165973 Cs 11.78218868 30.22337692 11.80937668 Pb 14.63970656 32.69947756 14.97851081 Br 14.75662967 32.95427574 11.78035743 Br 14.98288741 29.82540772 14.64516551 Br 11.79825579 32.31986557 14.72411789 Cs 11.49232045 29.48616507 17.72883640 Pb 14.80644579 32.70264309 20.64006488 Br 14.84477427 32.74224868 17.74187770 Br 14.70508553 29.91740116 20.47821473 Br 11.91166476 32.58576338 20.13745906 Cs 11.91742238 29.76685413 23.92667147 Pb 14.86540664 32.51401114 26.58505661 Br 14.67750266 32.22689358 23.65233934 Br 14.54531986 29.71100168 27.02880498 Br 11.80379309 32.61196918 27.09033325 Cs 11.79094326 30.07305359 29.51877785 Pb 14.85965925 32.80286429 32.43334697 Br 14.70240322 32.80006400 29.53610716 Br 14.23677071 29.66427503 32.26342117 Br 11.81279644 32.78580045 32.36021299 Cs 17.93220378 0.41418167 0.25572125 Pb 20.75483608 3.19221962 3.01965422 Br 20.96087673 3.20550802 35.55408096 Br 20.65274723 0.14087462 2.58529937 Br 17.78398432 3.47180154 2.86530669 Cs 17.55520546 0.17226715 6.16797714 Pb 20.86932223 3.05576598 8.91661093 Br 20.96927469 2.85336054 6.05483528 Br 20.62763625 35.54768443 8.74560965 Br 17.83512806 3.12029493 9.23396351 Cs 17.86175432 0.26515858 11.97643896 Pb 20.89211991 2.98306988 15.02611613 Br 20.97625298 3.12838287 11.90235904 Br 20.74598771 -0.06016821 14.86288922 Br 17.66137564 2.74942679 14.79004906 Cs 18.00776320 35.34705583 18.45974214 Pb 20.69249795 2.87669311 21.11904783 Br 21.43517028 2.46520213 17.89484701 Br 20.98658584 35.15111371 21.88529782 Br 17.79080958 2.73312292 20.24996803 Cs 17.97873313 35.33118882 24.30062759 Pb 20.76873816 3.07354409 26.82587939 Br 20.45075854 3.59121845 23.84807628 Br 20.74637432 0.10370819 26.58118970 Br 17.78608240 2.66275037 27.02317607 Cs 17.77518947 0.29223727 30.04152488 Pb 20.86122521 2.95583688 32.65488939 Br 20.47903494 2.89987817 29.67325547 Br 20.51758051 35.54196152 33.02746010 Br 17.79480327 3.25408719 32.85273794 Cs 17.89041862 6.32039749 0.61458650 Pb 20.64238098 9.01601671 2.87145882 Br 20.71482012 9.50862685 35.55293638 Br 20.62557379 6.18084586 3.06053040 Br 17.70489500 9.31290726 3.26257111 Cs 17.70310164 6.27567372 5.68675180 Pb 20.84728673 8.90749121 8.82796443 Br 20.92750987 9.11630866 5.90620353 Br 20.74111809 5.82137159 8.62586464 Br 17.92488317 8.90628277 8.24731186 Cs 18.01637644 6.01673684 11.59472791 Pb 20.75273968 8.93731753 14.93802527 Br 20.69570986 9.05869065 11.76189723 Br 21.87830378 5.79659268 14.73873128 Br 17.89233869 8.85942633 15.41476478 Cs 17.52092394 5.64756537 17.70497292 Pb 20.52604933 8.83364167 20.86207816 Br 20.84249469 8.86634413 17.86554170 Br 20.47409210 5.74810066 20.80530196 Br 17.55584207 8.91714964 20.77661960 Cs 17.65034367 6.09545043 23.50195219 Pb 20.52963585 8.73884644 26.56841880 Br 20.76531528 8.45039190 23.67529054 Br 20.59079632 6.04896493 27.04013455 Br 17.69459052 8.81036422 26.29320749 Cs 17.92396934 5.75088996 29.30430163 Pb 20.55843081 9.00537167 32.41252222 Br 20.52907571 9.48805996 29.62844155 Br 20.88348760 5.96808642 32.72696657 Br 17.66994836 8.88760618 32.26074478 Cs 17.78707027 12.02423830 0.58981419 Pb 20.87508050 14.94091836 3.04798814 Br 20.72943000 15.68555454 35.48252583 Br 21.27095411 12.11532606 3.37095063 Br 17.76069575 15.08323066 2.94571633 Cs 17.64028021 11.95367526 6.22021050 Pb 20.76093047 14.89441217 8.86619562 Br 20.64713700 15.11148068 6.07926987 Br 20.56362400 11.63363374 8.94644099 Br 17.79781582 14.42641656 9.49356547 Cs 17.76648145 11.50069605 11.87100377 Pb 21.13542221 14.77246420 14.76304477 Br 21.29259662 14.66914778 11.88239622 Br 20.46642265 11.82819334 14.88355717 Br 18.09396151 15.10872029 14.60425152 Cs 17.91755914 11.91933965 18.17165987 Pb 21.05063188 14.75993182 20.70400168 Br 20.99281142 14.55187974 17.87602583 Br 21.10612793 11.63691831 21.03708181 Br 18.09800016 14.65007809 20.51638922 Cs 18.16536985 11.85975706 23.84333157 Pb 20.88433794 14.70497002 26.60967713 Br 20.51489887 15.05350793 23.58071745 Br 20.61804008 11.81716840 26.69836744 Br 18.05019287 15.13759790 26.78445216 Cs 17.98289494 12.01335684 29.77265727 Pb 20.79800059 14.70129335 32.45937409 Br 21.04189888 14.65095524 29.72106459 Br 21.01547971 11.76488530 32.68557734 Br 17.84439576 14.53528645 32.41876688 Cs 18.06385154 17.82123894 35.15178803 Pb 20.77972579 20.84472699 3.02026612 Br 21.20724433 20.88550099 -0.01032884 Br 20.92572261 18.02598058 3.47409467 Br 17.85914038 20.88402183 3.33268288 Cs 18.14215108 17.85160695 6.18985700 Pb 20.86729099 20.77827976 9.01419561 Br 21.06816562 20.81242606 6.13685788 Br 20.56523809 17.73314435 9.31378106 Br 17.82158742 21.16184314 8.75446198 Cs 17.61371348 17.17007401 11.74673458 Pb 20.83897019 20.64731711 15.03940142 Br 20.61388748 21.02492297 12.00491800 Br 21.37828904 17.69838053 14.53808745 Br 17.93148235 21.04757862 14.86600819 Cs 17.62401898 17.47937735 17.72636777 Pb 20.61850226 20.39253794 20.88181604 Br 20.66890094 20.39759170 18.12104069 Br 19.99138644 17.48528726 20.80860370 Br 17.62283582 20.64273931 20.65790037 Cs 17.63235140 18.41106710 24.04263262 Pb 20.77371376 20.62856221 26.84896824 Br 20.71370716 20.83863552 24.01415526 Br 21.09956072 17.87983450 26.10129593 Br 17.82236142 20.87211458 26.80403210 Cs 17.74709868 17.37043425 29.65870861 Pb 20.87291801 20.76412952 32.61160478 Br 21.08263921 20.38787882 29.71708868 Br 20.31592807 17.83297753 32.29697446 Br 18.00539513 21.12185871 32.90195317 Cs 17.21640406 24.07741338 0.17863492 Pb 20.75963245 26.84050053 3.08458309 Br 20.95705846 27.10264313 35.58067841 Br 21.07490623 23.90209956 2.83722314 Br 17.67734409 26.99614757 2.64188623 Cs 17.68134695 23.56000678 6.15268022 Pb 20.81938279 26.59366120 8.95923089 Br 20.17086470 26.62246938 5.88686542 Br 20.63920201 23.84927788 8.52815358 Br 17.78912277 26.55899860 8.78834254 Cs 17.67858242 23.93288458 12.37119671 Pb 20.91555829 26.76731309 14.81419592 Br 20.92695528 26.32986926 12.04642108 Br 21.19066417 23.70348801 15.51121800 Br 17.87581836 26.80030252 14.76986425 Cs 18.16375898 23.26069127 17.85161298 Pb 20.60543097 26.53747778 20.67157614 Br 20.08835248 26.48306479 17.87010786 Br 20.49284965 23.38402809 20.84110851 Br 17.68591824 26.96390179 21.18721387 Cs 18.02857935 23.65717547 23.78846798 Pb 20.69821282 26.53244601 26.65318783 Br 21.18302814 26.97461934 23.66217890 Br 20.66536490 23.78819866 27.07812207 Br 17.73469727 26.50345093 25.89387280 Cs 18.07579426 23.44219953 30.01467280 Pb 20.76857010 26.69007436 32.56556853 Br 20.70122199 26.86972659 29.64778163 Br 21.20987656 23.61603027 32.51578737 Br 17.69837955 26.75153145 32.48088902 Cs 18.43052869 29.97031621 35.38935980 Pb 20.98107960 32.70736841 3.02334176 Br 21.04781842 32.55511759 35.63375018 Br 20.31553831 29.57765962 3.53831455 Br 17.95652044 32.84208184 2.79714469 Cs 17.68064451 29.26958300 5.84745222 Pb 20.65830137 32.55180587 8.91053381 Br 21.30572991 32.76174854 5.88146831 Br 20.65306245 29.46990838 9.64411959 Br 17.77139134 32.56388207 8.35578527 Cs 17.79906188 30.40400349 11.78217256 Pb 20.88893011 32.71879307 14.83997395 Br 20.57811357 32.81275543 11.85441985 Br 20.77477022 29.65486133 15.12659656 Br 17.80679920 32.88983414 15.27747670 Cs 17.50503771 29.46011330 17.69164853 Pb 20.91062584 32.39295454 20.99618292 Br 20.78482220 33.22401989 17.94655936 Br 20.62936804 29.35718741 20.36855038 Br 17.66473528 32.84888222 21.09239821 Cs 17.88605982 29.38380901 23.68615710 Pb 20.89841145 32.42639289 26.66265122 Br 20.72392197 31.84696503 23.79611784 Br 20.78627671 29.62253654 26.80981954 Br 17.91846110 32.18716900 26.86575584 Cs 17.63937106 29.62219082 30.05402423 Pb 20.66866903 32.63796837 32.56287252 Br 20.68986140 32.38456458 29.50510046 Br 20.34562144 29.69801956 32.80683078 Br 17.71043330 32.88527509 32.31192863 Cs 23.66782472 0.23174285 0.09242824 Pb 26.57406991 3.12969969 2.85771228 Br 27.33099207 3.26018209 0.09280290 Br 26.52321312 0.16324155 2.86540899 Br 23.65952050 3.77656981 2.98629078 Cs 23.85787232 0.01038572 5.59617464 Pb 26.68020122 2.87096399 8.91247697 Br 26.72241088 2.99159316 5.75478992 Br 26.52122488 35.45541576 8.93496364 Br 23.74221544 3.23543101 8.93358487 Cs 23.64780189 -0.06083336 11.92171441 Pb 26.51600918 3.05785051 14.85753152 Br 27.07883707 3.15610587 11.87742524 Br 27.14425933 35.65969801 14.90136583 Br 23.74954084 2.44231691 14.38368225 Cs 23.89188068 35.48587662 17.43014012 Pb 26.70219587 2.65149678 20.95095952 Br 26.49477726 3.22049086 17.77144850 Br 26.18190479 35.43200874 20.00248317 Br 23.61656252 2.98512039 21.06917422 Cs 23.82593906 0.42904798 23.47148907 Pb 26.61799336 3.06589043 26.79766332 Br 27.27373413 2.55954009 23.70784463 Br 26.58112810 0.08531449 26.70818604 Br 23.78076027 3.33971200 26.56358744 Cs 24.05082125 0.01513376 30.03684581 Pb 26.78525560 3.09402914 32.78628659 Br 26.47181229 3.02987317 29.79865764 Br 26.51607684 0.14357276 32.66860687 Br 23.82846847 3.40428593 33.01808599 Cs 23.48892834 6.25058090 -0.11106136 Pb 26.80099667 8.92902949 3.21487528 Br 26.25222837 8.76561336 35.55903580 Br 26.89012147 6.16254953 2.96184116 Br 23.53476610 9.05964467 3.17805808 Cs 23.75347533 5.73207122 6.10504444 Pb 26.83641003 8.64088610 9.08772683 Br 26.92791383 8.76879022 6.15725756 Br 26.67681625 5.61928974 8.67157354 Br 23.95061520 8.82134944 8.83894219 Cs 23.75983423 5.31438004 11.69256187 Pb 26.71793349 8.76398796 14.89880225 Br 27.06040490 9.04429252 12.09480023 Br 26.27324196 5.84948066 14.38971259 Br 23.85135232 9.02889592 14.95118703 Cs 23.64903400 5.68963171 18.65033070 Pb 26.59419108 8.68588570 20.83500127 Br 26.69355871 9.29925779 17.91087536 Br 26.57533869 5.78856783 21.27014714 Br 23.70398964 8.44703591 20.95763888 Cs 23.65800378 6.31045551 23.92041303 Pb 26.79051235 8.89789236 26.81478246 Br 26.91720828 8.92581152 23.81253333 Br 26.68213691 5.81623452 25.95031769 Br 23.59576944 9.01516959 26.49099078 Cs 23.84868467 5.92287317 29.91035200 Pb 26.63293489 9.12489473 32.53789950 Br 26.35646529 8.50062305 29.86376610 Br 26.98145005 6.14022665 32.55543349 Br 23.66224567 9.07822236 32.54968617 Cs 23.83729190 11.86431932 0.42443361 Pb 26.79208594 14.94670001 2.94165407 Br 26.29597886 14.34016476 0.01118091 Br 27.06280612 11.97725372 3.15639427 Br 23.85610114 14.73545254 2.96584454 Cs 24.60078422 12.16137107 5.58325519 Pb 26.64210467 14.89909449 8.84259207 Br 26.74692927 15.17982530 5.96744542 Br 26.69796063 11.64077347 8.85175277 Br 23.74933275 15.34640527 8.21472306 Cs 23.76317940 11.66812217 12.04713262 Pb 26.67048850 14.84262644 14.57401852 Br 26.79982012 14.64444565 11.67782400 Br 27.02443002 11.89843826 14.50912620 Br 23.81612314 14.74007939 14.60027044 Cs 23.23728657 11.64289637 17.98436596 Pb 26.66958915 14.65248595 20.90475743 Br 27.04124841 14.72671865 17.61010659 Br 26.14842609 11.76533366 20.55024837 Br 23.74505968 15.50140296 20.91262932 Cs 24.20295183 11.71802284 23.81270037 Pb 26.90888126 14.74936930 26.86810267 Br 27.08787955 14.45942541 23.77762573 Br 27.28883558 11.79960264 26.86242080 Br 23.85683741 14.27243076 26.44701761 Cs 23.81097130 11.85973707 29.26836869 Pb 26.65194909 14.85657989 32.86652398 Br 26.41894828 14.75287274 29.89006713 Br 26.81000026 12.01236235 32.39088560 Br 23.58345783 14.78003621 32.26517968 Cs 23.36478058 18.04168293 0.35884749 Pb 26.64714811 21.09648276 2.96068315 Br 26.72021301 21.29685188 35.46610352 Br 26.80580335 17.82555960 2.19363726 Br 23.80720915 20.89867332 3.28787289 Cs 23.50955573 18.22944382 5.81118953 Pb 26.93708726 20.85370036 8.91555370 Br 26.67150889 21.19815571 6.16028214 Br 27.71894703 17.80371852 8.74476951 Br 23.95260107 20.28183766 9.02483286 Cs 23.48276980 17.40503499 11.59945936 Pb 26.54530443 20.78890660 14.55926595 Br 25.87337908 20.22313366 12.01137093 Br 26.49156586 17.75391519 14.87381137 Br 23.78194520 20.71108207 15.22495991 Cs 24.24317264 17.07015897 17.56263952 Pb 26.36286435 20.56433635 20.79025203 Br 26.13867286 20.47539939 17.99804883 Br 26.72656551 17.55016665 21.21831124 Br 23.67520796 20.94630842 21.17636812 Cs 24.01397279 17.68279782 23.81450845 Pb 26.67674697 20.83555948 26.71751070 Br 26.92452796 20.35917222 23.69971048 Br 26.71925289 17.60770949 26.53272284 Br 23.90607584 21.22090890 26.61765185 Cs 23.40418470 17.59533905 30.10243770 Pb 26.82073190 21.04700573 32.56638181 Br 26.77830837 20.67725855 29.51999776 Br 27.10434118 17.99904862 32.99285841 Br 23.86027182 20.49918077 32.42174214 Cs 23.73767369 23.89512079 0.11022331 Pb 26.79589685 26.88499010 3.03631306 Br 26.40281826 26.25478496 0.11444112 Br 26.82416542 23.86793749 3.22452467 Br 23.77921676 26.69181391 3.76341181 Cs 24.07168519 24.33889436 6.38421586 Pb 26.72951665 26.94292016 8.94392562 Br 27.22073068 26.98013220 6.13693792 Br 26.63768539 23.72055398 8.97496958 Br 23.70558230 27.07866695 9.27513861 Cs 23.61128142 23.33513466 12.77910946 Pb 26.72665309 26.77156801 14.88860077 Br 26.74793222 26.71872087 11.99925490 Br 26.79784946 23.93586402 14.77238544 Br 23.80632376 26.51795838 15.07982347 Cs 23.86084372 23.61320130 18.24433111 Pb 26.63166081 26.71022683 20.98185444 Br 26.96664281 26.44780717 17.83386938 Br 26.42329077 23.55052059 21.19105392 Br 23.63757452 26.67813907 20.56043722 Cs 23.54454448 23.91439845 23.61431165 Pb 26.60518839 26.83988820 26.98255861 Br 26.63794230 26.92871651 23.94745733 Br 26.36424420 23.89369312 26.60401625 Br 23.55463946 26.65750147 26.85273824 Cs 23.93825930 23.38461628 29.27053816 Pb 26.47674402 26.79274976 32.73104801 Br 26.00165383 27.09693672 29.97235303 Br 26.14874488 23.99031085 32.55495401 Br 23.65741437 27.41909606 32.83341953 Cs 23.86769567 30.03967438 35.49297141 Pb 26.89398531 32.91610234 2.93731007 Br 26.77293359 32.85681136 0.16247831 Br 26.35230211 29.89455531 3.33651633 Br 23.85859403 32.94506223 2.65743479 Cs 23.54137344 29.77993694 5.97789577 Pb 26.72458451 32.62123294 8.82725033 Br 26.93101160 33.17996454 5.89343875 Br 25.78997609 29.83735434 8.82098540 Br 23.61389325 32.83566001 8.69704693 Cs 23.46856505 29.71240283 12.51911564 Pb 26.96949150 32.64720369 14.81818607 Br 26.88399035 32.64688120 11.86408801 Br 27.06833679 29.62785831 14.91658160 Br 23.73876538 32.54460502 14.74142374 Cs 24.17517889 29.54993483 17.85035303 Pb 26.73232882 32.71391939 20.91170006 Br 26.60978103 32.07784396 17.83072963 Br 26.44661775 29.69117413 21.43898343 Br 24.00030432 32.39491644 20.56393697 Cs 23.59409421 29.57535831 23.60941534 Pb 26.38166137 32.70741742 26.92915754 Br 26.77321836 32.47672636 23.82017596 Br 26.59099768 29.73853501 27.24124700 Br 23.57330711 33.24337211 26.66181935 Cs 22.90045232 29.35133366 29.82923923 Pb 26.62455933 32.88026727 32.68405109 Br 26.93055931 33.29013922 29.95529256 Br 26.61353676 29.82990992 33.10709322 Br 23.72356719 32.59101221 32.93526145 Cs 29.64660938 0.31563826 35.16239791 Pb 32.59353873 2.79688549 2.83383549 Br 32.65976347 2.57424509 35.48320196 Br 32.67039760 35.49365104 3.32512933 Br 29.59044037 2.94484738 3.13552539 Cs 29.98297658 0.53677386 6.02849486 Pb 32.58949278 2.79488543 9.03733917 Br 32.62986018 2.92669736 5.87633460 Br 32.07857251 -0.01557542 9.31919638 Br 29.67039702 3.27048076 8.98976515 Cs 29.41349469 0.07248848 12.01230116 Pb 32.52414891 3.06476260 14.96910072 Br 32.81768013 2.72187707 12.06042654 Br 32.23720655 -0.03746358 15.33008123 Br 29.65809704 3.18359060 15.14287358 Cs 29.51654002 0.22505953 17.50933820 Pb 32.48812587 2.85719878 20.91205414 Br 32.22029890 3.08769986 18.01862921 Br 33.02497873 0.06701182 21.60517432 Br 29.53302566 2.38391284 20.59273780 Cs 29.26573864 35.54746320 24.08792224 Pb 32.59085610 2.94096963 26.59824416 Br 32.20274852 3.56860817 23.82858585 Br 32.92795094 35.55106559 26.90242751 Br 29.68468406 2.68869464 26.74464646 Cs 29.58405956 0.11605030 29.24788502 Pb 32.65310711 2.87251539 32.60171417 Br 32.57013038 3.86909104 29.73448195 Br 33.63874535 35.44648076 32.27204810 Br 29.74029422 2.56139110 32.53320164 Cs 29.17964526 6.33008023 0.15286370 Pb 32.46067197 8.66215754 2.94602534 Br 32.35904595 9.16625290 0.05128294 Br 32.30179235 5.72236354 2.58332713 Br 29.67432738 9.21851765 2.59812932 Cs 29.78022864 6.00428860 6.10328089 Pb 32.62295206 8.97824582 8.84619784 Br 32.43072364 8.29209630 6.03676487 Br 32.64531217 5.83886484 8.61064087 Br 29.68002694 9.11159196 8.70006732 Cs 30.05646045 5.41561742 12.06198777 Pb 32.75685340 8.83428500 14.80743523 Br 32.67351075 8.72081289 11.70517313 Br 33.00123487 5.95753060 14.81469108 Br 29.66256764 8.45325718 15.23647439 Cs 29.81012797 5.95907427 17.57327097 Pb 32.44896995 9.02382774 20.58562700 Br 32.63093848 8.77046790 17.83821189 Br 32.84409949 6.06766856 20.79148839 Br 29.53457254 8.76331841 20.91355715 Cs 29.82548522 6.29247546 23.40573367 Pb 32.67125993 8.86715317 26.49848296 Br 32.40278553 8.96584264 23.62493064 Br 33.24990857 5.94504088 26.61243649 Br 29.72560115 8.73228327 27.12064435 Cs 29.89415040 6.38234407 29.48256597 Pb 32.59793576 8.60601419 32.92231789 Br 32.84167794 8.66623374 29.70444648 Br 32.19146274 5.70757552 33.25929881 Br 29.56539636 8.97073444 32.74712926 Cs 29.84862852 12.18723715 35.23374286 Pb 32.50525069 14.65090436 3.24347382 Br 32.94051688 14.75326091 0.12104159 Br 32.77855156 11.71952757 3.45858067 Br 29.64387351 14.74682912 2.29782189 Cs 29.94630755 11.54351670 6.02002494 Pb 32.58533943 14.96256012 8.92344303 Br 32.34837442 14.76731231 6.10280590 Br 32.18645089 11.94188806 8.49290805 Br 29.76904722 14.66472442 9.28701013 Cs 29.46253748 11.87531496 11.72641731 Pb 32.56846073 14.72637778 14.80651437 Br 32.67236479 14.39389282 11.71753450 Br 32.39784534 11.79484243 14.78953332 Br 29.47950913 14.71487653 14.48956438 Cs 30.04229587 11.48660803 17.44341288 Pb 32.58075264 15.09331292 20.93388020 Br 32.65656125 15.10394609 17.91442857 Br 32.73375892 12.11759894 21.05698560 Br 29.60734836 15.00230636 20.75103831 Cs 30.10641687 12.03341738 23.72739449 Pb 32.70326028 14.61012413 26.77560290 Br 32.94115181 14.40969351 23.95633459 Br 32.99186408 11.76349790 26.77451977 Br 29.74052800 14.72742918 26.33657691 Cs 30.07549568 11.40506623 29.65951272 Pb 32.70952590 14.85252632 32.77652444 Br 32.32303140 14.86595879 29.74824781 Br 32.51917967 11.69081168 32.38853742 Br 29.64210356 14.88936413 33.01153694 Cs 29.90031287 17.95899428 35.49212828 Pb 32.53757242 20.70705838 2.78496601 Br 32.64532462 20.47434996 35.37548508 Br 32.36442716 17.72170020 3.05766325 Br 29.57848153 20.75849087 2.92255967 Cs 29.82088468 17.91649259 5.86401021 Pb 32.58984741 20.89824546 8.52061032 Br 33.18493041 20.07592637 5.74031713 Br 32.60123962 17.87748238 8.86845106 Br 29.67245959 21.42618749 8.37340737 Cs 30.47904097 17.13960155 11.67541920 Pb 32.68371722 20.86413972 14.51753514 Br 32.64069030 21.35814483 11.59684927 Br 32.73863090 17.88275185 14.36828024 Br 29.66285825 21.12287435 14.91582035 Cs 29.50581187 17.57721933 17.58560677 Pb 32.30554411 20.77914433 20.39324593 Br 32.62525572 20.34249977 17.66158067 Br 32.16330025 17.95431002 21.37455118 Br 29.48627078 20.80640540 20.17615600 Cs 29.83286810 17.84040198 24.06885470 Pb 32.68975212 20.83868532 26.55642859 Br 32.52212176 21.18976974 23.60264919 Br 32.37198912 17.60015463 26.74043462 Br 29.79168805 21.55062160 26.45349980 Cs 29.54077950 17.76654085 29.65808473 Pb 32.84686710 20.84185834 32.30585384 Br 32.58038527 20.72609359 29.55381253 Br 33.10061994 17.85013103 32.71251340 Br 29.82975135 20.64954983 32.66531795 Cs 30.10319444 23.99918037 35.27247353 Pb 32.88017324 26.92235647 2.94095562 Br 32.59881807 26.53378029 35.53040567 Br 32.85625902 23.70719387 2.89076022 Br 29.78593131 26.57798240 3.14452765 Cs 29.48437555 24.16274793 5.69309129 Pb 32.70127241 26.73628856 9.02856522 Br 32.96796163 26.75654268 5.95094412 Br 33.39365035 23.55126706 8.53837493 Br 29.82198083 26.83891830 9.08348574 Cs 29.45664300 24.00250147 11.69196889 Pb 32.44652710 26.88835013 14.99612523 Br 32.67520560 26.44407355 12.00349469 Br 32.83158812 24.01180646 15.21351699 Br 29.64939218 26.29180902 15.04569958 Cs 29.40600187 23.39575597 17.72795617 Pb 32.69305972 26.79188305 20.81166374 Br 32.43689031 27.21195833 18.00223013 Br 32.22054341 23.71472699 20.05470780 Br 29.53193132 27.06801964 20.73442263 Cs 29.63836291 23.48515844 22.96328289 Pb 32.53035179 26.63177466 26.65322714 Br 32.04136125 26.56644231 23.78528467 Br 32.90436706 23.65352852 27.26032019 Br 29.56394638 26.31955826 27.13741419 Cs 29.85743033 23.49482961 29.74459884 Pb 32.71703639 26.76117894 32.58015449 Br 32.64837044 26.80284721 29.61490104 Br 32.32326328 23.85213439 32.44668033 Br 29.79230453 26.94971566 32.83739043 Cs 29.80254035 30.32461545 35.02991498 Pb 32.71159300 32.70752348 3.23065232 Br 32.92511075 32.76446620 0.14169363 Br 33.32952759 29.64822864 2.77821894 Br 29.76944481 32.30319183 2.78378814 Cs 30.12766420 30.27203589 5.79148570 Pb 32.59592039 32.61397880 9.08092241 Br 32.75474671 33.49790217 6.23623930 Br 32.85985010 29.53905591 8.92309708 Br 29.41082429 31.70814020 9.10446330 Cs 29.47115364 29.39111516 12.36437292 Pb 32.68729541 32.66875174 14.95126218 Br 32.28871154 32.96472232 11.95222588 Br 32.43086859 29.76918343 14.63550038 Br 29.86638431 32.50453899 14.76990564 Cs 29.16529251 29.19987254 17.79469330 Pb 32.69484543 32.68544383 20.70330463 Br 33.11777818 32.47466146 17.82582385 Br 32.59175593 29.81477676 20.87344059 Br 29.90790830 33.19262095 20.17735856 Cs 29.35367250 29.69635525 23.71458532 Pb 32.66833618 32.42587867 26.79653247 Br 32.34773056 33.35954023 23.96784217 Br 31.95083240 29.51305818 26.57068243 Br 29.79188893 33.10746042 27.11761714 Cs 29.66743106 29.34011294 29.25949443 Pb 32.90503628 32.53460813 32.84100106 Br 33.12423724 32.30699400 29.78637007 Br 33.16847231 29.70452275 32.82652305 Br 29.60312429 32.78390333 32.44358794 1080 Lattice="35.60125893 0.0 0.0 0.0 35.60125893 0.0 0.0 0.0 35.60125893" Properties=species:S:1:pos:R:3 Time=1000.0 energy=-38569.34277344 virial="-13.30114827 -1.7815178 -0.66453176 -1.7815178 -23.45749583 1.82833241 -0.66453176 1.82833241 -21.30598314" stress="0.00053024 -3.948e-05 -1.473e-05 -3.948e-05 0.00030515 4.052e-05 -1.473e-05 4.052e-05 0.00035284" pbc="T T T" Cs 0.08861874 0.17371122 0.11090065 Pb 2.65678043 2.98421346 2.97425056 Br 3.33619769 2.63258199 0.09155939 Br 2.86856166 0.12179521 3.05254420 Br 35.38658661 3.13659934 3.45524236 Cs 0.20382936 0.03750261 5.63828919 Pb 2.89528085 2.82804492 8.82178113 Br 2.67953472 3.22217399 5.86990665 Br 2.43456127 35.54532396 8.65951920 Br 35.47956708 3.20648729 8.73683439 Cs 35.35599186 35.17490979 11.59618112 Pb 2.89374896 2.83874565 14.55827359 Br 2.45691275 2.98465409 11.77173450 Br 3.06231576 0.16423640 14.24189262 Br 0.15483050 2.05253274 15.01940129 Cs 35.46148756 0.13482574 18.07139667 Pb 2.99382889 2.86509031 20.61365921 Br 2.86753075 2.68182313 17.80700311 Br 3.53348060 35.49461474 20.37981848 Br 35.38322812 2.80311066 20.70149326 Cs 35.34810464 35.16910494 24.21881936 Pb 3.07618239 2.82189124 26.80601870 Br 3.45901864 2.28867908 23.63538173 Br 2.42254184 0.06132204 26.76637658 Br 35.54161570 2.85943318 26.38890232 Cs 35.40785240 35.29684242 29.50567325 Pb 2.94490161 2.89181569 32.72977895 Br 2.91443152 2.72312060 29.87919506 Br 2.61580685 0.03598949 32.80917077 Br 0.14577946 3.31008792 33.59784192 Cs 35.48109435 6.14728116 0.23103053 Pb 2.88674539 8.81492058 3.04014522 Br 2.77344105 9.62869858 35.58128614 Br 3.29565882 5.83678063 2.20442376 Br 35.59055266 8.58505785 2.81886025 Cs 34.54196164 5.94706404 5.78130217 Pb 2.94410959 8.82658600 8.63170701 Br 3.15400477 7.99056024 5.64420780 Br 3.52395820 5.82835654 9.15244338 Br 0.11762782 8.22849970 8.89675434 Cs 35.26922738 6.17641571 11.98298782 Pb 2.98533110 8.67667876 14.81590017 Br 2.96014131 8.57380013 11.86436554 Br 2.38590205 5.88601133 15.20406861 Br 0.07077626 9.31131222 14.65516190 Cs 0.06316186 5.91685773 18.29756628 Pb 2.72320405 9.00669824 20.66994980 Br 2.57310406 8.57096588 17.94009265 Br 2.83170583 5.81845590 21.01497437 Br 35.53335539 8.73800142 20.72692525 Cs 0.04258211 6.13684584 23.77080363 Pb 3.01383004 9.06085702 26.54606358 Br 2.00457560 9.20265641 23.61581223 Br 2.82263592 5.79512256 26.24024203 Br 35.33914685 9.04582142 26.71676868 Cs 0.36838530 6.29972195 29.00000170 Pb 2.84937413 8.92229572 32.75912214 Br 3.52284682 8.60800929 29.57310124 Br 2.57925570 6.14340068 32.75034658 Br 0.03635271 8.95564994 32.49009216 Cs 35.53280917 11.44370523 0.15813626 Pb 2.95986662 14.80988076 2.99795762 Br 2.73732310 14.48704215 0.12358196 Br 2.92760776 12.04957623 3.26296754 Br 0.19479647 15.47100522 3.17011114 Cs 35.48392551 12.25310714 6.12151676 Pb 2.86071704 14.56447683 8.66500180 Br 2.27727810 14.87207248 5.97894581 Br 2.91447277 11.61330944 8.04439840 Br 0.18209286 14.37752442 9.62518547 Cs 35.33911169 11.36437778 11.64148161 Pb 3.30476671 14.89857245 14.82600546 Br 3.03852868 14.68518207 11.69948983 Br 3.33918541 11.82648033 14.97671970 Br 0.20484195 14.88607380 14.66986697 Cs 0.34452073 11.40079613 17.23446207 Pb 2.91060663 14.80152540 20.92501970 Br 2.90176578 14.51593280 17.72904659 Br 2.29554879 11.85763810 20.60701226 Br 0.01527839 15.14592466 20.74932007 Cs 35.42611821 11.89835144 24.07939799 Pb 2.95768449 14.86535213 26.78875582 Br 2.71233557 14.09775887 23.90210640 Br 3.01053523 11.73894322 27.39037604 Br 0.07197151 15.13713260 26.99511144 Cs 35.36689390 11.75545538 29.46819763 Pb 3.31336966 14.76852646 32.54913011 Br 2.86612014 15.09462375 29.70382136 Br 2.94613212 11.85005639 32.33902315 Br 0.11501631 14.44207523 32.24663593 Cs 34.93324112 17.52345566 0.08689284 Pb 2.85432937 21.26280587 2.77270336 Br 2.85430846 21.22131630 35.46867799 Br 3.09239249 18.48963187 3.04032078 Br 35.53497166 20.90767653 2.40124882 Cs 0.47489306 18.31183464 5.36389235 Pb 2.74801133 20.50851518 8.88989261 Br 2.23771749 21.11990033 5.85647136 Br 2.70813201 17.52240720 8.77684014 Br 35.51012834 20.38385388 9.14986907 Cs 0.29360145 17.12329342 11.84737701 Pb 3.04832143 20.88437060 14.86394479 Br 2.81851946 19.73788310 11.73110981 Br 3.26174095 17.93831894 15.27015216 Br 35.37767711 20.71975821 14.34093328 Cs 35.53980371 17.62902238 17.34999315 Pb 2.79217356 20.41870591 20.92962241 Br 2.55672487 21.01827611 17.69081035 Br 2.93467448 17.61239653 21.62830220 Br 35.47848909 20.80148190 20.88958713 Cs 35.35298801 17.90551116 24.10839831 Pb 3.02023046 20.75974968 26.57932097 Br 3.34814619 20.83181226 23.59490420 Br 3.15464304 17.67187635 26.57757722 Br 0.05589915 20.94749720 26.27974247 Cs 0.03481585 17.59361793 29.95848950 Pb 3.00732350 20.70501643 32.46362304 Br 3.47240300 20.73091777 29.42895863 Br 3.03643545 17.80461796 33.15652256 Br 0.11192968 20.82906534 32.43166575 Cs 35.46722908 23.21695192 35.19426685 Pb 2.91084847 26.67763515 3.16242760 Br 2.82500607 26.28144410 0.14401480 Br 3.22452749 23.97052176 4.13192896 Br 35.57395387 26.30620687 3.41534908 Cs 0.27402564 23.75061530 6.23460133 Pb 3.07284133 26.73715878 8.93481342 Br 2.52529623 27.08368681 6.16575730 Br 3.30183867 23.71635901 9.12148607 Br 0.01639870 26.44222323 8.91518191 Cs 0.06453041 23.72622172 11.56128971 Pb 2.81321827 26.69604476 14.89693225 Br 2.99684695 26.61946900 11.94435412 Br 3.11096730 23.70394419 14.78488746 Br 35.57230868 26.85843986 14.53451412 Cs 0.02289354 23.52937886 17.68682860 Pb 2.88209521 26.58959635 20.74954574 Br 2.47949969 26.36736859 17.76298335 Br 3.22308940 23.52019927 20.69896117 Br 0.05758121 27.10243601 21.09499503 Cs 35.49503902 23.59433457 23.47970689 Pb 3.07129913 26.87123638 26.63044902 Br 3.29198981 27.19823501 23.71704194 Br 2.81144562 23.85268212 26.25148991 Br 35.38313871 27.13451517 26.55806143 Cs 35.37931031 23.90109995 29.50709206 Pb 2.87852494 26.68793307 32.50460917 Br 2.60708948 26.69507653 29.57529384 Br 3.26124118 23.72312803 32.70417284 Br 35.54051776 26.20779800 32.54077302 Cs 35.50689382 29.72781888 35.38603260 Pb 3.11497462 32.88493225 3.12110125 Br 2.95090523 32.55822691 0.21541532 Br 3.00233446 29.96627158 3.20423712 Br 0.09487168 32.85892948 3.12966608 Cs 0.04261179 29.50235962 6.23980731 Pb 2.78563318 32.59438281 8.81953643 Br 3.23835859 32.63404087 5.99915356 Br 2.83680485 29.61672197 8.92141543 Br 35.50139461 32.73260536 9.39343970 Cs 0.09788421 29.11956072 11.93973793 Pb 2.94480663 32.81082169 14.73901423 Br 2.83817133 32.73587198 11.94478444 Br 3.09479917 29.74624617 14.72341203 Br 0.04214640 32.53204648 14.24126752 Cs 0.10150881 29.60504417 18.27634531 Pb 3.17799176 32.62757007 20.60095132 Br 2.89788424 32.34532145 17.72384263 Br 2.94593103 29.62909435 20.87551839 Br 0.15868293 33.15504172 20.84689955 Cs 0.49972218 29.78583424 23.81644938 Pb 3.25445401 32.59171386 26.76342043 Br 3.19555224 32.47926355 23.77412510 Br 3.46878952 29.83425902 27.22185039 Br 0.15649388 32.48641739 26.80463334 Cs 35.53141029 29.41600517 29.45383235 Pb 3.04895956 32.65537240 32.92905861 Br 3.49389893 33.24694973 29.66156196 Br 2.41797212 29.64125859 32.66461458 Br 0.06707166 32.38983554 33.18072399 Cs 6.29658044 0.03395082 35.51017339 Pb 8.42795925 3.13955394 3.06142704 Br 8.98707841 3.78290305 0.14460657 Br 8.81644851 0.26904112 3.17301518 Br 5.55046631 2.71861567 3.38908774 Cs 6.04707564 35.23422918 6.08818736 Pb 8.70504180 2.85123942 8.85694976 Br 8.73236076 2.67230982 5.86163657 Br 9.01624878 35.46488660 9.15073950 Br 5.86822310 2.58636100 9.48978343 Cs 6.36897914 34.78541071 11.89805322 Pb 9.00809531 3.13639373 14.60822017 Br 9.03872171 3.44826301 11.71954660 Br 9.07847071 0.01527680 14.36589792 Br 5.98871733 3.33309210 14.87322429 Cs 6.01158623 0.42844289 17.43439246 Pb 8.82763770 3.02926862 20.75672903 Br 8.92815351 2.95971628 17.67856830 Br 9.23090113 0.30623556 21.15822067 Br 6.08328484 2.57695027 20.53764551 Cs 6.05981331 0.03879020 23.29391730 Pb 9.30010655 2.70360215 26.78682548 Br 8.44943695 3.04679418 23.63218756 Br 8.60313414 35.56406414 26.97555753 Br 6.00120224 3.16255540 26.87388711 Cs 6.22666293 35.59741822 29.68163326 Pb 9.00206638 3.15887139 32.91437900 Br 9.18308872 3.35183695 29.61041694 Br 8.72925721 0.11303787 32.25526428 Br 6.01142253 3.10748974 32.78425372 Cs 6.15519015 6.39605929 35.32094399 Pb 8.73006815 9.05001229 3.07320876 Br 8.78533996 8.97170145 35.60400261 Br 8.72794314 6.13935260 3.48811059 Br 5.68109487 9.56649223 2.57457482 Cs 5.91481251 5.46992680 6.46082218 Pb 9.10196839 8.69471206 8.71512624 Br 8.79534642 9.70622517 5.89851955 Br 9.01386121 5.79004937 8.66491684 Br 6.08814748 8.56679417 8.74843197 Cs 6.32481527 6.04042631 11.44225197 Pb 8.91423741 8.96154239 14.98202437 Br 8.78607143 9.14041857 11.94066772 Br 9.17513111 5.97980324 14.95490361 Br 5.96040086 8.74521220 15.06966914 Cs 5.18511279 5.60292732 18.20249395 Pb 8.53451963 8.92767876 20.74931213 Br 9.17472777 8.70493540 17.91747465 Br 7.99639577 6.11418161 20.10677225 Br 5.75013464 9.40063748 20.73076363 Cs 5.78164904 5.44602275 23.94609976 Pb 8.79092241 8.50723879 26.64300510 Br 8.96343914 8.56765702 23.68792910 Br 9.30471085 5.72964389 26.68729819 Br 5.79096911 8.65385544 26.13227797 Cs 5.86181308 5.59754419 30.17813135 Pb 8.73875959 8.76961872 32.51787649 Br 8.55523447 8.52895759 29.67765177 Br 9.48675955 5.90761319 32.56700240 Br 5.99900676 8.65913450 32.65005255 Cs 5.60462586 12.30822039 0.17333225 Pb 8.98641272 15.14157385 2.72615766 Br 8.63858196 15.61652653 35.58829457 Br 9.32988338 12.19863636 2.89790241 Br 5.84897802 14.71445014 3.30075838 Cs 5.91752223 11.76115972 5.72840954 Pb 8.94255975 14.92620634 8.85710056 Br 9.39794676 14.78063535 6.08762563 Br 8.84868064 11.92512890 8.83269057 Br 5.79470753 14.88746146 8.72898017 Cs 6.20791911 11.95810148 11.48378277 Pb 9.31690799 14.77753744 14.68116228 Br 8.93194674 14.62657274 11.81634634 Br 8.91869047 11.95797190 15.33857307 Br 6.34006381 15.47897968 14.87115893 Cs 5.82034275 11.88705599 17.85156848 Pb 8.89028949 15.00772818 20.85465311 Br 9.13709549 15.33268671 18.03299220 Br 8.39819185 12.10650378 20.59223352 Br 5.77697282 15.09003979 20.51605297 Cs 5.97834401 11.64381739 23.34835538 Pb 8.76263060 14.40826015 26.74661126 Br 8.32577022 15.05962446 23.85099171 Br 8.53565824 11.66629765 26.19662825 Br 5.85734387 14.38444279 27.12691834 Cs 5.53056826 11.64694439 29.55099961 Pb 9.07519956 14.46460779 32.50745715 Br 9.38665657 13.73323130 29.68841273 Br 7.99039261 11.83380505 33.03568463 Br 6.17368632 14.86965882 31.75274249 Cs 5.74572791 18.38742695 0.43298502 Pb 8.92331670 21.02521372 2.97336273 Br 9.59884235 20.82914932 35.55998702 Br 8.35490558 18.31831860 2.86097707 Br 5.87054051 21.29637459 2.77093183 Cs 5.56054539 19.03611943 5.67900231 Pb 8.96011922 20.66930984 8.90315128 Br 8.88303187 20.91825108 5.84101699 Br 9.40957481 17.87302670 8.85525440 Br 5.66382175 20.22845947 8.84898037 Cs 5.76195992 17.60933747 11.62386287 Pb 8.83083857 21.06414137 14.73223726 Br 9.61118500 20.63402962 11.85336136 Br 9.13565959 18.05970255 14.56685977 Br 5.72348369 20.81243925 14.84819207 Cs 6.63760780 17.62489955 17.69626486 Pb 8.71753417 20.89190371 20.74784819 Br 8.49521802 20.68261041 17.78098536 Br 9.46549284 17.89021632 20.71393259 Br 5.70012469 20.55034888 20.72445566 Cs 6.26170782 17.87803959 23.99934380 Pb 8.81242878 20.66611361 26.79001121 Br 8.68906373 20.63303362 23.85054264 Br 8.79789658 17.63193549 26.78186256 Br 6.07609689 21.29250910 26.66883675 Cs 6.17529797 17.76284992 29.36215928 Pb 8.93987736 20.63805629 32.74602200 Br 8.37995204 21.05121861 29.61352455 Br 8.87601230 17.69248257 31.99566190 Br 6.06632838 20.89553516 33.13065972 Cs 5.80284977 24.16548321 0.23256544 Pb 8.76671204 27.11866063 2.93042630 Br 8.71040330 26.79100213 35.55020253 Br 9.01229542 24.19820714 3.08893155 Br 5.69388052 26.92918563 2.69501008 Cs 6.23476442 23.63798561 6.41068102 Pb 8.93319177 26.64927469 8.78480922 Br 8.37157966 27.05422631 5.87336659 Br 8.66629767 23.68259811 9.28615671 Br 5.89473219 27.18955161 8.82031390 Cs 5.62016304 23.91313044 11.98901805 Pb 8.71642242 27.08413178 14.95597187 Br 8.68224043 26.69248145 11.87888855 Br 8.94002541 23.98392238 15.06401279 Br 5.72246497 27.00788836 15.12202272 Cs 5.39888714 23.89256634 17.94155758 Pb 8.78048249 26.72554810 20.83156275 Br 9.32555775 27.16075660 17.82056703 Br 8.45110237 23.82240401 20.14772241 Br 5.84300974 26.59135178 20.15886178 Cs 5.72461141 23.96870359 23.73369978 Pb 9.27959511 26.57921563 26.62206722 Br 8.68693817 26.04323186 23.84773418 Br 9.23662993 23.54404257 26.53514284 Br 5.90940989 26.27339719 26.85235411 Cs 6.23913225 24.03629848 29.38022283 Pb 8.83230132 26.64238538 32.50787161 Br 8.65002132 26.42707465 29.45476081 Br 8.95023564 23.61348208 32.59473221 Br 5.92147150 26.75490273 32.49708514 Cs 5.46275268 29.19535205 35.39085726 Pb 8.84320636 32.97098477 2.97267447 Br 9.07808939 33.07798149 35.55483007 Br 9.47792053 29.91144751 2.53516000 Br 6.11466822 32.99551200 3.16747471 Cs 5.73621045 29.62687193 6.19763743 Pb 8.79971957 32.49537481 8.85497161 Br 9.02040459 32.72839272 5.86830851 Br 8.91612072 29.65128526 8.43114936 Br 5.82937497 32.70513323 8.89856440 Cs 5.32116958 29.65240947 12.20116715 Pb 8.83305102 32.82155439 14.74586998 Br 9.41386654 32.60237959 11.69558222 Br 8.31918394 29.92240395 14.33755968 Br 5.92232593 33.07904235 15.07362205 Cs 6.08524316 29.46885059 17.84596864 Pb 9.15287557 32.77714808 20.86741275 Br 9.33395115 32.42278114 17.60889717 Br 9.20337975 29.85144702 21.23868567 Br 6.12898423 32.68711550 21.29886479 Cs 6.37488195 29.51787229 23.67076520 Pb 9.10808535 32.66819470 26.64531040 Br 9.46306088 32.39070746 23.80517832 Br 9.07697716 29.66241531 26.65214735 Br 6.21261005 32.63691012 26.43011924 Cs 6.30019348 29.93177905 29.26221460 Pb 9.09879755 32.65792996 32.56458777 Br 9.19534404 32.20398949 29.49210350 Br 9.48854199 29.67184645 32.69446346 Br 5.86273668 32.37947113 32.50200038 Cs 11.97823240 0.03207158 0.22407312 Pb 14.90459945 3.08418177 2.93314328 Br 14.86647583 3.76072964 0.00804085 Br 15.25643112 0.14635562 2.62428729 Br 11.67087105 3.26415818 2.84324321 Cs 11.74465651 0.24422238 6.23915021 Pb 14.72640645 2.90848030 8.87103236 Br 14.69211857 2.64252855 5.76171286 Br 14.54698291 0.00053085 8.66740643 Br 12.00832844 2.82008874 8.98446452 Cs 11.74407455 0.08420987 11.88542651 Pb 14.88198501 2.83509973 14.83592483 Br 15.17001706 3.06826216 11.80637795 Br 14.55985626 35.22685720 15.34371126 Br 12.18341854 2.75184484 14.43403136 Cs 11.88679776 0.16294042 17.57733014 Pb 15.05433134 2.84489800 20.84525823 Br 15.10135137 3.33558629 17.64953167 Br 14.51249799 35.58400263 20.12985839 Br 12.10977077 3.40678496 20.73865742 Cs 12.19358510 0.16178007 23.82104966 Pb 14.99147363 2.85413503 26.75665048 Br 14.95643946 2.37593185 23.81372293 Br 15.29632648 35.52955418 26.51263287 Br 12.10073221 2.71147917 27.29537509 Cs 11.64634931 0.10261386 29.89794294 Pb 15.12665366 2.95578774 32.84832252 Br 14.96769978 2.69473644 29.82320151 Br 15.30139612 35.62946566 32.83237326 Br 11.90841853 2.43337503 33.16256339 Cs 11.63390355 6.31776343 35.34892322 Pb 14.55464403 9.04782193 2.73850290 Br 14.57096880 8.49216910 35.32443988 Br 15.13331671 6.15620417 3.14889590 Br 11.84991975 9.30448946 2.95920477 Cs 11.90944476 5.30664175 6.09036370 Pb 14.64290741 8.72394068 8.69701205 Br 14.44196805 8.88319645 5.81627509 Br 14.50219280 5.81569699 9.14966617 Br 11.94504144 9.20290177 9.34594561 Cs 12.18116857 5.82461610 12.31675333 Pb 15.06201778 9.01829029 14.69930705 Br 15.48365029 8.72046360 11.88136746 Br 14.53303586 5.93468346 14.79894903 Br 11.93778742 9.03895878 14.87491457 Cs 11.68948217 5.56413960 17.56601194 Pb 14.47408252 8.73899505 20.61716781 Br 15.28145345 9.15201550 17.86884303 Br 15.57485260 5.65456496 20.99625953 Br 11.62987006 9.16875250 20.68380643 Cs 12.05240408 6.21976883 23.99462882 Pb 14.55247381 8.83624291 26.53080475 Br 14.67815253 9.01199143 23.51705862 Br 14.93943107 5.83587051 25.99848260 Br 11.61382490 9.23745151 26.23421961 Cs 11.99805594 5.70513920 29.47632977 Pb 14.55172678 8.78048730 32.34092051 Br 14.93747060 8.73370928 29.42112064 Br 14.57777919 5.87263177 32.47538775 Br 11.72585604 9.33156772 33.00496499 Cs 11.79105774 12.51891588 0.44484493 Pb 14.89152884 14.78671198 3.13867040 Br 15.13525595 14.77512206 0.17242922 Br 14.85407631 11.79007985 2.59423467 Br 11.91628547 15.37220060 2.47386251 Cs 12.06297659 11.86045897 5.66660014 Pb 14.96669487 14.61989608 9.03014704 Br 14.61516039 14.62798410 6.02613593 Br 14.93962069 11.80860215 9.06691708 Br 11.90657155 14.60674322 8.82222592 Cs 11.63373674 11.68227687 11.83581960 Pb 14.90040545 14.67396052 14.70332723 Br 14.09926942 14.81365141 11.73884986 Br 14.47572653 11.84453831 14.31380800 Br 12.10415421 14.94287659 15.36972242 Cs 11.27962365 11.81987313 18.06768083 Pb 15.07764398 14.49837911 20.75864134 Br 14.67773005 15.02717897 17.77088124 Br 14.70579385 11.71593654 20.49821381 Br 12.01465705 14.82432190 21.00516728 Cs 11.97299015 12.12147115 24.10413239 Pb 14.83683188 14.86174890 26.74042824 Br 14.85877315 14.92577235 23.74764538 Br 15.18944157 11.86313431 26.56318951 Br 11.61675746 14.74665133 26.51611850 Cs 12.15945768 11.66970124 29.53698340 Pb 15.07984139 14.84190765 32.74152377 Br 14.64390518 14.28574118 29.63595748 Br 15.09115878 11.87696155 32.19546042 Br 11.86656600 14.48692575 33.05219649 Cs 11.96993045 17.85682455 35.01525715 Pb 14.81853060 20.70381817 3.12044658 Br 14.68291275 20.36382283 0.05703582 Br 14.69691792 17.78730870 3.32997714 Br 11.78125192 21.11454830 3.03369648 Cs 12.12899943 17.57713383 5.94007417 Pb 14.70649388 20.80129104 8.81778246 Br 14.33290689 21.37092605 6.11261567 Br 14.99349717 17.90126327 8.56207072 Br 11.63812789 20.89674481 8.36954653 Cs 11.92740624 18.09306897 12.14761301 Pb 14.81632978 20.60958341 14.60845036 Br 14.27044138 20.95295198 11.77717975 Br 14.71153707 17.74976746 14.24505677 Br 11.88512887 20.83834234 14.82542440 Cs 11.91083706 18.02847776 17.70778072 Pb 14.88263633 20.63116743 20.73357217 Br 15.24434281 20.44315860 17.63894963 Br 14.96130901 17.63111611 20.92404600 Br 11.71347747 21.08508568 20.54861663 Cs 12.13663381 17.69204651 23.19392731 Pb 14.52566069 20.96231864 26.87696055 Br 14.26886731 20.92702352 23.71007466 Br 14.68005299 17.73458949 27.13889542 Br 11.78987767 20.65586316 27.67320736 Cs 11.50599281 17.76670263 29.30746126 Pb 15.10480883 20.73359613 32.58224707 Br 15.43470512 20.93900015 29.58459361 Br 15.20836345 17.63998032 32.28390433 Br 12.07864225 20.79874033 32.23487035 Cs 12.05422288 23.57390210 35.43661221 Pb 14.57701920 26.50730704 2.67563322 Br 14.70775058 26.71215335 35.34042177 Br 14.79909148 23.66366751 2.85066862 Br 11.72092032 26.97395177 3.56502756 Cs 11.80902929 24.00439892 5.78156421 Pb 14.91606922 26.45753643 8.62248603 Br 15.21667256 25.80362656 5.93677368 Br 15.25380083 23.70998656 9.07754511 Br 11.82678508 26.78845134 8.67050751 Cs 11.44029592 23.74047787 11.71588491 Pb 14.75321884 26.59010772 14.79733711 Br 14.64326348 25.84978050 11.95904052 Br 14.61760640 23.68352640 15.20282401 Br 11.69336755 26.63029581 14.39293691 Cs 11.46362008 23.78590547 17.90422683 Pb 14.98552510 26.50238109 20.66037088 Br 14.25847607 27.27411854 17.74539481 Br 15.22510709 23.64577528 21.03824216 Br 11.75251620 26.45296711 21.15794271 Cs 11.66118063 23.57964105 23.17506786 Pb 14.74293876 26.71428946 26.60975450 Br 14.34566897 27.06318431 23.85550815 Br 14.09326936 23.82269962 26.40239624 Br 11.96119865 26.81730616 27.39819594 Cs 11.67950026 23.70508283 29.78658929 Pb 14.95648455 26.45025175 32.36312074 Br 15.79580327 26.10868352 29.31403383 Br 14.61038598 23.52647646 32.43192145 Br 11.92640130 26.53126654 32.30302139 Cs 12.18375415 30.03428443 35.55743213 Pb 14.90916224 32.59129988 3.03017974 Br 15.04731976 31.81133914 35.51458291 Br 14.79401947 29.68835239 3.49445320 Br 12.16044407 33.20622150 2.93044953 Cs 11.17134701 29.63958391 5.72221451 Pb 14.91474237 32.45490217 8.77286887 Br 14.88152101 32.70614202 5.81421121 Br 14.66754093 29.71850914 9.29755231 Br 11.78957775 32.42089595 8.67084100 Cs 11.16326456 29.19809151 11.54440009 Pb 14.83044747 32.32719625 14.76073530 Br 15.19266383 32.93234426 11.69521098 Br 14.41351080 29.48832572 13.82767787 Br 11.77210362 32.80127599 14.89912561 Cs 11.82891651 29.77602396 18.33515304 Pb 14.88875978 32.47518052 20.71686834 Br 14.90582274 31.68152162 17.68558391 Br 14.74777485 29.58069420 20.95366376 Br 11.98794877 33.00580744 20.68429341 Cs 12.23176035 29.84192800 23.35203232 Pb 14.92997218 32.71372854 26.65432293 Br 14.92873842 32.32326756 23.56146622 Br 15.02966397 29.71890665 26.90910818 Br 12.00976498 33.38079163 26.56644390 Cs 12.14432351 29.76109713 29.46503386 Pb 14.92600383 32.52621997 32.68570622 Br 14.87605963 32.86562076 29.53555280 Br 14.51418174 29.36139187 32.31876416 Br 11.96265492 33.32938350 32.96316150 Cs 17.74881138 0.63292616 0.10873764 Pb 20.86055567 3.27088488 2.96973765 Br 20.88113727 3.65885253 35.54546120 Br 20.29452807 0.24187769 2.65348211 Br 17.83457395 3.32490487 2.97089213 Cs 17.58289673 35.53806605 6.25407616 Pb 20.86924570 2.89866207 8.73386812 Br 21.53615805 2.95132005 5.85704719 Br 20.34742278 0.03930554 8.77329747 Br 17.78284703 3.40915022 8.76934857 Cs 17.38581542 0.34095336 12.26714875 Pb 21.12542164 2.89531076 14.92439988 Br 20.54746478 2.72285116 11.94473750 Br 20.86469899 35.59219959 14.59125554 Br 17.98500140 3.05441764 15.26808537 Cs 17.78531623 0.02403938 17.73882351 Pb 20.92360815 2.79405126 20.81010506 Br 20.98504497 2.34297295 18.01812860 Br 20.81678316 35.40490286 21.17972737 Br 17.99589106 2.50995396 20.74342990 Cs 17.74380987 35.06390945 23.65914627 Pb 20.60846412 2.77415426 26.65254946 Br 20.73517608 2.96817129 23.83034186 Br 20.24669261 35.32637585 26.78917677 Br 17.81684945 3.45869642 26.26599326 Cs 17.59585679 35.39098103 29.54037381 Pb 21.05717745 2.97558821 32.80952686 Br 20.70443425 2.80903335 29.60082635 Br 20.35255584 0.03688693 33.34888879 Br 18.11215763 2.92098769 32.83176446 Cs 17.35127064 6.67017535 0.42274019 Pb 20.61408892 8.96637900 3.27063364 Br 20.67325081 9.52599152 0.19815380 Br 20.60549768 6.18486916 2.99813420 Br 17.69449837 8.99764122 3.30660567 Cs 17.21356102 6.06800425 6.29140774 Pb 20.74024191 8.94704955 9.05230032 Br 20.95612252 9.07249015 6.11501308 Br 20.77288236 6.06896766 8.31835012 Br 17.67231837 8.47746207 8.73578741 Cs 17.94652312 5.81363861 11.44667261 Pb 21.08124112 8.90496877 14.78763201 Br 20.86055171 8.62574742 11.95099015 Br 21.57378595 5.87168897 14.81822716 Br 18.07972374 9.37268723 14.84563389 Cs 17.93165044 5.75895851 17.67707779 Pb 20.76529607 8.98551180 20.83861922 Br 21.20601949 8.90267781 17.76534125 Br 20.49292352 5.69994019 20.84880405 Br 17.93392310 8.97314153 20.56197104 Cs 18.02043838 6.20432431 23.47764347 Pb 20.63379685 9.07949306 26.78616711 Br 20.77668791 8.90818841 23.60800946 Br 20.50344015 6.04683031 26.66282158 Br 17.74401058 9.14068532 26.42070393 Cs 17.97745465 6.11542520 29.39281439 Pb 20.52026569 9.17943406 32.72007889 Br 21.23410709 9.28042686 29.64449399 Br 20.85814537 5.88683746 32.42317050 Br 17.55049436 9.01509130 32.53918838 Cs 18.11731886 12.16041862 -0.01075999 Pb 20.76783819 15.02396467 3.05425213 Br 20.93073217 14.53669129 0.24274888 Br 20.96461118 12.15876951 3.33262241 Br 17.88589437 14.59389120 3.42880383 Cs 17.93185460 11.90796735 6.03407725 Pb 20.88474814 14.97661596 8.82563943 Br 20.54338896 15.27235466 6.13237849 Br 20.86002440 11.89441002 8.54012366 Br 17.98330955 15.05520587 9.22562608 Cs 17.95270170 12.09135438 11.50868431 Pb 20.75252898 14.62957256 14.78284191 Br 21.35756002 14.89653937 12.00998932 Br 20.85187192 11.77854513 14.51164116 Br 18.03199031 15.25423788 14.56639323 Cs 18.23793236 11.51873639 17.93187978 Pb 20.79097667 14.90015163 20.63504350 Br 21.16931405 14.73340636 17.66701138 Br 21.13109741 11.88326615 21.00794317 Br 18.05038307 13.74728922 20.55716490 Cs 17.99177551 11.73628976 23.46386323 Pb 20.69103158 14.96778655 26.59343998 Br 20.28327295 14.74013118 23.54265102 Br 20.30395645 11.93262001 26.92126316 Br 17.78777379 15.32391968 26.71608420 Cs 17.93568487 11.68607654 29.96733567 Pb 20.81621625 14.98112625 32.61802836 Br 20.85410728 14.62629279 29.49974478 Br 20.48425565 12.04759063 32.81505930 Br 17.77229826 14.32572757 32.82007924 Cs 17.80244531 17.35288396 35.53453107 Pb 20.66459162 20.67381806 2.87785389 Br 20.48103750 20.31482141 35.52818146 Br 21.03237310 17.90500857 2.75615124 Br 17.82692231 20.62299437 3.17529275 Cs 17.79558196 17.55988148 5.76436158 Pb 20.53931796 20.87568429 9.29626405 Br 20.83035568 20.65474206 6.03116284 Br 20.55275708 17.97070853 8.81099135 Br 17.61537595 20.70319949 8.52926862 Cs 17.75433070 17.63862264 11.77363381 Pb 20.90726681 20.64002907 15.08832524 Br 20.37322078 20.98657432 12.17108363 Br 21.06241711 17.76020064 14.80772132 Br 17.73724575 20.22489977 14.57102520 Cs 17.60706847 17.61841547 17.15452218 Pb 20.58587905 20.68971511 20.75443410 Br 20.14607005 20.56735611 17.82602267 Br 20.21335729 17.81205651 20.75624645 Br 17.56499037 20.87284253 21.64048511 Cs 17.64869658 18.12816347 24.26051263 Pb 20.89720907 21.05917765 26.76078874 Br 20.71114525 20.87481532 23.76540016 Br 20.85911698 17.87973641 26.73852230 Br 18.07478363 20.62678932 27.08940555 Cs 17.70741951 18.04184451 29.58242215 Pb 20.80943094 20.55516375 32.62052102 Br 20.92704503 21.04634794 29.72580571 Br 19.82383890 17.67381225 32.41658688 Br 17.78242140 21.10868241 33.37118960 Cs 17.93423645 24.08195843 35.34448967 Pb 20.78050766 26.75023207 3.04469373 Br 21.06364183 26.22037266 0.21407807 Br 20.78978974 23.70667356 3.09010884 Br 17.56979123 26.69256132 2.68284661 Cs 18.39622735 23.50096993 6.34050452 Pb 20.70450219 26.78935949 9.12030276 Br 20.67087550 26.83562414 6.23879153 Br 20.60034252 23.93697147 9.26354991 Br 17.71532102 26.83314048 9.62212788 Cs 17.46808549 23.32608902 12.74884596 Pb 20.40827595 26.65165027 15.19838688 Br 21.10777547 26.38002477 12.21080431 Br 20.88618000 23.55517903 14.93905517 Br 17.60008678 27.37855773 14.44102070 Cs 17.42086110 23.22631510 17.95791979 Pb 20.80181185 26.64956101 20.99827169 Br 19.84520899 26.65275234 18.02162384 Br 20.38446735 23.75126598 20.77570230 Br 17.71922007 26.89293251 21.21431843 Cs 17.85913642 24.16609644 23.52803522 Pb 20.61207200 26.63196355 26.73179917 Br 20.97898557 26.87834472 23.81768768 Br 20.19822290 23.69360572 26.84837902 Br 17.87817594 26.84373189 26.07138410 Cs 18.27948267 24.04136177 30.16208534 Pb 21.06489805 26.86835260 32.68320775 Br 20.23696247 26.95551738 29.61941733 Br 20.58217531 23.69377334 32.87692115 Br 17.75811882 26.73183108 32.85047041 Cs 17.67890553 29.88764436 0.13528463 Pb 20.70956898 32.91847393 2.75288442 Br 20.67770843 32.48488185 35.47304988 Br 20.65248219 29.63458753 2.93581296 Br 17.83501399 32.65980651 2.55037384 Cs 17.44506128 29.15297637 5.80085648 Pb 20.61341206 32.76145230 9.05948902 Br 20.95189467 32.55330278 5.74511785 Br 20.48383584 29.73360223 9.41751919 Br 17.86972539 32.41567213 8.57907488 Cs 17.70003550 29.95903960 11.74437545 Pb 20.87247281 32.76245305 14.77883357 Br 20.75328555 32.94757003 11.92843243 Br 20.86055525 29.75796917 15.01567026 Br 17.73586646 32.65786046 14.93886110 Cs 17.80269849 29.65376657 17.72455066 Pb 20.78134790 32.38397073 20.84170785 Br 21.28834195 33.16371652 17.96138681 Br 20.78612324 29.46316983 20.39635955 Br 17.74615784 32.22905441 20.38272513 Cs 18.09576247 29.86803908 23.94464896 Pb 20.85835020 32.61531706 26.70931927 Br 20.97052070 31.96571497 23.81377098 Br 21.13330958 29.52633311 26.37227752 Br 17.94129363 32.26917300 26.68297740 Cs 17.38769510 29.57605296 29.41104944 Pb 20.84064425 32.69672389 32.50523515 Br 20.66027085 32.28242199 29.69593584 Br 20.75758106 29.86408387 32.59053981 Br 17.73469475 32.17987683 32.16325362 Cs 23.94744440 0.44017749 0.15254006 Pb 26.88366418 3.50702711 3.01520815 Br 27.25890669 3.61632595 35.27374246 Br 26.67110067 0.44547629 2.34980078 Br 23.90189121 3.41465144 2.48794845 Cs 23.83816592 35.53122868 5.68091834 Pb 26.88371577 2.97994426 8.98413887 Br 26.61888568 3.42215213 6.03416206 Br 27.71243309 0.20150090 9.23047224 Br 23.75846012 2.86283435 9.33237232 Cs 23.85923390 0.03699585 11.68485403 Pb 26.97322763 2.99324386 14.99502068 Br 26.61549163 2.67113614 12.06261875 Br 26.84517950 35.38736946 14.96668828 Br 24.07513464 2.68238071 15.01802380 Cs 23.83545467 35.59390142 17.45948274 Pb 26.75146410 3.15171343 20.74615071 Br 26.41428114 3.27413882 17.87454231 Br 26.25719175 0.24866318 20.16080339 Br 23.92147052 3.29641702 21.10508771 Cs 24.20657443 0.20102048 23.39562026 Pb 26.66889123 2.98713847 26.32799342 Br 27.38287914 2.49773911 23.57170921 Br 26.90631992 0.07661393 26.99423410 Br 23.71470677 2.86138641 26.08269326 Cs 23.04738219 35.25104177 29.68031455 Pb 26.78275498 3.09281131 32.45394059 Br 26.05581652 3.36186697 29.43321942 Br 26.74151549 0.08000040 32.87615398 Br 24.08345685 3.36884146 33.25406193 Cs 23.63934527 6.40337793 35.42974017 Pb 27.00894641 8.91129423 3.08462702 Br 26.45889594 9.16861967 35.43694383 Br 27.89328026 6.27022038 2.97450530 Br 24.05916279 8.44416208 3.19794923 Cs 23.62273305 5.90126225 6.16748008 Pb 26.82594526 8.94814912 8.98939085 Br 26.83005000 8.83144627 5.93679380 Br 26.51426317 5.88244854 9.12064489 Br 23.79533038 9.52278588 9.01497642 Cs 24.11761904 5.37091301 11.86780521 Pb 26.85997584 8.93068547 14.87891994 Br 26.98329357 9.27031208 11.88215065 Br 26.86478891 5.92308585 14.32979329 Br 23.96697557 9.83516170 15.17830168 Cs 24.01872126 6.14292908 18.26576861 Pb 26.94783231 8.92180008 20.66418940 Br 26.69273522 8.65159561 17.72414127 Br 27.35629186 6.06895253 20.52975367 Br 24.01150870 8.76772216 20.79966713 Cs 23.46166685 5.74174840 23.79973996 Pb 26.62255493 8.75095771 26.56911929 Br 27.36009048 8.86736352 23.57128425 Br 26.59193372 5.96947738 26.34774447 Br 23.73291781 8.95774751 26.01845057 Cs 23.87349873 6.09339178 30.39839186 Pb 26.50435860 8.96291204 32.37833239 Br 26.12512032 9.14734106 29.32924615 Br 26.93771843 6.18038110 32.54303187 Br 23.47914344 9.04853413 32.76926766 Cs 23.79890624 11.86303880 0.19123929 Pb 26.88510390 14.95206825 2.97195356 Br 26.11611952 14.28600443 35.50075706 Br 26.68006343 11.96375747 3.67141390 Br 23.62471118 14.83033008 3.01834143 Cs 23.67751917 11.77880891 5.82236547 Pb 26.87451069 14.83090008 8.62513993 Br 26.71761616 15.45603757 5.70956783 Br 26.94799111 11.83874962 8.25722437 Br 23.91471906 15.32104527 8.70214945 Cs 24.16692885 11.66099193 11.94074730 Pb 26.79830745 14.50226609 14.68428786 Br 26.67543144 14.51474405 11.64650749 Br 27.59606031 11.65059635 14.61278225 Br 23.84917262 14.58431513 14.58805389 Cs 24.00904626 11.58886653 18.10941405 Pb 26.64907423 14.92848370 20.77517300 Br 26.49747506 14.45728774 17.69671906 Br 26.64989043 11.96390503 20.71660134 Br 23.77037455 15.03850344 20.43717907 Cs 23.78149559 11.19526271 23.31805463 Pb 26.32379894 14.92834180 26.63962335 Br 26.71910785 15.35176142 23.73628950 Br 26.56024862 11.88518266 26.17437742 Br 23.51945440 14.34341718 26.16595473 Cs 23.57180904 11.68645802 28.59601490 Pb 26.62931744 14.78285454 32.73267985 Br 26.15054634 14.36551432 29.67972534 Br 26.46050146 12.08859010 32.30146116 Br 23.74376594 15.54213014 32.87074144 Cs 24.09639980 17.66725062 0.06705315 Pb 26.43712132 20.92338315 2.98450973 Br 27.60390565 21.46415624 35.50664110 Br 26.82000385 17.92996386 2.56281517 Br 23.85301513 20.86213064 1.93772984 Cs 23.59552936 17.60989840 5.75838340 Pb 26.95004434 20.73520023 9.02814888 Br 26.60415035 20.85268803 6.02885869 Br 27.75238614 17.81552426 9.04112750 Br 23.80589329 20.88647390 9.47563398 Cs 23.69185751 18.06731907 12.09873829 Pb 26.74570085 20.55795310 14.92513544 Br 26.80905665 20.54503549 12.09594915 Br 27.07743333 17.54447284 14.84658759 Br 23.65480609 20.87990135 14.35592939 Cs 24.04225307 17.21278221 17.42340378 Pb 26.77607541 20.70288059 20.83534148 Br 26.45733381 20.49621757 17.86355701 Br 26.59174233 17.87019500 20.05838321 Br 23.72683968 20.79999585 21.22593792 Cs 23.82245671 18.32447018 23.79490075 Pb 26.95363276 20.93845129 26.66844065 Br 27.35580010 20.37852163 23.72484542 Br 26.62124397 17.79617369 27.06720212 Br 23.88382267 21.44296532 26.49955405 Cs 23.86313746 17.47848596 29.62469528 Pb 26.54270910 20.91606190 32.63286775 Br 26.32189966 20.92589017 29.69974079 Br 26.57695751 18.08903259 33.46474893 Br 23.64990496 20.54052394 32.40525321 Cs 23.92634513 24.07024045 0.04960604 Pb 26.61836118 26.89080950 2.99520125 Br 26.82569638 26.32874413 0.11996154 Br 27.07829751 23.94638612 3.13614667 Br 23.73089749 27.11479968 2.64246167 Cs 23.41718422 23.84132573 6.52710959 Pb 26.58568067 26.76458173 8.95905218 Br 26.52517289 26.73854304 5.98395870 Br 26.63897350 23.83322761 9.18944868 Br 23.67847910 26.71318299 9.06652146 Cs 23.20070997 23.70692520 11.75708940 Pb 26.51812113 26.29548630 14.99136767 Br 26.51126521 26.67350475 11.82644798 Br 26.56564926 23.39006914 14.66796338 Br 23.31134491 26.57887857 15.45918158 Cs 24.14747249 23.84394697 17.88867643 Pb 26.68786694 26.72537150 20.89252512 Br 26.81840843 26.39257993 18.13453315 Br 26.90382150 23.77368395 21.07323580 Br 23.45566907 26.57175643 20.41245251 Cs 23.38501718 24.15441663 23.73916921 Pb 26.70141546 26.54436239 26.65853370 Br 26.46806420 26.72638839 23.71947323 Br 26.81512260 23.61066813 25.99985177 Br 23.77710115 26.49259695 26.96102712 Cs 23.81135966 23.28448333 29.69551059 Pb 26.86285064 26.60803197 32.86925506 Br 26.56739299 26.69604929 29.78581097 Br 26.14622152 23.90215465 32.73475582 Br 23.82560457 27.47811923 32.75454402 Cs 24.16548102 30.02095025 35.50288855 Pb 26.67436536 32.79273828 2.82965574 Br 27.07774819 32.52694307 35.52782802 Br 26.89297533 29.83204185 3.20215239 Br 23.97138741 32.39120909 2.95933237 Cs 23.80281782 29.50194735 5.64236770 Pb 26.52125207 32.59670711 8.54740755 Br 26.82205690 33.30746493 5.70383874 Br 26.38774825 29.70885496 8.67986442 Br 23.53897589 32.64508806 9.09231549 Cs 23.76090808 29.93518751 11.68713904 Pb 26.80415621 32.40491911 14.88390917 Br 26.26040066 32.58807265 11.85787603 Br 26.89768048 29.36068074 14.99281599 Br 23.82343588 32.45869136 14.60518934 Cs 23.64370143 30.13114375 17.81667598 Pb 26.76226168 32.71470341 20.61294647 Br 25.98938838 32.76642628 17.70797789 Br 26.56143229 29.72337051 20.46099897 Br 23.66118558 32.47662787 21.47430164 Cs 24.41657724 30.00261045 23.92620186 Pb 26.70426119 32.51694604 26.81725177 Br 26.99899485 32.70122234 23.55791843 Br 26.83799679 29.56930649 26.49439472 Br 23.74188103 33.35886237 26.70498563 Cs 23.26488520 29.29514468 29.87742917 Pb 26.91574130 32.73376565 32.65095093 Br 26.53745375 31.88527744 29.65690870 Br 26.97703494 29.52721366 33.01410606 Br 23.82541822 32.66773171 32.64572542 Cs 29.41567290 0.49022564 35.43066670 Pb 32.45963512 2.81013143 3.02857812 Br 32.73825879 2.21222735 0.06765233 Br 32.53030872 0.03625226 3.07713027 Br 29.71809097 3.03945900 3.70082043 Cs 29.93215345 0.31728315 6.69094547 Pb 32.80283339 2.74973607 9.04970916 Br 32.40103519 2.58301966 6.05532064 Br 32.12253161 35.21491331 9.73800568 Br 29.65533304 3.25987011 8.55570917 Cs 29.47743283 35.11460575 12.60246457 Pb 32.79813324 2.81537035 14.87175237 Br 32.03366835 2.76339893 11.99243030 Br 32.70422242 35.35927749 14.93600323 Br 29.93558400 3.06532680 15.13941749 Cs 29.52837689 35.58540145 17.61264237 Pb 32.45903517 3.03968029 21.03627966 Br 31.98726220 2.83541968 18.07579006 Br 32.57467003 35.58885650 21.08590408 Br 29.64106406 2.40586456 20.57281293 Cs 29.83587795 0.17049643 24.13215336 Pb 32.61288221 3.05413575 26.51574997 Br 32.02581296 3.11773716 23.76976676 Br 32.20676763 35.49666036 26.82242186 Br 29.62776858 3.01860302 26.84577478 Cs 29.34159838 0.16816805 29.31810032 Pb 32.98014651 2.76546520 32.58863995 Br 33.12908429 2.66253649 29.65499098 Br 33.10018732 35.57540228 32.24161339 Br 29.98864038 2.82190170 32.46573079 Cs 29.43481734 6.69565147 0.12235248 Pb 32.59598675 8.89311055 2.91133727 Br 32.55339859 9.42021288 35.60948502 Br 32.38843552 5.98178077 2.76207129 Br 29.91673434 10.03737548 2.75047381 Cs 29.41570138 6.43838177 6.36054370 Pb 32.66011976 8.88200812 8.92287217 Br 32.09563976 8.75431984 6.01191671 Br 32.70172073 5.86138887 8.78531654 Br 29.82740727 8.76118096 9.08109697 Cs 29.27170493 5.96878940 11.81033217 Pb 32.60763461 8.67380714 15.06257622 Br 32.53495293 8.51115440 11.79154329 Br 33.33957941 5.83328023 14.88260664 Br 29.74069556 8.07300444 15.01843946 Cs 29.44147188 5.61593149 17.62888359 Pb 32.55268152 9.07498108 20.60453736 Br 32.24852313 9.17228841 17.78687633 Br 32.05299406 6.06798427 20.60215600 Br 29.71839935 9.70356692 21.12508180 Cs 29.87767801 6.31788082 24.03541511 Pb 32.49695901 8.85569167 26.44744842 Br 33.13776429 8.80624942 23.72986920 Br 32.73319529 5.88707489 26.74835649 Br 29.51294009 8.58462680 26.92724085 Cs 29.84624152 6.40241692 30.05718413 Pb 32.70516547 8.79790952 32.58016030 Br 32.59092974 8.33849630 29.75449809 Br 32.54378489 5.83978898 33.47655165 Br 29.55952115 8.91379608 32.51889299 Cs 29.72140022 11.58822695 35.20957782 Pb 32.75505926 14.66610701 2.84569324 Br 33.06303380 14.42690753 35.54472754 Br 33.49627251 11.67369054 3.34003901 Br 29.83804047 14.59411070 2.33146549 Cs 30.01111106 11.78054892 5.96842330 Pb 32.73473091 14.85125369 8.90959925 Br 33.06744192 15.16720657 5.84169196 Br 32.75028416 11.69775344 8.47013283 Br 29.66769285 14.13212500 8.98836550 Cs 29.88499628 12.06653885 11.70381625 Pb 32.75606991 14.51308269 14.87198624 Br 32.71692881 14.25849926 11.80778573 Br 32.46057137 11.74820719 14.49670034 Br 29.88928235 14.48383949 14.58807136 Cs 29.28214146 11.99494079 17.84338414 Pb 32.59032351 15.06952030 20.82990677 Br 32.29965643 14.85162744 17.87537970 Br 33.01738356 11.85145726 20.72802761 Br 29.74124891 14.70290599 21.24479305 Cs 29.35339086 12.11201389 23.79377388 Pb 32.47288085 14.76146617 26.78129714 Br 32.99388141 15.00171403 23.82327522 Br 32.07665015 11.96638514 26.73209971 Br 29.68161963 15.07865716 26.98462293 Cs 29.28195287 12.04891719 29.82863956 Pb 32.83178214 14.53332929 32.50375275 Br 32.27481446 14.58288173 29.78815122 Br 33.03011429 11.67947115 32.09849200 Br 29.70924748 14.51415230 32.61131888 Cs 29.78936180 17.98428446 0.49134680 Pb 32.63518858 20.96612302 3.04915990 Br 32.57422807 20.72616705 35.51946962 Br 32.54111725 17.69993891 3.02175811 Br 29.44707024 21.24777630 2.91430584 Cs 29.44701079 18.66916529 5.30503792 Pb 32.52314967 20.57398107 8.69462682 Br 33.39126558 20.49276015 5.90014093 Br 32.51179748 17.80260892 9.08159880 Br 29.64072970 21.33441267 8.66493360 Cs 29.51831967 17.41366111 11.89385012 Pb 32.67939688 20.53437042 14.48353581 Br 32.50381579 21.30814336 11.57375020 Br 32.37699791 17.32546125 13.92699061 Br 29.53649252 21.17234756 14.91493302 Cs 29.51056655 17.58834343 18.33437905 Pb 32.56598801 21.08186553 20.62106908 Br 32.63963155 20.61040649 17.89332390 Br 32.10366561 17.85269701 21.04536132 Br 29.59632416 21.24168107 20.05984157 Cs 29.62658188 17.41408934 24.10212732 Pb 32.42174856 20.72542020 26.63668044 Br 32.29338015 21.17207272 23.55650415 Br 32.53722274 17.85779711 26.26071662 Br 29.78310593 21.02949855 27.40423219 Cs 29.63332642 17.72051239 29.60163585 Pb 32.54258807 20.67244890 32.45387707 Br 33.18638900 20.52402188 29.38611610 Br 32.77699431 17.52103650 32.60620059 Br 29.67973078 20.57778978 31.82769238 Cs 29.91408281 23.84254813 0.55449730 Pb 32.84223368 26.72710588 2.97107057 Br 33.29659730 26.61870487 35.61180345 Br 32.96428914 23.67621056 3.84700532 Br 29.82655896 27.11777443 3.03806320 Cs 29.47621589 24.27945917 5.89620430 Pb 32.70006135 26.50257162 8.86415212 Br 33.22235299 27.27950044 5.85073105 Br 32.93794656 23.60141062 8.19792576 Br 29.53708320 26.81713215 8.89862392 Cs 29.41589287 23.89859648 11.84474768 Pb 32.60487658 26.30386240 14.87370601 Br 32.65905450 26.13772590 11.93337556 Br 32.57446188 23.37643374 15.15005032 Br 29.32544943 26.61209926 14.93363165 Cs 29.61328006 23.61467043 17.60368456 Pb 32.45319240 26.85892865 21.11334792 Br 32.19982395 26.76796619 17.74778387 Br 32.67433447 23.97914549 20.09287227 Br 29.69119851 27.43763473 21.21296799 Cs 29.70840173 23.58552193 23.30584086 Pb 32.38274406 26.65124506 27.03058344 Br 32.30637153 26.38953968 23.79711823 Br 32.71419505 23.65457264 26.82716524 Br 29.74537268 25.90273280 26.62386443 Cs 29.02544351 23.67998401 29.94680345 Pb 32.60179295 26.67543047 32.89193896 Br 32.75140292 27.06246366 30.01226432 Br 31.99589939 23.55034199 32.78148622 Br 29.72403181 26.78936192 32.78645627 Cs 29.69520878 29.98454132 0.17809951 Pb 32.87049025 32.57597124 2.86058136 Br 32.54117098 33.55924906 0.02267531 Br 32.92719815 29.73336028 2.71339547 Br 29.79033677 32.25941340 2.76217056 Cs 29.97142687 30.06722978 5.79079637 Pb 32.60561439 32.44799516 9.02271927 Br 32.84955945 32.97673691 5.96619036 Br 33.27017880 29.35755594 9.27646148 Br 29.50215344 32.38706060 8.78122204 Cs 29.66634565 29.45331073 11.99170843 Pb 32.58127577 32.29795611 14.98992572 Br 31.82175015 31.99467078 12.17770672 Br 31.99441891 29.29810274 14.63370505 Br 29.70185633 32.66925769 15.67703304 Cs 29.60640481 29.14924243 17.49250414 Pb 32.76413867 32.58729217 20.84404201 Br 33.26818610 32.12425010 17.98299359 Br 33.00267105 29.81106951 21.56324753 Br 29.72574364 32.60190734 20.23308379 Cs 29.47924430 30.12444694 23.78510270 Pb 32.58177542 32.56960465 26.82669786 Br 32.08419791 33.01082796 23.99510877 Br 32.13889530 29.64537400 26.66584264 Br 29.57020990 32.50886688 27.29469458 Cs 29.69768802 29.92405487 30.40552700 Pb 32.72804356 32.52082474 32.90290372 Br 32.57289088 32.43662937 29.98636286 Br 32.19902105 29.64794350 33.45105519 Br 29.76519124 33.08767667 32.68588734 1080 Lattice="35.60125893 0.0 0.0 0.0 35.60125893 0.0 0.0 0.0 35.60125893" Properties=species:S:1:pos:R:3 Time=1500.0 energy=-38566.46902847 virial="-1.65835154 -0.39460889 4.03214396 -0.39460889 -44.66878079 4.91583021 4.03214396 4.91583021 -35.54259386" stress="0.00078826 -8.75e-06 8.936e-05 -8.75e-06 -0.00016492 0.00010894 8.936e-05 0.00010894 3.733e-05" pbc="T T T" Cs 0.31830451 35.15366493 35.47630503 Pb 2.82537386 3.12752018 2.85277167 Br 3.62374403 2.83473870 0.00885687 Br 2.69043449 0.14919798 2.90598450 Br 35.47543986 2.89038972 2.81821526 Cs 35.54898526 35.58994833 5.89950260 Pb 3.02636647 3.00844103 8.84184370 Br 2.31155650 3.42015996 5.81969355 Br 2.43948492 0.10219726 8.88068064 Br 35.44082757 2.60366748 8.65199109 Cs 0.33933238 35.57693058 11.99816209 Pb 2.99141385 3.12994938 14.54130857 Br 2.12003985 3.31433152 11.63163934 Br 2.90298895 0.03706219 14.83351965 Br 0.11246265 2.47233525 14.53087440 Cs 35.42500622 35.26334203 18.13597575 Pb 2.86153184 2.85625944 20.83877026 Br 3.02867036 3.10969586 17.86850584 Br 2.97333618 0.03378149 20.24459899 Br 35.53003716 2.87508569 20.93694704 Cs 35.41421494 0.11806396 23.65327590 Pb 3.15798528 2.83536293 26.50795184 Br 3.68899186 2.59548917 23.73001942 Br 2.94151831 35.47414967 26.56067303 Br 35.43309660 2.28873439 26.20785907 Cs 0.31403611 0.18087236 29.22915273 Pb 2.90195847 2.86753485 32.74290203 Br 2.68175246 3.01592718 29.51410151 Br 2.55874409 35.26992709 32.58307432 Br 0.17094402 2.82557686 33.42924240 Cs 35.40653223 5.52416823 0.37980102 Pb 3.00173544 8.86258282 2.94777118 Br 3.05424370 9.28032891 35.53500878 Br 3.50348465 6.03243216 2.14953498 Br 35.53259335 8.38924596 3.09526380 Cs 35.29848093 5.80623078 6.45753278 Pb 3.11571015 8.81211608 8.87301036 Br 3.32002981 8.01637487 5.88313088 Br 3.35761035 5.94681044 9.40440426 Br 0.02949457 8.55202883 8.88839945 Cs 35.50517561 6.05687253 12.13056437 Pb 3.00565986 8.94625851 14.85976879 Br 3.05869282 9.22974795 11.81593689 Br 3.37593822 5.91889848 14.59720935 Br 35.49657375 8.74918790 14.31018896 Cs 35.55060052 5.91378149 18.33141329 Pb 3.08530902 8.90299464 20.89597933 Br 2.74430792 8.75605438 17.89489475 Br 3.02773080 5.95485676 21.06746612 Br 35.55119294 9.18107441 20.33717674 Cs 35.57358076 6.20379663 23.44561440 Pb 3.05351395 8.58853701 26.62935224 Br 2.61201838 9.17237116 23.79463792 Br 2.20317309 5.83163971 26.36370187 Br 0.05133211 8.53406729 26.74720865 Cs 0.04761274 5.74189836 29.38247178 Pb 2.95224038 8.81580830 32.52953329 Br 2.93559329 8.50422350 29.56444763 Br 2.91581731 5.77982521 32.92237658 Br 0.01357274 8.99271124 32.93740637 Cs 35.17993292 11.52815877 0.30997851 Pb 3.01749283 14.89569081 3.03650547 Br 2.85512817 14.73728018 0.09837064 Br 2.49456645 11.89923721 3.13048421 Br 0.04345997 15.23762775 2.28569478 Cs 0.26386917 11.22066743 6.17764872 Pb 3.25608231 14.83892573 8.84268184 Br 2.03357149 14.39387053 5.87804833 Br 2.91313022 11.84174303 9.04546148 Br 0.05986002 14.56269260 9.25315893 Cs 35.60116560 11.10818527 11.64548090 Pb 3.09150216 14.91555519 14.90192502 Br 3.66441356 14.75433416 11.68432337 Br 3.35243535 11.68977565 15.21015347 Br 0.11210090 15.00227325 14.71677589 Cs 0.01954214 11.98001368 17.17128877 Pb 2.89679174 14.62155917 20.76729625 Br 2.65862949 14.78141204 17.93517130 Br 2.77715102 11.83055386 20.79284767 Br 35.56063679 15.08840419 20.85673009 Cs 35.09950791 12.02299121 24.12675326 Pb 3.11162182 14.73566350 26.67156369 Br 3.31002372 14.41662697 23.81927222 Br 3.03179029 11.69986874 26.56911505 Br 35.58701355 14.89608500 26.88771663 Cs 0.09812816 11.68589163 29.29265482 Pb 2.87970613 14.69793712 32.60343264 Br 2.72559125 15.17353923 29.66423577 Br 3.35219772 11.71440769 32.13242224 Br 0.09422328 14.39213888 32.53849612 Cs 0.06502421 18.64626124 35.35198056 Pb 2.88477931 21.23980933 2.91246394 Br 2.95286638 21.40533529 35.59791040 Br 2.90844684 18.22964438 2.41920557 Br 35.51929685 21.20806180 2.74818671 Cs 35.55727124 17.61744712 5.14157925 Pb 2.88722956 20.77817612 9.14385247 Br 3.06847239 20.57504731 6.01681859 Br 3.25700067 17.76297798 9.33028953 Br 0.10843332 20.35381397 9.45175570 Cs 35.12320026 17.21573151 11.49601745 Pb 2.97896511 20.73248578 14.90142188 Br 3.58835305 20.72329854 11.94225758 Br 2.72917946 17.80831866 15.28454485 Br 0.02370583 20.91200335 14.43572684 Cs 0.55700341 17.84383717 18.02109246 Pb 3.00033675 20.54364908 20.80337749 Br 2.34149844 21.20208386 17.70622646 Br 2.66360598 17.53430221 21.10336037 Br 35.60855701 20.73533021 20.88647815 Cs 0.07710760 17.03477536 24.04309448 Pb 2.74773861 20.79180037 26.85073327 Br 3.52822931 20.66726929 23.59779851 Br 3.03755168 17.88112678 26.13018079 Br 0.11648713 20.44624034 25.50470331 Cs 0.24021141 17.57185273 30.58002863 Pb 3.47764881 20.78697365 32.76010584 Br 3.03303491 20.73636894 29.76014328 Br 2.71341831 17.79861471 33.10324849 Br 0.27091569 20.78978843 32.77457899 Cs 35.27304070 23.48920144 35.03991865 Pb 2.87572336 26.76103212 2.87939560 Br 2.43042993 25.85861490 0.21335515 Br 3.38171774 24.09001536 4.02553336 Br 35.51249194 27.05501731 3.40710803 Cs 0.53358290 24.04187030 6.11508920 Pb 2.98784185 26.60682135 8.96796803 Br 2.78095777 27.31724925 6.05418013 Br 2.80822516 23.58215628 9.28411252 Br 35.43402478 27.04147104 9.24532447 Cs 35.40636959 23.61936225 12.00687212 Pb 2.74441407 26.62008054 14.80292111 Br 2.84969296 26.43093896 11.81342258 Br 3.17797074 23.72935786 14.42768686 Br 35.19693068 26.60763271 15.08848670 Cs 35.33943634 23.57863815 17.54611473 Pb 2.64762481 26.56274347 20.83229181 Br 2.56389387 26.24971931 18.03179605 Br 3.31759242 23.40512139 21.17517375 Br 35.41310459 27.12297998 21.06508516 Cs 35.41895396 23.75857124 23.22264509 Pb 2.91742848 26.65599253 26.70838435 Br 3.02294223 27.23146382 23.74779393 Br 2.44972551 23.75153865 26.36895791 Br 35.52319319 27.14048255 26.87360580 Cs 35.08563194 24.03872089 29.12499428 Pb 2.89323805 26.55912111 32.51148663 Br 2.59380184 26.06525636 29.78813190 Br 3.84179524 23.75391773 32.20949052 Br 35.40390837 26.27473382 32.40719084 Cs 0.47942561 29.60839525 0.03087068 Pb 2.86505034 32.62862493 3.07913643 Br 2.87595207 32.21727126 0.29665129 Br 2.77913131 29.73150017 3.17190193 Br 35.38451328 33.03600642 3.33052763 Cs 35.38795079 29.81689449 5.93307820 Pb 2.83764602 32.65683864 9.04751052 Br 3.06955254 32.48539729 6.04391730 Br 3.17761609 29.66506811 9.06649618 Br 35.55189191 32.42332082 9.65116709 Cs 35.23539953 29.49545747 12.15056420 Pb 2.81014585 32.58951998 14.84775523 Br 2.90069950 32.63519251 11.96896150 Br 2.60750403 29.75372633 14.20950021 Br 35.57058812 33.05185671 14.44170407 Cs 0.04595548 29.42971635 17.93121122 Pb 3.05603273 32.44230166 20.85037411 Br 2.92120412 31.85336007 17.73130165 Br 2.90358069 29.68479982 21.25130349 Br 0.11159977 32.75351358 21.43009085 Cs 0.04097302 29.60801086 23.98345731 Pb 3.17815607 32.59086057 26.97050110 Br 2.90086679 32.58263534 23.83764922 Br 2.87815762 29.73507746 27.09025086 Br 0.19648636 32.10735457 26.80810406 Cs 0.44590001 29.42082558 29.77808799 Pb 3.03960416 32.38978744 32.90474921 Br 2.83156853 33.10764025 29.88485651 Br 3.11359390 29.58141495 31.95527441 Br 0.10627581 32.22954504 32.82114227 Cs 6.31974281 0.11832136 0.04116856 Pb 8.57937415 3.27425504 3.15506923 Br 8.45214263 2.88886587 0.12372037 Br 9.06062947 0.17733689 2.89023489 Br 5.68337953 2.83635066 3.72836991 Cs 5.79794248 35.50862435 6.01726969 Pb 9.00195542 3.02748128 9.08576129 Br 9.19589874 2.62770861 5.88950222 Br 9.15362820 0.01078333 9.26139254 Br 5.88553980 2.75702732 9.70531246 Cs 6.36222440 35.05333033 12.42419075 Pb 8.89941547 3.09314069 14.58430835 Br 9.16488269 4.18813808 11.84999533 Br 9.36836872 0.21141655 14.51066938 Br 5.80063897 2.71906220 14.55758858 Cs 5.88468015 0.47588533 17.60661919 Pb 9.05410884 3.21725857 20.61480448 Br 8.65280052 2.99158417 17.64588166 Br 9.27444320 35.48956510 20.90837115 Br 5.83522913 3.02528106 20.46275621 Cs 5.80470211 35.55035506 23.41271275 Pb 9.13535259 2.97390413 26.66331745 Br 8.61503896 3.47081011 23.67125262 Br 8.81500081 0.10016377 26.10573110 Br 5.97477038 2.77761775 27.35100918 Cs 5.73566386 35.12523964 29.66233508 Pb 8.99065578 3.01897239 32.82515734 Br 9.04105500 2.55208226 29.89219053 Br 8.80005429 -0.02003346 33.04261494 Br 6.08426250 3.51187718 32.65085294 Cs 5.98988494 5.70504591 35.25091288 Pb 8.63151762 9.13260334 2.74222922 Br 9.22755636 8.86015040 35.53264744 Br 8.09437839 6.11528649 3.32216752 Br 5.83293425 9.50896736 2.86992964 Cs 5.62394534 5.66840087 6.01367485 Pb 8.94131849 8.77731269 8.84817177 Br 9.12872181 10.00323054 6.12485823 Br 8.59813921 6.01736425 8.39092692 Br 6.06218919 9.02534818 8.24108393 Cs 5.67234952 5.90922708 11.78000964 Pb 8.86825962 8.76145749 14.83236021 Br 8.81824940 8.58912963 11.91167027 Br 9.60361784 6.07098187 15.04700031 Br 5.81259023 8.33071514 14.98365637 Cs 5.14003004 5.80659850 18.01786035 Pb 8.80030222 9.00332613 20.72418566 Br 9.56838889 9.34430925 17.97811931 Br 8.39265563 6.05767618 20.04000351 Br 5.77816264 8.87179514 20.81348061 Cs 5.89126399 5.75347259 24.10679003 Pb 9.07194623 8.71753584 26.34451511 Br 8.73994795 8.14080734 23.48241580 Br 10.14753718 5.98677684 26.86252909 Br 5.86650932 8.03556443 26.52806754 Cs 6.29614152 5.66629984 29.93661837 Pb 8.62042812 9.04737540 32.37663730 Br 9.25978973 9.01189878 29.21435218 Br 8.98925784 5.99497638 32.33904063 Br 5.91375190 8.87484025 32.72864124 Cs 5.47586004 11.92908947 0.21417015 Pb 9.07898900 15.05273347 2.98532920 Br 8.48863339 15.76926919 35.54212255 Br 8.56775872 12.11523278 2.18576263 Br 5.99933167 15.16868912 2.80717669 Cs 6.05184367 11.89739688 6.26540711 Pb 9.02625261 14.62320270 8.82616662 Br 9.12130234 14.22148473 5.85758174 Br 9.19992000 11.78373230 9.66065908 Br 5.98379362 14.99263296 7.93862714 Cs 6.64037425 12.06297640 12.08112124 Pb 9.19209435 14.99035979 14.75422860 Br 9.25283133 14.86589092 11.88598123 Br 8.91995906 11.77918714 15.01432860 Br 6.12551512 15.11872930 14.89509949 Cs 5.99333633 12.60158109 17.49250344 Pb 8.89887379 14.84679209 20.71982087 Br 9.35213255 15.04354208 17.80992076 Br 8.85754119 11.98326742 21.06025794 Br 6.03157601 15.04597218 20.51773539 Cs 6.03740333 12.08988364 23.60671094 Pb 8.85129169 14.97991666 26.70300147 Br 8.86126589 15.34473511 23.62371333 Br 8.58507256 11.68919364 25.92829467 Br 5.91482557 15.04830446 27.60542468 Cs 6.25852542 11.40107492 28.91225963 Pb 8.85632799 14.67125171 32.66629370 Br 8.88808070 14.17841940 29.61304130 Br 8.31396353 11.72722952 32.96031838 Br 5.91491360 14.87717316 32.13358785 Cs 6.10926609 18.48304737 0.41741091 Pb 8.85438077 20.86456069 3.27408187 Br 9.02803217 20.28531000 35.51245771 Br 8.55883143 17.84955020 3.52428616 Br 5.97740174 21.22338551 2.94877662 Cs 5.43606426 18.33771642 5.26911197 Pb 8.97351090 20.64071339 9.10068292 Br 8.87516393 21.40749247 5.98519143 Br 8.77810447 17.87462717 9.71742808 Br 6.11156000 20.37246907 8.57735925 Cs 5.69192019 17.81716286 12.08441781 Pb 8.94594259 20.89665516 15.05508310 Br 8.89166852 21.01005427 12.33213905 Br 9.27486319 17.94235294 15.03543563 Br 5.81182661 20.35746162 14.91625306 Cs 6.40812107 17.72205223 17.48718477 Pb 8.87470333 20.52184665 20.81237739 Br 8.80697223 20.88753720 17.95587392 Br 9.26281271 17.71434696 20.47722266 Br 5.92938826 20.10052013 20.27733057 Cs 5.49297548 17.72991016 23.66664508 Pb 8.73907483 20.69351280 26.65379162 Br 9.28615612 20.24131256 23.86807858 Br 9.10178629 17.68691424 27.04548720 Br 5.94896026 21.24334285 26.52336251 Cs 5.61379802 17.99565297 29.98537586 Pb 9.10477277 20.81518002 32.78950488 Br 8.58357517 20.93913307 29.77455893 Br 8.81902426 17.68606158 32.04526720 Br 6.35571106 20.90522307 33.28881707 Cs 6.08519924 24.16053594 35.03871923 Pb 8.70548555 26.56406194 2.89956097 Br 8.94278264 26.61365243 35.42855459 Br 8.87184955 23.72337147 1.88756266 Br 5.73671362 26.94429120 2.86549701 Cs 5.97070294 23.36549717 6.51179819 Pb 8.98670299 26.60880633 9.01585038 Br 8.88543379 26.59173610 5.79570228 Br 8.47857888 23.78601143 9.48238144 Br 6.01194728 26.56507561 8.73813309 Cs 6.07151694 23.67303241 12.22658041 Pb 8.62422251 26.59937464 15.09325911 Br 9.09069271 26.30269439 12.00557229 Br 8.40453752 23.80953233 15.14791088 Br 5.68101771 26.85386095 14.40734757 Cs 5.24937751 23.21893115 17.45065979 Pb 8.75986782 26.64463440 20.89334927 Br 8.81972129 27.07509686 17.79448069 Br 8.43773573 23.66191436 20.97000862 Br 5.88785692 26.90311587 20.42976280 Cs 5.23790341 24.13556674 23.97526631 Pb 8.71332423 26.73511839 26.65998391 Br 8.13350256 26.69317778 23.61852877 Br 9.02473481 24.00135468 26.82694515 Br 5.75978936 26.76941630 26.77233643 Cs 6.45158059 24.06432853 29.58941886 Pb 8.85687377 26.82796445 32.49113102 Br 8.67355264 26.85527296 29.64877991 Br 9.36723694 23.67499542 32.81056268 Br 5.75745976 26.90439676 32.60816808 Cs 5.97030333 29.50375912 35.51623075 Pb 9.03497921 32.70749234 2.96616309 Br 9.63673102 33.30840732 0.17930623 Br 9.07211910 29.55783714 2.71298444 Br 5.84677368 32.88018380 3.11695340 Cs 5.41207020 29.75552307 6.51940324 Pb 9.00282449 32.61075093 8.97925316 Br 9.40980558 32.77661890 5.91834795 Br 8.95606081 29.74886319 8.44132182 Br 6.04711119 32.75782412 9.11264727 Cs 5.37290405 29.62243701 11.82555071 Pb 8.88061249 32.64682393 14.79028495 Br 9.64589200 32.78083651 11.84492038 Br 8.54053212 29.83845542 14.53194191 Br 5.92862563 32.45315584 14.85767423 Cs 5.66484884 29.71245694 17.97116892 Pb 8.79773548 32.62452564 20.62450035 Br 8.96799862 32.95608459 17.88656468 Br 9.14970046 29.59927608 20.98002506 Br 5.95225276 32.63325899 20.79836343 Cs 6.26239562 29.76808418 23.46379946 Pb 9.11799778 32.68955829 26.61588139 Br 8.96061290 32.76609162 23.57900320 Br 9.13312847 29.60706540 26.56038687 Br 5.98616681 32.59166211 26.99140938 Cs 6.74481190 29.99485966 29.27809090 Pb 9.28602339 32.49391833 32.68524840 Br 8.99768542 32.82689245 29.59430220 Br 8.92312523 29.74537237 32.75136817 Br 6.00011298 32.13879823 32.51191112 Cs 11.87754815 0.46778239 0.46148787 Pb 14.87613661 2.98838472 3.18648032 Br 14.98032036 2.92423511 0.14977603 Br 14.62666799 0.01275883 2.95396167 Br 11.98662652 3.41011583 3.16711913 Cs 11.80486859 0.09407242 5.42264788 Pb 14.87894762 2.87497129 8.72340156 Br 15.24317815 2.41147196 5.97450642 Br 15.12298714 0.04860768 8.95965214 Br 11.90084851 2.98016080 8.75342826 Cs 11.84857950 0.07149814 11.81245918 Pb 15.11411882 2.95644742 14.85587220 Br 15.18397591 2.96267201 11.88680168 Br 14.94784859 35.41023569 15.14610738 Br 11.79624319 2.81034280 14.85895926 Cs 11.53168801 0.14924121 17.36900537 Pb 14.84288339 2.78770133 20.91234465 Br 14.81109007 3.15933177 17.82352071 Br 14.15680040 0.09581258 19.99645446 Br 11.67032499 4.17092808 21.28088784 Cs 11.83702476 35.36056746 23.16258456 Pb 14.88637366 2.93502398 26.78976921 Br 14.58805681 2.29419421 23.65503789 Br 15.75926873 0.16397063 26.85297082 Br 11.99106137 2.29171679 26.85604118 Cs 12.17524222 0.18762569 30.24759394 Pb 14.84074350 2.99607382 32.74744780 Br 15.61183240 3.01576196 29.74088052 Br 15.06106600 35.49804966 32.75693366 Br 11.76145793 2.60465214 33.23782839 Cs 11.77786706 6.19874200 0.30518467 Pb 14.88859457 8.98060349 2.80181165 Br 14.81421009 8.49183798 35.54021734 Br 15.06900974 5.97897654 3.72112572 Br 11.72668659 9.21607236 2.91143929 Cs 11.66462166 5.96707847 5.48703980 Pb 14.86022260 8.88490123 8.65584679 Br 15.02137297 8.93537955 5.77807487 Br 14.69122444 5.99016694 8.54504633 Br 11.88375162 8.99039988 9.00357335 Cs 12.24669875 6.23724878 12.55299393 Pb 14.79365040 8.95086286 14.69266853 Br 15.00215702 8.71377294 11.59065213 Br 14.75973534 6.04716873 15.17833767 Br 11.95944822 9.30179300 14.69949635 Cs 11.54413163 6.32364388 18.19851888 Pb 14.87872723 8.94659585 20.75562071 Br 14.72774147 9.27260509 17.56838566 Br 14.91587339 5.76237779 21.11341087 Br 11.86952585 9.25578557 20.53096811 Cs 12.18570542 6.39619729 23.76075621 Pb 14.81566947 8.71553417 26.77832042 Br 14.78979774 8.65921417 23.74906653 Br 15.20282193 5.76835496 26.31319888 Br 11.80254491 9.52831472 26.20699168 Cs 11.89261798 5.36834652 29.73898701 Pb 14.77733224 8.71497083 32.72614463 Br 14.74409024 8.98942777 29.60224715 Br 13.87960521 5.80082305 32.83182914 Br 11.91183683 9.59306112 32.55392825 Cs 11.68446387 11.81343629 0.09488098 Pb 15.07126317 14.91309768 2.99402055 Br 15.22639266 15.21323643 0.15022579 Br 14.39848107 11.86552240 2.20331909 Br 11.93618550 15.47037492 2.39763870 Cs 12.46082430 11.84943748 6.07217433 Pb 14.87801379 14.91738915 8.73271576 Br 15.07865588 14.80342393 5.88956207 Br 15.24161315 11.90197909 8.66530911 Br 11.90365638 14.37880696 8.52034509 Cs 12.30499793 12.05505693 11.32122288 Pb 15.08135997 14.81068399 14.73351338 Br 15.22720947 14.39952634 11.56021323 Br 15.05425887 11.85349146 14.44390608 Br 12.21768982 15.08810547 15.14685437 Cs 11.67475875 12.21089150 17.68018988 Pb 14.94028299 14.97860153 21.08133954 Br 14.94995915 15.54616760 17.75508809 Br 14.94516014 11.84781779 20.78321613 Br 11.95755809 14.84335969 20.68618614 Cs 11.66984226 11.73391162 23.72158839 Pb 14.73909762 14.84923043 26.67642325 Br 14.78369698 14.72611565 23.84003399 Br 14.65248924 11.61238253 26.84400366 Br 11.67372800 14.65522996 26.54361244 Cs 12.38191208 11.51720020 29.67613313 Pb 14.73860439 14.66910623 32.78985962 Br 14.59319585 14.95946789 29.76784606 Br 14.84070212 11.84359111 33.04241655 Br 11.76337493 14.83368452 33.55844834 Cs 11.86993121 17.87204072 35.01424318 Pb 15.02798348 20.98008056 2.77597260 Br 14.23224530 20.79272376 0.06484522 Br 15.82944090 17.69605003 3.49421578 Br 11.80200787 20.86596283 3.17115545 Cs 12.17908639 17.89518390 5.53714982 Pb 14.77355516 20.86012028 8.91660334 Br 14.85126776 21.24637726 5.91205844 Br 14.47721999 17.82882286 8.70979594 Br 11.93153226 21.09775138 8.69068127 Cs 11.64109523 17.93738260 11.93875412 Pb 14.79127499 20.64831513 14.87062214 Br 14.09094504 20.99133402 11.95586457 Br 14.92016205 17.71081042 14.26154394 Br 11.92974517 20.98451641 15.37292808 Cs 11.98851600 17.92546731 17.35192524 Pb 14.79781459 20.67588163 20.71902618 Br 14.92374323 20.72295440 17.79143600 Br 15.63601955 17.91717535 21.06811131 Br 12.04368763 20.53640631 20.44653988 Cs 11.75766569 17.53590432 23.30331409 Pb 14.77486620 20.89281401 26.80660722 Br 14.43469508 20.42940387 23.89139875 Br 14.40637701 17.74884579 26.51265115 Br 11.75454250 20.77218708 26.94102535 Cs 11.84592271 17.77196573 29.25756206 Pb 14.80644546 21.04127523 32.69604845 Br 14.60298411 20.60505077 29.72742289 Br 14.89446642 17.73787579 33.15396421 Br 11.86455403 20.08773952 32.31374540 Cs 11.80489174 23.51646463 0.19054288 Pb 14.74396554 26.78453494 2.98219551 Br 14.60134915 26.46924913 0.03488950 Br 14.85849399 23.77215879 3.15268420 Br 11.91636161 26.67441518 2.54452750 Cs 11.82702199 23.51416676 5.54094739 Pb 14.87153580 26.59433145 8.87818095 Br 15.36976797 26.29289377 5.93341501 Br 15.61794714 23.70122619 9.20673068 Br 11.97073884 26.19022450 8.62869009 Cs 11.70367735 23.82020390 11.38454918 Pb 14.50129742 26.63049370 14.85203271 Br 14.14096601 26.11501012 11.84944657 Br 15.08613977 23.80346570 15.25376467 Br 11.59083044 26.40946873 14.62110493 Cs 11.56886449 24.38158837 17.28715158 Pb 14.76617115 26.82038758 20.67973044 Br 14.32629003 27.47919016 17.68325667 Br 15.25695303 23.98130755 21.00343036 Br 11.91875860 26.65720432 20.52082292 Cs 10.93724568 24.00190507 23.44267212 Pb 14.76389166 26.59070201 26.38980967 Br 13.96769559 26.25971690 23.49739519 Br 15.09846879 23.69840344 26.42582083 Br 11.87513318 26.84445484 27.11983626 Cs 11.94018034 23.67477088 29.46411107 Pb 14.84418927 26.58916961 32.52819577 Br 15.51836153 26.42816538 29.43870497 Br 13.98294734 23.64225309 32.90118772 Br 11.95802643 27.11701440 31.96910553 Cs 12.05426085 30.08620310 35.28886877 Pb 15.15532255 32.77967455 2.92325280 Br 14.65119643 32.54929237 35.58090765 Br 15.54882231 29.79823028 3.12151637 Br 12.04511015 32.76965602 2.80706347 Cs 11.14856490 29.32237866 5.57611668 Pb 14.87898402 32.53314637 9.00088401 Br 14.60374646 32.50310197 5.96333139 Br 14.98184627 29.60684962 8.79501606 Br 11.98617253 32.36551844 8.97263919 Cs 11.08267878 29.47551972 11.87737071 Pb 14.90626764 32.43142107 14.70850703 Br 14.76764313 33.16333220 11.74645212 Br 14.52971721 29.78471144 14.01558875 Br 11.71644025 32.21689527 15.24651188 Cs 11.77421354 30.06527203 18.04915455 Pb 14.86340126 32.62732607 20.53305829 Br 14.71821187 32.32026013 17.63080476 Br 14.63949175 29.91441080 20.99317601 Br 11.94709396 32.66578792 20.98987598 Cs 11.86619453 29.56058927 23.83455837 Pb 15.03423384 32.75241721 26.66739383 Br 14.97624862 33.19765356 23.71621327 Br 14.73845274 29.66374739 26.28671395 Br 11.89637535 32.82575293 26.94363290 Cs 11.99747715 29.84905739 29.59214034 Pb 15.19146138 32.68543207 32.71218662 Br 14.96528295 32.67118329 29.61852407 Br 15.40367183 29.70809272 32.48591817 Br 12.18030032 32.66037008 32.72866474 Cs 18.44277103 0.41420175 0.21136122 Pb 21.05046862 2.78501875 2.95680806 Br 20.61453286 3.67893888 35.52316276 Br 21.30796871 35.59112089 2.60507774 Br 17.80843980 3.04599989 3.37078221 Cs 17.35954530 35.07996188 6.50086504 Pb 20.85409203 2.69950940 8.86382137 Br 21.66495197 2.80647509 6.03144013 Br 19.91351830 35.43922625 9.17998458 Br 17.95803106 3.21343998 8.17850352 Cs 17.77304690 0.53634094 11.94113375 Pb 21.01040088 3.03009411 14.80074183 Br 20.96821942 2.77751332 11.97953675 Br 21.41170482 0.02519690 14.90993978 Br 17.86443917 3.18787365 14.76653620 Cs 18.04627950 0.21220697 17.67112855 Pb 21.04201062 3.00071519 20.73404742 Br 21.05733155 2.26473836 17.77864036 Br 20.71359913 35.41822524 21.45297996 Br 17.76683864 2.65682407 20.77740137 Cs 17.72312262 35.37255182 23.49336912 Pb 20.79995157 2.78844732 26.63999717 Br 20.91071973 3.08712171 23.60890832 Br 20.44705356 35.58281240 26.02430200 Br 17.79550568 3.02563820 26.59011256 Cs 17.72099305 35.28782777 29.72362966 Pb 21.10709124 3.08969424 32.60345704 Br 20.97306016 3.07209808 29.60637846 Br 21.23349125 0.07713444 33.57106849 Br 17.64645315 3.07245234 32.71473332 Cs 17.54149888 6.04800680 0.48561140 Pb 20.92725360 8.75856006 2.92002552 Br 20.56470683 8.98081555 35.52430974 Br 20.62670755 5.77099450 3.42992572 Br 17.92386873 8.91875690 2.81874019 Cs 17.50333860 6.24993899 6.03507084 Pb 20.92524423 8.72889353 8.97596355 Br 20.81413659 9.50202337 5.98138371 Br 21.02793304 5.71912934 8.64361252 Br 17.79372672 8.67340392 8.59322275 Cs 17.61549883 5.79221341 11.12992465 Pb 20.80219049 8.87202434 14.87085861 Br 20.99927007 8.63088836 11.96136804 Br 21.47085752 5.95552491 15.04490224 Br 17.76562879 9.32056536 14.45382288 Cs 16.93715797 6.05293111 18.15654623 Pb 20.74828034 8.65237559 20.71386885 Br 20.93573246 9.22586928 17.83314477 Br 20.26465694 5.80413545 20.11859263 Br 17.79482025 8.48453541 20.64988288 Cs 18.22670175 5.87579455 23.75189262 Pb 20.56863961 9.08806048 26.86048220 Br 20.80639955 8.63069612 23.85488170 Br 20.88820604 5.92059349 27.02920965 Br 17.57940572 8.99049981 26.72191798 Cs 17.61790195 6.06401694 29.79780011 Pb 20.65154703 8.82021145 32.49206710 Br 20.92112274 9.57058191 29.74157475 Br 20.70428310 6.08419532 31.68268753 Br 17.56857906 8.50956515 32.02270990 Cs 18.35019879 12.44736309 0.08883720 Pb 20.62275488 14.88981939 2.82048264 Br 20.99355931 14.63197533 35.54122737 Br 20.66483482 11.94981829 3.09076148 Br 17.79391264 14.15301582 3.28483877 Cs 17.99216721 11.86351983 6.16429328 Pb 20.73761842 14.99457240 8.74392154 Br 20.55198405 15.02818129 5.86576899 Br 20.63025884 11.81023328 9.22492256 Br 17.70439116 14.88857775 8.91378200 Cs 17.77486756 11.78363033 11.86171251 Pb 20.86861630 14.72793220 14.57220483 Br 20.92321244 14.95421540 11.75667837 Br 20.51148231 11.83114103 14.73922675 Br 18.00649824 15.23095348 14.96636500 Cs 17.91988327 11.36890351 18.21414487 Pb 20.58144929 14.94718751 20.77284263 Br 21.26902960 14.95039115 17.94024697 Br 21.12928943 11.56246719 20.97721847 Br 17.75905939 14.00640960 20.80870397 Cs 17.57783678 11.22423548 23.78204648 Pb 20.49683616 15.27741911 26.70756399 Br 20.07037214 15.06737828 23.76845760 Br 20.65488418 11.94533101 26.67675922 Br 17.74446489 15.54359829 26.68708405 Cs 18.04444307 12.11730073 29.65223673 Pb 20.56421625 14.71803857 32.39899377 Br 21.22475231 14.30702459 29.67105802 Br 20.36251064 11.74985907 32.84355735 Br 17.74827154 14.68138194 32.49198319 Cs 17.47072085 17.77014164 35.35583853 Pb 20.88624605 20.79365529 3.14339361 Br 21.21067344 20.65123277 0.01928860 Br 21.28643741 17.68747957 3.03277851 Br 18.01485829 20.60808949 2.17244415 Cs 17.79729141 17.84916161 6.13333082 Pb 20.71423984 20.76655469 9.14312916 Br 20.96976309 20.81386351 6.01992826 Br 20.58532736 17.88764600 8.64569643 Br 17.88722240 20.36991266 8.88400275 Cs 17.58076372 17.70672449 12.22729381 Pb 20.78858936 20.63525840 14.80953019 Br 20.65267639 19.89332948 11.83589171 Br 21.05015841 17.80723173 14.85764597 Br 17.65968207 20.66717197 14.81825599 Cs 18.02192153 17.75693348 17.81859813 Pb 20.65532804 20.61729414 20.77219312 Br 20.74744320 20.99291833 17.82610046 Br 19.85159164 17.65957492 20.80691956 Br 17.79971319 21.09049032 20.65427769 Cs 17.70973355 18.31939139 24.16029754 Pb 20.86703082 20.92946391 26.60252423 Br 20.44163630 20.88183038 23.60772244 Br 21.25369697 18.11355792 26.53634743 Br 18.07285376 21.15736984 27.26143742 Cs 17.46727811 18.23642447 29.39608835 Pb 20.78416358 20.65955828 32.48890310 Br 21.21469932 20.60898826 29.58081495 Br 19.69833279 17.73545621 32.14148156 Br 17.58864382 21.43337259 33.56049674 Cs 18.02535858 23.87196417 0.54367567 Pb 20.64600640 26.88020969 3.04716122 Br 20.30815319 26.69368843 35.59776497 Br 20.29574184 23.92813070 3.46787530 Br 17.61648044 26.68247776 2.95716728 Cs 17.97852241 23.76949486 6.10580161 Pb 20.64690387 26.70179991 9.08974013 Br 20.34096003 26.67932722 6.29081358 Br 20.54770703 23.78545728 9.88183462 Br 17.69556511 27.10712114 9.71894887 Cs 17.75502990 24.16908144 12.46437877 Pb 20.54128598 26.78776879 14.89749330 Br 21.07622165 26.91969485 11.94898353 Br 21.13191290 23.86518945 14.60254260 Br 17.56903341 26.98434670 14.39690956 Cs 17.64861473 23.29796311 17.87053407 Pb 20.51465611 26.79565974 20.55804931 Br 19.78062466 26.63965040 17.82803726 Br 20.49902331 23.90303542 20.77213958 Br 17.52163711 27.21746790 20.96469381 Cs 17.79948015 24.43545838 23.56354547 Pb 20.82784906 26.67818406 26.57517569 Br 20.63625767 26.82483607 23.57239518 Br 21.04146423 23.84830686 27.21890872 Br 17.67618376 26.96304488 26.37052687 Cs 18.06425905 24.00459201 30.24514779 Pb 20.76247490 26.75177772 32.57826455 Br 20.31542254 27.01763649 29.70918835 Br 20.99898796 23.70415602 32.56017587 Br 17.86466120 27.06458956 32.56623628 Cs 17.77191233 29.48743413 35.28403348 Pb 20.82947188 32.61051742 2.97398841 Br 21.04995737 32.09403164 35.58990891 Br 20.44315070 29.73264070 3.18618627 Br 18.02670125 33.03853613 3.33750122 Cs 17.91628367 29.74427110 6.26627458 Pb 21.12947827 32.79339198 8.92806868 Br 21.21823466 33.10418312 5.87087778 Br 21.40533701 29.81108681 8.57935800 Br 18.03485805 32.49319953 8.95466184 Cs 17.66299250 30.34613075 11.89277868 Pb 20.82173423 32.81243667 14.97328816 Br 20.93520937 32.63772893 11.97995702 Br 20.30333334 29.86373074 15.25007320 Br 17.81705515 33.14458874 14.94034314 Cs 17.62092487 29.56445779 17.74183916 Pb 20.86093743 32.55318725 20.77946861 Br 21.49460520 32.89671256 17.92533510 Br 21.23355094 29.67159243 20.53892997 Br 17.92013777 32.71576353 19.85402633 Cs 17.53822128 29.62897768 23.72023195 Pb 20.63694638 32.69515459 26.66654385 Br 20.78987444 32.28031550 23.82180808 Br 20.28615741 29.62923596 26.54338822 Br 17.75286849 32.01461973 26.40515763 Cs 17.66678386 29.40096874 29.21397909 Pb 21.19931294 32.53908932 32.58396132 Br 21.07130582 32.29211576 29.72705458 Br 21.12506472 29.67273155 32.66310495 Br 18.24600748 32.94554171 32.49774724 Cs 24.07925519 0.07411195 0.00956070 Pb 27.17267380 3.51820717 3.10613291 Br 27.55850075 3.42521137 0.15503002 Br 26.47363981 0.16335737 2.45001036 Br 23.81539240 3.17258778 2.48737932 Cs 23.81951385 0.19209704 5.17843471 Pb 26.57613462 3.02975265 8.94098402 Br 26.25735488 3.33534221 5.95769812 Br 26.78125431 35.54468181 9.63738235 Br 23.86627906 2.93221731 9.17674156 Cs 24.20858794 0.05281675 11.94617326 Pb 26.95963158 2.75274089 14.71246680 Br 26.91986814 3.35370331 11.85239759 Br 27.00362559 35.53837607 14.82975142 Br 23.82460750 2.71778200 14.46533999 Cs 24.00646289 35.37297318 17.48882697 Pb 26.79289411 2.92725133 20.64507031 Br 26.23721459 3.16792635 17.54803737 Br 26.26477617 35.56558940 20.25228151 Br 23.85037786 3.69539505 21.16902304 Cs 23.88485466 0.38181741 23.45529108 Pb 26.63485570 2.80047220 26.79711231 Br 26.25432335 2.52858874 23.60450372 Br 27.17149894 35.55536199 26.83268707 Br 23.82004447 3.31255441 26.99514636 Cs 23.00929177 35.29130969 29.58274737 Pb 26.75719506 3.07351481 32.56054901 Br 26.37643501 3.55588115 29.82595507 Br 26.56474102 0.18471779 32.95252805 Br 23.83358866 3.55118686 33.03247824 Cs 24.11826386 6.27721520 35.30880988 Pb 26.92640529 9.19764142 2.99311306 Br 26.43286464 9.37627016 35.52670347 Br 27.70813069 6.28539006 2.95984778 Br 23.80161946 8.46980912 2.91688897 Cs 23.40317087 5.67646333 5.35383376 Pb 26.57370529 9.20086615 8.71617131 Br 27.07626918 8.95055337 5.95566436 Br 26.10603441 5.88957396 8.76167027 Br 23.68860401 8.73126257 9.28463843 Cs 24.05940522 5.41727826 12.29266049 Pb 26.73492841 8.64322095 14.74364641 Br 27.18668173 9.13256724 11.84277913 Br 26.94283823 5.55302508 14.83058448 Br 23.92188132 9.26517284 15.23092649 Cs 23.66736715 6.09045092 17.63348799 Pb 27.04502682 8.93217476 20.42822898 Br 27.17184802 8.42172012 17.63159627 Br 27.82669894 5.87758823 20.75482940 Br 23.62515465 8.47425430 20.67518078 Cs 23.06950172 5.97494240 24.09857303 Pb 26.77317291 8.76083841 26.43012848 Br 26.97744450 8.69294432 23.62580214 Br 26.45107398 5.87636827 26.28727158 Br 23.75004819 8.96195529 26.68854457 Cs 23.57086915 6.20450418 29.39893051 Pb 26.55310501 9.06272012 32.40217580 Br 26.14020247 8.72746441 29.25888165 Br 26.87771735 6.11700198 32.94897432 Br 23.44523346 8.89055901 32.64278018 Cs 23.93526366 12.02104794 0.22518390 Pb 26.82522327 14.99577939 3.01741465 Br 26.87128559 14.22300048 35.50299497 Br 26.27172387 12.23411798 3.47036359 Br 23.90873405 15.05240969 2.84930269 Cs 23.49931588 12.55936858 5.73783417 Pb 26.85502005 14.80862345 8.82319249 Br 26.83079991 15.41928874 6.14099296 Br 26.38802435 12.04096996 8.17968865 Br 23.59820387 14.99889751 8.61784533 Cs 23.80931160 11.69910584 11.89237656 Pb 26.91091892 14.30865541 14.53422614 Br 27.03695716 13.57823071 11.72661440 Br 27.54451865 11.51298746 15.38082994 Br 23.93855043 14.16166861 14.88731094 Cs 23.26714662 12.09344073 18.29246093 Pb 26.36684548 15.01608277 20.80045128 Br 26.53438400 14.55290623 17.76512802 Br 26.09544193 11.81531387 20.42682091 Br 23.45886490 15.29405978 20.95745890 Cs 23.76861454 11.16106778 23.46466879 Pb 26.43880543 14.66532663 26.76558401 Br 26.71771247 14.87523712 23.53135373 Br 26.54730429 11.81657035 26.64579721 Br 23.64920034 14.72881417 26.10931202 Cs 23.60650671 12.07764297 28.86592089 Pb 26.76420808 14.83894345 32.75405900 Br 26.46955544 14.72702221 29.73520951 Br 26.45139171 11.99189956 32.57448752 Br 23.44538390 15.74414681 32.50409927 Cs 23.22319414 17.43982964 0.19791255 Pb 26.72091498 21.16407753 2.90840485 Br 26.98947760 21.65956037 35.40822636 Br 27.11958052 17.79478646 2.45581705 Br 23.86717229 20.85846655 2.78588809 Cs 23.71715424 17.81256803 5.99290894 Pb 26.67165620 20.95178214 8.87128251 Br 26.69085031 21.01278770 5.93182219 Br 26.73068017 17.88813925 8.88049345 Br 23.73739767 20.59310018 9.09774937 Cs 23.56422411 17.94310630 11.83463931 Pb 26.41787940 20.61334099 14.98818164 Br 26.96567168 20.89600876 11.81937226 Br 27.19861893 17.84552278 14.88505033 Br 23.62030265 20.85569259 14.38186784 Cs 24.25645275 17.30672397 17.39748813 Pb 26.52686844 20.70201769 20.77436608 Br 25.63093513 20.40184851 17.84459393 Br 26.51833942 17.78601977 21.47568183 Br 23.69616512 20.78073445 21.30561916 Cs 23.94779948 17.92450590 23.86560153 Pb 27.07533307 20.71489721 26.69255728 Br 27.21450243 20.70097696 23.62280382 Br 27.06393043 17.88587873 26.72320925 Br 23.83347415 20.73591463 26.36218595 Cs 23.77257542 17.97619442 29.70924538 Pb 26.59477954 20.79309127 32.50521354 Br 26.55346528 20.29151821 29.42271440 Br 27.24599424 18.04826286 33.03690399 Br 23.73170276 20.29391425 32.49925620 Cs 23.55126889 23.66823800 0.00881407 Pb 26.37680412 26.80286029 2.87171620 Br 26.67512998 26.56719893 35.49411480 Br 26.44520585 23.82824974 3.12281703 Br 23.61361780 26.88636225 2.78830060 Cs 23.50198378 23.45102049 6.50440301 Pb 26.62077685 26.98948977 8.92044843 Br 26.37414407 26.83517413 5.88948985 Br 26.26224348 23.91157665 9.02662372 Br 23.60191784 26.50923279 8.75049212 Cs 23.61248998 23.82553776 11.94165464 Pb 26.40640561 26.42029037 15.00234064 Br 26.50342597 27.13466202 11.88148667 Br 25.95461439 23.55975026 14.70198017 Br 23.42474955 26.66338627 15.33500337 Cs 23.78880897 23.62419401 17.65229993 Pb 26.69946402 26.62831234 20.89099616 Br 27.06249577 26.00887553 17.79107958 Br 26.24549352 23.56723554 21.38422786 Br 23.52994567 26.40184705 20.28125523 Cs 23.45789651 24.01291389 23.41865234 Pb 26.67370084 26.69587741 26.77768596 Br 26.68689262 27.21133994 23.80108693 Br 26.16287975 23.72551127 26.12122556 Br 23.77505423 26.77306476 27.09992053 Cs 23.90422035 23.64457019 29.42055266 Pb 26.58890084 26.64528396 32.37849201 Br 27.08509473 26.85904164 29.57464763 Br 26.04134050 23.49985454 31.76807722 Br 23.82527191 26.85733039 32.96786807 Cs 24.53117880 29.83041167 35.25626570 Pb 26.66218731 32.93222622 2.81791838 Br 27.03656211 32.91295205 0.02686881 Br 27.20762463 29.69931014 3.00102871 Br 24.02165557 32.47315930 2.60754202 Cs 23.84974188 29.58979110 5.57492572 Pb 26.75596997 32.71927512 8.64097812 Br 26.95040531 33.57587907 5.93377172 Br 26.52320607 29.83127709 8.38724035 Br 24.01940722 32.52541046 9.24595742 Cs 23.54230755 29.69163287 11.98571345 Pb 26.85239437 32.30276408 14.92087660 Br 27.25488285 32.35714831 11.89876433 Br 27.13553809 29.51759891 14.96677871 Br 23.82091845 31.64277556 14.94430814 Cs 23.75823775 29.40326941 17.95780929 Pb 26.82509452 32.61969719 20.84425682 Br 26.69309958 32.34932478 17.81591954 Br 26.64980704 29.61461650 20.78329207 Br 23.96788689 32.62915299 20.85483710 Cs 24.04282977 30.15371853 23.79473332 Pb 26.73138685 32.43746256 26.94024651 Br 27.32801249 32.89988232 23.75830267 Br 26.47344085 29.60719586 26.83192867 Br 23.61693492 32.98234148 26.79639406 Cs 23.50331825 29.47980224 29.48495327 Pb 27.13861769 32.61632575 32.60051788 Br 26.64719046 32.07325481 29.74549881 Br 27.27449807 29.63526488 32.87233572 Br 24.14964752 32.53660241 32.65329861 Cs 29.99322738 35.55456265 35.24667189 Pb 32.54476414 3.17165737 3.36317122 Br 32.34593041 2.83663595 0.03323570 Br 32.85290838 0.30150059 3.07705339 Br 29.67027941 2.83936319 4.11907658 Cs 30.18980482 35.49318344 6.17964190 Pb 32.54114117 2.51109914 9.25396477 Br 32.78453010 3.11357447 6.23158244 Br 32.02027560 35.33272216 9.26182018 Br 29.61638039 3.35426283 8.89083636 Cs 29.53465839 -0.00123217 12.25870217 Pb 32.70618254 2.64356845 15.08512942 Br 32.53540416 2.17620130 12.23618352 Br 32.64947663 35.29179309 15.70061928 Br 29.76475939 2.69955410 15.14371534 Cs 29.47072319 35.49710757 17.86445599 Pb 32.43545494 3.08530611 20.76990459 Br 32.78473783 2.45780888 18.12998745 Br 32.09101313 0.06148305 21.25360886 Br 29.59472877 2.74638086 20.35738114 Cs 29.67589330 0.16829753 23.97686157 Pb 32.67742324 2.94224184 26.67044973 Br 32.81231195 2.65979996 23.96168086 Br 32.03907489 35.40141341 26.73002597 Br 29.58265223 3.03726066 27.22287190 Cs 29.80524142 0.13235473 29.91722328 Pb 32.54873557 2.67166471 32.78169839 Br 33.42284121 3.02968872 29.88321285 Br 33.28307873 35.37188753 32.17141587 Br 29.73406784 3.00483602 32.02563513 Cs 29.36615372 6.38569963 35.41174971 Pb 32.54653579 8.84456469 2.94485179 Br 31.92944164 9.15988451 35.57789420 Br 32.17556369 5.89056666 3.41006868 Br 29.70075898 9.91658880 2.89183301 Cs 29.67844441 5.96763288 6.36244330 Pb 32.57620849 8.73546596 8.86886575 Br 32.36938629 8.60107853 5.77701685 Br 32.26412047 5.87960171 9.03251873 Br 29.70755267 8.97362708 9.01447535 Cs 29.20573441 6.05631051 11.54028417 Pb 32.62715460 8.57804683 14.80283573 Br 32.13429803 8.56699171 11.91797717 Br 32.85372182 5.60981296 15.24588176 Br 29.72543226 8.25436336 14.97979847 Cs 29.77840558 5.68431890 17.64114642 Pb 32.64587597 9.22728431 20.66717850 Br 32.84130750 9.33341363 17.72967139 Br 32.27673578 5.91126036 20.10681464 Br 29.65131977 9.98446323 21.03294557 Cs 29.60071050 5.71880934 24.16684412 Pb 32.53680161 8.71406645 26.78019612 Br 32.91220868 8.98713309 23.69802650 Br 33.23949166 5.73763824 26.62880995 Br 29.56163165 8.51740487 26.53620367 Cs 28.99455920 6.21279371 29.61868120 Pb 32.48310751 8.77305768 32.51379328 Br 32.72966943 8.34545505 29.64898844 Br 32.39169913 5.62110614 32.95822332 Br 29.66006615 9.01206640 32.82770185 Cs 30.10826588 12.07695888 35.49500448 Pb 32.76716417 15.01864226 2.93029074 Br 32.90550930 14.27054098 0.04545736 Br 33.16063700 11.82352101 2.93761074 Br 29.87897371 15.02783524 3.26405737 Cs 29.95515581 11.99034865 6.28524329 Pb 32.73006179 14.61767656 8.67891173 Br 33.18340482 14.44028522 5.86943249 Br 32.86119575 11.75401544 8.82880996 Br 29.84034902 14.37257390 8.94759324 Cs 30.15030016 12.07709822 11.83920044 Pb 32.54951398 14.88027436 14.76915254 Br 33.24421410 14.21950713 11.92473084 Br 32.82211102 11.88190130 14.70158716 Br 29.66002467 14.93851953 15.11384650 Cs 29.57309154 12.44895239 17.86358691 Pb 32.57298671 15.02536466 20.74786164 Br 32.91558264 15.08269977 17.68942784 Br 33.17642191 11.99268166 21.06727627 Br 29.46607448 14.86265299 20.55807950 Cs 29.28458634 12.17032197 23.78936610 Pb 32.62018135 14.81861550 26.73967618 Br 32.39360153 14.58664066 23.68181822 Br 32.39226250 11.65361657 26.77878762 Br 29.52961636 14.46684413 27.03794564 Cs 28.91269404 12.07102179 29.41399474 Pb 32.72401370 14.63284257 32.85061790 Br 32.56480424 15.32277800 29.59678852 Br 32.74312491 11.41552777 32.82221524 Br 29.59571048 14.51482009 32.24276180 Cs 29.85908872 18.48041756 0.35406188 Pb 32.62541211 20.98681470 2.84417520 Br 32.31945724 21.09297716 35.38331862 Br 32.78586065 18.04333943 2.29933018 Br 29.76661530 20.89104306 3.18286481 Cs 29.40122716 18.29973699 5.85939457 Pb 32.67058780 20.54514606 8.79438439 Br 32.83087728 20.01684558 5.78036985 Br 32.18469427 17.75818683 9.47872192 Br 29.72304032 21.32510752 8.59759324 Cs 29.09573107 18.04495607 11.84274416 Pb 32.32141382 20.65478059 14.58598728 Br 32.46051123 21.38660386 11.71835402 Br 31.99558575 17.57144773 13.96984283 Br 29.52544650 21.12737413 14.74257964 Cs 29.96983421 18.00939714 17.29185579 Pb 32.78400457 20.75898659 20.64436238 Br 33.32739354 20.09450334 17.33796121 Br 32.05751076 17.76596142 21.09298810 Br 29.72177535 20.95147079 20.52024865 Cs 29.57461207 17.31485268 23.86085881 Pb 32.81016141 20.83086342 26.35120766 Br 32.12669952 21.25484963 23.40683858 Br 32.49116236 17.87111797 26.35799587 Br 29.85496701 20.74098663 26.97050178 Cs 29.65023383 17.51241038 29.46814580 Pb 32.74039042 20.70922558 32.27683243 Br 33.46025058 20.61428765 29.36794059 Br 33.07956040 17.61338655 32.92480376 Br 29.72882276 20.75468931 32.06032898 Cs 29.88956036 23.93501044 0.47855875 Pb 32.59365183 26.70163774 3.28849610 Br 32.75706729 26.19754524 0.19250520 Br 32.72111702 23.89499001 4.00086516 Br 29.34647753 26.41817744 2.99470871 Cs 29.53541742 24.08803189 6.02839767 Pb 32.68374262 26.36864363 9.09864770 Br 32.04293942 26.94954405 6.02237720 Br 33.33829649 23.56976056 8.79339224 Br 29.55106221 26.77037329 9.14293124 Cs 29.77758070 23.73973044 11.96819472 Pb 32.27773581 26.44561640 14.98116279 Br 32.67346032 25.91648531 12.03071662 Br 32.56387802 23.68854742 15.23386379 Br 29.26153880 25.79405738 14.54610466 Cs 29.50896069 23.33841900 17.45590327 Pb 32.29612733 27.00860560 20.74176390 Br 31.80838107 26.83018104 17.80352453 Br 32.83378142 23.73889587 20.39404848 Br 29.51643414 26.95773175 21.42893267 Cs 29.49622679 23.60508153 23.01524451 Pb 32.28968459 26.96442826 26.91129017 Br 32.48551889 26.95167731 23.83495945 Br 32.42845366 23.68285911 26.60629492 Br 29.52877180 26.60081976 26.68576447 Cs 29.16432640 23.64829098 29.73734111 Pb 32.48612436 26.66069941 32.74940870 Br 32.83757994 26.72900562 29.83853840 Br 32.50924054 23.69862479 31.78542628 Br 29.63724248 26.35912261 32.47791143 Cs 29.91626501 29.38924895 0.56800373 Pb 32.57865231 32.95970818 2.99267127 Br 32.80471199 33.05487833 0.00007672 Br 32.69782114 29.93955942 3.06190215 Br 29.87936945 33.08199330 3.50666530 Cs 29.76013865 30.02408674 5.66281476 Pb 32.37193255 32.40675845 8.93582225 Br 32.69679702 32.56827917 5.98848337 Br 32.49450507 29.42450814 8.74383947 Br 29.66697149 32.43861470 8.38362839 Cs 29.57305657 29.43954220 12.02703977 Pb 32.57126045 32.35409124 15.08841595 Br 31.70301233 32.26783852 12.10633105 Br 32.49044905 29.39195099 14.76533187 Br 29.69224365 32.77602674 15.52923531 Cs 29.20632467 29.18569856 17.55049827 Pb 32.54690601 32.71207633 20.73749860 Br 33.38867836 32.39799524 18.00389374 Br 32.75949109 29.79856620 21.45034299 Br 29.69524376 32.34209074 19.82715497 Cs 29.63475172 29.83071856 23.50773869 Pb 32.62977782 32.43290929 26.79461387 Br 32.09545022 32.75085892 23.97778514 Br 31.77295908 29.70087267 26.94229529 Br 29.66610573 32.50039596 27.20390302 Cs 30.03356245 29.50429254 30.15455611 Pb 32.66645482 32.19154470 32.75880792 Br 32.75273294 31.92841181 29.90053274 Br 32.56582231 29.47522001 33.67421680 Br 30.05549280 33.09844026 32.69190633 1080 Lattice="35.60125893 0.0 0.0 0.0 35.60125893 0.0 0.0 0.0 35.60125893" Properties=species:S:1:pos:R:3 Time=2000.0 energy=-38569.24448013 virial="-22.42128116 -4.78426305 8.40518306 -4.78426305 -37.15916691 -2.04318393 8.40518306 -2.04318393 -29.83193797" stress="0.00032812 -0.00010603 0.00018627 -0.00010603 1.5e-06 -4.528e-05 0.00018627 -4.528e-05 0.00016389" pbc="T T T" Cs 0.38499154 35.02298845 35.39305128 Pb 3.05294971 3.01572045 3.03885601 Br 3.60258272 2.57059817 0.01144749 Br 3.35554067 35.55997924 3.21593114 Br 35.37470088 3.26528756 2.98039111 Cs 35.35909864 0.25844575 5.88722593 Pb 2.90721343 2.87210379 8.87284622 Br 3.14826408 2.55643654 5.97205008 Br 2.82389866 35.58911810 8.96471738 Br 35.47399587 2.90716071 9.37242979 Cs 0.09655739 0.31795244 11.93636620 Pb 2.92340629 2.72469323 15.00009529 Br 3.10393357 2.62274830 12.14985737 Br 3.02597779 0.03242001 14.93320773 Br 0.03102358 2.96593331 14.99975438 Cs 0.42068894 35.23546364 17.37025243 Pb 3.10170183 2.81119842 20.91346014 Br 2.79435435 3.07534193 17.78369379 Br 3.24601727 35.46274607 20.16100475 Br 35.51336581 3.16953061 21.00095353 Cs 35.31910689 0.27848865 23.46087013 Pb 2.75448786 2.89753066 26.77329176 Br 2.97626248 1.99601930 23.60485022 Br 2.47131880 0.00455632 26.90356986 Br 35.48400491 2.93761959 26.54042992 Cs 35.58797496 0.21658186 30.06889113 Pb 3.08923162 2.88111353 32.85724390 Br 2.23617256 3.01205831 29.95094635 Br 3.01355181 35.15005538 32.83771082 Br 35.55815919 3.26480317 33.70065898 Cs 35.26602569 6.09614737 0.53161642 Pb 3.07038549 8.92170091 3.04642070 Br 3.36265001 8.58056791 35.38483550 Br 3.85020153 5.91694985 2.85845593 Br 0.03350559 9.20943718 2.39293692 Cs 35.49676994 5.51286036 6.33775019 Pb 2.93082904 8.73014015 9.14829299 Br 2.92715592 9.24518544 5.90291876 Br 2.83457624 5.77965048 8.93322825 Br 0.09144664 8.35694342 8.84837291 Cs 35.12435122 5.54558973 12.20125290 Pb 3.01652366 8.75812682 14.92126745 Br 3.23212069 8.99176012 11.93709686 Br 3.19787414 5.77824824 14.73457990 Br 0.05017727 9.01193929 14.32418276 Cs 35.50662166 6.11602880 17.82295008 Pb 3.05532489 8.89437618 20.87492357 Br 2.98167762 8.55617202 17.93215720 Br 3.13637702 5.87006632 21.59858830 Br 0.05994477 8.70388068 20.89144584 Cs 0.42094823 6.37524830 24.35984630 Pb 3.21986389 8.69251517 26.63815312 Br 3.64400834 9.04782212 23.61190849 Br 3.02119691 5.72675908 26.79164416 Br 0.44127874 9.50705620 26.63812561 Cs 0.06207688 6.06951744 30.15260185 Pb 2.85369816 8.86251895 32.63515199 Br 3.03017002 8.34769942 29.66905451 Br 2.75806032 5.89002850 32.83943007 Br 35.46400761 9.77204156 33.36946470 Cs 0.04393666 12.00127042 0.47912938 Pb 2.94651434 14.78213318 3.09996180 Br 2.83641143 14.27611508 35.54362832 Br 2.71370865 11.91339337 3.05997022 Br 35.53093380 14.65142440 2.81713013 Cs 35.33233233 11.92023030 5.41133519 Pb 2.73555910 14.76991081 8.80945173 Br 2.71831521 14.49199413 5.90442170 Br 3.16211613 11.80736837 9.43230062 Br 35.45900137 14.55392668 9.38491564 Cs 35.42236157 11.49266238 11.51251222 Pb 2.96631969 14.63103566 14.79708625 Br 3.40312664 14.51909373 11.98092689 Br 3.14331012 11.75420805 15.25685120 Br 35.56142506 14.78316820 14.90367371 Cs 0.01546307 11.95475882 17.78123335 Pb 2.84298331 14.71663088 20.63263494 Br 2.38698691 14.65753976 17.82178360 Br 2.65959953 11.76532908 20.23053042 Br 35.47932814 15.09467634 21.15797723 Cs 35.28167934 11.92945342 23.79827467 Pb 3.06575380 14.50889932 26.85683637 Br 3.01897655 14.31131273 23.66437699 Br 3.63046717 11.60403994 27.41814846 Br 0.07854323 14.39369662 26.59698703 Cs 0.12554205 11.76546623 29.57442879 Pb 2.89153129 14.51556659 32.78061108 Br 2.71394754 15.11366691 29.81541379 Br 3.49851423 11.72610316 32.17153220 Br -0.00105689 14.33309013 32.20016306 Cs 0.11023725 18.43547637 0.39824534 Pb 2.84809243 20.92446659 2.90542351 Br 2.99263160 21.13541215 0.19712608 Br 2.80123569 17.67627505 2.54027474 Br 35.43531622 21.67237543 2.84459720 Cs 0.12943601 17.74284326 5.40299017 Pb 2.65920016 20.49024471 8.78643668 Br 3.28610152 19.92577610 5.69674446 Br 2.26689006 17.64206391 8.47801581 Br 35.43413796 20.32494340 9.57002084 Cs 0.26431180 17.46010489 12.06295455 Pb 2.85329658 20.72926128 14.70233904 Br 3.15305359 20.77214942 11.83400491 Br 2.44379793 17.57175038 14.95199087 Br 0.06578175 20.75324780 14.38168450 Cs 35.32858939 17.59033838 18.32290625 Pb 3.02259477 20.65921096 20.80318225 Br 3.05826305 20.41972050 17.59074705 Br 2.64619405 17.78900971 20.41943079 Br 35.37862827 20.87596416 20.74310619 Cs 35.35079788 17.12784538 24.21613106 Pb 2.82375238 20.40602870 26.41126918 Br 3.26753212 19.97640331 23.41035628 Br 2.50114729 17.49378281 26.93763679 Br 35.23544331 20.67934122 25.51535005 Cs 0.32175696 17.68184220 30.05313697 Pb 3.18783329 20.63926544 32.76232897 Br 3.23665300 20.56929581 29.84421932 Br 2.82905187 17.95035601 33.08987896 Br 35.55519388 20.83745046 32.27993422 Cs 0.06757186 23.46565834 34.64124773 Pb 3.15891632 26.58240782 2.90672662 Br 2.58792433 26.74105960 0.01520300 Br 2.79735217 23.65840963 3.91443942 Br 35.37786093 26.75576692 2.86961104 Cs 35.23828636 24.52973859 5.79400974 Pb 3.06964468 26.77364820 8.67010379 Br 2.50558675 27.04527256 5.84212607 Br 2.73956341 23.75933402 8.56763219 Br 35.58458020 27.09968078 8.39053567 Cs 0.22621252 23.32922861 11.73729593 Pb 2.78371889 26.83899277 14.46949616 Br 3.84195313 26.74379537 11.61418993 Br 2.44897903 23.83371404 14.57597703 Br 35.28543554 26.96756423 14.74975042 Cs 0.22402347 23.19444343 17.93910811 Pb 2.78873769 26.79514217 20.65422218 Br 2.75412370 26.25310243 17.55552534 Br 2.78387417 23.87814563 20.59950421 Br 35.23798541 26.32120529 20.43489538 Cs 35.52519698 23.59572153 23.38118745 Pb 2.61310247 26.95859759 26.68755270 Br 2.25477661 26.57869939 23.76041325 Br 2.64845489 23.32952498 26.62938445 Br 35.52001706 27.14330740 27.36123864 Cs 0.01397243 23.85403234 29.44668684 Pb 2.80811609 26.66898778 32.47629711 Br 2.92438120 26.18683498 29.57827984 Br 3.41917209 23.85349962 33.00404645 Br 35.52239126 27.08073212 32.63476078 Cs 35.36486265 29.49962207 0.09307820 Pb 3.14883649 32.54241036 2.88387246 Br 3.47326756 32.37127178 35.48495360 Br 3.07380410 29.59603079 2.84871526 Br 0.19490702 33.01779107 2.72811235 Cs 35.17058306 29.34332556 5.78873924 Pb 2.90829127 32.59529749 8.80345495 Br 3.28787622 32.42968705 5.88142223 Br 3.13199748 29.68037502 8.81672891 Br 0.02610553 32.77497559 9.06167586 Cs 35.24863281 29.63182962 12.06262694 Pb 2.69450118 32.75112823 14.92941187 Br 2.83080064 33.21442819 11.94427844 Br 2.76840137 29.95170630 14.18854290 Br 35.47140273 33.54947186 14.51710278 Cs 35.50042295 29.15391991 18.01427938 Pb 2.98437571 32.56703611 20.86956455 Br 2.75338527 32.27348320 17.85791439 Br 2.25220451 29.71804945 20.67268132 Br 35.54213668 33.04923358 20.68443612 Cs 0.18967787 29.90686072 23.99216817 Pb 2.98531100 32.70884871 26.89533620 Br 3.25070506 32.52225217 23.85868106 Br 2.92880214 29.70382315 26.78571779 Br 35.58286139 32.47294191 26.49772999 Cs 0.01728626 29.76609881 29.70415295 Pb 3.23570943 32.30014398 32.56837936 Br 2.41257341 32.83575323 29.69628807 Br 2.81554810 29.52710314 32.06239208 Br 0.19357725 32.26721495 33.02183299 Cs 6.22405740 35.24965597 0.26670219 Pb 8.85337217 3.10763123 3.11907053 Br 8.23896787 3.48314875 35.51713933 Br 9.48792886 0.38518287 2.45836418 Br 5.91905680 2.41982557 3.47698088 Cs 5.66469832 35.27334848 5.87230209 Pb 8.99971300 2.89899048 9.13082890 Br 8.94539737 2.96060236 6.10604043 Br 9.88660561 35.54772269 9.61868501 Br 5.90982975 2.74417661 9.30281746 Cs 5.68227648 35.54358918 12.25639918 Pb 8.76114731 3.39100639 14.69433844 Br 8.82411316 3.82370844 11.91699130 Br 8.92449922 0.16605248 14.07759430 Br 5.98876444 2.74744354 14.60860432 Cs 6.03472075 0.39109893 17.61536570 Pb 9.09441060 3.08755697 20.87447134 Br 9.31970641 2.81760825 17.77034929 Br 9.07007887 0.09992444 20.84870987 Br 6.20043570 2.98516264 20.60548312 Cs 5.99931260 0.00657749 23.75046527 Pb 8.82930607 3.15180809 26.77407363 Br 9.09804350 2.95841599 23.72087073 Br 7.92009612 35.40433598 26.45585459 Br 5.90341252 3.15212087 26.81989995 Cs 6.08632636 0.23543791 29.33330495 Pb 9.01367714 3.04404812 32.78271504 Br 8.74337577 2.62213468 29.58713998 Br 8.90307060 0.22400440 33.07624312 Br 5.87280988 3.38584454 32.30004167 Cs 6.11767930 5.89855398 0.18484764 Pb 8.69468530 9.13497613 2.96223264 Br 8.65412955 8.79552697 35.58157735 Br 8.80970947 5.93960373 3.13734560 Br 5.83298275 9.68536984 2.75275674 Cs 5.80298792 5.69121769 6.02500029 Pb 9.00513126 8.87614512 9.09889533 Br 8.81412181 9.41237039 5.92375385 Br 9.11157138 5.99871730 8.86414649 Br 6.05972575 8.58485758 8.70569009 Cs 5.56374122 5.31786689 11.90946179 Pb 8.93545728 8.92926735 14.90427276 Br 8.61542610 8.61265826 12.06695405 Br 9.18424494 5.95282352 15.83846969 Br 5.82658587 8.68355044 14.83400845 Cs 5.80850013 5.52106265 17.74090485 Pb 8.94251454 8.72914259 20.76586596 Br 9.24594250 8.91307497 17.92534088 Br 8.10581137 6.09010487 20.48878572 Br 6.07487598 9.44473401 20.69056190 Cs 5.73691639 5.74004194 24.06091889 Pb 8.84894199 8.76111232 26.69215816 Br 8.88773007 8.13808892 23.73348114 Br 9.56504612 5.99069356 26.95130500 Br 6.21304327 8.10617110 26.89659949 Cs 5.89112795 5.74669209 29.93621023 Pb 8.62690497 8.94803652 32.65456328 Br 9.31482321 9.26317457 29.75194262 Br 9.13680902 6.04860745 32.47284118 Br 5.68107217 8.79384679 32.08577500 Cs 5.55648080 11.97701635 35.30638624 Pb 9.00050854 14.72537389 2.81782784 Br 8.28338716 15.44183172 0.01492233 Br 9.13090296 12.14094006 1.86973554 Br 5.85217253 14.51025800 2.83283081 Cs 5.51582579 11.69437289 6.05760345 Pb 8.92434783 14.87070491 8.73394871 Br 8.95438247 13.77861732 5.67815864 Br 9.47357520 12.08331750 8.99280606 Br 5.86703589 15.18709139 8.59690682 Cs 6.16622456 11.32071405 12.38558530 Pb 8.87035187 14.91711284 14.65754145 Br 8.42451388 14.56118530 11.87871103 Br 8.52803077 11.76313875 14.85235776 Br 5.89792150 15.16602137 15.28153997 Cs 5.90789940 12.46948224 17.69782556 Pb 9.17038832 15.03966279 20.81326704 Br 9.47019724 14.71802198 17.72520311 Br 9.06915703 11.95338637 20.87691972 Br 5.69437835 14.93828112 20.53702343 Cs 6.13261177 11.82387879 23.14169687 Pb 8.76956882 14.98636828 26.86761245 Br 8.58521326 15.35807272 23.78458655 Br 8.29807529 11.84765744 26.10940449 Br 5.83110416 15.03081608 27.66710325 Cs 6.11043730 11.62708326 29.61702876 Pb 8.52442830 14.86291501 32.63349109 Br 9.40788093 14.98382905 29.76392835 Br 8.18369848 11.89613471 32.63273098 Br 5.79748886 14.84814260 31.87342775 Cs 5.86778706 18.02463714 0.59217852 Pb 8.88038810 20.91438234 3.00903603 Br 9.22457508 21.02089142 0.08760151 Br 8.83969028 17.86341301 3.20717888 Br 5.93836720 20.80606400 2.98491146 Cs 5.91073216 17.51229741 5.70876962 Pb 8.71215742 20.65428637 8.86284456 Br 8.71299400 20.91445016 5.97232582 Br 9.35137417 17.73128676 8.76343549 Br 5.95302940 20.29346294 8.68195111 Cs 6.01195040 17.69403044 12.54018228 Pb 8.93427256 20.76871132 14.65446387 Br 9.41796065 21.08514109 11.88667355 Br 9.00883193 17.88811128 14.57938954 Br 6.07412685 20.66160520 14.76791995 Cs 5.95407070 17.72251529 17.82402781 Pb 8.74663232 20.67040651 20.73550963 Br 9.06258938 20.70940087 17.70984642 Br 8.79317036 17.78718402 20.31024195 Br 5.92321617 20.23345864 20.78641706 Cs 5.45357124 17.12931113 23.59254817 Pb 8.68060099 20.80554074 26.63710259 Br 9.12082921 20.03464303 23.67342945 Br 8.74762818 17.88511992 27.32992759 Br 5.63574251 21.01738922 26.73292459 Cs 5.61170270 17.80872899 30.49971287 Pb 9.07756983 20.96995212 32.45435673 Br 8.71700210 21.41663382 29.65396102 Br 8.72258671 18.01177074 32.56101939 Br 6.26289711 21.06698136 33.10044356 Cs 6.15192572 24.15283213 34.91323241 Pb 9.00117485 26.84535666 2.84418534 Br 8.89688451 26.96680753 35.38548489 Br 9.38865306 23.91547558 2.54127058 Br 6.05565078 26.53477250 2.66602828 Cs 5.90494997 23.27196574 5.97175773 Pb 8.83289727 26.42938738 8.51910291 Br 8.87911027 25.92808023 5.68559248 Br 7.98183712 23.75559160 9.57215797 Br 5.88458493 26.93950225 7.90205609 Cs 5.82455779 23.71466621 12.20337281 Pb 8.56411765 26.78066863 14.78141915 Br 9.02707336 26.63849337 11.69004416 Br 8.64930485 23.73420377 15.59310708 Br 5.75840427 26.68798721 14.79670157 Cs 6.10039573 23.44842138 17.90213384 Pb 8.50394492 26.59036542 20.62171891 Br 8.69403356 27.50102283 17.83035520 Br 8.90683978 23.69466091 20.35166610 Br 5.47110971 26.83590596 21.53432826 Cs 5.19139239 23.74694071 23.77411631 Pb 8.64305337 26.66284091 26.39340451 Br 8.65571228 26.24996804 23.45172142 Br 8.91508171 23.83606697 26.20225245 Br 5.79921022 26.85522470 26.14581398 Cs 5.30573779 23.49448689 29.85761121 Pb 9.06323324 26.83411352 32.41651696 Br 8.49360583 26.99170480 29.40525941 Br 9.30432118 24.04546296 32.80477571 Br 5.86679551 26.92160707 32.46512393 Cs 6.18267703 29.61523243 0.23764868 Pb 9.13865986 32.94879567 2.86960068 Br 9.68071601 32.92387861 0.01693716 Br 8.78691506 30.00398041 3.22592175 Br 6.10330008 32.40190854 2.80772786 Cs 5.34231882 29.78339529 5.89695989 Pb 9.07626370 32.58551461 9.03125406 Br 8.93775442 33.80434683 5.99522839 Br 9.17950033 29.50428852 8.57035995 Br 6.02046844 32.24466531 8.99276260 Cs 5.60801705 30.36686934 12.10582371 Pb 8.91030114 32.90394108 14.71664125 Br 8.98617669 32.32234218 11.92858135 Br 8.36648162 29.68498503 14.80353962 Br 5.61309041 32.70670682 14.93413096 Cs 5.95071020 29.55045393 17.82340388 Pb 8.81689640 32.59273322 20.70936706 Br 9.15953960 33.11109483 17.69446392 Br 8.58066061 29.72055678 21.06441404 Br 5.94072517 32.36334877 20.70736082 Cs 6.08137215 30.04605547 24.14460414 Pb 8.93439142 32.74805731 26.66166421 Br 8.78710694 32.84098582 23.55470333 Br 9.07976294 29.81180345 26.79588210 Br 6.21080904 32.48298795 26.55434163 Cs 5.90431083 29.68596895 29.44943122 Pb 9.11814588 32.77486541 32.62856303 Br 9.14427246 33.30200508 29.86232050 Br 8.79739355 29.82837352 33.02290602 Br 6.13499825 32.57817428 32.25782063 Cs 11.91961969 0.43453761 0.06904304 Pb 14.81594897 2.90990821 2.89952656 Br 14.87203394 3.30006913 0.04955782 Br 14.20073038 35.56068530 2.98938695 Br 11.77079740 3.27057608 2.87973140 Cs 11.66917126 0.17669160 5.75342118 Pb 14.91622436 2.78798631 8.87585405 Br 14.94893281 2.76269554 5.79329380 Br 14.47420171 35.50207574 8.92625289 Br 11.88902269 2.89644370 9.06480576 Cs 12.27721655 35.44034718 12.28256739 Pb 14.67208737 2.95276031 14.79741673 Br 14.95680376 2.80856359 11.87985544 Br 14.59921053 0.11724690 15.25747206 Br 11.91161131 3.01241154 14.84791381 Cs 11.60163431 0.48205000 17.65784623 Pb 14.89895310 3.08319234 20.40591281 Br 14.94801685 3.44120863 17.60122770 Br 14.75033054 0.01898067 19.55902811 Br 12.09598456 3.83133014 21.01695349 Cs 12.02908372 35.48623493 23.19808678 Pb 14.97750865 2.45334033 26.80509486 Br 14.85932125 2.27012558 23.79476292 Br 15.70003282 35.30138477 26.90188800 Br 12.07836399 2.19518964 26.80420923 Cs 11.83732483 35.56163253 29.90795056 Pb 15.00585843 2.98048135 32.56589608 Br 14.80437522 3.02809480 29.61120447 Br 15.01047702 35.49037273 32.49869223 Br 11.86924727 2.65268791 32.34631271 Cs 11.50054247 6.70109058 0.26169737 Pb 15.18673170 9.22559511 2.67730396 Br 14.92134746 8.18864696 0.08017255 Br 15.21675899 5.72327772 3.59971078 Br 11.95045499 9.46220716 3.02329996 Cs 11.96036772 5.57377574 5.58884411 Pb 15.07666832 8.68982084 8.76303567 Br 15.54246586 9.18300684 5.68270824 Br 15.07315904 5.64602946 8.74447677 Br 12.11924578 8.66985299 9.46237267 Cs 12.05270043 6.25350975 12.08373008 Pb 14.72217233 8.93430089 14.74579568 Br 15.16001119 8.94028960 11.82169580 Br 15.03336544 6.16479362 14.40874542 Br 11.84913201 9.04633540 14.72451645 Cs 12.62052581 5.96576103 17.91420249 Pb 14.72527650 9.06959040 20.84752145 Br 14.63404667 8.93483421 17.78279504 Br 15.19642148 5.88542256 20.44129307 Br 11.98939545 8.88655524 21.75218687 Cs 12.21745443 5.76071950 24.03667058 Pb 14.71741246 8.61505302 26.73783906 Br 15.04336005 8.30198032 23.76864405 Br 14.56371807 5.66013605 26.70205651 Br 11.88058548 8.98035996 26.35613212 Cs 11.77469987 5.84209715 29.65827044 Pb 14.79803765 8.76361913 32.70499162 Br 14.43204953 8.84246035 29.67265392 Br 14.56331112 5.89661205 32.72668521 Br 11.80623984 8.75110671 32.77733486 Cs 11.89055114 11.38246096 35.11981712 Pb 14.94925928 14.90544536 2.95283695 Br 15.04628779 15.20576298 35.57394834 Br 14.94860111 12.10817605 1.97258073 Br 12.13004367 15.06660635 3.14450545 Cs 12.27364434 11.69357391 6.47218958 Pb 14.91398297 14.85579816 8.60974206 Br 15.16183610 13.90334104 5.85358294 Br 15.09815620 11.67725154 8.99271092 Br 12.13160472 14.66478456 8.71765632 Cs 12.50965732 11.96430799 11.46019302 Pb 14.90709949 14.80212579 14.52675030 Br 15.63671782 14.74770085 11.51766790 Br 15.05362518 11.83452137 14.53401802 Br 12.04714828 14.49014731 14.31015056 Cs 11.33089478 11.78721537 17.36094997 Pb 15.39507043 14.83458557 20.65509546 Br 15.06297813 14.61409061 17.45615584 Br 14.69871857 11.95754192 20.83570144 Br 12.02687103 14.66822689 21.04422855 Cs 11.60317040 11.83177071 24.14053988 Pb 14.90149794 14.66620424 26.50499575 Br 15.20392686 14.60312996 23.55575163 Br 14.98248336 11.87686009 27.01962767 Br 11.79377573 15.02506387 26.45133853 Cs 11.89993884 11.68758272 29.35821665 Pb 14.35071474 14.78111003 32.53844902 Br 14.49928197 14.72617897 29.55970674 Br 14.77212171 11.86030305 32.58082386 Br 11.60045082 14.37234962 33.27142727 Cs 11.68415749 17.78806725 35.22788704 Pb 14.88983662 20.71224577 3.04270431 Br 14.75314734 20.39042746 35.52179741 Br 15.16451751 17.79271472 2.92548770 Br 11.93938716 20.78862588 3.47805820 Cs 11.83772864 17.97952416 5.84186252 Pb 14.67996586 20.71356979 8.98219552 Br 15.07477382 20.33703089 5.96814215 Br 14.87492450 17.65809980 8.04776645 Br 11.72833548 20.97034609 8.75852553 Cs 11.46518608 18.05106554 12.18538613 Pb 14.89485434 20.70108727 14.87194830 Br 14.22966768 21.47216922 11.71658686 Br 14.73858462 17.81237551 14.17716237 Br 11.93130288 20.28236012 15.17273894 Cs 12.62820249 17.50452153 17.69340343 Pb 14.87624079 20.83315919 20.81676607 Br 15.20408137 19.87992810 17.53929384 Br 15.60061208 17.93280465 20.59771902 Br 11.72724678 20.44813445 21.03488536 Cs 12.24592306 17.75835096 23.47401237 Pb 14.91806781 20.75124756 26.80637515 Br 14.50823731 21.18218370 23.60210630 Br 14.93691874 17.91020041 26.22384674 Br 11.82408897 20.46725564 26.85440003 Cs 12.09261686 17.95054185 29.33455268 Pb 14.91179078 20.65441751 32.68015828 Br 14.81151458 20.16637257 29.51699682 Br 15.08661335 17.82076181 32.39488345 Br 11.96035599 20.16449057 32.12958421 Cs 11.60810590 23.73004242 35.38072290 Pb 14.96627166 26.74204091 2.71772744 Br 14.58799041 26.23608044 35.35850913 Br 14.46351840 23.56577098 2.89170757 Br 11.82645209 27.12740897 2.92133620 Cs 12.10282242 24.53121351 5.41858697 Pb 14.79222859 26.59334332 8.72046957 Br 15.17554503 26.65242438 5.76617504 Br 15.18397989 23.77985106 8.75174956 Br 11.91807038 26.35881881 8.45836217 Cs 11.41257927 23.78151070 11.17494883 Pb 14.34081673 26.90441315 14.83735051 Br 14.21344447 26.36261121 11.87264868 Br 14.67378145 23.80293216 15.70867276 Br 11.61056108 26.71443519 14.87196680 Cs 11.59684936 23.96544275 18.01610360 Pb 14.48936300 26.85239424 20.70926645 Br 14.18295660 26.97885144 17.87411779 Br 15.12623446 23.80166400 20.71775568 Br 11.36506103 26.79173183 20.75945361 Cs 11.95984479 23.48117768 24.12496930 Pb 14.61757907 26.49884412 26.65241249 Br 14.36502721 26.73944524 23.74463764 Br 14.43966133 23.68736528 26.90931613 Br 11.74466891 26.63966950 26.92124958 Cs 11.76079405 23.49467631 29.56392861 Pb 14.86762565 26.52257736 32.36325400 Br 14.75306144 26.30993293 29.52247857 Br 13.96634592 23.59207955 32.65734358 Br 11.87437757 26.64723895 32.10361159 Cs 12.09293908 29.11960542 35.43039052 Pb 14.94058708 32.76622590 3.03223442 Br 15.11402993 32.77915063 0.02796582 Br 14.94841745 29.57371175 3.15553116 Br 12.10078190 32.81487412 2.95906053 Cs 11.80260075 29.81860971 5.98724201 Pb 15.01879127 32.50582672 8.85365371 Br 14.48203581 32.88899518 6.19384328 Br 14.41192170 29.61174144 8.85426422 Br 11.95572129 32.43139474 9.32011628 Cs 11.60549102 29.35352040 11.50451417 Pb 14.66470629 32.65443234 14.90217704 Br 15.27061269 33.17834481 12.12265038 Br 14.13071756 29.79484396 14.22471961 Br 11.59134128 32.72300392 14.66317422 Cs 11.48468425 29.80392489 17.65479578 Pb 14.68618875 32.74250264 20.55792238 Br 14.71693696 31.83230970 17.67662160 Br 14.80762158 29.67245078 21.03679719 Br 11.82497995 32.12069939 21.08032658 Cs 11.23093572 28.97221694 24.11153287 Pb 14.97507096 32.54497678 26.80820733 Br 14.88620509 33.02350707 23.64558153 Br 14.34086300 29.58878759 26.80550068 Br 12.02266121 33.26563838 27.10826198 Cs 11.63784334 29.46405754 29.58157974 Pb 15.01916582 32.60499145 32.68853660 Br 15.74258991 31.99782082 29.43982035 Br 15.09809717 29.37867330 32.68850047 Br 12.17822109 32.36584606 32.31018130 Cs 18.63837834 35.40267183 0.42977933 Pb 20.86200162 2.97407292 3.01269082 Br 20.49492713 4.21398431 35.56727196 Br 21.39317049 -0.00064136 2.34797553 Br 17.95924053 2.91083888 3.15135816 Cs 17.16290928 35.26645467 6.37855794 Pb 20.86071181 2.88534519 8.82299317 Br 21.48232254 2.58206812 5.88491458 Br 20.59599360 35.46997577 8.87104417 Br 17.94803185 2.76503758 8.74190319 Cs 17.93628314 0.16174344 11.87015319 Pb 20.86876520 3.18247185 14.73778982 Br 20.64970732 3.21820505 11.76646554 Br 20.53637153 0.00821851 14.76625399 Br 17.73267189 3.50305471 14.74196509 Cs 17.92091341 0.16821494 17.30416438 Pb 20.92965721 3.19425525 20.88918703 Br 20.86966299 2.55583007 17.68859557 Br 21.10942474 0.21886088 20.81659666 Br 17.73840367 3.25517323 20.90589105 Cs 17.85188076 35.27206260 24.05979563 Pb 20.75364417 3.00697291 26.76042722 Br 20.97785156 3.25673295 23.95761549 Br 20.48122452 0.05663320 26.34948170 Br 17.93649036 2.84839723 26.75528051 Cs 18.31930636 0.08475872 29.86198013 Pb 20.71982379 3.40606221 32.88207476 Br 20.93821966 2.87679124 29.82805888 Br 21.33352565 0.22679010 33.04262817 Br 17.87511744 3.13406440 32.83341848 Cs 17.50566175 5.89740978 0.36476984 Pb 20.79442903 8.92922039 2.88578561 Br 21.35427972 8.51853844 35.59430701 Br 20.60359529 5.95916482 3.43025674 Br 18.10702733 9.12418757 2.03414393 Cs 17.92218805 6.04322636 5.77438259 Pb 20.69436944 8.94354472 9.00293219 Br 20.70382374 9.25540273 5.99654696 Br 21.39946029 5.85871185 8.83429105 Br 17.93897477 8.42493905 9.10719323 Cs 17.84289743 5.36279969 11.60629823 Pb 20.74457929 8.96649840 14.63260348 Br 20.83159834 9.02210300 11.95657558 Br 21.18680589 6.05807085 14.50073549 Br 17.82367241 8.37792328 14.78463854 Cs 17.45759471 6.18445862 17.42341170 Pb 20.71337158 8.83530221 20.68621801 Br 20.49388720 9.20622901 17.64589071 Br 20.30752013 5.87723182 20.41350945 Br 17.79932170 9.43640973 21.23106261 Cs 18.15932777 5.42144776 24.02404995 Pb 20.66315774 8.93706072 26.60528905 Br 20.40210101 8.57463373 23.64154527 Br 20.90695566 6.08894872 27.10477385 Br 17.76091488 8.74714314 26.76245983 Cs 17.24338346 5.38519711 29.22860967 Pb 20.64917537 8.95192672 32.69821917 Br 20.77601345 9.52457880 29.51308175 Br 20.13239851 6.08570251 31.90291466 Br 17.77718920 8.84069081 32.83922701 Cs 18.02058307 11.98071241 35.53444867 Pb 20.79120732 14.84358994 2.94101864 Br 20.93499554 14.97676461 35.53916327 Br 21.35930323 11.72860213 2.98587102 Br 17.94168186 14.55496435 3.05065807 Cs 17.82156260 11.78263880 6.19886355 Pb 20.73997379 14.69968479 8.86542313 Br 21.19341699 14.66800663 5.88906272 Br 20.13681949 11.84859605 9.01860790 Br 18.18778875 15.38296683 8.43793774 Cs 17.96660924 11.54493473 11.84952764 Pb 20.79316408 14.93628007 14.89973973 Br 21.10735055 15.26393757 11.90676379 Br 20.44094867 12.06034394 15.37088136 Br 17.79535454 15.17505203 14.95979433 Cs 17.54706580 11.66966570 17.55527501 Pb 20.92412616 14.74741160 20.61855320 Br 20.64806581 15.04185385 17.75714175 Br 21.32470697 11.93902318 20.63324095 Br 17.97430525 14.16284545 20.49734348 Cs 17.56315849 11.60152401 23.68541422 Pb 20.62868686 14.93423776 26.61237292 Br 20.21592322 15.09326305 23.81272490 Br 20.38959477 11.89976307 26.62488233 Br 17.72698267 15.11603369 26.81691881 Cs 17.66028188 12.04451137 30.24051167 Pb 20.63275571 14.99412217 32.50878871 Br 20.86856500 14.67347048 29.69857553 Br 20.56379188 11.82267756 32.88240312 Br 17.74211556 14.98838008 32.49711174 Cs 17.69979554 17.92959878 35.01821069 Pb 20.88629987 20.78647738 3.10148267 Br 20.59413665 20.60830968 35.50259945 Br 21.38487263 17.74492935 3.25309339 Br 17.86094776 20.68034397 3.10352054 Cs 17.64190115 18.03565572 5.91879401 Pb 20.59976465 20.59485453 8.66059671 Br 20.68901762 21.16684653 5.92635504 Br 20.59389364 17.77669343 8.49480912 Br 17.72811971 20.25184716 8.83879227 Cs 17.95820892 17.58616606 12.04393721 Pb 21.06004763 20.80877158 14.71862170 Br 20.73203990 20.06591910 11.94925863 Br 21.26551499 17.88501312 15.44218740 Br 17.93300737 20.61909932 14.89277116 Cs 18.21073839 17.93122879 17.84008678 Pb 20.81295898 20.69946659 20.58369104 Br 21.03825675 20.97399988 17.78497974 Br 20.00081327 17.84765085 20.79223600 Br 17.77954403 21.28483176 20.48880261 Cs 17.82977606 17.79632807 23.75804953 Pb 20.73557355 21.10381419 26.74966985 Br 20.08492609 21.59243507 23.76998253 Br 20.71467943 18.03226990 26.86394315 Br 17.84775890 21.13615432 27.19482349 Cs 17.68847812 18.00843466 29.58922326 Pb 20.89020843 20.95723710 32.68502271 Br 21.35844596 21.13959532 29.72496684 Br 20.14981492 17.99199031 32.14271798 Br 17.98327134 21.52861383 32.69762307 Cs 18.01138484 23.85547612 0.02425442 Pb 20.75782129 27.01163068 3.12367708 Br 20.16729323 27.20434172 35.52117378 Br 20.70706504 23.74639002 2.72388619 Br 17.72423957 26.32517867 3.24409926 Cs 17.80887001 23.66554256 6.29058011 Pb 20.48058651 26.86766780 9.00291835 Br 20.72224089 26.33202757 5.79357854 Br 20.15379397 24.01442759 9.02728377 Br 17.65241770 27.17248191 9.39166136 Cs 17.64036949 23.31886423 12.21386208 Pb 20.43667608 26.59334265 15.00414597 Br 20.46597048 27.08769227 11.91911864 Br 20.36501734 23.85025164 14.74500499 Br 17.57829059 26.79929353 14.80034261 Cs 17.82427483 23.53507974 17.47725514 Pb 20.54437437 26.67843673 20.84871084 Br 19.69932303 26.52319213 17.96051840 Br 21.14366048 23.88738803 20.36867755 Br 17.51941120 27.33642701 21.08630999 Cs 17.61960735 24.22533488 23.69381086 Pb 21.05896351 26.75357138 26.78947699 Br 21.09735642 26.82922203 23.84428390 Br 20.88474722 23.99546646 26.60192864 Br 17.93111354 26.76558919 26.26281852 Cs 18.01036841 23.67343570 29.62535881 Pb 20.89693845 26.96561470 32.75733760 Br 20.79213939 27.26383874 29.83816577 Br 21.52601158 23.99480514 32.43502814 Br 17.90843649 26.05580376 32.55289241 Cs 17.39097834 29.81703715 35.33862979 Pb 20.87770921 32.73947284 3.11879450 Br 20.48428053 32.27036638 0.20206782 Br 20.36816455 29.82755001 3.56408954 Br 17.80728730 33.00854950 3.94663594 Cs 17.81889156 29.46421337 5.79644657 Pb 20.78594554 32.60351334 8.81072663 Br 21.17342580 32.61283277 5.98375261 Br 21.63138872 29.40167398 8.14747096 Br 17.90298446 32.30488232 8.32559747 Cs 17.82395749 29.60168470 12.28555278 Pb 20.69846124 32.63873450 14.63948806 Br 21.38341174 32.86914533 11.81883975 Br 20.45745917 29.62572848 14.82234293 Br 17.77462319 32.65213902 14.97746193 Cs 17.60155419 30.05299489 18.13542418 Pb 20.83399762 32.79500745 20.56437316 Br 21.08305921 32.47046252 17.63930726 Br 20.86805651 29.71163048 20.38421667 Br 17.68765150 33.15686203 20.26932676 Cs 17.69057564 30.10780186 23.41532899 Pb 20.82207118 32.65010154 26.63638720 Br 20.50790317 32.89640437 23.37168864 Br 21.22616877 29.84179792 26.50247672 Br 17.86914446 31.96903423 26.46649528 Cs 17.99649754 29.49288296 29.58681274 Pb 20.74540708 32.76094509 32.90022117 Br 20.64159072 33.01979880 29.78421632 Br 20.92539309 29.89661829 32.57563585 Br 18.04261385 32.11422711 32.56388099 Cs 24.25224837 0.17994119 35.21937795 Pb 26.80084482 3.14635080 2.92641481 Br 27.34520309 2.82691793 35.55093076 Br 26.55766100 0.10911307 2.77994147 Br 23.71460073 3.64855262 2.65159379 Cs 24.23583062 0.16946722 5.59842332 Pb 26.46087107 2.67798697 8.92439576 Br 25.98728383 3.47581187 5.93033124 Br 26.72233983 35.54515301 8.87174391 Br 23.60157195 2.30733991 9.77893660 Cs 24.38208179 35.33594924 12.15809809 Pb 26.75566526 3.12817651 14.82110353 Br 27.41525349 2.55601727 11.84705460 Br 26.73491131 0.10843855 14.76477839 Br 23.76395992 2.69787179 14.72621592 Cs 23.67669136 0.04911655 17.39703909 Pb 26.78930287 2.92577495 20.89753566 Br 26.32902360 2.95398024 18.06261331 Br 26.84827586 35.55496108 20.86890407 Br 23.72984802 2.87735215 21.29379543 Cs 23.97667046 35.54965480 23.44023255 Pb 26.56030313 2.87014750 26.81372790 Br 27.02515375 2.82343103 23.58733254 Br 26.38438872 0.04318538 27.46468462 Br 23.54121917 2.55424989 26.53904907 Cs 23.55517132 35.37924216 29.87019113 Pb 26.86531169 3.09101815 32.76214477 Br 26.69191760 3.23872098 29.78775555 Br 26.54172760 0.01103674 32.27691260 Br 23.92181358 3.09725337 32.75105534 Cs 23.87369628 5.92103959 0.06798822 Pb 26.59077570 9.05339825 2.91267197 Br 26.83188818 8.75048353 35.48286510 Br 26.89029108 6.09588319 2.47472974 Br 23.66354622 8.65635457 3.46552305 Cs 23.68822954 5.99723386 5.63099726 Pb 26.75375883 9.08191074 8.68583969 Br 26.68520820 8.37265094 6.00092423 Br 27.04067679 6.02229446 9.28659182 Br 23.80794026 9.09426706 9.20873366 Cs 23.76125841 6.01236596 11.38170894 Pb 26.91764650 8.73272853 14.72770400 Br 26.99078974 9.50046288 11.85732731 Br 26.05968998 6.05015443 13.84282733 Br 23.97094769 9.26034569 14.86719901 Cs 24.52191121 5.84446143 17.97254280 Pb 26.74073465 8.89976844 20.74696356 Br 26.69344564 8.78927970 17.76639694 Br 27.23880890 6.03305184 20.63155005 Br 23.78316561 8.20571352 20.76886907 Cs 24.02281896 5.78960907 24.20501388 Pb 26.65640282 8.88845778 26.56256266 Br 26.21632413 8.73620361 23.73760363 Br 26.38857097 5.77244763 27.08816501 Br 23.83113431 8.76616654 26.93575216 Cs 23.08731632 6.07233122 29.89038559 Pb 26.85441716 8.81384802 32.39632164 Br 26.58899984 8.69525564 29.58451779 Br 27.10612390 5.83682813 32.52049676 Br 23.85512798 8.13549026 32.65074275 Cs 23.59380365 11.82984484 0.01399542 Pb 26.83018458 15.09123145 3.02804106 Br 26.59659854 14.16018981 0.11128621 Br 26.81590483 11.88734415 3.22344973 Br 23.97168044 15.14084319 3.22026707 Cs 23.40448784 12.14511724 5.88449382 Pb 26.83503616 14.97985652 8.83335852 Br 27.09216181 15.76973902 6.07503534 Br 26.19008668 11.98149072 7.96122183 Br 24.04948910 15.55784446 8.79647184 Cs 24.34116754 12.16893582 11.93961215 Pb 26.67312542 14.70653211 14.83981445 Br 27.01891393 14.56810694 11.90898666 Br 27.24561081 11.80956168 15.19552040 Br 23.74552303 14.29474066 14.84911799 Cs 23.30210999 12.02443852 17.68113619 Pb 26.71505625 14.89440308 20.90589144 Br 26.32637500 14.38056926 17.80445988 Br 26.23840779 11.80532128 21.09275566 Br 23.88853436 15.30071644 20.72085567 Cs 23.59148462 10.98851420 23.74009975 Pb 26.79637193 14.94214317 26.87325397 Br 26.84971349 14.97951959 23.89387190 Br 26.38678495 11.98850639 26.57567087 Br 23.60309816 15.26962751 26.35838529 Cs 23.66139319 11.93382446 28.92552218 Pb 26.69822249 14.76616915 32.80104056 Br 26.47162043 15.44449633 29.92461457 Br 26.38785212 11.81717463 32.17536449 Br 23.58966724 15.08795233 32.89968585 Cs 24.05160911 17.79694491 0.23000872 Pb 26.75527791 21.09678149 2.98103376 Br 27.35532723 21.07809654 0.08879654 Br 27.18723307 17.91601357 2.54534626 Br 23.97151687 20.52281100 2.65199690 Cs 23.80073966 18.12642482 5.78848820 Pb 26.68398718 20.74443801 8.64323263 Br 27.15405635 21.08946794 5.71998561 Br 27.35479292 17.84571695 9.03861538 Br 23.66329429 20.54009071 8.36732094 Cs 23.40522633 17.84960123 11.61316720 Pb 26.41204627 20.77998931 14.71328062 Br 26.63263508 20.63743324 11.70751466 Br 26.49090151 17.71399581 14.76817469 Br 23.69429992 21.33117655 13.81081318 Cs 24.06684622 17.06429915 17.51517146 Pb 26.72618262 20.55656505 20.75117422 Br 25.69345925 20.58076769 17.57621649 Br 26.79231377 17.67187255 20.02682329 Br 23.81776436 20.19895688 21.33112679 Cs 24.15344414 17.32123410 23.78117665 Pb 26.63568357 20.57937979 26.59058548 Br 27.10968401 20.32650467 23.54032278 Br 27.00345813 17.70601802 26.09157447 Br 23.56622078 20.88598696 26.54088088 Cs 23.65469181 18.26187742 29.69491634 Pb 26.96227925 20.80886337 32.81864048 Br 26.57828870 20.35471849 29.72337836 Br 26.97398796 17.65494146 33.32063521 Br 23.85446023 20.48144389 33.16580255 Cs 24.39074498 23.86105034 35.29485427 Pb 26.25923326 26.68464208 2.97012095 Br 26.63155758 26.58695848 0.11641886 Br 25.78419816 23.78445858 3.78046853 Br 23.51216045 27.89535375 2.73868672 Cs 23.74687170 23.90281940 6.58863746 Pb 26.64852323 26.66576519 8.94114445 Br 26.37142100 27.17375071 5.99391593 Br 26.79511176 23.77111330 9.49300708 Br 23.74418520 26.72972962 9.60137042 Cs 24.14987370 24.47834006 12.12944173 Pb 26.51250782 26.79858428 14.87813701 Br 26.67527363 26.90431364 11.98762073 Br 26.54995890 23.75664251 14.91680420 Br 23.53117492 26.82768327 15.33170258 Cs 23.79950380 23.76687267 17.31740234 Pb 26.50460252 26.60016300 20.85975886 Br 27.23962609 26.88111599 17.81016358 Br 26.49596057 23.42362878 21.16291908 Br 23.69040822 26.34552459 19.93549130 Cs 23.87218516 22.91831271 23.78561726 Pb 26.65142632 26.94932526 26.65357966 Br 26.31749286 26.97447695 23.69367473 Br 26.69513316 23.86758178 26.55526576 Br 23.81080712 26.34344827 27.39331131 Cs 24.18455608 23.58079660 29.56107753 Pb 26.88916467 26.81980625 32.71562503 Br 27.22195923 26.48890416 29.78656323 Br 26.64017434 23.75849602 32.58428774 Br 23.71532535 26.76080786 33.14012355 Cs 24.11037165 29.97345056 35.13992289 Pb 26.80110961 32.69180106 3.06313039 Br 26.80496722 32.58477385 0.02113938 Br 27.03889418 29.57279307 3.26632599 Br 23.87226311 32.39195428 2.73782060 Cs 24.05948105 29.85774258 5.68594510 Pb 26.88654239 32.55301871 8.75396490 Br 27.20885855 33.14444161 5.80829286 Br 26.76104400 29.69982241 8.47667816 Br 24.07724034 32.51738772 8.77765734 Cs 23.35518841 29.88723967 11.64015504 Pb 26.71977651 32.63170142 14.89898787 Br 26.68517501 32.49944853 11.84753679 Br 26.97013768 29.54323785 14.71256550 Br 23.74670509 32.44311989 14.83468280 Cs 23.53449592 29.71367798 17.90797075 Pb 26.66637712 32.55883867 20.79513448 Br 26.93453359 32.74820498 18.07379158 Br 26.29532751 29.60306840 20.55698893 Br 23.88869868 32.81911034 20.83884495 Cs 23.36226553 29.66211087 22.99017779 Pb 26.68422915 32.75363262 26.77635222 Br 27.23030128 32.38741815 23.86360896 Br 26.33879309 29.81892011 26.81409489 Br 23.79964377 32.75411754 26.35302766 Cs 23.72392416 30.13264509 29.31393472 Pb 26.84026255 32.84927203 32.68561408 Br 26.50111803 32.00085676 29.51474578 Br 26.74563709 29.61303970 32.78834279 Br 23.89024726 33.05213877 33.02425743 Cs 30.08102799 35.12683985 35.33084148 Pb 32.42158405 3.27456099 3.15345006 Br 32.09763428 3.46994806 0.41506324 Br 32.65507376 35.57161427 3.49577177 Br 29.52309033 2.47923097 3.73842874 Cs 30.14047062 0.01446575 6.14710192 Pb 32.34795573 2.88250344 9.16520459 Br 32.95105627 3.08824373 6.19430012 Br 32.80309111 0.02108503 9.30654598 Br 29.45472590 2.86274276 8.44386589 Cs 30.06225127 35.36364477 11.73050979 Pb 32.78471941 2.86972588 15.26323518 Br 32.38670284 2.75182524 12.21728011 Br 32.51395380 35.56279019 15.17010469 Br 29.69927311 3.49857428 15.10332998 Cs 29.66285470 0.39878393 17.83634972 Pb 32.58125893 2.95428038 20.90060122 Br 33.19405326 2.80205972 18.02224131 Br 32.39866928 0.00746277 21.34620358 Br 29.65072415 2.93914991 20.33249916 Cs 29.61383978 35.51624571 24.23218286 Pb 32.47470869 2.96918562 26.82055614 Br 32.10778090 2.78802484 23.96848396 Br 32.90987061 0.03029187 27.08692136 Br 29.46325600 2.79540134 26.87486094 Cs 29.47725272 0.11726422 29.93598010 Pb 32.89348529 2.82321523 32.81204308 Br 33.47817874 3.03616663 29.82059473 Br 33.21910147 35.41878062 32.70509383 Br 29.85206154 2.54523643 32.67966055 Cs 29.59815187 6.22952257 35.25966202 Pb 32.54141266 8.95080198 3.01083963 Br 32.74817041 8.81157425 0.02996290 Br 32.18749092 6.10512298 3.52246713 Br 29.55792345 9.29560206 3.58487686 Cs 29.44949881 6.00209789 6.47743659 Pb 32.48651475 8.84659292 8.93710242 Br 32.94847967 8.84326733 5.92375489 Br 32.64375839 5.80235809 8.97527530 Br 29.75944170 9.40167171 8.71494356 Cs 29.89151512 5.70301537 12.17024928 Pb 32.74101300 8.70547766 14.77029032 Br 32.77554381 8.68844026 11.78460630 Br 32.95074543 6.00114690 15.09582357 Br 29.99743925 7.93904820 14.85837697 Cs 29.51176625 6.07799548 17.90480831 Pb 32.88301616 9.12901146 20.79205304 Br 32.96154294 9.21884615 17.71262137 Br 32.24305682 6.08012391 20.49648479 Br 29.82890000 9.55380046 21.13021908 Cs 29.55027768 5.45967615 23.78802178 Pb 32.67388918 8.77501027 26.89671832 Br 32.88494413 9.24020701 23.85620279 Br 33.28805104 5.82388856 26.43481120 Br 29.72971508 8.53992774 26.72435825 Cs 29.26256033 6.20653797 29.43703239 Pb 32.86941660 8.90235643 32.64209494 Br 33.51978425 8.76696076 29.70759134 Br 32.95034139 5.69530291 32.92679825 Br 29.84753426 9.19171781 31.74630596 Cs 29.49081623 11.63140558 0.26414298 Pb 32.54676248 14.84752079 2.86741445 Br 31.93399559 14.23903577 35.57882717 Br 32.62463561 12.00188300 3.05778876 Br 29.66582748 14.59041623 3.51657139 Cs 29.34160647 11.94198158 5.82860655 Pb 32.31598313 14.66736564 8.75401969 Br 32.97315951 15.26798206 5.79654416 Br 33.20670184 11.73198836 8.48800523 Br 29.63698530 13.79046770 8.88844718 Cs 29.73404936 12.10493810 11.95161187 Pb 32.56680785 14.51784005 14.66547471 Br 32.45713604 14.46257980 11.86672439 Br 32.53354681 11.71753922 15.13038178 Br 29.52288241 15.15791823 15.03894764 Cs 29.50822889 12.18108902 18.28153585 Pb 32.59571102 15.11398817 20.66496733 Br 32.33322723 15.27540562 17.78883223 Br 32.72521517 12.24949712 20.55429188 Br 29.64588493 14.92118317 20.62275683 Cs 29.64187099 12.23896004 23.38935481 Pb 32.57966504 14.77628211 26.81911682 Br 32.07341927 14.96451138 23.72431351 Br 32.38659265 11.77353548 26.61202499 Br 29.77165308 14.74272725 26.95710009 Cs 29.14408117 12.16015090 29.55742774 Pb 32.74357526 14.87704181 32.57649399 Br 32.28525077 15.17883470 29.62065967 Br 32.30961879 11.91533465 32.08947885 Br 29.67631841 14.37081951 32.55813017 Cs 30.45238253 17.96470057 35.24437636 Pb 32.60658946 20.91139432 3.10712668 Br 32.54330759 21.21655657 35.51974276 Br 32.54049824 18.02141909 2.53265934 Br 29.80003093 21.09737911 3.26312000 Cs 30.02312351 18.04181196 5.83898550 Pb 32.60122333 20.82099027 8.81776893 Br 33.30301978 20.06867142 5.92756243 Br 32.15563476 17.62244395 8.77867350 Br 29.57840178 21.33130969 8.51163098 Cs 29.63142741 18.20248184 12.13156855 Pb 32.35092174 20.79729987 14.70565138 Br 32.18221719 21.18849013 11.79169462 Br 32.28634846 17.78345871 14.41365944 Br 29.42260606 20.86757232 14.79380579 Cs 29.95174244 18.13025975 17.74175810 Pb 32.54310341 21.08162067 20.87266080 Br 32.85415988 20.47875050 17.63691731 Br 32.85797274 18.10499057 21.58354125 Br 29.63762289 20.78556933 20.02370810 Cs 29.52686199 17.81332856 23.29650963 Pb 32.60317913 20.63540091 26.62712407 Br 31.90545535 21.30256450 23.80239091 Br 32.04212548 17.85823416 26.48642826 Br 29.53168626 20.99106362 27.26846154 Cs 29.67249182 17.64195461 29.88979754 Pb 32.80744366 20.67596060 32.50351512 Br 33.42853175 20.12024835 29.32065370 Br 33.29208586 17.82430904 32.90510329 Br 29.69885536 20.92017184 32.53796335 Cs 29.82586307 23.77833306 0.58916501 Pb 32.58067922 26.79857439 3.18534204 Br 32.75411627 26.46930422 0.02444324 Br 32.07943791 23.62926555 3.71702280 Br 29.19836070 26.77330843 2.80248336 Cs 29.20605835 24.43437730 5.99300281 Pb 32.47417806 26.65261969 9.12799584 Br 31.79923334 27.25309730 5.86915072 Br 33.10537655 23.66499299 8.35716072 Br 29.74850176 26.33917286 9.34861253 Cs 29.37616458 23.70548369 11.98283135 Pb 32.40768237 26.38007220 14.94371689 Br 32.87970359 26.52904935 11.92891795 Br 32.87249682 23.61993659 15.47057581 Br 29.47853848 26.54254854 14.60300051 Cs 29.49806752 23.91422752 17.92064688 Pb 32.52429161 26.65685164 21.15122253 Br 32.46466535 26.70650034 17.84812511 Br 31.82089062 23.92106882 20.44795119 Br 29.48756978 26.99146030 20.95081550 Cs 29.63106814 23.84299232 24.11249009 Pb 32.65357911 26.73488297 26.85554713 Br 33.21154934 26.95295580 23.85906278 Br 32.77784713 23.76323187 27.19657106 Br 29.63776572 26.71067121 26.32426158 Cs 29.59193142 23.73776210 29.52734785 Pb 32.23800168 26.65077424 32.75621987 Br 32.22290545 26.72609251 29.79200707 Br 33.05347743 23.63311784 32.34744767 Br 29.59651102 25.83191888 32.80346253 Cs 29.52269363 29.56489245 0.27108035 Pb 32.84172320 32.83245502 3.19261538 Br 32.71033820 32.67780697 0.22790785 Br 32.80879939 29.69492373 2.89913686 Br 29.89458729 32.56494352 2.70565671 Cs 29.61408190 30.24101451 6.03352372 Pb 32.81768985 32.72464338 9.21366680 Br 32.83271045 32.66916192 6.22091190 Br 32.36336888 29.70409648 9.31012608 Br 29.88242404 32.52274292 8.46349848 Cs 29.62953318 29.16698062 11.99543832 Pb 32.62805949 32.41730138 14.85994970 Br 32.41843549 32.30735900 12.07624489 Br 32.49663622 29.37727413 15.04235873 Br 29.60662294 32.91874692 15.45296846 Cs 29.93329792 29.45994944 17.67655871 Pb 32.39391042 32.75417074 20.61006486 Br 33.35499225 31.88282413 17.68627552 Br 32.52569361 29.79495205 20.76989296 Br 29.64550706 32.23819254 20.26574080 Cs 29.96157994 30.05242965 23.57408984 Pb 32.75053951 32.69893946 26.64579979 Br 32.17546083 32.86729487 23.51347554 Br 32.29247995 29.81698541 26.55508354 Br 29.70295436 32.97859985 26.97285802 Cs 30.20237432 30.08877688 30.08810890 Pb 32.90061028 32.42206118 32.78651853 Br 33.17856019 32.51184186 29.78146744 Br 31.90075865 29.50804774 33.16618962 Br 29.68603696 33.24153377 32.47353174 dump_long_with_velocities.xyz000066400000000000000000037073651522712672400362630ustar00rootroot00000000000000dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/tests/trajectory_reader/trajectory_files320 Lattice="23.77195271 0.0 0.0 0.0 23.77195271 0.0 0.0 0.0 23.77195271" Properties=species:S:1:pos:R:3:vel:R:3 Time=20009.99999999 energy=-11425.73629379 virial="-8.52731669 2.37909827 1.04281276 2.37909827 -11.81493511 -1.08412727 1.04281276 -1.08412727 -8.94770223" stress="0.00035771 0.0001771 7.763e-05 0.0001771 0.00013004 -8.07e-05 7.763e-05 -8.07e-05 0.0002261" pbc="T T T" Cs 0.28005796 0.46185748 23.67840617 -0.00380245 -0.00251500 0.00077767 Pb 3.14210325 2.92224607 2.93363169 -0.00240997 0.00117849 0.00045680 Br 3.35739066 2.88487261 23.63676504 -0.00087253 0.00086342 0.00256432 Br 3.00369191 23.75485519 3.17108827 0.00011848 0.00258703 0.00007991 Br 0.02598834 3.48354100 2.66459874 0.00164296 0.00016985 -0.00212640 Cs 23.56372643 23.53864041 6.25675129 0.00284554 0.00162126 -0.00074970 Pb 2.90821345 3.00229500 9.33350368 0.00083660 -0.00114938 0.00070674 Br 2.31213297 2.89410678 5.99320497 0.00094120 -0.00148782 0.00268168 Br 3.14946225 0.03247983 9.02396356 0.00010519 -0.00145219 0.00285068 Br 23.74171002 2.84677628 9.73022500 -0.00194067 0.00355332 0.00549220 Cs 0.62016866 23.69331027 12.13808238 -0.00277509 -0.00084395 0.00103249 Pb 3.21634208 3.23938591 15.00312111 -0.00220994 0.00258704 0.00009630 Br 4.25696681 3.10456913 11.97114981 0.00108315 0.00012234 -0.00080275 Br 2.76280754 0.37416647 15.04542479 0.00279831 -0.00009131 -0.00278449 Br 0.22315882 3.60600215 14.70639126 0.00179680 -0.00202261 0.00207715 Cs 0.27431257 23.54087642 17.95382120 -0.00082684 -0.00212420 -0.00172522 Pb 3.04196089 3.08847630 20.64861415 0.00140965 -0.00033091 -0.00035659 Br 2.68951171 2.99377143 17.71267555 -0.00193848 0.00175140 0.00068250 Br 3.19402550 0.00280227 20.03924545 -0.00150890 -0.00047180 0.00116701 Br 0.12668416 3.16289636 20.57603983 -0.00171150 0.00047044 -0.00093185 Cs 23.49125299 6.17691638 0.00601363 -0.00015368 0.00245161 -0.00015025 Pb 2.85587536 8.89683476 3.05839199 -0.00005335 0.00079183 0.00411461 Br 2.56984719 9.07377992 23.62795863 -0.00050595 -0.00387448 -0.00043269 Br 3.37654844 6.01028816 3.21947326 -0.00201120 0.00179041 -0.00036908 Br 23.69040512 8.76242620 3.37926180 -0.00061243 -0.00224485 -0.00119158 Cs 0.18966213 5.90969304 5.76094417 0.00247497 0.00175136 0.00185333 Pb 3.06772823 8.92543027 8.96819654 0.00305638 0.00325248 0.00008571 Br 2.89585414 9.15804547 5.88672732 -0.00046000 -0.00321863 -0.00122984 Br 2.92615671 6.12727341 8.61515418 0.00101470 -0.00030424 0.00045903 Br 0.17667046 8.90754041 8.98576219 -0.00188056 0.00339525 -0.00210229 Cs 23.75940594 6.16845441 11.77552435 -0.00164794 -0.00210134 0.00173005 Pb 3.01945911 8.80940272 15.04580754 -0.00168760 -0.00104515 -0.00261606 Br 3.11552833 8.76678641 11.88932180 0.00106266 0.00052453 -0.00429518 Br 3.86076390 6.03348213 15.04598226 -0.00249660 -0.00258500 0.00051355 Br 0.03387310 8.70619654 14.76558251 0.00031839 -0.00229843 -0.00203122 Cs 0.02318190 5.72710460 17.65855360 -0.00086354 -0.00008423 -0.00094283 Pb 2.75433559 8.84299711 20.66837357 0.00022573 0.00031792 -0.00157703 Br 1.94172750 8.78173784 17.80824286 0.00213698 -0.00052646 0.00036665 Br 2.18098486 5.97111778 20.63478904 -0.00333004 -0.00124332 0.00055661 Br 23.64566191 9.32230080 21.12541771 0.00245648 -0.00046267 0.00068000 Cs 23.00708190 11.86708560 0.20974846 0.00008879 -0.00155770 0.00070768 Pb 2.90428025 14.79420942 2.82539902 -0.00059271 0.00097404 0.00079009 Br 2.60439477 15.22533247 0.01077897 -0.00207454 -0.00036354 -0.00240168 Br 2.74084680 11.79592036 2.40035491 0.00274875 -0.00346151 -0.00093923 Br 23.51482103 14.54912203 3.39005793 -0.00333850 0.00085723 -0.00102468 Cs 23.52754871 11.88315466 6.37060838 -0.00304620 -0.00143504 0.00485864 Pb 2.92389708 15.07150962 8.92128213 0.00053568 -0.00077409 0.00226827 Br 3.45788360 14.99224444 5.99578577 -0.00064703 -0.00036510 -0.00022152 Br 2.94344508 11.81416996 8.69629828 0.00065596 -0.00445162 -0.00028405 Br 23.68734596 15.13880578 8.93734371 0.00008315 -0.00026427 -0.00083200 Cs 0.29652975 11.60285149 11.60883097 -0.00176515 0.00022349 -0.00212697 Pb 2.99174299 14.70785771 14.96453052 0.00114209 -0.00240155 0.00155749 Br 2.99023910 14.63260366 12.00825306 0.00202335 -0.00239507 -0.00248340 Br 2.92592870 11.87736593 15.20799528 -0.00386165 -0.00061586 0.00327820 Br 0.00175052 14.64221158 14.78570777 -0.00180233 0.00072247 -0.00373426 Cs 23.37056563 11.84517394 18.17051940 0.00012357 -0.00062479 0.00026343 Pb 2.94014223 14.79063286 20.92608824 0.00229339 -0.00297514 -0.00208160 Br 2.96233579 14.79208491 17.90290046 -0.00058748 0.00048923 0.00142937 Br 2.76738122 11.89416176 20.86167437 -0.00114488 -0.00161416 0.00122861 Br 23.65290918 14.44739783 21.07031047 0.00480232 0.00035604 -0.00114318 Cs 0.07156369 17.76027439 0.00593945 0.00051041 -0.00026464 -0.00150324 Pb 2.80312262 20.84302706 2.96834544 0.00209186 -0.00108141 -0.00005402 Br 2.86718203 20.67625442 23.32207582 0.00100854 0.00016034 0.00057387 Br 3.24921425 17.79273412 2.62871259 0.00062650 0.00235084 -0.00099749 Br 0.04617851 20.64226862 2.78928599 -0.00229149 0.00230658 0.00129822 Cs 23.24850604 17.70744236 6.47800134 -0.00132702 0.00084777 -0.00203698 Pb 2.72249856 20.93518437 8.82628694 0.00103388 0.00085825 -0.00079928 Br 2.84573888 20.98616140 5.81498007 0.00105850 -0.00176085 -0.00157803 Br 3.41289135 17.98089550 9.55856490 0.00163503 0.00246617 0.00029871 Br 23.62113325 21.24476103 8.86249102 -0.00370086 -0.00072442 -0.00048409 Cs 0.21419323 17.09411480 12.47489751 0.00342790 -0.00061093 -0.00190200 Pb 3.15852383 21.05718931 14.60355897 -0.00115410 0.00103733 -0.00017262 Br 3.54932658 20.80589805 11.89596140 -0.00254310 -0.00182507 -0.00003364 Br 3.06792370 17.99239978 14.65578544 -0.00026397 0.00185974 0.00443826 Br 0.09569936 20.36473041 14.98561905 0.00078766 0.00066934 -0.00003028 Cs 0.34650781 17.63480090 17.38215178 0.00069476 0.00055925 0.00082672 Pb 3.00958567 20.68954025 20.57896147 -0.00078456 -0.00007141 -0.00020276 Br 3.76538973 20.84834728 17.62141095 -0.00099362 -0.00420924 -0.00103344 Br 3.09411338 17.84378366 20.95429340 -0.00022134 0.00139054 -0.00440287 Br 0.06350141 20.67613794 20.36420844 -0.00268617 -0.00007053 0.00305922 Cs 6.19775088 0.03717139 22.93798180 -0.00242825 0.00036725 -0.00132113 Pb 9.11342080 2.81443554 3.36617265 0.00003535 0.00196593 -0.00038773 Br 9.22691064 2.37772230 0.09109791 -0.00140112 0.00167596 -0.00064853 Br 8.53282825 23.45459733 3.00011593 0.00168531 0.00130726 0.00415203 Br 6.12859493 2.75105796 3.42222723 -0.00187255 0.00282415 0.00101170 Cs 6.18408049 23.37922818 5.64445182 -0.00012536 0.00034902 0.00095599 Pb 8.81266239 2.79526863 8.80415395 0.00016325 -0.00112805 0.00008789 Br 10.02211742 3.32533651 6.11796079 -0.00145101 0.00240065 -0.00154291 Br 8.26181946 23.59269812 8.48650169 0.00058427 -0.00610182 0.00344821 Br 5.75001590 3.50197542 8.71164754 -0.00150223 0.00001267 -0.00227263 Cs 6.12562994 23.57430130 12.04121096 -0.00050952 0.00142027 0.00025678 Pb 8.99198269 2.83069918 14.66296796 0.00227334 0.00086898 -0.00078750 Br 8.90916520 2.76470469 11.74060520 0.00204427 0.00149805 0.00131084 Br 9.94311575 23.49373392 15.35212756 0.00257615 -0.00184110 -0.00031010 Br 6.17835740 2.40057246 15.42426056 0.00072679 -0.00141972 -0.00226297 Cs 6.60105684 23.63600023 17.59515079 -0.00110658 -0.00107448 0.00052188 Pb 9.26143223 2.97466956 21.10244478 -0.00065690 0.00089845 0.00000887 Br 9.83882144 3.10013785 17.34584476 -0.00130017 -0.00203583 -0.00079123 Br 9.21976437 0.18178967 20.34039550 -0.00113928 0.00171529 0.00328647 Br 6.22748318 3.80063389 20.73647080 -0.00089491 0.00278567 -0.00004170 Cs 5.74574099 6.55238386 23.62783819 -0.00096631 -0.00253950 0.00061700 Pb 8.95358817 8.62118256 2.86699992 -0.00037089 0.00324078 -0.00122046 Br 8.78404933 8.76147906 23.72510660 0.00128639 0.00072826 -0.00115309 Br 8.50969016 5.57755268 2.71970040 -0.00389576 0.00170825 0.00173930 Br 5.89461130 8.85685927 2.64815718 -0.00066836 0.00011936 0.00050244 Cs 5.61851441 5.82623523 5.77928218 0.00282111 0.00360640 0.00050133 Pb 8.81952953 8.83521638 8.90978099 -0.00000692 -0.00004569 -0.00269857 Br 8.91119709 8.79054251 5.89794713 -0.00237463 -0.00185585 0.00152810 Br 8.96061657 5.65843179 8.90913102 -0.00258759 0.00183494 0.00215042 Br 5.97889335 7.94384260 8.59661698 -0.00089747 0.00013244 -0.00043080 Cs 5.78723577 6.27988605 11.75594450 0.00043785 -0.00143824 0.00182291 Pb 9.07721122 8.90001753 14.81188102 0.00100727 -0.00083683 0.00074062 Br 8.15462626 9.44733829 11.82563300 -0.00372287 -0.00422689 -0.00041881 Br 9.08062729 5.69482010 14.74274307 0.00383624 -0.00064020 0.00456310 Br 5.83682812 9.58259225 15.22701261 0.00167860 -0.00394687 0.00188027 Cs 7.07972093 5.95716995 17.90525179 -0.00135114 0.00165490 -0.00036347 Pb 8.69798159 8.88793655 20.74810789 -0.00072105 -0.00017182 -0.00060705 Br 9.15812148 9.38231878 17.63640437 0.00145947 0.00066147 0.00322436 Br 9.23653130 5.98789356 21.01320928 -0.00025891 0.00092897 0.00062015 Br 5.87458581 8.77346431 20.49164826 -0.00076188 -0.00162809 0.00151866 Cs 5.65796856 11.68149459 23.24080905 -0.00143395 -0.00134615 0.00052487 Pb 8.62851980 14.67813863 2.99914788 0.00142452 -0.00110508 -0.00010014 Br 9.24742651 14.98916897 0.16409286 0.00030261 -0.00351573 0.00273808 Br 8.82050798 11.66900268 2.84144890 0.00192799 -0.00222995 0.00199130 Br 5.69151497 14.84855310 2.56148717 -0.00317813 -0.00091098 0.00077686 Cs 5.09330648 11.84604592 4.90343695 -0.00055854 0.00074240 -0.00149971 Pb 8.71855367 14.70612115 8.85547102 -0.00292905 -0.00226297 0.00079246 Br 7.78704395 14.30928523 6.14872788 0.00423161 -0.00301392 -0.00067794 Br 9.85885090 11.64912145 9.08683798 0.00014500 0.00360106 -0.00234756 Br 5.85000241 14.82683626 9.41788588 0.00236777 -0.00442196 -0.00001187 Cs 5.43284229 12.07613119 12.03173872 -0.00012925 0.00046875 -0.00225475 Pb 8.84868600 14.92736302 14.83714069 -0.00264469 0.00120237 0.00139303 Br 9.80700153 14.98794122 11.70112817 -0.00039263 0.00201098 -0.00041240 Br 9.34665009 11.89898151 14.91271533 0.00167294 -0.00139845 0.00281133 Br 6.09254479 14.53883607 14.88814867 0.00371665 0.00222837 -0.00605936 Cs 6.37866272 12.15702696 17.96658407 0.00016654 -0.00216071 -0.00172717 Pb 8.92475314 14.91464628 20.91523905 0.00027612 0.00016288 -0.00278806 Br 9.07182175 14.89726321 18.08286739 0.00375295 0.00041769 0.00156375 Br 8.92155912 12.09190946 21.50722091 0.00082954 -0.00120718 0.00017278 Br 5.90271209 14.54454085 21.12635261 0.00320748 0.00334312 0.00189239 Cs 5.69310317 18.20754106 0.00628048 -0.00311567 0.00340751 0.00015165 Pb 8.85862444 20.70917935 2.95313946 -0.00090562 0.00085599 0.00144684 Br 8.90068809 20.58180400 23.64865706 -0.00025606 0.00165351 0.00229119 Br 8.52238043 17.51853130 3.10797369 -0.00238292 0.00255303 -0.00218222 Br 5.99429631 20.61338245 2.77549065 0.00024332 -0.00054414 0.00107757 Cs 5.79710118 17.64612512 5.58429377 -0.00126159 0.00242370 -0.00070964 Pb 8.68093032 20.69809958 8.69353765 -0.00156237 -0.00047785 0.00069158 Br 8.61593997 20.42419993 5.94371194 -0.00120726 -0.00170250 0.00109097 Br 8.14303163 17.59094294 8.71518085 -0.00281070 -0.00146305 -0.00250298 Br 5.84280480 20.79689710 8.87172217 -0.00074444 -0.00236504 -0.00145292 Cs 6.38268381 17.95777659 11.62033021 0.00027197 0.00230563 -0.00309505 Pb 9.32796668 20.83041530 14.71034816 0.00173791 0.00071774 -0.00129232 Br 9.22962815 21.32712991 11.63768241 0.00287871 -0.00266610 0.00123437 Br 8.39313961 18.04511950 14.47874456 -0.00021435 -0.00195012 0.00087979 Br 6.13985136 20.99963452 14.64867202 -0.00068018 0.00064036 0.00318001 Cs 5.87698437 17.48499960 18.09058527 -0.00052896 -0.00188757 -0.00104469 Pb 9.05092197 20.75338740 20.77875240 0.00155588 -0.00072952 -0.00003128 Br 9.27675985 20.56055487 17.96774932 0.00456723 0.00147233 -0.00252354 Br 8.37228027 17.79158658 20.74230253 -0.00667963 0.00207659 -0.00138807 Br 6.08016972 21.25078023 20.68763349 0.00090044 -0.00387616 -0.00111731 Cs 12.06062242 0.01588533 0.49701548 0.00101948 -0.00078503 -0.00083607 Pb 15.04297133 3.21588449 3.02913534 0.00021875 0.00140133 -0.00276656 Br 15.41677353 2.80376424 0.05683176 -0.00081797 -0.00061122 0.00338560 Br 14.90822700 0.27025592 2.51357680 0.00573048 -0.00462266 -0.00059915 Br 11.93216493 3.21171503 2.77382303 -0.00166615 0.00207095 -0.00203284 Cs 12.28910738 0.49472654 6.15327768 -0.00086942 0.00178270 -0.00337012 Pb 14.77681954 3.01164380 8.93033379 0.00018166 -0.00023447 -0.00072451 Br 15.01879359 2.70871039 5.95710596 -0.00289326 0.00432395 -0.00004491 Br 14.62772792 0.11281507 8.91249363 0.00267997 -0.00009510 0.00028450 Br 11.75001654 3.02730795 9.50426266 0.00293288 0.00118927 0.00260893 Cs 11.96769443 23.57356227 11.92254196 -0.00088770 0.00392331 0.00019887 Pb 14.71527987 3.07595370 14.81378804 0.00002597 0.00059960 0.00030035 Br 15.63174230 3.08878334 11.71898610 -0.00066259 -0.00429485 0.00047180 Br 14.50253253 23.55498899 15.17108801 -0.00280224 0.00155930 -0.00081881 Br 11.96515373 2.79354980 14.28329625 -0.00045424 -0.00089816 0.00100156 Cs 12.16902336 23.39560612 18.06916159 -0.00093525 -0.00198081 0.00297748 Pb 15.14034462 2.75671804 20.97464004 -0.00115564 0.00281254 -0.00054652 Br 15.26269767 2.84813098 17.66209918 -0.00156281 0.00133331 0.00214968 Br 14.78466641 23.71481409 21.27902745 0.00281441 -0.00061680 -0.00281798 Br 12.28614814 3.33342434 21.27962871 -0.00282916 -0.00519653 0.00268456 Cs 11.73921451 6.37459292 23.38349196 -0.00016547 -0.00098069 -0.00166090 Pb 14.86640383 9.06493121 3.39839992 -0.00165738 -0.00098507 0.00150682 Br 14.71467151 9.34402355 0.11081040 -0.00007744 -0.00216457 0.00071562 Br 15.53809598 6.14238010 3.92891189 -0.00040431 0.00264706 -0.00217182 Br 12.00005475 8.80115820 2.76663432 -0.00102686 -0.00134387 -0.00130110 Cs 11.73706871 6.41206259 5.59448633 0.00166737 -0.00150454 -0.00054564 Pb 14.47102299 8.91739150 8.95307398 -0.00009068 -0.00029312 0.00100354 Br 14.32832617 9.54565686 6.22428606 0.00086012 -0.00030119 0.00189782 Br 15.17227797 6.04410532 8.39861428 0.00128175 0.00213077 -0.00165436 Br 11.62965581 8.23775442 9.12988221 -0.00057458 -0.00123734 -0.00150911 Cs 11.78063437 5.76922705 11.80804647 0.00236171 0.00189300 0.00009398 Pb 14.81728151 8.72459806 14.74207097 0.00054558 -0.00047810 -0.00118658 Br 15.14288893 8.91989569 12.03266950 -0.00054234 0.00204864 -0.00505213 Br 15.33407385 5.84210148 14.97151928 -0.00250926 0.00076333 -0.00016744 Br 11.81588815 8.35761176 14.47592766 0.00266874 0.00088070 0.00389626 Cs 12.39369310 5.83406560 18.21932596 0.00197761 -0.00091922 0.00091796 Pb 14.61322489 8.99876873 20.90045007 -0.00072033 0.00137553 -0.00031424 Br 14.75119760 9.00739525 18.01281090 0.00041795 -0.00215697 0.00233304 Br 15.40656904 5.92325923 21.02720715 0.00135382 -0.00241359 -0.00067942 Br 11.63442874 9.13996328 20.49117709 -0.00386180 0.00013862 -0.00194757 Cs 11.60974327 11.82655754 0.51572192 -0.00361761 0.00035725 0.00203211 Pb 14.65077048 15.11745964 3.30905945 0.00131002 -0.00135388 -0.00076549 Br 14.71645575 15.83856733 0.12841469 0.00155963 -0.00116440 -0.00295411 Br 14.83267831 11.88476276 2.97323538 -0.00242873 -0.00172627 0.00124964 Br 11.62142876 15.30512806 3.90223974 0.00046746 -0.00121665 -0.00207678 Cs 11.60579635 12.46400133 5.90896223 -0.00106752 0.00072481 0.00072488 Pb 14.38132660 14.74506572 9.07668203 0.00036051 0.00123666 0.00314978 Br 15.32439926 14.66681242 6.07794560 -0.00235186 0.00121237 0.00142426 Br 14.34824321 11.85324232 9.07488880 -0.00429456 0.00385987 -0.00101537 Br 11.83139194 15.56830898 8.18877898 0.00301590 0.00097673 0.00043786 Cs 11.93853463 12.06941729 11.95946634 -0.00011236 0.00025151 -0.00068770 Pb 15.03007889 14.71523122 14.82081205 0.00068396 0.00215845 0.00044008 Br 14.41258108 14.99817006 11.91353772 0.00297896 -0.00143943 0.00151752 Br 14.48264147 11.89122994 14.76340825 -0.00097385 0.00019205 0.00254801 Br 12.02599582 15.36571530 15.07536991 -0.00078666 0.00020858 0.00219426 Cs 12.13833597 11.81206362 17.64493349 0.00184060 -0.00230575 -0.00220074 Pb 14.58966451 14.86341989 21.10339189 0.00155050 0.00028639 0.00030457 Br 14.58111167 14.38111413 17.93061198 -0.00229104 0.00134371 -0.00228177 Br 14.14803566 11.82435597 20.72227963 -0.00009735 0.00106477 0.00246415 Br 11.75817895 15.02071725 21.13478711 0.00256053 0.00136245 0.00358443 Cs 11.71074469 18.03294001 0.11032881 -0.00095277 0.00093484 0.00351326 Pb 14.64729871 20.78886948 2.84943528 0.00056050 0.00030349 -0.00041802 Br 14.09401072 20.46216650 0.16448750 0.00429840 0.00262431 0.00347775 Br 14.63341741 17.98800856 3.42512665 -0.00083974 0.00355632 0.00072051 Br 11.90298544 21.25068400 3.32394664 -0.00221224 0.00158014 0.00156075 Cs 11.31055471 18.09704006 5.93130774 -0.00181004 0.00048038 0.00033046 Pb 14.62360591 20.86692605 8.72064013 0.00233319 0.00099354 0.00101453 Br 15.18327475 21.63860972 5.78178117 -0.00196599 -0.00684690 -0.00241139 Br 14.70999579 18.11521121 8.75768917 -0.00090240 -0.00270239 0.00039511 Br 11.89697261 20.77944399 8.52176330 -0.00095026 0.00433138 -0.00313989 Cs 12.14825177 18.24194428 11.64382265 0.00452979 0.00501162 0.00209800 Pb 14.93345488 20.64346838 14.94758027 -0.00041700 -0.00218178 0.00050647 Br 14.75018475 20.62651280 12.03607426 0.00025539 -0.00347687 0.00105713 Br 15.94058760 17.60871248 14.63771721 -0.00212303 -0.00064890 -0.00081105 Br 11.94455327 19.82681133 14.76832282 -0.00190309 -0.00250128 -0.00134176 Cs 11.91675823 17.66517671 17.58376447 0.00286129 -0.00005775 0.00155803 Pb 14.71747498 20.50816175 20.79059290 -0.00092159 0.00013363 -0.00239030 Br 14.63719968 20.40157058 17.90104830 -0.00372389 0.00101186 -0.00125408 Br 14.52001084 17.58028254 20.50873290 0.00126857 0.00212340 -0.00593353 Br 11.86617964 21.03397401 20.99668999 0.00384523 -0.00189867 -0.00044236 Cs 17.43505926 23.40513674 0.31307535 0.00448062 0.00019876 -0.00180288 Pb 20.84563509 2.96225624 2.82690663 -0.00163156 0.00123259 -0.00173993 Br 20.79046517 3.46735780 0.02911727 -0.00227089 0.00599436 -0.00245118 Br 21.08081996 0.11095662 3.29769345 -0.00055347 -0.00090953 0.00046714 Br 17.91039424 3.12286321 2.64620244 0.00546466 0.00131524 0.00190195 Cs 18.51679878 23.47218785 6.16722667 -0.00224278 0.00266524 0.00003858 Pb 20.86424554 3.14227718 8.94582317 0.00107499 0.00043284 0.00016589 Br 21.45344643 2.85095599 5.87054888 0.00247961 0.00025124 0.00204773 Br 21.07715586 0.07684175 8.68237975 0.00031371 -0.00120859 0.00223226 Br 18.10202828 2.44221382 8.55501002 -0.00161578 0.00077101 0.00052818 Cs 17.55892198 0.45255151 11.82509583 0.00081716 0.00146023 -0.00060419 Pb 20.87516841 2.91837658 14.62127743 -0.00080090 0.00120811 -0.00088600 Br 20.01096854 3.36569573 11.74245197 -0.00213651 0.00135248 -0.00106816 Br 21.12244205 23.73768990 14.80923742 0.00176174 0.00055673 0.00097387 Br 17.93315074 2.38141338 15.01403316 -0.00109129 0.00184017 0.00012207 Cs 17.85653149 0.06849329 18.37298011 0.00099930 -0.00043789 0.00098737 Pb 20.98085791 2.96584280 20.65310211 0.00256984 -0.00177844 0.00012047 Br 20.96114892 2.83270724 17.71094657 -0.00177963 0.00372044 0.00013481 Br 21.37053064 23.74895819 20.89347007 -0.00332081 -0.00384574 -0.00319959 Br 18.26755817 2.85469066 20.36679050 -0.00208578 -0.00081977 -0.00508288 Cs 17.71000257 6.11656628 23.63339062 -0.00118023 0.00218444 -0.00320418 Pb 20.74983321 8.96051067 3.05430867 0.00142762 0.00015520 -0.00007006 Br 20.55630188 9.55373757 23.70188207 -0.00190864 -0.00339192 0.00110958 Br 20.73124261 6.08296224 3.21062179 -0.00126250 0.00126491 0.00068517 Br 17.75983629 9.10413662 3.19012650 0.00139113 -0.00106416 0.00386586 Cs 18.27063617 5.38192731 6.38118468 0.00130290 -0.00063891 0.00131197 Pb 20.72226410 9.00462758 9.02597998 0.00198842 -0.00172295 -0.00169281 Br 21.05552855 8.97581813 6.09152830 -0.00110815 0.00516621 0.00187697 Br 21.28838322 6.00565738 8.87681473 0.00104920 -0.00146304 -0.00012533 Br 17.93822091 9.09037901 9.61044081 -0.00756275 -0.00106959 0.00024159 Cs 17.40837956 6.09672823 11.56324320 -0.00030322 0.00311642 -0.00169069 Pb 20.83914630 8.87373458 14.83310955 0.00114006 0.00091266 0.00007467 Br 21.05113303 8.62139846 11.99177007 -0.00081287 -0.00059524 -0.00114824 Br 20.97022377 5.94658911 15.28716282 -0.00016414 -0.00217029 0.00196556 Br 17.73735978 8.82737990 14.69572786 0.00177373 -0.00288988 -0.00088971 Cs 18.06884068 5.79715623 17.47345669 -0.00210966 -0.00012888 -0.00007869 Pb 20.46667763 9.02288306 20.71919307 0.00030274 0.00120007 -0.00138592 Br 21.35432803 8.81060228 18.03272452 0.00106051 -0.00073659 0.00139831 Br 20.49072645 6.06527141 20.91302901 -0.00059923 -0.00425455 -0.00124082 Br 17.67164417 9.66421374 20.61145884 -0.00122167 -0.00149678 -0.00173410 Cs 17.96077948 12.03151253 0.22823566 -0.00094781 -0.00056286 -0.00072069 Pb 20.37177873 14.86433215 2.93640573 -0.00055236 -0.00238193 0.00054787 Br 20.72138454 15.22602106 23.69298714 0.00065141 0.00167803 -0.00042824 Br 20.48025411 11.97055757 2.93658119 -0.00134837 -0.00417553 0.00253214 Br 17.56385088 15.13021225 2.95348995 0.00060238 -0.00075824 -0.00138567 Cs 17.62006687 11.25961478 6.31269183 0.00176921 -0.00213201 -0.00079800 Pb 20.58563249 14.70387872 8.76809962 0.00016664 0.00027247 0.00156496 Br 20.51064529 15.29588084 6.00639761 -0.00239862 -0.00232311 -0.00137564 Br 21.10775315 11.95594040 8.34160010 -0.00142965 -0.00155707 -0.00106100 Br 17.80881905 14.49648586 9.31056425 -0.00011248 -0.00039045 0.00025145 Cs 17.81595327 11.47512722 12.45196407 0.00070959 -0.00214673 -0.00059495 Pb 20.88153619 14.59006865 14.91916840 0.00017112 -0.00064537 -0.00038414 Br 21.04564413 14.02734981 12.08318120 -0.00240600 -0.00013030 0.00145089 Br 20.41231951 11.66392621 15.12395398 -0.00069470 -0.00092822 0.00098372 Br 17.94672401 14.05667636 14.61177522 0.00279963 -0.00095270 0.00127491 Cs 17.49082371 12.14729624 17.78169031 -0.00055547 -0.00109858 -0.00201689 Pb 20.66852164 14.73181047 20.81159558 0.00186384 0.00198624 0.00091688 Br 21.53189348 14.71730429 17.73296643 -0.00256611 -0.00373673 0.00113281 Br 20.63202980 11.89612485 20.16081483 -0.00270823 0.00059386 -0.00298166 Br 17.55783022 15.15419176 20.76750882 0.00144233 -0.00058857 -0.00155083 Cs 17.88423483 17.75617795 23.41494181 0.00007430 -0.00024088 0.00024445 Pb 20.49463455 20.91874279 2.95980889 0.00190772 0.00164543 0.00221543 Br 21.19232604 21.03069977 0.01598583 0.00243299 0.00098688 -0.00150981 Br 20.54746737 17.86459198 3.01284097 0.00103519 0.00023032 0.00113704 Br 17.76347328 20.27156319 2.10039520 0.00604616 -0.00204292 0.00061294 Cs 17.55828051 17.99257013 5.93580031 0.00130644 0.00010284 -0.00237661 Pb 20.63908296 20.86628685 9.18093253 0.00196332 0.00158721 -0.00112329 Br 20.93924755 20.86747139 5.99427579 0.00170032 -0.00064439 0.00128303 Br 20.26267224 18.01562112 8.64831716 -0.00110725 -0.00178555 -0.00002199 Br 17.86475447 20.93585446 9.46371537 0.00008279 -0.00364377 0.00431779 Cs 17.90450813 18.08297405 11.31283173 -0.00072593 0.00047212 0.00172712 Pb 21.05490231 20.71052773 14.70630801 -0.00069741 0.00119315 0.00103568 Br 20.96072754 20.84656756 11.91962546 -0.00052403 -0.00045973 -0.00454847 Br 20.83224088 17.73187427 14.04051891 -0.00275210 -0.00378535 -0.00204209 Br 17.88370674 21.19953983 14.40812018 0.00183340 0.00352272 0.00203638 Cs 17.48955022 18.13566431 17.66655783 -0.00112290 -0.00040706 0.00249230 Pb 20.97706713 20.79840460 20.63057740 0.00072670 0.00025428 0.00139496 Br 20.38963882 21.10815087 17.90942334 0.00279236 -0.00245156 -0.00093671 Br 20.79328069 17.67525268 20.68583572 -0.00036882 0.00255017 -0.00281734 Br 17.68499700 20.49188757 20.76637985 0.00113615 0.00110989 0.00320263 320 Lattice="23.77195271 0.0 0.0 0.0 23.77195271 0.0 0.0 0.0 23.77195271" Properties=species:S:1:pos:R:3:vel:R:3 Time=20019.99999999 energy=-11425.76148605 virial="-11.00193161 2.5321247 0.77788768 2.5321247 -13.96822141 -1.08373296 0.77788768 -1.08373296 -7.90303365" stress="0.00017639 0.00018849 5.791e-05 0.00018849 -2.333e-05 -8.067e-05 5.791e-05 -8.067e-05 0.00029779" pbc="T T T" Cs 0.24177142 0.43639281 23.68633600 -0.00385291 -0.00257787 0.00080936 Pb 3.11734178 2.93422815 2.93924866 -0.00253827 0.00121948 0.00066514 Br 3.34854753 2.89318746 23.66348153 -0.00089556 0.00079852 0.00276028 Br 3.00471277 0.00943051 3.17100691 0.00008495 0.00269228 -0.00009458 Br 0.04434807 3.48461704 2.64231370 0.00201841 0.00004685 -0.00232664 Cs 23.59289251 23.55582318 6.24805598 0.00298369 0.00181266 -0.00098564 Pb 2.91749007 2.99139732 9.33932397 0.00101256 -0.00102392 0.00045514 Br 2.32317573 2.87938086 6.01988768 0.00125860 -0.00145722 0.00266041 Br 3.15029312 0.01886839 9.05303845 0.00006451 -0.00126324 0.00296239 Br 23.72298571 2.88216218 9.78653447 -0.00179482 0.00351987 0.00576713 Cs 0.59169103 23.68497933 12.14808766 -0.00292001 -0.00082294 0.00096873 Pb 3.19430091 3.26428844 15.00264673 -0.00219420 0.00237333 -0.00018862 Br 4.26527296 3.10467225 11.96371421 0.00057860 -0.00010122 -0.00068074 Br 2.79040856 0.36927165 15.01660447 0.00271884 -0.00087933 -0.00297387 Br 0.24139783 3.58510672 14.72555028 0.00183575 -0.00215080 0.00175026 Cs 0.26576127 23.51999378 17.93621168 -0.00088239 -0.00205282 -0.00179661 Pb 3.05670423 3.08502900 20.64494450 0.00153436 -0.00035332 -0.00037304 Br 2.67020963 3.01211111 17.72174849 -0.00191641 0.00191062 0.00112458 Br 3.17877666 0.00018955 20.05326727 -0.00153746 -0.00004987 0.00163041 Br 0.11185833 3.16599689 20.56767818 -0.00124123 0.00014445 -0.00074166 Cs 23.48964965 6.20167440 0.00414266 -0.00016540 0.00249993 -0.00022388 Pb 2.85518584 8.90455966 3.09821379 -0.00008446 0.00074511 0.00382974 Br 2.56651042 9.03310792 23.62499243 -0.00015901 -0.00425612 -0.00016447 Br 3.35504740 6.02661726 3.21406789 -0.00227953 0.00146568 -0.00070048 Br 23.68473041 8.74044627 3.36602974 -0.00051739 -0.00214838 -0.00144919 Cs 0.21421847 5.92691720 5.77960723 0.00243771 0.00169104 0.00187889 Pb 3.09883233 8.95761719 8.96852738 0.00315612 0.00315495 -0.00002627 Br 2.89131813 9.12583210 5.87520299 -0.00044539 -0.00321216 -0.00103381 Br 2.93398587 6.11912119 8.62029030 0.00054759 -0.00130894 0.00057368 Br 0.15573212 8.94212798 8.96525323 -0.00229944 0.00351749 -0.00199709 Cs 23.74305883 6.14729489 11.79294620 -0.00162106 -0.00212956 0.00175158 Pb 3.00245519 8.79982502 15.01967594 -0.00170657 -0.00087155 -0.00260472 Br 3.12572843 8.77295441 11.84858892 0.00097676 0.00071036 -0.00381734 Br 3.83716589 6.00929557 15.05176917 -0.00221807 -0.00220420 0.00064343 Br 0.03646439 8.68343484 14.74356150 0.00021022 -0.00225353 -0.00236764 Cs 0.01393376 5.72648663 17.64929539 -0.00098513 -0.00003945 -0.00090426 Pb 2.75509476 8.84663477 20.65291809 -0.00007018 0.00040061 -0.00150445 Br 1.96518707 8.77721331 17.81190120 0.00254961 -0.00037868 0.00033427 Br 2.14972282 5.95844053 20.64163609 -0.00291405 -0.00128777 0.00081020 Br 23.66902899 9.31723128 21.13213866 0.00219239 -0.00054499 0.00066082 Cs 23.00990979 11.85234443 0.21652632 0.00047331 -0.00139433 0.00064830 Pb 2.89644734 14.80446837 2.83389444 -0.00097592 0.00107528 0.00089911 Br 2.58403318 15.22066601 23.75924475 -0.00199043 -0.00056955 -0.00226977 Br 2.76796412 11.76331960 2.39049569 0.00266626 -0.00301950 -0.00103790 Br 23.48186383 14.55823791 3.37932130 -0.00324486 0.00096113 -0.00112145 Cs 23.50037125 11.86861494 6.41623841 -0.00237434 -0.00147159 0.00425461 Pb 2.92995187 15.06390973 8.94404107 0.00067377 -0.00073913 0.00227833 Br 3.45003255 14.98834139 5.99364630 -0.00092110 -0.00041487 -0.00020429 Br 2.94970250 11.77503032 8.69423403 0.00059677 -0.00332479 -0.00013356 Br 23.68891408 15.13574270 8.92940676 0.00024152 -0.00035002 -0.00075472 Cs 0.27869463 11.60529204 11.58796447 -0.00180406 0.00026464 -0.00204600 Pb 3.00312324 14.68434679 14.98007249 0.00112698 -0.00229558 0.00154683 Br 3.00922264 14.60857776 11.98237719 0.00176741 -0.00240458 -0.00266919 Br 2.88684727 11.87142905 15.23974893 -0.00394431 -0.00057803 0.00307156 Br 23.75905715 14.64815236 14.74948929 -0.00109104 0.00046141 -0.00349999 Cs 23.37378100 11.83810577 18.17268596 0.00051850 -0.00078835 0.00017247 Pb 2.96252383 14.76075545 20.90501969 0.00218130 -0.00299332 -0.00212140 Br 2.95669620 14.79784707 17.91729376 -0.00053825 0.00065802 0.00142006 Br 2.75724060 11.87760578 20.87348495 -0.00087920 -0.00169011 0.00112995 Br 23.69992568 14.45194140 21.05872152 0.00458268 0.00055194 -0.00117815 Cs 0.07623571 17.75824896 23.76292246 0.00042192 -0.00013837 -0.00149058 Pb 2.82457764 20.83173783 2.96682422 0.00218438 -0.00116787 -0.00025420 Br 2.87695620 20.67939134 23.33341685 0.00094643 0.00046584 0.00167754 Br 3.25420459 17.81631803 2.62011912 0.00036666 0.00235041 -0.00071810 Br 0.02069332 20.66527350 2.80268762 -0.00276244 0.00229412 0.00138434 Cs 23.23506400 17.71651435 6.45668500 -0.00136515 0.00096442 -0.00222539 Pb 2.73406671 20.94301694 8.81850909 0.00127075 0.00070382 -0.00075327 Br 2.85655156 20.96788719 5.80170449 0.00110579 -0.00189209 -0.00106963 Br 3.42651127 18.00283550 9.55938646 0.00108410 0.00189489 -0.00013849 Br 23.58728053 21.23570696 8.85894899 -0.00302443 -0.00108643 -0.00023043 Cs 0.24849356 17.08919123 12.45446239 0.00342764 -0.00036783 -0.00218279 Pb 3.14762419 21.06667445 14.60414098 -0.00102185 0.00086112 0.00028805 Br 3.52261351 20.78902962 11.89133347 -0.00279518 -0.00154707 -0.00089018 Br 3.06538419 18.00976588 14.70086489 -0.00024441 0.00159314 0.00456980 Br 0.10486047 20.37234397 14.98504865 0.00102330 0.00085338 -0.00008372 Cs 0.35342153 17.64016564 17.39096434 0.00068640 0.00051504 0.00093584 Pb 3.00261463 20.69001246 20.57557595 -0.00061090 0.00016523 -0.00047191 Br 3.75432244 20.80516132 17.61028234 -0.00122418 -0.00442019 -0.00117461 Br 3.09220015 17.85552314 20.90894155 -0.00016292 0.00094360 -0.00466315 Br 0.03687474 20.67470741 20.39391278 -0.00260225 -0.00021641 0.00287903 Cs 6.17323578 0.04158588 22.92554596 -0.00247459 0.00051250 -0.00116852 Pb 9.11326697 2.83420812 3.36165522 -0.00007572 0.00198881 -0.00051955 Br 9.21216650 2.39502610 0.08445293 -0.00154646 0.00178042 -0.00067519 Br 8.55092515 23.47085962 3.04127689 0.00193176 0.00194651 0.00408080 Br 6.10786177 2.77900184 3.43291981 -0.00225953 0.00275973 0.00112830 Cs 6.18138916 23.38270555 5.65400711 -0.00041224 0.00034826 0.00095505 Pb 8.81367971 2.78487302 8.80540743 0.00004242 -0.00094368 0.00016321 Br 10.00702612 3.34837003 6.10095826 -0.00156832 0.00220233 -0.00182807 Br 8.26891383 23.53266048 8.52100412 0.00082904 -0.00584715 0.00344852 Br 5.73687744 3.50078399 8.68759877 -0.00110855 -0.00025898 -0.00253299 Cs 6.12062598 23.58878492 12.04336381 -0.00049241 0.00147262 0.00017914 Pb 9.01467093 2.83892613 14.65572470 0.00225573 0.00077193 -0.00065426 Br 8.92832628 2.77894379 11.75245310 0.00178724 0.00135062 0.00102598 Br 9.96839109 23.47812790 15.34820134 0.00248301 -0.00127119 -0.00047468 Br 6.18411178 2.38817031 15.40079680 0.00042867 -0.00106185 -0.00242806 Cs 6.58965524 23.62383560 17.60122178 -0.00117281 -0.00135521 0.00068751 Pb 9.25443652 2.98348624 21.10197908 -0.00074339 0.00086806 -0.00010275 Br 9.82578234 3.08048934 17.34339506 -0.00130337 -0.00189527 0.00031558 Br 9.20793547 0.19721783 20.37375505 -0.00122626 0.00135204 0.00338087 Br 6.21809060 3.82691033 20.73654086 -0.00099429 0.00246740 0.00005442 Cs 5.73562453 6.52618598 23.63395500 -0.00105660 -0.00269777 0.00060818 Pb 8.94898502 8.65384412 2.85560457 -0.00055122 0.00328250 -0.00105931 Br 8.79716648 8.76887196 23.71203507 0.00133432 0.00074904 -0.00144123 Br 8.47187442 5.59646807 2.73710889 -0.00366224 0.00207016 0.00174146 Br 5.88887826 8.85893680 2.65377474 -0.00047327 0.00029815 0.00061957 Cs 5.64738580 5.86203053 5.78497095 0.00294768 0.00355002 0.00062635 Pb 8.81844619 8.83485346 8.88306782 -0.00020847 -0.00002567 -0.00263881 Br 8.88798178 8.77168758 5.91258444 -0.00226597 -0.00191567 0.00138614 Br 8.93500089 5.67861992 8.93144909 -0.00253407 0.00216806 0.00230999 Br 5.96994631 7.94748851 8.59252350 -0.00088720 0.00060249 -0.00038008 Cs 5.79235911 6.26547521 11.77395329 0.00058740 -0.00144103 0.00177480 Pb 9.08557035 8.89218424 14.81850257 0.00066694 -0.00072638 0.00058980 Br 8.11807288 9.40370167 11.82213140 -0.00358154 -0.00449536 -0.00027642 Br 9.11801993 5.69120347 14.78757425 0.00364341 -0.00007161 0.00439811 Br 5.85528634 9.54239099 15.24593506 0.00199146 -0.00409249 0.00190141 Cs 7.06576069 5.97328819 17.90104756 -0.00143976 0.00156903 -0.00047565 Pb 8.69237508 8.88734074 20.74210167 -0.00040359 0.00005397 -0.00059341 Br 9.17185069 9.38735379 17.67081720 0.00127803 0.00034232 0.00362160 Br 9.23349036 5.99940776 21.01815952 -0.00034879 0.00136323 0.00036638 Br 5.86497088 8.75695230 20.50666474 -0.00116402 -0.00167398 0.00148094 Cs 5.64337772 11.66778787 23.24683597 -0.00148316 -0.00139521 0.00067840 Pb 8.64314087 14.66719650 2.99823752 0.00149624 -0.00107171 -0.00007855 Br 9.25026262 14.95420041 0.19415405 0.00026712 -0.00347720 0.00325053 Br 8.83990250 11.64584265 2.86159868 0.00194438 -0.00237934 0.00204185 Br 5.66315921 14.83820240 2.56878083 -0.00247459 -0.00115824 0.00068274 Cs 5.08931579 11.85359197 4.88974005 -0.00023690 0.00076484 -0.00123631 Pb 8.68968312 14.68354500 8.86386121 -0.00282887 -0.00224852 0.00088585 Br 7.83044983 14.27914753 6.13934014 0.00443872 -0.00301330 -0.00119981 Br 9.85946805 11.68690053 9.06206497 -0.00002652 0.00393216 -0.00259623 Br 5.87378183 14.78137576 9.41901574 0.00235412 -0.00466761 0.00024007 Cs 5.43215956 12.07979309 12.00936440 -0.00000182 0.00025806 -0.00221990 Pb 8.82487520 14.93984142 14.85079267 -0.00208969 0.00129936 0.00133242 Br 9.80094727 15.00845714 11.69850005 -0.00082002 0.00208704 -0.00011087 Br 9.36345777 11.88638602 14.94024156 0.00168260 -0.00112702 0.00268983 Br 6.12640961 14.56060836 14.82774376 0.00297880 0.00211059 -0.00601819 Cs 6.38016086 12.13519519 17.94909287 0.00013682 -0.00220688 -0.00176949 Pb 8.92700814 14.91627975 20.88847367 0.00018201 0.00016338 -0.00255173 Br 9.10870268 14.90093033 18.09261575 0.00361804 0.00031366 0.00037514 Br 8.92929341 12.07520356 21.50775049 0.00071308 -0.00210229 -0.00007118 Br 5.93337849 14.57847236 21.14448385 0.00292245 0.00343259 0.00173471 Cs 5.66230658 18.24129967 0.00725980 -0.00303911 0.00334218 0.00004006 Pb 8.84945283 20.71675604 2.96705555 -0.00092167 0.00065921 0.00133725 Br 8.89826916 20.59858068 23.67554966 -0.00022484 0.00170428 0.00304887 Br 8.49912768 17.54638821 3.08620793 -0.00226412 0.00298021 -0.00217190 Br 5.99607474 20.60836493 2.78685720 0.00009276 -0.00045245 0.00119624 Cs 5.78452936 17.67068909 5.57739594 -0.00125135 0.00248721 -0.00066992 Pb 8.66525063 20.69327177 8.70216926 -0.00156217 -0.00050060 0.00103249 Br 8.60308695 20.40742748 5.94999019 -0.00136742 -0.00164613 0.00017226 Br 8.11767556 17.57669506 8.68850900 -0.00226453 -0.00139172 -0.00283384 Br 5.83468922 20.77350412 8.85665816 -0.00089671 -0.00230866 -0.00155838 Cs 6.38422403 17.98117470 11.59015218 0.00004249 0.00237405 -0.00293280 Pb 9.34330854 20.83714752 14.69748187 0.00132174 0.00062420 -0.00127914 Br 9.25823757 21.29942929 11.65249587 0.00284162 -0.00286783 0.00171828 Br 8.39205109 18.02129461 14.48878444 -0.00001064 -0.00280641 0.00112513 Br 6.13315717 21.00756939 14.68042319 -0.00065609 0.00094665 0.00315974 Cs 5.87156056 17.46674745 18.07983879 -0.00055772 -0.00175902 -0.00110628 Pb 9.06471450 20.74609294 20.77785856 0.00120253 -0.00073078 -0.00014912 Br 9.32287502 20.57508265 17.93785280 0.00465231 0.00143615 -0.00341034 Br 8.30609474 17.81460353 20.72917184 -0.00654482 0.00250560 -0.00123296 Br 6.08910447 21.21050823 20.67621153 0.00088653 -0.00416714 -0.00116200 Cs 12.07039130 0.00821628 0.48804230 0.00093662 -0.00074750 -0.00095510 Pb 15.04505573 3.22987123 3.00174455 0.00020069 0.00139139 -0.00270797 Br 15.40830932 2.79925238 0.08822804 -0.00087276 -0.00029214 0.00287945 Br 14.96449682 0.22270831 2.51023041 0.00551565 -0.00485143 -0.00006903 Br 11.91746546 3.23259078 2.75313511 -0.00125181 0.00210320 -0.00210405 Cs 12.28025872 0.51162429 6.11920083 -0.00089821 0.00159282 -0.00344295 Pb 14.77863427 3.00934882 8.92270434 0.00018549 -0.00022046 -0.00079617 Br 14.99028102 2.75220989 5.95662597 -0.00280987 0.00437206 -0.00006079 Br 14.65429984 0.11324191 8.91699361 0.00263156 0.00017295 0.00060365 Br 11.78303178 3.03774119 9.52920497 0.00364092 0.00089746 0.00236919 Cs 11.95927884 23.61333904 11.92476812 -0.00079538 0.00402948 0.00024632 Pb 14.71749246 3.07968272 14.81712888 0.00041485 0.00014167 0.00036925 Br 15.62166466 3.04833909 11.72476612 -0.00135864 -0.00376994 0.00068499 Br 14.47451016 23.56985248 15.16186295 -0.00279646 0.00139729 -0.00102644 Br 11.96105846 2.78591935 14.29162586 -0.00035320 -0.00062324 0.00066227 Cs 12.15974237 23.37626020 18.09898894 -0.00092084 -0.00188463 0.00298402 Pb 15.12915229 2.78607639 20.96881160 -0.00107726 0.00304942 -0.00061998 Br 15.24508077 2.86206732 17.68548717 -0.00196238 0.00145495 0.00252123 Br 14.81308651 23.70627792 21.25002645 0.00286948 -0.00107203 -0.00298109 Br 12.25668848 3.28114648 21.30596178 -0.00305163 -0.00525368 0.00258180 Cs 11.73846550 6.36482043 23.36779772 0.00001529 -0.00097250 -0.00147599 Pb 14.85005227 9.05441615 3.41204884 -0.00160644 -0.00111492 0.00122361 Br 14.71400164 9.32118113 0.11830519 -0.00005466 -0.00240365 0.00078182 Br 15.53270097 6.16764911 3.90671811 -0.00067213 0.00237535 -0.00226853 Br 11.98665226 8.78693752 2.75334301 -0.00164470 -0.00150058 -0.00135387 Cs 11.75415424 6.39687020 5.58945945 0.00174968 -0.00152935 -0.00046051 Pb 14.47031417 8.91381713 8.96398828 -0.00004724 -0.00041460 0.00117737 Br 14.33635467 9.54291865 6.24111190 0.00074507 -0.00024282 0.00144453 Br 15.18482655 6.06526470 8.38290688 0.00122465 0.00209282 -0.00148448 Br 11.62836766 8.22516091 9.11502232 0.00030116 -0.00128106 -0.00146152 Cs 11.80353366 5.78782402 11.80922248 0.00221847 0.00182759 0.00014277 Pb 14.82270200 8.72062221 14.73225853 0.00054226 -0.00031046 -0.00079100 Br 15.13576112 8.94021809 11.97897425 -0.00089263 0.00201125 -0.00560251 Br 15.30821019 5.85139220 14.96930537 -0.00266182 0.00109339 -0.00027560 Br 11.84452112 8.36673597 14.51460677 0.00304436 0.00094240 0.00383852 Cs 12.41358228 5.82473579 18.22844765 0.00199739 -0.00094779 0.00090570 Pb 14.60693743 9.01280803 20.89772541 -0.00054121 0.00143184 -0.00022614 Br 14.75517578 8.98503482 18.03265692 0.00037728 -0.00231447 0.00161601 Br 15.41830453 5.89903163 21.01933017 0.00098438 -0.00242635 -0.00089963 Br 11.59696548 9.13960763 20.47282023 -0.00359337 -0.00020587 -0.00172085 Cs 11.57404355 11.83025188 0.53506308 -0.00351813 0.00038072 0.00183281 Pb 14.66356318 15.10320295 3.30089518 0.00124818 -0.00148851 -0.00085688 Br 14.73176508 15.82566627 0.10045372 0.00150263 -0.00140828 -0.00262675 Br 14.80793066 11.87098100 2.98611520 -0.00251953 -0.00100976 0.00132327 Br 11.62603962 15.29173082 3.87950644 0.00044907 -0.00145666 -0.00247091 Cs 11.59517157 12.47051968 5.91668686 -0.00105351 0.00058165 0.00082197 Pb 14.38655314 14.75817554 9.10710398 0.00068532 0.00138732 0.00292662 Br 15.30025873 14.67899250 6.09210258 -0.00247400 0.00122460 0.00137758 Br 14.30463469 11.89214102 9.06536515 -0.00442524 0.00388394 -0.00089200 Br 11.85922768 15.57633755 8.19403464 0.00253453 0.00063106 0.00060874 Cs 11.93712348 12.07184134 11.95325327 -0.00017003 0.00023654 -0.00055543 Pb 15.03639755 14.73799902 14.82614498 0.00057737 0.00238378 0.00062674 Br 14.44200973 14.98366003 11.92952793 0.00290252 -0.00145883 0.00169858 Br 14.47310984 11.88977686 14.78817765 -0.00092491 -0.00047937 0.00240095 Br 12.01804481 15.36626620 15.09605684 -0.00078653 -0.00009826 0.00193776 Cs 12.15623526 11.78908406 17.62312371 0.00173771 -0.00228929 -0.00215872 Pb 14.60595182 14.86495127 21.10607423 0.00170461 0.00003041 0.00022460 Br 14.55944552 14.39631545 17.90729153 -0.00203601 0.00169205 -0.00237133 Br 14.14790781 11.83517206 20.74808633 0.00007093 0.00109369 0.00269272 Br 11.78352520 15.03507681 21.17054658 0.00248717 0.00151154 0.00357011 Cs 11.70035542 18.04100548 0.14524223 -0.00111842 0.00068354 0.00347188 Pb 14.65452703 20.79309132 2.84697727 0.00088084 0.00054686 -0.00006069 Br 14.13806864 20.48900759 0.19458361 0.00448739 0.00272571 0.00247591 Br 14.62436944 18.02297731 3.43360682 -0.00096992 0.00337777 0.00097108 Br 11.87923571 21.26618349 3.34012719 -0.00250682 0.00151675 0.00166676 Cs 11.29290414 18.10246252 5.93432788 -0.00171640 0.00059925 0.00027430 Pb 14.64895443 20.87787171 8.73091878 0.00271914 0.00117521 0.00103647 Br 15.16372558 21.56891049 5.75972284 -0.00193369 -0.00707551 -0.00196335 Br 14.70186457 18.08328400 8.76132546 -0.00072305 -0.00362125 0.00033507 Br 11.88395422 20.82313764 8.48998654 -0.00160510 0.00440842 -0.00320941 Cs 12.19304916 18.29077222 11.66434750 0.00442045 0.00474061 0.00199600 Pb 14.93056435 20.62206368 14.95136959 -0.00016370 -0.00209074 0.00025041 Br 14.75220785 20.59311332 12.04367827 0.00016356 -0.00318915 0.00046599 Br 15.91798907 17.60344350 14.62941134 -0.00238783 -0.00038791 -0.00084074 Br 11.92862797 19.80211034 14.75535219 -0.00125505 -0.00243787 -0.00123839 Cs 11.94515217 17.66446191 17.60064255 0.00281573 -0.00008584 0.00181391 Pb 14.70905784 20.50971521 20.76729028 -0.00074676 0.00017890 -0.00226476 Br 14.59952743 20.41218412 17.88969732 -0.00380483 0.00111093 -0.00101980 Br 14.53246541 17.60670633 20.44834963 0.00122514 0.00311405 -0.00613024 Br 11.90613321 21.01472763 20.99242925 0.00409885 -0.00194761 -0.00040124 Cs 17.48088641 23.40734965 0.29409251 0.00468456 0.00023871 -0.00199510 Pb 20.83008798 2.97559480 2.81045278 -0.00146335 0.00143360 -0.00155425 Br 20.76825096 3.52652928 0.00210442 -0.00217107 0.00583176 -0.00292038 Br 21.07460756 0.10001616 3.30133687 -0.00069112 -0.00124869 0.00026374 Br 17.96467702 3.13564004 2.66666426 0.00533254 0.00124084 0.00219212 Cs 18.49366835 23.49945112 6.16764728 -0.00237847 0.00278086 0.00004586 Pb 20.87567718 3.14526212 8.94703024 0.00120689 0.00015970 0.00007516 Br 21.47684447 2.85485857 5.89268377 0.00219550 0.00052805 0.00236325 Br 21.07882897 0.06540296 8.70656436 0.00001988 -0.00106610 0.00259600 Br 18.08161521 2.45100031 8.55996001 -0.00245050 0.00098608 0.00046009 Cs 17.56879972 0.46474666 11.81951755 0.00116419 0.00097203 -0.00051163 Pb 20.86659995 2.93036711 14.61324431 -0.00091496 0.00118982 -0.00071870 Br 19.99017152 3.37936351 11.73274661 -0.00202134 0.00138261 -0.00086621 Br 21.14168654 23.74286930 14.81821957 0.00208557 0.00046821 0.00082266 Br 17.92301241 2.40120031 15.01473081 -0.00093300 0.00211555 0.00001759 Cs 17.86672067 0.06360515 18.38154102 0.00103790 -0.00054424 0.00072360 Pb 21.00708698 2.94860385 20.65465732 0.00264655 -0.00166635 0.00018777 Br 20.94414264 2.86987936 17.71188640 -0.00161665 0.00370754 0.00004565 Br 21.33689662 23.71132365 20.86116433 -0.00339893 -0.00365344 -0.00325843 Br 18.23982711 2.84666330 20.31727109 -0.00341227 -0.00077526 -0.00480542 Cs 17.69907679 6.13832427 23.60198081 -0.00099978 0.00216899 -0.00307514 Pb 20.76400716 8.96170825 3.05373289 0.00140117 0.00008301 -0.00004632 Br 20.53607504 9.51739243 23.71518166 -0.00212940 -0.00386767 0.00154055 Br 20.71811223 6.09453392 3.21727210 -0.00136118 0.00102431 0.00064869 Br 17.77538238 9.09418120 3.22894640 0.00169481 -0.00092697 0.00389581 Cs 18.28370694 5.37569022 6.39319326 0.00130925 -0.00060560 0.00108821 Pb 20.74321407 8.98681271 9.00895654 0.00218089 -0.00183736 -0.00170555 Br 21.04265503 9.02723776 6.10866170 -0.00145971 0.00510416 0.00153475 Br 21.29763101 5.99316174 8.87488258 0.00080424 -0.00102613 -0.00026370 Br 17.86134275 9.08011693 9.61047525 -0.00774474 -0.00097777 -0.00025563 Cs 17.40623997 6.12789845 11.54674206 -0.00012908 0.00310762 -0.00160860 Pb 20.85101788 8.88169277 14.83512549 0.00122981 0.00067335 0.00032459 Br 21.04262577 8.61640382 11.97930670 -0.00088872 -0.00040294 -0.00132693 Br 20.96745414 5.92366735 15.30557943 -0.00038843 -0.00239588 0.00171046 Br 17.75592210 8.79744963 14.68812291 0.00193222 -0.00309431 -0.00063558 Cs 18.04792231 5.79546380 17.47349221 -0.00207266 -0.00021193 0.00008735 Pb 20.47074960 9.03418914 20.70576105 0.00050780 0.00105475 -0.00129360 Br 21.36397732 8.80259141 18.04303759 0.00087416 -0.00086532 0.00065052 Br 20.48478481 6.02167761 20.90064368 -0.00058656 -0.00443081 -0.00124233 Br 17.65726574 9.64860332 20.59466704 -0.00164318 -0.00162761 -0.00162238 Cs 17.95034344 12.02561012 0.22086557 -0.00113770 -0.00061932 -0.00075178 Pb 20.36839536 14.84175169 2.94242804 -0.00012023 -0.00213149 0.00065796 Br 20.72749562 15.24140914 23.69015065 0.00057277 0.00139960 -0.00013607 Br 20.46633693 11.92830086 2.96270541 -0.00143457 -0.00423684 0.00268802 Br 17.56835611 15.12255488 2.93902180 0.00028397 -0.00077206 -0.00150838 Cs 17.63805913 11.23908311 6.30471287 0.00182755 -0.00197372 -0.00079531 Pb 20.58717517 14.70785465 8.78503242 0.00013485 0.00052212 0.00181379 Br 20.48584688 15.27147960 5.99145167 -0.00255585 -0.00255993 -0.00159397 Br 21.08858781 11.93910663 8.33518538 -0.00242864 -0.00178895 -0.00019429 Br 17.80577424 14.49237801 9.31293764 -0.00048208 -0.00043153 0.00022432 Cs 17.82343929 11.45404688 12.44462753 0.00078667 -0.00206445 -0.00087010 Pb 20.88230385 14.58453674 14.91440298 -0.00001996 -0.00045437 -0.00056408 Br 21.02216588 14.02585443 12.09478177 -0.00228639 -0.00017602 0.00085004 Br 20.40702059 11.65607238 15.13484218 -0.00036167 -0.00065584 0.00119340 Br 17.97134520 14.04949401 14.62606206 0.00209885 -0.00048538 0.00157708 Cs 17.48528128 12.13601008 17.76194608 -0.00055392 -0.00115623 -0.00193110 Pb 20.68759935 14.75260748 20.81999305 0.00195133 0.00217027 0.00075795 Br 21.50482875 14.68217420 17.74697442 -0.00284056 -0.00328326 0.00166138 Br 20.60312720 11.90290569 20.13255419 -0.00306914 0.00075886 -0.00266279 Br 17.57498219 15.14741587 20.75170723 0.00199195 -0.00076453 -0.00160845 Cs 17.88531353 17.75347206 23.41706994 0.00014208 -0.00029801 0.00018090 Pb 20.51502029 20.93418796 2.98123000 0.00217279 0.00143819 0.00206762 Br 21.21579174 21.04013453 0.00166689 0.00226032 0.00090223 -0.00135156 Br 20.55847240 17.86798989 3.02427065 0.00116812 0.00042605 0.00115081 Br 17.82219530 20.25296559 2.10800963 0.00567028 -0.00166904 0.00090715 Cs 17.57171310 17.99339638 5.91194593 0.00137927 0.00006258 -0.00239276 Pb 20.65913812 20.88235152 9.16747291 0.00203283 0.00161743 -0.00157701 Br 20.95549930 20.86017770 6.00541482 0.00154421 -0.00080551 0.00093363 Br 20.25240613 17.99586075 8.64842620 -0.00094163 -0.00216213 0.00004465 Br 17.86098508 20.90232570 9.50525703 -0.00083713 -0.00304700 0.00397788 Cs 17.89689434 18.08674815 11.33183485 -0.00079356 0.00027500 0.00207245 Pb 21.04609073 20.72240040 14.71756937 -0.00106203 0.00117925 0.00120226 Br 20.95689821 20.84265938 11.87623337 -0.00023546 -0.00032662 -0.00404513 Br 20.80452756 17.69353005 14.02182301 -0.00278053 -0.00386159 -0.00169657 Br 17.90124064 21.23384748 14.42977115 0.00166709 0.00333254 0.00229291 Cs 17.47928499 18.13135163 17.69241257 -0.00093170 -0.00045567 0.00267656 Pb 20.98383532 20.80071675 20.64573183 0.00061548 0.00020396 0.00163539 Br 20.41765908 21.08319819 17.89773998 0.00281197 -0.00253873 -0.00140639 Br 20.78988362 17.70234393 20.65741799 -0.00031065 0.00283773 -0.00286340 Br 17.69641897 20.50341683 20.79890727 0.00114420 0.00119584 0.00329611 320 Lattice="23.77195271 0.0 0.0 0.0 23.77195271 0.0 0.0 0.0 23.77195271" Properties=species:S:1:pos:R:3:vel:R:3 Time=20029.99999999 energy=-11425.67180252 virial="-12.73136128 2.67295283 0.44487392 2.67295283 -15.07985776 -1.14500227 0.44487392 -1.14500227 -7.18217677" stress="4.426e-05 0.00019897 3.312e-05 0.00019897 -0.00011308 -8.523e-05 3.312e-05 -8.523e-05 0.00034852" pbc="T T T" Cs 0.20304048 0.41030029 23.69461576 -0.00389101 -0.00264066 0.00084780 Pb 3.09142750 2.94666412 2.94690076 -0.00263936 0.00126901 0.00086329 Br 3.33949546 2.90082281 23.69161438 -0.00091374 0.00072751 0.00284848 Br 3.00536981 0.03623723 3.16923405 0.00004505 0.00264306 -0.00025780 Br 0.06611401 3.48451060 2.61814592 0.00232021 -0.00006633 -0.00250274 Cs 23.62332723 23.57483324 6.23711438 0.00309983 0.00198588 -0.00119865 Pb 2.92834094 2.98192924 9.34256418 0.00115111 -0.00086440 0.00019073 Br 2.33712917 2.86496550 6.04652031 0.00152269 -0.00142575 0.00267147 Br 3.15082136 0.00734050 9.08317377 0.00004463 -0.00103640 0.00306270 Br 23.70596523 2.91708767 9.84549502 -0.00160309 0.00346026 0.00601994 Cs 0.56178126 23.67683517 12.15746033 -0.00306107 -0.00080686 0.00090598 Pb 3.17253989 3.28645722 14.99942064 -0.00215352 0.00204047 -0.00045185 Br 4.26856753 3.10256857 11.95759854 0.00008273 -0.00031754 -0.00053942 Br 2.81713272 0.35679511 14.98606283 0.00262366 -0.00160228 -0.00312829 Br 0.25954831 3.56310029 14.74131992 0.00177614 -0.00224447 0.00140019 Cs 0.25668315 23.49981095 17.91789139 -0.00093237 -0.00198417 -0.00186726 Pb 3.07256994 3.08151363 20.64124112 0.00163530 -0.00034433 -0.00036290 Br 2.65129991 3.03187000 17.73494859 -0.00185916 0.00203537 0.00150070 Br 3.16334158 0.00177408 20.07170985 -0.00154626 0.00036275 0.00205030 Br 0.10202008 3.16568724 20.56118483 -0.00072220 -0.00021124 -0.00055801 Cs 23.48797725 6.22690882 0.00153926 -0.00016746 0.00254630 -0.00029654 Pb 2.85419225 8.91157956 3.13457265 -0.00011371 0.00065096 0.00341982 Br 2.56670137 8.98874418 23.62459510 0.00019848 -0.00461137 0.00008142 Br 3.33114104 6.03946355 3.20568493 -0.00249251 0.00109929 -0.00096494 Br 23.68018136 8.71950675 3.35038953 -0.00038485 -0.00203735 -0.00167308 Cs 0.23844208 5.94346889 5.79851482 0.00240821 0.00161712 0.00190231 Pb 3.13069717 8.98794509 8.96753990 0.00320937 0.00288143 -0.00017779 Br 2.88697582 9.09402176 5.86693977 -0.00042185 -0.00313941 -0.00057002 Br 2.93706804 6.10149822 8.62673593 0.00006841 -0.00219198 0.00072103 Br 0.13083590 8.97778564 8.94586255 -0.00267140 0.00360819 -0.00187912 Cs 23.72698600 6.12588573 11.81050828 -0.00159376 -0.00215090 0.00175873 Pb 2.98545507 8.79195051 14.99381871 -0.00168705 -0.00070431 -0.00256147 Br 3.13505470 8.78101085 11.81360194 0.00088817 0.00090137 -0.00315025 Br 3.81649707 5.99030126 15.05884237 -0.00191111 -0.00155131 0.00077074 Br 0.03828721 8.66112980 14.71834094 0.00016530 -0.00220746 -0.00267046 Cs 0.00350468 5.72631239 17.64055488 -0.00109905 0.00000436 -0.00083950 Pb 2.75302695 8.85083186 20.63846766 -0.00033743 0.00042956 -0.00137654 Br 1.99259092 8.77415032 17.81434573 0.00292332 -0.00023507 0.00012592 Br 2.12285002 5.94545674 20.65093406 -0.00245385 -0.00130361 0.00104601 Br 23.68901858 9.31152223 21.13857358 0.00177976 -0.00059064 0.00062336 Cs 23.01646944 11.83912075 0.22272444 0.00083409 -0.00125458 0.00059196 Pb 2.88475938 14.81567095 2.84320393 -0.00135973 0.00116312 0.00095443 Br 2.56472434 15.21395379 23.73780305 -0.00186454 -0.00077185 -0.00199667 Br 2.79401288 11.73623134 2.37949671 0.00253568 -0.00236566 -0.00116673 Br 23.45009519 14.56825619 3.36765061 -0.00309922 0.00103823 -0.00121161 Cs 23.48027247 11.85376070 6.45552252 -0.00163938 -0.00149663 0.00359706 Pb 2.93733237 15.05684916 8.96674401 0.00079974 -0.00066723 0.00225663 Br 3.43951068 14.98396162 5.99170396 -0.00118039 -0.00046023 -0.00018580 Br 2.95539877 11.74863332 8.69352884 0.00054302 -0.00190926 -0.00001257 Br 23.69236022 15.13177615 8.92225792 0.00045543 -0.00044460 -0.00067487 Cs 0.26040518 11.60814894 11.56791794 -0.00185605 0.00030720 -0.00196291 Pb 3.01416341 14.66204271 14.99539425 0.00107579 -0.00216036 0.00151404 Br 3.02547307 14.58461352 11.95530182 0.00147691 -0.00238343 -0.00272396 Br 2.84722752 11.86566550 15.26941259 -0.00397093 -0.00058241 0.00286074 Br 23.75244487 14.65137321 14.71587539 -0.00020992 0.00018027 -0.00321507 Cs 23.38089315 11.82942994 18.17402270 0.00090060 -0.00094497 0.00009778 Pb 2.98373850 14.73089555 20.88387045 0.00206024 -0.00297235 -0.00209759 Br 2.95160400 14.80514843 17.93073907 -0.00047895 0.00079749 0.00124086 Br 2.74985916 11.86048488 20.88421057 -0.00059480 -0.00172810 0.00101230 Br 23.74423130 14.45841643 21.04667950 0.00426265 0.00074176 -0.00123375 Cs 0.07996444 17.75754402 23.74808292 0.00032206 -0.00000095 -0.00147722 Pb 2.84654772 20.81983873 2.96320204 0.00219761 -0.00120314 -0.00047228 Br 2.88610908 20.68554079 23.35515800 0.00088375 0.00076222 0.00264037 Br 3.25645854 17.83943702 2.61440516 0.00007993 0.00225748 -0.00042228 Br 23.76368026 20.68813553 2.81700967 -0.00299236 0.00227701 0.00148156 Cs 23.22113758 17.72668237 6.43351361 -0.00142304 0.00106640 -0.00240769 Pb 2.74775841 20.94917671 8.81128091 0.00146049 0.00052400 -0.00068928 Br 2.86788304 20.94835783 5.79366605 0.00116164 -0.00201178 -0.00053753 Br 3.43449927 18.01832012 9.55573789 0.00051080 0.00118031 -0.00059300 Br 23.56140022 21.22305536 8.85775822 -0.00211948 -0.00144160 -0.00001433 Cs 0.28266795 17.08686046 12.43129161 0.00340374 -0.00009380 -0.00244843 Pb 3.13815766 21.07443178 14.60925080 -0.00086821 0.00069086 0.00072790 Br 3.49349235 20.77498947 11.87833219 -0.00302615 -0.00125918 -0.00169431 Br 3.06303237 18.02389061 14.74702242 -0.00022580 0.00121400 0.00465322 Br 0.11574307 20.38179948 14.98394962 0.00113156 0.00103785 -0.00013560 Cs 0.36020632 17.64513030 17.40086709 0.00066915 0.00047949 0.00104449 Pb 2.99734017 20.69281739 20.56961563 -0.00044547 0.00039346 -0.00071344 Br 3.74082127 20.76010425 17.59828732 -0.00148041 -0.00458247 -0.00120438 Br 3.09082197 17.86248064 20.86112599 -0.00011451 0.00044362 -0.00489451 Br 0.01219406 20.67179594 20.42174014 -0.00229570 -0.00036641 0.00268407 Cs 6.14826313 0.04736230 22.91456875 -0.00251970 0.00063986 -0.00102882 Pb 9.11172746 2.85421534 3.35570250 -0.00024083 0.00201275 -0.00067527 Br 9.19600087 2.41325192 0.07769264 -0.00168592 0.00186104 -0.00067201 Br 8.57141979 23.49349532 3.08175229 0.00216496 0.00257604 0.00401571 Br 6.08371869 2.80616895 3.44482389 -0.00255146 0.00266999 0.00125413 Cs 6.17585426 23.38622572 5.66354926 -0.00069351 0.00035731 0.00095307 Pb 8.81355159 2.77653275 8.80741901 -0.00006617 -0.00071778 0.00023878 Br 9.99072964 3.36931852 6.08197776 -0.00169196 0.00198447 -0.00193868 Br 8.27827106 23.47691344 8.55539630 0.00103475 -0.00524250 0.00342601 Br 5.72816337 3.49664772 8.66108330 -0.00061870 -0.00057540 -0.00276441 Cs 6.11575410 23.60368352 12.04489398 -0.00048362 0.00150375 0.00013180 Pb 9.03694771 2.84607057 14.65003451 0.00219291 0.00065456 -0.00047516 Br 8.94490186 2.79173462 11.76054573 0.00152773 0.00120847 0.00056659 Br 9.99283310 23.46842956 15.34262289 0.00240701 -0.00066533 -0.00064273 Br 6.18704650 2.37929052 15.37574653 0.00016541 -0.00071749 -0.00257894 Cs 6.57762134 23.60897343 17.60880072 -0.00123275 -0.00161254 0.00082285 Pb 9.24654405 2.99209886 21.10037246 -0.00083604 0.00085813 -0.00021960 Br 9.81280514 3.06219774 17.35231701 -0.00129107 -0.00176508 0.00147078 Br 9.19523630 0.20849334 20.40792819 -0.00131408 0.00088700 0.00344966 Br 6.20741164 3.84994089 20.73752595 -0.00114985 0.00213685 0.00014060 Cs 5.72461807 6.49847569 23.64003608 -0.00114412 -0.00284182 0.00060967 Pb 8.94253974 8.68663859 2.84578535 -0.00073882 0.00326538 -0.00090666 Br 8.81068722 8.77643817 23.69670975 0.00136753 0.00076326 -0.00160017 Br 8.43653572 5.61883656 2.75451464 -0.00340139 0.00239605 0.00173908 Br 5.88520459 8.86285584 2.66052171 -0.00026055 0.00048709 0.00072856 Cs 5.67736166 5.89717868 5.79161027 0.00304213 0.00347640 0.00069126 Pb 8.81541759 8.83471736 8.85711356 -0.00039272 -0.00000128 -0.00254610 Br 8.86591597 8.75222123 5.92542085 -0.00214569 -0.00197789 0.00116879 Br 8.90997103 5.70116319 8.95526164 -0.00246973 0.00231095 0.00244865 Br 5.96123056 7.95599086 8.58916105 -0.00085236 0.00110223 -0.00028533 Cs 5.79898875 6.25111475 11.79137034 0.00073846 -0.00142893 0.00170547 Pb 9.09061273 8.88555341 14.82379566 0.00034544 -0.00059675 0.00047477 Br 8.08311012 9.35753583 11.82020026 -0.00340557 -0.00473224 -0.00010498 Br 9.15351615 5.69355353 14.83061651 0.00345683 0.00054682 0.00420621 Br 5.87627177 9.50075381 15.26498423 0.00218781 -0.00423460 0.00190546 Cs 7.05094816 5.98855921 17.89577686 -0.00152161 0.00148576 -0.00057632 Pb 8.68980813 8.88903886 20.73625511 -0.00011686 0.00028667 -0.00057505 Br 9.18352184 9.38912188 17.70809037 0.00104816 0.00001013 0.00379340 Br 9.22957399 6.01488972 21.02047745 -0.00043307 0.00171633 0.00009463 Br 5.85129157 8.73999327 20.52120410 -0.00157027 -0.00171727 0.00142413 Cs 5.62832857 11.65358631 23.25433671 -0.00152533 -0.00144547 0.00081974 Pb 8.65838082 14.65691139 2.99767173 0.00154850 -0.00097575 -0.00002818 Br 9.25282857 14.91964101 0.22854461 0.00024969 -0.00343369 0.00359350 Br 8.85926525 11.62192123 2.88235216 0.00192137 -0.00237563 0.00211247 Br 5.64224652 14.82541559 2.57515595 -0.00170352 -0.00139726 0.00059299 Cs 5.08861713 11.86131070 4.87876838 0.00009939 0.00077758 -0.00095547 Pb 8.66231205 14.66122784 8.87317543 -0.00262706 -0.00221066 0.00097536 Br 7.87561619 14.24902399 6.12479246 0.00458460 -0.00301116 -0.00170306 Br 9.85822906 11.72730803 9.03513519 -0.00022555 0.00412520 -0.00277790 Br 5.89637747 14.73354228 9.42272937 0.00212660 -0.00489562 0.00050465 Cs 5.43290947 12.08118899 11.98735280 0.00015688 0.00001606 -0.00218134 Pb 8.80740794 14.95345911 14.86370241 -0.00137979 0.00142897 0.00124552 Br 9.79056935 15.02957701 11.69893646 -0.00125700 0.00213133 0.00019848 Br 9.38019187 11.87628073 14.96643777 0.00165894 -0.00090387 0.00254586 Br 6.15068762 14.58075930 14.76781293 0.00181052 0.00190597 -0.00596840 Cs 6.38146972 12.11286283 17.93122133 0.00012845 -0.00226107 -0.00180358 Pb 8.92852327 14.91789309 20.86442179 0.00012727 0.00015770 -0.00224996 Br 9.14408810 14.90351069 18.09030502 0.00345432 0.00020185 -0.00083245 Br 8.93574280 12.05054895 21.50571727 0.00057304 -0.00279047 -0.00033929 Br 5.96112372 14.61299778 21.16107189 0.00262604 0.00346300 0.00158458 Cs 5.63241066 18.27434366 0.00700799 -0.00293539 0.00326440 -0.00009381 Pb 8.84032424 20.72238702 2.97990537 -0.00089748 0.00046951 0.00123395 Br 8.89624989 20.61593225 23.70880315 -0.00017598 0.00176787 0.00355577 Br 8.47717207 17.57742472 3.06451637 -0.00212292 0.00319267 -0.00216744 Br 5.99575732 20.60446810 2.79941942 -0.00017666 -0.00032004 0.00131611 Cs 5.77209975 17.69583435 5.57089489 -0.00123338 0.00254009 -0.00063033 Pb 8.64991151 20.68781194 8.71409675 -0.00149410 -0.00060656 0.00134608 Br 8.58851332 20.39140303 5.94747841 -0.00155121 -0.00155176 -0.00064975 Br 8.09763661 17.56301425 8.65848341 -0.00174991 -0.00134883 -0.00317089 Br 5.82456956 20.75081811 8.84057751 -0.00113955 -0.00222378 -0.00165693 Cs 6.38365890 18.00525088 11.56181110 -0.00014915 0.00244039 -0.00272917 Pb 9.35427283 20.84281737 14.68481735 0.00086686 0.00050592 -0.00125012 Br 9.28642994 21.26989443 11.67179818 0.00279521 -0.00303289 0.00212652 Br 8.39278706 17.98922306 14.50118526 0.00015081 -0.00359242 0.00135173 Br 6.12680450 21.01855329 14.71166791 -0.00060930 0.00124869 0.00307927 Cs 5.86579237 17.44988434 18.06842140 -0.00059790 -0.00161078 -0.00117944 Pb 9.07499376 20.73874939 20.77574709 0.00085543 -0.00073877 -0.00027386 Br 9.36971711 20.58932482 17.90047258 0.00471049 0.00141405 -0.00401648 Br 8.24164675 17.84127219 20.71773621 -0.00633040 0.00280554 -0.00104974 Br 6.09788619 21.16766742 20.66448795 0.00086814 -0.00438836 -0.00117812 Cs 12.07939965 0.00095424 0.47797741 0.00086730 -0.00070423 -0.00105481 Pb 15.04701108 3.24364136 2.97506594 0.00019056 0.00135996 -0.00262217 Br 15.39937634 2.79788365 0.11419500 -0.00091000 0.00001544 0.00230438 Br 15.01842096 0.17388737 2.51217842 0.00526484 -0.00488038 0.00045579 Br 11.90750116 3.25375967 2.73176122 -0.00072497 0.00212953 -0.00216937 Cs 12.27117827 0.52651057 6.08445760 -0.00091645 0.00138111 -0.00350393 Pb 14.78061861 3.00732051 8.91451274 0.00021632 -0.00018052 -0.00083691 Br 14.96257595 2.79607062 5.95570107 -0.00273258 0.00439570 -0.00013381 Br 14.68030791 0.11606525 8.92434548 0.00256767 0.00037815 0.00085588 Br 11.82223293 3.04527177 9.55146764 0.00416694 0.00061006 0.00207543 Cs 11.95178832 23.65409611 11.92746693 -0.00070257 0.00411881 0.00029329 Pb 14.72353463 3.07872701 14.82119600 0.00079096 -0.00033445 0.00044510 Br 15.60448535 3.01376322 11.73271420 -0.00207967 -0.00313058 0.00090673 Br 14.44671945 23.58268195 15.15055903 -0.00275551 0.00115857 -0.00123413 Br 11.95831637 2.78117665 14.29652250 -0.00018380 -0.00032055 0.00031716 Cs 12.15060912 23.35798915 18.12876595 -0.00090566 -0.00176556 0.00296755 Pb 15.11889677 2.81749864 20.96220498 -0.00096941 0.00322349 -0.00070398 Br 15.22342434 2.87724590 17.71237392 -0.00236997 0.00158138 0.00284715 Br 14.84205129 23.69375983 21.21943350 0.00292307 -0.00141077 -0.00313580 Br 12.22533423 3.22845436 21.33126500 -0.00320789 -0.00527951 0.00247915 Cs 11.73950543 6.35516459 23.35400428 0.00019155 -0.00095774 -0.00128134 Pb 14.83438314 9.04271697 3.42288835 -0.00152277 -0.00121930 0.00094528 Br 14.71361647 9.29595711 0.12641118 -0.00002034 -0.00264073 0.00083713 Br 15.52471209 6.18930717 3.88350493 -0.00092221 0.00192835 -0.00237621 Br 11.96739635 8.77114635 2.73962508 -0.00219136 -0.00165739 -0.00138602 Cs 11.77206582 6.38156126 5.58526164 0.00183304 -0.00152826 -0.00037975 Pb 14.47016057 8.90923496 8.97654711 0.00002095 -0.00049505 0.00132840 Br 14.34322643 9.54086959 6.25275641 0.00062962 -0.00016365 0.00086454 Br 15.19671468 6.08579338 8.36896585 0.00115056 0.00200389 -0.00130210 Br 11.63525548 8.21214011 9.10067316 0.00104908 -0.00132280 -0.00140748 Cs 11.82501734 5.80580214 11.81092940 0.00207928 0.00176919 0.00019991 Pb 14.82819608 8.71851673 14.72596543 0.00055989 -0.00010413 -0.00048130 Br 15.12485976 8.96003375 11.92229713 -0.00129622 0.00194778 -0.00564607 Br 15.28086427 5.86385250 14.96600947 -0.00280619 0.00138797 -0.00038315 Br 11.87645364 8.37643280 14.55268512 0.00332282 0.00099587 0.00377676 Cs 12.43358454 5.81509236 18.23742383 0.00200009 -0.00098160 0.00088852 Pb 14.60232418 9.02739057 20.89600313 -0.00038526 0.00148358 -0.00011523 Br 14.75873082 8.96112637 18.04480342 0.00033290 -0.00246581 0.00080010 Br 15.42609962 5.87485851 21.00915697 0.00056752 -0.00240058 -0.00113719 Br 11.56329915 9.13592721 20.45682031 -0.00310181 -0.00052578 -0.00147587 Cs 11.53946449 11.83415046 0.55231779 -0.00339324 0.00039769 0.00161527 Pb 14.67572265 15.08783597 3.29212356 0.00118285 -0.00157869 -0.00088731 Br 14.74652098 15.81054236 0.07607208 0.00144945 -0.00160956 -0.00224116 Br 14.78231284 11.86487804 2.99963623 -0.00260275 -0.00019947 0.00137739 Br 11.63030376 15.27611580 3.85281606 0.00039843 -0.00166011 -0.00286673 Cs 11.58480826 12.47569209 5.92542912 -0.00101460 0.00045596 0.00092733 Pb 14.39503014 14.77284865 9.13506494 0.00100920 0.00154890 0.00265827 Br 15.27496891 14.69131891 6.10495142 -0.00258109 0.00124121 0.00116614 Br 14.25977780 11.93025130 9.05699377 -0.00454398 0.00370560 -0.00078523 Br 11.88178773 15.58097288 8.20086057 0.00196402 0.00029815 0.00075169 Cs 11.93513328 12.07420947 11.94834579 -0.00022795 0.00024015 -0.00042673 Pb 15.04157283 14.76267161 14.83334386 0.00045468 0.00253804 0.00081253 Br 14.47056034 14.96905369 11.94782588 0.00280439 -0.00145988 0.00197478 Br 14.46430083 11.88176242 14.81133014 -0.00082834 -0.00111475 0.00222441 Br 12.01055599 15.36376854 15.11403488 -0.00069865 -0.00039956 0.00165384 Cs 12.17305740 11.76629355 17.60180811 0.00162491 -0.00226807 -0.00210185 Pb 14.62370754 14.86424786 21.10773561 0.00184381 -0.00015947 0.00009997 Br 14.54049766 14.41486160 17.88342511 -0.00174881 0.00201209 -0.00238820 Br 14.14943685 11.84616899 20.77604578 0.00023393 0.00110422 0.00289466 Br 11.80753948 15.05098135 21.20622994 0.00229813 0.00167072 0.00356804 Cs 11.68850378 18.04671507 0.17981788 -0.00124549 0.00046374 0.00344606 Pb 14.66481148 20.79990297 2.84842492 0.00116971 0.00081904 0.00035739 Br 14.18330407 20.51635320 0.21289117 0.00453935 0.00272824 0.00113902 Br 14.61402052 18.05450800 3.44446153 -0.00109966 0.00287999 0.00119518 Br 11.85345094 21.28096499 3.35711270 -0.00261942 0.00143704 0.00172170 Cs 11.27629786 18.10892786 5.93680998 -0.00160121 0.00068872 0.00022302 Pb 14.67764664 20.89004435 8.74128240 0.00300203 0.00124064 0.00103215 Br 15.14479533 21.49742946 5.74321156 -0.00184249 -0.00720442 -0.00130482 Br 14.69556818 18.04402357 8.76445469 -0.00053357 -0.00416722 0.00029443 Br 11.86580400 20.86761626 8.45771009 -0.00197913 0.00448662 -0.00323823 Cs 12.23646008 18.33647802 11.68351756 0.00425037 0.00438524 0.00182595 Pb 14.93011878 20.60179952 14.95258436 0.00007085 -0.00195542 -0.00000690 Br 14.75377832 20.56303026 12.04546849 0.00016943 -0.00281041 -0.00010256 Br 15.89300674 17.60127262 14.62107746 -0.00259943 -0.00003064 -0.00081745 Br 11.91986504 19.77810151 14.74383670 -0.00048254 -0.00235987 -0.00104988 Cs 11.97303371 17.66344561 17.61995562 0.00275830 -0.00011829 0.00204343 Pb 14.70285021 20.51179409 20.74540323 -0.00047805 0.00024068 -0.00210724 Br 14.56121748 20.42380440 17.88049648 -0.00385110 0.00121462 -0.00083253 Br 14.54457638 17.64151543 20.38640996 0.00120071 0.00378861 -0.00624187 Br 11.94713165 20.99508361 20.98882967 0.00404302 -0.00197781 -0.00031026 Cs 17.52873056 23.40981682 0.27315065 0.00488243 0.00025015 -0.00219413 Pb 20.81667640 2.99089553 2.79573533 -0.00120190 0.00162481 -0.00139454 Br 20.74705884 3.58382192 23.74330156 -0.00206673 0.00561734 -0.00319645 Br 21.06696040 0.08659889 3.30301946 -0.00083996 -0.00140178 0.00007580 Br 18.01587705 3.14769413 2.69005847 0.00484689 0.00117072 0.00248657 Cs 18.46931562 23.52768686 6.16814551 -0.00248806 0.00286070 0.00005364 Pb 20.88828918 3.14540631 8.94733413 0.00131043 -0.00013295 -0.00001317 Br 21.49728270 2.86148281 5.91751002 0.00188914 0.00079451 0.00258681 Br 21.07754828 0.05575982 8.73412947 -0.00027546 -0.00085108 0.00290821 Br 18.05339780 2.46193041 8.56418880 -0.00317058 0.00119954 0.00038495 Cs 17.58229228 0.47190079 11.81485482 0.00153719 0.00045636 -0.00042162 Pb 20.85683221 2.94216645 14.60693213 -0.00104020 0.00116949 -0.00054271 Br 19.97057096 3.39337156 11.72523228 -0.00189729 0.00141989 -0.00063267 Br 21.16411317 23.74685303 14.82569650 0.00239719 0.00031899 0.00067324 Br 17.91453985 2.42368122 15.01439635 -0.00075981 0.00237786 -0.00008342 Cs 17.87727437 0.05751710 18.38743109 0.00107194 -0.00067823 0.00045346 Pb 21.03324035 2.93257243 20.65679266 0.00255789 -0.00153704 0.00023553 Br 20.92890090 2.90672857 17.71174999 -0.00142799 0.00365537 -0.00007661 Br 21.30268502 23.67645996 20.82835529 -0.00343683 -0.00328876 -0.00330100 Br 18.20025733 2.83938894 20.27098994 -0.00445410 -0.00066929 -0.00443491 Cs 17.69009967 6.15998115 23.57193450 -0.00079134 0.00216413 -0.00293200 Pb 20.77774083 8.96214196 3.05336218 0.00133963 0.00000201 -0.00002861 Br 20.51386420 9.47657238 23.73246442 -0.00230476 -0.00428649 0.00190237 Br 20.70406497 6.10296044 3.22366517 -0.00144591 0.00063641 0.00063328 Br 17.79326014 9.08557902 3.26798989 0.00185576 -0.00079537 0.00390972 Cs 18.29678163 5.36987355 6.40292676 0.00130361 -0.00055465 0.00085771 Pb 20.76555577 8.96793400 8.99198014 0.00227399 -0.00193547 -0.00168455 Br 21.02647407 9.07763486 6.12190760 -0.00176904 0.00496156 0.00109725 Br 21.30454956 5.98525126 8.87149097 0.00058392 -0.00055417 -0.00041713 Br 17.78444656 9.07094039 9.60495227 -0.00758592 -0.00085064 -0.00086698 Cs 17.40572065 6.15869709 11.53109334 0.00002160 0.00304319 -0.00151979 Pb 20.86364789 8.88711137 14.83951704 0.00129095 0.00040669 0.00054906 Br 21.03335911 8.61335178 11.96558757 -0.00096429 -0.00020701 -0.00139809 Br 20.96248872 5.89902319 15.32123507 -0.00060256 -0.00251499 0.00141392 Br 17.77589078 8.76554704 14.68292712 0.00205651 -0.00328261 -0.00040837 Cs 18.02740819 5.79287510 17.47522715 -0.00202937 -0.00030783 0.00026047 Pb 20.47675171 9.04388917 20.69343870 0.00068821 0.00088239 -0.00116581 Br 21.37189452 8.79330767 18.04554007 0.00071289 -0.00099042 -0.00015598 Br 20.47904681 5.97727011 20.88805497 -0.00055836 -0.00442065 -0.00128249 Br 17.63903136 9.63161554 20.57905388 -0.00198895 -0.00177231 -0.00149775 Cs 17.93806168 12.01909636 0.21323309 -0.00131668 -0.00068476 -0.00077286 Pb 20.36941823 14.82173968 2.94959071 0.00032516 -0.00186966 0.00077579 Br 20.73288020 15.25402306 23.69030964 0.00050631 0.00112418 0.00016937 Br 20.45157921 11.88660665 2.99024699 -0.00151576 -0.00406075 0.00281523 Br 17.56932526 15.11479546 2.92330811 -0.00009625 -0.00077853 -0.00163538 Cs 17.65658034 11.22015541 6.29683646 0.00187452 -0.00181087 -0.00077723 Pb 20.58819193 14.71429681 8.80422794 0.00006153 0.00076443 0.00201807 Br 20.45963517 15.24463980 5.97489262 -0.00268078 -0.00280953 -0.00169918 Br 21.05882482 11.92058844 8.33813893 -0.00353416 -0.00189200 0.00080021 Br 17.79947267 14.48785544 9.31507103 -0.00076263 -0.00047258 0.00020337 Cs 17.83166794 11.43392725 12.43461373 0.00085812 -0.00195524 -0.00112961 Pb 20.88111681 14.58109630 14.90798620 -0.00021710 -0.00022859 -0.00071411 Br 20.99996677 14.02370552 12.09986809 -0.00215145 -0.00025917 0.00015449 Br 20.40514060 11.65055384 15.14780293 -0.00001220 -0.00046059 0.00139730 Br 17.98828434 14.04691914 14.64320203 0.00127219 -0.00003304 0.00184459 Cs 17.47972953 12.12421414 17.74308256 -0.00055716 -0.00120103 -0.00184099 Pb 20.70753644 14.77515869 20.82666779 0.00203527 0.00233690 0.00057282 Br 21.47520255 14.65174006 17.76598256 -0.00307860 -0.00279923 0.00212610 Br 20.57073331 11.91119053 20.10769854 -0.00340390 0.00089010 -0.00230163 Br 17.59769303 15.13894571 20.73536335 0.00254878 -0.00092702 -0.00165895 Cs 17.88708416 17.75026586 23.41856193 0.00021223 -0.00034060 0.00011798 Pb 20.53814833 20.94740476 3.00114491 0.00245528 0.00120009 0.00191472 Br 21.23754278 21.04878620 23.76091733 0.00209075 0.00083021 -0.00119011 Br 20.57087605 17.87266217 3.03589424 0.00131507 0.00048559 0.00117580 Br 17.87635380 20.23830241 2.11846388 0.00513553 -0.00125873 0.00117904 Cs 17.58584676 17.99381975 5.88797182 0.00144631 0.00002173 -0.00240086 Pb 20.67943595 20.89847775 9.14927209 0.00201093 0.00159989 -0.00206641 Br 20.97003282 20.85152492 6.01277756 0.00135814 -0.00091704 0.00053275 Br 20.24391069 17.97247891 8.64922891 -0.00075452 -0.00250812 0.00011694 Br 17.84808947 20.87517778 9.54302466 -0.00173392 -0.00237068 0.00356302 Cs 17.88869858 18.08833159 11.35424916 -0.00084245 0.00003492 0.00240820 Pb 21.03373432 20.73408507 14.73012638 -0.00140475 0.00115693 0.00129991 Br 20.95611510 20.83995528 11.84013280 0.00008214 -0.00021770 -0.00311668 Br 20.77680880 17.65501054 14.00659427 -0.00275500 -0.00382639 -0.00134901 Br 17.91695626 21.26608030 14.45394631 0.00147258 0.00310901 0.00253977 Cs 17.47088559 18.12655140 17.72004908 -0.00074974 -0.00050412 0.00284867 Pb 20.98914206 20.80239255 20.66325704 0.00043347 0.00012606 0.00186736 Br 20.44589231 21.05737954 17.88123181 0.00283568 -0.00262486 -0.00189510 Br 20.78706643 17.73141359 20.62862560 -0.00025290 0.00294580 -0.00289198 Br 17.70785472 20.51579872 20.83218111 0.00114420 0.00128011 0.00335290 320 Lattice="23.77195271 0.0 0.0 0.0 23.77195271 0.0 0.0 0.0 23.77195271" Properties=species:S:1:pos:R:3:vel:R:3 Time=20039.99999999 energy=-11425.56390381 virial="-13.94363288 2.74813405 0.09209339 2.74813405 -15.29530829 -1.29072658 0.09209339 -1.29072658 -6.94949898" stress="-4.894e-05 0.00020457 6.86e-06 0.00020457 -0.00014556 -9.608e-05 6.86e-06 -9.608e-05 0.00036917" pbc="T T T" Cs 0.16399728 0.38357848 23.70331563 -0.00391511 -0.00270376 0.00089335 Pb 3.06466936 2.95962760 2.95647138 -0.00270561 0.00132428 0.00104843 Br 3.33030108 2.90771952 23.72012187 -0.00092338 0.00065098 0.00283708 Br 3.00558015 0.06180280 3.16589780 -0.00000501 0.00244600 -0.00040686 Br 0.09042448 3.48332825 2.59233980 0.00252251 -0.00016802 -0.00265434 Cs 23.65483045 23.59546833 6.22416135 0.00319824 0.00213720 -0.00138795 Pb 2.94038615 2.97420082 9.34309698 0.00125153 -0.00067722 -0.00008625 Br 2.35344185 2.85086628 6.07341458 0.00172998 -0.00139411 0.00271193 Br 3.15124943 23.77019412 9.11425324 0.00004397 -0.00077915 0.00315112 Br 23.69101165 2.95125729 9.90680736 -0.00138471 0.00336720 0.00623452 Cs 0.53049038 23.66882026 12.16621048 -0.00319588 -0.00079740 0.00084419 Pb 3.15132806 3.30473722 14.99371793 -0.00208315 0.00159903 -0.00068243 Br 4.26699036 3.09837423 11.95297854 -0.00039417 -0.00051788 -0.00038207 Br 2.84284132 0.33753929 14.95416111 0.00251615 -0.00223042 -0.00324562 Br 0.27654250 3.54033395 14.75349646 0.00160214 -0.00230291 0.00103269 Cs 0.24712856 23.48030265 17.89887118 -0.00097790 -0.00191784 -0.00193646 Pb 3.08935050 3.07824720 20.63778123 0.00171805 -0.00030361 -0.00032406 Br 2.63315534 3.05270842 17.75141913 -0.00176295 0.00212678 0.00177320 Br 3.14791336 0.00732067 20.09411523 -0.00153630 0.00073792 0.00242234 Br 0.09742188 3.16168160 20.55650202 -0.00020077 -0.00059430 -0.00037916 Cs 23.48633110 6.25258130 23.77017077 -0.00016023 0.00258694 -0.00036736 Pb 2.85292760 8.91743652 3.16618009 -0.00013811 0.00051397 0.00288037 Br 2.57049406 8.94099737 23.62655898 0.00056013 -0.00493128 0.00030859 Br 3.30536864 6.04857845 3.19497940 -0.00265365 0.00072548 -0.00116572 Br 23.67719827 8.69973405 3.33267905 -0.00020253 -0.00191578 -0.00186342 Cs 0.26240290 5.95922017 5.81764778 0.00238480 0.00153124 0.00192399 Pb 3.16287971 9.01472683 8.96485510 0.00322008 0.00245177 -0.00036436 Br 2.88289910 9.06322481 5.86474972 -0.00039286 -0.00301205 0.00017826 Br 2.93537743 6.07578592 8.63481744 -0.00040380 -0.00292243 0.00090062 Br 0.10248345 9.01416397 8.92770615 -0.00298873 0.00365997 -0.00175038 Cs 23.71117335 6.10431004 11.82808690 -0.00156956 -0.00216210 0.00175563 Pb 2.96883377 8.78572487 14.96854287 -0.00163134 -0.00054126 -0.00248908 Br 3.14348872 8.79098137 11.78609262 0.00079864 0.00109224 -0.00232975 Br 3.79901994 5.97900104 15.06717332 -0.00158136 -0.00067837 0.00089480 Br 0.03997699 8.63928167 14.69027176 0.00018271 -0.00216260 -0.00293717 Cs 23.76394315 5.72656796 17.63258599 -0.00120155 0.00004641 -0.00075035 Pb 2.74847828 8.85504541 20.62555357 -0.00056480 0.00040384 -0.00119840 Br 2.02348292 8.77248080 17.81390518 0.00324550 -0.00010085 -0.00023769 Br 2.10075653 5.93248335 20.66248421 -0.00196014 -0.00128460 0.00126004 Br 23.70412457 9.30553556 21.14455919 0.00121613 -0.00060085 0.00057179 Cs 23.02649640 11.82717090 0.22838118 0.00116614 -0.00113951 0.00054031 Pb 2.86933315 14.82769146 2.85283655 -0.00171922 0.00123899 0.00096536 Br 2.54686602 15.20525618 23.71968832 -0.00170124 -0.00096596 -0.00160950 Br 2.81853624 11.71655105 2.36707929 0.00236229 -0.00154731 -0.00132011 Br 23.42008342 14.57892447 3.35510972 -0.00289172 0.00109170 -0.00129549 Cs 23.46759488 11.83874818 6.48816336 -0.00090113 -0.00150174 0.00293453 Pb 2.94588732 15.05066543 8.98905895 0.00090779 -0.00056500 0.00220013 Br 3.42648627 14.97915573 5.98986613 -0.00142111 -0.00049983 -0.00018662 Br 2.96056778 11.73758391 8.69389083 0.00049086 -0.00027224 0.00008088 Br 23.69813402 15.12682934 8.91590805 0.00070294 -0.00054562 -0.00059544 Cs 0.24153308 11.61144962 11.54871497 -0.00192028 0.00035389 -0.00187718 Pb 3.02455372 14.64123035 15.01028825 0.00099872 -0.00199767 0.00146146 Br 3.03865267 14.56099305 11.92831741 0.00115369 -0.00233694 -0.00265156 Br 2.80758171 11.85962651 15.29696177 -0.00395113 -0.00063333 0.00264935 Br 23.75511990 14.65173043 14.68531783 0.00075021 -0.00010911 -0.00289085 Cs 23.39170761 11.81925434 18.17469854 0.00125690 -0.00108711 0.00004029 Pb 3.00370435 14.70142615 20.86327559 0.00193183 -0.00291568 -0.00201097 Br 2.94713319 14.81370934 17.94159882 -0.00041475 0.00091047 0.00090706 Br 2.74537103 11.84314978 20.89368346 -0.00030239 -0.00173396 0.00088030 Br 0.01294769 14.46674827 21.03397768 0.00385834 0.00092293 -0.00131005 Cs 0.08264702 17.75825726 23.73337969 0.00021316 0.00014468 -0.00146336 Pb 2.86832915 20.80784737 2.95736016 0.00215006 -0.00118624 -0.00069594 Br 2.89461479 20.69459795 23.38555366 0.00081602 0.00104726 0.00340138 Br 3.25572979 17.86115579 2.61171025 -0.00022899 0.00207009 -0.00011544 Br 23.73347769 20.71078100 2.83233889 -0.00301672 0.00225007 0.00158485 Cs 23.20655694 17.73778245 6.40855745 -0.00149491 0.00115024 -0.00258201 Pb 2.76315249 20.95342396 8.80478275 0.00161252 0.00032215 -0.00060735 Br 2.87979865 20.92769148 5.79089587 0.00122181 -0.00211941 -0.00002293 Br 3.43669856 18.02607604 9.54751535 -0.00007112 0.00035512 -0.00105082 Br 23.54537170 21.20693878 8.85853259 -0.00106920 -0.00177754 0.00016248 Cs 0.31651066 17.08738735 12.40555793 0.00336234 0.00020207 -0.00269464 Pb 3.13031309 21.08048916 14.61854148 -0.00069856 0.00051976 0.00112034 Br 3.46213829 20.76388107 11.85786433 -0.00324247 -0.00096079 -0.00237291 Br 3.06087583 18.03372761 14.79375910 -0.00020491 0.00073836 0.00468510 Br 0.12708745 20.39310297 14.98235048 0.00111777 0.00122289 -0.00018327 Cs 0.36678028 17.64978920 17.41184653 0.00064447 0.00045403 0.00115086 Pb 2.99367754 20.69781878 20.56146290 -0.00028837 0.00060265 -0.00090822 Br 3.72463483 20.71369465 17.58659642 -0.00176056 -0.00468930 -0.00111265 Br 3.08987289 17.86440010 20.81116681 -0.00007736 -0.00005522 -0.00509090 Br 23.76363075 20.66737412 20.44755231 -0.00177288 -0.00051788 0.00247647 Cs 6.12284709 0.05432950 22.90493878 -0.00256318 0.00075100 -0.00089845 Pb 9.10829828 2.87446416 3.34806615 -0.00045186 0.00203702 -0.00085626 Br 9.17845622 2.43218260 0.07110288 -0.00182301 0.00192220 -0.00064143 Br 8.59418222 23.52223799 3.12162478 0.00238538 0.00316267 0.00396077 Br 6.05719743 2.83234198 3.45803324 -0.00273305 0.00256213 0.00138924 Cs 6.16754755 23.38987915 5.67306048 -0.00096612 0.00037471 0.00094867 Pb 8.81238505 2.77063110 8.81016834 -0.00016608 -0.00045755 0.00030988 Br 9.97317373 3.38801242 6.06272733 -0.00181951 0.00175236 -0.00188543 Br 8.28944571 23.42893450 8.58945192 0.00119125 -0.00429883 0.00338172 Br 5.72478015 3.48914982 8.63243474 -0.00004530 -0.00092995 -0.00295826 Cs 6.11091804 23.61880014 12.04608864 -0.00048555 0.00151681 0.00011132 Pb 9.05841922 2.85199213 14.64639848 0.00209683 0.00052951 -0.00024269 Br 8.95888245 2.80313082 11.76338063 0.00126868 0.00107163 -0.00001458 Br 10.01653290 23.46481930 15.33529002 0.00233140 -0.00006012 -0.00082797 Br 6.18757608 2.37373743 15.34928762 -0.00005072 -0.00039819 -0.00270879 Cs 6.56502633 23.59168624 17.61757170 -0.00128479 -0.00183923 0.00092584 Pb 9.23770038 3.00071802 21.09756487 -0.00093331 0.00086901 -0.00034318 Br 9.79995016 3.04514730 17.37272495 -0.00128190 -0.00164699 0.00260020 Br 9.18163711 0.21468140 20.44267567 -0.00140697 0.00033852 0.00349648 Br 6.19495826 3.86961822 20.73930957 -0.00134633 0.00179752 0.00021368 Cs 5.71275550 6.46939751 23.64617712 -0.00122760 -0.00297137 0.00061986 Pb 8.93419580 8.71891687 2.83741974 -0.00093031 0.00317709 -0.00076956 Br 8.82447651 8.78412189 23.68050976 0.00138839 0.00077285 -0.00161468 Br 8.40391379 5.64422267 2.77188038 -0.00312023 0.00267174 0.00173359 Br 5.88364116 8.86870015 2.66832403 -0.00005576 0.00068255 0.00083084 Cs 5.70812932 5.93149112 5.79860061 0.00310663 0.00338240 0.00069704 Pb 8.81070533 8.83482439 8.83226675 -0.00054274 0.00002209 -0.00241681 Br 8.84508845 8.73212607 5.93573362 -0.00201919 -0.00204125 0.00088292 Br 8.88565172 5.72433225 8.98034170 -0.00239179 0.00230069 0.00256296 Br 5.95295766 7.96959603 8.58694384 -0.00079976 0.00162086 -0.00015221 Cs 5.80712024 6.23692953 11.80801145 0.00088706 -0.00140690 0.00162064 Pb 9.09256314 8.88030281 14.82810717 0.00004922 -0.00045093 0.00039272 Br 8.05006296 9.30917439 11.82011347 -0.00319875 -0.00493342 0.00009104 Br 9.18717353 5.70216914 14.87162729 0.00327535 0.00117400 0.00399278 Br 5.89874389 9.45770261 15.28398746 0.00229377 -0.00437552 0.00189237 Cs 7.03534945 6.00301737 17.88956632 -0.00159711 0.00140670 -0.00066335 Pb 8.68986979 8.89308946 20.73061901 0.00011917 0.00052397 -0.00055112 Br 9.19266535 9.38755510 17.74590950 0.00077342 -0.00032243 0.00373070 Br 9.22486442 6.03335675 21.02001236 -0.00050663 0.00195533 -0.00018915 Br 5.83364156 8.72262024 20.53510196 -0.00195350 -0.00175650 0.00135355 Cs 5.61289967 11.63886916 23.26319377 -0.00155886 -0.00149851 0.00094991 Pb 8.67404545 14.64784374 2.99782954 0.00158087 -0.00083063 0.00006928 Br 9.25533666 14.88555209 0.26526008 0.00025654 -0.00338237 0.00370512 Br 8.87819887 11.59895979 2.90392241 0.00185876 -0.00218129 0.00220559 Br 5.62903931 14.81030174 2.58065650 -0.00094712 -0.00162277 0.00050811 Cs 5.09133909 11.86912456 4.87067146 0.00044635 0.00078454 -0.00066227 Pb 8.63751080 14.63942304 8.88331709 -0.00231383 -0.00214512 0.00104938 Br 7.92195632 14.21893158 6.10547838 0.00467424 -0.00300680 -0.00214597 Br 9.85488057 11.76892130 9.00675064 -0.00044777 0.00417188 -0.00288570 Br 5.91555205 14.68353936 9.42914060 0.00166930 -0.00510059 0.00077867 Cs 5.43538787 12.08002177 11.96576813 0.00034295 -0.00025394 -0.00213352 Pb 8.79765422 14.96848659 14.87563186 -0.00055754 0.00157813 0.00113690 Br 9.77578468 15.05097263 11.70245834 -0.00170075 0.00214199 0.00050443 Br 9.39654388 11.86809639 14.99109417 0.00160723 -0.00074468 0.00238232 Br 6.16158915 14.59850602 14.70833989 0.00033327 0.00163448 -0.00592867 Cs 6.38279454 12.08994342 17.91304294 0.00013962 -0.00232434 -0.00183125 Pb 8.92966685 14.91939663 20.84358696 0.00010689 0.00014090 -0.00191421 Br 9.17769658 14.90497025 18.07623038 0.00326245 0.00009088 -0.00196023 Br 8.94068697 12.02017029 21.50089896 0.00041266 -0.00324409 -0.00062726 Br 5.98591249 14.64755958 21.17621446 0.00233358 0.00344113 0.00144621 Cs 5.60369054 18.30654258 0.00532604 -0.00280389 0.00317292 -0.00024508 Pb 8.83162139 20.72621851 2.99176019 -0.00083759 0.00030161 0.00113837 Br 8.89480514 20.63396522 23.74573928 -0.00011036 0.00183968 0.00378314 Br 8.45675131 17.60962918 3.04283883 -0.00195703 0.00321991 -0.00216916 Br 5.99216034 20.60209510 2.81317155 -0.00056099 -0.00014799 0.00143355 Cs 5.75988170 17.72145578 5.56478815 -0.00120941 0.00258228 -0.00059108 Pb 8.63558617 20.68083909 8.72892007 -0.00136033 -0.00080328 0.00160824 Br 8.57199070 20.37653309 5.93761298 -0.00175677 -0.00141491 -0.00128565 Br 8.08252711 17.54964598 8.62512766 -0.00128069 -0.00132776 -0.00349690 Br 5.81173705 20.72911854 8.82352885 -0.00143076 -0.00211167 -0.00175266 Cs 6.38135986 18.02995767 11.53567232 -0.00030487 0.00249916 -0.00249422 Pb 9.36061464 20.84720070 14.67257040 0.00040265 0.00036796 -0.00119350 Br 9.31410805 21.23889185 11.69466938 0.00273856 -0.00316155 0.00242671 Br 8.39493665 17.94981712 14.51575358 0.00027270 -0.00426596 0.00155851 Br 6.12109409 21.03250251 14.74182430 -0.00052540 0.00153846 0.00294304 Cs 5.85956366 17.43457590 18.05620170 -0.00064987 -0.00144922 -0.00126718 Pb 9.08187966 20.73130891 20.77237777 0.00052535 -0.00074935 -0.00039968 Br 9.41695643 20.60338160 17.85847416 0.00472982 0.00139731 -0.00433532 Br 8.17978058 17.87026490 20.70825634 -0.00602714 0.00296929 -0.00084251 Br 6.10640938 21.12300407 20.65273121 0.00083206 -0.00452988 -0.00116941 Cs 12.08777966 23.76609288 0.46700076 0.00081078 -0.00065837 -0.00113801 Pb 15.04885090 3.25703650 2.94942607 0.00017571 0.00131817 -0.00249865 Br 15.39019830 2.79948497 0.13415718 -0.00092035 0.00029995 0.00168097 Br 15.06974490 0.12569406 2.51925795 0.00499926 -0.00472964 0.00095406 Br 11.90320976 3.27515870 2.70978296 -0.00012448 0.00214897 -0.00222398 Cs 12.26195029 0.53919164 6.04915302 -0.00092837 0.00115275 -0.00355555 Pb 14.78306169 3.00583190 8.90606412 0.00027768 -0.00011234 -0.00084806 Br 14.93559455 2.84003614 5.95376938 -0.00266584 0.00439271 -0.00026125 Br 14.70561234 0.12049940 8.93391311 0.00249145 0.00049131 0.00104802 Br 11.86572675 3.04998142 9.57059059 0.00449819 0.00033446 0.00174450 Cs 11.94523223 23.69565113 11.93062765 -0.00060830 0.00418875 0.00033835 Pb 14.73325253 3.07298992 14.82604347 0.00114917 -0.00081177 0.00052473 Br 15.58007204 2.98591301 11.74295185 -0.00280015 -0.00243499 0.00114389 Br 14.41952317 23.59288420 15.13718886 -0.00267725 0.00087739 -0.00143924 Br 11.95759049 2.77960151 14.29799128 0.00004833 0.00001035 -0.00002125 Cs 12.14163214 23.34102968 18.15826789 -0.00088957 -0.00162206 0.00292929 Pb 15.10983701 2.85030941 20.95467080 -0.00083952 0.00332589 -0.00080635 Br 15.19766306 2.89370194 17.74222950 -0.00278314 0.00170990 0.00311225 Br 14.87153606 23.67846890 21.18734786 0.00297307 -0.00162687 -0.00327924 Br 12.19274903 3.17565763 21.35555247 -0.00329799 -0.00527461 0.00237871 Cs 11.74226748 6.34567944 23.34219141 0.00035905 -0.00093879 -0.00108046 Pb 14.81966880 9.03016147 3.43097538 -0.00141726 -0.00128404 0.00067325 Br 14.71363487 9.26837744 0.13499367 0.00002607 -0.00287450 0.00087597 Br 15.51433021 6.20572411 3.85914942 -0.00115015 0.00133267 -0.00249735 Br 11.94317015 8.75379967 2.72569705 -0.00263376 -0.00181119 -0.00139568 Cs 11.79082680 6.36637959 5.58185193 0.00191991 -0.00150460 -0.00030279 Pb 14.47081652 8.90404485 8.99039153 0.00011423 -0.00053649 0.00142937 Br 14.34895968 9.53970320 6.25806967 0.00051790 -0.00006719 0.00018415 Br 15.20780141 6.10516157 8.35688751 0.00106547 0.00186022 -0.00111282 Br 11.64872972 8.19870693 9.08688344 0.00161000 -0.00136398 -0.00135019 Cs 11.84514425 5.82322802 11.81324369 0.00194763 0.00171687 0.00026403 Pb 14.83395912 8.71866154 14.72239904 0.00059553 0.00013906 -0.00024213 Br 15.10968600 8.97910685 11.86771215 -0.00174533 0.00186421 -0.00518773 Br 15.25210677 5.87885526 14.96165610 -0.00294431 0.00159296 -0.00048658 Br 11.91055985 8.38663682 14.59013770 0.00347513 0.00104429 0.00371362 Cs 12.45352534 5.80509303 18.24619594 0.00198502 -0.00101868 0.00086462 Pb 14.59916064 9.04245164 20.89546063 -0.00025100 0.00152680 0.00000765 Br 14.76181464 8.93575349 18.04848627 0.00028274 -0.00260646 -0.00006779 Br 15.42953908 5.85120041 20.99656125 0.00011568 -0.00231983 -0.00138228 Br 11.53565449 9.12918062 20.44336836 -0.00239116 -0.00081893 -0.00121104 Cs 11.50626759 11.83817651 0.56731798 -0.00324143 0.00040583 0.00138247 Pb 14.68719714 15.07172595 3.28333571 0.00111051 -0.00163940 -0.00086127 Br 14.76077633 15.79360077 0.05575881 0.00140306 -0.00177281 -0.00181686 Br 14.75590072 11.86711603 3.01359113 -0.00267837 0.00064847 0.00140973 Br 11.63390840 15.25864958 3.82219679 0.00031765 -0.00182702 -0.00325495 Cs 11.57497430 12.47970229 5.93523639 -0.00094702 0.00034946 0.00103352 Pb 14.40670373 14.78917459 9.16013884 0.00132294 0.00171668 0.00235047 Br 15.24869786 14.70382240 6.11497484 -0.00266973 0.00125954 0.00081858 Br 14.21379944 11.96567538 9.04960234 -0.00464924 0.00335268 -0.00069603 Br 11.89828610 15.58233683 8.20898513 0.00132645 -0.00002384 0.00086963 Cs 11.93256738 12.07670123 11.94470566 -0.00028502 0.00026096 -0.00030195 Pb 15.04543123 14.78850812 14.84237963 0.00031395 0.00261624 0.00099352 Br 14.49804051 14.95450232 11.96922017 0.00268892 -0.00144887 0.00230993 Br 14.45671373 11.86769718 14.83256660 -0.00068015 -0.00168506 0.00201782 Br 12.00426226 15.35832175 15.12906684 -0.00055328 -0.00068676 0.00134963 Cs 12.18869616 11.74373519 17.58113699 0.00150085 -0.00224305 -0.00202974 Pb 14.64277085 14.86198242 21.10793018 0.00196567 -0.00028280 -0.00006788 Br 14.52454647 14.43645081 17.85981948 -0.00143825 0.00229985 -0.00231664 Br 14.15256713 11.85725770 20.80589320 0.00039109 0.00111495 0.00307042 Br 11.82919570 15.06850946 21.24192188 0.00202067 0.00183540 0.00357024 Cs 11.67556635 18.05038195 0.21421983 -0.00133601 0.00027470 0.00343729 Pb 14.67778051 20.80950450 2.85417866 0.00141602 0.00110078 0.00079122 Br 14.22853620 20.54331891 0.21673560 0.00449498 0.00265403 -0.00038682 Br 14.60238239 18.07977562 3.45741371 -0.00122753 0.00213988 0.00138987 Br 11.82742406 21.29488278 3.37440161 -0.00255778 0.00134481 0.00172865 Cs 11.26094960 18.11613788 5.93880608 -0.00146495 0.00074825 0.00017712 Pb 14.70867900 20.90235887 8.75149261 0.00318941 0.00120769 0.00100693 Br 15.12706146 21.42511948 5.73422964 -0.00169522 -0.00724326 -0.00046432 Br 14.69126626 18.00112145 8.76729084 -0.00032223 -0.00435606 0.00027697 Br 11.84516805 20.91284362 8.42537639 -0.00211271 0.00455668 -0.00322042 Cs 12.27781804 18.37816350 11.70062793 0.00400810 0.00393506 0.00158381 Pb 14.93189978 20.58307082 14.95125626 0.00028073 -0.00178499 -0.00025679 Br 14.75600615 20.53726327 12.04176092 0.00029912 -0.00232339 -0.00063054 Br 15.86618068 17.60311223 14.61321866 -0.00275652 0.00041171 -0.00074673 Br 11.91913628 19.75502443 14.73464195 0.00033812 -0.00224809 -0.00077456 Cs 12.00026744 17.66207720 17.64139415 0.00268547 -0.00015648 0.00223753 Pb 14.69982718 20.51462301 20.72525026 -0.00010964 0.00033103 -0.00191802 Br 14.52262656 20.43651932 17.87274085 -0.00386064 0.00133123 -0.00073742 Br 14.55655356 17.68126775 20.32384048 0.00119879 0.00409753 -0.00625441 Br 11.98581514 20.97523826 20.98638481 0.00363188 -0.00198789 -0.00017061 Cs 17.57848268 23.41226990 0.25020147 0.00506445 0.00023651 -0.00239567 Pb 20.80639477 3.00805863 2.78244689 -0.00083652 0.00180628 -0.00126955 Br 20.72692765 3.63868120 23.71080533 -0.00195892 0.00534389 -0.00326778 Br 21.05778251 0.07262862 3.30291732 -0.00099659 -0.00135907 -0.00009266 Br 18.06045330 3.15906849 2.71638152 0.00401048 0.00110473 0.00277695 Cs 18.44397591 23.55656698 6.16871283 -0.00257685 0.00291081 0.00005923 Pb 20.90178814 3.14258919 8.94680729 0.00138454 -0.00042977 -0.00008921 Br 21.51458626 2.87069534 5.94413483 0.00157041 0.00104482 0.00272398 Br 21.07334683 0.04857916 8.76455817 -0.00056261 -0.00057636 0.00316883 Br 18.01868391 2.47497804 8.56765286 -0.00374538 0.00140893 0.00030792 Cs 17.59956355 0.47387427 11.81106716 0.00191614 -0.00005916 -0.00033704 Pb 20.84577120 2.95374100 14.60240143 -0.00117292 0.00114430 -0.00036328 Br 19.95225343 3.40777181 11.72014244 -0.00176485 0.00146036 -0.00038418 Br 21.18957077 23.74908552 14.83169882 0.00269075 0.00012034 0.00052822 Br 17.90783492 2.44869250 15.01309207 -0.00058090 0.00262047 -0.00017544 Cs 17.88814035 0.04994619 18.39059469 0.00110023 -0.00084070 0.00017863 Pb 21.05779131 2.91791941 20.65928934 0.00233213 -0.00139065 0.00025957 Br 20.91564053 2.94284700 17.71032188 -0.00122192 0.00356079 -0.00020869 Br 21.26827867 23.64614363 20.79518123 -0.00343883 -0.00274455 -0.00333245 Br 18.15161897 2.83347023 20.22887278 -0.00523160 -0.00050510 -0.00397386 Cs 17.68332090 6.18163994 23.54337562 -0.00056151 0.00216925 -0.00277834 Pb 20.79068535 8.96171047 3.05315104 0.00124342 -0.00009046 -0.00001379 Br 20.49013646 9.43185502 23.75293282 -0.00243277 -0.00464709 0.00217461 Br 20.68924129 6.10681284 3.22999823 -0.00151624 0.00011299 0.00063621 Br 17.81201141 9.07821688 3.30706906 0.00186974 -0.00068102 0.00390187 Cs 18.30973773 5.36465777 6.41033830 0.00128542 -0.00048537 0.00062437 Pb 20.78848214 8.94816465 8.97535716 0.00230243 -0.00201492 -0.00163576 Br 21.00742653 9.12620599 6.13027066 -0.00203226 0.00473947 0.00055852 Br 21.30939984 5.98202735 8.86649274 0.00039095 -0.00009765 -0.00058488 Br 17.71043710 9.06324900 9.59281513 -0.00717955 -0.00067984 -0.00157534 Cs 17.40660877 6.18860037 11.51637606 0.00015309 0.00292974 -0.00142199 Pb 20.87673012 8.88977345 14.84601312 0.00131979 0.00012391 0.00074523 Br 21.02334534 8.61226984 11.95169981 -0.00103828 -0.00000922 -0.00136219 Br 20.95544986 5.87371195 15.33373439 -0.00080248 -0.00252977 0.00108003 Br 17.79697414 8.73188180 14.67986149 0.00215717 -0.00344541 -0.00020958 Cs 18.00734473 5.78927261 17.47870988 -0.00198308 -0.00041416 0.00043610 Pb 20.48442831 9.05181076 20.68251914 0.00084285 0.00070193 -0.00101560 Br 21.37828853 8.78281166 18.03988906 0.00056774 -0.00110680 -0.00097112 Br 20.47366869 5.93381863 20.87484649 -0.00051476 -0.00424251 -0.00136727 Br 17.61779974 9.61311391 20.56476595 -0.00224017 -0.00193002 -0.00135681 Cs 17.92405016 12.01189381 0.20544689 -0.00148348 -0.00075659 -0.00078230 Pb 20.37486631 14.80437232 2.95796381 0.00076080 -0.00160358 0.00089960 Br 20.73766500 15.26391971 23.69355078 0.00045294 0.00085690 0.00047860 Br 20.43605311 11.84787869 3.01890779 -0.00158744 -0.00364526 0.00291147 Br 17.56640427 15.10700831 2.90628995 -0.00048462 -0.00077775 -0.00176968 Cs 17.67550018 11.20288999 6.28922340 0.00190653 -0.00164067 -0.00074248 Pb 20.58827764 14.72309482 8.82526318 -0.00005049 0.00099207 0.00218281 Br 20.43234435 15.21527494 5.95781085 -0.00277154 -0.00306333 -0.00170065 Br 21.01788502 11.90170430 8.35134084 -0.00464536 -0.00186293 0.00184049 Br 17.79082027 14.48294241 9.31702444 -0.00095306 -0.00050880 0.00018832 Cs 17.84058454 11.41501671 12.42210022 0.00092441 -0.00182346 -0.00136949 Pb 20.87799520 14.58004622 14.90021559 -0.00040421 0.00002176 -0.00083559 Br 20.97916445 14.02058714 12.09770096 -0.00200813 -0.00036767 -0.00059253 Br 20.40680211 11.64663137 15.16275040 0.00034496 -0.00033455 0.00158977 Br 17.99656303 14.04875172 14.66282633 0.00037729 0.00039469 0.00207353 Cs 17.47412568 12.11202136 17.72513641 -0.00056416 -0.00123620 -0.00174782 Pb 20.72828465 14.79928209 20.83137705 0.00211314 0.00248426 0.00036584 Br 21.44337292 14.62625693 17.78916662 -0.00328125 -0.00229461 0.00249119 Br 20.53518460 11.92051063 20.08664342 -0.00369768 0.00096174 -0.00190352 Br 17.62587891 15.12892739 20.71855871 0.00308176 -0.00107381 -0.00170023 Cs 17.88955696 17.74671256 23.41944701 0.00028204 -0.00036735 0.00006035 Pb 20.56415981 20.95809435 3.01951255 0.00274789 0.00093311 0.00175812 Br 21.25762821 21.05677796 23.74976605 0.00192765 0.00077004 -0.00104405 Br 20.58482153 17.87727801 3.04782269 0.00147645 0.00041713 0.00121169 Br 17.92443360 20.22786058 2.13148895 0.00445796 -0.00082722 0.00142039 Cs 17.60061394 17.99381860 5.86394759 0.00150565 -0.00002284 -0.00240326 Pb 20.69906242 20.91419980 9.12615637 0.00190040 0.00153707 -0.00255186 Br 20.98259123 20.84198466 6.01600737 0.00115060 -0.00098379 0.00011314 Br 20.23735744 17.94583923 8.65078623 -0.00055477 -0.00281127 0.00019566 Br 17.82653296 20.85510343 9.57628557 -0.00256226 -0.00163662 0.00307815 Cs 17.88010585 18.08733189 11.37994369 -0.00087302 -0.00023972 0.00272717 Pb 21.01809503 20.74553557 14.74342764 -0.00171747 0.00113349 0.00135484 Br 20.95856565 20.83824974 11.81470795 0.00040707 -0.00012563 -0.00194423 Br 20.74956730 17.61725336 13.99484233 -0.00268714 -0.00371499 -0.00100159 Br 17.93064328 21.29594478 14.48050865 0.00126306 0.00286042 0.00276916 Cs 17.46426531 18.12127895 17.74934117 -0.00057537 -0.00054966 0.00300724 Pb 20.99226892 20.80313359 20.68302049 0.00018043 0.00001671 0.00208173 Br 20.47439310 21.03070590 17.85991017 0.00286545 -0.00270945 -0.00236194 Br 20.78482263 17.76068437 20.59963905 -0.00019607 0.00287986 -0.00290224 Br 17.71937166 20.52900741 20.86586257 0.00116472 0.00136087 0.00337855 320 Lattice="23.77195271 0.0 0.0 0.0 23.77195271 0.0 0.0 0.0 23.77195271" Properties=species:S:1:pos:R:3:vel:R:3 Time=20049.99999999 energy=-11425.54467392 virial="-15.05931457 2.71565698 -0.26009921 2.71565698 -15.04246893 -1.49529212 -0.26009921 -1.49529212 -7.31849334" stress="-0.0001278 0.00020215 -1.936e-05 0.00020215 -0.0001453 -0.00011131 -1.936e-05 -0.00011131 0.00035322" pbc="T T T" Cs 0.12479033 0.35622536 23.71250407 -0.00392341 -0.00276676 0.00094534 Pb 3.03746023 2.97315023 2.96781939 -0.00272792 0.00137962 0.00121847 Br 3.32106561 2.91382736 23.74807118 -0.00092158 0.00056991 0.00273951 Br 3.00522529 0.08472140 3.16114950 -0.00006850 0.00211718 -0.00053998 Br 0.11613534 3.48119436 2.56513732 0.00259471 -0.00025631 -0.00278232 Cs 23.68725069 23.61749760 6.20943093 0.00328412 0.00226444 -0.00155439 Pb 2.95325117 2.96844947 9.34080093 0.00131556 -0.00047050 -0.00037476 Br 2.37153558 2.83708323 6.10083467 0.00187879 -0.00136247 0.00277525 Br 3.15175112 23.76377140 9.14615367 0.00005853 -0.00050352 0.00322662 Br 23.67829782 2.98428728 9.96999800 -0.00115813 0.00323034 0.00639223 Cs 0.49789008 23.66085965 12.17434600 -0.00332269 -0.00079628 0.00078297 Pb 3.13100349 3.31817567 14.98590275 -0.00197440 0.00107869 -0.00087354 Br 4.26077417 3.09229111 11.95000256 -0.00084387 -0.00069397 -0.00021093 Br 2.86742126 0.31258690 14.92127647 0.00239818 -0.00273741 -0.00332477 Br 0.29117860 3.51715151 14.76193750 0.00130351 -0.00232830 0.00065422 Cs 0.23713573 23.46144799 17.87917011 -0.00102021 -0.00185337 -0.00200325 Pb 3.10688071 3.07554234 20.63485888 0.00178551 -0.00023234 -0.00025527 Br 2.61617344 3.07430091 17.76998368 -0.00162665 0.00218645 0.00191617 Br 3.13267220 0.01632479 20.11998967 -0.00150919 0.00105026 0.00274384 Br 0.09787066 3.15372089 20.55359480 0.00028019 -0.00100168 -0.00020235 Cs 23.48480150 6.27861777 23.76615142 -0.00014431 0.00261855 -0.00043619 Pb 2.85145742 8.92175699 3.19180948 -0.00015420 0.00034628 0.00222952 Br 2.57789203 8.89026287 23.63072174 0.00091819 -0.00520747 0.00052205 Br 3.27821812 6.05406626 3.18256490 -0.00276936 0.00037975 -0.00130760 Br 23.67631675 8.68121064 3.31322568 0.00003587 -0.00178833 -0.00202212 Cs 0.28615029 5.97405969 5.83698879 0.00236509 0.00143506 0.00194389 Pb 3.19496129 9.03662979 8.96017425 0.00318898 0.00191662 -0.00057447 Br 2.87912523 9.03390347 5.87127505 -0.00036186 -0.00284758 0.00115719 Br 2.92907386 6.04362585 8.64484707 -0.00085128 -0.00347852 0.00111005 Br 0.07128925 9.05082052 8.91088946 -0.00323680 0.00366194 -0.00161114 Cs 23.69557563 6.08269523 11.84560311 -0.00155116 -0.00215766 0.00174716 Pb 2.95293561 8.78112372 14.94411842 -0.00154310 -0.00037868 -0.00239210 Br 3.15103178 8.80283702 11.76732900 0.00071044 0.00127749 -0.00141218 Br 3.78490041 5.97713776 15.07672188 -0.00124258 0.00031578 0.00101380 Br 0.04211778 8.61786511 14.65971709 0.00025334 -0.00212158 -0.00316770 Cs 23.75147506 5.72723283 17.62561863 -0.00128936 0.00008609 -0.00063980 Pb 2.74188475 8.85872901 20.61463525 -0.00074577 0.00032371 -0.00097931 Br 2.05730496 8.77208681 17.80920452 0.00350847 0.00001933 -0.00071814 Br 2.08371812 5.91990014 20.67605400 -0.00144497 -0.00122461 0.00144963 Br 23.71287047 9.29961307 21.14998037 0.00051031 -0.00057842 0.00051145 Cs 23.03968916 11.81625546 0.23354975 0.00146703 -0.00104712 0.00049446 Pb 2.85053545 14.84041139 2.86239618 -0.00203029 0.00130289 0.00094148 Br 2.53080240 15.19467301 23.70588324 -0.00150681 -0.00114846 -0.00114057 Br 2.84114035 11.70562517 2.35305224 0.00215297 -0.00062624 -0.00148600 Br 23.39249990 14.59002361 3.34176315 -0.00261171 0.00112498 -0.00137257 Cs 23.46205192 11.82382077 6.51435283 -0.00022232 -0.00147815 0.00231381 Pb 2.95541419 15.04562296 9.01061916 0.00099348 -0.00044046 0.00210501 Br 3.41115703 14.97398943 5.98785092 -0.00164111 -0.00053202 -0.00022382 Br 2.96521942 11.74348827 8.69508542 0.00043992 0.00145386 0.00015598 Br 23.70644523 15.12085183 8.91033768 0.00095830 -0.00065022 -0.00051946 Cs 0.22196795 11.61525016 11.53038543 -0.00199405 0.00040768 -0.00178809 Pb 3.03408454 14.62216919 15.02455744 0.00090546 -0.00181084 0.00138878 Br 3.04844919 14.53793367 11.90268267 0.00080090 -0.00227264 -0.00245421 Br 2.76833009 11.85285260 15.32241123 -0.00389328 -0.00072837 0.00244147 Br 23.76739545 14.64920919 14.65814413 0.00169441 -0.00039295 -0.00254067 Cs 23.40590985 11.80775630 18.17488227 0.00157656 -0.00120854 -0.00000096 Pb 3.02235684 14.67269318 20.84384327 0.00179786 -0.00282526 -0.00186624 Br 2.94331234 14.82328087 17.94847674 -0.00034949 0.00100014 0.00045154 Br 2.74380213 11.82589182 20.90178831 -0.00001277 -0.00171372 0.00073975 Br 0.04922842 14.47683798 21.02041559 0.00338841 0.00109290 -0.00140534 Cs 0.08420748 17.76045138 23.71881654 0.00009818 0.00029445 -0.00144926 Pb 2.88941358 20.79628600 2.94930736 0.00206137 -0.00111725 -0.00091235 Br 2.90239197 20.70645022 23.42244200 0.00073691 0.00132155 0.00393816 Br 3.25182376 17.88052325 2.61210700 -0.00055455 0.00178717 0.00019456 Br 23.70388861 20.73309381 2.84870878 -0.00287641 0.00221014 0.00168878 Cs 23.19121601 17.74961597 6.38190655 -0.00157385 0.00121251 -0.00274629 Pb 2.77990327 20.95556740 8.79918995 0.00173244 0.00010452 -0.00050847 Br 2.89231715 20.90601108 5.79302105 0.00128134 -0.00221448 0.00043542 Br 3.43310069 18.02518398 9.53475986 -0.00064578 -0.00054203 -0.00149697 Br 23.54018719 21.18760041 8.86087877 0.00003263 -0.00208456 0.00030024 Cs 0.34987669 17.09094157 12.37747642 0.00330937 0.00050995 -0.00291734 Pb 3.12421481 21.08479540 14.63144016 -0.00052035 0.00033904 0.00144725 Br 3.42868518 20.75579938 11.83145612 -0.00344583 -0.00065480 -0.00287598 Br 3.05894944 18.03839658 14.84054531 -0.00017941 0.00018358 0.00466270 Br 0.13775432 20.40625603 14.98030682 0.00099995 0.00140751 -0.00022409 Cs 0.37307445 17.65424413 17.42387106 0.00061334 0.00043861 0.00125324 Pb 2.99155052 20.70477251 20.55162798 -0.00013801 0.00078227 -0.00104979 Br 3.70554890 20.66652663 17.57644281 -0.00205915 -0.00473297 -0.00089740 Br 3.08923246 17.86153612 20.75944179 -0.00005303 -0.00050608 -0.00524673 Br 23.74928508 20.66144852 20.47123954 -0.00107053 -0.00066622 0.00225983 Cs 6.09700720 0.06233593 22.89658038 -0.00260434 0.00084808 -0.00077396 Pb 9.10257915 2.89495532 3.33849878 -0.00069646 0.00206114 -0.00106094 Br 9.15953206 2.45164800 0.06494934 -0.00196262 0.00196890 -0.00058506 Br 8.61908486 23.55651804 3.16101016 0.00259291 0.00367973 0.00391863 Br 6.02945333 2.85737462 3.47263527 -0.00279501 0.00244307 0.00153234 Cs 6.15656918 23.39374496 5.68251181 -0.00122744 0.00039971 0.00094101 Pb 8.81024185 2.76745934 8.81358411 -0.00026236 -0.00017385 0.00037109 Br 9.95434335 3.40433773 6.04472099 -0.00194585 0.00151176 -0.00169561 Br 8.30191952 23.39187647 8.62297508 0.00129452 -0.00307228 0.00332069 Br 5.72746402 3.47795268 8.60206593 0.00059049 -0.00131352 -0.00310740 Cs 6.10600244 23.63397361 12.04719298 -0.00049974 0.00151589 0.00011280 Pb 9.07881690 2.85667473 14.64535950 0.00198031 0.00040861 0.00004366 Br 8.97028464 2.81318248 11.76009317 0.00101242 0.00093939 -0.00064420 Br 10.03939793 23.46709944 15.32596237 0.00223665 0.00050658 -0.00104372 Br 6.18621197 2.37121743 15.32165618 -0.00021248 -0.00011190 -0.00281281 Cs 6.55195653 23.57230550 17.62721373 -0.00132746 -0.00203066 0.00099749 Pb 9.22787017 3.00953317 21.09348239 -0.00103288 0.00089617 -0.00047476 Br 9.78710547 3.02922437 17.40400630 -0.00129120 -0.00153897 0.00363487 Br 9.16706731 0.21507834 20.47780095 -0.00150881 -0.00026593 0.00352607 Br 6.18040902 3.88587889 20.74174867 -0.00156579 0.00145446 0.00027135 Cs 5.70008378 6.43909512 23.65245596 -0.00130572 -0.00308676 0.00063693 Pb 8.92393397 8.74990629 2.83032784 -0.00112167 0.00300590 -0.00065245 Br 8.83841894 8.79188498 23.66490063 0.00139829 0.00077933 -0.00148286 Br 8.37417020 5.67207762 2.78917647 -0.00282725 0.00288895 0.00172504 Br 5.88397441 8.87651527 2.67711841 0.00011423 0.00088055 0.00092698 Cs 5.73940968 5.96475173 5.80537088 0.00314555 0.00326575 0.00064830 Pb 8.80471487 8.83514061 8.80890464 -0.00064686 0.00003986 -0.00224905 Br 8.82553867 8.71139579 5.94288821 -0.00189075 -0.00210475 0.00053943 Br 8.86217942 5.74682479 9.00643178 -0.00230052 0.00218371 0.00265030 Br 5.94527302 7.98842078 8.58621791 -0.00073560 0.00214323 0.00001141 Cs 5.81670776 6.22299092 11.82375243 0.00102893 -0.00138061 0.00152652 Pb 9.09168689 8.87657531 14.83174069 -0.00021993 -0.00029286 0.00033809 Br 8.01923344 9.25900726 11.82207044 -0.00296211 -0.00509224 0.00030198 Br 9.21903266 5.71691461 14.91042027 0.00309673 0.00176526 0.00376367 Br 5.92194828 9.41324664 15.30278024 0.00233939 -0.00451537 0.00186376 Cs 7.01902407 6.01671065 17.88255782 -0.00166713 0.00133292 -0.00073588 Pb 8.69197859 8.89952155 20.72525544 0.00029101 0.00076222 -0.00052030 Br 9.19886583 9.38271380 17.78195334 0.00046111 -0.00064277 0.00344093 Br 9.21949036 6.05354050 21.01667641 -0.00056540 0.00205663 -0.00047838 Br 5.81238489 8.70488294 20.54824763 -0.00228735 -0.00178974 0.00127462 Cs 5.59718506 11.62360516 23.27330336 -0.00158214 -0.00155482 0.00107048 Pb 8.68992411 14.64041526 2.99926817 0.00159075 -0.00065012 0.00023038 Br 9.25805504 14.85203605 0.30169638 0.00029224 -0.00331820 0.00352937 Br 8.89631853 11.57902809 2.92654527 0.00175932 -0.00176553 0.00232333 Br 5.62301228 14.79302150 2.58534199 -0.00027837 -0.00182966 0.00043044 Cs 5.09755909 11.87699522 4.86554536 0.00079779 0.00078958 -0.00036228 Pb 8.61640320 14.61843642 8.89407885 -0.00188980 -0.00204596 0.00109808 Br 7.96892865 14.18890037 6.08220369 0.00471167 -0.00299871 -0.00248901 Br 9.84920972 11.81024300 8.97769215 -0.00068932 0.00406640 -0.00291151 Br 5.92904951 14.63162386 9.43830584 0.00099730 -0.00527726 0.00105325 Cs 5.43983631 12.07603050 11.94473282 0.00054948 -0.00054807 -0.00207043 Pb 8.79637938 14.98500827 14.88637168 0.00030150 0.00172289 0.00100729 Br 9.75654696 15.07230438 11.70898029 -0.00214667 0.00211863 0.00079680 Br 9.41226555 11.86115460 15.01402780 0.00153404 -0.00065566 0.00220168 Br 6.15702945 14.61333217 14.64920188 -0.00124127 0.00132767 -0.00589965 Cs 6.38431726 12.06634935 17.89460613 0.00016746 -0.00239569 -0.00185597 Pb 8.93075987 14.92066982 20.82613679 0.00011646 0.00011168 -0.00157845 Br 9.20923702 14.90535547 18.05168598 0.00304030 -0.00001189 -0.00291146 Br 8.94394222 11.98647279 21.49312689 0.00023601 -0.00345411 -0.00092893 Br 6.00785083 14.68167304 21.19004397 0.00205778 0.00337461 0.00132230 Cs 5.57642479 18.33775103 0.00206724 -0.00264455 0.00306596 -0.00040820 Pb 8.82366512 20.72852943 3.00269760 -0.00074967 0.00016696 0.00105027 Br 8.89408592 20.65273617 0.01158878 -0.00003168 0.00191465 0.00373112 Br 8.43811183 17.64133974 3.02111216 -0.00176680 0.00310072 -0.00217725 Br 5.98422728 20.60163226 2.82806764 -0.00103878 0.00006149 0.00154414 Cs 5.74792244 17.74743925 5.55907282 -0.00118218 0.00261214 -0.00055201 Pb 8.62289467 20.67147470 8.74604064 -0.00116925 -0.00108154 0.00180377 Br 8.55331756 20.36324265 5.92257690 -0.00198054 -0.00123644 -0.00167679 Br 8.07184018 17.53642203 8.58863866 -0.00086658 -0.00131768 -0.00379465 Br 5.79597873 20.70866691 8.80552197 -0.00171435 -0.00197473 -0.00184903 Cs 6.37766435 18.05518794 11.51199243 -0.00042950 0.00254397 -0.00223933 Pb 9.36239989 20.85013578 14.66108001 -0.00003925 0.00021769 -0.00109669 Br 9.34116655 21.20677673 11.71987569 0.00267140 -0.00325591 0.00258869 Br 8.39812362 17.90442299 14.53228819 0.00035899 -0.00478188 0.00174488 Br 6.11645602 21.04925972 14.77036700 -0.00039335 0.00180923 0.00275784 Cs 5.85275591 17.42092178 18.04302324 -0.00071363 -0.00128104 -0.00137136 Pb 9.08558128 20.72377075 20.76776899 0.00021961 -0.00075738 -0.00052084 Br 9.46415069 20.61725265 17.81465390 0.00469988 0.00137501 -0.00438569 Br 8.12141647 17.90018535 20.70095033 -0.00562931 0.00298975 -0.00061586 Br 6.11440771 21.07736887 20.64116572 0.00075957 -0.00458071 -0.00114075 Cs 12.09565366 23.75974035 0.45525997 0.00076587 -0.00061224 -0.00120829 Pb 15.05048030 3.27000160 2.92524209 0.00014739 0.00127530 -0.00233011 Br 15.38108122 2.80377140 0.14769125 -0.00089693 0.00055087 0.00102021 Br 15.11842552 0.07981107 2.53111911 0.00473943 -0.00442246 0.00140997 Br 11.90510718 3.29671022 2.68733663 0.00050498 0.00215969 -0.00226194 Cs 12.25262088 0.54952993 6.01337298 -0.00093725 0.00091361 -0.00359915 Pb 14.78627822 3.00516594 8.89763581 0.00037108 -0.00001630 -0.00083377 Br 14.90921218 2.88383024 5.95032218 -0.00261331 0.00436114 -0.00043515 Br 14.73010947 0.12553881 8.94513085 0.00240696 0.00049818 0.00118713 Br 11.91153719 3.05202491 9.58629947 0.00462967 0.00007827 0.00139592 Cs 11.93963133 23.73780120 11.93421957 -0.00051135 0.00423747 0.00037901 Pb 14.74644295 3.06253969 14.83169104 0.00148463 -0.00127463 0.00060447 Br 15.54859635 2.96505777 11.75565651 -0.00348607 -0.00174077 0.00140043 Br 14.39330298 23.60019028 15.12179182 -0.00256009 0.00058409 -0.00163902 Br 11.95944361 2.78147817 14.29615774 0.00032888 0.00036988 -0.00034147 Cs 12.13282087 23.32563318 18.18728698 -0.00087254 -0.00145276 0.00287142 Pb 15.10214860 2.88375963 20.94600930 -0.00069691 0.00335049 -0.00092934 Br 15.16774640 2.91143866 17.77436515 -0.00320083 0.00183673 0.00330045 Br 14.90149330 23.66160168 21.15389123 0.00301721 -0.00172841 -0.00340985 Br 12.15958536 3.12306279 21.37884501 -0.00332442 -0.00523926 0.00228010 Cs 11.74664586 6.33639414 23.33240452 0.00051431 -0.00091821 -0.00087670 Pb 14.80607868 9.01720007 3.43637516 -0.00129930 -0.00129935 0.00040778 Br 14.71417673 9.23848500 0.14385077 0.00008424 -0.00310283 0.00089060 Br 15.50178887 6.21559486 3.83350777 -0.00135399 0.00062646 -0.00263362 Br 11.91514797 8.73494100 2.71178772 -0.00294717 -0.00195941 -0.00138229 Cs 11.81048034 6.35152805 5.57919495 0.00201168 -0.00146318 -0.00022912 Pb 14.47251256 8.89862917 9.00486877 0.00022782 -0.00054035 0.00145000 Br 14.35360371 9.53956001 6.25624295 0.00041193 0.00003953 -0.00055542 Br 15.21800939 6.12281259 8.34671699 0.00097590 0.00166057 -0.00092120 Br 11.66671102 8.18484849 9.07366952 0.00194616 -0.00140885 -0.00129287 Cs 11.86400335 5.84015235 11.81622894 0.00182608 0.00166843 0.00033392 Pb 14.84015743 8.72140451 14.72096405 0.00064658 0.00041445 -0.00005113 Br 15.08984554 8.99728464 11.82008886 -0.00222684 0.00177058 -0.00426177 Br 15.22199873 5.89525741 14.95629974 -0.00307608 0.00166080 -0.00058342 Br 11.94548143 8.39730907 14.62695519 0.00348398 0.00108983 0.00364971 Cs 12.47322512 5.79471426 18.25468891 0.00195181 -0.00105708 0.00083242 Pb 14.59723736 9.05788250 20.89615083 -0.00013699 0.00155663 0.00012892 Br 14.76436283 8.90905299 18.04345919 0.00022574 -0.00273013 -0.00093222 Br 15.42834942 5.82872546 20.98152436 -0.00035541 -0.00215891 -0.00162340 Br 11.51612635 9.11963697 20.43266737 -0.00148425 -0.00108535 -0.00092570 Cs 11.47472940 11.84223083 0.57992872 -0.00306129 0.00040303 0.00113803 Pb 14.69789633 15.05510795 3.27505708 0.00102709 -0.00168178 -0.00078713 Br 14.77461579 15.77518744 0.03978547 0.00136677 -0.00190564 -0.00137706 Br 14.72877110 11.87777893 3.02775349 -0.00274622 0.00147579 0.00141862 Br 11.63656766 15.23969181 3.78777655 0.00020983 -0.00195862 -0.00362518 Cs 11.56597363 12.48274884 5.94607198 -0.00084743 0.00026339 0.00113134 Pb 14.42142179 14.80717180 9.18197269 0.00161636 0.00188116 0.00201192 Br 15.22164423 14.71650636 6.12100780 -0.00273725 0.00127686 0.00037575 Br 14.16684485 11.99686256 9.04301729 -0.00473874 0.00286597 -0.00062371 Br 11.90818548 15.58051811 8.21820218 0.00064922 -0.00033911 0.00097224 Cs 11.92943838 12.07947859 11.94229525 -0.00034044 0.00029687 -0.00018064 Pb 15.04779593 14.81474849 14.85318831 0.00015638 0.00261950 0.00116666 Br 14.52428854 14.94009594 11.99404141 0.00255819 -0.00143185 0.00264997 Br 14.45087027 11.84835501 14.85159061 -0.00047981 -0.00216643 0.00178223 Br 11.99956198 15.35010345 15.14097957 -0.00038625 -0.00095277 0.00103085 Cs 12.20303437 11.72144097 17.56126560 0.00136469 -0.00221547 -0.00194179 Pb 14.66295439 14.85878766 21.10626250 0.00206734 -0.00034685 -0.00027054 Br 14.51177869 14.46073545 17.83743192 -0.00111376 0.00255026 -0.00214225 Br 14.15723798 11.86851857 20.83736965 0.00054198 0.00114086 0.00322057 Br 11.84776379 15.08768975 21.27761441 0.00168602 0.00200019 0.00356631 Cs 11.66189531 18.05230296 0.24862065 -0.00139265 0.00011409 0.00344571 Pb 14.69296313 20.82186214 2.86411034 0.00161136 0.00136537 0.00118324 Br 14.27304997 20.56926651 0.20516356 0.00440369 0.00252901 -0.00191020 Br 14.58948149 18.09681106 3.47214460 -0.00135189 0.00125011 0.00154983 Br 11.80280786 21.30783444 3.39155401 -0.00234096 0.00124440 0.00169563 Cs 11.24706469 18.12379763 5.94036947 -0.00130878 0.00077898 0.00013638 Pb 14.74116993 20.91396203 8.76137729 0.00329644 0.00110319 0.00096847 Br 15.11105514 21.35281853 5.73435630 -0.00149831 -0.00720520 0.00050605 Br 14.68922006 17.95791979 8.77007673 -0.00008183 -0.00423705 0.00028446 Br 11.82411187 20.95868874 8.39345643 -0.00207620 0.00460827 -0.00315583 Cs 12.31635604 18.41484813 11.71496039 0.00368547 0.00338490 0.00127120 Pb 14.93563658 20.56619360 14.94749716 0.00046132 -0.00158601 -0.00049205 Br 14.76022991 20.51695561 12.03304891 0.00057043 -0.00171771 -0.00110068 Br 15.83805399 17.60972793 14.60627906 -0.00285982 0.00092078 -0.00063480 Br 11.92652628 19.73331399 14.72861311 0.00112859 -0.00208376 -0.00041849 Cs 12.02667746 17.66029349 17.66456279 0.00259271 -0.00020148 0.00238822 Pb 14.70096761 20.51854936 20.70714422 0.00035211 0.00046209 -0.00169816 Br 14.48413383 20.45049584 17.86534311 -0.00383106 0.00146798 -0.00076421 Br 14.56863880 17.72221577 20.26166516 0.00122320 0.00402882 -0.00616336 Br 12.01860633 20.95538382 20.98556720 0.00287060 -0.00198064 0.00001427 Cs 17.62993578 23.41447723 0.22524789 0.00522111 0.00020174 -0.00259396 Pb 20.80027570 3.02699569 2.77021401 -0.00037212 0.00198008 -0.00118396 Br 20.70788967 3.69048754 23.67861724 -0.00184826 0.00500626 -0.00313604 Br 21.04701710 0.06004207 3.30123836 -0.00115671 -0.00112768 -0.00023934 Br 18.09504938 3.16979341 2.74557436 0.00286192 0.00104006 0.00306049 Cs 18.41782736 23.58582616 6.16931415 -0.00265082 0.00293763 0.00006000 Pb 20.91589392 3.13685192 8.94562572 0.00143267 -0.00071420 -0.00014230 Br 21.52868761 2.88230971 5.97172366 0.00125069 0.00127411 0.00278051 Br 21.06635404 0.04436782 8.79733903 -0.00083226 -0.00026084 0.00337884 Br 17.97904341 2.49007923 8.57035452 -0.00415279 0.00160924 0.00023308 Cs 17.62055855 0.47081260 11.80808928 0.00227801 -0.00054608 -0.00025992 Pb 20.83336398 2.96502459 14.59966121 -0.00130858 0.00111066 -0.00018545 Br 19.93529895 3.42257624 11.71754394 -0.00162486 0.00150008 -0.00013706 Br 21.21784886 23.74914904 14.83628446 0.00296026 -0.00011178 0.00039032 Br 17.90291573 2.47600308 15.01093476 -0.00040380 0.00283656 -0.00025315 Cs 17.89925848 0.04061727 18.39099599 0.00112236 -0.00102902 -0.00009848 Pb 21.07955727 2.90481623 20.66190071 0.00200766 -0.00122705 0.00025842 Br 20.90449002 2.97779280 17.70761821 -0.00100749 0.00342033 -0.00032817 Br 21.23400544 23.62211017 20.76172179 -0.00341151 -0.00203734 -0.00335921 Br 18.09638062 2.82945232 20.19177302 -0.00577988 -0.00029099 -0.00343419 Cs 17.67891135 6.20339357 23.51638828 -0.00031902 0.00218263 -0.00261845 Pb 20.80249612 8.96028909 3.05308990 0.00111294 -0.00019605 0.00000213 Br 20.46536017 9.38382084 0.00365717 -0.00251498 -0.00495008 0.00234200 Br 20.67379617 6.10486821 3.23644060 -0.00156958 -0.00051664 0.00065466 Br 17.83018936 9.07186173 3.34593312 0.00174295 -0.00059599 0.00386547 Cs 18.32244590 5.36022673 6.41541344 0.00125387 -0.00039778 0.00039076 Pb 20.81146091 8.92771161 8.95933945 0.00228703 -0.00207123 -0.00156442 Br 20.98599241 9.17218150 6.13277976 -0.00224590 0.00444397 -0.00006985 Br 21.31246459 5.98307901 8.85974949 0.00022711 0.00029261 -0.00076590 Br 17.64148018 9.05749496 9.57319369 -0.00658345 -0.00046343 -0.00235966 Cs 17.40873138 6.21715514 11.50268808 0.00026909 0.00277488 -0.00131376 Pb 20.88993131 8.88957091 14.85432581 0.00131464 -0.00016458 0.00091238 Br 21.01259545 8.61316665 11.93864661 -0.00111167 0.00018833 -0.00123491 Br 20.94649706 5.84876098 15.34273114 -0.00098487 -0.00244361 0.00071450 Br 17.81899546 8.69674706 14.67864417 0.00224601 -0.00357549 -0.00003842 Cs 17.98774606 5.78456922 17.48394143 -0.00193692 -0.00052732 0.00060941 Pb 20.49353141 9.05794957 20.67314786 0.00097413 0.00052791 -0.00085895 Br 21.38326652 8.77122031 18.02626756 0.00042786 -0.00120847 -0.00174095 Br 20.46879643 5.89292203 20.86054488 -0.00045761 -0.00391218 -0.00150187 Br 17.59457451 9.59298254 20.55197983 -0.00238664 -0.00209755 -0.00119711 Cs 17.90843463 12.00395384 0.19762895 -0.00163740 -0.00083166 -0.00077907 Pb 20.38452601 14.78966503 2.96759141 0.00116374 -0.00133840 0.00102598 Br 20.74198467 15.27120130 23.69985719 0.00041342 0.00060177 0.00078037 Br 20.41987797 11.81442057 3.04836573 -0.00164469 -0.00301273 0.00297417 Br 17.55978517 15.09926049 2.88788436 -0.00082712 -0.00077092 -0.00191308 Cs 17.69464629 11.18737717 6.28204178 0.00191908 -0.00145978 -0.00069103 Pb 20.58707760 14.73406648 8.84777642 -0.00019408 0.00119795 0.00231489 Br 20.40431646 15.18339175 5.94117640 -0.00282840 -0.00331149 -0.00161216 Br 20.96625776 11.88372810 8.37482245 -0.00565456 -0.00171369 0.00284309 Br 17.78067781 14.47771115 9.31885742 -0.00106308 -0.00053541 0.00017934 Cs 17.85014279 11.39751519 12.40729739 0.00098668 -0.00167439 -0.00158718 Pb 20.87311239 14.58157222 14.89134715 -0.00056731 0.00028440 -0.00093513 Br 20.95981271 14.01631458 12.08801318 -0.00186220 -0.00048754 -0.00134083 Br 20.41203045 11.64368452 15.17954239 0.00069929 -0.00026239 0.00176530 Br 17.99581718 14.05470807 14.68454108 -0.00052152 0.00079073 0.00226265 Cs 17.46843760 12.09950978 17.70813307 -0.00057380 -0.00126544 -0.00165255 Pb 20.74977166 14.82476932 20.83393581 0.00218267 0.00260899 0.00014414 Br 21.40969894 14.60589030 17.81538787 -0.00344708 -0.00177713 0.00272980 Br 20.49695936 11.93015512 20.06973537 -0.00393711 0.00095174 -0.00147291 Br 17.65915296 15.11752702 20.70139683 0.00356163 -0.00120311 -0.00173000 Cs 17.89271487 17.74297003 23.41980247 0.00034878 -0.00037867 0.00001289 Pb 20.59310241 20.96598031 3.03628979 0.00303923 0.00063995 0.00159612 Br 21.27612394 21.06422077 23.73994164 0.00177302 0.00072006 -0.00092650 Br 20.60044956 17.88064411 3.06016134 0.00165125 0.00023969 0.00125764 Br 17.96512156 20.22178582 2.14676324 0.00366205 -0.00038723 0.00162904 Cs 17.61592823 17.99334128 5.83991495 0.00155542 -0.00007395 -0.00240313 Pb 20.71720864 20.92908022 9.09841562 0.00171891 0.00143217 -0.00298274 Br 20.99300014 20.83198274 6.01510329 0.00092950 -0.00101001 -0.00028738 Br 20.23282630 17.91644753 8.65316415 -0.00035166 -0.00305556 0.00028105 Br 17.79720302 20.84254179 9.60439897 -0.00328193 -0.00087349 0.00253635 Cs 17.87129682 18.08346783 11.40870991 -0.00088584 -0.00053527 0.00302106 Pb 20.99950265 20.75676885 14.75714038 -0.00199457 0.00111419 0.00138436 Br 20.96419627 20.83741115 11.80137279 0.00071387 -0.00004313 -0.00073316 Br 20.72316617 17.58085515 13.98655396 -0.00258891 -0.00355958 -0.00065676 Br 17.94219352 21.32323830 14.50925253 0.00104617 0.00259660 0.00297531 Cs 17.45936179 18.11557501 17.78014115 -0.00040591 -0.00059013 0.00314977 Pb 20.99255375 20.80262592 20.70481339 -0.00013186 -0.00012296 0.00227251 Br 20.50321470 21.00320326 17.83420233 0.00289919 -0.00279015 -0.00276548 Br 20.78314000 17.78849133 20.57063843 -0.00014075 0.00265694 -0.00289513 Br 17.73128608 20.54299808 20.89966599 0.00122639 0.00143615 0.00337808 320 Lattice="23.77195271 0.0 0.0 0.0 23.77195271 0.0 0.0 0.0 23.77195271" Properties=species:S:1:pos:R:3:vel:R:3 Time=20059.99999999 energy=-11425.67568588 virial="-16.35387872 2.56344556 -0.59732041 2.56344556 -14.70018887 -1.71114183 -0.59732041 -1.71114183 -8.24277755" stress="-0.00020948 0.00019082 -4.446e-05 0.00019082 -0.00013403 -0.00012738 -4.446e-05 -0.00012738 0.00030345" pbc="T T T" Cs 0.08558911 0.32824823 23.72224058 -0.00391348 -0.00282816 0.00100275 Pb 3.01028886 2.98719232 2.98078634 -0.00269636 0.00142651 0.00137204 Br 3.31191372 2.91910757 0.00273218 -0.00090638 0.00048577 0.00257319 Br 3.00415738 0.10379272 3.15515452 -0.00014794 0.00168141 -0.00065609 Br 0.14177786 3.47825236 2.53676125 0.00250310 -0.00032934 -0.00288980 Cs 23.72048803 23.64067357 6.19314249 0.00336243 0.00236640 -0.00169997 Pb 2.96658971 2.96482343 9.33557169 0.00134704 -0.00025394 -0.00067231 Br 2.39082582 2.82361912 6.12896374 0.00196956 -0.00133011 0.00285187 Br 3.15245271 23.76013148 9.17873649 0.00008298 -0.00022565 0.00328719 Br 23.66782187 3.01567787 10.03439591 -0.00093965 0.00303697 0.00647238 Cs 0.46406711 23.65286217 12.18186967 -0.00344023 -0.00080497 0.00072170 Pb 3.11199766 3.32619549 14.97638729 -0.00181804 0.00052348 -0.00102227 Br 4.25022281 3.08459840 11.94879997 -0.00126041 -0.00083872 -0.00002765 Br 2.89077540 0.28326695 14.88779196 0.00227121 -0.00310037 -0.00336561 Br 0.30220051 3.49386053 14.76656812 0.00088050 -0.00232573 0.00027188 Cs 0.22673076 23.44323027 17.85881885 -0.00106053 -0.00179038 -0.00206621 Pb 3.12501169 3.07369278 20.63277497 0.00183806 -0.00013313 -0.00015649 Br 2.60075096 3.09633711 17.78926925 -0.00145142 0.00221573 0.00191614 Br 3.11777958 0.02804584 20.14882077 -0.00146692 0.00127798 0.00301352 Br 0.10276382 3.14158138 20.55245855 0.00068202 -0.00142918 -0.00002447 Cs 23.48346861 6.30491084 23.76145330 -0.00012123 0.00263759 -0.00050309 Pb 2.84988316 8.92431823 3.21054248 -0.00015833 0.00016532 0.00151006 Br 2.58882023 8.83702026 23.63696933 0.00126488 -0.00543147 0.00072619 Br 3.25010726 6.05637232 3.16900455 -0.00284692 0.00009399 -0.00139580 Br 23.67809432 8.66397070 3.29232982 0.00032804 -0.00165991 -0.00215259 Cs 0.30970647 5.98789198 5.85651897 0.00234584 0.00132990 0.00196180 Pb 3.22651154 9.05294885 8.95334607 0.00311297 0.00134808 -0.00079035 Br 2.87565910 9.00633337 5.88823862 -0.00033155 -0.00266463 0.00223916 Br 2.91848710 6.00683829 8.65710155 -0.00125788 -0.00384610 0.00134459 Br 0.03804121 9.08720501 8.89551938 -0.00339570 0.00360413 -0.00146098 Cs 23.68012457 6.06123131 11.86303165 -0.00154048 -0.00213064 0.00173916 Pb 2.93806546 8.77816514 14.92076316 -0.00142644 -0.00021188 -0.00227628 Br 3.15771170 8.81649494 11.75793630 0.00062658 0.00145182 -0.00046827 Br 3.77413228 5.98528280 15.08742039 -0.00091472 0.00129949 0.00112397 Br 0.04516822 8.59682994 14.62703256 0.00036155 -0.00208664 -0.00336344 Cs 23.73821193 5.72827946 17.61984746 -0.00136024 0.00012263 -0.00051187 Pb 2.73371932 8.86134506 20.60605991 -0.00087952 0.00019073 -0.00073228 Br 2.09344783 8.77280959 17.79933407 0.00370968 0.00012206 -0.00126143 Br 2.07188577 5.90814220 20.69139249 -0.00092113 -0.00111898 0.00161372 Br 23.71393173 9.29406256 21.15477732 -0.00031600 -0.00052705 0.00044781 Cs 23.05573255 11.80616772 0.23829224 0.00173633 -0.00097310 0.00045521 Pb 2.82894829 14.85370587 2.87158328 -0.00227473 0.00135361 0.00089251 Br 2.51680672 15.18233402 23.69702908 -0.00128910 -0.00131674 -0.00062555 Br 2.86150023 11.70413346 2.33737555 0.00191473 0.00032732 -0.00164689 Br 23.36812499 14.60136782 3.32768572 -0.00224814 0.00114122 -0.00144130 Cs 23.46279135 11.80930535 6.53468631 0.00034919 -0.00141822 0.00176714 Pb 2.96567286 15.04189861 9.03101953 0.00105371 -0.00030314 0.00196762 Br 3.39373507 14.96854552 5.98522778 -0.00183963 -0.00055505 -0.00030988 Br 2.96938432 11.76636284 8.69699103 0.00039456 0.00308954 0.00022508 Br 23.71723394 15.11382370 8.90549827 0.00119363 -0.00075517 -0.00044970 Cs 0.20163416 11.61963639 11.51296714 -0.00207321 0.00047151 -0.00169477 Pb 3.04263794 14.60507627 15.03798806 0.00080458 -0.00160504 0.00129305 Br 3.05458483 14.51556978 11.87964418 0.00042213 -0.00219940 -0.00213242 Br 2.72982283 11.84494261 15.34581573 -0.00380313 -0.00085852 0.00224103 Br 0.01671424 14.64393958 14.63454271 0.00253589 -0.00065616 -0.00217892 Cs 23.42309200 11.79516641 18.17472438 0.00185187 -0.00130492 -0.00002860 Pb 3.03964859 14.64503065 20.82611359 0.00165994 -0.00270154 -0.00167230 Br 2.94013933 14.83364419 17.95038868 -0.00028543 0.00106920 -0.00007655 Br 2.74507281 11.80894095 20.90847143 0.00026390 -0.00167361 0.00059707 Br 0.08057088 14.48856189 21.00581914 0.00287466 0.00124946 -0.00151626 Cs 0.08460199 17.76414387 23.70439430 -0.00001940 0.00044348 -0.00143522 Pb 2.90947573 20.78566996 2.93917530 0.00194801 -0.00099754 -0.00111002 Br 2.90929415 20.72100176 23.46360574 0.00064000 0.00158761 0.00426005 Br 3.24460454 17.89658967 2.61557977 -0.00089049 0.00141067 0.00049794 Br 23.67637407 20.75493768 2.86609975 -0.00260705 0.00215631 0.00178827 Cs 23.17508145 17.76195066 6.35367154 -0.00165234 0.00124995 -0.00289848 Pb 2.79770151 20.95549011 8.79466222 0.00182199 -0.00012041 -0.00039483 Br 2.90540643 20.88344583 5.79931286 0.00133509 -0.00229623 0.00080584 Br 3.42386450 18.01515457 9.51766621 -0.00119571 -0.00146328 -0.00191575 Br 23.54586258 21.16536547 8.86441532 0.00108548 -0.00235595 0.00040104 Cs 0.38267745 17.09759258 12.34729993 0.00325013 0.00081976 -0.00311317 Pb 3.11990559 21.08720671 14.64724162 -0.00034228 0.00013915 0.00170027 Br 3.39327146 20.75078312 11.80100961 -0.00363405 -0.00034955 -0.00317826 Br 3.05730864 18.03720083 14.88682565 -0.00014757 -0.00043128 0.00458351 Br 0.14682869 20.42124607 14.97789814 0.00080417 0.00158994 -0.00025601 Cs 0.37902908 17.65859219 17.43689377 0.00057675 0.00043251 0.00135031 Pb 2.99090188 20.71333894 20.54063054 0.00000763 0.00092408 -0.00114202 Br 3.68341707 20.61926819 17.56903553 -0.00236824 -0.00470610 -0.00056516 Br 3.08876549 17.85455007 20.70638519 -0.00004284 -0.00087519 -0.00535619 Br 23.74259735 20.65408079 20.49273530 -0.00025457 -0.00080511 0.00203908 Cs 6.07077048 0.07125153 22.88944957 -0.00264242 0.00093316 -0.00065252 Pb 9.09430667 2.91568498 3.32678494 -0.00095994 0.00208464 -0.00128442 Br 9.13918217 2.47153182 0.05948016 -0.00210877 0.00200690 -0.00050487 Br 8.64599628 23.59554514 3.20004400 0.00278714 0.00411012 0.00389059 Br 6.00170053 2.88118851 3.48869784 -0.00273504 0.00231947 0.00168080 Cs 6.14304335 23.39789804 5.69186960 -0.00147526 0.00043220 0.00093003 Pb 8.80713527 2.76719043 8.81753788 -0.00035935 0.00012085 0.00041643 Br 9.93427938 3.41823918 6.02913956 -0.00206538 0.00126860 -0.00140773 Br 8.31517004 23.36807282 8.65583746 0.00134765 -0.00167012 0.00325111 Br 5.73670492 3.46281999 8.57045128 0.00126098 -0.00171505 -0.00320675 Cs 6.10088179 23.64908630 12.04839729 -0.00052645 0.00150536 0.00013023 Pb 9.09799883 2.86020874 14.64742631 0.00185568 0.00030105 0.00037664 Br 8.97914709 2.82192988 11.75061062 0.00076110 0.00081057 -0.00123939 Br 10.06113854 23.47471632 15.31428298 0.00210342 0.00100258 -0.00129942 Br 6.18351631 2.37137558 15.29312641 -0.00031684 0.00013696 -0.00288810 Cs 6.53851268 23.55119756 17.63743016 -0.00135938 -0.00218442 0.00104156 Pb 9.21704565 3.01866754 21.08804035 -0.00113154 0.00093119 -0.00061522 Br 9.77403186 3.01434935 17.44493436 -0.00132885 -0.00143656 0.00452250 Br 9.15142068 0.20928783 20.51315595 -0.00162279 -0.00089308 0.00354316 Br 6.16363192 3.89871514 20.74468040 -0.00178840 0.00111376 0.00031201 Cs 5.68666329 6.40770628 23.65893295 -0.00137713 -0.00318884 0.00065922 Pb 8.91177737 8.77873922 2.82429837 -0.00130847 0.00274552 -0.00055719 Br 8.85240741 8.79970069 23.65129713 0.00139756 0.00078342 -0.00121677 Br 8.34737839 5.70180256 2.80636598 -0.00253123 0.00304603 0.00171170 Br 5.88572557 8.88630554 2.68684225 0.00022368 0.00107687 0.00101661 Cs 5.77097407 5.99672820 5.81141180 0.00316434 0.00312555 0.00055279 Pb 8.79794057 8.83559125 8.78741001 -0.00069886 0.00004845 -0.00204374 Br 8.80726776 8.69003344 5.94638685 -0.00176400 -0.00216753 0.00015516 Br 8.83967821 5.76780779 9.03325258 -0.00219815 0.00200634 0.00270883 Br 5.93827385 8.01241167 8.58723785 -0.00066271 0.00265066 0.00019491 Cs 5.82766217 6.20931159 11.83853188 0.00115969 -0.00135607 0.00142938 Pb 9.08824897 8.87447116 14.83493729 -0.00046354 -0.00012706 0.00030416 Br 7.99091968 9.20749254 11.82616539 -0.00269548 -0.00520169 0.00051663 Br 9.24910765 5.73721703 14.94686926 0.00291796 0.00227885 0.00352503 Br 5.94542811 9.36740535 15.32122062 0.00235348 -0.00465214 0.00182245 Cs 7.00202079 6.02969529 17.87489548 -0.00173292 0.00126503 -0.00079430 Pb 8.69546382 8.90832002 20.72024023 0.00039468 0.00099622 -0.00048128 Br 9.20179955 9.37477455 17.81405285 0.00012226 -0.00094054 0.00294704 Br 9.21361392 6.07399587 21.01044958 -0.00060681 0.00200885 -0.00076613 Br 5.78813271 8.68685386 20.56058439 -0.00254925 -0.00181437 0.00119265 Cs 5.58129662 11.60776439 23.28457746 -0.00159340 -0.00161361 0.00118321 Pb 8.70577572 14.63491925 3.00267873 0.00157499 -0.00044587 0.00046418 Br 9.26127580 14.81924482 0.33477970 0.00035634 -0.00323684 0.00303145 Br 8.91328393 11.56442518 2.95047595 0.00162926 -0.00111572 0.00246737 Br 5.62300983 14.77378763 2.58929786 0.00025135 -0.00201259 0.00036258 Cs 5.10728415 11.88492155 4.86342933 0.00114598 0.00079626 -0.00006123 Pb 8.60002111 14.59863500 8.90518006 -0.00137365 -0.00190711 0.00111715 Br 8.01603090 14.15897214 6.05613145 0.00470086 -0.00298611 -0.00270123 Br 9.84104947 11.84973805 8.94880922 -0.00094436 0.00380613 -0.00284998 Br 5.93498520 14.57810604 9.45014946 0.00017167 -0.00541991 0.00131049 Cs 5.44641461 12.06899460 11.92443018 0.00076721 -0.00086214 -0.00198594 Pb 8.80353004 15.00282249 14.89569676 0.00111474 0.00183049 0.00085314 Br 9.73286603 15.09323678 11.71831579 -0.00258807 0.00206246 0.00106534 Br 9.42717642 11.85475791 15.03507778 0.00144619 -0.00063471 0.00200588 Br 6.13721630 14.62505273 14.59037128 -0.00268087 0.00101863 -0.00586298 Cs 6.38618496 12.04200831 17.87591962 0.00020785 -0.00247341 -0.00188188 Pb 8.93208178 14.92159418 20.81192809 0.00015200 0.00007160 -0.00126945 Br 9.23839912 14.90478102 18.01882655 0.00278685 -0.00010008 -0.00361390 Br 8.94537103 11.95187218 21.48229669 0.00004843 -0.00342696 -0.00123779 Br 6.02714901 14.71492875 21.20271180 0.00180625 0.00327079 0.00121390 Cs 5.55088930 18.36780389 23.76909443 -0.00245804 0.00294147 -0.00057746 Pb 8.81668892 20.72968981 3.01278377 -0.00064330 0.00007217 0.00096763 Br 8.89419696 20.67225451 0.04755886 0.00005468 0.00198856 0.00342204 Br 8.42149055 17.67129340 2.99927433 -0.00155385 0.00287541 -0.00219121 Br 5.97119852 20.60343381 2.84401843 -0.00157270 0.00030377 0.00164385 Cs 5.73623667 17.77365100 5.55374682 -0.00115543 0.00262759 -0.00051328 Pb 8.61234694 20.65903673 8.76475447 -0.00093406 -0.00141115 0.00192649 Br 8.53233800 20.35192312 5.90497192 -0.00221694 -0.00102208 -0.00179786 Br 8.06499914 17.52330612 8.54938086 -0.00051184 -0.00130339 -0.00404825 Br 5.77768362 20.68969261 8.78654056 -0.00192723 -0.00181708 -0.00194762 Cs 6.37284891 18.08076944 11.49091492 -0.00053022 0.00256831 -0.00197554 Pb 9.35999498 20.85154358 14.65080756 -0.00043137 0.00006413 -0.00094817 Br 9.36750297 21.17387541 11.74590250 0.00259422 -0.00331950 0.00258714 Br 8.40201124 17.85487510 14.55058050 0.00041316 -0.00508667 0.00190977 Br 6.11340791 21.06860520 14.79684852 -0.00020668 0.00205522 0.00253253 Cs 5.84525532 17.40895625 18.02872032 -0.00078818 -0.00111250 -0.00149186 Pb 9.08636967 20.71618765 20.76199266 -0.00005651 -0.00075745 -0.00063244 Br 9.51076463 20.63082844 17.77153269 0.00461266 0.00133664 -0.00420230 Br 8.06751342 17.92955920 20.69598319 -0.00513497 0.00285856 -0.00037591 Br 6.12140690 21.03172999 20.62996437 0.00062834 -0.00452853 -0.00109752 Cs 12.10313039 23.75384440 0.44286435 0.00073105 -0.00056721 -0.00126968 Pb 15.05174065 3.28256004 2.90298087 0.00010161 0.00123760 -0.00211420 Br 15.37238238 2.81036692 0.15446931 -0.00083644 0.00076094 0.00033177 Br 15.16460889 0.03767764 2.54728572 0.00450226 -0.00398420 0.00181409 Br 11.91325492 3.31831791 2.66461821 0.00111807 0.00216000 -0.00227749 Cs 12.24320597 0.55744967 5.97719614 -0.00094589 0.00067024 -0.00363477 Pb 14.79058616 3.00558758 8.88945180 0.00049554 0.00010440 -0.00080042 Br 14.88327260 2.92716124 5.94495388 -0.00257761 0.00430000 -0.00064302 Br 14.75373962 0.13011683 8.95750475 0.00231887 0.00040066 0.00128049 Br 11.95765342 3.05163887 9.59851320 0.00455929 -0.00015008 0.00104853 Cs 11.93501747 0.00837144 11.93818235 -0.00041070 0.00426307 0.00041193 Pb 14.76285295 3.04758788 14.83812005 0.00179216 -0.00171018 0.00068033 Br 15.51057926 2.95093379 11.77102136 -0.00410294 -0.00109561 0.00167519 Br 14.36845212 23.60461037 15.10443585 -0.00240337 0.00030405 -0.00183050 Br 11.96425675 2.78709368 14.29125566 0.00063612 0.00075805 -0.00063333 Cs 12.12418338 23.31206285 18.21564129 -0.00085491 -0.00125672 0.00279691 Pb 15.09590251 2.91704697 20.93602606 -0.00055326 0.00329262 -0.00106969 Br 15.13363774 2.93041217 17.80793208 -0.00362117 0.00195617 0.00339584 Br 14.93185470 23.64421767 21.11919228 0.00305364 -0.00173414 -0.00352797 Br 12.12644587 3.07096874 21.40115956 -0.00329485 -0.00517474 0.00218301 Cs 11.75250513 6.32731201 23.32465659 0.00065491 -0.00089860 -0.00067318 Pb 14.79370206 9.00434772 3.43914994 -0.00117541 -0.00126245 0.00014800 Br 14.71535527 9.20634925 0.15269629 0.00015317 -0.00332252 0.00087211 Br 15.48732807 6.21804459 3.80642614 -0.00153439 -0.00014277 -0.00278525 Br 11.88470145 8.71463626 2.69812511 -0.00311710 -0.00210013 -0.00134654 Cs 11.83107715 6.33715467 5.57726001 0.00210849 -0.00141009 -0.00015832 Pb 14.47541449 8.89335890 9.01904432 0.00035399 -0.00050755 0.00136602 Br 14.35721632 9.54049636 6.24693508 0.00031137 0.00014698 -0.00130278 Br 15.22732275 6.13819638 8.33846007 0.00088724 0.00140750 -0.00073057 Br 11.68686214 8.17050373 9.06101514 0.00204351 -0.00146159 -0.00123887 Cs 11.88170518 5.85659988 11.81993819 0.00171643 0.00162100 0.00040873 Pb 14.84693663 8.72703046 14.72128835 0.00071155 0.00071392 0.00011302 Br 15.06510506 9.01452004 11.78381314 -0.00272144 0.00167732 -0.00293236 Br 15.19061345 5.91150790 14.95001392 -0.00319924 0.00155866 -0.00067236 Br 11.97974679 8.40842971 14.66312699 0.00334395 0.00113426 0.00358429 Cs 12.49249945 5.78395674 18.26281195 0.00189978 -0.00109389 0.00079034 Pb 14.59635765 9.07352130 20.89798947 -0.00004215 0.00156739 0.00023529 Br 14.76630984 8.88123201 18.03003553 0.00016288 -0.00282919 -0.00173830 Br 15.42242573 5.80839568 20.96414572 -0.00082799 -0.00188469 -0.00184861 Br 11.50646815 9.10755678 20.42491849 -0.00042704 -0.00132661 -0.00062106 Cs 11.44513779 11.84619568 0.59005306 -0.00285209 0.00038772 0.00088588 Pb 14.70768916 15.03812918 3.26771638 0.00092860 -0.00171224 -0.00067568 Br 14.78815248 15.75555456 0.02818288 0.00134278 -0.00201823 -0.00094795 Br 14.70100260 11.89638404 3.04188097 -0.00280611 0.00222827 0.00140247 Br 11.63802636 15.21958910 3.74978587 0.00007827 -0.00205633 -0.00396735 Cs 11.55813887 12.48504017 5.95780334 -0.00071351 0.00019848 0.00121106 Pb 14.43893184 14.82674341 9.20030993 0.00187981 0.00202909 0.00165295 Br 15.19402760 14.72934773 6.12233444 -0.00278203 0.00129059 -0.00011423 Br 14.11908875 12.02271231 9.03707901 -0.00480876 0.00229368 -0.00056624 Br 11.91123931 15.57556557 8.22842238 -0.00003760 -0.00065092 0.00107264 Cs 11.92576621 12.08267972 11.94108522 -0.00039354 0.00034523 -0.00006164 Pb 15.04851497 14.84067253 14.86567896 -0.00001442 0.00255455 0.00132956 Br 14.54915696 14.92586793 12.02203125 0.00241310 -0.00141396 0.00293309 Br 14.44728086 11.82473023 14.86812423 -0.00023005 -0.00253822 0.00152039 Br 11.99648910 15.33935550 15.14965067 -0.00023365 -0.00119180 0.00070199 Cs 12.21594910 11.69942942 17.54235555 0.00121618 -0.00218680 -0.00183740 Pb 14.68404091 14.85521135 21.10244876 0.00214574 -0.00036055 -0.00049451 Br 14.50228540 14.48731402 17.81735573 -0.00078498 0.00275760 -0.00185245 Br 14.16338468 11.88016102 20.87022234 0.00068621 0.00119273 0.00334576 Br 11.86283542 15.10849445 21.31319181 0.00132687 0.00215927 0.00354532 Cs 11.64781764 18.05274823 0.28318561 -0.00141773 -0.00002101 0.00346971 Pb 14.70982787 20.83666122 2.87754156 0.00175242 0.00158446 0.00148474 Br 14.31659261 20.59380863 0.17916047 0.00430656 0.00237629 -0.00324522 Br 14.57536267 18.10460359 3.48826939 -0.00147077 0.00030731 0.00166711 Br 11.78103825 21.31975172 3.40820413 -0.00199292 0.00113801 0.00162910 Cs 11.23483440 18.13163049 5.94154815 -0.00113434 0.00078340 0.00010004 Pb 14.77438894 20.92427800 8.77084646 0.00333689 0.00095469 0.00092527 Br 15.09722892 21.28121813 5.74454060 -0.00126071 -0.00710561 0.00153351 Br 14.68972830 17.91719062 8.77306040 0.00018829 -0.00387255 0.00031623 Br 11.80395104 21.00491302 8.36240278 -0.00194638 0.00463080 -0.00304796 Cs 12.35125822 18.44554554 11.72584696 0.00328166 0.00273945 0.00089673 Pb 14.94101746 20.55143182 14.94148047 0.00060901 -0.00136232 -0.00070764 Br 14.76788844 20.50329267 12.01998662 0.00098441 -0.00099646 -0.00149842 Br 15.80915476 17.62166096 14.60063377 -0.00291147 0.00146985 -0.00048919 Br 11.94139065 19.71356290 14.72648858 0.00182368 -0.00185509 0.00000284 Cs 12.05203818 17.65802365 17.68899405 0.00247465 -0.00025372 0.00248913 Pb 14.70709674 20.52403033 20.69137908 0.00088204 0.00064306 -0.00145050 Br 14.44614291 20.46596068 17.85702868 -0.00375979 0.00162933 -0.00091987 Br 14.58112565 17.76066525 20.20090923 0.00128042 0.00360397 -0.00597036 Br 12.04228987 20.93565171 20.98679317 0.00182740 -0.00196563 0.00023625 Cs 17.68279407 23.41624978 0.19835427 0.00534428 0.00015038 -0.00278256 Pb 20.79918549 3.04764519 2.75863200 0.00016207 0.00214930 -0.00113941 Br 20.68997057 3.73859086 23.64871676 -0.00173508 0.00460345 -0.00281303 Br 21.03465100 0.05062020 3.29820348 -0.00131605 -0.00073168 -0.00036397 Br 18.11697515 3.17985366 2.77757341 0.00149834 0.00097042 0.00333858 Cs 18.39099189 23.61526578 6.16988897 -0.00271507 0.00294807 0.00005353 Pb 20.93037601 3.12839672 8.94407105 0.00146097 -0.00097101 -0.00016218 Br 21.53963596 2.89609422 5.99949448 0.00094182 0.00147816 0.00276109 Br 21.05678408 0.04341971 8.83197278 -0.00107704 0.00007188 0.00353976 Br 17.93623104 2.50711189 8.57233216 -0.00437767 0.00179411 0.00016359 Cs 17.64499566 0.46312608 11.80584094 0.00260127 -0.00098076 -0.00019120 Pb 20.81960829 2.97591376 14.59867109 -0.00144157 0.00106478 -0.00001407 Br 19.91977721 3.43775886 11.71734867 -0.00147853 0.00153542 0.00009426 Br 21.24867770 23.74679849 14.83953750 0.00320000 -0.00035950 0.00026220 Br 17.89973283 2.50531389 15.00809267 -0.00023456 0.00301940 -0.00031158 Cs 17.91056772 0.02930289 18.38862955 0.00113850 -0.00123625 -0.00037420 Pb 21.09774708 2.89343525 20.66437666 0.00162319 -0.00104623 0.00023274 Br 20.89549141 3.01109375 17.70386251 -0.00079283 0.00323147 -0.00041631 Br 21.20011614 23.60579028 20.72798907 -0.00336361 -0.00121183 -0.00338826 Br 18.03668573 2.82776988 20.16038477 -0.00612625 -0.00004097 -0.00283527 Cs 17.67695132 6.22530894 23.49101223 -0.00007327 0.00220096 -0.00245688 Pb 20.81283215 8.95775049 3.05321212 0.00094853 -0.00031330 0.00002365 Br 20.43997503 9.33303749 0.02744316 -0.00255539 -0.00519732 0.00239541 Br 20.65792044 6.09627242 3.24313369 -0.00160144 -0.00120882 0.00068592 Br 17.84645423 9.06616372 3.38425953 0.00149029 -0.00055130 0.00379302 Cs 18.33476792 5.35675977 6.41815675 0.00120804 -0.00029278 0.00015810 Pb 20.83411234 8.90683946 8.94412377 0.00223786 -0.00209733 -0.00147646 Br 20.96268327 9.21487897 6.12868053 -0.00240652 0.00408600 -0.00075610 Br 21.31404482 5.98751116 8.85113667 0.00009442 0.00057166 -0.00095843 Br 17.57925208 9.05411660 9.54547169 -0.00584066 -0.00020591 -0.00318905 Cs 17.41194933 6.24399170 11.49013620 0.00037253 0.00258790 -0.00119487 Pb 20.90291191 8.88649313 14.86416569 0.00127591 -0.00044971 0.00105072 Br 21.00111147 8.61602638 11.92721560 -0.00118518 0.00038283 -0.00104303 Br 20.93581827 5.82515835 15.34794327 -0.00114735 -0.00226097 0.00032442 Br 17.84188744 8.66049574 14.67900920 0.00233255 -0.00366813 0.00010728 Cs 17.96859661 5.77871847 17.49087449 -0.00189368 -0.00064274 0.00077554 Pb 20.50384779 9.06242344 20.66530949 0.00108627 0.00037025 -0.00071148 Br 21.38683166 8.75870924 18.00538796 0.00028387 -0.00128991 -0.00241476 Br 20.46455180 5.85602959 20.84463429 -0.00038978 -0.00344417 -0.00168932 Br 17.57042131 9.57114672 20.54088830 -0.00242592 -0.00226996 -0.00101795 Cs 17.89134552 11.99526141 0.18990901 -0.00177818 -0.00090648 -0.00076268 Pb 20.39796643 14.77758822 2.97847610 0.00151389 -0.00107809 0.00115011 Br 20.74598126 15.27600464 23.70909196 0.00038840 0.00036167 0.00106200 Br 20.40322477 11.78818730 3.07826970 -0.00168209 -0.00221026 0.00300002 Br 17.55016844 15.09160517 2.86799482 -0.00107723 -0.00075948 -0.00206654 Cs 17.71380701 11.17374243 6.27545660 0.00190901 -0.00126433 -0.00062330 Pb 20.58432604 14.74695774 8.87147569 -0.00035880 0.00137470 0.00242105 Br 20.37588138 15.14909636 5.92581927 -0.00285347 -0.00354415 -0.00144732 Br 20.90540653 11.86774160 8.40786011 -0.00647961 -0.00147056 0.00374296 Br 17.76976610 14.47228171 9.32063288 -0.00111043 -0.00054764 0.00017692 Cs 17.86030968 11.38156705 12.39043330 0.00104638 -0.00151379 -0.00178170 Pb 20.86675905 14.58572991 14.88155482 -0.00069727 0.00054574 -0.00102198 Br 20.94191432 14.01084679 12.07104899 -0.00171811 -0.00060428 -0.00203941 Br 20.42074223 11.64127004 15.19798407 0.00103982 -0.00022463 0.00191895 Br 17.98633440 14.06444673 14.70794927 -0.00135942 0.00115062 0.00241239 Cs 17.46264528 12.08671917 17.69209057 -0.00058476 -0.00129267 -0.00155570 Pb 20.77190425 14.85137243 20.83424186 0.00224195 0.00270660 -0.00008296 Br 21.37456179 14.59073449 17.84328645 -0.00357343 -0.00125355 0.00282452 Br 20.45665859 11.93922478 20.05727918 -0.00411114 0.00084490 -0.00101383 Br 17.69684602 15.10493109 20.68400391 0.00396118 -0.00131243 -0.00174608 Cs 17.89651336 17.73918306 23.41975398 0.00040968 -0.00037675 -0.00001967 Pb 20.62489055 20.97082249 3.05140433 0.00331416 0.00032542 0.00142479 Br 21.29311829 21.07120409 23.73111668 0.00162719 0.00067743 -0.00084493 Br 20.61788155 17.88180382 3.07300341 0.00183659 -0.00001875 0.00131203 Br 17.99739027 20.22011069 2.16397279 0.00278017 0.00005123 0.00180845 Cs 17.63168450 17.99231027 5.81588156 0.00159374 -0.00013392 -0.00240402 Pb 20.73329805 20.94271846 9.06683258 0.00149438 0.00128956 -0.00331405 Br 21.00115908 20.82190782 6.01045024 0.00070164 -0.00099883 -0.00063033 Br 20.23030622 17.88498242 8.65642807 -0.00015396 -0.00322272 0.00037272 Br 17.76138288 20.83762057 9.62688858 -0.00385347 -0.00011432 0.00195710 Cs 17.86244326 18.07661351 11.44025310 -0.00088226 -0.00083473 0.00328109 Pb 20.97833490 20.76784328 14.77106000 -0.00223194 0.00110205 0.00139702 Br 20.97270795 20.83737656 11.79956424 0.00098005 0.00003609 0.00033454 Br 20.69785259 17.54612198 13.98168851 -0.00247138 -0.00338561 -0.00031762 Br 17.95155558 21.34786276 14.53992677 0.00082585 0.00232854 0.00315485 Cs 17.45614223 18.10949675 17.81227183 -0.00023789 -0.00062444 0.00327264 Pb 20.98951284 20.80059884 20.72838263 -0.00047990 -0.00028529 0.00243664 Br 20.53237334 20.97492641 17.80493789 0.00293181 -0.00286368 -0.00306686 Br 20.78200134 17.81339911 20.54178452 -0.00008730 0.00230584 -0.00287344 Br 17.74407255 20.55770533 20.93335090 0.00134020 0.00150383 0.00335537 320 Lattice="23.77195271 0.0 0.0 0.0 23.77195271 0.0 0.0 0.0 23.77195271" Properties=species:S:1:pos:R:3:vel:R:3 Time=20069.99999999 energy=-11425.94496155 virial="-17.83243535 2.25277796 -0.90401944 2.25277796 -14.491543 -1.88517754 -0.90401944 -1.88517754 -9.5111662" stress="-0.000296 0.0001677 -6.73e-05 0.0001677 -0.00012572 -0.00014033 -6.73e-05 -0.00014033 0.00023278" pbc="T T T" Cs 0.04659030 0.29967784 23.73257098 -0.00388254 -0.00288486 0.00106376 Pb 2.98374141 3.00161807 2.99520320 -0.00260176 0.00145449 0.00150835 Br 3.30298706 2.92354008 0.02742091 -0.00087633 0.00040082 0.00235800 Br 3.00220974 0.11810006 3.14808483 -0.00024453 0.00117013 -0.00075490 Br 0.16555389 3.47466335 2.50739112 0.00221694 -0.00038549 -0.00298221 Cs 23.75448645 23.66474090 6.17549088 0.00343680 0.00244270 -0.00182757 Pb 2.98010241 2.96336655 9.32733933 0.00135144 -0.00003868 -0.00097443 Br 2.41074397 2.81048864 6.15787674 0.00200503 -0.00129540 0.00292985 Br 3.15342429 23.75920323 9.21183849 0.00011155 0.00003548 0.00332986 Br 23.65943565 3.04479198 10.09912155 -0.00074198 0.00277245 0.00645441 Cs 0.42912083 23.64472518 12.18877624 -0.00354703 -0.00082425 0.00065928 Pb 3.09481685 3.32868813 14.96559718 -0.00160922 -0.00001905 -0.00112869 Br 4.23568591 3.07563834 11.94948433 -0.00164070 -0.00094667 0.00016615 Br 2.91281742 0.25111518 14.85409141 0.00213578 -0.00330133 -0.00336802 Br 0.30842627 3.47070466 14.76738685 0.00034831 -0.00230265 -0.00010689 Cs 0.21592923 23.42563706 17.83786344 -0.00109960 -0.00172837 -0.00212385 Pb 3.14358637 3.07295804 20.63182295 0.00187371 -0.00001024 -0.00002934 Br 2.58726486 3.11851926 17.80782821 -0.00123990 0.00221589 0.00177162 Br 3.10337753 0.04155162 20.18008889 -0.00141140 0.00140514 0.00323138 Br 0.11114331 3.12509084 20.55311790 0.00097274 -0.00187068 0.00015718 Cs 23.48239258 6.33131680 23.75609505 -0.00009341 0.00264067 -0.00056836 Pb 2.84834103 8.92508149 3.22197107 -0.00014730 -0.00001028 0.00077838 Br 2.60312780 8.78183322 23.64522485 0.00159286 -0.00559497 0.00092401 Br 3.22138335 6.05622391 3.15480941 -0.00289302 -0.00010795 -0.00143536 Br 23.68301443 8.64800063 3.27025306 0.00066129 -0.00153505 -0.00225905 Cs 0.33305558 6.00063100 5.87621757 0.00232312 0.00121669 0.00197751 Pb 3.25705164 9.06372600 8.94442160 0.00298570 0.00081964 -0.00098969 Br 2.87248588 8.98062122 5.91583773 -0.00030383 -0.00247843 0.00325476 Br 2.90409703 5.96735238 8.67179759 -0.00160983 -0.00401734 0.00159684 Br 0.00374984 9.12268442 8.88170750 -0.00344102 0.00348024 -0.00129945 Cs 23.66473669 6.04018272 11.88040906 -0.00153867 -0.00207323 0.00173805 Pb 2.92448736 8.77691752 14.89863445 -0.00128535 -0.00003557 -0.00214786 Br 3.16359109 8.83182314 11.75780480 0.00055112 0.00161096 0.00042783 Br 3.76650269 6.00264012 15.09915387 -0.00061790 0.00213648 0.00121974 Br 0.04940863 8.57610766 14.59255801 0.00048785 -0.00205910 -0.00352589 Cs 23.72433104 5.72967243 17.61542257 -0.00141274 0.00015522 -0.00037143 Pb 2.72443554 8.86237938 20.60003036 -0.00097062 0.00000808 -0.00047294 Br 2.13130027 8.77446520 17.78398136 0.00385083 0.00020570 -0.00180280 Br 2.06528062 5.89767614 20.70824548 -0.00040179 -0.00096637 0.00175270 Br 23.70626525 9.28915253 21.15894333 -0.00122831 -0.00045134 0.00038612 Cs 23.07431518 11.79675129 0.24267778 0.00197517 -0.00091184 0.00042316 Pb 2.80529251 14.86743335 2.88019546 -0.00244322 0.00138911 0.00082812 Br 2.50506728 15.16839119 23.69340282 -0.00105719 -0.00146896 -0.00010125 Br 2.87936325 11.71204723 2.32019333 0.00165471 0.00124353 -0.00178389 Br 23.34784674 14.61280131 3.31297281 -0.00179084 0.00114329 -0.00149919 Cs 23.46860317 11.79559535 6.54998145 0.00079051 -0.00131623 0.00130647 Pb 2.97639692 15.03956488 9.04982479 0.00108625 -0.00016444 0.00178587 Br 3.37443470 14.96292418 5.98146864 -0.00201688 -0.00056726 -0.00045165 Br 2.97314995 11.80438114 8.69959672 0.00036101 0.00445491 0.00029677 Br 23.73015961 15.10575842 8.90131512 0.00138112 -0.00085706 -0.00038857 Cs 0.18050239 11.62472213 11.49650759 -0.00215277 0.00054799 -0.00159613 Pb 3.05017449 14.59010895 15.05032636 0.00070314 -0.00138698 0.00116940 Br 3.05681883 14.49394574 11.86043669 0.00002123 -0.00212606 -0.00168897 Br 2.69236055 11.83560968 15.36726926 -0.00368470 -0.00101052 0.00205186 Br 0.04559312 14.63620377 14.61454930 0.00320595 -0.00088371 -0.00182187 Cs 23.44278671 11.78174850 18.17434239 0.00207864 -0.00137392 -0.00004653 Pb 3.05554699 14.61877498 20.81051843 0.00151940 -0.00254367 -0.00144170 Br 2.93759697 14.84460217 17.94690957 -0.00022338 0.00111921 -0.00061538 Br 2.74900812 11.79246433 20.91374378 0.00051881 -0.00161995 0.00045864 Br 0.10665784 14.50177773 20.99005314 0.00234165 0.00139105 -0.00163843 Cs 0.08382360 17.76930109 23.69011136 -0.00013572 0.00058649 -0.00142142 Pb 2.92833052 20.77649241 2.92719927 0.00182133 -0.00083028 -0.00127980 Br 2.91511624 20.73818686 23.50702637 0.00052021 0.00184917 0.00439545 Br 3.23400188 17.90845373 2.62200768 -0.00123009 0.00094873 0.00078374 Br 23.65208969 20.77617720 2.88444539 -0.00223361 0.00208957 0.00187908 Cs 23.15819561 17.77452322 6.32398335 -0.00172285 0.00125962 -0.00303663 Pb 2.81623778 20.95316757 8.79133025 0.00187954 -0.00034281 -0.00027007 Br 2.91898329 20.86013401 5.80877621 0.00137806 -0.00236356 0.00106705 Br 3.40932881 17.99603354 9.49658705 -0.00170253 -0.00234921 -0.00229147 Br 23.56140720 21.14061229 8.86878850 0.00199046 -0.00258776 0.00046816 Cs 0.41487341 17.10731227 12.31530871 0.00318908 0.00112216 -0.00328002 Pb 3.11733962 21.08748445 14.66520128 -0.00017315 -0.00008913 0.00187949 Br 3.35607096 20.74876386 11.76857769 -0.00380200 -0.00005790 -0.00327281 Br 3.05602223 18.02963876 14.93202178 -0.00010839 -0.00108615 0.00444566 Br 0.15368048 20.43804032 14.97521991 0.00056075 0.00176795 -0.00027790 Cs 0.38459515 17.66292192 17.45085714 0.00053582 0.00043475 0.00144130 Pb 2.99169281 20.72311413 20.52891948 0.00015012 0.00102362 -0.00119431 Br 3.65818059 20.57265939 17.56547696 -0.00267840 -0.00460214 -0.00013103 Br 3.08832701 17.84436152 20.65248867 -0.00004735 -0.00114531 -0.00541416 Br 23.74428852 20.64540087 20.51202602 0.00059005 -0.00092748 0.00181979 Cs 6.04417132 0.08096531 22.88352683 -0.00267668 0.00100801 -0.00053205 Pb 9.08336781 2.93664436 3.31277516 -0.00122725 0.00210699 -0.00151846 Br 9.11732322 2.49177623 0.05492541 -0.00226505 0.00204191 -0.00040241 Br 8.67478325 23.63840989 3.23886961 0.00296777 0.00444646 0.00387692 Br 5.97513810 2.90376865 3.50625663 -0.00255851 0.00219723 0.00183084 Cs 6.12711505 23.40241498 5.70110376 -0.00170760 0.00047259 0.00091647 Pb 8.80304497 2.76987121 8.82183971 -0.00045925 0.00041402 0.00043963 Br 9.91307174 3.42972061 6.01674113 -0.00217409 0.00102873 -0.00106696 Br 8.32873315 23.35857898 8.68799851 0.00135859 -0.00023628 0.00318183 Br 5.75269291 3.44363360 8.53810453 0.00193420 -0.00212202 -0.00325359 Cs 6.09543574 23.66406253 12.04982864 -0.00056454 0.00148929 0.00015710 Pb 9.11594044 2.86275830 14.65300260 0.00173399 0.00021233 0.00074294 Br 8.98552985 2.82940103 11.73568390 0.00051681 0.00068395 -0.00172034 Br 10.08128541 23.48684689 15.29983194 0.00191556 0.00140687 -0.00159786 Br 6.18006350 2.37382847 15.26399382 -0.00036422 0.00034708 -0.00293322 Cs 6.52480782 23.52874340 17.64797310 -0.00137953 -0.00229993 0.00106392 Pb 9.20525591 3.02814864 21.08114702 -0.00122516 0.00096372 -0.00076507 Br 9.76042118 3.00049085 17.49387141 -0.00139883 -0.00133490 0.00523373 Br 9.13456438 0.19725520 20.54863472 -0.00175098 -0.00150873 0.00355127 Br 6.14469977 3.90818445 20.74793000 -0.00199318 0.00078229 0.00033487 Cs 5.67256799 6.37535797 23.66565271 -0.00144047 -0.00327895 0.00068525 Pb 8.89779607 8.80453724 2.81911338 -0.00148570 0.00240104 -0.00048326 Br 8.86633304 8.80754516 23.64092719 0.00138559 0.00078504 -0.00084139 Br 8.32353025 5.73281586 2.82338087 -0.00223989 0.00314805 0.00168943 Br 5.88817578 8.89803332 2.69742565 0.00025088 0.00126733 0.00109864 Cs 5.80264698 6.02718707 5.81630699 0.00316817 0.00296245 0.00042102 Pb 8.79091628 8.83607037 8.76814042 -0.00069683 0.00004517 -0.00180503 Br 8.79024322 8.66805104 5.94593225 -0.00164189 -0.00222851 -0.00024686 Br 8.81823879 5.78688842 9.06050832 -0.00208904 0.00180955 0.00273707 Br 5.93205724 8.04131416 8.59013855 -0.00057793 0.00312168 0.00038508 Cs 5.83985041 6.19584379 11.85234962 0.00127497 -0.00133931 0.00133513 Pb 9.08249118 8.87404351 14.83786911 -0.00068438 0.00004159 0.00028396 Br 7.96542618 9.15516105 11.83237554 -0.00239780 -0.00525429 0.00072303 Br 9.27737988 5.76212296 14.98090964 0.00273558 0.00268111 0.00328298 Br 5.96899136 9.32022662 15.33919929 0.00235923 -0.00478197 0.00177207 Cs 6.98437518 6.04203117 17.86671354 -0.00179583 0.00120316 -0.00084013 Pb 8.69966791 8.91940958 20.71565879 0.00043674 0.00121914 -0.00043355 Br 9.20127107 9.36400000 17.84034497 -0.00022862 -0.00120906 0.00228722 Br 9.20741466 6.09323092 21.00138165 -0.00062994 0.00181409 -0.00104543 Br 5.76169110 8.66863426 20.57210707 -0.00272319 -0.00182727 0.00111260 Cs 5.56536109 11.59133166 23.29694960 -0.00159132 -0.00167275 0.00129049 Pb 8.72133285 14.63154748 3.00877583 0.00153174 -0.00022648 0.00076518 Br 9.26525086 14.78736578 0.36127388 0.00044101 -0.00313547 0.00221679 Br 8.92883309 11.55742610 2.97598075 0.00147784 -0.00025152 0.00263798 Br 5.62749927 14.75286566 2.59263178 0.00061838 -0.00216646 0.00030624 Cs 5.12044107 11.89293695 4.86430574 0.00148281 0.00080795 0.00023530 Pb 8.58910981 14.58044086 8.91633268 -0.00080354 -0.00172391 0.00110941 Br 8.06279789 14.12919669 6.02866885 0.00464516 -0.00296786 -0.00276544 Br 9.83030489 11.88585371 8.92098591 -0.00120474 0.00339090 -0.00269972 Br 5.93233023 14.52336571 9.46437440 -0.00069917 -0.00551996 0.00152467 Cs 5.45518205 12.05873448 11.90510465 0.00098540 -0.00119236 -0.00187398 Pb 8.81832519 15.02136906 14.90333939 0.00182365 0.00186254 0.00067030 Br 9.70482926 15.11345427 11.73017127 -0.00301619 0.00197624 0.00129876 Br 9.44116116 11.84826366 15.05410020 0.00134974 -0.00067320 0.00179638 Br 6.10444165 14.63378580 14.53205208 -0.00381211 0.00073369 -0.00579334 Cs 6.38850204 12.01686880 17.85695180 0.00025668 -0.00255486 -0.00191282 Pb 8.93387268 14.92207763 20.80061226 0.00020958 0.00002422 -0.00100103 Br 9.26487768 14.90341571 17.98035063 0.00250442 -0.00016959 -0.00403191 Br 8.94489443 11.91865743 21.46836790 -0.00014411 -0.00318078 -0.00154764 Br 6.04406018 14.74698631 21.21437158 0.00158002 0.00313582 0.00112048 Cs 5.52734664 18.39651693 23.76247069 -0.00224648 0.00279774 -0.00074683 Pb 8.81082676 20.73010977 3.02205665 -0.00052852 0.00001873 0.00088693 Br 8.89518488 20.69249460 0.07930692 0.00014266 0.00205866 0.00289439 Br 8.40709825 17.69863032 2.97727442 -0.00132177 0.00258412 -0.00220921 Br 5.95274747 20.60778946 2.86089550 -0.00211405 0.00057094 0.00172872 Cs 5.72479766 17.79993632 5.54880459 -0.00113360 0.00262647 -0.00047535 Pb 8.60431025 20.64323672 8.78433153 -0.00066948 -0.00174516 0.00197694 Br 8.50896235 20.34288950 5.88749813 -0.00245825 -0.00078094 -0.00165362 Br 8.06140869 17.51042295 8.50786061 -0.00021595 -0.00126826 -0.00424535 Br 5.75787164 20.67237278 8.76656570 -0.00200790 -0.00164512 -0.00204727 Cs 6.36711095 18.10646617 11.47247868 -0.00061546 0.00256606 -0.00171257 Pb 9.35399939 20.85143945 14.64231479 -0.00075509 -0.00008307 -0.00073980 Br 9.39302143 21.14047130 11.77102554 0.00250808 -0.00335756 0.00240641 Br 8.40627288 17.80359718 14.57040426 0.00043254 -0.00511591 0.00205055 Br 6.11250394 21.09026507 14.82091521 0.00003485 0.00227113 0.00227619 Cs 5.83696374 17.39865540 18.01313989 -0.00087136 -0.00094890 -0.00162626 Pb 9.08456271 20.70866410 20.75516440 -0.00029883 -0.00074473 -0.00073068 Br 9.55619669 20.64390650 17.73124083 0.00446273 0.00127450 -0.00382718 Br 8.01902967 17.95683239 20.69344787 -0.00454615 0.00256869 -0.00013112 Br 6.12670802 20.98717541 20.61924472 0.00041654 -0.00436222 -0.00104536 Cs 12.11030197 23.74839114 0.42988224 0.00070451 -0.00052364 -0.00132624 Pb 15.05245556 3.29477934 2.88310225 0.00003868 0.00120756 -0.00185481 Br 15.36447570 2.81884761 0.15428652 -0.00073869 0.00092799 -0.00036852 Br 15.20858408 0.00046974 2.56721802 0.00429923 -0.00344216 0.00216295 Br 11.92727660 3.33987378 2.64187510 0.00167323 0.00214930 -0.00226640 Cs 12.23369706 0.56294514 5.94070948 -0.00095633 0.00042994 -0.00366078 Pb 14.79627750 3.00731666 8.88166523 0.00064667 0.00024404 -0.00075561 Br 14.85760031 2.96973186 5.93740239 -0.00255992 0.00420905 -0.00086878 Br 14.77648929 0.13325959 8.97061270 0.00223153 0.00021493 0.00133506 Br 12.00206410 3.04913905 9.60732868 0.00428949 -0.00034341 0.00071873 Cs 11.93143277 0.05102990 11.94242056 -0.00030540 0.00426442 0.00043344 Pb 14.78217488 3.02845798 14.84527133 0.00206597 -0.00210890 0.00074824 Br 15.46685894 2.94287677 11.78919605 -0.00462279 -0.00053129 0.00196054 Br 14.34536501 23.60638161 15.08522055 -0.00220753 0.00005726 -0.00201027 Br 11.97216612 2.79673065 14.28361403 0.00094332 0.00117389 -0.00088797 Cs 12.11572219 23.30058793 18.24318232 -0.00083745 -0.00103372 0.00270942 Pb 15.09104005 2.94932897 20.92458546 -0.00042283 0.00314908 -0.00121898 Br 15.09532214 2.95051555 17.84192511 -0.00404169 0.00206144 0.00338313 Br 14.96253659 23.62715179 21.08336503 0.00308115 -0.00166935 -0.00363607 Br 12.09383458 3.01965652 21.42250765 -0.00322108 -0.00508341 0.00208663 Cs 11.75969142 6.31841084 23.31893040 0.00077960 -0.00088242 -0.00047283 Pb 14.78257840 8.99210699 3.43934668 -0.00104906 -0.00117856 -0.00010834 Br 14.71727040 9.17207677 0.16115553 0.00023126 -0.00352941 0.00081202 Br 15.47116794 6.21271120 3.77775620 -0.00169457 -0.00092042 -0.00295092 Br 11.85329175 8.69296790 2.68492568 -0.00314019 -0.00223193 -0.00128996 Cs 11.85266357 6.32334231 5.57602008 0.00220934 -0.00135216 -0.00009009 Pb 14.47960664 8.88859604 9.03181611 0.00048452 -0.00043908 0.00116986 Br 14.35984127 9.54246807 6.23034662 0.00021386 0.00024492 -0.00200192 Br 15.23576666 6.15080700 8.33209541 0.00080227 0.00110742 -0.00054301 Br 11.70682067 8.15558681 9.04886860 0.00191115 -0.00152326 -0.00119200 Cs 11.89837526 5.87256622 11.82441881 0.00161989 0.00157173 0.00048820 Pb 14.85443180 8.73572090 14.72319228 0.00078965 0.00102472 0.00026725 Br 15.03545822 9.03085626 11.76242454 -0.00320349 0.00159151 -0.00130710 Br 15.15805393 5.92582615 14.94287835 -0.00331028 0.00127416 -0.00075352 Br 12.01188788 8.41999701 14.69863170 0.00306110 0.00117955 0.00351604 Cs 12.51115478 5.77285273 18.27045616 0.00182780 -0.00112574 0.00073633 Pb 14.59633339 9.08914819 20.90077404 0.00003419 0.00155331 0.00031682 Br 14.76761217 8.85257738 18.00903997 0.00009761 -0.00289550 -0.00243978 Br 15.41184661 5.79152534 20.94464357 -0.00128349 -0.00146110 -0.00204631 Br 11.50785590 9.09317941 20.42029583 0.00071148 -0.00154523 -0.00030151 Cs 11.41778194 11.84994119 0.59763449 -0.00261437 0.00035908 0.00063015 Pb 14.71640746 15.02089619 3.26162567 0.00081172 -0.00173254 -0.00053935 Br 14.80151589 15.73486204 0.02068820 0.00133225 -0.00211887 -0.00056065 Br 14.67267592 11.92194196 3.05571430 -0.00285782 0.00285906 0.00135948 Br 11.63807345 15.19866915 3.70855303 -0.00007121 -0.00212257 -0.00427218 Cs 11.55182363 12.48678687 5.97020378 -0.00054320 0.00015426 0.00126375 Pb 14.45889287 14.84765022 9.21499815 0.00210542 0.00214572 0.00128375 Br 15.16608618 14.74229810 6.11873041 -0.00280196 0.00129831 -0.00060195 Br 14.07075190 12.04261623 9.03165310 -0.00485382 0.00168485 -0.00052069 Br 11.90749446 15.56751087 8.23969333 -0.00070594 -0.00095923 0.00118448 Cs 11.92157750 12.08641410 11.94106017 -0.00044365 0.00040296 0.00005665 Pb 15.04747995 14.86565161 14.87973969 -0.00019356 0.00243278 0.00148039 Br 14.57250471 14.91180722 12.05232194 0.00225416 -0.00139887 0.00310117 Br 14.44641394 11.79801621 14.88192802 0.00006347 -0.00278105 0.00123732 Br 11.99473830 15.32637219 15.15499997 -0.00012633 -0.00139913 0.00036725 Cs 12.22731953 11.67770240 17.52457253 0.00105604 -0.00215890 -0.00171640 Pb 14.70578273 14.85171781 21.09635426 0.00219796 -0.00033141 -0.00072394 Br 14.49606174 14.51572579 17.80079334 -0.00046145 0.00291604 -0.00143858 Br 14.17093919 11.89247957 20.90420435 0.00082349 0.00127661 0.00344661 Br 11.87432294 15.13083718 21.34842747 0.00097415 0.00230686 0.00349608 Cs 11.63363494 18.05195231 0.31805583 -0.00141427 -0.00013506 0.00350610 Pb 14.72784151 20.85332113 2.89342900 0.00184215 0.00173411 0.00167334 Br 14.35924887 20.61675337 0.14130880 0.00422940 0.00221153 -0.00426504 Br 14.56009219 18.10308097 3.50532105 -0.00158176 -0.00059862 0.00173426 Br 11.76329020 21.33057106 3.42403842 -0.00154173 0.00102454 0.00153305 Cs 11.22443135 18.13939063 5.94238202 -0.00094372 0.00076513 0.00006729 Pb 14.80771093 20.93298890 8.77989341 0.00331736 0.00078572 0.00088525 Br 15.08595094 21.21086245 5.76492866 -0.00098969 -0.00695861 0.00253116 Br 14.69306897 17.88106817 8.77647316 0.00048348 -0.00332627 0.00036972 Br 11.78526164 21.05117767 8.33262273 -0.00179259 0.00461487 -0.00290210 Cs 12.38175506 18.46937931 11.73274307 0.00280734 0.00201637 0.00047642 Pb 14.94769895 20.53902508 14.93341902 0.00072103 -0.00111494 -0.00090068 Br 14.78031521 20.49733877 12.00335665 0.00151773 -0.00018118 -0.00181265 Br 15.77998624 17.63913706 14.59658134 -0.00291409 0.00202218 -0.00031740 Br 11.96254852 19.69642932 14.72880182 0.00238281 -0.00156108 0.00046379 Cs 12.07606838 17.65519593 17.71416691 0.00232569 -0.00031297 0.00253604 Pb 14.71868720 20.53158387 20.67820902 0.00143543 0.00087619 -0.00118025 Br 14.40908436 20.48316458 17.84657417 -0.00364416 0.00181535 -0.00118736 Br 14.59438250 17.79329056 20.14260217 0.00137865 0.00287585 -0.00567299 Br 12.05463830 20.91604637 20.99036925 0.00062898 -0.00195816 0.00048143 Cs 17.73669162 23.41744741 0.16964972 0.00542822 0.00008773 -0.00295509 Pb 20.80357526 3.06997490 2.74729449 0.00071355 0.00231637 -0.00113456 Br 20.67320024 3.78235058 23.62292174 -0.00161821 0.00413843 -0.00231917 Br 21.02071052 0.04581705 3.29402666 -0.00147104 -0.00021243 -0.00046811 Br 18.12481118 3.18915492 2.81233288 0.00007345 0.00088657 0.00361254 Cs 18.36354047 23.64475379 6.17035292 -0.00277489 0.00294826 0.00003742 Pb 20.94507129 3.11756242 8.94252131 0.00147650 -0.00118838 -0.00013998 Br 21.54759774 2.91177729 6.02670769 0.00065526 0.00165328 0.00266961 Br 21.04490996 0.04576920 8.86798089 -0.00129254 0.00039341 0.00365426 Br 17.89212640 2.52589123 8.57365204 -0.00440996 0.00195779 0.00010187 Cs 17.67240267 0.45142052 11.80423801 0.00286987 -0.00134817 -0.00013073 Pb 20.80456351 2.98626894 14.59934353 -0.00156515 0.00100341 0.00014635 Br 19.90574455 3.45325964 11.71933987 -0.00132728 0.00156322 0.00029866 Br 21.28173116 23.74196369 14.84157263 0.00340407 -0.00060587 0.00014748 Br 17.89818223 2.53626070 15.00478195 -0.00007816 0.00316277 -0.00034611 Cs 17.92200961 0.01586554 18.38353202 0.00114892 -0.00145152 -0.00064385 Pb 21.11193706 2.88394823 20.66648138 0.00121304 -0.00084827 0.00018464 Br 20.88861134 3.04225801 17.69944171 -0.00058483 0.00299292 -0.00045927 Br 21.16677323 23.59803494 20.69393195 -0.00330356 -0.00033790 -0.00342460 Br 17.97448364 2.82868634 20.13518307 -0.00628177 0.00022517 -0.00220154 Cs 17.67742436 6.24741562 23.46724092 0.00016598 0.00222015 -0.00229824 Pb 20.82135648 8.95400323 3.05359586 0.00075074 -0.00043643 0.00005511 Br 20.41437204 9.28004982 0.05118086 -0.00255956 -0.00539142 0.00233254 Br 20.64185888 6.08066254 3.25019223 -0.00160549 -0.00190967 0.00072722 Br 17.85965064 9.06067426 3.42164888 0.00113341 -0.00055542 0.00367676 Cs 18.34655615 5.35442245 6.41857991 0.00114689 -0.00017228 -0.00007319 Pb 20.85610951 8.88589491 8.92984286 0.00215546 -0.00208404 -0.00137862 Br 20.93805493 9.25373979 6.11762702 -0.00250860 0.00367906 -0.00145140 Br 21.31446312 5.99403494 8.84055111 -0.00000490 0.00070664 -0.00115997 Br 17.52501678 9.05348165 9.50940749 -0.00499188 0.00008326 -0.00401972 Cs 17.41614588 6.26884471 11.47882226 0.00046498 0.00238024 -0.00106646 Pb 20.91534596 8.88061337 14.87524527 0.00120587 -0.00072376 0.00116025 Br 20.98889110 8.62080189 11.91788876 -0.00125889 0.00057086 -0.00082010 Br 20.92362063 5.80384003 15.34916575 -0.00128846 -0.00198798 -0.00008195 Br 17.86565356 8.62351641 14.68071232 0.00242108 -0.00372085 0.00022957 Cs 17.94985524 5.77172491 17.49941274 -0.00185565 -0.00075500 0.00092976 Pb 20.51520704 9.06542763 20.65884628 0.00118327 0.00023462 -0.00058563 Br 21.38891280 8.74550167 17.97842736 0.00013065 -0.00134732 -0.00295112 Br 20.46102525 5.82444072 20.82658574 -0.00031453 -0.00285447 -0.00192891 Br 17.54639963 9.54758716 20.53168050 -0.00236122 -0.00244121 -0.00082081 Cs 17.87291432 11.98583686 0.18241806 -0.00190585 -0.00097750 -0.00073338 Pb 20.41457794 14.76807577 2.99056727 0.00179578 -0.00082608 0.00126626 Br 20.74980149 15.27849241 23.72098526 0.00037817 0.00013902 0.00130975 Br 20.38632080 11.77055334 3.10823059 -0.00169384 -0.00130585 0.00298481 Br 17.53865673 15.08408212 2.84652230 -0.00120178 -0.00074463 -0.00222941 Cs 17.73274479 11.16215316 6.26962623 0.00187420 -0.00104990 -0.00054032 Pb 20.57987308 14.76143827 8.89612873 -0.00053215 0.00151439 0.00250629 Br 20.34734059 15.11258970 5.91244569 -0.00285021 -0.00375233 -0.00121702 Br 20.83741033 11.85450265 8.44921963 -0.00708039 -0.00117135 0.00450375 Br 17.75860200 14.46682087 9.32241983 -0.00111762 -0.00054093 0.00018177 Cs 17.87106650 11.36725788 12.37173887 0.00110488 -0.00134759 -0.00195340 Pb 20.85928758 14.59243693 14.87092053 -0.00079084 0.00079192 -0.00110476 Br 20.92543337 14.00428447 12.04754134 -0.00157921 -0.00070430 -0.00264245 Br 20.43274814 11.63914359 15.21783615 0.00135657 -0.00020163 0.00204681 Br 17.96902349 14.07759510 14.73266321 -0.00207815 0.00147256 0.00252405 Cs 17.45674348 12.07365023 17.67702426 -0.00059539 -0.00132174 -0.00145726 Pb 20.79457095 14.87879494 20.83229444 0.00228921 0.00277197 -0.00030464 Br 21.33836423 14.58081727 17.87138103 -0.00365906 -0.00073062 0.00276859 Br 20.41497995 11.94671181 20.04953823 -0.00421152 0.00063499 -0.00053073 Br 17.73803309 15.09135448 20.66652670 0.00425683 -0.00139871 -0.00174657 Cs 17.90088043 17.73546656 23.41947228 0.00046211 -0.00036535 -0.00003314 Pb 20.65927292 20.97244489 3.06473910 0.00355489 -0.00000226 0.00123938 Br 21.30868836 21.07777525 23.72291885 0.00148771 0.00063669 -0.00080075 Br 20.63720089 17.88011535 3.08642203 0.00202779 -0.00032366 0.00137249 Br 18.02056517 20.22278026 2.18286044 0.00184988 0.00048061 0.00196617 Cs 17.64775929 17.99062793 5.79181977 0.00161882 -0.00020444 -0.00240935 Pb 20.74706168 20.95476871 9.03254989 0.00125932 0.00111591 -0.00352033 Br 21.00702862 20.81212206 6.00279998 0.00047239 -0.00095248 -0.00088174 Br 20.22970383 17.85231176 8.66063505 0.00003075 -0.00329340 0.00046931 Br 17.72076085 20.84013214 9.64349267 -0.00423481 0.00060776 0.00136325 Cs 17.85369783 18.06681797 11.47419297 -0.00086469 -0.00112073 0.00349915 Pb 20.95500571 20.77883655 14.78503045 -0.00242636 0.00109795 0.00139424 Br 20.98361507 20.83813638 11.80718153 0.00119139 0.00011625 0.00113745 Br 20.67377124 17.51314830 13.98016984 -0.00234389 -0.00320966 0.00001186 Br 17.95870466 21.36983337 14.57225726 0.00060371 0.00206782 0.00330649 Cs 17.45461373 18.10310633 17.84551321 -0.00006690 -0.00065269 0.00337105 Pb 20.98293971 20.79689069 20.75345205 -0.00083280 -0.00045642 0.00257219 Br 20.56182543 20.94596481 17.77332333 0.00295673 -0.00292656 -0.00322942 Br 20.78138813 17.83431255 20.51320769 -0.00003559 0.00186543 -0.00284020 Br 17.75826992 20.57304278 20.96670809 0.00150828 0.00156186 0.00331281 320 Lattice="23.77195271 0.0 0.0 0.0 23.77195271 0.0 0.0 0.0 23.77195271" Properties=species:S:1:pos:R:3:vel:R:3 Time=20079.99999999 energy=-11426.27513504 virial="-19.21400483 1.75688059 -1.11878363 1.75688059 -14.37219681 -2.00850101 -1.11878363 -2.00850101 -10.83933637" stress="-0.00037251 0.00013078 -8.328e-05 0.00013078 -0.00011875 -0.00014951 -8.328e-05 -0.00014951 0.00015864" pbc="T T T" Cs 0.00801408 0.27057711 23.74352066 -0.00382864 -0.00293361 0.00112628 Pb 2.95848129 3.01618449 3.01089430 -0.00243841 0.00145296 0.00162676 Br 3.29443959 2.92713085 0.04979856 -0.00083031 0.00031795 0.00211433 Br 2.99921047 0.12706178 3.14011406 -0.00035812 0.00061839 -0.00083628 Br 0.18541382 3.47060083 2.47714472 0.00171856 -0.00042403 -0.00306611 Cs 0.01726413 23.68944253 6.15664019 0.00350893 0.00249327 -0.00194028 Pb 2.99355126 2.96400455 9.31609037 0.00133549 0.00016283 -0.00127425 Br 2.43076037 2.79772651 6.18752047 0.00199026 -0.00125600 0.00299558 Br 3.15467927 23.76072066 9.24526269 0.00013883 0.00026019 0.00335099 Br 23.65288643 3.07084161 10.16310053 -0.00057316 0.00242147 0.00632025 Cs 0.39316765 23.63634223 12.19504760 -0.00364135 -0.00085407 0.00059452 Pb 3.07997485 3.32598771 14.95394562 -0.00135180 -0.00050965 -0.00119514 Br 4.21752943 3.06579831 11.95215004 -0.00198451 -0.00101426 0.00036807 Br 2.93346398 0.21780611 14.82055404 0.00199220 -0.00333133 -0.00333340 Br 0.30891101 3.44784799 14.76446652 -0.00026083 -0.00226727 -0.00047470 Cs 0.20474208 23.40866176 17.81636394 -0.00113765 -0.00166667 -0.00217480 Pb 3.16241641 3.07354674 20.63226911 0.00188822 0.00013042 0.00012226 Br 2.57606141 3.14056518 17.82426010 -0.00099553 0.00218891 0.00149372 Br 3.08958855 0.05578987 20.21328134 -0.00134465 0.00142388 0.00339870 Br 0.12177685 3.10414875 20.55562103 0.00113002 -0.00231789 0.00034451 Cs 23.48160608 6.35766352 23.75008885 -0.00006389 0.00262535 -0.00063284 Pb 2.84699476 8.92418343 3.22624083 -0.00011888 -0.00016461 0.00008554 Br 2.62059335 8.72534623 23.65543397 0.00189540 -0.00569021 0.00111709 Br 3.19233269 6.05453353 3.14044317 -0.00291304 -0.00021327 -0.00143070 Br 23.69138088 8.63324588 3.24721224 0.00101258 -0.00141732 -0.00234623 Cs 0.35614715 6.01220453 5.89606036 0.00229381 0.00109704 0.00199056 Pb 3.28603047 9.06965843 8.93368652 0.00279934 0.00038616 -0.00114841 Br 2.86956255 8.95673516 5.95258638 -0.00028216 -0.00230102 0.00404591 Br 2.88650548 5.92714871 8.68906407 -0.00189679 -0.00398958 0.00185682 Br 23.74163414 9.15658498 8.86956814 -0.00334673 0.00328850 -0.00112653 Cs 23.64931984 6.01989274 11.89783621 -0.00154634 -0.00197768 0.00175012 Pb 2.91242657 8.77750185 14.87782735 -0.00112363 0.00015529 -0.00201294 Br 3.16877767 8.84865455 11.76611657 0.00048885 0.00175210 0.00121048 Br 3.76162780 6.02716251 15.11174726 -0.00036514 0.00271751 0.00129495 Br 0.05491717 8.55562391 14.55662276 0.00061181 -0.00203878 -0.00365557 Cs 23.71001984 5.73136745 17.61244153 -0.00144626 0.00018284 -0.00022410 Pb 2.71441956 8.86135934 20.59658745 -0.00102763 -0.00021913 -0.00021764 Br 2.17027558 8.77685646 17.76351986 0.00393473 0.00026900 -0.00227102 Br 2.06379179 5.88895967 20.72636807 0.00009994 -0.00077002 0.00186793 Br 23.68923238 9.28509521 21.16251988 -0.00218073 -0.00035760 0.00033047 Cs 23.09514185 11.78790943 0.24678065 0.00218553 -0.00085710 0.00039875 Pb 2.78033715 14.88143015 2.88812554 -0.00253521 0.00140707 0.00075760 Br 2.49567707 15.15301280 23.69491465 -0.00082098 -0.00160359 0.00039602 Br 2.89454147 11.72867990 2.30183539 0.00137861 0.00206206 -0.00187933 Br 23.33264116 14.62419508 3.29774764 -0.00123290 0.00113367 -0.00154322 Cs 23.47817452 11.78313301 6.56108115 0.00110301 -0.00116816 0.00092589 Pb 2.98730115 15.03857093 9.06659415 0.00108952 -0.00003760 0.00156101 Br 3.35346533 14.95723995 5.97601020 -0.00217359 -0.00056749 -0.00064905 Br 2.97665624 11.85412763 8.70293382 0.00034281 0.00541872 0.00037035 Br 23.74461635 15.09670412 8.89769283 0.00149614 -0.00095240 -0.00033772 Cs 0.15859459 11.63064469 11.48106650 -0.00222757 0.00063914 -0.00149082 Pb 3.05671627 14.57735219 15.06126524 0.00060633 -0.00116421 0.00101220 Br 3.05494935 14.47302531 11.84623255 -0.00039788 -0.00205965 -0.00113453 Br 2.65621595 11.82471112 15.38689915 -0.00353984 -0.00116893 0.00187667 Br 0.08010174 14.62642902 14.59804316 0.00365506 -0.00106176 -0.00148361 Cs 23.46450134 11.76778007 18.17381030 0.00225597 -0.00141510 -0.00005945 Pb 3.07003214 14.59427459 20.79735262 0.00137752 -0.00235025 -0.00118894 Br 2.93566636 14.85596641 17.93826983 -0.00016293 0.00115038 -0.00109709 Br 2.75535415 11.77656588 20.91767727 0.00074505 -0.00155903 0.00033016 Br 0.12742698 14.51632987 20.97303016 0.00181563 0.00151660 -0.00176676 Cs 0.08190534 17.77583663 23.67596490 -0.00024668 0.00071828 -0.00140790 Pb 2.94587719 20.76920298 2.91369436 0.00168681 -0.00062113 -0.00141480 Br 2.91961493 20.75798244 23.55101140 0.00037523 0.00210977 0.00437808 Br 3.22001278 17.91533664 2.63116239 -0.00156667 0.00041818 0.00104168 Br 23.63200005 20.79669436 2.90364188 -0.00176948 0.00201236 0.00195798 Cs 23.14067392 17.78704354 6.29299065 -0.00177840 0.00123915 -0.00315914 Pb 2.83517385 20.94867553 8.78928081 0.00190118 -0.00055262 -0.00013929 Br 2.93291768 20.83622698 5.82025933 0.00140592 -0.00241503 0.00120911 Br 3.39001637 17.96852053 9.47202660 -0.00214807 -0.00312873 -0.00260977 Br 23.58490298 21.11374664 8.87367822 0.00266346 -0.00277831 0.00050483 Cs 0.44646414 17.11998326 12.28179901 0.00312954 0.00140870 -0.00341677 Pb 3.11638677 21.08530576 14.68460690 -0.00002088 -0.00035314 0.00199080 Br 3.31732041 20.74952899 11.73622952 -0.00394297 0.00020467 -0.00316264 Br 3.05516762 18.01541693 14.97554419 -0.00006109 -0.00175942 0.00424891 Br 0.15798957 20.45658236 14.97237340 0.00030085 0.00193907 -0.00028975 Cs 0.38973485 17.66731056 17.46569898 0.00049165 0.00044406 0.00152603 Pb 2.99389698 20.73367018 20.51684193 0.00029037 0.00108052 -0.00121693 Br 3.62987784 20.52749226 17.56667102 -0.00297988 -0.00441747 0.00038041 Br 3.08777052 17.83197614 20.59828516 -0.00006640 -0.00131510 -0.00541663 Br 23.75420453 20.63560623 20.52915416 0.00137649 -0.00102718 0.00160744 Cs 6.01725303 0.09138311 22.87881065 -0.00270606 0.00107419 -0.00041097 Pb 9.06979431 2.95781906 3.29641639 -0.00148474 0.00212758 -0.00175225 Br 9.09383640 2.51237646 0.05150205 -0.00243485 0.00207883 -0.00027869 Br 8.70529850 23.68415236 3.27762614 0.00313229 0.00468532 0.00387644 Br 5.95087216 2.92515359 3.52530364 -0.00227866 0.00208114 0.00197753 Cs 6.10894870 23.40737857 5.71019542 -0.00192255 0.00052167 0.00090184 Pb 8.79794301 2.77542709 8.82623694 -0.00056124 0.00069398 0.00043449 Br 9.89084103 3.43884230 6.00781915 -0.00226971 0.00079753 -0.00072034 Br 8.34223206 23.36296321 8.71949826 0.00133620 0.00108249 0.00311971 Br 5.77528339 3.42040636 8.50555276 0.00257536 -0.00252085 -0.00324803 Cs 6.08956120 23.67886739 12.05154918 -0.00061167 0.00147176 0.00018700 Pb 9.13271949 2.86452433 14.66234103 0.00162459 0.00014441 0.00112618 Br 8.98951197 2.83561327 11.71680292 0.00028117 0.00055872 -0.00202042 Br 10.09923298 23.50252419 15.28220053 0.00166223 0.00171173 -0.00193397 Br 6.17641044 2.37819385 15.23456318 -0.00035759 0.00051989 -0.00294769 Cs 6.51096247 23.50532289 17.65866028 -0.00138746 -0.00237790 0.00107168 Pb 9.19257474 3.03790060 21.07270789 -0.00130899 0.00098380 -0.00092433 Br 9.74595182 2.98766150 17.54900466 -0.00150003 -0.00123026 0.00576250 Br 9.11635183 0.17925187 20.58415644 -0.00189402 -0.00208264 0.00355174 Br 6.12389752 3.91441521 20.75131908 -0.00215881 0.00046729 0.00033999 Cs 5.65788293 6.34215889 23.67264611 -0.00149496 -0.00335944 0.00071373 Pb 8.88211424 8.82654868 2.81457254 -0.00164747 0.00199306 -0.00042763 Br 8.88008088 8.81539390 23.63471306 0.00136188 0.00078425 -0.00039243 Br 8.30253846 5.76461855 2.84011168 -0.00196122 0.00320620 0.00165415 Br 5.89042386 8.91161829 2.70878431 0.00018166 0.00144757 0.00117134 Cs 5.83430460 6.05590692 5.81974992 0.00316204 0.00277810 0.00026433 Pb 8.78417968 8.83644923 8.75139641 -0.00064162 0.00002813 -0.00154004 Br 8.77441024 8.64547688 5.94148151 -0.00152573 -0.00228531 -0.00063919 Br 8.79789983 5.80404537 9.08789222 -0.00197919 0.00162655 0.00273449 Br 5.92679044 8.07464731 8.59490986 -0.00047001 0.00353275 0.00056634 Cs 5.85309713 6.18248043 11.86526104 0.00137073 -0.00133615 0.00124890 Pb 9.07462845 8.87529552 14.84063952 -0.00088483 0.00020795 0.00027074 Br 7.94307219 9.10261998 11.84056198 -0.00206733 -0.00524232 0.00090997 Br 9.30379558 5.79039464 15.01253708 0.00254603 0.00294903 0.00304340 Br 5.99263299 9.27180624 15.35664526 0.00237087 -0.00489948 0.00171659 Cs 6.96610942 6.05377781 17.85812670 -0.00185714 0.00114712 -0.00087573 Pb 8.70404529 8.93263861 20.71159535 0.00043247 0.00142259 -0.00037795 Br 9.19724267 9.35069816 17.85942170 -0.00057481 -0.00144594 0.00151401 Br 9.20107371 6.10984236 20.98959090 -0.00063536 0.00148782 -0.00130961 Br 5.73398636 8.65035783 20.58285656 -0.00280121 -0.00182520 0.00103862 Cs 5.54951886 11.57431854 23.31037649 -0.00157450 -0.00172909 0.00139449 Pb 8.73632176 14.63041992 3.01812423 0.00146168 0.00000214 0.00110855 Br 9.27010763 14.75660179 0.37825847 0.00052886 -0.00301399 0.00114428 Br 8.94280564 11.55990385 3.00331284 0.00131574 0.00076554 0.00283180 Br 5.63484792 14.73056884 2.59546254 0.00082564 -0.00228692 0.00026193 Cs 5.13687853 11.90110792 4.86810353 0.00180095 0.00082791 0.00052224 Pb 8.58396188 14.56430984 8.92730728 -0.00022986 -0.00149443 0.00108324 Br 8.10879375 14.09964186 6.00132659 0.00454693 -0.00294136 -0.00267733 Br 9.81696558 11.91705940 8.89509736 -0.00146184 0.00282537 -0.00246402 Br 5.92130370 14.46788932 9.48039754 -0.00147885 -0.00556456 0.00166458 Cs 5.46608709 12.04510355 11.88706062 0.00119289 -0.00153591 -0.00172867 Pb 8.83958995 15.03969098 14.90900569 0.00240852 0.00177891 0.00045821 Br 9.67261998 15.13267605 11.74413430 -0.00342074 0.00186412 0.00148471 Br 9.45415821 11.84114547 15.07096384 0.00124925 -0.00075644 0.00157428 Br 6.06225130 14.63985468 14.47467285 -0.00455924 0.00048717 -0.00567324 Cs 6.39133378 11.99090898 17.83763715 0.00031015 -0.00263694 -0.00195162 Pb 8.93633293 14.92206983 20.79177057 0.00028520 -0.00002580 -0.00077376 Br 9.28841399 14.90145671 17.93913588 0.00219983 -0.00021874 -0.00416357 Br 8.94249149 11.88889209 21.45135905 -0.00033588 -0.00274224 -0.00185295 Br 6.05881538 14.77755515 21.22516390 0.00137360 0.00297353 0.00103990 Cs 5.50603238 18.42369134 23.75417409 -0.00201294 0.00263364 -0.00091128 Pb 8.80611660 20.73019192 3.03051555 -0.00041424 0.00000384 0.00080417 Br 8.89703612 20.71341053 0.10489156 0.00022640 0.00212359 0.00219884 Br 8.39510110 17.72288734 2.95508889 -0.00107579 0.00226587 -0.00222760 Br 5.92908334 20.61490856 2.87853203 -0.00260509 0.00085503 0.00179520 Cs 5.71353338 17.82611832 5.54423494 -0.00112122 0.00260656 -0.00043889 Pb 8.59901195 20.62428724 8.80407004 -0.00038818 -0.00203303 0.00195980 Br 8.48318826 20.33636060 5.87267748 -0.00269486 -0.00052267 -0.00127440 Br 8.06050227 17.49806306 8.46468975 0.00002615 -0.00119628 -0.00437717 Br 5.73813059 20.65680911 8.74560498 -0.00190593 -0.00146748 -0.00214388 Cs 6.36056348 18.13198681 11.45663616 -0.00069342 0.00253243 -0.00145781 Pb 9.34514752 20.84993233 14.63621710 -0.00100207 -0.00021515 -0.00046940 Br 9.41764176 21.10678337 11.79343648 0.00241508 -0.00337780 0.00204640 Br 8.41051092 17.75372011 14.59149826 0.00040609 -0.00479304 0.00216304 Br 6.11427082 21.11390249 14.84229442 0.00032634 0.00244928 0.00199625 Cs 5.82781083 17.38994839 17.99616403 -0.00095976 -0.00079430 -0.00177004 Pb 9.08051534 20.70134715 20.74743162 -0.00050416 -0.00071567 -0.00081302 Br 9.59980354 20.65622728 17.69547400 0.00424756 0.00118487 -0.00330514 Br 7.97687915 17.98040055 20.69334717 -0.00386998 0.00211777 0.00010954 Br 6.12941517 20.94488639 20.60906844 0.00010734 -0.00407471 -0.00098974 Cs 12.11724097 23.74336972 0.41634411 0.00068416 -0.00048060 -0.00138140 Pb 15.05246890 3.30673169 2.86599500 -0.00003791 0.00118358 -0.00156215 Br 15.35772448 2.82879414 0.14714073 -0.00060587 0.00105500 -0.00105592 Br 15.25072456 23.74103327 2.59036595 0.00413572 -0.00282561 0.00245773 Br 11.94641270 3.36126886 2.61938261 0.00213569 0.00212802 -0.00222739 Cs 12.22406906 0.56607952 5.90401860 -0.00096994 0.00019903 -0.00367529 Pb 14.80357925 3.01050584 8.87435257 0.00081569 0.00039502 -0.00070683 Br 14.83201588 3.01124383 5.92758171 -0.00255984 0.00408828 -0.00109344 Br 14.79838598 0.13421515 8.98409863 0.00214905 -0.00003125 0.00135715 Br 12.04281711 3.04490363 9.61298600 0.00383037 -0.00049652 0.00041847 Cs 11.92892646 0.09357740 11.94680080 -0.00019499 0.00424078 0.00043970 Pb 14.80403984 3.00555311 14.85304577 0.00229963 -0.00246430 0.00080434 Br 15.41850335 2.93999107 11.81022165 -0.00502789 -0.00006250 0.00224252 Br 14.32442356 23.60591472 15.06428006 -0.00197477 -0.00014146 -0.00217502 Br 11.98302906 2.81065451 14.27364517 0.00122207 0.00161483 -0.00109748 Cs 12.10743008 23.29147509 18.26979996 -0.00082132 -0.00078452 0.00261292 Pb 15.08734752 2.97974581 20.91165903 -0.00032225 0.00292012 -0.00136432 Br 15.05281900 2.97156683 17.87518846 -0.00445760 0.00214417 0.00324748 Br 14.99344601 23.61097537 21.04649198 0.00309908 -0.00156058 -0.00373783 Br 12.06212253 2.96937737 21.44288988 -0.00311755 -0.00496882 0.00198953 Cs 11.76804429 6.30964481 23.31518087 0.00088831 -0.00087192 -0.00027827 Pb 14.77272546 8.98089239 3.43698211 -0.00092129 -0.00105969 -0.00036506 Br 14.72000438 9.13581963 0.16877823 0.00031662 -0.00371852 0.00070405 Br 15.45348494 6.19980296 3.74736908 -0.00184016 -0.00164750 -0.00312828 Br 11.82235865 8.67003062 2.67238866 -0.00302393 -0.00235374 -0.00121436 Cs 11.87527073 6.31010459 5.57545040 0.00231221 -0.00129631 -0.00002424 Pb 14.48509439 8.88468941 9.04212185 0.00061187 -0.00033669 0.00087762 Br 14.36149220 9.54533356 6.20723814 0.00011594 0.00032443 -0.00259780 Br 15.24338225 6.16022521 8.32758627 0.00072149 0.00077124 -0.00035956 Br 11.72442765 8.14001486 9.03713719 0.00158017 -0.00159196 -0.00115664 Cs 11.91414877 5.88802054 11.82971862 0.00153719 0.00151825 0.00057267 Pb 14.86276938 8.74750260 14.72664185 0.00087972 0.00132857 0.00042394 Br 15.00116244 9.04638123 11.75812192 -0.00364726 0.00151489 0.00045327 Br 15.12446043 5.93642105 14.93496408 -0.00340535 0.00081763 -0.00082849 Br 12.04055328 8.43203234 14.73343384 0.00265266 0.00122844 0.00344354 Cs 12.52898512 5.76147274 18.27749388 0.00173448 -0.00114824 0.00066880 Pb 14.59698238 9.10448941 20.90422720 0.00009265 0.00150959 0.00036865 Br 14.76827303 8.82345458 17.98169574 0.00003571 -0.00292169 -0.00300359 Br 15.39685995 5.77976814 20.92333975 -0.00170765 -0.00085810 -0.00220770 Br 11.52068363 9.07671945 20.41891132 0.00184556 -0.00174342 0.00002492 Cs 11.39293703 11.85333293 0.60265813 -0.00235047 0.00031704 0.00037506 Pb 14.72385686 15.00352017 3.25696689 0.00067470 -0.00174004 -0.00039166 Br 14.81484349 15.71319481 0.01673873 0.00133563 -0.00221406 -0.00024315 Br 14.64387409 11.95303259 3.06897537 -0.00290111 0.00332857 0.00128765 Br 11.63657345 15.17723287 3.66449171 -0.00022924 -0.00215995 -0.00453177 Cs 11.54739890 12.48818764 5.98296467 -0.00033534 0.00012887 0.00128215 Pb 14.48089740 14.86950957 9.22598001 0.00228785 0.00221756 0.00091296 Br 15.13807138 14.75528885 6.11046513 -0.00279676 0.00129843 -0.00103838 Br 14.02211939 12.05643283 9.02663888 -0.00486649 0.00108278 -0.00048317 Br 11.89726446 15.55640340 8.25218332 -0.00133091 -0.00126062 0.00131779 Cs 11.91690485 12.09075824 11.94222176 -0.00049025 0.00046656 0.00017598 Pb 15.04463245 14.88918893 14.89524161 -0.00037615 0.00226890 0.00161753 Br 14.59419794 14.89787505 12.08352676 0.00208249 -0.00138841 0.00311004 Br 14.44866721 11.76959698 14.89282493 0.00039235 -0.00287617 0.00094043 Br 11.99374683 15.31149052 15.15698886 -0.00008387 -0.00157086 0.00003045 Cs 12.23703687 11.65624255 17.50808069 0.00088593 -0.00213370 -0.00157932 Pb 14.72790678 14.84870565 21.08800412 0.00222194 -0.00026509 -0.00094310 Br 14.49301058 14.54545890 17.78899624 -0.00015173 0.00302123 -0.00090017 Br 14.17983067 11.90579828 20.93907928 0.00095354 0.00139211 0.00352466 Br 11.88242652 15.15457604 21.38298542 0.00065387 0.00243751 0.00340813 Cs 11.61961349 18.05009707 0.35333404 -0.00138622 -0.00023395 0.00355048 Pb 14.74652768 20.87106247 2.91065729 0.00188860 0.00179900 0.00175623 Br 14.40128245 20.63802854 0.09506564 0.00418273 0.00204338 -0.00492176 Br 14.54376234 18.09299959 3.52277712 -0.00168223 -0.00139375 0.00174780 Br 11.75044201 21.34021301 3.43877854 -0.00101835 0.00090209 0.00141070 Cs 11.21600517 18.14687273 5.94290395 -0.00073943 0.00072860 0.00003759 Pb 14.84052865 20.93998474 8.78858265 0.00323460 0.00061426 0.00085457 Br 15.07752914 21.14216166 5.79475945 -0.00069010 -0.00677642 0.00340652 Br 14.69945265 17.85106788 8.78051427 0.00079521 -0.00265777 0.00044123 Br 11.76799538 21.09705738 8.30446599 -0.00166968 0.00455264 -0.00272441 Cs 12.40724905 18.48571836 11.73529671 0.00228608 0.00124683 0.00003238 Pb 14.95531354 20.52921673 14.92354522 0.00079533 -0.00084210 -0.00107003 Br 14.79847498 20.49984633 11.98403737 0.00212062 0.00068720 -0.00203476 Br 15.75102673 17.66196202 14.59434882 -0.00287009 0.00253111 -0.00012654 Br 11.98853924 19.68252214 14.73578435 0.00278874 -0.00121258 0.00093063 Cs 12.09843285 17.65174463 17.73953138 0.00214067 -0.00037809 0.00252744 Pb 14.73569896 20.54170110 20.66782281 0.00195674 0.00115349 -0.00089533 Br 14.37341497 20.50233055 17.83303627 -0.00348149 0.00202019 -0.00152868 Br 14.60886227 17.81746096 20.08780804 0.00152645 0.00193009 -0.00526698 Br 12.05487996 20.89641015 20.99643789 -0.00056667 -0.00197422 0.00073150 Cs 17.79121919 23.41798729 0.13932470 0.00547006 0.00001992 -0.00310572 Pb 20.81330864 3.09396421 2.73582460 0.00122063 0.00248072 -0.00116485 Br 20.65762409 3.82118053 23.60279823 -0.00149600 0.00361941 -0.00168483 Br 21.00525473 0.04659103 3.28890128 -0.00161860 0.00037300 -0.00055402 Br 18.11882280 3.19750751 2.84980142 -0.00123782 0.00077937 0.00387941 Cs 18.33549380 23.67421428 6.17059802 -0.00283475 0.00294337 0.00000938 Pb 20.95988543 3.10478430 8.94143161 0.00148574 -0.00135889 -0.00006935 Br 21.55284868 2.92905389 6.05266078 0.00040121 0.00179627 0.00250968 Br 21.03103525 0.05114379 8.90491697 -0.00147730 0.00067133 0.00372602 Br 17.84868532 2.54618367 8.57440113 -0.00424433 0.00209599 0.00004971 Cs 17.70218345 0.43640328 11.80320217 0.00307522 -0.00164289 -0.00007754 Pb 20.78836240 2.99592411 14.60154875 -0.00167139 0.00092458 0.00029186 Br 19.89324326 3.46898972 11.72320856 -0.00117249 0.00158089 0.00046887 Br 21.31662146 23.73473573 14.84254439 0.00356639 -0.00083579 0.00005014 Br 17.89810868 2.56842201 15.00126606 0.00005979 0.00326155 -0.00035174 Cs 17.93352685 0.00028879 18.37578854 0.00115347 -0.00166188 -0.00090261 Pb 21.12201722 2.87652634 20.66800600 0.00080547 -0.00063319 0.00011734 Br 20.88375467 3.07079014 17.69485571 -0.00038894 0.00270536 -0.00044831 Br 21.13406112 23.59891783 20.65947184 -0.00323852 0.00050148 -0.00346841 Br 17.91169795 2.83224864 20.11637168 -0.00624064 0.00048384 -0.00156252 Cs 17.68021967 6.26969988 23.44502463 0.00038976 0.00223571 -0.00214649 Pb 20.82774125 8.94903351 3.05435775 0.00052098 -0.00055599 0.00009967 Br 20.38888307 9.22537472 0.07372530 -0.00253364 -0.00553534 0.00215819 Br 20.62592480 6.05824647 3.25770456 -0.00157501 -0.00255973 0.00077665 Br 17.86886541 9.05487876 3.45762653 0.00069875 -0.00061289 0.00350959 Cs 18.35765372 5.35335506 6.41669887 0.00107006 -0.00003942 -0.00030265 Pb 20.87708557 8.86532057 8.91656325 0.00203164 -0.00202176 -0.00127712 Br 20.91272825 9.28834430 6.09981373 -0.00254487 0.00323683 -0.00209788 Br 21.31406347 6.00111210 8.82791788 -0.00006890 0.00068108 -0.00136737 Br 17.47963048 9.05584256 9.46524445 -0.00407748 0.00039077 -0.00479983 Cs 17.42121399 6.29156928 11.46883168 0.00054683 0.00216431 -0.00093054 Pb 20.92693893 8.87207644 14.88727313 0.00110846 -0.00098006 0.00124016 Br 20.97593548 8.62740962 11.91079832 -0.00133203 0.00074862 -0.00060172 Br 20.91012223 5.78566796 15.34628028 -0.00140743 -0.00163358 -0.00049558 Br 17.89031310 8.58621172 14.68353207 0.00251062 -0.00373333 0.00033111 Cs 17.93146053 5.76364563 17.50941867 -0.00182458 -0.00085905 0.00106856 Pb 20.52747102 9.06719620 20.65350045 0.00126737 0.00012325 -0.00048882 Br 21.38941471 8.73184662 17.94691653 -0.00003150 -0.00137944 -0.00332099 Br 20.45827632 5.79927559 20.80590408 -0.00023467 -0.00216335 -0.00221430 Br 17.52351844 9.52234692 20.52451936 -0.00219914 -0.00260509 -0.00060943 Cs 17.85327101 11.97573476 0.17528053 -0.00202066 -0.00104147 -0.00069225 Pb 20.43362284 14.76102900 3.00375544 0.00199932 -0.00058546 0.00136852 Br 20.75359322 15.27884881 23.73513262 0.00038265 -0.00006433 0.00151082 Br 20.36945091 11.76214202 3.13781452 -0.00167447 -0.00037963 0.00292363 Br 17.52660783 15.07672130 2.82338312 -0.00118295 -0.00072713 -0.00239935 Cs 17.75120529 11.15282254 6.26469381 0.00181344 -0.00081181 -0.00044415 Pb 20.57369741 14.77709476 8.92154063 -0.00070117 0.00160839 0.00257298 Br 20.31895667 15.07415544 5.90166915 -0.00282285 -0.00392855 -0.00092901 Br 20.76454363 11.84435354 8.49744125 -0.00745630 -0.00086032 0.00511549 Br 17.74746624 14.46153980 9.32429463 -0.00110886 -0.00051101 0.00019461 Cs 17.88240710 11.35461507 12.35143609 0.00116331 -0.00118140 -0.00210366 Pb 20.85105769 14.60147418 14.85945510 -0.00084951 0.00100961 -0.00118874 Br 20.91030471 13.99685342 12.01863557 -0.00144796 -0.00077634 -0.00311391 Br 20.44776890 11.63724380 15.23882610 0.00164172 -0.00017693 0.00214622 Br 17.94531566 14.09377343 14.75830980 -0.00263257 0.00175682 0.00259922 Cs 17.45074508 12.06026664 17.66295229 -0.00060371 -0.00135615 -0.00135670 Pb 20.81764365 14.90668790 20.82820414 0.00232289 0.00279981 -0.00050967 Br 21.30151962 14.57609555 17.89815794 -0.00370264 -0.00021574 0.00256134 Br 20.37269006 11.95159490 20.04672476 -0.00423263 0.00032549 -0.00002947 Br 17.78157783 15.07704359 20.64912917 0.00443003 -0.00145873 -0.00172993 Cs 17.90572110 17.73189048 23.41916426 0.00050411 -0.00034961 -0.00002452 Pb 20.69581241 20.97077344 3.07613163 0.00374234 -0.00033099 0.00103578 Br 21.32288521 21.08392691 23.71499551 0.00135206 0.00059259 -0.00078849 Br 20.65843711 17.87530334 3.10046360 0.00221889 -0.00063684 0.00143610 Br 18.03435467 20.22967350 2.20325523 0.00090940 0.00089507 0.00211146 Cs 17.66401168 17.98818341 5.76767046 0.00162900 -0.00028645 -0.00242192 Pb 20.75855119 20.96496357 8.99685190 0.00104419 0.00092017 -0.00359808 Br 21.01061477 20.80296955 5.99319733 0.00024543 -0.00087239 -0.00101794 Br 20.23085988 17.81949564 8.66582190 0.00019674 -0.00324839 0.00056822 Br 17.67747113 20.84956054 9.65418303 -0.00437795 0.00126461 0.00077814 Cs 17.84518461 18.05429174 11.51007762 -0.00083648 -0.00137877 0.00366929 Pb 20.92995789 20.78982609 14.79888034 -0.00257529 0.00110104 0.00137178 Br 20.99633943 20.83971008 11.82126212 0.00134351 0.00019887 0.00162381 Br 20.65098309 17.48191302 13.98187760 -0.00221380 -0.00303805 0.00032671 Br 17.96362539 21.38928142 14.60596553 0.00038024 0.00182595 0.00343058 Cs 17.45483021 18.09645908 17.87959526 0.00011195 -0.00067610 0.00343991 Pb 20.97294371 20.79149803 20.77972268 -0.00115961 -0.00061923 0.00267620 Br 20.59146179 20.91643955 17.74092280 0.00296771 -0.00297605 -0.00321835 Br 20.78128622 17.85056586 20.48501026 0.00001512 0.00138214 -0.00279790 Br 17.77440166 20.58890488 20.99954555 0.00172574 0.00160850 0.00325157 320 Lattice="23.77195271 0.0 0.0 0.0 23.77195271 0.0 0.0 0.0 23.77195271" Properties=species:S:1:pos:R:3:vel:R:3 Time=20089.99999999 energy=-11426.5546608 virial="-20.01841015 1.07669322 -1.20859253 1.07669322 -14.11659693 -2.0574335 -1.20859253 -2.0574335 -11.86375209" stress="-0.00041153 8.015e-05 -8.997e-05 8.015e-05 -0.0001005 -0.00015316 -8.997e-05 -0.00015316 0.00010391" pbc="T T T" Cs 23.74205209 0.24104552 23.75509560 -0.00374991 -0.00297024 0.00118854 Pb 2.93519717 3.03055152 3.02767569 -0.00220753 0.00141348 0.00172611 Br 3.28644141 2.92991575 0.06967298 -0.00076611 0.00024017 0.00186022 Br 2.99499620 0.13045209 3.13141758 -0.00048715 0.00006176 -0.00089995 Br 0.19923874 3.46623818 2.44608239 0.00101347 -0.00044593 -0.00314588 Cs 0.05270565 23.71452401 6.13672575 0.00357892 0.00251888 -0.00204070 Pb 3.00677015 2.96653382 9.30189246 0.00130678 0.00033728 -0.00156242 Br 2.45040680 2.78539320 6.21769841 0.00193240 -0.00120917 0.00303401 Br 3.15618258 23.76422757 9.27877440 0.00016061 0.00043068 0.00334670 Br 23.64786669 3.09288767 10.22509479 -0.00043625 0.00196963 0.00605553 Cs 0.35634109 23.62761215 12.20065571 -0.00372133 -0.00089345 0.00052646 Pb 3.06789699 3.31874663 14.94181247 -0.00105945 -0.00092459 -0.00122587 Br 4.19610968 3.05549248 11.95685904 -0.00229383 -0.00103970 0.00057392 Br 2.95263681 0.18503935 14.78753428 0.00184118 -0.00319501 -0.00326523 Br 0.30310933 3.42537485 14.75795254 -0.00090014 -0.00222709 -0.00082445 Cs 0.19318174 23.39230529 17.79439532 -0.00117398 -0.00160446 -0.00221727 Pb 3.18126014 3.07560076 20.63432773 0.00187520 0.00028143 0.00029183 Br 2.56744983 3.16221748 17.83733973 -0.00072223 0.00213772 0.00110623 Br 3.07651628 0.06967380 20.24790436 -0.00126827 0.00133522 0.00351804 Br 0.13326889 3.07874860 20.56002913 0.00114387 -0.00276033 0.00053814 Cs 23.48110901 6.38375528 23.74343671 -0.00003612 0.00258934 -0.00069771 Pb 2.84602415 8.92189702 3.22391390 -0.00007210 -0.00028647 -0.00053784 Br 2.64093245 8.66827638 23.66755285 0.00216665 -0.00571054 0.00130598 Br 3.16319677 6.05227871 3.12632862 -0.00291049 -0.00022199 -0.00138565 Br 23.70322009 8.61962664 3.22338130 0.00134969 -0.00130799 -0.00241743 Cs 0.37890011 6.02255481 5.91601791 0.00225491 0.00097221 0.00200034 Pb 3.31281777 9.07185408 8.92166229 0.00254617 0.00007449 -0.00124396 Br 2.86681110 8.93454642 5.99566362 -0.00027008 -0.00213999 0.00450834 Br 2.86639487 5.88821292 8.70892177 -0.00211315 -0.00376432 0.00211311 Br 23.70931059 9.18824592 8.85921134 -0.00308830 0.00303376 -0.00094319 Cs 23.63377762 6.00077562 11.91547194 -0.00156377 -0.00183785 0.00178045 Pb 2.90207045 8.78008416 14.85838040 -0.00094505 0.00036447 -0.00187654 Br 3.17342712 8.86680091 11.78147663 0.00044444 0.00187383 0.00183082 Br 3.75904251 6.05592972 15.12496752 -0.00015976 0.00297895 0.00134437 Br 0.06157688 8.53531395 14.51955781 0.00071539 -0.00202387 -0.00375151 Cs 23.69546810 5.73330890 17.61094447 -0.00146094 0.00020427 -0.00007558 Pb 2.70396284 8.85787252 20.59561677 -0.00106038 -0.00048390 0.00001937 Br 2.20981429 8.77977427 17.73902895 0.00396385 0.00031082 -0.00259783 Br 2.06718026 5.88239377 20.74553166 0.00057166 -0.00053806 0.00196121 Br 23.66269294 9.28203914 21.16558128 -0.00312023 -0.00025210 0.00028349 Cs 23.11794009 11.77960860 0.25067934 0.00236998 -0.00080260 0.00038234 Pb 2.75482118 14.89550940 2.89535455 -0.00255676 0.00140522 0.00068919 Br 2.48862835 15.13638276 23.70112026 -0.00059059 -0.00171898 0.00083190 Br 2.90689655 11.75281691 2.28278824 0.00109069 0.00273833 -0.00191999 Br 23.32352163 14.63544339 3.28216519 -0.00057450 0.00111454 -0.00157008 Cs 23.49029517 11.77239129 6.56870918 0.00130417 -0.00097194 0.00060884 Pb 2.99808709 15.03872411 9.08092177 0.00106243 0.00006229 0.00129898 Br 3.33102704 14.95161488 5.96833136 -0.00231087 -0.00055547 -0.00089348 Br 2.98004327 11.91121863 8.70697696 0.00033546 0.00591881 0.00043563 Br 23.75978435 15.08674208 8.89452462 0.00152121 -0.00103815 -0.00029778 Cs 0.13598206 11.63755688 11.46671857 -0.00229300 0.00074599 -0.00137722 Pb 3.06232658 14.56681607 15.07044308 0.00051716 -0.00094405 0.00081652 Br 3.04881705 14.45271712 11.83803583 -0.00083037 -0.00200388 -0.00049297 Br 2.62164670 11.81226027 15.40485413 -0.00336986 -0.00131860 0.00171703 Br 0.11786364 14.61516982 14.58477533 0.00385309 -0.00117893 -0.00117606 Cs 23.48774921 11.75353454 18.17315354 0.00238583 -0.00142964 -0.00007231 Pb 3.08309823 14.57189079 20.78676597 0.00123589 -0.00212023 -0.00092809 Br 2.93433687 14.86754488 17.92537188 -0.00010293 0.00116184 -0.00145679 Br 2.76379883 11.76128778 20.92039419 0.00093794 -0.00149685 0.00021592 Br 0.14307997 14.53205487 20.95471534 0.00132271 0.00162554 -0.00189590 Cs 0.07892115 17.78361359 23.66195295 -0.00034822 0.00083402 -0.00139448 Pb 2.96203972 20.76417966 2.89903466 0.00154392 -0.00037881 -0.00151003 Br 2.92254049 20.78037642 23.59417220 0.00020601 0.00236855 0.00423436 Br 3.20269952 17.91668211 2.64272157 -0.00189385 -0.00015293 0.00126342 Br 23.61698491 20.81640120 2.92355872 -0.00121889 0.00192816 0.00202292 Cs 23.12269674 17.79920170 6.26085606 -0.00181304 0.00118703 -0.00326482 Pb 2.85412825 20.94218765 8.78854379 0.00188252 -0.00074051 -0.00000871 Br 2.94703968 20.81189057 5.83257019 0.00141505 -0.00244922 0.00123386 Br 3.36662348 17.93407233 9.44461920 -0.00251612 -0.00372229 -0.00285926 Br 23.61372432 21.08518191 8.87879120 0.00304928 -0.00292778 0.00051299 Cs 0.47747568 17.13541067 12.24707355 0.00307343 0.00167242 -0.00352318 Pb 3.11684553 21.08029117 14.70482318 0.00010829 -0.00065650 0.00204332 Br 3.27732109 20.75271240 11.70597220 -0.00405084 0.00042342 -0.00285641 Br 3.05482998 17.99446602 15.01681189 -0.00000484 -0.00242791 0.00399526 Br 0.15974043 20.47679063 14.96945493 0.00005384 0.00210076 -0.00029253 Cs 0.39442026 17.67182131 17.48135818 0.00044508 0.00045889 0.00160487 Pb 2.99749383 20.74459380 20.50465229 0.00042865 0.00109790 -0.00121776 Br 3.59864056 20.48457538 17.57323055 -0.00326398 -0.00415255 0.00093566 Br 3.08695309 17.81836443 20.54435750 -0.00009932 -0.00139277 -0.00535811 Br 23.77138771 20.62494493 20.54421290 0.00203337 -0.00110043 0.00140658 Cs 5.99006950 0.10242420 22.87531283 -0.00272959 0.00113282 -0.00028825 Pb 9.05374265 2.97918744 3.27777014 -0.00172121 0.00214558 -0.00197394 Br 9.06857331 2.53337136 0.04941947 -0.00262064 0.00212132 -0.00013442 Br 8.73736591 23.73178954 3.31643288 0.00327764 0.00482514 0.00388635 Br 5.92983785 2.94542327 3.54577704 -0.00191634 0.00197465 0.00211506 Cs 6.08872783 23.41287966 5.71914379 -0.00211818 0.00058021 0.00088821 Pb 8.79182776 2.78367269 8.83041786 -0.00066077 0.00095030 0.00039559 Br 9.86772437 3.44571804 6.00220926 -0.00235113 0.00058051 -0.00041134 Br 8.35536951 23.37947223 8.75042619 0.00128713 0.00217401 0.00306764 Br 5.80397175 3.39329024 8.47330612 0.00314722 -0.00289734 -0.00319343 Cs 6.08318237 23.69350431 12.05355915 -0.00066484 0.00145622 0.00021406 Pb 9.14849687 2.86571292 14.67552653 0.00153468 0.00009657 0.00150958 Br 8.99118408 2.84058017 11.69603178 0.00005481 0.00043494 -0.00209210 Br 10.11429875 23.52076685 15.26106893 0.00133903 0.00192156 -0.00229521 Br 6.17307333 2.38411459 15.20514102 -0.00030206 0.00065890 -0.00293157 Cs 6.49709822 23.48130395 17.66938352 -0.00138342 -0.00241995 0.00107240 Pb 9.17912424 3.04775782 21.06263153 -0.00137826 0.00098373 -0.00109230 Br 9.73033237 2.97590066 17.60856058 -0.00162812 -0.00112120 0.00612202 Br 9.09663815 0.15581723 20.61964161 -0.00205090 -0.00259173 0.00354370 Br 6.10172207 3.91760857 20.75467399 -0.00226434 0.00017602 0.00032825 Cs 5.64270198 6.30819287 23.67993183 -0.00153939 -0.00343275 0.00074359 Pb 8.86492097 8.84430311 2.81051854 -0.00178669 0.00155643 -0.00038473 Br 8.89352609 8.82322007 23.63317911 0.00132466 0.00078040 0.00008685 Br 8.28424254 5.79684271 2.85640561 -0.00170168 0.00323492 0.00160120 Br 5.89147529 8.92693839 2.72081450 0.00001176 0.00161379 0.00123254 Cs 5.86586820 6.08268724 5.82155011 0.00314993 0.00257502 0.00009446 Pb 8.77825180 8.83658326 8.73739658 -0.00053555 -0.00000395 -0.00125782 Br 8.75971019 8.62237200 5.93327549 -0.00141508 -0.00233382 -0.00099310 Br 8.77863584 5.81954598 9.11509656 -0.00187514 0.00148163 0.00270120 Br 5.92279955 8.11169614 8.60138588 -0.00031876 0.00386121 0.00072354 Cs 5.86718929 6.16905806 11.87736739 0.00144356 -0.00135199 0.00117468 Pb 9.06485879 8.87817618 14.84328516 -0.00106574 0.00036628 0.00025790 Br 7.92419071 9.05055155 11.85047347 -0.00170324 -0.00515860 0.00106623 Br 9.32826558 5.82061976 15.04180362 0.00234591 0.00307069 0.00281164 Br 6.01644955 9.22230026 15.37352652 0.00239455 -0.00499781 0.00165971 Cs 6.94723439 6.06499083 17.84922322 -0.00191782 0.00109631 -0.00090397 Pb 8.70822890 8.94776638 20.70811805 0.00040142 0.00159722 -0.00031686 Br 9.18984538 9.33518184 17.87046168 -0.00089976 -0.00165241 0.00069147 Br 9.19476034 6.12264163 20.97525907 -0.00062480 0.00105711 -0.00155275 Br 5.70598361 8.63218977 20.59290943 -0.00278350 -0.00180513 0.00097368 Cs 5.53392392 11.55677153 23.32483409 -0.00154167 -0.00177887 0.00149686 Pb 8.75048722 14.63160576 3.03094834 0.00136762 0.00023547 0.00145213 Br 9.27575247 14.72714924 0.38366588 0.00059368 -0.00287328 -0.00007691 Br 8.95514494 11.57291390 3.03266348 0.00115268 0.00183555 0.00303958 Br 5.64355973 14.70724731 2.59790844 0.00089622 -0.00237106 0.00022911 Cs 5.15637892 11.90953189 4.87470443 0.00209455 0.00085899 0.00079525 Pb 8.58436258 14.55070077 8.93797009 0.00029890 -0.00122005 0.00104865 Br 8.15359965 14.07041510 5.97560076 0.00440714 -0.00290082 -0.00244466 Br 9.80110840 11.94189476 8.87196462 -0.00170673 0.00211864 -0.00214975 Br 5.90348320 14.41231410 9.49732107 -0.00203879 -0.00553640 0.00170036 Cs 5.47897148 12.02797783 11.87065908 0.00137976 -0.00189114 -0.00154463 Pb 8.86612435 15.05645075 14.91242004 0.00288133 0.00154552 0.00022089 Br 9.63652802 15.15066793 11.75966864 -0.00379091 0.00173119 0.00161111 Br 9.46614131 11.83305763 15.08554692 0.00114719 -0.00086281 0.00134041 Br 6.01450803 14.64366772 14.41876172 -0.00492843 0.00028245 -0.00550076 Cs 6.39470862 11.96413818 17.81788841 0.00036472 -0.00271655 -0.00199974 Pb 8.93962318 14.92157002 20.78503089 0.00037492 -0.00007328 -0.00057850 Br 9.30883332 14.89910535 17.89795707 0.00188285 -0.00024844 -0.00402965 Br 8.93819752 11.86433773 21.43133908 -0.00052142 -0.00214475 -0.00214914 Br 6.07156272 14.80637383 21.23520070 0.00117637 0.00278603 0.00096873 Cs 5.48714712 18.44912188 23.74427392 -0.00176148 0.00244903 -0.00106704 Pb 8.80251863 20.73029538 3.03812256 -0.00030695 0.00002186 0.00071616 Br 8.89968145 20.73494744 0.12292663 0.00030066 0.00218279 0.00139525 Br 8.38560912 17.74397835 2.93273639 -0.00082174 0.00195677 -0.00224175 Br 5.90101140 20.62491649 2.89672928 -0.00298539 0.00114710 0.00184042 Cs 5.70232899 17.85199932 5.54001927 -0.00112222 0.00256599 -0.00040471 Pb 8.59657482 20.60286011 8.82332745 -0.00009817 -0.00223558 0.00188207 Br 8.45511752 20.33246675 5.86261370 -0.00291553 -0.00025497 -0.00071294 Br 8.06178145 17.48665751 8.42055089 0.00022275 -0.00107605 -0.00443830 Br 5.72045855 20.64300880 8.72371887 -0.00159115 -0.00129419 -0.00223113 Cs 6.35324462 18.15700220 11.44328005 -0.00077064 0.00246477 -0.00121582 Pb 9.33420949 20.84720775 14.63311403 -0.00117305 -0.00032576 -0.00014257 Br 9.44130951 21.07294290 11.81143093 0.00231794 -0.00338969 0.00152888 Br 8.41419673 17.70912601 14.61355847 0.00031972 -0.00405075 0.00224341 Br 6.11916164 21.13910511 14.86078524 0.00065732 0.00258337 0.00169963 Cs 5.81776591 17.38273061 17.97772732 -0.00104892 -0.00065140 -0.00191737 Pb 9.07460684 20.69441053 20.73896100 -0.00067098 -0.00066857 -0.00087813 Br 9.64093575 20.66751367 17.66547105 0.00396819 0.00106801 -0.00268214 Br 7.94187966 17.99867592 20.69559720 -0.00311832 0.00151220 0.00033766 Br 6.12850799 20.90608215 20.59944514 -0.00030647 -0.00366608 -0.00093551 Cs 12.12399799 23.73878292 0.40225041 0.00066773 -0.00043640 -0.00143767 Pb 15.05166952 3.31845187 2.85191905 -0.00012291 0.00115975 -0.00125145 Br 15.35245912 2.83984194 0.13331348 -0.00044244 0.00114994 -0.00169924 Br 15.29142813 23.71605283 2.61620857 0.00401133 -0.00216590 0.00270289 Br 11.96959444 3.38240435 2.59741434 0.00247846 0.00209776 -0.00216214 Cs 12.21428359 0.56697499 5.86724812 -0.00098796 -0.00001697 -0.00367629 Pb 14.81260449 3.01522626 8.86752099 0.00098852 0.00054881 -0.00066027 Br 14.80635137 3.05139930 5.91560542 -0.00257551 0.00393775 -0.00129632 Br 14.81950070 0.13255227 8.99766750 0.00207574 -0.00030231 0.00135241 Br 12.07811682 3.03935079 9.61581478 0.00320338 -0.00060661 0.00015311 Cs 11.92754868 0.13576183 11.95115211 -0.00007984 0.00419180 0.00042704 Pb 14.82801236 2.97932853 14.86130809 0.00248629 -0.00277231 0.00084529 Br 15.36670690 2.94130882 11.83397415 -0.00530997 0.00031025 0.00250271 Br 14.30597828 23.60374439 15.04177936 -0.00170916 -0.00028219 -0.00232181 Br 11.99642736 2.82909454 14.26183727 0.00144663 0.00207607 -0.00125496 Cs 12.09928652 23.28497793 18.29542417 -0.00080802 -0.00051098 0.00251137 Pb 15.08443710 3.00747293 20.89736552 -0.00026952 0.00261325 -0.00148946 Br 15.00620894 2.99329105 17.90643058 -0.00486165 0.00219417 0.00297748 Br 15.02448575 23.59600999 21.00861612 0.00310721 -0.00143043 -0.00383717 Br 12.03153359 2.92034191 21.46228902 -0.00299876 -0.00483552 0.00188962 Cs 11.77740813 6.30094753 23.31333818 0.00098204 -0.00086896 -0.00009181 Pb 14.76415801 8.97097650 3.43202632 -0.00079188 -0.00092161 -0.00062760 Br 14.72362114 9.09778214 0.17506906 0.00040751 -0.00388468 0.00054546 Br 15.43439390 6.18012442 3.71516662 -0.00197680 -0.00226463 -0.00331280 Br 11.79321755 8.64592967 2.66069578 -0.00278538 -0.00246446 -0.00112137 Cs 11.89890516 6.29738797 5.57552811 0.00241425 -0.00124895 0.00003943 Pb 14.49180840 8.88196428 9.04917499 0.00072838 -0.00020344 0.00052707 Br 14.36214723 9.54887562 6.17889378 0.00001416 0.00037953 -0.00304159 Br 15.25020589 6.16616087 8.32489016 0.00064355 0.00041369 -0.00018034 Br 11.73792946 8.12374279 9.02568239 0.00109974 -0.00166200 -0.00113745 Cs 11.92916498 5.90291143 11.83589105 0.00146838 0.00145881 0.00066287 Pb 14.87205675 8.76219146 14.73170949 0.00097896 0.00160176 0.00059222 Br 14.96270360 9.06117407 11.77127536 -0.00403365 0.00144425 0.00214857 Br 15.09001294 5.94171166 14.92632052 -0.00348042 0.00022014 -0.00089986 Br 12.06461638 8.44459131 14.76748434 0.00214623 0.00128508 0.00336554 Cs 12.54576613 5.74993754 18.28378287 0.00161744 -0.00115566 0.00058635 Pb 14.59813170 9.11923317 20.90805007 0.00013461 0.00143361 0.00039123 Br 14.76836066 8.79429427 17.94948887 -0.00001579 -0.00290231 -0.00340994 Br 15.37782744 5.77498761 20.90062699 -0.00209147 -0.00006715 -0.00232754 Br 11.54445397 9.05837513 20.42078110 0.00288510 -0.00192213 0.00034735 Cs 11.37084478 11.85624045 0.60515278 -0.00206472 0.00026243 0.00012515 Pb 14.72983737 14.98615913 3.25378328 0.00051837 -0.00172809 -0.00024677 Br 14.82827382 15.69058662 0.01552653 0.00135277 -0.00230742 -0.00001579 Br 14.61468173 11.98786794 3.08136667 -0.00293596 0.00360265 0.00118540 Br 11.63350436 15.15555940 3.61808848 -0.00038235 -0.00217028 -0.00473966 Cs 11.54524025 12.48941597 5.99572103 -0.00009018 0.00011913 0.00126256 Pb 14.50449879 14.89182092 9.23327369 0.00242461 0.00223488 0.00054703 Br 15.11023230 14.76823747 6.09828115 -0.00276693 0.00128970 -0.00137859 Br 13.97355575 12.06440321 9.02197535 -0.00483867 0.00052008 -0.00044998 Br 11.88107907 15.54234188 8.26613956 -0.00189472 -0.00154879 0.00147837 Cs 11.91178503 12.09575262 11.94458850 -0.00053306 0.00053238 0.00029791 Pb 15.03995915 14.91094429 14.91204016 -0.00055811 0.00207913 0.00173947 Br 14.61412108 14.88402332 12.11391869 0.00190073 -0.00138264 0.00293672 Br 14.45434206 11.74103687 14.90072121 0.00074575 -0.00280647 0.00063897 Br 11.99282735 15.29508258 15.15561252 -0.00011176 -0.00170397 -0.00030525 Cs 12.24501392 11.63501335 17.49303489 0.00070852 -0.00211307 -0.00142750 Pb 14.75012618 14.84652446 21.07757127 0.00221708 -0.00016590 -0.00113844 Br 14.49296025 14.57596601 17.78316993 0.00013778 0.00307058 -0.00024716 Br 14.18998461 11.92040281 20.97462908 0.00107588 0.00153195 0.00358196 Br 11.88757064 15.17951388 21.41643048 0.00038494 0.00254601 0.00327193 Cs 11.60597606 18.04730271 0.38907572 -0.00133835 -0.00032393 0.00359795 Pb 14.76550594 20.88900404 2.92829274 0.00190248 0.00177422 0.00176022 Br 14.44300529 20.65762465 0.04401621 0.00416675 0.00187631 -0.00523356 Br 14.52648910 18.07574120 3.54010431 -0.00177016 -0.00202755 0.00170911 Br 11.74305542 21.34857301 3.45217122 -0.00045490 0.00076753 0.00126370 Cs 11.20967792 18.15391853 5.94314367 -0.00052449 0.00067873 0.00001089 Pb 14.87215146 20.94530866 8.79702791 0.00307535 0.00045313 0.00083683 Br 15.07223490 21.07542067 5.83236951 -0.00036443 -0.00656790 0.00407356 Br 14.70899322 17.82813711 8.78534419 0.00111301 -0.00192108 0.00052685 Br 11.75163938 21.14205815 8.27821705 -0.00161541 0.00443830 -0.00252176 Cs 12.42743165 18.49429636 11.73339575 0.00175128 0.00047143 -0.00041017 Pb 14.96347636 20.52228163 14.91209677 0.00083049 -0.00053932 -0.00121566 Br 14.82272992 20.51107011 11.96299878 0.00272404 0.00155160 -0.00215456 Br 15.72273140 17.68944305 14.59408672 -0.00278151 0.00294413 0.00007529 Br 12.01781159 19.67229234 14.74730845 0.00303999 -0.00082947 0.00136543 Cs 12.11874898 17.64762348 17.76453718 0.00191547 -0.00044637 0.00246483 Pb 14.75754557 20.55473952 20.66031582 0.00239493 0.00145655 -0.00060643 Br 14.33962010 20.52359083 17.81592751 -0.00326869 0.00223166 -0.00189205 Br 14.62509655 17.83153240 20.03763537 0.00173007 0.00087544 -0.00474810 Br 12.04380437 20.87644818 21.00495504 -0.00161263 -0.00202440 0.00096833 Cs 17.84595592 23.41784831 0.10762466 0.00547042 -0.00004707 -0.00322930 Pb 20.82767198 3.11956103 2.72390514 0.00163295 0.00263624 -0.00122311 Br 20.64330165 3.85460792 23.58955161 -0.00136723 0.00306094 -0.00095200 Br 20.98837160 0.05327191 3.28299648 -0.00175611 0.00095683 -0.00062469 Br 18.10091286 3.20464732 2.88987325 -0.00229160 0.00064356 0.00413184 Cs 18.30683525 23.70361853 6.17049424 -0.00289763 0.00293763 -0.00003267 Pb 20.97478186 3.09055006 8.94130391 0.00149364 -0.00147938 0.00005255 Br 21.55575596 2.94758812 6.07669215 0.00018769 0.00190458 0.00228653 Br 21.01545787 0.05894662 8.94237309 -0.00163364 0.00087393 0.00375888 Br 17.80789795 2.56771418 8.57468246 -0.00387949 0.00220489 0.00000852 Cs 17.73369357 0.41879689 11.80266917 0.00321608 -0.00186691 -0.00002986 Pb 20.77122241 3.00470186 14.60512039 -0.00175132 0.00082805 0.00041912 Br 19.88230241 3.48483732 11.72859297 -0.00101537 0.00158643 0.00060154 Br 21.35289864 23.72534317 14.84264288 0.00368004 -0.00103694 -0.00002681 Br 17.89929728 2.60133234 14.99785672 0.00017327 0.00331210 -0.00032420 Cs 17.94505714 23.75463848 18.36552824 0.00115139 -0.00185482 -0.00114676 Pb 21.12812642 2.87134126 20.66877811 0.00042190 -0.00040091 0.00003488 Br 20.88078030 3.09621321 17.69066523 -0.00020883 0.00237183 -0.00038004 Br 21.10200161 23.60768707 20.62455610 -0.00317381 0.00122712 -0.00351436 Br 17.85037668 2.83825931 20.10384653 -0.00598543 0.00071120 -0.00094957 Cs 17.68514324 6.29210308 23.42427789 0.00059037 0.00224308 -0.00200481 Pb 20.83168015 8.94293101 3.05563608 0.00026224 -0.00066120 0.00015832 Br 20.36377961 9.16949909 0.09401513 -0.00248317 -0.00563183 0.00188419 Br 20.61048457 6.02983704 3.26575565 -0.00150649 -0.00309790 0.00083543 Br 17.87346456 9.04823995 3.49165539 0.00021521 -0.00072370 0.00328631 Cs 18.36790811 5.35366208 6.41253270 0.00097832 0.00010191 -0.00053033 Pb 20.89656097 8.84564582 8.90429873 0.00185288 -0.00190326 -0.00117603 Br 20.88740321 9.31839412 6.07601817 -0.00250667 0.00276953 -0.00263910 Br 21.31320526 6.00713186 8.81319669 -0.00009653 0.00049720 -0.00157687 Br 17.44356463 9.06130663 9.41374207 -0.00313327 0.00070114 -0.00548010 Cs 17.42704608 6.31214225 11.46022916 0.00061763 0.00195174 -0.00078926 Pb 20.93744062 8.86109089 14.89994326 0.00098848 -0.00121252 0.00128813 Br 20.96225712 8.63572813 11.90572946 -0.00140315 0.00091241 -0.00042098 Br 20.89554561 5.77139284 15.33926141 -0.00150412 -0.00121139 -0.00090702 Br 17.91585003 8.54897899 14.68727633 0.00259516 -0.00370672 0.00041504 Cs 17.91333643 5.75458676 17.52072476 -0.00180171 -0.00095011 0.00118944 Pb 20.54051030 9.06797115 20.64896584 0.00133796 0.00003562 -0.00042339 Br 21.38826964 8.71799186 17.91261378 -0.00019750 -0.00138777 -0.00350754 Br 20.45634057 5.78142286 20.78219180 -0.00015211 -0.00139693 -0.00253217 Br 17.50271076 9.49552759 20.51952009 -0.00194801 -0.00275625 -0.00038954 Cs 17.83254213 11.96503976 0.16860560 -0.00212305 -0.00109567 -0.00064128 Pb 20.45429014 14.75631978 3.01787423 0.00211997 -0.00035899 0.00145159 Br 20.75750159 15.27727376 23.75101379 0.00040138 -0.00024710 0.00165510 Br 20.35294640 11.76277150 3.16653031 -0.00162042 0.00048971 0.00281017 Br 17.51548099 15.06954652 2.79852510 -0.00101821 -0.00070750 -0.00257232 Cs 17.76892776 11.14600978 6.26077604 0.00172670 -0.00054561 -0.00033807 Pb 20.56590350 14.79342616 8.94752549 -0.00085395 0.00164793 0.00262055 Br 20.29094838 15.03414293 5.89403654 -0.00277583 -0.00406720 -0.00058920 Br 20.68894575 11.83719078 8.55106160 -0.00763211 -0.00058100 0.00558574 Br 17.73640043 14.45668969 9.32634030 -0.00110736 -0.00045415 0.00021600 Cs 17.89433537 11.34361406 12.32973008 0.00122255 -0.00101994 -0.00223441 Pb 20.84239889 14.61249451 14.84713879 -0.00087742 0.00118656 -0.00127466 Br 20.89644298 13.98887715 11.98578009 -0.00132616 -0.00081232 -0.00342909 Br 20.46546145 11.63564588 15.26066269 0.00189027 -0.00014009 0.00221602 Br 17.91700822 14.11261371 14.78453253 -0.00299487 0.00200541 0.00263952 Cs 17.44468461 12.04650020 17.64990059 -0.00060738 -0.00139871 -0.00125301 Pb 20.84097927 14.93465143 20.82218974 0.00234158 0.00278533 -0.00068790 Br 21.26445026 14.57644993 17.92212719 -0.00370425 0.00028322 0.00220873 Br 20.33059874 11.95293391 20.04898222 -0.00417162 -0.00007085 0.00048189 Br 17.82618675 15.06227696 20.63198843 0.00446802 -0.00148933 -0.00169503 Cs 17.91092342 17.72846964 23.41906064 0.00053424 -0.00033530 0.00000793 Pb 20.73388257 20.96587096 3.08538770 0.00385805 -0.00064581 0.00081197 Br 21.33573205 21.08959615 23.70708490 0.00121716 0.00053913 -0.00079540 Br 20.68155715 17.86747852 3.11514406 0.00240350 -0.00091986 0.00149966 Br 18.03883443 20.24061584 2.22507730 -0.00000668 0.00128962 0.00225293 Cs 17.68028589 17.98486064 5.74335169 0.00162298 -0.00038003 -0.00244340 Pb 20.76808621 20.97313295 8.96097538 0.00087130 0.00071255 -0.00355873 Br 21.01194779 20.79477826 5.98285204 0.00002171 -0.00076071 -0.00103027 Br 20.23355690 17.78779415 8.67199788 0.00033801 -0.00306710 0.00066661 Br 17.63418219 20.86513120 9.65915254 -0.00422380 0.00183309 0.00022242 Cs 17.83699050 18.03936798 11.54741004 -0.00080153 -0.00159899 0.00378841 Pb 20.90365567 20.80087441 14.81237368 -0.00267705 0.00110930 0.00132128 Br 21.01030067 20.84211663 11.83862478 0.00143984 0.00028235 0.00179831 Br 20.62948675 17.45239003 13.98663389 -0.00208624 -0.00286527 0.00062032 Br 17.96630619 21.40645050 14.64078335 0.00015580 0.00161377 0.00352881 Cs 17.45689554 18.08959367 17.91419817 0.00030377 -0.00069670 0.00347447 Pb 20.95991489 20.78458707 20.80685429 -0.00143608 -0.00075767 0.00274322 Br 20.62111855 20.88649217 17.70963192 0.00296030 -0.00301096 -0.00300290 Br 20.78169109 17.86197781 20.45727544 0.00006598 0.00090555 -0.00274775 Br 17.79291386 20.60517093 21.03168014 0.00198219 0.00164247 0.00317235 320 Lattice="23.77195271 0.0 0.0 0.0 23.77195271 0.0 0.0 0.0 23.77195271" Properties=species:S:1:pos:R:3:vel:R:3 Time=20099.99999999 energy=-11426.68151474 virial="-19.8243129 0.26517382 -1.18028488 0.26517382 -13.52085292 -1.98919134 -1.18028488 -1.98919134 -12.25718824" stress="-0.00038866 1.974e-05 -8.786e-05 1.974e-05 -5.978e-05 -0.00014808 -8.786e-05 -0.00014808 8.871e-05" pbc="T T T" Cs 23.70505659 0.21122875 23.76728639 -0.00364451 -0.00298975 0.00124927 Pb 2.91451944 3.04431418 3.04534495 -0.00191980 0.00133178 0.00180371 Br 3.27918061 2.93196187 0.08702229 -0.00068278 0.00017074 0.00161189 Br 2.98942604 0.12839045 3.12217627 -0.00062870 -0.00046670 -0.00094506 Br 0.20512537 3.46172320 2.41423648 0.00014060 -0.00045546 -0.00322257 Cs 0.08882755 23.73974027 6.11585913 0.00364440 0.00252029 -0.00213122 Pb 3.01967035 2.97061683 9.28491904 0.00127306 0.00047141 -0.00182719 Br 2.46929479 2.77357450 6.24805388 0.00184015 -0.00115286 0.00302786 Br 3.15786394 23.76911203 9.31210044 0.00017409 0.00053408 0.00331329 Br 23.64406064 3.10987513 10.28375672 -0.00032976 0.00140931 0.00565344 Cs 0.31879678 23.61844719 12.20556212 -0.00378441 -0.00094071 0.00045395 Pb 3.05883324 3.30777213 14.92952742 -0.00075281 -0.00125622 -0.00122658 Br 4.17175567 3.04514321 11.96362019 -0.00257198 -0.00102311 0.00077717 Br 2.97026681 0.15438578 14.75534428 0.00168386 -0.00291389 -0.00316833 Br 0.29098586 3.40330325 14.74805894 -0.00151607 -0.00218770 -0.00114954 Cs 0.18127212 23.37657723 17.77205260 -0.00120736 -0.00154082 -0.00224943 Pb 3.19980315 3.07918075 20.63813602 0.00182660 0.00043403 0.00047053 Br 2.56169762 3.18325140 17.84613705 -0.00042446 0.00206587 0.00064400 Br 3.06425158 0.08217146 20.28349580 -0.00118328 0.00114902 0.00359305 Br 0.14419031 3.04899807 20.56640090 0.00101755 -0.00318580 0.00073689 Cs 23.48086663 6.40937744 23.73613265 -0.00001351 0.00253128 -0.00076316 Pb 2.84561277 8.91858330 3.21573907 -0.00000716 -0.00036928 -0.00108448 Br 2.66380681 8.61139915 23.68153902 0.00240172 -0.00565102 0.00149042 Br 3.13419299 6.05037102 3.11285224 -0.00288672 -0.00014720 -0.00130363 Br 23.71819943 8.60705706 3.19890915 0.00163364 -0.00120740 -0.00247477 Cs 0.40120438 6.03163455 5.93605388 0.00220361 0.00084306 0.00200612 Pb 3.33671335 9.07155833 8.90907736 0.00222026 -0.00011342 -0.00125787 Br 2.86411674 8.91386630 6.04152882 -0.00027137 -0.00199983 0.00460136 Br 2.84447659 5.85249951 8.73127833 -0.00225875 -0.00334607 0.00235500 Br 23.68046479 9.21708764 8.85073278 -0.00264971 0.00272680 -0.00075126 Cs 23.61801158 5.98329069 11.93351371 -0.00159115 -0.00165127 0.00183139 Pb 2.89356823 8.78485419 14.84029013 -0.00075355 0.00059253 -0.00174198 Br 3.17773845 8.88606733 11.80209667 0.00042180 0.00197627 0.00225890 Br 3.75829050 6.08563705 15.13853746 0.00000265 0.00290723 0.00136436 Br 0.06911534 8.51514149 14.48171309 0.00078604 -0.00201041 -0.00381086 Cs 23.68086072 5.73542738 17.61091573 -0.00145757 0.00021796 0.00006866 Pb 2.69326393 8.85158740 20.59687957 -0.00107727 -0.00077706 0.00022766 Br 2.24937692 8.78299775 17.71221815 0.00393971 0.00032989 -0.00272686 Br 2.07508908 5.87827443 20.76552587 0.00100227 -0.00028323 0.00203435 Br 23.62705405 9.28007416 21.16822483 -0.00399124 -0.00014018 0.00024712 Cs 23.14246725 11.77187581 0.25445326 0.00253191 -0.00074259 0.00037378 Pb 2.72940298 14.90946183 2.90193837 -0.00251742 0.00138133 0.00062951 Br 2.48381396 15.11870346 23.71124676 -0.00037569 -0.00181305 0.00117524 Br 2.91632657 11.78288515 2.26364021 0.00079406 0.00324501 -0.00189888 Br 23.32144532 14.64646042 3.26641250 0.00017247 0.00108769 -0.00157684 Cs 23.50397066 11.76385232 6.57340208 0.00141826 -0.00072789 0.00033555 Pb 3.00844684 15.03967313 9.09248737 0.00100421 0.00011864 0.00101093 Br 3.30730839 14.94616836 5.95804366 -0.00242983 -0.00053202 -0.00116699 Br 2.98335799 11.97095508 8.71156723 0.00032569 0.00595061 0.00047679 Br 0.00276598 15.07597984 8.89170260 0.00144915 -0.00111221 -0.00026831 Cs 0.11277732 11.64561587 11.45355486 -0.00234552 0.00086836 -0.00125370 Pb 3.06708752 14.55844060 15.07746069 0.00043642 -0.00073310 0.00058009 Br 3.03831636 14.43291441 11.83652318 -0.00127052 -0.00195797 0.00019352 Br 2.58889879 11.79840922 15.42128984 -0.00317541 -0.00144716 0.00157228 Br 0.15629455 14.60307786 14.57439074 0.00378809 -0.00122737 -0.00090823 Cs 23.51207322 11.73926758 18.17235007 0.00247173 -0.00141986 -0.00008926 Pb 3.09475641 14.55199219 20.77877937 0.00109626 -0.00185325 -0.00067056 Br 2.93361060 14.87913265 17.90971084 -0.00004208 0.00115190 -0.00164246 Br 2.77399467 11.74661565 20.92205118 0.00109507 -0.00143863 0.00011843 Br 0.15406839 14.54878446 20.93512595 0.00088632 0.00171744 -0.00202088 Cs 0.07498460 17.79245065 23.64807629 -0.00043653 0.00092969 -0.00138077 Pb 2.97670813 20.76169774 2.88363787 0.00138650 -0.00011507 -0.00156163 Br 2.92366818 20.80533941 23.63533838 0.00001659 0.00262303 0.00398111 Br 3.18218771 17.91227295 2.65629178 -0.00220529 -0.00072529 0.00144311 Br 23.60790695 20.83524835 2.94405237 -0.00058216 0.00184120 0.00207337 Cs 23.10449798 17.81067830 6.22775442 -0.00182200 0.00110294 -0.00335227 Pb 2.87268189 20.93396284 8.78908397 0.00182084 -0.00089893 0.00011503 Br 2.96114855 20.78730259 5.84458881 0.00140300 -0.00246525 0.00115355 Br 3.33999134 17.89495970 9.41509259 -0.00279423 -0.00404788 -0.00303282 Br 23.64487983 21.05531930 8.88384511 0.00313077 -0.00303848 0.00049296 Cs 0.50794608 17.15333958 12.21143406 0.00302133 0.00190833 -0.00359971 Pb 3.11846241 21.07205151 14.72531339 0.00021023 -0.00099726 0.00204737 Br 3.23642730 20.75781187 11.67970854 -0.00412134 0.00058611 -0.00236632 Br 3.05510327 17.96695450 15.05527609 0.00006125 -0.00306737 0.00368917 Br 0.15919398 20.49855710 14.96654776 -0.00015467 0.00225020 -0.00028777 Cs 0.39863016 17.67650063 17.49777959 0.00039659 0.00047745 0.00167863 Pb 3.00246241 20.75551568 20.49254559 0.00056460 0.00108124 -0.00120060 Br 3.56467814 20.44468970 17.58539270 -0.00352396 -0.00381245 0.00149319 Br 3.08574364 17.80437225 20.49133975 -0.00014450 -0.00139443 -0.00523391 Br 0.02233863 20.61368613 20.55733440 0.00251522 -0.00114692 0.00122043 Cs 5.96268305 0.11401681 22.87305294 -0.00274652 0.00118456 -0.00016331 Pb 9.03546692 3.00071901 3.25701449 -0.00192844 0.00216006 -0.00217241 Br 9.04136236 2.55483142 0.04887789 -0.00282483 0.00217232 0.00002932 Br 8.77077763 0.00837022 3.35537339 0.00340067 0.00486428 0.00390235 Br 5.91272699 2.96468514 3.56755353 -0.00149913 0.00187979 0.00223703 Cs 6.06665515 23.41901576 5.72797131 -0.00229259 0.00064870 0.00087811 Pb 8.78476493 2.79432543 8.83402682 -0.00074881 0.00117405 0.00031989 Br 9.84386575 3.45051870 5.99935302 -0.00241820 0.00038357 -0.00017454 Br 8.36789823 23.40549894 8.78088241 0.00121482 0.00298123 0.00302498 Br 5.83786732 3.36258072 8.44182319 0.00360976 -0.00323712 -0.00309682 Cs 6.07625624 23.70800761 12.05580437 -0.00072048 0.00144544 0.00023330 Pb 9.16349345 2.86651491 14.69248174 0.00146902 0.00006667 0.00187764 Br 8.99063578 2.84431899 11.67580124 -0.00016324 0.00031329 -0.00190996 Br 10.12578889 23.54068477 15.23627416 0.00094796 0.00204953 -0.00266306 Br 6.17050885 2.39127687 15.17603213 -0.00020428 0.00076917 -0.00288504 Cs 6.48333064 23.45703561 17.68010872 -0.00136834 -0.00242817 0.00107320 Pb 9.16507107 3.05749363 21.05083978 -0.00142896 0.00095905 -0.00126692 Br 9.71331779 2.96524783 17.67097315 -0.00177770 -0.00100908 0.00633837 Br 9.07529693 0.12768117 20.65499451 -0.00221893 -0.00302111 0.00352473 Br 6.07887398 3.91803687 20.75783353 -0.00229016 -0.00008460 0.00030124 Cs 5.62713386 6.27351979 23.68752076 -0.00157210 -0.00350125 0.00077434 Pb 8.84648116 8.85771957 2.80686208 -0.00189541 0.00113244 -0.00034659 Br 8.90652189 8.83099052 23.63639477 0.00127163 0.00077313 0.00054939 Br 8.26842538 5.82926970 2.87205999 -0.00146617 0.00324922 0.00152533 Br 5.89034936 8.94383740 2.73338907 -0.00025155 0.00176288 0.00127978 Cs 5.89729071 6.10735451 5.82163358 0.00313401 0.00235595 -0.00007716 Pb 8.77362707 8.83631874 8.72626472 -0.00038150 -0.00005159 -0.00096807 Br 8.74609806 8.59884708 5.92183423 -0.00130755 -0.00236816 -0.00128214 Br 8.76035722 5.83385527 9.14181770 -0.00178309 0.00139013 0.00263811 Br 5.92063637 8.15153840 8.60925634 -0.00010076 0.00408906 0.00084320 Cs 5.88188467 6.15536192 11.88880333 0.00149107 -0.00139165 0.00111516 Pb 9.05338207 8.88257236 14.84577710 -0.00122584 0.00050981 0.00023887 Br 7.90911711 8.99970572 11.86175336 -0.00130594 -0.00499686 0.00118218 Br 9.35066962 5.85131978 15.06881062 0.00213245 0.00304456 0.00259217 Br 6.04055592 9.17193902 15.38984529 0.00242759 -0.00506918 0.00160459 Cs 6.92775299 6.07571805 17.84006133 -0.00197849 0.00104974 -0.00092794 Pb 8.71204967 8.96445504 20.70526430 0.00036293 0.00173312 -0.00025392 Br 9.17936286 9.31773920 17.87331962 -0.00118994 -0.00183184 -0.00011036 Br 9.18862240 6.13075714 20.95862089 -0.00060071 0.00055766 -0.00177018 Br 5.67860783 8.61432254 20.60236587 -0.00267764 -0.00176469 0.00091952 Cs 5.51874103 11.53877573 23.34031184 -0.00149201 -0.00181820 0.00159864 Pb 8.76360485 14.63513024 3.04700404 0.00125247 0.00046905 0.00174668 Br 9.28180812 14.69920125 0.37667098 0.00060614 -0.00271276 -0.00131366 Br 8.96588167 11.59639533 3.06409810 0.00099638 0.00283935 0.00324520 Br 5.65243514 14.68327305 2.60007899 0.00086475 -0.00241724 0.00020664 Cs 5.17867458 11.91833098 4.88395110 0.00235942 0.00090315 0.00105090 Pb 8.58967902 14.54004177 8.94827756 0.00074939 -0.00090553 0.00101305 Br 8.19680105 14.04170207 5.95285821 0.00422629 -0.00283693 -0.00208329 Br 9.78289458 11.95901724 8.85232847 -0.00193254 0.00128655 -0.00176710 Br 5.88154585 14.35746799 9.51399498 -0.00229231 -0.00541534 0.00161256 Cs 5.49358967 12.00724446 11.85631021 0.00153867 -0.00225734 -0.00131757 Pb 8.89692794 15.07005874 14.91336107 0.00326647 0.00114902 -0.00003539 Br 9.59694853 15.16724919 11.77612761 -0.00411666 0.00158283 0.00166821 Br 9.47709851 11.82390775 15.09773638 0.00104403 -0.00096336 0.00109574 Br 5.96478624 14.64563418 14.36479607 -0.00496318 0.00011724 -0.00528649 Cs 6.39862081 11.93659578 17.79761173 0.00041708 -0.00279077 -0.00205707 Pb 8.94386532 14.92062982 20.78014165 0.00047493 -0.00011300 -0.00040106 Br 9.32606606 14.89654077 17.85930628 0.00156432 -0.00026220 -0.00366469 Br 8.93209978 11.84639444 21.40841916 -0.00069590 -0.00142701 -0.00243239 Br 6.08233173 14.83319497 21.24455563 0.00097587 0.00257399 0.00090273 Cs 5.47084457 18.47260714 23.73287005 -0.00149737 0.00224482 -0.00121172 Pb 8.79994206 20.73071425 3.04481725 -0.00021036 0.00006566 0.00062173 Br 8.90300356 20.75704574 0.13265521 0.00036108 0.00223572 0.00054880 Br 8.37867379 17.76216013 2.91028514 -0.00056540 0.00168914 -0.00224645 Br 5.86993417 20.63784548 2.91526300 -0.00319660 0.00143763 0.00186224 Cs 5.69103503 17.87736537 5.53613005 -0.00113952 0.00250341 -0.00037376 Pb 8.59706669 20.57992402 8.84153972 0.00019745 -0.00233327 0.00175233 Br 8.42497244 20.33127772 5.85878686 -0.00310744 0.00001789 -0.00004032 Br 8.06483869 17.47671289 8.37616552 0.00038340 -0.00090444 -0.00442627 Br 5.70702132 20.63087917 8.70103805 -0.00106119 -0.00113479 -0.00230153 Cs 6.34514009 18.18116921 11.43227135 -0.00085095 0.00236297 -0.00098842 Pb 9.32191595 20.84349772 14.63350597 -0.00127461 -0.00041208 0.00022650 Br 9.46399265 21.03898578 11.82364295 0.00221834 -0.00340272 0.00089960 Br 8.41667068 17.67415548 14.63626405 0.00016269 -0.00287228 0.00229317 Br 6.12749543 21.16541226 14.87625209 0.00101190 0.00266975 0.00139238 Cs 5.80684740 17.37687675 17.95782552 -0.00113354 -0.00052168 -0.00206202 Pb 9.06721996 20.68803317 20.72992664 -0.00080025 -0.00060416 -0.00092581 Br 9.67896645 20.67751031 17.64201789 0.00362801 0.00092786 -0.00200245 Br 7.91470888 18.01019723 20.70003421 -0.00230738 0.00077169 0.00054580 Br 6.12296230 20.87192995 20.59034136 -0.00081765 -0.00314728 -0.00088631 Cs 12.13060375 23.73464851 0.38758428 0.00065375 -0.00039022 -0.00149575 Pb 15.05000163 3.32989374 2.84096623 -0.00021069 0.00112581 -0.00094065 Br 15.34896080 2.85171830 0.11340656 -0.00025322 0.00122220 -0.00226678 Br 15.33106287 23.69774911 2.64428143 0.00392065 -0.00149597 0.00290496 Br 11.99552542 3.40320345 2.57620733 0.00268311 0.00206124 -0.00207649 Cs 12.20429388 0.56580517 5.83054615 -0.00101082 -0.00021328 -0.00366115 Pb 14.82330021 3.02146386 8.86112729 0.00114628 0.00069713 -0.00061962 Br 14.78046349 3.08990013 5.90179592 -0.00260395 0.00375732 -0.00145665 Br 14.83994128 0.12821241 9.01107042 0.00201466 -0.00056035 0.00132445 Br 12.10643862 3.03291573 9.61615062 0.00244203 -0.00067304 -0.00008130 Cs 11.92734096 0.17733036 11.95526851 0.00003871 0.00411771 0.00039225 Pb 14.85358745 2.95027130 14.86989248 0.00261896 -0.00303054 0.00086838 Br 15.31272285 2.94590692 11.86014182 -0.00546560 0.00059548 0.00272210 Br 14.29032913 23.60047815 15.01791280 -0.00141671 -0.00036029 -0.00244774 Br 12.01172070 2.85221871 14.24873111 0.00159829 0.00255018 -0.00135614 Cs 12.09125450 23.28132630 18.32002211 -0.00079933 -0.00021601 0.00240821 Pb 15.08174029 3.03181347 20.88199372 -0.00028239 0.00224699 -0.00157711 Br 14.95565726 3.01530411 17.93428027 -0.00524395 0.00219984 0.00256914 Br 15.05555755 23.58238438 20.96974971 0.00310545 -0.00129464 -0.00393611 Br 12.00215892 2.87271388 21.48066290 -0.00287629 -0.00468780 0.00178366 Cs 11.78764161 6.29223589 23.31331214 0.00106260 -0.00087500 0.00008480 Pb 14.75689427 8.96247096 3.42439049 -0.00066055 -0.00078013 -0.00090210 Br 14.72816646 9.05821627 0.17952233 0.00050198 -0.00402354 0.00033698 Br 15.41397207 6.15505692 3.68111607 -0.00210638 -0.00271638 -0.00349632 Br 11.76697507 8.62078260 2.65001449 -0.00244878 -0.00256267 -0.00101223 Cs 11.92354055 6.28508011 5.57623332 0.00251170 -0.00121533 0.00010137 Pb 14.49959675 8.88070681 9.05262748 0.00082507 -0.00004416 0.00016541 Br 14.36175486 9.55283573 6.14703450 -0.00009379 0.00040796 -0.00329556 Br 15.25625599 6.16848675 8.32396798 0.00056644 0.00005249 -0.00000457 Br 11.74613187 8.10680020 9.01432170 0.00053142 -0.00172431 -0.00113834 Cs 11.94355990 5.91717335 11.84299758 0.00141273 0.00139230 0.00075962 Pb 14.88236252 8.77934766 14.73854855 0.00108236 0.00181738 0.00077933 Br 14.92071120 9.07526893 11.80021103 -0.00435314 0.00137448 0.00357808 Br 15.05493026 5.94051226 14.91697012 -0.00353176 -0.00047149 -0.00097023 Br 12.08327863 8.45777659 14.80072442 0.00157966 0.00135464 0.00328139 Cs 12.56124387 5.73843283 18.28916752 0.00147305 -0.00114084 0.00048771 Pb 14.59963061 9.13305615 20.91197010 0.00016334 0.00132579 0.00038913 Br 14.76801458 8.76556772 17.91404888 -0.00004995 -0.00283477 -0.00364922 Br 15.35517691 5.77895864 20.87693257 -0.00243115 0.00088409 -0.00240393 Br 11.57780592 9.03834236 20.42580574 0.00374966 -0.00208093 0.00065391 Cs 11.35169524 11.85854437 0.60519401 -0.00176304 0.00019659 -0.00011479 Pb 14.73417482 14.96905566 3.25197471 0.00034718 -0.00168659 -0.00011908 Br 14.84194694 15.66704796 0.01609269 0.00138430 -0.00240011 0.00011195 Br 14.58518097 12.02434391 3.09258311 -0.00296296 0.00365219 0.00105284 Br 11.62900179 15.13391219 3.56988277 -0.00051241 -0.00215485 -0.00489167 Cs 11.54571072 12.49060821 6.00809077 0.00018985 0.00012087 0.00120521 Pb 14.52923947 14.91401172 9.23695164 0.00251597 0.00219319 0.00019048 Br 15.08281129 14.78105062 6.08333583 -0.00271332 0.00127120 -0.00158505 Br 13.92550624 12.06703101 9.01763600 -0.00476232 0.00001651 -0.00041779 Br 11.85960354 15.52549717 8.28184697 -0.00238806 -0.00181585 0.00166798 Cs 11.90625589 12.10140124 11.94819188 -0.00057216 0.00059682 0.00042340 Pb 15.03348224 14.93073688 14.92997543 -0.00073646 0.00187875 0.00184455 Br 14.63219333 14.87021186 12.14166199 0.00171318 -0.00137995 0.00258267 Br 14.46362079 11.71406031 14.90562710 0.00111082 -0.00255736 0.00034444 Br 11.99129946 15.27754375 15.15089905 -0.00020371 -0.00179694 -0.00063631 Cs 12.25119379 11.61396044 17.47956946 0.00052701 -0.00209872 -0.00126398 Pb 14.77215693 14.84548294 21.06534788 0.00218458 -0.00003785 -0.00129981 Br 14.49568457 14.60668053 17.78435302 0.00040263 0.00306242 0.00049621 Br 14.20132212 11.93647341 21.01066211 0.00119031 0.00168255 0.00362223 Br 11.89033481 15.20541592 21.44823824 0.00017923 0.00263022 0.00307953 Cs 11.59289223 18.04362874 0.42528606 -0.00127653 -0.00041081 0.00364330 Pb 14.78450664 20.90626717 2.94570877 0.00189494 0.00166500 0.00171765 Br 14.48470267 20.67556794 23.76330032 0.00417649 0.00171324 -0.00525688 Br 14.50840410 18.05307035 3.55680552 -0.00184454 -0.00247366 0.00162390 Br 11.74137070 21.35551088 3.46397210 0.00011672 0.00061700 0.00109232 Cs 11.20553883 18.16041974 5.94313346 -0.00030246 0.00062050 -0.00001225 Pb 14.90171433 20.94911013 8.80536545 0.00281871 0.00031093 0.00083302 Br 15.07032420 21.01086891 5.87531372 -0.00001343 -0.00633952 0.00446360 Br 14.72169763 17.81271104 8.79108672 0.00142625 -0.00116458 0.00062327 Br 11.73539509 21.18564039 8.25408847 -0.00164903 0.00426859 -0.00230167 Cs 12.44235013 18.49526709 11.72717840 0.00123911 -0.00026784 -0.00082709 Pb 14.97179155 20.51855064 14.89930851 0.00082574 -0.00020008 -0.00133812 Br 14.85271251 20.53066319 11.94132435 0.00325423 0.00235114 -0.00215976 Br 15.69554041 17.72034835 14.59586446 -0.00264929 0.00320697 0.00028011 Br 12.04885860 19.66596862 14.76286344 0.00314580 -0.00043546 0.00173089 Cs 12.13660308 17.64281962 17.78866526 0.00164822 -0.00051387 0.00235302 Pb 14.78321083 20.57083454 20.65566549 0.00271673 0.00176033 -0.00032623 Br 14.30821579 20.54692936 17.79530580 -0.00300300 0.00243270 -0.00222216 Br 14.64364628 17.83498658 19.99322561 0.00198870 -0.00017464 -0.00411441 Br 12.02346212 20.85580446 21.01571548 -0.00240773 -0.00210992 0.00117840 Cs 17.90049927 23.41707060 0.07484265 0.00543194 -0.00010657 -0.00332136 Pb 20.84557144 3.14661663 2.71129698 0.00192620 0.00276955 -0.00130122 Br 20.63030174 3.88233470 23.58391201 -0.00123180 0.00248300 -0.00017273 Br 20.97017610 0.06549040 3.27644564 -0.00188039 0.00146910 -0.00068383 Br 18.07410086 3.21028501 2.93236230 -0.00301153 0.00047953 0.00436137 Cs 18.27752649 23.73297416 6.16989295 -0.00296496 0.00293412 -0.00009034 Pb 20.98976422 3.07536106 8.94264736 0.00150331 -0.00155014 0.00022424 Br 21.55675399 2.96702858 6.09821337 0.00001998 0.00197750 0.00200959 Br 20.99844345 0.06828556 8.97997725 -0.00176561 0.00097482 0.00375631 Br 17.77173633 2.59017768 8.57461110 -0.00332078 0.00228242 -0.00002071 Cs 17.76630661 0.39927270 11.80259153 0.00329692 -0.00202808 0.00001371 Pb 20.75345515 3.01243248 14.60986204 -0.00179516 0.00071564 0.00052548 Br 19.87294076 3.50067345 11.73511580 -0.00085676 0.00157839 0.00069686 Br 21.39003591 23.71412229 14.84208623 0.00373718 -0.00120022 -0.00008012 Br 17.90147337 2.63449885 14.99490126 0.00025625 0.00331269 -0.00026058 Cs 17.95653022 23.73523517 18.35290695 0.00114191 -0.00202061 -0.00137475 Pb 21.13058243 2.86856374 20.66866828 0.00007665 -0.00015180 -0.00005828 Br 20.87951815 3.11809476 17.68743756 -0.00004684 0.00199840 -0.00025662 Br 21.07057116 23.62288459 20.58920955 -0.00311318 0.00177935 -0.00355278 Br 17.79276694 2.84630719 20.09719482 -0.00549468 0.00088868 -0.00039270 Cs 17.69192798 6.31451731 23.40488661 0.00076105 0.00223714 -0.00187578 Pb 20.83290711 8.93588743 3.05756419 -0.00002036 -0.00074292 0.00022896 Br 20.33929071 9.11289111 0.11113898 -0.00241117 -0.00568195 0.00152856 Br 20.59591814 5.99686144 3.27445841 -0.00140084 -0.00346245 0.00090808 Br 17.87309881 9.04024306 3.52315717 -0.00028979 -0.00088338 0.00300398 Cs 18.37717328 5.35540727 6.40609653 0.00087241 0.00024747 -0.00075668 Pb 20.91391901 8.82744867 8.89303821 0.00160648 -0.00172644 -0.00107618 Br 20.86287074 9.34367502 6.04752740 -0.00238506 0.00228398 -0.00303079 Br 21.31225157 6.01060265 8.79638733 -0.00008835 0.00017656 -0.00178428 Br 17.41696894 9.06982053 9.35609054 -0.00218769 0.00099789 -0.00602528 Cs 17.43352689 6.33064638 11.45305587 0.00067644 0.00175189 -0.00064512 Pb 20.94665184 8.84792261 14.91291663 0.00085132 -0.00141571 0.00129996 Br 20.94788476 8.64560120 11.90216463 -0.00147056 0.00105906 -0.00030480 Br 20.88011281 5.76159963 15.32817805 -0.00157871 -0.00074114 -0.00130693 Br 17.94216619 8.51219922 14.69178576 0.00266441 -0.00364321 0.00048469 Cs 17.89539724 5.74470064 17.53314289 -0.00178761 -0.00102386 0.00129078 Pb 20.55417309 9.06797868 20.64493443 0.00139112 -0.00003083 -0.00038764 Br 21.38548144 8.70415555 17.87739597 -0.00035837 -0.00137651 -0.00350320 Br 20.45524042 5.77148171 20.75521877 -0.00006762 -0.00058680 -0.00286263 Br 17.48482049 9.46727623 20.51673256 -0.00161741 -0.00289101 -0.00016841 Cs 17.81084894 11.95386009 0.16247817 -0.00221364 -0.00113815 -0.00058330 Pb 20.47575190 14.75379514 3.03271116 0.00215891 -0.00014887 0.00151167 Br 20.76166515 15.27397704 23.76802618 0.00043352 -0.00040860 0.00173637 Br 20.33715679 11.77152806 3.19383039 -0.00153179 0.00123602 0.00263986 Br 17.50668717 15.06257630 2.77194670 -0.00071952 -0.00068634 -0.00274219 Cs 17.78565762 11.14201565 6.25794968 0.00161525 -0.00024754 -0.00022681 Pb 20.55670359 14.80984841 8.97387815 -0.00098104 0.00162548 0.00264580 Br 20.26349112 14.99294723 5.89003554 -0.00271335 -0.00416485 -0.00020394 Br 20.61244766 11.83250795 8.60874138 -0.00764221 -0.00036977 0.00593032 Br 17.72522794 14.45255572 9.32864350 -0.00113343 -0.00036723 0.00024617 Cs 17.90686294 11.33418877 12.30680524 0.00128327 -0.00086675 -0.00234780 Pb 20.83359583 14.62503473 14.83396709 -0.00087905 0.00131183 -0.00135888 Br 20.88374266 13.98073917 11.95061644 -0.00121593 -0.00080834 -0.00357385 Br 20.48544436 11.63449370 15.28304790 0.00209953 -0.00008779 0.00225599 Br 17.88608613 14.13377379 14.81099088 -0.00315538 0.00222135 0.00264672 Cs 17.43862147 12.03225958 17.63790674 -0.00060378 -0.00145119 -0.00114489 Pb 20.86442169 14.96224209 20.81456049 0.00234406 0.00272471 -0.00083160 Br 21.22756465 14.58168056 17.94189741 -0.00366657 0.00075812 0.00172454 Br 20.28952710 11.94996234 20.05636310 -0.00402913 -0.00053229 0.00099332 Br 17.87047101 15.04736260 20.61529156 0.00436478 -0.00148810 -0.00164111 Cs 17.91636363 17.72516061 23.41940285 0.00055157 -0.00032818 0.00006459 Pb 20.77268450 20.95795365 3.09230710 0.00388644 -0.00093151 0.00056890 Br 21.34722059 21.09465745 23.69908989 0.00107994 0.00046984 -0.00080128 Br 20.70646544 17.85712236 3.13044242 0.00257549 -0.00113759 0.00155893 Br 18.03439540 20.25538493 2.24832253 -0.00087018 0.00165965 0.00239693 Cs 17.69641455 17.98054713 5.71877289 0.00159981 -0.00048438 -0.00247382 Pb 20.77615362 20.97920740 8.92600690 0.00075149 0.00050267 -0.00341947 Br 21.01105924 20.78785176 5.97297218 -0.00019882 -0.00061994 -0.00092802 Br 20.23752332 17.75866063 8.67914120 0.00045004 -0.00273246 0.00076106 Br 17.59419354 20.88588925 9.65879197 -0.00370673 0.00230101 -0.00028476 Cs 17.82916365 18.02245289 11.58567632 -0.00076362 -0.00177665 0.00385625 Pb 20.87657397 20.81202074 14.82518047 -0.00273125 0.00112019 0.00123284 Br 21.02497639 20.84534979 11.85632814 0.00148768 0.00036360 0.00170062 Br 20.60923779 17.42466454 13.99418980 -0.00196488 -0.00267486 0.00088536 Br 17.96674062 21.42168640 14.67646647 -0.00006885 0.00144079 0.00360426 Cs 17.46096596 18.08252692 17.94895860 0.00051388 -0.00071675 0.00347084 Pb 20.94443571 20.77646290 20.83443847 -0.00164817 -0.00086051 0.00276512 Br 20.65060302 20.85626516 17.68160766 0.00293339 -0.00303234 -0.00256305 Br 20.78260985 17.86885466 20.43008212 0.00011805 0.00048254 -0.00268940 Br 17.81412467 20.62170755 21.06293388 0.00226249 0.00166225 0.00307536 320 Lattice="23.77195271 0.0 0.0 0.0 23.77195271 0.0 0.0 0.0 23.77195271" Properties=species:S:1:pos:R:3:vel:R:3 Time=20109.99999999 energy=-11426.60185242 virial="-18.51839268 -0.54051858 -1.06033541 -0.54051858 -12.55301945 -1.75881528 -1.06033541 -1.75881528 -11.84476105" stress="-0.00029808 -4.024e-05 -7.893e-05 -4.024e-05 3.89e-06 -0.00013093 -7.893e-05 -0.00013093 0.00012256" pbc="T T T" Cs 23.66925527 0.18132478 0.00812034 -0.00351084 -0.00298678 0.00130761 Pb 2.89692362 3.05705006 3.06366289 -0.00159514 0.00120870 0.00185487 Br 3.27284840 2.93336799 0.10197587 -0.00058053 0.00011265 0.00138320 Br 2.98239413 0.12130618 3.11258171 -0.00077874 -0.00093833 -0.00097035 Br 0.20173641 3.45714904 2.38165659 -0.00082667 -0.00045894 -0.00329130 Cs 0.12556425 23.76485058 6.09412495 0.00370085 0.00249767 -0.00221453 Pb 3.03223963 2.97579070 9.26546938 0.00124180 0.00055380 -0.00205535 Br 2.48712800 2.76237216 6.27804291 0.00172313 -0.00108559 0.00295719 Br 3.15963150 0.00271619 9.34493363 0.00017759 0.00056499 0.00324758 Br 23.64118844 3.12072367 10.33771967 -0.00024836 0.00074338 0.00511721 Cs 0.28071880 23.60877890 12.20971516 -0.00382749 -0.00099374 0.00037549 Pb 3.05281364 3.29388391 14.91735839 -0.00045425 -0.00150875 -0.00120380 Br 4.14476094 3.03516326 11.97236336 -0.00282255 -0.00096627 0.00096848 Br 2.98629800 0.12711135 14.72424592 0.00152169 -0.00252726 -0.00304769 Br 0.27303078 3.38160760 14.73506341 -0.00205886 -0.00215224 -0.00144385 Cs 0.16904777 23.36149708 17.74944515 -0.00123670 -0.00147468 -0.00226991 Pb 3.21764519 3.08425478 20.64373423 0.00173349 0.00057855 0.00064799 Br 2.55902400 3.20347887 17.85011424 -0.00010752 0.00197704 0.00014993 Br 3.05287689 0.09239002 20.31963253 -0.00109036 0.00088298 0.00362778 Br 0.15320725 3.01512836 20.57477706 0.00076652 -0.00358225 0.00093862 Cs 23.48081125 6.43430652 23.72817397 0.00000078 0.00245070 -0.00082844 Pb 2.84593700 8.91465139 3.20245017 0.00007473 -0.00040975 -0.00156286 Br 2.68883493 8.55552889 23.69734258 0.00259685 -0.00550886 0.00166918 Br 3.10553044 6.04952591 3.10036711 -0.00284227 -0.00001480 -0.00118796 Br 23.73558263 8.59545119 3.17392758 0.00182370 -0.00111507 -0.00251943 Cs 0.42292299 6.03940426 5.95612508 0.00213736 0.00071033 0.00200730 Pb 3.35697704 9.06995044 8.89681646 0.00182009 -0.00019101 -0.00117763 Br 2.86132879 8.89447404 6.08649051 -0.00028929 -0.00188262 0.00433149 Br 2.82143717 5.82190039 8.75594692 -0.00233866 -0.00274362 0.00257438 Br 23.65690544 9.24265356 8.84420597 -0.00203360 0.00238091 -0.00055338 Cs 23.60192070 5.96789362 11.95216178 -0.00162880 -0.00142152 0.00190092 Pb 2.88702818 8.79198272 14.82353201 -0.00055335 0.00083491 -0.00160997 Br 3.18194418 8.90626809 11.82597433 0.00042367 0.00206112 0.00248123 Br 3.75897775 6.11306928 15.15214885 0.00012937 0.00253193 0.00135221 Br 0.07717307 8.49512166 14.44347693 0.00081922 -0.00199203 -0.00382882 Cs 23.66637173 5.73763845 17.61228539 -0.00143753 0.00022264 0.00020314 Pb 2.68245186 8.84227426 20.60005488 -0.00108358 -0.00108745 0.00040122 Br 2.28843611 8.78629265 17.68527103 0.00386333 0.00032498 -0.00262049 Br 2.08705940 5.87675026 20.78615780 0.00138257 -0.00002211 0.00208904 Br 23.58327362 9.27924391 21.17056276 -0.00473942 -0.00002563 0.00022265 Cs 23.16851469 11.76479053 0.25818149 0.00267457 -0.00067242 0.00037325 Pb 2.70463964 14.92305559 2.90798885 -0.00242748 0.00133307 0.00058309 Br 2.48103526 15.10020029 23.72423132 -0.00018476 -0.00188330 0.00139946 Br 2.92275485 11.81711099 2.24501663 0.00049046 0.00356833 -0.00181554 Br 23.32717370 14.65717643 3.25070131 0.00098024 0.00105456 -0.00156154 Cs 23.51845373 11.75798158 6.57550505 0.00146963 -0.00043909 0.00008815 Pb 3.01806967 15.04089586 9.10110549 0.00091526 0.00011411 0.00071237 Br 3.28248834 14.94100585 5.94498158 -0.00253124 -0.00049910 -0.00144332 Br 2.98649285 12.02877443 8.71637477 0.00029649 0.00554164 0.00047646 Br 0.01650601 15.06453867 8.88912881 0.00128390 -0.00117394 -0.00024780 Cs 0.08912196 11.65497072 11.44168166 -0.00238285 0.00100481 -0.00111895 Pb 3.07107969 14.55210596 15.08191635 0.00036313 -0.00053666 0.00030489 Br 3.02340442 14.41354435 11.84186469 -0.00171139 -0.00191623 0.00086707 Br 2.55821965 11.78341580 15.43633719 -0.00295649 -0.00154592 0.00143891 Br 0.19276003 14.59085935 14.56645849 0.00346143 -0.00120421 -0.00068642 Cs 23.53705169 11.72520800 18.17134621 0.00251763 -0.00138868 -0.00011291 Pb 3.10503737 14.53494458 20.77331859 0.00096088 -0.00155037 -0.00042385 Br 2.93350140 14.89050477 17.89320136 0.00002062 0.00111829 -0.00162343 Br 2.78558124 11.73248959 20.92282018 0.00121622 -0.00138809 0.00003827 Br 0.16105326 14.56634610 20.91432535 0.00052450 0.00179189 -0.00213754 Cs 0.07024545 17.80213121 23.63433984 -0.00050822 0.00100232 -0.00136636 Pb 2.98968707 20.76190305 2.86795670 0.00120399 0.00015616 -0.00156641 Br 2.92282743 20.83281034 23.67345587 -0.00018637 0.00286942 0.00362504 Br 3.15866648 17.90232814 2.67143346 -0.00249466 -0.00125153 0.00157734 Br 23.60561317 20.85323235 2.96498077 0.00013629 0.00175641 0.00211021 Cs 23.08635381 17.82116029 6.19387663 -0.00180161 0.00098850 -0.00341970 Pb 2.89040644 20.92432254 8.79080202 0.00171736 -0.00102309 0.00022600 Br 2.97502318 20.76264881 5.85536134 0.00136778 -0.00246210 0.00098867 Br 3.31105883 17.85424986 9.38421977 -0.00297565 -0.00403018 -0.00312875 Br 23.67540189 21.02452517 8.88855533 0.00292916 -0.00311503 0.00044413 Cs 0.53791441 17.17347608 12.17517419 0.00297288 0.00211366 -0.00364750 Pb 3.12095386 21.06025044 14.74564451 0.00028305 -0.00136672 0.00201316 Br 3.19502369 20.76422208 11.65919635 -0.00415273 0.00068461 -0.00170967 Br 3.05608977 17.93329525 15.09044494 0.00013783 -0.00365335 0.00333752 Br 0.15684217 20.52174344 14.96371850 -0.00030425 0.00238407 -0.00027730 Cs 0.40234581 17.68137597 17.51491724 0.00034614 0.00049774 0.00174825 Pb 3.00877416 20.76612877 20.48070209 0.00069697 0.00103737 -0.00116455 Br 3.52825534 20.40854330 17.60294043 -0.00375551 -0.00340677 0.00200392 Br 3.08402954 17.79065822 20.43990884 -0.00019977 -0.00134083 -0.00504016 Br 0.04909452 20.60208762 20.56867719 0.00280234 -0.00116889 0.00105112 Cs 5.93516321 0.12609322 22.87205503 -0.00275615 0.00122956 -0.00003583 Pb 9.01528771 3.02237382 3.23442735 -0.00210127 0.00217006 -0.00233905 Br 9.01201428 2.57684907 0.05006667 -0.00304789 0.00223275 0.00021149 Br 8.80529673 0.05678493 3.39448226 0.00349870 0.00480122 0.00391903 Br 5.89992988 2.98305960 3.59044485 -0.00105936 0.00179715 0.00233695 Cs 6.04295211 23.42588610 5.73672606 -0.00244395 0.00072696 0.00087408 Pb 8.77693454 2.80702321 8.83669825 -0.00081154 0.00135836 0.00020884 Br 9.81940777 3.45347569 5.99839942 -0.00247091 0.00021275 -0.00003393 Br 8.37959479 23.43814278 8.81094522 0.00112097 0.00350058 0.00298818 Br 5.87566555 3.32871399 8.41147306 0.00392028 -0.00352662 -0.00296903 Cs 6.06877815 23.72243561 12.05818796 -0.00077448 0.00144146 0.00024136 Pb 9.17796417 2.86709753 14.71298334 0.00142961 0.00005230 0.00221669 Br 8.98793798 2.84685716 11.65867823 -0.00037567 0.00019504 -0.00147242 Br 10.13305819 23.56154419 15.20785871 0.00049654 0.00211294 -0.00301546 Br 6.16910213 2.39942255 15.14753992 -0.00007188 0.00085662 -0.00280831 Cs 6.46976295 23.43284448 17.69086755 -0.00134374 -0.00240492 0.00108002 Pb 9.15061422 3.06685367 21.03728364 -0.00145877 0.00090866 -0.00144435 Br 9.69473902 2.95571828 17.73494023 -0.00193922 -0.00089722 0.00643708 Br 9.05223664 0.09568210 20.69008759 -0.00239386 -0.00336371 0.00349100 Br 6.05623885 3.91603803 20.76065544 -0.00221915 -0.00030848 0.00026114 Cs 5.61130414 6.23817623 23.69542140 -0.00159141 -0.00356721 0.00080596 Pb 8.82713877 8.86711575 2.80360252 -0.00196602 0.00075762 -0.00030367 Br 8.91890021 8.83867378 23.64395698 0.00120090 0.00076319 0.00094846 Br 8.25482873 5.86182150 2.88681939 -0.00125800 0.00326163 0.00142135 Br 5.88619773 8.96213158 2.74635580 -0.00058925 0.00189245 0.00131056 Cs 5.92853721 6.12976259 5.82003468 0.00311464 0.00212366 -0.00024044 Pb 8.77076971 8.83550190 8.71803013 -0.00018263 -0.00011421 -0.00067979 Br 8.73356352 8.57507976 5.90791554 -0.00119864 -0.00238096 -0.00148578 Br 8.74291854 5.84754565 9.16776648 -0.00170783 0.00135799 0.00254716 Br 5.92105757 8.19311648 8.61809386 0.00019930 0.00420804 0.00091578 Cs 5.89692295 6.14113388 11.89972430 0.00151206 -0.00145890 0.00107184 Pb 9.04042159 8.88829764 14.84801991 -0.00136174 0.00063067 0.00020690 Br 7.89817592 8.95088632 11.87395868 -0.00087723 -0.00475281 0.00125028 Br 9.37086450 5.88104620 15.09370000 0.00190387 0.00287786 0.00238870 Br 6.06499042 9.12103078 15.40563248 0.00245690 -0.00510597 0.00155356 Cs 6.90766334 6.08599432 17.83066872 -0.00203950 0.00100576 -0.00095057 Pb 8.71551304 8.98227188 20.70303217 0.00033197 0.00182158 -0.00019324 Br 9.16619267 9.29862249 17.86855289 -0.00143592 -0.00198772 -0.00082198 Br 9.18278271 6.13370092 20.93995164 -0.00056554 0.00002972 -0.00195855 Br 5.65267947 8.59696816 20.61133707 -0.00249653 -0.00170240 0.00087662 Cs 5.50413965 11.52045420 23.35680642 -0.00142543 -0.00184343 0.00170023 Pb 8.77546907 14.64097031 3.06558045 0.00111671 0.00069765 0.00195058 Br 9.28761628 14.67296996 0.35773476 0.00054070 -0.00252914 -0.00244804 Br 8.97511502 11.62911513 3.09748618 0.00085292 0.00366684 0.00342608 Br 5.66064731 14.65903290 2.60207117 0.00077043 -0.00242391 0.00019321 Cs 5.20346330 11.92764004 4.89565759 0.00259282 0.00096095 0.00128694 Pb 8.59905001 14.53269469 8.95823604 0.00110953 -0.00055948 0.00097877 Br 8.23799525 14.01379160 5.93430684 0.00400600 -0.00273808 -0.00160927 Br 9.76252492 11.96730417 8.83679652 -0.00213814 0.00035747 -0.00133179 Br 5.85874892 14.30437307 9.52915035 -0.00221108 -0.00518416 0.00139766 Cs 5.50963930 11.98279851 11.84445552 0.00166573 -0.00263328 -0.00104586 Pb 8.93122868 15.07896731 14.91166839 0.00358288 0.00061352 -0.00030497 Br 9.55436965 15.18228839 11.79278324 -0.00438959 0.00142365 0.00165006 Br 9.48701616 11.81392353 15.10743042 0.00093912 -0.00102355 0.00084162 Br 5.91619687 14.64613085 14.31312095 -0.00470902 -0.00001212 -0.00504517 Cs 6.40303299 11.90835017 17.77672139 0.00046422 -0.00285670 -0.00212212 Pb 8.94914344 14.91935232 20.77700155 0.00058148 -0.00013993 -0.00022617 Br 9.34014688 14.89389508 17.82528239 0.00125391 -0.00026579 -0.00311242 Br 8.92432777 11.83605170 21.38274190 -0.00085580 -0.00063243 -0.00270037 Br 6.09103254 14.85777026 21.25325618 0.00076106 0.00233666 0.00083700 Cs 5.45722169 18.49395949 23.72008066 -0.00122662 0.00202279 -0.00134393 Pb 8.79827110 20.73166936 3.05054008 -0.00012593 0.00012790 0.00052221 Br 8.90684597 20.77963694 0.13397624 0.00040422 0.00228105 -0.00027560 Br 8.37429195 17.77798860 2.88785527 -0.00031181 0.00149019 -0.00223668 Br 5.83781112 20.65363379 2.93389430 -0.00318649 0.00171751 0.00185995 Cs 5.67948062 17.90199171 5.53253022 -0.00117434 0.00241789 -0.00034699 Pb 8.60054358 20.55654331 8.85823363 0.00049908 -0.00232579 0.00158006 Br 8.39310976 20.33283933 5.86190292 -0.00325659 0.00029534 0.00066125 Br 8.06936286 17.46871603 8.33226667 0.00051805 -0.00068810 -0.00434135 Br 5.69985315 20.62024493 8.67777011 -0.00034490 -0.00099584 -0.00234713 Cs 6.33621256 18.20415676 11.42346515 -0.00093513 0.00222948 -0.00077507 Pb 9.30891560 20.83904328 14.63771384 -0.00131609 -0.00047514 0.00061658 Br 9.48566920 21.00485535 11.82926800 0.00211652 -0.00342571 0.00022366 Br 8.41722470 17.65287906 14.65935800 -0.00006191 -0.00133466 0.00232371 Br 6.13941442 21.19233661 14.88861335 0.00137094 0.00270668 0.00107920 Cs 5.79512974 17.37225269 17.93651420 -0.00120768 -0.00040541 -0.00219839 Pb 9.05871338 20.68237362 20.72050023 -0.00089580 -0.00052581 -0.00095670 Br 9.71331248 20.68601576 17.62547529 0.00323228 0.00077106 -0.00130643 Br 7.89585960 18.01378397 20.70642751 -0.00145794 -0.00006693 0.00072804 Br 6.11191576 20.84342142 20.58169515 -0.00140064 -0.00254256 -0.00084430 Cs 12.13707602 23.73098510 0.37233336 0.00064072 -0.00034206 -0.00155444 Pb 15.04746308 3.34088808 2.83304743 -0.00029627 0.00106754 -0.00064751 Br 15.34746499 2.86423835 0.08831442 -0.00004269 0.00128006 -0.00273278 Br 15.36992164 23.68605507 2.67419016 0.00385426 -0.00084978 0.00307139 Br 12.02276268 3.42361811 2.55592640 0.00273833 0.00202133 -0.00197820 Cs 12.19405129 0.56278846 5.79408584 -0.00103850 -0.00038563 -0.00362766 Pb 14.83541000 3.02912721 8.85510666 0.00126770 0.00083290 -0.00058551 Br 14.75424190 3.12644667 5.88667554 -0.00264161 0.00354690 -0.00155558 Br 14.85984130 0.12150082 9.02408767 0.00196786 -0.00077158 0.00127556 Br 12.12668066 3.02603021 9.61426090 0.00159716 -0.00069699 -0.00029404 Cs 11.92832626 0.21803732 11.95891708 0.00015827 0.00401982 0.00033325 Pb 14.88019142 2.91888577 14.87861079 0.00269100 -0.00323786 0.00087186 Br 15.25779568 2.95296781 11.88821271 -0.00549980 0.00080496 0.00287987 Br 14.27770775 23.59674403 14.99290110 -0.00110499 -0.00037630 -0.00255053 Br 12.02810835 2.88010736 14.23492175 0.00166394 0.00302696 -0.00139460 Cs 12.08327928 23.28071542 18.34359258 -0.00079692 0.00009639 0.00230634 Pb 15.07852390 3.05231536 20.86599764 -0.00037541 0.00185147 -0.00161215 Br 14.90144599 3.03709895 17.95737751 -0.00559094 0.00214822 0.00202919 Br 15.08656061 23.57011019 20.92989470 0.00309335 -0.00116063 -0.00403474 Br 11.97399779 2.82662410 21.49792716 -0.00275666 -0.00452850 0.00166711 Cs 11.79862413 6.28341428 23.31499780 0.00113225 -0.00089109 0.00025040 Pb 14.75095139 8.95534275 3.41392752 -0.00052793 -0.00064791 -0.00119370 Br 14.73366561 9.01741257 0.18165911 0.00059791 -0.00413180 0.00008330 Br 15.39229260 6.12650451 3.64527819 -0.00222786 -0.00295401 -0.00366828 Br 11.74447214 8.59472398 2.64050117 -0.00204246 -0.00264639 -0.00088792 Cs 11.94911100 6.27302353 5.57755170 0.00260061 -0.00119916 0.00016215 Pb 14.50821121 8.88114427 9.05258215 0.00089194 0.00013421 -0.00016694 Br 14.36024971 9.55694966 6.11368345 -0.00020826 0.00041062 -0.00333784 Br 15.26153143 6.16726285 8.32479274 0.00048838 -0.00029289 0.00016939 Br 11.74848773 8.08931765 9.00284291 -0.00005825 -0.00176814 -0.00116114 Cs 11.95745774 5.93073203 11.85110697 0.00136860 0.00131811 0.00086350 Pb 14.89369470 8.79826237 14.74737789 0.00118294 0.00194938 0.00099116 Br 14.87586680 9.08865590 11.84143263 -0.00460433 0.00130250 0.00458452 Br 15.01946857 5.93215163 14.90691464 -0.00355555 -0.00120260 -0.00104092 Br 12.09615810 8.47174496 14.83309031 0.00099758 0.00144263 0.00319064 Cs 12.57511948 5.72722075 18.29347932 0.00129600 -0.00109568 0.00037157 Pb 14.60137475 9.14565590 20.91577439 0.00018502 0.00118985 0.00036940 Br 14.76743690 8.73775612 17.87706799 -0.00006141 -0.00271969 -0.00371782 Br 15.32934431 5.79298181 20.85269080 -0.00272856 0.00192897 -0.00243717 Br 11.61866582 9.01682630 20.43377066 0.00437824 -0.00221862 0.00093372 Cs 11.33561460 11.86014125 0.60290572 -0.00145209 0.00012138 -0.00033997 Pb 14.73675925 14.95256431 3.25129906 0.00016951 -0.00160360 -0.00002243 Br 14.85600536 15.64259425 0.01744597 0.00142961 -0.00248975 0.00014327 Br 14.55544308 12.06009414 3.10232923 -0.00298376 0.00345434 0.00089179 Br 11.62340851 15.11254605 3.52045089 -0.00059648 -0.00211411 -0.00498471 Cs 11.54913525 12.49185517 6.01971432 0.00049950 0.00012923 0.00111438 Pb 14.55467680 14.93549336 9.23712525 0.00256455 0.00209367 -0.00015338 Br 15.05604051 14.79362692 6.06711313 -0.00263714 0.00124226 -0.00163045 Br 13.87849427 12.06495400 9.01362656 -0.00462999 -0.00042054 -0.00038348 Br 11.83355679 15.50612185 8.29958890 -0.00280920 -0.00205354 0.00188467 Cs 11.90035287 12.10767381 11.95306838 -0.00060792 0.00065666 0.00055244 Pb 15.02524878 14.94852526 14.94886850 -0.00090911 0.00168002 0.00193060 Br 14.64838389 14.85642447 12.16505646 0.00152539 -0.00137716 0.00207304 Br 14.47655187 11.69051145 14.90767366 0.00147391 -0.00212018 0.00006919 Br 11.98858648 15.25927475 15.14291550 -0.00034607 -0.00185028 -0.00095851 Cs 12.25555261 11.59301353 17.46778069 0.00034505 -0.00209206 -0.00109276 Pb 14.79373675 14.84584837 21.05170810 0.00212756 0.00011469 -0.00142098 Br 14.50092325 14.63702036 17.79326211 0.00064038 0.00299547 0.00129003 Br 14.21376276 11.95402840 21.04703176 0.00129619 0.00182535 0.00364979 Br 11.89137913 15.23203897 21.47781718 0.00004115 0.00269044 0.00282545 Cs 11.58047436 18.03907894 0.46191682 -0.00120591 -0.00049991 0.00368113 Pb 14.80336817 20.92207023 2.96258353 0.00187628 0.00148515 0.00165588 Br 14.52659517 20.69191039 23.71155755 0.00420440 0.00155652 -0.00505990 Br 14.48964196 18.02690792 3.57245671 -0.00190575 -0.00272639 0.00150065 Br 11.74531800 21.36084801 3.47393669 0.00066670 0.00044680 0.00089643 Cs 11.20363879 18.16631623 5.94291358 -0.00007741 0.00055850 -0.00003085 Pb 14.92813282 20.95160737 8.81373311 0.00244350 0.00019292 0.00084282 Br 15.07204779 20.94867887 5.92058072 0.00036211 -0.00609637 0.00453337 Br 14.73747240 17.80476985 8.79783817 0.00172567 -0.00043079 0.00072839 Br 11.71836388 21.22724318 8.23221614 -0.00177145 0.00404230 -0.00207172 Cs 12.45238783 18.48917521 11.71700381 0.00077921 -0.00093615 -0.00119884 Pb 14.97985931 20.51842345 14.88540840 0.00078109 0.00018255 -0.00143812 Br 14.88737052 20.55770787 11.92022064 0.00365086 0.00303469 -0.00203882 Br 15.66988897 17.75292552 14.59967510 -0.00247367 0.00327129 0.00048079 Br 12.08029307 19.66354227 14.78159395 0.00311996 -0.00005371 0.00199603 Cs 12.15157999 17.63736467 17.81146235 0.00134070 -0.00057571 0.00220036 Pb 14.81145805 20.58986427 20.65372020 0.00291126 0.00203947 -0.00006750 Br 14.27973989 20.57215159 17.77175744 -0.00268281 0.00260557 -0.00246997 Br 14.66502023 17.82838647 19.95571506 0.00229233 -0.00112061 -0.00336953 Br 11.99662552 20.83415804 21.02841088 -0.00290889 -0.00222306 0.00135466 Cs 17.95447946 23.41576438 0.04131379 0.00535857 -0.00015171 -0.00337818 Pb 20.86581092 3.17481077 2.69784088 0.00210336 0.00285985 -0.00139145 Br 20.61867783 3.90427962 23.58606396 -0.00109277 0.00190863 0.00059650 Br 20.95082140 0.08221666 3.26934527 -0.00198721 0.00184952 -0.00073529 Br 18.04181039 3.21416271 2.97700309 -0.00339133 0.00029282 0.00456154 Cs 18.24751920 23.76231684 6.16863313 -0.00303730 0.00293532 -0.00016447 Pb 21.00485921 3.05970189 8.94593382 0.00151630 -0.00157410 0.00043975 Br 21.55631340 2.98702225 6.11675076 -0.00010002 0.00201535 0.00169243 Br 20.98020840 0.07805270 9.01739501 -0.00187848 0.00095791 0.00372201 Br 17.74207600 2.61325948 8.57430964 -0.00258261 0.00232878 -0.00003743 Cs 17.79945973 0.37840836 11.80293236 0.00332564 -0.00213681 0.00005390 Pb 20.73547016 3.01897492 14.61555566 -0.00179328 0.00059124 0.00060934 Br 19.86517077 3.51635709 11.74241717 -0.00069705 0.00155580 0.00075804 Br 21.42743077 23.70148263 14.84113309 0.00373021 -0.00131999 -0.00010537 Br 17.90430181 2.66742293 14.99276940 0.00030275 0.00326390 -0.00015939 Cs 17.96786821 23.71433602 18.33808443 0.00112423 -0.00215330 -0.00158728 Pb 21.12981082 2.86835636 20.66759204 -0.00022294 0.00011275 -0.00015767 Br 20.87977901 3.13607995 17.68568904 0.00009563 0.00159437 -0.00008603 Br 21.03971992 23.64259669 20.55356096 -0.00305798 0.00212730 -0.00357284 Br 17.74131934 2.85583042 20.09572102 -0.00475120 0.00100505 0.00008214 Cs 17.70024961 6.33678793 23.38671397 0.00089702 0.00221365 -0.00176139 Pb 20.83121745 8.92816788 3.06023612 -0.00031975 -0.00079592 0.00030580 Br 20.31562196 9.05601241 0.12439445 -0.00231913 -0.00568583 0.00111489 Br 20.58256920 5.96136367 3.28399036 -0.00126465 -0.00359208 0.00100303 Br 17.86768098 9.03043898 3.55154501 -0.00079124 -0.00108336 0.00266425 Cs 18.38531442 5.35861079 6.39740436 0.00075384 0.00039283 -0.00098142 Pb 20.92844976 8.81129493 8.88277742 0.00128756 -0.00149595 -0.00097562 Br 20.83999355 9.36402908 6.01598514 -0.00217530 0.00178492 -0.00324646 Br 21.31154431 6.01034087 8.77753180 -0.00004811 -0.00024126 -0.00198536 Br 17.39973801 9.08116395 9.29373623 -0.00126421 0.00126417 -0.00641959 Cs 17.44053490 6.34724435 11.44732919 0.00072311 0.00157129 -0.00050022 Pb 20.95442919 8.83289066 14.92580550 0.00070269 -0.00158438 0.00127027 Br 20.93286247 8.65684528 11.89935944 -0.00153296 0.00118633 -0.00027131 Br 20.86404354 5.75664112 15.31318989 -0.00163146 -0.00024957 -0.00168656 Br 17.96904328 8.47622679 14.69693219 0.00270482 -0.00354578 0.00054284 Cs 17.87755592 5.73417772 17.54647388 -0.00178205 -0.00107699 0.00137204 Pb 20.56825077 9.06741076 20.64112850 0.00141934 -0.00008031 -0.00037747 Br 21.38115448 8.69051262 17.84318914 -0.00050340 -0.00134991 -0.00330527 Br 20.45499411 5.76971486 20.72497501 0.00001875 0.00023184 -0.00318225 Br 17.47058876 9.43776824 20.51613057 -0.00121840 -0.00300738 0.00004629 Cs 17.78830589 11.94231909 0.15695125 -0.00229317 -0.00116783 -0.00052181 Pb 20.49722143 14.75328290 3.04802585 0.00212321 0.00004326 0.00154705 Br 20.76621289 15.26917204 0.01357403 0.00047804 -0.00054881 0.00175282 Br 20.32241502 11.78692892 3.21913066 -0.00141163 0.00181255 0.00241005 Br 17.50145316 15.05582202 2.74371914 -0.00031128 -0.00066447 -0.00290054 Cs 17.80115967 11.14117232 6.25623598 0.00148170 0.00008480 -0.00011679 Pb 20.54638766 14.82571711 9.00035043 -0.00107646 0.00153697 0.00264342 Br 20.23671850 14.95098152 5.89007880 -0.00263951 -0.00422139 0.00021784 Br 20.53653869 11.82948247 8.66930963 -0.00751974 -0.00025278 0.00616638 Br 17.71358591 14.44945319 9.33129289 -0.00120338 -0.00024771 0.00028514 Cs 17.92000645 11.32624351 12.28282461 0.00134572 -0.00072426 -0.00244589 Pb 20.82489338 14.63852811 14.81998921 -0.00085773 0.00137564 -0.00143458 Br 20.87208842 13.97284634 11.91489380 -0.00111658 -0.00076334 -0.00353986 Br 20.50732042 11.63393179 15.30568642 0.00226893 -0.00002290 0.00226679 Br 17.85454355 14.15694469 14.83736670 -0.00312154 0.00240820 0.00262358 Cs 17.43264201 12.01744173 17.62702163 -0.00059019 -0.00151413 -0.00103106 Pb 20.88780326 14.98898581 20.80568571 0.00232921 0.00261571 -0.00093666 Br 21.19123127 14.59150711 17.95625420 -0.00359488 0.00120115 0.00113040 Br 20.25027120 11.94216582 20.06880871 -0.00380930 -0.00103080 0.00149262 Br 17.91302320 15.03261891 20.59922897 0.00412254 -0.00145524 -0.00156817 Cs 17.92191096 17.72186554 23.42042896 0.00055561 -0.00033327 0.00014441 Pb 20.81129033 20.94737862 3.09671594 0.00381788 -0.00117560 0.00031087 Br 21.35731603 21.09891850 23.69115220 0.00093827 0.00037809 -0.00077894 Br 20.73300562 17.84503861 3.14629399 0.00272889 -0.00126114 0.00160947 Br 18.02167564 20.27371280 2.27303563 -0.00165970 0.00200058 0.00254679 Cs 17.71222623 17.97514258 5.69384976 0.00155977 -0.00059797 -0.00251193 Pb 20.78329023 20.98320388 8.89286080 0.00068419 0.00029800 -0.00319680 Br 21.00798326 20.78246199 5.96458055 -0.00041580 -0.00045407 -0.00073886 Br 20.24245409 17.73368026 8.68719390 0.00053075 -0.00223610 0.00084788 Br 17.56143282 20.91081592 9.65366831 -0.00277148 0.00266761 -0.00072848 Cs 17.82171801 18.00397628 11.62437326 -0.00072572 -0.00191159 0.00387511 Pb 20.84918424 20.82327839 14.83687939 -0.00273897 0.00113113 0.00109886 Br 21.03991746 20.84936892 11.87193013 0.00149407 0.00043887 0.00138905 Br 20.59015824 17.39903695 14.00422209 -0.00185286 -0.00244051 0.00111435 Br 17.96493337 21.43542102 14.71280726 -0.00029222 0.00131464 0.00366116 Cs 17.46725144 18.07525318 17.98347926 0.00074769 -0.00073843 0.00342614 Pb 20.92717996 20.76751030 20.86197445 -0.00179123 -0.00092312 0.00273176 Br 20.67973149 20.82587869 17.65911130 0.00288997 -0.00304364 -0.00189993 Br 20.78405812 17.87193319 20.40352030 0.00017191 0.00015138 -0.00262106 Br 17.83817731 20.63836336 21.09313097 0.00254678 0.00166610 0.00296131 320 Lattice="23.77195271 0.0 0.0 0.0 23.77195271 0.0 0.0 0.0 23.77195271" Properties=species:S:1:pos:R:3:vel:R:3 Time=20119.99999999 energy=-11426.32896423 virial="-16.34651322 -1.16241143 -0.87815972 -1.16241143 -11.37100399 -1.33452551 -0.87815972 -1.33452551 -10.6405228" stress="-0.00015526 -8.653e-05 -6.537e-05 -8.653e-05 7.919e-05 -9.934e-05 -6.537e-05 -9.934e-05 0.00020311" pbc="T T T" Cs 23.63493675 0.15158395 0.02147640 -0.00334781 -0.00295633 0.00136307 Pb 2.88265574 3.06836950 3.08233422 -0.00125856 0.00104978 0.00187325 Br 3.26762659 2.93426221 0.11478879 -0.00046115 0.00006883 0.00118547 Br 2.97383857 0.10988911 3.10283971 -0.00093261 -0.00132977 -0.00097430 Br 0.18859945 3.45254147 2.34847107 -0.00179147 -0.00046322 -0.00334161 Cs 0.16279199 0.01765999 6.07158793 0.00374133 0.00245050 -0.00229194 Pb 3.04453600 2.98149858 9.24397818 0.00121944 0.00057760 -0.00223320 Br 2.50370791 2.75190493 6.30691293 0.00159108 -0.00100554 0.00280018 Br 3.16138016 0.00823066 9.37693679 0.00017034 0.00052699 0.00314701 Br 23.63903450 3.12444679 10.38570269 -0.00018477 -0.00001173 0.00446063 Cs 0.24232466 23.59856039 12.21304584 -0.00384702 -0.00105041 0.00028911 Pb 3.04965876 3.27782361 14.90550723 -0.00018254 -0.00169269 -0.00116407 Br 4.11538581 3.02593993 11.98291141 -0.00304860 -0.00087248 0.00113621 Br 3.00069021 0.10401272 14.69445615 0.00135639 -0.00208867 -0.00290711 Br 0.25017349 3.36024187 14.71930129 -0.00249173 -0.00212173 -0.00170200 Cs 0.15655664 23.34709581 17.72669995 -0.00126044 -0.00140488 -0.00227657 Pb 3.23429819 3.09069233 20.65105673 0.00158751 0.00070508 0.00081364 Br 2.55958858 3.22274692 17.84918339 0.00022193 0.00187448 -0.00032972 Br 3.04246868 0.09964586 20.35593107 -0.00099009 0.00056093 0.00362602 Br 0.15919095 2.97748700 20.58517058 0.00041577 -0.00393820 0.00113949 Cs 23.48084557 6.45831802 23.71956935 0.00000399 0.00234790 -0.00089203 Pb 2.84715608 8.91053268 3.18466112 0.00017131 -0.00040651 -0.00198685 Br 2.71560322 8.50149964 23.71489995 0.00274939 -0.00528265 0.00184103 Br 3.07741664 6.05014898 3.08919310 -0.00277690 0.00013982 -0.00104196 Br 23.75424222 8.58473320 3.14856158 0.00188342 -0.00102950 -0.00255166 Cs 0.44389483 6.04583247 5.97618354 0.00205391 0.00057497 0.00200354 Pb 3.37288876 9.06803515 8.88585154 0.00135167 -0.00017866 -0.00099875 Br 2.85826745 8.87613390 6.12706779 -0.00032622 -0.00178945 0.00373156 Br 2.79789054 5.79817678 8.78267699 -0.00236198 -0.00197507 0.00276699 Br 23.64028284 9.26461944 8.83967339 -0.00126996 0.00200874 -0.00035295 Cs 23.58540073 5.95496571 11.97156945 -0.00167701 -0.00116011 0.00198155 Pb 2.88251567 8.80156575 14.80809069 -0.00034879 0.00108116 -0.00147787 Br 3.18629719 8.92724162 11.85103827 0.00045135 0.00213143 0.00249690 Br 3.76078019 6.13546951 15.16546662 0.00022627 0.00191302 0.00130524 Br 0.08538917 8.47534684 14.40529474 0.00081914 -0.00195963 -0.00379883 Cs 23.65215962 5.73984620 17.61493074 -0.00140250 0.00021704 0.00032317 Pb 2.67162171 8.82982391 20.60478240 -0.00108087 -0.00140220 0.00053803 Br 2.32647670 8.78941694 17.66063801 0.00373653 0.00029564 -0.00226365 Br 2.10255062 5.87778670 20.80724844 0.00170542 0.00022512 0.00212607 Br 23.53284863 9.27956494 21.17272597 -0.00531082 0.00009002 0.00021291 Cs 23.19590494 11.75847129 0.26194551 0.00280094 -0.00058900 0.00038097 Pb 2.68099034 14.93603416 2.91365141 -0.00229596 0.00125782 0.00055208 Br 2.48001773 15.08112518 23.73877455 -0.00002439 -0.00192690 0.00148409 Br 2.92611681 11.85363168 2.22751892 0.00018115 0.00370391 -0.00167485 Br 23.34110435 14.66753490 3.23525758 0.00180392 0.00101636 -0.00152329 Cs 23.53322715 11.75519786 6.57520566 0.00147963 -0.00011183 -0.00014679 Pb 3.02665895 15.04170270 9.10675741 0.00079821 0.00003325 0.00042062 Br 3.25674045 14.93620803 5.92927256 -0.00261540 -0.00045966 -0.00169071 Br 2.98917129 12.08047385 8.72092105 0.00023222 0.00473484 0.00042325 Br 0.02818032 15.05253956 8.88672458 0.00103899 -0.00122402 -0.00023393 Cs 0.06517341 11.66574973 11.43121474 -0.00240411 0.00115264 -0.00097244 Pb 3.07436801 14.54764730 15.08345332 0.00029525 -0.00035838 -0.00000198 Br 3.00411052 14.39459575 11.85360614 -0.00214555 -0.00187274 0.00146256 Br 2.52983983 11.76760634 15.45009572 -0.00271614 -0.00160979 0.00131386 Br 0.22471162 14.57922291 14.56050099 0.00288939 -0.00111176 -0.00051329 Cs 23.56231229 11.71155459 18.17006091 0.00252908 -0.00133912 -0.00014587 Pb 3.11399770 14.52109326 20.77025470 0.00083269 -0.00121471 -0.00019148 Br 2.93403098 14.90141117 17.87793299 0.00008575 0.00105822 -0.00139611 Br 2.79820557 11.71882371 20.92287024 0.00130305 -0.00134656 -0.00002566 Br 0.16484073 14.58456360 20.89241238 0.00024795 0.00184859 -0.00224297 Cs 0.06488392 17.81241545 23.62075259 -0.00056061 0.00105026 -0.00135090 Pb 3.00066535 20.76479746 2.85247226 0.00098448 0.00042032 -0.00152198 Br 2.91992364 20.86268642 23.70749915 -0.00039450 0.00310329 0.00316589 Br 3.13238777 17.88756160 2.68768431 -0.00275578 -0.00168115 0.00166490 Br 23.61084292 20.87040079 2.98622274 0.00091764 0.00167896 0.00213686 Cs 23.06857056 17.83035748 6.15943521 -0.00174950 0.00084658 -0.00346442 Pb 2.90691266 20.91361851 8.79354927 0.00157880 -0.00111179 0.00032050 Br 2.98842230 20.73812809 5.86416906 0.00130779 -0.00243848 0.00076540 Br 3.28079900 17.81567244 9.35275418 -0.00306030 -0.00361504 -0.00315317 Br 23.70269874 20.99311173 8.89263031 0.00249663 -0.00316340 0.00036583 Cs 0.56740903 17.19550948 12.13857578 0.00292608 0.00228779 -0.00366755 Pb 3.12402703 21.04467207 14.76547969 0.00032672 -0.00174968 0.00194951 Br 3.15349951 20.77128211 11.64598146 -0.00414578 0.00071605 -0.00091212 Br 3.05788841 17.89414196 15.12189960 0.00022317 -0.00416238 0.00294761 Br 0.15335166 20.54617297 14.96101313 -0.00038048 0.00249808 -0.00026331 Cs 0.40554371 17.68645452 17.53273257 0.00029289 0.00051775 0.00181426 Pb 3.01638112 20.77619744 20.46933508 0.00082300 0.00097361 -0.00110396 Br 3.48967017 20.37672470 17.62513739 -0.00395608 -0.00294958 0.00241345 Br 3.08172613 17.77766582 20.39077704 -0.00026175 -0.00125378 -0.00477361 Br 0.07774077 20.59037659 20.57841745 0.00289447 -0.00117022 0.00090018 Cs 5.90758705 0.13858382 22.87234481 -0.00275761 0.00126725 0.00009421 Pb 8.99356349 3.04410188 3.21035617 -0.00223721 0.00217449 -0.00246865 Br 8.98034894 2.59951417 0.05316567 -0.00328783 0.00230154 0.00041120 Br 8.84066205 0.10405825 3.43373649 0.00356960 0.00463639 0.00393038 Br 5.89150064 3.00066604 3.61419964 -0.00063140 0.00172596 0.00240877 Cs 6.01785745 23.43358383 5.74548039 -0.00257067 0.00081392 0.00087836 Pb 8.76867734 2.82134850 8.83811266 -0.00083079 0.00149912 0.00007049 Br 9.79449623 3.45487686 5.99831836 -0.00250891 0.00007314 -0.00000071 Br 8.39025310 23.47466747 8.84064880 0.00100758 0.00376501 0.00295231 Br 5.91562514 3.29225214 8.38249770 0.00403473 -0.00375450 -0.00282492 Cs 6.06078569 23.73686410 12.06059018 -0.00082272 0.00144571 0.00023690 Pb 9.19216999 2.86760555 14.73668088 0.00141571 0.00005148 0.00251499 Br 8.98312739 2.84823561 11.64712181 -0.00058660 0.00008160 -0.00080272 Br 10.13556122 23.58279168 15.17608931 -0.00000280 0.00213027 -0.00333029 Br 6.16915873 2.40835476 15.11996355 0.00008702 0.00092755 -0.00270197 Cs 6.45648081 23.40903282 17.70174347 -0.00131159 -0.00235270 0.00109720 Pb 9.13596540 3.07558749 21.02196260 -0.00146753 0.00083431 -0.00161873 Br 9.67452768 2.94728872 17.79939939 -0.00210247 -0.00078984 0.00643993 Br 9.02741502 0.06069691 20.72475093 -0.00257018 -0.00361860 0.00343800 Br 6.03485333 3.91200356 20.76302207 -0.00203860 -0.00049115 0.00021064 Cs 5.59535616 6.20217746 23.70364367 -0.00159542 -0.00363260 0.00083872 Pb 8.80730580 8.87311196 2.80083648 -0.00199280 0.00045499 -0.00024632 Br 8.93047779 8.84625137 23.65501489 0.00111138 0.00075238 0.00124209 Br 8.24316949 5.89451988 2.90037885 -0.00107885 0.00327909 0.00128463 Br 5.87841325 8.98161588 2.75953851 -0.00097247 0.00200066 0.00132263 Cs 5.95956767 6.14979355 5.81688023 0.00309039 0.00188100 -0.00038698 Pb 8.77011095 8.83399163 8.71263938 0.00005759 -0.00018978 -0.00040039 Br 8.72215074 8.55132546 5.89244045 -0.00108197 -0.00236418 -0.00159227 Br 8.72613363 5.86119699 9.19268034 -0.00165281 0.00138056 0.00243185 Br 5.92487688 8.23535570 8.62740362 0.00057542 0.00422345 0.00093756 Cs 5.91204002 6.12608531 11.91029429 0.00150709 -0.00155596 0.00104488 Pb 9.02624461 8.89508259 14.84984952 -0.00146810 0.00072027 0.00015505 Br 7.89166297 8.90492500 11.88658778 -0.00042118 -0.00442542 0.00126665 Br 9.38869497 5.90846073 15.11664708 0.00165954 0.00258527 0.00220413 Br 6.08960660 9.06995846 15.42092950 0.00245958 -0.00510083 0.00150650 Cs 6.88696206 6.09583179 17.82104794 -0.00210078 0.00096138 -0.00097383 Pb 8.71874388 9.00071131 20.70137991 0.00031741 0.00185708 -0.00013846 Br 9.15080761 9.27805552 17.85738170 -0.00163262 -0.00212210 -0.00138210 Br 9.17734163 6.13138948 20.91955116 -0.00052126 -0.00048687 -0.00211630 Br 5.62887418 8.58034801 20.61993369 -0.00225576 -0.00161788 0.00084444 Cs 5.49028564 11.50196354 23.37431503 -0.00134277 -0.00185164 0.00180138 Pb 8.78586056 14.64904851 3.08563983 0.00095709 0.00091578 0.00204141 Br 9.29232142 14.64871271 0.32831277 0.00038461 -0.00231699 -0.00340151 Br 8.98299827 11.66889284 3.13245440 0.00072714 0.00424152 0.00355699 Br 5.66776407 14.63492783 2.60396793 0.00065183 -0.00239031 0.00018728 Cs 5.23042372 11.93759349 4.90962181 0.00279368 0.00103181 0.00150241 Pb 8.61159147 14.52891434 8.96784825 0.00138576 -0.00019473 0.00094291 Br 8.27679835 13.98710150 5.92098702 0.00374856 -0.00259040 -0.00104012 Br 9.74018559 11.96598336 8.82580919 -0.00232761 -0.00062671 -0.00086117 Br 5.83834294 14.25416735 9.54157416 -0.00182234 -0.00483731 0.00106981 Cs 5.52679886 11.95455978 11.83553126 0.00176097 -0.00301486 -0.00073248 Pb 8.96837607 15.08208610 14.90723526 0.00383537 0.00000526 -0.00058246 Br 9.50934854 15.19570623 11.80888699 -0.00460451 0.00125965 0.00155891 Br 9.49587186 11.80368776 15.11454407 0.00083148 -0.00100785 0.00058005 Br 5.87143185 14.64549838 14.26393667 -0.00420460 -0.00010919 -0.00479041 Cs 6.40787880 11.87949885 17.75515263 0.00050336 -0.00291141 -0.00219222 Pb 8.95550420 14.91788859 20.77565408 0.00069084 -0.00014952 -0.00004056 Br 9.35119449 14.89123529 17.79750567 0.00095862 -0.00026625 -0.00242471 Br 8.91503835 11.83384040 21.35446338 -0.00099921 0.00019063 -0.00295278 Br 6.09747792 14.87984049 21.26127973 0.00052365 0.00207284 0.00076646 Cs 5.44631339 18.51301252 23.70603442 -0.00095548 0.00178551 -0.00146313 Pb 8.79738269 20.73331128 3.05525548 -0.00005383 0.00020203 0.00042092 Br 8.91102249 20.80263335 0.12740879 0.00042756 0.00231623 -0.00101998 Br 8.37241533 17.79225781 2.86561264 -0.00006496 0.00137996 -0.00220841 Br 5.80706858 20.67213526 2.95238550 -0.00291526 0.00197916 0.00183452 Cs 5.66749142 17.92564643 5.52917382 -0.00122634 0.00230916 -0.00032517 Pb 8.60707414 20.53371402 8.87303290 0.00080851 -0.00222583 0.00137506 Br 8.36002931 20.33720612 5.87183225 -0.00334858 0.00057938 0.00130874 Br 8.07515064 17.46305686 8.28956834 0.00063765 -0.00044010 -0.00418674 Br 5.70054612 20.61088979 8.65420479 0.00049864 -0.00087865 -0.00235959 Cs 6.32643056 18.22566864 11.41673005 -0.00102136 0.00206872 -0.00057378 Pb 9.29575988 20.83405676 14.64582569 -0.00130732 -0.00051953 0.00100310 Br 9.50631111 20.97041654 11.82820399 0.00201103 -0.00346528 -0.00042585 Br 8.41529385 17.64804510 14.68273140 -0.00032779 0.00037745 0.00235210 Br 6.15486205 21.21938363 14.89782877 0.00171422 0.00269451 0.00076377 Cs 5.78275090 17.36872389 17.91390372 -0.00126466 -0.00030265 -0.00232097 Pb 9.04939507 20.67754372 20.71084488 -0.00096376 -0.00043942 -0.00097177 Br 9.74345092 20.69290318 17.61582932 0.00278777 0.00060569 -0.00062832 Br 7.88559532 18.00871101 20.71449531 -0.00059485 -0.00094990 0.00088013 Br 6.09485619 20.82124232 20.57343043 -0.00201140 -0.00188853 -0.00080976 Cs 12.14341550 23.72781511 0.35649855 0.00062689 -0.00029160 -0.00161217 Pb 15.04409682 3.35110825 2.82790754 -0.00037564 0.00096797 -0.00038707 Br 15.34816190 2.87729626 0.05913465 0.00018448 0.00133077 -0.00308291 Br 15.40818822 23.68056499 2.70561689 0.00380008 -0.00026059 0.00320989 Br 12.04978152 3.44362612 2.53665820 0.00263914 0.00198024 -0.00187537 Cs 12.18350903 0.55818375 5.75805852 -0.00107066 -0.00053038 -0.00357435 Pb 14.84846900 3.03806275 8.84940205 0.00133323 0.00095081 -0.00055589 Br 14.72761341 3.16074057 5.87093573 -0.00268468 0.00330697 -0.00157855 Br 14.87934757 0.11301715 9.03651666 0.00193587 -0.00091160 0.00120694 Br 12.13830623 3.01910975 9.61029537 0.00072942 -0.00068041 -0.00049911 Cs 11.93049922 0.25765681 11.96185171 0.00027566 0.00390076 0.00024953 Pb 14.90718863 2.88568282 14.88726285 0.00269680 -0.00339405 0.00085513 Br 15.20310554 2.96180085 11.91746822 -0.00541939 0.00095219 0.00295562 Br 14.26826463 23.59313858 14.96698267 -0.00078263 -0.00033598 -0.00262896 Br 12.04469421 2.91272270 14.22106496 0.00163729 0.00349299 -0.00136472 Cs 12.07529068 23.28329502 18.36616138 -0.00080220 0.00042115 0.00220822 Pb 15.07394415 3.06886852 20.84995499 -0.00055520 0.00146366 -0.00158566 Br 14.84401146 3.05803311 17.97449324 -0.00588541 0.00202526 0.00137734 Br 15.11738820 23.55917172 20.88906227 0.00307024 -0.00102631 -0.00413109 Br 11.94701367 2.78217168 21.51395906 -0.00264049 -0.00436072 0.00153663 Cs 11.81025821 6.27437890 23.31828216 0.00119328 -0.00091782 0.00040452 Pb 14.74633306 8.94945649 3.40045310 -0.00039614 -0.00053255 -0.00150431 Br 14.74012217 8.97568735 0.18106675 0.00069310 -0.00420768 -0.00020711 Br 15.36945733 6.09681328 3.60782820 -0.00233647 -0.00293854 -0.00381669 Br 11.72625867 8.56791105 2.63230283 -0.00159588 -0.00271306 -0.00074945 Cs 11.97551222 6.26103354 5.57947440 0.00267725 -0.00120213 0.00022233 Pb 14.51730860 8.88342630 9.04946285 0.00092052 0.00032326 -0.00044719 Br 14.35757412 9.56097075 6.08098455 -0.00032737 0.00038981 -0.00316670 Br 15.26601679 6.16274591 8.32735748 0.00040832 -0.00060292 0.00034387 Br 11.74510825 8.07153178 8.99102970 -0.00060539 -0.00178348 -0.00120479 Cs 11.97096166 5.94351007 11.86029133 0.00133346 0.00123622 0.00097453 Pb 14.90598635 8.81799209 14.75846687 0.00127296 0.00197778 0.00123167 Br 14.82883748 9.10131497 11.89020655 -0.00479113 0.00122938 0.00507982 Br 14.98392399 5.91651075 14.89615352 -0.00354758 -0.00191862 -0.00111095 Br 12.10333032 8.48670479 14.86451549 0.00044559 0.00155340 0.00309335 Cs 12.58703479 5.71664869 18.29653729 0.00107981 -0.00101129 0.00023676 Pb 14.60334039 9.15678144 20.91932578 0.00020966 0.00103223 0.00033988 Br 14.76687000 8.71131924 17.84025805 -0.00004765 -0.00256065 -0.00361509 Br 15.30072899 5.81753424 20.82832801 -0.00298862 0.00297251 -0.00242830 Br 11.66446218 8.99404068 20.44436313 0.00473215 -0.00233492 0.00117816 Cs 11.32266243 11.86094922 0.59845749 -0.00113840 0.00003928 -0.00054613 Pb 14.73758163 14.93715735 3.25138358 -0.00000307 -0.00146813 0.00003115 Br 14.87057991 15.61727909 0.01869019 0.00148725 -0.00257145 0.00009375 Br 14.52551710 12.09257349 3.11033592 -0.00300114 0.00299738 0.00070565 Br 11.61729590 15.09171190 3.47037613 -0.00061290 -0.00204855 -0.00502104 Cs 11.55577260 12.49319839 6.03029414 0.00083084 0.00013928 0.00099806 Pb 14.58040341 14.95571310 9.23393366 0.00257466 0.00194214 -0.00048221 Br 15.03013699 14.80586071 6.05130744 -0.00254024 0.00120271 -0.00150045 Br 13.83311178 12.05883310 9.00998326 -0.00443551 -0.00079327 -0.00034405 Br 11.80364659 15.48454885 8.31961346 -0.00316161 -0.00225415 0.00212344 Cs 11.89410664 12.11451035 11.95924869 -0.00064090 0.00070921 0.00068387 Pb 15.01532520 14.96436867 14.96851552 -0.00107409 0.00149064 0.00199484 Br 14.66271993 14.84268399 12.18275343 0.00134326 -0.00136968 0.00145190 Br 14.49305146 11.67227567 14.90711370 0.00182275 -0.00149582 -0.00017493 Br 11.98426828 15.24066088 15.13177346 -0.00052160 -0.00186650 -0.00126720 Cs 12.25810776 11.57209086 17.45772772 0.00016701 -0.00209396 -0.00091744 Pb 14.81464236 14.84784065 21.03706999 0.00205062 0.00028656 -0.00149929 Br 14.50839936 14.66639298 17.81014571 0.00085003 0.00286883 0.00208116 Br 14.22721006 11.97288459 21.08362602 0.00139118 0.00193912 0.00366767 Br 11.89137678 15.25915266 21.50453502 -0.00003070 0.00272886 0.00250717 Cs 11.56878799 18.03360853 0.49886732 -0.00113100 -0.00059562 0.00370643 Pb 14.82202467 20.93580195 2.97882402 0.00185527 0.00125443 0.00159310 Br 14.56882689 20.70672989 23.66261972 0.00424330 0.00140936 -0.00470564 Br 14.47032807 17.99914889 3.58672247 -0.00195518 -0.00279576 0.00134825 Br 11.75454689 21.36437265 3.48182089 0.00116903 0.00025416 0.00067638 Cs 11.20398589 18.17158956 5.94253525 0.00014620 0.00049642 -0.00004377 Pb 14.95016413 20.95305713 8.82226910 0.00194199 0.00010168 0.00086706 Br 15.07763669 20.88897345 5.96488496 0.00075888 -0.00584359 0.00427221 Br 14.75614196 17.80390131 8.80568101 0.00200424 0.00024448 0.00084157 Br 11.69972290 21.26630178 8.21266277 -0.00196718 0.00376012 -0.00183912 Cs 12.45816518 18.47685250 11.70339363 0.00038877 -0.00151152 -0.00151262 Pb 14.98728556 20.52236082 14.87061924 0.00069776 0.00061321 -0.00151597 Br 14.92517451 20.59086730 11.90098059 0.00388039 0.00357064 -0.00178728 Br 15.64620635 17.78500864 14.60544903 -0.00225595 0.00310456 0.00067213 Br 12.11087176 19.66478900 14.80238246 0.00297710 0.00029625 0.00213989 Cs 12.16330473 17.63134146 17.83257643 0.00099928 -0.00062665 0.00201869 Pb 14.84103969 20.61147391 20.65420820 0.00298605 0.00227338 0.00015883 Br 14.25474322 20.59889892 17.74629880 -0.00230706 0.00273573 -0.00259949 Br 14.68957588 17.81315790 19.92616371 0.00262081 -0.00189084 -0.00252537 Br 11.96623704 20.81129456 21.04269177 -0.00312240 -0.00235093 0.00149559 Cs 18.00757207 23.41410323 0.00740378 0.00525533 -0.00017663 -0.00339725 Pb 20.88732313 3.20358550 2.68345141 0.00218504 0.00288098 -0.00148667 Br 20.60844037 3.92059766 23.59561958 -0.00095562 0.00136155 0.00129841 Br 20.93050393 0.10190606 3.26175123 -0.00207223 0.00205656 -0.00078333 Br 18.00725509 3.21609552 3.02349295 -0.00347453 0.00009214 0.00473153 Cs 18.21676776 0.01974578 6.16654881 -0.00311345 0.00294190 -0.00025516 Pb 21.02010191 3.04401759 8.95155351 0.00153279 -0.00155612 0.00068882 Br 21.55490666 3.00722292 6.13198746 -0.00017373 0.00201904 0.00135277 Br 20.96092528 0.08704377 9.05431892 -0.00197577 0.00082022 0.00365790 Br 17.72059168 2.63665797 8.57390502 -0.00169118 0.00234636 -0.00004126 Cs 17.83267936 0.35667329 11.80365822 0.00331192 -0.00220404 0.00009063 Pb 20.71776911 3.02423596 14.62197258 -0.00173704 0.00046042 0.00067014 Br 19.85900418 3.53173925 11.75018288 -0.00053598 0.00151803 0.00079090 Br 21.46440182 23.68787537 14.84008468 0.00365103 -0.00139348 -0.00009856 Br 17.90739082 2.69962215 14.99183544 0.00030763 0.00316837 -0.00002125 Cs 17.97898695 23.69228422 18.32120430 0.00109814 -0.00225122 -0.00178681 Pb 21.12627875 2.87085947 20.66550766 -0.00047568 0.00038959 -0.00025928 Br 20.88136467 3.14991961 17.68583003 0.00021811 0.00117142 0.00011869 Br 21.00939553 23.66474695 20.51785032 -0.00300759 0.00226894 -0.00356340 Br 17.69857233 2.86619580 20.09850683 -0.00375768 0.00105737 0.00045718 Cs 17.70974122 6.35871881 23.36960463 0.00099447 0.00216849 -0.00166342 Pb 20.82648378 8.92006548 3.06367065 -0.00062772 -0.00081984 0.00037968 Br 20.29298093 8.99933435 0.13333402 -0.00220490 -0.00564118 0.00067013 Br 20.57068495 5.92598533 3.29463133 -0.00111048 -0.00342959 0.00113208 Br 17.85735605 9.01848215 3.57627454 -0.00126797 -0.00131165 0.00227392 Cs 18.39221573 5.36324914 6.38647694 0.00062488 0.00053376 -0.00120347 Pb 20.93945518 8.79767090 8.87353818 0.00090400 -0.00122279 -0.00087135 Br 20.81965097 9.37934507 5.98321223 -0.00187919 0.00127738 -0.00327658 Br 21.31137521 6.00563067 8.75671690 0.00001803 -0.00070332 -0.00217528 Br 17.39153174 9.09494751 9.22819455 -0.00038724 0.00148305 -0.00666398 Cs 17.44794992 6.36214307 11.44304961 0.00075796 0.00141205 -0.00035588 Pb 20.96068729 8.81636576 14.93816719 0.00054850 -0.00171345 0.00119372 Br 20.91724484 8.66926018 11.89643762 -0.00158956 0.00129313 -0.00032861 Br 20.84755506 5.75657031 15.29453969 -0.00166251 0.00023032 -0.00203810 Br 17.99611607 8.44138543 14.70261099 0.00270078 -0.00341747 0.00059141 Cs 17.85973088 5.72323666 17.56052021 -0.00178416 -0.00110721 0.00143409 Pb 20.58243999 9.06640957 20.63731706 0.00141108 -0.00011860 -0.00038789 Br 21.37550029 8.67719113 17.81192077 -0.00062221 -0.00131299 -0.00291677 Br 20.45562591 5.77603035 20.69168502 0.00010831 0.00102386 -0.00346834 Br 17.46063079 9.40719008 20.51761336 -0.00076555 -0.00310518 0.00024741 Cs 17.76501897 11.93054654 0.15204113 -0.00236258 -0.00118449 -0.00046060 Pb 20.51801026 14.75459732 3.06357222 0.00202527 0.00021629 0.00155827 Br 20.77126342 15.26306928 0.03092413 0.00053393 -0.00066831 0.00170708 Br 20.30900955 11.80713769 3.24183855 -0.00126567 0.00219547 0.00212197 Br 17.50071431 15.04928618 2.71400745 0.00017343 -0.00064278 -0.00303717 Cs 17.81523181 11.14382612 6.25558345 0.00133007 0.00045175 -0.00001609 Pb 20.53528575 14.84037516 9.02663285 -0.00113826 0.00138429 0.00260658 Br 20.21073031 14.90865093 5.89447087 -0.00255675 -0.00423816 0.00066327 Br 20.46239145 11.82710054 8.73176700 -0.00729450 -0.00024199 0.00631083 Br 17.70098759 14.44770817 9.33437196 -0.00132537 -0.00009595 0.00033187 Cs 17.93378266 11.31966449 12.25793147 0.00140971 -0.00059356 -0.00253070 Pb 20.81651112 14.65231690 14.80533095 -0.00081521 0.00136964 -0.00149349 Br 20.86138213 13.96559906 11.88042065 -0.00102595 -0.00068004 -0.00332377 Br 20.53069568 11.63405608 15.32829058 0.00239990 0.00004802 0.00224958 Br 17.82422663 14.18185434 14.86337528 -0.00291492 0.00256978 0.00257403 Cs 17.42685944 12.00194528 17.61730803 -0.00056397 -0.00158664 -0.00091045 Pb 20.91094444 15.01439480 20.79595456 0.00229571 0.00245783 -0.00100332 Br 21.15575683 14.60557692 17.96423925 -0.00349601 0.00160590 0.00045582 Br 20.21357901 11.92932532 20.08613201 -0.00351747 -0.00153555 0.00196664 Br 17.95249086 15.01836272 20.58399113 0.00374999 -0.00139063 -0.00147615 Cs 17.92743155 17.71844198 23.42235954 0.00054623 -0.00035442 0.00024496 Pb 20.84871967 20.93460518 3.09849872 0.00365204 -0.00137032 0.00004511 Br 21.36596787 21.10212865 23.68370472 0.00079114 0.00025895 -0.00069795 Br 20.76096309 17.83227720 3.16258502 0.00285801 -0.00127062 0.00164577 Br 18.00149917 20.29528526 2.29927889 -0.00235910 0.00230778 0.00270278 Cs 17.72755944 17.96856292 5.66851702 0.00150443 -0.00071901 -0.00255523 Pb 20.78998082 20.98520048 8.86230250 0.00066018 0.00010325 -0.00290380 Br 21.00275363 20.77884022 5.95833685 -0.00062962 -0.00026708 -0.00050709 Br 20.24803722 17.71445473 8.69606287 0.00058090 -0.00158401 0.00092386 Br 17.54018242 20.93893625 9.64444044 -0.00140945 0.00294193 -0.00110688 Cs 17.81464250 17.98435239 11.66302964 -0.00068985 -0.00200680 0.00384888 Pb 20.82194051 20.83463504 14.84700310 -0.00270250 0.00113953 0.00091841 Br 21.05474282 20.85409454 11.88362970 0.00146556 0.00050428 0.00093233 Br 20.57214055 17.37610819 14.01633518 -0.00175293 -0.00212922 0.00130050 Br 17.96090774 21.44815160 14.74964549 -0.00051206 0.00124066 0.00370468 Cs 17.47601361 18.06774814 18.01734045 0.00100993 -0.00076317 0.00333880 Pb 20.90883526 20.75812923 20.88885544 -0.00186642 -0.00094683 0.00263232 Br 20.70836628 20.79541206 17.64424399 0.00283562 -0.00304893 -0.00104564 Br 20.78605300 17.87226951 20.37770229 0.00022725 -0.00006298 -0.00254016 Br 17.86499256 20.65497233 21.12211070 0.00281040 0.00165259 0.00283231 320 Lattice="23.77195271 0.0 0.0 0.0 23.77195271 0.0 0.0 0.0 23.77195271" Properties=species:S:1:pos:R:3:vel:R:3 Time=20129.99999999 energy=-11425.93354416 virial="-13.79490475 -1.48716449 -0.64039037 -1.48716449 -10.23329299 -0.72592612 -0.64039037 -0.72592612 -8.88520384" stress="1.023e-05 -0.0001107 -4.767e-05 -0.0001107 0.00014876 -5.404e-05 -4.767e-05 -5.404e-05 0.0003145" pbc="T T T" Cs 23.60239769 0.12230258 0.03537083 -0.00315493 -0.00289431 0.00141521 Pb 2.87171331 3.07795658 3.10099872 -0.00093357 0.00086395 0.00185256 Br 3.26367179 2.93479801 0.12581590 -0.00032778 0.00004113 0.00102722 Br 2.96374519 0.09503417 3.09317032 -0.00108551 -0.00162341 -0.00095565 Br 0.16625403 3.44786535 2.31492418 -0.00265158 -0.00047322 -0.00336182 Cs 0.20031265 0.04182340 6.04830309 0.00375811 0.00237780 -0.00236427 Pb 3.05667397 2.98715021 9.22101450 0.00121078 0.00054341 -0.00234777 Br 2.51892887 2.74230951 6.33368504 0.00145266 -0.00091093 0.00253365 Br 3.16300518 0.01306899 9.40775484 0.00015306 0.00043257 0.00301042 Br 23.63746159 3.12029433 10.42661103 -0.00013067 -0.00082546 0.00370698 Cs 0.20386823 23.58776451 12.21546346 -0.00383932 -0.00110897 0.00019244 Pb 3.04903369 3.26021526 14.89411439 0.00005031 -0.00182049 -0.00111296 Br 4.08386105 3.01782021 11.99497019 -0.00325261 -0.00074640 0.00126804 Br 3.01342142 0.08531188 14.66616387 0.00118975 -0.00165780 -0.00274819 Br 0.22362197 3.33915893 14.70115782 -0.00279621 -0.00209544 -0.00191944 Cs 0.14386329 23.33341363 17.70396805 -0.00127681 -0.00133080 -0.00226676 Pb 3.24920089 3.09826641 20.65993677 0.00138274 0.00080441 0.00095807 Br 2.56347726 3.24093219 17.84371613 0.00055580 0.00176093 -0.00075012 Br 3.03309671 0.10351440 20.39204291 -0.00088329 0.00021036 0.00359090 Br 0.16129604 2.93653215 20.59754644 -0.00000354 -0.00424342 0.00133417 Cs 23.48084653 6.48119526 23.71034598 -0.00000626 0.00222416 -0.00095178 Pb 2.84939936 8.90666644 3.16285281 0.00027875 -0.00035928 -0.00236832 Br 2.74367496 8.45015019 23.73412900 0.00285721 -0.00497339 0.00200232 Br 3.05006412 6.05225856 3.07961966 -0.00268988 0.00027504 -0.00086815 Br 0.00077831 8.57484734 3.12293582 0.00178606 -0.00094808 -0.00257136 Cs 0.46393842 6.05089958 5.99617946 0.00195143 0.00043841 0.00199482 Pb 3.38383525 9.06659830 8.87714701 0.00083045 -0.00009936 -0.00072779 Br 2.85474738 8.85861189 6.16019498 -0.00038025 -0.00171832 0.00285148 Br 2.77434584 5.78284437 8.81120021 -0.00234026 -0.00107237 0.00293355 Br 23.63180200 9.28277425 8.83714460 -0.00041752 0.00162003 -0.00015333 Cs 23.56834527 5.94473163 11.99178705 -0.00173588 -0.00088674 0.00206027 Pb 2.88005205 8.81356742 14.79399452 -0.00014431 0.00131564 -0.00133966 Br 3.19105546 8.94886098 11.87525950 0.00050452 0.00219091 0.00231505 Br 3.76340775 6.15078389 15.17813091 0.00029411 0.00112888 0.00122108 Br 0.09348349 8.45601374 14.36768420 0.00079731 -0.00190149 -0.00371310 Cs 23.63836524 5.74194118 17.61869076 -0.00135429 0.00019993 0.00042564 Pb 2.66086928 8.81426294 20.61069636 -0.00106757 -0.00170702 0.00063883 Br 2.36301553 8.79212173 17.64079200 0.00356407 0.00024095 -0.00166707 Br 2.12096180 5.88113316 20.82862417 0.00196594 0.00043606 0.00214645 Br 23.47782949 9.28105131 21.17488982 -0.00564802 0.00020791 0.00022415 Cs 23.22448956 11.75306020 0.26582838 0.00291389 -0.00049060 0.00039698 Pb 2.65883615 14.94811367 2.91908018 -0.00212927 0.00115275 0.00053602 Br 2.48043198 15.06176230 23.75340821 0.00010107 -0.00194022 0.00141612 Br 2.92636669 11.89057518 2.21168458 -0.00013144 0.00365364 -0.00148421 Br 23.36311056 14.67749033 3.22031144 0.00258445 0.00097411 -0.00146206 Cs 23.54796834 11.75584099 6.57258145 0.00146585 0.00024443 -0.00037787 Pb 3.03396039 15.04128028 9.10959321 0.00065903 -0.00013214 0.00015154 Br 3.23023799 14.93182194 5.91137403 -0.00268209 -0.00041744 -0.00187553 Br 2.99099111 12.12233827 8.72465279 0.00012400 0.00358536 0.00031373 Br 0.03708891 15.04009109 8.88443740 0.00073449 -0.00126420 -0.00022397 Cs 0.04109274 11.67804881 11.42227047 -0.00240935 0.00130812 -0.00081457 Pb 3.07699425 14.54487231 15.08181200 0.00023029 -0.00020006 -0.00032834 Br 2.98054416 14.37610971 11.87066310 -0.00256431 -0.00182325 0.00192146 Br 2.50395500 11.75134170 15.46262711 -0.00245820 -0.00163686 0.00119263 Br 0.24984073 14.56883080 14.55604115 0.00210363 -0.00095683 -0.00038590 Cs 23.58753657 11.69847674 18.16839474 0.00251117 -0.00127404 -0.00018910 Pb 3.12172673 14.51074102 20.76944030 0.00071527 -0.00085165 0.00002619 Br 2.93522599 14.91156998 17.86588554 0.00015378 0.00096803 -0.00098597 Br 2.81153929 11.70553426 20.92235318 0.00135875 -0.00131222 -0.00007560 Br 0.16630175 14.60325967 20.86950832 0.00005882 0.00188767 -0.00233560 Cs 0.05910319 17.82305389 23.60732548 -0.00059182 0.00107324 -0.00133437 Pb 3.00922678 20.77024171 2.83768199 0.00071990 0.00066404 -0.00142769 Br 2.91495013 20.89482074 23.73642746 -0.00059880 0.00332039 0.00260201 Br 3.10366176 17.86917890 2.70457946 -0.00298337 -0.00196759 0.00170647 Br 23.62404280 20.88685105 3.00770183 0.00172149 0.00161343 0.00215865 Cs 23.05147385 17.83801522 6.12467647 -0.00166406 0.00068149 -0.00348233 Pb 2.92190594 20.90219596 8.79715634 0.00141714 -0.00116749 0.00039824 Br 3.00109638 20.71395093 5.87057388 0.00122279 -0.00239308 0.00051321 Br 3.25014318 17.78330636 9.32134855 -0.00305727 -0.00279087 -0.00311960 Br 23.72481036 20.96132787 8.89577541 0.00190561 -0.00319049 0.00025852 Cs 0.59643199 17.21913431 12.10191017 0.00287802 0.00243242 -0.00366124 Pb 3.12739648 21.02527644 14.78455917 0.00034263 -0.00212667 0.00186288 Br 3.11222555 20.77832948 11.64129837 -0.00410329 0.00068290 -0.00001062 Br 3.06057413 17.85036855 15.14930109 0.00031476 -0.00457431 0.00252858 Br 0.14950573 20.57161978 14.95845916 -0.00037419 0.00258630 -0.00024722 Cs 0.40819080 17.69172372 17.55119188 0.00023576 0.00053558 0.00187706 Pb 3.02519675 20.78555897 20.45873591 0.00093761 0.00089712 -0.00100902 Br 3.44924251 20.34966552 17.65068837 -0.00412368 -0.00245798 0.00266519 Br 3.07878505 17.76562529 20.34468461 -0.00032656 -0.00115366 -0.00443193 Br 0.10637774 20.57873505 20.58674641 0.00280311 -0.00115586 0.00076913 Cs 5.88004054 0.15141122 22.87394783 -0.00275010 0.00129677 0.00022684 Pb 8.97066660 3.06583983 3.18518219 -0.00233590 0.00217169 -0.00255959 Br 8.94621131 2.62290006 0.05834297 -0.00354182 0.00237638 0.00062668 Br 8.87659397 0.14919356 3.47305168 0.00361181 0.00437510 0.00393014 Br 5.88715288 3.01761060 3.63851098 -0.00024841 0.00166438 0.00244752 Cs 5.99162451 23.44218649 5.75432593 -0.00267142 0.00090752 0.00089253 Pb 8.76053079 2.83685853 8.83806867 -0.00078584 0.00159543 -0.00007960 Br 9.76927673 3.45505296 5.99803971 -0.00253266 -0.00003187 -0.00007171 Br 8.39969169 23.51276618 8.86998022 0.00087771 0.00382440 0.00291306 Br 5.95557938 3.25385077 8.35496257 0.00391263 -0.00391378 -0.00268423 Cs 6.05235406 23.75137920 12.06288392 -0.00086179 0.00145885 0.00021976 Pb 9.20635291 2.86816769 14.76311606 0.00142434 0.00006299 0.00276274 Br 8.97619464 2.84851000 11.64323587 -0.00080095 -0.00002551 0.00005178 Br 10.13289685 23.60405422 15.14144026 -0.00053399 0.00211859 -0.00358853 Br 6.17090404 2.41793912 15.09359616 0.00026456 0.00098801 -0.00256665 Cs 6.44354875 23.38587764 17.71285374 -0.00127412 -0.00227406 0.00112711 Pb 9.12132571 3.08347314 21.00494494 -0.00145754 0.00073979 -0.00178215 Br 9.65271756 2.93989071 17.86346999 -0.00225725 -0.00069172 0.00636085 Br 9.00084920 0.02358984 20.75876865 -0.00274162 -0.00378874 0.00336113 Br 6.01584666 3.90635815 20.76484234 -0.00174334 -0.00063058 0.00015227 Cs 5.57945309 6.16552060 23.71220091 -0.00158202 -0.00369909 0.00087297 Pb 8.78743645 8.87648012 2.79875112 -0.00197314 0.00023216 -0.00016633 Br 8.94106606 8.85372661 23.66834288 0.00100318 0.00074314 0.00139808 Br 8.23315497 5.92741778 2.91239158 -0.00092900 0.00330057 0.00111142 Br 5.86671840 9.00207130 2.77274099 -0.00136461 0.00208672 0.00131440 Cs 5.99031823 6.16735750 5.81237304 0.00305799 0.00163073 -0.00050990 Pb 8.77204445 8.83167584 8.70997504 0.00033505 -0.00027452 -0.00013525 Br 8.71197526 8.52791988 5.87639712 -0.00094980 -0.00230985 -0.00159995 Br 8.70978944 5.87528523 9.21633844 -0.00161983 0.00144167 0.00229687 Br 5.93270110 8.27730735 8.63668569 0.00099157 0.00415502 0.00091125 Cs 5.92698634 6.10991574 11.92067305 0.00147843 -0.00168290 0.00103338 Pb 9.01117932 8.90257258 14.85103316 -0.00153816 0.00077040 0.00007663 Br 7.88982180 8.86264220 11.89912227 0.00005583 -0.00401813 0.00123186 Br 9.40400423 5.93240646 15.13785077 0.00139977 0.00218821 0.00204022 Br 6.11400179 9.01917072 15.43577264 0.00240752 -0.00504829 0.00146239 Cs 6.86564732 6.10520845 17.81118615 -0.00216217 0.00091288 -0.00099884 Pb 8.72192638 9.01923576 20.70023618 0.00032245 0.00183893 -0.00009174 Br 9.13370426 9.25625111 17.84156231 -0.00177968 -0.00223503 -0.00174550 Br 9.17238371 6.12411827 20.89772665 -0.00046901 -0.00095684 -0.00224348 Br 5.60770895 8.56468115 20.62825715 -0.00197111 -0.00151200 0.00082174 Cs 5.47733109 11.48348377 23.39283025 -0.00124602 -0.00184097 0.00190137 Pb 8.79451317 14.65923598 3.10603330 0.00076789 0.00111885 0.00201912 Br 9.29501088 14.62673945 0.29041263 0.00013870 -0.00207187 -0.00414156 Br 8.98973157 11.71299447 3.16838538 0.00062369 0.00452925 0.00361518 Br 5.67371636 14.61135680 2.60583590 0.00054285 -0.00231732 0.00018717 Cs 5.25922812 11.94831119 4.92564051 0.00296159 0.00111319 0.00169813 Pb 8.62654499 14.52880680 8.97706732 0.00159532 0.00017199 0.00089882 Br 8.31285608 13.96219181 5.91375295 0.00345778 -0.00237971 -0.00039574 Br 9.71599883 11.95476675 8.81963276 -0.00250922 -0.00161165 -0.00037244 Br 5.82310982 14.20799387 9.55025977 -0.00118960 -0.00437976 0.00065534 Cs 5.54476520 11.92251057 11.82991446 0.00182797 -0.00339349 -0.00038643 Pb 9.00769971 15.07912829 14.90001886 0.00401603 -0.00058656 -0.00085987 Br 9.46247738 15.20748758 11.82375259 -0.00475955 0.00109751 0.00140469 Br 9.50363411 11.79411969 15.11901537 0.00072031 -0.00088551 0.00031363 Br 5.83280223 14.64404068 14.21732305 -0.00348921 -0.00017797 -0.00453235 Cs 6.41306601 11.85016882 17.73287208 0.00053214 -0.00295191 -0.00226380 Pb 8.96295743 14.91643219 20.77625683 0.00079938 -0.00013786 0.00016502 Br 9.35938303 14.88855747 17.77704259 0.00068258 -0.00027045 -0.00166004 Br 8.90439907 11.83978337 21.32373171 -0.00112571 0.00098908 -0.00319153 Br 6.10141366 14.89913713 21.26855452 0.00025847 0.00178177 0.00068665 Cs 5.43809461 18.52963135 23.69085921 -0.00068963 0.00153664 -0.00156989 Pb 8.79715470 20.73573317 3.05896620 0.00000620 0.00028326 0.00032197 Br 8.91532662 20.82591600 0.11399115 0.00042956 0.00233758 -0.00163932 Br 8.37295821 17.80591635 2.84375816 0.00017153 0.00136890 -0.00215868 Br 5.78042935 20.69314006 2.97051574 -0.00236540 0.00221760 0.00178819 Cs 5.65490238 17.94810092 5.52600935 -0.00129389 0.00217805 -0.00030861 Pb 8.61674484 20.51227131 8.88565811 0.00112729 -0.00205176 0.00114680 Br 8.32637204 20.34445742 5.88765464 -0.00336976 0.00087262 0.00182865 Br 8.08209083 17.45994487 8.24875046 0.00074965 -0.00018280 -0.00396595 Br 5.70997181 20.60261589 8.63071529 0.00138593 -0.00077824 -0.00233053 Cs 6.31578976 18.24546015 11.41195832 -0.00110625 0.00188646 -0.00038196 Pb 9.28290458 20.82869060 14.65768690 -0.00125748 -0.00055245 0.00136288 Br 9.52586943 20.93548342 11.82106275 0.00189938 -0.00352484 -0.00098151 Br 8.41068354 17.66020371 14.70644361 -0.00058912 0.00202286 0.00239328 Br 6.17358293 21.24607214 14.90389178 0.00202249 0.00263552 0.00044926 Cs 5.76991087 17.36615547 17.89015279 -0.00129916 -0.00021330 -0.00242600 Pb 9.03950654 20.67358599 20.70111434 -0.00101097 -0.00035282 -0.00097182 Br 9.76893374 20.69812921 17.61276177 0.00230269 0.00044021 0.00000549 Br 7.88391925 17.99485333 20.72392186 0.00025515 -0.00181246 0.00099948 Br 6.07178620 20.80566781 20.56548390 -0.00259213 -0.00122926 -0.00078005 Cs 12.14960471 23.72515707 0.34010086 0.00061038 -0.00023999 -0.00166653 Pb 15.03998151 3.36005467 2.82516336 -0.00044557 0.00081011 -0.00016964 Br 15.35119221 2.89084448 0.02704713 0.00042299 0.00137848 -0.00331485 Br 15.44592245 23.68055816 2.73832105 0.00374557 0.00024260 0.00332791 Br 12.07504308 3.46322435 2.51840783 0.00238802 0.00193956 -0.00177590 Cs 12.17262569 0.55227977 5.72266649 -0.00110656 -0.00064518 -0.00350058 Pb 14.86184721 3.04807228 8.84398614 0.00133023 0.00104722 -0.00052708 Br 14.70054296 3.19249461 5.85538297 -0.00272938 0.00303937 -0.00151743 Br 14.89860335 0.10354648 9.04816302 0.00191735 -0.00096786 0.00111910 Br 12.14139717 3.01254880 9.60425203 -0.00009979 -0.00062543 -0.00071222 Cs 11.93382078 0.29599477 11.96383003 0.00038739 0.00376425 0.00014236 Pb 14.93389664 2.85117224 14.89564881 0.00263274 -0.00349948 0.00081883 Br 15.14975803 2.97184759 11.94699389 -0.00523292 0.00104962 0.00293113 Br 14.26205812 23.59017416 14.94040367 -0.00045938 -0.00025045 -0.00268266 Br 12.06054694 2.94987857 14.20786599 0.00151770 0.00393211 -0.00126259 Cs 12.06720717 23.28915929 18.38777490 -0.00081597 0.00075222 0.00211550 Pb 15.06715509 3.08172972 20.83448535 -0.00081460 0.00111823 -0.00149871 Br 14.78397577 3.07732052 17.98466137 -0.00610775 0.00181644 0.00064628 Br 15.14792675 23.54962300 20.84729310 0.00303539 -0.00087982 -0.00422130 Br 11.92119271 2.73943281 21.52860363 -0.00252272 -0.00418589 0.00138916 Cs 11.82246773 6.26502264 23.32305078 0.00124761 -0.00095522 0.00054733 Pb 14.74301392 8.94462876 3.38378926 -0.00026884 -0.00043610 -0.00183049 Br 14.74751736 8.93336738 0.17743413 0.00078519 -0.00425076 -0.00052254 Br 15.34562562 6.06865563 3.56905959 -0.00242619 -0.00264447 -0.00393032 Br 11.71259280 8.54052857 2.62555372 -0.00113762 -0.00275979 -0.00059844 Cs 12.00260501 6.24891879 5.58199814 0.00273848 -0.00122389 0.00028244 Pb 14.52648124 8.88761650 9.04382624 0.00090677 0.00051450 -0.00067094 Br 14.35370039 9.56467462 6.05098848 -0.00044701 0.00034732 -0.00280267 Br 15.26969093 6.15537861 8.33168049 0.00032621 -0.00086036 0.00052148 Br 11.73670264 8.05376525 8.97869068 -0.00105521 -0.00176347 -0.00126544 Cs 11.98414634 5.95543295 11.87061760 0.00130421 0.00114724 0.00109161 Pb 14.91909657 8.83744469 14.77210618 0.00134568 0.00189357 0.00150065 Br 14.78023043 9.11324993 11.94127745 -0.00492166 0.00115823 0.00504660 Br 14.94863732 5.89399081 14.88471009 -0.00350314 -0.00257083 -0.00117649 Br 12.10530009 8.50289712 14.89493583 -0.00003702 0.00168966 0.00298959 Cs 12.59656316 5.70715441 18.29814998 0.00081741 -0.00087862 0.00008241 Pb 14.60562229 9.16625629 20.92256429 0.00025082 0.00086123 0.00030798 Br 14.76656769 8.68666867 17.80532951 -0.00000857 -0.00236364 -0.00334180 Br 15.26967918 5.85209405 20.80426202 -0.00321611 0.00391428 -0.00237786 Br 11.71233611 8.97019436 20.45719601 0.00479183 -0.00243115 0.00138093 Cs 11.31283651 11.86091429 0.59205640 -0.00082773 -0.00004661 -0.00073012 Pb 14.73675721 14.92339565 3.25175106 -0.00015770 -0.00127421 0.00003311 Br 14.88578523 15.59121109 0.01913871 0.00155544 -0.00263928 -0.00001074 Br 14.49542011 12.11920875 3.11637460 -0.00301853 0.00228899 0.00049904 Br 11.61142963 15.07165380 3.42019641 -0.00054505 -0.00195924 -0.00500713 Cs 11.56578946 12.49463061 6.03962443 0.00117348 0.00014625 0.00086628 Pb 14.60606027 14.97419474 9.22753734 0.00255151 0.00174781 -0.00079399 Br 15.00529668 14.81764730 6.03767985 -0.00242493 0.00115294 -0.00119656 Br 13.79000368 12.04927454 9.00677231 -0.00417457 -0.00110973 -0.00029651 Br 11.77053164 15.46118353 8.34210792 -0.00345120 -0.00241099 0.00237734 Cs 11.88754263 12.12182712 11.96674636 -0.00067152 0.00075241 0.00081543 Pb 15.00380193 14.97837866 14.98868333 -0.00122839 0.00131340 0.00203426 Br 14.67528511 14.82906539 12.19390881 0.00117198 -0.00135172 0.00077454 Br 14.51292827 11.66116497 14.90431453 0.00214787 -0.00069957 -0.00037696 Br 11.97811162 15.22205428 15.11763160 -0.00070995 -0.00184957 -0.00155769 Cs 12.25891939 11.55110579 17.44943361 -0.00000302 -0.00210450 -0.00074159 Pb 14.83470130 14.85162301 21.02186408 0.00195919 0.00047155 -0.00153469 Br 14.51782980 14.69419724 17.83467322 0.00103117 0.00268165 0.00280795 Br 14.24154018 11.99264536 21.12036314 0.00147217 0.00200286 0.00367885 Br 11.89096847 15.28655590 21.52775199 -0.00004099 0.00274908 0.00212584 Cs 11.55785900 18.02713683 0.53598771 -0.00105471 -0.00070027 0.00371431 Pb 14.84048865 20.94706520 2.99447535 0.00183873 0.00099532 0.00153915 Br 14.61147448 20.72013995 23.61779779 0.00428628 0.00127507 -0.00424382 Br 14.45057132 17.97152862 3.59935108 -0.00199461 -0.00270300 0.00117435 Br 11.76847178 21.36585310 3.48738945 0.00160284 0.00003794 0.00043371 Cs 11.20654372 18.17625365 5.94205975 0.00036402 0.00043711 -0.00005015 Pb 14.96662101 20.95373169 8.83113447 0.00133524 0.00003790 0.00090960 Br 15.08727682 20.83181930 6.00501188 0.00117111 -0.00558704 0.00370530 Br 14.77747387 17.80937207 8.81469828 0.00225765 0.00083295 0.00096349 Br 11.67886702 21.30227079 8.19541961 -0.00220919 0.00342503 -0.00161077 Cs 12.46040590 18.45927824 11.68696342 0.00007153 -0.00198627 -0.00176235 Pb 14.99369624 20.53084139 14.85516139 0.00057864 0.00109011 -0.00157187 Br 14.96441720 20.62860061 11.88487527 0.00394086 0.00394967 -0.00141515 Br 15.62489813 17.81422851 14.61307975 -0.00199937 0.00270009 0.00085208 Br 12.13949064 19.66931637 14.82395925 0.00272967 0.00060083 0.00215310 Cs 12.17148975 17.62488378 17.85178814 0.00063499 -0.00066186 0.00182252 Pb 14.87084337 20.63513605 20.65676434 0.00295922 0.00244816 0.00034513 Br 14.23377963 20.62669848 17.72022317 -0.00187663 0.00281537 -0.00259156 Br 14.71742573 17.79128793 19.90546804 0.00294592 -0.00244426 -0.00160257 Br 11.93500575 20.78713759 21.05821085 -0.00308535 -0.00247942 0.00160262 Cs 18.05950572 23.41231501 23.74545090 0.00512774 -0.00017638 -0.00337714 Pb 20.90928099 3.23211382 2.66811742 0.00219660 0.00280645 -0.00157870 Br 20.59953423 3.93167296 23.61162927 -0.00082798 0.00086352 0.00187835 Br 20.90946112 0.12271109 3.25367376 -0.00213172 0.00207140 -0.00083288 Br 17.97308688 3.21598616 3.07154581 -0.00332528 -0.00011451 0.00487490 Cs 18.18524596 0.04921786 6.16347802 -0.00319090 0.00295321 -0.00036157 Pb 21.03552387 3.02869733 8.95977624 0.00155192 -0.00150247 0.00095782 Br 21.55297924 3.02728986 6.14379789 -0.00020494 0.00198847 0.00101067 Br 20.94073483 0.09408930 9.09046343 -0.00206032 0.00057150 0.00356672 Br 17.70862449 2.66010838 8.57352870 -0.00068752 0.00234031 -0.00003164 Cs 17.86559107 0.33443125 11.80473074 0.00326573 -0.00223979 0.00012302 Pb 20.70093066 3.02818347 14.62888349 -0.00161990 0.00032963 0.00070827 Br 19.85445829 3.54666677 11.75816762 -0.00037272 0.00146487 0.00080334 Br 21.50018284 23.67377247 14.83927763 0.00349055 -0.00141875 -0.00005671 Br 17.91030728 2.73065360 14.99245974 0.00026804 0.00303146 0.00015178 Cs 17.98980650 23.66942088 18.30238036 0.00106465 -0.00231609 -0.00197660 Pb 21.12044050 2.87617151 20.66241014 -0.00068512 0.00067341 -0.00035981 Br 20.88407292 3.15948826 17.68812249 0.00032001 0.00074236 0.00034133 Br 20.97955810 23.68736630 20.48242144 -0.00296020 0.00222652 -0.00351510 Br 17.66686762 2.87678521 20.10451662 -0.00255328 0.00105159 0.00072742 Cs 17.72000199 6.38007252 23.35338576 0.00105044 0.00209751 -0.00158357 Pb 20.81866206 8.91185395 3.06778209 -0.00093600 -0.00081874 0.00043911 Br 20.27161408 8.94336251 0.13778697 -0.00206323 -0.00554381 0.00022227 Br 20.56036494 5.89388241 3.30678677 -0.00095465 -0.00293162 0.00130796 Br 17.84246009 9.00416074 3.59689532 -0.00170285 -0.00155361 0.00184491 Cs 18.39778723 5.36925992 6.37335063 0.00048841 0.00066688 -0.00142067 Pb 20.94638765 8.78693079 8.86537134 0.00047775 -0.00092188 -0.00076082 Br 20.80266212 9.38957254 5.95105604 -0.00150683 0.00076869 -0.00312434 Br 21.31196228 5.99632818 8.73408345 0.00010154 -0.00114924 -0.00234790 Br 17.39174746 9.11061300 9.16091588 0.00041527 0.00163771 -0.00676931 Cs 17.45565862 6.37555164 11.44020634 0.00078209 0.00127282 -0.00021312 Pb 20.96540135 8.79876868 14.94951387 0.00039498 -0.00179801 0.00106701 Br 20.90109182 8.68263906 11.89249402 -0.00163990 0.00137903 -0.00047441 Br 20.83086732 5.76108585 15.27254215 -0.00167114 0.00066123 -0.00235505 Br 18.02285795 8.40797061 14.70873241 0.00263591 -0.00326089 0.00063150 Cs 17.84185171 5.71211157 17.57509809 -0.00179261 -0.00111381 0.00147873 Pb 20.59629867 9.06505162 20.63331810 0.00135002 -0.00015306 -0.00041428 Br 21.36882320 8.66427418 17.78543918 -0.00070691 -0.00126955 -0.00235142 Br 20.45717784 5.78999596 20.65577781 0.00020316 0.00175662 -0.00370326 Br 17.45539020 9.37572069 20.52101457 -0.00027880 -0.00318603 0.00042913 Cs 17.74108321 11.91867043 0.14772482 -0.00242319 -0.00118869 -0.00040373 Pb 20.53757457 14.75754251 3.07911940 0.00188122 0.00036925 0.00154774 Br 20.77692674 15.25587052 0.04753188 0.00060051 -0.00076824 0.00160577 Br 20.29716403 11.83018918 3.26139792 -0.00110085 0.00238229 0.00178178 Br 17.50505406 15.04296430 2.68308350 0.00069833 -0.00062168 -0.00314117 Cs 17.82771860 11.15031491 6.25585106 0.00116561 0.00085125 0.00006543 Pb 20.52372684 14.85322154 9.05234375 -0.00116856 0.00117694 0.00252786 Br 20.18560855 14.86634058 5.90336728 -0.00246636 -0.00421791 0.00111562 Br 20.39090603 11.82429268 8.79527106 -0.00699112 -0.00033685 0.00637787 Br 17.68690706 14.44763315 9.33795027 -0.00149896 0.00008573 0.00038463 Cs 17.94820278 11.31433484 12.23224830 0.00147430 -0.00047415 -0.00260406 Pb 20.80865672 14.66567076 14.79020226 -0.00075223 0.00128799 -0.00152734 Br 20.85154930 13.95935140 11.84901590 -0.00094139 -0.00056473 -0.00292682 Br 20.55520334 11.63488057 15.35059526 0.00249602 0.00011545 0.00220729 Br 17.79671067 14.20826999 14.88877610 -0.00256745 0.00271008 0.00250291 Cs 17.42141255 11.98568425 17.60883698 -0.00052266 -0.00166656 -0.00078250 Pb 20.93364957 15.03798509 20.78573026 0.00224162 0.00225234 -0.00103649 Br 21.12137682 14.62348670 17.96522352 -0.00337719 0.00196885 -0.00026321 Br 20.18013369 11.91154851 20.10801693 -0.00316155 -0.00201175 0.00240298 Br 17.98764739 15.00490734 20.56976858 0.00326394 -0.00129550 -0.00136517 Cs 17.93279165 17.71471621 23.42538460 0.00052356 -0.00039399 0.00036260 Pb 20.88404622 20.92014495 3.09762270 0.00340026 -0.00151283 -0.00021925 Br 21.37312110 21.10399569 23.67748595 0.00063864 0.00010901 -0.00052849 Br 20.79006795 17.82003432 3.17913950 0.00295746 -0.00115680 0.00166081 Br 17.97483470 20.31974222 2.32710470 -0.00295518 0.00257669 0.00286280 Cs 17.74227084 17.96074668 5.64273950 0.00143578 -0.00084479 -0.00260024 Pb 20.79659026 20.98530843 8.83498547 0.00066535 -0.00007957 -0.00254989 Br 20.99540048 20.77717420 5.95439955 -0.00084056 -0.00006382 -0.00028726 Br 20.25398315 17.70243418 8.70562898 0.00060418 -0.00080108 0.00098714 Br 17.53441049 20.96939766 9.63168723 0.00030248 0.00313833 -0.00143856 Cs 17.80791090 17.96395630 11.70121693 -0.00065703 -0.00206688 0.00378208 Pb 20.79527124 20.84605100 14.85512162 -0.00262452 0.00114243 0.00070013 Br 21.06913091 20.85941102 11.89033697 0.00140737 0.00055657 0.00040286 Br 20.55505303 17.35681660 14.03007555 -0.00166706 -0.00170793 0.00143935 Br 17.95471435 21.46041516 14.78687519 -0.00072513 0.00122142 0.00374040 Cs 17.48755549 18.05997722 18.05011374 0.00130408 -0.00079170 0.00320864 Pb 20.89006626 20.74868561 20.91437043 -0.00187649 -0.00093667 0.00245713 Br 20.73642554 20.76491128 17.63861506 0.00277563 -0.00305074 -0.00006604 Br 20.78860459 17.87109155 20.35276719 0.00028303 -0.00015122 -0.00244414 Br 17.89422053 20.67135106 21.14973665 0.00302397 0.00161961 0.00269106 320 Lattice="23.77195271 0.0 0.0 0.0 23.77195271 0.0 0.0 0.0 23.77195271" Properties=species:S:1:pos:R:3:vel:R:3 Time=20139.99999999 energy=-11425.51316071 virial="-11.43714396 -1.42546191 -0.3672934 -1.42546191 -9.44222647 -0.0174298 -0.3672934 -0.0174298 -7.00070204" stress="0.00016247 -0.00010611 -2.734e-05 -0.00010611 0.00019295 -1.3e-06 -2.734e-05 -1.3e-06 0.00043017" pbc="T T T" Cs 23.57193572 0.09381061 0.04976783 -0.00293251 -0.00279817 0.00146347 Pb 2.86389140 3.08559496 3.11924302 -0.00063638 0.00066192 0.00178884 Br 3.26110247 2.93513884 0.13548094 -0.00018488 0.00002981 0.00091359 Br 2.95214633 0.07778279 3.08380348 -0.00123300 -0.00180762 -0.00091377 Br 0.13621140 3.44305281 2.28135887 -0.00331755 -0.00049047 -0.00334468 Cs 0.23785101 0.06512876 6.02431660 0.00374363 0.00227863 -0.00243226 Pb 3.06880617 2.99220606 9.19726844 0.00121855 0.00046079 -0.00238802 Br 2.53276253 2.73374018 6.35715129 0.00131457 -0.00080001 0.00213561 Br 3.16441274 0.01676033 9.43702473 0.00012713 0.00030140 0.00283734 Br 23.63641998 3.10788113 10.45962373 -0.00007690 -0.00165614 0.00288723 Cs 0.16564097 23.57637940 12.21685188 -0.00380054 -0.00116805 0.00008283 Pb 3.05052063 3.24155936 14.88327231 0.00023966 -0.00190414 -0.00105444 Br 4.05040908 3.01110107 12.00809674 -0.00343369 -0.00059335 0.00134669 Br 3.02448458 0.07064794 14.63955763 0.00102289 -0.00128969 -0.00256940 Br 0.19467484 3.31832507 14.68105863 -0.00297223 -0.00207154 -0.00209266 Cs 0.13105102 23.32049463 17.68142943 -0.00128399 -0.00125225 -0.00223745 Pb 3.26175336 3.10666617 20.67012403 0.00111770 0.00086917 0.00107410 Br 2.57068829 3.25793720 17.83450571 0.00088473 0.00163880 -0.00107245 Br 3.02482051 0.10385216 20.42764701 -0.00077103 -0.00014056 0.00352497 Br 0.15900715 2.89281090 20.61181655 -0.00045706 -0.00449048 0.00151755 Cs 23.48067014 6.50274011 23.70055447 -0.00003170 0.00208194 -0.00100523 Pb 2.85274664 8.90348996 3.13741276 0.00039079 -0.00026883 -0.00271382 Br 2.77259298 8.40229070 23.75488160 0.00291823 -0.00458579 0.00214387 Br 3.02368880 6.05544522 3.07191291 -0.00258158 0.00034851 -0.00066910 Br 0.01744913 8.56576959 3.09717572 0.00151913 -0.00086722 -0.00257856 Cs 0.48285616 6.05460329 6.01606418 0.00182861 0.00030268 0.00198132 Pb 3.38940014 9.06618870 8.87153406 0.00027976 0.00002257 -0.00038512 Br 2.85063160 8.84171375 6.18340082 -0.00044335 -0.00166336 0.00176116 Br 2.75120010 5.77700469 8.84127810 -0.00228358 -0.00008675 0.00307902 Br 23.63196138 9.29698652 8.83658865 0.00044358 0.00122112 0.00004075 Cs 23.55064895 5.93718470 12.01271189 -0.00180509 -0.00062746 0.00211983 Pb 2.87961494 8.82777915 14.78134542 0.00005579 0.00152002 -0.00118693 Br 3.19646525 8.97103544 11.89674846 0.00058121 0.00224295 0.00195450 Br 3.76655560 6.15777794 15.18975702 0.00032952 0.00026293 0.00109720 Br 0.10131564 8.43744697 14.33124370 0.00076945 -0.00180395 -0.00356341 Cs 23.62511165 5.74380495 17.62337948 -0.00129462 0.00017078 0.00050872 Pb 2.65031610 8.79576754 20.61744909 -0.00104040 -0.00198635 0.00070637 Br 2.39763417 8.79415036 17.62797409 0.00335418 0.00016034 -0.00086592 Br 2.14165551 5.88631164 20.85013052 0.00216169 0.00058746 0.00215263 Br 23.42087358 9.28374391 21.17731383 -0.00568645 0.00033216 0.00026731 Cs 23.25414462 11.74870889 0.26991032 0.00301531 -0.00037722 0.00042063 Pb 2.63851099 14.95898113 2.92440987 -0.00193031 0.00101494 0.00053137 Br 2.48191728 15.04243236 23.76658365 0.00018968 -0.00191973 0.00119307 Br 2.92348984 11.92611486 2.19797300 -0.00044355 0.00342488 -0.00125173 Br 23.39243291 14.68700641 3.20609127 0.00325582 0.00092861 -0.00137814 Cs 23.56251078 11.76014186 6.56764762 0.00144179 0.00061741 -0.00060899 Pb 3.03979770 15.03879713 9.10990140 0.00050726 -0.00037654 -0.00008343 Br 3.20315758 14.92785488 5.89207423 -0.00273091 -0.00037652 -0.00196589 Br 2.99150593 12.15129187 8.72702787 -0.00002795 0.00216733 0.00015365 Br 0.04274946 15.02727722 8.88224207 0.00039345 -0.00129782 -0.00021499 Cs 0.01703669 11.69192246 11.41495612 -0.00239937 0.00146671 -0.00064680 Pb 3.07897557 14.54358052 15.07687373 0.00016589 -0.00006158 -0.00065857 Br 2.95290680 14.35815387 11.89144685 -0.00295809 -0.00176683 0.00220281 Br 2.48071904 11.73498848 15.47394338 -0.00218717 -0.00162783 0.00106991 Br 0.26623146 14.56025900 14.55265982 0.00115085 -0.00074965 -0.00029570 Cs 23.61245285 11.68611622 18.16624775 0.00246812 -0.00119610 -0.00024172 Pb 3.12835179 14.50412607 20.77073504 0.00061258 -0.00046870 0.00023070 Br 2.93711795 14.92065561 17.85864971 0.00022524 0.00084256 -0.00044421 Br 2.82529216 11.69257183 20.92139535 0.00138769 -0.00128006 -0.00011428 Br 0.16628510 14.62226121 20.84574407 -0.00004938 0.00190989 -0.00241503 Cs 0.05312077 17.83380118 23.59406721 -0.00060087 0.00107232 -0.00131726 Pb 3.01491904 20.77797481 2.82407523 0.00041209 0.00087654 -0.00128607 Br 2.90798841 20.92902723 23.75920523 -0.00079079 0.00351719 0.00193731 Br 3.07284234 17.84880289 2.72166950 -0.00317411 -0.00207485 0.00170440 Br 23.64514546 20.90271820 3.02939990 0.00248636 0.00156258 0.00218165 Cs 23.03540315 17.84393171 6.08989384 -0.00154414 0.00049959 -0.00346827 Pb 2.93522770 20.89036056 8.80146988 0.00124702 -0.00119540 0.00046267 Br 3.01279712 20.69034596 5.87443620 0.00111326 -0.00232360 0.00026182 Br 3.21988429 17.76103862 9.29048888 -0.00298452 -0.00161068 -0.00304775 Br 23.74055835 20.92934532 8.89771433 0.00123802 -0.00320427 0.00012503 Cs 0.62495398 17.24407233 12.06543230 0.00282525 0.00255119 -0.00363029 Pb 3.13079551 21.00222591 14.80267324 0.00033307 -0.00247731 0.00175675 Br 3.07153748 20.78474850 11.64595351 -0.00402948 0.00059177 0.00094663 Br 3.06419106 17.80302970 15.17240576 0.00040852 -0.00487302 0.00208959 Br 0.14615099 20.59779120 14.95607254 -0.00028229 0.00264182 -0.00022998 Cs 0.41024099 17.69715362 17.57026270 0.00017319 0.00054963 0.00193647 Pb 3.03506939 20.79411956 20.44930954 0.00103275 0.00081445 -0.00086716 Br 3.40731216 20.32761629 17.67774528 -0.00425625 -0.00195082 0.00270654 Br 3.07520179 17.75458082 20.30239163 -0.00038926 -0.00105691 -0.00401357 Br 0.13325648 20.56729472 20.59387115 0.00254625 -0.00113081 0.00065966 Cs 5.85261732 0.16448905 22.87689074 -0.00273281 0.00131716 0.00036225 Pb 8.94696437 3.08750381 3.15928908 -0.00239860 0.00215919 -0.00261279 Br 8.90947770 2.64704926 0.06573895 -0.00380641 0.00245347 0.00085411 Br 8.91279754 0.19128242 3.51227827 0.00362339 0.00402986 0.00391130 Br 5.88628899 3.03397618 3.66302858 0.00006106 0.00160964 0.00244951 Cs 5.96451796 23.45174668 5.76336640 -0.00274526 0.00100488 0.00091742 Pb 8.75323683 2.85311762 8.83655242 -0.00065744 0.00164958 -0.00022003 Br 9.74388508 3.45435777 5.99659310 -0.00254368 -0.00010111 -0.00023054 Br 8.40776907 23.55065179 8.89888687 0.00073632 0.00373098 0.00286684 Br 5.99298357 3.21420996 8.32874482 0.00352033 -0.00400258 -0.00256415 Cs 6.04358964 23.76607049 12.06494711 -0.00088885 0.00148089 0.00019106 Pb 9.22071525 2.86890461 14.79174345 0.00145063 0.00008640 0.00295234 Br 8.96708179 2.84775173 11.64854935 -0.00102327 -0.00012462 0.00102476 Br 10.12483560 23.62511291 15.10455188 -0.00107903 0.00209143 -0.00377632 Br 6.17448639 2.42809785 15.06872320 0.00045317 0.00104324 -0.00240328 Cs 6.43100794 23.36363056 17.72432875 -0.00123375 -0.00217152 0.00116997 Pb 9.10686125 3.09033291 20.98638872 -0.00143347 0.00063002 -0.00192454 Br 9.62944402 2.93340995 17.92635459 -0.00239345 -0.00060707 0.00620196 Br 8.97261941 23.75714143 20.79188158 -0.00290194 -0.00387807 0.00325643 Br 6.00034682 3.89953036 20.76604859 -0.00133943 -0.00072821 0.00008813 Cs 5.56378124 6.12818603 23.72110671 -0.00154877 -0.00376841 0.00090840 Pb 8.76799406 8.87800725 2.79760335 -0.00190779 0.00008558 -0.00005815 Br 8.95048486 8.86112720 23.68246115 0.00087789 0.00073783 0.00139852 Br 8.22449512 5.96051041 2.92247582 -0.00080771 0.00331545 0.00089844 Br 5.85122705 9.02328152 2.78576102 -0.00172463 0.00215188 0.00128627 Cs 6.02068673 6.18239175 5.80678121 0.00301310 0.00137550 -0.00060293 Pb 8.77691806 8.82848856 8.70987545 0.00064460 -0.00036305 0.00011208 Br 8.70323374 8.50527844 5.86074546 -0.00079366 -0.00221014 -0.00151578 Br 8.69366390 5.89007030 9.23857084 -0.00160895 0.00151461 0.00214769 Br 5.94463163 8.31828237 8.64549648 0.00138391 0.00403409 0.00084497 Cs 5.94154323 6.09233721 11.93100776 0.00142999 -0.00183705 0.00103575 Pb 8.99562602 8.91033932 14.85127558 -0.00156446 0.00077480 -0.00003397 Br 7.89282029 8.82479749 11.91107271 0.00054512 -0.00353979 0.00115107 Br 9.41664370 5.95196608 15.15752304 0.00112587 0.00171289 0.00189791 Br 6.13747382 8.96916077 15.45017454 0.00226974 -0.00494489 0.00141737 Cs 6.84371900 6.11406048 17.80106704 -0.00222348 0.00085558 -0.00102504 Pb 8.72525481 9.03732753 20.69951756 0.00034618 0.00177175 -0.00005330 Br 9.11537495 9.23343176 17.82320752 -0.00187798 -0.00232464 -0.00188673 Br 9.16798715 6.11249765 20.87477822 -0.00040895 -0.00135291 -0.00234142 Br 5.58954918 8.55017170 20.63639463 -0.00165691 -0.00138687 0.00080696 Cs 5.46540090 11.46521081 23.41233387 -0.00113842 -0.00181014 0.00199869 Pb 8.80110292 14.67136597 3.12570567 0.00054386 0.00130393 0.00190122 Br 9.29483573 14.60738583 0.24618566 -0.00018569 -0.00179344 -0.00466868 Br 8.99555915 11.75853513 3.20446082 0.00054658 0.00453216 0.00358356 Br 5.67874631 14.58870587 2.60772521 0.00047158 -0.00220665 0.00019130 Cs 5.28954767 11.95987826 4.94352694 0.00309682 0.00120086 0.00187648 Pb 8.64334363 14.53229862 8.98577079 0.00175801 0.00052195 0.00083842 Br 8.34586452 13.93975523 5.91324473 0.00313999 -0.00209401 0.00030077 Br 9.68999703 11.93394894 8.81836821 -0.00269195 -0.00253651 0.00011876 Br 5.81507577 14.16687598 9.55450843 -0.00039817 -0.00383058 0.00018867 Cs 5.56328593 11.88674309 11.82786163 0.00187309 -0.00375593 -0.00002264 Pb 9.04841316 15.07066710 14.89008034 0.00411077 -0.00108523 -0.00112425 Br 9.41435238 15.21768556 11.83682463 -0.00485563 0.00094421 0.00120328 Br 9.51026446 11.78639821 15.12081051 0.00060501 -0.00063622 0.00004535 Br 5.80218359 14.64201206 14.17328302 -0.00261179 -0.00022464 -0.00427613 Cs 6.41848139 11.82051813 17.70988559 0.00054873 -0.00297501 -0.00233263 Pb 8.97147842 14.91521140 20.77903436 0.00090390 -0.00010198 0.00039459 Br 9.36491630 14.88579709 17.76435469 0.00042777 -0.00028338 -0.00088023 Br 8.89257969 11.85335601 21.29066553 -0.00123550 0.00170694 -0.00342031 Br 6.10254595 14.91538446 21.27497360 -0.00003735 0.00146308 0.00059503 Cs 5.43248698 18.54372164 23.67467356 -0.00043399 0.00128059 -0.00166544 Pb 8.79746778 20.73899155 3.06171502 0.00005443 0.00036914 0.00022909 Br 8.91954097 20.84932470 0.09513095 0.00040956 0.00234066 -0.00210512 Br 8.37580304 17.81996135 2.82251368 0.00039504 0.00145605 -0.00208639 Br 5.76063031 20.71640470 2.98808812 -0.00155342 0.00243112 0.00172321 Cs 5.64157253 17.96913912 5.52298500 -0.00137383 0.00202629 -0.00029706 Pb 8.62965221 20.49286087 8.89592091 0.00145579 -0.00182243 0.00090397 Br 8.29290686 20.35469397 5.90779674 -0.00330841 0.00117651 0.00216491 Br 8.09013925 17.45935128 8.21045181 0.00086029 0.00005958 -0.00368385 Br 5.72809179 20.59530231 8.60775949 0.00222104 -0.00068463 -0.00225153 Cs 6.30432246 18.26334798 11.40906774 -0.00118603 0.00168909 -0.00019719 Pb 9.27071875 20.82301801 14.67293537 -0.00117472 -0.00058223 0.00167812 Br 9.54427165 20.89985414 11.80905179 0.00177958 -0.00360415 -0.00139310 Br 8.40368943 17.68758744 14.73065489 -0.00079714 0.00339480 0.00245154 Br 6.19514051 21.27195258 14.90682832 0.00227899 0.00253375 0.00013910 Cs 5.75685523 17.36441386 17.86545031 -0.00130718 -0.00013728 -0.00251088 Pb 9.02922525 20.67045940 20.69145786 -0.00104305 -0.00027468 -0.00095688 Br 9.78940322 20.70173434 17.61573322 0.00178692 0.00028285 0.00057717 Br 7.89056980 17.97274607 20.73437316 0.00106637 -0.00258926 0.00108502 Br 6.04332274 20.79653430 20.55782694 -0.00307929 -0.00060683 -0.00075109 Cs 12.15561016 23.72301048 0.32319024 0.00058997 -0.00018988 -0.00171412 Pb 15.03522600 3.36707549 2.82435785 -0.00050319 0.00058141 0.00000047 Br 15.35663962 2.90485882 23.76515849 0.00066690 0.00142409 -0.00343552 Br 15.48305477 23.68506656 2.77212311 0.00367752 0.00063991 0.00343018 Br 12.09707687 3.48242080 2.50110673 0.00199632 0.00189989 -0.00168651 Cs 12.16137051 0.54538277 5.68811407 -0.00114460 -0.00072891 -0.00340645 Pb 14.87484007 3.05892889 8.83887084 0.00125687 0.00111997 -0.00049514 Br 14.67303224 3.22144821 5.84086635 -0.00277215 0.00274766 -0.00137197 Br 14.91772770 0.09394265 9.05883633 0.00190880 -0.00093875 0.00101241 Br 12.13661336 3.00672128 9.59597758 -0.00083774 -0.00053408 -0.00094741 Cs 11.93821700 0.33290018 11.96463182 0.00049003 0.00361514 0.00001491 Pb 14.95960890 2.81585914 14.90357934 0.00249771 -0.00355462 0.00076430 Br 15.09876332 2.98266243 11.97571325 -0.00495068 0.00110729 0.00279245 Br 14.25904613 23.58822515 14.91340914 -0.00014550 -0.00013610 -0.00271226 Br 12.07475557 2.99121600 14.19605715 0.00130957 0.00432609 -0.00108661 Cs 12.05894293 23.29833765 18.40849262 -0.00083830 0.00108274 0.00202918 Pb 15.05746978 3.09145178 20.82013961 -0.00112869 0.00083850 -0.00136396 Br 14.72215842 3.09403563 17.98730272 -0.00623872 0.00150890 -0.00011972 Br 15.17805500 23.54168383 20.80467246 0.00298812 -0.00070034 -0.00430050 Br 11.89659691 2.69847767 21.54167767 -0.00239342 -0.00400381 0.00122215 Cs 11.83519208 6.25524105 23.32919363 0.00129633 -0.00100276 0.00067951 Pb 14.74092526 8.94068140 3.36382243 -0.00015096 -0.00035564 -0.00216283 Br 14.75580803 8.89077833 0.17057928 0.00087190 -0.00426165 -0.00084906 Br 15.32101209 6.04485769 3.52936098 -0.00249220 -0.00206864 -0.00400203 Br 11.70345828 8.51279076 2.62036677 -0.00069376 -0.00278365 -0.00043758 Cs 12.03022384 6.23650097 5.58512397 0.00278217 -0.00126220 0.00034279 Pb 14.53530851 8.89370075 9.03620524 0.00085214 0.00070114 -0.00084605 Br 14.34865193 9.56784372 6.02543223 -0.00056115 0.00028249 -0.00228733 Br 15.27253727 6.14575438 8.33780502 0.00024299 -0.00105270 0.00070448 Br 11.72446365 8.03638646 8.96568447 -0.00136627 -0.00170605 -0.00133710 Cs 11.99705498 5.96643623 11.88213626 0.00127773 0.00105264 0.00121253 Pb 14.93282768 8.85550743 14.78854854 0.00139658 0.00170198 0.00179006 Br 14.73055011 9.12449521 11.98953911 -0.00500793 0.00109167 0.00453043 Br 14.91399772 5.86543759 14.87265955 -0.00341732 -0.00311928 -0.00123109 Br 12.10291639 8.52058993 14.92428940 -0.00042127 0.00185359 0.00288041 Cs 12.60320867 5.69926293 18.29811987 0.00050224 -0.00068950 -0.00009180 Pb 14.60846192 9.17399221 20.92549776 0.00032378 0.00068606 0.00027975 Br 14.76677412 8.66414533 17.77396778 0.00005334 -0.00213669 -0.00290341 Br 15.23650575 5.89520712 20.78090594 -0.00341353 0.00467152 -0.00228647 Br 11.75931330 8.94547270 20.47183267 0.00455341 -0.00251073 0.00153862 Cs 11.30608225 11.86001701 0.58393399 -0.00052477 -0.00013251 -0.00089026 Pb 14.73452485 14.91185646 3.25186409 -0.00028300 -0.00102516 -0.00001977 Br 14.90171679 15.56455896 0.01840305 0.00163214 -0.00268732 -0.00013700 Br 14.46513749 12.13763871 3.12026658 -0.00303855 0.00136520 0.00027720 Br 11.60669881 15.05259456 3.37036528 -0.00038560 -0.00184943 -0.00495322 Cs 11.57924100 12.49609856 6.04760331 0.00151574 0.00014572 0.00072949 Pb 14.63134136 14.99056384 9.21811693 0.00250036 0.00152152 -0.00108666 Br 14.98168743 14.82888979 6.02789006 -0.00229477 0.00109412 -0.00073749 Br 13.74984617 12.03679078 9.00408943 -0.00384533 -0.00138000 -0.00023797 Br 11.73481569 15.43649081 8.36718054 -0.00368209 -0.00251887 0.00263735 Cs 11.88068424 12.12952241 11.97554703 -0.00069970 0.00078469 0.00094386 Pb 14.99080913 14.99067203 15.00910991 -0.00136693 0.00114678 0.00204622 Br 14.68620645 14.81570593 12.19824699 0.00101481 -0.00131670 0.00009775 Br 14.53590738 11.65873084 14.89974097 0.00244239 0.00023120 -0.00052865 Br 11.97011167 15.20376447 15.10069388 -0.00088513 -0.00180385 -0.00182572 Cs 12.25808510 11.52997465 17.44288525 -0.00016167 -0.00212293 -0.00056890 Pb 14.85379710 14.85729033 21.00651368 0.00185892 0.00066216 -0.00152844 Br 14.52892637 14.71982343 17.86588238 0.00118304 0.00243310 0.00340693 Br 14.25659584 12.01271663 21.15719032 0.00153572 0.00199855 0.00368615 Br 11.89074124 15.31408952 21.54686116 0.00000457 0.00275577 0.00168689 Cs 11.54769388 18.01957432 0.57308219 -0.00097835 -0.00081358 0.00370059 Pb 14.85882823 20.95568709 3.00965057 0.00183085 0.00072964 0.00149829 Br 14.65454208 20.73228288 23.57798520 0.00432620 0.00115642 -0.00370824 Br 14.43046490 17.94552797 3.61015658 -0.00202513 -0.00247729 0.00098418 Br 11.78632751 21.36505680 3.49043085 0.00195315 -0.00020080 0.00017166 Cs 11.21123363 18.18035060 5.94155709 0.00057190 0.00038341 -0.00004905 Pb 14.97670320 20.95390529 8.84053999 0.00067911 0.00000145 0.00097591 Br 15.10108068 20.77723134 6.03817858 0.00158987 -0.00533072 0.00289323 Br 14.80120613 17.82020655 8.82498465 0.00248422 0.00131454 0.00109566 Br 11.65549388 21.33464265 8.18041355 -0.00246489 0.00304160 -0.00139262 Cs 12.45981694 18.43744643 11.66835986 -0.00017886 -0.00236450 -0.00194760 Pb 14.99875639 20.54427735 14.83925413 0.00042868 0.00160126 -0.00160582 Br 15.00351708 20.66937336 11.87298042 0.00385812 0.00418143 -0.00095190 Br 15.60632173 17.83831317 14.62245791 -0.00171187 0.00208564 0.00102220 Br 12.16515802 19.67663193 14.84501879 0.00238926 0.00085294 0.00203762 Cs 12.17597699 17.61816808 17.86903105 0.00026239 -0.00067779 0.00162760 Pb 14.89995957 20.66021495 20.66096430 0.00285219 0.00255576 0.00048714 Br 14.21737687 20.65503932 17.69493191 -0.00139594 0.00284463 -0.00244340 Br 14.74837909 17.76504543 19.89428106 0.00323626 -0.00276398 -0.00062876 Br 11.90518974 20.76174643 21.07463762 -0.00284845 -0.00259581 0.00167732 Cs 18.11006482 23.41067119 23.71194436 0.00498134 -0.00014722 -0.00331748 Pb 20.93109086 3.25933628 2.65192863 0.00215800 0.00261792 -0.00165493 Br 20.59181815 3.93809100 23.63261823 -0.00071937 0.00043293 0.00228635 Br 20.88796281 0.14270828 3.24506932 -0.00216297 0.00189727 -0.00088982 Br 17.94129906 3.21380629 3.12092090 -0.00300926 -0.00032086 0.00499705 Cs 18.15295505 0.07882025 6.15927020 -0.00326671 0.00296762 -0.00048222 Pb 21.05114298 3.01406133 8.97072401 0.00157182 -0.00142063 0.00123109 Br 21.55093152 3.04687722 6.15226133 -0.00019870 0.00192301 0.00068679 Br 20.91975531 0.09817689 9.12557717 -0.00213380 0.00023314 0.00345245 Br 17.70702747 2.68341210 8.57331997 0.00037218 0.00231859 -0.00000760 Cs 17.89791814 0.31195320 11.80609891 0.00319653 -0.00225261 0.00014940 Pb 20.68558330 3.03084898 14.63606660 -0.00143862 0.00020496 0.00072481 Br 19.85156162 3.56098637 11.76621307 -0.00020586 0.00139651 0.00080472 Br 21.53391632 23.65966175 14.83907142 0.00324028 -0.00139515 0.00002170 Br 17.91260801 2.76014000 14.99497514 0.00018512 0.00286093 0.00035644 Cs 18.00026139 23.64606009 18.28169419 0.00102557 -0.00235138 -0.00215971 Pb 21.11269657 2.88432580 20.65832323 -0.00085817 0.00095658 -0.00045680 Br 20.88769890 3.16478838 17.69265278 0.00040196 0.00031983 0.00056280 Br 20.95019081 23.70879427 20.44769962 -0.00291302 0.00203796 -0.00342101 Br 17.64791767 2.88707992 20.11274815 -0.00122586 0.00100122 0.00090516 Cs 17.73060595 6.40057092 23.33786889 0.00106278 0.00199680 -0.00152321 Pb 20.80778778 8.90374989 3.07236489 -0.00123706 -0.00079972 0.00047196 Br 20.25183241 8.88865004 0.13785775 -0.00188638 -0.00538828 -0.00020200 Br 20.55154676 5.86850473 3.32097402 -0.00081225 -0.00208654 0.00153897 Br 17.82348292 8.98741894 3.61309747 -0.00208209 -0.00179285 0.00139316 Cs 18.40196979 5.37655391 6.35808990 0.00034763 0.00079008 -0.00162989 Pb 20.94897426 8.77927213 8.85834586 0.00004068 -0.00060914 -0.00064309 Br 20.78971230 9.39474845 5.92129355 -0.00107466 0.00026904 -0.00280014 Br 21.31343135 5.98289254 8.70983806 0.00019270 -0.00152080 -0.00249646 Br 17.39949803 9.12743610 9.09322028 0.00111528 0.00171198 -0.00674970 Cs 17.46356149 6.38765442 11.43877873 0.00079706 0.00115022 -0.00007295 Pb 20.96861081 8.78056716 14.95934525 0.00024873 -0.00183365 0.00089138 Br 20.88446954 8.69677052 11.88669719 -0.00168335 0.00144363 -0.00069631 Br 20.81421094 5.76950994 15.24756992 -0.00165590 0.00100594 -0.00263229 Br 18.04858336 8.37625373 14.71521498 0.00249506 -0.00307818 0.00066372 Cs 17.82386367 5.70103629 17.59004597 -0.00180560 -0.00109747 0.00150858 Pb 20.60919690 9.06333131 20.62899034 0.00121509 -0.00019273 -0.00045318 Br 21.36148970 8.65181281 17.76538116 -0.00075310 -0.00122222 -0.00163869 Br 20.45971380 5.81087154 20.61782435 0.00030545 0.00240122 -0.00387648 Br 17.45508338 9.34351522 20.52611874 0.00021650 -0.00325303 0.00058751 Cs 17.71658046 11.90680987 0.14394111 -0.00247614 -0.00118164 -0.00035455 Pb 20.55553860 14.76191222 3.09446637 0.00170798 0.00050096 0.00151884 Br 20.78330831 15.24776437 0.06288793 0.00067762 -0.00085003 0.00145857 Br 20.28703007 11.85418400 3.27733267 -0.00092460 0.00238786 0.00139876 Br 17.51470135 15.03685261 2.65132541 0.00122965 -0.00060052 -0.00320239 Cs 17.83852196 11.16094452 6.25679220 0.00099449 0.00127900 0.00011672 Pb 20.51199990 14.86378996 9.07702931 -0.00117308 0.00093221 0.00240053 Br 20.16142722 14.82440299 5.91674410 -0.00236865 -0.00416423 0.00155615 Br 20.32276242 11.82005405 8.85910564 -0.00662898 -0.00052519 0.00637850 Br 17.67086960 14.44950750 9.34207668 -0.00171425 0.00029300 0.00044104 Cs 17.96326520 11.31014993 12.20588619 0.00153781 -0.00036430 -0.00266650 Pb 20.80153226 14.67782300 14.77488973 -0.00066935 0.00112973 -0.00152930 Br 20.84254385 13.95438114 11.82245710 -0.00086001 -0.00042623 -0.00235666 Br 20.58051385 11.63632326 15.37236111 0.00256120 0.00017030 0.00214220 Br 17.77321572 14.23599852 14.91337980 -0.00211762 0.00283298 0.00241550 Cs 17.41646250 11.96860081 17.60168247 -0.00046433 -0.00175044 -0.00064720 Pb 20.95570015 15.05929479 20.77530644 0.00216429 0.00200244 -0.00104498 Br 21.08825731 14.64481584 17.95896016 -0.00324490 0.00229008 -0.00098676 Br 20.15052211 11.88931454 20.13403211 -0.00275300 -0.00241987 0.00279108 Br 18.01747786 14.99254147 20.55674810 0.00268961 -0.00117349 -0.00123589 Cs 17.93786029 17.71049821 23.42965278 0.00048803 -0.00045289 0.00049274 Pb 20.91651433 20.90451887 3.09415064 0.00308492 -0.00160380 -0.00047244 Br 21.37872531 21.10420015 23.67350379 0.00048147 -0.00007384 -0.00024775 Br 20.81999662 17.80954308 3.19570213 0.00302182 -0.00092155 0.00164573 Br 17.94277790 20.34667586 2.35653565 -0.00343534 0.00280216 0.00302302 Cs 17.75623885 17.95166179 5.61651968 0.00135618 -0.00097220 -0.00264299 Pb 20.80333301 20.98364926 8.81148221 0.00068424 -0.00025027 -0.00214216 Br 20.98595156 20.77759794 5.95236804 -0.00104873 0.00014976 -0.00013483 Br 20.26005262 17.69871758 8.71576376 0.00060683 0.00006744 0.00103780 Br 17.54681551 21.00148761 9.61570116 0.00218889 0.00326923 -0.00176071 Cs 17.80149101 17.94311389 11.73855250 -0.00062749 -0.00209691 0.00367928 Pb 20.76957948 20.85745299 14.86093804 -0.00250733 0.00113601 0.00046151 Br 21.08280243 20.86517357 11.89167772 0.00132254 0.00059306 -0.00012969 Br 20.53875144 17.34239237 14.04496526 -0.00159560 -0.00115314 0.00153089 Br 17.94644179 21.47275943 14.82444549 -0.00092703 0.00125645 0.00377365 Cs 17.50220536 18.05190012 18.08137617 0.00163148 -0.00082447 0.00303695 Pb 20.87151664 20.73948701 20.93772674 -0.00182245 -0.00089886 0.00219995 Br 20.76386957 20.73441017 17.64303426 0.00271266 -0.00304846 0.00094692 Br 20.79171015 17.86964060 20.32887635 0.00033785 -0.00012014 -0.00233081 Br 17.92519654 20.68729096 21.17590135 0.00315395 0.00156436 0.00254056 320 Lattice="23.77195271 0.0 0.0 0.0 23.77195271 0.0 0.0 0.0 23.77195271" Properties=species:S:1:pos:R:3:vel:R:3 Time=20149.99999999 energy=-11425.15390015 virial="-9.69844326 -0.99835221 -0.06441885 -0.99835221 -9.24874512 0.68980401 -0.06441885 0.68980401 -5.44067875" stress="0.00027306 -7.432e-05 -4.8e-06 -7.432e-05 0.00019663 5.135e-05 -4.8e-06 5.135e-05 0.00052238" pbc="T T T" Cs 23.54384146 0.06645431 0.06462490 -0.00268166 -0.00266724 0.00150706 Pb 2.85887212 3.09117797 3.13663335 -0.00037331 0.00045470 0.00168218 Br 3.25998903 2.93544642 0.14423833 -0.00003749 0.00003424 0.00084541 Br 2.93911646 0.05926127 3.07496979 -0.00137103 -0.00187705 -0.00084926 Br 0.10076331 3.43803727 2.24815244 -0.00372377 -0.00051336 -0.00329092 Cs 0.27505927 0.08730314 5.99967440 0.00369101 0.00215132 -0.00249521 Pb 3.08110070 2.99626304 9.17352445 0.00124311 0.00034713 -0.00234656 Br 2.54523622 2.72636915 6.37590995 0.00118124 -0.00067092 0.00159034 Br 3.16552554 0.01905454 9.46438112 0.00009446 0.00015740 0.00262777 Br 23.63595481 3.08726905 10.48425192 -0.00001366 -0.00245739 0.00203606 Cs 0.12797022 23.56440358 12.21706916 -0.00372762 -0.00122713 -0.00004216 Pb 3.05368820 3.22224260 14.87304274 0.00038712 -0.00195436 -0.00099066 Br 4.01527096 3.00602310 12.02166989 -0.00358937 -0.00041914 0.00135443 Br 3.03388111 0.05917412 14.61485281 0.00085659 -0.00102513 -0.00236728 Br 0.16455504 3.29772788 14.65945802 -0.00303408 -0.00204770 -0.00221946 Cs 0.11821736 23.30838355 17.65929141 -0.00128084 -0.00116922 -0.00218613 Pb 3.27137084 3.11552038 20.68131049 0.00079712 0.00089481 0.00115741 Br 2.58112286 3.27368667 17.82269003 0.00119883 0.00151015 -0.00126699 Br 3.01769107 0.10078840 20.46245024 -0.00065437 -0.00046583 0.00343106 Br 0.15215798 2.84692756 20.62784699 -0.00090965 -0.00467555 0.00168571 Cs 23.48015958 6.52278504 23.69027048 -0.00007315 0.00192488 -0.00104987 Pb 2.85719931 8.90142388 3.10869079 0.00049791 -0.00013785 -0.00302451 Br 2.80187930 8.35866618 0.00494840 0.00293077 -0.00412810 0.00225345 Br 2.99850254 6.05890860 3.06631487 -0.00245209 0.00032546 -0.00044665 Br 0.03061463 8.55751492 3.07140438 0.00108727 -0.00078270 -0.00257374 Cs 0.50044192 6.05696408 6.03579058 0.00168500 0.00017028 0.00196322 Pb 3.38941976 9.06710894 8.86957071 -0.00027432 0.00016249 -0.00000455 Br 2.84589988 8.82532313 6.19502661 -0.00050039 -0.00161482 0.00055268 Br 2.72876927 5.78116379 8.87273409 -0.00219798 0.00091513 0.00321037 Br 23.64042698 9.30717988 8.83792410 0.00123057 0.00081719 0.00022409 Cs 23.53221228 5.93204225 12.03405781 -0.00188375 -0.00041056 0.00214132 Pb 2.88113990 8.84381069 14.77033214 0.00024745 0.00167691 -0.00101119 Br 3.20274463 8.99370273 11.91385818 0.00067746 0.00228968 0.00144554 Br 3.76987434 6.15602902 15.19993839 0.00032746 -0.00060763 0.00093203 Br 0.10890548 8.42011216 14.29665320 0.00075152 -0.00165268 -0.00334191 Cs 23.61250539 5.74531820 17.62879975 -0.00122507 0.00012995 0.00057200 Pb 2.64011853 8.77467443 20.62472645 -0.00099590 -0.00222366 0.00074443 Br 2.43001125 8.79524272 17.62396468 0.00311770 0.00005377 0.00008351 Br 2.16398009 5.89260490 20.87163960 0.00229218 0.00065478 0.00214757 Br 23.36531238 9.28773692 21.18039182 -0.00535825 0.00046931 0.00035768 Cs 23.28476344 11.74555970 0.27426276 0.00310690 -0.00025060 0.00045083 Pb 2.62033962 14.96829682 2.92972256 -0.00169794 0.00084214 0.00053115 Br 2.48410474 15.02348907 0.00483913 0.00024172 -0.00186249 0.00082575 Br 2.91750928 11.95852677 2.18675940 -0.00075151 0.00303089 -0.00098606 Br 23.42765405 14.69605358 3.19282298 0.00375391 0.00088034 -0.00127168 Cs 23.57679870 11.76819709 6.56040372 0.00141577 0.00099279 -0.00083929 Pb 3.04410291 15.03355989 9.10805612 0.00035467 -0.00067799 -0.00027873 Br 3.17567996 14.92427495 5.87245697 -0.00276151 -0.00034058 -0.00193510 Br 2.99031790 12.16512344 8.72760514 -0.00021423 0.00057997 -0.00004278 Br 0.04491534 15.01414406 8.88014223 0.00003945 -0.00132859 -0.00020461 Cs 23.76510303 11.70737521 11.40935876 -0.00237573 0.00162295 -0.00047170 Pb 3.08030745 14.54357971 15.06868180 0.00010013 0.00005843 -0.00097640 Br 2.92149788 14.34079078 11.91406937 -0.00331696 -0.00170512 0.00228794 Br 2.46023840 11.71889469 15.48400268 -0.00190801 -0.00158562 0.00094040 Br 0.27250202 14.55396982 14.55004667 0.00009058 -0.00050249 -0.00022981 Cs 23.63682814 11.67458881 18.16353775 0.00240366 -0.00110785 -0.00030119 Pb 3.13403989 14.50140206 20.77401828 0.00052851 -0.00007518 0.00042427 Br 2.93974416 14.92828344 17.85721333 0.00030073 0.00067535 0.00016157 Br 2.83922046 11.67994942 20.92009621 0.00139462 -0.00124292 -0.00014426 Br 0.16553507 14.64140743 20.82124963 -0.00009084 0.00191698 -0.00248178 Cs 0.04715924 17.84442864 23.58097894 -0.00058771 0.00104973 -0.00130059 Pb 3.01737657 20.78764459 2.81209375 0.00007687 0.00105042 -0.00110428 Br 2.89919882 20.96508822 0.00293942 -0.00096329 0.00369085 0.00118786 Br 3.04030311 17.82834388 2.73853584 -0.00332754 -0.00198215 0.00166251 Br 23.67340182 20.91815080 3.05135558 0.00313988 0.00152622 0.00221084 Cs 23.02070784 17.84797697 6.05543815 -0.00138883 0.00030862 -0.00341566 Pb 2.94686348 20.87835891 8.80638576 0.00108193 -0.00120179 0.00051987 Br 3.02328432 20.66756749 5.87590228 0.00098037 -0.00222725 0.00003806 Br 3.19060834 17.75185989 9.26044456 -0.00286458 -0.00019997 -0.00296037 Br 23.74958837 20.89726772 8.89819946 0.00057556 -0.00321040 -0.00003159 Cs 0.65290853 17.27008913 12.02938012 0.00276387 0.00264910 -0.00357638 Pb 3.13398285 20.97587572 14.81963273 0.00030080 -0.00278405 0.00163196 Br 3.03172154 20.79000282 11.66023429 -0.00392966 0.00045152 0.00190477 Br 3.06873124 17.75332129 15.19104784 0.00049821 -0.00504677 0.00163685 Br 0.14413209 20.62432486 14.95386131 -0.00010814 0.00265757 -0.00021222 Cs 0.41162776 17.70269725 17.58990730 0.00010259 0.00055796 0.00199161 Pb 3.04575081 20.80184576 20.44158873 0.00109719 0.00073104 -0.00066605 Br 3.36424253 20.31064284 17.70399671 -0.00435112 -0.00144558 0.00249960 Br 3.07102619 17.74444075 20.26466583 -0.00044397 -0.00097396 -0.00351874 Br 0.15682873 20.55613844 20.60001816 0.00214587 -0.00109975 0.00057392 Cs 5.82541965 0.17772111 22.88120355 -0.00270482 0.00132747 0.00050093 Pb 8.92280617 3.10898069 3.13304368 -0.00242752 0.00213364 -0.00263043 Br 8.87005932 2.67196202 0.07544476 -0.00407822 0.00252829 0.00108738 Br 8.94894938 0.22957825 3.55118877 0.00360084 0.00362038 0.00386543 Br 5.88806012 3.04981525 3.68737199 0.00027574 0.00155848 0.00241240 Cs 5.93680999 23.46228555 5.77270994 -0.00279167 0.00110268 0.00095307 Pb 8.74769683 2.86972785 8.83377494 -0.00043418 0.00166674 -0.00032824 Br 9.71843737 3.45314554 5.99322611 -0.00254432 -0.00013561 -0.00045059 Br 8.41440066 23.58703938 8.92728639 0.00058973 0.00353228 0.00281139 Br 6.02505038 3.17401730 8.30356573 0.00284580 -0.00402552 -0.00247830 Cs 6.03462317 0.00907208 12.06667418 -0.00090197 0.00151132 0.00015301 Pb 9.23540303 2.86993488 14.82195345 0.00148827 0.00012167 0.00307859 Br 8.95569495 2.84605101 11.66387422 -0.00125583 -0.00021359 0.00204129 Br 10.11133199 23.64586216 15.06617187 -0.00161933 0.00205783 -0.00388613 Br 6.17997925 2.43880068 15.04561865 0.00064536 0.00109745 -0.00221323 Cs 6.41887543 23.34251749 17.73628887 -0.00119284 -0.00204771 0.00122356 Pb 9.09268097 3.09604107 20.96656519 -0.00140198 0.00051040 -0.00203316 Br 9.60494327 2.92769116 17.98720854 -0.00250093 -0.00054007 0.00595141 Br 8.94286570 23.71823690 20.82379452 -0.00304557 -0.00388984 0.00312063 Br 5.98935024 3.89191278 20.76659040 -0.00084756 -0.00078997 0.00001954 Cs 5.54855057 6.09013988 23.73037318 -0.00149344 -0.00384148 0.00094514 Pb 8.74942065 8.87841572 2.79768808 -0.00180006 0.00000688 0.00008018 Br 8.95857729 8.86850223 23.69579627 0.00073852 0.00073826 0.00124295 Br 8.21691097 5.99364217 2.93022120 -0.00071363 0.00330449 0.00064334 Br 5.83246851 9.04504288 2.79840446 -0.00201125 0.00219709 0.00123946 Cs 6.05051869 6.19485897 5.80042874 0.00294954 0.00111768 -0.00066136 Pb 8.78501957 8.82442328 8.71215064 0.00097922 -0.00044901 0.00033942 Br 8.69621067 8.48388769 5.84633882 -0.00060469 -0.00205878 -0.00135381 Br 8.67754218 5.90550400 9.25926519 -0.00161873 0.00156520 0.00199033 Br 5.96004123 8.35793458 8.65348854 0.00167420 0.00389653 0.00074945 Cs 5.95553835 6.07310293 11.94142694 0.00136709 -0.00201287 0.00104990 Pb 8.98005641 8.91790752 14.85023783 -0.00154047 0.00073054 -0.00017957 Br 7.90073056 8.79203366 11.92202134 0.00103630 -0.00300487 0.00103332 Br 9.42648184 5.96650707 15.17587855 0.00083992 0.00119003 0.00177661 Br 6.15902080 8.92044463 15.46409567 0.00201841 -0.00478954 0.00136488 Cs 6.82117824 6.12227354 17.79068940 -0.00228466 0.00078416 -0.00104996 Pb 8.72890043 9.05453845 20.69914797 0.00038517 0.00166456 -0.00002153 Br 9.09630239 9.20984499 17.80457279 -0.00192846 -0.00238798 -0.00180300 Br 9.16423266 6.09736287 20.85098710 -0.00034039 -0.00165758 -0.00241247 Br 5.57462829 8.53699604 20.64441718 -0.00132517 -0.00124592 0.00079848 Cs 5.45458835 11.44734889 23.43278553 -0.00102302 -0.00175874 0.00209036 Pb 8.80526963 14.68524991 3.14383030 0.00028346 0.00146942 0.00171447 Br 9.29109749 14.59096583 0.19769633 -0.00057047 -0.00148632 -0.00499651 Br 9.00075836 11.80278606 3.23972679 0.00049824 0.00427758 0.00345230 Br 5.68334284 14.56733970 2.60967168 0.00045911 -0.00206089 0.00019843 Cs 5.32105999 11.97233158 4.96312473 0.00320039 0.00128945 0.00204109 Pb 8.66160552 14.53913195 8.99375774 0.00189026 0.00083758 0.00075447 Br 8.37559564 13.92058676 5.91985009 0.00280403 -0.00172514 0.00102187 Br 9.66213844 11.90442883 8.82197270 -0.00288093 -0.00334292 0.00059933 Br 5.81535647 14.13158342 9.55398298 0.00045689 -0.00322065 -0.00029337 Cs 5.58218130 11.84749876 11.82945779 0.00190438 -0.00408598 0.00033995 Pb 9.08958364 15.05785935 14.87763210 0.00410512 -0.00145340 -0.00135887 Br 9.36555001 15.22642476 11.84772715 -0.00489560 0.00080696 0.00097437 Br 9.51571852 11.78183443 15.11992665 0.00048492 -0.00025447 -0.00022166 Br 5.78090571 14.63959730 14.13179487 -0.00163179 -0.00025634 -0.00402144 Cs 6.42399586 11.79073620 17.68624403 0.00055181 -0.00297765 -0.00239401 Pb 8.98101380 14.91447860 20.78422293 0.00100192 -0.00004007 0.00064647 Br 9.36801144 14.88285424 17.75928693 0.00019508 -0.00030700 -0.00014597 Br 8.87973634 11.87347277 21.25534699 -0.00133098 0.00228882 -0.00364257 Br 6.10056249 14.92831316 21.28041372 -0.00036462 0.00111859 0.00049091 Cs 5.42936852 18.55523481 23.65758340 -0.00019232 0.00102201 -0.00175098 Pb 8.79820525 20.74313136 3.06357704 0.00009115 0.00045978 0.00014496 Br 8.92344552 20.87265390 0.07243545 0.00036769 0.00232093 -0.00240574 Br 8.38080871 17.83531872 2.80210191 0.00060330 0.00162799 -0.00199251 Br 5.75004156 20.74168456 3.00491943 -0.00053655 0.00262108 0.00163984 Cs 5.62740013 17.98856794 5.52005430 -0.00146148 0.00185676 -0.00028968 Pb 8.64588936 20.47594955 8.90371466 0.00179292 -0.00155455 0.00065420 Br 8.26050422 20.36802076 5.93023061 -0.00315619 0.00149041 0.00228311 Br 8.09931015 17.46101590 8.17525441 0.00097497 0.00026564 -0.00334699 Br 5.75391450 20.58893951 8.58587338 0.00291188 -0.00058614 -0.00211529 Cs 6.29209713 18.27921344 11.40799660 -0.00125734 0.00148306 -0.00001788 Pb 9.25949516 20.81703136 14.69106646 -0.00106616 -0.00061635 0.00193821 Br 9.56143070 20.86334298 11.79377024 0.00165071 -0.00370075 -0.00163291 Br 8.39502317 17.72683330 14.75550636 -0.00092051 0.00438682 0.00251928 Br 6.21894842 21.29662594 14.90670095 0.00247069 0.00239533 -0.00016280 Cs 5.74386413 17.36336636 17.84000886 -0.00128589 -0.00007445 -0.00257346 Pb 9.01868594 20.66803644 20.68202962 -0.00106274 -0.00021343 -0.00092590 Br 9.80460905 20.70383782 17.62406683 0.00125207 0.00014108 0.00107686 Br 7.90504676 17.94353608 20.74551239 0.00181721 -0.00322485 0.00113733 Br 6.01071032 20.79330237 20.55047406 -0.00341225 -0.00005324 -0.00071849 Cs 12.16138793 23.72134157 0.30585318 0.00056468 -0.00014522 -0.00175119 Pb 15.02996780 3.37143992 2.82502325 -0.00054562 0.00027965 0.00012546 Br 15.36452760 2.91932129 23.73061111 0.00091001 0.00146817 -0.00345877 Br 15.51938957 23.69297520 2.80688558 0.00358432 0.00092217 0.00352053 Br 12.11457370 3.50122397 2.48462794 0.00148505 0.00186068 -0.00161205 Cs 12.14973545 0.53780301 5.65460265 -0.00118208 -0.00078194 -0.00329258 Pb 14.88678353 3.07039080 8.83410436 0.00112289 0.00116826 -0.00045688 Br 14.64511883 3.24738432 5.82819678 -0.00280904 0.00243659 -0.00115003 Br 14.93679516 0.08503118 9.06835196 0.00190503 -0.00083119 0.00088779 Br 12.12506854 3.00197962 9.58519819 -0.00144722 -0.00040871 -0.00121441 Cs 11.94358226 0.36827611 11.96407705 0.00058081 0.00345950 -0.00012801 Pb 14.98362266 2.78024538 14.91087921 0.00229364 -0.00355957 0.00069290 Br 15.05102213 2.99388632 12.00243986 -0.00458432 0.00113240 0.00253192 Br 14.25908264 23.58747891 14.88623250 0.00014852 -0.00001372 -0.00271946 Br 12.08647398 3.03618827 14.18637448 0.00102146 0.00465584 -0.00083782 Cs 12.05041506 23.31078797 18.42838125 -0.00086853 0.00140535 0.00194971 Pb 15.04453333 3.09874360 20.80729227 -0.00145712 0.00063220 -0.00120324 Br 14.65955308 3.10714347 17.98231989 -0.00626342 0.00109402 -0.00086974 Br 15.20764759 23.53580734 20.76133747 0.00292824 -0.00046299 -0.00436322 Br 11.87340674 2.65938559 21.55297583 -0.00223911 -0.00381294 0.00103370 Cs 11.84837590 6.24493773 23.33660912 0.00133945 -0.00105927 0.00080206 Pb 14.73994493 8.93748397 3.34055882 -0.00004819 -0.00028483 -0.00248717 Br 14.76493200 8.84823685 0.16046525 0.00095155 -0.00424154 -0.00117185 Br 15.29586662 6.02811332 3.48915732 -0.00253258 -0.00124275 -0.00403216 Br 11.69859568 8.48493966 2.61682198 -0.00028668 -0.00278206 -0.00027076 Cs 12.05818564 6.22363409 5.58885482 0.00280678 -0.00131273 0.00040339 Pb 14.54340911 8.90161075 9.02702240 0.00076276 0.00087922 -0.00098565 Br 14.34251940 9.57023929 6.00555540 -0.00066254 0.00019183 -0.00167716 Br 15.27455255 6.13455903 8.34579157 0.00016032 -0.00117427 0.00089394 Br 11.70992467 8.01975655 8.95193510 -0.00151193 -0.00161475 -0.00141286 Cs 12.00970098 5.97647534 11.89487022 0.00125127 0.00095492 0.00133403 Pb 14.94694795 8.87119365 14.80791387 0.00142319 0.00142272 0.00208107 Br 14.68018353 9.13509627 12.03060322 -0.00506042 0.00102887 0.00362717 Br 14.88044393 5.83205085 14.86015179 -0.00328522 -0.00353302 -0.00126649 Br 12.09724898 8.54005451 14.95253497 -0.00069252 0.00204334 0.00276838 Cs 12.60641904 5.69356895 18.29624907 0.00012993 -0.00043853 -0.00028563 Pb 14.61224707 9.17999498 20.92818512 0.00044171 0.00051618 0.00025926 Br 14.76769297 8.64400417 17.74775915 0.00013298 -0.00188871 -0.00231453 Br 15.20150790 5.94474140 20.75866367 -0.00358077 0.00519262 -0.00215537 Br 11.80245807 8.92002194 20.48781922 0.00402897 -0.00257754 0.00165118 Cs 11.30230344 11.85827774 0.57432956 -0.00023318 -0.00021429 -0.00102670 Pb 14.73121890 14.90302617 3.25118441 -0.00037161 -0.00073593 -0.00012433 Br 14.91844872 15.53754659 0.01644894 0.00171508 -0.00271059 -0.00024803 Br 14.43464019 12.14602495 3.12188514 -0.00306112 0.00029467 0.00004528 Br 11.60400286 15.03472058 3.32122783 -0.00014021 -0.00172292 -0.00487027 Cs 11.59606411 12.49750784 6.05422830 0.00184587 0.00013396 0.00059686 Pb 14.65598999 15.00455553 9.20587489 0.00242570 0.00127412 -0.00135787 Br 14.95943507 14.83950316 6.02332724 -0.00215441 0.00102734 -0.00015879 Br 13.71331887 12.02179626 9.00205788 -0.00344897 -0.00161335 -0.00016583 Br 11.69708621 15.41098147 8.39483965 -0.00385322 -0.00257361 0.00289273 Cs 11.87355914 12.13748119 11.98560044 -0.00072467 0.00080495 0.00106530 Pb 14.97653990 15.00133554 15.02951161 -0.00148221 0.00098657 0.00202912 Br 14.69562995 14.80280925 12.19603120 0.00087226 -0.00125815 -0.00052883 Br 14.56166580 11.66603742 14.89392574 0.00270362 0.00123661 -0.00062466 Br 11.96056791 15.18606028 15.08120257 -0.00101181 -0.00173292 -0.00206792 Cs 12.25573135 11.50862846 17.43803618 -0.00030661 -0.00214699 -0.00040205 Pb 14.87186760 14.86485556 20.99142864 0.00175494 0.00084948 -0.00148176 Br 14.54138751 14.74265508 17.90219609 0.00130359 0.00212274 0.00381972 Br 14.27218685 12.03234932 21.19408200 0.00157880 0.00191355 0.00369219 Br 11.89122725 15.34164333 21.56133058 0.00010087 0.00275396 0.00119983 Cs 11.53829216 18.01084287 0.60991621 -0.00090190 -0.00093361 0.00366166 Pb 14.87713805 20.96169764 3.02448872 0.00183265 0.00047579 0.00147180 Br 14.69797009 20.74333013 23.54380836 0.00435759 0.00105650 -0.00311920 Br 14.41009865 17.92230970 3.61898676 -0.00204654 -0.00215282 0.00077954 Br 11.80722770 21.36177464 3.49077581 0.00221056 -0.00045840 -0.00010454 Cs 11.21793637 18.18394648 5.94110369 0.00076609 0.00033714 -0.00004041 Pb 14.98029669 20.95385054 8.85073767 0.00005108 -0.00000779 0.00106759 Br 15.11905880 20.72520464 6.06236087 0.00200389 -0.00507425 0.00192546 Br 14.82707067 17.83527108 8.83665010 0.00268428 0.00167770 0.00123951 Br 11.62963593 21.36296259 8.16751707 -0.00270048 0.00261580 -0.00118961 Cs 12.45701019 18.41226866 11.64821308 -0.00037428 -0.00265786 -0.00207179 Pb 15.00219066 20.56290359 14.82311925 0.00025475 0.00212333 -0.00161721 Br 15.04124997 20.71181528 11.86598106 0.00367562 0.00428798 -0.00044557 Br 15.59070955 17.85545924 14.63350625 -0.00140966 0.00132597 0.00118713 Br 12.18702060 19.68619108 14.86432262 0.00197160 0.00104942 0.00180449 Cs 12.17676272 17.61139768 17.88439360 -0.00010264 -0.00067268 0.00144878 Pb 14.92768945 20.68601619 20.66635628 0.00268512 0.00259225 0.00058353 Br 14.20600109 20.68344648 17.67178428 -0.00087285 0.00283022 -0.00216554 Br 14.78194289 17.73678950 19.89295746 0.00346391 -0.00284712 0.00036465 Br 11.87851913 20.73529534 21.09165203 -0.00246525 -0.00268991 0.00172010 Cs 18.15908668 23.40947447 23.67922913 0.00482104 -0.00008663 -0.00321912 Pb 20.95230692 3.28409183 2.63513594 0.00207833 0.00231496 -0.00169540 Br 20.58504842 3.94060270 23.65664821 -0.00064059 0.00008434 0.00248017 Br 20.86630079 0.16010403 3.23583044 -0.00216422 0.00155623 -0.00096101 Br 17.91325391 3.20959061 3.17143624 -0.00258624 -0.00052064 0.00510378 Cs 18.11992839 0.10857296 6.15379501 -0.00333748 0.00298283 -0.00061453 Pb 21.06695349 3.00034958 8.98435954 0.00158960 -0.00131914 0.00149272 Br 21.54911056 3.06563189 6.15765346 -0.00016051 0.00182173 0.00039895 Br 20.89809326 0.09855851 9.15945006 -0.00219671 -0.00016359 0.00331926 Br 17.71603262 2.70646112 8.57342655 0.00142104 0.00229107 0.00003159 Cs 17.92947346 0.28942982 11.80769149 0.00311264 -0.00224989 0.00016758 Pb 20.67236644 3.03231829 14.64331351 -0.00119442 0.00009099 0.00072132 Br 19.85035903 3.57454932 11.77425893 -0.00003359 0.00131372 0.00080516 Br 21.56467145 23.64602683 14.83983109 0.00289407 -0.00132394 0.00013622 Br 17.91388805 2.78779416 14.99968205 0.00006557 0.00266673 0.00058935 Cs 18.01030710 23.62247760 18.25920097 0.00098323 -0.00236113 -0.00233829 Pb 21.10336872 2.89527065 20.65329163 -0.00100346 0.00122987 -0.00054851 Br 20.89205367 3.16594606 17.69930725 0.00046625 -0.00008423 0.00076270 Br 20.92130836 23.72779121 20.41416797 -0.00286259 0.00174810 -0.00327644 Br 17.64233165 2.89672191 20.12240523 0.00009547 0.00092427 0.00101809 Cs 17.74110810 6.41989867 23.32285444 0.00102986 0.00186281 -0.00148317 Pb 20.79396551 8.89589081 3.07709854 -0.00152466 -0.00077121 0.00046777 Br 20.23403209 8.83580835 0.13390174 -0.00166545 -0.00516872 -0.00057956 Br 20.54404904 5.85316528 3.33773402 -0.00069087 -0.00093658 0.00182015 Br 17.80104015 8.96836893 3.62474332 -0.00239412 -0.00201236 0.00093680 Cs 18.40473593 5.38502414 6.34078911 0.00020563 0.00090202 -0.00182810 Pb 20.94727773 8.77473879 8.85252975 -0.00037342 -0.00029911 -0.00051932 Br 20.78129604 9.39502527 5.89556674 -0.00060407 -0.00020878 -0.00232059 Br 21.31580738 5.96631323 8.68425878 0.00028124 -0.00177148 -0.00261341 Br 17.41364205 9.14454456 9.02629615 0.00169100 0.00169279 -0.00661668 Cs 17.47157430 6.39859739 11.43873422 0.00080429 0.00104017 0.00006323 Pb 20.97042282 8.76226875 14.96720476 0.00011666 -0.00181689 0.00067456 Br 20.86744929 8.71144073 11.87838768 -0.00171937 0.00148669 -0.00097251 Br 20.79783584 5.78081125 15.22004011 -0.00161443 0.00123178 -0.00286601 Br 18.07246486 8.34648819 14.72198240 0.00226516 -0.00287080 0.00068857 Cs 17.80573188 5.69023178 17.60523085 -0.00182095 -0.00106006 0.00152666 Pb 20.62028136 9.06114642 20.62422091 0.00098365 -0.00024774 -0.00050226 Br 21.35388913 8.63983806 17.75299041 -0.00076068 -0.00117233 -0.00082820 Br 20.46331626 5.83764435 20.57846187 0.00041661 0.00293200 -0.00398513 Br 17.45965360 9.31069209 20.53268115 0.00069163 -0.00331034 0.00072060 Cs 17.69158262 11.89506923 0.14060067 -0.00252234 -0.00116502 -0.00031539 Pb 20.57168652 14.76748648 3.10944677 0.00152012 0.00060986 0.00147495 Br 20.79051503 15.23892475 0.07659038 0.00076562 -0.00091524 0.00127719 Br 20.27868802 11.87743688 3.28926709 -0.00074344 0.00223940 0.00098368 Br 17.52957739 15.03096058 2.61920427 0.00174013 -0.00057729 -0.00321262 Cs 17.84760839 11.17596832 6.25804879 0.00082339 0.00172897 0.00012665 Pb 20.50032349 14.87181757 9.10018017 -0.00115995 0.00067291 0.00222059 Br 20.13826091 14.78315458 5.93439198 -0.00226322 -0.00408069 0.00196700 Br 20.25847225 11.81354923 8.92264467 -0.00622187 -0.00078557 0.00631923 Br 17.65254743 14.45355649 9.34677366 -0.00195184 0.00051938 0.00049823 Cs 17.97894598 11.30702245 12.17895422 0.00159733 -0.00026252 -0.00271810 Pb 20.79533150 14.68803415 14.75973568 -0.00056781 0.00090175 -0.00149518 Br 20.83434596 13.95086987 11.80239464 -0.00077963 -0.00027463 -0.00163192 Br 20.60634028 11.63822442 15.39337213 0.00259999 0.00020643 0.00205677 Br 17.75455908 14.26488548 14.93705137 -0.00160691 0.00294262 0.00231766 Cs 17.41218631 11.95067659 17.59591508 -0.00038773 -0.00183393 -0.00050524 Pb 20.97684836 15.07790472 20.76487512 0.00206061 0.00171347 -0.00104001 Br 21.05650657 14.66915981 17.94560608 -0.00310410 0.00257242 -0.00167479 Br 20.12519605 11.86351944 20.16365329 -0.00230760 -0.00271637 0.00312323 Br 18.04124878 14.98150939 20.54510783 0.00205752 -0.00102976 -0.00108933 Cs 17.94251215 17.70559645 23.43526355 0.00044036 -0.00053053 0.00063021 Pb 20.94563103 20.88823134 3.08824156 0.00273515 -0.00164557 -0.00070515 Br 21.38273818 21.10240500 23.67292815 0.00032052 -0.00029118 0.00015267 Br 20.85036887 17.80196316 3.21191916 0.00304506 -0.00057758 0.00158968 Br 17.90655715 20.37562152 2.38754839 -0.00378530 0.00297790 0.00317814 Cs 17.76936649 17.94130821 5.58990024 0.00126809 -0.00109795 -0.00267956 Pb 20.81027208 20.98033997 8.79229716 0.00070233 -0.00040981 -0.00168756 Br 20.97443614 20.78018093 5.95132781 -0.00125379 0.00036682 -0.00009540 Br 20.26607719 17.70386053 8.72635094 0.00059662 0.00095911 0.00107824 Br 17.57797895 21.03458463 9.59636830 0.00401237 0.00334000 -0.00211394 Cs 17.79535138 17.92210092 11.77469780 -0.00060089 -0.00210182 0.00354474 Pb 20.74524854 20.86872525 14.86435653 -0.00235261 0.00111565 0.00022421 Br 21.09549935 20.87121500 11.88793715 0.00121271 0.00061224 -0.00060392 Br 20.52310041 17.33420944 14.06055801 -0.00153635 -0.00046121 0.00158168 Br 17.93622992 21.48571198 14.86235449 -0.00111185 0.00134223 0.00380874 Cs 17.52028572 18.04347262 18.11072251 0.00198936 -0.00086185 0.00282591 Pb 20.85383634 20.73078131 20.95809652 -0.00170191 -0.00083888 0.00186035 Br 20.79066338 20.70397383 17.65733063 0.00264487 -0.00303642 0.00189285 Br 20.79535927 17.86901097 20.30622230 0.00039185 0.00000821 -0.00219592 Br 17.95690258 20.70255571 21.20052639 0.00316366 0.00148412 0.00238355 320 Lattice="23.77195271 0.0 0.0 0.0 23.77195271 0.0 0.0 0.0 23.77195271" Properties=species:S:1:pos:R:3:vel:R:3 Time=20159.99999999 energy=-11424.90086746 virial="-8.70117232 -0.25697196 0.24335753 -0.25697196 -9.75297983 1.30733658 0.24335753 1.30733658 -4.48685961" stress="0.00033176 -1.913e-05 1.812e-05 -1.913e-05 0.00015528 9.732e-05 1.812e-05 9.732e-05 0.00057506" pbc="T T T" Cs 23.51838949 0.04057600 0.07989016 -0.00240450 -0.00250299 0.00154492 Pb 2.85631887 3.09470578 3.15275985 -0.00014229 0.00025251 0.00153722 Br 3.26035021 2.93586837 0.15252531 0.00010917 0.00005216 0.00081829 Br 2.92476889 0.04061906 3.06688696 -0.00149601 -0.00183233 -0.00076401 Br 0.06272618 3.43277623 2.21563196 -0.00383074 -0.00053914 -0.00320963 Cs 0.31152722 0.10805774 5.97443315 0.00359479 0.00199450 -0.00255184 Pb 3.09371787 2.99911754 9.15061200 0.00128259 0.00022398 -0.00222211 Br 2.55641196 2.72038994 6.38846090 0.00105539 -0.00052116 0.00089518 Br 3.16628667 0.01994836 9.48946204 0.00005718 0.00002545 0.00238246 Br 23.63621258 3.05897307 10.50035518 0.00006955 -0.00318636 0.00118790 Cs 0.09120693 23.55183561 12.21595230 -0.00361886 -0.00128658 -0.00018418 Pb 3.05814309 3.20255143 14.86347579 0.00049832 -0.00198054 -0.00092179 Br 3.97871538 3.00276971 12.03489766 -0.00371667 -0.00022935 0.00127508 Br 3.04162115 0.04973088 14.59230127 0.00069173 -0.00088546 -0.00213817 Br 0.13429573 3.27737846 14.63682536 -0.00300436 -0.00202162 -0.00229908 Cs 0.10547375 23.29712443 17.63779031 -0.00126571 -0.00108189 -0.00210954 Pb 3.27754958 3.12442916 20.69315871 0.00043245 0.00088019 0.00120640 Br 2.59458469 3.28812471 17.80965165 0.00148871 0.00137681 -0.00131470 Br 3.01173779 0.09468919 20.49618029 -0.00053632 -0.00074482 0.00331048 Br 0.14092701 2.79950578 20.64547354 -0.00132820 -0.00479832 0.00183661 Cs 23.47915504 6.54120320 23.67959394 -0.00013038 0.00175740 -0.00108340 Pb 2.86264595 8.90084910 3.07704718 0.00058709 0.00002823 -0.00329733 Br 2.83105074 8.31992143 0.02784588 0.00289566 -0.00361214 0.00231689 Br 2.97471494 6.06156443 3.06305160 -0.00230205 0.00018491 -0.00020252 Br 0.03871994 8.55014339 3.04573665 0.00051211 -0.00068976 -0.00255812 Cs 0.51648781 6.05802884 6.05531523 0.00152073 0.00004394 0.00194107 Pb 3.38397682 9.06942101 8.87142936 -0.00080961 0.00029722 0.00037189 Br 2.84070649 8.80943928 6.19441077 -0.00053246 -0.00155957 -0.00066927 Br 2.70732738 5.79511865 8.90545986 -0.00208596 0.00186008 0.00333384 Br 23.65610761 9.31333573 8.84102008 0.00187764 0.00041503 0.00039222 Cs 23.51294770 5.92874977 12.05535564 -0.00197037 -0.00026118 0.00210761 Pb 2.88452446 8.86111573 14.76121707 0.00042720 0.00177295 -0.00080675 Br 3.21005472 9.01681196 11.92531519 0.00078582 0.00233116 0.00083279 Br 3.77296563 6.14581861 15.20826136 0.00028326 -0.00142029 0.00072563 Br 0.11642534 8.40461438 14.26466168 0.00075743 -0.00143457 -0.00304291 Cs 23.60063718 5.74636789 17.63475640 -0.00114741 0.00007820 0.00061610 Pb 2.63046396 8.75148971 20.63225427 -0.00093147 -0.00240177 0.00075724 Br 2.45994099 8.79514496 17.62989849 0.00286688 -0.00007725 0.00110979 Br 2.18728843 5.89904825 20.89305842 0.00235898 0.00061341 0.00213557 Br 23.31508491 9.29319827 21.18467281 -0.00461476 0.00062702 0.00051000 Cs 23.31625407 11.74373043 0.27894206 0.00318979 -0.00011387 0.00048559 Pb 2.60467998 14.97570720 2.93501481 -0.00142670 0.00063410 0.00052540 Br 2.48663757 15.00530896 0.01075280 0.00025924 -0.00176702 0.00033980 Br 2.90848728 11.98625282 2.17833483 -0.00105107 0.00249177 -0.00069520 Br 23.46675705 14.70460331 3.18073291 0.00402402 0.00082909 -0.00114244 Cs 23.59083050 11.77995663 6.55087929 0.00139106 0.00135578 -0.00106434 Pb 3.04692655 15.02515819 9.10445732 0.00021270 -0.00100346 -0.00043463 Br 3.14798681 14.92101752 5.85383392 -0.00277413 -0.00031241 -0.00176478 Br 2.98716433 12.16273245 8.72612018 -0.00041762 -0.00105514 -0.00025493 Br 0.04356782 15.00070582 8.87815945 -0.00030574 -0.00135924 -0.00019137 Cs 23.74151401 11.72435463 11.40553519 -0.00234020 0.00177104 -0.00029268 Pb 3.08096963 14.54469448 15.05743305 0.00003184 0.00016187 -0.00126794 Br 2.88671228 14.32405972 11.93656239 -0.00363204 -0.00164086 0.00217869 Br 2.44256873 11.70337142 15.49271544 -0.00162585 -0.00151452 0.00080007 Br 0.26791088 14.55029823 14.54803242 -0.00100970 -0.00022833 -0.00017339 Cs 23.66046768 11.66398536 18.16021353 0.00232152 -0.00101177 -0.00036375 Pb 3.13899437 14.50262231 20.77919102 0.00046633 0.00031826 0.00060890 Br 2.94314714 14.93400460 17.86184867 0.00038064 0.00046037 0.00075770 Br 2.85312732 11.66775566 20.91853215 0.00138422 -0.00119299 -0.00016756 Br 0.16462217 14.66055940 20.79614696 -0.00008578 0.00191156 -0.00253689 Cs 0.04143686 17.85473461 23.56804904 -0.00055330 0.00100861 -0.00128586 Pb 3.01645967 20.79884281 2.80208246 -0.00025718 0.00118187 -0.00089450 Br 2.88880540 21.00275814 0.01083303 -0.00111077 0.00383840 0.00038538 Br 3.00640494 17.80982911 2.75480557 -0.00344676 -0.00168739 0.00158606 Br 23.70735421 20.93328028 3.07364570 0.00361575 0.00150119 0.00224869 Cs 23.00774613 17.85010240 6.02173402 -0.00119728 0.00011714 -0.00331656 Pb 2.95691322 20.86637778 8.81186515 0.00093085 -0.00119204 0.00057637 Br 3.03233507 20.64590011 5.87535601 0.00082646 -0.00210085 -0.00013792 Br 3.16267804 17.75721265 9.23126512 -0.00271852 0.00126297 -0.00287770 Br 23.75233707 20.86514567 8.89702166 -0.00000623 -0.00321396 -0.00020661 Cs 0.68019001 17.29700395 11.99397505 0.00268998 0.00273175 -0.00350114 Pb 3.13674683 20.94673092 14.83524856 0.00024896 -0.00303462 0.00148777 Br 2.99301750 20.79364603 11.68386342 -0.00380762 0.00027097 0.00280692 Br 3.07411741 17.70252407 15.20510911 0.00057645 -0.00509050 0.00117389 Br 0.14422762 20.65078995 14.95182857 0.00013834 0.00262713 -0.00019432 Cs 0.41225961 17.70828916 17.61007637 0.00002195 0.00055911 0.00204111 Pb 3.05686600 20.80875017 20.43621014 0.00111697 0.00065051 -0.00039815 Br 3.32041475 20.29864745 17.72688044 -0.00440823 -0.00095752 0.00203447 Br 3.06636968 17.73504496 20.23225950 -0.00048434 -0.00090790 -0.00295049 Br 0.17578468 20.54530890 20.60543245 0.00162770 -0.00106586 0.00051327 Cs 5.79855986 0.19100267 22.88692276 -0.00266504 0.00132697 0.00064369 Pb 8.89851699 3.13012083 3.10679146 -0.00242524 0.00209118 -0.00261438 Br 8.82789915 2.69759143 0.08747629 -0.00435433 0.00259595 0.00131738 Br 8.98468568 0.26355954 3.58946560 0.00353923 0.00317142 0.00378279 Br 5.89145104 3.06514546 3.71114434 0.00038388 0.00150723 0.00233525 Cs 5.90877420 23.47379121 5.78246029 -0.00281092 0.00119774 0.00099859 Pb 8.74485724 2.88635409 8.83015292 -0.00011950 0.00165414 -0.00038671 Br 9.69302378 3.45175216 5.98748845 -0.00253733 -0.00013785 -0.00069869 Br 8.41957221 23.62108242 8.95508260 0.00044570 0.00326819 0.00274619 Br 6.04904416 3.13388038 8.27904504 0.00191365 -0.00399382 -0.00243258 Cs 6.02559948 0.02436859 12.06798641 -0.00090020 0.00154913 0.00010865 Pb 9.25049468 2.87137684 14.85309730 0.00153014 0.00016869 0.00313890 Br 8.94193672 2.84352259 11.68928363 -0.00149684 -0.00028963 0.00303062 Br 10.09252425 23.66626274 15.02709153 -0.00213688 0.00202202 -0.00391666 Br 6.18737870 2.45005362 15.02454072 0.00083300 0.00115368 -0.00199830 Cs 6.40714619 23.32273745 17.74882109 -0.00115342 -0.00190538 0.00128349 Pb 9.07882002 3.10052652 20.94589052 -0.00137101 0.00038641 -0.00209161 Br 9.57955512 2.92253511 18.04499075 -0.00256908 -0.00049535 0.00558149 Br 8.91177691 23.67959541 20.85418308 -0.00316837 -0.00382555 0.00295120 Br 5.98356783 3.88381454 20.76642612 -0.00030397 -0.00082653 -0.00005307 Cs 5.53399103 6.05134401 23.74001410 -0.00141432 -0.00391827 0.00098328 Pb 8.73211747 8.87834039 2.79930030 -0.00165457 -0.00001250 0.00024665 Br 8.96522208 8.87591469 23.70686261 0.00058909 0.00074543 0.00094931 Br 8.21013698 6.02643176 2.93520013 -0.00064548 0.00324243 0.00034513 Br 5.81136793 9.06716317 2.81049789 -0.00218728 0.00222411 0.00117686 Cs 6.07959390 6.20474313 5.79367937 0.00286065 0.00085917 -0.00068184 Pb 8.79655253 8.81953761 8.71659288 0.00132888 -0.00052614 0.00054526 Br 8.69127297 8.46428824 5.83386458 -0.00037553 -0.00185134 -0.00113271 Br 8.66122896 5.92117859 9.27837169 -0.00164680 0.00155658 0.00183124 Br 5.97755320 8.39627048 8.66042404 0.00179530 0.00377601 0.00063538 Cs 5.96885841 6.05203313 11.95203889 0.00129610 -0.00220275 0.00107405 Pb 8.96499581 8.92479320 14.84757255 -0.00146233 0.00063888 -0.00035881 Br 7.91351502 8.76482354 11.93165645 0.00151802 -0.00243263 0.00089056 Br 9.43341096 5.97572157 15.19311682 0.00054455 0.00065354 0.00167393 Br 6.17740671 8.87353866 15.47741670 0.00163574 -0.00458311 0.00129551 Cs 6.79802760 6.12967938 17.78008713 -0.00234527 0.00069325 -0.00106895 Pb 8.73299368 9.07052393 20.69907429 0.00043490 0.00152865 0.00000643 Br 9.07696097 9.18576876 17.78782572 -0.00193188 -0.00242209 -0.00151451 Br 9.16121233 6.07967307 20.82660823 -0.00026184 -0.00186348 -0.00245950 Br 5.56307196 8.52529042 20.65238018 -0.00098539 -0.00109373 0.00079482 Cs 5.44495617 11.43010214 23.45411013 -0.00090282 -0.00168730 0.00217263 Pb 8.80666471 14.70068475 3.15986263 -0.00000929 0.00161375 0.00148748 Br 9.28329793 14.57772567 0.14687357 -0.00099414 -0.00115920 -0.00513674 Br 9.00561914 11.84338337 3.27317261 0.00047863 0.00381037 0.00322012 Br 5.68816771 14.54759261 2.61170151 0.00051886 -0.00188350 0.00020791 Cs 5.35345648 11.98565015 4.98431774 0.00327400 0.00137282 0.00219630 Pb 8.68108156 14.54889248 9.00076683 0.00200184 0.00110562 0.00064238 Br 8.40192147 13.90553528 5.93365617 0.00246097 -0.00127086 0.00173493 Br 9.63236206 11.86763875 8.83028360 -0.00307460 -0.00398385 0.00105842 Br 5.82410669 14.10254259 9.54869981 0.00127974 -0.00258668 -0.00075779 Cs 5.60135772 11.80518537 11.83459041 0.00193090 -0.00436706 0.00068136 Pb 9.13014281 15.04201740 14.86305508 0.00398658 -0.00169570 -0.00154758 Br 9.31661387 15.23390033 11.85629341 -0.00488296 0.00069258 0.00073988 Br 9.51994361 11.78170909 15.11639120 0.00035900 0.00024799 -0.00048449 Br 5.76966994 14.63691226 14.09286669 -0.00061482 -0.00027984 -0.00376287 Cs 6.42947170 11.76104183 17.66204525 0.00054100 -0.00295707 -0.00244331 Pb 8.99149045 14.91449759 20.79201449 0.00109199 0.00004832 0.00091355 Br 9.36889386 14.87962537 17.76110372 -0.00001458 -0.00034018 0.00048820 Br 8.86599739 11.89851970 21.21782578 -0.00141518 0.00268543 -0.00386128 Br 6.09515616 14.93768683 21.28475445 -0.00072123 0.00075317 0.00037550 Cs 5.42858614 18.56416854 23.63968404 0.00003290 0.00076543 -0.00182739 Pb 8.79925495 20.74821117 3.06464852 0.00011705 0.00055775 0.00007118 Br 8.92682533 20.89565476 0.04755329 0.00030477 0.00227443 -0.00254395 Br 8.38781070 17.85271968 2.78272751 0.00079386 0.00185981 -0.00187920 Br 5.75026881 20.76875946 3.02081803 0.00059172 0.00279083 0.00153615 Cs 5.61233753 18.00622846 5.51718235 -0.00155074 0.00167340 -0.00028503 Pb 8.66552966 20.46185032 8.90900383 0.00213575 -0.00126220 0.00040408 Br 8.23009645 20.38452409 5.95269427 -0.00290915 0.00181085 0.00217021 Br 8.10966547 17.46449288 8.14366423 0.00109773 0.00042012 -0.00296409 Br 5.78560372 20.58362983 8.56566769 0.00338383 -0.00047256 -0.00191424 Cs 6.27921221 18.29300084 11.40869387 -0.00131761 0.00127447 0.00015644 Pb 9.24946037 20.81066069 14.71150436 -0.00093790 -0.00065953 0.00213931 Br 9.57726032 20.82579602 11.77698114 0.00151414 -0.00381042 -0.00169530 Br 8.38557688 17.77405715 14.78102830 -0.00095437 0.00499720 0.00258349 Br 6.24431091 21.31976440 14.90361404 0.00258875 0.00222824 -0.00045204 Cs 5.73123840 17.36288186 17.81406079 -0.00123405 -0.00002464 -0.00261192 Pb 9.00801334 20.66611155 20.67299961 -0.00106931 -0.00017611 -0.00087680 Br 9.81442452 20.70462865 17.63702143 0.00071117 0.00002130 0.00150124 Br 7.92666118 17.90884604 20.75701847 0.00249162 -0.00368017 0.00115902 Br 5.97575688 20.79518074 20.54348182 -0.00353990 0.00041326 -0.00067850 Cs 12.16688513 23.72007223 0.28821309 0.00053374 -0.00011084 -0.00177415 Pb 15.02437230 3.37246776 2.82674483 -0.00057032 -0.00008240 0.00021355 Br 15.37481515 2.93421809 23.69625061 0.00114565 0.00151101 -0.00340087 Br 15.55462904 23.70314162 2.84250209 0.00345726 0.00109327 0.00360122 Br 12.12648362 3.51962892 2.46880967 0.00088487 0.00181968 -0.00155441 Cs 12.13774187 0.52983937 5.62232394 -0.00121561 -0.00080611 -0.00316010 Pb 14.89715842 3.08221532 8.82976089 0.00094678 0.00119273 -0.00041028 Br 14.61688883 3.27013000 5.81806860 -0.00283462 0.00211039 -0.00086680 Br 14.95582602 0.07753341 9.07653931 0.00190062 -0.00065881 0.00074719 Br 12.10815645 2.99864912 9.57156981 -0.00190961 -0.00025248 -0.00151745 Cs 11.94978568 0.40208910 11.96203985 0.00065720 0.00330351 -0.00028052 Pb 15.00527070 2.74483569 14.91738475 0.00202575 -0.00351361 0.00060553 Br 15.00731175 3.00521732 12.02594991 -0.00414693 0.00112923 0.00214995 Br 14.26191563 23.58790043 14.85908510 0.00041195 0.00009341 -0.00270696 Br 12.09496291 3.08406074 14.17952645 0.00066628 0.00490328 -0.00052075 Cs 12.04155008 23.32639427 18.44750906 -0.00090548 0.00171272 0.00187696 Pb 15.02843058 3.10431924 20.79607750 -0.00175460 0.00049334 -0.00104156 Br 14.59726292 3.11555436 17.97012542 -0.00617554 0.00056999 -0.00155409 Br 15.23657716 23.53269302 20.71748004 0.00285543 -0.00014442 -0.00440416 Br 11.85194623 2.62225417 21.56227576 -0.00204502 -0.00361132 0.00082227 Cs 11.86196056 6.23403447 23.34520720 0.00137631 -0.00112217 0.00091618 Pb 14.73989142 8.93497931 3.31415854 0.00003337 -0.00021570 -0.00278749 Br 14.77481090 8.80604357 0.14720272 0.00102270 -0.00419230 -0.00147640 Br 15.27043838 6.02058142 3.44882497 -0.00254947 -0.00024307 -0.00403007 Br 11.69754388 8.45723999 2.61495505 0.00006579 -0.00275309 -0.00010290 Cs 12.08629182 6.21022638 5.59319088 0.00281095 -0.00136933 0.00046370 Pb 14.55047736 8.91125264 9.01656609 0.00064719 0.00104750 -0.00110277 Br 14.33546801 9.57157869 5.99199809 -0.00074370 0.00007040 -0.00103373 Br 15.27575125 6.12249652 8.35570366 0.00007996 -0.00122711 0.00108936 Br 11.69481347 8.00417622 8.93743664 -0.00147971 -0.00149789 -0.00148579 Cs 12.02207354 5.98553596 11.90880506 0.00122277 0.00085760 0.00145190 Pb 14.96120558 8.88377303 14.83007977 0.00142373 0.00108640 0.00234461 Br 14.62943038 9.14507009 12.06124864 -0.00508549 0.00096524 0.00247153 Br 14.84845742 5.79529549 14.84742073 -0.00310344 -0.00378996 -0.00127429 Br 12.08944490 8.56152393 14.97965240 -0.00084930 0.00225337 0.00265495 Cs 12.60561697 5.69070033 18.29234617 -0.00029985 -0.00012476 -0.00049795 Pb 14.61746519 9.18436242 20.93071454 0.00061047 0.00036039 0.00024824 Br 14.76947457 8.62641279 17.72809134 0.00022505 -0.00162802 -0.00160053 Br 15.16499742 5.99821330 20.73792365 -0.00371559 0.00545808 -0.00198644 Br 11.83905336 8.89395435 20.50471778 0.00325103 -0.00263440 0.00172190 Cs 11.30137173 11.85575806 0.56347224 0.00004421 -0.00028795 -0.00114129 Pb 14.72722100 14.89718970 3.24923743 -0.00042153 -0.00043112 -0.00027120 Br 14.93602967 15.51044014 0.01361479 0.00180145 -0.00270579 -0.00030697 Br 14.40391995 12.14336630 3.12115730 -0.00308208 -0.00082571 -0.00019121 Br 11.60411612 15.01817431 3.27302048 0.00017192 -0.00158507 -0.00476877 Cs 11.61608286 12.49873111 6.05957597 0.00215317 0.00010813 0.00047486 Pb 14.67978825 15.01600942 9.19104022 0.00233063 0.00101554 -0.00160461 Br 14.93861566 14.84941662 6.02493471 -0.00200905 0.00095453 0.00048636 Br 13.68107323 12.00462347 9.00082430 -0.00299019 -0.00181634 -0.00007816 Br 11.65797422 15.38520384 8.42498205 -0.00395648 -0.00257235 0.00313202 Cs 11.86620617 12.14557967 11.99681677 -0.00074497 0.00081255 0.00117578 Pb 14.96127245 15.01040714 15.04959562 -0.00156488 0.00082750 0.00198278 Br 14.70369287 14.79063692 12.18795614 0.00074212 -0.00117113 -0.00106934 Br 14.58987205 11.68345284 14.88743883 0.00293226 0.00223870 -0.00066290 Br 11.95018391 15.16918289 15.05942874 -0.00104485 -0.00163864 -0.00228189 Cs 12.25200192 11.48702835 17.43482090 -0.00043675 -0.00217298 -0.00024224 Pb 14.88889759 14.87423483 20.97701112 0.00165143 0.00102309 -0.00139486 Br 14.55488314 14.76207671 17.94150466 0.00138922 0.00175142 0.00399908 Br 14.28809827 12.05070249 21.23103809 0.00159966 0.00174242 0.00369945 Br 11.89291195 15.36915608 21.57074144 0.00024362 0.00274830 0.00067760 Cs 11.52966013 18.00089102 0.64622188 -0.00082412 -0.00105688 0.00359435 Pb 14.89550453 20.96528730 3.03913371 0.00184137 0.00024734 0.00145955 Br 14.74165303 20.75348510 23.51574049 0.00437658 0.00097847 -0.00248776 Br 14.38956654 17.90266574 3.62570438 -0.00205806 -0.00176960 0.00056184 Br 11.83021488 21.35584596 3.48831559 0.00236995 -0.00072892 -0.00038811 Cs 11.22650223 18.18712232 5.94077047 0.00094418 0.00029961 -0.00002524 Pb 14.97805656 20.95383846 8.86194877 -0.00047755 0.00000987 0.00117616 Br 15.14110185 20.67576596 6.07652185 0.00240080 -0.00481174 0.00090735 Br 14.85481081 17.85335897 8.84981592 0.00285993 0.00191952 0.00139577 Br 11.60165467 21.38684550 8.15655848 -0.00288457 0.00215582 -0.00100563 Cs 12.45246860 18.38451902 11.62710735 -0.00052806 -0.00288161 -0.00214045 Pb 15.00379845 20.58667565 14.80699070 0.00006475 0.00262467 -0.00160407 Br 15.07685930 20.75481146 11.86401328 0.00344130 0.00429701 0.00004385 Br 15.57811309 17.86465006 14.64620343 -0.00111214 0.00051120 0.00135274 Br 12.20439610 19.69744056 14.88077877 0.00149529 0.00119155 0.00147159 Cs 12.17399759 17.60478300 17.89809966 -0.00044554 -0.00064686 0.00129799 Pb 14.95351934 20.71182263 20.67248782 0.00247470 0.00255696 0.00063545 Br 14.20002358 20.71153564 17.65198388 -0.00031842 0.00278310 -0.00177795 Br 14.81738844 17.70887626 19.90153445 0.00361058 -0.00269600 0.00134616 Br 11.85619965 20.70804668 21.10893157 -0.00198642 -0.00275428 0.00173012 Cs 18.20645396 23.40904554 23.64768294 0.00465111 0.00000633 -0.00308412 Pb 20.97251351 3.30533261 2.61822899 0.00195469 0.00192092 -0.00167261 Br 20.57887217 3.94008006 23.68141082 -0.00060241 -0.00017288 0.00242891 Br 20.84478103 0.17339994 3.22577756 -0.00213443 0.00108425 -0.00105388 Br 17.88975518 3.20343508 3.22295726 -0.00210812 -0.00070783 0.00519847 Cs 18.08623339 0.13847075 6.14695207 -0.00339980 0.00299617 -0.00075518 Pb 21.08291442 2.98771320 9.00049126 0.00160109 -0.00120712 0.00172800 Br 21.54780947 3.08318923 6.16039745 -0.00009556 0.00168333 0.00015841 Br 20.87586543 0.09484273 9.19191091 -0.00224621 -0.00057901 0.00317060 Br 17.73518425 2.72924561 8.57400502 0.00239020 0.00226715 0.00008688 Cs 17.96014212 0.26699097 11.80941958 0.00302008 -0.00223635 0.00017634 Pb 20.66188291 3.03271169 14.65043292 -0.00089342 -0.00001008 0.00069964 Br 19.85091347 3.58721735 11.78234651 0.00014582 0.00121778 0.00081470 Br 21.59147124 23.63333087 14.84190647 0.00244908 -0.00120796 0.00028417 Br 17.91384028 2.81342877 15.00683883 -0.00007777 0.00245863 0.00084550 Cs 18.01992360 23.59891034 18.23494288 0.00094015 -0.00234865 -0.00251224 Pb 21.09268851 2.90885823 20.64737466 -0.00113021 0.00148348 -0.00063367 Br 20.89697615 3.16319839 17.70776978 0.00051611 -0.00045969 0.00092114 Br 20.89296255 23.74356911 20.38234657 -0.00280502 0.00140133 -0.00307866 Br 17.64934428 2.90553865 20.13300687 0.00127160 0.00083906 0.00109959 Cs 17.75105074 6.43770689 23.30813841 0.00095079 0.00169240 -0.00146345 Pb 20.77735559 8.88833058 3.08157622 -0.00179381 -0.00074115 0.00042014 Br 20.21869701 8.78550341 0.12647982 -0.00139190 -0.00488019 -0.00089276 Br 20.53767515 5.85043546 3.35746564 -0.00058548 0.00041232 0.00212755 Br 17.77585638 8.94729113 3.63187823 -0.00262866 -0.00219577 0.00049392 Cs 18.40608726 5.39455727 6.32157640 0.00006506 0.00100279 -0.00201159 Pb 20.94167461 8.77324062 8.84796875 -0.00073678 -0.00000395 -0.00039278 Br 20.77767840 9.39069125 5.87531580 -0.00011908 -0.00065076 -0.00170986 Br 21.31901905 5.94795644 8.65769809 0.00035860 -0.00187334 -0.00269152 Br 17.43285918 9.16094320 8.96123583 0.00212781 0.00156810 -0.00637749 Cs 17.47962489 6.40848706 11.44002392 0.00080473 0.00093901 0.00019350 Pb 20.97101302 8.74440986 14.97274767 0.00000544 -0.00174549 0.00043099 Br 20.85010991 8.72643034 11.86716530 -0.00174698 0.00150730 -0.00127327 Br 20.78201794 5.79367498 15.19040225 -0.00154401 0.00131548 -0.00305354 Br 18.09356179 8.31891741 14.72896505 0.00193705 -0.00263923 0.00070699 Cs 17.78744502 5.67989561 17.62055027 -0.00183623 -0.00100434 0.00153593 Pb 20.62849662 9.05829307 20.61891950 0.00063975 -0.00032793 -0.00055901 Br 21.34639048 8.62837457 17.74891278 -0.00073370 -0.00111984 0.00001085 Br 20.46807626 5.86906266 20.53832572 0.00053682 0.00332693 -0.00403214 Br 17.46875571 9.27732497 20.54044631 0.00111838 -0.00336266 0.00082814 Cs 17.66615360 11.88353400 0.13759527 -0.00256249 -0.00114094 -0.00028773 Pb 20.58592639 14.77402771 3.12392431 0.00132764 0.00069402 0.00141859 Br 20.79865868 15.22950822 0.08836154 0.00086514 -0.00096572 0.00107452 Br 20.27216143 11.89858336 3.29694055 -0.00056218 0.00197337 0.00054867 Br 17.54937019 15.02532391 2.58726163 0.00221030 -0.00054896 -0.00316565 Cs 17.85501201 11.19557408 6.25915542 0.00065903 0.00219410 0.00008529 Pb 20.48882964 14.87727947 9.12127022 -0.00113805 0.00042298 0.00198911 Br 20.11619135 14.74287589 5.95593637 -0.00214912 -0.00397086 0.00233327 Br 20.19846904 11.80421384 8.98528640 -0.00577087 -0.00108472 0.00619790 Br 17.63185028 14.45993095 9.35203394 -0.00218394 0.00075659 0.00055319 Cs 17.99517997 11.30487646 12.15156635 0.00164704 -0.00016777 -0.00275672 Pb 20.79022908 14.69568221 14.74510929 -0.00045028 0.00062085 -0.00142381 Br 20.82694950 13.94890137 11.79021787 -0.00069988 -0.00011902 -0.00078687 Br 20.63244131 11.64038017 15.41343622 0.00261684 0.00022091 0.00195308 Br 17.74114337 14.29481887 14.95971772 -0.00107609 0.00304249 0.00221517 Cs 17.40876973 11.93193969 17.59159500 -0.00029242 -0.00191209 -0.00035804 Pb 20.99681577 15.09345947 20.75451171 0.00192772 0.00139285 -0.00103333 Br 21.02619562 14.69615128 17.92570127 -0.00295721 0.00282069 -0.00229119 Br 20.10442815 11.83550125 20.19629841 -0.00184464 -0.00285730 0.00339539 Br 18.05854410 14.97199663 20.53501556 0.00139993 -0.00087078 -0.00092643 Cs 17.94663121 17.69983280 23.44226275 0.00038173 -0.00062468 0.00076938 Pb 20.97120047 20.87176147 3.08014213 0.00238003 -0.00164068 -0.00090928 Br 21.38512344 21.09825869 23.67691660 0.00015580 -0.00054422 0.00066125 Br 20.88073825 17.79827624 3.22731897 0.00301991 -0.00014758 0.00148003 Br 17.86755946 20.40604402 2.42006009 -0.00398728 0.00309604 0.00332156 Cs 17.78158039 17.92971966 5.56295971 0.00117389 -0.00121860 -0.00270667 Pb 20.81733714 20.97548490 8.77785538 0.00070770 -0.00055969 -0.00119543 Br 20.96088714 20.78492229 5.94999626 -0.00145542 0.00058023 -0.00019561 Br 20.27196887 17.71773883 8.73730766 0.00058142 0.00180208 0.00111229 Br 17.62610519 21.06808610 9.57323919 0.00554856 0.00334968 -0.00252163 Cs 17.78946559 17.90114437 11.80935649 -0.00057661 -0.00208648 0.00338271 Pb 20.72264579 20.87970400 14.86549041 -0.00216204 0.00107651 0.00000728 Br 21.10697841 20.87735974 11.87995439 0.00107915 0.00061368 -0.00097108 Br 20.50800088 17.33354705 14.07650821 -0.00148414 0.00034461 0.00160519 Br 17.92428608 21.49974742 14.90063114 -0.00127200 0.00147116 0.00384669 Cs 17.54207351 18.03464602 18.13777769 0.00237167 -0.00090442 0.00257960 Pb 20.83771549 20.72276995 20.97468383 -0.00150950 -0.00076046 0.00144531 Br 20.81673137 20.67374617 17.68038108 0.00256624 -0.00300480 0.00268529 Br 20.79954394 17.87001550 20.28504496 0.00044492 0.00020008 -0.00203461 Br 17.98795453 20.71688260 21.22355722 0.00301763 0.00137640 0.00222187 320 Lattice="23.77195271 0.0 0.0 0.0 23.77195271 0.0 0.0 0.0 23.77195271" Properties=species:S:1:pos:R:3:vel:R:3 Time=20169.99999999 energy=-11424.74987411 virial="-8.37987232 0.70960675 0.50935049 0.70960675 -10.8441964 1.793108 0.50935049 1.793108 -4.24228278" stress="0.00034046 5.282e-05 3.792e-05 5.282e-05 7.718e-05 0.00013348 3.792e-05 0.00013348 0.00058287" pbc="T T T" Cs 23.49582785 0.01649313 0.09550038 -0.00210424 -0.00230889 0.00157585 Pb 2.85594281 3.09627378 3.16727851 0.00006344 0.00006408 0.00136224 Br 3.26215420 2.93652104 0.16070785 0.00025034 0.00007956 0.00082244 Br 2.90924934 0.02297272 3.05974706 -0.00160489 -0.00167923 -0.00066140 Br 0.02519274 3.42725161 2.18400160 -0.00362189 -0.00056588 -0.00311565 Cs 0.34679858 0.12709346 5.94866432 0.00345119 0.00180751 -0.00260035 Pb 3.10678933 3.00077753 9.12933530 0.00133319 0.00011115 -0.00202129 Br 2.56637787 2.71602428 6.39337595 0.00093983 -0.00034772 0.00006816 Br 3.16666140 0.01967886 9.51191886 0.00001755 -0.00007170 0.00210343 Br 23.63744936 3.02389008 10.50812194 0.00018417 -0.00381027 0.00037345 Cs 0.05571226 23.53866854 12.21332866 -0.00347401 -0.00134710 -0.00034337 Pb 3.06356007 3.18268280 14.85462852 0.00058091 -0.00199124 -0.00084634 Br 3.94104040 3.00147079 12.04684085 -0.00381282 -0.00002902 0.00109572 Br 3.04772287 0.04104498 14.57218749 0.00052906 -0.00087250 -0.00187941 Br 0.10468659 3.25730917 14.61362924 -0.00290823 -0.00199140 -0.00233242 Cs 0.09294799 23.28675934 17.61719378 -0.00123702 -0.00099046 -0.00200474 Pb 3.27993040 3.13299772 20.70532759 0.00004080 0.00082734 0.00122184 Br 2.61079190 3.30121290 17.79690759 0.00174681 0.00124046 -0.00120783 Br 3.00695804 0.08608731 20.52857360 -0.00042029 -0.00096495 0.00316376 Br 0.12579959 2.75115918 20.66452218 -0.00168469 -0.00486117 0.00197030 Cs 23.47750358 6.55791485 23.66864428 -0.00020226 0.00158440 -0.00110430 Pb 2.86883098 8.90207611 3.04288984 0.00064287 0.00022076 -0.00352595 Br 2.85964794 8.28656503 0.05108141 0.00281680 -0.00305341 0.00231822 Br 2.95252356 6.06220854 3.06232656 -0.00213327 -0.00007563 0.00006033 Br 0.04050191 8.54376349 3.02027050 -0.00017028 -0.00058355 -0.00253388 Cs 0.53079202 6.05787177 6.07460057 0.00133695 -0.00007365 0.00191545 Pb 3.37333673 9.07296906 8.87685710 -0.00131214 0.00040654 0.00070280 Br 2.83539085 8.79420115 6.18192341 -0.00052227 -0.00148335 -0.00180661 Br 2.68713651 5.81795454 8.93939696 -0.00194775 0.00268124 0.00345304 Br 23.67739121 9.31552071 8.84570848 0.00234739 0.00002518 0.00054222 Cs 23.49278485 5.92653793 12.07598654 -0.00206304 -0.00019648 0.00200641 Pb 2.88963724 8.87904451 14.75429169 0.00059286 0.00180098 -0.00057389 Br 3.21846647 9.04030132 11.93036320 0.00089575 0.00236513 0.00017469 Br 3.77539348 6.12795999 15.21432426 0.00019493 -0.00213156 0.00048093 Br 0.12416196 8.39167166 14.23605767 0.00079579 -0.00114066 -0.00266449 Cs 23.58957827 5.74685054 17.64106262 -0.00106331 0.00001684 0.00064235 Pb 2.62155835 8.72688920 20.63980468 -0.00084610 -0.00250418 0.00074993 Br 2.48733962 8.79362553 17.64615798 0.00261315 -0.00023008 0.00213543 Br 2.21096118 5.90444195 20.91434694 0.00236564 0.00044165 0.00212231 Br 23.27438773 9.30036203 21.19081931 -0.00345879 0.00081015 0.00073066 Cs 23.34853141 11.74330184 0.28398037 0.00326420 0.00002876 0.00052208 Pb 2.59196245 14.98087403 2.94017419 -0.00110790 0.00039447 0.00050260 Br 2.48918644 14.98827253 0.01137397 0.00024556 -0.00163410 -0.00022490 Br 2.89653136 12.00797427 2.17291511 -0.00133745 0.00183536 -0.00038619 Br 23.50724802 14.71262466 3.17005049 0.00402624 0.00077455 -0.00099010 Cs 23.60463265 11.79522536 6.53914776 0.00137015 0.00169237 -0.00128006 Pb 3.04842081 15.01351966 9.09947782 0.00008992 -0.00132059 -0.00055600 Br 3.12025318 14.91799552 5.83764756 -0.00276989 -0.00029367 -0.00144759 Br 2.98197966 12.14429974 8.72253081 -0.00061675 -0.00260591 -0.00045985 Br 0.03889180 14.98695285 8.87632677 -0.00062342 -0.00139182 -0.00017452 Cs 23.71833240 11.74274930 11.40350489 -0.00229456 0.00190497 -0.00011375 Pb 3.08093591 14.54676758 15.04344047 -0.00003893 0.00025026 -0.00152407 Br 2.84902632 14.30797657 11.95705805 -0.00389589 -0.00157561 0.00189250 Br 2.42771537 11.68868377 15.49996086 -0.00134550 -0.00141930 0.00064674 Br 0.25240259 14.54945096 14.54659342 -0.00208153 0.00006034 -0.00011268 Cs 23.68321264 11.65436772 18.15627019 0.00222533 -0.00091123 -0.00042405 Pb 3.14344565 14.50772938 20.78617104 0.00042808 0.00070022 0.00078585 Br 2.94737177 14.93732243 17.87210625 0.00046502 0.00019452 0.00127515 Br 2.86686385 11.65615599 20.91676236 0.00136138 -0.00112303 -0.00018560 Br 0.16389370 14.67960887 20.77054595 -0.00005826 0.00189716 -0.00258169 Cs 0.03615659 17.86455100 23.55525057 -0.00049972 0.00095237 -0.00127448 Pb 3.01234946 20.81114011 2.79424305 -0.00055610 0.00127043 -0.00067286 Br 2.87707880 21.04175693 0.01062175 -0.00122938 0.00395582 -0.00042453 Br 2.97146089 17.79520541 2.77016350 -0.00353786 -0.00120870 0.00148128 Br 23.74498050 20.94819444 3.09635593 0.00386957 0.00148216 0.00229449 Cs 22.99688389 17.85034796 5.98928721 -0.00096887 -0.00006582 -0.00316287 Pb 2.96553785 20.85456160 8.81792609 0.00079700 -0.00116900 0.00063665 Br 3.03975454 20.62565945 5.87334101 0.00065471 -0.00194144 -0.00025450 Br 3.13627537 17.77666812 9.20283095 -0.00256081 0.00259053 -0.00281279 Br 23.74994232 20.83298296 8.89402687 -0.00044357 -0.00321919 -0.00039383 Cs 0.70665472 17.32469167 11.95942240 0.00259992 0.00280456 -0.00340615 Pb 3.13890905 20.91539144 14.84931817 0.00018110 -0.00322227 0.00132237 Br 2.95563401 20.79531421 11.71601847 -0.00366591 0.00005764 0.00360211 Br 3.08019871 17.65193627 15.21449955 0.00063609 -0.00500560 0.00070301 Br 0.14708448 20.67669944 14.94997504 0.00044088 0.00254587 -0.00017639 Cs 0.41202959 17.71385256 17.63070574 -0.00006994 0.00055210 0.00208341 Pb 3.06789281 20.81486903 20.43383944 0.00107697 0.00057384 -0.00006595 Br 3.27619113 20.29140008 17.74393080 -0.00443114 -0.00049722 0.00134214 Br 3.06140762 17.72624214 20.20587646 -0.00050406 -0.00085391 -0.00231560 Br 0.18909122 20.53482596 20.61036657 0.00102132 -0.00103044 0.00047769 Cs 5.77216169 0.20422390 22.89409454 -0.00261229 0.00131539 0.00079164 Pb 8.87439593 3.15073642 3.08086526 -0.00239431 0.00202820 -0.00256515 Br 8.78296557 2.72384106 0.10173771 -0.00463263 0.00265151 0.00153057 Br 9.01958103 0.29296259 3.62668743 0.00343121 0.00270897 0.00365249 Br 5.89537980 3.07994722 3.73394521 0.00038386 0.00145217 0.00221821 Cs 5.88067885 23.48622268 5.79271090 -0.00280374 0.00128749 0.00105294 Pb 8.74553720 2.90273914 8.82623072 0.00026440 0.00161986 -0.00038802 Br 9.66770709 3.45048387 5.97927831 -0.00252535 -0.00011115 -0.00093889 Br 8.42334989 23.65229574 8.98217850 0.00031234 0.00297130 0.00267142 Br 6.06269737 3.09427031 8.25479863 0.00079317 -0.00392286 -0.00242142 Cs 6.01666929 0.04007472 12.06883736 -0.00088328 0.00159299 0.00006133 Pb 9.26599501 2.87334558 14.88451402 0.00156872 0.00022684 0.00313345 Br 8.92575164 2.84031234 11.72420803 -0.00173970 -0.00034934 0.00393616 Br 10.06872453 23.68629519 14.98808703 -0.00261512 0.00198394 -0.00387213 Br 6.19659268 2.46188776 15.00572720 0.00100643 0.00121402 -0.00176085 Cs 6.39579690 23.30446179 17.76195951 -0.00111705 -0.00174736 0.00134369 Pb 9.06523188 3.10377280 20.92496592 -0.00134876 0.00026347 -0.00207938 Br 9.55372768 2.91769389 18.09830399 -0.00258694 -0.00047801 0.00504935 Br 8.87957587 23.64197823 20.88270289 -0.00326773 -0.00368475 0.00274677 Br 5.98328155 3.87541829 20.76551531 0.00024266 -0.00085219 -0.00012982 Cs 5.52034541 6.01176584 23.75004320 -0.00131062 -0.00399765 0.00102274 Pb 8.71643801 8.87833900 2.80269474 -0.00147609 0.00002067 0.00043531 Br 8.97034022 8.88343458 23.71443883 0.00043392 0.00075978 0.00055189 Br 8.20391895 6.05823335 2.93698460 -0.00060233 0.00310248 0.00000484 Br 5.78918532 9.08947540 2.82190429 -0.00222361 0.00223609 0.00110299 Cs 6.10762859 6.21204545 5.78692418 0.00274058 0.00060158 -0.00066230 Pb 8.81160141 8.81394632 8.72298041 0.00167968 -0.00058949 0.00072823 Br 8.68884845 8.44705142 5.82381212 -0.00010157 -0.00158630 -0.00087297 Br 8.64455502 5.93632647 9.29590556 -0.00169038 0.00145454 0.00167685 Br 5.99527299 8.43359379 8.66616976 0.00171359 0.00369724 0.00051318 Cs 5.98145536 6.02903161 11.96293439 0.00122360 -0.00239760 0.00110625 Pb 8.95098716 8.93054597 14.84297220 -0.00133067 0.00050515 -0.00056496 Br 7.93102307 8.74343106 11.93979393 0.00197923 -0.00184525 0.00073610 Br 9.43735150 5.97964904 15.20940625 0.00024273 0.00013862 0.00158609 Br 6.19129580 8.82894561 15.48991075 0.00112053 -0.00432722 0.00119724 Cs 6.77427609 6.13605818 17.76934968 -0.00240489 0.00057800 -0.00107602 Pb 8.73761834 9.08505379 20.69927498 0.00049069 0.00137520 0.00003391 Br 9.05781717 9.16150610 17.77482515 -0.00188919 -0.00242507 -0.00106216 Br 9.15903447 6.06041395 20.80186600 -0.00017162 -0.00197241 -0.00248564 Br 5.55492384 8.51514085 20.66032551 -0.00064448 -0.00093566 0.00079479 Cs 5.43653889 11.41366367 23.47619374 -0.00078050 -0.00159745 0.00224141 Pb 8.80500766 14.71744504 3.17353217 -0.00032498 0.00173380 0.00124584 Br 9.27116097 14.56781142 0.09556868 -0.00143411 -0.00082314 -0.00509268 Br 9.01041565 11.87848677 3.30382333 0.00048459 0.00319006 0.00289560 Br 5.69397781 14.52976004 2.61383721 0.00065647 -0.00167878 0.00021963 Cs 5.38644868 11.99974935 5.00703467 0.00331996 0.00144447 0.00234663 Pb 8.70158081 14.56106296 9.00650989 0.00209449 0.00131888 0.00050154 Br 8.42483375 13.89543290 5.95439771 0.00212360 -0.00073710 0.00240210 Br 9.60065919 11.82539890 8.84304346 -0.00326421 -0.00442930 0.00148802 Br 5.84054742 14.07980622 9.53897202 0.00198070 -0.00196572 -0.00117865 Cs 5.62081461 11.76036621 11.84295654 0.00196207 -0.00458507 0.00098401 Pb 9.16890899 15.02426138 14.84686975 0.00374507 -0.00184237 -0.00167921 Br 9.26805380 15.24037247 11.86258136 -0.00482082 0.00060723 0.00052243 Br 9.52287449 11.78710079 15.11025934 0.00022582 0.00084290 -0.00074054 Br 5.76850963 14.63400614 14.05657903 0.00037211 -0.00030161 -0.00349170 Cs 6.43477103 11.73167841 17.63743224 0.00051660 -0.00291119 -0.00247615 Pb 9.00282553 14.91552869 20.80250537 0.00117360 0.00016200 0.00118397 Br 9.36780047 14.87602961 17.76857552 -0.00019979 -0.00037972 0.00097885 Br 8.85145902 11.92644517 21.17812654 -0.00149139 0.00285956 -0.00407838 Br 6.08606368 14.94333411 21.28789537 -0.00110008 0.00037498 0.00025152 Cs 5.42996765 18.57056322 23.62106437 0.00024031 0.00051482 -0.00189508 Pb 8.80051577 20.75432526 3.06503769 0.00013376 0.00066753 0.00000859 Br 8.92947484 20.91804549 0.02205033 0.00022182 0.00219856 -0.00253268 Br 8.39661863 17.87260450 2.76457683 0.00096400 0.00211858 -0.00174805 Br 5.76187698 20.79744630 3.03556286 0.00172059 0.00294389 0.00140834 Cs 5.59640263 18.02200697 5.51434954 -0.00163474 0.00148130 -0.00028161 Pb 8.68860562 20.45074718 8.91181380 0.00247886 -0.00095717 0.00015924 Br 8.20263268 20.40423627 5.97288759 -0.00256788 0.00213075 0.00183148 Br 8.12129950 17.46922453 8.11608877 0.00123097 0.00051621 -0.00254608 Br 5.82071221 20.57955963 8.54782401 0.00359064 -0.00033760 -0.00164199 Cs 6.26578675 18.30471244 11.41110644 -0.00136517 0.00106875 0.00032506 Pb 9.24078595 20.80380788 14.73366095 -0.00079479 -0.00071252 0.00228242 Br 9.59169762 20.78710768 11.76041112 0.00137274 -0.00392827 -0.00159241 Br 8.37617966 17.82572166 14.80712984 -0.00091475 0.00528811 0.00263387 Br 6.27046308 21.34112568 14.89771679 0.00262800 0.00204146 -0.00072411 Cs 5.71928209 17.36283182 17.78785482 -0.00115227 0.00001253 -0.00262483 Pb 8.99735446 20.66441865 20.66456239 -0.00105924 -0.00016766 -0.00080685 Br 9.81885870 20.70435291 17.65384635 0.00017821 -0.00007155 0.00185138 Br 7.95459067 17.87059942 20.76860689 0.00307875 -0.00393398 0.00115475 Br 5.94071354 20.80125953 20.53693442 -0.00342539 0.00078671 -0.00062940 Cs 12.17204253 23.71907372 0.27042670 0.00049669 -0.00009192 -0.00178002 Pb 15.01862479 3.36968306 2.82921279 -0.00057588 -0.00047710 0.00027699 Br 15.38739372 2.94953871 23.66281338 0.00136710 0.00155304 -0.00327626 Br 15.58840324 23.71452766 2.87888187 0.00329031 0.00116994 0.00367349 Br 12.13210362 3.53759816 2.45348948 0.00023407 0.00177282 -0.00151197 Cs 12.12545147 0.52176598 5.59145856 -0.00124057 -0.00080449 -0.00301008 Pb 14.90565295 3.09417387 8.82592995 0.00075056 0.00119556 -0.00035425 Br 14.58848086 3.28955740 5.81099506 -0.00284368 0.00177363 -0.00054299 Br 14.97479119 0.07200719 9.08325302 0.00189132 -0.00044044 0.00059366 Br 12.08736581 2.99701759 9.55473352 -0.00222419 -0.00006978 -0.00185540 Cs 11.95667065 0.43436314 11.95845603 0.00071674 0.00315260 -0.00043624 Pb 15.02395545 2.71014605 14.92293804 0.00170278 -0.00341528 0.00050246 Br 14.96826622 3.01638528 12.04506798 -0.00365411 0.00109999 0.00165589 Br 14.26718464 23.58922044 14.83214618 0.00063386 0.00016227 -0.00267849 Br 12.09963424 3.13393033 14.17615760 0.00026108 0.00505277 -0.00014380 Cs 12.03228850 23.34496992 18.46594061 -0.00094757 0.00199818 0.00181030 Pb 15.00966541 3.10878538 20.78639315 -0.00198483 0.00040741 -0.00090015 Br 14.53639697 3.11821288 17.95159483 -0.00598063 -0.00005369 -0.00213080 Br 15.26471087 23.53323843 20.67334067 0.00276884 0.00027044 -0.00441896 Br 11.83268156 2.58720412 21.56934257 -0.00179800 -0.00339610 0.00058701 Cs 11.87587655 6.22248477 23.35490661 0.00140543 -0.00118799 0.00102242 Pb 14.74052313 8.93319031 3.28493579 0.00008791 -0.00014047 -0.00304958 Br 14.78535580 8.76448223 0.13103976 0.00108472 -0.00411536 -0.00174995 Br 15.24493623 6.02343464 3.40860880 -0.00254850 0.00081106 -0.00401197 Br 11.69968837 8.42997175 2.61474982 0.00035085 -0.00269573 0.00006071 Cs 12.11433652 6.19624778 5.59812449 0.00279447 -0.00142582 0.00052269 Pb 14.55629525 8.92253008 9.00500654 0.00051407 0.00120650 -0.00120776 Br 14.32773643 9.57152911 5.98480329 -0.00079748 -0.00008660 -0.00041331 Br 15.27616506 6.11021664 8.36759241 0.00000363 -0.00121953 0.00128886 Br 11.68091869 7.98984537 8.92224909 -0.00126960 -0.00136696 -0.00154974 Cs 12.03414578 5.99364232 11.92387909 0.00119109 0.00076487 0.00156098 Pb 14.97532849 8.89284779 14.85460480 0.00139575 0.00072756 0.00254736 Br 14.57856903 9.15437736 12.07969385 -0.00508143 0.00089446 0.00121255 Br 14.81854652 5.75681924 14.83478470 -0.00286972 -0.00387517 -0.00124621 Br 12.08060993 8.58515813 15.00563190 -0.00090115 0.00247428 0.00254075 Cs 12.60024992 5.69126285 18.28623529 -0.00078156 0.00024602 -0.00072687 Pb 14.62460635 9.18727358 20.93317883 0.00082416 0.00022600 0.00024591 Br 14.77222149 8.61146370 17.71604290 0.00032557 -0.00136124 -0.00079759 Br 15.12730724 6.05307577 20.71904788 -0.00381661 0.00547298 -0.00178346 Br 11.86682673 8.86736716 20.52214266 0.00227637 -0.00268100 0.00175801 Cs 11.30313313 11.85255657 0.55156517 0.00030508 -0.00035016 -0.00123733 Pb 14.72290605 14.89436214 3.24566511 -0.00043592 -0.00013875 -0.00044692 Br 14.95447851 15.48352568 0.01059021 0.00188805 -0.00267235 -0.00028116 Br 14.37302850 12.12970263 3.11805894 -0.00309388 -0.00188832 -0.00042829 Br 11.60756438 15.00303031 3.22588924 0.00052119 -0.00144368 -0.00465609 Cs 11.63902085 12.49961959 6.06377143 0.00242828 0.00006689 0.00036669 Pb 14.70253776 15.02485859 9.17387618 0.00221582 0.00075456 -0.00182300 Br 14.91925522 14.85858329 6.03306566 -0.00186329 0.00087847 0.00113480 Br 13.65369232 11.98556243 9.00054776 -0.00247782 -0.00199100 0.00002556 Br 11.61824373 15.35972509 8.45738869 -0.00397225 -0.00251333 0.00334364 Cs 11.85868230 12.15368915 12.00906768 -0.00075846 0.00080715 0.00127161 Pb 14.94538234 15.01787306 15.06907490 -0.00160514 0.00066472 0.00190851 Br 14.71049999 14.77949190 12.17499677 0.00062031 -0.00105228 -0.00150358 Br 14.62021315 11.71052405 14.88085588 0.00313120 0.00315397 -0.00064420 Br 11.94014406 15.15336395 15.03566301 -0.00093479 -0.00152116 -0.00246615 Cs 12.24704455 11.46517881 17.43316842 -0.00055234 -0.00219593 -0.00008935 Pb 14.90490736 14.88523456 20.96366741 0.00155125 0.00117161 -0.00126676 Br 14.56904145 14.77749336 17.98130247 0.00143533 0.00132269 0.00391400 Br 14.30410758 12.06692288 21.26808267 0.00159865 0.00148829 0.00371026 Br 11.89623856 15.39660893 21.57481662 0.00042841 0.00274248 0.00013541 Cs 11.52182128 17.98971277 0.68169846 -0.00074284 -0.00117794 0.00349540 Pb 14.91396576 20.96675742 3.05372352 0.00185003 0.00005298 0.00146054 Br 14.78545130 20.76298049 23.49417099 0.00438036 0.00092495 -0.00182071 Br 14.36897626 17.88696916 3.63018222 -0.00205777 -0.00137079 0.00033162 Br 11.85429555 21.34717849 3.48301520 0.00242906 -0.00100479 -0.00067117 Cs 11.23676159 18.18997083 5.94061553 0.00110460 0.00027181 -0.00000505 Pb 14.97121406 20.95413211 8.87425468 -0.00086557 0.00005294 0.00128274 Br 15.16697986 20.62902235 6.08068685 0.00276900 -0.00453326 -0.00005719 Br 14.88419757 17.87327464 8.86460576 0.00301384 0.00204433 0.00156420 Br 11.57220535 21.40600120 8.14733161 -0.00298974 0.00167233 -0.00084372 Cs 12.44654582 18.35481304 11.60556598 -0.00065240 -0.00305181 -0.00216005 Pb 15.00345765 20.61521799 14.79113159 -0.00013378 0.00307199 -0.00156247 Br 15.11004297 20.79752123 11.86660032 0.00319649 0.00423488 0.00045544 Br 15.56838674 17.86582223 14.66057017 -0.00083862 -0.00026199 0.00152110 Br 12.21680533 19.70985837 14.89349955 0.00098240 0.00128415 0.00106156 Cs 12.16796228 17.59852202 17.91047144 -0.00075515 -0.00060242 0.00118281 Pb 14.97708320 20.73692782 20.67893044 0.00223379 0.00245288 0.00064660 Br 14.19969056 20.73904136 17.63650200 0.00025350 0.00271561 -0.00130663 Br 14.85387035 17.68362945 19.91974591 0.00367162 -0.00231474 0.00228696 Br 11.83895823 20.68032412 21.12614069 -0.00145656 -0.00278407 0.00170569 Cs 18.25209013 23.40970537 23.61765622 0.00447551 0.00013084 -0.00291605 Pb 20.99121605 3.32235044 2.60199373 0.00177505 0.00147867 -0.00155613 Br 20.57283603 3.93745551 23.70437215 -0.00061396 -0.00033631 0.00211962 Br 20.82371500 0.18150092 3.21465772 -0.00207354 0.00052503 -0.00117550 Br 17.87112701 3.19549445 3.27536513 -0.00161899 -0.00087674 0.00528074 Cs 18.05197163 0.16848159 6.13867598 -0.00345026 0.00300503 -0.00090052 Pb 21.09893654 2.97621283 9.01879110 0.00160081 -0.00109334 0.00192444 Br 21.54727245 3.09917266 6.16098938 -0.00000842 0.00150684 -0.00003157 Br 20.85322891 0.08706479 9.22282465 -0.00227746 -0.00096825 0.00301058 Br 17.76337402 2.75184020 8.57521724 0.00321981 0.00225405 0.00015819 Cs 17.98986388 0.24472796 11.81118355 0.00292399 -0.00221510 0.00017460 Pb 20.65464996 3.03215788 14.65725299 -0.00054650 -0.00009877 0.00066182 Br 19.85330371 3.59886715 11.79061163 0.00033380 0.00111041 0.00084193 Br 21.61333280 23.62200133 14.84560718 0.00190725 -0.00105150 0.00046010 Br 17.91231274 2.83695045 15.01665046 -0.00022705 0.00224552 0.00111936 Cs 18.02911541 23.57557165 18.20898074 0.00089865 -0.00231583 -0.00267871 Pb 21.08079675 2.92484494 20.64064429 -0.00124715 0.00170827 -0.00071090 Br 20.90234176 3.15687229 17.71753314 0.00055563 -0.00079884 0.00102000 Br 20.86524416 23.75575807 20.35277353 -0.00273648 0.00103612 -0.00282680 Br 17.66692130 2.91351896 20.14438590 0.00219426 0.00075872 0.00117732 Cs 17.75997156 6.45361833 23.29352117 0.00082561 0.00148310 -0.00146314 Pb 20.75816397 8.88105398 3.08535575 -0.00204034 -0.00071514 0.00032863 Br 20.20639154 8.73844067 0.11629496 -0.00105872 -0.00452020 -0.00113091 Br 20.53234247 5.86156013 3.38023659 -0.00047902 0.00180588 0.00242007 Br 17.74875202 8.92461997 3.63471890 -0.00277686 -0.00232883 0.00008036 Cs 18.40604856 5.40504739 6.30062091 -0.00007210 0.00109375 -0.00217579 Pb 20.93276515 8.77457900 8.84466661 -0.00103300 0.00026681 -0.00026856 Br 20.77888454 9.38217182 5.86168988 0.00035697 -0.00104388 -0.00100253 Br 21.32292211 5.92936210 8.63057978 0.00041868 -0.00181960 -0.00272381 Br 17.45571580 9.17552878 8.89908832 0.00241895 0.00132920 -0.00603488 Cs 17.48764977 6.41739514 11.44257649 0.00079938 0.00084319 0.00031537 Pb 20.97061869 8.72754134 14.97579814 -0.00007947 -0.00161889 0.00017941 Br 20.83254150 8.74150749 11.85295832 -0.00176497 0.00150389 -0.00156302 Br 20.76705858 5.80661633 15.15912704 -0.00144243 0.00124686 -0.00319332 Br 18.11087058 8.29378494 14.73610702 0.00150764 -0.00238301 0.00072084 Cs 17.76901382 5.67019457 17.63592782 -0.00184954 -0.00093362 0.00153861 Pb 20.63271158 9.05448108 20.61303232 0.00018638 -0.00044022 -0.00061811 Br 21.33930643 8.61745457 17.75303537 -0.00067907 -0.00106319 0.00079772 Br 20.47407576 5.90367614 20.49799826 0.00066394 0.00356840 -0.00402488 Br 17.48178476 9.24344077 20.54916252 0.00147382 -0.00341426 0.00091096 Cs 17.64035050 11.87226908 0.13480689 -0.00259723 -0.00111122 -0.00027191 Pb 20.59824144 14.78127872 3.13778149 0.00113567 0.00075151 0.00135096 Br 20.80785821 15.21965305 0.09805203 0.00097691 -0.00100328 0.00086309 Br 20.26743443 11.91665520 3.30021521 -0.00038391 0.00163192 0.00010596 Br 17.57360702 15.02000684 2.55609485 0.00262719 -0.00051288 -0.00305714 Cs 17.86083131 11.21987324 6.25956439 0.00050745 0.00266614 -0.00001348 Pb 20.47756497 14.88037593 9.13981777 -0.00111530 0.00020272 0.00171404 Br 20.09531069 14.70381374 5.98087597 -0.00202527 -0.00383787 0.00264476 Br 20.14321638 11.79186277 9.04637684 -0.00527078 -0.00138128 0.00600811 Br 17.60899867 14.46869350 9.35782064 -0.00237648 0.00099533 0.00060308 Cs 18.01182847 11.30364958 12.12388279 0.00167899 -0.00007834 -0.00277612 Pb 20.78636436 14.70036269 14.73137825 -0.00032116 0.00031337 -0.00131648 Br 20.82033855 13.94848133 11.78688226 -0.00062325 0.00003423 0.00012574 Br 20.65861765 11.64256868 15.43237805 0.00261566 0.00021303 0.00183252 Br 17.73298332 14.32570435 14.98135213 -0.00056135 0.00313293 0.00211187 Cs 17.40639838 11.91247010 17.58876446 -0.00017883 -0.00197955 -0.00020777 Pb 21.01530041 15.10568825 20.74417988 0.00176391 0.00105000 -0.00103524 Br 20.99737477 14.72548849 17.90012056 -0.00280631 0.00304290 -0.00280530 Br 20.08829734 11.80701361 20.23136063 -0.00138347 -0.00280475 0.00360692 Br 18.06926656 14.96412151 20.52662836 0.00074799 -0.00070340 -0.00074865 Cs 17.95011630 17.69306163 23.45063744 0.00031389 -0.00073132 0.00090446 Pb 20.99329031 20.85556386 3.07017165 0.00204235 -0.00159150 -0.00107843 Br 21.38583780 21.09140083 23.68640310 -0.00001391 -0.00083353 0.00124496 Br 20.91058017 17.79919349 3.24129847 0.00293816 0.00033729 0.00130338 Br 17.82737635 20.43732115 2.45391378 -0.00401797 0.00314721 0.00344494 Cs 17.79283351 17.91696384 5.53580787 0.00107643 -0.00133089 -0.00272138 Pb 20.82435705 20.96917328 8.76846687 0.00069235 -0.00070133 -0.00067976 Br 20.94533907 20.79175109 5.94694769 -0.00165363 0.00078323 -0.00043664 Br 20.27770788 17.73950906 8.74858538 0.00056680 0.00252639 0.00114284 Br 17.68750842 21.10137044 9.54576802 0.00665136 0.00329655 -0.00297933 Cs 17.78381332 17.88042148 11.84227646 -0.00055417 -0.00205595 0.00319780 Pb 20.70211677 20.89017922 14.86460794 -0.00193858 0.00101435 -0.00017791 Br 21.11700785 20.88342948 11.86898102 0.00092301 0.00059721 -0.00119774 Br 20.49342165 17.34131629 14.09263288 -0.00143064 0.00121492 0.00162000 Br 17.91090061 21.51523867 14.93928688 -0.00139856 0.00163137 0.00388412 Cs 17.56776960 18.02536410 18.16221802 0.00276914 -0.00095310 0.00230403 Pb 20.82390314 20.71562675 20.98680110 -0.00123943 -0.00066541 0.00096939 Br 20.84192693 20.64398152 17.71034095 0.00246909 -0.00294180 0.00326825 Br 20.80425538 17.87308297 20.26563599 0.00049732 0.00041320 -0.00184120 Br 18.01665222 20.72998653 21.24494878 0.00268952 0.00123927 0.00205560 320 Lattice="23.77195271 0.0 0.0 0.0 23.77195271 0.0 0.0 0.0 23.77195271" Properties=species:S:1:pos:R:3:vel:R:3 Time=20179.99999999 energy=-11424.66172028 virial="-8.6046774 1.79737773 0.6631498 1.79737773 -12.17603185 2.13227162 0.6631498 2.13227162 -4.56369059" stress="0.00030712 0.0001338 4.936e-05 0.0001338 -1.569e-05 0.00015873 4.936e-05 0.00015873 0.00055614" pbc="T T T" Cs 23.47636640 23.76643267 0.11138053 -0.00178530 -0.00209003 0.00159871 Pb 2.85752893 3.09605618 3.17993871 0.00025130 -0.00010358 0.00116728 Br 3.26532621 2.93747248 0.16903060 0.00038224 0.00011083 0.00084364 Br 2.89273484 0.00735967 3.05369977 -0.00169444 -0.00142770 -0.00054647 Br 23.76327195 3.42145438 2.15330944 -0.00310174 -0.00059400 -0.00302442 Cs 0.38038778 0.14410942 5.92246089 0.00325819 0.00159071 -0.00263831 Pb 3.12040125 3.00141956 9.11038777 0.00138967 0.00002194 -0.00175964 Br 2.57525483 2.71351957 6.38951830 0.00083834 -0.00014861 -0.00085067 Br 3.16663665 0.01869120 9.53143184 -0.00002239 -0.00011543 0.00179453 Br 23.64003986 2.98318048 10.50801647 0.00034240 -0.00430955 -0.00038297 Cs 0.02184090 23.52488721 12.20903508 -0.00329462 -0.00140957 -0.00051759 Pb 3.06969418 3.16275194 14.84657965 0.00064315 -0.00199426 -0.00076159 Br 3.90256801 3.00221064 12.05645612 -0.00387598 0.00017779 0.00080881 Br 3.05221248 0.03190619 14.55481388 0.00036953 -0.00097273 -0.00159018 Br 0.07627489 3.23756839 14.59032318 -0.00276835 -0.00195577 -0.00232151 Cs 0.08078250 23.27732805 17.59779634 -0.00119346 -0.00089515 -0.00186941 Pb 3.27834273 3.14086336 20.71749281 -0.00035779 0.00074041 0.00120620 Br 2.62939772 3.31292790 17.78599379 0.00196769 0.00110225 -0.00095018 Br 3.00331259 0.07559847 20.55936908 -0.00031013 -0.00112228 0.00299085 Br 0.10750896 2.70246353 20.68482834 -0.00195792 -0.00486917 0.00208851 Cs 23.47506839 6.57288776 23.65755228 -0.00028668 0.00141031 -0.00111179 Pb 2.87534134 8.90531335 3.00670306 0.00064966 0.00042807 -0.00370152 Br 2.88726307 8.25893046 0.07394824 0.00270056 -0.00247141 0.00224010 Br 2.93210154 6.05971018 3.06430340 -0.00194876 -0.00043914 0.00033678 Br 0.03511074 8.53853146 2.99507756 -0.00091426 -0.00045945 -0.00250407 Cs 0.54316979 6.05659283 6.09361446 0.00113594 -0.00018005 0.00188687 Pb 3.35786127 9.07741217 8.88522863 -0.00177627 0.00047363 0.00095642 Br 2.83043769 8.77988158 6.15880169 -0.00045890 -0.00137416 -0.00278673 Br 2.66845814 5.84817388 8.97451344 -0.00178363 0.00333039 0.00356981 Br 23.70243969 9.31392659 8.85180025 0.00263127 -0.00033818 0.00067281 Cs 23.47167473 5.92451896 12.09524332 -0.00215938 -0.00022264 0.00183259 Pb 2.89633337 8.89690648 14.74981036 0.00074393 0.00175984 -0.00031970 Br 3.22793190 9.06407615 11.92888274 0.00099418 0.00238742 -0.00046101 Br 3.77672691 6.10360600 15.21777405 0.00006508 -0.00271652 0.00020415 Br 0.13245434 8.38205857 14.21162316 0.00086820 -0.00076907 -0.00221014 Cs 23.57939177 5.74667864 17.64755421 -0.00097291 -0.00005244 0.00065372 Pb 2.61360843 8.70170228 20.64720764 -0.00074063 -0.00251705 0.00072900 Br 2.51222165 8.79048342 17.67235630 0.00236467 -0.00040102 0.00308542 Br 2.23441547 5.90740428 20.93551542 0.00231603 0.00012588 0.00211233 Br 23.24701116 9.30947869 21.19947946 -0.00197175 0.00101639 0.00100921 Cs 23.38150712 11.74430966 0.28937771 0.00332920 0.00017260 0.00055676 Pb 2.58271011 14.98351976 2.94497467 -0.00073223 0.00013173 0.00045200 Br 2.49145877 14.97273996 0.00616229 0.00020462 -0.00146703 -0.00081742 Br 2.88179910 12.02268877 2.17065127 -0.00160539 0.00109696 -0.00006493 Br 23.54632993 14.72008006 3.16100732 0.00374094 0.00071568 -0.00081466 Cs 23.61825005 11.81367609 6.52532016 0.00135419 0.00199030 -0.00148309 Pb 3.04880387 14.99884449 9.09342611 -0.00000909 -0.00160857 -0.00065048 Br 3.09263779 14.91511050 5.82534938 -0.00275093 -0.00028508 -0.00098932 Br 2.97491042 12.11131127 8.71701300 -0.00079154 -0.00394624 -0.00063781 Br 0.03123797 14.97285900 8.87468164 -0.00089931 -0.00142750 -0.00015388 Cs 23.69565051 11.76238747 11.40324631 -0.00224054 0.00201873 0.00006097 Pb 3.08018737 14.54965038 15.02708208 -0.00011075 0.00032366 -0.00174084 Br 2.80898311 14.29255294 11.97392165 -0.00410263 -0.00150864 0.00145784 Br 2.41563986 11.67505069 15.50560675 -0.00107081 -0.00130425 0.00048030 Br 0.22658092 14.55151579 14.54582643 -0.00306267 0.00035234 -0.00003774 Cs 23.70493672 11.64576831 18.15175938 0.00211776 -0.00080842 -0.00047621 Pb 3.14763590 14.51655084 20.79488295 0.00041397 0.00105922 0.00095515 Br 2.95246104 14.93773167 17.88690301 0.00055347 -0.00012062 0.00165724 Br 2.88032916 11.64537740 20.91483502 0.00133071 -0.00102825 -0.00019910 Br 0.16344686 14.69848643 20.74454206 -0.00003374 0.00187787 -0.00261774 Cs 0.03149602 17.87374306 23.54254469 -0.00042994 0.00088433 -0.00126754 Pb 3.00555236 20.82411785 2.78860641 -0.00079041 0.00131861 -0.00045686 Br 2.86432174 21.08175734 0.00248989 -0.00131653 0.00403770 -0.00118992 Br 2.93571614 17.78613779 2.78436009 -0.00360825 -0.00058442 0.00135496 Br 0.01200982 20.96292383 3.11955075 0.00388712 0.00146329 0.00234489 Cs 22.98849098 17.84884272 5.95867975 -0.00070347 -0.00023166 -0.00294774 Pb 2.97290586 20.84304363 8.82461373 0.00067903 -0.00113192 0.00070150 Br 3.04538255 20.60718818 5.87047101 0.00046882 -0.00174663 -0.00030926 Br 3.11147845 17.80806815 9.17493530 -0.00239784 0.00363088 -0.00277008 Br 23.74409967 20.80074453 8.88912845 -0.00068949 -0.00322964 -0.00058609 Cs 0.73212406 17.35307874 11.92591248 0.00249041 0.00287235 -0.00329282 Pb 3.14033002 20.88250062 14.86161962 0.00010151 -0.00334485 0.00113384 Br 2.91975776 20.79471007 11.75541832 -0.00350652 -0.00018265 0.00425002 Br 3.08675750 17.60281576 15.21914966 0.00067103 -0.00479845 0.00022619 Br 0.15315958 20.70153380 14.94830006 0.00077796 0.00241209 -0.00015866 Cs 0.41082204 17.71930120 17.65171639 -0.00017357 0.00053601 0.00211717 Pb 3.07816261 20.82023349 20.43505436 0.00096358 0.00049905 0.00031532 Br 3.23188546 20.28859760 17.75322442 -0.00442574 -0.00006868 0.00050003 Br 3.05637450 17.71796537 20.18613084 -0.00049776 -0.00080021 -0.00162532 Br 0.19602786 20.52470632 20.61506395 0.00035949 -0.00099309 0.00046562 Cs 5.74636069 0.21727438 22.90277657 -0.00254543 0.00129288 0.00094594 Pb 8.85071756 3.17060934 3.05560257 -0.00233708 0.00194241 -0.00248125 Br 8.73524299 2.75056611 0.11796735 -0.00491203 0.00269028 0.00170699 Br 9.05312473 0.31776094 3.66231426 0.00326709 0.00225315 0.00346142 Br 5.89879542 3.09416429 3.75538041 0.00028323 0.00138968 0.00206238 Cs 5.85278235 23.49951823 5.80354447 -0.00277144 0.00137036 0.00111497 Pb 8.75024930 2.91870364 8.82256881 0.00067965 0.00157108 -0.00033621 Br 9.64252821 3.44962211 5.96886073 -0.00250992 -0.00005648 -0.00113389 Br 8.42588064 23.68048644 9.00848249 0.00019765 0.00266752 0.00258802 Br 6.06463591 3.05550353 8.23055899 -0.00040922 -0.00382743 -0.00242751 Cs 6.00798295 0.05624295 12.06921465 -0.00085152 0.00164126 0.00001449 Pb 9.28183284 2.87594515 14.91555944 0.00159632 0.00029448 0.00306545 Br 8.90717107 2.83660175 11.76760073 -0.00197392 -0.00038907 0.00471918 Br 10.04039703 23.70592648 14.94986237 -0.00304028 0.00194108 -0.00376286 Br 6.20742069 2.47435268 14.98938723 0.00115347 0.00127995 -0.00150416 Cs 6.38479224 23.28783175 17.77567337 -0.00108457 -0.00157675 0.00139735 Pb 9.05179265 3.10581611 20.90461035 -0.00134224 0.00014661 -0.00197412 Br 9.52801548 2.91286720 18.14527799 -0.00254456 -0.00049329 0.00430555 Br 8.84650315 23.60616224 20.90900024 -0.00334264 -0.00346457 0.00250660 Br 5.98825240 3.86675570 20.76381501 0.00073831 -0.00088237 -0.00021098 Cs 5.50785851 5.97139019 23.76047258 -0.00118277 -0.00407724 0.00106329 Pb 8.70269042 8.87890949 2.80804964 -0.00126887 0.00010100 0.00063699 Br 8.97389483 8.89113422 23.71770765 0.00027701 0.00078140 0.00009623 Br 8.19800949 6.08814769 2.93516971 -0.00058369 0.00286177 -0.00037389 Br 5.76741537 9.11184835 2.83254141 -0.00210299 0.00223700 0.00102421 Cs 6.13428948 6.21678335 5.78056707 0.00258546 0.00034662 -0.00060230 Pb 8.83009083 8.80780082 8.73107684 0.00201363 -0.00063691 0.00088677 Br 8.68939205 8.43274537 5.81646619 0.00021782 -0.00126594 -0.00059468 Br 8.62737970 5.94987254 9.31194290 -0.00174651 0.00123274 0.00153279 Br 6.01118096 8.47039948 8.67068860 0.00143807 0.00367393 0.00039118 Cs 5.99334611 6.00409598 11.97418407 0.00115580 -0.00258797 0.00114458 Pb 8.93853834 8.93478392 14.83622459 -0.00115197 0.00033743 -0.00078573 Br 7.95300046 8.72789267 11.94638366 0.00241049 -0.00126566 0.00058316 Br 9.43825482 5.97868012 15.22487185 -0.00006235 -0.00031995 0.00150829 Br 6.19944118 8.78715452 15.50121960 0.00049206 -0.00402273 0.00105595 Cs 6.74993233 6.14114582 17.75862570 -0.00246374 0.00043445 -0.00106524 Pb 8.74281353 9.09800030 20.69976160 0.00054834 0.00121327 0.00006413 Br 9.03932144 9.13737109 17.76693751 -0.00180287 -0.00239669 -0.00050256 Br 9.15782328 6.04051612 20.77695492 -0.00006830 -0.00199328 -0.00249374 Br 5.55017138 8.50657812 20.66828533 -0.00030683 -0.00077730 0.00079759 Cs 5.42934484 11.39820540 23.49887870 -0.00065856 -0.00149177 0.00229211 Pb 8.80013247 14.73526377 3.18479778 -0.00065051 0.00182441 0.00100955 Br 9.25463473 14.56125283 0.04564635 -0.00186839 -0.00049013 -0.00485860 Br 9.01537639 11.90691446 3.33083981 0.00051037 0.00248809 0.00249713 Br 5.70154857 14.51409104 2.61610273 0.00087003 -0.00145166 0.00023397 Cs 5.41977287 12.01447993 5.03124808 0.00334082 0.00149808 0.00249627 Pb 8.72288850 14.57508468 9.01071535 0.00216175 0.00147608 0.00033594 Br 8.44445737 13.89100631 5.98140814 0.00180555 -0.00013908 0.00298132 Br 9.56713672 11.77974290 8.85993001 -0.00343595 -0.00466608 0.00188318 Br 5.86306704 14.06308175 9.52532666 0.00248434 -0.00138881 -0.00153928 Cs 5.64064450 11.71372448 11.85409991 0.00200696 -0.00473036 0.00123514 Pb 9.20461793 15.00536697 14.82967410 0.00337469 -0.00192902 -0.00174948 Br 9.22035502 15.24615509 11.86686644 -0.00471096 0.00055524 0.00034260 Br 9.52443095 11.79873794 15.10160994 0.00008387 0.00148936 -0.00098766 Br 5.77680614 14.63086639 14.02310480 0.00126669 -0.00032749 -0.00319876 Cs 6.43976131 11.70290521 17.61258932 0.00047936 -0.00283894 -0.00248869 Pb 9.01493583 14.91781084 20.81565359 0.00124717 0.00029777 0.00144210 Br 9.36498481 14.87202360 17.78011239 -0.00035875 -0.00042151 0.00129791 Br 8.83618649 11.95491016 21.13626137 -0.00156248 0.00279181 -0.00429449 Br 6.07312796 14.94518067 21.28976895 -0.00148668 -0.00000487 0.00012275 Cs 5.43333196 18.57449524 23.60181079 0.00042946 0.00027337 -0.00195418 Pb 8.80191000 20.76161931 3.06485817 0.00014443 0.00079477 -0.00004256 Br 8.93120044 20.93952175 23.76928786 0.00012033 0.00209117 -0.00238953 Br 8.40701316 17.89505377 2.74782002 0.00111063 0.00236593 -0.00160069 Br 5.78433604 20.82758850 3.04889115 0.00274600 0.00308213 0.00125221 Cs 5.57968354 18.03584553 5.51155184 -0.00170639 0.00128650 -0.00027768 Pb 8.71507874 20.44271560 8.91222230 0.00281328 -0.00064944 -0.00007542 Br 8.17903349 20.42710683 5.98865241 -0.00213749 0.00244082 0.00128990 Br 8.13432178 17.47463065 8.09281575 0.00137532 0.00055611 -0.00210595 Br 5.85649481 20.57695204 8.53307314 0.00351929 -0.00018035 -0.00129561 Cs 6.25195616 18.31440009 11.41517422 -0.00139837 0.00087035 0.00048738 Pb 9.23360213 20.79639005 14.75697378 -0.00064022 -0.00077148 0.00237133 Br 9.60470751 20.74721606 11.74559562 0.00122900 -0.00405057 -0.00134963 Br 8.36743379 17.87901854 14.83364292 -0.00082907 0.00533557 0.00266532 Br 6.29660199 21.36055961 14.88920341 0.00258581 0.00184436 -0.00097450 Cs 5.70828412 17.36309308 17.76165163 -0.00104298 0.00003778 -0.00261121 Pb 8.98689914 20.66265477 20.65694075 -0.00102777 -0.00019048 -0.00071323 Br 9.81806057 20.70329506 17.67381790 -0.00033301 -0.00013488 0.00213141 Br 7.98792471 17.83085084 20.78004846 0.00357158 -0.00398081 0.00113081 Br 5.90811079 20.81061712 20.53092286 -0.00305063 0.00107008 -0.00057156 Cs 12.17679909 23.71816697 0.25267565 0.00045363 -0.00009326 -0.00176684 Pb 15.01291771 3.36293833 2.83224620 -0.00056245 -0.00086809 0.00032871 Br 15.40208985 2.96527939 23.63091163 0.00156821 0.00159522 -0.00309507 Br 15.62028692 23.72632717 2.91594783 0.00307857 0.00118186 0.00373841 Br 12.13113830 3.55505143 2.43853215 -0.00042484 0.00171567 -0.00148098 Cs 12.11297681 0.51382174 5.56217696 -0.00125145 -0.00078102 -0.00284350 Pb 14.91217109 3.10606701 8.82270893 0.00055459 0.00118037 -0.00028823 Br 14.56008897 3.30557905 5.80726279 -0.00283037 0.00142966 -0.00020285 Br 14.99362820 0.06880300 9.08838163 0.00187475 -0.00019831 0.00043081 Br 12.06411543 2.99732173 9.53436455 -0.00240493 0.00013355 -0.00222274 Cs 11.96405776 0.46517352 11.95332558 0.00075727 0.00301148 -0.00058879 Pb 15.03918371 2.67670926 14.92738198 0.00133665 -0.00326260 0.00038365 Br 14.93435932 3.02713286 12.05875629 -0.00312201 0.00104526 0.00106769 Br 14.27441972 23.59095605 14.80555205 0.00080329 0.00017393 -0.00263892 Br 12.10008572 3.18474829 14.17680634 -0.00017390 0.00509091 0.00028028 Cs 12.02258840 23.36626749 18.48373347 -0.00099280 0.00225633 0.00174915 Pb 14.98901736 3.11258354 20.77796368 -0.00212987 0.00035668 -0.00079201 Br 14.47794414 3.11422685 17.92796176 -0.00569705 -0.00075381 -0.00257098 Br 15.29190227 23.53842366 20.62919818 0.00266653 0.00078221 -0.00440431 Br 11.81619038 2.55438800 21.57393750 -0.00148935 -0.00316398 0.00032800 Cs 11.89003770 6.21027564 23.36563145 0.00142502 -0.00125351 0.00112133 Pb 14.74154381 8.93221143 3.25332503 0.00011042 -0.00005273 -0.00326397 Br 14.79647572 8.72382513 0.11234108 0.00113779 -0.00401153 -0.00198196 Br 15.21950470 6.03650689 3.36858064 -0.00253645 0.00177577 -0.00399513 Br 11.70431337 8.40341875 2.61613955 0.00056108 -0.00261004 0.00021531 Cs 12.14211299 6.18173121 5.60363559 0.00275731 -0.00147588 0.00057893 Pb 14.56072532 8.93535492 8.99242830 0.00037074 0.00135709 -0.00130735 Br 14.31962283 9.56972670 5.98351044 -0.00081973 -0.00027979 0.00014052 Br 15.27584245 6.09826626 8.38148487 -0.00006706 -0.00116339 0.00148955 Br 11.66997448 7.97684230 8.90648744 -0.00089226 -0.00123441 -0.00159993 Cs 12.04588391 6.00086467 11.93997923 0.00115597 0.00068164 0.00165628 Pb 14.98901616 8.89835257 14.88073249 0.00133616 0.00037696 0.00266136 Br 14.52792516 9.16291652 12.08562473 -0.00503990 0.00081081 -0.00000969 Br 14.79124126 5.71838741 14.82263616 -0.00258200 -0.00378025 -0.00117590 Br 12.07171512 8.61100573 15.03045962 -0.00086427 0.00269400 0.00242410 Cs 12.58985354 5.69576315 18.27776139 -0.00130313 0.00065969 -0.00097017 Pb 14.63403535 9.18897022 20.93565253 0.00106384 0.00011814 0.00024953 Br 14.77600868 8.59919471 17.71228298 0.00043305 -0.00109227 0.00004852 Br 15.08878447 6.10689463 20.70234481 -0.00388151 0.00525218 -0.00155310 Br 11.88419415 8.84038556 20.53979939 0.00118504 -0.00271190 0.00177032 Cs 11.30741245 11.84880014 0.53877244 0.00054747 -0.00039875 -0.00131920 Pb 14.71859751 14.89429541 3.24025098 -0.00042161 0.00011785 -0.00063728 Br 14.97377862 15.45707963 0.00835256 0.00197096 -0.00261282 -0.00014639 Br 14.34210228 12.10611482 3.11259918 -0.00308757 -0.00279582 -0.00066300 Br 11.61455008 14.98929186 3.17991932 0.00087371 -0.00130502 -0.00453702 Cs 11.66451950 12.50001579 6.06695610 0.00266441 0.00000960 0.00027249 Pb 14.72403182 15.03111797 9.15468976 0.00207883 0.00049880 -0.00200815 Br 14.90134068 14.86698269 6.04742704 -0.00172026 0.00080135 0.00172179 Br 13.63165382 11.96490226 9.00138547 -0.00192401 -0.00213564 0.00014436 Br 11.57892971 15.33513781 8.49172620 -0.00386615 -0.00239353 0.00351629 Cs 11.85106701 12.16168150 12.02219024 -0.00076284 0.00078930 0.00134967 Pb 14.92933773 15.02367796 15.08768198 -0.00159457 0.00049488 0.00180886 Br 14.71611039 14.76970022 12.15825432 0.00050195 -0.00090050 -0.00182617 Br 14.65240364 11.74599451 14.87473277 0.00330207 0.00390702 -0.00057168 Br 11.93209761 15.13883793 15.01020450 -0.00064097 -0.00138001 -0.00262055 Cs 12.24099908 11.44313960 17.43301571 -0.00065465 -0.00220983 0.00005807 Pb 14.91993813 14.89754767 20.95181560 0.00145570 0.00128404 -0.00109640 Br 14.58344565 14.78836915 18.01887941 0.00143783 0.00084533 0.00355556 Br 14.32000816 12.08023282 21.30526194 0.00157860 0.00116308 0.00372670 Br 11.90159883 15.42401196 21.57343720 0.00064907 0.00273850 -0.00041054 Cs 11.51482406 17.97735761 0.71601972 -0.00065529 -0.00129128 0.00336309 Pb 14.93246753 20.96647513 3.06838233 0.00184718 -0.00010262 0.00147297 Br 14.82920668 20.77207045 23.47941573 0.00436791 0.00089754 -0.00112673 Br 14.34845901 17.87515865 3.63229570 -0.00204295 -0.00099950 0.00008898 Br 11.87846530 21.33575931 3.47492204 0.00238805 -0.00127784 -0.00094525 Cs 11.24853321 18.19259300 5.94067992 0.00124657 0.00025442 0.00001828 Pb 14.96123025 20.95496963 8.88752225 -0.00110749 0.00011785 0.00136487 Br 15.19635388 20.58519213 6.07584088 0.00309848 -0.00422718 -0.00088250 Br 14.91502082 17.89389067 8.88113650 0.00314756 0.00206183 0.00174365 Br 11.54219722 21.42025674 8.13960173 -0.00299284 0.00117823 -0.00070682 Cs 12.43948518 18.32360958 11.58404633 -0.00075706 -0.00318352 -0.00213719 Pb 15.00111506 20.64784426 14.77585429 -0.00033472 0.00343747 -0.00148661 Br 15.14085029 20.83934859 11.87268803 0.00296977 0.00412395 0.00073602 Br 15.56121435 17.85982228 14.67663213 -0.00060313 -0.00091130 0.00169138 Br 12.22400688 19.72298445 14.90183946 0.00045829 0.00133464 0.00059987 Cs 12.15902705 17.59278540 17.92188337 -0.00102477 -0.00054261 0.00110611 Pb 14.99812646 20.76067843 20.68530666 0.00197198 0.00228787 0.00062356 Br 14.20509912 20.76581778 17.62603058 0.00082710 0.00263892 -0.00078103 Br 14.89056736 17.66331393 19.94705664 0.00365620 -0.00171192 0.00316214 Br 11.82711534 20.65248778 21.14292641 -0.00091222 -0.00277681 0.00164526 Cs 18.29595660 23.41175877 23.58945434 0.00429756 0.00028451 -0.00272000 Pb 21.00778409 3.33490823 2.58749390 0.00152590 0.00103672 -0.00132289 Br 20.56640794 3.93364403 23.72299107 -0.00068130 -0.00041215 0.00156549 Br 20.80341002 0.18376501 3.20215421 -0.00198248 -0.00007530 -0.00133139 Br 17.85728881 3.18597378 3.32851587 -0.00115574 -0.00102332 0.00534589 Cs 18.01727805 0.19854909 6.12893888 -0.00348564 0.00300718 -0.00104671 Pb 21.11487020 2.96582789 9.03881909 0.00158223 -0.00098506 0.00207218 Br 21.54770081 3.11320026 6.15992026 0.00009685 0.00129229 -0.00017501 Br 20.83039901 0.07571067 9.25209850 -0.00228356 -0.00128713 0.00284326 Br 17.79897923 2.77437517 8.57721549 0.00386803 0.00225554 0.00024358 Cs 18.01862052 0.22270999 11.81287636 0.00282743 -0.00218748 0.00016221 Pb 20.65105669 3.03076764 14.66362356 -0.00016819 -0.00017804 0.00061016 Br 19.85762111 3.60939464 11.79926327 0.00053135 0.00099369 0.00089271 Br 21.62932271 23.61241578 14.85117296 0.00127670 -0.00086023 0.00065548 Br 17.90934536 2.85834588 15.02926594 -0.00036242 0.00203435 0.00140533 Cs 18.03790778 23.55265499 18.18140400 0.00086051 -0.00226442 -0.00283440 Pb 21.06775388 2.94290398 20.63318993 -0.00136147 0.00189691 -0.00077799 Br 20.90806854 3.14735956 17.72792226 0.00058901 -0.00109627 0.00104399 Br 20.83827994 23.76432819 20.32598547 -0.00265371 0.00068162 -0.00252205 Br 17.69220636 2.92076250 20.15659443 0.00280892 0.00069318 0.00126710 Cs 17.76741636 6.46723461 23.27881957 0.00065609 0.00123317 -0.00147976 Pb 20.73663572 8.87400845 3.08802325 -0.00226052 -0.00069479 0.00019927 Br 20.19772657 8.69533088 0.10412183 -0.00066394 -0.00409023 -0.00129043 Br 20.52817051 5.88615733 3.40566317 -0.00034984 0.00308074 0.00265123 Br 17.72062779 8.90091831 3.63362300 -0.00283164 -0.00240025 -0.00029168 Cs 18.40466090 5.41640785 6.27814249 -0.00020457 0.00117734 -0.00231513 Pb 20.92125114 8.77847305 8.84256793 -0.00125788 0.00050619 -0.00015333 Br 20.78472245 9.37001006 5.85543435 0.00080427 -0.00137755 -0.00024421 Br 21.32732256 5.91203835 8.60339298 0.00045760 -0.00162273 -0.00270431 Br 17.48078092 9.18714400 8.84085395 0.00257195 0.00097456 -0.00559688 Cs 17.49559606 6.42535770 11.44629538 0.00078906 0.00074945 0.00042637 Pb 20.96952119 8.71220889 14.97637183 -0.00013493 -0.00143880 -0.00006142 Br 20.81484662 8.75642193 11.83606865 -0.00177204 0.00147437 -0.00180316 Br 20.75327524 5.81812650 15.12669449 -0.00130881 0.00103166 -0.00328507 Br 18.12339573 8.27134101 14.74337672 0.00098195 -0.00210148 0.00073322 Cs 17.75046603 5.66126008 17.65130546 -0.00185930 -0.00085172 0.00153612 Pb 20.63196590 9.04937970 20.60658510 -0.00034421 -0.00058521 -0.00066856 Br 21.33287421 8.60713503 17.76442947 -0.00060469 -0.00099914 0.00145245 Br 20.48136361 5.93988131 20.45797536 0.00079362 0.00364359 -0.00397299 Br 17.49794802 9.20903604 20.55858498 0.00174345 -0.00346696 0.00096939 Cs 17.61422671 11.86132331 0.13212083 -0.00262660 -0.00107733 -0.00026703 Pb 20.60864721 14.78896508 3.15090580 0.00094589 0.00078092 0.00127188 Br 20.81823883 15.20947937 0.10563089 0.00110138 -0.00102972 0.00065387 Br 20.26446754 11.93111885 3.29907251 -0.00021038 0.00125921 -0.00033292 Br 17.60170983 15.01510108 2.52632344 0.00298227 -0.00046631 -0.00288673 Cs 17.86521860 11.24888805 6.25869467 0.00037326 0.00313545 -0.00016991 Pb 20.46650778 14.88147492 9.15545457 -0.00109726 0.00002490 0.00140988 Br 20.07572089 14.66618595 6.00863453 -0.00189084 -0.00368441 0.00289672 Br 20.09324085 11.77674284 9.10519994 -0.00471381 -0.00163113 0.00574283 Br 17.58457900 14.47980831 9.36407146 -0.00249067 0.00122519 0.00064572 Cs 18.02867114 11.30329624 12.09613457 0.00168401 0.00000706 -0.00276796 Pb 20.78382417 14.70196780 14.71889038 -0.00018652 0.00001140 -0.00117558 Br 20.81445507 13.94956556 11.79272508 -0.00055565 0.00018137 0.00103568 Br 20.68470445 11.64457073 15.45003441 0.00259925 0.00018396 0.00169612 Br 17.72976981 14.35743986 15.00196295 -0.00009065 0.00321186 0.00201090 Cs 17.40524948 11.89240149 17.58744071 -0.00004819 -0.00203100 -0.00005721 Pb 21.03199244 15.11442111 20.73375380 0.00156944 0.00069550 -0.00105311 Br 20.97008355 14.75694339 17.87001252 -0.00265111 0.00324508 -0.00319323 Br 20.07669083 11.78012534 20.26824758 -0.00094275 -0.00253502 0.00376123 Br 18.07361130 14.95793400 20.52008448 0.00012834 -0.00053435 -0.00055817 Cs 17.95288593 17.68518062 23.46032365 0.00023906 -0.00084573 0.00103099 Pb 21.01214879 20.84007169 3.05870419 0.00173501 -0.00149981 -0.00120805 Br 21.38482483 21.08147001 23.70189761 -0.00018977 -0.00115853 0.00185310 Br 20.93928982 17.80507711 3.25312517 0.00279237 0.00083869 0.00104768 Br 17.78786263 20.46872999 2.48885869 -0.00384792 0.00312039 0.00353792 Cs 17.80310787 17.90313869 5.50857958 0.00097866 -0.00143200 -0.00272176 Pb 20.83110603 20.96148267 8.76427323 0.00065355 -0.00083555 -0.00016030 Br 20.92782458 20.80053354 5.94088400 -0.00184879 0.00097010 -0.00079177 Br 20.28331591 17.76768418 8.76015825 0.00055541 0.00307490 0.00117145 Br 17.75752710 21.13381922 9.51357394 0.00727014 0.00318393 -0.00346079 Cs 17.77837593 17.86005759 11.87325422 -0.00053363 -0.00201583 0.00299537 Pb 20.68396434 20.89990559 14.86204575 -0.00168788 0.00092633 -0.00032892 Br 21.12536877 20.88924388 11.85652231 0.00074573 0.00056253 -0.00126653 Br 20.47942639 17.35784402 14.10894519 -0.00136556 0.00208484 0.00164590 Br 17.89645711 21.53243498 14.97829617 -0.00148204 0.00180967 0.00391612 Cs 17.59746535 18.01555955 18.18378300 0.00316928 -0.00100918 0.00200565 Pb 20.81318948 20.70951566 20.99393796 -0.00089013 -0.00055399 0.00045306 Br 20.86603048 20.61505057 17.74498239 0.00234699 -0.00283636 0.00362083 Br 20.80949217 17.87820164 20.24834824 0.00055030 0.00060286 -0.00160925 Br 18.04111449 20.74156714 21.26464982 0.00217141 0.00107161 0.00188335 320 Lattice="23.77195271 0.0 0.0 0.0 23.77195271 0.0 0.0 0.0 23.77195271" Properties=species:S:1:pos:R:3:vel:R:3 Time=20189.99999999 energy=-11424.58690262 virial="-9.24572752 2.88498241 0.65979028 2.88498241 -13.24904219 2.34999396 0.65979028 2.34999396 -5.17397072" stress="0.00024225 0.00021476 4.911e-05 0.00021476 -9.212e-05 0.00017493 4.911e-05 0.00017493 0.00051329" pbc="T T T" Cs 23.46016592 23.74670611 0.12744387 -0.00145306 -0.00185278 0.00161228 Pb 2.86092888 3.09428779 3.19059078 0.00042695 -0.00024537 0.00096217 Br 3.26976005 2.93872772 0.17758182 0.00050239 0.00013906 0.00086519 Br 2.87543602 23.76665100 3.04883696 -0.00176107 -0.00109154 -0.00042572 Br 23.73602705 3.41535767 2.12346674 -0.00230538 -0.00062642 -0.00294696 Cs 0.41179922 0.15881455 5.89594225 0.00301582 0.00134577 -0.00266289 Pb 3.13458078 3.00131120 9.09426761 0.00144550 -0.00003879 -0.00146019 Br 2.58320634 2.71314771 6.37625781 0.00075552 0.00007943 -0.00180092 Br 3.16621835 0.01759122 9.54773052 -0.00006092 -0.00009240 0.00146175 Br 23.64448146 2.93813224 10.50071291 0.00055635 -0.00467795 -0.00106373 Cs 23.76187542 23.51046863 12.20294255 -0.00308412 -0.00147459 -0.00070213 Pb 3.07638257 3.14279931 14.83943732 0.00069312 -0.00199674 -0.00066455 Br 3.86363004 3.00503687 12.06266302 -0.00390612 0.00038775 0.00041504 Br 3.05512855 0.02129356 14.54047976 0.00021454 -0.00116276 -0.00127199 Br 0.04940561 3.21821352 14.56733490 -0.00260215 -0.00191421 -0.00226947 Cs 0.06913097 23.26886887 17.57991010 -0.00113413 -0.00079603 -0.00170244 Pb 3.27281616 3.14770899 20.72936091 -0.00074393 0.00062409 0.00116309 Br 2.65001322 3.32325419 17.77835409 0.00214851 0.00096288 -0.00055649 Br 3.00072331 0.06383516 20.58830200 -0.00020969 -0.00122125 0.00279109 Br 0.08695870 2.65393453 20.70624964 -0.00213525 -0.00482905 0.00219378 Cs 23.47173837 6.58612786 23.64645340 -0.00038065 0.00123822 -0.00110570 Pb 2.88162043 8.91064123 2.96907031 0.00059491 0.00063649 -0.00381315 Br 2.91356524 8.23713805 0.09556004 0.00255596 -0.00188915 0.00206400 Br 2.91358823 6.05319517 3.06908226 -0.00175218 -0.00087219 0.00061922 Br 0.02217555 8.53464834 2.97019325 -0.00167079 -0.00031305 -0.00247297 Cs 0.55346545 6.05431398 6.11233101 0.00092119 -0.00027336 0.00185618 Pb 3.33794019 9.08225490 8.89567235 -0.00220135 0.00048413 0.00111565 Br 2.82639339 8.76685106 6.12683178 -0.00034114 -0.00122484 -0.00357324 Br 2.65154169 5.88389991 9.01078033 -0.00159607 0.00377948 0.00368265 Br 23.72944229 9.30889551 8.85910163 0.00274147 -0.00065968 0.00078435 Cs 23.44959448 5.92180263 12.11240709 -0.00225651 -0.00033405 0.00158895 Pb 2.90447132 8.91402493 14.74792663 0.00088154 0.00165324 -0.00005685 Br 3.23826535 9.08799370 11.92144425 0.00106696 0.00239275 -0.00100574 Br 3.77657449 6.07407744 15.21832605 -0.00010125 -0.00316618 -0.00009725 Br 0.14161887 8.37652254 14.19207280 0.00096850 -0.00032749 -0.00169003 Cs 23.57014182 5.74578023 17.65409912 -0.00087592 -0.00012825 0.00065359 Pb 2.60680416 8.67687302 20.65436772 -0.00061751 -0.00243180 0.00070303 Br 2.53466563 8.78556272 17.70739689 0.00212595 -0.00058478 0.00389353 Br 2.25711193 5.90648658 20.95661831 0.00221494 -0.00033246 0.00210982 Br 23.23552376 9.32072679 21.21109637 -0.00031304 0.00123356 0.00131540 Cs 23.41507727 11.74674260 0.29509659 0.00338258 0.00031305 0.00058569 Pb 2.57752292 14.98347503 2.94909798 -0.00029480 -0.00014107 0.00036638 Br 2.49320199 14.95902630 23.76707545 0.00014037 -0.00127141 -0.00138044 Br 2.86450415 12.02977891 2.17164110 -0.00184890 0.00031793 0.00026384 Br 23.58114078 14.72692042 3.15382998 0.00317634 0.00065143 -0.00061728 Cs 23.63173265 11.83487249 6.50953502 0.00134309 0.00224020 -0.00167131 Pb 3.04832083 14.98146457 9.08652993 -0.00008337 -0.00186172 -0.00072623 Br 3.06527179 14.91225904 5.81826085 -0.00272066 -0.00028697 -0.00041046 Br 2.96627533 12.06642002 8.70990502 -0.00092836 -0.00497086 -0.00077659 Br 0.02107322 14.95839274 8.87326053 -0.00112462 -0.00146619 -0.00012982 Cs 23.67354365 11.78303902 11.40469705 -0.00217985 0.00210678 0.00022746 Pb 3.07872685 14.55318545 15.00875285 -0.00018075 0.00038024 -0.00191858 Br 2.76717641 14.27781759 11.98584252 -0.00424800 -0.00143741 0.00091051 Br 2.40627145 11.66265373 15.50952988 -0.00080447 -0.00117251 0.00030272 Br 0.19161707 14.55647785 14.54590072 -0.00390252 0.00063838 0.00005583 Cs 23.72553835 11.63820120 18.14679254 0.00200126 -0.00070503 -0.00051428 Pb 3.15179772 14.52880194 20.80524336 0.00042179 0.00138439 0.00111506 Br 2.95845195 14.93476990 17.90467048 0.00064518 -0.00047806 0.00186403 Br 2.89346503 11.63567701 20.91279545 0.00129608 -0.00090752 -0.00020800 Br 0.16313029 14.71716348 20.71821401 -0.00003589 0.00185773 -0.00264689 Cs 0.02759870 17.88220954 23.52988227 -0.00034777 0.00080775 -0.00126587 Pb 2.99681434 20.83739485 2.78503574 -0.00094247 0.00133128 -0.00026194 Br 2.85085696 21.12237290 23.75908267 -0.00137075 0.00407777 -0.00186289 Br 2.89933862 17.78381000 2.79721354 -0.00366565 0.00012822 0.00121373 Br 0.05003460 20.97744471 3.14325455 0.00368248 0.00143973 0.00239560 Cs 22.98293333 17.84579467 5.93054790 -0.00040214 -0.00037336 -0.00266750 Pb 2.97915914 20.83198515 8.83196149 0.00057340 -0.00107591 0.00076789 Br 3.04909750 20.59084866 5.86734065 0.00027279 -0.00151494 -0.00030822 Br 3.08833462 17.84802876 9.14736387 -0.00222990 0.00429241 -0.00274711 Br 23.73687357 20.76836373 8.88231270 -0.00071768 -0.00324807 -0.00077608 Cs 0.75639040 17.38213438 11.89362248 0.00235896 0.00293876 -0.00316236 Pb 3.14091823 20.84870550 14.87191381 0.00001559 -0.00340353 0.00092077 Br 2.88555427 20.79158644 11.80043532 -0.00333181 -0.00044565 0.00472137 Br 3.09352268 17.55634002 15.21901221 0.00067676 -0.00447837 -0.00025395 Br 0.16267429 20.72477193 14.94680059 0.00112426 0.00222712 -0.00014130 Cs 0.40852033 17.72454048 17.67301759 -0.00028864 0.00051013 0.00214139 Pb 3.08689430 20.82484217 20.44022262 0.00076868 0.00042186 0.00071972 Br 3.18774869 20.28992624 17.75381702 -0.00439836 0.00032955 -0.00037619 Br 3.05155062 17.71028991 20.17350158 -0.00046181 -0.00073074 -0.00089531 Br 0.19621197 20.51497442 20.61974884 -0.00032325 -0.00095260 0.00047466 Cs 5.72130244 0.23004719 22.91303767 -0.00246361 0.00125996 0.00110758 Pb 8.82773360 3.18950827 3.03136597 -0.00225581 0.00183357 -0.00235918 Br 8.68472202 2.77757739 0.13566566 -0.00519238 0.00270811 0.00181895 Br 9.08470360 0.33809529 3.69567715 0.00303655 0.00181817 0.00319810 Br 5.90076143 3.10770511 3.77507087 0.00009705 0.00131638 0.00186964 Cs 5.82532616 23.51360518 5.81503086 -0.00271609 0.00144573 0.00118325 Pb 8.75909104 2.93412899 8.81964054 0.00108334 0.00151259 -0.00024391 Br 9.61751697 3.44944115 5.95685905 -0.00249193 0.00002455 -0.00125079 Br 8.42739033 23.70568678 9.03391504 0.00010951 0.00237570 0.00249743 Br 6.05463300 3.01776413 8.20627045 -0.00157589 -0.00371863 -0.00242696 Cs 5.99968530 0.07290874 12.06913939 -0.00080571 0.00169220 -0.00002864 Pb 9.29785953 2.87925693 14.94563735 0.00160516 0.00036865 0.00294130 Br 8.88634282 2.83260867 11.81811141 -0.00218706 -0.00040523 0.00535709 Br 10.00813364 23.72508038 14.91300270 -0.00340048 0.00188713 -0.00360129 Br 6.21952577 2.48750623 14.97569611 0.00125912 0.00135177 -0.00123171 Cs 6.37409252 23.27295950 17.78986073 -0.00105602 -0.00139622 0.00143718 Pb 9.03832075 3.10673877 20.88585083 -0.00135567 0.00003992 -0.00175803 Br 9.50305568 2.90770622 18.18359080 -0.00243602 -0.00054539 0.00331338 Br 8.81280268 23.57296278 20.93271850 -0.00339344 -0.00316082 0.00223121 Br 5.99771262 3.85770926 20.76128254 0.00113293 -0.00093104 -0.00029614 Cs 5.49676382 5.93023280 23.77131162 -0.00103260 -0.00415324 0.00110462 Pb 8.69114314 8.88049646 2.81544195 -0.00103664 0.00022289 0.00084092 Br 8.97588551 8.89908743 23.71633906 0.00012163 0.00081057 -0.00036686 Br 8.19216597 6.11509215 2.92940114 -0.00058901 0.00250747 -0.00078456 Br 5.74765286 9.13419373 2.84239731 -0.00182315 0.00223136 0.00094809 Cs 6.15921895 6.21899577 5.77500608 0.00239445 0.00009711 -0.00050361 Pb 8.85175044 8.80125544 8.74062945 0.00231013 -0.00067012 0.00101916 Br 8.69334145 8.42189374 5.81192010 0.00057844 -0.00089693 -0.00031583 Br 8.60959675 5.96054260 9.32661029 -0.00181124 0.00087843 0.00140345 Br 6.02354881 8.50726919 8.67401295 0.00101622 0.00370999 0.00027503 Cs 6.00460237 5.97731652 11.98584084 0.00109734 -0.00276500 0.00118745 Pb 8.92806341 8.93721173 14.82726476 -0.00093835 0.00014458 -0.00100426 Br 7.97911217 8.71802054 11.95150092 0.00280530 -0.00071547 0.00044339 Br 9.43610296 5.97354613 15.23958781 -0.00036792 -0.00068886 0.00143526 Br 6.20089128 8.74865373 15.51083895 -0.00021035 -0.00366888 0.00085692 Cs 6.72500400 6.14464464 17.74812258 -0.00252175 0.00025991 -0.00103095 Pb 8.74857909 9.10931142 20.70057572 0.00060418 0.00104887 0.00009984 Br 9.02188812 9.11367005 17.76490975 -0.00167778 -0.00233875 0.00009812 Br 9.15771438 6.02079032 20.75204222 0.00004889 -0.00194098 -0.00248637 Br 5.54876795 8.49957825 20.67628422 0.00002501 -0.00062392 0.00080250 Cs 5.42335990 11.38386982 23.52196112 -0.00053896 -0.00137352 0.00232015 Pb 8.79201240 14.75381549 3.19378586 -0.00097156 0.00187936 0.00079205 Br 9.23387720 14.55795976 23.77101551 -0.00227732 -0.00017198 -0.00442261 Br 9.02066465 11.92823228 3.35360692 0.00054872 0.00178097 0.00205055 Br 5.71159951 14.50078328 2.61852745 0.00115033 -0.00120745 0.00025160 Cs 5.45319105 12.02963307 5.05696939 0.00333927 0.00152814 0.00264869 Pb 8.74468559 14.59040887 9.01317484 0.00218933 0.00158011 0.00015418 Br 8.46105133 13.89278578 6.01359116 0.00151972 0.00049954 0.00342947 Br 9.53204839 11.73276784 8.88058697 -0.00357498 -0.00469383 0.00224207 Br 5.88940186 14.05181302 9.50841064 0.00273670 -0.00087731 -0.00183218 Cs 5.66102542 11.66601523 11.86746626 0.00207346 -0.00479816 0.00142795 Pb 9.23598520 14.98578639 14.81207761 0.00287831 -0.00198328 -0.00176005 Br 9.17398747 15.25159544 11.86961433 -0.00455480 0.00053900 0.00021737 Br 9.52451922 11.81690155 15.09054079 -0.00006792 0.00213994 -0.00122428 Br 5.79336730 14.62742662 13.99269976 0.00201756 -0.00036232 -0.00287688 Cs 6.44431962 11.67498636 17.58773577 0.00043043 -0.00274044 -0.00247783 Pb 9.02774624 14.92153723 20.83124705 0.00131391 0.00044952 0.00166980 Br 9.36071598 14.86760568 17.79393085 -0.00049035 -0.00046142 0.00143489 Br 8.82021782 11.98148968 21.09224090 -0.00163097 0.00248499 -0.00450929 Br 6.05638046 14.94327515 21.29034973 -0.00185773 -0.00037305 -0.00000618 Cs 5.43849776 18.57606900 23.58200900 0.00060073 0.00004352 -0.00200473 Pb 8.80340165 20.77029826 3.06422314 0.00015435 0.00094549 -0.00008264 Br 8.93182856 20.95976083 23.74658283 0.00000284 0.00195108 -0.00213416 Br 8.41874215 17.91975416 2.73261166 0.00123027 0.00256223 -0.00143863 Br 5.81619123 20.85904293 3.06050370 0.00358888 0.00320644 0.00106485 Cs 5.56233865 18.04775028 5.50880372 -0.00175878 0.00109569 -0.00027139 Pb 8.74479832 20.43773925 8.91035414 0.00312541 -0.00034745 -0.00029530 Br 8.16014471 20.45298590 5.99814801 -0.00162796 0.00273065 0.00058605 Br 8.14883896 17.48019402 8.07399615 0.00152964 0.00054995 -0.00165782 Br 5.89024276 20.57601230 8.52214914 0.00318978 -0.00000511 -0.00087763 Cs 6.23787158 18.32215512 11.42083131 -0.00141573 0.00068271 0.00064284 Pb 9.22801293 20.78838595 14.78092491 -0.00047608 -0.00082798 0.00241095 Br 9.61627417 20.70608980 11.73376571 0.00108415 -0.00417486 -0.00100187 Br 8.35965734 17.93182002 14.86036903 -0.00072551 0.00519616 0.00267669 Br 6.32190784 21.37800854 14.87831219 0.00246146 0.00164590 -0.00119905 Cs 5.69849797 17.36355163 17.73572182 -0.00091077 0.00005221 -0.00257008 Pb 8.97688421 20.66050687 20.65038402 -0.00097055 -0.00024414 -0.00059349 Br 9.81231251 20.70175715 17.69625785 -0.00080975 -0.00016743 0.00234573 Br 8.02569604 17.79164384 20.79118159 0.00396563 -0.00382776 0.00109428 Br 5.88053904 20.82239132 20.52552489 -0.00242309 0.00127176 -0.00050719 Cs 12.18109673 23.71712866 0.23515615 0.00040501 -0.00011879 -0.00173363 Pb 15.00743039 3.35244646 2.83578321 -0.00053255 -0.00122159 0.00037913 Br 15.41867439 2.98144837 23.60108038 0.00174406 0.00163899 -0.00286274 Br 15.64981843 23.73805909 2.95361962 0.00281930 0.00116169 0.00379386 Br 12.12371998 3.57186094 2.42385233 -0.00104967 0.00164316 -0.00145524 Cs 12.10048958 0.50620282 5.53464063 -0.00124200 -0.00074021 -0.00266114 Pb 14.91679668 3.11773683 8.82019913 0.00037412 0.00115172 -0.00021197 Br 14.53196790 3.31813221 5.80690768 -0.00278846 0.00108002 0.00012811 Br 15.01225987 0.06804564 9.09185140 0.00185023 0.00004536 0.00026254 Br 12.03963030 2.99973338 9.51020995 -0.00247580 0.00035044 -0.00261086 Cs 11.97174678 0.49463617 11.94670984 0.00077681 0.00288348 -0.00073239 Pb 15.05059086 2.64508321 14.93055961 0.00094100 -0.00305260 0.00024928 Br 14.90589935 3.03720992 12.06619178 -0.00256773 0.00096617 0.00040994 Br 14.28304124 23.59247484 14.77938604 0.00090937 0.00011805 -0.00259382 Br 12.09614039 3.23534826 14.18186487 -0.00061350 0.00500798 0.00073507 Cs 12.01242913 23.38999473 18.50093805 -0.00103907 0.00248369 0.00169239 Pb 14.96734674 3.11597737 20.77042893 -0.00219091 0.00032374 -0.00072123 Br 14.42265541 3.10300137 17.90067236 -0.00535322 -0.00149466 -0.00286100 Br 15.31797913 23.54915067 20.58535841 0.00254530 0.00137470 -0.00435822 Br 11.80310616 2.52399020 21.57582831 -0.00111685 -0.00291199 0.00004655 Cs 11.90433976 6.19742619 23.37730905 0.00143334 -0.00131553 0.00121292 Pb 14.74261346 8.93219216 3.21982660 0.00009722 0.00005206 -0.00342698 Br 14.80808472 8.68433921 0.09156565 0.00118272 -0.00388114 -0.00216401 Br 15.19422612 6.05819557 3.32866222 -0.00251854 0.00251309 -0.00399128 Br 11.71065529 8.37786458 2.61901238 0.00069431 -0.00249598 0.00035673 Cs 12.16941438 6.16677087 5.60968849 0.00269944 -0.00151364 0.00063076 Pb 14.56369589 8.94964438 8.97886689 0.00022307 0.00149930 -0.00140461 Br 14.31144834 9.56582912 5.98729689 -0.00080983 -0.00050440 0.00059876 Br 15.27484714 6.08706567 8.39737593 -0.00013069 -0.00107183 0.00168795 Br 11.66355883 7.96512447 8.89030684 -0.00036819 -0.00111166 -0.00163311 Cs 12.05725361 6.00732206 11.95694211 0.00111740 0.00061258 0.00173268 Pb 15.00193904 8.90048585 14.90749022 0.00124253 0.00005591 0.00267236 Br 14.47793565 9.17053903 12.07998222 -0.00494854 0.00071076 -0.00108734 Br 14.76709068 5.68181275 14.81142150 -0.00223865 -0.00350422 -0.00105889 Br 12.06355502 8.63899296 15.05409249 -0.00075759 0.00289997 0.00230088 Cs 12.57411307 5.70452724 18.26679184 -0.00184710 0.00109457 -0.00122565 Pb 14.64587596 9.18973262 20.93817605 0.00130126 0.00003937 0.00025516 Br 14.78090782 8.58962658 17.71697795 0.00054825 -0.00082071 0.00088384 Br 15.04980643 6.15740066 20.68805216 -0.00390726 0.00481364 -0.00130266 Br 11.89045181 8.81321502 20.55751082 0.00007071 -0.00271668 0.00177098 Cs 11.31401557 11.84462679 0.52521247 0.00076959 -0.00043369 -0.00139153 Pb 14.71454020 14.89655711 3.23290990 -0.00038719 0.00032578 -0.00083073 Br 14.99386991 15.43133673 0.00806373 0.00204540 -0.00253294 0.00010948 Br 14.31136192 12.07453707 3.10481466 -0.00305567 -0.00347703 -0.00089313 Br 11.62494803 14.97689938 3.13516600 0.00119910 -0.00117559 -0.00441262 Cs 11.69216616 12.49975916 6.06926130 0.00285724 -0.00006356 0.00019044 Pb 14.74402212 15.03487451 9.13384286 0.00191379 0.00025510 -0.00215396 Br 14.88483671 14.87461058 6.06710663 -0.00158115 0.00072430 0.00218962 Br 13.61530087 11.94296306 9.00347550 -0.00134336 -0.00224580 0.00027538 Br 11.54148126 15.31206570 8.52754969 -0.00358982 -0.00220971 0.00363909 Cs 11.84346382 12.16943858 12.03599519 -0.00075563 0.00076041 0.00140780 Pb 14.91367721 15.02774275 15.10517960 -0.00152770 0.00031666 0.00168725 Br 14.72053501 14.76158625 12.13882222 0.00038241 -0.00071715 -0.00204326 Br 14.68615683 11.78794105 14.86958224 0.00344324 0.00444179 -0.00045074 Br 11.92798017 15.12584062 14.98334466 -0.00015019 -0.00121564 -0.00274704 Cs 12.23398868 11.42103359 17.43432033 -0.00074571 -0.00220813 0.00020267 Pb 14.93403550 14.91076828 20.94187548 0.00136436 0.00135207 -0.00088497 Br 14.59764734 14.79429178 18.05156848 0.00139483 0.00033544 0.00294215 Br 14.33563308 12.09001437 21.34264062 0.00154451 0.00078652 0.00375036 Br 11.90930786 15.45138475 21.56664638 0.00089626 0.00273622 -0.00094422 Cs 11.50874641 17.96393216 0.74884777 -0.00055838 -0.00139101 0.00319670 Pb 14.95082198 20.96483917 3.08321150 0.00181782 -0.00021764 0.00149403 Br 14.87275533 20.78101536 23.47168247 0.00433900 0.00089556 -0.00041894 Br 14.32817482 17.86675926 3.63192148 -0.00201068 -0.00069452 -0.00016584 Br 11.90173788 21.32165880 3.46416535 0.00225068 -0.00153965 -0.00120266 Cs 11.26163188 18.19509467 5.94098423 0.00137008 0.00024773 0.00004256 Pb 14.94948271 20.95654321 8.90141283 -0.00122291 0.00019911 0.00140519 Br 15.22879521 20.54460051 6.06368903 0.00338130 -0.00388441 -0.00151129 Br 14.94708994 17.91420991 8.89950705 0.00326346 0.00198811 0.00193162 Br 11.51274735 21.42958264 8.13309955 -0.00287514 0.00068931 -0.00059895 Cs 12.43144632 18.29122624 11.56294201 -0.00084898 -0.00328983 -0.00207797 Pb 14.99676991 20.68363581 14.76153375 -0.00053366 0.00370280 -0.00137009 Br 15.16955266 20.87989783 11.88075991 0.00277735 0.00398181 0.00084737 Br 15.55617590 17.84819081 14.69439181 -0.00041231 -0.00138146 0.00185981 Br 12.22602379 19.73644173 14.90541304 -0.00004987 0.00135200 0.00011272 Cs 12.14760722 17.58770789 17.93271613 -0.00125196 -0.00047127 0.00106642 Pb 15.01647822 20.78252853 20.69132044 0.00169659 0.00207558 0.00057600 Br 14.21618365 20.79181708 17.62095873 0.00138592 0.00256130 -0.00023196 Br 14.92680641 17.65005897 19.98270388 0.00358399 -0.00090733 0.00395118 Br 11.82066762 20.62491143 21.15892668 -0.00038158 -0.00273220 0.00154878 Cs 18.33803946 23.41547981 23.56332988 0.00411902 0.00046362 -0.00250163 Pb 21.02148662 3.34321522 2.57593757 0.00120239 0.00063362 -0.00096888 Br 20.55902958 3.92944985 23.73503036 -0.00080299 -0.00041636 0.00081525 Br 20.78415811 0.18000779 3.18790581 -0.00186338 -0.00067183 -0.00152477 Br 17.84783341 3.17511333 3.38219937 -0.00074633 -0.00114411 0.00538563 Cs 17.98231907 0.22859757 6.11775384 -0.00350267 0.00300100 -0.00118909 Pb 21.13049771 2.95647788 9.06004999 0.00153842 -0.00088686 0.00216404 Br 21.54925654 3.12489809 6.15761206 0.00021638 0.00104132 -0.00028132 Br 20.80766801 0.06167927 9.27967659 -0.00225623 -0.00149823 0.00267190 Br 17.84006823 2.79699987 8.58012203 0.00431504 0.00227172 0.00033899 Cs 18.04641605 0.20099724 11.81439646 0.00273188 -0.00215412 0.00014038 Pb 20.65133529 3.02861250 14.66941925 0.00022499 -0.00025266 0.00054733 Br 19.86396289 3.61871718 11.80855100 0.00073863 0.00086976 0.00096911 Br 21.63862262 23.60488969 14.85874383 0.00057239 -0.00064080 0.00085898 Br 17.90516898 2.87765822 15.04477895 -0.00046631 0.00182935 0.00169795 Cs 18.04634058 23.53034351 18.15234716 0.00082689 -0.00219469 -0.00297353 Pb 21.05356009 2.96264779 20.62512874 -0.00147788 0.00204464 -0.00083162 Br 20.91411408 3.13509501 17.73812769 0.00062000 -0.00134889 0.00098170 Br 20.81222460 23.76948789 20.30249544 -0.00255441 0.00035627 -0.00216803 Br 17.72208256 2.92745372 20.16978064 0.00311676 0.00064909 0.00137257 Cs 17.77296140 6.47814677 23.26388347 0.00044672 0.00094241 -0.00150922 Pb 20.71305275 8.86715147 3.08925306 -0.00245064 -0.00067626 0.00004332 Br 20.19330305 8.65684624 0.09073534 -0.00021177 -0.00359679 -0.00137468 Br 20.52547474 5.92234236 3.43293865 -0.00018201 0.00410512 0.00278546 Br 17.69244178 8.87684375 3.62904889 -0.00278881 -0.00240246 -0.00061429 Cs 18.40197441 5.42858010 6.25442183 -0.00033182 0.00125670 -0.00242305 Pb 20.90782693 8.78458268 8.84154546 -0.00141644 0.00070924 -0.00005463 Br 20.79482450 9.35484532 5.85679942 0.00120767 -0.00164331 0.00051207 Br 21.33199826 5.89729330 8.57667938 0.00047358 -0.00130921 -0.00262858 Br 17.50676384 9.19466504 8.78741192 0.00260678 0.00051274 -0.00508007 Cs 17.50341564 6.43238313 11.45106092 0.00077409 0.00065540 0.00052435 Pb 20.96801710 8.69892633 14.97465411 -0.00016113 -0.00121004 -0.00027693 Br 20.79714229 8.77090143 11.81718635 -0.00176652 0.00141654 -0.00195515 Br 20.74098472 5.82683976 15.09358050 -0.00114425 0.00069287 -0.00332993 Br 18.13023905 8.25183888 14.75078138 0.00037466 -0.00179467 0.00074863 Cs 17.73184337 5.65318363 17.66663539 -0.00186436 -0.00076271 0.00152910 Pb 20.62577171 9.04269542 20.59974668 -0.00089120 -0.00075457 -0.00069298 Br 21.32725424 8.59751052 17.78142926 -0.00051763 -0.00092328 0.00190945 Br 20.48993764 5.97597460 20.41865379 0.00092010 0.00354519 -0.00388604 Br 17.51634896 9.17409515 20.56847038 0.00192096 -0.00352141 0.00100347 Cs 17.58783774 11.85073253 0.12943576 -0.00265014 -0.00104036 -0.00027137 Pb 20.61716722 14.79680327 3.16317717 0.00075855 0.00078193 0.00118009 Br 20.82992722 15.19908967 0.11116503 0.00123834 -0.00104676 0.00045535 Br 20.26320700 11.94187690 3.29360444 -0.00004286 0.00089764 -0.00075740 Br 17.63302720 15.01072088 2.49855788 0.00326911 -0.00040760 -0.00265654 Cs 17.86836265 11.28253538 6.25599802 0.00025905 0.00359050 -0.00037705 Pb 20.45559555 14.88102787 9.16798022 -0.00108648 -0.00010651 0.00109508 Br 20.05753007 14.63018562 6.03861473 -0.00174547 -0.00351273 0.00308951 Br 20.04916221 11.75952990 9.16094763 -0.00408952 -0.00179345 0.00539082 Br 17.55957898 14.49313083 9.37070745 -0.00248562 0.00143494 0.00068003 Cs 18.04538165 11.30377902 12.06865209 0.00165050 0.00008889 -0.00272077 Pb 20.78262822 14.70071391 14.70797151 -0.00005361 -0.00025346 -0.00100283 Br 20.80916787 13.95208121 11.80732215 -0.00050555 0.00032015 0.00186275 Br 20.71055917 11.64618322 15.46625093 0.00256963 0.00013526 0.00154471 Br 17.73096450 14.38988981 15.02158470 0.00031805 0.00327510 0.00191433 Cs 17.40548513 11.87192061 17.58760914 0.00009774 -0.00206117 0.00009004 Pb 21.04659657 15.11959423 20.72305313 0.00134699 0.00033972 -0.00109040 Br 20.94436983 14.79033791 17.83672868 -0.00249067 0.00343130 -0.00343810 Br 20.06932389 11.75704144 20.30641843 -0.00053770 -0.00204545 0.00386506 Br 18.07200499 14.95342121 20.51549897 -0.00043960 -0.00036928 -0.00035738 Cs 17.95488364 17.67614052 23.47121661 0.00016003 -0.00096206 0.00114525 Pb 21.02810034 20.82570407 3.04615084 0.00146067 -0.00136681 -0.00129523 Br 21.38202338 21.06812274 23.72334496 -0.00037132 -0.00151597 0.00242477 Br 20.96620031 17.81588183 3.26196432 0.00257789 0.00131417 0.00070506 Br 17.75118431 20.49944302 2.52453577 -0.00344577 0.00300707 0.00358955 Cs 17.81241595 17.88836890 5.48142170 0.00088371 -0.00151946 -0.00270733 Pb 20.83736100 20.95248705 8.76519234 0.00059466 -0.00096221 0.00033832 Br 20.90837295 20.81108492 5.93090296 -0.00204099 0.00113636 -0.00120978 Br 20.28882662 17.80030111 8.77200719 0.00054717 0.00341024 0.00119776 Br 17.83137027 21.16489288 9.47658043 0.00742290 0.00302378 -0.00393362 Cs 17.77313297 17.84011124 11.90215134 -0.00051548 -0.00197358 0.00278297 Pb 20.66842304 20.90861338 14.85812868 -0.00141790 0.00081034 -0.00044997 Br 21.13185851 20.89461795 11.84417959 0.00054911 0.00050903 -0.00117541 Br 20.46618167 17.38280021 14.12564143 -0.00127901 0.00289091 0.00169901 Br 17.88142967 21.55143813 15.01755760 -0.00151420 0.00198969 0.00393226 Cs 17.63111709 18.00515157 18.20227779 0.00355788 -0.00107401 0.00169113 Pb 20.80633796 20.70460329 20.99580642 -0.00046932 -0.00042551 -0.00008023 Br 20.88877368 20.58742236 17.78202185 0.00219684 -0.00268012 0.00375109 Br 20.81526988 17.88492657 20.23359889 0.00060589 0.00072812 -0.00133255 Br 18.05951929 20.75131690 21.28258377 0.00148448 0.00087312 0.00170131 320 Lattice="23.77195271 0.0 0.0 0.0 23.77195271 0.0 0.0 0.0 23.77195271" Properties=species:S:1:pos:R:3:vel:R:3 Time=20199.99999999 energy=-11424.48653793 virial="-10.21691648 3.86736932 0.51321259 3.86736932 -13.61170291 2.51510684 0.51321259 2.51510684 -5.77970021" stress="0.00015476 0.00028789 3.82e-05 0.00028789 -0.00012377 0.00018722 3.82e-05 0.00018722 0.0004731" pbc="T T T" Cs 23.44732759 23.72941404 0.14359280 -0.00111404 -0.00160434 0.00161576 Pb 2.86603799 3.09124493 3.19917551 0.00059342 -0.00035810 0.00075496 Br 3.27533097 2.94021809 0.18627897 0.00060953 0.00015649 0.00086997 Br 2.85760426 23.75770433 3.04517890 -0.00180019 -0.00068807 -0.00030695 Br 23.71782791 3.40889550 2.09431647 -0.00130950 -0.00066809 -0.00288539 Cs 0.44054787 0.17094284 5.86925743 0.00272615 0.00107606 -0.00267100 Pb 3.14928340 3.00073034 9.08122114 0.00149285 -0.00007343 -0.00114941 Br 2.59043931 2.71521143 6.35362608 0.00069502 0.00033865 -0.00271312 Br 3.16542651 0.01708724 9.56061284 -0.00009697 0.00000436 0.00111264 Br 23.65138466 2.89003932 10.48702879 0.00083611 -0.00491997 -0.00165778 Cs 23.73219694 23.49538730 12.19498162 -0.00284764 -0.00154207 -0.00088989 Pb 3.08354158 3.12279808 14.83333639 0.00073853 -0.00200495 -0.00055309 Br 3.82454781 3.00996681 12.06444250 -0.00390537 0.00059809 -0.00007351 Br 3.05652169 0.00844665 14.52945658 0.00006514 -0.00141469 -0.00092886 Br 0.02428259 3.19930245 14.54505629 -0.00242035 -0.00186715 -0.00218036 Cs 0.05815239 23.26142027 17.56385026 -0.00105893 -0.00069302 -0.00150439 Pb 3.26355722 3.15326073 20.74067801 -0.00110207 0.00048210 0.00109677 Br 2.67223457 3.33218368 17.77523048 0.00228896 0.00082294 -0.00005202 Br 2.99907527 0.05132927 20.61509872 -0.00012240 -0.00127318 0.00256340 Br 0.06513273 2.60601871 20.72866946 -0.00221327 -0.00474762 0.00228856 Cs 23.46743586 6.59766354 23.63548147 -0.00048051 0.00106950 -0.00108654 Pb 2.88701437 8.91800094 2.93069126 0.00047220 0.00083218 -0.00384866 Br 2.93832517 8.22106465 0.11484331 0.00239409 -0.00133196 0.00177133 Br 2.89708491 6.04217993 3.07667498 -0.00154741 -0.00133136 0.00089771 Br 0.00180825 8.53235492 2.94560823 -0.00239326 -0.00014111 -0.00244486 Cs 0.56156311 6.05117456 6.13073387 0.00069721 -0.00035188 0.00182422 Pb 3.31396214 9.08686232 8.90722015 -0.00258759 0.00042416 0.00117785 Br 2.82376955 8.75552150 6.08798518 -0.00017669 -0.00103430 -0.00416409 Br 2.63659509 5.92307005 9.04814334 -0.00139093 0.00401892 0.00378848 Br 23.75676853 9.30091542 8.86743026 0.00269999 -0.00092604 0.00087870 Cs 23.42655385 5.91760841 12.12682453 -0.00235081 -0.00051478 0.00128559 Pb 2.91392736 8.92977851 14.74865172 0.00100799 0.00148830 0.00019946 Br 3.24914262 9.11185778 11.90926559 0.00110089 0.00237590 -0.00140029 Br 3.77460267 6.04071819 15.21577441 -0.00029760 -0.00348405 -0.00041522 Br 0.15187079 8.37567899 14.17798216 0.00108266 0.00016534 -0.00112177 Cs 23.56189692 5.74409579 17.66059919 -0.00077181 -0.00020948 0.00064532 Pb 2.60130488 8.65339281 20.66128936 -0.00048027 -0.00224796 0.00068357 Br 2.55477688 8.77876668 17.74957627 0.00189814 -0.00077493 0.00450484 Br 2.27856117 5.90035776 20.97774666 0.00206743 -0.00091033 0.00211778 Br 23.24066639 9.33412007 21.22573628 0.00132124 0.00144167 0.00160616 Cs 23.44910825 11.75054639 0.30105915 0.00342062 0.00044618 0.00060480 Pb 2.57699868 14.98071758 2.95218112 0.00019799 -0.00040804 0.00024430 Br 2.49420037 14.94738236 23.75079795 0.00005615 -0.00105441 -0.00185515 Br 2.84492448 12.02906295 2.17594098 -0.00206107 -0.00045634 0.00059659 Br 23.60908212 14.73309113 3.14872380 0.00237857 0.00058193 -0.00040119 Cs 23.64512468 11.85830238 6.49194587 0.00133584 0.00243638 -0.00184383 Pb 3.04721367 14.96170761 9.07894017 -0.00013427 -0.00208544 -0.00079019 Br 3.03825107 14.90933939 5.81742176 -0.00268236 -0.00029846 0.00025326 Br 2.95648354 12.01319596 8.70161944 -0.00102304 -0.00560183 -0.00087381 Br 0.00892163 14.94352907 8.87209390 -0.00129681 -0.00150671 -0.00010318 Cs 23.65207011 11.80442312 11.40775652 -0.00211415 0.00216458 0.00038216 Pb 3.07659051 14.55718839 14.98882878 -0.00024524 0.00041654 -0.00206052 Br 2.72423370 14.26383086 11.99189374 -0.00432972 -0.00135824 0.00029115 Br 2.39951726 11.65164909 15.51163744 -0.00054816 -0.00102599 0.00011800 Br 0.14912042 14.56423987 14.54700129 -0.00456401 0.00091131 0.00016687 Cs 23.74494051 11.63166473 18.14153806 0.00187828 -0.00060252 -0.00053293 Pb 3.15612795 14.54409528 20.81713951 0.00044655 0.00166618 0.00126131 Br 2.96537312 14.92806894 17.92352957 0.00073947 -0.00086614 0.00187322 Br 2.90624790 11.62730446 20.91069185 0.00126055 -0.00076340 -0.00021180 Br 0.16257738 14.73565053 20.69162018 -0.00008373 0.00184043 -0.00267122 Cs 0.02456672 17.88987779 23.51721013 -0.00025767 0.00072497 -0.00126936 Pb 2.98698922 20.85064799 2.78326293 -0.00100817 0.00131508 -0.00009851 Br 2.83701674 21.16315438 23.73760684 -0.00139170 0.00406991 -0.00240860 Br 2.86242310 17.78876393 2.80860835 -0.00371689 0.00085948 0.00106449 Br 0.08503605 20.99169084 3.16745081 0.00328937 0.00140790 0.00244272 Cs 22.98055704 17.84147124 5.90554078 -0.00006795 -0.00048615 -0.00232354 Pb 2.98440278 20.82161753 8.83995828 0.00047659 -0.00099187 0.00083025 Br 3.05081876 20.57701211 5.86445014 0.00007082 -0.00124611 -0.00026382 Br 3.06690524 17.89254911 9.11994851 -0.00205444 0.00454094 -0.00273764 Br 23.73047975 20.73575067 8.87363620 -0.00052431 -0.00327627 -0.00095722 Cs 0.77922527 17.41185567 11.86271784 0.00220390 0.00300557 -0.00301598 Pb 3.14064351 20.81463062 14.87995319 -0.00006974 -0.00340148 0.00068297 Br 2.85316159 20.78573317 11.84919209 -0.00314502 -0.00072787 0.00499479 Br 3.10018793 17.51358694 15.21407269 0.00065080 -0.00405569 -0.00073342 Br 0.17558412 20.74592202 14.94547220 0.00145214 0.00199552 -0.00012450 Cs 0.40501528 17.72946990 17.69451094 -0.00041396 0.00047397 0.00215557 Pb 3.09327170 20.82864712 20.44942650 0.00049411 0.00033761 0.00111755 Br 3.14397367 20.29510015 17.74602315 -0.00435412 0.00070092 -0.00115514 Br 3.04723985 17.70345743 20.16829401 -0.00039521 -0.00062903 -0.00014433 Br 0.18960817 20.50567187 20.62461484 -0.00099230 -0.00090679 0.00050112 Cs 5.69713986 0.24244156 22.92495396 -0.00236623 0.00121732 0.00127699 Pb 8.80567326 3.20721091 3.00856128 -0.00215275 0.00170365 -0.00219396 Br 8.63139185 2.80464718 0.15402006 -0.00547380 0.00270140 0.00183197 Br 9.11360566 0.35422088 3.72601209 0.00273034 0.00141222 0.00285488 Br 5.90052242 3.12044666 3.79265981 -0.00015382 0.00122939 0.00164258 Cs 5.79852758 23.52840751 5.82722554 -0.00264042 0.00151342 0.00125638 Pb 8.77176075 2.94893019 8.81777086 0.00144058 0.00144637 -0.00012724 Br 9.59269708 3.45019139 5.94418590 -0.00247163 0.00012933 -0.00126473 Br 8.42818183 23.72807859 9.05841282 0.00005490 0.00210741 0.00240128 Br 6.03357175 2.98116479 8.18211644 -0.00260636 -0.00359946 -0.00239743 Cs 5.99191067 0.09008929 12.06866406 -0.00074721 0.00174386 -0.00006505 Pb 9.31384848 2.88332492 14.97423091 0.00158745 0.00044482 0.00277043 Br 8.86354014 2.82858614 11.87422589 -0.00236690 -0.00039440 0.00583857 Br 9.97263164 23.74360496 14.87796438 -0.00368680 0.00181372 -0.00340100 Br 6.23241089 2.50140741 14.96479237 0.00130649 0.00142942 -0.00094736 Cs 6.36366112 23.25993268 17.80434912 -0.00103071 -0.00120800 0.00145651 Pb 9.02461394 3.10665668 20.86982958 -0.00138871 -0.00005408 -0.00142767 Br 9.47950746 2.90183259 18.21073043 -0.00226353 -0.00063572 0.00207650 Br 8.77871036 23.54323085 20.95351427 -0.00342122 -0.00277079 0.00192250 Br 6.01045587 3.84804122 20.75788200 0.00139033 -0.00100792 -0.00038435 Cs 5.48727027 5.88835393 0.01061358 -0.00086315 -0.00422058 0.00114636 Pb 8.68202795 8.88347918 2.82484031 -0.00078312 0.00037856 0.00103653 Br 8.97634018 8.90737332 23.71051106 -0.00002990 0.00084813 -0.00078757 Br 8.18615423 6.13792781 2.91940320 -0.00061701 0.00204191 -0.00121799 Br 5.73143090 9.15646888 2.85153751 -0.00139892 0.00222367 0.00088232 Cs 6.18206888 6.21875631 5.77061027 0.00217034 -0.00014304 -0.00037004 Pb 8.87610286 8.79442601 8.75136824 0.00254904 -0.00069508 0.00112375 Br 8.70106697 8.41493074 5.81010159 0.00097106 -0.00049048 -0.00005143 Br 8.59113663 5.96700921 9.34006709 -0.00188149 0.00039380 0.00129077 Br 6.03125640 8.54478323 8.67622036 0.00051910 0.00380160 0.00016826 Cs 6.01533332 5.94886313 11.99794495 0.00105105 -0.00292179 0.00123396 Pb 8.91983407 8.93761849 14.81620930 -0.00070573 -0.00006562 -0.00120162 Br 8.00897697 8.71342411 11.95532469 0.00316099 -0.00021340 0.00032568 Br 9.43090174 5.96530057 15.25357491 -0.00067190 -0.00093741 0.00136158 Br 6.19516617 8.71394647 15.51812522 -0.00093321 -0.00326370 0.00058753 Cs 6.69950149 6.14623823 17.73809949 -0.00257847 0.00005342 -0.00096863 Pb 8.75488011 9.11897968 20.70178418 0.00065486 0.00088497 0.00014335 Br 9.00586835 9.09068146 17.76880814 -0.00152169 -0.00225499 0.00067117 Br 9.15884651 6.00187371 20.72727164 0.00017978 -0.00183515 -0.00246560 Br 5.55064644 8.49406829 20.68434032 0.00034923 -0.00048000 0.00080891 Cs 5.41855342 11.37076392 23.54519302 -0.00042302 -0.00124651 0.00232135 Pb 8.78076036 14.77271614 3.20072456 -0.00127480 0.00189357 0.00060035 Br 9.20922455 14.55772735 23.72985723 -0.00264514 0.00012019 -0.00377154 Br 9.02636562 11.94275196 3.37178678 0.00059161 0.00113932 0.00158445 Br 5.72472237 14.48998028 2.62114808 0.00148103 -0.00095164 0.00027326 Cs 5.48649442 12.04495089 5.08423879 0.00331836 0.00153042 0.00280619 Pb 8.76647713 14.60652801 9.01378667 0.00215685 0.00163602 -0.00003122 Br 8.47499319 13.90103667 6.04943597 0.00127662 0.00115013 0.00370778 Br 9.49578614 11.68653339 8.90465561 -0.00366902 -0.00451953 0.00256596 Br 5.91688042 14.04528135 9.48890817 0.00271115 -0.00044251 -0.00205709 Cs 5.68220406 11.61801927 11.88246173 0.00216724 -0.00478787 0.00156115 Pb 9.26182453 14.96575616 14.79465228 0.00227358 -0.00202055 -0.00171637 Br 9.12940663 15.25705424 11.87142878 -0.00435414 0.00055874 0.00015706 Br 9.52304020 11.84139268 15.07716218 -0.00022946 0.00274705 -0.00144944 Br 5.81656588 14.62357914 13.96567636 0.00259001 -0.00040943 -0.00252210 Cs 6.44833590 11.64817917 17.56311706 0.00037120 -0.00261691 -0.00244144 Pb 9.04119841 14.92682176 20.84888611 0.00137596 0.00060735 0.00184802 Br 9.35526665 14.86281400 17.80822937 -0.00059512 -0.00049567 0.00139637 Br 8.80357067 12.00390887 21.04608466 -0.00169833 0.00196657 -0.00472131 Br 6.03612742 14.93780510 21.28966255 -0.00218191 -0.00071537 -0.00012985 Cs 5.44529276 18.57540976 23.56174156 0.00075572 -0.00017301 -0.00204766 Pb 8.80502040 20.78062382 3.06323787 0.00017144 0.00112483 -0.00011291 Br 8.93121697 20.97843660 23.72690481 -0.00012680 0.00177870 -0.00178787 Br 8.43151678 17.94600921 2.71909111 0.00131909 0.00267116 -0.00126334 Br 5.85534057 20.89167388 3.07008206 0.00419816 0.00331771 0.00084549 Cs 5.54458998 18.05779224 5.50613834 -0.00178640 0.00091484 -0.00026076 Pb 8.77745257 20.43572393 8.90638144 0.00339666 -0.00005837 -0.00049542 Br 8.14668430 20.48161835 6.00002832 -0.00105501 0.00298977 -0.00022181 Br 8.16494006 17.48552954 8.05964015 0.00169165 0.00051353 -0.00121546 Br 5.91959802 20.57688419 8.51572287 0.00265116 0.00018049 -0.00039870 Cs 6.22369794 18.32809742 11.42800737 -0.00141601 0.00050810 0.00079115 Pb 9.22410958 20.77987909 14.80504690 -0.00030313 -0.00086966 0.00240636 Br 9.62638484 20.66371785 11.72576828 0.00093751 -0.00429947 -0.00059057 Br 8.35289591 17.98241780 14.88712255 -0.00063013 0.00489708 0.00267170 Br 6.34556482 21.39350010 14.86531824 0.00225648 0.00145425 -0.00139463 Cs 5.69012784 17.36410691 17.71033918 -0.00076065 0.00005744 -0.00250198 Pb 8.96758204 20.65768142 20.64516377 -0.00088498 -0.00032514 -0.00044564 Br 9.80201540 20.70004746 17.72052730 -0.00124118 -0.00016943 0.00249779 Br 8.06690305 17.75490174 20.80191532 0.00425844 -0.00349124 0.00105202 Br 5.86035237 20.83581895 20.52078920 -0.00158371 0.00140271 -0.00043973 Cs 12.18488424 23.71570167 0.21806869 0.00035184 -0.00017139 -0.00168059 Pb 15.00230588 3.33870390 2.83984480 -0.00049081 -0.00151582 0.00043426 Br 15.43687614 2.99807022 23.57381767 0.00189118 0.00168611 -0.00258166 Br 15.67650461 23.74955343 2.99177741 0.00250905 0.00113897 0.00383487 Br 12.11037977 3.58784598 2.40943582 -0.00160370 0.00154985 -0.00142690 Cs 12.08822472 0.49905925 5.50900420 -0.00120594 -0.00068674 -0.00246361 Pb 14.91973569 3.12907303 8.81850397 0.00021836 0.00111443 -0.00012530 Br 14.50443546 3.32715823 5.80971127 -0.00271160 0.00072394 0.00042481 Br 15.03060745 0.06965338 9.09362623 0.00181803 0.00027203 0.00009237 Br 12.01486631 3.00434986 9.48211315 -0.00246582 0.00057317 -0.00300929 Cs 11.97951933 0.52289255 11.93872283 0.00077375 0.00277039 -0.00086237 Pb 15.05795555 2.61585591 14.93231919 0.00053076 -0.00278244 0.00010030 Br 14.88301909 3.04638401 12.06682045 -0.00200909 0.00086526 -0.00028857 Br 14.29236515 23.59309854 14.75367511 0.00094220 -0.00000351 -0.00254886 Br 12.08790092 3.28449766 14.19154582 -0.00102707 0.00480103 0.00120137 Cs 12.00180944 23.41583456 18.51759154 -0.00108460 0.00267876 0.00163874 Pb 14.94542995 3.11906655 20.76342422 -0.00218218 0.00029341 -0.00068511 Br 14.37095680 3.08434261 17.87122926 -0.00498566 -0.00223281 -0.00300340 Br 15.34273043 23.56608250 20.54214245 0.00240049 0.00201685 -0.00427920 Br 11.79404759 2.49622071 21.57480430 -0.00068573 -0.00263806 -0.00025429 Cs 11.91866305 6.18398581 23.38986350 0.00142914 -0.00137129 0.00129652 Pb 14.74336377 8.93331784 3.18495042 0.00004640 0.00017651 -0.00354012 Br 14.82010688 8.64628995 0.06924892 0.00122061 -0.00372419 -0.00228910 Br 15.16914658 6.08567070 3.28870225 -0.00249666 0.00291944 -0.00400318 Br 11.71795471 8.35359217 2.62322102 0.00075348 -0.00235375 0.00048212 Cs 12.19603293 6.15151575 5.61623095 0.00262071 -0.00153407 0.00067658 Pb 14.56518883 8.96530823 8.96434481 0.00007604 0.00163152 -0.00149914 Br 14.30352481 9.55956553 5.99511211 -0.00077016 -0.00075109 0.00094400 Br 15.27325588 6.07690364 8.41522352 -0.00018607 -0.00095782 0.00188019 Br 11.66299690 7.95454750 8.87388768 0.00027259 -0.00100743 -0.00164742 Cs 12.06822192 6.01317419 11.97455629 0.00107585 0.00056107 0.00178592 Pb 15.01375418 8.89960203 14.93384642 0.00111493 -0.00022571 0.00258284 Br 14.42915280 9.17707430 12.06457377 -0.00479739 0.00059335 -0.00195552 Br 14.74664689 5.64888042 14.80161561 -0.00184123 -0.00305358 -0.00089438 Br 12.05672640 8.66891404 15.07643482 -0.00060160 0.00307838 0.00216475 Cs 12.55290381 5.71763806 18.25321499 -0.00239343 0.00152447 -0.00149123 Pb 14.65995268 9.18985449 20.94074896 0.00150621 -0.00001009 0.00025875 Br 14.78700612 8.58279959 17.72973866 0.00067329 -0.00054334 0.00165232 Br 15.01077281 6.20251326 20.67633212 -0.00389264 0.00417692 -0.00103993 Br 11.88583736 8.78618139 20.57521619 -0.00097487 -0.00268228 0.00177066 Cs 11.32273350 11.84016619 0.51096002 0.00097028 -0.00045658 -0.00145831 Pb 14.71089128 14.90064525 3.22364875 -0.00034135 0.00048381 -0.00102062 Br 15.01464019 15.40646059 0.01093578 0.00210574 -0.00244106 0.00048408 Br 14.28108386 12.03744839 3.09475376 -0.00299518 -0.00389440 -0.00111804 Br 11.63837019 14.96572863 3.09169194 0.00147586 -0.00106172 -0.00428056 Cs 11.72151362 12.49869604 6.07079595 0.00300452 -0.00015137 0.00011778 Pb 14.76218682 15.03628063 9.11176431 0.00171212 0.00002976 -0.00225313 Br 14.86970766 14.88146960 6.09067665 -0.00144503 0.00064748 0.00249349 Br 13.60482180 11.92012434 9.00691844 -0.00075207 -0.00231399 0.00041405 Br 11.50788813 15.29116576 8.56431391 -0.00308483 -0.00195862 0.00370320 Cs 11.83600005 12.17686132 12.05027617 -0.00073458 0.00072290 0.00144472 Pb 14.89897601 15.02998610 15.12136680 -0.00140268 0.00013093 0.00154740 Br 14.72374201 14.75545025 12.11769450 0.00025792 -0.00050571 -0.00216796 Br 14.72115799 11.83400681 14.86585454 0.00355089 0.00472782 -0.00028849 Br 11.92964091 15.11459391 14.95533691 0.00050446 -0.00103052 -0.00285145 Cs 12.22611410 11.39905085 17.43707220 -0.00082791 -0.00218403 0.00034825 Pb 14.94723262 14.92443145 20.93423187 0.00127518 0.00137260 -0.00063871 Br 14.61120011 14.79504538 18.07704262 0.00130892 -0.00018420 0.00212383 Br 14.35087288 12.09587671 21.38029559 0.00150269 0.00038382 0.00378200 Br 11.91956902 15.47873471 21.55464280 0.00115699 0.00273340 -0.00145068 Cs 11.50369666 17.94960170 0.77984145 -0.00044930 -0.00147130 0.00299630 Pb 14.96868411 20.96225563 3.09827798 0.00174625 -0.00029234 0.00151958 Br 14.91592953 20.79005822 23.47102325 0.00429273 0.00091676 0.00028457 Br 14.30831200 17.86095058 3.62894149 -0.00195827 -0.00048535 -0.00043194 Br 11.92319115 21.30502885 3.45094522 0.00202641 -0.00178256 -0.00143701 Cs 11.27587601 18.19758236 5.94152699 0.00147579 0.00025153 0.00006557 Pb 14.93709215 20.95898495 8.91546273 -0.00124124 0.00029040 0.00139638 Br 15.26380776 20.50763708 6.04635422 0.00361203 -0.00350153 -0.00191702 Br 14.98023966 17.93341596 8.91978456 0.00336407 0.00184257 0.00212428 Br 11.48514859 21.43412019 8.12750953 -0.00261980 0.00022388 -0.00052542 Cs 12.42253331 18.25786093 11.54258911 -0.00093239 -0.00338155 -0.00198777 Pb 14.99045988 20.72153944 14.74860278 -0.00072713 0.00385899 -0.00120817 Br 15.19653212 20.91892483 11.88901620 0.00262558 0.00382130 0.00077192 Br 15.55282200 17.83287396 14.71380435 -0.00026560 -0.00164611 0.00202101 Br 12.22314975 19.74994620 14.90409261 -0.00051562 0.00134564 -0.00037490 Cs 12.13412558 17.58338530 17.94331905 -0.00143745 -0.00039225 0.00105917 Pb 15.03203318 20.80209578 20.69678589 0.00141355 0.00183480 0.00051611 Br 14.23271745 20.81705749 17.62136887 0.00191450 0.00248774 0.00031038 Br 14.96214159 17.64571138 20.02574515 0.00347980 0.00006145 0.00463891 Br 11.81937309 20.59796198 21.17379013 0.00011580 -0.00265184 0.00141849 Cs 18.37833741 23.42109838 23.53947481 0.00394061 0.00066301 -0.00226724 Pb 21.03162432 3.34777913 2.56844576 0.00081664 0.00029012 -0.00051582 Br 20.55020396 3.92547039 23.73892448 -0.00096808 -0.00037417 -0.00004660 Br 20.76622438 0.17047996 3.17153425 -0.00171948 -0.00122256 -0.00175580 Br 17.84212460 3.16318710 3.43610742 -0.00040856 -0.00123581 0.00538846 Cs 17.94729724 0.25853686 6.10519057 -0.00349773 0.00298496 -0.00132118 Pb 21.14553508 2.94805316 9.08190145 0.00146332 -0.00079993 0.00219587 Br 21.55206194 3.13391813 6.15437540 0.00034604 0.00075752 -0.00036297 Br 20.78541468 0.04619034 9.30553036 -0.00218631 -0.00157505 0.00249864 Br 17.88461547 2.81984902 8.58400959 0.00456119 0.00229977 0.00043871 Cs 18.07326131 0.17964534 11.81566099 0.00263722 -0.00211535 0.00011164 Pb 20.65555016 3.02571092 14.67454360 0.00061629 -0.00032834 0.00047647 Br 19.87242378 3.62677200 11.81872194 0.00095498 0.00074041 0.00106848 Br 21.64059775 23.59966883 14.86833555 -0.00018407 -0.00040043 0.00105731 Br 17.90016010 2.89495706 15.06322969 -0.00052782 0.00163156 0.00199200 Cs 18.05446262 23.50882630 18.12201537 0.00079843 -0.00210515 -0.00308773 Pb 21.03818396 2.98365467 20.61661511 -0.00159788 0.00214938 -0.00086779 Br 20.92047189 3.12053175 17.74724959 0.00065199 -0.00155614 0.00082669 Br 20.78724984 23.77157855 20.28276765 -0.00243754 0.00006851 -0.00177082 Br 17.75367535 2.93382699 20.18408985 0.00316236 0.00063004 0.00149100 Cs 17.77624568 6.48595499 23.24861072 0.00020567 0.00061299 -0.00154606 Pb 20.68773496 8.86051007 3.08884738 -0.00260682 -0.00064938 -0.00012550 Br 20.19364179 8.62356333 0.07684780 0.00028575 -0.00305259 -0.00139252 Br 20.52467251 5.96715648 3.46100814 0.00002853 0.00479652 0.00281007 Br 17.66517450 8.85310929 3.62151356 -0.00264838 -0.00233212 -0.00088358 Cs 18.39804187 5.44154020 6.22980895 -0.00045382 0.00133557 -0.00249232 Pb 20.89310848 8.79252698 8.84139625 -0.00151865 0.00087269 0.00002013 Br 20.80869770 9.33738587 5.86548263 0.00155724 -0.00183585 0.00120998 Br 21.33671756 5.88612640 8.55101476 0.00046639 -0.00091324 -0.00249435 Br 17.53261877 9.19710899 8.73942855 0.00255149 -0.00003655 -0.00451016 Cs 17.51106375 6.43845858 11.45673346 0.00075484 0.00055919 0.00060752 Pb 20.96638289 8.68814112 14.97094345 -0.00016187 -0.00094110 -0.00045927 Br 20.77956273 8.78465318 11.79737303 -0.00174692 0.00132857 -0.00198393 Br 20.73048122 5.83170602 15.06024254 -0.00095219 0.00027034 -0.00333041 Br 18.13070153 8.23552835 14.75837520 -0.00028907 -0.00146348 0.00077189 Cs 17.71319631 5.64601605 17.68187220 -0.00186412 -0.00067062 0.00151745 Pb 20.61432959 9.03426467 20.59287810 -0.00138179 -0.00093087 -0.00067129 Br 21.32254828 8.58872813 17.80182088 -0.00042242 -0.00082963 0.00212566 Br 20.49973362 6.01020369 20.38034774 0.00103692 0.00327080 -0.00377075 Br 17.53606619 9.13860980 20.57856808 0.00200743 -0.00357517 0.00101136 Cs 17.56124653 11.84052448 0.12667013 -0.00266682 -0.00100084 -0.00028275 Pb 20.62382828 14.80451371 3.17446003 0.00057429 0.00075572 0.00107387 Br 20.84304228 15.18856992 0.11479261 0.00138640 -0.00105598 0.00027326 Br 20.26358587 11.94922973 3.28400321 0.00011730 0.00058368 -0.00115812 Br 17.66684853 15.00699015 2.47337436 0.00348209 -0.00033650 -0.00237136 Cs 17.87046577 11.32060400 6.25103206 0.00016478 0.00401692 -0.00062085 Pb 20.44475530 14.87948803 9.17738238 -0.00108256 -0.00019464 0.00078808 Br 20.04084994 14.59598961 6.07024086 -0.00158836 -0.00332330 0.00322666 Br 20.01170870 11.74127288 9.21268283 -0.00338703 -0.00183528 0.00493767 Br 17.53538821 14.50840478 9.37764524 -0.00232287 0.00161374 0.00070630 Cs 18.06151317 11.30506142 12.04189404 0.00156611 0.00016692 -0.00262059 Pb 20.78272049 14.69710457 14.69893892 0.00006977 -0.00045627 -0.00079813 Br 20.80425589 13.95593162 11.82944750 -0.00048212 0.00044767 0.00252893 Br 20.73606007 11.64721283 15.48088198 0.00252878 0.00006741 0.00137918 Br 17.73589866 14.42287574 15.04026766 0.00065677 0.00331835 0.00182323 Cs 17.40724781 11.85126155 17.58922045 0.00025686 -0.00206611 0.00023092 Pb 21.05885586 15.12123975 20.71188286 0.00110146 -0.00000882 -0.00114666 Br 20.92028836 14.82551770 17.80176458 -0.00232467 0.00360191 -0.00352740 Br 20.06578887 11.73987747 20.34540716 -0.00017727 -0.00135653 0.00392611 Br 18.06502789 14.95052188 20.51296314 -0.00094460 -0.00021219 -0.00014864 Cs 17.95608324 17.66595379 23.48317724 0.00008001 -0.00107402 0.00124396 Pb 21.04145527 20.81286707 3.03294553 0.00121436 -0.00119398 -0.00133821 Br 21.37739092 21.05106739 23.75006615 -0.00055503 -0.00189843 0.00289763 Br 20.99062100 17.83113440 3.26693685 0.00229499 0.00172124 0.00027508 Br 17.71980115 20.52857762 2.56048216 -0.00278549 0.00280501 0.00359061 Cs 17.82080166 17.87280040 5.45447945 0.00079469 -0.00159151 -0.00267885 Pb 20.84296458 20.94226722 8.77088344 0.00052548 -0.00108013 0.00078969 Br 20.88701758 20.82318130 5.91669921 -0.00222927 0.00127859 -0.00162440 Br 20.29426236 17.83513087 8.78410015 0.00053978 0.00351650 0.00122005 Br 17.90459871 21.19417841 9.43503318 0.00715533 0.00282832 -0.00436693 Cs 17.76805499 17.82057490 11.92890270 -0.00050079 -0.00193467 0.00256723 Pb 20.65563716 20.91601537 14.85312174 -0.00113861 0.00066498 -0.00054816 Br 21.13629409 20.89936010 11.83350581 0.00033529 0.00043606 -0.00093567 Br 20.45394123 17.41529704 14.14304696 -0.00116382 0.00358727 0.00178869 Br 17.86635826 21.57217147 15.05683546 -0.00149042 0.00215227 0.00391611 Cs 17.66853801 17.99404686 18.21757412 0.00392077 -0.00114866 0.00136703 Pb 20.80397457 20.70106481 20.99235403 0.00000305 -0.00027907 -0.00060735 Br 20.90987718 20.56163208 17.81935389 0.00201951 -0.00246828 0.00368451 Br 20.82162651 17.89244552 20.22186099 0.00066650 0.00075731 -0.00100633 Br 18.07042401 20.75892966 21.29862371 0.00068334 0.00064440 0.00150336 320 Lattice="23.77195271 0.0 0.0 0.0 23.77195271 0.0 0.0 0.0 23.77195271" Properties=species:S:1:pos:R:3:vel:R:3 Time=20209.99999999 energy=-11424.3420372 virial="-11.43339297 4.68100907 0.26344369 4.68100907 -12.99285894 2.66291661 0.26344369 2.66291661 -6.10062234" stress="5.345e-05 0.00034845 1.961e-05 0.00034845 -9.271e-05 0.00019823 1.961e-05 0.00019823 0.00045346" pbc="T T T" Cs 23.43788420 23.71463264 0.15972526 -0.00077528 -0.00135183 0.00160900 Pb 2.87276870 3.08722619 3.20570074 0.00075115 -0.00044049 0.00055089 Br 3.28190700 2.94179503 0.19487274 0.00070347 0.00015502 0.00084195 Br 2.83953915 23.75303883 3.04266217 -0.00180693 -0.00023957 -0.00019902 Br 23.71010486 3.40194635 2.06573458 -0.00023334 -0.00072470 -0.00283088 Cs 0.46617929 0.18027119 5.84258774 0.00239324 0.00078695 -0.00265920 Pb 3.16437912 2.99990707 9.07123505 0.00152236 -0.00008856 -0.00085182 Br 2.59718113 2.72001971 6.32233597 0.00065685 0.00062770 -0.00352267 Br 3.16428952 0.01792239 9.56995690 -0.00012985 0.00017479 0.00075552 Br 23.66143795 2.84011204 10.46785838 0.00118657 -0.00504721 -0.00216085 Cs 23.70498922 23.47962061 12.18516272 -0.00259100 -0.00161151 -0.00107218 Pb 3.09116184 3.10266470 14.82842373 0.00078651 -0.00202391 -0.00042711 Br 3.78561173 3.01699135 12.06096313 -0.00387765 0.00080621 -0.00063118 Br 3.05645369 23.76484218 14.52196489 -0.00007748 -0.00170009 -0.00056687 Br 0.00103546 3.18088536 14.52383345 -0.00222716 -0.00181563 -0.00205922 Cs 0.04800209 23.25502170 17.54991689 -0.00096871 -0.00058599 -0.00127775 Pb 3.25090035 3.15727141 20.75123510 -0.00142235 0.00031590 0.00101190 Br 2.69566269 3.33971085 17.77757083 0.00239018 0.00068237 0.00053012 Br 2.99822345 0.03847477 20.63947384 -0.00005077 -0.00129389 0.00230658 Br 0.04300467 2.55909983 20.75198999 -0.00219720 -0.00463044 0.00237402 Cs 23.46212250 6.60752764 23.62476219 -0.00058207 0.00090368 -0.00105540 Pb 2.89084541 8.92720210 2.89238844 0.00028340 0.00100290 -0.00379598 Br 2.96142952 8.21033310 0.13054786 0.00222690 -0.00082487 0.00134597 Br 2.88265476 6.02663403 3.08698598 -0.00133815 -0.00177063 0.00116091 Br 23.74650671 8.53191635 2.92127449 -0.00304227 0.00005816 -0.00242326 Cs 0.56739720 6.04733017 6.14881310 0.00046945 -0.00041413 0.00179156 Pb 3.28632222 9.09045712 8.91893503 -0.00293317 0.00027903 0.00115108 Br 2.82297005 8.74628767 6.04413025 0.00002116 -0.00080674 -0.00457932 Br 2.62375267 5.96359893 9.08651673 -0.00117688 0.00405276 0.00388427 Br 0.01106515 9.29058633 8.87662851 0.00252971 -0.00112858 0.00095879 Cs 23.40260048 5.91135431 12.13797360 -0.00243831 -0.00074171 0.00093824 Pb 2.92460299 8.94362894 14.75184689 0.00112588 0.00127429 0.00043501 Br 3.26012052 9.13542494 11.89407197 0.00108569 0.00233283 -0.00160369 Br 3.77054747 6.00479434 15.20999232 -0.00051667 -0.00368147 -0.00074207 Br 0.16324994 8.37990632 14.16971946 0.00118997 0.00068130 -0.00052875 Cs 23.55473071 5.74157564 17.66698880 -0.00066011 -0.00029528 0.00063195 Pb 2.59723300 8.63220934 20.66810997 -0.00033261 -0.00197488 0.00068567 Br 2.57266188 8.77006631 17.79669725 0.00168045 -0.00096443 0.00487509 Br 2.29832184 5.88804153 20.99901215 0.00187782 -0.00155972 0.00213725 Br 23.26129995 9.34945969 21.24303158 0.00276163 0.00161955 0.00184051 Cs 23.48342218 11.75563262 0.30714686 0.00343819 0.00056916 0.00061006 Pb 2.58159404 14.97538492 2.95388022 0.00072398 -0.00065370 0.00009099 Br 2.49426746 14.93798389 23.73045039 -0.00004556 -0.00082352 -0.00218527 Br 2.82341181 12.02082284 2.18357870 -0.00223409 -0.00117892 0.00093117 Br 23.62823415 14.73854827 3.14584914 0.00143721 0.00050922 -0.00017216 Cs 23.65845670 11.88341682 6.47270991 0.00133076 0.00257716 -0.00200075 Pb 3.04570450 14.93982180 9.07074940 -0.00016425 -0.00228906 -0.00084701 Br 3.01164303 14.90626358 5.82344543 -0.00263845 -0.00031791 0.00095282 Br 2.94593634 11.95585925 8.69254193 -0.00108094 -0.00578621 -0.00093703 Br 23.76725305 14.92825868 8.87120100 -0.00141967 -0.00154718 -0.00007535 Cs 23.63127192 11.82622023 11.41229183 -0.00204506 0.00218897 0.00052221 Pb 3.07385342 14.56143532 14.96764478 -0.00030024 0.00042831 -0.00217148 Br 2.68079051 14.25069064 11.99157104 -0.00434836 -0.00126758 -0.00035679 Br 2.39527316 11.64218080 15.51188366 -0.00030244 -0.00086524 -0.00006860 Br 0.10101050 14.57464441 14.54927737 -0.00502172 0.00116625 0.00028980 Cs 23.76309039 11.62614425 18.13621150 0.00175120 -0.00050200 -0.00052816 Pb 3.16075881 14.56195707 20.83039968 0.00048032 0.00189699 0.00138625 Br 2.97324974 14.91739487 17.94146306 0.00083633 -0.00126998 0.00167941 Br 2.91868058 11.62046845 20.90857883 0.00122635 -0.00060132 -0.00020973 Br 0.16126454 14.75399108 20.66479949 -0.00018914 0.00182882 -0.00269257 Cs 0.02245549 17.89669321 23.50448093 -0.00016450 0.00063743 -0.00127709 Pb 2.97691464 20.86362749 2.78294855 -0.00099419 0.00127799 0.00002968 Br 2.82312884 21.20359684 23.71139076 -0.00138062 0.00400930 -0.00280939 Br 2.82500922 17.80082057 2.81849528 -0.00376545 0.00153678 0.00091276 Br 0.11534848 21.00556892 3.19208466 0.00275293 0.00136570 0.00248236 Cs 22.98166237 17.83617370 5.88426202 0.00029280 -0.00056808 -0.00192379 Pb 2.98871257 20.81228069 8.84853277 0.00038639 -0.00086751 0.00088246 Br 3.05050909 20.56604553 5.86215399 -0.00013259 -0.00094126 -0.00019224 Br 3.04727956 17.93749605 9.09259021 -0.00186878 0.00438059 -0.00273454 Br 23.72706006 20.70280485 8.86321480 -0.00012783 -0.00331452 -0.00112434 Cs 0.80038808 17.44224551 11.83335013 0.00202458 0.00307226 -0.00285533 Pb 3.13955424 20.78086130 14.88549709 -0.00014566 -0.00334331 0.00042220 Br 2.82267730 20.77698175 11.89962013 -0.00295119 -0.00102444 0.00505277 Br 3.10643316 17.47552987 15.20436368 0.00059292 -0.00354101 -0.00120681 Br 0.19156102 20.76455295 14.94430719 0.00173274 0.00172477 -0.00010873 Cs 0.40021374 17.73398473 17.71609611 -0.00054753 0.00042718 0.00215981 Pb 3.09656215 20.83156183 20.46246797 0.00015503 0.00024361 0.00148371 Br 3.10070974 20.30386277 17.73143090 -0.00429671 0.00104738 -0.00171819 Br 3.04374176 17.69785796 20.17061874 -0.00029982 -0.00048255 0.00060781 Br 0.17652034 20.49686487 20.62981343 -0.00161465 -0.00085301 0.00054034 Cs 5.67402962 0.25436394 22.93860205 -0.00225317 0.00116569 0.00145380 Pb 8.78474294 3.22352355 2.98764591 -0.00203020 0.00155628 -0.00198058 Br 8.57524284 2.83151495 0.17185823 -0.00575619 0.00266732 0.00170989 Br 9.13903936 0.36644318 3.75250035 0.00234224 0.00103745 0.00242871 Br 5.89755416 3.13224049 3.80782074 -0.00044422 0.00112654 0.00138473 Cs 5.77257399 23.54384372 5.84016584 -0.00254771 0.00157203 0.00133174 Pb 8.78768674 2.96303070 8.81712625 0.00173301 0.00137234 -0.00000119 Br 9.56809284 3.45209721 5.93195442 -0.00244879 0.00025512 -0.00116093 Br 8.42861257 23.74793434 9.08192542 0.00003787 0.00186900 0.00230051 Br 6.00317899 2.94582017 8.15846853 -0.00343407 -0.00346653 -0.00232388 Cs 5.98477720 0.10778204 12.06786911 -0.00067778 0.00179434 -0.00009217 Pb 9.32949540 2.88814023 15.00093090 0.00153547 0.00051698 0.00256476 Br 8.83914506 2.82481885 11.93434501 -0.00250430 -0.00035387 0.00615721 Br 9.93465763 23.76125976 14.84506426 -0.00389461 0.00171130 -0.00317592 Br 6.24540516 2.51611210 14.95677485 0.00127801 0.00151234 -0.00065514 Cs 6.35347239 23.24881914 17.81890543 -0.00100720 -0.00101381 0.00144999 Pb 9.01050063 3.10570095 20.85761139 -0.00143551 -0.00013491 -0.00100306 Br 9.45795336 2.89487570 18.22452788 -0.00204044 -0.00076109 0.00066236 Br 8.74445198 23.51783198 20.97106911 -0.00342659 -0.00229451 0.00158374 Br 6.02500853 3.83744191 20.75359103 0.00149368 -0.00111765 -0.00047391 Cs 5.47955169 5.84586961 0.02228624 -0.00067832 -0.00427324 0.00118816 Pb 8.67553696 8.88814221 2.83611606 -0.00051261 0.00055669 0.00121513 Br 8.97530603 8.91608064 23.70087309 -0.00017581 0.00089496 -0.00112214 Br 8.17975973 6.15562872 2.90500411 -0.00066494 0.00148557 -0.00166266 Br 5.72004645 9.17867312 2.86009962 -0.00086250 0.00221774 0.00083340 Cs 6.20253719 6.21618151 5.76770234 0.00191949 -0.00036904 -0.00020713 Pb 8.90248881 8.78735007 8.76300855 0.00271479 -0.00072125 0.00119932 Br 8.71282343 8.41215839 5.81080559 0.00138200 -0.00006161 0.00018707 Br 8.57195758 5.96804891 9.35247923 -0.00195457 -0.00020228 0.00119421 Br 6.03393436 8.58345361 8.67741650 0.00002266 0.00393928 0.00007309 Cs 6.02566639 5.91896189 12.01053132 0.00101775 -0.00305408 0.00128393 Pb 8.91395636 8.93586097 14.80336242 -0.00047071 -0.00028754 -0.00135969 Br 8.04220792 8.71354387 11.95810175 0.00347923 0.00022559 0.00023412 Br 9.42267509 5.95528132 15.26679911 -0.00097282 -0.00103974 0.00128176 Br 6.18234879 8.68355212 15.52233487 -0.00161948 -0.00280642 0.00024132 Cs 6.67344118 6.14561182 17.72885063 -0.00263319 -0.00018363 -0.00087593 Pb 8.76164789 9.12701432 20.70347449 0.00069691 0.00072207 0.00019649 Br 8.99152502 9.06863815 17.77802992 -0.00134421 -0.00215070 0.00115272 Br 9.16135021 5.98418698 20.70277076 0.00032279 -0.00169918 -0.00243246 Br 5.55571982 8.48993610 20.69246490 0.00066349 -0.00034879 0.00081604 Cs 5.41488573 11.35895495 23.56828832 -0.00031122 -0.00111484 0.00229234 Pb 8.76661269 14.79154666 3.20588328 -0.00154894 0.00186546 0.00043589 Br 9.18114575 14.56024472 23.69632084 -0.00296126 0.00037643 -0.00289795 Br 9.03248904 11.95141429 3.38531725 0.00063220 0.00061669 0.00112456 Br 5.74130963 14.48177073 2.62400777 0.00183859 -0.00068970 0.00029947 Cs 5.51950350 12.06014075 5.11311432 0.00328086 0.00150223 0.00297004 Pb 8.78755191 14.62299047 9.01258692 0.00204259 0.00164990 -0.00020559 Br 8.48674737 13.91573083 6.08708972 0.00108295 0.00178340 0.00378749 Br 9.45884136 11.64300818 8.93180242 -0.00371075 -0.00415417 0.00285845 Br 5.94271550 14.04270234 9.46747603 0.00241106 -0.00008636 -0.00221935 Cs 5.70446948 11.57050849 11.89850162 0.00229100 -0.00470172 0.00163759 Pb 9.28121273 14.94541630 14.77790597 0.00159534 -0.00204516 -0.00162549 Br 9.08703526 15.26288073 11.87297969 -0.00411366 0.00061196 0.00016413 Br 9.51990248 11.87155583 15.06159164 -0.00039928 0.00326749 -0.00166258 Br 5.84452856 14.61919317 13.94236720 0.00296911 -0.00047014 -0.00213403 Cs 6.45171470 11.62272398 17.53899553 0.00030320 -0.00247044 -0.00237825 Pb 9.05525984 14.93365862 20.86798568 0.00143633 0.00075729 0.00195922 Br 9.34889317 14.85772181 17.82134740 -0.00067583 -0.00052101 0.00120346 Br 8.78625421 12.02029123 20.99783270 -0.00176470 0.00128826 -0.00492782 Br 6.01301736 14.92909796 21.28779083 -0.00242268 -0.00101833 -0.00024195 Cs 5.45356657 18.57265637 23.54107594 0.00089692 -0.00037513 -0.00208448 Pb 8.80688931 20.79289965 3.06198917 0.00020643 0.00133582 -0.00013588 Br 8.92926989 20.99523475 23.71105194 -0.00026329 0.00157607 -0.00137301 Br 8.44501019 17.97279260 2.70738449 0.00137343 0.00266391 -0.00107602 Br 5.89927852 20.92536026 3.07731068 0.00454229 0.00341784 0.00059496 Cs 5.52670382 18.06609613 5.50360977 -0.00178585 0.00074883 -0.00024371 Pb 8.81252230 20.43650851 8.90053036 0.00360445 0.00021154 -0.00066986 Br 8.13918365 20.51264828 5.99361052 -0.00044004 0.00320863 -0.00106005 Br 8.18268800 17.49042489 8.04962820 0.00185853 0.00046505 -0.00079080 Br 5.94280465 20.57962609 8.51431057 0.00197302 0.00036731 0.00012098 Cs 6.20961039 18.33236414 11.43663068 -0.00139845 0.00034770 0.00093233 Pb 9.22197802 20.77109044 14.82892272 -0.00012184 -0.00088175 0.00236254 Br 9.63501061 20.62010401 11.72201536 0.00078664 -0.00442297 -0.00016073 Br 8.34695619 18.02924916 14.91376879 -0.00056446 0.00444244 0.00265633 Br 6.36678599 21.40713950 14.85052044 0.00197550 0.00127641 -0.00155970 Cs 5.68332680 17.36467631 17.68576570 -0.00059787 0.00005537 -0.00240861 Pb 8.95927904 20.65393507 20.64156920 -0.00077090 -0.00042697 -0.00026813 Br 9.78766601 20.69846293 17.74601471 -0.00161905 -0.00014286 0.00258947 Br 8.11052834 17.72235024 20.81222196 0.00444934 -0.00299390 0.00100966 Br 5.84931046 20.85024703 20.51672908 -0.00061040 0.00147336 -0.00037258 Cs 12.18812346 23.71360612 0.20160474 0.00029563 -0.00025263 -0.00160928 Pb 14.99763401 3.32234918 2.84448985 -0.00044303 -0.00174407 0.00049580 Br 15.45639535 3.01518753 23.54960060 0.00200723 0.00173836 -0.00225419 Br 15.69982206 23.76090614 3.03025279 0.00214503 0.00113638 0.00385636 Br 12.09196921 3.60277152 2.39535042 -0.00206024 0.00143037 -0.00138763 Cs 12.07647720 0.49249477 5.48541638 -0.00113771 -0.00062514 -0.00225155 Pb 14.92125919 3.14001104 8.81772723 0.00009137 0.00107272 -0.00002834 Br 14.47786799 3.33258337 5.81522064 -0.00259478 0.00035946 0.00066618 Br 15.04859638 0.07338684 9.09370178 0.00177848 0.00046882 -0.00007693 Br 11.99048271 3.01119007 9.45002841 -0.00240457 0.00079388 -0.00340640 Cs 11.98714353 0.55009421 11.92951799 0.00074701 0.00267247 -0.00097556 Pb 15.06120854 2.58963813 14.93252451 0.00012125 -0.00245061 -0.00006107 Br 14.86567492 3.05445810 12.06038573 -0.00146330 0.00074715 -0.00099761 Br 14.30161599 23.59224469 14.72839129 0.00089352 -0.00017284 -0.00250907 Br 12.07580349 3.33098010 14.20586407 -0.00137844 0.00447682 0.00165907 Cs 11.99074457 23.44346442 18.53371925 -0.00112791 0.00284191 0.00158701 Pb 14.92386970 3.12181372 20.75663423 -0.00212291 0.00025353 -0.00067695 Br 14.32291222 3.05849779 17.84105456 -0.00462694 -0.00292500 -0.00300966 Br 15.36589384 23.58952021 20.49988941 0.00222690 0.00266891 -0.00416533 Br 11.78954469 2.47130443 21.57069424 -0.00020836 -0.00234132 -0.00056972 Cs 11.93287911 6.17002843 23.40320910 0.00141180 -0.00141858 0.00137090 Pb 14.74341818 8.93578779 3.14917291 -0.00004168 0.00032065 -0.00360824 Br 14.83247668 8.60994440 0.04599457 0.00125234 -0.00354023 -0.00235020 Br 15.14431052 6.11530672 3.24856080 -0.00246947 0.00294055 -0.00402640 Br 11.72550452 8.33088106 2.62859761 0.00074601 -0.00218383 0.00059022 Cs 12.22175922 6.13615966 5.62319563 0.00252097 -0.00153328 0.00071500 Pb 14.56523533 8.98222998 8.94890456 -0.00006530 0.00175010 -0.00158740 Br 14.29612822 9.55077521 6.00576598 -0.00070535 -0.00100748 0.00116474 Br 15.27115598 6.06794353 8.43494632 -0.00023228 -0.00083338 0.00206232 Br 11.66926741 7.94489769 8.85742375 0.00099093 -0.00092673 -0.00164198 Cs 12.07876494 6.01860936 11.99257403 0.00103261 0.00052948 0.00181299 Pb 15.02413630 8.89610284 14.95886731 0.00095674 -0.00046777 0.00240879 Br 14.38219472 9.18235096 12.04164887 -0.00458381 0.00045927 -0.00258919 Br 14.73042925 5.62126717 14.79367979 -0.00139451 -0.00244385 -0.00068579 Br 12.05161086 8.70042518 15.09731890 -0.00041907 0.00321580 0.00200781 Cs 12.52630108 5.73491479 18.23694247 -0.00292276 0.00192371 -0.00176428 Pb 14.67581123 9.18962230 20.94333275 0.00165428 -0.00003188 0.00025682 Br 14.79441310 8.57879327 17.74963329 0.00081023 -0.00025588 0.00230268 Br 14.97208157 6.24035447 20.66726603 -0.00383937 0.00336322 -0.00077344 Br 11.87142954 8.75973831 20.59294365 -0.00187741 -0.00259685 0.00177612 Cs 11.33334757 11.83552629 0.49605431 0.00114868 -0.00047004 -0.00152262 Pb 14.70772516 14.90609293 3.21251514 -0.00029182 0.00059946 -0.00120530 Br 15.03591741 15.38252253 0.01808118 0.00214568 -0.00234705 0.00095998 Br 14.25154426 11.99754576 3.08247604 -0.00290876 -0.00404083 -0.00133625 Br 11.65427115 14.95559425 3.04959398 0.00169418 -0.00096925 -0.00413662 Cs 11.75210889 12.49669102 6.07163619 0.00310709 -0.00025148 0.00005100 Pb 14.77811391 15.03554992 9.08896289 0.00146495 -0.00017134 -0.00229696 Br 14.85593288 14.88755789 6.11633264 -0.00130986 0.00056994 0.00260270 Br 13.60023903 11.89685699 9.01176309 -0.00016696 -0.00232940 0.00055468 Br 11.48072391 15.27311968 8.60140412 -0.00229622 -0.00163912 0.00370408 Cs 11.82882540 12.18387702 12.06481789 -0.00069743 0.00067949 0.00145993 Pb 14.88581152 15.03034660 15.13607901 -0.00122081 -0.00005919 0.00139277 Br 14.72567031 14.75154634 12.09571185 0.00012649 -0.00027184 -0.00221726 Br 14.75705020 11.88165282 14.86391906 0.00362092 0.00475854 -0.00009403 Br 11.93833168 15.10528284 14.92635900 0.00123724 -0.00082954 -0.00294278 Cs 12.21745168 11.37744880 17.44130383 -0.00090369 -0.00213091 0.00049946 Pb 14.95953574 14.93807583 20.92917417 0.00118495 0.00134964 -0.00037055 Br 14.62370802 14.79066079 18.09363086 0.00118744 -0.00068785 0.00118074 Br 14.36568349 12.09769638 21.41830589 0.00145980 -0.00001721 0.00382124 Br 11.93243982 15.50603869 21.53776641 0.00141530 0.00272632 -0.00191671 Cs 11.49980631 17.93458830 0.80866431 -0.00032633 -0.00152677 0.00276275 Pb 14.98557031 20.95911857 3.11359841 0.00162153 -0.00032886 0.00154362 Br 14.95854451 20.79941865 23.47728886 0.00422657 0.00095856 0.00096235 Br 14.28908368 17.85668068 3.62325298 -0.00188342 -0.00038825 -0.00070700 Br 11.94203431 21.28609064 3.43551640 0.00173212 -0.00200035 -0.00164375 Cs 11.29109197 18.20015668 5.94228364 0.00156462 0.00026481 0.00008495 Pb 14.92488471 20.96236412 8.92918541 -0.00119062 0.00038564 0.00134079 Br 15.30085394 20.47469235 6.02608838 0.00378771 -0.00308173 -0.00209922 Br 15.01432636 17.95088803 8.94199198 0.00345101 0.00164441 0.00231671 Br 11.46086181 21.43420503 8.12245578 -0.00220973 -0.00019766 -0.00049295 Cs 12.41282045 18.22361545 11.52327285 -0.00100908 -0.00346709 -0.00187142 Pb 14.98225730 20.76044453 14.73752270 -0.00091146 0.00390283 -0.00100027 Br 15.22219953 20.95628989 11.89560039 0.00251467 0.00365026 0.00051604 Br 15.55073740 17.81595855 14.73477105 -0.00015714 -0.00170224 0.00216988 Br 12.21592765 19.76330463 14.89798596 -0.00091603 0.00132411 -0.00084117 Cs 12.11898752 17.57987703 17.95398555 -0.00158377 -0.00030894 0.00107797 Pb 15.04474097 20.81920048 20.70164563 0.00112799 0.00158669 0.00045712 Br 14.25433097 20.84159178 17.62705337 0.00239997 0.00242016 0.00081859 Br 14.99637862 17.65164361 20.07511855 0.00336826 0.00113735 0.00521682 Br 11.82283462 20.57198249 21.18720021 0.00056826 -0.00253883 0.00125909 Cs 18.41685217 23.42878508 23.51801427 0.00376235 0.00087596 -0.00202405 Pb 21.03771296 3.34923193 2.56580572 0.00039901 0.00001123 -0.00000715 Br 20.53958860 3.92202112 23.73409460 -0.00115709 -0.00031534 -0.00090953 Br 20.74983638 0.15583577 3.15267404 -0.00155503 -0.00168884 -0.00202182 Br 17.83940149 3.15050492 3.48979919 -0.00014939 -0.00129497 0.00534000 Cs 17.91244595 0.28825652 6.09138902 -0.00346812 0.00295675 -0.00143541 Pb 21.15964788 2.94044772 9.10376609 0.00135314 -0.00072249 0.00216684 Br 21.55619548 3.13995825 6.15038940 0.00048117 0.00044644 -0.00043345 Br 20.76411745 0.03067199 9.32964582 -0.00206316 -0.00150275 0.00232427 Br 17.93067903 2.84301998 8.58888830 0.00462189 0.00233534 0.00053605 Cs 18.09915768 0.15870735 11.81661911 0.00254168 -0.00207139 0.00007983 Pb 20.66360391 3.02202255 14.67893435 0.00099033 -0.00041101 0.00040129 Br 19.88308692 3.63351139 11.82997092 0.00117866 0.00060672 0.00118282 Br 21.63485742 23.59692571 14.87982795 -0.00096581 -0.00014651 0.00123689 Br 17.89475847 2.91030667 15.08460673 -0.00054548 0.00143891 0.00228234 Cs 18.06232745 23.48831743 18.09071128 0.00077550 -0.00199224 -0.00316587 Pb 21.02159673 3.00549405 20.60784304 -0.00171952 0.00221134 -0.00088278 Br 20.92716645 3.10411575 17.75435466 0.00068778 -0.00171995 0.00057896 Br 20.76353075 23.77098504 20.26719497 -0.00230344 -0.00018095 -0.00133854 Br 17.78466377 2.94014498 20.19961624 0.00300623 0.00063837 0.00161421 Cs 17.77701001 6.49029515 23.23295914 -0.00005489 0.00024986 -0.00158383 Pb 20.66104097 8.85424502 3.08674225 -0.00272528 -0.00059774 -0.00029472 Br 20.19910142 8.59590003 0.06306200 0.00080830 -0.00247653 -0.00135679 Br 20.52616564 6.01702703 3.48880584 0.00027538 0.00511106 0.00273391 Br 17.63977504 8.83043373 3.61155440 -0.00241705 -0.00219154 -0.00109929 Cs 18.39291402 5.45530298 6.20472671 -0.00057099 0.00141791 -0.00251576 Pb 20.87760194 8.80189992 8.84184858 -0.00157599 0.00099478 0.00006482 Br 20.82577432 9.31837832 5.88063348 0.00184785 -0.00195273 0.00179753 Br 21.34125254 5.87918087 8.52698426 0.00043694 -0.00047102 -0.00230209 Br 17.55759884 9.19374349 8.69727931 0.00243717 -0.00064334 -0.00391890 Cs 17.51849992 6.44355580 11.46315863 0.00073180 0.00045961 0.00067474 Pb 20.96484208 8.68019753 14.96558252 -0.00014374 -0.00064401 -0.00060730 Br 20.76225253 8.79736988 11.77800834 -0.00171259 0.00120939 -0.00186131 Br 20.72201841 5.83214679 15.02710318 -0.00073681 -0.00018212 -0.00329101 Br 18.12438534 8.22263943 14.76626062 -0.00097450 -0.00111113 0.00080769 Cs 17.69457611 5.63976889 17.69696989 -0.00185926 -0.00057923 0.00150138 Pb 20.59852357 9.02413377 20.58652862 -0.00175603 -0.00109023 -0.00058698 Br 21.31882560 8.58099751 17.82308827 -0.00032118 -0.00071189 0.00208396 Br 20.51062461 6.04082138 20.34331958 0.00113823 0.00282428 -0.00363090 Br 17.55621836 9.10261044 20.58860048 0.00200937 -0.00362321 0.00098979 Cs 17.53452930 11.83072384 0.12376536 -0.00267503 -0.00095885 -0.00029879 Pb 20.62866926 14.81183719 3.18460183 0.00039499 0.00070519 0.00095168 Br 20.85768562 15.17799144 0.11669304 0.00154363 -0.00105873 0.00011012 Br 20.26552581 11.95379995 3.27054515 0.00026924 0.00034492 -0.00152766 Br 17.70240427 15.00402780 2.45128885 0.00361482 -0.00025425 -0.00203836 Cs 17.87171657 11.36272482 6.24352347 0.00008810 0.00439818 -0.00088205 Pb 20.43393090 14.87725461 9.18381646 -0.00108265 -0.00024660 0.00050349 Br 20.02580613 14.56378178 6.10298080 -0.00141811 -0.00311496 0.00331310 Br 19.98170901 11.72329269 9.25931384 -0.00259765 -0.00173563 0.00436724 Br 17.51373721 14.52527356 9.38481395 -0.00197424 0.00175266 0.00072669 Cs 18.07649666 11.30710342 12.01646283 0.00141902 0.00024073 -0.00245308 Pb 20.78396573 14.69184240 14.69212061 0.00017561 -0.00058251 -0.00055954 Br 20.79941123 13.96098086 11.85715584 -0.00049323 0.00055882 0.00297017 Br 20.76110243 11.64746847 15.49379111 0.00247812 -0.00001948 0.00120051 Br 17.74387197 14.45617718 15.05806789 0.00092663 0.00333752 0.00173774 Cs 17.41065863 11.83068960 17.59219852 0.00042698 -0.00204348 0.00036303 Pb 21.06857061 15.11945989 20.70006913 0.00083925 -0.00034472 -0.00121823 Br 20.89789580 14.86232417 17.76671872 -0.00215281 0.00375679 -0.00345389 Br 20.06562342 11.73041810 20.38483017 0.00013642 -0.00051473 0.00395374 Br 18.05333332 14.94914273 20.51254419 -0.00138302 -0.00006559 0.00006554 Cs 17.95649219 17.65469681 23.49603627 0.00000254 -0.00117488 0.00132467 Pb 21.05244879 20.80194860 3.01953643 0.00098654 -0.00098356 -0.00133579 Br 21.37094062 21.03011063 0.00883163 -0.00073322 -0.00229397 0.00321531 Br 21.01189750 17.84994998 3.26720771 0.00195096 0.00202047 -0.00023271 Br 17.69635666 20.55526642 2.59616478 -0.00185912 0.00251940 0.00353645 Cs 17.82833869 17.85659307 5.42788546 0.00071444 -0.00164714 -0.00263803 Pb 20.84788830 20.93091918 8.78075153 0.00046177 -0.00118750 0.00117022 Br 20.86380741 20.83657010 5.89865631 -0.00241147 0.00139456 -0.00196622 Br 20.29961107 17.86988386 8.79639127 0.00052899 0.00339681 0.00123725 Br 17.97323415 21.22138040 9.38944533 0.00651082 0.00260890 -0.00473895 Cs 17.76310167 17.80139262 11.95350369 -0.00049080 -0.00190337 0.00235383 Pb 20.64564560 20.92181302 14.84721850 -0.00086088 0.00048935 -0.00063022 Br 21.13851542 20.90327457 11.82588437 0.00010668 0.00034357 -0.00056947 Br 20.44300962 17.45411494 14.16154263 -0.00101719 0.00415400 0.00191677 Br 17.85180571 21.59435624 15.09570100 -0.00141093 0.00227620 0.00384558 Cs 17.70940510 17.98214451 18.22960640 0.00424518 -0.00123355 0.00103922 Pb 20.80646779 20.69908284 20.98374745 0.00049611 -0.00011420 -0.00110794 Br 20.92908790 20.53824361 17.85516714 0.00181923 -0.00219994 0.00345275 Br 20.82862492 17.89969345 20.21364441 0.00073468 0.00067207 -0.00062807 Br 18.07307548 20.76411153 21.31257360 -0.00015100 0.00038745 0.00128198 320 Lattice="23.77195271 0.0 0.0 0.0 23.77195271 0.0 0.0 0.0 23.77195271" Properties=species:S:1:pos:R:3:vel:R:3 Time=20219.99999999 energy=-11424.15662766 virial="-12.78937819 5.26896379 -0.01228508 5.26896379 -11.44894669 2.79251096 -0.01228508 2.79251096 -5.98056584" stress="-5.232e-05 0.00039222 -9.1e-07 0.00039222 -1.69e-06 0.00020787 -9.1e-07 0.00020787 0.00046352" pbc="T T T" Cs 23.43179823 23.70236890 0.17573988 -0.00044370 -0.00110188 0.00159229 Pb 2.88102701 3.08253364 3.21021041 0.00089852 -0.00049310 0.00035188 Br 3.28935898 2.94323092 0.20296335 0.00078488 0.00012704 0.00076719 Br 2.82158684 23.75297009 3.04113213 -0.00177699 0.00022608 -0.00011119 Br 23.71295443 3.39433834 2.03773361 0.00078060 -0.00080034 -0.00276574 Cs 0.48828953 0.18664429 5.81615291 0.00202306 0.00048646 -0.00262341 Pb 3.17963390 2.99899891 9.06408164 0.00152248 -0.00009160 -0.00058516 Br 2.60363179 2.72784262 6.28367767 0.00063529 0.00093994 -0.00417981 Br 3.16284147 0.02080143 9.57572262 -0.00015912 0.00041111 0.00039817 Br 23.67533799 2.78942386 10.44410328 0.00160389 -0.00507485 -0.00257582 Cs 23.68042322 23.46315458 12.17358598 -0.00232031 -0.00168167 -0.00123996 Pb 3.09930078 3.08227268 14.82483366 0.00084323 -0.00205712 -0.00028926 Br 3.74706847 3.02607429 12.05171860 -0.00382755 0.00100923 -0.00121885 Br 3.05499848 23.74637579 14.51815745 -0.00021209 -0.00199225 -0.00019335 Br 23.75173465 3.16299855 14.50395923 -0.00202088 -0.00176145 -0.00191163 Cs 0.03882136 23.24971437 17.53837515 -0.00086544 -0.00047475 -0.00102698 Pb 3.23524907 3.15949463 20.76087116 -0.00170068 0.00012411 0.00091344 Br 2.71991859 3.34582885 17.78595904 0.00245520 0.00054111 0.00115035 Br 2.99800444 0.02549678 20.66113088 0.00000410 -0.00130078 0.00201962 Br 0.02145663 2.51351394 20.77612159 -0.00210018 -0.00448169 0.00245087 Cs 23.45580391 6.61573998 23.61440781 -0.00068075 0.00073867 -0.00101384 Pb 2.89249789 8.93794389 2.85509414 0.00003892 0.00113881 -0.00364578 Br 2.98288380 8.20432921 0.14129450 0.00206593 -0.00038965 0.00077927 Br 2.87032591 6.00697494 3.09980529 -0.00112767 -0.00214723 0.00139751 Br 23.71325704 8.53361184 2.89711183 -0.00358811 0.00028579 -0.00241107 Cs 0.57095844 6.04294894 6.16656470 0.00024360 -0.00045913 0.00175877 Pb 3.25544767 9.09211400 8.92999457 -0.00323355 0.00003444 0.00104951 Br 2.82425156 8.73948444 5.99688837 0.00023688 -0.00054958 -0.00484605 Br 2.61305411 6.00348740 9.12578780 -0.00096384 0.00389349 0.00396769 Br 0.03504656 9.27856061 8.88656893 0.00224925 -0.00126554 0.00102760 Cs 23.37782631 5.90270772 12.14550256 -0.00251409 -0.00098886 0.00056457 Pb 2.93642337 8.95513618 14.75724807 0.00123718 0.00102145 0.00063928 Br 3.27067429 9.15842061 11.87788219 0.00101548 0.00226144 -0.00159880 Br 3.76421801 5.96743666 15.20092794 -0.00075133 -0.00377339 -0.00107041 Br 0.17556622 8.38927137 14.16740351 0.00126573 0.00118709 0.00006324 Cs 23.54872029 5.73817719 17.67322918 -0.00054066 -0.00038503 0.00061580 Pb 2.59467800 8.61412936 20.67513290 -0.00017722 -0.00163105 0.00072720 Br 2.58841416 8.75950337 17.84616283 0.00147136 -0.00114637 0.00496822 Br 2.31598729 5.86915826 21.02052690 0.00164870 -0.00221012 0.00216735 Br 23.29492634 9.36635984 21.26227278 0.00390998 0.00175193 0.00199259 Cs 23.51778411 11.76189098 0.31320242 0.00342910 0.00068047 0.00059778 Pb 2.59146864 14.96775449 2.95393329 0.00124686 -0.00086593 -0.00008272 Br 2.49323613 14.93093542 23.70773607 -0.00016339 -0.00058547 -0.00232079 Br 2.80040015 12.00580689 2.19456861 -0.00235932 -0.00180395 0.00126715 Br 23.63776155 14.74327647 3.14529748 0.00047657 0.00043676 0.00006197 Cs 23.67173989 11.90966951 6.45198028 0.00132568 0.00266468 -0.00214270 Pb 3.04398765 14.91596723 9.06201548 -0.00017637 -0.00248016 -0.00089904 Br 2.98549655 14.90296079 5.83640573 -0.00259012 -0.00034356 0.00163033 Br 2.93495062 11.89905235 8.68294122 -0.00111293 -0.00549269 -0.00098147 Br 23.75261563 14.91259265 8.87058486 -0.00150178 -0.00158549 -0.00004816 Cs 23.61117469 11.84808702 11.41814580 -0.00197423 0.00217839 0.00064565 Pb 3.07062854 14.56566075 14.94548308 -0.00034231 0.00041170 -0.00225703 Br 2.63746594 14.23852717 11.98480841 -0.00430663 -0.00116245 -0.00098947 Br 2.39343535 11.63439230 15.51027756 -0.00006671 -0.00068992 -0.00025155 Br 0.04941465 14.58749310 14.55281613 -0.00525872 0.00139960 0.00041833 Cs 0.00800661 11.62161562 18.13105962 0.00162254 -0.00040425 -0.00049779 Pb 3.16572884 14.58184971 20.84475689 0.00051236 0.00207174 0.00147848 Br 2.98211006 14.90267083 17.95648201 0.00093635 -0.00167337 0.00129349 Br 2.93078402 11.61531419 20.90651958 0.00119488 -0.00042829 -0.00020088 Br 0.15859547 14.77225126 20.63777320 -0.00035449 0.00182451 -0.00271259 Cs 0.02126901 17.90261447 23.49165939 -0.00007367 0.00054628 -0.00128752 Pb 2.96732414 20.87616802 2.78374791 -0.00091403 0.00122883 0.00012511 Br 2.80950154 21.24315687 23.68190535 -0.00134015 0.00389312 -0.00306311 Br 2.78712700 17.81910612 2.82686477 -0.00380999 0.00209545 0.00076135 Br 0.13978975 21.01896410 3.21705859 0.00212419 0.00131134 0.00251018 Cs 22.98646807 17.83020986 5.86720073 0.00067012 -0.00061968 -0.00148310 Pb 2.99215006 20.80444177 8.85755845 0.00030215 -0.00069048 0.00091992 Br 3.04817806 20.55829750 5.86064012 -0.00033262 -0.00060294 -0.00011009 Br 3.02956935 17.97889366 9.06525477 -0.00167113 0.00383681 -0.00273247 Br 23.72846548 20.66943224 8.85120673 0.00043236 -0.00336117 -0.00127417 Cs 0.81963944 17.47329515 11.80564845 0.00182183 0.00313708 -0.00268339 Pb 3.13779620 20.74793023 14.88833689 -0.00020157 -0.00323497 0.00014333 Br 2.79413903 20.76521643 11.94948110 -0.00275683 -0.00132934 0.00487839 Br 3.11194748 17.44303264 15.18997794 0.00050516 -0.00294585 -0.00166761 Br 0.20998732 20.78031973 14.94329276 0.00193702 0.00142445 -0.00009423 Cs 0.39404621 17.73797813 17.73767620 -0.00068666 0.00036970 0.00215468 Pb 3.09625020 20.83348901 20.47894411 -0.00022083 0.00014053 0.00180275 Br 3.05807095 20.31596339 17.71265081 -0.00422979 0.00136823 -0.00198247 Br 3.04132400 17.69397269 20.18039184 -0.00018008 -0.00028602 0.00134234 Br 0.15756974 20.48864708 20.63544729 -0.00215995 -0.00078850 0.00058733 Cs 5.65212696 0.26572809 22.95405029 -0.00212487 0.00110576 0.00163673 Pb 8.76512585 3.23829344 2.96911761 -0.00189049 0.00139583 -0.00171650 Br 8.51626026 2.85789888 0.18768087 -0.00604068 0.00260464 0.00142565 Br 9.16017928 0.37506735 3.77431953 0.00187213 0.00069217 0.00192200 Br 5.89160112 3.14292087 3.82026854 -0.00074573 0.00100657 0.00110091 Cs 5.74761660 23.55981234 5.85385852 -0.00244187 0.00161975 0.00140648 Pb 8.80620048 2.97634826 8.81773975 0.00195901 0.00128962 0.00012277 Br 9.54372924 3.45535067 5.92136617 -0.00242359 0.00039811 -0.00093673 Br 8.42907036 23.76555716 9.10440912 0.00006049 0.00166023 0.00219552 Br 5.96566887 2.91189605 8.13579709 -0.00402652 -0.00331491 -0.00220274 Cs 5.97838460 0.12596636 12.06685775 -0.00059940 0.00184188 -0.00010807 Pb 9.34442056 2.89362794 15.02545546 0.00144195 0.00057804 0.00233769 Br 8.81361490 2.82161192 11.99681398 -0.00259313 -0.00028209 0.00630722 Br 9.89499710 0.00574794 14.81448623 -0.00402440 0.00156957 -0.00293858 Br 6.25766470 2.53166804 14.95170518 0.00115671 0.00159946 -0.00035811 Cs 6.34351680 23.23967251 17.83325108 -0.00098380 -0.00081476 0.00141390 Pb 8.99589305 3.10399680 20.84993398 -0.00148548 -0.00020418 -0.00052875 Br 9.43878825 2.88652315 18.22382908 -0.00179037 -0.00091287 -0.00079540 Br 8.71025455 23.49760991 20.98510676 -0.00340887 -0.00173684 0.00122008 Br 6.03982846 3.82557945 20.74840575 0.00144578 -0.00126023 -0.00056289 Cs 5.47374054 5.80295886 0.03437589 -0.00048239 -0.00430478 0.00122967 Pb 8.67181446 8.89463809 2.84906637 -0.00023040 0.00074235 0.00137064 Br 8.97284872 8.92530474 23.68846722 -0.00031424 0.00095157 -0.00133622 Br 8.17280503 6.16746252 2.88615681 -0.00072809 0.00087613 -0.00210522 Br 5.71439840 9.20083884 2.86827587 -0.00026049 0.00221634 0.00080568 Cs 6.22040050 6.21144086 5.76654063 0.00165108 -0.00057521 -0.00002215 Pb 8.93013127 8.77996056 8.77525768 0.00279984 -0.00075961 0.00124555 Br 8.72871293 8.41371534 5.81373264 0.00179472 0.00037233 0.00039229 Br 8.55204521 5.96271065 9.36399517 -0.00202764 -0.00087454 0.00111087 Br 6.03192744 8.62367770 8.67772550 -0.00040850 0.00410992 -0.00000894 Cs 6.03572834 5.88786769 12.02363658 0.00099656 -0.00316034 0.00133788 Pb 8.91038129 8.93184009 14.78919449 -0.00024720 -0.00051788 -0.00146370 Br 8.07845351 8.71769245 11.96008996 0.00376473 0.00059090 0.00016747 Br 9.41145813 5.94505042 15.27917256 -0.00126984 -0.00097741 0.00119055 Br 6.16306573 8.65797981 15.52270311 -0.00221861 -0.00230036 -0.00017989 Cs 6.64684635 6.14247888 17.72067979 -0.00268530 -0.00044686 -0.00075339 Pb 8.76877671 9.13342039 20.70575036 0.00072633 0.00055894 0.00026063 Br 8.97902159 9.04771410 17.79138535 -0.00115536 -0.00203226 0.00149021 Br 9.16533200 5.96790368 20.67866285 0.00047476 -0.00155913 -0.00238709 Br 5.56387079 8.48704415 20.70065991 0.00096393 -0.00023217 0.00082278 Cs 5.41231672 11.34846865 23.59093222 -0.00020311 -0.00098267 0.00223077 Pb 8.74990878 14.80989560 3.20952323 -0.00178455 0.00179822 0.00029584 Br 9.15018462 14.56511196 23.67260853 -0.00322139 0.00058921 -0.00181005 Br 9.03898592 11.95557902 3.39436247 0.00066567 0.00024267 0.00069001 Br 5.76148203 14.47618785 2.62715277 0.00219221 -0.00042728 0.00033028 Cs 5.55206472 12.07489103 5.14366129 0.00322911 0.00144247 0.00314042 Pb 8.80700954 14.63941147 9.00975875 0.00183215 0.00162915 -0.00035464 Br 8.49682373 13.93656197 6.12448030 0.00094107 0.00237360 0.00365390 Br 9.42174803 11.60402926 8.96173626 -0.00369908 -0.00361357 0.00312417 Br 5.96429948 14.04331501 9.44469755 0.00186876 0.00019751 -0.00232825 Cs 5.72812238 11.52421931 11.91504466 0.00244444 -0.00454459 0.00166291 Pb 9.29364420 14.92490705 14.76227373 0.00089070 -0.00205314 -0.00149484 Br 9.04725500 15.26939089 11.87490327 -0.00383634 0.00069468 0.00022897 Br 9.51503729 11.90633200 15.04395379 -0.00057428 0.00366363 -0.00186256 Br 5.87530235 14.61412983 13.92310799 0.00315300 -0.00054496 -0.00171199 Cs 6.45437630 11.59883748 17.51564198 0.00022804 -0.00230363 -0.00228786 Pb 9.06993103 14.94188565 20.88780750 0.00149850 0.00088238 0.00199107 Br 9.34181406 14.85243192 17.83189591 -0.00073718 -0.00053481 0.00088882 Br 8.76828563 12.02937844 20.94756126 -0.00182830 0.00052069 -0.00512427 Br 5.98807507 14.91760541 21.28488522 -0.00254199 -0.00127084 -0.00033538 Cs 5.46319499 18.56795803 23.52006971 0.00102717 -0.00056184 -0.00211593 Pb 8.80925042 20.80744445 3.06053090 0.00027215 0.00157825 -0.00015567 Br 8.92594843 21.00987219 23.69959349 -0.00040062 0.00134753 -0.00091309 Br 8.45886093 17.99884728 2.69760786 0.00139011 0.00252385 -0.00087740 Br 5.94523885 20.96000149 3.08187918 0.00459946 0.00350918 0.00031353 Cs 5.50897095 18.07283067 5.50129072 -0.00175568 0.00060154 -0.00021860 Pb 8.84925305 20.43987311 8.89309258 0.00372492 0.00045676 -0.00081145 Br 8.13793980 20.54563122 5.97899822 0.00019202 0.00337915 -0.00184659 Br 8.20211782 17.49484950 8.04373560 0.00202759 0.00042226 -0.00039285 Br 5.95885595 20.58420511 8.51818582 0.00123334 0.00054655 0.00065372 Cs 6.19578772 18.33509883 11.44663230 -0.00136312 0.00020164 0.00106694 Pb 9.22169612 20.76238956 14.85218394 0.00006642 -0.00084991 0.00228423 Br 9.64209136 20.57526886 11.72246695 0.00062781 -0.00454329 0.00024301 Br 8.34145305 18.07074028 14.94023612 -0.00054514 0.00382857 0.00263698 Br 6.38485568 21.41908679 14.83422479 0.00162832 0.00111617 -0.00169437 Cs 5.67819426 17.36519647 17.66224139 -0.00042804 0.00004788 -0.00229294 Pb 8.95225100 20.64910338 20.63990297 -0.00063054 -0.00054044 -0.00005981 Br 9.76982890 20.69727365 17.77211692 -0.00193809 -0.00009103 0.00262053 Br 8.15555923 17.69546584 20.82212147 0.00454041 -0.00236271 0.00097146 Br 5.84823363 20.86511176 20.51332983 0.00038967 0.00149098 -0.00030768 Cs 12.19079227 23.71055489 0.18593489 0.00023804 -0.00036234 -0.00152232 Pb 14.99344710 3.30402393 2.84977744 -0.00039463 -0.00191149 0.00056234 Br 15.47691213 3.03285917 23.52887049 0.00209036 0.00179716 -0.00188550 Br 15.71921716 0.00043476 3.06881752 0.00172405 0.00116562 0.00385140 Br 12.06954378 3.61635074 2.38174508 -0.00240550 0.00127981 -0.00132968 Cs 12.06558879 0.48656945 5.46401754 -0.00103375 -0.00055956 -0.00202608 Pb 14.92166084 3.15052305 8.81796890 -0.00000606 0.00102963 0.00007820 Br 14.45268147 3.33431201 5.82280213 -0.00243512 -0.00001573 0.00083727 Br 15.06615249 0.07890964 9.09209626 0.00173147 0.00062940 -0.00024367 Br 11.96685642 3.02019643 9.41403100 -0.00231836 0.00100523 -0.00378962 Cs 11.99437979 0.57638875 11.91927177 0.00069612 0.00258873 -0.00107058 Pb 15.06043134 2.56704403 14.93106926 -0.00027292 -0.00205817 -0.00023101 Br 14.85366048 3.06129266 12.04693029 -0.00094544 0.00061867 -0.00168794 Br 14.30994643 23.58960307 14.70346703 0.00075737 -0.00035336 -0.00247739 Br 12.06066921 3.37370515 14.22463328 -0.00162774 0.00405313 0.00208840 Cs 11.97926203 23.47257296 18.54933441 -0.00116796 0.00297484 0.00153611 Pb 14.90307849 3.12407548 20.74982096 -0.00203112 0.00019506 -0.00068828 Br 14.27831346 3.02611197 17.81142882 -0.00429954 -0.00353601 -0.00289707 Br 15.38715933 23.61936624 20.45895621 0.00202027 0.00329279 -0.00401505 Br 11.78997911 2.44946722 21.56337820 0.00029860 -0.00202254 -0.00089458 Cs 11.94685487 6.15564768 23.41724562 0.00138109 -0.00145576 0.00143441 Pb 14.74241719 8.93978352 3.11291372 -0.00016389 0.00048056 -0.00363752 Br 14.84513410 8.57557623 0.02248086 0.00127810 -0.00332874 -0.00233983 Br 15.11977888 6.14320142 3.20816307 -0.00243562 0.00257519 -0.00405301 Br 11.73269179 8.31000438 2.63496831 0.00068315 -0.00198703 0.00068104 Cs 12.24638344 6.12092877 5.63050294 0.00240037 -0.00150873 0.00074495 Pb 14.56392106 9.00024731 8.93263773 -0.00019499 0.00184956 -0.00166322 Br 14.28948133 9.53942883 6.01796157 -0.00062134 -0.00126000 0.00125048 Br 15.26864298 6.06023501 8.45642405 -0.00026860 -0.00070917 0.00223057 Br 11.68291724 7.93593158 8.84111424 0.00173995 -0.00087054 -0.00161652 Cs 12.08887397 6.02383245 12.01072418 0.00098935 0.00051865 0.00181227 Pb 15.03280825 8.89035855 14.98181791 0.00077414 -0.00067597 0.00217283 Br 14.33766849 9.18621083 12.01355608 -0.00431226 0.00031039 -0.00299119 Br 14.71889212 5.60044812 14.78802134 -0.00090690 -0.00169958 -0.00044048 Br 12.04835042 8.73305508 15.11649475 -0.00023491 0.00330025 0.00182169 Cs 12.49455625 5.75594306 18.21791586 -0.00341965 0.00227198 -0.00204137 Pb 14.69280320 9.18929634 20.94585851 0.00173148 -0.00002969 0.00024677 Br 14.80325182 8.57772628 17.77526890 0.00095951 0.00004489 0.00279501 Br 14.93409870 6.26927827 20.66084721 -0.00375198 0.00239865 -0.00051180 Br 11.84892218 8.73444025 20.61076446 -0.00258891 -0.00245235 0.00178927 Cs 11.34563068 11.83078693 0.48050886 0.00130393 -0.00047694 -0.00158651 Pb 14.70504653 14.91253276 3.19955438 -0.00024476 0.00068430 -0.00138648 Br 15.05746466 15.35949407 0.03036948 0.00215863 -0.00226071 0.00150646 Br 14.22297526 11.95747562 3.06805557 -0.00280219 -0.00393207 -0.00154633 Br 11.67206293 14.94625624 3.00901298 0.00185492 -0.00090323 -0.00397653 Cs 11.78351586 12.49363583 6.07182588 0.00316738 -0.00036084 -0.00001271 Pb 14.79131830 15.03294919 9.06603922 0.00116733 -0.00034362 -0.00227593 Br 14.84351684 14.89286124 6.14202575 -0.00117274 0.00049025 0.00249864 Br 13.60140742 11.87375737 9.01799790 0.00039541 -0.00227772 0.00069102 Br 11.46299251 15.25859323 8.63819641 -0.00119789 -0.00125640 0.00364498 Cs 11.82211135 12.19044127 12.07940344 -0.00064216 0.00063312 0.00145355 Pb 14.87473537 15.02880534 15.14918178 -0.00098578 -0.00024831 0.00122582 Br 14.72624862 14.75006415 12.07353901 -0.00001190 -0.00002260 -0.00220904 Br 14.79344641 11.92837222 14.86404342 0.00365164 0.00454546 0.00012147 Br 11.95423783 15.09803278 14.89649067 0.00192505 -0.00061961 -0.00303119 Cs 12.20805377 11.35654793 17.44709654 -0.00097538 -0.00204290 0.00066132 Pb 14.97091618 14.95131789 20.92683187 0.00109006 0.00129463 -0.00009911 Br 14.63486624 14.78141938 18.10057672 0.00104093 -0.00115205 0.00021338 Br 14.38008500 12.09562881 21.45673844 0.00142162 -0.00038917 0.00386578 Br 11.94780935 15.53323308 21.51647886 0.00165370 0.00271073 -0.00233124 Cs 11.49721770 17.91916206 0.83499331 -0.00018901 -0.00155332 0.00249800 Pb 15.00093849 20.95579117 3.12911849 0.00144397 -0.00033129 0.00155794 Br 15.00038107 20.80928666 23.49011887 0.00413618 0.00101770 0.00159446 Br 14.27072226 17.85281116 3.61478373 -0.00178475 -0.00040407 -0.00098738 Br 11.95768088 21.26512018 3.41817091 0.00139179 -0.00218845 -0.00182003 Cs 11.30711684 18.20290367 5.94320706 0.00163784 0.00028564 0.00009861 Pb 14.91343363 20.96669369 8.94215207 -0.00109310 0.00047971 0.00124692 Br 15.33937703 20.44609606 6.00504956 0.00390740 -0.00263367 -0.00207582 Br 15.04921477 17.96618529 8.96610015 0.00352423 0.00141010 0.00250350 Br 11.44152483 21.43038125 8.11749052 -0.00162672 -0.00055435 -0.00050895 Cs 12.40237383 18.18851902 11.50523220 -0.00107899 -0.00355259 -0.00173339 Pb 14.97227699 20.79922220 14.72873403 -0.00108170 0.00383338 -0.00075118 Br 15.24694933 20.99190419 11.89883593 0.00244114 0.00347134 0.00010910 Br 15.54958145 17.79947498 14.75714962 -0.00007832 -0.00156302 0.00230298 Br 12.20510196 19.77640118 14.88739781 -0.00123402 0.00129424 -0.00126864 Cs 12.10256844 17.57721164 17.96494077 -0.00169425 -0.00022406 0.00111567 Pb 15.05460009 20.83387198 20.70597276 0.00084458 0.00135127 0.00041154 Br 14.28054366 20.86548071 17.63754950 0.00283308 0.00235863 0.00126903 Br 15.02954983 17.66856807 20.12971872 0.00326954 0.00224618 0.00568509 Br 11.83057237 20.54727939 21.19889791 0.00097058 -0.00239734 0.00107730 Cs 18.45358395 23.43863443 23.49899604 0.00358395 0.00109398 -0.00178020 Pb 21.03963538 3.34820199 2.56831397 -0.00000935 -0.00020773 0.00050518 Br 20.52706142 3.91912870 23.72110511 -0.00134642 -0.00026684 -0.00166091 Br 20.73517530 0.13709598 3.13099958 -0.00137499 -0.00203631 -0.00231756 Br 17.83889089 3.13739716 3.54269234 0.00003544 -0.00132081 0.00522639 Cs 17.87802438 0.31762490 6.07656964 -0.00341138 0.00291425 -0.00152313 Pb 21.17248145 2.93358477 9.12504630 0.00120783 -0.00065066 0.00207976 Br 21.56168616 3.14278067 6.14570161 0.00061655 0.00011531 -0.00050528 Br 20.74436134 0.01663095 9.35201131 -0.00187654 -0.00128080 0.00214851 Br 17.97651970 2.86656512 8.59470113 0.00452100 0.00237376 0.00062451 Cs 18.12408195 0.13823276 11.81726198 0.00244216 -0.00202282 0.00004935 Pb 20.67525627 3.01744967 14.68256914 0.00133402 -0.00050609 0.00032606 Br 19.89601130 3.63889226 11.84238388 0.00140665 0.00046873 0.00129873 Br 21.62129638 23.59675978 14.89297179 -0.00174310 0.00011378 0.00138583 Br 17.88936831 2.92373737 15.10884652 -0.00052769 0.00124684 0.00256359 Cs 18.06999188 23.46907741 18.05886190 0.00075840 -0.00185019 -0.00319427 Pb 21.00380250 3.02774615 20.59903692 -0.00183839 0.00223233 -0.00087441 Br 20.93424845 3.08626497 17.75854566 0.00072988 -0.00184379 0.00024581 Br 20.74123269 23.76807048 20.25608214 -0.00215377 -0.00039684 -0.00088046 Br 17.81333335 2.94668978 20.21635731 0.00270847 0.00067556 0.00173244 Cs 17.77513469 6.49086431 23.21695345 -0.00031942 -0.00013988 -0.00161575 Pb 20.63336360 8.84870501 3.08298000 -0.00280313 -0.00050073 -0.00045592 Br 20.20980142 8.57406136 0.04983703 0.00132881 -0.00189182 -0.00128325 Br 20.53026368 6.06805393 3.51542571 0.00054761 0.00502489 0.00257898 Br 17.61708602 8.80948634 3.59969599 -0.00210946 -0.00198812 -0.00126409 Cs 18.38663541 5.46992263 6.17966550 -0.00068412 0.00150758 -0.00248723 Pb 20.86170077 8.81228467 8.84258147 -0.00159929 0.00107511 0.00007595 Br 20.84545648 9.29858093 5.90093242 0.00207850 -0.00199404 0.00223387 Br 21.34538804 5.87674578 8.50515332 0.00038681 -0.00001659 -0.00205527 Br 17.58126949 9.18417312 8.66101218 0.00229463 -0.00127103 -0.00333908 Cs 17.52568953 6.44763791 11.47017448 0.00070565 0.00035615 0.00072567 Pb 20.96354435 8.67530647 14.95889780 -0.00011459 -0.00033315 -0.00072501 Br 20.74535956 8.80873631 11.76070947 -0.00166359 0.00105855 -0.00156868 Br 20.71579968 5.82816768 14.99454516 -0.00050493 -0.00060314 -0.00321437 Br 18.11127460 8.21335627 14.77458272 -0.00164081 -0.00074354 0.00085970 Cs 17.67602351 5.63441870 17.71188449 -0.00185066 -0.00049168 0.00148067 Pb 20.57969623 9.01260027 20.58137040 -0.00198373 -0.00120726 -0.00043283 Br 21.31614134 8.57458783 17.84267752 -0.00021492 -0.00056467 0.00179392 Br 20.52243302 6.06616411 20.30780761 0.00121990 0.00221917 -0.00346751 Br 17.57600507 9.06618553 20.59825472 0.00193602 -0.00365912 0.00093514 Cs 17.50778066 11.82135680 0.12068727 -0.00267264 -0.00091403 -0.00031692 Pb 20.63175542 14.81855265 3.19343878 0.00022401 0.00063506 0.00081292 Br 20.87393656 15.16741261 0.11705859 0.00170740 -0.00105626 -0.00003377 Br 20.26894162 11.95643131 3.25357091 0.00041241 0.00019759 -0.00186063 Br 17.73885632 15.00193372 2.43273311 0.00366008 -0.00016337 -0.00166729 Cs 17.87227367 11.40836539 6.23340299 0.00002529 0.00471825 -0.00113973 Pb 20.42310420 14.87464836 9.18755603 -0.00108212 -0.00027056 0.00025018 Br 20.01253301 14.53375731 6.13634800 -0.00123412 -0.00288630 0.00335248 Br 19.96005214 11.70705370 9.29959444 -0.00171880 -0.00148696 0.00366595 Br 17.49654691 14.54330983 9.39216969 -0.00143164 0.00184688 0.00074422 Cs 18.08965590 11.30986134 11.99309280 0.00120012 0.00031010 -0.00220695 Pb 20.78614778 14.68572012 14.68786889 0.00025572 -0.00062852 -0.00028442 Br 20.79425964 13.96703009 11.88796889 -0.00054414 0.00064636 0.00314510 Br 20.78559240 11.64676355 15.50485238 0.00241834 -0.00012435 0.00100979 Br 17.75422551 14.48953518 15.07503982 0.00113426 0.00332918 0.00165751 Cs 17.41581618 11.81048535 17.59644579 0.00060581 -0.00199259 0.00048447 Pb 21.07560875 15.11439487 20.68748798 0.00056732 -0.00066570 -0.00129893 Br 20.87725268 14.90060482 17.73322430 -0.00197477 0.00389694 -0.00321776 Br 20.06837691 11.72985700 20.42440731 0.00040774 0.00040972 0.00395866 Br 18.03758122 14.94916991 20.51428292 -0.00175678 0.00006886 0.00028241 Cs 17.95615513 17.64251635 23.50960756 -0.00006852 -0.00125752 0.00138630 Pb 21.06120998 20.79330904 3.00638335 0.00076597 -0.00073889 -0.00128680 Br 21.36278112 21.00519285 0.04175120 -0.00089482 -0.00268799 0.00333086 Br 21.02948804 17.87107697 3.26209477 0.00156105 0.00217854 -0.00079708 Br 17.68341454 20.57873334 2.63103547 -0.00069325 0.00216370 0.00342936 Cs 17.83512811 17.83991225 5.40175242 0.00064557 -0.00168625 -0.00258706 Pb 20.85228044 20.91855950 8.79399955 0.00042237 -0.00128205 0.00146377 Br 20.83881905 20.85098040 5.87781587 -0.00258428 0.00148264 -0.00217498 Br 20.30481651 17.90237877 8.80882661 0.00051044 0.00306892 0.00124888 Br 18.03368669 21.24631038 9.34049310 0.00552449 0.00237548 -0.00503898 Cs 17.75821893 17.78247242 11.97600374 -0.00048688 -0.00188264 0.00214782 Pb 20.63837987 20.92570090 14.84055540 -0.00059510 0.00028301 -0.00070065 Br 21.13838734 20.90617032 11.82243551 -0.00013412 0.00023258 -0.00010673 Br 20.43369431 17.49796185 14.18149566 -0.00084131 0.00459546 0.00207908 Br 17.83830569 21.61749253 15.13347848 -0.00128131 0.00233861 0.00369354 Cs 17.75328161 17.96934381 18.23836179 0.00452121 -0.00132811 0.00071236 Pb 20.81384685 20.69883957 20.97033687 0.00097416 0.00006844 -0.00156576 Br 20.94620735 20.51780982 17.88796371 0.00160251 -0.00187825 0.00308591 Br 20.83635244 17.90549657 20.20946991 0.00081264 0.00046908 -0.00019834 Br 18.06759175 20.76659925 21.32416224 -0.00092886 0.00010650 0.00103008 320 Lattice="23.77195271 0.0 0.0 0.0 23.77195271 0.0 0.0 0.0 23.77195271" Properties=species:S:1:pos:R:3:vel:R:3 Time=20229.99999999 energy=-11423.95538712 virial="-14.198549 5.59752518 -0.21454092 5.59752518 -9.33110323 2.87501737 -0.21454092 2.87501737 -5.40376959" stress="-0.00015595 0.00041668 -1.597e-05 0.00041668 0.000128 0.00021402 -1.597e-05 0.00021402 0.0005032" pbc="T T T" Cs 23.42896510 23.69256820 0.19154113 -0.00012580 -0.00086001 0.00156653 Pb 2.89069450 3.07745496 3.21275002 0.00103235 -0.00051825 0.00015630 Br 3.29756656 2.94422637 0.21002540 0.00085473 0.00006592 0.00063466 Br 2.80413009 23.75750921 3.04034281 -0.00170749 0.00067611 -0.00005229 Br 23.72507407 3.38587301 2.01052260 0.00160133 -0.00089605 -0.00266965 Cs 0.50654167 0.18999191 5.79020845 0.00162297 0.00018345 -0.00256069 Pb 3.19468507 2.99809511 9.05939958 0.00147899 -0.00008848 -0.00035829 Br 2.60990379 2.73885576 6.23934941 0.00061867 0.00126336 -0.00465272 Br 3.16112010 0.02630981 9.57793930 -0.00018447 0.00069723 0.00004652 Br 23.69368773 2.73889120 10.41660053 0.00207292 -0.00501877 -0.00291268 Cs 23.65861032 23.44599151 12.16043852 -0.00204130 -0.00175052 -0.00138508 Pb 3.10807182 3.06146996 14.82266056 0.00091362 -0.00210612 -0.00014477 Br 3.70912297 3.03714707 12.03664989 -0.00375850 0.00120352 -0.00178705 Br 3.05224230 23.72505071 14.51810831 -0.00033749 -0.00226841 0.00018331 Br 23.73263599 3.14565853 14.48566790 -0.00179466 -0.00170660 -0.00174372 Cs 0.03072647 23.24554083 17.52943661 -0.00075215 -0.00035924 -0.00075827 Pb 3.21702502 3.15965475 20.76947652 -0.00193718 -0.00009771 0.00080670 Br 2.74466190 3.35053140 17.80055949 0.00248846 0.00039934 0.00176500 Br 2.99825142 0.01245492 20.67976267 0.00004263 -0.00130878 0.00170126 Br 0.00120463 2.46955969 20.80097945 -0.00194159 -0.00430453 0.00251920 Cs 23.44853259 6.62229310 23.60451423 -0.00077182 0.00057121 -0.00096352 Pb 2.89149612 8.94983671 2.81980592 -0.00024413 0.00123180 -0.00339500 Br 3.00280313 8.20225437 0.14568671 0.00192154 -0.00004065 0.00007807 Br 2.86009578 5.98401630 3.11481525 -0.00091886 -0.00242499 0.00159757 Br 23.67515328 8.53772629 2.87301601 -0.00401039 0.00054193 -0.00241008 Cs 0.57229282 6.03820540 6.18398892 0.00002488 -0.00048663 0.00172608 Pb 3.22181999 9.09079067 8.93973466 -0.00348281 -0.00031722 0.00089021 Br 2.82771761 8.73536598 5.94761877 0.00045569 -0.00027126 -0.00498848 Br 2.60444141 6.04089084 9.16582384 -0.00076114 0.00355927 0.00403702 Br 0.05573820 9.26548194 8.89715121 0.00187423 -0.00134011 0.00108750 Cs 23.35237454 5.89159194 12.14923378 -0.00257298 -0.00123171 0.00018134 Pb 2.94932697 8.96396469 14.76451040 0.00134246 0.00074034 0.00080673 Br 3.28024539 9.18056101 11.86275801 0.00088945 0.00216202 -0.00139414 Br 3.75548843 5.92962808 15.18860562 -0.00099577 -0.00377400 -0.00139232 Br 0.18837523 8.40350575 14.17090046 0.00128422 0.00164994 0.00063000 Cs 23.54394189 5.73386417 17.67930026 -0.00041380 -0.00047805 0.00059822 Pb 2.59371128 8.59974117 20.68284377 -0.00001489 -0.00124094 0.00082605 Br 2.60211393 8.74718073 17.89505367 0.00126976 -0.00131563 0.00475655 Br 2.33116798 5.84411634 21.04238719 0.00138075 -0.00277655 0.00220596 Br 23.33845147 9.38432689 21.28258643 0.00474236 0.00183264 0.00205519 Cs 23.55189434 11.76920322 0.31903385 0.00338673 0.00078005 0.00056475 Pb 2.60638309 14.95820108 2.95220371 0.00172507 -0.00103747 -0.00026313 Br 2.49094826 14.92627893 23.68480668 -0.00029680 -0.00034605 -0.00222297 Br 2.77641193 11.98520460 2.20893082 -0.00242810 -0.00228961 0.00160585 Br 23.63814590 14.74729442 3.14707702 -0.00036963 0.00036752 0.00029265 Cs 23.68496256 11.93655131 6.42990319 0.00131826 0.00270407 -0.00227031 Pb 3.04222455 14.89024784 9.05278255 -0.00017415 -0.00266227 -0.00094677 Br 2.95985633 14.89937761 5.85578084 -0.00253699 -0.00037370 0.00222585 Br 2.92372052 11.84758973 8.67291400 -0.00113181 -0.00472079 -0.00102539 Br 23.73731535 14.89656234 8.87022930 -0.00155420 -0.00161972 -0.00002358 Cs 23.59178767 11.86967196 11.42514655 -0.00190330 0.00213271 0.00075145 Pb 3.06705745 14.56956826 14.92256944 -0.00036922 0.00036457 -0.00232279 Br 2.59484163 14.22749705 11.97197012 -0.00420926 -0.00104054 -0.00156521 Br 2.39391103 11.62843308 15.50688054 0.00016061 -0.00049926 -0.00042610 Br 23.76856167 14.60255992 14.55763995 -0.00526159 0.00160982 0.00054571 Cs 0.02359176 11.61804858 18.12633997 0.00149488 -0.00030974 -0.00044197 Pb 3.17095814 14.60319985 20.85981375 0.00052987 0.00218841 0.00152357 Br 2.99199034 14.88398110 17.96678314 0.00104052 -0.00206055 0.00074145 Br 2.94258953 11.61191549 20.90458677 0.00116682 -0.00025131 -0.00018428 Br 0.15400262 14.79050597 20.61054787 -0.00057162 0.00182761 -0.00273255 Cs 0.02095600 17.90760924 23.47872837 0.00000924 0.00045221 -0.00129860 Pb 2.95879611 20.88819396 2.78536544 -0.00078478 0.00117656 0.00019475 Br 2.79641075 21.28127163 23.65059234 -0.00127396 0.00372011 -0.00317634 Br 2.74883855 17.84217656 2.83372775 -0.00384562 0.00248731 0.00061158 Br 0.15770685 21.03176050 3.24224096 0.00145740 0.00124635 0.00252366 Cs 22.99507654 17.82387291 5.85466849 0.00105095 -0.00064320 -0.00102174 Pb 2.99477765 20.79866745 8.86687692 0.00022462 -0.00045440 0.00094104 Br 3.04388616 20.55408485 5.85993640 -0.00052368 -0.00023492 -0.00003258 Br 3.01389963 18.01311033 9.03794325 -0.00146054 0.00295401 -0.00272981 Br 23.73605874 20.63556632 8.83779310 0.00109858 -0.00341234 -0.00140535 Cs 0.83675201 17.50496715 11.77970772 0.00159710 0.00319581 -0.00250380 Pb 3.13562742 20.71630261 14.88833470 -0.00022584 -0.00308405 -0.00014414 Br 2.76752463 20.75039709 11.99636683 -0.00256735 -0.00163351 0.00445555 Br 3.11645152 17.41683225 15.17107604 0.00039176 -0.00228436 -0.00210886 Br 0.22994910 20.79298026 14.94241813 0.00203475 0.00110542 -0.00008117 Cs 0.38647255 17.74134460 17.75916152 -0.00082820 0.00030188 0.00214100 Pb 3.09213814 20.83435512 20.49834701 -0.00059903 0.00003227 0.00206867 Br 3.01613518 20.33113637 17.69289555 -0.00415647 0.00166156 -0.00191091 Br 3.04019985 17.69229213 20.19735402 -0.00004228 -0.00004296 0.00204320 Br 0.13366572 20.48113861 20.64156990 -0.00260098 -0.00071077 0.00063732 Cs 5.63157819 0.27645259 22.97134974 -0.00198263 0.00103778 0.00182361 Pb 8.74698215 3.25140999 2.95347396 -0.00173593 0.00122618 -0.00140483 Br 8.45442869 2.88351785 0.19980658 -0.00632527 0.00251476 0.00097130 Br 9.17623104 0.38037433 3.79070443 0.00132629 0.00037348 0.00134402 Br 5.88270366 3.15231477 3.82977468 -0.00102781 0.00086932 0.00079766 Cs 5.72376744 23.57619930 5.86828371 -0.00232669 0.00165549 0.00147766 Pb 8.82667725 2.98879133 8.81954868 0.00212763 0.00119729 0.00023681 Br 9.51962591 3.46010003 5.91358478 -0.00239696 0.00055336 -0.00060239 Br 8.42995404 0.00926321 9.12582191 0.00012293 0.00147461 0.00208632 Br 5.92346929 2.87958960 8.11454401 -0.00437034 -0.00314290 -0.00204237 Cs 5.97281134 0.14460404 12.06574869 -0.00051432 0.00188471 -0.00011159 Pb 9.35817735 2.89964006 15.04765886 0.00130089 0.00062065 0.00210272 Br 8.78745145 2.81928330 12.05990445 -0.00263085 -0.00017813 0.00627998 Br 9.85441701 0.02054915 14.78629800 -0.00407934 0.00138275 -0.00269950 Br 6.26818356 2.54811064 14.94962420 0.00092777 0.00168940 -0.00005748 Cs 6.33380096 23.23253781 17.84707955 -0.00095905 -0.00061151 0.00134640 Pb 8.98082156 3.10164591 20.84700888 -0.00152627 -0.00026482 -0.00006252 Br 9.42214240 2.87657402 18.20904174 -0.00154119 -0.00107810 -0.00212588 Br 8.67635634 23.48332701 20.99541076 -0.00336629 -0.00110912 0.00083831 Br 6.05348558 3.81213937 20.74234055 0.00126529 -0.00143242 -0.00064966 Cs 5.46992651 5.75986401 0.04687651 -0.00027959 -0.00430907 0.00127022 Pb 8.67094741 8.90295814 2.86344065 0.00005739 0.00091863 0.00149949 Br 8.96905157 8.93514596 23.67462470 -0.00044350 0.00101849 -0.00140648 Br 8.16516861 6.17313852 2.86295517 -0.00080008 0.00026292 -0.00253094 Br 5.71486994 9.22301933 2.87628678 0.00035160 0.00222077 0.00080014 Cs 6.23553620 6.20476182 5.76730446 0.00137583 -0.00075580 0.00017649 Pb 8.95822424 8.77208732 8.78782545 0.00280595 -0.00081915 0.00126332 Br 8.74866639 8.41956263 5.81853118 0.00219193 0.00079351 0.00056126 Br 8.53141530 5.95046527 9.37472524 -0.00209749 -0.00157464 0.00103616 Br 6.02612617 8.66571113 8.67728420 -0.00073084 0.00429844 -0.00007688 Cs 6.04563084 5.85583825 12.03730579 0.00098542 -0.00324142 0.00139696 Pb 8.90894375 8.92548208 14.77430040 -0.00004428 -0.00075467 -0.00150364 Br 8.11741169 8.72509511 11.96151571 0.00402270 0.00087545 0.00012015 Br 9.39729371 5.93630271 15.29055593 -0.00156214 -0.00074296 0.00108291 Br 6.13839245 8.63767456 15.51852019 -0.00269227 -0.00175527 -0.00066654 Cs 6.61974435 6.13661294 17.71386917 -0.00273467 -0.00072868 -0.00060477 Pb 8.77611729 9.13818461 20.70872545 0.00073837 0.00039321 0.00033634 Br 8.96842572 9.02801806 17.80723631 -0.00096405 -0.00190616 0.00164719 Br 9.17086087 5.95292860 20.65506761 0.00063139 -0.00144193 -0.00233001 Br 5.57493463 8.48524237 20.70891447 0.00124502 -0.00013078 0.00082768 Cs 5.41081580 11.33929040 23.61279272 -0.00009734 -0.00085387 0.00213560 Pb 8.73107700 14.82740722 3.21186236 -0.00197308 0.00169964 0.00017464 Br 9.11689959 14.57187506 23.66071571 -0.00342660 0.00075563 -0.00054284 Br 9.04576980 11.95677744 3.39923981 0.00068931 0.00002242 0.00029233 Br 5.78501753 14.47320723 2.63062624 0.00250432 -0.00017030 0.00036497 Cs 5.58404473 12.08888788 5.17594272 0.00316497 0.00135178 0.00331689 Pb 8.82388438 14.65549023 9.00561620 0.00152847 0.00158327 -0.00046682 Br 8.50573302 13.96299353 6.15946058 0.00084883 0.00290062 0.00330649 Br 9.38502040 11.57123488 8.99421172 -0.00363888 -0.00292250 0.00336738 Br 5.97947775 14.04645449 9.42104688 0.00114133 0.00042148 -0.00239602 Cs 5.75344602 11.47982796 11.93161448 0.00262435 -0.00432347 0.00164418 Pb 9.29909742 14.90444309 14.74811497 0.00020704 -0.00203374 -0.00133212 Br 9.01042100 15.27685496 11.87768404 -0.00352495 0.00080184 0.00033137 Br 9.50841421 11.94430335 15.02439215 -0.00075005 0.00390055 -0.00204678 Br 5.90697773 14.60824697 13.90824122 0.00315216 -0.00063404 -0.00125545 Cs 6.45625710 11.57670898 17.49332636 0.00014731 -0.00211935 -0.00217083 Pb 9.08524548 14.95116681 20.90753636 0.00156534 0.00096535 0.00194138 Br 9.33419439 14.84707074 17.83885674 -0.00078495 -0.00053497 0.00049342 Br 8.74970859 12.03067429 20.89540091 -0.00188568 -0.00025626 -0.00530449 Br 5.96269407 14.90387275 21.28116788 -0.00250503 -0.00146545 -0.00040321 Cs 5.47408464 18.56147320 23.49877226 0.00114974 -0.00073230 -0.00214286 Pb 8.81247620 20.82455445 3.05886721 0.00038131 0.00184775 -0.00017809 Br 8.92127528 21.02212236 23.69285788 -0.00053257 0.00109998 -0.00043266 Br 8.47268213 18.02282612 2.68987386 0.00136740 0.00225002 -0.00066740 Br 5.99026817 20.99552560 3.08348267 0.00435415 0.00359499 0.00000226 Cs 5.49168584 18.07819795 5.49926877 -0.00169662 0.00047575 -0.00018410 Pb 8.88666299 20.44554718 8.88443547 0.00373731 0.00067282 -0.00091244 Br 8.14299942 20.58005142 5.95711405 0.00081679 0.00349524 -0.00250171 Br 8.22323781 17.49894186 8.04166360 0.00219611 0.00040108 -0.00002737 Br 5.96751195 20.59050856 8.52732971 0.00050539 0.00071117 0.00116989 Cs 6.18240372 18.33644179 11.45795072 -0.00131095 0.00006915 0.00119587 Pb 9.22331805 20.75427096 14.87450868 0.00025807 -0.00076421 0.00217601 Br 9.64752716 20.52925962 11.72665697 0.00045696 -0.00465708 0.00058081 Br 8.33586731 18.10530316 14.96651394 -0.00058189 0.00305857 0.00261926 Br 6.39918350 21.42952138 14.81672833 0.00123011 0.00097353 -0.00180021 Cs 5.67476610 17.36562092 17.63996051 -0.00025804 0.00003648 -0.00216087 Pb 8.94674171 20.64312535 20.64047693 -0.00046796 -0.00065416 0.00017996 Br 9.74910564 20.69671208 17.79821863 -0.00219617 -0.00001808 0.00258895 Br 8.20103276 17.67543568 20.83168260 0.00453936 -0.00162884 0.00094242 Br 5.85681079 20.87990222 20.51057026 0.00130160 0.00145864 -0.00024424 Cs 12.19288468 23.70627225 0.17119798 0.00018059 -0.00049838 -0.00142339 Pb 14.98973075 3.28428372 2.85574283 -0.00034938 -0.00202912 0.00063073 Br 15.49808709 3.05115433 23.51199534 0.00213847 0.00186310 -0.00148524 Br 15.73410575 0.01236930 3.10716540 0.00124332 0.00122582 0.00381144 Br 12.04422889 3.62825125 2.36883932 -0.00263912 0.00109400 -0.00124680 Cs 12.05592151 0.48130483 5.44493202 -0.00089380 -0.00049355 -0.00178937 Pb 14.92123281 3.16060451 8.81931754 -0.00007478 0.00098675 0.00019268 Br 14.42930815 3.33222787 5.83170681 -0.00223234 -0.00040323 0.00093002 Br 15.08319996 0.08585241 9.08883821 0.00167660 0.00075300 -0.00040746 Br 11.94413333 3.03124323 9.37432486 -0.00222639 0.00120107 -0.00414594 Cs 12.00098632 0.60190970 11.90816496 0.00062114 0.00251738 -0.00114792 Pb 15.05584303 2.54866043 14.92789339 -0.00063903 -0.00160957 -0.00040419 Br 14.84663470 3.06682509 12.02677661 -0.00046726 0.00048825 -0.00233305 Br 14.31646591 23.58531236 14.67880857 0.00053120 -0.00049269 -0.00245623 Br 12.04370961 3.41179525 14.24748123 -0.00173755 0.00355465 0.00247213 Cs 11.96739898 23.50286860 18.56444222 -0.00120387 0.00307970 0.00148547 Pb 14.88330841 3.12563347 20.74283305 -0.00192060 0.00011198 -0.00071041 Br 14.23677530 2.98812312 17.78343058 -0.00401609 -0.00404277 -0.00268844 Br 15.40618173 23.65519080 20.41971049 0.00177814 0.00386099 -0.00382778 Br 11.79554459 2.43091827 21.55279450 0.00081400 -0.00168424 -0.00122188 Cs 11.96045821 6.14094938 23.43185550 0.00133745 -0.00148202 0.00148528 Pb 14.74004735 8.94541975 3.07653167 -0.00031420 0.00064651 -0.00363368 Br 14.85801715 8.54345904 23.77140607 0.00129731 -0.00309020 -0.00225217 Br 15.09562411 6.16567831 3.16751495 -0.00239394 0.00186808 -0.00407535 Br 11.73903094 8.29122531 2.64216455 0.00057895 -0.00176455 0.00075555 Cs 12.26970045 6.10606668 5.63806378 0.00225975 -0.00145952 0.00076558 Pb 14.56139772 9.01913498 8.91570733 -0.00030577 0.00192289 -0.00171868 Br 14.28374194 9.52563077 6.03029188 -0.00052505 -0.00149574 0.00118966 Br 15.26581779 6.05372999 8.47950107 -0.00029469 -0.00059398 0.00238170 Br 11.70399679 7.92741391 8.82515956 0.00246787 -0.00083640 -0.00157101 Cs 12.09855791 6.02904814 12.02872802 0.00094790 0.00052771 0.00178389 Pb 15.03956469 8.88266989 15.00219534 0.00057505 -0.00085793 0.00189776 Br 14.29611050 9.18851881 11.98253276 -0.00399198 0.00014944 -0.00317885 Br 14.71238565 5.58761601 14.78495728 -0.00039083 -0.00085225 -0.00016883 Br 12.04683497 8.76622234 15.13363021 -0.00007420 0.00332184 0.00159851 Cs 12.45804943 5.78014539 18.19611662 -0.00387387 0.00255724 -0.00231793 Pb 14.71019507 9.18908655 20.94823502 0.00173447 -0.00001003 0.00022672 Br 14.81363767 8.57973755 17.80493136 0.00111909 0.00035960 0.00310552 Br 14.89712687 6.28795125 20.65698991 -0.00363895 0.00132057 -0.00026230 Br 11.82036603 8.71089120 20.62874807 -0.00308441 -0.00224745 0.00180813 Cs 11.35934632 11.82600104 0.46432205 0.00143499 -0.00047970 -0.00165097 Pb 14.70280685 14.91971502 3.18478881 -0.00020463 0.00074967 -0.00156659 Br 15.07898013 15.33725622 0.04830936 0.00213830 -0.00219003 0.00208271 Br 14.19554472 11.91964610 3.05158128 -0.00268217 -0.00359888 -0.00174675 Br 11.69120625 14.93742891 2.97012711 0.00196627 -0.00086771 -0.00379718 Cs 11.81532808 12.48945524 6.07138689 0.00318876 -0.00047595 -0.00007488 Pb 14.80130430 15.02878119 9.04368929 0.00082279 -0.00048457 -0.00218075 Br 14.83249470 14.89735126 6.16557346 -0.00103058 0.00040708 0.00217317 Br 13.60802068 11.85158093 9.02554970 0.00091952 -0.00214164 0.00081714 Br 11.45769558 15.24814701 8.67414877 0.00017940 -0.00082708 0.00353928 Cs 11.81604671 12.19653927 12.09381952 -0.00056737 0.00058671 0.00142615 Pb 14.86625446 15.02540864 15.16055785 -0.00070276 -0.00042881 0.00104742 Br 14.72541558 14.75112101 12.05166393 -0.00015531 0.00023471 -0.00216046 Br 14.82995686 11.97182475 14.86637726 0.00364421 0.00410902 0.00034565 Br 11.97626094 15.09289443 14.86571722 0.00244080 -0.00040852 -0.00312489 Cs 12.19795069 11.33672096 17.45458132 -0.00104508 -0.00191553 0.00083863 Pb 14.98131187 14.96391801 20.92713482 0.00098762 0.00122438 0.00015513 Br 14.64448146 14.76780573 18.09816669 0.00088067 -0.00156024 -0.00067314 Br 14.39414114 12.09008905 21.49562156 0.00139116 -0.00070799 0.00391037 Br 11.96539093 15.56021187 21.49133974 0.00185499 0.00268266 -0.00268588 Cs 11.49606973 17.90362522 0.85853170 -0.00003855 -0.00154863 0.00220522 Pb 15.01432641 20.95258484 3.14469228 0.00122959 -0.00030587 0.00155258 Br 15.04116958 20.81981846 23.50898128 0.00401592 0.00109074 0.00216724 Br 14.25347013 17.84827270 3.60350318 -0.00166141 -0.00051835 -0.00126841 Br 11.96981770 21.24242604 3.39921954 0.00103564 -0.00234497 -0.00196491 Cs 11.32380288 18.20588569 5.94423064 0.00169717 0.00031140 0.00010461 Pb 14.90312724 20.97194107 8.95403288 -0.00096367 0.00056861 0.00112545 Br 15.37881822 20.42207308 5.98515459 0.00397142 -0.00216924 -0.00187608 Br 15.08475882 17.97901319 8.99202524 0.00358159 0.00115238 0.00267918 Br 11.42895112 21.42339337 8.11209540 -0.00085506 -0.00082785 -0.00057979 Cs 12.39126739 18.15254984 11.48866301 -0.00114076 -0.00364220 -0.00157781 Pb 14.96069099 20.83673488 14.72260176 -0.00123137 0.00364961 -0.00047113 Br 15.27112406 21.02569247 11.89744757 0.00239833 0.00328499 -0.00039888 Br 15.54910090 17.78526645 14.78077438 -0.00002072 -0.00125198 0.00241915 Br 12.19155290 19.78917760 14.87278304 -0.00145962 0.00126072 -0.00164479 Cs 12.08520916 17.57539476 17.97633756 -0.00177243 -0.00013948 0.00116507 Pb 15.06165380 20.84631926 20.70995502 0.00056758 0.00114394 0.00038964 Br 14.31080107 20.88878278 17.65217919 0.00320830 0.00230269 0.00164245 Br 15.06185797 17.69640224 20.18848589 0.00319740 0.00330512 0.00605223 Br 11.84207740 20.52411356 21.20869886 0.00132181 -0.00223224 0.00088117 Cs 18.48852853 23.45065086 23.48238492 0.00340470 0.00130777 -0.00154394 Pb 21.03768791 3.34524432 2.57577156 -0.00036912 -0.00037614 0.00097641 Br 20.51273104 3.91658300 23.70153780 -0.00151453 -0.00024867 -0.00221344 Br 20.72237026 0.11559433 3.10624926 -0.00118472 -0.00223724 -0.00263558 Br 17.83990193 3.12420501 3.59406756 0.00015730 -0.00131136 0.00503422 Cs 17.84431536 0.34648387 6.06104759 -0.00332526 0.00285423 -0.00157409 Pb 21.18370291 2.92743185 9.14518966 0.00103188 -0.00057966 0.00194078 Br 21.56850924 3.14222534 6.14024637 0.00074672 -0.00022767 -0.00058830 Br 20.72681897 0.00550271 9.37260758 -0.00161970 -0.00092401 0.00197018 Br 18.02065462 2.89048990 8.60133206 0.00428545 0.00241063 0.00069895 Cs 18.14797486 0.11826245 11.81762503 0.00233475 -0.00197077 0.00002460 Pb 20.69014971 3.01184569 14.68547132 0.00163702 -0.00061780 0.00025562 Br 19.91122104 3.64287195 11.85589625 0.00163494 0.00032645 0.00139963 Br 21.60010675 23.59919799 14.90741517 -0.00248678 0.00037309 0.00149573 Br 17.88426952 2.93522559 15.13582899 -0.00049037 0.00104934 0.00282982 Cs 18.07751678 23.45143547 18.02704206 0.00074777 -0.00167116 -0.00315715 Pb 20.98485688 3.05001372 20.59043181 -0.00194880 0.00221484 -0.00084279 Br 20.94179160 3.06735415 17.75904334 0.00078029 -0.00193304 -0.00015596 Br 20.72049558 23.76313699 20.24963720 -0.00199180 -0.00058577 -0.00040689 Br 17.83854570 2.95375268 20.23422258 0.00232325 0.00074195 0.00183788 Cs 17.77066394 6.48743589 23.20068514 -0.00057118 -0.00054832 -0.00163526 Pb 20.60511619 8.84444647 3.07766268 -0.00283932 -0.00033857 -0.00060547 Br 20.22557078 8.55801195 0.03746284 0.00181697 -0.00132265 -0.00118981 Br 20.53716936 6.11616941 3.54021723 0.00083564 0.00452874 0.00237289 Br 17.59776240 8.79084195 3.58642454 -0.00174863 -0.00173307 -0.00138292 Cs 18.37924204 5.48549038 6.15516709 -0.00079410 0.00160807 -0.00240277 Pb 20.84569737 8.82326790 8.84325799 -0.00159790 0.00111480 0.00005403 Br 20.86715531 9.27873892 5.92472591 0.00225198 -0.00196212 0.00249362 Br 21.34892688 5.87879217 8.48603852 0.00031797 0.00042081 -0.00176014 Br 17.60348921 9.16837842 8.63036601 0.00215122 -0.00188204 -0.00279884 Cs 17.53260556 6.45066624 11.47762001 0.00067725 0.00024891 0.00076085 Pb 20.96256292 8.67353123 14.95115990 -0.00008153 -0.00002324 -0.00081926 Br 20.72902592 8.81843990 11.74721222 -0.00160092 0.00087724 -0.00110169 Br 20.71194308 5.82038400 14.96293727 -0.00026599 -0.00093370 -0.00310087 Br 18.09176859 8.20779270 14.78351403 -0.00224692 -0.00036866 0.00092963 Cs 17.65757397 5.62991518 17.72656426 -0.00183872 -0.00041020 0.00145422 Pb 20.55933552 9.00019819 20.57808753 -0.00206461 -0.00126111 -0.00021373 Br 21.31453726 8.56981106 17.85826124 -0.00010569 -0.00038512 0.00129095 Br 20.53495281 6.08477508 20.27404186 0.00128049 0.00148447 -0.00328214 Br 17.59472247 9.02949090 20.60718388 0.00179710 -0.00367599 0.00084438 Cs 17.48112208 11.81245474 0.11743085 -0.00265650 -0.00086572 -0.00033391 Pb 20.63319198 14.82449431 3.20080825 0.00006594 0.00055162 0.00065847 Br 20.89184383 15.15687890 0.11607904 0.00187423 -0.00104995 -0.00015903 Br 20.27374421 11.95806554 3.23346359 0.00054658 0.00014490 -0.00215398 Br 17.77529150 15.00077805 2.41802844 0.00361006 -0.00006717 -0.00127040 Cs 17.87225227 11.45684137 6.22080348 -0.00002848 0.00496319 -0.00137501 Pb 20.41230979 14.87191132 9.18893474 -0.00107511 -0.00027389 0.00003149 Br 20.00117250 14.50613040 6.16987478 -0.00103533 -0.00263499 0.00334450 Br 19.94760341 11.69402430 9.33219161 -0.00075850 -0.00109612 0.00283164 Br 17.48567791 14.56206568 9.39969789 -0.00071642 0.00189721 0.00076155 Cs 18.10025114 11.31329191 11.97259534 0.00090644 0.00037541 -0.00187899 Pb 20.78896755 14.67951982 14.68655643 0.00030172 -0.00059966 0.00002818 Br 20.78838977 13.97380686 11.91913174 -0.00063698 0.00070337 0.00304048 Br 20.80944020 11.64493437 15.51395236 0.00234973 -0.00024335 0.00080861 Br 17.76638292 14.52266158 15.09123474 0.00128900 0.00329096 0.00158235 Cs 17.42279577 11.79092817 17.60184756 0.00079100 -0.00191437 0.00059370 Pb 21.07990679 15.10619464 20.67408574 0.00029228 -0.00097196 -0.00138105 Br 20.85841985 14.94021780 17.70287534 -0.00179087 0.00402341 -0.00282684 Br 20.07366716 11.73861353 20.46395819 0.00064545 0.00133458 0.00394977 Br 18.01839431 14.95047608 20.51819160 -0.00207101 0.00019004 0.00049901 Cs 17.95515597 17.62962891 23.52369836 -0.00012922 -0.00131520 0.00142860 Pb 21.06775664 20.78726822 2.99395822 0.00054198 -0.00046477 -0.00119001 Br 21.35313895 20.97640774 0.07466434 -0.00102781 -0.00306483 0.00320918 Br 21.04303937 17.89297264 3.25118283 0.00114730 0.00217093 -0.00138648 Br 17.68304876 20.59838119 2.66461293 0.00063994 0.00175994 0.00328040 Cs 17.84129459 17.82292084 5.37617012 0.00059015 -0.00170938 -0.00252825 Pb 20.85647078 20.90532833 8.80971982 0.00042353 -0.00136139 0.00166463 Br 20.81216594 20.86612701 5.85573928 -0.00274371 0.00154171 -0.00220792 Br 20.30978289 17.93066899 8.82135166 0.00048073 0.00256155 0.00125533 Br 18.08272431 21.26887187 9.28890258 0.00423613 0.00213648 -0.00526759 Cs 17.75334095 17.76369945 11.99649786 -0.00048992 -0.00187408 0.00195322 Pb 20.63367534 20.92737291 14.83323817 -0.00034991 0.00004634 -0.00076108 Br 21.13580148 20.90787259 11.82395336 -0.00038450 0.00010539 0.00041759 Br 20.42626038 17.54567591 14.20321283 -0.00064221 0.00493156 0.00226820 Br 17.82631051 21.64084802 15.16920231 -0.00111214 0.00231610 0.00342955 Cs 17.79964661 17.95555715 18.24386888 0.00474201 -0.00143025 0.00039008 Pb 20.82579200 20.70050287 20.95261340 0.00140434 0.00026655 -0.00196881 Br 20.96110949 20.50082958 17.91652273 0.00137713 -0.00151097 0.00260900 Br 20.84491490 17.90872376 20.20983332 0.00090183 0.00016014 0.00027846 Br 18.05494249 20.76618556 21.33305983 -0.00157320 -0.00019130 0.00074338 320 Lattice="23.77195271 0.0 0.0 0.0 23.77195271 0.0 0.0 0.0 23.77195271" Properties=species:S:1:pos:R:3:vel:R:3 Time=20239.99999999 energy=-11423.78686905 virial="-15.59862893 5.64560413 -0.31250319 5.64560413 -7.17662852 2.91228569 -0.31250319 2.91228569 -4.51766776" stress="-0.0002537 0.00042026 -2.326e-05 0.00042026 0.0002636 0.00021679 -2.326e-05 0.00021679 0.0005624" pbc="T T T" Cs 23.42921848 23.68512836 0.20704493 0.00017283 -0.00063030 0.00153304 Pb 2.90161563 3.07224694 3.21333141 0.00114847 -0.00051972 -0.00004089 Br 3.30641710 2.94442314 0.21544211 0.00091347 -0.00003339 0.00043735 Br 2.78757060 23.76632450 3.03996618 -0.00159760 0.00107535 -0.00002973 Br 23.74404753 3.37635814 1.98449450 0.00214122 -0.00100961 -0.00252727 Cs 0.52067750 0.19033754 5.76503501 0.00120125 -0.00011221 -0.00246894 Pb 3.20901478 2.99723841 9.05678163 0.00137521 -0.00008249 -0.00017191 Br 2.61596866 2.75309552 6.19128763 0.00059092 0.00158241 -0.00492461 Br 3.15916589 0.03483167 9.57668381 -0.00020563 0.00100906 -0.00029586 Br 23.71687893 2.68927589 10.38605506 0.00256659 -0.00489366 -0.00318741 Cs 23.63960821 23.42815700 12.14598017 -0.00175891 -0.00181547 -0.00150127 Pb 3.11762830 3.04010026 14.82193786 0.00100062 -0.00217021 -0.00000038 Br 3.67195809 3.05010267 12.01621844 -0.00367126 0.00138503 -0.00228230 Br 3.04828239 23.70111737 14.52180509 -0.00045268 -0.00251144 0.00055431 Br 23.71594101 3.12886210 14.46913054 -0.00153823 -0.00165309 -0.00156196 Cs 0.02379847 23.24254331 17.52324499 -0.00063279 -0.00023959 -0.00047890 Pb 3.19663566 3.15741937 20.77699705 -0.00213427 -0.00035624 0.00069751 Br 2.76959849 3.35381595 17.82109433 0.00249465 0.00025766 0.00232981 Br 2.99880967 23.77123855 20.69504765 0.00006685 -0.00132723 0.00135006 Br 23.75470864 2.42751050 20.82647416 -0.00174320 -0.00410094 0.00257798 Cs 23.44040766 6.62714311 23.59516155 -0.00085072 0.00039737 -0.00090589 Pb 2.88755583 8.96241235 2.78750509 -0.00054521 0.00127398 -0.00305041 Br 3.02139624 8.20321960 0.14250879 0.00180178 0.00021891 -0.00072741 Br 2.85193484 5.95889826 3.13161312 -0.00071425 -0.00257478 0.00175423 Br 23.63349787 8.54454429 2.84887165 -0.00429674 0.00082665 -0.00242079 Cs 0.57149292 6.03327178 6.20108550 -0.00018271 -0.00049736 0.00169312 Pb 3.18597648 9.08544329 8.94766791 -0.00367611 -0.00076690 0.00069122 Br 2.83333395 8.73410055 5.89746845 0.00066505 0.00001960 -0.00502481 Br 2.59777361 6.07417238 9.20647890 -0.00057579 0.00307360 0.00409142 Br 0.07226659 9.25194078 8.90829486 0.00141910 -0.00135949 0.00114007 Cs 23.32643749 5.87815340 12.14914360 -0.00261046 -0.00145060 -0.00019770 Pb 2.96324791 8.96988428 14.77325836 0.00144017 0.00044144 0.00093664 Br 3.28829195 9.20157660 11.85055458 0.00071163 0.00203706 -0.00102189 Br 3.74428873 5.89221157 15.17312658 -0.00124445 -0.00369732 -0.00170061 Br 0.20098403 8.42203855 14.17985494 0.00122215 0.00204293 0.00115198 Cs 23.54046737 5.72860871 17.68518895 -0.00027999 -0.00057331 0.00057922 Pb 2.59440769 8.58937852 20.69188559 0.00015583 -0.00083010 0.00099470 Br 2.61382999 8.73324591 17.94025089 0.00107440 -0.00146828 0.00422985 Br 2.34346836 5.81418437 21.06466685 0.00107217 -0.00317476 0.00225092 Br 23.38880472 9.40284701 21.30310283 0.00528166 0.00186333 0.00203508 Cs 23.58538844 11.77745777 0.32442075 0.00330493 0.00086929 0.00050852 Pb 2.62570445 14.94714857 2.94869328 0.00212333 -0.00116564 -0.00043680 Br 2.48725095 14.92400157 23.66411941 -0.00044503 -0.00011054 -0.00187067 Br 2.75204600 11.96058935 2.22669861 -0.00243397 -0.00260151 0.00194847 Br 23.63111969 14.75063999 3.15111417 -0.00099000 0.00030214 0.00051242 Cs 23.69808988 11.96361574 6.40662069 0.00130624 0.00270255 -0.00238377 Pb 3.04053769 14.86275141 9.04309668 -0.00016193 -0.00283532 -0.00098943 Br 2.93477910 14.89547769 5.88047223 -0.00247706 -0.00040662 0.00268533 Br 2.91232710 11.80606173 8.66238306 -0.00114662 -0.00351942 -0.00108429 Br 23.72159231 14.88021848 8.87009756 -0.00158819 -0.00164785 -0.00000376 Cs 23.57310321 11.89062940 11.43311561 -0.00183401 0.00205319 0.00083932 Pb 3.06329734 14.57285323 14.89907565 -0.00038006 0.00028756 -0.00237386 Br 2.55344433 14.21777816 11.95381333 -0.00406230 -0.00089993 -0.00204753 Br 2.39662904 11.62445993 15.50179285 0.00038231 -0.00029266 -0.00058941 Br 23.71694573 14.61961435 14.56370823 -0.00501901 0.00179732 0.00066657 Cs 0.03791641 11.61540874 18.12229567 0.00137093 -0.00021888 -0.00036336 Pb 3.17623346 14.62542967 20.87502274 0.00051931 0.00224815 0.00150651 Br 3.00293781 14.86156002 17.97089033 0.00114995 -0.00241749 0.00006226 Br 2.95413171 11.61028253 20.90286283 0.00114219 -0.00007588 -0.00015895 Br 0.14705168 14.80882304 20.58311972 -0.00082222 0.00183659 -0.00275322 Cs 0.02140991 17.91165016 23.46569279 0.00007885 0.00035556 -0.00130802 Pb 2.95172966 20.89971593 2.78758790 -0.00062485 0.00112927 0.00024772 Br 2.78409217 21.31737169 23.61881438 -0.00118666 0.00349039 -0.00315739 Br 2.71026744 17.86821172 2.83910302 -0.00386522 0.00268588 0.00046380 Br 0.16898984 21.04386532 3.26747706 0.00080620 0.00117357 0.00252055 Cs 23.00745147 17.81742783 5.84675859 0.00142104 -0.00064192 -0.00056254 Pb 2.99666954 20.79552994 8.87633036 0.00015532 -0.00016552 0.00094744 Br 3.03775613 20.55368145 5.85993311 -0.00069905 0.00015802 0.00002830 Br 3.00040406 18.03708933 9.01065275 -0.00123601 0.00180430 -0.00272899 Br 23.75055133 20.60118976 8.82316160 0.00179908 -0.00346206 -0.00151779 Cs 0.85151497 17.53717045 11.75558346 0.00135224 0.00324212 -0.00232090 Pb 3.13341933 20.68635948 14.88547450 -0.00020786 -0.00289976 -0.00042527 Br 2.74275961 20.73258646 12.03774831 -0.00238760 -0.00192539 0.00377762 Br 3.11971762 17.39750401 15.14788702 0.00025868 -0.00157498 -0.00252402 Br 0.25024739 20.80240161 14.94165908 0.00200101 0.00077831 -0.00007122 Cs 0.37748528 17.74398427 17.78047094 -0.00096882 0.00022456 0.00211966 Pb 3.08436998 20.83413666 20.52014155 -0.00094717 -0.00007531 0.00228133 Br 2.97495098 20.34910358 17.67550667 -0.00408029 0.00192751 -0.00151548 Br 3.04051438 17.69322835 20.22110373 0.00010642 0.00023468 0.00269810 Br 0.10597404 20.47448096 20.64818830 -0.00291373 -0.00061819 0.00068560 Cs 5.61251579 0.28645730 22.99052655 -0.00182786 0.00096179 0.00201168 Pb 8.73044783 3.26279528 2.94114150 -0.00156910 0.00104988 -0.00105697 Br 8.38977348 2.90811613 0.20660896 -0.00660402 0.00240134 0.00036716 Br 9.18650111 0.38261487 3.80101621 0.00071894 0.00007842 0.00071053 Br 5.87121168 3.16025339 3.83618467 -0.00125944 0.00071579 0.00048321 Cs 5.70110190 23.59287711 5.88338784 -0.00220570 0.00167755 0.00154157 Pb 8.84860414 3.00025898 8.82242504 0.00225102 0.00109431 0.00033547 Br 9.49578874 3.46643866 5.90960861 -0.00237073 0.00071489 -0.00018047 Br 8.43165524 0.02313546 9.14612157 0.00022359 0.00130063 0.00197290 Br 5.87911550 2.84910310 8.09502672 -0.00445541 -0.00295127 -0.00185881 Cs 5.96811211 0.16363940 12.06466766 -0.00042502 0.00192115 -0.00010250 Pb 9.37026690 2.90595838 15.06752144 0.00110796 0.00063832 0.00187114 Br 8.76116469 2.81815548 12.12180078 -0.00261820 -0.00004193 0.00606708 Br 9.81363721 0.03324949 14.76047712 -0.00406558 0.00114954 -0.00246598 Br 6.27583390 2.56545934 14.95056818 0.00058212 0.00178033 0.00024701 Cs 6.32434307 23.22745431 17.86007583 -0.00093214 -0.00040463 0.00124766 Pb 8.96543120 3.09871830 20.84848437 -0.00154799 -0.00032004 0.00034418 Br 9.40788343 2.86495904 18.18225426 -0.00131615 -0.00124413 -0.00317280 Br 8.64302277 23.47560376 21.00184081 -0.00329517 -0.00042814 0.00044684 Br 6.06478888 3.79685032 20.73542311 0.00098022 -0.00162906 -0.00073330 Cs 5.46815851 5.71688532 0.05977358 -0.00007383 -0.00428071 0.00130873 Pb 8.67295912 8.91292687 2.87896226 0.00034426 0.00106965 0.00159990 Br 8.96401379 8.94571080 23.66085129 -0.00056213 0.00109641 -0.00132152 Br 8.15679862 6.17288075 2.83564455 -0.00087353 -0.00030213 -0.00292437 Br 5.72127709 9.24527302 2.88434394 0.00091755 0.00223087 0.00081431 Cs 6.24792994 6.19642540 5.77008634 0.00110441 -0.00090600 0.00037998 Pb 8.98602149 8.76349602 8.80043765 0.00274288 -0.00090311 0.00125495 Br 8.77244819 8.42948953 5.82484336 0.00255797 0.00118573 0.00069572 Br 8.51011768 5.93130962 9.38473015 -0.00216061 -0.00224690 0.00096505 Br 6.01773884 8.70965242 8.67623528 -0.00092414 0.00448878 -0.00013047 Cs 6.05546036 5.82311494 12.05159787 0.00098142 -0.00329958 0.00146274 Pb 8.90941473 8.91673202 14.75935111 0.00013428 -0.00099588 -0.00147420 Br 8.15883463 8.73492101 11.96251815 0.00425856 0.00107507 0.00008029 Br 9.38023602 5.93073827 15.30076096 -0.00184811 -0.00034330 0.00095411 Br 6.10971349 8.62294852 15.50921425 -0.00301690 -0.00118792 -0.00120089 Cs 6.59215991 6.12787767 17.70864929 -0.00278198 -0.00101880 -0.00043653 Pb 8.78346820 9.14126554 20.71251326 0.00072737 0.00022167 0.00042293 Br 8.95972621 9.00959743 17.82367362 -0.00077702 -0.00177805 0.00160628 Br 9.17795842 5.93891261 20.63209873 0.00078759 -0.00137066 -0.00226200 Br 5.58868730 8.48437968 20.71720166 0.00150072 -0.00004419 0.00082900 Cs 5.41036763 11.33136844 23.63353479 0.00000774 -0.00073194 0.00200730 Pb 8.71063086 14.84382080 3.21305589 -0.00210592 0.00158062 0.00006553 Br 9.08181894 14.58008361 23.66211670 -0.00358140 0.00087959 0.00083385 Br 9.05273625 11.95648781 3.40034762 0.00070213 -0.00005876 -0.00006352 Br 5.81129058 14.47274189 2.63445965 0.00273214 0.00007454 0.00040190 Cs 5.61533012 12.10183146 5.21001869 0.00309052 0.00123229 0.00349929 Pb 8.83735559 14.67103215 9.00056933 0.00115781 0.00152384 -0.00053457 Br 8.51394609 13.99432041 6.18994493 0.00080069 0.00335073 0.00275782 Br 9.34910057 11.54595225 9.02901758 -0.00353931 -0.00211893 0.00359039 Br 5.98675841 14.05159940 9.39686879 0.00030278 0.00060146 -0.00243600 Cs 5.78067741 11.43793356 11.94780639 0.00282477 -0.00404674 0.00158852 Pb 9.29797286 14.88437765 14.73571147 -0.00042038 -0.00197013 -0.00114512 Br 8.97685592 15.28549059 11.88156216 -0.00318333 0.00092808 0.00044314 Br 9.50005042 11.98369557 15.00308449 -0.00092154 0.00394113 -0.00221088 Br 5.93780236 14.60140239 13.89811328 0.00298692 -0.00073727 -0.00076435 Cs 6.45730894 11.55649791 17.47230821 0.00006247 -0.00192068 -0.00202872 Pb 9.10125583 14.96100684 20.92640355 0.00163750 0.00099225 0.00182202 Br 9.32614066 14.84178237 17.84164918 -0.00082501 -0.00052006 0.00006326 Br 8.73060789 12.02447957 20.84154671 -0.00193217 -0.00096509 -0.00546194 Br 5.93858049 14.88849762 21.27692441 -0.00228489 -0.00159928 -0.00043981 Cs 5.48617537 18.55336778 23.47722514 0.00126792 -0.00088587 -0.00216594 Pb 8.81704896 20.84446107 3.05693678 0.00054236 0.00213580 -0.00021032 Br 8.91533476 21.03183800 23.69092666 -0.00065314 0.00084224 0.00004366 Br 8.48608029 18.04345708 2.68430086 0.00130583 0.00185838 -0.00044492 Br 6.03131045 21.03189339 3.08183376 0.00380320 0.00367833 -0.00033638 Cs 5.47512440 18.08242230 5.49764284 -0.00161162 0.00037313 -0.00013929 Pb 8.92360191 20.45322670 8.87499954 0.00362979 0.00085786 -0.00096637 Br 8.15418577 20.61534394 5.92961771 0.00141454 0.00355324 -0.00295950 Br 8.24603221 17.50298571 8.04307375 0.00236227 0.00041476 0.00030366 Br 5.96919842 20.59836450 8.54144512 -0.00015209 0.00085648 0.00164441 Cs 6.16961879 18.33652192 11.47053509 -0.00124362 -0.00005123 0.00132038 Pb 9.22684632 20.74728888 14.89561989 0.00044621 -0.00062328 0.00204227 Br 9.65117846 20.48216522 11.73376222 0.00027033 -0.00475935 0.00082176 Br 8.32962876 18.13146355 14.99263965 -0.00067474 0.00215365 0.00260699 Br 6.40935272 21.43860332 14.79830751 0.00080011 0.00084473 -0.00187972 Cs 5.67301696 17.36591783 17.61906184 -0.00009298 0.00002252 -0.00201734 Pb 8.94294800 20.63606145 20.64360514 -0.00028836 -0.00075537 0.00045087 Br 9.72610432 20.69696682 17.82368088 -0.00239408 0.00007146 0.00249227 Br 8.24607446 17.66310437 20.84100040 0.00445602 -0.00082989 0.00092257 Br 5.87364923 20.89411645 20.50845189 0.00202817 0.00137519 -0.00017868 Cs 12.19440871 23.70051326 0.15749209 0.00012456 -0.00065679 -0.00131691 Pb 14.98644366 3.26356703 2.86238495 -0.00030885 -0.00210864 0.00069696 Br 15.51955687 3.07014287 23.49922423 0.00214879 0.00193556 -0.00106729 Br 15.74388078 0.02501329 3.14489985 0.00070123 0.00130456 0.00372710 Br 12.01709560 3.63810299 2.35690581 -0.00277180 0.00086959 -0.00113465 Cs 12.04781836 0.47669013 5.42825543 -0.00072204 -0.00043002 -0.00154505 Pb 14.92025508 3.17025908 8.82183967 -0.00011625 0.00094413 0.00031231 Br 14.40816831 3.32620818 5.84113828 -0.00198915 -0.00080251 0.00094285 Br 15.09965402 0.09385557 9.08395463 0.00161247 0.00084210 -0.00056897 Br 11.92231634 3.04415182 9.33124247 -0.00213777 0.00137701 -0.00446282 Cs 12.00672607 0.62676888 11.89636475 0.00052300 0.00245588 -0.00120974 Pb 15.04777644 2.53501163 14.92299634 -0.00096716 -0.00111290 -0.00057404 Br 14.84415720 3.07108132 12.00049471 -0.00003687 0.00036498 -0.00290999 Br 14.32027731 23.58008680 14.65430311 0.00021637 -0.00052992 -0.00244699 Br 12.02648234 3.44465257 14.27387930 -0.00167682 0.00301212 0.00279643 Cs 11.95520041 23.53408114 18.57904378 -0.00123504 0.00315845 0.00143482 Pb 14.86469869 3.12622258 20.73560481 -0.00180017 0.00000093 -0.00073499 Br 14.19783598 2.94563680 17.75788842 -0.00377948 -0.00443456 -0.00241068 Br 15.42260737 23.69635220 20.38252119 0.00150135 0.00435872 -0.00360403 Br 11.80621340 2.41583290 21.53896049 0.00131563 -0.00133069 -0.00154308 Cs 11.97356480 6.12604309 23.44690392 0.00128199 -0.00149744 0.00152185 Pb 14.73606766 8.95268006 3.04033500 -0.00048432 0.00080209 -0.00360102 Br 14.87105462 8.51385695 23.74965468 0.00130874 -0.00282604 -0.00208456 Br 15.07192898 6.17970371 3.12668865 -0.00234343 0.00090142 -0.00408789 Br 11.74418365 8.27479414 2.65002990 0.00044823 -0.00151756 0.00081509 Cs 12.29151854 6.09181648 5.64578167 0.00210106 -0.00138671 0.00077625 Pb 14.55789559 9.03859335 8.89835897 -0.00038926 0.00196239 -0.00174544 Br 14.27899742 9.50960538 6.04123363 -0.00042354 -0.00170389 0.00097111 Br 15.26278266 6.04830518 8.50399366 -0.00031064 -0.00049388 0.00251337 Br 11.73203189 7.91914583 8.80975996 0.00312241 -0.00081974 -0.00150551 Cs 12.10784343 6.03444486 12.04631676 0.00090993 0.00055437 0.00172972 Pb 15.04428150 8.87326371 15.01970619 0.00036753 -0.00102049 0.00160220 Br 14.25795254 9.18917189 11.95061290 -0.00363419 -0.00002001 -0.00317448 Br 14.71111696 5.58362817 14.78469257 0.00013771 0.00006333 0.00011728 Br 12.04671418 8.79926198 15.14832274 0.00004056 0.00327393 0.00133233 Cs 12.41723768 5.80686780 18.17157800 -0.00428008 0.00277608 -0.00258819 Pb 14.72726151 9.18912147 20.95035610 0.00166756 0.00001736 0.00019556 Br 14.82565283 8.58495547 17.83675732 0.00128441 0.00068550 0.00322810 Br 14.86136655 6.29548242 20.65553961 -0.00351168 0.00018008 -0.00003144 Br 11.78797983 8.68967504 20.64693310 -0.00335418 -0.00198699 0.00182883 Cs 11.37424637 11.82120131 0.44748765 0.00154058 -0.00047990 -0.00171595 Pb 14.70092218 14.92748986 3.16822339 -0.00017407 0.00080389 -0.00174631 Br 15.10010291 15.31562592 0.07196731 0.00207930 -0.00213965 0.00264181 Br 14.16935802 11.88610618 3.03315750 -0.00255411 -0.00308165 -0.00193601 Br 11.71126302 14.92879229 2.93313799 0.00203960 -0.00086540 -0.00359710 Cs 11.84717527 12.48411010 6.07033244 0.00317504 -0.00059309 -0.00013577 Pb 14.80766658 15.02336176 9.02269187 0.00044602 -0.00059410 -0.00200471 Br 14.82293050 14.90098843 6.18477152 -0.00088079 0.00031959 0.00163088 Br 13.61962685 11.83126515 9.03429004 0.00139193 -0.00190235 0.00092789 Br 11.46716664 15.24211334 8.70889810 0.00173222 -0.00037930 0.00340821 Cs 11.81083040 12.20218327 12.10785935 -0.00047249 0.00054254 0.00137832 Pb 14.86081672 15.02028826 15.17008903 -0.00037843 -0.00059137 0.00085650 Br 14.72313785 14.75476114 12.03041326 -0.00030006 0.00049286 -0.00208647 Br 14.86621737 12.00989348 14.87094179 0.00360256 0.00347300 0.00056548 Br 12.00216870 15.08984307 14.83396204 0.00269048 -0.00020310 -0.00322779 Cs 12.18715214 11.31837533 17.46393275 -0.00111480 -0.00174644 0.00103516 Pb 14.99063873 14.97581538 20.92982003 0.00087621 0.00115702 0.00037494 Br 14.65246605 14.75042499 18.08768869 0.00071616 -0.00190497 -0.00138644 Br 14.40794128 12.08170483 21.53492554 0.00137062 -0.00095560 0.00394873 Br 11.98473997 15.58683812 21.46297516 0.00200513 0.00264007 -0.00297582 Cs 11.49648073 17.88829481 0.87901782 0.00012215 -0.00151207 0.00188825 Pb 15.02550875 20.94973988 3.16007131 0.00100889 -0.00026044 0.00151738 Br 15.08058094 20.83113685 23.53325167 0.00385977 0.00117451 0.00267610 Br 14.23757700 17.84220884 3.58943215 -0.00151294 -0.00070362 -0.00154437 Br 11.97844885 21.21832366 3.37897445 0.00069611 -0.00247035 -0.00207899 Cs 11.34102069 18.20913823 5.94526870 0.00174454 0.00033914 0.00010137 Pb 14.89423613 20.97803877 8.96460284 -0.00081109 0.00064933 0.00098626 Br 15.41862363 20.40272312 5.96799224 0.00398031 -0.00170118 -0.00153578 Br 15.12077992 17.98918650 9.01962976 0.00361864 0.00088053 0.00283852 Br 11.42506218 21.41413483 8.10570558 0.00010928 -0.00100769 -0.00070767 Cs 12.37959166 18.11565321 11.47372097 -0.00119253 -0.00373837 -0.00140861 Pb 14.94774275 20.87183388 14.71936739 -0.00135282 0.00335062 -0.00017416 Br 15.29498861 21.05757619 11.89073132 0.00237750 0.00309021 -0.00094526 Br 15.54912131 17.77489576 14.80548156 0.00002293 -0.00080166 0.00251993 Br 12.17622451 19.80161279 14.85469696 -0.00158998 0.00122630 -0.00196202 Cs 12.06721240 17.57441595 17.98825794 -0.00182255 -0.00005665 0.00121933 Pb 15.06598529 20.85687431 20.71386711 0.00030073 0.00097387 0.00039839 Br 14.34451410 20.91155408 17.67009678 0.00352433 0.00225263 0.00192463 Br 15.09360851 17.73424204 20.25047534 0.00315855 0.00423473 0.00633211 Br 11.85684391 20.50269529 21.21650199 0.00162316 -0.00204872 0.00067916 Cs 18.52166990 23.46474554 23.46807030 0.00322304 0.00150815 -0.00132185 Pb 21.03250244 3.34080925 2.58761465 -0.00065390 -0.00050515 0.00137940 Br 20.49689058 3.91402032 23.67764524 -0.00164666 -0.00027108 -0.00252246 Br 20.71149696 0.09289519 3.07824458 -0.00098957 -0.00227377 -0.00296672 Br 17.84188055 3.11129762 3.64307889 0.00023179 -0.00126317 0.00475194 Cs 17.81162223 0.37463788 6.04524601 -0.00320795 0.00277251 -0.00157711 Pb 21.19304722 2.92200366 9.16371953 0.00083425 -0.00050502 0.00175871 Br 21.57658634 3.13821607 6.13387932 0.00086654 -0.00057408 -0.00068823 Br 20.71220291 23.77045238 9.39140116 -0.00129170 -0.00046193 0.00178767 Br 18.06187595 2.91476249 8.60862306 0.00394296 0.00244294 0.00075618 Cs 18.17073793 0.09882340 11.81778594 0.00221555 -0.00191690 0.00000951 Pb 20.70783699 3.00503088 14.68771430 0.00189171 -0.00074843 0.00019511 Br 19.92869387 3.64540559 11.87026462 0.00185829 0.00017943 0.00146661 Br 21.57175754 23.60419544 14.92274391 -0.00317105 0.00062450 0.00156261 Br 17.87956055 2.94468547 15.16537209 -0.00045312 0.00084014 0.00307456 Cs 18.08497264 23.43580578 17.99598901 0.00074481 -0.00144621 -0.00303829 Pb 20.96487339 3.07192315 20.58224981 -0.00204504 0.00216102 -0.00079041 Br 20.94988617 3.04769657 17.75528681 0.00084032 -0.00199427 -0.00059977 Br 20.70142894 23.75642744 20.24795577 -0.00182006 -0.00075270 0.00007012 Br 17.85965889 2.96162211 20.25305253 0.00189546 0.00083658 0.00192446 Cs 17.76380406 6.47985989 23.18430911 -0.00079490 -0.00096834 -0.00163638 Pb 20.57670977 8.84218576 3.07091072 -0.00283541 -0.00010066 -0.00074284 Br 20.24593704 8.54748653 0.02604381 0.00224355 -0.00079016 -0.00109543 Br 20.54700965 6.15732238 3.56279548 0.00113366 0.00363846 0.00213993 Br 17.58219032 8.77494990 3.57216813 -0.00136462 -0.00144020 -0.00146223 Cs 18.37076080 5.50212871 6.13179803 -0.00090183 0.00172207 -0.00226151 Pb 20.82979787 8.83445531 8.84357021 -0.00157975 0.00111655 0.00000442 Br 20.89032660 9.25957031 5.95019292 0.00237414 -0.00186020 0.00256831 Br 21.35169335 5.88502883 8.47008161 0.00023277 0.00081811 -0.00142533 Br 17.62436386 9.14669797 8.60484106 0.00202876 -0.00244316 -0.00231713 Cs 17.53923067 6.45260530 11.48534415 0.00064770 0.00013842 0.00078171 Pb 20.96191076 8.67479531 14.94256403 -0.00004927 0.00027274 -0.00089793 Br 20.71338026 8.82619417 11.73920648 -0.00152646 0.00066971 -0.00047456 Br 20.71047246 5.80994797 14.93265122 -0.00002925 -0.00112732 -0.00294994 Br 18.06665129 8.20597899 14.79323262 -0.00275792 0.00000500 0.00101685 Cs 17.63925888 5.62618996 17.74094646 -0.00182382 -0.00033618 0.00142098 Pb 20.53883153 8.98762424 20.57726038 -0.00201605 -0.00124081 0.00005501 Br 21.31402258 8.56699236 17.86798738 0.00000191 -0.00017330 0.00063433 Br 20.54797456 6.09558116 20.24222928 0.00132051 0.00066779 -0.00307732 Br 17.61175976 8.99275036 20.61501827 0.00160140 -0.00366728 0.00071614 Cs 17.45470523 11.80405700 0.11402294 -0.00262384 -0.00081300 -0.00034668 Pb 20.63313481 14.82956573 3.20656468 -0.00007374 0.00046236 0.00049087 Br 20.91141679 15.14642074 0.11393778 0.00203969 -0.00104149 -0.00026613 Br 20.27984406 11.95961008 3.21062404 0.00067191 0.00017684 -0.00240720 Br 17.81071711 15.00059579 2.40736364 0.00345694 0.00003067 -0.00086185 Cs 17.87171640 11.50734883 6.20602290 -0.00007853 0.00512336 -0.00157415 Pb 20.40164613 14.86921928 9.18829862 -0.00105469 -0.00026242 -0.00015309 Br 19.99188114 14.48114074 6.20306329 -0.00081994 -0.00235845 0.00328339 Br 19.94507965 11.68553237 9.35584358 0.00026111 -0.00058415 0.00188194 Br 17.48261118 14.58112975 9.40740455 0.00011680 0.00191000 0.00077997 Cs 18.10755818 11.31736034 11.95575229 0.00054452 0.00043797 -0.00147901 Pb 20.79204215 14.67393514 14.68854528 0.00030534 -0.00050785 0.00037470 Br 20.78138352 13.98098549 11.94791218 -0.00077120 0.00072668 0.00267443 Br 20.83255797 11.64187686 15.52099768 0.00227230 -0.00036813 0.00059929 Br 17.77985730 14.55525725 15.10670477 0.00139871 0.00322340 0.00151264 Cs 17.43164965 11.77228007 17.60827560 0.00098029 -0.00181132 0.00068954 Pb 21.08146247 15.09499832 20.65989020 0.00001967 -0.00126529 -0.00145615 Br 20.84145068 14.98102994 17.67714449 -0.00160233 0.00413680 -0.00229779 Br 20.08120706 11.75628554 20.50337598 0.00085917 0.00218031 0.00393265 Br 17.99633717 14.95292413 20.52425143 -0.00233181 0.00029711 0.00071205 Cs 17.95361776 17.61631399 23.53811768 -0.00017569 -0.00134214 0.00145215 Pb 21.07201011 20.78408910 2.98274281 0.00030617 -0.00016784 -0.00104491 Br 21.34235144 20.94399909 0.10508253 -0.00112257 -0.00341063 0.00283001 Br 21.05243961 17.91390558 3.23440515 0.00073522 0.00198509 -0.00196379 Br 17.69639478 20.61386436 2.69656441 0.00202747 0.00133564 0.00310751 Cs 17.84698021 17.80577442 5.35120673 0.00054957 -0.00171743 -0.00246355 Pb 20.86090633 20.89138982 8.82700080 0.00047116 -0.00142310 0.00177749 Br 20.78400304 20.88171805 5.83431124 -0.00288549 0.00157156 -0.00204349 Br 20.31438787 17.95314034 8.83392004 0.00043801 0.00191220 0.00125780 Br 18.11762149 21.28904203 9.23534392 0.00271042 0.00189797 -0.00543500 Cs 17.74839775 17.74495006 12.01511511 -0.00049980 -0.00187784 0.00177285 Pb 20.63129328 20.92653300 14.82537025 -0.00013142 -0.00021894 -0.00081038 Br 21.13067269 20.90823702 11.83086442 -0.00064234 -0.00003422 0.00096524 Br 20.42089768 17.59632767 14.22692031 -0.00042876 0.00518710 0.00247570 Br 17.81614553 21.66346168 15.20159986 -0.00091799 0.00218682 0.00302345 Cs 17.84792861 17.94072537 18.24618710 0.00490439 -0.00153643 0.00007495 Pb 20.84169364 20.70420784 20.93116944 0.00176211 0.00047588 -0.00230868 Br 20.97374824 20.48769930 17.93984634 0.00115130 -0.00111089 0.00204191 Br 20.85442753 17.90843397 20.21516852 0.00100266 -0.00022879 0.00079436 Br 18.03673043 20.76275234 21.33891456 -0.00203608 -0.00049537 0.00042192 320 Lattice="23.77195271 0.0 0.0 0.0 23.77195271 0.0 0.0 0.0 23.77195271" Properties=species:S:1:pos:R:3:vel:R:3 Time=20249.99999999 energy=-11423.71601105 virial="-16.93433824 5.4160277 -0.2800315 5.4160277 -5.57592926 2.89845393 -0.2800315 2.89845393 -3.59634402" stress="-0.00034312 0.00040317 -2.085e-05 0.00040317 0.00036897 0.00021576 -2.085e-05 0.00021576 0.00062421" pbc="T T T" Cs 23.43234529 23.67991305 0.22218072 0.00044835 -0.00041545 0.00149312 Pb 2.91358974 3.06712283 3.21190127 0.00124213 -0.00050233 -0.00024752 Br 3.31579652 2.94342255 0.21855327 0.00096019 -0.00017384 0.00017388 Br 2.77230637 0.00678530 3.03961161 -0.00144892 0.00139052 -0.00004845 Br 23.76686365 3.36563781 1.96015177 0.00236927 -0.00113613 -0.00233228 Cs 0.53052398 0.18780540 5.74092885 0.00076659 -0.00039039 -0.00234717 Pb 3.22193346 2.99645313 9.05584229 0.00119403 -0.00007406 -0.00002160 Br 2.62162807 2.77043352 6.14154613 0.00053470 0.00188016 -0.00498792 Br 3.15702253 0.04647361 9.57205820 -0.00022224 0.00131544 -0.00062733 Br 23.74497916 2.64120556 10.35299473 0.00304791 -0.00471159 -0.00341896 Cs 23.62343020 23.40970478 12.13052131 -0.00147707 -0.00187354 -0.00158476 Pb 3.12814086 3.01802504 14.82263052 0.00110469 -0.00224657 0.00013726 Br 3.63576692 3.06478860 11.99141532 -0.00356295 0.00154865 -0.00265318 Br 3.04322554 23.67497162 14.52914526 -0.00055673 -0.00270926 0.00091040 Br 23.70200867 3.11258553 14.45445248 -0.00124025 -0.00160297 -0.00137298 Cs 0.01807577 23.24076086 17.51986806 -0.00051188 -0.00011636 -0.00019666 Pb 3.17445842 3.15238558 20.78343957 -0.00229540 -0.00065843 0.00059226 Br 2.79448004 3.35568906 17.84685142 0.00247812 0.00011733 0.00280256 Br 2.99955350 23.75782029 20.70665320 0.00008054 -0.00135837 0.00096540 Br 23.73835756 2.38762421 20.85250113 -0.00152521 -0.00387210 0.00262527 Cs 23.43157233 6.63020651 23.58641667 -0.00091325 0.00021324 -0.00084212 Pb 2.88059944 8.97511969 2.75904871 -0.00084411 0.00125657 -0.00263007 Br 3.03893535 8.20636772 0.13097587 0.00171117 0.00039861 -0.00158150 Br 2.84578893 5.93301537 3.14974521 -0.00051628 -0.00257498 0.00186417 Br 23.58969181 8.55435681 2.82456298 -0.00443998 0.00114078 -0.00244271 Cs 0.56868348 6.02830794 6.21784680 -0.00037676 -0.00049304 0.00165879 Pb 3.14848911 9.07523603 8.95348666 -0.00381155 -0.00128142 0.00047021 Br 2.84095404 8.73577319 5.84742804 0.00085494 0.00031509 -0.00496848 Br 2.59285547 6.10195995 9.24760208 -0.00041157 0.00246605 0.00413063 Br 0.08390730 9.23844381 8.91993075 0.00089957 -0.00133290 0.00118601 Cs 23.30024694 5.86270289 12.14532627 -0.00262325 -0.00163247 -0.00056277 Pb 2.97809191 8.97276897 14.78312921 0.00152612 0.00013508 0.00103209 Br 3.29433323 9.22123114 11.84270802 0.00049003 0.00189059 -0.00053251 Br 3.73060212 5.85589341 15.15465892 -0.00149235 -0.00355643 -0.00198907 Br 0.21249455 8.44407478 14.19374970 0.00106228 0.00234855 0.00161654 Cs 23.53836162 5.72239583 17.69087728 -0.00014025 -0.00066920 0.00055797 Pb 2.59686628 8.58313420 20.70297108 0.00033821 -0.00042054 0.00123345 Br 2.62361809 8.71787590 17.97866870 0.00088393 -0.00160263 0.00340696 Br 2.35246776 5.78140190 21.08742277 0.00072014 -0.00333743 0.00230112 Br 23.44324651 9.42144901 21.32306024 0.00556685 0.00185031 0.00194603 Cs 23.61784731 11.78656368 0.32912270 0.00317910 0.00095089 0.00042762 Pb 2.64851580 14.93502912 2.94352488 0.00242099 -0.00125122 -0.00059327 Br 2.48200745 14.92403728 23.64822340 -0.00060530 0.00011559 -0.00126795 Br 2.72795288 11.93382725 2.24791605 -0.00237319 -0.00271605 0.00229586 Br 23.61931425 14.75334237 3.15727303 -0.00131879 0.00023824 0.00071641 Cs 23.71106576 11.99049503 6.38227540 0.00128769 0.00266852 -0.00248278 Pb 3.03900238 14.83358014 9.03301554 -0.00014477 -0.00299657 -0.00102556 Br 2.91034796 14.89124188 5.90890091 -0.00240715 -0.00044056 0.00296769 Br 2.90079151 11.77824550 8.65115345 -0.00116021 -0.00200393 -0.00116548 Br 23.70557805 14.86363159 8.87013098 -0.00161413 -0.00166803 0.00000905 Cs 23.55509684 11.91063146 11.44187485 -0.00176791 0.00194208 0.00090960 Pb 3.05950822 14.57523308 14.87512669 -0.00037518 0.00018445 -0.00241446 Br 2.51373884 14.20956448 11.93142618 -0.00387191 -0.00073937 -0.00240688 Br 2.40154883 11.62263091 15.49513163 0.00060143 -0.00007060 -0.00074089 Br 23.66902073 14.63843741 14.57094387 -0.00452256 0.00196391 0.00077909 Cs 0.05103293 11.61365691 18.11913255 0.00125382 -0.00013228 -0.00026668 Pb 3.18121198 14.64798869 20.88970049 0.00046879 0.00225520 0.00141596 Br 3.01500948 14.83576826 17.96776805 0.00126547 -0.00273301 -0.00069566 Br 2.96544396 11.61038284 20.90143956 0.00112080 0.00009512 -0.00012401 Br 0.13753486 14.82724734 20.55548077 -0.00107972 0.00184842 -0.00277467 Cs 0.02247362 17.91471232 23.45258143 0.00013050 0.00025646 -0.00131334 Pb 2.94634144 20.91081591 2.79029561 -0.00045198 0.00109292 0.00029328 Br 2.77272714 21.35089895 23.58786159 -0.00108433 0.00320604 -0.00301190 Br 2.67161267 17.89524339 2.84300974 -0.00386105 0.00268801 0.00031782 Br 0.17404199 21.05521740 3.29258998 0.00021859 0.00109639 0.00249868 Cs 23.02341964 17.81110395 5.84333869 0.00176767 -0.00061965 -0.00012711 Pb 2.99791729 20.79545974 8.88579026 0.00009612 0.00015403 0.00094312 Br 3.02998048 20.55730932 5.86042105 -0.00085160 0.00057035 0.00006470 Br 2.98923644 18.04865894 8.98334177 -0.00099424 0.00049275 -0.00273473 Br 23.77188961 20.56635651 8.80749710 0.00245400 -0.00350232 -0.00161207 Cs 0.86373685 17.56974332 11.73328434 0.00108922 0.00326831 -0.00213964 Pb 3.13163229 20.65838354 14.87991480 -0.00014101 -0.00269239 -0.00068045 Br 2.71973385 20.71197638 12.07111014 -0.00221944 -0.00219078 0.00285538 Br 3.12158310 17.38541364 15.12069899 0.00011285 -0.00084120 -0.00290792 Br 0.26948919 20.80855006 14.94098030 0.00182228 0.00045222 -0.00006536 Cs 0.36710982 17.74580968 17.80153242 -0.00110531 0.00013936 0.00209154 Pb 3.07337119 20.83287143 20.54380420 -0.00124226 -0.00017610 0.00244262 Br 2.93452596 20.36961060 17.66346649 -0.00400495 0.00217044 -0.00085448 Br 3.04234072 17.69703975 20.25113617 0.00025894 0.00052857 0.00329862 Br 0.07588551 20.46882334 20.65526007 -0.00307657 -0.00051091 0.00072734 Cs 5.59505682 0.29566099 23.01157603 -0.00166227 0.00087746 0.00219751 Pb 8.71563001 3.27238953 2.93238868 -0.00139325 0.00086806 -0.00069291 Br 8.32240372 2.93148557 0.20682126 -0.00686543 0.00226975 -0.00033573 Br 9.19048070 0.38201286 3.80480008 0.00007266 -0.00019536 0.00004228 Br 5.85777750 3.16658352 3.83943297 -0.00141164 0.00054805 0.00016696 Cs 5.67966244 23.60969695 5.89907715 -0.00208195 0.00168343 0.00159396 Pb 8.87158174 3.01063636 8.82619436 0.00233910 0.00097881 0.00041487 Br 9.47220202 3.47439716 5.91015670 -0.00234732 0.00087623 0.00029617 Br 8.43454194 0.03526581 9.16526674 0.00035939 0.00112360 0.00185550 Br 5.83525630 2.82061913 8.07737650 -0.00426931 -0.00274305 -0.00167261 Cs 5.96431646 0.18300096 12.06373821 -0.00033403 0.00194972 -0.00008149 Pb 9.38016025 2.91230939 15.08511689 0.00086175 0.00062670 0.00165005 Br 8.73523786 2.81855406 12.18061040 -0.00256000 0.00012717 0.00566202 Br 9.77330726 0.04339196 14.73695362 -0.00399080 0.00087189 -0.00224005 Br 6.27943808 2.58371323 14.95457911 0.00011933 0.00187003 0.00055592 Cs 6.31516493 23.22445438 17.87193655 -0.00090320 -0.00019499 0.00111982 Pb 8.94993978 3.09525402 20.85359675 -0.00154629 -0.00037256 0.00066221 Br 9.39569657 2.85171831 18.14685789 -0.00112793 -0.00140220 -0.00383673 Br 8.61056240 23.47487228 21.00434688 -0.00319090 0.00028553 0.00005518 Br 6.07284781 3.77949684 20.72768345 0.00062173 -0.00184430 -0.00081430 Cs 5.46844731 5.67437232 0.07303892 0.00013095 -0.00421509 0.00134351 Pb 8.67780898 8.92422827 2.89534198 0.00062401 0.00118366 0.00167102 Br 8.95784771 8.95711329 23.64870372 -0.00066902 0.00118621 -0.00108262 Br 8.14771773 6.16740444 2.80462481 -0.00094102 -0.00077418 -0.00327023 Br 5.73291016 9.26765245 2.89262414 0.00138987 0.00224570 0.00084396 Cs 6.25766749 6.18675220 5.77489252 0.00084596 -0.00102282 0.00058003 Pb 9.01290114 8.75395984 8.81285031 0.00262508 -0.00100656 0.00122415 Br 8.79968258 8.44313840 5.83235247 0.00288077 0.00153604 0.00080210 Br 8.48823572 5.90580550 9.39402491 -0.00221387 -0.00283502 0.00089361 Br 6.00807122 8.75544170 8.67472119 -0.00098830 0.00466556 -0.00017003 Cs 6.06527170 5.78991291 12.06658822 0.00098121 -0.00333767 0.00153688 Pb 8.91155061 8.90556046 14.74505045 0.00028909 -0.00123825 -0.00137396 Br 8.20252592 8.74631049 11.96309031 0.00447714 0.00118809 0.00003089 Br 9.36036290 5.92990707 15.30955441 -0.00212450 0.00019832 0.00079997 Br 6.07858443 8.61391638 15.49442187 -0.00318118 -0.00062057 -0.00175956 Cs 6.56410603 6.11624882 17.70517625 -0.00282892 -0.00130532 -0.00025689 Pb 8.79056799 9.14258618 20.71721237 0.00068708 0.00004047 0.00051804 Br 8.95285637 8.99244762 17.83871843 -0.00059868 -0.00165275 0.00137109 Br 9.18659388 5.92531343 20.60985904 0.00093809 -0.00136020 -0.00218449 Br 5.60484909 8.48431414 20.72547670 0.00172625 0.00002896 0.00082494 Cs 5.41097390 11.32461993 23.65283616 0.00011380 -0.00061955 0.00184796 Pb 8.68917262 14.85899225 3.21319042 -0.00217390 0.00145330 -0.00003823 Br 9.04542329 14.58936186 23.67742427 -0.00369010 0.00097189 0.00221967 Br 9.05977775 11.95597069 3.39810863 0.00070447 -0.00002852 -0.00037728 Br 5.83923275 14.47463224 2.63866268 0.00283042 0.00029945 0.00043831 Cs 5.64582699 12.11344985 5.24594686 0.00300752 0.00108743 0.00368724 Pb 8.84696752 14.68596120 8.99508384 0.00076513 0.00146251 -0.00055423 Br 8.52186831 14.02972746 6.21403244 0.00078933 0.00371619 0.00203255 Br 9.31432736 11.52905628 9.06595156 -0.00341153 -0.00125514 0.00379277 Br 5.98543807 14.05839448 9.37237652 -0.00056464 0.00075433 -0.00246084 Cs 5.80998208 11.39904503 11.96328483 0.00303743 -0.00372403 0.00150253 Pb 9.29093294 14.86525606 14.72526758 -0.00097473 -0.00184120 -0.00094143 Br 8.94683713 15.29546227 11.88647040 -0.00281670 0.00106819 0.00053177 Br 9.49001572 12.02234436 14.98025908 -0.00108324 0.00374497 -0.00234937 Br 5.96625948 14.59345598 13.89306190 0.00268335 -0.00085433 -0.00024072 Cs 6.45749657 11.53833265 17.45282596 -0.00002539 -0.00171071 -0.00186420 Pb 9.11800300 14.97080147 20.94382963 0.00171177 0.00095574 0.00165832 Br 9.31770173 14.83672213 17.84016408 -0.00086268 -0.00048925 -0.00035428 Br 8.71111816 12.01181208 20.78625657 -0.00196261 -0.00154144 -0.00559086 Br 5.91764610 14.87208108 21.27248546 -0.00186858 -0.00167451 -0.00044209 Cs 5.49943812 18.54381273 23.45546594 0.00138456 -0.00102219 -0.00218521 Pb 8.82349401 20.86729302 3.05460273 0.00075464 0.00243065 -0.00026010 Br 8.90826698 21.03896569 23.69363944 -0.00075724 0.00058405 0.00049230 Br 8.49868127 18.05970630 2.68102324 0.00120878 0.00137999 -0.00020794 Br 6.06538964 21.06908795 3.07667981 0.00296791 0.00376027 -0.00069771 Cs 5.45952306 18.08573856 5.49651807 -0.00150558 0.00029410 -0.00008386 Pb 8.95886904 20.46260947 8.86527212 0.00340479 0.00101433 -0.00097079 Br 8.17115473 20.65092243 5.89872377 0.00197140 0.00355264 -0.00317581 Br 8.27047239 17.50739189 8.04763019 0.00252514 0.00047508 0.00060236 Br 5.96483982 20.60756654 8.56002562 -0.00069808 0.00098012 0.00206101 Cs 6.15757327 18.33545110 11.48434795 -0.00116358 -0.00016137 0.00144174 Pb 9.23219675 20.74195694 14.91528411 0.00062047 -0.00043679 0.00188733 Br 9.65287222 20.43412955 11.74270820 0.00006510 -0.00484436 0.00094678 Br 8.32222364 18.14807794 15.01867515 -0.00081227 0.00115913 0.00260113 Br 6.41515001 21.44644028 14.77921148 0.00035926 0.00072323 -0.00193574 Cs 5.67287696 17.36606489 17.59963494 0.00006312 0.00000662 -0.00186738 Pb 8.94101232 20.62810261 20.64959097 -0.00009731 -0.00083094 0.00075096 Br 9.70141143 20.69818057 17.84784531 -0.00253530 0.00017293 0.00232944 Br 8.28992819 17.65890455 20.85015841 0.00430474 -0.00001113 0.00091012 Br 5.89655992 20.90722520 20.50701984 0.00250947 0.00123709 -0.00010614 Cs 12.19538382 23.69308089 0.14486880 0.00007094 -0.00083200 -0.00120771 Pb 14.98353980 3.24220527 2.86966059 -0.00027250 -0.00215947 0.00075668 Br 15.54092800 3.08987928 23.49065820 0.00211821 0.00201212 -0.00064664 Br 15.74792995 0.03845293 3.18153102 0.00009851 0.00138087 0.00358937 Br 11.98906304 3.64550987 2.34625232 -0.00282274 0.00060489 -0.00099043 Cs 12.04155679 0.47268833 5.41403941 -0.00052737 -0.00037134 -0.00129824 Pb 14.91899293 3.17948314 8.82556712 -0.00013190 0.00090028 0.00043292 Br 14.38963969 3.31614916 5.85031227 -0.00171135 -0.00121036 0.00087963 Br 15.11541118 0.10259581 9.07746180 0.00153676 0.00090136 -0.00072958 Br 11.90137106 3.05871024 9.28523650 -0.00205085 0.00153087 -0.00472887 Cs 12.01137606 0.65104961 11.88401008 0.00040358 0.00240113 -0.00125948 Pb 15.03664748 2.52652311 14.91644832 -0.00125066 -0.00057973 -0.00073305 Br 14.84572360 3.07417723 11.96886616 0.00034097 0.00025757 -0.00339936 Br 14.32052049 23.57523594 14.62982567 -0.00018089 -0.00040981 -0.00245081 Br 12.01080052 3.47200076 14.30318239 -0.00142663 0.00245831 0.00305198 Cs 11.94271144 23.56595069 18.59313851 -0.00126210 0.00321105 0.00138415 Pb 14.84732193 3.12555632 20.72814929 -0.00167453 -0.00013905 -0.00075476 Br 14.16105199 2.89982283 17.73534748 -0.00358339 -0.00470832 -0.00209336 Br 15.43610770 23.74211938 20.34774216 0.00119404 0.00478212 -0.00334628 Br 11.82174485 2.40433583 21.52198667 0.00178332 -0.00096776 -0.00184802 Cs 11.98606501 6.11103361 23.46224097 0.00121654 -0.00150288 0.00154281 Pb 14.73032599 8.96135845 3.00459794 -0.00066505 0.00092645 -0.00354205 Br 14.88416152 8.48701513 23.72997073 0.00131095 -0.00253862 -0.00183964 Br 15.04878599 6.18320727 3.08579924 -0.00228372 -0.00021620 -0.00408781 Br 11.74795645 8.26095011 2.65842171 0.00030566 -0.00124761 0.00086111 Cs 12.31167341 6.07840037 5.65355358 0.00192779 -0.00129337 0.00077623 Pb 14.55373161 9.05824711 8.88091708 -0.00043668 0.00196087 -0.00173638 Br 14.27526694 9.49166911 6.04917202 -0.00032323 -0.00187697 0.00058871 Br 15.25963620 6.04379467 8.52969973 -0.00031716 -0.00041111 0.00262426 Br 11.76604539 7.91097839 8.79511487 0.00365619 -0.00081545 -0.00142014 Cs 12.11677284 6.04018258 12.06324856 0.00087687 0.00059527 0.00165321 Pb 15.04691088 8.86230741 15.03421530 0.00015862 -0.00116864 0.00129918 Br 14.22351488 9.18809597 11.91960020 -0.00324964 -0.00019618 -0.00300160 Br 14.71512113 5.58899600 14.78731025 0.00066057 0.00101338 0.00040567 Br 12.04743571 8.83146329 15.16012564 0.00009176 0.00315418 0.00102048 Cs 12.37261208 5.83545906 18.14439525 -0.00463657 0.00293207 -0.00284561 Pb 14.74334233 9.18941977 20.95210870 0.00153886 0.00004052 0.00015307 Br 14.83932321 8.59346618 17.86890402 0.00144890 0.00101697 0.00317253 Br 14.82690351 6.29155171 20.65628022 -0.00338122 -0.00096059 0.00017491 Br 11.75403113 8.67130638 20.66531725 -0.00339721 -0.00167966 0.00184733 Cs 11.39006919 11.81640885 0.43000428 0.00161931 -0.00047830 -0.00178054 Pb 14.69929030 14.93577035 3.14987290 -0.00015411 0.00085114 -0.00192275 Br 15.12042630 15.29439434 0.10092666 0.00197800 -0.00210998 0.00313457 Br 14.14447994 11.85846435 3.01290155 -0.00242063 -0.00242787 -0.00211302 Br 11.73190840 14.92000871 2.89825199 0.00208557 -0.00089703 -0.00337687 Cs 11.87872637 12.47759932 6.06867631 0.00313031 -0.00070845 -0.00019527 Pb 14.81019705 15.01699616 9.00386390 0.00006098 -0.00067427 -0.00174759 Br 14.81490987 14.90372872 6.19755050 -0.00072166 0.00022777 0.00089539 Br 13.63565273 11.81393833 9.04403721 0.00180195 -0.00154084 0.00101757 Br 11.49239113 15.24050979 8.74230226 0.00330013 0.00005308 0.00327367 Cs 11.80666135 12.20740095 12.12131992 -0.00035811 0.00050169 0.00131056 Pb 14.85879291 15.01367255 15.17763768 -0.00002178 -0.00072632 0.00065034 Br 14.71942712 14.76095844 12.00997824 -0.00044089 0.00074505 -0.00199910 Br 14.90191588 12.04071736 14.87763549 0.00353302 0.00266531 0.00076984 Br 12.02906219 15.08879710 14.80113086 0.00263661 -0.00000829 -0.00333970 Cs 12.17564847 11.30192897 17.47535462 -0.00118639 -0.00153600 0.00125284 Pb 14.99880748 14.98711795 20.93448598 0.00075620 0.00110745 0.00055030 Br 14.65881167 14.72991259 18.07122966 0.00055362 -0.00218742 -0.00186102 Br 14.42158706 12.07124677 21.57454987 0.00136017 -0.00112176 0.00397304 Br 12.00528962 15.61296412 21.43204338 0.00209358 0.00258262 -0.00319908 Cs 11.49853184 17.87348529 0.89623131 0.00028860 -0.00144480 0.00155150 Pb 15.03460455 20.94741452 3.17491563 0.00081836 -0.00020334 0.00144470 Br 15.11823018 20.84333510 23.56230677 0.00366287 0.00126631 0.00312564 Br 14.22329421 17.83408511 3.57265469 -0.00133936 -0.00092361 -0.00180855 Br 11.98389449 21.19311516 3.35773537 0.00040319 -0.00256663 -0.00216416 Cs 11.35866148 18.21266331 5.94622621 0.00178217 0.00036530 0.00008850 Pb 14.88696804 20.98489325 8.97372443 -0.00063946 0.00071963 0.00083676 Br 15.45824066 20.38802270 5.95477457 0.00393359 -0.00124114 -0.00109399 Br 15.15703945 17.99660008 9.04872249 0.00362783 0.00060139 0.00297587 Br 11.43171078 21.40354059 8.09777306 0.00124638 -0.00109689 -0.00088636 Cs 12.36745565 18.07775745 11.46052317 -0.00123256 -0.00384206 -0.00122958 Pb 14.93375261 20.90337306 14.71912518 -0.00143859 0.00293851 0.00012487 Br 15.31871440 21.08746524 11.87863389 0.00236885 0.00288590 -0.00146412 Br 15.54952952 17.76956390 14.83113407 0.00005739 -0.00025129 0.00260897 Br 12.16005667 19.81370438 14.83374720 -0.00162859 0.00119206 -0.00221739 Cs 12.04883552 17.57425631 18.00072065 -0.00184925 0.00002452 0.00127242 Pb 15.06771245 20.86593224 20.71803560 0.00004723 0.00084480 0.00044121 Br 14.38109753 20.93385478 17.69034137 0.00378281 0.00220844 0.00210688 Br 15.12513833 17.78045355 20.31488858 0.00315265 0.00497018 0.00653931 Br 11.87438106 20.48318228 21.22228958 0.00187611 -0.00185214 0.00047939 Cs 18.55297691 23.48074312 23.45588529 0.00303762 0.00168717 -0.00111865 Pb 21.02489001 3.33523463 2.60310787 -0.00085428 -0.00060582 0.00170663 Br 20.47993768 3.91102491 23.65189298 -0.00173661 -0.00033461 -0.00258892 Br 20.70257744 0.07068075 3.04690614 -0.00079481 -0.00214052 -0.00330051 Br 17.84443474 3.09908359 3.68878336 0.00027495 -0.00117194 0.00437198 Cs 17.78026295 0.40184750 6.02970050 -0.00305834 0.00266453 -0.00152107 Pb 21.20035658 2.91735471 9.18025494 0.00062714 -0.00042336 0.00154364 Br 21.58579144 3.13075824 6.12642171 0.00097161 -0.00091609 -0.00080624 Br 20.70120029 23.76842681 9.40834123 -0.00089868 0.00006346 0.00159916 Br 18.09924989 2.93932830 8.61639537 0.00352044 0.00246912 0.00079522 Cs 18.19223515 0.07992343 11.81785685 0.00208107 -0.00186331 0.00000698 Pb 20.72780750 2.99681591 14.68942316 0.00209301 -0.00089748 0.00014963 Br 19.94835008 3.64644287 11.88505287 0.00207050 0.00002708 0.00148045 Br 21.53694915 23.61164120 14.93852607 -0.00377575 0.00086177 0.00158679 Br 17.87514466 2.95197249 15.19722616 -0.00043462 0.00061406 0.00329082 Cs 18.09244283 23.42268736 17.96658840 0.00075086 -0.00116803 -0.00282512 Pb 20.94401865 3.09311929 20.57467773 -0.00212236 0.00207236 -0.00072166 Br 20.95863172 3.02754031 17.74702154 0.00091052 -0.00203378 -0.00105144 Br 20.68412006 23.74814724 20.25100982 -0.00164051 -0.00090006 0.00053811 Br 17.87643319 2.97057450 20.27263599 0.00146086 0.00095848 0.00198806 Cs 17.75489709 6.46804802 23.16803346 -0.00097905 -0.00139484 -0.00161455 Pb 20.54852824 8.84266058 3.06284839 -0.00279507 0.00020534 -0.00086719 Br 20.27015778 8.54203774 0.01549744 0.00258435 -0.00030918 -0.00101818 Br 20.55986307 6.18784327 3.58298174 0.00143803 0.00241847 0.00189608 Br 17.57043582 8.76211192 3.55728674 -0.00099094 -0.00112490 -0.00150917 Cs 18.36121095 5.51998138 6.11011540 -0.00100783 0.00185104 -0.00206627 Pb 20.81413584 8.84548790 8.84328764 -0.00155143 0.00108471 -0.00006290 Br 20.91449319 9.24174226 5.97549357 0.00245240 -0.00169558 0.00246198 Br 21.35353668 5.89496683 8.45762963 0.00013376 0.00115881 -0.00106095 Br 17.64418285 9.11976213 8.58380082 0.00194191 -0.00292985 -0.00190255 Cs 17.54556100 6.45342686 11.49321445 0.00061858 0.00002556 0.00079053 Pb 20.96156973 8.67890641 14.93322830 -0.00001927 0.00054471 -0.00096838 Br 20.69852409 8.83177283 11.73813751 -0.00144374 0.00044372 0.00027815 Br 20.71132372 5.79837773 14.90406333 0.00019714 -0.00115812 -0.00276122 Br 18.03700676 8.20786247 14.80389878 -0.00314913 0.00036957 0.00111850 Cs 17.62110641 5.62316496 17.75495831 -0.00180624 -0.00027019 0.00138001 Pb 20.51936938 8.97562341 20.57927961 -0.00185989 -0.00114774 0.00035151 Br 21.31454511 8.56643995 17.87069566 0.00010023 0.00006712 -0.00009844 Br 20.56130299 6.09806944 20.21254840 0.00134235 -0.00016718 -0.00285651 Br 17.62659026 8.95624725 20.62138627 0.00135725 -0.00362776 0.00055152 Cs 17.42870874 11.79621261 0.11052242 -0.00257197 -0.00075488 -0.00035182 Pb 20.63179847 14.83374909 3.21059498 -0.00018878 0.00037540 0.00031400 Br 20.93261619 15.13604952 0.11081722 0.00219861 -0.00103293 -0.00035474 Br 20.28715520 11.96180773 3.18544644 0.00078894 0.00027067 -0.00262210 Br 17.84406856 15.00138568 2.40077975 0.00319432 0.00012676 -0.00045679 Cs 17.87067600 11.55901503 6.18946425 -0.00013029 0.00519478 -0.00172996 Pb 20.39128737 14.86669757 9.18597752 -0.00101258 -0.00024041 -0.00030598 Br 19.98483521 14.45905205 6.23532980 -0.00058592 -0.00205446 0.00315764 Br 19.95290013 11.68263239 9.36958374 0.00130293 0.00001571 0.00085819 Br 17.48813240 14.60017411 9.41529966 0.00098546 0.00189504 0.00079911 Cs 18.11097312 11.32204983 11.94317388 0.00013190 0.00050011 -0.00103131 Pb 20.79491177 14.66951686 14.69412902 0.00025982 -0.00036944 0.00074475 Br 20.77283271 13.98823964 11.97191915 -0.00094556 0.00071974 0.00209706 Br 20.85485535 11.63759825 15.52592037 0.00218553 -0.00048470 0.00038457 Br 17.79422324 14.58704359 15.12150884 0.00146771 0.00312987 0.00144935 Cs 17.44240784 11.75477119 17.61559122 0.00117149 -0.00168727 0.00077109 Pb 21.08032191 15.08092427 20.64501390 -0.00024640 -0.00154785 -0.00151578 Br 20.82638126 15.02290943 17.65728887 -0.00141134 0.00423670 -0.00165715 Br 20.09079570 11.78174566 20.54259025 0.00105611 0.00288317 0.00390906 Br 17.97191856 14.95636964 20.53240862 -0.00254388 0.00038945 0.00091781 Cs 17.95170105 17.60290244 23.55268311 -0.00020433 -0.00133391 0.00145806 Pb 21.07382362 20.78395785 2.97321355 0.00005342 0.00014309 -0.00085348 Br 21.33083252 20.90833125 0.13041647 -0.00117358 -0.00371537 0.00219489 Br 21.05783016 17.93209531 3.21206706 0.00034915 0.00162445 -0.00249287 Br 17.72334909 20.62511840 2.72674369 0.00333998 0.00091855 0.00292864 Cs 17.85233628 17.78861796 5.32691434 0.00052420 -0.00171162 -0.00239420 Pb 20.86601547 20.87693295 8.84501946 0.00055502 -0.00146463 0.00181467 Br 20.75452961 20.89746410 5.81552213 -0.00300497 0.00157290 -0.00168200 Br 20.31850202 17.96859302 8.84649951 0.00038293 0.00116614 0.00125781 Br 18.13648293 21.30684083 9.18035153 0.00104961 0.00166220 -0.00555726 Cs 17.74332885 17.72610519 12.03201033 -0.00051461 -0.00189291 0.00160918 Pb 20.63094717 20.92291172 14.81707888 0.00005691 -0.00050911 -0.00084495 Br 21.12293801 20.90716411 11.84320510 -0.00090514 -0.00018111 0.00149679 Br 20.41769802 17.64922218 14.25276080 -0.00021145 0.00538301 0.00269352 Br 17.80797824 21.68417574 15.22912162 -0.00071550 0.00193401 0.00245126 Cs 17.89754367 17.92483018 18.24539655 0.00500907 -0.00164208 -0.00023148 Pb 20.86074391 20.71003730 20.90666755 0.00203214 0.00069028 -0.00257961 Br 20.98416043 20.47866131 17.95711802 0.00093294 -0.00069590 0.00140111 Br 20.86500534 17.90400877 20.22581286 0.00111465 -0.00065970 0.00133815 Br 18.01487482 20.75629967 21.34139827 -0.00230171 -0.00079288 0.00007031 320 Lattice="23.77195271 0.0 0.0 0.0 23.77195271 0.0 0.0 0.0 23.77195271" Properties=species:S:1:pos:R:3:vel:R:3 Time=20259.99999999 energy=-11423.80234146 virial="-18.16315138 4.9411291 -0.11245415 4.9411291 -4.970079 2.85387841 -0.11245415 2.85387841 -3.01078971" stress="-0.00042314 0.00036782 -8.37e-06 0.00036782 0.00041698 0.00021244 -8.37e-06 0.00021244 0.00066629" pbc="T T T" Cs 23.43810105 23.67676548 0.23688986 0.00069833 -0.00021697 0.00144793 Pb 2.92636855 3.06224600 3.20831918 0.00130863 -0.00047109 -0.00047285 Br 3.32557188 2.94080963 0.21871922 0.00099197 -0.00035561 -0.00015008 Br 2.75870726 0.02181617 3.03885525 -0.00126546 0.00159516 -0.00011003 Br 0.01850799 3.35361420 1.93801630 0.00230379 -0.00126909 -0.00208674 Cs 0.53599480 0.18261900 5.71818974 0.00032749 -0.00064157 -0.00219572 Pb 3.23259499 2.99577130 9.05624859 0.00092218 -0.00006133 0.00009799 Br 2.62651691 2.79057695 6.09222529 0.00043450 0.00214112 -0.00484052 Br 3.15473742 0.06100176 9.56417746 -0.00023389 0.00157983 -0.00094666 Br 0.00570890 2.59520090 10.31775626 0.00347627 -0.00448190 -0.00362598 Cs 23.61005446 23.39071866 12.11439668 -0.00119898 -0.00192176 -0.00163450 Pb 3.13977144 2.99514360 14.82464341 0.00122363 -0.00233054 0.00026298 Br 3.60078825 3.08099843 11.96370848 -0.00342761 0.00168889 -0.00285674 Br 3.03718953 23.64710740 14.53993459 -0.00064805 -0.00285397 0.00124271 Br 23.69130588 3.09678497 14.44167356 -0.00089095 -0.00155819 -0.00118320 Cs 0.01355093 23.24022469 17.51929573 -0.00039397 0.00000946 0.00008076 Pb 3.15083241 3.14409343 20.78887899 -0.00242484 -0.00100805 0.00049820 Br 2.81909781 3.35617346 17.87671524 0.00244244 -0.00001968 0.00314515 Br 3.00040345 23.74404535 20.71424896 0.00008910 -0.00139710 0.00054849 Br 23.72421604 2.35014914 20.87893324 -0.00130351 -0.00361877 0.00265843 Cs 23.42220748 6.63136212 23.57833688 -0.00095616 0.00001530 -0.00077297 Pb 2.87073745 8.98731701 2.73506435 -0.00112382 0.00117054 -0.00216117 Br 3.05571525 8.21099594 0.11096195 0.00164942 0.00051916 -0.00241023 Br 2.84157880 5.90793536 3.16874281 -0.00032755 -0.00241292 0.00192765 Br 23.54517084 8.56745177 2.79998648 -0.00444018 0.00148268 -0.00247399 Cs 0.56400555 6.02345254 6.23425142 -0.00055636 -0.00047613 0.00162146 Pb 3.10993401 9.05978182 8.95705269 -0.00388991 -0.00180570 0.00024325 Br 2.85034312 8.74039493 5.79837020 0.00101774 0.00060838 -0.00482986 Br 2.58947024 6.12320877 9.28904131 -0.00026907 0.00177245 0.00415461 Br 0.09010626 9.22540326 8.93199344 0.00033416 -0.00126976 0.00122539 Cs 23.27405998 5.84564813 12.13795452 -0.00260956 -0.00177085 -0.00090783 Pb 2.99370837 8.97259389 14.79380470 0.00159322 -0.00016889 0.00109867 Br 3.29798320 9.23933331 11.84008999 0.00023537 0.00172744 0.00001303 Br 3.71445849 5.82125630 15.13342793 -0.00173518 -0.00336297 -0.00225249 Br 0.22187614 8.46869669 14.21197714 0.00079564 0.00255971 0.00201811 Cs 23.53767795 5.71523011 17.69633634 0.00000421 -0.00076343 0.00053308 Pb 2.60122378 8.58090978 20.71673127 0.00053632 -0.00002833 0.00152512 Br 2.63151997 8.70124961 18.00759796 0.00069690 -0.00172002 0.00234540 Br 2.35772490 5.74833922 21.11070540 0.00032400 -0.00322771 0.00235618 Br 23.49942476 9.43973502 21.34184918 0.00563449 0.00180149 0.00180420 Cs 23.64881830 11.79646296 0.33289142 0.00300729 0.00102868 0.00032202 Pb 2.67377662 14.92225512 2.93690694 0.00261374 -0.00129716 -0.00072591 Br 2.47512653 14.92626226 23.63947227 -0.00077098 0.00032621 -0.00045068 Br 2.70480263 11.90695463 2.27263104 -0.00224576 -0.00262315 0.00264781 Br 23.60575644 14.75539444 3.16538324 -0.00134228 0.00017119 0.00090256 Cs 23.72381780 12.01690797 6.35701499 0.00126130 0.00261079 -0.00256668 Pb 3.03763973 14.80287554 9.02261245 -0.00012843 -0.00314087 -0.00105354 Br 2.88668098 14.88666950 5.93915901 -0.00232364 -0.00047357 0.00304850 Br 2.88913282 11.76643823 8.63900820 -0.00117079 -0.00035157 -0.00126619 Br 23.68930497 14.84688937 8.87024818 -0.00164150 -0.00167867 0.00001265 Cs 23.53773097 11.92937671 11.45125282 -0.00170606 0.00180242 0.00096328 Pb 3.05584104 14.57647734 14.85081014 -0.00035600 0.00006173 -0.00244786 Br 2.47612659 14.20305626 11.90615299 -0.00364524 -0.00055904 -0.00262199 Br 2.40865792 11.62309295 15.48700955 0.00082066 0.00016495 -0.00088195 Br 23.62732415 14.65883190 14.57926131 -0.00377554 0.00211219 0.00088295 Cs 0.06302717 11.61274593 18.11700093 0.00114711 -0.00005092 -0.00015823 Pb 3.18545314 14.67038150 20.90308859 0.00037125 0.00221617 0.00124905 Br 3.02826888 14.80706504 17.95689206 0.00138755 -0.00299853 -0.00147938 Br 2.97655913 11.61217180 20.90041643 0.00110291 0.00026229 -0.00007886 Br 0.12553883 14.84578643 20.52762627 -0.00131247 0.00185858 -0.00279602 Cs 0.02394908 17.91677123 23.43944770 0.00016074 0.00015488 -0.00131208 Pb 2.94268283 20.92161937 2.79345483 -0.00028117 0.00107007 0.00033916 Br 2.76243445 21.38132665 23.55898993 -0.00097324 0.00287156 -0.00274097 Br 2.63314849 17.92138212 2.84546451 -0.00382602 0.00251199 0.00017341 Br 0.17369438 21.06578951 3.31738376 -0.00026875 0.00101810 0.00245656 Cs 23.04269579 17.80508929 5.84408119 0.00208144 -0.00058091 0.00026759 Pb 2.99863161 20.79859856 8.89517283 0.00004896 0.00046986 0.00093273 Br 3.02082234 20.56513315 5.86113359 -0.00097443 0.00099625 0.00007287 Br 2.98058955 18.04684335 8.95592329 -0.00073116 -0.00084933 -0.00275082 Br 0.02726811 20.53119985 8.79097967 0.00298484 -0.00352552 -0.00168829 Cs 0.87324602 17.60244096 11.71276775 0.00081001 0.00326548 -0.00196533 Pb 3.13076006 20.63255477 14.87202020 -0.00002569 -0.00247191 -0.00088893 Br 2.69834269 20.68891388 12.09416275 -0.00205960 -0.00241287 0.00172421 Br 3.12195628 17.38066900 15.08984420 -0.00003856 -0.00011080 -0.00325684 Br 0.28621492 20.81147127 14.94033232 0.00149916 0.00013355 -0.00006545 Cs 0.35540221 17.74675337 17.82228112 -0.00123479 0.00004876 0.00205709 Pb 3.05973717 20.83065361 20.56882925 -0.00147333 -0.00026515 0.00255396 Br 2.89485003 20.39245534 17.65898761 -0.00393003 0.00239604 -0.00002122 Br 3.04568504 17.70378108 20.28688016 0.00040906 0.00081679 0.00383986 Br 0.04501302 20.46430730 20.66270257 -0.00306658 -0.00039020 0.00075922 Cs 5.57929944 0.30397533 23.03445616 -0.00148789 0.00078365 0.00237709 Pb 8.70259910 3.28013967 2.92725337 -0.00121248 0.00068114 -0.00033790 Br 8.25259351 2.95345701 0.19980849 -0.00708804 0.00212203 -0.00106451 Br 9.18792078 0.37877268 3.80182479 -0.00058371 -0.00044943 -0.00063720 Br 5.84332192 3.17117696 3.83954911 -0.00146018 0.00036899 -0.00014168 Cs 5.65946365 23.62648255 5.91521463 -0.00195796 0.00167019 0.00163051 Pb 8.89528764 3.01978327 8.83065098 0.00239712 0.00084752 0.00047266 Br 9.44882466 3.48394176 5.91558144 -0.00232926 0.00103109 0.00078770 Br 8.43894489 0.04554647 9.18322081 0.00052592 0.00092822 0.00173490 Br 5.79465189 2.79428325 8.06151554 -0.00380432 -0.00252233 -0.00150407 Cs 5.96142898 0.20260371 12.06307158 -0.00024378 0.00196921 -0.00005032 Pb 9.38733563 2.91839145 15.10056242 0.00056538 0.00058463 0.00144086 Br 8.71009173 2.82079938 12.23441260 -0.00246338 0.00032667 0.00506685 Br 9.73399144 0.05056749 14.71565103 -0.00386444 0.00055811 -0.00202166 Br 6.27786700 2.60284706 14.96169999 -0.00045019 0.00195580 0.00086882 Cs 6.30628251 23.22355897 17.88238911 -0.00087326 0.00001595 0.00096685 Pb 8.93457758 3.09126868 20.86142329 -0.00152288 -0.00042461 0.00088839 Br 9.38519458 2.83695537 18.10689142 -0.00097903 -0.00154812 -0.00408691 Br 8.57932722 23.48134963 21.00297632 -0.00304960 0.00100992 -0.00032681 Br 6.07708086 3.75992248 20.71914045 0.00021992 -0.00207225 -0.00089430 Cs 5.47075373 5.63272493 0.08662275 0.00032872 -0.00410688 0.00137191 Pb 8.68539725 8.93645897 2.91228486 0.00089104 0.00125515 0.00171250 Br 8.95067403 8.96947872 23.63966163 -0.00076362 0.00128928 -0.00070392 Br 8.13802035 6.15780739 2.77044258 -0.00099579 -0.00112255 -0.00355478 Br 5.74865959 9.29019802 2.90126087 0.00173708 0.00226385 0.00088506 Cs 6.26491446 6.17608212 5.78165230 0.00060720 -0.00110543 0.00076966 Pb 9.03839359 8.74334186 8.82486243 0.00246795 -0.00111682 0.00117574 Br 8.82989647 8.46004250 5.84082553 0.00315311 0.00183564 0.00089041 Br 8.46588065 5.87505783 9.40259480 -0.00225496 -0.00328752 0.00081995 Br 5.99835183 8.80287438 8.67287804 -0.00093796 0.00481542 -0.00019644 Cs 6.07508499 5.75641946 12.08236738 0.00098116 -0.00335820 0.00162066 Pb 8.91512932 8.89198065 14.73210032 0.00042356 -0.00147639 -0.00120461 Br 8.24833480 8.75839836 11.96304152 0.00468281 0.00121499 -0.00004706 Br 9.33779529 5.93504415 15.31666658 -0.00238590 0.00084231 0.00061748 Br 6.04663036 8.61045653 15.47402651 -0.00318180 -0.00007756 -0.00231725 Cs 6.53557444 6.10182280 17.70352064 -0.00287801 -0.00157630 -0.00007446 Pb 8.79709336 9.14203026 20.72288874 0.00061156 -0.00015425 0.00061754 Br 8.94771582 8.97652054 17.85053143 -0.00043140 -0.00153404 0.00096564 Br 9.19668346 5.91149457 20.58843125 0.00107743 -0.00141415 -0.00210019 Br 5.62310613 8.48492155 20.73367668 0.00191975 0.00009081 0.00081370 Cs 5.41265105 11.31894090 23.67040375 0.00022207 -0.00051819 0.00166132 Pb 8.66739833 14.87289903 3.21229022 -0.00216753 0.00132963 -0.00014224 Br 9.00816654 14.59946615 23.70616990 -0.00375328 0.00104742 0.00350291 Br 9.06679546 11.95617206 3.39293298 0.00069769 0.00007885 -0.00065141 Br 5.86732560 14.47863593 2.64321144 0.00275519 0.00049557 0.00047034 Cs 5.67545642 12.12351142 5.28377926 0.00291717 0.00092179 0.00387993 Pb 8.85274557 14.70030322 8.98964445 0.00039837 0.00140726 -0.00052563 Br 8.52982837 14.06837001 6.23014204 0.00080703 0.00399877 0.00117024 Br 9.28092799 11.52084441 9.10479514 -0.00326599 -0.00039283 0.00397148 Br 5.97564318 14.06664736 9.34766780 -0.00137846 0.00089532 -0.00248073 Cs 5.84143341 11.36356962 11.97777590 0.00325231 -0.00336599 0.00139194 Pb 9.27872241 14.84783909 14.71691580 -0.00145543 -0.00162603 -0.00072772 Br 8.92058324 15.30688463 11.89201580 -0.00243154 0.00121750 0.00056554 Br 9.47843633 12.05769102 14.95619849 -0.00122963 0.00327555 -0.00245722 Br 5.99110960 14.58427228 13.89339245 0.00227093 -0.00098453 0.00031126 Cs 6.45679311 11.52231118 17.43508566 -0.00011569 -0.00149241 -0.00168108 Pb 9.13547308 14.97991653 20.95952939 0.00178042 0.00085739 0.00148219 Br 9.30888469 14.83205062 17.83474916 -0.00090100 -0.00044233 -0.00071582 Br 8.69142455 11.99423626 20.72983547 -0.00197219 -0.00194120 -0.00568777 Br 5.90183110 14.85517915 21.26819291 -0.00126473 -0.00169780 -0.00041113 Cs 5.51386804 18.53298090 23.43353674 0.00150159 -0.00114129 -0.00219968 Pb 8.83226846 20.89305407 3.05165380 0.00100485 0.00271947 -0.00033430 Br 8.90025761 21.04354859 23.70061088 -0.00084087 0.00033478 0.00089203 Br 8.51015910 18.07090545 2.68019704 0.00108248 0.00085579 0.00004567 Br 6.08991269 21.10708736 3.06782972 0.00190477 0.00383858 -0.00107399 Cs 5.44506119 18.08837897 5.49599905 -0.00138492 0.00023765 -0.00001831 Pb 8.99137249 20.47344414 8.85573609 0.00308178 0.00115002 -0.00092927 Br 8.19344785 20.68621265 5.86696510 0.00247806 0.00349636 -0.00313125 Br 8.29652041 17.51266468 8.05502480 0.00268357 0.00058903 0.00087198 Br 5.95569219 20.61789299 8.58245243 -0.00110641 0.00108136 0.00241337 Cs 6.14637871 18.33332106 11.49936254 -0.00107400 -0.00026346 0.00156086 Pb 9.23916734 20.73863857 14.93331035 0.00076795 -0.00022462 0.00171537 Br 9.65241418 20.38535355 11.75229199 -0.00016019 -0.00490652 0.00094922 Br 8.31330842 18.15456970 15.04467773 -0.00097226 0.00014178 0.00259987 Br 6.41656765 21.45306513 14.75966129 -0.00007272 0.00060086 -0.00197097 Cs 5.67423693 17.36604578 17.58171944 0.00020640 -0.00001066 -0.00171583 Pb 8.94102148 20.61956824 20.65870634 0.00009971 -0.00086850 0.00107578 Br 9.67556639 20.70044965 17.87005879 -0.00262562 0.00028178 0.00210289 Br 8.33201059 17.66276660 20.85922028 0.00410547 0.00077255 0.00090297 Br 5.92296726 20.91867379 20.50636695 0.00272801 0.00104320 -0.00002222 Cs 12.19583819 23.68383970 0.13333036 0.00002055 -0.00101737 -0.00110076 Pb 14.98098604 3.22045099 2.87748220 -0.00023844 -0.00218797 0.00080539 Br 15.56177790 3.11038502 23.48625645 0.00204428 0.00208855 -0.00023645 Br 15.74566289 0.05253904 3.21648733 -0.00056106 0.00142942 0.00339122 Br 11.96083337 3.65006761 2.33720794 -0.00281533 0.00029990 -0.00081271 Cs 12.03730476 0.46924098 5.40227841 -0.00032255 -0.00031944 -0.00105514 Pb 14.91769836 3.18825142 8.83048331 -0.00012292 0.00085239 0.00054900 Br 14.37402797 3.30199344 5.85850402 -0.00140734 -0.00162037 0.00074774 Br 15.13034150 0.11180527 9.06936092 0.00144659 0.00093719 -0.00089083 Br 11.88133282 3.07469668 9.23686397 -0.00195342 0.00166283 -0.00493475 Cs 12.01473582 0.67480291 11.87120127 0.00026545 0.00234985 -0.00130132 Pb 15.02292215 2.52349122 14.90839782 -0.00148611 -0.00002411 -0.00087313 Br 14.85079648 3.07630913 11.93285083 0.00066431 0.00017323 -0.00378478 Br 14.31642835 23.57251723 14.60524064 -0.00064790 -0.00010130 -0.00246862 Br 11.99857801 3.49388117 14.33467821 -0.00098690 0.00192317 0.00323482 Cs 11.92997157 23.59821648 18.60672753 -0.00128515 0.00323759 0.00133372 Pb 14.83121872 3.12335039 20.72054415 -0.00154561 -0.00030657 -0.00076415 Br 14.12606894 2.85184722 17.71605275 -0.00341728 -0.00486792 -0.00176645 Br 15.44641115 0.01980007 20.31569499 0.00086352 0.00513220 -0.00305838 Br 11.84170844 2.39648157 21.50208987 0.00219915 -0.00060366 -0.00212570 Cs 11.99787128 6.09601346 23.47770747 0.00114374 -0.00149987 0.00154781 Pb 14.72275950 8.97103910 2.96957699 -0.00084804 0.00099957 -0.00345789 Br 14.89723930 8.46315043 23.71308811 0.00130277 -0.00223127 -0.00152637 Br 15.02627983 6.17527452 3.04497681 -0.00221633 -0.00136608 -0.00407429 Br 11.75030913 8.24990585 2.66721223 0.00016674 -0.00095836 0.00089503 Cs 12.33004088 6.06600157 5.66126805 0.00174441 -0.00118419 0.00076461 Pb 14.54930718 9.07765842 8.86376329 -0.00044019 0.00191334 -0.00168749 Br 14.27251242 9.47219368 6.05248260 -0.00022900 -0.00201149 0.00004776 Br 15.25646680 6.04003279 8.55640874 -0.00031540 -0.00034339 0.00271394 Br 11.80462980 7.90281044 8.78142197 0.00403103 -0.00081919 -0.00131511 Cs 12.12539986 6.04638427 12.07932247 0.00084954 0.00064647 0.00155900 Pb 15.04746717 8.84992813 15.04569283 -0.00004635 -0.00130545 0.00099696 Br 14.19301670 9.18523269 11.89104520 -0.00284774 -0.00037708 -0.00268820 Br 14.72424859 5.60390956 14.79277775 0.00115930 0.00196775 0.00068559 Br 12.04830056 8.86211666 15.16858704 0.00006775 0.00296524 0.00066477 Cs 12.32466854 5.86532817 18.11472960 -0.00494388 0.00303328 -0.00308377 Pb 14.75786462 9.18987817 20.95338316 0.00135737 0.00004749 0.00010020 Br 14.85460265 8.60528764 17.89969306 0.00160487 0.00134620 0.00296120 Br 14.79372414 6.27649097 20.65893764 -0.00325611 -0.00203459 0.00035092 Br 11.72077096 8.65620108 20.68386259 -0.00321763 -0.00133623 0.00186066 Cs 11.40653971 11.81164124 0.41188408 0.00166985 -0.00047489 -0.00184288 Pb 14.69780638 14.94448958 3.12980318 -0.00014430 0.00089127 -0.00208846 Br 15.13951971 15.27337072 0.13427607 0.00183348 -0.00209702 0.00351141 Br 14.12096553 11.83782424 2.99093976 -0.00228091 -0.00169077 -0.00227714 Br 11.75290716 14.91074594 2.86565531 0.00211096 -0.00096066 -0.00314008 Cs 11.90969407 12.46995779 6.06643070 0.00305911 -0.00081874 -0.00025371 Pb 14.80894864 15.00996333 8.98797559 -0.00030529 -0.00072810 -0.00141951 Br 14.80853037 14.90553165 6.20220092 -0.00055252 0.00013229 0.00001585 Br 13.65543860 11.80089504 9.05456060 0.00214328 -0.00104436 0.00108280 Br 11.53264824 15.24301806 8.77441238 0.00471155 0.00043810 0.00315091 Cs 11.80372715 12.21223224 12.13401171 -0.00022591 0.00046539 0.00122500 Pb 14.86044610 15.00587937 15.18303396 0.00035442 -0.00082580 0.00042546 Br 14.71435424 14.76962237 11.99044679 -0.00057132 0.00098522 -0.00190689 Br 14.93680997 12.06274859 14.88626076 0.00344293 0.00172128 0.00095074 Br 12.05395756 15.08962952 14.76715434 0.00229842 0.00017212 -0.00345563 Cs 12.16341331 11.28778030 17.48905732 -0.00126132 -0.00128780 0.00149093 Pb 15.00573994 14.99805105 20.94067320 0.00062931 0.00108387 0.00067957 Br 14.66355425 14.70685700 18.05136238 0.00039577 -0.00241531 -0.00206586 Br 14.43517244 12.05954574 21.61430971 0.00135815 -0.00120451 0.00397401 Br 12.02638206 15.63844431 21.39921417 0.00211251 0.00251123 -0.00335560 Cs 11.50225301 17.85949105 0.90999924 0.00045513 -0.00134963 0.00120005 Pb 15.04207821 20.94568607 3.18883263 0.00068858 -0.00014224 0.00133203 Br 15.15369699 20.85648263 23.59560563 0.00342323 0.00136406 0.00352710 Br 14.21087095 17.82375793 3.55332120 -0.00114122 -0.00113710 -0.00205456 Br 11.98674429 21.16707792 3.33577651 0.00018010 -0.00263663 -0.00222350 Cs 11.37664004 18.21642788 5.94700783 0.00181253 0.00038653 0.00006628 Pb 14.88150569 20.99239110 8.98131793 -0.00045001 0.00077772 0.00068104 Br 15.49711265 20.37784288 5.94630542 0.00383147 -0.00079839 -0.00059380 Br 15.19321911 18.00120544 9.07906142 0.00360144 0.00031952 0.00308730 Br 11.45038698 21.39243298 8.08786667 0.00250277 -0.00111517 -0.00109858 Cs 12.35498374 18.03878973 11.44915111 -0.00125947 -0.00395229 -0.00104381 Pb 14.91910722 20.93026271 14.72182861 -0.00148322 0.00242340 0.00041306 Br 15.34237062 21.11525921 11.86174121 0.00236185 0.00267113 -0.00189481 Br 15.55024499 17.77005839 14.85763836 0.00008447 0.00035617 0.00269116 Br 12.14392705 19.82545427 14.81055104 -0.00158422 0.00115788 -0.00241167 Cs 12.03028885 17.57490309 18.01368044 -0.00185734 0.00010474 0.00131767 Pb 15.06698357 20.87390756 20.72280492 -0.00019000 0.00075731 0.00051836 Br 14.41999175 20.95573904 17.71189440 0.00398720 0.00216904 0.00218624 Br 15.15675053 17.83286098 20.38106219 0.00317357 0.00546862 0.00668594 Br 11.89420768 20.46567696 21.22612112 0.00208100 -0.00164816 0.00028909 Cs 18.58240563 23.49839925 23.44562567 0.00284712 0.00183911 -0.00093714 Pb 21.01568077 3.32875528 2.62152032 -0.00097496 -0.00068746 0.00196535 Br 20.46229710 3.90721281 23.62655532 -0.00178463 -0.00043301 -0.00244733 Br 20.69558498 0.05062067 3.01226161 -0.00060473 -0.00184531 -0.00362600 Br 17.84732016 3.08801487 3.73018833 0.00029996 -0.00103344 0.00389217 Cs 17.75056378 0.42782609 6.01505170 -0.00287596 0.00252555 -0.00139634 Pb 21.20560267 2.91356644 9.19451644 0.00042380 -0.00033265 0.00130552 Br 21.59595517 3.11993316 6.11770331 0.00105764 -0.00124642 -0.00093969 Br 20.69440125 23.77176386 9.42335863 -0.00045370 0.00060152 0.00140290 Br 18.13210481 2.96412341 8.62447058 0.00304331 0.00248880 0.00081707 Cs 18.21230180 0.06155030 11.81797219 0.00192917 -0.00181184 0.00001854 Pb 20.74951160 2.98703270 14.69077155 0.00223817 -0.00106112 0.00012370 Br 19.97004504 3.64592831 11.89964144 0.00226487 -0.00013099 0.00142409 Br 21.49654962 23.62136637 14.95435050 -0.00428765 0.00107957 0.00157188 Br 17.87075976 2.95690117 15.23107168 -0.00044870 0.00036820 0.00347169 Cs 18.10002239 23.41263212 17.93981394 0.00076673 -0.00083345 -0.00251318 Pb 20.92249934 3.11325735 20.56785362 -0.00217756 0.00194943 -0.00064167 Br 20.96812947 3.00707522 17.73435745 0.00099068 -0.00205679 -0.00147301 Br 20.66864013 23.73849317 20.25864803 -0.00145444 -0.00102703 0.00098494 Br 17.88894660 2.98087548 20.29272825 0.00104740 0.00110573 0.00202579 Cs 17.74437502 6.45195112 23.15210268 -0.00111732 -0.00182503 -0.00156716 Pb 20.52091475 8.84642153 3.05362307 -0.00272242 0.00054988 -0.00097439 Br 20.29728576 8.54110974 0.00557224 0.00282290 0.00011351 -0.00097349 Br 20.57578783 6.20500607 3.60068796 0.00174763 0.00099209 0.00164290 Br 17.56222705 8.75247570 3.54206737 -0.00066088 -0.00080233 -0.00153102 Cs 18.35061197 5.53919733 6.09063023 -0.00111148 0.00199450 -0.00182338 Pb 20.79878510 8.85605811 8.84229621 -0.00151827 0.00102522 -0.00013505 Br 20.93925595 9.22583029 5.99888220 0.00249465 -0.00147904 0.00218917 Br 21.35433113 5.90798367 8.44892504 0.00002341 0.00143264 -0.00067766 Br 17.66334232 9.08839370 8.56656824 0.00189727 -0.00332819 -0.00155498 Cs 17.55160984 6.45311180 11.50112475 0.00059174 -0.00008874 0.00079027 Pb 20.96152284 8.68558647 14.92320354 0.00001004 0.00078554 -0.00103661 Br 20.68451676 8.83504376 11.74501238 -0.00135763 0.00021029 0.00110403 Br 20.71436058 5.78733269 14.87754987 0.00040702 -0.00102393 -0.00253528 Br 18.00411021 8.21331977 14.81563528 -0.00340685 0.00071890 0.00123003 Cs 17.60314217 5.62076010 17.76851891 -0.00178621 -0.00021207 0.00133064 Pb 20.50192211 8.96486961 20.58430339 -0.00161629 -0.00099423 0.00065197 Br 21.31596523 8.56840205 17.86604822 0.00017981 0.00032780 -0.00082238 Br 20.57477160 6.09241861 20.18513631 0.00134905 -0.00094732 -0.00262445 Br 17.63877961 8.92030750 20.62594143 0.00107538 -0.00355428 0.00035449 Cs 17.40333774 11.78898005 0.10702438 -0.00249833 -0.00069050 -0.00034550 Pb 20.62946305 14.83710804 3.21282955 -0.00027235 0.00029879 0.00013253 Br 20.95534914 15.12575420 0.10690881 0.00234546 -0.00102677 -0.00042338 Br 20.29559641 11.96512144 3.15829618 0.00089794 0.00039396 -0.00280246 Br 17.87423231 15.00311516 2.39816905 0.00281942 0.00021806 -0.00006945 Cs 17.86908792 11.61095745 6.17157018 -0.00018888 0.00517940 -0.00184154 Pb 20.38149966 14.86443685 9.18227415 -0.00093855 -0.00021055 -0.00043002 Br 19.98022962 14.44014657 6.26594817 -0.00033169 -0.00172173 0.00295057 Br 19.97105405 11.68599640 9.37296002 0.00231918 0.00066078 -0.00017969 Br 17.50212183 14.61897102 9.42338776 0.00179417 0.00186190 0.00081865 Cs 18.11011683 11.32737130 11.93516751 -0.00030464 0.00056491 -0.00057116 Pb 20.79706671 14.66663739 14.70346038 0.00016251 -0.00020346 0.00112070 Br 20.76235312 13.99531606 11.98942030 -0.00115597 0.00069354 0.00138920 Br 20.87623545 11.63227415 15.52868082 0.00208870 -0.00057342 0.00016734 Br 17.80907988 14.61778704 15.13571694 0.00149678 0.00301590 0.00139368 Cs 17.45507793 11.73858784 17.62364852 0.00136227 -0.00154712 0.00083782 Pb 21.07656531 15.06407217 20.62965226 -0.00050317 -0.00182099 -0.00155188 Br 20.81322351 15.06571317 17.64425020 -0.00122050 0.00432118 -0.00094146 Br 20.10228522 11.81333782 20.58153156 0.00123955 0.00340157 0.00387763 Br 17.94561517 14.96066333 20.54257028 -0.00270863 0.00046671 0.00111224 Cs 17.94960063 17.58976063 23.56722550 -0.00021193 -0.00128791 0.00144782 Pb 21.07301750 20.78695994 2.96580344 -0.00021766 0.00045666 -0.00062285 Br 21.31902489 20.86984509 0.14823487 -0.00118082 -0.00397377 0.00133506 Br 21.05957209 17.94589080 3.18480381 0.00000810 0.00111183 -0.00294475 Br 17.76256454 20.63233509 2.75516234 0.00446309 0.00053141 0.00275711 Cs 17.85751197 17.77158296 5.30333695 0.00051326 -0.00169341 -0.00232051 Pb 20.87204130 20.86217134 8.86309514 0.00064908 -0.00148377 0.00179148 Br 20.72399519 20.91308680 5.80125523 -0.00309665 0.00154721 -0.00114414 Br 20.32201740 17.97631635 8.85907293 0.00031912 0.00037580 0.00125682 Br 18.13861192 21.32228932 9.12429757 -0.00061141 0.00142733 -0.00564918 Cs 17.73809686 17.70706134 12.04735683 -0.00053202 -0.00191722 0.00146310 Pb 20.63232868 20.91628863 14.80853929 0.00021414 -0.00081802 -0.00085881 Br 21.11256717 20.90461069 11.86061275 -0.00116873 -0.00032923 0.00197141 Br 20.41664520 17.70383127 14.28080137 -0.00000125 0.00553105 0.00291460 Br 17.80180829 21.70170970 15.25004223 -0.00052118 0.00155072 0.00170321 Cs 17.94793294 17.90790312 18.24159016 0.00506014 -0.00174187 -0.00052800 Pb 20.88201802 20.71800533 20.87981692 0.00220577 0.00090222 -0.00277799 Br 20.99245012 20.47376105 17.96767151 0.00072761 -0.00028700 0.00070068 Br 20.87674885 17.89524915 20.24197953 0.00123516 -0.00108781 0.00189646 Br 17.99131617 20.74696157 21.34024787 -0.00238002 -0.00107016 -0.00030330 320 Lattice="23.77195271 0.0 0.0 0.0 23.77195271 0.0 0.0 0.0 23.77195271" Properties=species:S:1:pos:R:3:vel:R:3 Time=20269.99999999 energy=-11424.07070923 virial="-19.25453177 4.32370234 0.15187436 4.32370234 -5.42748067 2.78192201 0.15187436 2.78192201 -3.0699204" stress="-0.00049361 0.00032186 1.131e-05 0.00032186 0.0004037 0.00020709 1.131e-05 0.00020709 0.0006703" pbc="T T T" Cs 23.44622392 23.67551589 0.25112585 0.00092172 -0.00003597 0.00139860 Pb 2.93965890 3.05773226 3.20235514 0.00134380 -0.00043034 -0.00072503 Br 3.33556955 2.93618364 0.21539488 0.00100357 -0.00057566 -0.00052130 Br 2.74709162 0.03827152 3.03727706 -0.00105345 0.00167394 -0.00021206 Br 0.04017931 3.34025741 1.91855975 0.00199406 -0.00140164 -0.00179801 Cs 0.53708462 0.17508914 5.69710616 -0.00010834 -0.00085795 -0.00201653 Pb 3.24006574 2.99525081 9.05770921 0.00055653 -0.00004126 0.00018924 Br 2.63013813 2.81309517 6.04541895 0.00028000 0.00235356 -0.00448649 Br 3.15236348 0.07781032 9.55316795 -0.00023985 0.00176566 -0.00125296 Br 0.04225736 2.55170153 10.28050736 0.00381555 -0.00421159 -0.00382316 Cs 23.59942984 23.37131095 12.09793842 -0.00092716 -0.00195746 -0.00165182 Pb 3.15264741 2.97140854 14.82784128 0.00135276 -0.00241621 0.00037399 Br 3.56732574 3.09847140 11.93493409 -0.00325847 0.00180042 -0.00286271 Br 3.03031292 23.61808470 14.55389413 -0.00072475 -0.00294048 0.00154318 Br 23.68437742 3.08139870 14.43077115 -0.00048502 -0.00152036 -0.00099865 Cs 0.01017267 23.24095405 17.52144420 -0.00028321 0.00013647 0.00034639 Pb 3.12604754 3.13207880 20.79346775 -0.00252831 -0.00140145 0.00042361 Br 2.84327506 3.35531476 17.90921850 0.00239045 -0.00015083 0.00332564 Br 3.00133944 23.72989069 20.71752799 0.00009887 -0.00143190 0.00010288 Br 23.71226525 2.31532798 20.90561128 -0.00108831 -0.00334157 0.00267397 Cs 23.41252111 6.63045944 23.57097303 -0.00097729 -0.00019867 -0.00069902 Pb 2.85822721 8.99828092 2.71588299 -0.00137213 0.00100909 -0.00167467 Br 3.07200655 8.21664328 0.08311407 0.00161276 0.00060704 -0.00313553 Br 2.83919900 5.88529811 3.18815511 -0.00015057 -0.00208738 0.00194782 Br 23.50134365 8.58408981 2.77506009 -0.00430255 0.00184857 -0.00251225 Cs 0.55760284 6.01881683 6.25025980 -0.00072188 -0.00044968 0.00157921 Pb 3.07087151 9.03929348 8.95837870 -0.00391354 -0.00227856 0.00002445 Br 2.86120158 8.74791516 5.75107533 0.00114809 0.00089396 -0.00461708 Br 2.58740948 6.13725891 9.33064247 -0.00014610 0.00103349 0.00416285 Br 0.09050935 9.21314161 8.94441557 -0.00025573 -0.00117838 0.00125782 Cs 23.24814492 5.82743380 12.12724708 -0.00256887 -0.00186454 -0.00122948 Pb 3.00986231 8.96942895 14.80503039 0.00163176 -0.00046146 0.00114337 Br 3.29897213 9.25574207 11.84294629 -0.00004001 0.00155281 0.00055199 Br 3.69592693 5.78877363 15.10970524 -0.00196942 -0.00312716 -0.00248684 Br 0.22805635 8.49496409 14.23390465 0.00042300 0.00267876 0.00235702 Cs 23.53845754 5.70714224 17.70152059 0.00015224 -0.00085309 0.00050267 Pb 2.60765700 8.58247570 20.73353521 0.00075388 0.00033617 0.00183514 Br 2.63756274 8.68351895 18.02510038 0.00051192 -0.00182428 0.00114024 Br 2.35881852 5.71774174 21.13455782 -0.00011098 -0.00284859 0.00241457 Br 23.55531265 9.45738637 21.35902475 0.00551288 0.00172466 0.00162613 Cs 23.67784572 11.80713938 0.33548766 0.00279094 0.00110711 0.00019363 Pb 2.70047133 14.90920421 2.92909664 0.00270996 -0.00130726 -0.00083135 Br 2.46660745 14.93048786 23.63969365 -0.00093059 0.00051452 0.00051230 Br 2.68324363 11.88203328 2.30088063 -0.00205612 -0.00232806 0.00300226 Br 23.59338063 14.75673856 3.17526587 -0.00109040 0.00009584 0.00107107 Cs 23.73626363 12.04266157 6.33099353 0.00122638 0.00253810 -0.00263492 Pb 3.03640975 14.77084599 9.01197693 -0.00011940 -0.00325977 -0.00107177 Br 2.86393012 14.88178083 5.96917680 -0.00222354 -0.00050341 0.00291942 Br 2.87739167 11.77098692 8.62579626 -0.00117669 0.00123268 -0.00137705 Br 23.67271443 14.83009413 8.87034737 -0.00167906 -0.00167843 0.00000515 Cs 23.52095957 11.94659876 11.46109073 -0.00164908 0.00163820 0.00100191 Pb 3.05242749 14.57642945 14.82618504 -0.00032494 -0.00007255 -0.00247653 Br 2.44092743 14.19844271 11.87950634 -0.00339023 -0.00036089 -0.00267978 Br 2.41797266 11.62595735 15.47751840 0.00104307 0.00040870 -0.00101524 Br 23.59425368 14.68063481 14.58857614 -0.00280425 0.00224641 0.00097874 Cs 0.07402172 11.61261639 18.11598304 0.00105452 0.00002374 -0.00004520 Pb 3.18848424 14.69218612 20.91445721 0.00022778 0.00213898 0.00101455 Br 3.04278231 14.77598358 17.93826792 0.00151628 -0.00320781 -0.00223676 Br 2.98751764 11.61562985 20.89989642 0.00108973 0.00042993 -0.00002343 Br 0.11147494 14.86439214 20.49956899 -0.00148793 0.00186066 -0.00281478 Cs 0.02561101 17.91780119 23.42636986 0.00016759 0.00005064 -0.00130174 Pb 2.94067497 20.93225756 2.79709773 -0.00012327 0.00105928 0.00039069 Br 2.75327148 21.40819043 23.53346466 -0.00085938 0.00249486 -0.00234201 Br 2.59521252 17.94500831 2.84648381 -0.00375461 0.00219236 0.00003080 Br 0.16906934 21.07558503 3.34165285 -0.00063476 0.00094151 0.00239378 Cs 23.06492513 17.79952240 5.84852056 0.00235803 -0.00053108 0.00061104 Pb 2.99894272 20.80472214 8.90443698 0.00001582 0.00074518 0.00091988 Br 3.01060987 20.57725948 5.86178445 -0.00106156 0.00142974 0.00005271 Br 2.97469577 18.03201670 8.92829178 -0.00044314 -0.00208798 -0.00277692 Br 0.05899456 20.49592304 8.77379338 0.00332259 -0.00352549 -0.00174570 Cs 0.87988931 17.63492485 11.69393630 0.00051634 0.00322382 -0.00180358 Pb 3.13124929 20.60896160 14.86234412 0.00012897 -0.00224641 -0.00103477 Br 2.67854495 20.66391828 12.10511611 -0.00189859 -0.00257456 0.00044905 Br 3.12081712 17.38308926 15.05568640 -0.00018842 0.00058684 -0.00356812 Br 0.29905022 20.81125504 14.93964250 0.00104873 -0.00017487 -0.00007431 Cs 0.34244510 17.74677951 17.84265165 -0.00135488 -0.00004347 0.00201582 Pb 3.04411883 20.82761707 20.59471955 -0.00163968 -0.00033954 0.00261565 Br 2.85593881 20.41749029 17.66324289 -0.00385091 0.00260902 0.00087301 Br 3.05049762 17.71328136 20.32773105 0.00055188 0.00107653 0.00431989 Br 0.01519915 20.46105467 20.67040187 -0.00286118 -0.00025884 0.00077820 Cs 5.56532154 0.31129597 23.05908268 -0.00130670 0.00067826 0.00254602 Pb 8.69138119 3.28599871 2.92551869 -0.00103144 0.00049013 -0.00001661 Br 8.18085046 2.97387997 0.18570226 -0.00724757 0.00196007 -0.00174179 Br 9.17887728 0.37308596 3.79209731 -0.00121871 -0.00068476 -0.00130429 Br 5.82897010 3.17393694 3.83665283 -0.00138903 0.00018192 -0.00043419 Cs 5.64049735 23.64302814 5.93162297 -0.00183576 0.00163496 0.00164758 Pb 8.91942517 3.02751903 8.83557379 0.00242494 0.00069568 0.00050814 Br 9.42559310 3.49497941 5.92581872 -0.00231838 0.00117401 0.00125141 Br 8.44514209 0.05372519 9.19995960 0.00071731 0.00070106 0.00161273 Br 5.76005392 2.77020378 8.04718989 -0.00307263 -0.00229201 -0.00136691 Cs 5.95943119 0.22235250 12.06275633 -0.00015643 0.00197882 -0.00001175 Pb 9.39133264 2.92390858 15.11395848 0.00022823 0.00051438 0.00123889 Br 8.68606894 2.82517085 12.28136256 -0.00233686 0.00055136 0.00429493 Br 9.69615552 0.05448017 14.69650136 -0.00369653 0.00022205 -0.00180903 Br 6.27015204 2.62280506 14.97196054 -0.00110479 0.00203423 0.00118319 Cs 6.29769710 23.22477036 17.89120853 -0.00084416 0.00022592 0.00079423 Pb 8.91953427 3.08675595 20.87111122 -0.00148377 -0.00047846 0.00103805 Br 9.37599860 2.82079665 18.06641129 -0.00086564 -0.00168179 -0.00394677 Br 8.54969897 23.49503654 20.99787404 -0.00286946 0.00172406 -0.00068961 Br 6.07719274 3.73802767 20.70979421 -0.00019828 -0.00230755 -0.00097534 Cs 5.47498183 5.59238487 0.10044543 0.00051430 -0.00395313 0.00139070 Pb 8.69557122 8.94919387 2.92949424 0.00114034 0.00128519 0.00172437 Br 8.94261549 8.98294888 23.63500065 -0.00084605 0.00140749 -0.00021186 Br 8.12786236 6.14540673 2.73376722 -0.00103226 -0.00133413 -0.00376735 Br 5.76719283 9.31293378 2.91035262 0.00194578 0.00228331 0.00093445 Cs 6.26988985 6.16475241 5.79023448 0.00039210 -0.00115515 0.00094382 Pb 9.06217438 8.73165644 8.83632540 0.00228471 -0.00121713 0.00111529 Br 8.86257013 8.47966804 5.85014050 0.00337267 0.00207986 0.00097257 Br 8.44317969 5.84064391 9.41041729 -0.00228308 -0.00356205 0.00074434 Br 5.98961212 8.85162668 8.67083153 -0.00079681 0.00492800 -0.00021097 Cs 6.08488355 5.72280097 12.09903520 0.00097770 -0.00336291 0.00171463 Pb 8.91996952 8.87607345 14.72117136 0.00054215 -0.00170204 -0.00097079 Br 8.29615242 8.77033527 11.96199750 0.00487946 0.00115866 -0.00017078 Br 9.31271898 5.94691355 15.32180658 -0.00262480 0.00153446 0.00040559 Br 6.01548594 8.61220983 15.44815780 -0.00301961 0.00041857 -0.00285060 Cs 6.50652854 6.08480846 17.70367110 -0.00293232 -0.00182145 0.00010319 Pb 8.80266799 9.13944678 20.72955939 0.00049640 -0.00036536 0.00071589 Br 8.94419005 8.96173719 17.85760596 -0.00027569 -0.00142427 0.00043175 Br 9.20808653 5.89684565 20.56787055 0.00119996 -0.00152368 -0.00201134 Br 5.64314361 8.48610164 20.74172163 0.00208292 0.00014396 0.00079367 Cs 5.41542448 11.31421877 23.68598988 0.00033310 -0.00042809 0.00145269 Pb 8.64609597 14.88563237 3.21033203 -0.00207824 0.00122028 -0.00025029 Br 8.97052208 14.61030317 23.74681821 -0.00376663 0.00112077 0.00458635 Br 9.07370884 11.95768284 3.38519941 0.00068405 0.00022723 -0.00088952 Br 5.89364008 14.48441645 2.64804101 0.00246940 0.00065321 0.00049377 Cs 5.70414806 12.13183487 5.32355625 0.00282004 0.00074079 0.00407585 Pb 8.85513429 14.71413348 8.98471873 0.00009118 0.00135999 -0.00045252 Br 8.53808425 14.10945655 6.23716406 0.00084766 0.00420689 0.00022567 Br 9.24904463 11.52099592 9.14528439 -0.00310906 0.00040826 0.00412084 Br 5.95829915 14.07629883 9.32275681 -0.00206229 0.00103548 -0.00250201 Cs 5.87500015 11.33180496 11.99105885 0.00345869 -0.00298379 0.00126169 Pb 9.26203936 14.83306524 14.71072911 -0.00187105 -0.00131150 -0.00050915 Br 8.89824299 15.31983017 11.89751466 -0.00203532 0.00137225 0.00051891 Br 9.46548674 12.08689296 14.93122405 -0.00135684 0.00251592 -0.00253207 Br 6.01141756 14.57372608 13.89935750 0.00177950 -0.00112659 0.00088444 Cs 6.45517481 11.50850245 17.41924923 -0.00020846 -0.00126865 -0.00148445 Pb 9.15355659 14.98777854 20.97352749 0.00183243 0.00070751 0.00132202 Br 9.29967529 14.82792739 17.82615346 -0.00094121 -0.00037967 -0.00098504 Br 8.67175667 11.97364055 20.67261094 -0.00195679 -0.00214427 -0.00575167 Br 5.89285255 14.83826517 21.26435239 -0.00051007 -0.00167866 -0.00035320 Cs 5.52947361 18.52104146 23.41149458 0.00161971 -0.00124389 -0.00220737 Pb 8.84363282 20.92162346 3.04782175 0.00126749 0.00299053 -0.00043710 Br 8.89152663 21.04571710 23.71125887 -0.00090119 0.00010235 0.00122487 Br 8.52026233 18.07681559 2.68199497 0.00093546 0.00032944 0.00031688 Br 6.10303186 21.14582725 3.05518863 0.00070502 0.00390700 -0.00145360 Cs 5.43184926 18.09055873 5.49618167 -0.00125682 0.00020151 0.00005626 Pb 9.02029380 20.48558209 8.84680665 0.00269515 0.00127734 -0.00085155 Br 8.22052736 20.72068542 5.83694639 0.00292761 0.00339040 -0.00283062 Br 8.32412568 17.51936153 8.06498977 0.00283655 0.00076010 0.00111742 Br 5.94322119 20.62911972 8.60808467 -0.00136055 0.00116015 0.00270276 Cs 6.13611303 18.33020111 11.51555933 -0.00097841 -0.00035972 0.00167820 Pb 9.24742409 20.73746518 14.94954885 0.00087562 -0.00001223 0.00153045 Br 9.64960030 20.33609001 11.76130238 -0.00040610 -0.00494119 0.00083369 Br 8.30279820 18.15109043 15.07067335 -0.00112626 -0.00082229 0.00259879 Br 6.41377828 21.45842671 14.73985330 -0.00047982 0.00046920 -0.00198765 Cs 5.67695143 17.36584729 17.56530985 0.00033348 -0.00002930 -0.00156679 Pb 8.94300941 20.61089321 20.67116378 0.00029761 -0.00085748 0.00141783 Br 9.64904286 20.70382746 17.88971763 -0.00267241 0.00039408 0.00182039 Br 8.37195367 17.67404280 20.86822863 0.00388073 0.00146156 0.00089914 Br 5.95031438 20.92791115 20.50662181 0.00270326 0.00079551 0.00007565 Cs 12.19580799 23.67272343 0.12283087 -0.00002584 -0.00120581 -0.00100065 Pb 14.97877157 3.19850719 2.88571735 -0.00020422 -0.00219783 0.00083863 Br 15.58166471 3.13162556 23.48586277 0.00192520 0.00215725 0.00015381 Br 15.73654638 0.06686606 3.24914591 -0.00126966 0.00142497 0.00312980 Br 11.93287041 3.65138034 2.33010871 -0.00277333 -0.00004367 -0.00060152 Cs 12.03509030 0.46627178 5.39290348 -0.00012242 -0.00027596 -0.00082205 Pb 14.91661217 3.19650501 8.83650924 -0.00009040 0.00079662 0.00065355 Br 14.36154714 3.28376787 5.86506913 -0.00108690 -0.00202238 0.00055590 Br 15.14428533 0.12128950 9.05963900 0.00133898 0.00095789 -0.00105393 Br 11.86240895 3.09190204 9.18676211 -0.00182348 0.00177522 -0.00507388 Cs 12.01663324 0.69804810 11.85799521 0.00011164 0.00229903 -0.00133966 Pb 15.00708586 2.52606408 14.89907530 -0.00167295 0.00053851 -0.00098611 Br 14.85883115 3.07773457 11.89356070 0.00093357 0.00011688 -0.00405196 Br 14.30739978 23.57380439 14.58040675 -0.00116394 0.00038566 -0.00250043 Br 11.99160970 3.51060257 14.36764572 -0.00038230 0.00142964 0.00334724 Cs 11.91702077 23.63061340 18.61981397 -0.00130451 0.00323712 0.00128360 Pb 14.81641961 3.11934660 20.71291176 -0.00141367 -0.00049773 -0.00075983 Br 14.09264986 2.80281980 17.69995543 -0.00326826 -0.00491979 -0.00145852 Br 15.45333558 0.07256387 20.28665753 0.00052015 0.00540714 -0.00274542 Br 11.86550181 2.39223608 21.47960291 0.00254647 -0.00024763 -0.00236387 Cs 12.00892567 6.08105762 23.49314563 0.00106666 -0.00149039 0.00153722 Pb 14.71337784 8.98113754 2.93552278 -0.00102731 0.00100876 -0.00334869 Br 14.91018124 8.44244121 23.69961898 0.00128382 -0.00190836 -0.00116007 Br 15.00448053 6.15613830 3.00436173 -0.00214253 -0.00244027 -0.00404611 Br 11.75134927 8.24182954 2.67628534 0.00004529 -0.00065506 0.00091771 Cs 12.34654502 6.05474841 5.66880470 0.00155602 -0.00106539 0.00074053 Pb 14.54509055 9.09635839 8.84729718 -0.00039455 0.00181890 -0.00159941 Br 14.27066062 9.45156630 6.04967231 -0.00014263 -0.00210765 -0.00063034 Br 15.25335260 6.03689868 8.58390889 -0.00030630 -0.00028407 0.00278258 Br 11.84605030 7.89457679 8.76887981 0.00421982 -0.00082837 -0.00118975 Cs 12.13378320 6.05313261 12.09438735 0.00082810 0.00070394 0.00145231 Pb 15.04600786 8.83623212 15.05417382 -0.00024411 -0.00143198 0.00070052 Br 14.16659357 9.18054696 11.86618990 -0.00243558 -0.00056021 -0.00226780 Br 14.73817082 5.62828002 14.80096565 0.00161683 0.00290069 0.00094850 Br 12.04852261 8.89056515 15.17329897 -0.00003732 0.00271494 0.00027226 Cs 12.27389383 5.89597731 18.08280046 -0.00520296 0.00308984 -0.00329750 Pb 14.77034345 9.19028445 20.95408591 0.00113154 0.00002884 0.00003923 Br 14.87136614 8.62035370 17.92771414 0.00174438 0.00166441 0.00262456 Br 14.76174579 6.25129133 20.66317997 -0.00314165 -0.00297821 0.00049099 Br 11.69038053 8.64466328 20.70250733 -0.00282563 -0.00096807 0.00186700 Cs 11.42336810 11.80691817 0.39316405 0.00169075 -0.00046931 -0.00190002 Pb 14.69637604 14.95355949 3.10818008 -0.00014305 0.00092032 -0.00223083 Br 15.15696669 15.25242431 0.17060962 0.00164985 -0.00209294 0.00372283 Br 14.09889689 11.82473548 2.96740064 -0.00213042 -0.00092749 -0.00242860 Br 11.77406218 14.90070407 2.83548697 0.00211627 -0.00105191 -0.00289227 Cs 11.93983552 12.46125090 6.06360264 0.00296579 -0.00092104 -0.00031205 Pb 14.80423114 15.00251110 8.97563701 -0.00062947 -0.00075861 -0.00104226 Br 14.80389209 14.90636582 6.19763757 -0.00037352 0.00003418 -0.00093279 Br 13.67828720 11.79350120 9.06560846 0.00241478 -0.00041210 0.00112238 Br 11.58565713 15.24903990 8.80536879 0.00583292 0.00075348 0.00304254 Cs 11.80219265 12.21672459 12.14577061 -0.00007875 0.00043384 0.00112455 Pb 14.86588904 14.99728700 15.18607623 0.00073284 -0.00088593 0.00017929 Br 14.70805856 14.78060609 11.97184106 -0.00068426 0.00120822 -0.00181435 Br 14.97073019 12.07484217 14.89655790 0.00333939 0.00068650 0.00110378 Br 12.07429678 15.09218608 14.73203379 0.00173864 0.00033638 -0.00356677 Cs 12.15040749 11.27627514 17.50522759 -0.00134065 -0.00100875 0.00174538 Pb 15.01137802 15.00888405 20.94794161 0.00049765 0.00108696 0.00076782 Br 14.66674427 14.68175150 18.03079478 0.00024305 -0.00259930 -0.00200440 Br 14.44876877 12.04741508 21.65391507 0.00136161 -0.00120916 0.00394024 Br 12.04728686 15.66315103 21.36514014 0.00205510 0.00242845 -0.00344895 Cs 11.50761382 17.84657123 0.92020283 0.00061543 -0.00123069 0.00083972 Pb 15.04863071 20.94456209 3.20143609 0.00063532 -0.00008338 0.00118316 Br 15.18655778 20.87063065 23.63273470 0.00314220 0.00146616 0.00389392 Br 14.20054521 17.81150440 3.53164151 -0.00092027 -0.00130180 -0.00227700 Br 11.98777154 21.14045900 3.31333939 0.00003997 -0.00268346 -0.00226043 Cs 11.39489676 18.22036904 5.94752311 0.00183823 0.00040027 0.00003536 Pb 14.87802580 21.00040228 8.98732787 -0.00024302 0.00082205 0.00051995 Br 15.53469179 20.37197676 5.94293797 0.00367537 -0.00037925 -0.00008165 Br 15.22892772 18.00299124 9.11037605 0.00353250 0.00003784 0.00317077 Br 11.48188026 21.38135882 8.07577906 0.00379299 -0.00109668 -0.00131739 Cs 12.34231371 17.99870265 11.43966422 -0.00127205 -0.00406522 -0.00085284 Pb 14.90422924 20.95157596 14.72731934 -0.00148526 0.00182843 0.00068105 Br 15.36592756 21.14085596 11.84119370 0.00234772 0.00244675 -0.00218782 Br 15.55119323 17.77673231 14.88495126 0.00010371 0.00097739 0.00277149 Br 12.12860510 19.83685917 14.78570441 -0.00146933 0.00112284 -0.00254822 Cs 12.01173457 17.57635105 18.02702818 -0.00185153 0.00018494 0.00134905 Pb 15.06397565 20.88121486 20.72850874 -0.00040807 0.00071121 0.00062753 Br 14.46067884 20.97724527 17.73373502 0.00414230 0.00213256 0.00216547 Br 15.18866365 17.88897250 20.44843429 0.00321076 0.00570908 0.00678006 Br 11.91583911 20.45022481 21.22812324 0.00223675 -0.00144245 0.00011445 Cs 18.60989799 23.51742559 23.43706720 0.00265018 0.00196093 -0.00077838 Pb 21.00561156 3.32152610 2.64223721 -0.00102884 -0.00075668 0.00217028 Br 20.44437101 3.90227857 23.60346503 -0.00179449 -0.00055753 -0.00214864 Br 20.69045757 0.03424406 2.97444908 -0.00042225 -0.00140818 -0.00393248 Br 17.85040546 3.07858176 3.76630946 0.00031623 -0.00084443 0.00331642 Cs 17.72284874 0.45224323 6.00202186 -0.00266186 0.00235172 -0.00119658 Pb 21.20888428 2.91073446 9.20631750 0.00023600 -0.00023204 0.00105280 Br 21.60687386 3.10588681 6.10759279 0.00112239 -0.00155953 -0.00108377 Br 20.69223907 0.00836846 9.43636984 0.00002535 0.00109851 0.00119775 Br 18.16001271 2.98908440 8.63268768 0.00253477 0.00250242 0.00082408 Cs 18.23075666 0.04367282 11.81827461 0.00175858 -0.00176453 0.00004430 Pb 20.77238410 2.97556962 14.69197287 0.00232682 -0.00123191 0.00012063 Br 19.99356548 3.64380352 11.91326004 0.00243443 -0.00029494 0.00128474 Br 21.45152321 23.63315454 14.96985247 -0.00470068 0.00127379 0.00152330 Br 17.86604022 2.95927009 15.26652203 -0.00050202 0.00010230 0.00361082 Cs 18.10780846 23.40619407 17.91662652 0.00079192 -0.00044594 -0.00211034 Pb 20.90054546 3.13199385 20.56186641 -0.00220917 0.00179193 -0.00055492 Br 20.97847705 2.98644689 17.71778227 0.00108044 -0.00206692 -0.00182775 Br 20.65505052 23.72768804 20.27060119 -0.00126253 -0.00112922 0.00139927 Br 17.89752068 2.99276036 20.31306123 0.00067604 0.00127455 0.00203607 Cs 17.73270739 6.43154247 23.13677546 -0.00120823 -0.00225665 -0.00149405 Pb 20.49417819 8.85363888 3.04344257 -0.00261987 0.00088861 -0.00105670 Br 20.32624976 8.54411630 23.76783582 0.00295080 0.00047832 -0.00097244 Br 20.59482048 6.20753548 3.61577153 0.00205850 -0.00047980 0.00136888 Br 17.55697855 8.74604436 3.52672742 -0.00040318 -0.00048603 -0.00153429 Cs 18.33899246 5.55991323 6.07377411 -0.00121172 0.00215062 -0.00154230 Pb 20.78377355 8.86592533 8.84061381 -0.00148410 0.00094549 -0.00019908 Br 20.96428934 9.21229025 6.01880320 0.00250743 -0.00122349 0.00177305 Br 21.35397535 5.92338367 8.44410428 -0.00009588 0.00163513 -0.00028590 Br 17.68226307 9.05350422 8.55249926 0.00189319 -0.00363409 -0.00126851 Cs 17.55740991 6.45165035 11.50900139 0.00056918 -0.00020357 0.00078443 Pb 20.96178003 8.69449847 14.91248975 0.00004225 0.00099042 -0.00110670 Br 20.67136291 8.83599822 11.76024680 -0.00127412 -0.00001719 0.00193847 Br 20.71939635 5.77837823 14.85347384 0.00059643 -0.00074515 -0.00227428 Br 17.96932650 8.22217741 14.82851410 -0.00352635 0.00104919 0.00134599 Cs 17.58538938 5.61890049 17.78154209 -0.00176399 -0.00016093 0.00127250 Pb 20.48727175 8.95587560 20.59226077 -0.00130355 -0.00079930 0.00093498 Br 21.31805076 8.57302227 17.85454489 0.00023210 0.00059642 -0.00145690 Br 20.58824302 6.07954440 20.16007845 0.00134330 -0.00160020 -0.00238662 Br 17.64802662 8.88527591 20.62839124 0.00077215 -0.00344627 0.00013182 Cs 17.37882151 11.78242552 0.10366350 -0.00240066 -0.00061919 -0.00032369 Pb 20.62647885 14.83978307 3.21324542 -0.00031734 0.00023973 -0.00004903 Br 20.97946732 15.11549665 0.10242267 0.00247472 -0.00102589 -0.00046988 Br 20.30508707 11.96965895 3.12949651 0.00099876 0.00050871 -0.00295280 Br 17.90009740 15.00572180 2.39929360 0.00233587 0.00030172 0.00028858 Cs 17.86686155 11.66233921 6.15276667 -0.00025858 0.00508421 -0.00191284 Pb 20.37266294 14.86250808 9.17746348 -0.00082006 -0.00017413 -0.00052789 Br 19.97826952 14.42471084 6.29400743 -0.00005685 -0.00136075 0.00264231 Br 19.99903442 11.69583746 9.36616034 0.00326017 0.00130263 -0.00116633 Br 17.52352006 14.63737226 9.43168074 0.00245335 0.00181651 0.00084087 Cs 18.10490285 11.33336675 11.93166490 -0.00073448 0.00063536 -0.00013677 Pb 20.79800697 14.66547297 14.71648847 0.00001862 -0.00002974 0.00148011 Br 20.74961744 14.00210038 11.99960734 -0.00139536 0.00066433 0.00065245 Br 20.89659218 11.62630155 15.52926876 0.00198059 -0.00061002 -0.00004942 Br 17.82402473 14.64731689 15.14941205 0.00148524 0.00288838 0.00134697 Cs 17.46964429 11.72386332 17.63229793 0.00155035 -0.00139652 0.00088950 Pb 21.07029526 15.04453745 20.61407911 -0.00074883 -0.00208403 -0.00155685 Br 20.80196038 15.10927091 17.63855715 -0.00103297 0.00438679 -0.00019645 Br 20.11554056 11.84909898 20.62010658 0.00140890 0.00371502 0.00383519 Br 17.91791322 14.96565413 20.55460212 -0.00282247 0.00052891 0.00129111 Cs 17.94754058 17.57727091 23.58159182 -0.00019587 -0.00120358 0.00142315 Pb 21.06941782 20.79305412 2.96084033 -0.00050454 0.00075896 -0.00036708 Br 21.30734186 20.82901458 0.15658641 -0.00115009 -0.00418410 0.00031526 Br 21.05817876 17.95396784 3.15348799 -0.00027669 0.00048938 -0.00330127 Br 17.81173237 20.63589554 2.78193029 0.00532161 0.00018912 0.00259876 Cs 17.86264282 17.75478485 5.28052076 0.00051480 -0.00166454 -0.00224173 Pb 20.87892014 20.84733334 8.88069597 0.00072000 -0.00148008 0.00172173 Br 20.69271086 20.92832635 5.79309895 -0.00315367 0.00149668 -0.00046740 Br 20.32487582 17.97615451 8.87163674 0.00025277 -0.00040068 0.00125606 Br 18.12472796 21.33537761 9.06745003 -0.00213001 0.00118953 -0.00571576 Cs 17.73268816 17.68773907 12.06132894 -0.00054952 -0.00194810 0.00133416 Pb 20.63513125 20.90651922 14.79999788 0.00034149 -0.00113656 -0.00084374 Br 21.09957955 20.90059722 11.88230050 -0.00142739 -0.00047199 0.00234508 Br 20.41761413 17.75968590 14.31104298 0.00019115 0.00563093 0.00313273 Br 17.79748115 21.71478207 15.26264238 -0.00034897 0.00104438 0.00079143 Cs 17.99859007 17.89002953 18.23487388 0.00506389 -0.00183049 -0.00081319 Pb 20.90452281 20.72804583 20.85135189 0.00227747 0.00110330 -0.00290248 Br 20.99877073 20.47283290 17.97098588 0.00053977 0.00009517 -0.00004372 Br 20.88972116 17.88241299 20.26374402 0.00135948 -0.00146756 0.00245541 Br 17.96780918 20.73500573 21.33529265 -0.00229647 -0.00131438 -0.00068889 320 Lattice="23.77195271 0.0 0.0 0.0 23.77195271 0.0 0.0 0.0 23.77195271" Properties=species:S:1:pos:R:3:vel:R:3 Time=20279.99999999 energy=-11424.49562454 virial="-20.16340917 3.65232082 0.50090021 3.65232082 -6.66104594 2.66154708 0.50090021 2.66154708 -3.9192572" stress="-0.00055276 0.00027188 3.729e-05 0.00027188 0.00034656 0.00019813 3.729e-05 0.00019813 0.00062713" pbc="T T T" Cs 23.45644845 23.67598687 0.26484825 0.00111881 0.00012706 0.00134515 Pb 2.95313019 3.05366139 3.19371637 0.00134437 -0.00038282 -0.00100791 Br 3.34555249 2.92919045 0.20820275 0.00098763 -0.00082769 -0.00091976 Br 2.73770621 0.05487322 3.03450139 -0.00082090 0.00162543 -0.00034811 Br 0.05779102 3.32560372 1.90216977 0.00150302 -0.00152746 -0.00147485 Cs 0.53386239 0.16559295 5.67793957 -0.00053376 -0.00103412 -0.00181302 Pb 3.24345471 2.99497891 9.05993080 0.00010940 -0.00001131 0.00024922 Br 2.63192607 2.83746659 6.00312865 0.00006796 0.00251117 -0.00394120 Br 3.14996258 0.09595229 9.53916498 -0.00023913 0.00184190 -0.00154506 Br 0.08163051 2.51108248 10.24128977 0.00403731 -0.00390693 -0.00402114 Cs 23.59148587 23.35161750 12.08145887 -0.00066303 -0.00197864 -0.00163928 Pb 3.16683726 2.94683579 14.83207322 0.00148500 -0.00249692 0.00046991 Br 3.53575678 3.11689726 11.90715995 -0.00304763 0.00187880 -0.00265550 Br 3.02274373 23.58849557 14.57067167 -0.00078656 -0.00296708 0.00180538 Br 23.68178690 3.06635073 14.42166591 -0.00002444 -0.00149072 -0.00082456 Cs 0.00785163 23.24295280 17.52616511 -0.00018312 0.00026311 0.00059435 Pb 3.10032608 3.11596628 20.79744529 -0.00261370 -0.00182419 0.00037752 Br 2.86686090 3.35318520 17.94261372 0.00232455 -0.00027345 0.00332040 Br 3.00240654 23.71547282 20.71623200 0.00011639 -0.00144680 -0.00036523 Br 23.70241206 2.28338874 20.93234703 -0.00088439 -0.00304287 0.00266964 Cs 23.40273649 6.62733369 23.56437080 -0.00097579 -0.00042928 -0.00062072 Pb 2.84342228 9.00724505 2.70153522 -0.00158198 0.00077108 -0.00119873 Br 3.08803890 8.22311138 0.04881533 0.00159691 0.00068697 -0.00369072 Br 2.83852250 5.86668560 3.20757163 0.00001285 -0.00161151 0.00192931 Br 23.45954210 8.60448281 2.74972719 -0.00403710 0.00223246 -0.00255483 Cs 0.54961000 6.01447881 6.26581302 -0.00087468 -0.00041719 0.00153013 Pb 3.03183421 9.01453614 8.95761024 -0.00388538 -0.00265380 -0.00017364 Br 2.87318655 8.75823450 5.70625412 0.00124263 0.00116758 -0.00433582 Br 2.58649517 6.14387233 9.37224052 -0.00003925 0.00029260 0.00415354 Br 0.08499231 9.20190462 8.95712546 -0.00084532 -0.00106606 0.00128290 Cs 23.22277039 5.80849724 12.11344923 -0.00250157 -0.00191590 -0.00152561 Pb 3.02621119 8.96342741 14.81662432 0.00163015 -0.00073509 0.00117356 Br 3.29715686 9.27036938 11.85091925 -0.00032332 0.00137210 0.00102755 Br 3.67510728 5.75882342 15.08379595 -0.00219242 -0.00285787 -0.00268948 Br 0.23002099 8.52199980 14.25892485 -0.00004483 0.00271545 0.00263755 Cs 23.54073170 5.69819462 17.70636541 0.00030299 -0.00093478 0.00046489 Pb 2.61637265 8.58752001 20.75334729 0.00099294 0.00066645 0.00211892 Br 2.64176373 8.66479243 18.03030970 0.00032862 -0.00191987 -0.00009343 Br 2.35542194 5.69210852 21.15899063 -0.00057093 -0.00224621 0.00247090 Br 23.60912645 9.47415960 21.37430426 0.00522265 0.00162699 0.00142752 Cs 23.70450677 11.81862230 0.33670207 0.00253520 0.00119072 0.00004655 Pb 2.72770758 14.89621627 2.92037564 0.00272492 -0.00128510 -0.00090778 Br 2.45658530 14.93645642 23.64988855 -0.00106880 0.00067371 0.00152651 Br 2.66385474 11.86099146 2.33267272 -0.00181374 -0.00185230 0.00335560 Br 23.58466594 14.75726994 3.18675228 -0.00062156 0.00000804 0.00122375 Cs 23.74831610 12.06764765 6.30436824 0.00118259 0.00245860 -0.00268753 Pb 3.03520353 14.73780748 9.00121481 -0.00012499 -0.00333985 -0.00107857 Br 2.84226919 14.87662043 5.99686940 -0.00210562 -0.00052747 0.00258526 Br 2.86560712 11.79024788 8.61146302 -0.00118032 0.00256299 -0.00148942 Br 23.65566349 14.81336039 8.87030824 -0.00173474 -0.00166622 -0.00001525 Cs 23.50473263 11.96207554 11.47124871 -0.00159718 0.00145427 0.00102773 Pb 3.04937058 14.57501521 14.80128927 -0.00028533 -0.00021023 -0.00250222 Br 2.40839843 14.19588548 11.85309860 -0.00311211 -0.00014857 -0.00257406 Br 2.42953862 11.63126638 15.46671961 0.00127129 0.00065223 -0.00114403 Br 23.57179603 14.70372927 14.59881440 -0.00166528 0.00237126 0.00106798 Cs 0.08417571 11.61319279 18.11608768 0.00097954 0.00008995 0.00006504 Pb 3.18989189 14.71306226 20.92322015 0.00004918 0.00203179 0.00073196 Br 3.05861643 14.74310709 17.91239914 0.00165160 -0.00335700 -0.00292100 Br 2.99837502 11.62079533 20.89997980 0.00108301 0.00060517 0.00004166 Br 0.09606517 14.88295217 20.47134784 -0.00157732 0.00184836 -0.00282829 Cs 0.02722227 17.91777508 23.41345066 0.00015066 -0.00005631 -0.00127996 Pb 2.94015400 20.94282791 2.80129612 0.00001554 0.00105536 0.00045046 Br 2.74523773 21.43112240 23.51258174 -0.00074822 0.00208753 -0.00181232 Br 2.55818952 17.96489740 2.84608895 -0.00364292 0.00177220 -0.00010922 Br 0.16141543 21.08463284 3.36519427 -0.00087513 0.00086884 0.00231132 Cs 23.08973527 17.79448425 5.85611846 0.00259805 -0.00047622 0.00089906 Pb 2.99900127 20.81327486 8.91356736 -0.00000119 0.00095222 0.00090602 Br 2.99972464 20.59373082 5.86210604 -0.00110830 0.00186409 0.00000787 Br 2.97181320 18.00578969 8.90036746 -0.00012905 -0.00311432 -0.00280815 Br 0.09291048 20.46077834 8.75612973 0.00341588 -0.00349884 -0.00178374 Cs 0.88352987 17.66675526 11.67663628 0.00020963 0.00313304 -0.00165996 Pb 3.13341254 20.58762595 14.85155203 0.00030560 -0.00202101 -0.00111219 Br 2.66042570 20.63767691 12.10296635 -0.00172082 -0.00265924 -0.00087940 Br 3.11821559 17.39220724 15.01861370 -0.00032989 0.00122450 -0.00383945 Br 0.30688230 20.80801191 14.93880486 0.00050562 -0.00047182 -0.00009569 Cs 0.32834042 17.74589275 17.86257278 -0.00146410 -0.00013307 0.00196704 Pb 3.02713849 20.82391379 20.62097787 -0.00174703 -0.00039853 0.00262758 Br 2.81787132 20.44459624 17.67628788 -0.00375971 0.00280978 0.00171954 Br 3.05668773 17.72514626 20.37307720 0.00068416 0.00128623 0.00473912 Br 23.76044947 20.45915124 20.67821491 -0.00244185 -0.00012122 0.00078154 Cs 5.55318173 0.31749144 23.08532677 -0.00112057 0.00055794 0.00269986 Pb 8.68195702 3.28993764 2.92674753 -0.00085438 0.00029779 0.00025240 Br 8.10794296 2.99260102 0.16536458 -0.00731591 0.00178048 -0.00230114 Br 9.16371620 0.36513887 3.77584953 -0.00180251 -0.00090142 -0.00193804 Br 5.81595508 3.17479961 3.83094113 -0.00119294 -0.00000995 -0.00070380 Cs 5.62273704 23.65910029 5.94809268 -0.00171700 0.00157507 0.00164241 Pb 8.94366911 3.03361148 8.84074387 0.00241707 0.00051799 0.00052248 Br 9.40243087 3.50736918 5.94038132 -0.00231540 0.00130101 0.00164615 Br 8.45335725 0.05942517 9.21547602 0.00092879 0.00043086 0.00149075 Br 5.73393366 2.74847745 8.03407140 -0.00211970 -0.00205103 -0.00126122 Cs 5.95828506 0.24214588 12.06284926 -0.00007371 0.00197807 0.00003065 Pb 9.39181617 2.92860426 15.12533363 -0.00013423 0.00042153 0.00103488 Br 8.66342556 2.83188523 12.31981419 -0.00218876 0.00079375 0.00337301 Br 9.66016469 0.05499358 14.67946052 -0.00349712 -0.00011846 -0.00159940 Br 6.25560060 2.64349372 14.98535247 -0.00181066 0.00210115 0.00149408 Cs 6.28938910 23.22806432 17.89823081 -0.00081818 0.00043191 0.00060862 Pb 8.90492782 3.08168411 20.88200655 -0.00143690 -0.00053698 0.00113379 Br 9.36778713 2.80334928 18.02906306 -0.00078098 -0.00180644 -0.00347170 Br 8.52206191 23.51573530 20.98927583 -0.00265191 0.00240931 -0.00102467 Br 6.07313464 3.71376271 20.69962007 -0.00061081 -0.00254553 -0.00106052 Cs 5.48097539 5.55382410 0.11439369 0.00068027 -0.00375032 0.00139643 Pb 8.70813014 8.96204678 2.94667681 0.00136724 0.00128018 0.00170733 Br 8.93379132 8.99768515 23.63567717 -0.00091689 0.00154282 0.00035655 Br 8.11745225 6.13156723 2.69535656 -0.00104542 -0.00141217 -0.00390112 Br 5.78712506 9.33585908 2.91996928 0.00201847 0.00230119 0.00098972 Cs 6.27283960 6.15307735 5.80046757 0.00020215 -0.00117517 0.00109952 Pb 9.08403345 8.71908444 8.84714775 0.00208489 -0.00129182 0.00104861 Br 8.89718028 8.50145326 5.86029470 0.00354099 0.00226777 0.00106013 Br 8.42025730 5.80450310 9.41748094 -0.00229960 -0.00362910 0.00066850 Br 5.98262399 8.90128796 8.66869206 -0.00059257 0.00499631 -0.00021542 Cs 6.09461725 5.68921103 12.11669213 0.00096769 -0.00335248 0.00181832 Pb 8.92593349 8.85802030 14.71287073 0.00064891 -0.00190315 -0.00068073 Br 8.34590480 8.78131623 11.95943563 0.00507032 0.00102509 -0.00035174 Br 9.28540241 5.96567270 15.32468796 -0.00283246 0.00220782 0.00016648 Br 5.98676588 8.61861430 15.41715693 -0.00269760 0.00085022 -0.00334102 Cs 6.47690074 6.06550380 17.70554998 -0.00299488 -0.00203339 0.00027048 Pb 8.80688586 9.13466694 20.73718305 0.00034033 -0.00059332 0.00080716 Br 8.94216591 8.94800280 17.85892062 -0.00013091 -0.00132425 -0.00017644 Br 9.22061601 5.88090349 20.54821206 0.00130234 -0.00166850 -0.00192017 Br 5.66468069 8.48778078 20.74951656 0.00222074 0.00019105 0.00076348 Cs 5.41932222 11.31034552 23.69940642 0.00044689 -0.00034813 0.00122861 Pb 8.62612551 14.89738107 3.20726623 -0.00190070 0.00113399 -0.00036377 Br 8.93301568 14.62190622 0.02507105 -0.00372456 0.00120134 0.00540593 Br 9.08046381 11.96073704 3.37524856 0.00066660 0.00038192 -0.00109551 Br 5.91594015 14.49154502 2.65304684 0.00195001 0.00076397 0.00050517 Cs 5.73183651 12.13829587 5.36529989 0.00271663 0.00055030 0.00427281 Pb 8.85480462 14.72751765 8.98071315 -0.00014540 0.00131704 -0.00034349 Br 8.54684506 14.15230463 6.23461153 0.00090765 0.00435355 -0.00073189 Br 9.21877623 11.52865546 9.18709389 -0.00294310 0.00110286 0.00423442 Br 5.93504313 14.08737287 9.29761543 -0.00255098 0.00118028 -0.00252682 Cs 5.91054562 11.30393622 12.00295961 0.00364649 -0.00258873 0.00111624 Pb 9.24148059 14.82192803 14.70673690 -0.00223229 -0.00090111 -0.00028932 Br 8.87988567 15.33433716 11.90207853 -0.00163643 0.00152930 0.00037718 Br 9.45136546 12.10712323 14.90566051 -0.00146423 0.00148976 -0.00257572 Br 6.02652748 14.56170821 13.91111327 0.00123594 -0.00127834 0.00146727 Cs 6.45261398 11.49694670 17.40542079 -0.00030424 -0.00104227 -0.00128054 Pb 9.17202803 14.99394963 20.98608076 0.00185615 0.00052221 0.00119499 Br 9.29005557 14.82450677 17.81543964 -0.00098288 -0.00030203 -0.00113613 Br 8.65238392 11.95198720 20.61490631 -0.00191253 -0.00215515 -0.00578382 Br 5.89191507 14.82171334 21.26118166 0.00033038 -0.00162691 -0.00027939 Cs 5.54626500 18.50815245 23.38942124 0.00173862 -0.00133152 -0.00220535 Pb 8.85755317 20.95277584 3.04281843 0.00151050 0.00323477 -0.00056796 Br 8.88231628 21.04567025 23.72484084 -0.00093656 -0.00010754 0.00147680 Br 8.52883264 18.07761802 2.68658864 0.00077764 -0.00015974 0.00060435 Br 6.10393023 21.18516456 3.03879674 -0.00051996 0.00395642 -0.00182137 Cs 5.41992607 18.09246581 5.49714925 -0.00112830 0.00018249 0.00013842 Pb 9.04520456 20.49900919 8.83877964 0.00228692 0.00141008 -0.00075123 Br 8.25179305 20.75388416 5.81110913 0.00331449 0.00324312 -0.00230147 Br 8.35323349 17.52805136 8.07731364 0.00298410 0.00098694 0.00134483 Br 5.92902288 20.64102161 8.63631898 -0.00145079 0.00121638 0.00293505 Cs 6.12681865 18.32613931 11.53292016 -0.00088032 -0.00045213 0.00179357 Pb 9.25651387 20.73831377 14.96388906 0.00093320 0.00017612 0.00133631 Br 9.64422412 20.28663108 11.76861937 -0.00067261 -0.00494522 0.00061339 Br 8.29089611 18.13852469 15.09663286 -0.00124626 -0.00166576 0.00259142 Br 6.40708991 21.46239380 14.71996368 -0.00085115 0.00032085 -0.00198754 Cs 5.68084313 17.36545351 17.55036590 0.00044135 -0.00004985 -0.00142305 Pb 8.94696084 20.60260591 20.68708513 0.00049160 -0.00078983 0.00176651 Br 9.62223337 20.70833196 17.90632850 -0.00268431 0.00050664 0.00149625 Br 8.40961299 17.69149765 20.87721045 0.00365214 0.00199945 0.00089756 Br 5.97637497 20.93442601 20.50792675 0.00247970 0.00050010 0.00018762 Cs 12.19533740 23.65973634 0.11328250 -0.00006737 -0.00139040 -0.00091107 Pb 14.97690988 3.17654871 2.89418948 -0.00016754 -0.00219120 0.00085209 Br 15.60013147 3.15347172 23.48925129 0.00176061 0.00220823 0.00051954 Br 15.72014955 0.08079149 3.27888465 -0.00201448 0.00134588 0.00280844 Br 11.90540898 3.64907849 2.32528326 -0.00271843 -0.00042224 -0.00035835 Cs 12.03479275 0.46368928 5.38578761 0.00005854 -0.00024236 -0.00060405 Pb 14.91596431 3.20414354 8.84348839 -0.00003546 0.00072863 0.00073805 Br 14.35231064 3.26161333 5.86945697 -0.00076017 -0.00240391 0.00031409 Br 15.15705495 0.13094630 9.04827490 0.00121135 0.00097356 -0.00121920 Br 11.84506895 3.11015046 9.13561765 -0.00163106 0.00187230 -0.00514304 Cs 12.01692805 0.72077742 11.84440558 -0.00005448 0.00224635 -0.00137872 Pb 14.98961869 2.53423137 14.88878966 -0.00181268 0.00109200 -0.00106461 Br 14.86929638 3.07874627 11.85224861 0.00115090 0.00009062 -0.00418673 Br 14.29308168 23.58063943 14.55519366 -0.00170037 0.00099538 -0.00254373 Br 11.99130803 3.52265730 14.40141730 0.00033541 0.00099136 0.00339743 Cs 11.90388799 23.66286505 18.63239865 -0.00132190 0.00320817 0.00123325 Pb 14.80295679 3.11333971 20.70539707 -0.00127823 -0.00070574 -0.00074057 Br 14.06069300 2.75378875 17.68673434 -0.00312251 -0.00486936 -0.00119528 Br 15.45681299 0.12765524 20.26084766 0.00017655 0.00559438 -0.00241396 Br 11.89235615 2.39147093 21.45498632 0.00280839 0.00009080 -0.00254936 Cs 12.01920032 6.06621952 23.50840274 0.00098830 -0.00147672 0.00151177 Pb 14.70223624 8.99099805 2.90268658 -0.00119971 0.00095284 -0.00321423 Br 14.92288257 8.42501661 23.68999280 0.00125489 -0.00157536 -0.00076177 Br 14.98344729 6.12699892 2.96410820 -0.00206327 -0.00335495 -0.00400165 Br 11.75130890 8.23682826 2.68552994 -0.00004782 -0.00034466 0.00092931 Cs 12.36116215 6.04470198 5.67603532 0.00136787 -0.00094409 0.00070333 Pb 14.54158272 9.11389674 8.83188529 -0.00029864 0.00168236 -0.00147809 Br 14.26963665 9.43015501 6.03955592 -0.00006322 -0.00216927 -0.00140477 Br 15.25035938 6.03435820 8.61199449 -0.00029145 -0.00022261 0.00283120 Br 11.88836742 7.88622925 8.75769504 0.00420940 -0.00084179 -0.00104377 Cs 12.14197959 6.06047176 12.10834468 0.00081199 0.00076408 0.00133832 Pb 15.04261351 8.82132632 15.05973222 -0.00043327 -0.00154694 0.00041285 Br 14.14432477 9.17403136 11.84591596 -0.00201734 -0.00074243 -0.00177881 Br 14.75640121 5.66177507 14.81167061 0.00201870 0.00378860 0.00118811 Br 12.04728553 8.91625414 15.17395140 -0.00022358 0.00241586 -0.00014459 Cs 12.22076336 5.92701148 18.04887499 -0.00541535 0.00311198 -0.00348237 Pb 14.78037461 9.19035341 20.95415225 0.00086909 -0.00002040 -0.00002644 Br 14.88941122 8.63851195 17.95188355 0.00185993 0.00196333 0.00219678 Br 14.73084777 6.21753703 20.66862321 -0.00304025 -0.00373738 0.00059044 Br 11.66489840 8.63688554 20.72117742 -0.00224008 -0.00058596 0.00186572 Cs 11.44025640 11.80226426 0.37390938 0.00168189 -0.00046094 -0.00194939 Pb 14.69492466 14.96283685 3.08532186 -0.00014809 0.00093141 -0.00233205 Br 15.17241483 15.23151738 0.20801319 0.00143550 -0.00208717 0.00371612 Br 14.07841711 11.81916956 2.94240627 -0.00196170 -0.00019583 -0.00256839 Br 11.79514298 14.88963929 2.80782315 0.00209416 -0.00116383 -0.00264049 Cs 11.96895105 12.45157156 6.06018300 0.00285462 -0.00101288 -0.00037247 Pb 14.79655105 14.99486049 8.96719191 -0.00089577 -0.00076810 -0.00064645 Br 14.80108920 14.90620911 6.18361742 -0.00018569 -0.00006583 -0.00185960 Br 13.70351845 11.79305052 9.07692368 0.00262113 0.00033989 0.00113640 Br 11.64811111 15.25783603 8.83528953 0.00659401 0.00099299 0.00294192 Cs 11.80218881 12.22092154 12.15646629 0.00007949 0.00040606 0.00101311 Pb 14.87504088 14.98828691 15.18655026 0.00109243 -0.00090804 -0.00008776 Br 14.70074917 14.79371589 11.95416137 -0.00077291 0.00140970 -0.00172111 Br 15.00357350 12.07636466 14.90824063 0.00322845 -0.00038254 0.00122784 Br 12.08828065 15.09630285 14.69587154 0.00104262 0.00048421 -0.00366197 Cs 12.13658413 11.26767498 17.52399459 -0.00142486 -0.00070867 0.00200874 Pb 15.01568221 15.01986148 20.95592265 0.00036270 0.00111153 0.00082369 Br 14.66843049 14.65498156 18.01204593 0.00009501 -0.00274939 -0.00170970 Br 14.46240337 12.03558319 21.69296502 0.00136464 -0.00114709 0.00386116 Br 12.06722230 15.68698976 21.33042378 0.00191801 0.00233843 -0.00348482 Cs 11.51452044 17.83493892 0.92678591 0.00076328 -0.00109309 0.00047705 Pb 15.05503486 20.94399523 3.21240614 0.00065745 -0.00003151 0.00100721 Br 15.21642023 20.88581575 23.67340784 0.00282465 0.00157125 0.00423744 Br 14.19253021 17.79801500 3.50787184 -0.00067979 -0.00137788 -0.00247205 Br 11.98782553 21.11347631 3.29062845 -0.00001510 -0.00270981 -0.00227905 Cs 11.41339734 18.22440337 5.94768888 0.00186170 0.00040500 -0.00000347 Pb 14.87670053 21.00878182 8.99169376 -0.00001935 0.00085114 0.00035180 Br 15.57045078 20.37016694 5.94455970 0.00346805 0.00001238 0.00039653 Br 15.26370951 18.00196827 9.14238158 0.00341530 -0.00024202 0.00322543 Br 11.52601366 21.37049484 8.06160084 0.00501277 -0.00107928 -0.00151151 Cs 12.32959154 17.95749272 11.43210707 -0.00126990 -0.00417576 -0.00065793 Pb 14.88953330 20.96668905 14.73536298 -0.00144767 0.00119070 0.00092285 Br 15.38928018 21.16417015 11.81855771 0.00232013 0.00221511 -0.00230777 Br 15.55228794 17.78951773 14.91307501 0.00011346 0.00157200 0.00285383 Br 12.11472738 19.84790388 14.75976272 -0.00129784 0.00108561 -0.00263139 Cs 11.99328747 17.57860543 18.04059536 -0.00183679 0.00026622 0.00136049 Pb 15.05889596 20.88827099 20.73544684 -0.00060376 0.00070735 0.00076429 Br 14.50269334 20.99839378 17.75490343 0.00425359 0.00209712 0.00205387 Br 15.22098256 17.94617590 20.51650976 0.00325284 0.00568696 0.00682734 Br 11.93877168 20.43681560 21.22847799 0.00234064 -0.00124047 -0.00003966 Cs 18.63538497 23.53751706 23.42998604 0.00244590 0.00205227 -0.00064140 Pb 20.99527329 3.31364917 2.66480084 -0.00103138 -0.00081752 0.00233720 Br 20.42651570 3.89600361 23.58392340 -0.00177140 -0.00069990 -0.00174639 Br 20.68710785 0.02282601 2.93370471 -0.00024945 -0.00085955 -0.00421091 Br 17.85363627 3.07129571 3.79623362 0.00032985 -0.00060423 0.00265508 Cs 17.69742105 0.47473897 5.99137192 -0.00241922 0.00214118 -0.00092077 Pb 21.21040183 2.90895872 9.21554485 0.00007203 -0.00012140 0.00079163 Br 21.61833026 3.08881295 6.09601007 0.00116516 -0.00185135 -0.00123329 Br 20.69495320 0.02148179 9.44728240 0.00051804 0.00150511 0.00098319 Br 18.18276518 3.01415392 8.64091319 0.00201550 0.00251059 0.00081928 Cs 18.24741162 0.02624045 11.81890158 0.00156932 -0.00172303 0.00008317 Pb 20.79586819 2.96240519 14.69326446 0.00236106 -0.00139942 0.00014179 Br 20.01862996 3.64001099 11.92504060 0.00257262 -0.00046450 0.00105607 Br 21.40286115 23.64675564 14.98472541 -0.00501535 0.00144187 0.00144710 Br 17.86059284 2.95888975 15.30313403 -0.00059344 -0.00018093 0.00370331 Cs 18.11588367 23.40384705 17.89783171 0.00082397 -0.00001823 -0.00163996 Pb 20.87839397 3.14897857 20.55676818 -0.00221719 0.00159887 -0.00046415 Br 20.98976738 2.96577226 17.69812867 0.00117903 -0.00206625 -0.00208391 Br 20.64340750 23.71601591 20.28649312 -0.00106519 -0.00119882 0.00177113 Br 17.90265895 3.00642289 20.33335726 0.00036253 0.00146041 0.00201839 Cs 17.72035647 6.40682072 23.12229900 -0.00125501 -0.00268743 -0.00139758 Pb 20.46861691 8.86402957 3.03260014 -0.00248708 0.00117819 -0.00110545 Br 20.35593337 8.55050389 23.75791114 0.00296728 0.00079101 -0.00102118 Br 20.61693250 6.19578167 3.62794421 0.00236156 -0.00184071 0.00105837 Br 17.55385508 8.74269774 3.51142618 -0.00023860 -0.00018699 -0.00152399 Cs 18.32639501 5.58224148 6.05987023 -0.00130670 0.00231634 -0.00123513 Pb 20.76909986 8.87492929 8.83837405 -0.00145085 0.00085409 -0.00024530 Br 20.98932051 9.20144253 6.03397214 0.00249463 -0.00094308 0.00124476 Br 21.35239180 5.94045180 8.44320332 -0.00022175 0.00176671 0.00010476 Br 17.70130930 9.01600610 8.54102425 0.00192010 -0.00385101 -0.00103469 Cs 17.56301398 6.44904109 11.51680776 0.00055285 -0.00031818 0.00077689 Pb 20.96239101 8.70526504 14.90105734 0.00008147 0.00115597 -0.00118038 Br 20.65900414 8.83476558 11.78358219 -0.00119974 -0.00022484 0.00271256 Br 20.72621534 5.77278341 14.83216644 0.00076356 -0.00035956 -0.00198235 Br 17.93403671 8.23423474 14.84255011 -0.00350852 0.00135869 0.00146045 Cs 17.56786878 5.61752373 17.79394010 -0.00173982 -0.00011516 0.00120565 Pb 20.47601972 8.94895230 20.60289139 -0.00093974 -0.00058343 0.00118442 Br 21.32050033 8.58031019 17.83742573 0.00025219 0.00085897 -0.00193595 Br 20.60160296 6.06103757 20.13740504 0.00132702 -0.00206501 -0.00214842 Br 17.65422729 8.85148876 20.62852224 0.00047051 -0.00330601 -0.00010762 Cs 17.35541003 11.77662001 0.10061509 -0.00227710 -0.00054073 -0.00028238 Pb 20.62326532 14.84197932 3.21186037 -0.00031722 0.00020381 -0.00022720 Br 21.00476772 15.10520840 0.09759186 0.00258103 -0.00103347 -0.00049196 Br 20.31554142 11.97514023 3.09932151 0.00109041 0.00057661 -0.00307847 Br 17.92063075 15.00911545 2.40381885 0.00175604 0.00037512 0.00060997 Cs 17.86386981 11.71241368 6.13342340 -0.00034232 0.00492004 -0.00195084 Pb 20.36529208 14.86097543 9.17179407 -0.00064291 -0.00013129 -0.00060225 Br 19.97915906 14.41301713 6.31839224 0.00023797 -0.00097394 0.00221242 Br 20.03587103 11.71187408 9.34998520 0.00408462 0.00189161 -0.00204716 Br 17.55046091 14.65526481 9.44023811 0.00289299 0.00175996 0.00087309 Cs 18.09554127 11.34010399 11.93224130 -0.00113008 0.00071350 0.00024027 Pb 20.79733691 14.66600738 14.73293285 -0.00015586 0.00013345 0.00180037 Br 20.73438078 14.00865067 12.00272454 -0.00165444 0.00064956 -0.00000749 Br 20.91580510 11.62033385 15.52770391 0.00185970 -0.00056780 -0.00026272 Br 17.83865048 14.67553374 15.16268892 0.00143312 0.00275452 0.00131012 Cs 17.48606816 11.71067175 17.64138910 0.00173329 -0.00124161 0.00092616 Pb 21.06163043 15.02243643 20.59864122 -0.00098183 -0.00233335 -0.00152368 Br 20.79254660 15.15336670 17.64024612 -0.00085073 0.00442741 0.00052574 Br 20.13040628 11.88694480 20.65818775 0.00156099 0.00381865 0.00377843 Br 17.88936543 14.97119298 20.56832623 -0.00287557 0.00057643 0.00144989 Cs 17.94576735 17.56581077 23.59564697 -0.00015434 -0.00108243 0.00138591 Pb 21.06289508 20.80205079 2.95847763 -0.00080085 0.00103446 -0.00010662 Br 21.29611436 20.78632487 0.15431665 -0.00109178 -0.00434533 -0.00077183 Br 21.05423570 17.95550010 3.11911645 -0.00050184 -0.00018707 -0.00355579 Br 17.86801849 20.63628906 2.80717513 0.00588485 -0.00010139 0.00245163 Cs 17.86783966 17.73832167 5.25852514 0.00052590 -0.00162675 -0.00215604 Pb 20.88626932 20.83263555 8.89740851 0.00073932 -0.00145658 0.00161510 Br 20.66106241 20.94294863 5.79220656 -0.00316807 0.00142455 0.00030003 Br 20.32708986 17.96854735 8.88419804 0.00019161 -0.00110318 0.00125645 Br 18.09686938 21.34606163 9.01008843 -0.00339054 0.00094634 -0.00574991 Cs 17.72711446 17.66808786 12.07409274 -0.00056463 -0.00198247 0.00122118 Pb 20.63906885 20.89356653 14.79179346 0.00044173 -0.00145222 -0.00078956 Br 21.08405928 20.89521009 11.90702759 -0.00167388 -0.00060281 0.00257120 Br 20.42037684 17.81624566 14.34342741 0.00035591 0.00566827 0.00334231 Br 17.79472467 21.72227011 15.26545499 -0.00020815 0.00043945 -0.00024548 Cs 18.04907837 17.87135031 18.22537124 0.00502756 -0.00190188 -0.00108475 Pb 20.92721745 20.74000634 20.82200675 0.00224319 0.00128469 -0.00295455 Br 21.00331285 20.47551630 17.96671967 0.00037231 0.00043267 -0.00081135 Br 20.90393102 17.86618950 20.29104725 0.00148152 -0.00175937 0.00300216 Br 17.94581668 20.72081968 21.32646457 -0.00208263 -0.00151451 -0.00107620 320 Lattice="23.77195271 0.0 0.0 0.0 23.77195271 0.0 0.0 0.0 23.77195271" Properties=species:S:1:pos:R:3:vel:R:3 Time=20289.99999999 energy=-11425.00474167 virial="-20.850556 3.04747872 0.91635933 3.04747872 -8.10279615 2.46780431 0.91635933 2.46780431 -5.45917132" stress="-0.00059848 0.00022685 6.821e-05 0.00022685 0.0002801 0.0001837 6.821e-05 0.0001837 0.000542" pbc="T T T" Cs 23.46851774 23.67799591 0.27801363 0.00129099 0.00027152 0.00128708 Pb 2.96642480 3.05009552 3.18210572 0.00130818 -0.00032930 -0.00131812 Br 3.35520328 2.91955613 0.19699090 0.00093567 -0.00110196 -0.00132080 Br 2.73071108 0.07040208 3.03023499 -0.00057702 0.00146281 -0.00050828 Br 0.06985234 3.30974942 1.88914902 0.00089444 -0.00164084 -0.00112550 Cs 0.52646542 0.15454955 5.66091061 -0.00094224 -0.00116708 -0.00158987 Pb 3.24207959 2.99505960 9.06256573 -0.00039009 0.00002918 0.00027040 Br 2.63132228 2.86313418 5.96712189 -0.00019683 0.00261306 -0.00323691 Br 3.14760255 0.11420704 9.52232343 -0.00023173 0.00178482 -0.00182001 Br 0.12255976 2.47365568 10.20006230 0.00412500 -0.00357414 -0.00422569 Cs 23.58614108 23.33178943 12.06524039 -0.00040744 -0.00198431 -0.00160023 Pb 3.18232627 2.92150932 14.83719454 0.00161073 -0.00256579 0.00055224 Br 3.50652920 3.13592586 11.88252025 -0.00278969 0.00192036 -0.00223808 Br 3.01463112 23.55894241 14.58986072 -0.00083344 -0.00293327 0.00202495 Br 23.68403239 3.05155391 14.41423208 0.00047936 -0.00147028 -0.00066493 Cs 0.00646546 23.24621036 17.53325716 -0.00009657 0.00038793 0.00081994 Pb 3.07380068 3.09558003 20.80114088 -0.00269090 -0.00225124 0.00036853 Br 2.88972914 3.34988087 17.97497290 0.00224741 -0.00038545 0.00311751 Br 3.00371230 23.70107765 20.71017806 0.00014751 -0.00142450 -0.00084698 Br 23.69453842 2.25453038 20.95893274 -0.00069221 -0.00272606 0.00264371 Cs 23.39307801 6.62182222 23.55857180 -0.00095234 -0.00067549 -0.00053842 Pb 2.82672274 9.01347436 2.69180275 -0.00175101 0.00046431 -0.00075449 Br 3.10400245 8.23041382 0.01001311 0.00159865 0.00077603 -0.00403053 Br 2.83940786 5.85347335 3.22662870 0.00016172 -0.00101321 0.00187662 Br 23.42097827 8.62877135 2.72395794 -0.00365760 0.00262610 -0.00259908 Cs 0.54014518 6.01048178 6.28083451 -0.00101661 -0.00038204 0.00147261 Pb 2.99332273 8.98660351 8.95501145 -0.00380907 -0.00291292 -0.00033984 Br 2.88593062 8.77121662 5.66457773 0.00129976 0.00142604 -0.00398811 Br 2.58658003 6.14324701 9.41364383 0.00005405 -0.00040724 0.00412316 Br 0.07369776 9.19186675 8.97004940 -0.00140610 -0.00093966 0.00130067 Cs 23.19819868 5.78924182 12.09682498 -0.00240845 -0.00192912 -0.00179445 Pb 3.04229353 8.95480808 14.82847659 0.00157655 -0.00098430 0.00119613 Br 3.29251992 9.28318289 11.86313645 -0.00060240 0.00119102 0.00139426 Br 3.65212159 5.73170214 15.05602489 -0.00240238 -0.00256251 -0.00285907 Br 0.22690874 8.54904521 14.28648109 -0.00058841 0.00268326 0.00286522 Cs 23.54452372 5.68848473 17.71078827 0.00045569 -0.00100496 0.00041800 Pb 2.62758431 8.59567629 20.77568387 0.00125260 0.00095785 0.00233348 Br 2.64414587 8.64514020 18.02349493 0.00014865 -0.00200971 -0.00124854 Br 2.34738862 5.67328245 21.18393465 -0.00103415 -0.00150358 0.00251457 Br 23.65926110 9.48987805 21.38755344 0.00477951 0.00151469 0.00122221 Cs 23.72844698 11.83098397 0.33637755 0.00224842 0.00128342 -0.00011291 Pb 2.75475823 14.88359937 2.91104305 0.00267578 -0.00123349 -0.00095326 Br 2.44535496 14.94384731 23.67004477 -0.00116970 0.00079821 0.00248667 Br 2.64709877 11.84546008 2.36797398 -0.00153205 -0.00123379 0.00370328 Br 23.58142762 14.75684988 3.19969804 -0.00000789 -0.00009471 0.00136345 Cs 23.75988500 12.09183425 6.27729137 0.00112956 0.00237924 -0.00272553 Pb 3.03383552 14.70424310 8.99044909 -0.00015312 -0.00336084 -0.00107218 Br 2.82187495 14.87125838 6.02025824 -0.00197042 -0.00054336 0.00206255 Br 2.85376935 11.82094899 8.59601006 -0.00118887 0.00350173 -0.00160141 Br 23.63794469 14.79681101 8.86999889 -0.00181295 -0.00164153 -0.00004878 Cs 23.48899935 11.97563767 11.48161260 -0.00155033 0.00125626 0.00104365 Pb 3.04673503 14.57223664 14.77614791 -0.00024145 -0.00034439 -0.00252568 Br 2.37874176 14.19550121 11.82854627 -0.00281685 0.00007278 -0.00231097 Br 2.44342431 11.63896770 15.45464333 0.00150722 0.00088526 -0.00127111 Br 23.56121998 14.72804179 14.60992223 -0.00044451 0.00249051 0.00115316 Cs 0.09367991 11.61438121 18.11725177 0.00092493 0.00014583 0.00016559 Pb 3.18941371 14.73274621 20.92902283 -0.00014572 0.00190158 0.00042707 Br 3.07583222 14.70905074 17.88021679 0.00179235 -0.00344346 -0.00349381 Br 3.00920520 11.62778705 20.90075756 0.00108458 0.00079669 0.00011518 Br 0.08028208 14.90129080 20.44303165 -0.00156000 0.00181530 -0.00283313 Cs 0.02854878 17.91666666 23.40081551 0.00011090 -0.00016576 -0.00124461 Pb 2.94091526 20.95336156 2.80613420 0.00013308 0.00105045 0.00051841 Br 2.73828546 21.44988836 23.49764273 -0.00064355 0.00166429 -0.00115432 Br 2.52249397 17.98026782 2.84431304 -0.00348892 0.00129595 -0.00024507 Br 0.15194967 21.09297782 3.38782201 -0.00099984 0.00080097 0.00221153 Cs 23.11677870 17.78999398 5.86632030 0.00280556 -0.00042265 0.00113211 Pb 2.99897866 20.82350037 8.92255121 -0.00000013 0.00107919 0.00089023 Br 2.98858771 20.61452163 5.86188022 -0.00111151 0.00229250 -0.00005561 Br 2.97219111 17.97064923 8.87214068 0.00020803 -0.00386389 -0.00283571 Br 0.12641176 20.42603440 8.73818202 0.00323736 -0.00344543 -0.00180242 Cs 0.88404117 17.69739061 11.66066102 -0.00010947 0.00298322 -0.00153936 Pb 3.13735997 20.56853361 14.84030695 0.00048181 -0.00179789 -0.00112731 Br 2.64424803 20.61104064 12.08769390 -0.00150685 -0.00265091 -0.00215780 Br 3.11426981 17.40730773 14.97903409 -0.00045610 0.00178012 -0.00406936 Br 0.30900981 20.80185427 14.93767389 -0.00008364 -0.00075797 -0.00013365 Cs 0.31320199 17.74414144 17.88196451 -0.00156157 -0.00021561 0.00190977 Pb 3.00934490 20.81969479 20.64710996 -0.00180386 -0.00044292 0.00259075 Br 2.78081447 20.47362958 17.69718275 -0.00364743 0.00299338 0.00243051 Br 3.06413941 17.73877704 20.42231682 0.00080396 0.00142668 0.00509879 Br 23.73904473 20.45863356 20.68597423 -0.00180218 0.00001740 0.00076722 Cs 5.54292106 0.32239059 23.11301612 -0.00093114 0.00041812 0.00283441 Pb 8.67427008 3.29196704 2.93036293 -0.00068436 0.00010932 0.00045998 Br 8.03492893 3.00939830 0.14021721 -0.00726410 0.00157305 -0.00269836 Br 9.14307736 0.35512303 3.75350090 -0.00231075 -0.00109816 -0.00252215 Br 5.80550165 3.17373129 3.82266789 -0.00087901 -0.00020387 -0.00094591 Cs 5.60614168 23.67444079 5.96439229 -0.00160298 0.00148830 0.00161338 Pb 8.96761737 3.03777960 8.84596344 0.00236395 0.00031035 0.00051871 Br 9.37926071 3.52093981 5.95839689 -0.00231977 0.00140996 0.00193672 Br 8.46377209 0.06217560 9.22978179 0.00115671 0.00011037 0.00137095 Br 5.71812886 2.72924634 8.02191301 -0.00102550 -0.00179083 -0.00117089 Cs 5.95793737 0.26187900 12.06336841 0.00000306 0.00196675 0.00007276 Pb 9.38863015 2.93228968 15.13461489 -0.00050209 0.00031367 0.00081854 Br 8.64234371 2.84107251 12.34846626 -0.00202552 0.00104410 0.00234341 Br 9.62628447 0.05216316 14.66451242 -0.00327606 -0.00044314 -0.00139009 Br 6.23392155 2.66477357 15.00180821 -0.00252134 0.00215156 0.00179468 Cs 6.28131536 23.23338382 17.90336200 -0.00079770 0.00063041 0.00041728 Pb 8.89079550 3.07598955 20.89367999 -0.00139023 -0.00060361 0.00119689 Br 9.36030383 2.78468727 17.99784370 -0.00071919 -0.00192514 -0.00273451 Br 8.49676803 23.54307179 20.97749494 -0.00240194 0.00304885 -0.00132516 Br 6.06505060 3.68712214 20.68855754 -0.00100107 -0.00278200 -0.00115365 Cs 5.48848717 5.51755583 0.12831967 0.00081582 -0.00349373 0.00138571 Pb 8.72282683 8.97471280 2.96355103 0.00156709 0.00124955 0.00166311 Br 8.92431292 9.01386688 23.64223767 -0.00097708 0.00169676 0.00095701 Br 8.10704587 6.11754870 2.65601049 -0.00103068 -0.00137353 -0.00395429 Br 5.80716074 9.35894004 2.93016088 0.00196975 0.00231373 0.00104923 Cs 6.27401499 6.14133233 5.81216015 0.00003707 -0.00117002 0.00123562 Pb 9.10383678 8.70593987 8.85729622 0.00187424 -0.00133046 0.00098148 Br 8.93323686 8.52484733 5.87139231 0.00366293 0.00240222 0.00116219 Br 8.39721838 5.76879560 9.42379181 -0.00230683 -0.00347449 0.00059398 Br 5.97787262 8.95139402 8.66654822 -0.00035436 0.00501665 -0.00021221 Cs 6.10420680 5.65580381 12.13542862 0.00094846 -0.00332609 0.00193016 Pb 8.93291720 8.83814762 14.70769974 0.00074637 -0.00206248 -0.00034757 Br 8.39754999 8.79060706 11.95477381 0.00525853 0.00082190 -0.00058953 Br 9.25619816 5.99074773 15.32506515 -0.00300138 0.00278311 -0.00009403 Br 5.96204318 8.62896512 15.38152072 -0.00222149 0.00120664 -0.00377612 Cs 6.44659531 6.04426366 17.70903564 -0.00306822 -0.00220816 0.00042418 Pb 8.80934990 9.12753509 20.74566165 0.00014665 -0.00083484 0.00088609 Br 8.94153940 8.93522163 17.85402871 0.00000404 -0.00123344 -0.00079935 Br 9.23406205 5.86345465 20.52946749 0.00138318 -0.00181949 -0.00182890 Br 5.68749839 8.48991040 20.75695410 0.00234038 0.00023440 0.00072205 Cs 5.42437002 11.30722980 23.71053566 0.00056304 -0.00027613 0.00099654 Pb 8.60836997 14.90840853 3.20304167 -0.00163625 0.00107679 -0.00048174 Br 8.89624104 14.63435801 0.08199086 -0.00361892 0.00129022 0.00592541 Br 9.08704091 11.96524087 3.36338335 0.00064916 0.00051247 -0.00127297 Br 5.93186339 14.49952390 2.65810231 0.00119624 0.00082284 0.00050403 Cs 5.75846222 12.14282877 5.40900565 0.00260765 0.00035612 0.00446766 Pb 8.85243002 14.74046559 8.97792445 -0.00032061 0.00127147 -0.00021168 Br 8.55630097 14.19637932 6.22273720 0.00098687 0.00445513 -0.00162548 Br 9.19021604 11.54262721 9.22983274 -0.00276699 0.00166927 0.00430562 Br 5.90808847 14.09992084 9.27221411 -0.00279529 0.00132992 -0.00255348 Cs 5.94784196 11.28003585 12.01334760 0.00380775 -0.00219196 0.00095987 Pb 9.21754476 14.81527705 14.70493832 -0.00254759 -0.00042050 -0.00007084 Br 8.86549403 15.35041428 11.90474122 -0.00124373 0.00168578 0.00013988 Br 9.43625455 12.11606417 14.87979384 -0.00155591 0.00027634 -0.00259393 Br 6.03605566 14.54814074 13.92869129 0.00066794 -0.00143570 0.00204651 Cs 6.44907911 11.48765729 17.39364108 -0.00040330 -0.00081580 -0.00107586 Pb 9.19055486 14.99816358 20.99755029 0.00184222 0.00031889 0.00110485 Br 9.28001755 14.82193093 17.80386386 -0.00102456 -0.00021103 -0.00115650 Br 8.63361350 11.93109333 20.55704236 -0.00183572 -0.00199835 -0.00578324 Br 5.89946730 14.80580956 21.25877086 0.00117295 -0.00155000 -0.00020366 Cs 5.56424543 18.49445449 23.36743280 0.00185731 -0.00140601 -0.00218967 Pb 8.87367828 20.98620934 3.03639206 0.00170402 0.00344586 -0.00071994 Br 8.87287951 21.04365123 23.74049555 -0.00094649 -0.00029177 0.00163820 Br 8.53581307 18.07384403 2.69412251 0.00061916 -0.00058166 0.00090401 Br 6.09290930 21.22486500 3.01885272 -0.00166202 0.00397835 -0.00216139 Cs 5.40926464 18.09425069 5.49897100 -0.00100536 0.00017628 0.00022687 Pb 9.06609239 20.51383859 8.83181079 0.00189653 0.00155923 -0.00064208 Br 8.28659884 20.78544334 5.79151419 0.00363533 0.00306419 -0.00159191 Br 8.38379059 17.53926231 8.09184768 0.00312641 0.00126273 0.00156051 Br 5.91475551 20.65337261 8.66662474 -0.00137462 0.00124993 0.00311860 Cs 6.11850461 18.32116654 11.55142130 -0.00078280 -0.00054210 0.00190605 Pb 9.26590514 20.74084956 14.97625503 0.00093554 0.00032298 0.00113607 Br 9.63608228 20.23729150 11.77328877 -0.00095903 -0.00491722 0.00030797 Br 8.27805915 18.11832094 15.12245535 -0.00131015 -0.00234495 0.00256997 Br 6.39689098 21.46477046 14.70015379 -0.00118152 0.00015039 -0.00197190 Cs 5.68570562 17.36484069 17.53682766 0.00052717 -0.00007331 -0.00128567 Pb 8.95281243 20.59530026 20.70647289 0.00067679 -0.00066058 0.00210874 Br 9.59544306 20.71395283 17.91956973 -0.00267003 0.00061699 0.00115012 Br 8.44504211 17.71338535 20.88618618 0.00343716 0.00234216 0.00089790 Br 5.99944149 20.93778896 20.51041402 0.00211478 0.00016717 0.00031162 Cs 12.19447972 23.64494883 0.10456678 -0.00010308 -0.00156492 -0.00083439 Pb 14.97543466 3.15473209 2.90268096 -0.00012668 -0.00216976 0.00084189 Br 15.61673591 3.17570178 23.49616835 0.00155333 0.00223238 0.00085951 Br 15.69619746 0.09348499 3.30515170 -0.00277710 0.00117652 0.00243796 Br 11.87848340 3.64283700 2.32303652 -0.00266900 -0.00083063 -0.00008651 Cs 12.03615923 0.46138655 5.38076034 0.00020877 -0.00022031 -0.00040480 Pb 14.91597239 3.21102367 8.85117063 0.00004051 0.00064431 0.00079232 Br 14.34633221 3.23579950 5.87122058 -0.00043686 -0.00275214 0.00003286 Br 15.16843932 0.14078143 9.03525225 0.00106169 0.00099551 -0.00138530 Br 11.83010481 3.12931584 9.08413075 -0.00134271 0.00195964 -0.00514281 Cs 12.01551437 0.74296258 11.83040600 -0.00022948 0.00218990 -0.00142222 Pb 14.97097925 2.54781949 14.87791574 -0.00190786 0.00161988 -0.00110300 Br 14.88168888 3.07964585 11.81031215 0.00131967 0.00009425 -0.00417414 Br 14.27343652 23.59382854 14.52951550 -0.00222345 0.00164031 -0.00259194 Br 11.99844450 3.53063011 14.43542738 0.00109117 0.00061362 0.00339710 Cs 11.89058052 23.69467404 18.64447649 -0.00133998 0.00314806 0.00118213 Pb 14.79086892 3.10521048 20.69814854 -0.00113856 -0.00092006 -0.00070674 Br 14.03023537 2.70576052 17.67583327 -0.00296602 -0.00471950 -0.00099743 Br 15.45691699 0.18407478 20.23841698 -0.00015165 0.00566689 -0.00207079 Br 11.92132814 2.39396342 21.42883307 0.00296681 0.00040238 -0.00266903 Cs 12.02869702 6.05152896 23.52333632 0.00091155 -0.00146130 0.00147281 Pb 14.68940962 9.00001285 2.87132227 -0.00136427 0.00084220 -0.00305429 Br 14.93525299 8.41094606 23.68440623 0.00121810 -0.00123875 -0.00035671 Br 14.96323025 6.08974968 2.92439005 -0.00197940 -0.00405525 -0.00393859 Br 11.75050971 8.23493217 2.69483331 -0.00010571 -0.00003554 0.00092932 Cs 12.37391907 6.03585091 5.68282732 0.00118472 -0.00082753 0.00065282 Pb 14.53927090 9.12990104 8.81781095 -0.00015652 0.00151433 -0.00133393 Br 14.26938052 9.40827263 6.02145158 0.00001145 -0.00220313 -0.00221686 Br 15.24754099 6.03249870 8.64047091 -0.00027133 -0.00014533 0.00286101 Br 11.92958487 7.87772901 8.74806885 0.00400152 -0.00085877 -0.00087831 Cs 12.15003635 6.06841365 12.12114439 0.00079989 0.00082408 0.00122146 Pb 15.03737069 8.80534616 15.06246656 -0.00061389 -0.00164574 0.00013601 Br 14.12626163 9.16571569 11.83070184 -0.00159448 -0.00091936 -0.00126313 Br 14.77832222 5.70381637 14.82463538 0.00235320 0.00460472 0.00139973 Br 12.04380134 8.93877400 15.17038217 -0.00048540 0.00208397 -0.00056902 Cs 12.16573673 5.95813432 18.01326054 -0.00558225 0.00310907 -0.00363466 Pb 14.78762847 9.18977454 20.95355452 0.00057732 -0.00010037 -0.00009293 Br 14.90846331 8.65953480 17.97146253 0.00194488 0.00223645 0.00171231 Br 14.70089661 6.17728909 20.67484530 -0.00295221 -0.00427117 0.00064651 Br 11.64611848 8.63295904 20.73979746 -0.00149147 -0.00019925 0.00185709 Cs 11.45690992 11.79771029 0.35420939 0.00164401 -0.00044926 -0.00198873 Pb 14.69340155 14.97210137 3.06174698 -0.00015700 0.00091636 -0.00237026 Br 15.18561716 15.21072472 0.24402994 0.00120327 -0.00206818 0.00343622 Br 14.05975266 11.82053656 2.91606754 -0.00176582 0.00045066 -0.00269763 Br 11.81580587 14.87739032 2.78266371 0.00202959 -0.00128694 -0.00239277 Cs 11.99688180 12.44103366 6.05613702 0.00272945 -0.00109252 -0.00043783 Pb 14.78653808 14.98721280 8.96266016 -0.00109477 -0.00075828 -0.00026498 Br 14.80020108 14.90504243 6.16081259 0.00000903 -0.00016793 -0.00267594 Br 13.73052959 11.80057712 9.08825578 0.00277300 0.00117550 0.00112600 Br 11.71629651 15.26866376 8.86418841 0.00698007 0.00116120 0.00283552 Cs 11.80380380 12.22485200 12.16601275 0.00024416 0.00038013 0.00089559 Pb 14.88760254 14.97923057 15.18426886 0.00141118 -0.00089868 -0.00037043 Br 14.69270375 14.80871243 11.93744205 -0.00083037 0.00158474 -0.00162064 Br 15.03528966 12.06728987 14.92102064 0.00311459 -0.00142163 0.00132345 Br 12.09499002 15.10181808 14.65888695 0.00029788 0.00061622 -0.00372914 Cs 12.12189444 11.26213239 17.54539835 -0.00151384 -0.00039929 0.00227083 Pb 15.01862211 15.03115871 20.96433942 0.00022468 0.00114943 0.00085651 Br 14.66866266 14.62686202 17.99719394 -0.00004749 -0.00286961 -0.00123585 Br 14.47603543 12.02464359 21.73095446 0.00135971 -0.00103348 0.00372641 Br 12.08536676 15.70990900 21.29562582 0.00169613 0.00224522 -0.00346551 Cs 11.52282021 17.82475141 0.92975767 0.00089323 -0.00094277 0.00011858 Pb 15.06198360 20.94389738 3.22152992 0.00074113 0.00000992 0.00081597 Br 15.24295643 20.90206083 23.71742552 0.00247863 0.00167790 0.00456313 Br 14.18699818 17.78435084 3.48229780 -0.00042457 -0.00133192 -0.00263770 Br 11.98772414 21.08632413 3.26780214 0.00000664 -0.00271751 -0.00228437 Cs 11.43213224 18.22843852 5.94743139 0.00188554 0.00040050 -0.00004908 Pb 14.87768593 21.01736929 8.99433115 0.00021854 0.00086333 0.00017378 Br 15.60389880 20.37212443 5.95062210 0.00321416 0.00037371 0.00080044 Br 15.29706368 17.99816118 9.17478978 0.00324668 -0.00051869 0.00325134 Br 11.58157447 21.35966739 8.04571962 0.00606346 -0.00109338 -0.00165433 Cs 12.31696150 17.91522239 11.42651970 -0.00125384 -0.00427569 -0.00045867 Pb 14.87538508 20.97540137 14.74567716 -0.00137705 0.00055641 0.00113464 Br 15.41227356 21.18514448 11.79567639 0.00227549 0.00197935 -0.00223495 Br 15.55342522 17.80796869 14.94204074 0.00011197 0.00210516 0.00293998 Br 12.10278368 19.85855978 14.73323547 -0.00108539 0.00104496 -0.00266662 Cs 11.97501483 17.58168244 18.05415604 -0.00181717 0.00034953 0.00134722 Pb 15.05198650 20.89550770 20.74386473 -0.00077335 0.00074766 0.00092225 Br 14.54562378 21.01918380 17.77456370 0.00432642 0.00206052 0.00186711 Br 15.25369134 18.00185207 20.58483420 0.00328611 0.00540462 0.00683018 Br 11.96246732 20.42538681 21.22740972 0.00238859 -0.00104714 -0.00016966 Cs 18.65878901 23.55837615 23.42417425 0.00223345 0.00211493 -0.00052406 Pb 20.98510540 3.30519485 2.68889882 -0.00099702 -0.00087255 0.00247898 Br 20.40903813 3.88824202 23.56872183 -0.00171943 -0.00085405 -0.00128789 Br 20.68543313 0.01729337 2.89035051 -0.00008736 -0.00023833 -0.00445312 Br 17.85700473 3.06665423 3.81918337 0.00034408 -0.00031664 0.00192493 Cs 17.67454063 0.49494906 5.98383751 -0.00215355 0.00189509 -0.00057520 Pb 21.21041672 2.90833945 9.22213522 -0.00006425 -0.00000075 0.00052590 Br 21.63010508 3.06893965 6.08292356 0.00118616 -0.00211901 -0.00138388 Br 20.70257886 0.03804433 9.45600608 0.00100443 0.00178305 0.00076036 Br 18.20034583 3.03927941 8.64904404 0.00150311 0.00251364 0.00080561 Cs 18.26208666 0.00919043 11.81997439 0.00136301 -0.00168810 0.00013305 Pb 20.81944110 2.94763220 14.69488515 0.00234563 -0.00155150 0.00018603 Br 20.04489580 3.63449613 11.93409133 0.00267389 -0.00063932 0.00073978 Br 21.35152402 23.66190343 14.99872212 -0.00523685 0.00158317 0.00134893 Br 17.85407158 2.95561063 15.34042371 -0.00071496 -0.00047646 0.00374603 Cs 18.12429634 23.40589174 17.88392740 0.00085853 0.00042816 -0.00113951 Pb 20.85627621 3.16385035 20.55259474 -0.00220265 0.00136921 -0.00036986 Br 21.00208261 2.94515586 17.67650903 0.00128519 -0.00205524 -0.00221770 Br 20.63376325 23.70385531 20.30585639 -0.00086286 -0.00122480 0.00209232 Br 17.90499868 3.02200716 20.35333796 0.00011775 0.00165792 0.00197317 Cs 17.70772887 6.37780346 23.10888671 -0.00126497 -0.00311521 -0.00128211 Pb 20.44454380 8.87694758 3.02146361 -0.00232179 0.00139128 -0.00111500 Br 20.38524567 8.55979200 23.74724569 0.00287795 0.00105992 -0.00112017 Br 20.64198449 6.17146779 3.63678981 0.00264439 -0.00297757 0.00070321 Br 17.55186204 8.74222174 3.49628158 -0.00017815 0.00008714 -0.00150339 Cs 18.31288236 5.60625792 6.04911447 -0.00139440 0.00248746 -0.00091494 Pb 20.75475433 8.88299863 8.83578060 -0.00141820 0.00076011 -0.00026968 Br 21.01410226 9.19346101 6.04345508 0.00245756 -0.00065282 0.00064292 Br 21.34952602 5.95849772 8.44616798 -0.00035195 0.00183167 0.00048590 Br 17.72071281 8.97675001 8.53166010 0.00196124 -0.00398741 -0.00084497 Cs 17.56849328 6.44528974 11.52454677 0.00054448 -0.00043190 0.00077166 Pb 20.96344372 8.71747956 14.88887440 0.00013104 0.00127965 -0.00125624 Br 20.64731641 8.83160806 11.81407994 -0.00114099 -0.00040030 0.00336023 Br 20.73459481 5.77138078 14.81391011 0.00090870 0.00008467 -0.00166523 Br 17.89959631 8.24928278 14.85769873 -0.00335734 0.00164752 0.00156756 Cs 17.55059892 5.61658745 17.80562783 -0.00171386 -0.00007236 0.00113057 Pb 20.46858433 8.94421700 20.61580563 -0.00054339 -0.00036433 0.00139058 Br 21.32298836 8.59013902 17.81649178 0.00024025 0.00110267 -0.00221401 Br 20.61475322 6.03900150 20.11709628 0.00130159 -0.00230121 -0.00191436 Br 17.65753228 8.81924792 20.62621870 0.00019780 -0.00313783 -0.00035316 Cs 17.33336957 11.77163223 0.09809136 -0.00212624 -0.00045577 -0.00021822 Pb 20.62029901 14.84394730 3.20871837 -0.00026736 0.00019446 -0.00040032 Br 21.03099679 15.09478930 0.09266935 0.00265977 -0.00105255 -0.00048806 Br 20.32685815 11.98093041 3.06799053 0.00117079 0.00056588 -0.00318490 Br 17.93497500 15.01318511 2.41136891 0.00110284 0.00043659 0.00089391 Cs 17.85996311 11.76055765 6.11383223 -0.00044174 0.00470066 -0.00196386 Pb 20.36004348 14.85990455 9.16548509 -0.00039357 -0.00008172 -0.00065648 Br 19.98308606 14.40530396 6.33780382 0.00055002 -0.00056542 0.00164596 Br 20.08025766 11.73334295 9.32567617 0.00476759 0.00238195 -0.00278980 Br 17.58051349 14.67252431 9.44920598 0.00307116 0.00168895 0.00092442 Cs 18.08247789 11.34766411 11.93621872 -0.00147253 0.00079988 0.00054161 Pb 20.79487588 14.66805888 14.75230655 -0.00033433 0.00027163 0.00206355 Br 20.71649856 14.01518739 12.00001464 -0.00192264 0.00066363 -0.00049970 Br 20.93373964 11.61529401 15.52403468 0.00172473 -0.00042009 -0.00046987 Br 17.85256020 14.70240859 15.17564748 0.00134265 0.00262102 0.00128326 Cs 17.50428441 11.69902414 17.65077225 0.00190847 -0.00108884 0.00094802 Pb 21.05070725 14.99793829 20.58375244 -0.00120005 -0.00256200 -0.00144601 Br 20.78492680 15.19770922 17.64881790 -0.00067415 0.00443463 0.00117102 Br 20.14668879 11.92479387 20.69562115 0.00169132 0.00371692 0.00370529 Br 17.86065453 14.97713723 20.58351780 -0.00285196 0.00061015 0.00158380 Cs 17.94454086 17.55573269 23.60927513 -0.00008648 -0.00092791 0.00133808 Pb 21.05340246 20.81360436 2.95864727 -0.00109654 0.00126800 0.00013557 Br 21.28555444 20.74227965 0.14127214 -0.00101890 -0.00445441 -0.00182224 Br 21.04832990 17.95024695 3.08269997 -0.00067017 -0.00085743 -0.00371115 Br 17.92846105 20.63404182 2.83097797 0.00615516 -0.00033928 0.00230902 Cs 17.87318053 17.72227295 5.23742993 0.00054296 -0.00158197 -0.00206126 Pb 20.89348633 20.81824485 8.91289536 0.00069245 -0.00142009 0.00147730 Br 20.62951774 20.95676146 5.79922715 -0.00313153 0.00133545 0.00110611 Br 20.32875085 17.95452027 8.89677190 0.00014338 -0.00167602 0.00125868 Br 18.05798890 21.35428940 8.95260659 -0.00432802 0.00069881 -0.00573712 Cs 17.72140969 17.64808965 12.08580037 -0.00057547 -0.00201678 0.00112272 Pb 20.64388911 20.87753875 14.78437230 0.00051875 -0.00174833 -0.00068524 Br 21.06617096 20.88860479 11.93308129 -0.00189947 -0.00071447 0.00260277 Br 20.42461185 17.87274089 14.37784691 0.00048425 0.00561102 0.00353899 Br 17.79320245 21.72337596 15.25752501 -0.00010218 -0.00022430 -0.00134487 Cs 18.09903237 17.85206902 18.21323550 0.00495817 -0.00194988 -0.00133914 Pb 20.94903164 20.75364477 20.79248404 0.00210157 0.00143731 -0.00293913 Br 21.00629148 20.48129774 17.95477327 0.00022741 0.00071329 -0.00157437 Br 20.91932077 17.84761007 20.32371036 0.00159424 -0.00193518 0.00352569 Br 17.92648346 20.70489240 21.31379459 -0.00176956 -0.00166102 -0.00145565 320 Lattice="23.77195271 0.0 0.0 0.0 23.77195271 0.0 0.0 0.0 23.77195271" Properties=species:S:1:pos:R:3:vel:R:3 Time=20299.99999999 energy=-11425.50002289 virial="-21.26822518 2.56774877 1.38397469 2.56774877 -9.15950369 2.20440023 1.38397469 2.20440023 -7.46489551" stress="-0.00062732 0.00019114 0.00010302 0.00019114 0.00023905 0.0001641 0.00010302 0.0001641 0.00042655" pbc="T T T" Cs 23.48219331 23.68135333 0.29057208 0.00144050 0.00039659 0.00122364 Pb 2.97916908 3.04709845 3.16730207 0.00123423 -0.00026877 -0.00164380 Br 3.36411988 2.90712028 0.18186611 0.00083954 -0.00138555 -0.00169783 Br 2.72617143 0.08383856 3.02429761 -0.00033143 0.00121243 -0.00068000 Br 0.07548675 3.29284460 1.87972283 0.00022712 -0.00173679 -0.00075698 Cs 0.51509292 0.14239703 5.64618939 -0.00132794 -0.00125596 -0.00135241 Pb 3.23561379 2.99559204 9.06518305 -0.00090137 0.00007857 0.00024455 Br 2.62785553 2.88955495 5.93874791 -0.00050197 0.00266264 -0.00242482 Br 3.14535589 0.13116447 9.50283235 -0.00021583 0.00158143 -0.00207451 Br 0.16367209 2.43967424 10.15675038 0.00407417 -0.00321875 -0.00443786 Cs 23.58330692 23.31198249 12.04952888 -0.00016101 -0.00197450 -0.00153859 Pb 3.19898938 2.89558105 14.84308166 0.00171738 -0.00261620 0.00062353 Br 3.48011905 3.15517096 11.86301483 -0.00248470 0.00192144 -0.00163461 Br 3.00612220 23.53002977 14.61102301 -0.00086604 -0.00283898 0.00219987 Br 23.69144619 3.03691195 14.40831122 0.00100467 -0.00145989 -0.00052224 Cs 0.00587093 23.25069942 17.54247898 -0.00002492 0.00050914 0.00101985 Pb 3.04650045 3.07102885 20.80495777 -0.00277036 -0.00265191 0.00040232 Br 2.91178069 3.34551634 18.00431565 0.00216164 -0.00048531 0.00271886 Br 3.00541692 23.68715596 20.69928463 0.00019680 -0.00134992 -0.00133100 Br 23.68853588 2.22891296 20.98514631 -0.00050971 -0.00239550 0.00259508 Cs 23.38375391 6.61378085 23.55361479 -0.00090951 -0.00093468 -0.00045234 Pb 2.80853218 9.01636227 2.68629786 -0.00188070 0.00010662 -0.00035471 Br 3.12006808 8.23869002 23.74094525 0.00161750 0.00088236 -0.00413202 Br 2.84170901 5.84669404 3.24500345 0.00029613 -0.00033245 0.00179331 Br 23.38671262 8.65700441 2.69774807 -0.00318043 0.00301961 -0.00264261 Cs 0.52930730 6.00683665 6.29523332 -0.00114960 -0.00034728 0.00140543 Pb 2.95579778 8.95664150 8.95094875 -0.00368887 -0.00306231 -0.00046518 Br 2.89905739 8.78669719 5.62671816 0.00131924 0.00166688 -0.00357173 Br 2.58753467 6.13598921 9.45461833 0.00013468 -0.00102808 0.00406690 Br 0.05706779 9.18313722 8.98311609 -0.00190703 -0.00080542 0.00131158 Cs 23.17468352 5.77002433 12.07765689 -0.00229042 -0.00190917 -0.00203400 Pb 3.05753880 8.94383214 14.84054076 0.00146151 -0.00120613 0.00121674 Br 3.28515698 9.29420719 11.87834064 -0.00086698 0.00101519 0.00162094 Br 3.62710552 5.70764060 15.02672417 -0.00259857 -0.00224655 -0.00299545 Br 0.21809361 8.57549188 14.31607389 -0.00117999 0.00259862 0.00304578 Cs 23.54984959 5.67814405 17.71469061 0.00060963 -0.00106044 0.00036059 Pb 2.64147602 8.60653600 20.79968235 0.00152767 0.00120657 0.00244723 Br 2.64476096 8.62461570 18.00585154 -0.00002391 -0.00209420 -0.00224974 Br 2.33481235 5.66216488 21.20919086 -0.00147519 -0.00072192 0.00253053 Br 23.70426233 9.50442215 21.39876238 0.00419892 0.00139281 0.00102097 Cs 23.74940745 11.84432940 0.33443004 0.00194115 0.00138773 -0.00027651 Pb 2.78106611 14.87163583 2.90142309 0.00257898 -0.00115477 -0.00096452 Br 2.43335578 14.95229527 23.69911319 -0.00122100 0.00088487 0.00329325 Br 2.63329268 11.83661139 2.40670230 -0.00122644 -0.00052612 0.00403997 Br 23.58473775 14.75532404 3.21398809 0.00067546 -0.00021307 0.00149308 Cs 23.77087467 12.11525023 6.24989795 0.00106665 0.00230521 -0.00275140 Pb 3.03204212 14.67087706 8.97982074 -0.00021118 -0.00329481 -0.00105081 Br 2.80291279 14.86578721 6.03759513 -0.00181947 -0.00054897 0.00138118 Br 2.84178106 11.85868455 8.57941635 -0.00121216 0.00395713 -0.00171917 Br 23.61933307 14.78057068 8.86929372 -0.00191258 -0.00160448 -0.00009403 Cs 23.47370958 11.98717373 11.49210093 -0.00150845 0.00105007 0.00105327 Pb 3.04453952 14.56815601 14.75078420 -0.00019819 -0.00046995 -0.00254649 Br 2.35209729 14.19735217 11.80732974 -0.00251068 0.00029749 -0.00191192 Br 2.45970810 11.64889369 15.44129547 0.00175068 0.00109458 -0.00139876 Br 23.56283726 14.75352688 14.62187520 0.00075566 0.00260588 0.00123762 Cs 0.10274659 11.61606969 18.11934815 0.00089217 0.00018970 0.00025062 Pb 3.18699649 14.75103498 20.93177290 -0.00033496 0.00175349 0.00012540 Br 3.09447598 14.67445103 17.84298701 0.00193693 -0.00346537 -0.00392630 Br 3.02009846 11.63681020 20.90230539 0.00109577 0.00101264 0.00019534 Br 0.06524734 14.91916854 20.41472790 -0.00142732 0.00175512 -0.00282500 Cs 0.02937169 17.91445350 23.38860876 0.00005039 -0.00027714 -0.00119405 Pb 2.94274650 20.96380340 2.81168407 0.00022971 0.00103540 0.00059227 Br 2.73233958 21.46440809 23.48987967 -0.00054704 0.00124099 -0.00038036 Br 2.48855256 17.99076502 2.84120894 -0.00329213 0.00080388 -0.00037440 Br 0.14173381 21.10066649 3.40937799 -0.00102926 0.00073739 0.00209752 Cs 23.14575813 17.78600499 5.87858784 0.00298634 -0.00037692 0.00131288 Pb 2.99906193 20.83460613 8.93135595 0.00002002 0.00113000 0.00086951 Br 2.97764300 20.63954023 5.86095635 -0.00106976 0.00270858 -0.00013037 Br 2.97602509 17.92948636 8.84370024 0.00056050 -0.00431943 -0.00284914 Br 0.15676047 20.39195406 8.72014744 0.00278797 -0.00336662 -0.00180118 Cs 0.88129906 17.72619741 11.64576125 -0.00044111 0.00276618 -0.00144530 Pb 3.14297210 20.55166081 14.82916323 0.00063496 -0.00157697 -0.00109483 Br 2.63047182 20.58501287 12.06030120 -0.00123759 -0.00253541 -0.00328825 Br 3.10916471 17.42748799 14.93736598 -0.00056075 0.00223853 -0.00425704 Br 0.30522506 20.79288423 14.93606522 -0.00066836 -0.00103446 -0.00019177 Cs 0.29714973 17.74161897 17.90073497 -0.00164675 -0.00028653 0.00184247 Pb 2.99119909 20.81509875 20.67264361 -0.00181891 -0.00047417 0.00250883 Br 2.74501467 20.50438079 17.72425804 -0.00350738 0.00315176 0.00294863 Br 3.07272409 17.75339742 20.47486060 0.00091080 0.00148172 0.00540013 Br 23.72507947 20.45948667 20.69349883 -0.00095960 0.00015230 0.00073465 Cs 5.53456504 0.32577101 23.14193958 -0.00073987 0.00025319 0.00294615 Pb 8.66824226 3.29216099 2.93574354 -0.00052260 -0.00006790 0.00060610 Br 7.96314624 3.02391705 0.11199863 -0.00706544 0.00132160 -0.00291410 Br 9.11780541 0.34325259 3.72560582 -0.00272712 -0.00127145 -0.00304608 Br 5.79869902 3.17072237 3.81212198 -0.00046729 -0.00039779 -0.00115808 Cs 5.59065753 23.68877269 5.98028023 -0.00149498 0.00137322 0.00156013 Pb 8.99076322 3.03971740 8.85107360 0.00225490 0.00007230 0.00050151 Br 9.35601520 3.53550683 5.97868354 -0.00233023 0.00150028 0.00209678 Br 8.47653594 0.06146215 9.24290849 0.00139816 -0.00026154 0.00125522 Br 5.71355174 2.71276690 8.01069913 0.00010764 -0.00149850 -0.00106659 Cs 5.95832308 0.28144580 12.06428933 0.00007280 0.00194481 0.00011028 Pb 9.38182560 2.93485297 15.14164180 -0.00085458 0.00019824 0.00058348 Br 8.62294833 2.85275453 12.36650565 -0.00185182 0.00129059 0.00126064 Br 9.59468546 0.04625397 14.65166397 -0.00304230 -0.00073054 -0.00117925 Br 6.20535177 2.68645110 15.02118675 -0.00317757 0.00217948 0.00207736 Cs 6.27340988 23.24063605 17.90658217 -0.00078487 0.00081786 0.00022765 Pb 8.87709979 3.06957382 20.90588997 -0.00135061 -0.00068173 0.00124330 Br 9.35334105 2.76486150 17.97495586 -0.00067635 -0.00203918 -0.00182110 Br 8.47410418 23.57650811 20.96290247 -0.00212745 0.00362645 -0.00158636 Br 6.05322607 3.65814051 20.67650990 -0.00135727 -0.00301310 -0.00125800 Cs 5.49714551 5.48414585 0.14204437 0.00090698 -0.00317764 0.00135579 Pb 8.73936676 8.98698710 2.97985925 0.00173506 0.00120353 0.00159477 Br 8.91428112 9.03168316 23.65476716 -0.00102775 0.00186966 0.00154230 Br 8.09694817 6.10440625 2.61654038 -0.00098273 -0.00124122 -0.00392585 Br 5.82619264 9.38210118 2.94096431 0.00182192 0.00231642 0.00111192 Cs 6.27365871 6.12974123 5.82511475 -0.00010449 -0.00114534 0.00135204 Pb 9.12149185 8.69260604 8.86679357 0.00165551 -0.00132967 0.00091925 Br 8.97030587 8.54933241 5.88359051 0.00374459 0.00248667 0.00127981 Br 8.37414357 5.73572969 9.42936616 -0.00230705 -0.00310360 0.00052105 Br 5.97555722 9.00146256 8.66446712 -0.00010978 0.00498898 -0.00020326 Cs 6.11354900 5.62274980 12.15531318 0.00091790 -0.00328127 0.00204737 Pb 8.94083202 8.81698696 14.70599954 0.00083503 -0.00215602 0.00000970 Br 8.45107703 8.79755468 11.94749566 0.00544719 0.00055827 -0.00087225 Br 9.22552294 6.02070909 15.32277443 -0.00312550 0.00316829 -0.00036428 Br 5.94280612 8.64248690 15.34183723 -0.00160335 0.00148447 -0.00415001 Cs 6.41549481 6.02146585 17.71398442 -0.00315411 -0.00234505 0.00056303 Pb 8.80972290 9.11795019 20.75485140 -0.00007592 -0.00108229 0.00094890 Br 8.94221791 8.92330857 17.84307841 0.00013024 -0.00115031 -0.00137864 Br 9.24820889 5.84460661 20.51162862 0.00144264 -0.00194229 -0.00173927 Br 5.71145408 8.49246179 20.76391816 0.00244962 0.00027562 0.00066871 Cs 5.43058921 11.30480646 23.71933692 0.00068109 -0.00020908 0.00076437 Pb 8.59364976 14.91902164 3.19762445 -0.00129673 0.00105119 -0.00060177 Br 8.86086705 14.64772544 0.14254759 -0.00344381 0.00138348 0.00613270 Br 9.09346005 11.97082730 3.34987241 0.00063554 0.00059510 -0.00142526 Br 5.93918654 14.50783157 2.66310257 0.00023829 0.00083037 0.00049557 Cs 5.78397525 12.14542359 5.45463298 0.00249437 0.00016351 0.00465638 Pb 8.84852998 14.75291523 8.97650397 -0.00045412 0.00121637 -0.00007243 Br 8.56664644 14.24130676 6.20256391 0.00108545 0.00452610 -0.00237963 Br 9.16347819 11.56162339 9.27305083 -0.00257823 0.00210981 0.00432976 Br 5.88003987 14.11397579 9.24655292 -0.00276701 0.00148090 -0.00257799 Cs 5.98659658 11.26006822 12.02213527 0.00393761 -0.00180387 0.00079691 Pb 9.19066422 14.81360255 14.70530668 -0.00282174 0.00008548 0.00014353 Br 8.85496067 15.36804037 11.90460642 -0.00086630 0.00183849 -0.00017873 Br 9.42026882 12.11245408 14.85384062 -0.00164112 -0.00099570 -0.00259427 Br 6.03989992 14.53299236 13.95198355 0.00010408 -0.00159359 0.00260758 Cs 6.44453681 11.48062347 17.38388342 -0.00050573 -0.00059151 -0.00087731 Pb 9.20872981 15.00031619 21.00827697 0.00178522 0.00011197 0.00104461 Br 9.26957097 14.82032387 17.79273721 -0.00106422 -0.00010863 -0.00104777 Br 8.61578833 11.91242867 20.49935787 -0.00172293 -0.00171826 -0.00574731 Br 5.91509988 14.79078456 21.25706905 0.00193308 -0.00145140 -0.00013974 Cs 5.58340536 18.48006841 23.34568617 0.00197423 -0.00146954 -0.00215633 Pb 8.89140317 21.02156829 3.02839476 0.00182819 0.00361914 -0.00087930 Br 8.86346869 21.03992205 23.75728980 -0.00093157 -0.00044963 0.00170436 Br 8.54124496 18.06626993 2.70468861 0.00046944 -0.00091758 0.00120961 Br 6.07124243 21.26461953 2.99570894 -0.00263783 0.00396649 -0.00245914 Cs 5.39978589 18.09601973 5.50170114 -0.00089245 0.00017868 0.00031965 Pb 9.08328909 20.53026546 8.82593165 0.00155226 0.00172987 -0.00053470 Br 8.32428280 20.81509653 5.77965496 0.00389062 0.00286354 -0.00076619 Br 8.41574383 17.55342888 8.10850340 0.00326347 0.00157557 0.00177000 Br 5.90205914 20.66594505 8.69856131 -0.00113866 0.00126083 0.00326286 Cs 6.11115337 18.31530295 11.57102665 -0.00068806 -0.00063035 0.00201411 Pb 9.27504469 20.74460814 14.98659840 0.00088355 0.00042005 0.00093216 Br 9.62498397 20.18839212 11.77457740 -0.00126341 -0.00485730 -0.00005838 Br 8.26492450 18.09221672 15.14795815 -0.00130420 -0.00284598 0.00252605 Br 6.38359541 21.46531610 14.68057349 -0.00147081 -0.00004588 -0.00194178 Cs 5.69130488 17.36397321 17.52463448 0.00058824 -0.00010103 -0.00115369 Pb 8.96045015 20.58960301 20.72919172 0.00084788 -0.00046824 0.00243030 Br 9.56889226 20.72065875 17.92933129 -0.00263768 0.00072343 0.00080409 Br 8.47843921 17.73761545 20.89517492 0.00324712 0.00246592 0.00090017 Br 6.01840650 20.93768990 20.51418731 0.00166976 -0.00018980 0.00044413 Cs 12.19329856 23.62848834 0.09654954 -0.00013188 -0.00172422 -0.00077136 Pb 14.97439392 3.13319575 2.91093867 -0.00008048 -0.00213554 0.00080487 Br 15.63107007 3.19799695 23.50635697 0.00130726 0.00221944 0.00117385 Br 15.66462618 0.10399556 3.32754119 -0.00353401 0.00090863 0.00203656 Br 11.85196488 3.63238988 2.32363088 -0.00263878 -0.00126196 0.00020886 Cs 12.03884216 0.45923899 5.37762385 0.00032100 -0.00021165 -0.00022599 Pb 14.91683765 3.21696612 8.85919741 0.00013558 0.00054076 0.00080496 Br 14.34353518 3.20672263 5.87002486 -0.00012505 -0.00305431 -0.00027536 Br 15.17821433 0.15091347 9.02057345 0.00088957 0.00103461 -0.00154997 Br 11.81862921 3.14933072 9.03297712 -0.00092935 0.00204318 -0.00507741 Cs 12.01232042 0.76455799 11.81593642 -0.00040993 0.00212817 -0.00147318 Pb 14.95160051 2.56649018 14.86687254 -0.00196086 0.00210573 -0.00109820 Br 14.89554252 3.08072719 11.76931142 0.00144384 0.00012691 -0.00399616 Br 14.24876751 23.61325811 14.50337413 -0.00269983 0.00222936 -0.00263424 Br 12.01297187 3.53513219 14.46923448 0.00179830 0.00029715 0.00335862 Cs 11.87707480 23.72571230 18.65603656 -0.00136217 0.00305344 0.00112958 Pb 14.78020166 3.09496626 20.69130675 -0.00099404 -0.00112580 -0.00065942 Br 14.00145071 2.65972198 17.66652490 -0.00278594 -0.00447167 -0.00087831 Br 15.45389146 0.24047626 20.21944633 -0.00044594 0.00558233 -0.00172328 Br 11.95128782 2.39940198 21.40186272 0.00300265 0.00067866 -0.00271091 Cs 12.03744536 6.03699132 23.53782120 0.00083905 -0.00144655 0.00142243 Pb 14.67497680 9.00772096 2.84168499 -0.00152093 0.00069495 -0.00286886 Br 14.94722931 8.40022906 23.68278941 0.00117672 -0.00090599 0.00002737 Br 14.94386862 6.04669892 2.88540607 -0.00189275 -0.00451227 -0.00385443 Br 11.74931784 8.23608293 2.70407458 -0.00012648 0.00026310 0.00091665 Cs 12.38488599 6.02811347 5.68904879 0.00101043 -0.00072241 0.00058933 Pb 14.53857410 9.14412892 8.80524054 0.00002234 0.00132986 -0.00117947 Br 14.26986103 9.38615906 5.99531872 0.00008469 -0.00221676 -0.00299870 Br 15.24494936 6.03155045 8.66915482 -0.00024621 -0.00003813 0.00287268 Br 11.96780099 7.86904705 8.74018293 0.00361334 -0.00087768 -0.00069644 Cs 12.15798406 6.07694652 12.13277584 0.00078979 0.00088202 0.00110515 Pb 15.03036183 8.78849429 15.06249387 -0.00078656 -0.00171922 -0.00012825 Br 14.11245558 9.15568163 11.82060351 -0.00116541 -0.00108491 -0.00076250 Br 14.80321235 5.75350615 14.83955496 0.00261095 0.00530982 0.00157789 Br 12.03736215 8.95788050 15.16260655 -0.00081214 0.00173593 -0.00098255 Cs 12.10926472 5.98913697 17.97629708 -0.00570480 0.00308919 -0.00375207 Pb 14.79184895 9.18825696 20.95230275 0.00026373 -0.00020723 -0.00015672 Br 14.92819374 8.68314339 17.98605330 0.00199487 0.00248007 0.00120445 Br 14.67176248 6.13294762 20.68140993 -0.00287663 -0.00455273 0.00065920 Br 11.63546530 8.63289146 20.75829725 -0.00062320 0.00018478 0.00184182 Cs 11.47304672 11.79329049 0.33417313 0.00157893 -0.00043412 -0.00201646 Pb 14.69177855 14.98105331 3.03819839 -0.00016778 0.00086788 -0.00232335 Br 15.19647638 15.19023128 0.27567156 0.00096987 -0.00202586 0.00283408 Br 14.04321807 11.82775624 2.88848655 -0.00153449 0.00096846 -0.00281683 Br 11.83552955 14.86390126 2.75992940 0.00190283 -0.00140977 -0.00215661 Cs 12.02350534 12.42976483 6.05140062 0.00259378 -0.00115895 -0.00051096 Pb 14.77490252 14.97975338 8.96175132 -0.00121884 -0.00073073 0.00007409 Br 14.80128339 14.90284015 6.13068962 0.00020787 -0.00027320 -0.00331436 Br 13.75884810 11.81665719 9.09936783 0.00288541 0.00204000 0.00109264 Br 11.78651904 15.28085841 8.89192773 0.00700437 0.00126835 0.00270725 Cs 11.80707551 12.22852150 12.17437573 0.00040989 0.00035338 0.00077734 Pb 14.90306531 14.97038337 15.17912521 0.00166979 -0.00086816 -0.00065794 Br 14.68426106 14.82531122 11.92181324 -0.00085173 0.00172963 -0.00150038 Br 15.06586485 12.04825166 14.93462164 0.00300059 -0.00236400 0.00139236 Br 12.09432549 15.10858030 14.62141849 -0.00042127 0.00073395 -0.00375706 Cs 12.10629497 11.25968060 17.56936832 -0.00160659 -0.00009250 0.00251995 Pb 15.02016110 15.04286568 20.97300485 0.00008217 0.00119209 0.00087475 Br 14.66750792 14.59768207 17.98768759 -0.00018170 -0.00296167 -0.00065356 Br 14.48954372 12.01502740 21.76727973 0.00133844 -0.00088543 0.00352699 Br 12.10085820 15.73189702 21.26129083 0.00138763 0.00215289 -0.00339250 Cs 11.53231208 17.81610538 0.92919230 0.00100112 -0.00078583 -0.00022936 Pb 15.06999372 20.94415085 3.22871375 0.00086607 0.00003839 0.00062108 Br 15.26593451 20.91937373 23.76460533 0.00211497 0.00178452 0.00486881 Br 14.18406933 17.77185306 3.45521456 -0.00016012 -0.00114211 -0.00277408 Br 11.98815521 21.05918766 3.24496860 0.00008783 -0.00270652 -0.00228133 Cs 11.45111527 18.23238442 5.94668836 0.00191152 0.00038726 -0.00010040 Pb 14.88110430 21.02598632 8.99513072 0.00046634 0.00085669 -0.00001581 Br 15.63460601 20.37753251 5.96022824 0.00292155 0.00070215 0.00110160 Br 15.32847211 17.99161347 9.20731499 0.00302634 -0.00078953 0.00324895 Br 11.64648244 21.34847204 8.02873282 0.00687288 -0.00115385 -0.00173144 Cs 12.30455639 17.87204610 11.42295522 -0.00122522 -0.00435519 -0.00025283 Pb 14.86207439 20.97797945 14.75794766 -0.00128152 -0.00002911 0.00131370 Br 15.43472933 21.20375432 11.77450697 0.00221249 0.00174271 -0.00196630 Br 15.55448652 17.83132594 14.97188539 0.00009816 0.00254921 0.00302930 Br 12.09309684 19.86878931 14.70656584 -0.00084963 0.00100010 -0.00266074 Cs 11.95694997 17.58560508 18.06744856 -0.00179576 0.00043558 0.00130631 Pb 15.04352894 20.90337364 20.75393481 -0.00091260 0.00083311 0.00109317 Br 14.58911029 21.03959328 17.79206834 0.00436547 0.00202065 0.00162687 Br 15.28662366 18.05344249 20.65297556 0.00329485 0.00487257 0.00679138 Br 11.98634322 20.41583078 21.22517189 0.00237591 -0.00086650 -0.00027329 Cs 18.68002429 23.57973235 23.41944960 0.00201199 0.00215240 -0.00042343 Pb 20.97541490 3.29620831 2.71432549 -0.00093771 -0.00092455 0.00260396 Br 20.39221431 3.87889591 23.55822363 -0.00164099 -0.00101637 -0.00081152 Br 20.68532402 0.01814936 2.84478663 0.00006363 0.00040998 -0.00465164 Br 17.86052153 3.06508779 3.83458010 0.00035932 0.00000856 0.00114886 Cs 17.65439970 0.51253890 5.98005319 -0.00187286 0.00161827 -0.00017372 Pb 21.20920783 2.90897593 9.22605442 -0.00017308 0.00012965 0.00025759 Br 21.64198407 3.04651926 6.06833978 0.00118615 -0.00236049 -0.00153229 Br 20.71496267 0.05664083 9.46247367 0.00146698 0.00190947 0.00053277 Br 18.21289700 3.06440959 8.65700371 0.00101171 0.00251147 0.00078535 Cs 18.27462598 23.76440977 11.82159025 0.00114285 -0.00165963 0.00019129 Pb 20.84264101 2.93146629 14.69704860 0.00228794 -0.00167603 0.00024945 Br 20.07197327 3.62720940 11.93957948 0.00273448 -0.00081874 0.00034600 Br 21.29840558 23.67833312 15.01164742 -0.00537314 0.00169845 0.00123359 Br 17.84623605 2.94934155 15.37788839 -0.00085382 -0.00077770 0.00373848 Cs 18.13304400 23.41238472 17.87499271 0.00088997 0.00086684 -0.00065374 Pb 20.83441067 3.17624195 20.54938769 -0.00216700 0.00110304 -0.00027048 Br 21.01549141 2.92470271 17.65422827 0.00139749 -0.00203353 -0.00221434 Br 20.62616426 23.69170839 20.32815157 -0.00065634 -0.00119364 0.00235661 Br 17.90525947 3.03959974 20.37273479 -0.00005282 0.00186098 0.00190197 Cs 17.69515010 6.34454150 23.09670049 -0.00124671 -0.00353532 -0.00115342 Pb 20.42229571 8.89158238 3.01043065 -0.00212185 0.00152232 -0.00108518 Br 20.41317723 8.57158595 23.73536020 0.00269325 0.00129351 -0.00126363 Br 20.66971541 6.13715763 3.64188056 0.00289548 -0.00383489 0.00030983 Br 17.54994400 8.74434341 3.48138392 -0.00022268 0.00033197 -0.00147494 Cs 18.29853876 5.63199192 6.04157035 -0.00147246 0.00265895 -0.00059506 Pb 20.74074286 8.89015208 8.83304592 -0.00138335 0.00067234 -0.00027438 Br 21.03838569 9.18837050 6.04672995 0.00239435 -0.00036728 0.00001053 Br 21.34534546 5.97688793 8.45286622 -0.00048437 0.00183688 0.00085038 Br 17.74050652 8.93648692 8.52400350 0.00199385 -0.00405441 -0.00069209 Cs 17.57393413 6.44040765 11.53226133 0.00054536 -0.00054423 0.00077260 Pb 20.96505165 8.73071437 14.87594395 0.00019259 0.00135985 -0.00132849 Br 20.63611268 8.82689647 11.85017454 -0.00110392 -0.00053443 0.00382266 Br 20.74432175 5.77450452 14.79891964 0.00103340 0.00053751 -0.00133063 Br 17.86731040 8.26712370 14.87386027 -0.00307864 0.00191796 0.00166227 Cs 17.53359739 5.61607679 17.81652704 -0.00168615 -0.00002946 0.00104811 Pb 20.46519850 8.94163332 20.63054635 -0.00013287 -0.00015485 0.00154941 Br 21.32521545 8.60227042 17.79388798 0.00020116 0.00131834 -0.00226783 Br 20.62760957 6.01582149 20.09909368 0.00126855 -0.00229345 -0.00168758 Br 17.65836845 8.78880627 20.62147755 -0.00001905 -0.00294729 -0.00059309 Cs 17.31297963 11.76752372 0.09633551 -0.00194680 -0.00036502 -0.00012841 Pb 20.61808736 14.84595921 3.20386922 -0.00016636 0.00021250 -0.00056890 Br 21.05786114 15.08411086 0.08791708 0.00270764 -0.00108573 -0.00045805 Br 20.33891046 11.98612684 3.03567040 0.00123695 0.00045530 -0.00327708 Br 17.94255283 15.01779966 2.42158890 0.00040905 0.00048362 0.00114524 Cs 17.85498397 11.80629708 6.09420284 -0.00055659 0.00444192 -0.00195974 Pb 20.35768837 14.85936315 9.15871521 -0.00006345 -0.00002549 -0.00069540 Br 19.99019955 14.40176508 6.35084770 0.00087417 -0.00013969 0.00093941 Br 20.13073435 11.75906407 9.29467442 0.00530304 0.00273656 -0.00338592 Br 17.61098189 14.68897735 9.45881627 0.00297710 0.00159781 0.00100281 Cs 18.06629379 11.35612536 11.94281068 -0.00175375 0.00089341 0.00076371 Pb 20.79074472 14.67132567 14.77398167 -0.00048426 0.00037537 0.00225972 Br 20.69593249 14.02204802 11.99348970 -0.00218952 0.00071528 -0.00076320 Br 20.95025075 11.61235746 15.51833458 0.00157494 -0.00014374 -0.00066855 Br 17.86539293 14.72797366 15.18838490 0.00121876 0.00249338 0.00126570 Cs 17.52420546 11.68886685 17.66030457 0.00207395 -0.00094455 0.00095627 Pb 21.03768948 14.97129756 20.56988588 -0.00140003 -0.00275994 -0.00131843 Br 20.77904997 15.24191710 17.66324318 -0.00050158 0.00439875 0.00168834 Br 20.16414108 11.96063507 20.73223520 0.00179395 0.00341889 0.00361453 Br 17.83264461 14.98335281 20.59990533 -0.00273188 0.00063080 0.00168836 Cs 17.94412372 17.54734614 23.62238005 0.00000738 -0.00074509 0.00128158 Pb 21.04100959 20.82723369 2.96106281 -0.00137835 0.00144810 0.00033978 Br 21.27573931 20.69742864 0.11830371 -0.00094509 -0.00450522 -0.00274285 Br 21.04099602 17.93855966 3.04519719 -0.00078897 -0.00146517 -0.00377457 Br 17.99021246 20.62966714 2.85334372 0.00614984 -0.00052776 0.00216271 Cs 17.87870581 17.70669914 5.21733765 0.00056214 -0.00153207 -0.00195505 Pb 20.89990121 20.80424680 8.92686303 0.00058016 -0.00137963 0.00131176 Br 20.59862505 20.96961425 5.81428407 -0.00303640 0.00123324 0.00189898 Br 20.33002089 17.93559957 8.90937922 0.00011426 -0.00207603 0.00126320 Br 18.01142552 21.36004028 8.89554684 -0.00492830 0.00045213 -0.00566368 Cs 17.71562260 17.62776573 12.09658994 -0.00058101 -0.00204702 0.00103721 Pb 20.64938277 20.85873592 14.77828594 0.00057729 -0.00200304 -0.00052134 Br 21.04616684 20.88100165 11.95831217 -0.00209574 -0.00080169 0.00240145 Br 20.42991819 17.92799150 14.41414729 0.00056910 0.00540934 0.00371749 Br 17.79257347 21.71774871 15.23860190 -0.00002864 -0.00089886 -0.00243095 Cs 18.14815054 17.83245048 18.19865958 0.00486100 -0.00196822 -0.00157195 Pb 20.96891112 20.76862612 20.76341948 0.00185800 0.00155160 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I?A?k_c?%6+zWbտI^ޠ?`B;W ^??#~xgͿŨ1ʨ} i?Q{Z^? F˿qD3A>=~TDĿrg?3~V ԿuO?qwy"? _O?3͚D¿|[PUe?_OըC4Yd?9?dZο)Ѷ?H?%Z ѿADϿݔ?D ?')Ŀ()v?v>?SGBS'aT~⮫{)>v馩?i?n?4USȿD莂ʿޣ:vJz?$%ۈs[?5?p- >ZLU8?1=?dX?H=E.ſ!ӑASePdh]qÿ8L^?˷`x??÷'}^G Cοxs??\Q?<܊?L|\ d ?[yqt-zIyd?.iJeڿ잜B\r?Sr鶴?@YZ?ix?5q?Ϳ{Y嘒[kԿHVX Pm¿\Q ¿_ ѿdJE?N(<ήMk/?/ﰿzn0 a׿@9;2mRvG? 5}` _?5'd? g˿!b?1RLUп:%I/?ÿ &ȉ?6Շ?2?r3?T?{ޤ? =Q[?yU{ǿɩ?hx ?db̿dh_ſ K+ſ2U9aj?}~=zz?'}z?}d?(%;?>B<ĿtcGȋT?;Ca ٿ:jn?J5? g8?DB+1S˿N?>{K?&& ?CUfZ?Pk?'/?Խ^Ϳ_ )?(.pſȏ`߉zއ?T h┟?qU&v?~LkHӔ~ʿ;EbO?F9zs?i:kY5¿!v?f?H'ݥ?R1?]ҿ9#i?EֹϿa0^?vc,?9#?1H~;οI(ԴAr1?xr?77ʿˉZ֯=[R3㥿ܟϫ?2?`ѿ[ĒL?FB׿B@gIп\x*ɿT̿YV?ttup˿AgѿJT ʿ9_v@?̌2?j[.?Rң?W ? ^|?4?HhP?RO¿QYX?ʿ:uW %sw5<ɿ?Z#?̴w?Z,?l+̖?"97?X?7D߳>}?J ?vο*gLA?bn?Xo`?C}}𸞩ۧ%?F![vQп 3C[t̿¼"!|? .ο}K{C|?glK?Kd?ntLJjrB$? un؃ ǿ&U:?Au?_w@?/;4c΍y?ǘdccE4?b܄W?2nJ3mڟο'|Z%\%?]?Y ?E(6q?ژ+ҿ9DKΌ׿Xܻxe?jlKO7ކ𦢿4bײ˿P9^? $鼿8 ?H nۗRs˿Bvs7y?{}Xѿwu bh?C;Û?4Z[хƿIPMqտKM? Gߐ?o}?~ĿZ[?ܑo?sd2?&s?K`(?nb}vȇӴȿ鞢?6| ˿qe7?~(0ڰ?Ę9?RR?FU`]l\?(,eW?XORoAǣJ?6JĿe7;?۹p տW|ο& c=ݦڿt1?BKx?/^Ո?UY亿eA`ſ+ 0I?kףf?R ?نKÿО C?6(Ҷ>&`# п$W-?!+%ۿ@?*VtDa=Y?"h8?E?S"E?rh9?bFy9?,'?FS?Dz?TDԿwW?CLۿj?+iQ95;xV@6NNxO?[d(?ue3Iǿ)¿7{ww?W6=!G?BDʶ@^޿IYgĿ)nE:?E5ܿjyҿt Ͽi@¿l:Y?g0^}?5¢঎G?Ӆ,Æ[em U_T?eտ׉o8*?O `pw=?R³?&-M+E5p?ф?yu*s/_?N?Ssm3 p~Sst?YK?b3b43@t݀?%Tӿ mwDӿ Ȅ?|'ÿG.I3|?,#sh:ŊK%!ÿ9* &?I ?Im̻ˉZ֯?r4ҿƋ?lu%?tq9 >u#Na$9!O?g8 0b:u|14ȿ?4?I?fNי?d7K?20vel?gߔ?4$枺aNV,X?##å?|Gi?vqȿsE?ɬbb¿H@"VШTSb?>?h(]\:?Ra=G?nA?*xs޿',zrѿBbFH2z'?W3?ƛ?_/ǿ :?Z? +l? `eȿ{"positions.": {"ndarray": [[1080, 3], "float64", 166904]}, "cell": [[35.60125893, 0.0, 0.0], [0.0, 35.60125893, 0.0], [0.0, 0.0, 35.60125893]], "momenta.": {"ndarray": [[1080, 3], "float64", 192824]}, "calculator.": {"name": "unknown", "parameters": {}, "energy": -38569.24448013, "stress": [0.00032812, 1.5e-06, 0.00016389, -4.528e-05, 0.00018627, -0.00010603]}, "info": {"Time": 2000.0, "energy": -38569.24448013, "virial": {"__ndarray__": [[3, 3], "float64", [-22.42128116, -4.78426305, 8.40518306, -4.78426305, -37.15916691, -2.04318393, 8.40518306, -2.04318393, -29.83193797]]}, "stress": {"__ndarray__": [[3, 3], "float64", [0.00032812, -0.00010603, 0.00018627, -0.00010603, 1.5e-06, -4.528e-05, 0.00018627, -4.528e-05, 0.00016389]]}}}dump_with_velocities.xyz000066400000000000000000015707311522712672400352350ustar00rootroot00000000000000dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/tests/trajectory_reader/trajectory_files1080 Lattice="35.60125893 0.0 0.0 0.0 35.60125893 0.0 0.0 0.0 35.60125893" Properties=species:S:1:pos:R:3:vel:R:3 Time=500.0 energy=-38569.18799591 virial="-8.94988471 2.4137949 -5.50609137 2.4137949 -11.40798285 -3.67371139 -5.50609137 -3.67371139 14.0956178" stress="0.00062667 5.349e-05 -0.00012202 5.349e-05 0.00057219 -8.142e-05 -0.00012202 -8.142e-05 0.0011374" pbc="T T T" Cs 35.49864674 35.46639586 0.17918569 0.00110336 0.00111412 0.00155735 Pb 3.01637108 3.02819058 2.89811028 -0.00100465 0.00085346 -0.00125993 Br 3.33355666 3.05802449 0.00510028 0.00139634 0.00269955 -0.00236018 Br 2.38218237 -0.02001685 2.92525985 0.00059981 -0.00173836 -0.00372708 Br 35.51266599 3.65396702 2.90811710 0.00247676 0.00041072 -0.00134462 Cs 0.14795431 0.57606010 5.84829266 0.00025091 0.00183596 0.00005673 Pb 2.76570460 2.87591327 9.00896951 -0.00125924 0.00092048 0.00104950 Br 3.03593227 2.99864610 5.92615797 -0.00228339 -0.00259377 0.00123477 Br 3.07807400 35.49146320 8.52016982 0.00179520 0.00291556 0.00062531 Br 35.44251522 3.06317454 8.80987203 -0.00306424 -0.00029107 -0.00196764 Cs 0.30279477 35.48785711 11.84911825 -0.00068355 -0.00232293 -0.00104394 Pb 2.65562721 2.58922045 14.75993085 -0.00152291 0.00071395 -0.00163812 Br 1.94953929 2.89983318 12.02300203 0.00138145 -0.00018348 -0.00204889 Br 3.20361457 35.32715925 14.13414003 -0.00019793 0.00231531 -0.00216412 Br 35.33556604 2.29688903 15.14658724 -0.00138296 -0.00394866 0.00151536 Cs 35.30097815 0.20513132 18.13059156 -0.00013956 0.00091795 -0.00094903 Pb 2.88601334 2.62153115 20.74476594 -0.00098528 -0.00227864 -0.00072755 Br 2.84413027 2.47813597 17.68129031 -0.00044860 -0.00243145 0.00006064 Br 3.51093574 35.51838268 20.71799293 0.00034377 -0.00322430 0.00134509 Br 35.52841903 3.46231077 20.73207201 -0.00006183 0.00128551 -0.00144023 Cs 0.05965226 35.17066249 24.19081820 -0.00094664 -0.00235806 0.00122515 Pb 2.74381535 3.21308043 26.58197623 0.00245066 0.00118486 -0.00080326 Br 2.93560144 2.41641168 23.61054173 0.00073681 -0.00280315 -0.00041812 Br 3.19872905 0.28937623 26.88769578 0.00151979 0.00010991 0.00093270 Br 35.57807754 3.01651067 26.06719663 -0.00170755 0.00269754 -0.00219394 Cs 35.46322695 35.03104270 29.52404953 -0.00147089 -0.00061917 -0.00272406 Pb 2.88938908 2.97582174 32.67013970 -0.00129164 -0.00045200 0.00170524 Br 3.01799540 3.07112768 29.86401280 -0.00202215 -0.00043091 0.00069106 Br 2.76719896 35.52259823 32.45548220 -0.00215375 0.00241589 -0.00227633 Br 0.04924286 2.58527094 33.28728817 -0.00005598 0.00009420 -0.00382196 Cs 0.31210638 5.82105730 35.63819371 0.00045892 0.00146475 0.00290708 Pb 2.99107131 9.13096268 3.09427436 0.00055032 0.00073625 -0.00021370 Br 2.77979441 8.65080560 35.50689943 -0.00137121 -0.00191606 0.00069359 Br 3.27108273 5.95471479 2.52113436 0.00268627 -0.00312643 -0.00108327 Br 35.51047768 8.33529332 2.67407999 0.00037888 -0.00520030 0.00210755 Cs 35.23666389 5.84297755 5.77080164 -0.00010509 -0.00146392 0.00083818 Pb 3.01233610 8.86316869 8.98828995 0.00051792 -0.00263141 0.00025706 Br 3.40348239 8.78575810 5.80725083 -0.00176179 -0.00064073 0.00149978 Br 3.02315186 5.84440702 9.22323097 -0.00071272 -0.00317099 0.00021512 Br 0.06808259 9.13230103 8.59706592 -0.00186365 -0.00058842 -0.00055328 Cs -0.02855399 5.90886037 12.31501676 -0.00062157 -0.00170024 0.00129282 Pb 3.06073148 8.59596842 14.94715671 0.00128209 0.00194631 -0.00027998 Br 3.28514788 8.87699280 11.90534854 0.00002607 0.00160709 -0.00315849 Br 2.52823158 5.60979229 15.15263104 0.00002217 0.00057924 0.00043578 Br 35.65033906 8.98276021 14.93869453 0.00123915 0.00076005 0.00014971 Cs 0.20840538 5.90929413 17.96266311 0.00079429 -0.00065235 0.00090228 Pb 2.64067920 8.94190916 20.65652928 0.00163539 0.00097738 -0.00174515 Br 3.18462973 9.05365100 17.72478037 -0.00227910 0.00100929 -0.00119562 Br 2.95412953 5.70875806 20.60852442 -0.00379170 0.00049299 -0.00331766 Br 35.31426035 9.06485052 20.45405361 -0.00195723 0.00291342 -0.00114061 Cs 0.23223848 6.02076510 23.73788594 0.00300401 -0.00106719 -0.00257263 Pb 2.92138101 9.07943346 26.41521710 -0.00002352 -0.00055496 -0.00125385 Br 2.77013464 8.70740520 23.63971906 -0.00162582 0.00257871 -0.00115551 Br 2.69365411 6.18760996 26.57044436 -0.00318789 -0.00012107 -0.00208855 Br 0.04573228 8.96977098 26.79335127 -0.00116066 -0.00317752 -0.00334794 Cs 35.45927747 6.17602951 29.62266285 0.00121740 0.00045723 -0.00128815 Pb 2.80936458 8.93552832 32.66031315 -0.00305787 0.00154611 -0.00077416 Br 2.92850921 8.97383876 29.59822936 0.00129073 -0.00237715 0.00067362 Br 2.94725607 6.01510251 32.97015639 0.00149628 -0.00314297 -0.00055834 Br 35.57714778 8.28763007 32.69933843 0.00028519 0.00091653 0.00201552 Cs 35.36740811 11.21273731 0.06600194 -0.00245639 0.00056992 -0.00019799 Pb 2.89879238 15.00046295 2.99743800 -0.00122281 -0.00003495 0.00180968 Br 3.02663371 14.96022303 0.09821733 -0.00078667 -0.00245896 -0.00029613 Br 2.75044817 11.87107876 2.82845418 0.00052338 0.00195840 0.00288626 Br 35.65746516 15.14126660 3.40654610 0.00148388 0.00107698 -0.00043201 Cs 35.35013895 12.45350017 6.01863675 0.00139431 0.00112970 -0.00105718 Pb 2.74383248 14.67921797 8.85150345 0.00065710 0.00047988 0.00164105 Br 2.81942582 15.21205793 6.08671841 0.00044455 0.00269389 -0.00003360 Br 3.56703587 11.64270492 8.42788993 -0.00307847 0.00026322 0.00025016 Br 35.37559298 14.08757652 9.09789774 -0.00184736 0.00103665 -0.00130072 Cs 0.08198437 11.50558928 11.71811729 0.00088305 -0.00145825 -0.00101683 Pb 2.96733036 14.87435208 14.90753192 0.00000908 0.00255589 0.00068263 Br 3.05669923 14.62878711 11.88228795 -0.00309023 0.00022672 -0.00218961 Br 3.30066005 11.98253946 14.98108768 -0.00237336 -0.00182134 -0.00089797 Br 35.49457177 14.58871976 15.04973339 -0.00146651 -0.00163105 -0.00037136 Cs 35.45236569 11.51127507 17.80983704 -0.00351364 0.00306421 -0.00014913 Pb 2.98181746 14.59848602 20.90357953 -0.00234969 0.00104995 0.00159448 Br 2.70613710 14.76630005 17.88670532 0.00202060 -0.00262016 -0.00029511 Br 2.40155584 11.81926078 20.46693532 -0.00262183 0.00111244 0.00145675 Br 35.55760516 15.21885005 20.76420103 0.00110782 0.00292754 -0.00569244 Cs 0.15112744 11.68715239 23.97801590 0.00111608 -0.00028674 0.00063426 Pb 2.92077525 15.02598158 26.70976690 -0.00003237 -0.00085163 0.00184337 Br 3.19314892 13.68991397 24.01031369 0.00159297 -0.00535675 0.00348594 Br 2.85592547 11.85408720 26.88555717 -0.00245881 0.00115444 0.00150358 Br 0.07662368 15.09361181 26.93314531 -0.00003331 -0.00102585 -0.00082826 Cs 0.16651650 11.95052039 29.52347212 -0.00331360 -0.00178951 -0.00165911 Pb 3.22919419 14.99472169 32.64862276 0.00254724 -0.00226808 -0.00113939 Br 2.97031244 14.84134334 29.68196124 0.00133223 -0.00358411 -0.00139754 Br 3.46130937 12.13698107 32.44811554 0.00105826 0.00402159 -0.00124287 Br 35.57388300 14.95041014 32.72291122 0.00421345 0.00275783 -0.00396721 Cs 35.41766321 17.55733342 -0.05332566 0.00263448 0.00021551 -0.00332057 Pb 2.83050684 20.87738677 2.65030851 -0.00004428 0.00253590 0.00089394 Br 2.34221058 21.64668572 0.00631303 -0.00064857 0.00128292 -0.00011559 Br 3.38688505 18.11491559 2.74772861 -0.00039031 0.00330401 -0.00127368 Br 35.48206655 20.34950282 2.88619888 0.00064340 0.00049028 0.00001530 Cs 0.17228042 17.91095878 5.49554769 -0.00026488 -0.00157664 -0.00200685 Pb 2.76660390 20.78366742 8.55380570 -0.00161631 0.00164250 -0.00095558 Br 2.54313491 20.52946349 5.71180967 0.00245023 -0.00184704 -0.00434562 Br 2.34869710 17.68108665 9.09764243 0.00135262 0.00017088 0.00055889 Br -0.00110650 20.28894605 8.80095688 -0.00193062 0.00270967 -0.00109162 Cs 0.26332668 17.32529991 11.98328443 -0.00100025 -0.00089933 0.00004474 Pb 2.94456584 20.68019912 14.57727732 0.00121947 0.00090479 -0.00055418 Br 2.88670444 20.48413205 11.83906155 0.00218557 0.00013002 -0.00247689 Br 2.27419992 17.71743568 15.28255706 -0.00168753 -0.00246290 -0.00119294 Br 35.62862497 21.00064204 14.56350738 0.00087047 -0.00283462 -0.00322187 Cs 34.97105475 17.57026058 17.19230162 0.00022215 -0.00134545 -0.00010820 Pb 2.62384813 20.41425726 20.50850897 0.00083464 0.00133601 -0.00013649 Br 3.07384385 21.29471934 17.77510891 0.00060826 0.00026589 0.00243823 Br 2.90406812 17.63158751 21.74280770 0.00005305 0.00333685 0.00207695 Br 35.42186476 21.39977660 20.49626927 -0.00007592 -0.00243082 -0.00054609 Cs 35.62953639 17.62266086 23.70440437 0.00039189 0.00392786 -0.00212138 Pb 2.94722832 20.91956873 26.63914240 -0.00260766 0.00196117 0.00074891 Br 3.27536394 20.92844432 23.72604417 0.00155432 -0.00076199 -0.00239200 Br 2.85718087 17.90301866 26.35155124 0.00159700 0.00062166 0.00115189 Br 35.60942411 20.80951460 26.25105966 0.00163476 -0.00062180 -0.00070614 Cs 0.19249111 17.54784366 29.99962739 -0.00258028 0.00202112 -0.00084090 Pb 2.99853406 20.90914702 32.61712571 0.00176989 0.00015307 -0.00111917 Br 3.21804613 20.28693753 29.83829424 0.00150223 -0.00004550 -0.00038919 Br 2.82435780 17.90899868 32.88302386 -0.00274564 -0.00102838 0.00316169 Br 0.17773880 21.19469607 32.20829438 -0.00022561 0.00105617 -0.00038481 Cs 35.54004263 23.88578078 35.40830268 0.00283275 -0.00372532 0.00290039 Pb 3.04899977 26.72336727 3.04570285 0.00029511 0.00064507 0.00107476 Br 2.86068434 26.71848850 -0.06291978 -0.00356242 0.00245698 -0.00186080 Br 2.76218791 23.75466107 3.36974564 0.00118434 0.00089597 0.00356156 Br 0.25018379 26.24817527 2.94604768 -0.00043997 -0.00315393 -0.00419432 Cs 0.36842683 23.70523546 5.76947366 -0.00067234 -0.00297434 0.00024637 Pb 3.11275162 26.67846304 9.05544494 0.00039861 -0.00050259 0.00060143 Br 2.87136853 26.77540082 5.96637785 -0.00203398 0.00247292 0.00152545 Br 2.91769889 23.74856457 8.80394698 -0.00207328 0.00004199 -0.00003884 Br 0.10469274 26.71360339 9.00774291 -0.00295401 -0.00057370 -0.00372275 Cs 0.20256004 22.91652660 11.49668545 -0.00126392 -0.00005739 -0.00243451 Pb 2.91989882 26.48013571 14.95838399 0.00059590 -0.00052461 0.00051284 Br 3.11571750 26.69613009 11.90041863 0.00123735 0.00395277 0.00343178 Br 3.22088845 23.83070636 14.18903093 0.00350885 0.00047244 -0.00184106 Br 35.35990492 26.79286421 15.49112837 0.00388133 -0.00063955 -0.00221854 Cs 35.53020065 24.05038239 17.76308715 0.00140241 0.00030049 0.00241980 Pb 3.12682389 26.60699507 20.84243498 0.00012373 0.00142782 -0.00167212 Br 2.91623541 26.31081135 17.90733675 -0.00347255 0.00290765 0.00050975 Br 2.74880897 23.49422201 21.09137246 0.00064847 0.00121900 -0.00002612 Br -0.02865200 26.91206381 21.42201736 -0.00213775 0.00154119 0.00001043 Cs 35.52838051 23.84932370 23.52300506 -0.00190993 -0.00111918 0.00249419 Pb 3.10499046 26.85570504 26.61475331 -0.00023885 -0.00021961 0.00024423 Br 3.44337031 26.52954902 23.76388482 0.00308983 0.00218289 0.00318506 Br 3.39836319 23.99351693 26.49571488 -0.00122430 -0.00300758 -0.00194500 Br 0.07088413 26.96206193 26.22127988 0.00181774 0.00106068 0.00070986 Cs 0.24938989 24.02490551 29.89983191 -0.00042265 0.00010321 -0.00126062 Pb 3.06326081 26.66601671 32.56779965 0.00063136 0.00022744 -0.00042897 Br 2.67990741 27.30252172 29.81764395 0.00122358 -0.00225004 -0.00243434 Br 3.24062322 23.69642567 31.80608914 -0.00332394 -0.00025608 0.00031780 Br 35.42052036 26.38593172 32.58928663 -0.00083845 -0.00021283 0.00351756 Cs 0.30365921 29.72496572 35.52598864 0.00148905 0.00241410 -0.00072346 Pb 3.04042283 32.57724644 3.06868438 0.00272477 0.00056236 -0.00189003 Br 3.28664214 32.51190893 35.49558809 0.00046086 -0.00146351 0.00155133 Br 2.85191686 29.61398109 2.85166011 0.00163119 -0.00205354 0.00041731 Br 35.52113215 32.36781321 2.85893049 0.00010502 -0.00008668 0.00037526 Cs 0.01760032 29.30679817 6.39812627 -0.00036075 0.00151361 0.00155323 Pb 3.02225758 32.55476943 8.82534631 0.00094388 0.00059757 0.00144219 Br 2.73899059 32.87705463 5.83691847 -0.00264110 0.00345655 0.00267239 Br 3.04360449 29.57441630 8.34782874 -0.00329346 -0.00428754 -0.00262555 Br 0.04961802 32.54595491 9.33290954 -0.00032220 -0.00054571 0.00062598 Cs 35.34187600 29.39791143 12.19153225 -0.00201075 -0.00071316 0.00211472 Pb 2.86026907 32.43563587 14.59113918 0.00165880 -0.00083951 0.00061903 Br 3.68524847 32.80833550 11.81423646 0.00074285 -0.00288685 -0.00139419 Br 2.68868888 29.51191509 14.69969065 0.00175806 -0.00176435 -0.00165213 Br 35.57842484 32.86861371 14.50448414 0.00227655 0.00218853 -0.00083447 Cs 0.13897368 29.91087246 18.11523302 -0.00165105 -0.00028056 0.00183973 Pb 3.27518390 32.51073793 20.89763509 0.00211125 -0.00128919 -0.00054718 Br 3.10464019 32.07646933 17.62476554 -0.00177592 -0.00098610 -0.00250453 Br 3.55111931 29.46173626 21.33889642 0.00196013 0.00381185 0.00114554 Br 0.29804470 32.85068244 21.01799287 -0.00142299 0.00085014 -0.00125013 Cs 0.30466168 29.64985908 23.87432193 0.00266492 -0.00037596 -0.00294437 Pb 2.91916175 32.76204667 26.89334331 0.00009226 0.00216063 -0.00026250 Br 2.99014753 32.49677012 23.83987551 -0.00027847 -0.00284698 -0.00127594 Br 3.16897893 29.88188830 26.61837735 0.00162051 -0.00139767 0.00166212 Br 0.04348633 31.92286760 26.69037123 -0.00347323 -0.00055870 -0.00071293 Cs 35.45782997 29.31208233 29.70389040 0.00278090 0.00016838 -0.00126173 Pb 2.87178978 32.59468453 32.51456834 0.00286100 0.00134908 -0.00035731 Br 2.93883015 32.97663139 29.66771175 -0.00092422 0.00011394 0.00199593 Br 3.16752830 29.63290056 32.96611082 -0.00378805 0.00067688 0.00383575 Br 0.02501656 32.53685879 33.39932281 0.00172696 -0.00174519 -0.00217000 Cs 5.87911626 35.33633203 35.51301465 0.00118239 0.00228267 -0.00133847 Pb 8.89008843 3.04907157 2.90414245 0.00035400 0.00009791 -0.00151320 Br 8.65527256 3.06917741 35.58505990 0.00186814 -0.00260722 0.00308831 Br 9.16890865 0.11594554 3.05350169 -0.00175925 0.00036594 -0.00227455 Br 5.96061573 2.24931626 2.68419993 0.00172347 -0.00213368 0.00113002 Cs 5.90028258 35.46930054 5.91460968 0.00129581 0.00114019 0.00036803 Pb 8.81002030 3.00163979 9.11668292 -0.00030389 -0.00027072 -0.00003013 Br 8.97509907 2.81912505 5.75116453 -0.00473163 -0.00363919 0.00153784 Br 9.07517881 -0.02274229 9.41331407 -0.00010482 -0.00023032 -0.00142014 Br 5.95352578 2.76603830 9.48778479 -0.00143938 -0.00122920 -0.00017819 Cs 6.17960417 0.06665708 11.80335601 0.00069012 0.00020769 0.00255371 Pb 8.68986046 2.86006789 14.55841083 -0.00061262 0.00130788 0.00084246 Br 9.24590322 2.86976169 11.87915669 0.00284492 -0.00201567 -0.00138419 Br 9.26453449 35.45806489 14.65941952 -0.00117582 0.00177482 0.00006702 Br 5.55321716 3.25567232 13.94930775 -0.00341891 0.00142493 0.00106506 Cs 5.73170569 35.59087292 17.86371474 0.00057526 0.00075476 0.00144084 Pb 8.88309578 3.13751158 21.02319914 -0.00190301 -0.00226941 0.00040795 Br 9.14068454 3.02969926 18.04395480 0.00083735 -0.00310665 0.00245613 Br 9.32113188 -0.02645055 20.86144366 0.00035187 -0.00115902 -0.00206360 Br 5.88931959 2.81637233 20.67073741 -0.00544826 -0.00221745 -0.00327151 Cs 6.04291868 35.28163627 23.65052946 0.00297227 -0.00069293 0.00202599 Pb 8.63010777 3.20387804 26.74356788 0.00152376 -0.00180447 0.00020706 Br 8.62520495 3.31965922 23.89326163 -0.00064930 -0.00096396 0.00267401 Br 8.52824559 0.01035505 26.31458910 0.00042310 -0.00219224 -0.00223895 Br 5.63466002 3.66809733 26.85442002 0.00367665 -0.00033274 0.00466821 Cs 6.05286407 0.05643724 29.63568803 0.00171295 0.00095564 -0.00080145 Pb 8.82193661 2.90716465 32.65626102 0.00041679 -0.00093681 -0.00026078 Br 9.55298060 2.91342098 29.74272834 -0.00319281 0.00318643 -0.00187148 Br 8.48186213 35.59685308 32.80364490 -0.00181464 -0.00133589 0.00225888 Br 6.02752141 3.08918917 32.58433625 0.00192906 -0.00053186 0.00186005 Cs 5.72843386 6.02072955 35.56411777 0.00179152 0.00086398 0.00057217 Pb 8.91175563 8.88918012 3.06765952 -0.00173425 0.00280427 0.00121975 Br 9.27746396 8.61440984 -0.04764987 -0.00040110 0.00174995 -0.00185256 Br 8.98602853 6.03358730 2.70220302 0.00067717 0.00087573 0.00073696 Br 5.82503032 8.95008073 2.43540682 -0.00231759 -0.00156799 -0.00056572 Cs 5.55509353 5.82760959 6.39270665 0.00106353 -0.00054324 0.00131897 Pb 8.90433731 8.91787638 9.02104359 -0.00095036 -0.00004246 0.00188830 Br 9.38072432 8.76701730 6.04667731 0.00232042 0.00426380 -0.00230380 Br 9.33086171 5.80404152 9.39378906 0.00252928 -0.00095535 -0.00434745 Br 6.12288408 8.46328219 8.75736249 -0.00052094 -0.00333733 0.00111619 Cs 6.37006517 5.96017280 11.75022404 0.00002376 0.00225853 0.00009921 Pb 9.12624440 8.95630796 15.08930297 0.00086573 0.00085026 -0.00030217 Br 8.96023935 9.04975505 12.01941164 -0.00132123 0.00286353 0.00092171 Br 8.93860940 5.66684865 14.57473925 -0.00108763 0.00025509 0.00030813 Br 5.99439565 8.10011312 14.87705534 0.00089100 0.00151266 0.00101332 Cs 5.66060601 5.40300224 18.15976179 0.00087167 -0.00217695 0.00144943 Pb 8.61595331 8.87916006 20.81089817 0.00041836 -0.00024638 -0.00029231 Br 9.54377289 8.33516251 17.81725201 0.00211139 0.00113752 -0.00308906 Br 8.37462586 5.96764233 20.98096331 -0.00075185 -0.00100826 -0.00297233 Br 5.70493634 8.77673412 20.56810681 -0.00088271 0.00091059 -0.00236480 Cs 5.53295261 5.57749495 23.67952452 0.00120107 -0.00190385 0.00087850 Pb 8.65582700 8.63616145 26.58197550 -0.00104330 -0.00013354 -0.00209970 Br 8.90784422 8.86957390 23.64430397 -0.00173718 -0.00272873 0.00177182 Br 9.29040065 5.93305852 26.59234391 0.00181354 -0.00114059 -0.00052311 Br 5.80348804 8.29285804 26.29942468 -0.00347509 -0.00137027 0.00334277 Cs 6.23673948 5.91843262 29.94032851 -0.00149997 0.00149404 0.00232563 Pb 8.81604407 8.78003275 32.67643537 0.00074117 -0.00148593 -0.00012346 Br 8.65642842 8.77255027 29.42927505 -0.00094737 -0.00543773 -0.00089148 Br 8.86747265 5.83984281 32.62587775 -0.00113710 -0.00001606 -0.00081797 Br 5.90890491 8.54553554 32.74450319 0.00174623 -0.00106636 0.00125634 Cs 5.79035249 11.83723687 0.24706827 -0.00255817 0.00108879 -0.00101820 Pb 8.93759138 14.81078088 2.70097933 -0.00073080 -0.00092001 0.00106858 Br 8.90745614 14.77513315 35.51162859 0.00150535 0.00164710 -0.00211699 Br 8.74013808 11.87839437 3.01617179 0.00287832 0.00126941 -0.00060532 Br 5.95881880 14.93691315 3.48883732 -0.00003510 0.00096311 -0.00236803 Cs 6.33432275 12.04688780 5.74885271 0.00025517 -0.00030881 0.00019272 Pb 8.82041629 14.89161943 8.78567067 0.00085531 0.00130076 -0.00203681 Br 9.15133692 14.49594437 5.73534467 0.00014498 0.00213616 0.00267650 Br 8.46388314 11.91724496 9.11724804 0.00357530 0.00078208 0.00207136 Br 5.82101017 15.28792433 8.86732223 -0.00287765 -0.00015917 0.00148113 Cs 5.94618949 11.88801705 11.56296708 -0.00066750 -0.00188221 0.00042365 Pb 8.89785350 14.93574963 14.70116663 -0.00028243 0.00020028 0.00072266 Br 9.22711597 14.81152501 11.87037164 0.00250617 0.00225190 -0.00078369 Br 8.96908999 12.02878401 15.02138214 -0.00113703 -0.00186824 0.00080542 Br 5.77045951 14.94432613 14.76266643 -0.00092868 0.00031000 -0.00233779 Cs 5.85171252 12.40019916 17.24385636 0.00070925 0.00003131 0.00009107 Pb 8.80806529 14.72551504 20.66563548 -0.00162553 -0.00184171 -0.00066960 Br 8.99734691 14.80385668 17.76014257 0.00129445 0.00079471 -0.00371373 Br 9.03342280 11.75200699 20.58037539 -0.00129908 -0.00144283 -0.00182895 Br 5.76059521 15.05941260 20.40660166 0.00098038 0.00269063 0.00227840 Cs 5.97709727 11.18296396 23.73246406 0.00162865 0.00280009 -0.00253536 Pb 8.78655272 14.48298509 26.42213212 -0.00272279 -0.00138113 -0.00064569 Br 8.46080747 14.18163686 23.62819860 -0.00232771 -0.00044544 -0.00045661 Br 8.78841554 11.58085992 26.51695409 -0.00035616 -0.00111833 -0.00129603 Br 5.95619787 14.82333392 27.04091010 0.00030571 -0.00175925 -0.00239691 Cs 5.82860377 11.54483842 29.70647108 -0.00040567 0.00028725 0.00026073 Pb 8.94814156 14.72565726 32.26548906 -0.00183140 0.00015694 -0.00234395 Br 8.65012760 14.36431803 29.42520978 -0.00013190 -0.00241085 0.00341429 Br 8.81417662 11.89683933 32.53112862 0.00096715 -0.00157505 0.00036770 Br 5.91349708 15.30438336 32.00822361 0.00288995 0.00072634 0.00127380 Cs 5.67108172 18.45978527 35.59939986 -0.00352142 0.00016876 0.00200340 Pb 8.88058972 20.90840598 3.03833532 -0.00065841 -0.00124487 -0.00235603 Br 9.39373308 20.88416662 35.33918392 -0.00055966 0.00115104 -0.00196674 Br 8.24918660 17.99678825 2.87587753 0.00040015 0.00089499 0.00034731 Br 5.81714644 21.12550507 3.13451505 0.00191950 -0.00126615 -0.00090309 Cs 5.67352532 18.48281140 5.60486928 0.00032686 0.00150051 -0.00125884 Pb 8.74966198 20.68620699 8.92433616 0.00070582 0.00009785 -0.00177770 Br 9.31855792 21.17181491 5.86172244 0.00011909 -0.00301028 0.00278275 Br 9.32912756 17.93221933 9.07635027 -0.00100262 -0.00255368 0.00112090 Br 5.83705446 20.00702437 8.60600025 -0.00034566 -0.00343491 -0.00199846 Cs 5.59194970 17.74526134 11.88757113 -0.00189157 0.00150013 0.00084621 Pb 8.64786558 20.68918495 14.81513588 -0.00180368 0.00047469 0.00007121 Br 8.94858151 20.49236649 11.94896702 -0.00087777 -0.00105228 -0.00068893 Br 9.11911099 17.86267869 14.57231035 0.00182873 0.00179543 0.00101114 Br 5.64763558 20.57349262 14.07636260 -0.00123375 -0.00021288 0.00166036 Cs 5.69759007 17.83281250 17.73287652 -0.00016731 0.00124208 -0.00259372 Pb 8.76091253 20.75687381 20.69162490 -0.00066941 -0.00216539 0.00215727 Br 8.65739974 21.02492702 17.66044932 0.00119994 -0.00274081 -0.00390447 Br 8.66204193 17.62481629 21.22342516 0.00276079 0.00219322 0.00133476 Br 5.82231912 20.23845314 20.39765422 0.00100353 -0.00089866 0.00026508 Cs 6.11976347 18.10803567 23.63390580 0.00193292 0.00222022 0.00157753 Pb 8.67696813 20.48200641 26.60968857 -0.00017900 0.00250139 0.00027063 Br 8.61399349 21.05181738 23.63676645 0.00178145 -0.00014360 0.00035387 Br 8.52248749 17.51120414 26.50693371 -0.00005805 0.00054055 -0.00285252 Br 5.97508539 21.07353114 26.56944204 -0.00036416 0.00308980 0.00286620 Cs 5.73385998 17.77042131 29.22463722 -0.00078654 0.00388393 -0.00166352 Pb 8.68780369 20.99788107 32.37780261 0.00115904 -0.00021130 -0.00046833 Br 8.25658595 20.94682732 29.63693006 -0.00009689 -0.00178718 0.00285723 Br 8.79754352 18.04003141 32.07688019 0.00004702 -0.00274929 0.00252587 Br 5.87440290 21.75169180 33.09264876 0.00112573 -0.00015689 -0.00007400 Cs 5.65400996 24.10027603 0.55161016 0.00083182 0.00191204 -0.00128756 Pb 8.90709733 26.86982768 3.33197708 -0.00020807 0.00103895 -0.00039217 Br 8.46337199 27.06598324 0.06881422 -0.00069208 -0.00016424 0.00154121 Br 9.24776660 23.88439331 2.83764591 0.00166618 -0.00264222 0.00005024 Br 5.88478867 26.36535654 3.55675945 0.00053260 -0.00187543 -0.00053739 Cs 6.27227702 23.92799094 6.29941619 -0.00132579 -0.00309860 0.00012254 Pb 8.97128474 26.76759170 9.06161411 -0.00170195 0.00172904 -0.00011545 Br 8.86955109 27.20270314 6.06230478 0.00311896 0.00382174 -0.00261730 Br 9.17753557 23.79326949 8.77874249 -0.00351360 -0.00159454 0.00248866 Br 5.92201899 26.74077343 8.77225474 0.00375693 -0.00115873 -0.00204257 Cs 5.72242235 23.86811007 12.07602316 -0.00302988 -0.00035125 0.00211811 Pb 8.95854444 26.72182762 14.87067100 0.00156594 0.00223312 0.00122309 Br 8.36700962 26.42901543 12.08566897 -0.00247816 -0.00366966 0.00400159 Br 8.01305848 23.76080840 14.75594999 0.00078074 -0.00209017 0.00055109 Br 5.93621143 26.63511807 15.36221508 0.00148041 0.00038238 0.00205203 Cs 5.81730953 23.65419184 17.85770386 -0.00260362 -0.00008127 0.00001409 Pb 8.96217297 26.78157695 20.83646195 0.00058658 0.00045027 -0.00099865 Br 8.69680459 26.67304707 18.00106894 0.00038542 0.00147486 -0.00270695 Br 8.45803739 23.70049011 20.58349820 -0.00044901 0.00525017 0.00008353 Br 5.97937623 26.86764313 20.75251940 0.00339439 0.00283743 0.00110763 Cs 5.74764732 23.59779896 23.58495349 0.00163324 -0.00222301 -0.00007494 Pb 8.96058767 26.70916673 26.81121212 0.00090529 -0.00064312 -0.00033458 Br 9.26070437 26.57563365 23.92547971 0.00099625 0.00233048 -0.00144784 Br 9.05270496 23.45539820 26.74691049 -0.00040791 -0.00077406 -0.00051969 Br 6.02427876 26.76763660 26.19052461 0.00103148 -0.00264992 -0.00524376 Cs 5.69651786 23.46504004 29.68721122 0.00369118 0.00014461 -0.00117202 Pb 9.00422266 26.65114252 32.63149982 0.00150401 0.00057805 -0.00153633 Br 8.39406089 26.48248043 29.81571082 0.00242226 -0.00044887 0.00000862 Br 9.48785829 23.82907942 32.62180967 -0.00202979 0.00033750 0.00189918 Br 5.84648508 26.93698030 32.51102743 -0.00202562 0.00060419 0.00338876 Cs 5.80833366 29.62546728 35.60047977 0.00097273 0.00024288 0.00104171 Pb 8.83945092 32.85105910 2.85700137 -0.00102712 0.00008115 -0.00020788 Br 8.56174690 32.59784655 35.49924908 0.00271703 -0.00423193 0.00111854 Br 9.03201469 29.76796379 2.73346360 0.00159676 0.00027895 -0.00192767 Br 6.04822871 33.33333794 2.91688204 0.00080644 -0.00039375 -0.00159085 Cs 5.13694627 29.87117923 5.80320587 -0.00140332 0.00064252 0.00249965 Pb 8.89305521 32.91001864 8.94807210 0.00102005 0.00038084 0.00031157 Br 8.52109627 32.65535052 5.91216315 0.00160890 0.00243376 0.00483057 Br 9.04498044 29.78871064 9.24333387 -0.00183259 0.00348049 -0.00122835 Br 5.91639311 32.48770111 8.99335668 -0.00100333 0.00070252 0.00157807 Cs 5.97037906 29.41552020 11.04301352 0.00056464 -0.00179420 0.00002424 Pb 8.78236985 32.40626105 15.04469196 0.00234594 -0.00062108 0.00174018 Br 9.13868474 32.57666681 12.18437206 0.00169092 -0.00296184 0.00034303 Br 9.00413343 29.47844339 14.28109624 0.00184342 0.00301927 -0.00173698 Br 5.88170228 32.62076712 15.21628751 -0.00059401 0.00028570 -0.00006413 Cs 6.72745408 29.61062235 17.34283829 0.00239613 0.00213918 0.00233483 Pb 9.02140514 32.73371099 20.97922184 -0.00053585 -0.00026587 0.00148893 Br 9.08501439 32.90823260 17.96035906 0.00181093 -0.00073598 -0.00156481 Br 8.87304133 29.70486045 20.70432740 -0.00274708 0.00158446 0.00103216 Br 5.98617303 32.46294172 21.40533968 -0.00120268 0.00041864 0.00031983 Cs 5.99351387 29.90430888 23.98076356 0.00275984 -0.00174433 -0.00166113 Pb 9.13767950 32.80747537 26.69517110 0.00195500 0.00056087 0.00001395 Br 9.47647766 32.72330964 23.59995208 0.00203272 0.00377279 0.00256764 Br 8.81143642 29.64142821 26.58297383 -0.00282505 -0.00308688 -0.00071097 Br 6.01640316 32.41401742 27.01091726 0.00246948 0.00459133 0.00562413 Cs 6.15320461 29.57504319 29.30533669 -0.00001756 -0.00049537 0.00128599 Pb 9.04451766 32.60111037 32.70904536 0.00280617 0.00139873 -0.00089227 Br 8.85744162 32.68154880 29.47750923 -0.00276622 0.00017402 -0.00278865 Br 9.56039757 29.76613974 32.36917834 -0.00054676 0.00014418 -0.00007778 Br 5.96209429 32.83147266 32.38803665 0.00203894 -0.00033971 -0.00056343 Cs 11.58791421 0.18340849 35.73358046 -0.00084370 -0.00342988 0.00387903 Pb 14.78207065 3.02609376 2.98527645 -0.00208513 -0.00123258 0.00181790 Br 14.94618963 4.24149565 35.50367088 -0.00144802 -0.00002263 0.00140589 Br 15.22560770 0.29637431 3.08572829 0.00249457 0.00268826 0.00189057 Br 11.99909019 2.83877365 3.13366672 0.00012511 -0.00418600 -0.00138794 Cs 11.76680862 0.03299819 5.89057407 0.00025714 -0.00093574 0.00284263 Pb 14.72885354 2.85925882 8.91565056 0.00165891 -0.00085592 0.00074418 Br 15.17468453 3.14652090 5.92320368 0.00139278 -0.00189889 -0.00348810 Br 14.55976265 35.64535733 8.29907028 0.00206471 0.00035383 -0.00112139 Br 11.82510413 2.59773395 8.71536270 -0.00492582 0.00061652 -0.00203721 Cs 12.09487263 35.30049919 11.66411242 -0.00285697 -0.00054983 -0.00085274 Pb 14.58451228 2.93217016 14.66213463 0.00141053 0.00148155 0.00053735 Br 14.59023646 2.87431862 11.85604353 0.00059649 0.00143251 0.00107896 Br 14.17009694 0.11561259 15.21337256 -0.00099280 -0.00091999 -0.00113354 Br 11.75952846 3.08450799 14.65187885 -0.00130226 0.00232603 0.00138354 Cs 11.82443823 0.34777363 17.77140777 -0.00042109 0.00200606 -0.00121080 Pb 14.74600766 2.96584326 20.73731976 -0.00003905 0.00038766 0.00390950 Br 14.51897845 3.31862390 17.57048566 -0.00051187 0.00041916 -0.00081075 Br 14.71710267 -0.00985612 20.73989054 0.00427073 -0.00065796 -0.00106573 Br 11.76343509 3.23588299 21.14821326 0.00190895 0.00210358 0.00304309 Cs 11.97548038 0.61760660 23.79965814 0.00074531 -0.00138686 -0.00099874 Pb 14.84229517 3.04125749 26.64716304 0.00015466 0.00112119 0.00274377 Br 15.01946319 2.44929120 23.76221970 -0.00174184 0.00092002 -0.00259782 Br 15.00422692 35.58089556 27.01996724 0.00004661 0.00353010 -0.00237600 Br 11.86821928 3.45331168 26.19610322 -0.00148130 -0.00173015 0.00148102 Cs 12.01096065 0.18162796 30.19223360 0.00111264 -0.00224128 0.00039562 Pb 14.88786831 3.09694116 32.75047868 -0.00133810 0.00055195 0.00451616 Br 14.80933663 3.24373580 29.71030608 -0.00145300 0.00237492 0.00093137 Br 15.21783870 -0.02178097 32.53480746 0.00109800 -0.00434941 -0.00095870 Br 11.87433673 2.69720488 32.96903324 0.00210464 -0.00061433 -0.00380623 Cs 11.65147534 5.75567500 35.30777670 0.00230328 -0.00065553 -0.00052474 Pb 14.67043682 9.01699058 2.90343039 0.00000753 0.00045945 -0.00261986 Br 15.14050176 9.12822222 35.24231375 0.00031561 -0.00095613 -0.00017941 Br 14.76623890 6.17676159 3.61519471 -0.00041325 -0.00078959 -0.00306563 Br 11.85643918 9.02085699 3.35024678 -0.00052743 -0.00422877 0.00118223 Cs 12.17201656 5.81218600 6.11145482 0.00068926 0.00086717 -0.00221573 Pb 15.04806721 8.72511888 8.98220049 -0.00193901 0.00003447 -0.00025013 Br 14.82613193 9.17819336 5.77755106 0.00376681 -0.00535121 0.00057870 Br 14.78649320 5.75098404 8.95471682 0.00230501 0.00270629 0.00172837 Br 12.10818256 8.79904304 9.16254653 0.00017303 0.00211344 0.00262882 Cs 11.94959946 6.28492620 11.96352481 0.00052929 0.00128301 -0.00269014 Pb 14.93181437 8.90882271 14.80080489 0.00127068 0.00128135 -0.00127694 Br 15.28369320 8.88671196 11.87615406 0.00208290 0.00067810 0.00154013 Br 14.83458186 5.75853466 14.29043299 -0.00105986 0.00015687 -0.00165133 Br 12.09607323 8.86543079 14.54511165 -0.00102756 -0.00080884 0.00565831 Cs 12.09807116 5.64308876 17.12172219 -0.00120642 -0.00216947 -0.00083231 Pb 14.36077059 8.91631633 20.54421653 -0.00046853 -0.00079491 -0.00112513 Br 14.67131671 8.52835719 17.71652245 0.00024216 0.00029848 0.00408543 Br 14.83412245 6.04093219 20.80024715 -0.00099467 -0.00183188 0.00255298 Br 11.61595699 9.34936499 20.66614913 -0.00241840 -0.00101638 0.00168157 Cs 11.37357594 5.84506826 23.47880485 0.00166187 0.00241255 0.00454977 Pb 14.76033001 8.89554829 26.30636840 0.00155390 0.00282248 -0.00079964 Br 13.98648970 8.85950606 23.61131956 0.00123842 0.00120117 -0.00225946 Br 14.94380974 5.83729344 26.19163409 -0.00189072 -0.00361852 0.00140622 Br 11.74080529 8.87762648 26.72943456 -0.00004947 0.00012212 0.00139791 Cs 11.77952654 5.67897875 29.32034703 -0.00360989 -0.00245840 0.00226274 Pb 14.75954775 8.75873841 32.50258672 -0.00000702 0.00217372 0.00057464 Br 14.45488267 9.21759792 29.40606908 -0.00159246 0.00010828 -0.00288030 Br 14.98122010 5.89766829 32.44027863 -0.00150728 -0.00203722 -0.00035935 Br 11.83112424 8.85699570 32.36342638 0.00076039 0.00022480 -0.00010418 Cs 12.23404697 12.37064292 0.34529351 -0.00101666 0.00114515 0.00138789 Pb 14.71166051 15.09982902 2.87947542 0.00095432 0.00158819 0.00057741 Br 15.31413710 14.80868809 35.55836355 0.00089362 0.00332723 0.00075672 Br 14.92595097 11.84868569 3.04100034 0.00254165 -0.00007915 -0.00020520 Br 11.87927589 15.22833309 2.66152474 0.00102674 0.00033844 0.00042497 Cs 11.88012745 11.48586898 6.33319747 -0.00175834 0.00052919 -0.00013909 Pb 14.78097992 14.46873694 8.75702707 -0.00011263 -0.00172697 0.00078174 Br 14.94775188 14.47468095 5.74572871 -0.00090803 -0.00057696 0.00104493 Br 14.57697771 11.67852694 8.87916689 0.00146446 0.00451543 -0.00130610 Br 11.65212798 14.82549767 8.60334007 -0.00063462 -0.00122572 0.00041561 Cs 12.06258017 11.95137284 12.03670546 -0.00175374 0.00140569 0.00045812 Pb 15.21522456 14.85456889 14.86539234 0.00015510 0.00018845 0.00087651 Br 14.86244485 14.90225494 11.90386626 0.00077637 -0.00075682 0.00104489 Br 14.73342222 11.87824684 14.97532609 0.00124294 -0.00019390 0.00136707 Br 12.03864388 14.78364028 14.93638434 0.00007295 0.00033941 -0.00022341 Cs 11.62692243 12.19488378 18.04428496 -0.00090688 0.00201094 -0.00100933 Pb 15.24760079 14.69830231 20.87179443 0.00007332 0.00120758 -0.00292382 Br 15.20956414 14.67131958 17.68423000 -0.00139298 -0.00049150 0.00097018 Br 14.84643650 11.86483677 20.21333789 0.00385172 0.00143174 -0.00282105 Br 12.14681049 14.56716683 21.27267784 -0.00246180 0.00197201 0.00439066 Cs 11.70800551 11.65591656 23.89544399 0.00188221 0.00041657 0.00022827 Pb 14.79225290 14.73645516 26.50178736 0.00084164 -0.00009594 0.00008476 Br 15.05835976 14.88778307 23.70196051 -0.00233713 -0.00055862 0.00026227 Br 15.20966731 11.76263941 26.41194007 0.00062801 0.00026390 -0.00087463 Br 11.93471499 14.30906340 27.02609950 0.00264796 0.00093092 -0.00073079 Cs 11.92187184 11.48616436 29.69972862 0.00239772 0.00054413 -0.00207165 Pb 14.85928948 14.70701953 32.50617892 -0.00014272 -0.00167389 0.00104810 Br 15.14625478 14.20779971 29.66906995 -0.00078418 -0.00029939 -0.00203146 Br 14.63672962 11.88097209 32.73759264 -0.00186669 0.00097866 -0.00047820 Br 11.70539738 14.97611324 32.65278649 -0.00236471 -0.00207566 0.00248325 Cs 11.94541750 18.13820824 34.62600191 0.00132993 0.00129708 0.00040902 Pb 14.89967190 20.67375236 2.91551182 0.00174540 -0.00123243 -0.00185024 Br 14.88341320 20.40023817 35.44397206 -0.00262623 0.00058680 -0.00140865 Br 15.07050436 17.90614032 3.49171596 -0.00240119 0.00028878 0.00018058 Br 11.89672079 20.75584973 2.78692614 0.00126492 0.00234162 0.00198104 Cs 12.61304461 17.83969817 6.31492472 0.00036225 -0.00388906 0.00104675 Pb 14.97136231 20.70337464 8.95284045 -0.00052854 -0.00161696 -0.00124469 Br 15.03238471 21.12279026 5.94431829 -0.00191093 0.00129386 0.00435986 Br 15.19289342 17.39465562 8.87935505 -0.00159439 -0.00040951 -0.00681089 Br 11.83616955 20.73968554 9.26664669 0.00336222 -0.00188753 0.00092691 Cs 11.94563521 17.79283504 11.84825664 0.00223629 -0.00089798 0.00024044 Pb 14.73070792 20.77398503 14.75021249 0.00237086 0.00170988 -0.00165537 Br 14.57133689 20.86388940 11.81061975 0.00375891 -0.00035521 0.00075345 Br 15.00924425 17.87680050 14.36784097 0.00086210 0.00485996 0.00128316 Br 11.87818961 20.44883941 14.70841433 -0.00219053 -0.00057511 -0.00216064 Cs 12.70371484 17.38342152 17.47751814 0.00159912 -0.00046489 0.00279017 Pb 14.69156938 20.99595000 20.46253454 -0.00155561 -0.00013980 -0.00066278 Br 15.10194853 20.40139123 17.68695859 -0.00076816 -0.00201929 -0.00016731 Br 15.19629293 17.81416450 20.45868603 -0.00131155 0.00203085 0.00386004 Br 11.77543825 20.74875893 20.12807905 0.00541813 -0.00216454 -0.00153731 Cs 11.93354789 17.55609302 23.27012079 0.00454304 0.00113645 0.00020154 Pb 14.65796144 20.72064948 26.65156642 0.00161835 -0.00158803 0.00237605 Br 14.14973870 20.88313479 23.64119434 0.00109750 -0.00149389 0.00131601 Br 14.36889107 17.69212904 26.42757196 -0.00001968 0.00255082 0.00239071 Br 11.89173181 20.72824566 26.96680792 -0.00248812 -0.00189169 0.00350593 Cs 11.35185680 17.80229866 29.36364505 -0.00068960 0.00045297 0.00090655 Pb 14.93850349 20.71729751 32.45521645 -0.00087788 -0.00188115 0.00044640 Br 15.47978666 20.56847819 29.62747256 0.00073481 0.00101084 0.00048142 Br 15.01292909 17.67007891 32.41060583 0.00209531 0.00263107 -0.00158234 Br 11.80352270 20.53009145 32.14997924 -0.00090892 0.00132090 -0.00136299 Cs 11.94469665 23.75912414 35.02363965 0.00135832 0.00164745 -0.00241010 Pb 14.92265060 26.63402779 3.00246223 0.00062686 0.00231197 0.00006389 Br 15.05753495 26.98924491 35.34891276 0.00251523 -0.00192146 0.00004035 Br 14.41173215 23.69877868 2.52852126 0.00279477 -0.00117110 -0.00134319 Br 11.67173329 26.51382889 3.63375714 0.00113787 0.00299962 0.00192543 Cs 11.95354819 23.59277308 5.94184520 -0.00130512 -0.00013254 -0.00078889 Pb 14.96032972 26.71188246 9.00396393 -0.00007881 0.00056934 -0.00143490 Br 14.90901920 26.68077011 5.82833086 -0.00045207 0.00111353 0.00072022 Br 15.03727924 23.84339912 8.71951413 -0.00309429 -0.00255572 0.00206606 Br 11.79103513 26.76665351 8.43151312 0.00090210 0.00022078 -0.00028101 Cs 11.94367499 24.03522295 11.73612143 -0.00019796 -0.00088244 -0.00008471 Pb 14.87195494 26.65472309 14.83750103 -0.00154963 0.00083799 -0.00087247 Br 13.94164789 26.61232380 11.93959441 0.00210733 -0.00009695 -0.00552467 Br 15.25472434 23.67311637 15.32952684 0.00004410 -0.00090798 -0.00055295 Br 11.76047947 26.57605496 15.54192021 -0.00076977 -0.00359806 -0.00313247 Cs 11.60859150 23.83402585 17.75542848 -0.00320376 -0.00025488 -0.00180174 Pb 14.74072809 26.88625656 20.68459877 -0.00060676 0.00060127 0.00118489 Br 14.96439128 27.35301892 17.60021224 0.00197421 0.00185795 -0.00230950 Br 15.13729313 24.09633107 20.55867337 0.00228030 -0.00309281 -0.00002383 Br 11.77333090 26.12263518 20.77726199 -0.00108651 0.00110900 0.00293291 Cs 11.62384394 23.72304766 24.28172826 0.00015882 0.00003543 -0.00062220 Pb 14.87806076 26.65564345 26.71120725 0.00163499 -0.00181547 0.00154970 Br 14.09826037 26.73465670 23.71183596 0.00098581 0.00153218 -0.00066736 Br 14.61704772 23.61035889 26.57383625 -0.00056689 0.00086529 -0.00088714 Br 11.96460878 26.84748597 27.44444474 0.00117258 0.00172466 -0.00111569 Cs 11.99814657 23.85537578 29.53022685 -0.00005540 -0.00152104 -0.00133553 Pb 14.79686956 26.87365507 32.50148153 0.00198371 0.00077325 0.00236186 Br 15.54949327 26.89829380 29.32867312 0.00060046 -0.00251750 0.00180441 Br 14.94588632 23.69949609 32.54136647 0.00249296 -0.00038811 0.00037074 Br 11.93222110 26.36879884 32.58729969 -0.00304374 -0.00207022 0.00278703 Cs 12.12868366 29.56281328 35.50266006 0.00169726 0.00324039 -0.00220719 Pb 14.90294367 32.75010410 2.90187110 0.00127987 0.00149265 -0.00204659 Br 14.88957586 32.42756022 35.32024364 -0.00183588 -0.00196852 0.00276192 Br 14.39678501 29.73750660 2.96217434 -0.00259352 0.00173958 -0.00140220 Br 11.70340687 33.00572477 2.87747666 -0.00187394 -0.00077068 0.00226429 Cs 11.40869852 29.93074980 5.39506912 -0.00095769 -0.00156586 0.00104098 Pb 14.84156276 32.55345344 8.72982611 -0.00053382 -0.00198123 -0.00059787 Br 14.62981549 32.42476783 5.94953125 -0.00015926 0.00187843 -0.00249166 Br 14.92117437 29.69852043 9.27241671 -0.00211243 -0.00000611 -0.00010812 Br 11.75128010 32.79985657 8.42165973 0.00052500 0.00288681 0.00173809 Cs 11.78218868 30.22337692 11.80937668 0.00050083 -0.00009874 0.00029494 Pb 14.63970656 32.69947756 14.97851081 0.00067850 -0.00021398 0.00109396 Br 14.75662967 32.95427574 11.78035743 -0.00102723 -0.00142973 0.00138331 Br 14.98288741 29.82540772 14.64516551 -0.00012146 -0.00011723 -0.00003049 Br 11.79825579 32.31986557 14.72411789 -0.00471507 -0.00035871 -0.00089463 Cs 11.49232045 29.48616507 17.72883640 0.00065569 -0.00214522 -0.00168063 Pb 14.80644579 32.70264309 20.64006488 -0.00007312 -0.00224102 0.00009562 Br 14.84477427 32.74224868 17.74187770 -0.00040196 -0.00154082 -0.00265972 Br 14.70508553 29.91740116 20.47821473 -0.00565092 -0.00020782 -0.00214102 Br 11.91166476 32.58576338 20.13745906 0.00334134 -0.00087188 -0.00081855 Cs 11.91742238 29.76685413 23.92667147 0.00071583 0.00125560 0.00044025 Pb 14.86540664 32.51401114 26.58505661 -0.00187230 0.00082278 0.00290211 Br 14.67750266 32.22689358 23.65233934 -0.00147376 0.00067362 -0.00059935 Br 14.54531986 29.71100168 27.02880498 -0.00105876 0.00373037 0.00115185 Br 11.80379309 32.61196918 27.09033325 0.00162612 -0.00021021 0.00123461 Cs 11.79094326 30.07305359 29.51877785 0.00141310 -0.00043936 -0.00241287 Pb 14.85965925 32.80286429 32.43334697 0.00167751 -0.00038253 -0.00058202 Br 14.70240322 32.80006400 29.53610716 -0.00263766 0.00087522 -0.00195331 Br 14.23677071 29.66427503 32.26342117 0.00137939 -0.00117146 -0.00188903 Br 11.81279644 32.78580045 32.36021299 0.00098877 0.00586562 0.00096533 Cs 17.93220378 0.41418167 0.25572125 0.00149726 0.00204307 -0.00198894 Pb 20.75483608 3.19221962 3.01965422 -0.00039164 -0.00200513 0.00021598 Br 20.96087673 3.20550802 35.55408096 -0.00011188 0.00349865 0.00312048 Br 20.65274723 0.14087462 2.58529937 -0.00197275 0.00024277 -0.00318242 Br 17.78398432 3.47180154 2.86530669 -0.00230791 -0.00006665 -0.00201077 Cs 17.55520546 0.17226715 6.16797714 -0.00003259 -0.00110207 -0.00039530 Pb 20.86932223 3.05576598 8.91661093 -0.00132178 0.00032566 0.00119660 Br 20.96927469 2.85336054 6.05483528 0.00215050 -0.00062752 -0.00081082 Br 20.62763625 35.54768443 8.74560965 -0.00179134 0.00010385 -0.00036954 Br 17.83512806 3.12029493 9.23396351 0.00377831 0.00224393 0.00063526 Cs 17.86175432 0.26515858 11.97643896 -0.00041269 -0.00371218 0.00051357 Pb 20.89211991 2.98306988 15.02611613 0.00069653 -0.00011752 0.00087487 Br 20.97625298 3.12838287 11.90235904 -0.00017011 0.00337580 -0.00482468 Br 20.74598771 -0.06016821 14.86288922 -0.00211719 -0.00087617 -0.00458732 Br 17.66137564 2.74942679 14.79004906 0.00201784 -0.00187575 0.00310888 Cs 18.00776320 35.34705583 18.45974214 -0.00065998 -0.00039579 0.00015546 Pb 20.69249795 2.87669311 21.11904783 0.00006981 -0.00062225 0.00159534 Br 21.43517028 2.46520213 17.89484701 -0.00338747 -0.00108345 0.00224513 Br 20.98658584 35.15111371 21.88529782 -0.00104972 -0.00545050 0.00317169 Br 17.79080958 2.73312292 20.24996803 -0.00150948 0.00153852 0.00090711 Cs 17.97873313 35.33118882 24.30062759 -0.00076520 -0.00345554 -0.00134550 Pb 20.76873816 3.07354409 26.82587939 -0.00009083 0.00032031 -0.00130509 Br 20.45075854 3.59121845 23.84807628 -0.00083150 -0.00144407 -0.00067312 Br 20.74637432 0.10370819 26.58118970 -0.00082383 -0.00262501 -0.00167975 Br 17.78608240 2.66275037 27.02317607 0.00173246 -0.00031526 -0.00338266 Cs 17.77518947 0.29223727 30.04152488 -0.00013979 -0.00133309 0.00009111 Pb 20.86122521 2.95583688 32.65488939 0.00020428 -0.00037509 -0.00021356 Br 20.47903494 2.89987817 29.67325547 -0.00212828 -0.00329342 0.00058773 Br 20.51758051 35.54196152 33.02746010 -0.00097505 -0.00052030 0.00248108 Br 17.79480327 3.25408719 32.85273794 -0.00271383 0.00238728 -0.00155621 Cs 17.89041862 6.32039749 0.61458650 -0.00181621 0.00131977 0.00019086 Pb 20.64238098 9.01601671 2.87145882 0.00178460 0.00056292 -0.00141853 Br 20.71482012 9.50862685 35.55293638 -0.00008881 0.00028635 -0.00001763 Br 20.62557379 6.18084586 3.06053040 0.00124588 -0.00053311 -0.00275088 Br 17.70489500 9.31290726 3.26257111 -0.00147868 -0.00220982 -0.00057816 Cs 17.70310164 6.27567372 5.68675180 0.00010137 0.00060017 -0.00020878 Pb 20.84728673 8.90749121 8.82796443 -0.00005240 -0.00202715 0.00156664 Br 20.92750987 9.11630866 5.90620353 0.00048695 0.00092993 0.00321194 Br 20.74111809 5.82137159 8.62586464 0.00449204 0.00355519 -0.00070710 Br 17.92488317 8.90628277 8.24731186 0.00019342 -0.00199867 -0.00001637 Cs 18.01637644 6.01673684 11.59472791 0.00014253 -0.00019834 -0.00426919 Pb 20.75273968 8.93731753 14.93802527 -0.00057804 0.00012697 -0.00332268 Br 20.69570986 9.05869065 11.76189723 0.00375232 0.00382505 0.00078073 Br 21.87830378 5.79659268 14.73873128 0.00261984 -0.00021173 0.00158673 Br 17.89233869 8.85942633 15.41476478 -0.00037082 -0.00290382 0.00395952 Cs 17.52092394 5.64756537 17.70497292 0.00035230 0.00122982 -0.00043760 Pb 20.52604933 8.83364167 20.86207816 -0.00151259 0.00122168 0.00014151 Br 20.84249469 8.86634413 17.86554170 0.00019157 0.00041249 -0.00051790 Br 20.47409210 5.74810066 20.80530196 -0.00003090 -0.00182564 0.00040336 Br 17.55584207 8.91714964 20.77661960 0.00054421 0.00008630 0.00137045 Cs 17.65034367 6.09545043 23.50195219 -0.00166057 -0.00031144 0.00150899 Pb 20.52963585 8.73884644 26.56841880 -0.00022209 -0.00080305 0.00032120 Br 20.76531528 8.45039190 23.67529054 0.00180111 0.00150204 -0.00077293 Br 20.59079632 6.04896493 27.04013455 0.00084214 -0.00108819 0.00030240 Br 17.69459052 8.81036422 26.29320749 -0.00000662 0.00130955 -0.00166351 Cs 17.92396934 5.75088996 29.30430163 -0.00008911 -0.00283725 -0.00092317 Pb 20.55843081 9.00537167 32.41252222 0.00063973 0.00129589 0.00128133 Br 20.52907571 9.48805996 29.62844155 -0.00177347 -0.00092626 -0.00042876 Br 20.88348760 5.96808642 32.72696657 0.00317435 0.00115657 -0.00033830 Br 17.66994836 8.88760618 32.26074478 0.00185788 0.00493396 -0.00187449 Cs 17.78707027 12.02423830 0.58981419 0.00145408 0.00220816 -0.00042988 Pb 20.87508050 14.94091836 3.04798814 0.00298102 -0.00090484 -0.00056202 Br 20.72943000 15.68555454 35.48252583 0.00039588 -0.00174541 0.00050425 Br 21.27095411 12.11532606 3.37095063 -0.00087880 -0.00017511 -0.00095807 Br 17.76069575 15.08323066 2.94571633 -0.00179208 0.00228665 0.00258919 Cs 17.64028021 11.95367526 6.22021050 0.00231500 -0.00031107 -0.00154135 Pb 20.76093047 14.89441217 8.86619562 -0.00170694 -0.00077116 -0.00152767 Br 20.64713700 15.11148068 6.07926987 0.00113172 -0.00178445 0.00214641 Br 20.56362400 11.63363374 8.94644099 0.00064097 -0.00027050 0.00227268 Br 17.79781582 14.42641656 9.49356547 0.00108374 -0.00114240 0.00157226 Cs 17.76648145 11.50069605 11.87100377 -0.00358121 0.00040509 0.00135319 Pb 21.13542221 14.77246420 14.76304477 -0.00025395 0.00063927 0.00127906 Br 21.29259662 14.66914778 11.88239622 0.00061463 0.00024448 -0.00135102 Br 20.46642265 11.82819334 14.88355717 0.00154456 -0.00325916 -0.00090590 Br 18.09396151 15.10872029 14.60425152 0.00098543 -0.00214181 -0.00192416 Cs 17.91755914 11.91933965 18.17165987 -0.00047348 -0.00066820 -0.00047402 Pb 21.05063188 14.75993182 20.70400168 0.00087829 -0.00061035 -0.00298319 Br 20.99281142 14.55187974 17.87602583 -0.00007227 -0.00003561 -0.00310466 Br 21.10612793 11.63691831 21.03708181 -0.00342212 0.00140381 -0.00205258 Br 18.09800016 14.65007809 20.51638922 -0.00097559 -0.00280582 -0.00205645 Cs 18.16536985 11.85975706 23.84333157 0.00056847 0.00079507 -0.00195264 Pb 20.88433794 14.70497002 26.60967713 -0.00096106 -0.00018894 -0.00033459 Br 20.51489887 15.05350793 23.58071745 0.00053737 0.00231363 0.00036674 Br 20.61804008 11.81716840 26.69836744 -0.00242924 0.00445070 0.00065784 Br 18.05019287 15.13759790 26.78445216 0.00136041 0.00202168 0.00084176 Cs 17.98289494 12.01335684 29.77265727 0.00232900 -0.00122701 -0.00103569 Pb 20.79800059 14.70129335 32.45937409 -0.00061809 -0.00208335 -0.00070591 Br 21.04189888 14.65095524 29.72106459 0.00021663 0.00233254 0.00166770 Br 21.01547971 11.76488530 32.68557734 -0.00341718 0.00121388 0.00075277 Br 17.84439576 14.53528645 32.41876688 0.00057068 0.00040309 -0.00110471 Cs 18.06385154 17.82123894 35.15178803 0.00223249 -0.00197246 -0.00163106 Pb 20.77972579 20.84472699 3.02026612 0.00077087 0.00097999 -0.00201891 Br 21.20724433 20.88550099 -0.01032884 -0.00513102 -0.00168088 -0.00277151 Br 20.92572261 18.02598058 3.47409467 -0.00061162 -0.00080552 -0.00241843 Br 17.85914038 20.88402183 3.33268288 -0.00119419 0.00103946 -0.00273117 Cs 18.14215108 17.85160695 6.18985700 -0.00029937 0.00028266 0.00078043 Pb 20.86729099 20.77827976 9.01419561 0.00080297 0.00179346 0.00121660 Br 21.06816562 20.81242606 6.13685788 -0.00021648 0.00162185 0.00059587 Br 20.56523809 17.73314435 9.31378106 -0.00070556 -0.00094773 0.00194227 Br 17.82158742 21.16184314 8.75446198 -0.00129937 -0.00070604 0.00077715 Cs 17.61371348 17.17007401 11.74673458 -0.00072960 0.00266260 0.00109179 Pb 20.83897019 20.64731711 15.03940142 -0.00210244 0.00018099 -0.00008600 Br 20.61388748 21.02492297 12.00491800 -0.00096481 -0.00014753 0.00143103 Br 21.37828904 17.69838053 14.53808745 0.00239193 -0.00321853 0.00350635 Br 17.93148235 21.04757862 14.86600819 -0.00273015 -0.00044612 -0.00157219 Cs 17.62401898 17.47937735 17.72636777 -0.00298542 -0.00039211 -0.00099428 Pb 20.61850226 20.39253794 20.88181604 0.00126598 -0.00061130 -0.00000840 Br 20.66890094 20.39759170 18.12104069 -0.00291168 0.00039126 0.00403069 Br 19.99138644 17.48528726 20.80860370 -0.00104379 0.00103313 0.00014486 Br 17.62283582 20.64273931 20.65790037 -0.00447405 -0.00069963 0.00275953 Cs 17.63235140 18.41106710 24.04263262 0.00276418 0.00094842 0.00025302 Pb 20.77371376 20.62856221 26.84896824 -0.00054702 -0.00125649 -0.00347022 Br 20.71370716 20.83863552 24.01415526 -0.00063135 0.00059599 0.00352436 Br 21.09956072 17.87983450 26.10129593 -0.00084062 -0.00243541 0.00240701 Br 17.82236142 20.87211458 26.80403210 -0.00234721 -0.00105667 0.00045305 Cs 17.74709868 17.37043425 29.65870861 -0.00104236 0.00051997 -0.00022223 Pb 20.87291801 20.76412952 32.61160478 -0.00187687 0.00037975 -0.00025591 Br 21.08263921 20.38787882 29.71708868 -0.00089871 -0.00095890 -0.00308004 Br 20.31592807 17.83297753 32.29697446 -0.00141433 -0.00222746 -0.00092272 Br 18.00539513 21.12185871 32.90195317 0.00014819 -0.00082214 0.00018716 Cs 17.21640406 24.07741338 0.17863492 0.00195398 -0.00209003 -0.00293825 Pb 20.75963245 26.84050053 3.08458309 -0.00000227 -0.00167336 -0.00192837 Br 20.95705846 27.10264313 35.58067841 -0.00223761 -0.00100619 -0.00014318 Br 21.07490623 23.90209956 2.83722314 -0.00010672 -0.00040116 -0.00088572 Br 17.67734409 26.99614757 2.64188623 -0.00083331 0.00157440 -0.00164409 Cs 17.68134695 23.56000678 6.15268022 0.00301311 -0.00084283 -0.00277017 Pb 20.81938279 26.59366120 8.95923089 0.00079122 -0.00040254 0.00061397 Br 20.17086470 26.62246938 5.88686542 -0.00407884 -0.00293890 0.00050346 Br 20.63920201 23.84927788 8.52815358 -0.00019972 -0.00341892 -0.00104598 Br 17.78912277 26.55899860 8.78834254 -0.00102678 0.00087211 0.00109981 Cs 17.67858242 23.93288458 12.37119671 -0.00123238 0.00081351 0.00381763 Pb 20.91555829 26.76731309 14.81419592 0.00022455 -0.00008185 -0.00072486 Br 20.92695528 26.32986926 12.04642108 -0.00306248 0.00159256 0.00005420 Br 21.19066417 23.70348801 15.51121800 -0.00429884 0.00226705 -0.00012872 Br 17.87581836 26.80030252 14.76986425 -0.00248682 0.00004384 0.00204720 Cs 18.16375898 23.26069127 17.85161298 0.00080170 0.00046326 -0.00115830 Pb 20.60543097 26.53747778 20.67157614 -0.00063814 -0.00027773 -0.00030226 Br 20.08835248 26.48306479 17.87010786 -0.00199817 0.00329159 -0.00476745 Br 20.49284965 23.38402809 20.84110851 -0.00046025 -0.00192949 0.00295152 Br 17.68591824 26.96390179 21.18721387 0.00207701 -0.00162542 0.00462072 Cs 18.02857935 23.65717547 23.78846798 0.00028369 0.00046114 -0.00265732 Pb 20.69821282 26.53244601 26.65318783 -0.00008735 0.00069447 -0.00071915 Br 21.18302814 26.97461934 23.66217890 -0.00085030 0.00246283 -0.00039116 Br 20.66536490 23.78819866 27.07812207 -0.00225743 0.00638112 0.00022429 Br 17.73469727 26.50345093 25.89387280 0.00018485 0.00147303 -0.00306890 Cs 18.07579426 23.44219953 30.01467280 0.00037051 -0.00059368 0.00246903 Pb 20.76857010 26.69007436 32.56556853 -0.00083638 0.00080667 0.00265533 Br 20.70122199 26.86972659 29.64778163 -0.00109096 0.00074660 -0.00027606 Br 21.20987656 23.61603027 32.51578737 0.00129441 -0.00254398 0.00189921 Br 17.69837955 26.75153145 32.48088902 -0.00219870 0.00305047 0.00202319 Cs 18.43052869 29.97031621 35.38935980 0.00059508 -0.00144629 0.00141527 Pb 20.98107960 32.70736841 3.02334176 0.00012376 -0.00124525 0.00162173 Br 21.04781842 32.55511759 35.63375018 -0.00198053 0.00084416 -0.00028159 Br 20.31553831 29.57765962 3.53831455 -0.00150530 -0.00393499 0.00191660 Br 17.95652044 32.84208184 2.79714469 0.00151635 -0.00133075 -0.00080096 Cs 17.68064451 29.26958300 5.84745222 -0.00054830 0.00133741 -0.00173414 Pb 20.65830137 32.55180587 8.91053381 -0.00201523 0.00125418 0.00154339 Br 21.30572991 32.76174854 5.88146831 0.00035107 0.00193235 0.00031838 Br 20.65306245 29.46990838 9.64411959 -0.00055955 -0.00132484 -0.00341783 Br 17.77139134 32.56388207 8.35578527 -0.00095278 0.00247827 0.00068657 Cs 17.79906188 30.40400349 11.78217256 -0.00168779 0.00228592 0.00263795 Pb 20.88893011 32.71879307 14.83997395 0.00120815 -0.00046601 0.00295605 Br 20.57811357 32.81275543 11.85441985 0.00070334 0.00066583 0.00188587 Br 20.77477022 29.65486133 15.12659656 0.00243967 -0.00000681 -0.00014995 Br 17.80679920 32.88983414 15.27747670 0.00078746 -0.00231718 -0.00160914 Cs 17.50503771 29.46011330 17.69164853 -0.00177878 0.00089171 -0.00221204 Pb 20.91062584 32.39295454 20.99618292 0.00029692 0.00046308 -0.00188791 Br 20.78482220 33.22401989 17.94655936 0.00208831 -0.00082810 -0.00064608 Br 20.62936804 29.35718741 20.36855038 -0.00283546 0.00125327 -0.00540733 Br 17.66473528 32.84888222 21.09239821 0.00339419 0.00189621 0.00340670 Cs 17.88605982 29.38380901 23.68615710 0.00078212 0.00032169 0.00235047 Pb 20.89841145 32.42639289 26.66265122 -0.00012203 -0.00162052 -0.00139372 Br 20.72392197 31.84696503 23.79611784 0.00207898 0.00413347 0.00328923 Br 20.78627671 29.62253654 26.80981954 -0.00138150 0.00150728 -0.00268895 Br 17.91846110 32.18716900 26.86575584 0.00080620 0.00102967 -0.00049896 Cs 17.63937106 29.62219082 30.05402423 -0.00034596 -0.00120993 -0.00218951 Pb 20.66866903 32.63796837 32.56287252 -0.00088329 0.00155479 -0.00127775 Br 20.68986140 32.38456458 29.50510046 0.00188692 0.00098883 -0.00127125 Br 20.34562144 29.69801956 32.80683078 0.00254647 0.00175384 0.00129459 Br 17.71043330 32.88527509 32.31192863 -0.00281335 -0.00109718 0.00064051 Cs 23.66782472 0.23174285 0.09242824 -0.00302586 -0.00018107 -0.00138612 Pb 26.57406991 3.12969969 2.85771228 -0.00000664 0.00326096 0.00020549 Br 27.33099207 3.26018209 0.09280290 0.00098403 0.00333094 0.00234494 Br 26.52321312 0.16324155 2.86540899 0.00118109 0.00192806 -0.00281025 Br 23.65952050 3.77656981 2.98629078 -0.00012397 -0.00211419 -0.00178125 Cs 23.85787232 0.01038572 5.59617464 -0.00031268 0.00002216 -0.00261291 Pb 26.68020122 2.87096399 8.91247697 -0.00169325 -0.00005574 -0.00133666 Br 26.72241088 2.99159316 5.75478992 -0.00079634 0.00751079 -0.00085941 Br 26.52122488 35.45541576 8.93496364 0.00164039 -0.00190275 -0.00082742 Br 23.74221544 3.23543101 8.93358487 0.00080164 -0.00008986 -0.00128426 Cs 23.64780189 -0.06083336 11.92171441 -0.00191886 -0.00253537 0.00097873 Pb 26.51600918 3.05785051 14.85753152 -0.00144185 -0.00176459 -0.00131852 Br 27.07883707 3.15610587 11.87742524 0.00172461 -0.00095173 -0.00239531 Br 27.14425933 35.65969801 14.90136583 -0.00173960 0.00440254 -0.00011810 Br 23.74954084 2.44231691 14.38368225 -0.00016985 0.00104467 -0.00016166 Cs 23.89188068 35.48587662 17.43014012 -0.00013849 -0.00295146 -0.00069000 Pb 26.70219587 2.65149678 20.95095952 -0.00176199 -0.00087084 0.00121339 Br 26.49477726 3.22049086 17.77144850 -0.00078810 -0.00351550 0.00005711 Br 26.18190479 35.43200874 20.00248317 0.00350915 0.00335911 -0.00055353 Br 23.61656252 2.98512039 21.06917422 0.00313266 0.00001453 -0.00101315 Cs 23.82593906 0.42904798 23.47148907 0.00276134 0.00004892 0.00006938 Pb 26.61799336 3.06589043 26.79766332 0.00082849 -0.00171961 0.00156660 Br 27.27373413 2.55954009 23.70784463 0.00127703 -0.00081747 -0.00161894 Br 26.58112810 0.08531449 26.70818604 -0.00167243 -0.00032855 -0.00181855 Br 23.78076027 3.33971200 26.56358744 -0.00066912 0.00066215 -0.00020702 Cs 24.05082125 0.01513376 30.03684581 0.00017945 0.00010659 0.00010504 Pb 26.78525560 3.09402914 32.78628659 -0.00069696 -0.00107449 -0.00085095 Br 26.47181229 3.02987317 29.79865764 -0.00212803 -0.00106825 -0.00287834 Br 26.51607684 0.14357276 32.66860687 -0.00238984 0.00371195 0.00346900 Br 23.82846847 3.40428593 33.01808599 0.00202718 -0.00094509 -0.00106396 Cs 23.48892834 6.25058090 -0.11106136 0.00107595 -0.00050857 -0.00162592 Pb 26.80099667 8.92902949 3.21487528 -0.00012311 -0.00074085 0.00070181 Br 26.25222837 8.76561336 35.55903580 -0.00021807 0.00406498 0.00170541 Br 26.89012147 6.16254953 2.96184116 0.00254097 0.00260576 -0.00102977 Br 23.53476610 9.05964467 3.17805808 0.00419729 0.00012641 -0.00205635 Cs 23.75347533 5.73207122 6.10504444 0.00100907 0.00255914 0.00009582 Pb 26.83641003 8.64088610 9.08772683 -0.00060469 -0.00072607 0.00313316 Br 26.92791383 8.76879022 6.15725756 0.00222876 -0.00011698 -0.00003633 Br 26.67681625 5.61928974 8.67157354 -0.00179834 0.00396040 0.00065224 Br 23.95061520 8.82134944 8.83894219 -0.00327322 0.00020285 -0.00014098 Cs 23.75983423 5.31438004 11.69256187 -0.00233590 0.00208045 0.00037435 Pb 26.71793349 8.76398796 14.89880225 0.00119700 0.00066248 -0.00144200 Br 27.06040490 9.04429252 12.09480023 0.00087577 -0.00285988 -0.00199489 Br 26.27324196 5.84948066 14.38971259 -0.00091920 -0.00270285 0.00091082 Br 23.85135232 9.02889592 14.95118703 -0.00163678 -0.00245401 -0.00143338 Cs 23.64903400 5.68963171 18.65033070 -0.00227441 0.00070237 -0.00042597 Pb 26.59419108 8.68588570 20.83500127 -0.00226378 -0.00170587 -0.00201247 Br 26.69355871 9.29925779 17.91087536 0.00463974 0.00144160 -0.00146756 Br 26.57533869 5.78856783 21.27014714 0.00184728 -0.00020531 0.00179353 Br 23.70398964 8.44703591 20.95763888 0.00183827 0.00304505 0.00369501 Cs 23.65800378 6.31045551 23.92041303 -0.00077409 -0.00149643 -0.00102203 Pb 26.79051235 8.89789236 26.81478246 0.00283707 0.00047320 -0.00034105 Br 26.91720828 8.92581152 23.81253333 0.00182967 -0.00335728 0.00052085 Br 26.68213691 5.81623452 25.95031769 0.00078692 0.00028883 -0.00029887 Br 23.59576944 9.01516959 26.49099078 0.00136779 0.00048415 -0.00237718 Cs 23.84868467 5.92287317 29.91035200 0.00128093 -0.00015189 0.00064753 Pb 26.63293489 9.12489473 32.53789950 0.00209565 -0.00058981 0.00127739 Br 26.35646529 8.50062305 29.86376610 0.00245065 -0.00105666 0.00397285 Br 26.98145005 6.14022665 32.55543349 -0.00067440 0.00237432 -0.00093947 Br 23.66224567 9.07822236 32.54968617 -0.00059957 -0.00046486 0.00540537 Cs 23.83729190 11.86431932 0.42443361 -0.00195142 0.00182029 -0.00070175 Pb 26.79208594 14.94670001 2.94165407 0.00093695 -0.00116611 0.00080921 Br 26.29597886 14.34016476 0.01118091 0.00096739 0.00292331 -0.00064939 Br 27.06280612 11.97725372 3.15639427 -0.00064149 -0.00222574 0.00040670 Br 23.85610114 14.73545254 2.96584454 0.00161938 0.00115646 0.00123155 Cs 24.60078422 12.16137107 5.58325519 0.00086738 -0.00188218 0.00091995 Pb 26.64210467 14.89909449 8.84259207 0.00068710 0.00024592 -0.00002192 Br 26.74692927 15.17982530 5.96744542 -0.00000875 -0.00283092 0.00005910 Br 26.69796063 11.64077347 8.85175277 -0.00142081 0.00336507 -0.00034709 Br 23.74933275 15.34640527 8.21472306 -0.00254961 0.00257549 -0.00311213 Cs 23.76317940 11.66812217 12.04713262 0.00091307 -0.00041993 -0.00184779 Pb 26.67048850 14.84262644 14.57401852 0.00220713 -0.00136288 -0.00065930 Br 26.79982012 14.64444565 11.67782400 -0.00422660 -0.00239001 -0.00270571 Br 27.02443002 11.89843826 14.50912620 -0.00056327 -0.00258214 0.00188455 Br 23.81612314 14.74007939 14.60027044 -0.00038429 0.00051509 -0.00283805 Cs 23.23728657 11.64289637 17.98436596 -0.00109017 0.00250548 0.00096836 Pb 26.66958915 14.65248595 20.90475743 -0.00022627 0.00090317 0.00136783 Br 27.04124841 14.72671865 17.61010659 -0.00170951 -0.00285518 0.00111125 Br 26.14842609 11.76533366 20.55024837 0.00010979 0.00042221 0.00129937 Br 23.74505968 15.50140296 20.91262932 -0.00176799 -0.00137876 -0.00127601 Cs 24.20295183 11.71802284 23.81270037 -0.00079689 -0.00173560 0.00195742 Pb 26.90888126 14.74936930 26.86810267 0.00129057 -0.00029711 -0.00048843 Br 27.08787955 14.45942541 23.77762573 -0.00357819 0.00318738 0.00081539 Br 27.28883558 11.79960264 26.86242080 -0.00428783 0.00065301 -0.00269097 Br 23.85683741 14.27243076 26.44701761 -0.00120192 0.00048564 0.00074512 Cs 23.81097130 11.85973707 29.26836869 -0.00145926 -0.00044886 0.00000619 Pb 26.65194909 14.85657989 32.86652398 0.00179943 -0.00169819 0.00054446 Br 26.41894828 14.75287274 29.89006713 0.00127820 -0.00313737 0.00060673 Br 26.81000026 12.01236235 32.39088560 -0.00144049 -0.00376387 -0.00282883 Br 23.58345783 14.78003621 32.26517968 0.00157123 0.00012522 0.00207010 Cs 23.36478058 18.04168293 0.35884749 0.00164992 0.00180398 0.00197128 Pb 26.64714811 21.09648276 2.96068315 0.00100084 0.00038254 0.00128742 Br 26.72021301 21.29685188 35.46610352 0.00115874 0.00249834 0.00005121 Br 26.80580335 17.82555960 2.19363726 0.00070400 0.00015303 -0.00033284 Br 23.80720915 20.89867332 3.28787289 0.00247471 -0.00263015 0.00207600 Cs 23.50955573 18.22944382 5.81118953 -0.00048562 -0.00081854 -0.00279910 Pb 26.93708726 20.85370036 8.91555370 0.00189293 0.00053020 0.00073318 Br 26.67150889 21.19815571 6.16028214 -0.00206859 -0.00035100 -0.00022230 Br 27.71894703 17.80371852 8.74476951 0.00187892 0.00265771 0.00068516 Br 23.95260107 20.28183766 9.02483286 -0.00128399 0.00326371 0.00454149 Cs 23.48276980 17.40503499 11.59945936 -0.00142682 -0.00055699 0.00337243 Pb 26.54530443 20.78890660 14.55926595 0.00147121 -0.00110269 0.00014067 Br 25.87337908 20.22313366 12.01137093 -0.00040321 -0.00207255 0.00262077 Br 26.49156586 17.75391519 14.87381137 -0.00132850 0.00222316 0.00133373 Br 23.78194520 20.71108207 15.22495991 0.00198230 0.00016581 0.00397740 Cs 24.24317264 17.07015897 17.56263952 0.00204867 -0.00128727 -0.00355614 Pb 26.36286435 20.56433635 20.79025203 -0.00033468 -0.00030918 0.00044037 Br 26.13867286 20.47539939 17.99804883 0.00187408 0.00126530 -0.00306103 Br 26.72656551 17.55016665 21.21831124 0.00006353 0.00077641 -0.00009339 Br 23.67520796 20.94630842 21.17636812 -0.00313032 -0.00004385 0.00024156 Cs 24.01397279 17.68279782 23.81450845 -0.00330388 0.00188203 -0.00022046 Pb 26.67674697 20.83555948 26.71751070 0.00110119 -0.00050076 0.00037300 Br 26.92452796 20.35917222 23.69971048 0.00425916 0.00139650 0.00024514 Br 26.71925289 17.60770949 26.53272284 0.00029717 0.00163643 0.00200297 Br 23.90607584 21.22090890 26.61765185 0.00288204 0.00244088 0.00069974 Cs 23.40418470 17.59533905 30.10243770 0.00121575 0.00066983 0.00071955 Pb 26.82073190 21.04700573 32.56638181 0.00010727 0.00081663 0.00092379 Br 26.77830837 20.67725855 29.51999776 -0.00357341 -0.00351731 -0.00028981 Br 27.10434118 17.99904862 32.99285841 0.00528150 0.00390534 0.00106382 Br 23.86027182 20.49918077 32.42174214 0.00129783 -0.00115901 -0.00569882 Cs 23.73767369 23.89512079 0.11022331 0.00066031 0.00271977 0.00198143 Pb 26.79589685 26.88499010 3.03631306 0.00091147 -0.00063824 -0.00106174 Br 26.40281826 26.25478496 0.11444112 -0.00011125 -0.00087969 -0.00211381 Br 26.82416542 23.86793749 3.22452467 -0.00311663 -0.00032315 -0.00035589 Br 23.77921676 26.69181391 3.76341181 0.00027673 -0.00033492 0.00098970 Cs 24.07168519 24.33889436 6.38421586 -0.00094800 0.00243739 -0.00012322 Pb 26.72951665 26.94292016 8.94392562 0.00093306 0.00118710 0.00121667 Br 27.22073068 26.98013220 6.13693792 0.00012063 -0.00434336 -0.00034891 Br 26.63768539 23.72055398 8.97496958 -0.00067464 0.00014700 0.00515992 Br 23.70558230 27.07866695 9.27513861 -0.00172961 -0.00117908 0.00418340 Cs 23.61128142 23.33513466 12.77910946 -0.00082528 0.00125694 0.00015091 Pb 26.72665309 26.77156801 14.88860077 -0.00137913 -0.00068299 -0.00263651 Br 26.74793222 26.71872087 11.99925490 -0.00284381 0.00022695 -0.00423137 Br 26.79784946 23.93586402 14.77238544 -0.00220924 -0.00128106 -0.00115033 Br 23.80632376 26.51795838 15.07982347 -0.00034365 -0.00031351 -0.00095756 Cs 23.86084372 23.61320130 18.24433111 0.00142086 -0.00031757 -0.00095998 Pb 26.63166081 26.71022683 20.98185444 0.00105903 0.00059677 -0.00054932 Br 26.96664281 26.44780717 17.83386938 -0.00022444 -0.00058010 0.00168864 Br 26.42329077 23.55052059 21.19105392 -0.00016349 -0.00224085 0.00446569 Br 23.63757452 26.67813907 20.56043722 0.00033549 -0.00430606 0.00055643 Cs 23.54454448 23.91439845 23.61431165 -0.00146238 0.00038869 0.00138388 Pb 26.60518839 26.83988820 26.98255861 -0.00071256 0.00131781 -0.00078013 Br 26.63794230 26.92871651 23.94745733 0.00063549 -0.00130219 0.00496460 Br 26.36424420 23.89369312 26.60401625 0.00010660 0.00285321 0.00284867 Br 23.55463946 26.65750147 26.85273824 0.00216545 -0.00130704 -0.00055665 Cs 23.93825930 23.38461628 29.27053816 -0.00198346 -0.00043672 0.00368677 Pb 26.47674402 26.79274976 32.73104801 0.00005486 0.00109067 -0.00062178 Br 26.00165383 27.09693672 29.97235303 0.00358736 -0.00075608 -0.00283962 Br 26.14874488 23.99031085 32.55495401 -0.00210248 0.00046466 -0.00187369 Br 23.65741437 27.41909606 32.83341953 0.00197314 0.00004564 0.00060437 Cs 23.86769567 30.03967438 35.49297141 -0.00030618 -0.00087331 -0.00017160 Pb 26.89398531 32.91610234 2.93731007 0.00064402 -0.00060743 0.00106633 Br 26.77293359 32.85681136 0.16247831 0.00107752 0.00032033 0.00093853 Br 26.35230211 29.89455531 3.33651633 0.00078147 -0.00142295 0.00216034 Br 23.85859403 32.94506223 2.65743479 -0.00033270 -0.00263271 -0.00194933 Cs 23.54137344 29.77993694 5.97789577 -0.00022613 -0.00024496 -0.00035294 Pb 26.72458451 32.62123294 8.82725033 0.00221647 -0.00022634 -0.00013133 Br 26.93101160 33.17996454 5.89343875 0.00012749 0.00341044 0.00073302 Br 25.78997609 29.83735434 8.82098540 0.00244926 -0.00082639 -0.00098741 Br 23.61389325 32.83566001 8.69704693 0.00380235 0.00117898 -0.00135260 Cs 23.46856505 29.71240283 12.51911564 0.00183151 0.00283938 -0.00064690 Pb 26.96949150 32.64720369 14.81818607 -0.00011905 -0.00071248 0.00064886 Br 26.88399035 32.64688120 11.86408801 0.00089407 0.00001421 -0.00076094 Br 27.06833679 29.62785831 14.91658160 -0.00259563 0.00050745 -0.00452554 Br 23.73876538 32.54460502 14.74142374 -0.00053708 -0.00247779 -0.00323844 Cs 24.17517889 29.54993483 17.85035303 0.00011329 -0.00182931 -0.00177649 Pb 26.73232882 32.71391939 20.91170006 -0.00118694 -0.00281480 0.00077374 Br 26.60978103 32.07784396 17.83072963 0.00035686 -0.00096571 0.00004899 Br 26.44661775 29.69117413 21.43898343 -0.00117440 -0.00027627 -0.00034965 Br 24.00030432 32.39491644 20.56393697 -0.00055669 -0.00439668 0.00074992 Cs 23.59409421 29.57535831 23.60941534 -0.00106386 -0.00215716 0.00148809 Pb 26.38166137 32.70741742 26.92915754 -0.00114584 -0.00048653 -0.00072986 Br 26.77321836 32.47672636 23.82017596 -0.00264141 -0.00186566 -0.00139076 Br 26.59099768 29.73853501 27.24124700 0.00033715 -0.00016864 0.00175436 Br 23.57330711 33.24337211 26.66181935 -0.00044889 0.00186008 -0.00107328 Cs 22.90045232 29.35133366 29.82923923 0.00235834 -0.00262858 0.00092428 Pb 26.62455933 32.88026727 32.68405109 -0.00051435 0.00196971 0.00215062 Br 26.93055931 33.29013922 29.95529256 0.00022349 -0.00337147 -0.00189467 Br 26.61353676 29.82990992 33.10709322 0.00034240 0.00107338 0.00091267 Br 23.72356719 32.59101221 32.93526145 -0.00241985 0.00357339 0.00019928 Cs 29.64660938 0.31563826 35.16239791 -0.00229340 0.00156560 0.00091369 Pb 32.59353873 2.79688549 2.83383549 0.00037828 -0.00099496 0.00074990 Br 32.65976347 2.57424509 35.48320196 -0.00007854 -0.00053126 0.00058288 Br 32.67039760 35.49365104 3.32512933 0.00296720 0.00107167 0.00036836 Br 29.59044037 2.94484738 3.13552539 0.00138712 0.00349909 -0.00086841 Cs 29.98297658 0.53677386 6.02849486 0.00175941 0.00286720 0.00196736 Pb 32.58949278 2.79488543 9.03733917 -0.00152423 0.00061402 -0.00112695 Br 32.62986018 2.92669736 5.87633460 0.00214204 0.00044712 0.00033900 Br 32.07857251 -0.01557542 9.31919638 0.00034754 -0.00221104 0.00289484 Br 29.67039702 3.27048076 8.98976515 -0.00424866 0.00232276 0.00041329 Cs 29.41349469 0.07248848 12.01230116 0.00254926 0.00020431 0.00017931 Pb 32.52414891 3.06476260 14.96910072 -0.00021770 -0.00059705 0.00102003 Br 32.81768013 2.72187707 12.06042654 -0.00043547 0.00016923 0.00052752 Br 32.23720655 -0.03746358 15.33008123 -0.00126981 -0.00165901 0.00339798 Br 29.65809704 3.18359060 15.14287358 -0.00009131 -0.00084519 0.00049686 Cs 29.51654002 0.22505953 17.50933820 -0.00140136 0.00202288 0.00221957 Pb 32.48812587 2.85719878 20.91205414 -0.00203824 0.00090285 -0.00082134 Br 32.22029890 3.08769986 18.01862921 0.00244601 -0.00031793 0.00281198 Br 33.02497873 0.06701182 21.60517432 0.00023200 0.00017912 0.00229068 Br 29.53302566 2.38391284 20.59273780 0.00207059 -0.00052376 0.00171402 Cs 29.26573864 35.54746320 24.08792224 0.00127272 0.00187908 0.00114186 Pb 32.59085610 2.94096963 26.59824416 0.00017538 -0.00243574 0.00158597 Br 32.20274852 3.56860817 23.82858585 0.00122899 0.00028976 -0.00497940 Br 32.92795094 35.55106559 26.90242751 -0.00173529 0.00020433 -0.00069878 Br 29.68468406 2.68869464 26.74464646 -0.00172106 0.00093698 -0.00097629 Cs 29.58405956 0.11605030 29.24788502 -0.00035918 0.00240941 0.00091279 Pb 32.65310711 2.87251539 32.60171417 -0.00179694 0.00100154 0.00229824 Br 32.57013038 3.86909104 29.73448195 0.00022838 0.00032453 0.00325373 Br 33.63874535 35.44648076 32.27204810 -0.00036177 0.00019837 -0.00310677 Br 29.74029422 2.56139110 32.53320164 -0.00062730 0.00125653 -0.00142204 Cs 29.17964526 6.33008023 0.15286370 -0.00107863 0.00025696 -0.00227882 Pb 32.46067197 8.66215754 2.94602534 0.00111872 0.00104885 -0.00178056 Br 32.35904595 9.16625290 0.05128294 -0.00064581 0.00049624 -0.00025846 Br 32.30179235 5.72236354 2.58332713 -0.00025942 0.00114255 -0.00201011 Br 29.67432738 9.21851765 2.59812932 0.00289767 0.00303130 0.00027154 Cs 29.78022864 6.00428860 6.10328089 -0.00074994 0.00279985 0.00405191 Pb 32.62295206 8.97824582 8.84619784 -0.00167287 -0.00141566 -0.00114129 Br 32.43072364 8.29209630 6.03676487 -0.00313248 0.00060751 0.00202422 Br 32.64531217 5.83886484 8.61064087 -0.00012222 -0.00140634 0.00346993 Br 29.68002694 9.11159196 8.70006732 -0.00016417 -0.00167592 -0.00091652 Cs 30.05646045 5.41561742 12.06198777 -0.00137625 -0.00157347 0.00006213 Pb 32.75685340 8.83428500 14.80743523 -0.00003045 0.00083056 -0.00056934 Br 32.67351075 8.72081289 11.70517313 -0.00054695 -0.00259712 -0.00392004 Br 33.00123487 5.95753060 14.81469108 0.00023186 -0.00037209 0.00246742 Br 29.66256764 8.45325718 15.23647439 -0.00377372 -0.00106787 0.00007173 Cs 29.81012797 5.95907427 17.57327097 0.00089957 0.00081850 -0.00112772 Pb 32.44896995 9.02382774 20.58562700 0.00092425 -0.00206250 -0.00170216 Br 32.63093848 8.77046790 17.83821189 -0.00195779 0.00213123 -0.00224554 Br 32.84409949 6.06766856 20.79148839 -0.00214714 0.00101267 0.00054205 Br 29.53457254 8.76331841 20.91355715 -0.00070508 0.00167626 0.00475188 Cs 29.82548522 6.29247546 23.40573367 -0.00191657 -0.00009893 0.00045746 Pb 32.67125993 8.86715317 26.49848296 -0.00049708 -0.00050277 0.00052312 Br 32.40278553 8.96584264 23.62493064 0.00238823 -0.00091943 0.00002540 Br 33.24990857 5.94504088 26.61243649 -0.00318191 -0.00087954 0.00102322 Br 29.72560115 8.73228327 27.12064435 -0.00220808 0.00221915 -0.00068307 Cs 29.89415040 6.38234407 29.48256597 0.00071406 0.00044281 -0.00214809 Pb 32.59793576 8.60601419 32.92231789 -0.00151244 -0.00114836 -0.00038375 Br 32.84167794 8.66623374 29.70444648 -0.00008991 0.00152008 -0.00017868 Br 32.19146274 5.70757552 33.25929881 -0.00252740 -0.00240374 -0.00216071 Br 29.56539636 8.97073444 32.74712926 0.00243101 0.00192989 -0.00471837 Cs 29.84862852 12.18723715 35.23374286 -0.00217844 -0.00104210 -0.00097982 Pb 32.50525069 14.65090436 3.24347382 -0.00134573 -0.00039517 0.00133382 Br 32.94051688 14.75326091 0.12104159 0.00009641 -0.00004805 -0.00092296 Br 32.77855156 11.71952757 3.45858067 -0.00182960 -0.00087946 -0.00057375 Br 29.64387351 14.74682912 2.29782189 -0.00168892 -0.00261032 -0.00063632 Cs 29.94630755 11.54351670 6.02002494 0.00074300 -0.00122220 -0.00154564 Pb 32.58533943 14.96256012 8.92344303 -0.00147587 -0.00086913 -0.00001611 Br 32.34837442 14.76731231 6.10280590 0.00524920 -0.00208685 0.00330854 Br 32.18645089 11.94188806 8.49290805 0.00126676 -0.00099413 0.00098912 Br 29.76904722 14.66472442 9.28701013 0.00013055 -0.00130133 -0.00111704 Cs 29.46253748 11.87531496 11.72641731 -0.00029539 0.00131109 0.00092098 Pb 32.56846073 14.72637778 14.80651437 -0.00032035 -0.00069448 0.00027380 Br 32.67236479 14.39389282 11.71753450 0.00214327 -0.00179154 -0.00077968 Br 32.39784534 11.79484243 14.78953332 0.00014141 -0.00454996 0.00109347 Br 29.47950913 14.71487653 14.48956438 0.00163079 -0.00254236 -0.00127763 Cs 30.04229587 11.48660803 17.44341288 0.00089026 0.00006234 0.00044996 Pb 32.58075264 15.09331292 20.93388020 -0.00191300 -0.00068255 0.00179532 Br 32.65656125 15.10394609 17.91442857 -0.00023600 0.00384435 0.00100283 Br 32.73375892 12.11759894 21.05698560 0.00123488 0.00049667 0.00250784 Br 29.60734836 15.00230636 20.75103831 -0.00132986 0.00135052 -0.00319644 Cs 30.10641687 12.03341738 23.72739449 -0.00019049 0.00215228 -0.00074698 Pb 32.70326028 14.61012413 26.77560290 0.00057519 -0.00148338 0.00133304 Br 32.94115181 14.40969351 23.95633459 -0.00026889 0.00268980 -0.00019355 Br 32.99186408 11.76349790 26.77451977 0.00088902 0.00348150 0.00019311 Br 29.74052800 14.72742918 26.33657691 -0.00146895 0.00099209 0.00159604 Cs 30.07549568 11.40506623 29.65951272 0.00088009 0.00164234 0.00182413 Pb 32.70952590 14.85252632 32.77652444 -0.00011298 0.00075630 0.00121183 Br 32.32303140 14.86595879 29.74824781 0.00143413 0.00269990 0.00170738 Br 32.51917967 11.69081168 32.38853742 -0.00459538 -0.00118803 0.00123289 Br 29.64210356 14.88936413 33.01153694 0.00003434 -0.00120621 -0.00358217 Cs 29.90031287 17.95899428 35.49212828 -0.00198516 -0.00127854 -0.00092951 Pb 32.53757242 20.70705838 2.78496601 -0.00080422 -0.00017650 -0.00098941 Br 32.64532462 20.47434996 35.37548508 -0.00003554 0.00108590 0.00125206 Br 32.36442716 17.72170020 3.05766325 -0.00108614 0.00208527 0.00118364 Br 29.57848153 20.75849087 2.92255967 0.00014956 0.00129982 -0.00079424 Cs 29.82088468 17.91649259 5.86401021 -0.00179074 0.00172986 0.00078908 Pb 32.58984741 20.89824546 8.52061032 -0.00150060 0.00056228 -0.00120249 Br 33.18493041 20.07592637 5.74031713 0.00130344 0.00156909 0.00036179 Br 32.60123962 17.87748238 8.86845106 0.00066351 -0.00232972 -0.00076495 Br 29.67245959 21.42618749 8.37340737 0.00065528 0.00209038 0.00022251 Cs 30.47904097 17.13960155 11.67541920 -0.00263229 -0.00045948 0.00156951 Pb 32.68371722 20.86413972 14.51753514 0.00230666 -0.00182925 0.00016637 Br 32.64069030 21.35814483 11.59684927 0.00218012 0.00336845 0.00253911 Br 32.73863090 17.88275185 14.36828024 0.00083360 0.00321523 -0.00248737 Br 29.66285825 21.12287435 14.91582035 -0.00206119 0.00003353 -0.00180973 Cs 29.50581187 17.57721933 17.58560677 0.00063882 -0.00299770 -0.00082802 Pb 32.30554411 20.77914433 20.39324593 0.00132916 -0.00164120 -0.00171380 Br 32.62525572 20.34249977 17.66158067 -0.00047203 -0.00109356 0.00134265 Br 32.16330025 17.95431002 21.37455118 0.00077200 -0.00025575 0.00264053 Br 29.48627078 20.80640540 20.17615600 -0.00000983 -0.00262835 -0.00227377 Cs 29.83286810 17.84040198 24.06885470 0.00208943 -0.00233555 -0.00428747 Pb 32.68975212 20.83868532 26.55642859 0.00011437 0.00040513 0.00058591 Br 32.52212176 21.18976974 23.60264919 -0.00031382 0.00183156 -0.00023484 Br 32.37198912 17.60015463 26.74043462 -0.00382839 0.00166281 0.00030588 Br 29.79168805 21.55062160 26.45349980 -0.00058003 0.00114164 0.00033634 Cs 29.54077950 17.76654085 29.65808473 0.00116624 -0.00081431 -0.00119648 Pb 32.84686710 20.84185834 32.30585384 0.00059876 -0.00066557 -0.00096113 Br 32.58038527 20.72609359 29.55381253 -0.00023905 -0.00183239 0.00159032 Br 33.10061994 17.85013103 32.71251340 0.00214048 0.00334695 -0.00002435 Br 29.82975135 20.64954983 32.66531795 -0.00073956 0.00158706 0.00332998 Cs 30.10319444 23.99918037 35.27247353 -0.00134458 0.00123372 -0.00112796 Pb 32.88017324 26.92235647 2.94095562 0.00208451 0.00402096 -0.00087565 Br 32.59881807 26.53378029 35.53040567 0.00214852 -0.00050513 0.00209650 Br 32.85625902 23.70719387 2.89076022 -0.00048159 -0.00038555 0.00327242 Br 29.78593131 26.57798240 3.14452765 0.00145385 0.00150005 0.00249254 Cs 29.48437555 24.16274793 5.69309129 -0.00169153 0.00081735 0.00016234 Pb 32.70127241 26.73628856 9.02856522 -0.00009259 0.00145089 0.00081521 Br 32.96796163 26.75654268 5.95094412 -0.00103048 0.00086943 0.00255439 Br 33.39365035 23.55126706 8.53837493 0.00206198 0.00009516 0.00003266 Br 29.82198083 26.83891830 9.08348574 0.00169067 0.00167363 -0.00300761 Cs 29.45664300 24.00250147 11.69196889 -0.00073836 -0.00144855 0.00061783 Pb 32.44652710 26.88835013 14.99612523 0.00054024 -0.00380050 0.00085239 Br 32.67520560 26.44407355 12.00349469 0.00357819 0.00248345 -0.00014875 Br 32.83158812 24.01180646 15.21351699 -0.00279012 -0.00118903 -0.00057634 Br 29.64939218 26.29180902 15.04569958 -0.00333049 0.00035402 -0.00173531 Cs 29.40600187 23.39575597 17.72795617 -0.00176909 -0.00077679 -0.00207789 Pb 32.69305972 26.79188305 20.81166374 0.00023121 -0.00078314 0.00094718 Br 32.43689031 27.21195833 18.00223013 0.00129770 0.00405124 -0.00006247 Br 32.22054341 23.71472699 20.05470780 -0.00222738 -0.00164972 0.00483203 Br 29.53193132 27.06801964 20.73442263 -0.00011391 0.00077734 -0.00146669 Cs 29.63836291 23.48515844 22.96328289 -0.00437259 -0.00262396 -0.00074960 Pb 32.53035179 26.63177466 26.65322714 -0.00079440 0.00051436 0.00006744 Br 32.04136125 26.56644231 23.78528467 -0.00087060 -0.00248231 -0.00110584 Br 32.90436706 23.65352852 27.26032019 0.00498077 -0.00023590 0.00026255 Br 29.56394638 26.31955826 27.13741419 0.00071134 0.00063333 0.00067691 Cs 29.85743033 23.49482961 29.74459884 -0.00125061 -0.00141902 0.00140947 Pb 32.71703639 26.76117894 32.58015449 -0.00135411 0.00151883 0.00138667 Br 32.64837044 26.80284721 29.61490104 0.00253504 -0.00141110 -0.00140172 Br 32.32326328 23.85213439 32.44668033 -0.00228991 -0.00042084 0.00172525 Br 29.79230453 26.94971566 32.83739043 -0.00140569 0.00152927 0.00129616 Cs 29.80254035 30.32461545 35.02991498 -0.00009232 -0.00022739 -0.00121921 Pb 32.71159300 32.70752348 3.23065232 0.00183756 -0.00028946 0.00083282 Br 32.92511075 32.76446620 0.14169363 -0.00200404 0.00138196 0.00218715 Br 33.32952759 29.64822864 2.77821894 0.00081933 -0.00245256 0.00045581 Br 29.76944481 32.30319183 2.78378814 0.00170930 0.00131152 -0.00046605 Cs 30.12766420 30.27203589 5.79148570 0.00172214 -0.00236802 -0.00082167 Pb 32.59592039 32.61397880 9.08092241 -0.00189419 0.00112042 0.00209463 Br 32.75474671 33.49790217 6.23623930 0.00163720 -0.00148317 -0.00057159 Br 32.85985010 29.53905591 8.92309708 -0.00052638 -0.00034120 0.00053579 Br 29.41082429 31.70814020 9.10446330 0.00079897 0.00063618 0.00252725 Cs 29.47115364 29.39111516 12.36437292 -0.00066102 -0.00048068 -0.00257811 Pb 32.68729541 32.66875174 14.95126218 0.00006522 -0.00040437 -0.00001468 Br 32.28871154 32.96472232 11.95222588 0.00233979 -0.00189236 -0.00082914 Br 32.43086859 29.76918343 14.63550038 -0.00291102 0.00031530 0.00043549 Br 29.86638431 32.50453899 14.76990564 0.00080605 -0.00319805 0.00013027 Cs 29.16529251 29.19987254 17.79469330 -0.00203256 -0.00044457 -0.00195926 Pb 32.69484543 32.68544383 20.70330463 0.00007660 -0.00091657 -0.00069978 Br 33.11777818 32.47466146 17.82582385 -0.00082751 -0.00309460 -0.00302996 Br 32.59175593 29.81477676 20.87344059 -0.00044213 -0.00213841 0.00210933 Br 29.90790830 33.19262095 20.17735856 -0.00106516 -0.00290117 -0.00095404 Cs 29.35367250 29.69635525 23.71458532 0.00111903 0.00060822 0.00161958 Pb 32.66833618 32.42587867 26.79653247 0.00096539 0.00028820 -0.00023381 Br 32.34773056 33.35954023 23.96784217 0.00192110 -0.00264905 -0.00179000 Br 31.95083240 29.51305818 26.57068243 0.00025573 -0.00094242 0.00426976 Br 29.79188893 33.10746042 27.11761714 0.00054425 0.00043125 0.00393391 Cs 29.66743106 29.34011294 29.25949443 -0.00031654 -0.00019951 0.00043263 Pb 32.90503628 32.53460813 32.84100106 0.00217143 0.00121597 0.00103557 Br 33.12423724 32.30699400 29.78637007 0.00036236 0.00096423 0.00219545 Br 33.16847231 29.70452275 32.82652305 0.00214618 -0.00245090 0.00392563 Br 29.60312429 32.78390333 32.44358794 0.00080758 0.00199521 -0.00098365 1080 Lattice="35.60125893 0.0 0.0 0.0 35.60125893 0.0 0.0 0.0 35.60125893" Properties=species:S:1:pos:R:3:vel:R:3 Time=1000.0 energy=-38569.34277344 virial="-13.30114827 -1.7815178 -0.66453176 -1.7815178 -23.45749583 1.82833241 -0.66453176 1.82833241 -21.30598314" stress="0.00053024 -3.948e-05 -1.473e-05 -3.948e-05 0.00030515 4.052e-05 -1.473e-05 4.052e-05 0.00035284" pbc="T T T" Cs 0.08861874 0.17371122 0.11090065 0.00190958 0.00006849 0.00164979 Pb 2.65678043 2.98421346 2.97425056 -0.00136961 -0.00049027 0.00038383 Br 3.33619769 2.63258199 0.09155939 0.00014587 -0.00214059 -0.00305013 Br 2.86856166 0.12179521 3.05254420 0.00268449 -0.00359064 0.00202907 Br 35.38658661 3.13659934 3.45524236 -0.00009907 0.00139074 -0.00407403 Cs 0.20382936 0.03750261 5.63828919 -0.00290120 -0.00142608 0.00329474 Pb 2.89528085 2.82804492 8.82178113 0.00075751 -0.00201617 -0.00149985 Br 2.67953472 3.22217399 5.86990665 -0.00077260 0.00010320 0.00022786 Br 2.43456127 35.54532396 8.65951920 0.00348435 -0.00265942 -0.00080165 Br 35.47956708 3.20648729 8.73683439 0.00321140 -0.00117165 0.00197216 Cs 35.35599186 35.17490979 11.59618112 0.00141052 -0.00119755 -0.00060316 Pb 2.89374896 2.83874565 14.55827359 0.00272750 0.00141895 -0.00114895 Br 2.45691275 2.98465409 11.77173450 -0.00064259 0.00113789 -0.00229508 Br 3.06231576 0.16423640 14.24189262 0.00182582 0.00023976 -0.00134991 Br 0.15483050 2.05253274 15.01940129 -0.00029762 -0.00111929 -0.00019908 Cs 35.46148756 0.13482574 18.07139667 0.00216404 0.00211724 -0.00129970 Pb 2.99382889 2.86509031 20.61365921 0.00053321 -0.00038296 0.00007690 Br 2.86753075 2.68182313 17.80700311 0.00209008 0.00016379 -0.00095552 Br 3.53348060 35.49461474 20.37981848 -0.00249218 0.00316796 -0.00210735 Br 35.38322812 2.80311066 20.70149326 0.00398447 0.00159558 -0.00028896 Cs 35.34810464 35.16910494 24.21881936 -0.00119966 0.00205913 -0.00188815 Pb 3.07618239 2.82189124 26.80601870 0.00120397 -0.00187361 0.00040157 Br 3.45901864 2.28867908 23.63538173 0.00250503 -0.00148007 -0.00319913 Br 2.42254184 0.06132204 26.76637658 -0.00297146 0.00073814 -0.00002467 Br 35.54161570 2.85943318 26.38890232 -0.00215506 -0.00372475 -0.00046667 Cs 35.40785240 35.29684242 29.50567325 0.00210254 0.00027759 0.00083769 Pb 2.94490161 2.89181569 32.72977895 -0.00189404 -0.00036967 -0.00062737 Br 2.91443152 2.72312060 29.87919506 -0.00196931 0.00184123 0.00195622 Br 2.61580685 0.03598949 32.80917077 0.00171594 -0.00375321 -0.00051635 Br 0.14577946 3.31008792 33.59784192 -0.00341917 0.00120617 0.00271088 Cs 35.48109435 6.14728116 0.23103053 -0.00115728 -0.00047507 0.00034470 Pb 2.88674539 8.81492058 3.04014522 -0.00196970 0.00012619 0.00104437 Br 2.77344105 9.62869858 35.58128614 -0.00013430 -0.00304191 -0.00049850 Br 3.29565882 5.83678063 2.20442376 -0.00381863 0.00205400 -0.00177625 Br 35.59055266 8.58505785 2.81886025 -0.00067612 -0.00419764 0.00359654 Cs 34.54196164 5.94706404 5.78130217 -0.00069889 0.00198641 -0.00023287 Pb 2.94410959 8.82658600 8.63170701 0.00142164 0.00045817 0.00156630 Br 3.15400477 7.99056024 5.64420780 -0.00166543 -0.00394475 0.00114412 Br 3.52395820 5.82835654 9.15244338 0.00157425 -0.00008840 0.00059564 Br 0.11762782 8.22849970 8.89675434 0.00004855 0.00094511 -0.00353458 Cs 35.26922738 6.17641571 11.98298782 0.00273206 0.00084428 -0.00071693 Pb 2.98533110 8.67667876 14.81590017 0.00030458 -0.00078433 0.00036815 Br 2.96014131 8.57380013 11.86436554 -0.00251364 0.00249887 -0.00249806 Br 2.38590205 5.88601133 15.20406861 -0.00125911 0.00108318 -0.00092254 Br 0.07077626 9.31131222 14.65516190 -0.00471076 -0.00048084 0.00119234 Cs 0.06316186 5.91685773 18.29756628 -0.00068143 -0.00209491 0.00015212 Pb 2.72320405 9.00669824 20.66994980 -0.00002195 -0.00216063 -0.00016760 Br 2.57310406 8.57096588 17.94009265 0.00042447 -0.00039582 -0.00201993 Br 2.83170583 5.81845590 21.01497437 -0.00171538 0.00090126 0.00236355 Br 35.53335539 8.73800142 20.72692525 0.00169076 -0.00010229 0.00119137 Cs 0.04258211 6.13684584 23.77080363 0.00410924 0.00368762 0.00106424 Pb 3.01383004 9.06085702 26.54606358 -0.00061357 -0.00122109 -0.00047613 Br 2.00457560 9.20265641 23.61581223 -0.00235473 -0.00264326 0.00113645 Br 2.82263592 5.79512256 26.24024203 -0.00169910 0.00153086 -0.00125445 Br 35.33914685 9.04582142 26.71676868 0.00297363 0.00169622 0.00131713 Cs 0.36838530 6.29972195 29.00000170 -0.00022325 -0.00021807 0.00232486 Pb 2.84937413 8.92229572 32.75912214 0.00082623 -0.00001792 -0.00021832 Br 3.52284682 8.60800929 29.57310124 -0.00264837 -0.00171648 0.00067096 Br 2.57925570 6.14340068 32.75034658 0.00083076 0.00220579 -0.00032775 Br 0.03635271 8.95564994 32.49009216 0.00167475 -0.00045162 -0.00163472 Cs 35.53280917 11.44370523 0.15813626 0.00144502 0.00055393 -0.00273537 Pb 2.95986662 14.80988076 2.99795762 0.00071877 -0.00078095 0.00028946 Br 2.73732310 14.48704215 0.12358196 0.00364251 -0.00117329 0.00343120 Br 2.92760776 12.04957623 3.26296754 -0.00377851 0.00146624 0.00169022 Br 0.19479647 15.47100522 3.17011114 0.00055602 -0.00197594 0.00333787 Cs 35.48392551 12.25310714 6.12151676 -0.00181894 -0.00125461 0.00090024 Pb 2.86071704 14.56447683 8.66500180 0.00125979 -0.00004428 -0.00069587 Br 2.27727810 14.87207248 5.97894581 -0.00145199 -0.00171150 0.00213897 Br 2.91447277 11.61330944 8.04439840 0.00030862 0.00103927 -0.00616564 Br 0.18209286 14.37752442 9.62518547 0.00154280 -0.00111873 -0.00191135 Cs 35.33911169 11.36437778 11.64148161 -0.00015080 -0.00044061 -0.00069944 Pb 3.30476671 14.89857245 14.82600546 0.00006079 -0.00129826 -0.00019584 Br 3.03852868 14.68518207 11.69948983 0.00199569 0.00301366 -0.00307901 Br 3.33918541 11.82648033 14.97671970 0.00135064 -0.00050697 0.00171836 Br 0.20484195 14.88607380 14.66986697 0.00038024 -0.00156868 -0.00376301 Cs 0.34452073 11.40079613 17.23446207 0.00217221 -0.00145589 -0.00137321 Pb 2.91060663 14.80152540 20.92501970 -0.00148063 -0.00228456 -0.00009431 Br 2.90176578 14.51593280 17.72904659 -0.00316857 -0.00116440 0.00267650 Br 2.29554879 11.85763810 20.60701226 -0.00159329 0.00144526 -0.00102549 Br 0.01527839 15.14592466 20.74932007 0.00124414 0.00215593 0.00625276 Cs 35.42611821 11.89835144 24.07939799 -0.00186297 0.00070391 -0.00179961 Pb 2.95768449 14.86535213 26.78875582 -0.00074047 0.00033685 -0.00167182 Br 2.71233557 14.09775887 23.90210640 -0.00188973 0.00171933 -0.00485864 Br 3.01053523 11.73894322 27.39037604 -0.00077292 0.00168620 -0.00435217 Br 0.07197151 15.13713260 26.99511144 0.00426705 -0.00115378 0.00032733 Cs 35.36689390 11.75545538 29.46819763 -0.00094010 -0.00073477 -0.00233047 Pb 3.31336966 14.76852646 32.54913011 -0.00008792 -0.00215340 0.00040123 Br 2.86612014 15.09462375 29.70382136 0.00108798 -0.00028041 0.00078891 Br 2.94613212 11.85005639 32.33902315 0.00059196 -0.00056354 0.00052482 Br 0.11501631 14.44207523 32.24663593 -0.00036257 0.00036379 -0.00187786 Cs 34.93324112 17.52345566 0.08689284 -0.00197747 0.00091207 0.00037349 Pb 2.85432937 21.26280587 2.77270336 -0.00140085 0.00219131 0.00121204 Br 2.85430846 21.22131630 35.46867799 0.00258996 -0.00215110 0.00420830 Br 3.09239249 18.48963187 3.04032078 0.00147890 0.00259987 0.00189353 Br 35.53497166 20.90767653 2.40124882 -0.00012153 0.00105185 -0.00447908 Cs 0.47489306 18.31183464 5.36389235 0.00059668 -0.00151299 0.00104530 Pb 2.74801133 20.50851518 8.88989261 0.00052851 0.00053248 0.00144773 Br 2.23771749 21.11990033 5.85647136 -0.00322515 -0.00290223 -0.00196248 Br 2.70813201 17.52240720 8.77684014 0.00114452 -0.00069687 -0.00121670 Br 35.51012834 20.38385388 9.14986907 -0.00160813 0.00089502 -0.00207118 Cs 0.29360145 17.12329342 11.84737701 -0.00048040 -0.00137272 -0.00089635 Pb 3.04832143 20.88437060 14.86394479 -0.00122225 -0.00190377 -0.00132573 Br 2.81851946 19.73788310 11.73110981 -0.00196056 0.00008948 -0.00046829 Br 3.26174095 17.93831894 15.27015216 0.00140050 0.00013800 0.00182882 Br 35.37767711 20.71975821 14.34093328 -0.00168922 0.00013492 -0.00153610 Cs 35.53980371 17.62902238 17.34999315 0.00112300 -0.00091705 0.00199879 Pb 2.79217356 20.41870591 20.92962241 -0.00193713 0.00017571 -0.00132906 Br 2.55672487 21.01827611 17.69081035 -0.00210649 -0.00229624 -0.00052187 Br 2.93467448 17.61239653 21.62830220 -0.00359751 -0.00178459 -0.00200910 Br 35.47848909 20.80148190 20.88958713 -0.00140674 0.00063130 0.00222368 Cs 35.35298801 17.90551116 24.10839831 -0.00323867 0.00007882 0.00368200 Pb 3.02023046 20.75974968 26.57932097 0.00053721 -0.00051919 -0.00018994 Br 3.34814619 20.83181226 23.59490420 -0.00220753 -0.00020445 -0.00076229 Br 3.15464304 17.67187635 26.57757722 0.00002021 0.00411658 -0.00539776 Br 0.05589915 20.94749720 26.27974247 -0.00068594 0.00107783 -0.00112507 Cs 0.03481585 17.59361793 29.95848950 0.00007520 0.00004473 -0.00032347 Pb 3.00732350 20.70501643 32.46362304 -0.00118425 -0.00015227 -0.00239238 Br 3.47240300 20.73091777 29.42895863 -0.00352400 -0.00051053 0.00017274 Br 3.03643545 17.80461796 33.15652256 -0.00167544 -0.00071598 0.00232680 Br 0.11192968 20.82906534 32.43166575 0.00251028 0.00012774 0.00169776 Cs 35.46722908 23.21695192 35.19426685 -0.00038287 -0.00104278 0.00021652 Pb 2.91084847 26.67763515 3.16242760 -0.00043886 -0.00003925 -0.00000972 Br 2.82500607 26.28144410 0.14401480 -0.00044309 0.00041897 0.00041533 Br 3.22452749 23.97052176 4.13192896 0.00282585 0.00002025 -0.00191176 Br 35.57395387 26.30620687 3.41534908 0.00059668 0.00321600 0.00063857 Cs 0.27402564 23.75061530 6.23460133 0.00347759 -0.00058065 0.00048750 Pb 3.07284133 26.73715878 8.93481342 -0.00073740 0.00067035 0.00059783 Br 2.52529623 27.08368681 6.16575730 -0.00203061 -0.00253231 -0.00271531 Br 3.30183867 23.71635901 9.12148607 -0.00112279 0.00243533 -0.00126289 Br 0.01639870 26.44222323 8.91518191 -0.00189912 0.00600459 0.00038527 Cs 0.06453041 23.72622172 11.56128971 0.00278328 0.00055453 0.00057178 Pb 2.81321827 26.69604476 14.89693225 -0.00171116 -0.00070153 0.00001426 Br 2.99684695 26.61946900 11.94435412 0.00017354 -0.00073365 0.00116040 Br 3.11096730 23.70394419 14.78488746 0.00005462 0.00122457 0.00095038 Br 35.57230868 26.85843986 14.53451412 0.00038479 0.00222134 0.00096255 Cs 0.02289354 23.52937886 17.68682860 0.00050241 -0.00087190 0.00166443 Pb 2.88209521 26.58959635 20.74954574 0.00103063 0.00035935 0.00049062 Br 2.47949969 26.36736859 17.76298335 0.00025599 -0.00171261 -0.00164723 Br 3.22308940 23.52019927 20.69896117 0.00388390 -0.00061997 0.00243789 Br 0.05758121 27.10243601 21.09499503 0.00202804 -0.00153109 -0.00072438 Cs 35.49503902 23.59433457 23.47970689 0.00100936 0.00028805 -0.00224567 Pb 3.07129913 26.87123638 26.63044902 0.00004871 -0.00030692 0.00232860 Br 3.29198981 27.19823501 23.71704194 -0.00168623 0.00329053 0.00059642 Br 2.81144562 23.85268212 26.25148991 -0.00215925 -0.00202893 -0.00329458 Br 35.38313871 27.13451517 26.55806143 -0.00216248 0.00235290 -0.00277119 Cs 35.37931031 23.90109995 29.50709206 -0.00081226 0.00248610 -0.00048945 Pb 2.87852494 26.68793307 32.50460917 -0.00063891 -0.00066577 -0.00072812 Br 2.60708948 26.69507653 29.57529384 -0.00226066 -0.00350995 0.00349100 Br 3.26124118 23.72312803 32.70417284 0.00281698 0.00059732 0.00118298 Br 35.54051776 26.20779800 32.54077302 -0.00152103 0.00127957 0.00149208 Cs 35.50689382 29.72781888 35.38603260 -0.00064565 0.00064228 -0.00132227 Pb 3.11497462 32.88493225 3.12110125 0.00104214 -0.00088264 -0.00094885 Br 2.95090523 32.55822691 0.21541532 0.00107137 -0.00206726 -0.00050786 Br 3.00233446 29.96627158 3.20423712 0.00177902 0.00179274 -0.00024228 Br 0.09487168 32.85892948 3.12966608 0.00225239 -0.00096255 -0.00032721 Cs 0.04261179 29.50235962 6.23980731 -0.00095658 0.00181930 0.00049486 Pb 2.78563318 32.59438281 8.81953643 -0.00054913 -0.00039604 -0.00106683 Br 3.23835859 32.63404087 5.99915356 -0.00039889 0.00006575 -0.00309083 Br 2.83680485 29.61672197 8.92141543 -0.00011063 0.00050767 0.00031880 Br 35.50139461 32.73260536 9.39343970 0.00105643 -0.00171683 0.00065729 Cs 0.09788421 29.11956072 11.93973793 -0.00003306 -0.00103034 0.00022643 Pb 2.94480663 32.81082169 14.73901423 -0.00300371 -0.00161139 -0.00093157 Br 2.83817133 32.73587198 11.94478444 -0.00264631 -0.00276013 -0.00060012 Br 3.09479917 29.74624617 14.72341203 0.00122814 -0.00100271 0.00233211 Br 0.04214640 32.53204648 14.24126752 0.00308710 0.00253985 0.00336427 Cs 0.10150881 29.60504417 18.27634531 -0.00117958 0.00067290 -0.00044048 Pb 3.17799176 32.62757007 20.60095132 -0.00017138 -0.00055270 0.00142733 Br 2.89788424 32.34532145 17.72384263 0.00135541 0.00097441 0.00085715 Br 2.94593103 29.62909435 20.87551839 -0.00258372 -0.00142646 -0.00283329 Br 0.15868293 33.15504172 20.84689955 0.00112987 0.00050144 0.00097441 Cs 0.49972218 29.78583424 23.81644938 -0.00251937 -0.00336218 0.00068587 Pb 3.25445401 32.59171386 26.76342043 0.00081082 -0.00077101 -0.00163942 Br 3.19555224 32.47926355 23.77412510 0.00033558 -0.00057778 0.00228692 Br 3.46878952 29.83425902 27.22185039 -0.00423276 0.00123560 0.00126533 Br 0.15649388 32.48641739 26.80463334 0.00146779 0.00092866 0.00077062 Cs 35.53141029 29.41600517 29.45383235 -0.00144140 0.00250302 0.00009099 Pb 3.04895956 32.65537240 32.92905861 -0.00140195 -0.00105224 0.00092976 Br 3.49389893 33.24694973 29.66156196 -0.00044141 -0.00137408 -0.00227856 Br 2.41797212 29.64125859 32.66461458 0.00043230 0.00072660 -0.00134965 Br 0.06707166 32.38983554 33.18072399 -0.00124864 -0.00392008 -0.00254151 Cs 6.29658044 0.03395082 35.51017339 0.00252652 0.00197949 0.00120712 Pb 8.42795925 3.13955394 3.06142704 0.00010022 0.00125679 0.00017635 Br 8.98707841 3.78290305 0.14460657 -0.00094101 0.00161655 -0.00238428 Br 8.81644851 0.26904112 3.17301518 -0.00029971 -0.00107334 0.00075182 Br 5.55046631 2.71861567 3.38908774 -0.00119758 -0.00167029 0.00095886 Cs 6.04707564 35.23422918 6.08818736 0.00214329 -0.00025440 0.00096212 Pb 8.70504180 2.85123942 8.85694976 0.00128752 -0.00010962 0.00075693 Br 8.73236076 2.67230982 5.86163657 -0.00247191 0.00209108 -0.00222246 Br 9.01624878 35.46488660 9.15073950 0.00580637 -0.00074843 0.00298681 Br 5.86822310 2.58636100 9.48978343 -0.00193731 0.00358747 0.00220932 Cs 6.36897914 34.78541071 11.89805322 0.00142085 -0.00095219 0.00116991 Pb 9.00809531 3.13639373 14.60822017 0.00036423 -0.00131010 -0.00151454 Br 9.03872171 3.44826301 11.71954660 0.00077794 0.00394745 0.00081845 Br 9.07847071 0.01527680 14.36589792 0.00175811 -0.00345235 0.00108953 Br 5.98871733 3.33309210 14.87322429 -0.00193528 -0.00029924 -0.00237886 Cs 6.01158623 0.42844289 17.43439246 -0.00318156 0.00093781 -0.00119608 Pb 8.82763770 3.02926862 20.75672903 0.00274240 0.00318396 -0.00038716 Br 8.92815351 2.95971628 17.67856830 -0.00055210 -0.00272601 -0.00253985 Br 9.23090113 0.30623556 21.15822067 -0.00064363 0.00132710 -0.00079498 Br 6.08328484 2.57695027 20.53764551 -0.00117953 0.00113058 -0.00287045 Cs 6.05981331 0.03879020 23.29391730 -0.00203415 -0.00437061 0.00208878 Pb 9.30010655 2.70360215 26.78682548 0.00034527 -0.00229264 0.00240089 Br 8.44943695 3.04679418 23.63218756 -0.00082154 -0.00286760 0.00315833 Br 8.60313414 35.56406414 26.97555753 -0.00385581 0.00085851 -0.00026050 Br 6.00120224 3.16255540 26.87388711 0.00289336 -0.00273186 0.00077436 Cs 6.22666293 35.59741822 29.68163326 -0.00028039 -0.00083330 -0.00300324 Pb 9.00206638 3.15887139 32.91437900 0.00016438 -0.00019314 -0.00238445 Br 9.18308872 3.35183695 29.61041694 0.00393043 0.00253721 -0.00186790 Br 8.72925721 0.11303787 32.25526428 -0.00263806 -0.00048463 0.00056400 Br 6.01142253 3.10748974 32.78425372 -0.00045975 0.00010911 -0.00122313 Cs 6.15519015 6.39605929 35.32094399 0.00017061 -0.00041663 -0.00146241 Pb 8.73006815 9.05001229 3.07320876 -0.00114014 -0.00013932 0.00152603 Br 8.78533996 8.97170145 35.60400261 -0.00289016 -0.00079954 0.00250186 Br 8.72794314 6.13935260 3.48811059 -0.00333566 0.00113504 -0.00035574 Br 5.68109487 9.56649223 2.57457482 0.00065708 -0.00130331 -0.00341934 Cs 5.91481251 5.46992680 6.46082218 0.00235154 -0.00213384 0.00269785 Pb 9.10196839 8.69471206 8.71512624 -0.00116023 0.00010509 0.00078481 Br 8.79534642 9.70622517 5.89851955 0.00169478 -0.00163114 0.00266239 Br 9.01386121 5.79004937 8.66491684 0.00101467 -0.00130948 -0.00166830 Br 6.08814748 8.56679417 8.74843197 0.00068772 0.00326203 -0.00081272 Cs 6.32481527 6.04042631 11.44225197 0.00259509 0.00138965 -0.00215371 Pb 8.91423741 8.96154239 14.98202437 0.00049673 -0.00132171 -0.00073645 Br 8.78607143 9.14041857 11.94066772 0.00396605 0.00134964 -0.00169233 Br 9.17513111 5.97980324 14.95490361 0.00174628 -0.00146156 0.00239955 Br 5.96040086 8.74521220 15.06966914 0.00321412 -0.00267184 0.00028103 Cs 5.18511279 5.60292732 18.20249395 -0.00225216 0.00193006 -0.00270413 Pb 8.53451963 8.92767876 20.74931213 -0.00033643 0.00058748 -0.00019447 Br 9.17472777 8.70493540 17.91747465 -0.00306481 0.00044989 -0.00265709 Br 7.99639577 6.11418161 20.10677225 0.00246447 0.00395212 0.00016163 Br 5.75013464 9.40063748 20.73076363 0.00056185 0.00057819 0.00226128 Cs 5.78164904 5.44602275 23.94609976 0.00297551 0.00016896 0.00028078 Pb 8.79092241 8.50723879 26.64300510 -0.00068397 -0.00025688 0.00080205 Br 8.96343914 8.56765702 23.68792910 -0.00556448 0.00045310 0.00094204 Br 9.30471085 5.72964389 26.68729819 0.00030870 0.00341572 0.00047852 Br 5.79096911 8.65385544 26.13227797 0.00000522 -0.00038188 0.00041462 Cs 5.86181308 5.59754419 30.17813135 0.00139502 0.00214252 -0.00020205 Pb 8.73875959 8.76961872 32.51787649 0.00065958 0.00156196 -0.00065193 Br 8.55523447 8.52895759 29.67765177 -0.00037336 -0.00340888 -0.00026581 Br 9.48675955 5.90761319 32.56700240 -0.00180469 -0.00037341 0.00023557 Br 5.99900676 8.65913450 32.65005255 0.00049694 -0.00443615 -0.00078574 Cs 5.60462586 12.30822039 0.17333225 -0.00039282 0.00074520 0.00241818 Pb 8.98641272 15.14157385 2.72615766 -0.00084060 0.00033964 0.00004531 Br 8.63858196 15.61652653 35.58829457 -0.00048353 -0.00047339 0.00095154 Br 9.32988338 12.19863636 2.89790241 0.00217020 0.00163220 -0.00405505 Br 5.84897802 14.71445014 3.30075838 -0.00004615 0.00293858 0.00061535 Cs 5.91752223 11.76115972 5.72840954 0.00041267 -0.00144084 0.00048314 Pb 8.94255975 14.92620634 8.85710056 0.00055574 0.00052593 0.00158776 Br 9.39794676 14.78063535 6.08762563 0.00060526 -0.00242579 -0.00125785 Br 8.84868064 11.92512890 8.83269057 -0.00071350 0.00075574 0.00066558 Br 5.79470753 14.88746146 8.72898017 -0.00502347 0.00142192 0.00102795 Cs 6.20791911 11.95810148 11.48378277 -0.00003071 0.00371203 0.00098786 Pb 9.31690799 14.77753744 14.68116228 0.00185169 -0.00107624 -0.00061842 Br 8.93194674 14.62657274 11.81634634 -0.00220907 0.00026620 -0.00341237 Br 8.91869047 11.95797190 15.33857307 0.00023657 -0.00107129 -0.00316854 Br 6.34006381 15.47897968 14.87115893 -0.00423652 0.00228355 0.00089042 Cs 5.82034275 11.88705599 17.85156848 0.00092663 -0.00263010 0.00188012 Pb 8.89028949 15.00772818 20.85465311 0.00190459 -0.00104982 -0.00056733 Br 9.13709549 15.33268671 18.03299220 -0.00528637 -0.00232348 0.00119721 Br 8.39819185 12.10650378 20.59223352 -0.00049217 0.00270398 -0.00036510 Br 5.77697282 15.09003979 20.51605297 -0.00261538 -0.00012715 -0.00093631 Cs 5.97834401 11.64381739 23.34835538 -0.00170332 -0.00316260 -0.00038485 Pb 8.76263060 14.40826015 26.74661126 0.00084371 0.00160243 0.00018798 Br 8.32577022 15.05962446 23.85099171 0.00086485 0.00169103 0.00207599 Br 8.53565824 11.66629765 26.19662825 0.00214944 -0.00397493 0.00292236 Br 5.85734387 14.38444279 27.12691834 -0.00012875 -0.00033284 0.00095865 Cs 5.53056826 11.64694439 29.55099961 0.00226762 -0.00096405 -0.00142399 Pb 9.07519956 14.46460779 32.50745715 -0.00014634 0.00214486 0.00042452 Br 9.38665657 13.73323130 29.68841273 -0.00042142 -0.00288054 -0.00376281 Br 7.99039261 11.83380505 33.03568463 -0.00139741 0.00139356 -0.00012823 Br 6.17368632 14.86965882 31.75274249 -0.00008161 -0.00182726 0.00169698 Cs 5.74572791 18.38742695 0.43298502 0.00309936 0.00101563 0.00160698 Pb 8.92331670 21.02521372 2.97336273 0.00359734 -0.00163424 0.00221321 Br 9.59884235 20.82914932 35.55998702 0.00104065 -0.00151916 0.00026468 Br 8.35490558 18.31831860 2.86097707 -0.00002422 -0.00125118 0.00194083 Br 5.87054051 21.29637459 2.77093183 0.00334549 0.00154133 0.00070590 Cs 5.56054539 19.03611943 5.67900231 -0.00194095 -0.00084609 0.00251123 Pb 8.96011922 20.66930984 8.90315128 0.00006565 -0.00092828 -0.00107382 Br 8.88303187 20.91825108 5.84101699 0.00087676 -0.00011213 -0.00006645 Br 9.40957481 17.87302670 8.85525440 -0.00211651 0.00062642 0.00452177 Br 5.66382175 20.22845947 8.84898037 0.00304510 -0.00118561 -0.00020088 Cs 5.76195992 17.60933747 11.62386287 0.00124949 -0.00048025 -0.00168953 Pb 8.83083857 21.06414137 14.73223726 0.00176959 -0.00028064 0.00327469 Br 9.61118500 20.63402962 11.85336136 -0.00135137 0.00017484 -0.00104095 Br 9.13565959 18.05970255 14.56685977 0.00117506 0.00057639 0.00060298 Br 5.72348369 20.81243925 14.84819207 -0.00022638 0.00243614 0.00160780 Cs 6.63760780 17.62489955 17.69626486 -0.00278752 0.00060512 0.00054709 Pb 8.71753417 20.89190371 20.74784819 -0.00113477 0.00041082 -0.00022301 Br 8.49521802 20.68261041 17.78098536 0.00022156 0.00319873 0.00123544 Br 9.46549284 17.89021632 20.71393259 0.00012930 -0.00178739 -0.00284592 Br 5.70012469 20.55034888 20.72445566 -0.00094227 0.00169161 -0.00068155 Cs 6.26170782 17.87803959 23.99934380 -0.00019030 -0.00070089 0.00121739 Pb 8.81242878 20.66611361 26.79001121 -0.00005776 0.00189042 -0.00013357 Br 8.68906373 20.63303362 23.85054264 -0.00103076 -0.00435047 -0.00140612 Br 8.79789658 17.63193549 26.78186256 -0.00400658 -0.00026401 0.00393332 Br 6.07609689 21.29250910 26.66883675 -0.00164455 -0.00293550 0.00145536 Cs 6.17529797 17.76284992 29.36215928 0.00053557 0.00222082 -0.00297618 Pb 8.93987736 20.63805629 32.74602200 -0.00073333 -0.00004895 0.00448339 Br 8.37995204 21.05121861 29.61352455 -0.00397104 -0.00159643 -0.00173688 Br 8.87601230 17.69248257 31.99566190 -0.00226694 0.00078158 -0.00024666 Br 6.06632838 20.89553516 33.13065972 0.00026379 0.00222813 -0.00170382 Cs 5.80284977 24.16548321 0.23256544 0.00148913 0.00048118 -0.00151298 Pb 8.76671204 27.11866063 2.93042630 -0.00077623 -0.00049136 -0.00070081 Br 8.71040330 26.79100213 35.55020253 0.00115995 -0.00032598 0.00198205 Br 9.01229542 24.19820714 3.08893155 -0.00194513 0.00000709 -0.00225127 Br 5.69388052 26.92918563 2.69501008 -0.00553956 0.00101136 0.00092055 Cs 6.23476442 23.63798561 6.41068102 0.00139160 0.00353508 0.00338848 Pb 8.93319177 26.64927469 8.78480922 -0.00001709 -0.00230070 0.00035655 Br 8.37157966 27.05422631 5.87336659 -0.00135528 -0.00441047 -0.00458705 Br 8.66629767 23.68259811 9.28615671 -0.00017612 0.00333302 0.00268606 Br 5.89473219 27.18955161 8.82031390 -0.00171447 0.00467758 0.00354158 Cs 5.62016304 23.91313044 11.98901805 -0.00052589 0.00410745 0.00051670 Pb 8.71642242 27.08413178 14.95597187 -0.00089907 -0.00115235 -0.00279016 Br 8.68224043 26.69248145 11.87888855 -0.00272558 -0.00130432 0.00003135 Br 8.94002541 23.98392238 15.06401279 -0.00112773 0.00249688 0.00150812 Br 5.72246497 27.00788836 15.12202272 0.00028478 0.00093927 0.00113675 Cs 5.39888714 23.89256634 17.94155758 -0.00418710 -0.00032134 -0.00052851 Pb 8.78048249 26.72554810 20.83156275 0.00120436 0.00010192 0.00043889 Br 9.32555775 27.16075660 17.82056703 -0.00538440 0.00146177 0.00107376 Br 8.45110237 23.82240401 20.14772241 -0.00055062 -0.00039480 -0.00190545 Br 5.84300974 26.59135178 20.15886178 0.00152742 0.00239979 -0.00004205 Cs 5.72461141 23.96870359 23.73369978 -0.00027731 0.00220412 0.00129678 Pb 9.27959511 26.57921563 26.62206722 -0.00006329 0.00104105 -0.00114062 Br 8.68693817 26.04323186 23.84773418 -0.00182232 0.00108448 -0.00362194 Br 9.23662993 23.54404257 26.53514284 0.00429216 -0.00462137 0.00037552 Br 5.90940989 26.27339719 26.85235411 -0.00083075 0.00062271 0.00218477 Cs 6.23913225 24.03629848 29.38022283 0.00170078 0.00008234 -0.00045142 Pb 8.83230132 26.64238538 32.50787161 0.00045356 0.00043295 -0.00050462 Br 8.65002132 26.42707465 29.45476081 0.00007482 0.00092863 0.00267507 Br 8.95023564 23.61348208 32.59473221 -0.00286807 -0.00226011 0.00043432 Br 5.92147150 26.75490273 32.49708514 0.00383260 -0.00052306 -0.00150166 Cs 5.46275268 29.19535205 35.39085726 0.00148912 0.00158025 -0.00107506 Pb 8.84320636 32.97098477 2.97267447 -0.00107289 -0.00069361 -0.00117562 Br 9.07808939 33.07798149 35.55483007 0.00293466 0.00013036 0.00060510 Br 9.47792053 29.91144751 2.53516000 -0.00460665 -0.00038886 0.00181519 Br 6.11466822 32.99551200 3.16747471 0.00006823 -0.00092349 -0.00122991 Cs 5.73621045 29.62687193 6.19763743 0.00293680 -0.00215347 0.00140227 Pb 8.79971957 32.49537481 8.85497161 -0.00172503 -0.00010323 0.00109807 Br 9.02040459 32.72839272 5.86830851 0.00314467 -0.00081227 0.00195100 Br 8.91612072 29.65128526 8.43114936 -0.00325020 0.00316911 -0.00076635 Br 5.82937497 32.70513323 8.89856440 0.00051493 -0.00067194 -0.00099138 Cs 5.32116958 29.65240947 12.20116715 -0.00068347 0.00081372 -0.00002003 Pb 8.83305102 32.82155439 14.74586998 -0.00393638 0.00208382 0.00046229 Br 9.41386654 32.60237959 11.69558222 0.00333708 -0.00277156 -0.00256011 Br 8.31918394 29.92240395 14.33755968 0.00082880 0.00241467 -0.00016421 Br 5.92232593 33.07904235 15.07362205 -0.00078846 -0.00125467 -0.00107951 Cs 6.08524316 29.46885059 17.84596864 0.00103936 -0.00087677 0.00199500 Pb 9.15287557 32.77714808 20.86741275 -0.00091817 0.00039552 0.00060153 Br 9.33395115 32.42278114 17.60889717 -0.00140871 0.00058492 -0.00289692 Br 9.20337975 29.85144702 21.23868567 0.00317359 0.00149314 -0.00451006 Br 6.12898423 32.68711550 21.29886479 0.00110997 0.00259281 -0.00259643 Cs 6.37488195 29.51787229 23.67076520 -0.00136355 -0.00048982 0.00165432 Pb 9.10808535 32.66819470 26.64531040 0.00031987 0.00238792 0.00029711 Br 9.46306088 32.39070746 23.80517832 -0.00126206 -0.00107187 -0.00430938 Br 9.07697716 29.66241531 26.65214735 0.00097182 -0.00427761 -0.00092712 Br 6.21261005 32.63691012 26.43011924 0.00077822 0.00059186 0.00045806 Cs 6.30019348 29.93177905 29.26221460 0.00016861 0.00189587 -0.00115910 Pb 9.09879755 32.65792996 32.56458777 0.00012445 0.00103426 -0.00072219 Br 9.19534404 32.20398949 29.49210350 -0.00021597 0.00106277 0.00157069 Br 9.48854199 29.67184645 32.69446346 -0.00206208 -0.00112820 0.00093264 Br 5.86273668 32.37947113 32.50200038 -0.00318457 -0.00059448 0.00125968 Cs 11.97823240 0.03207158 0.22407312 0.00180906 -0.00233327 0.00192947 Pb 14.90459945 3.08418177 2.93314328 0.00064404 0.00020847 0.00049460 Br 14.86647583 3.76072964 0.00804085 0.00071072 -0.00441676 -0.00032088 Br 15.25643112 0.14635562 2.62428729 0.00009018 -0.00157251 -0.00095331 Br 11.67087105 3.26415818 2.84324321 -0.00324203 0.00481650 0.00292868 Cs 11.74465651 0.24422238 6.23915021 0.00101529 0.00036522 0.00093380 Pb 14.72640645 2.90848030 8.87103236 0.00171096 -0.00088618 -0.00046869 Br 14.69211857 2.64252855 5.76171286 -0.00371810 0.00017133 0.00026566 Br 14.54698291 0.00053085 8.66740643 -0.00247508 -0.00257710 0.00210780 Br 12.00832844 2.82008874 8.98446452 0.00064840 -0.00055493 0.00140415 Cs 11.74407455 0.08420987 11.88542651 0.00108064 -0.00136311 -0.00021259 Pb 14.88198501 2.83509973 14.83592483 -0.00129401 -0.00023535 0.00075408 Br 15.17001706 3.06826216 11.80637795 0.00188919 0.00142821 0.00124019 Br 14.55985626 35.22685720 15.34371126 0.00324485 0.00092552 -0.00043435 Br 12.18341854 2.75184484 14.43403136 -0.00015884 0.00182344 -0.00073206 Cs 11.88679776 0.16294042 17.57733014 0.00025700 -0.00344040 -0.00377701 Pb 15.05433134 2.84489800 20.84525823 0.00081535 0.00103544 0.00015217 Br 15.10135137 3.33558629 17.64953167 0.00220610 -0.00191952 0.00320954 Br 14.51249799 35.58400263 20.12985839 -0.00410009 0.00308395 -0.00112888 Br 12.10977077 3.40678496 20.73865742 -0.00135474 -0.00156209 -0.00124865 Cs 12.19358510 0.16178007 23.82104966 0.00222688 -0.00112384 -0.00016114 Pb 14.99147363 2.85413503 26.75665048 0.00034210 -0.00011027 -0.00081960 Br 14.95643946 2.37593185 23.81372293 -0.00178604 -0.00067804 0.00192908 Br 15.29632648 35.52955418 26.51263287 0.00173036 -0.00378567 -0.00037286 Br 12.10073221 2.71147917 27.29537509 0.00036666 -0.00249964 0.00145082 Cs 11.64634931 0.10261386 29.89794294 0.00102145 0.00272328 -0.00087571 Pb 15.12665366 2.95578774 32.84832252 0.00229957 0.00171848 0.00099722 Br 14.96769978 2.69473644 29.82320151 0.00470324 0.00014691 -0.00098509 Br 15.30139612 35.62946566 32.83237326 -0.00075126 0.00270468 0.00520517 Br 11.90841853 2.43337503 33.16256339 0.00134642 -0.00045949 -0.00034581 Cs 11.63390355 6.31776343 35.34892322 -0.00110296 -0.00076898 0.00055783 Pb 14.55464403 9.04782193 2.73850290 -0.00023335 -0.00066560 -0.00069288 Br 14.57096880 8.49216910 35.32443988 0.00018574 0.00126170 0.00384578 Br 15.13331671 6.15620417 3.14889590 -0.00164174 -0.00061451 0.00169639 Br 11.84991975 9.30448946 2.95920477 0.00606517 0.00044730 -0.00069986 Cs 11.90944476 5.30664175 6.09036370 -0.00320337 0.00062336 -0.00152382 Pb 14.64290741 8.72394068 8.69701205 0.00240229 0.00063508 0.00094042 Br 14.44196805 8.88319645 5.81627509 0.00266552 0.00077839 -0.00163128 Br 14.50219280 5.81569699 9.14966617 -0.00019188 -0.00042370 -0.00360596 Br 11.94504144 9.20290177 9.34594561 -0.00129031 -0.00280267 -0.00385956 Cs 12.18116857 5.82461610 12.31675333 -0.00150475 0.00098124 0.00215461 Pb 15.06201778 9.01829029 14.69930705 -0.00189137 0.00217685 -0.00130745 Br 15.48365029 8.72046360 11.88136746 -0.00459123 0.00144523 0.00150613 Br 14.53303586 5.93468346 14.79894903 -0.00016917 0.00270625 -0.00085308 Br 11.93778742 9.03895878 14.87491457 0.00280696 0.00162565 0.00066226 Cs 11.68948217 5.56413960 17.56601194 -0.00120707 0.00235437 0.00153956 Pb 14.47408252 8.73899505 20.61716781 0.00095472 -0.00183770 -0.00051717 Br 15.28145345 9.15201550 17.86884303 0.00110457 0.00359046 -0.00196295 Br 15.57485260 5.65456496 20.99625953 -0.00186623 0.00134972 0.00218185 Br 11.62987006 9.16875250 20.68380643 0.00105635 0.00147186 0.00256882 Cs 12.05240408 6.21976883 23.99462882 -0.00064179 -0.00069718 -0.00156722 Pb 14.55247381 8.83624291 26.53080475 -0.00052306 -0.00089167 0.00147784 Br 14.67815253 9.01199143 23.51705862 0.00008811 0.00048283 0.00242482 Br 14.93943107 5.83587051 25.99848260 0.00264143 -0.00071443 0.00190358 Br 11.61382490 9.23745151 26.23421961 -0.00176839 -0.00053355 0.00315495 Cs 11.99805594 5.70513920 29.47632977 0.00006347 -0.00225510 -0.00156973 Pb 14.55172678 8.78048730 32.34092051 -0.00048791 0.00003932 -0.00098179 Br 14.93747060 8.73370928 29.42112064 0.00117207 -0.00183148 -0.00070267 Br 14.57777919 5.87263177 32.47538775 -0.00064724 -0.00210728 -0.00147801 Br 11.72585604 9.33156772 33.00496499 0.00037853 0.00353025 -0.00097762 Cs 11.79105774 12.51891588 0.44484493 -0.00128892 -0.00137497 0.00160864 Pb 14.89152884 14.78671198 3.13867040 0.00129401 -0.00151347 0.00039682 Br 15.13525595 14.77512206 0.17242922 0.00070759 -0.00144373 0.00392767 Br 14.85407631 11.79007985 2.59423467 -0.00508456 0.00550142 0.00260452 Br 11.91628547 15.37220060 2.47386251 0.00467194 -0.00212654 -0.00273954 Cs 12.06297659 11.86045897 5.66660014 -0.00044213 0.00011361 -0.00230491 Pb 14.96669487 14.61989608 9.03014704 -0.00051418 0.00100089 -0.00218926 Br 14.61516039 14.62798410 6.02613593 -0.00072544 -0.00186174 -0.00253859 Br 14.93962069 11.80860215 9.06691708 0.00062422 0.00200295 -0.00059185 Br 11.90657155 14.60674322 8.82222592 0.00115251 -0.00025238 -0.00325401 Cs 11.63373674 11.68227687 11.83581960 0.00116120 0.00087049 0.00098608 Pb 14.90040545 14.67396052 14.70332723 0.00154603 -0.00160597 -0.00025438 Br 14.09926942 14.81365141 11.73884986 -0.00109595 -0.00320359 -0.00008595 Br 14.47572653 11.84453831 14.31380800 -0.00131172 -0.00367386 0.00049505 Br 12.10415421 14.94287659 15.36972242 0.00181631 0.00072677 0.00033682 Cs 11.27962365 11.81987313 18.06768083 -0.00055956 0.00089711 0.00072332 Pb 15.07764398 14.49837911 20.75864134 -0.00069757 0.00031103 -0.00246099 Br 14.67773005 15.02717897 17.77088124 0.00056952 -0.00152330 -0.00181294 Br 14.70579385 11.71593654 20.49821381 -0.00023042 -0.00241004 0.00243064 Br 12.01465705 14.82432190 21.00516728 0.00044699 0.00253774 -0.00022887 Cs 11.97299015 12.12147115 24.10413239 -0.00162278 -0.00073774 -0.00403115 Pb 14.83683188 14.86174890 26.74042824 -0.00160325 0.00113595 -0.00024262 Br 14.85877315 14.92577235 23.74764538 0.00224478 -0.00018257 -0.00055999 Br 15.18944157 11.86313431 26.56318951 0.00171500 0.00076091 -0.00016914 Br 11.61675746 14.74665133 26.51611850 0.00172129 0.00327057 -0.00161898 Cs 12.15945768 11.66970124 29.53698340 0.00125904 -0.00156622 0.00035294 Pb 15.07984139 14.84190765 32.74152377 0.00116677 0.00236437 -0.00141719 Br 14.64390518 14.28574118 29.63595748 -0.00217190 0.00069442 -0.00061434 Br 15.09115878 11.87696155 32.19546042 0.00018188 -0.00011633 -0.00153704 Br 11.86656600 14.48692575 33.05219649 0.00073816 0.00189793 0.00093883 Cs 11.96993045 17.85682455 35.01525715 -0.00115460 -0.00166206 -0.00031766 Pb 14.81853060 20.70381817 3.12044658 -0.00110679 -0.00200948 0.00019600 Br 14.68291275 20.36382283 0.05703582 -0.00406000 0.00041801 -0.00025759 Br 14.69691792 17.78730870 3.32997714 -0.00008549 -0.00050718 0.00185151 Br 11.78125192 21.11454830 3.03369648 -0.00256939 0.00053530 0.00271646 Cs 12.12899943 17.57713383 5.94007417 0.00177369 -0.00222316 0.00145946 Pb 14.70649388 20.80129104 8.81778246 -0.00163238 0.00192983 0.00041716 Br 14.33290689 21.37092605 6.11261567 -0.00157878 0.00425297 0.00309374 Br 14.99349717 17.90126327 8.56207072 0.00096212 0.00200533 0.00076473 Br 11.63812789 20.89674481 8.36954653 -0.00047135 0.00113873 0.00247073 Cs 11.92740624 18.09306897 12.14761301 -0.00114773 0.00219060 0.00249895 Pb 14.81632978 20.60958341 14.60845036 -0.00249476 0.00030387 0.00186938 Br 14.27044138 20.95295198 11.77717975 -0.00009672 0.00097004 0.00465898 Br 14.71153707 17.74976746 14.24505677 0.00092270 0.00170448 0.00110680 Br 11.88512887 20.83834234 14.82542440 -0.00052183 0.00050445 0.00121581 Cs 11.91083706 18.02847776 17.70778072 0.00109555 0.00073075 -0.00193525 Pb 14.88263633 20.63116743 20.73357217 0.00201577 0.00033128 0.00105475 Br 15.24434281 20.44315860 17.63894963 -0.00067396 0.00080125 -0.00137467 Br 14.96130901 17.63111611 20.92404600 0.00425608 -0.00086614 0.00070872 Br 11.71347747 21.08508568 20.54861663 0.00078280 -0.00001232 0.00336439 Cs 12.13663381 17.69204651 23.19392731 0.00134689 -0.00051535 0.00079334 Pb 14.52566069 20.96231864 26.87696055 -0.00058900 0.00042766 0.00085608 Br 14.26886731 20.92702352 23.71007466 -0.00331810 -0.00003685 0.00212378 Br 14.68005299 17.73458949 27.13889542 0.00076752 0.00106841 0.00006326 Br 11.78987767 20.65586316 27.67320736 0.00289789 -0.00085662 -0.00069189 Cs 11.50599281 17.76670263 29.30746126 -0.00032780 -0.00000188 -0.00010255 Pb 15.10480883 20.73359613 32.58224707 0.00207578 -0.00244918 0.00034946 Br 15.43470512 20.93900015 29.58459361 0.00105853 0.00269079 0.00184027 Br 15.20836345 17.63998032 32.28390433 -0.00108968 -0.00321427 0.00139528 Br 12.07864225 20.79874033 32.23487035 0.00088433 0.00022845 -0.00291596 Cs 12.05422288 23.57390210 35.43661221 -0.00354247 -0.00040218 0.00372975 Pb 14.57701920 26.50730704 2.67563322 0.00145142 -0.00106386 -0.00121683 Br 14.70775058 26.71215335 35.34042177 0.00180122 -0.00131572 0.00185553 Br 14.79909148 23.66366751 2.85066862 0.00257099 0.00461727 0.00320339 Br 11.72092032 26.97395177 3.56502756 0.00196808 0.00066737 -0.00224835 Cs 11.80902929 24.00439892 5.78156421 -0.00021065 0.00169187 -0.00323143 Pb 14.91606922 26.45753643 8.62248603 -0.00007734 -0.00353160 0.00001213 Br 15.21667256 25.80362656 5.93677368 0.00137827 0.00207840 -0.00347725 Br 15.25380083 23.70998656 9.07754511 -0.00082057 -0.00062149 0.00333799 Br 11.82678508 26.78845134 8.67050751 -0.00165702 -0.00117392 -0.00242253 Cs 11.44029592 23.74047787 11.71588491 0.00080084 -0.00123586 0.00339254 Pb 14.75321884 26.59010772 14.79733711 0.00100107 0.00068293 -0.00119270 Br 14.64326348 25.84978050 11.95904052 0.00084324 -0.00023771 0.00152465 Br 14.61760640 23.68352640 15.20282401 0.00226654 0.00021329 -0.00002097 Br 11.69336755 26.63029581 14.39293691 -0.00354158 0.00153047 -0.00193502 Cs 11.46362008 23.78590547 17.90422683 0.00263381 0.00131558 -0.00079655 Pb 14.98552510 26.50238109 20.66037088 -0.00056556 0.00116050 0.00093185 Br 14.25847607 27.27411854 17.74539481 -0.00185274 0.00081031 -0.00064255 Br 15.22510709 23.64577528 21.03824216 -0.00115432 -0.00320902 -0.00185082 Br 11.75251620 26.45296711 21.15794271 0.00233930 0.00184579 0.00233827 Cs 11.66118063 23.57964105 23.17506786 -0.00233543 0.00010203 -0.00093410 Pb 14.74293876 26.71428946 26.60975450 -0.00168148 0.00152879 0.00087818 Br 14.34566897 27.06318431 23.85550815 -0.00017164 -0.00119939 -0.00193743 Br 14.09326936 23.82269962 26.40239624 0.00351811 0.00315111 0.00092566 Br 11.96119865 26.81730616 27.39819594 -0.00063088 0.00021110 0.00021700 Cs 11.67950026 23.70508283 29.78658929 -0.00104292 0.00012614 -0.00017808 Pb 14.95648455 26.45025175 32.36312074 -0.00137897 0.00143539 0.00256740 Br 15.79580327 26.10868352 29.31403383 0.00068332 0.00001899 0.00266941 Br 14.61038598 23.52647646 32.43192145 -0.00005978 0.00210251 -0.00002502 Br 11.92640130 26.53126654 32.30302139 0.00287557 0.00216368 -0.00031344 Cs 12.18375415 30.03428443 35.55743213 0.00307964 0.00258883 0.00060950 Pb 14.90916224 32.59129988 3.03017974 0.00086652 -0.00049129 -0.00053953 Br 15.04731976 31.81133914 35.51458291 0.00136227 0.00167818 -0.00204884 Br 14.79401947 29.68835239 3.49445320 0.00156808 -0.00459819 0.00042350 Br 12.16044407 33.20622150 2.93044953 0.00081685 -0.00215129 0.00280377 Cs 11.17134701 29.63958391 5.72221451 0.00015248 -0.00023849 -0.00040328 Pb 14.91474237 32.45490217 8.77286887 -0.00008150 0.00102281 0.00019177 Br 14.88152101 32.70614202 5.81421121 0.00030175 0.00097471 -0.00064165 Br 14.66754093 29.71850914 9.29755231 0.00109173 0.00026874 -0.00296998 Br 11.78957775 32.42089595 8.67084100 0.00013555 -0.00224350 -0.00091616 Cs 11.16326456 29.19809151 11.54440009 -0.00349704 -0.00126828 -0.00021499 Pb 14.83044747 32.32719625 14.76073530 -0.00015881 -0.00100861 0.00011170 Br 15.19266383 32.93234426 11.69521098 -0.00022571 0.00682748 -0.00116349 Br 14.41351080 29.48832572 13.82767787 -0.00324942 -0.00352339 -0.00267862 Br 11.77210362 32.80127599 14.89912561 -0.00205240 -0.00051501 0.00061426 Cs 11.82891651 29.77602396 18.33515304 -0.00132143 0.00013020 0.00054069 Pb 14.88875978 32.47518052 20.71686834 0.00032992 0.00004336 0.00095567 Br 14.90582274 31.68152162 17.68558391 0.00004787 0.00132028 0.00082430 Br 14.74777485 29.58069420 20.95366376 -0.00225832 -0.00040827 0.00244781 Br 11.98794877 33.00580744 20.68429341 0.00079674 0.00234266 0.00351687 Cs 12.23176035 29.84192800 23.35203232 -0.00108903 -0.00019439 -0.00162415 Pb 14.92997218 32.71372854 26.65432293 0.00035546 0.00058656 -0.00013120 Br 14.92873842 32.32326756 23.56146622 0.00084244 0.00066488 -0.00258495 Br 15.02966397 29.71890665 26.90910818 0.00377687 0.00129305 -0.00317309 Br 12.00976498 33.38079163 26.56644390 0.00029002 0.00062376 -0.00324050 Cs 12.14432351 29.76109713 29.46503386 0.00089655 -0.00065724 0.00008859 Pb 14.92600383 32.52621997 32.68570622 0.00330918 0.00022396 0.00214129 Br 14.87605963 32.86562076 29.53555280 0.00115487 -0.00001945 0.00126188 Br 14.51418174 29.36139187 32.31876416 0.00014675 0.00150435 0.00022687 Br 11.96265492 33.32938350 32.96316150 -0.00080282 -0.00075632 -0.00096345 Cs 17.74881138 0.63292616 0.10873764 0.00009271 0.00071775 -0.00054297 Pb 20.86055567 3.27088488 2.96973765 -0.00015133 -0.00147238 -0.00064991 Br 20.88113727 3.65885253 35.54546120 -0.00133956 -0.00046268 -0.00096229 Br 20.29452807 0.24187769 2.65348211 -0.00114176 0.00122862 -0.00096586 Br 17.83457395 3.32490487 2.97089213 0.00004974 -0.00268537 -0.00387188 Cs 17.58289673 35.53806605 6.25407616 -0.00188015 0.00228848 -0.00009480 Pb 20.86924570 2.89866207 8.73386812 0.00138423 0.00039924 -0.00037094 Br 21.53615805 2.95132005 5.85704719 -0.00106292 0.00238463 0.00003733 Br 20.34742278 0.03930554 8.77329747 -0.00364895 0.00505723 -0.00059024 Br 17.78284703 3.40915022 8.76934857 -0.00004588 0.00043292 -0.00014579 Cs 17.38581542 0.34095336 12.26714875 -0.00057427 -0.00014399 -0.00170036 Pb 21.12542164 2.89531076 14.92439988 -0.00058891 -0.00160218 -0.00026509 Br 20.54746478 2.72285116 11.94473750 0.00387818 -0.00072955 0.00067794 Br 20.86469899 35.59219959 14.59125554 0.00328966 0.00085878 0.00382780 Br 17.98500140 3.05441764 15.26808537 -0.00153724 0.00251696 -0.00056642 Cs 17.78531623 0.02403938 17.73882351 0.00075875 -0.00159137 -0.00413289 Pb 20.92360815 2.79405126 20.81010506 0.00140925 0.00150479 0.00114105 Br 20.98504497 2.34297295 18.01812860 -0.00080585 0.00119557 0.00135769 Br 20.81678316 35.40490286 21.17972737 0.00091722 -0.00094803 -0.00325438 Br 17.99589106 2.50995396 20.74342990 0.00038327 0.00096929 0.00221441 Cs 17.74380987 35.06390945 23.65914627 -0.00112849 -0.00082780 0.00022089 Pb 20.60846412 2.77415426 26.65254946 0.00191762 -0.00086572 -0.00159761 Br 20.73517608 2.96817129 23.83034186 0.00372925 -0.00123462 -0.00137822 Br 20.24669261 35.32637585 26.78917677 0.00394017 -0.00015585 0.00011620 Br 17.81684945 3.45869642 26.26599326 0.00276597 0.00052037 0.00369372 Cs 17.59585679 35.39098103 29.54037381 0.00123985 -0.00245914 0.00038081 Pb 21.05717745 2.97558821 32.80952686 -0.00020294 -0.00213812 0.00095395 Br 20.70443425 2.80903335 29.60082635 0.00148670 -0.00256732 -0.00400853 Br 20.35255584 0.03688693 33.34888879 -0.00015991 -0.00052676 0.00053858 Br 18.11215763 2.92098769 32.83176446 -0.00078634 -0.00100357 0.00024043 Cs 17.35127064 6.67017535 0.42274019 -0.00178134 0.00040411 -0.00008502 Pb 20.61408892 8.96637900 3.27063364 0.00007372 0.00093251 -0.00055883 Br 20.67325081 9.52599152 0.19815380 0.00177642 -0.00151630 0.00023818 Br 20.60549768 6.18486916 2.99813420 -0.00176002 0.00254383 0.00102345 Br 17.69449837 8.99764122 3.30660567 0.00089089 -0.00028846 0.00327013 Cs 17.21356102 6.06800425 6.29140774 0.00114371 0.00157302 0.00002140 Pb 20.74024191 8.94704955 9.05230032 0.00182663 -0.00131018 -0.00073778 Br 20.95612252 9.07249015 6.11501308 0.00146460 -0.00167421 0.00177863 Br 20.77288236 6.06896766 8.31835012 -0.00189295 0.00517356 -0.00123269 Br 17.67231837 8.47746207 8.73578741 -0.00140623 -0.00047387 -0.00480482 Cs 17.94652312 5.81363861 11.44667261 -0.00277776 -0.00184737 0.00225038 Pb 21.08124112 8.90496877 14.78763201 0.00093319 0.00160726 0.00170812 Br 20.86055171 8.62574742 11.95099015 -0.00210426 0.00012687 0.00002909 Br 21.57378595 5.87168897 14.81822716 0.00006896 -0.00059556 0.00092434 Br 18.07972374 9.37268723 14.84563389 -0.00265406 -0.00023120 0.00008156 Cs 17.93165044 5.75895851 17.67707779 -0.00050327 0.00115370 -0.00174455 Pb 20.76529607 8.98551180 20.83861922 0.00142411 -0.00025815 0.00265650 Br 21.20601949 8.90267781 17.76534125 -0.00135547 -0.00291629 0.00005912 Br 20.49292352 5.69994019 20.84880405 0.00183980 0.00086284 -0.00017477 Br 17.93392310 8.97314153 20.56197104 -0.00101504 -0.00042102 -0.00035652 Cs 18.02043838 6.20432431 23.47764347 -0.00015809 0.00039954 0.00017557 Pb 20.63379685 9.07949306 26.78616711 -0.00070693 0.00178837 0.00022524 Br 20.77668791 8.90818841 23.60800946 -0.00348926 -0.00399746 0.00261969 Br 20.50344015 6.04683031 26.66282158 -0.00467613 -0.00283767 -0.00063527 Br 17.74401058 9.14068532 26.42070393 0.00177482 0.00068815 -0.00068528 Cs 17.97745465 6.11542520 29.39281439 0.00123422 0.00155817 -0.00087795 Pb 20.52026569 9.17943406 32.72007889 -0.00076036 0.00334475 0.00146567 Br 21.23410709 9.28042686 29.64449399 0.00409450 0.00164625 -0.00023691 Br 20.85814537 5.88683746 32.42317050 0.00071212 0.00070083 -0.00159599 Br 17.55049436 9.01509130 32.53918838 0.00075828 -0.00383411 0.00376269 Cs 18.11731886 12.16041862 -0.01075999 0.00028820 -0.00106969 -0.00169178 Pb 20.76783819 15.02396467 3.05425213 -0.00041201 0.00015348 -0.00150829 Br 20.93073217 14.53669129 0.24274888 -0.00038463 -0.00041603 0.00344898 Br 20.96461118 12.15876951 3.33262241 -0.00377206 -0.00131236 -0.00105246 Br 17.88589437 14.59389120 3.42880383 -0.00266261 0.00053300 0.00272871 Cs 17.93185460 11.90796735 6.03407725 -0.00195745 -0.00136940 0.00054056 Pb 20.88474814 14.97661596 8.82563943 -0.00334305 -0.00333480 -0.00054881 Br 20.54338896 15.27235466 6.13237849 0.00030514 -0.00187151 -0.00003832 Br 20.86002440 11.89441002 8.54012366 0.00149331 0.00002025 -0.00157289 Br 17.98330955 15.05520587 9.22562608 -0.00020335 0.00146154 0.00082289 Cs 17.95270170 12.09135438 11.50868431 0.00059915 0.00131500 -0.00074240 Pb 20.75252898 14.62957256 14.78284191 -0.00244592 -0.00020470 0.00017019 Br 21.35756002 14.89653937 12.00998932 0.00062183 -0.00135627 0.00298382 Br 20.85187192 11.77854513 14.51164116 -0.00295033 -0.00333487 -0.00133883 Br 18.03199031 15.25423788 14.56639323 -0.00238807 -0.00032108 0.00181395 Cs 18.23793236 11.51873639 17.93187978 0.00242135 -0.00079622 -0.00086097 Pb 20.79097667 14.90015163 20.63504350 0.00039674 0.00147676 -0.00255573 Br 21.16931405 14.73340636 17.66701138 0.00319819 -0.00198986 0.00014619 Br 21.13109741 11.88326615 21.00794317 -0.00043780 -0.00253523 0.00028688 Br 18.05038307 13.74728922 20.55716490 0.00190154 -0.00321571 0.00037299 Cs 17.99177551 11.73628976 23.46386323 -0.00108982 -0.00180473 -0.00368177 Pb 20.69103158 14.96778655 26.59343998 0.00169364 0.00241236 0.00108571 Br 20.28327295 14.74013118 23.54265102 0.00019273 -0.00189302 -0.00213951 Br 20.30395645 11.93262001 26.92126316 0.00146571 0.00051079 -0.00095877 Br 17.78777379 15.32391968 26.71608420 0.00071210 0.00066616 -0.00151879 Cs 17.93568487 11.68607654 29.96733567 -0.00121523 0.00223950 0.00111176 Pb 20.81621625 14.98112625 32.61802836 -0.00075580 0.00016268 -0.00081606 Br 20.85410728 14.62629279 29.49974478 -0.00256605 0.00052346 -0.00355844 Br 20.48425565 12.04759063 32.81505930 0.00135487 -0.00261937 0.00164542 Br 17.77229826 14.32572757 32.82007924 0.00142929 -0.00152539 0.00075433 Cs 17.80244531 17.35288396 35.53453107 -0.00141327 0.00193713 -0.00036988 Pb 20.66459162 20.67381806 2.87785389 0.00184839 -0.00021363 0.00018282 Br 20.48103750 20.31482141 35.52818146 0.00049532 -0.00179948 0.00154479 Br 21.03237310 17.90500857 2.75615124 -0.00286007 0.00015832 0.00060752 Br 17.82692231 20.62299437 3.17529275 -0.00140702 -0.00022263 -0.00068926 Cs 17.79558196 17.55988148 5.76436158 -0.00067220 0.00184316 0.00029325 Pb 20.53931796 20.87568429 9.29626405 -0.00019973 -0.00111124 0.00248230 Br 20.83035568 20.65474206 6.03116284 -0.00136807 0.00213044 -0.00332230 Br 20.55275708 17.97070853 8.81099135 0.00008474 0.00366251 -0.00138370 Br 17.61537595 20.70319949 8.52926862 -0.00029367 -0.00168800 -0.00151129 Cs 17.75433070 17.63862264 11.77363381 0.00058455 -0.00234207 -0.00117829 Pb 20.90726681 20.64002907 15.08832524 -0.00012949 0.00158451 -0.00143230 Br 20.37322078 20.98657432 12.17108363 0.00062063 -0.00352744 0.00406385 Br 21.06241711 17.76020064 14.80772132 -0.00180290 -0.00021538 -0.00208614 Br 17.73724575 20.22489977 14.57102520 -0.00175280 -0.00268398 0.00223729 Cs 17.60706847 17.61841547 17.15452218 -0.00161885 0.00041206 0.00158822 Pb 20.58587905 20.68971511 20.75443410 0.00085588 0.00203354 0.00095193 Br 20.14607005 20.56735611 17.82602267 0.00211168 -0.00003793 -0.00185094 Br 20.21335729 17.81205651 20.75624645 -0.00214443 0.00015697 0.00079401 Br 17.56499037 20.87284253 21.64048511 0.00133344 0.00085901 -0.00584090 Cs 17.64869658 18.12816347 24.26051263 -0.00010386 -0.00143954 -0.00087630 Pb 20.89720907 21.05917765 26.76078874 -0.00043765 -0.00070608 -0.00068501 Br 20.71114525 20.87481532 23.76540016 -0.00092231 -0.00135203 -0.00360185 Br 20.85911698 17.87973641 26.73852230 0.00353442 -0.00278762 0.00397424 Br 18.07478363 20.62678932 27.08940555 0.00184881 -0.00179390 -0.00104367 Cs 17.70741951 18.04184451 29.58242215 -0.00247458 0.00116273 0.00289215 Pb 20.80943094 20.55516375 32.62052102 -0.00120192 0.00002717 -0.00237049 Br 20.92704503 21.04634794 29.72580571 0.00178314 0.00006888 0.00074419 Br 19.82383890 17.67381225 32.41658688 -0.00066404 0.00232859 -0.00234629 Br 17.78242140 21.10868241 33.37118960 -0.00310730 0.00177239 0.00129165 Cs 17.93423645 24.08195843 35.34448967 -0.00120763 0.00048957 0.00225173 Pb 20.78050766 26.75023207 3.04469373 0.00146687 -0.00155106 0.00242968 Br 21.06364183 26.22037266 0.21407807 0.00151501 0.00089350 0.00101644 Br 20.78978974 23.70667356 3.09010884 0.00091857 0.00062511 0.00206676 Br 17.56979123 26.69256132 2.68284661 0.00120199 0.00267859 0.00317234 Cs 18.39622735 23.50096993 6.34050452 0.00122960 0.00276939 -0.00168556 Pb 20.70450219 26.78935949 9.12030276 -0.00056204 0.00257625 0.00061022 Br 20.67087550 26.83562414 6.23879153 0.00104376 -0.00125157 0.00066414 Br 20.60034252 23.93697147 9.26354991 -0.00244486 -0.00073450 -0.00339564 Br 17.71532102 26.83314048 9.62212788 0.00083354 0.00139133 0.00554771 Cs 17.46808549 23.32608902 12.74884596 0.00162922 -0.00131432 -0.00010451 Pb 20.40827595 26.65165027 15.19838688 -0.00129785 -0.00010801 -0.00045527 Br 21.10777547 26.38002477 12.21080431 0.00180219 0.00076090 0.00169433 Br 20.88618000 23.55517903 14.93905517 0.00310046 0.00225431 -0.00194758 Br 17.60008678 27.37855773 14.44102070 -0.00236290 0.00016673 0.00333131 Cs 17.42086110 23.22631510 17.95791979 -0.00003683 -0.00212763 0.00130957 Pb 20.80181185 26.64956101 20.99827169 0.00169491 -0.00067853 -0.00075316 Br 19.84520899 26.65275234 18.02162384 -0.00100018 0.00244588 -0.00057526 Br 20.38446735 23.75126598 20.77570230 -0.00098120 0.00128500 -0.00038334 Br 17.71922007 26.89293251 21.21431843 0.00082419 0.00022867 -0.00076863 Cs 17.85913642 24.16609644 23.52803522 -0.00063692 -0.00089149 -0.00099378 Pb 20.61207200 26.63196355 26.73179917 0.00151751 0.00023552 0.00331124 Br 20.97898557 26.87834472 23.81768768 -0.00279896 0.00093260 0.00076605 Br 20.19822290 23.69360572 26.84837902 0.00504806 -0.00174304 -0.00118181 Br 17.87817594 26.84373189 26.07138410 0.00377317 0.00358532 -0.00090871 Cs 18.27948267 24.04136177 30.16208534 0.00010060 -0.00125330 -0.00085682 Pb 21.06489805 26.86835260 32.68320775 0.00058480 0.00037319 -0.00194963 Br 20.23696247 26.95551738 29.61941733 0.00238436 0.00073849 -0.00115024 Br 20.58217531 23.69377334 32.87692115 0.00041689 -0.00108840 0.00215406 Br 17.75811882 26.73183108 32.85047041 -0.00008682 0.00118340 0.00298449 Cs 17.67890553 29.88764436 0.13528463 0.00044946 0.00033243 0.00023475 Pb 20.70956898 32.91847393 2.75288442 -0.00096710 -0.00052449 0.00045043 Br 20.67770843 32.48488185 35.47304988 -0.00089060 0.00152258 -0.00200998 Br 20.65248219 29.63458753 2.93581296 -0.00376661 -0.00250151 0.00010585 Br 17.83501399 32.65980651 2.55037384 0.00046153 0.00277601 -0.00032299 Cs 17.44506128 29.15297637 5.80085648 -0.00035200 -0.00078603 0.00160685 Pb 20.61341206 32.76145230 9.05948902 0.00010729 -0.00002789 -0.00065313 Br 20.95189467 32.55330278 5.74511785 -0.00105464 -0.00119218 0.00345406 Br 20.48383584 29.73360223 9.41751919 0.00154631 0.00234872 -0.00306986 Br 17.86972539 32.41567213 8.57907488 0.00331017 -0.00305104 0.00342332 Cs 17.70003550 29.95903960 11.74437545 -0.00270778 -0.00068552 -0.00272604 Pb 20.87247281 32.76245305 14.77883357 0.00042382 -0.00106342 0.00075821 Br 20.75328555 32.94757003 11.92843243 -0.00232314 -0.00121919 0.00224753 Br 20.86055525 29.75796917 15.01567026 0.00250260 0.00077504 -0.00016994 Br 17.73586646 32.65786046 14.93886110 -0.00072087 0.00494864 0.00039723 Cs 17.80269849 29.65376657 17.72455066 0.00051664 -0.00133628 -0.00186671 Pb 20.78134790 32.38397073 20.84170785 0.00078632 -0.00014202 -0.00115761 Br 21.28834195 33.16371652 17.96138681 0.00358114 -0.00231738 0.00226850 Br 20.78612324 29.46316983 20.39635955 -0.00012107 -0.00180522 0.00350339 Br 17.74615784 32.22905441 20.38272513 -0.00418933 -0.00067707 -0.00319476 Cs 18.09576247 29.86803908 23.94464896 -0.00313950 0.00009350 0.00016754 Pb 20.85835020 32.61531706 26.70931927 0.00280318 0.00080295 -0.00113540 Br 20.97052070 31.96571497 23.81377098 0.00080554 0.00139437 0.00118332 Br 21.13330958 29.52633311 26.37227752 -0.00023282 0.00043405 -0.00168891 Br 17.94129363 32.26917300 26.68297740 0.00060678 0.00176672 0.00027219 Cs 17.38769510 29.57605296 29.41104944 -0.00143096 0.00088385 -0.00147281 Pb 20.84064425 32.69672389 32.50523515 0.00147183 -0.00011704 0.00033793 Br 20.66027085 32.28242199 29.69593584 -0.00136019 -0.00030002 -0.00077207 Br 20.75758106 29.86408387 32.59053981 0.00106823 0.00244086 -0.00273659 Br 17.73469475 32.17987683 32.16325362 -0.00441037 -0.00183527 0.00435722 Cs 23.94744440 0.44017749 0.15254006 0.00106478 -0.00098386 -0.00163738 Pb 26.88366418 3.50702711 3.01520815 0.00232746 -0.00124185 0.00289573 Br 27.25890669 3.61632595 35.27374246 -0.00022306 0.00253303 -0.00020937 Br 26.67110067 0.44547629 2.34980078 0.00150786 0.00022397 -0.00173333 Br 23.90189121 3.41465144 2.48794845 0.00103744 0.00046907 -0.00070445 Cs 23.83816592 35.53122868 5.68091834 -0.00059921 0.00173606 -0.00120243 Pb 26.88371577 2.97994426 8.98413887 0.00005519 0.00103201 0.00196980 Br 26.61888568 3.42215213 6.03416206 0.00065474 0.00108305 -0.00034912 Br 27.71243309 0.20150090 9.23047224 0.00067107 -0.00025796 0.00010217 Br 23.75846012 2.86283435 9.33237232 0.00075544 0.00066802 -0.00249338 Cs 23.85923390 0.03699585 11.68485403 0.00194424 -0.00206091 -0.00013006 Pb 26.97322763 2.99324386 14.99502068 0.00020185 -0.00130913 -0.00270252 Br 26.61549163 2.67113614 12.06261875 -0.00155251 0.00067431 -0.00222611 Br 26.84517950 35.38736946 14.96668828 0.00352744 -0.00192493 0.00217057 Br 24.07513464 2.68238071 15.01802380 0.00256455 -0.00079464 -0.00108236 Cs 23.83545467 35.59390142 17.45948274 0.00043762 -0.00119766 -0.00069047 Pb 26.75146410 3.15171343 20.74615071 0.00121795 0.00101911 0.00175811 Br 26.41428114 3.27413882 17.87454231 0.00029779 -0.00100391 0.00057347 Br 26.25719175 0.24866318 20.16080339 -0.00026532 0.00354703 -0.00154599 Br 23.92147052 3.29641702 21.10508771 -0.00168295 -0.00037209 -0.00016638 Cs 24.20657443 0.20102048 23.39562026 -0.00088974 -0.00121032 0.00109979 Pb 26.66889123 2.98713847 26.32799342 0.00233881 -0.00036010 0.00113295 Br 27.38287914 2.49773911 23.57170921 -0.00214381 0.00157265 0.00063902 Br 26.90631992 0.07661393 26.99423410 -0.00048341 -0.00101800 -0.00234304 Br 23.71470677 2.86138641 26.08269326 -0.00294421 -0.00378787 0.00127903 Cs 23.04738219 35.25104177 29.68031455 -0.00004558 -0.00283550 -0.00146109 Pb 26.78275498 3.09281131 32.45394059 0.00007615 -0.00008670 -0.00026548 Br 26.05581652 3.36186697 29.43321942 -0.00351086 0.00054813 0.00027282 Br 26.74151549 0.08000040 32.87615398 0.00022619 -0.00125780 -0.00195378 Br 24.08345685 3.36884146 33.25406193 -0.00023659 0.00159604 -0.00007329 Cs 23.63934527 6.40337793 35.42974017 0.00056723 0.00191634 -0.00103279 Pb 27.00894641 8.91129423 3.08462702 -0.00043434 0.00221403 -0.00150890 Br 26.45889594 9.16861967 35.43694383 0.00092305 -0.00170527 -0.00100026 Br 27.89328026 6.27022038 2.97450530 0.00052500 -0.00147877 0.00369336 Br 24.05916279 8.44416208 3.19794923 -0.00071109 -0.00171769 -0.00384473 Cs 23.62273305 5.90126225 6.16748008 -0.00192084 0.00021830 0.00022910 Pb 26.82594526 8.94814912 8.98939085 -0.00033363 0.00000327 -0.00072779 Br 26.83005000 8.83144627 5.93679380 0.00199275 -0.00022809 0.00007930 Br 26.51426317 5.88244854 9.12064489 0.00198458 -0.00010046 0.00028857 Br 23.79533038 9.52278588 9.01497642 -0.00150673 0.00049733 -0.00011003 Cs 24.11761904 5.37091301 11.86780521 0.00111485 0.00014289 -0.00003299 Pb 26.85997584 8.93068547 14.87891994 0.00259298 -0.00011779 -0.00196472 Br 26.98329357 9.27031208 11.88215065 0.00525049 -0.00109912 -0.00386308 Br 26.86478891 5.92308585 14.32979329 -0.00098727 0.00078421 0.00079146 Br 23.96697557 9.83516170 15.17830168 0.00346069 -0.00007160 0.00096082 Cs 24.01872126 6.14292908 18.26576861 0.00270730 0.00024955 0.00004200 Pb 26.94783231 8.92180008 20.66418940 0.00188174 0.00045219 0.00031523 Br 26.69273522 8.65159561 17.72414127 0.00133146 -0.00324441 -0.00094758 Br 27.35629186 6.06895253 20.52975367 0.00237872 0.00097644 -0.00140705 Br 24.01150870 8.76772216 20.79966713 0.00010214 -0.00008431 0.00281614 Cs 23.46166685 5.74174840 23.79973996 -0.00135533 0.00092002 0.00032598 Pb 26.62255493 8.75095771 26.56911929 0.00033457 -0.00242018 0.00045119 Br 27.36009048 8.86736352 23.57128425 0.00179655 0.00196605 0.00438181 Br 26.59193372 5.96947738 26.34774447 -0.00396810 -0.00102889 -0.00306284 Br 23.73291781 8.95774751 26.01845057 -0.00240195 0.00204150 -0.00006420 Cs 23.87349873 6.09339178 30.39839186 0.00225306 -0.00009602 -0.00267742 Pb 26.50435860 8.96291204 32.37833239 0.00228254 -0.00074392 -0.00105955 Br 26.12512032 9.14734106 29.32924615 -0.00032571 0.00241280 0.00022174 Br 26.93771843 6.18038110 32.54303187 0.00205155 0.00181334 -0.00374480 Br 23.47914344 9.04853413 32.76926766 0.00186224 0.00169356 0.00165547 Cs 23.79890624 11.86303880 0.19123929 0.00142619 -0.00038584 -0.00066514 Pb 26.88510390 14.95206825 2.97195356 0.00111885 -0.00158873 0.00167886 Br 26.11611952 14.28600443 35.50075706 -0.00017539 -0.00130980 0.00171732 Br 26.68006343 11.96375747 3.67141390 -0.00095225 -0.00024973 0.00163585 Br 23.62471118 14.83033008 3.01834143 -0.00036734 0.00008967 -0.00281120 Cs 23.67751917 11.77880891 5.82236547 -0.00211104 0.00183843 -0.00265762 Pb 26.87451069 14.83090008 8.62513993 -0.00084068 -0.00116997 0.00026004 Br 26.71761616 15.45603757 5.70956783 0.00034066 -0.00200217 -0.00250712 Br 26.94799111 11.83874962 8.25722437 0.00416263 0.00162497 -0.00035223 Br 23.91471906 15.32104527 8.70214945 -0.00257984 -0.00170796 0.00265182 Cs 24.16692885 11.66099193 11.94074730 -0.00388802 0.00040988 -0.00005844 Pb 26.79830745 14.50226609 14.68428786 -0.00014339 0.00167060 -0.00045351 Br 26.67543144 14.51474405 11.64650749 0.00220541 -0.00100117 0.00011487 Br 27.59606031 11.65059635 14.61278225 0.00114402 0.00160492 -0.00101949 Br 23.84917262 14.58431513 14.58805389 -0.00069890 -0.00029260 0.00287795 Cs 24.00904626 11.58886653 18.10941405 -0.00240051 0.00067869 -0.00269662 Pb 26.64907423 14.92848370 20.77517300 -0.00009047 0.00037142 -0.00085265 Br 26.49747506 14.45728774 17.69671906 -0.00055871 0.00237793 0.00111574 Br 26.64989043 11.96390503 20.71660134 0.00205104 -0.00379259 -0.00040653 Br 23.77037455 15.03850344 20.43717907 0.00357846 -0.00014853 0.00296818 Cs 23.78149559 11.19526271 23.31805463 -0.00022035 0.00097066 0.00209053 Pb 26.32379894 14.92834180 26.63962335 0.00101894 0.00180591 -0.00110708 Br 26.71910785 15.35176142 23.73628950 0.00092030 -0.00023270 -0.00360880 Br 26.56024862 11.88518266 26.17437742 -0.00147021 -0.00276336 -0.00031548 Br 23.51945440 14.34341718 26.16595473 0.00354671 -0.00027648 -0.00143240 Cs 23.57180904 11.68645802 28.59601490 0.00156423 -0.00006045 -0.00035976 Pb 26.62931744 14.78285454 32.73267985 -0.00303209 0.00064293 -0.00060024 Br 26.15054634 14.36551432 29.67972534 0.00030364 -0.00277122 -0.00147484 Br 26.46050146 12.08859010 32.30146116 0.00053864 0.00136660 -0.00204170 Br 23.74376594 15.54213014 32.87074144 -0.00158903 -0.00162112 0.00173370 Cs 24.09639980 17.66725062 0.06705315 0.00183266 0.00140131 -0.00030142 Pb 26.43712132 20.92338315 2.98450973 -0.00086244 0.00105249 0.00134250 Br 27.60390565 21.46415624 35.50664110 0.00266501 0.00128738 0.00161567 Br 26.82000385 17.92996386 2.56281517 0.00022375 0.00050827 0.00107237 Br 23.85301513 20.86213064 1.93772984 0.00107549 -0.00170803 -0.00029053 Cs 23.59552936 17.60989840 5.75838340 0.00311173 0.00328154 0.00005728 Pb 26.95004434 20.73520023 9.02814888 -0.00110513 0.00046290 -0.00160556 Br 26.60415035 20.85268803 6.02885869 -0.00087159 0.00252094 0.00183912 Br 27.75238614 17.81552426 9.04112750 -0.00290304 -0.00001205 -0.00014789 Br 23.80589329 20.88647390 9.47563398 -0.00141709 0.00251792 -0.00219129 Cs 23.69185751 18.06731907 12.09873829 0.00003310 0.00022342 0.00036209 Pb 26.74570085 20.55795310 14.92513544 0.00090452 0.00175493 0.00132198 Br 26.80905665 20.54503549 12.09594915 0.00429747 0.00022936 0.00130743 Br 27.07743333 17.54447284 14.84658759 -0.00010807 -0.00053623 -0.00082522 Br 23.65480609 20.87990135 14.35592939 0.00019879 -0.00401385 -0.00209641 Cs 24.04225307 17.21278221 17.42340378 -0.00150517 -0.00021155 -0.00070719 Pb 26.77607541 20.70288059 20.83534148 0.00038553 0.00081649 -0.00126935 Br 26.45733381 20.49621757 17.86355701 0.00013141 0.00131060 0.00002877 Br 26.59174233 17.87019500 20.05838321 -0.00157292 -0.00346151 -0.00121523 Br 23.72683968 20.79999585 21.22593792 0.00205337 0.00094652 -0.00142687 Cs 23.82245671 18.32447018 23.79490075 0.00054308 -0.00062842 0.00017966 Pb 26.95363276 20.93845129 26.66844065 0.00007587 -0.00031034 0.00086409 Br 27.35580010 20.37852163 23.72484542 -0.00004697 0.00030148 0.00125067 Br 26.62124397 17.79617369 27.06720212 0.00181162 0.00141891 0.00117764 Br 23.88382267 21.44296532 26.49955405 0.00000453 -0.00085599 -0.00072341 Cs 23.86313746 17.47848596 29.62469528 -0.00177434 -0.00188515 0.00120264 Pb 26.54270910 20.91606190 32.63286775 0.00076086 -0.00080312 0.00115762 Br 26.32189966 20.92589017 29.69974079 0.00181767 -0.00010278 0.00002296 Br 26.57695751 18.08903259 33.46474893 -0.00251745 -0.00095407 -0.00311968 Br 23.64990496 20.54052394 32.40525321 0.00137663 0.00365328 0.00101780 Cs 23.92634513 24.07024045 0.04960604 0.00024251 0.00018335 -0.00051396 Pb 26.61836118 26.89080950 2.99520125 0.00046128 0.00028764 -0.00247689 Br 26.82569638 26.32874413 0.11996154 0.00082422 0.00070024 -0.00361855 Br 27.07829751 23.94638612 3.13614667 -0.00293003 -0.00006631 -0.00037001 Br 23.73089749 27.11479968 2.64246167 -0.00457769 0.00060173 -0.00008448 Cs 23.41718422 23.84132573 6.52710959 -0.00062690 -0.00004462 0.00163147 Pb 26.58568067 26.76458173 8.95905218 0.00005684 0.00205016 0.00005885 Br 26.52517289 26.73854304 5.98395870 -0.00136790 0.00107628 -0.00122828 Br 26.63897350 23.83322761 9.18944868 -0.00120358 -0.00075686 -0.00091685 Br 23.67847910 26.71318299 9.06652146 -0.00144049 0.00124396 -0.00213569 Cs 23.20070997 23.70692520 11.75708940 0.00093701 0.00087862 -0.00278703 Pb 26.51812113 26.29548630 14.99136767 -0.00005309 0.00075846 0.00100675 Br 26.51126521 26.67350475 11.82644798 -0.00093621 0.00088094 -0.00320654 Br 26.56564926 23.39006914 14.66796338 -0.00293107 0.00161176 -0.00270445 Br 23.31134491 26.57887857 15.45918158 -0.00049678 -0.00016299 -0.00251061 Cs 24.14747249 23.84394697 17.88867643 0.00074397 -0.00237439 0.00161870 Pb 26.68786694 26.72537150 20.89252512 -0.00031639 -0.00002861 -0.00217406 Br 26.81840843 26.39257993 18.13453315 0.00048604 0.00066351 0.00041307 Br 26.90382150 23.77368395 21.07323580 0.00151582 -0.00036978 -0.00355591 Br 23.45566907 26.57175643 20.41245251 -0.00640371 -0.00229891 0.00089085 Cs 23.38501718 24.15441663 23.73916921 0.00222786 -0.00351615 -0.00143254 Pb 26.70141546 26.54436239 26.65853370 -0.00095127 -0.00183489 -0.00056681 Br 26.46806420 26.72638839 23.71947323 0.00033185 0.00091755 -0.00119495 Br 26.81512260 23.61066813 25.99985177 0.00241738 -0.00410843 0.00161609 Br 23.77710115 26.49259695 26.96102712 -0.00551945 -0.00128589 -0.00041246 Cs 23.81135966 23.28448333 29.69551059 0.00283430 0.00009305 -0.00135088 Pb 26.86285064 26.60803197 32.86925506 0.00013256 0.00058348 -0.00032802 Br 26.56739299 26.69604929 29.78581097 -0.00033852 -0.00057770 0.00166803 Br 26.14622152 23.90215465 32.73475582 0.00439310 0.00184916 0.00043066 Br 23.82560457 27.47811923 32.75454402 -0.00291412 0.00006075 -0.00213961 Cs 24.16548102 30.02095025 35.50288855 -0.00075779 -0.00056057 0.00094119 Pb 26.67436536 32.79273828 2.82965574 0.00044747 -0.00092090 -0.00088425 Br 27.07774819 32.52694307 35.52782802 0.00338465 -0.00188659 -0.00111063 Br 26.89297533 29.83204185 3.20215239 0.00045946 0.00239959 0.00233367 Br 23.97138741 32.39120909 2.95933237 -0.00703230 -0.00125510 -0.00020914 Cs 23.80281782 29.50194735 5.64236770 0.00413661 -0.00123744 -0.00033312 Pb 26.52125207 32.59670711 8.54740755 -0.00297116 -0.00158224 -0.00063817 Br 26.82205690 33.30746493 5.70383874 0.00103273 -0.00362710 -0.00253418 Br 26.38774825 29.70885496 8.67986442 0.00114439 0.00002244 0.00067401 Br 23.53897589 32.64508806 9.09231549 0.00068979 0.00160394 0.00162162 Cs 23.76090808 29.93518751 11.68713904 -0.00242710 -0.00196753 -0.00045438 Pb 26.80415621 32.40491911 14.88390917 -0.00198341 0.00048044 0.00054556 Br 26.26040066 32.58807265 11.85787603 0.00079025 0.00157590 0.00003882 Br 26.89768048 29.36068074 14.99281599 0.00534472 0.00081706 -0.00001017 Br 23.82343588 32.45869136 14.60518934 0.00237675 -0.00125185 -0.00062880 Cs 23.64370143 30.13114375 17.81667598 -0.00106062 0.00063684 -0.00065001 Pb 26.76226168 32.71470341 20.61294647 -0.00216465 0.00118458 0.00269032 Br 25.98938838 32.76642628 17.70797789 -0.00046839 -0.00400322 -0.00132888 Br 26.56143229 29.72337051 20.46099897 0.00016918 0.00086175 0.00042855 Br 23.66118558 32.47662787 21.47430164 0.00136971 -0.00101175 -0.00038902 Cs 24.41657724 30.00261045 23.92620186 -0.00004483 0.00109868 0.00032950 Pb 26.70426119 32.51694604 26.81725177 -0.00019166 0.00082985 0.00028667 Br 26.99899485 32.70122234 23.55791843 0.00281938 -0.00018786 -0.00074159 Br 26.83799679 29.56930649 26.49439472 -0.00165236 0.00073410 0.00209903 Br 23.74188103 33.35886237 26.70498563 0.00174266 -0.00253646 0.00169166 Cs 23.26488520 29.29514468 29.87742917 0.00389183 -0.00106532 -0.00069352 Pb 26.91574130 32.73376565 32.65095093 -0.00104966 -0.00053000 -0.00115295 Br 26.53745375 31.88527744 29.65690870 0.00227068 -0.00044738 -0.00194156 Br 26.97703494 29.52721366 33.01410606 0.00052391 0.00072500 0.00282468 Br 23.82541822 32.66773171 32.64572542 0.00184870 0.00209010 -0.00001948 Cs 29.41567290 0.49022564 35.43066670 -0.00037288 -0.00085811 0.00099825 Pb 32.45963512 2.81013143 3.02857812 -0.00143115 -0.00040037 0.00121549 Br 32.73825879 2.21222735 0.06765233 -0.00157612 -0.00020125 0.00490999 Br 32.53030872 0.03625226 3.07713027 -0.00026436 -0.00278747 -0.00040720 Br 29.71809097 3.03945900 3.70082043 0.00033064 -0.00234211 0.00029199 Cs 29.93215345 0.31728315 6.69094547 -0.00002129 -0.00090215 0.00094711 Pb 32.80283339 2.74973607 9.04970916 0.00012314 -0.00102140 -0.00190073 Br 32.40103519 2.58301966 6.05532064 0.00291214 -0.00050150 0.00009179 Br 32.12253161 35.21491331 9.73800568 -0.00009698 0.00236518 -0.00079020 Br 29.65533304 3.25987011 8.55570917 0.00045097 -0.00031510 0.00390500 Cs 29.47743283 35.11460575 12.60246457 0.00102283 0.00063602 0.00197673 Pb 32.79813324 2.81537035 14.87175237 -0.00028846 -0.00201436 -0.00073140 Br 32.03366835 2.76339893 11.99243030 -0.00075733 0.00197871 -0.00106093 Br 32.70422242 35.35927749 14.93600323 -0.00146982 0.00141217 0.00163255 Br 29.93558400 3.06532680 15.13941749 -0.00164957 0.00216737 -0.00134461 Cs 29.52837689 35.58540145 17.61264237 0.00103229 -0.00237635 0.00219870 Pb 32.45903517 3.03968029 21.03627966 -0.00143430 -0.00071398 0.00018619 Br 31.98726220 2.83541968 18.07579006 -0.00049577 0.00028037 0.00190945 Br 32.57467003 35.58885650 21.08590408 -0.00244066 0.00050392 -0.00128963 Br 29.64106406 2.40586456 20.57281293 -0.00201776 0.00119700 0.00088971 Cs 29.83587795 0.17049643 24.13215336 -0.00102569 -0.00026271 -0.00135654 Pb 32.61288221 3.05413575 26.51574997 -0.00034407 -0.00066560 0.00043548 Br 32.02581296 3.11773716 23.76976676 -0.00112545 -0.00103647 0.00440405 Br 32.20676763 35.49666036 26.82242186 -0.00259595 0.00086686 -0.00029420 Br 29.62776858 3.01860302 26.84577478 -0.00201651 0.00098765 -0.00082679 Cs 29.34159838 0.16816805 29.31810032 -0.00204645 0.00171519 0.00201453 Pb 32.98014651 2.76546520 32.58863995 0.00001601 -0.00098441 -0.00108645 Br 33.12908429 2.66253649 29.65499098 -0.00004374 -0.00282091 -0.00157300 Br 33.10018732 35.57540228 32.24161339 0.00048897 0.00263005 -0.00150911 Br 29.98864038 2.82190170 32.46573079 0.00164759 0.00068349 -0.00353886 Cs 29.43481734 6.69565147 0.12235248 -0.00081190 -0.00056361 -0.00026669 Pb 32.59598675 8.89311055 2.91133727 -0.00131298 0.00038418 0.00001003 Br 32.55339859 9.42021288 35.60948502 0.00062418 -0.00323988 0.00211484 Br 32.38843552 5.98178077 2.76207129 -0.00235667 -0.00054203 0.00448906 Br 29.91673434 10.03737548 2.75047381 -0.00154973 0.00001627 0.00112350 Cs 29.41570138 6.43838177 6.36054370 0.00063305 0.00008696 -0.00152398 Pb 32.66011976 8.88200812 8.92287217 0.00153242 0.00118013 0.00017724 Br 32.09563976 8.75431984 6.01191671 0.00193504 0.00069931 0.00491641 Br 32.70172073 5.86138887 8.78531654 -0.00183390 -0.00125126 0.00200254 Br 29.82740727 8.76118096 9.08109697 0.00072684 0.00056576 -0.00068907 Cs 29.27170493 5.96878940 11.81033217 -0.00190978 0.00191968 -0.00243832 Pb 32.60763461 8.67380714 15.06257622 -0.00099826 0.00231065 -0.00037401 Br 32.53495293 8.51115440 11.79154329 -0.00237190 -0.00171494 0.00023215 Br 33.33957941 5.83328023 14.88260664 -0.00027036 0.00068855 -0.00195922 Br 29.74069556 8.07300444 15.01843946 0.00107356 0.00073368 -0.00051709 Cs 29.44147188 5.61593149 17.62888359 -0.00093888 -0.00173705 0.00050864 Pb 32.55268152 9.07498108 20.60453736 -0.00010540 0.00045610 -0.00017672 Br 32.24852313 9.17228841 17.78687633 -0.00116911 0.00255235 0.00235428 Br 32.05299406 6.06798427 20.60215600 0.00100586 -0.00179688 0.00055838 Br 29.71839935 9.70356692 21.12508180 -0.00082390 0.00160734 0.00176450 Cs 29.87767801 6.31788082 24.03541511 0.00123491 -0.00137140 0.00407569 Pb 32.49695901 8.85569167 26.44744842 0.00218144 -0.00010321 -0.00036405 Br 33.13776429 8.80624942 23.72986920 0.00031602 -0.00120079 0.00244101 Br 32.73319529 5.88707489 26.74835649 -0.00243104 -0.00155676 0.00279312 Br 29.51294009 8.58462680 26.92724085 0.00065823 -0.00364059 -0.00097039 Cs 29.84624152 6.40241692 30.05718413 -0.00101894 0.00163301 0.00052986 Pb 32.70516547 8.79790952 32.58016030 0.00080754 0.00026942 0.00215215 Br 32.59092974 8.33849630 29.75449809 0.00090736 0.00100310 0.00015551 Br 32.54378489 5.83978898 33.47655165 0.00096833 -0.00158568 0.00270780 Br 29.55952115 8.91379608 32.51889299 0.00132016 -0.00065154 -0.00158404 Cs 29.72140022 11.58822695 35.20957782 -0.00135261 0.00056886 -0.00104568 Pb 32.75505926 14.66610701 2.84569324 0.00053650 0.00064994 0.00150511 Br 33.06303380 14.42690753 35.54472754 -0.00258274 -0.00104102 -0.00249720 Br 33.49627251 11.67369054 3.34003901 0.00153189 0.00378738 -0.00036290 Br 29.83804047 14.59411070 2.33146549 -0.00035652 0.00288880 -0.00004239 Cs 30.01111106 11.78054892 5.96842330 0.00213137 -0.00041756 0.00350362 Pb 32.73473091 14.85125369 8.90959925 0.00006294 -0.00044971 0.00009834 Br 33.06744192 15.16720657 5.84169196 0.00084900 -0.00012304 -0.00096877 Br 32.75028416 11.69775344 8.47013283 0.00111417 0.00253519 0.00030149 Br 29.66769285 14.13212500 8.98836550 0.00200775 -0.00166587 -0.00349931 Cs 29.88499628 12.06653885 11.70381625 0.00114245 0.00104088 0.00027602 Pb 32.75606991 14.51308269 14.87198624 0.00083190 0.00003966 -0.00175280 Br 32.71692881 14.25849926 11.80778573 0.00092716 -0.00134419 -0.00005909 Br 32.46057137 11.74820719 14.49670034 0.00231238 0.00296182 -0.00421651 Br 29.88928235 14.48383949 14.58807136 0.00158788 -0.00246145 0.00275347 Cs 29.28214146 11.99494079 17.84338414 -0.00091817 0.00184329 -0.00085834 Pb 32.59032351 15.06952030 20.82990677 0.00081517 0.00239333 -0.00097529 Br 32.29965643 14.85162744 17.87537970 -0.00049591 0.00028236 -0.00142106 Br 33.01738356 11.85145726 20.72802761 0.00466006 -0.00306919 0.00121843 Br 29.74124891 14.70290599 21.24479305 0.00002686 0.00528267 -0.00403146 Cs 29.35339086 12.11201389 23.79377388 0.00104614 -0.00009117 0.00208384 Pb 32.47288085 14.76146617 26.78129714 0.00013195 -0.00050154 -0.00053408 Br 32.99388141 15.00171403 23.82327522 0.00326265 -0.00467410 0.00298450 Br 32.07665015 11.96638514 26.73209971 -0.00135323 -0.00152301 -0.00292555 Br 29.68161963 15.07865716 26.98462293 -0.00123351 -0.00039524 0.00150566 Cs 29.28195287 12.04891719 29.82863956 -0.00033824 0.00197812 0.00015518 Pb 32.83178214 14.53332929 32.50375275 0.00075704 -0.00075158 0.00017696 Br 32.27481446 14.58288173 29.78815122 0.00096323 -0.00054015 0.00149099 Br 33.03011429 11.67947115 32.09849200 0.00013774 0.00137729 -0.00075570 Br 29.70924748 14.51415230 32.61131888 -0.00356811 -0.00125337 0.00157577 Cs 29.78936180 17.98428446 0.49134680 -0.00048197 0.00099680 -0.00060924 Pb 32.63518858 20.96612302 3.04915990 -0.00181231 -0.00004880 0.00207239 Br 32.57422807 20.72616705 35.51946962 -0.00232239 0.00056654 0.00086391 Br 32.54111725 17.69993891 3.02175811 -0.00140140 0.00073827 -0.00146602 Br 29.44707024 21.24777630 2.91430584 -0.00213609 0.00193901 -0.00126148 Cs 29.44701079 18.66916529 5.30503792 -0.00066183 0.00289445 -0.00131658 Pb 32.52314967 20.57398107 8.69462682 0.00176844 -0.00010430 -0.00061025 Br 33.39126558 20.49276015 5.90014093 -0.00198187 -0.00092602 0.00042707 Br 32.51179748 17.80260892 9.08159880 -0.00140339 -0.00309337 0.00112139 Br 29.64072970 21.33441267 8.66493360 -0.00127703 -0.00126832 0.00011207 Cs 29.51831967 17.41366111 11.89385012 -0.00289907 -0.00072155 0.00227807 Pb 32.67939688 20.53437042 14.48353581 0.00022673 -0.00027661 0.00091330 Br 32.50381579 21.30814336 11.57375020 -0.00017310 -0.00429129 0.00352531 Br 32.37699791 17.32546125 13.92699061 -0.00174288 -0.00186379 0.00188407 Br 29.53649252 21.17234756 14.91493302 0.00101342 0.00032736 -0.00046668 Cs 29.51056655 17.58834343 18.33437905 0.00297323 0.00099537 -0.00166751 Pb 32.56598801 21.08186553 20.62106908 0.00050723 0.00007505 0.00129019 Br 32.63963155 20.61040649 17.89332390 -0.00121765 0.00023969 0.00264660 Br 32.10366561 17.85269701 21.04536132 -0.00315775 -0.00177265 -0.00100656 Br 29.59632416 21.24168107 20.05984157 0.00054450 0.00238019 0.00123825 Cs 29.62658188 17.41408934 24.10212732 0.00097066 -0.00067377 -0.00085823 Pb 32.42174856 20.72542020 26.63668044 -0.00008146 0.00193040 -0.00046479 Br 32.29338015 21.17207272 23.55650415 0.00107625 0.00291315 -0.00040410 Br 32.53722274 17.85779711 26.26071662 -0.00505282 -0.00086341 -0.00367448 Br 29.78310593 21.02949855 27.40423219 -0.00005885 0.00039783 0.00019649 Cs 29.63332642 17.72051239 29.60163585 -0.00156620 0.00065882 0.00115957 Pb 32.54258807 20.67244890 32.45387707 0.00094242 -0.00040793 0.00047335 Br 33.18638900 20.52402188 29.38611610 0.00495448 0.00176956 0.00121117 Br 32.77699431 17.52103650 32.60620059 -0.00302974 -0.00303031 0.00115032 Br 29.67973078 20.57778978 31.82769238 -0.00360137 0.00181337 0.00597270 Cs 29.91408281 23.84254813 0.55449730 -0.00078041 -0.00156894 -0.00045683 Pb 32.84223368 26.72710588 2.97107057 -0.00170721 -0.00068439 -0.00088198 Br 33.29659730 26.61870487 35.61180345 0.00326548 -0.00155713 0.00286020 Br 32.96428914 23.67621056 3.84700532 0.00104811 0.00140293 0.00119621 Br 29.82655896 27.11777443 3.03806320 -0.00204488 -0.00031742 0.00040889 Cs 29.47621589 24.27945917 5.89620430 -0.00180871 0.00108680 -0.00206848 Pb 32.70006135 26.50257162 8.86415212 0.00022095 -0.00112136 0.00053639 Br 33.22235299 27.27950044 5.85073105 0.00039892 -0.00088289 0.00073770 Br 32.93794656 23.60141062 8.19792576 -0.00388000 -0.00405774 -0.00193529 Br 29.53708320 26.81713215 8.89862392 -0.00147930 -0.00103265 0.00083361 Cs 29.41589287 23.89859648 11.84474768 0.00008409 0.00145276 -0.00147343 Pb 32.60487658 26.30386240 14.87370601 0.00104608 -0.00080952 -0.00086510 Br 32.65905450 26.13772590 11.93337556 0.00152048 0.00161437 -0.00231687 Br 32.57446188 23.37643374 15.15005032 0.00163016 0.00217218 -0.00061397 Br 29.32544943 26.61209926 14.93363165 0.00064370 0.00323988 -0.00235515 Cs 29.61328006 23.61467043 17.60368456 0.00306636 -0.00086536 -0.00156229 Pb 32.45319240 26.85892865 21.11334792 0.00042252 -0.00071146 -0.00042260 Br 32.19982395 26.76796619 17.74778387 -0.00555502 -0.00108102 0.00316145 Br 32.67433447 23.97914549 20.09287227 0.00048853 -0.00013002 0.00122287 Br 29.69119851 27.43763473 21.21296799 -0.00062536 0.00519336 -0.00169577 Cs 29.70840173 23.58552193 23.30584086 -0.00069059 0.00093621 -0.00108646 Pb 32.38274406 26.65124506 27.03058344 -0.00176219 -0.00110869 0.00039961 Br 32.30637153 26.38953968 23.79711823 -0.00175974 0.00204477 -0.00153951 Br 32.71419505 23.65457264 26.82716524 -0.00198793 0.00314333 0.00155086 Br 29.74537268 25.90273280 26.62386443 0.00061532 -0.00471673 -0.00054702 Cs 29.02544351 23.67998401 29.94680345 0.00043683 0.00311995 -0.00088970 Pb 32.60179295 26.67543047 32.89193896 0.00197955 -0.00067454 -0.00052693 Br 32.75140292 27.06246366 30.01226432 -0.00332981 -0.00034680 0.00313603 Br 31.99589939 23.55034199 32.78148622 -0.00010676 0.00096134 0.00150385 Br 29.72403181 26.78936192 32.78645627 0.00150827 -0.00172517 -0.00259768 Cs 29.69520878 29.98454132 0.17809951 -0.00252356 -0.00226764 0.00260108 Pb 32.87049025 32.57597124 2.86058136 0.00176142 0.00008332 -0.00023699 Br 32.54117098 33.55924906 0.02267531 -0.00175436 -0.00115462 -0.00009123 Br 32.92719815 29.73336028 2.71339547 -0.00298875 0.00181551 0.00028577 Br 29.79033677 32.25941340 2.76217056 -0.00057162 0.00097118 0.00032294 Cs 29.97142687 30.06722978 5.79079637 0.00014678 0.00337929 0.00203838 Pb 32.60561439 32.44799516 9.02271927 -0.00233375 0.00146460 -0.00076995 Br 32.84955945 32.97673691 5.96619036 0.00326459 0.00024378 -0.00160530 Br 33.27017880 29.35755594 9.27646148 0.00067829 -0.00295593 -0.00263441 Br 29.50215344 32.38706060 8.78122204 0.00156930 -0.00053837 0.00134044 Cs 29.66634565 29.45331073 11.99170843 -0.00066246 0.00176944 0.00116902 Pb 32.58127577 32.29795611 14.98992572 0.00056748 -0.00021914 -0.00095029 Br 31.82175015 31.99467078 12.17770672 -0.00103851 -0.00201926 0.00019102 Br 31.99441891 29.29810274 14.63370505 0.00313327 -0.00260594 0.00107354 Br 29.70185633 32.66925769 15.67703304 -0.00314308 -0.00105151 0.00305530 Cs 29.60640481 29.14924243 17.49250414 -0.00123483 -0.00069971 -0.00317060 Pb 32.76413867 32.58729217 20.84404201 -0.00137511 0.00004851 0.00006412 Br 33.26818610 32.12425010 17.98299359 0.00190968 -0.00240103 0.00026674 Br 33.00267105 29.81106951 21.56324753 0.00072792 -0.00115022 -0.00115674 Br 29.72574364 32.60190734 20.23308379 -0.00212358 0.00003504 -0.00256205 Cs 29.47924430 30.12444694 23.78510270 0.00127585 0.00198220 0.00053945 Pb 32.58177542 32.56960465 26.82669786 -0.00019950 0.00169117 0.00166136 Br 32.08419791 33.01082796 23.99510877 0.00107481 -0.00015361 0.00100213 Br 32.13889530 29.64537400 26.66584264 0.00034415 -0.00474451 -0.00200840 Br 29.57020990 32.50886688 27.29469458 0.00332594 0.00163982 0.00184206 Cs 29.69768802 29.92405487 30.40552700 0.00132411 0.00061852 -0.00045832 Pb 32.72804356 32.52082474 32.90290372 -0.00096012 -0.00024095 0.00248884 Br 32.57289088 32.43662937 29.98636286 0.00040076 0.00016646 0.00067848 Br 32.19902105 29.64794350 33.45105519 0.00001081 0.00079704 -0.00206923 Br 29.76519124 33.08767667 32.68588734 -0.00076509 0.00047712 -0.00176185 1080 Lattice="35.60125893 0.0 0.0 0.0 35.60125893 0.0 0.0 0.0 35.60125893" Properties=species:S:1:pos:R:3:vel:R:3 Time=1500.0 energy=-38566.46902847 virial="-1.65835154 -0.39460889 4.03214396 -0.39460889 -44.66878079 4.91583021 4.03214396 4.91583021 -35.54259386" stress="0.00078826 -8.75e-06 8.936e-05 -8.75e-06 -0.00016492 0.00010894 8.936e-05 0.00010894 3.733e-05" pbc="T T T" Cs 0.31830451 35.15366493 35.47630503 0.00105383 -0.00085412 -0.00000065 Pb 2.82537386 3.12752018 2.85277167 0.00066997 0.00206115 0.00012385 Br 3.62374403 2.83473870 0.00885687 -0.00004374 -0.00298332 0.00062592 Br 2.69043449 0.14919798 2.90598450 -0.00108447 -0.00187335 0.00135101 Br 35.47543986 2.89038972 2.81821526 -0.00277559 -0.00279878 0.00073288 Cs 35.54898526 35.58994833 5.89950260 -0.00141641 -0.00040357 0.00114441 Pb 3.02636647 3.00844103 8.84184370 -0.00013034 0.00064952 0.00047431 Br 2.31155650 3.42015996 5.81969355 -0.00119469 0.00116382 -0.00212173 Br 2.43948492 0.10219726 8.88068064 0.00222890 -0.00224546 0.00211697 Br 35.44082757 2.60366748 8.65199109 0.00012166 -0.00123040 -0.00013314 Cs 0.33933238 35.57693058 11.99816209 -0.00163586 0.00170034 0.00042471 Pb 2.99141385 3.12994938 14.54130857 -0.00018834 -0.00193485 0.00116216 Br 2.12003985 3.31433152 11.63163934 0.00129192 0.00122768 0.00141768 Br 2.90298895 0.03706219 14.83351965 0.00004721 -0.00193455 0.00274298 Br 0.11246265 2.47233525 14.53087440 0.00094094 -0.00126299 -0.00006556 Cs 35.42500622 35.26334203 18.13597575 -0.00098127 -0.00164207 -0.00130156 Pb 2.86153184 2.85625944 20.83877026 0.00272993 -0.00072826 -0.00182373 Br 3.02867036 3.10969586 17.86850584 0.00296063 0.00155276 -0.00096956 Br 2.97333618 0.03378149 20.24459899 0.00320019 -0.00095765 -0.00160372 Br 35.53003716 2.87508569 20.93694704 0.00254950 0.00107833 0.00229386 Cs 35.41421494 0.11806396 23.65327590 -0.00182653 0.00215954 -0.00203151 Pb 3.15798528 2.83536293 26.50795184 -0.00176332 0.00255111 0.00164348 Br 3.68899186 2.59548917 23.73001942 0.00050132 0.00070243 0.00314555 Br 2.94151831 35.47414967 26.56067303 -0.00184541 0.00289599 0.00438327 Br 35.43309660 2.28873439 26.20785907 0.00021315 -0.00347474 0.00251819 Cs 0.31403611 0.18087236 29.22915273 0.00126922 0.00115726 -0.00010791 Pb 2.90195847 2.86753485 32.74290203 0.00059712 -0.00201336 0.00117222 Br 2.68175246 3.01592718 29.51410151 -0.00136572 0.00155618 0.00154582 Br 2.55874409 35.26992709 32.58307432 0.00039807 0.00040604 0.00310225 Br 0.17094402 2.82557686 33.42924240 -0.00217078 -0.00175686 0.00076769 Cs 35.40653223 5.52416823 0.37980102 -0.00019486 -0.00064008 0.00122749 Pb 3.00173544 8.86258282 2.94777118 0.00001104 0.00148070 0.00190830 Br 3.05424370 9.28032891 35.53500878 0.00053393 -0.00026216 0.00157290 Br 3.50348465 6.03243216 2.14953498 0.00319517 -0.00260469 0.00286009 Br 35.53259335 8.38924596 3.09526380 0.00029196 0.00281643 0.00248244 Cs 35.29848093 5.80623078 6.45753278 0.00021213 0.00273303 0.00036766 Pb 3.11571015 8.81211608 8.87301036 0.00058272 0.00039498 -0.00067826 Br 3.32002981 8.01637487 5.88313088 -0.00173859 0.00029313 -0.00070750 Br 3.35761035 5.94681044 9.40440426 -0.00274309 -0.00083714 -0.00403808 Br 0.02949457 8.55202883 8.88839945 0.00319422 -0.00155567 0.00197118 Cs 35.50517561 6.05687253 12.13056437 0.00190284 -0.00064944 0.00059815 Pb 3.00565986 8.94625851 14.85976879 -0.00034184 0.00010674 0.00033585 Br 3.05869282 9.22974795 11.81593689 -0.00020295 -0.00183070 0.00198393 Br 3.37593822 5.91889848 14.59720935 -0.00018892 -0.00096528 0.00039757 Br 35.49657375 8.74918790 14.31018896 0.00122564 -0.00113812 -0.00080440 Cs 35.55060052 5.91378149 18.33141329 -0.00019534 0.00112762 -0.00251301 Pb 3.08530902 8.90299464 20.89597933 -0.00000057 -0.00103275 -0.00076089 Br 2.74430792 8.75605438 17.89489475 -0.00351489 -0.00352473 0.00149166 Br 3.02773080 5.95485676 21.06746612 0.00028639 -0.00178761 -0.00126453 Br 35.55119294 9.18107441 20.33717674 0.00274645 0.00212800 -0.00190569 Cs 35.57358076 6.20379663 23.44561440 -0.00086532 0.00135372 0.00053007 Pb 3.05351395 8.58853701 26.62935224 0.00152623 -0.00113540 -0.00309534 Br 2.61201838 9.17237116 23.79463792 -0.00334636 0.00022212 -0.00367225 Br 2.20317309 5.83163971 26.36370187 -0.00243809 0.00164164 0.00028367 Br 0.05133211 8.53406729 26.74720865 0.00012263 -0.00269621 -0.00141995 Cs 0.04761274 5.74189836 29.38247178 -0.00328402 0.00242868 0.00043672 Pb 2.95224038 8.81580830 32.52953329 0.00096707 -0.00174628 -0.00001312 Br 2.93559329 8.50422350 29.56444763 -0.00149037 -0.00414589 0.00236909 Br 2.91581731 5.77982521 32.92237658 0.00003250 -0.00207395 -0.00231219 Br 0.01357274 8.99271124 32.93740637 -0.00012832 0.00127757 -0.00084593 Cs 35.17993292 11.52815877 0.30997851 0.00220840 0.00121028 -0.00104727 Pb 3.01749283 14.89569081 3.03650547 -0.00152798 0.00187680 0.00014984 Br 2.85512817 14.73728018 0.09837064 0.00022641 0.00213688 0.00078381 Br 2.49456645 11.89923721 3.13048421 -0.00150656 -0.00399797 -0.00260061 Br 0.04345997 15.23762775 2.28569478 0.00010901 0.00003064 -0.00071055 Cs 0.26386917 11.22066743 6.17764872 -0.00040871 -0.00270063 -0.00008252 Pb 3.25608231 14.83892573 8.84268184 0.00136491 0.00005319 0.00102229 Br 2.03357149 14.39387053 5.87804833 -0.00115896 -0.00349349 0.00207114 Br 2.91313022 11.84174303 9.04546148 0.00152893 -0.00130036 0.00074707 Br 0.05986002 14.56269260 9.25315893 0.00162436 0.00118358 -0.00060302 Cs 35.60116560 11.10818527 11.64548090 0.00106723 0.00200984 -0.00010238 Pb 3.09150216 14.91555519 14.90192502 0.00079244 0.00037342 0.00207632 Br 3.66441356 14.75433416 11.68432337 -0.00037306 0.00402079 0.00071466 Br 3.35243535 11.68977565 15.21015347 -0.00065628 -0.00028243 0.00141461 Br 0.11210090 15.00227325 14.71677589 -0.00104287 0.00041686 0.00053585 Cs 0.01954214 11.98001368 17.17128877 -0.00201598 0.00454145 0.00060761 Pb 2.89679174 14.62155917 20.76729625 -0.00228220 -0.00013534 0.00543606 Br 2.65862949 14.78141204 17.93517130 -0.00059412 -0.00190879 0.00317123 Br 2.77715102 11.83055386 20.79284767 -0.00164877 0.00082702 0.00159727 Br 35.56063679 15.08840419 20.85673009 -0.00261828 -0.00113033 -0.00033227 Cs 35.09950791 12.02299121 24.12675326 0.00134305 -0.00095153 -0.00022023 Pb 3.11162182 14.73566350 26.67156369 -0.00055756 -0.00064249 -0.00055148 Br 3.31002372 14.41662697 23.81927222 -0.00328523 0.00126871 -0.00054691 Br 3.03179029 11.69986874 26.56911505 0.00063240 -0.00084646 0.00141655 Br 35.58701355 14.89608500 26.88771663 0.00017242 0.00007981 -0.00177988 Cs 0.09812816 11.68589163 29.29265482 0.00177502 -0.00024703 -0.00250384 Pb 2.87970613 14.69793712 32.60343264 0.00091801 -0.00049621 -0.00036702 Br 2.72559125 15.17353923 29.66423577 -0.00128763 -0.00206333 0.00273069 Br 3.35219772 11.71440769 32.13242224 0.00098810 -0.00130619 -0.00107225 Br 0.09422328 14.39213888 32.53849612 -0.00066563 -0.00395041 -0.00148952 Cs 0.06502421 18.64626124 35.35198056 0.00289443 -0.00059073 -0.00086534 Pb 2.88477931 21.23980933 2.91246394 0.00091081 -0.00239551 0.00142254 Br 2.95286638 21.40533529 35.59791040 -0.00034161 0.00046158 0.00241486 Br 2.90844684 18.22964438 2.41920557 -0.00327741 -0.00237617 -0.00193280 Br 35.51929685 21.20806180 2.74818671 0.00253735 -0.00195795 0.00184748 Cs 35.55727124 17.61744712 5.14157925 -0.00080725 0.00265268 -0.00026391 Pb 2.88722956 20.77817612 9.14385247 -0.00220998 0.00046439 -0.00058692 Br 3.06847239 20.57504731 6.01681859 0.00289416 -0.00231620 -0.00074608 Br 3.25700067 17.76297798 9.33028953 0.00092489 0.00059266 -0.00208323 Br 0.10843332 20.35381397 9.45175570 -0.00211927 0.00054620 0.00028246 Cs 35.12320026 17.21573151 11.49601745 -0.00076820 -0.00222848 -0.00304294 Pb 2.97896511 20.73248578 14.90142188 0.00117828 0.00078951 0.00068392 Br 3.58835305 20.72329854 11.94225758 0.00206826 0.00270980 0.00347047 Br 2.72917946 17.80831866 15.28454485 0.00057938 0.00169945 0.00030567 Br 0.02370583 20.91200335 14.43572684 -0.00266494 0.00052467 0.00050849 Cs 0.55700341 17.84383717 18.02109246 0.00236096 -0.00015271 -0.00080878 Pb 3.00033675 20.54364908 20.80337749 -0.00003106 0.00026741 -0.00035073 Br 2.34149844 21.20208386 17.70622646 -0.00065241 -0.00237767 0.00029315 Br 2.66360598 17.53430221 21.10336037 0.00214332 -0.00390406 -0.00147236 Br 35.60855701 20.73533021 20.88647815 0.00340319 -0.00019115 -0.00158710 Cs 0.07710760 17.03477536 24.04309448 0.00044293 -0.00199645 -0.00264756 Pb 2.74773861 20.79180037 26.85073327 -0.00162680 0.00085609 0.00054161 Br 3.52822931 20.66726929 23.59779851 -0.00049929 0.00045537 -0.00159146 Br 3.03755168 17.88112678 26.13018079 0.00213843 -0.00276427 0.00138885 Br 0.11648713 20.44624034 25.50470331 -0.00336187 0.00127121 -0.00042795 Cs 0.24021141 17.57185273 30.58002863 -0.00025959 0.00176270 0.00326457 Pb 3.47764881 20.78697365 32.76010584 0.00009964 -0.00066415 0.00309794 Br 3.03303491 20.73636894 29.76014328 0.00000144 0.00028782 0.00121227 Br 2.71341831 17.79861471 33.10324849 -0.00422505 0.00008378 -0.00202584 Br 0.27091569 20.78978843 32.77457899 0.00279489 -0.00107933 -0.00137606 Cs 35.27304070 23.48920144 35.03991865 0.00262830 0.00303429 0.00036459 Pb 2.87572336 26.76103212 2.87939560 -0.00147315 0.00000412 0.00004597 Br 2.43042993 25.85861490 0.21335515 -0.00254697 -0.00359349 0.00308644 Br 3.38171774 24.09001536 4.02553336 0.00019237 -0.00097114 -0.00155240 Br 35.51249194 27.05501731 3.40710803 -0.00172317 0.00183893 0.00003457 Cs 0.53358290 24.04187030 6.11508920 -0.00216242 -0.00202069 -0.00216369 Pb 2.98784185 26.60682135 8.96796803 0.00072932 0.00024771 0.00062267 Br 2.78095777 27.31724925 6.05418013 0.00091252 -0.00303947 -0.00006077 Br 2.80822516 23.58215628 9.28411252 0.00047498 -0.00056581 -0.00331718 Br 35.43402478 27.04147104 9.24532447 0.00551962 -0.00013216 0.00090249 Cs 35.40636959 23.61936225 12.00687212 -0.00024289 -0.00354968 0.00167080 Pb 2.74441407 26.62008054 14.80292111 -0.00019813 -0.00098957 -0.00041957 Br 2.84969296 26.43093896 11.81342258 0.00022949 0.00189154 -0.00238783 Br 3.17797074 23.72935786 14.42768686 -0.00126385 -0.00204856 -0.00050753 Br 35.19693068 26.60763271 15.08848670 -0.00016348 0.00256039 -0.00026198 Cs 35.33943634 23.57863815 17.54611473 -0.00026723 0.00013945 -0.00033134 Pb 2.64762481 26.56274347 20.83229181 -0.00032448 -0.00013989 0.00068080 Br 2.56389387 26.24971931 18.03179605 -0.00089364 -0.00200778 -0.00146318 Br 3.31759242 23.40512139 21.17517375 -0.00211845 -0.00114836 -0.00045193 Br 35.41310459 27.12297998 21.06508516 -0.00037746 0.00068845 -0.00096520 Cs 35.41895396 23.75857124 23.22264509 0.00022845 0.00014585 -0.00196297 Pb 2.91742848 26.65599253 26.70838435 0.00001658 0.00090103 -0.00067120 Br 3.02294223 27.23146382 23.74779393 -0.00066061 0.00041457 -0.00267565 Br 2.44972551 23.75153865 26.36895791 0.00005133 -0.00154979 0.00118865 Br 35.52319319 27.14048255 26.87360580 0.00231730 0.00253805 0.00035405 Cs 35.08563194 24.03872089 29.12499428 -0.00212675 -0.00076922 0.00149898 Pb 2.89323805 26.55912111 32.51148663 -0.00001186 0.00218717 0.00189629 Br 2.59380184 26.06525636 29.78813190 0.00075357 0.00004456 0.00120809 Br 3.84179524 23.75391773 32.20949052 -0.00038242 0.00272354 0.00348904 Br 35.40390837 26.27473382 32.40719084 -0.00317591 0.00056523 0.00045555 Cs 0.47942561 29.60839525 0.03087068 -0.00067010 -0.00259512 0.00092734 Pb 2.86505034 32.62862493 3.07913643 0.00007599 -0.00098638 -0.00119210 Br 2.87595207 32.21727126 0.29665129 0.00227810 0.00095715 0.00400836 Br 2.77913131 29.73150017 3.17190193 -0.00028396 0.00186010 0.00198436 Br 35.38451328 33.03600642 3.33052763 0.00329434 -0.00281718 0.00117622 Cs 35.38795079 29.81689449 5.93307820 0.00166527 0.00019794 -0.00109667 Pb 2.83764602 32.65683864 9.04751052 0.00168875 0.00071668 0.00009881 Br 3.06955254 32.48539729 6.04391730 0.00055208 -0.00354256 -0.00199204 Br 3.17761609 29.66506811 9.06649618 0.00005091 -0.00403852 0.00262602 Br 35.55189191 32.42332082 9.65116709 -0.00310497 -0.00171100 0.00010066 Cs 35.23539953 29.49545747 12.15056420 -0.00473325 -0.00019384 0.00017437 Pb 2.81014585 32.58951998 14.84775523 -0.00073882 0.00142406 -0.00037987 Br 2.90069950 32.63519251 11.96896150 0.00151402 0.00077042 -0.00044283 Br 2.60750403 29.75372633 14.20950021 -0.00258803 0.00294469 -0.00550068 Br 35.57058812 33.05185671 14.44170407 0.00077752 0.00063799 0.00085286 Cs 0.04595548 29.42971635 17.93121122 0.00266682 0.00291167 -0.00124217 Pb 3.05603273 32.44230166 20.85037411 -0.00074643 0.00101169 -0.00045727 Br 2.92120412 31.85336007 17.73130165 -0.00349700 -0.00025832 -0.00569536 Br 2.90358069 29.68479982 21.25130349 0.00103995 0.00262929 0.00372071 Br 0.11159977 32.75351358 21.43009085 -0.00142068 -0.00137226 0.00146220 Cs 0.04097302 29.60801086 23.98345731 0.00099968 -0.00102660 0.00313103 Pb 3.17815607 32.59086057 26.97050110 -0.00055926 0.00126750 0.00099679 Br 2.90086679 32.58263534 23.83764922 0.00128562 0.00207011 -0.00027363 Br 2.87815762 29.73507746 27.09025086 0.00194831 -0.00230751 -0.00286190 Br 0.19648636 32.10735457 26.80810406 -0.00300049 -0.00144913 -0.00002030 Cs 0.44590001 29.42082558 29.77808799 0.00132469 0.00018102 0.00192761 Pb 3.03960416 32.38978744 32.90474921 -0.00148450 -0.00130907 -0.00096534 Br 2.83156853 33.10764025 29.88485651 -0.00000138 0.00268446 0.00099346 Br 3.11359390 29.58141495 31.95527441 0.00026572 0.00083237 0.00426373 Br 0.10627581 32.22954504 32.82114227 0.00247165 -0.00017576 0.00032437 Cs 6.31974281 0.11832136 0.04116856 -0.00286775 -0.00200245 -0.00018893 Pb 8.57937415 3.27425504 3.15506923 -0.00138703 -0.00051745 0.00084255 Br 8.45214263 2.88886587 0.12372037 0.00000943 -0.00199578 -0.00374266 Br 9.06062947 0.17733689 2.89023489 0.00167302 -0.00080160 0.00300974 Br 5.68337953 2.83635066 3.72836991 -0.00020430 0.00107142 0.00038529 Cs 5.79794248 35.50862435 6.01726969 0.00024626 0.00213511 -0.00040679 Pb 9.00195542 3.02748128 9.08576129 0.00160132 0.00057753 -0.00048057 Br 9.19589874 2.62770861 5.88950222 -0.00004077 0.00404290 -0.00267644 Br 9.15362820 0.01078333 9.26139254 -0.00254843 -0.00049728 0.00104140 Br 5.88553980 2.75702732 9.70531246 0.00144936 0.00109802 -0.00001099 Cs 6.36222440 35.05333033 12.42419075 -0.00075527 0.00137779 -0.00109313 Pb 8.89941547 3.09314069 14.58430835 0.00005795 0.00027937 -0.00000204 Br 9.16488269 4.18813808 11.84999533 -0.00263848 -0.00431829 -0.00116319 Br 9.36836872 0.21141655 14.51066938 -0.00340463 0.00099535 0.00146409 Br 5.80063897 2.71906220 14.55758858 -0.00237900 -0.00059989 -0.00146196 Cs 5.88468015 0.47588533 17.60661919 0.00088868 0.00217855 -0.00177748 Pb 9.05410884 3.21725857 20.61480448 -0.00088333 0.00074853 -0.00033238 Br 8.65280052 2.99158417 17.64588166 0.00064298 0.00123348 0.00453744 Br 9.27444320 35.48956510 20.90837115 0.00157962 -0.00087484 0.00178449 Br 5.83522913 3.02528106 20.46275621 0.00143167 0.00078317 0.00068948 Cs 5.80470211 35.55035506 23.41271275 -0.00150895 -0.00156719 0.00006285 Pb 9.13535259 2.97390413 26.66331745 -0.00152617 0.00141159 0.00213555 Br 8.61503896 3.47081011 23.67125262 0.00006030 0.00217559 -0.00176253 Br 8.81500081 0.10016377 26.10573110 0.00027940 -0.00065185 -0.00099715 Br 5.97477038 2.77761775 27.35100918 -0.00048195 0.00040776 -0.00257667 Cs 5.73566386 35.12523964 29.66233508 0.00208924 0.00112838 0.00344901 Pb 8.99065578 3.01897239 32.82515734 0.00235526 -0.00116282 0.00015135 Br 9.04105500 2.55208226 29.89219053 -0.00255909 -0.00000372 0.00012876 Br 8.80005429 -0.02003346 33.04261494 -0.00066857 -0.00307900 -0.00131059 Br 6.08426250 3.51187718 32.65085294 -0.00249763 0.00010281 0.00148761 Cs 5.98988494 5.70504591 35.25091288 0.00235672 0.00135396 -0.00157896 Pb 8.63151762 9.13260334 2.74222922 0.00218870 0.00127317 -0.00006471 Br 9.22755636 8.86015040 35.53264744 -0.00395668 0.00052559 0.00059082 Br 8.09437839 6.11528649 3.32216752 0.00195830 -0.00152982 -0.00538556 Br 5.83293425 9.50896736 2.86992964 0.00102875 0.00108513 0.00086707 Cs 5.62394534 5.66840087 6.01367485 0.00132486 0.00271567 -0.00273484 Pb 8.94131849 8.77731269 8.84817177 0.00007459 0.00118518 0.00052962 Br 9.12872181 10.00323054 6.12485823 -0.00092615 0.00033999 0.00049499 Br 8.59813921 6.01736425 8.39092692 -0.00079948 0.00002517 0.00031628 Br 6.06218919 9.02534818 8.24108393 -0.00115029 0.00025317 -0.00084065 Cs 5.67234952 5.90922708 11.78000964 -0.00044837 -0.00109305 -0.00002310 Pb 8.86825962 8.76145749 14.83236021 -0.00046896 -0.00097613 0.00105295 Br 8.81824940 8.58912963 11.91167027 0.00072234 -0.00113032 0.00243237 Br 9.60361784 6.07098187 15.04700031 0.00044207 -0.00018029 0.00223834 Br 5.81259023 8.33071514 14.98365637 -0.00261804 -0.00148858 -0.00280151 Cs 5.14003004 5.80659850 18.01786035 0.00053539 -0.00063163 -0.00073184 Pb 8.80030222 9.00332613 20.72418566 -0.00049454 -0.00100499 0.00011670 Br 9.56838889 9.34430925 17.97811931 -0.00065224 0.00164394 0.00041796 Br 8.39265563 6.05767618 20.04000351 -0.00213638 -0.00102820 0.00191878 Br 5.77816264 8.87179514 20.81348061 -0.00116159 -0.00100111 -0.00151301 Cs 5.89126399 5.75347259 24.10679003 0.00082258 -0.00072570 0.00149554 Pb 9.07194623 8.71753584 26.34451511 -0.00056093 -0.00025625 0.00041458 Br 8.73994795 8.14080734 23.48241580 -0.00196782 -0.00131982 -0.00200827 Br 10.14753718 5.98677684 26.86252909 -0.00090239 0.00163793 -0.00074448 Br 5.86650932 8.03556443 26.52806754 0.00057205 0.00448183 0.00053741 Cs 6.29614152 5.66629984 29.93661837 0.00216604 -0.00072230 0.00281836 Pb 8.62042812 9.04737540 32.37663730 -0.00048793 -0.00106158 0.00049197 Br 9.25978973 9.01189878 29.21435218 -0.00031840 0.00131250 -0.00013782 Br 8.98925784 5.99497638 32.33904063 0.00116675 0.00082073 -0.00172390 Br 5.91375190 8.87484025 32.72864124 -0.00072645 -0.00050702 0.00010831 Cs 5.47586004 11.92908947 0.21417015 0.00064226 -0.00349488 0.00013881 Pb 9.07898900 15.05273347 2.98532920 -0.00190787 -0.00124863 -0.00047283 Br 8.48863339 15.76926919 35.54212255 -0.00173742 0.00103985 -0.00044349 Br 8.56775872 12.11523278 2.18576263 -0.00242208 -0.00272029 0.00011252 Br 5.99933167 15.16868912 2.80717669 0.00030057 0.00000128 -0.00344735 Cs 6.05184367 11.89739688 6.26540711 -0.00163755 -0.00123335 -0.00051191 Pb 9.02625261 14.62320270 8.82616662 0.00194924 0.00123891 -0.00094588 Br 9.12130234 14.22148473 5.85758174 -0.00004293 0.00035131 -0.00052430 Br 9.19992000 11.78373230 9.66065908 0.00432058 -0.00224089 0.00175375 Br 5.98379362 14.99263296 7.93862714 0.00216433 0.00119263 -0.00225815 Cs 6.64037425 12.06297640 12.08112124 -0.00099142 0.00181935 -0.00190423 Pb 9.19209435 14.99035979 14.75422860 -0.00217655 -0.00123798 -0.00146099 Br 9.25283133 14.86589092 11.88598123 0.00380557 -0.00348780 -0.00302997 Br 8.91995906 11.77918714 15.01432860 0.00143507 0.00142076 0.00168798 Br 6.12551512 15.11872930 14.89509949 -0.00024429 -0.00158014 -0.00051394 Cs 5.99333633 12.60158109 17.49250344 -0.00247479 -0.00085022 0.00021632 Pb 8.89887379 14.84679209 20.71982087 0.00170057 0.00054286 -0.00045552 Br 9.35213255 15.04354208 17.80992076 -0.00098294 0.00017109 0.00100001 Br 8.85754119 11.98326742 21.06025794 -0.00191020 -0.00045547 -0.00209885 Br 6.03157601 15.04597218 20.51773539 -0.00037316 -0.00231454 -0.00095221 Cs 6.03740333 12.08988364 23.60671094 0.00323057 0.00187000 -0.00367716 Pb 8.85129169 14.97991666 26.70300147 0.00067652 0.00033234 0.00052544 Br 8.86126589 15.34473511 23.62371333 -0.00187911 0.00280586 0.00051625 Br 8.58507256 11.68919364 25.92829467 -0.00172501 0.00077890 -0.00035458 Br 5.91482557 15.04830446 27.60542468 0.00269697 -0.00196001 0.00129120 Cs 6.25852542 11.40107492 28.91225963 -0.00036266 -0.00189326 -0.00025170 Pb 8.85632799 14.67125171 32.66629370 -0.00070961 -0.00050214 0.00112831 Br 8.88808070 14.17841940 29.61304130 -0.00025346 0.00121981 -0.00017905 Br 8.31396353 11.72722952 32.96031838 -0.00067307 0.00059348 -0.00186085 Br 5.91491360 14.87717316 32.13358785 -0.00009411 -0.00190023 -0.00179732 Cs 6.10926609 18.48304737 0.41741091 -0.00030265 -0.00090349 -0.00114437 Pb 8.85438077 20.86456069 3.27408187 -0.00015563 -0.00105238 -0.00158298 Br 9.02803217 20.28531000 35.51245771 -0.00100710 -0.00095376 0.00180096 Br 8.55883143 17.84955020 3.52428616 0.00046243 0.00167384 -0.00032929 Br 5.97740174 21.22338551 2.94877662 -0.00026929 -0.00224353 -0.00142337 Cs 5.43606426 18.33771642 5.26911197 0.00144517 -0.00442347 -0.00286822 Pb 8.97351090 20.64071339 9.10068292 0.00031287 -0.00119655 0.00002267 Br 8.87516393 21.40749247 5.98519143 0.00536260 -0.00668067 -0.00105116 Br 8.77810447 17.87462717 9.71742808 0.00235396 0.00133399 0.00114818 Br 6.11156000 20.37246907 8.57735925 0.00151676 0.00025907 -0.00163767 Cs 5.69192019 17.81716286 12.08441781 -0.00056279 -0.00066269 0.00196048 Pb 8.94594259 20.89665516 15.05508310 0.00047499 -0.00009795 -0.00187264 Br 8.89166852 21.01005427 12.33213905 0.00346626 0.00063349 -0.00185700 Br 9.27486319 17.94235294 15.03543563 0.00448483 -0.00091171 0.00055253 Br 5.81182661 20.35746162 14.91625306 0.00072902 0.00284515 0.00078127 Cs 6.40812107 17.72205223 17.48718477 -0.00177470 -0.00087863 0.00101926 Pb 8.87470333 20.52184665 20.81237739 -0.00047050 -0.00078954 0.00184533 Br 8.80697223 20.88753720 17.95587392 0.00316180 0.00018259 0.00161189 Br 9.26281271 17.71434696 20.47722266 0.00342867 -0.00063962 -0.00036268 Br 5.92938826 20.10052013 20.27733057 -0.00314247 0.00249837 -0.00059292 Cs 5.49297548 17.72991016 23.66664508 -0.00049005 0.00087883 0.00077057 Pb 8.73907483 20.69351280 26.65379162 0.00046313 0.00088530 -0.00009935 Br 9.28615612 20.24131256 23.86807858 -0.00329439 0.00265999 -0.00165300 Br 9.10178629 17.68691424 27.04548720 0.00298525 -0.00009850 0.00429182 Br 5.94896026 21.24334285 26.52336251 -0.00351003 -0.00103450 0.00149207 Cs 5.61379802 17.99565297 29.98537586 -0.00202941 0.00169949 0.00171394 Pb 9.10477277 20.81518002 32.78950488 0.00246109 -0.00010957 -0.00073363 Br 8.58357517 20.93913307 29.77455893 0.00152214 -0.00168103 -0.00014868 Br 8.81902426 17.68606158 32.04526720 0.00030421 0.00443329 0.00580210 Br 6.35571106 20.90522307 33.28881707 -0.00290479 -0.00033156 -0.00660956 Cs 6.08519924 24.16053594 35.03871923 0.00186295 0.00021186 0.00158110 Pb 8.70548555 26.56406194 2.89956097 0.00159724 -0.00182681 0.00020484 Br 8.94278264 26.61365243 35.42855459 0.00077892 -0.00000727 0.00182550 Br 8.87184955 23.72337147 1.88756266 0.00112355 -0.00071360 -0.00478684 Br 5.73671362 26.94429120 2.86549701 0.00269718 -0.00020356 0.00236474 Cs 5.97070294 23.36549717 6.51179819 0.00227706 -0.00146716 -0.00008016 Pb 8.98670299 26.60880633 9.01585038 0.00106737 0.00059870 0.00040922 Br 8.88543379 26.59173610 5.79570228 -0.00025239 -0.00187110 -0.00349984 Br 8.47857888 23.78601143 9.48238144 0.00179816 0.00393248 -0.00204828 Br 6.01194728 26.56507561 8.73813309 -0.00003796 0.00027567 -0.00356282 Cs 6.07151694 23.67303241 12.22658041 -0.00093047 -0.00022237 -0.00134353 Pb 8.62422251 26.59937464 15.09325911 -0.00058406 0.00233881 -0.00117617 Br 9.09069271 26.30269439 12.00557229 -0.00054681 0.00050922 -0.00148377 Br 8.40453752 23.80953233 15.14791088 0.00068526 0.00112486 0.00180341 Br 5.68101771 26.85386095 14.40734757 0.00129546 -0.00217112 0.00398665 Cs 5.24937751 23.21893115 17.45065979 -0.00137770 -0.00043242 -0.00075043 Pb 8.75986782 26.64463440 20.89334927 0.00180782 -0.00132840 -0.00150695 Br 8.81972129 27.07509686 17.79448069 0.00081818 -0.00160955 -0.00101942 Br 8.43773573 23.66191436 20.97000862 0.00051483 -0.00124864 0.00235096 Br 5.88785692 26.90311587 20.42976280 0.00084991 0.00084817 0.00447626 Cs 5.23790341 24.13556674 23.97526631 -0.00046033 -0.00079038 -0.00104655 Pb 8.71332423 26.73511839 26.65998391 -0.00094950 -0.00138590 -0.00145576 Br 8.13350256 26.69317778 23.61852877 0.00111150 0.00218271 -0.00336689 Br 9.02473481 24.00135468 26.82694515 -0.00133853 -0.00203009 -0.00109424 Br 5.75978936 26.76941630 26.77233643 -0.00112225 -0.00219921 -0.00084542 Cs 6.45158059 24.06432853 29.58941886 -0.00350462 0.00217402 -0.00274101 Pb 8.85687377 26.82796445 32.49113102 -0.00124444 -0.00089033 -0.00092693 Br 8.67355264 26.85527296 29.64877991 0.00109025 0.00115855 0.00020029 Br 9.36723694 23.67499542 32.81056268 -0.00262269 -0.00087204 0.00096007 Br 5.75745976 26.90439676 32.60816808 -0.00289544 -0.00238031 -0.00400112 Cs 5.97030333 29.50375912 35.51623075 0.00138047 -0.00183755 0.00169857 Pb 9.03497921 32.70749234 2.96616309 -0.00116351 0.00253052 0.00180323 Br 9.63673102 33.30840732 0.17930623 -0.00236431 -0.00170722 -0.00242255 Br 9.07211910 29.55783714 2.71298444 0.00166127 -0.00087388 0.00352558 Br 5.84677368 32.88018380 3.11695340 0.00034687 -0.00224286 0.00159851 Cs 5.41207020 29.75552307 6.51940324 -0.00263198 -0.00095312 -0.00075716 Pb 9.00282449 32.61075093 8.97925316 -0.00030758 0.00056154 0.00025113 Br 9.40980558 32.77661890 5.91834795 -0.00196608 -0.00219870 0.00208311 Br 8.95606081 29.74886319 8.44132182 0.00142979 -0.00279242 -0.00358175 Br 6.04711119 32.75782412 9.11264727 0.00189718 -0.00092527 0.00232879 Cs 5.37290405 29.62243701 11.82555071 0.00029158 0.00000392 0.00095346 Pb 8.88061249 32.64682393 14.79028495 0.00013815 -0.00147522 0.00234924 Br 9.64589200 32.78083651 11.84492038 0.00060806 0.00176714 -0.00212093 Br 8.54053212 29.83845542 14.53194191 0.00344499 0.00128988 -0.00290751 Br 5.92862563 32.45315584 14.85767423 0.00110136 -0.00207862 0.00356416 Cs 5.66484884 29.71245694 17.97116892 -0.00125661 0.00292889 0.00153449 Pb 8.79773548 32.62452564 20.62450035 -0.00161465 0.00041666 0.00003337 Br 8.96799862 32.95608459 17.88656468 -0.00012393 0.00156102 -0.00204183 Br 9.14970046 29.59927608 20.98002506 0.00659285 0.00564907 0.00011944 Br 5.95225276 32.63325899 20.79836343 -0.00326081 0.00172830 -0.00137492 Cs 6.26239562 29.76808418 23.46379946 0.00155828 -0.00016528 0.00042477 Pb 9.11799778 32.68955829 26.61588139 -0.00072175 -0.00152464 0.00086450 Br 8.96061290 32.76609162 23.57900320 -0.00214194 -0.00090214 0.00172874 Br 9.13312847 29.60706540 26.56038687 -0.00121283 0.00151721 0.00062154 Br 5.98616681 32.59166211 26.99140938 0.00154162 -0.00149714 0.00296976 Cs 6.74481190 29.99485966 29.27809090 -0.00176948 0.00061077 -0.00023962 Pb 9.28602339 32.49391833 32.68524840 0.00031201 0.00041585 0.00002969 Br 8.99768542 32.82689245 29.59430220 0.00273820 0.00224059 -0.00059892 Br 8.92312523 29.74537237 32.75136817 -0.00072303 0.00261992 0.00081256 Br 6.00011298 32.13879823 32.51191112 0.00135012 -0.00053485 -0.00425150 Cs 11.87754815 0.46778239 0.46148787 -0.00195651 0.00160285 0.00024157 Pb 14.87613661 2.98838472 3.18648032 0.00151895 -0.00071289 0.00090971 Br 14.98032036 2.92423511 0.14977603 0.00029874 -0.00096723 0.00020061 Br 14.62666799 0.01275883 2.95396167 -0.00230096 -0.00247626 0.00030118 Br 11.98662652 3.41011583 3.16711913 -0.00149148 0.00088318 0.00220722 Cs 11.80486859 0.09407242 5.42264788 0.00221184 -0.00101360 -0.00071228 Pb 14.87894762 2.87497129 8.72340156 0.00232240 0.00145495 -0.00077952 Br 15.24317815 2.41147196 5.97450642 -0.00017809 0.00134906 -0.00797076 Br 15.12298714 0.04860768 8.95965214 -0.00139113 -0.00092840 -0.00021254 Br 11.90084851 2.98016080 8.75342826 -0.00343511 0.00009698 0.00286802 Cs 11.84857950 0.07149814 11.81245918 -0.00035586 0.00219020 0.00059746 Pb 15.11411882 2.95644742 14.85587220 0.00023844 -0.00127007 0.00044813 Br 15.18397591 2.96267201 11.88680168 -0.00061189 0.00129339 0.00228165 Br 14.94784859 35.41023569 15.14610738 -0.00542502 -0.00015889 -0.00034557 Br 11.79624319 2.81034280 14.85895926 0.00171002 0.00073000 -0.00212866 Cs 11.53168801 0.14924121 17.36900537 0.00158268 -0.00125416 -0.00037598 Pb 14.84288339 2.78770133 20.91234465 0.00286832 0.00046770 -0.00210888 Br 14.81109007 3.15933177 17.82352071 -0.00130892 0.00032473 -0.00048442 Br 14.15680040 0.09581258 19.99645446 0.00353010 0.00001141 -0.00077906 Br 11.67032499 4.17092808 21.28088784 -0.00131421 -0.00146513 -0.00109238 Cs 11.83702476 35.36056746 23.16258456 -0.00281698 0.00044816 0.00039190 Pb 14.88637366 2.93502398 26.78976921 -0.00058282 0.00013426 0.00067132 Br 14.58805681 2.29419421 23.65503789 0.00014093 -0.00455437 0.00145504 Br 15.75926873 0.16397063 26.85297082 0.00303269 -0.00240193 0.00105057 Br 11.99106137 2.29171679 26.85604118 0.00310675 0.00165744 -0.00023174 Cs 12.17524222 0.18762569 30.24759394 0.00139223 -0.00135182 -0.00014137 Pb 14.84074350 2.99607382 32.74744780 -0.00013866 0.00016439 0.00002252 Br 15.61183240 3.01576196 29.74088052 0.00135292 -0.00111025 0.00048853 Br 15.06106600 35.49804966 32.75693366 -0.00001313 -0.00146217 -0.00651964 Br 11.76145793 2.60465214 33.23782839 -0.00425709 0.00144294 -0.00440564 Cs 11.77786706 6.19874200 0.30518467 -0.00077362 0.00217603 0.00119436 Pb 14.88859457 8.98060349 2.80181165 0.00052532 0.00325947 -0.00143918 Br 14.81421009 8.49183798 35.54021734 0.00240673 -0.00137523 -0.00162586 Br 15.06900974 5.97897654 3.72112572 0.00089810 -0.00056733 -0.00153014 Br 11.72668659 9.21607236 2.91143929 0.00222183 0.00204752 0.00058789 Cs 11.66462166 5.96707847 5.48703980 -0.00333777 0.00141536 -0.00182161 Pb 14.86022260 8.88490123 8.65584679 -0.00083467 0.00228252 0.00112755 Br 15.02137297 8.93537955 5.77807487 0.00245005 -0.00016024 0.00135011 Br 14.69122444 5.99016694 8.54504633 0.00124776 -0.00363159 0.00025074 Br 11.88375162 8.99039988 9.00357335 -0.00285743 0.00058269 -0.00142567 Cs 12.24669875 6.23724878 12.55299393 -0.00214416 -0.00093734 -0.00023428 Pb 14.79365040 8.95086286 14.69266853 0.00083635 0.00050223 0.00087864 Br 15.00215702 8.71377294 11.59065213 0.00190459 0.00026338 -0.00121862 Br 14.75973534 6.04716873 15.17833767 -0.00306765 0.00215506 -0.00115447 Br 11.95944822 9.30179300 14.69949635 -0.00089195 0.00056404 -0.00160629 Cs 11.54413163 6.32364388 18.19851888 0.00115845 0.00281377 0.00131765 Pb 14.87872723 8.94659585 20.75562071 -0.00104603 0.00128071 -0.00137115 Br 14.72774147 9.27260509 17.56838566 0.00080206 0.00205240 0.00034530 Br 14.91587339 5.76237779 21.11341087 -0.00137561 0.00251270 -0.00167832 Br 11.86952585 9.25578557 20.53096811 0.00068819 -0.00118439 0.00292448 Cs 12.18570542 6.39619729 23.76075621 -0.00332525 0.00006571 -0.00155730 Pb 14.81566947 8.71553417 26.77832042 -0.00126150 0.00140641 0.00009396 Br 14.78979774 8.65921417 23.74906653 -0.00317698 0.00038806 0.00058027 Br 15.20282193 5.76835496 26.31319888 0.00012733 -0.00000535 -0.00063074 Br 11.80254491 9.52831472 26.20699168 -0.00128489 -0.00372174 -0.00030241 Cs 11.89261798 5.36834652 29.73898701 -0.00109261 -0.00094270 -0.00023561 Pb 14.77733224 8.71497083 32.72614463 0.00087915 -0.00213415 0.00250874 Br 14.74409024 8.98942777 29.60224715 0.00114562 -0.00123686 0.00018403 Br 13.87960521 5.80082305 32.83182914 0.00137849 0.00163339 0.00083156 Br 11.91183683 9.59306112 32.55392825 0.00265683 0.00162514 -0.00328015 Cs 11.68446387 11.81343629 0.09488098 0.00042582 -0.00326647 -0.00402447 Pb 15.07126317 14.91309768 2.99402055 0.00252325 0.00080389 -0.00038135 Br 15.22639266 15.21323643 0.15022579 -0.00286585 0.00125837 -0.00253735 Br 14.39848107 11.86552240 2.20331909 0.00162278 -0.00283663 -0.00175858 Br 11.93618550 15.47037492 2.39763870 -0.00147082 0.00213523 -0.00260159 Cs 12.46082430 11.84943748 6.07217433 -0.00046550 0.00272618 -0.00101034 Pb 14.87801379 14.91738915 8.73271576 0.00084992 -0.00011762 -0.00056725 Br 15.07865588 14.80342393 5.88956207 0.00187741 -0.00096007 0.00026945 Br 15.24161315 11.90197909 8.66530911 0.00160937 0.00136874 0.00032517 Br 11.90365638 14.37880696 8.52034509 -0.00177583 -0.00102553 0.00360507 Cs 12.30499793 12.05505693 11.32122288 0.00298740 -0.00005454 0.00254845 Pb 15.08135997 14.81068399 14.73351338 -0.00102595 0.00340848 -0.00217047 Br 15.22720947 14.39952634 11.56021323 0.00341551 -0.00100753 -0.00162465 Br 15.05425887 11.85349146 14.44390608 0.00131811 -0.00107124 0.00080326 Br 12.21768982 15.08810547 15.14685437 -0.00137820 -0.00199980 -0.00134918 Cs 11.67475875 12.21089150 17.68018988 -0.00299579 0.00117695 0.00055260 Pb 14.94028299 14.97860153 21.08133954 -0.00015323 0.00117097 -0.00027845 Br 14.94995915 15.54616760 17.75508809 -0.00271061 -0.00005235 -0.00160241 Br 14.94516014 11.84781779 20.78321613 -0.00107541 0.00145510 0.00072576 Br 11.95755809 14.84335969 20.68618614 -0.00070819 -0.00080036 -0.00281276 Cs 11.66984226 11.73391162 23.72158839 0.00122722 -0.00057156 0.00031400 Pb 14.73909762 14.84923043 26.67642325 0.00094804 -0.00004736 -0.00133039 Br 14.78369698 14.72611565 23.84003399 0.00181115 -0.00165982 -0.00269595 Br 14.65248924 11.61238253 26.84400366 0.00201657 0.00139626 -0.00130347 Br 11.67372800 14.65522996 26.54361244 -0.00218306 -0.00111970 0.00065116 Cs 12.38191208 11.51720020 29.67613313 -0.00058136 0.00204181 0.00137357 Pb 14.73860439 14.66910623 32.78985962 -0.00057028 0.00149528 0.00151226 Br 14.59319585 14.95946789 29.76784606 0.00024269 -0.00016403 -0.00260562 Br 14.84070212 11.84359111 33.04241655 -0.00054956 0.00476133 -0.00075798 Br 11.76337493 14.83368452 33.55844834 -0.00118001 -0.00234336 0.00343124 Cs 11.86993121 17.87204072 35.01424318 -0.00079528 -0.00199283 -0.00132679 Pb 15.02798348 20.98008056 2.77597260 -0.00043090 -0.00055781 0.00161444 Br 14.23224530 20.79272376 0.06484522 -0.00144259 0.00398496 -0.00002110 Br 15.82944090 17.69605003 3.49421578 0.00277672 0.00054218 -0.00507679 Br 11.80200787 20.86596283 3.17115545 0.00004024 -0.00342276 0.00195879 Cs 12.17908639 17.89518390 5.53714982 -0.00100835 0.00193398 0.00147346 Pb 14.77355516 20.86012028 8.91660334 0.00058333 0.00044228 0.00227797 Br 14.85126776 21.24637726 5.91205844 0.00000452 -0.00159931 -0.00319087 Br 14.47721999 17.82882286 8.70979594 -0.00161274 -0.00160484 -0.00011540 Br 11.93153226 21.09775138 8.69068127 -0.00059657 -0.00272614 -0.00189245 Cs 11.64109523 17.93738260 11.93875412 0.00101422 0.00027948 0.00054324 Pb 14.79127499 20.64831513 14.87062214 -0.00119819 0.00039243 0.00231730 Br 14.09094504 20.99133402 11.95586457 -0.00032273 -0.00000730 -0.00002839 Br 14.92016205 17.71081042 14.26154394 0.00005764 0.00125911 -0.00411045 Br 11.92974517 20.98451641 15.37292808 0.00098484 -0.00328833 -0.00285554 Cs 11.98851600 17.92546731 17.35192524 0.00187211 -0.00052441 -0.00253055 Pb 14.79781459 20.67588163 20.71902618 0.00108116 0.00100426 -0.00033399 Br 14.92374323 20.72295440 17.79143600 -0.00090690 -0.00338364 0.00174208 Br 15.63601955 17.91717535 21.06811131 -0.00445756 -0.00175207 -0.00037041 Br 12.04368763 20.53640631 20.44653988 -0.00428127 0.00020343 -0.00208524 Cs 11.75766569 17.53590432 23.30331409 -0.00226387 -0.00061092 -0.00058663 Pb 14.77486620 20.89281401 26.80660722 -0.00157500 0.00017385 0.00171680 Br 14.43469508 20.42940387 23.89139875 0.00203156 -0.00326374 -0.00308263 Br 14.40637701 17.74884579 26.51265115 -0.00003447 -0.00011965 -0.00139809 Br 11.75454250 20.77218708 26.94102535 0.00170147 -0.00013667 0.00080336 Cs 11.84592271 17.77196573 29.25756206 0.00141477 0.00069077 -0.00031025 Pb 14.80644546 21.04127523 32.69604845 -0.00138418 -0.00111391 0.00228728 Br 14.60298411 20.60505077 29.72742289 -0.00151824 -0.00214954 0.00061173 Br 14.89446642 17.73787579 33.15396421 -0.00230375 0.00213357 -0.00318287 Br 11.86455403 20.08773952 32.31374540 0.00460882 -0.00008186 -0.00003746 Cs 11.80489174 23.51646463 0.19054288 -0.00020797 -0.00102217 -0.00229110 Pb 14.74396554 26.78453494 2.98219551 0.00036955 -0.00064954 0.00043481 Br 14.60134915 26.46924913 0.03488950 -0.00257890 0.00179245 0.00054685 Br 14.85849399 23.77215879 3.15268420 0.00345328 -0.00228597 0.00137617 Br 11.91636161 26.67441518 2.54452750 0.00116615 0.00400899 0.00000084 Cs 11.82702199 23.51416676 5.54094739 0.00003205 -0.00027414 0.00121230 Pb 14.87153580 26.59433145 8.87818095 -0.00029446 0.00055574 -0.00246553 Br 15.36976797 26.29289377 5.93341501 -0.00224530 0.00562705 -0.00045032 Br 15.61794714 23.70122619 9.20673068 -0.00202410 0.00227004 0.00302850 Br 11.97073884 26.19022450 8.62869009 -0.00426683 -0.00357472 0.00232198 Cs 11.70367735 23.82020390 11.38454918 -0.00025503 0.00077361 -0.00101262 Pb 14.50129742 26.63049370 14.85203271 0.00036373 -0.00097806 0.00119014 Br 14.14096601 26.11501012 11.84944657 -0.00301122 -0.00098776 0.00314617 Br 15.08613977 23.80346570 15.25376467 0.00044362 0.00084621 -0.00031820 Br 11.59083044 26.40946873 14.62110493 0.00161051 -0.00330329 -0.00029365 Cs 11.56886449 24.38158837 17.28715158 -0.00089209 0.00196984 0.00309620 Pb 14.76617115 26.82038758 20.67973044 0.00056201 0.00146847 -0.00074743 Br 14.32629003 27.47919016 17.68325667 0.00019100 0.00071087 -0.00055731 Br 15.25695303 23.98130755 21.00343036 0.00267044 0.00382672 0.00061784 Br 11.91875860 26.65720432 20.52082292 0.00062339 0.00099944 0.00056261 Cs 10.93724568 24.00190507 23.44267212 0.00192991 0.00227656 -0.00108490 Pb 14.76389166 26.59070201 26.38980967 0.00114212 -0.00021346 0.00128027 Br 13.96769559 26.25971690 23.49739519 -0.00038212 -0.00183752 0.00193290 Br 15.09846879 23.69840344 26.42582083 -0.00102808 -0.00064817 0.00157462 Br 11.87513318 26.84445484 27.11983626 -0.00094316 -0.00094303 -0.00184666 Cs 11.94018034 23.67477088 29.46411107 0.00296401 0.00112207 0.00259725 Pb 14.84418927 26.58916961 32.52819577 0.00032086 -0.00033278 0.00108549 Br 15.51836153 26.42816538 29.43870497 -0.00104140 -0.00030803 -0.00245581 Br 13.98294734 23.64225309 32.90118772 0.00219507 -0.00032827 -0.00513574 Br 11.95802643 27.11701440 31.96910553 0.00083514 0.00340662 0.00083377 Cs 12.05426085 30.08620310 35.28886877 0.00057641 -0.00082332 -0.00001258 Pb 15.15532255 32.77967455 2.92325280 0.00135679 0.00142137 0.00161459 Br 14.65119643 32.54929237 35.58090765 -0.00247986 0.00126051 -0.00216914 Br 15.54882231 29.79823028 3.12151637 0.00016009 0.00268464 0.00224364 Br 12.04511015 32.76965602 2.80706347 -0.00013052 -0.00053221 -0.00162123 Cs 11.14856490 29.32237866 5.57611668 0.00128234 0.00295094 0.00177613 Pb 14.87898402 32.53314637 9.00088401 -0.00125146 -0.00026991 0.00135437 Br 14.60374646 32.50310197 5.96333139 0.00302058 0.00168051 -0.00129848 Br 14.98184627 29.60684962 8.79501606 0.00095168 0.00268977 0.00019297 Br 11.98617253 32.36551844 8.97263919 -0.00289458 -0.00093123 -0.00159650 Cs 11.08267878 29.47551972 11.87737071 0.00003108 -0.00215031 -0.00004517 Pb 14.90626764 32.43142107 14.70850703 0.00119570 -0.00146277 0.00074920 Br 14.76764313 33.16333220 11.74645212 0.00005637 0.00012296 -0.00043550 Br 14.52971721 29.78471144 14.01558875 0.00316062 0.00216143 -0.00138092 Br 11.71644025 32.21689527 15.24651188 0.00070749 -0.00135814 -0.00210520 Cs 11.77421354 30.06527203 18.04915455 -0.00112707 -0.00039336 -0.00232642 Pb 14.86340126 32.62732607 20.53305829 0.00066790 -0.00083673 0.00277665 Br 14.71821187 32.32026013 17.63080476 0.00316608 -0.00105212 0.00078869 Br 14.63949175 29.91441080 20.99317601 -0.00077997 0.00053988 -0.00252639 Br 11.94709396 32.66578792 20.98987598 0.00098720 -0.00316904 -0.00355962 Cs 11.86619453 29.56058927 23.83455837 -0.00044555 -0.00160158 0.00172382 Pb 15.03423384 32.75241721 26.66739383 0.00150465 -0.00084542 0.00046269 Br 14.97624862 33.19765356 23.71621327 0.00275865 0.00030030 -0.00044632 Br 14.73845274 29.66374739 26.28671395 -0.00195506 0.00001169 -0.00158833 Br 11.89637535 32.82575293 26.94363290 -0.00187075 0.00245722 0.00277608 Cs 11.99747715 29.84905739 29.59214034 -0.00156291 0.00051710 0.00045384 Pb 15.19146138 32.68543207 32.71218662 -0.00070142 -0.00007565 0.00205546 Br 14.96528295 32.67118329 29.61852407 0.00097233 -0.00146970 0.00210860 Br 15.40367183 29.70809272 32.48591817 -0.00128776 0.00264519 0.00124499 Br 12.18030032 32.66037008 32.72866474 0.00122696 0.00000317 -0.00059036 Cs 18.44277103 0.41420175 0.21136122 0.00012494 0.00127742 0.00003591 Pb 21.05046862 2.78501875 2.95680806 -0.00062814 -0.00101744 0.00102685 Br 20.61453286 3.67893888 35.52316276 -0.00007309 0.00084725 0.00295818 Br 21.30796871 35.59112089 2.60507774 0.00129904 -0.00092218 0.00007689 Br 17.80843980 3.04599989 3.37078221 0.00140748 -0.00128671 -0.00531025 Cs 17.35954530 35.07996188 6.50086504 0.00118993 -0.00352635 0.00042132 Pb 20.85409203 2.69950940 8.86382137 -0.00099830 -0.00198562 0.00096086 Br 21.66495197 2.80647509 6.03144013 0.00059698 0.00035300 -0.00121756 Br 19.91351830 35.43922625 9.17998458 0.00453790 0.00010092 0.00281461 Br 17.95803106 3.21343998 8.17850352 0.00054197 0.00038415 0.00561731 Cs 17.77304690 0.53634094 11.94113375 0.00134671 -0.00043632 0.00386753 Pb 21.01040088 3.03009411 14.80074183 0.00214501 0.00070122 0.00036112 Br 20.96821942 2.77751332 11.97953675 -0.00130801 0.00052087 -0.00126708 Br 21.41170482 0.02519690 14.90993978 -0.00121871 0.00386392 -0.00364362 Br 17.86443917 3.18787365 14.76653620 -0.00055480 0.00065499 -0.00063541 Cs 18.04627950 0.21220697 17.67112855 -0.00132017 0.00366709 -0.00190676 Pb 21.04201062 3.00071519 20.73404742 0.00113673 -0.00088448 0.00142080 Br 21.05733155 2.26473836 17.77864036 0.00071705 0.00209864 -0.00231759 Br 20.71359913 35.41822524 21.45297996 0.00221700 0.00265974 -0.00299602 Br 17.76683864 2.65682407 20.77740137 -0.00050944 0.00318542 -0.00024590 Cs 17.72312262 35.37255182 23.49336912 0.00111917 0.00177295 0.00137641 Pb 20.79995157 2.78844732 26.63999717 0.00028535 -0.00042418 -0.00061254 Br 20.91071973 3.08712171 23.60890832 -0.00062451 0.00022977 -0.00049438 Br 20.44705356 35.58281240 26.02430200 0.00241157 0.00167433 0.00165398 Br 17.79550568 3.02563820 26.59011256 -0.00542741 0.00117868 0.00372896 Cs 17.72099305 35.28782777 29.72362966 0.00031107 0.00102736 -0.00102448 Pb 21.10709124 3.08969424 32.60345704 -0.00096551 0.00029347 0.00266156 Br 20.97306016 3.07209808 29.60637846 -0.00145245 -0.00059469 -0.00124847 Br 21.23349125 0.07713444 33.57106849 0.00123680 0.00094314 -0.00427765 Br 17.64645315 3.07245234 32.71473332 -0.00205054 -0.00099795 -0.00124333 Cs 17.54149888 6.04800680 0.48561140 -0.00191347 -0.00262423 -0.00179817 Pb 20.92725360 8.75856006 2.92002552 0.00141051 -0.00058468 0.00162127 Br 20.56470683 8.98081555 35.52430974 0.00037619 0.00067422 -0.00235661 Br 20.62670755 5.77099450 3.42992572 -0.00122330 -0.00084235 -0.00156498 Br 17.92386873 8.91875690 2.81874019 0.00244627 -0.00457347 -0.00141468 Cs 17.50333860 6.24993899 6.03507084 -0.00009170 0.00022742 -0.00036146 Pb 20.92524423 8.72889353 8.97596355 -0.00112787 -0.00052913 0.00106611 Br 20.81413659 9.50202337 5.98138371 -0.00330789 -0.00243757 -0.00060857 Br 21.02793304 5.71912934 8.64361252 -0.00023286 0.00115540 0.00305932 Br 17.79372672 8.67340392 8.59322275 0.00131871 0.00255432 0.00052953 Cs 17.61549883 5.79221341 11.12992465 -0.00009705 -0.00193921 -0.00091258 Pb 20.80219049 8.87202434 14.87085861 0.00064889 0.00031383 0.00091360 Br 20.99927007 8.63088836 11.96136804 0.00018804 0.00069883 -0.00038885 Br 21.47085752 5.95552491 15.04490224 0.00024509 -0.00011325 -0.00331452 Br 17.76562879 9.32056536 14.45382288 -0.00099037 -0.00271330 0.00290969 Cs 16.93715797 6.05293111 18.15654623 0.00035211 0.00124569 -0.00081438 Pb 20.74828034 8.65237559 20.71386885 0.00005098 0.00104324 0.00034246 Br 20.93573246 9.22586928 17.83314477 -0.00057127 -0.00310195 -0.00064553 Br 20.26465694 5.80413545 20.11859263 0.00015446 0.00077177 -0.00296906 Br 17.79482025 8.48453541 20.64988288 0.00339306 0.00270106 -0.00145000 Cs 18.22670175 5.87579455 23.75189262 0.00189611 -0.00020172 0.00330663 Pb 20.56863961 9.08806048 26.86048220 0.00096841 -0.00094329 0.00072724 Br 20.80639955 8.63069612 23.85488170 -0.00183684 0.00229489 -0.00151483 Br 20.88820604 5.92059349 27.02920965 0.00241223 0.00394955 -0.00097015 Br 17.57940572 8.99049981 26.72191798 -0.00105526 -0.00092998 -0.00286136 Cs 17.61790195 6.06401694 29.79780011 -0.00107118 0.00015589 -0.00124581 Pb 20.65154703 8.82021145 32.49206710 0.00075656 -0.00020839 0.00133868 Br 20.92112274 9.57058191 29.74157475 -0.00051621 -0.00229239 0.00128433 Br 20.70428310 6.08419532 31.68268753 0.00252281 -0.00106479 -0.00002173 Br 17.56857906 8.50956515 32.02270990 -0.00035818 -0.00073977 -0.00349825 Cs 18.35019879 12.44736309 0.08883720 -0.00147269 0.00032807 0.00198064 Pb 20.62275488 14.88981939 2.82048264 -0.00064891 -0.00105925 0.00253776 Br 20.99355931 14.63197533 35.54122737 -0.00163887 -0.00163670 0.00102980 Br 20.66483482 11.94981829 3.09076148 0.00176083 -0.00094840 -0.00164621 Br 17.79391264 14.15301582 3.28483877 0.00041264 0.00129341 0.00068575 Cs 17.99216721 11.86351983 6.16429328 -0.00080939 -0.00234846 -0.00130840 Pb 20.73761842 14.99457240 8.74392154 0.00291209 0.00178135 0.00018649 Br 20.55198405 15.02818129 5.86576899 -0.00112360 -0.00560802 0.00039172 Br 20.63025884 11.81023328 9.22492256 0.00007887 -0.00356417 -0.00167940 Br 17.70439116 14.88857775 8.91378200 0.00058325 -0.00026378 -0.00062022 Cs 17.77486756 11.78363033 11.86171251 -0.00049791 -0.00049411 0.00171760 Pb 20.86861630 14.72793220 14.57220483 0.00008845 0.00142010 0.00043169 Br 20.92321244 14.95421540 11.75667837 0.00111787 0.00117706 -0.00243332 Br 20.51148231 11.83114103 14.73922675 -0.00174457 -0.00000949 0.00332148 Br 18.00649824 15.23095348 14.96636500 -0.00171350 -0.00035395 0.00110787 Cs 17.91988327 11.36890351 18.21414487 0.00014855 0.00052306 0.00000564 Pb 20.58144929 14.94718751 20.77284263 0.00099049 0.00042545 -0.00193504 Br 21.26902960 14.95039115 17.94024697 0.00097045 -0.00089026 -0.00076551 Br 21.12928943 11.56246719 20.97721847 0.00213380 -0.00155240 0.00219612 Br 17.75905939 14.00640960 20.80870397 -0.00134176 0.00175734 0.00094924 Cs 17.57783678 11.22423548 23.78204648 0.00077671 0.00068062 0.00133922 Pb 20.49683616 15.27741911 26.70756399 0.00017376 -0.00017141 0.00008277 Br 20.07037214 15.06737828 23.76845760 0.00149035 0.00293637 -0.00415807 Br 20.65488418 11.94533101 26.67675922 0.00367829 0.00064314 -0.00387995 Br 17.74446489 15.54359829 26.68708405 -0.00367331 0.00073548 0.00069857 Cs 18.04444307 12.11730073 29.65223673 0.00008985 -0.00012554 -0.00037738 Pb 20.56421625 14.71803857 32.39899377 0.00018309 0.00002850 -0.00104195 Br 21.22475231 14.30702459 29.67105802 -0.00081651 -0.00087073 -0.00148416 Br 20.36251064 11.74985907 32.84355735 -0.00255576 0.00066833 -0.00232198 Br 17.74827154 14.68138194 32.49198319 -0.00035456 -0.00266661 0.00006065 Cs 17.47072085 17.77014164 35.35583853 -0.00161016 -0.00099847 -0.00263257 Pb 20.88624605 20.79365529 3.14339361 -0.00105064 -0.00023943 0.00019673 Br 21.21067344 20.65123277 0.01928860 0.00089014 0.00384727 -0.00042174 Br 21.28643741 17.68747957 3.03277851 0.00068525 -0.00170480 -0.00226770 Br 18.01485829 20.60808949 2.17244415 -0.00087191 -0.00066806 -0.00040237 Cs 17.79729141 17.84916161 6.13333082 -0.00339408 -0.00039139 -0.00083104 Pb 20.71423984 20.76655469 9.14312916 0.00055932 -0.00095776 -0.00107534 Br 20.96976309 20.81386351 6.01992826 -0.00467641 -0.00021546 0.00437937 Br 20.58532736 17.88764600 8.64569643 -0.00208561 0.00065337 -0.00040056 Br 17.88722240 20.36991266 8.88400275 0.00157590 -0.00207489 -0.00055953 Cs 17.58076372 17.70672449 12.22729381 0.00002659 0.00046436 -0.00062440 Pb 20.78858936 20.63525840 14.80953019 -0.00101297 0.00163481 -0.00106513 Br 20.65267639 19.89332948 11.83589171 -0.00521719 0.00272048 -0.00211045 Br 21.05015841 17.80723173 14.85764597 -0.00278002 0.00006315 -0.00254608 Br 17.65968207 20.66717197 14.81825599 -0.00426776 0.00085191 0.00113741 Cs 18.02192153 17.75693348 17.81859813 0.00244082 0.00027450 0.00395515 Pb 20.65532804 20.61729414 20.77219312 0.00061059 -0.00125651 0.00058275 Br 20.74744320 20.99291833 17.82610046 0.00136479 -0.00079263 0.00074105 Br 19.85159164 17.65957492 20.80691956 0.00261746 -0.00005305 0.00335495 Br 17.79971319 21.09049032 20.65427769 0.00122934 0.00166153 -0.00409326 Cs 17.70973355 18.31939139 24.16029754 -0.00044911 -0.00260676 0.00033762 Pb 20.86703082 20.92946391 26.60252423 -0.00195497 0.00014244 -0.00075535 Br 20.44163630 20.88183038 23.60772244 -0.00374103 0.00155578 -0.00127862 Br 21.25369697 18.11355792 26.53634743 -0.00442742 0.00051743 0.00111810 Br 18.07285376 21.15736984 27.26143742 -0.00002956 0.00066465 0.00164005 Cs 17.46727811 18.23642447 29.39608835 0.00179710 -0.00058453 -0.00039358 Pb 20.78416358 20.65955828 32.48890310 -0.00063428 -0.00074641 -0.00078640 Br 21.21469932 20.60898826 29.58081495 0.00041825 -0.00052248 -0.00568839 Br 19.69833279 17.73545621 32.14148156 0.00200036 -0.00240930 -0.00126084 Br 17.58864382 21.43337259 33.56049674 -0.00007019 -0.00012669 0.00070022 Cs 18.02535858 23.87196417 0.54367567 -0.00135462 -0.00000025 0.00156915 Pb 20.64600640 26.88020969 3.04716122 -0.00138795 0.00091504 0.00076791 Br 20.30815319 26.69368843 35.59776497 -0.00086914 0.00122812 -0.00043224 Br 20.29574184 23.92813070 3.46787530 0.00101876 -0.00116018 -0.00085621 Br 17.61648044 26.68247776 2.95716728 0.00163152 0.00237215 0.00138167 Cs 17.97852241 23.76949486 6.10580161 0.00052442 0.00172936 -0.00074620 Pb 20.64690387 26.70179991 9.08974013 -0.00042452 0.00041617 -0.00127920 Br 20.34096003 26.67932722 6.29081358 0.00194454 0.00200299 -0.00000938 Br 20.54770703 23.78545728 9.88183462 -0.00454166 0.00495876 -0.00398837 Br 17.69556511 27.10712114 9.71894887 0.00003115 0.00261137 -0.00173519 Cs 17.75502990 24.16908144 12.46437877 0.00176277 -0.00116670 -0.00133111 Pb 20.54128598 26.78776879 14.89749330 -0.00248374 -0.00122030 0.00140882 Br 21.07622165 26.91969485 11.94898353 -0.00314583 0.00465133 0.00346765 Br 21.13191290 23.86518945 14.60254260 -0.00256016 -0.00052889 -0.00008658 Br 17.56903341 26.98434670 14.39690956 -0.00063589 -0.00022689 -0.00301813 Cs 17.64861473 23.29796311 17.87053407 -0.00019258 -0.00216609 0.00210765 Pb 20.51465611 26.79565974 20.55804931 0.00126978 0.00136066 0.00195493 Br 19.78062466 26.63965040 17.82803726 0.00236092 -0.00067031 0.00137357 Br 20.49902331 23.90303542 20.77213958 -0.00121210 0.00115295 0.00086029 Br 17.52163711 27.21746790 20.96469381 -0.00082384 -0.00234225 -0.00164849 Cs 17.79948015 24.43545838 23.56354547 -0.00251857 -0.00091885 -0.00249532 Pb 20.82784906 26.67818406 26.57517569 0.00022927 0.00317669 -0.00033771 Br 20.63625767 26.82483607 23.57239518 0.00325892 0.00218424 -0.00192435 Br 21.04146423 23.84830686 27.21890872 0.00231391 -0.00566666 0.00015572 Br 17.67618376 26.96304488 26.37052687 0.00069498 0.00052581 0.00247727 Cs 18.06425905 24.00459201 30.24514779 -0.00012372 0.00084451 0.00055597 Pb 20.76247490 26.75177772 32.57826455 -0.00162858 -0.00051405 0.00082207 Br 20.31542254 27.01763649 29.70918835 -0.00300871 0.00179253 -0.00331105 Br 20.99898796 23.70415602 32.56017587 -0.00075681 -0.00239525 -0.00025787 Br 17.86466120 27.06458956 32.56623628 -0.00028165 -0.00136800 0.00180006 Cs 17.77191233 29.48743413 35.28403348 -0.00178857 0.00155771 -0.00048524 Pb 20.82947188 32.61051742 2.97398841 -0.00252029 0.00044989 0.00057347 Br 21.04995737 32.09403164 35.58990891 0.00014695 0.00021703 0.00203017 Br 20.44315070 29.73264070 3.18618627 -0.00079458 -0.00080743 0.00320759 Br 18.02670125 33.03853613 3.33750122 0.00171133 0.00362069 0.00539210 Cs 17.91628367 29.74427110 6.26627458 0.00145132 0.00288785 0.00055330 Pb 21.12947827 32.79339198 8.92806868 -0.00039016 -0.00082658 -0.00165242 Br 21.21823466 33.10418312 5.87087778 0.00123898 0.00096566 -0.00051590 Br 21.40533701 29.81108681 8.57935800 0.00044566 -0.00153340 -0.00240575 Br 18.03485805 32.49319953 8.95466184 -0.00124362 -0.00424159 0.00002664 Cs 17.66299250 30.34613075 11.89277868 0.00320312 -0.00013555 -0.00044580 Pb 20.82173423 32.81243667 14.97328816 -0.00127158 -0.00129357 0.00064762 Br 20.93520937 32.63772893 11.97995702 0.00045639 -0.00246579 0.00080302 Br 20.30333334 29.86373074 15.25007320 -0.00232818 0.00346282 0.00032768 Br 17.81705515 33.14458874 14.94034314 -0.00244844 -0.00072128 -0.00278530 Cs 17.62092487 29.56445779 17.74183916 -0.00003060 0.00069923 0.00057012 Pb 20.86093743 32.55318725 20.77946861 -0.00070244 -0.00105369 0.00311174 Br 21.49460520 32.89671256 17.92533510 -0.00149205 -0.00002743 0.00267265 Br 21.23355094 29.67159243 20.53892997 -0.00139961 -0.00090825 -0.00184628 Br 17.92013777 32.71576353 19.85402633 -0.00363355 -0.00082787 0.00337238 Cs 17.53822128 29.62897768 23.72023195 -0.00305236 0.00052242 -0.00125237 Pb 20.63694638 32.69515459 26.66654385 -0.00027503 -0.00198914 0.00110197 Br 20.78987444 32.28031550 23.82180808 -0.00024297 0.00203802 -0.00021906 Br 20.28615741 29.62923596 26.54338822 -0.00174183 0.00049918 0.00322561 Br 17.75286849 32.01461973 26.40515763 0.00102654 0.00115408 0.00018512 Cs 17.66678386 29.40096874 29.21397909 -0.00165535 0.00035733 -0.00116273 Pb 21.19931294 32.53908932 32.58396132 -0.00080592 -0.00094644 0.00288633 Br 21.07130582 32.29211576 29.72705458 0.00040399 0.00121833 -0.00089267 Br 21.12506472 29.67273155 32.66310495 0.00019685 0.00425647 -0.00300003 Br 18.24600748 32.94554171 32.49774724 0.00060456 0.00368282 0.00291033 Cs 24.07925519 0.07411195 0.00956070 0.00063435 0.00001361 -0.00088736 Pb 27.17267380 3.51820717 3.10613291 -0.00176342 -0.00142431 0.00036209 Br 27.55850075 3.42521137 0.15503002 0.00297808 -0.00400734 0.00111018 Br 26.47363981 0.16335737 2.45001036 -0.00063769 0.00091439 -0.00071221 Br 23.81539240 3.17258778 2.48737932 0.00304367 0.00253553 0.00159053 Cs 23.81951385 0.19209704 5.17843471 -0.00220984 0.00092688 0.00125305 Pb 26.57613462 3.02975265 8.94098402 0.00228763 -0.00176347 0.00039017 Br 26.25735488 3.33534221 5.95769812 -0.00163578 -0.00244856 -0.00107975 Br 26.78125431 35.54468181 9.63738235 0.00148965 0.00192019 -0.00202565 Br 23.86627906 2.93221731 9.17674156 -0.00173201 0.00038117 -0.00040939 Cs 24.20858794 0.05281675 11.94617326 0.00098981 -0.00000126 0.00101040 Pb 26.95963158 2.75274089 14.71246680 -0.00194683 -0.00119362 0.00088956 Br 26.91986814 3.35370331 11.85239759 0.00109867 0.00207994 -0.00184384 Br 27.00362559 35.53837607 14.82975142 0.00101537 0.00047557 0.00026686 Br 23.82460750 2.71778200 14.46533999 -0.00158628 -0.00266956 0.00231992 Cs 24.00646289 35.37297318 17.48882697 0.00028115 0.00024313 0.00072613 Pb 26.79289411 2.92725133 20.64507031 -0.00018235 -0.00035130 0.00054473 Br 26.23721459 3.16792635 17.54803737 -0.00110566 0.00254557 0.00238844 Br 26.26477617 35.56558940 20.25228151 -0.00088769 -0.00176117 0.00435115 Br 23.85037786 3.69539505 21.16902304 -0.00423113 0.00183000 -0.00216069 Cs 23.88485466 0.38181741 23.45529108 -0.00008124 0.00050299 0.00187375 Pb 26.63485570 2.80047220 26.79711231 0.00113712 0.00013306 0.00033922 Br 26.25432335 2.52858874 23.60450372 -0.00071568 -0.00111227 -0.00089955 Br 27.17149894 35.55536199 26.83268707 0.00055536 -0.00083642 -0.00211319 Br 23.82004447 3.31255441 26.99514636 0.00193373 -0.00047854 0.00093869 Cs 23.00929177 35.29130969 29.58274737 -0.00084511 0.00002050 0.00079437 Pb 26.75719506 3.07351481 32.56054901 -0.00032334 -0.00115493 -0.00040047 Br 26.37643501 3.55588115 29.82595507 0.00126590 -0.00107406 0.00234139 Br 26.56474102 0.18471779 32.95252805 0.00090657 0.00167288 0.00507818 Br 23.83358866 3.55118686 33.03247824 -0.00311065 0.00326730 -0.00180366 Cs 24.11826386 6.27721520 35.30880988 0.00105419 -0.00195812 0.00042225 Pb 26.92640529 9.19764142 2.99311306 -0.00093317 0.00071833 0.00080475 Br 26.43286464 9.37627016 35.52670347 0.00080168 0.00040844 -0.00055321 Br 27.70813069 6.28539006 2.95984778 -0.00020550 0.00078848 -0.00152079 Br 23.80161946 8.46980912 2.91688897 0.00065960 -0.00134357 0.00208242 Cs 23.40317087 5.67646333 5.35383376 0.00014280 0.00274431 0.00106084 Pb 26.57370529 9.20086615 8.71617131 0.00288019 -0.00075130 -0.00093643 Br 27.07626918 8.95055337 5.95566436 -0.00079314 -0.00244145 -0.00128256 Br 26.10603441 5.88957396 8.76167027 -0.00130222 0.00289938 0.00053861 Br 23.68860401 8.73126257 9.28463843 0.00553561 -0.00162669 0.00118269 Cs 24.05940522 5.41727826 12.29266049 0.00020256 0.00077830 -0.00207424 Pb 26.73492841 8.64322095 14.74364641 -0.00139225 0.00022682 0.00125784 Br 27.18668173 9.13256724 11.84277913 -0.00254028 0.00006597 0.00072158 Br 26.94283823 5.55302508 14.83058448 -0.00088009 0.00213055 0.00175174 Br 23.92188132 9.26517284 15.23092649 0.00085461 0.00214250 -0.00154148 Cs 23.66736715 6.09045092 17.63348799 0.00056443 0.00048996 0.00011269 Pb 27.04502682 8.93217476 20.42822898 0.00149278 0.00042400 -0.00070092 Br 27.17184802 8.42172012 17.63159627 0.00164784 0.00208867 0.00271987 Br 27.82669894 5.87758823 20.75482940 -0.00130097 0.00056402 -0.00123070 Br 23.62515465 8.47425430 20.67518078 0.00274695 -0.00420764 -0.00005916 Cs 23.06950172 5.97494240 24.09857303 -0.00168800 -0.00014203 -0.00045625 Pb 26.77317291 8.76083841 26.43012848 0.00014362 0.00165205 0.00138585 Br 26.97744450 8.69294432 23.62580214 0.00011746 -0.00275280 -0.00098997 Br 26.45107398 5.87636827 26.28727158 0.00274495 -0.00092113 -0.00187837 Br 23.75004819 8.96195529 26.68854457 -0.00234601 0.00163134 0.00136440 Cs 23.57086915 6.20450418 29.39893051 -0.00121503 -0.00049556 -0.00266521 Pb 26.55310501 9.06272012 32.40217580 0.00166467 -0.00243139 -0.00303430 Br 26.14020247 8.72746441 29.25888165 -0.00034076 0.00086933 -0.00279634 Br 26.87771735 6.11700198 32.94897432 0.00259738 0.00323421 0.00005142 Br 23.44523346 8.89055901 32.64278018 0.00068296 -0.00251499 -0.00328305 Cs 23.93526366 12.02104794 0.22518390 -0.00100283 0.00084248 0.00218191 Pb 26.82522327 14.99577939 3.01741465 0.00345352 0.00011806 -0.00138806 Br 26.87128559 14.22300048 35.50299497 -0.00037635 0.00085817 0.00249164 Br 26.27172387 12.23411798 3.47036359 0.00131886 0.00096362 -0.00269433 Br 23.90873405 15.05240969 2.84930269 -0.00366455 0.00067495 -0.00050034 Cs 23.49931588 12.55936858 5.73783417 -0.00083318 0.00116630 0.00052366 Pb 26.85502005 14.80862345 8.82319249 -0.00076283 -0.00019477 -0.00025403 Br 26.83079991 15.41928874 6.14099296 -0.00086843 0.00124285 -0.00004845 Br 26.38802435 12.04096996 8.17968865 -0.00064048 0.00464444 -0.00303776 Br 23.59820387 14.99889751 8.61784533 0.00159610 0.00335768 -0.00019944 Cs 23.80931160 11.69910584 11.89237656 -0.00040079 -0.00115000 -0.00063823 Pb 26.91091892 14.30865541 14.53422614 -0.00104501 0.00072294 -0.00006687 Br 27.03695716 13.57823071 11.72661440 0.00061292 0.00369092 0.00178495 Br 27.54451865 11.51298746 15.38082994 -0.00218525 -0.00120260 0.00007315 Br 23.93855043 14.16166861 14.88731094 0.00217591 0.00250745 -0.00059659 Cs 23.26714662 12.09344073 18.29246093 0.00358324 0.00066629 -0.00228825 Pb 26.36684548 15.01608277 20.80045128 0.00054322 -0.00211088 0.00193050 Br 26.53438400 14.55290623 17.76512802 -0.00011424 -0.00109722 0.00263797 Br 26.09544193 11.81531387 20.42682091 0.00338893 -0.00129558 -0.00114466 Br 23.45886490 15.29405978 20.95745890 -0.00140374 0.00170380 0.00370524 Cs 23.76861454 11.16106778 23.46466879 -0.00304422 -0.00125928 0.00134132 Pb 26.43880543 14.66532663 26.76558401 0.00315652 -0.00013558 0.00006956 Br 26.71771247 14.87523712 23.53135373 -0.00168567 -0.00022146 -0.00174101 Br 26.54730429 11.81657035 26.64579721 -0.00081977 0.00084653 0.00246585 Br 23.64920034 14.72881417 26.10931202 -0.00328797 -0.00097840 -0.00190752 Cs 23.60650671 12.07764297 28.86592089 -0.00166444 -0.00055846 0.00057902 Pb 26.76420808 14.83894345 32.75405900 -0.00043078 -0.00072528 -0.00063673 Br 26.46955544 14.72702221 29.73520951 0.00030373 0.00204190 -0.00067778 Br 26.45139171 11.99189956 32.57448752 0.00012889 0.00105415 -0.00118730 Br 23.44538390 15.74414681 32.50409927 -0.00385816 0.00135114 0.00208853 Cs 23.22319414 17.43982964 0.19791255 0.00100537 0.00441409 0.00104730 Pb 26.72091498 21.16407753 2.90840485 -0.00022323 0.00028897 -0.00066251 Br 26.98947760 21.65956037 35.40822636 -0.00191295 -0.00168390 -0.00140527 Br 27.11958052 17.79478646 2.45581705 -0.00089694 0.00069574 0.00053324 Br 23.86717229 20.85846655 2.78588809 0.00073937 -0.00083091 0.00258040 Cs 23.71715424 17.81256803 5.99290894 0.00096341 -0.00375880 -0.00149986 Pb 26.67165620 20.95178214 8.87128251 -0.00092269 -0.00043223 -0.00112797 Br 26.69085031 21.01278770 5.93182219 0.00153100 -0.00126529 -0.00111142 Br 26.73068017 17.88813925 8.88049345 -0.00327271 -0.00070092 0.00005335 Br 23.73739767 20.59310018 9.09774937 0.00027703 0.00001038 0.00130188 Cs 23.56422411 17.94310630 11.83463931 -0.00081125 -0.00003489 0.00015358 Pb 26.41787940 20.61334099 14.98818164 -0.00042573 -0.00092199 0.00190011 Br 26.96567168 20.89600876 11.81937226 -0.00045265 0.00124145 -0.00264710 Br 27.19861893 17.84552278 14.88505033 -0.00224259 -0.00148639 -0.00138413 Br 23.62030265 20.85569259 14.38186784 0.00042369 0.00033429 -0.00170803 Cs 24.25645275 17.30672397 17.39748813 -0.00007723 -0.00166307 0.00113230 Pb 26.52686844 20.70201769 20.77436608 -0.00100386 0.00145767 0.00029813 Br 25.63093513 20.40184851 17.84459393 -0.00133668 0.00068458 0.00269039 Br 26.51833942 17.78601977 21.47568183 0.00062008 0.00047988 0.00199327 Br 23.69616512 20.78073445 21.30561916 0.00245669 0.00039975 0.00016274 Cs 23.94779948 17.92450590 23.86560153 0.00153427 -0.00091406 -0.00327150 Pb 27.07533307 20.71489721 26.69255728 0.00129940 -0.00146426 0.00041625 Br 27.21450243 20.70097696 23.62280382 -0.00327899 0.00290488 -0.00274012 Br 27.06393043 17.88587873 26.72320925 0.00065645 -0.00145020 0.00253854 Br 23.83347415 20.73591463 26.36218595 0.00043855 -0.00301716 -0.00133674 Cs 23.77257542 17.97619442 29.70924538 0.00324548 -0.00049532 0.00109673 Pb 26.59477954 20.79309127 32.50521354 0.00162795 0.00145561 0.00096972 Br 26.55346528 20.29151821 29.42271440 -0.00307591 -0.00290485 -0.00418839 Br 27.24599424 18.04826286 33.03690399 -0.00144804 0.00000562 0.00144603 Br 23.73170276 20.29391425 32.49925620 -0.00322154 -0.00171515 0.00234550 Cs 23.55126889 23.66823800 0.00881407 0.00068662 -0.00131441 -0.00181584 Pb 26.37680412 26.80286029 2.87171620 0.00049197 -0.00236431 -0.00000027 Br 26.67512998 26.56719893 35.49411480 -0.00261329 -0.00039390 -0.00077507 Br 26.44520585 23.82824974 3.12281703 -0.00149313 -0.00207986 -0.00091325 Br 23.61361780 26.88636225 2.78830060 0.00092891 0.00138722 -0.00065287 Cs 23.50198378 23.45102049 6.50440301 0.00477958 0.00030795 -0.00004049 Pb 26.62077685 26.98948977 8.92044843 0.00107521 0.00047429 0.00016849 Br 26.37414407 26.83517413 5.88948985 0.00076818 -0.00042613 0.00166611 Br 26.26224348 23.91157665 9.02662372 -0.00108683 -0.00058226 -0.00015822 Br 23.60191784 26.50923279 8.75049212 -0.00060592 -0.00088665 0.00170382 Cs 23.61248998 23.82553776 11.94165464 0.00067101 0.00203543 0.00118878 Pb 26.40640561 26.42029037 15.00234064 -0.00005445 0.00064741 -0.00068237 Br 26.50342597 27.13466202 11.88148667 -0.00239036 0.00044691 0.00294822 Br 25.95461439 23.55975026 14.70198017 0.00092703 0.00391942 -0.00051551 Br 23.42474955 26.66338627 15.33500337 0.00312727 0.00021633 0.00112061 Cs 23.78880897 23.62419401 17.65229993 -0.00061087 -0.00397315 -0.00063935 Pb 26.69946402 26.62831234 20.89099616 -0.00002752 -0.00022564 0.00194183 Br 27.06249577 26.00887553 17.79107958 -0.00342584 0.00045573 0.00146868 Br 26.24549352 23.56723554 21.38422786 -0.00087543 0.00160566 -0.00306876 Br 23.52994567 26.40184705 20.28125523 0.00382816 0.00043948 -0.00265284 Cs 23.45789651 24.01291389 23.41865234 -0.00035711 -0.00152958 -0.00193064 Pb 26.67370084 26.69587741 26.77768596 0.00061528 0.00215817 0.00011204 Br 26.68689262 27.21133994 23.80108693 -0.00212955 0.00093108 -0.00052056 Br 26.16287975 23.72551127 26.12122556 -0.00328471 0.00165620 0.00426531 Br 23.77505423 26.77306476 27.09992053 -0.00180025 -0.00228634 -0.00213278 Cs 23.90422035 23.64457019 29.42055266 0.00024336 -0.00080468 -0.00357965 Pb 26.58890084 26.64528396 32.37849201 0.00233250 -0.00008041 -0.00048799 Br 27.08509473 26.85904164 29.57464763 0.00359265 0.00320155 -0.00445374 Br 26.04134050 23.49985454 31.76807722 0.00169436 -0.00638928 -0.00121458 Br 23.82527191 26.85733039 32.96786807 -0.00065614 -0.00171485 0.00072273 Cs 24.53117880 29.83041167 35.25626570 0.00077737 0.00166327 -0.00092303 Pb 26.66218731 32.93222622 2.81791838 -0.00012653 0.00095886 -0.00056633 Br 27.03656211 32.91295205 0.02686881 -0.00177223 0.00018051 -0.00015437 Br 27.20762463 29.69931014 3.00102871 -0.00237076 -0.00096634 -0.00174257 Br 24.02165557 32.47315930 2.60754202 -0.00003876 -0.00227049 -0.00013847 Cs 23.84974188 29.58979110 5.57492572 0.00173193 0.00130055 0.00051538 Pb 26.75596997 32.71927512 8.64097812 0.00109895 -0.00034686 -0.00098839 Br 26.95040531 33.57587907 5.93377172 0.00545324 0.00135201 0.00006357 Br 26.52320607 29.83127709 8.38724035 0.00446102 0.00311524 -0.00117232 Br 24.01940722 32.52541046 9.24595742 -0.00046054 0.00030130 -0.00108469 Cs 23.54230755 29.69163287 11.98571345 0.00122558 -0.00254321 -0.00030483 Pb 26.85239437 32.30276408 14.92087660 0.00181934 0.00007719 -0.00082022 Br 27.25488285 32.35714831 11.89876433 0.00041415 -0.00177881 -0.00221970 Br 27.13553809 29.51759891 14.96677871 0.00049379 -0.00134047 0.00063362 Br 23.82091845 31.64277556 14.94430814 0.00109159 0.00056790 -0.00181272 Cs 23.75823775 29.40326941 17.95780929 0.00065299 -0.00056452 0.00035975 Pb 26.82509452 32.61969719 20.84425682 -0.00092829 -0.00039515 -0.00055816 Br 26.69309958 32.34932478 17.81591954 -0.00061725 0.00203783 0.00052278 Br 26.64980704 29.61461650 20.78329207 -0.00060698 -0.00304919 -0.00003019 Br 23.96788689 32.62915299 20.85483710 0.00281252 0.00105130 -0.00087228 Cs 24.04282977 30.15371853 23.79473332 -0.00404258 -0.00186338 0.00036878 Pb 26.73138685 32.43746256 26.94024651 -0.00189973 0.00180817 -0.00064339 Br 27.32801249 32.89988232 23.75830267 0.00089168 -0.00360248 0.00170114 Br 26.47344085 29.60719586 26.83192867 0.00217634 -0.00091093 0.00187963 Br 23.61693492 32.98234148 26.79639406 0.00044476 0.00278768 0.00267481 Cs 23.50331825 29.47980224 29.48495327 -0.00018039 0.00080851 0.00059589 Pb 27.13861769 32.61632575 32.60051788 -0.00098537 -0.00044485 0.00159574 Br 26.64719046 32.07325481 29.74549881 -0.00452798 -0.00148740 -0.00207846 Br 27.27449807 29.63526488 32.87233572 -0.00155595 -0.00068344 -0.00013217 Br 24.14964752 32.53660241 32.65329861 -0.00129550 -0.00048415 -0.00199674 Cs 29.99322738 35.55456265 35.24667189 -0.00039746 0.00172983 0.00207204 Pb 32.54476414 3.17165737 3.36317122 -0.00156124 0.00082804 0.00145289 Br 32.34593041 2.83663595 0.03323570 -0.00274731 0.00217326 0.00144671 Br 32.85290838 0.30150059 3.07705339 -0.00113695 -0.00244900 0.00086738 Br 29.67027941 2.83936319 4.11907658 0.00070408 0.00157574 -0.00224600 Cs 30.18980482 35.49318344 6.17964190 0.00094213 -0.00189730 -0.00145980 Pb 32.54114117 2.51109914 9.25396477 0.00056443 -0.00126013 0.00065891 Br 32.78453010 3.11357447 6.23158244 0.00276241 0.00012359 -0.00224045 Br 32.02027560 35.33272216 9.26182018 -0.00318460 -0.00215359 0.00019994 Br 29.61638039 3.35426283 8.89083636 0.00085510 0.00116232 -0.00212148 Cs 29.53465839 -0.00123217 12.25870217 0.00040749 -0.00137481 0.00155632 Pb 32.70618254 2.64356845 15.08512942 0.00215320 -0.00041034 0.00104277 Br 32.53540416 2.17620130 12.23618352 0.00194817 0.00255508 -0.00392063 Br 32.64947663 35.29179309 15.70061928 -0.00095672 -0.00275225 0.00125156 Br 29.76475939 2.69955410 15.14371534 -0.00071215 0.00094412 0.00198154 Cs 29.47072319 35.49710757 17.86445599 -0.00133167 0.00076459 -0.00183357 Pb 32.43545494 3.08530611 20.76990459 0.00020317 0.00214289 -0.00001880 Br 32.78473783 2.45780888 18.12998745 0.00107326 0.00274546 -0.00095547 Br 32.09101313 0.06148305 21.25360886 0.00057225 -0.00052462 -0.00067344 Br 29.59472877 2.74638086 20.35738114 0.00508729 -0.00122341 0.00067069 Cs 29.67589330 0.16829753 23.97686157 -0.00252658 0.00098070 0.00107031 Pb 32.67742324 2.94224184 26.67044973 -0.00049506 0.00006030 -0.00080971 Br 32.81231195 2.65979996 23.96168086 0.00089064 -0.00495753 0.00097504 Br 32.03907489 35.40141341 26.73002597 0.00001434 0.00247191 -0.00092557 Br 29.58265223 3.03726066 27.22287190 0.00241701 0.00029065 -0.00186170 Cs 29.80524142 0.13235473 29.91722328 -0.00133135 -0.00170589 -0.00196047 Pb 32.54873557 2.67166471 32.78169839 -0.00050889 0.00046555 -0.00098140 Br 33.42284121 3.02968872 29.88321285 0.00058745 0.00117811 -0.00166898 Br 33.28307873 35.37188753 32.17141587 0.00176187 -0.00033528 0.00145621 Br 29.73406784 3.00483602 32.02563513 -0.00168576 -0.00321129 0.00182951 Cs 29.36615372 6.38569963 35.41174971 0.00095253 0.00077779 -0.00136596 Pb 32.54653579 8.84456469 2.94485179 0.00275151 -0.00009587 -0.00078299 Br 31.92944164 9.15988451 35.57789420 -0.00001263 0.00224112 -0.00211408 Br 32.17556369 5.89056666 3.41006868 -0.00002162 -0.00276067 -0.00215846 Br 29.70075898 9.91658880 2.89183301 0.00139548 -0.00057751 -0.00241587 Cs 29.67844441 5.96763288 6.36244330 -0.00248157 -0.00001868 0.00109948 Pb 32.57620849 8.73546596 8.86886575 0.00034932 -0.00124572 -0.00051612 Br 32.36938629 8.60107853 5.77701685 0.00000082 -0.00033647 0.00074921 Br 32.26412047 5.87960171 9.03251873 0.00084055 0.00367646 0.00229977 Br 29.70755267 8.97362708 9.01447535 0.00292147 0.00137344 -0.00077211 Cs 29.20573441 6.05631051 11.54028417 -0.00061724 0.00162432 0.00118277 Pb 32.62715460 8.57804683 14.80283573 0.00024582 -0.00118916 0.00096461 Br 32.13429803 8.56699171 11.91797717 -0.00155293 -0.00544165 0.00523563 Br 32.85372182 5.60981296 15.24588176 -0.00198096 0.00413838 0.00035444 Br 29.72543226 8.25436336 14.97979847 0.00197648 0.00115661 0.00063772 Cs 29.77840558 5.68431890 17.64114642 0.00027839 0.00382700 0.00090057 Pb 32.64587597 9.22728431 20.66717850 0.00282936 -0.00006848 -0.00080158 Br 32.84130750 9.33341363 17.72967139 0.00269186 -0.00278259 0.00390803 Br 32.27673578 5.91126036 20.10681464 0.00288248 0.00025632 0.00148581 Br 29.65131977 9.98446323 21.03294557 -0.00225635 -0.00123596 -0.00205316 Cs 29.60071050 5.71880934 24.16684412 0.00094488 -0.00005517 -0.00187673 Pb 32.53680161 8.71406645 26.78019612 0.00137520 -0.00168394 0.00110661 Br 32.91220868 8.98713309 23.69802650 0.00024792 0.00522021 0.00017977 Br 33.23949166 5.73763824 26.62880995 -0.00365636 -0.00191964 -0.00016769 Br 29.56163165 8.51740487 26.53620367 -0.00086671 0.00000927 -0.00185214 Cs 28.99455920 6.21279371 29.61868120 -0.00065961 0.00286391 -0.00345325 Pb 32.48310751 8.77305768 32.51379328 0.00015643 0.00001192 -0.00069935 Br 32.72966943 8.34545505 29.64898844 0.00367924 0.00088931 0.00199217 Br 32.39169913 5.62110614 32.95822332 0.00029791 -0.00105117 0.00051657 Br 29.66006615 9.01206640 32.82770185 -0.00069288 -0.00318513 -0.00013910 Cs 30.10826588 12.07695888 35.49500448 -0.00111001 -0.00130465 0.00114598 Pb 32.76716417 15.01864226 2.93029074 -0.00172187 0.00107087 0.00110954 Br 32.90550930 14.27054098 0.04545736 -0.00087212 0.00237282 0.00179114 Br 33.16063700 11.82352101 2.93761074 0.00339459 0.00431925 0.00182638 Br 29.87897371 15.02783524 3.26405737 -0.00011518 0.00188718 0.00043335 Cs 29.95515581 11.99034865 6.28524329 -0.00345318 0.00040687 -0.00138931 Pb 32.73006179 14.61767656 8.67891173 0.00021958 0.00001163 -0.00008368 Br 33.18340482 14.44028522 5.86943249 -0.00090849 0.00039913 -0.00293676 Br 32.86119575 11.75401544 8.82880996 -0.00375769 0.00393512 -0.00284119 Br 29.84034902 14.37257390 8.94759324 -0.00345528 -0.00008711 -0.00180448 Cs 30.15030016 12.07709822 11.83920044 0.00052175 -0.00090295 0.00250354 Pb 32.54951398 14.88027436 14.76915254 -0.00065513 0.00049151 -0.00086684 Br 33.24421410 14.21950713 11.92473084 -0.00145426 -0.00186774 -0.00112175 Br 32.82211102 11.88190130 14.70158716 -0.00038002 0.00266816 0.00281135 Br 29.66002467 14.93851953 15.11384650 0.00257984 0.00030918 -0.00075417 Cs 29.57309154 12.44895239 17.86358691 -0.00253669 0.00064039 0.00131699 Pb 32.57298671 15.02536466 20.74786164 -0.00193826 -0.00074955 0.00032770 Br 32.91558264 15.08269977 17.68942784 0.00058433 -0.00141905 -0.00155611 Br 33.17642191 11.99268166 21.06727627 0.00020323 0.00140167 -0.00016223 Br 29.46607448 14.86265299 20.55807950 0.00136764 0.00196967 0.00153439 Cs 29.28458634 12.17032197 23.78936610 0.00171126 0.00094302 -0.00250718 Pb 32.62018135 14.81861550 26.73967618 -0.00008516 -0.00259182 -0.00133856 Br 32.39360153 14.58664066 23.68181822 -0.00369867 -0.00372003 0.00284538 Br 32.39226250 11.65361657 26.77878762 0.00131069 -0.00269199 -0.00035529 Br 29.52961636 14.46684413 27.03794564 0.00214375 -0.00206345 0.00135502 Cs 28.91269404 12.07102179 29.41399474 0.00272515 -0.00189546 -0.00144298 Pb 32.72401370 14.63284257 32.85061790 0.00012930 -0.00013470 0.00098558 Br 32.56480424 15.32277800 29.59678852 -0.00073009 0.00195041 0.00103654 Br 32.74312491 11.41552777 32.82221524 -0.00039842 -0.00066596 0.00136787 Br 29.59571048 14.51482009 32.24276180 0.00038738 0.00067039 -0.00077772 Cs 29.85908872 18.48041756 0.35406188 -0.00067801 -0.00049190 -0.00031703 Pb 32.62541211 20.98681470 2.84417520 -0.00021511 0.00195292 -0.00095790 Br 32.31945724 21.09297716 35.38331862 0.00358530 -0.00064834 -0.00062646 Br 32.78586065 18.04333943 2.29933018 0.00239397 0.00119105 -0.00097168 Br 29.76661530 20.89104306 3.18286481 -0.00247629 -0.00091481 0.00095694 Cs 29.40122716 18.29973699 5.85939457 0.00344426 -0.00039327 0.00165838 Pb 32.67058780 20.54514606 8.79438439 -0.00010772 0.00168327 0.00032243 Br 32.83087728 20.01684558 5.78036985 -0.00164457 0.00356075 -0.00105762 Br 32.18469427 17.75818683 9.47872192 -0.00169865 0.00326248 0.00349802 Br 29.72304032 21.32510752 8.59759324 0.00231303 0.00102601 0.00156346 Cs 29.09573107 18.04495607 11.84274416 0.00026612 0.00038176 0.00022905 Pb 32.32141382 20.65478059 14.58598728 0.00078762 0.00179633 -0.00139751 Br 32.46051123 21.38660386 11.71835402 -0.00087153 0.00359697 -0.00174726 Br 31.99558575 17.57144773 13.96984283 0.00014007 -0.00070223 0.00020191 Br 29.52544650 21.12737413 14.74257964 -0.00207069 -0.00099509 0.00324930 Cs 29.96983421 18.00939714 17.29185579 -0.00060533 0.00091398 -0.00164682 Pb 32.78400457 20.75898659 20.64436238 0.00006485 -0.00080665 0.00006580 Br 33.32739354 20.09450334 17.33796121 -0.00141119 0.00227155 -0.00173688 Br 32.05751076 17.76596142 21.09298810 0.00024912 0.00159770 0.00031257 Br 29.72177535 20.95147079 20.52024865 -0.00179597 -0.00133765 -0.00350538 Cs 29.57461207 17.31485268 23.86085881 -0.00123607 -0.00260983 -0.00010168 Pb 32.81016141 20.83086342 26.35120766 0.00254961 -0.00163005 0.00003594 Br 32.12669952 21.25484963 23.40683858 0.00252167 -0.00088846 -0.00014021 Br 32.49116236 17.87111797 26.35799587 -0.00048764 0.00008456 0.00017776 Br 29.85496701 20.74098663 26.97050178 -0.00174651 -0.00097969 0.00019484 Cs 29.65023383 17.51241038 29.46814580 0.00044451 0.00262427 -0.00099849 Pb 32.74039042 20.70922558 32.27683243 0.00004831 0.00031910 0.00004317 Br 33.46025058 20.61428765 29.36794059 -0.00272994 -0.00305378 -0.00306562 Br 33.07956040 17.61338655 32.92480376 0.00111690 0.00187407 0.00190781 Br 29.72882276 20.75468931 32.06032898 0.00030323 0.00473117 -0.00208309 Cs 29.88956036 23.93501044 0.47855875 -0.00027141 -0.00493595 -0.00125426 Pb 32.59365183 26.70163774 3.28849610 -0.00355517 0.00294341 -0.00002550 Br 32.75706729 26.19754524 0.19250520 -0.00114518 0.00034490 -0.00131582 Br 32.72111702 23.89499001 4.00086516 0.00324591 -0.00126754 -0.00374775 Br 29.34647753 26.41817744 2.99470871 -0.00493662 0.00085285 0.00184164 Cs 29.53541742 24.08803189 6.02839767 -0.00119220 -0.00132053 0.00227969 Pb 32.68374262 26.36864363 9.09864770 -0.00022037 0.00051238 -0.00111240 Br 32.04293942 26.94954405 6.02237720 0.00029699 0.00136937 -0.00154509 Br 33.33829649 23.56976056 8.79339224 0.00050187 -0.00093068 0.00238493 Br 29.55106221 26.77037329 9.14293124 -0.00021609 -0.00083602 0.00173321 Cs 29.77758070 23.73973044 11.96819472 0.00145439 -0.00146870 0.00029487 Pb 32.27773581 26.44561640 14.98116279 -0.00068512 0.00029537 -0.00006711 Br 32.67346032 25.91648531 12.03071662 -0.00069079 0.00091827 0.00172136 Br 32.56387802 23.68854742 15.23386379 -0.00087753 0.00146608 0.00196374 Br 29.26153880 25.79405738 14.54610466 0.00383091 0.00089204 0.00156758 Cs 29.50896069 23.33841900 17.45590327 0.00026481 -0.00117926 0.00163093 Pb 32.29612733 27.00860560 20.74176390 -0.00267620 0.00075192 -0.00024459 Br 31.80838107 26.83018104 17.80352453 0.00387828 -0.00231001 0.00151165 Br 32.83378142 23.73889587 20.39404848 -0.00140412 -0.00131177 -0.00014754 Br 29.51643414 26.95773175 21.42893267 0.00173151 0.00174338 -0.00152956 Cs 29.49622679 23.60508153 23.01524451 -0.00152934 -0.00183713 0.00569129 Pb 32.28968459 26.96442826 26.91129017 0.00157942 -0.00057406 -0.00023332 Br 32.48551889 26.95167731 23.83495945 0.00122656 0.00239597 -0.00018767 Br 32.42845366 23.68285911 26.60629492 -0.00258940 0.00133949 0.00008467 Br 29.52877180 26.60081976 26.68576447 -0.00255540 -0.00001996 0.00000255 Cs 29.16432640 23.64829098 29.73734111 0.00167947 -0.00045112 -0.00100283 Pb 32.48612436 26.66069941 32.74940870 -0.00188353 -0.00057397 0.00052477 Br 32.83757994 26.72900562 29.83853840 -0.00173170 0.00245206 0.00460039 Br 32.50924054 23.69862479 31.78542628 0.00115374 -0.00069674 0.00254208 Br 29.63724248 26.35912261 32.47791143 -0.00036047 -0.00159428 -0.00407183 Cs 29.91626501 29.38924895 0.56800373 -0.00183804 0.00012497 -0.00030280 Pb 32.57865231 32.95970818 2.99267127 0.00075033 -0.00148644 -0.00150558 Br 32.80471199 33.05487833 0.00007672 0.00006672 -0.00286861 -0.00098223 Br 32.69782114 29.93955942 3.06190215 0.00074079 -0.00030080 0.00253934 Br 29.87936945 33.08199330 3.50666530 0.00015584 0.00090910 0.00222972 Cs 29.76013865 30.02408674 5.66281476 0.00118908 -0.00125751 -0.00055473 Pb 32.37193255 32.40675845 8.93582225 0.00057229 -0.00012206 0.00142503 Br 32.69679702 32.56827917 5.98848337 0.00090736 -0.00278560 -0.00085095 Br 32.49450507 29.42450814 8.74383947 0.00054251 -0.00124765 0.00162260 Br 29.66697149 32.43861470 8.38362839 0.00153304 0.00056476 -0.00587513 Cs 29.57305657 29.43954220 12.02703977 0.00024869 0.00030173 0.00045722 Pb 32.57126045 32.35409124 15.08841595 0.00045571 -0.00073277 0.00207914 Br 31.70301233 32.26783852 12.10633105 0.00469284 0.00188282 -0.00252605 Br 32.49044905 29.39195099 14.76533187 0.00185770 -0.00038131 0.00088998 Br 29.69224365 32.77602674 15.52923531 -0.00001416 -0.00215383 -0.00021797 Cs 29.20632467 29.18569856 17.55049827 0.00120937 0.00016464 0.00071976 Pb 32.54690601 32.71207633 20.73749860 -0.00079267 0.00121684 -0.00180207 Br 33.38867836 32.39799524 18.00389374 -0.00086965 -0.00018725 -0.00164134 Br 32.75949109 29.79856620 21.45034299 0.00365626 -0.00025569 -0.00187345 Br 29.69524376 32.34209074 19.82715497 -0.00009069 0.00166380 -0.00200002 Cs 29.63475172 29.83071856 23.50773869 0.00071191 -0.00506934 -0.00081733 Pb 32.62977782 32.43290929 26.79461387 0.00187565 -0.00019003 -0.00072349 Br 32.09545022 32.75085892 23.97778514 0.00005763 0.00010538 0.00071838 Br 31.77295908 29.70087267 26.94229529 0.00031657 0.00021360 0.00143689 Br 29.66610573 32.50039596 27.20390302 -0.00281148 0.00098630 0.00194927 Cs 30.03356245 29.50429254 30.15455611 0.00114097 0.00176172 -0.00300071 Pb 32.66645482 32.19154470 32.75880792 0.00355044 -0.00015782 -0.00210809 Br 32.75273294 31.92841181 29.90053274 -0.00346754 0.00232152 -0.00197233 Br 32.56582231 29.47522001 33.67421680 0.00118238 0.00047557 -0.00121286 Br 30.05549280 33.09844026 32.69190633 0.00281000 -0.00117289 0.00058552 1080 Lattice="35.60125893 0.0 0.0 0.0 35.60125893 0.0 0.0 0.0 35.60125893" Properties=species:S:1:pos:R:3:vel:R:3 Time=2000.0 energy=-38569.24448013 virial="-22.42128116 -4.78426305 8.40518306 -4.78426305 -37.15916691 -2.04318393 8.40518306 -2.04318393 -29.83193797" stress="0.00032812 -0.00010603 0.00018627 -0.00010603 1.5e-06 -4.528e-05 0.00018627 -4.528e-05 0.00016389" pbc="T T T" Cs 0.38499154 35.02298845 35.39305128 -0.00074392 0.00002293 0.00205198 Pb 3.05294971 3.01572045 3.03885601 0.00059842 0.00116231 -0.00003432 Br 3.60258272 2.57059817 0.01144749 0.00051396 -0.00120516 0.00103015 Br 3.35554067 35.55997924 3.21593114 0.00430283 0.00087015 -0.00084383 Br 35.37470088 3.26528756 2.98039111 0.00112292 0.00146524 -0.00073160 Cs 35.35909864 0.25844575 5.88722593 0.00245491 0.00097609 0.00178400 Pb 2.90721343 2.87210379 8.87284622 0.00024719 0.00035310 0.00057694 Br 3.14826408 2.55643654 5.97205008 0.00116985 0.00218933 0.00092454 Br 2.82389866 35.58911810 8.96471738 0.00009927 0.00079498 0.00061126 Br 35.47399587 2.90716071 9.37242979 -0.00197287 -0.00016509 -0.00218873 Cs 0.09655739 0.31795244 11.93636620 -0.00179573 0.00055957 0.00289319 Pb 2.92340629 2.72469323 15.00009529 -0.00012550 -0.00044703 0.00013794 Br 3.10393357 2.62274830 12.14985737 0.00545604 0.00053315 -0.00080807 Br 3.02597779 0.03242001 14.93320773 -0.00103906 0.00181916 0.00372826 Br 0.03102358 2.96593331 14.99975438 0.00237214 0.00129949 0.00097488 Cs 0.42068894 35.23546364 17.37025243 -0.00024341 0.00012898 -0.00089759 Pb 3.10170183 2.81119842 20.91346014 0.00032564 0.00050181 0.00021777 Br 2.79435435 3.07534193 17.78369379 -0.00089094 -0.00184958 -0.00165771 Br 3.24601727 35.46274607 20.16100475 -0.00023100 -0.00171434 -0.00120671 Br 35.51336581 3.16953061 21.00095353 0.00497878 -0.00330955 -0.00339207 Cs 35.31910689 0.27848865 23.46087013 -0.00199455 -0.00074595 0.00024431 Pb 2.75448786 2.89753066 26.77329176 0.00077753 -0.00116532 0.00276987 Br 2.97626248 1.99601930 23.60485022 0.00052288 -0.00060398 -0.00355026 Br 2.47131880 0.00455632 26.90356986 -0.00240556 -0.00194447 -0.00050046 Br 35.48400491 2.93761959 26.54042992 -0.00017570 0.00158565 -0.00091291 Cs 35.58797496 0.21658186 30.06889113 -0.00012940 0.00199648 0.00093227 Pb 3.08923162 2.88111353 32.85724390 0.00109400 -0.00041669 0.00027256 Br 2.23617256 3.01205831 29.95094635 0.00018351 -0.00124267 -0.00184988 Br 3.01355181 35.15005538 32.83771082 0.00204252 0.00001030 0.00113130 Br 35.55815919 3.26480317 33.70065898 -0.00015346 0.00111303 0.00124686 Cs 35.26602569 6.09614737 0.53161642 -0.00015747 0.00399508 -0.00172857 Pb 3.07038549 8.92170091 3.04642070 0.00083776 0.00078161 0.00051062 Br 3.36265001 8.58056791 35.38483550 0.00011134 0.00073217 -0.00010102 Br 3.85020153 5.91694985 2.85845593 -0.00237851 -0.00086485 0.00030018 Br 0.03350559 9.20943718 2.39293692 0.00137458 0.00190824 0.00209844 Cs 35.49676994 5.51286036 6.33775019 0.00249769 -0.00024386 -0.00052747 Pb 2.93082904 8.73014015 9.14829299 -0.00037120 0.00087181 0.00030925 Br 2.92715592 9.24518544 5.90291876 0.00090552 -0.00091915 -0.00055843 Br 2.83457624 5.77965048 8.93322825 0.00067717 0.00188419 -0.00163293 Br 0.09144664 8.35694342 8.84837291 0.00366963 0.00150784 0.00126560 Cs 35.12435122 5.54558973 12.20125290 0.00019269 0.00034377 -0.00073735 Pb 3.01652366 8.75812682 14.92126745 -0.00108362 -0.00062311 -0.00019418 Br 3.23212069 8.99176012 11.93709686 -0.00185172 -0.00389234 -0.00057568 Br 3.19787414 5.77824824 14.73457990 -0.00076702 0.00186677 0.00452609 Br 0.05017727 9.01193929 14.32418276 -0.00193230 -0.00040722 -0.00034009 Cs 35.50662166 6.11602880 17.82295008 -0.00103107 -0.00011832 0.00230446 Pb 3.05532489 8.89437618 20.87492357 -0.00019497 0.00076713 -0.00106457 Br 2.98167762 8.55617202 17.93215720 -0.00059451 -0.00177945 -0.00042019 Br 3.13637702 5.87006632 21.59858830 0.00101682 0.00062998 0.00025036 Br 0.05994477 8.70388068 20.89144584 0.00090724 -0.00296759 -0.00082463 Cs 0.42094823 6.37524830 24.35984630 -0.00032223 0.00122460 0.00185796 Pb 3.21986389 8.69251517 26.63815312 0.00172270 -0.00172429 0.00103637 Br 3.64400834 9.04782212 23.61190849 0.00135007 0.00132926 -0.00283081 Br 3.02119691 5.72675908 26.79164416 0.00559312 0.00067053 -0.00519442 Br 0.44127874 9.50705620 26.63812561 -0.00170157 0.00041803 0.00005528 Cs 0.06207688 6.06951744 30.15260185 -0.00266190 -0.00039406 0.00067196 Pb 2.85369816 8.86251895 32.63515199 0.00083315 -0.00020659 -0.00013313 Br 3.03017002 8.34769942 29.66905451 -0.00168215 0.00019912 -0.00053007 Br 2.75806032 5.89002850 32.83943007 -0.00003565 -0.00244814 0.00259949 Br 35.46400761 9.77204156 33.36946470 0.00090535 -0.00020126 0.00129697 Cs 0.04393666 12.00127042 0.47912938 -0.00119943 0.00083172 -0.00052510 Pb 2.94651434 14.78213318 3.09996180 -0.00100154 -0.00120230 -0.00152702 Br 2.83641143 14.27611508 35.54362832 0.00333361 0.00161863 -0.00184892 Br 2.71370865 11.91339337 3.05997022 -0.00202058 -0.00071615 0.00179468 Br 35.53093380 14.65142440 2.81713013 0.00150557 0.00240333 0.00283791 Cs 35.33233233 11.92023030 5.41133519 -0.00042074 0.00157289 0.00031981 Pb 2.73555910 14.76991081 8.80945173 -0.00114340 0.00043412 0.00226603 Br 2.71831521 14.49199413 5.90442170 0.00015424 0.00028338 -0.00167346 Br 3.16211613 11.80736837 9.43230062 0.00120269 0.00135879 0.00252145 Br 35.45900137 14.55392668 9.38491564 -0.00069848 -0.00071273 -0.00442846 Cs 35.42236157 11.49266238 11.51251222 0.00111541 0.00084769 0.00098813 Pb 2.96631969 14.63103566 14.79708625 -0.00141215 -0.00069944 0.00131220 Br 3.40312664 14.51909373 11.98092689 0.00104814 0.00032324 0.00524295 Br 3.14331012 11.75420805 15.25685120 -0.00251121 0.00198116 -0.00186889 Br 35.56142506 14.78316820 14.90367371 0.00418094 -0.00291707 0.00022999 Cs 0.01546307 11.95475882 17.78123335 0.00039016 -0.00137848 -0.00057497 Pb 2.84298331 14.71663088 20.63263494 0.00043983 -0.00068221 -0.00072804 Br 2.38698691 14.65753976 17.82178360 -0.00206176 0.00182192 0.00097276 Br 2.65959953 11.76532908 20.23053042 0.00381798 -0.00052122 -0.00379098 Br 35.47932814 15.09467634 21.15797723 0.00089878 0.00119758 0.00055608 Cs 35.28167934 11.92945342 23.79827467 -0.00032940 0.00157148 0.00071992 Pb 3.06575380 14.50889932 26.85683637 0.00187955 0.00099790 -0.00067079 Br 3.01897655 14.31131273 23.66437699 0.00175272 -0.00050907 -0.00268108 Br 3.63046717 11.60403994 27.41814846 0.00102713 -0.00009667 0.00105105 Br 0.07854323 14.39369662 26.59698703 -0.00067345 0.00197617 0.00313763 Cs 0.12554205 11.76546623 29.57442879 0.00015726 0.00180800 0.00178328 Pb 2.89153129 14.51556659 32.78061108 0.00067633 0.00377693 -0.00158123 Br 2.71394754 15.11366691 29.81541379 0.00615780 -0.00173271 -0.00030911 Br 3.49851423 11.72610316 32.17153220 -0.00310447 0.00030857 0.00186550 Br -0.00105689 14.33309013 32.20016306 -0.00237201 -0.00209378 -0.00067183 Cs 0.11023725 18.43547637 0.39824534 0.00021815 -0.00133934 0.00057398 Pb 2.84809243 20.92446659 2.90542351 -0.00139814 0.00017049 -0.00009208 Br 2.99263160 21.13541215 0.19712608 -0.00291244 0.00334100 -0.00275028 Br 2.80123569 17.67627505 2.54027474 0.00049657 0.00176160 0.00053693 Br 35.43531622 21.67237543 2.84459720 0.00101690 -0.00175238 0.00335066 Cs 0.12943601 17.74284326 5.40299017 -0.00107471 0.00294178 0.00080978 Pb 2.65920016 20.49024471 8.78643668 -0.00181433 -0.00088559 -0.00115621 Br 3.28610152 19.92577610 5.69674446 -0.00298626 0.00130877 -0.00014562 Br 2.26689006 17.64206391 8.47801581 -0.00399082 -0.00016241 -0.00064704 Br 35.43413796 20.32494340 9.57002084 0.00078440 0.00152099 0.00301336 Cs 0.26431180 17.46010489 12.06295455 0.00126683 0.00148984 0.00368662 Pb 2.85329658 20.72926128 14.70233904 -0.00069414 0.00229629 0.00109230 Br 3.15305359 20.77214942 11.83400491 0.00104179 -0.00390038 -0.00242290 Br 2.44379793 17.57175038 14.95199087 -0.00103017 -0.00345586 0.00163189 Br 0.06578175 20.75324780 14.38168450 -0.00148576 0.00104509 0.00059703 Cs 35.32858939 17.59033838 18.32290625 0.00063491 -0.00086590 0.00064304 Pb 3.02259477 20.65921096 20.80318225 -0.00175278 0.00081165 -0.00035931 Br 3.05826305 20.41972050 17.59074705 -0.00147645 -0.00004735 -0.00101630 Br 2.64619405 17.78900971 20.41943079 -0.00072731 0.00143488 0.00048381 Br 35.37862827 20.87596416 20.74310619 0.00070446 -0.00141883 0.00251161 Cs 35.35079788 17.12784538 24.21613106 0.00001002 -0.00197145 -0.00016795 Pb 2.82375238 20.40602870 26.41126918 -0.00118807 -0.00103025 0.00141868 Br 3.26753212 19.97640331 23.41035628 -0.00225373 -0.00088852 -0.00111727 Br 2.50114729 17.49378281 26.93763679 -0.00191787 -0.00394182 -0.00153250 Br 35.23544331 20.67934122 25.51535005 0.00066094 0.00047732 0.00056632 Cs 0.32175696 17.68184220 30.05313697 0.00173622 0.00034185 0.00141005 Pb 3.18783329 20.63926544 32.76232897 -0.00074500 -0.00050327 -0.00255341 Br 3.23665300 20.56929581 29.84421932 0.00053341 0.00095363 0.00299069 Br 2.82905187 17.95035601 33.08987896 -0.00328584 0.00002087 0.00149139 Br 35.55519388 20.83745046 32.27993422 0.00069807 0.00185850 -0.00354171 Cs 0.06757186 23.46565834 34.64124773 -0.00120203 0.00051543 -0.00095362 Pb 3.15891632 26.58240782 2.90672662 0.00055980 -0.00060975 -0.00027108 Br 2.58792433 26.74105960 0.01520300 0.00215132 -0.00049135 0.00042097 Br 2.79735217 23.65840963 3.91443942 -0.00002339 -0.00054452 0.00279469 Br 35.37786093 26.75576692 2.86961104 -0.00400073 -0.00183913 0.00424912 Cs 35.23828636 24.52973859 5.79400974 0.00130786 0.00125992 -0.00228572 Pb 3.06964468 26.77364820 8.67010379 -0.00035337 -0.00016257 0.00062501 Br 2.50558675 27.04527256 5.84212607 -0.00128056 0.00386660 -0.00108318 Br 2.73956341 23.75933402 8.56763219 0.00032515 -0.00352319 -0.00227460 Br 35.58458020 27.09968078 8.39053567 0.00183993 0.00155030 -0.00102974 Cs 0.22621252 23.32922861 11.73729593 0.00121735 0.00043223 -0.00102947 Pb 2.78371889 26.83899277 14.46949616 0.00209622 -0.00025155 0.00012723 Br 3.84195313 26.74379537 11.61418993 0.00281676 0.00038932 -0.00063714 Br 2.44897903 23.83371404 14.57597703 -0.00081786 0.00020016 -0.00041721 Br 35.28543554 26.96756423 14.74975042 0.00263141 -0.00167823 0.00251239 Cs 0.22402347 23.19444343 17.93910811 0.00113554 0.00063660 -0.00076467 Pb 2.78873769 26.79514217 20.65422218 0.00226104 0.00073839 -0.00063730 Br 2.75412370 26.25310243 17.55552534 -0.00030241 0.00023527 -0.00368576 Br 2.78387417 23.87814563 20.59950421 -0.00216342 -0.00223952 -0.00074078 Br 35.23798541 26.32120529 20.43489538 -0.00158646 -0.00113530 0.00143877 Cs 35.52519698 23.59572153 23.38118745 -0.00009465 0.00115430 0.00246093 Pb 2.61310247 26.95859759 26.68755270 0.00121444 -0.00213085 -0.00054649 Br 2.25477661 26.57869939 23.76041325 -0.00239382 0.00035348 -0.00049603 Br 2.64845489 23.32952498 26.62938445 0.00181685 0.00054414 -0.00258926 Br 35.52001706 27.14330740 27.36123864 -0.00157013 -0.00155806 -0.00080964 Cs 0.01397243 23.85403234 29.44668684 0.00029888 -0.00050518 0.00094173 Pb 2.80811609 26.66898778 32.47629711 0.00025415 -0.00081688 -0.00073978 Br 2.92438120 26.18683498 29.57827984 0.00071074 -0.00051375 0.00440306 Br 3.41917209 23.85349962 33.00404645 -0.00109867 -0.00030402 -0.00030975 Br 35.52239126 27.08073212 32.63476078 -0.00193586 0.00261245 -0.00108958 Cs 35.36486265 29.49962207 0.09307820 -0.00144345 0.00319956 -0.00056834 Pb 3.14883649 32.54241036 2.88387246 -0.00063891 0.00043123 0.00189007 Br 3.47326756 32.37127178 35.48495360 -0.00161957 -0.00241963 -0.00421273 Br 3.07380410 29.59603079 2.84871526 -0.00401830 -0.00078101 -0.00232012 Br 0.19490702 33.01779107 2.72811235 -0.00320432 0.00425970 -0.00189621 Cs 35.17058306 29.34332556 5.78873924 0.00147321 -0.00156473 0.00065053 Pb 2.90829127 32.59529749 8.80345495 0.00167419 0.00034081 -0.00131129 Br 3.28787622 32.42968705 5.88142223 0.00007299 0.00278110 -0.00349685 Br 3.13199748 29.68037502 8.81672891 -0.00099600 0.00104464 -0.00441628 Br 0.02610553 32.77497559 9.06167586 0.00081527 0.00013644 -0.00129289 Cs 35.24863281 29.63182962 12.06262694 -0.00045278 0.00201981 -0.00020801 Pb 2.69450118 32.75112823 14.92941187 -0.00009356 0.00032967 -0.00293276 Br 2.83080064 33.21442819 11.94427844 -0.00021587 0.00012353 -0.00324158 Br 2.76840137 29.95170630 14.18854290 0.00007954 -0.00220325 -0.00245367 Br 35.47140273 33.54947186 14.51710278 0.00183232 -0.00013311 -0.00206380 Cs 35.50042295 29.15391991 18.01427938 0.00050046 -0.00126994 -0.00127384 Pb 2.98437571 32.56703611 20.86956455 -0.00157922 -0.00104724 0.00094250 Br 2.75338527 32.27348320 17.85791439 -0.00056568 0.00259191 -0.00037739 Br 2.25220451 29.71804945 20.67268132 0.00202026 0.00004627 0.00117177 Br 35.54213668 33.04923358 20.68443612 0.00185622 -0.00020818 -0.00170892 Cs 0.18967787 29.90686072 23.99216817 0.00070777 -0.00104491 -0.00017692 Pb 2.98531100 32.70884871 26.89533620 0.00013408 -0.00002760 -0.00123329 Br 3.25070506 32.52225217 23.85868106 0.00138339 -0.00339584 0.00104600 Br 2.92880214 29.70382315 26.78571779 -0.00068889 -0.00256703 0.00176586 Br 35.58286139 32.47294191 26.49772999 0.00044243 0.00075719 -0.00134688 Cs 0.01728626 29.76609881 29.70415295 -0.00007845 -0.00028259 -0.00066789 Pb 3.23570943 32.30014398 32.56837936 -0.00158414 0.00090246 -0.00036956 Br 2.41257341 32.83575323 29.69628807 -0.00447088 0.00461929 -0.00313797 Br 2.81554810 29.52710314 32.06239208 0.00057483 -0.00233823 -0.00018680 Br 0.19357725 32.26721495 33.02183299 -0.00106822 0.00383657 0.00060924 Cs 6.22405740 35.24965597 0.26670219 0.00121688 0.00146790 -0.00110068 Pb 8.85337217 3.10763123 3.11907053 0.00068067 -0.00126281 -0.00003521 Br 8.23896787 3.48314875 35.51713933 -0.00042160 0.00054853 0.00396067 Br 9.48792886 0.38518287 2.45836418 -0.00032813 0.00149430 -0.00019876 Br 5.91905680 2.41982557 3.47698088 0.00010934 0.00444290 -0.00359834 Cs 5.66469832 35.27334848 5.87230209 -0.00212894 0.00331928 0.00280850 Pb 8.99971300 2.89899048 9.13082890 0.00030497 0.00153832 0.00058028 Br 8.94539737 2.96060236 6.10604043 -0.00225655 -0.00090511 -0.00078680 Br 9.88660561 35.54772269 9.61868501 0.00051902 -0.00026427 0.00222671 Br 5.90982975 2.74417661 9.30281746 0.00085331 -0.00142106 -0.00437330 Cs 5.68227648 35.54358918 12.25639918 -0.00042578 -0.00331153 0.00248757 Pb 8.76114731 3.39100639 14.69433844 -0.00054675 -0.00022922 0.00062050 Br 8.82411316 3.82370844 11.91699130 -0.00385258 0.00063835 -0.00324687 Br 8.92449922 0.16605248 14.07759430 0.00435843 -0.00080355 -0.00167295 Br 5.98876444 2.74744354 14.60860432 -0.00090333 0.00097823 -0.00060329 Cs 6.03472075 0.39109893 17.61536570 -0.00091829 -0.00279891 0.00028221 Pb 9.09441060 3.08755697 20.87447134 -0.00033040 -0.00119027 -0.00013723 Br 9.31970641 2.81760825 17.77034929 0.00458942 -0.00086360 0.00069601 Br 9.07007887 0.09992444 20.84870987 0.00469399 -0.00131656 -0.00068894 Br 6.20043570 2.98516264 20.60548312 0.00267580 0.00033212 -0.00147609 Cs 5.99931260 0.00657749 23.75046527 0.00301416 -0.00119696 0.00151083 Pb 8.82930607 3.15180809 26.77407363 -0.00188594 0.00164389 -0.00019077 Br 9.09804350 2.95841599 23.72087073 0.00306167 -0.00132567 0.00003039 Br 7.92009612 35.40433598 26.45585459 -0.00068518 0.00140628 -0.00314322 Br 5.90341252 3.15212087 26.81989995 -0.00023599 -0.00301707 -0.00255389 Cs 6.08632636 0.23543791 29.33330495 0.00122577 0.00211402 -0.00103019 Pb 9.01367714 3.04404812 32.78271504 -0.00100964 -0.00175762 0.00039298 Br 8.74337577 2.62213468 29.58713998 -0.00275621 0.00376299 0.00156639 Br 8.90307060 0.22400440 33.07624312 -0.00297679 -0.00002069 -0.00328859 Br 5.87280988 3.38584454 32.30004167 0.00036226 0.00109413 0.00352253 Cs 6.11767930 5.89855398 0.18484764 0.00020266 -0.00161999 0.00048546 Pb 8.69468530 9.13497613 2.96223264 0.00178946 0.00000750 -0.00052725 Br 8.65412955 8.79552697 35.58157735 0.00014725 -0.00110114 0.00242016 Br 8.80970947 5.93960373 3.13734560 -0.00071432 0.00142439 -0.00172485 Br 5.83298275 9.68536984 2.75275674 0.00003056 -0.00113428 -0.00245512 Cs 5.80298792 5.69121769 6.02500029 0.00098018 0.00047949 -0.00096150 Pb 9.00513126 8.87614512 9.09889533 -0.00044028 0.00001431 0.00174859 Br 8.81412181 9.41237039 5.92375385 -0.00388829 0.00056799 -0.00031541 Br 9.11157138 5.99871730 8.86414649 -0.00001810 0.00050935 0.00318796 Br 6.05972575 8.58485758 8.70569009 0.00067014 0.00043236 0.00188846 Cs 5.56374122 5.31786689 11.90946179 -0.00265988 0.00182231 -0.00142892 Pb 8.93545728 8.92926735 14.90427276 0.00183446 0.00233491 -0.00143124 Br 8.61542610 8.61265826 12.06695405 0.00143047 0.00315755 -0.00056367 Br 9.18424494 5.95282352 15.83846969 0.00434441 -0.00016292 -0.00136912 Br 5.82658587 8.68355044 14.83400845 -0.00085463 0.00323950 0.00221945 Cs 5.80850013 5.52106265 17.74090485 0.00083579 -0.00069565 0.00018153 Pb 8.94251454 8.72914259 20.76586596 -0.00068412 -0.00229765 -0.00152005 Br 9.24594250 8.91307497 17.92534088 0.00174812 0.00202181 0.00027704 Br 8.10581137 6.09010487 20.48878572 0.00142526 0.00195727 -0.00178906 Br 6.07487598 9.44473401 20.69056190 0.00059426 -0.00261702 -0.00063096 Cs 5.73691639 5.74004194 24.06091889 0.00206839 0.00289959 -0.00099995 Pb 8.84894199 8.76111232 26.69215816 -0.00111287 -0.00048321 0.00106593 Br 8.88773007 8.13808892 23.73348114 -0.00396707 -0.00150790 -0.00248548 Br 9.56504612 5.99069356 26.95130500 0.00238146 -0.00089918 -0.00158573 Br 6.21304327 8.10617110 26.89659949 -0.00165195 0.00002759 -0.00135130 Cs 5.89112795 5.74669209 29.93621023 -0.00183981 -0.00076249 -0.00042365 Pb 8.62690497 8.94803652 32.65456328 -0.00007011 -0.00053708 -0.00105266 Br 9.31482321 9.26317457 29.75194262 -0.00249450 -0.00182626 -0.00076919 Br 9.13680902 6.04860745 32.47284118 -0.00108218 -0.00562389 -0.00290252 Br 5.68107217 8.79384679 32.08577500 -0.00026172 0.00281059 -0.00083373 Cs 5.55648080 11.97701635 35.30638624 0.00046063 -0.00213771 -0.00001906 Pb 9.00050854 14.72537389 2.81782784 0.00035687 -0.00227074 -0.00119641 Br 8.28338716 15.44183172 0.01492233 -0.00018534 -0.00281336 -0.00055387 Br 9.13090296 12.14094006 1.86973554 0.00265655 0.00233722 -0.00073929 Br 5.85217253 14.51025800 2.83283081 0.00128846 -0.00275407 0.00145750 Cs 5.51582579 11.69437289 6.05760345 -0.00233944 -0.00175849 -0.00216758 Pb 8.92434783 14.87070491 8.73394871 -0.00091058 0.00045497 0.00194023 Br 8.95438247 13.77861732 5.67815864 0.00245885 -0.00026722 -0.00054774 Br 9.47357520 12.08331750 8.99280606 0.00132271 -0.00060728 0.00101271 Br 5.86703589 15.18709139 8.59690682 -0.00126777 -0.00033447 0.00266349 Cs 6.16622456 11.32071405 12.38558530 -0.00127953 -0.00074104 0.00174143 Pb 8.87035187 14.91711284 14.65754145 0.00231154 -0.00085934 0.00018302 Br 8.42451388 14.56118530 11.87871103 0.00127048 0.00032757 0.00065503 Br 8.52803077 11.76313875 14.85235776 -0.00093922 -0.00148138 0.00187050 Br 5.89792150 15.16602137 15.28153997 0.00022730 -0.00078644 -0.00086135 Cs 5.90789940 12.46948224 17.69782556 0.00083067 -0.00134392 -0.00241049 Pb 9.17038832 15.03966279 20.81326704 0.00014225 0.00210192 -0.00023333 Br 9.47019724 14.71802198 17.72520311 0.00142705 0.00026147 -0.00073204 Br 9.06915703 11.95338637 20.87691972 -0.00616343 0.00046681 0.00061422 Br 5.69437835 14.93828112 20.53702343 0.00102833 -0.00415824 -0.00049220 Cs 6.13261177 11.82387879 23.14169687 0.00184122 0.00018635 0.00113530 Pb 8.76956882 14.98636828 26.86761245 -0.00178312 0.00295822 -0.00066520 Br 8.58521326 15.35807272 23.78458655 0.00142984 -0.00181975 -0.00137280 Br 8.29807529 11.84765744 26.10940449 -0.00232289 -0.00578467 0.00016514 Br 5.83110416 15.03081608 27.66710325 0.00391661 0.00207555 0.00210982 Cs 6.11043730 11.62708326 29.61702876 0.00047380 0.00202588 -0.00089513 Pb 8.52442830 14.86291501 32.63349109 0.00087216 -0.00042537 -0.00005613 Br 9.40788093 14.98382905 29.76392835 -0.00016978 -0.00079358 0.00075205 Br 8.18369848 11.89613471 32.63273098 0.00271604 0.00080719 0.00444148 Br 5.79748886 14.84814260 31.87342775 0.00404545 -0.00302307 -0.00087419 Cs 5.86778706 18.02463714 0.59217852 -0.00037354 0.00188607 0.00026126 Pb 8.88038810 20.91438234 3.00903603 0.00066994 0.00252471 -0.00102059 Br 9.22457508 21.02089142 0.08760151 0.00246715 0.00076654 -0.00292007 Br 8.83969028 17.86341301 3.20717888 -0.00150876 0.00163670 -0.00105972 Br 5.93836720 20.80606400 2.98491146 -0.00006056 -0.00016248 0.00209163 Cs 5.91073216 17.51229741 5.70876962 -0.00145260 -0.00035712 0.00063490 Pb 8.71215742 20.65428637 8.86284456 -0.00001536 -0.00184955 0.00194836 Br 8.71299400 20.91445016 5.97232582 0.00122898 -0.00040428 0.00229769 Br 9.35137417 17.73128676 8.76343549 0.00136219 0.00007880 -0.00196246 Br 5.95302940 20.29346294 8.68195111 -0.00585628 0.00216032 0.00056404 Cs 6.01195040 17.69403044 12.54018228 -0.00032976 -0.00276597 -0.00104310 Pb 8.93427256 20.76871132 14.65446387 -0.00147519 -0.00031145 0.00015972 Br 9.41796065 21.08514109 11.88667355 -0.00302863 -0.00205985 0.00141878 Br 9.00883193 17.88811128 14.57938954 0.00298797 -0.00000080 0.00101910 Br 6.07412685 20.66160520 14.76791995 -0.00059903 0.00625922 -0.00247702 Cs 5.95407070 17.72251529 17.82402781 0.00141095 -0.00232490 -0.00002231 Pb 8.74663232 20.67040651 20.73550963 0.00257627 0.00150320 -0.00059051 Br 9.06258938 20.70940087 17.70984642 0.00140178 -0.00236533 -0.00066578 Br 8.79317036 17.78718402 20.31024195 -0.00098408 -0.00087186 0.00061556 Br 5.92321617 20.23345864 20.78641706 -0.00149199 0.00056288 -0.00064439 Cs 5.45357124 17.12931113 23.59254817 0.00130263 0.00251405 0.00090244 Pb 8.68060099 20.80554074 26.63710259 0.00013112 0.00001023 -0.00167559 Br 9.12082921 20.03464303 23.67342945 0.00548735 -0.00218767 -0.00012656 Br 8.74762818 17.88511992 27.32992759 -0.00271331 0.00012480 0.00082961 Br 5.63574251 21.01738922 26.73292459 -0.00244619 -0.00320408 0.00150899 Cs 5.61170270 17.80872899 30.49971287 -0.00021300 -0.00017338 0.00186353 Pb 9.07756983 20.96995212 32.45435673 -0.00024475 0.00001840 0.00127908 Br 8.71700210 21.41663382 29.65396102 0.00055155 -0.00392466 -0.00016128 Br 8.72258671 18.01177074 32.56101939 0.00123565 0.00083869 0.00107085 Br 6.26289711 21.06698136 33.10044356 0.00114992 0.00169559 0.00183331 Cs 6.15192572 24.15283213 34.91323241 -0.00204197 -0.00059030 0.00050153 Pb 9.00117485 26.84535666 2.84418534 0.00049661 0.00291758 -0.00177981 Br 8.89688451 26.96680753 35.38548489 -0.00531284 -0.00223847 -0.00270762 Br 9.38865306 23.91547558 2.54127058 0.00275046 0.00000751 -0.00028341 Br 6.05565078 26.53477250 2.66602828 0.00237531 -0.00079358 -0.00179488 Cs 5.90494997 23.27196574 5.97175773 -0.00222716 -0.00168457 -0.00093585 Pb 8.83289727 26.42938738 8.51910291 0.00080607 -0.00092530 -0.00053475 Br 8.87911027 25.92808023 5.68559248 -0.00369540 0.00016068 0.00042600 Br 7.98183712 23.75559160 9.57215797 0.00121047 0.00162260 0.00194557 Br 5.88458493 26.93950225 7.90205609 -0.00033256 0.00017758 0.00106281 Cs 5.82455779 23.71466621 12.20337281 -0.00085921 -0.00099613 -0.00104299 Pb 8.56411765 26.78066863 14.78141915 0.00173024 -0.00048204 -0.00263211 Br 9.02707336 26.63849337 11.69004416 0.00023983 0.00080640 0.00093229 Br 8.64930485 23.73420377 15.59310708 0.00219683 0.00001802 0.00011795 Br 5.75840427 26.68798721 14.79670157 0.00362967 0.00091582 0.00022039 Cs 6.10039573 23.44842138 17.90213384 -0.00050713 -0.00213455 -0.00280337 Pb 8.50394492 26.59036542 20.62171891 0.00063370 -0.00014312 0.00188144 Br 8.69403356 27.50102283 17.83035520 0.00161723 -0.00566329 -0.00266314 Br 8.90683978 23.69466091 20.35166610 -0.00227492 0.00526412 -0.00146432 Br 5.47110971 26.83590596 21.53432826 -0.00311059 -0.00019107 0.00338724 Cs 5.19139239 23.74694071 23.77411631 0.00314334 -0.00182372 0.00259571 Pb 8.64305337 26.66284091 26.39340451 -0.00022719 -0.00166193 -0.00041704 Br 8.65571228 26.24996804 23.45172142 -0.00304440 0.00098794 -0.00025845 Br 8.91508171 23.83606697 26.20225245 -0.00044946 -0.00322787 -0.00305644 Br 5.79921022 26.85522470 26.14581398 0.00129063 0.00125995 0.00011857 Cs 5.30573779 23.49448689 29.85761121 -0.00135297 0.00137159 -0.00069147 Pb 9.06323324 26.83411352 32.41651696 0.00069031 0.00091902 -0.00084670 Br 8.49360583 26.99170480 29.40525941 0.00000236 0.00096187 0.00081312 Br 9.30432118 24.04546296 32.80477571 0.00143062 0.00058234 0.00445806 Br 5.86679551 26.92160707 32.46512393 -0.00044019 0.00017406 0.00217777 Cs 6.18267703 29.61523243 0.23764868 0.00095069 -0.00140061 -0.00428433 Pb 9.13865986 32.94879567 2.86960068 0.00217594 0.00137297 0.00014632 Br 9.68071601 32.92387861 0.01693716 0.00316531 0.00086281 0.00203951 Br 8.78691506 30.00398041 3.22592175 -0.00047490 0.00240686 -0.00193273 Br 6.10330008 32.40190854 2.80772786 0.00127648 -0.00046819 -0.00018829 Cs 5.34231882 29.78339529 5.89695989 -0.00220020 0.00342968 -0.00044771 Pb 9.07626370 32.58551461 9.03125406 -0.00269402 -0.00097339 0.00154801 Br 8.93775442 33.80434683 5.99522839 0.00450475 -0.00171771 -0.00018395 Br 9.17950033 29.50428852 8.57035995 0.00153134 -0.00133130 0.00117833 Br 6.02046844 32.24466531 8.99276260 -0.00140438 -0.00034829 -0.00153608 Cs 5.60801705 30.36686934 12.10582371 0.00026697 0.00085229 -0.00036223 Pb 8.91030114 32.90394108 14.71664125 0.00047729 -0.00020097 -0.00357147 Br 8.98617669 32.32234218 11.92858135 0.00061554 0.00125315 -0.00412478 Br 8.36648162 29.68498503 14.80353962 0.00036188 -0.00235890 0.00250188 Br 5.61309041 32.70670682 14.93413096 0.00062879 0.00127935 0.00190699 Cs 5.95071020 29.55045393 17.82340388 0.00033075 0.00021091 -0.00124729 Pb 8.81689640 32.59273322 20.70936706 0.00122299 -0.00084552 0.00168366 Br 9.15953960 33.11109483 17.69446392 -0.00084039 -0.00022242 0.00118365 Br 8.58066061 29.72055678 21.06441404 0.00226433 0.00113482 -0.00283117 Br 5.94072517 32.36334877 20.70736082 0.00311028 -0.00140864 -0.00300537 Cs 6.08137215 30.04605547 24.14460414 -0.00132360 0.00146912 0.00331703 Pb 8.93439142 32.74805731 26.66166421 0.00027599 -0.00106928 0.00056509 Br 8.78710694 32.84098582 23.55470333 -0.00052840 -0.00059661 0.00052239 Br 9.07976294 29.81180345 26.79588210 0.00148725 0.00066611 -0.00222870 Br 6.21080904 32.48298795 26.55434163 0.00064506 -0.00167556 -0.00284352 Cs 5.90431083 29.68596895 29.44943122 -0.00005163 -0.00125272 0.00031735 Pb 9.11814588 32.77486541 32.62856303 0.00118098 0.00035449 -0.00162609 Br 9.14427246 33.30200508 29.86232050 0.00107947 -0.00310745 -0.00081045 Br 8.79739355 29.82837352 33.02290602 0.00012772 0.00413909 -0.00136199 Br 6.13499825 32.57817428 32.25782063 0.00138455 -0.00272076 -0.00139292 Cs 11.91961969 0.43453761 0.06904304 0.00077728 -0.00045360 -0.00054494 Pb 14.81594897 2.90990821 2.89952656 -0.00210387 -0.00040255 -0.00116993 Br 14.87203394 3.30006913 0.04955782 -0.00192285 0.00147384 0.00061399 Br 14.20073038 35.56068530 2.98938695 -0.00202081 -0.00171816 -0.00156652 Br 11.77079740 3.27057608 2.87973140 -0.00046466 -0.00482271 0.00204247 Cs 11.66917126 0.17669160 5.75342118 0.00057448 0.00159425 0.00154183 Pb 14.91622436 2.78798631 8.87585405 0.00118183 0.00045802 0.00180139 Br 14.94893281 2.76269554 5.79329380 -0.00116950 0.00200605 0.00068022 Br 14.47420171 35.50207574 8.92625289 0.00077506 -0.00006300 -0.00168335 Br 11.88902269 2.89644370 9.06480576 -0.00170307 0.00309925 -0.00039971 Cs 12.27721655 35.44034718 12.28256739 0.00044212 -0.00100816 0.00219337 Pb 14.67208737 2.95276031 14.79741673 0.00057800 0.00197389 -0.00070690 Br 14.95680376 2.80856359 11.87985544 0.00023345 0.00046415 -0.00228567 Br 14.59921053 0.11724690 15.25747206 0.00181096 0.00117505 -0.00144663 Br 11.91161131 3.01241154 14.84791381 -0.00090844 -0.00016673 -0.00012144 Cs 11.60163431 0.48205000 17.65784623 -0.00189423 0.00281123 -0.00047760 Pb 14.89895310 3.08319234 20.40591281 0.00290320 -0.00255726 -0.00143318 Br 14.94801685 3.44120863 17.60122770 -0.00027222 0.00073633 -0.00130289 Br 14.75033054 0.01898067 19.55902811 -0.00270856 0.00004380 0.00097108 Br 12.09598456 3.83133014 21.01695349 -0.00160272 0.00548990 0.00035773 Cs 12.02908372 35.48623493 23.19808678 -0.00022548 0.00215447 0.00039285 Pb 14.97750865 2.45334033 26.80509486 -0.00093263 -0.00121658 0.00186060 Br 14.85932125 2.27012558 23.79476292 -0.00103601 0.00271400 -0.00082359 Br 15.70003282 35.30138477 26.90188800 -0.00469252 0.00366526 0.00171806 Br 12.07836399 2.19518964 26.80420923 -0.00063460 -0.00029249 -0.00153338 Cs 11.83732483 35.56163253 29.90795056 -0.00175766 -0.00005525 -0.00008627 Pb 15.00585843 2.98048135 32.56589608 0.00110353 -0.00145042 -0.00131684 Br 14.80437522 3.02809480 29.61120447 -0.00009197 0.00123404 -0.00007251 Br 15.01047702 35.49037273 32.49869223 0.00238376 -0.00146131 -0.00219127 Br 11.86924727 2.65268791 32.34631271 -0.00167393 0.00164898 -0.00013677 Cs 11.50054247 6.70109058 0.26169737 -0.00288775 0.00003581 0.00083946 Pb 15.18673170 9.22559511 2.67730396 -0.00208891 -0.00143222 -0.00051005 Br 14.92134746 8.18864696 0.08017255 0.00092062 0.00248229 0.00175877 Br 15.21675899 5.72327772 3.59971078 -0.00532099 -0.00046174 -0.00005484 Br 11.95045499 9.46220716 3.02329996 0.00194626 0.00019651 -0.00077879 Cs 11.96036772 5.57377574 5.58884411 0.00310639 -0.00090181 0.00087651 Pb 15.07666832 8.68982084 8.76303567 -0.00077678 -0.00106751 0.00174409 Br 15.54246586 9.18300684 5.68270824 0.00300942 -0.00310249 -0.00251335 Br 15.07315904 5.64602946 8.74447677 0.00228332 0.00106836 -0.00018018 Br 12.11924578 8.66985299 9.46237267 -0.00049739 0.00121288 -0.00021594 Cs 12.05270043 6.25350975 12.08373008 0.00148758 0.00077984 -0.00126937 Pb 14.72217233 8.93430089 14.74579568 0.00094640 0.00165297 0.00162464 Br 15.16001119 8.94028960 11.82169580 0.00032546 -0.00041002 0.00483344 Br 15.03336544 6.16479362 14.40874542 -0.00001397 -0.00194321 -0.00129335 Br 11.84913201 9.04633540 14.72451645 0.00121448 0.00076092 0.00376296 Cs 12.62052581 5.96576103 17.91420249 0.00193684 0.00150710 -0.00162788 Pb 14.72527650 9.06959040 20.84752145 0.00101919 -0.00116357 -0.00006923 Br 14.63404667 8.93483421 17.78279504 0.00163666 -0.00328467 -0.00057228 Br 15.19642148 5.88542256 20.44129307 -0.00057413 -0.00124764 -0.00106589 Br 11.98939545 8.88655524 21.75218687 -0.00309117 -0.00186316 -0.00098618 Cs 12.21745443 5.76071950 24.03667058 -0.00172249 -0.00264553 0.00025971 Pb 14.71741246 8.61505302 26.73783906 -0.00091327 -0.00075207 -0.00019066 Br 15.04336005 8.30198032 23.76864405 -0.00126526 -0.00258829 0.00039459 Br 14.56371807 5.66013605 26.70205651 -0.00221600 0.00028129 0.00276621 Br 11.88058548 8.98035996 26.35613212 -0.00019043 0.00016713 -0.00090128 Cs 11.77469987 5.84209715 29.65827044 0.00019865 0.00328875 0.00087930 Pb 14.79803765 8.76361913 32.70499162 -0.00302729 -0.00095452 -0.00109630 Br 14.43204953 8.84246035 29.67265392 -0.00503755 0.00073568 -0.00011947 Br 14.56331112 5.89661205 32.72668521 0.00061043 0.00198675 0.00028331 Br 11.80623984 8.75110671 32.77733486 -0.00015984 -0.00051628 0.00238736 Cs 11.89055114 11.38246096 35.11981712 -0.00204916 0.00067277 -0.00103122 Pb 14.94925928 14.90544536 2.95283695 0.00031317 -0.00044639 0.00121475 Br 15.04628779 15.20576298 35.57394834 0.00252396 -0.00032949 0.00101287 Br 14.94860111 12.10817605 1.97258073 0.00325535 -0.00108794 -0.00220617 Br 12.13004367 15.06660635 3.14450545 -0.00165750 0.00027102 -0.00011506 Cs 12.27364434 11.69357391 6.47218958 -0.00051367 0.00083442 0.00238896 Pb 14.91398297 14.85579816 8.60974206 0.00017947 -0.00004038 -0.00033537 Br 15.16183610 13.90334104 5.85358294 -0.00224861 -0.00365502 0.00099942 Br 15.09815620 11.67725154 8.99271092 0.00168804 -0.00001383 0.00016426 Br 12.13160472 14.66478456 8.71765632 -0.00076379 -0.00024066 -0.00240281 Cs 12.50965732 11.96430799 11.46019302 0.00026765 -0.00028930 0.00056035 Pb 14.90709949 14.80212579 14.52675030 -0.00011148 0.00055357 -0.00091745 Br 15.63671782 14.74770085 11.51766790 0.00120824 0.00014806 -0.00051838 Br 15.05362518 11.83452137 14.53401802 -0.00321585 -0.00058706 0.00412616 Br 12.04714828 14.49014731 14.31015056 0.00203648 0.00043441 -0.00390052 Cs 11.33089478 11.78721537 17.36094997 0.00313659 0.00006906 -0.00306751 Pb 15.39507043 14.83458557 20.65509546 0.00145535 -0.00096193 -0.00106539 Br 15.06297813 14.61409061 17.45615584 -0.00357391 -0.00022416 -0.00136591 Br 14.69871857 11.95754192 20.83570144 0.00118816 -0.00385293 0.00009402 Br 12.02687103 14.66822689 21.04422855 -0.00036198 0.00071674 0.00166848 Cs 11.60317040 11.83177071 24.14053988 0.00116512 0.00063810 -0.00110483 Pb 14.90149794 14.66620424 26.50499575 -0.00053242 -0.00119475 -0.00107932 Br 15.20392686 14.60312996 23.55575163 0.00086816 0.00142002 0.00335809 Br 14.98248336 11.87686009 27.01962767 -0.00483647 0.00119578 -0.00159144 Br 11.79377573 15.02506387 26.45133853 -0.00101947 0.00199577 -0.00035027 Cs 11.89993884 11.68758272 29.35821665 -0.00247368 0.00171477 0.00196174 Pb 14.35071474 14.78111003 32.53844902 0.00021259 0.00047073 -0.00213449 Br 14.49928197 14.72617897 29.55970674 0.00038088 -0.00083947 0.00194980 Br 14.77212171 11.86030305 32.58082386 -0.00025612 -0.00408924 -0.00057178 Br 11.60045082 14.37234962 33.27142727 0.00128864 0.00099538 0.00106087 Cs 11.68415749 17.78806725 35.22788704 0.00290950 0.00146716 0.00155469 Pb 14.88983662 20.71224577 3.04270431 -0.00066853 -0.00063876 0.00003136 Br 14.75314734 20.39042746 35.52179741 0.00079040 0.00254175 -0.00349771 Br 15.16451751 17.79271472 2.92548770 0.00016507 -0.00053052 0.00315229 Br 11.93938716 20.78862588 3.47805820 -0.00099390 -0.00282907 -0.00106579 Cs 11.83772864 17.97952416 5.84186252 0.00120587 -0.00058710 -0.00193820 Pb 14.67996586 20.71356979 8.98219552 0.00042762 0.00118743 -0.00146582 Br 15.07477382 20.33703089 5.96814215 -0.00181138 0.00056931 -0.00054693 Br 14.87492450 17.65809980 8.04776645 0.00082873 -0.00161709 0.00093413 Br 11.72833548 20.97034609 8.75852553 -0.00352035 0.00530141 0.00133851 Cs 11.46518608 18.05106554 12.18538613 0.00013727 -0.00043497 0.00050470 Pb 14.89485434 20.70108727 14.87194830 -0.00048820 0.00246057 -0.00129012 Br 14.22966768 21.47216922 11.71658686 0.00182214 -0.00382031 -0.00042152 Br 14.73858462 17.81237551 14.17716237 -0.00310296 0.00155908 -0.00384910 Br 11.93130288 20.28236012 15.17273894 0.00285729 -0.00189167 -0.00095945 Cs 12.62820249 17.50452153 17.69340343 0.00084961 -0.00163642 -0.00062719 Pb 14.87624079 20.83315919 20.81676607 -0.00117319 -0.00035380 0.00233231 Br 15.20408137 19.87992810 17.53929384 0.00280404 0.00101975 0.00015973 Br 15.60061208 17.93280465 20.59771902 -0.00027987 -0.00030903 0.00297077 Br 11.72724678 20.44813445 21.03488536 0.00091882 0.00011273 0.00063485 Cs 12.24592306 17.75835096 23.47401237 -0.00097298 -0.00144318 0.00005971 Pb 14.91806781 20.75124756 26.80637515 -0.00034153 0.00133203 -0.00081721 Br 14.50823731 21.18218370 23.60210630 0.00286923 -0.00099894 0.00031095 Br 14.93691874 17.91020041 26.22384674 -0.00029238 -0.00093634 0.00539239 Br 11.82408897 20.46725564 26.85440003 -0.00166879 -0.00264880 -0.00364800 Cs 12.09261686 17.95054185 29.33455268 -0.00095076 -0.00289232 -0.00166267 Pb 14.91179078 20.65441751 32.68015828 0.00063378 -0.00090373 0.00050155 Br 14.81151458 20.16637257 29.51699682 0.00098707 0.00126668 0.00285352 Br 15.08661335 17.82076181 32.39488345 0.00229792 -0.00120765 -0.00165188 Br 11.96035599 20.16449057 32.12958421 0.00216473 -0.00289139 0.00210913 Cs 11.60810590 23.73004242 35.38072290 0.00200991 0.00055942 0.00213940 Pb 14.96627166 26.74204091 2.71772744 -0.00001853 0.00089055 0.00009395 Br 14.58799041 26.23608044 35.35850913 0.00302488 0.00111532 -0.00227378 Br 14.46351840 23.56577098 2.89170757 0.00175767 0.00327006 -0.00061512 Br 11.82645209 27.12740897 2.92133620 0.00168161 -0.00158399 0.00148361 Cs 12.10282242 24.53121351 5.41858697 -0.00070836 -0.00020428 0.00207448 Pb 14.79222859 26.59334332 8.72046957 0.00181972 0.00049150 0.00048220 Br 15.17554503 26.65242438 5.76617504 -0.00055592 -0.00103478 0.00268586 Br 15.18397989 23.77985106 8.75174956 0.00075496 0.00192487 -0.00361438 Br 11.91807038 26.35881881 8.45836217 -0.00151374 0.00094211 -0.00036559 Cs 11.41257927 23.78151070 11.17494883 -0.00198262 0.00063052 0.00179961 Pb 14.34081673 26.90441315 14.83735051 -0.00054962 -0.00192797 0.00042299 Br 14.21344447 26.36261121 11.87264868 -0.00137875 0.00035063 -0.00031493 Br 14.67378145 23.80293216 15.70867276 -0.00131732 -0.00220898 0.00326424 Br 11.61056108 26.71443519 14.87196680 -0.00272777 -0.00082126 -0.00032496 Cs 11.59684936 23.96544275 18.01610360 -0.00194914 -0.00053575 -0.00057939 Pb 14.48936300 26.85239424 20.70926645 0.00002651 -0.00270088 0.00162856 Br 14.18295660 26.97885144 17.87411779 -0.00105870 0.00049130 0.00306296 Br 15.12623446 23.80166400 20.71775568 -0.00242075 -0.00060116 0.00003391 Br 11.36506103 26.79173183 20.75945361 -0.00276589 -0.00017723 0.00376138 Cs 11.95984479 23.48117768 24.12496930 -0.00063674 0.00003098 0.00482185 Pb 14.61757907 26.49884412 26.65241249 -0.00082561 -0.00106091 0.00036624 Br 14.36502721 26.73944524 23.74463764 0.00236483 -0.00247852 -0.00042076 Br 14.43966133 23.68736528 26.90931613 0.00252931 -0.00097176 0.00065522 Br 11.74466891 26.63966950 26.92124958 -0.00145796 -0.00065097 -0.00326557 Cs 11.76079405 23.49467631 29.56392861 -0.00171089 0.00040111 -0.00010037 Pb 14.86762565 26.52257736 32.36325400 0.00206382 -0.00100692 -0.00057732 Br 14.75306144 26.30993293 29.52247857 0.00071740 -0.00368699 -0.00147744 Br 13.96634592 23.59207955 32.65734358 -0.00198833 -0.00130149 0.00084644 Br 11.87437757 26.64723895 32.10361159 -0.00160935 0.00073050 0.00251978 Cs 12.09293908 29.11960542 35.43039052 -0.00093266 0.00000183 -0.00039967 Pb 14.94058708 32.76622590 3.03223442 -0.00277388 0.00110091 -0.00067344 Br 15.11402993 32.77915063 0.02796582 -0.00066630 0.00602046 -0.00080266 Br 14.94841745 29.57371175 3.15553116 0.00353613 0.00089385 -0.00591772 Br 12.10078190 32.81487412 2.95906053 0.00073939 -0.00128221 0.00014586 Cs 11.80260075 29.81860971 5.98724201 -0.00015342 -0.00007960 0.00032729 Pb 15.01879127 32.50582672 8.85365371 0.00117665 -0.00106497 0.00131340 Br 14.48203581 32.88899518 6.19384328 -0.00210566 0.00265446 -0.00300653 Br 14.41192170 29.61174144 8.85426422 -0.00476870 0.00026435 -0.00168866 Br 11.95572129 32.43139474 9.32011628 0.00099180 -0.00007864 0.00209486 Cs 11.60549102 29.35352040 11.50451417 -0.00088837 0.00131458 -0.00065296 Pb 14.66470629 32.65443234 14.90217704 0.00167576 -0.00025289 0.00032827 Br 15.27061269 33.17834481 12.12265038 0.00002568 -0.00001212 -0.00245644 Br 14.13071756 29.79484396 14.22471961 0.00031360 -0.00200478 0.00241512 Br 11.59134128 32.72300392 14.66317422 -0.00054281 0.00183754 0.00385461 Cs 11.48468425 29.80392489 17.65479578 -0.00139228 -0.00173224 -0.00222911 Pb 14.68618875 32.74250264 20.55792238 -0.00044808 0.00088636 0.00035917 Br 14.71693696 31.83230970 17.67662160 -0.00089705 0.00159982 -0.00270358 Br 14.80762158 29.67245078 21.03679719 -0.00055293 -0.00051628 -0.00317811 Br 11.82497995 32.12069939 21.08032658 -0.00183872 -0.00048956 -0.00047012 Cs 11.23093572 28.97221694 24.11153287 0.00241808 -0.00024569 0.00044824 Pb 14.97507096 32.54497678 26.80820733 -0.00005291 -0.00071719 -0.00131423 Br 14.88620509 33.02350707 23.64558153 -0.00304850 -0.00030671 -0.00260209 Br 14.34086300 29.58878759 26.80550068 -0.00511563 0.00068836 -0.00156966 Br 12.02266121 33.26563838 27.10826198 -0.00027656 0.00440712 -0.00236151 Cs 11.63784334 29.46405754 29.58157974 -0.00084857 0.00158854 -0.00128426 Pb 15.01916582 32.60499145 32.68853660 -0.00053530 -0.00280503 -0.00085711 Br 15.74258991 31.99782082 29.43982035 -0.00016262 0.00033341 -0.00246260 Br 15.09809717 29.37867330 32.68850047 -0.00059106 0.00089228 0.00013782 Br 12.17822109 32.36584606 32.31018130 0.00059144 -0.00291397 -0.00134431 Cs 18.63837834 35.40267183 0.42977933 0.00256441 0.00001421 0.00238530 Pb 20.86200162 2.97407292 3.01269082 -0.00201212 -0.00098670 -0.00178981 Br 20.49492713 4.21398431 35.56727196 0.00012909 0.00201369 -0.00120162 Br 21.39317049 -0.00064136 2.34797553 -0.00028772 -0.00208672 0.00054916 Br 17.95924053 2.91083888 3.15135816 -0.00011153 -0.00056704 0.00102493 Cs 17.16290928 35.26645467 6.37855794 -0.00036731 0.00027436 -0.00097519 Pb 20.86071181 2.88534519 8.82299317 -0.00155862 0.00181354 0.00089150 Br 21.48232254 2.58206812 5.88491458 -0.00208757 0.00220249 0.00367884 Br 20.59599360 35.46997577 8.87104417 -0.00217482 -0.00010495 -0.00116293 Br 17.94803185 2.76503758 8.74190319 -0.00040927 -0.00053524 0.00065708 Cs 17.93628314 0.16174344 11.87015319 0.00050671 -0.00056475 -0.00033597 Pb 20.86876520 3.18247185 14.73778982 -0.00111274 -0.00049202 0.00151086 Br 20.64970732 3.21820505 11.76646554 -0.00001942 0.00174442 -0.00254763 Br 20.53637153 0.00821851 14.76625399 0.00224319 0.00076280 -0.00038476 Br 17.73267189 3.50305471 14.74196509 -0.00175403 0.00116085 -0.00190010 Cs 17.92091341 0.16821494 17.30416438 -0.00187087 -0.00067337 -0.00013633 Pb 20.92965721 3.19425525 20.88918703 -0.00301301 -0.00014309 -0.00080163 Br 20.86966299 2.55583007 17.68859557 0.00185698 0.00372683 0.00047914 Br 21.10942474 0.21886088 20.81659666 -0.00113952 0.00003902 -0.00003038 Br 17.73840367 3.25517323 20.90589105 0.00005696 -0.00510844 0.00315209 Cs 17.85188076 35.27206260 24.05979563 -0.00167781 -0.00102464 0.00075494 Pb 20.75364417 3.00697291 26.76042722 -0.00085552 0.00037249 0.00191016 Br 20.97785156 3.25673295 23.95761549 0.00024161 -0.00113934 -0.00198008 Br 20.48122452 0.05663320 26.34948170 0.00137071 0.00014831 -0.00265850 Br 17.93649036 2.84839723 26.75528051 -0.00032209 -0.00324367 0.00119806 Cs 18.31930636 0.08475872 29.86198013 -0.00103593 -0.00131537 0.00115251 Pb 20.71982379 3.40606221 32.88207476 -0.00003838 0.00111356 -0.00027587 Br 20.93821966 2.87679124 29.82805888 -0.00098983 0.00198827 -0.00077064 Br 21.33352565 0.22679010 33.04262817 -0.00074827 0.00297311 0.00203149 Br 17.87511744 3.13406440 32.83341848 -0.00032104 0.00106967 -0.00213925 Cs 17.50566175 5.89740978 0.36476984 0.00175777 -0.00139469 -0.00316067 Pb 20.79442903 8.92922039 2.88578561 -0.00192581 -0.00036841 -0.00204708 Br 21.35427972 8.51853844 35.59430701 0.00333724 0.00386863 -0.00009092 Br 20.60359529 5.95916482 3.43025674 -0.00119321 -0.00011613 -0.00057155 Br 18.10702733 9.12418757 2.03414393 -0.00220418 -0.00266220 -0.00020965 Cs 17.92218805 6.04322636 5.77438259 0.00317787 -0.00172964 0.00075184 Pb 20.69436944 8.94354472 9.00293219 0.00017551 0.00091742 0.00043537 Br 20.70382374 9.25540273 5.99654696 0.00128566 0.00314135 -0.00134009 Br 21.39946029 5.85871185 8.83429105 0.00437616 0.00243382 0.00197259 Br 17.93897477 8.42493905 9.10719323 -0.00022551 -0.00179226 0.00152000 Cs 17.84289743 5.36279969 11.60629823 -0.00156374 -0.00276058 0.00168152 Pb 20.74457929 8.96649840 14.63260348 -0.00279419 -0.00024273 0.00019654 Br 20.83159834 9.02210300 11.95657558 0.00165911 -0.00032458 -0.00316599 Br 21.18680589 6.05807085 14.50073549 0.00081413 0.00031340 -0.00006931 Br 17.82367241 8.37792328 14.78463854 -0.00052714 0.00039386 0.00080242 Cs 17.45759471 6.18445862 17.42341170 -0.00044103 0.00080102 -0.00232666 Pb 20.71337158 8.83530221 20.68621801 -0.00215670 0.00248897 -0.00057491 Br 20.49388720 9.20622901 17.64589071 -0.00116440 0.00127645 0.00003961 Br 20.30752013 5.87723182 20.41350945 0.00042176 0.00348486 0.00089305 Br 17.79932170 9.43640973 21.23106261 0.00346919 0.00272760 0.00250929 Cs 18.15932777 5.42144776 24.02404995 0.00002636 0.00310375 -0.00062365 Pb 20.66315774 8.93706072 26.60528905 -0.00002221 0.00054878 -0.00001646 Br 20.40210101 8.57463373 23.64154527 -0.00198565 -0.00040255 0.00150018 Br 20.90695566 6.08894872 27.10477385 -0.00116192 -0.00079972 -0.00184637 Br 17.76091488 8.74714314 26.76245983 0.00202370 -0.00087325 0.00536055 Cs 17.24338346 5.38519711 29.22860967 0.00235543 -0.00010526 0.00144808 Pb 20.64917537 8.95192672 32.69821917 -0.00007137 -0.00168092 0.00077364 Br 20.77601345 9.52457880 29.51308175 -0.00283676 -0.00166497 0.00210517 Br 20.13239851 6.08570251 31.90291466 -0.00100357 0.00093689 0.00153715 Br 17.77718920 8.84069081 32.83922701 -0.00186530 0.00040272 0.00146536 Cs 18.02058307 11.98071241 35.53444867 -0.00098647 0.00143955 -0.00034181 Pb 20.79120732 14.84358994 2.94101864 -0.00054670 0.00049480 0.00413849 Br 20.93499554 14.97676461 35.53916327 -0.00006007 0.00258239 0.00242234 Br 21.35930323 11.72860213 2.98587102 0.00173762 0.00077653 0.00010295 Br 17.94168186 14.55496435 3.05065807 -0.00167323 0.00141379 -0.00107450 Cs 17.82156260 11.78263880 6.19886355 -0.00115409 0.00168715 -0.00128965 Pb 20.73997379 14.69968479 8.86542313 0.00067406 0.00138118 0.00058677 Br 21.19341699 14.66800663 5.88906272 -0.00151212 -0.00028862 -0.00025880 Br 20.13681949 11.84859605 9.01860790 -0.00150455 -0.00066419 0.00256323 Br 18.18778875 15.38296683 8.43793774 -0.00031100 0.00306384 -0.00032466 Cs 17.96660924 11.54493473 11.84952764 0.00240255 0.00051209 -0.00005841 Pb 20.79316408 14.93628007 14.89973973 -0.00019147 -0.00207376 -0.00076404 Br 21.10735055 15.26393757 11.90676379 -0.00090087 0.00081942 0.00163767 Br 20.44094867 12.06034394 15.37088136 0.00083941 -0.00142623 -0.00191664 Br 17.79535454 15.17505203 14.95979433 0.00168214 -0.00148149 0.00004615 Cs 17.54706580 11.66966570 17.55527501 0.00045141 0.00263965 0.00159699 Pb 20.92412616 14.74741160 20.61855320 0.00093945 -0.00114176 -0.00009085 Br 20.64806581 15.04185385 17.75714175 -0.00005757 0.00045526 0.00102779 Br 21.32470697 11.93902318 20.63324095 0.00029512 0.00315684 -0.00224190 Br 17.97430525 14.16284545 20.49734348 -0.00170583 -0.00333429 0.00068209 Cs 17.56315849 11.60152401 23.68541422 -0.00151482 0.00001489 -0.00063893 Pb 20.62868686 14.93423776 26.61237292 -0.00186394 0.00054311 0.00179288 Br 20.21592322 15.09326305 23.81272490 -0.00312043 0.00170021 0.00043795 Br 20.38959477 11.89976307 26.62488233 0.00292228 -0.00229679 0.00102976 Br 17.72698267 15.11603369 26.81691881 -0.00091388 -0.00191626 0.00089260 Cs 17.66028188 12.04451137 30.24051167 -0.00075381 -0.00208507 -0.00116385 Pb 20.63275571 14.99412217 32.50878871 0.00149159 0.00028848 0.00079901 Br 20.86856500 14.67347048 29.69857553 0.00050438 0.00148871 0.00347693 Br 20.56379188 11.82267756 32.88240312 0.00200965 -0.00106878 0.00148100 Br 17.74211556 14.98838008 32.49711174 0.00167359 -0.00088880 0.00183875 Cs 17.69979554 17.92959878 35.01821069 -0.00027187 0.00102655 -0.00061446 Pb 20.88629987 20.78647738 3.10148267 -0.00131146 -0.00172836 -0.00099550 Br 20.59413665 20.60830968 35.50259945 -0.00108786 0.00168162 0.00165168 Br 21.38487263 17.74492935 3.25309339 -0.00010971 0.00130763 -0.00019672 Br 17.86094776 20.68034397 3.10352054 -0.00042878 -0.00137677 0.00038782 Cs 17.64190115 18.03565572 5.91879401 0.00371427 -0.00067791 -0.00225180 Pb 20.59976465 20.59485453 8.66059671 -0.00084689 -0.00051562 0.00222524 Br 20.68901762 21.16684653 5.92635504 -0.00145225 -0.00245844 -0.00314404 Br 20.59389364 17.77669343 8.49480912 -0.00077900 -0.00029642 -0.00069665 Br 17.72811971 20.25184716 8.83879227 -0.00048892 -0.00016119 -0.00421652 Cs 17.95820892 17.58616606 12.04393721 0.00201930 -0.00027901 0.00010684 Pb 21.06004763 20.80877158 14.71862170 -0.00099619 0.00145471 0.00076429 Br 20.73203990 20.06591910 11.94925863 -0.00208897 -0.00037556 0.00086048 Br 21.26551499 17.88501312 15.44218740 -0.00150340 -0.00016382 0.00125768 Br 17.93300737 20.61909932 14.89277116 0.00037286 -0.00123317 0.00152965 Cs 18.21073839 17.93122879 17.84008678 0.00034815 0.00110446 -0.00146753 Pb 20.81295898 20.69946659 20.58369104 0.00016247 0.00093823 -0.00161614 Br 21.03825675 20.97399988 17.78497974 0.00342181 0.00276273 -0.00186326 Br 20.00081327 17.84765085 20.79223600 -0.00240720 0.00096956 -0.00135694 Br 17.77954403 21.28483176 20.48880261 -0.00275791 0.00356789 0.00097806 Cs 17.82977606 17.79632807 23.75804953 -0.00125456 -0.00084817 -0.00073683 Pb 20.73557355 21.10381419 26.74966985 -0.00084180 0.00143800 0.00026120 Br 20.08492609 21.59243507 23.76998253 0.00165143 0.00075155 -0.00118402 Br 20.71467943 18.03226990 26.86394315 0.00309479 0.00466325 0.00302572 Br 17.84775890 21.13615432 27.19482349 0.00456993 -0.00207183 -0.00292009 Cs 17.68847812 18.00843466 29.58922326 0.00211061 -0.00052844 -0.00032373 Pb 20.89020843 20.95723710 32.68502271 -0.00083994 0.00012073 0.00033809 Br 21.35844596 21.13959532 29.72496684 0.00342514 0.00194667 -0.00013213 Br 20.14981492 17.99199031 32.14271798 -0.00210241 -0.00490385 -0.00354567 Br 17.98327134 21.52861383 32.69762307 0.00202621 0.00377263 0.00083533 Cs 18.01138484 23.85547612 0.02425442 0.00121476 -0.00002184 -0.00098458 Pb 20.75782129 27.01163068 3.12367708 0.00070706 0.00103937 0.00119893 Br 20.16729323 27.20434172 35.52117378 -0.00078788 -0.00115232 0.00381515 Br 20.70706504 23.74639002 2.72388619 -0.00037451 0.00355890 -0.00245124 Br 17.72423957 26.32517867 3.24409926 -0.00099353 0.00096089 -0.00365819 Cs 17.80887001 23.66554256 6.29058011 0.00113882 -0.00183536 -0.00005639 Pb 20.48058651 26.86766780 9.00291835 -0.00124254 -0.00081311 0.00037040 Br 20.72224089 26.33202757 5.79357854 0.00105605 -0.00189069 0.00061101 Br 20.15379397 24.01442759 9.02728377 0.00115865 0.00222175 0.00283464 Br 17.65241770 27.17248191 9.39166136 0.00235761 -0.00136526 -0.00164449 Cs 17.64036949 23.31886423 12.21386208 -0.00193464 0.00230453 0.00101533 Pb 20.43667608 26.59334265 15.00414597 -0.00071237 -0.00059473 0.00214327 Br 20.46597048 27.08769227 11.91911864 -0.00191939 -0.00009931 0.00057968 Br 20.36501734 23.85025164 14.74500499 0.00079699 0.00296379 0.00127033 Br 17.57829059 26.79929353 14.80034261 0.00190462 0.00132714 0.00284363 Cs 17.82427483 23.53507974 17.47725514 0.00311053 0.00177910 -0.00488621 Pb 20.54437437 26.67843673 20.84871084 0.00125867 -0.00175511 0.00080383 Br 19.69932303 26.52319213 17.96051840 0.00354087 -0.00014716 0.00247335 Br 21.14366048 23.88738803 20.36867755 0.00271639 -0.00295801 -0.00184176 Br 17.51941120 27.33642701 21.08630999 0.00143068 -0.00145212 -0.00062784 Cs 17.61960735 24.22533488 23.69381086 -0.00143896 0.00004559 0.00066279 Pb 21.05896351 26.75357138 26.78947699 0.00104531 0.00122291 0.00052033 Br 21.09735642 26.82922203 23.84428390 -0.00107513 -0.00332605 -0.00106493 Br 20.88474722 23.99546646 26.60192864 0.00176845 -0.00079004 0.00180322 Br 17.93111354 26.76558919 26.26281852 0.00243387 -0.00009756 0.00141876 Cs 18.01036841 23.67343570 29.62535881 0.00001265 -0.00074634 0.00014748 Pb 20.89693845 26.96561470 32.75733760 -0.00181546 -0.00131655 -0.00111149 Br 20.79213939 27.26383874 29.83816577 0.00222237 0.00222529 -0.00047195 Br 21.52601158 23.99480514 32.43502814 -0.00170344 0.00027552 0.00139816 Br 17.90843649 26.05580376 32.55289241 0.00215067 0.00067341 0.00004941 Cs 17.39097834 29.81703715 35.33862979 0.00115899 -0.00201686 0.00153180 Pb 20.87770921 32.73947284 3.11879450 -0.00199566 0.00214850 -0.00049664 Br 20.48428053 32.27036638 0.20206782 -0.00215615 -0.00166418 0.00227660 Br 20.36816455 29.82755001 3.56408954 -0.00017643 -0.00107963 0.00069501 Br 17.80728730 33.00854950 3.94663594 -0.00267445 0.00245230 0.00076801 Cs 17.81889156 29.46421337 5.79644657 0.00018877 0.00100081 -0.00167772 Pb 20.78594554 32.60351334 8.81072663 -0.00000180 -0.00023805 0.00033602 Br 21.17342580 32.61283277 5.98375261 0.00128441 -0.00123491 -0.00271150 Br 21.63138872 29.40167398 8.14747096 0.00127376 -0.00011605 0.00154905 Br 17.90298446 32.30488232 8.32559747 -0.00234166 -0.00468402 -0.00057594 Cs 17.82395749 29.60168470 12.28555278 0.00004029 0.00049799 0.00019081 Pb 20.69846124 32.63873450 14.63948806 -0.00128864 -0.00029738 -0.00106348 Br 21.38341174 32.86914533 11.81883975 -0.00106516 0.00043384 -0.00078610 Br 20.45745917 29.62572848 14.82234293 -0.00077872 -0.00402078 -0.00285358 Br 17.77462319 32.65213902 14.97746193 -0.00290637 -0.00036086 0.00120069 Cs 17.60155419 30.05299489 18.13542418 -0.00033907 0.00182550 0.00228411 Pb 20.83399762 32.79500745 20.56437316 0.00033774 -0.00180497 -0.00124946 Br 21.08305921 32.47046252 17.63930726 0.00069553 -0.00261360 0.00024919 Br 20.86805651 29.71163048 20.38421667 -0.00180613 0.00387793 0.00212244 Br 17.68765150 33.15686203 20.26932676 -0.00359667 -0.00000138 -0.00166506 Cs 17.69057564 30.10780186 23.41532899 0.00173291 0.00051270 0.00263229 Pb 20.82207118 32.65010154 26.63638720 -0.00093050 -0.00045408 -0.00097500 Br 20.50790317 32.89640437 23.37168864 0.00185992 0.00087138 -0.00026955 Br 21.22616877 29.84179792 26.50247672 0.00239780 -0.00182648 -0.00035411 Br 17.86914446 31.96903423 26.46649528 -0.00124626 -0.00404949 0.00067127 Cs 17.99649754 29.49288296 29.58681274 -0.00119716 0.00183316 0.00001458 Pb 20.74540708 32.76094509 32.90022117 0.00048786 -0.00072231 0.00006480 Br 20.64159072 33.01979880 29.78421632 0.00404352 -0.00311095 -0.00191019 Br 20.92539309 29.89661829 32.57563585 0.00194677 -0.00020264 -0.00205506 Br 18.04261385 32.11422711 32.56388099 -0.00204604 -0.00145341 0.00299888 Cs 24.25224837 0.17994119 35.21937795 -0.00082512 -0.00181605 -0.00138887 Pb 26.80084482 3.14635080 2.92641481 -0.00037443 -0.00171473 -0.00039962 Br 27.34520309 2.82691793 35.55093076 0.00101678 0.00041188 -0.00048164 Br 26.55766100 0.10911307 2.77994147 0.00134787 -0.00267217 0.00311556 Br 23.71460073 3.64855262 2.65159379 0.00101694 -0.00168565 0.00084663 Cs 24.23583062 0.16946722 5.59842332 0.00032177 0.00037061 -0.00218009 Pb 26.46087107 2.67798697 8.92439576 0.00213967 0.00082200 -0.00084659 Br 25.98728383 3.47581187 5.93033124 0.00162825 0.00068117 0.00222689 Br 26.72233983 35.54515301 8.87174391 0.00003839 0.00192486 0.00126458 Br 23.60157195 2.30733991 9.77893660 -0.00102067 0.00194402 0.00106498 Cs 24.38208179 35.33594924 12.15809809 -0.00023471 -0.00006823 -0.00036720 Pb 26.75566526 3.12817651 14.82110353 -0.00108375 0.00266499 -0.00098514 Br 27.41525349 2.55601727 11.84705460 -0.00059450 -0.00453806 -0.00110538 Br 26.73491131 0.10843855 14.76477839 0.00334252 -0.00047974 -0.00250139 Br 23.76395992 2.69787179 14.72621592 -0.00258383 -0.00195183 -0.00068061 Cs 23.67669136 0.04911655 17.39703909 0.00076144 0.00015759 0.00042189 Pb 26.78930287 2.92577495 20.89753566 0.00248497 -0.00253719 -0.00211210 Br 26.32902360 2.95398024 18.06261331 -0.00273814 -0.00122072 -0.00097202 Br 26.84827586 35.55496108 20.86890407 -0.00130056 -0.00101314 -0.00071251 Br 23.72984802 2.87735215 21.29379543 0.00474540 -0.00355543 0.00095494 Cs 23.97667046 35.54965480 23.44023255 0.00113975 -0.00188730 0.00119911 Pb 26.56030313 2.87014750 26.81372790 -0.00067127 -0.00226328 0.00069084 Br 27.02515375 2.82343103 23.58733254 -0.00130462 0.00068190 0.00127034 Br 26.38438872 0.04318538 27.46468462 0.00173470 -0.00153704 -0.00020655 Br 23.54121917 2.55424989 26.53904907 0.00121561 0.00076997 -0.00237481 Cs 23.55517132 35.37924216 29.87019113 0.00245989 -0.00018806 -0.00077639 Pb 26.86531169 3.09101815 32.76214477 -0.00024662 0.00187494 0.00005094 Br 26.69191760 3.23872098 29.78775555 0.00237857 0.00019038 0.00224302 Br 26.54172760 0.01103674 32.27691260 -0.00234776 0.00058088 0.00105463 Br 23.92181358 3.09725337 32.75105534 0.00176056 -0.00088938 0.00144698 Cs 23.87369628 5.92103959 0.06798822 -0.00031309 0.00174651 0.00118328 Pb 26.59077570 9.05339825 2.91267197 -0.00333925 0.00123982 0.00146280 Br 26.83188818 8.75048353 35.48286510 -0.00208017 0.00162469 -0.00179906 Br 26.89029108 6.09588319 2.47472974 -0.00491869 0.00130052 -0.00105648 Br 23.66354622 8.65635457 3.46552305 0.00221405 0.00088555 -0.00287848 Cs 23.68822954 5.99723386 5.63099726 0.00041060 0.00065989 0.00054098 Pb 26.75375883 9.08191074 8.68583969 -0.00059670 -0.00167948 0.00052486 Br 26.68520820 8.37265094 6.00092423 -0.00250316 0.00242544 -0.00062867 Br 27.04067679 6.02229446 9.28659182 0.00110906 -0.00003525 -0.00251911 Br 23.80794026 9.09426706 9.20873366 0.00102348 0.00578227 0.00178249 Cs 23.76125841 6.01236596 11.38170894 -0.00086472 0.00013240 0.00046030 Pb 26.91764650 8.73272853 14.72770400 -0.00036422 -0.00281872 -0.00010253 Br 26.99078974 9.50046288 11.85732731 0.00436272 0.00213865 -0.00292294 Br 26.05968998 6.05015443 13.84282733 0.00031218 0.00062062 0.00102489 Br 23.97094769 9.26034569 14.86719901 -0.00077313 -0.00648859 -0.00143876 Cs 24.52191121 5.84446143 17.97254280 -0.00001944 -0.00090485 0.00075440 Pb 26.74073465 8.89976844 20.74696356 -0.00104394 -0.00019050 -0.00237145 Br 26.69344564 8.78927970 17.76639694 0.00134504 -0.00257301 -0.00193145 Br 27.23880890 6.03305184 20.63155005 0.00042708 -0.00400619 -0.00225219 Br 23.78316561 8.20571352 20.76886907 -0.00173317 0.00032873 -0.00086993 Cs 24.02281896 5.78960907 24.20501388 -0.00361511 0.00014728 0.00207623 Pb 26.65640282 8.88845778 26.56256266 -0.00023496 -0.00030879 -0.00004840 Br 26.21632413 8.73620361 23.73760363 0.00219279 0.00262647 -0.00280307 Br 26.38857097 5.77244763 27.08816501 0.00327855 0.00016176 -0.00003429 Br 23.83113431 8.76616654 26.93575216 0.00442212 -0.00369009 0.00058568 Cs 23.08731632 6.07233122 29.89038559 0.00041082 -0.00266718 0.00210573 Pb 26.85441716 8.81384802 32.39632164 -0.00004693 -0.00121511 -0.00046707 Br 26.58899984 8.69525564 29.58451779 0.00053366 -0.00079042 -0.00246006 Br 27.10612390 5.83682813 32.52049676 -0.00438960 -0.00115061 -0.00221498 Br 23.85512798 8.13549026 32.65074275 -0.00071178 0.00176597 0.00139617 Cs 23.59380365 11.82984484 0.01399542 -0.00240789 -0.00103349 0.00225160 Pb 26.83018458 15.09123145 3.02804106 -0.00186526 0.00132470 -0.00191925 Br 26.59659854 14.16018981 0.11128621 -0.00043331 0.00186490 -0.00388109 Br 26.81590483 11.88734415 3.22344973 0.00093065 0.00149670 -0.00041511 Br 23.97168044 15.14084319 3.22026707 -0.00373206 -0.00030230 0.00288265 Cs 23.40448784 12.14511724 5.88449382 -0.00260761 0.00056061 0.00370385 Pb 26.83503616 14.97985652 8.83335852 -0.00008733 -0.00183690 -0.00052439 Br 27.09216181 15.76973902 6.07503534 0.00007661 -0.00191100 0.00092244 Br 26.19008668 11.98149072 7.96122183 -0.00309939 -0.00127757 0.00163909 Br 24.04948910 15.55784446 8.79647184 0.00065903 -0.00137412 -0.00015997 Cs 24.34116754 12.16893582 11.93961215 -0.00161892 0.00140729 0.00026807 Pb 26.67312542 14.70653211 14.83981445 -0.00073740 -0.00018240 0.00006336 Br 27.01891393 14.56810694 11.90898666 0.00042023 0.00178798 0.00041387 Br 27.24561081 11.80956168 15.19552040 -0.00154386 0.00055954 -0.00146630 Br 23.74552303 14.29474066 14.84911799 -0.00101624 0.00206556 0.00319297 Cs 23.30210999 12.02443852 17.68113619 -0.00105557 0.00221452 0.00154783 Pb 26.71505625 14.89440308 20.90589144 0.00194367 -0.00032234 -0.00348301 Br 26.32637500 14.38056926 17.80445988 0.00302764 -0.00139026 -0.00238156 Br 26.23840779 11.80532128 21.09275566 -0.00215217 0.00148320 -0.00163819 Br 23.88853436 15.30071644 20.72085567 0.00142358 0.00031101 0.00052360 Cs 23.59148462 10.98851420 23.74009975 -0.00009778 0.00301899 -0.00089836 Pb 26.79637193 14.94214317 26.87325397 -0.00031380 -0.00187230 0.00154264 Br 26.84971349 14.97951959 23.89387190 -0.00200187 0.00198889 0.00039216 Br 26.38678495 11.98850639 26.57567087 0.00023619 0.00081187 -0.00202675 Br 23.60309816 15.26962751 26.35838529 0.00141149 0.00098178 0.00161716 Cs 23.66139319 11.93382446 28.92552218 -0.00030941 0.00178225 -0.00011377 Pb 26.69822249 14.76616915 32.80104056 0.00085171 -0.00128404 0.00085389 Br 26.47162043 15.44449633 29.92461457 -0.00073204 0.00053400 -0.00072336 Br 26.38785212 11.81717463 32.17536449 0.00151899 0.00081983 -0.00129025 Br 23.58966724 15.08795233 32.89968585 -0.00375816 0.00128547 -0.00056650 Cs 24.05160911 17.79694491 0.23000872 -0.00130026 -0.00098121 0.00064163 Pb 26.75527791 21.09678149 2.98103376 -0.00033079 -0.00054232 -0.00071718 Br 27.35532723 21.07809654 0.08879654 0.00240392 0.00270575 0.00169054 Br 27.18723307 17.91601357 2.54534626 -0.00090526 -0.00001007 -0.00179411 Br 23.97151687 20.52281100 2.65199690 0.00019295 0.00129778 0.00044928 Cs 23.80073966 18.12642482 5.78848820 0.00271687 0.00029212 -0.00231350 Pb 26.68398718 20.74443801 8.64323263 0.00091828 -0.00076136 -0.00100123 Br 27.15405635 21.08946794 5.71998561 -0.00115471 -0.00338127 -0.00127801 Br 27.35479292 17.84571695 9.03861538 0.00185150 -0.00127077 0.00383519 Br 23.66329429 20.54009071 8.36732094 -0.00005702 -0.00308328 -0.00166816 Cs 23.40522633 17.84960123 11.61316720 -0.00054633 0.00046991 -0.00102258 Pb 26.41204627 20.77998931 14.71328062 -0.00054556 -0.00137689 0.00080387 Br 26.63263508 20.63743324 11.70751466 0.00452694 -0.00065411 0.00046630 Br 26.49090151 17.71399581 14.76817469 -0.00139844 -0.00381962 0.00068774 Br 23.69429992 21.33117655 13.81081318 0.00172592 -0.00180516 -0.00023718 Cs 24.06684622 17.06429915 17.51517146 0.00042574 0.00117390 0.00040395 Pb 26.72618262 20.55656505 20.75117422 0.00049582 0.00125056 0.00096400 Br 25.69345925 20.58076769 17.57621649 0.00089042 -0.00064850 0.00212308 Br 26.79231377 17.67187255 20.02682329 0.00239547 -0.00195226 -0.00207074 Br 23.81776436 20.19895688 21.33112679 0.00046549 0.00298818 0.00118498 Cs 24.15344414 17.32123410 23.78117665 0.00046497 -0.00173447 0.00269156 Pb 26.63568357 20.57937979 26.59058548 0.00008358 0.00140668 0.00072579 Br 27.10968401 20.32650467 23.54032278 0.00271334 -0.00379314 0.00380845 Br 27.00345813 17.70601802 26.09157447 -0.00218209 -0.00113925 0.00090616 Br 23.56622078 20.88598696 26.54088088 -0.00212304 0.00118825 0.00067167 Cs 23.65469181 18.26187742 29.69491634 -0.00172408 -0.00095296 0.00049416 Pb 26.96227925 20.80886337 32.81864048 -0.00039439 -0.00037433 -0.00073914 Br 26.57828870 20.35471849 29.72337836 0.00246034 -0.00051119 -0.00459632 Br 26.97398796 17.65494146 33.32063521 0.00196213 0.00161413 0.00098228 Br 23.85446023 20.48144389 33.16580255 -0.00301267 -0.00108630 0.00165859 Cs 24.39074498 23.86105034 35.29485427 -0.00054864 0.00033713 0.00105435 Pb 26.25923326 26.68464208 2.97012095 -0.00321990 0.00038575 -0.00164963 Br 26.63155758 26.58695848 0.11641886 0.00102453 0.00010183 -0.00277227 Br 25.78419816 23.78445858 3.78046853 -0.00167672 -0.00071899 0.00526738 Br 23.51216045 27.89535375 2.73868672 -0.00070958 -0.00613977 0.00092998 Cs 23.74687170 23.90281940 6.58863746 -0.00079003 0.00142250 0.00181074 Pb 26.64852323 26.66576519 8.94114445 0.00109021 0.00061502 -0.00123014 Br 26.37142100 27.17375071 5.99391593 -0.00110492 0.00043992 -0.00134496 Br 26.79511176 23.77111330 9.49300708 -0.00334817 -0.00182839 0.00004901 Br 23.74418520 26.72972962 9.60137042 -0.00157776 -0.00237081 0.00228980 Cs 24.14987370 24.47834006 12.12944173 0.00098431 -0.00043147 0.00191494 Pb 26.51250782 26.79858428 14.87813701 0.00039510 0.00111086 0.00005199 Br 26.67527363 26.90431364 11.98762073 0.00377763 -0.00043634 -0.00038907 Br 26.54995890 23.75664251 14.91680420 0.00064451 -0.00058900 0.00128182 Br 23.53117492 26.82768327 15.33170258 -0.00391746 0.00042455 0.00028589 Cs 23.79950380 23.76687267 17.31740234 0.00022172 -0.00062335 -0.00035280 Pb 26.50460252 26.60016300 20.85975886 0.00006359 -0.00063489 -0.00226864 Br 27.23962609 26.88111599 17.81016358 0.00447508 0.00382736 0.00015150 Br 26.49596057 23.42362878 21.16291908 0.00018262 -0.00222772 0.00178919 Br 23.69040822 26.34552459 19.93549130 -0.00053443 0.00005951 -0.00130261 Cs 23.87218516 22.91831271 23.78561726 -0.00005445 -0.00133394 0.00132253 Pb 26.65142632 26.94932526 26.65357966 0.00009109 -0.00115414 0.00006688 Br 26.31749286 26.97447695 23.69367473 -0.00034363 0.00232404 -0.00111270 Br 26.69513316 23.86758178 26.55526576 -0.00087751 0.00279267 -0.00276864 Br 23.81080712 26.34344827 27.39331131 -0.00009813 -0.00085986 0.00174192 Cs 24.18455608 23.58079660 29.56107753 0.00233476 0.00207437 0.00174934 Pb 26.88916467 26.81980625 32.71562503 -0.00000787 0.00028989 -0.00001415 Br 27.22195923 26.48890416 29.78656323 0.00296982 -0.00137432 0.00022751 Br 26.64017434 23.75849602 32.58428774 -0.00017841 -0.00038191 -0.00140798 Br 23.71532535 26.76080786 33.14012355 -0.00160412 -0.00384155 -0.00124946 Cs 24.11037165 29.97345056 35.13992289 -0.00115071 0.00110116 0.00022416 Pb 26.80110961 32.69180106 3.06313039 0.00013474 -0.00196451 0.00110197 Br 26.80496722 32.58477385 0.02113938 -0.00114648 -0.00038062 0.00103489 Br 27.03889418 29.57279307 3.26632599 -0.00546167 -0.00162933 0.00011794 Br 23.87226311 32.39195428 2.73782060 0.00114819 0.00165289 -0.00083568 Cs 24.05948105 29.85774258 5.68594510 -0.00067757 0.00183090 -0.00004139 Pb 26.88654239 32.55301871 8.75396490 0.00084824 0.00263102 -0.00202692 Br 27.20885855 33.14444161 5.80829286 0.00328487 -0.00165863 0.00176699 Br 26.76104400 29.69982241 8.47667816 0.00351933 -0.00251859 -0.00285106 Br 24.07724034 32.51738772 8.77765734 -0.00039715 -0.00118031 -0.00285888 Cs 23.35518841 29.88723967 11.64015504 0.00405555 -0.00329759 -0.00180718 Pb 26.71977651 32.63170142 14.89898787 -0.00065273 -0.00005744 0.00145499 Br 26.68517501 32.49944853 11.84753679 0.00264278 0.00234255 0.00128437 Br 26.97013768 29.54323785 14.71256550 0.00488386 0.00202082 0.00232752 Br 23.74670509 32.44311989 14.83468280 0.00025730 0.00211386 -0.00067385 Cs 23.53449592 29.71367798 17.90797075 -0.00100206 0.00230909 0.00072958 Pb 26.66637712 32.55883867 20.79513448 0.00028594 -0.00012980 0.00025947 Br 26.93453359 32.74820498 18.07379158 -0.00001448 -0.00177577 0.00335633 Br 26.29532751 29.60306840 20.55698893 -0.00166450 0.00000887 0.00114261 Br 23.88869868 32.81911034 20.83884495 -0.00039658 -0.00142386 0.00092306 Cs 23.36226553 29.66211087 22.99017779 0.00208196 0.00073355 0.00122150 Pb 26.68422915 32.75363262 26.77635222 0.00051638 -0.00037066 -0.00161268 Br 27.23030128 32.38741815 23.86360896 0.00250578 -0.00035729 0.00351402 Br 26.33879309 29.81892011 26.81409489 0.00040672 -0.00284260 -0.00098728 Br 23.79964377 32.75411754 26.35302766 0.00185098 -0.00169656 -0.00273089 Cs 23.72392416 30.13264509 29.31393472 -0.00104573 -0.00020201 -0.00122347 Pb 26.84026255 32.84927203 32.68561408 0.00063879 -0.00151781 0.00028883 Br 26.50111803 32.00085676 29.51474578 0.00334330 0.00029711 -0.00178625 Br 26.74563709 29.61303970 32.78834279 0.00094820 -0.00060698 0.00179825 Br 23.89024726 33.05213877 33.02425743 0.00204891 -0.00298726 0.00653680 Cs 30.08102799 35.12683985 35.33084148 0.00127996 -0.00207807 -0.00016917 Pb 32.42158405 3.27456099 3.15345006 -0.00117258 0.00070782 0.00284794 Br 32.09763428 3.46994806 0.41506324 -0.00197119 0.00220963 0.00356763 Br 32.65507376 35.57161427 3.49577177 -0.00167717 -0.00067667 -0.00088789 Br 29.52309033 2.47923097 3.73842874 0.00105902 0.00010331 0.00019588 Cs 30.14047062 0.01446575 6.14710192 -0.00144254 -0.00158219 0.00299528 Pb 32.34795573 2.88250344 9.16520459 -0.00023127 0.00223325 0.00018748 Br 32.95105627 3.08824373 6.19430012 -0.00037855 0.00065350 0.00415316 Br 32.80309111 0.02108503 9.30654598 0.00403266 -0.00059358 -0.00210182 Br 29.45472590 2.86274276 8.44386589 -0.00066799 -0.00133910 -0.00193321 Cs 30.06225127 35.36364477 11.73050979 0.00067891 0.00208745 0.00133246 Pb 32.78471941 2.86972588 15.26323518 -0.00055198 -0.00116311 0.00106516 Br 32.38670284 2.75182524 12.21728011 0.00010583 0.00253250 -0.00067184 Br 32.51395380 35.56279019 15.17010469 -0.00039180 0.00346556 -0.00006274 Br 29.69927311 3.49857428 15.10332998 -0.00072050 0.00144559 -0.00516185 Cs 29.66285470 0.39878393 17.83634972 -0.00083911 0.00228625 0.00043587 Pb 32.58125893 2.95428038 20.90060122 0.00042141 0.00020239 0.00040428 Br 33.19405326 2.80205972 18.02224131 -0.00284616 -0.00022729 -0.00410618 Br 32.39866928 0.00746277 21.34620358 -0.00038309 -0.00181203 -0.00177252 Br 29.65072415 2.93914991 20.33249916 -0.00333299 0.00094216 -0.00132760 Cs 29.61383978 35.51624571 24.23218286 0.00088718 -0.00049771 -0.00023232 Pb 32.47470869 2.96918562 26.82055614 -0.00176302 -0.00065707 0.00043884 Br 32.10778090 2.78802484 23.96848396 -0.00009047 0.00040017 0.00199396 Br 32.90987061 0.03029187 27.08692136 -0.00269475 0.00051082 -0.00319391 Br 29.46325600 2.79540134 26.87486094 -0.00188183 0.00161700 0.00154143 Cs 29.47725272 0.11726422 29.93598010 0.00081161 0.00256627 0.00208888 Pb 32.89348529 2.82321523 32.81204308 0.00019670 0.00150124 -0.00089218 Br 33.47817874 3.03616663 29.82059473 0.00130348 0.00221135 -0.00279395 Br 33.21910147 35.41878062 32.70509383 -0.00208979 -0.00206978 0.00130761 Br 29.85206154 2.54523643 32.67966055 0.00008090 0.00420027 0.00124136 Cs 29.59815187 6.22952257 35.25966202 0.00041901 -0.00118962 -0.00080180 Pb 32.54141266 8.95080198 3.01083963 0.00325992 -0.00194595 0.00138991 Br 32.74817041 8.81157425 0.02996290 0.00042069 0.00246573 -0.00267166 Br 32.18749092 6.10512298 3.52246713 0.00095454 0.00108995 0.00173973 Br 29.55792345 9.29560206 3.58487686 0.00153282 0.00087568 0.00009774 Cs 29.44949881 6.00209789 6.47743659 -0.00174312 0.00272326 -0.00127248 Pb 32.48651475 8.84659292 8.93710242 -0.00003127 0.00024581 -0.00307771 Br 32.94847967 8.84326733 5.92375489 0.00234008 0.00405643 -0.00039284 Br 32.64375839 5.80235809 8.97527530 -0.00259563 -0.00021111 0.00136290 Br 29.75944170 9.40167171 8.71494356 0.00005951 -0.00178030 0.00214787 Cs 29.89151512 5.70301537 12.17024928 0.00436404 0.00052951 0.00354973 Pb 32.74101300 8.70547766 14.77029032 -0.00138501 0.00042254 -0.00119623 Br 32.77554381 8.68844026 11.78460630 0.00022452 0.00066421 0.00240764 Br 32.95074543 6.00114690 15.09582357 -0.00295593 -0.00101825 0.00163358 Br 29.99743925 7.93904820 14.85837697 0.00190126 -0.00256339 -0.00162613 Cs 29.51176625 6.07799548 17.90480831 -0.00038150 -0.00032165 0.00040871 Pb 32.88301616 9.12901146 20.79205304 0.00244316 -0.00133393 0.00061456 Br 32.96154294 9.21884615 17.71262137 -0.00460243 -0.00021420 -0.00318493 Br 32.24305682 6.08012391 20.49648479 -0.00246056 -0.00281431 0.00047272 Br 29.82890000 9.55380046 21.13021908 -0.00271644 -0.00346134 -0.00254644 Cs 29.55027768 5.45967615 23.78802178 0.00047766 0.00033299 0.00050499 Pb 32.67388918 8.77501027 26.89671832 0.00018686 0.00257965 0.00142469 Br 32.88494413 9.24020701 23.85620279 0.00081439 0.00253765 -0.00185529 Br 33.28805104 5.82388856 26.43481120 0.00034886 0.00543208 -0.00007280 Br 29.72971508 8.53992774 26.72435825 -0.00344703 0.00132426 0.00116002 Cs 29.26256033 6.20653797 29.43703239 0.00030618 0.00047335 0.00113347 Pb 32.86941660 8.90235643 32.64209494 -0.00161432 0.00256645 0.00002580 Br 33.51978425 8.76696076 29.70759134 -0.00109399 0.00171930 -0.00196251 Br 32.95034139 5.69530291 32.92679825 0.00329784 0.00023215 -0.00107858 Br 29.84753426 9.19171781 31.74630596 -0.00159237 0.00101895 0.00091307 Cs 29.49081623 11.63140558 0.26414298 -0.00014118 0.00066942 0.00196337 Pb 32.54676248 14.84752079 2.86741445 -0.00100526 -0.00007869 -0.00095159 Br 31.93399559 14.23903577 35.57882717 0.00120226 -0.00082388 -0.00129096 Br 32.62463561 12.00188300 3.05778876 0.00122814 0.00039009 -0.00220713 Br 29.66582748 14.59041623 3.51657139 -0.00278981 -0.00126367 -0.00078780 Cs 29.34160647 11.94198158 5.82860655 -0.00133084 -0.00228655 -0.00376065 Pb 32.31598313 14.66736564 8.75401969 0.00157510 0.00141105 -0.00088005 Br 32.97315951 15.26798206 5.79654416 -0.00004245 0.00002575 0.00217719 Br 33.20670184 11.73198836 8.48800523 -0.00237826 0.00402787 -0.00452395 Br 29.63698530 13.79046770 8.88844718 0.00245807 0.00095701 0.00246138 Cs 29.73404936 12.10493810 11.95161187 0.00092844 0.00165872 0.00028975 Pb 32.56680785 14.51784005 14.66547471 -0.00039484 0.00078116 0.00015049 Br 32.45713604 14.46257980 11.86672439 -0.00300771 -0.00133448 0.00229899 Br 32.53354681 11.71753922 15.13038178 0.00221800 -0.00062624 0.00032646 Br 29.52288241 15.15791823 15.03894764 -0.00319098 -0.00278210 -0.00129946 Cs 29.50822889 12.18108902 18.28153585 0.00264343 -0.00180457 0.00005192 Pb 32.59571102 15.11398817 20.66496733 0.00093678 0.00018012 -0.00025727 Br 32.33322723 15.27540562 17.78883223 -0.00005733 0.00251887 -0.00027649 Br 32.72521517 12.24949712 20.55429188 -0.00232558 0.00207576 0.00006669 Br 29.64588493 14.92118317 20.62275683 0.00242136 -0.00002934 0.00209967 Cs 29.64187099 12.23896004 23.38935481 0.00091806 0.00002742 -0.00029121 Pb 32.57966504 14.77628211 26.81911682 -0.00061210 0.00112720 0.00061754 Br 32.07341927 14.96451138 23.72431351 0.00002320 -0.00244709 -0.00164589 Br 32.38659265 11.77353548 26.61202499 0.00005618 -0.00082274 -0.00085483 Br 29.77165308 14.74272725 26.95710009 0.00160868 -0.00191513 -0.00151214 Cs 29.14408117 12.16015090 29.55742774 0.00054375 0.00005537 0.00193605 Pb 32.74357526 14.87704181 32.57649399 -0.00112039 0.00136108 0.00251837 Br 32.28525077 15.17883470 29.62065967 0.00309531 -0.00437615 -0.00138713 Br 32.30961879 11.91533465 32.08947885 0.00259379 0.00364634 -0.00149165 Br 29.67631841 14.37081951 32.55813017 -0.00004325 -0.00171074 -0.00147899 Cs 30.45238253 17.96470057 35.24437636 -0.00169692 0.00044908 -0.00068747 Pb 32.60658946 20.91139432 3.10712668 0.00095029 0.00003931 -0.00049144 Br 32.54330759 21.21655657 35.51974276 -0.00299426 -0.00035576 0.00101108 Br 32.54049824 18.02141909 2.53265934 0.00157101 0.00262959 -0.00139045 Br 29.80003093 21.09737911 3.26312000 0.00071093 -0.00361767 -0.00460514 Cs 30.02312351 18.04181196 5.83898550 -0.00081886 -0.00095512 -0.00102379 Pb 32.60122333 20.82099027 8.81776893 -0.00015285 -0.00017582 -0.00104804 Br 33.30301978 20.06867142 5.92756243 0.00046255 -0.00141342 0.00204906 Br 32.15563476 17.62244395 8.77867350 -0.00129216 -0.00271994 -0.00005947 Br 29.57840178 21.33130969 8.51163098 0.00282308 -0.00333526 0.00361717 Cs 29.63142741 18.20248184 12.13156855 0.00067446 -0.00132394 -0.00048763 Pb 32.35092174 20.79729987 14.70565138 -0.00161713 0.00133172 0.00177378 Br 32.18221719 21.18849013 11.79169462 0.00322457 -0.00195757 0.00212526 Br 32.28634846 17.78345871 14.41365944 0.00067066 0.00316759 0.00129318 Br 29.42260606 20.86757232 14.79380579 -0.00110064 0.00025057 -0.00119592 Cs 29.95174244 18.13025975 17.74175810 -0.00141668 0.00033063 -0.00159461 Pb 32.54310341 21.08162067 20.87266080 0.00070930 0.00148489 0.00025664 Br 32.85415988 20.47875050 17.63691731 0.00069234 -0.00049701 0.00109310 Br 32.85797274 18.10499057 21.58354125 0.00169553 -0.00012898 -0.00048345 Br 29.63762289 20.78556933 20.02370810 0.00286395 -0.00196303 0.00327330 Cs 29.52686199 17.81332856 23.29650963 -0.00247378 -0.00176853 -0.00066769 Pb 32.60317913 20.63540091 26.62712407 -0.00200416 0.00220136 0.00099813 Br 31.90545535 21.30256450 23.80239091 0.00062415 -0.00131470 -0.00209029 Br 32.04212548 17.85823416 26.48642826 0.00000962 -0.00064582 0.00202306 Br 29.53168626 20.99106362 27.26846154 0.00092425 -0.00194298 0.00152830 Cs 29.67249182 17.64195461 29.88979754 -0.00091742 -0.00067078 -0.00188707 Pb 32.80744366 20.67596060 32.50351512 0.00036020 -0.00204077 0.00182889 Br 33.42853175 20.12024835 29.32065370 -0.00130633 0.00339276 0.00532307 Br 33.29208586 17.82430904 32.90510329 0.00318191 0.00030206 0.00036226 Br 29.69885536 20.92017184 32.53796335 -0.00051272 -0.00013221 0.00367478 Cs 29.82586307 23.77833306 0.58916501 0.00079135 0.00004934 -0.00244694 Pb 32.58067922 26.79857439 3.18534204 0.00208749 -0.00057518 -0.00112622 Br 32.75411627 26.46930422 0.02444324 0.00056549 -0.00087963 0.00089166 Br 32.07943791 23.62926555 3.71702280 -0.00081876 -0.00084034 0.00081959 Br 29.19836070 26.77330843 2.80248336 -0.00185205 0.00029641 -0.00215317 Cs 29.20605835 24.43437730 5.99300281 0.00263533 0.00070378 0.00331364 Pb 32.47417806 26.65261969 9.12799584 -0.00170799 0.00204021 0.00097791 Br 31.79923334 27.25309730 5.86915072 0.00189965 0.00366072 0.00133850 Br 33.10537655 23.66499299 8.35716072 -0.00217718 0.00255261 0.00589813 Br 29.74850176 26.33917286 9.34861253 -0.00136129 -0.00425560 0.00355630 Cs 29.37616458 23.70548369 11.98283135 0.00245082 -0.00028965 0.00107802 Pb 32.40768237 26.38007220 14.94371689 -0.00148483 0.00083982 -0.00124452 Br 32.87970359 26.52904935 11.92891795 -0.00227935 0.00192973 -0.00010861 Br 32.87249682 23.61993659 15.47057581 -0.00173155 -0.00311086 0.00280140 Br 29.47853848 26.54254854 14.60300051 0.00353074 0.00070617 0.00048437 Cs 29.49806752 23.91422752 17.92064688 0.00141677 -0.00141732 0.00312803 Pb 32.52429161 26.65685164 21.15122253 0.00281176 -0.00086723 -0.00107689 Br 32.46466535 26.70650034 17.84812511 0.00051022 0.00160356 -0.00013936 Br 31.82089062 23.92106882 20.44795119 0.00102224 -0.00060014 0.00211462 Br 29.48756978 26.99146030 20.95081550 0.00383001 -0.00077419 -0.00152517 Cs 29.63106814 23.84299232 24.11249009 0.00218533 0.00126694 0.00018403 Pb 32.65357911 26.73488297 26.85554713 -0.00076481 0.00066305 -0.00070485 Br 33.21154934 26.95295580 23.85906278 -0.00380384 0.00336130 -0.00008598 Br 32.77784713 23.76323187 27.19657106 -0.00154823 0.00048596 0.00281881 Br 29.63776572 26.71067121 26.32426158 0.00046015 0.00153644 0.00116296 Cs 29.59193142 23.73776210 29.52734785 -0.00084402 -0.00200237 0.00171506 Pb 32.23800168 26.65077424 32.75621987 0.00177177 -0.00205850 0.00058446 Br 32.22290545 26.72609251 29.79200707 -0.00056728 -0.00147889 -0.00051187 Br 33.05347743 23.63311784 32.34744767 0.00044759 0.00076164 -0.00227705 Br 29.59651102 25.83191888 32.80346253 -0.00182224 0.00322035 0.00032116 Cs 29.52269363 29.56489245 0.27108035 -0.00066981 -0.00362493 -0.00119952 Pb 32.84172320 32.83245502 3.19261538 0.00144996 -0.00212732 -0.00142174 Br 32.71033820 32.67780697 0.22790785 -0.00303584 -0.00182724 0.00407773 Br 32.80879939 29.69492373 2.89913686 -0.00101300 0.00269776 -0.00161886 Br 29.89458729 32.56494352 2.70565671 0.00403643 -0.00155238 -0.00071855 Cs 29.61408190 30.24101451 6.03352372 0.00044574 -0.00247664 0.00028164 Pb 32.81768985 32.72464338 9.21366680 0.00000217 0.00037249 -0.00019012 Br 32.83271045 32.66916192 6.22091190 0.00004953 0.00210538 -0.00010786 Br 32.36336888 29.70409648 9.31012608 -0.00006075 0.00014283 0.00082548 Br 29.88242404 32.52274292 8.46349848 -0.00009300 0.00273690 0.00070253 Cs 29.62953318 29.16698062 11.99543832 -0.00049794 0.00063201 -0.00227234 Pb 32.62805949 32.41730138 14.85994970 -0.00149002 0.00177355 -0.00073805 Br 32.41843549 32.30735900 12.07624489 0.00180736 -0.00124424 -0.00016359 Br 32.49663622 29.37727413 15.04235873 -0.00187058 0.00197000 0.00146413 Br 29.60662294 32.91874692 15.45296846 -0.00135906 0.00162613 -0.00368933 Cs 29.93329792 29.45994944 17.67655871 0.00028724 0.00000125 -0.00034859 Pb 32.39391042 32.75417074 20.61006486 -0.00018591 -0.00004835 0.00079421 Br 33.35499225 31.88282413 17.68627552 -0.00002779 -0.00017521 -0.00235272 Br 32.52569361 29.79495205 20.76989296 0.00136407 0.00310439 0.00326876 Br 29.64550706 32.23819254 20.26574080 0.00031584 0.00104109 -0.00098694 Cs 29.96157994 30.05242965 23.57408984 0.00031483 0.00085140 -0.00098888 Pb 32.75053951 32.69893946 26.64579979 -0.00026364 0.00099332 -0.00037963 Br 32.17546083 32.86729487 23.51347554 0.00345092 0.00042562 -0.00239715 Br 32.29247995 29.81698541 26.55508354 0.00076442 -0.00179769 -0.00133647 Br 29.70295436 32.97859985 26.97285802 -0.00016091 0.00309347 0.00278946 Cs 30.20237432 30.08877688 30.08810890 0.00059457 0.00261927 0.00113189 Pb 32.90061028 32.42206118 32.78651853 -0.00233137 -0.00129083 -0.00057076 Br 33.17856019 32.51184186 29.78146744 -0.00046578 0.00097471 0.00084088 Br 31.90075865 29.50804774 33.16618962 0.00224791 -0.00232143 0.00103782 Br 29.68603696 33.24153377 32.47353174 0.00213244 0.00067030 -0.00238545 index_file_dump_long000066400000000000000000000027451522712672400343220ustar00rootroot00000000000000dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/tests/trajectory_reader/trajectory_files[ Cs ] 1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91 96 101 106 111 116 121 126 131 136 141 146 151 156 161 166 171 176 181 186 191 196 201 206 211 216 221 226 231 236 241 246 251 256 261 266 271 276 281 286 291 296 301 306 311 316 [ Pb ] 2 7 12 17 22 27 32 37 42 47 52 57 62 67 72 77 82 87 92 97 102 107 112 117 122 127 132 137 142 147 152 157 162 167 172 177 182 187 192 197 202 207 212 217 222 227 232 237 242 247 252 257 262 267 272 277 282 287 292 297 302 307 312 317 [ Br ] 3 4 5 8 9 10 13 14 15 18 19 20 23 24 25 28 29 30 33 34 35 38 39 40 43 44 45 48 49 50 53 54 55 58 59 60 63 64 65 68 69 70 73 74 75 78 79 80 83 84 85 88 89 90 93 94 95 98 99 100 103 104 105 108 109 110 113 114 115 118 119 120 123 124 125 128 129 130 133 134 135 138 139 140 143 144 145 148 149 150 153 154 155 158 159 160 163 164 165 168 169 170 173 174 175 178 179 180 183 184 185 188 189 190 193 194 195 198 199 200 203 204 205 208 209 210 213 214 215 218 219 220 223 224 225 228 229 230 233 234 235 238 239 240 243 244 245 248 249 250 253 254 255 258 259 260 263 264 265 268 269 270 273 274 275 278 279 280 283 284 285 288 289 290 293 294 295 298 299 300 303 304 305 308 309 310 313 314 315 318 319 320positions.lammpstrj000066400000000000000000000064221522712672400342030ustar00rootroot00000000000000dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/tests/trajectory_reader/trajectory_filesITEM: TIMESTEP 103000 ITEM: NUMBER OF ATOMS 24 ITEM: BOX BOUNDS pp pp pp 0 6.25 0 6.25 0 6.25 ITEM: ATOMS type id x y z 1 1 1.91468 3.02071 0.528818 2 2 1.29395 3.5652 6.21471 2 3 1.39919 2.29819 0.989518 1 4 3.626 4.14767 2.28901 2 5 4.16502 3.38633 2.64931 2 6 3.00361 3.81053 1.58263 1 7 2.42769 6.19665 3.54307 2 8 3.06452 0.52461 4.05338 2 9 2.90869 5.38709 3.20654 1 10 3.80235 1.58899 5.41742 2 11 4.36344 2.15982 4.81798 2 12 3.13529 2.16145 5.8942 1 13 4.80923 5.75237 0.272529 2 14 4.24226 0.143661 6.00552 2 15 4.22911 5.2088 0.879155 1 16 0.417374 1.05515 1.90862 2 17 6.07448 0.524278 1.30311 2 18 0.957417 0.433166 2.47562 1 19 5.4205 2.90285 3.66719 2 20 5.97449 2.21136 3.20358 2 21 6.01398 3.63229 4.00734 1 22 0.616258 4.77853 5.1262 2 23 1.20745 5.48874 4.74398 2 24 6.16478 5.20174 5.69963 ITEM: TIMESTEP 103010 ITEM: NUMBER OF ATOMS 24 ITEM: BOX BOUNDS pp pp pp 0 6.25 0 6.25 0 6.25 ITEM: ATOMS type id x y z 1 1 1.91806 3.02462 0.537276 2 2 1.45901 3.74756 6.2709 2 3 1.24646 2.53311 1.09168 1 4 3.53456 4.12772 2.27293 2 5 4.14475 3.4532 2.68848 2 6 2.95012 3.67734 1.59796 1 7 2.47109 6.24789 3.53127 2 8 3.15164 0.497842 4.06691 2 9 2.8912 5.42956 3.13904 1 10 3.79734 1.60358 5.37577 2 11 4.59475 2.08325 5.00964 2 12 3.18476 2.26147 5.81388 1 13 4.78059 5.76109 0.282028 2 14 4.23941 0.261148 6.15184 2 15 4.25918 5.30656 1.0042 1 16 0.429525 1.03259 1.89422 2 17 6.25565 0.466682 1.18706 2 18 0.974104 0.454438 2.50181 1 19 5.45756 2.89291 3.71349 2 20 6.05988 2.32996 3.14754 2 21 5.9286 3.73936 3.96178 1 22 0.612162 4.70999 5.13274 2 23 1.24029 5.35184 4.69288 2 24 6.14482 5.21885 5.60866 ITEM: TIMESTEP 103020 ITEM: NUMBER OF ATOMS 24 ITEM: BOX BOUNDS pp pp pp 0 6.25 0 6.25 0 6.25 ITEM: ATOMS type id x y z 1 1 1.91315 3.03918 0.550044 2 2 1.48509 3.66826 6.15118 2 3 1.20457 2.51921 1.02707 1 4 3.45039 4.1245 2.24681 2 5 4.10151 3.67898 2.86127 2 6 2.93593 3.42899 1.74522 1 7 2.49116 6.28034 3.52679 2 8 3.09109 0.582952 4.10534 2 9 3.03607 5.60285 3.03275 1 10 3.78894 1.62531 5.33295 2 11 4.54583 2.15077 4.94435 2 12 3.16619 2.24309 5.81308 1 13 4.77698 5.75647 0.315634 2 14 4.34464 0.364064 6.28703 2 15 4.24151 5.33577 1.04795 1 16 0.442036 1.02591 1.88035 2 17 6.29003 0.354262 1.25804 2 18 1.14402 0.584303 2.4391 1 19 5.50131 2.87751 3.7384 2 20 6.09836 2.40101 3.09304 2 21 6.02416 3.58185 4.21854 1 22 0.618073 4.66276 5.13836 2 23 1.21059 5.24685 4.58359 2 24 6.10409 5.20268 5.49166 ITEM: TIMESTEP 103030 ITEM: NUMBER OF ATOMS 24 ITEM: BOX BOUNDS pp pp pp 0 6.25 0 6.25 0 6.25 ITEM: ATOMS type id x y z 1 1 1.88343 3.02189 0.535152 2 2 1.37093 3.57604 6.12921 2 3 1.29211 2.3007 0.896034 1 4 3.42109 4.15174 2.26632 2 5 4.03666 3.64818 2.87253 2 6 2.87094 3.50507 1.73797 1 7 2.48542 6.31641 3.52174 2 8 3.00448 0.635016 4.15991 2 9 3.11014 5.70483 3.03625 1 10 3.77663 1.65095 5.3047 2 11 4.27621 2.24376 4.67303 2 12 3.12429 2.19772 5.82957 1 13 4.80097 5.75228 0.371818 2 14 4.35032 0.306132 6.23356 2 15 4.13772 5.21982 0.897723 1 16 0.469709 1.03389 1.86811 2 17 6.19299 0.285638 1.46476 2 18 1.25849 0.660776 2.35659 1 19 5.54475 2.87608 3.74827 2 20 6.0391 2.31915 3.08085 2 21 6.18653 3.22714 4.43009 1 22 0.629838 4.63427 5.12616 2 23 1.17089 5.24826 4.55146 2 24 6.1288 5.14496 5.54465 positions_and_velocities.lammpstrj000066400000000000000000000147441522712672400372610ustar00rootroot00000000000000dynasor-2.4-c7370550a3212194be3784c60d67152563ff8d49/tests/trajectory_reader/trajectory_filesITEM: TIMESTEP 103000 ITEM: NUMBER OF ATOMS 24 ITEM: BOX BOUNDS pp pp pp 0 6.25 0 6.25 0 6.25 ITEM: ATOMS type id x y z vx vy vz 1 1 1.91468 3.02071 0.528818 0.00105876 0.000389945 0.000540445 2 2 1.29395 3.5652 6.21471 0.0116399 0.0238521 0.0115804 2 3 1.39919 2.29819 0.989518 -0.0190113 0.0234721 0.0142206 1 4 3.626 4.14767 2.28901 -0.00775121 -0.00332156 0.00045625 2 5 4.16502 3.38633 2.64931 -0.00916183 -0.00898758 -0.0093385 2 6 3.00361 3.81053 1.58263 -0.00415819 0.00792349 -0.00812868 1 7 2.42769 6.19665 3.54307 0.00516741 0.00576708 -0.00187862 2 8 3.06452 0.52461 4.05338 0.0147389 -0.00707458 0.000731629 2 9 2.90869 5.38709 3.20654 -0.00964617 -0.00368993 -0.00034467 1 10 3.80235 1.58899 5.41742 0.000160311 0.000400954 -0.00271396 2 11 4.36344 2.15982 4.81798 0.0317508 -0.00780526 0.0190895 2 12 3.13529 2.16145 5.8942 0.00501066 0.0133524 -0.0114753 1 13 4.80923 5.75237 0.272529 -0.0041924 0.00248113 5.02226e-05 2 14 4.24226 0.143661 6.00552 -0.00864071 0.00125601 0.00341038 2 15 4.22911 5.2088 0.879155 0.00156727 0.00697033 0.00957028 1 16 0.417374 1.05515 1.90862 0.00143456 -0.00280331 -0.00114098 2 17 6.07448 0.524278 1.30311 0.0218267 -0.00281436 -0.0211203 2 18 0.957417 0.433166 2.47562 -0.0062517 -0.00294501 0.00603581 1 19 5.4205 2.90285 3.66719 0.00245983 0.000292166 0.00455708 2 20 5.97449 2.21136 3.20358 0.00989121 0.00880165 0.000767886 2 21 6.01398 3.63229 4.00734 -0.00868655 0.0196349 -0.0174331 1 22 0.616258 4.77853 5.1262 -0.000836257 -0.00691637 0.000166677 2 23 1.20745 5.48874 4.74398 0.00678782 -0.0119932 0.00252133 2 24 6.16478 5.20174 5.69963 0.00200832 -0.001067 -0.00067658 ITEM: TIMESTEP 103010 ITEM: NUMBER OF ATOMS 24 ITEM: BOX BOUNDS pp pp pp 0 6.25 0 6.25 0 6.25 ITEM: ATOMS type id x y z vx vy vz 1 1 1.91806 3.02462 0.537276 -7.69697e-05 0.0011057 0.00138714 2 2 1.45901 3.74756 6.2709 0.0132496 0.0052182 -0.00472884 2 3 1.24646 2.53311 1.09168 -0.00995092 0.0150081 0.00180022 1 4 3.53456 4.12772 2.27293 -0.00974017 -0.000996474 -0.0031475 2 5 4.14475 3.4532 2.68848 0.000399266 0.0202446 0.0163603 2 6 2.95012 3.67734 1.59796 -0.00427778 -0.0296536 0.0113008 1 7 2.47109 6.24789 3.53127 0.00327942 0.00414788 -0.000659915 2 8 3.15164 0.497842 4.06691 0.000631192 0.00383505 0.00299396 2 9 2.8912 5.42956 3.13904 0.00904231 0.0121781 -0.0112 1 10 3.79734 1.60358 5.37577 -0.000732717 0.00212546 -0.0048415 2 11 4.59475 2.08325 5.00964 0.00926688 -0.00268902 0.0105133 2 12 3.18476 2.26147 5.81388 0.00195439 0.00387369 -0.00370498 1 13 4.78059 5.76109 0.282028 -0.00172197 -0.00023565 0.00205876 2 14 4.23941 0.261148 6.15184 0.00892234 0.0164616 0.0198433 2 15 4.25918 5.30656 1.0042 0.00365635 0.00898675 0.0117617 1 16 0.429525 1.03259 1.89422 0.00105961 -0.00148984 -0.00161911 2 17 6.25565 0.466682 1.18706 0.0111244 -0.0094255 -0.0013067 2 18 0.974104 0.454438 2.50181 0.011001 0.00848514 -0.00106207 1 19 5.45756 2.89291 3.71349 0.00444433 -0.0018541 0.00389442 2 20 6.05988 2.32996 3.14754 0.00640523 0.0119555 -0.00776534 2 21 5.9286 3.73936 3.96178 -0.00269868 -0.000188289 0.0117462 1 22 0.612162 4.70999 5.13274 0.000137718 -0.00610904 0.000994942 2 23 1.24029 5.35184 4.69288 -0.000862432 -0.0135273 -0.0112443 2 24 6.14482 5.21885 5.60866 -0.0046787 0.00171199 -0.0146299 ITEM: TIMESTEP 103020 ITEM: NUMBER OF ATOMS 24 ITEM: BOX BOUNDS pp pp pp 0 6.25 0 6.25 0 6.25 ITEM: ATOMS type id x y z vx vy vz 1 1 1.91315 3.03918 0.550044 -0.00152697 0.000879521 0.000320957 2 2 1.48509 3.66826 6.15118 -0.00622131 -0.0151365 -0.0121246 2 3 1.20457 2.51921 1.02707 0.00363719 -0.0174163 -0.0120113 1 4 3.45039 4.1245 2.24681 -0.00613918 0.000796462 -0.000791446 2 5 4.10151 3.67898 2.86127 -0.00794544 0.0155087 0.0118607 2 6 2.93593 3.42899 1.74522 -0.00126796 -0.0112123 0.0107813 1 7 2.49116 6.28034 3.52679 0.000721665 0.0028145 -0.000399492 2 8 3.09109 0.582952 4.10534 -0.0104955 0.00957257 0.0047806 2 9 3.03607 5.60285 3.03275 0.0145924 0.0190311 -0.00737087 1 10 3.78894 1.62531 5.33295 -0.000926339 0.00222064 -0.00345869 2 11 4.54583 2.15077 4.94435 -0.019044 0.0138952 -0.022821 2 12 3.16619 2.24309 5.81308 -0.00451462 -0.00586593 0.00229066 1 13 4.77698 5.75647 0.315634 0.00123517 -0.000531559 0.0046138 2 14 4.34464 0.364064 6.28703 0.00659118 0.00207806 0.0043116 2 15 4.24151 5.33577 1.04795 -0.00937022 -0.00423903 -0.00530556 1 16 0.442036 1.02591 1.88035 0.0017731 0.000119745 -0.0011502 2 17 6.29003 0.354262 1.25804 -0.00346259 -0.0115702 0.0148717 2 18 1.14402 0.584303 2.4391 0.0182037 0.0144337 -0.0104606 1 19 5.50131 2.87751 3.7384 0.00431278 -0.000537781 0.00137471 2 20 6.09836 2.40101 3.09304 -0.0001322 -0.00118246 -0.00225833 2 21 6.02416 3.58185 4.21854 0.0194731 -0.0330873 0.0324963 1 22 0.618073 4.66276 5.13836 0.000878532 -0.00354398 -0.000375304 2 23 1.21059 5.24685 4.58359 -0.00366967 -0.00536017 -0.00715421 2 24 6.10409 5.20268 5.49166 -0.00159224 -0.00464713 -0.00401843 ITEM: TIMESTEP 103030 ITEM: NUMBER OF ATOMS 24 ITEM: BOX BOUNDS pp pp pp 0 6.25 0 6.25 0 6.25 ITEM: ATOMS type id x y z vx vy vz 1 1 1.88343 3.02189 0.535152 -0.00385339 -0.00471696 -0.00314589 2 2 1.37093 3.57604 6.12921 -0.0156995 -0.000622159 0.00959868 2 3 1.29211 2.3007 0.896034 0.00840018 -0.0190589 -0.0116593 1 4 3.42109 4.15174 2.26632 -8.49087e-05 0.00433746 0.00416711 2 5 4.03666 3.64818 2.87253 -0.00351108 -0.0179241 -0.010904 2 6 2.87094 3.50507 1.73797 -0.0102032 0.0239435 -0.00924329 1 7 2.48542 6.31641 3.52174 -0.00180879 0.00471469 -0.000594351 2 8 3.00448 0.635016 4.15991 -0.0048423 4.02817e-05 0.00603585 2 9 3.11014 5.70483 3.03625 -0.00127582 -0.0016753 0.00817086 1 10 3.77663 1.65095 5.3047 -0.00199776 0.00279613 -0.00254015 2 11 4.27621 2.24376 4.67303 -0.0253459 0.00193674 -0.0218887 2 12 3.12429 2.19772 5.82957 -0.00207471 -0.000896097 0.00121416 1 13 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