diff --git a/.git-blame-ignore-revs b/.git-blame-ignore-revs new file mode 100644 index 00000000..f49cfb3a --- /dev/null +++ b/.git-blame-ignore-revs @@ -0,0 +1,2 @@ +# Autoformat all files using `black` +8f061e7da99eb78353e9392d6929673da5b352a3 diff --git a/.github/workflows/black.yaml b/.github/workflows/black.yaml new file mode 100644 index 00000000..0a8b8e92 --- /dev/null +++ b/.github/workflows/black.yaml @@ -0,0 +1,10 @@ +name: Lint + +on: [push, pull_request] + +jobs: + lint: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v3 + - uses: psf/black@stable \ No newline at end of file diff --git a/.github/workflows/ci.yaml b/.github/workflows/ci.yaml index e25704ec..54830999 100644 --- a/.github/workflows/ci.yaml +++ b/.github/workflows/ci.yaml @@ -32,20 +32,17 @@ jobs: os: [ubuntu-latest] python-version: ["3.10"] - gromacs-version: ["4.6.5", "2018.6", "2020.6", "2021.1"] + gromacs-version: ["4.6.5", "2018.6", "2020.6", "2021.1", "2022.4", "2023.1"] include: - - os: ubuntu-latest - python-version: "3.7" - gromacs-version: "2021.1" - os: ubuntu-latest python-version: "3.8" - gromacs-version: "2021.1" + gromacs-version: "2023.1" - os: ubuntu-latest python-version: "3.9" - gromacs-version: "2021.1" + gromacs-version: "2023.1" - os: macos-latest python-version: "3.10" - gromacs-version: "2021.1" + gromacs-version: "2023.1" env: MPLBACKEND: agg @@ -55,13 +52,17 @@ jobs: - uses: actions/checkout@v3 - name: mamba environment and package installation - uses: mamba-org/provision-with-micromamba@main + uses: mamba-org/setup-micromamba@v1 with: environment-file: devtools/conda-envs/test_env.yaml - channel-priority: "flexible" + condarc: | + channels: + - conda-forge + - bioconda + channel_priority: flexible cache-downloads: true - cache-env: true - extra-specs: | + cache-environment: true + create-args: >- python=${{ matrix.python-version }} - name: Python version information @@ -78,8 +79,10 @@ jobs: python -m pip install pybol - name: Install GROMACS (${{ matrix.gromacs-version }}) + # include mdanalysis here AGAIN to work around micromamba removing mdanalysis + # while downgrading libxml2 (see issue #252) run: | - micromamba install 'gromacs==${{ matrix.gromacs-version }}' pocl + micromamba install 'gromacs==${{ matrix.gromacs-version }}' pocl mdanalysis - name: Install package (with no dependencies) run: | diff --git a/.gitignore b/.gitignore index 89916e83..ed42a5df 100644 --- a/.gitignore +++ b/.gitignore @@ -12,3 +12,6 @@ dist doc/examples/benzene/Equilibrium/ .coverage coverage.xml +*.lock +*.npz +dir.csv diff --git a/.vscode/extensions.json b/.vscode/extensions.json new file mode 100644 index 00000000..876fcd74 --- /dev/null +++ b/.vscode/extensions.json @@ -0,0 +1,6 @@ +{ + "recommendations": [ + "ms-python.black-formatter", + "ms-python.python" + ] +} \ No newline at end of file diff --git a/.vscode/settings.json b/.vscode/settings.json new file mode 100644 index 00000000..ea9d4d09 --- /dev/null +++ b/.vscode/settings.json @@ -0,0 +1,5 @@ +{ + "[python]": { + "editor.formatOnSave": true + } +} \ No newline at end of file diff --git a/AUTHORS b/AUTHORS index 0acf2990..19708124 100644 --- a/AUTHORS +++ b/AUTHORS @@ -39,3 +39,7 @@ their first commit. GitHub handle is optional. - Cade Duckworth (cadeduckworth) +2023 +---- + +- Alexander Moriarty (@a-ws-m) diff --git a/CHANGES b/CHANGES index 1b9f55e7..d9cc73f0 100644 --- a/CHANGES +++ b/CHANGES @@ -5,11 +5,16 @@ CHANGES for MDPOW Add summary of changes for each release. Use ISO 8061 dates. Reference GitHub issues numbers and PR numbers. -2022-??-?? 0.9.0 -cadeduckworth, orbeckst, VOD555 +2023-??-?? 0.9.0 +cadeduckworth, orbeckst, VOD555, a-ws-m Changes +* added support for Python 3.10 (#202) +* dropped testing on Python 3.6 (PR #220, #202) +* dropped testing on Python 3.7 (minimally supported Python >= 3.8, #248) +* support Gromacs 2022.4 and 2023.1 (#256) +* use pymbar >= 4 and alchemlyb >= 2 (#246) * for ensemble.EnsembleAnalysis._single_frame() changed 'pass' to 'raise NotImplementedError' (#216) * for ensemble.EnsembleAnalysis._single_universe() @@ -18,16 +23,38 @@ Changes and use _single_frame OR _single_universe (#216) * _prepare_universe and _conclude_universe removed from EnsembleAnalysis.run() method, no longer needed (per comments, #199) -* added support for Python 3.10 -* dropped testing on Python 3.6 +* internal log_banner() now uses logger as argument (PR #247) +* use `black` formatter for codebase (#271) + +Enhancements + +* convert figure components to SVG, save as individual PDFs, + and generate PDF of all figures combined in one file, + for workflows dihedrals module (#243) +* add RDKit mol object to dihedrals plot with dihedral atom + indices labeled and dihedral atom group bonds highlighted + for workflows dihedrals module (#243) +* new workflows registry that contains each EnsembleAnalysis for which + a workflows module exists, for use with workflows base module (#229) +* new workflows base module that provides iterative workflow use for + directories that contain multiple projects (#229) +* new workflows module (#217) +* new automated dihedral analysis workflow (detect dihedrals with SMARTS, + analyze with EnsembleAnalysis, and generate seaborn violinplots) (#217) +* new workflows.base.guess_elements() function to guess elements + from masses (PR #264) +* add new exit_on_error=False|True argument to run.runMD_or_exit() so + that failures just raise exceptions and not call sys.exit() (PR #249) Fixes +* for mdpow.workflows.dihedrals.periodic_angle() implement .copy(deep=True) + to explicitly make a copy DataFrame of the data for angle padding (#242) * fix rcoulomb in CHARMM energy minimization MDP template file (PR #210) * fix ensemble.EnsembleAnalysis.check_groups_from_common_ensemble (#212) -2021-01-03 0.8.0 +2022-01-03 0.8.0 ALescoulie, orbeckst Changes diff --git a/INSTALL.rst b/INSTALL.rst index bed03b5c..093a4740 100644 --- a/INSTALL.rst +++ b/INSTALL.rst @@ -2,7 +2,7 @@ Quick installation instructions for *MDPOW* ============================================= -MDPOW is compatible with Python 3.7+ and tested +MDPOW is compatible with Python >=3.8 and tested on Ubuntu and Mac OS. We recommend that you install MDPOW in a virtual environment. @@ -28,11 +28,11 @@ GROMACS_. Conda environment with pre-requisites ------------------------------------- -Make a conda environment with the latest packages for Python 3.7 and +Make a conda environment with the latest packages for Python 3.8 or higher with the name *mdpow*; this installs the larger dependencies that are pre-requisites for MDPOW:: - conda create -c conda-forge -n mdpow numpy scipy matplotlib mdanalysis pyyaml alchemlyb pandas gromacswrapper + conda create -c conda-forge -n mdpow numpy scipy matplotlib seaborn mdanalysis pyyaml alchemlyb pandas gromacswrapper rdkit conda activate mdpow Install MDPOW with ``pip``:: diff --git a/README.rst b/README.rst index c82edd00..7c4e37c1 100644 --- a/README.rst +++ b/README.rst @@ -2,10 +2,11 @@ README for MDPOW =================== -|build| |cov| |docs| +|build| |cov| |docs| |black| |zenodo| .. |P_ow| replace:: *P*\ :sub:`OW` .. |P_cw| replace:: *P*\ :sub:`CW` +.. |P_tw| replace:: *P*\ :sub:`TW` *MDPOW* is a python package that automates the calculation of solvation free energies via molecular dynamics (MD) simulations. In @@ -14,7 +15,8 @@ coefficients. Currently implemented: - *water-octanol* partition coefficient (|P_ow|) - *water-cyclohexane* partition coefficient (|P_cw|) - +- *water-toluene* partition coefficient (|P_tw|) + Calculations are performed with the Gromacs_ MD software package [#GromacsWrapperNote]_. Currently, *OPLS-AA*, *CHARMM/CGENFF*, and *AMBER/GAFF* parameters are supported. @@ -44,10 +46,10 @@ Installation ------------ See `INSTALL`_ for detailed instructions. MDPOW currently supports and -is tested with Python 3.7 to 3.9. +is tested with Python 3.8 to 3.10. You will also need `Gromacs`_ (currently tested with versions 4.6.5, -2018, 2020, 2021 but 2016 and 2019 should also work). +2018, 2020, 2021, 2022, 2023 but 2016 and 2019 should also work). Development version @@ -72,9 +74,14 @@ Source code *MDPOW* is open source and published under the `GNU General Public License v3`_. Source code is available at https://github.com/Becksteinlab/MDPOW . +We use `black`_ for uniform code formatting. + .. _`GNU General Public License v3`: http://www.gnu.org/licenses/gpl-3.0.html +.. _`black`: https://github.com/psf/black + + Footnotes --------- @@ -92,5 +99,13 @@ Footnotes .. |docs| image:: https://readthedocs.org/projects/mdpow/badge/?version=latest :target: http://mdpow.readthedocs.org/en/latest/?badge=latest :alt: Documentation + +.. |zenodo| image:: https://zenodo.org/badge/44999898.svg + :target: https://zenodo.org/badge/latestdoi/44999898 + :alt: Zenodo + +.. |black| image:: https://img.shields.io/badge/code%20style-black-000000.svg + :target: https://github.com/psf/black + :alt: black .. _INSTALL: INSTALL.rst diff --git a/devtools/conda-envs/test_env.yaml b/devtools/conda-envs/test_env.yaml index 5544ee54..c283e3e4 100644 --- a/devtools/conda-envs/test_env.yaml +++ b/devtools/conda-envs/test_env.yaml @@ -14,10 +14,16 @@ dependencies: - mdanalysis >=2 - numkit - gromacswrapper -- alchemlyb <2 +- alchemlyb >=2 +- pymbar >=4 +- rdkit +- seaborn +- svgutils +- cairosvg +- pypdf # Testing - pytest - pytest-pep8 - pytest-cov -- codecov \ No newline at end of file +- codecov \ No newline at end of file diff --git a/doc/examples/benzene/session.py b/doc/examples/benzene/session.py index b0ff5276..da1ac8d9 100644 --- a/doc/examples/benzene/session.py +++ b/doc/examples/benzene/session.py @@ -1,31 +1,39 @@ import mdpow.equil + S = mdpow.equil.WaterSimulation(molecule="BNZ") S.topology("benzene.itp") S.solvate(struct="benzene.pdb") S.energy_minimize() -S.MD_relaxed(runtime=5) # should be at least 1e3 ps for production not just 5 ps +S.MD_relaxed(runtime=5) # should be at least 1e3 ps for production not just 5 ps # run simulation externally or use MDrunner # (see docs for using mpi etc) import gromacs -r = gromacs.run.MDrunner(dirname=S.dirs['MD_relaxed'], deffnm="md", c="md.pdb", cpi=True, append=True, v=True) -r.run() # runs mdrun in the python shell + +r = gromacs.run.MDrunner( + dirname=S.dirs["MD_relaxed"], deffnm="md", c="md.pdb", cpi=True, append=True, v=True +) +r.run() # runs mdrun in the python shell -S.MD(runtime=10, qscript=['local.sh']) # should be at least 10e3 ps for production, not just 10 ps +S.MD( + runtime=10, qscript=["local.sh"] +) # should be at least 10e3 ps for production, not just 10 ps # run simulation -r = gromacs.run.MDrunner(dirname=S.dirs['MD_NPT'], deffnm="md", c="md.pdb", cpi=True, append=True, v=True) -r.run() # runs mdrun in the python shell +r = gromacs.run.MDrunner( + dirname=S.dirs["MD_NPT"], deffnm="md", c="md.pdb", cpi=True, append=True, v=True +) +r.run() # runs mdrun in the python shell import mdpow.fep + gwat = mdpow.fep.Ghyd(simulation=S, runtime=10) gwat.setup() # run multiple simulations on cluster - O = mdpow.equil.OctanolSimulation(molecule="BNZ") O.topology("benzene.itp") O.solvate(struct="benzene.pdb") diff --git a/doc/requirements.txt b/doc/requirements.txt index e487171e..3e243106 100644 --- a/doc/requirements.txt +++ b/doc/requirements.txt @@ -4,5 +4,12 @@ scipy pandas pyyaml GromacsWrapper>=0.5.1 -alchemlyb +alchemlyb>=2 +pymbar>=4 mdanalysis +rdkit +seaborn +matplotlib +svgutils +cairosvg +pypdf diff --git a/doc/sphinx/source/analysis.txt b/doc/sphinx/source/analysis.txt index 648249c5..240a0d36 100644 --- a/doc/sphinx/source/analysis.txt +++ b/doc/sphinx/source/analysis.txt @@ -20,12 +20,11 @@ all FEP simulations. They then make data available (typically, as a :class:`pandas.DataFrame`). .. toctree:: - :maxdepth: 1 + :maxdepth: 2 analysis/solvation analysis/dihedral - .. _framework: Ensemble Analysis Framework @@ -45,10 +44,9 @@ in more detail in [Lescoulie2021]_. .. toctree:: :maxdepth: 1 - analysis/ensemble_analysis + analysis/ensemble_analysis analysis/ensemble - References ---------- .. [Lescoulie2021] A. Lescoulie, "SPIDAL Summer REU 2021: Upgrading MDPOW and diff --git a/doc/sphinx/source/conf.py b/doc/sphinx/source/conf.py index 6aa70864..b55c6df5 100644 --- a/doc/sphinx/source/conf.py +++ b/doc/sphinx/source/conf.py @@ -14,6 +14,7 @@ import sys import os import datetime + # https://sphinx-rtd-theme.readthedocs.io/en/stable/ import sphinx_rtd_theme @@ -22,45 +23,51 @@ # is relative to the documentation root, use os.path.abspath to make it # absolute, like shown here. # make sure sphinx always uses the current branch -sys.path.insert(0, os.path.abspath('../../..')) +sys.path.insert(0, os.path.abspath("../../..")) # -- General configuration ----------------------------------------------------- # Add any Sphinx extension module names here, as strings. They can be extensions # coming with Sphinx (named 'sphinx.ext.*') or your custom ones. # 'sphinx.ext.pngmath', 'sphinx.ext.jsmath' -extensions = ['sphinx.ext.autodoc', 'sphinx.ext.intersphinx', - 'sphinx.ext.mathjax', 'sphinx.ext.viewcode', - 'sphinx_rtd_theme'] +extensions = [ + "sphinx.ext.autodoc", + "sphinx.ext.intersphinx", + "sphinx.ext.mathjax", + "sphinx.ext.viewcode", + "sphinx_rtd_theme", +] -mathjax_path = 'https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.0/MathJax.js?config=TeX-AMS-MML_HTMLorMML' +mathjax_path = "https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.0/MathJax.js?config=TeX-AMS-MML_HTMLorMML" # Add any paths that contain templates here, relative to this directory. -templates_path = ['_templates'] +templates_path = ["_templates"] # The suffix of source filenames. -source_suffix = '.txt' +source_suffix = ".txt" # The encoding of source files. -#source_encoding = 'utf-8' +# source_encoding = 'utf-8' # The master toctree document. -master_doc = 'index' +master_doc = "index" # General information about the project. -project = u'MDPOW' +project = "MDPOW" now = datetime.datetime.now() -copyright = u'2010–{}, Shujie Fan, Ian Kenney, Alia Lescoulie, Bogdan Iorga, and Oliver Beckstein'.format(now.year) +copyright = "2010–{}, Shujie Fan, Ian Kenney, Alia Lescoulie, Bogdan Iorga, and Oliver Beckstein".format( + now.year +) # The version info for the project you're documenting, acts as replacement for # |version| and |release|, also used in various other places throughout the # built documents. # # Dynamically calculate the version -packageversion = __import__('mdpow').__version__ +packageversion = __import__("mdpow").__version__ # The short X.Y version. -version = '.'.join(packageversion.split('.')[:2]) +version = ".".join(packageversion.split(".")[:2]) # The full version, including alpha/beta/rc tags. ##release = packageversion @@ -73,7 +80,7 @@ except ValueError: ver, rc = packageversion, None -if not rc or rc.startswith('0'): +if not rc or rc.startswith("0"): release = ver else: release = ver + "+" + rc.replace(".dirty", "") @@ -81,73 +88,71 @@ # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. -#language = None +# language = None # There are two options for replacing |today|: either, you set today to some # non-false value, then it is used: -#today = '' +# today = '' # Else, today_fmt is used as the format for a strftime call. -#today_fmt = '%B %d, %Y' +# today_fmt = '%B %d, %Y' # List of documents that shouldn't be included in the build. -#unused_docs = [] +# unused_docs = [] # List of directories, relative to source directory, that shouldn't be searched # for source files. exclude_trees = [] # The reST default role (used for this markup: `text`) to use for all documents. -#default_role = None +# default_role = None # If true, '()' will be appended to :func: etc. cross-reference text. -#add_function_parentheses = True +# add_function_parentheses = True # If true, the current module name will be prepended to all description # unit titles (such as .. function::). -#add_module_names = True +# add_module_names = True # If true, sectionauthor and moduleauthor directives will be shown in the # output. They are ignored by default. -#show_authors = False +# show_authors = False # The name of the Pygments (syntax highlighting) style to use. -pygments_style = 'sphinx' +pygments_style = "sphinx" # A list of ignored prefixes for module index sorting. -#modindex_common_prefix = [] +# modindex_common_prefix = [] # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. -html_theme = 'sphinx_rtd_theme' +html_theme = "sphinx_rtd_theme" html_theme_options = { - 'logo_only': False, - 'display_version': True, - 'prev_next_buttons_location': 'bottom', - 'style_external_links': False, - 'style_nav_header_background': 'white', + "logo_only": False, + "display_version": True, + "prev_next_buttons_location": "bottom", + "style_external_links": False, + "style_nav_header_background": "white", # Toc options - 'collapse_navigation': True, - 'sticky_navigation': True, - 'navigation_depth': 4, - 'includehidden': True, - 'titles_only': False, + "collapse_navigation": True, + "sticky_navigation": True, + "navigation_depth": 4, + "includehidden": True, + "titles_only": False, } # Add any paths that contain custom themes here, relative to this directory. -html_theme_path = [ - sphinx_rtd_theme.get_html_theme_path() -] +html_theme_path = [sphinx_rtd_theme.get_html_theme_path()] # The name for this set of Sphinx documents. If None, it defaults to # " v documentation". -#html_title = None +# html_title = None # A shorter title for the navigation bar. Default is the same as html_title. -#html_short_title = None +# html_short_title = None # The name of an image file (relative to this directory) to place at the top # of the sidebar. @@ -161,91 +166,102 @@ # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". -html_static_path = ['_static'] +html_static_path = ["_static"] # If not '', a 'Last updated on:' timestamp is inserted at every page bottom, # using the given strftime format. -html_last_updated_fmt = '%b %d, %Y' +html_last_updated_fmt = "%b %d, %Y" # If true, SmartyPants will be used to convert quotes and dashes to # typographically correct entities. -#html_use_smartypants = True +# html_use_smartypants = True # Custom sidebar templates, maps document names to template names. -#html_sidebars = {} +# html_sidebars = {} # Additional templates that should be rendered to pages, maps page names to # template names. -#html_additional_pages = {} +# html_additional_pages = {} # If false, no module index is generated. -#html_use_modindex = True +# html_use_modindex = True # If false, no index is generated. -#html_use_index = True +# html_use_index = True # If true, the index is split into individual pages for each letter. -#html_split_index = False +# html_split_index = False # If true, links to the reST sources are added to the pages. -#html_show_sourcelink = True +# html_show_sourcelink = True # If true, an OpenSearch description file will be output, and all pages will # contain a tag referring to it. The value of this option must be the # base URL from which the finished HTML is served. -#html_use_opensearch = '' +# html_use_opensearch = '' # If nonempty, this is the file name suffix for HTML files (e.g. ".xhtml"). -#html_file_suffix = '' +# html_file_suffix = '' # Output file base name for HTML help builder. -htmlhelp_basename = 'MDPOWdoc' +htmlhelp_basename = "MDPOWdoc" # -- Options for LaTeX output -------------------------------------------------- # The paper size ('letter' or 'a4'). -#latex_paper_size = 'letter' +# latex_paper_size = 'letter' # The font size ('10pt', '11pt' or '12pt'). -#latex_font_size = '10pt' +# latex_font_size = '10pt' # Grouping the document tree into LaTeX files. List of tuples # (source start file, target name, title, author, documentclass [howto/manual]). latex_documents = [ - ('index', 'MDpow.tex', u'MDpow Documentation', - u'Ian Kenney, Bogdan Iorga, and Oliver Beckstein', 'manual'), + ( + "index", + "MDpow.tex", + "MDpow Documentation", + "Ian Kenney, Bogdan Iorga, and Oliver Beckstein", + "manual", + ), ] # The name of an image file (relative to this directory) to place at the top of # the title page. -#latex_logo = None +# latex_logo = None # For "manual" documents, if this is true, then toplevel headings are parts, # not chapters. -#latex_use_parts = False +# latex_use_parts = False # Additional stuff for the LaTeX preamble. -#latex_preamble = '' +# latex_preamble = '' # Documents to append as an appendix to all manuals. -#latex_appendices = [] +# latex_appendices = [] # If false, no module index is generated. -#latex_use_modindex = True - +# latex_use_modindex = True # Options for ext.intersphinx # --------------------------- # intersphinx: reference standard lib and RecSQL # http://sphinx.pocoo.org/latest/ext/intersphinx.html -intersphinx_mapping = {'https://docs.python.org/': None, - 'https://numpy.org/doc/stable/': None, - 'https://docs.scipy.org/doc/scipy/reference/': None, - 'https://gromacswrapper.readthedocs.io/en/latest': None, - } - +intersphinx_mapping = { + "https://docs.python.org/": None, + "https://numpy.org/doc/stable/": None, + "https://docs.scipy.org/doc/scipy/reference/": None, + "https://gromacswrapper.readthedocs.io/en/latest": None, + "https://docs.mdanalysis.org/stable/": None, + "https://www.rdkit.org/docs/": None, + "https://pandas.pydata.org/docs/": None, + "https://seaborn.pydata.org": None, + "https://cairosvg.org/documentation/": None, + "https://svgutils.readthedocs.io/en/latest/": None, + "https://pypdf.readthedocs.io/en/stable/": None, +} # Options for ext.autodoc @@ -255,5 +271,4 @@ # This value selects what content will be inserted into the main body of an autoclass directive. # "class", "init", "both" autoclass_content = "both" - - +automodule_content = "both" diff --git a/doc/sphinx/source/index.txt b/doc/sphinx/source/index.txt index 24864c28..b5a3d884 100644 --- a/doc/sphinx/source/index.txt +++ b/doc/sphinx/source/index.txt @@ -7,7 +7,7 @@ .. |POW| replace:: *P*\ :sub:`OW` .. |P_ow| replace:: *P*\ :sub:`OW` .. |P_cw| replace:: *P*\ :sub:`CW` - +.. |P_tw| replace:: *P*\ :sub:`TW` MDPOW documentation @@ -19,15 +19,16 @@ MDPOW documentation **MDPOW** is a python package that automates the calculation of solvation free energies via molecular dynamics (MD) simulations. In -particular, it facilitates the computation of partition coeffcients. Currently implemented: +particular, it facilitates the computation of partition +coeffcients. Currently implemented: - *water-octanol* partition coefficient (|P_ow|) - *water-cyclohexane* partition coefficient (|P_cw|) - -.. warning:: Development is still very much in flux and the - documentation is at least partially out of date. If - something appears unclear or just wrong, then please ask - questions on the `MDPOW Issue Tracker`_. +- *water-toluene* partition coefficient (|P_tw|) + +The package is being actively developed and incorporates recent ideas +and advances. If something appears unclear or just wrong, then please +ask questions on the `MDPOW Issue Tracker`_. .. _`MDPOW Issue Tracker`: https://github.com/Becksteinlab/mdpow/issues @@ -44,6 +45,8 @@ software package [#GromacsWrapperFramework]_. MDPOW is tested with * Gromacs 2018.6 * Gromacs 2020.6 * Gromacs 2021.1 +* Gromacs 2022.4 +* Gromacs 2023.1 but versions 5.x, 2016.x, and 2019.x should also work. It should be possible to use any of these Gromacs versions without @@ -139,7 +142,8 @@ For current issues and open feature requests please look through the scripts equil fep - analysis + analysis + workflows utilities forcefields diff --git a/doc/sphinx/source/tutorials/workflows-tutorials_automated-dihedrals.ipynb b/doc/sphinx/source/tutorials/workflows-tutorials_automated-dihedrals.ipynb new file mode 100644 index 00000000..3c91c9dc --- /dev/null +++ b/doc/sphinx/source/tutorials/workflows-tutorials_automated-dihedrals.ipynb @@ -0,0 +1,2951 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "e862c743", + "metadata": {}, + "source": [ + "# MDPOW Workflows Tutorial: Automated Dihedral Analysis\n", + "##### - Cade Duckworth, January 2023, incomplete documentation" + ] + }, + { + "cell_type": "markdown", + "id": "b58f2ddb", + "metadata": {}, + "source": [ + "# Demonstration of stand-alone and top-level function use" + ] + }, + { + "cell_type": "markdown", + "id": "03173bf7", + "metadata": {}, + "source": [ + "## Imports" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "14c3d0da", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "babbd0fac2bd44dc844c6a52180685d3", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "mdpow : INFO MDPOW 0.8.0+130.g9fe35ec.dirty starting.\n", + "mdpow : INFO Copyright (c) 2010-2021 Shujie Fan, Ian Kenney, Alia Lescoulie, Bogdan Iorga, and Oliver Beckstein\n", + "mdpow : INFO Released under the GNU Public Licence, version 3.\n", + "mdpow : INFO For bug reports and help: https://github.com/Becksteinlab/MDPOW/issues\n" + ] + } + ], + "source": [ + "import os\n", + "import sys\n", + "\n", + "import pathlib\n", + "import pandas as pd\n", + "import nglview as nv\n", + "import MDAnalysis as mda\n", + "\n", + "sys.path.append('/nfs/homes3/cade/Projects/Repositories/MDPOW-ADA/')\n", + "import mdpow\n", + "sys.path.append('/nfs/homes3/cade/Projects/Repositories/MDPOW-ADA/mdpow/workflows/')\n", + "import dihedrals" + ] + }, + { + "cell_type": "markdown", + "id": "1cbadb7e", + "metadata": {}, + "source": [ + "## Basic Parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "ac09fb23", + "metadata": {}, + "outputs": [], + "source": [ + "dirname = '/nfs/homes3/cade/Projects/Repositories/MDPOW-ADA/mdpow/tests/testing_resources/states/workflows/SM25'" + ] + }, + { + "cell_type": "markdown", + "id": "d12fc376", + "metadata": {}, + "source": [ + "### Using the following directory to save plots (.pdf) and results-DataFrame (compressed .bz2 file)\n", + "##### serving as df_save_dir & figdir kwargs" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "b4a1e328", + "metadata": {}, + "outputs": [], + "source": [ + "df_save_directory = '/nfs/homes3/cade/Projects/Notebooks/tmp_data/'" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "ac4f3e99", + "metadata": {}, + "outputs": [], + "source": [ + "path = pathlib.Path(df_save_directory)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "3084cba8", + "metadata": {}, + "outputs": [], + "source": [ + "fig_save_directory = path / 'SM25_figures'" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "33f985e6", + "metadata": {}, + "outputs": [], + "source": [ + "os.mkdir(fig_save_directory)" + ] + }, + { + "cell_type": "markdown", + "id": "c27ed995", + "metadata": {}, + "source": [ + "## Naming conventions specified in top / traj" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "26e6ade5", + "metadata": {}, + "outputs": [], + "source": [ + "resname = 'UNK' # recognized name" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "73ca3a17", + "metadata": {}, + "outputs": [], + "source": [ + "molname = 'SM25' # desired name for data storage, if not recognized defaults to^" + ] + }, + { + "cell_type": "markdown", + "id": "71b06058", + "metadata": {}, + "source": [ + "## DihedralAnalysis Level Kwargs" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "5eaad9db", + "metadata": {}, + "outputs": [], + "source": [ + "solvents = ('water',)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "3ffefc66", + "metadata": {}, + "outputs": [], + "source": [ + "interactions = ('Coulomb', 'VDW')" + ] + }, + { + "cell_type": "markdown", + "id": "41f3f4b1", + "metadata": {}, + "source": [ + "## Periodic Angle Padding and Violin KDE Width" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "d3535b66", + "metadata": {}, + "outputs": [], + "source": [ + "padding = 45" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "2329af7b", + "metadata": {}, + "outputs": [], + "source": [ + "width = 0.9" + ] + }, + { + "cell_type": "markdown", + "id": "730d02c3", + "metadata": {}, + "source": [ + "## Defined/specified at the module level, shown here for clarity" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "8e46859d", + "metadata": {}, + "outputs": [], + "source": [ + "SMARTS_DEFAULT = '[!#1]~[!$(*#*)&!D1]-!@[!$(*#*)&!D1]~[!#1]'" + ] + }, + { + "cell_type": "markdown", + "id": "67c79fd9", + "metadata": {}, + "source": [ + "# Individual, stand-alone function usage" + ] + }, + { + "cell_type": "markdown", + "id": "7e15e2e0", + "metadata": {}, + "source": [ + "## Build MDAnalysis Universe" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "eb2a1c05", + "metadata": {}, + "outputs": [], + "source": [ + "u = dihedrals.build_universe(dirname)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "e2212608", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "u" + ] + }, + { + "cell_type": "markdown", + "id": "f714163c", + "metadata": {}, + "source": [ + "## Convert MDA Universe Object to RDKit Mol Object" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "a91f3b34", + "metadata": {}, + "outputs": [], + "source": [ + "mol = dihedrals.rdkit_conversion(u, resname)[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "081bb221", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mol" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "fa4eb2fa", + "metadata": {}, + "outputs": [], + "source": [ + "solute = dihedrals.rdkit_conversion(u, resname)[1]" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "906c2fb9", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "solute" + ] + }, + { + "cell_type": "markdown", + "id": "1ac24a0e", + "metadata": {}, + "source": [ + "## View Molecule (SM25)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "e0809834", + "metadata": {}, + "outputs": [], + "source": [ + "view = nv.show_mdanalysis(solute)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "8de5718d", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "746b035d0ed3418f8584115b13c7fe29", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "NGLWidget(max_frame=100)" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "view" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "06bd36e1", + "metadata": {}, + "outputs": [], + "source": [ + "view2 = nv.show_rdkit(mol)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "19eb72b4", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "bb58d01190bb4707bec3f37faa464357", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "NGLWidget()" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "view2" + ] + }, + { + "cell_type": "markdown", + "id": "90bd9992", + "metadata": {}, + "source": [ + "## Retrieving atom indices for desired dihedral atom groups\n", + "##### encompasses functions displayed above" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "134dc3d1", + "metadata": {}, + "outputs": [], + "source": [ + "atom_group_indices = dihedrals.dihedral_indices(dirname, resname, SMARTS=SMARTS_DEFAULT)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "2e6fa5d3", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "((0, 1, 2, 3),\n", + " (0, 1, 12, 13),\n", + " (1, 2, 3, 11),\n", + " (1, 2, 3, 10),\n", + " (1, 2, 3, 4),\n", + " (1, 12, 13, 14),\n", + " (2, 3, 4, 5),\n", + " (2, 3, 4, 9),\n", + " (2, 1, 12, 13),\n", + " (3, 2, 1, 12),\n", + " (5, 4, 3, 11),\n", + " (5, 4, 3, 10),\n", + " (9, 4, 3, 11),\n", + " (9, 4, 3, 10),\n", + " (12, 13, 14, 15),\n", + " (12, 13, 14, 19))" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "atom_group_indices" + ] + }, + { + "cell_type": "markdown", + "id": "1be94e17", + "metadata": {}, + "source": [ + "## Translation of atom group indices to atom names per group\n", + "##### not required for full functionality, convenient for checking results" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "c54bd76e", + "metadata": {}, + "outputs": [], + "source": [ + "dihedral_groups = dihedrals.dihedral_groups(dirname, resname, SMARTS=SMARTS_DEFAULT)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "5269b68a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[array(['O1', 'C2', 'N3', 'S4'], dtype=object),\n", + " array(['O1', 'C2', 'C13', 'C14'], dtype=object),\n", + " array(['C2', 'N3', 'S4', 'O12'], dtype=object),\n", + " array(['C2', 'N3', 'S4', 'O11'], dtype=object),\n", + " array(['C2', 'N3', 'S4', 'C5'], dtype=object),\n", + " array(['C2', 'C13', 'C14', 'C15'], dtype=object),\n", + " array(['N3', 'S4', 'C5', 'C6'], dtype=object),\n", + " array(['N3', 'S4', 'C5', 'C10'], dtype=object),\n", + " array(['N3', 'C2', 'C13', 'C14'], dtype=object),\n", + " array(['S4', 'N3', 'C2', 'C13'], dtype=object),\n", + " array(['C6', 'C5', 'S4', 'O12'], dtype=object),\n", + " array(['C6', 'C5', 'S4', 'O11'], dtype=object),\n", + " array(['C10', 'C5', 'S4', 'O12'], dtype=object),\n", + " array(['C10', 'C5', 'S4', 'O11'], dtype=object),\n", + " array(['C13', 'C14', 'C15', 'C16'], dtype=object),\n", + " array(['C13', 'C14', 'C15', 'C20'], dtype=object)]" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dihedral_groups" + ] + }, + { + "cell_type": "markdown", + "id": "9ef4f3e5", + "metadata": {}, + "source": [ + "## Obtaining DihedralAnalysis results for each identified dihedral atom group" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "6bdccb4a", + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "mdpow._ensemble: INFO Searching . directory for systems\n", + "mdpow._ensemble: INFO Searching . directory for systems\n", + "mdpow._ensemble: INFO Setting up systems\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "57a3c45ed7b04e53acd7320a0ffb70cd", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/7 [00:00\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + 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selectionsolventinteractionlambdatimedihedral
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1O1-C2-C13-C14waterCoulomb00000.055.665298
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indexselectionsolventinteractionlambdatimedihedral
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........................
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selectionsolventinteractionlambdatimedihedral
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.....................
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selectionsolventinteractionlambdatimedihedral
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.....................
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selectionsolventinteractionlambdatimedihedral
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.....................
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selectionsolventinteractionlambdatimedihedral
13C10-C5-S4-O11waterCoulomb00000.0188.229160
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25S4-N3-C2-C13waterCoulomb0000500.0182.877619
29C10-C5-S4-O11waterCoulomb0000500.0206.330222
35C2-N3-S4-O11waterCoulomb00001000.0211.566012
.....................
11273S4-N3-C2-C13waterVDW100049000.0200.308016
11277C10-C5-S4-O11waterVDW100049000.0195.636933
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11305S4-N3-C2-C13waterVDW100050000.0182.638040
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" + ], + "text/plain": [ + " selection solvent interaction lambda time dihedral\n", + "13 C10-C5-S4-O11 water Coulomb 0000 0.0 188.229160\n", + "19 C2-N3-S4-O11 water Coulomb 0000 500.0 192.601235\n", + "25 S4-N3-C2-C13 water Coulomb 0000 500.0 182.877619\n", + "29 C10-C5-S4-O11 water Coulomb 0000 500.0 206.330222\n", + "35 C2-N3-S4-O11 water Coulomb 0000 1000.0 211.566012\n", + "... ... ... ... ... ... ...\n", + "11273 S4-N3-C2-C13 water VDW 1000 49000.0 200.308016\n", + "11277 C10-C5-S4-O11 water VDW 1000 49000.0 195.636933\n", + "11289 S4-N3-C2-C13 water VDW 1000 49500.0 200.110494\n", + "11305 S4-N3-C2-C13 water VDW 1000 50000.0 182.638040\n", + "11309 C10-C5-S4-O11 water VDW 1000 50000.0 207.513849\n", + "\n", + "[1417 rows x 6 columns]" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "DF2" + ] + }, + { + "cell_type": "markdown", + "id": "4049e5ea", + "metadata": {}, + "source": [ + "### Combining df, DF1, and DF2 for the padded, augmented DataFrame (df_aug) used in violins plots" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "4cee1966", + "metadata": {}, + "outputs": [], + "source": [ + "DF_AUG = pd.concat([DF1, df, DF2]).reset_index()" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "cf59e6ba", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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indexselectionsolventinteractionlambdatimedihedral
03C2-N3-S4-O11waterCoulomb00000.0-183.546313
19S4-N3-C2-C13waterCoulomb00000.0-190.786529
226C6-C5-S4-O12waterCoulomb0000500.0-189.376818
341S4-N3-C2-C13waterCoulomb00001000.0-202.434820
444C10-C5-S4-O12waterCoulomb00001000.0-194.875231
........................
1417311273S4-N3-C2-C13waterVDW100049000.0200.308016
1417411277C10-C5-S4-O11waterVDW100049000.0195.636933
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" + ], + "text/plain": [ + " index selection solvent interaction lambda time dihedral\n", + "0 3 C2-N3-S4-O11 water Coulomb 0000 0.0 -183.546313\n", + "1 9 S4-N3-C2-C13 water Coulomb 0000 0.0 -190.786529\n", + "2 26 C6-C5-S4-O12 water Coulomb 0000 500.0 -189.376818\n", + "3 41 S4-N3-C2-C13 water Coulomb 0000 1000.0 -202.434820\n", + "4 44 C10-C5-S4-O12 water Coulomb 0000 1000.0 -194.875231\n", + "... ... ... ... ... ... ... ...\n", + "14173 11273 S4-N3-C2-C13 water VDW 1000 49000.0 200.308016\n", + "14174 11277 C10-C5-S4-O11 water VDW 1000 49000.0 195.636933\n", + "14175 11289 S4-N3-C2-C13 water VDW 1000 49500.0 200.110494\n", + "14176 11305 S4-N3-C2-C13 water VDW 1000 50000.0 182.638040\n", + "14177 11309 C10-C5-S4-O11 water VDW 1000 50000.0 207.513849\n", + "\n", + "[14178 rows x 7 columns]" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "DF_AUG" + ] + }, + { + "cell_type": "markdown", + "id": "ab5aba48", + "metadata": {}, + "source": [ + "## Violin plotting function runs dihedral_violins for each dihedral atom group" + ] + }, + { + "cell_type": "markdown", + "id": "98f3066b", + "metadata": {}, + "source": [ + "### unpadded angles" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "35fbd054", + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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\n", 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\n", 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BhUIB4PmPV7FYjLCwMJw/fx69evWCubk5Ll26hKlTp+K9994rcoXpn0QiEbZv344+ffqge/fu+Oabb9CsWTNkZWXhzz//xN9//42QkBAAz6+mpKamIjY2FkqlUv0DuVGjRjAxMcGRI0eQmJiIN954AyYmJoiKisLXX38NDw8PODs7v/T9mjJlCv766y/07t0b3333Hbp27ap+z5YvX46tW7eiXbt2yMrKwr1799TLxcTEIDIyElZWVmjQoAGsra2LXTHU19eHvb09mjZt+tLt169fH2vXrsXEiROhUCgwfPhwODs748mTJ/jll19gYmKiHg4yKioK+fn5SE1NRWZmpvo9eHEl0sfHB87OzmjZsiXy8/Oxe/du+Pv7lzis4j/5+vqiS5cuePPNN7F48WK0adMGhYWFCAwMxMaNG3Hr1q1S7QPNmzfHO++8g7Fjx2Lz5s0AgC+++ALvvvvuK98DTT+T//znP5g5c6b6M1ixYgVycnLUne917NgRRkZG+OabbzBp0iRcvHixxKb6/zRz5kwsWLBA3Q/C9u3bERkZiV9//bXU+V8mPz8fY8aMwdy5c/Ho0SMsWLAAEydOLLFTSSKqmniM5TG2uh9jXxgzZgzGjh0LAC/tKyczMxMJCQkoKChATEwMdu/ejZ9//hne3t5FOqF1dXVFgwYNsH79egwbNkw93dHREU5OTti0aRM+/vjjMmckokpUeQMQEVUPubm5wuzZs4UOHToI5ubmgpGRkdC0aVNh7ty5Qk5Ojnq+1w1T+M+hIF8MMVjSX0xMjCAIghAeHi507NhRMDc3FwwMDISmTZsKCxYsELKzs4us18nJSViwYEGRadHR0cLw4cMFR0dHQSqVCk5OTsKQIUOKDM84YsSIErcfFBQkCIIgnDp1SujcubN6+40bNxZmzZpVqmERc3NzBW9vb6F169aCgYGBYGVlJXh4eAg7duxQDx/4svdgxIgRL11vWYbGDAwMFDw9PQVLS0vBwMBAaNasmTBjxgwhLi6uyPpKyvDC8uXLhYYNGwoGBgaCpaWl0LVrV+Gvv/4q1fbj4uKECRMmCE5OToJUKhXq1q0rvPfee+r3tzT7gCAIQkpKijBs2DDB1NRUMDU1FYYNG1bsMyhpH/i30nwmz549EyZNmiTY2NiUONyyIDwfXrlRo0aCgYGB8O677wpbtmwp9XDL+vr6Lx1uOSIiQj2tpCE4/z1k6YvhO+fPny9YW1sLJiYmwueffy7k5ua+8n0goqqFx1geY2vCMVYQBCEnJ0cwNzcXzM3Ni+y7Jb0fUqlUaNCggfDJJ58Ip06dKnF9L/ahvXv3Fpk+ZswYAYCwa9eu12YiIt0RCcK/bqAnoirr2bNnsLKywpEjR3ifbS3FfYCIqGLw+5W4DxCRpth+mqgaCQkJwVtvvcWDfS3GfYCIqGLw+5W4DxCRpthipQzy8/MxZ84ciEQiLF26FFKpVNeRiIiIiIiIiEiHqnWLldDQUAwYMACOjo4QiUQ4cOBAkecLCwsxd+5cuLi4wNDQEK6urli8eHGxnt19fX3h4uICAwMDuLm54fTp0yVuz8/PDx06dICHhwd27dpVUS+LiIiIiIiIiKqJal1Yyc7ORtu2bfHjjz+W+Pzy5cuxadMm/Pjjj7h16xZWrFiBlStXYv369ep5/Pz8MGXKFHz77beIiIhAt27d0K9fP8TGxhZbn0qlglgshlKpLHHYPSIiIiIiIiKqXWrMrUAikQgBAQFFxoR/9913YWdnh61bt6qnffjhhzAyMlK3OOnYsSM6dOiAjRs3qudp3rw5Bg0aBG9v7yLbyMvLw6xZsyASibBs2TLIZLKKfVFEREREREREVKVJdB2gInXt2hWbNm3CnTt30KRJE1y9ehVnzpyBj48PgOd9poSHh2P27NlFluvbty/OnTtXbH0ymUy97Kvk5eUhLy9P/VgQBOTn58PGxgYikahcr4mIiIiIiIiIqo5yFVauXLmC06dPQyqVwsPDA23atNFWLq2YNWsWMjIy0KxZM/UtPEuWLMGQIUMAAMnJyVAqlbCzsyuynJ2dHRISEjTerre3NxYtWlRsekZGBszMzDReLxERERERERFVLRoXVnx8fDBt2jRYWFhAIpEgOTkZLVu2xI4dO+Dm5qbNjBrz8/PD7t27sWfPHrRs2RKRkZGYMmUKHB0dMWLECPV8/25FIghCuVqWzJkzB9OmTVM/VigUqF+/vsbrIyIiIiIiIqKqqUyd127btg1XrlxBXl4eli5dimXLliElJQVyuRyPHj3CwIED0bNnT5w5c6ai8pbJzJkzMXv2bAwePBitW7eGl5cXpk6dqu47xcbGBmKxuFjrFLlcXqwVS1nIZDKYmZkV+SMiIiIiIiKimqdMhZWVK1eiY8eOMDExQUpKCi5duoS1a9ciODgYpqam+P7777FixQrMmDGjovKWSU5ODvT0ir5EsVisHtFHKpXCzc0NgYGBReYJDAxEly5dKi0nEREREREREVVPZboV6NatW8jNzcX169fRrVs36Onp4ffff8e8efOQm5uLevXqoWHDhoiIiMCff/6JVq1awcXFpaKyIysrC/fu3VM/jomJQWRkJKysrNCgQQMMGDAAS5YsQYMGDdCyZUtERERgzZo1GD16tHqZadOmwcvLC+7u7ujcuTO2bNmC2NhYjBs3rsJyExEREREREVHNoPFwy71790bv3r3xzTffQKVS4datW7h69SpCQ0OxZcsWWFpaIi0tDSYmJlAoFNrODQAIDg5Gr169ik0fMWIEduzYgczMTMybNw8BAQGQy+VwdHTEkCFDMH/+fEilUvX8vr6+WLFiBeLj49GqVSusXbsW3bt311pOhUIBc3Nzdl5LREREREREVMNoXFiJjIxEz5498dFHH2HcuHFo27YtcnNzsWjRIuzduxdPnjzBkydPcOPGDbzzzjvazl2tsLBCREREREREVDNpPCpQu3btEB4ejnHjxqFTp054UZ+RSCTYtm0bAKBevXqoV6+edpISEREREREREVUxGrdY+afExEScP38e+fn56NSpE4cW/he2WCEiIiIiIiKqmbRSWKGSbdiwARs2bIBSqcSdO3dYWCEiIiIiIiKqYVhYqQRssUJUNomJifj7VBDOnr+MgsJC7a1YEPD15PFo2rSp9tZJRERERES1msZ9rBARaYtKpcLt27dx/GQILkdeQ6ZKH3KJHQrMWgBS7X1NSVPuIT4+noUVIiIiIiLSGhZWiEgnUlJSEHbhIgKDzyBOnoxMsRlSpHZQWXYERHq6jkdERERERFQqLKyUQX5+PubMmQORSISlS5dCKpXqOhJRtZGTk4PIyKs4GXoWt+/eR5ZKgmSxFfJMGkCowxYkpD03b97E7j8CkJubV2HbaN+mFYYP/bTC1k9ERERE1Ue5CiunT5/G5s2bcf/+fezbtw9169bFrl274OLigq5du2or40uFhoZi5cqVCA8PR3x8PAICAjBo0CD1897e3ti/fz9u374NQ0NDdOnSBcuXLy92G4Cvry9WrlyJ+Ph4tGzZEj4+PujWrVux7fn5+aFDhw4wMjLCrl27MGbMmIp+iUTVVl5eHm7cuIHgsxdw7WYUFHlKpIstkWVoB5VlJ0Ak0nVEqkEEQcDFS5exZeeviMvRQ5KJKwSJrMK2l3H6HAsrRERaUlhYiNu3b6OgoKBCtyMSidCqVStIJLy2TETapfG3ir+/P7y8vDBs2DBEREQgL+/5lcHMzEwsXboUR44c0VrIl8nOzkbbtm0xatQofPjhh8WeDwkJwYQJE/DGG2+gsLAQ3377Lfr27YuoqCgYGxsDeF4smTJlCnx9feHh4YHNmzejX79+iIqKQoMGDYqsT6VSQV9fH0qlEiqVqsJfX3VRWFhY4e+Hnp4eD4JVXH5+PqKiohAadhER124gIycfGRJLKKQ2UJm6A2a8vYe0T6VSIeT0GWzf7YcEpTFSzVpAsDGo+A2zLkhEVG5JSUnY6x+A0LBLyJBYQSmq2HM9meIJNq5cDCcnpwrdTm1w7949BJ0+i8JCZYVvy9zMFEM//bjCt0NUHhqPCtS+fXtMnToVw4cPh6mpKa5evQpXV1dERkbinXfeQUJCgrazvpJIJCrWYuXfkpKSYGtri5CQEHTv3h0A0LFjR3To0AEbN25Uz9e8eXMMGjQI3t7eRZbPy8vDrFmzIBKJsGzZMshkpbsaWlNHBSooKMDO3b/hyMmQCr0yDADISUPA779V7DaoTAoLCxEdHY3QcxdwKeIq0rPzkCmxQLq+NVTGNoCeWNcRi5Gm3IP38LfQs2dPXUehclIqlQg8eQq79vojQc8S6WYNAXHl3Z7ZNjsc23zXVdr2iEg34uLi8MOmn3Hn4ROI9P53gSAnPRmzp05Cjx49dJiuelKpVLhw8RJ2/74fT1OzEG9QH4UmDpXSktU+7Rp8501iYUVDGRkZ+PPIMRw7GYw0lSGS9O0gVML5Xr2MmwjYs73Ct0NUHhqXhaOjo9XFiX8yMzNDenp6eTJVmIyMDACAlZUVgOdX2MPDwzF79uwi8/Xt2xfnzp0rtrxMJoOPj89rt5OXl6duwQM8L6zUNBcvX8baDVvwRL8ecuy7V/jB0CUprELXT6+nUqlw//59nD53AecvX0FKZg6yxOZI07eG0rgdYFz1CilU86hUKvx94iR2/vYHEvXqIMPiTUDM1mxEpD2FhYW4ePES9vgfRGxKFuKNG0FlV/QWd4k4BjnPnukoYfWUnp6O3/cfxMmQM0iRWCPd1AVCHSNdx6LXKCwsRNj5C/j9wJ94kqxAgtQR+eZvVuoFNEnu/UrbFpGmND4bdXBwwL179+Ds7Fxk+pkzZ+Dq6lreXFonCAKmTZuGrl27olWrVgCA5ORkKJVK2NnZFZnXzs6uXC1uvL29sWjRonLlraoyMzOxdOVaRD5Og9zyzUq9QkyVLzk5GWfDwnDqdBji5CnIFpsiWWwNpUkroA5/zFLlEQQBZ8PCsHHrL4iHFTKsOgF63AeJSDsyMzNx8dIlHDsVioeP45Cqb40MY2f+8C8nQRBw/foN7Njjh5j4FMTL6qGgjgdH/6sGYmNj8Zv/AVyOvI5USR1kmLpAqGOo61hEVZbGZ6VffvklJk+ejG3btkEkEiEuLg5hYWGYMWMG5s+fr82MWjFx4kRcu3YNZ86cKfac6F+tLQRBKDatLObMmYNp06apHysUCtSvX1/j9VUVFy5ewqofNyPWuBkKbZx1HYcqgEqlQnR0NP4KPIXIazeRWShGksQG+SZOEOo003U8qqUePHgA7zXr8ShHHykWbwBifV1HIiq3U0HByFA8b0nbvFkzNGvK0dEqU0pKCq5evYYzFy7jzv0YZBWKkCqxQo6RAwTbqneBsLrJy8vD0eOB2HfwLyQLxkg2cYFg20TXseg18vLycOLkKew7dARJeXpINGgAVZ2uHHCAqBQ0Lqx8/fXXyMjIQK9evZCbm4vu3btDJpNhxowZmDhxojYzltukSZNw6NAhhIaGol69eurpNjY2EIvFxVqnyOXyYq1YykImk5W6/5XqQKVS4cfNP+PY+etItOnMHzU1jEqlwo0bN7Dv0FHcvnsfCok5UqT2UFl05IGUdCovLw/rfDfjdGQ04s1bQrA20XUkIq1IT0/Hmi2/QG7sCr3CZ3i74S18N2/26xckjRQWFuLevXu4FBGJi+FXkZSahhxBijQ9C+Qa2vB4p0UZGRnYvfd3BJ29ALm0LrIt3mDrwmogISEBO/f44VLkDcilDsg2aweY8nyfqCzK9U23ZMkSfPvtt4iKioJKpUKLFi1gYlJ1TnwFQcCkSZMQEBCA4OBguLi4FHleKpXCzc0NgYGBeP/999XTAwMDMXDgwMqOWyUVFhbimwXf4ZJcgML2DV3HIS1KSkqCn/8BhIZdRIbEEqmGdaGyrfhh0olK4+7de1iwdCUeyZyQa9tR13GItCo6OhrZRg5QWjaAKi8LQM3ri02XcnNzcePGDYSGXcT1qNtQ5OQjW98cqSIzKE1cAeuac/GrqkhLS8PmbTtx4eotxBs6o8C2G4tV1cCtW7ewcdsveJSUiThD5+fngfzciDRS7hKykZER3N3dtZGlzLKysnDv3j3145iYGERGRsLKygoNGjTAhAkTsGfPHhw8eBCmpqbqlinm5uYwNHx+j+C0adPg5eUFd3d3dO7cGVu2bEFsbCzGjRunk9dUlahUKsyevxjnU2R4ZlH9b2Wi5548eYL1m7fidmwiEgwboJBNPKmKOXEqGD9s24M4GzcI+ryfm2qem7fvIltiqusYNcaLztVPhpzBhfAIZOQUIENiAYXUBiqjDoAJ+/OoKDk5Odi8dQdCL0XiqXEjKO08dB2JSuHmzSis2/QznmQJkJs2hlCn6lwYJ6quylRY+We/Ia+zZs2aMocpq8uXL6NXr17qxy/yjRgxAjt27FAPofzvoVW3b9+OkSNHAgA+/fRTpKSkYPHixYiPj0erVq1w5MgRDsMGYOvO3QhPElhUqSFUKhU2/rQNf5+9jDiz5lDZurx+IaJK9veJU1j3yz4k2HUB9PhjiGqmR0+eQiXlD5nyyMzMxPkLF3E8KBSxTxOQKTZFiqQOlCbtAROOUlfRBEHA8cCT+Hn3Xjw1bIh8O7Z4rQ6SkpLgvXodbidmQW7WDIINO2cm0pYyFVYiIiJKNV95On4ti549e0IQhJc+/6rn/mn8+PEYP368tmLVCKmpqfjr1BkoeOWhxpj//TKceZyLTNvObKFCVdLTp0+xYcdvSLDzYFGFarS09HQI+ta6jlGtPHv2DDdu3EDw2fO4dvMWMgtESBFb45mJA4Q6DXUdr1bJycnBvO+8cV2eh5Q6HpU67C5p7s+/jmLbb/vxxLwVVDbsSJhI28pUWAkKCqqoHFTFHD76N+INGvAHeA3x6NEjRMYkItPGTddRiF5qre8WPLVsw6IK1Xi5uXmAjB16vkpKSgquXruOsxcu486DGGTlC8gQWyBTZgOV+ZscrldH0tLSMGnGHNyTNUSBleYDPVDlEQQBa9ZvxN+R95Fsz6GuiSoKj+pUott370NpxKtpNUopW3AR6Urs00ReeaZaQU9P7/l3Mq9dqMXHx+PCpcsIDbuE+EQ5cgQpUvXMn4/aY/YmL/RUAYWFhZg6ex6iDZtDZWip6zhUSnv/8MexyEdItW6r6yhENZrGhZWX9bciEolgYGCARo0aYeDAgbCystI4XHW3YcMGbNiwAUqlUtdRyszU1BiipDwIUmNdRyEtcHJygnuTugiJuQuFeSOeoFKVxNIf1RYymQwoKKz1t1Dcv38ffgGHcO3mbWTDAMliKxSYOEKwbqTraFQC/wOH8EBlzaJKNVJQUIB9fx5Dql03XUchqvE0LqxERETgypUrUCqVaNq0KQRBwN27dyEWi9GsWTP4+vpi+vTpOHPmDFq0aKHNzNXGhAkTMGHCBCgUCpibm+s6Tpm83aMrTt38HalGtbcwVtMsmPM1ftr+C44EncNTs2ZQsUUSVTF1LM0QU5ADQZ+d6VHNZmFhBlF8HgRJ7Rz2NyUlBfO+X4aH6QVIMmwAlXUXFvyrgZOhZ5Bt1krXMagM4uPjkSc15/8vokqg8U12AwcORJ8+fRAXF4fw8HBcuXIFT58+xdtvv40hQ4bg6dOn6N69O6ZOnarNvFRJ3NzcUE/6DKK8LF1HIS0RiUT4YvQIbFq5CP3sc9Eg8RykGbGAoNJ1NCIAwKQvRsM+9RpvW6Maz962DvQKnuk6hs58t3w1LhXUQ6JNB6iMbfijr5oQ64n5/VzN2NjYQL+A5/JElUHjwsrKlSvx3XffwczMTD3NzMwMCxcuxIoVK2BkZIT58+cjPDxcK0GpcolEInw3dxbqp14BlAW6jkNaZG9vj8Xz5mD7D8vwRTdXNE09D/uUSOhlJvKEiXSqefNm+KhvV1inXOe+SDWac7260CvI1nUMnWnerAkssh8DqkJdR6EyeNezD8wzHug6BpWBkZERPNxaw1jxUNdRiGo8jQsrGRkZkMvlxaYnJSVBoVAAACwsLJCfn695uiomPz8f06dPx4wZM2rU63qZevXqYcHXX6GePAwozNN1HNIyCwsLjPIail+3bsT6uRMxtKUJmqSGwTHlCiTpsYCSJ7xU+UZ+NhRD3+oAu6TL/NFFNVa9uo4wFdXe4+oXo0Zg5rB+aKG4DIeUCIjTH/MiTjXQz/NtNDPNg35Woq6jUBlMmTAObha5MMmI0XUUohqtXLcCjR49GgEBAXjy5AmePn2KgIAAjBkzBoMGDQIAXLx4EU2a6H6c9KdPn+Kzzz6DtbU1jIyM0K5duyItaXx9feHi4gIDAwO4ubnh9OnTJa7Hz88PHTp0gIeHB3bt2lVZ8XWqXZs2WDZ3BlxSLkDvWbqu41AFEIlEcHV1xdRJ/8Fv2zZh03czMbFXI7TPuwYn+TmYp96GXk4qWxBQpRnpNRTffDEEzvJz0MtJ0XUcIq2rV68ejJS1t8WKSCRC3z69sesnX/gunIoJPV3whvIWGiadg2PKFRil3oVelpzFlipGT08Pq5YuRjv9eLaAqEYkEglWLf0OA1vVgb38Ii+WElUQjTuv3bx5M6ZOnYrBgwejsPD5VUWJRIIRI0Zg7dq1AIBmzZrh559/1k5SDaWlpcHDwwO9evXC0aNHYWtri/v378PCwgLA82LJlClT4OvrCw8PD2zevBn9+vVDVFQUGjRoUGRdKpUK+vr6UCqVUKlqT78UzZs1w+a1yzF7/ne4k26OLHNX3g9dg9WtWxdeQwfDa+hg5OXlITLyKk6EnMGtOxeQVShCqsQKOYa2EGRm3A+ownT16IIWzZvhu+VrcCvpCZIsWgBifV3HItIKCwsLSIWa3/K1NBo0aIARw4ZixLChEAQBiYmJuHv3Lq5cj0L03WhkpGXjWYEKeRJjZMAIefqmUBlaALW0419dMzIywo9rVmDNel+ERF5CglUbfhbVgFgsxrSvxqPP9etYuc4XT2ANhXnDWj8yGZE2iQShfJehs7Ky8ODBAwiCgIYNG8LExERb2bRi9uzZOHv27EtboXTs2BEdOnTAxo0b1dOaN2+OQYMGwdvbu8i8eXl5mDVrFkQiEZYtW/Z8uMRSeDEqUEZGRpE+aaobpVKJrTt343BQGOKs2lbqUMwuSWHYt2Pj62ekCpWRkYHwKxEIPnse92IeIVspRprEstoUWqQp9+A9/C307NlT11GoDM6Fncf6LdsQJ3FAlrkLINK4saXWtM0OxzbfdbqOQdXYiC8n4YapO0T52ehXR4Hv5s3WdaQqq7CwEE+fPsXDh49wM/oe7t5/gJT0DOTkFSBfT4ZskTEy9YyhMrR8fm5SCcciSWoMFn7cEf3eeafCt1VVXb9xAyt8NlSrH+n2adfgO28SnJycdB1FZ5RKJQ4fPYY9fxxAor4dMs1dAT2Nr7VXCtfkMPyxnb8DqGor9/8iExMTtGnTRhtZKsShQ4fg6emJjz/+GCEhIahbty7Gjx+PsWPHIj8/H+Hh4Zg9u+jJTN++fXHu3Lli65LJZPDx8XntNvPy8pCX979mdi/6nKnuxGIxvhg9Am/36o7Fy9fgYbY5FBaNqsSPHKoc5ubmeKtXT7zVqyeA5y3CIiKvIuTcBdyLuYXsQhFSxZZ4ZlgHKgOLKl9ooeqhS+dOePMNd/yx/wD8Dx9DvIETcs0acP+iaq1+XQfcTM4CwP34dSQSCZycnODk5IQePbqrpwuCgJSUFDx69Ai37txDVPQ9PI2/hezcfOSLDZEuMsEzqQVURlZV/odjddS6VSvs2Pwj/vzrKPb4H0SC1BHZZi6AHs8LqzKxWIyB7/ZH/3c8cTzwBPb8cQBJMEOqmSsEfSNdxyOqtsp1lDl58iROnjwJuVxe7NaYbdu2lSuYtjx48AAbN27EtGnT8M033+DixYv46quvIJPJ0KdPHyiVStjZ2RVZxs7ODgkJCRpv09vbG4sWLSpv9CrLxcUFW33Xwf/AIewNOIzHJk2hNLF7/YJU41haWhYptCgUCly9ehXB5y4i+u4FZBUC6WJLZBvUgcrQkj+ESWMSiQRDPvkI77/3Lnb/9juOngpFgiELLFR9dWjTEoGHr0JpZKPrKNWWSCSCjY0NbGxs4Obmpp4uCAKSkpJw585dXLl+Ezdv3URaZjaeiQyQIjJHvrEdBFnVamFdXYnFYgx671307+eJA38exr6DR5AksUWGuStv36ziJBIJ+vd7B//3jifCw8Ox7dff8TQpB/Gy+lCa2vPYSlRGGhdWFi1ahMWLF8Pd3R0ODg4QVdH/fCqVCu7u7li6dCkAoH379rh58yY2btyIPn36AECx7IIglOv1zJkzB9OmTVM/VigUqF+/vsbrq4r09PTw8QeD0Ld3L6z5cSPC71xEgkXLSr09iKoeMzMzdOvWDd26dQPw/FbBa9euI+TcBUTduYisfAFpYktkG9qyRQtpxMDAAJ+PGo6hn36E3373x9FTIUiQ1cczcye2nqNqpXXLFrD68wySwMKKtolEItja2sLW1hZdu3oAeH5ul5CQgIjIqwgNu4RHT64hS5AhWWKDfFNHFgHKSV9fHx9/8D4+GPgeTgWHYPfv+yEvkCHJ2AWCQfW9Db42EIlEcHd3h7u7O+RyOfb87o+zl84gVWKDDFNnCPqGuo5IVC1oXFjZtGkTduzYAS8vL23m0ToHBwe0aNGiyLTmzZvD398fNjY2EIvFxVqnyOXyYq1YykImk5W6/5XqztzcHIu+nY179+5h2dr1eJRlgFSLpmxySwCe3yrYpUtndOnSGcDzQsvVa9dw6nQYbt+9gOxCPSSLrZBr4sCiHJWJkZERxoz0wmdDPsG+gIM4eDQQifr/7YOlGtznT+Ti4gKjwkxdx6g1RCIRHBwc4ODggP/r97xflPj4eJw+G4YToWeQlPIMKfp1kG1aD5AY6Dht9SUWi/F277fwdu+3cOvWLWzdtRf3n95EvEF9FJg6sgBexdna2mLKxP9gYmEhws5fgF/AITxJykSi1BH5pvV4mxfRK2j86zc/Px9dunTRZpYK4eHhgejo6CLT7ty5AycnJ0ilUri5uSEwMBDvv/+++vnAwEAMHDiwsqNWa40aNcJPP/rgZFAwftq5B09kDdhEn4oxMTGBR5cu8Pjvd0d6ejouXLqEEyFn8ehJPLLFJkgS2zxvgsofx1QKMpkMwwZ/gk8+fB+Hjx6H3/5DkEvqIMOMzdCpapNIJDAxkADgUPa64uDggE8++gCffPQBcnJyEHL6DP48Foj4pCwk6jsgz6weLxSVQ/PmzbFq6SKkp6fj9/0HcTLkDFIk1kg3dWEriCpOIpGgW1cPdOvqgbS0NBw8fBSBQaFIhTGSDes/77eIiIrQ+Gjx+eefY8+ePZg3b54282jd1KlT0aVLFyxduhSffPIJLl68iC1btmDLli0AgGnTpsHLywvu7u7o3LkztmzZgtjYWIwbN07HyasfkUiEPm/1QjePLti6czf+PnMOT81aPO9bg6gEFhYW8Hz7bXi+/TYEQUBMTAxOhZzG2YvhSHtWiGRJHTwzrcurh/Ra+vr6eP+9d/Fe/344GRSMX/buQ6JgjlTzRoBYqut4RCVq5OqCW/fSAbCQrGtGRkbo59kX/Tz7QqFQ4Mjxv/HX36eQmY8Sr9LnpskhkXhUftBqyMLCAl+MHoHPR3rhwsVL2OXnj7ikbMQb1EehiQMvwlVxlpaWGOk1FCM+G4I7d+7gN/+DuHH7NJKldsg0deJw20T/pXFhJTc3F1u2bMGJEyfQpk0b6OsXvTK4Zs2acofThjfeeAMBAQGYM2cOFi9eDBcXF/j4+GDYsGEAgE8//RQpKSlYvHgx4uPj0apVKxw5cqRWD8NWXjKZDOO/GIOPBg3AkpVrcTs5FsmWLQExr/rQy4lEIri6usLV1RWfjxoBhUKB02fO4ciJU4iXK5Csb8sm2vRaYrEYffv0xtu938Lps2fx887fEK8yQap5YxZYqMrp2KEtjl8/AsBW11HoH8zMzDD4448w+OOPkJOTA0EouVWRoSFbXZSFnp4eOnfqiM6dOiIpKQl7/QNwOuwMkiV1kGHmwh/oVZxIJELTpk2x8JuvkZ+fj5DQM/jj0GEkpuUjQVYXhaaOLJJRrSYSXna0eI1evXq9fKUiEU6dOqVxqJpGoVDA3NwcGRkZMDOrfR14nT5zDut/2o5Yw4bP76/VgEtSGPbt4Pj1tVVOTg5Cz5zFwSN/Iz49G4n6Dsg3q1/me32lKffgPfwt9OzZs2KCUpUjCALOnD2HLTt+RZxgjnSLxlpp2t82OxzbfNdpISHVZk+ePMHYmQvh3qIRvps3W9dxiCpdYWEhzp49h91/BCAhW4kEQ2eojCuuQ2f7tGvwnTeJF1C1SC6X44+Agwg5dxGpFXSrl2tyGP7Yzt8BVLVpfHYZFBSkzRxUg3Xr2gVuHdphpc+PuHAnHIlWbdj3AZWJkZER3un7Nt7p+zbS09Nx6K+jOHoiGCl65kg1dYWgb6TriFRFiUQidOvqga4eXRB48iS27vZDvLQuss1ceGWNdM7R0RF6+Vm6jkGkMxKJBD16dEePHt3x+PFjbN+9F5G3ziBBVh955vXZ2W01YGtriwlfjsW4z0fjwsVL2P37fjxNykGcoTNUxnV4rK3hHj58CBcXF0RERKBdu3a6jqNT5b5sFxUVhdjYWOTn56uniUQiDBgwoLyrphrEyMgIC775GmEXLmD1jz/hkXlr9r1CGrGwsMDwYUPgNXQwwq9cwdZdv+FxUgESzJpAkJnqOh5VUSKRCH379EGvHj3w694/cPDvIDwxbQGVsbWuo1EtpqenB6mYPxyJAKB+/fqYP2cmsrOzsS/gIP4KDIJc3x6Z5i7sRLgaEIvF6NK5E7p07oSEhATs3OOHi5FnkSirh2fmDVgkI63q2bMn2rVrBx8fH11HUdP4W+rBgwd4//33cf36dYhEIvX9p6L/ViWVSqV2ElZjGzZswIYNG/he/EPnjh2xpXFjfD13EW5l2iLXtL6uI1E1JRKJ4O7mBnc3N9y7dw8+G3/C/eR8yC1a8D5teil9fX2M9BqK9/q/g6Ur1+J64hMkW7biKFSkMybGRsjNzdF1DKIqw9jYGCM+G4phgz/B0eOB2LPvABLENlCYN2SBpZqwt7fHrGmTkZOTg/0H/8ShYycg13f4b5GMx1uqmTQuHU6ePBkuLi5ITEyEkZERbt68idDQULi7uyM4OFiLEauvCRMmICoqCpcuXdJ1lCrFysoKG9etQrc6hTDLuK/rOFQDNGrUCD+uXo4lXw1Hs4zLMFDE6joSVXFWVlZY5f0dZg1/D07ysxDlZug6EtVS9vb2SEtJ0XUMoipHIpFgQP9+2P2zL2Z+1B2NU87DOOMBoFn3kKQDRkZG+GzIp9izdSOmvPcmGqWEwTj9PiCodB2N/mXfvn1o3bo1DA0NYW1tjT59+iA7OxsqlQqLFy9GvXr1IJPJ0K5dOxw7dqzEdahUKtSrVw+bNm0qMv3KlSsQiUR48OABACAjIwNffPEFbG1tYWZmhrfeegtXr15Vz79w4UK0a9cOu3btgrOzM8zNzTF48GBkZmYCAEaOHImQkBCsW7cOIpEIIpEIDx8+rJg3pgw0LqyEhYVh8eLFqFOnDvT09KCnp4euXbvC29sbX331lTYzUg2kr6+PpYvmoVt9GYwVMbqOQzVEh/btsX3TegxqZg5beTigYmsxerW3evbA5jXeaJUfDVlmnK7jUC3kYG8LRXqarmMQVVlisRj/944ndv20AaO7NUGDxDPQy+H/mepEIpFg0Hvv4teffTHBsy1c5Gegz2NulREfH48hQ4Zg9OjRuHXrFoKDg/HBBx9AEASsW7cOq1evxqpVq3Dt2jV4enrivffew927d4utR09PD4MHD8avv/5aZPqePXvQuXNnuLq6QhAE9O/fHwkJCThy5AjCw8PRoUMH9O7dG6mpqepl7t+/jwMHDuDw4cM4fPgwQkJCsGzZMgDAunXr0LlzZ4wdOxbx8fGIj49H/fq6vwtC48KKUqmEiYkJAMDGxgZxcc//czg5OSE6Olo76ahG09PTw4JvZqG1URb0snm1jrRDKpVi5tRJmD5iEOrKzwOqQl1HoiquTp062PzDarxhkg6DzMe6jkO1TB1rK2RnZeo6BlGV9+JWzp99lqGjQRws0m6z9Uo1I5FI8OlHH+CXTT9gUGMDOMovQFTwTNexar34+HgUFhbigw8+gLOzM1q3bo3x48fDxMQEq1atwqxZszB48GA0bdoUy5cvf2XfJsOGDcPZs2fx6NEjAM9bsezduxefffYZgOcD4Fy/fh1//PEH3N3d0bhxY6xatQoWFhbYt2+fej0qlQo7duxAq1at0K1bN3h5eeHkyZMAAHNzc0ilUhgZGcHe3h729vYQi3V/i5nGhZVWrVrh2rVrAICOHTtixYoVOHv2LBYvXgxXV1etBaxK8vPzMX36dMyYMaNIZ72kObFYjCULvkWDzCgeHEmr+rzVC99MHAPHpEvct+i1ZDIZVi/7Hm1kqRBnJ+k6DtUipiYmyH3GPlaISsva2hrrVy3D6D7t4CC/wAso1ZCRkRFmT5+CNXOnoFlWBAwUT3QdqVZr27YtevfujdatW+Pjjz/GTz/9hLS0NCgUCsTFxcHDw6PI/B4eHrh161aJ62rfvj2aNWuG3377DQAQEhICuVyOTz75BAAQHh6OrKwsWFtbw8TERP0XExOD+/f/10WEs7MzTE3/NyiFg4MD5HK5tl+6VmlcWJk7dy5Uquf3x33//fd49OgRunXrhiNHjuCHH37QWsDK4uvrCxcXFxgYGMDNzQ2nT58uNo+fnx86dOgADw8P7Nq1SwcpayYLCwv06d4JEsVTXUehGqZTxzcx7L0+sEhnKzp6PYlEghXfL3xe6OVtZFRJZDIZO7knKiORSIRhgz/BnHHDUVd+AVCxz47qqHHjxtjm+wPecdGHTfI19r2iI2KxGIGBgTh69ChatGiB9evXo2nTpoiJed5dg+hfQ2YLglBs2j8NGzYMe/bsAfD8NiBPT0/Y2NgAeN4SxcHBAZGRkUX+oqOjMXPmTPU69PX1i6xTJBKpaw9VlcaFFU9PT3zwwQcAAFdXV0RFRSE5ORlyuRxvvfWW1gJWBj8/P0yZMgXffvstIiIi0K1bN/Tr1w+xsUU7wFSpVBCLxVAqlVX+g61u+nu+jTrKZF3HoBro0w8/gJN+FkQFvCJMr2dqaorBH7wHI8UjXUehWqIqNF8mqq48unTCf7w+gk3aDV1HIQ1JpVLMmz0TXwzsDgf5RV7Y0BGRSAQPDw8sWrQIERERkEqlOHnyJBwdHXHmzJki8547dw7Nmzd/6bqGDh2K69evIzw8HPv27cOwYcPUz3Xo0AEJCQmQSCRo1KhRkb8XxZfSkEqlVe6ihFYHFLeysnpl9aqqWrNmDcaMGYPPP/8czZs3h4+PD+rXr4+NGzcWmW/w4ME4f/48zp49i+HDh+sobc1kZ2cHfWWurmNQDSQSiTBh7EhYKTgCFZVOv759YF3A24GIiKqDfn3fhqspIMrnBZTq7MNB72HqqE/gkHSJLVcq2YULF7B06VJcvnwZsbGx2L9/P5KSktC8eXPMnDkTy5cvh5+fH6KjozF79mxERkZi8uTJL12fi4sLunTpgjFjxqCwsBADBw5UP9enTx907twZgwYNwvHjx/Hw4UOcO3cOc+fOxeXLl0ud2dnZGRcuXMDDhw+RnJxcJRo91PrB4PPz8xEeHo7Zs2cXmd63b1+cO3euyDSZTPbSjnr+KS8vD3l5eerHCoVCK1lrsqSkJBTqyXQdg2qoDu3bw1K1CZl6hrqOQtWAsbEx9KH7AzTVDgUFBbqOQFTtfTzoXdzYfRI5Vo11HYXKoXevnsjMysLGgCAkW7fVdZxaw8zMDKGhofDx8YFCoYCTkxNWr16Nfv36wdPTEwqFAtOnT4dcLkeLFi1w6NAhNG786v9rw4YNw4QJEzB8+HAYGv7v/FskEuHIkSP49ttvMXr0aCQlJcHe3h7du3eHnZ1dqTPPmDEDI0aMQIsWLfDs2TPExMTA2dlZ07dAK2p9YSU5ORlKpbLYB2lnZ4eEhASN1unt7Y1FixZpI16tcfjo30iS1NF1DKqhRCIR6jrY4/FTtoqi18vKykKBdht0Er1UTk4OpDJeWCAqDxtrKxiICsE2K9XfoAHvIvLaDRx7HIcCU0ddx6kVmjdvjmPHjpX4nJ6eHubPn4/58+eX+LyzszOEEgaJGD9+PMaPH1/iMqampvjhhx9e2i/rwoULsXDhwiLTpkyZgilTpqgfN2nSBGFhYSUurys8c/yvsnbK8ypz5sxBRkaG+u/xYw7f+SpyuRzHQ8NQYFZX11GoBmvc0BmiXLYeo9c79NcRJOvb6joG1RJxCXKYmVvoOgZRtXb1ZhQUImNdxyAt+XrqV6ifc4+dElO1UusLKzY2NhCLxcVap8jl8jI1R/onmUwGMzOzIn9UsqysLMz4diEeW7QBqmH/PFR92FhaQFSY9/oZqVaTy+XYd/hvPDN30nUUqiXuxTxEHTsHXccgqrZyc3Px57GTKOQFuhrDyMgIHw54B4bsSJ6qkTLdCjRt2rRSz7tmzZoyh9EFqVQKNzc3BAYG4v3331dPDwwMLNLRDmmfXC7H9G8WIFraCIKBua7jUA0nCGDxjl4pIyMD0+bMR6xFW0BU6687UCV5/PgxWrVuo+sYRNWSSqXCvMVL8dioEaDHEbZqkoHv9scfhwPxEC66jkJUKmUqrERERJRqvuo2MtC0adPg5eUFd3d3dO7cGVu2bEFsbCzGjRun62g1VlBIKNb//AtiLdpBMGCLHqp4SSmpEMTsx4BK9uTJE3w97zvcNWrGQi9VGrlcjnyRFCIW8ojKrKCgAHMXL8WlZD3km7PVV01jaGgIOytzPCzM13UUolIpU2ElKCioonLo1KeffoqUlBQsXrwY8fHxaNWqFY4cOQInJzYF1za5XI6lq9fhdlIukmy7AHq1vv9kqiT3H8ZCMDDVdQyqYgRBwIE//8LOPw7iiVUHCFLeo0+VZ+svvyLVhFdjicoqPj4ecxYuwV2RI3LN6+k6DlWQDm1a4fL5eF3HICoV/qr9r1f1XEzll5GRgS3bf8HZKzfx1LQZVNZWuo5EtUyCPAkCR56if7h//z6WrV2PB7mGSLfz4O0/VKkePHiAsGt3UGjRGgA71iYqjcLCQvz2xz74HzmJJxZt2eq5hmvs6gT9M3d1HYOoVMpdWImKikJsbCzy84s203rvvffKu2qqAeRyObbt2oOLV2/hqZErCu266DoS1UIpKSnIVkmA6nWXIlWQuLg4/LDpZ9yMlSPerAUESxNdR6JaJj09Hd8s9kaclRtHvSAqpfMXLmD9lu14qmeHTLuu7DetFjA2NoYU/I6k6kHjwsqDBw/w/vvv4/r16xCJROrxq1/0r6JUKrWTkKodQRBw7dp1bP/VDzGJaYgzcoXKzkPXsagWO37iFOQSW0BZoOsopEP379/Hxm2/4O6TZMSZNIaqjrOuI1EtlJCQgGnfLMB941YQ9I0gysvSdSSiKi0iMhI/btmOJ7n6SDZ3ByRSXUeiSpKfnw8lW5NSNaFxYWXy5MlwcXHBiRMn4OrqiosXLyIlJQXTp0/HqlWrtJmx2tqwYQM2bNhQa4pMaWlpOHj4KP4OCkGqyAzJxi4QbJvqOhbVcoIg4EjgKeSbvwFp2kNdx6FKVlhYiNDTZ/DrHwFIeAYkmjSCYNtQ17GolgoKOY31P+9ErGV7CDL2+UT0MiqVCufOhWHr7r2Iz5ci2awFBGNDXceiSvb4aTxyRRx44J8KCwsRGxtbadtr0KABJBL2HlIaGr9LYWFhOHXqFOrUqQM9PT3o6emha9eu8Pb2xldffVXqEYRqsgkTJmDChAlQKBQwN6+Zo0zk5uYi5PQZHPjrOOLTc5AodUC+VWf2VUBVRkRkJJJVxuwouZZ5/Pgx/PwP4kLEVaRIbJFh1gow5skZ6UZ8fDyWr/0Rt5PZcTvRqzx79gwHDx/BwSN/I0nPEmlmbQBTfnfXVpE3b0FlaAVkPtV1lCojNjYWQ5buhkpW8b8t9fIy8Ns3n8HV1bXCt1UTaHxkVyqVMDF5fl+6jY0N4uLi0LRpUzg5OSE6OlprAanqycvLw/kLFxHw1zE8TkhGkr4tsk2bAHV44KOqRRAE/LhlO5LNWuo6ClWC9PR0HAs8gaOBwUgtlCBBWheqOrwPn3QnISEBG7fuQOSdR4gzbQaVtaWuIxFVSU+ePMGOX/0QcTMaibK6eGbVCdAT6zoW6ZAgCIiJfQLBqr6uo1Q5Kpk5VEa143jSs2dPtGvXDj4+PrqO8loaF1ZatWqFa9euwdXVFR07dsSKFSsglUqxZcsWVrVqoJycHJw7fx5/HjuJJ4nJSNW3QaZxfd7qQ1XaqeBQPMk3hGDC5sM1VWZmJoJCQnH475OQK/KQqG+PfLMObBFAOiMIAiIjr2L7Hj88kisQZ9wQKtvOuo5FVOUUFhYiJPQ0ftt/EInZKiQYOkNl11XXsaiKuHfvHhQw4sUR0or8/HxIpRXbP5PGZ55z585FdnY2AOD777/Hu+++i27dusHa2hp+fn5aC1iV5OfnY86cORCJRFi6dGmFfzi6lpaWhuDQMzh+KgTyjGwkS2yQY8JiClUPOTk52LR9F1LqcCSqmkahUOBUcAiOngiGXJGDJIkdnpm2AOrU7O9kqtoSEhJw4PBRBJ8NQ6rIAqkmzhBsOeIU0b/Fx8djz+/7cT48EslSOyhMWwI2bPVMRe3Y8zuSjJx0HYPK6M8//4SXlxdSU1Ohp6eHyMhItG/fHjNmzMDKlSsBAF9++SUUCgV+/PFHTJw4EadPn0ZqaioaNmyIb775BkOGDAEAjBw5EiEhIQgJCcG6desAADExMXB2dkZUVBRmzJiB0NBQGBsbo2/fvli7di1sbGwAPG/p0qpVK0ilUvzyyy9o2bIlQkJCKvS1a1xY8fT0VP/b1dUVUVFRSE1NhaWlpXpkoKrg6dOnmDVrFo4ePYpnz56hSZMm2Lp1K9zc3NTz+Pr6YuXKlYiPj0fLli3h4+ODbt26FVuXn58fOnToACMjI+zatQtjxoypzJdS4QRBQGxsLAJPBeP0+UtIzxOQJLFFnmkz/mChasd7tQ8eGzVmy4UaQi6XI/BUME6GnkVqTgGSJLbINW3O7ybSqcTERBw7cRKnQs8hLV+ERKkjCq27sJ8xon950ZH4b/4HkZhViHiDBlDZ8lZNKplcLsfNB0/Y2q8a6t69OzIzMxEREQE3NzeEhITAxsamSFEjODgYU6dORW5uLtzc3DBr1iyYmZnhr7/+gpeXl/qOmHXr1uHOnTto1aoVFi9eDACoU6cO4uPj0aNHD4wdOxZr1qzBs2fPMGvWLHzyySc4deqUejs7d+7Ef/7zH5w9e1Y9gnFF0ugXR0FBAfr27YvNmzejSZMm6ulWVlZaC6YNaWlp8PDwQK9evXD06FHY2tri/v37sLCwUM/j5+eHKVOmwNfXFx4eHti8eTP69euHqKgoNGjQoMj6VCoV9PX1oVQqoVLVjDHVCwsLcf36dfz19yncuH0HWSJjyCV1oDR1A8x4bytVT6eCQ3DpQTIKbNrpOgppSBAEPHjwAEcDT+FCeAQyCiVIktgi37QtYMxiGemGSqXC3bt38fepEFwIj4RCKYFcYocCMzf2B0FUArlcjt1++xB2OQLJElsozFqwTz56rVU/+OKpSZPXz0hVjrm5Odq1a4fg4GC4ubmpiyiLFi1CZmYmsrOzcefOHfTs2RN169bFjBkz1MtOmjQJx44dwx9//IGOHTvC3NwcUqkURkZGsLe3V8+3ceNGdOjQAUuXLlVP27ZtG+rXr487d+6o6xONGjXCihUrKu21a3R2qq+vjxs3blSpliklWb58OerXr4/t27erpzk7OxeZZ82aNRgzZgw+//xzAICPjw+OHz+OjRs3wtvbu8i8gwcPxqxZsyASibBs2bIKz19RsrKycC7sPI6cCMKThGRk6FsiXWYPlXUXXjmgai8xMRHrt+5Ckq2HrqNQGRUWFuLatWv48/hJREXfQ6aeCZKldlCav8kWAKQzGRkZuHDxEv4OOo3YuAQoxGZI1a8DpQX3S6KSCIKA8PBwbN/zB56kZiPeoAGU7EicSuly+BXceJIOVR322Vld9ezZE8HBwZg2bRpOnz6N77//Hv7+/jhz5gzS09NhZ2eHZs2aQalUYtmyZfDz88PTp0+Rl5eHvLw8GBsbv3L94eHhCAoKUg+k80/3799XF1bc3d0r5PW9jMaX/YYPH46tW7dW6QLDoUOH4OnpiY8//hghISGoW7cuxo8fj7FjxwJ43mdKeHg4Zs+eXWS5vn374ty5c8XWJ5PJStUj8Yud4gWFQlG+F6IFSUlJOBkUghMhZ5GSnYsUSR32l0I1TkFBAWbN/w6PLdrx6nE1kZOTg/PnL+Bw4CnExiUiQ2KFdEMHqGxY6CXdyM/Px82bN3Ey9Cyu3ohCZoEeksVWyDVxgFCnoa7jEVVZBQUF+Ovocfxx4DCSRGZINXGFUMdI17GoGsnOzsbKHzYi0bqjrqNQOfTs2RNbt27F1atXoaenhxYtWqBHjx4ICQlBWloaevToAQBYvXo11q5dCx8fH7Ru3RrGxsaYMmUK8vPzX7l+lUqFAQMGYPny5cWec3BwUP/7dQUabdO4sJKfn4+ff/4ZgYGBcHd3LxZ8zZo15Q5XXg8ePMDGjRsxbdo0fPPNN7h48SK++uoryGQyDB8+HMnJyVAqlbCzsyuynJ2dHRISEjTerre3NxYtWlTe+OUWFxeHI8cDERp2ERkFepBLbJFv2gow0td1NKIKsWy1D+6KHCAYmOk6Cr1CRkYGgkJCcexkCOTp2UjWt0GOcV0Ito11HY1qIZVKhQcPHiD07HmEXQpHauYzKCQWSJfWgYqtpYheKy8vD7/vD8ChYyeRqO+AbMs32b8ZlZkgCFi4dDkeGTUGxOw/rTp70c+Kj48PevToAZFIhB49esDb2xtpaWmYPHkyAOD06dMYOHAgPvvsMwD/u922efPm6nVJpVIolcoi6+/QoQP8/f3h7OwMiaTqfNdonOTGjRvo0KEDAODOnTtaC/Q6CxcufG3R4tKlS3B3d4dKpYK7u7v6/qv27dvj5s2b2LhxI4YPH66e/9+3NAmCUK7bnObMmYNp06apHysUCtSvXzljsCcmJuLPo8cRfOY8MpQSyPXtUWj6BqDHE0Oq2Y79fQJnohOQa9NW11GoBOnp6fj75Cn8fSoUydkFSJLUQa5pM8CWJ09U+ZKSknA27DyCzoQhXp6CLLEpksXWUJq0BmyrzkkaUVWmUqlw8PAR7PE/iHhpPTyr48FCJGnMz38/IhPzUWBp//qZazm9vIwqvZ0X/azs3r1bPZpP9+7d8fHHH6OgoAA9e/YE8LwPFH9/f5w7dw6WlpZYs2YNEhISihRWnJ2dceHCBTx8+BAmJiawsrLChAkT8NNPP2HIkCGYOXMmbGxscO/ePezduxc//fQTxGLdtFrX+OwhKChImzlKbeLEiRg8ePAr53nRj4qDgwNatGhR5LnmzZvD398fAGBjYwOxWFysdYpcLi/WiqUsZDIZZLLK65grJycHf584iUNHA5H8TECi1B6F5m+ymEK1Rnx8PDbt8kMy+1WpUnJycnDiVBD+PHYCSVn5SNS3Q75pa7aao0pXUFCAa9eu4dipENy8fRdZKn0ki62Rb+IEoU4zXccjqnbu37+PxcvXIFZlCYWNB885qVyu37iJXw8GItWOowC9ToMGDfDbN59V6vY00atXL1y5ckVdRLG0tESLFi0QFxenLpzMmzcPMTEx8PT0hJGREb744gsMGjQIGRn/K+jMmDEDI0aMQIsWLfDs2TP1cMtnz57FrFmz4Onpiby8PDg5OeGdd96Bng6/i8pUWPlnK4xXEYlEWL16tUaBXsfGxkY9PvXreHh4IDo6usi0O3fuwMnp+ZjoUqkUbm5uCAwMxPvvv6+eJzAwEAMHDtRe6Ary4MEDbNu9F7fuP0KivgOembUFTPiDhWoXlUqFbxcvxRPLdjyxqwIEQUBkZCR2/x6AmDg55Pr2eGbWksMiU6XLzs7GmbPncPjvU4hLSkWGvhUyZHZQWXZi/z1E5fDbH/7Yc+hvxFu1hyBlHypUPikpKVi0fC3ibDryu7kUJBIJXF2rfse+q1atwqpVq4pMi4yMLPLYysoKBw4ceOV6mjRpgrCwsGLTGzdujP379790ueDg4NJG1ZoyFVYiIiKKPA4PD4dSqUTTps87QL1z5w7EYjHc3Ny0l7Acpk6dii5dumDp0qX45JNPcPHiRWzZsgVbtmxRzzNt2jR4eXnB3d0dnTt3xpYtWxAbG4tx48bpMPmrRV69Ct+fdyIuS4lEY1eoeJWeajG/ffsRU2AOwcRU11FqtZycHAQcOow/j59Aqp4FUo2dIdg20nUsqmWUSiXCzl+AX8CfeJqUhmR9W+SYOEOwbf76hYnotdZv3II/L91Fih07GKfyKywsxNdzF+KReWtAwmG4qXorU2Hln7f/rFmzBqampti5cycsLS0BAGlpaRg1ahS6deum3ZQaeuONNxAQEIA5c+Zg8eLFcHFxgY+PD4YNG6ae59NPP0VKSgoWL16M+Ph4tGrVCkeOHFG3aqlKYmNj4b1mPWIylEg2bwrBxlDXkYh0KisrC/v+PAaFXVddR6m1MjMzsX33bwgJu4REWX08s+rClkNU6fLz8+Hnvx+Hj59CstgaGaZOLKYQaVlw6GkcvnALKXXa6zoK1RAr1v6AOyo7qEwsdR2FqNw07mNl9erV+Pvvv9VFFeD5vVPff/89+vbti+nTp2slYHm9++67ePfdd185z/jx4zF+/PhKSlR2giBg997f8ceRU4izbAPBhlfmiYDnzZHjDJ3ZWZ4OqFQq/LH/AH4/eARPjRqiwLYrr16STly7dh1L16zHU/26yLHpwu8Dogqy/Vc/JFt10HUMqiFCz5zF6ZuxyKnDfYpqBo0LKwqFAomJiWjZsmWR6XK5HJmZmeUORv+zat2POH79KdLY7JKoiFOh55Bv3UXXMWodhUKBWfMW4Xa2wfPWQvwhSzoSExODeSt+wJM6HQEJ+/Ehqkg5eYWAGUfNovLLzMzED1u2Q16H53BUc2h8Nvz+++9j1KhR2LdvH548eYInT55g3759GDNmDD744ANtZqzVLly8hBORD5Bm1YJFFaJ/SExMRLbIkP8vKllOTg4mTJuFywV1obBsyqIK6VRgUCjijRuyqEJUCYwN9AFlga5jUA3gs2EzHhs1AfRYqKOaQ+Mz4k2bNqF///747LPP4OTkBCcnJwwbNgz9+vWDr6+vNjNWWxs2bECLFi3wxhtvaLyOA0eOI8WssRZTEdUMjx49Qqaesa5j1Do+GzbjrsQZKuPSjc5GVJHe6u4B++wH/LFHVAnGDh8G29RrgCDoOgpVY3K5HOG3HqDQ1F7XUYi0SuPCipGREXx9fZGSkoKIiAhcuXIFqamp8PX1hbExf+wAwIQJExAVFYVLly5pvA5zU1OICnO1mIqoZsjNzUW+INZ1jFrnRvRdFJg66DoGEQCgUaNGmD91HBqmnIdhxkNAUOk6ElGN5dGlEz7o4Qab5Ej+XyON7fljP+IMqt4gIUTlVe423MbGxmjTpg3atm3LgkoFGD7kY9RV3AJUhbqOQlSlWFhYwFDEq9SVzVAmBZT5uo5BpObu1gG/bF6Psd0boWHSWVik3oIoP0fXsYhqpLGjhmP8h31QL/EcRHnsU5HK7lLENSjZWoVqoHLd2Hb69Gls3rwZ9+/fx759+1C3bl3s2rULLi4u6NqVw59qg6OjI2ZNGotl63/CU2s3CFIjXUciqhJcXV1hUpiBNF0HqWX+M8oL83/YhkTbN9m/DVUZBgYGGPHZUHw25FNcuHgJfxw8jNj4JKTq2yDTyBGCjKPpEWnLwAH94da+LRYtW4WHqQZItWBfGVQ6BQUFyCkQeP5QDoWFhYiNja207TVo0AASCf9/l4bG75K/vz+8vLwwbNgwREREIC8vD8DzXp6XLl2KI0eOaC1kVZKfn485c+ZAJBJh6dKlkEorvsO8zh3fxDo7W8xbshyPs22QadGQHUZSrWdiYgJrYykeK/MBMTuurCzu7m4Y+1E8tv7xF+JtOgASma4jEamJxWJ06dwJXTp3Ql5eHi5evIS/AoPwIPYqsvRMkSyxgdLUjj8CicqpXr162LJ+LU6cCsK23XsRL7ZFprkr/2/RK2VnZ0MQ6+s6RrUWGxuLyLUfw95QWeHbSngmBqb+AVdX1wrfVlUQHByMXr16IS0tDRYWFmVeXuNf599//z02bdqEn376Cfr6//sP0qVLF1y5ckXT1ZZJaGgoBgwYAEdHR4hEIhw4cKDYPN7e3njjjTdgamoKW1tbDBo0CNHR0cXm8/X1hYuLCwwMDODm5obTp0+XuE0/Pz906NABHh4e2LVrl7Zf0ks5Oztjx6Yf8Z932sFVfgaG6TGAive3Uu025MOBsMh4oOsYtc77770L71kT0CT9EowyHrIjQ6qSZDIZunXrimWL58Fv+2as/3Y8vuhcF+1zr8FJfg6WqVHQy5KzrwgiDYlEIrzd+y3s+skX0wd1QZPUC7BIvQUU5uk6GlVRBgYGELF7g3KzN1SinknF/1VG8ebfRo4ciUGDBlX6drVB48JKdHQ0unfvXmy6mZkZ0tPTy5Op1LKzs9G2bVv8+OOPL50nJCQEEyZMwPnz5xEYGIjCwkL07dsX2dnZ6nn8/PwwZcoUfPvtt4iIiEC3bt3Qr1+/EptZqVQqiMViKJVKqCq5sCEWi/HpRx9i908bML5PCzROfX5iKCp4Vqk5iKqKt3r2gIMojSdxOtC6VSv8smUDRnRxgXPiGcgyYllgoSpLJBKhYcOG+HzUcPy8wQd7f/oBKyd+iqEtTdA6KxxOSWGwSL0FvawkFlqIykgikWDAu/+HX7duxIKR/4e2uTdgn3wFes94sy4VZWBgAJkev2OpZtK4sOLg4IB79+4Vm37mzJlKay7Ur18/fP/99/jggw9eOs+xY8cwcuRItGzZEm3btsX27dsRGxuL8PBw9Txr1qzBmDFj8Pnnn6N58+bw8fFB/fr1sXHjxmLrGzx4MM6fP4+zZ89i+PDhFfK6Xkcmk2Hopx9jz9ZNWDxmIDrq3Uf9pAuQZjxmKxaqVfT09DB94jjYpd3QdZRaSV9fH5+P9MKuzeswposTGiadgVlqNAtdVOVJpVK0b98eUyeOw45N67Fn01os/8+HGNLCEK0yL8NJ/t9CS3YyCy1EpaSnp4fu3bph28Z1+GHuJHjWyYJT4lkYpMcAqsq/8k1VU0On+hDlZug6BlWgvLw8fPXVV7C1tYWBgQG6du1aZJTcmzdvon///jAzM4OpqSm6deuG+/fvY+HChdi5cycOHjwIkUgEkUiE4OBgAMCsWbPQpEkTGBkZwdXVFfPmzUNBwf8GsVi4cCHatWuHXbt2wdnZGebm5hg8eDAyMzNLnau8NL4R8ssvv8TkyZOxbds2iEQixMXFISwsDDNmzMD8+fO1FlDbMjKe/0e2srIC8LzPlPDwcMyePbvIfH379sW5c+eKLS+TyeDj4/PKbeTl5an7nAEAhUJRztQl09PTQ5cundGlS2ekpKTg0JHjOBF8GhkqGeQyRyhN7Ng5FNV47du1Raem9XDsURzyTB11HadWMjIywpiRXhjx2RCEhJ6GX8CfSEgrQKKsLgpN7dknFFV5BgYGcHd3h7u7O4Dnw7nfuHEDwWfP41rUZWTmKZEutkKWoS1UBhY8thK9RsOGDfH9gm+RnZ2NP48cw+HjJ5CiMkKykRNUhha6jkc69NknH+DKyp8gN2in6yhUQb7++mv4+/tj586dcHJywooVK+Dp6Yl79+7h2bNn6N69O3r27IlTp07BzMwMZ8+eRWFhIWbMmIFbt25BoVBg+/btAP73m93U1BQ7duyAo6Mjrl+/jrFjx8LU1BRff/21erv379/HgQMHcPjwYaSlpeGTTz7BsmXLsGTJktfmerGd8tC4sPL1118jIyMDvXr1Qm5uLrp37w6ZTIYZM2Zg4sSJ5Q5WEQRBwLRp09C1a1e0atUKAJCcnAylUgk7O7si89rZ2SEhIUGj7Xh7e2PRokXlzlsW1tbWGOU1FKO8huLRo0c4cPgYLoSHQQFDyPXtn3fWxx83VEN9PXUSbo+fjDsFFhD0OXKWrkgkEvR+qxd6v9ULCQkJCPjzCELPhSFDzwRJsrpQGVnzBylVC/8utOTk5CAy8ipOhp7FnfsXoYL29mOVSgmnDl20tj6iqsTY2BiDP/4Qgz/+ENHR0djl54/HT+9Ci/+FykSkp4SJiYluNk4AgObNm6O+MZCUnw1BaqzrOKRl2dnZ2LhxI3bs2IF+/foBAH766ScEBgZi69atSEtLg7m5Ofbu3avup7VJkybq5Q0NDZGXlwd7+6JDcs+dO1f9b2dnZ0yfPh1+fn5FCisqlQo7duyAqenzkQC9vLxw8uRJLFmy5LW5Zs6cWe7XXq6uu5csWYJvv/0WUVFRUKlUaNGiRbm/rBYuXPjaosSlS5fUJztlMXHiRFy7dg1nzpwp9pzoXyf7giAUm1Zac+bMwbRp09SPFQoF6tevr9G6NOHk5ITJE74EADx8+BB/HT+B85cvIqNQH3KJLQrMHNhrO9UoBgYG+H7uLEydvwxP7LuwiFgF2Nvb4z9jR2Pc56Nw9+5d+B/6CxHXz0AhMUeKzJFFFqpWjIyM1C1EiUgzTZs2xffzv9F1DKoCvp48HlO/W4s42466jkJadv/+fRQUFMDDw0M9TV9fH2+++SZu3bqFhIQEdOvWrcjgN6Wxb98++Pj44N69e8jKykJhYSHMzMyKzOPs7KwuqgDPuy6Ry+WlyqUN5f51bWRkpFGR42UmTpyIwYMHv3IeZ2fnMq930qRJOHToEEJDQ1GvXj31dBsbG4jF4mKtU+RyebFWLKUlk8kgk1WNIUidnZ0x4cvPMeFLICEhASeCQnDq9Dmk5RQgSVwHuaaOEPQNdR2TqNxcXV0x8pP3sHHfCaQblPx/lx3pVT6RSIQmTZpgzowmEAQB0dHROHjkb0RcP4dMkTGSpPZQmtiyGEZERFRLNGrUCN3bNcOB27HINWug6zikRcJ/BzJ4WaMFQ8Oy/+48f/48Bg8ejEWLFsHT01Pd4mX16tVF5vt3sUYkEqkHm3ldLm0oV2Hl5MmTOHnyJORyebERcrZt26bROm1sbGBjY1OeWEUIgoBJkyYhICAAwcHBcHFxKfK8VCqFm5sbAgMD8f7776unBwYGYuDAgVrLURXY29vjsyGf4rMhnyIzMxNnz53HkRNBiJOnIF1iiQxDB947TtXa+++9CwOZFMkpqS+ZwwUtWrSo1Ez0PyKRCM2aNUOzZs0gCAIePnyII3+f/G+LOgmSJHWQb+oIiMt2FYOIiIiql8kTvsTV8ZNxO98GgpS3cdcUjRo1glQqxZkzZzB06FAAQEFBAS5fvowpU6YgOzsbO3fuREFBQYmtVqRSKZTKop1dnz17Fk5OTvj222/V0x49eqTVXNqgcWFl0aJFWLx4Mdzd3eHg4KC1Sk9ZZGVlFRmZKCYmBpGRkbCyskKDBs+rnxMmTMCePXtw8OBBmJqaqlummJubqytm06ZNg5eXF9zd3dG5c2ds2bIFsbGxGDduXKW/pspiamqKdzzfxjueb6OwsBBXr17F4b9PISr6HLLEJkgRjKGzG2BLkP8sS9cRqJro59lX1xGoFEQiEVxcXNQt6uRyOU4Gh+Jk6FmkZOUiRWKDHOO6PNkiIiKqgaRSKZbMm42v5i7BEzvexl0WCc/ElbYd+9fPVoSxsTH+85//YObMmerf5CtWrEBOTg7GjBkDlUqF9evXY/DgwZgzZw7Mzc1x/vx5vPnmm2jatCmcnZ1x/PhxREdHw9raGubm5mjUqBFiY2Oxd+9evPHGG/jrr78QEBCg1VzaoHFhZdOmTdixYwe8vLy0EkQTly9fRq9evdSPX/RrMmLECOzYsQMA1EMm9+zZs8iy27dvx8iRIwEAn376KVJSUrB48WLEx8ejVatWOHLkCJycnCr8NVQFEokEbm5ucHNzU19FjomJ0XWsIoyM+uk6AhFVIFtbWwz55CMM+eQjZGdnI+z8BfwVeAoJSak6Kdy/Tn3nyus3i4iIqCZycnLCmMGD8KN/EFKsW+s6TrXQoEEDYOoflbIt+xfbK6Nly5ZBpVLBy8sLmZmZcHd3x/Hjx2FpaQkAOHXqFGbOnIkePXpALBajXbt26r5Pxo4di+DgYLi7uyMrKwtBQUEYOHAgpk6diokTJyIvLw/9+/fHvHnzsHDhQq3mKi+R8OKGozKytrbGxYsX0bBhQ60EqckUCgXMzc2RkZFRrJMdIiIiIiKi2mr+d0tx8lEe8qXmJT5fL+MGAn7VrJsJosqicWFl1qxZMDExwbx587SdqcZhYYWIiIiIiKi4vLw87Ni9B8/y8kt83tLMFCM+G1rJqYjKpkyFlX8OIaxSqbBz5060adMGbdq0Kdb5zJo1a7SXsppjYYWIiIiIiIioZipTHysRERFFHrdr1w4AcOPGjSLTq+L98LqwYcMGbNiwoVjPxkRERERERERUM2h8KxCVHlusEBEREREREdVMHNeKiIiIiIiIiEhDZboVaNq0afjuu+9gbGxcpL+VktTUPlby8/MxZ84ciEQiLF26FFKpVNeRiIiIiIiIiEhHytRiJSIiAgUFBep/v+qvMoSGhmLAgAFwdHSESCTCgQMHis1TWFiIuXPnwsXFBYaGhnB1dcXixYuhUqmKzOfr6wsXFxcYGBjAzc0Np0+fLnGbfn5+6NChAzw8PLBr166KeFlEREREREREVE2UqcVKUFBQif/WlezsbLRt2xajRo3Chx9+WOI8y5cvx6ZNm7Bz5060bNkSly9fxqhRo2Bubo7JkycDeF4smTJlCnx9feHh4YHNmzejX79+iIqKQoMGDYqsT6VSQV9fH0qlslhxhoiIiIiIiIhqF42HW37lSkUirF69WuNQmhCJRAgICMCgQYOKTH/33XdhZ2eHrVu3qqd9+OGHMDIyUrc46dixIzp06ICNGzeq52nevDkGDRoEb2/vIuvLy8vDrFmzIBKJsGzZMshkstdmY+e1RERERERERDVTuYZbDg8Ph1KpRNOmTQEAd+7cgVgshpubm/YSllPXrl2xadMm3LlzB02aNMHVq1dx5swZ+Pj4AHjeZ0p4eDhmz55dZLm+ffvi3LlzxdYnk8nUyxIRERERERFR7abxrUBr1qyBqakpdu7cCUtLSwBAWloaRo0ahW7dumk3ZTnMmjULGRkZaNasGcRiMZRKJZYsWYIhQ4YAAJKTk6FUKmFnZ1dkOTs7OyQkJGi0zby8POTl5akfKxQKzV8AEREREREREVVZGg+3vHr1anh7e6uLKgBgaWmJ77//vly3AS1cuBAikeiVf5cvXy71+vz8/LB7927s2bMHV65cwc6dO7Fq1Srs3LmzyHwikajIY0EQik0rLW9vb5ibm6v/6tevr9F6iIiIiIiIiKhqK1OLlX9SKBRITExEy5Yti0yXy+XIzMzUONDEiRMxePDgV87j7Oxc6vXNnDkTs2fPVq+zdevWePToEby9vTFixAjY2NhALBYXa50il8uLtWIprTlz5hTpj0YQBOTn58PU1FSj9RERERERERFR1aRxYeX999/HqFGjsHr1anTq1AkAcP78ecycORMffPCBxoFsbGxgY2Oj8fL/lpOTAz29og1zxGKxekQfqVQKNzc3BAYG4v3331fPExgYiIEDB2q0TZlMVqpObYmIiIiIiIioetO4sLJp0ybMmDEDn332GQoKCp6vTCLBmDFjsHLlSq0FfJWsrCzcu3dP/TgmJgaRkZGwsrJSD5M8YMAALFmyBA0aNEDLli0RERGBNWvWYPTo0erlpk2bBi8vL7i7u6Nz587YsmULYmNjMW7cuEp5HURERERERERUPZVpuOWSZGdn4/79+xAEAY0aNYKxsbG2sr1WcHAwevXqVWz6iBEjsGPHDgBAZmYm5s2bh4CAAMjlcjg6OmLIkCGYP38+pFKpehlfX1+sWLEC8fHxaNWqFdauXYvu3btX1kshIiIiIiIiomqo3IUVIiIiIiIiIqLaSuNbgUg7BEEoV2e/REREVZWpqanGI+z9E4+VRERUU2nrWEm6xcKKjmVmZsLc3FzXMYiIiLQuIyMDZmZm5V4Pj5VERFRTaetYSbrFW4F0jFfhXk+hUKB+/fp4/Pgxv3RIY9yPSFu4L5UeW6y8Hven6omfW/XDz6x6qg2fG1us1AxssaJjIpGoxn5JaJuZmRnfKyo37kekLdyXKk9tOFZyf6qe+LlVP/zMqid+blTV6ek6ABERERERERFRdcXCChERERERERGRhlhYoSpPJpNhwYIFkMlkuo5C1Rj3I9IW7kukTdyfqid+btUPP7PqiZ8bVRfsvJaIiIiIiIiISENssUJEREREREREpCEWVoiIiIiIiIiINMTCChERERERERGRhlhYISIiIiIiIiLSEAsrVCX4+vrCxcUFBgYGcHNzw+nTp185f0hICNzc3GBgYABXV1ds2rSpkpJSVVaW/Sg4OBgikajY3+3btysxMVU1oaGhGDBgABwdHSESiXDgwIHXLsPvI3odHuOqn7J8Zvv378fbb7+NOnXqwMzMDJ07d8bx48crMS29wPOA6ofHXaopWFghnfPz88OUKVPw7bffIiIiAt26dUO/fv0QGxtb4vwxMTH4v//7P3Tr1g0RERH45ptv8NVXX8Hf37+Sk1NVUtb96IXo6GjEx8er/xo3blxJiakqys7ORtu2bfHjjz+Wan5+H9Hr8BhX/ZT1MwsNDcXbb7+NI0eOIDw8HL169cKAAQMQERFRyclrN54HVE887lKNIRDp2JtvvimMGzeuyLRmzZoJs2fPLnH+r7/+WmjWrFmRaV9++aXQqVOnCstIVV9Z96OgoCABgJCWllYJ6ag6AiAEBAS8ch5+H9Hr8BhX/ZT1MytJixYthEWLFmk7Gr0CzwOqPx53qTpjixXSqfz8fISHh6Nv375Fpvft2xfnzp0rcZmwsLBi83t6euLy5csoKCiosKxUdWmyH73Qvn17ODg4oHfv3ggKCqrImFQD8fuIXoXHuOqnPMeTF1QqFTIzM2FlZVUREakEPA+oPfgdSVUVCyukU8nJyVAqlbCzsysy3c7ODgkJCSUuk5CQUOL8hYWFSE5OrrCsVHVpsh85ODhgy5Yt8Pf3x/79+9G0aVP07t0boaGhlRGZagh+H9Gr8BhX/Wjymf3b6tWrkZ2djU8++aQiIlIJeB5Qe/A7kqoqia4DEAGASCQq8lgQhGLTXjd/SdOpdinLftS0aVM0bdpU/bhz5854/PgxVq1ahe7du1doTqpZ+H1Er8NjXPVT1s/shd9++w0LFy7EwYMHYWtrW1Hx6CV4HlA78DuSqiK2WCGdsrGxgVgsLnY1QS6XF6tGv2Bvb1/i/BKJBNbW1hWWlaouTfajknTq1Al3797Vdjyqwfh9RK/CY1z1U57jiZ+fH8aMGYPff/8dffr0qciY9C88D6g9+B1JVRULK6RTUqkUbm5uCAwMLDI9MDAQXbp0KXGZzp07F5v/77//hru7O/T19SssK1VdmuxHJYmIiICDg4O241ENxu8jehUe46ofTY8nv/32G0aOHIk9e/agf//+FR2T/oXnAbUHvyOpytJZt7lE/7V3715BX19f2Lp1qxAVFSVMmTJFMDY2Fh4+fCgIgiDMnj1b8PLyUs//4MEDwcjISJg6daoQFRUlbN26VdDX1xf27dunq5dAVUBZ96O1a9cKAQEBwp07d4QbN24Is2fPFgAI/v7+unoJVAVkZmYKERERQkREhABAWLNmjRARESE8evRIEAR+H1HZ8RhX/ZT1M9uzZ48gkUiEDRs2CPHx8eq/9PR0Xb2EWonnAdUTj7tUU7CwQlXChg0bBCcnJ0EqlQodOnQQQkJC1M+NGDFC6NGjR5H5g4ODhfbt2wtSqVRwdnYWNm7cWMmJqSoqy360fPlyoWHDhoKBgYFgaWkpdO3aVfjrr790kJqqkhfDb/77b8SIEYIg8PuINMNjXPVTls+sR48er/zeoMrD84Dqh8ddqilEgvDf3n6IiIiIiIiIiKhM2McKEREREREREZGGWFghIiIiIiIiItIQCytERERERERERBpiYYWIiIiIiIiISEMsrBARERERERERaYiFFSIiIiIiIiIiDbGwQkRERERERESkIRZWiIiIiIiIiIg0xMIKEREREREREZGGWFgholph7ty5kMlkGDp0qK6jEBERVUk8VhIRaUYkCIKg6xBERBVNoVBg165dmDhxIu7evYtGjRrpOhIREVGVwmMlEZFm2GKFiGoFMzMzjB49Gnp6erh+/bqu4xAREVU5PFYSEWmGhRUiqjUKCwthZGSEGzdu6DoKERFRlcRjJRFR2bGwQkS1xty5c5GVlcWTRSIiopfgsZKIqOzYxwoR1Qrh4eHo0qUL3n77bcTExODmzZu6jkRERFSl8FhJRKQZFlaIqMZTqVR488030aNHD3Ts2BHDhg1DdnY2pFKprqMRERFVCTxWEhFpjrcCEVGNt379eiQlJWHx4sVo3bo1CgsLER0dretYREREVQaPlUREmmNhhYhqtKdPn2LevHnw9fWFsbExGjduDJlMxnvHiYiI/ovHSiKi8mFhhYhqtK+++gr9+vVD//79AQASiQTNmzfnySIREdF/8VhJRFQ+El0HICKqKIcPH8apU6dw69atItNbt27Nk0UiIiLwWElEpA3svJaIiIiIiIiISEO8FYiIiIiIiIiISEMsrBARERERERERaYiFFSIiIiIiIiIiDbGwQkRERERERESkIRZWiIiIiIiIiIg0xMIKEREREREREZGGWFghIiIiIiIiItIQCytERERERERERBpiYYWIiIiIiIiISEMsrBARERERERERaYiFFSIiIiIiIiIiDf0/vS3vjDDibhEAAAAASUVORK5CYII=\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "dihedrals.plot_violins(df, resname, figdir=None, molname=molname, width=width, solvents=solvents)" + ] + }, + { + "cell_type": "markdown", + "id": "8937d5d1", + "metadata": {}, + "source": [ + "### padded angles" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "9bbd177d", + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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r3Lkze/r0KUtPT5fd3owsHx4eznx9fVlUVBRLSEhgwcHBrHnz5uyzzz57Z95bt24xPT095uzszP78808WHx/P7t69y9atW8f69+8va5ecnMyioqLYmjVrmK6uLouKimJRUVGssLCQMcZYeXk569SpE/vwww9ZZGQku3DhAmvZsiWbOXPmO7dfWlrK+vXrx4yMjNiOHTtYdHQ0i4+PZ8HBwczBwUE2qv6CBQtYWFgYS0hIYDdv3mSffvop09PTY0lJSW9d9++//87Gjh3Lfv/9dxYXF8cePXrENm3axNTU1NhPP/1U5WNCQ0OrNWMBY4z5+voyPp/Pli5dymJjY9nTp0+Zj48PEwqFbMGCBRXaPnnyhEVFRbFp06ax9u3by14/kUgka/Pw4UMWFRXFhg0bxlxcXGRtaiIhIYE1bdqU2dnZsePHj7PHjx+zmJgYtnXrVmZra8sYez1TRbdu3Vi/fv1YREQEu3XrFnN0dKww48+5c+cYj8djBw8eZI8fP2YrV65k+vr675wVaPjw4czOzo5dvXqVRUdHs48//pi1bduWicVixljVs11UNYPDf2dssLS0ZPr6+mzjxo0sLi6O7dixg6mpqbFz587V6LUhhKg+2sfSPpbLfewbkydPZkZGRozP57Pk5OQK972ZFejChQssPT2dxcfHs9OnT7OBAwcyLS0tdunSpQrtExISGACmp6fHjh49Klv+7Nkzpq6uzjQ1Nd/6ehJCVAMVVgiRQ2lpKXN3d2cODg7MwMCAaWtrMxsbG7Z8+fIKB3Xvm7bx3wd9bw5EqrolJiYyxhiLiIhgPXv2ZAYGBkxTU5PZ2NiwVatWseLi4grrtbS0ZKtWraqwLC4ujo0fP541b96caWhoMEtLSzZmzJgK0zNOmDChyu2HhobK2iQnJ7OhQ4cyLS0tZmxszGbOnMlKS0ur9Zp5eXmxzp07M01NTWZsbMycnZ3ZgQMHZNM4fv3116xZs2ZMXV2dNW/enI0aNYo9fPjwneuNj49nU6dOlU3ta2hoyLp37/7OKTZrctDHGGOnT59m/fr1Yzo6OkxTU5M5OjqyoKCgSu0GDBjwzvePsdfvTVVt3nhzsPbv17wqaWlpzM3NjVlaWjINDQ3WokUL9tlnn1V6r9413TJjjK1cuZKZm5szAwMDNm/ePDZz5sxqTbdsYGDAtLS0mKura5XTLf9bdQsra9asYV999RXT1tZm5ubmzM/P752vASGkYaJ9LO1jud7HMva60AaADR48uNJ9b9bz5qatrc06dOjAZsyYwZ48eVLl+t5kS09Pr7C8TZs2DABLTU19byZCiPLiMfaOudMIISrn1atXMDY2xtmzZ2l0eRUUFhaGkSNHIiEhoVqzDxBCCKk/tI9VbbSPJYTUFRpjhZAG5vLly/jggw/ogE9FnTt3DkuXLqUDPkIIUUK0j1VttI8lhNQV6rFSA2KxGEuWLAGPx4Onpyc0NDS4jkQIIYQQQgghhBAOqXSPlStXrmDYsGFo3rw5eDweTp06VeH+8vJyLF++HNbW1tDS0kLr1q3h4eFRadT2gIAAWFtbQ1NTE46Ojrh69WqV2wsODoaDgwOcnZ1x6NChunpahBBCCCGEEEIIUREqXVgpLi5G165dsWPHjirv37hxI3bu3IkdO3YgNjYW3t7e2LRpE7Zv3y5rExwcjLlz52LZsmWIiopCv379MGTIEKSkpFRan1QqhZqaGiQSSZVT6hFCCCGEEEIIIaRxaTCXAvF4PJw8eRIjRoyQLfv0009hbm6Offv2yZZ9/vnn0NbWlvU46dmzJxwcHBAYGChr06FDB4wYMQJeXl4VtiESibB48WLweDxs2LABQqGwbp8UIYQQQgghhBBClJqA6wB1qW/fvti5cyceP36M9u3b4+7du7h27Rr8/PwAvB4zJSIiAu7u7hUeN3jwYISHh1dan1AolD32XUQiEUQikexvxhjEYjFMTU3B4/Fq9ZwIIYQQQgghhBCiPGpVWImMjMTVq1ehoaEBZ2dndOnSRVG5FGLx4sXIz8+Hra2t7BKe9evXY8yYMQCArKwsSCQSmJubV3icubk5MjIy5N6ul5cX1qxZU2l5fn4+9PX15V6vIhQWFmLijHlIMnMG+GqcZqkJ65c3cPxA4PsbEqUglUoxY+5CREgsINU24TqOjEb2U3iN/wAuLi5cR2lQTv9xFrt/PYsMMyeAp9JXmFZb1+IIBAVs5ToG+Zfb/9zB6h0/4YWZE9dRaoQnKsKQJgVYu8L9/Y0bmCUrPRCWqQGxXnOuo9SYICcRq7/siSEff8x1FE4sX7MeFzLUUabblOsoNdI09x4CVsyCpaUl11FUwsOYGCzeGIB0sx6c5middQPH9tPvAKLc5D4C9vPzg5OTE9asWYNVq1bB3t4enTt3RkREhCLz1UpwcDB+/vlnHD58GJGRkTh48CA2b96MgwcPVmj3314kjLFa9SxZsmQJ8vPzZbfU1FS516Voenp6mDbhGzTJecB1FNKA8fl8bPBYiTbFseCJiriOQ+rY8E8/waxvR6DFixtAuZjrOKSR6tHdCR85dYBeXjzXUUg1rVr6I9pLUsB/lct1FFJDSxbOhdWrJ+CVveI6CqlDHe3sMLhXF+jlJ3AdhRClV6PCSlBQECIjIyESieDp6YkNGzYgOzsbmZmZSE5OxvDhw+Hi4oJr167VVd4aWbRoEdzd3TF69Gh07twZ48aNw7x582Rjp5iamkJNTa1S75TMzMxKvVhqQigUQl9fv8JNmQwe9AH62TaHTn4S11FIA2ZoaAif9atgnRcJnriY6zikjn08eBA83WejdfZN8EuyuY5DGqk5btPgaCKBZsEzrqOQatDU1MSWjevQtugheKX5XMchNaCjowOvVUthkfUPICnjOg6pQ27fT0Yn3RKoF2dyHYUQpVajwsqmTZvQs2dP6OrqIjs7G//88w+2bNmCsLAw6OnpYd26dfD29sbChQvrKm+NlJSUgM+v+BTV1NRkM/poaGjA0dERISEhFdqEhISgT58+9ZaTC4sXzIG9fjGEhWlcRyENWIsWLeC7fhWscyPAExVyHYfUsU4dO2LPts3ooZEGw9w4gNHsaaR+8fl8bFi7Co66+dAqUJ7eouTtjI2NsX2zJ9oXP6CirIqxtrbGigVuaJl5i4orDZiamhq8162GTXki+CXUu4yQt6lRYSU2NhaFhYUIDw+Huro6+Hw+fv31VwwdOhQmJiawtLTEsWPHEBUVhd9//x2JiYl1lRsAUFRUhOjoaERHRwMAEhMTER0dLZsqediwYVi/fj3+/PNPJCUl4eTJk/D19cXIkSNl65g/fz727t2LoKAgxMbGYt68eUhJScH06dPrNDvXBAIBNq33QBeNTGgUyT+eDCHvY2FhgR2b1qNd4V06aG4EDA0NscPXG99/7IhWL66D/yqP60ikkVFXV4fvxvXoYyqGft5TruOQajA1NUXgFm90YYnQLHzOdRxSAw7dumHl/B9gkXkTKBe9/wFEJWlra2Or93q0exVD+3VC3qLGY6xoamqie/fucHZ2RteuXXHz5k0UFhbi3r178PLyQvv27VFWVoaJEyeiTZs2dXoZzJ07d9CtWzd069YNwOsiSbdu3bBy5UoAwPbt2/HFF19gxowZ6NChAxYuXIhp06Zh7dq1snV8/fXX8PPzg4eHB+zt7XHlyhWcPXu2UQxqJRQK4eftia7qGRAWUc8VUnfMzc2x028TOksTqJdUI8Dj8fD1F6Owy2c9emmmwTT7Hp3NJPVKIBBgw9pVGNbZHKYvo6j3lAowMDBAoN9muDSVwDjnIcAY15FINTk6dMPGFYtglXWLeqc2YIaGhti+yRPtSh7SuEiEVEHuwWt9fHzg7e2NKVOmIDIyEu3bt8ewYcOgq6uL5s2bIzs7GykpKfj1118VmbcCFxcXMMYq3Q4cOADg9UCtfn5+SE5OxqtXrxAfH49169ZBQ0OjwnpmzJiBpKQkiEQiREREoH///nWWWdloampi6yYvOGnnQrsghes4pAF7c9Dc27AI+jS4ZKNgZmaG7Zs3YPmkUWiXcws6+Yn0Y4nUGz6fj/mz3TB79Mdo+SKcBtlUARoaGli3ahmmfNILLeg9Uyk2Nu3hv2kdbEseQJ16QjdYJiYm8PfZAJtXsdQLmZD/kLuwYm9vj4iICCQnJ6NXr17Q1NR8Xcncvh0bN24EALRs2RIfN9Jp6FSJUCiE78b1cDaTQD+ffvCSuiMUCrHJ0wPDupjDNCuafmQ3Ev369sGhPf6Y2LcNLF9cg6CQDrpJ/Rn6sSu2rHFH+/xIqBemcx2HvAePx8PXn4+Ez8pFaJ8fCQ3q5agymjZtit3bfNDHIB8GeY9pH99AGRkZwd93I+zKnkJQ/JLrOIQoDbkLKwDQpk0bhISE4Pnz5/jtt99w9OhRPH36FGPHjlVUPlJPBAIBPNeswKC2+jDKjeU6DmnA+Hw+5s+age+Hu6BZ5i1AWs51JFIP1NXVMWn8tzjg74PhbdRhkXkD/JIcrmORRqJ169bYF+CHD5uWwTTrLiCVcB2JvEe7dm0RFLgVQyzVYPYygi4nVBHa2trw8VqHb5xtXu/jJWKuI5E6YGBgAH/fjejEkqmHEiH/r1aFlTfMzc0xfPhwfPnll7CwsFDEKhsEf39/2NnZoXv37lxHqRY+n4+li+bjMwcrmGTfpzMNpE59PuIzLP1hAlq+uAGU04FXY6Gvr4+li+Zh58ZVGGRSiOYvb9M0q6ReaGlpYf3q5VgwdigsM69TYU8FaGpqYuWSRVj1w1i0ybpBP+BUBI/Hw5SJ47BuwXS0fnkD/OIsriOROqCrq4sdvhvRRS2dJsIgBAoqrJCqubm5ISYmBv/88w/XUaqNx+Nh9oxp+KJvJ5hScYXUsT69e2L9krlo9fIGUF7KdRxSj5o2bQovjxXYsWYR+uu8QLOX/9Cgh6ReDB70Afb4bURPYdrrHpo0sK3S69WzB/YHbsVHzcth/jKCekGoCPuuXbB3hy96ambAIDeOjikbIC0tLWzb7IXOaulQL3rBdRxCOEWFFVKl7ydNwIg+djDOjeE6CmngOnXsCO9V7mj18jZN1dgIWVpawnfDOmxdMRe91VPR9GUEeKIirmORBs7ExATbfTZi1vC+aPXiGvglNMOFstPR0cHaFUvhMXsi2uXchmZBKteRSDUYGBhgu89GTPygM1pk3qABiRug1xNheMKOpYBfTAPaksaLCivkrX6YOgku7c2gm5/IdRTSwLVr1w7rlsxDi5f/AFI6e9wYtWnTBtt9NsBv2Uz0EiSjaRYVWEjd4vF4GD5sKPZs2YCemmmvp/il7x+l5+TQDQd3bcdIOwM0z7wJnriE60jkPXg8HsZ/Mxqbly9Au/wIGkS6AdLS0oKf93q0fRULnriY6ziEcIIKKzUgFouxYMECLFy4EGJx4+iG6r5wLjpo5tO16KTOdezYETPGfwXT3PtcRyEcateuHXb4boSv+wz0VEuCeVYkHaSROmVqaortmzdg3pcfwirzGo0HoQKEQiEWzpmJLSvmo+Or+9DLe0qXmaiA9u3bIShgKwaai2GS/YAuw2tg9PX14e2xAi2zI2iAcNIo1aqwcvXqVXz77bfo3bs3nj9/DgA4dOgQrl27ppBw73PlyhUMGzYMzZs3B4/Hw6lTpyrc7+Xlhe7du0NPTw9mZmYYMWIE4uLiKq0nICAA1tbW0NTUhKOjI65evVrl9oKDg+Hg4ABnZ2ccOnSoLp6S0lFTU4PXmhWwKKAdIKl7Hw8ehG4t9OmHDYGNjQ38t3jD58fp6M5LgFlWFHhldGaa1A0ej4dPPh6Mfds3Y4BBLkyz79KMZSqgbdu2CArchskf2MHixTXwX+VxHYm8h5aWFrzWrMDMz13Q8sV16nHUwFhYWGDauK9hTDOMkkZI7sLKiRMn4OrqCi0tLURFRUEkej02QmFhITw9PRUW8F2Ki4vRtWtX7Nixo8r7L1++DDc3N9y8eRMhISEoLy/H4MGDUVz8v7OfwcHBmDt3LpYtW4aoqCj069cPQ4YMQUpKSqX1SaVSqKmpQSKRQNqIugsbGRnhi09doZOfxHUU0ggsmjsTLYufcB2DKIkOHWwR6LcJmxdOgYMkDk2y74JXRgMdk7phaGiIzV5rsWTiCLR+GQ5BUSbXkch7qKmpYdyY0djtsx49NZ7BOPsBnS1XAZ8N/QTb1i2HTWEUzfbUwHw8eBDa6UtpQHrS6MhdWFm3bh127tyJPXv2QF1dXba8T58+iIyMVEi49xkyZAjWrVuHUaNGVXn/uXPnMHHiRHTs2BFdu3bF/v37kZKSgoiICFkbX19fTJ48GVOmTEGHDh3g5+cHCwsLBAYGVlrf6NGjcfPmTVy/fh3jx4+vs+eljD4f8RnMxHRNLKl7JiYmsDAzogHuSAUdO3bE7u1b4DnrW3QuvQ+TnAc0MwipMy79+yHIfws+NC+FWVYUIKHeK8rOzMwM2302YuGYwbDKvA61YiqKKTtLS0vs9feDs2EhDGnWoAaDx+Nhnts0mBc95ToKIfVK7sJKXFwc+vfvX2m5vr4+8vLyapOpzuTn5wMAjI2NAbweMyUiIgKDBw+u0G7w4MEIDw+v9HihUAg/Pz9s2bIFQqHwrdsRiUQoKCiocFN1QqEQ5iaGQDn9kCF1r7dTN7ociFTJoVs3BAVuw8pJI2Cbfwf6uXF0dprUCT09PXiuXo4V33+N1lnUe0UV8Hg8uH40CEE7fDDQpAhNsuiSLmWnpaWFzZ4eGNPXFk1f3qHv8waiXbt2MFYT0TACpFGRu7DSrFkzPH1auRJ57do1tG7dulah6gJjDPPnz0ffvn3RqVMnAEBWVhYkEgnMzc0rtDU3N0dGhvzdEr28vGBgYCC7WVhY1Cq7srBo2QL8MhpEktQ9A3098KRlXMcgSorH46Gvcx8c2hOA+SOd0ebldWjlJ9HZTlIn+vTuhSD/LRhoWgzT7Hv0w08FGBgYYOPa1Vg2+XO6pEsF8Hg8TJk4Dj9O/RotX4QD5SKuIxEF6GDTnsY9Io2K3IWVadOmYc6cObh16xZ4PB7S0tLwyy+/YOHChZgxY4YiMyrEzJkzce/ePRw5cqTSfTwer8LfjLFKy2piyZIlyM/Pl91SU1PlXpcyefXqFRhPjesYpBGIeRIPqYYe1zGIkuPz+Rg29BMc2uOP8b1aodWL69TTidQJPT09bFi7Cou+/QSWmdfpx4KK6Ne3D4L8t2CASRFMqCim9Ab07YsNyxfAKusWDWrbAFg0b0qXdZNGRe7Cyo8//ogRI0Zg4MCBKCoqQv/+/TFlyhRMmzYNM2fOVGTGWps1axbOnDmD0NBQtGzZUrbc1NQUampqlXqnZGZmVurFUhNCoRD6+voVbg3B04QkMCH92CV1q7y8HP9E3oVUx5TrKERFCIVCfD9pIvZt3YBBTYrR9OUdOpgjdWLQBx9g95YNcOAl0hS/KkJPTw/e61Zj4TdDYJkZDl5pPteRyDt0sLWFn+cqWOfeAU9UxHUcUgtFJSUAn07IksajVtMtr1+/HllZWbh9+zZu3ryJly9fYu3atYrKVmuMMcycORO//fYbLl26BGtr6wr3a2howNHRESEhIRWWh4SEoE+fPvUZVek9fPgQOVItoBY9eQipjl+CjyFDvRl91kiNGRsbw2vNCngvmo4ORdHQzYunH75E4UxNTRG41Qdf97RCs8zbgIQuW1QFgwd9iJ0+62EveQqd/ESu45B3sLCwwNYNHrDOi6SeKyrsQcwjSLSMuI5BSL2pVWEFALS1teHk5IQePXpAV1dXEZmqraioCNHR0YiOjgYAJCYmIjo6WjZVspubG37++WccPnwYenp6yMjIQEZGBl69+t+ZzPnz52Pv3r0ICgpCbGws5s2bh5SUFEyfPr1en4syKy8vx4YtO/DSoD3XUUgDFxkVjePnLqPIQPnGaSKqo2NHO+zfuR2TBtjC4sV18EpVfwBxolz4fD5+mDIJHvOmwvrlDbo0SEWYmZlh5zYfjOxsBvOXd2hgWyXWvHlz+KxbiVbZd6h4qYLy8vKQnl0ICN4+2QchDY2gJo3nz59f7ba+vr41DlNTd+7cwcCBA2V/v8k3YcIEHDhwQDZlsouLS4XH7d+/HxMnTgQAfP3118jOzoaHhwfS09PRqVMnnD17FpaWlnWeXxUwxrDG0xtJgpZg6tpcxyENWERUFNb6+CPNrDf1ViG1JhAIMOHbMRj8oQtWrt+Ip7l6KDBsS58tolAO3eyxy28jFi5bhceFLVCq14LrSOQ9BAIB5s36AU7Xw+EbGIRUE0cwDR2uY5EqtGrVCisWzcbKzQFIM6djA1XivzsIz7Ws39+QkAakRoWVqKioarWrzcCvNeHi4gL2jm7e77rv32bMmKGUA+5yTSqVYoOPH8JTS1BiSL1VSN05eeYP7D/2B56b9QbU1LmOQxqQZs2aYdc2Xxz85QhO/n0VaSYOYOqaXMciDYiJiQl2bfPFyrVeuJ3xGPm0v1QJ/Zz7wNqyFX5csRZPtWwgoXG9lFK3rl0weugHCLoQjTwjW67jkGq4ExmF8IeJKDfrznUUQupVjQoroaGhdZWDKJnS0lIsX7MeEdl8FNBBIqkjhYWFWLfRB1HpJcg27wXwan11IiGV8Pl8fDduLJx79sBKT28katmgXNeM61ikAdHQ0ICXx0ps37kHf96+iyyTLnR2XQW0bNkSu7ZtxoIlK/GwUEQ9jpTUN19/ifBb/+D2q1xIacwOpZaamgpP3x3IMKOxKknjQ79iSCXJycmY4jYX1/N0UWDQhus4pAFijOHvCxfw3Yy5CMvWRbZxJyqqkDrXvn077PP3Qw/tl9DLT+A6DmlgeDweZv/wPSYM6QXzrAgaOFlF6OnpYYfvRvQyLIZOAQ1qq4x4PB5WLVmElgUx9P9KiaWmpmL+sjVIMXGi3sekUapRj5V/e9t4KzweD5qammjbti2GDx8OY2NjucOpOn9/f/j7+0MikXAdpVqkUikOHQnGb+fC8NzYnq45JnUiLi4O3lsDkCLSQo5ZX5qKj9QrHR0dbN3kBZ+t/gi5fw/Zxp2pZwFRqG+++hLaWlrYffw8XjRxos+XCtDQ0ID3+jVY4eGJ8PQEFNIA6krHzMwMLj274ci9NJTpU88iZfPw4UOs3OCLZOPuYBo0JiNpnOQurERFRSEyMhISiQQ2NjZgjOHJkydQU1ODra0tAgICsGDBAly7dg12dnaKzKwy3Nzc4ObmhoKCAhgYGHAd552ePn2K9Zu3IkliiALzPnQgSBQuJSUFW/x34cmLQmQY2IHp0I6XcIPP52PRvFlodvRXHP7rGl40caTvPKJQI4Z9ColEir2nLiHTtBt9vlSAmpoa1q1ahqWr1iI8MxnF+jSJgbKZNH4sLs9chGQqrCiV07//iX2/nsFz0140CxBp1OQurLzpjbJ//37o6+sDAAoKCjB58mT07dsXU6dOxTfffIN58+bh/PnzCgtMFKugoABbA3bjdmwi0g07U5WZKFxSUhL89+zHo+fZSNezAWtiw3UkQgAA347+Clqamth38gIVV4jCfT7iM2Tn5OLX64+Qa9yB6zikGvh8PtatWob5i5fhdmE6yvSacR2J/Iuenh46WFsgJSsfTEu5T1g2BiUlJVi70QcRqYXINu9Nl3STRk/u/wGbNm3C2rVrZUUVANDX18fq1avh7e0NbW1trFy5EhEREQoJShSrrKwMhw4fxUS3+fgjSYo0s55UVCEK9fDhQ8xc4I5Zq30Rmm+KtCY9wDTpQIgol89HfIZxQ/vDNOce11FIAzT1u/HobakHzcJnXEch1SQQCOC9fg3aS5LBKy3gOg75jy+GfwKjV/T/iWvRd+/hux/mIPSFBrJNaJw8QoBa9FjJz89HZmZmpct8Xr58iYKC1zsiQ0NDiMXi2iVUImKxGEuWLAGPx4Onpyc0NDS4jlRjUqkU50Mu4MCRY0hXb4Fi8350lpYojEQiweWr1/DTkWN4USbES902YE10uY5FyDuN+eoLpGe8wJkHNLYCUSwej4cV7guROms+HpTqg2nqv/9BhHOamprYvH41fliwFElmfQC+3IfLRME6d+4M/fKdyOE6SCNVUlIC3+2BuBGThAzjHnTpDyH/Ind5cfjw4Zg0aRJOnjyJZ8+e4fnz5zh58iQmT56MESNGAABu376N9u25n6r3+fPn+Pbbb2FiYgJtbW3Y29tX6EkTEBAAa2traGpqwtHREVevXq1yPcHBwXBwcICzszMOHTpUX/EVgjGGa9fDMf57N2z+NQxPTXqj2LA1FVWIQhQXF+PgL4fxzeTpWH/oHO5r2yPTpCuYkIoqRDXMnfkD7LSLwC/J5joKaWA0NDTgtXoZWubdBZiU6zikmszMzDBn2ncwo95sSkUgEMBQVwuQ0v+l+nb1Wjgm/jAHfyaWIcOsOxVVCPkPuUvwu3btwrx58zB69GiUl5e/XplAgAkTJmDLli0AAFtbW+zdu1cxSeWUm5sLZ2dnDBw4EH/99RfMzMwQHx8PQ0NDAK+LJXPnzkVAQACcnZ2xa9cuDBkyBDExMWjVqlWFdUmlUqirq0MikUCqQl/oEZFR2L57H9LKtJFt4AioqV5PG6KcUlNTEXToCO7GxSNDoyVEps7UHZSoJD6fD8/VyzFl1kIkatIZaqJYzZo1w7ejPkXAn7eRb0TjTKmK/n2dERJ6GSFpGSjTbcp1HPL/rC1b4V5KAaRahlxHaRQyMzPhudkPj7LEeGnSG1Cj/SMhVZH7f4auri727NmDLVu2ICEhAYwxtGnTBrq6/ztDbW9vr4iMtbJx40ZYWFhg//79smVWVlayf/v6+mLy5MmYMmUKAMDPzw/nz59HYGAgvLy8Kqxr9OjRWLx4MXg8HjZs2FAv+WsjJiYWfoG7kVLMR5Z+FzA9Ta4jkQZAKpXixs1b+OnocaQVliFD2xpSM2euYxFSa4aGhpj+3Tfw/vnc62vGCVGgz0d8hj/PX8B9cQmNaaZCFs+bjZgf5iJJx4xOHCiJ1pYW4D15DFBhpU6Vl5fj4M9H8MfFq3hm0BFSEyOuIxGi1GpdctTV1UWXLl0UkaVOnDlzBq6urvjyyy9x+fJltGjRAjNmzMDUqVMhFosREREBd3f3Co8ZPHgwwsPDK61LKBTCz8/vvdsUiUQQiUSyv9+MOVNfkpKS4LNjJ+KzS5GpbwtmQgdwpPZKSkrw2+nf8cffF5HFN0aeng2YKRXrSMPy4cCBOHbqD+SIi8E0dLiOQxoQHo+HxXPdsGBjIDJMHbmOQ6pJV1cXY0Z+ih1nI1Fo1JbrOARAc/MmUJPch4TrIA3YnYhI+PrvwjNBcxSbO9PQAYRUQ60KKxcvXsTFixeRmZlZ6dKYoKCgWgVTlISEBAQGBmL+/PlYunQpbt++jdmzZ0MoFGLQoEGQSCQwNzev8Bhzc3NkZGTIvU0vLy+sWbOmttFrLCsrC1v8d+JeYgbS9WzBTGmQPFJ7GRkZCDp0BBEPHuGFRgu8MulDZ+1Ig8Xj8TDfbRrmbQjEC1MHruOQBsbW1hbWxpp4ISqiMahUyIjPPsXxM2dRKLUG+Gpcx2n0tLW1IeRL0XCmx1AeOTk58PLZigdp+cg0dKJxVAipAbkLK2vWrIGHhwecnJzQrFkz8JS0kimVSuHk5ARPT08AQLdu3fDw4UMEBgZi0KBBAFApO2OsVs9nyZIlmD9/vuzvgoICWFhYyL2+9yktLcXu/T/h0o0IPNe1gbRJjzrbFmk84uLisDPoEBJe5CFNy4ou9yGNho2NDVro8PCivBQQUK8solg/TBqP+M1ByBR25ToKqSaBQIBRw4Zg+7l7KDG05jpOoycQCMAH4zpGgyKVSnHi1GkcOXkWqXodIDVtw3UkQlSO3IWVnTt34sCBAxg3bpwi8yhcs2bNKk0J3aFDB5w4cQKmpqZQU1Or1DslMzOzUi+WmhAKhRAK677CyxjDxdDL2H3wFzzXtEKpWR/qqkdqLfruXQTsPYjnRQwv9NqCNeF+Zi9C6tvYL0fg6U/nUWBsy3UU0sB07NgRpoJXyJRKqPeDChn2yccIPnMOSaDCCmlYnj17hlWem5Ao1kO+OU1CQIi85C6siMVi9OnTR5FZ6oSzszPi4uIqLHv8+DEsLS2hoaEBR0dHhISEYOTIkbL7Q0JCMHz48PqOWiMvX77EGq/NeJzHkG3am2awILV27/59bN25D89fCZClbwNmqsV1JEI449ynD0yCfkH9jpBFGgvXDwbg6aUnEBu2en9johS0tLTQplVzJOcXggn1uI7TqJWVlUHK6ERibUmlUhw68it+OxeK50ZdwXTo8kRCakPukuSUKVNw+PBhRWapE/PmzcPNmzfh6emJp0+f4vDhw9i9ezfc3NwAAPPnz8fevXsRFBSE2NhYzJs3DykpKZg+fTrHyd/u7wsXMW3+EtwSN389cwUVVUgtpKamYuaCxXD3DcJd9Q54adIFTJ2KKqRxU1dXR8umTcAre8V1FNIADf5wIJqUZ3Idg9TQiE8GQ7/4OdcxGj2xWIxyKqzUSm5uLtzm/4gDoQ/wzLwPjflEiALI/Yu8tLQUu3fvxoULF9ClSxeoq6tXuN/X17fW4RShe/fuOHnyJJYsWQIPDw9YW1vDz88PY8eOBQB8/fXXyM7OhoeHB9LT09GpUyecPXsWlpaWHCevrLy8HN5btuFyzDNkmTlTF2JSKyKRCDt27sWViAd4btCRBjsm5D9cnHvh1pkoiI2suI5CGhgzMzPo8ssARuNEqBJHBwcYlh9EPtdBGrnikhKUgY6B5fUwJgarN/giSa8TpEbGXMchpMGQu7By79492NvbAwAePHhQ4T5lG8j2008/xaeffvrW+2fMmIEZM2bUY6KaE4vFWLBkBe4W6qDY1J7rOETFxcU9xpoNPkjWaIVS895cxyFEKdl36Qzj02GQf444Qt7OupUFHmcXAVCuYybydkKhEPqa6gCTvr8xqTMFhcVgaurvb0gquRR2GX57f8HzJj1pxh9CFEzuwkpoaKgic5B3kEqlWLRsFSKLDVFq0ILrOETF/XnuPHb98hvSTBzokh9C3qFly5YQlhdzHYM0UN3tO+HC79GQaJtyHYXUQGurVnjwvJDrGI1aYXEJXQYvh3N/X8D2n08iw7w39XonCpOUlARra2tERUXJOl00VrX+VoqJiUFKSgrE4v/NJs/j8TBs2LDarpr8v30Hf8a9PHWUGlJRhdTOsd9OIej0JWSa96ZR3wl5D4FAAKGA/p+QutG2TWsYIBw5XAchNdLRth3+jL/PdYxGrbCYeqzUVGRUFPwPHUeGeS86/iMNgouLC+zt7eHn58d1FBm5CysJCQkYOXIk7t+/Dx6PB/b/1wm/uQxIIpEoJqEK8/f3h7+/f61ei7y8PJy9dA0F5s4KTEYao+h793Dw1HlkmvWiabkJqSahOp0VJXWjadOm0JSWch2D1JClRUto4x+UQIPrKI1WSckr6nFRA0VFRfDa4o+0JnRSjZC6JPf/rjlz5sDa2hovXryAtrY2Hj58iCtXrsDJyQlhYWEKjKi63NzcEBMTg3/++UfudZw9H4IMTQv6IUxqhTGGLf67kW7iQJ8lQmpAjc+nAUZJnTA0NISahAorqsbIyAhaKOM6RqMmEonAeFRYqa59B39GqmYbgHr5kHc4fvw4OnfuDC0tLZiYmGDQoEEoLi6GVCqFh4fH68ujhULY29vj3LlzVa5DKpWiZcuW2LlzZ4XlkZGR4PF4SEhIAADk5+fj+++/h5mZGfT19fHBBx/g7t27svarV6+Gvb09Dh06BCsrKxgYGGD06NEoLHx9GebEiRNx+fJlbN26FTweDzweD0lJSXXzwtSA3IWVGzduwMPDA02aNAGfzwefz0ffvn3h5eWF2bNnKzJjo/bw0WOUaZlwHYOouOzsbOSK1WigMkJqSE2ND4AKK0TxBAKB/AdhhDPa2tpQY+Vcx2jUysvLqcdKNTHGcO1WBMr0m3MdhSix9PR0jBkzBpMmTUJsbCzCwsIwatQoMMawdetW+Pj4YPPmzbh37x5cXV3x2Wef4cmTJ5XWw+fzMXr0aPzyyy8Vlh8+fBi9e/dG69atwRjD0KFDkZGRgbNnzyIiIgIODg748MMPkZPzv4tj4+PjcerUKfzxxx/4448/cPnyZWzYsAEAsHXrVvTu3RtTp05Feno60tPTYWFhUbcvUjXIvU+XSCTQ1X0957mpqSnS0tIAAJaWloiLi1NMOgIdbR3wpHRmhNROTk4OxGpUVCGkpqRMCpq1hdQV6kCoetTU1MCjYiunpFIG+l6unszMTIjUdenLhrxTeno6ysvLMWrUKFhZWaFz586YMWMGdHV1sXnzZixevBijR4+GjY0NNm7c+M6xTcaOHYvr168jOTkZwOteLEePHsW3334L4PUEOPfv38exY8fg5OSEdu3aYfPmzTA0NMTx48dl65FKpThw4AA6deqEfv36Ydy4cbh48SIAwMDAABoaGtDW1kbTpk3RtGlTqKlxX2yVu7DSqVMn3Lt3DwDQs2dPeHt74/r16/Dw8EDr1q0VFlCZiMViLFiwAAsXLqwwWG9d6tvLCXqvXtTLtkjD1bJlS2iKC7iOQYjKKS+X0gEpqTN0lZnqkUgkYPSjXgnQf57qEIlEkNBlU+Q9unbtig8//BCdO3fGl19+iT179iA3NxcFBQVIS0uDs3PFsT6dnZ0RGxtb5bq6desGW1tbHDlyBABw+fJlZGZm4quvvgIAREREoKioCCYmJtDV1ZXdEhMTER8fL1uPlZUV9PT0ZH83a9YMmZmZin7qCiV3YWX58uWQSqUAgHXr1iE5ORn9+vXD2bNnsW3bNoUFrC8BAQGwtraGpqYmHB0dcfXq1UptgoOD4eDgAGdnZxw6dKhecvXp3QtmkiygXFQv2yMNk7a2NuysW4Bf9JLrKISolLJyGoid1A2RSAQpDSSpcoqLiyHh0aDWXFJTU6OqZDWZmZlBWFbMdQyi5NTU1BASEoK//voLdnZ22L59O2xsbJCYmAjgf5PTvMEYq7Ts38aOHYvDhw8DeH0ZkKurK0xNTQG87onSrFkzREdHV7jFxcVh0aJFsnWoq1ccE4jH48lqD8pK7j26q6srRo0aBQBo3bo1YmJikJWVhczMTHzwwQcKC1gfgoODMXfuXCxbtgxRUVHo168fhgwZgpSUlArtpFIp1NTUIJFI6u2NFQgEcJ83E82yowCm3B8motx+nDcL1sWx4JW94joKISqBMYbSMhpLgdSNnJwclKtpch2D1FBeXh5egQYB5ZKGhjodE1eTpqYmzAx16NiPvBePx4OzszPWrFmDqKgoaGho4OLFi2jevDmuXbtWoW14eDg6dOjw1nV98803uH//PiIiInD8+HGMHTtWdp+DgwMyMjIgEAjQtm3bCrc3xZfq0NDQULpZiBV6qsTY2Pid1Stl5evri8mTJ2PKlCno0KED/Pz8YGFhgcDAwArtRo8ejZs3b+L69esYP358veWz79oFU778BGYvI2lHQuRmaGgIb4/lsM75B7xSuiyIkPfJyMiAWE2b6xikgUpPT0cJjworqiY59RlKQO8blzQ1heBJqehdXTMmj4dZ3kOuYxAlduvWLXh6euLOnTtISUnBb7/9hpcvX6JDhw5YtGgRNm7ciODgYMTFxcHd3R3R0dGYM2fOW9dnbW2NPn36YPLkySgvL8fw4cNl9w0aNAi9e/fGiBEjcP78eSQlJSE8PBzLly/HnTt3qp3ZysoKt27dQlJSErKyspSiN0uj78soFosREREBd3f3CssHDx6M8PDwCsuEQuFbB+r5N5FIBJHof5fuFBTU/kfsiGGfAgD2/fon0po40ZRpRC5WVlbw3+yJRctWI17UHK8MWnEdiRCl9TA2Frk8vfc3JEQOsY+fopCnw3UMUkMPHj2BRMsQaiU5729M6oS2lhYgzeM6hsro0qUL+nSwxLmEVLzS537mFKJ89PX1ceXKFfj5+aGgoACWlpbw8fHBkCFD4OrqioKCAixYsACZmZmws7PDmTNn0K5du3euc+zYsXBzc8P48eOhpaUlW87j8XD27FksW7YMkyZNwsuXL9G0aVP0798f5ubm1c68cOFCTJgwAXZ2dnj16hUSExNhZWUl70ugEI3+4t6srCxIJJJKb6S5uTkyMjLkWqeXlxcMDAxkN0VN/zRi2KdYNXcqrLNugE87dCInMzMz7A3YipEdDdEs8zZ44hKuIxGilC5fv4VS7SZcxyAN1J3o+5DqVL/bM1EOCUkpYEIquHJJT0ebeqzUkPuCOegkzIF6kXy/bUjD1qFDB5w7dw6ZmZkoLS1FXFwcZs6cCeD1FMorV67Es2fPIBaLER0djY8//lj2WCsrKzDGYG9vX2GdM2bMAGMMBw8erLQ9PT09bNu2Dc+fP4dYLEZKSgp+/vln2W/m1atXIzo6usJj5s6di6SkJNnf7du3x40bN1BSUgLGGOdFFYAKKzI1HZTnXZYsWYL8/HzZLTU1VRERAQBOjg7Ys3UTemg8h1FuLKAE3Z6I6lFXV8eCOTOxZfkcdBY9hGFOLEAHKYTIMMbwOD4JTKjPdRTSADHGkJaRCaau9f7GRGmUlpaiUFQO0KDDnNLT0QEkdMxSEwKBAL4b1sJePQOahc+5jkNIg9To9wympqZQU1Or1DslMzOzRt2R/k0oFEJfX7/CTZGMjIyww9cbs0f2g2XmNagVK/fUU0R5tWvXDvsCtmLhVwPRLvsmdPLiqVhHCIDY2Fjk8/VoqmVSJ5KSklDEp8uAVE1kZCTy1Iy5jtHo6epoUY8VOWhra2Pb5g3oY/IKhrlxNLMSIQpWozFW5s+fX+22vr6+NQ7DBQ0NDTg6OiIkJAQjR46ULQ8JCakw0I6y4fF4+GzoJ+jv3Ac+2wIQmfAPXhh2BFOngRZJzfD5fAxxHYyPPvwAv53+HSfOnMUL9WYoNLAG+GpcxyOEEz8dPY4sbUuuY5AG6vTZ83ip0ZTrGKSGTv0VggKd5lzHaPQ0NTUhgBRUWqk5DQ0NeK9bg30HD+HUpVtIN+kGCDS4jkVIg1CjwkpUVFS12qnazEDz58/HuHHj4OTkhN69e2P37t1ISUnB9OnTuY72XoaGhli7cikePXoE720BSC3UQo6BDaDW6MclJjUkEAjw1ecjMWr4MPx57jyOnjiNl3xj5Oq3AQRCruMRUm+ys7MRl5wGZmbFdRTSAEkkEoTfjoCkSV+uo5AaePXqFeJT0sDMrLmO0ugJBALwedTbQl48Hg9TJo5HD4duWOezDSna7VCmS4VeQmqrRr++Q0ND6yoHp77++mtkZ2fDw8MD6enp6NSpE86ePQtLS9U5W2lra4t9/lsRevkK9hz8BemCpig0aE3XAZMaEwgEGP7pUAz7ZAjCw28g6JdfkVHKR6ZuazBNA67jEVLnAvbsxzOtNlzHIA3UlWvXkS0wpcvMVMyp3//ECw3qrUIaji5dOmN/gB88N21BZHIUMo060ayjhNQC/er+fzNmzEBSUhJEIhEiIiLQv39/riPVGI/HwwcuA3BoTwDchnRD68xr0MxPoWsoiVz4fD769nVGUOBW+C35AQP1s2GReQOCguf0mSINVnJyMv6JTYBUT74xtgh5F8YY9v98FHn6VLhTJeXl5Tj153m80ledE24NWVlZGSSMCpOKoKOjg/Wrl2P51K/QJvsm1AvTuI5EiMqq9fUiMTExSElJgVgsrrD8s88+q+2qiZwEAgG+HDUSwz4ZgkNHgnHu0jU812mLMr1mXEcjKqp9+/bYtH41cnJycPjXE7gcfg0vNcxRqG9NZzdIgyGRSLBmgw/SDDpxHYU0UJfCLiNNqk9jGqiYE6fOIEO9GcCn85HKoLCoCBIeXfKuSM59eqObfVf4bg/EzZjbyDDqTLOWEVJDcn8rJSQkYOTIkbh//z54PB7Y/5/BfjO+ikQiUUxCIjdNTU1M/W4CRn8xCruCDuJaxHU817WBVMeU62hERRkbG2Pm9KmYNnkiLlwKQ/DJ08gUqdNlQqRB2B10EAnlRmC6ulxHIQ2QSCTCrgO/INekN9dRSA0UFhbi19NnUWROY+Ioi7QXWWA09pvCaWtrY/niBXj06BHW+2xFKsz+f1gB6h1ESHXIXViZM2cOrK2tceHCBbRu3Rq3b99GdnY2FixYgM2bNysyo8ry9/eHv78/50UmPT09LJwzExOzsuAXsAt3428j3aADmFCP01xEdamrq2OI60cY4voR4uLisO/QETxJjUG6ZiuU6TWnnTBROeE3buGPa5EobNKd6yikgdqyIxDPtaxpcHkV4+WzFc90bWnMOiWSnPoMUqER1zEaLFtbWxzYuQOHg4/ht78u4pl+J0i16fVWFuXl5UhJSam37bVq1QoCAe23qkPuV+nGjRu4dOkSmjRpAj6f///jMfSFl5cXZs+eXe0ZhBoyNzc3uLm5oaCgAAYG3J/NNzU1xbqVy5CYmIhN2wKQkM3w0rADXcpBasXGxgbe61YjNzcXh389gbDr1/BCoymKDawBPn0RE+X39Gk8NvnvRYZ5byoKkjpx504Ert5PQGkTJ66jkBoIvXwVkck5KDe15zoK+ZdnaRlgei25jtGgqampYdw3o+E66AOs37QFj7KfIcuoAx3XKYGUlBSM8fwZUmHd/7bki/JxZOm3aN26dZ1vqyGQ+3+HRCKB7v93lzY1NUVaWhpsbGxgaWmJuLg4hQUkimdtbY2ALZtw4+YtbN8dhGeC5ijWt6IfFKRWjIyM4DZtCr6fNAF/nQ9B8Mnf8RJ6yNZvCwg0uY5HSJVSU1OxeI0nUk170gEjqRPZ2dnYsC0QGaZ0CZAqSU9Px/a9B/HSzJnrKORfxGIxikRlgD4ds9YHMzMzbN3khUthlxEYdAipOjYo06XB3bkmFRo0ml5ELi4usLe3h5+fH9dR3kvufo2dOnXCvXv3AAA9e/aEt7c3rl+/Dg8PD6pqqYjevXrip93+GN/bGhYvroNXms91JNIAqKur47NPP8HPewPg8f0XsBfFwCwrGjxxMdfRCKkgKSkJ85atQbJxdzB1Kv4RxROJRFi4bBWS9btQ71AVUlJSgh9XeCDFqBvAV+M6DvmXqKho5Kk1jh+UyuQDlwHYH+CHD81FaJIVBUjKuY5ESI38d6KduiB3YWX58uWQSqUAgHXr1iE5ORn9+vXD2bNnsW3bNoUFVCZisRgLFizAwoUL6+XNqQ8CgQBTvhuPQO816MbioZ/3hKbSJQrB4/HQq2cP7Avww+aFk+GEeJhlRYInLuE6GiGIunsP85avRZJxdzANba7jkAZIIpFg8Yo1iENLSLUMuY5Dqqm8vByLlq3CE43WNBadEjrx+1nka7fgOkajpKuri/Wrl2PZ5C9h9TIc/JIcriMRJfT777/D0NBQVieIjo4Gj8fDokWLZG2mTZuGMWPGIDs7G2PGjEHLli2hra2Nzp0748iRI7J2EydOxOXLl7F161bweDzweDwkJSUBeD0z8SeffAJdXV2Ym5tj3LhxyMrKkj3WxcUFM2fOxPz582FqaoqPPvqozp+73IUVV1dXjBo1CgDQunVrxMTEICsrC5mZmfjggw8UFrC2nj9/jm+//RYmJibQ1taGvb09IiIiKrQJCAiAtbU1NDU14ejoiKtXr1a5ruDgYDg4OMDZ2RmHDh2qj/j1plmzZti1zRcT+ndAsxc3AUnDKBwR5dCxY0fs3LoZmxdORTdJHEyy7wOSMq5jkUbq5Jk/sHKTP1LM+tB0kqROSKVSrPDwRGS+JkR6zbmOQ6pJIpHAfcUa3C0xRJkOXe6gbPLy8vAkNQNMU5/rKI1av759sHfbJnRXfwb9/Hiu4xAl079/fxQWFsrGW718+TJMTU1x+fJlWZuwsDAMGDAApaWlcHR0xB9//IEHDx7g+++/x7hx43Dr1i0AwNatW9G7d29MnToV6enpSE9Ph4WFBdLT0zFgwADY29vjzp07OHfuHF68eIGvvvqqQpaDBw9CIBDg+vXr2LVrV50/d7kKK2VlZRg4cCAeP35cYbmxsbFsumVlkJubC2dnZ6irq+Ovv/5CTEwMfHx8YGhoKGsTHByMuXPnYtmyZYiKikK/fv0wZMiQKkdblkqlUFNTg0QikVXhGhI+n4+J476B56IZsH55EzxREdeRSAPTsWNH7N7ui1VTRsEm7zZ08hOphxSpN2KxGGs8vbHzVBjSmvamSzNInZBIJFi+Zj3C0yWvxy8jKqG8vBzuK9bgdo46XulbcB2HVGHfwV+QpmnNdQyC1+Pq7fD1xiiHFjB/GQGwhve7iMjHwMAA9vb2CAsLA/C6iDJv3jzcvXsXhYWFyMjIwOPHj+Hi4oIWLVpg4cKFsLe3R+vWrTFr1iy4urri2LFjsnVpaGhAW1sbTZs2RdOmTaGmpobAwEA4ODjA09MTtra26NatG4KCghAaGlqhPtG2bVt4e3vDxsYGtra2df7c5SqsqKur48GDB0pVRKnKxo0bYWFhgf3796NHjx6wsrLChx9+iDZt2sja+Pr6YvLkyZgyZQo6dOgAPz8/WFhYIDAwsNL6Ro8ejZs3b+L69esYP358fT6VetWlS2ds916LtgVRNO4KUTgejwfnPr3x025/fNvLCi1fhFMRj9S55ORkTHabg7+Ty5Fl0oWmTiV1QiwWY9GylbiWwVBoQOPNqQqRSIQFS1bgZo4GFcOUVEZGBq5G3Ee5XlOuo5D/x+fzMWv695j9zTA0f3ETkNK4K+Q1FxcXhIWFgTGGq1evYvjw4ejUqROuXbuG0NBQmJubw9bWFhKJBOvXr0eXLl1gYmICXV1d/P333++dTjoiIgKhoaHQ1dWV3d4UTuLj/9eLysmpfmfik/vIcvz48di3b58isyjcmTNn4OTkhC+//BJmZmbo1q0b9uzZI7tfLBYjIiICgwcPrvC4wYMHIzw8vNL6hEIh/Pz8sGXLFgiFwrduVyQSoaCgoMJN1TRr1gzbN3nCOv8uUF7KdRzSAAkEAnw/aQL8vVagY+lDaBW8+0uUEHlIpVL8fCQYs5evR6x2F5Tq0xSdpG4UFhbCbf6PuJWnSz/OVUheXh5mzFuEfwoNUKLXius4pAqMMaz29MYz/Y40g6US+njwICyfNRktMm8BUgnXcYgScHFxwdWrV3H37l3w+XzY2dlhwIABuHz5suwyIADw8fHBli1b8OOPP+LSpUuIjo6Gq6vre8cylUqlGDZsGKKjoyvcnjx5gv79+8va6ejo1Onz/C+555YUi8XYu3cvQkJC4OTkVCm4r69vrcPVVkJCAgIDAzF//nwsXboUt2/fxuzZsyEUCjF+/HhkZWVBIpHA3LzidbTm5ubIyMiQe7teXl5Ys2ZNbeNzrkmTJvBYshA/btiONLOeXMchDVTLli2x198PXpv9cPVpDHKMOtCBE1GI5ORkrNngg8QyAxSYO9PnitSZ5ORkLF61Hgk6tpDomXIdh1RTUlIS3FevR7yOHaR6JlzHIW/x0y9H8aREC8zIgOso5C169uiOBdNE8N4TjIwmTrS/beTejLPi5+eHAQMGgMfjYcCAAfDy8kJubi7mzJkDALLeLN9++y2A1wWTJ0+eoEOHDrJ1aWhoQCKpWLBzcHDAiRMnYGVlBYFA7nKGwsndY+XBgwdwcHCAvr4+Hj9+jKioqAq3urJ69WrZqMBvu925cwfA6zfnzfVX3bp1w7Rp0zB16tRKl/n895ImxlitLnNasmQJ8vPzZbfU1FS518W1Dh1s0cPWCvyiTK6jkAZMXV0dK9wX4qu+HWCS84DrOETFiUQi+O0IxOwVG3BP3RYFRu3oII/UmUthlzFn+To8NXCARJuKKqriYmgY5i5fj6cGjpBqU1FFWd25E4Fjf19FnmF7rqOQ9xjQty+GOttDpyCJ6ygNHl+UD35Jbt3fRPINCfFmnJWff/4ZLi4uAF4XWyIjI2XjqwCvx0AJCQlBeHg4YmNjMW3atEqdG6ysrHDr1i0kJSUhKysLUqkUbm5uyMnJwZgxY3D79m0kJCTg77//xqRJkyoVYeqT3CWe0NBQReaotpkzZ2L06NHvbGNlZQXg9eUsdnZ2Fe7r0KEDTpw4AQAwNTWFmppapTcwMzOzUi+WmhAKhe+8VEjVfDduDG6t2Ix0XTOuo5AGjMfjYep3E5BfsB2/P0xEkQENUEdqhjGGi6Fh2H3wMJ5rWqHUvDfXkUgDVl5eji3bAxF6Nx4vzPoAfDWuI5FqKC8vx+atO3D5QRIyzel9U2YJCQlY7xeIdLPeVBxXEd9PmoDrN93wSNKSBoivI61atcKRpd/W6/bkMXDgQERGRsqKKEZGRrCzs0NaWpqsR8qKFSuQmJgIV1dXaGtr4/vvv8eIESOQn/+/gs7ChQsxYcIE2NnZ4dWrV0hMTISVlRWuX7+OxYsXw9XVFSKRCJaWlvj444/B53M3hl6NCivz58+vVjsejwcfHx+5Ar2PqakpTE2rd0bI2dkZcXFxFZY9fvwYlpaWAF53LXJ0dERISAhGjhwpaxMSEoLhw4crLrSKa9myJbRA46yQ+jFv5gzc/2E2HpY1palwSbXFxcVh07YAJJdqIse0N8BXnq6hpOFJT0/H0jXrEc/MUdzEges4pJrevG9PWVOUmNL7psxSUlKwaJUnUk270w90FSIQCDDhm6/gdTQUBUbUy6guCAQCtG6t/IOjb968GZs3b66wLDo6usLfxsbGOHXq1DvX0759e9y4caPS8nbt2uG333576+PezEpUn2p05PnfS3wiIiIgkUhgY2MD4HXRQk1NDY6OjopLWAvz5s1Dnz594Onpia+++gq3b9/G7t27sXv3blmb+fPnY9y4cXByckLv3r2xe/dupKSkYPr06RwmVz58OlNA6omamhp+mDweK3afRI6x3fsfQBq19PR0+O7YiZjnuXhhYAdmrM11JNKAMcbw57nz2Hf4BFINu4Jp6nMdiVQDYwxn/jiLA7+eQqqRPZhQj+tI5B2ePo3H4jVeSDZ2ohMsKsilfz/sOnQMBaDCCmlcalRY+fflP76+vtDT08PBgwdhZGQEAMjNzcV3332Hfv36KTalnLp3746TJ09iyZIl8PDwgLW1Nfz8/DB27FhZm6+//hrZ2dnw8PBAeno6OnXqhLNnz8p6tZDXMx2IpTQ1Kak/Pbp3h0HgfuRwHYQorZycHOzYtQ8RjxKQpmsLaZM2XEciDVx+fj5We3rjYVYZss2dacpuFZGXl/f6fcuWIofeN6UXERmFdb7+SDXtAaauyXUcIgcNDQ1oa1CvUdL4yP2p9/Hxwd9//y0rqgCvr51at24dBg8ejAULFigkYG19+umn+PTTT9/ZZsaMGZgxY0Y9JVI95/6+gJfqTbiOQRoRPp8PoYCueyeV5efnY1fQQdyIeojnOm0hMaNxVEjdu3ApFIH7f0GKnh2kxjRAraq4cOkSAvcfpvdNRZz+4yz2Bp9BmllvuvxHxfH51NOdND5yF1YKCgrw4sULdOzYscLyzMxMFBYW1joYUQ6lpaX49fSfeNWkL9dRSCPCGIOYw1G9ifIpKCjA3gM/49qdaDzXboNyc2euI5FGIC8vD2s3+uDBi1fIMnOmgU5VRE5ODtZu9MHDl2Jk0/um9N4MKBz2MBUvzXtRryIVxxhDqbiM6xiE1Du5CysjR47Ed999Bx8fH/Tq1QsAcPPmTSxatAijRo1SWEDCrfXevnim3RbgcIRl0vg8fvwYRTwdrmMQJZCXl4d9B3/B9Yi7eK7VBuVmzjQ7BKlzjDH88dc57D9yAql6dpCatOM6EqkGxhhO//4nfjp2Cqn6HSE1oWmUlV1OTg4Wr/BAXLkJik26ch2HKEBqaiqKeTQ2Dml85C6s7Ny5EwsXLsS3336LsrLXVUmBQIDJkydj06ZNCguoyvz9/eHv78/pfNq1cfCXI7iVlAexSSeuo5BGZteBn/FSx4rrGIRDOTk52HPgEG5GxyBNuzUVVEi9SU9Ph8cGHzwpEiDXrC+dWFARz58/x5oNmxH/ShN55n2p14MKuP3PHXhv24lkg86Q6hu9/wFEJfx05BheaFpwHYOQeid3YUVbWxsBAQHYtGkT4uPjwRhD27ZtoaNDZ5nfcHNzg5ubGwoKCmBgYMB1nBo59tspBP99E9k0jSSpZ//cuYNH6QVgTegMcWP08uVL7Nx3EJExT/FcpzUkdMkPqScSiQQ/HT6K039fxjPDzmDGqrXfbqzKy8sR9NMv+DM0HM8NO4MZ0UxNyq68vBzbAnfjYsQjZDSh8VQakoyMDNx5+ARS8z5cRyGk3tV6yGYdHR106dJFEVmIEmCMYf+hX3Ds0h1kNnGgM8SkXmVnZ8N72068MKUBSRubjIwM+O/Zj/tPU/Bcuw0dlJF69fjxE6zbtAUpaIJCc+odpSoePnwIL9/tSFFrhmLzPvS+qYBnz55h+doNSIAZips4cR2HKBBjDGs2bMZzfTuuoxDCiVoVVq5evYpdu3YhPj4ex48fR4sWLXDo0CFYW1ujb18a7FTVlJeXY91GH1xPyEWOaTc6QCH1qri4GPPcVyBJvwudvWpEnj9/jh27ghCTnI40nXaQ0iw/pB6JRCJsDdiFq9FxSDfqCqZO4wKogpKSEvhsC8Ct2GRkGDnQtLwqgDGG307/jkMnfsczo25gQl2uIxEF23/oF8QVa0JqaMh1lAatvLwcKSkp9ba9Vq1aQSCg6bOrQ+5X6cSJExg3bhzGjh2LqKgoiEQiAEBhYSE8PT1x9uxZhYVUJmKxGEuWLAGPx4Onpyc0NDS4jqQQOTk5+HHFGsSVN0GJMVWaSf0qLi7G7IXuiFNvA6mWIddxSD149uwZtu/ah9iUTKTrtoPUzJLrSKSRuRMZhc3bApGqaYVSs55cxyHVdO16OLbtCkKKdluUmXXnOg6phry8PKxctwExeTzkmjvT+DcNUPiNWzhx8Sbym9D/ybqWkpKC6C1foqlW3Y/hmfFKDZh3DK1bt67zbSmDsLAwDBw4ELm5uTCUo0Ao9zfbunXrsHPnTuzZswfq6v87u9ynTx9ERkbKu9oauXLlCoYNG4bmzZuDx+Ph1KlTldp4eXmhe/fu0NPTg5mZGUaMGIG4uLhK7QICAmBtbQ1NTU04Ojri6tWrVW4zODgYDg4OcHZ2xqFDhxT9lDgRFX0X389ZhLtojRL9VlzHIY1MXl4eZsz7EQ9giXKdJlzHIXUsPT0d7is8MGOZFy7lGuK5WU9ItY25jkUakZKSEqxatwErtx3AE+MeKNVryXUkUg0FBQX4cflqrNt7AvGmfVCm15zrSKQaroXfwJRZC3GjxAy5Rh2oqNIAxT56hI3+e5FhSkMI1JemWhK01K37W30Ub/5r4sSJGDFiRL1vVxHk7rESFxeH/v37V1qur6+PvLy82mSqtuLiYnTt2hXfffcdPv/88yrbXL58GW5ubujevTvKy8uxbNkyDB48GDExMbKBdoODgzF37lwEBATA2dkZu3btwpAhQxATE4NWrSoWGqRSKdTV1SGRSCCVSuv8OdYlqVSKoIM/41ToTaQ36Q2oNYzeN0R1PHv2DAuXeyBex45+XDdwWVlZ2LZzL+4+SUGarg2kZlZcRyKN0D937mDT9l1I0WqLsiaOXMch1RR25Sp27D2IFF1bSEwbx5lTVScSieC9ZTvCHz1DZpM+gBpdStAQJSQkYNn6zXjWpCfAp/eYNG5yl42bNWuGp0+fVlp+7dq1eusuNGTIEKxbtw6jRo16a5tz585h4sSJ6NixI7p27Yr9+/cjJSUFERERsja+vr6YPHkypkyZgg4dOsDPzw8WFhYIDAystL7Ro0fj5s2buH79OsaPH18nz6s+5OfnY9aCxfgl/AnSzXpSUYXUu6i79zDbfTWeGnSjokoDVlRUBJ+t/pgyfxn+StPAM7NekGrTtJqkfolEInhu8sWqHYcQb9ILZXrNuI5EqqGkpARLV63FhgOnkNikDyQ6ZlxHItWQmJiISTPm4FyS+PVECFRUaZCSk5OxaJUnUky6AwIh13GIEhGJRJg9ezbMzMygqamJvn374p9//pHd//DhQwwdOhT6+vrQ09NDv379EB8fj9WrV+PgwYM4ffo0eDweeDwewsLCAACLFy9G+/btoa2tjdatW2PFihUoKyuTrXP16tWwt7fHoUOHYGVlBQMDA4wePRqFhYXVzlVbcn/TTZs2DXPmzEFQUBB4PB7S0tJw48YNLFy4ECtXrlRYQEXLz88HABgbv/4hJxaLERERAXd39wrtBg8ejPDw8EqPFwqF8PPze+c2RCKRbMwZ4HX3VWVy7/4DrNvkh0RdO0gNTbmOQxqh03+cxd7gM0gzo2kWGyqJRILjJ0/h19N/4bl2G5TRtMmEI8nJyVi21guJaq1QSr1UVMbDhw/hsckPyVrtUGbSles4pJp+O/07Dh7/Hc+NHcA0tLmOQ+pIcnIyFixfi2QTJxr0m1Ty448/4sSJEzh48CAsLS3h7e0NV1dXPH36FK9evUL//v3h4uKCS5cuQV9fH9evX0d5eTkWLlyI2NhYFBQUYP/+/QD+95tdT08PBw4cQPPmzXH//n1MnToVenp6+PHHH2XbjY+Px6lTp/DHH38gNzcXX331FTZs2ID169e/N9eb7dSG3IWVH3/8Efn5+Rg4cCBKS0vRv39/CIVCLFy4EDNnzqx1sLrAGMP8+fPRt29fdOrUCcDr7ukSiQTm5uYV2pqbmyMjI0Ou7Xh5eWHNmjW1zqtojDEcDj6Go3+GIs20FyCgXiqkfkmlUvj578TfkU/x0rwXXWvdQN27fx/efv54xjdDoXlfep8JZ/746zz2HD6O58aO9CNPRTDGcPDnIzgRchVpJj3oTLiKEIlEWLthM24l5yPHvA997zdgKSkpWLB8LZKMncDU6XuVVFRcXIzAwEAcOHAAQ4YMAQDs2bMHISEh2LdvH3Jzc2FgYICjR4/Kxmlt37697PFaWloQiURo2rRphfUuX75c9m8rKyssWLAAwcHBFQorUqkUBw4cgJ6eHgBg3LhxuHjxItavX//eXIsWLar1c69V37z169dj2bJliImJgVQqhZ2dHXR1azd92urVq99blPjnn3/g5ORU43XPnDkT9+7dw7Vr1yrdx/vPYEuMsUrLqmvJkiWYP3++7O+CggJYWFjItS5FEYvFWL1+I24/L0GueS8aXIrUO5FIhKWr1iIym48CU3uu45A6UFxcjI2+2xARn44MI0f6QUQ4I5FIsNF3K67EPsdLmoVEZYhEIixbsw5RmVLkmdGxiqrIzs7GgqWr8ITXAqUmnbiOQ+rQs2fPMH+5x+uiChWrSRXi4+NRVlYGZ+f/9VRWV1dHjx49EBsbi4yMDPTr16/C5DfVcfz4cfj5+eHp06coKipCeXk59PX1K7SxsrKSFVWA10OXZGZmViuXItT6okdtbW25ihxvM3PmTIwePfqdbaysrGq83lmzZuHMmTO4cuUKWrb83wwApqamUFNTq9Q7JTMzs1IvluoSCoUQCpXnB0VBQQHmuS/HI2lTlBp14DoOaYQKCgowb/FyxLLmEBnSTA4N0Z07EfDeFogUnfYoa6K4fQIhNfXq1Sv8uGwV7hbropguIVEZOTk5mOe+Ao/VWkFsRGPgqIqEhAQsXu2JJP0ukGoZch2H1KHMzEzMX7YGSUbUA5C8HWMMwNs7LWhp1fzSsZs3b2L06NFYs2YNXF1dZT1efHx8KrT7b7GGx+PJJpt5Xy5FqFVh5eLFi7h48SIyMzMrzZATFBQk1zpNTU1haqq4cT8YY5g1axZOnjyJsLAwWFtbV7hfQ0MDjo6OCAkJwciRI2XLQ0JCMHz4cIXl4EpWVhbm/LgMjzVtIdUz4ToOaYRycnIwe9FSxAnbQ6pLY/o0NFKpFAG79+GvG3eRYUpj5hBuFRUVYfaiJYiBBcr0m77/AUQppKenY96SlYjX7QSpFg1urSruP3iIlRu2INW0O42z0cAVFhZinvsKJOh3BdPQ4ToOUWJt27aFhoYGrl27hm+++QYAUFZWhjt37mDu3LkoLi7GwYMHUVZWVmWvFQ0NDUgkFad5vn79OiwtLbFs2TLZsuTkZIXmUgS5Cytr1qyBh4cHnJyc0KxZM4VVemqiqKiowsxEiYmJiI6OhrGxsWyaZDc3Nxw+fBinT5+Gnp6erGeKgYGBrGI2f/58jBs3Dk5OTujduzd2796NlJQUTJ8+vd6fkyLl5uZi1qKleKLbGUzTgOs4pBHKzc3FzIVL8ES7I53JaoBEIhEWr1iD6FwBCpt0p277hFOvXr3C7IVL8JBvhXKdJlzHIdWUmZmJue4rEW/QDUxYu8vJSf15GBODFRu2ILUJjdnX0JWXl2PRslV4otkeTFP//Q8gdS7jlVq9baempyh0dHTwww8/YNGiRbLf5N7e3igpKcHkyZMhlUqxfft2jB49GkuWLIGBgQFu3ryJHj16wMbGBlZWVjh//jzi4uJgYmICAwMDtG3bFikpKTh69Ci6d++OP//8EydPnlRoLkWQu7Cyc+dOHDhwAOPGjVNIEHncuXMHAwcOlP39ZlyTCRMm4MCBAwAgmzLZxcWlwmP379+PiRMnAgC+/vprZGdnw8PDA+np6ejUqRPOnj0LS0vLOn8OdUUsFmOe+wo81elIRRXCiZKSEsz5cRmeaNtRUaUBKi0txayF7nhY3pQu7yKck0ql+HH5asTwLKiookJKSkowf8lKxOt3paKKCklLS8NKTx8qqjQS2wJ2I0ZkAIk+fbcqg1atWgHzjtXLtpq+2V4NbdiwAVKpFOPGjUNhYSGcnJxw/vx5GBm97pF46dIlLFq0CAMGDICamhrs7e1lY59MnToVYWFhcHJyQlFREUJDQzF8+HDMmzcPM2fOhEgkwtChQ7FixQqsXr1aoblqi8feXHBUQyYmJrh9+zbatGmjkCANWUFBAQwMDJCfn19pkJ264uG1CeeTy1Cq1/L9jVWA9csbOH4gkOsYpJoYY1iwZDnC8w1Qpqs83fE1sp/Ca/wHlQqtpGakUilmL3RHxCtTiJXo/a1PXYsjEBSwlesY5P/t2rcfR28mosCwLddRaownKsKQJgVYu8Kd6yj1buHSFbiWq48yXfnGtOOSICcRq7/siSEff8x1lHpVVlaGyTPmIEark0oWw5rm3kPAilkqffK0Pt1/8ADum3Yiw6wHpzlaZ93Asf30O4AoN7mHyZ8yZQoOHz6syCxEQR49eoRbcSkNpqhCVM+ZP//CvZcSpSqqEMXZHXQQDwq1Gm1RhSiXlJQUnL18CwUGdKJHlVy4FIp7GaUqWVRpzAL37kcCr7lKFlVIzUilUmzaGoAMYxoEnJDqqNGlQP+eQlgqlWL37t24cOECunTpUmnwGV9fX8UkJDW2c/8hvNCn2X8IN0QiEQ4F/4Zc875cRyF1ICsrC+eu3EShWR+uoxACANjivxvPDTrRGD8qRCKRYN+hI8g27sV1FFIDOTk5CA2/g1fmzu9vTFTezVu3kS7VBwTKM9MpIcqsRoWVqKioCn/b29sDAB48eFBhORcD2Sojf39/+Pv7VxrZuC5JJBKkpmeBmdvU2zYJ+bffz55DhrAlwJO7QxxRYkePn0SaVmv6EUuUQn5+PhIzssHM2nMdhdRAZGQUsvjGAL9Wk1OSenb81O9I17Ki7/9G4uhvZ5CrZ/3+hoQQADUsrISGhtZVjgbJzc0Nbm5usjFW6kNmZibE6jQNGuHOxcvX8EqfCnsNVcTd+5DoOXAdgxAAwD//3EGWgAZUVDXnLl1GnlYzrmOQGgq/fQdl+o5cxyD1JONlNlgTW65jEKIy6JRyA8Pn88GTbzxiQhSioLgEUKNZAhoqcbmEeiMRpRH7NBEiDZr+U9Ukp6RCSjMWqpwSUTl9/zcSIpEIYlY/U/oS0lDUeIyVtWvXQkdHp8J4K1VpqGOsiMViLFmyBDweD56entDQUK4fkKamptCQlHAdgzRmVNdr0ITqAoBJ6eCaKAWJlAp9qkgqlQKgy0lUDe3eGxe64ouQmqnR0UhUVBTKyspk/37XrT5cuXIFw4YNQ/PmzcHj8XDq1KlKbcrLy7F8+XJYW1tDS0sLrVu3hoeHx//v1P8nICAA1tbW0NTUhKOjI65evVrlNoODg+Hg4ABnZ2ccOnSoLp5WraipqaGtZQvwX+VyHYU0Uro6WoCkjOsYpI70cnKAekE61zEIAQC0at4UauJirmOQGjI2NgKv/BXXMUgNCdV4APWKbhSEQiHUIX1/Q0KITI0KK6GhoTA0NJT9+123+lBcXIyuXbtix44db22zceNG7Ny5Ezt27EBsbCy8vb2xadMmbN++XdYmODgYc+fOxbJlyxAVFYV+/fphyJAhSElJqbQ+qVQKNTU1SCSSSsUZZTHz+0loVhBLOz/CiY8G9IVW4TOuY5A68sWIYWj2KpG+X4hScHLoBlNpNtcxSA317ekEYVEG1zFIDdnZtAP/VQ7XMUg9aW5uAp6YesETUl1yT7f8LjweDz4+PnIFqokhQ4ZgyJAh72xz48YNDB8+HEOHDgUAWFlZ4ciRI7hz546sja+vLyZPnowpU6YAAPz8/HD+/HkEBgbCy8urwvpGjx6NxYsXg8fjYcOGDQp+RorRsmVLjPyoHw5djkG+EQ0iSurXp598jKOn/kCi1BLgUxf9hsbY2BgjPv4AP4XeRx59vxCOtWrVCibq5UiXiGlsJxXy4UAX/HT8dyQza7reQIWM/nw4wtf6IV3bhOsopB5888UIxAQeQ7ZJJ66jEKISajXdckREBCQSCWxsXh9cP378GGpqanB0VJ4Rw/v27YudO3fi8ePHaN++Pe7evYtr167Bz88PwOsxUyIiIuDu7l7hcYMHD0Z4eHil9QmFQtljldnEb79BfMJ6XElLRrG+JddxSCOiqamJ78Z8Cb9jF5FDO+MGafw3X+P+wxjcyHmGUr2WXMchjdy0id9izZ7jyDLpwnUUUk16enro62SPjIfPIdKn7xBV0bp1a9g1N8KL3CxIdUy5jkPqWHcnJ1hoHkKOuBhMg2YcJeR9anwp0JvbsGHD4OLigmfPniEyMhKRkZFITU3FwIEDZb1DlMHixYsxZswY2NraQl1dHd26dcPcuXMxZswYAEBWVhYkEgnMzc0rPM7c3BwZGfJ1UxWJRCgoKKhwq288Hg+rl7nD2ZxBP+9pvW+fNG6ffDwYPVrpQbMwlesopA7weDx4rVkBR918aBVUvmSSkPrUp3cvdDTThFrxS66jkBqY8f0ktBIlAuWlXEchNbB00TxYFsUAEjHXUUgd4/F4WPHjfLTIiaLLfwmpBrn76fv4+MDLywtGRkayZUZGRli3bl2tLgNavXo1eDzeO2//voznfYKDg/Hzzz/j8OHDiIyMxMGDB7F582YcPHiwQjvef7qiMsYqLasuLy8vGBgYyG4WFhZyrae2BAIBPNeswLDOTWH2MhKQSjjJQRqfN4W9zho50CiigU4bInV1dfhuWI8PWvJhnBNDB12EU2uWLUbrkjgaD0CFaGpqYrX7QrR4eQdQ0jHrSGWGhoZYuXAOmr/8h963RsDKygrjP/8Upjn3uY5CiNLjMSbf0bCenh5Onz6NDz74oMLyS5cuYfjw4SgsLJQrUFZWFrKyst7ZxsrKCpqamhWW8Xg8nDx5EiNGjKiw3MLCAu7u7nBzc5MtW7duHX7++Wc8evQIYrEY2traOHbsGEaOHClrM2fOHERHR+Py5cs1fg4ikQgikUj2N2MMYrEYpqamchdrCCGEEEIIIYQQonxqNMbKv40cORLfffcdfHx80KtXLwDAzZs3sWjRIowaNUruQKampjA1Vdx1myUlJeD/ZwBNNTU12Yw+GhoacHR0REhISIXCSkhICIYPHy7XNoVCIYRCofyhCSGEEEIIIYQQohLkLqzs3LkTCxcuxLfffouysrLXKxMIMHnyZGzatElhAd+lqKgIT5/+b/yQxMREREdHw9jYGK1atQIADBs2DOvXr0erVq3QsWNHREVFwdfXF5MmTZI9bv78+Rg3bhycnJzQu3dv7N69GykpKZg+fXq9PA9CCCGEEEIIIYSoJrkvBXqjuLgY8fHxYIyhbdu20NGpv1Gjw8LCMHDgwErLJ0yYgAMHDgAACgsLsWLFCpw8eRKZmZlo3rw5xowZg5UrV0JD439TMwYEBMDb2xvp6eno1KkTtmzZgv79+9fXUyGEEEIIIYQQQogKqnVhhRBCCCGEEEIIIaSxkvtSIKIYjDG5B/olhBBClJmenp5CBm2nfSUhhJCGSlH7SsItKqxwrLCwEAYGBlzHIIQQQhQuPz8f+vr6tV4P7SsJIYQ0VIraVxJu0aVAHKOzcO9XUFAACwsLpKam0pcOkRt9joii0Gep+qjHyvvR50k10fumeug9U02N4X2jHisNA/VY4RiPx2uwXxKKpq+vT68VqTX6HBFFoc9S/WkM+0r6PKkmet9UD71nqoneN6Ls+FwHIIQQQgghhBBCCFFVVFghhBBCCCGEEEIIkRMVVojSEwqFWLVqFYRCIddRiAqjzxFRFPosEUWiz5NqovdN9dB7pprofSOqggavJYQQQgghhBBCCJET9VghhBBCCCGEEEIIkRMVVgghhBBCCCGEEELkRIUVQgghhBBCCCGEEDlRYYUQQgghhBBCCCFETlRYIUohICAA1tbW0NTUhKOjI65evfrO9pcvX4ajoyM0NTXRunVr7Ny5s56SEmVWk89RWFgYeDxepdujR4/qMTFRNleuXMGwYcPQvHlz8Hg8nDp16r2Poe8j8j60j1M9NXnPfvvtN3z00Udo0qQJ9PX10bt3b5w/f74e05I36DhA9dB+lzQUVFghnAsODsbcuXOxbNkyREVFoV+/fhgyZAhSUlKqbJ+YmIhPPvkE/fr1Q1RUFJYuXYrZs2fjxIkT9ZycKJOafo7eiIuLQ3p6uuzWrl27ekpMlFFxcTG6du2KHTt2VKs9fR+R96F9nOqp6Xt25coVfPTRRzh79iwiIiIwcOBADBs2DFFRUfWcvHGj4wDVRPtd0mAwQjjWo0cPNn369ArLbG1tmbu7e5Xtf/zxR2Zra1th2bRp01ivXr3qLCNRfjX9HIWGhjIALDc3tx7SEVUEgJ08efKdbej7iLwP7eNUT03fs6rY2dmxNWvWKDoaeQc6DlB9tN8lqox6rBBOicViREREYPDgwRWWDx48GOHh4VU+5saNG5Xau7q64s6dOygrK6uzrER5yfM5eqNbt25o1qwZPvzwQ4SGhtZlTNIA0fcReRfax6me2uxP3pBKpSgsLISxsXFdRCRVoOOAxoO+I4myosIK4VRWVhYkEgnMzc0rLDc3N0dGRkaVj8nIyKiyfXl5ObKysuosK1Fe8nyOmjVrht27d+PEiRP47bffYGNjgw8//BBXrlypj8ikgaDvI/IutI9TPfK8Z//l4+OD4uJifPXVV3URkVSBjgMaD/qOJMpKwHUAQgCAx+NV+JsxVmnZ+9pXtZw0LjX5HNnY2MDGxkb2d+/evZGamorNmzejf//+dZqTNCz0fUTeh/Zxqqem79kbR44cwerVq3H69GmYmZnVVTzyFnQc0DjQdyRRRtRjhXDK1NQUampqlc4mZGZmVqpGv9G0adMq2wsEApiYmNRZVqK85PkcVaVXr1548uSJouORBoy+j8i70D5O9dRmfxIcHIzJkyfj119/xaBBg+oyJvkPOg5oPOg7kigrKqwQTmloaMDR0REhISEVloeEhKBPnz5VPqZ3796V2v/9999wcnKCurp6nWUlykuez1FVoqKi0KxZM0XHIw0YfR+Rd6F9nOqRd39y5MgRTJw4EYcPH8bQoUPrOib5DzoOaDzoO5IoLc6GzSXk/x09epSpq6uzffv2sZiYGDZ37lymo6PDkpKSGGOMubu7s3HjxsnaJyQkMG1tbTZv3jwWExPD9u3bx9TV1dnx48e5egpECdT0c7RlyxZ28uRJ9vjxY/bgwQPm7u7OALATJ05w9RSIEigsLGRRUVEsKiqKAWC+vr4sKiqKJScnM8bo+4jUHO3jVE9N37PDhw8zgUDA/P39WXp6uuyWl5fH1VNolOg4QDXRfpc0FFRYIUrB39+fWVpaMg0NDebg4MAuX74su2/ChAlswIABFdqHhYWxbt26MQ0NDWZlZcUCAwPrOTFRRjX5HG3cuJG1adOGaWpqMiMjI9a3b1/2559/cpCaKJM302/+9zZhwgTGGH0fEfnQPk711OQ9GzBgwDu/N0j9oeMA1UP7XdJQ8Bj7/9F+CCGEEEIIIYQQQkiN0BgrhBBCCCGEEEIIIXKiwgohhBBCCCGEEEKInKiwQgghhBBCCCGEECInKqwQQgghhBBCCCGEyIkKK4QQQgghhBBCCCFyosIKIYQQQgghhBBCiJyosEIIIYQQQgghhBAiJyqsEEIIIYQQQgghhMiJCiuEEEIIIYQQQgghcqLCCiGkUVi+fDmEQiG++eYbrqMQQgghSon2lYQQIh8eY4xxHYIQQupaQUEBDh06hJkzZ+LJkydo27Yt15EIIYQQpUL7SkIIkQ/1WCGENAr6+vqYNGkS+Hw+7t+/z3UcQgghROnQvpIQQuRDhRVCSKNRXl4ObW1tPHjwgOsohBBCiFKifSUhhNQcFVYIIY3G8uXLUVRURAeLhBBCyFvQvpIQQmqOxlghhDQKERER6NOnDz766CMkJibi4cOHXEcihBBClArtKwkhRD5UWCGENHhSqRQ9evTAgAED0LNnT4wdOxbFxcXQ0NDgOhohhBCiFGhfSQgh8qNLgQghDd727dvx8uVLeHh4oHPnzigvL0dcXBzXsQghhBClQftKQgiRHxVWCCEN2vPnz7FixQoEBARAR0cH7dq1g1AopGvHCSGEkP9H+0pCCKkdKqwQQhq02bNnY8iQIRg6dCgAQCAQoEOHDnSwSAghhPw/2lcSQkjtCLgOQAghdeWPP/7ApUuXEBsbW2F5586d6WCREEIIAe0rCSFEEWjwWkIIIYQQQgghhBA50aVAhBBCCCGEEEIIIXKiwgohhBBCCCGEEEKInKiwQgghhBBCCCGEECInKqwQQgghhBBCCCGEyIkKK4QQQgghhBBCCCFyosIKIYQQQgghhBBCiJyosEIIIYQQQgghhBAiJyqsEEIIIYQQQgghhMiJCiuEEEIIIYQQQgghcqLCCiGEEEIIIYQQQoicqLBCCCGEEEIIIYQQIqf/A3jbqrNJPRJxAAAAAElFTkSuQmCC\n", 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VV3B1dS025k3v3r1x9uxZBAQEoFevXtrlPXv2RGBgIAIDA9lbhai6q8KBcolqjOTkZNG7d2+xZ88ecfXqVRERESF+/PFH4ejoKEaNGqVt98/ZBZKTk0Vubq727ydnLMjMzBSdOnUSbdq0EXfv3hVxcXHa2+OR5c+ePSvWrl0rgoODRUREhPD39xf16tUTb7755nPzXrhwQVhYWAhvb2/x+++/i3v37omrV6+KJUuWiB49emjb3b9/XwQHB4tFixYJc3NzERwcLIKDg0VWVpYQQoiioiLRunVr8corr4grV66Iv//+WzRo0ECMHz/+uY+fn58vunfvLmxsbMSGDRtESEiIuHfvnvD39xeenp7aUfWnTZsmAgMDRUREhDh//rx44403hIWFhYiKinrmtn/99Vfx4Ycfil9//VXcvn1b3Lp1S6xatUoYGhqKb7/99qnrHD9+vFQzFgghxNq1a4WBgYGYM2eOCAsLE3fv3hVr1qwRSqVSTJs2rVjbO3fuiODgYDFmzBjRvHlz7etXUFCgfVxTU1Mxe/bsYu9vSkrKC3M8KSIiQtStW1e4ubmJ/fv3i/DwcBEaGiq+/PJL0bJlSyHEo5kq2rdvL7p37y6CgoLEhQsXhJeXV7EZf44cOSJkMpnYvXu3CA8PF59//rmwtLR87qxAAwcOFG5ubuLUqVMiJCREvP7666Jp06ZCpVIJIZ4+28XTZnD454xEzs7OwtLSUqxYsULcvn1bbNiwQRgaGoojR46U6bUhouqP+1juY6Xcxz42evRoYWNjIwwMDMT9+/eL3fd4VqC///5bxMXFiXv37omff/5Z9O7dW5iYmIhjx44Vax8RESEACAsLC/HDDz9olz98+FAYGRkJY2PjZ76eRFQ9sLBCpIP8/Hwxa9Ys4enpKaysrISpqalo0aKFmDdvXrGDuhdN2/jkQd/jA5Gn3SIjI4UQQgQFBYlOnToJKysrYWxsLFq0aCEWLFggcnJyim3X2dlZLFiwoNiy27dvi2HDhol69eoJhUIhnJ2dxZAhQ4pNzzh8+PCnPv7x48e1be7fvy/69+8vTExMhK2trRg/frzIz88v1Wvm6+sr2rRpI4yNjYWtra3w9vYWu3bt0k7j+P777wsnJydhZGQk6tWrJ95++21x8+bN52733r174pNPPtFO7WttbS06duz43Ck2y3LQJ4QQP//8s+jevbswMzMTxsbGwsvLS+zYsaNEu549ez73/XvW6/tkIePxwdqTr/nTxMbGCh8fH+Hs7CwUCoWoX7++ePPNN0u8V8+bblkIIT7//HPh6OgorKysxJQpU8T48eNLNd2ylZWVMDExEX379n3qdMtPKm1hZdGiReK9994TpqamwtHRUfj5+T33NSCimon7WO5jpd7HCvGo0AZA9OnTp8R9j7fz+GZqaipatWolxo0bJ+7cufPU7Tk7OwsAIi4urtjyJk2aCADiwYMHL8xERPpLJsRz5k4jomonLy8Ptra2OHz4MEeXr4YCAwPx1ltvISIiolSzDxARUdXhPrZ64z6WiCoLx1ghqmFOnDiBl19+mQd81dSRI0cwZ84cHvAREekh7mOrN+5jiaiysMdKGahUKsyePRsymQzLli2DQqGQOhIRERERERERSaha91g5efIkBgwYgHr16kEmk+HQoUPF7i8qKsK8efPg6uoKExMTNG7cGIsXLy4xavumTZvg6uoKY2NjeHl54dSpU099PH9/f3h6esLb2xt79uyprKdFRERERERERNVEtS6s5OTkwMPDAxs2bHjq/StWrMCWLVuwYcMGhIWFYeXKlVi1ahXWr1+vbePv74/Jkydj7ty5CA4ORvfu3dGvXz9ER0eX2J5Go4GhoSHUavVTp9QjIiIiIiIiotqlxlwKJJPJcPDgQQwaNEi77I033oCjoyO++eYb7bJ33nkHpqam2h4nnTp1gqenJzZv3qxt06pVKwwaNAi+vr7FHqOgoAAzZ86ETCbD8uXLoVQqK/dJEREREREREZFek0sdoDJ169YNW7ZsQXh4OJo3b46rV6/i9OnT8PPzA/BozJSgoCDMmjWr2Hp9+vTB2bNnS2xPqVRq132egoICFBQUaP8WQkClUsHe3h4ymaxcz4mIiIiIiIiI9Ee5CitXrlzBqVOnoFAo4O3tjbZt21ZUrgoxc+ZMZGRkoGXLltpLeJYuXYohQ4YAAJKTk6FWq+Ho6FhsPUdHR8THx+v8uL6+vli0aFGJ5RkZGbC0tNR5u8CjcWO+2rQVR4NuId62LSCvHb1mXJPOYf+uzS9uSKWSnZ2NWZ8vRmiWAhlWzYBaUvBTpNyF77CX0atXL6mjEBERUTnFxcUhLy+vTOu4uLjAwKBaj4ags9jYWEydswCJcgepo5SJRWYUfv7xe6ljED2XzoUVPz8/TJ06FdbW1pDL5UhOToa7uzt27doFLy+visyoM39/f3z33XfYu3cv3N3dERISgsmTJ6NevXoYPny4tt0/e5EIIcrVs2T27NmYOnWq9u/MzEw0bNhQ5+09uZ1pcz7HLZUdch06lnt7VDvFxcVh6pwFiDBpgSLrOlLHISIiIiqzhIQEjJkyE0XmdUu9jiwjBpvX+KJRo0aVmEw/JSQkYNKszxFp7QmhNJc6TpnYqVOljkD0QmUqrOzYsQPt2rWDu7s7li1bhuXLl2PGjBmQyWR48OABtm7dil69euGPP/5At27dKitzqc2YMQOzZs3C4MGDAQBt2rTB/fv34evri+HDh8Pe3h6GhoYleqckJiaW6MVSFkqlssLHX0lNTcX46bNxx7glNFZ2Fbptqj0e71QjrNpXu50qkdRycnJQWFgIADA2NoaxsbHEiYiIaq/v/A8g3ro11BalL6zUhQY1ZHjJMsnJycGUWZ8j0prHf0SVpUyFlVWrVuHu3bsAHs2Qc+nSJaxbtw6enp5o164dlixZgvr162P69Ok4f/58pQQui9zc3BJd/QwNDbUz+igUCnh5eSEgIABvvfWWtk1AQAAGDhxYpVmfJy8vD5M+m4s7pu7QmFhLHYeqqYKCAsyYtwgRlh7cqRKVUX5+Pj4Y8TFkFo96eTmZy7H5q7USpyIiqp2EELh0JQRqe2+po1QLi3xXIsKkKYTSQuooRDVWmQorYWFhyM/Px/Xr19G9e3cYGBjgxx9/xPz585Gfn48GDRqgSZMmCA4Oxq+//orWrVvD1dW1srIjOztbW+gBgMjISISEhMDW1haNGjXCgAEDsHTpUjRq1Aju7u4IDg7G2rVrMWrUKO06U6dOxdChQ9GhQwd06dIF27ZtQ3R0NMaOHVtpuctqld963JU7s6hC5bL7+32IQF0I4/KN80NUG0VFRSHfqiGSbVoDAGxzgiRORERUe8XFxSHHwLTWjBFXHsHBIbgek4lC+yZSRyGq0co8xoqxsTE6duwIb29veHh4wN/fHxqNBmFhYbh69SpOnjyJwMBAjBgxAmlpaTA3N0dmZmZlZMfly5fRu3dv7d+PxzUZPnw4du3ahfXr12P+/PkYN24cEhMTUa9ePYwZMwaff/65dp33338fKSkpWLx4MeLi4tC6dWscPnwYzs7OlZK5rBISEnD51n2oHDpJHYWqMbVajb8CzyDPQfpL9Iiqo4cxMcjU8NIfIiJ9cOv2baTJeKKoNL7esxdJVi2ljkFU4+k8eO2aNWvQq1cvREREYOzYsfDw8ECjRo1w5coV1KtXDw8fPsTDhw9x48aNisxbTK9evZ57naSFhQX8/PxeOEXyuHHjMG7cuApOVzF+O/IX4pUNpI5B1VxUVBRyjax4ZodIR4nJqVDLWVghItIHD2LioJKbSh1D7xUVFSEuOQPC0UTqKEQ1ns6FlXbt2iEoKAhjx45F586dtQUOuVyOHTt2AAAaNGiABg1YFCiPe5HR0BjbSB2DqrmcnBwUyoykjkFUbRUWFgKy2jk9JxGRvsnNy4cw0PlnTK2RkJAAlZGZ1DGIaoVyfSM1adIEAQEBSEhIwPnz56FSqdC5c+cKmVqYHrG0MIcsSYXaN345VaR69erBRJ0jdQyiasvczAxQF0kdg4iIAFiYm0Gmjn9xw1pOxp7KRFWmQkq9jo6OejWLjr7YuHEjNm7cCLVarfM2unfpiICw35BualuByai2sbe3h7WRGjFqFWCokDoOUbVT18EeSpGPXKmDEBERGjs3hIn6GrKlDqLnHBwcoCziiTWiqsB+zZXIx8cHoaGhuHTpks7b6PTSS6hTlASoCyswGdVGY0cNQ520m1LHIKqWGjRoAEvkSR2DiIgAuLm5wVpTOZNj1CRyuRxO9taQqXhagKiysbCi5+RyOab6jIFjSgjwnIF6iV6ka+dO6Nq8Hkwz70sdhajaadCgAYx51o+ISC9YW1vDQq4GhEbqKHrv42EfoE7mbaljENV4LKxUAy917IC3e3eETVqY1FGompszYwo8LXNgnPVA6ihE1YqhoSEsTRWAhgfxRET64JXu3lBkPJQ6ht5r5+EBdycLGOYkSh2FqEZjYaUMVCoVpk2bhunTp0OlUlXpY48ePhQDPF1gl3KdPVdIZ3K5HKuXfYFudQphlXaHnyWiMmjXpjUMcpKkjkFERADeGTQAdQt4oqg0FsyeAdec25AV8pJWospSrsLKqVOn8NFHH6FLly6IiYkBAOzZswenT5+ukHAvcvLkSQwYMAD16tWDTCbDoUOHit3v6+uLjh07wsLCAg4ODhg0aBBu3y7ZFW7Tpk1wdXWFsbExvLy8cOrUqac+nr+/Pzw9PeHt7Y09e/ZUxlN6JplMhkk+YzH01Q5wSrzAMVdIZ0ZGRli+eAHe7eSKukmX+FkiKqXe3TrDpjBZ6hhERATA0tISXq2bwzA7Qeooes/CwgIrFs1Fw+RLQFHVnhwmqi10LqwcOHAAffv2hYmJCYKDg1FQUAAAyMrKwrJlyyos4PPk5OTAw8MDGzZseOr9J06cgI+PD86fP4+AgAAUFRWhT58+yMn533Xy/v7+mDx5MubOnYvg4GB0794d/fr1Q3R0dIntaTQaGBoaQq1WQyNRd/ChH7yPzyeMgkvSORjkpkqSgao/mUyGTz8ZhUUTR6FJynnIeVBC9EJubm6wKEqXOgYREf2XzyejUD+bPXBLw8XFBV/MnoqGSeeAogKp4xDVODoXVpYsWYItW7Zg+/btMDIy0i7v2rUrrly5UiHhXqRfv35YsmQJ3n777afef+TIEYwYMQLu7u7w8PDAzp07ER0djaCgIG2btWvXYvTo0fj444/RqlUr+Pn5oWHDhti8eXOJ7Q0ePBjnz5/HmTNnMGzYsEp7Xi/yUscO2P7lSnQ0egjr1FscuIt01sHLEzs2rsMrDvlwSLrCsxhEzyGXy2FvZc5eXkREesLGxgavdn8Jxpm8JKg0Wru7YcXnM+GafAGyAk5WTVSRdC6s3L59Gz169Cix3NLSEunp6eXJVGkyMjIAALa2tgAejZkSFBSEPn36FGvXp08fnD17tsT6SqUSfn5+WLduHZRK5TMfp6CgAJmZmcVuFc3W1hYb163C2P4voVH8GRjkpVX4Y1DtYG5ujmWL5mPx+KFonn4RphlRPPND9AydO3rCMJsDABIR6YuPhw9F/YL7gKZI6ijVQovmzbF+5RdokX0VRhzQlqjC6FxYcXJywt27d0ssP336NBo3blyuUJVBCIGpU6eiW7duaN26NQAgOTkZarUajo6Oxdo6OjoiPj5e58fy9fWFlZWV9tawYcNyZX8WmUyGd98ehK1rl6KLcRzsUq5xp0I669jBC7u3bsDwLi5olHAaBjkcS4Lon7p09IKtmoVsIiJ9oVQqMWbER7BLvyV1lGrDyckJ2zesw0smSbBMv8sTakQVQOfCypgxYzBp0iRcuHABMpkMsbGx+P777zF9+nSMGzeuIjNWiPHjx+PatWvYt29fiftkMlmxv4UQJZaVxezZs5GRkaG9PXhQud0THRwc8NXq5Zg9/E00ST4HZSanniPdKBQKfDxyGL72W45X6+TAKekSZKqcF69IVEs0adIEZuosqWMQEdETXu7VA43N1JAV8Pu5tExNTeG3yhfvdXLhZAZEFUDnwspnn32GQYMGoXfv3sjOzkaPHj3w8ccfY8yYMRg/fnxFZiy3CRMm4JdffsHx48fRoEED7XJ7e3sYGhqW6J2SmJhYohdLWSiVSlhaWha7VYXePXvg263r8X+trdEg4Rxk+RV/CRLVDnZ2dvBdNB/r5k5Ee3X4o2m+1Rx/hcjIyAimRuWaUI+IiCqYTCbD7GkTUS/jptRRqhUDAwN8+vEoLJwwAq7J52CQyx6ZRLoq19Hh0qVLkZycjIsXL+L8+fNISkrCF198UVHZyk0IgfHjx+M///kPjh07BldX12L3KxQKeHl5ISAgoNjygIAAdO3atSqjVhhjY2NMnTAOG5bOQUeDKNil3ODlQaSzZs2aYdv6tVj4yTtomX4JlmnhgEYtdSwiSdnb2iA/lz25iIj0ibOzMzq2aszpl3XQsUMHbPNbCS/D+7DgpUFEOin3aTdTU1N06NABL730EszNzSsiU6llZ2cjJCQEISEhAIDIyEiEhIRop0r28fHBd999h71798LCwgLx8fGIj49HXl6edhtTp07F119/jR07diAsLAxTpkxBdHQ0xo4dW6XPpaI1bNgQm/xWYfbw/miSfI6jpZPOZDIZunbpjG+3b8LENzujcfJZGGfc506Xai1Xl0ZIT+GAf0RE+mbC2I9RP6fkGJD0Yra2ttjktxpDOrvCKfECeyoTlZG8LI2nTp1a6rZr164tc5iyunz5Mnr37q39+3G+4cOHY9euXdopk3v16lVsvZ07d2LEiBEAgPfffx8pKSlYvHgx4uLi0Lp1axw+fBjOzs6Vnr+yyWQy9O7ZE106dcKm7TsQeOk8Yq1bQyirtgBGNYOhoSHeevMN/Kvva9j13V4cCTyNGPMWUJs7SB2NqEo1blQfuVm81JKISN9YW1ujk0crPLyTAI2F7pf111YGBgYYM3okXurghWVrvkKUuTs0ZnZSxyKqFspUWAkODi5Vu/IM/FoWvXr1gnjOWfPn3fekcePG6eWAuxXl8eVBb0VG4ouV6xCZa4VM66ZAFb1PVLMolUqMGT0S77/zFtZt3IIr4RcQZ+XOgh3VGg516kgdgYiInmHkR0NwbuYSPGRhRWftPdpi+1erMXvhEoSlpyPLuonUkYj0XpkKK8ePH6+sHFQFXF1d8fVGP+z7cT9+/P1vxNi0449h0pm1tTUWzZ2FiIgILF+7HpEpCqTatAAMyvS1QlTtVNWA5EREVHaOjo6wMzXAQ00Rj0nKwdraGhvXrsSGrV/jyIXLSLBrDxgYSh2LSG9xaoNaxsDAAB8Ofg8bfT9H64JQmGXelzoSVXONGzfG1vVrMfOj19E46SyMsmKljkRUqap6PDEiIiqbnl07wTAr/sUN6bkMDAww8dN/Y8bId9Ag4SxkhflSRyLSWzqXcZ813opMJoOxsTGaNm2KgQMHwtbWVudw1d3GjRuxceNGqNX6N4tKgwYN8PVGP6xbvxnHrl5Bol07wIB1NtKNTCbDqy/3RtfOnbBuwxacvXkJ8bZtAblS6mhEFc7Y2FjqCERE9Bzt27aG1fFrSJU6SA3Ru2dPNKhXD7MXL0eUjRd7vBM9hc6FleDgYFy5cgVqtRotWrSAEAJ37tyBoaEhWrZsiU2bNmHatGk4ffo03NzcKjJzteHj4wMfHx9kZmbCyspK6jglyOVyzJgyAR5/H8OG3f6IqfMSYGgkdSyqxkxNTTH3s6m4eu0afNeux32TplCZO0kdi6hCKRQKqSMQEdFz1KlTB8bgrDYVqVmzZli/cgkmz/4c9yw8IIx5WSzRk3TuojBw4EC8+uqriI2NRVBQEK5cuYKYmBi89tprGDJkCGJiYtCjRw9MmTKlIvNSJejz6stYPMMHDRPPAepCqeNQDeDRti12bv4KvRxUsE25wamZqUYxNOQ15kRE+qy0E1hQ2Tg5OWH9yqVonBkCmSpH6jhEekXnwsqqVavwxRdfFBvEz9LSEgsXLsTKlSthamqKzz//HEFBQRUSlCpXu7ZtseiziWiQdAEQGqnjUA1gYmIC30Xz8e83uqJewnkW7ajGqKqZ74iISDcJCQnIk/Fy5Mrg4OCANUsWoFFKEKAukjoOkd7QubCSkZGBxMTEEsuTkpKQmZkJ4NFo0ipVzemGp1KpMG3aNEyfPr1GPa/HPNq2xScfvAO71JtSR6Ea5N23B+HzSaPRIPEcoK55/2+o9tFoWHwmItJnZy5cRqbcRuoYNVbDhg3x2YR/wzE1ROooRHqjXJcCjRo1CgcPHsTDhw8RExODgwcPYvTo0Rg0aBAA4OLFi2jevHlFZdVZTEwMPvroI9jZ2cHU1BTt2rUr1pNm06ZNcHV1hbGxMby8vHDq1Kmnbsff3x+enp7w9vbGnj17qip+lXqjX1+0tJNDlp8pdRSqQTp26IAvPpv0qEeURv8GcyYqi8JC9r4iItJXarUap89fgtrcQeooNVrnTi+hS8tGMMqKkzoKkV7QubCydetWvPLKKxg8eDCcnZ3RqFEjDB48GK+88gq2bNkCAGjZsiW+/vrrCguri7S0NHh7e8PIyAh//PEHQkNDsWbNGlhbWwN4VCyZPHky5s6di+DgYHTv3h39+vVDdHR0iW1pNBoYGhpCrVbX6DOWkz/9BHVz7kkdg2qYtm3bYOq/h6NuSrDUUYjKJTc3V+oIRET0DD//+jsS5A6AjLNdVrYp48eifu49jqVHhHLMCmRubo7t27dj3bp1iIiIgBACTZo0gbn5/6bfateuXUVkLJcVK1agYcOG2Llzp3aZi4uL9t9r167F6NGj8fHHHwMA/Pz88Oeff2Lz5s3w9fUttq3Bgwdj5syZkMlkWL58eZXkl0Ljxo1hKcsD689U0Xp274azFy7h1zsxKLCsL3UcIp2kp6dLHYGIiJ4iISEBew78gkzHblJHqRXMzc3h1aYVoiNToDGzlzoOkaTKXco1NzdH27Zt4eHhUayooi9++eUXdOjQAe+++y4cHBzQvn17bN++HcCjMVOCgoLQp0+fYuv06dMHZ8+eLbEtpVIJPz8/rFu3DkrlswfEKigoQGZmZrFbdWNuZsZLNqhSTBn/KerlRfDsBlVbDx/GSB2BiIj+ITc3F9PmLMAD63bsrVKF3uj7CmwLEqSOQSQ5nXusAMDRo0dx9OhRJCYmlrg0ZseOHeUKVlEiIiKwefNmTJ06FXPmzMHFixcxceJEKJVKvPrqq1Cr1XB0dCy2jqOjI+Lj43V+TF9fXyxatKi80SWlUasBBXdKVPGMjY3RpUN73A9LgobXP1M1dPnqDVjZ1ZE6BhER/VdWVhYmzpiNO8pmEMaWL16BKkyzZs1gos6WOgaR5HT+5bxo0SL06dMHR48eRXJyMtLS0ord9IVGo4GnpyeWLVuG9u3bY8yYMfjkk0+wefNmbZt/Tp0phCjXdJqzZ89GRkaG9vbgwQOdtyUFIQQyc/IATilKlaTvyz1go0qWOgZRmQkhcPNWOOwc6kkdhYiI8GiSijGTpuOmQWMUmbHoXdVMTExgINjLnUjnHitbtmzBrl27MHTo0IrMU+GcnJzg5uZWbFmrVq1w4MAB2Nvbw9DQsETvlMTExBK9WMpCqVQ+91IhfRccEoIMQyupY1ANVr9+fZhoOAAoVT/Xrl1HKsxRT8aZgYiIpPb3sePYuON7PLD1hFDq35AEtUFBQQEEL70i0r3HikqlQteuXSsyS6Xw9vbG7du3iy0LDw+Hs7MzFAoFvLy8EBAQUOz+gICAavHcKoMQAhu27USKRROpo1BNxx5RVM2o1Wqs2bAFyZbNpI5CRFSrZWVlYc7CL7B6z2+IdvRmUUVCkZGRyJPz9SfSubDy8ccfY+/evRWZpVJMmTIF58+fx7Jly3D37l3s3bsX27Ztg4+PDwBg6tSp+Prrr7Fjxw6EhYVhypQpiI6OxtixYyVOLo2f/nMI91XmEEYmUkehGiw2Nhb5BvyMUfUhhMCS5asRZeDE70ciIokIIfD7H39i5LgpOJpggiR7D8DAUOpYtdrRE6eQamgndQwiyel8KVB+fj62bduGv//+G23btoWRkVGx+9euXVvucBWhY8eOOHjwIGbPno3FixfD1dUVfn5++PDDDwEA77//PlJSUrB48WLExcWhdevWOHz4MJydnSVOXvVCw8Lw3cE/kF63dvbWoapz6txFpBlaSx2DqFQKCwux2HcVzj7IRY51c6njEBHVSjdu3MTq9ZvxQG2JdAdvFlT0gFqtxsmzF6Guw+mtiXQurFy7dg3t2rUDANy4caPYfeUZ+LUyvPHGG3jjjTeeef+4ceMwbty4Kkykfx48eID5y1Yjpk5nTlFHlUoIgRNnzkNt20XqKEQvFB5+B0tWrcM9w/rIZ1GFiKjKRUREYM2GLYhMUyHRqg17DeqRv/7+G4lGDry8mwjlKKwcP368InOQhB4+fIipcxfhvm1HQF59B92l6uHM2XNIllkDBizgkf6Ki4vD5q934crdh4i3aQthZCp1JCKiWuXu3bv4aus3iEzKRpxlSwh7C6kj0RMKCgqwc+9+ZNrzRFltFhUVBVdXVwQHB2s7XdRWOhdWHgsNDUV0dDRUKpV2mUwmw4ABA8q7aaoCd+/ew6xFvoiy9YJQ8IcDVa6ioiJs3vEtUq07SB2FqAQhBG7cuIFd+37C3dgUxJo2hcahk9SxiIhqlWvXrmHLzj2ITlMh3qIZRB1LqSPRU3y1eRtijF0Ag3L/nCQqs169eqFdu3bw8/OTOoqWzv8TIiIi8NZbb+H69euQyWQQQgD432VAajXnM9+4cSM2btyot6/FxcuXsezLrXho9xKEkbHUcagW2LPPHw8NHAFDhdRRiLRiY2Pxy+E/EXjmPNIMLJFi6gxRp6nUsYiIag21Wo0TJ0/j2x9+QnyhMZItmkLU4Qk/fXXt2nWcCL6NfIeXpI5CpDd07os/adIkuLq6IiEhAaamprh58yZOnjyJDh06IDAwsAIjVl8+Pj4IDQ3FpUuXpI5Swn9+/hVffPUNHtTpwqIKVYn79+/j0F8nkW3VWOooVMtpNBqEhYVh9Zcb8MHosRgzbyV2XorHHbsuSLZtA2HMs6NERFUhNzcX3+3zxwejP8WS7/7EdbN2SLJry17UeiwrKwtLVn+JOFsPqaNQBdq/fz/atGkDExMT2NnZ4dVXX0VOTg40Gg0WL16MBg0aQKlUol27djhy5MhTt6HRaNCgQQNs2bKl2PIrV65AJpMhIiICAJCRkYF///vfcHBwgKWlJV5++WVcvXpV237hwoVo164d9uzZAxcXF1hZWWHw4MHIysoCAIwYMQInTpzAl19+CZlMBplMhqioqMp5YcpA5x4r586dw7Fjx1CnTh0YGBjAwMAA3bp1g6+vLyZOnIjg4OCKzEkVRKPRYM1XG3H0WhSSHDpzsCmqEoWFhZi/dAVibDz4maMqJ4TAgwcPcPb8RZw8dxFJqRnIklsiRVEXGpsu/EwSEVWxuLg47NizD1du3EK8sgHy7bty8oRqQAiBeYuX4b55K47LWIPExcVhyJAhWLlyJd566y1kZWXh1KlTEELgyy+/xJo1a7B161a0b98eO3bswJtvvombN2+iWbNmxbZjYGCAwYMH4/vvv8fYsWO1y/fu3YsuXbqgcePGEEKgf//+sLW1xeHDh2FlZYWtW7filVdeQXh4OGxtbQEA9+7dw6FDh/Dbb78hLS0N7733HpYvX46lS5fiyy+/RHh4OFq3bo3FixcDAOrUqVN1L9gz6FxYUavVMDc3BwDY29sjNjYWLVq0gLOzM27fvl1hAani5OXlYeb8RbiWaYwsO1aZqeqs/nIDImT1IZTmUkehWqCwsBB3797FxaBgXLwSgqTUDOQamCHJ0AZF5s0ABx4MEhFVNSEEQkJC8M2eH/AgNQexJq7QOHKa3upk57ff43qaAYpspP8RSxUnLi4ORUVFePvtt+Hs7AwAaNOmDQBg9erVmDlzJgYPHgwAWLFiBY4fPw4/Pz9s3LixxLY+/PBDrF27Fvfv34ezszM0Gg1++OEHzJkzB8CjCXCuX7+OxMREKJVK7WMcOnQI+/fvx7///W8AjzoD7Nq1CxYWjwatHjp0KI4ePYqlS5fCysoKCoUCpqamqFu3buW+OGWgc2GldevWuHbtGho3boxOnTph5cqVUCgU2LZtGxo3rpld/VUqFWbPng2ZTIZly5ZBoag+40QkJydj6uzPES53QaGV/nwAqeY7fuIUTt68jzx7T6mjUA0khEBsbCxCw27hwpWruHsvElkFhciWWyHNwBpqs+ZAnerzXU1EVNMUFhbiyJ8B8D/4K5JgjhTzJhw/pRoKDrmK/xw9iwwO6l7jeHh44JVXXkGbNm3Qt29f9OnTB//3f/8HQ0NDxMbGwtvbu1h7b2/vYpfuPKl9+/Zo2bIl9u3bh1mzZuHEiRNITEzEe++9BwAICgpCdnY27Ozsiq2Xl5eHe/fuaf92cXHRFlUAwMnJCYmJiRX1lCuFzoWVefPmIScnBwCwZMkSvPHGG+jevTvs7Ozg7+9fYQGryqZNm7Bq1SrExcXB3d0dfn5+6N69e7E2/v7+8PT0hKmpKfbs2YPRo0dLlLZsIiIiMHPhMkRaeUAYW0kdh2qRxMRErP96NxIdvF/cmOgFhBCIj4/H7du3cfnqTdwKv4OMnDzkG5giTWaJAhNbaCy8AEt2JyciklpWVhb2/XQAAYFnkKioi2yrjoAhZ5CpjtLT07F0zVeIq9OVl8/WQIaGhggICMDZs2fx119/Yf369Zg7dy4CAgIA/G9ymseEECWWPenDDz/E3r17MWvWLOzduxd9+/aFvb09gEc9UZycnJ46Jqu1tbX230ZGRsXuk8lk0Gg0Oj7DqqHzt1vfvn21/27cuDFCQ0ORmpoKGxub577Q+sjf3x+TJ0/Gpk2b4O3tja1bt6Jfv34IDQ1Fo0aNtO00Gg2MjIygVqv1/o197Nr1G1iwwg8P7DtCGJlIHYdqEbVajTkLlyLa2gMwMJQ6DlUzarUaDx8+xK3wcARdvYm7EZHIyi1AgaEJUmGBAqU1NCYegCk/W1S9CCFw+sxZbN35HVQaoLAgD906d8S0iT5SRyOqECkpKdi+aw8uXg1DrHEjFDp044/xakwIgTkLl+K+ZWvA0OjFK1C1JJPJ4O3tDW9vb3z++edwdnbG0aNHUa9ePZw+fRo9evTQtj179ixeeunZM0J98MEHmDdvHoKCgrB//35s3rxZe5+npyfi4+Mhl8vh4uKic16FQqF3M+9WaNn48WAz1c3atWsxevRofPzxxwAAPz8//Pnnn9i8eTN8fX217QYPHoyZM2dCJpNh+fLlUsUtteCQq1i4egMeOnTm9LZU5XZ/tw93C60gzNhLip6vsLAQUVFRuBl2C0FXb+D+wxjk5BciX26ONJij0MQWGtP2gBl7olD1VVhYiL/+Pop9B35GgsYSqdYdAEMjyAqykZ6RKXU8onJLSUnBhq3fIOR2BGJMG0PtyN6qNcFe/59wK1sJjY3dixtTtXThwgUcPXoUffr0gYODAy5cuICkpCS0atUKM2bMwIIFC9CkSRO0a9cOO3fuREhICL7//vtnbs/V1RVdu3bF6NGjUVRUhIEDB2rve/XVV9GlSxcMGjQIK1asQIsWLRAbG4vDhw9j0KBB6NChQ6kyu7i44MKFC4iKioK5uTlsbW1hYCDtcWKt74+nUqkQFBSEWbNmFVvep08fnD17ttgypVIJPz+/F26zoKAABQUF2r8zM6v+gCns1i0sXL0eDx26sLpMVe7hw4f4+e9TyHLsKnUU0jMqlQoRERG4fjMUQdduIiY2HjkqNfLklkiVmUNtYgth0RGw5NlNqhlyc3Phv/8gDv8diCRFXWRZduB+WU8JIZCeno6EhATExcXj9r1IhN+LRGJyCnILiiCeaJuXnoTPpkzAy717S5ZXX2RnZ2PLN7twJug6Hpo1h8aB+/6aIj4+Hj/9/jcyWCSr0SwtLXHy5En4+fkhMzMTzs7OWLNmDfr164e+ffsiMzMT06ZNQ2JiItzc3PDLL7+UmBHonz788EP4+Phg2LBhMDH531UTMpkMhw8fxty5czFq1CgkJSWhbt266NGjBxwdHUudefr06Rg+fDjc3NyQl5eHyMjIcvWAqQgyIYR4cbOaKzY2FvXr18eZM2fQtev/dgTLli3D7t27dZrhaOHChVi0aFGJ5RkZGbC0tCxX3tJITEzEuGlzEGnfqcZMheaadA77d21+cUPSC+OmzMDFImcI48r/vJeFIuUufIe9jF69ekkdpVZ4PLDs9Rs3ce5yMCLuRyOnQI1cIyukwRxqUzsIhVm17CLukROEHZu+lDoG6SkhBO7evYufDv2GoOuhiDduhHzLhk+dTlZWkI1+dTLxxfxZT9kSlZdKpUJGRob2lpaWjvikZMQnJSM5JRWpaWkoUBWioFANVZEGakMlCgyMkaVRolBhDo2x1VMvpZanRmLhu53Q7/XXJXhW+kEIgV9+O4w9Px3EQ9OmKLSoJ3WkF6qbdg2b5k/QznxCzzdpxmyczXOCxsRa0hyNk8/hp538HUD6rdb3WHmsrIPyPM/s2bMxdepU7d+ZmZlo2LBhufKVllqtxuyFSxBl7VFjiipUvQSHhCAyXQNhr19FFap8Qgjcv38fZy9cxOnzl5CUloV8AxOkyCyhMq0DYflStSyiED2PEAIpKSmIiorCtZu3EHz9JhKSU5FtaI4kZX1oOL5EhRFCIDs7G2lpaUhPT0dqaioSU1KRkJSKlNRUpKalIzs7B4VqNQrVGhSpNVALGTRyYxTKjJAPI+Rq5NDIjSHkxhBGdSCMGgBKjtVUVikpKZi32Bfh2UZIr9MNkLgLPlW8sLAw3EnKg6aOtdRRiKqFWl9Ysbe3h6GhIeLj44stT0xMLFN3pCcplUrtvNxV7ccDB3FPZQ1hw3EtSBrbdn2HJKvmUsegKqJSqXDu/AUcOvwnYuKTkGNojmQDGxSZt+A0x1Qj5ObmIjU1FSkpKUhMTMT9mHg8iIlFXEICcvMKkK8qgsrQGNkyc2TLLaA2bQzUaSV17GqpoKAAMTExiI5+gIj7D3D/YQwSEpOQm18AVZEahUUaqA2MUGioRAGMkKsxQpGh4lGRRG4KYWQLmBmxkFXJrl27jkWrvkS0VRtobG2kjkOVZPOOb5FoyeM5otIqU2HlyV4YL7J27doyh5GCQqGAl5cXAgIC8NZbb2mXBwQEFBtopzpQqVQ48NsRZDl0kzoK1VIpKSmITc+HqMMZqGq627fD8fW3exH5MA4pRvbIMm8IUYcHYKT/CgsL/3tJSBoyMjKQkpqK+KRkJCSlIDnlUU+IfFUhitQaqIrUKIIhigyNkS9TIFujgMbIFBqFGYSiDXs6VICIiAjs2vsT7kXdR26hQIHcHJkwgUpu9t/X2QGw5OusLy5evowlftsQw4kRarSsrCw8SEyHcGCRmKi0ylRYCQ4OLlW76jbd8tSpUzF06FB06NABXbp0wbZt2xAdHY2xY8dKHa1MTp85i2RF3adew01UFU6cOoNEuYPUMagSZWVlYfnar3A1Mh7xli0hHBpLHYkIAKDRaJCcnIz4+HjExsUjOiYOsfEJSEpORk5uPgrVGqgK1SgCoDZQotBAiXwhRx6M/ndpiNwBQtEIMOZ+tKpMmzUPD23bQ2PdiT1N9FxiYiKW+21BjENXwLDWd3qv0S5cvIRkeR2pYxBVK2X6Vjx+/Hhl5ZDU+++/j5SUFCxevBhxcXFo3bo1Dh8+XO0Gtjpx7gKyTepKHYNqsQtXQlBoxh1xTfbV5u0IiJFBXaej1FGIkJSUhO27vkNY+F3kqtQoNDRGnoEJsjRKaBSm0BiZQhi1ACw4C4++WrJwHhavWIt7ClMIpYXUceg5Nm7fgQeWbiyq1AJXrt1EvrGt1DGIqhV+M/7XuHHjMG7cOKljlEtcXDyE0kPqGFSLJSWnQJhWr4IklU1CYiJk7JVEeuL8pSCcOH0GOfZuKLCwg8bYir02q5GHDx/i3IVL0Ghq9QSV1UZ4xH1obLtIHYOqQFxCIjSKRlLHIKpWyl1YCQ0NRXR0NFQqVbHlb775Znk3TWUklxsBGg1gyINKkoYQgl25a7hlC+dh0mdzEJ2agFTLJoDcWOpIVIsN+Nfr6N61My5fuYILQVcRERmC7Lx8FAhD5MnNkSFMoDa2gkZpxbPseuT4iVPY/u33yNIYI9HIAUWO3VkQqwZY/6o91Bo1AB7PEZWFzkcZEREReOutt3D9+nXIZLJHP6jwv/FV1Gp1xSSkUmvRrAkuXU+Fxpxnk0kaSoUC0BQBBvwBU1OZm5vj641f4tyFi9jx3T4kpmuQKHdEoYUTf7iSJKytrfHqyy/j1Zdf1i7Lzs7GgwcPEBl1Hzdv30VEVBgyMnOQVyiQLzdHuswchSa2EEpLFoMlcDcyEvmqImgUhpCr8yCyE6FRmEMoTFlg0WMNnRxwLzMDwpgzT9Z0dezsYPAgFxo5BygmKi2dj4InTZoEV1dX/P3332jcuDEuXryIlJQUTJs2DatXr67IjNXWxo0bsXHjxiorMv3rtZfx5+XNSGBhhSTStLErgm6nQ2NmL3UUqkQymQxdO3dC186dEBcXh6OBJxF4+hxSclRIMbRFnrE9NCY2/MFKkjE3N0erVq3QqlUr/Kvf/5arVCpER0fj1u1wXL56AxFRt5FdUIQ8I0ukwgJqM3sII1N+divZJyOGYdTQD/HgwQNERUXhdkQUoqIfIikpHPmFRVAVqlGoAdRyYxTIlMgRRlAZKCGMTCGMTCCMTFiAkcBUnzG489kCPHT0Bgz4+tdknm3c8ced8ygwsZY6Cv1DUVERoqOjq+zxGjVqBLmcJ85KQ+dX6dy5czh27Bjq1KkDAwMDGBgYoFu3bvD19cXEiRNLPYNQTebj4wMfHx9kZmbCyqryq/tNmzZFXWUREgvzHh10EFWxnl1fwh/X/oN0FlZqDScnJ3w05H18NOR95OTk4EpwME6du4xbdy4hW6VGhtwaWXIbqE3tAEMOIErSUigUaNq0KZo2bYo3+v8LwKOD1IiICFy9fgNB124iJi4BuQVFUMlNkQFT5MvNoTG2YsGlghkaGsLFxQUuLi7o1avk/YWFhUhLS0NycjJSUlIQl5iE2IQkJCQkISU1FfkFKhQUqVGoFo+mwzYwRpZQQm1kBo3Sgu9XJahXrx4mjP4Q63b+iPg6HQEDToNdU3Xu1BF1fvwND8GZ//RNdHQ0hiz77tElrpXMoCAD++Z8hMaN+TkoDZ0LK2q1Gubm5gAAe3t7xMbGokWLFnB2dsbt27crLCCVnkwmw4yJ4zDDdz3iHF7iAQVVufbt28OmcBvSOdZKrWRmZobu3bqhe7duAID8/HyEhobi/OUQXAsNRVpaDvKhQJqBJfKUNo96tfDAnCQml8vRvHlzNG/eHO++8zaAR1M3JyQkICoqCmF37iH8XiQSEpOQW1AIlUaGAkMzZMAEhUYW0BhbQsiN+Z1XwYyMjODg4AAHh+f3wi0qKkJaWhoSExMRFx+Pe1EPEBn9QPt+FWgMkGtogQyZGdSmdhAKM75X5fBq714wNJDhy+3f4aGd16PLt6jGsbOzg6OlAg+L8jmWmh7SKK2gMbWROkaV6NWrF9q1awc/Pz+po7yQzoWV1q1b49q1a2jcuDE6deqElStXQqFQYNu2baxqSahVq5bo29UDB4IikGXdROo4VMvI5XJ08WqHB2HxKLJwkjoOSczY2Bienp7w9PTULktJSUFoaBguBl/DrfBryMzJQz4UyDCwRK6RFdSmtuzZQpIzMDCAk5MTnJyc0KVL8VlQ8vPzERMTgwcPHiDsTgTuREQhOSX1UdHF0ASZMnPkGlmyl1YVkcvlqFOnDurUqQN3d3e8+o/7c3JyEBUVhdBbt3Hleigext5EToEauXJLpBlYQG3CYktZ9e7ZE67Ozpi/dAXu59ZFjpULX78aaNSHgxG19Sek2LWROgpRualUKigUlTtmkM6FlXnz5iEnJwcAsGTJErzxxhvo3r077Ozs4O/vX2EB9YlKpcLs2bMhk8mwbNmySn9zdOXz749xb9Y8XMiMhcqintRxqJYZNewDnJ40E9HmdXmgRSXY2dmhe/du6N69m3ZZSkoK7ty5gyvXQnEjLAxpqTnIU8uQLbdEpoElNKa2vLyR9IaxsTGaNGmCJk2aoNcT17AIIZCUlIR79+4h5GYYrt+8hdSULOQJI6QYWCPf1AFCacHvxSpmZmYGd3d3uLu7a3skFRYWIjIyEtdu3MTlkOuIibuJHJUaeUYWSIMFikztIBTmfK+ew8XFBTs3r8eOb7/HH4Fn8NC8FTRmdlLHogrU6aWOcN6zF6kF2RBKc6njUDXx66+/YujQoUhNTYWBgQFCQkLQvn17TJ8+HatWrQIAjBkzBpmZmdiwYQPGjx+PU6dOITU1FU2aNMGcOXMwZMgQAMCIESNw4sQJnDhxAl9++SUAIDIyEi4uLggNDcX06dNx8uRJmJmZoU+fPli3bh3s7R8NR9CrVy+0bt0aCoUC3377Ldzd3XHixIlKfe46F1b69u2r/Xfjxo0RGhqK1NRU2NjYaGcG0gcxMTGYOXMm/vjjD+Tl5aF58+b45ptv4OXlpW2zadMmrFq1CnFxcXB3d4efnx+6d+9eYlv+/v7w9PSEqakp9uzZg9GjR1flUyk1AwMDrPhiASZMm4Vr2QYoNK8rdSSqRWxsbPBK147Yd+UB8q0aSR2HqgE7OzvY2dmhc+fO2mXZ2dm4d+8ert4IQ8iNUCQmpyBXpUHuf3/4cJBR0jcymUx76cqTvVxSUlIQfPUqTpy5gAcPbwMGxT+zRaoCNOvcu6rj1mpGRkbay7/+7+23ADwqtkRFReFmaBguhdxA9MNQ5Kg0yJJbIcPIFmozBw7Y+g9yuRz/HjUc7wx8A6u/3IQb0RGIs2jOWYNqCJlMhjnTJmHSghWIcejC/S2VSo8ePZCVlYXg4GB4eXnhxIkTsLe3L1bUCAwMxJQpU5Cfnw8vLy/MnDkTlpaW+P333zF06FDtFTFffvklwsPD0bp1ayxevBgAUKdOHcTFxaFnz5745JNPsHbtWuTl5WHmzJl47733cOzYMe3j7N69G59++inOnDmjncG4MulUWCksLESfPn2wdetWNG/eXLvc1ta2woJVhLS0NHh7e6N37974448/4ODggHv37sHa2lrbxt/fH5MnT8amTZvg7e2NrVu3ol+/fggNDUWjRsV/FGo0GhgZGUGtVkOj0VTxsykbpVKJL1ctw5SZc3E9S418i/pSR6Ja5JORw3D6/HjcUdcFDPWzZxfpN3Nzc3h4eMDDwwPD/rtMpVIhIiICN0LDcPnqDcTExiO3UINcuQVSYAmN+X+LLUR6xM7OrsR00KR/jIyM0KxZMzRr1gyDBr4JACgoKMDt27dx8uwFXLxyCdn5RXh8aJ6XngSlsod0gfWInZ0dfBfPx/379/Hl5q8RHh+OBPNm0HBGmWrP1dUV/bq/hB8uRSLbikM90ItZWVmhXbt2CAwMhJeXl7aIsmjRImRlZSEnJwfh4eHo1asX6tevj+nTp2vXnTBhAo4cOYKffvoJnTp1gpWVFRQKBUxNTVG37v86CmzevBmenp5YtmyZdtmOHTvQsGFDhIeHa+sTTZs2xcqVK6vsuetUWDEyMsKNGzf0qmfK06xYsQINGzbEzp07tctcXFyKtVm7di1Gjx6Njz/+GADg5+eHP//8E5s3b4avr2+xtoMHD8bMmTMhk8mwfPnySs9fXiYmJvhq9XLM+nwRrqRGIItfiFRFlEolpk8ch8+/2oWEOl4vXoGoFBQKBVq2bImWLVsWO8scGRmJkGs3cDH4KuISkpBTJEOWoSWyjGyhNrPnALlEpBOlUom2bduibdu2UkepFpydnbF2+Re4f/8+Nn29C7eibyHWtDE05s8fgJj02ycjhyEoeBqu5qWzWEal0qtXLwQGBmLq1Kk4deoUlixZggMHDuD06dNIT0+Ho6MjWrZsCbVajeXLl8Pf3x8xMTEoKChAQUEBzMzMnrv9oKAgHD9+XDuRzpPu3bunLax06NChUp7fs+h8KdCwYcPwzTff6HWB4ZdffkHfvn3x7rvv4sSJE6hfvz7GjRuHTz75BMCjs59BQUGYNWtWsfX69OmDs2fPltieUqks1YjEjz8Uj2VmZpbviZSDQqHA6mVfYJXfehy7cQ3Jtm3YlY+qRAfP9uja6igOR8SgwJI9pqhyPNml/73/ezR+Qk5ODm7duoVzl64g+PpVpGfnIUduhVRDa6jNHTmgKBFRJXJ2dsaKLxYgMTER33y7F5evnUaccUMUWDYEZLycqrqRy+VYvng+xkyZhShFF+5D6YV69eqFb775BlevXoWBgQHc3NzQs2dPnDhxAmlpaejZsycAYM2aNVi3bh38/PzQpk0bmJmZYfLkyVCpVM/dvkajwYABA7BixYoS9zk5/W/yjBcVaCqazoUVlUqFr7/+GgEBAejQoUOJ4GvXri13uPKKiIjA5s2bMXXqVMyZMwcXL17ExIkToVQqMWzYMCQnJ0OtVsPR0bHYeo6OjoiPj9f5cX19fbFo0aLyxq8wBgYGmDl1Epoc/Bm7/3MEsXU68EuRqsSMyRMQOnYiwgvtIIw4XR9VDTMzM3h5eWnH0ioqKsK9e/dw/lIQzly8jJSUHKTLbZFp7ACNiS2LzURElcDBwQGzp09GTk4OfvrPz/jj7+NIktdBhmVjQM7LhKsTe3t7zJ48Dgu//BpxDp2436TnejzOip+fH3r27AmZTIaePXvC19cXaWlpmDRpEgDg1KlTGDhwID766CMAjwomd+7cQatWrbTbUigUUKvVxbbv6emJAwcOwMXFBXK5zuWMCqdz2fjGjRvw9PSEpaUlwsPDERwcXOxWWRYuXAiZTPbc2+XLlwE8enMeX3/Vvn17jBkzBp988gk2b95cbJv/vKRJCFGuy5xmz56NjIwM7e3Bgwc6b6si/d9bA7F42li4JJ2DLF+6XjRUeyiVSnw+cyqcUkOAKhg0iuhp5HI5WrRogeEffYBtX63Fvu3rsXLc/+HtxgZonnIWdVNCYJiVwM8oEVElMDMzw4ihH2Dvji2YP6wvPPKvwzH5CmR5GVJHozLo4OWJ917vAZv0W1JHqfUMCjJgkJtW+bcC3f6PPh5n5bvvvtPOntejRw9cuXJFO74K8GgMlICAAJw9exZhYWEYM2ZMic4NLi4uuHDhAqKiopCcnAyNRgMfHx+kpqZiyJAhuHjxIiIiIvDXX39h1KhRJYowVUnnEs/x48crMkepjR8/HoMHD35um8fjqDg5OcHNza3Yfa1atcKBAwcAPKq+GhoalngDExMTS/RiKQulUgmlUqnz+pWpfTsPbFq9FNPnLcTdIleoOGMQVbKWLVvg9W6e8L98HzlWLlLHIYJCodD2aBFCIDIyEr/88RfOXz6DNANrpJi5cGpJIqIKZmhoiN49e6J3z564c+cOvtmzD7fv30ScsTMKLeqxF0Q1MHTI+wi7FY4TiTEo4MQYkmjUqBH2zfmoSh9PF71798aVK1e0RRQbGxu4ubkhNjZW2yNl/vz5iIyMRN++fWFqaop///vfGDRoEDIy/lfQmT59OoYPHw43Nzfk5eVpp1s+c+YMZs6cib59+6KgoADOzs54/fXXYSDh7G0yUYa5h6ZOnVq6jcpkWLNmjc6hKsoHH3yABw8e4NSpU9plU6ZMwYULF7RjqHTq1AleXl7YtGmTto2bmxsGDhxYYvBaXWVmZsLKygoZGRmwtLSskG2WV35+PuYuXIIrKQbItG4qdZwXck06h/27Nr+4IemloqIijBw7AWHm7SCMTCTLoUi5C99hL2u/5ImeJIRASMhV7N73E6IS0xBj1gwaU7untvXICcKOTV9WcUIiopolPT0d3/vvx/EzF5CocHp0Asag8rr21027hk3zJ8DZ2bnSHqOmKygowCfjpyDUxB1CaVElj9k4+Rx+2snfAaTfyvTN9c9LfIKCgqBWq9GiRQsAQHh4OAwNDbXXtUttypQp6Nq1K5YtW4b33nsPFy9exLZt27Bt2zZtm6lTp2Lo0KHo0KEDunTpgm3btiE6Ohpjx46VMHnlMzY2xmrfL/DVpq3443IIkuw8eKaAKo1cLsfsqRMxY+VmxNep2hG6iUpLJpOhfft2aN++HRITE/Hlpm24FnkXcdatIRRVOwAaEVFtYG1tDZ8xH+OTkcNw+Mhf+PHQr0iCNVKtmgByjs2mj5RKJVYsno9xn81HtIM3Z94j+q8yFVaevPxn7dq1sLCwwO7du2FjYwMASEtLw8iRI9G9e/eKTamjjh074uDBg5g9ezYWL14MV1dX+Pn54cMPP9S2ef/995GSkoLFixcjLi4OrVu3xuHDh2tFJVsmk2GSz1g0/PlX7Nj/B+IcOvLLkSqNm1sruDewQWJaGjSmNlLHIXouBwcHLF04D1FRUfh86UpE5tdDrqVu3WGJiOj5FAoFBr35BgYO6I9zFy7im2/3IjbdEEmWzVjY1kNOTk7wGfkhVu35Dcn2HlLHIdILZboU6En169fHX3/9BXd392LLb9y4gT59+iA2NrZCAtYE+ngp0D8FnjyNNdv3INahU6V2wdQVLwWqGWJiYvDpXF/E1HlJksfnpUCki6KiIviuXofAu2lIt2kJgJcCERFVths3bmLj17twP6MQiZYtKqTAwkuBKtas+YtwLMUMajOHSn0cXgpE1YHOo7tkZmYiISGhxPLExERkZWWVKxRVvV49umHmpyNQL/EiIDRSx6Eaqn79+nCyVAJFBVJHISo1uVyOeTOn47VWDjDLiJQ6DhFRrdC6tTs2+63C6hlj0EHcRZ2Uqzx+0DOzp09Gg6xbnFWPCOUorLz11lsYOXIk9u/fj4cPH+Lhw4fYv38/Ro8ejbfffrsiM1IV6da1C8Z+8BYcUkKkjkI1WP8+vaHMYo82ql5kMhlmTJ6AJgZJkKlypY5DRFRruLm1wpav1mCJz4dwzw6GVdptngTUE1ZWVhjQ52WYZN6XOgqR5HQurGzZsgX9+/fHRx99BGdnZzg7O+PDDz9Ev379is2wU5tt3LgRbm5u6Nixo9RRSq1/v754uW0TmPELkiqJR5s2sBGZUscgKjNDQ0NMnzAODpnhUkchIqp1Onh5YtfWDZgwoDNcEk7DMCdR6kgE4IP33oFj/kOpYxBJTufCiqmpKTZt2oSUlBQEBwfjypUrSE1NxaZNm2BmxkGmAMDHxwehoaG4dOmS1FHKZMqET9Gg8CFQpJI6CtVA9vb2MNKwKy9VT+7ubrAxyIPQ8GwpEVFVMzAwwFsDB2DnxrXobZeDOslXAY1a6li1mlKphFszV8jyM6SOQiQpnQsrj5mZmaFt27bw8PBgQaWGkMvl+HTUMNhk3pU6CtVAKpUKovxfPUSSaePeEsnxMVLHICKqtSwtLbHiiwX4bPibaJRwBrICju8opT69u8Mst+TYm0S1Sbmmfzl16hS2bt2Ke/fuYf/+/ahfvz727NkDV1dXdOvWraIykgS8u3aB7dffIk3qIFTjREVFIdfAVOoYRDrr2K4NjvxyUOoYRES13su9eqJVi+aYPnch7hY1R5FZHakj1UpNGjeGBXKRLXWQWqCoqAjR0dFV9niNGjWCXK5/M8bqI51fpQMHDmDo0KH48MMPERwcjIKCR137s7KysGzZMhw+fLjCQuoTlUqF2bNnQyaTYdmyZVAoFFJHqhQGBgaoW8cO94oKALlS6jhUgxwOOI50ZeVOy0dUmerXqyd1BCIi+i8nJyds/WoNJn82FzdyNCg0c5Q6Uq1jamoKQ02R1DFqhejoaISsexd1TSr/Erj4PENgyk9o3LhxpT+WPggMDETv3r2RlpYGa2vrMq+vc3/8JUuWYMuWLdi+fTuMjIy0y7t27YorV67outkyOXnyJAYMGIB69epBJpPh0KFDJdr4+vqiY8eOsLCwgIODAwYNGoTbt2+XaLdp0ya4urrC2NgYXl5eOHXq1FMf09/fH56envD29saePXsq+inpFRtrK8jUHGeFKk5WVhau3LgFjZm91FGIdGZlZSV1BCIieoK5uTm+Wu2LlkVRMMhLlzpOrZOVlQW1Yc082ayP6pqo0cC88m9VUbz5pxEjRmDQoEFV/rgVQefCyu3bt9GjR48Syy0tLZGenl6eTKWWk5MDDw8PbNiw4ZltTpw4AR8fH5w/fx4BAQEoKipCnz59kJOTo23j7++PyZMnY+7cuQgODkb37t3Rr1+/p3az0mg0MDQ0hFqthqaGD16YmpYGwS9JqkAr/dbjoVlzqWMQlQvHEyMi0j+mpqZY47sYjTKuAew9UaVu3Q5HuuBl3lS76VxYcXJywt27JQc3PX36dJV1F+rXrx+WLFmCt99++5ltjhw5ghEjRsDd3R0eHh7YuXMnoqOjERQUpG2zdu1ajB49Gh9//DFatWoFPz8/NGzYEJs3by6xvcGDB+P8+fM4c+YMhg0bVinPSx+o1WokJKfyMiCqMIf//AuXIpJRZM7LgKh6Uyr5vUhEpI/s7OwwZvhg2GaESx2lVvk94BjyzZ2kjkF6oqCgABMnToSDgwOMjY3RrVu3YrPk3rx5E/3794elpSUsLCzQvXt33Lt3DwsXLsTu3bvx888/QyaTQSaTITAwEAAwc+ZMNG/eHKampmjcuDHmz5+PwsJC7TYXLlyIdu3aYc+ePXBxcYGVlRUGDx6MrKysUucqL50LK2PGjMGkSZNw4cIFyGQyxMbG4vvvv8f06dMxbty4CgtY0TIyHk0FZmtrC+DRmClBQUHo06dPsXZ9+vTB2bNnS6yvVCrh5+eHdevWPfPguqCgAJmZmcVu1c2Jk6eRIuflGlQxLly6jC3fHUCSXVupoxCVm6GhodQRiIjoGV575RU4IoO9VqpIYmIiouLTIBTszUmPfPbZZzhw4AB2796NK1euoGnTpujbty9SU1MRExODHj16wNjYGMeOHUNQUBBGjRqFoqIiTJ8+He+99x5ef/11xMXFIS4uDl27dgUAWFhYYNeuXQgNDcWXX36J7du3Y926dcUe9969ezh06BB+++03/Pbbbzhx4gSWL19eqlwVQefBaz/77DNkZGSgd+/eyM/PR48ePaBUKjF9+nSMHz++QsJVNCEEpk6dim7duqF169YAgOTkZKjVajg6Fh/oytHREfHx8To9jq+vLxYtWlTuvFLJz8/Htt3fI922s9RRqAY4ffYsVm3ehZg6nQEZp1mm6s/AgJ9jIiJ9JZPJ8K/XeuPu33dQaN1Q6jg13qovNyLGvJnUMUhP5OTkYPPmzdi1axf69esHANi+fTsCAgLwzTffIC0tDVZWVvjhhx+047Q2b/6/YQJMTExQUFCAunXrFtvuvHnztP92cXHBtGnT4O/vj88++0y7XKPRYNeuXbCwsAAADB06FEePHsXSpUtfmGvGjBnlfu7lOjpcunQpkpOTcfHiRZw/fx5JSUn44osvyhVo4cKF2q4/z7pdvnxZp22PHz8e165dw759+0rcJ5PJiv0thCixrLRmz56NjIwM7e3Bgwc6bUcKQggsWrYS902aAIacWovKx//AQSzf8h0eOnTh54mIiIiqROeOHWCrSZM6Ro13/MRJXI/NgsbUTuoopCfu3buHwsJCeHt7a5cZGRnhpZdeQlhYGEJCQtC9e/dik9+Uxv79+9GtWzfUrVsX5ubmmD9/fonxUF1cXLRFFeDR0CWJiYmlylURyv1Lx9TUFB06dKiILAAeFT8GDx783DYuLi5l3u6ECRPwyy+/4OTJk2jQoIF2ub29PQwNDUv0TklMTCzRi6W0lEpltb0Gf/2W7bgUmw+VjYvUUagay8/Px5IVa3AhOhOpDp0AHYuURPqopg9cTkRU3Tk6OkKhzpc6Ro324MEDfLX9WyQ5dpU6CukRIQSAZ3daMDExKfM2z58/j8GDB2PRokXo27evtsfLmjVrirX7Z7FGJpNpj9lelKsilKuwcvToURw9ehSJiYklDjR37Nih0zbt7e1hb19xY3sIITBhwgQcPHgQgYGBcHV1LXa/QqGAl5cXAgIC8NZbb2mXBwQEYODAgRWWQ98JIbBx69f47dIdpNm1kToOVWN37tzBAt/ViFS4QmXrLnUcogqnVlf99INERFR6j34oCalj1FipqamYMX8xHth5AQbskUz/07RpUygUCpw+fRoffPABAKCwsBCXL1/G5MmTkZOTg927d6OwsPCpvVYUCkWJ46wzZ87A2dkZc+fO1S67f/9+heaqCDr/T1i0aBEWL16MDh06wMnJqcIqPWWRnZ1dbGaiyMhIhISEwNbWFo0aNQIA+Pj4YO/evfj5559hYWGh7ZliZWWlrZhNnToVQ4cORYcOHdClSxds27YN0dHRGDt2bJU/JykUFRVh6Yo1OBmZjnQWVUhHRUVF2Pz1Dvx1Nhhxtp4QRmWvSBNVByysEBHpt6SkJBQaGksdo0ZKT0/HxBlzcM+8DQeslUh8XtUMoh+fZ4i6L25WjJmZGT799FPMmDFD+5t85cqVyM3NxejRo6HRaLB+/XoMHjwYs2fPhpWVFc6fP4+XXnoJLVq0gIuLC/7880/cvn0bdnZ2sLKyQtOmTREdHY0ffvgBHTt2xO+//46DBw9WaK6KoHNhZcuWLdi1axeGDh1aIUF0cfnyZfTu3Vv799SpUwEAw4cPx65duwBAO2Vyr169iq27c+dOjBgxAgDw/vvvIyUlBYsXL0ZcXBxat26Nw4cPw9nZudKfg9TS09Px2bxFCCu0Q65NK6njUDV17fp1rFi3AdHy+sh16MxLf6hGKygokDoCERE9x/WboUiTWby4IZVJYmIiJs+ajzumbtCYWEsdp1Zq1KgRMOWnKnmsuo8fr4yWL18OjUaDoUOHIisrCx06dMCff/4JGxsbAMCxY8cwY8YM9OzZE4aGhmjXrp127JNPPvkEgYGB6NChA7Kzs3H8+HEMHDgQU6ZMwfjx41FQUID+/ftj/vz5WLhwYYXmKi+ZeHzBURnZ2dnh4sWLaNKkSYUEqckyMzNhZWWFjIwMWFpaSh1HK/jqNSxb8xWiLNz1ftAp16Rz2L9rs9Qx6B/S09Ox+quNCI5IQLxNG0Cu32MLKVLuwnfYyyUKrURlERsbiw8++ACBgYFSRyEioqeYOGMOzhY2gjAyLba8bto1bJo/oVacPK1okZGR+GzBUkRatYMwrtrfM42Tz+GnnfwdQPpN51mBPv74Y+zdu7cis1AVUavV2LBlO+av2YoI+856X1Qh/aNWq+G//wBGT5iBv+KUiK/TQe+LKkQVJSsrS+oIRET0DFlZWXiYmFqiqEK6O3/hIqbMX4pI245VXlQhqi7KdCnQ40ttgEezImzbtg1///032rZtW2LwmbVr11ZMQqpQsbGxmPeFL+6q7ZHj8BIv2aAyCw65ijUbtuAh6iDL0RuQlWvWdqJqJyMjQ+oIRET0DP77/4M4ZUOpY9QIQgh87/8TfjgciDjHrhyolug5yvS/Izg4uNjf7dq1AwDcuHGj2HIpBrLVRxs3bsTGjRv1YqBDIQQOHPoF3/3nNzy0aQdhzutOqWwSEhKwYt16hCXkINHakz1UqNaKjU+QOgIRET1FXl4ejhw/BZVDd6mjVHuFhYVYvHwVzkdlINWhE0/GEr1AmQorx48fr6wcNZKPjw98fHy0Y6xIJTU1FZ8vWY6wTEOks4cBlVFBQQG27/wWf5+9jBhLN2jsm0sdiUhSdyOjYW5VMQOdERFRxdmx53vEGruyCFBOqampmD53AW4LJ+Tbuksdh6haYH+uGu74iZPY8PW3iLZsDY2NrdRxqBoRQuD4iZPYsvM7xBi7IN+hKw9UiADci4yCrUNZJyAkIqLKlJycjL9PXUSBo7fUUaq1sFu38Pmy1YiybAONCU8iEJVWmcdY+eKLL2BmZlZsvJWnqaljrKhUKsyePRsymQzLli2DQqGQOtJTFRQUwHf1Opy/m4gkB14TSWUTFxeHL1aswd1MA6TYd+Hnh+gJaZnZMDXWz+9+IqLaavnar/DQshVPApXDnwFHsXG3P2LqdOIl30RlVKZrQoKDg1FYWKj99/NuVeHkyZMYMGAA6tWrB5lMhkOHDpVoU1RUhHnz5sHV1RUmJiZo3LgxFi9eDI1GU6zdpk2b4OrqCmNjY3h5eeHUqVNPfUx/f394enrC29sbe/bsqYynVW4PHjzA6HGTEPBAIMm+HX8UU6kVFRVh+87d+PSzBbiodkGKXWt+foiekJaWhjwN/08QEemTK8HBuBmbyZkudSSEwNYdu+D33c+IcezKogqRDnQeY0UfxlvJycmBh4cHRo4ciXfeeeepbVasWIEtW7Zg9+7dcHd3x+XLlzFy5EhYWVlh0qRJAB4VSyZPnoxNmzbB29sbW7duRb9+/RAaGopGjRoV255Go4GRkRHUanWJ4ow+CDx5Gn7bduGhnReEwkzqOFSNREVFYcGyVYhCHWQ7evOMD9FTBIdcRZrcGnbIlDoKERHh0UmhNRu2ItHGS+oo1ZJGo8HSlWtw4m4q0up48fiPSEc6T7f8PDKZDGvWrNEpUFn069cP/fr1e26bc+fOYeDAgejfvz8AwMXFBfv27cPly5e1bdauXYvRo0fj448/BgD4+fnhzz//xObNm+Hr61tse4MHD8bMmTMhk8mwfPnyCn5G5bNnrz9+OHIK8Y7egIGh1HGomhBCYP/BQ/ju4B+IsW3PghzRc/x98gxyTRwBNQsrRET6wH//f/DQwJG9LHSgVqsxd+ESnE8QyLJpJXUcomqtXNMtBwUFQa1Wo0WLFgCA8PBwGBoawstLfyrG3bp1w5YtWxAeHo7mzZvj6tWrOH36NPz8/AA8GjMlKCgIs2bNKrZenz59cPbs2RLbUyqV2nX1yaZt3+DQ+TCkOHRkpZlKTaVSYcGS5bgck4tU9lIhei4hBO5FRUPYewM5UqchIqLs7Gz85/e/kO3YTeoo1Y4QAp9/4YtziUC2VWOp4xBVezpfCrR27VpYWFhg9+7dsLF5NGJ0WloaRo4cie7d9Wfu+JkzZyIjIwMtW7aEoaEh1Go1li5diiFDhgB4NIK4Wq2Go6NjsfUcHR0RHx+v02MWFBSgoKBA+3dmZuWe2fx27w84dP4WUmzb8IcxlVp2djYmfTYHtzROyLd1kzoOkd6Ljo5Gjow9uoiI9MU3336PGNPGgKxMw0YSgK82b8O5mAJkWzeTOgpRjaDzt9CaNWvg6+urLaoAgI2NDZYsWVKuy4AWLlwImUz23NuTl/G8iL+/P7777jvs3bsXV65cwe7du7F69Wrs3r27WDvZPwoSQogSy0rL19cXVlZW2lvDhg112k5pXL4cBP8jp5Bi25pFFSq1nJwcjJ86EzfggnyL+lLHIaoWAk+fQaIhB0YkItIHubm5OHnuMgp5HFNmJ0+fwZGLochgUYWowsiEEEKXFS0sLPDzzz/j5ZdfLrb82LFjGDhwILKysnQKlJycjOTk5Oe2cXFxgbGxcbFlMpkMBw8exKBBg4otb9iwIWbNmgUfHx/tsiVLluC7777DrVu3oFKpYGpqip9++glvvfWWts2kSZMQEhKCEydOlPk5/LPHihACKpUK9vb2OhdriIiIiIiIiEj/6Dxn5FtvvYWRI0dizZo16Ny5MwDg/PnzmDFjBt5++22dA9nb28Pe3l7n9f8pNzcXBgbFO+YYGhpqZ/RRKBTw8vJCQEBAscJKQEAABg4cqNNjKpVKKJUcQIuIiIiIiIioptO5sLJlyxZMnz4dH330EQoLCx9tTC7H6NGjsWrVqgoL+DzZ2dm4e/eu9u/IyEiEhITA1tZWO03ygAEDsHTpUjRq1Aju7u4IDg7G2rVrMWrUKO16U6dOxdChQ9GhQwd06dIF27ZtQ3R0NMaOHVslz4OIiIiIiIiIqiedLwV6LCcnB/fu3YMQAk2bNoWZWdUN7BcYGIjevXuXWD58+HDs2rULAJCVlYX58+fj4MGDSExMRL169TBkyBB8/vnnUCgU2nU2bdqElStXIi4uDq1bt8a6devQo0ePqnoqRERERERERFQNlbuwQkRERERERERUW+l8KRBVDCGEzgP9EhER6TMLC4sKGbSd+0oiIqqpKmpfSdJiYUViWVlZsLKykjoGERFRhcvIyIClpWW5t8N9JRER1VQVta8kafFSIInxLNyLZWZmomHDhnjw4AG/dEhn/BxRReFnqfTYY+XF+Hmqnvi+VT98z6qn2vC+scdKzcAeKxKTyWQ19kuiollaWvK1onLj54gqCj9LVac27Cv5eaqe+L5VP3zPqie+b6TvDKQOQERERERERERUXbGwQkRERERERESkIxZWSO8plUosWLAASqVS6ihUjfFzRBWFnyWqSPw8VU9836ofvmfVE983qi44eC0RERERERERkY7YY4WIiIiIiIiISEcsrBARERERERER6YiFFSIiIiIiIiIiHbGwQkRERERERESkIxZWSC9s2rQJrq6uMDY2hpeXF06dOvXc9idOnICXlxeMjY3RuHFjbNmypYqSkj4ry+coMDAQMpmsxO3WrVtVmJj0zcmTJzFgwADUq1cPMpkMhw4deuE6/D6iF+E+rvopy3v2n//8B6+99hrq1KkDS0tLdOnSBX/++WcVpqXHeBxQ/XC/SzUFCyskOX9/f0yePBlz585FcHAwunfvjn79+iE6Ovqp7SMjI/Gvf/0L3bt3R3BwMObMmYOJEyfiwIEDVZyc9ElZP0eP3b59G3Fxcdpbs2bNqigx6aOcnBx4eHhgw4YNpWrP7yN6Ee7jqp+yvmcnT57Ea6+9hsOHDyMoKAi9e/fGgAEDEBwcXMXJazceB1RP3O9SjSGIJPbSSy+JsWPHFlvWsmVLMWvWrKe2/+yzz0TLli2LLRszZozo3LlzpWUk/VfWz9Hx48cFAJGWllYF6ag6AiAOHjz43Db8PqIX4T6u+inre/Y0bm5uYtGiRRUdjZ6DxwHVH/e7VJ2xxwpJSqVSISgoCH369Cm2vE+fPjh79uxT1zl37lyJ9n379sXly5dRWFhYaVlJf+nyOXqsffv2cHJywiuvvILjx49XZkyqgfh9RM/DfVz1U579yWMajQZZWVmwtbWtjIj0FDwOqD34HUn6ioUVklRycjLUajUcHR2LLXd0dER8fPxT14mPj39q+6KiIiQnJ1daVtJfunyOnJycsG3bNhw4cAD/+c9/0KJFC7zyyis4efJkVUSmGoLfR/Q83MdVP7q8Z/+0Zs0a5OTk4L333quMiPQUPA6oPfgdSfpKLnUAIgCQyWTF/hZClFj2ovZPW061S1k+Ry1atECLFi20f3fp0gUPHjzA6tWr0aNHj0rNSTULv4/oRbiPq37K+p49tm/fPixcuBA///wzHBwcKisePQOPA2oHfkeSPmKPFZKUvb09DA0NS5xNSExMLFGNfqxu3bpPbS+Xy2FnZ1dpWUl/6fI5eprOnTvjzp07FR2PajB+H9HzcB9X/ZRnf+Lv74/Ro0fjxx9/xKuvvlqZMekfeBxQe/A7kvQVCyskKYVCAS8vLwQEBBRbHhAQgK5duz51nS5dupRo/9dff6FDhw4wMjKqtKykv3T5HD1NcHAwnJycKjoe1WD8PqLn4T6u+tF1f7Jv3z6MGDECe/fuRf/+/Ss7Jv0DjwNqD35Hkt6SbNhcov/64YcfhJGRkfjmm29EaGiomDx5sjAzMxNRUVFCCCFmzZolhg4dqm0fEREhTE1NxZQpU0RoaKj45ptvhJGRkdi/f79UT4H0QFk/R+vWrRMHDx4U4eHh4saNG2LWrFkCgDhw4IBUT4H0QFZWlggODhbBwcECgFi7dq0IDg4W9+/fF0Lw+4jKjvu46qes79nevXuFXC4XGzduFHFxcdpbenq6VE+hVuJxQPXE/S7VFCyskF7YuHGjcHZ2FgqFQnh6eooTJ05o7xs+fLjo2bNnsfaBgYGiffv2QqFQCBcXF7F58+YqTkz6qCyfoxUrVogmTZoIY2NjYWNjI7p16yZ+//13CVKTPnk8/eY/b8OHDxdC8PuIdMN9XPVTlvesZ8+ez/3eoKrD44Dqh/tdqilkQvx3tB8iIiIiIiIiIioTjrFCRERERERERKQjFlaIiIiIiIiIiHTEwgoRERERERERkY5YWCEiIiIiIiIi0hELK0REREREREREOmJhhYiIiIiIiIhIRyysEBERERERERHpiIUVIiIiIiIiIiIdsbBCRERERERERKQjFlaIqFaYN28elEolPvjgA6mjEBER6SXuK4mIdCMTQgipQxARVbbMzEzs2bMH48ePx507d9C0aVOpIxEREekV7iuJiHTDHitEVCtYWlpi1KhRMDAwwPXr16WOQ0REpHe4ryQi0g0LK0RUaxQVFcHU1BQ3btyQOgoREZFe4r6SiKjsWFgholpj3rx5yM7O5sEiERHRM3BfSURUdhxjhYhqhaCgIHTt2hWvvfYaIiMjcfPmTakjERER6RXuK4mIdMPCChHVeBqNBi+99BJ69uyJTp064cMPP0ROTg4UCoXU0YiIiPQC95VERLrjpUBEVOOtX78eSUlJWLx4Mdq0aYOioiLcvn1b6lhERER6g/tKIiLdsbBCRDVaTEwM5s+fj02bNsHMzAzNmjWDUqnkteNERET/xX0lEVH5sLBCRDXaxIkT0a9fP/Tv3x8AIJfL0apVKx4sEhER/Rf3lURE5SOXOgARUWX57bffcOzYMYSFhRVb3qZNGx4sEhERgftKIqKKwMFriYiIiIiIiIh0xEuBiIiIiIiIiIh0xMIKEREREREREZGOWFghIiIiIiIiItIRCytERERERERERDpiYYWIiIiIiIiISEcsrBARERERERER6YiFFSIiIiIiIiIiHbGwQkRERERERESkIxZWiIiIiIiIiIh0xMIKEREREREREZGOWFghIiIiIiIiItLR/wPvxQ9cwiON9AAAAABJRU5ErkJggg==\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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06YK6desiKCgIu3fvxjfffAMjI6MyPdbPzw/Gxsb46KOPSmzv/v37OHjwINq3b6/Yp3Xq1AlhYWEICwuDh4dHmb5vhBANUmXD5BKi5dLT01mXLl3Yvn372I0bN1h8fDw7cuQIs7GxYSNGjFCs998ZBtLT01lBQYHi73/PWpCTk8PatGnDmjVrxh48eMCSk5MVN6lUyhhjLCIigvn6+rLo6GgWHx/PgoODma2tLfv888/fm/fy5cvMyMiIeXp6sl9++YU9fPiQ3bhxg61YsYJ17NhRsd7kyZNZrVq12G+//cb++usvNnLkSGZtba0YAf9t5HI569evH9PX12erVq1iV69eZY8ePWI//fQT69q1q2Jk/SVLlrAzZ86whw8fsujoaDZ8+HAmEonY5cuX37nt6Oho9vnnn7OjR4+y2NhYdv/+fbZz505mYGDAli1b9tbHJCQklGnWAsYYO3r0KBMKhWz06NHsxo0bLCEhge3cuZOZmZmxr776qsSsNY8fP2bR0dFs6dKlzNDQkEVHR7Po6GiWm5vLGGPs9u3bzMrKig0cOLDEZ5eamvrBHP+WmZnJGjVqxGrXrs327t3LYmNj2b1799iuXbuYk5OTYpaJ3r17M2dnZ3b+/HkWExPDPvnkE+bk5MSKiooYY69nJ+A4jq1evZo9ePCAbdmyhZmZmb13VqDJkyczW1tb9uuvv7LY2Fg2dOhQZmZmpvj838wK9CYDY2+fxeG/MxJ16tSJGRoasqlTp7K//vqLHTx4kBkYGLCtW7eW670hhGg/2r/+g/avqt2/vrF8+XJmZmbGALALFy6Uuh8A27NnD0tOTmaPHj1iv//+O/vyyy+ZUChkBw4cKLHuy5cvmVgsZkZGRmz16tWK5UVFRUwikTAjI6N3vp+EEM1FhRVCVOTVq1dszpw5zNXVlZmYmDCJRMIaNmzIFixYUOLA7kNTN/77wO/NSevbbgkJCYwxxqKiolibNm2YiYkJ09PTYw0bNmSLFy8uNYWfvb09W7x4cYlld+/eZUOGDGG2trZMV1eX2dvbswEDBrDr168r1ikqKmLTp09n1tbWzMjIiH300Ufs9u3bH3w/ZDIZCwwMZK1atWISiYQZGxszNzc3tnHjRsX7MWXKFFanTh2mq6vLrKysWPfu3VlERMR7t5uWlsYmTZrEmjZtygwNDZmRkRFr1qwZW79+/VuntmSsfAd+jDF27tw59sknnzATExOmq6vLnJ2d2fr16xUH228MHTr0rZ9NaGgoY+x1MeFt9/+7kMHYPwds7/PixQs2Z84cVr9+faarq8tsbGzYRx99xE6cOKE4GH0z3bKJiQnT19dnXl5eJaZbZoyxwMBAZmdnxwwMDNiQIUPYypUr31tYefnyJZs4cSKztLR873TLyhRWxo0bx8aOHcuMjY2ZmZkZmzNnDk23TAgphfavJdH+VbX7V8YYe/LkCRMIBCWmSv7vdt7c9PT0WL169djQoUNZVFTUW9fv1KkTA8AiIyNLLO/WrRsDwM6fP//BTIQQzcIx9o6O+YQQrfHy5UuYm5vj9OnTNAq9mnn06BHq16+PuLg41K9fn+84hBBCyoH2r+qL9q+EkKpEY6wQUg2Eh4eja9eudNCnhs6cOYPvvvuODvoIIUQD0f5VfdH+lRBSlajFSjkUFRVh7ty54DgOq1atgq6uLt+RCCGEEEIIIYQQwiONbrFy7tw59OrVC7a2tuA4DidPnixxv1QqxYIFC+Do6Ah9fX3UrVsXy5YtKzVKe0BAABwdHaGnpwc3NzecP3/+rc8XHBwMV1dXeHp6Yt++fZX1sgghhBBCCCGEEKIhNLqwkp+fjxYtWmDLli1vvX/NmjXYunUrtmzZgjt37mDt2rVYt24dNm/erFgnODgYU6ZMwfz58xEdHY0OHTqgR48eSExMLLU9uVwOoVAImUz21in0CCGEEEIIIYQQUr1oTVcgjuNw4sQJ9OnTR7Hss88+g42NDXbt2qVY9uWXX0IikShanLRp0waurq4IDAxUrNO4cWP06dMH3t7eJZ6jsLAQs2fPBsdxWL16NcRiceW+KEIIIYQQQgghhKg1Ed8BKlP79u2xdetW3Lt3Dw0aNMCNGzdw4cIF+Pn5AXg9ZkpUVBTmzJlT4nHdu3dHREREqe2JxWLFY9+nsLAQhYWFir8ZYygqKoKlpSU4jqvQayKEEEIIIYQQQoj6qFBh5fr16zh//jx0dXXh6emJ5s2bqyqXSsyePRvZ2dlo1KiRogvPypUrMWDAAABAeno6ZDIZbGxsSjzOxsYGKSkpSj+vt7c3li5dWmp5dnY2jI2Nld5uecjlcqxa54vwe2nINHMGNLyg45h2CceCAj+8IlErcrkcK9b6IPzBC7wwa8h3HOhmPID3kK7o3Lkz31G0wpZtO/Fz5B2kWTTX+N+Y8mqRH4XdARv5jkH+JpVKMW7KTMTIa0NmYMV3nHLhCvPQwyoHyxfO+fDKWqawsBBjJ0/HbaET5BJzvuOUiygzAUu+boMen3zCd5QqxxjDvMXLcfE5kG/swHeccqmRdRMBCyfC3t6e7yga5dCR44i6cYu357cwM8HcGVN5e35CykLpwoqfnx+mTZsGU1NTiEQipKeno0mTJggKCoKbm5sqMyotODgY+/fvx8GDB9GkSRPExMRgypQpsLW1xdChQxXr/bcVCWOsQi1L5s6di2nTpin+zsnJgZ2dndLbKy/GGBYuW4mLSVLkmjepsucl5N8YY1i1zhehD3ORowZFFaJaO/Z8jx8v30OmZQu+oxCCjf7b8FexOWTGmlVUqe5WrduAe6ilcUWV6s5/205cflaMfDOaxrg6+PGnX7D31wtIN2nMWwb7J9d4e25CyqpchZXdu3fDxcUFTZo0wapVq7B69WrMnDkTHMfhyZMn2LZtGzp37oxff/0V7du3r6zMZTZz5kzMmTMH/fv3BwA0a9YMjx8/hre3N4YOHQpLS0sIhcJSrVNSU1NLtWIpD7FYzOv4K5u37sClpCLkmtIOj/Bn/6EjCL+XjhxzZ76jEBWLiLyME2FXkWnlzncUQvDgwQOEXY9DvnUbvqOQcrh6LQpXHqTglZUr31FIOURHx+DXSzeRbd2K7yikCiQkJGD3kR+RZtMO4Pib80QgFPL23ISUVbn+h6xbtw5t2rSBoaEhMjIycPXqVWzYsAFhYWEwMjLCihUrsHbtWsyYMaOy8pZLQUEBBIKSL1EoFCpm9NHV1YWbmxtCQkJKrBMSEoJ27dpVWU5Vuh0bi98ibyKbiiqER/Hx8Tj6aygyzfi7ukEqR2FhIfwCduC5Rctq1/2HqKcNATuQbNqU7xikHBhj2LxtF1LN6HPTJDKZDOs2ByLFnFoqVgeMMSxb44skc1deiyqEaIpytVi5c+cOXr16hVu3bqFDhw4QCAQ4cuQIFi5ciFevXqF27dqoV68eoqOj8dNPP6Fp06ZwdHSsrOzIy8vDgwcPFH8nJCQgJiYG5ubmqFOnDnr16oWVK1eiTp06aNKkCaKjo+Hr64sRI0YoHjNt2jQMHjwY7u7u8PDwwPbt25GYmIixY8dWWu7KtMF/O1LM1WusG1L9rPXzxzOz6jfuRnXw0y+/4plubUCo1WOfEw2RkZGBp5l5YFYGfEch5RAbG4tUmQQQ6fIdhZRDWPg5JHMW9LlVE+cvXMCTYgmYkYTvKIRohHIfGevp6aFVq1bw9PREixYtEBwcDLlcjjt37uDGjRs4d+4cwsLCMGzYMGRlZcHQ0BA5OTmVkR3Xrl1Dly5dFH+/Gddk6NChCAoKwubNm7Fw4UKMGzcOqampsLW1xZgxY7Bo0SLFY/r164eMjAwsW7YMycnJaNq0KU6fPq2Rg1o9f/4caS8ZYKDHdxRSjT19+hRJucVgloZ8RyGVICT8Al4aU0skoh6uXLuGNKEl3zFIOf12NhwZ4pp8xyDldPL078g1qrwLpkS9HDj2I7KMaYw8QspK6UuOPj4+6Ny5M+Lj4zF27Fi0aNECderUwfXr12Fra4unT5/i6dOnuH37tirzltC5c2cwxt55v5GREfz8/D44RfK4ceMwbtw4Faereg8fPkQWVzWzDhHyLucvXsJzkTXfMUglyS94BZjq8B2DEADA3YePUKxL+z1Nc/9hAuQSGn9L06RnZYOZ08W76kAqlSL9RS5gzd+YkYRoGqULKy4uLoiKisLYsWPRtm1bRYFDJBJh9+7dAIDatWujdu3aqklKPkgul4OBul4QfsUnPgUT04mOthIIADBG3byIWmBy+i5qouLiYkCHBqPUNFL5uy9mEu2SlZWFYqE+3zEI0SgV6iRfr149hISE4Pnz54iMjERRURHatm1bpVMLk3/Y2dnBlCvAS76DkGpNX08MyF/xHYNUknoO9rjzNIumRyVqoa59bYhirqPIgLoDaRJTUxNwBa/AdOjETZMIqYZZbcjlcipaE1JOKhni2cbGBr1798bXX39NRZV/8ff3h7OzM1q1qpop6erUqQMjeR4gl1bJ8xHyNi5NnaFXmMV3DFJJBn79Bazz4/mOQQgAoJWbKyxlGXzHIOXU1q0ldPKe8x2DlJO5iSEgLeQ7BqkCZmZmEEnpUi0h5UFzZ1Wi8ePHIy4uDlevXq2S5+M4DoO+6QvTF/er5PkIeZvWrdxhJU3lOwapJE5OTnBxtIFOXgrfUQiBra0trPUBrphayWmS7h91RY3iZ3zHIOX0eY/uMMpN5DsGqQK6urowlYgBGV2sJaSsqLCiZT79pDsaGUshyKcreIQfhoaGaN28MXRyk/mOQirJ/JlTUa/wIbjCXL6jEILxo4fB6kUc3zFIOZiYmMC1sROEVKDVKB917YIa0ufUMrqa+LzHxzDIecx3DEI0BhVWtAzHcVi1ZAGcXt4BV5jHdxxSTU3633ewK7gPyIr4jkIqgUQiga/3MtTLjgH3KofvOKSac23ZEm6OVlTM1TCTx32HOvn36Iq4BhGJRJgwehisM2/xHYVUgZ49vFCz+BkV0ggpIyqslENRURGmT5+OGTNmoKhIfU8YTUxM4LtqKeplR9NJD+GFRCLBghmTYJsW9XoGGaJ1bGxssHndSjQsuA2dfOr6Rfg1b+ZUOEkfUSsqDWJsbIwZ479DjXTaT2iS9p7t0K6BLSQ51CVI2+no6GDC6OGwyrzNdxRCNEKFCivnz5/HoEGD4OHhgaSkJADAvn37cOHCBZWE+5Bz586hV69esLW1BcdxOHnyZIn7vb290apVKxgZGcHa2hp9+vTB3bt3S20nICAAjo6O0NPTg5ubG86fP//W5wsODoarqys8PT2xb9++ynhJKlOzZk1sXrcSDfJvQZSfxnccUg21aN4cI77uCev0aDpo1lLW1tbYsdkXbQwyYJp1lz5nwhuJRAKflUvgmB0DrqiA7zikjNq2aY0Bn3aCRcYt+v3QIHNnTkUTcRaNtVUNdGjfDq3rWUEv9ynfUQhRe0oXVo4fPw4vLy/o6+sjOjoahYWvRwnPzc3FqlWrVBbwffLz89GiRQts2bLlrfeHh4dj/PjxiIyMREhICKRSKbp37478/HzFOsHBwZgyZQrmz5+P6OhodOjQAT169EBiYulKvFwuh1AohEwmez0NmZqztrbG9k0+cNV5BoOcBL7jkGroi88/Q7+PWsMq4wYdNGspiUQCv7WrMKxbc9R6HkFdEAlvbGxssGHlYjhmXaOWKxpkUP9v8IWnMywyb9N+QkOIRCJsWLMCTbinNLtTNbBg1nQ0wDMICjL5jkKIWlO6sLJixQps3boVO3bsgI6OjmJ5u3btcP36dZWE+5AePXpgxYoV6Nu371vvP3PmDIYNG4YmTZqgRYsW2LNnDxITExEVFaVYx9fXFyNHjsSoUaPQuHFj+Pn5wc7ODoGBgaW2179/f0RGRuLixYsYMmRIpb0uVTI0NMQW37Xo7WwJm7Qo6idJqtywQd9iYPe2r79/TP0LkqT8OI7D4AH9sHnFPDQrioNx1n36rAkvateujS3rVqJB7k0I89P5jkPK6H+jRuDbLi1hk3adfjs0hL6+PjavXw0X3RRqzaDldHV1sWH1CtTLj6WLJ4S8h9KFlbt376Jjx46llhsbG+PFixcVyVRpsrOzAQDm5uYAXo+ZEhUVhe7du5dYr3v37oiIiCj1eLFYDD8/P2zYsAFisfidz1NYWIicnJwSNz4JhUJMnzwes0d9DfvUCAhevuA1D6l+Bg/4BuP794Tt80hAVsx3HFJJ7O3tsdN/I8Z+4gKH5xchzKOxV0jVs7GxwbZN6+EqegqD7Ed8xyFlNGzwt5jwbU/USokApIV8xyFlIJFIsHn9anS0llJ3UC1namqKDd7L4JgVBa4o/8MPIKQaUrqwUrNmTTx48KDU8gsXLqBu3boVClUZGGOYNm0a2rdvj6ZNmwIA0tPTIZPJYGNjU2JdGxsbpKQo32/U29sbJiYmipudnV2FsqtKp/btsX3DarhyCTB+8YB2gKRK9ezhhcVTvkOd1Es0BoIWEwqF6PfVl9i1eR161JbCNvUydcsgVc7IyAhbfNfis8amsE67DshlfEciZfCpV3esWTAN9TIuQ0AtjjSCjo4OVi1diP6eDVEj9SrN8qTFatasCd9VS+CQScUVQt5G6cLKmDFjMHnyZFy+fBkcx+HZs2c4cOAAZsyYgXHjxqkyo0pMmDABN2/exKFDh0rdx3Fcib8ZY6WWlcfcuXORnZ2tuD158kTpbamapaUlAjf6YHCHBqiVeglc8Uu+I5FqxN3NFX4rF6JedhQEBRl8xyGVyNTUFEvnz8HmpTPRRvgI1unR9HtDqpRIJMKsqZMwa/iXsE+9SK01NUTjRo2wY7MPWouTYZx1jy4CaQCO4zB6+BAsmjAUDmnUMlqb2dnZwc97yeuWK9QtiJASlC6szJo1C3369EGXLl2Ql5eHjh07YtSoURgzZgwmTJigyowVNnHiRJw6dQqhoaGoXbu2YrmlpSWEQmGp1impqamlWrGUh1gshrGxcYmbOhEIBBgxZBB8Fs5Aw5zr1DeWVCl7e3ts81uH5iwBerlJfMchlczBwQH+G9Zh9dThaF4Y+3r2D2rmT6pQl04dsM3XGy25BBi/uE8n6hrAxMQEm33WYGgnZ9g+jwBXTK0cNUGb1q2wbcNqtJA/hGF2PP1f01K1a9fGxtXLUDc7mlqkEvIvFZpueeXKlUhPT8eVK1cQGRmJtLQ0LF++XFXZKowxhgkTJuCHH37A2bNn4ejoWOJ+XV1duLm5ISQkpMTykJAQtGvXriqj8qJ+fSfsDtyEHo7iv5tKU/NNUjVMTU0R6LcenhavYJxduksh0T4tmjfHroCNWDbmazTNj4F5xm0qsJAqY2Vlha0bfTC8cxPUptmrNIJAIMCwwd9iw+KZaJQbA/3s0rM1EvVjaWmJbZt98Y1bLdRMvUK/81rK1tYWm9eugFNODLhX/I4lSYi6qFBhBXg9cJW7uztat24NQ0NDVWQqs7y8PMTExCAmJgYAkJCQgJiYGMVUyePHj8f+/ftx8OBBGBkZISUlBSkpKXj58p/m6NOmTcPOnTuxe/du3LlzB1OnTkViYiLGjh1bpa+FL2KxGAtmT8fcvwe25V5l8x2JVBO6urpYvXwxejpbwSIzlq5sVQMcx6Ftm9YI2rYFS0d9gaYFN15PsUoH3qQKCAQCDBnYH1tWLUDz4r+o9YqGcHJywp6tm9G3mfnrMZuoS6HaEwqFGD9mNFbNGAunzCvQyVN+3EKivmxsbLBp7Uo45d6gliuEABCVZ+Vp06aVeV1fX99yhymva9euoUuXLoq/3+QbOnQogoKCFFMmd+7cucTj9uzZg2HDhgEA+vXrh4yMDCxbtgzJyclo2rQpTp8+DXt7+0rPr046tvdE44YNMHvRMtzNqYFXxuox4C7RbgKBADOmTITJnu9x/FwM0ixaABUY34hoBo7j0K6dBzw82iLyyhVs37MfyUVipBvXB9PR5zse0XJ2dnbY6e+Hg8FHcez0H3hq2hxMz4TvWOQ9dHR0MG3iOHz6112sWLcBiSJb5BvZ0/5CzTVv3gx7Ajdi5VpfRCXeQJpZU0Ag5DsWUSFra2tsWrsSE2bOR7ypG5iuhO9IhPCmXIWV6OjoMq1XkYFfy6Nz585g77na9L77/m3cuHFqOeBuVbOyssK2Tb5YsWY9IhLikGnWmA5aSJUYPXwIBAIBjoTfQLpFM77jkCrCcRw82rSBR5s2uB4djYBde5GULUCacQM6OCOVSiAQYNCAfvioSycs9V6PB5kiZJo2AgQVbshLKlGjRg0RtG0Ltu/ei98uXEKSaXMwcdW2liblI5FIsHLJAvwZGgb/3fvxxLgZ5BIzvmMRFbK2tobPikWYPG8pHlu1BYS6fEcihBflKqyEhoZWVg6iJnR0dLBk/hzs+f4AjoVexXNLVyqukCoxcuggZGZm4Zc78cg1Ub8p20nlcm3ZEju3tMTt27Hw3xmExPQiPDdqACY24jsa0WI1atRAgN86nD7zO3YfPIonRo0gM7DmOxZ5D5FIhHHfjURPr4+wdLUPEgqMkWPqBHBUFFNn3bp0hkvzZli4fDX+ykpFtmkDOr7UInZ2dlg0YxIW+gTimXVb+mxJtUR7IVIKx3EYMXQQRvTuCpu069QHnVSZ6ZPHo7F+LgQvs/iOQnjStGkTBPqtg9+8CWgnfgrbtKs09hOpVBzHoWcPL+zx90U3q5ewTr8OyIr5jkU+wN7eHjv9/TD2k5ao8/wiBAWZfEciH2BhYQH/DWsx8qMWqEWzPWkdlxbN8bVXJ5i8uM93FEJ4Ua4WK//2rvFWOI6Dnp4enJyc0Lt3b5ibmysdTtP5+/vD398fMpmM7yhK+eqL3sjPL8D+0BvINHfmOw6pBgQCAZYvnIvRU+chUc+TrnhUY/Xr18emdd5ISEjA5u27cT/5LlIMG0Cub8p3NKKljI2N4b10Ia5ei4LPlq14oueIV0a1+Y5F3kMgEKDfV1+iW+dOWL7GB3cykpBh1hgQKH14SyoZx3EY2P8beLR2x4IVa/BI1xGFRrZ8xyIqMmjANwi/eAk3ivLBdA34jkNIlVK6xUp0dDR27dqF7du3Izw8HGFhYdixYwd27dqFP//8E9OmTYOTkxPi4uJUmVejjB8/HnFxcbh69SrfUZQ2ZGB/uNbUgzAvle8opJqwtLSEh2tTCPOe8x2FqAFHR0f4ei9HwPLZ6GySgVppVyB4+YLvWESLtXJ3w95tW9CnoRHNQqMhLC0tsXGdN2YN6gnHtAiI6JhF7dWtWxe7Azaim60cFhm3ACbnOxJRAYFAgDlTJ6BGzl98RyGkyildWOnduzc++ugjPHv2DFFRUbh+/TqSkpLw8ccfY8CAAUhKSkLHjh0xdepUVeYlVYzjOMyfNQ12eXepSxCpMl/1/gxWRVRYIf+ws7PDupVLEbBiDjqbZMA27Qp1ESKVRiwWY+bUiVg/bzIa5cXAIPsR7QM1wEddO2OP/wZ8VKMQ1unR1KVLzenp6WH5onkY37cLaqVEANJXfEciKlC/fn3Y6IP+/5FqR+nCyrp167B8+XIYGxsrlhkbG2PJkiVYu3YtJBIJFi1ahKioKJUEJfwxNjZGW7cWEOSn8R2FVBP29vYQy/L5jkHUUO3atbFu5VJsWToTHSQpqJF2DVwRfVdI5WjYsAGCtm5GP3db1EqNpDEhNICRkRFWLp6PRWP7o15GJHTyUviORD6g92efYv2imaiXeRWCAhpjTRt07egJYS793yPVi9KFlezsbKSmlm5qmZaWhpycHACAqakpioqKlE+nZoqKijB9+nTMmDFDq15XWXh17QjT4gy+Y5BqQiaTgYHGVyHvZm9vjw1rVmLDvAlwZw9glXEDkBbyHYtoIZFIhPHfjYLvoulonHcD+jmJfEciZeDRpg2CAjfi45rS161X5FK+I5H3aNCgPrb5rUUz2QOI857xHYdUkHPD+jCS00UPUr1UqCvQiBEjcOLECTx9+hRJSUk4ceIERo4ciT59+gAArly5ggYNGqgqq9KSkpIwaNAgWFhYQCKRwMXFpURLmoCAADg6OkJPTw9ubm44f/78W7cTHBwMV1dXeHp6Yt++fVUVXy2Ym5tDzKpXMYnw586dO8gX0TS75MMaNGiArZt8sPR/A9A49zqMs+4CcuqrT1TPyckJuwM34QtnU9RIvUrN3DWARCLB8kXzMG/k13BMjYAgny4QqTMzMzMEblyP1kY5MMh5xHccUgFCoRAcR90nSfWidGFl27Zt6NatG/r37w97e3vUqVMH/fv3R7du3bB161YAQKNGjbBz506VhVVGVlYWPD09oaOjg19//RVxcXHw8fGBqakpgNfFkilTpmD+/PmIjo5Ghw4d0KNHDyQmlr4iJZfLIRQKIZPJIK9mB+7p6ekohA7fMUg1sWv/YWRK6vAdg2iQNq3c8f12f0z63AOOaRehk0tXPInq6ejoYPrkCVg6eQTqpl+CsCCd70ikDDq0b4edm9ejrX4KTLNozDh1JhaL4eO9HO1rcDDKjuc7DlHSo8eJyGN6fMcgpEopXVgxNDTEjh07kJGRgejoaFy/fh0ZGRnYvn07DAxeT6/l4uICFxcXVWVVypo1a2BnZ4c9e/agdevWcHBwQLdu3VCvXj0AgK+vL0aOHIlRo0ahcePG8PPzg52dHQIDA0ttq3///oiMjMTFixcxZMiQqn4pvPr1jzC8EFvzHYNUA2fDwnEvowhMz/jDKxPyLwKBAF98/hm+37oRferrwfZ5JI2/QiqFu2tL7Nrii9biFJi8uE8n6hrA1NQUm9avxrCuzWD7/BJ1HVRjQqEQKxbPR7uaQhjkPOY7DlHCmbPnUGxck+8YhFQppQsrbxgaGqJ58+Zo0aIFDA0NVZFJpU6dOgV3d3d8/fXXsLa2RsuWLbFjxw4Ar8dMiYqKQvfu3Us8pnv37oiIiCi1LbFYDD8/P2zYsAFisfidz1lYWIicnJwSN02WkZGBKzfvQC6x4DsK0XKxsbHYuPMA0sya8h2FaDCJRII506fAb/F0NHl5G0YvHtCJL1E5Y2NjbPZZg/4eTq+7BtEYHmqP4zgM/rY/Vs+dhHqZVyAooK5B6kogEGDZwrloaZQPXRqAWKM8evQITzILABG1WCHVS4UKK3/++SfmzZuHUaNGYcSIESVu6iI+Ph6BgYGoX78+fvvtN4wdOxaTJk3C999/j/T0dMhkMtjY2JR4jI2NDVJSlP8R9/b2homJieJmZ2dX0ZfBG5lMhnlLVuCpUWOAo8FESeW5EBGBed5+eGrVChAI+Y5DtEC9evWwO3AThndqjNrPI6j1ClE5juPw3YihmD9uMOo8jwBXRLMGaYImzs7YvnEdWnKJkOTSYMTqSiQSYfXyxXAqTgBXmMd3HFIGMpkMy9f4ItmkMd9RCKlyShdWli5diu7du+PPP/9Eeno6srKyStzUhVwuh6urK1atWoWWLVtizJgxGD16dImuPtx/CgaMsVLLymPu3LnIzs5W3J48eaL0tvgkl8uxaLk37hSZQy4x5zsO0VIymQxbtu6A97aDeGrjAYje3RqMkPISCoUYOmgANq2YhyYvb9GMLqRStGvbBhtXLYJTdhRNF6shTE1NEeC3Dt3sRDDPjKNWbWpKX18fq5cugF1WNMCq1/iGmsh3UwAeyizAxDQBAal+lC6sbN26FUFBQbh8+TJOnjyJEydOlLipi5o1a8LZ2bnEssaNGyMxMRGWlpYQCoWlWqekpqaWasVSHmKxGMbGxiVumqawsBBzFi7FxWdS5Bvb8x2HaKmEhASMGj8ZR68/Q4pVK0Ag4jsS0VL29vbY6b8RPesbwjr9OiCX8R2JaJk6depgq986OBffg05+Kt9xSBmIRCIsnjcb/To0eT0lMxVX1FKtWrUw5KvPYfriPt9RyHvs/n4/Qm4lItfEke8ohPBC6cJKUVER2rVrp8oslcLT0xN3794tsezevXuwt7eHrq4u3NzcEBISUuL+kJAQjXhtlSU1NRVjJ0/HxQw95JrU5TsO0UL5+flY57cZk5eswy1xE+SZOFJXM1LpdHR0MHfGFEwf3Pt116Dil3xHIlrG1NQUgX7r0Ix7QuNCaAiO4zBq2GB81/ej12PlUKsItdTn889QR5RDv9tqiDGGzYHbcTg0BhnmNEYeqb6ULqyMGjUKBw8eVGWWSjF16lRERkZi1apVePDgAQ4ePIjt27dj/PjxAIBp06Zh586d2L17N+7cuYOpU6ciMTERY8eO5Tk5P/44exZjps/DLZ2GeGVUi+84RMsUFxfjQPARDBk7Gcfv5CHJui2YrgHfsUg181HXLvBZMgeOmVfBvdLswcWJ+pFIJNjsswZNhc+gk/+c7zikjL74/DOMG9ALNdKuUXFFDXEchynjRsMq5x7fUci/FBYWYvaCJThxPREZls3pIhmp1pRud//q1Sts374df/zxB5o3bw4dHZ0S9/v6+lY4nCq0atUKJ06cwNy5c7Fs2TI4OjrCz88PAwcOBAD069cPGRkZWLZsGZKTk9G0aVOcPn0a9vbVq/tLZmYmlq/xQVxaIdKtPWnwUKJSxcXFOHHqZxz78RekiGsj36Y9wFV4UjJClObkVA/+61dhyuwFuM+aQK5vxnckokX09PSwce0qjJs6C7EFupBL6PulCXr28EKRtBjbj4cg1dKVThLVTIvmzWGjW4RUWTEg1PnwA0ilevz4MeYv80a8jgMKTW35jkMI75QurNy8eRMuLi4AgNu3b5e4ryIDv1aGzz77DJ999tk77x83bhzGjRtXhYnUh1QqRfCxH3D8lxA8MXaG3IKmVCaqU1BQgKM/nMTPIaF4rmuLfKv2gIAKKkQ9WFtbw993DSbMmIu7aAKmb8J3JKJFJBIJ/NaswJjJM/FQxx1MR5/vSKQMvuj1GTLSM3H44h1kmdHMJuqm72ef4MHJq3hpRl3V+cIYQ/DxH3Dox9+QZN6SWh4T8jelCyuhoaGqzEF4cPnKVWzauhNJwhrItWlPV2aIyqSnp2PvgWBERN1Aip4dXll5UgsVopbMzMywae1KjJs2Bw+FbmC6Er4jES1iamoK78VzMXXxajy1aUe/gxpi5LDBeJS4CqHJSdQtWs107dwJe479gkRQYYUPz58/x+JVa3E/X4wXNp507kDw6NEjODo6Ijo6WtHoorqq8BQccXFxSExMRFFRkWIZx3Ho1atXRTdNKkl8fDzWbQxAQi5DmkkrQKTLdySiJe7cuYOd+w7h4bN0PNN3gMyadrpE/VlYWGDtsgWYMn8FEm08aHYqolJ169bF6G+/xKajfyDDohnfcUgZcByHhXNmIHHCVMQVmoCJDfmORP5maGgII13u9QxOdHxRZWQyGQ4dOYZjp//AU5PmYGbUwpPwq3PnznBxcYGfnx/fURSUPnqMj4/HF198gVu3boHjOLC/p6h70w1IJqOpLP39/eHv768278Xz58/hu2UrYp+kIcW4MZgFzTFPKq64uBh/nA3F4eM/IlUqRppBXTArJ75jEVIu9vb2mPzdEKzZ8wNSLVvyHYdomc96eCH8QgTCs9IgM7DiOw4pA7FYjBUL52DS/BV4Qlfm1UpdB3vEJeeA6dHJfVW4f/8+VqzzQyKzQA61cCfknZRukzp58mQ4Ojri+fPnkEgkiI2Nxblz5+Du7o6wsDAVRtRc48ePR1xcHK5evcprjqysLCxfvR5jZi3B2UwTJFu1BhNTUYVUTFpaGvy2BGLAyHFYc/Q8bhu6ItXCBUzPmO9ohCilc8cOaFu/JnRyk/mOQrTQglnTYJd7h2ac0SB16tRBn+6dYZQdz3cU8i+N69eD4FU23zG0XkFBAVau9cHUFX6I1W+GHFMnKqposWPHjqFZs2bQ19eHhYUFPvroI+Tn50Mul2PZsmWoXbs2xGIxXFxccObMmbduQy6Xo3bt2ti6dWuJ5devXwfHcYiPf/1bmp2dje+++w7W1tYwNjZG165dcePGDcX6S5YsgYuLC/bt2wcHBweYmJigf//+yM3NBQAMGzYM4eHh2LhxIziOA8dxePToUeW8MeWgdGHl0qVLWLZsGaysrCAQCCAQCNC+fXt4e3tj0qRJqsxIlJSXl4eN/lsxYvJs/PSY4Ym1B+QSc75jEQ3GGEN0dAwmzZyLUTOX4NDtHCRYt0euWX0aoZ9ohZlTJsCu4D4gV4+WhkR7mJiYoF+fnnSSrmEGf9sPteTPAWkh31HI3+xq1YSe/BXfMbQWYwxnfv8DQ8ZMwk/xxUi2bkPjj2m55ORkDBgwACNGjMCdO3cQFhaGvn37gjGGjRs3wsfHB+vXr8fNmzfh5eWFzz//HPfv3y+1HYFAgP79++PAgQMllh88eBAeHh6oW7cuGGPo2bMnUlJScPr0aURFRcHV1RXdunVDZmam4jEPHz7EyZMn8fPPP+Pnn39GeHg4Vq9eDQDYuHEjPDw8MHr0aCQnJyM5ORl2dnaV+yaVgdKFFZlMBkPD131OLS0t8ezZMwCvm1PfvXtXNemIUgoLC7Hn+wMY+r+pOHQzC4nWnpAZ2vAdi2iwwsJCHD/5IwaN+h/mbDmIiKI6eGrVBlKjmnT1gmgViUSCIf36wiT7Id9RiBbq27sXahSnAHIp31FIGQmFQkz8bgQssu/xHYX8zcTEBHoc/R+qDElJSfjf5BnwCQ5BgrUnio1oGuXqIDk5GVKpFH379oWDgwOaNWuGcePGwdDQEOvXr8fs2bPRv39/NGzYEGvWrHnv2CYDBw7ExYsX8fjxYwCvW7EcPnwYgwYNAvB6Apxbt27h6NGjcHd3R/369bF+/XqYmpri2LFjiu3I5XIEBQWhadOm6NChAwYPHow///wTwOvfAF1dXUgkEtSoUQM1atSAUCis3DepDJQeY6Vp06a4efMm6tatizZt2mDt2rXQ1dXF9u3bUbeudo7UXVRUhLlz54LjOKxatQq6uuo16KtcLsfPp89g/9ETeCa2w0vqB0kqKD09HUEHDiMy6gaei2uhwLQ1DexJtN6nn3jh4LGTyJbXAwT876iJ9hCJROjf93NsOHUFeWY0FpWmaNO6FWx3f48MaREN+K8G9PT0IAJ1qVMlqVSKnUH78Gt4JJJMm4GZU7fu6qRFixbo1q0bmjVrBi8vL3Tv3h1fffUVhEIhnj17Bk9PzxLre3p6lui6828tW7ZEo0aNcOjQIcyZMwfh4eFITU3FN998AwCIiopCXl4eLCwsSjzu5cuXePjwn4taDg4OMDL6Z+iKmjVrIjU1VVUvuVIo3WJlwYIFkMtf/6itWLECjx8/RocOHXD69Gls2rRJZQGrSkBAABwdHaGnpwc3NzecP3++1DrBwcFwdXWFp6cn9u3bx0PKd7seHY2hYybA7+RFPLRsh5cmDlRUIUqLj4/HrAVLMGr6Ihy5U4AE6/YoMK1LRRVSLQiFQnzR8xPo5yTyHYVooU8/6Q6rouTXs5oQjcBxHIYM+BomuQl8RyF43d0AoP8/qvLXX3cxbMwEHLzyBE9t2tFYedWQUChESEgIfv31Vzg7O2Pz5s1o2LAhEhJe/+Zx/zmnZIyVWvZvAwcOxMGDBwG87gbk5eUFS0tLAK8bAtSsWRMxMTElbnfv3sXMmTMV29DRKTnEAMdxitqDulL6LMnLy0vx77p16yIuLg6ZmZkwMzN77xutjoKDgzFlyhQEBATA09MT27ZtQ48ePRAXF4c6deoo1pPL5dDR0YFMJlObDzYtLQ3ePhtx53k+Uk1bAiIx35GIBvvrr7+wZfsePMosQIphAzBr7Wx9RsiHfPbpJzj80+94DEe+oxAto6uri5ZNG+Px40zIJRYffgBRCx3be8JizwFkswZ04YpnMpkMDPQZVJRUKkXAjt0IuRSDZPOWYDr6fEciPOI4Dp6envD09MSiRYtgb2+PP//8E7a2trhw4QI6duyoWDciIgKtW7d+57a+/fZbLFiwAFFRUTh27BgCAwMV97m6uiIlJQUikQgODg5K59XV1VWbmXffUOnlZ3NzzRwY1dfXFyNHjsSoUaMAAH5+fvjtt98QGBgIb29vxXr9+/fH7NmzwXGcYvAcvsjlcgQf+wFHfvoNT42bQG7ZgNc8RLPFx8fD138b4jNeIdW4IZiVId+RCOGVRCJBTQtjPJYWUsGaqNyXn3+KC+t2IZUKKxpDJBKhWeMGePQkiyYC4FlxcTHkyje6JwCeP3+OuUtW4IHMCvnWbahYWM1dvnwZf/75J7p37w5ra2tcvnwZaWlpaNy4MWbOnInFixejXr16cHFxwZ49exATE1NqgNp/c3R0RLt27TBy5EhIpVL07t1bcd9HH30EDw8P9OnTB2vWrEHDhg3x7NkznD59Gn369IG7u3uZMjs4OODy5ct49OgRDA0NYW5u/ndrNv5U+3b9RUVFiIqKwpw5c0os7969OyIiIkosE4vF7xyo598KCwtRWPjP6PE5OTkqyfpGamoq5i9dhYeFhsimcVRIBbx48QLrN/njRnwKko0bgVnRNNyEvNGtoyeu/3wDRWYOfEchWqZBgwYwkuVAvXuLk//q5dUN57cEI5MKK7x69eoVpFRYUdrVa9fg7ReIRLOWYIbU7YcAxsbGOHfuHPz8/JCTkwN7e3v4+PigR48e8PLyQk5ODqZPn47U1FQ4Ozvj1KlTqF+//nu3OXDgQIwfPx5DhgyBvv4/raE4jsPp06cxf/58jBgxAmlpaahRowY6duwIG5uyT7YyY8YMDB06FM7Oznj58iUSEhIq1AJGFap9YSU9PR0ymazUB2ljY4OUlBSltunt7Y2lS5eqIl4pFyIuwTdwFxJNW4CZmVTKcxDtxxjDjz/9gu+PnsRTo0aQWbXiOxIhasfdtSUsfjqHZL6DEK0jEAhQw8oCD2kKX43StGlTGEu3IfPDq5JK9PLlSxSDBhZXxo8/n8aO4J+QbN0OEOp8+AGkWmjcuDHOnDnz1vsEAgEWLVqERYsWvfV+BwcHsLeMGTZu3DiMGzfurY8xMjLCpk2b3jku65IlS7BkyZISy6ZMmYIpU6Yo/m7QoAEuXbr01sfzhcq9fyvvoDzvM3fuXGRnZytuT548UUVEBB8/Ae+t+/DYuh2YHhVViHIKCgowbc58+P94AY9t2kNmYM13JELUUu3atSGW5vEdg2gpN5emEOan8x2DlIOOjg6M9HVo4GGe5ecXoFBOpzDldfTEj9h69AySbdpQUYWQSlDtf5UsLS0hFApLtU5JTU0tV3OkfxOLxTA2Ni5xq6gjP5zAnp/CkWLdhmZmIUrLyMjAmEnTcSnXHJnmzgBX7X8CCHkngUAAiZgOPknlaNqoIQxlVLjTNPZ2tcEV5vIdo1rLycsDE9Bvc3lcvBSJoBO/IdXKjY79CKkk5TpDnzZtWpnX9fX1LXcYPujq6sLNzQ0hISH44osvFMtDQkJKDLTDp+vRMdh7MgRpNLgUqYCXL19iyuwFuKPnDKZPLZ4IKQuxrghgcjoQJSpna2sLA1YA1Y7CRipbg3qO+PXRfb5jVGs5eQWAkC4yllV2djY2BOxEinU7Oo8gpBKV61cpOjq6TOtp2nTL06ZNw+DBg+Hu7g4PDw9s374diYmJGDt2LN/RIJVKsXZjAFIsW9OPIakQn80BuC+yp6IKIeVgZGgEFBbTzEBE5SwtLSGSveI7BimnOrVqQld+C8U0TCFvCgoKwKj1dplt3/M9nhjUpxbvhFSycv0PCw0NrawcvOrXrx8yMjKwbNkyJCcno2nTpjh9+jTs7e35joaIS5FIFVoCQl2+oxANVlBQgOu376HYxpPvKIRoFF1dHXAvZaARFYiq6ejoQMjRN0vTmJiYQI+ToZjRSSpfXhUVAQIavLYsGGO4En0LUis6/iOkstFe4W/vG7mYT+cjryJXX7mxXgh5IykpCS91qaUKIeXFGAOosSCpJJrWwpe8HkdPBBnfMao1abEUNExk2aSmpuKVUEKt3gmpAhUurMTFxSExMRFFRUUlln/++ecV3TTBm+aOZnzHIBrOxMQEOnKa1pOQ8nr1qpAGSSSEEDUik8upUFBGxcXFYBy17iGkKihdWImPj8cXX3yBW7dugeM4xfzVb66+yGRUzVeFxg2c8OfZ+ygWG/IdhWgwKysrmAiLkSQtpLEiCCmHvPx8QEKNO4nqMcYgk8n5jkHKqbCwEDJqLcErARVVyqxGjRrQlebzHYOQakHpo8XJkyfD0dERf/zxB+rWrYsrV64gIyMD06dPx/r161WZUWP5+/vD39+/QkWmnp90x7Ffz+IJ6qgwGaluOI7D1HHfYaHvNqTYtKUrPYSU0asiKWBA/1+I6hUUFEBOg0lqnJycHLyk8VV4JRAKAEYXcMtCJBKhjo0FEl/lgomN+I5DVEAqlSIxMbHKnq9OnToQieg3ryyUfpcuXbqEs2fPwsrKCgKBAAKBAO3bt4e3tzcmTZpU5hmEtNn48eMxfvx45OTkwMREufEtzMzM0LWtK47FPEK+iYNqA5JqxbWlC4b3/QS7Tv6BVEs3QEBX3Ah5H6lUilfFdPBOKsfz589RLJLwHYOU09NnKSgS6IGTS/mOUm3p6YoBeS7fMTTGxDEj8HCpD57ZtOU7ClGBxMREDFi1H3Jx5Y+dKCjMxqF5g1C3bt1Kfy5toPSZlUwmg6Hh6+4plpaWePbsGQDA3t4ed+/eVU06AgAY991INBZnQSc/le8oRMN99UVvTB38BWo9jwBXVMB3HELU2uPHj/FKRN0wSeVITHyCbKbHdwxSTncfJkCuZ8x3jGpNT08MjlqslFm9evXwiacrjF/c5zsKURG52ARyiVnl36qgePMhnTt3xpQpU/iOUSZKF1aaNm2KmzdvAgDatGmDtWvX4uLFi1i2bBlVtVRMJBJhw5qVaMIeQ5yXzHccouG6f9QVfsvmwLngJgyyEwBG030S8jZXoqKRITDlOwbRUlE3Y1EopsHpNc2jxCfUpYJnEn09QE6FlfIY991ItLIWQJJbdV1ICFEn/51opzIoXVhZsGAB5PLXg66tWLECjx8/RocOHXD69Gls2rRJZQHVSVFREaZPn44ZM2ZUyYfzbxKJBAF+69HGOAfGWffoZJhUSN26dbE7cBMGezigTupFCPLT+Y5EiNoJj4iE1LAG3zGIloq9cxdyfSqsaBKpVIqcgkKAo660fDKUSKgrVjlxHIdVSxfCw7IYRi8e8B2HaLGffvoJpqamijpBTEwMOI7DzJkzFeuMGTMGAwYMQEZGBgYMGIDatWtDIpGgWbNmOHTokGK9YcOGITw8HBs3bgTHceA4Do8ePQLwembiTz/9FIaGhrCxscHgwYORnv7P+Uznzp0xYcIETJs2DZaWlvj4448r/bUrvWfw8vJC3759Abw+SYuLi0N6ejpSU1PRtWtXlQWsqKSkJAwaNAgWFhaQSCRwcXFBVFRUiXUCAgLg6OgIPT09uLm54fz582/dVnBwMFxdXeHp6Yl9+/ZVRfwSxGIxfLyXY3DHRrBNjQRX/KrKMxDtIRKJMHr4UOzc4I3uNi9RK/UyBC+z+I5FiFrIz89HalY+INLlOwrRQi9fvsSLgiJAQNOgapK4uDjkikz5jlHtGUj0ASqslJtIJMLqZYvxefMasEqPBuQ0KxlRvY4dOyI3N1cx3mp4eDgsLS0RHh6uWCcsLAydOnXCq1ev4Obmhp9//hm3b9/Gd999h8GDB+Py5csAgI0bN8LDwwOjR49GcnIykpOTYWdnh+TkZHTq1AkuLi64du0azpw5g+fPn+Obb74pkWXv3r0QiUS4ePEitm3bVumvXanCSnFxMbp06YJ79+6VWG5ubq6YblkdZGVlwdPTEzo6Ovj1118RFxcHHx8fmJqaKtYJDg7GlClTMH/+fERHR6NDhw7o0aPHW0dblsvlEAqFkMlkiipcVRMIBBgxZBDWzpuMhjlR0M9+TK1XSIVYWFhg5eL5CFw1D11Ms1A77QoE+Rl8xyKEV7+F/IFUHRu+YxAtdSnyMjJ1LPiOQcrpl5CzyBLT7wLf9PTEEHFUFFCGQCDAtInjMPXbz1Dn+UVwhTQIMFEtExMTuLi4ICwsDMDrIsrUqVNx48YN5ObmIiUlBffu3UPnzp1Rq1YtzJgxAy4uLqhbty4mTpwILy8vHD16VLEtXV1dSCQS1KhRAzVq1IBQKERgYCBcXV2xatUqNGrUCC1btsTu3bsRGhpaoj7h5OSEtWvXomHDhmjUqFGlv3alCis6Ojq4ffu2WhVR3mbNmjWws7PDnj170Lp1azg4OKBbt26oV6+eYh1fX1+MHDkSo0aNQuPGjeHn5wc7OzsEBgaW2l7//v0RGRmJixcvYsiQIVX5Ukpp3KgRgrZtQT9XG9ROvQTuVQ6veYjmq1WrFtauWIKt3vPRs1YR6qRGQCc7iQp3pNphjOGHn87gpQlNc08qx/GfTiPP0I7vGKQcZDIZom/GQi6hghjfRCIRqK1XxXh93A0B65ahedFfMHrxkI71iEp17twZYWFhYIzh/Pnz6N27N5o2bYoLFy4gNDQUNjY2aNSoEWQyGVauXInmzZvDwsIChoaG+P333z84nXRUVBRCQ0NhaGiouL0pnDx8+FCxnru7e6W+zv9SuivQkCFDsGvXLlVmUblTp07B3d0dX3/9NaytrdGyZUvs2LFDcX9RURGioqLQvXv3Eo/r3r07IiIiSm1PLBbDz88PGzZsgFgsfufzFhYWIicnp8StMujo6GDi2O8Q4L0QrUWPYZlxE5BV7dgvRPvUrFkTS+bPxp6NqzHY3Qp1Uy/AMOs+ICvmOxohVeJixCU8hzEgEPEdhWih9PR0PM3IA9PR5zsKKYfIy1eQKbIA1PyiYnUgEAgg4KgQUFE1a9bEDn8/DPJ0Qu3nEdR6hahM586dcf78edy4cQMCgQDOzs7o1KkTwsPDFd2AAMDHxwcbNmzArFmzcPbsWcTExMDLy+uDY5nK5XL06tULMTExJW73799Hx44dFesZGBhU6uv8L6WPGouKirBz506EhITA3d29VHBfX98Kh6uo+Ph4BAYGYtq0aZg3bx6uXLmCSZMmQSwWY8iQIUhPT4dMJoONTclmnTY2NkhJSVH6eb29vbF06dKKxi+zWrVqIWDDOkRcisSW7XuQJLRGnmk9GlyNVIipqSnGfzcK3w0fit9C/sCRkz8jTSpGmkFdMJpqkmgpmUyGwN3fI9O4aq9ykOpj1/cHkKzvwHcMUk57DgYjy9iZ7xgEr3+n5VRXUQmhUIhRwwbDq1tnLF29HgmZBnhh1oDOIUiFvBlnxc/PD506dQLHcejUqRO8vb2RlZWFyZMnA4CiNcugQYMAvC6Y3L9/H40bN1ZsS1dXFzJZyVnAXF1dcfz4cTg4OEAkUp+LYEr/r7l9+zZcXV1hbGyMe/fuITo6usStsixZskQxKvC7bteuXQPw+sN50/+qZcuWGDNmDEaPHl2qm89/uzQxxirUzWnu3LnIzs5W3J48eaL0tsqjnUdbfL/DH+M+aQnH5xegm/OUmvaRCtPR0cFnn/bA99v9sWHWd+hqko46qRHQfZEIMOrjTLTL/sNHkSSwpkFrSaXIzMxEZEwsZEY025QmuXHzJpLyBYBIj+8oBMCrV69QzOjEX5Xs7OywY4sfJvZuB4fnFyDMS+U7EnkPQWE2BAVZlX8rzFYq35txVvbv34/OnTsDeF1suX79umJ8FeD1GCghISGIiIjAnTt3MGbMmFKNGxwcHHD58mU8evQI6enpkMvlGD9+PDIzMzFgwABcuXIF8fHx+P333zFixIhSRZiqpHSJJzQ0VJU5ymzChAno37//e9dxcHAA8LqJm7NzyasLjRs3xvHjxwEAlpaWEAqFpT7A1NTUUq1YykMsFr+3q1BlEolE6PdVX/T69BPs3LsfoZcu4qlBA8gNrXnJQ7RLo0aNsHbFEuTk5OD4yZ/w29lwZAjNkGXkCKYj4TseIRXy5MkT/HDmLHJtPPmOQrTUBv+teGrQgO8YpBwYY9jgvx1pJs34jkL+lvkiBzIhFb9VjeM49Pn8M3Tp1AHrN/rjevw1PDdtQt0W1UydOnVwaN6gKn0+ZXTp0gXXr19XFFHMzMzg7OyMZ8+eKVqkLFy4EAkJCfDy8oJEIsF3332HPn36IDv7n4LOjBkzMHToUDg7O+Ply5dISEiAg4MDLl68iNmzZ8PLywuFhYWwt7fHJ598AoGAv6JruQor06ZNK9N6HMfBx8dHqUAfYmlpCUtLyzKt6+npibt375ZYdu/ePdjb2wN43bTIzc0NISEh+OKLLxTrhISEoHfv3qoLzQOJRIJJ//sOg/p9hY2B2xF97zKeGTemLhxEJYyNjTF8yEAMHTQAV69ew/fBx/A0LR/JevaQGdpQH3SicQoLCzFv6SokmbnQ95dUituxsbj+MBlyq1Z8RyHlcPKnX/BEakwnl2rkybNkuphTiUxMTLB80Tzcjo3Fmg1b8ARWyDWtR/tGNSESiVC3bl2+Y3zQ+vXrsX79+hLLYmJiSvxtbm6OkydPvnc7DRo0wKVLl0otr1+/Pn744Yd3Pu7NrERVqVyFlf928YmKioJMJkPDhg0BvC5aCIVCuLm5qS5hBUydOhXt2rXDqlWr8M033+DKlSvYvn07tm/frlhn2rRpGDx4MNzd3eHh4YHt27cjMTERY8eO5TG56pibm2Pp/DlITEzE+k2BuJ/+EKmmjak5K1EJgUCANm1ao02b1khNTcX+w8cQce0C0nRrIM/YARDq8B2RkA9ijGHh8lV4KLIHExvyHYdoocLCQqxcvxEpZlRU0SRpaWnYd/RHvKBWbGol/lEi5OJ6H16RVEjTJk0QtG0Lgo/9gKM//46nxs40KxYh71Guwsq/u//4+vrCyMgIe/fuhZmZGQAgKysLw4cPR4cOHVSbUkmtWrXCiRMnMHfuXCxbtgyOjo7w8/PDwIEDFev069cPGRkZWLZsGZKTk9G0aVOcPn1a0apFW9SpUweb1nsj5sYN+Ppvx1O5KbJN6gM8Npci2sXa2hrTJo3DhKIihPx5FsEnfkKaVA9phvXoZJWotQ2bA3DtuRyFprZ8RyFaauU6XzwW1wVE/HQTJuUnk8kwf+kqPDFpRgN5qpn0zBeABXUFqgpCoRDf9vsaXh91hbfPRtxOfoo0M2e6cEbIWyg9xoqPjw9+//13RVEFeN13asWKFejevTumT5+ukoAV9dlnn+Gzzz577zrjxo3DuHHjqigRv1xatEDQ1s346Zdfse/ICTw1qI9io5p8xyJaRFdXFz17fIKePT5BbGwstgcdwKO0HDyT1IXcoGzd+AipKv7bd+LMjcfINm/KdxSipY6fPIXI+AwUWjTnOwoph43+W3G3yARyE1O+o5B/SU9PRwGjk/qqZmFhgfWrliHiUiT8tu5Cor4TnT8Q8h9Kl+BzcnLw/PnzUstTU1ORm0vzoKszgUCA3r16Yu/WjfjMUYSaqVfAFb/iOxbRQk2aNMHGdauw1Xs+PqstRZ3UCAhzlZ/KnBBVYYzBZ+MW/Hj5PjKoqEIqybWo6wg6fpq+Yxrm1C+nERL9AHkm6j+OQXXzZ2g4UkV0kYYv7TzaIihwI7xqM1inXQdkUr4jEaI2lC6sfPHFFxg+fDiOHTuGp0+f4unTpzh27BhGjhyJvn37qjIjqSQGBgZYMHs61s4ej4Y5UdDLecp3JKKlatasicXzZmG3nze+aqiHOs8vQphXujBLSFUoLi7G/CUr8MvtFGSYN+E7DtFS9+7dx4oNAXhm1Yq6kmiQiMjL2HH4FFItWvAdhbzF6ZCzKDKqzXeMak0ikWDpgjlY8F1/OKRFQFCQyXckQtSC0nv6rVu3omfPnhg0aBDs7e1hb2+PgQMHokePHggICFBlRo3l7+8PZ2dntGql3oPVOTs3xu7ATfisvj5s0qIAOX/zfxPtZmZmhllTJ2HPpjX43EGA2qmXIHj5gu9YpBrJzMzE2MnTEZ4iRLYpTXtLKsfDh/GYs2wNnli1obEINMj16Bis3rILz6xbUzFMDcXGxiG1WAwIlR7JgKiQZ7u22LlpHVwFj2GQ85jvOITwTum9hkQiQUBAADIyMhAdHY3r168jMzMTAQEBMDAwUGVGjTV+/HjExcXh6tWrfEf5ILFYjLkzpmLm8K9g9/wiuOKXfEciWszU1BQLZk/H1tWL0FbvGawybgCyYr5jES13PToG302ZhZuCenhlTFc8SeW4e/ceZi5ZhUTLVjRYrQa5di0KS338kWTdBhDQibs62rRtJ9KM6/Mdg/yLmZkZ/DesQ4/6hjDPuAUwxnckQnhT4XK8gYEBmjdvjhYtWlBBRQt06dQBfisWwCHjKrjCPL7jEC1Xs2ZNbF6/GotGf4166Zcgou5BpBJIpVL4+W/FQr+dSLD0gFzf7MMPIkQJl69ew+zl6/DYsg2Yjj7fcUgZnQ0Lx9KN2/HUui21MFJT4ecvICFPAKZL5xrqRiQSYe6MqRjYpSWs06OpuEKqrQqV5M+fP49t27bh4cOHOHbsGGrVqoV9+/bB0dER7du3V1VGUsUcHBywee1yTJy1AAmWbemKG6l07TzaonmzpljqvRbRKZnIMmsEcBzfsYgWiI+PxxLv9XjE1UC+dWu+4xAtduLHn7D72C94Zu1BXRU0yP7DR3Do9DmkWLcFBEK+45C3yM3NxZYdQciwbMd3FPIewwZ/C07A4eCf15Fu0YzvOFpLKpUiMTGxyp6vTp06EIlon1YWSr9Lx48fx+DBgzFw4EBER0ejsLAQwOsfv1WrVuH06dMqC6lOioqKMHfuXHAch1WrVkFXV5fvSJWiZs2aWDF/Fmat3IAkGw86ySWVztDQEGtXLMXe/Ydw9I9IpFi5Uh93orSioiJs3RWEPy5F45lZCzBdCd+RiJaSyWTw2eiPs7ceI82mLf1uaQipVIpV6zbgwv00ZFi3ouMcNcUYw4Jlq/DYsBEVLDXA0IEDkPQsGb8+SESBcR2+42ilxMRExGz4GjX0K39MzJSXQmDqUdStWz1mSAsLC0OXLl2QlZUFU1PTcj9e6b3/ihUrsHXrVuzYsQM6Ov80m2zXrh2uX7+u7GbL5dy5c+jVqxdsbW3BcRxOnjxZah1vb2+0atUKRkZGsLa2Rp8+fXD37t1S6wUEBMDR0RF6enpwc3PD+fPn3/qcwcHBcHV1haenJ/bt26fql6RWGjVqiE86tIJe7hO+o5BqguM4DBv8Lb77+hPYpF+n5qREKZevXsPQMRMQHP0cSdZtqahCKs2LFy8wfupM/Hw3C2mWLaiooiEyMzPxvykzEPKoEBkWTamoosYCduzCrRciyAys+Y5CymjW1Emwlz4FpIV8R9FaNfRlqG1Y+beqKN7817Bhw9CnT58qf15VUPoI4O7du+jYsWOp5cbGxnjx4kVFMpVZfn4+WrRogS1btrxznfDwcIwfPx6RkZEICQmBVCpF9+7dkZ+fr1gnODgYU6ZMwfz58xEdHY0OHTqgR48eb21mJZfLIRQKIZPJIJfLK+V1qZNhgwagxksqrJCq1fuzT9G7Q0sYZz/kOwrRICkpKZgyez4WBxzEPbPWeGViTydMpNLcuHkToyfNQJTMDvnGjnzHIWUUc+Mmvps8CzHMka6oq7kTp37GzxG3kW3ixHcUUg4ikQhTxo2GZc49vqMQUqWULqzUrFkTDx48KLX8woULVdZcqEePHlixYgX69u37znXOnDmDYcOGoUmTJmjRogX27NmDxMREREVFKdbx9fXFyJEjMWrUKDRu3Bh+fn6ws7NDYGBgqe31798fkZGRuHjxIoYMGVIpr0udSCQSWJsb04wtpMqNHj4UdkinKx7kgwoKCuC7KQBjZi3BxXxrpFm6AELt7KZJ+CeTyRCwfRfmr9+GeIu2kEss+I5EykAul2Pbrj1Y4LMVCZZtIZfQINbq7Mzvf2DnsV+RaulCBXIN5ObqCnN5DrU8rqYKCwsxadIkWFtbQ09PD+3bty8xS25sbCx69uwJY2NjGBkZoUOHDnj48CGWLFmCvXv34scffwTHceA4DmFhYQCA2bNno0GDBpBIJKhbty4WLlyI4uJ/zk+XLFkCFxcX7Nu3Dw4ODjAxMUH//v2Rm5tb5lwVpXRnxTFjxmDy5MnYvXs3OI7Ds2fPcOnSJcyYMQOLFi1SWUBVy87OBgCYm5sDeN0PPyoqCnPmzCmxXvfu3REREVHq8WKxGH5+fu99jsLCQsWYMwCQk5NTwdT8qlnDBlxSAZjQhO8opBoRCAQYOuArLD94FrlmDfiOQ9SQVCrFkeMncPzn3/BMUg9FNjSwIalcKSkpmL9sFR5KzZFr3ZpO+DREamoqFixbhftFxsixbkOfm5o79ctpbAv+BSnWrah7nYbiOA4NnOrifkoumJ4x33FIFZs1axaOHz+OvXv3wt7eHmvXroWXlxcePHiAly9fomPHjujcuTPOnj0LY2NjXLx4EVKpFDNmzMCdO3eQk5ODPXv2APjnnN3IyAhBQUGwtbXFrVu3MHr0aBgZGWHWrFmK53348CFOnjyJn3/+GVlZWfjmm2+wevVqrFy58oO53jxPRShdWJk1axays7PRpUsXvHr1Ch07doRYLMaMGTMwYcKECgerDIwxTJs2De3bt0fTpk0BAOnp6ZDJZLCxsSmxro2NDVJSUpR6Hm9vbyxdurTCedVFbl4eIDDkOwaphlq3agWT748j98OrkmpELpfjzO8h+P7wcTzTqYl8m/Z08E0qFWMMJ0/9jO+PnsITsxZgBnSioCl+/S0E2/cF46lpc8hNTPmOQz4gaP9BHPkjEs+pqKLxbGvYQPAkFTIqrFQr+fn5CAwMRFBQEHr06AEA2LFjB0JCQrBr1y5kZWXBxMQEhw8fVozT2qDBPxdQ9fX1UVhYiBo1apTY7oIFCxT/dnBwwPTp0xEcHFyisCKXyxEUFAQjIyMAwODBg/Hnn39i5cqVH8w1c+bMCr/2Cg2vvXLlSsyfPx9xcXGQy+VwdnaGoWHFTsCXLFnywaLE1atX4e7uXu5tT5gwATdv3sSFCxdK3cf95+oFY6zUsrKaO3cupk2bpvg7JycHdnZ2Sm2Lb4wxPElKBrOsHqNBE/ViaGgIgVzKdwyiJhhjCDt3Hju/P4hkzgLZFm0BAc0SQSpXRkYGFq9ai79ecMiq4UknexoiOzsby9esx82UV8iw8aSplNWcTCbDGt+NCL2TgkxLN2pVpAXyCwrABDofXpFolYcPH6K4uBienp6KZTo6OmjdujXu3LmDlJQUdOjQocTkN2Vx7Ngx+Pn54cGDB8jLy4NUKoWxccminYODg6KoArweuiQ1NbVMuVShwkekEolEqSLHu0yYMAH9+/d/7zoODg7l3u7EiRNx6tQpnDt3DrVr11Yst7S0hFAoLNU6JTU1tVQrlrISi8UQi8VKPVbdREVFIZOjLkCEH2lpaZCJ9PmOQXjGGMPFiEvYFrQfyXJjZJm0AoR0sEYqF2MMP53+FUGHT+CJcVPIzWlMDk0Rdu48tuwIQqKRM2QWVnzHIR+Ql5eH2QuX4laBIfItmvIdh6jI3fsPIddz5jsGqWLs73F13tVoQV+//Mf1kZGR6N+/P5YuXQovLy9FixcfH58S6/23WMNxnGKymQ/lUoUKFVb+/PNP/Pnnn0hNTS01Q87u3buV2qalpSUsLS0rEqsExhgmTpyIEydOICwsDI6OJUfu19XVhZubG0JCQvDFF18oloeEhKB3794qy6GJpFIpNm7dhUzjlnxHIdVU2LkLyBDSyUx1xRhDxKVL2BZ0AMlSQ2SauAIiGpSWVL7U1FQs9V6HuzlCZFl7AgJqpaIJcnNzsXyND24+y0WatSe1aNMACQkJmLvUGwmShpAaUxFMW+Tk5CAtuwCwposg1Y2TkxN0dXVx4cIFfPvttwCA4uJiXLt2DVOmTEF+fj727t2L4uLit7Za0dXVhUxWcprnixcvwt7eHvPnz1cse/z4sUpzqYLSe5ylS5di2bJlcHd3R82aNVVW6SmPvLy8EjMTJSQkICYmBubm5qhT5/UUeuPHj8fBgwfx448/wsjISNEyxcTERFExmzZtGgYPHgx3d3d4eHhg+/btSExMxNixY6v8NakT/2078VhYA0xHj+8opBqSSqU48csZvDJvy3cUUsXetFDZvvcAUmQGyDB2AUTa0QqQqDfGGI4cP4HDP/6KpybUSkWTnA0LR8DufUg0bASZBXVf1gQ///obdhw8jiQLVzAdCd9xiArtP3wUz8S1P7wiUUrKy6rp2pjyUogaH16tBAMDA/zvf//DzJkzFefka9euRUFBAUaOHAm5XI7Nmzejf//+mDt3LkxMTBAZGYnWrVujYcOGcHBwwG+//Ya7d+/CwsICJiYmcHJyQmJiIg4fPoxWrVrhl19+wYkTJ1SaSxWULqxs3boVQUFBGDx4sEqCKOPatWvo0qWL4u8345oMHToUQUFBAKCYMrlz584lHrtnzx4MGzYMANCvXz9kZGRg2bJlSE5ORtOmTXH69GnY29tX+mtQV7//cRa/XbmDfGs3vqOQamrfoSN4JqpJVxyrEcYYLl6MwPa9B5EiN0SGcUtqoUKqzNOnT7HEex3iCw2RbUNjqWiKFy9eYPkaH9x+/hLpVu1on6EBCgsLsWrdBkTGpyPdph39X9Myqamp+P18JIpt2vMdRSvVqVMHmHq0Sp6rxpvnK6fVq1dDLpdj8ODByM3Nhbu7O3777TeYmb2+WHH27FnMnDkTnTp1glAohIuLi2Lsk9GjRyMsLAzu7u7Iy8tDaGgoevfujalTp2LChAkoLCxEz549sXDhQixZskSluSqKY0y5CcYtLCxw5coV1KtXTyVBtFlOTg5MTEyQnZ1dapAddXT5ylWs2LwTz6w91Kb5s2PaJRwLCuQ7Bqki9+7dx4zlPkiy8VDpAHa6GQ/gPaRrqUIr4RdjDOcvXsTOvQeRLDdCpnF9tS+otMiPwu6AjXzHICogk8nw/cHDOPn7OSSZNQcTG334QSrEFeahh1UOli+cU6XPqw1+/+NPbNt7CIlGzpAbqK4beVmIMhOw5Os26PHJJ1X6vJouMTER85d5I15UB6+MalX589fIuomAhROr9cXTyiSXy/G/ydMRxeqC6atujMa66ZdwdA+dBxD1pnRZf9SoUTh48CAWLlyoyjyEZ5cuX4H35p14Zt1WbYoqpHrJyMjA/BVrkGTZmmYF0HJvWqhs23sAKXLj12OoCNW7oEK0y+PHj7F41To8klsg16Yd/eZoiBcvXmDZ6vW4nVqEDGua8UdT/PTLr9h1+Ac8NXcF0zXgOw6pBJsDt+PuK2MwU5r4glQ/5Sqs/HsKYblcju3bt+OPP/5A8+bNSw0+4+vrq5qEpMqE/HkWm4KO4JkNTWFK+JGbm4vJs+bjkXFzQERj+2grxhguX7mKwF178UxmSAUVUuVkMhn27DuIn85eQJKZC53kaZCzYeHw37XvdSsVi6ptpUKU8+rVKyxbvQ7XEnNeT31NXX+00k+nz+DMtb+Qa+nKdxRCeFGus+fo6OgSf7u4uAAAbt++XWI5HwPZqiN/f3/4+/uXGtlYHR0+ehz7fjqLFGqpQniSm5uLCdPn4J5eA8j1TfmOQyrJ7dux8AvcgScvdZBu4kKD0pIql5SUhIUr1iBeaoZca2qloiny8/OxYq0vrie+QDq1UtEYCQkJmL98NRLEdVFk0YzvOKSSRFy6jG2HfkSqTRu+oxDCm3IVVkJDQysrh1YaP348xo8frxhjRR0xxuC3ZSvORD9AujV1vSD8ePHiBSbNnIe/dJ0gk9AVSG30+PFjrN8ciIcZhUg1aQwm0ec7EqlmGGM49fNp7DlyEk/NXMAMq3YsFaK82NhYLFvrh8eS+ii2bMF3HFJGP50+g50HjyPJwg1Ml2b90VbXo2OwJmAXkq3bUmskUq1Rf49qTCqVYtFyb0QmFSHbgg5UCD8yMjIwaeY83NNvDLnEnO84RMVycnKw0X8brv71CM+MG4NZqv8A3kT7FBQUYMnKNYhOeYVM6oqgMRhj2LPvAE78cRHPLFpTCzcNIZVK4b1+Ay7ce470GvT/TZvduHkTS3388dSahhEgpNxjrCxfvhwGBgYlxlt5G20dY6WoqAhz584Fx3FYtWoVdHU1c1yAV69eYca8RbiRb4wCs/p8xyHVVEpKCqbMWYgHhs1VOno84Z9cLsfRH07gyI9n8NSwAaTW1DyY8OPx48eYs2QlEvScUGxOMxlqioKCAsxdvBy3XgiRbd2WWtRqiBcvXmDm/MX4S2qFAovmfMchlejmrdtYtHYTnlp7AEKdDz+AEC1XrhJydHQ0iouLFf9+360qnDt3Dr169YKtrS04jsPJkydLrSOVSrFgwQI4OjpCX18fdevWxbJlyyCXy0usFxAQAEdHR+jp6cHNzQ3nz59/63MGBwfD1dUVnp6e2LdvX2W8rEpXUFCAidNnI+qlJQqMyz83OSGq8PTpU0yatQAPjF2oqKJl4uPjMXzsRGw9E4NHNp6QGtrwHYlUU+HnL2Dy/OW4b+yKYsMafMchZZSUlITRE6bicr4Fsk0bUFFFQzx+/Bhjp8xEDBzp+FLLxcbFYdEav9ctVaioQgiACoyxog7jreTn56NFixYYPnw4vvzyy7eus2bNGmzduhV79+5FkyZNcO3aNQwfPhwmJiaYPHkygNfFkilTpiAgIACenp7Ytm0bevTogbi4ONSpU3LHIJfLoaOjA5lMVqo4owlevnyJidNn4zbsITW05jsOqaaePn2KqfOWIMHMjWbj0CIymQzbd+/F6XNXkGzuQn3qCa8OBB/FwV/CkWJDA51qkrg7d7Bw5Xo8NncDExvyHYeUUWxcHBau8kGiRSv67ddy9+8/wIJVvnhi1ZZm9CPkX5Sebvl9OI6Dj4+PUoHKo0ePHujRo8d717l06RJ69+6Nnj17AgAcHBxw6NAhXLt2TbGOr68vRo4ciVGjRgEA/Pz88NtvvyEwMBDe3t4ltte/f3/Mnj0bHMdh9erVKn5FlUsqlWLanAWIhR0VVQhv0tLSMG3+UiqqaJn09HTMXbwCd4tNkW/jQVeYCa+27tyDExdvI926FX0XNUj0jZtYsm4Tnlq1pfFUNMit27FYuHrD6xNt+ty0WlJSEuYsW40nlq0BERVVCPm3Ck23HBUVBZlMhoYNGwIA7t27B6FQCDc3N9UlrKD27dtj69atuHfvHho0aIAbN27gwoUL8PPzA/B6zJSoqCjMmTOnxOO6d++OiIiIUtsTi8WKx2oa7/UbcPuVGYqNqTk04Ud+fj6mzFmIeOMWVFTRIrFxcVjs7YPHJi0gNzHlOw6p5vZ8fwA/RMQhw6I5FVU0SNydO1iybjON16BhHj6Mf11UsabWC9ouNzcXM+YvxWMzVzAdPb7jEKJ2lO4K5OvrCyMjI+zduxdmZmYAgKysLAwfPhwdOnRQbcoKmD17NrKzs9GoUSMIhULIZDKsXLkSAwYMAPD6KqtMJoONTckxAGxsbJCSkqLUcxYWFqKwsFDxd05OjvIvQEXOXbiIi3eTUWDpwncUUk0xxjB38XI80HUC06OZYbTF5StXsXLTDiTRlUqiBn7/4yyO/HkZGVZuVFTRIM+fP8fCVetft1ShoorGyMrKwpylq/DUshUVVbScXC7H7IVL8VDSiLroEfIOSs9/5uPjA29vb0VRBQDMzMywYsWKCnUDWrJkCTiOe+/t3914PiQ4OBj79+/HwYMHcf36dezduxfr16/H3r17S6zH/ecAjDFWallZeXt7w8TERHGzs7NTajuqUlxcjC07gpBm3pTXHKR6O/rDScRmCagbmhaJvnHzdVHFhooqhH9Pnz5FQNAhpFq6UlFFg0ilUsxZvByPTVvS74gGYYxh/tJVeGTUFExHn+84pJLt/v4A4vIlkBlY8h2FELXFMcaYMg80MjLCjz/+iK5du5ZYfvbsWfTu3Ru5ublKBUpPT0d6evp713FwcICeXskmaBzH4cSJE+jTp0+J5XZ2dpgzZw7Gjx+vWLZixQrs378ff/31F4qKiiCRSHD06FF88cUXinUmT56MmJgYhIeHl/s1/LfFCmMMRUVFsLS0VLpYQwghhBBCCCGEEPVTrq5A//bFF19g+PDh8PHxQdu2bQEAkZGRmDlzJvr27at0IEtLS1haqq4aWlBQAIGgZMMcoVComNFHV1cXbm5uCAkJKVFYCQkJQe/evZV6TrFYDLGYrroQQgghhBBCCCHaTunCytatWzFjxgwMGjQIxcXFrzcmEmHkyJFYt26dygK+T15eHh48eKD4OyEhATExMTA3N1dMk9yrVy+sXLkSderUQZMmTRAdHQ1fX1+MGDFC8bhp06Zh8ODBcHd3h4eHB7Zv347ExESMHTu2Sl4HIYQQQgghhBBCNJPSXYHeyM/Px8OHD8EYg5OTEwwMqm6mj7CwMHTp0qXU8qFDhyIoKAjA6xGsFy5ciBMnTiA1NRW2trYYMGAAFi1aBF3dfwbaCggIwNq1a5GcnIymTZtiw4YN6NixY1W9FEIIIYQQQgghhGigChdWCCGEEEIIIYQQQqorpbsCEdVgjCk90C8hhBCizoyMjFQyaDvtKwkhhGgrVe0rCb+osMKz3NxcmJiY8B2DEEIIUbns7GwYGxtXeDu0rySEEKKtVLWvJPyirkA8o6twH5aTkwM7Ozs8efKEfnSI0uh7RFSFvktlRy1WPoy+T5qJPjfNQ5+ZZqoOnxu1WNEO1GKFZxzHae2PhKoZGxvTe0UqjL5HRFXou1R1qsO+kr5Pmok+N81Dn5lmos+NqDsB3wEIIYQQQgghhBBCNBUVVgghhBBCCCGEEEKURIUVovbEYjEWL14MsVjMdxSiweh7RFSFvktElej7pJnoc9M89JlpJvrciKagwWsJIYQQQgghhBBClEQtVgghhBBCCCGEEEKURIUVQgghhBBCCCGEECVRYYUQQgghhBBCCCFESVRYIYQQQgghhBBCCFESFVaIWggICICjoyP09PTg5uaG8+fPv3f98PBwuLm5QU9PD3Xr1sXWrVurKClRZ+X5HoWFhYHjuFK3v/76qwoTE3Vz7tw59OrVC7a2tuA4DidPnvzgY+j3iHwI7eM0T3k+sx9++AEff/wxrKysYGxsDA8PD/z2229VmJa8QccBmof2u0RbUGGF8C44OBhTpkzB/PnzER0djQ4dOqBHjx5ITEx86/oJCQn49NNP0aFDB0RHR2PevHmYNGkSjh8/XsXJiTop7/fojbt37yI5OVlxq1+/fhUlJuooPz8fLVq0wJYtW8q0Pv0ekQ+hfZzmKe9ndu7cOXz88cc4ffo0oqKi0KVLF/Tq1QvR0dFVnLx6o+MAzUT7XaI1GCE8a926NRs7dmyJZY0aNWJz5sx56/qzZs1ijRo1KrFszJgxrG3btpWWkai/8n6PQkNDGQCWlZVVBemIJgLATpw48d516PeIfAjt4zRPeT+zt3F2dmZLly5VdTTyHnQcoPlov0s0GbVYIbwqKipCVFQUunfvXmJ59+7dERER8dbHXLp0qdT6Xl5euHbtGoqLiystK1FfynyP3mjZsiVq1qyJbt26ITQ0tDJjEi1Ev0fkfWgfp3kqsj95Qy6XIzc3F+bm5pURkbwFHQdUH/QbSdQVFVYIr9LT0yGTyWBjY1NiuY2NDVJSUt76mJSUlLeuL5VKkZ6eXmlZifpS5ntUs2ZNbN++HcePH8cPP/yAhg0bolu3bjh37lxVRCZagn6PyPvQPk7zKPOZ/ZePjw/y8/PxzTffVEZE8hZ0HFB90G8kUVcivgMQAgAcx5X4mzFWatmH1n/bclK9lOd71LBhQzRs2FDxt4eHB548eYL169ejY8eOlZqTaBf6PSIfQvs4zVPez+yNQ4cOYcmSJfjxxx9hbW1dWfHIO9BxQPVAv5FEHVGLFcIrS0tLCIXCUlcTUlNTS1Wj36hRo8Zb1xeJRLCwsKi0rER9KfM9epu2bdvi/v37qo5HtBj9HpH3oX2c5qnI/iQ4OBgjR47EkSNH8NFHH1VmTPIfdBxQfdBvJFFXVFghvNLV1YWbmxtCQkJKLA8JCUG7du3e+hgPD49S6//+++9wd3eHjo5OpWUl6kuZ79HbREdHo2bNmqqOR7QY/R6R96F9nOZRdn9y6NAhDBs2DAcPHkTPnj0rOyb5DzoOqD7oN5KoLd6GzSXkb4cPH2Y6Ojps165dLC4ujk2ZMoUZGBiwR48eMcYYmzNnDhs8eLBi/fj4eCaRSNjUqVNZXFwc27VrF9PR0WHHjh3j6yUQNVDe79GGDRvYiRMn2L1799jt27fZnDlzGAB2/Phxvl4CUQO5ubksOjqaRUdHMwDM19eXRUdHs8ePHzPG6PeIlB/t4zRPeT+zgwcPMpFIxPz9/VlycrLi9uLFC75eQrVExwGaifa7RFtQYYWoBX9/f2Zvb890dXWZq6srCw8PV9w3dOhQ1qlTpxLrh4WFsZYtWzJdXV3m4ODAAgMDqzgxUUfl+R6tWbOG1atXj+np6TEzMzPWvn179ssvv/CQmqiTN9Nv/vc2dOhQxhj9HhHl0D5O85TnM+vUqdN7fzdI1aHjAM1D+12iLTjG/h7thxBCCCGEEEIIIYSUC42xQgghhBBCCCGEEKIkKqwQQgghhBBCCCGEKIkKK4QQQgghhBBCCCFKosIKIYQQQgghhBBCiJKosEIIIYQQQgghhBCiJCqsEEIIIYQQQgghhCiJCiuEEEIIIYQQQgghSqLCCiGEEEIIIYQQQoiSqLBCCCGEEEIIIYQQoiQqrBBCqoUFCxZALBbj22+/5TsKIYQQopZoX0kIIcrhGGOM7xCEEFLZcnJysG/fPkyYMAH379+Hk5MT35EIIYQQtUL7SkIIUQ61WCGEVAvGxsYYMWIEBAIBbt26xXccQgghRO3QvpIQQpRDhRVCSLUhlUohkUhw+/ZtvqMQQgghaon2lYQQUn5UWCGEVBsLFixAXl4eHSwSQggh70D7SkIIKT8aY4UQUi1ERUWhXbt2+Pjjj5GQkIDY2Fi+IxFCCCFqhfaVhBCiHCqsEEK0nlwuR+vWrdGpUye0adMGAwcORH5+PnR1dfmORgghhKgF2lcSQojyqCsQIUTrbd68GWlpaVi2bBmaNWsGqVSKu3fv8h2LEEIIURu0rySEEOVRYYUQotWSkpKwcOFCBAQEwMDAAPXr14dYLKa+44QQQsjfaF9JCCEVQ4UVQohWmzRpEnr06IGePXsCAEQiERo3bkwHi4QQQsjfaF9JCCEVI+I7ACGEVJaff/4ZZ8+exZ07d0osb9asGR0sEkIIIaB9JSGEqAINXksIIYQQQgghhBCiJOoKRAghhBBCCCGEEKIkKqwQQgghhBBCCCGEKIkKK4QQQgghhBBCCCFKosIKIYQQQgghhBBCiJKosEIIIYQQQgghhBCiJCqsEEIIIYQQQgghhCiJCiuEEEIIIYQQQgghSqLCCiGEEEIIIYQQQoiSqLBCCCGEEEIIIYQQoiQqrBBCCCGEEEIIIYQoiQorhBBCCCGEEEIIIUr6P3I6y1V/FSGyAAAAAElFTkSuQmCC\n", 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ASkpKuHv3Lu7du4e+fftCS0sLDx8+xPz58/H9999Xqz/xkCFD0KVLF+zZswdGRkaS5R4eHjA1NZX0Dw4ODsa2bdswe/bsr27P09MT3bt3R+fOneHk5IT27dujtLQUAQEB8PLywvPnz6v0+irSpUsXLF68GAsWLEB8fDxGjBiBJk2aICYmBt7e3ujRowfmzp0L4H9Fgby8PKSmpiIyMhJ8Pl/SJ9rFxQX29vZo1qwZiouLcfnyZRw+fBheXl5ffG0cDgcHDx7EgAED0KtXLyxfvhwtW7ZEXl4efv/9d/z555+Sq1mxsbHIyMhAbGwsRCKRJI+VlRXU1dXRrFmzMtv+3FS8VatW1Rqgz9nZGYGBgejSpQs2btwIe3t7KCsr4/bt23BxccHDhw/RvHlzDBs2DL/88gv27NkDDQ0NLF26FE2bNpV8Rvv06YPU1FRs2bIF//d//4erV6/iypUr0NTUrHC/ampq+O2337Bo0SLo6urC1NQUW7ZsQUFBQZWm9KzMhw8f4OjoiF9//RXh4eHYtWsXXF1da7xdQohioWNsWXSMrdtj7OdckydPhpubGzIzM7F169YK18vKykJSUhKKiook00WfP38ehw8fLrPP7t27QyAQYNeuXVixYoVkeadOnZCdnQ1/f38sWrSoWhkJIXKmjmYfIqReKSwsZJYuXcrY2toyWlpajFAoZKytrZmVK1cyBQUFkvUqm7bxn1NBfp6mtqLb27dvGYZhmLCwMKZLly6MlpYWo6KiwlhbWzNr1qwpNzWfmZkZs2bNGsn9iqZGDAkJYQCUmQpy586dTJs2bRihUMhoamoyHTt2ZDw9PSucAvHfEhISmJkzZzJmZmYMn89nmjZtynz//ffMzZs3q/z6vsbPz4/p1asXo6GhwaipqTHt27dnnJycykyjWNG2//n6VqxYwVhZWTEqKiqMjo4O061bN+bkyZOV7pthGCY6OpqZMGEC06RJE4bP5zNmZmbMmDFjmPDwcMk6EydOrDDD57/Bv33+m/x7KkgAX50ilGEYJisri1m6dCnTvHlzhs/nM0ZGRsyAAQOYc+fOSaYo/jzdspaWFqOqqsoMGjSozHTLDMMwXl5ejImJCaOmpsZMmDCB2bhx41enW/748SMze/ZsRl9f/6vTLf/zNVU0Nea/p3ru3bs3M2PGDGb69OmMpqYmo6OjwyxdupSmWyakAaJjbHl0jK3bYyzDMMyHDx8YLpdbZqrkf2/n801FRYVp1qwZM3HiRCYsLKzC9Xv37s0AYO7du1dmef/+/RkAzO3btyvNRAiRXxyG+UJHfEKIQvr48SN0dXVx+fJlGl1eAb179w7NmzdHVFQUmjdvznYcQggh/0DHWMVGx1hCSG2hMVYIqWeCgoLQr18/OuFTUFevXsV///tfOuEjhBA5RMdYxUbHWEJIbaEWK9VQXFyMZcuWgcPhwNnZGXw+n+1IhBBCCCGEEEIIYZFCt1i5desWhg4diiZNmoDD4eD8+fNlHi8tLcXKlSthYWEBVVVVWFpawsnJqdyo456enrCwsICKigrs7Oxw+/btCvfn5+cHW1tbODg44MiRI7X1sgghhBBCCCGEEKIgFLqwkp+fjw4dOmD37t0VPr5582Z4e3tj9+7deP78ObZs2YKtW7di165dknX8/Pwwb948rFixAhEREejZsycGDx6M2NjYctsTi8VQUlKCSCSqcEo4QgghhBBCCCGENCz1pisQh8PBuXPnMHz4cMmy7777DkZGRti/f79k2Q8//AChUChpcdKlSxfY2tqWmQquVatWGD58OFxcXMrso6ioCEuWLAGHw8GmTZsgEAhq90URQgghhBBCCCFErvHYDlCbevToAW9vb7x8+RItWrTAo0ePEBwcDHd3dwCfxkwJCwvD0qVLyzxv4MCBCAkJKbc9gUAgee7XFBUVoaioSHKfYRgUFxdDX18fHA6nRq+JEEIIIYQQQggh8qNGhZXw8HDcvn0bfD4fDg4OaN++vaxyycSSJUuQnZ2Nli1bSrrwbNy4EWPGjAEApKWlQSQSwcjIqMzzjIyMkJSUJPV+XVxcsG7dunLLs7OzoampKfV2a0IsFsP//AWcOPcH4tStIVI3qvxJcsQi9S7O+HpVviKpd549i8KWHR74INZBtrYVwJG+ByM/PQYuE/qhT58+sgtIFB7DMFiyci1uZwhRrN64Ss/pkB+GA547ajkZUSRisRg7PffgWmg0UvRtqvxdxSnKw2CDHKxftbTylQkhX/TXjZvYfugskgzsgS9cyGyU+Rieq2bDzMysjtMRQuo7qQsr7u7ucHR0hLa2Nng8HtLS0tCmTRv4+vrCzs5Olhml5ufnh6NHj+L48eNo06YNIiMjMW/ePDRp0gQTJ06UrPfvViQMw9SoZcmyZcvg6OgouZ+TkwMTExOpt1dTL168gLPrTnxg9JBj1KNGP0wJqStxcXHYutMTr1ILkKLVHoyyKtuRSD2123svQlNEKNauWlGFkH/LysrCinUb8SxPiFwDW7bjENLg3Am5hx0HTyLJsNsXiyqEEFKbqlVYOXDgAGxsbNCmTRs4Oztj06ZNWLRoETgcDj58+IA9e/agT58+uHLlCnr06FFbmats0aJFWLp0KUaPHg0AaNeuHd6/fw8XFxdMnDgR+vr6UFJSKtc6JSUlpVwrluoQCARyMf5Kbm4uXHd6IvRVHJK0O9APU6IQ4uPjsct7P6Jik5Gg2RKMvhbbkUg9duSEHy49iEa2fge2oxAFdf/BQ2zbvQfvNNpArK3HdhxCGpy/btzEjoN+SDDsAnDp4iEhhB3VKqxs3boVMTExAD41eX348CG2b98OW1tb2NjYYMOGDWjatCkWLlyIe/fu1Urg6igoKAD3X1+wSkpKkhl9+Hw+7OzsEBAQgBEjRkjWCQgIwLBhw+o0qywxDIOrf/6F/Uf98EGtBUoNOrEdiZBKvXnzBl77DyE6Lg2JGi0gNjRnOxKp546fOo3j10KQpk8tDEj1FRQUYKv7Ltx/mYhk/W6AkjLbkQhpUBiGwT7fIzgXGIpkw65UVCGEsKpahZXnz5+jsLAQT548Qc+ePcHlcnHq1CmsWrUKhYWFMDY2RrNmzRAREYHff/8dbdu2hYWFRW1lR15enqTQAwBv375FZGQkdHV1YWpqiqFDh2Ljxo0wNTVFmzZtEBERATc3N0yZMkXyHEdHR4wfPx729vbo1q0bfHx8EBsbi+nTp9da7tqUnJyMdZu24VUOFxmGDgBXie1IhHwRwzCIiIzE3kPH8CG7BElqVmAMm7EdizQA+3yPwD8oHCn6ttRsnFRb4K3b8Nx3CB/UmqOYuv4QUufy8/Ox0skZj9OBLAM7+h4nhLCu2mOsqKiooFOnTnBwcECHDh3g5+cHsViM58+f49GjR7h16xYCAwMxadIkZGZmQl1dHTk5ObWRHaGhoejbt6/k/udxTSZOnAhfX1/s2rULq1atwowZM5CSkoImTZrg119/xerVqyXPGTVqFNLT0+Hk5ITExES0bdsWly9fVrhBrRiGwemz53H83B+I124Psa4225EI+aKioiJcuRaA0xf+QBqjjnQNKzD6QrZjkQZALBZjs9tO3IhKRIaeDZ2Mk2pJSEiA87YdeJUlQppBd4BbrydXJEQuPXr8GM6uu/BOaI1SHUO24xBCCIAaDF7r6uqKPn364M2bN5g+fTo6dOgAU1NThIeHo0mTJoiLi0NcXByePn0qy7xl9OnTBwzDfPFxDQ0NuLu7VzpF8owZMzBjxgwZp6s76enpWOnkgpd5fGQ1osFpifxKTEzEEb8zeBD+GCn8JsjX7kQ/TEid+fjxI5audsKjbAFy9dqwHYcokPz8fHjtPYDb4c8Qr9UGjB6N/URIXSssLMROTx8EPX6FJN3OAI/98QwJIeQzqX/R2NjYICwsDNOnT0fXrl0lBQ4ej4cDBw4AAIyNjWFsbCybpKRCN4NuY9e+Q/ig1Q5iXR224xBSTmlpKULu3sXxMxeQkFOERBUziA17UEsBUqcSExOxaOU6xPAtUaLViO04REGUlpbCz/8szl76E/HCZigx6s52JEIapJC797Bjz37EqVigkMYOJITIoRpdKm7WrBkCAgKQnJyMe/fuobi4GF27dmV1auGGorS0FK47PRD4+C1SaCwVIoeSkpJw0v88Qh6EIU3ZEDnqLcDoq7AdizRAd+/fx9ZdPojVtQUj0GA7DlEAYrEY1wL+gu/x00jkN0G+EbUGJYQN8fHx2OS2E68yS5Gq25UGia4nHoaG4d3791VeX11dHYMHDazFRITUnEza4BsZGSn0LDq1xcPDAx4eHhCJRDLdbm5uLhatWIOoIm3k06B5RI4UFxcj6FYwTp3/Hcl5pUgSGENkQK1TCDvEYjG89x3EH8ERSDJ0AJSo2xn5OoZhcCs4GHt9jyMROsjS70bdFQlhQW5uLjx89uPuo2jEa7Wm7nf1BMMw8N53EBeDI5HON6ry8xrnvqTCCpF7dLZQi2bOnImZM2ciJycHWlqyOSCkpqZi7pKViFFpAZGmgUy2SUhNvXnzBsdOncWj5y+RpmyEXI3WgAH1fSbsyc7OxvK1GxCVr4ocw05U3CNfxTAM7t5/AO8Dh5Eo0kCGlj1dGSeEBUVFRTh64hT+uH4L8WpWKDXqxnYkIiOFhYVY6eSMiBRRtWdy4pfG1WIyQmSDCisKJDk5GXMWr8RrzQ5gVDTZjkMauIKCAly++icuXg1AegkfKULTT2OnEMKyx0+eYv1Wd7xTbw2xlj7bcYicCw0Ng8c+X8SXqCJd04YGxCSEBSUlJfA/fxH+v19BosAUH42otWt9kpiYiMWrnBCjbI5incZsxyGkVlBhRUHk5uZi/rLVeK1lQ2MEENYwDIOoqCgcPnkGr2ITkaLcGB+17KipPJELDMPg8HE/nLkWhAS9rgCPz3YkIscePX6Mnd77EV+ojFStdoAGjQFFSF0rLS3FxUuXcfLc70jiN0G+IY1nVN+E3LuPbR57EavTkX7DkHqNfg1VQ3FxMZYtWwYOhwNnZ2fw+XVz0s4wDJaudkKMijV9IRFW5Obm4uIfV/DHnzeQAXWkqZmDMbRgOxYhEh8/fsRKJ2dEpoqQZdiVrnSSL4qOjsZ2Dx+8z2WQqtUajJoq25EIaXBKS0tx6fJVnPC/gGR+Y+Tqd6eJGOoZhmHgc8AXF2+FIcmwO12EI/VejT7ht2/fxp49e/D69WucOXMGTZs2xZEjR2BhYYEePWq/S8CtW7ewdetWhIWFITExEefOncPw4cMlj7u4uODs2bN48eIFVFVV0b17d2zevBnW1tZltuPp6YmtW7ciMTERbdq0gbu7O3r27Fluf35+frC1tYVQKMSRI0cwderU2n6JAIDzFy/hRa4yRDrUpJ3UrZiYGBw8ehJRbz4gWdAURTpd6MSHyJ34+HgsXr0er/mWKNGhqZRJxeLi4uC6ywsvk/OQrNUSjL4a25FIDaWlpcHH9wjiE5JY2f+sXyajVauWrOxbUZWWluKPq9dw4swFJPGMqKBSTxUUFGD52g14nKmEbAMa54w0DFIXVvz9/TF+/HiMHTsWERERKCoqAvDpyrazszMuX74ss5Bfkp+fjw4dOmDy5Mn44Ycfyj0eFBSEmTNnolOnTigtLcWKFSswcOBAREVFQU3t0wmVn58f5s2bB09PTzg4OGDPnj0YPHgwoqKiYGpqWmZ7YrEYysrKEIlEEIvFtf76gE99Tk+cvYgsA4c62R8hYrEYt4Pv4NCJ00gu5CJFzQJiI/r8EfkUFh6Bjds9EKtjC0agznYcIodycnKww2MPHr54iwSNVmAMaHYRRZeVlQXPvQfw4MlLxKtZQaxqXfmTZIyX9R7v3r+jwkoViUQiXP3zLxzx80cyzxA5et2ooFJPJSQkYNEqp08XO7TpYgdpOKQurGzYsAHe3t6YMGECTp48KVnevXt3ODk5ySRcZQYPHozBgwd/8fGrV6+WuX/w4EEYGhoiLCwMvXr1AgC4ublh6tSpmDZtGgDA3d0d165dg5eXF1xcXMo8f/To0ViyZAk4HA42bdok41dTsbCwcKTxDKi/Kal1JSUluHDpMs5c+AOpPD1kabQD1GkQRyK/rgVcx+7Dp5Bg0I1mcCHliEQinDp7HmcuXkWceguUGnZlOxKpocLCQhw4fAwBt+8jXt0KIqPu7IWh87IqYRgGNwNvYd+R40jk6iNHryt1CanHHj9+grVbdiBWly52kIZH6m+26OhoSXHinzQ1NZGVlVWTTLUmOzsbAKCrqwvg05gpYWFhWLp0aZn1Bg4ciJCQkHLPFwgEcHd3r3Q/RUVFkhY8wKerZdK6dfcBclVoWmVSe0pLS3H+4iWcuvAHkpSbIJ+uIhEFcOH3P7Dn9BUkG3YDuPQDh5T15s0bOG12w3uxDnKMHOhHsIJjGAZX/wzAgWOnkKBqjkIjB+paoAAiIiLh7rUX8WJNZGp3pgJ4PXcjMAju+44h3pAudpCGSerCSuPGjRETEwNzc/Myy4ODg2FpaVnTXDLHMAwcHR3Ro0cPtG3bFsCnvrkikQhGRkZl1jUyMkJSkvT9dV1cXLBu3boa5f0sLSMTDM9QJtsi5N/uhNzF7r0HkcBrhDx9B/qBShTCn3/d+LuoYk8/mEkZYrEYvkeP4/xfwUjQsQHDF7IdidRQYmIi1jpvxesCPjINHKjwrwDi4+Ph4roDrzNLkaLdAeDRjFv13R9XrsHzxAUkGdHFOdJwSV1Y+fXXXzF37lwcOHAAHA4HCQkJuHv3LhYuXIjVq1fLMqNMzJo1C48fP0ZwcHC5xzj/uurBMEy5ZdWxbNkyODo6Su7n5OTAxMREqm0Z6OmCk/GRTg6JTGVmZmLNxs14mVGKVJ0udGWBKIznL15gt+9JJDfqTkUVUkZBQQEWr1yLZ7kC5Bh2oxYNCo5hGJy98DuO+F9CnHZ7MLqabEcilSgqKoLXvoO4eT8S8VptwejTe9YQXL8Z+KmoYtiZjsukQZO6sLJ48WJkZ2ejb9++KCwsRK9evSAQCLBw4ULMmjVLlhlrbPbs2bh48SJu3boFY2NjyXJ9fX0oKSmVa52SkpJSrhVLdQgEAggEshmbolsnW1x98juyhHoy2R4h9+4/xLbde/Besy3EerpsxyGkyoqKiuC0yQ3xBp3o5I2UkZmZiTmLluMVvxlKtKmVp6IrKSnB2o2b8eBDLjKMqIiqCJ49e4YN23Yglm+KQipsNhgvXkRjx/7jn1qq0P9T0sDV6H/Axo0bkZaWhgcPHuDevXtITU3F+vXrZZWtxhiGwaxZs3D27FncuHEDFhYWZR7n8/mws7NDQEBAmeUBAQHo3p3FAdH+oWuXztArTQPEpWxHIfXAn3/dwIbdB/DWsDvEQiqqEMVy5LgfYvkmAI8GVSb/U1BQgHmLV+CFSkuUqFFRRdGVlJRg3pLlCEzkIEO3Df1Yk3MMw2DPAV8s2eyJl9qdUKhpSkWVBqKgoABrXLZ9uthB3X8Ikb7FymdCoRD29vayyFJteXl5iImJkdx/+/YtIiMjoaurC1NTU8ycORPHjx/HhQsXoKGhIWmZoqWlBVVVVQCAo6Mjxo8fD3t7e3Tr1g0+Pj6IjY3F9OnTWXlN/8bj8TBt/GhsPX4VGXrt2I5DFNizqCjs9PVDohEN9kkUj1gsxrWbt1Fo2IPtKETObN6+E9FKZhCr6rAdhcjAWuctiMzXQaGmceUrE1aVlJRg6ap1iEgDsg07U0Glgdm9Zx9iVZvRxQ5C/latwso/xw2pjJubW7XDVFdoaCj69u0ruf8538SJE+Hr6wsvLy8AQJ8+fco87+DBg5g0aRIAYNSoUUhPT4eTkxMSExPRtm1bXL58GWZmZrWev6q+6d8Pt+7cxc2EDyjUlG6sFkI2bd+NRAM7KqoQhfTmzRvkK2vRiTspIz4+HuEv41Bi2JntKEQG7j94iLB3GSjU78B2FFKJ0tJSLFi2CmF5mijUoSJYQ5Obm4t7Ec9QbOTAdhRC5Ea1CisRERFVWq8mA79WR58+fcAwzBcf/9pj/zRjxgzMmDFDVrFkjsPhYM3yJchcvByRORy6ikOqLTExEVkiZRqZnyis5ORk5IAG8SZl/Xk9EMmCJmzHIDLie/wUUrWt2Y5BqmCn5x5E5AhRqEXnpA3Rrdt3kMJvxHYMQuRKtQorN2/erK0cpBLKyspw3+KMJavWIiI9BjnaVmxHIgqksLAQIg7N/EMUF4/HAw9itmMQOZOSngFGWY3tGERGsnLzAV26ACDv4uLicDP0GQqMurIdhbAk7PFTFKrosx2DELlCfQIUiLKyMrY5r8dIO2MYpTwERDSgLamapk2bQrU4C6hiKy5C5I2VlRW0xNlsxyByxtLUGLwi+lzUF1wO6DilAE6du4gkNUu2YxAW5RcUAEp0wY6Qf5J68NovjbfC4XCgoqICKysrDBs2DLq6DXfmEQ8PD3h4eEAkEslsm1wuF7On/xd2Nvex3XMv3qu3hIhmQSCV4PP5GDqwH3yDopCr04LtOIRUm56eHgyESogrLaQubUSif9/eOH7+CmIZMxp/px7o0KYlXr1MhkiduhjIs7fvYyFWbc52DMKixkaG4KTmg+FTF11CPpO6xUpERAT2798PHx8fBAUFITAwEHv37sX+/ftx/fp1ODo6wsrKClFRUbLMq1BmzpyJqKgoPHz4UObb7t61Cw54bEcf3TwYpkUAomKZ74PULxPGjkbXxspQy37HdhRCpDLn1ykwynzGdgwiR3R1dTG4rwM0smIqX5nIvV8mjYdJ3ktATC1y5ZmOtjY4JQVsxyAs6tWtC7SLU9iOQYhckbqwMmzYMAwYMAAJCQkICwtDeHg44uPj8c0332DMmDGIj49Hr169MH/+fFnmJf+goaGBLRvWYt3MsWiR+QBq2W+pCS35Ii6Xiw1rVmBgMzUYpEUCYtm1pCKkLrRv3x7dW5lCmBPLdhQiR6ZNGo9Ohhyo0udC4eno6GDBzF/QOOUBHaPk2JBv+kEnn/6/NWQ2Nh2gJ8qkIigh/yB1YWXr1q1Yv349NDU1Jcs0NTWxdu1abNmyBUKhEKtXr0ZYWJhMgpIv62xvj8M+HpjcsznMkoOhlJfEdiQip3g8HlYuWYAF476Decod8HLps0IUy9IFc9GanwHlvGS2oxA5weVy4bxuFXoaiaGdGU0XGBRc965dsGDaz2iafBcoLWI7DqlA586d0ExdDO7HTLajEJZwuVxMmzAaupnP2Y5CiNyQurCSnZ2NlJTyTcBSU1ORk5MDANDW1kZxcf3polJcXIwFCxZg4cKFcve6lJWVMXn8zziwayuGWSjBJOUuuAUZbMcicuqb/v3g67kd35lx0CTlPrgfs9iOREiV8Hg8uG/ZiLbcOPCpiEz+pqysDOd1qzCmhzWapNwDp+Qj25FIDfTt3QubVzrCKuMBeHnU3UDecDgcuKxbCcu8p+AU5bEdh7BkQL9+aG+kQhc6CPlbjboCTZkyBefOnUNcXBzi4+Nx7tw5TJ06FcOHDwcAPHjwAC1asD9QZnx8PMaNGwc9PT0IhULY2NiUaUnj6ekJCwsLqKiowM7ODrdv365wO35+frC1tYWDgwOOHDlSV/GrRVtbGysWL4D35jXor5eDpvSjmXyBhoYGVi1bBI/1S9BLMw1NUh/SZ4UoBFVVVex23YxOahmfukASgk8/9qZNmoBtK+ajRU44hNnvqPWKAmvVsiX2e2xHX4N8GKZFAqIStiORf9DR0cF253VonhsJpfxUtuMQlqxftQxWxa/BKaTZ2QiRurCyZ88e9O/fH6NHj4aZmRlMTU0xevRo9O/fH97e3gCAli1bYt++fTILK43MzEw4ODhAWVkZV65cQVRUFFxdXaGtrQ3gU7Fk3rx5WLFiBSIiItCzZ08MHjwYsbHl+46KxWIoKSlBJBJBLBbX8SupnkaNGmGT02p4blyG3pppaJLygH40kwqZmprC1dkJu9ctxADdLJgkh4CXk0A/SIhcU1FRwfbNGzGkpQ4M0iIAOf9OJnXH2roFDu3ZjfFdTWGSEkKtNxWYuro6NjmtwZrpo9Ai8z4Vy+RMkyZN4LPTFfb8RGhRN7wGSSgUYvum9WiW8xicoly24xDCKg7D1OxbMC8vD2/evAHDMGjWrBnU1dVllU0mli5dijt37nyxFUqXLl1ga2sLLy8vybJWrVph+PDhcHFxKbNuUVERlixZAg6Hg02bNkEgEFQpQ05ODrS0tJCdnV1mTJq69OHDB+zasx8vPqQiUb0FxEIdVnJIyyL1Ls74elW+IqmxjIwMHD5xCsH3w5DON0KOhjnA47MdSyb46TFwmdAPffr0YTsKkaFrAdfhdfgk4nQ6ghHUzTGoQ34YDnjuqJN9EemlpKTAdZcXnn1IQ5Jmqzr7fFQHpygPgw1ysH7VUrajyLXi4mIcPXkKlwKCEK/WHKUa7E7JzMt4i7U/dsHgb79lNYc8YBgGx/1O49QffyFeux0YFS22I31Ro8zH8Fw1G2ZmZmxHqVeSkpIwb+kqxKi3B6Mq+/ffMu0uTh+k3wFEvkndYuUzdXV1tG/fHh06dJC7ogoAXLx4Efb29vjxxx9haGiIjh07Yu/evQA+HaTDwsIwcODAMs8ZOHAgQkJCym1LIBDA3d0d27dv/2pRpaioCDk5OWVubDMxMcGWDWvhuXEZBujnwDjlHrj5aWzHInJIV1cX82ZOx4n9nlg2ui9sip6gSVoouLnJdDWKyKVB3/SHx6a1aFP4DGo579mOQ+SIoaEhNq9fg+0r5qAT9y0M0yJomlgFxefzMWXCOPh6uGJEcz5Mk0PAzaMuKPKAw+Fg7Oif4L1lHboof4Be+hPqutXANGrUCB6um9CqMArK+TQuEmmYeDV58vXr13H9+nWkpKSU6xpz4MCBGgWTlTdv3sDLywuOjo5Yvnw5Hjx4gDlz5kAgEGDAgAEQiUQwMjIq8xwjIyMkJUk/KKKLiwvWrVtX0+i1wtjYGC7rViE5ORme+w7i0cu7iBc2g1jdkO1oRM4oKytj4ID+GDigP+Li4nDyzHncDw9GhrI+stXNwPCFbEckRKJp06bY77kDu7334q+HD5Co2wHgVa1VIan/mjdvDi/3rXj69Bl27tmHD9kcpGq2oO8xBaSpqYmlC+YhLS0Nu332IzL6LuLVmkOsps92tAavcePG8HDbglvBd+C1/xASlRohV9sS4NT4Oi5RAHp6evDesQ2Llq/B05yPyNekVkGkYZH6m27dunUYOHAgrl+/jrS0NGRmZpa5yQuxWAxbW1s4OzujY8eO+PXXX/HLL7+U6frD4XDKPIdhmHLLqmPZsmXIzs6W3D58+CD1tmqLkZER1q1Yin2u6zHMggPT5BAo0dS75AuMjY2xcN4snDjghXVThqKb8nuYptyFIPMtIC5lOx4hAD7NGDRv1m9wXjgdzTMfgp+bwHYkImfatm0Dn13bsXXBVNgjBkZp4TSriYLS19fH2uVL4LN1HYY0Lfk0GyLNICQXevVwwJG9npj5n46wTAmGatY7avHaQKipqWGn6yYMtFT91HKJ3nfSgEjdYsXb2xu+vr4YP368LPPIXOPGjdG6desyy1q1agV/f3/o6+tDSUmpXOuUlJSUcq1YqkMgEFR5/BW26evrY+XiBcjMzITPgUO49+gOEoTNWO+7TOQTj8dDD4fu6OHQHbm5ubhyLQB//HkDGSVcJAuMIVI3AmpQlCREFtq3a4uDXjuw2W0n7sdEIEWnHaBUowaapJ5p27YNvHdsw4sXL+CxzxfvUvORqNFCrseGIBUzNDTE2hVLkJqaCu/9vgiLCkGCmiVE6nQewyYej4cfR47A90P+g5NnzuL3a9eRJDDFRy0zOk+o53g8HlYuWYjTZ8/j8LmrSDCwB5Tqx1h9hHyN1C1WiouL0b17d1lmqRUODg6Ijo4us+zly5cwMzMDn8+HnZ0dAgICyjweEBCgEK9NlnR0dLBkwTwc3LkZI1uowDT5DpRoXnryFRoaGvjp/0bikM9ueK1fjLHttGCVdgf6GU9p2j3COlVVVaxdsQSr/jsKFmkhNKYUqVDLli2xa9sm7FzjiL6aaWiach/cgnS2YxEpGBgYYNXSRdi/fSOGWyrDNDkYvNxEtmM1eAKBABPHjsFRn92Y2rMZLFKCoZr9DmBoJrf67seRw7F+0QyYp94Dp5D98SYJqW1SF1amTZuG48ePyzJLrZg/fz7u3bsHZ2dnxMTE4Pjx4/Dx8cHMmTMBAI6Ojti3bx8OHDiA58+fY/78+YiNjcX06dNZTs4ObW1tLHGcg/3uLhhmwf3UtJamqiSVMDY2xtyZv+LkwT3YOnccBuplwzw5GBqZL4HSIrbjkQase7eu2L/LFd3VUqGT+ZyaJZMKWVhYYOvGddizaSUGGX38u1sJDYyqiPT09LB80Xwc3LkFP1ir/n2hiLo6s01FRQWTJ4zFsb278d/ezWGZEgxh1hsqsNRzNu3bw8vVGa0Lo6BMhU5Sz0ndNrqwsBA+Pj7466+/0L59eygrK5d53M3NrcbhZKFTp044d+4cli1bBicnJ1hYWMDd3R1jx44FAIwaNQrp6elwcnJCYmIi2rZti8uXLzf4adh0dXWxYvECJCcnw223N57FxiBJuw0Yvhrb0Ygc43A4aN++Pdq3b4+ioiLcDr6DMxcvIym1AEn8pijRaApwaRA7Ure0tLTgvsUZJ0/748TvAYjXtwN4KmzHInKocePG2LB6OVJSUrDnwCGERYUgXs2KBnhXQNra2lg8fw6ysrLgvd8X9x6FIF69BQ1yyzKBQIAJY8dg1P+NxNkLv+PcH9eQxG+CfE0LOj+opwwNDeGzyw1LVzvhUVYBcrSasR2JkFrBYRjpLt/17dv3yxvlcHDjxg2pQ9U3OTk50NLSQnZ2NjQ1NdmOI5WYmBhs2r4L7z+qIEPHGuDW7XgFFql3ccaX5q9XVOnp6bhw6QoCAm8jk6OBNDUzVsYy4KfHwGVCP/Tp06fO903kQ3T0S6xy3oq36m0gFupKvZ0O+WE44LlDhsmIPEpPT4fn3oMIi4qptR/lnKI8DDbIwfpVS2W+bfI/aWlp2OG1F49i4pCg2QqMSs3Ox3gZb7H2xy4Y/O23MkrYMBUXF+PcxUvw//0KkvhNka9lXquzCDXKfAzPVbMb/AVUNojFYmx134XrT+ORrtumWmPtWKbdxemD9DuAyDepfx3fvHlTljmInLOyssLe3e64fjMQe3yP4YPQCiUaTdiORRSEnp4epkwch8kTxiIqKgrHTp/Di7dPkcJvgo+apgBXie2IpIGwtm4Bnx1bsWDZajzPLUQRfY+Rr9DT08OqpQv/8aP8PhI0W9IgtwpIX18f61ctw/v377Flhwdep4uQqt0aUFKu/Mmk1vD5fIz6v5EYOWwozpy7gLOXriJRxRQfNWmQ2/qGy+ViieNcGB09Dr+/HiBFvyO9x/XAu3fvYGFhgYiICNjY2LAdh1U1bnYQFRWF2NhYFBcXS5ZxOBwMHTq0ppsmcobD4WBAv77o3rULXHd64u7zUCTrdaCTElJlHA4Hbdq0gXObNigoKMDlq3/i/OVrSBcLkapuCUagwXZE0gBoa2vD030rFi1fjfBcMT5qGLMdici5zz/K4+LisGWHB16lvUGKdmuApxgzAJL/MTMzg4fbFty9dx87vPfhg8AMhZqmbMdq8JSVlTHmp//DyGFDcfTEKfxx/Rbi1axQqtGY7WhExiaN+xlCoSoOXghEioEtFVeIVPr06QMbGxu4u7uzHUVC6sLKmzdvMGLECDx58gQcDgefexRx/v7PIRKJZJNQgXl4eMDDw6Pe/S2EQiFWLV2I0NAwbN7phfcarSESUp9lUj1CoRD/N3I4fhgxDFFRUThw9CReJzxDgooZRBqN6EBLapVAIIDb5o2Yu2gZwvN4KKapWUkVGBsbY+dWF4SFR2C7xx584BohT8uSvq8UULeuXWDb0QaePgdw4+E9JOh1pEKZHBAIBJg6aTx+HDkMu/fsw70n95Cg3RaMQJ3taESGfho5AiXFJTj050Ok67VjOw4hMiF1J8a5c+fCwsICycnJEAqFePbsGW7dugV7e3sEBgbKMKLimjlzJqKiovDw4UO2o9QKe3s77N/tBlulOKjlvGM7DlFQn1uxuLqsxz5XJ4xqrQ7zlGAIsmNpBhdSq3g8Hlxd1qNZ8RtwivPZjkMUiJ1tR/ju2Y2JPZvDJPkOTSWqoAQCAebP/g2bls6CVeYD8PJS2I5E/qapqYnlixyx02kxOopfQjvzBc0gVM+MHf0T+rVpCrWc92xHIQDOnDmDdu3aQVVVFXp6ehgwYADy8/MhFovh5OQEY2NjCAQC2NjY4OrVqxVuQywWw9jYGN7e3mWWh4eHg8Ph4M2bNwCA7Oxs/Pe//4WhoSE0NTXRr18/PHr0SLL+2rVrYWNjgyNHjsDc3BxaWloYPXo0cnNzAQCTJk1CUFAQduzYAQ6HAw6Hg3fv3tXOH6YapC6s3L17F05OTjAwMACXywWXy0WPHj3g4uKCOXPmyDIjkWOamprY7bYF/c0E0Mp6yXYcouD09fWxYO5MHPHegandzWCREgzV7HdUYCG1RkVFBRtWLUWTrCdsRyEKhsfjYcqEcfDasg7tS6Ohkf2G7UhESm1at8b+3dvRWTUF6tlv2Y5D/sHc3Bw+u7Zj+n86w5SKmPXOwrmzYMVNAacoj+0oDVpiYiLGjBmDKVOm4Pnz5wgMDMTIkSPBMAx27NgBV1dXbNu2DY8fP8agQYPw/fff49WrV+W2w+VyMXr0aBw7dqzM8uPHj6Nbt26wtLQEwzAYMmQIkpKScPnyZYSFhcHW1hb9+/dHRkaG5DmvX7/G+fPncenSJVy6dAlBQUHYtGkTAGDHjh3o1q0bfvnlFyQmJiIxMREmJia1+0eqAqkLKyKRCOrqn5rl6evrIyEhAcCnvqvR0dGySUcUAo/Hw+pli/GNtR40s1+zHYfUA0KhEFMnjcexvbsxxcES5sm3wc+NZzsWqafMzc1h09wU3IJ0tqMQBdS4cWPs3e2OkR2bwCglFBDXr+6/DYW6ujrct7qgn7kKtLJi2I5D/oHD4eDHkcPhucUJbYqeQyU3ju1IREZ4PB7WLluEJtlP2Y7SoCUmJqK0tBQjR46Eubk52rVrhxkzZkBdXR3btm3DkiVLMHr0aFhbW2Pz5s1fHdtk7NixuHPnDt6//9QSSSwW4+TJkxg3bhyATxPgPHnyBKdPn4a9vT2aN2+Obdu2QVtbG2fOnJFsRywWw9fXF23btkXPnj0xfvx4XL9+HQCgpaUFPp8PoVCIRo0aoVGjRlBSYn8iDKkLK23btsXjx48BAF26dMGWLVtw584dODk5wdLSUmYB5UlxcTEWLFiAhQsXlhmsl3w66C1xnIt2mkXg5aeyHYfUEwKBAFMmjsNh7x34oZUGjJND6GoVqRWjRgyFXmEC2zGIglJSUsKc3/6LxVN/gnFyCCCicwRFpKSkhNXLFsPBRAXC3A9sxyH/8qmIuR09DUugmU3Fr/rC1NQU7SybgluQyXaUBqtDhw7o378/2rVrhx9//BF79+5FZmYmcnJykJCQAAcHhzLrOzg44Pnz5xVuq2PHjmjZsiVOnDgBAAgKCkJKSgp++uknAEBYWBjy8vKgp6cHdXV1ye3t27d4/fp/F+jNzc2hofG/SS0aN26MlBT57q4pdWFl5cqVEIs/9XXcsGED3r9/j549e+Ly5cvYuXOnzALWFU9PT1hYWEBFRQV2dna4fft2uXX8/Pxga2sLBwcHHDlyhIWU8o3L5WLD6uUwzqV+sES21NTUsHDuLHg4r4Q99y20M5/TZ4zIlJWVFVRFNM4KqZk+vXpg/eI5MEm5B4hK2I5DpMDhcLBm+WJYihNo7CU5xOfz4bx2FfpaqFP3u3pk3E8joVdILZHYoqSkhICAAFy5cgWtW7fGrl27YG1tjbdvP3WN5PxrgHaGYcot+6exY8fi+PHjAD51Axo0aBD09T9NdCIWi9G4cWNERkaWuUVHR2PRokWSbSgrl511lsPhSGoP8krqwsqgQYMwcuRIAIClpSWioqKQlpaGlJQU9OvXT2YB64Kfnx/mzZuHFStWICIiAj179sTgwYMRGxtbZj2xWAwlJSWIRCK5f2PZoqmpiW96dwcvN5HtKKQeMjY2hpf7Nsz4rtunvtbUJ5fIiEgkAgOa2YXUXPv27bB6wSw0SX1A40MpKB6Ph1WL56NRdsVXZAm7uFwuVi5ZiLbqBVAqSGM7DpGBFi1aQE2Uy3aMBo3D4cDBwQHr1q1DREQE+Hw+rl+/jiZNmiA4OLjMuiEhIWjVqtUXt/Xzzz/jyZMnCAsLw5kzZzB27FjJY7a2tkhKSgKPx4OVlVWZ2+fiS1Xw+Xy5m3lX6sJKRXR1db9avZJXbm5umDp1KqZNm4ZWrVrB3d0dJiYm8PLyKrPe6NGjce/ePdy5cwcTJkxgKa38++7bb2BQIt9NtYji4nA4+GH499jlsgYt8x+Dn5fEdiRSDzx+8gS5PE22Y5B6wrajDcYNGwTtLBpzTlFZWVnBUJUDiEvZjkIq8LmVdNOc51TArAeUlJSgLAdjZDRU9+/fh7OzM0JDQxEbG4uzZ88iNTUVrVq1wqJFi7B582b4+fkhOjoaS5cuRWRkJObOnfvF7VlYWKB79+6YOnUqSktLMWzYMMljAwYMQLdu3TB8+HBcu3YN7969Q0hICFauXInQ0NAqZzY3N8f9+/fx7t07pKWlyUWjB5kWVhRRcXExwsLCMHDgwDLLBw4ciJCQkDLLBAIB3N3dsX37dggEgi9us6ioCDk5OWVuDUmjRo2gLPrIdgxSzxkbG2Ofhzs6qWVAmBtb+RMI+QKGYbD/yAlkq5uzHYXUIz/9MALm/HxwSgrYjkKk1L5tK3A/ZrEdg3yBtrY2OrVvDe7HjMpXJnJNJBKhpFS+Wh80JJqamrh16xb+85//oEWLFli5ciVcXV0xePBgzJkzBwsWLMCCBQvQrl07XL16FRcvXkTz5s2/us2xY8fi0aNHGDlyJFRVVSXLORwOLl++jF69emHKlClo0aIFRo8ejXfv3sHIyKjKmRcuXAglJSW0bt0aBgYG5XqasKHBF1bS0tIgEonKvZFGRkZISpLuSriLiwu0tLQkN3mY/qku5efng+EqV74iITWkoqICt00b0E2vBKo51DeXSMf//EW8LVQFo6xa+cqEVBGHw8GcX6dCP4cG2VRUqioq4FCLFbnWr2c3qBXSjG6K7tmzZ9RqlEWtWrXC1atXkZKSgsLCQkRHR2PWrFkAPrUOW716NeLi4lBcXIzIyEh8++23kueam5uDYRjY2NiU2eaMGTPAMAwOHTpUbn8aGhrYuXMn4uPjUVxcjNjYWBw9elTym3nt2rWIjIws85x58+bh3bt3kvstWrTA3bt3UVBQAIZhYG5uLpO/RU00+MLKZ9UdlOdrli1bhuzsbMntw4eGNbJ8WEQkspW02I5BGggejwfndavQVpAGbj6dXJHquRl0C4fOXkWmtjXbUUg91L59O2gzNG6AooqOeQOxCv3Yk2cCgQBKHPa7AJCa2Xf4BDLUzNiOQUiNNPjCir6+PpSUlMq1TklJSalWc6R/EggE0NTULHNrKBiGwdGTZ5CjYcp2FNKA8Hg8bHJaDbPcZ9QfnlSZ35mzcNt/AgmGnQFOgz8cklrA4XBgqK8HlBaxHYVUU15eHt4nJINRFrIdhXzF46jnyOWosR2D1MCNwCC8zCgGI9CofGVC5BivOis7OjpWeV03N7dqh2EDn8+HnZ0dAgICMGLECMnygICAMgPtkKq58PsfeF+qDvBU2I5CGhhtbW1MHfcjtp6+hWwdan1AviwvLw/OW7cjNC4X6YZdAQUcdJ0oDkMDfXBjP0LM+/LYbET+uO3yQryqJdsxyFeIRCJcux4EkXYXtqMQKb169Qo79x1BqpED21EIqbFqFVYiIiKqtJ6izQzk6OiI8ePHw97eHt26dYOPjw9iY2Mxffp0tqMplCdPn+HA6d+RZdSN7SikgRr0zTc4fNIf2UxzaoFAymEYBkG3guGx/xA+qFmjRJeaHZPaV1paCobDZzsGqYZLV64h5GUCSvQ7sh2FfMXRk6eQqGQIKFXr5wyRE8+ePcMqFzd8MOgCcGlGIKL4qvVNdPPmzdrKwapRo0YhPT0dTk5OSExMRNu2bXH58mWYmdFJd1U9fvIUaza7I96gK/2gJazhcrno7dANrx8mQ6TZmO04RI68evUKW3Z44n2BMtL1u9OJOKkzCYlJYPjt2I5BqujKnwHwPnEBqYad2Y5CvuL+w1CcvnYLOYZd2Y5CpPD7H1ew9+S5T78bqDUfqSfozPJvM2bMwIwZM9iOoZBuBAZhx76jiDPsCijRVTnCrp7dOuPcvSNIAxVWCPD69Wvs3LMfr1PzkKTZCowe9cUndUckEiEzJx8wpKux8k4sFsN730FcuvMIyTTukly7E3IPmz0PIJG6ciqc3NxcbNjihoj4fKQbdaf/Z6ReqXFhJSoqCrGxsSguLi6z/Pvvv6/ppomcKy0txQ4Pb/wVEYMUo+7UjI/IhaZNm0JFXMB2DMIihmHw+PET+Bw6hvcZH5Gk0QKMfsMZRJzIj9DQUGTydNiOQSqRmpqKVetdEP1RHTkGdvRjXU4xDAPfo8fhHxCCRKOuAJeuDysKhmFw9c8A7D96Ch/UW6JUz4LtSITInNTfSG/evMGIESPw5MkTcDgcMAwD4H/jq4hEItkkJHLp/fv3WLVhM95xGyHfwJbtOIRIqKqqgiOimYEaouLiYgRcvwG/sxeRIhIiTaMZGANqoULYc+jkGWSpN2M7BvkCkUiEU/7ncOr3a4jTagextjbbkcgXZGZmYq3zFjzN5CDLsDMVvxTIs2dR2LbLCx9K1ZFp6EAXYkm9JXVhZe7cubCwsMBff/0FS0tLPHjwAOnp6ViwYAG2bdsmy4wKy8PDAx4eHvWqyFRcXIw9B3wREBKOBJ0OYPj0o4XIl4KCAjBcZbZjkDqUkJCAIydP42HkU6TyGyNP0w5Qos8AYdfLly/xPrMYjAFN1yuPQsPC4e61F/EcfeQYOVCXBDnFMAwuX/0TB46fRqxmG4h19NiORKooJiYG7l578SajCClabcEoq7IdqV4oLS1FbGxsne3P1NQUPB61DqsKqf9Kd+/exY0bN2BgYAAulwsul4sePXrAxcUFc+bMqfIMQvXZzJkzMXPmTOTk5EBLS4vtODUWHBKC3T4H8YFvikLDbnS1gMilDx8+oECJfsjUd0VFRfjr+g34X7qK1I8MklRMITbsQd9LRC4wDIMtOzyQrNWK7SjkX6Kjo7Hdwwfvc4EUbVsaOFOOvX37Fs6uO/C2UJVaOiiQqKgoeO47hHeZhUjSsAajr852pHolNjYWY5yPQiyo/d+W3KJsnFg+DpaWNPV8VUhdWBGJRFBX//QfRV9fHwkJCbC2toaZmRmio6NlFpCw782bN9js7oG3eVyk63alK8FErj0Ii0Q2l8bTqI8YhsGTJ09w3P8CXr2LQ6pyI+RrtgfU6DuJyJffL1/B20IhGF0q8sqLZ8+i4LHPF++yipBCP/bkWnp6OrZ7eOPR60QkarUGo0PvlbwTi8UICbmLg8dPI/EjBykazen/WC0SC7QgFjaM8bv69OkDGxsbuLu7sx2lUlIXVtq2bYvHjx/D0tISXbp0wZYtW8Dn8+Hj40NVrXoiJSUFbru98Sw2BYmarcDoabAdiZBKhTwIhUjDhu0YRIY+fPgA/4t/4O7DcGQqaSNDaALG0IztWIRUKD09Hb4nzyLLsAfbURo8hmFw99597D96EgkFXKRqNgejT12Y5VVubi58DhxGcNgjxKlZQ0xTXsu9jx8/4sKly7hw5U+kcnWRqdEGUKNWYET+FBcXg8+v3dlrpS6srFy5Evn5+QCADRs24LvvvkPPnj2hp6cHPz8/mQWUJ8XFxVi2bBk4HA6cnZ1r/c1hS0ZGBjz3HkRoVAzi1VtAbGDOdiRCqiQlJQXpH8WAOvUFVXQZGRn44+qf+PPmLWSWKiNF0BQifQfq6kPkGsMwWL1hEz5otgW4NGYHW4qKinDpylX4X7yCVI4WMjRbA0IVtmORL8jNzcWhYydxI+QhEoTNUGJI3/XyLi4uDoeOn0L40xdIFjTFR52u1FWLAAB+//13jB8/HhkZGeByuYiMjETHjh2xcOFCbN26FQDw66+/IicnB7t378asWbNw+/ZtZGRkoFmzZli+fDnGjBkDAJg0aRKCgoIQFBSEHTt2APjURdDc3BxRUVFYuHAhbt26BTU1NQwcOBDbt2+Hvr4+gE8tXdq2bQs+n4/Dhw+jTZs2CAoKqtXXLvWvj0GDBkn+bWlpiaioKGRkZEBHR0cyM5A8iI+Px5IlS3DlyhV8/PgRLVq0wP79+2FnZydZx9PTE1u3bkViYiLatGkDd3d39OzZs9y2/Pz8YGtrC6FQiCNHjmDq1Kl1+VJqXVZWFnwOHsbdiGeIV7OCyLAb25EIqZYjJ04jUcWU7RhESvn5+bhxMwgXrgYgLa8YycqNUKzZiU7WiMI4evIUXuTyIdbRZTtKg5SSkoIjJ07jblgkUgRNkK/Thb4/5FhOTg4OHjmOoPvhSFC1oIKKnBOLxQi5dw9HTvojMbcEiSpmEBtRyzxSVq9evZCbm4uIiAjY2dkhKCgI+vr6ZYoagYGBmD9/PgoLC2FnZ4clS5ZAU1MTf/zxB8aPHy/pEbNjxw68fPkSbdu2hZOTEwDAwMAAiYmJ6N27N3755Re4ubnh48ePWLJkCX766SfcuHFDsp9Dhw7ht99+w507dyQzGNcmqQorJSUlGDhwIPbs2YMWLVpIluvqyteJRGZmJhwcHNC3b19cuXIFhoaGeP36NbT/MZ2en58f5s2bB09PTzg4OGDPnj0YPHgwoqKiYGpa9geaWCyGsrIyRCIRxGJxHb+a2pOVlYW9vkcQEv4UCcJmKDVyYDsSIdWWl5eHkLBIiKj5vUIpKSnB3Xv3cebiZcSlZCBFuRE+arYBDOpni0BSf718+QqnL99ElhFdlKhLDMMgMjISB4+dwvu0HCSqmH06DtAPdLmVnp6Ofb5HcP/RcyQIqaAi73JycnD67AX8GXgbaTw9ZKtbg9GnFmCkYlpaWrCxsUFgYCDs7OwkRZR169YhNzcX+fn5ePnyJfr06YOmTZti4cKFkufOnj0bV69exenTp9GlSxdoaWmBz+dDKBSiUaNGkvW8vLxga2sLZ2dnybIDBw7AxMQEL1++lNQnrKyssGXLljp77VIVVpSVlfH06VO5aplSkc2bN8PExAQHDx6ULDM3Ny+zjpubG6ZOnYpp06YBANzd3XHt2jV4eXnBxcWlzLqjR4/GkiVLwOFwsGnTplrPX9s+F1Tuhj9FvNCSCipEoR04chwJqhZ0cqYAGIbB8+fPcfLsRUS9eoMMngFy1E3BGLZkOxohUikoKMAal61I0LOn76A6UlBQgN8vX8WFy38iDRrI0LAEY0Djp8iz5ORkeO33xaPot4gXWkJE551y7dWrV9h3+DhexiYiSWCCYv3uNC05qZI+ffogMDAQjo6OuH37NjZs2AB/f38EBwcjKysLRkZGaNmyJUQiETZt2gQ/Pz/Ex8ejqKgIRUVFUFP7+nd5WFgYbt68KZlI559ev34tKazY29vXyuv7Eqm7Ak2YMAH79++X6wLDxYsXMWjQIPz4448ICgpC06ZNMWPGDPzyyy8APo2ZEhYWhqVLl5Z53sCBAxESElJuewKBoEojEn/+UHyWk5NTsxciY/9rofIECUIrKqgQhZeRkYGbd0NRbEifZXmWmpqKM+d/x62Q+8jkaiJd1fjTFMmEKLh1LlvxVsUKjDJdxa1tcXFx8D3mh4hn0X+P7UDdfeRdQkICdu/Zj2fvEpGgZgWxYXe2I5EvEIlECLoVjCOn/JFSqIRkdUswhhZsxyIKpk+fPti/fz8ePXoELpeL1q1bo3fv3ggKCkJmZiZ69+4NAHB1dcX27dvh7u6Odu3aQU1NDfPmzUNxcfFXty8WizF06FBs3ry53GONGzeW/LuyAo2sSV1YKS4uxr59+xAQEAB7e/tywd3c3GocrqbevHkDLy8vODo6Yvny5Xjw4AHmzJkDgUCACRMmIC0tDSKRCEZGRmWeZ2RkhKSkJKn36+LignXr1tU0vsxlZ2fD5+DhfxRU6AcNqR+27fBAnHpLulIsh0pLSxF85w5Onv0didkfkSQwRqleN7rqReqNi39cRkRCPkp06cdHbWEYBqFhYThw9CTisoqQJLSgsR0UQFxcHHZ578eLuNS/Cyo0m5u8KioqwtkLv+P85T+RqqSPbM32gDp1ySXS+TzOiru7O3r37g0Oh4PevXvDxcUFmZmZmDt3LgDg9u3bGDZsGMaNGwfgU8Hk1atXaNWqlWRbfD4fIpGozPZtbW3h7+8Pc3Nz8HjyM2GF1EmePn0KW1tbAMDLly9lFqgya9eurbRo8fDhQ9jb20MsFsPe3l7S/6pjx4549uwZvLy8MGHCBMn6/+7SxDBMjbo5LVu2DI6OjpL7OTk5MDExkXp7NZWfn499h44i6G4Y4tWaUUGF1CvPnj3Do3epdMImZzIyMnDizFkEBt9HurIhcjRagDGgq/mkfklMTMSBk+eQTi0/a0VpaSmu/hmAE/4XkMpoIkPTCoyBkO1YpBJJSUnY4bkXz94nIVGjBcQGVHSUVx8/fsRxvzO4cuMWkgQm+KjXnWY0UwDcomy53s/ncVaOHj0qmc2nV69e+PHHH1FSUoI+ffoA+DQGir+/P0JCQqCjowM3NzckJSWVKayYm5vj/v37ePfuHdTV1aGrq4uZM2di7969GDNmDBYtWgR9fX3ExMTg5MmT2Lt3L5SU2GnFKHVh5ebNm7LMUWWzZs3C6NGjv7rO53FUGjdujNatW5d5rFWrVvD39wcA6OvrQ0lJqVzrlJSUlHKtWKpDIBBAIGB/DveioiIc9zuDSwGBiBdaoMSIBgcj9YtIJMJmdw8k63RgOwr52/v37+G51xfRH5KRqGKKUhpEktRTYrEYqzduRpx2B2qBJWPFxcXwP38R5y5dRQq/MXK1OwNc+bkqSSqWk5OD3Xv24f7TV0hQt6YLHnKstLQUp/zP4eyla0hUNUMhHasVhqmpKU4sH1en+5NG3759ER4eLimi6OjooHXr1khISJAUTlatWoW3b99i0KBBEAqF+O9//4vhw4cjO/t/BZ2FCxdi4sSJaN26NT5+/CiZbvnOnTtYsmQJBg0ahKKiIpiZmeHbb78Fl8XCYLWOUv9shfE1HA4Hrq6uUgWqjL6+vmR+6so4ODggOjq6zLKXL1/CzOzTFz2fz4ednR0CAgIwYsQIyToBAQEYNmyY7ELXMYZhcPXPABw8fhoJAhN8NKIvS1I/nTl3Hh+gB0ZZle0oDd7bt2+x3dMHb1PzkKTeHGLqk03qubMXfsfrQjUwOppsR6k3SktLce7i7zh14TKS+E1RYNiDilYKQCQS4eRpf5z9IwBxas1RakgzY8mz0PAIuO7yQjyvMfKN6P+YouHxeLC0tGQ7RqW2bduGbdu2lVkWGRlZ5r6uri7Onz//1e20aNECd+/eLbe8efPmOHv27BefFxgYWNWoMlOtwkpERESZ+2FhYRCJRLC2tgbwqWihpKQEOzs72SWsgfnz56N79+5wdnbGTz/9hAcPHsDHxwc+Pj6SdRwdHTF+/HjY29ujW7du8PHxQWxsLKZPn85icuk9f/4Cm91344NIE1n63WlAN1JvZWdn49SFq8ihJvisyszMhNsuL0S+TkCiViswBhpsRyKk1uXm5uLE2d+RTV1rZeZBaCh2eO5FPNcIufoO1B1BQURHv8TGbe6IhT5y6UKeXCsuLobztu148DIBybqdASUaQ4UQWapWYeWf3X/c3NygoaGBQ4cOQUdHB8CnE+zJkyejZ8+esk0ppU6dOuHcuXNYtmwZnJycYGFhAXd3d4wdO1ayzqhRo5Ceng4nJyckJiaibdu2uHz5sqRVi6LIzc3Ftp0eCH0Vj2TtdnQFn9R7bru98EG9BV1pYdFfN27A6+BxxKq3gtiwM9txCKkznnsPIE6tOX3/yEB+fj42bHFDZGwGUnQ60Y89BSEWi7HP9wh+D7yHRF0bOu+Uc1lZWZi3ZCVecZqg0EA+LoATUt9I3WHV1dUVf/75p6SoAnzqO7VhwwYMHDgQCxYskEnAmvruu+/w3XfffXWdGTNmYMaMGXWUSPZC7t7Ddq99iBW2QKlBJ7bjEFLr3r9/j/CXHyAy7MJ2lAaJYRhscnVH4LM4pBk6UMs40qBkZWXh3qPnKKUpY2vs3bt3WLp2I96ptkCJfke245AqKiwsxOKVa/Ekm48cw67USkXOFRQUYPbCZXghaAmxUKfyJxBCpCJ1YSUnJwfJyclo06ZNmeUpKSnIzc2tcTBSuZKSEmxydUfIiwSk6HcHlGhgN9IwuO7yRqJmq8pXJLViw+ZtuPE6Fzn6NGgwaXj8/M8hUSDdYH7kf169isHidZvwQd8ejDLN9KMoCgsLMXP+IkTBBMXajdiOQ6rAbZcXXvHMqahCSC2Tug3riBEjMHnyZJw5cwZxcXGIi4vDmTNnMHXqVIwcOVKWGUkF0tPT8cus+bj6rhgpBh2pqEIajDdv3uBNWj4YFRowkg13Qu7hTnQicrSt2I5CSJ1jGAY3g++hRLMp21EUWkFBAVZu2IxYg85UVFEgDMNg2RqnT0UVdSqqKIKSkhKEP4tGiUZjtqMQUu9J/Wvc29sbCxcuxLhx41BSUvJpYzwepk6diq1bt8osoCLz8PCAh4cHRCKRTLf7/v17LFq9AW8024NR0ZLptgmRd3sOHkGSenO2YzRYx06fRap268pXJKQeiouLQx5HSF0fauj4KX/ECswBngrbUUg13LodjGdppSjWpaKKokhPT0cJT43tGIQ0CFK3WBEKhfD09ER6ejoiIiIQHh6OjIwMeHp6Qk2N/gMDwMyZMxEVFYWHDx/KbJuJiYlYsNIJb7TtqKhCGpz8/Hy8ik0Eo0qffbbk5OUBPBpckjRMYeERSONSc/qauhcajmINavWjaI74nUW6ljXbMUg16OrqQrk0n+0YhDQINe4/oqamhvbt28siC6lEYWEhFqxYi3c6dmD41HSWNDw3g4KQomzEdowGrWkjI7zIyqbCLmmQnkbHQKSizXYMhafM4wGMCDW4vkdYkFPwERAqsx2DVAOfz0fnDq2R/CoOhRrGbMchpF6rUWHl9u3b2LNnD16/fo0zZ86gadOmOHLkCCwsLNCjRw9ZZSR/2+q+C2/5FmAE6mxHIYQVfwXdQaE6DRrJptm/TsWLZU6IM+pOU82SBic1LR1iZXO2Yyi8b/v3xvML95Cr04LtKKQaeFz6zldE82f9hug5C/AsXwUiNX2245AaKi0tRWxsbJ3tz9TUFDwejeVZFVL/lfz9/TF+/HiMHTsWERERKCoqAgDk5ubC2dkZly9flllIeVJcXIxly5aBw+HA2dkZfH7dNIlPTk5G6PO3KDbsWif7I0QeJadlgNGlZshsMjY2xi8//wCvExeRZNiJiiukQRGJRPSZl4Ehg7/FxSt/4nFBBsRCXbbjkCpq3aIZXr1LhVjdgO0opBoEAgF2bnPB/CUr8TynAIWadIFKkcXGxiJy+49opCrbMTwrkvRRCZh/GpaWlrW+L3kQGBiIvn37IjMzE9ra2tV+vtRnBxs2bIC3tzf27t0LZeX/NQvs3r07wsPDpd1stdy6dQtDhw5FkyZNwOFwcP78+XLruLi4oFOnTtDQ0IChoSGGDx+O6Ojocut5enrCwsICKioqsLOzw+3btyvcp5+fH2xtbeHg4IAjR47I+iV9kf+FS0hUMauz/REibwoLC1EoogEj5cF3gwdh3sT/g0nyHXCKctmOQ0id0VBXB0dUzHYMhcfj8eDq7ATrwhdQyk9hOw6pov9OHg/j3OeAuPZ/0BHZ0tDQgKf7VnxvrQGjlFCgtIjtSKQGGqmKYKxe+7e6KN7826RJkzB8+PA6368sSF1YiY6ORq9evcot19TURFZWVk0yVVl+fj46dOiA3bt3f3GdoKAgzJw5E/fu3UNAQABKS0sxcOBA5Of/byAnPz8/zJs3DytWrEBERAR69uyJwYMHV9jMSiwWQ0lJCSKRCGKxuFZeV0XCHz2FSN2wzvZHiLzJyMhAiZIq2zHI377p3xcem9aifUk0tDKjgTr8PiSELRZmJuAW5rAdo17Q1taGl/tWdBIkQyvzJcAwbEcilTA0NMTsaeNhlBoKMPSdr2j4fD6WOM7FxnlT0DI7FGpZr+l9JESGpC6sNG7cGDExMeWWBwcH11lzocGDB2PDhg0YOXLkF9e5evUqJk2ahDZt2qBDhw44ePAgYmNjERYWJlnHzc0NU6dOxbRp09CqVSu4u7vDxMQEXl5e5bY3evRo3Lt3D3fu3MGECRNq5XVVpLi0FOAq1dn+CJE3xcXFYKgJvlxp2rQp9nm4Y+6w7rBIDYYgO5Z+HJF6zbZ9G2iIstiOUW9oaGhgt9sWTB3QHibJweAWZLAdiVSiX5/emDHmezROvg+IStiOQ6TQsaMNDvl44Nf+bWCREgxB9gc6dhOZKioqwpw5c2BoaAgVFRX06NGjzCy5z549w5AhQ6CpqQkNDQ307NkTr1+/xtq1a3Ho0CFcuHABHA4HHA4HgYGBAIAlS5agRYsWEAqFsLS0xKpVq1BS8r/voLVr18LGxgZHjhyBubk5tLS0MHr0aOTm5lY5V01J/Svl119/xdy5c3H//n1wOBwkJCTg2LFjWLhwIWbMmCGzgLKWnZ0N4NP0Y8CnH2thYWEYOHBgmfUGDhyIkJCQcs8XCARwd3fH9u3bIRAIKtxHUVERcnJyytxqSsBXpqaXpEETCATg0pUVucPlcjFs6BAc9t6JKd1MYZESDJXs93QVjNRL7dq1g1ZJJtsx6hUOh4Oxo3+Cj+tG9NbKQKPUMHCKC9iORb7iu8GDsGbuVFik3gW3gP4/KCIej4exo3/CYe+dGN+pMcwlx24qsJCaW7x4Mfz9/XHo0CGEh4fDysoKgwYNQkZGBuLj49GrVy+oqKjgxo0bCAsLw5QpU1BaWoqFCxfip59+wrfffovExEQkJiaie/fuAD4V4n19fREVFYUdO3Zg79692L59e5n9vn79GufPn8elS5dw6dIlBAUFYdOmTVXKJQtSF1YWL16M4cOHo2/fvsjLy0OvXr0wbdo0/Prrr5g1a5ZMwskawzBwdHREjx490LZtWwBAWloaRCIRjIzKTuFqZGSEpKQkqfbj4uICLS0tyc3ExKTG2du3bgml/NQab4cQRaWlpQUl0Ue2Y5AvEAqFmDZ5Ao7s2YlpPZuhWeodaGZGAzQeBalHBAIBjA116Yd/LTA0NMQ2Zye4Lv0NtuKXMEyLpL+zHOtkbw+fHVvQmR8H3YxndPFPQQmFQvz2yxQc3bMTU3tYwDI1GOqZrwBxKdvRiILKz8+Hl5cXtm7disGDB6N169bYu3cvVFVVsX//fnh4eEBLSwsnT56Evb09WrRogcmTJ8Pa2hrq6upQVVWFQCBAo0aN0KhRI8lEMStXrkT37t1hbm6OoUOHYsGCBTh16lSZfYvFYvj6+qJt27bo2bMnxo8fj+vXr1cplyzUqF39xo0bkZaWhgcPHuDevXtITU3F+vXraxRo7dq1kqY/X7qFhoZKte1Zs2bh8ePHOHHiRLnHOJyyg2IyDFNuWVUtW7YM2dnZktuHDx+k2s4/jRj6HxgV1nw7hCgqoVAIAYeupMg7VVVVTB7/M47v98KKn/ujfcEjNEoLpyb+pN4Y99MI6OS+ZTtGvdWyZUv47HLDFscpsEcMGqeFgVOYzXYsUgFdXV3sdtsCx58GwCLlDpRz4tmORKSkqqqKKRPG4fg+Tywc3g0tsx5AL+MpOCVU3CTV8/r1a5SUlMDBwUGyTFlZGZ07d8bz588RGRmJnj17lpn8pirOnDmDHj16oFGjRlBXV8eqVavKjYdqbm4ODQ0Nyf3GjRsjJSWlSrlkocaTUguFQtjb28siC4BPxY/Ro0d/dR1zc/Nqb3f27Nm4ePEibt26BWNjY8lyfX19KCkplWudkpKSUq4VS1UJBIIvdhOSlrGxMayb6CApm6YmJA2Xjqbapz7dStX7MiZ1j8fjYUC/fhjQrx/evHmDQydO42n0HaTwG6FA05TeQ6KwOtnbw4hzABn0XVSr2rVrC+8d2xATE4M9B4/gVdxzJKiaQaTeCJDywheRPQ6Hg8GDvkGvHt3hufcAgsNDkKDRks5VFZSysjKGfvcffDdkMMLCw7H/yEnEpxUiUdUCYjV9tuMRBcD83Z3sS40WVFWrPxHFvXv3MHr0aKxbtw6DBg2StHhxdXUts96/izUcDkcy2UxluWShRoWV69ev4/r160hJSSk3Q86BAwek2qa+vj709WX3H5dhGMyePRvnzp1DYGAgLCwsyjzO5/NhZ2eHgIAAjBgxQrI8ICAAw4YNk1kOWVgyfzbeOC7DexUHGsiWNEjdOtsiPPAtRNo1715H6o6lpSXWrViCjx8/4s+/buD8H1eRVsRFkooJxGoG9COJKBQOh4PJ40bB5ehVZOq2ZjtOvWdlZYWtG9chLS0Nh4+fwt3QYDBycA5UXJAHoWoftmPIDTU1NSyaNxvjEhOxw3Mvnn2IQaJGSzAqmmxHI1LgcDiwt7ODvZ0d4uPjcfDoSUQ8C0aywBgfNU0BLk0mQCpmZWUFPp+P4OBg/PzzzwCAkpIShIaGYt68ecjPz8ehQ4dQUlJSYasVPp8Pkahs18I7d+7AzMwMK1askCx7//69THPJgtSFlXXr1sHJyQn29vZo3LixzCo91ZGXl1dmZqK3b98iMjISurq6MDU1BQDMnDkTx48fx4ULF6ChoSFpmaKlpSWpmDk6OmL8+PGwt7dHt27d4OPjg9jYWEyfPr3OX9PXGBgYYPa08dh6wB/JBnb0Y4Q0OP1798Lp6w+QCCqsKCJVVVUMGzoEw4YOwYcPH+B39gLuhwcjk6eHLHUzMHw1tiMSUiX9+vTGEb8zyCr5CEaZpoGvC/r6+nCcI7+TI5BPGjdujE3rV+P9+/dw99yLmORoJGq0AKOixXY0IqWmTZti5ZIFKCgowPnf/8DvV/9CGlcbmRqW9P3HkqSPdVNcTvqohEbVfI6amhp+++03LFq0SPKbfMuWLSgoKMDUqVMhFouxa9cujB49GsuWLYOWlhbu3buHzp07w9raGubm5rh27Rqio6Ohp6cHLS0tWFlZITY2FidPnkSnTp3wxx9/4Ny5czLNJQtSF1a8vb3h6+uL8ePHyySINEJDQ9G3b1/JfUdHRwDAxIkT4evrCwCSKZP79OlT5rkHDx7EpEmTAACjRo1Ceno6nJyckJiYiLZt2+Ly5cswMzOr9ddQXX1798bbdx9wIugxMvTash2HkDplbGwMLU4REhkxQFMvKzQTExMsnDsLIpEIoWFhOOF/AbFJ6UhRboyPmiaAUo17qhJSazgcDpbMnYWFmz2QZNCJ7TiEyB0zMzNs37wB7969w849+/EqMRpJ6s0hVtVhOxqRklAoxM+jfsSYn/4P9x88wMFjp5CQVoIkoSV1/apDpqamwPzTdbKvRp/3V02bNm2CWCzG+PHjkZubC3t7e1y7dg06Op/+/9+4cQOLFi1C7969oaSkBBsbG8nYJ7/88gsCAwNhb2+PvLw83Lx5E8OGDcP8+fMxa9YsFBUVYciQIVi1ahXWrl0r01w1xWEY6ebV0tPTw4MHD9CsWTOZBKnPcnJyoKWlhezsbGhq1rxJJMMw2Om5B5fC3yJDp3WDaLlikXoXZ3y92I5B5MCBQ0fhc+c9SrSq32qFnx4Dlwn9yhVaiXzIy8vDtb+u49LVv5BWxEGywBgidSO5/I7rkB+GA5472I5BWLZ242ZcjeOgRL261/T+h1OUh8EGOVi/aqkMkxEiX+Li4uDhcwBR75OQIGwGsboBKzkaZT6G56rZcnnxVBHFxsZir+8xPH39HokqZijRaFIrx2zLtLs4fZB+BxD5JvUl32nTpuH48eOyzEKqiMPhYO7M6RjZ1RoG6Y9oznnSoAwf+h80KqKZB+ojdXV1/DB8GA5674L3hiX4uZ0mmqUGQzf9GThFeWzHI6ScRfNmwST/FSCiqUkJ+RpjY2O4OK2Gz5Y1GGoqhmlyCHi5iXQOq+BMTU2xfvUyHNyxCeM66sMiJRiqWe8ARlzpcwmpb6rV1vpzVxvg0zzRPj4++Ouvv9C+fftyg8+4ubnJJiH5ot9+mQJDg4s4ePYKEvXtaXYC0iDo6urCzFAbccX5NCZHPda0aVPMmzkdc34TIzw8Asf9z+NdQiqS+Y1RqGkCcKmrEGGfmpoa5v46GRv2nUGqvg3bcQiRe0ZGRli9bBGysrKw/9Ax3Am7gwQVM5RoGstl60RSNdra2pg1/RdMnTgOZ85dwMVr15HEb4oCLXPquk0ajGqdmUZERJS5b2NjAwB4+vRpmeVsDGQrjzw8PODh4VFuZGNZ+mH49zAzMYazuyditW1o9HXSIEz6+Se82HUMaXrt2I5CahmXy4W9vR3s7e2Ql5eHy1f/xO/X/kJ6qQpS1cyovz5hXQ+H7rD98zpupCSjRN2I7TiEKARtbW0smDsT/83NxeETfrhxOxiJqmYo0jShAosCU1VVxfifR2PU/42E//mLOHvpKhJUzFCoaUrvK6n3pB5jhVSdrMdYqUhSUhKWrHZCDNcYhRrGtbIPNtEYK+SfGIbB2Km/IVqnS7WmHqcxVuqP6OhoHDl5BlGvY5EsaPrppK0Op3+kMVbIP+Xl5WHyjHl4o9+t2q1HaYwVQoCCggIcPXka1wKDkahSewUWGmOlbhUVFeHIcT9cvnEbcerWEKkbSrUdGmOFKAJqm1VPNGrUCHt3u+MbEyUYpEUC4tprJUMI2zgcDob/ZyBUc2LZjkJYYm1tjQ1rVuCIlxtmDGiJFhl3oZfxDJySj2xHIw2Quro6Fsz8LwwznrAdhRCFJBQK8d8pE3HEewcmdmkK8+RgKOfE0xgsCk4gEGDa5Ak4sGsrBhuXonHqQzpOk3qr2mOsrF+/HmpqamXGW6lIfR1jpbi4GMuWLQOHw4GzszP4fD7bkST4fD7WrliCgOs34Ol7HB+0O4BR0WI7FiG14rv/fAu/i1fxDhZsRyEsUlNTw9hRP+Hnn37EgwcPsf+YHxLSSpGkZgVGlb7/SN3p2qUzOgfcwLW4RJRoNGY7DiEKSSgU4tepkzF21I/Ye/Awgh6GIF7dGmI1fbajkRrQ1tbGuhVL8eLFC2zctgPvlRojX9Oc7ViEyFS1WqxERESgpKRE8u+v3erCrVu3MHToUDRp0gQcDgfnz58vt05paSlWrlwJCwsLqKqqwtLSEk5OThCLy45W7enpCQsLC6ioqMDOzg63b9+ucJ9+fn6wtbWFg4MDjhw5Uhsvq8a+6d8Pe1ydYSOOgUbWa6r2k3pJKBSibQtLcAsy2I5C5ACHw0GXLp3hs9MVO1fNRV+tdDRNuU+fD1KnljjOgenHGEBUwnYUQhSauro65s+egX1uGzHAIJ9aOtQTLVu2xEHvXfihvSEapzyg70pSr1SrxcrNmzcr/Ddb8vPz0aFDB0yePBk//PBDhets3rwZ3t7eOHToENq0aYPQ0FBMnjwZWlpamDt3LoBPxZJ58+bB09MTDg4O2LNnDwYPHoyoqCiYmpqW2Z5YLIaysjJEIlG54ow8MTQ0xJ6dbjhw+Bh+v3kXCbo2YJRV2Y5FiEyNG/UDQl08kSTUZTsKkSOWlpbYunEtEhIS4LH3IJ6+eYUEDWuIVbXZjkbqOaFQiHnTp8LJ5xTNEkSIDOjr68Nl3So8f/4Cm7bvQiyjixwtKxoIVYHxeDzMnTkdPbpHwNnNA+9p8g1ST0g93fLXcDgcuLq6ShWoOgYPHozBgwd/dZ27d+9i2LBhGDJkCADA3NwcJ06cQGhoqGQdNzc3TJ06FdOmTQMAuLu749q1a/Dy8oKLi0uZ7Y0ePRpLliwBh8PBpk2bZPyKZEtJSQm/TJ6APj26Ye0mV7zjGX8a4JGQeqJ58+bQ4RYiSSyq1iC2pGFo0qQJNq5Zgbi4OLh57EF0YgyStdtQkZnUqu7duqLtxT8QlJ0OsZoe23EIqRdatfrU0sHP/xxOXbyKOK12VCxXcHYdO8LL1RmOy1bjpagFRNTdiyi4Gk23HBYWBpFIBGtrawDAy5cvoaSkBDs7O9klrKEePXrA29sbL1++RIsWLfDo0SMEBwfD3d0dwKcxU8LCwrB0adnR+AcOHIiQkJBy2xMIBJLnKormzZvjoNdO7PDcg6CIB0jU7QDwBGzHIkQmBvbtjdeBMSjWpqIhqZixsTHcXNYjKuo5NrvvxgexNrK1mwMcGr+d1I5lC+fh9byliBX2oCvrhMgIl8vFmB9/QP/ePbFyvQteZWkgR6sZ/R9TYIaGhvBy34rZC5YiqgAQCam4QhRXtc4qb968KbkNHToUffr0QVxcHMLDwxEeHo4PHz6gb9++ktYh8mDJkiUYM2YMWrZsCWVlZXTs2BHz5s3DmDFjAABpaWkQiUQwMjIq8zwjIyMkJSVJtc+ioiLk5OSUubGNz+dj0bzZcF74K5pnPoQgJ57tSITIxHeDB8KoRLr/q6Rhad26FQ5678LM77rANDkY3IJMtiORekpPTw/fD+wLtey3bEchpN4xNDSE9w5XjO1uhcYp92mcDgWnoaGBXa6b0PxjNDhFuWzHIURqUl+uc3V1hYuLC3R0dCTLdHR0sGHDhhp1A1q7di04HM5Xb//sxlMZPz8/HD16FMePH0d4eDgOHTqEbdu24dChQ2XW4/yr2s0wTLllVeXi4gItLS3JzcTERKrt1Ib27drB13snhjQToFFqKFBazHYkQmpEV1cXukIlQFTKdhSiALhcLn4Y/j32bt+Ezvw4aGVG0wDfpFaMG/0jmpQkAGL6biJE1rhcLqZNnoA1c6bALCUEnOJ8tiORGtDQ0MC2jWthmhkJiEVsxyFEKhyGke6MUkNDAxcuXEC/fv3KLL9x4waGDRuG3FzpKo5paWlIS0v76jrm5uZQUVEps4zD4eDcuXMYPnx4meUmJiZYunQpZs6cKVm2YcMGHD16FC9evEBxcTGEQiFOnz6NESNGSNaZO3cuIiMjERQUVO3XUFRUhKKiIsl9hmFQXFwMfX19qYs1hBBCCCGEEEIIkT/VGmPln0aMGIHJkyfD1dUVXbt2BQDcu3cPixYtwsiRI6UOpK+vD3192fWvKygoAJdbtmGOkpKSZEYfPp8POzs7BAQElCmsBAQEYNiwYVLtUyAQQCCgMUwIIYQQQgghhJD6TurCire3NxYuXIhx48ahpORT30Yej4epU6di69atMgv4NXl5eYiJiZHcf/v2LSIjI6GrqyuZJnno0KHYuHEjTE1N0aZNG0RERMDNzQ1TpkyRPM/R0RHjx4+Hvb09unXrBh8fH8TGxmL69Ol18joIIYQQQgghhBCimKTuCvRZfn4+Xr9+DYZhYGVlBTU1NVllq1RgYCD69u1bbvnEiRPh6+sLAMjNzcWqVatw7tw5pKSkoEmTJhgzZgxWr14NPp8veY6npye2bNmCxMREtG3bFtu3b0evXr3q6qUQQgghhBBCCCFEAdW4sEIIIYQQQgghhBDSUEndFYjIBsMwUg/0SwghhMgzDQ0NmQzaTsdKQggh9ZWsjpWEXVRYYVlubi60tLTYjkEIIYTIXHZ2NjQ1NWu8HTpWEkIIqa9kdawk7KKuQCyjq3CVy8nJgYmJCT58+EBfOkRq9DkiskKfpaqjFiuVo8+TYqL3TfHQe6aYGsL7Ri1W6gdqscIyDodTb78kZE1TU5P+VqTG6HNEZIU+S3WnIRwr6fOkmOh9Uzz0nikmet+IvOOyHYAQQgghhBBCCCFEUVFhhRBCCCGEEEIIIURKVFghck8gEGDNmjUQCARsRyEKjD5HRFbos0RkiT5PioneN8VD75lioveNKAoavJYQQgghhBBCCCFEStRihRBCCCGEEEIIIURKVFghhBBCCCGEEEIIkRIVVgghhBBCCCGEEEKkRIUVQgghhBBCCCGEEClRYYXIBU9PT1hYWEBFRQV2dna4ffv2V9cPCgqCnZ0dVFRUYGlpCW9v7zpKSuRZdT5HgYGB4HA45W4vXryow8RE3ty6dQtDhw5FkyZNwOFwcP78+UqfQ99HpDJ0jFM81XnPzp49i2+++QYGBgbQ1NREt27dcO3atTpMSz6j8wDFQ8ddUl9QYYWwzs/PD/PmzcOKFSsQERGBnj17YvDgwYiNja1w/bdv3+I///kPevbsiYiICCxfvhxz5syBv79/HScn8qS6n6PPoqOjkZiYKLk1b968jhITeZSfn48OHTpg9+7dVVqfvo9IZegYp3iq+57dunUL33zzDS5fvoywsDD07dsXQ4cORURERB0nb9joPEAx0XGX1BsMISzr3LkzM3369DLLWrZsySxdurTC9RcvXsy0bNmyzLJff/2V6dq1a61lJPKvup+jmzdvMgCYzMzMOkhHFBEA5ty5c19dh76PSGXoGKd4qvueVaR169bMunXrZB2NfAWdByg+Ou4SRUYtVgiriouLERYWhoEDB5ZZPnDgQISEhFT4nLt375Zbf9CgQQgNDUVJSUmtZSXyS5rP0WcdO3ZE48aN0b9/f9y8ebM2Y5J6iL6PyNfQMU7x1OR48plYLEZubi50dXVrIyKpAJ0HNBz0HUnkFRVWCKvS0tIgEolgZGRUZrmRkRGSkpIqfE5SUlKF65eWliItLa3WshL5Jc3nqHHjxvDx8YG/vz/Onj0La2tr9O/fH7du3aqLyKSeoO8j8jV0jFM80rxn/+bq6or8/Hz89NNPtRGRVIDOAxoO+o4k8orHdgBCAIDD4ZS5zzBMuWWVrV/RctKwVOdzZG1tDWtra8n9bt264cOHD9i2bRt69epVqzlJ/ULfR6QydIxTPNV9zz47ceIE1q5diwsXLsDQ0LC24pEvoPOAhoG+I4k8ohYrhFX6+vpQUlIqdzUhJSWlXDX6s0aNGlW4Po/Hg56eXq1lJfJLms9RRbp27YpXr17JOh6px+j7iHwNHeMUT02OJ35+fpg6dSpOnTqFAQMG1GZM8i90HtBw0HckkVdUWCGs4vP5sLOzQ0BAQJnlAQEB6N69e4XP6datW7n1//zzT9jb20NZWbnWshL5Jc3nqCIRERFo3LixrOOReoy+j8jX0DFO8Uh7PDlx4gQmTZqE48ePY8iQIbUdk/wLnQc0HPQdSeQWa8PmEvK3kydPMsrKysz+/fuZqKgoZt68eYyamhrz7t07hmEYZunSpcz48eMl679584YRCoXM/PnzmaioKGb//v2MsrIyc+bMGbZeApED1f0cbd++nTl37hzz8uVL5unTp8zSpUsZAIy/vz9bL4HIgdzcXCYiIoKJiIhgADBubm5MREQE8/79e4Zh6PuIVB8d4xRPdd+z48ePMzwej/Hw8GASExMlt6ysLLZeQoNE5wGKiY67pL6gwgqRCx4eHoyZmRnD5/MZW1tbJigoSPLYxIkTmd69e5dZPzAwkOnYsSPD5/MZc3NzxsvLq44TE3lUnc/R5s2bmWbNmjEqKiqMjo4O06NHD+aPP/5gITWRJ5+n3/z3beLEiQzD0PcRkQ4d4xRPdd6z3r17f/V7g9QdOg9QPHTcJfUFh2H+Hu2HEEIIIYQQQgghhFQLjbFCCCGEEEIIIYQQIiUqrBBCCCGEEEIIIYRIiQorhBBCCCGEEEIIIVKiwgohhBBCCCGEEEKIlKiwQgghhBBCCCGEECIlKqwQQgghhBBCCCGESIkKK4QQQgghhBBCCCFSosIKIYQQQgghhBBCiJSosEIIIYQQQgghhBAiJSqsEEIahJUrV0IgEODnn39mOwohhBAil+hYSQgh0uEwDMOwHYIQQmpbTk4Ojhw5glmzZuHVq1ewsrJiOxIhhBAiV+hYSQgh0qEWK4SQBkFTUxNTpkwBl8vFkydP2I5DCCGEyB06VhJCiHSosEIIaTBKS0shFArx9OlTtqMQQgghcomOlYQQUn1UWCGENBgrV65EXl4enSwSQgghX0DHSkIIqT4aY4UQ0iCEhYWhe/fu+Oabb/D27Vs8e/aM7UiEEEKIXKFjJSGESIcKK4SQek8sFqNz587o3bs3unTpgrFjxyI/Px98Pp/taIQQQohcoGMlIYRIj7oCEULqvV27diE1NRVOTk5o164dSktLER0dzXYsQgghRG7QsZIQQqRHhRVCSL0WHx+PVatWwdPTE2pqamjevDkEAgH1HSeEEEL+RsdKQgipGSqsEELqtTlz5mDw4MEYMmQIAIDH46FVq1Z0skgIIYT8jY6VhBBSMzy2AxBCSG25dOkSbty4gefPn5dZ3q5dOzpZJIQQQkDHSkIIkQUavJYQQgghhBBCCCFEStQViBBCCCGEEEIIIURKVFghhBBCCCGEEEIIkRIVVgghhBBCCCGEEEKkRIUVQgghhBBCCCGEEClRYYUQQgghhBBCCCFESlRYIYQQQgghhBBCCJESFVYIIYQQQgghhBBCpESFFUIIIYQQQgghhBApUWGFEEIIIYQQQgghREpUWCGEEEIIIYQQQgiREhVWCCGEEEIIIYQQQqT0/30fQYvisASmAAAAAElFTkSuQmCC\n", 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\n", 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\n", 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\n", 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "dihedrals.plot_violins(df_aug, resname, figdir=fig_save_directory, molname=molname, width=width, solvents=solvents)" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "79dfac55", + "metadata": {}, + "outputs": [], + "source": [ + "placeholder = path / 'SM25_figs_top_level_run'\n", + "placeholder2 = path / 'SM25_top_level_results_no_pad'" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "3a861283", + "metadata": {}, + "outputs": [], + "source": [ + "os.mkdir(placeholder)\n", + "os.mkdir(placeholder2)" + ] + }, + { + "cell_type": "markdown", + "id": "5d95ad5e", + "metadata": {}, + "source": [ + "# Full, top-level functionality\n", + "* ##### encompasses all previously used functions" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "d634cbd0", + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "mdpow._ensemble: INFO Searching . directory for systems\n", + "mdpow._ensemble: INFO Searching . directory for systems\n", + "mdpow._ensemble: INFO Setting up systems\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "4efb690ee9d94880bea040695bf3570d", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/7 [00:00" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "dihedrals.automated_dihedral_analysis(dirname=dirname, df_save_dir=placeholder2, figdir=placeholder,\n", + " resname=resname, molname=molname, SMARTS=SMARTS_DEFAULT,\n", + " dataframe=None, padding=padding, width=width,\n", + " solvents=solvents, interactions=interactions)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3f7cfd83", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/doc/sphinx/source/workflows.txt b/doc/sphinx/source/workflows.txt new file mode 100644 index 00000000..8d2e42ec --- /dev/null +++ b/doc/sphinx/source/workflows.txt @@ -0,0 +1,19 @@ +========= +Workflows +========= + +.. _workflows: + +.. versionadded:: 0.9.0 + +MDPOW analysis workflows provide automation functions for use with +analysis :ref:`tools` based on the :ref:`framework`. Existing workflow +modules, :mod:`~mdpow.workflows.dihedrals`, include functions +for use with :class:`~mdpow.analysis.dihedral.DihedralAnalysis`. + +.. toctree:: + :maxdepth: 1 + + workflows/base + workflows/registry + workflows/dihedrals diff --git a/doc/sphinx/source/workflows/base.txt b/doc/sphinx/source/workflows/base.txt new file mode 100644 index 00000000..e16d813c --- /dev/null +++ b/doc/sphinx/source/workflows/base.txt @@ -0,0 +1,7 @@ +============== +Workflows Base +============== + +.. versionadded:: 0.9.0 + +.. automodule:: mdpow.workflows.base diff --git a/doc/sphinx/source/workflows/dihedrals.txt b/doc/sphinx/source/workflows/dihedrals.txt new file mode 100644 index 00000000..a0d6e7ed --- /dev/null +++ b/doc/sphinx/source/workflows/dihedrals.txt @@ -0,0 +1,7 @@ +=========================== +Automated Dihedral Analysis +=========================== + +.. versionadded:: 0.9.0 + +.. automodule:: mdpow.workflows.dihedrals diff --git a/doc/sphinx/source/workflows/registry.txt b/doc/sphinx/source/workflows/registry.txt new file mode 100644 index 00000000..a4465e37 --- /dev/null +++ b/doc/sphinx/source/workflows/registry.txt @@ -0,0 +1,7 @@ +================== +Workflows Registry +================== + +.. versionadded:: 0.9.0 + +.. automodule:: mdpow.workflows.registry diff --git a/mdpow/__init__.py b/mdpow/__init__.py index 2fb2484e..371ec35f 100644 --- a/mdpow/__init__.py +++ b/mdpow/__init__.py @@ -5,10 +5,12 @@ from . import log from ._version import get_versions -__version__ = get_versions()['version'] + +__version__ = get_versions()["version"] del get_versions -__all__ = ['fep', 'equil'] +__all__ = ["fep", "equil"] + def create_logger(logfile="mdpow.log"): """Create the default logger. @@ -16,20 +18,28 @@ def create_logger(logfile="mdpow.log"): Channels the output from :mod:`mdpow`, :mod:`gromacs`, and :mod:`numkit` into the file *logfile*. """ - logger = log.create('mdpow', logfile) - log.create('numkit', logfile) # capture numkit messages to same file - log.create('gromacs', logfile) # and the GromacsWrapper messages + logger = log.create("mdpow", logfile) + log.create("numkit", logfile) # capture numkit messages to same file + log.create("gromacs", logfile) # and the GromacsWrapper messages return logger -def log_banner(): + +def log_banner(logger): """Log program name and licence at INFO level.""" logger.info("MDPOW %s starting.", __version__) - logger.info("Copyright (c) 2010-2021 Shujie Fan, Ian Kenney, Alia Lescoulie, Bogdan Iorga, and Oliver Beckstein") + logger.info( + "Copyright (c) 2010-2023 Shujie Fan, Ian Kenney, " + "Alia Lescoulie, Cade Duckworth, Bogdan Iorga, and " + "Oliver Beckstein" + ) logger.info("Released under the GNU Public Licence, version 3.") - logger.info("For bug reports and help: https://github.com/Becksteinlab/MDPOW/issues") + logger.info( + "For bug reports and help: https://github.com/Becksteinlab/MDPOW/issues" + ) + logger = create_logger() -log_banner() +log_banner(logger) # AVOID IMPORTS OF OTHER PACKAGES IN __init__.py; only standard # library and mdpow.log are allowed. Anything else will break 'pip @@ -41,5 +51,4 @@ def log_banner(): #: Avogadro's constant |NA| in mol^-1 (`NA NIST value`_). N_AVOGADRO = 6.02214179e23 #: Boltzmann's constant |kB| in kJ mol^-1 (`kB NIST value`_). -kBOLTZ = 1.3806504e-23 *1e-3 * N_AVOGADRO - +kBOLTZ = 1.3806504e-23 * 1e-3 * N_AVOGADRO diff --git a/mdpow/_version.py b/mdpow/_version.py index 2484e7bd..e5328388 100644 --- a/mdpow/_version.py +++ b/mdpow/_version.py @@ -1,4 +1,3 @@ - # This file helps to compute a version number in source trees obtained from # git-archive tarball (such as those provided by githubs download-from-tag # feature). Distribution tarballs (built by setup.py sdist) and build @@ -57,17 +56,18 @@ class NotThisMethod(Exception): def register_vcs_handler(vcs, method): # decorator """Decorator to mark a method as the handler for a particular VCS.""" + def decorate(f): """Store f in HANDLERS[vcs][method].""" if vcs not in HANDLERS: HANDLERS[vcs] = {} HANDLERS[vcs][method] = f return f + return decorate -def run_command(commands, args, cwd=None, verbose=False, hide_stderr=False, - env=None): +def run_command(commands, args, cwd=None, verbose=False, hide_stderr=False, env=None): """Call the given command(s).""" assert isinstance(commands, list) p = None @@ -75,10 +75,13 @@ def run_command(commands, args, cwd=None, verbose=False, hide_stderr=False, try: dispcmd = str([c] + args) # remember shell=False, so use git.cmd on windows, not just git - p = subprocess.Popen([c] + args, cwd=cwd, env=env, - stdout=subprocess.PIPE, - stderr=(subprocess.PIPE if hide_stderr - else None)) + p = subprocess.Popen( + [c] + args, + cwd=cwd, + env=env, + stdout=subprocess.PIPE, + stderr=(subprocess.PIPE if hide_stderr else None), + ) break except EnvironmentError: e = sys.exc_info()[1] @@ -115,16 +118,22 @@ def versions_from_parentdir(parentdir_prefix, root, verbose): for i in range(3): dirname = os.path.basename(root) if dirname.startswith(parentdir_prefix): - return {"version": dirname[len(parentdir_prefix):], - "full-revisionid": None, - "dirty": False, "error": None, "date": None} + return { + "version": dirname[len(parentdir_prefix) :], + "full-revisionid": None, + "dirty": False, + "error": None, + "date": None, + } else: rootdirs.append(root) root = os.path.dirname(root) # up a level if verbose: - print("Tried directories %s but none started with prefix %s" % - (str(rootdirs), parentdir_prefix)) + print( + "Tried directories %s but none started with prefix %s" + % (str(rootdirs), parentdir_prefix) + ) raise NotThisMethod("rootdir doesn't start with parentdir_prefix") @@ -180,7 +189,7 @@ def git_versions_from_keywords(keywords, tag_prefix, verbose): # starting in git-1.8.3, tags are listed as "tag: foo-1.0" instead of # just "foo-1.0". If we see a "tag: " prefix, prefer those. TAG = "tag: " - tags = set([r[len(TAG):] for r in refs if r.startswith(TAG)]) + tags = set([r[len(TAG) :] for r in refs if r.startswith(TAG)]) if not tags: # Either we're using git < 1.8.3, or there really are no tags. We use # a heuristic: assume all version tags have a digit. The old git %d @@ -189,7 +198,7 @@ def git_versions_from_keywords(keywords, tag_prefix, verbose): # between branches and tags. By ignoring refnames without digits, we # filter out many common branch names like "release" and # "stabilization", as well as "HEAD" and "master". - tags = set([r for r in refs if re.search(r'\d', r)]) + tags = set([r for r in refs if re.search(r"\d", r)]) if verbose: print("discarding '%s', no digits" % ",".join(refs - tags)) if verbose: @@ -197,19 +206,26 @@ def git_versions_from_keywords(keywords, tag_prefix, verbose): for ref in sorted(tags): # sorting will prefer e.g. "2.0" over "2.0rc1" if ref.startswith(tag_prefix): - r = ref[len(tag_prefix):] + r = ref[len(tag_prefix) :] if verbose: print("picking %s" % r) - return {"version": r, - "full-revisionid": keywords["full"].strip(), - "dirty": False, "error": None, - "date": date} + return { + "version": r, + "full-revisionid": keywords["full"].strip(), + "dirty": False, + "error": None, + "date": date, + } # no suitable tags, so version is "0+unknown", but full hex is still there if verbose: print("no suitable tags, using unknown + full revision id") - return {"version": "0+unknown", - "full-revisionid": keywords["full"].strip(), - "dirty": False, "error": "no suitable tags", "date": None} + return { + "version": "0+unknown", + "full-revisionid": keywords["full"].strip(), + "dirty": False, + "error": "no suitable tags", + "date": None, + } @register_vcs_handler("git", "pieces_from_vcs") @@ -224,8 +240,7 @@ def git_pieces_from_vcs(tag_prefix, root, verbose, run_command=run_command): if sys.platform == "win32": GITS = ["git.cmd", "git.exe"] - out, rc = run_command(GITS, ["rev-parse", "--git-dir"], cwd=root, - hide_stderr=True) + out, rc = run_command(GITS, ["rev-parse", "--git-dir"], cwd=root, hide_stderr=True) if rc != 0: if verbose: print("Directory %s not under git control" % root) @@ -233,10 +248,19 @@ def git_pieces_from_vcs(tag_prefix, root, verbose, run_command=run_command): # if there is a tag matching tag_prefix, this yields TAG-NUM-gHEX[-dirty] # if there isn't one, this yields HEX[-dirty] (no NUM) - describe_out, rc = run_command(GITS, ["describe", "--tags", "--dirty", - "--always", "--long", - "--match", "%s*" % tag_prefix], - cwd=root) + describe_out, rc = run_command( + GITS, + [ + "describe", + "--tags", + "--dirty", + "--always", + "--long", + "--match", + "%s*" % tag_prefix, + ], + cwd=root, + ) # --long was added in git-1.5.5 if describe_out is None: raise NotThisMethod("'git describe' failed") @@ -259,17 +283,16 @@ def git_pieces_from_vcs(tag_prefix, root, verbose, run_command=run_command): dirty = git_describe.endswith("-dirty") pieces["dirty"] = dirty if dirty: - git_describe = git_describe[:git_describe.rindex("-dirty")] + git_describe = git_describe[: git_describe.rindex("-dirty")] # now we have TAG-NUM-gHEX or HEX if "-" in git_describe: # TAG-NUM-gHEX - mo = re.search(r'^(.+)-(\d+)-g([0-9a-f]+)$', git_describe) + mo = re.search(r"^(.+)-(\d+)-g([0-9a-f]+)$", git_describe) if not mo: # unparseable. Maybe git-describe is misbehaving? - pieces["error"] = ("unable to parse git-describe output: '%s'" - % describe_out) + pieces["error"] = "unable to parse git-describe output: '%s'" % describe_out return pieces # tag @@ -278,10 +301,12 @@ def git_pieces_from_vcs(tag_prefix, root, verbose, run_command=run_command): if verbose: fmt = "tag '%s' doesn't start with prefix '%s'" print(fmt % (full_tag, tag_prefix)) - pieces["error"] = ("tag '%s' doesn't start with prefix '%s'" - % (full_tag, tag_prefix)) + pieces["error"] = "tag '%s' doesn't start with prefix '%s'" % ( + full_tag, + tag_prefix, + ) return pieces - pieces["closest-tag"] = full_tag[len(tag_prefix):] + pieces["closest-tag"] = full_tag[len(tag_prefix) :] # distance: number of commits since tag pieces["distance"] = int(mo.group(2)) @@ -292,13 +317,13 @@ def git_pieces_from_vcs(tag_prefix, root, verbose, run_command=run_command): else: # HEX: no tags pieces["closest-tag"] = None - count_out, rc = run_command(GITS, ["rev-list", "HEAD", "--count"], - cwd=root) + count_out, rc = run_command(GITS, ["rev-list", "HEAD", "--count"], cwd=root) pieces["distance"] = int(count_out) # total number of commits # commit date: see ISO-8601 comment in git_versions_from_keywords() - date = run_command(GITS, ["show", "-s", "--format=%ci", "HEAD"], - cwd=root)[0].strip() + date = run_command(GITS, ["show", "-s", "--format=%ci", "HEAD"], cwd=root)[ + 0 + ].strip() pieces["date"] = date.strip().replace(" ", "T", 1).replace(" ", "", 1) return pieces @@ -329,8 +354,7 @@ def render_pep440(pieces): rendered += ".dirty" else: # exception #1 - rendered = "0+untagged.%d.g%s" % (pieces["distance"], - pieces["short"]) + rendered = "0+untagged.%d.g%s" % (pieces["distance"], pieces["short"]) if pieces["dirty"]: rendered += ".dirty" return rendered @@ -444,11 +468,13 @@ def render_git_describe_long(pieces): def render(pieces, style): """Render the given version pieces into the requested style.""" if pieces["error"]: - return {"version": "unknown", - "full-revisionid": pieces.get("long"), - "dirty": None, - "error": pieces["error"], - "date": None} + return { + "version": "unknown", + "full-revisionid": pieces.get("long"), + "dirty": None, + "error": pieces["error"], + "date": None, + } if not style or style == "default": style = "pep440" # the default @@ -468,9 +494,13 @@ def render(pieces, style): else: raise ValueError("unknown style '%s'" % style) - return {"version": rendered, "full-revisionid": pieces["long"], - "dirty": pieces["dirty"], "error": None, - "date": pieces.get("date")} + return { + "version": rendered, + "full-revisionid": pieces["long"], + "dirty": pieces["dirty"], + "error": None, + "date": pieces.get("date"), + } def get_versions(): @@ -484,8 +514,7 @@ def get_versions(): verbose = cfg.verbose try: - return git_versions_from_keywords(get_keywords(), cfg.tag_prefix, - verbose) + return git_versions_from_keywords(get_keywords(), cfg.tag_prefix, verbose) except NotThisMethod: pass @@ -494,13 +523,16 @@ def get_versions(): # versionfile_source is the relative path from the top of the source # tree (where the .git directory might live) to this file. Invert # this to find the root from __file__. - for i in cfg.versionfile_source.split('/'): + for i in cfg.versionfile_source.split("/"): root = os.path.dirname(root) except NameError: - return {"version": "0+unknown", "full-revisionid": None, - "dirty": None, - "error": "unable to find root of source tree", - "date": None} + return { + "version": "0+unknown", + "full-revisionid": None, + "dirty": None, + "error": "unable to find root of source tree", + "date": None, + } try: pieces = git_pieces_from_vcs(cfg.tag_prefix, root, verbose) @@ -514,6 +546,10 @@ def get_versions(): except NotThisMethod: pass - return {"version": "0+unknown", "full-revisionid": None, - "dirty": None, - "error": "unable to compute version", "date": None} + return { + "version": "0+unknown", + "full-revisionid": None, + "dirty": None, + "error": "unable to compute version", + "date": None, + } diff --git a/mdpow/analysis/dihedral.py b/mdpow/analysis/dihedral.py index 69b3c0a9..05ca6c53 100644 --- a/mdpow/analysis/dihedral.py +++ b/mdpow/analysis/dihedral.py @@ -14,7 +14,7 @@ import logging -logger = logging.getLogger('mdpow.analysis.dihedral') +logger = logging.getLogger("mdpow.analysis.dihedral") class DihedralAnalysis(EnsembleAnalysis): @@ -31,7 +31,7 @@ class DihedralAnalysis(EnsembleAnalysis): Data is returned in a :class:`pandas.DataFrame` with observations sorted by selection, solvent, interaction, lambda, time. - .. ruberic:: Example + .. rubric:: Example Typical Workflow:: @@ -48,7 +48,9 @@ def __init__(self, dihedral_groups: List[EnsembleAtomGroup]): self.check_groups_from_common_ensemble(dihedral_groups) self.check_dihedral_inputs(dihedral_groups) super(DihedralAnalysis, self).__init__(dihedral_groups[0].ensemble) - self.g1, self.g2, self.g3, self.g4, self.names = self._reorg_groups(dihedral_groups) + self.g1, self.g2, self.g3, self.g4, self.names = self._reorg_groups( + dihedral_groups + ) @staticmethod def _reorg_groups(groups: List[EnsembleAtomGroup]): @@ -63,14 +65,30 @@ def _reorg_groups(groups: List[EnsembleAtomGroup]): ag2 += [mda.AtomGroup([ag[1]]) for ag in [group[k] for k in group.keys()]] ag3 += [mda.AtomGroup([ag[2]]) for ag in [group[k] for k in group.keys()]] ag4 += [mda.AtomGroup([ag[3]]) for ag in [group[k] for k in group.keys()]] - names.append('-'.join([ag1[-1].atoms[0].name, ag2[-1].atoms[0].name, - ag3[-1].atoms[0].name, ag4[-1].atoms[0].name])) + names.append( + "-".join( + [ + ag1[-1].atoms[0].name, + ag2[-1].atoms[0].name, + ag3[-1].atoms[0].name, + ag4[-1].atoms[0].name, + ] + ) + ) for k in group.keys(): ag_keys.append((names[-1], k[0], k[1], k[2])) - eag1 = EnsembleAtomGroup({ag_keys[i]: ag1[i] for i in range(len(ag_keys))}, groups[0].ensemble) - eag2 = EnsembleAtomGroup({ag_keys[i]: ag2[i] for i in range(len(ag_keys))}, groups[0].ensemble) - eag3 = EnsembleAtomGroup({ag_keys[i]: ag3[i] for i in range(len(ag_keys))}, groups[0].ensemble) - eag4 = EnsembleAtomGroup({ag_keys[i]: ag4[i] for i in range(len(ag_keys))}, groups[0].ensemble) + eag1 = EnsembleAtomGroup( + {ag_keys[i]: ag1[i] for i in range(len(ag_keys))}, groups[0].ensemble + ) + eag2 = EnsembleAtomGroup( + {ag_keys[i]: ag2[i] for i in range(len(ag_keys))}, groups[0].ensemble + ) + eag3 = EnsembleAtomGroup( + {ag_keys[i]: ag3[i] for i in range(len(ag_keys))}, groups[0].ensemble + ) + eag4 = EnsembleAtomGroup( + {ag_keys[i]: ag4[i] for i in range(len(ag_keys))}, groups[0].ensemble + ) return eag1, eag2, eag3, eag4, names @staticmethod @@ -78,14 +96,22 @@ def check_dihedral_inputs(selections): for group in selections: for k in group.keys(): if len(group[k]) != 4: - msg = ("Dihedral calculations require AtomGroups with " - f"only 4 atoms, {len(group)} selected") + msg = ( + "Dihedral calculations require AtomGroups with " + f"only 4 atoms, {len(group)} selected" + ) logger.error(msg) raise SelectionError(msg) def _prepare_ensemble(self): - self._col = ['selection', 'solvent', 'interaction', - 'lambda', 'time', 'dihedral'] + self._col = [ + "selection", + "solvent", + "interaction", + "lambda", + "time", + "dihedral", + ] self.results = pd.DataFrame(columns=self._col) self._res_dict = {key: [] for key in self._col} @@ -99,8 +125,9 @@ def _single_frame(self): cord2 = np.concatenate(tuple([cord_dict2[k] for k in key_list])) cord3 = np.concatenate(tuple([cord_dict3[k] for k in key_list])) cord4 = np.concatenate(tuple([cord_dict4[k] for k in key_list])) - angle = calc_dihedrals(cord1, cord2, cord3, cord4, - box=self.g1[key_list[0]].dimensions) + angle = calc_dihedrals( + cord1, cord2, cord3, cord4, box=self.g1[key_list[0]].dimensions + ) angle = np.rad2deg(angle) for i in range(len(self.names)): result = list(key_list[i]) + [self._ts.time, angle[i]] diff --git a/mdpow/analysis/ensemble.py b/mdpow/analysis/ensemble.py index d4ba40a6..308f5115 100644 --- a/mdpow/analysis/ensemble.py +++ b/mdpow/analysis/ensemble.py @@ -9,17 +9,22 @@ import MDAnalysis as mda from MDAnalysis.lib.log import ProgressBar -from MDAnalysis.exceptions import FileFormatWarning, NoDataError, MissingDataWarning, SelectionError +from MDAnalysis.exceptions import ( + FileFormatWarning, + NoDataError, + MissingDataWarning, + SelectionError, +) from gromacs.utilities import in_dir import logging -logger = logging.getLogger('mdpow._ensemble') +logger = logging.getLogger("mdpow._ensemble") class Ensemble(object): - """ Collection of related :class:`MDAnalysis.Universe ` + """Collection of related :class:`MDAnalysis.Universe ` objects. Stores systems produced by running mdpow-fep organized @@ -86,9 +91,14 @@ class Ensemble(object): .. versionadded:: 0.8.0 """ - def __init__(self, dirname=None, solvents=('octanol', 'water'), - topology_paths=None, interactions=('Coulomb', 'VDW'), - **universe_kwargs): + def __init__( + self, + dirname=None, + solvents=("octanol", "water"), + topology_paths=None, + interactions=("Coulomb", "VDW"), + **universe_kwargs, + ): self.top_dict = topology_paths self._num_systems = 0 self._ensemble = {} @@ -102,8 +112,7 @@ def __init__(self, dirname=None, solvents=('octanol', 'water'), if not os.path.exists(dirname): logger.error(f"Directory {dirname} does not exist") - raise FileNotFoundError(errno.ENOENT, 'Directory does not' - 'exist', dirname) + raise FileNotFoundError(errno.ENOENT, "Directory does not" "exist", dirname) self._ensemble_dir = dirname self._build_ensemble() @@ -119,7 +128,9 @@ def __getitem__(self, index): return self._ensemble[index] @staticmethod - def _load_universe_from_dir(solv_dir=None, **universe_kwargs) -> Optional[mda.Universe]: + def _load_universe_from_dir( + solv_dir=None, **universe_kwargs + ) -> Optional[mda.Universe]: """Loads system simulation files in directory into an :class:`MDAnalysis.Universe ` @@ -141,10 +152,12 @@ def _sort_trajectories(trajectories: list) -> list: def _sort_topologies(topologies: list) -> list: """sorts list of trajectory files with .tpr first""" tops = [] - logger.info('If more than one topology is present the tpr will be the one used') + logger.info( + "If more than one topology is present the tpr will be the one used" + ) for i in range(len(topologies)): f = topologies[i] - if f.endswith('.tpr'): + if f.endswith(".tpr"): topologies.pop(i) tops = [f] + topologies break @@ -159,16 +172,20 @@ def _sort_topologies(topologies: list) -> list: top = [solv_dir] for file in cur_dir: - if file.endswith('.xtc'): + if file.endswith(".xtc"): # Saving trajectory directories trj.append(file) - elif (file.endswith('gro') or file.endswith('.tpr') or file.endswith('gro.bz2') - or file.endswith('gro.gz')) and solv_dir is None: + elif ( + file.endswith("gro") + or file.endswith(".tpr") + or file.endswith("gro.bz2") + or file.endswith("gro.gz") + ) and solv_dir is None: # Saving topology directories top.append(file) if len(top) == 0 or len(trj) == 0: - logger.warning('No MD files detected in %s', os.curdir) + logger.warning("No MD files detected in %s", os.curdir) return trj = _sort_trajectories(trj) @@ -177,8 +194,14 @@ def _sort_topologies(topologies: list) -> list: try: return mda.Universe(os.path.abspath(top[0]), trj, **universe_kwargs) - except (ValueError, FileFormatWarning, NoDataError, MissingDataWarning, OSError) as err: - logger.error(f'{err} raised while loading {top[0]} {trj} in dir {cur_dir}') + except ( + ValueError, + FileFormatWarning, + NoDataError, + MissingDataWarning, + OSError, + ) as err: + logger.error(f"{err} raised while loading {top[0]} {trj} in dir {cur_dir}") raise NoDataError def keys(self): @@ -193,9 +216,11 @@ def _build_ensemble(self): Run if :code:`dirname` argument is given when initializing the class. First enters FEP directory, then traverses solvent and interaction directories to search lambda directories for system files.""" - fep_dir = os.path.join(self._ensemble_dir, 'FEP') + fep_dir = os.path.join(self._ensemble_dir, "FEP") solv_top_path = None - for solvent in self._solvents: # Ugly set of loops, may have to find way to clean up + for ( + solvent + ) in self._solvents: # Ugly set of loops, may have to find way to clean up if self.top_dict is not None: solv_top_path = self.top_dict[solvent] for dirs in self._interactions: # Attribute folder names @@ -206,10 +231,11 @@ def _build_ensemble(self): for file in sorted(files): # Traversing lambda directories if os.path.isdir(file): with in_dir(file, create=False): - u = self._load_universe_from_dir(solv_dir=solv_top_path, - **self.unv_kwargs) + u = self._load_universe_from_dir( + solv_dir=solv_top_path, **self.unv_kwargs + ) if u is None: - logger.warning(f'No system loaded in {file}') + logger.warning(f"No system loaded in {file}") else: self.add_system((solvent, dirs, file), u) @@ -236,20 +262,33 @@ def select_atoms(self, *args, **kwargs): Uses the same `selection commands `_ - as MDAnalysis, and has the same keys as the :class:`~mdpow.analysis.ensemble.Ensemble`""" + as MDAnalysis, and has the same keys as the :class:`~mdpow.analysis.ensemble.Ensemble` + """ selections = {} for key in self.keys(): try: ag = self[key].select_atoms(*args, **kwargs) except SelectionError as err: - logger.error("%r on system %r with selection settings %r %r", err, key, args, kwargs) + logger.error( + "%r on system %r with selection settings %r %r", + err, + key, + args, + kwargs, + ) raise else: selections[key] = ag return EnsembleAtomGroup(selections, ensemble=self) - def select_systems(self, keys=None, solvents=None, interactions=None, - lambdas=None, lambda_range=None): + def select_systems( + self, + keys=None, + solvents=None, + interactions=None, + lambdas=None, + lambda_range=None, + ): """ Select specific subset of systems and returns them in an Ensemble. @@ -318,10 +357,14 @@ def select_systems(self, keys=None, solvents=None, interactions=None, elif lambda_range is not None: # Selecting range of lambdas for k in self.keys(): - if lambda_range[0] <= int(k[2]) / 1000 <= lambda_range[1]: + if ( + lambda_range[0] + <= int(k[2]) / 1000 + <= lambda_range[1] + ): new_key.append((s, i, k[2])) for k in new_key: - logger.info('adding system %r to ensemble', k) + logger.info("adding system %r to ensemble", k) new_ens.add_system(k, universe=self[k]) new_ens._ensemble_dir = self._ensemble_dir return new_ens @@ -374,13 +417,20 @@ def select_atoms(self, *args, **kwargs): Uses the same `selection commands `_ - as MDAnalysis, and has the same keys as :class:`~mdpow.analysis.ensemble.EnsembleAtomGroup`""" + as MDAnalysis, and has the same keys as :class:`~mdpow.analysis.ensemble.EnsembleAtomGroup` + """ selections = {} for key in self.keys(): try: ag = self[key].select_atoms(*args, **kwargs) except SelectionError as err: - logger.error("%r on system %r with selection settings %r %r", err, key, args, kwargs) + logger.error( + "%r on system %r with selection settings %r %r", + err, + key, + args, + kwargs, + ) raise else: selections[key] = ag @@ -458,39 +508,41 @@ def _setup_system(self, key, start=None, stop=None, step=None): def _setup_frames(self, trajectory): self._trajectory = trajectory - start, stop, step = trajectory.check_slice_indices(self.start, self.stop, self.step) + start, stop, step = trajectory.check_slice_indices( + self.start, self.stop, self.step + ) self.n_frames = len(range(start, stop, step)) self.frames = np.zeros(self.n_frames, dtype=int) self.times = np.zeros(self.n_frames) def _single_universe(self): - """Calculations on a single :class:`MDAnalysis.Universe - ` object. - - Run on each :class:`MDAnalysis.Universe - ` - in the :class:`~mdpow.analysis.ensemble.Ensemble` - during when :meth:`run` in called. - :exc:`NotImplementedError` will detect whether - :meth:`~EnsembleAnalysis._single_universe` - or :meth:`~EnsembleAnalysis._single_frame` - should be implemented, based on which is defined - in the :class:`~mdpow.analysis.ensemble.EnsembleAnalysis`. + """Calculations on a single :class:`MDAnalysis.Universe + ` object. + + Run on each :class:`MDAnalysis.Universe + ` + in the :class:`~mdpow.analysis.ensemble.Ensemble` + during when :meth:`run` in called. + :exc:`NotImplementedError` will detect whether + :meth:`~EnsembleAnalysis._single_universe` + or :meth:`~EnsembleAnalysis._single_frame` + should be implemented, based on which is defined + in the :class:`~mdpow.analysis.ensemble.EnsembleAnalysis`. """ raise NotImplementedError def _single_frame(self): """Calculate data from a single frame of trajectory. - Called on each frame for each - :class:`MDAnalysis.Universe ` - in the :class:`~mdpow.analysis.ensemble.Ensemble`. - - :exc:`NotImplementedError` will detect whether - :meth:`~EnsembleAnalysis._single_universe` - or :meth:`~EnsembleAnalysis._single_frame` - should be implemented, based on which is defined - in the :class:`~mdpow.analysis.ensemble.EnsembleAnalysis`. + Called on each frame for each + :class:`MDAnalysis.Universe ` + in the :class:`~mdpow.analysis.ensemble.Ensemble`. + + :exc:`NotImplementedError` will detect whether + :meth:`~EnsembleAnalysis._single_universe` + or :meth:`~EnsembleAnalysis._single_frame` + should be implemented, based on which is defined + in the :class:`~mdpow.analysis.ensemble.EnsembleAnalysis`. """ raise NotImplementedError @@ -521,15 +573,15 @@ def _conclude_ensemble(self): pass # pragma: no cover def run(self, start=None, stop=None, step=None): - """Runs :meth:`~EnsembleAnalysis._single_universe` + """Runs :meth:`~EnsembleAnalysis._single_universe` on each system or :meth:`~EnsembleAnalysis._single_frame` on each frame in the system. - First iterates through keys of ensemble, then runs - :meth:`~EnsembleAnalysis._setup_system`which defines + First iterates through keys of ensemble, then runs + :meth:`~EnsembleAnalysis._setup_system`which defines the system and trajectory. Then iterates over each - system universe or trajectory frames of each universe - as defined by :meth:`~EnsembleAnalysis._single_universe` + system universe or trajectory frames of each universe + as defined by :meth:`~EnsembleAnalysis._single_universe` or :meth:`~EnsembleAnalysis._single_frame`. """ logger.info("Setting up systems") @@ -539,8 +591,13 @@ def run(self, start=None, stop=None, step=None): try: self._single_universe() except NotImplementedError: - for i, ts in enumerate(ProgressBar(self._trajectory[self.start:self.stop:self.step], verbose=True, - postfix=f'running system {self._key}')): + for i, ts in enumerate( + ProgressBar( + self._trajectory[self.start : self.stop : self.step], + verbose=True, + postfix=f"running system {self._key}", + ) + ): self._frame_index = i self._ts = ts self.frames[i] = ts.frame @@ -566,9 +623,11 @@ def check_groups_from_common_ensemble(groups: List[EnsembleAtomGroup]): for j in range(i + 1, len(groups)): # Checking if EnsembleAtomGroup.ensemble references same object in memory if groups[i].ensemble is not groups[j].ensemble: - msg = ('Dihedral selections from different Ensembles, ' - 'ensure that all EnsembleAtomGroups are created ' - 'from the same Ensemble. ' - f'mismatch: group[{i}].ensemble != group[{j}].ensemble') + msg = ( + "Dihedral selections from different Ensembles, " + "ensure that all EnsembleAtomGroups are created " + "from the same Ensemble. " + f"mismatch: group[{i}].ensemble != group[{j}].ensemble" + ) logger.error(msg) raise ValueError(msg) diff --git a/mdpow/analysis/solvation.py b/mdpow/analysis/solvation.py index 6ed6137a..47be995d 100644 --- a/mdpow/analysis/solvation.py +++ b/mdpow/analysis/solvation.py @@ -13,7 +13,7 @@ import logging -logger = logging.getLogger('mdpow.dihedral') +logger = logging.getLogger("mdpow.dihedral") class SolvationAnalysis(EnsembleAnalysis): @@ -48,7 +48,13 @@ class SolvationAnalysis(EnsembleAnalysis): solv_dist = SolvationAnalysis(solute, solvent, [1.2, 2.4]).run(stop=10) """ - def __init__(self, solute: EnsembleAtomGroup, solvent: EnsembleAtomGroup, distances: List[float]): + + def __init__( + self, + solute: EnsembleAtomGroup, + solvent: EnsembleAtomGroup, + distances: List[float], + ): self.check_groups_from_common_ensemble([solute, solvent]) super(SolvationAnalysis, self).__init__(solute.ensemble) self._solute = solute @@ -56,21 +62,37 @@ def __init__(self, solute: EnsembleAtomGroup, solvent: EnsembleAtomGroup, distan self._dists = distances def _prepare_ensemble(self): - self._col = ['distance', 'solvent', 'interaction', - 'lambda', 'time', 'N_solvent'] + self._col = [ + "distance", + "solvent", + "interaction", + "lambda", + "time", + "N_solvent", + ] self.results = pd.DataFrame(columns=self._col) self._res_dict = {key: [] for key in self._col} def _single_frame(self): solute = self._solute[self._key] solvent = self._solvent[self._key] - pairs, distances = capped_distance(solute.positions, solvent.positions, - max(self._dists), box=self._ts.dimensions) + pairs, distances = capped_distance( + solute.positions, + solvent.positions, + max(self._dists), + box=self._ts.dimensions, + ) solute_i, solvent_j = np.transpose(pairs) for d in self._dists: close_solv_atoms = solvent[solvent_j[distances < d]] - result = [d, self._key[0], self._key[1],self._key[2], - self._ts.time, close_solv_atoms.n_atoms] + result = [ + d, + self._key[0], + self._key[1], + self._key[2], + self._ts.time, + close_solv_atoms.n_atoms, + ] for i in range(len(self._col)): self._res_dict[self._col[i]].append(result[i]) diff --git a/mdpow/config.py b/mdpow/config.py index 3d2e2878..18f2890d 100644 --- a/mdpow/config.py +++ b/mdpow/config.py @@ -104,6 +104,7 @@ import warnings import logging + logger = logging.getLogger("mdpow.config") # Reading of configuration files @@ -112,7 +113,8 @@ #: Locations of default run input files and configurations. defaults = { "runinput": resource_filename(__name__, "templates/runinput.yml"), - } +} + class NoSectionError(ValueError): """Section entry is missing. @@ -120,13 +122,16 @@ class NoSectionError(ValueError): .. versionadded:: 0.8.0 """ + # not used at the moment # class NoOptionError(ValueError): # """Option entry is missing from section""" + class NoOptionWarning(UserWarning): """Warning that an option is missing.""" + def merge_dicts(user, default): """Merge two dictionaries recursively. @@ -186,7 +191,7 @@ def merge(self, fn): return self.conf def write(self, filename): - with open(filename, 'w') as f: + with open(filename, "w") as f: f.write(yaml.dump(self.conf)) def get(self, section, option): @@ -213,10 +218,12 @@ def get(self, section, option): value = value if value != "None" else None # still needed?? except KeyError: # Returning None has been standard behavior. - #raise NoOptionError(f"Config file section {section} contains " + # raise NoOptionError(f"Config file section {section} contains " # f"no option {option}.") - msg = (f"Config file section {section} contains " - f"no option {option}. Using 'None'.") + msg = ( + f"Config file section {section} contains " + f"no option {option}. Using 'None'." + ) warnings.warn(msg, category=NoOptionWarning) logger.warning(msg) value = None @@ -240,8 +247,9 @@ def getpath(self, section, option): instead of raising a :exc:`TypeError`. """ item = self.get(section, option) - return os.path.expanduser( - os.path.expandvars(item)) if item is not None else None + return ( + os.path.expanduser(os.path.expandvars(item)) if item is not None else None + ) def findfile(self, section, option): """Return location of a file ``option`` or ``None``. @@ -263,8 +271,7 @@ def getlist(self, section, option): instead of raising a :exc:`TypeError`. """ item = self.get(section, option) - return [x.strip() - for x in str(item).split(",")] if item is not None else [] + return [x.strip() for x in str(item).split(",")] if item is not None else [] def getarray(self, section, option): """Return option as a numpy array of floats or ``np.array([])``. @@ -277,6 +284,7 @@ def getarray(self, section, option): """ return np.asarray(self.getlist(section, option), dtype=float) + # def getintarray(self, section, option): # """Return option as a numpy array of integers. # @@ -285,6 +293,7 @@ def getarray(self, section, option): # """ # return np.asarray(self.getlist(section, option), dtype=int) + def get_configuration(filename=None): """Reads and parses a run input config file. @@ -294,47 +303,61 @@ def get_configuration(filename=None): cfg.readfp(open(defaults["runinput"])) logger.debug("Loaded runinput defaults from %r", defaults["runinput"]) if filename is not None: - cfg.merge(open(filename)) # override package defaults + cfg.merge(open(filename)) # override package defaults logger.debug("Loaded user runinput from %r (replacing defaults)", filename) else: - logger.warning("Running with package defaults for the run; you should supply a runinput file!") + logger.warning( + "Running with package defaults for the run; you should supply a runinput file!" + ) return cfg + def modify_gromacs_environment(name, value): from gromacs.environment import flags + if flags[name] != value: - logger.warning("Changing GromacsWrapper environment: flags[%(name)r] = %(value)r", vars()) + logger.warning( + "Changing GromacsWrapper environment: flags[%(name)r] = %(value)r", vars() + ) flags[name] = value + def set_gromacsoutput(cfg): # maybe allow setting this on a per protocol basis? - modify_gromacs_environment('capture_output', not cfg.getboolean('setup', 'gromacsoutput')) + modify_gromacs_environment( + "capture_output", not cfg.getboolean("setup", "gromacsoutput") + ) # Functions to locate template files # ---------------------------------- + def _generate_template_dict(dirname): """Generate a list of included top-level files *and* extract them to a temp space. Only lists files and directories at the *top level* of the *dirname*; however, all directories are extracted recursively and will be available. """ - return dict((resource_basename(fn), resource_filename(__name__, dirname+'/'+fn)) - for fn in resource_listdir(__name__, dirname) - if not fn.endswith('~')) + return dict( + (resource_basename(fn), resource_filename(__name__, dirname + "/" + fn)) + for fn in resource_listdir(__name__, dirname) + if not fn.endswith("~") + ) + def resource_basename(resource): - """Last component of a resource (which always uses '/' as sep).""" - if resource.endswith('/'): - resource = resource[:-1] - parts = resource.split('/') - return parts[-1] + """Last component of a resource (which always uses '/' as sep).""" + if resource.endswith("/"): + resource = resource[:-1] + parts = resource.split("/") + return parts[-1] # Functions to access configuration data # -------------------------------------- + def get_template(t): """Find template file *t* and return its real path. @@ -358,9 +381,10 @@ def get_template(t): # Not sure if this is the best way to get asiterables templates = [_get_template(s) for s in gromacs.utilities.asiterable(t)] if len(templates) == 1: - return templates[0] + return templates[0] return templates + def get_templates(t): """Find template file(s) *t* and return their real paths. @@ -381,26 +405,27 @@ def get_templates(t): """ return [_get_template(s) for s in gromacs.utilities.asiterable(t)] + def _get_template(t): """Return a single template *t*.""" - if os.path.exists(t): # 1) Is it an accessible file? + if os.path.exists(t): # 1) Is it an accessible file? pass - else: # 2) check the packaged template files + else: # 2) check the packaged template files _t = os.path.basename(t) _t_found = False for p in templates.values(): if _t == os.path.basename(p): t = p - _t_found = True # NOTE: in principle this could match multiple - break # times if more than one template dir existed. - if not _t_found: # 3) try it as a key into templates + _t_found = True # NOTE: in principle this could match multiple + break # times if more than one template dir existed. + if not _t_found: # 3) try it as a key into templates try: t = templates[t] except KeyError: pass else: _t_found = True - if not _t_found: # 4) nothing else to try... or introduce a PATH? + if not _t_found: # 4) nothing else to try... or introduce a PATH? raise ValueError("Failed to locate the template file %(t)r." % vars()) return os.path.realpath(t) @@ -408,19 +433,21 @@ def _get_template(t): # utility functions (from gromacs.utilities) # Copied so that config does not have a dependency on gromacs.utilities + def iterable(obj): """Returns ``True`` if *obj* can be iterated over and is *not* a string.""" if isinstance(obj, str): return False # avoid iterating over characters of a string - if hasattr(obj, 'next'): - return True # any iterator will do + if hasattr(obj, "next"): + return True # any iterator will do try: - len(obj) # anything else that might work + len(obj) # anything else that might work except TypeError: return False return True + def asiterable(obj): """Returns obj so that it can be iterated over; a string is *not* treated as iterable""" if not iterable(obj): @@ -429,9 +456,9 @@ def asiterable(obj): # Setting up configuration variables and paths -#--------------------------------------------- +# --------------------------------------------- -templates = _generate_template_dict('templates') +templates = _generate_template_dict("templates") """*POW* comes with a number of templates for run input files and queuing system scripts. They are provided as a convenience and examples but **WITHOUT ANY GUARANTEE FOR CORRECTNESS OR SUITABILITY FOR @@ -459,8 +486,8 @@ def asiterable(obj): #: List of all topology files that are included in the package. #: (includes force field files under ``top/oplsaa.ff``) -topfiles = _generate_template_dict('top') -topfiles.update(_generate_template_dict('top/oplsaa.ff')) # added manually! +topfiles = _generate_template_dict("top") +topfiles.update(_generate_template_dict("top/oplsaa.ff")) # added manually! # Find the top include dir by looking for an important file 'ffoplsaa.itp'. # Since Gromacs 4.5.x, force fields are ONLY found in @@ -471,21 +498,30 @@ def asiterable(obj): #: The package's include directory for :func:`gromacs.grompp`; the #: environment variable :envvar:`GMXLIB` is set to :data:`includedir` #: so that the bundled version of the force field is picked up. - includedir = os.path.dirname(topfiles['ffoplsaa.itp']) + includedir = os.path.dirname(topfiles["ffoplsaa.itp"]) except KeyError: errmsg = "Missing required data files (ffoplsaa.itp). Check your installation." logger.fatal(errmsg) raise ImportError(errmsg) -if not 'GMXLIB' in os.environ: +if not "GMXLIB" in os.environ: if not os.path.exists(includedir): - errmsg = "Likely installation problem: cannot access the package GMXLIB " \ + errmsg = ( + "Likely installation problem: cannot access the package GMXLIB " "directory (try re-installing): " + ) logger.fatal(errmsg + includedir) raise OSError(errno.ENOENT, errmsg, includedir) - os.environ['GMXLIB'] = includedir + os.environ["GMXLIB"] = includedir logger.info("Using the bundled force fields from GMXLIB=%(includedir)r.", vars()) - logger.info("If required, override this behaviour by setting the environment variable GMXLIB yourself.") + logger.info( + "If required, override this behaviour by setting the environment variable GMXLIB yourself." + ) else: - logger.warning("Using user-supplied environment variable GMXLIB=%r to find force fields", os.environ['GMXLIB']) - logger.info("(You can use the MDPOW default by executing 'unset GMXLIB' in your shell before running MDPOW.)") + logger.warning( + "Using user-supplied environment variable GMXLIB=%r to find force fields", + os.environ["GMXLIB"], + ) + logger.info( + "(You can use the MDPOW default by executing 'unset GMXLIB' in your shell before running MDPOW.)" + ) diff --git a/mdpow/equil.py b/mdpow/equil.py index a14140e4..d1ac36ff 100644 --- a/mdpow/equil.py +++ b/mdpow/equil.py @@ -47,7 +47,8 @@ from .restart import Journalled import logging -logger = logging.getLogger('mdpow.equil') + +logger = logging.getLogger("mdpow.equil") # ITP <-- forcefields.get_solvent_model(id).itp # BOX <-- forcefields.get_solvent_model(id).coordinates @@ -56,7 +57,14 @@ # TODO: change to water distance 1.2 in the future (1.0 for # compatibility with our SAMPL5 runs) #: minimum distance between solute and box surface (in nm) -DIST = {'water': 1.0, 'octanol': 1.5, 'cyclohexane': 1.5, 'wetoctanol': 1.5} +DIST = { + "water": 1.0, + "octanol": 1.5, + "cyclohexane": 1.5, + "wetoctanol": 1.5, + "toluene": 1.5, +} + class Simulation(Journalled): """Simple MD simulation of a single compound molecule in water. @@ -76,33 +84,60 @@ class Simulation(Journalled): """ #: Keyword arguments to pre-set some file names; they are keys in :attr:`Simulation.files`. - filekeys = ('topology', 'processed_topology', 'structure', 'solvated', 'ndx', - 'energy_minimized', 'MD_relaxed', 'MD_restrained', 'MD_NPT') + filekeys = ( + "topology", + "processed_topology", + "structure", + "solvated", + "ndx", + "energy_minimized", + "MD_relaxed", + "MD_restrained", + "MD_NPT", + ) topdir_default = "Equilibrium" dirname_default = os.path.curdir - solvent_default = 'water' + solvent_default = "water" #: Coordinate files of the full system in increasing order of advancement of #: the protocol; the later the better. The values are keys into :attr:`Simulation.files`. - coordinate_structures = ('solvated', 'energy_minimized', 'MD_relaxed', - 'MD_restrained', 'MD_NPT') - checkpoints = ('solvated','energy_minimized','MD_relaxed','MD_restrained','MD_NPT') - + coordinate_structures = ( + "solvated", + "energy_minimized", + "MD_relaxed", + "MD_restrained", + "MD_NPT", + ) + checkpoints = ( + "solvated", + "energy_minimized", + "MD_relaxed", + "MD_restrained", + "MD_NPT", + ) #: Check list of all methods that can be run as an independent protocol; see also #: :meth:`Simulation.get_protocol` and :class:`restart.Journal` - protocols = ("MD_NPT", "MD_NPT_run", # *_run as dummies for the ... - "MD_relaxed", "MD_relaxed_run", # ...checkpointing logic - "MD_restrained", "MD_restrained_run", - "energy_minimize", "solvate", "topology") + protocols = ( + "MD_NPT", + "MD_NPT_run", # *_run as dummies for the ... + "MD_relaxed", + "MD_relaxed_run", # ...checkpointing logic + "MD_restrained", + "MD_restrained_run", + "energy_minimize", + "solvate", + "topology", + ) #: Default Gromacs *MDP* run parameter files for the different stages. #: (All are part of the package and are found with :func:`mdpow.config.get_template`.) - mdp_defaults = {'MD_relaxed': 'NPT_opls.mdp', - 'MD_restrained': 'NPT_opls.mdp', - 'MD_NPT': 'NPT_opls.mdp', - 'energy_minimize': 'em_opls.mdp', - } + mdp_defaults = { + "MD_relaxed": "NPT_opls.mdp", + "MD_restrained": "NPT_opls.mdp", + "MD_NPT": "NPT_opls.mdp", + "energy_minimize": "em_opls.mdp", + } def __init__(self, molecule=None, **kwargs): """Set up Simulation instance. @@ -123,7 +158,7 @@ def __init__(self, molecule=None, **kwargs): *forcefield* 'OPLS-AA' or 'CHARMM' or 'AMBER' *solvent* - 'water' or 'octanol' or 'cyclohexane' or 'wetoctanol' + 'water' or 'octanol' or 'cyclohexane' or 'wetoctanol' or 'toluene' *solventmodel* ``None`` chooses the default (e.g, :data:`mdpow.forcefields.DEFAULT_WATER_MODEL` for ``solvent == "water"``. Other options are the models defined in @@ -142,11 +177,11 @@ def __init__(self, molecule=None, **kwargs): """ self.__cache = {} - filename = kwargs.pop('filename', None) - dirname = kwargs.pop('dirname', self.dirname_default) + filename = kwargs.pop("filename", None) + dirname = kwargs.pop("dirname", self.dirname_default) - forcefield = kwargs.pop('forcefield', 'OPLS-AA') - solvent = kwargs.pop('solvent', self.solvent_default) + forcefield = kwargs.pop("forcefield", "OPLS-AA") + solvent = kwargs.pop("solvent", self.solvent_default) # mdp files --- should get values from default runinput.cfg # None values in the kwarg mdp dict are ignored # self.mdp: key = stage, value = path to MDP file @@ -154,25 +189,36 @@ def __init__(self, molecule=None, **kwargs): # 'water' will choose the default ('tip4p'), other choices are # 'tip3p', 'spc', 'spce', 'm24', for water; no choices # available for 'cyclohexane' and 'octanol' - solventmodel = kwargs.pop('solventmodel', None) - - mdp_kw = kwargs.pop('mdp', {}) - self.mdp = dict((stage, config.get_template(fn)) for stage,fn in self.mdp_defaults.items()) - self.mdp.update(dict((stage, config.get_template(fn)) for stage,fn in mdp_kw.items() if fn is not None)) + solventmodel = kwargs.pop("solventmodel", None) + + mdp_kw = kwargs.pop("mdp", {}) + self.mdp = dict( + (stage, config.get_template(fn)) for stage, fn in self.mdp_defaults.items() + ) + self.mdp.update( + dict( + (stage, config.get_template(fn)) + for stage, fn in mdp_kw.items() + if fn is not None + ) + ) if molecule is None and filename is not None: # load from pickle file self.load(filename) self.filename = filename - kwargs = {} # for super + kwargs = {} # for super else: - self.molecule = molecule or 'DRUG' + self.molecule = molecule or "DRUG" self.dirs = AttributeDict( - basedir=realpath(dirname), # .../Equilibrium/ - includes=list(asiterable(kwargs.pop('includes',[]))) + [config.includedir], - ) + basedir=realpath(dirname), # .../Equilibrium/ + includes=list(asiterable(kwargs.pop("includes", []))) + + [config.includedir], + ) # pre-set filenames: keyword == variable name - self.files = AttributeDict([(k, kwargs.pop(k, None)) for k in self.filekeys]) + self.files = AttributeDict( + [(k, kwargs.pop(k, None)) for k in self.filekeys] + ) self.deffnm = kwargs.pop("deffnm", "md") if self.files.topology: @@ -184,28 +230,31 @@ def __init__(self, molecule=None, **kwargs): self.forcefield = forcefield self.solvent_type = solvent self.solventmodel_identifier = forcefields.get_solvent_identifier( - solvent, - model=solventmodel, - forcefield=forcefield, - ) + solvent, + model=solventmodel, + forcefield=forcefield, + ) if self.solventmodel_identifier is None: msg = "No parameters for solvent {0} and solventmodel {1} available.".format( - solvent, solventmodel) + solvent, solventmodel + ) logger.error(msg) raise ValueError(msg) self.solventmodel = forcefields.get_solvent_model( self.solventmodel_identifier, forcefield=forcefield, - ) + ) - distance = kwargs.pop('distance', None) + distance = kwargs.pop("distance", None) distance = distance if distance is not None else DIST[solvent] - self.solvent = AttributeDict(itp=self.solventmodel.itp, - box=self.solventmodel.coordinates, - distance=distance) + self.solvent = AttributeDict( + itp=self.solventmodel.itp, + box=self.solventmodel.coordinates, + distance=distance, + ) - self.filename = filename or self.solvent_type+'.simulation' + self.filename = filename or self.solvent_type + ".simulation" super(Simulation, self).__init__(**kwargs) @@ -220,12 +269,14 @@ def save(self, filename=None): """ if filename is None: if self.filename is None: - self.filename = filename or self.solvent_type+'.simulation' - logger.warning("No filename known, saving instance under name %r", self.filename) + self.filename = filename or self.solvent_type + ".simulation" + logger.warning( + "No filename known, saving instance under name %r", self.filename + ) filename = self.filename else: self.filename = filename - with open(filename, 'wb') as f: + with open(filename, "wb") as f: pickle.dump(self, f) logger.debug("Instance pickled to %(filename)r" % vars()) @@ -233,10 +284,10 @@ def load(self, filename=None): """Re-instantiate class from pickled file.""" if filename is None: if self.filename is None: - self.filename = self.molecule.lower() + '.pickle' + self.filename = self.molecule.lower() + ".pickle" logger.warning("No filename known, trying name %r", self.filename) filename = self.filename - with open(filename, 'rb') as f: + with open(filename, "rb") as f: instance = pickle.load(f) self.__dict__.update(instance.__dict__) logger.debug("Instance loaded from %(filename)r" % vars()) @@ -248,6 +299,7 @@ def make_paths_relative(self, prefix=os.path.curdir): check :attr:`mdpow.equil.Simulation.dirs.includes` and adjust manually if necessary. """ + def assinglet(m): if len(m) == 1: return m[0] @@ -272,10 +324,13 @@ def assinglet(m): self.mdp[key] = fn.replace(basedir, prefix) except AttributeError: pass - logger.warning("make_paths_relative(): check/manually adjust %s.dirs.includes = %r !", - self.__class__.__name__, self.dirs.includes) + logger.warning( + "make_paths_relative(): check/manually adjust %s.dirs.includes = %r !", + self.__class__.__name__, + self.dirs.includes, + ) - def topology(self, itp='drug.itp', prm=None, **kwargs): + def topology(self, itp="drug.itp", prm=None, **kwargs): """Generate a topology for compound *molecule*. :Keywords: @@ -290,22 +345,22 @@ def topology(self, itp='drug.itp', prm=None, **kwargs): *kwargs* see source for *top_template*, *topol* """ - self.journal.start('topology') + self.journal.start("topology") - dirname = kwargs.pop('dirname', self.BASEDIR('top')) + dirname = kwargs.pop("dirname", self.BASEDIR("top")) self.dirs.topology = realpath(dirname) setting = forcefields.get_ff_paths(self.forcefield) template = forcefields.get_top_template(self.solvent_type) - top_template = config.get_template(kwargs.pop('top_template', template)) - topol = kwargs.pop('topol', os.path.basename(top_template)) + top_template = config.get_template(kwargs.pop("top_template", template)) + topol = kwargs.pop("topol", os.path.basename(top_template)) self.top_template = top_template itp = os.path.realpath(itp) _itp = os.path.basename(itp) if prm is None: - prm_kw = '' + prm_kw = "" else: prm = os.path.realpath(prm) _prm = os.path.basename(prm) @@ -315,45 +370,48 @@ def topology(self, itp='drug.itp', prm=None, **kwargs): shutil.copy(itp, _itp) if prm is not None: shutil.copy(prm, _prm) - gromacs.cbook.edit_txt(top_template, - [(r'#include +"oplsaa\.ff/forcefield\.itp"', - r'oplsaa\.ff/', - setting[0]), - (r'#include +"compound\.itp"', - r'compound\.itp', - _itp), - (r'#include +"oplsaa\.ff/tip4p\.itp"', - r'oplsaa\.ff/tip4p\.itp', - setting[0] + self.solvent.itp), - (r'#include +"oplsaa\.ff/ions_opls\.itp"', - r'oplsaa\.ff/ions_opls\.itp', - setting[1]), - (r'#include +"compound\.prm"', - r'#include +"compound\.prm"', - prm_kw), - (r'#include +"water\.itp"', - r'water\.itp', - setting[2]), - (r'Compound', - 'solvent', - self.solvent_type), - (r'Compound', - 'DRUG', - self.molecule), - (r'DRUG\s*1', - 'DRUG', - self.molecule), - ], - newname=topol) - logger.info('[%(dirname)s] Created topology %(topol)r that includes %(_itp)r', vars()) + gromacs.cbook.edit_txt( + top_template, + [ + ( + r'#include +"oplsaa\.ff/forcefield\.itp"', + r"oplsaa\.ff/", + setting[0], + ), + (r'#include +"compound\.itp"', r"compound\.itp", _itp), + ( + r'#include +"oplsaa\.ff/tip4p\.itp"', + r"oplsaa\.ff/tip4p\.itp", + setting[0] + self.solvent.itp, + ), + ( + r'#include +"oplsaa\.ff/ions_opls\.itp"', + r"oplsaa\.ff/ions_opls\.itp", + setting[1], + ), + ( + r'#include +"compound\.prm"', + r'#include +"compound\.prm"', + prm_kw, + ), + (r'#include +"water\.itp"', r"water\.itp", setting[2]), + (r"Compound", "solvent", self.solvent_type), + (r"Compound", "DRUG", self.molecule), + (r"DRUG\s*1", "DRUG", self.molecule), + ], + newname=topol, + ) + logger.info( + "[%(dirname)s] Created topology %(topol)r that includes %(_itp)r", vars() + ) # update known files and dirs self.files.topology = realpath(dirname, topol) if not self.dirs.topology in self.dirs.includes: self.dirs.includes.append(self.dirs.topology) - self.journal.completed('topology') - return {'dirname': dirname, 'topol': topol} + self.journal.completed("topology") + return {"dirname": dirname, "topol": topol} @staticmethod def _setup_solvate(**kwargs): @@ -385,43 +443,49 @@ def solvate(self, struct=None, **kwargs): All other arguments are passed on to :func:`gromacs.setup.solvate`, but set to sensible default values. *top* and *water* are always fixed. """ - self.journal.start('solvate') - - self.dirs.solvation = realpath(kwargs.setdefault('dirname', self.BASEDIR('solvation'))) - kwargs['struct'] = self._checknotempty(struct or self.files.structure, 'struct') - kwargs['top'] = self._checknotempty(self.files.topology, 'top') - kwargs['water'] = self.solvent.box - kwargs.setdefault('mainselection', '"%s"' % self.molecule) # quotes are needed for make_ndx - kwargs.setdefault('distance', self.solvent.distance) - - boxtype = kwargs.pop('bt', None) + self.journal.start("solvate") + + self.dirs.solvation = realpath( + kwargs.setdefault("dirname", self.BASEDIR("solvation")) + ) + kwargs["struct"] = self._checknotempty(struct or self.files.structure, "struct") + kwargs["top"] = self._checknotempty(self.files.topology, "top") + kwargs["water"] = self.solvent.box + kwargs.setdefault( + "mainselection", '"%s"' % self.molecule + ) # quotes are needed for make_ndx + kwargs.setdefault("distance", self.solvent.distance) + + boxtype = kwargs.pop("bt", None) boxtype = boxtype if boxtype is not None else "dodecahedron" if boxtype not in ("dodecahedron", "triclinic", "cubic", "octahedron"): - msg = "Invalid boxtype '{0}', not suitable for 'gmx editconf'.".format(boxtype) + msg = "Invalid boxtype '{0}', not suitable for 'gmx editconf'.".format( + boxtype + ) logger.error(msg) raise ValueError(msg) - kwargs['bt'] = boxtype + kwargs["bt"] = boxtype - kwargs['includes'] = asiterable(kwargs.pop('includes',[])) + self.dirs.includes + kwargs["includes"] = asiterable(kwargs.pop("includes", [])) + self.dirs.includes params = self._setup_solvate(**kwargs) - self.files.structure = kwargs['struct'] - self.files.solvated = params['struct'] - self.files.ndx = params['ndx'] + self.files.structure = kwargs["struct"] + self.files.solvated = params["struct"] + self.files.ndx = params["ndx"] # we can also make a processed topology right now self.processed_topology(**kwargs) - self.journal.completed('solvate') + self.journal.completed("solvate") return params def processed_topology(self, **kwargs): """Create a portable topology file from the topology and the solvated system.""" if self.files.solvated is None or not os.path.exists(self.files.solvated): self.solvate(**kwargs) - kwargs['topol'] = self.files.topology - kwargs['struct'] = self.files.solvated - kwargs['includes'] = self.dirs.includes + kwargs["topol"] = self.files.topology + kwargs["struct"] = self.files.solvated + kwargs["includes"] = self.dirs.includes self.files.processed_topology = gromacs.cbook.create_portable_topology(**kwargs) return self.files.processed_topology @@ -432,55 +496,71 @@ def energy_minimize(self, **kwargs): :meth:`~mdpow.equil.Simulation.solvate` step has been carried out previously all the defaults should just work. """ - self.journal.start('energy_minimize') + self.journal.start("energy_minimize") - self.dirs.energy_minimization = realpath(kwargs.setdefault('dirname', self.BASEDIR('em'))) - kwargs['top'] = self.files.topology - kwargs.setdefault('struct', self.files.solvated) - kwargs.setdefault('mdp', self.mdp['energy_minimize']) - kwargs['mainselection'] = None - kwargs['includes'] = asiterable(kwargs.pop('includes',[])) + self.dirs.includes + self.dirs.energy_minimization = realpath( + kwargs.setdefault("dirname", self.BASEDIR("em")) + ) + kwargs["top"] = self.files.topology + kwargs.setdefault("struct", self.files.solvated) + kwargs.setdefault("mdp", self.mdp["energy_minimize"]) + kwargs["mainselection"] = None + kwargs["includes"] = asiterable(kwargs.pop("includes", [])) + self.dirs.includes params = gromacs.setup.energy_minimize(**kwargs) - self.files.energy_minimized = params['struct'] + self.files.energy_minimized = params["struct"] - self.journal.completed('energy_minimize') + self.journal.completed("energy_minimize") return params def _MD(self, protocol, **kwargs): """Basic MD driver for this Simulation. Do not call directly.""" self.journal.start(protocol) - kwargs.setdefault('dirname', self.BASEDIR(protocol)) - kwargs.setdefault('deffnm', self.deffnm) - kwargs.setdefault('mdp', config.get_template('NPT_opls.mdp')) - self.dirs[protocol] = realpath(kwargs['dirname']) - setupMD = kwargs.pop('MDfunc', gromacs.setup.MD) - kwargs['top'] = self.files.topology - kwargs['includes'] = asiterable(kwargs.pop('includes',[])) + self.dirs.includes - kwargs['ndx'] = self.files.ndx - kwargs['mainselection'] = None # important for SD (use custom mdp and ndx!, gromacs.setup._MD) - self._checknotempty(kwargs['struct'], 'struct') - if not os.path.exists(kwargs['struct']): + kwargs.setdefault("dirname", self.BASEDIR(protocol)) + kwargs.setdefault("deffnm", self.deffnm) + kwargs.setdefault("mdp", config.get_template("NPT_opls.mdp")) + self.dirs[protocol] = realpath(kwargs["dirname"]) + setupMD = kwargs.pop("MDfunc", gromacs.setup.MD) + kwargs["top"] = self.files.topology + kwargs["includes"] = asiterable(kwargs.pop("includes", [])) + self.dirs.includes + kwargs["ndx"] = self.files.ndx + kwargs[ + "mainselection" + ] = None # important for SD (use custom mdp and ndx!, gromacs.setup._MD) + self._checknotempty(kwargs["struct"], "struct") + if not os.path.exists(kwargs["struct"]): # struct is not reliable as it depends on qscript so now we just try everything... - struct = gromacs.utilities.find_first(kwargs['struct'], suffices=['pdb', 'gro']) + struct = gromacs.utilities.find_first( + kwargs["struct"], suffices=["pdb", "gro"] + ) if struct is None: - logger.error("Starting structure %(struct)r does not exist (yet)" % kwargs) - raise IOError(errno.ENOENT, "Starting structure not found", kwargs['struct']) + logger.error( + "Starting structure %(struct)r does not exist (yet)" % kwargs + ) + raise IOError( + errno.ENOENT, "Starting structure not found", kwargs["struct"] + ) else: - logger.info("Found starting structure %r (instead of %r).", struct, kwargs['struct']) - kwargs['struct'] = struct + logger.info( + "Found starting structure %r (instead of %r).", + struct, + kwargs["struct"], + ) + kwargs["struct"] = struct # now setup the whole simulation (this is typically gromacs.setup.MD() ) - params = setupMD(**kwargs) + params = setupMD(**kwargs) # params['struct'] is md.gro but could also be md.pdb --- depends entirely on qscript - self.files[protocol] = params['struct'] + self.files[protocol] = params["struct"] # Gromacs 4.5.x 'mdrun -c PDB' fails if it cannot find 'residuetypes.dat' # so instead of fuffing with GMXLIB we just dump it into the directory try: - shutil.copy(config.topfiles['residuetypes.dat'], self.dirs[protocol]) + shutil.copy(config.topfiles["residuetypes.dat"], self.dirs[protocol]) except IOError: - logger.warning("Failed to copy 'residuetypes.dat': mdrun will likely fail to write a final structure") + logger.warning( + "Failed to copy 'residuetypes.dat': mdrun will likely fail to write a final structure" + ) self.journal.completed(protocol) return params @@ -513,11 +593,11 @@ def MD_relaxed(self, **kwargs): """ # user structure or restrained or solvated - kwargs.setdefault('struct', self.files.energy_minimized) - kwargs.setdefault('dt', 0.0001) # ps - kwargs.setdefault('runtime', 5) # ps - kwargs.setdefault('mdp', self.mdp['MD_relaxed']) - return self._MD('MD_relaxed', **kwargs) + kwargs.setdefault("struct", self.files.energy_minimized) + kwargs.setdefault("dt", 0.0001) # ps + kwargs.setdefault("runtime", 5) # ps + kwargs.setdefault("mdp", self.mdp["MD_relaxed"]) + return self._MD("MD_relaxed", **kwargs) def MD_restrained(self, **kwargs): """Short MD simulation with position restraints on compound. @@ -551,11 +631,13 @@ def MD_restrained(self, **kwargs): :class:`gromacs.manager.Manager` """ - kwargs.setdefault('struct', - self._lastnotempty([self.files.energy_minimized, self.files.MD_relaxed])) - kwargs.setdefault('mdp', self.mdp['MD_restrained']) - kwargs['MDfunc'] = gromacs.setup.MD_restrained - return self._MD('MD_restrained', **kwargs) + kwargs.setdefault( + "struct", + self._lastnotempty([self.files.energy_minimized, self.files.MD_relaxed]), + ) + kwargs.setdefault("mdp", self.mdp["MD_restrained"]) + kwargs["MDfunc"] = gromacs.setup.MD_restrained + return self._MD("MD_restrained", **kwargs) def MD_NPT(self, **kwargs): """Short NPT MD simulation. @@ -595,10 +677,12 @@ def MD_NPT(self, **kwargs): """ # user structure or relaxed or restrained or solvated - kwargs.setdefault('struct', self.get_last_structure()) - kwargs.setdefault('t',self.get_last_checkpoint()) # Pass checkpoint file from md_relaxed - kwargs.setdefault('mdp', self.mdp['MD_NPT']) - return self._MD('MD_NPT', **kwargs) + kwargs.setdefault("struct", self.get_last_structure()) + kwargs.setdefault( + "t", self.get_last_checkpoint() + ) # Pass checkpoint file from md_relaxed + kwargs.setdefault("mdp", self.mdp["MD_NPT"]) + return self._MD("MD_NPT", **kwargs) # for convenience and compatibility MD = MD_NPT @@ -617,44 +701,64 @@ def _lastnotempty(l): def get_last_structure(self): """Returns the coordinates of the most advanced step in the protocol.""" - return self._lastnotempty([self.files[name] for name in self.coordinate_structures]) + return self._lastnotempty( + [self.files[name] for name in self.coordinate_structures] + ) def get_last_checkpoint(self): """Returns the checkpoint of the most advanced step in the protocol. Relies on md.gro being present from previous simulation, assumes that checkpoint is then present. """ - return self._lastnotempty([self.files[name] for name in self.checkpoints]).replace('.gro','.cpt') + return self._lastnotempty( + [self.files[name] for name in self.checkpoints] + ).replace(".gro", ".cpt") + class WaterSimulation(Simulation): """Equilibrium MD of a solute in a box of water.""" - solvent_default = 'water' + + solvent_default = "water" dirname_default = os.path.join(Simulation.topdir_default, solvent_default) + class CyclohexaneSimulation(Simulation): """Equilibrium MD of a solute in a box of cyclohexane.""" - solvent_default = 'cyclohexane' + + solvent_default = "cyclohexane" dirname_default = os.path.join(Simulation.topdir_default, solvent_default) + class OctanolSimulation(Simulation): """Equilibrium MD of a solute in a box of octanol.""" - solvent_default = 'octanol' + + solvent_default = "octanol" dirname_default = os.path.join(Simulation.topdir_default, solvent_default) + class WetOctanolSimulation(Simulation): """Equilibrium MD of a solute in a box of wet octanol.""" - solvent_default = 'wetoctanol' + + solvent_default = "wetoctanol" dirname_default = os.path.join(Simulation.topdir_default, solvent_default) - def _setup_solvate(self, **kwargs): + def _setup_solvate(self, **kwargs): sol = gromacs.setup.solvate_sol(**kwargs) with in_dir(self.dirs.solvation, create=False): - u = mda.Universe('solvated.gro') - octanol = u.select_atoms('resname OcOH') + u = mda.Universe("solvated.gro") + octanol = u.select_atoms("resname OcOH") n = octanol.n_residues with in_dir(self.dirs.topology, create=False): - gromacs.cbook.edit_txt(self.files.topology, - [('OcOH 1', '1', n)]) + gromacs.cbook.edit_txt( + self.files.topology, [("OcOH 1", "1", n)] + ) ionkwargs = kwargs - ionkwargs['struct'] = sol['struct'] + ionkwargs["struct"] = sol["struct"] params = gromacs.setup.solvate_ion(**ionkwargs) return params + + +class TolueneSimulation(Simulation): + """Equilibrium MD of a solute in a box of toluene.""" + + solvent_default = "toluene" + dirname_default = os.path.join(Simulation.topdir_default, solvent_default) diff --git a/mdpow/fep.py b/mdpow/fep.py index b0ce0dba..4edd0362 100644 --- a/mdpow/fep.py +++ b/mdpow/fep.py @@ -87,10 +87,15 @@ :members: :inherited-members: +.. autoclass:: Gtol + :members: + :inherited-members: + .. autofunction:: pOW .. autofunction:: pCW +.. autofunction:: pTW Developer notes --------------- @@ -154,10 +159,11 @@ from alchemlyb.parsing.gmx import extract_dHdl, extract_u_nk from alchemlyb.estimators import TI, BAR, MBAR from alchemlyb.parsing.gmx import _extract_dataframe -from pymbar.timeseries import (statisticalInefficiency, - subsampleCorrelatedData, ) +from alchemlyb.preprocessing.subsampling import statistical_inefficiency + import gromacs import gromacs.utilities + try: import gromacs.setup except (ImportError, OSError): @@ -165,16 +171,19 @@ from gromacs.utilities import asiterable, AttributeDict, in_dir, openany import logging -logger = logging.getLogger('mdpow.fep') + +logger = logging.getLogger("mdpow.fep") from . import config from .restart import Journalled from . import kBOLTZ, N_AVOGADRO + def molar_to_nm3(c): """Convert a concentration in Molar to nm|^-3|.""" return c * N_AVOGADRO * 1e-24 + def bar_to_kJmolnm3(p): """Convert pressure in bar to kJ mol|^-1| nm|^-3|. @@ -182,17 +191,20 @@ def bar_to_kJmolnm3(p): """ return p * N_AVOGADRO * 1e-25 + def kcal_to_kJ(x): """Convert a energy in kcal to kJ.""" return 4.184 * x + def kJ_to_kcal(x): """Convert a energy in kJ to kcal.""" return x / 4.184 + def kBT_to_kJ(x, T): """Convert a energy in kBT to kJ/mol.""" - return x * constants.N_A*constants.k*T*1e-3 + return x * constants.N_A * constants.k * T * 1e-3 class FEPschedule(AttributeDict): @@ -227,19 +239,23 @@ class FEPschedule(AttributeDict): lambdas = 0.0, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95, 1 """ - mdp_keywords = dict((('sc_alpha', float), - ('sc_power', int), - ('sc_sigma', float), - ('couple_lambda0', str), - ('couple_lambda1', str), - )) - meta_keywords = dict((('name', str), ('description', str), ('label', str))) - other_keywords = dict((('lambdas', list), )) + + mdp_keywords = dict( + ( + ("sc_alpha", float), + ("sc_power", int), + ("sc_sigma", float), + ("couple_lambda0", str), + ("couple_lambda1", str), + ) + ) + meta_keywords = dict((("name", str), ("description", str), ("label", str))) + other_keywords = dict((("lambdas", list),)) @property def mdp_dict(self): """Dict of key-values that can be set in a mdp file.""" - return dict(((k,v) for k,v in self.items() if k in self.mdp_keywords)) + return dict(((k, v) for k, v in self.items() if k in self.mdp_keywords)) @staticmethod def load(cfg, section): @@ -249,12 +265,14 @@ def load(cfg, section): keys.update(FEPschedule.meta_keywords) keys.update(FEPschedule.other_keywords) - cfg_get = {float: cfg.getfloat, - int: cfg.getint, - str: cfg.getstr, # literal strings, no conversion of None (which we need for the MDP!) - # CHECK: THIS IS LIKELY NOT GUARANTEED ANYMORE since getstr == get - list: cfg.getarray # numpy float array from list - } + cfg_get = { + float: cfg.getfloat, + int: cfg.getint, + str: cfg.getstr, # literal strings, no conversion of None (which we need for the MDP!) + # CHECK: THIS IS LIKELY NOT GUARANTEED ANYMORE since getstr == get + list: cfg.getarray, # numpy float array from list + } + def getter(type, section, key): value = cfg_get[type](section, key) try: @@ -264,9 +282,13 @@ def getter(type, section, key): except TypeError: pass return value + # skip any None values - return FEPschedule((key, getter(keytype, section, key)) for key,keytype in keys.items() - if getter(keytype, section, key) is not None) + return FEPschedule( + (key, getter(keytype, section, key)) + for key, keytype in keys.items() + if getter(keytype, section, key) is not None + ) def __deepcopy__(self, memo): x = FEPschedule() @@ -274,6 +296,7 @@ def __deepcopy__(self, memo): x[k] = copy.deepcopy(v) return x + class Gsolv(Journalled): r"""Simulations to calculate and analyze the solvation free energy. @@ -321,36 +344,58 @@ class Gsolv(Journalled): protocols = ["setup", "fep_run"] #: Estimators in alchemlyb - estimators = {'TI': {'extract': extract_dHdl, 'estimator': TI}, - 'BAR': {'extract': extract_u_nk, 'estimator': BAR}, - 'MBAR': {'extract': extract_u_nk, 'estimator': MBAR} - } + estimators = { + "TI": {"extract": extract_dHdl, "estimator": TI}, + "BAR": {"extract": extract_u_nk, "estimator": BAR}, + "MBAR": {"extract": extract_u_nk, "estimator": MBAR}, + } # TODO: initialize from default cfg - schedules_default = {'coulomb': - FEPschedule(name='coulomb', - description="dis-charging vdw+q --> vdw", - label='Coul', - couple_lambda0='vdw-q', couple_lambda1='vdw', - sc_alpha=0, # linear scaling for coulomb - lambdas=np.array([0.0, 0.25, 0.5, 0.75, 1.0]), # default values - ), - 'vdw': - FEPschedule(name='vdw', - description="decoupling vdw --> none", - label='VDW', - couple_lambda0='vdw', couple_lambda1='none', - sc_alpha=0.5, sc_power=1, sc_sigma=0.3, # recommended values - lambdas=np.array([0.0, 0.05, 0.1, 0.2, 0.3, - 0.4, 0.5, 0.6, 0.65, 0.7, 0.75, 0.8, - 0.85, 0.9, 0.95, 1]), # defaults - ), - } + schedules_default = { + "coulomb": FEPschedule( + name="coulomb", + description="dis-charging vdw+q --> vdw", + label="Coul", + couple_lambda0="vdw-q", + couple_lambda1="vdw", + sc_alpha=0, # linear scaling for coulomb + lambdas=np.array([0.0, 0.25, 0.5, 0.75, 1.0]), # default values + ), + "vdw": FEPschedule( + name="vdw", + description="decoupling vdw --> none", + label="VDW", + couple_lambda0="vdw", + couple_lambda1="none", + sc_alpha=0.5, + sc_power=1, + sc_sigma=0.3, # recommended values + lambdas=np.array( + [ + 0.0, + 0.05, + 0.1, + 0.2, + 0.3, + 0.4, + 0.5, + 0.6, + 0.65, + 0.7, + 0.75, + 0.8, + 0.85, + 0.9, + 0.95, + 1, + ] + ), # defaults + ), + } #: Default Gromacs *MDP* run parameter file for FEP. #: (The file is part of the package and is found with :func:`mdpow.config.get_template`.) - mdp_default = 'bar_opls.mdp' - + mdp_default = "bar_opls.mdp" def __init__(self, molecule=None, top=None, struct=None, method="BAR", **kwargs): """Set up Gsolv from input files or a equilibrium simulation. @@ -431,41 +476,49 @@ def __init__(self, molecule=None, top=None, struct=None, method="BAR", **kwargs) parameters. """ - required_args = ('molecule', 'top', 'struct') + required_args = ("molecule", "top", "struct") # should this be below somewhere? if not method in ("TI", "BAR", "MBAR"): raise ValueError("method can only be TI, BAR, or MBAR") self.method = method - filename = kwargs.pop('filename', None) - basedir = kwargs.pop('basedir', os.path.curdir) # all other dirs relative to basedir - simulation = kwargs.pop('simulation', None) - solvent = kwargs.pop('solvent', self.solvent_default) - if (None in (molecule, top, struct) and simulation is None) and filename is not None: + filename = kwargs.pop("filename", None) + basedir = kwargs.pop( + "basedir", os.path.curdir + ) # all other dirs relative to basedir + simulation = kwargs.pop("simulation", None) + solvent = kwargs.pop("solvent", self.solvent_default) + if ( + None in (molecule, top, struct) and simulation is None + ) and filename is not None: # load from pickle file self.load(filename) self.filename = filename - kwargs = {} # for super + kwargs = {} # for super else: if simulation is not None: # load data from Simulation instance self.molecule = simulation.molecule - self.top = simulation.files.processed_topology or simulation.files.topology + self.top = ( + simulation.files.processed_topology or simulation.files.topology + ) self.struct = simulation.files.MD_NPT self.ndx = simulation.files.ndx if simulation.solvent_type == solvent: self.solvent_type = simulation.solvent_type else: - errmsg = "Solvent mismatch: simulation was run for %s but Gsolv is set up for %s" % \ - (simulation.solvent_type, solvent) + errmsg = ( + "Solvent mismatch: simulation was run for %s but Gsolv is set up for %s" + % (simulation.solvent_type, solvent) + ) logger.error(errmsg) raise ValueError(errmsg) else: - self.molecule = molecule # should check that this is in top (?) + self.molecule = molecule # should check that this is in top (?) self.top = top self.struct = struct - self.ndx = kwargs.pop('ndx', None) + self.ndx = kwargs.pop("ndx", None) self.solvent_type = solvent for attr in required_args: @@ -475,54 +528,71 @@ def __init__(self, molecule=None, top=None, struct=None, method="BAR", **kwargs) # fix struct (issue with qscripts being independent from rest of code) if not os.path.exists(self.struct): # struct is not reliable as it depends on qscript so now we just try everything... - struct = gromacs.utilities.find_first(self.struct, suffices=['pdb', 'gro']) + struct = gromacs.utilities.find_first( + self.struct, suffices=["pdb", "gro"] + ) if struct is None: logger.error("Starting structure %r does not exist.", self.struct) - raise IOError(errno.ENOENT, "Starting structure not found", self.struct) + raise IOError( + errno.ENOENT, "Starting structure not found", self.struct + ) else: - logger.info("Found starting structure %r (instead of %r).", struct, self.struct) + logger.info( + "Found starting structure %r (instead of %r).", + struct, + self.struct, + ) self.struct = struct - self.Temperature = kwargs.pop('temperature', 300.0) - self.qscript = kwargs.pop('qscript', ['local.sh']) - self.deffnm = kwargs.pop('deffnm', 'md') + self.Temperature = kwargs.pop("temperature", 300.0) + self.qscript = kwargs.pop("qscript", ["local.sh"]) + self.deffnm = kwargs.pop("deffnm", "md") - self.mdp = kwargs.pop('mdp', config.get_template(self.mdp_default)) + self.mdp = kwargs.pop("mdp", config.get_template(self.mdp_default)) # schedules (deepcopy because we might modify) # For some reason 2.7 tests failed with deepcopy in 2.7 so used merge_dict instead self.schedules = config.merge_dicts(self.schedules_default, {}) - schedules = kwargs.pop('schedules', {}) + schedules = kwargs.pop("schedules", {}) self.schedules.update(schedules) self.lambdas = { - 'coulomb': kwargs.pop('lambda_coulomb', self.schedules['coulomb'].lambdas), - 'vdw': kwargs.pop('lambda_vdw', self.schedules['vdw'].lambdas), - } - self.runtime = kwargs.pop('runtime', 5000.0) # ps - self.dirname = kwargs.pop('dirname', self.dirname_default) - self.includes = list(asiterable(kwargs.pop('includes',[]))) + [config.includedir] - self.component_dirs = {'coulomb': os.path.join(self.dirname, 'Coulomb'), - 'vdw': os.path.join(self.dirname, 'VDW')} + "coulomb": kwargs.pop( + "lambda_coulomb", self.schedules["coulomb"].lambdas + ), + "vdw": kwargs.pop("lambda_vdw", self.schedules["vdw"].lambdas), + } + self.runtime = kwargs.pop("runtime", 5000.0) # ps + self.dirname = kwargs.pop("dirname", self.dirname_default) + self.includes = list(asiterable(kwargs.pop("includes", []))) + [ + config.includedir + ] + self.component_dirs = { + "coulomb": os.path.join(self.dirname, "Coulomb"), + "vdw": os.path.join(self.dirname, "VDW"), + } # for analysis - self.stride = kwargs.pop('stride', 1) - self.start = kwargs.pop('start', 0) - self.stop = kwargs.pop('stop', None) - self.SI = kwargs.pop('SI', True) + self.stride = kwargs.pop("stride", 1) + self.start = kwargs.pop("start", 0) + self.stop = kwargs.pop("stop", None) + self.SI = kwargs.pop("SI", True) # other variables #: Results from the analysis - self.results = AttributeDict(xvg=AttributeDict(), - dvdl=AttributeDict(), - DeltaA=AttributeDict(), # contains QuantityWithError - ) + self.results = AttributeDict( + xvg=AttributeDict(), + dvdl=AttributeDict(), + DeltaA=AttributeDict(), # contains QuantityWithError + ) #: Generated run scripts self.scripts = AttributeDict() # sanity checks if os.path.exists(self.dirname): - wmsg = "Directory %(dirname)r already exists --- will overwrite " \ - "existing files." % vars(self) + wmsg = ( + "Directory %(dirname)r already exists --- will overwrite " + "existing files." % vars(self) + ) warnings.warn(wmsg) logger.warning(wmsg) @@ -536,16 +606,23 @@ def __init__(self, molecule=None, top=None, struct=None, method="BAR", **kwargs) self.filename = os.path.abspath(self.filename) except (AttributeError, TypeError): # default filename if none was provided - self.filename = self.frombase(self.dirname, self.__class__.__name__+os.extsep+'fep') + self.filename = self.frombase( + self.dirname, self.__class__.__name__ + os.extsep + "fep" + ) # override pickle file for this dangerous option: must be set # on a case-by-case basis - self.permissive = kwargs.pop('permissive', False) + self.permissive = kwargs.pop("permissive", False) - logger.info("Solvation free energy calculation for molecule " - "%(molecule)s in solvent %(solvent_type)s.", vars(self)) + logger.info( + "Solvation free energy calculation for molecule " + "%(molecule)s in solvent %(solvent_type)s.", + vars(self), + ) logger.info("Base directory is %(basedir)r", vars(self)) - logger.info("Using setup directories under %(dirname)r: %(component_dirs)r", vars(self)) + logger.info( + "Using setup directories under %(dirname)r: %(component_dirs)r", vars(self) + ) logger.info("Default checkpoint file is %(filename)r", vars(self)) logger.debug("Coulomb lambdas = %(coulomb)r", self.lambdas) logger.debug("VDW lambdas = %(vdw)r", self.lambdas) @@ -580,11 +657,16 @@ def label(self, component): def tasklabel(self, component, lmbda): """Batch submission script name for a single task job.""" - return self.molecule[:3]+'_'+self.schedules[component].label+"%04d" % (1000 * lmbda) + return ( + self.molecule[:3] + + "_" + + self.schedules[component].label + + "%04d" % (1000 * lmbda) + ) def arraylabel(self, component): """Batch submission script name for a job array.""" - return self.molecule[:3]+'_'+self.schedules[component].label + return self.molecule[:3] + "_" + self.schedules[component].label def fep_dirs(self): """Generator for all simulation sub directories""" @@ -620,39 +702,46 @@ def setup(self, **kwargs): .. versionchanged:: 0.6.0 Gromacs now uses option ``-dhdl`` instead of ``-dgdl``. """ - self.journal.start('setup') + self.journal.start("setup") # -dgdl for FEP output (although that seems to have been changed to -dHdl in Gromacs 4.5.3) # NOW use -dhdl - kwargs['mdrun_opts'] = " ".join([kwargs.pop('mdrun_opts',''), '-dhdl']) - kwargs['includes'] = asiterable(kwargs.pop('includes',[])) + self.includes - kwargs['deffnm'] = self.deffnm - kwargs.setdefault('maxwarn', 1) + kwargs["mdrun_opts"] = " ".join([kwargs.pop("mdrun_opts", ""), "-dhdl"]) + kwargs["includes"] = asiterable(kwargs.pop("includes", [])) + self.includes + kwargs["deffnm"] = self.deffnm + kwargs.setdefault("maxwarn", 1) qsubargs = kwargs.copy() - qsubargs['dirname'] = self.frombase(self.dirname) + qsubargs["dirname"] = self.frombase(self.dirname) # handle templates separately (necessary for array jobs) - qscripts = qsubargs.pop('sge', None) or self.qscript - qscripts.extend(qsubargs.pop('qscript',[])) # also allow canonical 'templates' + qscripts = qsubargs.pop("sge", None) or self.qscript + qscripts.extend(qsubargs.pop("qscript", [])) # also allow canonical 'templates' # make sure that the individual batch scripts are also written - kwargs.setdefault('qscript', qscripts) + kwargs.setdefault("qscript", qscripts) for component, lambdas in self.lambdas.items(): for l in lambdas: - params = self._setup(component, l, - foreign_lambdas=lambdas, **kwargs) + params = self._setup(component, l, foreign_lambdas=lambdas, **kwargs) # generate queuing system script for array job directories = [self.wdir(component, l) for l in lambdas] - qsubargs['jobname'] = self.arraylabel(component) - qsubargs['prefix'] = self.label(component)+'_' - self.scripts[component] = gromacs.qsub.generate_submit_array(qscripts, directories, **qsubargs) - logger.info("[%s] Wrote array job scripts %r", component, self.scripts[component]) - - self.journal.completed('setup') + qsubargs["jobname"] = self.arraylabel(component) + qsubargs["prefix"] = self.label(component) + "_" + self.scripts[component] = gromacs.qsub.generate_submit_array( + qscripts, directories, **qsubargs + ) + logger.info( + "[%s] Wrote array job scripts %r", component, self.scripts[component] + ) + + self.journal.completed("setup") self.save(self.filename) - logger.info("Saved state information to %r; reload later with G = %r.", self.filename, self) + logger.info( + "Saved state information to %r; reload later with G = %r.", + self.filename, + self, + ) logger.info("Finished setting up all individual simulations. Now run them...") - params.pop('struct', None) # scrub window-specific params + params.pop("struct", None) # scrub window-specific params return params def _setup(self, component, lmbda, foreign_lambdas, **kwargs): @@ -663,36 +752,41 @@ def _setup(self, component, lmbda, foreign_lambdas, **kwargs): logger.info("Preparing %(component)s for lambda=%(lmbda)g" % vars()) wdir = self.wdir(component, lmbda) - kwargs.setdefault('couple-intramol', 'no') + kwargs.setdefault("couple-intramol", "no") ### XXX Issue 20: if an entry is None then the dict will not be updated: ### I *must* keep "none" as a legal string value - kwargs.update(self.schedules[component].mdp_dict) # sets soft core & lambda0/1 state + kwargs.update( + self.schedules[component].mdp_dict + ) # sets soft core & lambda0/1 state - if kwargs.pop('edr', True): - logger.info('Setting dhdl file to edr format') - kwargs.setdefault('separate-dhdl-file', 'no') + if kwargs.pop("edr", True): + logger.info("Setting dhdl file to edr format") + kwargs.setdefault("separate-dhdl-file", "no") else: - logger.info('Setting dhdl file to xvg format') - kwargs.setdefault('separate-dhdl-file', 'yes') + logger.info("Setting dhdl file to xvg format") + kwargs.setdefault("separate-dhdl-file", "yes") foreign_lambdas = np.asarray(foreign_lambdas) lambda_index = np.where(foreign_lambdas == lmbda)[0][0] - kwargs.update(dirname=wdir, struct=self.struct, top=self.top, - mdp=self.mdp, - ndx=self.ndx, - mainselection=None, - runtime=self.runtime, - ref_t=self.Temperature, # TODO: maybe not working yet, check _setup() - gen_temp=self.Temperature, # needed until gromacs.setup() is smarter - qname=self.tasklabel(component,lmbda), - free_energy='yes', - couple_moltype=self.molecule, - init_lambda_state=lambda_index, - fep_lambdas=foreign_lambdas, - calc_lambda_neighbors=-1, - ) + kwargs.update( + dirname=wdir, + struct=self.struct, + top=self.top, + mdp=self.mdp, + ndx=self.ndx, + mainselection=None, + runtime=self.runtime, + ref_t=self.Temperature, # TODO: maybe not working yet, check _setup() + gen_temp=self.Temperature, # needed until gromacs.setup() is smarter + qname=self.tasklabel(component, lmbda), + free_energy="yes", + couple_moltype=self.molecule, + init_lambda_state=lambda_index, + fep_lambdas=foreign_lambdas, + calc_lambda_neighbors=-1, + ) return gromacs.setup.MD(**kwargs) @@ -713,9 +807,9 @@ def dgdl_xvg(self, *args): :Raises: :exc:`IOError` with error code ENOENT if no file could be found - """ - EXTENSIONS = ('', os.path.extsep+'bz2', os.path.extsep+'gz') - root = os.path.join(*args + (self.deffnm + '.xvg',)) + """ + EXTENSIONS = ("", os.path.extsep + "bz2", os.path.extsep + "gz") + root = os.path.join(*args + (self.deffnm + ".xvg",)) for ext in EXTENSIONS: fn = root + ext if os.path.exists(fn): @@ -736,8 +830,8 @@ def dgdl_edr(self, *args): :Raises: :exc:`IOError` with error code ENOENT if no file could be found - """ - pattern = os.path.join(*args + (self.deffnm + '*.edr',)) + """ + pattern = os.path.join(*args + (self.deffnm + "*.edr",)) edrs = glob(pattern) if not edrs: logger.error("Missing dgdl.edr file %(pattern)r.", vars()) @@ -757,8 +851,8 @@ def dgdl_tpr(self, *args): :Raises: :exc:`IOError` with error code ENOENT if no file could be found - """ - fn = os.path.join(*args + (self.deffnm + '.tpr',)) + """ + fn = os.path.join(*args + (self.deffnm + ".tpr",)) if not os.path.exists(fn): logger.error("Missing TPR file %(fn)r.", vars()) raise IOError(errno.ENOENT, "Missing TPR file", fn) @@ -797,7 +891,7 @@ def convert_edr(self): total_edr = edr[0] else: total_edr = self.dgdl_total_edr(dirct) - logger.info(" {0} --> {1}".format('edrs', total_edr)) + logger.info(" {0} --> {1}".format("edrs", total_edr)) gromacs.eneconv(f=edr, o=total_edr) xvgfile = os.path.join(dirct, self.deffnm + ".xvg") # hack logger.info(" {0} --> {1}".format(total_edr, xvgfile)) @@ -824,14 +918,20 @@ def collect(self, stride=None, autosave=True, autocompress=True): # must be done before adding to results.xvg or we will not find the file later self.compress_dgdl_xvg() - logger.info("[%(dirname)s] Finding dgdl xvg files, reading with " - "stride=%(stride)d permissive=%(permissive)r." % vars(self)) + logger.info( + "[%(dirname)s] Finding dgdl xvg files, reading with " + "stride=%(stride)d permissive=%(permissive)r." % vars(self) + ) for component, lambdas in self.lambdas.items(): xvg_files = [self.dgdl_xvg(self.wdir(component, l)) for l in lambdas] - self.results.xvg[component] = (np.array(lambdas), - [XVG(xvg, permissive=self.permissive, stride=self.stride) - for xvg in xvg_files]) + self.results.xvg[component] = ( + np.array(lambdas), + [ + XVG(xvg, permissive=self.permissive, stride=self.stride) + for xvg in xvg_files + ], + ) if autosave: self.save() @@ -847,23 +947,30 @@ def compress_dgdl_xvg(self): for component, lambdas in self.lambdas.items(): xvg_files = [self.dgdl_xvg(self.wdir(component, l)) for l in lambdas] for xvg in xvg_files: - root,ext = os.path.splitext(xvg) - if ext == os.path.extsep+"xvg": + root, ext = os.path.splitext(xvg) + if ext == os.path.extsep + "xvg": fnbz2 = xvg + os.path.extsep + "bz2" - logger.info("[%s] Compressing dgdl file %r with bzip2", self.dirname, xvg) + logger.info( + "[%s] Compressing dgdl file %r with bzip2", self.dirname, xvg + ) # speed is similar to 'bzip2 -9 FILE' (using a 1 Mio buffer) # (Since GW 0.8, openany() does not take kwargs anymore so the write buffer cannot be # set anymore (buffering=1048576) so the performance might be lower in MDPOW >= 0.7.0) - with open(xvg, 'rb', buffering=1048576) as source: - with openany(fnbz2, 'wb') as target: + with open(xvg, "rb", buffering=1048576) as source: + with openany(fnbz2, "wb") as target: target.writelines(source) if os.path.exists(fnbz2) and os.path.exists(xvg): os.unlink(xvg) if not os.path.exists(fnbz2): - logger.error("[%s] Failed to compress %r --- mysterious!", self.dirname, fnbz2) + logger.error( + "[%s] Failed to compress %r --- mysterious!", + self.dirname, + fnbz2, + ) else: - logger.info("[%s] Compression complete: %r", self.dirname, fnbz2) - + logger.info( + "[%s] Compression complete: %r", self.dirname, fnbz2 + ) def contains_corrupted_xvgs(self): """Check if any of the source datafiles had reported corrupted lines. @@ -875,18 +982,22 @@ def contains_corrupted_xvgs(self): :attr:`Gsolv._corrupted` as dicts of dicts with the component as primary and the lambda as secondary key. """ + def _lencorrupted(xvg): try: return len(xvg.corrupted_lineno) except AttributeError: # backwards compatible (pre gw 0.1.10 are always ok) return 0 - except TypeError: # len(None): XVG.parse() has not been run yet - return 0 # ... so we cannot conclude that it does contain bad ones + except TypeError: # len(None): XVG.parse() has not been run yet + return 0 # ... so we cannot conclude that it does contain bad ones + corrupted = {} - self._corrupted = {} # debugging ... + self._corrupted = {} # debugging ... for component, (lambdas, xvgs) in self.results.xvg.items(): corrupted[component] = np.any([(_lencorrupted(xvg) > 0) for xvg in xvgs]) - self._corrupted[component] = dict(((l, _lencorrupted(xvg)) for l,xvg in zip(lambdas, xvgs))) + self._corrupted[component] = dict( + ((l, _lencorrupted(xvg)) for l, xvg in zip(lambdas, xvgs)) + ) return np.any([x for x in corrupted.values()]) def analyze(self, force=False, stride=None, autosave=True, ncorrel=25000): @@ -972,7 +1083,7 @@ def analyze(self, force=False, stride=None, autosave=True, ncorrel=25000): .. _p526: http://books.google.co.uk/books?id=XmyO2oRUg0cC&pg=PA526 """ stride = stride or self.stride - logger.info("Analysis stride is %s.",stride) + logger.info("Analysis stride is %s.", stride) if force or not self.has_dVdl(): try: @@ -988,36 +1099,52 @@ def analyze(self, force=False, stride=None, autosave=True, ncorrel=25000): logger.info("Analyzing stored data.") # total free energy difference at const P (all simulations are done in NPT) - GibbsFreeEnergy = QuantityWithError(0,0) + GibbsFreeEnergy = QuantityWithError(0, 0) for component, (lambdas, xvgs) in self.results.xvg.items(): - logger.info("[%s %s] Computing averages and errors for %d lambda values.", - self.molecule, component, len(lambdas)) + logger.info( + "[%s %s] Computing averages and errors for %d lambda values.", + self.molecule, + component, + len(lambdas), + ) # for TI just get the average dv/dl value (in array column 1; col 0 is the time) # (This can take a while if the XVG is now reading the array from disk first time) # Use XVG class properties: first data in column 0! Y = np.array([x.mean[0] for x in xvgs]) - stdY = np.array([x.std[0] for x in xvgs]) + stdY = np.array([x.std[0] for x in xvgs]) # compute auto correlation time and error estimate for independent samples # (this can take a while). x.array[0] == time, x.array[1] == dHdl # nstep is calculated to give ncorrel samples (or all samples if less than ncorrel are # available) - tc_data = [numkit.timeseries.tcorrel(x.array[0], x.array[1], - nstep=int(np.ceil(len(x.array[0])/float(ncorrel)))) - for x in xvgs] - DY = np.array([tc['sigma'] for tc in tc_data]) - tc = np.array([tc['tc'] for tc in tc_data]) - - self.results.dvdl[component] = {'lambdas':lambdas, 'mean':Y, 'error':DY, - 'stddev':stdY, 'tcorrel':tc} + tc_data = [ + numkit.timeseries.tcorrel( + x.array[0], + x.array[1], + nstep=int(np.ceil(len(x.array[0]) / float(ncorrel))), + ) + for x in xvgs + ] + DY = np.array([tc["sigma"] for tc in tc_data]) + tc = np.array([tc["tc"] for tc in tc_data]) + + self.results.dvdl[component] = { + "lambdas": lambdas, + "mean": Y, + "error": DY, + "stddev": stdY, + "tcorrel": tc, + } # Combined Simpson rule integration: # even="last" because dV/dl is smoother at the beginning so using trapezoidal # integration there makes less of an error (one hopes...) - a = scipy.integrate.simps(Y, x=lambdas, even='last') - da = numkit.integration.simps_error(DY, x=lambdas, even='last') + a = scipy.integrate.simps(Y, x=lambdas, even="last") + da = numkit.integration.simps_error(DY, x=lambdas, even="last") self.results.DeltaA[component] = QuantityWithError(a, da) - GibbsFreeEnergy += self.results.DeltaA[component] # error propagation is automagic! + GibbsFreeEnergy += self.results.DeltaA[ + component + ] # error propagation is automagic! # hydration free energy Delta A = -(Delta A_coul + Delta A_vdw) GibbsFreeEnergy *= -1 @@ -1029,45 +1156,59 @@ def analyze(self, force=False, stride=None, autosave=True, ncorrel=25000): self.logger_DeltaA0() return self.results.DeltaA.Gibbs - def collect_alchemlyb(self, SI=True, start=0, stop=None, stride=None, autosave=True, autocompress=True): + def collect_alchemlyb( + self, SI=True, start=0, stop=None, stride=None, autosave=True, autocompress=True + ): """Collect the data files using alchemlyb. Unlike :meth:`collect`, this method can subsample with the statistical inefficiency (parameter `SI`). """ - extract = self.estimators[self.method]['extract'] + extract = self.estimators[self.method]["extract"] if autocompress: # must be done before adding to results.xvg or we will not find the file later self.compress_dgdl_xvg() - logger.info("[%(dirname)s] Finding dgdl xvg files, reading with " - "stride=%(stride)d permissive=%(permissive)r." % vars(self)) + logger.info( + "[%(dirname)s] Finding dgdl xvg files, reading with " + "stride=%(stride)d permissive=%(permissive)r." % vars(self) + ) for component, lambdas in self.lambdas.items(): val = [] for l in lambdas: xvg_file = self.dgdl_xvg(self.wdir(component, l)) xvg_df = extract(xvg_file, T=self.Temperature).iloc[start:stop:stride] + full_len = len(xvg_df) if SI: - logger.info("Performing statistical inefficiency analysis for window %s %04d" % (component, 1000 * l)) + logger.info( + "Performing statistical inefficiency analysis for window %s %04d" + % (component, 1000 * l) + ) ts = _extract_dataframe(xvg_file).iloc[start:stop:stride] - ts = pd.DataFrame({'time': ts.iloc[:,0], 'dhdl': ts.iloc[:,1]}) - ts = ts.set_index('time') - # calculate statistical inefficiency of series - statinef = statisticalInefficiency(ts, fast=False) - logger.info("The statistical inefficiency value is {:.4f}.".format(statinef)) - logger.info("The data are subsampled every {:d} frames.".format(int(np.ceil(statinef)))) - # use the subsampleCorrelatedData function to get the subsample index - indices = subsampleCorrelatedData(ts, g=statinef, - conservative=True) - xvg_df = xvg_df.iloc[indices] + ts = pd.DataFrame({"time": ts.iloc[:, 0], "dhdl": ts.iloc[:, 1]}) + ts = ts.set_index("time") + # use the statistical_inefficiency function to subsample the data + xvg_df = statistical_inefficiency(xvg_df, ts, conservative=True) + logger.info( + "The statistical inefficiency value is {:.4f}.".format( + full_len / len(xvg_df) / 2 + ) + ) + logger.info( + "The data are subsampled every {:d} frames.".format( + int(np.ceil(full_len / len(xvg_df) / 2)) + ) + ) val.append(xvg_df) self.results.xvg[component] = (np.array(lambdas), pd.concat(val)) if autosave: self.save() - def analyze_alchemlyb(self, SI=True, start=0, stop=None, stride=None, force=False, autosave=True): + def analyze_alchemlyb( + self, SI=True, start=0, stop=None, stride=None, force=False, autosave=True + ): """Compute the free energy from the simulation data with alchemlyb. Unlike :meth:`analyze`, the MBAR estimator is available (in @@ -1079,12 +1220,12 @@ def analyze_alchemlyb(self, SI=True, start=0, stop=None, stride=None, force=Fals start = start or self.start stop = stop or self.stop - logger.info("Analysis stride is %s.",stride) - logger.info("Analysis starts from frame %s.",start) + logger.info("Analysis stride is %s.", stride) + logger.info("Analysis starts from frame %s.", start) logger.info("Analysis stops at frame %s.", stop) - if self.method in ['TI', 'BAR', 'MBAR']: - estimator = self.estimators[self.method]['estimator'] + if self.method in ["TI", "BAR", "MBAR"]: + estimator = self.estimators[self.method]["estimator"] else: errmsg = "The method is not supported." logger.error(errmsg) @@ -1104,13 +1245,13 @@ def analyze_alchemlyb(self, SI=True, start=0, stop=None, stride=None, force=Fals logger.info("Analyzing stored data.") # total free energy difference at const P (all simulations are done in NPT) - GibbsFreeEnergy = QuantityWithError(0,0) + GibbsFreeEnergy = QuantityWithError(0, 0) for component, (lambdas, xvgs) in self.results.xvg.items(): result = estimator().fit(xvgs) - if self.method == 'BAR': - DeltaA = QuantityWithError(0,0) - a_s= np.diagonal(result.delta_f_, offset=1) + if self.method == "BAR": + DeltaA = QuantityWithError(0, 0) + a_s = np.diagonal(result.delta_f_, offset=1) da_s = np.diagonal(result.d_delta_f_, offset=1) for a, da in zip(a_s, da_s): DeltaA += QuantityWithError(a, da) @@ -1118,8 +1259,12 @@ def analyze_alchemlyb(self, SI=True, start=0, stop=None, stride=None, force=Fals else: a = result.delta_f_.loc[0.00, 1.00] da = result.d_delta_f_.loc[0.00, 1.00] - self.results.DeltaA[component] = kBT_to_kJ(QuantityWithError(a, da), self.Temperature) - GibbsFreeEnergy += self.results.DeltaA[component] # error propagation is automagic! + self.results.DeltaA[component] = kBT_to_kJ( + QuantityWithError(a, da), self.Temperature + ) + GibbsFreeEnergy += self.results.DeltaA[ + component + ] # error propagation is automagic! # hydration free energy Delta A = -(Delta A_coul + Delta A_vdw) GibbsFreeEnergy *= -1 @@ -1134,7 +1279,7 @@ def analyze_alchemlyb(self, SI=True, start=0, stop=None, stride=None, force=Fals if autosave: self.save() - def write_DeltaA0(self, filename, mode='w'): + def write_DeltaA0(self, filename, mode="w"): """Write free energy components to a file. :Arguments: @@ -1148,7 +1293,7 @@ def write_DeltaA0(self, filename, mode='w'): molecule solvent total coulomb vdw """ with open(filename, mode) as tab: - tab.write(self.summary() + '\n') + tab.write(self.summary() + "\n") def summary(self): """Return a string that summarizes the energetics. @@ -1163,21 +1308,29 @@ def summary(self): """ fmt = "%-10s %-14s %+8.2f %8.2f %+8.2f %8.2f %+8.2f %8.2f" d = self.results.DeltaA - return fmt % ((self.molecule, self.solvent_type) + \ - d.Gibbs.astuple() + d.coulomb.astuple() + \ - d.vdw.astuple()) + return fmt % ( + (self.molecule, self.solvent_type) + + d.Gibbs.astuple() + + d.coulomb.astuple() + + d.vdw.astuple() + ) def logger_DeltaA0(self): """Print the free energy contributions (errors in parentheses).""" - if not 'DeltaA' in self.results or len(self.results.DeltaA) == 0: + if not "DeltaA" in self.results or len(self.results.DeltaA) == 0: logger.info("No DeltaA free energies computed yet.") return logger.info("DeltaG0 = -(DeltaG_coul + DeltaG_vdw)") for component, energy in self.results.DeltaA.items(): - logger.info("[%s] %s solvation free energy (%s) %g (%.2f) kJ/mol", - self.molecule, self.solvent_type.capitalize(), component, - energy.value, energy.error) + logger.info( + "[%s] %s solvation free energy (%s) %g (%.2f) kJ/mol", + self.molecule, + self.solvent_type.capitalize(), + component, + energy.value, + energy.error, + ) def has_dVdl(self): """Check if dV/dl data have already been collected. @@ -1190,7 +1343,9 @@ def has_dVdl(self): return False except AttributeError: return False - return np.all(np.array([len(xvgs) for (lambdas,xvgs) in self.results.xvg.values()]) > 0) + return np.all( + np.array([len(xvgs) for (lambdas, xvgs) in self.results.xvg.values()]) > 0 + ) def plot(self, **kwargs): """Plot the TI data with error bars. @@ -1204,9 +1359,9 @@ def plot(self, **kwargs): import matplotlib import matplotlib.pyplot as plt - kwargs.setdefault('color', 'black') - kwargs.setdefault('capsize', 0) - kwargs.setdefault('elinewidth', 2) + kwargs.setdefault("color", "black") + kwargs.setdefault("capsize", 0) + kwargs.setdefault("elinewidth", 2) try: if self.results.DeltaA.Gibbs is None or len(self.results.dvdl) == 0: @@ -1219,20 +1374,32 @@ def plot(self, **kwargs): nplots = len(dvdl) fig, axs = plt.subplots(nrows=1, ncols=nplots) for i, component in enumerate(np.sort(dvdl.keys())): # stable plot order - x,y,dy = [dvdl[component][k] for k in ('lambdas', 'mean', 'error')] + x, y, dy = [dvdl[component][k] for k in ("lambdas", "mean", "error")] iplot = i ax = axs[i] energy = self.results.DeltaA[component] - label = r"$\Delta A^{\rm{%s}}_{\rm{%s}} = %.2f\pm%.2f$ kJ/mol" \ - % (component, self.solvent_type, energy.value, energy.error) + label = r"$\Delta A^{\rm{%s}}_{\rm{%s}} = %.2f\pm%.2f$ kJ/mol" % ( + component, + self.solvent_type, + energy.value, + energy.error, + ) ax.errorbar(x, y, yerr=dy, label=label, **kwargs) - ax.set_xlabel(r'$\lambda$') - ax.legend(loc='best') + ax.set_xlabel(r"$\lambda$") + ax.legend(loc="best") ax.set_xlim(-0.05, 1.05) - axs[0].set_ylabel(r'$dV/d\lambda$ in kJ/mol') - fig.suptitle(r"Free energy difference $\Delta A^{0}_{\rm{%s}}$ for %s: $%.2f\pm%.2f$ kJ/mol" % - ((self.solvent_type, self.molecule,) + self.results.DeltaA.Gibbs.astuple())) - fig.savefig('DeltaA.png') + axs[0].set_ylabel(r"$dV/d\lambda$ in kJ/mol") + fig.suptitle( + r"Free energy difference $\Delta A^{0}_{\rm{%s}}$ for %s: $%.2f\pm%.2f$ kJ/mol" + % ( + ( + self.solvent_type, + self.molecule, + ) + + self.results.DeltaA.Gibbs.astuple() + ) + ) + fig.savefig("DeltaA.png") plt.close() return fig @@ -1257,8 +1424,10 @@ def choose_script_from(scripts): with in_dir(self.dirname, create=False): for component, scripts in self.scripts.items(): - s = relpath(choose_script_from(scripts), self.dirname) # relative to dirname - cmd = ['qsub', s] + s = relpath( + choose_script_from(scripts), self.dirname + ) # relative to dirname + cmd = ["qsub", s] logger.debug("[%s] submitting locally: %s", " ".join(cmd), component) rc = call(cmd) if rc != 0: @@ -1266,7 +1435,9 @@ def choose_script_from(scripts): logger.error(errmsg) raise OSError(errmsg) - logger.info("[%r] Submitted jobs locally for %r", self.dirname, self.scripts.keys()) + logger.info( + "[%r] Submitted jobs locally for %r", self.dirname, self.scripts.keys() + ) def __repr__(self): return "%s(filename=%r)" % (self.__class__.__name__, self.filename) @@ -1274,6 +1445,7 @@ def __repr__(self): class Ghyd(Gsolv): """Sets up and analyses MD to obtain the hydration free energy of a solute.""" + solvent_default = "water" dirname_default = os.path.join(Gsolv.topdir_default, solvent_default) @@ -1290,28 +1462,50 @@ class Goct(Gsolv): part of the dV/dl curve is quite sensitive. By adding two additional points we hope to reduce the overall error on the dis-charging free energy. """ + solvent_default = "octanol" dirname_default = os.path.join(Gsolv.topdir_default, solvent_default) - schedules = {'coulomb': - FEPschedule(name='coulomb', - description="dis-charging vdw+q --> vdw", - label='Coul', - couple_lambda0='vdw-q', couple_lambda1='vdw', - sc_alpha=0, # linear scaling for coulomb - #lambdas=[0, 0.25, 0.5, 0.75, 1.0], # default - lambdas=[0, 0.125, 0.25, 0.375, 0.5, 0.75, 1.0], # +0.125, 0.375 enhanced - ), - 'vdw': - FEPschedule(name='vdw', - description="decoupling vdw --> none", - label='VDW', - couple_lambda0='vdw', couple_lambda1='none', - sc_alpha=0.5, sc_power=1, sc_sigma=0.3, # recommended values - lambdas=[0.0, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, # defaults - 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95, 1], - ), - } + schedules = { + "coulomb": FEPschedule( + name="coulomb", + description="dis-charging vdw+q --> vdw", + label="Coul", + couple_lambda0="vdw-q", + couple_lambda1="vdw", + sc_alpha=0, # linear scaling for coulomb + # lambdas=[0, 0.25, 0.5, 0.75, 1.0], # default + lambdas=[0, 0.125, 0.25, 0.375, 0.5, 0.75, 1.0], # +0.125, 0.375 enhanced + ), + "vdw": FEPschedule( + name="vdw", + description="decoupling vdw --> none", + label="VDW", + couple_lambda0="vdw", + couple_lambda1="none", + sc_alpha=0.5, + sc_power=1, + sc_sigma=0.3, # recommended values + lambdas=[ + 0.0, + 0.05, + 0.1, + 0.2, + 0.3, + 0.4, + 0.5, + 0.6, # defaults + 0.65, + 0.7, + 0.75, + 0.8, + 0.85, + 0.9, + 0.95, + 1, + ], + ), + } class Gwoct(Goct): @@ -1321,10 +1515,18 @@ class Gwoct(Goct): part of the dV/dl curve is quite sensitive. By adding two additional points we hope to reduce the overall error on the dis-charging free energy. """ + solvent_default = "wetoctanol" dirname_default = os.path.join(Gsolv.topdir_default, solvent_default) +class Gtol(Gsolv): + """Sets up and analyses MD to obtain the solvation free energy of a solute in toluene.""" + + solvent_default = "toluene" + dirname_default = os.path.join(Gsolv.topdir_default, solvent_default) + + def p_transfer(G1, G2, **kwargs): """Compute partition coefficient from two :class:`Gsolv` objects. @@ -1379,10 +1581,13 @@ def p_transfer(G1, G2, **kwargs): """ - kwargs.setdefault('force', False) - estimator = kwargs.pop('estimator', 'alchemlyb') - if not estimator in ('mdpow', 'alchemlyb'): - errmsg = "estimator = %r is not supported, must be 'mdpow' or 'alchemlyb'" % estimator + kwargs.setdefault("force", False) + estimator = kwargs.pop("estimator", "alchemlyb") + if not estimator in ("mdpow", "alchemlyb"): + errmsg = ( + "estimator = %r is not supported, must be 'mdpow' or 'alchemlyb'" + % estimator + ) logger.error(errmsg) raise ValueError(errmsg) @@ -1391,40 +1596,49 @@ def p_transfer(G1, G2, **kwargs): if G1.Temperature != G2.Temperature: raise ValueError("The two simulations were done at different temperatures.") - logger.info("[%s] transfer free energy %s --> %s calculation", - G1.molecule, G1.solvent_type, G2.solvent_type) + logger.info( + "[%s] transfer free energy %s --> %s calculation", + G1.molecule, + G1.solvent_type, + G2.solvent_type, + ) for G in (G1, G2): G_kwargs = kwargs.copy() # for fep files generated with old code which doesn't have these attributes - if not hasattr(G, 'start'): - G.start = G_kwargs.pop('start', 0) - if not hasattr(G, 'stop'): - G.stop = G_kwargs.pop('stop', None) - if not hasattr(G, 'SI'): - G_kwargs.setdefault('SI', True) + if not hasattr(G, "start"): + G.start = G_kwargs.pop("start", 0) + if not hasattr(G, "stop"): + G.stop = G_kwargs.pop("stop", None) + if not hasattr(G, "SI"): + G_kwargs.setdefault("SI", True) else: - G_kwargs.setdefault('SI', G.SI) + G_kwargs.setdefault("SI", G.SI) # for this version. use the method given instead of the one in the input cfg file - G.method = G_kwargs.pop('method', 'MBAR') - if estimator == 'mdpow': + G.method = G_kwargs.pop("method", "MBAR") + if estimator == "mdpow": if G.method != "TI": - errmsg = "Method %s is not implemented in MDPOW, use estimator='alchemlyb'" % G.method + errmsg = ( + "Method %s is not implemented in MDPOW, use estimator='alchemlyb'" + % G.method + ) logger.error(errmsg) raise ValueError(errmsg) logger.info("The solvent is %s .", G.solvent_type) - if kwargs['force'] or 'Gibbs' not in G.results.DeltaA: + if kwargs["force"] or "Gibbs" not in G.results.DeltaA: # write out the settings when the analysis is performed logger.info("Estimator is %s.", estimator) logger.info("Free energy calculation method is %s.", G.method) - if estimator == 'mdpow': - G_kwargs.pop('SI', None) # G.analyze() does not know SI + if estimator == "mdpow": + G_kwargs.pop("SI", None) # G.analyze() does not know SI G.analyze(**G_kwargs) - elif estimator == 'alchemlyb': - logger.info("Statistical inefficiency analysis will %s be performed." % - ("" if G_kwargs['SI'] else "not")) + elif estimator == "alchemlyb": + logger.info( + "Statistical inefficiency analysis will %s be performed." + % ("" if G_kwargs["SI"] else "not") + ) G.analyze_alchemlyb(**G_kwargs) else: logger.info("Using already calculated free energy DeltaA") @@ -1440,19 +1654,27 @@ def p_transfer(G1, G2, **kwargs): # lower case initials, in reverse order of transfer, e.g. # water -> octanol: P_ow # water -> cyclohexane: P_cw + # water -> toluene: P_tw coefficient = "P_{0}{1}".format( - G2.solvent_type.lower()[0], G1.solvent_type.lower()[0]) + G2.solvent_type.lower()[0], G1.solvent_type.lower()[0] + ) logger.info("[%s] Values at T = %g K", molecule, G1.Temperature) - logger.info("[%s] Free energy of transfer %s --> %s: %.3f (%.3f) kJ/mol", - molecule, - G1.solvent_type, G2.solvent_type, - transferFE.value, transferFE.error) - logger.info("[%s] log %s: %.3f (%.3f)", - molecule, coefficient, logPow.value, logPow.error) + logger.info( + "[%s] Free energy of transfer %s --> %s: %.3f (%.3f) kJ/mol", + molecule, + G1.solvent_type, + G2.solvent_type, + transferFE.value, + transferFE.error, + ) + logger.info( + "[%s] log %s: %.3f (%.3f)", molecule, coefficient, logPow.value, logPow.error + ) return transferFE, logPow + def pOW(G1, G2, **kwargs): """Compute water-octanol partition coefficient from two :class:`Gsolv` objects. @@ -1493,11 +1715,13 @@ def pOW(G1, G2, **kwargs): args = (G2, G1) else: msg = "For pOW need water and octanol simulations but instead got {0} and {1}".format( - G1.solvent_type, G2.solvent_type) + G1.solvent_type, G2.solvent_type + ) logger.error(msg) raise ValueError(msg) return p_transfer(*args, **kwargs) + def pCW(G1, G2, **kwargs): """Compute water-cyclohexane partition coefficient from two :class:`Gsolv` objects. @@ -1538,7 +1762,55 @@ def pCW(G1, G2, **kwargs): args = (G2, G1) else: msg = "For pCW need water and cyclohexane simulations but instead got {0} and {1}".format( - G1.solvent_type, G2.solvent_type) + G1.solvent_type, G2.solvent_type + ) + logger.error(msg) + raise ValueError(msg) + return p_transfer(*args, **kwargs) + + +def pTW(G1, G2, **kwargs): + """Compute water-toluene partition coefficient from two :class:`Gsolv` objects. + + transfer free energy from water into toluene:: + + DeltaDeltaG0 = DeltaG0_tol - DeltaG0_water + + toluene/water partition coefficient:: + + log P_tol/wat = log [X]_tol/[X]_wat + + :Arguments: + *G1*, *G2* + *G1* and *G2* should be a :class:`Ghyd` and a :class:`Gtol` instance, + but order does not matter + *force* + force rereading of data files even if some data were already stored [False] + *stride* + analyze every *stride*-th datapoint in the dV/dlambda files + *start* + Start frame of data analyzed in every fep window. + *stop* + Stop frame of data analyzed in every fep window. + *SI* + Set to ``True`` if you want to perform statistical inefficiency + to preprocess the data. + *estimator* + Set to ``alchemlyb`` if you want to use alchemlyb estimators, + or ``mdpow`` if you want the default TI method. + *method* + Use `TI`, `BAR` or `MBAR` method in `alchemlyb`, or `TI` in `mdpow`. + + :Returns: (transfer free energy, log10 of the toluene/water partition coefficient = log Ptw) + """ + if G1.solvent_type == "water" and G2.solvent_type == "toluene": + args = (G1, G2) + elif G1.solvent_type == "toluene" and G2.solvent_type == "water": + args = (G2, G1) + else: + msg = "For pTW need water and toluene simulations but instead got {0} and {1}".format( + G1.solvent_type, G2.solvent_type + ) logger.error(msg) raise ValueError(msg) return p_transfer(*args, **kwargs) diff --git a/mdpow/filelock.py b/mdpow/filelock.py index f01dc0fc..948b2a96 100644 --- a/mdpow/filelock.py +++ b/mdpow/filelock.py @@ -30,18 +30,20 @@ import time import errno + class FileLockException(Exception): pass + class FileLock(object): - """ A file locking mechanism that has context-manager support so - you can use it in a with statement. This should be relatively cross - compatible as it doesn't rely on msvcrt or fcntl for the locking. + """A file locking mechanism that has context-manager support so + you can use it in a with statement. This should be relatively cross + compatible as it doesn't rely on msvcrt or fcntl for the locking. """ - def __init__(self, file_name, timeout=10, delay=.05): - """ Prepare the file locker. Specify the file to lock and optionally - the maximum timeout and the delay between each attempt to lock. + def __init__(self, file_name, timeout=10, delay=0.05): + """Prepare the file locker. Specify the file to lock and optionally + the maximum timeout and the delay between each attempt to lock. """ self.is_locked = False self.lockfile = os.path.join(os.getcwd(), "%s.lock" % file_name) @@ -49,17 +51,16 @@ def __init__(self, file_name, timeout=10, delay=.05): self.timeout = timeout self.delay = delay - def acquire(self): - """ Acquire the lock, if possible. If the lock is in use, it check again - every `wait` seconds. It does this until it either gets the lock or - exceeds `timeout` number of seconds, in which case it throws - an exception. + """Acquire the lock, if possible. If the lock is in use, it check again + every `wait` seconds. It does this until it either gets the lock or + exceeds `timeout` number of seconds, in which case it throws + an exception. """ start_time = time.time() while True: try: - self.fd = os.open(self.lockfile, os.O_CREAT|os.O_EXCL|os.O_RDWR) + self.fd = os.open(self.lockfile, os.O_CREAT | os.O_EXCL | os.O_RDWR) break except OSError as e: if e.errno != errno.EEXIST: @@ -69,37 +70,33 @@ def acquire(self): time.sleep(self.delay) self.is_locked = True - def release(self): - """ Get rid of the lock by deleting the lockfile. - When working in a `with` statement, this gets automatically - called at the end. + """Get rid of the lock by deleting the lockfile. + When working in a `with` statement, this gets automatically + called at the end. """ if self.is_locked: os.close(self.fd) os.unlink(self.lockfile) self.is_locked = False - def __enter__(self): - """ Activated when used in the with statement. - Should automatically acquire a lock to be used in the with block. + """Activated when used in the with statement. + Should automatically acquire a lock to be used in the with block. """ if not self.is_locked: self.acquire() return self - def __exit__(self, type, value, traceback): - """ Activated at the end of the with statement. - It automatically releases the lock if it isn't locked. + """Activated at the end of the with statement. + It automatically releases the lock if it isn't locked. """ if self.is_locked: self.release() - def __del__(self): - """ Make sure that the FileLock instance doesn't leave a lockfile - lying around. + """Make sure that the FileLock instance doesn't leave a lockfile + lying around. """ self.release() diff --git a/mdpow/forcefields.py b/mdpow/forcefields.py index a9aed7c4..348944da 100644 --- a/mdpow/forcefields.py +++ b/mdpow/forcefields.py @@ -58,6 +58,7 @@ from collections import defaultdict import logging + logger = logging.getLogger("mdpow.forecefields") #: Default force field. At the moment, OPLS-AA, CHARMM/CGENFF, and AMBER/GAFF @@ -65,13 +66,13 @@ #: default here as this behavior is not tested. DEFAULT_FORCEFIELD = "OPLS-AA" -#------------------------------------------------------------ +# ------------------------------------------------------------ # Gromacs water models -#------------------------------------------------------------ +# ------------------------------------------------------------ #: See the file ``top/oplsaa.ff/watermodels.dat`` for a description of #: available water models that are bundled with MDPOW. -GMX_WATERMODELS_DAT=""" +GMX_WATERMODELS_DAT = """ tip4p TIP4P TIP 4-point, recommended tip3p TIP3P TIP 3-point tip5p TIP5P TIP 5-point @@ -82,14 +83,25 @@ tip4pew TIP4PEW TIP 4-point modified for use with Ewald techniques (TIP4PEW) """ + class GromacsSolventModel(object): """Data for a solvent model in Gromacs.""" - def __init__(self, identifier, name=None, itp=None, coordinates=None, - description=None, forcefield="OPLS-AA"): + + def __init__( + self, + identifier, + name=None, + itp=None, + coordinates=None, + description=None, + forcefield="OPLS-AA", + ): self.identifier = identifier self.name = name if name is not None else str(identifier).upper() - self.itp = itp if itp is not None else self.guess_filename('itp') - self.coordinates = coordinates if coordinates is not None else self.guess_filename('gro') + self.itp = itp if itp is not None else self.guess_filename("itp") + self.coordinates = ( + coordinates if coordinates is not None else self.guess_filename("gro") + ) self.description = description self.forcefield = forcefield @@ -98,35 +110,44 @@ def guess_filename(self, extension): return self.identifier.lower() + os.extsep + str(extension) def __repr__(self): - return "<{0[name]} water: identifier={0[identifier]}, ff={0[forcefield]}>".format(vars(self)) + return ( + "<{0[name]} water: identifier={0[identifier]}, ff={0[forcefield]}>".format( + vars(self) + ) + ) + #: For some water models we cannot derive the filename for the equilibrated box #: so we supply them explicitly. SPECIAL_WATER_COORDINATE_FILES = defaultdict( lambda: None, - spc='spc216.gro', - spce='spc216.gro', - tip3p='spc216.gro', - m24='spc216.gro', - tip4pd='tip4p.gro', - tip4pew='tip4p.gro', + spc="spc216.gro", + spce="spc216.gro", + tip3p="spc216.gro", + m24="spc216.gro", + tip4pd="tip4p.gro", + tip4pew="tip4p.gro", ) + def _create_water_models(watermodelsdat): models = {} - for line in GMX_WATERMODELS_DAT.split('\n'): + for line in GMX_WATERMODELS_DAT.split("\n"): line = line.strip() - if not line or line.startswith('#'): + if not line or line.startswith("#"): continue fields = line.split() identifier, name = fields[:2] description = " ".join(fields[2:]) models[identifier] = GromacsSolventModel( - identifier, name=name, + identifier, + name=name, coordinates=SPECIAL_WATER_COORDINATE_FILES[identifier], - description=description) + description=description, + ) return models + #: Use the default water model unless another water model is chosen in the #: :ref:`run input file ` file by setting the #: ``setup.watermodel`` parameter. @@ -145,6 +166,7 @@ def _create_water_models(watermodelsdat): #: For OPLS-AA the following ones are available. GROMACS_WATER_MODELS = _create_water_models(GMX_WATERMODELS_DAT) + def get_water_model(watermodel=DEFAULT_WATER_MODEL): """Return a :class:`GromacsSolventModel` corresponding to identifier *watermodel*""" @@ -152,57 +174,84 @@ def get_water_model(watermodel=DEFAULT_WATER_MODEL): return GROMACS_WATER_MODELS[watermodel] except KeyError: msg = "{0} is not a valid water model: choose one from {1}".format( - watermodel, ", ".join(GROMACS_WATER_MODELS.keys())) + watermodel, ", ".join(GROMACS_WATER_MODELS.keys()) + ) logger.error(msg) raise ValueError(msg) + #: Other solvents (not water, see :data:`GROMACS_WATER_MODELS` for those). -new_octanol = '''Zangi R (2018) Refinement of the OPLSAA force-field +new_octanol = """Zangi R (2018) Refinement of the OPLSAA force-field for liquid alcohols.; ACS Omega 3(12):18089-18099. - doi: 10.1021/acsomega.8b03132''' + doi: 10.1021/acsomega.8b03132""" OPLS_SOLVENT_MODELS = { - 'octanol': GromacsSolventModel( - identifier="octanol", itp="1oct.itp", coordinates="1oct.gro"), - 'octanolnew': GromacsSolventModel( - identifier="octanol", itp="1octnew.itp", coordinates="1oct.gro", - description=new_octanol), - 'cyclohexane': GromacsSolventModel( - identifier="cyclohexane", itp="1cyclo.itp", coordinates="1cyclo.gro"), - 'wetoctanol': GromacsSolventModel( - identifier="wetoctanol", itp="1octwet.itp", coordinates="1octwet.gro"), - 'wetoctanolnew': GromacsSolventModel( - identifier="wetoctanol", itp="1octwetnew.itp", coordinates="1octwet.gro", - description=new_octanol) - } + "octanol": GromacsSolventModel( + identifier="octanol", itp="1oct.itp", coordinates="1oct.gro" + ), + "octanolnew": GromacsSolventModel( + identifier="octanol", + itp="1octnew.itp", + coordinates="1oct.gro", + description=new_octanol, + ), + "cyclohexane": GromacsSolventModel( + identifier="cyclohexane", itp="1cyclo.itp", coordinates="1cyclo.gro" + ), + "wetoctanol": GromacsSolventModel( + identifier="wetoctanol", itp="1octwet.itp", coordinates="1octwet.gro" + ), + "wetoctanolnew": GromacsSolventModel( + identifier="wetoctanol", + itp="1octwetnew.itp", + coordinates="1octwet.gro", + description=new_octanol, + ), + "toluene": GromacsSolventModel( + identifier="toluene", itp="1tol.itp", coordinates="1tol_oplsaa.gro" + ), +} CHARMM_SOLVENT_MODELS = { - 'octanol': GromacsSolventModel( - identifier="octanol", itp="1oct.itp", coordinates="1oct_charmm.gro"), - 'wetoctanol': GromacsSolventModel( - identifier="wetoctanol", itp="1octwet.itp", coordinates="1octwet_charmm.gro"), - 'cyclohexane': GromacsSolventModel( - identifier="cyclohexane", itp="1cyclo.itp", coordinates="1cyclo_charmm.gro"), - } + "octanol": GromacsSolventModel( + identifier="octanol", itp="1oct.itp", coordinates="1oct_charmm.gro" + ), + "wetoctanol": GromacsSolventModel( + identifier="wetoctanol", itp="1octwet.itp", coordinates="1octwet_charmm.gro" + ), + "cyclohexane": GromacsSolventModel( + identifier="cyclohexane", itp="1cyclo.itp", coordinates="1cyclo_charmm.gro" + ), + "toluene": GromacsSolventModel( + identifier="toluene", itp="1tol.itp", coordinates="1tol_charmm.gro" + ), +} AMBER_SOLVENT_MODELS = { - 'octanol': GromacsSolventModel( - identifier="octanol", itp="1oct.itp", coordinates="1oct_amber.gro"), - 'wetoctanol': GromacsSolventModel( - identifier="wetoctanol", itp="1octwet.itp", coordinates="1octwet_amber.gro"), - 'cyclohexane': GromacsSolventModel( - identifier="cyclohexane", itp="1cyclo.itp", coordinates="1cyclo_amber.gro"), - } + "octanol": GromacsSolventModel( + identifier="octanol", itp="1oct.itp", coordinates="1oct_amber.gro" + ), + "wetoctanol": GromacsSolventModel( + identifier="wetoctanol", itp="1octwet.itp", coordinates="1octwet_amber.gro" + ), + "cyclohexane": GromacsSolventModel( + identifier="cyclohexane", itp="1cyclo.itp", coordinates="1cyclo_amber.gro" + ), + "toluene": GromacsSolventModel( + identifier="toluene", itp="1tol.itp", coordinates="1tol_amber.gro" + ), +} #: Solvents available in GROMACS; the keys of the dictionary #: are the forcefields. GROMACS_SOLVENT_MODELS = { - 'OPLS-AA': OPLS_SOLVENT_MODELS, - 'CHARMM': CHARMM_SOLVENT_MODELS, - 'AMBER': AMBER_SOLVENT_MODELS, - } + "OPLS-AA": OPLS_SOLVENT_MODELS, + "CHARMM": CHARMM_SOLVENT_MODELS, + "AMBER": AMBER_SOLVENT_MODELS, +} -def get_solvent_identifier(solvent_type, model=None, forcefield='OPLS-AA'): + +def get_solvent_identifier(solvent_type, model=None, forcefield="OPLS-AA"): """Get the identifier for a solvent model. The identifier is needed to access a water model (i.e., a @@ -226,9 +275,9 @@ def get_solvent_identifier(solvent_type, model=None, forcefield='OPLS-AA'): :Returns: Either an identifier or ``None`` """ - + if solvent_type == "water": - identifier = model if not model in (None, 'water') else DEFAULT_WATER_MODEL + identifier = model if not model in (None, "water") else DEFAULT_WATER_MODEL return identifier if identifier in GROMACS_WATER_MODELS else None if model not in GROMACS_SOLVENT_MODELS[forcefield]: if solvent_type in GROMACS_SOLVENT_MODELS[forcefield]: @@ -238,7 +287,7 @@ def get_solvent_identifier(solvent_type, model=None, forcefield='OPLS-AA'): return model -def get_solvent_model(identifier, forcefield='OPLS-AA'): +def get_solvent_model(identifier, forcefield="OPLS-AA"): """Return a :class:`GromacsSolventModel` corresponding to identifier *identifier*. If identifier is "water" then the :data:`DEFAULT_WATER_MODEL` is assumed. @@ -257,19 +306,20 @@ def get_solvent_model(identifier, forcefield='OPLS-AA'): raise ValueError(msg) -def get_ff_paths(forcefield='OPLS-AA'): +def get_ff_paths(forcefield="OPLS-AA"): """Return a :list: containing the forcefield directory, paths of ions and default watermodel itp files. """ - + settings = { - 'OPLS-AA': ['oplsaa.ff/', 'oplsaa.ff/ions_opls.itp', - 'oplsaa.ff/tip4p.itp'], - 'AMBER': ['amber99sb.ff/', 'amber99sb.ff/ions.itp', - 'amber99sb.ff/tip3p.itp'], - 'CHARMM': ['charmm36-mar2019.ff/', 'charmm36-mar2019.ff/ions.itp', - 'charmm36-mar2019.ff/tip3p.itp'], - } + "OPLS-AA": ["oplsaa.ff/", "oplsaa.ff/ions_opls.itp", "oplsaa.ff/tip4p.itp"], + "AMBER": ["amber99sb.ff/", "amber99sb.ff/ions.itp", "amber99sb.ff/tip3p.itp"], + "CHARMM": [ + "charmm36-mar2019.ff/", + "charmm36-mar2019.ff/ions.itp", + "charmm36-mar2019.ff/tip3p.itp", + ], + } try: return settings[forcefield] except KeyError: @@ -280,9 +330,14 @@ def get_ff_paths(forcefield='OPLS-AA'): def get_top_template(identifier): """Return the topology file template suitable for the solvent model.""" - - templates = {'water': 'system.top', 'octanol': 'system.top', - 'cyclohexane': 'system.top', 'wetoctanol': 'system_octwet.top'} + + templates = { + "water": "system.top", + "octanol": "system.top", + "cyclohexane": "system.top", + "wetoctanol": "system_octwet.top", + "toluene": "system.top", + } try: return templates[identifier] except KeyError: diff --git a/mdpow/log.py b/mdpow/log.py index f2727293..fbe0b31d 100644 --- a/mdpow/log.py +++ b/mdpow/log.py @@ -26,6 +26,7 @@ import logging + def create(logname, logfile): """Create a top level logger. @@ -38,7 +39,9 @@ def create(logname, logfile): logger.setLevel(logging.DEBUG) logfile = logging.FileHandler(logfile) - logfile_formatter = logging.Formatter('%(asctime)s %(name)-12s %(levelname)-8s %(message)s') + logfile_formatter = logging.Formatter( + "%(asctime)s %(name)-12s %(levelname)-8s %(message)s" + ) logfile.setFormatter(logfile_formatter) logger.addHandler(logfile) @@ -46,12 +49,13 @@ def create(logname, logfile): console = logging.StreamHandler() console.setLevel(logging.INFO) # set a format which is simpler for console use - formatter = logging.Formatter('%(name)-12s: %(levelname)-8s %(message)s') + formatter = logging.Formatter("%(name)-12s: %(levelname)-8s %(message)s") console.setFormatter(formatter) logger.addHandler(console) return logger + def clear_handlers(logger): """clean out handlers in the library top level logger @@ -59,5 +63,3 @@ def clear_handlers(logger): """ for h in logger.handlers: logger.removeHandler(h) - - diff --git a/mdpow/restart.py b/mdpow/restart.py index 02d55f6e..2b2e362a 100644 --- a/mdpow/restart.py +++ b/mdpow/restart.py @@ -26,16 +26,20 @@ import os import logging -logger = logging.getLogger('mdpow.checkpoint') + +logger = logging.getLogger("mdpow.checkpoint") + def checkpoint(name, sim, filename): """Execute the :meth:`Journalled.save` method and log the event.""" logger.info("checkpoint: %(name)s", vars()) sim.save(filename) + class JournalSequenceError(Exception): """Raised when a stage is started without another one having been completed.""" + class Journal(object): """Class that keeps track of the stage in a protocol. @@ -69,6 +73,7 @@ class Journal(object): # raises JournalSequenceError """ + def __init__(self, stages): """Initialise the journal that keeps track of stages. @@ -92,8 +97,10 @@ def current(self): @current.setter def current(self, stage): if not stage in self.stages: - raise ValueError("Can only assign a registered stage from %r, not %r" % - (self.stages, stage)) + raise ValueError( + "Can only assign a registered stage from %r, not %r" + % (self.stages, stage) + ) self.__current = stage @current.deleter @@ -108,7 +115,9 @@ def incomplete(self): @incomplete.setter def incomplete(self, stage): if not stage in self.stages: - raise ValueError("can only assign a registered stage from %(stages)r" % vars(self)) + raise ValueError( + "can only assign a registered stage from %(stages)r" % vars(self) + ) self.__incomplete = stage @incomplete.deleter @@ -126,15 +135,19 @@ def history(self): def completed(self, stage): """Record completed stage and reset :attr:`Journal.current`""" - assert stage == self.current, "Program logic error: can only complete the current stage" + assert ( + stage == self.current + ), "Program logic error: can only complete the current stage" self.__history.append(self.current) del self.current def start(self, stage): """Record that *stage* is starting.""" if self.current is not None: - errmsg = "Cannot start stage %s because previously started stage %s " \ + errmsg = ( + "Cannot start stage %s because previously started stage %s " "has not been completed." % (stage, self.current) + ) logger.error(errmsg) raise JournalSequenceError(errmsg) self.current = stage @@ -155,12 +168,14 @@ def clear(self): def __repr__(self): return "%s(%r)" % (self.__class__.__name__, self.stages) + class Journalled(object): """A base class providing methods for journalling and restarts. It installs an instance of :class:`Journal` in the attribute :attr:`Journalled.journal` if it does not exist already. """ + #: Class-attribute that contains the names of computation protocols #: supported by the class. These are either method names or dummy names, #: whose logic is provided by an external callback function. @@ -199,7 +214,9 @@ def get_protocol(self, protocol): """ if protocol not in self.protocols: - raise ValueError("%r: protocol must be one of %r" % (protocol, self.protocols)) + raise ValueError( + "%r: protocol must be one of %r" % (protocol, self.protocols) + ) try: return self.__getattribute__(protocol) except AttributeError: @@ -216,6 +233,7 @@ def dummy_protocol(*args, **kwargs): if success: self.journal.completed(protocol) return success + return dummy_protocol def save(self, filename=None): @@ -237,7 +255,7 @@ def save(self, filename=None): raise ValueError(errmsg) else: self.filename = os.path.abspath(filename) - with open(self.filename, 'wb') as f: + with open(self.filename, "wb") as f: pickle.dump(self, f) logger.debug("Instance pickled to %(filename)r" % vars(self)) @@ -264,12 +282,12 @@ def load(self, filename=None): # Do not remove this code when dropping Py 2.7 support as it is needed to # be able to read old data files with Python 3 MDPOW. - with open(filename, 'rb') as f: + with open(filename, "rb") as f: try: instance = pickle.load(f) except UnicodeDecodeError: logger.debug("Reading old Python 2 Pickle file %(filename)r" % vars()) - instance = pickle.load(f, encoding='latin1') + instance = pickle.load(f, encoding="latin1") self.__dict__.update(instance.__dict__) logger.debug("Instance loaded from %(filename)r" % vars()) diff --git a/mdpow/run.py b/mdpow/run.py index f0b4d94c..44469626 100644 --- a/mdpow/run.py +++ b/mdpow/run.py @@ -42,15 +42,16 @@ import errno import gromacs.run +import gromacs.exceptions -from .config import (get_configuration, set_gromacsoutput, - NoSectionError) +from .config import get_configuration, set_gromacsoutput, NoSectionError from . import equil from . import fep from .restart import checkpoint import logging -logger = logging.getLogger('mdpow.run') + +logger = logging.getLogger("mdpow.run") def setupMD(S, protocol, cfg): @@ -62,38 +63,49 @@ def setupMD(S, protocol, cfg): maxwarn = 0 simulation_protocol = S.get_protocol(protocol) - params = simulation_protocol(runtime=cfg.getfloat(protocol, "runtime"), - qscript=cfg.getlist(protocol, "qscript"), - maxwarn=maxwarn, - ) + params = simulation_protocol( + runtime=cfg.getfloat(protocol, "runtime"), + qscript=cfg.getlist(protocol, "qscript"), + maxwarn=maxwarn, + ) return params + def get_mdp_files(cfg, protocols): """Get file names of MDP files from *cfg* for all *protocols*""" mdpfiles = {} for protocol in protocols: try: - mdp = cfg.findfile(protocol, 'mdp') + mdp = cfg.findfile(protocol, "mdp") except NoSectionError: # skip anything for which we do not define sections, such as # the dummy run protocols mdp = None except ValueError: # But it is a problem if we can't find a file! - logger.critical("Failed to find custom MDP file %r for protocol [%s]", - cfg.get(protocol, 'mdp'), protocol) + logger.critical( + "Failed to find custom MDP file %r for protocol [%s]", + cfg.get(protocol, "mdp"), + protocol, + ) raise else: if mdp is None: # Should not happen... let's continue and wait for hard-coded defaults - logger.warning("No 'mdp' config file entry for protocol [%s]---check input files! " - "Using package defaults.", protocol) + logger.warning( + "No 'mdp' config file entry for protocol [%s]---check input files! " + "Using package defaults.", + protocol, + ) if mdp: mdpfiles[protocol] = mdp - logger.debug("%(protocol)s: Using MDP file %(mdp)r from config file", vars()) + logger.debug( + "%(protocol)s: Using MDP file %(mdp)r from config file", vars() + ) return mdpfiles -def runMD_or_exit(S, protocol, params, cfg, **kwargs): + +def runMD_or_exit(S, protocol, params, cfg, exit_on_error=True, **kwargs): """run simulation Can launch :program:`mdrun` itself (:class:`gromacs.run.MDrunner`) or exit so @@ -107,10 +119,16 @@ def runMD_or_exit(S, protocol, params, cfg, **kwargs): - The directory in which the simulation input files reside can be provided as keyword argument *dirname* or taken from `S.dirs[protocol]`. + - The `exit_on_error` kwarg determines if :func:`sys.exit` is called if + mdrun fails to complete (``True``, default) or if instead we raise an + :exc:`gromacs.exceptions.GromacsError` (``False``). + - Other *kwargs* are interpreted as options for :class:`~gromacs.tools.Mdrun`. - It never returns ``False`` but instead does a :func:`sys.exit`. + .. versionchanged:: 0.9.0 + New kwarg `exit_on_error`. + """ dirname = kwargs.pop("dirname", None) if dirname is None: @@ -122,31 +140,49 @@ def runMD_or_exit(S, protocol, params, cfg, **kwargs): raise ValueError("supply dirname as a keyword argument") simulation_done = False if cfg.getboolean(protocol, "runlocal"): - logger.info("Running %s (%s.log) ... stand by.", protocol, params['deffnm']) + logger.info("Running %s (%s.log) ... stand by.", protocol, params["deffnm"]) logger.info("Run directory: %(dirname)s", vars()) mdrun = gromacs.run.MDrunner( - dirname=dirname, deffnm=params['deffnm'], - v=cfg.getboolean('mdrun','verbose'), - stepout=cfg.getint('mdrun','stepout'), - nice=cfg.getint('mdrun','nice'), - nt=cfg.get('mdrun','maxthreads'), - cpi=True) + dirname=dirname, + deffnm=params["deffnm"], + v=cfg.getboolean("mdrun", "verbose"), + stepout=cfg.getint("mdrun", "stepout"), + nice=cfg.getint("mdrun", "nice"), + nt=cfg.get("mdrun", "maxthreads"), + cpi=True, + ) simulation_done = mdrun.run_check() if not simulation_done: # should probably stop logger.critical("Failed %(protocol)s, investigate manually.", vars()) - sys.exit(1) + if exit_on_error: + sys.exit(1) + else: + raise gromacs.exceptions.GromacsError( + f"Failed {protocol}, investigate manually." + ) else: # must check if the simulation was run externally - logfile = os.path.join(dirname, params['deffnm']+os.extsep+"log") + logfile = os.path.join(dirname, params["deffnm"] + os.extsep + "log") logger.debug("Checking logfile %r if simulation has been completed.", logfile) - simulation_done = gromacs.run.check_mdrun_success(logfile) ### broken?? + simulation_done = gromacs.run.check_mdrun_success(logfile) ### broken?? if simulation_done is None: logger.info("Now go and run %(protocol)s in directory %(dirname)r.", vars()) - sys.exit(0) + if exit_on_error: + sys.exit(0) + else: + return simulation_done elif simulation_done is False: - logger.warning("Simulation %(protocol)s in directory %(dirname)r is incomplete (log=%)logfile)s).", vars()) - sys.exit(1) + logger.warning( + "Simulation %(protocol)s in directory %(dirname)r is incomplete (log=%(logfile)s).", + vars(), + ) + if exit_on_error: + sys.exit(1) + else: + raise gromacs.exceptions.MissingDataError( + f"Simulation {protocol} in directory {dirname} is incomplete (log={logfile})." + ) logger.info("Simulation %(protocol)s seems complete (log=%(logfile)s)", vars()) return simulation_done @@ -160,18 +196,20 @@ def equilibrium_simulation(cfg, solvent, **kwargs): (``runlocal``), :program:`mdrun` is executed at various stages, and hence this process can take a while. """ - deffnm = kwargs.pop('deffnm', "md") + deffnm = kwargs.pop("deffnm", "md") Simulations = { - 'water': equil.WaterSimulation, - 'octanol': equil.OctanolSimulation, - 'wetoctanol': equil.WetOctanolSimulation, - 'cyclohexane':equil.CyclohexaneSimulation, - } + "water": equil.WaterSimulation, + "octanol": equil.OctanolSimulation, + "wetoctanol": equil.WetOctanolSimulation, + "cyclohexane": equil.CyclohexaneSimulation, + "toluene": equil.TolueneSimulation, + } try: Simulation = Simulations[solvent] except KeyError: - raise ValueError("solvent must be one of {0}".format( - ", ".join(Simulations.keys()))) + raise ValueError( + "solvent must be one of {0}".format(", ".join(Simulations.keys())) + ) # generate a canonical path under dirname topdir = kwargs.get("dirname", None) @@ -189,18 +227,17 @@ def equilibrium_simulation(cfg, solvent, **kwargs): # custom mdp files mdpfiles = get_mdp_files(cfg, Simulation.protocols) try: - distance = cfg.get('setup', 'distance') + distance = cfg.get("setup", "distance") except KeyError: - distance = None # if no distance is specified, None = default + distance = None # if no distance is specified, None = default try: - boxtype = cfg.get('setup', 'boxtype') + boxtype = cfg.get("setup", "boxtype") except KeyError: - boxtype = None # if no distance is specified, None = default - + boxtype = None # if no distance is specified, None = default solventmodel = None try: - solventmodel = cfg.get('setup', 'solventmodel') + solventmodel = cfg.get("setup", "solventmodel") logger.info("Selected solvent model: {0}".format(solventmodel)) except KeyError: solventmodel = None @@ -209,22 +246,28 @@ def equilibrium_simulation(cfg, solvent, **kwargs): # parameterization included and hence there is no mechanism to # choose between different models. - S = Simulation(molecule=cfg.get("setup", "molecule"), - forcefield=cfg.get("setup", "forcefield"), - dirname=dirname, deffnm=deffnm, mdp=mdpfiles, - distance=distance, - solventmodel=solventmodel) + S = Simulation( + molecule=cfg.get("setup", "molecule"), + forcefield=cfg.get("setup", "forcefield"), + dirname=dirname, + deffnm=deffnm, + mdp=mdpfiles, + distance=distance, + solventmodel=solventmodel, + ) if S.journal.has_not_completed("energy_minimize"): maxwarn = cfg.getint("setup", "maxwarn") or None prm = cfg.get("setup", "prm") or None - maxthreads = cfg.get('mdrun', 'maxthreads') or None + maxthreads = cfg.get("mdrun", "maxthreads") or None S.topology(itp=cfg.getpath("setup", "itp"), prm=prm) - S.solvate(struct=cfg.getpath("setup", "structure"), - bt=cfg.get("setup", "boxtype"), - maxwarn=maxwarn) - S.energy_minimize(maxwarn=maxwarn, mdrun_args={'nt': maxthreads}) - checkpoint('energy_minize', S, savefilename) + S.solvate( + struct=cfg.getpath("setup", "structure"), + bt=cfg.get("setup", "boxtype"), + maxwarn=maxwarn, + ) + S.energy_minimize(maxwarn=maxwarn, mdrun_args={"nt": maxthreads}) + checkpoint("energy_minize", S, savefilename) else: logger.info("Fast-forwarding: setup + energy_minimize done") @@ -232,12 +275,14 @@ def equilibrium_simulation(cfg, solvent, **kwargs): params = setupMD(S, "MD_relaxed", cfg) checkpoint("MD_relaxed", S, savefilename) else: - params = {'deffnm': deffnm} + params = {"deffnm": deffnm} logger.info("Fast-forwarding: MD_relaxed (setup) done") if S.journal.has_not_completed("MD_relaxed_run"): wrapper = S.get_protocol("MD_relaxed_run") - success = wrapper(runMD_or_exit, S, "MD_relaxed", params, cfg) # note: MD_relaxed! + success = wrapper( + runMD_or_exit, S, "MD_relaxed", params, cfg + ) # note: MD_relaxed! checkpoint("MD_relaxed_run", S, savefilename) else: logger.info("Fast-forwarding: MD_relaxed (run) done") @@ -250,13 +295,15 @@ def equilibrium_simulation(cfg, solvent, **kwargs): if S.journal.has_not_completed("MD_NPT_run"): wrapper = S.get_protocol("MD_NPT_run") - success = wrapper(runMD_or_exit, S, "MD_NPT", params, cfg) # note: MD_NPT + success = wrapper(runMD_or_exit, S, "MD_NPT", params, cfg) # note: MD_NPT checkpoint("MD_NPT_run", S, savefilename) else: logger.info("Fast-forwarding: MD_NPT (run) done") - logger.info("Equilibrium simulation phase complete, use %(savefilename)r to continue.", - vars()) + logger.info( + "Equilibrium simulation phase complete, use %(savefilename)r to continue.", + vars(), + ) return savefilename @@ -271,25 +318,29 @@ def fep_simulation(cfg, solvent, **kwargs): recommended to use ``runlocal = False`` in the run input file and submit all window simulations to a cluster. """ - - deffnm = kwargs.pop('deffnm', "md") + exit_on_error = kwargs.pop("exit_on_error", True) + deffnm = kwargs.pop("deffnm", "md") EquilSimulations = { - 'water': equil.WaterSimulation, - 'octanol': equil.OctanolSimulation, - 'wetoctanol': equil.WetOctanolSimulation, - 'cyclohexane':equil.CyclohexaneSimulation - } + "water": equil.WaterSimulation, + "octanol": equil.OctanolSimulation, + "wetoctanol": equil.WetOctanolSimulation, + "cyclohexane": equil.CyclohexaneSimulation, + "toluene": equil.TolueneSimulation, + } Simulations = { - 'water': fep.Ghyd, - 'octanol': fep.Goct, - 'wetoctanol': fep.Gwoct, - 'cyclohexane':fep.Gcyclo - } + "water": fep.Ghyd, + "octanol": fep.Goct, + "wetoctanol": fep.Gwoct, + "cyclohexane": fep.Gcyclo, + "toluene": fep.Gtol, + } try: EquilSimulation = EquilSimulations[solvent] Simulation = Simulations[solvent] except KeyError: - raise ValueError("solvent must be 'water', 'octanol', 'wetoctanol' or 'cyclohexane'") + raise ValueError( + "solvent must be 'water', 'octanol', 'wetoctanol', 'cyclohexane' or 'toluene'" + ) # generate a canonical path under dirname topdir = kwargs.get("dirname", None) if topdir is None: @@ -299,15 +350,19 @@ def fep_simulation(cfg, solvent, **kwargs): # Gsolv ... should be a static method or something else I can use before # the class is instantiated. Note that the pickle files live under dirname # and NOT topdir (bit of an historic inconsistency) - savefilename = os.path.join(dirname, Simulation.__name__ + os.extsep + 'fep') + savefilename = os.path.join(dirname, Simulation.__name__ + os.extsep + "fep") # need pickle files for the equilibrium simulation ... another nasty guess: equil_savefilename = os.path.join(topdir, "%(solvent)s.simulation" % vars()) try: equil_S = EquilSimulation(filename=equil_savefilename) except IOError as err: if err.errno == errno.ENOENT: - logger.critical("Missing the equilibrium simulation %(equil_savefilename)r.", vars()) - logger.critical("Run `mdpow-equilibrium -S %s %s' first!", solvent, "RUNINPUT.cfg") + logger.critical( + "Missing the equilibrium simulation %(equil_savefilename)r.", vars() + ) + logger.critical( + "Run `mdpow-equilibrium -S %s %s' first!", solvent, "RUNINPUT.cfg" + ) raise # output to screen or hidden? @@ -326,33 +381,44 @@ def fep_simulation(cfg, solvent, **kwargs): logger.debug("Using [FEP] MDP file %r from config file", mdp) # lambda schedules can be read from [FEP_schedule_*] sections - schedules = {'coulomb': fep.FEPschedule.load(cfg, "FEP_schedule_Coulomb"), - 'vdw': fep.FEPschedule.load(cfg, "FEP_schedule_VDW"), - } + schedules = { + "coulomb": fep.FEPschedule.load(cfg, "FEP_schedule_Coulomb"), + "vdw": fep.FEPschedule.load(cfg, "FEP_schedule_VDW"), + } logger.debug("Loaded FEP schedules %r from config file", schedules.keys()) # Note that we set basedir=topdir (and *not* dirname=dirname!)...FEP is a bit convoluted - S = Simulation(simulation=equil_S, runtime=cfg.getfloat("FEP", "runtime"), - basedir=topdir, deffnm=deffnm, mdp=mdp, schedules=schedules, - method=method) + S = Simulation( + simulation=equil_S, + runtime=cfg.getfloat("FEP", "runtime"), + basedir=topdir, + deffnm=deffnm, + mdp=mdp, + schedules=schedules, + method=method, + ) if S.journal.has_not_completed("setup"): - params = S.setup(qscript=cfg.getlist("FEP", "qscript"), - maxwarn=cfg.getint("FEP", "maxwarn")) + params = S.setup( + qscript=cfg.getlist("FEP", "qscript"), maxwarn=cfg.getint("FEP", "maxwarn") + ) checkpoint("setup", S, savefilename) else: - params = {'deffnm': deffnm} + params = {"deffnm": deffnm} logger.info("Fast-forwarding: FEP setup done") if S.journal.has_not_completed("fep_run"): + def run_all_FEPS(): for wdir in S.fep_dirs(): runMD_or_exit(S, "FEP", params, cfg, dirname=wdir, dgdl=True) + wrapper = S.get_protocol("fep_run") wrapper(run_all_FEPS) checkpoint("fep_run", S, savefilename) else: logger.info("Fast-forwarding: fep (run) done") - logger.info("FEP simulation phase complete, use %(savefilename)r to continue.", - vars()) + logger.info( + "FEP simulation phase complete, use %(savefilename)r to continue.", vars() + ) return savefilename diff --git a/mdpow/tests/__init__.py b/mdpow/tests/__init__.py index 732ab050..a59ab5ed 100644 --- a/mdpow/tests/__init__.py +++ b/mdpow/tests/__init__.py @@ -4,7 +4,9 @@ from pkg_resources import resource_filename -RESOURCES = py.path.local(resource_filename(__name__, 'testing_resources')) +RESOURCES = py.path.local(resource_filename(__name__, "testing_resources")) + +MANIFEST = RESOURCES / "manifest.yml" MOLECULES = { "benzene": RESOURCES.join("molecules", "benzene"), @@ -13,5 +15,6 @@ "FEP": RESOURCES.join("states", "FEP"), "base": RESOURCES.join("states", "base"), "md_npt": RESOURCES.join("states", "FEP"), + "workflows": RESOURCES.join("states", "workflows"), } CONFIGURATIONS = RESOURCES.join("test_configurations") diff --git a/mdpow/tests/tempdir.py b/mdpow/tests/tempdir.py index 3e865d58..28c5c943 100644 --- a/mdpow/tests/tempdir.py +++ b/mdpow/tests/tempdir.py @@ -28,11 +28,11 @@ class TempDir(object): - """ class for temporary directories -creates a (named) directory which is deleted after use. -All files created within the directory are destroyed -Might not work on windows when the files are still opened -""" + """class for temporary directories + creates a (named) directory which is deleted after use. + All files created within the directory are destroyed + Might not work on windows when the files are still opened""" + def __init__(self, suffix="", prefix="tmp", basedir=None): self.name = tempfile.mkdtemp(suffix=suffix, prefix=prefix, dir=basedir) @@ -57,13 +57,15 @@ def dissolve(self): def __str__(self): if self.name: - return "temporary directory at: {}".format(self.name,) + return "temporary directory at: {}".format( + self.name, + ) else: return "dissolved temporary directory" class in_tempdir(object): - """Create a temporary directory and change to it. """ + """Create a temporary directory and change to it.""" def __init__(self, *args, **kwargs): self.tmpdir = TempDir(*args, **kwargs) @@ -82,10 +84,13 @@ def run_in_tempdir(*args, **kwargs): """Make a function execute in a new tempdir. Any time the function is called, a new tempdir is created and destroyed. """ + def change_dird(fnc): @wraps(fnc) def wrapper(*funcargs, **funckwargs): with in_tempdir(*args, **kwargs): return fnc(*funcargs, **funckwargs) + return wrapper + return change_dird diff --git a/mdpow/tests/test_Gsolv.py b/mdpow/tests/test_Gsolv.py index c39b352a..6fda59ec 100644 --- a/mdpow/tests/test_Gsolv.py +++ b/mdpow/tests/test_Gsolv.py @@ -9,40 +9,41 @@ from . import RESOURCES -class Test_Gsolv_manual(object): - def setup(self): +class Test_Gsolv_manual(object): + def setup_method(self): self.tmpdir = td.TempDir() - self.m = pybol.Manifest(str(RESOURCES / 'manifest.yml')) - self.m.assemble('md_npt',self.tmpdir.name) - simulation_filename = os.path.join(self.tmpdir.name,'benzene', - 'water.simulation') - self.S = equil.Simulation(filename = simulation_filename) - - self.S.make_paths_relative(prefix=os.path.join( - self.tmpdir.name,'benzene', 'Equilibrium', 'water')) - self.S.dirs.includes = os.path.join(self.tmpdir.name, 'top') + self.m = pybol.Manifest(str(RESOURCES / "manifest.yml")) + self.m.assemble("md_npt", self.tmpdir.name) + simulation_filename = os.path.join( + self.tmpdir.name, "benzene", "water.simulation" + ) + self.S = equil.Simulation(filename=simulation_filename) + + self.S.make_paths_relative( + prefix=os.path.join(self.tmpdir.name, "benzene", "Equilibrium", "water") + ) + self.S.dirs.includes = os.path.join(self.tmpdir.name, "top") self.S.save() - def teardown(self): + def teardown_method(self): self.tmpdir.dissolve() def _setup(self, **kwargs): mdp = config.get_template("bar_opls.mdp") with in_dir(self.tmpdir.name, create=False): - self.Gsolv = fep.Gsolv(simulation=self.S, molecule='BNZ', - mdp=mdp ,**kwargs) + self.Gsolv = fep.Gsolv(simulation=self.S, molecule="BNZ", mdp=mdp, **kwargs) self.Gsolv.setup(maxwarn=1) def test_default_setup(self): self._setup() def test_list_foreign_lambdas(self): - lambda_coulomb = [0,0.5,1.0] - lambda_vdw = [0,0.2,1.0] + lambda_coulomb = [0, 0.5, 1.0] + lambda_vdw = [0, 0.2, 1.0] self._setup(lambda_coulomb=lambda_coulomb, lambda_vdw=lambda_vdw) def test_array_foreign_lambdas(self): - lambda_coulomb = np.array([0,0.5,1.0]) - lambda_vdw = np.array([0,0.2,1.0]) + lambda_coulomb = np.array([0, 0.5, 1.0]) + lambda_vdw = np.array([0, 0.2, 1.0]) self._setup(lambda_coulomb=lambda_coulomb, lambda_vdw=lambda_vdw) diff --git a/mdpow/tests/test_analysis.py b/mdpow/tests/test_analysis.py index 70464b62..d4b21e22 100644 --- a/mdpow/tests/test_analysis.py +++ b/mdpow/tests/test_analysis.py @@ -17,6 +17,7 @@ MANIFEST = RESOURCES.join("manifest.yml") + def fix_manifest(topdir): """Create a temporary manifest with a custom `path`. @@ -46,7 +47,7 @@ def fix_manifest(topdir): manifest = yaml.safe_load(MANIFEST.open()) # simple heuristic: last element of the recorded manifest::path is the name # of the states directory, typically 'states' (from .../testing_resources/states) - manifest['path'] = RESOURCES.join(os.path.basename(manifest['path'])).strpath + manifest["path"] = RESOURCES.join(os.path.basename(manifest["path"])).strpath new_manifest = topdir.join("local_manifest.yml") yaml.dump(manifest, stream=new_manifest.open("w")) return new_manifest @@ -54,13 +55,15 @@ def fix_manifest(topdir): # session scope if read-only use + @pytest.fixture(scope="function") def fep_benzene_directory(tmpdir_factory): - topdir = tmpdir_factory.mktemp('analysis') + topdir = tmpdir_factory.mktemp("analysis") m = pybol.Manifest(fix_manifest(topdir).strpath) - m.assemble('FEP', topdir.strpath) + m.assemble("FEP", topdir.strpath) return topdir.join("benzene") + class TestAnalyze(object): def get_Gsolv(self, pth): gsolv = pth.join("FEP", "water", "Gsolv.fep") @@ -74,16 +77,25 @@ def get_Gsolv(self, pth): @staticmethod def assert_DeltaA(G): + # Tests are sensitive to the versions of dependencies (probably primarily + # scipy/numpy). Only the error estimate varies (which is computed in a + # a complicated manner via numkit) but the mean is robust. + # - July 2021: with more recent versions of pandas/alchemlyb/numpy the + # original values are only reproduced to 5 decimals, see PR #166" + # - June 2023: in CI, >= 3.8 results differ from reference values (although + # locally no changes are obvious) after ~4 decimals for unknown reasons. DeltaA = G.results.DeltaA - assert_array_almost_equal(DeltaA.Gibbs.astuple(), - (-3.7217472974883794, 2.3144288928034911), - decimal=5) # with more recent versions of pandas/alchemlyb/numpy the original values are only reproduced to 5 decimals, see PR #166") - assert_array_almost_equal(DeltaA.coulomb.astuple(), - (8.3346255170099575, 0.73620918517131495), - decimal=5) # with more recent versions of pandas/alchemlyb/numpy the original values are only reproduced to 5 decimals, see PR #166") - assert_array_almost_equal(DeltaA.vdw.astuple(), - (-4.6128782195215781, 2.1942144688960972), - decimal=5) # with more recent versions of pandas/alchemlyb/numpy the original values are only reproduced to 5 decimals, see PR #166") + assert_array_almost_equal( + DeltaA.Gibbs.astuple(), (-3.7217472974883794, 2.3144288928034911), decimal=3 + ) + assert_array_almost_equal( + DeltaA.coulomb.astuple(), + (8.3346255170099575, 0.73620918517131495), + decimal=3, + ) + assert_array_almost_equal( + DeltaA.vdw.astuple(), (-4.6128782195215781, 2.1942144688960972), decimal=3 + ) def test_convert_edr(self, fep_benzene_directory): G = self.get_Gsolv(fep_benzene_directory) @@ -91,11 +103,13 @@ def test_convert_edr(self, fep_benzene_directory): with pytest.warns(numkit.LowAccuracyWarning): G.analyze(force=True, autosave=False) except IOError as err: - raise AssertionError("Failed to auto-convert edr to xvg: {0}: {1}".format( - err.strerror, err.filename)) + raise AssertionError( + "Failed to auto-convert edr to xvg: {0}: {1}".format( + err.strerror, err.filename + ) + ) self.assert_DeltaA(G) - def test_TI(self, fep_benzene_directory): G = self.get_Gsolv(fep_benzene_directory) # ensure conversion EDR to XVG.bz2; if the fixture is session scoped @@ -107,6 +121,9 @@ def test_TI(self, fep_benzene_directory): with pytest.warns(numkit.LowAccuracyWarning): G.analyze(force=True, autosave=False) except IOError as err: - raise AssertionError("Failed to convert edr to xvg: {0}: {1}".format( - err.strerror, err.filename)) + raise AssertionError( + "Failed to convert edr to xvg: {0}: {1}".format( + err.strerror, err.filename + ) + ) self.assert_DeltaA(G) diff --git a/mdpow/tests/test_analysis_alchemlyb.py b/mdpow/tests/test_analysis_alchemlyb.py index 2452b6bc..9dbb0f1f 100644 --- a/mdpow/tests/test_analysis_alchemlyb.py +++ b/mdpow/tests/test_analysis_alchemlyb.py @@ -15,6 +15,7 @@ MANIFEST = RESOURCES.join("manifest.yml") + def fix_manifest(topdir): """Create a temporary manifest with a custom `path`. @@ -44,7 +45,7 @@ def fix_manifest(topdir): manifest = yaml.safe_load(MANIFEST.open()) # simple heuristic: last element of the recorded manifest::path is the name # of the states directory, typically 'states' (from .../testing_resources/states) - manifest['path'] = RESOURCES.join(os.path.basename(manifest['path'])).strpath + manifest["path"] = RESOURCES.join(os.path.basename(manifest["path"])).strpath new_manifest = topdir.join("local_manifest.yml") yaml.dump(manifest, stream=new_manifest.open("w")) return new_manifest @@ -52,13 +53,15 @@ def fix_manifest(topdir): # session scope if read-only use + @pytest.fixture(scope="function") def fep_benzene_directory(tmpdir_factory): - topdir = tmpdir_factory.mktemp('analysis') + topdir = tmpdir_factory.mktemp("analysis") m = pybol.Manifest(fix_manifest(topdir).strpath) - m.assemble('FEP', topdir.strpath) + m.assemble("FEP", topdir.strpath) return topdir.join("benzene") + class TestAnalyze(object): def get_Gsolv(self, pth): gsolv = pth.join("FEP", "water", "Gsolv.fep") @@ -70,25 +73,26 @@ def get_Gsolv(self, pth): G.filename = gsolv.strpath return G - @pytest.mark.parametrize('method, Gibbs, coulomb, vdw', [ - ('TI', - (-3.901068, 0.550272), - (8.417035, 0.22289), - (-4.515967, 0.50311)), - ('BAR', - (-4.091241, 0.385413), - (8.339705, 0.166802), - (-4.248463, 0.347449)), - ('MBAR', - (-6.793117, 0.475149), - (8.241836, 0.219235), - (-1.448719, 0.421548)) - ]) - - @pytest.mark.xfail(pandas.__version__.startswith("1.3.0"), - reason="bug in pandas 1.3.0 see alchemistry/alchemlyb#147") - def test_estimator_alchemlyb(self, fep_benzene_directory, method, - Gibbs, coulomb, vdw): + @pytest.mark.parametrize( + "method, Gibbs, coulomb, vdw", + [ + ("TI", (-3.901068, 0.550272), (8.417035, 0.22289), (-4.515967, 0.50311)), + ("BAR", (-4.091241, 0.385413), (8.339705, 0.166802), (-4.248463, 0.347449)), + ( + "MBAR", + (-6.793117, 0.475149), + (8.241836, 0.219235), + (-1.448719, 0.421548), + ), + ], + ) + @pytest.mark.xfail( + pandas.__version__.startswith("1.3.0"), + reason="bug in pandas 1.3.0 see alchemistry/alchemlyb#147", + ) + def test_estimator_alchemlyb( + self, fep_benzene_directory, method, Gibbs, coulomb, vdw + ): G = self.get_Gsolv(fep_benzene_directory) G.method = method G.start = 0 @@ -101,42 +105,58 @@ def test_estimator_alchemlyb(self, fep_benzene_directory, method, try: G.analyze_alchemlyb(force=True, autosave=False, SI=False) except IOError as err: - raise AssertionError("Failed to convert edr to xvg: {0}: {1}".format( - err.strerror, err.filename)) + raise AssertionError( + "Failed to convert edr to xvg: {0}: {1}".format( + err.strerror, err.filename + ) + ) DeltaA = G.results.DeltaA - assert_array_almost_equal(DeltaA.Gibbs.astuple(), Gibbs, - decimal=5) # with more recent versions of pandas/alchemlyb/numpy the original values are only reproduced to 5 decimals, see PR #166") - assert_array_almost_equal(DeltaA.coulomb.astuple(), coulomb, - decimal=5) # with more recent versions of pandas/alchemlyb/numpy the original values are only reproduced to 5 decimals, see PR #166") - assert_array_almost_equal(DeltaA.vdw.astuple(), vdw, - decimal=5) # with more recent versions of pandas/alchemlyb/numpy the original values are only reproduced to 5 decimals, see PR #166") - - @pytest.mark.xfail(pandas.__version__.startswith("1.3.0"), - reason="bug in pandas 1.3.0 see alchemistry/alchemlyb#147") + assert_array_almost_equal( + DeltaA.Gibbs.astuple(), Gibbs, decimal=5 + ) # with more recent versions of pandas/alchemlyb/numpy the original values are only reproduced to 5 decimals, see PR #166") + assert_array_almost_equal( + DeltaA.coulomb.astuple(), coulomb, decimal=5 + ) # with more recent versions of pandas/alchemlyb/numpy the original values are only reproduced to 5 decimals, see PR #166") + assert_array_almost_equal( + DeltaA.vdw.astuple(), vdw, decimal=5 + ) # with more recent versions of pandas/alchemlyb/numpy the original values are only reproduced to 5 decimals, see PR #166") + + @pytest.mark.xfail( + pandas.__version__.startswith("1.3.0"), + reason="bug in pandas 1.3.0 see alchemistry/alchemlyb#147", + ) def test_SI(self, fep_benzene_directory): G = self.get_Gsolv(fep_benzene_directory) - G.method = 'TI' + G.method = "TI" G.start = 0 G.stop = None G.convert_edr() try: G.analyze_alchemlyb(force=True, SI=True, autosave=False) except IOError as err: - raise AssertionError("Failed to convert edr to xvg: {0}: {1}".format( - err.strerror, err.filename)) + raise AssertionError( + "Failed to convert edr to xvg: {0}: {1}".format( + err.strerror, err.filename + ) + ) DeltaA = G.results.DeltaA - assert_array_almost_equal(DeltaA.Gibbs.astuple(), (-2.908885, 2.175976), - decimal=5) # with more recent versions of pandas/alchemlyb/numpy the original values are only reproduced to 5 decimals, see PR #166") - assert_array_almost_equal(DeltaA.coulomb.astuple(), (7.755779, 0.531481), - decimal=5) # with more recent versions of pandas/alchemlyb/numpy the original values are only reproduced to 5 decimals, see PR #166") - assert_array_almost_equal(DeltaA.vdw.astuple(), (-4.846894, 2.110071), - decimal=5) # with more recent versions of pandas/alchemlyb/numpy the original values are only reproduced to 5 decimals, see PR #166") - - @pytest.mark.xfail(pandas.__version__.startswith("1.3.0"), - reason="bug in pandas 1.3.0 see alchemistry/alchemlyb#147") + assert_array_almost_equal( + DeltaA.Gibbs.astuple(), (-2.908885, 2.175976), decimal=5 + ) # with more recent versions of pandas/alchemlyb/numpy the original values are only reproduced to 5 decimals, see PR #166") + assert_array_almost_equal( + DeltaA.coulomb.astuple(), (7.755779, 0.531481), decimal=5 + ) # with more recent versions of pandas/alchemlyb/numpy the original values are only reproduced to 5 decimals, see PR #166") + assert_array_almost_equal( + DeltaA.vdw.astuple(), (-4.846894, 2.110071), decimal=5 + ) # with more recent versions of pandas/alchemlyb/numpy the original values are only reproduced to 5 decimals, see PR #166") + + @pytest.mark.xfail( + pandas.__version__.startswith("1.3.0"), + reason="bug in pandas 1.3.0 see alchemistry/alchemlyb#147", + ) def test_start_stop_stride(self, fep_benzene_directory): G = self.get_Gsolv(fep_benzene_directory) - G.method = 'TI' + G.method = "TI" G.start = 10 G.stride = 2 G.stop = 200 @@ -144,12 +164,18 @@ def test_start_stop_stride(self, fep_benzene_directory): try: G.analyze_alchemlyb(force=True, autosave=False, SI=False) except IOError as err: - raise AssertionError("Failed to convert edr to xvg: {0}: {1}".format( - err.strerror, err.filename)) + raise AssertionError( + "Failed to convert edr to xvg: {0}: {1}".format( + err.strerror, err.filename + ) + ) DeltaA = G.results.DeltaA - assert_array_almost_equal(DeltaA.Gibbs.astuple(), (-3.318109, 0.905128), - decimal=5) # with more recent versions of pandas/alchemlyb/numpy the original values are only reproduced to 5 decimals, see PR #166") - assert_array_almost_equal(DeltaA.coulomb.astuple(), (8.146806, 0.348866), - decimal=5) # with more recent versions of pandas/alchemlyb/numpy the original values are only reproduced to 5 decimals, see PR #166") - assert_array_almost_equal(DeltaA.vdw.astuple(), (-4.828696, 0.835195), - decimal=5) # with more recent versions of pandas/alchemlyb/numpy the original values are only reproduced to 5 decimals, see PR #166") + assert_array_almost_equal( + DeltaA.Gibbs.astuple(), (-3.318109, 0.905128), decimal=5 + ) # with more recent versions of pandas/alchemlyb/numpy the original values are only reproduced to 5 decimals, see PR #166") + assert_array_almost_equal( + DeltaA.coulomb.astuple(), (8.146806, 0.348866), decimal=5 + ) # with more recent versions of pandas/alchemlyb/numpy the original values are only reproduced to 5 decimals, see PR #166") + assert_array_almost_equal( + DeltaA.vdw.astuple(), (-4.828696, 0.835195), decimal=5 + ) # with more recent versions of pandas/alchemlyb/numpy the original values are only reproduced to 5 decimals, see PR #166") diff --git a/mdpow/tests/test_automated_dihedral_analysis.py b/mdpow/tests/test_automated_dihedral_analysis.py new file mode 100644 index 00000000..0bc1850f --- /dev/null +++ b/mdpow/tests/test_automated_dihedral_analysis.py @@ -0,0 +1,414 @@ +import os +import sys +import yaml +import py.path +import pathlib +import logging + +import scipy +from scipy.stats import circvar, circmean +import seaborn +import numpy as np +from numpy.testing import assert_almost_equal +import pandas as pd +import pybol +import pytest + +from . import RESOURCES +from pkg_resources import resource_filename +from mdpow.workflows import dihedrals + +RESOURCES = pathlib.PurePath(resource_filename(__name__, "testing_resources")) +MANIFEST = RESOURCES / "manifest.yml" + +resname = "UNK" +molname = "SM25" + + +@pytest.fixture +def molname_workflows_directory(tmp_path, molname="SM25"): + m = pybol.Manifest(str(MANIFEST)) + m.assemble("workflows", tmp_path) + return tmp_path / molname + + +class TestAutomatedDihedralAnalysis(object): + @pytest.fixture + def SM25_tmp_dir(self, molname_workflows_directory): + dirname = molname_workflows_directory + return dirname + + @pytest.fixture + def mol_sol_data(self, SM25_tmp_dir): + u = dihedrals.build_universe(dirname=SM25_tmp_dir) + mol, solute = dihedrals.rdkit_conversion(u=u, resname=resname) + return mol, solute + + @pytest.fixture + def atom_indices(self, mol_sol_data): + mol, _ = mol_sol_data + atom_group_indices = dihedrals.get_atom_indices(mol=mol) + + # testing optional user input of alternate SMARTS string + # for automated dihedral atom group selection + atom_group_indices_alt = dihedrals.get_atom_indices( + mol=mol, SMARTS="[!$(*#*)&!D1]-!@[!$(*#*)&!D1]" + ) + return atom_group_indices, atom_group_indices_alt + # fixture output, tuple: + # atom_indices[0]=atom_group_indices + # atom_indices[1]=atom_group_indices_alt + + @pytest.fixture + def bond_indices(self, mol_sol_data, atom_indices): + mol, _ = mol_sol_data + atom_index, _ = atom_indices + bond_indices = dihedrals.get_bond_indices(mol=mol, atom_indices=atom_index) + return bond_indices + + @pytest.fixture + def dihedral_groups(self, mol_sol_data, atom_indices): + _, solute = mol_sol_data + atom_index, _ = atom_indices + dihedral_groups = dihedrals.get_dihedral_groups( + solute=solute, atom_indices=atom_index + ) + return dihedral_groups + + @pytest.fixture + def dihedral_data(self, SM25_tmp_dir, atom_indices): + atom_group_indices, _ = atom_indices + df = dihedrals.dihedral_groups_ensemble( + atom_indices=atom_group_indices, dirname=SM25_tmp_dir, solvents=("water",) + ) + df_aug = dihedrals.periodic_angle_padding(df) + return df, df_aug + # fixture output, tuple: + # dihedral_data[0]=df + # dihedral_data[1]=df_aug + + # tuple-tuples of dihedral atom group indices + # collected using mdpow.workflows.dihedrals.SMARTS_DEFAULT + check_atom_group_indices = ( + (0, 1, 2, 3), + (0, 1, 12, 13), + (1, 2, 3, 11), + (1, 2, 3, 10), + (1, 2, 3, 4), + (1, 12, 13, 14), + (2, 3, 4, 5), + (2, 3, 4, 9), + (2, 1, 12, 13), + (3, 2, 1, 12), + (5, 4, 3, 11), + (5, 4, 3, 10), + (9, 4, 3, 11), + (9, 4, 3, 10), + (12, 13, 14, 15), + (12, 13, 14, 19), + ) + + # tuple-tuples of dihedral atom group indices + # collected using alternate SMARTS input (explicitly defined) + # see: fixture - atom_indices().atom_group_indices_alt + check_atom_group_indices_alt = ((1, 2), (1, 12), (2, 3), (3, 4), (12, 13), (13, 14)) + + check_atom_name_index_pairs = { + "O1-C2-N3-S4": (0, 1, 2, 3), + "O1-C2-C13-C14": (0, 1, 12, 13), + "C2-N3-S4-O12": (1, 2, 3, 11), + "C2-N3-S4-O11": (1, 2, 3, 10), + "C2-N3-S4-C5": (1, 2, 3, 4), + "C2-C13-C14-C15": (1, 12, 13, 14), + "N3-S4-C5-C6": (2, 3, 4, 5), + "N3-S4-C5-C10": (2, 3, 4, 9), + "N3-C2-C13-C14": (2, 1, 12, 13), + "S4-N3-C2-C13": (3, 2, 1, 12), + "C6-C5-S4-O12": (5, 4, 3, 11), + "C6-C5-S4-O11": (5, 4, 3, 10), + "C10-C5-S4-O12": (9, 4, 3, 11), + "C10-C5-S4-O11": (9, 4, 3, 10), + "C13-C14-C15-C16": (12, 13, 14, 15), + "C13-C14-C15-C20": (12, 13, 14, 19), + } + + check_groups = [ + np.array(["O1", "C2", "N3", "S4"], dtype=object), + np.array(["O1", "C2", "C13", "C14"], dtype=object), + np.array(["C2", "N3", "S4", "O12"], dtype=object), + np.array(["C2", "N3", "S4", "O11"], dtype=object), + np.array(["C2", "N3", "S4", "C5"], dtype=object), + np.array(["C2", "C13", "C14", "C15"], dtype=object), + np.array(["N3", "S4", "C5", "C6"], dtype=object), + np.array(["N3", "S4", "C5", "C10"], dtype=object), + np.array(["N3", "C2", "C13", "C14"], dtype=object), + np.array(["S4", "N3", "C2", "C13"], dtype=object), + np.array(["C6", "C5", "S4", "O12"], dtype=object), + np.array(["C6", "C5", "S4", "O11"], dtype=object), + np.array(["C10", "C5", "S4", "O12"], dtype=object), + np.array(["C10", "C5", "S4", "O11"], dtype=object), + np.array(["C13", "C14", "C15", "C16"], dtype=object), + np.array(["C13", "C14", "C15", "C20"], dtype=object), + ] + + universe_solute_atom_names = np.array( + [ + "O1", + "C2", + "N3", + "S4", + "C5", + "C6", + "C7", + "C8", + "C9", + "C10", + "O11", + "O12", + "C13", + "C14", + "C15", + "C16", + "C17", + "C18", + "C19", + "C20", + "H21", + "H22", + "H23", + "H24", + "H25", + "H26", + "H27", + "H28", + "H29", + "H30", + "H31", + "H32", + "H33", + "H34", + "H35", + ], + dtype=object, + ) + + check_hydrogens = np.array( + [ + "H21", + "H22", + "H23", + "H24", + "H25", + "H26", + "H27", + "H28", + "H29", + "H30", + "H31", + "H32", + "H33", + "H34", + "H35", + ], + dtype=object, + ) + + # pre 'angle padding' - scipy.stats for + # dihedral atom group: O1-C2-N3-S4 + DG_O1C2N3S4_mean = -0.5933808752787115 + DG_O1C2N3S4_var = 0.031351024457919485 + + # pre 'angle padding' - scipy.stats for + # dihedral atom group: C13-C14-C15-C20 + DG_C13141520_mean = 89.22382649857468 + DG_C13141520_var = 0.8753980937068645 + + # post 'angle padding' - scipy.stats for + # dihedral atom group: C13-C14-C15-C20 + # 'A' = 'augmented', referencing those + # results included in 'df_aug' + ADG_C13141520_mean = 91.71943996962284 + ADG_C13141520_var = 0.8773028474908289 + + def test_build_universe(self, SM25_tmp_dir): + u = dihedrals.build_universe(dirname=SM25_tmp_dir) + solute = u.select_atoms("resname UNK") + solute_names = solute.atoms.names + assert solute_names.all() == self.universe_solute_atom_names.all() + + # Use set comparison because ordering of indices appears to change + # between RDKIT versions; issue raised (#239) to identify and + # resolve exact package/version responsible + def test_dihedral_indices(self, atom_indices): + atom_group_indices = atom_indices[0] + assert set(atom_group_indices) == set(self.check_atom_group_indices) + + # Possible ordering issue (#239) + def test_SMARTS(self, atom_indices): + _, atom_group_indices_alt = atom_indices + assert atom_group_indices_alt == self.check_atom_group_indices_alt + + # Use set comparison because ordering of indices appears to change + # between RDKIT versions; issue raised (#239) to identify and + # resolve exact package/version responsible + def test_dihedral_groups(self, dihedral_groups): + groups = dihedral_groups + + values = [g.all() for g in groups] + reference = [g.all() for g in self.check_groups] + + assert set(values) == set(reference) + + # atom indices are determined by RDKit Mol object + # bond indices are determined by atom indices and are subsequently self-consistent + # dihedral group names are determined by the MDAnalysis solute object from RDKit-derived atom indices + # this test checks if indexing schemes for RDKit and MDAnalysis are consistent + def test_RDKit_MDAnalysis_atom_index_consistency( + self, atom_indices, bond_indices, dihedral_groups + ): + atom_index, _ = atom_indices + bond_index = bond_indices + groups = dihedral_groups + + name_index_pairs = dihedrals.get_paired_indices( + atom_indices=atom_index, bond_indices=bond_index, dihedral_groups=groups + ) + + atom_name_index_pairs = {} + + for key in name_index_pairs.keys(): + atom_name_index_pairs[key] = name_index_pairs[key][0] + + assert atom_name_index_pairs == self.check_atom_name_index_pairs + + # Possible ordering issue (#239) + def test_dihedral_groups_ensemble(self, dihedral_data): + df, _ = dihedral_data + + dh1_result = df.loc[df["selection"] == "O1-C2-N3-S4"]["dihedral"] + dh1_mean = circmean(dh1_result, high=180, low=-180) + dh1_var = circvar(dh1_result, high=180, low=-180) + + dh2_result = df.loc[df["selection"] == "C13-C14-C15-C20"]["dihedral"] + dh2_mean = circmean(dh2_result, high=180, low=-180) + dh2_var = circvar(dh2_result, high=180, low=-180) + + # default relative tolerance for pytest.approx is 1e-6 + dh1_mean == pytest.approx(self.DG_O1C2N3S4_mean) + dh1_var == pytest.approx(self.DG_O1C2N3S4_var) + + dh2_mean == pytest.approx(self.DG_C13141520_mean) + dh2_var == pytest.approx(self.DG_C13141520_var) + + def test_save_df(self, dihedral_data, SM25_tmp_dir): + df, _ = dihedral_data + dihedrals.save_df( + df=df, df_save_dir=SM25_tmp_dir, resname="UNK", molname="SM25" + ) + assert ( + SM25_tmp_dir / "SM25" / "SM25_full_df.csv.bz2" + ).exists(), "Compressed csv file not saved" + + def test_save_df_info(self, dihedral_data, SM25_tmp_dir, caplog): + df, _ = dihedral_data + caplog.clear() + caplog.set_level(logging.INFO, logger="mdpow.workflows.dihedrals") + dihedrals.save_df( + df=df, df_save_dir=SM25_tmp_dir, resname="UNK", molname="SM25" + ) + assert ( + f"Results DataFrame saved as {SM25_tmp_dir}/SM25/SM25_full_df.csv.bz2" + in caplog.text + ), "Save location not logged or returned" + + # Possible ordering issue (#239) + def test_periodic_angle(self, dihedral_data): + _, df_aug = dihedral_data + + aug_dh2_result = df_aug.loc[df_aug["selection"] == "C13-C14-C15-C20"][ + "dihedral" + ] + + aug_dh2_mean = circmean(aug_dh2_result, high=180, low=-180) + aug_dh2_var = circvar(aug_dh2_result, high=180, low=-180) + + aug_dh2_mean == pytest.approx(self.ADG_C13141520_mean) + aug_dh2_var == pytest.approx(self.ADG_C13141520_var) + + # Possible ordering issue (#239) + # Tests using similar instances of the automated analyses + # will use module or class-scoped fixtures, pending #235 + def test_save_fig(self, SM25_tmp_dir): + dihedrals.automated_dihedral_analysis( + dirname=SM25_tmp_dir, + figdir=SM25_tmp_dir, + resname=resname, + molname="SM25", + solvents=("water",), + ) + assert ( + SM25_tmp_dir / "SM25" / "SM25_C10-C5-S4-O11_violins.pdf" + ).exists(), "PDF file not generated" + + # Possible ordering issue (#239) + # Tests using similar instances of the automated analyses + # will use module or class-scoped fixtures, pending #235 + def test_save_fig_info(self, SM25_tmp_dir, caplog): + caplog.clear() + caplog.set_level(logging.INFO, logger="mdpow.workflows.dihedrals") + dihedrals.automated_dihedral_analysis( + dirname=SM25_tmp_dir, + figdir=SM25_tmp_dir, + resname=resname, + molname="SM25", + solvents=("water",), + ) + assert ( + f"Figure saved as {SM25_tmp_dir}/SM25/SM25_C10-C5-S4-O11_violins.pdf" + in caplog.text + ), "PDF file not saved" + + # Tests using similar instances of the automated analyses + # will use module or class-scoped fixtures, pending #235 + def test_DataFrame_input(self, SM25_tmp_dir, dihedral_data): + df, _ = dihedral_data + dihedrals.automated_dihedral_analysis( + dirname=SM25_tmp_dir, + figdir=SM25_tmp_dir, + resname=resname, + molname=molname, + solvents=("water",), + dataframe=df, + ) + assert ( + SM25_tmp_dir / "SM25" / "SM25_C10-C5-S4-O11_violins.pdf" + ).exists(), "PDF file not generated" + + # Tests using similar instances of the automated analyses + # will use module or class-scoped fixtures, pending #235 + def test_DataFrame_input_info(self, SM25_tmp_dir, dihedral_data, caplog): + caplog.clear() + caplog.set_level(logging.INFO, logger="mdpow.workflows.dihedrals") + df, _ = dihedral_data + dihedrals.automated_dihedral_analysis( + dirname=SM25_tmp_dir, + figdir=SM25_tmp_dir, + resname=resname, + molname=molname, + solvents=("water",), + dataframe=df, + ) + assert ( + "Proceeding with results DataFrame provided." in caplog.text + ), "No dataframe provided or dataframe not recognized" + + # testing resources only contain analyses with single solvent input + def test_single_solvent(self, dihedral_data): + df, _ = dihedral_data + # all analysis data in one violin plot + g = dihedrals.dihedral_violins( + df=df, width=0.9, solvents=("water",), plot_title="test" + ) + # number of solvents in DataFrame used to generate plot + number_of_solvents = g.data["solvent"].nunique() + assert number_of_solvents == 1 diff --git a/mdpow/tests/test_config.py b/mdpow/tests/test_config.py index 4a34dd25..a3646539 100644 --- a/mdpow/tests/test_config.py +++ b/mdpow/tests/test_config.py @@ -7,33 +7,39 @@ import mdpow.config + @pytest.fixture def cfg(): # default bundled config return mdpow.config.get_configuration() + @pytest.fixture def minicfg(): s = StringIO("setup:\n name: 'Alice'\n gromacsoutput: False\n") yield s s.close() + class TestConfigurationParser: def test_get_NoSectionError(self): cfg = mdpow.config.POWConfigParser() - with pytest.raises(mdpow.config.NoSectionError, - match="Config file has no section Jabberwocky"): - cfg.get('Jabberwocky', 'elump') + with pytest.raises( + mdpow.config.NoSectionError, match="Config file has no section Jabberwocky" + ): + cfg.get("Jabberwocky", "elump") def test_get_NoOptionWarning_gives_None(self, cfg): - with pytest.warns(mdpow.config.NoOptionWarning, - match="Config file section FEP contains no " - "option Jabberwocky. Using 'None'."): + with pytest.warns( + mdpow.config.NoOptionWarning, + match="Config file section FEP contains no " + "option Jabberwocky. Using 'None'.", + ): item = cfg.get("FEP", "Jabberwocky") assert item is None def test_get(self, cfg): - item = cfg.get("setup","solventmodel") + item = cfg.get("setup", "solventmodel") assert isinstance(item, str) assert item == "tip4p" @@ -42,7 +48,7 @@ def test_get_None(self, cfg): assert item is None def test_getstr(self, cfg): - args = "setup","solventmodel" + args = "setup", "solventmodel" item = cfg.getstr(*args) assert isinstance(item, str) assert item == "tip4p" @@ -75,7 +81,7 @@ def test_getfloat(self, cfg): def test_getpath(self, cfg): pth = "~/mirrors/jbwck.itp" - cfg.conf['setup']['itp'] = pth + cfg.conf["setup"]["itp"] = pth item = cfg.getpath("setup", "itp") @@ -99,7 +105,7 @@ def test_getlist(self, cfg): item = cfg.getlist("FEP_schedule_Coulomb", "lambdas") assert isinstance(item, list) - assert item == ['0', '0.25', '0.5', '0.75', '1.0'] + assert item == ["0", "0.25", "0.5", "0.75", "1.0"] def test_getlist_empty(self, cfg): # get an option with None for this test diff --git a/mdpow/tests/test_dihedral.py b/mdpow/tests/test_dihedral.py index 21025b58..0a48a62f 100644 --- a/mdpow/tests/test_dihedral.py +++ b/mdpow/tests/test_dihedral.py @@ -19,7 +19,7 @@ from pkg_resources import resource_filename -RESOURCES = py.path.local(resource_filename(__name__, 'testing_resources')) +RESOURCES = py.path.local(resource_filename(__name__, "testing_resources")) MANIFEST = RESOURCES.join("manifest.yml") @@ -29,51 +29,57 @@ class TestDihedral(object): DG48910_var = 0.20311120667628546 DG491011_var = 0.006976126708773456 - def setup(self): + def setup_method(self): self.tmpdir = td.TempDir() - self.m = pybol.Manifest(str(RESOURCES / 'manifest.yml')) - self.m.assemble('example_FEP', self.tmpdir.name) - self.Ens = Ensemble(dirname=self.tmpdir.name, solvents=['water']) + self.m = pybol.Manifest(str(RESOURCES / "manifest.yml")) + self.m.assemble("example_FEP", self.tmpdir.name) + self.Ens = Ensemble(dirname=self.tmpdir.name, solvents=["water"]) - def teardown(self): + def teardown_method(self): self.tmpdir.dissolve() def test_dataframe(self): - dh1 = self.Ens.select_atoms('name C4', 'name C17', 'name S2', 'name N3') + dh1 = self.Ens.select_atoms("name C4", "name C17", "name S2", "name N3") dh_run = DihedralAnalysis([dh1]).run(start=0, stop=4, step=1) results = dh_run.results - assert results['selection'][0] == 'C4-C17-S2-N3' - for s in results['solvent']: - assert s == 'water' - for i in results['interaction'][:12]: - assert i == 'Coulomb' + assert results["selection"][0] == "C4-C17-S2-N3" + for s in results["solvent"]: + assert s == "water" + for i in results["interaction"][:12]: + assert i == "Coulomb" def test_selection_error(self): - dh1 = self.Ens.select_atoms('name C17', 'name S2', 'name N3') + dh1 = self.Ens.select_atoms("name C17", "name S2", "name N3") with pytest.raises(SelectionError): dh_run = DihedralAnalysis([dh1]).run(start=0, stop=4, step=1) def test_results_recursive1(self): - dh1 = self.Ens.select_atoms('name C11', 'name C10', 'name C9', 'name C4') - dh2 = self.Ens.select_atoms('name C11', 'name C10', 'name C9', 'name C4') + dh1 = self.Ens.select_atoms("name C11", "name C10", "name C9", "name C4") + dh2 = self.Ens.select_atoms("name C11", "name C10", "name C9", "name C4") dh_run1 = DihedralAnalysis([dh1]).run(start=0, stop=4, step=1) dh_run2 = DihedralAnalysis([dh2]).run(start=0, stop=4, step=1) - assert len(dh_run1.results['dihedral']) == len(dh_run2.results['dihedral']) - for i in range(len(dh_run1.results['dihedral'])): - assert dh_run1.results['dihedral'][i] == dh_run2.results['dihedral'][i] + assert len(dh_run1.results["dihedral"]) == len(dh_run2.results["dihedral"]) + for i in range(len(dh_run1.results["dihedral"])): + assert dh_run1.results["dihedral"][i] == dh_run2.results["dihedral"][i] - @pytest.mark.skipif(sys.version_info < (3, 8), reason="scipy circvar gives wrong answers") + @pytest.mark.skipif( + sys.version_info < (3, 8), reason="scipy circvar gives wrong answers" + ) def test_results_recursive2(self): - dh1 = self.Ens.select_atoms('name C11', 'name C10', 'name C9', 'name C4') - dh2 = self.Ens.select_atoms('name C8', 'name C4', 'name C9', 'name C10') + dh1 = self.Ens.select_atoms("name C11", "name C10", "name C9", "name C4") + dh2 = self.Ens.select_atoms("name C8", "name C4", "name C9", "name C10") dh_run = DihedralAnalysis([dh1, dh2]).run(start=0, stop=4, step=1) - dh1_result = dh_run.results.loc[dh_run.results['selection'] == 'C11-C10-C9-C4']['dihedral'] - dh2_result = dh_run.results.loc[dh_run.results['selection'] == 'C8-C4-C9-C10']['dihedral'] + dh1_result = dh_run.results.loc[dh_run.results["selection"] == "C11-C10-C9-C4"][ + "dihedral" + ] + dh2_result = dh_run.results.loc[dh_run.results["selection"] == "C8-C4-C9-C10"][ + "dihedral" + ] dh1_mean = circmean(dh1_result, high=180, low=-180) dh2_mean = circmean(dh2_result, high=180, low=-180) @@ -86,15 +92,15 @@ def test_results_recursive2(self): assert_almost_equal(dh2_var, self.DG491011_var, 6) def test_ValueError_different_ensemble(self): - other = Ensemble(dirname=self.tmpdir.name, solvents=['water']) - dh1 = self.Ens.select_atoms('name C11', 'name C10', 'name C9', 'name C4') - dh2 = other.select_atoms('name C8', 'name C4', 'name C9', 'name C10') - with pytest.raises(ValueError, - match='Dihedral selections from different Ensembles, '): + other = Ensemble(dirname=self.tmpdir.name, solvents=["water"]) + dh1 = self.Ens.select_atoms("name C11", "name C10", "name C9", "name C4") + dh2 = other.select_atoms("name C8", "name C4", "name C9", "name C10") + with pytest.raises( + ValueError, match="Dihedral selections from different Ensembles, " + ): DihedralAnalysis([dh1, dh2]) def test_single_universe(self): - dh = self.Ens.select_atoms('name C4', 'name C17', 'name S2', 'name N3') + dh = self.Ens.select_atoms("name C4", "name C17", "name S2", "name N3") with pytest.raises(NotImplementedError): DihedralAnalysis([dh])._single_universe() - diff --git a/mdpow/tests/test_ensemble.py b/mdpow/tests/test_ensemble.py index e361dcb3..75c7bb39 100644 --- a/mdpow/tests/test_ensemble.py +++ b/mdpow/tests/test_ensemble.py @@ -21,110 +21,120 @@ from pkg_resources import resource_filename -RESOURCES = py.path.local(resource_filename(__name__, 'testing_resources')) +RESOURCES = py.path.local(resource_filename(__name__, "testing_resources")) MANIFEST = RESOURCES.join("manifest.yml") -ensemble_keys = [('water', 'Coulomb', '0000'), - ('water', 'Coulomb', '0500'), - ('water', 'Coulomb', '1000'), - ('water', 'VDW', '0000'), - ('water', 'VDW', '0250'), - ('water', 'VDW', '0500'), - ('water', 'VDW', '1000')] +ensemble_keys = [ + ("water", "Coulomb", "0000"), + ("water", "Coulomb", "0500"), + ("water", "Coulomb", "1000"), + ("water", "VDW", "0000"), + ("water", "VDW", "0250"), + ("water", "VDW", "0500"), + ("water", "VDW", "1000"), +] class TestEnsemble(object): - def setup(self): + def setup_method(self): self.tmpdir = td.TempDir() - self.m = pybol.Manifest(str(RESOURCES / 'manifest.yml')) - self.m.assemble('example_FEP', self.tmpdir.name) + self.m = pybol.Manifest(str(RESOURCES / "manifest.yml")) + self.m.assemble("example_FEP", self.tmpdir.name) - def teardown(self): + def teardown_method(self): self.tmpdir.dissolve() def test_build_ensemble(self): # Octanol will be added later - Sim = Ensemble(dirname=self.tmpdir.name, solvents=['water']) + Sim = Ensemble(dirname=self.tmpdir.name, solvents=["water"]) diff = set(Sim.keys()) ^ set(ensemble_keys) assert not diff def test_kwargs(self): - l_dir = os.path.abspath(os.path.join(self.tmpdir.name, 'FEP', 'md.gro')) - bnz = Ensemble(dirname=self.tmpdir.name, solvents=['water'], topology_paths={'water': l_dir}) + l_dir = os.path.abspath(os.path.join(self.tmpdir.name, "FEP", "md.gro")) + bnz = Ensemble( + dirname=self.tmpdir.name, + solvents=["water"], + topology_paths={"water": l_dir}, + ) diff = set(bnz.keys()) ^ set(ensemble_keys) assert not diff def test_add_remove_systems(self): with in_dir(self.tmpdir.name, create=False): bnz = Ensemble() - l_dir = os.path.join(os.curdir, 'FEP', 'water', 'Coulomb', '0000') - top_dir = os.path.join(l_dir, 'md.gro') - trj_dir = os.path.join(l_dir, 'md_red.xtc') + l_dir = os.path.join(os.curdir, "FEP", "water", "Coulomb", "0000") + top_dir = os.path.join(l_dir, "md.gro") + trj_dir = os.path.join(l_dir, "md_red.xtc") U = mda.Universe(top_dir, trj_dir) - bnz.add_system(('water', 'Coulomb', '0000'), U) - assert bnz.keys() == [('water', 'Coulomb', '0000')] + bnz.add_system(("water", "Coulomb", "0000"), U) + assert bnz.keys() == [("water", "Coulomb", "0000")] assert bnz._num_systems == 1 assert bnz.__repr__() == "" assert len(bnz) == 1 - bnz.pop(('water', 'Coulomb', '0000')) + bnz.pop(("water", "Coulomb", "0000")) assert bnz._num_systems == 0 assert len(bnz) == 0 def test_select_atoms(self): - Sim = Ensemble(dirname=self.tmpdir.name, solvents=['water']) - solute = Sim.select_atoms('not resname SOL') + Sim = Ensemble(dirname=self.tmpdir.name, solvents=["water"]) + solute = Sim.select_atoms("not resname SOL") assert len(solute) == 7 for k in solute.keys(): assert len(solute[k]) == 42 def test_select_systems(self): - Sim = Ensemble(dirname=self.tmpdir.name, solvents=['water']) - Sel1 = Sim.select_systems(keys=[('water', 'Coulomb', '0000'), - ('water', 'VDW', '0500')]) - assert Sel1.keys() == [('water', 'Coulomb', '0000'), - ('water', 'VDW', '0500')] - Sel2 = Sim.select_systems(solvents=['water'], interactions=['Coulomb'], - lambdas=['0000', '1000']) - assert Sel2.keys() == [('water', 'Coulomb', '0000'), - ('water', 'Coulomb', '1000')] - Sel3 = Sim.select_systems(solvents=['water'], interactions=['VDW'], - lambda_range=[0, 1]) + Sim = Ensemble(dirname=self.tmpdir.name, solvents=["water"]) + Sel1 = Sim.select_systems( + keys=[("water", "Coulomb", "0000"), ("water", "VDW", "0500")] + ) + assert Sel1.keys() == [("water", "Coulomb", "0000"), ("water", "VDW", "0500")] + Sel2 = Sim.select_systems( + solvents=["water"], interactions=["Coulomb"], lambdas=["0000", "1000"] + ) + assert Sel2.keys() == [ + ("water", "Coulomb", "0000"), + ("water", "Coulomb", "1000"), + ] + Sel3 = Sim.select_systems( + solvents=["water"], interactions=["VDW"], lambda_range=[0, 1] + ) diff = set(Sel3.keys()) ^ set(ensemble_keys[3:]) assert not diff def test_ensemble_ag_methods(self): - Solv_system = Ensemble(dirname=self.tmpdir.name, solvents=['water']) - Sol1 = Solv_system.select_atoms('resname SOL') - Sol2 = Sol1.select_atoms('resid 2') + Solv_system = Ensemble(dirname=self.tmpdir.name, solvents=["water"]) + Sol1 = Solv_system.select_atoms("resname SOL") + Sol2 = Sol1.select_atoms("resid 2") Sol2_pos = Sol2.positions() assert len(Sol2_pos) > 0 for k in Sol2_pos: assert np.shape(Sol2_pos[k]) == (3, 3) assert not Sol1 == Sol2 assert isinstance(Sol2, EnsembleAtomGroup) - assert Sol2 == Sol1.select_atoms('resid 2') + assert Sol2 == Sol1.select_atoms("resid 2") assert ensemble_keys.sort() == Sol1.ensemble.keys().sort() - Sol1._groups.pop(('water', 'Coulomb', '0000')) + Sol1._groups.pop(("water", "Coulomb", "0000")) Sol1._keys = Sol1._groups.keys() assert not Sol1 == Sol2 - pos2 = Sol2.positions(keys=[('water', 'Coulomb', '0000')]) - assert np.shape(pos2[('water', 'Coulomb', '0000')]) == (3, 3) + pos2 = Sol2.positions(keys=[("water", "Coulomb", "0000")]) + assert np.shape(pos2[("water", "Coulomb", "0000")]) == (3, 3) def test_ensemble_init_exception(self): with pytest.raises(FileNotFoundError): - Ens = Ensemble(dirname='foo') + Ens = Ensemble(dirname="foo") def test_ensemble_build_exceptions(self): with pytest.raises(NoDataError): - ens = Ensemble(self.tmpdir.name, solvents=['test_solv']) + ens = Ensemble(self.tmpdir.name, solvents=["test_solv"]) def test_ensemble_selection_error(self): - ens = Ensemble(dirname=self.tmpdir.name, solvents=['water']) - sel1 = ens.select_atoms('resid 1') + ens = Ensemble(dirname=self.tmpdir.name, solvents=["water"]) + sel1 = ens.select_atoms("resid 1") with pytest.raises(SelectionError): - ens.select_atoms('foo') + ens.select_atoms("foo") with pytest.raises(SelectionError): - sel1.select_atoms('foo') + sel1.select_atoms("foo") def test_ensemble_analysis(self): class TestAnalysis(EnsembleAnalysis): @@ -140,12 +150,12 @@ def _single_universe(self): self.key_list.append(self._key) def _single_frame(self): - assert len(self._system.select_atoms('not resname SOL')) == 42 + assert len(self._system.select_atoms("not resname SOL")) == 42 def _conclude_universe(self): assert self.n_frames == self.stop - Sim = Ensemble(dirname=self.tmpdir.name, solvents=['water']) + Sim = Ensemble(dirname=self.tmpdir.name, solvents=["water"]) TestRun = TestAnalysis(Sim).run(start=0, step=1, stop=10) assert Sim.keys() == TestRun.key_list @@ -159,37 +169,37 @@ def __init__(self, test_ensemble): def _single_universe(self): pass - Sim = Ensemble(dirname=self.tmpdir.name, solvents=['water']) + Sim = Ensemble(dirname=self.tmpdir.name, solvents=["water"]) TestRun = TestAnalysis(Sim) - + with pytest.raises(NotImplementedError): TestRun._single_frame() - + def test_ensemble_analysis_run_universe(self): class TestAnalysis(EnsembleAnalysis): def __init__(self, test_ensemble): super(TestAnalysis, self).__init__(test_ensemble) self._ens = test_ensemble - + def _single_frame(self): pass - Sim = Ensemble(dirname=self.tmpdir.name, solvents=['water']) + Sim = Ensemble(dirname=self.tmpdir.name, solvents=["water"]) TestRun = TestAnalysis(Sim) - + with pytest.raises(NotImplementedError): TestRun._single_universe() def test_value_error(self): - ens = Ensemble(dirname=self.tmpdir.name, solvents=['water']) + ens = Ensemble(dirname=self.tmpdir.name, solvents=["water"]) copy_ens = Ensemble() copy_ens._ensemble_dir = self.tmpdir.name for k in ens.keys(): copy_ens.add_system(k, ens[k]) - dh1 = ens.select_atoms('name C4 or name C17 or name S2 or name N3') - dh2 = copy_ens.select_atoms('name C4 or name C17 or name S2 or name N3') - dh3 = ens.select_atoms('name C4 or name C17 or name S2 or name N3') - dh4 = ens.select_atoms('name C4 or name C17 or name S2 or name N3') + dh1 = ens.select_atoms("name C4 or name C17 or name S2 or name N3") + dh2 = copy_ens.select_atoms("name C4 or name C17 or name S2 or name N3") + dh3 = ens.select_atoms("name C4 or name C17 or name S2 or name N3") + dh4 = ens.select_atoms("name C4 or name C17 or name S2 or name N3") with pytest.raises(ValueError): dh_run = DihedralAnalysis([dh1, dh2, dh4, dh3]).run(start=0, stop=4, step=1) diff --git a/mdpow/tests/test_equilibration_script.py b/mdpow/tests/test_equilibration_script.py index 79ddaf2a..c0b5f752 100644 --- a/mdpow/tests/test_equilibration_script.py +++ b/mdpow/tests/test_equilibration_script.py @@ -10,36 +10,52 @@ from . import RESOURCES + class TestEquilibriumScript(object): - def setup(self): + def setup_method(self): self.tmpdir = td.TempDir() self.old_path = os.getcwd() self.resources = RESOURCES - m = pybol.Manifest(str(self.resources / 'manifest.yml')) - m.assemble('base', self.tmpdir.name) + m = pybol.Manifest(str(self.resources / "manifest.yml")) + m.assemble("base", self.tmpdir.name) - def teardown(self): + def teardown_method(self): self.tmpdir.dissolve() def _run_equil(self, solvent, dirname): - cfg = get_configuration('runinput.yml') + cfg = get_configuration("runinput.yml") self.S = equilibrium_simulation(cfg, solvent, dirname=dirname) def test_basic_run(self): with in_dir(self.tmpdir.name, create=False): try: - self._run_equil('water','benzene/') + self._run_equil("water", "benzene/") self._new_structures() except Exception as err: - raise AssertionError('Equilibration simulations failed with exception:\n{0}'.format(str(err))) + raise AssertionError( + "Equilibration simulations failed with exception:\n{0}".format( + str(err) + ) + ) def _new_structures(self): assert os.path.exists( - os.path.join(self.tmpdir.name, - 'benzene', 'Equilibrium', 'water', 'em', 'em.pdb')) + os.path.join( + self.tmpdir.name, "benzene", "Equilibrium", "water", "em", "em.pdb" + ) + ) assert os.path.exists( - os.path.join(self.tmpdir.name, - 'benzene', 'Equilibrium', 'water', 'solvation', 'solvated.gro')) + os.path.join( + self.tmpdir.name, + "benzene", + "Equilibrium", + "water", + "solvation", + "solvated.gro", + ) + ) assert os.path.exists( - os.path.join(self.tmpdir.name, - 'benzene', 'Equilibrium', 'water', 'MD_NPT', 'md.gro')) + os.path.join( + self.tmpdir.name, "benzene", "Equilibrium", "water", "MD_NPT", "md.gro" + ) + ) diff --git a/mdpow/tests/test_fep.py b/mdpow/tests/test_fep.py index 6bda9432..d9447ac3 100644 --- a/mdpow/tests/test_fep.py +++ b/mdpow/tests/test_fep.py @@ -10,46 +10,72 @@ import mdpow.config import mdpow.fep + def test_molar_to_nm3(): assert_almost_equal(mdpow.fep.molar_to_nm3(1.5), 0.9033212684) assert_almost_equal(mdpow.fep.molar_to_nm3(55.5), 33.42288693449999) + def test_bar_to_kJmolnm3(): assert_almost_equal(mdpow.fep.bar_to_kJmolnm3(1.0), 0.0602214179) + def test_kcal_to_kJ(): assert_almost_equal(mdpow.fep.kcal_to_kJ(10.0), 41.84) + def test_kJ_to_kcal(): assert_almost_equal(mdpow.fep.kJ_to_kcal(41.84), 10.0) + def test_kBT_to_kJ(): - ref = constants.N_A*constants.k*1e-3 + ref = constants.N_A * constants.k * 1e-3 assert_almost_equal(mdpow.fep.kBT_to_kJ(1, 1), ref) + class TestFEPschedule(object): reference = { - 'VDW': - {'couple_lambda0': 'vdw', - 'couple_lambda1': 'none', - 'description': 'decoupling vdw --> none', - 'label': 'VDW', - 'lambdas': np.array([ 0. , 0.05, 0.1 , 0.2 , 0.3 , 0.4 , 0.5 , 0.6 , 0.65, - 0.7 , 0.75, 0.8 , 0.85, 0.9 , 0.95, 1. ]), - 'name': 'vdw', - 'sc_alpha': 0.5, - 'sc_power': 1, - 'sc_sigma': 0.3}, - 'Coulomb': - {'couple_lambda0': 'vdw-q', - 'couple_lambda1': 'vdw', - 'description': 'dis-charging vdw+q --> vdw', - 'label': 'Coul', - 'lambdas': np.array([ 0. , 0.25, 0.5 , 0.75, 1. ]), - 'name': 'Coulomb', - 'sc_alpha': 0, - 'sc_power': 1, - 'sc_sigma': 0.3} + "VDW": { + "couple_lambda0": "vdw", + "couple_lambda1": "none", + "description": "decoupling vdw --> none", + "label": "VDW", + "lambdas": np.array( + [ + 0.0, + 0.05, + 0.1, + 0.2, + 0.3, + 0.4, + 0.5, + 0.6, + 0.65, + 0.7, + 0.75, + 0.8, + 0.85, + 0.9, + 0.95, + 1.0, + ] + ), + "name": "vdw", + "sc_alpha": 0.5, + "sc_power": 1, + "sc_sigma": 0.3, + }, + "Coulomb": { + "couple_lambda0": "vdw-q", + "couple_lambda1": "vdw", + "description": "dis-charging vdw+q --> vdw", + "label": "Coul", + "lambdas": np.array([0.0, 0.25, 0.5, 0.75, 1.0]), + "name": "Coulomb", + "sc_alpha": 0, + "sc_power": 1, + "sc_sigma": 0.3, + }, } @pytest.fixture @@ -58,14 +84,14 @@ def cfg(self): return mdpow.config.get_configuration() def test_VDW(self, cfg): - return self._test_schedule(cfg, 'VDW') + return self._test_schedule(cfg, "VDW") def test_Coulomb(self, cfg): - return self._test_schedule(cfg, 'Coulomb') + return self._test_schedule(cfg, "Coulomb") - @pytest.mark.parametrize('component', ['VDW', 'Coulomb']) + @pytest.mark.parametrize("component", ["VDW", "Coulomb"]) def test_copy(self, cfg, component): - section = 'FEP_schedule_{0}'.format(component) + section = "FEP_schedule_{0}".format(component) schedule = deepcopy(mdpow.fep.FEPschedule.load(cfg, section)) reference = self.reference[component] @@ -77,30 +103,39 @@ def test_copy(self, cfg, component): for k in schedule.keys(): if k == "lambdas": - assert_array_almost_equal(schedule[k], reference[k], - err_msg="FEP schedule {0} mismatch".format(k)) + assert_array_almost_equal( + schedule[k], + reference[k], + err_msg="FEP schedule {0} mismatch".format(k), + ) else: - assert schedule[k] == reference[k], \ - "mismatch between loaded FEP schedule entry {0} and reference".format(k) + assert ( + schedule[k] == reference[k] + ), "mismatch between loaded FEP schedule entry {0} and reference".format( + k + ) - @pytest.mark.parametrize('component', ['VDW', 'Coulomb']) + @pytest.mark.parametrize("component", ["VDW", "Coulomb"]) def test_write(self, cfg, component, tmp_path): filename = tmp_path / "cfg.yaml" cfg.write(filename) new_cfg = mdpow.config.get_configuration(filename) assert new_cfg.conf == cfg.conf - @pytest.mark.parametrize("x,ref", [ - ("test", False), - ([1, 1, 2, 3], True), - ({1, 2, 3}, True), - (None, False), - ]) + @pytest.mark.parametrize( + "x,ref", + [ + ("test", False), + ([1, 1, 2, 3], True), + ({1, 2, 3}, True), + (None, False), + ], + ) def test_iterable(self, x, ref): assert mdpow.config.iterable(x) == ref def _test_schedule(self, cfg, component): - section = 'FEP_schedule_{0}'.format(component) + section = "FEP_schedule_{0}".format(component) schedule = mdpow.fep.FEPschedule.load(cfg, section) reference = self.reference[component] @@ -112,20 +147,26 @@ def _test_schedule(self, cfg, component): for k in schedule.keys(): if k == "lambdas": - assert_array_almost_equal(schedule[k], reference[k], - err_msg="FEP schedule {0} mismatch".format(k)) + assert_array_almost_equal( + schedule[k], + reference[k], + err_msg="FEP schedule {0} mismatch".format(k), + ) else: - assert schedule[k] == reference[k], \ - "mismatch between loaded FEP schedule entry {0} and reference".format(k) + assert ( + schedule[k] == reference[k] + ), "mismatch between loaded FEP schedule entry {0} and reference".format( + k + ) def test_skip_empty_entries(self, cfg, section="FEP_schedule_Coulomb"): # remove some entries - del cfg.conf[section]['name'] # string - del cfg.conf[section]['lambdas'] # array + del cfg.conf[section]["name"] # string + del cfg.conf[section]["lambdas"] # array with pytest.warns(mdpow.config.NoOptionWarning): schedule = mdpow.fep.FEPschedule.load(cfg, section) - assert schedule['label'] == "Coul" - assert schedule['sc_power'] == 1 - assert 'name' not in schedule - assert 'lambdas' not in schedule + assert schedule["label"] == "Coul" + assert schedule["sc_power"] == 1 + assert "name" not in schedule + assert "lambdas" not in schedule diff --git a/mdpow/tests/test_fep_analysis.py b/mdpow/tests/test_fep_analysis.py index dff2a72d..e9bf3981 100644 --- a/mdpow/tests/test_fep_analysis.py +++ b/mdpow/tests/test_fep_analysis.py @@ -8,14 +8,16 @@ from . import STATES + @pytest.fixture def FEP_dir(tmpdir): - name = STATES['FEP'].basename # a py.path.local - fepdir = tmpdir.join(name) # a py.path.local - shutil.copytree(STATES['FEP'].strpath, fepdir.strpath) + name = STATES["FEP"].basename # a py.path.local + fepdir = tmpdir.join(name) # a py.path.local + shutil.copytree(STATES["FEP"].strpath, fepdir.strpath) assert os.path.isdir(fepdir.strpath) return fepdir + def setup_Ghyd(fepdir): basedir = fepdir.join("benzene") gsolv = basedir.join("FEP", "water", "Gsolv.fep") @@ -30,25 +32,31 @@ def setup_Ghyd(fepdir): def Ghyd(FEP_dir): return setup_Ghyd(FEP_dir) + @pytest.fixture def Ghyd_other(FEP_dir): return setup_Ghyd(FEP_dir) + def test_load_Ghyd(Ghyd): assert isinstance(Ghyd, mdpow.fep.Ghyd) -@pytest.mark.parametrize("kwargs", ( - {}, - {'SI': True, 'estimator': 'alchemlyb', 'method': 'TI'}, - {'SI': False, 'estimator': 'alchemlyb', 'method': 'MBAR', 'force': False}, - {'SI': False, 'estimator': 'mdpow', 'method': 'TI', 'force': True}, + +@pytest.mark.parametrize( + "kwargs", + ( + {}, + {"SI": True, "estimator": "alchemlyb", "method": "TI"}, + {"SI": False, "estimator": "alchemlyb", "method": "MBAR", "force": False}, + {"SI": False, "estimator": "mdpow", "method": "TI", "force": True}, ), - ids=["defaults", - "SI=True, estimator='alchemlyb', method='TI'", - "SI=False, estimator='alchemlyb', method='MBAR', force=False", - "SI=False, estimator='mdpow', method='TI', force=True", - ] - ) + ids=[ + "defaults", + "SI=True, estimator='alchemlyb', method='TI'", + "SI=False, estimator='alchemlyb', method='MBAR', force=False", + "SI=False, estimator='mdpow', method='TI', force=True", + ], +) def test_p_transfer(Ghyd, Ghyd_other, kwargs): """Test transfer water <-> water with same data.""" G1 = Ghyd @@ -59,13 +67,16 @@ def test_p_transfer(Ghyd, Ghyd_other, kwargs): assert transferFE == pytest.approx(0.0) assert logPow == pytest.approx(0.0) + def test_p_transfer_wrong_method(Ghyd, Ghyd_other): """Test transfer water <-> water with same data.""" G1 = Ghyd G2 = Ghyd_other - with pytest.raises(ValueError, - match="Method MBAR is not implemented in MDPOW, use estimator='alchemlyb'"): + with pytest.raises( + ValueError, + match="Method MBAR is not implemented in MDPOW, use estimator='alchemlyb'", + ): mdpow.fep.p_transfer(G1, G2, estimator="mdpow", method="MBAR") @@ -73,6 +84,12 @@ def test_pOW_error(Ghyd, Ghyd_other): with pytest.raises(ValueError): mdpow.fep.pOW(Ghyd, Ghyd_other) + def test_pCW_error(Ghyd, Ghyd_other): with pytest.raises(ValueError): mdpow.fep.pCW(Ghyd, Ghyd_other) + + +def test_pTW_error(Ghyd, Ghyd_other): + with pytest.raises(ValueError): + mdpow.fep.pTW(Ghyd, Ghyd_other) diff --git a/mdpow/tests/test_fep_script.py b/mdpow/tests/test_fep_script.py index 2ebed629..ff4c29b5 100644 --- a/mdpow/tests/test_fep_script.py +++ b/mdpow/tests/test_fep_script.py @@ -1,8 +1,9 @@ -from . import tempdir as td - import os +import pytest import pybol +from . import tempdir as td + import gromacs import mdpow @@ -12,27 +13,30 @@ from . import RESOURCES + class TestFEPScript(object): - def setup(self): + def setup_method(self): self.tmpdir = td.TempDir() self.old_path = os.getcwd() self.resources = RESOURCES - self.m = pybol.Manifest(str(self.resources / 'manifest.yml')) - self.m.assemble('md_npt', self.tmpdir.name) - - S = Simulation(filename=os.path.join( - self.tmpdir.name, 'benzene', 'water.simulation')) - S.make_paths_relative(prefix=os.path.join( - self.tmpdir.name,'benzene', 'Equilibrium', 'water')) - S.dirs.includes = os.path.join(self.tmpdir.name, 'top') + self.m = pybol.Manifest(str(self.resources / "manifest.yml")) + self.m.assemble("md_npt", self.tmpdir.name) + + S = Simulation( + filename=os.path.join(self.tmpdir.name, "benzene", "water.simulation") + ) + S.make_paths_relative( + prefix=os.path.join(self.tmpdir.name, "benzene", "Equilibrium", "water") + ) + S.dirs.includes = os.path.join(self.tmpdir.name, "top") S.save() - def teardown(self): + def teardown_method(self): self.tmpdir.dissolve() def _run_fep(self, solvent, dirname): - cfg = get_configuration('runinput.yml') - if gromacs.release.startswith('4'): + cfg = get_configuration("runinput.yml") + if gromacs.release.startswith("4"): # For GROMACS 4.6.5 explicitly enable the group neighbor # scheme by creating a copy of the MDP file in the current # directory with MDP cutoff-scheme option changed. The local @@ -40,17 +44,43 @@ def _run_fep(self, solvent, dirname): # in the templates. fep_mdp_name = cfg.get("FEP", "mdp") mdp = mdpow.config.get_template(fep_mdp_name) - gromacs.cbook.edit_mdp(mdp, - new_mdp=os.path.join(os.getcwd(), fep_mdp_name), - cutoff_scheme="group") - self.S = fep_simulation(cfg, solvent, dirname=dirname) + gromacs.cbook.edit_mdp( + mdp, + new_mdp=os.path.join(os.getcwd(), fep_mdp_name), + cutoff_scheme="group", + ) + self.savefilename = fep_simulation( + cfg, solvent, dirname=dirname, exit_on_error=False + ) - def test_default_run(self): + def test_default_run(self, capsys): with gromacs.utilities.in_dir(self.tmpdir.name, create=False): try: - self._run_fep('water', 'benzene/') + self._run_fep("water", "benzene/") except Exception as err: - raise AssertionError('FEP simulations failed with exception:\n{0}'.format(str(err))) + # check if log file contains + # 'There are 2 perturbed non-bonded pair interactions beyond the pair-list cutoff' + # This error can happen and still counts as passing. (It is stochastic and is due + # to a poorly designed test system together with GROMACS having become better at detecting + # internal problems.) + captured = capsys.readouterr() + for line in captured: + if ( + "perturbed non-bonded pair interactions beyond the pair-list cutoff'" + in line + ): + pytest.xfail( + "Stochastic test failure (perturbed non-bonded beyond cutoff). " + "Still works as expected, see #175." + ) + break + else: + raise AssertionError( + "FEP simulations failed with exception:\n{0}".format(str(err)) + ) - assert os.path.exists(os.path.join(self.tmpdir.name, - 'benzene', 'FEP', 'water', 'VDW', '0000', 'md.edr')) + assert os.path.exists( + os.path.join( + self.tmpdir.name, "benzene", "FEP", "water", "VDW", "0000", "md.edr" + ) + ) diff --git a/mdpow/tests/test_filelock.py b/mdpow/tests/test_filelock.py index a6c4d4ed..a337cdae 100644 --- a/mdpow/tests/test_filelock.py +++ b/mdpow/tests/test_filelock.py @@ -2,7 +2,8 @@ import pytest -from .. import filelock +from mdpow import filelock + def test_FileLock_acquire(tmpdir, filename="test.txt"): with tmpdir.as_cwd(): @@ -11,6 +12,7 @@ def test_FileLock_acquire(tmpdir, filename="test.txt"): f.write("Humpty Dumpty sat on a wall") assert os.path.exists(filename) + def test_FileLock_lock(filename="test.txt"): with filelock.FileLock(filename, timeout=2) as lock: with pytest.raises(filelock.FileLockException): diff --git a/mdpow/tests/test_forcefields.py b/mdpow/tests/test_forcefields.py index db556f80..bc5c3112 100644 --- a/mdpow/tests/test_forcefields.py +++ b/mdpow/tests/test_forcefields.py @@ -6,8 +6,9 @@ import mdpow.forcefields # currently supported -WATERMODELS = ('tip4p', 'tip3p', 'tip5p', 'spc', 'spce', 'm24', 'tip4pd') -SOLVENTMODELS = ('water', 'cyclohexane', 'octanol') +WATERMODELS = ("tip4p", "tip3p", "tip5p", "spc", "spce", "m24", "tip4pd") +SOLVENTMODELS = ("water", "cyclohexane", "octanol", "toluene") + class TestIncludedForcefiels(object): @staticmethod @@ -18,31 +19,40 @@ def test_default_forcefield(): def test_oplsaa_itp(): assert "ffoplsaa.itp" in mdpow.config.topfiles assert mdpow.config.topfiles["ffoplsaa.itp"].endswith( - os.path.join('mdpow', 'top', 'ffoplsaa.itp')) + os.path.join("mdpow", "top", "ffoplsaa.itp") + ) @staticmethod def test_oplsaa_ff(): assert "oplsaa.ff" in mdpow.config.topfiles assert mdpow.config.topfiles["oplsaa.ff"].endswith( - os.path.join('mdpow', 'top', 'oplsaa.ff')) + os.path.join("mdpow", "top", "oplsaa.ff") + ) + class TestIncludedSolvents(object): solvents = { - 'tip4p': { - 'tip4p.itp': os.path.join('mdpow', 'top', 'oplsaa.ff', 'tip4p.itp'), - 'tip4p.gro': os.path.join('mdpow', 'top', 'tip4p.gro') + "tip4p": { + "tip4p.itp": os.path.join("mdpow", "top", "oplsaa.ff", "tip4p.itp"), + "tip4p.gro": os.path.join("mdpow", "top", "tip4p.gro"), + }, + "octanol": { + "1oct.gro": os.path.join("mdpow", "top", "1oct.gro"), + "1oct.itp": os.path.join("mdpow", "top", "oplsaa.ff", "1oct.itp"), }, - 'octanol': { - '1oct.gro': os.path.join('mdpow', 'top', '1oct.gro'), - '1oct.itp': os.path.join('mdpow', 'top', 'oplsaa.ff', '1oct.itp'), + "cyclohexane": { + "1cyclo.gro": os.path.join("mdpow", "top", "1cyclo.gro"), + "1cyclo.itp": os.path.join("mdpow", "top", "oplsaa.ff", "1cyclo.itp"), }, - 'cyclohexane': { - '1cyclo.gro': os.path.join('mdpow', 'top', '1cyclo.gro'), - '1cyclo.itp': os.path.join('mdpow', 'top', 'oplsaa.ff', '1cyclo.itp') + "toluene": { + "1tol_oplsaa.gro": os.path.join("mdpow", "top", "1tol_oplsaa.gro"), + "1tol.itp": os.path.join("mdpow", "top", "oplsaa.ff", "1tol.itp"), }, } - @pytest.mark.parametrize("solvent_name", ["tip4p", "octanol", "cyclohexane"]) + @pytest.mark.parametrize( + "solvent_name", ["tip4p", "octanol", "cyclohexane", "toluene"] + ) def test_solvent(self, solvent_name): solvent = self.solvents[solvent_name] for filename, path in solvent.items(): @@ -56,12 +66,14 @@ def test_default_water_model(): assert mdpow.forcefields.DEFAULT_WATER_MODEL == "tip4p" def test_watermodelsdat(self): - included_watermodels = open(mdpow.config.topfiles['watermodels.dat']).read() - for line, ref in zip(self._simple_line_parser(mdpow.forcefields.GMX_WATERMODELS_DAT), - self._simple_line_parser(included_watermodels)): + included_watermodels = open(mdpow.config.topfiles["watermodels.dat"]).read() + for line, ref in zip( + self._simple_line_parser(mdpow.forcefields.GMX_WATERMODELS_DAT), + self._simple_line_parser(included_watermodels), + ): assert line.strip() == ref.strip() - @pytest.mark.parametrize('identifier', WATERMODELS) + @pytest.mark.parametrize("identifier", WATERMODELS) def test_gromacs_water_models(self, identifier): models = mdpow.forcefields.GROMACS_WATER_MODELS @@ -71,33 +83,37 @@ def test_gromacs_water_models(self, identifier): assert model.itp in mdpow.config.topfiles assert model.coordinates in mdpow.config.topfiles - @staticmethod def _simple_line_parser(string): - for line in string.split('\n'): + for line in string.split("\n"): line = line.strip() - if not line or line.startswith('#'): + if not line or line.startswith("#"): continue yield line @staticmethod def test_get_water_model(): model = mdpow.forcefields.DEFAULT_WATER_MODEL - assert (mdpow.forcefields.get_water_model(model) is - mdpow.forcefields.GROMACS_WATER_MODELS[model]) + assert ( + mdpow.forcefields.get_water_model(model) + is mdpow.forcefields.GROMACS_WATER_MODELS[model] + ) @staticmethod def test_get_water_model_ValueError(): with pytest.raises(ValueError): mdpow.forcefields.get_water_model("The Jabberwock is an imaginary beast.") + class TestSolventModels(object): @staticmethod def test_get_solvent_default_water(): model = "water" defaultmodel = mdpow.forcefields.DEFAULT_WATER_MODEL - assert (mdpow.forcefields.get_solvent_model(model) is - mdpow.forcefields.GROMACS_WATER_MODELS[defaultmodel]) + assert ( + mdpow.forcefields.get_solvent_model(model) + is mdpow.forcefields.GROMACS_WATER_MODELS[defaultmodel] + ) @staticmethod def test_get_solvent_model_ValueError(): @@ -106,40 +122,57 @@ def test_get_solvent_model_ValueError(): @staticmethod def test_get_solvent_cyclohexane(): - model = 'cyclohexane' - forcefield = 'OPLS-AA' - assert (mdpow.forcefields.get_solvent_model(model) is - mdpow.forcefields.GROMACS_SOLVENT_MODELS[forcefield][model]) - - @pytest.mark.parametrize("forcefield", ['OPLS-AA', 'CHARMM', 'AMBER']) + model = "cyclohexane" + forcefield = "OPLS-AA" + assert ( + mdpow.forcefields.get_solvent_model(model) + is mdpow.forcefields.GROMACS_SOLVENT_MODELS[forcefield][model] + ) + + @pytest.mark.parametrize("forcefield", ["OPLS-AA", "CHARMM", "AMBER"]) def test_get_solvent_octanol(self, forcefield): - model = 'octanol' - assert (mdpow.forcefields.get_solvent_model(model, forcefield=forcefield) is - mdpow.forcefields.GROMACS_SOLVENT_MODELS[forcefield][model]) + model = "octanol" + assert ( + mdpow.forcefields.get_solvent_model(model, forcefield=forcefield) + is mdpow.forcefields.GROMACS_SOLVENT_MODELS[forcefield][model] + ) - @pytest.mark.parametrize("forcefield", ['OPLS-AA', 'CHARMM', 'AMBER']) + @pytest.mark.parametrize("forcefield", ["OPLS-AA", "CHARMM", "AMBER"]) def test_get_solvent_wetoctanol(self, forcefield): - model = 'wetoctanol' - assert (mdpow.forcefields.get_solvent_model(model, forcefield=forcefield) is - mdpow.forcefields.GROMACS_SOLVENT_MODELS[forcefield][model]) + model = "wetoctanol" + assert ( + mdpow.forcefields.get_solvent_model(model, forcefield=forcefield) + is mdpow.forcefields.GROMACS_SOLVENT_MODELS[forcefield][model] + ) + + @pytest.mark.parametrize("forcefield", ["OPLS-AA", "CHARMM", "AMBER"]) + def test_get_solvent_toluene(self, forcefield): + model = "toluene" + assert ( + mdpow.forcefields.get_solvent_model(model, forcefield=forcefield) + is mdpow.forcefields.GROMACS_SOLVENT_MODELS[forcefield][model] + ) @staticmethod def test_get_solvent_identifier_default_is_water(): - assert (mdpow.forcefields.get_solvent_identifier('water') is - mdpow.forcefields.DEFAULT_WATER_MODEL) + assert ( + mdpow.forcefields.get_solvent_identifier("water") + is mdpow.forcefields.DEFAULT_WATER_MODEL + ) @pytest.mark.parametrize("model", WATERMODELS) def test_get_solvent_identifier_water(self, model): - assert mdpow.forcefields.get_solvent_identifier('water', model=model) is model + assert mdpow.forcefields.get_solvent_identifier("water", model=model) is model - @pytest.mark.parametrize('solvent', - [model for model in SOLVENTMODELS if model != "water"]) - @pytest.mark.parametrize('model', [None, "Jabberwock model"]) + @pytest.mark.parametrize( + "solvent", [model for model in SOLVENTMODELS if model != "water"] + ) + @pytest.mark.parametrize("model", [None, "Jabberwock model"]) def test_get_solvent_identifier_solvents(self, solvent, model): # The model="Jabberwock model" checks that "model" is properly ignored. assert mdpow.forcefields.get_solvent_identifier(solvent, model=model) is solvent @staticmethod def test_get_solvent_identifier_None(): - assert mdpow.forcefields.get_solvent_identifier('water', model="foobar") is None - assert mdpow.forcefields.get_solvent_identifier('benzene') is None + assert mdpow.forcefields.get_solvent_identifier("water", model="foobar") is None + assert mdpow.forcefields.get_solvent_identifier("benzene") is None diff --git a/mdpow/tests/test_run.py b/mdpow/tests/test_run.py index 0dd0b8b9..d774d31b 100644 --- a/mdpow/tests/test_run.py +++ b/mdpow/tests/test_run.py @@ -1,37 +1,147 @@ import pytest +import pybol + +import gromacs.run +import gromacs.exceptions from numpy.testing import assert_equal import mdpow.run import mdpow.config + @pytest.fixture def cfg(): # default bundled config return mdpow.config.get_configuration() -@pytest.mark.parametrize("protocols", [["energy_minimize"], - ["MD_relaxed"], - ["MD_NPT", "FEP"], - ]) + +@pytest.mark.parametrize( + "protocols", + [ + ["energy_minimize"], + ["MD_relaxed"], + ["MD_NPT", "FEP"], + ], +) def test_get_mdp_files(cfg, protocols): mdpfiles = mdpow.run.get_mdp_files(cfg, protocols) assert len(mdpfiles) == len(protocols) assert set(mdpfiles.keys()) == set(protocols) assert all([mdp.endswith(".mdp") for mdp in mdpfiles.values()]) -@pytest.mark.parametrize("protocols", [["FEP"], - ["Jabberwocky", "Mad Hatter"] - ]) + +@pytest.mark.parametrize("protocols", [["FEP"], ["Jabberwocky", "Mad Hatter"]]) def test_get_mdp_files_None(cfg, protocols): # modify cfg - del cfg.conf['FEP']['mdp'] + del cfg.conf["FEP"]["mdp"] with pytest.warns(mdpow.config.NoOptionWarning): mdpfiles = mdpow.run.get_mdp_files(cfg, ["FEP"]) assert mdpfiles == {} + def test_get_mdp_files_ValueError(cfg): # modify cfg with a non-existant file - cfg.conf['FEP']['mdp'] = "smoke_and_mirror.mdp" + cfg.conf["FEP"]["mdp"] = "smoke_and_mirror.mdp" with pytest.raises(ValueError): mdpow.run.get_mdp_files(cfg, ["MD_NPT", "FEP"]) + + +# To test the failure modes of runMD_or_exit() we mock the functions +# and methods that would return failures, so that we don't have to +# actually run simulations. + + +@pytest.fixture +def MDrunner_failure(monkeypatch): + # mock gromacs.run.MDrunner: pretend that the simulation failed + def mock_run_check(*args, **kwargs): + return False + + monkeypatch.setattr(gromacs.run.MDrunner, "run_check", mock_run_check) + + +# mock gromacs.run.check_mdrun_success(logfile) +@pytest.fixture +def check_mdrun_success_failure(monkeypatch): + # pretend simulation has not completed as indicated by log file + def mock_check_mdrun_success(arg): + return False + + monkeypatch.setattr(gromacs.run, "check_mdrun_success", mock_check_mdrun_success) + + +@pytest.fixture +def check_mdrun_success_none(monkeypatch): + # pretend no simulation has been run so there's no logfile to + # check and check_mdrun_success() returns None + def mock_check_mdrun_success(arg): + return None + + monkeypatch.setattr(gromacs.run, "check_mdrun_success", mock_check_mdrun_success) + + +@pytest.mark.parametrize( + "runlocal,exception", + [ + (True, gromacs.exceptions.GromacsError), + (False, gromacs.exceptions.MissingDataError), + ], +) +def test_runMD_or_exit_exceptions( + runlocal, + exception, + cfg, + MDrunner_failure, + check_mdrun_success_failure, + monkeypatch, + tmpdir, +): + params = {"deffnm": "md"} + S = {} + + def mock_getboolean(*args): + return runlocal + + monkeypatch.setattr(cfg, "getboolean", mock_getboolean) + + with pytest.raises(exception): + mdpow.run.runMD_or_exit( + S, "FEP", params, cfg, dirname=str(tmpdir), exit_on_error=False + ) + + +def test_runMD_or_exit_None(cfg, check_mdrun_success_none, monkeypatch, tmpdir): + # special case where runlocal=False and no simulation has been run + # so there's no logfile to check and check_mdrun_success() returns + # None + params = {"deffnm": "md"} + S = {} + + def mock_getboolean(*args): + return False + + monkeypatch.setattr(cfg, "getboolean", mock_getboolean) + + return_value = mdpow.run.runMD_or_exit( + S, "FEP", params, cfg, dirname=str(tmpdir), exit_on_error=False + ) + assert return_value is None + + +@pytest.mark.parametrize("runlocal", [True, False]) +def test_runMD_or_exit_SysExit( + runlocal, cfg, MDrunner_failure, check_mdrun_success_failure, monkeypatch, tmpdir +): + params = {"deffnm": "md"} + S = {} + + def mock_getboolean(*args): + return runlocal + + monkeypatch.setattr(cfg, "getboolean", mock_getboolean) + + with pytest.raises(SystemExit): + mdpow.run.runMD_or_exit( + S, "FEP", params, cfg, dirname=str(tmpdir), exit_on_error=True + ) diff --git a/mdpow/tests/test_runinput.py b/mdpow/tests/test_runinput.py index dc416279..cbf4db9e 100644 --- a/mdpow/tests/test_runinput.py +++ b/mdpow/tests/test_runinput.py @@ -7,98 +7,85 @@ from mdpow import config + class TestAlteredConfig(object): params_altered = { - 'DEFAULT': - { - 'qscripts':'custom.sh' - }, - 'setup': - { - 'name': 'custom_name', - 'molecule': 'some_molecule_ident', - 'itp': 'some_molecules_itp', - 'structure': 'some_molecules_structure', - 'watermodel': 'spce', - 'maxwarn': 2, - 'distance': None, # default (not in this input file) - 'boxtype': 'dodecahedron', # default (not in this input file) - 'gromacsoutput': True, - }, - 'energy_minimize': - { - 'mdp': 'custom_emin.mdp' - }, - 'MD_relaxed': - { - 'qscript': 'MD_relaxed.sge', - 'runtime': 10, - 'runlocal': False, - 'mdp': 'MD_relaxed_NPT_opls.mdp' - }, - 'MD_NPT': - { - 'qscript': 'MD_NPT.sge', - 'runtime': 10000, - 'runlocal': True, - 'mdp': 'MD_NPT_opls.mdp', - }, - 'FEP': - { - 'method': 'TI', - 'qscript': 'FEP.sge', - 'runtime': 1000, - 'runlocal': True, - 'maxwarn': 3, - 'mdp': 'fep_custom_opls.mdp' - }, - 'FEP_schedule_Coulomb': - { - 'name': 'Coul', - 'description': 'transition_1', - 'label': 'coulomb', - 'couple_lambda0': 'vdw', - 'couple_lambda1': 'vdw-q', - 'sc_alpha': 0.2, - 'sc_power': 2, - 'sc_sigma': 0.6, - 'lambdas': np.array([ 0 , 0.1 , 0.2 , 0.3 , 0.4 , 0.5 , 0.6 , 0.7 , 0.8 , 0.9 , 1. ]) - }, - 'FEP_schedule_VDW': - { - 'name': 'VANDERWAALS', - 'description': 'transition_2', - 'label': 'vanderwaals', - 'couple_lambda0': 'none', - 'couple_lambda1': 'vdw', - 'sc_alpha': 0, - 'sc_power': 3, - 'sc_sigma': 0.1, - 'lambdas': np.array([ 0.0 , 0.25 , 0.50 , 0.75 , 1 ]) - }, - 'mdrun': - { - 'stepout': 12000, - 'verbose': False, - 'nice': 12, - 'maxthreads': 1 - } + "DEFAULT": {"qscripts": "custom.sh"}, + "setup": { + "name": "custom_name", + "molecule": "some_molecule_ident", + "itp": "some_molecules_itp", + "structure": "some_molecules_structure", + "watermodel": "spce", + "maxwarn": 2, + "distance": None, # default (not in this input file) + "boxtype": "dodecahedron", # default (not in this input file) + "gromacsoutput": True, + }, + "energy_minimize": {"mdp": "custom_emin.mdp"}, + "MD_relaxed": { + "qscript": "MD_relaxed.sge", + "runtime": 10, + "runlocal": False, + "mdp": "MD_relaxed_NPT_opls.mdp", + }, + "MD_NPT": { + "qscript": "MD_NPT.sge", + "runtime": 10000, + "runlocal": True, + "mdp": "MD_NPT_opls.mdp", + }, + "FEP": { + "method": "TI", + "qscript": "FEP.sge", + "runtime": 1000, + "runlocal": True, + "maxwarn": 3, + "mdp": "fep_custom_opls.mdp", + }, + "FEP_schedule_Coulomb": { + "name": "Coul", + "description": "transition_1", + "label": "coulomb", + "couple_lambda0": "vdw", + "couple_lambda1": "vdw-q", + "sc_alpha": 0.2, + "sc_power": 2, + "sc_sigma": 0.6, + "lambdas": np.array([0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0]), + }, + "FEP_schedule_VDW": { + "name": "VANDERWAALS", + "description": "transition_2", + "label": "vanderwaals", + "couple_lambda0": "none", + "couple_lambda1": "vdw", + "sc_alpha": 0, + "sc_power": 3, + "sc_sigma": 0.1, + "lambdas": np.array([0.0, 0.25, 0.50, 0.75, 1]), + }, + "mdrun": {"stepout": 12000, "verbose": False, "nice": 12, "maxthreads": 1}, } @pytest.fixture def cfg(self): - return config.get_configuration(str(CONFIGURATIONS / 'altered_runinput.yml')) + return config.get_configuration(str(CONFIGURATIONS / "altered_runinput.yml")) def _test_section(self, cfg, section): section_dict = self.params_altered[section] for k in section_dict.keys(): - if k == 'lambdas': - parsed = np.array([float(x.strip()) for x in cfg.get(section,k).split(",")]) - assert_array_almost_equal(parsed, section_dict[k], - err_msg="mismatch in lambdas") + if k == "lambdas": + parsed = np.array( + [float(x.strip()) for x in cfg.get(section, k).split(",")] + ) + assert_array_almost_equal( + parsed, section_dict[k], err_msg="mismatch in lambdas" + ) else: - assert cfg.get(section,k) == section_dict[k], \ - "mismatch in {}:{}".format(section,k) + assert ( + cfg.get(section, k) == section_dict[k] + ), "mismatch in {}:{}".format(section, k) def test_DEFAULT(self, cfg): return self._test_section(cfg, "DEFAULT") diff --git a/mdpow/tests/test_solv_shell.py b/mdpow/tests/test_solv_shell.py index 259b3247..0f020c98 100644 --- a/mdpow/tests/test_solv_shell.py +++ b/mdpow/tests/test_solv_shell.py @@ -10,50 +10,55 @@ from numpy.testing import assert_almost_equal -from ..analysis.ensemble import Ensemble +from mdpow.analysis.ensemble import Ensemble -from ..analysis.solvation import SolvationAnalysis +from mdpow.analysis.solvation import SolvationAnalysis from pkg_resources import resource_filename -RESOURCES = py.path.local(resource_filename(__name__, 'testing_resources')) -MANIFEST = RESOURCES.join("manifest.yml") +from . import RESOURCES, MANIFEST class TestSolvShell(object): - def setup(self): + def setup_method(self): self.tmpdir = td.TempDir() - self.m = pybol.Manifest(str(RESOURCES / 'manifest.yml')) - self.m.assemble('example_FEP', self.tmpdir.name) - self.ens = Ensemble(dirname=self.tmpdir.name, solvents=['water']) - self.solute = self.ens.select_atoms('not resname SOL') - self.solvent = self.ens.select_atoms('resname SOL and name OW') + self.m = pybol.Manifest(str(RESOURCES / "manifest.yml")) + self.m.assemble("example_FEP", self.tmpdir.name) + self.ens = Ensemble(dirname=self.tmpdir.name, solvents=["water"]) + self.solute = self.ens.select_atoms("not resname SOL") + self.solvent = self.ens.select_atoms("resname SOL and name OW") - def teardown(self): + def teardown_method(self): self.tmpdir.dissolve() def test_dataframe(self): - solv = SolvationAnalysis(self.solute, self.solvent, [1.2]).run(start=0, stop=4, step=1) + solv = SolvationAnalysis(self.solute, self.solvent, [1.2]).run( + start=0, stop=4, step=1 + ) assert isinstance(solv.results, pd.DataFrame) - for d in solv.results['distance']: + for d in solv.results["distance"]: assert d == 1.2 - for s in solv.results['solvent']: - assert s == 'water' - for i in solv.results['interaction'][:12]: - assert i == 'Coulomb' + for s in solv.results["solvent"]: + assert s == "water" + for i in solv.results["interaction"][:12]: + assert i == "Coulomb" - @pytest.fixture(scope='class') + @pytest.fixture(scope="class") def solvation_analysis_list_results(self): - self.setup() # Won't have solute and solvent without this - return SolvationAnalysis(self.solute, self.solvent, [2, 10]).run(start=0, stop=4, step=1) - - @pytest.mark.parametrize("d,ref_mean,ref_std", [(2, 1.10714285,2.07604166), - (10, 5306.89285714, 129.16720594)]) + self.setup_method() # Won't have solute and solvent without this + return SolvationAnalysis(self.solute, self.solvent, [2, 10]).run( + start=0, stop=4, step=1 + ) + + @pytest.mark.parametrize( + "d,ref_mean,ref_std", + [(2, 1.10714285, 2.07604166), (10, 5306.89285714, 129.16720594)], + ) def test_selection(self, solvation_analysis_list_results, d, ref_mean, ref_std): results = solvation_analysis_list_results.results - mean = np.mean(results.loc[results['distance'] == d]['N_solvent']) - std = np.std(results.loc[results['distance'] == d]['N_solvent']) + mean = np.mean(results.loc[results["distance"] == d]["N_solvent"]) + std = np.std(results.loc[results["distance"] == d]["N_solvent"]) assert mean == pytest.approx(ref_mean) assert std == pytest.approx(ref_std) diff --git a/mdpow/tests/test_solvation.py b/mdpow/tests/test_solvation.py index 9ffadccb..d2d75404 100644 --- a/mdpow/tests/test_solvation.py +++ b/mdpow/tests/test_solvation.py @@ -10,53 +10,67 @@ from . import RESOURCES -sims = {"water" : equil.WaterSimulation, - "octanol" : equil.OctanolSimulation, - "cyclohexane" : equil.CyclohexaneSimulation, - "wetoctanol" : equil.WetOctanolSimulation, - } +sims = { + "water": equil.WaterSimulation, + "octanol": equil.OctanolSimulation, + "cyclohexane": equil.CyclohexaneSimulation, + "wetoctanol": equil.WetOctanolSimulation, + "toluene": equil.TolueneSimulation, +} + +test_file = { + "OPLS-AA": "benzene.itp", + "CHARMM": "benzene_charmm.itp", + "AMBER": "benzene_amber.itp", +} -test_file = {"OPLS-AA": 'benzene.itp', - "CHARMM": 'benzene_charmm.itp', - "AMBER": 'benzene_amber.itp', - } @pytest.fixture def setup(tmpdir): - newdir = tmpdir.mkdir('resources') - files = ['benzene.pdb', 'benzene.itp', - 'benzene_charmm.itp', 'benzene_amber.itp'] + newdir = tmpdir.mkdir("resources") + files = ["benzene.pdb", "benzene.itp", "benzene_charmm.itp", "benzene_amber.itp"] for f in files: - orig = RESOURCES / 'molecules' / 'benzene' / f + orig = RESOURCES / "molecules" / "benzene" / f shutil.copy(orig, newdir.dirname) return newdir.dirname -def solvation(setup, solvent, ff='OPLS-AA'): + +def solvation(setup, solvent, ff="OPLS-AA"): itp = test_file[ff] with in_dir(setup, create=False): try: - S = sims[solvent](molecule='BNZ', forcefield=ff) + S = sims[solvent](molecule="BNZ", forcefield=ff) S.topology(itp=itp) - S.solvate(struct='benzene.pdb') + S.solvate(struct="benzene.pdb") except Exception: - raise AssertionError('Solvation failed.') + raise AssertionError("Solvation failed.") -@pytest.mark.parametrize("ff", ['OPLS-AA', 'CHARMM', 'AMBER']) + +@pytest.mark.parametrize("ff", ["OPLS-AA", "CHARMM", "AMBER"]) def test_solvation_water(setup, ff): solvation(setup, "water", ff) -@pytest.mark.parametrize("ff", ['OPLS-AA', 'CHARMM', 'AMBER']) + +@pytest.mark.parametrize("ff", ["OPLS-AA", "CHARMM", "AMBER"]) def test_solvation_octanol(setup, ff): solvation(setup, "octanol", ff) + def test_solvation_cyclohexane(setup): solvation(setup, "cyclohexane") -@pytest.mark.xfail(gromacs.release.startswith('4') - or gromacs.release.startswith('5') - or gromacs.release.startswith('2016'), - reason="GROMACS < 2018 cannot easily work with mixed solvents " - "(see issue #111)") -@pytest.mark.parametrize("ff", ['OPLS-AA', 'CHARMM', 'AMBER']) + +@pytest.mark.parametrize("ff", ["OPLS-AA", "AMBER"]) +def test_solvation_toluene(setup, ff): + solvation(setup, "toluene", ff) + + +@pytest.mark.xfail( + gromacs.release.startswith("4") + or gromacs.release.startswith("5") + or gromacs.release.startswith("2016"), + reason="GROMACS < 2018 cannot easily work with mixed solvents " "(see issue #111)", +) +@pytest.mark.parametrize("ff", ["OPLS-AA", "CHARMM", "AMBER"]) def test_solvation_wetoctanol(setup, ff): solvation(setup, "wetoctanol", ff) diff --git a/mdpow/tests/test_version.py b/mdpow/tests/test_version.py index 187f27d4..28968abd 100644 --- a/mdpow/tests/test_version.py +++ b/mdpow/tests/test_version.py @@ -2,16 +2,17 @@ import mdpow + @pytest.fixture(scope="module") def version(): return mdpow.__version__ + def test_version_string(version): assert isinstance(version, str) + def test_version(version): # generic non-empty check because versioneer can provide different # answers depending on VCS status assert version - - diff --git a/mdpow/tests/test_workflows_base.py b/mdpow/tests/test_workflows_base.py new file mode 100644 index 00000000..d46e1080 --- /dev/null +++ b/mdpow/tests/test_workflows_base.py @@ -0,0 +1,162 @@ +import re +import os +import sys +import yaml +import pathlib +import logging + +import pybol +import pytest +import numpy as np +from numpy.testing import assert_equal +import pandas as pd +import MDAnalysis as mda + +from . import RESOURCES, MANIFEST, STATES +from pkg_resources import resource_filename +from mdpow.workflows import base + + +@pytest.fixture +def molname_workflows_directory(tmp_path): + m = pybol.Manifest(str(MANIFEST)) + m.assemble("workflows", tmp_path) + return tmp_path + + +@pytest.fixture +def universe(request): + masses, names = request.param + # build minimal test universe + u = mda.Universe.empty(n_atoms=len(names)) + u.add_TopologyAttr("names", names) + u.add_TopologyAttr("masses", masses) + return u + + +@pytest.mark.parametrize( + "universe,elements", + [ + [ + ( + np.array([12.011, 14.007, 0, 12.011, 35.45, 12.011]), + np.array(["C", "Nx", "DUMMY", "C0S", "Cl123", "C0U"]), + ), + np.array(["C", "N", "DUMMY", "C", "CL", "C"]), + ], + [ + ( + np.array([12.011, 14.007, 0, 35.45]), + np.array(["C", "Nx", "DUMMY", "Cl123"]), + ), + np.array(["C", "N", "DUMMY", "CL"]), + ], + [ + ( + np.array([15.999, 0, 40.08, 40.08, 40.08, 24.305, 132.9]), + np.array(["OW", "MW", "C0", "CAL", "CA2+", "MG2+", "CES"]), + ), + np.array(["O", "DUMMY", "CA", "CA", "CA", "MG", "CS"]), + ], + [ + (np.array([16, 1e-6, 40.085, 133]), np.array(["OW", "MW", "CA2+", "CES"])), + np.array(["O", "DUMMY", "CA", "CS"]), + ], + ], + indirect=["universe"], +) +def test_guess_elements(universe, elements): + u = universe + guessed_elements = base.guess_elements(u.atoms) + + assert_equal(guessed_elements, elements) + + +class TestWorkflowsBase(object): + @pytest.fixture + def SM_tmp_dir(self, molname_workflows_directory): + dirname = molname_workflows_directory + return dirname + + @pytest.fixture + def csv_input_data(self): + csv_path = STATES["workflows"] / "project_paths.csv" + csv_df = pd.read_csv(csv_path).reset_index(drop=True) + return csv_path, csv_df + + @pytest.fixture + def test_df_data(self): + test_dict = {"molecule": ["SM25", "SM26"], "resname": ["SM25", "SM26"]} + test_df = pd.DataFrame(test_dict).reset_index(drop=True) + return test_df + + @pytest.fixture + def project_paths_data(self, SM_tmp_dir): + project_paths = base.project_paths(parent_directory=SM_tmp_dir) + return project_paths + + def test_project_paths(self, test_df_data, project_paths_data): + test_df = test_df_data + project_paths = project_paths_data + + assert project_paths["molecule"][0] == test_df["molecule"][0] + assert project_paths["molecule"][1] == test_df["molecule"][1] + assert project_paths["resname"][0] == test_df["resname"][0] + assert project_paths["resname"][1] == test_df["resname"][1] + + def test_project_paths_csv_input(self, csv_input_data): + csv_path, csv_df = csv_input_data + project_paths = base.project_paths(csv=csv_path) + + pd.testing.assert_frame_equal(project_paths, csv_df) + + def test_dihedral_analysis_figdir_requirement(self, project_paths_data, caplog): + caplog.clear() + caplog.set_level(logging.ERROR, logger="mdpow.workflows.base") + + project_paths = project_paths_data + # change resname to match topology (every SAMPL7 resname is 'UNK') + # only necessary for this dataset, not necessary for normal use + project_paths["resname"] = "UNK" + + with pytest.raises( + AssertionError, + match="figdir MUST be set, even though it is a kwarg. Will be changed with #244", + ): + base.automated_project_analysis( + project_paths, solvents=("water",), ensemble_analysis="DihedralAnalysis" + ) + + assert "all analyses completed" in caplog.text, ( + "automated_dihedral_analysis " + "did not iteratively run to completion for the provided project" + ) + + def test_automated_project_analysis_KeyError(self, project_paths_data, caplog): + caplog.clear() + caplog.set_level(logging.ERROR, logger="mdpow.workflows.base") + + project_paths = project_paths_data + # change resname to match topology (every SAMPL7 resname is 'UNK') + # only necessary for this dataset, not necessary for normal use + project_paths["resname"] = "UNK" + + # test error output when raised + with pytest.raises( + KeyError, + match="Invalid ensemble_analysis 'DarthVaderAnalysis'. " + "An EnsembleAnalysis type that corresponds to an existing " + "automated workflow module must be input as a kwarg. ex: " + "ensemble_analysis='DihedralAnalysis'", + ): + base.automated_project_analysis( + project_paths, + ensemble_analysis="DarthVaderAnalysis", + solvents=("water",), + ) + + # test logger error recording + assert "'DarthVaderAnalysis' is an invalid selection" in caplog.text, ( + "did not catch incorrect " + "key specification for workflows.registry that results in KeyError" + ) diff --git a/mdpow/tests/test_workflows_registry.py b/mdpow/tests/test_workflows_registry.py new file mode 100644 index 00000000..50143818 --- /dev/null +++ b/mdpow/tests/test_workflows_registry.py @@ -0,0 +1,7 @@ +import pytest + +from mdpow.workflows import registry + + +def test_registry(): + assert list(registry.registry.keys()) == ["DihedralAnalysis"] diff --git a/mdpow/tests/testing_resources/manifest.yml b/mdpow/tests/testing_resources/manifest.yml index 3d55f814..2ba2a245 100644 --- 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-0.1916 0.6525 + 510SOL H11 7641 3.580 1.958 1.906 0.9796 -0.3431 -2.1003 + 510SOL C4 7642 3.505 2.115 1.785 0.4601 -0.8268 0.3211 + 510SOL H12 7643 3.449 2.053 1.716 1.0257 -1.0842 0.0865 + 510SOL C5 7644 3.493 2.257 1.768 0.0574 0.3050 0.4794 + 510SOL H13 7645 3.440 2.296 1.682 0.7866 -1.1045 -0.6699 + 510SOL C6 7646 3.544 2.341 1.866 -0.0594 -0.7932 0.0796 + 510SOL H14 7647 3.526 2.446 1.851 0.4800 -0.3030 2.4017 + 510SOL C7 7648 3.610 2.289 1.979 1.0687 -0.4825 0.4610 + 510SOL H15 7649 3.649 2.363 2.048 0.4703 1.8771 -1.5886 + 510SOL C2 7650 3.624 2.151 1.999 0.3626 -0.0681 -0.4194 + 511SOL C1 7651 2.067 2.172 0.337 -0.1608 -0.2843 0.5809 + 511SOL H8 7652 1.994 2.177 0.256 1.1642 1.2282 -0.6057 + 511SOL H9 7653 2.022 2.222 0.422 0.8010 -0.5553 1.2671 + 511SOL H10 7654 2.153 2.234 0.310 -0.6665 0.2728 0.2458 + 511SOL C3 7655 2.022 1.929 0.391 -0.2487 0.7881 0.4667 + 511SOL H11 7656 1.920 1.927 0.356 0.2074 0.5539 -0.9313 + 511SOL C4 7657 2.069 1.802 0.432 1.2586 0.1717 -0.3566 + 511SOL H12 7658 2.001 1.718 0.433 2.1637 -0.6080 -1.8618 + 511SOL C5 7659 2.201 1.777 0.473 -0.4101 -0.1367 0.0044 + 511SOL H13 7660 2.233 1.683 0.514 -1.2556 -2.1863 -3.6756 + 511SOL C6 7661 2.291 1.887 0.466 -0.2103 -0.0227 0.1763 + 511SOL H14 7662 2.389 1.872 0.507 0.3420 -0.3986 -1.2083 + 511SOL C7 7663 2.253 2.012 0.408 0.0532 -0.0922 -0.2285 + 511SOL H15 7664 2.319 2.097 0.409 -2.3224 1.9744 -2.1809 + 511SOL C2 7665 2.115 2.039 0.377 0.4275 0.3373 -0.7311 + 512SOL C1 7666 0.298 2.724 1.390 0.2046 1.2635 0.0652 + 512SOL H8 7667 0.363 2.810 1.401 2.9835 -0.7912 0.9577 + 512SOL H9 7668 0.266 2.688 1.488 -0.8753 -0.4849 -0.8663 + 512SOL H10 7669 0.360 2.649 1.339 3.0161 2.6183 1.2808 + 512SOL C3 7670 0.179 2.827 1.186 -0.1739 0.4737 -0.1419 + 512SOL H11 7671 0.277 2.859 1.153 -0.3860 -0.1616 -1.4161 + 512SOL C4 7672 0.066 2.864 1.114 -0.5183 -0.1852 0.1610 + 512SOL H12 7673 0.063 2.935 1.032 -1.5132 0.7412 0.9667 + 512SOL C5 7674 -0.058 2.823 1.167 -0.2566 0.0015 -0.4342 + 512SOL H13 7675 -0.143 2.861 1.112 -1.0323 0.0906 0.7890 + 512SOL C6 7676 -0.066 2.741 1.278 0.3277 -0.1105 -0.0695 + 512SOL H14 7677 -0.162 2.712 1.319 0.9695 -1.8805 0.2819 + 512SOL C7 7678 0.051 2.708 1.348 0.1441 0.0345 -0.0037 + 512SOL H15 7679 0.049 2.655 1.443 1.6372 -1.5870 -0.8564 + 512SOL C2 7680 0.176 2.747 1.304 -0.2385 0.6568 0.5053 + 4.49984 4.49984 4.49984 diff --git a/mdpow/top/amber99sb.ff/1tol.itp b/mdpow/top/amber99sb.ff/1tol.itp new file mode 100644 index 00000000..098f9f02 --- /dev/null +++ b/mdpow/top/amber99sb.ff/1tol.itp @@ -0,0 +1,148 @@ +;[ atomtypes ] +;name bond_type mass charge ptype sigma epsilon Amb +; c3 c3 0.00000 0.00000 A 3.39771e-01 4.51035e-01 ; 1.91 0.1078 +; hc hc 0.00000 0.00000 A 2.60018e-01 8.70272e-02 ; 1.46 0.0208 +; ca ca 0.00000 0.00000 A 3.31521e-01 4.13379e-01 ; 1.86 0.0988 +; ha ha 0.00000 0.00000 A 2.62548e-01 6.73624e-02 ; 1.47 0.0161 + +[ moleculetype ] +;name nrexcl + SOL 3 + +[ atoms ] +; nr type resi res atom cgnr charge mass ; qtot bond_type + 1 CT2 1 SOL C1 1 -0.053800 12.01000 ; qtot -0.054 + 2 HC2 1 SOL H8 2 0.044033 1.00800 ; qtot -0.010 + 3 HC2 1 SOL H9 3 0.044033 1.00800 ; qtot 0.034 + 4 HC2 1 SOL H10 4 0.044033 1.00800 ; qtot 0.078 + 5 CA2 1 SOL C3 5 -0.131000 12.01000 ; qtot -0.053 + 6 HA2 1 SOL H11 6 0.130000 1.00800 ; qtot 0.077 + 7 CA2 1 SOL C4 7 -0.127000 12.01000 ; qtot -0.050 + 8 HA2 1 SOL H12 8 0.130000 1.00800 ; qtot 0.080 + 9 CA2 1 SOL C5 9 -0.134999 12.01000 ; qtot -0.055 + 10 HA2 1 SOL H13 10 0.130000 1.00800 ; qtot 0.075 + 11 CA2 1 SOL C6 11 -0.127000 12.01000 ; qtot -0.052 + 12 HA2 1 SOL H14 12 0.130000 1.00800 ; qtot 0.078 + 13 CA2 1 SOL C7 13 -0.131000 12.01000 ; qtot -0.053 + 14 HA2 1 SOL H15 14 0.130000 1.00800 ; qtot 0.077 + 15 CA2 1 SOL C2 15 -0.077300 12.01000 ; qtot -0.000 + +[ bonds ] +; ai aj funct r k + 1 2 1 1.0970e-01 3.1455e+05 ; C1 - H8 + 1 3 1 1.0970e-01 3.1455e+05 ; C1 - H9 + 1 4 1 1.0970e-01 3.1455e+05 ; C1 - H10 + 1 15 1 1.5160e-01 2.0945e+05 ; C1 - C2 + 5 6 1 1.0860e-01 3.3112e+05 ; C3 - H11 + 5 7 1 1.3980e-01 3.1681e+05 ; C3 - C4 + 5 15 1 1.3980e-01 3.1681e+05 ; C3 - C2 + 7 8 1 1.0860e-01 3.3112e+05 ; C4 - H12 + 7 9 1 1.3980e-01 3.1681e+05 ; C4 - C5 + 9 10 1 1.0860e-01 3.3112e+05 ; C5 - H13 + 9 11 1 1.3980e-01 3.1681e+05 ; C5 - C6 + 11 12 1 1.0860e-01 3.3112e+05 ; C6 - H14 + 11 13 1 1.3980e-01 3.1681e+05 ; C6 - C7 + 13 14 1 1.0860e-01 3.3112e+05 ; C7 - H15 + 13 15 1 1.3980e-01 3.1681e+05 ; C7 - C2 + +[ pairs ] +; ai aj funct + 1 6 1 ; C1 - H11 + 1 7 1 ; C1 - C4 + 1 11 1 ; C1 - C6 + 1 14 1 ; C1 - H15 + 2 5 1 ; H8 - C3 + 2 13 1 ; H8 - C7 + 3 5 1 ; H9 - C3 + 3 13 1 ; H9 - C7 + 4 5 1 ; H10 - C3 + 4 13 1 ; H10 - C7 + 5 10 1 ; C3 - H13 + 5 11 1 ; C3 - C6 + 5 14 1 ; C3 - H15 + 6 8 1 ; H11 - H12 + 6 9 1 ; H11 - C5 + 6 13 1 ; H11 - C7 + 7 12 1 ; C4 - H14 + 7 13 1 ; C4 - C7 + 8 10 1 ; H12 - H13 + 8 11 1 ; H12 - C6 + 8 15 1 ; H12 - C2 + 9 14 1 ; C5 - H15 + 9 15 1 ; C5 - C2 + 10 12 1 ; H13 - H14 + 10 13 1 ; H13 - C7 + 12 14 1 ; H14 - H15 + 12 15 1 ; H14 - C2 + +[ angles ] +; ai aj ak funct theta cth + 1 15 5 1 1.2077e+02 5.4894e+02 ; C1 - C2 - C3 + 1 15 13 1 1.2077e+02 5.4894e+02 ; C1 - C2 - C7 + 2 1 3 1 1.0758e+02 3.2635e+02 ; H8 - C1 - H9 + 2 1 4 1 1.0758e+02 3.2635e+02 ; H8 - C1 - H10 + 2 1 15 1 1.1047e+02 3.9581e+02 ; H8 - C1 - C2 + 3 1 4 1 1.0758e+02 3.2635e+02 ; H9 - C1 - H10 + 3 1 15 1 1.1047e+02 3.9581e+02 ; H9 - C1 - C2 + 4 1 15 1 1.1047e+02 3.9581e+02 ; H10 - C1 - C2 + 5 7 8 1 1.1988e+02 4.0752e+02 ; C3 - C4 - H12 + 5 7 9 1 1.2002e+02 5.7572e+02 ; C3 - C4 - C5 + 5 15 13 1 1.2002e+02 5.7572e+02 ; C3 - C2 - C7 + 6 5 7 1 1.1988e+02 4.0752e+02 ; H11 - C3 - C4 + 6 5 15 1 1.1988e+02 4.0752e+02 ; H11 - C3 - C2 + 7 5 15 1 1.2002e+02 5.7572e+02 ; C4 - C3 - C2 + 7 9 10 1 1.1988e+02 4.0752e+02 ; C4 - C5 - H13 + 7 9 11 1 1.2002e+02 5.7572e+02 ; C4 - C5 - C6 + 8 7 9 1 1.1988e+02 4.0752e+02 ; H12 - C4 - C5 + 9 11 12 1 1.1988e+02 4.0752e+02 ; C5 - C6 - H14 + 9 11 13 1 1.2002e+02 5.7572e+02 ; C5 - C6 - C7 + 10 9 11 1 1.1988e+02 4.0752e+02 ; H13 - C5 - C6 + 11 13 14 1 1.1988e+02 4.0752e+02 ; C6 - C7 - H15 + 11 13 15 1 1.2002e+02 5.7572e+02 ; C6 - C7 - C2 + 12 11 13 1 1.1988e+02 4.0752e+02 ; H14 - C6 - C7 + 14 13 15 1 1.1988e+02 4.0752e+02 ; H15 - C7 - C2 + +[ dihedrals ] ; propers +; for gromacs 4.5 or higher, using funct 9 +; i j k l func phase kd pn + 1 15 5 6 9 180.00 15.16700 2 ; C1- C2- C3- H11 + 1 15 5 7 9 180.00 15.16700 2 ; C1- C2- C3- C4 + 1 15 13 11 9 180.00 15.16700 2 ; C1- C2- C7- C6 + 1 15 13 14 9 180.00 15.16700 2 ; C1- C2- C7- H15 + 2 1 15 5 9 180.00 0.00000 2 ; H8- C1- C2- C3 + 2 1 15 13 9 180.00 0.00000 2 ; H8- C1- C2- C7 + 3 1 15 5 9 180.00 0.00000 2 ; H9- C1- C2- C3 + 3 1 15 13 9 180.00 0.00000 2 ; H9- C1- C2- C7 + 4 1 15 5 9 180.00 0.00000 2 ; H10- C1- C2- C3 + 4 1 15 13 9 180.00 0.00000 2 ; H10- C1- C2- C7 + 5 7 9 10 9 180.00 15.16700 2 ; C3- C4- C5- H13 + 5 7 9 11 9 180.00 15.16700 2 ; C3- C4- C5- C6 + 5 15 13 11 9 180.00 15.16700 2 ; C3- C2- C7- C6 + 5 15 13 14 9 180.00 15.16700 2 ; C3- C2- C7- H15 + 6 5 7 8 9 180.00 15.16700 2 ; H11- C3- C4- H12 + 6 5 7 9 9 180.00 15.16700 2 ; H11- C3- C4- C5 + 6 5 15 13 9 180.00 15.16700 2 ; H11- C3- C2- C7 + 7 5 15 13 9 180.00 15.16700 2 ; C4- C3- C2- C7 + 7 9 11 12 9 180.00 15.16700 2 ; C4- C5- C6- H14 + 7 9 11 13 9 180.00 15.16700 2 ; C4- C5- C6- C7 + 8 7 5 15 9 180.00 15.16700 2 ; H12- C4- C3- C2 + 8 7 9 10 9 180.00 15.16700 2 ; H12- C4- C5- H13 + 8 7 9 11 9 180.00 15.16700 2 ; H12- C4- C5- C6 + 9 7 5 15 9 180.00 15.16700 2 ; C5- C4- C3- C2 + 9 11 13 14 9 180.00 15.16700 2 ; C5- C6- C7- H15 + 9 11 13 15 9 180.00 15.16700 2 ; C5- C6- C7- C2 + 10 9 11 12 9 180.00 15.16700 2 ; H13- C5- C6- H14 + 10 9 11 13 9 180.00 15.16700 2 ; H13- C5- C6- C7 + 12 11 13 14 9 180.00 15.16700 2 ; H14- C6- C7- H15 + 12 11 13 15 9 180.00 15.16700 2 ; H14- C6- C7- C2 + +[ dihedrals ] ; impropers +; treated as propers in GROMACS to use correct AMBER analytical function +; i j k l func phase kd pn + 1 15 13 5 4 180.00 4.60240 2 ; C1- C2- C7- C3 + 5 9 7 8 4 180.00 4.60240 2 ; C3- C5- C4- H12 + 7 11 9 10 4 180.00 4.60240 2 ; C4- C6- C5- H13 + 7 15 5 6 4 180.00 4.60240 2 ; C4- C2- C3- H11 + 9 13 11 12 4 180.00 4.60240 2 ; C5- C7- C6- H14 + 11 15 13 14 4 180.00 4.60240 2 ; C6- C2- C7- H15 + diff --git a/mdpow/top/amber99sb.ff/forcefield.itp b/mdpow/top/amber99sb.ff/forcefield.itp index 8624d69d..dff7f80a 100644 --- a/mdpow/top/amber99sb.ff/forcefield.itp +++ b/mdpow/top/amber99sb.ff/forcefield.itp @@ -17,6 +17,7 @@ 1 2 yes 0.5 0.8333 #include "ffnonbonded.itp" +#include "gaff2_nonbonded.itp" #include "ffbonded.itp" #include "gbsa.itp" diff --git a/mdpow/top/amber99sb.ff/gaff2_nonbonded.itp b/mdpow/top/amber99sb.ff/gaff2_nonbonded.itp new file mode 100644 index 00000000..30eec4f0 --- /dev/null +++ b/mdpow/top/amber99sb.ff/gaff2_nonbonded.itp @@ -0,0 +1,8 @@ +[ atomtypes ] +; name at.num mass charge ptype sigma epsilon +; CT2, HC2, CA2 and HA2 are the GAFF2 equivalents of CT, HC, CA and HA from GAFF, respectively. + CT2 0 0.0000 0.0000 A 3.39771e-01 4.51035e-01 ; 1.91 0.1078 + HC2 0 0.0000 0.0000 A 2.60018e-01 8.70272e-02 ; 1.46 0.0208 + CA2 0 0.0000 0.0000 A 3.31521e-01 4.13379e-01 ; 1.86 0.0988 + HA2 0 0.0000 0.0000 A 2.62548e-01 6.73624e-02 ; 1.47 0.0161 + \ No newline at end of file diff --git a/mdpow/top/charmm36-mar2019.ff/1tol.itp b/mdpow/top/charmm36-mar2019.ff/1tol.itp new file mode 100644 index 00000000..0176c060 --- /dev/null +++ b/mdpow/top/charmm36-mar2019.ff/1tol.itp @@ -0,0 +1,133 @@ +; Created by cgenff_charmm2gmx.py + +[ moleculetype ] +; Name nrexcl +SOL 3 + +[ atoms ] +; nr type resnr residue atom cgnr charge mass typeB chargeB massB +; residue 1 SOL rtp SOL q qsum + 1 CG331 1 SOL C1 1 -0.264 12.011 ; + 2 CG2R61 1 SOL C2 2 0.028 12.011 ; + 3 CG2R61 1 SOL C3 3 -0.114 12.011 ; + 4 CG2R61 1 SOL C4 4 -0.133 12.011 ; + 5 CG2R61 1 SOL C5 5 -0.115 12.011 ; + 6 CG2R61 1 SOL C6 6 -0.133 12.011 ; + 7 CG2R61 1 SOL C7 7 -0.114 12.011 ; + 8 HGA3 1 SOL H8 8 0.090 1.008 ; + 9 HGA3 1 SOL H9 9 0.090 1.008 ; + 10 HGA3 1 SOL H10 10 0.090 1.008 ; + 11 HGR61 1 SOL H11 11 0.115 1.008 ; + 12 HGR61 1 SOL H12 12 0.115 1.008 ; + 13 HGR61 1 SOL H13 13 0.115 1.008 ; + 14 HGR61 1 SOL H14 14 0.115 1.008 ; + 15 HGR61 1 SOL H15 15 0.115 1.008 ; + +[ bonds ] +; ai aj funct c0 c1 c2 c3 + 1 9 1 + 1 2 1 + 1 10 1 + 1 8 1 + 2 3 1 + 2 7 1 + 3 11 1 + 3 4 1 + 4 12 1 + 4 5 1 + 5 13 1 + 5 6 1 + 6 14 1 + 6 7 1 + 7 15 1 + +[ pairs ] +; ai aj funct c0 c1 c2 c3 + 1 11 1 + 1 4 1 + 1 6 1 + 1 15 1 + 2 12 1 + 2 5 1 + 2 14 1 + 3 6 1 + 3 8 1 + 3 9 1 + 3 10 1 + 3 13 1 + 3 15 1 + 4 14 1 + 4 7 1 + 5 11 1 + 5 15 1 + 6 12 1 + 7 8 1 + 7 9 1 + 7 10 1 + 7 11 1 + 7 13 1 + 11 12 1 + 12 13 1 + 13 14 1 + 14 15 1 + +[ angles ] +; ai aj ak funct c0 c1 c2 c3 + 9 1 2 5 + 9 1 10 5 + 9 1 8 5 + 2 1 10 5 + 2 1 8 5 + 10 1 8 5 + 1 2 3 5 + 1 2 7 5 + 3 2 7 5 + 2 3 11 5 + 2 3 4 5 + 11 3 4 5 + 3 4 12 5 + 3 4 5 5 + 12 4 5 5 + 4 5 13 5 + 4 5 6 5 + 13 5 6 5 + 5 6 14 5 + 5 6 7 5 + 14 6 7 5 + 2 7 6 5 + 2 7 15 5 + 6 7 15 5 + +[ dihedrals ] +; ai aj ak al funct c0 c1 c2 c3 c4 c5 + 9 1 2 3 9 + 9 1 2 7 9 + 10 1 2 3 9 + 10 1 2 7 9 + 8 1 2 3 9 + 8 1 2 7 9 + 1 2 3 11 9 + 1 2 3 4 9 + 7 2 3 11 9 + 7 2 3 4 9 + 1 2 7 6 9 + 1 2 7 15 9 + 3 2 7 6 9 + 3 2 7 15 9 + 2 3 4 12 9 + 2 3 4 5 9 + 11 3 4 12 9 + 11 3 4 5 9 + 3 4 5 13 9 + 3 4 5 6 9 + 12 4 5 13 9 + 12 4 5 6 9 + 4 5 6 14 9 + 4 5 6 7 9 + 13 5 6 14 9 + 13 5 6 7 9 + 5 6 7 2 9 + 5 6 7 15 9 + 14 6 7 2 9 + 14 6 7 15 9 + diff --git a/mdpow/top/oplsaa.ff/1tol.itp b/mdpow/top/oplsaa.ff/1tol.itp new file mode 100644 index 00000000..770c2cb6 --- /dev/null +++ b/mdpow/top/oplsaa.ff/1tol.itp @@ -0,0 +1,155 @@ +; input pdb SMILES : CC1=CC=CC=C1 + +[ moleculetype ] +; Name nrexcl +SOL 3 + +[ atoms ] +; atomnr atomtype resnr resname atomname chargegrp charge mass + 1 opls_148 1 SOL C1 1 -0.065 12.01100 ; CT # Charge adjusted with contributions from: ` C: CH3, toluene' AND ` Benzene C - 12 site JACS,112,4768-90. Use #145B for biphenyl', changes on the exocyclic atom. + 2 opls_140 1 SOL H8 1 0.060 1.00800 ; HC # alkane H. + 3 opls_140 1 SOL H9 1 0.060 1.00800 ; HC # alkane H. + 4 opls_140 1 SOL H10 1 0.060 1.00800 ; HC # alkane H. + 5 opls_145 1 SOL C3 7 -0.115 12.01100 ; CA # Benzene C - 12 site JACS,112,4768-90. Use #145B for biphenyl + 6 opls_146 1 SOL H11 7 0.115 1.00800 ; HA # Benzene H - 12 site. + 7 opls_145 1 SOL C4 9 -0.115 12.01100 ; CA # Benzene C - 12 site JACS,112,4768-90. Use #145B for biphenyl + 8 opls_146 1 SOL H12 9 0.115 1.00800 ; HA # Benzene H - 12 site. + 9 opls_145 1 SOL C5 11 -0.115 12.01100 ; CA # Benzene C - 12 site JACS,112,4768-90. Use #145B for biphenyl + 10 opls_146 1 SOL H13 11 0.115 1.00800 ; HA # Benzene H - 12 site. + 11 opls_145 1 SOL C6 13 -0.115 12.01100 ; CA # Benzene C - 12 site JACS,112,4768-90. Use #145B for biphenyl + 12 opls_146 1 SOL H14 13 0.115 1.00800 ; HA # Benzene H - 12 site. + 13 opls_145 1 SOL C7 15 -0.115 12.01100 ; CA # Benzene C - 12 site JACS,112,4768-90. Use #145B for biphenyl + 14 opls_146 1 SOL H15 15 0.115 1.00800 ; HA # Benzene H - 12 site. + 15 opls_145 1 SOL C2 17 -0.115 12.01100 ; CA # Benzene C - 12 site JACS,112,4768-90. Use #145B for biphenyl +; Total charge : 0.000 ### Number of unassigned atoms : 0/15 + +[ bonds ] +; ai aj funct r k + 1 15 1 ; CT-CA # (CA-CT) PHE,TYR + 1 2 1 ; CT-HC # CHARMM 22 parameter file + 1 3 1 ; CT-HC # CHARMM 22 parameter file + 1 4 1 ; CT-HC # CHARMM 22 parameter file + 15 13 1 ; CA-CA # TRP,TYR,PHE + 15 5 1 ; CA-CA # TRP,TYR,PHE + 5 7 1 ; CA-CA # TRP,TYR,PHE + 5 6 1 ; CA-HA # PHE, etc. + 7 9 1 ; CA-CA # TRP,TYR,PHE + 7 8 1 ; CA-HA # PHE, etc. + 9 11 1 ; CA-CA # TRP,TYR,PHE + 9 10 1 ; CA-HA # PHE, etc. + 11 13 1 ; CA-CA # TRP,TYR,PHE + 11 12 1 ; CA-HA # PHE, etc. + 13 14 1 ; CA-HA # PHE, etc. + +[ angles ] +; ai aj ak funct theta cth + 15 1 2 1 ; CA-CT-HC # + 15 1 3 1 ; CA-CT-HC # + 15 1 4 1 ; CA-CT-HC # + 2 1 3 1 ; HC-CT-HC # CHARMM 22 parameter file + 2 1 4 1 ; HC-CT-HC # CHARMM 22 parameter file + 3 1 4 1 ; HC-CT-HC # CHARMM 22 parameter file + 1 15 13 1 ; CT-CA-CA # (CA-CA-CT) PHE(OL) + 1 15 5 1 ; CT-CA-CA # (CA-CA-CT) PHE(OL) + 5 15 13 1 ; CA-CA-CA # PHE(OL) + 15 5 7 1 ; CA-CA-CA # PHE(OL) + 15 5 6 1 ; CA-CA-HA # + 7 5 6 1 ; CA-CA-HA # + 5 7 9 1 ; CA-CA-CA # PHE(OL) + 5 7 8 1 ; CA-CA-HA # + 9 7 8 1 ; CA-CA-HA # + 7 9 11 1 ; CA-CA-CA # PHE(OL) + 7 9 10 1 ; CA-CA-HA # + 11 9 10 1 ; CA-CA-HA # + 9 11 13 1 ; CA-CA-CA # PHE(OL) + 9 11 12 1 ; CA-CA-HA # + 13 11 12 1 ; CA-CA-HA # + 15 13 11 1 ; CA-CA-CA # PHE(OL) + 15 13 14 1 ; CA-CA-HA # + 11 13 14 1 ; CA-CA-HA # + +[ dihedrals ] +; ai aj ak al funct C0 ... C5 + 2 1 15 13 3 ; HC-CT-CA-CA # (CA-CA-CT-HC) ethyl benzene + 2 1 15 5 3 ; HC-CT-CA-CA # (CA-CA-CT-HC) ethyl benzene + 3 1 15 13 3 ; HC-CT-CA-CA # (CA-CA-CT-HC) ethyl benzene + 3 1 15 5 3 ; HC-CT-CA-CA # (CA-CA-CT-HC) ethyl benzene + 4 1 15 13 3 ; HC-CT-CA-CA # (CA-CA-CT-HC) ethyl benzene + 4 1 15 5 3 ; HC-CT-CA-CA # (CA-CA-CT-HC) ethyl benzene + 1 15 13 11 3 ; CT-CA-CA-CA # (X-CA-CA-X generic proper dihedral) aromatic ring + 1 15 13 14 3 ; CT-CA-CA-HA # (X-CA-CA-X generic proper dihedral) aromatic ring + 5 15 13 11 3 ; CA-CA-CA-CA # (X-CA-CA-X generic proper dihedral) aromatic ring + 5 15 13 14 3 ; CA-CA-CA-HA # (X-CA-CA-X generic proper dihedral) aromatic ring + 1 15 5 7 3 ; CT-CA-CA-CA # (X-CA-CA-X generic proper dihedral) aromatic ring + 1 15 5 6 3 ; CT-CA-CA-HA # (X-CA-CA-X generic proper dihedral) aromatic ring + 13 15 5 7 3 ; CA-CA-CA-CA # (X-CA-CA-X generic proper dihedral) aromatic ring + 13 15 5 6 3 ; CA-CA-CA-HA # (X-CA-CA-X generic proper dihedral) aromatic ring + 15 5 7 9 3 ; CA-CA-CA-CA # (X-CA-CA-X generic proper dihedral) aromatic ring + 15 5 7 8 3 ; CA-CA-CA-HA # (X-CA-CA-X generic proper dihedral) aromatic ring + 6 5 7 9 3 ; HA-CA-CA-CA # (X-CA-CA-X generic proper dihedral) aromatic ring + 6 5 7 8 3 ; HA-CA-CA-HA # (X-CA-CA-X generic proper dihedral) aromatic ring + 5 7 9 11 3 ; CA-CA-CA-CA # (X-CA-CA-X generic proper dihedral) aromatic ring + 5 7 9 10 3 ; CA-CA-CA-HA # (X-CA-CA-X generic proper dihedral) aromatic ring + 8 7 9 11 3 ; HA-CA-CA-CA # (X-CA-CA-X generic proper dihedral) aromatic ring + 8 7 9 10 3 ; HA-CA-CA-HA # (X-CA-CA-X generic proper dihedral) aromatic ring + 7 9 11 13 3 ; CA-CA-CA-CA # (X-CA-CA-X generic proper dihedral) aromatic ring + 7 9 11 12 3 ; CA-CA-CA-HA # (X-CA-CA-X generic proper dihedral) aromatic ring + 10 9 11 13 3 ; HA-CA-CA-CA # (X-CA-CA-X generic proper dihedral) aromatic ring + 10 9 11 12 3 ; HA-CA-CA-HA # (X-CA-CA-X generic proper dihedral) aromatic ring + 9 11 13 15 3 ; CA-CA-CA-CA # (X-CA-CA-X generic proper dihedral) aromatic ring + 9 11 13 14 3 ; CA-CA-CA-HA # (X-CA-CA-X generic proper dihedral) aromatic ring + 12 11 13 15 3 ; HA-CA-CA-CA # (X-CA-CA-X generic proper dihedral) aromatic ring + 12 11 13 14 3 ; HA-CA-CA-HA # (X-CA-CA-X generic proper dihedral) aromatic ring + +[ dihedrals ] +; impropers +; ai aj ak al funct improper + 13 5 15 1 1 improper_Z_CA_X_Y ; CA-CA-CA-CT + 7 6 5 15 1 improper_Z_CA_X_Y ; CA-HA-CA-CA + 9 8 7 5 1 improper_Z_CA_X_Y ; CA-HA-CA-CA + 11 10 9 7 1 improper_Z_CA_X_Y ; CA-HA-CA-CA + 13 12 11 9 1 improper_Z_CA_X_Y ; CA-HA-CA-CA + 11 14 13 15 1 improper_Z_CA_X_Y ; CA-HA-CA-CA + +[ pairs ] +; ai aj funct + 1 11 1 + 1 14 1 + 1 7 1 + 1 6 1 + 15 9 1 + 15 12 1 + 15 8 1 + 5 2 1 + 5 3 1 + 5 4 1 + 5 11 1 + 5 14 1 + 5 10 1 + 7 13 1 + 7 12 1 + 9 6 1 + 9 14 1 + 11 8 1 + 13 2 1 + 13 3 1 + 13 4 1 + 13 6 1 + 13 10 1 + 6 8 1 + 8 10 1 + 10 12 1 + 12 14 1 + +#ifdef POSRES +[ position_restraints ] +; atom type fx fy fz + 1 1 1000 1000 1000 + 15 1 1000 1000 1000 + 5 1 1000 1000 1000 + 7 1 1000 1000 1000 + 9 1 1000 1000 1000 + 11 1 1000 1000 1000 + 13 1 1000 1000 1000 +#endif + diff --git a/mdpow/workflows/__init__.py b/mdpow/workflows/__init__.py new file mode 100644 index 00000000..f9889125 --- /dev/null +++ b/mdpow/workflows/__init__.py @@ -0,0 +1,2 @@ +# Workflows Subpackage +# Cade Duckworth (cadeduckworth) 2022 diff --git a/mdpow/workflows/base.py b/mdpow/workflows/base.py new file mode 100644 index 00000000..93ddb561 --- /dev/null +++ b/mdpow/workflows/base.py @@ -0,0 +1,258 @@ +# MDPOW: base.py +# 2022 Cade Duckworth + +""" +:mod:`mdpow.workflows.base` --- Automated workflow base functions +================================================================= + +To analyze multiple MDPOW projects, provide :func:`project_paths` +with the top-level directory containing all MDPOW projects' simulation data +to obtain a :class:`pandas.DataFrame` containing the project information +and paths. Then, :func:`automated_project_analysis` takes as input the +aforementioned :class:`pandas.DataFrame` and runs the specified +:class:`~mdpow.analysis.ensemble.EnsembleAnalysis` for all MDPOW projects +under the top-level directory provided to :func:`project_paths`. + +.. seealso:: :mod:`~mdpow.workflows.registry` + +.. autofunction:: project_paths +.. autofunction:: automated_project_analysis +.. autofunction:: guess_elements + +""" + +import os +import re +import logging + +import numpy as np +import pandas as pd +from MDAnalysis.topology import guessers, tables + +logger = logging.getLogger("mdpow.workflows.base") + + +def project_paths(parent_directory=None, csv=None, csv_save_dir=None): + """Takes a top directory containing MDPOW projects and determines + the molname, resname, and path, of each MDPOW project within. + + Optionally takes a .csv file containing `molname`, `resname`, and + `paths`, in that order. + + :keywords: + + *parent_directory* + the path for the location of the top directory + under which the subdirectories of MDPOW simulation + data exist, additionally creates a 'project_paths.csv' file + for user manipulation of metadata and for future reference + + *csv* + .csv file containing the molecule names, resnames, + and paths, in that order, for the MDPOW simulation + data to be iterated over must contain header of the + form: `molecule,resname,path` + + *csv_save_dir* + optionally provided directory to save .csv file, otherwise, + data will be saved in current working directory + + :returns: + + *project_paths* + :class:`pandas.DataFrame` containing MDPOW project metadata + + .. rubric:: Example + + Typical Workflow:: + + project_paths = project_paths(parent_directory='/foo/bar/MDPOW_projects') + automated_project_analysis(project_paths) + + or:: + + project_paths = project_paths(csv='/foo/bar/MDPOW.csv') + automated_project_analysis(project_paths) + + """ + + if parent_directory is not None: + locations = [] + + reg_compile = re.compile("FEP") + for dirpath, dirnames, filenames in os.walk(parent_directory): + result = [ + dirpath.strip() for dirname in dirnames if reg_compile.match(dirname) + ] + if result: + locations.append(result[0]) + + resnames = [] + + for loc in locations: + res_temp = loc.strip().split("/") + resnames.append(res_temp[-1]) + + project_paths = ( + pd.DataFrame({"molecule": resnames, "resname": resnames, "path": locations}) + .sort_values(by=["molecule", "resname", "path"]) + .reset_index(drop=True) + ) + if csv_save_dir is not None: + project_paths.to_csv(f"{csv_save_dir}/project_paths.csv", index=False) + logger.info(f"project_paths saved under {csv_save_dir}") + else: + current_directory = os.getcwd() + project_paths.to_csv("project_paths.csv", index=False) + logger.info(f"project_paths saved under {current_directory}") + + elif csv is not None: + locations = pd.read_csv(csv) + project_paths = locations.sort_values( + by=["molecule", "resname", "path"] + ).reset_index(drop=True) + + return project_paths + + +def automated_project_analysis(project_paths, ensemble_analysis, **kwargs): + """Takes a :class:`pandas.DataFrame` created by :func:`~mdpow.workflows.base.project_paths` + and iteratively runs the specified :class:`~mdpow.analysis.ensemble.EnsembleAnalysis` + for each of the projects by running the associated automated workflow + in each project directory returned by :func:`~mdpow.workflows.base.project_paths`. + + Compatibility with more automated analyses in development. + + :keywords: + + *project_paths* + :class:`pandas.DataFrame` that provides paths to MDPOW projects + + *ensemble_analysis* + name of the :class:`~mdpow.analysis.ensemble.EnsembleAnalysis` + that corresponds to the desired automated workflow module + + *kwargs* + keyword arguments for the supported automated workflows, + see the :mod:`~mdpow.workflows.registry` for all available + workflows and their call signatures + + .. rubric:: Example + + A typical workflow is the automated dihedral analysis from + :mod:`mdpow.workflows.dihedrals`, which applies the *ensemble analysis* + :class:`~mdpow.analysis.dihedral.DihedralAnalysis` to each project. + The :data:`~mdpow.workflows.registry.registry` contains this automated + workflow under the key *"DihedralAnalysis"* and so the automated execution + for all `project_paths` (obtained via :func:`project_paths`) is performed by + passing the specific key to :func:`automated_project_analysis`:: + + project_paths = project_paths(parent_directory='/foo/bar/MDPOW_projects') + automated_project_analysis(project_paths, ensemble_analysis='DihedralAnalysis', **kwargs) + + """ + # import inside function to avoid circular imports + from .registry import registry + + for row in project_paths.itertuples(): + molname = row.molecule + resname = row.resname + dirname = row.path + + logger.info(f"starting {molname}") + + try: + registry[ensemble_analysis]( + dirname=dirname, resname=resname, molname=molname, **kwargs + ) + logger.info(f"{molname} completed") + except KeyError as err: + msg = ( + f"Invalid ensemble_analysis {err}. An EnsembleAnalysis type that corresponds " + "to an existing automated workflow module must be input as a kwarg. " + "ex: ensemble_analysis='DihedralAnalysis'" + ) + logger.error(f"{err} is an invalid selection") + raise KeyError(msg) + except TypeError as err: + msg = ( + f"Invalid ensemble_analysis {ensemble_analysis}. An EnsembleAnalysis type that " + "corresponds to an existing automated workflow module must be input as a kwarg. " + "ex: ensemble_analysis='DihedralAnalysis'" + ) + logger.error(f"workflow module for {ensemble_analysis} does not exist yet") + raise TypeError(msg) + + logger.info("all analyses completed") + return + + +def guess_elements(atoms, rtol=1e-3): + """guess elements for atoms from masses + + Given masses, we perform a reverse lookup on + :data:`MDAnalysis.topology.tables.masses` to find the corresponding + element. Only atoms where the standard MDAnalysis guesser finds elements + with masses contradicting the topology masses are corrected. + + .. Note:: This function *requires* correct masses to be present. + No sanity checks because MDPOW always uses TPR files that + contain correct masses. + + :arguments: + + *atoms* + MDAnalysis AtomGroup *with masses defined* + + :keywords: + + *rtol* + relative tolerance for a match (as used in :func:`numpy.isclose`); + atol=1e-6 is at a fixed value, which means that "zero" is only + recognized for values =< 1e-6 + + .. note:: In order to reliably match GROMACS masses, *rtol* should + be at least 1e-3. + + :returns: + + *elements* + array of guessed element symbols, in same order as `atoms` + + .. rubric:: Example + + As an example we guess masses and then set the elements for all atoms:: + + elements = guess_elements(atoms) + atoms.add_TopologyAttr("elements", elements) + + """ + ATOL = 1e-6 + + names = atoms.names + masses = atoms.masses + + mda_elements = np.fromiter(tables.masses.keys(), dtype="U5") + mda_masses = np.fromiter(tables.masses.values(), dtype=np.float64) + + guessed_elements = guessers.guess_types(names) + guessed_masses = np.array([guessers.get_atom_mass(n) for n in guessed_elements]) + problems = np.logical_not(np.isclose(masses, guessed_masses, atol=ATOL, rtol=rtol)) + + # match only problematic masses against the MDA reference masses + iproblem, ielem = np.nonzero( + np.isclose(masses[problems, np.newaxis], mda_masses, atol=ATOL, rtol=rtol) + ) + # We should normally find a match for each problem but just in case, assert and + # give some useful information for debugging. + assert len(ielem) == sum(problems), ( + "Not all masses could be assigned an element, " + f"missing names {set(names[problems]) - set(names[problems][iproblem])}" + ) + + guessed_elements[problems] = mda_elements[ielem] + + # manually fix some dummies that are labelled "D": set ALL zero masses to DUMMY + guessed_elements[np.isclose(masses, 0, atol=ATOL)] = "DUMMY" + + return guessed_elements diff --git a/mdpow/workflows/dihedrals.py b/mdpow/workflows/dihedrals.py new file mode 100644 index 00000000..05e678a9 --- /dev/null +++ b/mdpow/workflows/dihedrals.py @@ -0,0 +1,972 @@ +# MDPOW: dihedrals.py +# 2022 Cade Duckworth + +""" +:mod:`mdpow.workflows.dihedrals` --- Automation for :class:`DihedralAnalysis` +============================================================================== + +:mod:`~mdpow.workflows.dihedrals` module provides functions for automated +workflows that encompass :class:`~mdpow.analysis.dihedral.DihedralAnalysis`. +See each function for requirements and examples. + +Most functions can be used as standalone, individually, or in combination +depending on the desired results. Details of the completely automated workflow +are discussed under :func:`~mdpow.workflows.dihedrals.automated_dihedral_analysis`. + +Atom indices obtained by :func:`get_atom_indices` are 0-based, +atom index labels on the molecule in the plots are 0-based, +but atom `names` in plots and file names are 1-based. + +.. autodata:: SOLVENTS_DEFAULT + :annotation: = ('water', 'octanol') +.. autodata:: INTERACTIONS_DEFAULT + :annotation: = ('Coulomb', 'VDW') +.. autodata:: SMARTS_DEFAULT + :annotation: = [!#1]~[!$(*#*)&!D1]-!@[!$(*#*)&!D1]~[!#1] +.. autodata:: PLOT_WIDTH_DEFAULT + :annotation: = 190 +.. autofunction:: automated_dihedral_analysis +.. autofunction:: build_universe +.. autofunction:: rdkit_conversion +.. autofunction:: get_atom_indices +.. autofunction:: get_bond_indices +.. autofunction:: get_dihedral_groups +.. autofunction:: get_paired_indices +.. autofunction:: dihedral_groups_ensemble +.. autofunction:: save_df +.. autofunction:: periodic_angle_padding +.. autofunction:: dihedral_violins +.. autofunction:: build_svg +.. autofunction:: plot_dihedral_violins + +""" + +import os +import logging +import pathlib + +import numpy as np +import pandas as pd +import MDAnalysis as mda +from MDAnalysis.topology.guessers import guess_types +from rdkit import Chem +from rdkit.Chem import rdCoordGen +from rdkit.Chem.Draw import rdMolDraw2D +import matplotlib.pyplot as plt +import seaborn as sns +import pypdf +import cairosvg +import svgutils +import svgutils.compose +import svgutils.transform + +from .base import guess_elements +from ..analysis import ensemble, dihedral + +logger = logging.getLogger("mdpow.workflows.dihedrals") + +SOLVENTS_DEFAULT = ("water", "octanol") +"""Default solvents are water and octanol: + + * must match solvents used in project directory + * one or two solvents can be specified + * current solvents supported, + + .. seealso:: :mod:`mdpow.forcefields` + +""" + +INTERACTIONS_DEFAULT = ("Coulomb", "VDW") +"""Default interactions set to Coulomb and VDW: + + * default values should not be changed + * order should not be changed + +""" + +SMARTS_DEFAULT = "[!#1]~[!$(*#*)&!D1]-!@[!$(*#*)&!D1]~[!#1]" +"""Default SMARTS string to identify relevant dihedral atom groups: + + * ``[!#1]`` : any atom, not Hydrogen + * ``~`` : any bond + * ``[!$(*#*)&!D1]`` : any atom that is not part of linear triple + bond and not atom with 1 explicit bond + * ``-!@`` : single bond that is not ring bond + * ``[!$(*#*)&!D1]-!@[!$(*#*)&!D1]`` : the central portion selects + two atoms that are not involved in a triple bond and are not terminal, + that are connected by a single, non-ring bond + * ``[!#1]~` or `~[!#1]`` : the first and last portion specify any bond, + to any atom that is not hydrogen + +""" + +PLOT_WIDTH_DEFAULT = 190 +"""Plot width (`plot_pdf_width`) should be provided in millimeters (mm), + and is converted to pixels (px) for use with :mod:`cairosvg`. + + conversion factor: 1 mm = 3.7795275591 px + default value: 190 mm = 718.110236229 pixels +""" + + +def build_universe(dirname, solvents=SOLVENTS_DEFAULT): + """Builds :class:`~MDAnalysis.core.universe.Universe` from the + ``./Coulomb/0000`` topology and trajectory of the project for + the first solvent specified. + + Output used by :func:`~mdpow.workflows.dihedrals.rdkit_conversion` + and :func:`~mdpow.workflows.dihedrals.get_atom_indices` to obtain atom indices + for each dihedral atom group. + + :keywords: + + *dirname* + Molecule Simulation directory. Loads simulation files present in + lambda directories into the new instance. With this method for + generating an :class:`~mdpow.analysis.ensemble.Ensemble` the + lambda directories are explored and + :meth:`~mdpow.analysis.ensemble.Ensemble._load_universe_from_dir` + searches for .gro, .gro.bz2, .gro.gz, and .tpr files for topology, + and .xtc files for trajectory. It will default to using the tpr file + available. + + *solvents* + The default solvents are documented under :data:`SOLVENTS_DEFAULT`. + Normally takes a two-tuple, but analysis is compatible with single solvent selections. + Single solvent analyses will result in a figure with fully filled violins for the single solvent. + + :returns: + + *u* + :class:`~MDAnalysis.core.universe.Universe` object + + """ + + path = pathlib.Path(dirname) + topology = path / f"FEP/{solvents[0]}/Coulomb/0000" / "md.tpr" + trajectory = path / f"FEP/{solvents[0]}/Coulomb/0000" / "md.xtc" + u = mda.Universe(str(topology), str(trajectory)) + + return u + + +def rdkit_conversion(u, resname): + """Converts the solute, `resname`, of the + :class:`~MDAnalysis.core.universe.Universe` to :class:`rdkit.Chem.rdchem.Mol` object + for use with a SMARTS selection string to identify dihedral atom groups. + + Accepts :class:`~MDAnalysis.core.universe.Universe` object made with + :func:`~mdpow.workflows.dihedrals.build_universe` and a `resname` as input. + Uses `resname` to select the solute for conversion by + :class:`~MDAnalysis.converters.RDKit.RDKitConverter` to :class:`rdkit.Chem.rdchem.Mol`, + and will add element attributes for Hydrogen if not listed in the topology, + using :func:`MDAnalysis.topology.guessers.guess_atom_element`. + + :keywords: + + *u* + :class:`~MDAnalysis.core.universe.Universe` object + + *resname* + `resname` for the molecule as defined in + the topology and trajectory + + :returns: + + *tuple(mol, solute)* + function call returns tuple, see below + + *mol* + :class:`rdkit.Chem.rdchem.Mol` object converted from `solute` + + *solute* + the :any:`MDAnalysis` `AtomGroup` for the solute + + """ + + try: + solute = u.select_atoms(f"resname {resname}") + mol = solute.convert_to("RDKIT") + except AttributeError: + guessed_elements = guess_elements(u.atoms) + u.add_TopologyAttr("elements", guessed_elements) + solute = u.select_atoms(f"resname {resname}") + mol = solute.convert_to("RDKIT") + + rdCoordGen.AddCoords(mol) + + for atom in mol.GetAtoms(): + atom.SetProp("atomNote", str(atom.GetIdx())) + + return mol, solute + + +def get_atom_indices(mol, SMARTS=SMARTS_DEFAULT): + """Uses a SMARTS selection string to identify atom indices + for relevant dihedral atom groups. + + Requires a :class:`rdkit.Chem.rdchem.Mol` object as input + for the :data:`SMARTS_DEFAULT` kwarg to match patterns to and + identify relevant dihedral atom groups. + + :keywords: + + *mol* + :class:`rdkit.Chem.rdchem.Mol` object converted from `solute` + + *SMARTS* + The default SMARTS string is described in detail under :data:`SMARTS_DEFAULT`. + + :returns: + + *atom_indices* + tuple of tuples of indices for each dihedral atom group + + """ + + pattern = Chem.MolFromSmarts(SMARTS) + atom_indices = mol.GetSubstructMatches(pattern) + + return atom_indices + + +def get_bond_indices(mol, atom_indices): + """From the :class:`rdkit.Chem.rdchem.Mol` object, uses + `atom_indices` to determine the indices of the bonds + between those atoms for each dihedral atom group. + + :keywords: + + *mol* + :class:`rdkit.Chem.rdchem.Mol` object converted from `solute` + + *atom_indices* + tuple of tuples of indices for each dihedral atom group + + :returns: + + *bond_indices* + tuple of tuples of indices for the bonds in each dihedral atom group + + """ + + bonds = [] + + for atom_index in atom_indices: + x = mol.GetBondBetweenAtoms(atom_index[0], atom_index[1]).GetIdx() + y = mol.GetBondBetweenAtoms(atom_index[1], atom_index[2]).GetIdx() + z = mol.GetBondBetweenAtoms(atom_index[2], atom_index[3]).GetIdx() + bix = (x, y, z) + + bonds.append(bix) + + bond_indices = tuple(bonds) + + return bond_indices + + +def get_dihedral_groups(solute, atom_indices): + """Uses the 0-based `atom_indices` of the relevant dihedral atom groups + determined by :func:`~mdpow.workflows.dihedrals.get_atom_indices` + and returns the 1-based index names for each atom in each group. + + Requires the `atom_indices` from :func:`~mdpow.workflows.dihedrals.get_atom_indices` + to index the `solute` specified by :func:`~MDAnalysis.core.groups.select_atoms` + and return an array of the names of each atom within its respective + dihedral atom group as identified by the SMARTS selection string. + + :keywords: + + *solute* + the :any:`MDAnalysis` `AtomGroup` for the solute + + *atom_indices* + tuple of tuples of indices for each dihedral atom group + + :returns: + + *dihedral_groups* + list of :func:`numpy.array` for atom names in each dihedral atom group + + """ + # currently uses RDKit Mol object atom indices to retrieve + # atom names from the MDAnalysis solute object + # RDKit-MDAnalysis index consistency is currently tested + dihedral_groups = [ + solute.atoms[list(atom_index)].names for atom_index in atom_indices + ] + + return dihedral_groups + + +def get_paired_indices(atom_indices, bond_indices, dihedral_groups): + """Combines `atom_indices` and `bond_indices` in tuples + to be paired with their respective dihedral atom groups. + + A dictionary is created with key-value pairs as follows: + `atom_indices` and `bond_indices` are joined in a tuple + as the value, with the key being the respective member + of `dihedral_groups` to facilitate highlighting the + relevant dihedral atom group when generating violin plots. + As an example, `'C1-N2-O3-S4': ((0, 1, 2, 3), (0, 1, 2))`, + would be one key-value pair in the dictionary. + + :keywords: + + *atom_indices* + tuple of tuples of indices for each dihedral atom group + + *bond_indices* + tuple of tuples of indices for the bonds in each dihedral atom group + + *dihedral_groups* + list of :func:`numpy.array` for atom names in each dihedral atom group + + :returns: + + *name_index_pairs* + dictionary with key-value pair for dihedral atom group, + atom indices, and bond indices + + """ + + all_dgs = [f"{dg[0]}-{dg[1]}-{dg[2]}-{dg[3]}" for dg in dihedral_groups] + + name_index_pairs = {} + name_index_pairs = { + all_dgs[i]: (atom_indices[i], bond_indices[i]) for i in range(len(all_dgs)) + } + + return name_index_pairs + + +def dihedral_groups_ensemble( + dirname, + atom_indices, + solvents=SOLVENTS_DEFAULT, + interactions=INTERACTIONS_DEFAULT, + start=None, + stop=None, + step=None, +): + """Creates one :class:`~mdpow.analysis.ensemble.Ensemble` for the MDPOW + project and runs :class:`~mdpow.analysis.dihedral.DihedralAnalysis` + for each dihedral atom group identified by the SMARTS + selection string. + + .. seealso:: + + :func:`~mdpow.workflows.dihedrals.automated_dihedral_analysis`, + :class:`~mdpow.analysis.dihedral.DihedralAnalysis` + + :keywords: + + *dirname* + Molecule Simulation directory. Loads simulation files present in + lambda directories into the new instance. With this method for + generating an :class:`~mdpow.analysis.ensemble.Ensemble` the + lambda directories are explored and + :meth:`~mdpow.analysis.ensemble.Ensemble._load_universe_from_dir` + searches for .gro, .gro.bz2, .gro.gz, and .tpr files for topology, + and .xtc files for trajectory. It will default to using the tpr file + available. + + *atom_indices* + tuples of atom indices for dihedral atom groups + + .. seealso:: :func:`~mdpow.workflows.dihedrals.get_atom_indices`, :data:`SMARTS_DEFAULT` + + *solvents* + The default solvents are documented under :data:`SOLVENTS_DEFAULT`. + Normally takes a two-tuple, but analysis is compatible with single solvent selections. + Single solvent analyses will result in a figure with fully filled violins for the single solvent. + + *interactions* + The default interactions are documented under :data:`INTERACTIONS_DEFAULT`. + + *start, stop, step* + arguments passed to :func:`~mdpow.analysis.ensemble.EnsembleAnalysis.run`, + as parameters for iterating through the trajectories of the current ensemble + + .. seealso:: :class:`~mdpow.analysis.ensemble.EnsembleAnalysis` + + :returns: + + *df* + :class:`pandas.DataFrame` of :class:`~mdpow.analysis.dihedral.DihedralAnalysis` + results, including all dihedral atom groups for molecule of current project + + """ + + dih_ens = ensemble.Ensemble( + dirname=dirname, solvents=solvents, interactions=interactions + ) + indices = atom_indices + all_dihedrals = [ + dih_ens.select_atoms( + f"index {i[0]}", f"index {i[1]}", f"index {i[2]}", f"index {i[3]}" + ) + for i in indices + ] + + da = dihedral.DihedralAnalysis(all_dihedrals) + da.run(start=start, stop=stop, step=step) + df = da.results + + return df + + +def save_df(df, df_save_dir, resname, molname=None): + """Takes a :class:`pandas.DataFrame` of results from + :class:`~mdpow.analysis.dihedral.DihedralAnalysis` + as input before padding the angles to optionaly save the raw + data. + + Optionally saves results before padding the angles for periodicity + and plotting dihedral angle frequencies as KDE violins + with :func:`~mdpow.workflows.dihedrals.dihedral_violins`. + Given a parent directory, creates subdirectory for molecule, + saves fully sampled, unpadded results :class:`pandas.DataFrame` + as a compressed csv file, default: .csv.bz2. + + :keywords: + + *df* + :class:`pandas.DataFrame` of :class:`~mdpow.analysis.dihedral.DihedralAnalysis` + results, including all dihedral atom groups for molecule of current project + + *df_save_dir* + optional, path to the location to save results :class:`pandas.DataFrame` + + *resname* + `resname` for the molecule as defined in + the topology and trajectory + + *molname* + molecule name to be used for labelling + plots, if different from `resname` + + """ + + df = df.sort_values( + by=["selection", "solvent", "interaction", "lambda"] + ).reset_index(drop=True) + + if molname is None: + molname = resname + + subdir = molname + newdir = os.path.join(df_save_dir, subdir) + os.mkdir(newdir) + + # time and compression level can be adjusted as kwargs + df.to_csv(f"{newdir}/{molname}_full_df.csv.bz2", index=False, compression="bz2") + + logger.info(f"Results DataFrame saved as {newdir}/{molname}_full_df.csv.bz2") + + +def periodic_angle_padding(df, padding=45): + """Pads the angles from the results :class:`~pandas.DataFrame` + to maintain periodicity in the violin plots. + + Takes a :class:`pandas.DataFrame` of results from + :class:`~mdpow.analysis.dihedral.DihedralAnalysis` + or :func:`~mdpow.workflows.dihedrals.dihedral_groups_ensemble` + as input and pads the angles to maintain periodicity + for properly plotting dihedral angle frequencies as KDE violins + with :func:`~mdpow.workflows.dihedrals.dihedral_violins` and + :func:`~mdpow.workflows.dihedrals.plot_dihedral_violins`. + Creates two new :class:`pandas.DataFrame` based on the + `padding` value specified, pads the angle values, concatenates + all three :class:`pandas.DataFrame`, maintaining original data and + adding padded values, and returns new augmented :class:`pandas.DataFrame`. + + :keywords: + + *df* + :class:`pandas.DataFrame` of :class:`~mdpow.analysis.dihedral.DihedralAnalysis` + results, including all dihedral atom groups for molecule of current project + + *padding* + value in degrees to specify angle augmentation threshold + default: 45 + + :returns: + + *df_aug* + augmented results :class:`pandas.DataFrame` containing + padded dihedral angles as specified by `padding` + + """ + + df1 = df[df.dihedral > 180 - padding].copy(deep=True) + df1.dihedral -= 360 + df2 = df[df.dihedral < -180 + padding].copy(deep=True) + df2.dihedral += 360 + df_aug = pd.concat([df1, df, df2]).reset_index(drop=True) + + return df_aug + + +def dihedral_violins(df, width=0.9, solvents=SOLVENTS_DEFAULT, plot_title=None): + """Plots kernel density estimates (KDE) of dihedral angle frequencies for + one dihedral atom group as violin plots, using as input the augmented + :class:`pandas.DataFrame` from :func:`~mdpow.workflows.dihedrals.periodic_angle_padding`. + + Output is converted to SVG by :func:`~mdpow.workflows.dihedrals.build_svg` + and final output is saved as PDF by :func:`~mdpow.workflows.dihedrals.plot_dihedral_violins` + + :keywords: + + *df* + augmented results :class:`pandas.DataFrame` from + :func:`~mdpow.workflows.dihedrals.periodic_angle_padding` + + *width* + width of the violin element (>1 overlaps) + default: 0.9 + + *solvents* + The default solvents are documented under :data:`SOLVENTS_DEFAULT`. + Normally takes a two-tuple, but analysis is compatible with single solvent selections. + Single solvent analyses will result in a figure with fully filled violins for the single solvent. + + *plot_title* + generated by :func:`~mdpow.workflows.dihedrals.build_svg` using + `molname`, `dihedral_groups`, `atom_indices`, and `interactions` + in this order and format: f'{molname}, {name[0]} {a} | ''{col_name}' + + :returns: + + *g* + returns a :class:`seaborn.FacetGrid` object containing a violin plot of the + kernel density estimates (KDE) of the dihedral angle frequencies for each + dihedral atom group identified by :data:`SMARTS_DEFAULT` + + """ + + df["lambda"] = df["lambda"].astype("float") / 1000 + df = df.sort_values( + by=["selection", "solvent", "interaction", "lambda"] + ).reset_index(drop=True) + + width_ratios = [ + len(df[df["interaction"] == "Coulomb"]["lambda"].unique()) + 1, + len(df[df["interaction"] == "VDW"]["lambda"].unique()), + len(df[df["interaction"] == "Coulomb"]["lambda"].unique()) - 1, + ] + + # Usage in Jupyter causes matplotlib figure object output, not the completed figure + # Upcoming fix in issue #260 + assert ( + 0 < len(solvents) < 3 + ), "one or two solvents must be specified, otherwise SOLVENTS_DEFAULT is used" + split = len(solvents) > 1 + g = sns.catplot( + data=df, + x="lambda", + y="dihedral", + hue="solvent", + col="interaction", + kind="violin", + split=split, + width=width, + inner=None, + cut=0, + linewidth=0.5, + hue_order=list(solvents), + col_order=["Coulomb", "VDW", "Structure"], + sharex=False, + sharey=True, + height=3.0, + aspect=2.0, + facet_kws={ + "ylim": (-180, 180), + "gridspec_kws": { + "width_ratios": width_ratios, + }, + }, + ) + + g.set_xlabels(r"$\lambda$") + g.set_ylabels(r"dihedral angle $\phi$") + g.despine(offset=5) + + axC = g.axes_dict["Coulomb"] + axC.yaxis.set_major_locator(plt.matplotlib.ticker.MultipleLocator(60)) + axC.yaxis.set_minor_locator(plt.matplotlib.ticker.MultipleLocator(30)) + axC.yaxis.set_major_formatter( + plt.matplotlib.ticker.FormatStrFormatter(r"$%g^\circ$") + ) + + axV = g.axes_dict["VDW"] + axV.yaxis.set_visible(False) + axV.spines["left"].set_visible(False) + + axIM = g.axes_dict["Structure"] + axIM.axis("off") + + g.set_titles(plot_title) + + return g + + +def build_svg( + mol, + molname, + name_index_pairs, + atom_group_selection, + solvents=SOLVENTS_DEFAULT, + width=0.9, +): + """Converts and combines figure components into an + SVG object to be converted and saved as a publication + quality PDF. + + :keywords: + + *mol* + :class:`rdkit.Chem.rdchem.Mol` object converted from `solute` + + *molname* + molecule name to be used for labelling + plots, if different from `resname` + (in this case, carried over from an upstream + decision between the two) + + *name_index_pairs* + dictionary with key-value pair for dihedral atom group, + atom indices, and bond indices + + .. seealso:: :func:`~mdpow.workflows.dihedrals.get_paired_indices` + + *atom_group_selection* + `name` of each section in the `groupby` series of atom group selections + + .. seealso:: :func:`~mdpow.workflows.dihedrals.plot_dihedral_violins` + + *solvents* + The default solvents are documented under :data:`SOLVENTS_DEFAULT`. + Normally takes a two-tuple, but analysis is compatible with single solvent selections. + Single solvent analyses will result in a figure with fully filled violins for the single solvent. + + *width* + width of the violin element (>1 overlaps) + default: 0.9 + + :returns: + + *fig* + :mod:`svgutils` SVG figure object + + """ + + atom_index = name_index_pairs[atom_group_selection[0]][0] + bond_index = name_index_pairs[atom_group_selection[0]][1] + + drawer = rdMolDraw2D.MolDraw2DSVG(250, 250) + drawer.DrawMolecule(mol=mol, highlightAtoms=atom_index, highlightBonds=bond_index) + drawer.FinishDrawing() + svg = drawer.GetDrawingText().replace("svg:", "") + + mol_svg = svgutils.transform.fromstring(svg) + m = mol_svg.getroot() + m.scale(0.0125).moveto(21.8, 0.35) + + plot_title = f"{molname}, {atom_group_selection[0]} {atom_index} | " "{col_name}" + plot = dihedral_violins( + atom_group_selection[1], width=width, solvents=solvents, plot_title=plot_title + ) + + plot_svg = svgutils.transform.from_mpl(plot) + p = plot_svg.getroot() + p.scale(0.02) + + # order matters here, plot down first, mol on top (p, m) + fig = svgutils.compose.Figure("28cm", "4.2cm", p, m) + + return fig + + +def plot_dihedral_violins( + df, + resname, + mol, + name_index_pairs, + figdir=None, + molname=None, + width=0.9, + plot_pdf_width=PLOT_WIDTH_DEFAULT, + solvents=SOLVENTS_DEFAULT, +): + """Coordinates plotting and saving figures for all dihedral atom groups. + + Makes a subdirectory for the current project within the specified + `figdir` using `resname` or `molname` as title and saves production + quality PDFs for each dihedral atom group separately. + + .. seealso:: + + :func:`~mdpow.workflows.dihedrals.automated_dihedral_analysis`, + :func:`~mdpow.workflows.dihedrals.dihedral_violins`, + :func:`~mdpow.workflows.dihedrals.build_svg` + + :keywords: + + *df* + augmented results :class:`pandas.DataFrame` from + :func:`~mdpow.workflows.dihedrals.periodic_angle_padding` + + *resname* + `resname` for the molecule as defined in + the topology and trajectory + + *mol* + :class:`rdkit.Chem.rdchem.Mol` object converted from `solute` + + *name_index_pairs* + dictionary with key-value pair for dihedral atom group, + atom indices, and bond indices + + .. seealso:: :func:`~mdpow.workflows.dihedrals.get_paired_indices` + + *figdir* + path to the location to save figures (REQUIRED but marked + as a kwarg for technical reasons; will be changed in #244) + + *molname* + molecule name to be used for labelling + plots, if different from `resname` + + *width* + width of the violin element (>1 overlaps) + default: 0.9 + + .. seealso:: :func:`~mdpow.workflows.dihedrals.dihedral_violins` + + *plot_pdf_width* + The default value for width of plot output is described in detail under + :data:`PLOT_WIDTH_DEFAULT`. + + *solvents* + The default solvents are documented under :data:`SOLVENTS_DEFAULT`. + Normally takes a two-tuple, but analysis is compatible with single solvent selections. + Single solvent analyses will result in a figure with fully filled violins for the single solvent. + + """ + + assert ( + figdir is not None + ), "figdir MUST be set, even though it is a kwarg. Will be changed with #244" + + if molname is None: + molname = resname + + subdir = molname + newdir = os.path.join(figdir, subdir) + os.mkdir(newdir) + + section = df.groupby(by="selection") + + plot_pdf_width_px = plot_pdf_width * 3.7795275591 + + pdf_list = [] + + for name in section: + fig = build_svg( + mol=mol, + molname=molname, + atom_group_selection=name, + name_index_pairs=name_index_pairs, + solvents=solvents, + width=width, + ) + + figfile = pathlib.Path(newdir) / f"{molname}_{name[0]}_violins.pdf" + if figdir is not None: + plot_pdf = cairosvg.svg2pdf( + bytestring=fig.tostr(), + write_to=str(figfile), + output_width=plot_pdf_width_px, + ) + + # add PDF for each dihedral atom group to all_PDFs list + pdf_list.append(f"{figfile}") + + logger.info(f"Figure saved as {figfile}") + + logger.info(f"All figures generated and saved in {figdir}") + + merger = pypdf.PdfWriter() + + for pdf in pdf_list: + merger.append(pdf) + merger.write(f"{figdir}/{molname}/{molname}_all_figures.pdf") + merger.close() + logger.info( + f"PDF of combined figures generated and saved as {figdir}/{molname}/{molname}_all_figures.pdf" + ) + + return None + + +def automated_dihedral_analysis( + dirname, + resname, + figdir=None, + # figdir is required and will cause issues if not specified + # figdir=None is a temporary way to satisfy + # workflows base tests until issue #244 is resolved + # because it currently uses a **kwargs convention and the + # positional argument figdir will not carry over nicely + df_save_dir=None, + molname=None, + SMARTS=SMARTS_DEFAULT, + plot_pdf_width=PLOT_WIDTH_DEFAULT, + dataframe=None, + padding=45, + width=0.9, + solvents=SOLVENTS_DEFAULT, + interactions=INTERACTIONS_DEFAULT, + start=None, + stop=None, + step=None, +): + """Runs :class:`~mdpow.analysis.dihedral.DihedralAnalysis` for a single MDPOW + project and creates violin plots of dihedral angle frequencies for each + relevant dihedral atom group. + + For one MDPOW project, automatically determines all relevant dihedral atom groups + in the molecule, runs :class:`~mdpow.analysis.dihedral.DihedralAnalysis` for each group, + pads the dihedral angles to maintain periodicity, creates violin plots of dihedral angle + frequencies (KDEs), and saves publication quality PDF figures for each group, separately. + + Optionally saves all pre-padded :class:`~mdpow.analysis.dihedral.DihedralAnalysis` results + as a single :class:`pandas.DataFrame` in `df_save_dir` provided. + + :keywords: + + *dirname* + Molecule Simulation directory. Loads simulation files present in + lambda directories into the new instance. With this method for + generating an :class:`~mdpow.analysis.ensemble.Ensemble` the + lambda directories are explored and + :meth:`~mdpow.analysis.ensemble.Ensemble._load_universe_from_dir` + searches for .gro, .gro.bz2, .gro.gz, and .tpr files for topology, + and .xtc files for trajectory. It will default to using the tpr file + available. + + *figdir* + path to the location to save figures (REQUIRED but marked + as a kwarg for technical reasons; will be changed in #244) + + *resname* + `resname` for the molecule as defined in + the topology and trajectory + + *df_save_dir* + optional, path to the location to save results :class:`pandas.DataFrame` + + *molname* + molecule name to be used for labelling + plots, if different from `resname` + + *SMARTS* + The default SMARTS string is described in detail under :data:`SMARTS_DEFAULT`. + + *plot_pdf_width* + The default value for width of plot output is described in detail under + :data:`PLOT_WIDTH_DEFAULT`. + + *dataframe* + optional, if :class:`~mdpow.analysis.dihedral.DihedralAnalysis` + was done prior, then results :class:`pandas.DataFrame` can be + input to utilize angle padding and violin plotting functionality + + *padding* + value in degrees + default: 45 + + .. seealso:: :func:`~mdpow.workflows.dihedrals.periodic_angle_padding` + + *width* + width of the violin element (>1 overlaps) + default: 0.9 + + .. seealso:: :func:`~mdpow.workflows.dihedrals.dihedral_violins` + + *solvents* + The default solvents are documented under :data:`SOLVENTS_DEFAULT`. + Normally takes a two-tuple, but analysis is compatible with single solvent selections. + Single solvent analyses will result in a figure with fully filled violins for the single solvent. + + *interactions* + The default interactions are documented under :data:`INTERACTIONS_DEFAULT`. + + *start, stop, step* + arguments passed to :func:`~mdpow.analysis.ensemble.EnsembleAnalysis.run`, + as parameters for iterating through the trajectories of the current ensemble + + .. seealso:: :class:`~mdpow.analysis.ensemble.EnsembleAnalysis` + + .. rubric:: Example + + Typical Workflow:: + + import dihedrals + + dihedrals.automated_dihedral_analysis(dirname='/foo/bar/MDPOW_project_data', + figdir='/foo/bar/MDPOW_figure_directory', + resname='UNK', molname='benzene', + padding=45, width=0.9, + solvents=('water','octanol'), + interactions=('Coulomb','VDW'), + start=0, stop=100, step=10) + """ + + u = build_universe(dirname=dirname, solvents=solvents) + mol, solute = rdkit_conversion(u=u, resname=resname) + atom_indices = get_atom_indices(mol=mol, SMARTS=SMARTS) + bond_indices = get_bond_indices(mol=mol, atom_indices=atom_indices) + dihedral_groups = get_dihedral_groups(solute=solute, atom_indices=atom_indices) + name_index_pairs = get_paired_indices( + atom_indices=atom_indices, + bond_indices=bond_indices, + dihedral_groups=dihedral_groups, + ) + + if dataframe is not None: + df = dataframe + logger.info(f"Proceeding with results DataFrame provided.") + + else: + df = dihedral_groups_ensemble( + dirname=dirname, + atom_indices=atom_indices, + solvents=solvents, + interactions=interactions, + start=start, + stop=stop, + step=step, + ) + + if df_save_dir is not None: + save_df(df=df, df_save_dir=df_save_dir, resname=resname, molname=molname) + + df_aug = periodic_angle_padding(df, padding=padding) + + plot_dihedral_violins( + df=df_aug, + resname=resname, + mol=mol, + name_index_pairs=name_index_pairs, + figdir=figdir, + molname=molname, + width=width, + plot_pdf_width=plot_pdf_width, + solvents=solvents, + ) + + logger.info(f"DihedralAnalysis completed for all projects in {dirname}") + + return diff --git a/mdpow/workflows/registry.py b/mdpow/workflows/registry.py new file mode 100644 index 00000000..5320b6b8 --- /dev/null +++ b/mdpow/workflows/registry.py @@ -0,0 +1,49 @@ +# MDPOW: registry.py +# 2023 Cade Duckworth + +""" +:mod:`mdpow.workflows.registry` --- Registry of currently supported automated workflows +======================================================================================= + +The :mod:`mdpow.workflows.registry` module hosts a dictionary with keys that correspond to an +:class:`~mdpow.analysis.ensemble.EnsembleAnalysis` for which exists a corresponding automated workflow. + +.. table:: Currently supported automated workflows. + :widths: auto + :name: workflows_registry + + +-------------------------------+------------------------------------------------------------------------------------------------------+ + | key/keyword: EnsembleAnalysis | value: . | + +===============================+======================================================================================================+ + | DihedralAnalysis | :any:`dihedrals.automated_dihedral_analysis ` | + +-------------------------------+------------------------------------------------------------------------------------------------------+ + +.. autodata:: registry + +.. seealso:: :mod:`~mdpow.workflows.base` + +""" + +# import analysis functions +# NOTE: analysis modules should NOT import registry to avoid circular imports +from . import dihedrals + +registry = {"DihedralAnalysis": dihedrals.automated_dihedral_analysis} +""" +In the `registry`, each entry corresponds to an +:class:`~mdpow.analysis.ensemble.EnsembleAnalysis` +for which exists a corresponding automated workflow. + +Intended for use with :mod:`mdpow.workflows.base` to specify which +:class:`~mdpow.analysis.ensemble.EnsembleAnalysis` should run iteratively over +the provided project data directory. + +To include a new automated workflow for use with :mod:`mdpow.workflows.base`, +create a key that is the name of the corresponding +:class:`~mdpow.analysis.ensemble.EnsembleAnalysis`, with the value defined as +`.`. + +The available automated workflows (key-value pairs) are listed in the +following table :any:`Currently supported automated workflows. ` + +""" diff --git a/scripts/mdpow-cfg2yaml.py b/scripts/mdpow-cfg2yaml.py index a46f7c4c..4931750c 100755 --- a/scripts/mdpow-cfg2yaml.py +++ b/scripts/mdpow-cfg2yaml.py @@ -18,7 +18,7 @@ full_file = [] -with open(sys.argv[1],'r') as f: +with open(sys.argv[1], "r") as f: for line in f: newline = line.strip() if not newline.startswith("#") and not newline == "": @@ -32,28 +32,31 @@ sections = [] -for x in range(len(heads)-1): - sections.append(full_file[heads[x]:heads[x+1]]) +for x in range(len(heads) - 1): + sections.append(full_file[heads[x] : heads[x + 1]]) -sections.append(full_file[heads[-1]:]) +sections.append(full_file[heads[-1] :]) yaml_formatting = {} for x in sections: options = x[1:] - split = [[str(y.split("=")[0].strip()),str(y.split("=")[1].strip()).strip()] for y in options] - yaml_formatting[x[0][1:-1]]=split + split = [ + [str(y.split("=")[0].strip()), str(y.split("=")[1].strip()).strip()] + for y in options + ] + yaml_formatting[x[0][1:-1]] = split for x in yaml_formatting: - d = dict((key,value) for (key,value) in yaml_formatting[x]) + d = dict((key, value) for (key, value) in yaml_formatting[x]) yaml_formatting[x] = d -with open("result.yml",'w') as f: - f.write(yaml.dump(yaml_formatting,default_flow_style=False)) +with open("result.yml", "w") as f: + f.write(yaml.dump(yaml_formatting, default_flow_style=False)) -with open("result.yml",'r') as infile, open(sys.argv[-1],'w') as outfile: +with open("result.yml", "r") as infile, open(sys.argv[-1], "w") as outfile: data = infile.read() - data = data.replace("'","") + data = data.replace("'", "") outfile.write(data) os.remove("result.yml") diff --git a/scripts/mdpow-equilibrium b/scripts/mdpow-equilibrium index f8523e97..9f8d47db 100755 --- a/scripts/mdpow-equilibrium +++ b/scripts/mdpow-equilibrium @@ -36,7 +36,7 @@ if __name__ == "__main__": parser = OptionParser(usage=__doc__) parser.add_option('--solvent', '-S', dest='solvent', metavar="NAME", - help="solvent NAME for compound, 'water' or 'octanol' [%default]") + help="solvent NAME for compound, 'water', 'octanol', 'cyclohexane' or 'toluene' [%default]") parser.add_option('--dirname', '-d', dest='dirname', metavar="DIRECTORY", help="generate files and directories in DIRECTORY, which is created " diff --git a/scripts/mdpow-fep b/scripts/mdpow-fep index 4f06a8e3..1fbc203e 100755 --- a/scripts/mdpow-fep +++ b/scripts/mdpow-fep @@ -36,7 +36,7 @@ if __name__ == "__main__": parser = OptionParser(usage=__doc__) parser.add_option('--solvent', '-S', dest='solvent', metavar="NAME", - help="solvent NAME for compound, 'water' or 'octanol' [%default]") + help="solvent NAME for compound, 'water', 'octanol', 'cyclohexane' or 'toluene' [%default]") parser.add_option('--dirname', '-d', dest='dirname', metavar="DIRECTORY", help="generate files and directories in DIRECTORY, which is created " diff --git a/scripts/mdpow-ptw b/scripts/mdpow-ptw new file mode 100755 index 00000000..a851750b --- /dev/null +++ b/scripts/mdpow-ptw @@ -0,0 +1,269 @@ +#!/usr/bin/env python +"""%(prog)s [options] DIRECTORY [DIRECTORY ...] + +Run the free energy analysis for water and toluene in /FEP +and return the toluene-water partition coefficient log P_cw. + +DIRECTORY should contain all the files resulting from running +``mdpow.fep.Ghyd.setup()`` and ``mdpow.fep.Gtol.setup()`` and the results of +the MD FEP simulations. It relies on the canonical naming scheme (basically: +just use the defaults as in the tutorial). + +The dV/dlambda plots can be produced automatically (--plot=auto). If multiple +DIRECTORY arguments are provided then one has to choose the auto option (or +None). + +Results are *appended* to a data file with **Output file format**:: + + molecule DeltaG0(kJ/mol) ErrorDeltaG0 "logPtw" log P_TW Error logPtw directory + +molecule + molecule name as used in the itp +DeltaG0, ErrorDeltaG0 + transfer free energy water --> toluene, in kJ/mol; error from correlation time + and error propagation through Simpson's rule (see :meth:`mdpow.fep.Gsolv`) +log P_TW + base-10 logarithm of the toluene-water partition coefficient; >0: partitions into + toluene, <0: partitions preferrably into water +wat_ok, tol_ok + "OK": input data was clean; "BAD": some input data xvg files contained + unparseable lines that were skipped; this can mean that the data became + corrupted and caution should be used. +directory + folder in which the simulations were stored +""" +from __future__ import absolute_import, print_function, division + +import os +import mdpow.fep +import mdpow.filelock + +import logging +logger = logging.getLogger('mdpow') + +def load_gsolv(directory, **kwargs): + FEPdir = os.path.join(directory, "FEP") + + files = {'water': os.path.join(FEPdir, 'water', 'Ghyd.fep'), + 'toluene': os.path.join(FEPdir, 'toluene', 'Gtol.fep'), + } + files_not_found = dict(((solvent,fn) for solvent,fn in files.items() + if not os.path.exists(fn))) + if len(files_not_found) > 0: + raise OSError("Missing input files: %r" % files_not_found) + + permissive = kwargs.pop('permissive', False) + logger.info("Reading water data %(water)r", files) + gwat = mdpow.fep.Ghyd(filename=files['water'], basedir=directory, permissive=permissive) + logger.info("Reading toluene data %(toluene)r", files) + gtol = mdpow.fep.Gtol(filename=files['toluene'], basedir=directory, permissive=permissive) + + return gwat, gtol + + +def run_ptw(directory, **kwargs): + """Do the P_TW analysis + + :Arguments: + *directory* + directory under which the project is stored + *plotfile* + name of a file to plot dV/dlambda graphs to + *outfile* + name of a file to append results to + *energyfile* + append individual energy terms to this file + *force* + force rereading data files [False] + + **Output file format (energies and log P_tw with errors) :** + + molecule DeltaG0(kJ/mol) log P_TW directory + + Removed from output (>0.2.1): + * wat_ok and tol_ok are "OK" if no lines were skipped in the input data files due to + file corruption, and "BAD" otherwise. + + """ + import gc + + fmt_result = "%-8s %+8.2f %8.2f logPtw %+8.2f %8.2f %s\n" + fmt_energy = "%s %+8.2f %8.2f logPtw %+8.2f %8.2f %s\n" + _datastatus = {True: 'BAD', # suspect file corruption + False: 'OK'} + + def datstat(g): + return _datastatus[g.contains_corrupted_xvgs()] + + estimator=kwargs.pop('estimator', 'alchemlyb') + gwat, gtol = load_gsolv(directory, permissive=kwargs.pop('permissive',False),) + transferFE, logPtw = mdpow.fep.pTW(gtol, gwat, stride=kwargs.pop('stride', None), + start=kwargs.pop('start', 0), stop=kwargs.pop('stop', None), + force=kwargs.pop('force', False), SI=kwargs.pop('SI', False), + estimator=estimator, + method=kwargs.pop('method', 'MBAR')) + + if datstat(gwat) == 'BAD' or datstat(gtol) == 'BAD': + logger.warning("Possible file corruption: water:%s, toluene:%s", + datstat(gwat), datstat(gtol)) + + if kwargs.get('outfile', None): + with mdpow.filelock.FileLock(kwargs['outfile'], timeout=2): + with open(kwargs['outfile'], mode='a') as out: + out.write(fmt_result % \ + (gwat.molecule, transferFE.value, transferFE.error, + logPtw.value, logPtw.error, directory)) + # removed (>0.2.1): + # datstat(gwat), datstat(gtol) --> wat_ok tol_ok + logger.info("Wrote results to %(outfile)r", kwargs) + + if kwargs.get('energyfile', None): + with mdpow.filelock.FileLock(kwargs['energyfile'], timeout=2): + with open(kwargs['energyfile'], mode='a') as out: + for g in gwat, gtol: + out.write(fmt_energy % \ + (g.summary(), transferFE.value, transferFE.error, + logPtw.value, logPtw.error, directory)) + logger.info("Wrote solvation energy terms to %(energyfile)r", kwargs) + + plotfile = kwargs.get('plotfile', None) + if estimator == 'alchemlyb': + plotfile = False + if plotfile: + if plotfile == 'auto': + plotfile = auto_plotfile(directory, gwat) + import matplotlib + from matplotlib.pyplot import clf, title, savefig + clf() + gwat.plot(color='k', ls='--') + gtol.plot(color='r', ls='-') + activate_subplot(1) + title(r"[%s] $\Delta A^0 = %.2f\pm%.2f$ kJ/mol" % \ + (gtol.molecule, transferFE.value, transferFE.error)) + activate_subplot(2) + title(r"$\log P_{CW} = %.2f\pm%.2f$" % logPtw.astuple()) + savefig(plotfile) + logger.info("Wrote graph to %(plotfile)r", vars()) + + del gwat + del gtol + gc.collect() # try to free as much memory as possible + + +def auto_plotfile(directory, gsolv): + return os.path.join(directory, + "dVdl_{0}_ptw.pdf".format(gsolv.molecule)) + + +def activate_subplot(numPlot): + """Make subplot *numPlot* active on the canvas. + + Use this if a simple ``subplot(numRows, numCols, numPlot)`` + overwrites the subplot instead of activating it. + """ + # see http://www.mail-archive.com/matplotlib-users@lists.sourceforge.net/msg07156.html + from pylab import gcf, axes + numPlot -= 1 # index is 0-based, plots are 1-based + return axes(gcf().get_axes()[numPlot]) + + +def parse_filename_option(fn): + if fn.lower() == "none": + fn = None + elif fn.lower() == "auto": + fn = "auto" + return fn + +def realpath_or_none(fn): + if not fn is None: + fn = os.path.realpath(fn) + return fn + +if __name__ == "__main__": + import sys + import os.path + import argparse + + parser = argparse.ArgumentParser(usage=__doc__, prog='mdpow-ptw', + formatter_class=argparse.ArgumentDefaultsHelpFormatter) + parser.add_argument("directory", nargs='+', + help="directory or directories which contain all the files " + "resulting from running mdpow.fep.Ghyd.setup() and mdpow.fep.Gtol.setup()" + "and the results of the MD FEP simulations.",) + parser.add_argument('--plotfile', dest="plotfile", default='none', + help="plot dV/dlambda to FILE; use png or pdf suffix to " + "determine the file type. 'auto' generates a pdf file " + "DIRECTORY/dVdl__ptw.pdf and 'none' disables it " + "The plot function is only available for mdpow estimator," + "and is disabled when using alchemlyb estimators.", + metavar="FILE") + parser.add_argument('-o', '--outfile', dest="outfile", default="ptw.txt", + help="append one-line results summary to FILE ", + metavar="FILE") + parser.add_argument('-e', '--energies', dest="energyfile", default="energies.txt", + help="append solvation free energies to FILE ", + metavar="FILE") + parser.add_argument('--estimator', dest="estimator", default='alchemlyb', + choices=['mdpow', 'alchemlyb'], + help="choose the estimator to be used, alchemlyb or mdpow estimators") + parser.add_argument('--method', dest="method", default='MBAR', + choices=['TI', 'MBAR', 'BAR'], + help="choose the method to calculate free energy ",) + parser.add_argument('--force', dest='force', default=False, + action="store_true", + help="force rereading all data ") + SIs = parser.add_mutually_exclusive_group() + SIs.add_argument('--SI', dest='SI', + action="store_true", default=True, + help="enable statistical inefficiency (SI) analysis. " + "Statistical inefficiency analysis is performed by default when using" + "alchemlyb estimators and is always disabled when using mdpow estimator.") + SIs.add_argument('--no-SI', dest='noSI', + action="store_true", default=False, + help="disable statistical inefficiency analysis. " + "Statistical inefficiency analysis is performed by default when using" + "alchemlyb estimators and is disabled when using mdpow estimator. ") + parser.add_argument('-s', '--stride', dest="stride", type=int, default=1, + help="use every N-th datapoint from the original dV/dlambda data. ", + metavar="N") + parser.add_argument('--start', dest="start", type=int, default=0, + help="start point for the data used from the original dV/dlambda data. ") + parser.add_argument('--stop', dest="stop", type=int, default=None, + help="stop point for the data used from the original dV/dlambda data. ",) + parser.add_argument('--ignore-corrupted', dest="permissive", + action="store_true", default=False, + help="skip lines in the md.xvg files that cannot be parsed. " + "WARNING: Other lines in the file might have been corrupted in " + "such a way that they appear correct but are in fact wrong. " + "WRONG RESULTS CAN OCCUR! USE AT YOUR OWN RISK ") + opts = parser.parse_args() + + if len(opts.directory) > 1 and not opts.plotfile.lower() in ('none', 'auto'): + logger.fatal("Can only use --plotfile=None or --plotfile=auto with multiple directories.") + sys.exit(1) + + if opts.estimator == 'mdpow' and opts.SI and not opts.noSI: + logger.fatal("Statistical inefficiency analysis is only available for estimator 'alchemlyb'.") + sys.exit(1) + + for directory in opts.directory: + if not os.path.exists(directory): + logger.warning("Directory %r not found, skipping...", directory) + continue + logger.info("Analyzing directory %r... (can take a while)", directory) + + opts.plotfile = parse_filename_option(opts.plotfile) + + try: + run_ptw(directory, plotfile=opts.plotfile, + outfile=realpath_or_none(opts.outfile), + energyfile=realpath_or_none(opts.energyfile), + force=opts.force, stride=opts.stride, start=opts.start, + stop=opts.stop, estimator=opts.estimator, + method=opts.method, permissive=opts.permissive, SI=opts.SI and not opts.noSI) + except (OSError, IOError) as err: + logger.error("Running analysis in directory %r failed: %s", directory, str(err)) + except Exception as err: + logger.fatal("Running analysis in directory %r failed", directory) + logger.exception("Catastrophic problem occurred, see the stack trace for hints.") + raise diff --git a/scripts/mdpow-solvationenergy b/scripts/mdpow-solvationenergy index 59fefb55..5be47612 100755 --- a/scripts/mdpow-solvationenergy +++ b/scripts/mdpow-solvationenergy @@ -56,7 +56,8 @@ logger = logging.getLogger('mdpow') suffix = {'water':'hyd', 'octanol':'oct', - 'cyclohexane':'cyclo' + 'cyclohexane':'cyclo', + 'toluene':'tol', } def load_gsolv(solvent, directory, **kwargs): @@ -228,7 +229,7 @@ if __name__ == "__main__": "and is disabled when using alchemlyb estimators.", metavar="FILE") parser.add_argument('--solvent', '-S', dest='solvent', default='water', - choices=['water', 'octanol', 'cyclohexane'], metavar="NAME", + choices=['water', 'octanol', 'cyclohexane', 'toluene'], metavar="NAME", help="solvent NAME for compound, 'water', 'octanol', or 'cyclohexane' ") parser.add_argument('-o', '--outfile', dest="outfile", default="gsolv.txt", help="append one-line results summary to FILE ", diff --git a/setup.py b/setup.py index 9ade6c7d..d6944529 100644 --- a/setup.py +++ b/setup.py @@ -6,61 +6,77 @@ from setuptools import setup, find_packages import versioneer -setup(name="MDPOW", - version=versioneer.get_version(), - cmdclass=versioneer.get_cmdclass(), - description="A library for computing solvation/water partitioning coefficients using molecular dynamics simulations", - long_description=open("README.rst").read(), - long_description_content_type="text/x-rst", - author="Oliver Beckstein", - author_email="orbeckst@gmail.com", - license="GPLv3", - url="https://github.com/Becksteinlab/MDPOW", - keywords="science Gromacs analysis 'molecular dynamics'", - classifiers=[ - "Development Status :: 4 - Beta", - "Environment :: Console", - "Intended Audience :: Science/Research", - "License :: OSI Approved :: GNU General Public License v3 or later (GPLv3+)", - "Operating System :: POSIX", - 'Operating System :: MacOS :: MacOS X', - 'Programming Language :: Python', - "Programming Language :: Python :: 3", - "Programming Language :: Python :: 3.7", - "Programming Language :: Python :: 3.8", - "Programming Language :: Python :: 3.9", - "Programming Language :: Python :: 3.10", - "Topic :: Scientific/Engineering :: Chemistry", - "Topic :: Scientific/Engineering :: Physics", - ], - packages=find_packages(exclude=['examples']), - scripts=['scripts/mdpow-pow', - 'scripts/mdpow-pcw', - 'scripts/mdpow-check', - 'scripts/mdpow-rebuild-fep', - 'scripts/mdpow-rebuild-simulation', - 'scripts/mdpow-equilibrium', - 'scripts/mdpow-fep', - 'scripts/mdpow-cfg2yaml.py', - 'scripts/mdpow-solvationenergy', - 'scripts/mdpow-get-runinput' - ], - package_data={'mdpow': ['top/*.dat', 'top/*.gro', 'top/*.itp', - 'top/oplsaa.ff/*', - 'top/charmm36-mar2019.ff/*', - 'top/amber99sb.ff/*', - 'templates/*'], }, - install_requires=['numpy>=1.6', 'scipy', - 'pyyaml', - 'GromacsWrapper>=0.5.1', - 'numkit', - 'six', - 'mdanalysis>=2', - 'alchemlyb<2', - 'pandas', - 'pymbar', - ], - #setup_requires=['pytest-runner',], - tests_require=['pytest', 'pybol', 'py'], - zip_safe=True, +setup( + name="MDPOW", + version=versioneer.get_version(), + cmdclass=versioneer.get_cmdclass(), + description="A library for computing solvation/water partitioning coefficients using molecular dynamics simulations", + long_description=open("README.rst").read(), + long_description_content_type="text/x-rst", + author="Oliver Beckstein", + author_email="orbeckst@gmail.com", + license="GPLv3", + url="https://github.com/Becksteinlab/MDPOW", + keywords="science Gromacs analysis 'molecular dynamics'", + classifiers=[ + "Development Status :: 4 - Beta", + "Environment :: Console", + "Intended Audience :: Science/Research", + "License :: OSI Approved :: GNU General Public License v3 or later (GPLv3+)", + "Operating System :: POSIX", + "Operating System :: MacOS :: MacOS X", + "Programming Language :: Python", + "Programming Language :: Python :: 3", + "Programming Language :: Python :: 3.8", + "Programming Language :: Python :: 3.9", + "Programming Language :: Python :: 3.10", + "Topic :: Scientific/Engineering :: Chemistry", + "Topic :: Scientific/Engineering :: Physics", + ], + packages=find_packages(exclude=["examples"]), + scripts=[ + "scripts/mdpow-pow", + "scripts/mdpow-pcw", + "scripts/mdpow-ptw", + "scripts/mdpow-check", + "scripts/mdpow-rebuild-fep", + "scripts/mdpow-rebuild-simulation", + "scripts/mdpow-equilibrium", + "scripts/mdpow-fep", + "scripts/mdpow-cfg2yaml.py", + "scripts/mdpow-solvationenergy", + "scripts/mdpow-get-runinput", + ], + package_data={ + "mdpow": [ + "top/*.dat", + "top/*.gro", + "top/*.itp", + "top/oplsaa.ff/*", + "top/charmm36-mar2019.ff/*", + "top/amber99sb.ff/*", + "templates/*", + ], + }, + install_requires=[ + "numpy>=1.6", + "scipy", + "pyyaml", + "GromacsWrapper>=0.5.1", + "numkit", + "six", + "mdanalysis>=2", + "alchemlyb>=2", + "pandas", + "pymbar>=4", + "matplotlib", + "seaborn", + "rdkit", + "svgutils", + "cairosvg", + "pypdf", + ], + # setup_requires=['pytest-runner',], + tests_require=["pytest", "pybol", "py"], + zip_safe=True, ) diff --git a/versioneer.py b/versioneer.py index 64fea1c8..2b545405 100644 --- a/versioneer.py +++ b/versioneer.py @@ -1,4 +1,3 @@ - # Version: 0.18 """The Versioneer - like a rocketeer, but for versions. @@ -277,6 +276,7 @@ """ from __future__ import print_function + try: import configparser except ImportError: @@ -308,11 +308,13 @@ def get_root(): setup_py = os.path.join(root, "setup.py") versioneer_py = os.path.join(root, "versioneer.py") if not (os.path.exists(setup_py) or os.path.exists(versioneer_py)): - err = ("Versioneer was unable to run the project root directory. " - "Versioneer requires setup.py to be executed from " - "its immediate directory (like 'python setup.py COMMAND'), " - "or in a way that lets it use sys.argv[0] to find the root " - "(like 'python path/to/setup.py COMMAND').") + err = ( + "Versioneer was unable to run the project root directory. " + "Versioneer requires setup.py to be executed from " + "its immediate directory (like 'python setup.py COMMAND'), " + "or in a way that lets it use sys.argv[0] to find the root " + "(like 'python path/to/setup.py COMMAND')." + ) raise VersioneerBadRootError(err) try: # Certain runtime workflows (setup.py install/develop in a setuptools @@ -325,8 +327,10 @@ def get_root(): me_dir = os.path.normcase(os.path.splitext(me)[0]) vsr_dir = os.path.normcase(os.path.splitext(versioneer_py)[0]) if me_dir != vsr_dir: - print("Warning: build in %s is using versioneer.py from %s" - % (os.path.dirname(me), versioneer_py)) + print( + "Warning: build in %s is using versioneer.py from %s" + % (os.path.dirname(me), versioneer_py) + ) except NameError: pass return root @@ -348,6 +352,7 @@ def get(parser, name): if parser.has_option("versioneer", name): return parser.get("versioneer", name) return None + cfg = VersioneerConfig() cfg.VCS = VCS cfg.style = get(parser, "style") or "" @@ -372,17 +377,18 @@ class NotThisMethod(Exception): def register_vcs_handler(vcs, method): # decorator """Decorator to mark a method as the handler for a particular VCS.""" + def decorate(f): """Store f in HANDLERS[vcs][method].""" if vcs not in HANDLERS: HANDLERS[vcs] = {} HANDLERS[vcs][method] = f return f + return decorate -def run_command(commands, args, cwd=None, verbose=False, hide_stderr=False, - env=None): +def run_command(commands, args, cwd=None, verbose=False, hide_stderr=False, env=None): """Call the given command(s).""" assert isinstance(commands, list) p = None @@ -390,10 +396,13 @@ def run_command(commands, args, cwd=None, verbose=False, hide_stderr=False, try: dispcmd = str([c] + args) # remember shell=False, so use git.cmd on windows, not just git - p = subprocess.Popen([c] + args, cwd=cwd, env=env, - stdout=subprocess.PIPE, - stderr=(subprocess.PIPE if hide_stderr - else None)) + p = subprocess.Popen( + [c] + args, + cwd=cwd, + env=env, + stdout=subprocess.PIPE, + stderr=(subprocess.PIPE if hide_stderr else None), + ) break except EnvironmentError: e = sys.exc_info()[1] @@ -418,7 +427,9 @@ def run_command(commands, args, cwd=None, verbose=False, hide_stderr=False, return stdout, p.returncode -LONG_VERSION_PY['git'] = ''' +LONG_VERSION_PY[ + "git" +] = ''' # This file helps to compute a version number in source trees obtained from # git-archive tarball (such as those provided by githubs download-from-tag # feature). Distribution tarballs (built by setup.py sdist) and build @@ -993,7 +1004,7 @@ def git_versions_from_keywords(keywords, tag_prefix, verbose): # starting in git-1.8.3, tags are listed as "tag: foo-1.0" instead of # just "foo-1.0". If we see a "tag: " prefix, prefer those. TAG = "tag: " - tags = set([r[len(TAG):] for r in refs if r.startswith(TAG)]) + tags = set([r[len(TAG) :] for r in refs if r.startswith(TAG)]) if not tags: # Either we're using git < 1.8.3, or there really are no tags. We use # a heuristic: assume all version tags have a digit. The old git %d @@ -1002,7 +1013,7 @@ def git_versions_from_keywords(keywords, tag_prefix, verbose): # between branches and tags. By ignoring refnames without digits, we # filter out many common branch names like "release" and # "stabilization", as well as "HEAD" and "master". - tags = set([r for r in refs if re.search(r'\d', r)]) + tags = set([r for r in refs if re.search(r"\d", r)]) if verbose: print("discarding '%s', no digits" % ",".join(refs - tags)) if verbose: @@ -1010,19 +1021,26 @@ def git_versions_from_keywords(keywords, tag_prefix, verbose): for ref in sorted(tags): # sorting will prefer e.g. "2.0" over "2.0rc1" if ref.startswith(tag_prefix): - r = ref[len(tag_prefix):] + r = ref[len(tag_prefix) :] if verbose: print("picking %s" % r) - return {"version": r, - "full-revisionid": keywords["full"].strip(), - "dirty": False, "error": None, - "date": date} + return { + "version": r, + "full-revisionid": keywords["full"].strip(), + "dirty": False, + "error": None, + "date": date, + } # no suitable tags, so version is "0+unknown", but full hex is still there if verbose: print("no suitable tags, using unknown + full revision id") - return {"version": "0+unknown", - "full-revisionid": keywords["full"].strip(), - "dirty": False, "error": "no suitable tags", "date": None} + return { + "version": "0+unknown", + "full-revisionid": keywords["full"].strip(), + "dirty": False, + "error": "no suitable tags", + "date": None, + } @register_vcs_handler("git", "pieces_from_vcs") @@ -1037,8 +1055,7 @@ def git_pieces_from_vcs(tag_prefix, root, verbose, run_command=run_command): if sys.platform == "win32": GITS = ["git.cmd", "git.exe"] - out, rc = run_command(GITS, ["rev-parse", "--git-dir"], cwd=root, - hide_stderr=True) + out, rc = run_command(GITS, ["rev-parse", "--git-dir"], cwd=root, hide_stderr=True) if rc != 0: if verbose: print("Directory %s not under git control" % root) @@ -1046,10 +1063,19 @@ def git_pieces_from_vcs(tag_prefix, root, verbose, run_command=run_command): # if there is a tag matching tag_prefix, this yields TAG-NUM-gHEX[-dirty] # if there isn't one, this yields HEX[-dirty] (no NUM) - describe_out, rc = run_command(GITS, ["describe", "--tags", "--dirty", - "--always", "--long", - "--match", "%s*" % tag_prefix], - cwd=root) + describe_out, rc = run_command( + GITS, + [ + "describe", + "--tags", + "--dirty", + "--always", + "--long", + "--match", + "%s*" % tag_prefix, + ], + cwd=root, + ) # --long was added in git-1.5.5 if describe_out is None: raise NotThisMethod("'git describe' failed") @@ -1072,17 +1098,16 @@ def git_pieces_from_vcs(tag_prefix, root, verbose, run_command=run_command): dirty = git_describe.endswith("-dirty") pieces["dirty"] = dirty if dirty: - git_describe = git_describe[:git_describe.rindex("-dirty")] + git_describe = git_describe[: git_describe.rindex("-dirty")] # now we have TAG-NUM-gHEX or HEX if "-" in git_describe: # TAG-NUM-gHEX - mo = re.search(r'^(.+)-(\d+)-g([0-9a-f]+)$', git_describe) + mo = re.search(r"^(.+)-(\d+)-g([0-9a-f]+)$", git_describe) if not mo: # unparseable. Maybe git-describe is misbehaving? - pieces["error"] = ("unable to parse git-describe output: '%s'" - % describe_out) + pieces["error"] = "unable to parse git-describe output: '%s'" % describe_out return pieces # tag @@ -1091,10 +1116,12 @@ def git_pieces_from_vcs(tag_prefix, root, verbose, run_command=run_command): if verbose: fmt = "tag '%s' doesn't start with prefix '%s'" print(fmt % (full_tag, tag_prefix)) - pieces["error"] = ("tag '%s' doesn't start with prefix '%s'" - % (full_tag, tag_prefix)) + pieces["error"] = "tag '%s' doesn't start with prefix '%s'" % ( + full_tag, + tag_prefix, + ) return pieces - pieces["closest-tag"] = full_tag[len(tag_prefix):] + pieces["closest-tag"] = full_tag[len(tag_prefix) :] # distance: number of commits since tag pieces["distance"] = int(mo.group(2)) @@ -1105,13 +1132,13 @@ def git_pieces_from_vcs(tag_prefix, root, verbose, run_command=run_command): else: # HEX: no tags pieces["closest-tag"] = None - count_out, rc = run_command(GITS, ["rev-list", "HEAD", "--count"], - cwd=root) + count_out, rc = run_command(GITS, ["rev-list", "HEAD", "--count"], cwd=root) pieces["distance"] = int(count_out) # total number of commits # commit date: see ISO-8601 comment in git_versions_from_keywords() - date = run_command(GITS, ["show", "-s", "--format=%ci", "HEAD"], - cwd=root)[0].strip() + date = run_command(GITS, ["show", "-s", "--format=%ci", "HEAD"], cwd=root)[ + 0 + ].strip() pieces["date"] = date.strip().replace(" ", "T", 1).replace(" ", "", 1) return pieces @@ -1167,16 +1194,22 @@ def versions_from_parentdir(parentdir_prefix, root, verbose): for i in range(3): dirname = os.path.basename(root) if dirname.startswith(parentdir_prefix): - return {"version": dirname[len(parentdir_prefix):], - "full-revisionid": None, - "dirty": False, "error": None, "date": None} + return { + "version": dirname[len(parentdir_prefix) :], + "full-revisionid": None, + "dirty": False, + "error": None, + "date": None, + } else: rootdirs.append(root) root = os.path.dirname(root) # up a level if verbose: - print("Tried directories %s but none started with prefix %s" % - (str(rootdirs), parentdir_prefix)) + print( + "Tried directories %s but none started with prefix %s" + % (str(rootdirs), parentdir_prefix) + ) raise NotThisMethod("rootdir doesn't start with parentdir_prefix") @@ -1205,11 +1238,13 @@ def versions_from_file(filename): contents = f.read() except EnvironmentError: raise NotThisMethod("unable to read _version.py") - mo = re.search(r"version_json = '''\n(.*)''' # END VERSION_JSON", - contents, re.M | re.S) + mo = re.search( + r"version_json = '''\n(.*)''' # END VERSION_JSON", contents, re.M | re.S + ) if not mo: - mo = re.search(r"version_json = '''\r\n(.*)''' # END VERSION_JSON", - contents, re.M | re.S) + mo = re.search( + r"version_json = '''\r\n(.*)''' # END VERSION_JSON", contents, re.M | re.S + ) if not mo: raise NotThisMethod("no version_json in _version.py") return json.loads(mo.group(1)) @@ -1218,8 +1253,7 @@ def versions_from_file(filename): def write_to_version_file(filename, versions): """Write the given version number to the given _version.py file.""" os.unlink(filename) - contents = json.dumps(versions, sort_keys=True, - indent=1, separators=(",", ": ")) + contents = json.dumps(versions, sort_keys=True, indent=1, separators=(",", ": ")) with open(filename, "w") as f: f.write(SHORT_VERSION_PY % contents) @@ -1251,8 +1285,7 @@ def render_pep440(pieces): rendered += ".dirty" else: # exception #1 - rendered = "0+untagged.%d.g%s" % (pieces["distance"], - pieces["short"]) + rendered = "0+untagged.%d.g%s" % (pieces["distance"], pieces["short"]) if pieces["dirty"]: rendered += ".dirty" return rendered @@ -1366,11 +1399,13 @@ def render_git_describe_long(pieces): def render(pieces, style): """Render the given version pieces into the requested style.""" if pieces["error"]: - return {"version": "unknown", - "full-revisionid": pieces.get("long"), - "dirty": None, - "error": pieces["error"], - "date": None} + return { + "version": "unknown", + "full-revisionid": pieces.get("long"), + "dirty": None, + "error": pieces["error"], + "date": None, + } if not style or style == "default": style = "pep440" # the default @@ -1390,9 +1425,13 @@ def render(pieces, style): else: raise ValueError("unknown style '%s'" % style) - return {"version": rendered, "full-revisionid": pieces["long"], - "dirty": pieces["dirty"], "error": None, - "date": pieces.get("date")} + return { + "version": rendered, + "full-revisionid": pieces["long"], + "dirty": pieces["dirty"], + "error": None, + "date": pieces.get("date"), + } class VersioneerBadRootError(Exception): @@ -1415,8 +1454,9 @@ def get_versions(verbose=False): handlers = HANDLERS.get(cfg.VCS) assert handlers, "unrecognized VCS '%s'" % cfg.VCS verbose = verbose or cfg.verbose - assert cfg.versionfile_source is not None, \ - "please set versioneer.versionfile_source" + assert ( + cfg.versionfile_source is not None + ), "please set versioneer.versionfile_source" assert cfg.tag_prefix is not None, "please set versioneer.tag_prefix" versionfile_abs = os.path.join(root, cfg.versionfile_source) @@ -1470,9 +1510,13 @@ def get_versions(verbose=False): if verbose: print("unable to compute version") - return {"version": "0+unknown", "full-revisionid": None, - "dirty": None, "error": "unable to compute version", - "date": None} + return { + "version": "0+unknown", + "full-revisionid": None, + "dirty": None, + "error": "unable to compute version", + "date": None, + } def get_version(): @@ -1521,6 +1565,7 @@ def run(self): print(" date: %s" % vers.get("date")) if vers["error"]: print(" error: %s" % vers["error"]) + cmds["version"] = cmd_version # we override "build_py" in both distutils and setuptools @@ -1553,14 +1598,15 @@ def run(self): # now locate _version.py in the new build/ directory and replace # it with an updated value if cfg.versionfile_build: - target_versionfile = os.path.join(self.build_lib, - cfg.versionfile_build) + target_versionfile = os.path.join(self.build_lib, cfg.versionfile_build) print("UPDATING %s" % target_versionfile) write_to_version_file(target_versionfile, versions) + cmds["build_py"] = cmd_build_py if "cx_Freeze" in sys.modules: # cx_freeze enabled? from cx_Freeze.dist import build_exe as _build_exe + # nczeczulin reports that py2exe won't like the pep440-style string # as FILEVERSION, but it can be used for PRODUCTVERSION, e.g. # setup(console=[{ @@ -1581,17 +1627,21 @@ def run(self): os.unlink(target_versionfile) with open(cfg.versionfile_source, "w") as f: LONG = LONG_VERSION_PY[cfg.VCS] - f.write(LONG % - {"DOLLAR": "$", - "STYLE": cfg.style, - "TAG_PREFIX": cfg.tag_prefix, - "PARENTDIR_PREFIX": cfg.parentdir_prefix, - "VERSIONFILE_SOURCE": cfg.versionfile_source, - }) + f.write( + LONG + % { + "DOLLAR": "$", + "STYLE": cfg.style, + "TAG_PREFIX": cfg.tag_prefix, + "PARENTDIR_PREFIX": cfg.parentdir_prefix, + "VERSIONFILE_SOURCE": cfg.versionfile_source, + } + ) + cmds["build_exe"] = cmd_build_exe del cmds["build_py"] - if 'py2exe' in sys.modules: # py2exe enabled? + if "py2exe" in sys.modules: # py2exe enabled? try: from py2exe.distutils_buildexe import py2exe as _py2exe # py3 except ImportError: @@ -1610,13 +1660,17 @@ def run(self): os.unlink(target_versionfile) with open(cfg.versionfile_source, "w") as f: LONG = LONG_VERSION_PY[cfg.VCS] - f.write(LONG % - {"DOLLAR": "$", - "STYLE": cfg.style, - "TAG_PREFIX": cfg.tag_prefix, - "PARENTDIR_PREFIX": cfg.parentdir_prefix, - "VERSIONFILE_SOURCE": cfg.versionfile_source, - }) + f.write( + LONG + % { + "DOLLAR": "$", + "STYLE": cfg.style, + "TAG_PREFIX": cfg.tag_prefix, + "PARENTDIR_PREFIX": cfg.parentdir_prefix, + "VERSIONFILE_SOURCE": cfg.versionfile_source, + } + ) + cmds["py2exe"] = cmd_py2exe # we override different "sdist" commands for both environments @@ -1643,8 +1697,10 @@ def make_release_tree(self, base_dir, files): # updated value target_versionfile = os.path.join(base_dir, cfg.versionfile_source) print("UPDATING %s" % target_versionfile) - write_to_version_file(target_versionfile, - self._versioneer_generated_versions) + write_to_version_file( + target_versionfile, self._versioneer_generated_versions + ) + cmds["sdist"] = cmd_sdist return cmds @@ -1699,11 +1755,13 @@ def do_setup(): root = get_root() try: cfg = get_config_from_root(root) - except (EnvironmentError, configparser.NoSectionError, - configparser.NoOptionError) as e: + except ( + EnvironmentError, + configparser.NoSectionError, + configparser.NoOptionError, + ) as e: if isinstance(e, (EnvironmentError, configparser.NoSectionError)): - print("Adding sample versioneer config to setup.cfg", - file=sys.stderr) + print("Adding sample versioneer config to setup.cfg", file=sys.stderr) with open(os.path.join(root, "setup.cfg"), "a") as f: f.write(SAMPLE_CONFIG) print(CONFIG_ERROR, file=sys.stderr) @@ -1712,15 +1770,18 @@ def do_setup(): print(" creating %s" % cfg.versionfile_source) with open(cfg.versionfile_source, "w") as f: LONG = LONG_VERSION_PY[cfg.VCS] - f.write(LONG % {"DOLLAR": "$", - "STYLE": cfg.style, - "TAG_PREFIX": cfg.tag_prefix, - "PARENTDIR_PREFIX": cfg.parentdir_prefix, - "VERSIONFILE_SOURCE": cfg.versionfile_source, - }) - - ipy = os.path.join(os.path.dirname(cfg.versionfile_source), - "__init__.py") + f.write( + LONG + % { + "DOLLAR": "$", + "STYLE": cfg.style, + "TAG_PREFIX": cfg.tag_prefix, + "PARENTDIR_PREFIX": cfg.parentdir_prefix, + "VERSIONFILE_SOURCE": cfg.versionfile_source, + } + ) + + ipy = os.path.join(os.path.dirname(cfg.versionfile_source), "__init__.py") if os.path.exists(ipy): try: with open(ipy, "r") as f: @@ -1762,8 +1823,10 @@ def do_setup(): else: print(" 'versioneer.py' already in MANIFEST.in") if cfg.versionfile_source not in simple_includes: - print(" appending versionfile_source ('%s') to MANIFEST.in" % - cfg.versionfile_source) + print( + " appending versionfile_source ('%s') to MANIFEST.in" + % cfg.versionfile_source + ) with open(manifest_in, "a") as f: f.write("include %s\n" % cfg.versionfile_source) else: