pax_global_header00006660000000000000000000000064151247410350014514gustar00rootroot0000000000000052 comment=8f4a8f91507ab3c3721e8d3f73e73ecca373546a ewoks-v4.0.0-8f4a8f91507ab3c3721e8d3f73e73ecca373546a/000077500000000000000000000000001512474103500205015ustar00rootroot00000000000000ewoks-v4.0.0-8f4a8f91507ab3c3721e8d3f73e73ecca373546a/.flake8000066400000000000000000000001211512474103500216460ustar00rootroot00000000000000[flake8] extend-ignore = E203,E501,E701 max-line-length = 88 exclude = [".eggs"] ewoks-v4.0.0-8f4a8f91507ab3c3721e8d3f73e73ecca373546a/.flake8_nb000066400000000000000000000000771512474103500223370ustar00rootroot00000000000000[flake8_nb] extend-ignore = E203,E501,E701 max-line-length = 88ewoks-v4.0.0-8f4a8f91507ab3c3721e8d3f73e73ecca373546a/.gitignore000066400000000000000000000004041512474103500224670ustar00rootroot00000000000000# Hidden files .* !.gitignore !.gitlab-ci.yml !.readthedocs.yaml !.flake8 !.flake8_nb # Byte / compiled / optimized *.py[cod] __pycache__/ *.so # Distribution / packaging /build/ /dist/ *.egg-info/ .eggs/ /doc/reference/_generated # Dask dask-worker-space/ ewoks-v4.0.0-8f4a8f91507ab3c3721e8d3f73e73ecca373546a/.gitlab-ci.yml000066400000000000000000000053101512474103500231340ustar00rootroot00000000000000include: - project: dau/ci/pyci file: .gitlab-ci-template.yml variables: WARNING1: "ignore::DeprecationWarning:jupyter_client.session" WARNING2: "ignore::DeprecationWarning:orangecanvas.utils.localization" WARNING3: "ignore::DeprecationWarning:orangecanvas.utils.localization.si" WARNING4: "ignore::DeprecationWarning:matplotlib._fontconfig_pattern" # https://github.com/matplotlib/matplotlib/issues/30617 WARNING5: "ignore::DeprecationWarning:matplotlib._mathtext" IGNORE_WARNINGS: "-W ${WARNING1} -W ${WARNING2} -W ${WARNING3} -W ${WARNING4} -W ${WARNING5}" flake8_nb: extends: .flake8_nb isort: extends: .isort test-3.8_glx: extends: .test-3.8_glx variables: JUPYTER_PLATFORM_DIRS: 1 PYTEST_WARNINGS: "-W error ${IGNORE_WARNINGS}" test-3.9_glx-win: extends: .test-3.9_glx-win variables: JUPYTER_PLATFORM_DIRS: 1 PYTEST_WARNINGS: "-W error ${IGNORE_WARNINGS}" timeout: 15m test-3.13_glx: extends: .test-3.13_glx variables: JUPYTER_PLATFORM_DIRS: 1 PYTEST_WARNINGS: "-W error ${IGNORE_WARNINGS}" build_sdist: extends: .build_sdist test_sdist-3.8_glx-win: extends: .test_sdist-3.8_glx-win script: - !reference [.test_sdist-3.8_glx-win, script] - python -m pip install ewokscore[test] - python -m pytest -v --pyargs ewokscore.tests - python -m pytest -v --pyargs ewoksppf.tests - python -m pytest -v --pyargs ewoksdask.tests #- python -m pytest -v --pyargs ewoksorange.tests variables: JUPYTER_PLATFORM_DIRS: 1 PYTEST_WARNINGS: "-W error ${IGNORE_WARNINGS}" timeout: 15m test_sdist-3.9_glx: extends: .test_sdist-3.9_glx script: - !reference [.test_sdist-3.9_glx, script] - python -m pip install ewokscore[test] - python -m pytest -v --pyargs ewokscore.tests - python -m pytest -v --pyargs ewoksppf.tests - python -m pytest -v --pyargs ewoksdask.tests - python -m pytest -v --pyargs ewoksorange.tests variables: JUPYTER_PLATFORM_DIRS: 1 PYTEST_WARNINGS: "-W error ${IGNORE_WARNINGS} -W ignore::RuntimeWarning:networkx.utils.backends" #https://github.com/networkx/networkx/issues/7372 timeout: 15m test_sdist-3.13_glx: extends: .test_sdist-3.13_glx script: - !reference [.test_sdist-3.13_glx, script] - python -m pip install ewokscore[test] - python -m pytest -v --pyargs ewokscore.tests - python -m pytest -v --pyargs ewoksppf.tests - python -m pytest -v --pyargs ewoksdask.tests - python -m pytest -v --pyargs ewoksorange.tests variables: JUPYTER_PLATFORM_DIRS: 1 PYTEST_WARNINGS: "-W error ${IGNORE_WARNINGS}" timeout: 15m build_doc: extends: .build_doc pages: extends: .pages assets: extends: .assets testpypi: extends: .testpypi pypi: extends: .pypi ewoks-v4.0.0-8f4a8f91507ab3c3721e8d3f73e73ecca373546a/.readthedocs.yaml000066400000000000000000000011421512474103500237260ustar00rootroot00000000000000# Read the Docs configuration file # See https://docs.readthedocs.io/en/stable/config-file/v2.html for details # Required version: 2 # Set the version of Python and other tools you might need build: os: ubuntu-20.04 tools: python: "3.9" # Build documentation in the docs/ directory with Sphinx sphinx: configuration: doc/conf.py # If using Sphinx, optionally build your docs in additional formats such as PDF # formats: # - pdf # Optionally declare the Python requirements required to build your docs python: install: - method: pip path: . extra_requirements: - docewoks-v4.0.0-8f4a8f91507ab3c3721e8d3f73e73ecca373546a/CHANGELOG.md000066400000000000000000000133321512474103500223140ustar00rootroot00000000000000# Changelog All notable changes to this project will be documented in this file. The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.1.0/), and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html). ## [Unreleased] ## [4.0.0] - 2025-12-30 ### Changes - Depend on `ewokscore 4.x`. ## [3.0.0] - 2025-12-12 ### Added - `execute_graph`: ICAT startDate and endDate are the start and end of the workflow by default. ### Changes - Depend on `ewoksorange 3.x`. ## [2.1.2] - 2025-11-07 ### Fixed - Handle failure of requirement generation when `pip` is not installed. ## [2.1.1] - 2025-11-03 ### Fixed - Improved "requires package" error messages. ## [2.1.0] - 2025-10-31 ### Added - Add `ewoks cancel` CLI command. ### Changed - CLI: refactor and rename modules. ## [2.0.1] - 2025-08-15 ### Fixed - Try the `core` engine first when (de)serializing graphs. ## [2.0.0] - 2025-07-25 ### Added - Use `ewoks.engines` entry points to find engines by name. - Use `ewoks.engines.serialization.representations` entry points to find engines by graph representation. ## [1.3.0] - 2025-06-26 ### Added - Ewoks CLI `install` command: sanitize requirements, especially handling editable installations without a remote. ## [1.2.0] - 2025-06-12 ### Added - Ewoks CLI `show` command: print all workflow parameters that are not connected to outputs from other nodes. ## [1.1.0] - 2025-04-07 ### Added - The base class to create Ewoks tasks can be imported from `ewoks`: `from ewoks import Task`. - `submit_graph` can be imported from `ewoks`: `from ewoks import submit_graph`. - Task inputs can be defined via a Pydantic model (`input_model`) instead of `input_names`, `optional_input_names`. The model needs to derive from `BaseInputModel`: `from ewoks import BaseInputModel`. ### Changed - Drop Python 3.6 and 3.7 and add support for Python 3.13. ## [1.0.0] - 2024-12-25 ### Changed - Remove deprecated argument `binding` from `execute_graph`. ## [0.6.0] - 2024-11-08 ### Added - Add graph installation to the python API (`install_graph`) and CLI (`ewoks install`). - Add requirements when converting a graph so the graph becomes installable. This is an opt-out feature. - Add task options to python API (`execute_graph` and `submit_graph`) and CLI (`ewoks install` and `ewoks submit`). ## [0.5.0] - 2024-06-23 ### Changed - Client-side graph resolution by default for submitting workflows. ### Fixed - The pyyaml 6.0.2rc1 package is broken. ## [0.4.3] - 2024-02-13 ### Changed - Ewoks CLI workflow search: sort by creation date. - Ewoks CLI `convert` command: support multiple workflow arguments. ## [0.4.2] - 2024-02-05 ### Added - CLI support for multiple workflows (explicit or with search pattern). ## [0.4.1] - 2023-12-15 ### Added - Improved documentation. ### Fixed - Fix test dependencies. ## [0.4.0] - 2023-12-15 ### Added - Add support for jupyter notebooks as workflow tasks. ## [0.3.0] - 2023-06-09 ### Changed - Update dependency bounds. ## [0.2.0] - 2023-03-27 ### Changed - CLI: rename --output to --outputs. - Add celery options to `submit_graph` and `ewoks submit`. ## [0.1.5] - 2023-03-23 ### Changed - Add 'esrf-data-portal' as extra pip requirement. ## [0.1.4] - 2023-03-23 ### Added - Add saving and uploading to graph execution. ## [0.1.3] - 2023-03-20 ### Added - Workflow caching when loading. ### Fixed - CLI binding argument no longer exists for `ewoks submit`. ## [0.1.2] - 2023-03-09 ### Deprecated - Ewoks event field "binding" is deprecated in favor of "engine". ## [0.1.1] - 2023-03-08 ### Changed - Pin minor versions of ewoks projects. ## [0.1.0] - 2023-01-11 ### Added - `ewokscore` dependency. - `ewoksdask` dependency. - `ewoksorange` dependency. - `ewoksppf` dependency. - Command-line interface. [unreleased]: https://gitlab.esrf.fr/workflow/ewoks/ewoks/compare/v4.0.0...HEAD [4.0.0]: https://gitlab.esrf.fr/workflow/ewoks/ewoks/compare/v3.0.0...v4.0.0 [3.0.0]: https://gitlab.esrf.fr/workflow/ewoks/ewoks/compare/v2.1.2...v3.0.0 [2.1.2]: https://gitlab.esrf.fr/workflow/ewoks/ewoks/compare/v2.1.1...v2.1.2 [2.1.1]: https://gitlab.esrf.fr/workflow/ewoks/ewoks/compare/v2.1.0...v2.1.1 [2.1.0]: https://gitlab.esrf.fr/workflow/ewoks/ewoks/compare/v2.0.1...v2.1.0 [2.0.1]: https://gitlab.esrf.fr/workflow/ewoks/ewoks/compare/v2.0.0...v2.0.1 [2.0.0]: https://gitlab.esrf.fr/workflow/ewoks/ewoks/compare/v1.3.0...v2.0.0 [1.3.0]: https://gitlab.esrf.fr/workflow/ewoks/ewoks/compare/v1.2.0...v1.3.0 [1.2.0]: https://gitlab.esrf.fr/workflow/ewoks/ewoks/compare/v1.1.0...v1.2.0 [1.1.0]: https://gitlab.esrf.fr/workflow/ewoks/ewoks/compare/v1.0.0...v1.1.0 [1.0.0]: https://gitlab.esrf.fr/workflow/ewoks/ewoks/compare/v0.6.0...v1.0.0 [0.6.0]: https://gitlab.esrf.fr/workflow/ewoks/ewoks/compare/v0.5.0...v0.6.0 [0.5.0]: https://gitlab.esrf.fr/workflow/ewoks/ewoks/compare/v0.4.3...v0.5.0 [0.4.3]: https://gitlab.esrf.fr/workflow/ewoks/ewoks/compare/v0.4.2...v0.4.3 [0.4.2]: https://gitlab.esrf.fr/workflow/ewoks/ewoks/compare/v0.4.1...v0.4.2 [0.4.1]: https://gitlab.esrf.fr/workflow/ewoks/ewoks/compare/v0.4.0...v0.4.1 [0.4.0]: https://gitlab.esrf.fr/workflow/ewoks/ewoks/compare/v0.3.0...v0.4.0 [0.3.0]: https://gitlab.esrf.fr/workflow/ewoks/ewoks/compare/v0.2.0...v0.3.0 [0.2.0]: https://gitlab.esrf.fr/workflow/ewoks/ewoks/compare/v0.1.5...v0.2.0 [0.1.5]: https://gitlab.esrf.fr/workflow/ewoks/ewoks/compare/v0.1.4...v0.1.5 [0.1.4]: https://gitlab.esrf.fr/workflow/ewoks/ewoks/compare/v0.1.3...v0.1.4 [0.1.3]: https://gitlab.esrf.fr/workflow/ewoks/ewoks/compare/v0.1.2...v0.1.3 [0.1.2]: https://gitlab.esrf.fr/workflow/ewoks/ewoks/compare/v0.1.1...v0.1.2 [0.1.1]: https://gitlab.esrf.fr/workflow/ewoks/ewoks/compare/v0.1.0...v0.1.1 [0.1.0]: https://gitlab.esrf.fr/workflow/ewoks/ewoks/-/tags/v0.1.0 ewoks-v4.0.0-8f4a8f91507ab3c3721e8d3f73e73ecca373546a/CONTRIBUTING.md000066400000000000000000000001541512474103500227320ustar00rootroot00000000000000CONTRIBUTING.mdewoks-v4.0.0-8f4a8f91507ab3c3721e8d3f73e73ecca373546a/LICENSE.md000066400000000000000000000021161512474103500221050ustar00rootroot00000000000000# MIT License **Copyright (c) 2021 European Synchrotron Radiation Facility** 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. ewoks-v4.0.0-8f4a8f91507ab3c3721e8d3f73e73ecca373546a/README.md000066400000000000000000000024411512474103500217610ustar00rootroot00000000000000# Ewoks: Extensible Workflow System Many [workflow management systems](https://s.apache.org/existing-workflow-systems) exist to deal with data processing problems that can be expressed as a graph of tasks, also referred to as a *computational graph* or *workflow*. The main purpose of a workflow management system is to provide a framework for implementing tasks, creating graphs of tasks and executing these graphs. The purpose of *ewoks* is to provide an abstraction layer between graph representation and execution. This allows using the same tasks and graphs in different workflow management systems. *ewoks* itself is **not** a workflow management system. ## Install ```bash pip install ewoks[orange,dask,ppf,test] ``` ## Test ```bash pytest --pyargs ewoks.tests ``` ## Getting started Workflows can be executed from the command line ```bash ewoks execute /path/to/graph.json [--engine dask] ``` or for an installation with the system python ```bash python3 -m ewoks execute /path/to/graph.json [--engine dask] ``` Workflows can also be executed from python ```python from ewoks import execute_graph result = execute_graph("/path/to/graph.json", engine="dask") ``` When no engine is specified it will use sequential execution from `ewokscore`. ## Documentation https://ewoks.readthedocs.io/ ewoks-v4.0.0-8f4a8f91507ab3c3721e8d3f73e73ecca373546a/doc/000077500000000000000000000000001512474103500212465ustar00rootroot00000000000000ewoks-v4.0.0-8f4a8f91507ab3c3721e8d3f73e73ecca373546a/doc/_static/000077500000000000000000000000001512474103500226745ustar00rootroot00000000000000ewoks-v4.0.0-8f4a8f91507ab3c3721e8d3f73e73ecca373546a/doc/_static/custom.css000066400000000000000000000001171512474103500247170ustar00rootroot00000000000000button.copybtn { opacity: 0.3; } button.copybtn:hover { opacity: 1; } ewoks-v4.0.0-8f4a8f91507ab3c3721e8d3f73e73ecca373546a/doc/_templates/000077500000000000000000000000001512474103500234035ustar00rootroot00000000000000ewoks-v4.0.0-8f4a8f91507ab3c3721e8d3f73e73ecca373546a/doc/_templates/autosummary/000077500000000000000000000000001512474103500257715ustar00rootroot00000000000000ewoks-v4.0.0-8f4a8f91507ab3c3721e8d3f73e73ecca373546a/doc/_templates/autosummary/class.rst000066400000000000000000000002571512474103500276340ustar00rootroot00000000000000{{ fullname | escape | underline}} .. currentmodule:: {{ module }} .. autoclass:: {{ objname }} :members: :undoc-members: :show-inheritance: :inherited-members: ewoks-v4.0.0-8f4a8f91507ab3c3721e8d3f73e73ecca373546a/doc/_templates/autosummary/module.rst000066400000000000000000000021451512474103500300120ustar00rootroot00000000000000{{ fullname | escape | underline}} .. automodule:: {{ fullname }} {% block attributes %} {% if attributes %} .. rubric:: Module Attributes .. autosummary:: :toctree: {% for item in attributes %} {{ item }} {%- endfor %} {% endif %} {% endblock %} {% block functions %} {% if functions %} .. rubric:: {{ _('Functions') }} .. autosummary:: :toctree: {% for item in functions %} {{ item }} {%- endfor %} {% endif %} {% endblock %} {% block classes %} {% if classes %} .. rubric:: {{ _('Classes') }} .. autosummary:: :toctree: {% for item in classes %} {{ item }} {%- endfor %} {% endif %} {% endblock %} {% block exceptions %} {% if exceptions %} .. rubric:: {{ _('Exceptions') }} .. autosummary:: :toctree: {% for item in exceptions %} {{ item }} {%- endfor %} {% endif %} {% endblock %} {% block modules %} {% if modules %} .. rubric:: Modules .. autosummary:: :toctree: :recursive: {% for item in modules %} {{ item }} {%- endfor %} {% endif %} {% endblock %}ewoks-v4.0.0-8f4a8f91507ab3c3721e8d3f73e73ecca373546a/doc/_templates/footer_end.html000066400000000000000000000000341512474103500264120ustar00rootroot00000000000000{{ project }} {{ release }} ewoks-v4.0.0-8f4a8f91507ab3c3721e8d3f73e73ecca373546a/doc/conf.py000066400000000000000000000036441512474103500225540ustar00rootroot00000000000000# -- Project information ----------------------------------------------------- # https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information import importlib.metadata release = importlib.metadata.version("ewoks") project = "ewoks" version = ".".join(release.split(".")[:2]) copyright = "2021-present, ESRF" author = "ESRF" docstitle = f"{project} {version}" # -- General configuration --------------------------------------------------- # https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration extensions = [ "sphinxarg.ext", "sphinx.ext.autodoc", "sphinx.ext.autosummary", "sphinx.ext.viewcode", "sphinx_autodoc_typehints", "nbsphinx", "nbsphinx_link", "sphinx_copybutton", ] templates_path = ["_templates"] exclude_patterns = ["build", "**.ipynb_checkpoints"] always_document_param_types = True autosummary_generate = True autodoc_default_flags = [ "members", "undoc-members", "show-inheritance", ] copybutton_prompt_text = r">>> |\.\.\. |\$ |In \[\d*\]: | {2,5}\.\.\.: | {5,8}: " copybutton_prompt_is_regexp = True # -- Options for HTML output ------------------------------------------------- # https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output html_theme = "pydata_sphinx_theme" html_title = docstitle # html_logo = "_static/logo.png" html_static_path = ["_static"] html_template_path = ["_templates"] html_css_files = ["custom.css"] html_theme_options = { "icon_links": [ { "name": "gitlab", "url": "https://gitlab.esrf.fr/workflow/ewoks/ewoks", "icon": "fa-brands fa-gitlab", }, { "name": "pypi", "url": "https://pypi.org/project/ewoks", "icon": "fa-brands fa-python", }, ], "logo": { "text": docstitle, }, "footer_start": ["copyright"], "footer_end": ["footer_end"], } ewoks-v4.0.0-8f4a8f91507ab3c3721e8d3f73e73ecca373546a/doc/explanations.rst000066400000000000000000000002431512474103500245040ustar00rootroot00000000000000Explanations ============ Explanations of some Ewoks concepts. .. toctree:: :maxdepth: 1 explanations/task_errors explanations/task_input_priority ewoks-v4.0.0-8f4a8f91507ab3c3721e8d3f73e73ecca373546a/doc/explanations/000077500000000000000000000000001512474103500237535ustar00rootroot00000000000000ewoks-v4.0.0-8f4a8f91507ab3c3721e8d3f73e73ecca373546a/doc/explanations/task_errors.rst000066400000000000000000000045751512474103500270560ustar00rootroot00000000000000Errors raised when executing an Ewoks task ========================================== .. warning:: To showcase the errors raised when executing a task, we use the task execution mechanism described in `Execute a task from Python <../howtoguides/task_python>`_. Be sure to read `Execute a task from Python <../howtoguides/task_python>`_ before reading this page. The principle stays the same when executing a task in a workflow. An exception encountered when running an Ewoks task will always be chained with a ``RuntimeError`` telling which task failed: .. code-block:: python >>> from ewokscore.tests.examples.tasks.sumtask import SumTask >>> task = SumTask(inputs={"a": 'a_string', "b": 10}) >>> task.execute() Traceback (most recent call last): <...> TypeError: can only concatenate str (not "int") to str The above exception was the direct cause of the following exception: Traceback (most recent call last): <...> RuntimeError: Task 'ewokscore.tests.examples.tasks.sumtask.SumTask' failed The top-level exception will always be ``RuntimeError`` while the real exception (in this case ``TypeError``) is one level below. This is particularly important when testing or catching exceptions: .. code-block:: python >>> task = SumTask(inputs={"a": 'a_string', "b": 10}) >>> try: ... task.execute() ... except TypeError as e: ... print('TYPE_ERROR') ... exception = e ... except RuntimeError as e: ... print('RUNTIME_ERROR') ... exception = e RUNTIME_ERROR >>> exception RuntimeError("Task 'ewokscore.tests.examples.tasks.sumtask.SumTask' failed") We see here that the error caught is indeed the ``RuntimeError`` and not the ``TypeError``. To retrieve the original error, we have to go back to the ``RuntimeError`` cause: .. code-block:: python >>> original_exception = exception.__cause__ >>> original_exception TypeError('can only concatenate str (not "int") to str') Full code --------- .. code-block:: python from ewokscore.tests.examples.tasks.sumtask import SumTask task = SumTask(inputs={"a": 'a_string', "b": 10}) try: task.execute() except TypeError as e: print('TYPE_ERROR') exception = e except RuntimeError as e: print('RUNTIME_ERROR') exception = e original_exception = exception.__cause__ewoks-v4.0.0-8f4a8f91507ab3c3721e8d3f73e73ecca373546a/doc/explanations/task_input_priority.rst000066400000000000000000000100561512474103500306310ustar00rootroot00000000000000Priority of task inputs ======================= Details ------- A node in a workflow can get inputs from three different sources: 1. Via the ``data_mapping`` link attribute of an incoming link (see `Link attributes `_) 2. Via the ``parameters`` CLI argument (or ``inputs`` for Python) when executing/submitting the workflow (see `ewoks execute reference `_) 3. Via the ``default_inputs`` node attribute of the node itself (see `Node attributes `_) If the same input is specified by these different sources, *ewoks* applies the following priorities: - **Data mapping takes precedence over workflow parameters and default inputs** - If the input is not specified by the data mapping, **the workflow parameters takes precedence over the default inputs** - If the input is not specified by the data mapping nor the workflow parameters, **default inputs are used as last resort** TL;DR ----- .. code-block:: Data mapping > Workflow parameters > Node default inputs Example ------- Consider the following workflow made of `SumTask` nodes from `ewokscore` (`SumTask` takes two inputs `a` and `b` and sums them): .. code-block:: python wf = { "graph": {"id": "Sum"}, "nodes": [ { "id": "sum_node", "task_type": "class", "task_identifier": "ewokscore.tests.examples.tasks.sumtask.SumTask", "default_inputs": [{"name": "a", "value": 1}, {"name": "b", "value": 2}], }, ], "links": [], } In this case, we have given two ``default_inputs`` to our ``sum_node``: - ``a = 1`` - ``b = 2`` If we execute the workflow with no parameters/inputs: .. code-block:: python-console >>> execute_graph(graph=wf) {'result': 3} There is no incoming link with data mapping, no workflow inputs, the two default inputs are summed to get ``3``. --- If we specify a value for ``b`` when executing the workflow: .. code-block:: python-console >>> execute_graph( ... graph=wf, ... inputs=[ ... { ... "name": "b", ... "value": 20, ... "id": "sum_node", ... } ... ], ... ) {'result': 21} ``a`` is still ``1`` but the default value of ``b`` was replaced by the workflow input value (``20``), changing to result to ``21``. --- If we change our workflow to include a ``SumTask`` before our ``sum_node`` with an incoming data mapping that sets ``b`` value to its result (in this case ``200``): .. code-block:: python new_wf = { "graph": {"id": "Sum"}, "nodes": [ { "id": "first_node", "task_type": "class", "task_identifier": "ewokscore.tests.examples.tasks.sumtask.SumTask", "default_inputs": [{"name": "a", "value": 100}, {"name": "b", "value": 100}], }, { "id": "sum_node", "task_type": "class", "task_identifier": "ewokscore.tests.examples.tasks.sumtask.SumTask", "default_inputs": [{"name": "a", "value": 1}, {"name": "b", "value": 2}], }, ], "links": [ { "source": "first_node", "target": "sum_node", "data_mapping": [{"source_output": "result", "target_input": "b"}], } ], } Then, if we execute as before (with the workflow input): .. code-block:: python-console >>> execute_graph( ... graph=new_wf, ... inputs=[ ... { ... "name": "b", ... "value": 20, ... "id": "sum_node", ... } ... ], ... ) {'result': 201} We see that both the default input and the workflow input for ``b`` of ``sum_node`` are ignored, the data mapping taking precedence to change the result to ``201``. ewoks-v4.0.0-8f4a8f91507ab3c3721e8d3f73e73ecca373546a/doc/howtoguides.rst000066400000000000000000000011501512474103500243360ustar00rootroot00000000000000How-to Guides ============= *Ewoks* is versatile with respect to workflow creation, edition, and execution across different environments and interfaces. This guide presents several possible ways of handling workflows with *Ewoks*, whether through command-line tools, Python APIs, graphical interfaces, or REST services. .. toctree:: :maxdepth: 1 howtoguides/running_workflows howtoguides/python howtoguides/inspect_workflows howtoguides/job howtoguides/gui howtoguides/rest howtoguides/benchmark howtoguides/ewoks_events howtoguides/task_python howtoguides/task_inputs ewoks-v4.0.0-8f4a8f91507ab3c3721e8d3f73e73ecca373546a/doc/howtoguides/000077500000000000000000000000001512474103500236075ustar00rootroot00000000000000ewoks-v4.0.0-8f4a8f91507ab3c3721e8d3f73e73ecca373546a/doc/howtoguides/benchmark.nblink000066400000000000000000000001011512474103500267300ustar00rootroot00000000000000{ "path": "../../src/ewoks/tests/notebooks/benchmark.ipynb" }ewoks-v4.0.0-8f4a8f91507ab3c3721e8d3f73e73ecca373546a/doc/howtoguides/ewoks_events.rst000066400000000000000000000016041512474103500270560ustar00rootroot00000000000000Enable Ewoks events during execution ==================================== Events can be emitted by *ewoks* during the execution of a workflow to notify about progress and potential errors. For this you can specify one or more destinations where the events should be send to. To specify event handlers from python: .. code-block:: python from ewoks import execute_graph execinfo = { "job_id": "1234", "handlers": [ { "class": "ewokscore.events.handlers.Sqlite3EwoksEventHandler", "arguments": [{"name": "uri", "value": "file:/tmp/ewoks_event.db"}], } ], } results = execute_graph("/path/to/file.json", execinfo=execinfo) To specify event handlers from the command line: .. code-block:: bash ewoks execute /path/to/file.json -j 1234 --sqlite3 /shared/path/test.dbewoks-v4.0.0-8f4a8f91507ab3c3721e8d3f73e73ecca373546a/doc/howtoguides/gui.rst000066400000000000000000000017141512474103500251300ustar00rootroot00000000000000.. _gui: Use Ewoks graphical interfaces ============================== Ewoks comes with two graphical interfaces. You can either of the two to create and edit workflows. .. _ewoksweb: ``ewoksweb``: full-stack web application ---------------------------------------- Install ``ewoksweb`` .. code-block:: bash pip install ewoksweb Start ``ewoksweb`` .. code-block:: bash ewoksweb ``ewoksweb`` will create folders to store the tasks/workflows that you will create when using it. 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V ?кt-~fVyի^wߜtICx"VY`A,X0@h`@h#0Bt*%Uƌ5`wRJIHzTUl:('IW&W6ۅFRJJI))Q{ZU|wIcvY61np ~nFvHURľjzKRi%uRꔴ69nt>ao `=Cxv|@֨u彫-uZk!z*2.|?ݩJ=sL*uJ] n<-/ɜ}&κܗҏWdQkԮY9ԹyGVN]p~a2.|d^z!驞ʒ|)dzĤrlz0?z7yZ%3Sr׻yTV' ;*2eƔVeœVwtH2}O>;G`ks[{r|-'=W|7qv^Kkη0'{ubNI>++fLOW3} /`UǏ/}}}}`;[R.zzzzzzzzzzz;hgvJR:Pe?{03KrtL │ SumTask │ task1 │ ├────────┼────────────────┼───────────────────┼───────┤ │ a │ 2 │ SumTask │ task2 │ ├────────┼────────────────┼───────────────────┼───────┤ │ delay │ 0 │ SumTask │ task2 │ ├────────┼────────────────┼───────────────────┼───────┤ │ b │ │ SumTask │ task2 │ ├────────┼────────────────┼───────────────────┼───────┤ │ delay │ 0 │ SumTask │ task3 │ ├────────┼────────────────┼───────────────────┼───────┤ │ b │ 3 │ SumTask │ task3 │ ├────────┼────────────────┼───────────────────┼───────┤ │ delay │ 0 │ SumTask │ task4 │ ├────────┼────────────────┼───────────────────┼───────┤ │ b │ 4 │ SumTask │ task4 │ ├────────┼────────────────┼───────────────────┼───────┤ │ delay │ 0 │ SumTask │ task5 │ ├────────┼────────────────┼───────────────────┼───────┤ │ delay │ 0 │ SumTask │ task6 │ ├────────┼────────────────┼───────────────────┼───────┤ │ b │ 6 │ SumTask │ task6 │ ╘════════╧════════════════╧═══════════════════╧═══════╛ Parameters with `` do not have a value. If a missing parameter is **required**, it is marked with a `(*)`. For example: .. code-block:: bash Workflow: example.json Id: demo Description: demo ╒════════╤════════════════╤═══════════════════╤═══════╕ │ Name │ Value │ Task identifier │ Id │ ╞════════╪════════════════╪═══════════════════╪═══════╡ │ a⁽*⁾ │ │ SumTask │ task2 │ ├────────┼────────────────┼───────────────────┼───────┤ │ list │ [0, 1, 2] │ SumList │ task0 │ ├────────┼────────────────┼───────────────────┼───────┤ │ delay │ 0 │ SumList │ task0 │ ├────────┼────────────────┼───────────────────┼───────┤ │ b │ │ SumTask │ task1 │ ├────────┼────────────────┼───────────────────┼───────┤ │ delay │ 0 │ SumTask │ task1 │ ├────────┼────────────────┼───────────────────┼───────┤ │ b │ │ SumTask │ task2 │ ├────────┼────────────────┼───────────────────┼───────┤ │ delay │ 0 │ SumTask │ task2 │ ├────────┼────────────────┼───────────────────┼───────┤ │ b │ 3 │ SumTask │ task3 │ ├────────┼────────────────┼───────────────────┼───────┤ │ delay │ 0 │ SumTask │ task3 │ ├────────┼────────────────┼───────────────────┼───────┤ │ b │ 4 │ SumTask │ task4 │ ├────────┼────────────────┼───────────────────┼───────┤ │ delay │ 0 │ SumTask │ task4 │ ├────────┼────────────────┼───────────────────┼───────┤ │ delay │ 0 │ SumTask │ task5 │ ├────────┼────────────────┼───────────────────┼───────┤ │ b │ 6 │ SumTask │ task6 │ ├────────┼────────────────┼───────────────────┼───────┤ │ delay │ 0 │ SumTask │ task6 │ ╘════════╧════════════════╧═══════════════════╧═══════╛ ⁽*⁾ Value is required for execution. In this case, the `a` parameter for task `SumTask` at node `task2` is required and must be provided before execution. Workflow nodes can be identified by: - **Task identifier** – the identifier of the code to be executed. - **Id** – the unique identifier of the node within the workflow. - **Label** (if present) – a human-readable tag, which may not be unique. If no labels are defined, the `Label` column is omitted from the output. Validating Execution Arguments ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ To override workflow parameters for execution, use the `-p` option: .. code-block:: bash ewoks execute example.json -p SumTask:delay=99 --input-node-id taskid Before executing the workflow, you can verify that your arguments are applied as intended using `ewoks show` with the same arguments: .. code-block:: bash ewoks show example.json -p SumTask:delay=99 --input-node-id taskid The output will reflect the overridden `delay` values: .. code-block:: bash Workflow: example.json Id: demo Description: demo ╒════════╤════════════════╤═══════════════════╤═══════╕ │ Name │ Value │ Task identifier │ Id │ ╞════════╪════════════════╪═══════════════════╪═══════╡ │ list │ [0, 1, 2] │ SumList │ task0 │ ├────────┼────────────────┼───────────────────┼───────┤ │ delay │ 0 │ SumList │ task0 │ ├────────┼────────────────┼───────────────────┼───────┤ │ delay │ 99 │ SumTask │ task1 │ ├────────┼────────────────┼───────────────────┼───────┤ │ b │ │ SumTask │ task1 │ ├────────┼────────────────┼───────────────────┼───────┤ │ a │ 2 │ SumTask │ task2 │ ├────────┼────────────────┼───────────────────┼───────┤ │ delay │ 99 │ SumTask │ task2 │ ├────────┼────────────────┼───────────────────┼───────┤ │ b │ │ SumTask │ task2 │ ├────────┼────────────────┼───────────────────┼───────┤ │ delay │ 99 │ SumTask │ task3 │ ├────────┼────────────────┼───────────────────┼───────┤ │ b │ 3 │ SumTask │ task3 │ ├────────┼────────────────┼───────────────────┼───────┤ │ delay │ 99 │ SumTask │ task4 │ ├────────┼────────────────┼───────────────────┼───────┤ │ b │ 4 │ SumTask │ task4 │ ├────────┼────────────────┼───────────────────┼───────┤ │ delay │ 99 │ SumTask │ task5 │ ├────────┼────────────────┼───────────────────┼───────┤ │ delay │ 99 │ SumTask │ task6 │ ├────────┼────────────────┼───────────────────┼───────┤ │ b │ 6 │ SumTask │ task6 │ ╘════════╧════════════════╧═══════════════════╧═══════╛ The value before the colon in `-p :=` refers to the node identifier, which defaults to the `Id` column. You can change this behavior with the `--input-node-id` option to use the `Label` or `Task identifier` instead. Graphical Interfaces -------------------- You can also inspect input parameters using graphical tools. Desktop GUI ~~~~~~~~~~~ To use the :ref:`desktop GUI ` based on Orange: .. code-block:: bash ewoks execute example.json --engine=orange -p SumTask:delay=99 --input-node-id taskid Then double-click on each node to inspect or edit parameters: .. image:: images/inspect_desktop.png :alt: Double-click on each node to inspect input parameters. Web GUI ~~~~~~~ To inspect parameters via the :ref:`web interface `: 1. Convert the workflow while applying overrides: .. code-block:: bash ewoks convert example.json example_with_params.json --test -p SumTask:delay=99 --input-node-id taskid 2. Start the web server: .. code-block:: bash ewoksweb You should see: .. code-block:: bash INFO: Uvicorn running on http://127.0.0.1:8000 (Press CTRL+C to quit) 3. Open the workflow file in your browser: .. image:: images/inspect_web_open.png :alt: Open the workflow file from disk in the web UI. 4. Click on a node to view or edit its parameters: .. image:: images/inspect_web_node.png :alt: Click on each node to inspect input parameters. ewoks-v4.0.0-8f4a8f91507ab3c3721e8d3f73e73ecca373546a/doc/howtoguides/job.rst000066400000000000000000000032651512474103500251210ustar00rootroot00000000000000Submit a job to a worker ======================== The client may want to start a workflow execution remotely and wait for the result while doing other work. Installation ------------ Install on the client side .. code-block:: bash pip install ewoksjob[redis] Install on the worker side .. code-block:: bash pip install ewoksjob[worker,redis,monitor] The communication between client and worker goes through *Redis*, *RabbitMQ* or *Sqlite3*. Depending on which one you choose, the `redis` installation option may vary. Both client and worker need access to a configuration that specifies the URL of the database and/or broker. For more information see the `ewoksjob documentation `_. Submit a workflow ----------------- Start a worker that can execute *ewoks* graphs .. code-block:: bash ewoksjob worker Submit a workflow from python, possible from another machine .. code-block:: python from ewoksjob.client import submit workflow = {"graph": {"id": "mygraph"}} future = submit(args=(workflow,)) result = future.get() Submit a workflow from the command line, possible from another machine .. code-block:: bash ewoks submit empty --test Submit a workflow in *BLISS* ---------------------------- In the context of *Bliss* .. code-block:: bash BEACON_HOST=hostname:25000 ewoksjob worker Submit a workflow as usual .. code-block:: bash BEACON_HOST=hostname:25000 ewoks submit empty --test The environment variables are needed for communication between worker and client via the message broker. Workflow monitoring ------------------- Start a web server for monitoring jobs .. code-block:: bash ewoksjob monitor ewoks-v4.0.0-8f4a8f91507ab3c3721e8d3f73e73ecca373546a/doc/howtoguides/python.rst000066400000000000000000000017001512474103500256600ustar00rootroot00000000000000.. _execute_python: Execute a workflow from Python ============================== Install requirements .. code-block:: bash pip install ewoks Execute a workflow .. code-block:: python from ewoks import execute_graph result = execute_graph("/path/to/graph.json", engine=None) The :ref:`hello world ` example provides a small but complete example of task implementation, workflow definition and workflow execution. The *engine* argument can be * *ppf*: required to execute cyclic workflows * *dask*: required to parallelize workflow execution (thread, processes, cluster) * *orange*: required to execute workflows with a graphical interface When no *engine* is specified it will use sequential execution in the current process. Bindings can be installed as follows .. code-block:: bash pip install ewoks[orange,dask,ppf] For more information see the `ewokscore documentation `_. ewoks-v4.0.0-8f4a8f91507ab3c3721e8d3f73e73ecca373546a/doc/howtoguides/rest.rst000066400000000000000000000006101512474103500253130ustar00rootroot00000000000000Set up a REST Ewoks server ========================== Install requirements .. code-block:: bash pip install ewoksserver Start the REST server .. code-block:: bash ewoks-server or for an installation with the system python .. code-block:: bash python3 -m ewoksserver.server For more information see the `ewoksserver documentation `_. ewoks-v4.0.0-8f4a8f91507ab3c3721e8d3f73e73ecca373546a/doc/howtoguides/running_workflows.nblink000066400000000000000000000001111512474103500305740ustar00rootroot00000000000000{ "path": "../../src/ewoks/tests/notebooks/running_workflows.ipynb" }ewoks-v4.0.0-8f4a8f91507ab3c3721e8d3f73e73ecca373546a/doc/howtoguides/task_inputs.rst000066400000000000000000000204131512474103500267050ustar00rootroot00000000000000Define Task class inputs ======================== There are two ways of defining task inputs in Ewoks when using the `Task` class: - :ref:`Input names`: inputs are defined by their names, given as lists of strings - :ref:`Input model`: inputs are defined by a `Pydantic model `_ These two methods are incompatible and only one should be picked (see :ref:`Incompatibility`). .. _Input names: Input names ----------- Required input names are given as a list of strings via the ``input_names`` subclass argument of ``Task``. They can then be retrieved in the task via ``self.inputs`` or ``self.get_input_value``: .. code-block:: python from ewoks import Task class ExampleTask(Task, input_names=["a", "b"]): def run(self): a = self.inputs.a b = self.get_input_value("b") print(f"a={a} b={b}") For demonstration purposes, we can `execute a task from Python <./task_python.rst>`_. .. code-block:: python-console >>> task = ExampleTask(inputs={"a": 1, "b": 2}) >>> task.run() a=1 b=2 Since these are **required** input names, Ewoks will throw a ``TaskInputError`` if one is missing .. code-block:: python-console >>> task = ExampleTask(inputs={"a": 1}) ewokscore.task.TaskInputError: Missing inputs for : {'b'} We can allow optional inputs by giving a list of strings to a ``optional_input_names`` subclass argument of ``Task`` .. code-block:: python class ExampleTaskWithOptional(Task, input_names=["a", "b"], optional_input_names=["c"]): def run(self): a = self.inputs.a b = self.get_input_value("b") c = self.inputs.c print(f"a={a} b={b} c={c}") These optional inputs can be specified .. code-block:: python-console >>> task = ExampleTaskWithOptional(inputs={"a": 1, "b": 2, "c": 3}) >>> task.run() a=1 b=2 c=3 or omitted. In this case, they will be set to the special object ``MISSING_DATA``: .. code-block:: python-console >>> task = ExampleTaskWithOptional(inputs={"a": 1, "b": 2}) >>> task.run() a=1 b=2 c= Default values can be given thanks to ``get_input_value`` .. code-block:: python class ExampleTaskWithDefault(Task, input_names=["a", "b"], optional_input_names=["c"]): def run(self): a = self.inputs.a b = self.get_input_value("b") c = self.get_input_value("c", 0) # <-- Default value print(f"a={a} b={b} c={c}") .. code-block:: python-console >>> task = ExampleTaskWithDefault(inputs={"a": 1, "b": 2}) >>> task.run() a=1 b=2 c=0 Subclassing ^^^^^^^^^^^ Subclasses of a task will inherit the input names from the base class and any additional input name will be added to those: .. code-block:: python class ChildExampleTask(ExampleTaskWithOptional, input_names=["d"]): def run(self): print(self.get_input_values()) .. code-block:: python-console >>> task = ChildExampleTask(inputs={"a": 1, "b": 2, "c": 3, "d": 4}) >>> # Accepts `a`, `b` and `c` in addition to `d` ^ >>> task.run() {'a': 1, 'b': 2, 'c': 3, 'd': 4} .. _Input model: Input model ----------- .. versionadded:: 1.1.0 Instead of input names, it is possible to provide a `Pydantic model `_ for inputs. The model needs to derive from ``ewoks.BaseInputModel`` and must be provided via ``input_model`` to the task. Inputs can then be retrieved in the task via ``self.inputs`` or ``self.get_input_value``: .. code-block:: python from ewoks import Task from ewoks import BaseInputModel class Inputs(BaseInputModel): a: int b: int class ExampleTaskWithModel(Task, input_model=Inputs): def run(self): a = self.inputs.a b = self.get_input_value("b") print(f"a={a} b={b}") .. code-block:: python-console >>> task = ExampleTaskWithModel(inputs={"a": 1, "b": 2}) >>> task.run() a=1 b=2 The advantage of using a model is that **inputs are validated by Pydantic** .. code-block:: python-console >>> task = ExampleTaskWithModel(inputs={"a": 1}) ewokscore.task.TaskInputError: 1 validation error for Inputs b Field required [type=missing, input_value={'a': 1}, input_type=dict] For further information visit https://errors.pydantic.dev/2.10/v/missing >>> task = ExampleTaskWithModel(inputs={"a": 1, "b": "not_a_number"}) ewokscore.task.TaskInputError: 1 validation error for Inputs b Input should be a valid integer, unable to parse string as an integer [type=int_parsing, input_value='not_a_number', input_type=str] For further information visit https://errors.pydantic.dev/2.10/v/int_parsing All Pydantic features such has `Default values `_, `Constraints `_ or `Custom validation `_ are available. For example, we can add an optional input to our model by giving it a default value .. code-block:: python from ewoks import Task from ewoks import BaseInputModel class Inputs(BaseInputModel): a: int b: int c: int = 0 # <-- `c` is optional class ExampleTaskWithModel(Task, input_model=Inputs): def run(self): a = self.inputs.a b = self.get_input_value("b") c = self.inputs.c print(f"a={a} b={b} c={c}") .. code-block:: python-console >>> task = ExampleTaskWithModel(inputs={"a": 1, "b": 2}) >>> task.run() a=1 b=2 c=0 .. note:: It is possible to reproduce the default behaviour of `optional_input_names` that are set to ``MISSING_DATA`` if not set. For this, add ``MissingData`` as a possible type for the input in the model and use ``MISSING_DATA`` as the default value .. code-block:: python from ewoks import Task from ewoks import BaseInputModel from ewokscore.missing_data import MissingData, MISSING_DATA class Inputs(BaseInputModel): a: int b: int c: int | MissingData = MISSING_DATA # <-- `c` is optional class ExampleTaskWithModel(Task, input_model=Inputs): def run(self): a = self.inputs.a b = self.get_input_value("b") c = self.inputs.c print(f"a={a} b={b} c={c}") .. code-block:: python-console >>> task = ExampleTaskWithModel(inputs={"a": 1, "b": 2}) >>> task.run() a=1 b=2 c= .. _Subclassing models: Subclassing ^^^^^^^^^^^ Subclasses of tasks with input models will inherit the model if they do not implement a model themselves .. code-block:: python class ChildTask(ExampleTaskWithModel): def run(self): print(self.get_input_values()) .. code-block:: python-console >>> task = ChildTask(inputs={"a": 1, "b": 2, "c": 3}) >>> task.run() {'a': 1, 'b': 2, 'c': 3} If the subclass does have an input model, it must be inherit from the model of the base class to have compliant inputs .. code-block:: python class NewInputs(Inputs): d: int class ChildTaskWithModel(ExampleTaskWithModel, input_model=NewInputs): def run(self): print(self.get_input_values()) .. code-block:: python-console >>> task = ChildTaskWithModel(inputs={"a": 1, "b": 2, "c": 3, "d": 4}) >>> task.run() {'a': 1, 'b': 2, 'c': 3, 'd': 4} .. _Incompatibility: Incompatibility between methods ------------------------------- .. danger:: It is **not possible to mix** ``input_names``/``optional_input_names`` and ``input_model``! This means a ``Task`` **cannot** have both ``input_names``/``optional_input_names`` and ``input_model`` defined. But also, **a subclass must use the same input definition method as its base class**: - If the base class task uses ``input_names``/``optional_input_names``, the subclass must use ``input_names``/``optional_input_names`` as well. - If the base class task uses ``input_model``, the subclass must use an ``input_model`` that subclasses the model of the base task (see :ref:`above `). ewoks-v4.0.0-8f4a8f91507ab3c3721e8d3f73e73ecca373546a/doc/howtoguides/task_python.rst000066400000000000000000000043161512474103500267100ustar00rootroot00000000000000Execute a task from Python ========================== Walkthrough ----------- This page shows to instantiate a task from Python, set its inputs, execute it and retrieve its outputs. It can be useful for debugging a single task for example. To illustrate it, we will use the `SumTask` below that adds two numbers `a` and `b`, and stores the result in `result`: .. code-block:: python class SumTask( Task, input_names=["a"], optional_input_names=["b"], output_names=["result"] ): def run(self): result = self.inputs.a if self.inputs.b: result += self.inputs.b self.outputs.result = result .. admonition:: Tip This task can be imported from ``ewokscore.tests.examples.tasks.sumtask`` Task inputs need to be set via the ``inputs`` argument when creating the task before executing via ``execute``: .. code-block:: python >>> task = SumTask(inputs={"a": 5, "b": 10}) >>> task.execute() The results can then be retrieved from ``outputs`` or using ``get_output_value``: .. code-block:: python >>> task.outputs["result"] 15 >>> task.get_output_value('result') 15 Limitations ----------- Inputs cannot be modified once the task is instantiated .. code-block:: python >>> task = SumTask(inputs={"a": 5, "b": 10}) >>> task.inputs.a = 10 ... ewokscore.variable.ReadOnlyVariableError: a A new task instance needs to be created to execute it with different inputs .. code-block:: python >>> task = SumTask(inputs={"a": 10, "b": 10}) >>> task.execute() >>> task.get_output_value("result") 20 Outputs will be missing before the task is executed. More specifically, the outputs are set to `MISSING_DATA `_ until they are set by the execution: .. code-block:: python >>> task = SumTask(inputs={"a": 5, "b": 10}) >>> task.get_output_value("result") Full code --------- .. code-block:: python from ewokscore.tests.examples.tasks.sumtask import SumTask task = SumTask(inputs={"a": 5, "b": 10}) task.execute() print(task.get_output_value("result")) ewoks-v4.0.0-8f4a8f91507ab3c3721e8d3f73e73ecca373546a/doc/index.rst000066400000000000000000000022611512474103500231100ustar00rootroot00000000000000ewoks |version| =============== .. image:: https://img.shields.io/badge/DOI-10.1080/08940886.2024.2432305-blue :target: https://doi.org/10.1080/08940886.2024.2432305 Many `workflow management systems `_ exist to deal with data processing problems that can be expressed as a graph of tasks, also referred to as a *computational graph* or *workflow*. The main purpose of a workflow management system is to provide a framework for implementing tasks, creating graphs of tasks and executing these graphs. The purpose of *ewoks* (Extensible Workflow System) is to provide an abstraction layer between graph representation and execution. This allows using the same tasks and graphs in different workflow management systems. *ewoks* has been developed by the `Software group `_ of the `European Synchrotron `_. .. admonition:: Quick Start Install *ewoks* .. code-block:: bash pip install ewoks Execute the demo workflow .. code-block:: bash ewoks execute demo --test .. toctree:: :hidden: tutorials howtoguides explanations reference ewoks-v4.0.0-8f4a8f91507ab3c3721e8d3f73e73ecca373546a/doc/reference.rst000066400000000000000000000002201512474103500237300ustar00rootroot00000000000000Reference ========= Command-Line Interface (CLI) and Python API reference. .. toctree:: :maxdepth: 1 reference/cli reference/api ewoks-v4.0.0-8f4a8f91507ab3c3721e8d3f73e73ecca373546a/doc/reference/000077500000000000000000000000001512474103500232045ustar00rootroot00000000000000ewoks-v4.0.0-8f4a8f91507ab3c3721e8d3f73e73ecca373546a/doc/reference/api.rst000066400000000000000000000001421512474103500245040ustar00rootroot00000000000000API reference ============= .. autosummary:: :toctree: _generated :recursive: ewoks ewoks-v4.0.0-8f4a8f91507ab3c3721e8d3f73e73ecca373546a/doc/reference/cli.rst000066400000000000000000000065641512474103500245200ustar00rootroot00000000000000.. _cli: CLI reference ============= ewoks install ------------- .. argparse:: :module: ewoks.__main__ :func: create_argument_parser :prog: ewoks :path: install **Install the packages required to run a workflow**. **ewoks install** relies on the existence of the ``requirements`` field in the ``graph`` field of the workflow. If no ``requirements`` field exist, **ewoks install** will try to extract requirements from the tasks in the workflows before installing them. Unless ``--yes`` is provided, **ewoks install** will ask for confirmation before installing the packages. By default, packages are installed in the current Python environment: if **ewoks install** is run in a virtual environment, the packages will be installed in this virtual environment. ewoks convert ------------- .. argparse:: :module: ewoks.__main__ :func: create_argument_parser :prog: ewoks :path: convert **Convert a source workflow in another format supported by Ewoks**. The source workflow is untouched: a new destination workflow is created. .. important:: **ewoks convert** will save the packages installed in the current environment as `requirements` in the destination workflow. ⚠️ If the source workflow has a `requirements` field, it will therefore be replaced by this package list. This can be disabled by using the ``--exclude-requirements`` argument. **ewoks convert** can also be used to store `inputs` inside the destination workflow. ewoks execute ------------- .. argparse:: :module: ewoks.__main__ :func: create_argument_parser :prog: ewoks :path: execute **Execute a workflow using one of the Ewoks-supported engine**. The workflow can have any format as long it is supported by Ewoks. Inputs can be provided only for this execution. .. tip:: Using the execution option ``convert_destination`` (``-o convert_destination=``) runs `ewoks convert` on the executed workflow, producing a new workflow that stored the inputs and the requirements inside. The produced workflow can then be used as a provenance document since it can reproduce the source workflow execution in exactly the same way. ewoks submit ------------ .. argparse:: :module: ewoks.__main__ :func: create_argument_parser :prog: ewoks :path: submit **Execute a workflow remotely, in a worker**. Very similar to ``ewoks execute`` except the execution does not run in this environment (client) but in a remote one (worker). Requires the `EWOKS_CONFIG_URI` environment variable to be set. .. tip:: Before running this command, the client and the worker must have agreed on a messaging protocol to communicate. See the `ewoksjob documentation `_ to see how to set-up this. ewoks show ---------- .. argparse:: :module: ewoks.__main__ :func: create_argument_parser :prog: ewoks :path: show **Display the workflow parameters with their default values and descriptions**. Required parameters without default value are highlighted. Parameters which get their value at runtime from upstream nodes in the workflow are not shown. Parameters values in the displayed table can be provided in the same way as inputs for ``ewoks execute``. ewoks-v4.0.0-8f4a8f91507ab3c3721e8d3f73e73ecca373546a/doc/tutorials.rst000066400000000000000000000003521512474103500240260ustar00rootroot00000000000000Tutorials ========= .. toctree:: :maxdepth: 1 tutorials/getting_started tutorials/hello_world tutorials/create_and_edit tutorials/execute tutorials/infrastructure tutorials/external tutorials/develop ewoks-v4.0.0-8f4a8f91507ab3c3721e8d3f73e73ecca373546a/doc/tutorials/000077500000000000000000000000001512474103500232745ustar00rootroot00000000000000ewoks-v4.0.0-8f4a8f91507ab3c3721e8d3f73e73ecca373546a/doc/tutorials/create_and_edit.rst000066400000000000000000000011021512474103500271120ustar00rootroot00000000000000Create and edit workflows ========================= Documentation on the different interfaces to create and edit workflows: * `ewoks tutorial on workflow creation `_: create a workflow from the Python interface * `ewoksorange `_ : create and execute workflows with a desktop GUI * `ewoksweb `_: web frontend to create, visualize and execute workflows * `ewokscore `_ : create workflows and implement tasks ewoks-v4.0.0-8f4a8f91507ab3c3721e8d3f73e73ecca373546a/doc/tutorials/develop.rst000066400000000000000000000004661512474103500254720ustar00rootroot00000000000000Ewoks core development ====================== Documentation on libraries used for core development: * `ewoksutils `_ : developer utilities shared by client and worker side * `ewokscore `_ : developer utilities for engine implementation ewoks-v4.0.0-8f4a8f91507ab3c3721e8d3f73e73ecca373546a/doc/tutorials/execute.rst000066400000000000000000000020461512474103500254720ustar00rootroot00000000000000Execute workflows ================= Ewoks workflows can be executed via several interfaces and with several workflow engines. The :ref:`python interface ` for executing workflows is a function .. code-block:: python from ewoks import execute_graph result = execute_graph("/path/to/graph.json", engine=None) The equivalent exists for the :ref:`command-line `. The :code:`engine=None` argument selects the default engine. Documentation on different engines: * `ewoksppf `_ : execute cyclic workflows * `ewoksdask `_ : parallel and distributed workflow execution * `ewoksorange `_ : execute with desktop GUI Documentation on other interfaces than python and the command-line interface: * `ewoksweb `_ : execute with web GUI * `ewoksserver `_ : execute with REST API * `ewoksjob `_ : job scheduling for Ewoks workflows ewoks-v4.0.0-8f4a8f91507ab3c3721e8d3f73e73ecca373546a/doc/tutorials/external.rst000066400000000000000000000015011512474103500256450ustar00rootroot00000000000000More tutorials ============== - Tutorial on `workflow creation in Python `_. - `Documentation `_ on creating and executing workflows with a desktop GUI. - `Documentation `_ on creating and executing workflows with a web GUI. - `Ewoks training `_ on task implementation, workflow creation/execution and job scheduling. - Tutorial on `Ewoks for Online Data Processing `_ with `blissdata `_ which is a python library to store and access scientific data during and after experiment at large-sclae facilities like `ESRF `_. ewoks-v4.0.0-8f4a8f91507ab3c3721e8d3f73e73ecca373546a/doc/tutorials/getting_started.rst000066400000000000000000000017211512474103500272160ustar00rootroot00000000000000Getting started =============== Install *ewoks* .. code-block:: bash pip install ewoks Create a test workflow in JSON format (“acyclic1” is an test workflow that ships with ewoks) .. code-block:: bash ewoks convert acyclic1 --test test.json Inspect workflow parameters before executing the workflow .. code-block:: bash Inspect the demo workflow .. code-block:: bash ewoks show test.json Execute a workflow and print the output of all tasks .. code-block:: bash ewoks execute test.json --outputs all For a desktop GUI, install *ewoks* with the orange binding .. code-block:: bash pip install ewoks[orange] Open the test workflow in the GUI .. code-block:: bash ewoks execute test.json --engine orange For a web GUI, install *ewoksserver* with the frontend .. code-block:: bash pip install ewoksserver[frontend] Start the *ewoks* web server, open the link in a web browser and load the `test.json` file .. code-block:: bash ewoks-server ewoks-v4.0.0-8f4a8f91507ab3c3721e8d3f73e73ecca373546a/doc/tutorials/hello_world.rst000066400000000000000000000057521512474103500263510ustar00rootroot00000000000000.. _hello_world: Hello world =========== This script demonstrates how to define an Ewoks workflow from the ground up. The only thing needed to run it is the ``ewoks`` Python package .. code-block:: bash pip install ewoks The script defines defines two tasks, one that ask for a name and the other that creates a greeting, creates a workflow out of these and executes it. .. code-block:: python from ewokscore import Task from ewoks import execute_graph # Define a workflow task with an output class AskForName(Task, output_names=["input_name"]): def run(self): name = input("What is your name?") if not name: name = "World" self.outputs.input_name = name # Define a workflow task with inputs and outputs class SayHello( Task, input_names=["name"], optional_input_names=["in_french"], output_names=["greeting"], ): def run(self): name = self.inputs.name if self.inputs.in_french: hello = "Bonjour" else: hello = "Hello" self.outputs.greeting = f"{hello}, {name}!" # Define nodes: entities that will execute tasks nodes = [ { "id": "ask_for_name", "task_type": "class", "task_identifier": "__main__.AskForName", }, { "id": "say_hello", "task_type": "class", "task_identifier": "__main__.SayHello", }, ] # Define links that make data flow from one node to another links = [ { "source": "ask_for_name", "target": "say_hello", "data_mapping": [{"source_output": "input_name", "target_input": "name"}], } ] # Define a workflow from nodes, links and graph metadata workflow = {"graph": {"id": "hello_world"}, "nodes": nodes, "links": links} # Execute a workflow result = execute_graph(workflow) print(result) print('----') # Optionally, define inputs inputs = [{"id": "say_hello", "name": "in_french", "value": True}] # Execute a workflow with inputs result = execute_graph(workflow, inputs=inputs) print(result) This is the output you should get .. code-block:: bash $ python hello_world.py What is your name? Ewoks # <--- `input` waits for some input. `Ewoks` was typed here and Enter pressed. {'greeting': 'Hello, Ewoks!'} ---- What is your name? # <--- Enter was pressed without typing anything {'greeting': 'Bonjour, World!'} .. note:: Ewoks developers rarely design workflows by hand like this but instead use graphical interfaces to produce the dictionnary (usually stored in JSON). See :ref:`this page about the Ewoks graphical interfaces `. .. note:: For all the fields that can be set when creating the dictionnary representing the workflow, see the `Ewoks specification `_. ewoks-v4.0.0-8f4a8f91507ab3c3721e8d3f73e73ecca373546a/doc/tutorials/infrastructure.rst000066400000000000000000000005261512474103500271110ustar00rootroot00000000000000Infrastructure for workflow management ====================================== Documentation related to workflow infrastructure (servers, message brokers): * `ewoksjob `_: job scheduling for Ewoks workflows * `ewoksserver `_: REST server to manage and execute workflows ewoks-v4.0.0-8f4a8f91507ab3c3721e8d3f73e73ecca373546a/pyproject.toml000066400000000000000000000040021512474103500234110ustar00rootroot00000000000000[build-system] requires = ["setuptools>=46.4"] build-backend = "setuptools.build_meta" [project] name = "ewoks" version = "4.0.0" authors = [{ name = "ESRF", email = "dau-pydev@esrf.fr" }] description = "Extensible Workflow System" readme = { file = "README.md", content-type = "text/markdown" } license = { file = "LICENSE.md" } classifiers = [ "Intended Audience :: Science/Research", "License :: OSI Approved :: MIT License", "Programming Language :: Python :: 3", ] requires-python = ">=3.8" dependencies = [ "ewokscore >=4.0.1,<5", "ewoksutils >=1.6.0,<2", "tabulate", "importlib_resources; python_version < '3.9'", ] [project.urls] Homepage = "https://gitlab.esrf.fr/workflow/ewoks/ewoks/" Documentation = "https://ewoks.readthedocs.io/" Repository = "https://gitlab.esrf.fr/workflow/ewoks/ewoks/" Issues = "https://gitlab.esrf.fr/workflow/ewoks/ewoks/issues" Changelog = "https://gitlab.esrf.fr/workflow/ewoks/ewoks/-/blob/main/CHANGELOG.md" [project.optional-dependencies] dask = ["ewoksdask >=2,<3"] ppf = ["ewoksppf >=2,<3"] orange = ["ewoksorange >=3,<4"] esrf-data-portal = ["pyicat-plus"] notebooks = ["ewokscore[notebooks]"] test = [ "ewoksutils[test]", "ewoks[dask]", "ewoks[ppf]", "ewoks[orange]", "ewoks[esrf-data-portal]", "pytest >=7", "testbook", "ipykernel", "importlib_resources", "pyqt5", "pytest-venv", ] dev = [ "ewoks[test]", "black[jupyter] >=25", "flake8 >=4", "flake8_nb >= 0.3.1", "isort", ] doc = [ "ewoks[test]", "sphinx >=4.5", "sphinx-autodoc-typehints >=1.16", "ipykernel", "nbsphinx", "nbsphinx_link", "pydata-sphinx-theme", "sphinx-argparse", "sphinx-copybutton", ] [tool.setuptools] package-dir = { "" = "src" } [tool.setuptools.packages.find] where = ["src"] [tool.coverage.run] omit = ['*/tests/*'] [tool.isort] profile = "black" force_single_line = "True" [project.scripts] ewoks = "ewoks.__main__:main" [tool.setuptools.package-data] "ewoks.tests.notebooks"= ["*.ipynb"] ewoks-v4.0.0-8f4a8f91507ab3c3721e8d3f73e73ecca373546a/src/000077500000000000000000000000001512474103500212705ustar00rootroot00000000000000ewoks-v4.0.0-8f4a8f91507ab3c3721e8d3f73e73ecca373546a/src/ewoks/000077500000000000000000000000001512474103500224205ustar00rootroot00000000000000ewoks-v4.0.0-8f4a8f91507ab3c3721e8d3f73e73ecca373546a/src/ewoks/__init__.py000066400000000000000000000006051512474103500245320ustar00rootroot00000000000000from ewokscore import Task # noqa: F401 from ewokscore.model import BaseInputModel # noqa: F401 from .bindings import convert_graph # noqa: F401 from .bindings import execute_graph # noqa: F401 from .bindings import load_graph # noqa: F401 from .bindings import save_graph # noqa: F401 from .bindings import show_graph # noqa: F401 from .bindings import submit_graph # noqa: F401 ewoks-v4.0.0-8f4a8f91507ab3c3721e8d3f73e73ecca373546a/src/ewoks/__main__.py000066400000000000000000000157521512474103500245240ustar00rootroot00000000000000import sys import traceback from argparse import ArgumentDefaultsHelpFormatter from argparse import ArgumentParser from argparse import Namespace from pprint import pprint from subprocess import CalledProcessError from typing import Any from typing import List from typing import Literal from typing import Optional from typing import Union from ewoksutils.cli_utils import cli_cancel_utils from ewoksutils.cli_utils import cli_execute_utils from ewoksutils.cli_utils import cli_submit_utils from ewoksutils.cli_utils.cli_argparse import add_to_parser from .bindings import _load_graph from .bindings import convert_graph from .bindings import execute_graph from .bindings import install_graph from .bindings import show_graph from .cli_utils import cli_convert_utils from .cli_utils import cli_install_utils from .cli_utils import cli_show_utils from .errors import AbortException try: from ewoksjob.cli_utils.cancel import command_cancel as _command_cancel from ewoksjob.cli_utils.submit import command_submit as _command_submit except ImportError: _command_submit = None _command_cancel = None def create_argument_parser(shell: bool = False) -> ArgumentParser: parser = ArgumentParser( description="Extensible WOrKflow System CLI", prog="ewoks", formatter_class=ArgumentDefaultsHelpFormatter, ) subparsers = parser.add_subparsers(help="Commands", dest="command") execute = subparsers.add_parser( "execute", help="Execute a workflow", formatter_class=ArgumentDefaultsHelpFormatter, ) submit = subparsers.add_parser( "submit", help="Schedule a workflow execution", formatter_class=ArgumentDefaultsHelpFormatter, ) cancel = subparsers.add_parser( "cancel", help="Abort a scheduled workflow execution", formatter_class=ArgumentDefaultsHelpFormatter, ) convert = subparsers.add_parser( "convert", help="Convert a workflow", formatter_class=ArgumentDefaultsHelpFormatter, ) install = subparsers.add_parser( "install", help="Install requirements of a workflow", formatter_class=ArgumentDefaultsHelpFormatter, ) show = subparsers.add_parser( "show", help="Show workflow information", formatter_class=ArgumentDefaultsHelpFormatter, ) add_to_parser(execute, cli_execute_utils.execute_arguments(shell=shell)) add_to_parser(submit, cli_submit_utils.submit_arguments(shell=shell)) add_to_parser(cancel, cli_cancel_utils.cancel_arguments(shell=shell)) add_to_parser(convert, cli_convert_utils.convert_arguments(shell=shell)) add_to_parser(install, cli_install_utils.install_arguments(shell=shell)) add_to_parser(show, cli_show_utils.show_arguments(shell=shell)) return parser def command_execute( cli_args: Namespace, shell: bool = False ) -> Union[List[dict], Literal[0, 1]]: cli_execute_utils.parse_execute_argument(cli_args, shell=shell) return_code = 0 keep_results = [] for workflow, graph in zip(cli_args.workflows, cli_args.graphs): print("###################################") print(f"# Execute workflow '{workflow}'") print("###################################") try: results = execute_graph( graph, engine=cli_args.engine, **cli_args.execute_options ) except Exception as ex: traceback.print_exc() print("FAILED") results = ex return_code = 1 else: if cli_args.outputs == "none": if results is None: print("FAILED") else: print("FINISHED") else: print("") print("RESULTS:") pprint(results) print("") print("FINISHED") if results is None: return_code = 1 finally: print() if not shell: keep_results.append(results) if shell: return return_code return keep_results def command_submit( cli_args: Namespace, shell: bool = False ) -> Union[List[dict], Literal[0, 1]]: if _command_submit is None: raise RuntimeError("requires the 'ewoksjob>=1.3' package") return _command_submit(cli_args, _convert_graph=_load_graph, shell=shell) def command_cancel(cli_args: Namespace, shell: bool = False) -> Optional[Literal[0, 1]]: if _command_cancel is None: raise RuntimeError("requires the 'ewoksjob>=1.3' package") return _command_cancel(cli_args, shell) def command_convert( cli_args: Namespace, shell: bool = False ) -> Optional[Literal[0, 1]]: cli_convert_utils.parse_convert_arguments(cli_args, shell=shell) for workflow, graph, destination in zip( cli_args.workflows, cli_args.graphs, cli_args.destinations ): convert_graph(graph, destination, **cli_args.convert_options) print(f"Converted {workflow} -> {destination}") if shell: return 0 return None def command_install( cli_args: Namespace, shell: bool = False ) -> Optional[Literal[0, 1]]: cli_install_utils.parse_install_arguments(cli_args, shell=shell) for workflow, graph in zip(cli_args.workflows, cli_args.graphs): try: install_graph(graph, cli_args.yes, cli_args.python) except CalledProcessError as e: print(f"Install failed for {workflow}: {e}") except AbortException: print(f"Install aborted for {workflow}") else: print(f"Installed requirements for {workflow}") if shell: return 0 return None def command_show(cli_args: Namespace, shell: bool = False) -> Optional[Literal[0, 1]]: cli_show_utils.parse_show_arguments(cli_args, shell=shell) for workflow, graph in zip(cli_args.workflows, cli_args.graphs): show_graph(graph, original_source=workflow, **cli_args.show_options) if shell: return 0 return None def command_default( cli_args: Namespace, shell: bool = False ) -> Optional[Literal[0, 1]]: if shell: return 1 return None def main(argv=None, shell: bool = True) -> Union[Any, Literal[0, 1]]: parser = create_argument_parser(shell=shell) if argv is None: argv = sys.argv cli_args = parser.parse_args(argv[1:]) if cli_args.command == "execute": return command_execute(cli_args, shell=shell) elif cli_args.command == "submit": return command_submit(cli_args, shell=shell) elif cli_args.command == "cancel": return command_cancel(cli_args, shell=shell) elif cli_args.command == "convert": return command_convert(cli_args, shell=shell) elif cli_args.command == "install": return command_install(cli_args, shell=shell) elif cli_args.command == "show": return command_show(cli_args, shell=shell) else: parser.print_help() return command_default(cli_args, shell=shell) if __name__ == "__main__": sys.exit(main()) ewoks-v4.0.0-8f4a8f91507ab3c3721e8d3f73e73ecca373546a/src/ewoks/_engines.py000066400000000000000000000064341512474103500245700ustar00rootroot00000000000000import logging from functools import lru_cache from typing import Any from typing import Callable from typing import Generator from typing import List from typing import Optional from typing import Tuple from ewokscore.engine_interface import WorkflowEngine from ewokscore.engine_interface import WorkflowEngineWithSerialization from ewokscore.entry_points import EntryPoint from ewokscore.entry_points import entry_points _logger = logging.getLogger(__name__) @lru_cache(1) def get_engine_names() -> List[str]: return [name for name, _ in _iter_engine_class_loaders_with_name()] @lru_cache(1) def get_graph_representations() -> List[str]: return [ representation for representation, _ in _iter_engine_class_loaders_with_representation() ] def get_execution_engine(engine_name: Optional[str]) -> WorkflowEngine: if not engine_name or engine_name.lower() == "none": engine_name = "core" for name, load_engine_cls in _iter_engine_class_loaders_with_name(): if name == engine_name: try: engine_cls = load_engine_cls() except Exception as e: _logger.warning( f"Unable to properly load '{name}' engine. Error is {e}" ) continue else: return engine_cls() raise RuntimeError(f"No engine found for graph execution: '{engine_name}'") def get_serialization_engine( graph: Any, representation: Optional[str] = None ) -> Tuple[WorkflowEngine, str]: core_representation = representation if representation: for ( representations, load_engine_cls, ) in _iter_engine_class_loaders_with_representation(): if representation in representations: engine_cls = load_engine_cls() engine = engine_cls() return engine, representation else: for name, load_engine_cls in _iter_engine_class_loaders_with_name(): try: engine_cls = load_engine_cls() except Exception as e: _logger.warning( f"Unable to properly load serialization engine '{name}'. Error is {e}" ) continue if not issubclass(engine_cls, WorkflowEngineWithSerialization): continue engine = engine_cls() representation = engine.get_graph_representation(graph) if representation: return engine, representation return get_execution_engine("core"), core_representation def _iter_engine_class_loaders_with_name() -> ( Generator[Tuple[str, Callable[[], None]], None, None] ): try: eps = entry_points(group="ewoks.engines") except Exception: return eps = sorted(eps, key=_sort_ewoks_engines) for ep in eps: yield ep.name, ep.load def _iter_engine_class_loaders_with_representation() -> ( Generator[Tuple[str, Callable[[], None]], None, None] ): try: eps = entry_points(group="ewoks.engines.serialization.representations") except Exception: return eps = sorted(eps, key=_sort_ewoks_engines) for ep in eps: yield ep.name, ep.load def _sort_ewoks_engines(item: EntryPoint): return (item.name != "core", item) ewoks-v4.0.0-8f4a8f91507ab3c3721e8d3f73e73ecca373546a/src/ewoks/_requirements/000077500000000000000000000000001512474103500253025ustar00rootroot00000000000000ewoks-v4.0.0-8f4a8f91507ab3c3721e8d3f73e73ecca373546a/src/ewoks/_requirements/__init__.py000066400000000000000000000000351512474103500274110ustar00rootroot00000000000000"""Workflow requirements.""" ewoks-v4.0.0-8f4a8f91507ab3c3721e8d3f73e73ecca373546a/src/ewoks/_requirements/pip/000077500000000000000000000000001512474103500260725ustar00rootroot00000000000000ewoks-v4.0.0-8f4a8f91507ab3c3721e8d3f73e73ecca373546a/src/ewoks/_requirements/pip/__init__.py000066400000000000000000000000561512474103500302040ustar00rootroot00000000000000"""Workflow requirements managed by `pip`.""" ewoks-v4.0.0-8f4a8f91507ab3c3721e8d3f73e73ecca373546a/src/ewoks/_requirements/pip/extract.py000066400000000000000000000035261512474103500301240ustar00rootroot00000000000000import logging import subprocess import sys from typing import List from ewokscore.graph import TaskGraph logger = logging.getLogger(__file__) def extract_pip_requirements(graph: TaskGraph) -> List[str]: imports: set[str] = set() for node_id, node in graph.graph.nodes.items(): task_identifier = node["task_identifier"] task_type = node["task_type"] if task_type in ("class", "method", "ppfmethod", "ppfport"): package = task_identifier.split(".")[0] if package in ("__main__", ""): logger.warning( f"Could not extract requirements for node {node_id}: the task identifier is a relative import or an import from __main__." ) continue imports.add(package) elif task_type == "notebook": logger.warning( f"Requirement extraction may be incomplete for node {node_id}: {task_type} is only partially supported." ) imports.add("ewokscore[notebook]") elif task_type == "script": logger.warning( f"Requirement extraction cannot be done for scripts (node {node_id})." ) else: logger.warning( f"Could not extract requirements for node {node_id}: unsupported task type {task_type}." ) return list(imports) def add_current_env_pip_requirements(graph: TaskGraph) -> TaskGraph: try: freeze_output = subprocess.check_output( [sys.executable, "-m", "pip", "freeze"], text=True ) except subprocess.CalledProcessError as ex: logger.warning("Cannot generate list of requirements with 'pip' (%s).", ex) return graph requirements = freeze_output.strip().split("\n") graph.graph.graph["requirements"] = requirements return graph ewoks-v4.0.0-8f4a8f91507ab3c3721e8d3f73e73ecca373546a/src/ewoks/_requirements/pip/install.py000066400000000000000000000007721512474103500301200ustar00rootroot00000000000000import logging import subprocess from typing import Sequence from .sanitize import sanitize_requirements logger = logging.getLogger(__name__) def pip_install(requirements: Sequence[str], python_path: str) -> int: requirements, warnings = sanitize_requirements(requirements) for warning in warnings: logger.warning(warning) # https://pip.pypa.io/en/stable/user_guide/#using-pip-from-your-program return subprocess.check_call([python_path, "-m", "pip", "install", *requirements]) ewoks-v4.0.0-8f4a8f91507ab3c3721e8d3f73e73ecca373546a/src/ewoks/_requirements/pip/sanitize.py000066400000000000000000000113161512474103500302740ustar00rootroot00000000000000import os import re from typing import List from typing import Sequence from typing import Tuple from urllib.parse import ParseResult from urllib.parse import parse_qs from urllib.parse import urlparse from urllib.parse import urlunparse from packaging.requirements import InvalidRequirement from packaging.requirements import Requirement def sanitize_requirements(requirements: Sequence[str]) -> Tuple[List[str], List[str]]: """Sanitize a list of requirements coming from 'pip freeze'. Returns a sanitized with warnings regarding applied changes. """ sanitized = [] warnings = [] previous_comment = "" for requirement in requirements: requirement = requirement.strip() if not requirement: continue if requirement.startswith("#"): previous_comment = requirement continue editable = requirement.startswith("-e ") if editable: requirement = requirement[2:].strip() if editable and _is_path(requirement): requirement, warning = _handle_editable_path(requirement, previous_comment) if warning: warnings.append(warning) if _is_path(requirement): sanitized.append(f"-e {requirement}") continue requirement, warning = _normalize_vcs_url(requirement) if warning: warnings.append(warning) requirement, warning = _normalize_requirement(requirement) if warning: warnings.append(warning) sanitized.append(requirement) previous_comment = "" return sanitized, warnings def _is_path(requirement: str) -> bool: """Return True if requirement looks like a local path (not a URL).""" return "://" not in requirement and ("/" in requirement or "\\" in requirement) def _normalize_requirement(requirement: str) -> Tuple[str, str]: """Normalize the requirement or pass-through with warning when not valid.""" try: req = Requirement(requirement) except InvalidRequirement: warning = f"Possibly invalid requirement format: {requirement!r}" return requirement, warning replacement = _format_requirement(req) if requirement.replace(" ", "") != replacement.replace(" ", ""): warning = f"Normalize requirement {requirement!r} to {replacement!r}" return replacement, warning return requirement, "" def _format_requirement(req: Requirement) -> str: """Convert requirement to a string and replace ssh with https""" if req.url and req.url.startswith("git+ssh://"): https_url = re.sub(r"^git\+ssh://git@", "git+https://", req.url) return f"{req.name} @ {https_url}" return str(req) def _normalize_vcs_url(requirement: str) -> Tuple[str, str]: """Normalize VCS URLs to the PEP 508 spec (name @ url).""" parsed = urlparse(requirement) if not parsed.fragment or "egg=" not in parsed.fragment: return requirement, "" qs = parse_qs(parsed.fragment) egg_names = qs.get("egg") if not egg_names: return requirement, "" egg_name = egg_names[0] base_url = _remove_url_fragment(parsed) new_requirement = f"{egg_name} @ {base_url}" warning = f"Normalize VCS requirement '{requirement}' to '{new_requirement}'" return new_requirement, warning def _remove_url_fragment(parsed: ParseResult) -> str: return urlunparse( ParseResult( scheme=parsed.scheme, netloc=parsed.netloc, path=parsed.path, params=parsed.params, query=parsed.query, fragment="", ) ) def _handle_editable_path(requirement: str, comment: str) -> Tuple[str, str]: """Replace editable path with comment substitution if available.""" replacement = _requirement_from_comment(comment) if replacement: warning = f"Replaced editable install '{requirement}' with '{replacement}'." return replacement, warning if os.path.exists(requirement): warning = f"Editable path exists locally: '{requirement}'" else: warning = f"Editable path does not exist locally: '{requirement}'" return requirement, warning _REQUIREMENT_COMMENT_RE = re.compile(r"\(([\w\.\-\[\]]+[=> str: """Extract requirement from a 'pip freeze' comment.""" # When doing "pip freeze" in the same envrinment where "git status" gives this # fatal: detected dubious ownership in repository at '/path/to/repo' # the requirements list will contain "-e /path/to/repo" with a warning that # contains a requirement message. match = _REQUIREMENT_COMMENT_RE.search(comment) if match: return match.group(1) return "" ewoks-v4.0.0-8f4a8f91507ab3c3721e8d3f73e73ecca373546a/src/ewoks/bindings.py000066400000000000000000000211451512474103500245720ustar00rootroot00000000000000import datetime import logging import os import sys from contextlib import contextmanager from pathlib import Path from typing import Any from typing import Dict from typing import Generator from typing import List from typing import Optional from typing import Union from ewokscore.events.contexts import RawExecInfoType from ewokscore.events.contexts import job_context from ewokscore.graph import TaskGraph from ewokscore.graph.inputs import graph_inputs_as_table from tabulate import tabulate from . import _engines from . import graph_cache from ._requirements.pip.extract import add_current_env_pip_requirements from ._requirements.pip.extract import extract_pip_requirements from ._requirements.pip.install import pip_install from .errors import AbortException try: from ewoksjob.bindings import submit_graph as _submit_graph from ewoksjob.client.futures import FutureInterface except ImportError: _submit_graph = None FutureInterface = Any try: from pyicat_plus.client import defaults as icat_defaults from pyicat_plus.client.main import IcatClient except ImportError: IcatClient = None icat_defaults = None __all__ = ["execute_graph", "load_graph", "save_graph", "convert_graph", "submit_graph"] logger = logging.getLogger(__name__) def execute_graph( graph, engine: Optional[str] = None, inputs: Optional[List[dict]] = None, load_options: Optional[dict] = None, varinfo: Optional[dict] = None, execinfo: RawExecInfoType = None, task_options: Optional[dict] = None, outputs: Optional[List[dict]] = None, merge_outputs: Optional[bool] = True, environment: Optional[dict] = None, convert_destination: Optional[Any] = None, save_options: Optional[dict] = None, upload_parameters: Optional[dict] = None, **execute_options, ) -> Optional[dict]: with job_context(execinfo, engine=engine) as execinfo: with _upload_context(upload_parameters): if environment: environment = {k: str(v) for k, v in environment.items()} os.environ.update(environment) # Load the graph if load_options is None: load_options = dict() graph = load_graph(graph, inputs=inputs, **load_options) # Save the graph (with inputs) if convert_destination is not None: convert_graph(graph, convert_destination, save_options=save_options) # Execute the graph engine_api = _engines.get_execution_engine(engine) result = engine_api.execute_graph( graph, varinfo=varinfo, execinfo=execinfo, task_options=task_options, outputs=outputs, merge_outputs=merge_outputs, **execute_options, ) return result @contextmanager def _upload_context(upload_parameters: Optional[dict]) -> Generator[None, None, None]: if upload_parameters is None: yield return if IcatClient is None: raise RuntimeError("requires the 'pyicat-plus' package") metadata = upload_parameters.setdefault("metadata", {}) if "startDate" not in metadata: metadata["startDate"] = datetime.datetime.now().astimezone() yield # Only upload when no exception is raised. if "endDate" not in metadata: metadata["endDate"] = datetime.datetime.now().astimezone() metadata_urls = upload_parameters.pop( "metadata_urls", icat_defaults.METADATA_BROKERS ) client = IcatClient(metadata_urls=metadata_urls) logger.info( "Upload processed dataset '%s' metadata to ICAT: %s", upload_parameters.get("dataset"), upload_parameters.get("path"), ) client.store_processed_data(**upload_parameters) def submit_graph( graph, _celery_options: Optional[dict] = None, resolve_graph_remotely: Optional[bool] = None, load_options: Optional[dict] = None, **options, ) -> FutureInterface: """Submit a workflow to be executed remotely. The workflow is resolved on the client-side by default (e.g. load from a file) but can optionally be resolved remotely. """ if _submit_graph is None: raise RuntimeError("requires the 'ewoksjob' package") return _submit_graph( graph, _convert_graph=_load_graph, _celery_options=_celery_options, resolve_graph_remotely=resolve_graph_remotely, load_options=load_options, **options, ) def _load_graph(graph, load_options: Optional[dict] = None) -> dict: return convert_graph( graph, None, load_options=load_options, save_requirements=False ) @graph_cache.cache def load_graph( graph: Any, inputs: Optional[List[dict]] = None, representation: Optional[str] = None, root_dir: Optional[Union[str, Path]] = None, root_module: Optional[str] = None, **load_options, ) -> TaskGraph: """When load option `graph_cache_max_size > 0` is provided, the graph will cached in memory. When the graph comes from external storage (for example a file) any changes to the external graph will require flushing the cache with `graph_cache_max_size = 0`. """ engine_api, representation = _engines.get_serialization_engine( graph, representation=representation ) return engine_api.deserialize_graph( graph, inputs=inputs, representation=representation, root_dir=root_dir, root_module=root_module, **load_options, ) def save_graph( graph: TaskGraph, destination, representation: Optional[str] = None, **save_options, ) -> Union[str, dict]: engine_api, representation = _engines.get_serialization_engine( destination, representation=representation ) return engine_api.serialize_graph( graph, destination, representation=representation, **save_options ) def convert_graph( source, destination, inputs: Optional[List[dict]] = None, load_options: Optional[dict] = None, save_options: Optional[dict] = None, save_requirements: bool = True, ) -> Union[str, dict]: if load_options is None: load_options = dict() if save_options is None: save_options = dict() graph = load_graph(source, inputs=inputs, **load_options) if save_requirements: graph = add_current_env_pip_requirements(graph) return save_graph(graph, destination, **save_options) def show_graph( source, inputs: Optional[List[dict]] = None, load_options: Optional[dict] = None, original_source: Optional[str] = None, column_widths: Optional[Dict[str, Optional[int]]] = None, ) -> Union[str, dict]: if load_options is None: load_options = dict() graph = load_graph(source, inputs=inputs, **load_options) _print_graph(graph, column_widths=column_widths, original_source=original_source) def _print_graph( graph: TaskGraph, column_widths: Optional[Dict[str, Optional[int]]] = None, original_source: Optional[str] = None, ) -> None: column_names, rows, metadata, footnotes = graph_inputs_as_table( graph, column_widths=column_widths ) print() if original_source: print(f"Workflow: {original_source}") else: print("Workflow:") for key, value in metadata.items(): label = key.replace("_", " ").capitalize() print(f"{label}: {value}") if rows: print(tabulate(rows, headers=column_names, tablefmt="fancy_grid")) else: print("No workflow inputs parameters detected!") for footnote in footnotes: print(footnote) def install_graph( source, skip_prompt: bool = False, python_path: Optional[str] = None, load_options: Optional[dict] = None, ): if load_options is None: load_options = dict() graph = load_graph(source, **load_options) requirements = graph.requirements if requirements is None: logger.warning( "Requirements field is empty. Trying to extract requirements automatically..." ) requirements = extract_pip_requirements(graph) logger.info(f"Extracted the following requirements: {requirements}") if python_path is None: python_path = sys.executable if skip_prompt: pip_install(requirements, python_path) return requirements_as_str = "\n".join(requirements) answer = input( f"{requirements_as_str}\nThis will install the above packages via {python_path} -m pip install. Do you want to proceed (y/N)?" ) if answer.lower() == "y" or answer.lower() == "yes": pip_install(requirements, python_path) else: raise AbortException() ewoks-v4.0.0-8f4a8f91507ab3c3721e8d3f73e73ecca373546a/src/ewoks/cli_utils/000077500000000000000000000000001512474103500244075ustar00rootroot00000000000000ewoks-v4.0.0-8f4a8f91507ab3c3721e8d3f73e73ecca373546a/src/ewoks/cli_utils/__init__.py000066400000000000000000000000001512474103500265060ustar00rootroot00000000000000ewoks-v4.0.0-8f4a8f91507ab3c3721e8d3f73e73ecca373546a/src/ewoks/cli_utils/cli_convert_utils.py000066400000000000000000000062161512474103500305150ustar00rootroot00000000000000from argparse import Namespace from typing import List from ewoksutils.cli_utils import cli_arguments from ewoksutils.cli_utils import cli_log_utils from ewoksutils.cli_utils import cli_parse from ewoksutils.cli_utils.cli_spec import CLIArg from .._engines import get_graph_representations from .cli_parse import parse_destinations def convert_arguments( shell: bool = False, default_log_level: str = "warning" ) -> List[CLIArg]: if shell: args_list = cli_log_utils.log_arguments(default_log_level=default_log_level) else: args_list = [] args_list += cli_arguments.workflow_arguments("convert") args_list += cli_arguments.ewoks_inputs_arguments() args_list += [ CLIArg( "destination", [], type=str, help="Destination of the conversion (e.g., JSON filename).", ), CLIArg( "source_representation", ["--src-format"], type=str.lower, choices=get_graph_representations(), help="Source format.", ), CLIArg( "destination_representation", ["--dst-format"], type=str.lower, choices=get_graph_representations(), help="Destination format.", ), CLIArg( "load_options", ["-o", "--load-option"], action="append", metavar="OPTION=VALUE", help="Load options.", ), CLIArg( "save_options", ["-s", "--save-option"], action="append", metavar="OPTION=VALUE", help="Save options.", ), CLIArg( "exclude_requirements", ["--exclude-requirements"], action="store_true", help="Do not include the packages of the current Python environment as requirements in the destination workflow.", ), ] return args_list def parse_convert_arguments(cli_args: Namespace, shell: bool = False) -> None: if shell: cli_log_utils.parse_log_arguments(cli_args) cli_args.workflows, cli_args.graphs = cli_parse.parse_workflows(cli_args) cli_args.destinations = parse_destinations(cli_args) load_options = dict(cli_parse.parse_option(item) for item in cli_args.load_options) if cli_args.source_representation: load_options["representation"] = cli_args.source_representation if cli_args.test: load_options["representation"] = "test_core" if cli_args.root_module: load_options["root_module"] = cli_args.root_module if cli_args.root_dir: load_options["root_dir"] = cli_args.root_dir save_options = dict(cli_parse.parse_option(item) for item in cli_args.save_options) if cli_args.destination_representation: save_options["representation"] = cli_args.destination_representation convert_options = { "save_options": save_options, "load_options": load_options, "inputs": cli_parse.parse_ewoks_inputs_parameters(cli_args), } if cli_args.exclude_requirements: convert_options["save_requirements"] = False cli_args.convert_options = convert_options ewoks-v4.0.0-8f4a8f91507ab3c3721e8d3f73e73ecca373546a/src/ewoks/cli_utils/cli_install_utils.py000066400000000000000000000023601512474103500304770ustar00rootroot00000000000000import logging from argparse import Namespace from typing import List from ewoksutils.cli_utils import cli_arguments from ewoksutils.cli_utils import cli_log_utils from ewoksutils.cli_utils import cli_parse from ewoksutils.cli_utils.cli_spec import CLIArg logger = logging.getLogger(__name__) def install_arguments( shell: bool = False, default_log_level: str = "info" ) -> List[CLIArg]: if shell: args_list = cli_log_utils.log_arguments(default_log_level=default_log_level) else: args_list = [] args_list += cli_arguments.workflow_arguments("install") args_list += [ CLIArg( "yes", ["--yes"], action="store_true", help="Automatically accept installation prompts.", ), CLIArg( "python", ["-p", "--python"], type=str, help="Python interpreter of the environment where the packages should be installed. Default: current environment Python.", ), ] return args_list def parse_install_arguments(cli_args: Namespace, shell: bool = False) -> None: if shell: cli_log_utils.parse_log_arguments(cli_args) cli_args.workflows, cli_args.graphs = cli_parse.parse_workflows(cli_args) ewoks-v4.0.0-8f4a8f91507ab3c3721e8d3f73e73ecca373546a/src/ewoks/cli_utils/cli_parse.py000066400000000000000000000014521512474103500267240ustar00rootroot00000000000000import os from typing import List def parse_destinations(args) -> List[str]: dest_dirname = os.path.dirname(args.destination) basename = os.path.basename(args.destination) dest_basename, dest_ext = os.path.splitext(basename) if not dest_ext: dest_ext = dest_basename dest_basename = "" if not dest_ext.startswith("."): dest_ext = f".{dest_ext}" if len(args.workflows) == 1 and dest_basename: return [os.path.join(dest_dirname, f"{dest_basename}{dest_ext}")] destinations = list() for workflow in args.workflows: basename, _ = os.path.splitext(os.path.basename(workflow)) destination = os.path.join(dest_dirname, f"{basename}{dest_basename}{dest_ext}") destinations.append(destination) return destinations ewoks-v4.0.0-8f4a8f91507ab3c3721e8d3f73e73ecca373546a/src/ewoks/cli_utils/cli_show_utils.py000066400000000000000000000036031512474103500300120ustar00rootroot00000000000000from argparse import Namespace from typing import List from ewoksutils.cli_utils import cli_arguments from ewoksutils.cli_utils import cli_log_utils from ewoksutils.cli_utils import cli_parse from ewoksutils.cli_utils.cli_spec import CLIArg from .._engines import get_graph_representations def show_arguments( shell: bool = False, default_log_level: str = "warning" ) -> List[CLIArg]: if shell: args_list = cli_log_utils.log_arguments(default_log_level=default_log_level) else: args_list = [] args_list += cli_arguments.workflow_arguments("show") args_list += cli_arguments.ewoks_inputs_arguments() args_list += [ CLIArg( "source_representation", ["--src-format"], type=str.lower, choices=get_graph_representations(), help="Source format.", ), CLIArg( "load_options", ["-o", "--load-option"], action="append", metavar="OPTION=VALUE", help="Load options.", ), ] return args_list def parse_show_arguments(cli_args: Namespace, shell: bool = False) -> None: if shell: cli_log_utils.parse_log_arguments(cli_args) cli_args.workflows, cli_args.graphs = cli_parse.parse_workflows(cli_args) load_options = dict(cli_parse.parse_option(item) for item in cli_args.load_options) if cli_args.source_representation: load_options["representation"] = cli_args.source_representation if cli_args.root_module: load_options["root_module"] = cli_args.root_module if cli_args.root_dir: load_options["root_dir"] = cli_args.root_dir if cli_args.test: load_options["representation"] = "test_core" show_options = { "load_options": load_options, "inputs": cli_parse.parse_ewoks_inputs_parameters(cli_args), } cli_args.show_options = show_options ewoks-v4.0.0-8f4a8f91507ab3c3721e8d3f73e73ecca373546a/src/ewoks/errors.py000066400000000000000000000000521512474103500243030ustar00rootroot00000000000000class AbortException(Exception): pass ewoks-v4.0.0-8f4a8f91507ab3c3721e8d3f73e73ecca373546a/src/ewoks/graph_cache.py000066400000000000000000000022221512474103500252140ustar00rootroot00000000000000from collections import OrderedDict from functools import wraps from typing import Dict from typing import Optional from ewokscore.graph import TaskGraph from ewokscore.hashing import uhash _GRAPH_CACHE: Dict[int, TaskGraph] = OrderedDict() _GRAPH_CACHE_MAX_SIZE: int = 0 def cache(load_method): @wraps(wraps) def wrapper(*args, graph_cache_max_size: Optional[int] = None, **kw): set_cache_max_size(graph_cache_max_size) if _GRAPH_CACHE_MAX_SIZE <= 0: return load_method(*args, **kw) graph_id = uhash((args, kw)) graph = _GRAPH_CACHE.pop(graph_id, None) if graph is None: graph = load_method(*args, **kw) _GRAPH_CACHE[graph_id] = graph _check_cache_size() return graph return wrapper def set_cache_max_size(graph_cache_max_size: Optional[int] = None) -> None: global _GRAPH_CACHE_MAX_SIZE if graph_cache_max_size is not None: _GRAPH_CACHE_MAX_SIZE = graph_cache_max_size _check_cache_size() def _check_cache_size() -> None: while _GRAPH_CACHE and len(_GRAPH_CACHE) > _GRAPH_CACHE_MAX_SIZE: _GRAPH_CACHE.popitem(last=False) ewoks-v4.0.0-8f4a8f91507ab3c3721e8d3f73e73ecca373546a/src/ewoks/tests/000077500000000000000000000000001512474103500235625ustar00rootroot00000000000000ewoks-v4.0.0-8f4a8f91507ab3c3721e8d3f73e73ecca373546a/src/ewoks/tests/__init__.py000066400000000000000000000000001512474103500256610ustar00rootroot00000000000000ewoks-v4.0.0-8f4a8f91507ab3c3721e8d3f73e73ecca373546a/src/ewoks/tests/conftest.py000066400000000000000000000011601512474103500257570ustar00rootroot00000000000000from unittest.mock import MagicMock import pytest @pytest.fixture def mock_icat_client(monkeypatch): mock_defaults = MagicMock() mock_defaults.METADATA_BROKERS = ["mock://broker"] # Create a mock for IcatClient mock_client = MagicMock() mock_client_instance = mock_client.return_value mock_client_instance.store_processed_data.return_value = None # Patch the imports in your module (replace 'your_module' with actual module name) monkeypatch.setattr("ewoks.bindings.icat_defaults", mock_defaults) monkeypatch.setattr("ewoks.bindings.IcatClient", mock_client) return mock_client ewoks-v4.0.0-8f4a8f91507ab3c3721e8d3f73e73ecca373546a/src/ewoks/tests/notebooks/000077500000000000000000000000001512474103500255655ustar00rootroot00000000000000ewoks-v4.0.0-8f4a8f91507ab3c3721e8d3f73e73ecca373546a/src/ewoks/tests/notebooks/__init__.py000066400000000000000000000010241512474103500276730ustar00rootroot00000000000000import sys from contextlib import contextmanager from pathlib import Path from typing import Generator if sys.version_info < (3, 9): import importlib_resources else: import importlib.resources as importlib_resources @contextmanager def notebook_path(*args) -> Generator[Path, None, None]: source = importlib_resources.files(__name__).joinpath(*args) with importlib_resources.as_file(source) as path: if not path.is_file(): raise FileNotFoundError(f"Not a file: '{source}'") yield path ewoks-v4.0.0-8f4a8f91507ab3c3721e8d3f73e73ecca373546a/src/ewoks/tests/notebooks/benchmark.ipynb000066400000000000000000007260671512474103500306040ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "id": "090e06c3-02d4-48f7-9442-e81db4ae06bc", "metadata": {}, "source": [ "# Benchmark workflow execution\n", "\n", "This notebook checks the overhead added to the actual computations by ewoks." ] }, { "cell_type": "code", "execution_count": 1, "id": "75f296a5-0dbd-4313-9c15-bb9e31f10acd", "metadata": {}, "outputs": [], "source": [ "import time\n", "import matplotlib.pyplot as plt\n", "from ewoksutils.import_utils import qualname\n", "from ewoks import execute_graph\n", "from ewokscore.tests.examples.tasks.addfunc import addfunc" ] }, { "cell_type": "markdown", "id": "92f9a606-7cbf-45b8-b9f8-0a1aa182e0a8", "metadata": {}, "source": [ "Exclude importing from the benchmark" ] }, { "cell_type": "code", "execution_count": 2, "id": "d008ea63-82c4-4802-9d6a-c6320b83f706", "metadata": {}, "outputs": [], "source": [ "import ewoksppf # noqa F401\n", "import ewoksdask # noqa F401" ] }, { "cell_type": "markdown", "id": "c69d9db9-6056-4492-be02-1f32784d92e9", "metadata": {}, "source": [ "Define a workflow for benchmarking" ] }, { "cell_type": "code", "execution_count": 3, "id": "adfb071b-7f84-4822-86dc-126c6a27af0b", "metadata": {}, "outputs": [], "source": [ "def generate_graph(nbranches=1, nodes_per_branch=10):\n", " task_identifier = qualname(addfunc)\n", " nodes = []\n", " links = []\n", " for ibranch in range(nbranches):\n", " node_id1 = None\n", " for inode in range(nodes_per_branch):\n", " node_id2 = f\"{ibranch}-{inode}\"\n", " nodes.append(\n", " {\n", " \"id\": node_id2,\n", " \"task_type\": \"method\",\n", " \"task_identifier\": task_identifier,\n", " \"default_inputs\": [{\"name\": \"arg\", \"value\": 0}],\n", " }\n", " )\n", " if node_id1:\n", " links.append(\n", " {\n", " \"source\": node_id1,\n", " \"target\": node_id2,\n", " \"data_mapping\": [\n", " {\"source_output\": \"return_value\", \"target_input\": \"arg\"}\n", " ],\n", " }\n", " )\n", " node_id1 = node_id2\n", " return {\"graph\": {\"id\": \"test\"}, \"nodes\": nodes, \"links\": links}" ] }, { "cell_type": "markdown", "id": "854582a9-3c7b-445d-9875-176922853d48", "metadata": {}, "source": [ "Pure python equivalent of the workflow" ] }, { "cell_type": "code", "execution_count": 4, "id": "0a144804-e8af-4439-ab19-cc53108d2999", "metadata": {}, "outputs": [], "source": [ "def prefunc_raw(**kw):\n", " return kw\n", "\n", "\n", "def timefunc_raw(kw, **_):\n", " result = dict()\n", " for ibranch in range(kw[\"nbranches\"]):\n", " arg = 0\n", " for inode in range(kw[\"nodes_per_branch\"]):\n", " arg = addfunc(arg)\n", " name = f\"{ibranch}-{inode}\"\n", " result[name] = arg\n", " return result" ] }, { "cell_type": "markdown", "id": "efa80a05-b2fa-4d0a-b035-91d346a4b5e9", "metadata": {}, "source": [ "Benchmark with varying number of nodes and branches" ] }, { "cell_type": "code", "execution_count": 5, "id": "d2f19a58-ad55-4696-9ac1-f9f8802c5d9c", "metadata": {}, "outputs": [], "source": [ "def bench(prefunc, timefunc, title, nrepeats=3, nnodesmax=200, **timefuncopts):\n", " plt.title(title)\n", " for i in range(0, 7, 2):\n", " nbranches = max(i, 1)\n", " x = []\n", " y = []\n", " nnodespbmax = nnodesmax // nbranches\n", " for j in range(0, nnodespbmax, max(nnodespbmax // 10, 1)):\n", " n = dt = 0\n", " for k in range(nrepeats):\n", " nodes_per_branch = max(j, 1)\n", " graph = prefunc(nbranches=nbranches, nodes_per_branch=nodes_per_branch)\n", " t0 = time.perf_counter_ns()\n", " timefunc(graph, **timefuncopts)\n", " t1 = time.perf_counter_ns()\n", " nk = nbranches * nodes_per_branch\n", " n += nk\n", " dt += (t1 - t0) * 1e-6 / nk\n", " x.append(n / nrepeats)\n", " y.append(dt / nrepeats)\n", " x, y = list(zip(*sorted(zip(x, y))))\n", "\n", " plt.plot(x, y, label=f\"{nbranches} branches\")\n", " plt.xlabel(\"# nodes\")\n", " plt.ylabel(\"Time/# nodes (msec)\")\n", " plt.legend()\n", " plt.gca().set_prop_cycle(None)" ] }, { "cell_type": "code", "execution_count": 6, "id": "3e7b8d52-0717-4818-939d-9e6ecd39c30e", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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uueSSVvfTZag1reMcraxjzY5DfJpykE3Zh1EKogN6c/+cSObFBRDU38vRIToFPUbQDpRS3HDDDURFRXHPPfe0ua8uQ61p9lVRa+ajpDx+/Z/NjPnbOh78KI3CYzXceUE46+6ZzBd3nc+tU4fqJNCMPtVrBxs3bmTFihXExsY2ddv8/e9/Z+7cuafsq8tQa1r7q6lv4Ps9RaxOOcg36UXUmi0M6uPJDeeFMn/kIGIG9e7Sg73nym5lqO1Fl6F2PH28NWdQ32BhQ2YJn6YcZO3OQipqzQzo6c5FcQEsGDmIhOB+Dl3sxdk4qgy1pmlau7JYFJtzjrA65SBr0go4WlWPt6cbF44YyIL4QUwIG4Cbq+7xPlM6EWia5tSUUqTkNd7odZDCY7V4mlyYEeXPgpGDmDLcFw8356313xnoRKBpmlPKKCznk+SDfJp6kP2HqzC5ClMi/HhwbgAzovzp2YVKPDiaPpKapjmNOrOFr3YeYsXP+9mccwQXgYlDfbht6jBmxwykj5fJ0SF2SToRaJrmcAdLq3l38wHe3ZxLSUUtQf178IcLI1mUMLhb3+jVUXQi0DTNISwWxcZ9Jaz4eT/r0gtRwLThflw9YQhTwn31jJ8OpBNBO2poaCAxMZHBgwc3FZRrburUqTz99NMkJrY4g6vdLV++nK1bt/LSSy91yOdpmi3KqupZtT2PtzftJ6ukkv493bl58lCWjAvWN3k5iE4E7ej5558nKiqKY8dOWW3TZroMtdZV7cgvY8XP+/kkJZ+aegsJwX35xxUjuXBEAJ4mPevHkfSE23aSl5fH559/zo033tjmfitWrCA+Pp4RI0awebOxKufDDz/M1VdfzaRJk7j66qvJycnh/PPPJyEhgYSEBH766SfAWGhm6tSpLZaV3rJlCxMnTmTkyJGMHTuW8vJyAA4ePMicOXMIDw/nvvvua4pj7dq1TJgwgYSEBH71q181lZJ44IEHmspQ33vvve1+nLTupaa+gQ+357Ho5Y3Me3EDq1MOsmjUYD674zw+/O0kFo0K1EnACXS5U88nNj/B7iO72/U9I/tHcv/Y+9vc5+677+bJJ59saoBbU1VVRXJyMuvXr+f6669nx44dAOzatYsNGzbQo0cPqqqq+Prrr/H09CQjI4PFixfTeDd1S2Wlx44dyxVXXMHKlSsZM2YMx44do0ePHgAkJyeTlJSEh4cHw4cP54477qBHjx48+uijrFu3jp49e/LEE0/w7LPPctttt/HRRx+xe/duRESXodbOWu6RKt76ZT//25LL0ap6wnx68ud50Vw6OpA+PfTMH2djt0QgIm8A84AipdSIVvaZCjwHmIASpdQUe8VjT5999hl+fn6MHj2a77//vs19Fy82Fm6bPHkyx44da2psFyxY0NR419fXc/vtt5OcnIyrqyt79+5ten1LZaX79OlDQEAAY8aMATihBPb06dPp06cPANHR0ezfv5/S0lJ27drFpEmTAKirq2PChAn06dMHT09PbrjhBubNm6eXudTOSINFsX5vMW/+nMP3e4txEWFmlD9XTxjCxKFdd3WvrsCeVwTLgZeAN1t6UkT6Ai8Dc5RSB0TErz0+9HRn7vawceNGVq9ezRdffEFNTQ3Hjh1j6dKlvPXWW6fsa0sZ6n/84x/4+/uTkpKCxWI5oTBdS2Wl29JaGeqZM2fy7rvvnrL/5s2b+eabb1i1ahUvvfQS33777Wl+e627O1JZx/+25vL2L/vJPVKNr7cHd0wbxuJxwQT06eHo8DQb2HPN4vUiEtLGLlcBHyqlDlj3L7JXLPb22GOP8dhjjwFGP/7TTz/dYhIAWLlyJdOmTWPDhg306dOn6Wy9ubKyMgIDA3FxceG///0vDQ0NbX7+8OHDKSgoYMuWLYwZM4by8vKmq4uWjB8/nttuu43MzEyGDRtGZWUl+fn5DBo0iKqqKubOncukSZMICws7g6OgdSdKKZJzS1nx834+SyugzmxhXGh/7p8Tyazogbi76eHHzsSRYwQRgElEvge8geeVUq1dPdwM3AxGGefOzNPTk1GjRlFfX88bb7zR4j6//e1vufTSS3nzzTeZM2fOCVcLLXF3d2flypXccccdVFdX06NHD9atW9fq/r6+vixfvpzFixdTW1sLwKOPPoq3tzcLFy6kpqYGpRTPPvvs2f+iWpdUXdfA6pR8Vmzaz478Y/TycOPKMUEsHT+ECH9vR4ennSW7lqG2XhF81tIYgYi8BCQC04EewM/ARUqpvSfv25wuQ+14+nh3P1nFFby16QCrtuVyrMbMcH9vlk4YwqJRg+mla/50Cs5ahjoPOKyUqgQqRWQ9MBJoMxFomtYxzA0WvtldxFub9vNjRgluLsKFsQFcPX4IY0L66cHfLsSRieAT4CURcQPcgXHAPxwYj6ZpQHF5Le9tPsA7mw9QUFZDQB9P7p0VweVjgnTdny7KntNH3wWmAj4ikgf8H8Y0UZRSryql0kXkSyAVsAD/VkrtsFc8mqadXmZRBZe8vJFjNWbOD/fh4QUxTI/004u9dHH2nDW02IZ9ngKeslcMmqbZrqyqnpve3Iq7mwtrfzdZD/52I3qUR9M0GiyKO95LIu9oFe/cNF4ngW5GJwJN03jiy92s31vMY5fEMiakv6PD0TqY7vhrJ6WlpU3F4KKiovj5559P2ee6665j1apVHRbT999/r8tEaKf14fY8lq3P4poJQ1g8tnPfp6OdHX1F0E7uuusu5syZw6pVq6irq6Oqquqs3keXodY6UnJuKQ98mMb4sP78aV60o8PRHERfEbSDsrIy1q9fzw033AAYd/r27du3xX3XrVtHYmIiERERTYvXLF++nAULFnDBBRcwffp0KioqmD59OgkJCcTGxvLJJ58AkJOTQ1RUFDfddBMxMTHMmjWL6upqADIzM5kxYwYjR44kISGBffv2AVBRUdFi2ept27YxZcoURo8ezezZsykoKADghRdeaCpDfeWVV9rtmGmOV3SshltWbMXP24OXl4zGpGcGdVunPfUUkQnAUuB8IACoBnYAnwNvKaXK7BrhGTr0979Tm96+Zag9oiIZ+OCDrT6fnZ2Nr68vv/71r0lJSWH06NE8//zzLZaGyMnJYfPmzezbt49p06aRmZkJwPbt20lNTaV///6YzWY++ugjevfuTUlJCePHj2fBggUAZGRk8O677/Laa69x+eWX88EHH7B06VKWLFnCAw88wKJFi6ipqcFisZCbm9ti2epx48Zxxx138Mknn+Dr68vKlSt56KGHeOONN3j88cfJzs7Gw8NDl6HuwmrqG7jlrW2U15j54NaJ9O/p7uiQNAdq8xRARNYANwJfAXMwEkE08EfAE/hERBbYO0hnZzab2b59O7feeitJSUn07NmTxx9/vMV9L7/8clxcXAgPDycsLIzdu42kNXPmTPr3NwbplFI8+OCDxMXFMWPGDPLz8yksLAQgNDSU+Ph4AEaPHk1OTg7l5eXk5+ezaNEiwKhn5OVlLPnXWLbaxcWlqWz1nj172LFjBzNnziQ+Pp5HH32UvLw8AOLi4liyZAlvvfWW7qLqopRS/PHjHSQdKOWZX40kKqD36V+kdWmn+59+tVKq5KRtFcB269czIuJjl8jOUltn7vYSGBhIYGAg48aNA+Cyyy5rNRHYUob67bffpri4mG3btmEymQgJCaGmpgY4tax0Y9dQa1orQx0TE9PigPbnn3/O+vXr+fTTT/nb3/5GWlqaTghdzBsbc1i1LY87p4dzYWyAo8PRnECbVwSNSUBEQkWk6d5yEenRWGK6hUTR7QwcOJCgoCD27NkDwDfffEN0dMsDb++//z4Wi4V9+/aRlZXF8OHDT9mnrKwMPz8/TCYT3333Hfv372/z8729vQkMDOTjjz8GoLa2ts3B6uHDh1NcXNyUCOrr69m5c2dTd9K0adN44oknKCsra1rCUusaNmSU8LfPdzE7xp+7p4c7OhzNSdh6qvc+MLHZ4wbrtjHtHlEn9eKLL7JkyRLq6uoICwvjP//5T4v7BQcHM3bsWI4dO8arr756wqIzjZYsWcL8+fOJjY0lMTGRyMjI037+ihUruOWWW/jzn/+MyWTi/fffb3Vfd3d3Vq1axZ133klZWRlms5m7776biIgIli5dSllZGUop7rzzzlYHvbXOJ6ekktve2U64nzfPXh6Pi4suGqcZbCpDLSLJSqn4k7alKKVG2iuw1ugy1I6nj3fnU15TzyUv/0RxRS2rbzuP4AFejg5J62BtlaG2db5YcfNBYRFZCHT7LiFN6wwsFsXvViaTVVLJy1cl6CSgncLWrqHfAG+LyD8BhbGWwDV2i0rTtHbzj3V7WZdexMPzo5k4zKnmdmhOwqZEoJTaB4wXkV7Wx043gqiU0gtldAB7rmintb/PUwt48dtMrkgM4tqJIY4OR3NSNnUNiYi/iLwOvK+UqhCRaBG5wc6x2czT05PDhw/rRsrOlFIcPny4xQFuzfnsPFjGve+nMHpIP/5ycYw+UdJaZWvX0HLgP8BD1sd7gZXA63aI6YwFBgaSl5dHcXGxo0Pp8jw9PQkMDHR0GNppHK6o5eY3t9HXy8QrSxPwcHN1dEiaE7M1Efgopf4nIn8AUEqZRaTBjnGdEZPJRGhoqKPD0DSnUGe2cOvb2ympqGXVbybq5SW107J11lCliAzAGChGRMYDbdYYEpE3RKRIRNpcflJExoiIWUQuszEWTdPa8MinO9mcfYQnL4sjNrCPo8PROgFbE8E9wGpgqIhsBN4E7jjNa5Zj1CdqlYi4Ak8Aa22MQ9O0Nry1aT9v/3KAW6aEsTB+sKPD0ToJW2cNbReRKcBwQIA9Sqn607xmfWMZijbcAXyAvkNZ087ZL1mHeXj1TqYN9+W+2ae/G13TGtk6a+hXQA+l1E7gYmCliCScyweLyGBgEfDKubyPpmmQd7SKW9/eTvAAL55fPApXXT5COwO2dg39SSlVLiLnAdMxZgudawP+HHC/Uspyuh1F5GYR2SoiW/XMIE07UVWdmZve3EZ9g4XXrkmkt6fJ0SFpnYytiaBxhtBFwGtKqc+Bc13JIhF4T0RygMuAl0Xk4pZ2VEotU0olKqUSfX19z/FjNa3rUErx+/dT2X3oGC8sHsVQ316ODknrhGydPpovIv8CZgJPiIgH57jMpVKqab6niCwHPlNKfXwu76lp3c0/v8vk87QC/nBhJNOG+zk6HK2TsjURXI4xA+hppVSpiAQAv2/rBSLyLjAV8BGRPOD/ABOAUurVs45Y0zQAvt5VyNNr93Jx/CBunhzm6HC0TszWWUNVIvIdENRskLjN6qNKqcW2BqGUus7WfTVNg72F5dz9XhJxgX14/NI4XT5COyc2JQIR+StwHbAP601l1u8X2CcsTdNaU1pVx01vbqWHuxv/uno0niZdPkI7N2fSNTRUKVVnz2A0TWubucHC7e8kUVBaw7s3jyegTw9Hh6R1AbYO+O4A+toxDk3TbPD3L3azIbOERxeNYPSQfo4OR+sibL0ieAxIstYNqm3cqJRa0PpLNE1rT+9vzeWNjdlcNzGEyxODHB2O1oXYmgj+i1ETKA047Q1gmqa1r+0HjvLQRzuYNGwAf7xIrxettS9bE0GVUuoFu0aiaVqLDpXVcMuKbQzs48lLixNwcz2nW3g07RS2JoIfReQxjAqkzbuGttslKk3TAKipb+CWFVupqjXz9o3j6NfzXG/o17RT2ZoIRlm/j2+2TU8f1TQ7Ukrxhw/TSMkrY9nVo4nw93Z0SFoXZesNZdPsHYimaSf694/ZfJSUzz0zI5gVM9DR4WhdWJudjSKyVERa3UdEhlorkmqa1o5+2FvMY2vSmRs7kDsuGObocLQu7nRXBAMwpo1uA7YBxYAnMAyYglFm4gG7Rqhp3UxWcQW3v7OdCH9vnrpspC4fodldm4lAKfW8iLyEMRYwCYgDqoF04Gql1AH7h6hp3cexmnpufHMrJlcXXrsmkZ4etg7jadrZO+1fmVKqAfja+qVpmp00WBR3v5fMgcNVvHXjOIL6ezk6JK2b0BOSNc0JKKV48svdfLu7iP9bEMP4sAGODknrRvR1p6Y5WFl1PQ9+mMbnaQVcNS6YpeOCHR2S1s3oRKBpDpScW8rt72ynoKyG++dEcsvkMD04rHU4m7qGROQuEekthtdFZLuIzLJ3cJrWVVksimXr93HZKz+hFPzvlgncOnUoLi46CWgdz9YrguutM4hmA/2Aq4EVwFq7RaZpXdThilr+3/spfL+nmDkxA3ni0jj6eJkcHZbWjdmaCBpPU+YCK5RSO+U0168i8gYwDyhSSo1o4fklwP3W9y4HblVKpdgcuaZ1Qj/vO8zdK5M4WlXPXxfGsHT8EN0VpDmcrbOGtonIWoxE8JWIeHP6ctTLMRa8b002MEUpFQv8FVhmYyya1uk0WBTPfr2Xq/69iZ4ebnz820lcPSFEJwHNKdh6RXADEA9kWReyHwD8uq0XKKXWi0hIG8//1OzhJiDQxlg0rVMpKKvmrveS2Zx9hEsTAvnLwhh9o5jmVGz9a1RANEZXz1+AnhilJtrLDcCa1p4UkZuBmwGCg/XUOq3z+Ca9kHvfT6HWbOHZy0dySYI+39Gcj62J4GWMrqALMBJBOfABMOZcAxCRaRiJoNXidUqpZVi7jhITE9W5fqam2Vud2cITX+7m9Q3ZRAX05qWrRjHUt5ejw9K0FtmaCMYppRJEJAlAKXVURM55hQwRiQP+DVyolDp8ru+nac5g/+FK7ng3idS8Mq6dMIQ/zI3C0+Tq6LA0rVW2JoJ6EXHF6CJCRHw5x7WLRSQY+BCjeN3ec3kvTXMWn6Yc5A8fpuEi8OrS0cwZodcR0JyfrYngBeAjwE9E/gZcBvyxrReIyLvAVMBHRPKA/wNMAEqpV4E/Y5S5ftk6c8KslEo8i99B0xyuuq6Bv3y2k3c355IQ3JcXFo8isJ8uGqd1DrauUPa2dU2C6Rjz/i9WSqWf5jWLT/P8jcCNtgaqac5qb2E5t7+znb2FFdw6dSj3zIzApBeY1zqRNhOBiPRv9rAIeLf5c0qpI/YKTNOcnVKKlVtyefjTnfTycOPN68cyOcLX0WFp2hk73RXBNoxxAQGCgaPWn/sCB4BQewanac6qvKaeP3yYxmepBZw3zIdnrxiJn3d7zqjWtI5zuhXKQgFE5DXgI6XUF9bHFwIX2z06TXNCqXml3P5OEvml1fx+9nBunaKLxWkdoHAXuPeEfkPa/a1t7cgc35gEAJRSa4CJ7R6NpjkxpRT//jGLS1/5CXODhZU3j+e2acN0EtDsp7oUtvwblk2FVybAzy/Z5WNsnTV0UET+CLxlfbwEOGiXiDTNCR2prOP376fwze4iZkb789RlcfT1OudbaTTtVBYLZH8PSW9D+qfQUAv+I2DO4xB7uV0+0tZEsBhj+udH1sfrrds6lcyicob5eTs6DK2T+SXrMHe9l8yRyjoenh/NtRN1sTjNDo7mQPI7xldZLnj2hYRrYNRSCBgJdvybs3X66BHgLmvVUaWUqrBbRHby/tZcfr8qlc/uOI8Rg/s4OhytE2iwKF76NpPnv9lLcH8vPvztRP23o7WvuipIXw1Jb0HOj4DA0Gkw8xEYfhGYOmYCgk2JQERigTeB/tbHJcC1SqkddoytXc0eMZC/f5HO379I5+0bx+kzOq1NhcdquPu9ZH7OOszF8YN4dFEsvXTFUK09KAV5WyFpBez4EOrKoV8ITPsjxC+GPh1fmNDWv+x/Afcopb4DEJGpGEXgOs2AcW9PE3dOD+eRT3fxw95ipg73c3RImpP6bk8R9/4vhaq6Bp68LI5fjQ7UJw7auSsvhNT3jLP/kr1g8oLoi2HUEgieCC6OuwnR1kTQszEJACilvheRnnaKyW6WjBvC8p9yeOyL3Zwf7ournu2hNVNntvD02j0sW59F5EBvXrpqlB5T0s6NuQ4yvjIGfjPWgmqAoHGw4EWIWQQezvH3ZWsiyBKRP2GsUwywFMiyT0j24+7mwn2zI7ntne18sD2PyxODHB2S5iRyj1Rx+7tJpOSWsmRcMH+aF60rhmpnr3CXceafuhKqSqCXP0y8A+KXgG+Eo6M7hc2L1wOPYFQLBfjRuq3TmRs7kJFBfXl27V7mxw2ih7v+z96dKaX4PK2AP3yYBsA/r0rgorgAB0eldUrVpbBjlZEADiaBiwmGz4FRV8PQ6eDqvGNMts4aOgrcaedYOoSI8NDcKC7/18+8sTGb26YNc3RImoOkFxzj0c93sTHzMPFBfXlx8SiC+uuKoU6htgLS/gf1NeDtb5xRN355eNt1KuUZaWnOv18MzH4M4i6Hnj6OjtAmts4aigDuBUKav0YpdYF9wrKvsaH9mRntzyvf7+PKMUEM6OXh6JC0DlRcXsuzX+9h5ZZcevcw8fD8aJaMH6IrhjqD2nLY/JpxB21VK2tVufWAXn7gPdD43ssfejX7uTFx9PQFV5N94jxlzn8f65z/JRAQ7zyJyka2Xqu8D7yKsZpYg/3C6Tj3z4lk9nPrefHbTB5eEOPocLQOUFPfwBsbs3n5u33U1Ddw3cRQ7pw+TN8h7AxqymDzMvj5n1B9FIbNgCn3w4BhUFEI5Yegosj4uflX8V7I/hFqSlt4UwGvAdZEcXLiaPbl7Q8evU/feDvJnH97sDURmJVSr9g1kg42zK8XV4wJ4q1N+7luYgghPp1uEpRmI6UUn6UW8Pia3eSXVjMjyp8H50YSptcQdrzqUvjlVdj0spEMwmcbCSBw9PF9vPqDX1Tb72OutSaHotYTx+FM43tD3amvd/O0JonmVxbWnz37wr5vT53zP/JK6Ns1JpzYmgg+FZHfYpSYqG3c2NnXI7h7RjgfJ+Xz5Fe7eXnJ6NO/QOt0knNL+etnu9i2/yiRA71558ZxTBzWOfptu7SqI7DpFSMJ1B6D4XNhyn0waNTZvZ+bB/QNNr7aopRxxVFRBBUnJYvyxoSxD/ZvNPZrZPKC6IVGuQcHz/m3B1sTwbXW779vtk0BYa29QETeAOYBRUqpES08L8DzwFygCrhOKbXdxnjahZ+3JzdPDuO5dRlsP3CUhOB+Hfnxmh0dLK3myS9383HyQXx6efD4JbH8KjFI3zviaFVHjP7/X5YZZ9dR82Hy741aOh1BxLjC8OoPfpFt72uuhcpiI1kMGAaevTsmRgewddbQ2SxAsxx4CaM0RUsuBMKtX+OAV6zfO9RN54fx1qYDPPZFOv+7ZYK+g7STq6w1868f9rHsxywsCm6bNpRbpw7T5SEcrbIEfnrRKKlcV2mcXU/+PQw85RzRebh5GOUeHFDyoaPZ7X+HUmq9iIS0sctC4E2llAI2iUhfEQlQShXYK6aW9PRw43czw3noox2s3VXI7JiBHfnxWjuxWBQfbM/jqa/2UFRey/yRg7h/znC9gLyjVRTBTy/AltehvhpGXGIkgNP1+WsdypGnSYOB3GaP86zbTkkEInIzcDNAcPBp+gDPwhWJQbyxIZsn1uzmgkg/PY2wk9mUdZhHP9/FjvxjxAf15ZWloxk9RHfzOVT5Idj4Amx9w5hbP+IymHwv+A53dGRaCzrF9bJSahlGkTsSExNVe7+/m6sLD1wYxU1vbmXlllyWjm//peC09rf/cCV//yKdr3YWMqiPJ89fGc/8uEF6xTBHOnYQNj4P25ZDQ71xU9X5/w98wh0dmdYGW28om6OU+rKdPzsfaD73KtC6zSFmRPkxNqQ/z63by8WjBus+ZSdWVl3PS99msPynHEyuLtw7K4Ibzw/TtYEcqSwPNjwH298EixlGLobz74EBQx0dmWaDNls7ERkHbAX+Dnxp3bZCKXV1O3z2auB2EXkPY5C4rKPHB5oTEf4wN5JFL//EsvVZ3DPT+QpDdXfmBgvvbj7AP9ZlcLSqjl+NDuTeWcPx6915b+Tp9EpzYcOzxk1WygLxV8F590D/s5lfojnK6U57rwSeAcJE5AkgFUiw5Y1F5F1gKuAjInkYS12aAJRSrwJfYEwdzcSYPvrrs4i/XY0K7sdFcQG8tj6LpeOCdQPjRL7fU8TfPk8no6iC8WH9+eNF0Xq1MEc6uh9+fMYosQDG/Prz7zn9PH7NKZ0uEdyvlKoTkVTgM2AkMFBENgH7lVJXtPZCpVSbaxpbZwvddqYB29t9s4ezduch/rEug8cuiXV0ON1eRmE5j36ezg97iwkZ4MW/rh7NrGh/Pc3XUY5kGQkg5T0QFxh9LZz3u24xxbIrO10i+FJEGgBfwAdYA1yvlBovIl3yX37IgJ4sGTeEN3/O4fpJIYT7O8fCEd3Nkco6/vH1Xt7ZfAAvd1f+eFEU10wIwd1Nz+hyiMP7YP3TRn19FzdIvAHOuxt6D3J0ZFo7aDMRKKUuEJEeGF1CYcCvgGEi8jGwAXja7hE6wJ3Tw/lgWx5PfLmbf187xtHhdCu15gbe/Gk/L3ybQVVdA0vGBXP3jAj699SF4RyieC/8+DSkvQ+u7jDuFph0l1GHR+syTjs1RilVLSK5SqlnAEQkCbgJmGzv4Bylf093bp02lCe/3MOmrMOMDxvg6JC6PKUUX+0s5LE16ew/XMXU4b48NDdKX5E5StFuWP8U7PgATD1gwm0w4Q6jUqfW5dhaYqL5ugMvKaWKgQ/sE5JzuH5SKCt+3s9jX6Tz8W2TdJ+0He3IL+Ovn+3il+wjRPj34r/Xj2VKhK+jw+p+lIL9P8Hmf8Gu1UahtUl3wYTboZf+9+jKTjd9dBnGuMA6pVQ5gFLq9Y4IzNE8Ta7cMzOC369K5bPUAuaP1H2h7a3wWA1PfbWHD7bn0c/LnUcvHsGVY4Jw03d2d6zaCqPvf8vrULTTWGTlvN8ZCaCnvhruDk53RfA6RnG4e0SkDlgLfKmUSrF7ZE7gkoRAXt+QzZNf7WZWjD8ebvqGpfaQVVzB6xuyWbUtD6Xg5vPDuO2CYfT2tNNqUlrLivcaReBS3jVKQQ+MhfkvQOyvwF3XaOpOTjdY/AvwC/CwiAwAZgH/T0RigSSMpPA/+4fpGK4uwh/mRnHtG5t5e9MBrj9P3yRztpRSbNt/lGXrs/g6vRCTiwuXJAzm1qlDGTJALwrUYRrMsOcLIwFk/2AMAEdfDGNvgsAxnW6JRa192FxHQSl1GHjX+oWIjAbm2CkupzE53IfzhvnwwrcZXDo6kD499FnrmWiwKNbuPMSyH7NIOlBKXy8Td0wbxtUTQvD11mtFd5iKItj2X9j2HziWD70D4YI/QcK1uv9fs7nWkD9GmYlBSqkLRSQaiFdK/c2u0TkBEeGBCyOZ/9IGXvl+Hw9ceJrFLDQAqurMrNqWx79/zObAkSqGDPDirwtjuHR0IF7uuo5Th1AKcn8xFoPf9QlY6iFsKlz4JETMAVf976AZbP1LWA78B3jI+ngvsBJjDKHLGzG4D4viB/PGxmyunjCEwX17ODokp1VUXsObP+3nrV/2U1pVz6jgvjw4N5KZ0QP16mAdpa4SUv9nDP4WpoFHHxhzI4y5QVcB1VpkayLwUUr9T0T+AKCUMlvvOO427pkVwWdpBTy7di/PXN5By+p1IhmF5fz7x2w+Ssqn3mJhVrQ/N08OY/SQ/o4OrfsoyTT6/pPfgdoy8B8B854zSkG763GYzkwpRV55HiZXEwN7tv/NfLYmgkrrYLECEJHxQFm7R+PEAvt58euJISz7MYsbzgslelDXXb/UVkopNmUd4bUfs/h2dxGeJheuGBPE9eeFEuqjG54O0WCGjK+M7p+s78DFBNELYMxNEDxeD/52UsfqjrGjeAepJamkFqeyo2QHR2uPcv2I6/nd6N+1++fZmgjuwSgbPVRENmLUHrqs3aNxcr+dNoyVW3N5bE06K27o8OWVnYa5wcIXOw7x2vos0vLLGNDTnXtmRrB0/BBdCqKjVBTD9v8aC8CU5YL3IJj2R0i4Rt/928mYLWYyjmaQVpJGanEqqSWpZJdlAyAIYX3CmBo0lVjfWMYOHGuXGGy9s3i7iEwBhgMC7FFK1dslIifWp4eJ26cN49HP01m/t5jJ3ezu14paM+9tPsB/NuaQX1pNmG9PHrsklkWjButFYTqCUpC3xTr4+zE01EHoZJj9dxg+Vw/+dhKFlYWklqSSVpxGSnEK6UfSqTZXA9Dfsz9xPnHMC5tHnG8cMQNi8Ha3f5kVW2cNuWKsHRBifc0sEUEp9awdY3NKV08YwvKfcnhszW4mDfPpFgOgh8pq+M9P2bzzywHKa8yMDe3PIwtiuCDSTy8L2RHqqmDHKiMBHEoFd28YfZ0xAKzXAHZqVfVV7Dq864Sz/aKqIgBMLiai+kdxafilxPrEEucbx+Begx1SzsbWU4hPgRogDbDYLxzn5+Hmyu9nD+eu95L5OCmfS0d3yWrcAKQXHOO1H7NYnXwQi1JcGBvATeeHER/U19GhdQ+H9xkzf5Lfgpoy8IuGi56FuCvAo5ejo9NOYlEWco7lkFpsnO2nlqSScTSDBmXMqwnsFUiifyJxvnHE+sQS2T8Sd1fn6Eq1NREEKqXi7BpJJzI/bhCvb8jmmbV7uCguoEt1iyil+DGjhNd+zOLHjBK83F25esIQrp8USlB/XXbA7iwNkLHWOPvf941R+z9qvjH4O2SiHvx1Ikdrjh4/07cO6JbXlwPQy9SLET4juCH2BuJ84oj1jaW/p/POoLM1EawRkVlKqbVn8uYiMgd4HnAF/q2Uevyk54OB/wJ9rfs8oJT64kw+wxFcXIybzK567ReW/5TDb6Z0/gW668wWPk05yGs/ZrH7UDl+3h7cN2c4S8YOoY+Xvpva7sryjcJv2/4DpQfAOwCmPmisAKZr/ztcfUM9e47uIaU4panxzy3PBcBFXAjvG86c0DlNXTyhfUJxkc5TPNHWRLAJ+EhEXIB6jAFjpZRqdQ6ldVzhn8BMIA/YIiKrlVK7mu32R+B/SqlXrHcrf4ExDuH0Jg714YJIP/75XSZXJAbRr5POlimrrufdzQf4z8ZsCo/VEuHfi6cui2NB/CBdZM/eao5B+mojAWT/CCgIOR9m/hUiLwJXnYAdpaS6hOSiZOOrOJn0w+nUWeoA8O3hS5xvHJdFXEasTywxA2LwMnXuq2VbE8GzwAQgzbrWsC3GAplKqSwAEXkPWAg0TwQKaEwmfYCDNr63U3jgwkjmPLeeF7/N5M/zox0dzhnJO1rFfzbm8N7mA1TWNTBp2ACeuDSOKRG+eu0Fe2oww75vIfU92P0FmKuhXyhMfcCo+jmg819ddjYNlgYySzObGv3komTyKvIAcHdxJ3pANIsjFxPnG0ecbxz+Xl1vzWxbE0EusOMMkgDAYOvrGuUBJ0++fxhYKyJ3AD2BGS29kYjcDNwMEBwcfAYh2FeEvzeXJwaxYlMO100MIXiA858V7D9cybNf7+Wz1AIEmD9yEDeeH0rMoD6ODq3rUgoOJhln/js+gMpi6NEP4q+CkVfqqp8drKKugtSSVFKKUkgqSiK1JJXK+koABngOYJTfKK6MvJKRviOJHhDtNAO69mRrIsgCvheRNUBt48Z2mD66GFiulHpGRCYAK0RkhFLqhJlJSqllwDKAxMTEM0lGdve7mRF8nJzPk1/t5qWrEhwdTqsOV9Ty4reZvLVpPyZXF66fFMKvJ4UySNdNsp/SA0bNn9SVULLXKPkcMcdo/IfNBLeu38A4mlKKvIo8kouSSSk2Gv6MoxkoFIIQ0S+CeWHzGOk7kni/eAJ7BXa5s31b2JoIsq1f7tYvW+QDQc0eB1q3NXcD1lLWSqmfRcQT8AGKbPwMh/Pv7clN54fx4reZ3Hh+qdNNrayua+D1DVm8+kMW1fUNXDEmiLunh+PX29PRoXVN1aVGpc/UlbB/o7EteCLMvw2iFxpXAprd1DXUsevwLlKKU5q6ekqqSwDoaerJSN+RzAiewUi/kcT5xNHLXU/DBdvvLH7kLN57CxAuIqEYCeBK4KqT9jkATAeWi0gU4AkUn8VnOdQtU4byzi8HeOyLdN67ebxTnFGYGyys2pbHP9btpfBYLbOi/blvTiTD/PQffrsz10HmOqPff8+X0FALA4YZJR/ifgX9QhwdYZd1uPowycXJpBSlkFyczM6SnU2DuoG9ApkQMIF4v3hG+o5kWN9huLroCRAtOd2axS8ppW4XkU+xFpxrTim1oLXXWiuU3g58hTE19A2l1E4R+QuwVSm1Gvh/wGsi8jvr+193huMQTqGXhxt3zwjnT5/s5NvdRUyPclytF6UU36QX8cSXu8koqiAhuC//vCqBxBDnncPcKSkFeVuP9/tXHwEvH+OO35FXwKAE3e/fzizKwr7SfU0DuslFyRwoPwAYd+lGD4jmqqiriPeNZ6TfSHx6+Dg44s5D2mp3ReSYUqq3tc7QKZRSP9gtslYkJiaqrVu3dvTHnlZ9g4VZ/1iPq4vw5V3nO2QB9qQDR3lszW42Zx8hzKcn980ZzuyYgU5xhdJlHMk+3u9/ZB+4eRp1fkZeCUMv0FM+21FlfSVpJWlNjX5qcWrTDVv9PfsT7xvPKL9RxPvFEzUgCg9XveJdW0Rkm1IqsaXnTtc1tA8c0+B3NiZXF+6fM5zfvLWd97flsXhsx81uyimp5Kmv9vB5WgE+vdz568UjuHJMECYHJKMuqeoI7PzISAC5mwCBkPPgvN8ZJZ899Yyr9nCo8hBJRUkkFSWRXJTMnqN7sCgLgjCs3zDmhM4xGn7feAK9u9+gbkNFBZjNuPbt2+7vfbpE4Csi97T2ZHcsOteW2TEDGT2kH89+vZeF8YPsviRjSUUtL36Twdu/HMDdzYW7podz0+QwennoKpTnzFxrlHpIec/43lAHvpEw/f+MhV76dN0aUx2hwdJARmkG2wu3k1yUTFJxEocqDwHQw60HcT5x3BR7E6P8RhHnG9chFTidgaqvpy4vj7rsHOqys6nLMb7X7s+hobiEAb+5Bb+77273zz1di+EK9MK4k1g7DRHhwbmRXPrKz7y2Ppu7ZthnWcCqOjOv/5jNv9YbM4GuHBPEXTPC8fPWM4HOSeMavynvGVcANaXQ08+o8zPyChgYp/v9z1JlfSWpxalGo3/S3H0/Lz9G+Y3iupjriPeLZ3i/4bi5dN2TGaUU5uJiayOf09TY1+XkUJeXB2Zz076u/frhHhJCr/Mn4x4SQs9xjlmPoEAp9Re7fHIXNXpIf+bEDORf6/dx1bhgfL3br9/S3GDh/W15/OPrvRSV1zI7xpgJNNRXzwQ6JyWZkGbt9z+aAyYviJxnVPkMm6rr/J+Ftrp5Gufuj/IbxSi/UQT0DOiS3TyWykrq9u+ntunM3tro5+Rgqaho2k88PHAfMgSPiAi8Z8/GPSQEj9AQ3ENC7NIN1JLT/YV3vX+dDnDfnOGsSy/k+W/28ujFsef8fkop1llnAmVaZwK9vETPBDprSkHhDkj/1Pgq2gXiAqFTYMoDEDUPPLpHV0R7aOzmaWz4k4q6TzePMpupz8+nLifnlAbfXFh4fEcRTAEBuIeE0GfhQtxDQnAPDcU9JATToADExbHjeadLBNM7JIouJsy3F1eNC+btXw7w60mh53TGnnTgKI99sZvNOcZMoFeXjmZ2TNerdWJ3FgvkbzWKvKV/apz5i4txs9ecxyH6Yugd4OgoO4Xu2M2jzGbqsrOp2bWL2owMahvP7g8cgPrjizW69O6Ne2gIPcePxz00BPeQUKPBHxKMi6fzdt22OX3UGTnr9NGTlVTUMuXJ75g0zIdl17Q4Y6tN2SWVPPXVbr5IO4RPLw/unhHOFXom0JlpqIecDUbDv/tzqDhkLO4eNtWo8T98LvTqXsuNno22unnC+4U3dfF0lW4eS20ttXszqNm1i5r0XdTsSqd2zx5UrVFdR0wmTEOCrV04oSec3bv26+e0v/+5TB/VzpJPLw9+M2Uoz3y9ly05RxhjYzfOyTOB7p4Rzk3nh9FTzwSyTX017PvOaPz3fGEM+Jq8IHwmRC0wvuvpnq1qXEg9uTi5W3TzNFRUUrs7nZpd6daGP53affuaBmxdvL3xjIyk35VX4hkdhWd0NO6hoYhb1/r/qK8I7Ki6roGpT3/HoL49+PDWiW2eKTTOBHr1h33UmC16JtCZqDlmTPFM/xQyvob6SqOxHz7XOPMfegGYdHG9kymlyK/IZ0fJDtJK0kgrSSP9cDo1DTUA+PXwY5T/qKabtjp7N4/5yBGjwU/fZXTx7Eqnbv/+puddfXzwjDIae8+oKDxjojEFdp37FfQVgYP0cHflnpkR3P9BGmt2HGJu7Kl90OYGC//bmsdz6/RMoDNSWWKc8ad/ClnfG/P8e/kb0zyj5hsLvOi7fE9QVlvGjpIdpJYYyyruKNnBkZojAHi4ehDVP6ppsZWRfiMZ1HNQp2wElVKYDx2iJj2dmp3GWX7Nrl2YDx1q2sc0eDCe0VH0uXghHtbG3+Tn58CoHUsnAju7bHQQr2/I5skvdzMjyh93N6OPv3Em0ONr0tlXXMnoIf14ZWkCo4fomUCtKssz+vrTPzUqeyoL9B0CY282un0Cx4CDZ184i9qGWnYf2W00/Nb1dBvr8ghCWJ8wJgdOJtYnlhE+IwjvF47JpfMlTmWxGFM0rY19YxdPQ2mpsYMI7mFheCUmGmf60VF4RkZ22LTMzkInAjtzdRH+cGEUv16+hXc3H+DaiSFsP3CUx75IZ0vOUcJ8e/Kvq0czK1rPBGpRSaYx02f3Z5C/zdjmGwXn32uc+Q+M7fY3eVmUhZxjOSc0+nuO7sFsMfq5/bz8iPWJZVH4IuJ84ogeEN0pyy9bamupzcykdvduanbvMbp30tOxVFUZO5hMeIaH02vG9ONdPMOH4+Ll/AtGOZoeI+gASimueu0X9hSWMy60P2t2GDOBfjcznCsSgxxSoM5pKQWH0o7P8S9ON7YPHm00/JHzwWeYY2N0sJLqkqYGP60kjZ0lO5uKsXm5eTHCZwSxPrFNZ/v+PR1XDfdsmQ8fpmb3bmp377F+301tVhY0NAAgXl54Dh9+/Cw/OhqPoUMRd73YT2vaGiPQiaCDpOaVsuCljXi5u3Lz5DA9E6g5iwXyNh9v/Ev3G3P8h0yyNv4XddvaPlX1Vew8vPOEAd3GWTxu4kZ4v/CmBj/ON46Q3iGdqua+amigbv9+Y7ZOs0bfXHx8WRK3gQPxHD4cj6hIPCONL1NwsMNvwups9GCxE4gL7MuHv51IUD+vdi070WlZGiB7vbXb53OoKDSWcgybBpPvNWb89Oxe9eTNFjP7Svc1DeamlaSxr3QfFuvKrYG9AhnlO4rYaONsP7J/JJ5unWdWWUNFJbV7rY19+m5q9uyhdu9eVI0xSwk3NzyGDqXnxIl4REbiGRWJx/DhuPXTq7rZm04EHSghWP9BU7jTKOqW9j6UF4Cpp3WO/3wInwWevR0dYYepb6hnx+EdbD20la2FW0kqSqLaXA1AX4++jPAZwYzgGU1dPf08O8ffj1IKc0GB0Y+/+/iZfv2BA037uPTpY8zPv+JyPCKj8IwcjvvQobjorh2H0IlAs7/yQqPhT3kPCtPAxc1o9OMeh4jZ3WaOf11DHWklaWw5tIWthVtJKUppmrM/rO8wFg5dyCi/UcT6xHaaevuWujrqMjNPbPT37MFSVta0j2lIMJ6RkfRddDEew40zfbeBesEkZ2LXRCAic4DnMcpZ/1sp9XgL+1wOPIyxVGWKUurkdY21zqiuyujySX0P9n1rTPUcPBoufApGXNItun1qG2pJLU5l66GtbCncQmpxKrUNtU0VOC+NuJQx/mNI8E/oFGf7lupqanbvNubm77LelJWZ2XQXrnh64hERQe/Zs/GIHI5nZBQeERG49urp4Mi107FbIhARV+CfwEwgD9giIquVUrua7RMO/AGYpJQ6KiLd946OrsBigZwfjXLOuz6BugroEwzn3WMs5ehjn/UZnEW1uZqU4hSj4T+0hbSSNOot9QhCZP9ILh9+OYn+iYz2H00fD+cuc9FUemHnzuON/r4s498Yo06+Z0wMvc4/3+jLj4zEfcgQxLXzDFRrx9nzimAskKmUygIQkfeAhcCuZvvcBPxTKXUUQClVZMd4DHVVRteEm+6LbDdFu40z/9T34VgeePSGmEVG4x88scve5FVVX0VyUTJbC40+/rSSNMwWMy7iQnT/aJZELSHRP5FR/qPo7e68Yx8Nx44dr7VjbfjrcnKMqbyAq68PPaJj8J45E8+YGDyjo3XXThdjz0QwGMht9jgPGHfSPhEAIrIRo/voYaXUlye/kYjcDNwMEBx8jmsBf/1nOPAzLPwnDIo/t/fqziqKYccqo9+/IBnEFYZNh1l/MWb8dMF+/8r6SpKKkpr6+HeV7MKszLiKKzEDYrgm+hqj4fcb5bQ3bJmPHm06w2/s4mk+iOs2cCCeMTH0nndRU6PfnUsvdBeOHix2A8KBqUAgsF5EYpVSpc13UkotA5aBcR/BOX3i0AuMueqvXWAsPj7lPnDT0zltUl8Ne9YYjX/mOlANEDDSqOc/4lLo1bUajPK68uMN/6GtpB9Jp0E14ObixogBI7huxHWM8R9DvF88Xibnu3vVfPjw8bP8ncb3+oMHm543DR6MZ0wMfS+5xNroR+E2YIADI9YcxZ6JIB8IavY40LqtuTzgF6VUPZAtInsxEsMWu0UVOReGTICvHoIfnzZKFyx8GQJH2+0jOzWLxbiCSn0Pdn4Mtceg92CYeIfR9eMX5egI201ZbRnbC7eztdDo42+su29yMRHrE8sNsTeQ6J/ISN+RTtfw1xcWUbNr5/GB3J07T1ghyzQkmB7xI+l31WLr3bjRut6O1sRudxaLiBuwF2OVs3yMxv0qpdTOZvvMARYrpa4VER8gCYhXSh1u7X3b9c7ijK/h07uM+ewTbodpD3bJLo2zUpJpNP4pK6HsgDHfP3qhUd0z5HzoRHevtqairoJthdvYVLCJrYVb2XNkDwqFu4s7cb5xjBk4hkT/ROJ845zmxq2mOfq7dlHdbCC3objE2EEE99BQo7G3du14Rkfh6t251w3Qzp1D7ixWSplF5HbgK4z+/zeUUjtF5C/AVqXUautzs0RkF9AA/L6tJNDuwmfCb3+GtX+Cn14wyhovfBmCTx7K6CYqD8POD42un/ytRpmHsGkw/U9GmQf3zj0NsK6hjpTiFH4p+IVNBZvYUbKDBtWAh6sH8b7x3Bp/K2P8xxDrG4uHq+O7C5VS1OfmHu/Ptzb8TZU1XVzwGDqUXhMnGY1+TDSekZG49Ozc/05ax9O1hhrt+w5W3wlluTD+Vrjgj52+4bOJuRb2fmmc+Wd8BRYz+I8wun1ifwXeAx0d4VmzKAt7juxhU8Emfin4hW2F26hpqMFFXBgxYATjAsYxPmA8I/1GOrzhVxYLdTn7T5iuWbNrF5Zyo5gcJhMe4cOaunV6xMTgERGBSw99BavZRheds1VtOax7BLa8Bv1CYeFLEHKefT7LkZSC3M2Q8i7s/MhYzrHXQIj7FcRdCQNHODrCs6KUIrc8l00Fm9hUsIkth7ZQWlsKwNA+QxkXMI5xAeNIHJjo0OmcymymNivrhJk7zcspi7u7UWvHWlXTMzoGj4hwXX5BOyc6EZypnA3wye1wNBvG3AgzHgEP55wOeEaOHYTkdyD5bTiSZazlGznPOPsPm9op+/1Lqkv4peCXpq+DlcasGH8v/6Yz/nEB4/DzcsyMJlVXR21mZtMZfvXOndTubrYQeo8eRkXNxj79mGg8wsIQU+dbJEZzbjoRnI26Svj2Udj0CvQJggUvwNBp9v/c9tZQb3T9bF8BmV8bpR5Czof4q4xCbx6daxCx+QDvpoJNZJZmAuDt7s24geOaGv8hvYd0+A1PltpaavfsOfFMf+9eVH09AC49ezZ17XjGGA2/e0iIvhtX6xA6EZyLA5vgk9vgcCYkXAuz/mosjO7sivdC0pvGwG9lMXgHGI1//BIYMNTR0dmsrQHeUX6jGB8wnvEB44nsH9lhdfiVUtTnH6Q2Yy+1ezOozcigdu9eavfta1o4xaVPHzyjo+jRNHMnWtfQ1xxKJ4JzVV8N3/0dfn7JaFDnvwDhMzo2BlvUVhh9/kkrIPcXo5RGxBxIuAaGTgdXR98/eHonD/BuL9pOtbn6hAHecQHjiPeL75ABXvORI0Yj37zBz8zEUlnZtI/boAA8wsPxjIxq6uIxDe6cC79rXZdOBO0lb6txdVC82ziznv036OHgqpFKQd4W2P6mkQTqKsAnAkZdbfT9O/ndvm0N8Ib1CWvq47f3AK+lstJYDzcjg5q9e62NfgYNh4/PZnbt2xePiAg8wsONr4gIPMKH6Tn6WqegVyhrL4GJcMt6+OEJ2PAcZH4D85+D4Rd2fCwVxcYNX9tXQMke44avEYtg1DUQNNZpF3S3KAs5ZTkkFyeTVJTE5oLNJwzwTg6cbNcBXlVfT212dlND33iWX5+X17SP9OiBx7Bh9JoyBY8Io9H3jIjA1cdHn+VrXZK+IjhbB5ONq4PCHcZ8+wufBK/+9v1MS4ORfJLeNGr+WMwQOBYSrjaqfTrhwG+1uZodJTtILkomuTiZlOIUymqNRUv6ePQh0T+xqeEP6R3Sbg2tslioP3jQaOybzvD3UpuTA9bBW1xdcQ8NaWroG8/yTYGBui9f63J011ArymrL+Me2fzA7ZDZjBo7BzeUML5DMdbDhWVj/lNFFdNEzRhmG9nYkG5LeMqZ+lh8ELx+j22fU1eAX2f6fdw4OVR4yGvyiFJKKkthzZA9mZSxcEtYnjHi/eOJ944n3i2+3ht9cUmI09Navmr17qcvIbJqXD2AaNOh4t05EBB4R4biHhuq5+Vq3oRNBK7Yc2sId395BZX0l/T37MztkNheGXshI35G4yBmcER7aAZ/8FgpSIPpimPs09PI9t+Dqq40qqdvfNBZ7ERcYNsNo/CPmOMV6CmaLmb1H95JUlERKUQrJxckUVBYA4OnqyQifEYzyG0W8XzxxPnH09ex7Tp/XUF5ObUbmCY1+bUYGDUeONO3j2q9fswY/vKk/37VXF7gPRNPOgU4Ebagx17AhfwNrstfwQ94P1DbUMrDnQOaEzGFO6Byi+0fbdtbaUA8bnzfGD9x7wdynjNLMZ3LGq5RR23/7CkhbBbVl0C8ERi2FkVdBn8Fn+2u2i2N1x5oa/JSiFFJLUpsWW/fz8jMafd94RvmNIqJ/BCaXs7spylJTQ11W1vFB24wMajMyMRcUNO3j4uV1SmPvERGhyyhrWit0IrBRZX0l3+V+x5fZX7Lx4EbMFjNDeg9hTsgcLgy9kKF9bZh/X5RujB3kb4PhF8G8Z09fr6fqiLG4+/YVxuLubp4QtcDo+x9ynkNW+FJKcaD8AMlFxqBuSnFK081bruJKRL+IprP9eN94AnoFnPlnmM3U7d9/4sBtRgZ1Bw40LYkoJhPuQ4c2a+zD8QwPx22Qnp6paWdCJ4KzUFZbxrr961iTs4Yth7ZgURbC+4VzYciFzAmdQ5B3UOsvbjDDpn/Ct38zylrPedzo02/ecFkskP2DMec//TNoqDUWeRl1tTH43KOv3X/H5mobatl1eBdJRUkkFxmDukdqjC4Xb3dvRvqObOrbj/WJPaN6/MbAbYFxA1ZGZtPgbV1WVtNdt7i44B4c3GxaptHouwcHI256cpumnSudCM5RSXUJa3PW8mXOlyQVJQEQ6xPLnJA5zA6ZjX9P/1ZemGFcHeT+AuGzYN5zgIKktyH5LSg9AJ59Ie5yIwEExHXUr0RJdYkxk6comaTiJHYd3oXZYgzqDuk9hJG+I5u6esL6htk0ZqKUouHw4aaGvqbxDP+kgVu3gICmM/vGM333sDBcPJ2j5r+mdUU6EbSjgxUH+SrnK9ZkryH9SDqCkOCfwNzQucwYMoP+nidNIbU0wOZlRlVTEWMQGAWhU4w7fiPngal9G0CzxUxJdQmHKg9RWFVIYWWh8d36c0FlAYVVxupV7i7ujPAZwUi/42f8p/wOLWioqDhlLn5tRgYNR4827XPCwG3Tl74BS9McQScCO8kpy+HLnC9Zk72GrLIsXMWV8QHjmRM6hwuCLzjxTtgjWfDDU9AnEEYtMQaBz0JdQ90JjXtRVdGJjX1lISU1JViU5YTXebp64t/TH38vf/y8/IjsH0m8XzxR/aNwd219BpKlro66rKxmc/EzqMnYi/ng8YFb8fLCI3zY8fn4w4YZA7c+Pmf1O2qa1v50IrAzpRR7j+5tSgr5FfmYXEycN/g85obOZXLgZJv61Kvqq4437Cc17o3bGvvtm+tl6oW/l39TQ+/f02js/b2MxwN7DqS3e+82B1dVQ4OxGlbT2b0xTbMuJ6epkBomEx6hoSeWWIgIxzRokL4BS9OcnMMSgXVN4ucxlqr8t1Lq8Vb2uxRYBYxRSrXZyjtjImhOKcWOkh18kf0FX+V8RXF1MT3cejA1cCqzQ2bj6eZ5QuN+qOqQ0fhXFnKs7tgp79fXo2+Ljbt/T38Geg3Ez8uPXu62z5FXSmEuKjqlS6d23z5UTU3TfqagoKZaOo133boPGYLoG7A0rVNySCIQEVeMxetnAnkYi9cvVkrtOmk/b+BzwB24vbMnguYaLA1sL9rOl9lfsnb/2qZiagCCMKDHgKaumuZn9AN7Dmzafi6LpjeUlZ1yx21tRiaWsrKmfVx9fayDtsbZvUdEBB5hYXrdW03rYhxVdG4skKmUyrIG8R6wENh10n5/BZ4Afm/HWBzC1cWVMQPHMGbgGB4Y9wDJRcm4ubjh7+WPbw9fTK7ntgqVUoqGkhLqcnOpz82l7kAu9Xm51OXmUZd7gIbikqZ9XXr1wiM8nN5z5pwwJ9+tn4Orp2qa5nD2TASDgdxmj/OAcc13EJEEIEgp9bmIODwRFL/wIqq+DlNgEO7BQZiCgjEFDGyXFaRMLibGDBxzxq+z1NVRn5dPfe4B6nLzmn3PpS4vD1VdfXxnEdwGDsQ9MJBekyfjHhLS1K3jFhCgb8DSNK1FDrtTR0RcgGeB62zY92bgZoDg4GC7xVS1ZQtVycnHq1MCmEyYBgXgHhTclBzcgwKbvrt42X5jVUuUUjSUlp50Rp9L/QGjoTcfOmSUnrCSHj1wDwzEFBREz4kTMQVZk1ZgEKbBg3DxsP9iLZqmdS32TAT5QPPbbwOt2xp5AyOA761nqgOB1SKy4ORxAqXUMmAZGGME9gp4yIo3UQ0NmAsLqTuQS13uAWuDbDTMZampWI6dOKDr6uuDe7MriKYkERyE64ABiAiqvp76goLjXTjNGvr63FwsFRWnvmdQMD3Hjjkl8eia+JqmtTd7Dha7YQwWT8dIAFuAq5RSO1vZ/3vgXmcfLG4oLT3eRdOYLHLzqMvNPfXs3csL1z59MBcVHZ+CiVE/xxQYiCk4qFkSCcI9KAjT4MHnfJWhaZp2MocMFiulzCJyO/AVxvTRN5RSO0XkL8BWpdRqe322Pbn27UuPvn3pETvilOcstbXU5+c3dfPU5R6gobQU06BBuAcFYwoKxD04GDc/Pz3vXtM0p6FvKNM0TesG2roi0KelmqZp3ZxOBJqmad2cTgSapmndnE4EmqZp3ZxOBJqmad2cTgSapmndnE4EmqZp3ZxOBJqmad1cp7uhTESKgf1n8VIfoOS0ezleZ4kTOk+snSVO6DyxdpY4ofPEau84hyilfFt6otMlgrMlIltbu6vOmXSWOKHzxNpZ4oTOE2tniRM6T6yOjFN3DWmapnVzOhFomqZ1c90pESxzdAA26ixxQueJtbPECZ0n1s4SJ3SeWB0WZ7cZI9A0TdNa1p2uCDRN07QW6ESgaZrWzXX5RCAic0Rkj4hkisgDjo6nOREJEpHvRGSXiOwUkbus2x8WkXwRSbZ+zXWCWHNEJM0az1brtv4i8rWIZFi/93OCOIc3O27JInJMRO52hmMqIm+ISJGI7Gi2rcVjKIYXrH+3qSKS4ASxPiUiu63xfCQifa3bQ0SkutmxfdXBcbb6by0if7Ae0z0iMruj4mwj1pXN4swRkWTr9o49pkqpLvuFsUTmPiAMcAdSgGhHx9UsvgAgwfqzN8Yaz9HAwxjrNzs8xmax5gA+J217EnjA+vMDwBOOjrOFf/9DwBBnOKbAZCAB2HG6YwjMBdYAAowHfnGCWGcBbtafn2gWa0jz/Zwgzhb/ra3/t1IADyDU2ja4OjLWk55/BvizI45pV78iGAtkKqWylFJ1wHvAQgfH1EQpVaCU2m79uRxIBwY7NqozshD4r/Xn/wIXOy6UFk0H9imlzuZO9HanlFoPHDlpc2vHcCHwpjJsAvqKSECHBErLsSql1iqlzNaHm4DAjoqnNa0c09YsBN5TStUqpbKBTIw2okO0FauICHA58G5HxdNcV08Eg4HcZo/zcNKGVkRCgFHAL9ZNt1svwd9whi4XQAFrRWSbiNxs3eavlCqw/nwI8HdMaK26khP/YznbMYXWj6Gz/+1ej3HF0ihURJJE5AcROd9RQTXT0r+1Mx/T84FCpVRGs20ddky7eiLoFESkF/ABcLdS6hjwCjAUiAcKMC4ZHe08pVQCcCFwm4hMbv6kMq5nnWYusoi4AwuA962bnPGYnsDZjmFrROQhwAy8bd1UAAQrpUYB9wDviEhvR8VHJ/i3bsFiTjxp6dBj2tUTQT4Q1OxxoHWb0xARE0YSeFsp9SGAUqpQKdWglLIAr9GBl6+tUUrlW78XAR9hxFTY2F1h/V7kuAhPcSGwXSlVCM55TK1aO4ZO+bcrItcB84Al1sSFtavlsPXnbRh97xGOirGNf2tnPaZuwCXAysZtHX1Mu3oi2AKEi0io9QzxSmC1g2NqYu0XfB1IV0o922x7877gRcCOk1/bkUSkp4h4N/6MMWi4A+NYXmvd7VrgE8dE2KITzrCc7Zg209oxXA1cY509NB4oa9aF5BAiMge4D1iglKpqtt1XRFytP4cB4UCWY6Js8996NXCliHiISChGnJs7Or4WzAB2K6XyGjd0+DHtqFFpR31hzL7Yi5FRH3J0PCfFdh5GV0AqkGz9mgusANKs21cDAQ6OMwxjtkUKsLPxOAIDgG+ADGAd0N/Rx9QaV0/gMNCn2TaHH1OMxFQA1GP0T9/Q2jHEmC30T+vfbRqQ6ASxZmL0sTf+rb5q3fdS699FMrAdmO/gOFv9twYesh7TPcCFjj6m1u3Lgd+ctG+HHlNdYkLTNK2b6+pdQ5qmadpp6ESgaZrWzelEoGma1s3pRKBpmtbN6USgaZrWzelEoGmAiDwmItNE5GIR+YOdPmO5iFxmj/fWtHOhE4GmGcZhFFKbAqx3cCya1qF0ItC6NWuN/VRgDPAzcCPwioj8uYV9l1vXCPhJRLIaz+6td/8+JSI7xFiz4Ypm21+y1r5fB/g1e6/R1mJi20Tkq2ZlJu4UY32KVBF5rwMOgabpG8o0TUTGANdgFPf6Xik1qZX9lmPctXwFEAmsVkoNE5FLgd8AcwAfjNIm44AJwK3W7f7ALoxE8wnwA7BQKVVsTRyzlVLXi8hBIFQpVSsifZVSpXb6tTWtiZujA9A0J5CAUT4jEmNNiLZ8rIxiZrtEpLFk9HnAu0qpBowicj9gXGFMbrb9oIh8a91/ODAC+NooN4UrRukBMMoivC0iHwMft8PvpmmnpROB1m2JSDxGnZdAoATwMjZLMjBBKVXdwstqm7/F2X40sFMpNaGF5y7CSCDzgYdEJFYdXwxG0+xCjxFo3ZZSKlkpFc/xJUK/xeiiiW8lCbTmR+AKEXEVEV+MhnwzxqBz4/YAYJp1/z2Ar4hMAKMUuYjEiIgLEKSU+g64H+gD9Dr331TT2qavCLRuzdpwH1VKWUQkUim16yze5iOM8YAUjGqy9ymlDonIR8AFGGMDBzAGo1FK1VkHml8QkT4Y/w+fw0hIb1m3CfCCHiPQOoIeLNY0TevmdNeQpmlaN6cTgaZpWjenE4GmaVo3pxOBpmlaN6cTgaZpWjenE4GmaVo3pxOBpmlaN/f/AdweBhNitzJLAAAAAElFTkSuQmCC\n", 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "bench(generate_graph, execute_graph, \"ewoks: dask - sequential\", engine=\"dask\")" ] }, { "cell_type": "code", "execution_count": 9, "id": "aff976e9-7a8c-41ed-ab34-ec45c750555f", "metadata": {}, "outputs": [ { "data": { "image/png": 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jI6POsjoMtaa145aRp+KrQ01kFjKut86B7PIseRSydjXvNaP6w7RnmlQ0LS2Nbdu2MWLEiHqP6zDUmtaMWwpBaIAP0SF+JOsegaYRiouLufLKK3nxxRcJDg6us4wOQ61p7bilEIARgC5Jh5poHTSx5d7cVFVVceWVVzJnzhx+8Ytf1FtOh6HWtHbccowAjJDUB3KKqbTanW2KxgVRSnHbbbeRkJDAQw891GBZHYZa09pxWyFIiLZQZVMcPKH/KTRns3btWhYsWMAPP/xAYmIiiYmJfPvtt3WW1WGoNa0dtwtDXU1KVhFTXlzNi9cmMntQp2awTNOc6NDILYd+1u6BDkNdB90jA/H2FB1zSKPRuD1uKwTenh70bG8hRc8c0mg0bo7bCgEYoSZ08DmNRuPuuLUQxEdbyCosJ6+k0tmmaDQajdNwbyGIMhYI6YVlGo3GnXFvIYg2Q03oAWONRuPGOEwIRKSziKwUkb0iskdEHqijzBwR2Skiu0RknYgMdJQ9dREZ5EtEoI8eJ9DUi81mY9CgQfWGfh4/fjzNMZ25qcyfP5977723xe6ncQ8c2SOwAr9TSvUBRgL3iEifM8ocAsYppfoD/wDecKA9ZyEixEfrJDWa+nnppZcueI69DkOtcXUcJgRKqUyl1FbzfRGQBHQ6o8w6pVSeubkBiHGUPfURHxVMSnYRNnvrWlincTzp6el88803/PrXv26w3IIFC0hMTKRfv35s3LgRgCeeeIIbb7yR0aNHc+ONN5KWlsaYMWMYPHgwgwcPZt26dYCRaGb8+PF1hpXetGkTF110EQMHDmT48OEUFRk912PHjjF16lR69erFww8/XGPHsmXLGDVqFIMHD+bqq6+uCSXx6KOP1oSh/v3vf9/sz0nT+mmRyFMiEgsMAn5uoNhtwJJ6zr8DuAOM5fzNSXyUhfIqO4dPltA9MqhZr61pHp7d+CzJucnNes348HgeGf5Ig2UefPBBnnvuuZoKuD5KS0vZvn07q1ev5tZbb2X37t0A7N27lzVr1uDv709paSnff/89fn5+pKamcv3119e4lOoKKz18+HCuvfZaFi1axLBhwygsLMTf3x+A7du3s23bNnx9fYmLi+O+++7D39+fJ598kuXLlxMYGMizzz7L888/zz333MPnn39OcnIyIqLDUGvqxOFCICJBwKfAg0qpOn0wIjIBQwguruu4UuoNTLfR0KFDm7XpnhB9auaQFgJNNV9//TXt27dnyJAhrFq1qsGy119/PQBjx46lsLCwprKdOXNmTeVdVVXFvffey/bt2/H09GTfvn0159cVVjokJITo6GiGDRsGcFoI7EmTJhESEgJAnz59OHz4MPn5+ezdu5fRo0cDUFlZyahRowgJCcHPz4/bbruNyy+/XKe51NSJQ4VARLwxROADpdRn9ZQZALwFTFNKnXSkPXXRs30QHmIkqZneP7qlb69pAo213B3B2rVrWbx4Md9++y3l5eUUFhZyww038P77759VtilhqF944QU6dOjAjh07sNvtpwWmqyusdEPUF4b60ksvZeHChWeV37hxIytWrOCTTz7hlVde4Ycffmjk02vcDUfOGhJgHpCklHq+njJdgM+AG5VS++oq42j8vD3pHhmk01ZqTuPpp58mPT2dtLQ0PvroIyZOnFinCAAsWrQIgDVr1hASElLTWq9NQUEB0dHReHh4sGDBAmw2W4P3j4uLIzMzk02bNgFQVFTUoECMHDmStWvX1mRHKykpYd++fRQXF1NQUMD06dN54YUX2LFjR5M+v8a9cGSPYDRwI7BLRLab+x4DugAopV4H/gJEAP81W1HW+qLjOZL4KAs70vNb+raaNoKfnx+DBg2iqqqKt99+u84yd999N1deeSXvvfceU6dOPa23UBc+Pj4sWrSI++67j7KyMvz9/Vm+fHm95SMjI5k/fz7XX389FRUVADz55JNYLBZmzZpFeXk5Simef77ONpnGzXHbMNS1eXXlfv71XQq7npiMxc+7Wa+tOT90aOSWQz9r90CHoW6E6mT2+7K1e0ij0bgfWgiAeHPmUJJeYazRaNwQLQRAxxA/LH5eeoWxRqNxS7QQYEz3S4gK1jGHNBqNW6KFwMSIOVREaxs812g0mgtFC4FJfFQwxRVW0vPKnG2KRqPRtChaCExO5SbQ7iGNQX5+fk0wuISEBNavX39WmZtvvplPPvmkxWxatWqVDhOhaXa0EJjEdTCFIFMPGGsMHnjgAaZOnUpycjI7duw477n2Ogy1xtXRQmAS6OtF14gA3SPQAEZIiNWrV3PbbbcBxkrf0NDQOssuX76coUOH0rt3b77++mvASCAzc+ZMJk6cyKRJkyguLmbSpEkMHjyY/v378+WXXwKQlpZGQkICt99+O3379mXy5MmUlRnuyf3793PJJZcwcOBABg8ezIEDBwAoLi6uM2z1li1bGDduHEOGDGHKlClkZmYCMHfu3Jow1Nddd53Dnpmm9dJoiAkRGQXcAIwBooEyYDfwDfC+UqrAoRa2IHEdLCTpKaQuR9Y//0lFUvOGofZNiCfqscfqPX7o0CEiIyO55ZZb2LFjB0OGDOGll16qMzREWloaGzdu5MCBA0yYMKEm3s/WrVvZuXMn4eHhWK1WPv/8c4KDgzlx4gQjR45k5syZAKSmprJw4ULefPNNrrnmGj799FNuuOEG5syZw6OPPsoVV1xBeXk5drudo0eP1hm2esSIEdx33318+eWXREZGsmjRIh5//HHefvttnnnmGQ4dOoSvr68OQ62pkwZ7BCKyBPg18B0wFUMI+gB/AvyAL0VkpqONbCnio4NJO1FCWWXDAcE0bR+r1crWrVv5zW9+w7Zt2wgMDOSZZ56ps+w111yDh4cHvXr1onv37iQnG6J16aWXEh4eDoBSiscee4wBAwZwySWXkJGRQXZ2NgDdunUjMTERgCFDhpCWlkZRUREZGRlcccUVgBHPKCAgADgVttrDw6MmbHVKSgq7d+/m0ksvJTExkSeffJL09HQABgwYwJw5c3j//ffx8mqRFCSaVkZjv4oblVInzthXDGw1X/8RkXYOscwJJERZsCtIPV7EgJhQZ5ujMWmo5e4oYmJiiImJYcSIEQBcddVV9QpBU8JQf/DBB+Tk5LBlyxa8vb2JjY2lvLwcODusdLVrqD7qC0Pdt2/fOge0v/nmG1avXs1XX33FU089xa5du7QgaE6jwR5BtQiISDcRqQmgLiL+ZtYx6hCKVkt1qAm9sEwTFRVF586dSUlJAWDFihX06XNmym2Djz/+GLvdzoEDBzh48CBxcXFnlSkoKKB9+/Z4e3uzcuVKDh8+3OD9LRYLMTExfPHFFwBUVFRQWlpab/m4uDhycnJqhKCqqoo9e/bUuJMmTJjAs88+S0FBQU0KS00roqoM0tZCTopDLt/UZsHHwEW1tm3mvmHNbpET6RIegL+3px4n0ADw8ssvM2fOHCorK+nevTvvvPNOneW6dOnC8OHDKSws5PXXXz8t6Uw1c+bMYcaMGfTv35+hQ4cSHx/f6P0XLFjAnXfeyV/+8he8vb35+OOP6y3r4+PDJ598wv33309BQQFWq5UHH3yQ3r17c8MNN1BQUIBSivvvv7/eQW+NC1FyEo5ugCPr4cgGOLYd7FUw8m6Y+nSz365JYahFZLtSKvGMfTuUUgOb3aJGcEQY6trMenUtgT6efHj7SIfdQ9M4OjRyy6GftZNRCvIOGRV+dcV/wszT5ekDHQdDl5HQZRR0Hg4B4ed1m4bCUDe1R5AjIjOVUovNC84C2oxLqDYJURa+25OFUuos369Go9FcMDYrZO08veIvOW4c8wuBziNh4PVGxd9xEHif3cNsbpoqBHcBH4jIq4AC0oGbHGaVE4mPsvDRpqPkFFXQPtjxX4BGo2njVBRB+uZTFX/6ZqgqMY6FdoEeE4wWf+eREBkPHi2/vKtJQqCUOgCMFJEgc7vNjjbV5CbIKtJC4GR0r8zx6CCLDqAo61RL/8h6yNoFyg4IRPWDQXNOVfwhnZxtLdBEIRCRDsA/gY5KqWki0gcYpZSa51DrnEB1trLkzELG9Y50sjXui5+fHydPniQiIkKLgYNQSnHy5Mk6B7c1TUQpw59fu+LPSzOOeflDzFAY8zuj4o8ZZrh+XJCmuobmA+8Aj5vb+4BFQJsTgtAAH6JD/HSoCScTExNDeno6OTk5zjalTePn50dMTIyzzWg9WCuMGTzVFf/Rn6Es1zgW0M6o8Ifdbvj3oweAZ+vIgd5UIWinlPo/EfkjgFLKKiJtdvltfJSFJB18zql4e3vTrVs3Z5uhcXeslZC22pjDf2QDZGwBW4VxLLwHxE0/NaMnogc4sPe6/3gxXcID8PFq/jGEpgpBiYhEYAwUIyIjgTYTY+hM4qODWbP/BJVWu0MeukajcWGqymD/CkhaDClLoaIAPLwgeiAMv93074+AoPYtZtLmtFxumb+JXwzqxN9m9Wv26zdVCB4CFgM9RGQtEAlc1ezWuAjxURaqbIqDJ4qJjwp2tjkajcbRVBTBvu+Myj/1e6gqBb9QSLgcEmZAt7Hgc3bAwZbgp9Qc7nhvC1EhftwxrodD7tHUWUNbRWQcEAcIkKKUqnKIRS5AQq1QE1oINJo2SlkepCyBvYvhwA+GyycwEgZcC31mQuwYp/v4v9uTxX0fbqN7ZCALbhtBpMW38ZPOg6bOGroaWKqU2iMifwIGi8iTSqmtDrHKyXRrF4iPpwdJWYXMxjWmd2k0mmagOAeSvzZa/odWg90KwZ1g6C2QMNNw+3h4OttKAD7fls7vP95J/04hvHvLcEICHCdKTXUN/Vkp9bGIXAxMAv4NvAaMcJhlTsTb04Oe7YN08DmNpi1QkAFJXxmV/5H1xpz+sG4w6h5ImAWdBjt0kPd8WLDhMH/+Yjejukfw5q+GEuTr2GixTb169Qyhy4A3lVLfiMiTDrLJJYiPtrB2f5uMoqHRtH1yDxkV/97FkGHGJouMhzG/N9w+Hfq5XOVfzWurDvDs0mQmxbfn1TmD8fN2fA+lqUKQISL/Ay4FnhURX9p4msuEqGA+25pBbkkl4YE+zjZHo9E0Rk6KUfEnfWms5gVjps/EPxtun8jezrWvEZRS/HtZCq+uPMCMgR15/pqBeHu2TDXbVCG4BiND2b+VUvkiEg38oaETRKQz8B7QAWPa6RtKqZfOKCPAS8B0oBS42VXGHeKjzRXGWYVc1KPN5N7RaNoOShnB2/YuNlr/1RE7Y4bD5CeN2T5hsU41sanY7Yq/fbWHd9cf5vrhnXlydn88PVqux9LUWUOlIrIS6Cwig83djflNrMDvzBlHFmCLiHyvlNpbq8w0oJf5GoELjTtUzxZKzizSQqDRuAp2u7GoK+lLw++flwbiAV1HGyt6Ey6H4I7OtvKcsNrsPPLpLj7dms7tY7rx2PSEFg+r0tRZQ/8AbgYOYC4qM/9OrO8cpVQmkGm+LxKRJKATUFsIZgHvKSPy1QYRCRWRaPNcpxJp8aVdkA/JOkmNRuNc7DZjkHfvYqPyLzoGHt7QfRxc/BDEXwaBrbOxVmG18eBH21myO4uHLu3NfRN7OiW21rm4hnoopSrP5yZmWstBwM9nHOoEHK21nW7uO00IROQO4A4wskG1FPFRwTrmkEbT0lSWQP4ROHkAUpdB8jdQegK8/KDHJOjzV+g9FfxDnW3pBVFWaePO97ewel8Of768D7dd7LyQKk0Vgt1AKHD8XG9ghq7+FHhQKXVezWul1BvAG2BkKDufa5wP8VEWFmw4jM2uWtRfp9G0aarKoeAo5B+GvMNGpZ9v/s07bFT61XgHQu/JxmBvr8ngG+Q8u5uRwvIqbpu/ic2H83j2yv5cO6zlGrh10VQheBrYJiK7gYrqnUqpmQ2dJCLeGCLwgVLqszqKZACda23HmPtcgvjoYCqsdtJOltAjsm38ADUah2OrgoL00yv32u+Ls04v7+ENoZ2NJC3xlxl/w2KNv1H9wdvfKR/DUeSWVHLT2z+TnFnEy9cP4vIBzh/TaKoQvAs8C+wC7E05wZwRNA9IUko9X0+xxcC9IvIRxiBxgSuMD1RzKjdBkRYCjaYauw0Kj51R0ddq1RdmmIlYTMQDgmMgrCv0nAShXY33oV2M95Yol1nN62iyC8u54a2fOZJbyps3DWVCfMsFrmuIpgpBqVJq7jleezRwI7BLRLab+x4DugAopV4HvsWYOrofY/roLed4D4fSs30Qnh5CclYhlw2IdrY5Gk3LYLdDcXatyv3w6a36gnQjNEMNApZoo3LvepFRuYd2OVXZB3dyesweV+Bobilz3vqZk8UVvHvrcEZ2j3C2STU0VQh+EpGnMVrwtV1D9c75V0qtwQhQVy/mbKF7mmhDi+Pn7Un3doEk6VATmraKtRJykiBzJ2TuMOblZ+0yom/WJrC9Ual3GgJ9r6hV2cdCSAx4OSYYWlshNbuIG+b9THmVnQ9uH0li51Bnm3QaTRWCQebfkbX2NTh9tK0QHx3MtiN5zjZDo7lwKksge49R4Ve/jieB3Qwk7BNk+OQH3Qjtep1y4YR0Bp8A59reitmdUcBNb2/EQ4RFd450yYjGTV1QNsHRhrgq8VEWvtpxjMLyKoL9dPdW00ooyzNa9pk7TrX2T6ae8t37hxupFEfdDVEDIDoRwruDR5uOHNPibErL5dZ3NhHs7837vx5Bt3bOyWnQGA0KgYjcAHyolKpzgFhEegDRphuoTVI9YLwvq4ihseFOtkajqYOibMOlk7n9VMWff/jUcUtHI+ZO39nG36gBhjvHRYOutRVW78vhjgWb6Rjiz/u/HkHHUNed/dRYjyACY9roFmALkAP4AT2BcRhhJh51qIVOJt5MUpOkhUDjbJQyBmurffnVlX7t6Zjh3aHjIBhys9HijxoIQZFOM9ldWbo7i/sXbqNH+yAW3DacdkGuPYbSoBAopV4SkVcwxgJGAwOAMiAJuFEpdcTxJjqXjiF+WPy8SNGhJjQtid1mrKzN3AFZO05V+uX5xnHxhMg46DHBdO0MhKh+4BfiVLM18OmWdB7+dCcDY0J452bHJpRpLhodI1BK2YDvzZfbISIkRAXrJDUax6GUETzt8Do4tu3smTuevtChTy3XzkBju40ttGoLvLc+jb98uYfRPSN448ahBDo4oUxz0TqsdDLx0RY+25qBUsopAaE0bQylIPcgpK2Bw2shbS0UphvHfIKMFv7gm0758yPj9Dz8VsCrK/fzr+9SuCShA6/8clCLJJRpLrQQNIH4qGCKKw6TnldG53A9jU5zjigFJ/dD2k9GpX94LRSZC+gDI40QyrEPQuzF0C5Oz9xpZSileO67FF5bdYBZiR3599Utl1CmudBC0AROJakp0kKgaRyljGxZh9cYrf60tVBixmsMioLY0Ual3/ViY76+7mW2Wux2xV8X72HBhsP8ckQXnpzVD49WGKCyqfkIHgDeAYqAtzAWmD2qlFrmQNtchrgO1TGHCrm0TwcnW6NxOex2Y3Vu2lqj1X943akImpaO0H28WfmPMWb16Iq/TWC12Xn4k518ti2DO8d259Fp8a3WddzUHsGt5gyiKUAYRgyhBYBbCEGgrxddIwJ0bgKNgd0O2btN//4ao+IvyzWOhXSGXpea7p7RENZNV/xtkAqrjfsXbuO7Pdn8fnJv7pngnIQyzUVThaD6E04HFiil9khr/tTnQXyUhSQ9hdQ9sduMmTzV/v3Da6G8wDgW2hXippmuntFGSAZNm6a00sqdC7bwU+oJ/jqjD7eMdl5CmeaiqUKwRUSWAd2AP5o5iJsUjrqtEB8VzPd7symrtOHv03pmA2jOA5vVmLtf7d8/sgEqzIo/vLuRJKW64g/t3PC1NG2KgjIjoczWI3k8d9UArhnaNr7/pgrBbUAicNBMZB+Bi4WMdjQJ0RbsClKPFzEgJtTZ5miaE1sVHNt+anD3yM9QaboBI3pCvyuMgd3Y0a0uMbqm+ThZXMFNb29kX3YRL18/uE2Fpm+qECigD3A58HcgECPUhNtQHTEwOVMLwQWjlBEUrSgTqsrAWgG2SqNCtlU28L6i1vsGyp51vYauW3F6bP12cTDgGqPS7zraSJqicXuyCsqZ89YG0vPKeOOmoUyIc42EMs1FU4XgvxiuoIkYQlCEkYJymIPscjm6hAfg7+2pxwkaw24zkpoUHjNeRZlGxqrCTHPb3G8tP/97eHgb8e89vcHTx3x5n/HXxyjjazl7f/V7L59T+yLjjYpfx+XRnMGRk6XMmbeBvJIq3rt1OCNcKKFMc9FUIRihlBosItsAlFJ5IuLjQLtcDg8PIS7K4t6hJqrK6q7gqyv3wkwjANqZwWo9fYyWdXAnI9xx3HTDxWKJAt/geip0n3r2eetZOJoWo7C8iuveWE9plY0Pbx/RZr0BTRWCKhHxxHARISKRuNlgMRjjBEt3Z7W9UBO1XTXVFX1N671WS76sjgQ9vsFGmsLgjtAj3qzgo41KPzjamEcfEKFXy2paJc8v20dmYTmf/eaiNisC0HQhmAt8DrQXkaeAq4A/OcwqFyU+KpiFG49yvKiCDsGtdIhEKdj4JqRvOr1Vby07o6AY4Q+COxpTIruMNN5XvywdjYre1+KUj6HROJo9xwp4b30ac0Z0YVCXMGeb41CamqHsAzMnwSSMNQWzlVJJDrXMBalOUpOUWdh6hWDnIljyBwiOMaY+1nbV1K7gg6IMH7pG44bY7Yo/fbGbsAAf/jA53tnmOJzGMpTVzsRyHFhY+5hSKtdRhrkiNTOHsooY3xpnDRQfh6WPQucRcMtS7a7RaOrh4y1H2XYkn39fPbBV5BO4UBrrEWzBGBcQoAuQZ74PBY5gLDBzG0ICvOkY4kdyZiudOfTt76GyFGa+okVAo6mH3JJKnl6SzPDYcK4c3MnZ5rQIDdYGSqluSqnuwHJghlKqnVIqAmM9gVvEGTqT+Ojg1hlzaO+Xxmv8oxDZ29nWaDQuy3NLkykqt/L32X3b1qSQBmhqs3CkUurb6g2l1BLgIseY5NrER1nYf7yYSmsrmjRVmgvf/N5IdHLR/c62RqNxWbYeyeOjTUe5dXRsjSvYHWiqEBwTkT+JSKz5ehw45kjDXJX46GCsdsWBnGJnm9J0vnvMiI4561Xw1CkoNJq6sNrs/Onz3UQF+/HAJe7Va26qEFwPRGJMIf0caG/uczsSoqqT1LSScYLU72HHQrj4txDV39nWaDQuy/sbDrM3s5A/X96HoFaSa7i5aOr00VzgATPqqFJKtaLmcPPSrV0gPp4exgrjQc62phHKC+GrB43wCWP/4GxrNBqX5XhhOf9Zto8xvdoxvb/7xZdqUo9ARPqb4SV2A3tEZIuI9GvknLdF5LiI7K7neIiIfCUiO0Rkj4g4JZppal4qSqkml/fy9KBXhyCSWsOA8fInjBXBs1414u5oNJo6eerbJCqsdv4+q5/bDBDXpqmuof8BDymluiqlugK/A95o5Jz5wNQGjt8D7FVKDQTGA/9p6fhFazLW8IvFv2B1+upzOi8+Ktj1p5Ae+gk2z4ORd0PMUGdbo9G4LOsOnODL7ce4a1x3urULdLY5TqGpQhColFpZvaGUWoURirpelFKrgYYWnCnAYmY6CzLLWhso3+yMiB5B1+CuvLDlBaz2pt86IdrC8aIKThZXONC6C6CyFBbfZ6RJnPC4s63RaFyWSqudP3+xm87h/tw9oaezzXEaTRWCgyLy51qzhv4EHLzAe78CJGDMPtoFPKDUmWErDUTkDhHZLCKbc3JyLvC2p/D28Ob+QfdzoOAAiw8sbvJ5ceaAcYqruodWPgV5h2Dmy+AT4GxrNBqXZd6aQxzIKeGJGX3x83bfzINNFYJbMWYNfWa+Is19F8IUYDvQESP72SsiUufEXaXUG0qpoUqpoZGRzRsv/tKulzKg3QBe3fYqZWcFXqub2qEmXI70LbDhvzDkFug2xtnWaDQuS0Z+GXNXpDK5TwcmJXRwtjlOpUlCoJTKU0rdr5QabL4eUErVEZP4nLgF+EwZ7AcOAS0e3UlEeGjoQxwvO84HSR806ZxIiy/tgnxcbwqptQK+vMcIA33p351tjUbj0vz9qz0oFH+Z0cfZpjidps4a6i0ib4jIMhH5ofp1gfc+ghHNFBHpAMRx4e6m82JIhyGM7zyeebvmkVfeNH2Lj3LBUBOr/w05STDjJfBzn1WRGs258kNyNt/tyeb+Sb2ICdPu06a6hj4GtmHkIPhDrVe9iMhCYD0QJyLpInKbiNwlIneZRf4BXCQiu4AVwCNKqRPn8yGagwcHP0iptZQ3djY2GcogPspCSlYRNnvTp546lKxdsOZ5GHAd9LrU2dZoNC5LeZWNvy7eQ4/IQH59cXdnm+MSNHX5nFUp9dq5XFgp1eDKY6XUMWDyuVzTkfQI7cEVPa/go5SP+GXCL+ls6dxg+fjoYCqsdtJOltAjMqiFrKwHmxW+vBf8w2Dq0861RaNxcf676gBHc8v48Ncj8PHSUXih6T2Cr0TkbhGJFpHw6pdDLXMCdyfejZd48fLWlxstW52kxiVyGK9/GTK3w/R/Q0Cb+1o0mmbj0IkSXl91gFmJHbmoZztnm+MyNFUIfoXhClqHkaNgC7DZUUY5i/YB7bmxz40sSVvCnhN7Gizbs30Qnh7i/AHjE6mw8mlImAl9ZzvXFo3GhVFK8dfFe/D18uDx6QnONselaOqsoW51vNqkc+3WfrcS5hvG81uebzD0hJ+3J93bBZLkzB6B3W64hLz9jd6ARqOplyW7s1i9L4eHJvemfWtNNesgtIPsDIJ8grhz4J1szNrImow1DZY1ktQ4sUew6U04ugGmPgMW954HrdE0RHGFlb9/tZc+0cHcOLKrs81xObQQ1ME1va+hs6Uzz295HpvdVm+5+CgL6XllFJZXtaB1JnmHYfnfoOclMPC6lr+/RtOKmLsilazCcv4xux9enrraOxP9ROrA29Ob+wffz/78/Xx18Kt6yyVEGwPG+1p6PYFS8NX9IAKXv2j81Wg0dZKSVcS8NYe4dmhnhnQNc7Y5LklTF5Q1FEW0TTKl6xT6RfTjlW2vUG4tr7NMdaiJFg9Jve19OLjKWD0c2vA0V43GnVFK8ecvdmPx8+KRaS0euKDV0KAQiMgIEfEE/llr3wKHW+UCVIeeyC7Nrjf0RHSIH8F+Xi0bkrowE757HLpebMQT0mg09fLZ1gw2puXy6NR4wgNbNMp9q6KxHsF1wI9AdxF5VkTmAIMdb5ZrMCxqGGNjxjJv1zzyy/PPOi4i5oBxC/UIlIJvHgJbJcycCx7as6fR1EdBaRVPL0liUJdQrhmqe84N0VhN8ohS6mKMuEBfA2FAlIhsEJFFDrfOBXhw8IOUWEt4Y1fdoScSzFAT9pYINbH7U0j5FiY+DhE9HH8/jaYV8+9lKeSWVPKPWf3w8NDjaA3RWIiJpSJiwwg73Q5YAtyqlBopIjEOt84F6BXWi1k9ZvFR8kfMSZhDp6BOpx2Pjw6muOIwGflldA53YPCqkhOw5GHoNMTIOqbRtHEKKgo4WX4Sm92G1W41Xsr4W2Wvwmq3GsfMfdWvKnsVR3OLWbQvlRGDQlh/8hg/5ZjnKOs5X8+qrIT4hBAdGE10UDRRgVFEBUQRHRRN+4D2eHt4O/tRXTANCoFSaqKI+AM7ge7A1UBPEfkCWAO4xSqmuxPv5ttD3/Lytpd5Zswzpx2rDjWRlFnoWCFY8oiRjH7Wq+Dhvgk0NG2fQwWHeHv323x94Gus6vyTFvp2gN1lsHubse0hHniJF14etV5nbtex30d8OFF2gl0ndpFfkX/aPQQhMiCSqMAoQygCTaGotR3qG+ryeZAbDTqnlCoTkaNKqf8AmEnsbwfGOto4VyEqMIobEm5g3u553NTnJvpEnIpf3ruDBREjSc3kvlGOMSD5W9j9iZF2sr1eGq9pmySdTOKtXW/x/eHv8fH04eq4q0mMTMTLwwtPD0+8PbzrrLg9xTxmbn+5PYvnlqTy9C8SmTmgc81+D7nwMbXSqlKySrPIKjFemSWZZBZnklWSRXJuMiuPrKTSXnnaOX6efmeJQ+3tqMAo/Lycu9K5SdFHlVITa22+opTKAT51jEmuyW39b+PT1E95fsvzvHnpmzUKH+jrRdfwAMetMC7Lh69/Cx36wcW/dcw9NBonsiV7C2/uepO1GWsJ8g7i1/1/zZyEOUT4R5zztU4UV/D6it2M7NqVawf3bvaWeIB3AN1DutM9pO4IO0opcstzTxcK85Vdks2ajDXklJ2dbjfcL5wOAR1q3E/RgdF0COxQIxzt/Ns1i5DVR4NCICJvYIwLLFdKFQEopeY5zBoXxuJj4Y4Bd/DcpudYd2wdozuNrjkWHxXsuCiky/4EJTnwy4/As/X7IjUaMCrMNRlreGvXW2w9vpVwv3AeGPwA18Zdi8XHct7XfWZJMiUVVv4xu69T3DEiQoR/BBH+EfRt17fOMpW2SrJLs88Si6ySLI4UHWFD5gZKraWnnePl4UWHgA5cH389v+r7q2a3u7EewTxgGvCQiFQCy4ClSqkdzW5JK+DauGv5IOkDXtjyAqM6jqpR6PhoC9/tzaKs0oa/TzP67w+shG0LYPSD0HFQ811Xo3ESNruN7498z7xd80jOTSYqMIo/Dv8jV/S6An8v/wu69qa0XD7Zks5d43rQs/35i4mj8fH0obOlc705T5RSFFUVkVmcSXZpNpnFp3oV59NLagqNDRb/DPwMPCEiERiJZH4nIv0xMpYtVUr9n0Msc0F8PH24f9D9PPLTI3xz8Btm9JgBGD0CpWBfdhEDO4c2z80qio0wEhE9YfyjzXNNjcZJVNmq+Prg18zbPY/DhYeJDY7lH6P/wWXdLsO7GXq6VpudP3+xm44hftw/qWczWOw8RIRgn2CCw4OJC49rkXs2NUMZSqmTwELzhYgMAdwu9MTUblN5d++7vLztZSbHTsbX07cm5lByVmHzCcEP/4D8o3DrUiPMtEbTCimtKuWz1M+Yv2c+2aXZJIQn8Pz455nYeSKezTj7bf66NJKzinj9hiEE+DS5WtOYNDXWUAcRmSciS8ztPkCiUuoph1rngniIBw8NeYjMkkwWJi0EoHNYAAE+ns2Xm+DIBvj5fzD8DugysnmuqdG0IIWVhbyx8w2mfjqVZzc9S4wlhtcveZ1Fly/i0q6XNqsIZBWU88L3+5gQF8mUvjoc+/nQVOmcD7wDPG5u7wMWYYwhuB0jokcwutNo3tj1Blf0uoIQ3xDioizNM3OoqtxINhPaGSb95cKvp9G0ICfKTrBg7wIWpSyipKqEsTFj+XX/XzOovePGuP7xzV6sdsXfZvZz+fn6rkpT5yO1M8cC7ABKKStQf6B+N+C3g39LcWUxb+16CzBnDmUVNZjVrEn8+AycTIUZL4FvUDNYqtE4nmPFx3hqw1NM/XQq7+x+hzGdxvDxjI95ddKrDhWBn1Jz+GZnJneP70mXCAcu6GzjNLVHUGIOFisAERkJFDjMqlZAXHgcM3rM4MOkD7k+/noSoi0s3HiE7MIKokLOc3HIse2wdi4MugF6TGy0uEbjbA7mH2Te7nl8e/BbEJjZYya39L2F2JBYh9+7wmrjL1/uITYigDvHtcnMuS1GU4XgIWAx0ENE1mLEHrrKYVa1Eu5NvJelh5byyrZXmB3zewCSsgrPTwislfDlPRAYCZPdbuhF08rYc2IPb+16ixVHVuDn5cd18dfxq76/IirQQavr6+DN1Qc5dKKEd28djp+3DrtyITR1ZfFWERkHxAECpCilnJCf0bWIDopmTp85zN89n1/0mAMY2ZAmxLU/94utfRGyd8N1C8E/tFnt1GiaA6UUm7M389aut1h3bF3NIss5CXMI82vZzF9Hc0t5+Yf9TO8fxbjekS1677ZIk4TATE4zHYg1z5ksIiilnnegbY5hw+twfA/0vQJix4LnhU01u63fbXy671Pe2vMyHUOuPr8kNceT4MfnoN+VED/9guzRaJobpRSr01fz5q432ZGzgwi/CH475Ldc0/sagnycM471xOI9eHoIf768T+OFNY3S1FrwK6Ac2IU5YNxqKcmBXZ/C1vfAPxwSZkDf2ectCiG+Idwx4A7+vfnf9I4eSXLWOS6OsduMWUJ+wTDtuXO+v6ZtUFpVSlJuElX2Kvw8/fD38sfPyw8/Tz/8vIxtbw/vFp0VY7PbWHZ4GW/teot9efvoGNiRx0c8zuyes50aJO37vdmsSD7OY9PjiQ7Ra2yag6bWfDFKqQEOtaSlmPRnGPt72L8c9nwOuz6Bre9ekChcH389HyZ9yImKz8g4fgeVVjs+Xk2ckLXhNcjYDFfOg8B25/eZNK2OzOJMtudsZ/vx7WzP2U5Kbgo21fBEPA/xOE0Yqt9Xv/w9/U9t1yMmZ23Xukb1tk3ZWHxgMW/vfpujRUfpHtKdf178T6Z2m+r02PtllTaeWLyH3h2CuGV0N6fa0pZoam23REQmK6WWOdSalsLb36j0E2ZAVdnZohAQAfGXN1kUfDx9uHfQvTy25jEI2s6BnLEkRAc3bsfJA/DDkxA33XALadokVfYqUnJTair97ce3k12aDYC/lz/92/Xn1n63ktg+kUDvQMqt5ZRbyymzldW8L7eVU2atZ9tWTmFFIdnW7JrtcqtxvDFxqQvBA4WduNAEXhz/IhO6THBo5Mtz4ZWVqWTkl7HojpF4e7qGTW2BpgrBBuBzEfEAqjAGjJVSqt7aTkTeBi4Hjiul+tVTZjzwIuANnFBKjWuy5c2FKQoq7jJy/vNvvC2FhHU8ds6icFn3y3hzxzsciFzG7mO/bFwI7HZYfD94+sBlz4MbLoQprCzEz9MPH8+2lVQ8vzyfHTk72HZ8G9tztrPnxB7KbeUARAdGM7j9YAa2H8ig9oPoHdYbLw/HhUSosledEo96xCWvrIStR46zLf04h/PyQaqwlfZgc1JPnkr1ZHn3PYzsHs7I7hF0CHaeS2j/8WLeWH2QXwzuxIjujgm+5q409Rf4PDAK2KWavmJqPvAK8F5dB0UkFPgvMFUpdUREzmOqTTNit1O+bz8n163D+3//I2jWq+fUU/AQDx4e/jt+s+Iuvj70KVcP+UPD99vyDhxeAzNfhuBox342FyCnNIek3CT2ntzL3pN7ScpNIqskC0HoENiBzpbOxATFEGOJISYoxti2xLh8die7spNWkMb2nO1GxX98O2mFaQB4iRfx4fFc1fsqEtsnMjByYItOrwTw9vDG28f7rNDOlVY7q1KO8+X2DJYn2am0dqZbu3juTuzIjIEdKamwsuHgSTYczOXrHcdYuPEIAN3aBTKimyEKI7qHt5iPXinFXxfvxs/bkz9O08mZmhtpSr0uIquB8UqpcxooFpFY4Ou6egQicjfQUSn1p3O55tChQ9XmzZvP5ZQmYysu5vCcG6hKT6frhx/iF9fbOFDbfZSyFKpKaonCFRA7pkYUhr11DVVeR1j9y2UE+9TTKyhIh1dHQswQuPGLNtUbUEqRXZp9WoWfdDKpJhmHIHQN7kpCRAJxYXFU2CpIL0onvTido0VHOVF24rTrBXoHniYM1WLR2dKZ6MDoZolceS6UVpWy+8TuGhfPjpwdFFYaM8VCfENIjEwksX0iiZGJ9G3X94JDKzcndrtiy5E8Pt+WwTc7MykoqyIi0IcZAzsye1AnBsaE1Cm6Nrti77FCNhw8yc+HTvLzoVyKyo0Ukl0jAhjZLYKRPcIZ0S2CjqGO+byLdxzj/oXb+Mesvtw4KtYh92jriMgWpdTQOo81UQjmY+QsXgJUVO9vbPpoI0LwIoZLqC9gAV5SStXXe7gDuAOgS5cuQw4fPtyozedLVVYWaddcCx4exC5ahHeHMzoqjYjCnRvzWFfxBLf2v4XfDqkjo5hS8MHVcHgt3L0ewmId9lkcjVKK9OJ0kk4m1bT2k04mkVeRBxi9pO4h3UkIT6BPRB8SIhKID48n0Duw3muWVpVyrPgYR4uOkl6cfppIZBRlnJYG0EM8iAqIMgTCFIfavYoQ37ortnOhoUHd7iHdGdR+EAMjB5LYPpHY4FiX7L2kZhfxxfYMvth2jIz8Mvy9PZnStwOzBnXi4p7tztnXbrMrkjILa3oMGw+dpNAUhi7hATU9hpE9IujUDMJQVF7FpP/8SIdgP764ZzSeHq73jFsDzSEEf61rv1Lqb42cF0v9QvAKMBSYBPgD64HLlFL7GrqmI3sE1ZQnJXF4zg14x3YldsECPALrqbjqEIUy71BuCe5EakgZ38z+iqjgmNPP2fERfH4nTH0WRt7l0M/RnNiVnSOFR06r8Pfm7qWo0oi46iVe9AzrSUJ4AgkRRsXfO6x3s7aI7cpOTmlOjTBUi0R6kbGdW557WnmLt6VGJGoEwhJD56DORAVFnTUDprFB3X7t+tW0+AdGDiTEN6TZPltzk11Yzlc7jvH5tgz2HCvEQ2BMr0hmD+rI5D5RBPo237iEza5Izipkw8Fcfj5o9BgKyoz1pjFh/oYodI9gRLdwOoefezygv3+1l3fWHeKLu0c3X5h3N+SCheACbhxL/ULwKOCvlPqruT0PI9HNxw1dsyWEAKB49WqO/uZugi6+mJhXX0G8GvnHMUUhZ8NHFKSv4JrO7ZhebuXJTlOgz2zDfVR6El4dDpHxcMsS8HDNWQ9Wu5W0grTTfPrJuck16fO8PbzpHda7psLvE96HnmE98fX0dardpVWlp4tELaHIKM6gyn5qMbyneBIVaPQmogOjOVp09LRB3ajAKAZFDmJge6O13zust9OnTjZGUXkVS3dn8eX2Y6w9cAKlYGBMCLMSO3H5wGjaW1pmoNduV6RkF5k9hpNsPJRLXqnx7DuF+teML4zqHkFMmH+Dvai9xwq5/OWfuH54F566on+L2N9WOW8hEJFXlFL3ishXmAHnaqOUmtnIjWOpXwgSMAaTpwA+wEbgOqXU7oau2VJCAJD30UdkPfE3wn55PR3+/OcmdftPFFcw+slvmTzgC36s2sonWfn0Lis03EeBkZB7CH6zFtr1aoFP0DhV9ioO5h88zaefkptSUyH6efoRFx53mnunR2iPeitFpRS2/Hw8Q11rkNdmt5FTllMjEtWup4yiDDKKM4gOjDZa+u0HkhiZ2OKDuudLpdXO6n05fLE9g+/3ZlNhtdMlPIDZiR2ZNagTPSKdH8HWblfsO17EhgNGb+HnQ7nklhguvo4hfqd6DN3D6RIeUPO7sdsVV/9vPYdOlPDD78YRGtC2Zpe1NA0JQWP9w5uAe4F/n8dNFwLjgXYikg78FWNMAKXU60qpJBFZCuzEWK38VmMi0NKEXXcdlUePkjvvbbw7dyHilpsbPaddkC+WIAseXncQJH/ghUEX8Vrnyw33Uer3cMlfnSYCpVWl7M/fT3JuMsm5yew9uZd9eftqWsoBXgE1s1z6RPQhITyB2JDYRqc32isrKd24ieIff6R41Sqqjh7Fq2M0lvETCJo4kYDhw/Dwce4/saeH0QOICoxiWNQwp9pyoSil2Hokjy+2HePrncfIK60iLMCba4Z2ZvagTgzu4loi7OEhxEcFEx8VzM2ju2G3K1KPF/PzIaPH8OO+HD7blgFAtCkMI7qFk19WxZbDefzrqgFaBBxMYz2CbUopl8qa3pI9AgBlt5Px24coWraMTi+9SPDkyY2ec+O8n8kvreIXE1J5fsvzvDX5LUZEj2gBaw2qZ+7sy9tHSm4KKXkppOSmcLjwMMrs2Fm8LTWunWq/ftfgrk1eOFR1/Dglq1cblf/adajSUsTXl8CRI/EfNIiynTspWbcOVV6OR0AAgRdfTNDECQSNG4dXWMsGKGsr7D9ezJfbM/hy+zGO5Jbi6+XB5L5RzE7syNjeka12gZVSiv3Hiw1X0iFjnOFEsdFjGBYbxqI7RuGhB4gvmAtxDaVjrCGoE2cEnWtpIQCwl5dz5Fc3U56cTNf33sV/4MAGyz/1zV7eW3+YrX8Zz+zFMwn3C2fhZQsdsjqzylbFwYKDpOSlkJybzL7cfaTkpZBfkV9TplNQJ+LC4ogLjyMuLI7e4b2JCYo5p1ajstsp37OX4lWrKF61ivI9ewDwio4maNxYgsaPJ3DECDz8Tw0O28vLKdmwgeIfVlK8ahXW48fBwwP/xESCJozHMnEiPt27u1Tr9Vyx5uVRvmsX5Xv24NW+AwEjhuPdqVOzfabjReV8tSOTL7dnsDO9AA+B0T3bMSuxE1P6dsDi59rjFueDUooDOcVsPZzPxb3aOWxKqrtxIUKQCbyGsZL4LBqbNeQInCEEANbcXNKuvQ57SQmxiz7Cp3Pnest+uiWd3328g+UPjWNv0Q88vuZxnhv7HNO6TbsgG/LL809V+GZr/0DBAax2Y+qer6cvPUN71lT4ceFx9A7rfdZioqZiKy6hZN1ao9X/42psJ06AiFGRjx9P0Phx+Pbu3aRKTyllCMkPP1C0aiUVe5MA8O7SBcuE8QRNmEjAkMGIt+tWbKqqivKUfZTt2E75zp2Ubd9BZR1Tmb2iowkYNpSAYcMIHD4c7y5dzkkYSiqsfLcniy+2H2NNag52Bf06BTM7sRMzBnZ06upeTevlQoRgq1JqsMMsOw+cJQQAFQcPkXb99XhFRBC78EM8Q+qePrjnWAGXzV3DK78cxLR+Hbjm62soqSph8ezFTQqnYLPbOFJ0hJS8FPbl7iM5N5mUvBSOlx6vKdPOv92pCj8sjvjweLoEd7ngcAWVhw/X+PpLNm2Gqio8LBaCxlxstPrHjGkW105VZibFq1ZRtHIlpRt+RlVW4hEcTNDFFxM0cSJBYy6u9/m2BEoprFlZlO3YQdmOnZTt2EH5nj2oCmMZjWe7dvgPHFjz8uvbh6pjxyjdtInSjZso3bQJW64xndWrfXsChg0jYPhwAoYNw6fb2esNlFKs2X+CT7aks2xPNmVVNjqF+jN7UEdmJ3aiV4fzE3ONppoLEQK3HyM4k9JNmzhy6234DxpE57ferHMQtMJqo89fvuM343rw+ylxrM1Yy13L7+KRYY9wQ58bTitbUlVCal4qKbkpJOcZrp3U/FTKrGWAMc2xW0g34sLjiA+Lp3d4b+LC4ojwb55YK6qqitItW2sq/8pDhwDw6dGDoPHjCBo3joBBgxzaUreXlFC8bh3FKw23ky03Fzw9CRgyhKCJE7BMmIBP164Ouz+AvbSUst27jZb+jh2Ubd+BNcdcDe3jg1+fPkalnzgQ/wED8OrYscFWvlKKyoMHTWHYSMmmTdhyjFXTnpHtCBg6lEBTGHZ7hfPs0hQ2puUS4u/N5QOimT2oE0O6hGnfuKbZuBAhCFdK5dZbwAk4WwgACr76mmN/+AMhs2YS/cwzdVYIk1/4kS7hAbz1q2Eopbj9+9tJyU3hr6P+yv78/ezLM1r6R4uO1pwT7BN8yo8f1pv48Hh6hPZo9qBs1txcilevpnjVj5SsWYO9uBjx9iZg+PAal09Dri9Homw2ynbuNERh5UoqUlMB8OneHcvECQRNmIB/YiLief6pCZXdTuWhQzUt/bIdO4z72IwVw95du+A/oLq1PwC/uDjkAmc9KaWoTEs7rcdgzTYWq+X7BLIvqhcdxl7E6F9cQlB8HOKia0w0rRenLShzBK4gBAAnXnuNnJfm0u6ee4i8796zjt+/cBtbDuex9lEjCf3ek3u59utra453sXQ5zZcfFxZHVGCUQwZOlVJUJCcbrf6VqyjbuROUwjOyHUHjxmEZP57AUaPqX0HtRCrT083B5pWUbNwEViueoaEEjRtH0IQJBF48Gs+ghufKW/PyTmvpl+3ahb3IWBHtYbHg378//okD8RswAP+BAx0+qymzoIwXl+3jp9XbGZKfxhWSTczRZGyZmQB4hobWjDEEDBuGb5wWBs2Fo4XAASilyHzscQo+/5zop58m9IrZpx3/76r9PLc0hZ1PTCbYnNmxNXsrHuJBr7BeDcbbaQ7spaWUbPjZHOj9EWtWFgB+/fubLp/x+PVJaFUVjK2oiJI1ayhauZKSH1djKygAb28Chw0jaOJELBPG4xUZaQ7oGi39sp07qDpsRM7EwwPf3r2Nlv6AAfgnDsSnW7cWewYFpVX898f9zF+bhlJww8iu3DuxJ+GBRm+jMj2jxpVUumkTVenphtnBwQQMPSUMfgnxF9Qj0rgnWggchKqs5Midd1K6aTNd3nqTwJEja46tTD7OLfM38fFdoxgWG+54W6qqKNu1m5IN6yldv4HS7duNgd6AAAJHjzZcPmPH4BXZNhJ9K6uVsm3bKDJdSNVjG3h7Q5WxQM4rMtLw6Q80W/t9+zql11NeZWP+ujT+u3I/RRVWrkjsxG8v7d1o3J2qzExKN22ipFoYTEHzsFgIGDyYgOGmMPTp03gIFI3bo4XAgdgKC0n75S+xZh8nduGH+PbsCRjd/1FP/+CwsLlKKSr2pVK6YT0l6zdQumkT9pISEME3IZ7AUaMIGj0a/6FDnb6qtyWoOHSI4pWrsJ48Ybh6Bg7EK8oxrramYrXZ+XRrOi98n0pWYTkT4iJ5eGp807LX1UFVdjalmzbX9BiqxU+8vfGMiMAzPAyvsHA8w8PxCg/DMyzc2Bdu7PMMM957BAe36rUbmvNDC4GDqcrI4NB11+Hh7UPsoo/wioxEKUXi37/nsgHR/LOZgmVVpmcYFf+69ZT8/DO2kycB8OnalYBRIwkcOYqAEcP1yl0no5Ri2d5s/vVdCvuPF5PYOZRHp8UzspmzallzcijdvJnyPXuwnszFlpuLNS8PW67x3l5aWveJXl54hoXiFRpmCEQtAakRjjBTTMLDjbhR2hXV6tFC0AKU7drN4ZtuwrdnT7q+9y4e/v5c+7/1VNnsfHb36PO6pjU3l9KffzYq/g0bqDpqzDDyjGxH4MhRBI4cSeCokXh37NicH0VzAWw8lMszS5LYeiSf7pGBPDwlnil9OzilBW6vqDDEITcXW24etrwz3uflGe9NAbEXFNR9IRE8Q0LwDAs7q7fhF59A0Lixp60o17gmFxJ0TtNE/Pv3o9N//k36PfeS8Yc/EPPSSyREB/Px5qPY7apJ88HtJSWUbt5MyfoNlGzYQEVyMgAeQUEEDB9O+I03EnjRKHx69NBdexcjJauI55YmsyL5OB2CfXn6F/25ekgMXk6M/+Ph64tHdDTe0U1LhaqqqrDl52M1hcIQkWqhyK0RjYpDh7Bt2YotPx/sdsTfH8uE8VimTSNozBg8/PTK59aGFoJmxDJxIh3++Eey//lPjj/3HPGT5lBSaSM9r4wuEWcPDKrKSiM4m1nxl+3YAVYr4u2N/+DBRD74AIGjRuHXt68eDHRRMvLLeH7ZPj7blk6QrxcPT43jlou64e/T+lwp4u2NV2RkkycUKKuV0s1bKFy6hKLvllH47RI8AgIImjSJ4GlTCbz4YrcYn2oL6NqlmQm/6UYq04+S++579AlqB3QkOauQLhEBKLudipQUo+Jfv57SLVtQpaUggl/fvkTccguBo0biP3iwblW5OHkllby6cj/vbTBiDd0+pjt3j+/hVuGSxcuLwJEjCBw5gqg//YnSjRspXLKEomXfU/jVV3hYLFiqRWHUqAtelOeu1F5kGTBkMEHjxjX7PfQYgQNQNhvp9z9A8cqVvJB4FVPi2jEi7yClP/+MLc/I5+vTvTuBI0cag7zDhzs1ro6m6ZRWWnlnbRqvrzpASaWVKwfH8NtLe+sImbVQVVWUbNhA4bdLKFq+HHtRER4hIVgumUTwtOkEjhju0sEFXYH6wq60+81viLz3nvO6ph4sdgL20lIO3/QryncbuXa8OnQ4VfGPGoV3hw5OtlBzLlTZ7Pzf5qO8tDyV40UVXJLQgYenxtFbB4NrEHtlJSVr11K0dClFy1dgLynBMzQUy+TJBE+bSsCwYdrtaVKVlUXxypWnB2K0WAgaO5agCRMuOBCjFgInYc3L479PvcN6v458+I9r9QBvK0QpxdLdWfzruxQOnihhaNcwHpkW3yKLBNsa9ooKSn76icIlSylauRJVWopnRASWyZcSPG0aAUOGuNU01YZDsxtxtZozNLueNeQkvMLCsE+9nA0rUimvsrfKAUR3Zv2BkzyzNJkdR/Pp1T6IN28ayiUJ7bWgnycevr5YLrkEyyWXYC8ro3j1TxQuWULB51+Qv9BYf2OZMoXg6dOMwIKtKPxJU6kzWZMI/oMGEfm7h5yWrEkLgYOJjwpGKdiXXcTAzqHONkfTBPYeK+TZpcn8uC+H6BA/nrtqAFcOjsFTh4RuNjz8/QmeMpngKZOxl5ZSvGoVhUuWkP9//0fe++/jFRVF8JQpBE+bit/Aga1afK05ORT/+CNFK1cZ6VvLyk6lb50wgaBxY/EKd24PUwuBg0mINnzIyVmFWghcnKO5pfxnWQpf7jhGsJ83j02P56ZRsfh5656cI/EICCB4+nSCp0/HVlxC8cofKFyylLwPPyT33Xfx7tgRy7SpBE+dhl+/vi4vCkb4l301/v7yHTsBI3Nd6BWzjWx8I4a71NRaLQQOpnNYAAE+niRlFjnbFE09nCyu4OUf9vPBz4fx9BDuGteDu8b1IMRfz2xpaTyDAgmZMYOQGTOwFRZS9MMPFC5ZQu6775E77228O3cmeOpULFOm4BPTCY+gIJcYbFaVlZRs2mS4fFaupOrYMcCI9tvu/vuwTJxohBN3URHTg8UtwBX/XYuvlwcf3THK2aZozmBVynHuX7iN4gor1w7rzAOTehMVotdwuBq2/HyKVqyg8NsllGzYUJNECEACAvAMCsLDYqn562EJwjPIYuyzBOERZO6zVO+znCp/nmJizcujZPVqin5YaSR4KilBfH0JvOgigiaMJ2j8eLzbt2/Gp3Bh6MFiJxMfFcyS3ZkopVy2ReBuKKX476oD/HtZCvFRwbx8fSI92+upoK6KZ2gooVdeSeiVVxoV8Jq12HJPYisqxl5UhK24CHtRMfbiImwFBVSlp2MrNo5V55luiHMRE9vJkxStXEnZ1m1gt+MZ2Y7g6dONREmjRrbKuEtaCFqAhGgLCzceIbuwQrc2XYCSCit/+GQH3+7KYsbAjjx35QA9o6sV4RUWRsiMy5tcXlVW1oiCrbDIEIuiWsJxHmLiGx9Pu7vuJGjCBCMETCuf4aSFoAWIjzLizydlFWohcDJpJ0q4c8EWUo8X8dj0eG4f0/JT9TQti/j4GLNyLmBmTm0xET+/NrcgVAtBCxAXZc4cyixiQpzr+AzdjerxAA8P4d1bhzOmV9vI1qZxPM0hJq6MFoIWIMTfm06h/iRnFTrbFLdEKcVrPx7gX9+lENfBwps3DW00TaRG4044zLElIm+LyHER2d1IuWEiYhWRqxxliysQH2UhWU8hbXFKKqzc++E2nluawuUDOvLZ3RdpEdBozsCRIxzzgakNFRART+BZYJkD7XAJ4qMtHMgppsJqa7ywplk4fLKEX/x3HUt2Z/LY9HjmXpdIgI/uBGs0Z+IwIVBKrQZyGyl2H/ApcNxRdrgK8VHBWO2KA8dLnG2KW7Aq5TgzXl5DdlE57946nDvG6qxuGk19OK15JCKdgCuACcAwZ9nRUlSHmnhh+T56tQ/Cx8vDeHl64Fv93ssDH0/P0475eNU67lmrXK1zdQV3Cj0eoNGcO87sJ78IPKKUsjdWkYnIHcAdAF26dHG8ZQ4gNiKQvh2DWZN6gh+Sj2OzN9+Kbm9PqVMkfLwMUfH1PH2/n7cHExM6cFn/6DYVSK2kwsrDn+zkm12ZXD4gmueuGqBdQRpNE3BoiAkRiQW+Vkr1q+PYIaC6FmoHlAJ3KKW+aOiarTHERF3Y7IpKq51Kq50Km63mfaXNfuq91U7FGdunHbfZqTjt2NnXqajjvPyyKnKKKugRGch9E3tx+YBopyZZbw4OnzTWB+zLLuKRqfHcMVavD9BoauOSISaUUt2q34vIfAzB+MJZ9rQ0nh6Cv4+nuaK1ZYOb2e2KJbuzmLsilQcXbeelFancO6EnsxI7tkpB+HFfDvcv3Aag1wdoNOeBI6ePLgTWA3Eiki4it4nIXSJyl6PuqWkaHh7CZQOiWfLAGF6/YTB+3p787uMdTHr+R/5v81GqbHZnm9gklFK8tuoAt7yzkegQP76692ItAhrNeaCjj2pQSrE86TgvrdjH7oxCYsL8uWdCT64cHIOPl2v2EPR4gEZzbuicxZomoZRiZcpxXlqeyo70AjqF+vOb8T24emgMvl6uE5RNjwdoNOeOFgLNOaGU4sd9Oby0IpVtR/KJDvHjN+N7cM3Qzk7P1lV7PODl6wcxtrd2BWk0TUELgea8UEqxZv8JXlqeyubDeXQI9uU343pw3fAuLS4ISin+t/ogzy1NpncHC2/cOJQuEXp9gEbTVLQQaC4IpRTrD5zkxRWpbDyUS6TFlzvHdmfOiK4tEse/tNLKHz7ZyTc7M7lsQDT/0uMBGs05o4VA02ysP3CSuStSWX/wJO2CfLhjbHduGNnVYRXzkZOl3LFgM/uyi3h4ajx36vEAjea80EKgaXY2Hspl7opU1uw/QXigD7eP6c5No7oS6Nt8grB6Xw736fEAjaZZ0EKgcRhbDufy0or9rN6XQ1iAN782BcHid/6L5PR4gEbT/Ggh0DicrUfyeHlFKitTcgjx9+a2i7tx8+hYgs9REEorjfUBX+vxAI2mWdFCoGkxdqbnM3dFKsuTjmPx8+LW0d24dXQ3QgIaFwQ9HqDROA4tBJoWZ3dGAXNXpLJsbzYWXy9uHh3LraO7ERboU2f5n1JzuPdDPR6g0TgKLQQap7H3WCEv/5DKkt1ZBPp48quLYvn1mO6Em4Jw5njA/24cQteIQCdbrdG0PbQQaJxOSlYRc39I5dtdmfh7e3LjqK7cMKIrzy5N1uMBGk0LoIVA4zKkZhfx8g/7+WrnMZQCD0GPB2g0LYAWAo3Lsf94Me9vOMykhPY6dLRG0wK4ZGIajXvTs30QT8zs62wzNBoNDkxMo9FoNJrWgRYCjUajcXO0EGg0Go2bo4VAo9Fo3BwtBBqNRuPmaCHQaDQaN0cLgUaj0bg5Wgg0Go3GzWl1K4tFJAc4fB6ntgNONLM5jqC12Amtx9bWYie0Hltbi53Qemx1tJ1dlVJ1LuNvdUJwvojI5vqWV7sSrcVOaD22thY7ofXY2lrshNZjqzPt1K4hjUajcXO0EGg0Go2b405C8IazDWgircVOaD22thY7ofXY2lrshNZjq9PsdJsxAo1Go9HUjTv1CDQajUZTB1oINBqNxs1p80IgIlNFJEVE9ovIo862pzYi0llEVorIXhHZIyIPmPufEJEMEdluvqa7gK1pIrLLtGezuS9cRL4XkVTzb5gL2BlX67ltF5FCEXnQFZ6piLwtIsdFZHetfXU+QzGYa/5ud4rIYBew9V8ikmza87mIhJr7Y0WkrNazfd3Jdtb7XYvIH81nmiIiU1rKzgZsXVTLzjQR2W7ub9lnqpRqsy/AEzgAdAd8gB1AH2fbVcu+aGCw+d4C7AP6AE8Av3e2fWfYmga0O2Pfc8Cj5vtHgWedbWcd338W0NUVnikwFhgM7G7sGQLTgSWAACOBn13A1smAl/n+2Vq2xtYu5wJ21vldm/9bOwBfoJtZN3g609Yzjv8H+Isznmlb7xEMB/YrpQ4qpSqBj4BZTrapBqVUplJqq/m+CEgCOjnXqnNiFvCu+f5dYLbzTKmTScABpdT5rERvdpRSq4HcM3bX9wxnAe8pgw1AqIhEt4ih1G2rUmqZUspqbm4AYlrKnvqo55nWxyzgI6VUhVLqELAfo45oERqyVUQEuAZY2FL21KatC0En4Git7XRctKIVkVhgEPCzueteswv+tiu4XAAFLBORLSJyh7mvg1Iq03yfBXRwjmn1ch2n/2O52jOF+p+hq/92b8XosVTTTUS2iciPIjLGWUbVoq7v2pWf6RggWymVWmtfiz3Tti4ErQIRCQI+BR5UShUCrwE9gEQgE6PL6GwuVkoNBqYB94jI2NoHldGfdZm5yCLiA8wEPjZ3ueIzPQ1Xe4b1ISKPA1bgA3NXJtBFKTUIeAj4UESCnWUfreC7roPrOb3R0qLPtK0LQQbQudZ2jLnPZRARbwwR+EAp9RmAUipbKWVTStmBN2nB7mt9KKUyzL/Hgc8xbMqudleYf487z8KzmAZsVUplg2s+U5P6nqFL/nZF5GbgcmCOKVyYrpaT5vstGL733s6ysYHv2lWfqRfwC2BR9b6WfqZtXQg2Ab1EpJvZQrwOWOxkm2ow/YLzgCSl1PO19tf2BV8B7D7z3JZERAJFxFL9HmPQcDfGs/yVWexXwJfOsbBOTmthudozrUV9z3AxcJM5e2gkUFDLheQURGQq8DAwUylVWmt/pIh4mu+7A72Ag86xssHvejFwnYj4ikg3DDs3trR9dXAJkKyUSq/e0eLPtKVGpZ31wph9sQ9DUR93tj1n2HYxhitgJ7DdfE0HFgC7zP2LgWgn29kdY7bFDmBP9XMEIoAVQCqwHAh39jM17QoETgIhtfY5/ZliCFMmUIXhn76tvmeIMVvoVfN3uwsY6gK27sfwsVf/Vl83y15p/i62A1uBGU62s97vGnjcfKYpwDRnP1Nz/3zgrjPKtugz1SEmNBqNxs1p664hjUaj0TSCFgKNRqNxc7QQaDQajZujhUCj0WjcHC0EGo1G4+ZoIdBoABF5WkQmiMhsEfmjg+4xX0SucsS1NZoLQQuBRmMwAiOQ2jhgtZNt0WhaFC0EGrfGjLG/ExgGrAd+DbwmIn+po+x8M0fAOhE5WN26N1f//ktEdouRs+HaWvtfMWPfLwfa17rWEDOY2BYR+a5WmIn7xchPsVNEPmqBR6DR6AVlGo2IDANuwgjutUopNbqecvMxVi1fC8QDi5VSPUXkSuAuYCrQDiO0yQhgFPAbc38HYC+G0HwJ/AjMUkrlmMIxRSl1q4gcA7oppSpEJFQple+gj63R1ODlbAM0GhdgMEb4jHiMnBAN8YUygpntFZHqkNEXAwuVUjaMIHI/YvQwxtbaf0xEfjDLxwH9gO+NcFN4YoQeACMswgci8gXwRTN8No2mUbQQaNwWEUnEiPMSA5wAAozdsh0YpZQqq+O0itqXON9bA3uUUqPqOHYZhoDMAB4Xkf7qVDIYjcYh6DECjduilNqulErkVIrQHzBcNIn1iEB9/ARcKyKeIhKJUZFvxBh0rt4fDUwwy6cAkSIyCoxQ5CLSV0Q8gM5KqZXAI0AIEHThn1SjaRjdI9C4NWbFnaeUsotIvFJq73lc5nOM8YAdGNFkH1ZKZYnI58BEjLGBIxiD0SilKs2B5rkiEoLxf/gihiC9b+4TYK4eI9C0BHqwWKPRaNwc7RrSaDQaN0cLgUaj0bg5Wgg0Go3GzdFCoNFoNG6OFgKNRqNxc7QQaDQajZujhUCj0WjcnP8Hd/Lcj4xdg3IAAAAASUVORK5CYII=\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "bench(\n", " generate_graph,\n", " execute_graph,\n", " \"ewoks: dask - multithreading\",\n", " engine=\"dask\",\n", " scheduler=\"multithreading\",\n", ")" ] }, { "cell_type": "code", "execution_count": 10, "id": "40fb0fdf-d166-495c-9f9b-e24d7afa167c", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "bench(\n", " generate_graph,\n", " execute_graph,\n", " \"ewoks: dask - multiprocessing (spawn)\",\n", " engine=\"dask\",\n", " scheduler=\"multiprocessing\",\n", " scheduler_options={\"context\": \"spawn\"},\n", ")" ] }, { "cell_type": "code", "execution_count": 11, "id": "494c1759-d9a0-4b1f-942e-a271ec4816e0", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "bench(\n", " generate_graph,\n", " execute_graph,\n", " \"ewoks: dask - multiprocessing (fork)\",\n", " engine=\"dask\",\n", " scheduler=\"multiprocessing\",\n", " scheduler_options={\"context\": \"fork\"},\n", ")" ] }, { "cell_type": "code", "execution_count": 12, "id": "481d39f0-8098-4917-8ceb-ed33d22aef40", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "bench(\n", " generate_graph,\n", " execute_graph,\n", " \"ewoks: pypushflow - multiprocessing (fork)\",\n", " engine=\"ppf\",\n", ")" ] }, { "cell_type": "code", "execution_count": 13, "id": "7c1bf6a5-7444-4a5d-b542-5009e9b3acf0", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "bench(\n", " generate_graph,\n", " execute_graph,\n", " \"ewoks: pypushflow - scaling multiprocessing (fork)\",\n", " engine=\"ppf\",\n", " scaling_workers=False,\n", ")" ] } ], "metadata": { "language_info": { "name": "python" } }, "nbformat": 4, "nbformat_minor": 5 } running_workflows.ipynb000066400000000000000000000477721512474103500323670ustar00rootroot00000000000000ewoks-v4.0.0-8f4a8f91507ab3c3721e8d3f73e73ecca373546a/src/ewoks/tests/notebooks{ "cells": [ { "cell_type": "markdown", "id": "68fe18a0", "metadata": {}, "source": [ "# Use different engines\n", "\n", "This notebook demonstrates how to execute a workflow with different engines." ] }, { "cell_type": "code", "execution_count": 1, "id": "9c0eb2c7", "metadata": {}, "outputs": [], "source": [ "import os\n", "import shutil\n", "\n", "from ewoks import execute_graph\n", "from ewokscore.variable import value_from_transfer\n", "from ewokscore.tests.utils.show import show_graph\n", "from ewokscore.tests.examples.graphs import get_graph" ] }, { "cell_type": "markdown", "id": "47adb79c", "metadata": {}, "source": [ "## Select and configure a task scheduler" ] }, { "cell_type": "code", "execution_count": 2, "id": "e0b355d2", "metadata": {}, "outputs": [], "source": [ "scheduler = None # select any of the schedulers shown below\n", "\n", "execute_options = dict()\n", "\n", "if scheduler == \"dask_multithreading\":\n", " # tasks are distributed by a local scheduler over multiple threads\n", " execute_options[\"engine\"] = \"dask\"\n", " execute_options[\"scheduler\"] = \"multithreading\"\n", "elif scheduler == \"dask_multiprocessing\":\n", " # tasks are distributed by a local scheduler over multiple processes\n", " execute_options[\"engine\"] = \"dask\"\n", " execute_options[\"scheduler\"] = \"multiprocessing\"\n", "elif scheduler == \"dask_local_cluster\":\n", " # tasks are distributed by a local scheduler\n", " execute_options[\"engine\"] = \"dask\"\n", " execute_options[\"scheduler\"] = \"cluster\"\n", "elif scheduler == \"dask_remote_cluster\":\n", " # tasks are distributed by a remote scheduler\n", " execute_options[\"engine\"] = \"dask\"\n", " execute_options[\"scheduler\"] = \"127.0.0.1:40331\"\n", "\n", " # There are different ways to start a remote scheduler\n", " #\n", " # Start a dask scheduler on any host:\n", " # >>> from ewoksdask.schedulers import local_scheduler\n", " # >>> scheduler = local_scheduler(n_workers=5)\n", " #\n", " # Start a dask scheduler on a host that can launch slurm jobs:\n", " # >>> from ewoksdask.schedulers import slurm_scheduler\n", " # >>> scheduler = slurm_scheduler(maximum_jobs=5)\n", "\n", "elif scheduler == \"pypushflow\":\n", " # tasks are distributed by a local scheduler over multiple processes\n", " execute_options[\"engine\"] = \"ppf\"\n", "else:\n", " # tasks are executed in a single thread\n", " scheduler = None\n", " pass" ] }, { "cell_type": "markdown", "id": "7bce49c6", "metadata": {}, "source": [ "## Select a task graph" ] }, { "cell_type": "code", "execution_count": 7, "id": "52a3e417", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "taskgraph, expected_results = get_graph(\"acyclic1\")\n", "show_graph(taskgraph, stdout=False, plot=True)" ] }, { "cell_type": "markdown", "id": "af5e69b3", "metadata": {}, "source": [ "## Configure storage of results (if any)" ] }, { "cell_type": "code", "execution_count": 4, "id": "085f154b", "metadata": {}, "outputs": [], "source": [ "def prepare_storage(clean=True):\n", " if not varinfo[\"root_uri\"]:\n", " return # results are not persisted by ewoks\n", " if clean:\n", " shutil.rmtree(varinfo[\"root_uri\"], ignore_errors=True)\n", " os.makedirs(varinfo[\"root_uri\"], exist_ok=True)\n", "\n", "\n", "varinfo = {\"root_uri\": None} # folder where all results will be saved\n", "execute_options[\"varinfo\"] = varinfo\n", "prepare_storage(clean=True)" ] }, { "cell_type": "markdown", "id": "0b3cfedd", "metadata": {}, "source": [ "## Execute the workflow" ] }, { "cell_type": "code", "execution_count": 5, "id": "0e52aac1", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Execution options:\n", "{'varinfo': {'root_uri': None}}\n" ] } ], "source": [ "print(\"Execution options:\")\n", "print(execute_options)\n", "results = execute_graph(taskgraph, **execute_options)" ] }, { "cell_type": "markdown", "id": "577fc5a9-c215-458f-afe1-b5b354e90d10", "metadata": {}, "source": [ "## Inspect results" ] }, { "cell_type": "code", "execution_count": 6, "id": "8fb1abbb-9c09-4465-85c5-237b9c8a193c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " result = 16\n" ] } ], "source": [ "for name, result in results.items():\n", " value = value_from_transfer(result, varinfo=varinfo)\n", " print(f\" {name} =\", value)" ] } ], "metadata": { "language_info": { "name": "python" } }, "nbformat": 4, "nbformat_minor": 5 } ewoks-v4.0.0-8f4a8f91507ab3c3721e8d3f73e73ecca373546a/src/ewoks/tests/test_cli.py000066400000000000000000000041451512474103500257460ustar00rootroot00000000000000from ewoksutils.tests.conftest import cli_interface # noqa F401 from ewoksutils.tests.conftest import graph_directory # noqa F401 from ..cli_utils import cli_convert_utils def test_cli_convert(cli_interface): # noqa F811 argv = [ "acyclic1", "test.json", "--test", "-p", "a=1", "-p", "task1:b=test", "--src-format", "yaml", "--dst-format", "json", ] cli_args = cli_interface( argv, cli_convert_utils.convert_arguments, cli_convert_utils.parse_convert_arguments, ) assert list(cli_args.graphs) == ["acyclic1"] convert_options = { "inputs": [ {"all": False, "name": "a", "value": 1}, {"id": "task1", "name": "b", "value": "test"}, ], "load_options": {"representation": "test_core"}, "save_options": {"representation": "json"}, } assert cli_args.convert_options == convert_options argv = ["acyclic1", "test.json"] cli_args = cli_interface( argv, cli_convert_utils.convert_arguments, cli_convert_utils.parse_convert_arguments, ) assert cli_args.destinations == ["test.json"] argv = ["acyclic1", ".json"] cli_args = cli_interface( argv, cli_convert_utils.convert_arguments, cli_convert_utils.parse_convert_arguments, ) assert cli_args.destinations == ["acyclic1.json"] argv = ["acyclic1", "json"] cli_args = cli_interface( argv, cli_convert_utils.convert_arguments, cli_convert_utils.parse_convert_arguments, ) assert cli_args.destinations == ["acyclic1.json"] def test_cli_convert_search(cli_interface, tmp_path, graph_directory): # noqa F811 argv = [ str(tmp_path / "subdir" / "*.json"), str(tmp_path / "*.json"), "_suffix.json", "--search", ] cli_args = cli_interface( argv, cli_convert_utils.convert_arguments, cli_convert_utils.parse_convert_arguments, ) assert len(cli_args.graphs) == 22 assert cli_args.graphs == graph_directory ewoks-v4.0.0-8f4a8f91507ab3c3721e8d3f73e73ecca373546a/src/ewoks/tests/test_convert_cli.py000066400000000000000000000063621512474103500275110ustar00rootroot00000000000000import os import sys from xml.etree import ElementTree import pytest from ewokscore import load_graph from ewokscore.graph import TaskGraph from ewokscore.task import Task from ewokscore.tests.examples.graphs import graph_names from ewoksutils.import_utils import import_qualname from orangewidget.widget import OWBaseWidget from ewoks.__main__ import main from ewoks.tests.utils import has_default_input from ewoks.tests.utils import no_widget_registry def _ewokscore_in_graph_requirements(graph: TaskGraph) -> bool: ewokscore_in_req = False for requirement in graph.graph.graph["requirements"]: if "ewokscore" in requirement: ewokscore_in_req = True break return ewokscore_in_req @pytest.mark.parametrize("graph_name", graph_names()) def test_convert_to_json(graph_name, tmpdir): destination = str(tmpdir / f"{graph_name}.json") argv = [ sys.executable, "convert", graph_name, destination, "--test", "-s", "indent=2", ] main(argv=argv, shell=False) assert os.path.exists(destination) graph = load_graph(destination) assert graph.graph.graph["requirements"] is not None assert _ewokscore_in_graph_requirements(graph) def test_convert_with_all_inputs(tmpdir): graph_name = "demo" destination = str(tmpdir / f"{graph_name}.json") argv = [ sys.executable, "convert", graph_name, destination, "--test", "-p", "value=10", "--inputs=all", ] main(argv=argv, shell=False) assert os.path.exists(destination) graph = load_graph(destination) for node in graph.graph.nodes.values(): assert has_default_input(node, "value", 10) def test_convert_with_taskid_inputs(tmpdir): graph_name = "demo" destination = str(tmpdir / f"{graph_name}.json") taskid = "ewokscore.tests.examples.tasks.sumtask.SumTask" argv = [ sys.executable, "convert", graph_name, destination, "--test", "-p", f"{taskid}:value=test", "--input-node-id", "taskid", ] main(argv=argv, shell=False) assert os.path.exists(destination) graph = load_graph(destination) for node in graph.graph.nodes.values(): if node["task_identifier"] == taskid: assert has_default_input(node, "value", "test") @pytest.mark.parametrize("graph_name", graph_names()) def test_convert_to_ows(graph_name, tmpdir): destination = str(tmpdir / f"{graph_name}.ows") argv = [ sys.executable, "convert", graph_name, destination, "--test", ] DAGs = ["acyclic1", "demo", "empty"] if graph_name not in DAGs: with pytest.raises(RuntimeError): main(argv=argv, shell=False) return # Run `convert` with no_widget_registry(): main(argv=argv, shell=False) assert os.path.exists(destination) tree = ElementTree.parse(destination) root = tree.getroot() for node in root.findall("./nodes/node"): try: assert issubclass(import_qualname(node.get("qualified_name")), OWBaseWidget) except ImportError: assert issubclass(import_qualname(node.get("name")), Task) ewoks-v4.0.0-8f4a8f91507ab3c3721e8d3f73e73ecca373546a/src/ewoks/tests/test_engines.py000066400000000000000000000025261512474103500266300ustar00rootroot00000000000000import pytest from .._engines import get_engine_names from .._engines import get_execution_engine from .._engines import get_serialization_engine def test_engine_name_discovery(): names = get_engine_names() expected = {"core", "dask", "ppf", "orange"} assert expected.issubset(names) def test_execution_engine_discovery(): core_engine = get_execution_engine("core") for name in (None, "none", "None"): engine = get_execution_engine(name) assert engine.__class__ is core_engine.__class__ for engine in ("ppf", "dask", "orange"): _ = get_execution_engine(engine) with pytest.raises(RuntimeError): _ = get_execution_engine("__wrong_value__") def test_serialization_engine_discovery(): orange_engine = get_execution_engine("orange") engine, representation = get_serialization_engine("test.ows") assert engine.__class__ is orange_engine.__class__ assert representation == "ows" core_engine = get_execution_engine("core") engine, representation = get_serialization_engine("test.json") assert engine.__class__ is core_engine.__class__ assert representation == "json" core_engine = get_execution_engine("core") engine, representation = get_serialization_engine("{}") assert engine.__class__ is core_engine.__class__ assert representation == "json_string" ewoks-v4.0.0-8f4a8f91507ab3c3721e8d3f73e73ecca373546a/src/ewoks/tests/test_execute_cli.py000066400000000000000000000061571512474103500274750ustar00rootroot00000000000000import os import sys import pytest from ewokscore import load_graph from ewokscore.graph import TaskGraph from ewokscore.tests.examples.graphs import get_graph from ewokscore.tests.examples.graphs import graph_names from ewokscore.tests.utils.results import assert_execute_graph_default_result from ewoks.__main__ import main from ewoks.tests.utils import has_default_input def _ewokscore_in_graph_requirements(graph: TaskGraph) -> bool: ewokscore_in_req = False for requirement in graph.graph.graph["requirements"]: if "ewokscore" in requirement: ewokscore_in_req = True break return ewokscore_in_req @pytest.mark.parametrize("graph_name", graph_names()) @pytest.mark.parametrize("scheme", (None, "json")) @pytest.mark.parametrize("engine", (None, "dask", "ppf")) def test_execute(graph_name, scheme, engine, tmpdir): if graph_name == "self_trigger": pytest.skip( "Self-triggering workflow execution is inconsistent: https://gitlab.esrf.fr/workflow/ewoks/ewoksppf/-/issues/16" ) graph, expected = get_graph(graph_name) argv = [sys.executable, "execute", graph_name, "--test", "--merge-outputs"] if engine: argv += ["--engine", engine] if engine == "ppf": argv += ["--outputs", "end"] else: argv += ["--outputs", "all"] if scheme: argv += ["--data-root-uri", str(tmpdir), "--data-scheme", scheme] varinfo = {"root_uri": str(tmpdir), "scheme": scheme} else: varinfo = None keep = graph ewoksgraph = load_graph(graph) non_dag = ewoksgraph.is_cyclic or ewoksgraph.has_conditional_links results = main(argv=argv, shell=False) assert len(results) == 1 if non_dag and engine != "ppf": assert isinstance(results[0], RuntimeError) else: assert_execute_graph_default_result(ewoksgraph, results[0], expected, varinfo) assert keep == graph def test_execute_with_convert_destination(tmpdir): destination = str(tmpdir / "convert.json") argv = [ sys.executable, "execute", "demo", "--test", "-p", "task1:b=42", "-o", f"convert_destination={destination}", ] main(argv=argv, shell=False) assert os.path.exists(destination) graph = load_graph(destination) task1_node = graph.graph.nodes["task1"] assert has_default_input(task1_node, "b", 42) assert graph.graph.graph["requirements"] is not None assert _ewokscore_in_graph_requirements(graph) def test_execute_with_convert_destination_inputs_all(tmpdir): destination = str(tmpdir / "convert.json") argv = [ sys.executable, "execute", "demo", "--test", "-p", "b=42", "--inputs=all", "-o", f"convert_destination={destination}", ] main(argv=argv, shell=False) assert os.path.exists(destination) graph = load_graph(destination) for node in graph.graph.nodes.values(): assert has_default_input(node, "b", 42) assert graph.graph.graph["requirements"] is not None assert _ewokscore_in_graph_requirements(graph) ewoks-v4.0.0-8f4a8f91507ab3c3721e8d3f73e73ecca373546a/src/ewoks/tests/test_icat.py000066400000000000000000000030341512474103500261130ustar00rootroot00000000000000from ..bindings import execute_graph def test_upload_context(mock_icat_client): graph = { "graph": {"id": "demo", "label": "demo", "schema_version": "1.1"}, "links": [], "nodes": [ { "default_inputs": [ {"name": "a", "value": 42}, {"name": "delay", "value": 0}, ], "id": "task0", "task_identifier": "ewokscore.tests.examples.tasks.sumtask.SumTask", "task_type": "class", }, ], } execute_graph( graph, inputs=[{"name": "delay", "value": 0, "all": False}], upload_parameters={}, ) args, kwargs = mock_icat_client.return_value.store_processed_data.call_args assert not args assert set(kwargs) == {"metadata"} assert set(kwargs["metadata"]) == {"startDate", "endDate"} duration = kwargs["metadata"]["endDate"] - kwargs["metadata"]["startDate"] delay = 1 margin = 0.5 execute_graph( graph, inputs=[{"name": "delay", "value": delay + margin, "all": False}], upload_parameters={}, ) args, kwargs = mock_icat_client.return_value.store_processed_data.call_args assert not args assert set(kwargs) == {"metadata"} assert set(kwargs["metadata"]) == {"startDate", "endDate"} duration_with_delay = ( kwargs["metadata"]["endDate"] - kwargs["metadata"]["startDate"] ) difference = duration_with_delay - duration assert difference.total_seconds() > delay ewoks-v4.0.0-8f4a8f91507ab3c3721e8d3f73e73ecca373546a/src/ewoks/tests/test_install_cli.py000066400000000000000000000024331512474103500274720ustar00rootroot00000000000000import json import subprocess import pytest def test_install(venv): with pytest.raises(Exception, match="package is not installed"): venv.get_version("ewoksdata") subprocess.check_call( [ "ewoks", "install", "--yes", '{"graph": {"id": "test_install", "requirements": ["ewoksdata"]}}', "-p", f"{venv.python}", ] ) assert venv.get_version("ewoksdata") is not None def test_install_with_extract(venv): with pytest.raises(Exception, match="package is not installed"): venv.get_version("ewoksdata") nodes = [ { "id": 1, "task_identifier": 'ewoksdata.tasks.normalization.Normalization"', "task_type": "class", }, { "id": 2, "task_identifier": "path/to/my/script", "task_type": "script", }, # Check that unsupported task type goes though without error ] graph = {"graph": {"id": "test_install"}, "nodes": nodes} subprocess.check_call( [ "ewoks", "install", "--yes", json.dumps(graph), "-p", f"{venv.python}", ] ) assert venv.get_version("ewoksdata") is not None ewoks-v4.0.0-8f4a8f91507ab3c3721e8d3f73e73ecca373546a/src/ewoks/tests/test_notebooks.py000066400000000000000000000024001512474103500271720ustar00rootroot00000000000000import asyncio import sys from typing import Generator if sys.platform.startswith("win"): asyncio.set_event_loop_policy(asyncio.WindowsSelectorEventLoopPolicy()) import pytest import testbook from testbook.client import TestbookNotebookClient from .notebooks import notebook_path _NOTEBOOK_OUTPUTS = {"running_workflows.ipynb": "result = 16"} @pytest.mark.parametrize("name", _NOTEBOOK_OUTPUTS) def test_notebooks(name): expected_output = _NOTEBOOK_OUTPUTS[name] with notebook_path(name) as filename: decorator = testbook.testbook(filename) output_found = False def verify_notebook(tb: TestbookNotebookClient) -> None: tb.execute() nonlocal output_found for output in _iter_cell_outputs(tb): print(output) if expected_output in output: output_found = True break decorator(verify_notebook)() assert output_found def _iter_cell_outputs(tb: TestbookNotebookClient) -> Generator[str, None, None]: for cell in tb.nb.cells: output_nodes = cell.get("outputs", []) for output_node in output_nodes: if output_node["output_type"] == "stream": yield output_node["text"] ewoks-v4.0.0-8f4a8f91507ab3c3721e8d3f73e73ecca373546a/src/ewoks/tests/test_projects.py000066400000000000000000000003021512474103500270170ustar00rootroot00000000000000import importlib import pytest @pytest.mark.parametrize( "project", ["ewokscore", "ewoksdask", "ewoksppf", "ewoksorange"] ) def test_import(project): importlib.import_module(project) ewoks-v4.0.0-8f4a8f91507ab3c3721e8d3f73e73ecca373546a/src/ewoks/tests/test_requirements.py000066400000000000000000000061151512474103500277210ustar00rootroot00000000000000import pytest from .._requirements.pip.sanitize import sanitize_requirements def test_normal_requirement(): req = ["ewoks==1.1.0"] sanitized, warnings = sanitize_requirements(req) assert sanitized == ["ewoks==1.1.0"] assert warnings == [] def test_editable_ssh_vcs_url_normalized(): ssh_project_name = "querypool" ssh_project_url = "gitlab.esrf.fr/dau/querypool.git" ssh_project_commit = "ab6acc7e140ed33eb896b1336a5a5aac6b60cc0f" req = [ f"-e git+ssh://git@{ssh_project_url}@{ssh_project_commit}#egg={ssh_project_name}" ] sanitized, warnings = sanitize_requirements(req) expected_sanitized = [ f"{ssh_project_name} @ git+https://{ssh_project_url}@{ssh_project_commit}" ] expected_warnings = [ f"Normalize VCS requirement 'git+ssh://git@{ssh_project_url}@{ssh_project_commit}#egg={ssh_project_name}' " f"to '{ssh_project_name} @ git+ssh://git@{ssh_project_url}@{ssh_project_commit}'", f"Normalize requirement '{ssh_project_name} @ git+ssh://git@{ssh_project_url}@{ssh_project_commit}' " f"to '{ssh_project_name} @ git+https://{ssh_project_url}@{ssh_project_commit}'", ] assert sanitized == expected_sanitized assert warnings == expected_warnings @pytest.mark.parametrize("exists", [True, False]) def test_editable_local_path_with_comment_replacement(tmp_path, exists): path = tmp_path / "repo_name" if exists: path.mkdir() comment = "# Editable Git install with no remote (project_name==1.0.0)" replacement = "project_name==1.0.0" req = [comment, f"-e {path}"] sanitized, warnings = sanitize_requirements(req) assert sanitized == [replacement] assert warnings == [f"Replaced editable install '{path}' with '{replacement}'."] @pytest.mark.parametrize("exists", [True, False]) def test_editable_local_path_without_comment_replacement(tmp_path, exists): path = tmp_path / "repo_name" if exists: path.mkdir() warning = f"Editable path exists locally: '{path}'" else: warning = f"Editable path does not exist locally: '{path}'" req = [f"-e {path}"] sanitized, warnings = sanitize_requirements(req) assert sanitized == [f"-e {path}"] if exists: assert warnings == [warning] else: assert warnings == [warning] def test_branch_specified_requirement(): project_name = "ewoksutils" project_url = "gitlab.esrf.fr/workflow/ewoks/ewoksutils.git" project_branch = "main" req = [f"{project_name}@ git+https://{project_url}@{project_branch}"] sanitized, warnings = sanitize_requirements(req) # No warnings expected here (assuming valid format) assert sanitized == [f"{project_name}@ git+https://{project_url}@{project_branch}"] assert warnings == [] def test_invalid_requirement_warning(): project_url = "gitlab.esrf.fr/workflow/ewoks/ewoksutils.git" req = [f"git+https://{project_url}"] sanitized, warnings = sanitize_requirements(req) assert sanitized == [f"git+https://{project_url}"] assert any("Possibly invalid requirement format" in w for w in warnings) ewoks-v4.0.0-8f4a8f91507ab3c3721e8d3f73e73ecca373546a/src/ewoks/tests/test_show_cli.py000066400000000000000000000304731512474103500270110ustar00rootroot00000000000000import os import sys from ewoks.__main__ import main from .utils import no_widget_registry def test_show(capsys): argv = [sys.executable, "show", "demo", "--test"] main(argv=argv, shell=False) captured = capsys.readouterr() expected = """ Workflow: demo Id: demo Description: demo ╒════════╤════════════════╤═══════════════════╤═══════╕ │ Name │ Value │ Task identifier │ Id │ ╞════════╪════════════════╪═══════════════════╪═══════╡ │ list │ [0, 1, 2] │ SumList │ task0 │ ├────────┼────────────────┼───────────────────┼───────┤ │ delay │ 0 │ SumList │ task0 │ ├────────┼────────────────┼───────────────────┼───────┤ │ b │ │ SumTask │ task1 │ ├────────┼────────────────┼───────────────────┼───────┤ │ delay │ 0 │ SumTask │ task1 │ ├────────┼────────────────┼───────────────────┼───────┤ │ a │ 2 │ SumTask │ task2 │ ├────────┼────────────────┼───────────────────┼───────┤ │ b │ │ SumTask │ task2 │ ├────────┼────────────────┼───────────────────┼───────┤ │ delay │ 0 │ SumTask │ task2 │ ├────────┼────────────────┼───────────────────┼───────┤ │ b │ 3 │ SumTask │ task3 │ ├────────┼────────────────┼───────────────────┼───────┤ │ delay │ 0 │ SumTask │ task3 │ ├────────┼────────────────┼───────────────────┼───────┤ │ b │ 4 │ SumTask │ task4 │ ├────────┼────────────────┼───────────────────┼───────┤ │ delay │ 0 │ SumTask │ task4 │ ├────────┼────────────────┼───────────────────┼───────┤ │ delay │ 0 │ SumTask │ task5 │ ├────────┼────────────────┼───────────────────┼───────┤ │ b │ 6 │ SumTask │ task6 │ ├────────┼────────────────┼───────────────────┼───────┤ │ delay │ 0 │ SumTask │ task6 │ ╘════════╧════════════════╧═══════════════════╧═══════╛ """ assert captured.out == expected assert captured.err == "" def test_show_with_inputs(capsys): argv = [sys.executable, "show", "demo", "--test", "-p", "b=999", "--inputs=all"] main(argv=argv, shell=False) captured = capsys.readouterr() expected = """ Workflow: demo Id: demo Description: demo ╒════════╤═══════════╤═══════════════════╤═══════╕ │ Name │ Value │ Task identifier │ Id │ ╞════════╪═══════════╪═══════════════════╪═══════╡ │ list │ [0, 1, 2] │ SumList │ task0 │ ├────────┼───────────┼───────────────────┼───────┤ │ delay │ 0 │ SumList │ task0 │ ├────────┼───────────┼───────────────────┼───────┤ │ b │ 999 │ SumTask │ task1 │ ├────────┼───────────┼───────────────────┼───────┤ │ delay │ 0 │ SumTask │ task1 │ ├────────┼───────────┼───────────────────┼───────┤ │ a │ 2 │ SumTask │ task2 │ ├────────┼───────────┼───────────────────┼───────┤ │ b │ 999 │ SumTask │ task2 │ ├────────┼───────────┼───────────────────┼───────┤ │ delay │ 0 │ SumTask │ task2 │ ├────────┼───────────┼───────────────────┼───────┤ │ b │ 999 │ SumTask │ task3 │ ├────────┼───────────┼───────────────────┼───────┤ │ delay │ 0 │ SumTask │ task3 │ ├────────┼───────────┼───────────────────┼───────┤ │ b │ 999 │ SumTask │ task4 │ ├────────┼───────────┼───────────────────┼───────┤ │ delay │ 0 │ SumTask │ task4 │ ├────────┼───────────┼───────────────────┼───────┤ │ delay │ 0 │ SumTask │ task5 │ ├────────┼───────────┼───────────────────┼───────┤ │ b │ 999 │ SumTask │ task6 │ ├────────┼───────────┼───────────────────┼───────┤ │ delay │ 0 │ SumTask │ task6 │ ╘════════╧═══════════╧═══════════════════╧═══════╛ """ assert captured.out == expected assert captured.err == "" def test_show_with_convert_ows(capsys, tmpdir): # Generate an OWS file graph_name = "demo" destination = str(tmpdir / f"{graph_name}.ows") argv = [ sys.executable, "convert", graph_name, destination, "--test", ] with no_widget_registry(): main(argv=argv, shell=False) assert os.path.exists(destination) capsys.readouterr() # Show the OWS file argv = [sys.executable, "show", destination] main(argv=argv, shell=False) captured = capsys.readouterr() expected = f""" Workflow: {destination} Id: demo Description: demo ╒════════╤════════════════╤═══════════════════╤══════╤═════════╕ │ Name │ Value │ Task identifier │ Id │ Label │ ╞════════╪════════════════╪═══════════════════╪══════╪═════════╡ │ list │ [0, 1, 2] │ SumList │ 0 │ task0 │ ├────────┼────────────────┼───────────────────┼──────┼─────────┤ │ delay │ 0 │ SumList │ 0 │ task0 │ ├────────┼────────────────┼───────────────────┼──────┼─────────┤ │ b │ │ SumTask │ 1 │ task1 │ ├────────┼────────────────┼───────────────────┼──────┼─────────┤ │ delay │ 0 │ SumTask │ 1 │ task1 │ ├────────┼────────────────┼───────────────────┼──────┼─────────┤ │ a │ 2 │ SumTask │ 2 │ task2 │ ├────────┼────────────────┼───────────────────┼──────┼─────────┤ │ b │ │ SumTask │ 2 │ task2 │ ├────────┼────────────────┼───────────────────┼──────┼─────────┤ │ delay │ 0 │ SumTask │ 2 │ task2 │ ├────────┼────────────────┼───────────────────┼──────┼─────────┤ │ b │ 3 │ SumTask │ 3 │ task3 │ ├────────┼────────────────┼───────────────────┼──────┼─────────┤ │ delay │ 0 │ SumTask │ 3 │ task3 │ ├────────┼────────────────┼───────────────────┼──────┼─────────┤ │ b │ 4 │ SumTask │ 4 │ task4 │ ├────────┼────────────────┼───────────────────┼──────┼─────────┤ │ delay │ 0 │ SumTask │ 4 │ task4 │ ├────────┼────────────────┼───────────────────┼──────┼─────────┤ │ delay │ 0 │ SumTask │ 5 │ task5 │ ├────────┼────────────────┼───────────────────┼──────┼─────────┤ │ b │ 6 │ SumTask │ 6 │ task6 │ ├────────┼────────────────┼───────────────────┼──────┼─────────┤ │ delay │ 0 │ SumTask │ 6 │ task6 │ ╘════════╧════════════════╧═══════════════════╧══════╧═════════╛ """ assert captured.out == expected assert captured.err == "" ewoks-v4.0.0-8f4a8f91507ab3c3721e8d3f73e73ecca373546a/src/ewoks/tests/utils.py000066400000000000000000000007771512474103500253070ustar00rootroot00000000000000from contextlib import contextmanager from typing import Any def has_default_input(node: dict, input_name: str, value: Any) -> bool: for default_input in node["default_inputs"]: if default_input["name"] == input_name: return default_input["value"] == value else: return False @contextmanager def no_widget_registry(): try: yield finally: from orangecontrib.ewoksnowidget import global_cleanup_ewoksnowidget global_cleanup_ewoksnowidget() ewoks-v4.0.0-8f4a8f91507ab3c3721e8d3f73e73ecca373546a/src/ewoks/utils.py000066400000000000000000000000001512474103500241200ustar00rootroot00000000000000